From 9b624c06daad25876a8064d323abe35e23c6ac0a Mon Sep 17 00:00:00 2001 From: Dylan Bourgeois Date: Fri, 15 Mar 2019 11:07:55 -0700 Subject: [PATCH] ipynb state --- notebook/BERT-freq-100k_epochs_top100.png | Bin 54288 -> 53971 bytes notebook/Inspect Attention.ipynb | 815 +- notebook/Inspect Predictions - MLM.ipynb | 6566 +-- .../Inspect Predictions - Method Naming.ipynb | 42336 ++++++---------- .../Inspect Predictions - Var Naming.ipynb | 40914 ++++++++++++--- 5 files changed, 51959 insertions(+), 38672 deletions(-) diff --git a/notebook/BERT-freq-100k_epochs_top100.png b/notebook/BERT-freq-100k_epochs_top100.png index 6643d095e4490bd4f02de6e80cdaf679da1438d1..133d781e62778c58f973b0cf604298429a2d06f7 100644 GIT binary patch literal 53971 zcmeFa2UL~WvMs!A+qT748x<3&MHB@EK_v&``6Ed+|VK;jtA}2{CmyUT!uD`&Dpls9tG;@P^~w#}((zQ&_o6tiIR&zMH$l zy*w4kx5XMNq3r{G;sHf9&aRb5dvjzbx*a}^On2)k2lCFseKQ!IrYW(4%crc^^kcO$tt{(ptJrKMmWm9_XFT#>ynXf~mGxp`V!ML**$hiU z{Jhx&6l>GX^b37ghJUKhwhstgyIZm5uHhCrKXx&HFRv4?uPdbL6^M+qx3wuY=DKc~ zaC48z8Ga{V9evlZq%f^QHoL&dX{eJ;#(CJ+%gb?~P2q-0R4iV(7!}32Ni*FyRllfh z}h%MJx+f8t2C<_=wal+jMlPakQh+r40wy{r>hATZPPs)>Z9{sJDB? z>X+s?j|g^(9C(}OK9g`De%D6L^o>2)y}7Z1e7YyAlKtijJ>2{}RES$G@vz@SZ=QQ% z^UUXSa`JC)sl~aB^;>@MTweI@{>d%1*Y`#&E8?`*x+JJAxlp3zg2U8M)t1_5r_P(= zlb@f`doKnBEfH&Q(07^ml%Xk87oJB{X3&SEm^n6>**pn z>0P0MoaeaR9QK55{xG-bNQSxgn?!XXD=RCOsZ_?J=v|Bj45^!;x^5DOZy#Z`ZYw=M zHPR@ToYi?ty4K=hU*VBdJ%zS*?ECXH*W$pn*b{GuzJle=|B(h~p%6jsRZ1L|@rcEJ7KEjHr7gSU)FiQw&tG6 zOP-KTn#-Dt0)#pb9N6MIKA51J=eAVfu&GaN23zj*A@l4{>0y$#zwe2WmBA-);)X1I zIV7xn^mSc_-geYx(37zX;}=K!ON8R~#mI(#ijuVTUbpvh{u9sn&De1Ng`AT4lVbyG zc=WKheUD}@%0E44zFv2ADt^-$qQ~;}c;3V*SRdUYTfq)gldN-n;YQLpDtBT(pD*pM=Z2g|POkrH*L2HPn z-eAW|);{e!F5@3>D1{1Y>-9AiyiC%}puN?v&$4y&f0?42S8dKuy!8({GqtQZ8)v;6IOX`j?>V)rd&L(+G5t?@u99QBik$*q%*M+8IIALGN*=X zgM0+DOcM2<2MdJya?8oWp=6G=MFgotNgAfRVvCi}FJ2pnv$_@=yK~p2^%q^H$J)a` zX=m9en`eEz&Z`_~@W^A9a!JUhEw#yc)8p6hURcO(w^QW{*hP8sraoB&;VAG0^Q-MD zhu<>z@Wj(G`7uoC@}b1Tn{FPx|NAREPiZd9S2;O%yz?4fQNI8C+rY$>TuF^IqrFBG zFRuO;ROrXf*ZMqoH&!vqcZDzyZuA7UIYH_CK{!Jc}l zu_hm(z%>EVA{*3`wXB9~jQMcl%Y5C(T0@z4QBzY>d{K!j+-2~kQn*<}vL+l>5Dw^C zI+$B(Mav%BR%wSGO4z@;xU6iij$>d4VhiZ+38(l8vF6MyLDi>rJovd zocY=#t;?4c*>%3zKpVCtL)U$3$Y5q_WM3e!vh2*{z%_&7z+!kwk=g*+@s5@c=eQf1 zQptz4++(L1O|}H>TPGnQq3p@R76?BfzEaFw>(!m(PkE)J zq%xbl1w@+jMw@(E9yI&!rWHMutR*4Q`*0wzz-PH}y4vj{!q(B)tF>yk4k_cPMOkQP zcTJj`4@gM%KU))2#O>~qT`~1iiF++i}{ZV|l2mF;56xz5_XRVli3pV#0@ zm5cm2gRt)w_lFgHmaR3bP2YR#(9J;Fi4$-)A_z03!=Ce(uS2BScS|iXgnlA{W3{EW z-#KUFPjkkkG;>`XaZ(;S4v36sTNSTW#sS272CD12Zo8?I<8;@uxv+Hj^0t$0H%Cf@ z^7fEVZwXe{QU3i+f~rZtY{syS+_%aYs9B@#!a6X$eghempi*Z>DBdfVOB{x0V zEI#`F%s{>D@h5+hCvF>!aT`BjByqvZ>&!yQ^}9kgt~_{s&-o1pugf1keE3b0rdV1I zK3A^8qv3(2z7}pv*s>{q#kSuM!LVoEKU^d`dh91o$*m1817%?{&US~e9mx-xyruQY z>m9tI;-?y?SkUV>l3l8l(VIKb<1kWhum9xFd41TCf(Mq(P086k-d?KpVR zR2Yv#S5gKKg0*!iHbsy;*+5&Fk8M|_q2#(4M6e@SHYZBMgx4$+)LKEo<`Taef0{0K z$?ZkF&i<6#@!ucLOi%RD)8~vdu!rh6osb?V-6D)_7=^j%eSZHK-XC^Z=(${H1&q{W zqt=7XPJ2M)KJzPhGti9=lUdY>F; znsJ3cOrfwee9M50F`z}<4o{Y`Y187S0w4YIND0U27)_^Xv%6!~nOGKrG)EqsI!-=W zHQVmfdiCTwJ5H=mgZB>$R<%qxw3UW0v2HK-L+EIcGD3I|#%7ZNG+#cMd>)5RN(aYt zbnx}QW<0EySfk4p9-ctn^AQ%g5=Ir#oI!m1{8w%<$~ViXU&h*4HbtA&@hX1z?W6bk z1s}XW-2n%};oO}%7Hw}I)mY~0?k{Ru{WC&ez}P^$5`q>FA0J=x!~W+-a-3{)C-VBA z3%pD>t98l9o9r*K#C;Uux#E`uX*ZeE}(X z(~<<})O8#UjV8w0W4IF!CwcFao7#$)zdUcO%z{U+HYK#t{@sZitx_>JqU_!+8`6#Uj`*$G_WX50V}fd&bP<3@VpFtpPw~LJ1&pPqWB26E^4OFJ zv_;O7d{XiA=ey_#?}-!_PRr5EFfVR!?v28EFm}tb`*fMNc*b_O5#0VnciKK2X!6K6 zy1FLXYYxwac|?acfT%F)09-1;i&G0;}xho*|glT z(A2ElJa1}PI_g$LR_pw<--f$V@>FmX|s_U*j^TUE& zZqoxX?JM0&Hf8VvnoK;ljCN=`e@)SERk-=sCdbLXqAe8_WoAyY6_FBKtaame1qZJF z?W}=$hDD?7*i-K5I|Wm^P9Ni2?mS&sV%;d@K33B5;fTD_V?1BhSYTW-_&1llPLMD{Xl&17>+khrExdqU&ye{{{S@u9K87I?$ zRW6MPg<%vWlmmO)QUQXCkvX_+Hb0pX>Us4ScVnzNl8ilc@C=4J-xdSl#*p{D34_EI zTLJn{USXepI8e?E_Sp2vsb2SeDiW=i@I4tCqQ#Y;KYIM&5%mTrN1w2@QQ2jOj;rTl zVeNfoVvPpr6xqp*l~x?wClhQuGiUy?d}K~d@Z>)5ruo2i_6h!YrLe!X#QoGv=i9{Q z(7Z`Q4$FL2%Yia6;~uPmDWxzK)j($onO3doS!O}NK|E&{urCAtJ&!=!rdc)k@pW_Q zmWooF7l@)l-phr!;M2-{Zz8g{jUWUUbl0Q-2@iMIsA9P`nMVlgD{|Pp_;oCr;$3(n zymXYt#Q5YuMF9L&5e^nRE)>9S@ld#8n=V3YZdAk#BtN2gKZ@|^0jpF@>L5aM*EJj} zq0hzxmApPKPH|eFW2?!24!i2(;b!C=$lW&Gf=Yqi%96V{EjnNHO0|E_6-qgcRXE{d_N0RT!YlN$1vJ_4G`;&%@<2 zb(z-9$PM9newQEXZ;itPIJSxLGzIP!JikwNjF%FiYUkdZ45x<4?#_Yi;*d6zgLAvp z)V0|F#(aQ;@&V-7Q?UQb0%(gyyVEMt$JYi%RHy^+#ya#hIhJQ2SqhVNb-JyaCrd6X zGepK|keRFI50_cEr5qe1dxR~lp)9nuBs4(ZUt(RzMvcYnqNZMm3i7z^SX@M|+CnoW zRIYM(I?AjcA5;ib!nW;Xr7w_(be7G>z%h}>Nt`Evk}aG4#0q1k2XEML{mAJ4@cj$M zoge|7hYt5}?r2*r@}}(m7(pU^Qa{FZ_&ZnlHvZk47*S7#F6h)P~mbN zd?{VKYOBcw3IWB~fiKETXFB2n`Zw;~pQ4kqV)L=>k$?w% zhy?AL>cHK!^o~15ai{WFX%T&+(y)tTQ*bW+uv8^^d7K=5U}4R=!)Zokqm47u8QN8C z6Ua4K$utqP3f$plM%(3PR>J2zH;s{Yw2F?Om1}|PYEJPyJADfVI@tMEpyEEQqzxeP z^?fm1p;}hT(g2pJr#?J6a@Rna!>Qw{rna8GlG(JUt!6|TR=YJk1*fb4$=-m>u*^tZ z(7aC>gK2Bo1P=SsPaWONrza zn8~Yy+|;D{&hgv;!>RG1zP1R9LS??FOTA&IDXEKv@966*;w}OpZwfb#F5Z$yiSo(h z%yoiU9kCBSrPu1cj8%}biPm;?ThkvCN6d(dib`}*SmW}|iY^(_<@oKGd6(RM0X_>6 z>p15x+xSeY2sUpR;$2-i+2b|QlPyHRe}$AC^RPEKbtQR?n$Pp0-+sbY5C7Mb^oIm>FN7iK6OMef`I$=iqjHN>kUVM258=-HRv-6Zo01 zBsiG@A-6uC1ULk;CxSlsb$Z>bs(Ch}pO3tmRv@piY4x2W4;?H)G+3PTe~A$J0C}fA z3}Q}0#@-0g@cV{qa_5&w+I0L31NX-5@Ppaubyp!6N6R@nk~hOCpq}iDV7%>Jh6Oh) z+egIkx%|ZNr{+pc3%_6i^+KuMhfAKGUEniNAtzTMENR=hbfnIDUnIiN*je^aZ95Gz zj3vAkF>-Qnb|J>4QT7et8W}c7S}n^Z+70~OtF-M4kTlXWBhK2(1M>6mG{yP|de55u zLq43>MU9vtP))UMwyR{EY+Ml4Qjb49yB-eh(t1^%iu;c6u>%VgI{scagRoFIG>FRk#<{TI6wdRVMk+ zvx=J6h4rRVc-v!=7auce^Xc8To~*89T1^)uqIjw6<|NWZt9xf#F&Ur8q^u zSmaNdoEbSGLb_b!N4vYb!?v9~JlItwR545d14gtgKyKRMo>{G^V0QQ29TL6CqItEK zD|c`KQAxhCCSaXkH3!5Lo(3^P@y27j}ydwj?2k} zl9qLQ!hR-h45_K~aHIQ7*yiI;7D-Oc@;JSONXkx%pOl#r$p=ixw9b81RY+taZk#ba z$=z##jFLh<7ZAjERWI+Ic>S!r+#htKqP#(tvy$H`Ha7l4&mw2w&g|t~stkvptqG7v zqOT8xO%TDN%|A{eX%u^T^|y0m&%_DhSO?FmY(&&subJL0dgj@aSGP6N z9J*7Bnw|@!_>yBnz9FK!<8Yqa)B~7h6Q0bM=t5a!wk9=qTlXEPs%*Rc>zN<=;Ibt^ z5k5=+z`Z{E{+)WDSIZZ_&SjFZa2l3R7l`F=xDmOSwbAxTD3&`HXxht5y!Fz-Ch%*% z&Hhq?3&r1iB;dW#$IPEySS{=}Ihp`xI@n#aMaQMDV9y=*_9**8VEH(>l-llo4q2B` z(a^^AUf>w=ktzNRx`t9A6XbYCwMYu}0Kxl^-USSJG6J3^EkQ=_v;=>~RRY zs-RXKS}rQlU52Nw-5uhKttq_n>zP>Or+rA93jq`3fHW=!2eV;S$|i{-LLg5ee|**E z{_IE5$wvYU+c(YfsDDmJ0;qa7*nxWqyK4kt3_i0LNpG%9(dE|A&`{7;A^yqorNVNy z-=96;dsbHFM+ZVhx-UOgaIG57Ucho)7vWBjEN|i`ui1r;ae^1p0;MHvUiTnYa1P_x zjYESoNk>Hnsw<9pF|Li8Umk|{a!OmodMVP-)y$A5Z z&O~naDds;fu*}-Z{O0D*+h@}^7rdj47MRd?9%dSOZmy-sSt@U99DEbs9RjBt^Q^cS zT6DvV(b*^}uingF+`_cYbRaJu<%RRw9cz51Ms; z%)$M)kE9Z4y=oUapIM^u(Dwc;A>-+4Hs{O3&DmpD7^EMKSNaTlURm?!)$3Ke3B$q|}Ix9ubVhPEu5%*NOK58RU-?TtS}v zXq<6Hv}IekaUqvDYq;?y-I;N9!Xc_K zB<$jED0*xpa{ZI6A{ax{x(CA6&WQbv>7l4?4LAcT=y$xhN?W@EG?_1Qio_x!d-UHw z`h#-Je2`KP>bJ!gd@(ad?Z<-Uy1oRd2uPFHAaV+RYX!XN7xYp_g&eH50#GWEk32k- z7$7EjQ=&+a1c8@HBO({+muW<#j>At<-KNGJVANYiVop7o$MnPb_PU23*nE5izmJvE zC-)3|YnT=96ZoZWc}!{nm$Z1=wZe5|UtsL`P*7No=ks~GP->$a@C0^+NIi=8vEVu_ z->51TAlY9+$&o*&RN>WgT&p;x?0kuybIwIT+Cs#I5{eeh7k*nhpV}r6rKhsJ}7n zzDFEudiM_5hF4|_HvqVyoVdF2Q={92nSmjjxKsTmwgj~UGy&yINQZEYyu3U>MJ(#LFA=&rz%Ckf z*V{L8Qocz=X97uiGgG~5c$xr#{rZ}Vu=6O$5lzj*LzJjk-KpQ&ju4qFFAqCk=Z}&K zO#e3yt7oeT_wEej<>7&OwE(I&55yB& zzb8s^8{Ers2Qg5s^lS5-F1qoh&M52kn!U z;Nc}4~R7aJFI@I~1iFdFir$^y137FjwgE0u+*;JQ&aSGpWr|9Vsj<>l|4tTK;i zXQu@~8cCqd%Zq9Uv>XHgHXXg^6&V@nUL{*VohuAQf?(rc8Pl9d6;hzFnp-S zIvm0Au9N_HSH+p>sW`LR8O`)ox);)H;O%Ra1F1$$ac3+#8wbA+v|tf2XO!^(9T$KA zVy2qpZz;`Mu662Hr$y7w-fJUrm!*2aQa27P2zZoS@J`)Xu*++i@3t$eIV(K`SEST4 zJ~xkLB%A!h(W;{;c}dwuYCJ=`TXx zGq>ef`xb=WQNR)|+@^i*2kYYkgF>tSyyqCoiZ^)F7oZk192Av^;xGBIm~IzVkhS!? zJI{ZKOV#n+yt*edH2J$v3ViX)b}OmgfHKzpAEQoxL4v6eE-ZZ~ob(00c8frAp$FI~ z3=%t5&@kg3ek_j=dX;k@BpCSW79rvbws}l7^s(LDf56`|uGPSP-}%KA>Q%D_Rkn99!(+Gq?SFge9bcaJAAtUEFYqKL%=eH{Gq3odpiMWxxCWVu z=AAVkJ_G(9O!%H*r#&q*hr!tRn7J+g!Ii#U`mT2tqguOVm_z5yT1NMalP~|e5`*#e z*%p7z3h-=W{lez-x$lc8MY#5iJ*&IZr2?NQca|(*z15rrSvjh+r50JFe|Kja#b^(g z9}8zO+&JhW*S4RU2NjwLC_tlijs7?nfKbHypy+TnJ@c-qE_|V|ORZILEU0^cgJ`*gN{V zwu834`}>XY-!UHb?EHrfEa*pGzrA!^1Fe~f-{{YKdumXG|W<_?04f{r8^B0)taV~K=H_{6Jg()z?~b^vL*jD9#L zZ1C&?QGaSCbC|Yu=j8fR3>oBN34o+;lC`A>FrZ#*P7$~w40xje2newdqA^v_G*50W zqfhnFR{SIMJrS61Zfk6@mO>o^31ozEDb?4BCzA$FBTkm|c$FnfE9yBne+Uuu<(BYX11NH6!)#7Kab7r62ps-!t(?B8i;P}Y6I zwf6e{dD-6I2|WfX@umF|qrIcopauB+hIM8vH5G)kRP50s6A3z4ug!t%#fUZPH0f`_b zlRJdC=)*mtsum{Ast21wU?F;<2fClqUZR6grEV`$W7^Oo!K6ojna3R&y9DMj_ulcB zx8BeAc5uFmPmi3Q?I9B#&<}UDUGl5D{hQzW+Y4VD;g!vQIc+$?m0ykW+u_B*UpkLF zn5d%aslS={@pZ&IHp9QHFWNqI+*wt!VtN_cMQgbf)uRDfYj8qPw|5iu>fj+1o$- z4HtY{`7dEzPPof+_MIDFVpn&*i}1QT0^gXsuMzEUJNxa0XZ)Sa`2H>0Ny+?ei~r`Z zzrH{yftK{u#ae3z)lkMGE|sq#?qB_6JGO68Hhby6`^itfkR#iFck}K&(C4~IO6cEx z>$m0kdXs;9;p<&{)N6k^F-zwWB-qZncqib&mTy7gU&INFYT>Vsa7XQLC-2|Yf&XR2 z5#P?1=Qnupzh1ll!aKaboBM(ar)L()ig&)bxdh}}eN)(S9f#GRnz`OTiwW4KoU8?2q@Sqx&_ayOK4Ee4DEROQGxCoOzK?oqMZ&@l_ln-8=0~Mjs z$JNtQ6G}*#KoL$)j(KV`BM3twVLRuGZNI;vdcT1=$z`A@ylA`!sR8n5!LK<1i2?Dj zSq077$a9H*S|R7=LRCen{j#)J45`HL=DeWAA^wI00#sCl=!PhVeqRYx^(iYvx{K&T znI(d1_6ruKLqArv{|ZAAIxsF&%1uyKqxuJNU$h}qmqh3QS0Ilo#vyj9f~pZmWQav# zPId7^-b||gSi9Q$ZC7gVomc{jso1)a)4uNFSPl|XOC-xhD7nluCo1 zoqydgUQ~Mn1Wus!pJ3wRWA<_XI?nvtdH4c65p<{j`I_%E-WCK2bM{m9=Y;JfXr z-+=hPd%XX4-v9dr+OvNpMf=}e(@FQ?9_d})ozv`}?SDSM`{(nQtG3PBYB`IIbN~MB zTBp6e*?k^g;W@on!IypAuisa#7&y2&`GL?cUJ1wQ7RQA#_#-_2{KfnIY4%_K)IOFl zvPfm*xIvb2*43p!?GiUC!h3FZX4R{K7rJ3lkE6a*VW>BY9%#}&!TRk$qb)1Lqs*7< zFRc9khrPb#Ud23R?D&-m7Mh$A{{8N6R=XqKX*_OltX7O6PQR(CtP2MEDRAgha zfBXkaAH3Y|#L_cM`|#mqh%(0?qV_?3AK+N|^dLQA-;)7`imKxz5{kHXg?UFVc}qFA z2e69?GRv!wo@_{(l5rS+XiuuXq0YHF6XLg zhhKIsjgrc!+ZwcPuQ%iq;ws`F{`3=C&Afi&ZUw(tQ5B#Kn<)u^*6_ir6LS}Gte_q% zW;c^rXDsub5-WrvEPi_|bH%>aEMs)EIUucqN=ns8XYN*nLMaB6?t^9+s?0Z`p5rTO z_AdX=c}qkcQc#2~z<0zjr@mH^VjClN3ox034e14nbHs&{Xq?J+lwOe2p3Xysj2J4A z^(s_vp&~fNYh*HJufBlgJB|Y49?7WV)j>34iN`I3dXN26ZAJj(h)NKZQU&|OCFZAj z^j?iE^uV3i-oyXZokXXKndfW&RoiZbDKPKmvyA$wMPI^o!Z%d-U%(gteGTTaeyeQ% z_j>%#>*3wmXgkX*EKRNGbot~H0Ie}#mV!Q}h7a?i%x;Lv>LEm=pN7Cn7-{MXhq zKg_g^h2;u6L=Py_=#)mwNWq)3Q5Pq?@}-D2)cO3Ww2s7KC9yGLCysxK9na^HW=^sGEB~kgRRVN!kE6tr6LD+Sf1xrTm2utUe zn95LF`+p;p|GzfcuaM%u?}H5DKcINOEzsM%uJ86oN@&_{G#Qr^n0w<8vo7!9J@3kl z($ZB&lzvPi%k=Qm|B)E9|I@V9|B3ygC%7|q`QLi-+rvH<_!JtL@>L74^Xlr1M^q5;n6W$ip5F2wZPbT#e}3lNFnHawz$)!G zdT*}4cP67k>LxEX-Jz8I*g@9qvs*jDsdD(}I*-Q>xl0-qUuXRusu|SaUY1EXD%E0qEFV-mfdQk~MULHZBnN--k%CXn38N9x<%44s&^@75& z-uH81jUF?5aYxj12ZhI)2^X|9GP|tXw^GXqX^^N<8+z`#XHDNR8kOAMGp_KsUHRT) z#;x(=-XH$~v#G%X6n$<1zdzi-@*LKabaXet)bqPAft;b9%X~bDSa@cgD0j!1Ij< zD}%oCI9w32sg!x?r1eon;eqv`R`C)6RvW6??)mDqsS394vgT?%ki4~h%t8Y{cF}C* z$@ty#hf12FKmrnKF(hdg@j$4}Jl~6aA%bM45W3&I5$zr4FZq150(-+?%;>XlohE4s zY2ov!gigEOli98arj2^!Ntr`c0VuZCk{-wT(|z^H4kbCd_%&5K=0!yHH3yLjSX|vQ z)om>2gKjGMifEZQh*YrG_*iBG18digPIS^0L2AZ8Ol?G_&o@pwu7E7X0C}t^t!2>w}Kky=d)NuPBnk1v=2I zyvfkLFta0FTU4FREF!(i8{LZB5G&bh|CBp7*UzkA_<`d%8YejBFBhCl_UVga>egY4 zy0~6*wjj%$m5aAq7?G-;B=98U{~|vch4#CjGxXMG>LWsn^PL1ob)WCL}9 zO_La?JCSt$4$xTgN6Ds@9U4AbNb+fP8ekJqH!FfNbQ$tm{sdpoqiBwWp2s#Qr;)ak zl@;o@dV}8Z-NHk=Qf*&C>a+>~>1d?e_(w~muxdx-%x>=(4x;Txy2_y(NDXbVN&#Gb z=u1d{Wk^E?sO{7NhSvnJlwvc!Lv8}?XNiMHnK{2>TX=AY{=u8ig?Z9Gp$Y!x`;i&Z zcRxS`dY+_D^7(*oT-0(*RYf+H2{bIWx|;4P=+>MmfzT&-K>$hgso!AZ(R=HArP&UQd4=;h+-O=?B*DQd2g0m&<25t0hwrG=&oAU$ z^XgVM2{BFT9#~Q{4XKwBo^Q(Vp5pL~j=S!<=M#_~iGb0j8;}4Bf>tm%cLrKC(VBjN zG&GpKc_+{%U{gB|*;q3=(a(F?wvU7lq-*&@#>Sr$(r5N5ZzXLpq<`v?+E?4#8$)J7 zd=0Hz*8Ld~j`0@qCr+G@hT=^anLkqqhX_ClAgkAEmDSmUuXbBcTi~HzVj>D|R7eln zV8ZYOgQ?e`U7CcTO5Cnn@unpxL>A{FLUpMr*wi$suM9X9RSwW~j5TQge10;peP z7)^Ij;*nc|d$J(;pG3O^YO-QKzFH{Ram4{wur5zS`*;z;%yNt>fQt#K*_a@jOjaU9=#sN<}LQ@f>>~ z(*)zQ1a!vjQ{y`-NhdpyoZ&L}Qjm(|i8GET(St|56NJ#y-~FDrpzWrK%;CXz<1{Bi z36yu!%v9dY^0&8=CN?EqyT;r8I2Za_60MPhznR)SsEr)~hz;8FicI26h=8%0o{1n4 zGsJp28@02xlE{$w_d4(|)Vb3d*|zFKI+uB5{wITiiMprtrKH2E`@{?2EYhu(2RA@^6>o< zp+h67G}{D&C4Qzbi_LF|5{;{Nx*O&cALd9MRwS6!o$6FakCMaZ_ebuTsFp8zhkjFe zbWiBRrKuh`uow&GL(LTwudG*k_V7VQ>qCNWbQw_tE2**-(Fk~V;Bd?IL~j71yr{oq zX96~yn&3&*8HwkL=uT4~oC?8R;`git<4|eoLz6j{YnzqIz+Td$Pxjd7`A{P{i3X{K zlIFF%gbX}#;Ly;Dm*|QZt?zYPPMU-|tCCIZ4=YHlZoN{?jGZKY7WGkGVVtSS(X`H_ zHiSSjQXEq&nvg>J!qdB+Ie}BozIN@}i@eG-&V%3!Zj)M(O~-p~D6(Q4=p9eZBkWQ*LV8m1xgI-bHOUsY8{TMTn%!$+43} zE!)rSHFc%Xz#^*Oqakz|9TNH(^rWHID+}Hv(nO(h{oi{Va>S}FE*?l$Y zfNb@jU!`Tex7?Bc-Ptvja~83=RuN)C^P$BVYk!v^dF;F%M1QFe1qUadv%Y_R4U0uH z+v~%dY&196xCVqcI;3W_tjYH^$_CBJ;T7IdW|IuzMZ;b$4gUDWoPxq^6T)=twV9DOT7Ia8?QU5M= z9#Aa|8=4POjIkc~vX$!|`cx}MBHAN3CF!zbQ0($YPJ;*aqjpR>^WCx`C&%>yel0N7 zWM!pb{u77zW{jpuBt^+Xcr44ubC&1hsE;hTuAOORNCHkGDM;j-**uK>4jph7w3*p- z-S0Id9}gf{(ev4q?nUZJn!`Y?3&`SIB5zz67=Qs4UNnX;h+-HykuuAJ|A zb6R`3M?ZNuwD0@zMLY2D@TAumMXO7kJsUY9`bD` z$p_B3>5KQJt@KD#w!5XC!bRIx?tR>qIwGlrO013oqF2+OOH>jzd$27N%pl4gkawl_gn zfAdr{spqNY3gN6DdRzr3oD3T^$d@4A(e=EJRE3pRS;t=(iXP*jbBq}Vf-h0J#3{sN zjin^tZ0NlL?;>o`@X+dy-{E|y>xLAW)RK1hHAPQ43i;^eF7dobErXqJ2h{fgJ9&W* z60;ah&!y>0r0Ac^X|rEmo+_TNbcTyKtoKZQ+~@91Bc3o0D8f~yN_04)adI7Lal4)5 zkj@Bv%DgD?-Y7ixoo}9bFw$yX;eA^_G4@1gjrQ(jfNgnZ64^yAuvLR?Ct$TWryaG%8 zzI6D+YV-ipm@U#eQCm&C?ewOMy2bcqJG+%=l=7lx01Jlp+X`74%Yq2R#_ZIgNeH>Q zKo`55eX39eUWm>_vWv_qvBpLBFnWmDq)T)dB=4l5r4~5nShctM`DmkFLMmv0{Q{KP z)qC9GUma(6+)7^npR$y-OQn?mT}D4m)46o%5@mf%k-DIERz5`P`#;ALMpCUz?Za4&f9lBFAV3>R=9bp}IZ* z>2vmFlW^UBM6@xL%v2#Ed<&P*@{^Dhs@qf$8om{b^d+ArP&}0DW;>WdW9?E4mTAgE zJC#dR$-AmZ6996sg5phnaoXlw-=@`x?FL3vaLs-~SM>gH6SqnnK}#o6&SpoUY41Kyv|5c4C(seZ1(F zR`RX%KGEw*M?2CFbWBHU6cQ>1c)R&Zgoh=Oj|O->Ku9GEsTsgXidaNnnl%<#vcApX z=rgB{!z)xx>e*E5gyR8q>=5Uw0Y)f0q^5eJaWOa~&x;n9eM*9dMM`OtorGsHWhas} zn~fqH4Ss`LXhw^TWy9SZGoC1-A3;)N=`BE(`w&tdDu+@Mj90GV;MPBnbpt}zVN4y63N7hToqdu1_->X!Qj zUggVT7SYs7J>i0>CCb>{fpSXNPls3{9RT>Tpy5hsPo%tOLAljhx4qOt+&w@;iB8}L4UV_QYY;_hTF}0rjCjVqmb}+6@fMvC`zj74aFs2PxtZWu&=X3Xxhzk`Nl1? zhkhp#mt`d_&B`+l>4VCNk5XSNq*;6zJyo9V%ex^t`T>S6^&?I3u4$jI5m3Ojh9xPx za>iXPU;_lN`xeTMzP|>~SBzGcaHQlSoNQLJ3Y&E+scnQ(>i&M`KJ{P#Tn(Z-;xOWh>a{)*rNf!-n&4mz(h!F_8nfMWzaZbPQg$T`j09mSx}GF%m>M zV6BfG;PZJ1*e_-Oe%v*DcP$A+#4%!qlCtvQ{>0lwMM!4TVr2BRWoYA#^CI?CBvIQQ zb;pDC6=UI=k+N2HxIR7hVUhchkJ1$WH)6O^c6}a(6sdxweMq_OMTwBN2qao5Pkmiv zu3+9t_OG??aqQs@g(oU$QWVU9IgS)MNe<%j8gv9$Ss__JN!wGyb)6uXwtfA!_Q7x` zB4h$6mg3EkX`%ask@g`(-owKZ1tV5(a}b@$*Iq?WlHtS8$ujd+>bykoNj&u|!FNw@ zbZMU@&?TJ!(|{!JcS=Z9DgA;t->S$9j?v4DrB-T@%;0{IJ2HJ3@*#_Wz>cvm$u^Np zL?DWK?Wq+c1%vtkjxX?$3yX24kaxm)iFOYW(`!rxTe)taT{Y$|#F zoIgjy_-gI0s}s)oSvpTg8r|I^?cSXLTR=mGCw_m_EE3U1X9M^RWR^MmuD_SGN_AAY6~k1#LCQ+Hp;^20aQiVWg+PNR72w z0PQ7>%_5|5IMaXq;EF>Xmcq?6GGzF`G=6oNeZ8$Z4N7%v{|Nts<7;>3s&~0$*KL*? zG@4T2A%6y-JHW*~KWnhC)JNQexwpQQLqno;q^GmT+n~IHa07o#jJjH?qXy-x!aoce znNVhkxUCq-`x3y1MTpH2WPg8d5zbcBfIHe3f2B?lqQQ{A`NNWogcg;w!YX|mkxZwe ziMgFq>}Iv)c^)1b4LU%n1DpTUFg~9egIQKN--8LzD5385(F*tJCB*QSm#nt-dugA~ zs??%YxInD#XJq~cG=U0fv1K*JXRuhb4VCrziujE^Uo}M2c-hS#te(ao08EJ_Ujizv z-0`m3OQ#C0yP+k(I%_g8~fux%*h!)4!Q5 zwWsK^cMJQuQy61i!~H99UAvxspy@w2fNTI7{&2_C(yDdp@f?Y~W*XT@sS2^Rz}$$b z1S%`{e)T%?@?@8Qk@JQ()Xf3s&(wH<#s{yrW_ov zror3|wKOhFJc+SX00m}2WogsZdQ|N}A*=|At>%Z^u#=GwTqZ0G#bTbTEw4gL48VDa z%tsAWK+;!7p42Gl=2Hvq%1ONIyCd(6Ug5%MQM03D;k8E^KxQ;!OlX-z{@(noW0PMy zJmFsS@I*A@iRN5bmmhqqFE3p5U`Q?A>;iM%4x(f;Cd1tB&sFoqFi?KT!a`+ko&wqUIy1;q+ zAW+CI+j!(>9FC;Ha~J9I0E29rrl|_V#Vixj;h+HH`I@F7wNJE2y0N{UUWJr{c#Kq3 zu0St1J6d2xiIT3E{w0`pfclx@Q<8e=o{(G`WCRp(Id6p4`@n}qYgcy7^&ZnH8L4?~ za^`m4LJX@}wmW3w%~PChiFt^Bv8aqm%rs(>F(wJ^c!=;vBS4sK+Nf=j8;TrQ+KEVT z9>p+&#x%6J(??Pjb)Y;0V~{BCyM=0AA#V!ubeeG7{-B&KT-;KR?zsiK-YvKSEm}q1Wk(e~1lRAW{805!9?H?nc-@}l)UBgACgaBY1 zG{RZ}L7ehJu2ggJ2qGhExt{&xe!=2LfA}+G0tJu$m@P2*YaFrMo6nMh5%+#cDw+Tj zTjf)BYaPv<5lKf1xDK+YmZm0OuzsWWE|JwU2|Z3yJrC9}$66sA#sO7Pb26O(4%;`X z5^_aoS0@!Z(~b)RxMMi2N6`dUBC+pkZ8L^y62)A5O#G!w?cvo$R}flgm{qe_-q_yq zkrG3ASZ3s-1R48C9sWe>_E)BQ`p@LJUC9C0K`9~#@V3cZ?-R|33~;?*yM?8ZF=zls zslq0{y!$pHA$3r)Hf=w>cUMWY6n-8REj^T3dZm9F6#<*hwL9xfI*@1vhGGs&XuQSj^ zAc4_nL3rG4jwn?Gp~WKQ)rTLwDZKDenWP_qdoA(q@vn^q4`u<6q=g=9&DQ!D5o(jH zk+!Y|+*jhE^d0G;6n8P#)h&qJY%$Hbb8Weczzc8BrA*nf0eGEa8lL$O1USHA>Ds7* z(lob~G05*|h_l+|+@HDKMvr?2Y$fFJz9i`NFwGRjhyrCAIp^B)3NxFofk9VBp^Z6_ znwdjtA7(Cb@NI;O*=|OQd_nIQBs~ro-8G7y+X8@%|7pKjUHm znh0rhW)~aBxQ19~QNuZs7eZiR)E_wUOghDz%3~)cZY3nCYE0;3HTHll(22LY-HXrJ zMWggu09z|$`Kd95mnN!mA+~j|Sx~@nj&fpD5ffg0RTZa$D^oug1wF^9sGL2JXN&(m7qC-N?qP`ud}=>t>jY151TT2{0!QuI?b{oBw`Lx) zWuH!`vLa0gPm6KHpmN8I3BBQ>R_6Z|!4oSKk5FU~jW|4_T?z$DV zn7zYeFhZ79qF4FN(sAxhKhppk8avtM+DLx!Bj`}fI1PT6=*d5PWW;Q}flLE%S20E! zYkYp8h$_#%9!P?(0edL}Lj)4x7MYG4TM2c^ue~#7KXu)?d2VkR_GDNYtcm8}B;i;q zObIr7vN$O*!)+j6i*AR#qwRkS(AB}*9ypRgwG%V5- zz>EH11x>Hv&nZm_Yv95lz2BH7@(@gXaKp~)FQg(45BgZP`M;SNai7U}Tz?EGF&1+g zX#P-Sj+AKSGELKqoL+e%1$DDTtv6^1X1Zx7AyL%=grLN`{gd$MUZfefJ{tvPnu21S zGqN=cd4xV@rC8F)e_$ufDWRz-4bifm4ZJ;Aa(`5t49-0{f-1X~U5%lmvnY{)R6{Uu zo;3mnl4b=G9arv1$sDDAO%${nE*K$PBIWJ&K%1`zTpOqI&8WY zq;l(3*)A$6DdE0ojumNo=(DKU%+@d_9v4>fz$cuK0s+lu>cGxV;_y6^sE0E>^r0(! z^*VkljBhbWu{#3phxj3yoI!MQuyypsHA+U#5$UEiBF6iYeD$1WE$<{;a3}YfJx3Ob zD$OW4`GYl1t5e&S+2%;!EPOP zxM_G*%vPfjo?{7#pfW#o!Hb;-05~M>dVMN2_xU^SnO+oqPdh)Y!$c|y+cdwNk_RT( z1vNKd9z^Jja#`3N2{0*jR>S}jE1mKz(4`Lm2ueN$P%Cl6hie2~@ct4Vwb!5^s36*4 zS3)AJr{@+mz89&r7=BS7#;#$8p~k}CmQ8TG^0&K+$(O2&e*!(mN&*8wh~SgD#1l## z+OB|sUX2Anlo~ywqwcUxN1^*s2vv3P+3YUET19X0M>&*;7gg%S-^Ex)UBsIE8fcIg zJku^+O2k+_!D%7xq zM%PD9C@ZRE1d|+>%9gFBY3&nkQuaL@G@UUsQP8^n7jym2^Iq#a0%xc+LIqSx_b@R; zmg+bJ*t5Cl&l*_el&ar8qIyDT0Kl^>9g&~fuI;$F5xtqca%j{`$r(QCIiYbq2ey{N zoF-3aIbD6eT4`zzHA}#|<2)5z zOQScNDH)ggiB3!t(73**`u8nXn8;NG%+$<0Qm0~BJ$2VwA%`nslF)v8t<464wUq;V z`Om_*@?i@)tpni}?*IXrvVrm$`m-cNN1FC_A{R+svZ1m#z9iTl}$* zCy6&flc%+BXT3PZ709nOM?0akZ{3zX9hl$SM0Gzf0f}`sG^?gz=%U4_+AS4EPPii+ zW62zyU@cd8Is|C~tCy``DvYd%)Pa!$EvuZjzV=8=czpO})ZPI#D{`>ihzHaI z57b7jCNy1%IzY>D3(V5#$Bb${ADXvZ;&&$UEd4D6gmlWN1?(?Ov<0vgM2;F1dT@ft zqd)0FgJW5{Rt1fHSB;M@B6$eP|A$*kw#?@&640x6N_^g4u>xbHsbkZ5tdwTYKrK?r zzMbaiFpn-UEstJ(l+%{Ry$X zBhO6>=@tfNU42!OOl6PmMzsY&ji{$sRq*Y-eD7C%6IYs0M}LKd2urUcBWoVRn){P$ z>0+cVTA)XwCFB!H{l!{pR3Ujj%|yGf_W0F>$Szi5O>eytcW3qV^#sjdcmT!n^;Y6cZ4d%)T8tO-`q_>Qk(XwB%l7e1x1at-*{YS?%X;uND`+s;rqakRPp+5juxAG}j&+qBB^_YJI0bK@GMM(q` zkGkInrYe`4nIwS>dDuQ=Wrf(uQ0sAO6QEzWN|C{w2S=ePW(JMXvjGtNg|em!ir>C{ zrT|l8ReKy!3m29vryP}-GTiB{L5C>Sx01ayC=pY;LTcT%^iOSHnJTFD4g!K$czslP zF(QL>T3_pt2JW|Y$$3-YuBng%$NX^O2#sWdouq=W02xCFzX;np;5O`?9t?kPzPTPs zzcv4Zt@i-u`fuOH5rxRk$Ra5J@4CSymZonPo&MdslWsGRkOJ$x14_NmfcF zD=Ye+ulxQz|KoX{|Nna&?xVY`&--(|uIs$c^SrKCcD3RP2I+WEQakwL$<(jglke$@ z`L|nr+5$@iXV8;H0+|wf!pKzMKi|Yo8f+u9_!Ypo5B0xd&`>h8@*S~S0sFKCg#G&T zgh-M0kVOAu>`*!+^Ru_GJ)the{u5FlI~jo>-wtjlf=;4Qeb2;sITj0)KfrpcwY7!1 z7KrK@-p@EkptL$uYhc#K#clWJ22bb!n+%dJaoZ z$M}w^*`44^a~{+#-g5l`QXZUwz}EY5itNy9-D7e$=@mE~xilTpNSC==j>wr1?2qxx zI16h|4;szqqgV0HeemBhfi}nq^y7b>+dar$Jqz(fDC3-KD z#0h(3I-mU>5gV>VW#>Q~yJ51J8syN|pWN(azv#n(rT72mC}=e%9Y4bjiuv7o0stl4 zgZkh^AonSm0|^qR^hXZnbeG%nlP&0z2u~^TOz(7-t=E1cRXr7Vl6v}Eli2-M_->&M zBzuR)7&9Zk(O4C9b#-~YCiEWT@IQQR_UVd<^FeJM#*A`p$>5#|=ZZ%sIx?eaBNe2?b>yj$`W?w4YUmV0p{%?2~Up*{8J*^mNU9<3hb-3`Y?nWunOne?_inOPI8CVk6D!G zV>~DQ;SL~D%GfP2yOA>vj}V8=z=)(u5Pt?)!#s?ia$h>8{h`8ZP;gJ}j|G~Jhq3c51>NIoV&%?IXD`+yV=^?+lwhDFh5)W;lmafW3K9oUd_wf>N=jv%;w-6U-9M3 zm*Nk!-%P#y$3sIxC<<=#7I4SJD46(65AToYa6WP31Q$2A(5_w4o{bi1HAV6x7wAn* zO+P;KO%6yp?c&ld|7QI1eFsNJ%7R~mWsZY#whj(W_r8})zK%#pV0UnE@CXp6=iW%) zCj0fnsh2NbYMGd@sBYdVDXDYjj3hfdJJ%gf&E+C1mb3c?FYp=PiZZjXprfIoxp?tn z>krNuM>KG(fnUFV9RcSM_hDgOFXidl%X2+X;^{wA{h)NLZJwd{ClNa^U~WBl=$V$i zot-FaQo!Pu4fvhQj#@7-Els~qzDSjroh^){+&=M+;>b7aMGtO3{}txN!)LqX(cL{OpO6q4)JpSUp(c;Y8cR2+~u-z<>e#au|SYJ42yA zdfKNIsv+k>mQ>O!&wSz$H_FJAJDii9J#jxVCUpQ&W9MM^vX@T+*x8j&Gzo;kvW6wWY)!C}vG&4vtWe^W|sk&z`*r1oXCF zbxd|=G&1WD*vhD_#|>Ubpvf7*N-Av2&D8FrKTeVMU}U#3Ux=#TqO_z24 zmL>D>1O}RZ*7NkW{Yml;H1U*egGbEFLVJ3SkSF7l{`arnazJi5$2;aYnhwtC3!4)C zMT^|Q+y!N237tE4PRa3%@w-sKoJ5PH{{0T)~JRLlg zvNQJ1&JkduWt_%)wZmh3QM7>|NT{`Cq@1Ys5TaVQ4xYTd_ojsMeJ>(cmR3~curX?C zX*vGc#mCF*vG(^;^94J@TpIS&5&?gI|B1=T=kMNGI5C%rq3ne|dGbVCP)1ZV#N@q% zni@ZnYrb3q6&2OQ1vNJGTEmGCOMMs4kca>VWkOyK!z0kAcFOf!o^uAwTwhsP`S9LP z;4xb_H>(}4(JE60Tie~-$5uMxH^;)T#9by_hr2#x)XM(SHnqAU0q#jWK7Y^)&bjX%=Ul6QXyOm)4P^9Wj7}z zIR*f0^tmtAiumD8IeAjhHfR+}L^i(YXdV^Koiz!Y?_FQrK0Q`wfBDk(flBJ$?49Ebw1IpJzMZ)|c*>)!5^IK& zBlqZQS+jQ=uS`+@1c@Yl;g=Qohz%jkd;J!fVeR~~mGeCVZXR=$?=uMQsyb@}J5XMd z){gG_?J6ofSRH-w@1hKTf{D2~?Y@2c=Avfv%+*|G#=6w3J?x(Qr%*KZgU&YQh zj>nH5g9s7;ffj}^DNCBdj$D#;VVv7=>n95@?@qJkY)L-b+RpqVLrE^lH8sjO(d6z= zAq9oR+hav-D0>s-Tjk{BI#UCzK+QbIW}IozAbQlIDV;g^d?3PqZ-Z|G_Szyf7HgEh zd1Jh|wA9eom-H)lX=$l!6HlJ=SxZaHVR(a!>%KxSeiX1VeWU8&qs3-=N zAr-d)X`@~Sax?5f@%zQar}^sA_WJ%|fZMG@tQ-{UG#k(DkKo|^7P311i4SV)X^+cm2zhK_TW0xjXju$K zvUgng^TQ%y;gpflrpCrb^E>^kXz|xkJj~y}e`ns=h;6*IjEsq` zv=1T*3ni{cMt=LsoYzkmB%{06V@UmDU7!FD4^IXw8>Glc1mH{;Ywz2)4FEpM_tVqT z=#gxiT3QaSUZ~WVhG|oCfd;pC2+1uP@ks&I!Fb_GpAS_xZPT{2^z?&_+pddf@2SR{ zy$BC&ng7c9Wk1t*m0eh_{{n9;LNZ+Z(CzvcFKFNovVSEyRus648uz{B=H{M+&qOdp z5Fvc|@?|Vd4#R#Z9MK7%Nw3d&saNh-0jvSMao5z4x>52u4vc25D^ zHHk)h{q^hD)oC@*4VI$4JMHQ^f**VMk=@T9KT0o7+eOgbfIirrI=~(f5P%0+)H{`1 za-h;1HZ%!d>=sY@iE*)np4r-S-wyxy^n`Q&{{5`C_gIxIJ=UacK&#_{*{{B+gU62_ zN1G7r-BuSp=sVKj5fMNlOz} zR^|>13p*!PdgTh`F6Rd7ixTY zzR~WcR)`*BWo32Xc?r*68y(U|czAfwg9lQL&!2m!#=+ip2-Ax<}3Ej88PcHN%AUqfsRV35v0$K%sA_wnGF~K-YQDH9a|>^O=pT~Pd*8wq~-C>yL(qeNQhE>o;q)5s<3m*9e1w{ zLxZQfdwUbPnTsQ0LfC7Ky_6JQu|U^~Mk^=Rei(yNU94B;uPk0VcH{^dvU&5&_d?At zLbeRl`tQM`iAhQdD=66ZW3WyD1ntHVMJw^~$;)rE9zv^)J@AJho2$~KT`RGhplN89}RT{l1xB7tdrYh-dPCXj0;Ii$MCy;9UHSn z`x!M|o@URkNs9mE7A!wnI1at>`_mRZQ}ITen#F_zd?fANFXghRvGBz|hdq zsfD6I;ll~_BrS2*#J|bMHS3o47g)QYWnZ{QrmS!!H+5`IJZXiI*}-g;DC-iQfV>43i%jV^yd z^^|oqcWG2uWYDeq_2TB14?eFs+f%knNiiY)WZdAi@weN(yXvw)0#$E0Jv>2IZ`{}< zu4+&_wJWG@y@-v-$!Tw?pG&XK{X)YF{b!G5H|`anf(~^zIw21Np(re>Y=8dzk-f>O zP}a8pD}OOof zFCB#Sg|zo@QQ&r&2*8Ysmo7CR*+R}OUNJT^+Ye-pUY(p~o#!6(CCmLjGSO@B*KI9f zf#>V##c3)D35iEnpBr@9u)FnNM>`14CiDJ%L#g=9N6vYB2jgxZquYBeif*y+Zg3_lHP4Igkdi zXfHt_kShuoLDFvRb1xXGV6JeiZBy0I&=`1fL2C7fr>AF@_!AdR$GYUCq+xh_6RNK{ zrZUg`tl$P&psbwwR!)yc^W4WJ;U?Z`%qZisK{|8*)4#uHq13MfaQFH-qM&^^nOXI6 zNppiwP2iHg3vI0Vy25p@*Vfj~$-Y8o^<{E$N5A^H{rf|;15q~C(AS-``UeZwB1Ge9 z{LmpF<1tb3#ul!)I6i1D=Fc`{VkT;P>_lFEzL-8S zZ&KeagY1arz2jYwdiXaBL-Y;TpkpTc@J$;D_nLfpB4F{`XU{RL$pehU@2#BJd$=+5gHJ;--tBFl2pG|z0FyFKV2D z0wuFvFZk-!3Ab@D6)Neej6(0z6Erk1yEr}f&6HfBd=WPeO)4tH`tbQ)yjVXtC;+BL z)6H!sX&!IKKYhAabnDhEoP&!qqjcDpFFNP2eH*8}P}*sHkLRF%zx_x-+Ldu?q4hkl zAqd5)JC36~Q~yxtgjeK2{T@zm7SeKZIga{9chq)yQ`GOS7Ir9ae(@sonBb;@Bv)q) zq@mV-^XJ*JrJr71PA`^R{85$B~;j%4~ z99rk|$@kJFy;tK&;+o``4To_hP}M`q>y%hBWBZ@;RsWhcMP&iD?iSST*pw97SY2vt z)x;5y`4ud&npgVhQ6l&6*_j#5)2Fwg+xjv(O3lQ?)J%6ZjV*ncCbH}EJ0F>HyO*;6 z*&aIZ*vIug!>2UknSGj{ZLC%V(Z^|^`Mj;>PZ0L9IV$ufLOy=F#{BbSq0K;{*$xyS zI}9#GSvXLDq@}qRzvT9M^M4N+tbII?AGMPg?x^~1?(FQ`CMj9sv&(dqSO1u$)xxdq z(!1mfPMKZ|B&K|%PfJr{;CVag@z5Pswio5Y^?@89!4keSPk zxpPMVrp}j-Z)Ro+@~WJpg;2`9S5>vlb3_L)KdZ2i9&&dPN6_Z%FOt-1&HkF=v45rp zZ%+0<=zcwfa?=1rE3#Njb!%LMvsb6X=rXHqZUR#Fc|VQ{5Qz|vNG=JV(#FpRVpE;H$izfW48(cLWP)DeV)sd{uD=$^fM_nH@7 zTe7pWqdIixkjfehKYu6B$k56M%7Eq=q)1RKMP{oO0{xu*7gHC#%Ye+!{rN{CLZ$5^ z!y#R{DFq-3Z4fb-nYd0q&}zov?3A79u5>@1U0A69uL@;eR6^pq&ldb==jwXU&E35X z3}-7uEx_sgy@B*^`}@B>dxd5`A|is0j+R!C5GMQFxVCKZwiJuTW!Q&}jRn1*VQMo5 z=`aqBGAj>{{*RwOBW~Vgy6f-Zh+$&U-Mf>UUc6voWMrIyA`}n0@1>7`n3z~d%y(}9 zXB}E02kZgQsJa;$DS~T8gg~k}o{$n03>qC9iv_gk!Uo;08t+r2dV zRe$?k_Zn~tn!i3jd+C~v_bwK5^YNvn(#s-|KYqBevaxB=3guQ;r?79{Oz$GKW&8H+ zqCg|m>(|FUd?;_^9{0yq^Tdf;(>b8je9{pC-VtIEV4-=jP@{AKygxO4d@uqeE%i%jt7PWsQ!G zuBo{>O#8qAHgFX+zj8`iCeaMYzRrn_Wodi;T73Qb^>ecB?(PllPQyS14$ug2$&WubLY-h2!*bQ7<`FRo_oE$z4ag~I#%vN#S6FtKIEm9&EHf_ zTO=``Pk1@32;MG)w4J@ZRv?+pSkA!Mh*UT_E8NyD3=d<|>(__Ir&%>$ojRniPe1+p z_em@GDA(qEbZLbMrny;$&mEbS5E04gE61dV?uj)u%@YgkKn<+^9?q>iH}!+h-ibbY zCno0m*AzS;4F0gc?duZ*npM45SUCLYs3P9G13kUH5l^1%@$~i<0YTw_(9cEsLXW8Z zT@hlBaER{bbL@mTb#$KsIiA`!-fd zMI{c>YH4Nk)LG7U$mC;dw-i`drCjsZPIm^KvHPts;pyWOiUWO10V_1mcd-Vq(7TRdk6j;Trl+rzm&<~nqTaA! z171$sPMT3)KGdjgZgI3jWPo|1e5aVC_0w=*H)?9?`u=`S*qt?SLEZiNlu?Oo1MLe- zsJ|MzilXtAJRj!EFHBj7goFt0-pz?OdJ*}LkdLYB6a4T5)q%(+PO%)n=MTV7_F=qK z8b)b+RI<;aKew)KAHviDXKU`8T}#M`^PlIQs0y3N_FtSl^> z!tfJC{qkC#L8y5KO7`EDY zl$+0)`y+d~XyM`?jcqx5Eb@;GqjMNWM{xMjZdaau!&F%sC@Cisa!#sz)y!>M{No9h zag&zTbIbg<<r0G78e&! zVgZq{iOCi4nP)yfjX>L@zpL%;s5=@;(WiUl<>gDEt~LAX7S(%QxNypy(~SGiRNX&) zt9Wd%NN}<1M@gtj|DdN8{!Z2gwGvmO3L@^ibgsF*i&uYSz+z+SRm)x1BbPg_Z2SD< z$EQg*R)8Tad_FG8dXF1`$PZIIU{x2O>W({Y54`E?!{RS$i5)wLx`W_+-qm&Zj63Ur z_X<0uZtuNBi08z9MbMPtQBfh$(Tpf?jyxN1Hi4);61Nma;EhAYaNgdAPNb{1VMf!! zf*bFAFfuPhTS`Mi!#;X1AuqJ&TL;P=H^TZA5);Gx@yP`y2#X#b9w$HM3prd{YPq>RbR!+ z3%?jOX%QNwr(W~x>^|SVR}jzzFzHCLw>|nv`feVuiPe|qCH(ne zi+*)_lc4nsc=#|hhz+f+1xXg`*runaw_!6R-XQTdHZ@uqzAvtd2^kqt@ZJdS+b4h* zs-vMnNhFCgXF98yAmlwf{gx_O%BC5B4c*u^q8+6%)5_TAWTfUX79!1yBwq=~xk0I6 zK+-l6v!)2_mdjkW*3;K-geQ1nl~%^r)*hVu?}Y_&JxFF|X8pZ$URd`qQ0czqYiD`P z;(6F@R zIdbI4Nhc?S}N>itQDGavu4o zrKQQPbFL5USIKM8)uS@uiE^`i;%DH#&BZ7TpK)m1Atf?$BNTuGb!>$8!dR5F(P!Dz zb%%9*^Y^Hz2+PT_p&Pgc*(xMK_tl`YtLw(hWgcBUy(_h~YP3StmuAHb3=Fs!i$ZxU zrbAr>6qL5g%2!O%C#%sOGnMfCT)M*~$1|8H{LDx4#YAGo^SzGXpnp=Drbg5H>w zpC5+k&Da-syX*vFAr$ZkvRW{xP{Cch7~x8A@V}`VxR%iuCje91vy>jt5q20kI#*wd za0F2w3IsHw2vq?B`Ow}kr)_PofSuH*wugM)=+b30-y696O&mDJ9+gQN%H@^XK7_|7CF!Jpw>W;hV(m=kGWP<^kwfsi;Bp=j1hh2R&%a2mhx=_xPg36A z7ub35`3YLhj-*g9PdYbuUt{1Fp1nvXvvot^GY6N?&kfiP?fdsv4#PMdOBs;Vq6!NO zS;Gnt&R($-g^_e#kgZ^r*<^9^T$L zcZytGT##<<84G5|06VcDRuXQ;b(*yNYJTC!V$zo-Jy(AY`z?(ngOh5;z?cUrJMe3L zS65tbNNn6?p^(h%Y$^^84y?e4{y4JF?{vY%k1hJ%>(8-ig=;X`)UGYNg0Q;t>)eme z&$h_O$go~kRbY{oYvUA3eTPj;>JAQ~?oC9=#-tKGCYQI2D572WS!gdOFl!bdFh?ai-ykb;zG*Sj5s>guiqj}tS8;w4NpacT)Q@c>8K`f zP&|L5 z8@WoHR-#_W9MrS6kk%|0s&Ofb$bn0rh3n$NuUj3yp;F*MT01%%d~8eTj;RhjP8kvv z7x$)biXiRF@;}($f8)0(y*)cR>JZT|M`qMj+UtB5Hlg%BhYo$3uPUEnkf938Z|m#p zDUdH@qva>B#{3vv)=sOu53L_@afHwCI#wWj!RCr!AQRXZ^qKU>qG8>Tm6dfkYTdg( zIQHf}#uqr36K&EIM0m{UlTUnQX=0*rNW0)B;8wn6DOD{7wvls7(l zw1mUq?zzP}6xCdW&AM6On+m5&dYN)osyt^ z$*5pkzwq%N#=$5GA6$TNqY~2@mYmGl)!iKni3E#)Sd!vtKO%bbb2XBw)m>4slh{_ z32OmXhuy?;c&R|~3`!ZgrEIj0b7!~gIbYnfQiS#&s5t!Q%>zi?v_fxsdK~Uo=hn^w z5i3C*4RYB>Z?W*~;@@k93Cs)EoEdn8`4?omd4Z5=WhTCCOnjxs$PN-%RDXXipj8B_ zL7UHSO_Su*K@_>JhEti3&*6oSZL{*C+lo zv$VVp5}4feiP46v4%U-u(0~oHM=H?FTK>JW5eoem(N89K!s|+*K^fHL4peWE-o$)etE3;(X@*z1OR;(Y7j2U^sDWp@4P6o_Z7>MQK;^G*DK2l5jqZ7oMg2r-YW`-FjF2!8*$eD1> zzcugSVZ5YZLj_JsWs!jbf#mdKOC?|yE}#DQ8{JNO6iY;Rx#K|$9d<3)VL!uzfNQ#< zjp#y}#`9l_@?o5u}KpQF`W+3jE&u(26sH#jQxg2}G)W$^&Yu z`S2A{Mj^R)E0(Ff2fdFuP43;XZJRFj6e_X9dG*E41PEF-nxZvwU( zO@`b1hkC!{P60YJ!ES##x)J0}-w=n{48xJvZcYvkJzcxqPKP`#y%*GhktlOH3fJM$ z(GWOB$Q4Jj!ROh*{9en+5m{SZuqn2xVFivd#}2rWWvqF5jsaqq8g$bI?~Dl&(sm)Sjpg27PXQNAJs@vZQBfo+?McjhVuf1L$AAsSpKQy^${Nt& zZdFkjrW{H0=ncMqa6{Z#K_ebubHencl~SvWXopxJiza{P~`f6Jn)*54PBH3bo#+&Kr^x7G4Q z;cX->+5O!wIh^Bdho{s%LmXsh_dk8Cxp$x=@3AJt-z0C<4;2H*;$U}5%gVAI1)**q zXXoU!M@F7m(iqxzrCA}2P-n|#A9sqeFObaZrKS=urBJs7LWK)llS z_4TEIwndw}?QMS%^6;UdpFg7k{HZ7qyv*$EP09nWIV86DqvW!_yvesm%=_xi9q>Bp z9y)YaoQgDt>(;I7qj(q`yl!T8_RGkKR^5YbueXTdNdNx3_pbq#%7$daMq;`dK5k}s1FuAr>M}y;oOe6!n1x{a3kej_;?WJ1l z?#Op~io!Or>B>U`UeJP%pBfRbr|+L85Kz;glUg60&}81%45w}Oqsn(TpdrZ+Lt{;j zlbc%<_|VVpPka|7F>Fb#N&Dl+4|BPvm?Hm;2`9~C$2h^3KL=#+0K3#T*pRSlCJ-m? z2Qd2_7J!S49apbjbxEw%JARb2$tV6rmj2(ohGR^9gM&8TXRrKJ-Z{#>{fq;wr4cBP zjs5+}3i1DNQ0FoafSVZi9z(AIUF+;F_ojTiTn*K`4rYlA+_Kd8WN(lmzreRsF_BU7=kuu$Tq znOj(VfmRhB8EJ2j#O!s4RwMlA4jX?{3x19+LKmQchB>ngDHlM{7PLpPSpv)-_?c<| zOrgN1tqL_^aPx6NET9si#`!_tU5}l6T#ij}E>Xg4WXV6$8Y@D}#B^$Y@O0t8`}f0G z<&nL^i~w&eJ}wp8oS2kk2Y{!mt2?xJGaRWe02@fl`c~Xdf0R@l^mqWgO1Ie5Q*P|B z37Pn6{rDUmmXD1&2E9s#qhb>t6%i+I@G3WcyQm-vjjsXZ7BM*!3lo4<;)e^~^E9?R z>BgneCKR@>&1#YTq}S5rU-L+GqE)9rF*Qn5!+9gt2x7g^q=n=Y0@G`k#aj9MJfswO z%{tN)4*q)g7VNwHE|l~ z(bLl-Fv^ui=w0Wtm@BCdU5jhizd+ff5PeynI^=o-{kPNdzZVBzj`2O@V6xmHA(0Iw z#`uVXq(NapK@6?-yMcj|of*o-X>M>*l$Mtl&Kt2D7T$J<7?ePR=}s)*L8q2A*7x>u zVAQ7p;B>3(SqH~Nziq!24kVl(^z^hldmS7yHFChya*wE}DCW(Zb(qGnt>$`ty?V_S zH_!clQ-w=H^;h{XS+)4Y%CS)m(#h9SnhE_@5MLi+$gdc(9l7+QxF$1eY2eDAUFcjz zMHL3(Ttlesk29Z-`(Ivwf;Bg2?U)%3#o&@K*3|436^K{&#LA-cu8Q6LawJrh~P*yw71iPP>KJb79zTtE=!Uv%P<0+1nf%u zFwb)FoeLCLx$V~xk^bdS1@!^89JPbeJbUVQMxs)Ph=>HQedy~untxlu(Pd<4CJ+1Q)arf`1Ap`(Lua1$mwe`Z_S7%H7PYU0?jw+T|<~Npz6Ybcesj$ z>9>B)c>O~lmluyJY78>nKRt$i-&1kwPz{Y+Xd2tP-q&B?_#xaUKR-XAQv7RE3J#f; zU=yaGj7(Y5=ZD3U)~)`+)Iyf`{kAHEw5~-Yx8t`up1)zk@4-FQPGV2?G8b}^b@9W< ziVscxBK=No?M`^Wo5c(vX=~x0T<+vP?N;-csl_DO>w$~Eg$P{Q@}j^0qGfnM3G+I) zT1cGhF|*cyZrT%q6e+Uc<4(yxJG%`Ak>8-8>IVi==0BNoRxe-ui>-0LAzoBfRrRb` zC)rlUe3h^XX=Y%%$b9U#1S7wk-ndq(YzK1NaK${AkA#7ZoQ zqnCKidsCP-b-{5GZ|iE-+8H-Yz3X78hG}^UT|g{|uZlA{MMVekUbP~W!ojYQ(*Y-+ z(BCWB(a&-H+O-Wb zii%>eCNx2Tu72`_jfv@|B<3+QD~C zD>Moe-PF^Q;JHfFQGgyzS|LhGN(V5~q6!LT0sAxs5%i-tnN9WBC4}YRz`Q|NhGSK- zlKE?OS$NN$boAw{Q1K#sC1Ah?E^YO(K`tAG4SsGAghvw$l|Jc6IdP~$H;77&vK|8l zCk}+>mSr+6kehZJ9K1SBwe*@QihTa}^m_$%m1!wiX~6H#kVa&tqlE!lYdJkqHU zKJLBF_h%j$7?2KtsJ-RMB}PpYLvHDl6hym2qvrf$oLQ5$th`(ptp3~}{-+7&^ZVnT zCzo-jw*x*#!OD^kWP@v1r%{}hwsUvEg-1&GkzPx`kb1LAN{p}^Ko2Zt_~XYaZ{NHT zMDy(c7kBXo48^cO`WJ5S+<$f4-{AJ0wX}KqKw{0- z9$?1-zC>*;tze>igN{o5IttPx7g;G6GcH6*#f*v3RMxU6o+{Q_-XOVQ6fBK67Ih2W zWt=2>4UMY)g8ps@XR@zTtXMUtbr16Wg~oU>g!mi_PIct7?xc z=Esl6V}2`2Y0n-aK9ez@KKGBGKD9yf=oS14*9SKwT`^QIFW>Wl+sKaD5N1taN`1Jp z%qP3qxzzlWls$$h?iCaW0s)B1%O^mZ%J5u8rxpuRJQkoSo_s*Oe#v`>hl}gYKNz_6 z(FEv1@H4L}D=ig7H!_OSN8AW&@?w627O#HikcJg^BT``byD+|991k8O^4H+NR?|8Y zkZyPwU6{;!ETRO4xX=9_Wi&LscU5QaG|65vv9QpCfw&FwwXUDvUS%`@coj~ckn!Z^ zGIMKVeB3^Z7uaVcCm%qNqaqaZ_2N9TZrX>~8M_rt(YMzt;W2`4aF*eVgwQ03k@0RJA)!%tvZ5wUUyGtXWWz$Hh6X2f!B6WX zPS&r%x<`K7XD&A$QztF+6N;k(>_z-$V!A&_s5V1Z>w zAG-i|_qoIC&+2EA|7fyMv~fBeP3W{ z%eL*?wh3avl^CqzO_;7CX5R0QeYiKykd;hL7$QI$5pvPjHy$aff2C}w5zx66Qu|@x_^x`U)V^ht?70bJSqu`CgRfn`@nYQ}26SV{lp!Boj0w`c1M(aN@J?-#_!MY8piLkiBE$=6-r7&+x7J|JqOsrdXqpKZtuD zKDYVD0&3yOo-?6dbi~U8V8eByk@AaUG_LR#a4=XKH_J zXqs4r5n1lJ`T1|Goj6k!?HTa8Y&l-(b_JhEAt))i34LiJbllH^Km7+Ut}ZM5U0roH z9Yp1a(qGki(_mncM?g-8$5vxo6lSy<16HSs{%w2i{j)SP_jaLni?w=e!WWH3#K^-5Wq{LJF_; zTOun%sUTb)eie<+@0mzYy@9-{^4688Uu=dQOYIJ^WG zBPX&g6-BE5Ss*TAEd=z{M7xPvgYjg|nn!xHnA!~i{#sqYaQ|T`H6Fh)+E8N*O~689 zi)ly?I53c|a;6=$)Seu=rD8UvGp(7T>@9|1Av71H5H22`#`o{jdRw2=I_!CI2_sSn z8s&oteo@5L+S$1YSR#f?%J}u;<|JS0khYc4uV1^ToZGdNqVfCd{&k17(NzC=Ljxu8 z1fnN%2nf`A%RE(lGp#w&e|NVzD8SngFyzn6jW(j~Z^e;rL(N52z~9Ee3RUw<3n_tc zi*o1CoCw{xaYMEWbf6$MtcVdu%lP;>!HcYURX$r`ctYd$E4VdjVR~8!ra57Nb+JvG zHZ?UgTp`uUXA81Z95&cq63}dy+j13cH8B8TBrqICK8uH^S!ym!-L_f~+VT9Czc5T= zIMZxk6$ui-WV2N4E>T7a(?y&w$a5j&(BlQml?br2YonGBzmEmPytlo*?yTHz;N5Wm zYuJRDE#ilP1{DuYzt5eZDik6;b#)4%kQj6mvB)~RwY@E%4}N(VrKvl<($axN5abgs z{T`wu9~dy-RaQ~q`opO$lzattGh~;g3#EWgVddh|#k-Ow3t)HV%xBTt z3s(0c|LST9X&(-jQW%qmcRdY?L*Hp!UQdOU7X@?3>m3 zd|4xsC-F{;;|7dfWJ}hwgT0<(IPjw|)C=NOii0K+g;o&#Dc9*DY%(94&rpeUjrtD^ zYoPu3PfdCCD!TgdV^*Ypuek}6nn%|PTXQ88j$_-5gM`8slI=uw2D2z8R$CM{O#So?dN0&K5TUtS3KT68j@+$Zh@QTFL4uN|Us2C(kZ`s$EaQfj* zi2{&9{-s!$8f^KyG;2|Dx90yH5G=E@e|%x%ep6F69H!C4DZJ~dujTRDsC3l6urxw-su#{wpm z-oT#|L)zEi&1b5 zU;94dX(==spkb#5lzZBo3Z?`)7igsbChM%PG7`DX~%j2D5n5 z;=f~qT&VG|md8NoLI)C+PWCzm;!pRShwk`x)ew3yq4i*7aO+W2n6)(qAviR25>-3A z|2!ezS~j9j^txFluYkUV*ks>N)QZiNyk9nlX*@6laQom1YnzhV`8N19n3b!cJ69PhJI;M(31MI!yO{2O;O;sj6D}N5nUB1aKh0bjtSvH^Z=j=X&7EWfOCM)fTziD-J_!_A0E?0}jmI%>3uRG6?P#5LIkm{U9X z9q45JE*W`DzSf8_$&cwJgxO&h;apIEPY*kY=I|Rg&d8?aANRZhe@6_e2DSCn&h_t-MLeC16eeBPMllBIJsTx^4o+_O~}0v z@RTMSHt{MkLog=W+#%8*WQyCKR>6{+Q&@N%Np3myFdCp!b9HAm!ic5$AI?V>+SN&+ zKnC2}#0k_A9vvMDvnI$u(8SFt@4gKWQ=z{m&MBz%*k8q5v`E(HP zatgh?%8%R$dYrN1R6RR0c92Y~B4@DDZ7!F$o!kQ24scfk--5ye1Mhg2kJ=3S$nYTo zv{u=x_Y--u|JhLy zQT%S9tSdqyo|7pbY~p5Aq%)&Q3Jgd7MJ=C&W<;cEW<(eBTm|9t z|DA4>k67cearu)oh-aay`b;M=ltp6SOx7FrF?qG~J9q7}b#ye_(Lf~e=z+rT^jat# zunCb_!)u%PG|!!TXmBZK*$K=D=^OjnZ-0(6H8Ibs{aS z6V@N%P6Lro@VpAeCsYZ@1{>k1jCXxfu;7f^MpiRmyjefmOhF!H`qA>6F4m1;gS{!1 zMXpNtcfuh8nEj0;)hqDxphMVF=JS^@n-oLP@W@UBxB@63<-sSQe&`SrqRg%5&rbv; zdUs5J?}wUBW|k2C?a$oCw6dQ*|l`BjcsW z5DT&%+o@gRy72d8jtUwPJ>UnihI%66Q{cg(-YEDZmS$?|lvxY3l%t-J# zfWsVWwStTr+{#j*dsG6(UJ~B1+8r*1Kv2$jUZVS9zsCOhk z@o~jhQTF_As&qkT7Xv-LCU%jGRo)B}M!R|D%o*cj$2JI2!$_oKZa(0vAk9JF&+U9E z4nwP;ii=a? zU(SnGj%ijwf%y*mlCnpWi(LbhP8OJgKX_Uj z?mDAiD%Zf;dz?S717t`s1pXZ}PwEB+QC17x%vcJ_1y@86A|IzDgaBIwg-5Z+kN(q@ z3oATpXdohzBbMXS!hxiV4J9-fCvL>#%C|9eWia2nHYF*z!x*wYFfb4#79Bc6!{(sF z0B`8JUJ!#MUK)iuTKNAc^DqiMj2H*x&dSR(JNub-9+MRA#1ncfG&etAF~aRp7`Zi; zEUrLPJ&{=s58cQzC|X@Z!xwm=xZTeei8M=(%i=^`z%4~>8Val#NJviJ?UKIeweai2 zX=i8HuUvpQ1TGtVsp z+IXA5tvkhcH!y%;LWaYoZ~B;T8bVS@!R49uyC;K51#5l%n%q)Cw8MgX_nsZg&l$!r zRU@WzF_#2$1bjs;n5o?I#DXylQ?}5|v1ZYaLLJ6@FodLZ-wEX|GBsnv;4*|cAR4FKuGm@yqupCV?eubTb&oFG*CiVX8)^0Rlrko zV|{D^OA$#I=X`w1AX$^Q94ldBC)IEfmnpBZ7c~@Q8sl9iZ+fm8SOKnI0Zd!9+sMRp zEh0isq^i)iKsn|FkT@n&4Xx44Eny zH4Tr*nI-zuqGa515_#a1=FCIdGuUUqDNzKW(m#hh<>?QcN_b8L=(e3Z$t8HxFznY< zxxx-WhJK0oUlUkmtd^^G&NSCz-Li-S_QgS(YXDp}yg;ih{B}dP5NVCEM3oyPwGJ$bzlc<;&RY6|`b`L(- zYOtiX8HgJu$aWWL@E?XNWIKoO?7h@~>-$J!B`YUKY&sDu5pCykYg1(%>4`(~U}S7| zS)N9kGr4W>Bw#HkJJGraFnfD^;*i93jT4RT~GEn;GPJPdpfjz49A8o9@!0YikNsMY`DegSZ% z3i?-dVL`?$DUi2i--YZWAh`;NM@o9SEK{AF1(i5{gd2z&$M1Y`SN~E<=&5$*CKs}es|Tr(Pvu;}iz)9W#^f|<+pDNKb7(*dvAzzJ z0FSJLZYzTj^0EnL?NycLl5GM^0F1Fei7NY!?>5}O5Qi$*1Ho{`Khn{{`mRclaf_f- zed=ERo0uf{VdxHk+Jot{Z^@~90L$7tI(jfO^Hi}t#ZQ$_&M69A@oB%4zNOg|d{?i2 zW7GiG;%JeCuYORucZU|l1Dh*0^h)A#lfJL(f-q_M0If<MbSL|_KsIw3A@xYtF<{Df8b#*G_IQH}g-UEKjc3h&>4^x+YviuF-PtFARDIa4@8 zb;K141@Kz5j!YLI;ghSup#n&CzooK-1g87(jmB>%utD%pe4Ah1tbdn(?VHmpnvV~i z{gLG)q@)&B>QDalg^_O8TA-D)sgCrg3?^;Zu6l4X$b)eotUUpD)c5YYuN^DGDX96A zU;4buJQ}ELU~mMzrIloIzg0NIqm|Y7{~TbYli0Nj+at1})BTu7I^urGZa_8Z#&5Y_ z+VGDbc3UQ-r4<5fO=35(sh(cMTj7^^E$wJ??*YFs^5Axdvp17>@7_%|iCFK+lv1Pr zE{Yju>*tGDd%wB2x0lT60) z)z8#z(i?7r3j2;V6$?vlOjdP~$7i59pT_4M`}x(6ToioXTTNE>Fu2ivf%p=ynmMEc&`k>ZUj!|+dX~xiLQE968y0!0r@Xk+_Jo7yF zbKl>?_5FUY>v{W$@AdGja!gK?)r?2j|CRUJYKIzTFPm>7UbSR=rOQlFxt>3;O??|>UYtq3XJ%kK66GMw){x9_aKku^ByuvRYSXO zZ>?mS{A4tsxuOHF^K^Q8G5U^iXPtAMqq@g~z%eLBb1XXK2FJu=P%*zBWpz9O*evdg z)vkUpq*T&!rmZr_Snajsx>-fj!Iz-HY{5Db#!*Ozxz@ia6PSGlOGgJXw&9*`WFp?X zckeFn?@a-rsy|3|3XogBiLjqQb_A&0vb7|x9#p_tXpZTICK-Oh3h9%;iHH!tX1Sa# zBE=XHHlQWUa(1}mr#UG)3$DMao;HQmSZa-+=Oa#zrIl53!&7f$Fb?jnE^^S%ZgO*x zZA7{`gzn`{D!W?I{#8vp)@^0WHXVuV3Q$t+wR?cM8Ei2G--c}(OF91j-Ig6Soq$RZ z)m|JL3M`K9fXhmO#QUS6$T&kyx!&_pyD1QCOhDe2i&{h=>VJW|cXRgdzpU^nTwrVa zpv3w4F&a86kQH3sT#FKM0l?Du4Vnpqg95OgI3fTW*N&CTg;lisc-WoGdTa{7>;;bC z?X39ikXiIc)SATiCBj;OCJgoUx3gxyDz0m2D5$LT!-&B$y5_F~s<-!QvlKFY) z;TA(UuK3;Z$tMKvx3;;tIY2X-x`#IF9Jq*$T(0Hv2@7N(E zuNvIGqYwjoq(s}_+5;ZY)0*P6oK^ZXQ2v^54R&r%SA+JK7W3l$eMQ;-5bva z29OG@o&cjZ$JaLx0dn#A=!$3$y2nE)@)(SUx>}_)iVR#^$pI+KZHDbbHRF2o>|9U5w zKsHx1Pkc^v43O+cE0QPLhB@#LV5umEAvIsQ(jT2Et0GnSSr8h){UDHXtk(qSvuDv^ z*)1L6ibr^=;jt<0a6j=kS9pQ70e%NfHuDrO&y4^8GsrkJ0Cwv(VE;B~nVqdIW3n9` zW6_&k1PImA-u~>iHbXEY>_Qy%=l%P3bj^z3!mt?z6{%Qg_Gqc@8U(`J;NT+oAYy|J z=ge7TVR0vTL&q%J@88WiTunXq12H_PUPj)#q2TtnBB&Q*A2w0%LJGIk1SInxra!k* zvDH`+-V&{?JU9si&HT5qMCPZie@1U46r)ZhC1nAn?1oTbVo%{1@Yb}%J|h$(z}9^z z8&eKFB7?q4X4txAk_Sm?X$8O%h0r0K0PIc}(XV6Nf0OE~|J@FBzt1rmOYN-AtvvYP z;t7+lzRE+)2l4d|D|^>a)mjT4T~kQaqcg-~Y;0`&mk$YemL7ouFBnmGGc%(N_K|9S7; zl@3N@<&%;f6m@n0QbBEiUTqMS{$jM$3CmSIq?m^F(I&4e4U-VI!H+M&R2E#g;Dipa zd{mA4FE_-XC3F7A?nO6X&~bL8+2E8vn%U}@Xm*5Bzlr_?4v)t;(U66%U?s?b?C2K4 z3ZB&m;Sh4Zk*vJW*6(UD$h)4H8l+KaiRu(JqpOsEjP$l)N-7`zVezNAxB!r1ArfW1>?h38)>Z1jB0 z9W<7_xU(?*RM!X>B^%K#n%%lt`XPtp;>27nf*XH$dG{{Cdr|;T3YK_pJqTem8&pFI z1Fri?Pr);R4~hty$0w;#TRozps`Cp9m@jBl3Ub9OWN%`yp-PC!5ry24t$<9ceCsm7$Dm;>V^FRFU+1DBiJ@fOC_+~MK)B&r;J?V(R^zFZ#`R$eQLU4$?P%}?c$o#h|OU2+-bdiE8*QiuyEv zdJ}aVy>UNyd!JMoIe3Lub+aN~WuR|%Z}Gk(I`}WL9Ab`fpp>Y!z!4;MjXc2XMP%?T zwpKzGMUz$RJRi9h#dt2NQ-^XX6EW7|S$I|ZMNg9tA~hE|`KGb~h~G9L?6AS$34)RJ zz?GOGg+@+OWfc<>;-d)(O30>_;~Zb0Xet*JWhdVtlYo8kqp3T|mwUXIiQ^$i~ zK_Jdt2Hp%CYwJ#B*)-}?8t@Jw2>pgya*hGzdGr|7a!khN!|&$- z^%{D3`68xyuygn<1KonKF$%mkPlh({n%7!-u?TNg2>WeNX@#ui)pOdV%k zV)OTOaiwjUtF;ld!#%A<#{77AkjX% z1%)UQ5Rz_Y+^IpvzHVX+g5mk0nRzN8=403||6b{bc)uCM{Q}nPz6f}i>AJcdV3!Ii z_DyzHGsZP#eB?5N<@<85n4svr6kMrTP#o6hN1Wqh&_$l;9L zG*@hKcmD;Wx*Q)Lb9N=v=0ZN9!jhjK2CMCTn$PFZ$<1&>rzR^hRvG`Kx~QF#e%_b z^R$0`1ZlN+%t7IqZue|J5btI_-uU4;;=y}$3Rdr ziIHx!W4?pkf*cXGbjD);%C;{{;BYBJQyc&7&yn!1x}EGwXj*gp`~sKEB$qI4!*Woj z>ukQHg5w>4@!H^rXbZkPPwJ9ZEQ2Hl_2Fr+#;!sGmcwAn|g-_M_r_ zA|tQa?(Jc)jaNmHWQMlz%N1r}MgIBJWWefv`N+P-4*TcNs>#Qg{LBC8pFinxY|Adx SkFY0~mhJ4ZrpRe)+HXx6fuE90}28Pwqih31O-Hr5+#XbK~ig*P{0NOk)$F) zB?w4P+6qWkBuAT!B*~d|XDxg0bMD^zjC=2RW862+d)u*tF4kKA|9@Y7RkLQzDsG)P zerV}p&czG{W2yXMIb{Z8!9xb)oA=)>#4Cmyt8d`{zO~*bf9gB@ar#dGCjR~X#lz~> z491e5=)d#I8(n7bqNL4%(>5n93~lVsSs5_Q&)Hlwv#>EU)?H_3U}bG=abca9(9c4m z+t!`8vAHNEEc_pD5VEi`65iz|9m8O(W5~<>a_Ul0Z=*x1szH{-mjSGRNg zG5?mEE3TivB`J6O(e?Su&PzV=b=b1`=-~bR>JEih1ERK?j4dqn=06i6cX8hVou8M# z~AbB9$dgU>-_!0 zc@I~Au6TNqQ{3$SrehDD9Jsw&a^llfG3$=v@{!k97e%_vP8pX!k?)Q?b|{cmQ<;#o_WbKHm&1q-18huxo&@bGL$VRkZqXsqQD4cwe~j$4JGp?CJi%gC3j)`8@W;8@zTa zKd>7vk%&}_)26r9rC5Xxmr4r@sz#sgNuQf6b2W_18k@4=I(@gSb+FOC+4$NL{G+Yb`kf*y?LHI(@#^bG6&K+Llok)v&y5C!bUz<3YP5|*Gw|kahYuI zO=))7DlDwnSsFa-pEDC)IAT!b*FN%5u}oFN+;?bbXr!+`Wk=tKL%bz)hj_sTyAoA0 z&#i3Zyd8`RWjug|pODsrH0#ce+d4D4rBXf70w2qy2Qs&5rN0~*o*6Dv3&_6s=O6gz z4&11{P}WHK%xIkJF>irkv#imo@KO57)rCvX#PF`ze7yMaf!hiBYcJnx>S;)`j?Ek@ zOSNnj)yun+IaX(yHqmlh;iH#i$Nk=fA|dq`XMefA zoHqpfC2Em9Y3RNDN~~#ImiTaIS=n#juMoY^kgDlCT4~x>KRezWdUcu9`8Vdh3r8Gw zXXi{+#);bZH*C|++_mB0op86Pc6$sUd2 zzT6w{04ufrG{d1ysfeKmjiQ5;n@Q`dU)l=i^p8N;cuzCfJLwsHmG z66T~VbF+fqwgT+Ek1TVtEDf9R@rk@oFHQr;&u8~uz;5r1oz9n?vm9%>Jh1J|%kxRb z&sAdzSE*_K-j`f2v|S^)_|>oT6@06BRe$wbBf@81pL{G(-2B;Zt^--)+NF}6K`+hg z#qnwh{(3Te$P&9&>(Cke5y#9drO{61n`jO(!zYTNGRAc;ji<&3LMMkiH_HnM2w3&T z=SRS_jhiwZ9LM{EVJyBKJ=JkK{W;7z+uC;1Q>v+&(lv|2#r3h%P3R{d$6 z5%5^GhJofXmJ@g3uR1#dElPdWv;%0lem}If0Cs1EzjV~-c^ykMED^P8dzZ8}kv|QN z=VQEH{!6o3(Kk1iE5e^WJGIK?`=e^A#mvm#CFMs-VQPrBgu9sw*Z9X3Be*b7ceP>TxjttJ_(eeJqk2rTh`0DU6 z=2Ue~LbJ(&g2dY91nhgZ;rUom|p8L-KX&J@<>`*8CO%d%6oNbV09*0WZ?y0V@0CDp(h!}yP%$d&c7p4oW1@?%9!#bRBCfS4YK3#S&iTM1FCQq?mB_ zgf1eYFTcZBt&w%P{O0x?=5$Wt+1FQs?COlkp={M}zL3#3G@irUb9?pn!d!Np%rgd^ z@VvBC`g1drk^`B;R`~9?ov%10t@L5)r=As(?S`~nUB`U4!K?U>YfHpnyEu7UPe!oL z26-u%#O5Wdx4nZ~d8XA#?x~|BP;EMKlSR&~T}NrKM*EEB$I0ii`chKURhl|Aatf}Ggj`nHo`R4BXP&6Qhd2T?T+MFjb5TzZ9>qc zmRpj4{Loo`LI*9*n)a9RJcvL+lcD zotrUBYxk?QF5B}l(WtCAR95!TS_gfXi8s8PCO=INkau?+>H57k(Z~pxC=J1H7y)s! zJiLpK&fJiHfUK(v3yI*TlYJJh8U|Y9jrKa5jtAW@@Y&oMo6#>&q7j@FEGDQHTfF`T zx3?#cv>^^gz@E!?o-$Ki`~{(NBauIFiEm43uu`BHeYrnvGeUr7tCURU>4b<-ucnQ| zO2Szq%IWq4+Tlalm&f~ioT`r+7#PsO;OBKpvG1)BVb^i8Os+F|F2H_=;igNt(N7BT zn|#$hfB()n*I84SnO0u40GH89HMPn4bHA-P9wqAhJSO?bo9jyxJ3}sa0Pg4v-1tsN zGxef}Z)v4^x=pvqua*uD4u0Fzy~7p!Bh(WOO@qcBwHNt!-q-Ui(6Gqzy*!X5;la7{ zOsivT&fL@wyfV81l*;@xE(E~kw)bZY#EPzb1&a(q#5IZxwky1~Ipp->^A-Rknl>K} z7&oTdo=GzCy)B%bdTOJ6tA|7fh2^>Ffza*AHGF+_NkyL0{in}#;A8gzJ7tz~iGPia z?W3P7i>@qKGW7X*oS#6COnMALWQ?*>stjC5CSgE)b&*>3knhMqvuyo^4D-UGB5SyI zKY=ZW?yUJ8kzjkqK=x(Y#}&tSt=G7aWGgWrx&*cF-jFn5_Crwg1e_gcp zokaGahsll9!!m0wXZ><@VRv(mE2qSTAU#jXO~53_Gwk|?D&t&|Y-XhG`-9+UWa$ZL zEqUj@+z7uYC4c<~4)4-nX(QZN88D=n)5I5(>NN4d;lbJW&f|^#8x{Q8aG}G@x!LNv z&d&lhvNkp6ZiExo1`J~1{qyhF<@35a(Hez$N?H`VR#> z_6@)DcUL}F!-@2&h|!P;ma;80jto|9pKwWKK9H~o0rrxbhCOU_2Za>hpRZvg?aGXHE&RD&nwt~vFWnPQAc%Kz?n%&^B zI}D#!JK0gQeIx3Vs(NY0_R5wbH8aI4; ztJ?s!A8UDXy0-vd)>CH2gf`)8k;%2EnD82-K| zwbip|-XiY1VO}y)g!%yW)C@v})f1l76_zZwDAK9Fy3VJK(vI%&>r1&}$EMz7(^acA zt&jOGSt({zy3M?GdrE^cXGjpO*b#hp{)mO6hB_P*+|H=3ugX)uiTenF(%Y+h*AUpyB&5$#u`_vYnML1w>B| zhDumylRJB9S}j!aU}q5`t4908Cf?uPQ~aWAXy-DHcp>r5)@da=xiGx@*x8Pd%l+!D z3EG$byaq27m#irBY*T16P|c~y8FKjH1IY;L9XMB8_v{{wl%4H1$xlfic=|cIFDO9L z+EB`__cW(xTbNAu^w7sM)n)2KuPr(EINA`LWhI=nalg)A6oK?wUZy6WF1>(V^X}AW zk5Q=eSOKC#&!gWNUpRi;JAdzXHK0sx#9~4ybAw*46_=XmFp+U4tuhRK0?(_mwq-;< z-L^;7AnI1N_T}va$diEp&2nbP)Z=vz05YpSn4@fus zWz%(c5wFvWBj4#LDgAEOSLa*jWRqD{t>e0fP`)=g9x=n>)2%O;@kq7lAV^DD4BYr! z`P?5aa@Wa-qXo>FGG-g@yNEXQ!5s1$wX{ZN7;yDASKG6}~oRI;HLd zQ-1=>Rw*qbI~X0bo+J#!o->h4$edg((7$SXOL4ycD14#8ySwYtkP9cmy{cqvFY*^I z)AQQn2ltdyC%$v=W9Zxt#4QJcaX2_LSmS6bCt%D60F|3JhdNood{qsCIv&&nc?~>> zJ{|wKZs59Yn%j$5dMxwg+K57znHP(tC(k+!!smz}-6(3wy=^_&?(h1!Y@n_*fR=5v zT1Th#>L$}6=UTW*6R%GDoasiB&VbzxR=$0K_}`@bm)J`OBtryPc7v;N=u4_jK$P%} zaBisU-^E~vb5mY#1%OMZxe1Wcrn}rA%TL6n>%FE!yYJJesHnlrMLag|O;YTIb+UJ_ zTD3|&$wURz#P9Cz?$MfQqVkjuG$2AwPB$|{D5E8}xlDHE%MSCp9Ufc~rGMVw4Y@6p zz6JQ~cnCnu9S*TgU_LZ`+j*^vMbc)5iiSZEWYt}4&1jyzP%6uuQNBBa49XB&s#2%v z*d0!Ltd0L~K&m47IV$W(Q4V0u5WNS$Z?d+`#Sa@GE>G~UGa~RpAu^2wlgeksA$z3b zkmGOek37WdyhAwi%Rbdu%>cua2jSXTjw%}VEYqP0QIz(XJRAkawyUl6fXX|J%lWaiM_k4;nw>w}#`Xo(B21R!Q$O%X?Vznj zB7S6f#qRcdC#?X+I=dr7j}w{$M_}SPcZP(dR)1XMCHsp?6W_xrxK_C>- zA?B|I0I#ZXK~RJlzDqDDqVF;4Z%7N*14q_Oe9~kY90d*#pW^O8xwXzCAGj~RnkNr1 zk%5SZbE$|c;y#}3ZeU}ac4dXcY~)1|2x>&Nk*zg24VS1q%O)yFV3odUMi%qa)#b;L zVpzRdDp8TjyXW%QQJ}+3NAI6-w7|hPu6c2eGL=8y+=vAF3m3VUVcX+Ji8E-eBZ$|D z`UTzsmDRvk7Q=u75lDjJp7e&uxIHS(HV0kYCzRgxn8>&Q8RsdJk7b~LepvJKy4mS* zMI7}+Bn79s^}Kr%%VfF%X|l(X;@Xjy9Rq~%;b+&p_zS^&O0T8+UY>Lu>kU{Ul)A29 z(Yc~y5{>tGXFe?=P=N0|Y5s6Xi85*;dk8BMl|ZjAuG)-^guyuJ{+RZtH@zn|?a%96 zlt2@YLDxL!$sNE?+?mTr7_R{s#A0%WU?i4+do436kHY~#4%(hG*ON08Jm9n$q?$a| z>_|$f#bJLNnIRPE=r06;gNtxPOf zq!%hXTr5V9{dy5^(3|D5nLEtkJRG_n-8k~7o#@!z__-<1l!2D{@%)s81W{xIf*vcF z&vj-6aL$deifMIH>_VEXBMG{-Ze9He#K{Jbm=54~h<4CN7E!o0GRPZiv~lw643m;z zM2!-#Ge_8~WIT}m*nX}kfc*xl={WC1;$TZ$E`YjN!J^iJP!Q6SrS{oAbg~ddvgiE2c=$X^1zA&~?T^b$wk_opby*7zcq{o3a?;Lv_TeYAGVDsQXTh;r z85eqEog&2jTQ37$QXW(ahm#ba@eSjIp!rPBJBHhtI9KMJz-yUn#1V33EU8c)$YHv8 z%Fa!cX-#BG5<>?nh1ab20)tU;=K3cF<81J$aRv(`b7hzYMiYkVdjE(kgG-*fd9*Ue zmDvk^8~+ULVjZZpjPDpP9<8}DpGq2T`=b7_q@iTGc3*0xszx~63qHjvd^i<@YZ60J z%vq*p`c2pIU?~F>7yjd?NN3ZvhdKWCoghu=V_SX$Z-YN>`zjuYe0@o#V5q%F3{d>k zsZ(aXv1WdpJI>!7>#L7Mbm7u>_*!Q<>m!19P%uQga710bHm|?bFdR1c7wJH@Wp>wq z^Xuhm^zmI!Ko0%U`OQ6FU*g=jUZ!g@meQrolz%JBt$jbO`t_eD_Jr^HBa;FE_?O6$t`8gb-&l6cI}^Zvk~kHZ&J&}ukV$ZH zzQ`FUDco9O4;NzC_U?CiZ>(v%>|CpCIpWbwx1wv|Qi+DUO<9+zfWzV_L1^$7AZ@pz zYz!}UgQ=S9be&_NhgclIa;q|U=b6uHIa5E39UeWkm$kzcuTbz{xelB@XF$1t$WRw4 zCaO)qllPC_|9L&~^g_zn`8jr+KXH8-&jZjy+x~(r$V1Yq+Eak(%61PQ_0|VZ#UZMH z*t_k@5}|h#kP}O#1e@_~aJ|!QAkh_Qorn|wY?TCn0)Xq(2NFnY^^_h0f3+$@Oy<|G zX7O+#MFo6NTHb2p!y%)JD5a;-(Z!U~;|Ck%v%g#uMr7DXlnAAr{PKtwRmPKtcx+E~ zkAv6{A=kbdE(q7_aGSsEu=fm@76QQD{1hCK*`L(b4)NQr(SguTWf4>*GRRc8%ra2Z zWEtmK9(Ym>Vk`aFwJ)tl_)|}_GC&1n2;X%%*k38-LSyuqq-P`Pj~_oS93hY5!n^y@ zMe;$|QhjV5hpfxY8GmQEI{KFb*a|D~aWU4R?z%sGwG>+$c;rsX*8t^2ou@{K!6OGk zed zX{s}QhM>j9$xr95QDE`rwyQZu4qeo;<*jnkNZw!7%+rU$4tfYIeLDDPgXb(0^= zw|VmudSWvS5nDx>>4cd%crS$Gt?F4We8g52&!tL28H+d}wq@ zu~7MH;ARUWjs_5;(TOFfbG%W;_29;j-_RGCEljuTdw_JzT+cpi@ha9yXRO@!)fMbr z|Ls4%V8XpO)TUgX0tfh^xM)j9SVJ}(c_db;8)uQ2d&Ne1)=kzu#)+lsih?X2y}3r~ zS<>e!o9k4|C>AR#1_hjx*S%b3;w)m3AJQm;KYsguvS~cq|N9l2oIZQ9ZZO*L^yEVA zrpJnA^awJ{-ndJf-&_=`8gqoTQiQOlx_<+;k38T3*y;W@4nLT9_zM+GK*&$bjU% zZ>`u&rPd=}Yxhu=WPsD!U7up%`^gCja4FJFoi3N!Pa-qDwTT}=ca`_R)f1rRIK2d16;gFX8$$N@tXuN*!< zjii)g-u#7aSQN$9JP&JRNNtF8MeeHv$%&$pYZ}v&0rG~N0PeH{CrBYw#I!{4I;`St4X_M!5j*e?ZYaHv_-G)Jox zr;Iuwx_PEULh_T_{AVwDWnG+-8?1`Tdk~KGbKO@}3gzI5YEkc{h}M8i$YH8GvfBKO z%(bO!_bx}CKujL7P>6Pq?jG-q?b z$-4~sgwiF5yHHqfi^DMlHrxnyBB&zmni}0a+iy=vmooeuEF-DzaWjAIxf?uGygnMV zON5LFt4^nz*w4ryt0R<6bpS=1pn9k@;Sy!Za4r_f*Podfq>=%_Uj?Bwe#*qWo#4(( z0N(E{T6<|-u(bVgIBqLM3-d3>hbNQM8mt!4`RRBLz~}&`gUI?#^2E={0+e*4G(j{q z0THkrLQ8#q=O+#mh1&b5xV3${%5?puo6tFlL*QT$dyk@CD2lR%Nwrpv%WMOjn`BRP z;t=IzNZcGykHr%CkVJyGJfiFy@?7OS=67NJV{9FPtB~EynPy3UoAm3WU)&U1bk|^u z`+lCh$DxvEq}@6BjHAavi?XVu@rBFRX4c&=1l?v=iWIA|E(B;_k~uT%!%wM3Rt^Az zAx?dGHEdYjTmSa<=Glm$f3hNXjgPXNzsgtA?&?(MQooK2tf_MVh}+O+^{=vLPmw(9)>Y2qGc2J>AF zdlbHXpLC@@RW_8{o(x6Y{+sh|imw~#&0lJt9W_AcI;pCf9G{V({wzAW;bGB=y>Avi ze$2s;*~31@U`+6ftrk)&^1FR9MM-S^$U^9g0zk1n0b$Q&7Cp{_IK{1FKfQ@4y2egxWQ(u${!V&cRh~URu6V-txcOg@L)`d} z@j3;HyQhkHp&4`H)G+H^hR@*69O%yQ=frpWFEU;Hr&-A+?Csz zywnCppe%bZVBk93<1%|lR*9ZPjxeH}V5BK(17yV3!ZQp_&bdCRXG4O%^ z^9#$@W0B8Z{qjkL@u`(|?TaMex^Eeu67|3P`po|!BfI{IF>xntl%!`9nJ4D4QkH)K zOzzBEYL;AO6y4sf#;v(mkuE0_if`WgrzRnt2`ABi{Y+-MCQ>hdKj*=4^EkjlJ+BPk z9FU6A`r$Qed;j&7zOe1&5&Qv{aQ`%LQk?x?t_MS0=|af@#>3>3|9H=T{K$=uz6cOK zwVc5f__tmBC*9mR0ONrV%hfe8$X9vv^??&)@IuYyMK9yECRE#3lFjwYd3b zwVt~DCCH<@ay#aEWDoxL>OWlt%Zfj4S2$$c%LM<|xbL5U;O7f0>pU@S!|BM(>;H!F zf(iln%FwOsyBIRE_&|lp+%7Dl+cAGu8gX{d7C**>{@Q)t(UteEr<-?L_Ou4OBeUe6 zmhkWS@z<<4%2(!gr|Ko~g)4DJoJ{88=^@V^`=3|h)uva$HyCE_f2DONzWXP;`!8RJ z%7~Z7`tAz8-joxcK{vROhcMy15{MAicm7K;X0%cR|%a$Q0s? znw`f>kN|l1H)c>(a-xEVTqEP`N99(AV|$^mWa}NVA<%+`h4;2{Ae#n6R}L>R@YnJ~ z)rFOfQEBj_qO9{(Tf_)j$o+^T00{yJbfqA}0^#C2kw%mveSP*4RX-y1efTM}sZTbS zIBLN%;TrvlnC>5d_Kx3J#&aYN*fSLP`joMlSTZu&Tw#S07(@hVFBd~+7+p^c= z#;5a82O&LHciaQA0`;xfBY|8NnIcGyj(QX0LKN)GU^&CJ@PAb0o0^Tc3+f&z~7 z7-#ngJOpzZh_p?Jq>QM^ds96Npee2E@og&*ko@u_w`{ohW+{PHD)`(%hJcKQD|48x zJu*|~6p@CCGYQH7Ufr)$fOshHo&t|Nnto zzsb4i{qj8{C&2sMfG|!{P17=$vz`(nDomg{?Kj}mDIJ>b!#e(Ej-RWZ3>NwdTIX>X zwUH2rk`MAnuiWO*2cDLIVHyf$PKG+P+}ccHxET~In?419jlpBjl@^SU>W#!CP$wKi z+GtHY9;*mReushpeT-iou1Z4l?;d-LTL>sURBlf=HfB3dqvn`#BoM5SAw&>JMifab zLLy_Xh{&&Zs%*03+x+y?U+d_8B)C8+iiidh2tc#wl$1;|;hSt5y!lC>alM~985j&M zB@cmr%157?a!;N-obr!~Sukjzcu^o_3(YDdKuw^;aP;>fAk)(y1> zC1F$zdVE``kR*`&l-ib~U}ppGi!{X|HvRK6RMHdfmVSk_mn$#*&;>WW1(oF!zT+-q ziJ`tO!U&2~s+nm)t!X5S_0F?CDq(*)h0|((a|Ztezy2G(U@KwAg84NU|8h(Z<^JuL z$jJXA?fIJzhbw!<)Eg*$^CYT&cF^@8Tgp1CO3hZQ1^nyX`M%?Sc;%rs`zg(N!tU-@ z6jsVN++6s5D%T1=?bg`TUmyOoVAW0$lb`n9J+SwW13$D}=UTU={<|MWXZ2hfWf!Jj zj^H{X;*b?$6mrA(xNUGqtW@M@EwH(`ji-M((7TKOx{ZIoz&eosm)67G`xk@owBLkGdiK;@j)wwQ%Fzcik*VQ48Sv61G4%F|S8y9Clh4=$DKo(3vf zKdjxo%Vp|g=ml^0zdoXD!hOAh?gV-P_~5E<_3d-+ip6&tR5866KyAA>K8r>fk4 z{>UEG3u_>tP0SepUoC=v2^c_*&B^OBagN$GxODcD{W7wBOw2z^XlB@%lX7JcgIiNE2LgJ7Q zMapB886iaH;Y;++VyCtNRE(ZbN zV~}zppwf1HW@Uu0>`%yB?Yss1L@d7Nv=pdn&Bk`NE?%@*N->{zQHM?-NAp z@NWn5f9yb_M$OgSZjL8+tB#0{7TpLxv4R%T?O6U_rltH>;9s}uFKF=Z7g%8SU#!Q0 zMJ$Ob^)_{Mi#~{)_pjZ$E z#R#==h?rDF2^at=|48y*Ik}e?8l_Oj2s@^xK^x1+7IReoGF?ct~9{U83(#ZC|S>$o_ynzC-|mA$sohOaV| zhY^2qT~Gd24Ho$K3$zFSKU|~MiX54>S&|y?RSVnv5FB0hgtrrf&SY&)-mfchzq4A-pLYUWY-!ayG4e^gqU!hd=)PE7;!8Xl${oc**vWZsf(_ zov)<*x>X1F?q3#OET*+BESi>iLe1#gueFnZj`Tk-+To;-eaj(YLopU!JvU9}Z(0Bl zu_=cbOPMY1OVbxr(G|=DzB&KUUHPElk{J5hcPMq3y|mDkVp~sgl2`U#49rpy6a}iO( zdp$S;X#2nP2}rrcMn&BuarWFyr!0i0y=*aOlH8#M$)yhP?Zap#lgn}(Z3$)07*XF7 zdVsk=PSiYEwWx;MX~p_+i39hk3}j}42t!)a9VQi9(8j-pjSYgCGhn5k9bxQ&-(LfU zLQakvl`SFOIONT*go2~7$xZ=TIW>tNIS4uszM>Fq9;%%zjUytBtbjS zD!|p(Hp<`eX>~{9veBvPsOp?IMi#ZAuCmO5Pq>bt`RgZ#Tl2jXNm<)F06meO72=)h zR%S#3NYiprxHp_L!m2XdT*VG3zE;AU0qvz*qQX3(-FM&B8D#w! zME(Z|NY0}dmUN!fnMEW%?IP5Ba~r7R{ovQ~Y0VvT$+IBSa)n0T@Xq~=#%dl&ziy$s zJ^XC;l!rIp={q>zL9jiU1*f5c&=vQqdL`0+FRr^}n0eGh_Rf%X`1Q!RvCrZrZha zZmpRkwF{HzSLE_oFQ+e4ttM`+@=4?Iz40$s{oFFgo( zwsTREKM?lP$LvbEXRi`eJ*Bbf@sMuG3dP+@VF^8s=#9_=TgS#` zi6zJ{*c{4@lydu&vv?8)J*dYS#~=~oXtvuae&VB} z^NLy3So56p*>mXk5P^Pcf)tLk1>t!1+*3)IRJ)v;S=paZ~$5$_&YhL!E}?N5G7d2P1jQ z>(P4&I=+0>;_8idXG{Wh?&#+DD}O%KD{7pwf>v+h$uV&Vn@kbgdNl!_Djy7v`j^?UW>;n^gp-fa6v4(Of|&-XpPxOa z=a(!(H{>2j0J(5#Z$tDd$|oQlKg!gGq<-tf_#_%&hvu4rpt%SG6T3nF4-EG0x!LLb zt`A|>Rj#m?4JeLthN0&YV3I`n-u`{~5)Y6%Yog zQ9PJ!I()ilc(z33dW$?Ez@7))&38zQHp|o$wOxbeP0}33rnb1JqP<$L&|P?YmHV;N#Op=Se(v=gRB1WuVL_AkOAs z4$CrEut5QJ=O*?`8r=i3ZQF9t)zbwBf&m{JF|LIZ2I+{=E)vth9m>5fq2J#cE~xCU zw3#*mEkwF>=#y6=MT_}x+2W|QQm+w2chjwPrg0u5o%ZIxm4luGiV_qthGN5N2KJ~0 zlZ{cMUWVt*!af%~Il2+dx6Cu?cBE$oTvO64UUq|dIU17h;t0)4sAt+JyF#8%U~WTz#>&=*e3wfQJE zeM-Vy0y!KXe)*qxoxc2n04a*%bXpLvzi$pUA569I_k_{G^?aE`X`jI*Ebzn z*<9_b9Cx#DxMFm=4+pe!yUtPO8a8@@RJ0nkz{`8Vx0t;bj<&!^0TJMerG+^t6>;6$ z9?sl@u?D)mHSt{BR}HI9cGV=J<7SWsKhOasJ9r7A(*%NraE~);vDqTlYirPh9MmZg z&Yif@XQ%$kjOFk%vm$6>KMlL0{bRKjK!_Ze;uK$RAvajBDrfAh7T+0_TVbl_sqQ|z zE%$;K8ygJ#I8hpq9V)kH#-3mvE~%JG7Tl`>y@&@+*;{nitYu>(`r?$m$3t|H)84Y2 z3Y+huNt~zpPiHrZrH8M?1gSe1)M9b*p*LJY!wQJ~-H8kUt9&x7^fTy%+tegPWDJ|H z>uqXZG>W`Cxs8TSkpH3P3^w0Ky^0WRE-i!)YZZYAgqYoAvkqs%W?^+@qzhreIGmzE za5MSvev8-E9K#q8?1+l_M93sJwMU~TO!g+kEr8wB**O7ATr4DwMJELD7$0U+a@gw| z7f)nN7gH}?|u`NNp@YV42$=k*MwVNm1MH&f(Zpm^sz+!w66-| zP&$eORv?DCquKK1kx7V@^8iVy8Jmqw37S%qINjx7zqgIPwkEO&x zh$=*V+?O!63OlfRK~bzm^UapJTgtN2pq4mkT-2B~>5-R7bv?W?&*oUpIP|QTjWp;r z0Uql49li;iT$3Hj*>EICj8>yG;ZCj4gvT$TKXu5DiH3odr2lDwOY@|n0F6!|)lTV; zdU{j&!Z|adR4Gp--wnzB9q4CNjU}q8;V~!B;mJ*lNvsoEVmPTQuQY)I)Xl;bC7ShZ z5z#Df8Vq-evVa$zQ9izwL$Xs!geN)+^3Z&@l8W(8aN#Q7%)&a~QooEn4>i&dWQ|AL zNp;k5Os=?tw)tIn!>RCN+ilp^KNhv=K^2_lrNpBf`Vzh%jM|dtcaeR-?pKfYRC}R6 zHoNY{I=+N_u>iKP0H08rZgeCK?n;_&m<6Olos$ch5bC%8@K9{l753CZQ#3s59ATT! zc%GjAC}W^EHK{v}rT4t0U_NY8S8d*F*+!T=$)wVqU>PMXSdh}SZ`!Pg@I?C-VVV!g z=Joe+zsu=*b)lmRnZ^6-yHNKwDlg;WRkJ8jk;WNu>fnRw_xp| zP4Yis9F!+bZR->bPoB(u*_i7h9A06pHx)}y2`8+F#)4-`(n`hg)d)amE;e4Hr99yA zviQzg&FXaa?N(du0=>3mXW_qbg5lcK3`B_r^~-j_Say$AoRln+s8%6AY6(!C56e2! zYTbvDl!OcJ`w&vBXN|53-L9D5!NnHN!@E)bCvsG~zr311m>4>{jappKqb}AoL|iHj zg!zCv(9N4`WSlP1rKqixBDh4?BE%lUt(#2WZL#C+$Hj?Ppi{dB%~AvymLdC~?5Fhh z!Q2ZuY`NwdlXdUnvZpX9;dza9h3;pSLJ@_hvlrv*_2k~7c(qvjbhynoT_r~$px1+* zt~L*XjKbQfZG8Za^o%~pKV z4(!`bpXhsCN7Ep_EFMXRgk?4Dgr;q!iX{dBs!eXIw5=lU+qe0-^cHUD_2lGy*>WZi z{;W?;NtSbyo%tYQItklR9xJK1tZEdA+*_LeFq?%|(puY2^;{GWD@CBe+|G4*6Mc@dI;RQlNPYN=_DPe9ncy>63)L6&=83ZFpah)yhZr*~0AptR1Tn#vMLw`@J;-y+;h1=`an*(&Ap1)Q};oTCJiczQ^S z10GIccQ4DGTIi3(pGwx{k`1arHH=?Rm7Ff)zdCA1vhuumdC{YC6ZepZ8ca_riYAM% z`?2MY0!d}`CMfuxh>=3x6_dP-BYZ3;{D?O{A@emD$~m@A4|#Lb5TcfqFIx-&XS;hn zIHrEq0m^`51)QSpjW;$3$o8PuN)Iho@nm$E4)Y`7D4HDwVoE#QjIf+#eO2CwR&Gex z>Wq>@EA5MwKO1eeyL=}q=z$@eUFEW|hnL`bXr59l1h}3s{hx_4SOe~XWLXRRsqIaV z7=igCq}Vmm(=0S^=O70HKq`#MOsN8k7EJz;ioWvQn1z*dYPv2R8_huwkLu$<^F-8q zfU)(SzpA;xg|cgbl-eq!5cJojg&4b`9SWu@aiVPVrzS^U(C-JuKd5%Y)_|5lnLG!XcJB<>F7(v!~=6e7)k-Y28iIGerZHoI`F zsOVWdFyWCaMJp4X56H37QyfC}ow@SxW$~eVtbdKir&z;Wj_>;Ox8C!)PK#^<25d20 zr*IAw(g))%3+CbUk-O&8r-9%!6gN11qzaU>G1)bQ(v84N&&E^LH#X~j9*^gfdOt_G&6zAkK3{95#{fhMQE@92mH8ljfQbwL;_lu(B^dT`K{FwTWg8?ShqWkoP8nn7XED zw5ELkzq~FoxLRprxPwr!t`hEX%4s`;uBLh35QTCuB^46;#IOT50OM#tYtsT28{Hl;g>Oq0DscM0fF9l!e2<&;8CXPi?3l;6YQdlwru?Bo(`8 z4=8Q+=-3qu<>m}nPETsM4Kis*xufAzl5+iwZe2f%>U$~TOa$^Oq}(1vx`U--qrUy* zV`=xSdToI`^I+7iMzYiDplgvnS<*{LZ$s?^5TNquA&HY_jggY(V-lyt;*OET(1~!x zE1kgt+r#dg%ZN3eitlrl8+6u+pVpPr7oMT1kF2x~NBvY^@Uz@8FX-{_KyG^BOYLyd zIC_|G3dW}L(WnN2cI&>|92kOCEGcXG=&9ThODugZCW5To9BiXZi46^MxpFcO;|1PA z*{{^Dxo*Xe9ny-z?f1j)ML()>o`CG~04Z&;hOfsFM{-HFqTv%2Aqqh)7zUt1 zLAdtd#_Clsz+01w78&h&BKZZZkMdvy%!1CxJxF4jm_-turBvak*)T!qghrCPuLd|h zbNNP^eyWS+?E@tioJzG9M=^SIx|SGD;-H6{WFA-?f3tu2o*kV%H2R7rlS<1DfR^IM zj%Z-unXE-H5vvjn7x&Y210X6ow$#Qqyv}*nAyd@jyzQc(wB(rL?fH`{#Wt(wq3=Wd zzE2ODH}0ToI3Ih#P9rfSP8%Y+)h^+r*=b3?3?NEbXKSBt(~BQ|yInK&XTZ}H6eK|x z5iJRT?#Pe z+R~m8*Z-?>Pl`O-}FjQdKT2b(@lw^ zWw;z@ZYbAAlolCShNp7fEnDV7Dn20z$VZ^bd2Ljt#@rVgc5guXajN89i7Ke~*obo_ zCYP2<$9YtRCe1h$`4449b8T7exV;tgTf9ylm)wY;5krZG9(=MTk`M!nv{IU}MYIrR zf9&^2DbQV2Z*kz%b#0n0L)eI}c^VU$Dex{hG#+YIxHTU0lMH(15aaW(VZ$;F8=K=5 zvdd(^d6UN)TQ82%p*0G28x};4-HI|>5qT_dHRY?&XNXW`ktR7`o)-Zz%pf%d)qg0q z`TpAR<7oEziFl#0bJ~Q)Xmk?Sw+q9$mm6R(B|EK~*{fzS3=^1-q7`P$Cj(YoU|PRJ zw`J-~$^{o8bZUp@{VpyLw-|vGTJ(`VT%eqR^g39_N8ESVJ>&90V;p zQqLlLi535CH_D|gK({3h<=Tx-Z+M+Yl&HW#j58Y>84g&^5by&yW>Q^hA5f<%;LAQEf>B$=%!o0#n*W06&BS9Cms9VQ3BWx%uIhOn~vW%fp zh0rqO)z5j>`6v~O=ZJt~6R^{{b2Alu522btYKf-_NCaNs%kmHf{p|ygG#o@v_VW=h z#XX~^nq=CEvPJG$eW|ooc`TRJo9ycE@1Ku^mmS7ZmLe0^hj`zDxvIAi^AC9Q*KXZP zAPBM1$?i*W`VrJg!Y#Jj@vcSMiU-@+=`w`8UiEu@K>-04Jko*I+@Z%;A)e^K;dOCH zg7Fk9iI^oR(I8IiTNoM-dhr`JJ+DsB-v*Zc1NOQLb1Gl!`6*BTi8|X`L^-t=M-PIu z(M97m*U?)iqqav}|4CdLyoVx91))}@>67n(;{y57zz($!)ia)DCO-|@eitR$uX6!* zU=Sru8Bb~ldz<_O4Z-rrdwp<6{Gdn_X|@a{-!ywf+&Ea2=0r5$(gg_We$=jgww8Cc z>rervYCu5>-!b{Y%jHzqD5droIx=>!()J`+o}FV^w9~opvZ4(+yoPRQ0&I2 zd_6%&X zJ_?+bex~pvnCh4kIe*!%hc6dbzelk3A^NAgK4oB&b2Fy8$#yErP!B&1)1|rD1#ols zW)Pk2X_9u^c2^8c+l-rPo^BfVMuM;JDw@MSgmLs6X*vRpGVWDeAMPaFGvGC;UG%O} zw)#mK5&YhSl`vPs$Q!xple!_)x-JZ1W+fl}f&g!@$1>B2Ph+&Rq-d@q>-*qEpCvoR z=5$6hjMcw1_5n#FSlbMDSxF-acb*x;sXk1D*JzG@$y{3^+?-1Ha~e!TBgA%|DML#> z{^M5>W%e{1BQQ9V4&cUNj7LB&-y;k`<4SyJ(rhh2)Ucm$hjhdJ+3^=|mZ1hax$+|` z8HKs~z&a{ZmvN-k{kAWEMM{&Sspz>se$xlw-HE5K9-tS+An9Nb-WYYsk9?wcQ_M+JhP^QePn5b5X@ZV{ z>tY&APFpD8S_Z~sr6z0m4K`ZCAOJurr*3MMb#EmQUmJ-~#>O!^e|7Iwd~IE_c_7eC zMHj}R$>Xm4$({CV+z9$z7ZOv>Rq)FBDw6%X zz{>lGmG0V*hJp5rLt33zIJZt^1MXg7>J-Xo7RSp`hyrot|jiUiPLv)_O?YmIfm@EaU5dAx4z@lRop=9hyL|vYHkn} zRUDpe9B^T$xOtPw^hP7uuM3(mpwy)pY!->hKzLMVzYrXc2;$B(~x;hF_pAr(1V&_?P8?>^X>Fa#@l=8FAp2>sbv%N;LC<)i_k+=brl{;M@ zQzeuQLWV|HNqHyc+*>Q6Np!gdv)?|aNIIt46_q{J%lVb*N1V@>r!QX_eG3%t z9%*M~xpqra47<_=s^Q{3sUPw2-A>* zy&1-I9ufjOxmi?7*P?e@=u)wAv~EX^wtawZrx7B*9kMoq`WK(;sL+#({Y>wdNMT{)D%206$NJq7 zZ0{o#r8Y-CK4K{`DB~uY2lHV74;rh)BeXHHrl4P#W(v}*#9B%BC6&+bOLH$rL5Z_W zG9!1j##;-Q;lQ8`S8vDT_ZLh628(~*W3_ec&mkSxSz90wtE+b5?<>~P4ge14qeGiX zN@nEVk3TgEvH9lDr$l;enLjjP2g*YBENR+)qE|+v=U zGrMuE?$0N`Io!SN@Z&|f_W23;g(_X-OfT@3pmybs;-5q#kiu?y0orN5xm27xyWpaJr$s;q6NP3Yd zorrsEE{6FNZO9_q@heJJfLcEzCub*A4g%Q!oZt?LI&Ho(-*=uhT%A&C*38c^xoZ3(_L zN>tVcMb>A#FADhUu5C~&-0vnzqR%`6! zU;k$}7xcjD;$|c}`${|UD==CNgSr=j_%Y7CanonZAAI`+zYpXN)m}o~ zwGqxwWBTuF+aQw4qkF*Y%P4dr2k`n($d1`Rm%HAi8W=GGz`B>mk_Yw?Ek@-7Zq#op zWhyr4HD;fK&!VK_%EyeEv6TP8)_Z_+-TwW_Vd6qMDEd5uclA_c$UX}9sGJ1H}{u%WUtW6WwbsQ8Wwe3(gXMp)qB_nmAGU?KDUQKQgs?$ON+eQ*75L{;Vl8xqM^IA+Yez6HwA58qzFCgD zCjKWP2bc>=;i^BC_|KQn;0NHWT)+b=0c$6?Z&%(^34#vHKp&oi>;g%PK*rm-oc8Fi zMd#rx&1!dsrtQ{s_8W2YQw5;N7vRjhV6?Wn-q#3A7<1xji*g5~e-Pz{nCh^80}F_- z^Y?7)N%`#xxJ=l*r!gmiBQ5^YTqCXX?0x4VBFDvdql7pLyEwH9LHVG{5M1(!L3-q@ z2nVX@{|2@?R!)H#y+1zySEr@ax(~m=c`?1i(#rxW;!Bne@g8Fph%)_WU;(NUMo~;)*S`r~{YH_$6buWzAp3mtQiHnN}k_6Yv`1_)c zst#A_EAdahn17n!vtIlA)eqPphiF73p`#P(ez`oc`w5Q-e>hVoI0jUFs zuQD$+y)j5{uSTIGLQ;ev?+&E&Iy01$1KDDsiX!Op;{PoM|GS;1H!$X14IBImkj-JX zovKC@y3LW%VUPFb;jE8aZPUGJK!SNG;RB>~m+;coZ`fz6U*R7<>-zjXMcfQ4hrTq< zULjLM;+`-9J>a}OY<${Ff}hwtrjh%OOYd2>aI4?GU2U7H(N!5@s&T?jCqt>o;3<`& z(>ZY|=*LO8&N8b`tZyP?*@rI0a!l8AQX9rBUXf`&OODSsh)ZpKF=!usT zB7})D3TE~yDYu*$g$yNNoMDII9pZoM+mjJVB6h@O1mfGh_ z-N2y4nKMjw%rp7~lDsmQAp8iYD z&*@>$ZXZkBWI`@0D=Q!nr`Zpy1Tmvk%@yJ}WHngRIhF=PTII*B9?u zEDH%Cn?$n4UV#%QP+C?tHa*?)ZEt&B@)La30ZB=wGiT0t|NARDE$(}hb!zSF)KpG9 zUuutowBG%5>!bBsN&NN-jw-QFpNj6?yEp#)jtMQ0%>~#{qljkSG829cNr^>-=jyi) zbmz~XSJ%{3x_q%Ft# zF7vO|pu^bUy1^O!B_(HdL`A_63g zyA0`_oScfEJ)79^XMyfI75D!AWFUJ6nw$3(KWsUebW=$0e%sA|e|@0XFeCI7ke<#p z_x=0i{3duxUce}fEf$e^=H0u$U@gVvyFHNiJ2riy?OJMgi)|H8zg+q(O~|unNAMBK zmY$`i?#<50$-;;QgZ`-Kt1vBg*Vos>yb3moH9!QGH04=3YB)wk2JcY+u;a2j=a6Gvi1}VMPTSWZGtIWtnLyg*?k% zh>KR2FW*Cnq&a$NXmoiy`RYicZ_6$?Ed~pM9c6fkdA0A}y<0?N$AiMpOnxmd z7h|L>Gb`&jmXTR*xERr<&sF;l9F-fo_BS1sh&B* z;qV|o%f|QT1T{!gU~)3s&XFx&0t*7!urZ(qOG z4-a$VEz9`-lk4p49B?^lPH!YX(vR2(D3AdV90w^>o1v;f(+j6y^;@ySUN8Z-fh6I8Xm?SfWb&|08 z7A7X9nQz|?eOLcJGZTi;q*lV$#WwX_x3kE;!UcyP>Q?Qyz8e_%0DjP9qK%}$gDd8Y zK6|*?&-$)Yw>gl&zUGB7f7CrG_{ z&eqCmuMd@kHYX>?&CN|u<{=ws&JigoY|?zUF$MuThV!*I)6P+aM@89ns2(g^8Xq5D z{5h!=6cj|P__O5IPOG7?nvqPRaV}H#-p=cVzF(<#?SP;@?Deo@YIst#>R$>MDCa~l0<1^Z)75I z_fSW~<@D(7JIF|{iyl6FI8f>7Q0bR4{Q-zC{`w_x zmrvz%FeB>Wq(;CSe1x8QgIl`hRN^#o#^>De-MW^%eSGQ%2F%)J{(u)KoIZWJGM_eJ zm_WQ_Qg>Th%wR2!_%HCG4A{Tjx!CuZQU2y%Dm+d6_)P3e z8XG!%UIjBVwjztl+Zgsz|N1oro>)?Tyl@A~C^6T8^^<>}{9iK+a7ehodQGpO*4espJUR_t#G9Aq-*ZSs7 zGp4dySy=^O0gpp{tngcxc38WqsYye85gze_H_i0UGvE69vdHTv~QGL z{qtvwz?+aLY+S9byId!CN=izeX5UFiM>jDsvCuy7r7Emx`ImAKxRK)7vwMbzhbR9C zo2OH+Zbmi&x3_=iLP;n=uG2&Dyh;ii%NWRhYiFmC*}rcyGp&iYH0`Zgg^cgryBDg? z`{jF+NcIm-Sr6Vz!`0N-swCUufZoL>ByM~(zAQ|_12(V;jgM!>vEjDz`_G?06`s>~ zd#A&qqKJO)n6xxH9wn5$63^+=Ghe@s;m=$$_{78*@Z_4~_;s_leCqUTa3~pvuG-q# z8t~}RBhA{A<~tWD={e)~*fM+(=w4QdjpO6u;u7=`$i(WZYiY?HT8dx0TUj*L*JtGI z?Y$o`Nc4PlKtaJF5UHjVjYGOE=MtOf0;7YM@U+Xu>2BP((G`#`Y`#Y|E;TRTa2w+o z>Oxp}cuaxLxfHR|Ek#8|1DHS+Ul!wnH&Wib4w@4d5zz?Rr>KlQzikH{-6DKInd8SBp-m5oW7n<%=9c&@*_TV) z(mI~((rP{wcU}*YPOpLxP-C6TbWMkcjxzWr=4H0>o^!|*VUH}XXbz{@``1sa+ zKXx&mul6Rta^;Fe?ah4(j=>cDJl=!j36-pf4@PnM|BW$?F2;kz2xP@d8a4dAlr}qZ za&i|pHZt7!qzgGXN>Zlt+O4jh9tNzyqc?OWCno9|8k8C@}rpAi|@M zBEy+&_wL=*T8V6FVi{f+)+4I@529gK%lasgT!!i;-7JU!pOyyDyyX#y}YGdp_$Zv4W^iYk5{B#4lJ z0J4PVZ9P53LyeZh8Hx3cjaw@#D=WOd?i%|1nURx|*s&iL6n0ZkP!ygwxp0A}Ajw#| zBcEsCmF{YEN=i7+?e;eqf^Jh*T6+88!}FD;wnfw{7?$sbH!YYZXXpKEHlgoARAFh^ z9qleQzoYVEDckL~KF@FJ?@!wISc5{{_*`!XC5GIm%K5}5c(=BVWWj7&tlY6imYqu^|`6O zR!ts$>F(!%LN`$3cU)9suU%g2wNCBFYqrP8H!YuM3Tz#C_l^n6aq`!2Y|iTh4MjzA z#3CcGlLNw-DNJ{A$`MFu91IT183Xj$5QsExH*4M9-B)qSXW#bw$@kNjP5@eDw}_tj zrrj{tXwfKgWwhueA=Fq^Xw$-CZ?5$7Tervy#&4pinzW=jZxQesvoL3)4k-H{!&d>XSaQtC1r!1 zjm?eXTfE}i%RId50;1u;nJyBvBcsL9+mG4TG;a%BT3UJw_m#%q-)`(i6s^W8x#q#R zy=@bT(Q8U?k7Xq=n7#^?N39@MILEecSv)aR&CShNOI1p*q%|h& zv?b%;tgb=`9-W*#_@Xhc|Lx+H&g}s>8OKNK17)r+3&2?ogI+7x7d6#^%}{LrhQk*= zot~kg%_L*8LP25OEcEkWEWvslc<@77fcV954s|Upyzd}HWps}kTza2=s4=SB&)}*= z*Z#`}@|T8WQf1t?@WiN3@jfg#r_>$`hO!L^i&Vm3w5=GQ@m#@41ob>OqjQTLyNny5d>wrM0;-hiL3S>sfvbk4zkE_@4X{l9C9U z?)60@m&EVx?ru{76$3`zr`G1b5A;h3XYYWcRsif{Ej7sU`M1)ad63DU4=<8(b5EI? zvLa7lbj(oS-1Zk!?1)tVwY~fHMU9SH1*e<_1_;J-7;;uQUmSNCcMjS+32&pNrRC-2 zRZkIf*O_8Fl@4ZW@zP5pZ~bj^^L7M;18Zt(lm>ZgDB>eS_zmlkofKnWJX>Gi3;>>y znRyG?^T5fIT#y+IE(RP{ZaFt7fA8`_nxc7QS?Pk5TA{WO&!PRi)~C!jZ~y)4J25r& z1#m}QODnFu)5P{tzOeSxLx#!TjOh4gJ%{c>)#PS{*Cd(37atW*%SEC9fDj3 zn=bl5MMEnE>sd!{P>=Hl^zB)Nm2z=)k0R+c%>l&j;t_noL;>~Fr&V#NSHF5C+aja= z;n*X6KO9qkk%LOD5mWfOO5AsjF8+gk@88FO^5gg**efD!5q&k_A_m>dL^r}GzHo-T z!`vIi4Adq%Iyx5B-i$aEUW1@8-zO!#`Eu>bmC4R8xU4{g2&o+>OMCF(?4?fb4zVLH8xSD%tb(m<)^NpQI9>CAd+{k!i_tix4s>3c#v`(M>)~J>#;6rXWyP& zr{2&n&bqou84kb;uObj)zsJ9MB`R9lt$I$o1lrKzG(lmOT$+Q7%@{m=qL*qB`pSP> zlSl&j`uU0D4sfhBY8-6UQQs=&nQA@;;Px}J{o3~qA3jWnx}XLl7G#|^J3Cw5F{%9m zRQE!!KpxxevFx_n$Au(hZFZMlxNt$Ho4XC;MPaVdNS9ebMMzEAuH<&*it(}ijjVh< z^~)^nh)ZlsPD#sqybonQJvTWmW+-IIBjXFDZQfAh(aQ_}hYWs~~9$$D?o5SFZ z*tP&j4N6K%TlO5mwjS&2*%pA~v(4fnQszN36^A}eFVfXW3OIY4?Ea5012$p$Qn$%4 zaBJhIPiO5HPF<|y)EllHg=7_2TYK_+jW0Ej-Y`FSPc!`JU|;3atL>~66%|BdWMN-B z1~G`^Bm{$KrLz}Xx`kS|UqFBgS12DsjhKAch}w?#k}T!CJ2fp0#tCn5#9bs9iToZx zRxUa@5rgMEelXcrx6h<&CMJ1C9bXf+rxl-zcUZ~I%*^bW`+;l^6&;-lX!+X;cbnjD zSMJ|g(fh~Hrdn)OzopuDZ5L287Mvo?;05UQtwZUT-70W(K{e|PJ|F#_J$uSGX=!Kx zC#k2!mGnMX)VOT9XIpnpe+%deeUWADPW%-z(qP`5DQi5iW|*0Svhgu7F%9jF0A+K- zul69V!vYir&*lsSW_rL;!mg<#^BR=3K#D-dhHsCwOC4Z+OniJ0%-6DBi_*~t`wtQ~ zCF2|2LcFVNSK%F@t6@~Lv$F%GcC@zx1K;NAa-F`H4;Vd$&p5~Cgso9rTs#^d18zl+ z@qppb8q}j+Rm3JC_H&5$;K7m+6>skfZObk8=bz*9R%JD{GDRnPdivmSF5fqv&ST{7 zf405L)?n)G>9N}U1a*Rjo<6C{UtOK*3a)5Hr?@6e8WKQMFDww*ZfhHxQ_w+dot?FS zevhD|@A|^%EzOgmF)@rkY|wV4zH{eJW@ct(yu*o^8Fh%g9=~y)iQKQ5w}F5E{=JCU z(VP||mVBOTsep(`4#b=;Ka@;GWzvY^&71O!j8}dPr!6I;kwOpJn~JKc0ThlIG)WNm zZ3({5X>aQ8z6Yu*096kS=Zff9%*FsMAwOSRTi$&quE*2R&`iwRI5}x5D}yFZmbV2~ zo=i^4S`VUMTwF{Q6@fRRjq#2=JXu3S7LcHKo12*l$T?iPB=Wh|-}2!1q!#qQe7$$_ zUq*B^!-}yv=vgml!DFcU6JukFuV24jhT>Z=fkiq0B=)y{BG}Ys2DwgfrK(oRkH+_by)0P=JT$HV+R^>*vpD*d4O>eEuxwb+WSB z+uK)vY+n8h=3w&jhqROwX?Yn!gtYw&h-!jz{a~kG(ebXV93K~VDkUYQ`eQ!T)HO_e z;lVOK22%Os$Da+w0dMI4YH-rKRVbA+iwRg~}G{N-iV_6_Cfs1gs+r z_JJhfDlGGf^z^ANOt?8S7!5a$K&R}o;ng^O+9co7 zxV(FPVQlNP7ANlS=EVa7+XSICtaI(sXE9MU4I|^=*S-r3hUCq(NCjLt_HE z#J8jRiKtr9_wRE^!4q(*;kj2=r;zX>Q&?VJ-WID9I~&Db6`9uzh!8?@BGS@{ewSKa zzcx9u-uv#IBdTm)x$D`|;^GW6gr|Xd3V2$YcfUk+bg!wMC=04@YEqv6hlTs>`Y$I< zWPOpD>ct_U4e7&I{7CZ%4 zElmB-nJJDNyEZnxb$+s`bQ@j1e7Um!9i)&B*FEySGTXOp>p`Xb3Is6m)#4W|yfAX-(yTr9HR@HT&_*NO+bE;gE ztuI6LSzddP`9FFFh_&~a*W=Wj{Yh^bEOBhm;F$!o@3I@SvEmaw5ZN{xqlUxQ?dOC> zdG%pgS#~rnG+_tX&Ue4y^#jMI!fA{8fyLcZ?c+(9Ac#h!e$RKg`eXR8=W){zbBe+WJSf0P36Xe$2uW9+d(sa}f$DP1z0> zYLK!WYtQ4JaU#C?1?UcwyjrV9i2@A}K(Sm;;gKlwFg|aVl|!!Do(4NEbPCDW`DEb71BokuQ zFoV0p(WCw2>-|QQaC4e*MiS#FWJnIS=V*B|GdoD>&X+FT1p^a|QZVP(hy}>piZ`x? z!!kBDb^$#vkg6Ult0mlWBvPP+912{TXED+TksfEc3f9XQ%mlC(Yeao%w4#EeV`FdQ zy2VT&GKi=u?(QWSy;eR8333b`CttpN`2`TH=iNJ-L-Ne*+qV2&p2Ykf{-EGs4ZB%* zqZH`Fj#>W~`60WcM4TYo=-%3L{rYu}GV)vP@3$l#u{a!&)meq=zbGxol$CjQu+KJ1 zt7fpxmoZDUD-_iBfUvN3(ZqJs5n_?feXe3fLP8PCZ|uvL+cPs4hid%xpde1JOs^Mi z)%2eR^%^$)%JZ5!W@d(G7Sd$1-K+bwGTJ3_DVbdBoNj&{v+RF$mYxP&s6 z3VV!$gTuvyKgkQ9^aKMdE9HR$2cWHOMJg%g>!ZdOyMpWAzi0XI;RAGL`1`jER6C4= zOJm*dc(qrSTIFh|5C#HR76pzeB`d4_(Dh3YBdBaa^C=C|Xe~fH?nq2*+qP}Lh{#UL zoqP3GQ{rFM4A=NY03ICR;n_mCgJoraNwpRycXGd+_WiW2^}}T1(eq5RuFtF+DWGjy zVJj5Y=RSC_2V-vzd2YtW?g|PGEwk@!-|qt{e<#E==DY81-@ZLMKE4&&Ap;}i_$^Q^ zyV?yQ_N>TSM%>AJJ$mi11RLtf;d3wJy}Y~xWMq(5`_fh4x ze|lf*@6RD=zk_h~iSe5&B+XTOb%q4{EIHp*J<|8+W`K*Y=xpV7c6J6{%tB3i2c>;t zzT0*c?<-X0&eGh;Di)lwFGU@=$9`_JTYOo%vs^mrAw$CId1RMMLRD8E$;levcktR zuN{dJ^9MvC54GCX%1QyaLi0DHa{F&4WA~GPR#%Hf@gvUE_{uuG4}^aO@$>OBx>7fH z_n28$(1eS?_NguC&y0u2@13S5Vbl2GgUO{8ilu#ofB_{|84D;Ef|ViiuWilE!ApCg zS?7QqNQjALgwk{MVsSa*j7n``V`tZg3u<&SQ-CR0R7t{XtwBzgo=aYv_ySb@g!I6$ zivYfTkJRXU-@fJM$-kY-!_Y9! z#l__@a4^lu8@|4BPic1Y^Yarm3q&f;OgINMzZZgt?fe6bz<~U<|J12dnmRgmWx{cB zad`kpG~MWhG^U}WGj{ydg-sf!ezQ4_2L^!(yz(3f!a~Br*@RAT<9R@3r7Ze=OZ?T; z)Z(C8ws$k_+ov-)IEWdr4&9va0685_9y)S7g&u#OH>-EiNL7_0AwFKeL~EK)-}hql(%uqFB35;efwLeNxbcI?Q; z+k^wkN31jnTdFrHl4?`pfJnLECWeTmwY9W_;&HIp)4_O*C*BUi06YLHkZ;EecQSd1 z(zHpuK$%3t=Y3gEK0jzfM{caRq_EtLP2k6Ql8OGFSX@85sgED3j$JyxuBNOUjc^1p z)&jF`70@uS-{0TAOWK&F-K1JAiR;T5 z(Bbv1rxx%aiRcsJ8Y(ymr79~b1X7Q4q3yW;7fBfCAq$_s_x*cPP|&s&a}NZ{gZ&ss zoF9e_ObY^La82B$ro^-%<(~!F6;`FITeoga;MgN@R{#848w8D1)b8%hL?ol9!1O8e z{7ESAe~|Q=0mrR=`0Keq4ngd#`0i-$C*%j0@NhY~6DQh+hacYD+_?4!J2ns0> zgmSGEgOS;~PoEyRxVjRXotPd-JbReyt)FXAX$&U$A%9Q6`0K0uxwIrGE*`UsiHSvi z)HWfz;CjOl8tU-JkN9;T_F!pWL^K7#humJz_=ATJN5P{LUD`CBP&|`jFCsp;mSOJ#zMKW+xOb( zm-js@E{^W!36%f|2LB5k2p$DS=|zz+zP{7`FCnrX^ZRpzG4G#`rjZo+dVaK=8;&v@ z)}j64ED0^65n0=hVDhBxQ%mDEAVn_>5ZRGW@82f~3k$ctdlz?3VS3gwp6)K; z_J#!|&@#2%-ciozS z;Uw+phjQkZ9%RXePLve^^&@hk3Pg}f`w4jS8B(KXDm@OqNjq*j=Bd@XT8<)5w37Zi z2P6s$3;&(>w|Qr3=-Op;B%+}*bV<&aBoZ_fQTnmqq8v;+9h?9JY(@5g8E%X0yyvxR zNq;tU(~haY%o~?e1Gpk8!T%0-dfxSdckB@RQSb>Og1P=1REbJ!qz-sp-vc%doNe z#mmYn!+`t~f_iU3t%K$?wK7Gz?9$>Jg! z;lqdB*H>ImhhEMcs`cl>^YPHqsc65_9`z7J37qOn665)|ss z){JM_apI)-{v05^FL*|&b~Y@{y}i5ep^OEjj2MBkk$U%sSVM_py9qnDhe$lE`y&oW zL+2u-q_V?r)!@U72W}of!(8pqMp|m>=)^>Jue5qYvM@CfL94!pxGeSh58!+V=C`G? z6Ph8;W=Fa~5WELo?Dp^x$Qohg6k|FZLOX&7X|3TJAf>TBcEs4Ze^oKFu(;p%pyn17qz1yfun8qg zHZm&8_&R~L+4k&NlIV5m$sk`(+s(|ZU~2lp;NX)9jT>Sp%#;Xl+(MR%Kx}p%c{8#| zA`#gzp^Z;IFb=V&Dtwmsh;YiaYu6&VPI6+mCw_d&L-C5t|I|Q!29Wr|&!0+o^5CcF z#Fs=aUwP8*FJvEB4vj#_;+|95g!~NIBDTsO0N&?8!Q!p3V%xS|(8G}Y*$QD=C`pvT zX?Hm~-aHI~;6bKNt|I$2hHN$TRzpK36c&W8pEPU)M_vwQyGkQ2@znGwr?Zl!fx#Bk zsLF$z0mV1o_VqEtaS}Uvlv2;h_i?M+`CdJpn6@K&AFurwC^?m%_Mf1G;&%#Q6ekTd zNya^U4w+76=8rjxY$6Gm$3`gk7Aaa32;-6HUSlr@W2~DMGj`OGLnZQXvm>!wM3W&x$=p_#NJeJ?92jCpR-s)qCRzrM@mWxS29yUFE@F4?&i&# zGTsaO5f7*4X+TPCbZ)M5&{V91_BC6A?9rqhiKQ)=v_KA4BzjcWdL@bFC>0e|9Wde)5=8d2|F9(tr<_R6>gyw`!?HofBy)h%~w3QegbsOg7HbMtuU4QinI zG~(6bVI}NQwzJ|ZMNsC8*O>8k^nKJ5awwvN7$8QqefxIgJjdjSu5mU%XM%Cym|z?m z8^gG=gVW;px(3~o8$kp!gLh3dM38nLuqC9)fsY@R)YK@IgYa8dUaA2@skpgu>_^)m z!bsvs7}?s|k_ns}rQW%7N{$cOIEbAXB6J*uo?p$ERMZthM3JR42t2cI-@ZHrLvxS~ zD;t|YAV>|JrV6isHQeN6V-6rri0LVtPvP_d;hws9@ggZL-8uFV6&OXzu{w(dx;out zF>>fw27fAn!c+ghU`Im2_6#GnC3G7hB@%lBqa2-!F~^OKjg63;8xaOTUx`uIQlBz1 zF(L|V zG`C=9#K(Up@CFb^Ux-{Y=Vah5I7lqZ%V~Ja{a#;7#lRX!ueT* z`vZ^wMWdqblsJrFRitUFtE&g5A5>KxpY}n-hgg~LIjkH7YC{F#M)0Jhqy$Sl5C9LgjRIsk zMU_Pf%1=z?xdf(oRB{YJ-2V3MTb#*(X2Kze_BrIlq4>aEL)*)K!Vzg`xczQ!9wi}J z+Lfd5$rDB8use4ez(iOmE>&Im_WIBxK4gLc7;h8VfX|;%#OWX@guIgp6NNkha%*ks z2S_pydM_(9uN1x!(HBFe$@LKbXhb1d#8{?|6RJPUaH_IUQ&S6V&cWV>5?Os0(b0P3 z)O>U~*7i4v(Fi=*QsKSG6GnCQ>ebZ2IF`(ejDR;z-#$J+H2n&VwaOTnVE9!rl(+MF zuJ#B2L19S>6?PBb$9MjHgU$#eDl}gI(EbqxlgHdbmaxq1R?g-hiB33TE(ARawQcnK z_rot5GIZ1B!@|O%+ohifK5nQG#Q{N7eVl!h^KsG9b@z{)!BF6)rl6%0NR2H*#4n{S z#3RJu3yNtTnz-i7i)VGF;kskx1mTgO!!jJa1tVtS%Z_PM;f<(kYd4{}S3wzI;Us2d zl6oDK!Kf=$UPebpbJ*3D2U7Ydqhdwu75{wD_{U8Y3eUhh#vvQt4mx2e)$jlss z;~Lw57Qo7*tOj$4kVXo^=Nv~+5Q-O)D0Wd|^WzcN`|uPEzfSvX{E@plZnVCD3fDZ*Fj4S!<>}<%>yA7qyGO zWM@ZOxW;-xOdiJhFzo=E0ibG;hpFd%Bl_3|s(mEkAjCvR2P`bO&3$}M3za+ZqwcU= zz%6I_1@ueSzj;FkZ>0(HJxjTU08p*>-&Ju`uX|W!hqOmaoWU z1(Er`Gq17l_gw)W6^0wKfFn)wpqGj2&tCt8k7QnSjc?yFLNE`+QQkfm51UlftudpW z?6k4i_2r91EM7!Jge(9=0~<2%TwbD@y**#i`B!%!0}^>DX#3E66RS2MFfk%iMh2YH zg1MulVpo(QBHDTEQHZ;&{EZc1^Z_2ll!Gl;i<=WBy|$GL*kB%4Yhu&=K=Dx*N$v8- z4LMoa*r?$7;u-@Br=EH#_4QP~D7;Rhqpi}4zyF5;ydkNIvXT;_;F=GnmoDt{BCEsk zONLT&b(FBx!RZfm&ivbp;D3;rut@*4L+$eTZ7B919?#GO>g)b@)__y#yp;u2EYlHW z=P=S($-bPj5lWRCx}_is841~(G9DaZLClwpFrUHV2kP^|%p9nBM#!oeZ0R1dRAzVl z+4QCSE=4|-0-+=dme&_#fkJ^h9d;KoXzS=O?%LHrVY~8UgdW^5;bCq@1{n~5K`iCZ z*d0!SZ$ez&<Y}NkbGs6v2Q-A{&H$Dhj+6gbjn+htqFe-f|Cw@q&$w_2tX- z_&!8~0r5mSbs?Bn+Tg@vWM>b@K}Spkk^vhB`3b~Msr-Hh=>z~@u?tO9q}Z)%^P7Ku zIb(*Zm)=cHlz_uG${OyV;?-|FlX0G#8+G4*PhF2#|{m~YACR% zO@AsE#IU;aHLi#YySwjrR(5vKwG2^UlX*gfqY?~g?)Q4XIel1$0aPs%FzsBpo$uL^goHhHg`8+ z5?@I0D4-ECJWnt1 zMu48u*4_bKh7eka4hP`C%at3+%`*@p_Df6e27TMgIEFy;Wc!-2;-HJRF^}@K1jSa1 z1w83w^!z}|0OzAOGdD*}Ch1*&QoL-X`*=v-FOvFH5Ismfh2BduDf><~8ChAugiM&J zr$9i8hN@2w|4v9xzXu%|?Id}=o%+V=BBLF*3q~L$>?NXYuq%l^uDXvOIbgsO=_}@&tiD#S zf7>0BnIIZa@|@`4A_up?rx*uB5fc|z)YD@?3awE@p>3Q1>97+sii*1Ib3Kjymp<1b zm6VM3y~?#Wxn?S6OlwDO6m+x6+xz=cUX|L%O+gQvEd(87pi77+>Q)Y>?ufOQB*GFf zz{L=!2_Bq)*IO+gq0Woyw-t}81Wv83ot@x50i#50hH-$`Nt2t#i~*duE0SUdTL4C( zSfTFBT?GsYCXa0YI~RExx)U4V0Vy(^*>OK@+K~^+7}McA{1HA@i2?f=6ro_)PD2~P zg$ro*dIbEmaenFNPv*oe*rQB9{Q*daVJ{co__)->RMF(DC)NR7zd}1w4ChS6=H#bQ!L2e>4;{3^@fo zmkp?Kq%CBqT2h*`if|_H(I|qc5kD6v7ua(svBE91;-1dW`tOtHOHyiSg7hK!!rS}L zvSTIf9UOjuRZVaf2qO)PUTFCk+3JG>CfgI(gSOR~91yDN5pd)%E`%x3S zo^($6W0}LfPL*tc33jZ&nSm05EOdRd*nx6S#Mf|dh8DVZi~~@{#Nr&12#SHnP_+(oyLh9m6}B+`-9f+R)*F%7u{? zW?#wp4KE<65Gw8Z(F&7=5)7^y8yMV%?~fBO3w4u7g4b^TE9fQng48El`(XC=+8mOr z#OZ#AqS4$wdP<(*wL5N7@myIV^ z3vx6$|FT*Oy@EBI?Zt_hx0R!#V86h7Se+=x7YB;DLD>nlUo*yI9ep;_p+MV-S`vDs z`5S{J{!j;Z09DBs&G$d$y1Q5QHa5~I4&$EQUL~X%hnlAd9T3~iWz$ekj|{7bBzeMl zm+y|_ha^~qoz+K>R|;#sQ2G1Jm1N07feii+y0>#n*q|f&6mUaSL<$Z$qE};%#{gIs zS+FL}{Ssb3hWD|GSOBGNMJC0(NGR#hm^02*qLdTV14(F{k4qA)M!VtRVjgmQ!DzGt zX7<|zLfy!G{Fn-y2y18NbJYv&IF%4~NqZ{{&LleTh?Xa$N9q8y5K!{CC%Ar20>47r zP?y?=@I0Q#J9lEQpXAFtonWB1Y3wA4Fu!jZBdo*B+}u#?M^l3cA+ENcb~SLnt#QJ! zp+7- z+d(GAS?Io)z-PR=(cu9}2jLGg)K*)%&Wa-={MyN6;Oga(8)$-qO=9R%m46#8*^0co zO1f9D!RrPG&8L>0@oYdy4E15}9jWwGv!}?-f4=&vy1Llws{sLqA)i*jPmsPeBw%`; z=qn@gcA~^;r6h@VLR7Z+C zc~ap7apwN`VR_nK^}PTh%h8DQffHVU^pz}Ty#?Tsg)ajW7Pga(j*XGS|0HQU93)l1 z?sDJwb0thU%fmxT$udGq#0dmytA(+iNpW=(nU;2q|dtAg)J z{Px3uiYJz}51}|IDJu&K3Nr87mGx8l>$h))z%=+OM#lf+QHvU)wCz7j*7RWV;Q1EOhrcQ-$FdzTGi zn}f5MIv&rDz~JKrc|K9md*JaJsCz{03!I$=cO>LJJBPDf(=~1a44Sw=0lyLAT&G}3 zgBft*UmGjpX!|}`7K)T$SQx_S4#fP4%Jtlw9AbD}D^vt@SzLt18GrwN{?DsOK}CbF z``ozk4lBMDt>as^ZZ*EiDc}0il;v}+u?p1Jl;q?dY#K+x564NQsjK^R-X6*LjaFm` zAIkYCp?e||%^}ff$hdN}K$8gn!@mtj7Ek%($B#z2r}v>(>Bg0#S`1J+64vrbf6(~r zTj>hFDzycU!COSgbkO6Sh0M4JcpVk zar9^|7It2>PUqeQEM_8JhEFv8_b-0c$@6$IdOlT7JfLy57cZ(q$;-tr1q8zghspF> z*|rkmd%zfnm;Vm&cET7ZS_iKA)6?G*a@lthERUGJh=hg)#>pLi^YFw2@SKVHr&t}} zdc0_EyV~_cK>97BR-|%wVy|;xU|Z#PvAZuWkcUM_TnuCOh9(sv!UtKh`Q#k#ar0){q z;({-}8*bcP`+~$LI@0!^J9myph)zs$L*dE=1V9i|m>a?Wd;jkNKsXc^u7mvihDez` zF)O*9nwpybTqjn}dm#WOr79L+E`olumv%BTB6E@ODd*`^OS;2(*LTp-eZ{|YVL&$M zHp+;_n?1)JmN6>swNm!jpM34fgk3@V%u;l#W=bLmvyzC1H zj~%=3CyiSfLIJ>Yd&Q41k-5S`bG&%5Saj;g<`%d?@nB_l=}-~0LGFy!d@haD=pb62 ziM+5Lun7_QglU$uyoe`k>+1UC=T-6U??j*m`!)RWlyG^!5!@-~rYwJzHH+ObND1cQ zmPmx41YwPA*|KE@pTy%gJuU44L<)W`FC(#$z0>@bbyjcFW7LdjtO*i!GSJfu-cEdK zYGGei5s217;tt2k%@NcT2?>d?Fk)xo0WyoYA^Y>YtYWt_&+}b-)5+0%{CJ+vXb+LN z1HIrpc{36-t%=wifkmLx6EloX|0K@Xg{7raAd8C11VKej`BV~mGk8U zu|E#y#p5*0R$z37euCjGi?!35|4El<3t}lZpaAJ4rDP- zvSEsePtgJR>G;}d%o16TNX={HP6lrbw71g$Z{B$REIhMtryHVtz#MNemV_+-Jvkxq zZQY4hVt}5l0*CcYc`q+j*1g#FEb+{$+uBdNLY(@C?T8r{kZmXDFJHVEwz}#~=naI@ zhBgUe7ju~0yc8>AtO(8n_7Vsdrvyt7dn;C`(qwyV&IHz4C?LB*&O|#;)AF3>smIp* zmP8vap&19==f+I>U!QfHamL<88=PYh)s0Y4#0~D{uGBgj@mbT=DtE+;y0XH+`GcF zw~$JoxjIXr6Q+S^3Il!O;pQgC(H$5VcuLkV0q+7`@0D>Du(}e;ok4~HsswZS&G(VZ zX*4E)a+L(?gv|EI#$VGFe|()hMl`bRpyxe!$Yoo<`Uv$8 zrkN=!3Bd~El-n| zG%s(^#*3FPk3D<-T$bq}TU-aIL|t7S8GJg!i{7F>J2m2|oBA7cT*>}ODtrhax>`R< zmgnEWCX>6qOohIIQvfm;PH8qhScK5Xo0qp_0kfGtJvISjjS_fY%z1M-&h4G#A zXiKxgU3~bwEl56*B*=LY;udf?bI|#Lkh;3MRar^X1@Q-@bMLoNP+UMz)f*#3F#53Z z692&+rcQ!st@78G)rF3$zo*?T6+_7V0IbSQ_q~zT^7V)?dfulE;B_0X(dOqwTWd#0 zBVHvEsr=V0h=qvR4czkboG9u@qT)Zq%`Pl2suS;6xw_H;ENyafJvP?h z;8Z6BE!-ox6>B#HxPh3Wf>!Ta7KS$1fnCcDsOp zfCAJ4+xe5;3syvM8`6au`j9XqkqU4gKQwM>$rmpdwPTy!{U)=g=TCHPIP2g2NVd$s z!*Jyxnr4V;=AZtD(uVi+^fr12hP|dtW6ac^pwGMkG35kPu=p zG2R4o2f0#@UD2Eu$G27mNk*RncY^q)U}6z^NVsqsZDXwO>e`8;KCbF%Vel!Hg2i-N zV*JO;E6!Jrj>i#YipV73%dK2oB9Sn`icY-o;k|*@;lD&OT*R^_a$>^P`^F8ze1f8r z*V%rI4E^i-j@O%*J~fv3YxIBjs8WXN!3{8|RcK;zvP6-QM+rNJ(4x>vf9BwH-d7wf z2cSYC)Zc)yK19nSLQ9sD(>|*U+$g*~xS-@OZr$-X=pNA9ynr?wD|kEmd5}rj1|izm zhhiD-9VVa0ZoPc4k{+Vlz@&GKKK`0t$0_xzr3i(JSX2anqmz*VV~mZC?!PuB1EUyG z7L);UH19+rWi|$L4%23$Z`>u6H3UbnTlVwF`TyIC4s4GWisu5lI7gUrcGkUdJQYmvAUqAskEYx!?~A8m^0ifHyX%KS zc0@mkKCn}2YPt8%m~>1Bz;wpf2ZAUQZP$*Dj(7+P$n0J@`XsTNP&xlP+GA z6;)L>SUvxza3bVts$+|!7V*^3v9W*{J~_n#=(Rcdy<$tmeg_K z@6(wGKaA8jc-fAqpoCO}d^1@Y50*y|(QFInm@EJ>W8$96_viDUqqz?a>qX^_MM^Nl zoP#1&P?n4RH*XS$9$I23$sq7yK$VS+;iU&W-{0JC1*IMR_6?ZgOtGqeX(TtiK-}4L zsM>#%6IXgzy>JGVVwd?~5W$N9L{@Ji&|H>LP;fSHc)L_Ml(H-=6pVn_8%70PRskgj zKbC--%Brd+*O;8wg7iNqlqYsa0P@YHT~}xpZ6Ve z9%y5gR}G9cN|5!$Lyy5!`@4>Dv9S{z&kFMLh=e@UM`-Mn(5z!pQr6Php^;S(^G)C@ zGgn!mHPrm`n?K!#(wX-(ykpfA6v#mDf{A*EFp6QmCCi0lpU@47;#RzI(bu=S{SQkB z)F|mh^7!^Eg7@Ej$l6PAv;efw<56#>Sf#^1Kl|nCzrQ|2@&TM9p{n-DlP8^{A99mj z*KlEg;J%Bnf}#Vo(n+Kfld? zVR*4gLxr!if6(?NLptOngkd5?#Zfx<5gVJB8bDa%_!fg17f|$x;UuoE--Rx^9!@7r z2o7?$e3qS3YC3xWp_lIzC$wdRTz07BxDrO7a-sSiuh$=@YNoa08 zv{7)KMs<0Pb7144L)gMGn6{V%#_xFZ1T-ePeaDt_y4$+CKEQjCLT;eaLL?2v9{I7^pOu&SjvYHg z43zwZt0dK5)_mhXcI;!X=jtI4sEVb7=d`r;THG^3ZACV!k5J|%P?Rw?N2Ks0IMl&oev8uE6q`jzPlSoVft?J)%hWx4oO7}^?8oX{v!A> zA~spy_ly)kbHzQrX$Y2oJ{I7c@a}6LTB4RQ5mqA>*P~-lon2mbcGJ&9t=04*k?7#d zxbQJnwqfVPw`r_buiMCaDk}#;aC(FY7aj#t#!dhLY4Y-DSOOaOfW9oWy7oEpr-^#V z>^&+xH8;anUGV63F4`A~9*i{k+?twyA;E(W*%~2f7?&~iFs(HstzG*+lxZJ+tmv$^ z(n%v5ot%=yG7U-_B2qKR6j`M*AvugBS-Wf6lqtK3QXz_wL&9K~4pS+qG)P7c6`Coh z6|Iim>(1x1@B0V5eyNXn#`DZQ_x(Lw-|PFmT8#Ml8{7=fzkT~wefsn|pPBrkZ7DRX zsI7o3nVU8xG>;6meeN=&S>|87y=B)mC8fizEEee?eRmDbldcd8iO2I=0${QO(9~R22UA&v$O$ zelC4QXV2}O(&7Yt&|?_2as{D+eWR^*B?fb1g4{K8;`rPQMgpL`L-IsWx$qKg87 zE(3FdWyp0OTbjb|waJXqIiA!@WyZ zLvZZ^OC!qnz1;8SW=F?kfJIW#r}YN9w>;nl>YAES5^*72okq*WS6kl6xYD~|^2CXC zh;(Z{6{52NfRh5SLE<@y*U)oOWD-UQa>Pm|W@dW&`d4gS2gB}+1P)<)Y<6&nBmDmC zcZnmI-|m1ItsL8Q2;;xyz+M?8#=_!a*3i&UfO>6D^QUfUeSJrXJDN(O`~bQwU(j}q z7@{pV(Z#UBY7jKuN=;|PYCJPHVd0l&Zic#XadE{M1+`5axdo;7O!4r`i5Qf7@LWJ# zRL4nM9LsbR-yQaQ-5u9{SSDnmr+W`dU@bo@bfeGbpbLX+sCjY>?`DG8RStn)1IA-m zT^z^{ecJ>oZLjnig{?Op3l5TD`+-ZsC#b0r4bDOEblNudcjW%wgaO(vK0X%@^np0C z=R?+GfC$3$RAA5UK(`amOT11-zgKFFxXN7&2QJEeUHPHw?3oq2&^3Ov2(drN)cxHC>i zG1TbQ=)-6D8rGB~;8Xw$$U@B*k-Zb{R|imf5Is5`5$C}+Bccy=u%A0-$Kp0<&h~Md z-G5t59bWyNY;5?eJFP9x17a%uzsfN8`x0h+d7?1;5h577cJB6khtsDE-YsUpe1QX*xqK7W3n>1l{-u*&y$9z|ueTkUb+ml2gz z|2{L?0Vtk^LNS$e5$emwl&7YruW5^Xcf7@$lHc^>a9X;otzg!wA3ZAhyb9P?d1u>n zsv=EK#TmgYFe5WtCXusyZ|4~6P1>kPxg7*q96d|}xCkByJ~2>2lzQATtScd7{?~q< z-`>6T5XaG#7};63DNU=jyt{c>m{$;dsRHxDYq8oxM63v27y)4zFSL1{nyt{zLO2Uu z?5HRv`D|ZfW7B{j=V_34iyTYS5s7A@IAHF_GQVU4+ z6dng8p3slO;NTuYj_rADsytc7A6`Dm2B-NcRG0jNnN&m}fGkw+&*pZb3dppxJJR|3 zg?N&+bHk~a%S6W)9wF4h{os1gEd>GrW(Ra!4F){+$fvEj?doq+qcYRdR)cI%hRkK~ z;`}dmfbEJ=X}APpoXFN+> zXhTdi0!fUJiC@Mfrqot?>9`Wr@)Nqycq+~`%(*T^-8~&!8j#7o zRRKfy(lFHaoaG@8^bl^`xbegXv8+k0pgE|kW)8gnK&;Cj$DGT9byAhw{lI0NYZX-i z0}7K%#*twTmI6j1jMudSfM`dE#Y!}mnC5Vmu~VuaK5Rg66!5`Zjb}rm8=EN%(kaQ` zx=Kmhj&in}lT$o6<0fIN1s@gWZaW4Lv3I=-S#g$mxpBjVZZixd+n} zR2`H+wbN^XM&L*irI5Q z-4+xkiJ9jOZP00oE7Wd_b%F!bVKbI}o4Pd!Yb0o6pq{-L3b#J4i(im{eLs_l!ccN6!<>a6&!r+)~#DL zG-YZZ-Gj|$d3p7@Dz%6o&ZRzYgF8p@IRjza`UL3f7=|)MjoS_EII{C^(~D zz^CfLhcp3p4(;q{CnbtYM6zJP&oB;_=wzp+TEo{|!-kbx1{7!Mo11TeUgB@iQ(Q^( z9E|v5ky#*~7{Zf-$(ICHMLrhb@gRZRCDuX~7!6Rwa>D0)Q+Ju)@&Z;3(cr@!w=g!@kSFmDUQ5+7O1B!K zy6p)SwqNif)@l39Q(rn3TV18E;%k`i&%iIso(@vF*!I0h!i1oIkn%A+id%vSHaW3F zMHyTxBwnWqj!Ir=DJD@gqe(WXJJkXg ztw}Egkb8Zpt_CBb$k4NbG)Xtnjz0cDY4VLtKngYjVL=~Oi-)V7HJg_>*!vpFnZFZm z2q=>d31}io1zg|X1Sg>nqRB?U1*EOt zU`yfJ1awxWLRn!WfE8oCW+mL}hMKpxH+oEqfLo$5KnsQp`GZut3HGlPTUw1@_XqcN zRFu3%$@xOw4>qw&jErjYqu3Kh)JM;ElFu%j zBJ~+FbQB-Qoq-OERYC?LVJM?{0R0QnD0}kWB3mFBi0UWH)m}AfGcypk%!&2Z3=}zY zL>DAI+TWb4uJMWWJ~8@q z!;E$Nr~)QztZ88D6=aof;3*RF7Qe)dLjU;D)udYQUeciwBM>1G&3ob?0G*Ku56H`R zaNVPA8|v%jM4BDcvJ4#AJ#guQ?Ch&_eez_~URxDd+2f-pD2%5R-T~rrORz&V%ri1waE?*&t-Cvr9^z_7;p@O&h

UkQ5Q#3R-p7m;MXi$J@ zX1Z<+dDZ%#soS~ucQ6DsO9G_;BwA>XrW5F%C*%7k*KD`)wN|FK{{oZI(^|<+7#x1Z zfAC`8vw7c>SE=_noZtRKQT^k{m7_naV&LpXe{N`Fv;1Fw{c?o5?|9a5wcf)H@^|fj Ma$1|WW_!$k023^Z1ONa4 diff --git a/notebook/Inspect Attention.ipynb b/notebook/Inspect Attention.ipynb index aef1a69..06f6805 100644 --- a/notebook/Inspect Attention.ipynb +++ b/notebook/Inspect Attention.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 216, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -27,11 +27,12 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "path = '../sparse/pretraining_output-small-300k/eval_results_att.txt'" + "#path = '../sparse/pretraining_output-small-300k/eval_results_att.txt'\n", + "path = '../large-corpus/pretraining_output-400k/eval_results_att.txt'" ] }, { @@ -43,22 +44,23 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1156, 1)" + "(9769, 1)" ] }, - "execution_count": 219, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "vocab = pd.read_csv('../sparse/sparse_tmp_vocab-code.txt', header=None)\n", + "#vocab = pd.read_csv('../sparse/sparse_tmp_vocab-code.txt', header=None)\n", + "vocab = pd.read_csv('../large-corpus/global_vocab.csv', header=None)\n", "vocab.shape" ] }, @@ -71,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -121,16 +123,16 @@ " \n", " \n", " 0\n", - " 53\n", - " 53\n", - " 11\n", + " 221\n", + " 377\n", + " 14\n", " 2\n", - " 6\n", - " 111\n", - " 127\n", - " 7\n", - " 576\n", - " 1142\n", + " 11\n", + " 8\n", + " 3130\n", + " 156\n", + " 654\n", + " 262\n", " ...\n", " 0\n", " 0\n", @@ -145,16 +147,16 @@ " \n", " \n", " 1\n", - " 38\n", - " 38\n", - " 8\n", + " 1814\n", + " 1814\n", + " 16\n", " 2\n", - " 139\n", - " 25\n", - " 387\n", - " 81\n", - " 38\n", - " 25\n", + " 11\n", + " 22\n", + " 3597\n", + " 102\n", + " 212\n", + " 8\n", " ...\n", " 0\n", " 0\n", @@ -169,16 +171,16 @@ " \n", " \n", " 2\n", - " 206\n", - " 206\n", - " 9\n", + " 8\n", + " 8\n", + " 7\n", " 2\n", - " 6\n", - " 44\n", - " 426\n", - " 104\n", " 25\n", - " 237\n", + " 20\n", + " 22\n", + " 26\n", + " 8\n", + " 20\n", " ...\n", " 0\n", " 0\n", @@ -193,16 +195,16 @@ " \n", " \n", " 3\n", - " 25\n", - " 25\n", - " 30\n", + " 8\n", + " 8\n", " 2\n", - " 6\n", - " 53\n", - " 25\n", - " 25\n", - " 426\n", - " 237\n", + " 2\n", + " 11\n", + " 4\n", + " 3618\n", + " 2928\n", + " 415\n", + " 8\n", " ...\n", " 0\n", " 0\n", @@ -217,16 +219,16 @@ " \n", " \n", " 4\n", - " 289\n", - " 289\n", - " 11\n", - " 2\n", + " 22\n", + " 22\n", " 6\n", - " 44\n", - " 25\n", - " 140\n", - " 43\n", - " 237\n", + " 2\n", + " 399\n", + " 24\n", + " 24\n", + " 24\n", + " 24\n", + " 22\n", " ...\n", " 0\n", " 0\n", @@ -241,15 +243,15 @@ " \n", " \n", " 5\n", - " 25\n", - " 25\n", + " 60\n", + " 60\n", " 6\n", " 2\n", - " 7\n", - " 576\n", - " 111\n", - " 678\n", - " 53\n", + " 20\n", + " 22\n", + " 43\n", + " 8\n", + " 27\n", " 4\n", " ...\n", " 0\n", @@ -265,16 +267,16 @@ " \n", " \n", " 6\n", - " 1142\n", - " 1142\n", - " 16\n", + " 8\n", + " 8\n", + " 21\n", " 2\n", - " 53\n", - " 1142\n", - " 193\n", - " 25\n", - " 253\n", - " 426\n", + " 31\n", + " 32\n", + " 33\n", + " 414\n", + " 37\n", + " 24\n", " ...\n", " 0\n", " 0\n", @@ -289,16 +291,16 @@ " \n", " \n", " 7\n", - " 25\n", - " 25\n", - " 14\n", + " 24\n", + " 24\n", + " 38\n", " 2\n", - " 398\n", - " 253\n", - " 426\n", - " 426\n", - " 426\n", - " 426\n", + " 6\n", + " 15\n", + " 17\n", + " 7\n", + " 24\n", + " 74\n", " ...\n", " 0\n", " 0\n", @@ -313,16 +315,16 @@ " \n", " \n", " 8\n", - " 53\n", - " 53\n", - " 34\n", + " 65\n", + " 65\n", + " 4\n", " 2\n", - " 57\n", - " 58\n", - " 59\n", - " 107\n", - " 59\n", - " 42\n", + " 6\n", + " 7\n", + " 8\n", + " 4\n", + " 24\n", + " 11\n", " ...\n", " 0\n", " 0\n", @@ -337,16 +339,16 @@ " \n", " \n", " 9\n", - " 25\n", - " 25\n", - " 12\n", + " 7\n", + " 7\n", + " 13\n", " 2\n", - " 398\n", - " 38\n", - " 38\n", - " 53\n", - " 1142\n", - " 50\n", + " 15\n", + " 17\n", + " 20\n", + " 8\n", + " 8\n", + " 24\n", " ...\n", " 0\n", " 0\n", @@ -365,40 +367,41 @@ "" ], "text/plain": [ - " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n", - "0 53 53 11 2 6 111 127 \n", - "1 38 38 8 2 139 25 387 \n", - "2 206 206 9 2 6 44 426 \n", - "3 25 25 30 2 6 53 25 \n", - "4 289 289 11 2 6 44 25 \n", - "5 25 25 6 2 7 576 111 \n", - "6 1142 1142 16 2 53 1142 193 \n", - "7 25 25 14 2 398 253 426 \n", - "8 53 53 34 2 57 58 59 \n", - "9 25 25 12 2 398 38 38 \n", + " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n", + "0 221 377 14 2 11 8 3130 \n", + "1 1814 1814 16 2 11 22 3597 \n", + "2 8 8 7 2 25 20 22 \n", + "3 8 8 2 2 11 4 3618 \n", + "4 22 22 6 2 399 24 24 \n", + "5 60 60 6 2 20 22 43 \n", + "6 8 8 21 2 31 32 33 \n", + "7 24 24 38 2 6 15 17 \n", + "8 65 65 4 2 6 7 8 \n", + "9 7 7 13 2 15 17 20 \n", "\n", - " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n", - "0 7 576 1142 ... 0 0 0 0 0 0 0 0 0 0 \n", - "1 81 38 25 ... 0 0 0 0 0 0 0 0 0 0 \n", - "2 104 25 237 ... 0 0 0 0 0 0 0 0 0 0 \n", - "3 25 426 237 ... 0 0 0 0 0 0 0 0 0 0 \n", - "4 140 43 237 ... 0 0 0 0 0 0 0 0 0 0 \n", - "5 678 53 4 ... 0 0 0 0 0 0 0 0 0 0 \n", - "6 25 253 426 ... 0 0 0 0 0 0 0 0 0 0 \n", - "7 426 426 426 ... 0 0 0 0 0 0 0 0 0 0 \n", - "8 107 59 42 ... 0 0 0 0 0 0 0 0 0 0 \n", - "9 53 1142 50 ... 0 0 0 0 0 0 0 0 0 0 \n", + " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n", + "0 156 654 262 ... 0 0 0 0 0 0 0 0 0 0 \n", + "1 102 212 8 ... 0 0 0 0 0 0 0 0 0 0 \n", + "2 26 8 20 ... 0 0 0 0 0 0 0 0 0 0 \n", + "3 2928 415 8 ... 0 0 0 0 0 0 0 0 0 0 \n", + "4 24 24 22 ... 0 0 0 0 0 0 0 0 0 0 \n", + "5 8 27 4 ... 0 0 0 0 0 0 0 0 0 0 \n", + "6 414 37 24 ... 0 0 0 0 0 0 0 0 0 0 \n", + "7 7 24 74 ... 0 0 0 0 0 0 0 0 0 0 \n", + "8 4 24 11 ... 0 0 0 0 0 0 0 0 0 0 \n", + "9 8 8 24 ... 0 0 0 0 0 0 0 0 0 0 \n", "\n", "[10 rows x 67 columns]" ] }, - "execution_count": 220, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results_df = pd.read_csv('../sparse/pretraining_output-300k/eval_results_masked_lm.txt')\n", + "#results_df = pd.read_csv('../sparse/pretraining_output-300k/eval_results_masked_lm.txt')\n", + "results_df = pd.read_csv('../large-corpus/pretraining_output-400k/eval_results_masked_lm.txt')\n", "results_df.head(10)" ] }, @@ -411,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 6, "metadata": { "scrolled": false }, @@ -420,24 +423,24 @@ "data": { "text/plain": [ "['[CLS]',\n", - " 'if',\n", - " 'boolop',\n", - " 'and',\n", - " 'unaryop',\n", - " 'not',\n", + " 'assign',\n", + " 'name',\n", + " 'optimizer',\n", + " 'weight',\n", + " 'names',\n", + " 'listcomp',\n", + " 'call',\n", " 'attribute',\n", - " 'inputs',\n", + " 'decode',\n", " 'name',\n", + " 'str',\n", + " 'comprehension',\n", " 'name',\n", - " 'expr',\n", " '[MASK]',\n", - " 'attribute',\n", - " 'build',\n", - " 'name',\n", + " 'subscript',\n", " 'name',\n", - " '[PAD]',\n", - " '[PAD]',\n", - " '[PAD]',\n", + " 'index',\n", + " 'str',\n", " '[PAD]',\n", " '[PAD]',\n", " '[PAD]',\n", @@ -485,7 +488,7 @@ " '[PAD]']" ] }, - "execution_count": 221, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -498,26 +501,38 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "19" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Actual sentence length, unpadded\n", - "emb_len = np.count_nonzero(pred)" + "emb_len = np.count_nonzero(pred)\n", + "emb_len" ] }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[CLS] if boolop and unaryop not attribute inputs name name expr [MASK] attribute build name name\n", - "Label = call\n", - "Pred = call\n" + "[CLS] assign name optimizer weight names listcomp call attribute decode name str comprehension name [MASK] subscript name index str\n", + "Label = layer\n", + "Pred = n\n" ] } ], @@ -536,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -545,7 +560,7 @@ "(18, 4096)" ] }, - "execution_count": 224, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -557,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -576,14 +591,14 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "

" ] @@ -619,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -628,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -637,20 +652,38 @@ "(64, 64)" ] }, - "execution_count": 229, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "m = io.mmread('../sparse/adj/'+str(0)+'_sparse_mlm_split_magret_adj_val.mtx')\n", + "m = io.mmread('../large-corpus/adj/'+str(0)+'_keras_mlm_split_magret_adj_val.mtx')\n", "A = m.toarray()\n", "A.shape" ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "pd_adj = pd.read_csv('../large-corpus/pretraining_output-400k/eval_results_adj.txt', header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.asarray(pd_adj)[0].reshape((64,64))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -660,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -669,12 +702,12 @@ }, { "cell_type": "code", - "execution_count": 232, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -694,18 +727,18 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n", + "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, - "execution_count": 233, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -716,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -726,20 +759,12 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 54, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.5/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n", - " if cb.is_numlike(alpha):\n" - ] - }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -766,16 +791,16 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15))" + "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18))" ] }, - "execution_count": 236, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -786,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -796,12 +821,12 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAI/CAYAAABAoBw9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XecXFX9//HXZ2Z7yaZPSAaSEHooUgKhd6RDAJWiiKhoRkUUUfmKEmxfv4J+UXCC+hWwgCAlwA8MqAQMkd6U3tKYTTJpm2y2lzm/P87dzezubEkyk9lk38/HYx+bvffccz8zKfPJOed+jjnnEBEREZHcCOU7ABEREZHtmZItERERkRxSsiUiIiKSQ0q2RERERHJIyZaIiIhIDinZEhEREckhJVsiIiIiOaRkS0RERCSHlGyJiIiI5JCSLREREZEcUrIlIiIikkNKtkRERERySMmWiIiISA4p2RIRERHJISVbIiIiIjmkZEtEREQkh5RsiYiIiOSQki0RERGRHFKyJSIiIpJDSrZEREREckjJloiIiEgOKdkSERERySElWyIiIiI5pGRLREREJIeUbImIiIjkUEG+AxAR2R5FY8kRwGSgGGgGFiXikZr8RiUi+WDOuXzHICKyzYvGkgYcBVwOHAGMABrSmpQBNcAC4JfA/EQ8on+ARYYAJVsiIlsoGkueAswGRgHlgPXR3AH1wBpgZiIemZv7CEUkn5RsiYhspmgsWQXcApyJH7naVA3Ag/ika302YxORwUML5EVENkM0lhwLvACcTbdEq/7tu6iePY76t+/qr5syYAbwQtBfF2Y2ycycmd2enahFJB+0QF5EZBMFI1oLgElA4RZ2VxL0syAaS07rb4Srv4X3ZnYJcBvwGefc7VsYm4hkgZItEZFNNxvYiV4SrdLJp1IUOZBwWWSg/RUCOwb9Xtj9ZLhyx0g0lryPXhbeR2PJtIX3hl8WJiKDhaYRRUQ2QbAY/iz8yFJGoeJhFI7YlVDxsE3pugQ4K+gfgBEn/uZogKIdDjkBP904Fp+YVaV9FQbHZwAPDz/qJzds0gsSkZzTyJaIbNfMrAJYC7zgnDs87XgpvhRDMXCxc+6PaedmAnHgs865W4NjI4GrwlWTr2yvW15o4UKKxuxHxf5fpmTHY7rcs/7tu1j3xBUMP/ZGyvc4v8u5pqVPsOGln9O6+nUIF1O8w3SGTf8Oda/cRMM7fymLXPjMb6MxpgK3FI3d7yx/71BBW+1Sap/7Ec2Jp0i11lM4cncqD/oGpZNO6gx71YMzKlqWPVMR/Hybmd2WduvJzrnFW/Rmishm0ciWiGzXnHN1wPPAwWZWmXbqcDaOTh3f7bKOnx8HMLOJwEvAt8Olo0PlUy+mdMpZtNa8x5qHL6D+zT8NKJaG9x5gzSMX0rr6dUqnnEH5Xp8i1byOVfefTtuGD30jC40AXgfOBisFaNuQYNV9p9BW+yGlu51H6ZQzaV37DmsfvYTm6gWd/Zft/glKJp0MQMmkk1IVH4mtDZWMvAG4Dlg3oCBFJOs0siUiQ8E8fHJ1FPBIcOx4oB34J2nJlpmFgGOBhc65JcHh3wMTqw7//vMV+142raNtqnk9qx88h3ULrqFk0kcJl43pNYBUSx3r5n8LQoWMmfEwhaOndp5b/+wPqXvl5o4IyoBS0mp1tSx7msqDvsGwad/ovKZp13NY88gFbHg1TvGEIwA6R9GaFj9KyeRTQ+V7nF9Zdej3zgL6XXgvIrmjkS0RGQoeD76nj2Adjx+tuh+ImtluwfGPACPZOKq1H3A0cF/FvpdNIi0JChVXUTntKmhvonHhw30G0LT4UVzLesp2O6dLogVQeeDXsKKq9ENdiqKGK6NUHvi1LteU7HQs4YoJtK58ta/bpi+8F5E80ciWiAwFzwCNBMmWmVUBBwA/xY96EZx7Fzgu+Lnj+KEAhApH1T7/09FY1+LwqcY1ALTVvNdnAK2rXwOgaNzBPc6FCsspHD2VlmVPZ7y2cNTeWCjc43i4YgItyRf7vC9pC+9VrV4kP5Rsich2zznXYmYLgBPMbAxwGBAGHnfOvWVmy/HJ1uzgu2NjsjUKgFTrsRte+nmv92ha8neWv/8AqdZ6wmU96pPS3uCTsrrXfkvtC9eTalxNqKiSoshBVBxwecYpyOQdPjGzcBE1T1xB05J/4FrrKRw1lWHTr4FQGFyK9U9fR+MHD9HesIpQychM4ZUBs6Ox5OTq2ePOBy4D9scnYouAO4DrnXPN6ReZ2ZHAN4O2Y/APFCwG5jrnruv1zRCRLpRsichQMQ84EZ9MHQY0Af9KO3eKmRUDRwJvOOdWBufWAxRFDvrfMec8fCm+3AJttUtJ3nEwReMPo23tO4RKx1A0+RRSTTU0vj8HgNa00S6XavO/aG+jZOIJhIqraN9QTdPix2j6cB4Fw3ftNfCmpfMoqJpE6S4zSDXX0Pj+g6x55ALCI/w1jYsfo2TiibhUGw3v3gNA2/rF3bsZteJPBz8MnAokgPvwi+anAz8AjjezE51zbQBmdjJ+fVst8BBQjZ9e3ROI4Rfdi8gAKNkSkaEifd3WocDTzrmmtHMXATPxG0k/nnbdswBttYv3ydRppsXr60pGUv+fX9O8dB4c+l0ASiYcRtMHD1A07kBGHH19Z9v2umWsvO9k2ta+02vg4bKxjDnvb/i1+9AQPZqaeV+hbfUbAEQ+Pg8rKAEgVDqKupd/QfPSf8Ah3+7so/7tu8rbNyw9FZgDXOSca+w4Z2azgGuBLwG/CA5/Hr+u9xjn3L/T4zGz0b0GKyI9aIG8iAwVL+NHqc4CptI1oeqYMry62884514Enko1rj6u/q07KugmXBmlZNLJtDes6jxWOGpPANo3LO08VrrrDKxoGA3v3k9rkCQBhCvGEy4fB6R6Dbxw7H6diZbv6xwIFYBrB+hMtACKx/mHJdtql3bpo/61/zOsAODS9EQr8ANgDT7h7K57W5xzq3sNVkR60MiWiAwJzrl2M3sSn2xBWrLlnFtiZh8AU9hYDiLdhcC8dU9euWv9a7dSGDmgc0ecVNM6Vt17AmNmPNJj3ZVr3birTqiokop9PseGl37OyntOADNwGRIs6/l/YAsVdvs5TKh0DKmGlZ0JV4eicQcBIVxLLeueuppQ2Vhceyutq98gVDISl2r5tpk10VMzfoqwwx3AOcBzZnY38ATwL+dcIsO1ItIHJVsiMpQ8jk+2aoHuj/E9jk+2XnLOdalJ5ZxLmNmB5Xtf+kzLihemNr57X+caLCuqpOqwaykYtUeG223co7Bx4V/Z8PKNEC7GCkpxLXUQKiRcGSVUVEnrKl/CIVRUmaGfniwU9qNb7V2TrVDxcApG7k7b2rdoePtuXNvGhC/VtAbgWwPp3zl3v5mdDlwJXAp8AcDMXgKuds79fUCBioiSLREZOpxzNwE39XLuCwQJRS/nN0RjyS8BDwMVHQvkS6JHUr7Xp7q0Ld/jfBreuZuWZc90Hqt9/n8gVMTYj/2NwhG7dWlf8+SVtK56FSseTqi4S70tisYfyojjfpkxpnDZGMZ9smfph1DJcADGf34hAKnWepb/3xQKR01tH/vxx49MxCPP9Lgo82t+BHjEzMqBQ4DT8evaHjaz/Z1zbw6kH5GhTmu2REQGbj5+bdMma6tdTMHwKYQrol2Op1LtNC16FIDiHQ7Z4gAzCRWWUzBid1pr3g03LX2i1w20e+Ocq3fOzXPOfR34MVAEnNLPZSISULIlIjJAiXjE4Ud2Gvpr211B5Y60r1/I8t/vw5rHPsf6p6+j5smrSP7hIx3Te5Tu/oksR7xRxX5fgFQra+Z++iozG979vJmNMLMD0n4+yswyzX5Egu+b/B6IDFWaRhQR2QSJeGRuNJZ8EL94fMCjRBX7Xsa6+d+EcDHNHz5BU1tT5wL5cOVOtG9YSrjbFGI2le95IS3Jlxoa3rrjVOADM3sMWIqvnTUZv2/kbcAXg0t+CUwws3/hC5m2AAfiK+wvAe7KWbAi2xmNbImIbLqZpNqWbcoF5VMvZvixN1I4fBdwKULFVZRMPpmxH59H2e4fc/33sMXciGN+Nhc4A7990QnA14Ez8YVarwduTGv/Y+Af+DIZn8MnYZHg+DTnXM1WiFlku2DObY2/4yIi25doLDkWWIDf6Lmkn+Z9aQJWAyPwBVVzpQ44LRGPzM/hPUQkA41siYhshkQ8shKYhq/IvrnrlxqA+4G98QlXLq0BnsrxPUQkA41siYhsoWgseQp+E+tR+NEp66O5A+rxyc/MRDwyN62Pe/GbRmdbA3Bex71EZOtSsiUikgXRWNLwm1j/FT81OAy/1Y3DJ1+lwFr81OMvgaeCpxvT+7gT+BjZfXipCZiTiEcuzGKfIrIJ9DSiiEgWJOIRF40lVwDr8Ou4qoCd8U8sNgMLE/HIun66WYTfJLEVKOyn7UC0Ah/iy1WISJ4o2RIRyZ6TgUeDEat1+M2vByQaS34XmAEcgF8Hlo2F9x8CRyTikfX9NRaR3NECeRGR7DkZeHRTLwoSrQuB4xLxyBtkb+H9tGAhv4jkkdZsiYhkQTSWLAFWAjsNYLow/bprgIuAYxPxyIpu57Z44b2I5J9GtkREsuNI4LVNTLS+A3wSP6K1ovv5IGGaDJyGH6lK4tdh1QLrg++twfH7g3aTlWiJDC5asyUikh2bNIUYjSX/C/gUfkRreW/tgvVf84MvorHkcDZ94b2I5JGSLRGR7DgZuGQgDaOx5NXAxfSTaGUSJFYDXngvIvmnZEtEZAtFY8mdgLHASwNoezU+KTtmUxMtEdk2ac2WiMiW+yjwt0Q8kuqrUTSW/DY+0drkES0R2XZpZEtEZBNEY8kR+EXrHWumFuGnEB/o57pvAZfiR7SW5TpOERk8lGyJiPQh2IbnKOBy4AhgBF3rX5Xh/y0tjsaSS4H5Gbbh+SbwWfyIlhItkSFGdbZERHqRpQ2mvwl8Hj+iVZ3biEVkMFKyJSLSTTSWrAJuAc7Ej1xtqgbgQeAtNj51mMhehCKyLdECeREZ0szsGDNzZjYLIBpLjgVeAM5m8xItgus+BnwXOK969riC4B63ZyFkEdnGKNkSke2amU0aaKITjGgtACaxZZtAw8Y1sfcU73R85Rb2JSLbMC2QF5Gh7nlgT2A1fn3WTkBhlvouBHYc9dFb/2vZbyfuid9iR0SGGI1siciQ5pxrcM69PWHmimnAWfiSDtlUYgXFZ06YuWKyc061tUSGICVbIjIomNnHzWy+ma03s0Yze83Mrjaz4m7tFgdfVWZ2s5lVm1mTmb1pZpebmaW1nYWvgwXw6WA6sePrkqDNMWbm1j/7wz+TtkZr1YMzqJ49DtfeSu2LP2PFHYdQ/ZuJJO88nPo3/9QZT/0bvyd59zFU/2YSy/+wP7XP/xTnutY2batdWlY9e9xf06cyzeySbvFk+prU7bUfYmb3mtkKM2sxsw/N7NdmNj7D+/lk0EeRmX3PzN4xs2atGxPZ+jSNKCJ5Z2Y/Bq7GT+XdCdQBpwA/Bj5qZic551rSLikC/gEMB+4Kfj4X+AWwO/CloN2TQZuvAv+ma+HRV7vEECoozRTb2r9/kZaVL1Oy0/FYqIDGhQ+z7p/fgFABrWveouGdv1Ay8QSKJxxJ0+LH2PDSz7HCUir3/0qPvsKVO0a63f+6DLesCuJ1QFPae3Qp8Bt8IdWHgA+BXYHPAWeY2XTn3NIM/d0HTAPmBq9/ZabXKSK5o2RLRPLKzA7FJ1ofAgc751YEx68G5gCnA9/AJ14ddgAWAns755qD9tfinyKMmdndzrn5zrknzWwxPnl51Tk3q/dAwkWZDrfXJYh84klCxVUAVHxkJsk/H8H6f11LqHgYkY/PI1yxAwCpad8geeeh1L06m4r9ZmKhrv/EFozYdc+OXzvnXqV7wmdWiE+KDPha2nuxG74UxWLgaOdcddo1xwN/wyeaMzK8hInB+7S619cuIjmlaUQRybdLg+8/7EguAJxzbcCVQAo/etPd1R2JVtB+LfCD4MfPZCu4YdOv6Uy0AAqGTaRo3MG4lvVUHvi1zkQLIFRcRcnEE0k1raW9vufyrFBRVaTHwa5+DRwP3OSc+0Xa8Zn4xfZfTU+0AJxzj+NHus4ws0xPPX5XiZZIfmlkS0Ty7YDg+7zuJ5xz75pZAphsZlXOuY6n+dqApzP09WTwff+B3rxgxG4VbTXv9nq+aMx+PY6Fy8f1cc4nX+11yymo3LHryVBBUTSWHJ6IR9Z1v87MvoNPEv8fcEW304cG3482s2kZwhwLhIHdgJe6nXs+Q3sR2YqUbIlIvnUMG/X2pN5yfDmG4WwsnbDaOdeeoW3HyFhVhnMZFY7Zb4e+kq1Q8bAexywU9t+LMgwkBedItfY851LtwM7Ay136M7sAPyr3EnCB677C3m8XBHBVr4F6FRmOrchwTES2IiVbIpJvHQnUOOCDDOd36NYOYLSZhTMkXOMytO2TFRRnq6bWQHV/uvJI4Db8mrUznHP1Ga7peD1VzrnaTbmZ055sInmnNVsikm+vBN+P6X7CzHYBosAi51z61FsBcFiGvjr6eCXtWEdCFs50c9fWnGEIKqc615kFC98fCI6d1kcdrmeD70fmODYRyQElWyKSb7cG368xszEdB80sDNyA/3fqdxmu++/0GlxmNhK4JvjxtrR2NfgyCjtlunnrqn9vvUKjfv5xIYCZjQb+CgwDznPOvd7HlTcDrcD/Bgla1259LS0lYiKDlKYRRSSvnHNPm9lPgW8Cr5vZvUA9vs7W3vi9Cq/vdtly/HTc62b2EP5JvfPwU45x59z8tP7rzOw54EgzuwN4Fz/a9ZBz7j9tNe/W5fYVpkm1taQtjv8+MAW/futwMzs8wxU3OufWOefeDups3Qq8YWaP4l9HIT6JPBJYBeyR89cgIptMyZaI5J1z7ltm9grwZeBifBLxAX6k6mfdCpoCtAAn4GtvnQ+Mxo8Y/QS4KcMtPgX8L3AycAG+jlUC+E/WX0wfUi3rk2k/dlSrP4CNT2R2dzuwDsA59ycz+ze+HMaxwEn4pHQZcC9wdw5CFpEsMK2dFJFtSVCkFOfcpGz1GY0ljwYeJvPTfNlSB5yWiEfm99tSRLYrWrMlIgLzgTU5vsca4Kkc30NEBiElWyIy5CXiEYev0t6Qo1s0ADOD+4jIEKNkS0QESMQjc4EHSdv8OUuagAeD/kVkCFKyJSLbFOfcpGyu1+pmJr64aLZqb7UG/c3MUn8isg1SsiUiEkjEI+uBI4DFpBUf3UxNQT9HBP2KyBClZEtEJE0iHlkJTMPX8trcEa4G4H5gWtCfiAxhqrMlItLT/vj/jH4cuBG/EXQ5vj5Xbxy+7tUa/GJ4rdESEUAjWyIiXURjySLgV8DXEvHIA8Bk4DT8SFUSP9pVi98cujb4ORmcPw2YrERLRNJpZEtEpKsrgCXAHOgsCzE/+CIaSw4HdsZvF9QMLEzbgkdEpAdVkBcRCURjyR2BV4BDEvHIB/mOR0S2D5pGFBHZ6EbgJiVaIpJNmkYUEQGiseQpwH7ARfmORUS2LxrZEpEhLxpLlgA3AV9JxCPZriAvIkOcki0REfgW8G89RSgiuaBpRBEZ0qKx5BTgK/jaWiIiWaeRLREZsqKxpAE3Az9NxCMf5jseEdk+KdkSkaFsBrAT/ilEEZGcUJ0tERmSorFkBfAmcHEiHnkyz+GIyHZMI1siMlR9F/inEi0RyTUtkBeRIScaS+4FXArsk+9YRGT7p5EtERlSgkXxceC6RDyyIt/xiMj2T8mWiAw1FwLDgNn5DkREhgYtkBeRISMaSw7HL4qfkYhHnst3PCIyNGhkS0SGku8DDyvREpGtSQvkRWRIiMaSBwCfAPbKdywiMrRoZEtEtnvRWDKEXxT/X4l4ZE2+4xGRoUXJlogMBZ8FUsBt+Q5ERIYeLZAXke1aNJYcDbwBfDQRj7ya73hEZOjRyJaIbO9+AvxZiZaI5IsWyIvIdisaSx4KnIIWxYtIHmlkS0S2S9FYsgBfuPQbiXhkfb7jEZGhS8mWiGyvYsBa4K58ByIiQ5sWyIvINiUaS44AJgPFQDOwKBGP1HRrswPwH+CoRDzy1taPUkRkIyVbIjKoBRtHHwVcDhwBjAAa0pqUATXAAuCXwHzgT8DSRDxy9daNVkSkJyVbIjJoRWPJU/DrrkYB5YD10dwB9cGXATsn4pH6nAcpItIPJVsiMuhEY8kq4BbgTPzI1aZqBu4HZmpxvIjkm5ItERlUorHkWPyU4I5AyRZ01QR8CByRiEdWZiM2EZHNoWRLRAaNYETrBWASUJiFLluBxcA0jXCJSL6o9IOIDCazgZ3ITqJF0M+OQb8iInmhZEtEBoVgMfxZ+JIO2VQCnBX0LyKy1WkaUUSyyswmAYuA3wOz8HsTngBUAK8Ds5xzD6e1rwK7rGjctO+3bfiwJNW4mlBRJUWRg6g44HKKxx3U4x7Vs8dRNP5QRp74G2qf+xFNS/6Ba62ncNRUhk2/huLx00m11rPhhRto/OAh2htWUVAZbW1bv+gi51L39BL3BcBlwP74BG0RcAdwvXOuOXvvkIgMNUq2RCSr0pKtJ4GpwELgGWAk8An81N4JzrkngvbTwZ4qGn+YFQzfORwqrqJ9QzVNix/DpVoYdcofKNnpuC73qJ49jsJRU0m11hMqqqBoh0NJNdfQ+P6DWKiAMTMepuafV5FqXkdJ9Chcqo3G9+fgWusdcJhz7tluMd8KfAZIAH8D1gHTgcOC13Gic64tJ2+YiGz3lGyJSFalJVvgR7GuSzv3UeBRYK5z7tTgWFXkUy//qaBi/Gmk1dFqr1vGyvtOIVQ0jMgFT3W5R/XscQCU7XUxw4/6CWZ+RUTDO/dQM+8rWPFwisdNY+RJv8UK/AONzcuedasfPNuAB5xzM9JiugS4DZgDXOSca0w7Nwu4FrjCOfeLLX5zRGRI0potEcmVJcAP0w845x4DlgIHpx1bX1Ax/mC6FSwNV4yndMrptK17j7YNiR6dW0EpVYd+rzPRAijd9RwIFeCa11F1xA87Ey2A4vHTLVwxoR34SLeuvgq0AZemJ1qBHwBrgIsG+qJFRLoryHcAIrLdetU5157h+IfAoR0/RGPJEc3LnxtZ/9r/0bLiJdobV0OqpcsF7fUrKKiMdjlWMHwKoaKKLscsFCZUOgbX2kDBsIk9bhyuGB9qr6vu7MjMyoD9gNXAFWYZC9Q3A3v2/VJFRHqnZEtEcmVdL8fbSBtVX/PXT17atHRegYWLKY4eTWnVRKygDCxE87KnaVn2DLT3XJ9uRZUZO7dQGCvOfA4Lpej6794I/IjaGPx0oYhI1inZEpG8aq5+OkaokDHnPUbhiN26nKv551U+2cqdjkKnrzjnDsjljURk6NKaLRHJK9fWGC0cvkt790TLuRQty5/L7b2dqwPeAKaa2cic3kxEhiwlWyKSX2ZL2mqXhNvrV3Qecs6x4YXraat5N9s3y/Rv3s+BIuBWMxveMzwbYWYa9RKRzaZpRBHJL5f6mWutu2XlPSdQuvNpECqkZcXztNW8R8nEk2ha8rcs3qu9jW5bATnnbjWzA4EY8IGZdTwxORKYDByFLw3xxewFIiJDiUa2RCSvnHO/HnbIf70ULovQ8M5faHzvfsIVExhzzl8pHLNPVm+Vaq3PuBm1c+5LwBn44qsnAF8HzgSqgOuBG7MZiIgMLSpqKiJ5F40ljwYexm/pkyt1wGmJeGR+Du8hItKDRrZEZDCYjy8emktrgKf6bSUikmVKtkQk7xLxiANmAg05ukUDMDO4j4jIVqVkS0QGhUQ8Mhd4EGjKctdNwINB/yIiW52SLREZTGbit/NpzVJ/rUF/M7PUn4jIJlOyJSKDRiIeWQ8cASxmy0e4moJ+jgj6FRHJCyVbIjKoJOKRlcA0YA7QuJndNAD3A9OC/kRE8kalH0Rk0IrGkvcAH8VvFl0efO+NA+rxTx3O1BotERkslGyJyKAUjSUjwJvAfsDOwOX4KcaR+BEvh0++SoG1wALgl8BTeupQRAYTbdcjIoPVN4E7EvFIAkjga3ERjSWH45OvYqAZWJiIR9blLUoRkX5oZEtEBp1oLLkD8AawdyIeWZbveEREtoQWyIvIYPQt4HYlWiKyPdA0oogMKtFYcgLwKWCvfMciIpINGtkSkcHmauDWRDySzHcgIiLZoJEtERk0orHkjsAFwB75jkVEJFs0siUig8l/Ab9NxCOr8h2IiEi2aGRLRAaFaCw5Efg4sHu+YxERySaNbInIYPEd4JZEPLI634GIiGSTRrZEJO+iseRk4Bxgt3zHIiKSbRrZEpHB4BrgV4l4ZG2+AxERyTaNbIlIXkVjyV2As4Bd8h2LiEguaGRLRPLtu8Avtb+hiGyvNLIlInkTjSV3A05Fo1oish3TyJaI5NP3gBsT8cj6fAciIpIrGtkSkbyIxpJ7AicBM/Mdi4hILmlkS0Ty5XvAzxPxyIZ8ByIikksa2RKRrS4aS04FjgM+n+9YRERyTSNbIpIP1wLXJ+KRunwHIiKSaxrZEpGtKhpL7gscCXwm37GIiGwNGtkSka1tFvDTRDxSn+9ARES2Bo1sichWE40l9wemAxflOxYRka1FI1sisjXNAn6SiEca8x2IiMjWopEtEdkqorHkQcCBwCfyHYuIyNakkS0R2VpmAf+diEea8h2IiMjWpJEtEcm5aCx5CLAvcG6+YxER2do0siUiW8Ms4EeJeKQ534GIiGxtGtkSkZyKxpKHAXsCZ+U7FhGRfNDIlojk2nXADxPxSEu+AxERyQclWyKSM9FY8khgCvD7fMciIpIvSrZEJJeuA36QiEda8x2IiEi+KNkSkZyIxpLHADsBf8xzKCIieaUF8iKyWaKx5AhgMlAMNAOLEvFITXDO8KNa30/EI235i1JEJP+UbG2hvj5wRLYnQQJ1FHA5cAQwAmhIa1IWjSVrgAXB1zjgzq0dp4jIYGPOuXzHsE0ZyAcO0PGB80tgfiIe0Zss27RoLHkKMBsYBZQD1kdzF3ytBS5OxCNzcx+hiMjgpWRrE2zGB049sAaYqQ8c2RZFY8kq4BbgTPx/JDZVA/Ag/u/A+mzGJiKyrVCyNQD6wJGhKBpLjsWP0O4R7oD9AAAgAElEQVQIlPTXvnr2OIrGH8qYs+Z0P9UEfAgckYhHVmY9UBGRQU5PI/Yj+MB5ATibzUu0CK6bAbwQ9CcyqAX/wVgATGIAiVY/SoJ+FgT9iogMKUq2+qAPHBnCZuPLNhQO9IKx5z/FiONu6u10IX6EbPaWhyYism1RstW3Tf7A6Yc+cGTQC9YmnoV/wnbACkfsSkFltK8mJcBZQf8iIkOG1mz1IvhAuJfNnzrsSwNwnhbNSzaZ2SXAGcD+wA5AK/AaMNs596dubXcGvg0cB0wAGoFq4F/jLnn91HDp6B0BXHsL9W/8gYZ37qatdimkWgiVjqJw1FTK9/ksJdGjOvvsbc1We32S2ud+TNOSf5Bqradg2E6tbTXvfR7cEuAJ4Drn3Ky02J4Ejsb/5+SbwGfw/+lZiS8l8V3nnPZZFJFtxnZdZ8vMJgGL8PuyzQJ+ApwAVACvA7Occw+nta8CLgNOCZWNPSrVVBMOFVVSFDmIigMup3jcQT3u0fEBM/LE31D73I9oWvIPXGs9haOmMmz6NRSPn06qtZ4NL9xA4wcP0d6wioKqSWWV+3/l99HYxyKZykKY2QVBHPvjRwMWAXcA1zvnmrP5Hsl2ZTbwBjAfWI5/avZU4I9mtrtz7rsAZrYDfh3iMOCvwH34P2eTsdCnUw0rU+HS0QDUzPsqje/PoWDkHpTt/jGsoIT2+hW0LH+e5qXzuiRbmbQ3rGLVnNNo35CgaIfpFI2bRnv9Cmtb9/4tOPdoP6/nTuBIYC5QG7yWbwJj8QmYiMg2YbtOttJMBJ4HFuK3DhkJfAJ40MxOcM49EbTbE/hRqGTkf0p2OiEVKh0Zbt9QTdPix2j6cB6jTvkDJTsd16Nz11zLqjlnECqqoHSXGaSaa2h8/0HWPHIBY2Y8TM0/ryLVvI6SiSfiUm00vj+HmnmXj2ld+/YX4OZb0vsys1vxHyQJ/IfgOmA68APgeDM70TmnitySyd7OuQ/SD5hZET5Z+baZ3eKcqwbOw/8duMI594v09uM/++4DFi46EyDVXEvj+w9QOGZfxpwzFwuFu9ysvWltvwHVPvdj2jckqPjIl6g69Lsdh8PNe5z/5OqHzj21n8unAFOdc2uD1/Id4N/AxWZ2tXNuRb8BiIgMAkMl2ToGP4p1XccBM7sTeBS4Cj+VAfAWMH6Hz7z5a+CAjrbtdctYed8prP/XtRmTrdY1b1C218UMP+onmPllcA3Ro6mZ9xVWPXQuxeOmMeas+7ECv8a+bLfzWP3g2bSsePG7+JISHTFdgk+05gAXOeca087NAq4FvgR0+YAUAeieaAXHWszsV/jpwuOBP6SdbuzePlRcdSgd9ePMAIeFi8F6Lu8Ml4zsO572FhrffwArGkblgV9LP2XFEw7fJ4jlc3108a2ORCt4LfVmdgfwPeAg4OFerxQRGUSGygL5JcAP0w845x4DlgIHpx1b75xbja8M31mwNFwxntIpp9O27j3aNiR6dG4FpVQd+r3ORAugdNdzIFSAa15H1RE/7Ey0AIrHTydcuSPtdYlIt66+CrQBl6YnWoEf4AukXrQpL1yGDjPbycx+ZWZvm1mDmTkzc/gRUvBrswAeAuqAX5nZfWZ2mZlNHf+590fgd0QAIFRUScnEk2hZ8QIr7zme2hd/RnP1AlKtDQxE27oPcG2NFI7ai1BRRffTI0NlY1/up4sXMxz7MPg+IsM5EZFBaaiMbL3qnGvPcPxD4ND0A4Wjp55cULXz6Nbky7Q3roZU13W47fUrejxxVTB8So8PEwuFCZWOwbU2UDBsYo8bh8t3oGXly+FoLDk8EY+sM7MyYD9gNXCFWcbi9M34qU6RLoIF78/jk5CngL8B64F2fMmRTxM8XeicW2JmB+PXMZ4MnAOw/La9Vgyb/p22yv2+2Pn07ciTfs2GV26m4b05bHjhejYAhEsonXI6VYdeS7hsTK8xpVpqAQgF67+6aSwad0ioaeH/6/V659y6DIc7ptDDGc6JiAxKQyXZyvSPNvh/uDuHo8xsBnBv+/pFoeLo0ZRWTcQKysBCNC97mpZlz0B7z/XpVlSZsXMLhbHizOcIhSHVBrAz8DL+Q9KAMfjpQpFN8XX8gvjPOOduTz8RPHDx6fRjzrm3gE+YWQE+yT8B7Mrap2eVhoqGUb7nhf7aglKGTbuKYdOuoq2umpZlz9Lwzt00vnsv7bUfMmbGg70GFAr+XqQaV2c67VxLbfeRXRGR7dJQSbYG6gdgbWPOndtcOHKPLllSzT+v8slW9nXUMurYxucV59wBvTUW6cUuwff7Mpw7ureLgoctXgJeKt/zwtUNb//5/5oWze1MttIVVEygYLdzKd11Bsk/H07Liudob1rb69qtguG7YAWltK55k1RLXffRX2ureW/vgb44EZFt2VBZszVQu1hB6QeFI/dIpR90LkXL8udydc9mfw9Xh39sf6qZ9b3yWKSnxcH3Y9IPmtlH6bYI3cwODMqcdNGcmN8GfjQLoL1xNa1r3upxI9fagGuth1ABFirqNSALF1E65UxcSy0bXvrfLudaVr9e1l6/7LT+X5aIyLZPI1tdLXZtDdH2+uUl4fIdAHDOseGF62mreTdX91yY9uufA78DbjWzS7qvWTGzEcBk51x/C4tl6Injn2S9x8zuBZYBe+PXZP0FX+qkw6eAL5jZAuADoAZfZuEMQkWU73sZ4NcnrrrnBApG7knhqL0IV4zHtWygacnfSTWspHyfz2Va+N7FsOnX0Fy9gLpXf0XLypcpikwj1ZCk8YOHwvinCc8GUn12IiKyjVOy1dX/Ares/MsJqdIpp0OokJYVz9NW8x4lE0+iacnfsn7DRDzSmVA55241swOBGPCBmXU8MTkSmAwcBdwGfDHrgcg2zTn3HzM7Fv/U7Wn4v9v/xi9+X0fXZOvP+Onrw4ADgVJ89fi7Rp95T7R43EHHA1ZQuSOV066iufppmpf9i1TjWkIlwykYPoVh06+hdJez+40rXDaGMTMeZv1zP6Z56eO0JF+hcPgUKg/8+ku1z/3oDnyyVZu9d0JEZPBRspXGOfdrM2u2ooobG975S5UVlFK0wyGMOPYXNC58OCfJVoYYvmRmc/EJ1QnAcGAtPum6HvhTH5fLEOacexpfTysTS2v3HJBxXjwaSx6NL6JbESquYthBV8JBVw7o/hNmZq4xGq7YgZHHd9mgug64sva5H300+LnLXKVz7pje7hEs/r99QAGJiAwS2hsxg+AD52H8tj65UgeclohH5ufwHiKbJBpLGn57qJ71SjZTe/0KwuXj0g8tqb4legau7WmgBZjgnGvK1v1ERAYbjWxlNh9fQDSXydYafD0kkUEjEY+4aCw5kyxuwr7y3o9SUDWZwpG7Y+GS1saF/28Zru1l/AM6X1CiJSLbO41s9SIaS55CFj9wumkAzkvEI3Nz0LfIFovGkncCM/AbVG+R2hduoGnxo7TVLsW11jlcai3wLHCDc+7JLe1fRGSwU7LVh2x+4KRpAuYk4pGehYxEBoloLFkFvICvPl/Yd+sBacWXp5iWiEfW99NWRGS7ojpbfZuJ39KnNUv9tQf9zcxSfyI5ESRER+ATpC2d5msK+jlCiZaIDEVKtvqQ5Q+cZsABP9EHjmwLEvHISmAaMAc/9b05GoD78SNaK7MVm4jItkTTiAMQTKnMBs5i89ZwNQAPBH3cD5yfiEfmZS9CkdyKxpKn4/8MN+PrcmXcKT3ggHr8QyAztTZRRIY6jWwNQCIeWR+ssToPWIIv29BfluqCdkvwi+EvSsQjC4CPAXdFY8kDcxmzSJaVAQuAU/D/YUjip9dr8ft61gY/J4PzpwGTlWiJiGhka5MFdYiOBC7HTzGOBBrxyZXh/9e/Fv/B9EvgqUQ84rr1cTZ+lOvoRDySs32ARLIlGksuAH6eiEfuTzs2HNgZX42+GViYviOCiIh4Sra20OZ+4ERjyc8C1wCHJ+KRZbmNUmTzBaOw9wNTEvFIW77jERHZ1ijZyqNoLPkt4JPAUYl4pCbf8YhkEo0lbwfeTMQjP813LCIi2yKt2cqvnwJ/Ax6OxpK5KJ4qskWiseRY/IMhv8t3LCIi2yolW3kUrOW6CvgA+Es0lsxG8UiRbLoMuDcRj6zJdyAiItsqJVt5lohHUsBngx9/F40l9Xsig0KQ/M8Ebsp3LCIi2zJ9sA8CiXikFfg4MAW4IXjiUSTfzgHeTcQj/8l3ICIi27KCfAcgXiIeaQgKR84Hvgn8T3/XRGPJEcBkNj4JuUgL7SWLLgduyHcQIiLbOj2NOMhEY8kJ+BpdP0zEI7/rds6Ao9hY42sEXbdRKQNq2Fjja373Gl8iAxGNJQ8C7gV2UbkHEZEto2RrEIrGkrsBTwKxRDzyQHDsFHwh1FFAOdouRXIoGkv+Hng9EY9cn+9YRES2dUq2BqmgkORc4BLgU8CZbP6+jA/iky5tgC39isaSEeBtfBHTtfmOR0RkW6cF8oNUIh55Cfgi8BAwg81LtAiumwG8ENRMEunPZcA9SrRERLJDydYgFY0lq4CfBD8WA9TMu5zq2eNoq13a2a6tdinVs8dRM+/yvrorASYBC4J+RTKKxpJFqNyDiEhWKdnKIjN70sx6nZc1s8VmtniA3c0GdgLC2YgNKAR2DPrdZGZ2u5k5M5uUpXhkcDoXeDsRj7yW70BERLYXSrYGoWAx/FkEI1odhh3yHcae/xTh8h02t+sS4Kygf5FMvoJ/klVERLJEydYgE5R3mE2GNVrh8giFI3bFwlu0q08ZMFuFU6W7aCw5DRgP/L98xyIisj1RstUPM7vEzO4zs4Vm1mhmtWb2LzP7ZFqbScH04dHBzy7t60kzOyY4PxGY2O387Wn9uOW37vlye31ydM0TX2f5Hz5C9S3jqX/7LiDzmq10rTXvsWbuJSy7dQ+W/XYyq+acSdOHT/ZoV/v8T3aonj0uZWbHZHi9kzLFBXw6+HFRWuyLu1070sz+28zeCt6r9Wb2uJmdNIC3WvLvK8DNiXikPd+BiIhsT1RBvpsMVdlnA2/gK7svx9e5OhX4o5nt7pz7LrAOuA5fpmFi8OsOi4Ov64ArgmM3pp1/Nf3+VlSx86o5p5VbQTmlk08FCxEuHdNv3G21S1l1/+kUjtqT8r0+RaohScP7D7HmkQsZcUKcsl3OTrtJuGhAb8ZG1wFnA/sBv8C/XtK+Y2YT8bXBJgFPAY/i64GdDjxqZl9wzv12E+8rW0k0lhwHnMHGP6MiIpIlQz7Z6q8qe+TCZ8IFVZN3BJYAtwHzq2ePK8TXwPq2md3inKsGZgUjRROdc7My3GqWmV0C0Mt5ANo3fDisdLfzGHHsjVho4L89LcufpWK/mVQddm3nsfK9L2XVnNNZ989vUbLT8YSKKgfcXzrn3KxgYfx+wI3OucUZmv0en2he4Jy7q+OgmQ3HJ2G/NLOHnHPJzQpCcu0y4C8q9yAikn1DehoxWCi+CHgYX4tqLP6pvaqOr4KqyYXB8RlBu0UTZq44HvgVPlk9PqtBhYqoOvTaTUq0AKxoGJUHXdnlWNHYj1C26zm4lvU0Lvxrj2sKhu9SsUWxdtzbbD/8FOp96YkWgHNuHXAtfnH+udm4n2RXUO7hi6jcg4hITgzJka2g1tQtDKAqe9uGBHWv3Exz9QJrr6uucG2NFUB65jIhm7GFKyekwmVjNjkJLhy9D6GinrlT0fjDaHjnL7Sufh34RNdrxu6/2Y81dnNo8L3KzGZlON8xD7pnlu4n2XUe8FYiHnk934GIiGyPtqlkq2NBtnNu0ub2EVRRX4CvOVXSV9u22iWsuu8UUs3rKNrhEIp3PJpQ0TCwEG21S9sb370nHCoZOXKAsc/CT7P1OY0WLh2zWfsnhcsyr+sKl/mi8a6ltmdMBcVb9FhjmlHB9xODr95kZSRNsu4rbCygKyIiWbZNJVtbKhjRWoBfxN1volH371tINa1l+LE3Ur7H+V3ONbw3J9z47j2U7XH+pdFY8odZ23fQNq8iQ3vDql6Or/TdFg1Lu4cfOHMt9akMlwzfjNt3vPavOudUo2kbEo0lDwbG4afIRUQkB4bamq2OquwDGtFpW78YgNKdT+9xrnnZMwBYuHgYG6uytwOYWV9V33u859FYMhSNJff2HYY2q2J86+rXSLXU9TjesuxpAApH770xgGK/Y09z9YJMv/8H9XKLjnIAmeJ7Nvh+5ICClcFE5R5ERHJsyCRbvVVl70u4ckcAmoOEpUPT0idoeOsO/4Nfyd5RlX1N0GSnProdPuKYG8ZGY8kTo7HktdFY8jFgLXA/AC61WR96rqWWDS/+rMuxlpWv0vDe/VjRMEp3PrXzeNHYAwBINa4638w6RzfNbEfge73cotfX5px7EV/u4RwzuzTTxWa2j5lpI+xBJCj3cDpwa75jERHZng3aaUQzewOYgv+QnwN8p4+2F+AfXd8fvw5rEXAHcL1zrrl7VfbWmveoe/VXNFcvoL1+JaGiSgqG70LprjOo2PuSzn4r9r6EhrfuZO3cT0OoAFw7Fi7GtTVSMvlUmhZ1rpMv8/3bf4P7GPBvMysGHPB+yc6nzyqOHrVfc2I+QGHDO/cub13zZiLVXLskVFT5z/b65bOaFj/2BWDXluXPhZffvjfFE46k8qArKRyxy4Der6IdplP/1p20rHyFonHTOuts4VIMP/qnXco+FEUOoChyYHNL8qUjgefNbB4QwddZegy/nq27x4GrgN+a2X3ABmCdc+7m4PyFwDzgd2Z2OfAcvg5XFNgX2Bu/kH7lgF6QbA1fAO5OxCM1+Q5ERGR7NmiTLXy9q98ArfgRqUOAIqAlvZGZ3Qp8BkgA9+E/4KcDPwCON7MTJ8xccTjBIu6mJX9n7d8uw7U3U7zjsZTuMgPXvJ7WNW9S9+qvuiRbLSteBNe+cR2VhTvXO7Wu/k/3eEeV7/u5gvr//LYNqMQnWmZFVXs1LX7snsJRU1cAhEpGzW9Z8dyUlhXPTcCPEjUDV9IxtRkqWlk8/rCRjYv+WtC09B+MPvM+isbs2++bVTBsJ4Yf/VNqn/0R9W/8AVItFI3Zh8oDv07JTsd2b14/8qT/+8P6p689t7l6wX6p5vX7WrhosWtr+ia4vwEf736Bc+4xM7sS+Dy+8GURvvbYzcH5hJkdiJ+WOhe4CD/luAJ4E19WQJsbDxJp5R5OyHcsIiLbO3Nusx5+yzkzG+mcWxv8ugR4Ap9ELel4GjEoEnobfuTrIudcY9r1s/D1na6YMHPFUcCM9sY1lrxzOq6tidFn3E3x+MO63LO9bhnhivEAtG34kOSdh2PhIsacO5fCEbt2tls3/1vUv/F7yvb8JCOOuQEA55xL/vlw175+YWjEcTe9W7b7x+YCTwPPVM8edw4bq8Yf65x7MohxBLAQvx7qKOfcm8Eo3KLWNW9NXHX/aRQMn8LYj/09K+9pmiXA5EQ84qKx5G7AJ4FP4Yu5/hG4IxGPfJjtm8rgEY0lLwI+k4hHlGyJiOTYoF2z1ZFoBb9uAq7O0OyrQBtwaXqiFfgBfgryInxleGt45y+4lg2UT/10j0QL6Ey0ABrfvQ9SLZTvfWmXRAtg2CFXY4UVNLx7L669GYCWFS9Y+/qFISy0YO3jX949EY9ckYhH/hIkLTcDH2SI/2L803/XOufeBEjEIw6YWThqz4ayvT5J6+rXaF37Th/v1CZrAGYG9yERj7ybiEe+h5+y/SKwM/BqNJZ8PBpLXhKNJTev7LwMdl8B9OSoiMhWMJinEbtbwMYn4jCzMvz2MauBKyxzyYRmfCHNYoCW5EsAlOx0XL83a1nlZ7yKo0f0OBcqHk7h6L1pWf4sbTXvUzh6aue0ohWUP929vXOu3cwW4BOadB3FQPfrXgy0bM+LFrbXLd8LCLXVvEfhyN37jXkAmoAHE/HI3O4nEvFICr/I/aloLHk5fuH0xcCN0VjyEfyI1z8S8UhbNgLZXBn2rlykNUebJhpLHoLfFeGRfMciIjIUbDPJlnOuzcxWpx0aARi+Ovm1ma/qtB6o6ijsGS7vv3B6Z9uyzA/QhcsjAKRa1gffNwBQMGKX3uZlV2Q41lEM9PPdT3Q+7QikWuv7jXcAWoEPgZn9NUzEI03AvcC90VhyDL70/HXAbdFY8s/4xOvVjtGxXOpv70qgLBpL1uCT8V8C87dGXNs4lXsQEdmKBu00YndBiYLRaYc6Cmm+4pyz3r4mzFzROV/YUdizvX55//fraNtbsdD6ZJd2HU/7ubam3sobjMtwrOM17NdL7JEJM1e8V77HJ5r6DbhvTcBi4IhNLb6aiEdWJeKRmxPxyCHAMfhEZw7wn2gs+c1oLJnV7YrSDWTvyuDnLntXBtdJBtFYcgfgVFTuQURkq9lmki38qEZnQU3nXB3wBjDVzPraMqe54xdFkQMBaFo6r9+bFY7xRUBblvWYFSTVvJ7WNW9AuKRzPVfhaP/EYHtdYr/u7YMipz3nI/spBpqIR1YC0/DJTUOmNgPQgK/hNS3ob7Ml4pF3EvHINfh1XV8CdgVei8aS/4jGkhdHY8msbMcTjSWrghG0e/FbHFXgRzH7YkG7ifgRuTuDHQOkq45yD+vyHYiIyFAxaJOt9AQqeBrxvzM0+zm+BMGtZtZjmxkzG7HyvlMqCeprle3+cayokvo3ft9ZAT5de92yzl+X7XouhAqpe+13tK1f1KVd7fP/g2vZQNlu52JhXyO1aNw0CoZPwbVsOMDMzurW9ZfpuV4L/JOU64BrzezgDPGHqmeP2z8Rj1yI3yx4CVCHLyvRFxe0WwKcl4hHLsradkL49V2JeGR+Ih75PH4j7t8AHwMS0VjyT9FY8qRoLLlZlfCDvStfAM6mn03C+1CGH+l6IehPgGgsWYx/COKmfMciIjKUDObSD8vwIxsddbZq8B/sLekbUZvZr4AYvgr7Y8BSYCR+EfVRwG0TZq7omIKiacnfWfPY5yHVQvFOx1E4ci9c6wZa17xFe1014z75QmcMda/fxvqn/JOHpVPOJFQ6ipZlz9CSfJGC4bsyZsZDhEpGdLZvrn567eqHzinFJ4D3A+8DHwGOxxf8PJm00g9B/MfjR64q8IVD38AnSzviF9CPcs6VQOf6pSPZuH5pJNAYtDegNHgfOtYvPbU11y8F67vOxy+snwDcCfwxEY/8e4DXV+ETrUkMcEulTFY9OIOWZc8wYeaKVvz06bTq2eP2x5cPuc45N2tz+96WRWPJTwKfTsQjfW0WLiIiWTaYF8ivx095dFSQ/y+gx4e2c+5LZjaXjQUah+MTjqXA9cCf8IvoZwBWMvFExp73KBteuZnm6gU0f/hPQsVVFAzfhcoDLu/Sd8Xen6GgajJ1r86mceEjuLZGwhXjqfhIjMoDvtq5x2BHKMUTDnsCPwL3I6Bj3dBz+LVOH8UnW93jf9zM9gW+EbQ5El+4dRk+Qbuvo22QOM0PvojGksPxU3odT+YtzOf0UCIeWYUfNbkpGkvuga/d9VA0llwP/AG4MxGPLOuji03au3IACvFJ62z86NtQdzm+JIqIiGxFg3ZkK5uiseTR+MXTWVlT1Is64LREPDI/h/fY5kRjyRB+hPFT+IT3RfzTjHMS8UhdWrtT8COZmzt12CltZKvjUMP6Z75/Xd2r8f9hiI5sBeUe7gR201OIIiJb12Ae2cqm+fgRslwmW2vwdaokTVC/60ngyWgs+WXgTPw0403RWPIhfOI1j7S9K3OgrDh61NfrXo3nqPvBoZ8aZJejcg8iInkxJJKtYFuamWRp5CSDLlXZJbNEPNII3A3cHY0lI8D5zdX/+kXda7/buSX5UnGqqYZQyXAKR+5J2Z4XUraLf86g/u27aFr8d1pXv0Z7w0osVEDhqD0pn/ppynY7b2A3t/CwXL2ufNmEGmQv4qenv7TVgxQRkaGRbAEk4pG50VjyQfxUVkkWu+61Krv0LhGPJM2sAdiNUEGoZNLJFFTtTKpxNa2r/k39G7d3Jlvr5n+bwpG7UTx+OqGyCKmmGpqWPk7N41+mbd0HDDv4W/3ezyyUzd/zvAumXWfjC+OWs7E0RvdyF2Px6wfb8NswzdSfVRGRrWvIJFuBmcBBbOHTbmkGXJVdujKzvYA4UDv23MdShaOnjko/n16GI/KJJymomtTletfewppHLmTDKzdRvtfFhCv63RWgvzpd24Tgic1b8NOxAx2lNfyf944aZA/iR2KzVg5ERER6N2jrbOVC8OFyBL4cQN6qsgvgE9SCUMmIGwpHT+0xxZe+KXj3RAvAwkWU7/0ZSLXRXD3wpXJWVLnNjnCpBpmIyLZpSCVbMPiqsg9h0wHK97nsbfr5fWjbkGDd/G+T/PMRLPvtZKpnj6N69jjWPvZZANrrM207mVm4fNyI/lv1zswmmZkzs9vTjt0eHJu0JX33JRjRWoAfld3ShLEk6GeBquyL/H/27jw+zqrs//jnTLZJmqXpNl2mG5R9E2ihYCllEUSUtsiOPwEVteMj8qiAPqJUeXwUFRfQqYILa1kESgFZKkIpZSlr2UrL1m26TLekzTpJZs7vj3MnzTJJmmSmk+X7fr3ySnsv5z53UpKLc65zHZH0G2jTiEDTCNdFHeS9JGOBKtyqQ+W99NxgAP/4UzscFWzYtZatD55BIlZO7qhjyRt7Ir7cYjA+4hXrqV51PzYe66iJlnw53apsn07GmMXAidbajv79pbMG2UUpalNERJIYkMFWIy9pfiK9uCp7P1YOULv26ZLc4Ye1e1HlW38mUbuDwSf9nkEHXtDiXPWHC6hedX+XHpo3+vjTgqHoLUAEl2/X9BEJB7o70vlD4JfAhm7e3yHvfwpm4ko6pJIfmBkMRc/Q/zyIiKTPgA62oPdXZe/HXgYmV71zy4HFk/+73fyjhp1rAMjf5/NtziXb37Iz9dtXLAPewI3qnAQEvT8Hg6FoNS7wahOIeR9Jgylr7SZgU5c7swe88g5prUEGzAuGogH8cFUAACAASURBVBP1PxEiIukx4IOt1rzA6o1M92MAmAd8M1Fb9v367St25Qw9uM1qxKzC0WQVjQUgtvFF8iec1nS+dt2zVL9/d5cfWrfxxfci4cCfWx/3gpphuMCr8SOI22Kp8e+jAxe9XBGdP5W8caecHAxFbwbWR++bcWbDjpXTC4/8rxNKpl67LBIO1AMYY84CvgMcjBsx3Q58CNxnrQ17OV5Nu5wbY5oHO89Za2fg6mgNjVdupGL5n6hd9wzxyk2YbD/ZJRPwjz+N4snfbfmOW9+i4vWbqNv0Mom6CrIKhuMffypFR3+XrEGBFteWPXMF1avuHz/4pN+fZ8yVw3H7jO4DbMZtcfQLa601xpwLXAUcgptOvx+4ylpb07w97x2ew01N3oDbgqoIWAHcaK2d39H3R0SkP1KwJRlhrV1hjAkBf97yz9PI3+cMskr2IVFbRv2W5ZjcQobPfIjCQy+leuW97Fh0Ofn7fJ6sQQHqd6wktu5Z8iedRc1HC/f4kXSQk+eN6mz1PpIG28FQ1FexPHw08Ep817qPcBuNj80aNGpcw46VDDro4vuBYcFQdFvFm3+qBSaa3KLqnCEHvg3mpXjVxux41ZaxJOouw5W9KAd+ClyKK8vw02aPW+N9vqJuy/JB2x67EBsrI3fUVPInfg7bUEND2QdUvPabFsFWzZpF7Hjqa4B1X6+iIPVb36bqvdupWfMUw2ctJLt4fJt3q1557424KfNHgUW40hI/B3KNMTtw06QP43ZJ+AyuQGoWycuelAIveu/3D1x+3nnA3caYMdbaX7fzbRAR6ZcUbEnGWGtvNca86ysY9svYhhdPSKx+0vj8Q8gZejCDDnI52zlDD2bYzAfZteyX1K59GmwDOUMPYchn/44vt3iPgy1rE7W4YKLbIuFAwpg7tgI0lH+4LhIO/AHAzHt2GDAhOv+448fM2RwBRla8/runwdcwfNYjv8oZelApbmRsJBCI12wfFgxF14+Zs3k9sD56z6ezGso/ZsyczW+zewozCmDjddN2LLrc2FgZpaeEKdj/7BZ9al6PLFFfRdkz3wHbwLCzHiJv9NSmcxVv3syul39O+XNXM+wL97V5t3jFulHAOGvtBgBjzFxcMHkVbrXo0dba971zecCbwFeMMddZa1uvyD0c+CdwgbU24d3zS+B14OfGmAettZ905WsvItKXKdiSjLLWvhQMRWfgptPaDrkAeSOnMHzmg0nvb7bZdJPhMxe0OeYPnrBlzJzNac9LioQDDUDEzKusAuq23H/STdbasubXBEPkAqPxpidtomGyd+rL7J6yLAmGoptr1ywaHq9Yj3/C6W0CLWhZj6x29ZPYWBn5k2a3CLQACo+YQ9V7dxCLPEdDRYTsomCL80VHf9cOOvhLVY1/t9aWG2MeAS7DTf+93+xczBhzHzAXOAhoHWzFgWsaAy3vntXGmJuA63Cbkv8UEZEBYsDV2ZLexwuA5tD9umedycTelXfjks9XGGN+Z4yZZYwZDhAJB+oi4cCaSDjwfCQcmB/ftXa9d3xWJBw4OhIOjMBtu/Ptus2vNgD4x53c6QPrt70NQN6YaW3OGV82uaOmete92+Z8zvAjanC5Ws01Dpu9nuRxjYsFgknOrbPWrk5yfLH3+cgk50RE+i0FW9IreKUHFtLzyv6tZWTvSmvtb4FLgLW4siILgKgx5lljzOQObwYi4UAtsDURK7cAvkGdbkdEIlYBQNag5IXhG5PjE7G2pc18ecWWtqUlGrzPyWqhNZ5LVvcr2k4XG4chVUhVRAYUBVvSm8zB1TRLlYzuXWmtvcNaOxVXNPdM4G+41YVPNY5ydSJm8kosQKKq88oSvrwiAOLVW5Oej1dFveva7I7U9Lw96NOeCLRzfKT3WdtbiciAomBLepPDgFxgHf1o70prbbm19nFr7eXAbbgyENObXRIHMMa0rm6/Om/klCyA2nXPdPqcnGGuOGxs44tt+5BooG7TshbXtWT8QKqS1se1s3XRDO/zmyl6johIn6BgS3qFYCh6GPAgcDFuNVuf3rvSGHOSMSZZqYnGOb7m77bd+zyu+YWRcKDMP+H0HVlFY6ld8xTVH7ZN/G++GtE/8QxMXik1Hy6gbnPLNKvKt28hXrGOvOD0NsnxANjEzhQW780CbjDGNP18McZMxE2nNgB3peg5IiJ9glYjSsYFQ9HxwOPAlZFwYJF3uK/vXbkAqDTGvIwbYTO4baGm4BLOn2527X+Ac4GHjDGP47aLWmutvdNk5S4dctqts7c9doEpe3oOVSvuIDdwNLYhRkP5h8QizzPmmy5X3ZcziNKTfseORZezdeFs8vf9AlmFY6jf9jax9YvxFYxg8InJS1zZhppXU/jubwPHAq8bYxaxu87WYOBqa+3HKXyWiEivp5EtyahgKDoMeAr4TSQcuKf5OS9gmojLd3oIl3hdD+zC5f3s8v4e9c6fCUzsBYEWwA+AV4GjcFXZL8Mlk18DnGStrW927V+BX+ASx68Grge+6p27KXfEp6pGnPs0gw65hHhFhMq3/kL1Bw+QiO2keMrVLR6aP/GzDJ/9CP7xpxBb/yyVb82joexDBh38ZUacsyhpQVOAum1v35ayN4cy4HjgPdx7X4Ir7XGxCpqKyEBkrNV2aJIZwVC0EHgGeDoSDvzPHt4zoPau9LYRarcGWYqsxQWpPf5h0Lhdj7fVkIiIoGBLMiQYiubitoZZD1yuTZDb502nPkB6NqOuBs5J1Wiggi0RkbYUbEmPBUPRUtx0X+No0+pIOFDWwfU+4E6gEPiiV3VdOhAMRecDswF/CputBRZEwoGLUtWggi0RkbaUIC9d5k1tTcetLpuG23i4+eq6gmAoWgYsBW4CljSOXHn33ohbeXeaAq09NgeYDEwgeSHRrspoDTIRkYFEI1vSJT1dIRgMRX+AK+8wvaPRL2krGIqOwAWwY+nZCFc9u2uQZaQ0hojIQKJgS/ZIMBQtAf4MnEX3coeqgXdwVcSPj4QDGzu5XpLwvg/zgJl07/tQiwuQj42EA2+lsm8iIpKcgi3pVApHVCxu5duxGlHpmZ6MMAL7AV8GPh0JB1K1RY+IiLRDdbakQ95IylJcrlDSQGvrwtlsmDcy2anWDDAGWOq1K93UwxpkNwMR4Ia933MRkYFHCfLSmXm4ZPZUJGXjtTPWazdlq+AGIm/RwRLvY49rkEXCARsMRb8CvBEMRRdHwoGH92K3RUQGHE0jSrv2tL7T1oWzqdv4EmPmbO5K8ymt7yRdFwxFpwILgWMi4cDaTPdHRKS/UrAlSTVWLq9Z/dT4qndupb7sAxK15fj8pWSXTCR/0kz8404hevcxSe/PHX0cw2e6jZM33zUZgBHnPcuuV39D7ep/Ea/aTOER39xZMvXaUhU0zZxgKHoVrn7XiZFwoL6z60VEpOs0jSjtmV757m0jdz7/A3wFI/CPPw2ffwiJmm3U71hB9cp7KdjvbIomf4/qVfcRr4hQNPl7TTdnFY1t0ZiN17PtkS+SqC0nLzgDX24h2SX75uE2Z16yd19NmrkROAn4X9y+jSIikmIKtqQ9V1S/f3cevlxGnPsfsgqGtzgZr9mOL6+E4ilXEdv4IvGKCMVTrmq3sUR1lJzS/Rk2cwG+nEGNh/NwhVEVbGVIJBxIBEPRS4A3vfwtTeuKiKSYViNKe6aBwfiyMFltc+Oz8od2ucGS4+c2D7TArU6c1u0eSkpEwoGtuEKz/wiGomMy3R8Rkf5GwZa04e11WJq//9nYhhqi906n/IWfULP6CeI127rXaJaf7KEHJzszxFtFJxkUCQeeA/4EzA+GohrxFhFJIQVbksxEoLroiG9SevJNZBUGqXrnr+x48jI233YYWxeeTd2W5V1qMCt/KMYkrbtZgytXIJn3f0AD8ONMd0REpD9RsCXJ5DX+oeCA8xjxxccZddn7DP3cXRQcdBF1m15m22MXdm2UK3mgBa66eV57J2XviYQDcdx04uXBUPTkTPdHRKS/ULAlybTZwsWXV4J//KmUzriRggPOx8bKqNv4MgDGZAFgE/HuPMske55kRiQc2IzbyufOYCgayHR/RET6AwVbksxqoCC2YSnJ6rAlvBEtk50PgM9fCkC8MtKdZ+UDn3Svm5IOkXDgaeAfuIBLPyNERHpIibDSRiQcKAuGomXbn/zKCJMziNzAUWQXjQUssU3LqN+ynJzhh5MXnA5A3pgTqPn4UXY89VXyxp2CyfaTXRik4IBz9+RxO5JtKSMZNxd4Fld76xeZ7YqISN+m/2uV9iwtnvojmzviU9RvfYeq926nauV9kGigeOq1DDvroaaSEAUHXUzhkVeQiO2icvmfqHjlBqpWzt+TZ1jcJtfSy0TCgQbgQuA7wVBU5TlERHpA2/VIUsFQ9ETgMaAwjY+pBM6MhAMqatpLBUPRM3Gbhh8VCQe6WfdDRGRg08iWtGcJsD3Nz9gOPJ/mZ0gPRMKBfwH3A7d5+2WKiEgXKdiSpLzNoecA1Wl6RDUwR5tQ9wn/AwwH/jvTHRER6Ys0jSgdCoai84HZgD+FzdYCCyLhwEUpbFPSKBiKTgSWAZ+PhAOvZLo/IiJ9iUa2pDNzgPVAfYraq/fam5Oi9mQviIQDq4FvAvdqeyURka7RyJZ0KhiKjsCtGhxLz0a4anGB1rRIOLAlFX2TvSsYit4MjALO1RSwiMie0ciWdMoLjKYAC+h+Dlc18BAwRYFWn3YVbi9LjUyKiOwhjWxJlwRD0TNwpQCGAoNw2+20xwJVuFWHcyLhwBPp76GkWzAU3Q94ETgtEg68men+iIj0dgq2pMu8EgAnAFcA04AhQA0uuDK4LXh24KYebwKe15RT/xIMRS8Efoarv1WR6f6IiPRmCrakx7yE6X2APNym0p9oC57+LxiK3oob3bxYwbSISPu0N6L0mBdYvZHpfshe9x3gFeArwN8y3BcRkV5LI1si0m3BUPRg3G4DMyLhwLuZ7o+ISG+k1Ygi0m2RcGAFboXifcFQdFCm+yMi0hsp2BKRnroNN418U4b7ISLSKynYEpEeabaP5rRgKPqlTPdHRKS3Uc6WiKREMBQ9Anga+HQkHPgg0/0REektNLIlIikRCQfeAn4M3B8MRVO5cbmISJ+mYEtEUukvwAfAbzPdERGR3kLBloikjJe/dTlwejAUPTfT/RER6Q2UsyUiKRcMRacAjwPHRsKBTzLdHxGRTNLIloikXCQceBX4Oa7+Vm6m+yMikkkKtkQkXf4AbAR+memOiIhkkqYRRSRtgqHoEOBN4NuRcOCRTPdHRCQTFGyJSFoFQ9HjgQXAlEg4sK6D60qBiUAeEANWR8KBsr3TSxGR9FGwJSJpFwxFrwHOwm1YXe8dM8B04ApgGlAKVDe7rQAoA5bitgJa4q12FBHpU5SzJSJ7w6+BCuBnAMFQ9AxgNfAYMBsYAeQAJc0+crzjs73rVnv3iYj0KRrZEpG9IhiKjsDlb30AHIMbueqqamAhMCcSDuxMYfdERNJGI1sisjdZ4ES6F2jh3TcbeNUL3kREej0FWyKSdsFQtASXezUCMO1dt3XhbDbMG9lZc35gArDUa7fXMMbMNcZYY8yMTPdFRHoPBVsisjfMA8bh8rBSIQcY67UrItKrKdgSkbTyktpn4ko6pJIfmNnLkub/CBwEvJLpjohI76FgS0TSxivvMI/u52h1pgCY5z0n46y126y1K6211Z1fLSIDhYItkb3AGHOMMeY+Y8wGY0zMGLPJGLPIGHNeq+vOM8YsMcbsNMbUGGPeMcb80BjTZlTIGLPG+yg0xvzOGLPeu2e5MWaWd022MeZHxpgPjTG1xpiPjTH/laStGV6u0VxjzHHGmKe9PlQYY54yxkxOck9TfpIx5iJjzDJjTKUxZk2zy6bHNr82fPtTX2XTbYex4S9j2XzHUZQ9dxXxqs3tfr1sooGK1//A5vnHseEv49h8x1HsfOl6bLwu2eVDK9++9WJjzG3e16DOGBM1xsw3xhyQpN+3ef2eYIz5hvc1rvXuucUY0yYPzBhzuDHmHu/rHTPGbDXGvGGM+b0xJqfZde3mbBljTjHGPGmM2eG18YEx5pftPG+x1062MeZ/vO9fzHu/G4wx2m9SpA/JznQHRPo7Y8zluNGdOPAI8CEuUXwyEALu9677P+CHwDZgPlAJnAH8H3C6MeY0a23raCMH+DcwBFcSIRe4EHjQGHOa1/6xwBO4quznAjcbY7Zaa+9L0t1jvT48DfwJmAScDUz3nv98knu+B3wGeBR4FlcjC4CK139/465Xf11gsnLxTzidrMLRNOxcTfX7d1O7ZhHDz/4X2UXBNg2WPT2H2KZl+MedjBlXRO26/1C5/E8karZRevIfWlxbu+6ZQTtfuv423ErHR4GPgKDX7zONMSdZa99I0u9fAad79ywCTgIu99755MaLjDGHA8u89h/B1Qcr9q4LAdcC9Unab2KM+Qbu30AV8E9gCzADuAb4gjHm09ba8iS3zgdOwH3/dgGfA67G/fu5rKNnikjvoWBLJI2MMQcDYdwvyhOste+1Oh/0Ph+HC3LWA8dYazd7x3+I2+rm88D3cYFXc6OBN4AZ1tqYd8+dwBLcL/WPgUMbf5EbY34LrAR+ACQLtj4LfNta+8dmfZwJPAz83RhzgLU20eqek4HjrLVvtnq3/fFlH51VNJbhMxeQVTiq6Vxt5Hm2P3Y+O1/4MUM/+482nWjYuZbA+c/h85cCkKj/AVvuP4XqD/5J8dQfkVXgqj4kYuXs+PccY3IKfDZWd6i1dkWz5x8KvAz8FTgqybtOBQ6z1q7zrs8GngFOMsYcY61tzLu6BJcfNstau7DVO7auet+GMWY8rgJ+Je57u7LZuTAwBxf4fT3J7fsCh1hrd3jX/wh4C/iyMeaHjf9ORKR30zSiSHrNwf1PzfWtAy0Aa23E++NXvM//2/wXqLW2ATdylAC+1s4zrmwMtLx7nseNvpQC1zQfMbHWfgK8ABxqjMlK0tZHuOCweR8XAs/hRnJOSHLPLa0DLQCTM+hKEg0MnnZ9i0ALwB88Af+E06lds4hEXWWbBouPu7Yp0ALw5QyiYL+zwSao27K86Xj1qn9i63ZSPOWqxJg5mze26ve7wK3AkV7Q29rPGgMt7/oGoDHyOybJ9TWtD1hry5IEn619CTfi+MfmgZbnR7jK+v8v2VQx7vu3o9nzqoC7cT+720ztikjvpJEtkfSa6n1+opPrGkdenml9wlr7gTEmAkw0xpRYa5tXTi+31n6cpL2NuE2dX09ybgPuv/2R3p+be76d4GExrhjpkbjAq7l2Vt6ZaQCxjS+1CJAaJWq2gY3TsPNjcocf0eJc678DZBWOAcDGdr9+XfQ1AOq3vZuI3nvCDWbeh5ta3ba/9/kgYEWrc68l6fR673Nps2P3Ad8BHjbGPICbYn2hna97Mh19b8uMMW/i9og8EDdq1Z0+ikgvpmBLJL0Ge59bBzWtNeY5tQ4WaHZ8nNde82CrvS1rGgBaBWYtzpG85lW0nfYaR9uSFRFNOpVlEw2lAJXLw8lO776uvqrNMV9eksf43ECctfGmQ4naMgCqV96TQ/JpuEaFSY4ly5Fq/No0jfpZa18xxpyAG4U6B/h/AMaYVcBPrbX3dPBc2LPvLez+t9KknTyuNn0Ukd5NwZZIejX+shyDy5VqT2NQNBKXZ9XaqFbXpUugneONZd2TPT/pBqvGZFVYYNRXP8SXW5SKvrV9htfu8HOfrswdduiJkXAgWSJ8j1lrXwI+7031HY2X2wbM9xYbPN3B7c2/t22mktl731sRyRDlbImk18ve584KbzbmPM1ofcIYMwm3um51OyMdqTTNGJPs50Jjv9rkZrXPvgBQt+nlzi7sttzA0bhnLMsHPknbgzzW2pi19kVr7U+AK7zDMzu5raPv7WDgU0At8H6q+ikivYuCLZH0moeb9vlxsiTtxtWIwN+9z9caY4Y3O58F/Ab33+rf0txXgP1w5Qya93EmLl/rIyBZ6YekbEP1jfiyKX/hOurL2w7W2XgdsY09C8QKDrwAk1tCxWs3mg3zRu7f+rwxxpes5lVXGGOON8bkJznVOArYWQHTu3ClIb7tBc7NXY8rI3FX80UOItK/aBpRJI2stSuMMSHgz8CbxpiFuDpbQ4EpuJIQJ1lrXzTG/ApXQ+ldLxG7CjcidihuE+df74UuPwncaIw5A5es3Vhnqxb4yh6svGtirV1Zcvx1r+5a9ospW+47Ef/Yk8gevA820UC8YgOxTcvIyh9K4MKl3e5sln8IQ06/1e544pIE8LIx5j+4qTqL2zvxONzX2t/th7jvycnGmMZVnpXAIbjvTRlwS0c3W2vXGGOuxNUte8MYcz+wFRfAHoebXr6mB/0TkV5OwZZImllrbzXGvIurkzUDmIUrXPo2rgZU43XXeCvT/gv4Mi6B/WNc0cwbkxQ0TYdlwM9wIy7/BRjcKrofWWtf7WpjRZ8KXeUPnvh45dt/KYhteIHa9c9hcgrIKgiQv+/nKdi3sxm4zvmD0xtGnL+4dteyX5TXrv3PYba+4gSgDrci8xngwR4+IowLqo4FpuF+bka84zdaa9d21oC1NmyM+Qj3b+CLuG2G1uMC6P/bC9PDIpJBxtqkua0iMoB4U23P4lbXzU1Vu96ehauB8alqM4m1uLIJZwGX4sptPADcBrwUCQf0Q05EMko5WyKSNl6gM4fO85q6qxqYEwkHaiPhwP2RcOBzuGnXj3E5bquCoej/BEPRsWl6vohIpzSyJSJpG9lqFAxF5wOz6VnuVGu1wIJIOHBRO880uErwlwLn4Qq83gY8HAkH0hX8iYi0oZwtEdkb5uC2l5lA8mKqXVWPy3ma094F3qjaMmBZMBT9b1yJhkuBPwZDUU0zplkwFC3F7WKQh9sEfXUkHCjLbK9EMkMjWyKyVwRD0RG4VZVj6dkIVy0u0JoWCQe2dKMfY3D7FV6GWwBwG3BnJByIdHRfuvSXoMQbSZyOqz82DbedUPMRxALcQoOluI25lyjQlYFCwZaI7DXBULQEV3tsJu6Xb1dVAw8DoUg40KOK682mGS8DzsXtQ3gbbpqxzabTqdIfg5JgKHoG7vs6FBiEC2LbY3FlTbbj8u062zc0rfpLsCu9m4ItEdnrWv1yTrZvYXNp/+UcDEXz2T3NeAzwT1zg9XIqA52+HJQk4wXPf8atBO1u8LwQ9357Zbui/hjsSu+nYEtEMsL7pfdFXIX1cmAIUIMLMgyQD+xg9y+95/fGL71gKBrETTNeSoqmGXtjUGKMmYAry3G7tfbSrt6/J9PCZc9cQfWq+wlc/ArZxePaa6pH08Jd0d+CXek7FGyJSMYEQ9GLgdmRcOCcYCg6GNiH3dM5n0TCgYwV+/SCwWNxQde5wKvAbRvmjbwHeM5aO2MP28lIrpoxZi5wHW6HgsVJzk+gm8GWFzy+SicLHvYw2AK34GENMCUdI1y9MdiVgUWrEUUkk07A22/RC6zeyGx3dvNG0V4GXvZWM87C5XeRXbr//sFQdCqwrKPRNu+X/FJSswrT77WzNBiKpiIo2QAcBHTYTjtlQeYB40jNylK8dsZ67SYt5dFdKQp2C3ClSyYHQ9G0j8BJ/6OipiKSSdOBJZnuRGci4UBNJBy4JxIOnA5g47Fa4Hbg/WAo+gNvhWMy6QxKOvNHXDD1SrKT1tp6a+1Ka+2mrnTAm4qbiRuBTCU/MNNrPyVaBbs9rfHWPNgt6WFbMsAo2BKRjAiGosOB0bg9IvuU+K6163BbBF2GW8n2TjAUfTIYil7gJdtnPCix1m7zgqmkBVyNMROMMdYYc1uzY7d5xyYYY75hjHkHWOSd/oIvr7hxNWmLqbjayBK2LpjJxlsnsvHvB7L9iUupL/uww5eoi77B9qe+yqbbDmPDX8ay+Y6jKHvuqoKGig23eFO4jX062+vTy8aYFkGrMeZQY0y1MWajMWZEksd0GuxuXTibDfNGdtjXZroS7Io0Uc6WiGREMBSdDVzubbGTcsaYs4DvAAfjku+3Ax8C91lrw941awCstROS3D+XJDlPxhgLPIeb7roBOB0o8vmHbCo6+rsVhYd/LQjcj5t2HGmtpXrV/VSvuJOGnZ+QqK8iyz+U7CH7U3DgBRRMmtXiufHKjVQs/xO1654hXrkJk+0nu2QC/vGnUTz5u03Xbb7jyHi8alMEOByYC5wNjAF+bq2duwf9vxpX9LXWO7UCqMSNNv7Te69tuDy6Noad9SB5Yz5NzcePsuPf3wBfLgWTzsJXEKBu0yvU71hJztCDqNv0cpucrar351P+3FWYrFz8E04nq3A0DTtXU7vmKXz5w2zJcdedt+Pp0APN+vxH4FvAr621V3vHCnDlOg4ATrXWPtu8f14w+gCd5GhtXTibuo0vMWbO5o4ua60aOEdJ87KnlLMlIpmStilEY8zXgb8Am4FHcUHDCFxgchkQ7uEjSoEXcaso/wEMTtTuOG/nC9dOrHxr3s9H/r/XRwABgF3LfkHlmzeRVTSO/H3PwuQWk6iOUrdlOTUfP9oi2Krbspxtj12IjZWRO2oq+RM/h22ooaHsAype+02LYAvj8+HLLSRR9wwumFwE7MIlve9J/x/y/vyR9y7nAYO9Y1OBw4CjcEHjJbjcrpKCAy8iq3AUWUVjSdRXUfbc1WB8DJ/1MLkjPtX0gPIXfkLV27e0eXB9+ceUL7mGrKKxDJ+5gKzCUU3naiPPs/2x803Ve3f8AXYHW8D3gOOB7xtjnrHWPgn8CTdN+rMkgZYhyQhcChUA84Kh6ESVhZA9oWBLRDKlsdZROnwDqAOOsNa2SGY2xgxLQfuH40Z/LrDWJrx2fwm8Hq/ccHX9tveeyRl2CABVK+7EN2gUI85fjC+n5e/+eM32pj/beB07Fl2OjZVRekqYgv3Pbnlt5caWPTDGkKgbCiwHTrTWVnWx///CjYS9bq39htf/lUAucT6odwAAIABJREFUELbWrgPWGWPKccHWYmBmzrBDKDzsqwBUf/AgNlZG/v7ntgi0AIonf5/qlfdi63a1OF713u2QqGfwtOtbBFoA/uAJ+CecTu2aRaONMUXW2goAa23MGHM+bgHFHcaYX+FWiS4Bfpbk/aYDQ2tWP0XVO7dSX/YBidpyfP5Ssksmkj9pJv5xpxC9+5imG5pPJeaOPo7hMxcAsPmuyQCMOO9Zdr36G2pX/4t41WaKjvyvUcXH/OAE+kDOoWSegi0R2euCoWgxbvrntTQ+pgFXUqAFa+22FLQdB65pDLS8dlcbY24Crqv++JFpJcMO2Z135MvG+LLaNJKVP7Tpz7VrFhGvWI9/wultAi2ArMLR7fXle10MtBr7/0vgzFb9fx84AhjftgP+GuK1JGK7Fy/Wb3Ppdnmjj2tzuS+vmJxhh1C38aUWx+s2u295bONL1G1Z3ua+RM02sHFyhh9+FG66s7F/Hxpjvomry/Zr3GjlRdbaeJL3u6LqvTsHlS+5Cl/BCPzjT8PnH0KiZhv1O1ZQvfJeCvY7m6LJ36N61X3EKyIUTf7e7lctGtuiMRuvZ9sjXyRRW05ecAa+3EKyiifm4P5nQcGWdErBlohkwvHAq5FwIJam9u8GbgRWGGPuxf3SfsFauzVF7a+z1iabrlsMXNewY2XTEFbB/mdT9c7fiN47nfx9zyJv9HHkBibjyytucWNd9HUA/ONO3vNeZPkZ/bWPioOh6GTcz/Omj5zAUZPqo29QdPSV07zFCI3nMDlFZUM/+48Ttz16DjkjPnVwMBS9GsjOHrzfoIbyD8ku3e+0YCj6ayC79OSbxpc9cwV5Y44/PrbuGWgW2yRiFa4bBcOTd6+gbc56IuZ2wqlc3vFMbs6QA/ejWbDlaZwqLQb+aa3d0M7t06pW3Gnw5TLi3P+06V+8Zju+vBKKp1xFbOOLxCsiFE+5qt2+JKqj5JTuz7CZC/DlDGo8bHAV6EU6pWBLRDJhOl59rXSw1v7WGLMNCOFGH64ErDHmOeAqa21PR9Si7RzfDJCI7Yzj/XwtOf5nZBeNp2rVvVS+eTOVb94Mvmz8406h5Pi5ZJdMBCDhTbf5Bo1qp+m2svKHYnzZfwMqcKN4DY0fucOOmFAffYOcoYd+Fjda1XgOX8HwhMkrPgjAZBcMAoYBDWTlNQAk6quzga1AQ6K23Adg66va1OPy5RUBEK9OHsPGq9uWo/LlFhEHRn31Q3y5RR293vXBUPTbuK1zyqxNlGcPPXhGw/YVxWT5K4nHvlF89JVri4/94VJc7lyZ9+HH5aRhfFmYrLYLEZuPKO6pkuPnNg+0Gg0JhqKDM1l8V/oGBVsikgknAD9N5wOstXfg8nsG40bSZgNfAZ4yxhzojXIlcDlKyQxu5zh4ye9JjARaBBHGl0XhEV+n8IivE6/eSt3mV6j56GFqPn6U+h2rCFzwHCYrD1+uG+lKVHWp7JUFLomEAy+1PmHMP+YC1+1Y9LVrW6xGnMeX4js/qdr6wGnXAhfXbXzx1Ug44Fb4zXv3KODAROWGVZFw4FeunZ/MAK5o2LlmDS5pvknOsMMBNyU46KCWtUgTsV3Ub3uvTYdzA0dTv/Vt6ja9jH/8Z9p7r13A14CNuMBp8I4nv3Jhw/YVE3IDk98tmnLVu9sf/9K5VSvunDvosK++m1UwotC7rhQ34pSdv//Z7HpxrhtRnDTLjSiOnEJWfjdS9rL8ZA89ONmZGtxqzV5TjFd6JwVbIrJXeXWojgTaBAjpYK0tBx4HHjfG+HAB13TgQdxIyOHGmBxrbev8rskdNDvOGDPBWrum1fEZADnDDm0gyc/XrILh5O9zJvn7nMm2R84htmEp9TtWkjv8CHIDRwNQu+4ZBh1ySVdesTtTseOAYJLjjVnizaOkOEAitrNNvS7/xM9i8gZT89EC6g77aosk+V2v/aZNcjzAoEO/QtWKuyh/4TqGluxDzuB9W5y38Trqoq8X5I0+7oXGESNjzFTcqsiP6qKvHb/t0fMqjDn/uUTtjnmbbz88ChxjvTpGwVD0RODRoiO+WZTlH0Llu7dR9c5fvZWRhtzRx1Fy3E/aJPR3JCt/KMYk3UbRkvo6atIPKdgSkb3tGOC9SDjQ1aTuPWaMOQlY3PgLuJnGJKLGwOEVXHmDy4CmOgXGmEuBT3fwiCzgBmPMhc1WI07ETVk2FBxwXhaAjceo2/IWeaOOaXGzjdeTiLmZJ5OdD4B/wmlkFY2lds1TVH+4gIL9Zre4J165MUmSvDHAJx30s6P+X9OyJTMRV0oBYEGzU27JZLy2zUifL2cQpSf+mh3//gZbH57Vps5W7qip1G16ucU9OaX7UXrS7yh79r/Zct+J+MeeRPbgfbCJBuIVG4htWobPP9g0lH/SGGgNBu7BjUJe0GyF4p+NMacA5wDfxeXogZtSTQAUHHAeBQecRyK2k7rNr1Kz+gmqV97DtscuJHDh83s+ypU80AI3ipauvEPpRxRsicjetjeWyy8AKo0xL+M2ODbec6cArwNPe9fdjAu05nm/uNcDnwKOAx4DPt9O+2/jNql+3RizCDfl2Fin6urskonfB0bYhlq2PXwWWSUTyR1+OFmFQWw8RizyHA1lH+KfcDo5pfsDYLJyGXLarWx77ALKnp5D1Yo7yA0cjW2I0VD+IbHI84z5Zut88ESim/lCb+NGFwGONsb8xet/45TqumbXrsLto3gyuCnDXa//joL9zyG7aCz5+36BobnFVLx2IzUfPwq+XPJGT2X42Y9R+ebNbYItgIL9zyFn6CFUvjWP2IYXqF3/HCangKyCAPn7nknemGkvNLv8b7htcr5rrX29VVNfA44GfmGMed5a+wquzliLGhu+vBL840/FP/5UsAmqV95D3caXyd/38xjjVonaRDzpitFO5NO9YFcGGAVbIrK3TccFOen0A1wF9KOAz+GqpK/FjebMa5wytNauMMacCvwf8AVcAvnzuGDrbNoPtsqAM4Bf4YK1YlwF9t9Ya+d7m1TPNtkFpnjqtcQ2vEDd5teI1zyJL2cQWcUTGDz9BgoOvLBFo7kjPsWIc5+m8s2bqV33DJWbX8PkFJJdMoHiKVe36YSN19W2Obhnmvf/NFwdrab+t3iGtXFjzGzgl5isY+s2vjSobuOL5I08hmyvRIJ/7In4x57Y5iGlJ99E6ck3Je1AztCD2jtXCfy42fO/2N5LWGt30qrCfSQcKAuGomWxDUtH5I7+dJvpv0SNq/zROKLo85cCEK+MkF3ctuJFJ3YoOV72hLbrEZG9JhiKZgM7gAmRcGBHpvuTLl7e0GNAYRofUwmcGQkHujRK2Lhdj7V2Rlcf6FVmX02yOlypsxboUWX2YCj64Ma/7X+2yRlEbuAoLyi0xDYto37LcnKGH87w2f/CZOVQ9d4dlC+5mpxhh5I37hS3PVJhkIIDzgV2FzUd+aU2C1gt8FAkHDinu/2UgUMbUYvI3nQksKY/B1qeJTTmOqXPdtJYPiMZLwCaw+6ct1SrBuakYAucm4qP/UEsd8SnqN/6DlXv3U7Vyvsg0UDx1GsZdtZDTSUhCg66mMIjryAR20Xl8j9R8coNVK2c30nzAFQByYftRFrRyJaI7DXBUPR7wD6RcOBbme5Luu3pRsjd1O2NkHsystUoGIrOx5XS8He3jSRqgQWRcOCiTq/sRF8ZgZOBQyNbIrI3DZi95LxAaCEuiEilWmBhdwKtFJqDW0zQZjukbqr32puTisb60AicDBAKtkRkrwiGoj5csLVXp74yrNcFJdZa05NRLYBIOLATt1XNGnoeTNZ67Uzz2k2Jfh7sSh+jYEtE9paDgbJIOLAx0x3ZW/pCUNJdkXBgC66UxgK6P4JUDTwETPHaS7VeF+zKwKRgS0T2lgEzhdhcHwlKuiUSDuz0cqzOweUwVeJW6XXEetetxeWdXZyu4LE/B7vStyjYEpG9ZToDMNiCHgUl4EZS0hqU9JQ3pTYROBMXFEZxo0C7gJ3e53rv+EPedRP3xlRcfw52pe/QakQRSTtvdVgEmB4JBz7OdH8yyftanIDb2mcaMAS3obHFVbrPx9UiWwqUAE9GwoEbk7fWewVD0cG4gqN5uC1tPsl0AVBvheg8YCgwCPf1bo/FlXfYjkuGV46WdJuCLRFJu2Aoug8uMT6oFVwtdRSUBEPRKcA/gUmRcKAhc73sP7oY7N4EPK9/s9JTCrZEJO2CoeilwOmRcODCzq6VloKh6FLg95Fw4IFM96U/6o0jcNL/aG9EEdkbpjOwSj6k0u+BK3EFUiXFvMDqjUz3Q/o3JciLyN4wIFcipsjDQNCbUhSRPkjBloikVTAUHY3Li1mR6b70RV6u1s240S0R6YMUbIlIup0ALI2EA4lMd6QP+xtwRjAUHZPpjohI1ylnS0RSKhiKluJqLjUmHJ+KphB7JBIOlAdD0buAbwH/k+n+iEjXaDWiiPSIt5R+OruX0pfSsnhkMVAGLMYtpV+ipfRdFwxFJwEvAeMj4UC6NlgWkTTQNKKIdJtXJHI18BgwGxgB5OCKcTZ+GFzO1mzvutXefdIFkXDgI+BF4P9lui8i0jUa2RKRLguGoiXAn4GzgIJuNFENLMRV5u6VW9D0RsFQ9CQgDByiHDiRvkMjWyLSJcFQdATwKjCL7gVaePfNBl712pM9sxiXB3dahvshIl2gYEtE9pg3orUUmAD4e9ic32tnqdeudMLLdWsscioifYSCLRHpinnAOFxeVirkAGO9dmXP3At8KhiKHpzpjojInlHOlojsES+p/QG6P3XYkWrgnEg48EQa2u53gqHodcDoSDjwjWbHWpfcWB0JB8oy1EURaUbBloh0yivvsBoYn8bHrAUmqixE54KhaABYCXwZuJTkJTcKcCU3lqKSGyIZpaKmIgOIMWYCLmi6HZgL/BJXdLQQeBeYa619rNn1JcDXff4hF+LLHZeo3Y4vt4jcwGQKj7qCvJGT2zxjw7yR5I4+jiGfuYVdy35O7dqnsfVV5Aw9hOKp15I3eiqJ+ioqXv0NNR8/Qrx6K9klEyg6+ruBgv1mJd1D0RhzIfB14Ehcrtdq4G7g19baWCq/Rn3EUbgp2Ae8z8Y73jr3bQRuIcJpwPZgKDpHo4cie59GtkQGkGbB1mLgEOATXKHMIcD5uF/cp1prn/WunwosyRl2WFnOiCNG+PIGE6/YQO2ap7CJOoaecQf+cSe3eMaGeSPJGXoIifoqfLmF5I46jkSsjJqPFmJ82Qyf/Rhlz11FIlaOPzgdm2ig5qMF2Ppqhn7ujme3/etLLRo0xvwduAyIAIuAcmAqcLz3Hp+x1jak5QvWy6jkhkjfpGBLZABpFmyBG8X6abNzpwNPAk9Yaz/nHSsBcsbM2fwebpQEgHjlRrY8eAa+3GICFz7f4hkb5o0EoODgLzN4+i8xxq3DqV71T8qe+TYmbzB5I6cw5LRbMdluQWNs48tsWziLvPGnxmrX/LtplaMx5lLgH8AC4GJrbU2zc3OB64ArrbV/6PEXp5fzSmQsxS0o6MlK0FpgPTAtEg5sSUXfRKRjWo0oMjCtBf63+QFr7VPAOuCYZsd2jpmzOY7LB2qSVTia/H0/T0P5hzRURNo0brLzKTnuJ02BFkD+fmeDLxsbK6dk2v82BVoAeaOnklU0loYdq/KCoejgZk19B2gAvtI80PJcD2wHLu7Sm/dBKrkh0rcpZ0tkYFpurY0nOb4eOK75gbLF35uVqC2jfsty4jXbIFHX4oZ41Wayi4ItjmUP3hdfbmGLY8aXhS9/OLa+muzitnn2WYNGURd9A2Af4A1jTAFwBLANuNIY0+Ye3Kq7gzp80/4hnSU3LkpRmyLSDgVbIgNTeTvHG2g24m2MmQ381WTn+/KCJ5JfMh6TXQDGR2zji9RtfAnibfPTTW5R0saNLwuTl/wcvixwqVd53pFSXOL3cNx04YDkldyYye6vS6r4gZnBUPQMJc2LpJeCLRHpyPVg6od/8Ym6nCEHtoiSyp67ygVbqdcYvTUmcL9prT0qHQ/q7bySG/NIT20zvHbnBUNRldwQSSMFWyLSkUkY38qcIQe2qFZubYK6TcvS9cxP3DNspTHmPeAQY8wQa+2OdD0w3VRyQ2RgU4K8iHRkDTa+T7xyY1OZAGstFa/+moayD9LywEg40HyK87dALvB3Y8zg1tcaY0qNMX1p1Gs88AouQf1O4D7gUGChMeakZtcdBPw8q3DMGP+EU03hEd8gL3gisQ1L2bZwFrXrnknauI3tYuuCL1C/7V3yJ83Gv8+Z1G19i+3/upD6be+x7ZFzqVnzFP7xn6HggPOIV26g7Ok5/tq1/57bui2v5MZ8YBLwIPAnYAduYcKTxhj9z7rIHtJ/LCLSkd8Bf47eNyO3YL/Z4MuhbvMrNJR9iH/8adSuXZTWh1tr/26MORoIAR8bYxpXTA7BbU0zHVca4ptp7UjqzKBtyY35uJIbVwHPeoffB0aPOPff7zW/ubHkxs4XrmtT3wygfvt7bUtuBE+k7Jlvs/WRL5I3cgrDZz7UtBK0YP9z2LZwFpXv3X48fKmpHa/kxmV0XHLjW0C/L7khkgoa2RKRdllr/wJchk2sr151PzUfPkRW4RiGn/04OcMPS/HDEslWR2Kt/RbwBVzx1VOB7+KKepYAvwZ+n9qOpJVKbogMQBrZEhlArLVr2L21S7LzM5Icuy0Yit5Oq70Rc4YeRPGUq9q0MWbO5nafP/JLr7V7bvishRHcaFWyfj0GPJbsXB+jkhsiA5CCLRHpVCQcsMFQdA5uL750rIyrxm0h099XxKnkhsgApGBLRPZIJBx4IhiKLsRtbNzTKubN1QILVeupBZXcEOlHlLMlIl0xBzflVZ+i9uq99uakqL3+orHkRougdm+V3ACaSm6k62EiA4mCLRHZY5FwYCcwDViDG5HqiVqvnWleu7KbSm6I9COaRhSRLomEA1uCoegUXGXzmXQvh6saeBgIKdBKSiU3RPoRBVsi0mVegHSRt2/fPGAoMIgOVjoCFqjClQ2Yoxyt9llr/2KMiWETP6pedf8kk51P7qhjKT3pD9R88lhqgy1XciMrSR++ZYx5AhdQnQoMxhU1XYcruXFX6joh0r8Za/v74h8RSSdv/74TcNXQ/bigqwYXXBkgH/dLeilwE/D8AFh1mBLe17ZFyY00WAtob0SRNFKwJSI95gUFW4HDcIHWPrhSAjHgk1b5QNIF3uhhOktunKNRRpH0UrAlIj0WDEX3BZ6NhAPjMt2X/igYis4nPSU3FkTCgYtS2KaIJKHViCKSCsfiNliW9FDJDZE+TMGWiKTCMSjYShuV3BDp2xRsiUgqHAukrdqmuJIbwBRgAS7XqjuqgYeAKV57IrIXKGdLRHokGIrmAmVAIBIOVGa6PwOBSm6I9C0a2RKRnjoc+FiB1t7jBUwTgTNxI1VRXB7WLtzehru8v0e982fiyjso0BLJABU1FZGeUr5WBnh1sZZ4HwRD0cGo5IZIr6RgS0R66lhcwVLJIC+weiPT/RCRtjSNKCI9pZEtEZEOKNgSkW4LhqIlQBB4L9N9ERHprRRsiUhPTAHejIQDDZnuiIhIb6VgS0R64hhUX0tEpEMKtkSkJ7RNj4hIJxRsiUi3BENRgyrHi4h0SsGWiHTXWO/z+oz2QkSkl1OwJSLddQywzCuuKSIi7VCwJSLdpXwtEZE9oGBLRLpLxUxFRPaAgi0R6bJgKJoNHAW8mum+iIj0dtobUUQ6FQxFS4GJ7N7kOB/YoI2ORUQ6Z6xVbquItOSVdZgOXAFMA0qB6maXFAL1wOPATcASJcqLiCSnYEtEWgiGomcA84ChwCDAdHC5BaqA7cCcSDjwRPp7KCLStyjYEhGgaVPpPwNnAQXdaKIaWIgLunamsm8iIn2Zgi0RIRiKjgCW4gqV+nvQVC2uyOm0SDiwJRV9ExHp67QaUaQPMMbMMMbYZh8rU9W2N6K1FJhAzwItvPsnAEu9dlPCGPP9Vu9/W6raFhFJNwVbIn3Lc8BPgT82HmgViK02xiTNsTLGFBpjdjW7doJ3ah4wDshpvLb6gwfZMG8kG+aNpHb94g47lIjtZNcrN7Dl/lPYeOs+bPjLuJxNtx+xX/SeT39sjLnRGHNkq37M9Z4/t51+/rTZu+zvHX7Re+8/dNgZEZFeSKUfRPqWxdbaue2ca8CNKn0GWJTk/AVAkXddNjQlw8/ElXRoUrXiTlxevKVqxZ34x85I+sB41Wa2LvgC8Yr1ZBWPJ3//L+LzD8HGyqnb8lYpcCVQA7zZ2YsZY7KAMPB14C3gDGvtJgBr7YvAi16A+J3O2hIR6U0UbIn0H08DJwGXkzzYuhzYBKwDjsWXC25Uq0UyfH3ZR9Rtepm84HQSsXJq1ywiXr2VrILhbRrc9cqviFesp+DACxk847e0GlTzxSs3RjbfNeWhzjpujPED9wCzgMXALGutkuxFpF/QNKJI/7EdeAiYaYxpERkZYw7Hba/zD9zIFqUzfnMsrrxDC9Xv3wVAwQEXUHDA+ZCop3rVvUkfWBd1BeQHHfbV1oEWAFmFoweP+eaGwo46bYwZjAsOZwEPAJ9VoCUi/YmCLZH+5VZc7tUlrY5fjquJ9bfGAznDDrsUV0eriY3XUb3qfkxuMfn7nEHBfmeDL5eq9+eTbOWyL28IAA3lH7fXn0G4wqhJGWPGAM8DJ+CmEM+31sY6ekERkb5GwZZI/7IY+Aj4WuMBY0w+8CXgP9baT5qOZ+dPplXB0ppPHidRu4P8STMx2fn4/KX4J3yG+M7VxDYsbfOw/ElnAVC++HvsfOln1EaWEK/d0fwSg6tAn8wBuMT3Q4GfWGu/Za1NdPmNRUR6OQVbIv2IdcNPfwUOMMZM9w6fAwzGjXrtZnxtSjPsnkI8v+lY45+rV9zZ5nmDDv0KhUdegU00ULk8zPZHz2PzPw5m811TKFv8Peq3vQcwJBiKDk7S3QtwqyD/Zq29vouvKiLSZyjYEul/bsPtW3i59/evA9uAh1teZmub/61h52piG14ge/Ak8kZObjruH3cyvoIR1Kx+knjN9hYtGGMomfo/jLrkLUo/82cGHf51ckdNJV69her372bLA6dR+d7tDcA+Sfq5BFcE9VJjzJd68sIiIr2Zgi2RfsZaGwUeBb5ojDkON413u7W2rqP7qlbcBdgWo1oAxpftcrcSdVSvui/pvb68EgomzWLwp3/G8FkPM+qyFRQd/d9g4+x84cf5NaufHJnktmeBz+MCrtuNMV9Lco2ISJ+n0g8i/dMtwNnA/d7fb+3gWmy8vimQ2rXs5+xa9vOk11WvuJuiT4U6fbgvZxDFx1xDbMOL1G1eRtWKOw+CSx5v81xr/2OM+SzwL+AWY4zfWvvHti2KiPRdCrZE+qd/A2uB8cASa+2qtpeYpq15atc8SaJmG9mDJ5E78pikDcY2vkDDzo+JbXyRvNHH71EnTK5b7Niw4/1290m01i41xpwKPAXcbIwpsNb+ao8eICLSByjYEumHrLUJY8zZuAT095NflNgJDIPGKUQomnIVBZNmJr286v35lC/+LlUr7moKtire/BP+8aeQM+TANtfHNi0jtuFFMNnEKzf+p5P+vmqMOQkXJN5gjMm31v50z95WRKR3U7Al0k9Za98A3mj3fEPNa8DpDbvWmlhkCT7/EPInntFue/mTZrLzhR9T88m/SNSW4fOXUvPhQ+x6+XqyB+9HbuAofAUBbEM1DTtWeaUiLIVHzHm7Ynl44x709y1jzInAf4C5XsD1g66/uYhI76IEeZEBqn7bO7cBVVXvzwcs+fufi8nKbfd6X84g8ifNhniM6lUuFWzwSb+n6Jhr8BWMILbxRSrfvoWqFXfSULGO/P1mM2zmgpqS46/79p72yVr7PjAdt6XQNcaYP7S3sbaISF9hklWFFpHexRgzA7d676cdbETdJcFQ1ACrcXld6bIWmBgJB1Lyg8bbiHo1bnXlpaloU0Qk3TSyJdK3XGeMscaYlT1tyAuA5gDVPe9WUtXAnFQEWsaY7xtjLC7QEhHpUzSyJdIHeCM6lzY7tC1VJRKCoeh8YDbg7+zaLqgFFkTCgYtS0Zgx5njgtGaHlltrH27vehGR3kTBlsgAFwxFS4BXgQm4Tax7qh5YA0yJhAM7U9CeiEifpmlEkQHOC4im4QKk2o6v7lSt1840BVoiIo6CLREhEg5sAaYAC+h+Dlc18BBuRKvdIqYiIgONphFFpIVgKHoGMA8YCgwCOiq9YIEqYDsuGf6J9PdQRKRvUbAlIm14ZSFOAK7ATTEOAWpwwZUB8oEdwFLgJuD5VJV3EBHpbxRsiUingqHoYGAfIA+IAZ9EwoHyzPZKRKRvULAlIiIikkZKkBcRERFJIwVbIiIiImmkYEtEREQkjRRsiYiIiKSRgi0RERGRNFKwJSIiIpJGCrZERERE0kjBloiIiEgaKdgSERERSSMFWyIiIiJppGBLREREJI0UbImIiIikkYItERERkTRSsCUiIiKSRgq2RERERNJIwZaIiIhIGinYEhEREUkjBVsiIiIiaaRgS0RERCSNFGyJiIiIpJGCLREREZE0UrAlIiIikkYKtkRERETSSMGWiIiISBop2BIRERFJIwVbIiIiImmkYEtEREQkjRRsiYiIiKSRgi0RERGRNFKwJSIi8v/bu7cYu6oCjsP/1aGdthRKaWAqDNIKxhgS8AUUKBc1IsRwi/Kg8cKDEjsm6AMJT0QwPhmfiJkaYghgAgkotxCIMdxqRQOBIIEEDFCRATploEVoO+10unw4p1DaZtpOz+q0yfclzfScs/de67z9svc6e0NDYgsAoCGxBQDQkNgCAGhIbAEANCS2AAAaElsAAA2JLQCAhsQWAEBDYgsAoCGxBQDQkNgCAGhIbAEANCS2AAAaElsAAA2JLQCAhsQWAEBDYgsAoCGxBQDQkNgCAGhIbAEANCS2AAAEswt/AAAG/0lEQVQaElsAAA2JLQCAhsQWAEBDYgsAoCGxBQDQkNgCAGhIbAEANCS2AAAaElsAAA2JLQCAhsQWAEBDYgsAoCGxBQDQ0BEzPQEAgAMxODS6KMmyJP1JtiRZMzI8sH5mZ/WJUmud6TkAAOyzwaHRkuT8JNcmWZ5kUZJNO20yP8n6JKuT3Jxk1cjwwIwFj9gCAA4bg0OjlyRZmWRxkiOTlCk2r0k2JnkvyYqR4YFH2s9wd2ILADjkDQ6NLkzy+ySXpXPman9tSvJAOtH1QS/ntjdiCwA4pA0OjR6fziXBk5LMPYBDjSd5M8nykeGBdb2Y277wa0QAoLlSyoWllLrTv5f3Zb/uGa3VSZbmwEIr3f2XJlndPe6USinX7TLn26YzqF8jAgAH05NJnkgytqcPSynfSHJ1knOSDGTW7Dl9C06YNWfJmWX+qVdm7slf/3jbLW/9PWMPfjtzTjg7x11+314HrtvG89GLt87e/OqDp25b/8q7ZeXmpLOe6+0k/0hyT631yZ12eSrJTUmOSfLz/f+qHWILADiYnqi13rjrm6WUo5LckeSKdC73PTZ78WnP9Z90waWTH470jb/xaDb/+09ZcMZPs/Cc3Xbfq+0TGzN2/5WZGHshs+YfX+adcmmZeP+VP0+8+691Sc5Ick06UfVxbNVan0ryVCllacQWAHC4KqXMSnJPkm8meTzJ909csfadJGuSzE6SOrklG1+6I9s2vDatMT564ZZMjL2Q/pMuzOJL7kjpm3NEkq8kWTYyPFBLKYuSfLEX32dX1mwBADPtu+mE1qtJLq21vp3OfbQW79ig9PVnwek/ycJzb5rWAFvXPpMkOfK0H6X0zdnx9uIk5yVJrXV990xWz4ktAGCmXdP9+9ta68bu/69N5z5an1L6+qc1wKy5xybJrmfGjuyO05TYAgBmTCllx+W8JHl0p4+WZ+oblu6XeadcliT539O/yYZV12f8jb9mcuNo6Y7TlNgCAGbSsUl2XNcbST5+1uGiXg4yb+lFWXjur1OOmJuNL92e9x7+QdbecUbeuf30gXLE3LtLKef3crydiS0A4FCzLJ9+1mFPLDj9x1nyw+dz7MW3ZcGXfpb+wQtSt36UTG65KsmTpZRf9XrMxK8RAYCZ9X6Sremc3ToxyWtJprcwax/Mmj0/85ZdnHnLLk6S1MmtH4w9+J0/bF379C+S3FBKubfW+nxPx+zlwQAA9ketdVuSf3Zf7rhj6ZaDNX7pm1OOu/LBO5Pc1X3ra70eQ2wBADPtlu7f60op89O5v9YeHzZdJ3veYfOSvJ7kw+7rni3K30FsAQAz7a4kf0ny+SQPvLVyydwk63feoE5uzUcv3poPnrpxWgNsfOn2bF377J4+ev+tlUuWJLmq+3rVtAaYgjVbAMCMqrVuL6VcleSPSS5P8vq6e7+1oX/JWcmsvkx++Ga2jKzO9vH3suCMFbvtv239q1n/2J5vl9W34MQcfdb1Gf/v49mw6vr0HXVS5iw5K30LTkid3JKJ0ee2J3khnTvV31xrfabX309sAQAzrtb6YZIrSikXJbl6Yuylr06MvZgk6Zs/kP7B8zP/C1dl7md3X1K1ffO72fTK3Xs87uzFp+Xos67P0WffkDmf+XK2jKzK1tFns33Nw6nbJ5NS+pI8lOTWWutDLb5bqbW2OC4AwMdKKRem89zDm/b0IOpdDQ6NlnTWbp3ccFpvpPtsxKk26j6Iek2S22utV+/vINZsAQAH0y9LKbWU8vJUG3UDaEUa3G+ra1OSFVOFVinlulJKTSe0ps2ZLQCgue7Zoat3emus1vq7ve03ODR6Z5Irk8zt4XTGk9w3Mjzwvak2KqWck+Sind56vtZ6//4OJrYAgEPW4NDowiTPJFmaziL2AzWR5D9JzhwZHvigB8fbK5cRAYBDVjeIlqcTSOMHeLjx7nGWH6zQSsQWAHCIGxkeWJfkzCT3ZfpruDYluTedM1rrejW3feEyIgBw2BgcGr0kycoki5Mcmanv+F6TbEzyXjqL4R9pP8PdiS0A4LDSvS3EeUmuTecS47FJNqcTVyWdR/C8n2R1kpuT/G1vt3doSWwBAIe1waHRY5J8Lkl/Og+xfn1keGDDzM7qE2ILAKAhC+QBABoSWwAADYktAICGxBYAQENiCwCgIbEFANCQ2AIAaEhsAQA0JLYAABoSWwAADYktAICGxBYAQENiCwCgIbEFANCQ2AIAaEhsAQA0JLYAABoSWwAADYktAICGxBYAQENiCwCgIbEFANCQ2AIAaEhsAQA0JLYAABoSWwAADYktAICGxBYAQENiCwCgIbEFANCQ2AIAaEhsAQA0JLYAABoSWwAADYktAICGxBYAQENiCwCgIbEFANCQ2AIAaEhsAQA0JLYAABr6P+5MATt9qgLLAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -828,14 +853,14 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 26, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -847,13 +872,14 @@ } ], "source": [ + "############# WITHOUT CLS\n", "f, axes = plt.subplots(nb_heads, 2, sharex='col', figsize=(20,60))\n", "\n", "#G0 = nx.from_numpy_matrix(attention[0, 1:emb_len, 1:emb_len])\n", "#pos = nx.spring_layout(G0) # positions for all nodes\n", "labels = dict(zip(range(emb_len),pred_str[:emb_len]))\n", "\n", - "d= 0\n", + "d = 0\n", "#f.suptitle('Attention head probabilities (Layer #12)')\n", "for i, (ax0, ax1) in enumerate(axes):\n", " # Attention map\n", @@ -868,11 +894,12 @@ " # graph\n", " Gi = nx.from_numpy_matrix(attention[d, i, :emb_len, :emb_len])\n", " #Gi.remove_node(0)\n", + " show_cls = lambda u:(u>0)\n", + "\n", " \n", - " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.8) & (u>0)]\n", - " #elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & (d['weight'] <= 0.8)& (u>0)]\n", - " #esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 0.5) & (d['weight'] > 0.01) & (u>0)]\n", - " esmall = [(u, v) for (u, v) in G.edges() if (u>0)]\n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.8) & show_cls(u)]\n", + " elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & (d['weight'] <= 0.8) & show_cls(u)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 0.5) & show_cls(u)]\n", " \n", " lg_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] > 0.5) &(u==v)]\n", " sm_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] <= 0.5)&(u==v)]\n", @@ -883,7 +910,7 @@ "\n", " # edges\n", " nx.draw_networkx_edges(Gi, pos, edgelist=evlarge, width=6, ax=ax1, edge_color=red)\n", - " #nx.draw_networkx_edges(Gi, pos, edgelist=elarge, width=2, ax=ax1, edge_color=red, style='dashed')\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=elarge, width=2, ax=ax1, edge_color=red, style='dashed')\n", " nx.draw_networkx_edges(Gi, pos, edgelist=esmall, width=1, ax=ax1, alpha=0.5, edge_color=blue)\n", "\n", " # labels\n", @@ -898,31 +925,280 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############# WITHOUT CLS\n", + "f, axes = plt.subplots(1, nb_heads, sharex='col', figsize=(60,10))\n", + "\n", + "#G0 = nx.from_numpy_matrix(attention[0, 1:emb_len, 1:emb_len])\n", + "#pos = nx.spring_layout(G0) # positions for all nodes\n", + "labels = dict(zip(range(emb_len),pred_str[:emb_len]))\n", + "\n", + "d = 2\n", + "#f.suptitle('Attention head probabilities (Layer #12)')\n", + "for i, ax in enumerate(axes):\n", + " # Attention map\n", + " #im = ax0.imshow(attention[d, i, :emb_len, :emb_len])\n", + " #ax0.set_title(\"Attention head {}\".format(i+1))\n", + " #im.set_clim(0, 1)\n", + " #divider = make_axes_locatable(ax0)\n", + " #cax = divider.append_axes('right', size='5%', pad=0.05)\n", + " #f.colorbar(im, cax=cax, orientation='vertical')\n", + "\n", + " ax.set_title(\"Attention head {} - Layer {}\".format(i+1, d))\n", + " # -----------------------------------------------------\n", + " # graph\n", + " Gi = nx.from_numpy_matrix(attention[d, i, :emb_len, :emb_len])\n", + " #Gi.remove_node(0)\n", + " show_cls = lambda u:(u>0)\n", + "\n", + " weights = [d['weight']*5 for (u, v, d) in Gi.edges(data=True) if show_cls(u)]\n", + " edges = [(u,v) for (u, v, d) in Gi.edges(data=True) if show_cls(u)]\n", + " weights = [weight*len(edges)*3.0/sum(weights) for weight in weights]\n", + " \n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & show_cls(u)]\n", + " elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & (d['weight'] <= 0.8) & show_cls(u)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 0.5) & show_cls(u)]\n", + "\n", + " lg_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] > 0.5) &(u==v)]\n", + " sm_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] <= 0.5)&(u==v)]\n", + "\n", + " # nodes\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=lg_self_att, node_size=900, ax=ax, node_color=red)\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=sm_self_att, node_size=200, ax=ax, node_color=blue)\n", + "\n", + " # edges\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=edges, width=weights, ax=ax, edge_color=red)\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=evlarge, width=6, ax=ax, edge_color=red)\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=elarge, width=2, ax=ax, edge_color=red, style='dashed')\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=esmall, width=1, ax=ax, alpha=0.5, edge_color=blue)\n", + "\n", + " # labels\n", + " nx.draw_networkx_labels(Gi, pos, labels=labels, font_size=20, font_family='sans-serif', ax=ax)\n", + "\n", + " ax.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'AxesSubplot' object is not iterable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'AxesSubplot' object is not iterable" + ] + } + ], + "source": [ + "list(axs)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "################### WITH CLS ################\n", + "f, axes = plt.subplots(nb_heads, 2, sharex='col', figsize=(20,60))\n", + "\n", + "#G0 = nx.from_numpy_matrix(attention[0, 1:emb_len, 1:emb_len])\n", + "#pos = nx.spring_layout(G0) # positions for all nodes\n", + "labels = dict(zip(range(emb_len),pred_str[:emb_len]))\n", + "\n", + "d = 2\n", + "#f.suptitle('Attention head probabilities (Layer #12)')\n", + "for i, (ax0, ax1) in enumerate(axes):\n", + " # Attention map\n", + " im = ax0.imshow(attention[d, i, :emb_len, :emb_len])\n", + " ax0.set_title(\"Attention head {}\".format(i+1))\n", + " im.set_clim(0, 1)\n", + " divider = make_axes_locatable(ax0)\n", + " cax = divider.append_axes('right', size='5%', pad=0.05)\n", + " f.colorbar(im, cax=cax, orientation='vertical')\n", + " \n", + " # -----------------------------------------------------\n", + " # graph\n", + " Gi = nx.from_numpy_matrix(attention[d, i, :emb_len, :emb_len])\n", + " #Gi.remove_node(0)\n", + " #show_cls = lambda u:(u>0)\n", + " show_cls = lambda u:(u==0)\n", + " \n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.8) & show_cls(u)]\n", + " elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & (d['weight'] <= 0.8) & show_cls(u)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 0.5) & show_cls(u)]\n", + " \n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & show_cls(u)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 0.5) & show_cls(u)]\n", + " #esmall = [(u, v) for (u, v) in G.edges() if (u>0)]\n", + " \n", + " sm_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] <= 1) &(u==v)]\n", + "\n", + " # nodes\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=sm_self_att, node_size=200, ax=ax1, node_color=blue)\n", + "\n", + " # edges\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=evlarge, width=6, ax=ax1, edge_color=red)\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=esmall, width=1, ax=ax1, alpha=0.5, edge_color=blue)\n", + "\n", + " # labels\n", + " nx.draw_networkx_labels(Gi, pos, labels=labels, font_size=20, font_family='sans-serif', ax=ax1)\n", + "\n", + " ax1.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "############# WITH CLS\n", + "f, axes = plt.subplots(1, nb_heads, sharex='col', figsize=(60,10))\n", + "\n", + "#G0 = nx.from_numpy_matrix(attention[0, 1:emb_len, 1:emb_len])\n", + "#pos = nx.spring_layout(G0) # positions for all nodes\n", + "labels = dict(zip(range(emb_len),pred_str[:emb_len]))\n", + "\n", + "d = 2\n", + "#f.suptitle('Attention head probabilities (Layer #12)')\n", + "for i, ax in enumerate(axes):\n", + " # Attention map\n", + " #im = ax0.imshow(attention[d, i, :emb_len, :emb_len])\n", + " #ax0.set_title(\"Attention head {}\".format(i+1))\n", + " #im.set_clim(0, 1)\n", + " #divider = make_axes_locatable(ax0)\n", + " #cax = divider.append_axes('right', size='5%', pad=0.05)\n", + " #f.colorbar(im, cax=cax, orientation='vertical')\n", + "\n", + " ax.set_title(\"Attention head {} - Layer {}\".format(i+1, d))\n", + " # -----------------------------------------------------\n", + " # graph\n", + " Gi = nx.from_numpy_matrix(attention[d, i, :emb_len, :emb_len])\n", + " #Gi.remove_node(0)\n", + " show_cls = lambda u:(u==0)\n", + "\n", + " weights = [d['weight'] for (u, v, d) in Gi.edges(data=True) if show_cls(u)]\n", + " edges = [(u,v) for (u, v, d) in Gi.edges(data=True) if show_cls(u)]\n", + " weights = [weight*len(edges)*2.0/sum(weights) for weight in weights]\n", + " \n", + " evlarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & show_cls(u)]\n", + " elarge = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] > 0.5) & (d['weight'] <= 0.8) & show_cls(u)]\n", + " esmall = [(u, v) for (u, v, d) in Gi.edges(data=True) if (d['weight'] <= 1) & ~(show_cls(u))]\n", + "\n", + " lg_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] > 0.5) &(u==v)]\n", + " sm_self_att = [u for (u,v,d) in Gi.edges(data=True) if (d['weight'] <= 0.5)&(u==v)]\n", + "\n", + " # nodes\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=lg_self_att, node_size=900, ax=ax, node_color=red)\n", + " nx.draw_networkx_nodes(Gi, pos, nodelist=sm_self_att, node_size=200, ax=ax, node_color=blue)\n", + "\n", + " # edges\n", + " nx.draw_networkx_edges(Gi, pos, edgelist=edges, width=weights, ax=ax, edge_color=red)\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=evlarge, width=6, ax=ax, edge_color=red)\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=elarge, width=2, ax=ax, edge_color=red, style='dashed')\n", + " #nx.draw_networkx_edges(Gi, pos, edgelist=esmall, width=1, ax=ax, alpha=0.5, edge_color=blue)\n", + "\n", + " # labels\n", + " nx.draw_networkx_labels(Gi, pos, labels=labels, font_size=20, font_family='sans-serif', ax=ax)\n", + "\n", + " ax.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: '[CLS]',\n", - " 1: 'if',\n", - " 2: 'boolop',\n", - " 3: 'and',\n", - " 4: 'unaryop',\n", - " 5: 'not',\n", - " 6: 'attribute',\n", - " 7: 'inputs',\n", - " 8: 'name',\n", - " 9: 'name',\n", - " 10: 'expr',\n", - " 11: '[MASK]',\n", - " 12: 'attribute',\n", - " 13: 'build',\n", - " 14: 'name',\n", - " 15: 'name'}" + " 1: 'assign',\n", + " 2: 'name',\n", + " 3: 'optimizer',\n", + " 4: 'weight',\n", + " 5: 'names',\n", + " 6: 'listcomp',\n", + " 7: 'call',\n", + " 8: 'attribute',\n", + " 9: 'decode',\n", + " 10: 'name',\n", + " 11: 'str',\n", + " 12: 'comprehension',\n", + " 13: 'name',\n", + " 14: '[MASK]',\n", + " 15: 'subscript',\n", + " 16: 'name',\n", + " 17: 'index',\n", + " 18: 'str'}" ] }, - "execution_count": 242, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -933,16 +1209,16 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "EdgeView([(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (0, 15), (1, 1), (1, 2), (1, 10), (2, 2), (2, 3), (2, 4), (2, 9), (3, 3), (4, 4), (4, 5), (4, 6), (5, 5), (6, 8), (6, 6), (6, 7), (7, 7), (8, 8), (9, 9), (10, 10), (10, 11), (11, 11), (11, 12), (11, 15), (12, 12), (12, 13), (12, 14), (13, 13), (14, 14), (15, 15)])" + "EdgeView([(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12), (0, 13), (0, 14), (0, 15), (0, 16), (0, 17), (0, 18), (1, 1), (1, 2), (1, 6), (2, 2), (2, 3), (3, 3), (3, 4), (3, 5), (4, 4), (4, 5), (5, 5), (6, 12), (6, 6), (6, 7), (7, 8), (7, 11), (7, 7), (8, 8), (8, 10), (8, 9), (9, 9), (10, 10), (11, 11), (12, 12), (12, 13), (12, 15), (13, 13), (13, 14), (14, 14), (15, 16), (15, 17), (15, 15), (16, 16), (17, 17), (17, 18), (18, 18)])" ] }, - "execution_count": 243, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -953,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -962,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 261, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -976,14 +1252,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "64" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(uniform_ent)" + ] }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -992,7 +1281,7 @@ "(3, 6, 64, 64)" ] }, - "execution_count": 263, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1017,7 +1306,14 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 112, "metadata": { "scrolled": false }, @@ -1025,45 +1321,47 @@ "source": [ "def plot_entropy_for_layer(attention, nb_heads, uniform_ent, d=0):\n", " \n", - " ent = [entropy(a[:emb_len,:emb_len],base=2) for a in attention[d]]\n", - " f, axes = plt.subplots(nb_heads//2, 2, sharex='col', figsize=(20,12))\n", + " ent = [entropy(a,base=2) for a in attention[d]]\n", + " f, axes = plt.subplots(nb_heads//3+1, 3, sharex=False, figsize=(20,6))\n", " \n", - " for i, (ax0, ax1) in enumerate(axes):\n", - " ent[i][np.isnan(ent[i])]=5.36\n", - " ax0.hist(ent[i],bins=20);\n", - " ax0.set_xticks(range(7))\n", - " ax0.set_yticks(range(5))\n", - " ax1.hist(ent[i+1],bins=20);\n", - " ax1.set_xticks(range(7))\n", - " ax1.set_yticks(range(5))\n", - " plt.figure(figsize=(20,4));\n", - " plt.hist(uniform_ent, bins=20);\n", - " plt.xticks(range(7));\n", + " for i, (ax0, ax1,ax2) in enumerate(axes):\n", + " if i < 2:\n", + " ent[i][np.isnan(ent[i])]=5.36\n", + " ax0.hist(ent[i],bins=20);\n", + " ax0.set_title('Entropy - Head {} / Layer {}'.format(i,d))\n", + " ax0.set_xticks(range(7))\n", + " #ax0.set_yticks(range(5))\n", + " ax1.set_title('Entropy - Head {} / Layer {}'.format(i+1,d))\n", + " ax1.hist(ent[i+1],bins=20);\n", + " ax1.set_xticks(range(7))\n", + " #ax1.set_yticks(range(5))\n", + " ax2.set_title('Entropy - Head {} / Layer {}'.format(i+2,d))\n", + " ax2.hist(ent[i+2],bins=20);\n", + " ax2.set_xticks(range(7))\n", + " else:\n", + " ax0.axis('off')\n", + " ax2.axis('off')\n", + " ax1.set_title('Entropy - Random Uniform Edge weights')\n", + " ax1.hist(uniform_ent, bins=20);\n", + " ax1.set_xticks(range(7))\n", + " plt.tight_layout()\n", + " \n", + " #plt.figure(figsize=(20,3));\n", + " #plt.hist(uniform_ent, bins=20);\n", + " #plt.xticks(range(7));\n", " #plt.yticks(range(5));" ] }, { "cell_type": "code", - "execution_count": 296, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAAD8CAYAAADgxbTNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADuNJREFUeJzt3V2oZQd5x+H/a8bSNtqq5DQM+ejxIgihUJUhtChia5XYSGNvioFKaIXphRalhTL1xpbepBe1vSlCalJT6geiBkMT1JAKUWitMzatidEawogTojNBiqY3En17MVuY2owz2XufWTPnfR4Y9l5rr7PXe7EmnPxmfVR3BwAAAID97XlLDwAAAADA3hOBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGOHAhd3bFFVf07u7uhdwlAAAAwL527Nixp7p751zbXdAItLu7m6NHj17IXQIAAADsa1X1jfPZzuVgAAAAAAOIQAAAAAADiEAAAAAAA4hAAAAAAAOIQAAAAAADiEAAAAAAA4hAAAAAAAOIQAAAAAADiEAAAAAAAxxYegDg/9s9cu/SI2zN8dtuWnoEAAAA4kwgAAAAgBFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAFEIAAAAIABRCAAAACAAUQgAAAAgAHOGYGq6pqq+mxVfaWqHqmqd67Wv6Sq7q+qr69eX7z34wIAAACwjvM5E+iZJH/c3dcn+ZUkb6+q65McSfJAd1+X5IHVMgAAAAAXoXNGoO5+sru/tHr/vSSPJrkqyc1J7lptdleSN+/VkAAAAABs5jndE6iqdpO8IskXklzZ3U+uPvpWkiu3OhkAAAAAW3PeEaiqXpDk40ne1d3fPfOz7u4kfZafO1xVR6vq6KlTpzYaFgAAAID1nFcEqqrn53QA+mB3f2K1+ttVdXD1+cEkJ5/tZ7v79u4+1N2HdnZ2tjEzAAAAAM/R+TwdrJLckeTR7n7vGR/dk+TW1ftbk3xy++MBAAAAsA0HzmObVyV5a5IvV9VDq3XvTnJbko9W1duSfCPJ7+zNiAAAAABs6pwRqLs/n6TO8vHrtjsOAAAAAHvhOT0dDAAAAIBLkwgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADDAOSNQVd1ZVSer6uEz1v1ZVT1RVQ+t/vzm3o4JAAAAwCbO50ygDyS58VnW/3V3v3z1577tjgUAAADANp0zAnX3g0m+cwFmAQAAAGCPbHJPoHdU1X+uLhd78dYmAgAAAGDrDqz5c+9L8hdJevX6V0l+/9k2rKrDSQ4nybXXXrvm7uDcdo/cu/QIAAAAcNFa60yg7v52d/+gu3+Y5O+S3PATtr29uw9196GdnZ115wQAAABgA2tFoKo6eMbibyd5+GzbAgAAALC8c14OVlUfTvLaJFdU1Ykk70ny2qp6eU5fDnY8yR/s4YwAAAAAbOicEai7b3mW1XfswSwAAAAA7JFNng4GAAAAwCVCBAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGOCcEaiq7qyqk1X18BnrXlJV91fV11evL97bMQEAAADYxPmcCfSBJDf+2LojSR7o7uuSPLBaBgAAAOAidc4I1N0PJvnOj62+Ocldq/d3JXnzlucCAAAAYIvWvSfQld395Or9t5JcuaV5AAAAANgDG98Yurs7SZ/t86o6XFVHq+roqVOnNt0dAAAAAGtYNwJ9u6oOJsnq9eTZNuzu27v7UHcf2tnZWXN3AAAAAGxi3Qh0T5JbV+9vTfLJ7YwDAAAAwF44n0fEfzjJvyR5WVWdqKq3Jbktyeur6utJfmO1DAAAAMBF6sC5NujuW87y0eu2PAsAAAAAe2TjG0MDAAAAcPETgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABhCBAAAAAAYQgQAAAAAGEIEAAAAABjiwyQ9X1fEk30vygyTPdPehbQwFAAAAwHZtFIFWfq27n9rC9wAAAACwR1wOBgAAADDAphGok3ymqo5V1eFtDAQAAADA9m16Odiru/uJqvqFJPdX1Ve7+8EzN1jFocNJcu211264O7Zt98i9S48AXGD76e/98dtuWnoEAAC4ZGx0JlB3P7F6PZnk7iQ3PMs2t3f3oe4+tLOzs8nuAAAAAFjT2hGoqi6vqhf+6H2SNyR5eFuDAQAAALA9m1wOdmWSu6vqR9/zoe7+1FamAgAAAGCr1o5A3f14kl/e4iwAAAAA7BGPiAcAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYYJOng422e+TepUeAS4K/KwAAABcHZwIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAxwYOkBAGBdu0fuXXqErTh+201LjwAAwADOBAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGEAEAgAAABhABAIAAAAYQAQCAAAAGODA0gMAwHS7R+5deoStOX7bTUuPAADsIb+3XNqcCQQAAAAwgAgEAAAAMIAIBAAAADCACAQAAAAwwEYRqKpurKqvVdVjVXVkW0MBAAAAsF1rR6CquizJ3yZ5Y5Lrk9xSVddvazAAAAAAtmeTM4FuSPJYdz/e3d9P8pEkN29nLAAAAAC2aZMIdFWSb56xfGK1DgAAAICLzIG93kFVHU5yeLX4dFV9ba/3+RNckeSpBfcP2+R4Zr9wLO8j9ZdLT7AoxzL7ieOZ/cKxzFldgr+3/KTj+RfP5ws2iUBPJLnmjOWrV+v+j+6+PcntG+xna6rqaHcfWnoO2AbHM/uFY5n9wrHMfuJ4Zr9wLLOfbON43uRysC8mua6qXlpVP5XkLUnu2WQYAAAAAPbG2mcCdfczVfWOJJ9OclmSO7v7ka1NBgAAAMDWbHRPoO6+L8l9W5rlQrgoLkuDLXE8s184ltkvHMvsJ45n9gvHMvvJxsdzdfc2BgEAAADgIrbJPYEAAAAAuESMiUBVdWNVfa2qHquqI0vPA+uqqjur6mRVPbz0LLCJqrqmqj5bVV+pqkeq6p1LzwTrqKqfrqp/q6r/WB3Lf770TLCJqrqsqv69qv5p6VlgE1V1vKq+XFUPVdXRpeeBdVXVi6rqY1X11ap6tKp+de3vmnA5WFVdluS/krw+yYmcfrLZLd39lUUHgzVU1WuSPJ3kH7r7l5aeB9ZVVQeTHOzuL1XVC5McS/Jm/23mUlNVleTy7n66qp6f5PNJ3tnd/7rwaLCWqvqjJIeS/Fx3v2npeWBdVXU8yaHufmrpWWATVXVXks919/tXT2f/2e7+73W+a8qZQDckeay7H+/u7yf5SJKbF54J1tLdDyb5ztJzwKa6+8nu/tLq/feSPJrkqmWngueuT3t6tfj81Z/9/69s7EtVdXWSm5K8f+lZAEiq6ueTvCbJHUnS3d9fNwAlcyLQVUm+ecbyifgfDYCLRlXtJnlFki8sOwmsZ3X5zENJTia5v7sdy1yq/ibJnyT54dKDwBZ0ks9U1bGqOrz0MLCmlyY5leTvV5fqvr+qLl/3y6ZEIAAuUlX1giQfT/Ku7v7u0vPAOrr7B9398iRXJ7mhqlyuyyWnqt6U5GR3H1t6FtiSV3f3K5O8McnbV7dVgEvNgSSvTPK+7n5Fkv9JsvZ9jqdEoCeSXHPG8tWrdQAsaHX/lI8n+WB3f2LpeWBTq9OzP5vkxqVngTW8Kslvre6j8pEkv15V/7jsSLC+7n5i9Xoyyd05fZsQuNScSHLijLOMP5bTUWgtUyLQF5NcV1UvXd1E6S1J7ll4JoDRVjfTvSPJo9393qXngXVV1U5VvWj1/mdy+kEUX112KnjuuvtPu/vq7t7N6d+X/7m7f3fhsWAtVXX56sETWV0684Yknq7LJae7v5Xkm1X1stWq1yVZ+0EqB7Yy1UWuu5+pqnck+XSSy5Lc2d2PLDwWrKWqPpzktUmuqKoTSd7T3XcsOxWs5VVJ3prky6t7qSTJu7v7vgVngnUcTHLX6mmkz0vy0e72aG2AZV2Z5O7T/+aUA0k+1N2fWnYkWNsfJvng6qSWx5P83rpfNOIR8QAAAADTTbkcDAAAAGA0EQgAAABgABEIAAAAYAARCAAAAGAAEQgAAABgABEIAAAAYAARCAAAAGAAEQgAAABggP8FtGh9Ulv9ZEkAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -1078,35 +1376,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_entropy_for_layer(attention, nb_heads, uniform_ent, d=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 291, + "execution_count": 114, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" - }, + } + ], + "source": [ + "plot_entropy_for_layer(attention, nb_heads, uniform_ent, d=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -1121,22 +1420,22 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 260, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAEStJREFUeJzt3X+s1fV9x/HniwuXq1ctID9EMEMQdaSZaBF1uoZKbak2pcmMUZuFbSwkndts2qbilph22RJNs9p26ezIdGWJFW3VQVynZUyydOuQS8UWRQX8EUDgWoWpiMiF9/44X+495/Ze7oHz/X7PPffzeiQ35/P9cc73ree+7+fz+X4+fL6KCMwsLaOaHYCZlc+Jb5YgJ75Zgpz4Zgly4pslyIlvliAnvlmCGkp8SYskvSRpu6TleQVlZsXSqU7gkdQGvAxcB+wCNgK3RMQL+YVnZkUY3cB75wPbI+IVAEmrgMXAoInfrrHRQeeQH9w5p/aP0cGtfQ0TjekLOT48Unewx8ad3lsedeD9ut9n1ko+4CAfxmENdV4jiT8N2Fm1vQu44kRv6KCTK7RwyA++/KGjNdtdV/QlbdvkSb3lnl27a994gtbLoQXze8unrX5myBjMWtGGWFfXeY0kfl0kLQOWAXRw+hBnm1kZGkn83cB5VdvTs301ImIFsALgrDOnxbFL5wLQvnt/7XntY3rLG+du7/cph3tLPTt39ZY/uqn23uSWjw1e47uWN+vTyF39jcBsSedLagduBtbkE5aZFemUa/yI6JH0Z8BTQBvwQEQ8n1tkZlaYhvr4EfET4Cc5xWJmJSn85l61Y+2jODi9A4BXPz+15th5a3t6y21TL6s51rb+FwN+3paPHcs5QrM0eMquWYKc+GYJKrWpH6PgyOmVSUWzvvrzmmNtvz27t/zGJyfVHBt32uW95Y43D/V9XteWIsI0G/Fc45slyIlvliAnvlmCSu3jj/ow6NzTM+Cxo1u39Za1sLaPf/qOt3vL2/5kcm95ZlfOAZolwjW+WYKc+GYJKrWp3zPpGG8tOwjAuf8++HlTn9xTs/3GZ/pm+V343df7Pi/f8MyS4RrfLEFOfLMEldrUH/3mKCZ+v7IKT9uFs2qOHX15R195+6s1x6b8fd92dfO+bfbMmvOis6O3fGzzqa35ueOHc3vLs27dfEqfYTbcucY3S5AT3yxBTnyzBJW7EMdocWhi5ZLtT+0Y4uyhHZo5oWa7Y897veW4em7NMf33wP317tt+t2Z71q3/03BcZsOda3yzBDnxzRJU7nDewR4mbHwTgKNDnFuP9qdq/5XOvi9e1Vs+Z/2va47tvr2vST9+W9+jtyZ/z017S49rfLMEOfHNEuTEN0uQ4gRPmM3bjI+eEXc9dgkAD118bqHXOnzD5TXbnVv29pZ7Xt/Z/3SzEWFDrOOdeHvIx2QPWeNLekBSt6QtVfsmSForaVv2Or7RgM2sPPU09X8ALOq3bzmwLiJmA+uybTNrEUMO50XEf0ma0W/3YmBBVl4JrAfuGOqz9r01nr974EYAzqXYYbTdv1f7nzbz3/qa9/uX9A37jV9Zu76/WQpO9ebelIg4vkzOXmBKTvGYWQkavqsflbuDg94hlLRMUpekrqPvH2z0cmaWg1OdubdP0tSI2CNpKtA92IkRsQJYATB2xvR4b9aRwU5tWPef9s3Om7l88K5EdfP+3ZuvrDl25qr/zT8ws2HmVGv8NcCSrLwEWJ1POGZWhnqG8x4Cfg5cJGmXpKXA3cB1krYBn8y2zaxF1HNX/5ZBDi3MORYzK0nJ/zpPTNyQ3yVHn1M7mDD5H05+iLB/n/7Q5+f3lts+qL1n2f7kxt7y23/cNyQ44QEPCVpr8Vx9swQ58c0SVGpTv+2DY4x7+VBun9ezd19un3XcoQltveWJmw7UHHvxvr5uwIVfdPPeWpdrfLMEOfHNEuTEN0tQqX38w+NHseOmsQDM/lmZV65f9dDcgVtrp/Oe9bL/TtrI4N9kswQ58c0SVGpTv+OND7j4rheBfNbVL8JTb/Q9auvT/ZYFPKuqXD1rMHp6as47+tbbfRslrmloVi/X+GYJcuKbJajUpv6RCR3s+/05AEz8x+E58+3T584d+iRg6z3TesvnrhlTc6zz0Q25xmSWN9f4Zgly4pslyIlvlqBS+/gxCo6cMeTTfVrCpHVje8udj9ber2i7cFZvefcNtYuFnHOvH8ttzeca3yxBTnyzBJXa1NcxaMtvHY6mGvcvgw9H7vlm3/De5G++X0Y4ZifFNb5Zgpz4Zgly4pslqNw+/lEY+3/HyrxkU0xe/OKgx/5ie9+x715wcRnhmP2Geh6hdZ6kpyW9IOl5Sbdn+ydIWitpW/Y6vvhwzSwP9TT1e4CvRMQc4ErgNklzgOXAuoiYDazLts2sBdTz7Lw9wJ6s/K6krcA0YDGwIDttJbAeuKOQKEeQ6uZ9dbO//zGzIp3UzT1JM4BLgQ3AlOyPAsBeYMogbzOzYabuxJd0BvAo8KWIeKf6WEQEMOAaU5KWSeqS1NVz+GBDwZpZPupKfEljqCT9gxHxWLZ7n6Sp2fGpQPdA742IFRExLyLmjR7bmUfMZtagIfv4kgTcD2yNiG9VHVoDLAHuzl5XFxLhCNa/T++hPitLPeP4VwN/APxK0vElaP+SSsI/Imkp8DpwUzEhmlne6rmr/zNgsH9EvzDfcMysDKXO3LMTG2yoz81+y5vn6pslyIlvliA39Ycpz/CzIrnGN0uQE98sQU58swS5j98CPMPP8uYa3yxBTnyzBLmp34I81GeNco1vliAnvlmCnPhmCXIfv8V5qM9OhWt8swQ58c0S5Kb+COPFPKwervHNEuTEN0uQm/ojmGf42WBc45slyIlvliAnvlmC3MdPhGf4WbUha3xJHZKekfScpOclfSPbf76kDZK2S3pYUnvx4ZpZHupp6h8Gro2IS4C5wCJJVwL3APdGxAXAfmBpcWGaWZ7qeXZeAO9lm2OynwCuBW7N9q8Evg7cl3+IVgQP9aWtrpt7ktqyJ+V2A2uBHcCBiOjJTtkFTCsmRDPLW12JHxFHI2IuMB2YD9RdJUhaJqlLUlfP4YOnGKaZ5emkhvMi4gDwNHAVME7S8a7CdGD3IO9ZERHzImLe6LGdDQVrZvkYso8vaRJwJCIOSDoNuI7Kjb2ngRuBVcASYHWRgVpxPNSXnnrG8acCKyW1UWkhPBIRT0h6AVgl6W+AZ4H7C4zTzHJUz139XwKXDrD/FSr9fTNrMZ65Z7/Bi3mMfJ6rb5YgJ75ZgtzUtxPyDL+RyTW+WYKc+GYJcuKbJch9fKubZ/iNHK7xzRLkxDdLkJv6dso81Ne6XOObJciJb5YgJ75ZgtzHt1x4qK+1uMY3S5AT3yxBbupbIbyYx/DmGt8sQU58swS5qW+F8wy/4cc1vlmCnPhmCXLimyXIfXwrlWf4DQ911/jZo7KflfREtn2+pA2Stkt6WFJ7cWGaWZ5Opql/O7C1avse4N6IuADYDyzNMzAzK05dTX1J04EbgL8FvixJwLXArdkpK4GvA/cVEKONYB7qa456a/xvA18DjmXbZwMHIqIn294FTMs5NjMryJCJL+mzQHdEbDqVC0haJqlLUlfP4YOn8hFmlrN6mvpXA5+TdD3QAZwFfAcYJ2l0VutPB3YP9OaIWAGsAOg8+7zIJWoza8iQiR8RdwJ3AkhaAHw1Ir4g6UfAjcAqYAmwusA4LQEe6itPIxN47qByo287lT7//fmEZGZFO6kJPBGxHliflV8B5ucfkpkVzTP3bNjyYh7F8Vx9swQ58c0S5Ka+tQTP8MuXa3yzBDnxzRLkxDdLkPv41nI8w69xrvHNEuTEN0uQm/rW8jzUd/Jc45slyIlvliAnvlmC3Me3EcVDffVxjW+WICe+WYLc1LcRzYt5DMw1vlmCnPhmCXJT35LhGX59XOObJciJb5YgJ75ZgtzHtySlPsOvrsSX9BrwLnAU6ImIeZImAA8DM4DXgJsiYn8xYZpZnk6mqf+JiJgbEfOy7eXAuoiYDazLts2sBTTS1F8MLMjKK6k8U++OBuMxa4rUhvrqrfED+KmkTZKWZfumRMSerLwXmJJ7dGZWiHpr/GsiYrekycBaSTV/EiMiJMVAb8z+UCwDaO8c31CwZpaPumr8iNidvXYDj1N5PPY+SVMBstfuQd67IiLmRcS80WM784nazBoyZI0vqRMYFRHvZuVPAX8NrAGWAHdnr6uLDNSsLCkM9dXT1J8CPC7p+Pk/jIgnJW0EHpG0FHgduKm4MM0sT0MmfkS8AlwywP63gIVFBGVmxfLMPbMhjMShPs/VN0uQE98sQU58swS5j292EkbKUJ9rfLMEOfHNEuSmvlkDWnXdftf4Zgly4pslyE19s5y00gw/1/hmCXLimyXIiW+WIPfxzQow3Gf4ucY3S5AT3yxBbuqblWC4DfW5xjdLkBPfLEFOfLMEuY9vVrLhMNTnGt8sQU58swS5qW/WZM1YzKOuGl/SOEk/lvSipK2SrpI0QdJaSduyVz8K16xF1NvU/w7wZERcTOVxWluB5cC6iJgNrMu2zawF1PO03I8AHwf+ECAiPgQ+lLQYWJCdthJYD9xRRJBmqShrhl89Nf75wJvAP0t6VtI/ZY/LnhIRe7Jz9lJ5qq6ZtYB6En80cBlwX0RcChykX7M+IgKIgd4saZmkLkldPYcPNhqvmeWgnsTfBeyKiA3Z9o+p/CHYJ2kqQPbaPdCbI2JFRMyLiHmjx3bmEbOZNWjIPn5E7JW0U9JFEfESsBB4IftZAtydva4uNFKzxBQ5w6/ecfw/Bx6U1A68AvwRldbCI5KWAq8DNzUUiZmVpq7Ej4jNwLwBDi3MNxwzK4Nn7pm1iDyH+jxX3yxBTnyzBDnxzRLkPr5ZCxpsqO/Liw/V9X7X+GYJcuKbJUiVafYlXUx6k8pkn4nAr0u78MCGQwzgOPpzHLVONo7fiohJQ51UauL3XlTqioiBJgQlFYPjcBzNisNNfbMEOfHNEtSsxF/RpOtWGw4xgOPoz3HUKiSOpvTxzay53NQ3S1CpiS9pkaSXJG2XVNqqvJIekNQtaUvVvtKXB5d0nqSnJb0g6XlJtzcjFkkdkp6R9FwWxzey/edL2pB9Pw9n6y8UTlJbtp7jE82KQ9Jrkn4labOkrmxfM35HSlnKvrTEl9QGfA/4DDAHuEXSnJIu/wNgUb99zVgevAf4SkTMAa4Ebsv+H5Qdy2Hg2oi4BJgLLJJ0JXAPcG9EXADsB5YWHMdxt1NZsv24ZsXxiYiYWzV81ozfkXKWso+IUn6Aq4CnqrbvBO4s8fozgC1V2y8BU7PyVOClsmKpimE1cF0zYwFOB34BXEFlosjogb6vAq8/PftlvhZ4AlCT4ngNmNhvX6nfC/AR4FWye29FxlFmU38asLNqe1e2r1maujy4pBnApcCGZsSSNa83U1kkdS2wAzgQET3ZKWV9P98GvgYcy7bPblIcAfxU0iZJy7J9ZX8vpS1l75t7nHh58CJIOgN4FPhSRLzTjFgi4mhEzKVS484Hynk+cxVJnwW6I2JT2dcewDURcRmVruhtkj5efbCk76WhpexPRpmJvxs4r2p7eravWepaHjxvksZQSfoHI+KxZsYCEBEHgKepNKnHSTr+T7XL+H6uBj4n6TVgFZXm/neaEAcRsTt77QYep/LHsOzvpaGl7E9GmYm/EZid3bFtB24G1pR4/f7WUFkWHEpaHlySgPuBrRHxrWbFImmSpHFZ+TQq9xm2UvkDcGNZcUTEnRExPSJmUPl9+M+I+ELZcUjqlHTm8TLwKWALJX8vEbEX2CnpomzX8aXs84+j6Jsm/W5SXA+8TKU/+VclXvchYA9whMpf1aVU+pLrgG3AfwATSojjGirNtF8Cm7Of68uOBfgd4Nksji3AXdn+mcAzwHbgR8DYEr+jBcATzYgju95z2c/zx383m/Q7Mhfoyr6bfwXGFxGHZ+6ZJcg398wS5MQ3S5AT3yxBTnyzBDnxzRLkxDdLkBPfLEFOfLME/T/2m9VtjL9GUQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] diff --git a/notebook/Inspect Predictions - MLM.ipynb b/notebook/Inspect Predictions - MLM.ipynb index cadaab3..cea9abb 100644 --- a/notebook/Inspect Predictions - MLM.ipynb +++ b/notebook/Inspect Predictions - MLM.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -20,20 +20,30 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ - "#path = \"../sparse/\"\n", - "#prefix = \"sparse_tmp_\"\n", + "path = \"../large-corpus/\"\n", + "prefix = \"sparse_tmp\"\n", "\n", - "path = \"../../bert-cmp/bert/\"\n", - "prefix=\"\"" + "#path = \"../../bert-cmp/bert/\"\n", + "#prefix=\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams.update({'font.size': 22})" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -83,16 +93,16 @@ " \n", " \n", " 0\n", - " 24\n", - " 24\n", - " 22\n", + " 414\n", + " 377\n", + " 14\n", " 2\n", - " 236\n", - " 24\n", - " 229\n", - " 37\n", - " 24\n", - " 241\n", + " 11\n", + " 8\n", + " 3130\n", + " 156\n", + " 654\n", + " 262\n", " ...\n", " 0\n", " 0\n", @@ -107,64 +117,64 @@ " \n", " \n", " 1\n", - " 24\n", - " 426\n", - " 25\n", + " 102\n", + " 1814\n", + " 16\n", " 2\n", - " 752\n", - " 24\n", - " 603\n", - " 564\n", - " 24\n", - " 199\n", + " 11\n", + " 22\n", + " 3597\n", + " 102\n", + " 212\n", + " 8\n", " ...\n", - " 1142\n", - " 52\n", - " 1142\n", - " 769\n", - " 24\n", - " 24\n", - " 24\n", - " 654\n", - " 24\n", - " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", " 2\n", - " 24\n", - " 97\n", - " 3\n", + " 8\n", + " 8\n", + " 7\n", " 2\n", - " 460\n", - " 6\n", - " 4\n", - " 318\n", - " 52\n", - " 24\n", + " 25\n", + " 20\n", + " 22\n", + " 26\n", + " 8\n", + " 20\n", " ...\n", - " 24\n", - " 236\n", - " 29\n", - " 1142\n", - " 135\n", - " 24\n", - " 256\n", - " 24\n", - " 37\n", - " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " \n", " \n", " 3\n", - " 24\n", - " 47\n", - " 7\n", + " 8\n", + " 8\n", " 2\n", - " 56\n", - " 57\n", - " 58\n", - " 106\n", - " 236\n", - " 24\n", + " 2\n", + " 11\n", + " 4\n", + " 3618\n", + " 2928\n", + " 415\n", + " 8\n", " ...\n", " 0\n", " 0\n", @@ -179,16 +189,16 @@ " \n", " \n", " 4\n", - " 24\n", - " 57\n", - " 18\n", + " 22\n", + " 22\n", + " 6\n", " 2\n", - " 52\n", + " 399\n", " 24\n", - " 37\n", - " 52\n", " 24\n", - " 52\n", + " 24\n", + " 24\n", + " 22\n", " ...\n", " 0\n", " 0\n", @@ -203,16 +213,16 @@ " \n", " \n", " 5\n", - " 24\n", - " 24\n", - " 22\n", + " 60\n", + " 60\n", + " 6\n", " 2\n", - " 5\n", + " 20\n", + " 22\n", " 43\n", - " 24\n", - " 152\n", - " 318\n", - " 10\n", + " 8\n", + " 27\n", + " 4\n", " ...\n", " 0\n", " 0\n", @@ -227,16 +237,16 @@ " \n", " \n", " 6\n", - " 24\n", - " 24\n", - " 32\n", + " 8\n", + " 8\n", + " 21\n", " 2\n", + " 31\n", + " 32\n", + " 33\n", + " 414\n", " 37\n", " 24\n", - " 80\n", - " 37\n", - " 318\n", - " 75\n", " ...\n", " 0\n", " 0\n", @@ -252,15 +262,15 @@ " \n", " 7\n", " 24\n", - " 113\n", - " 3\n", - " 2\n", - " 112\n", - " 24\n", - " 4\n", " 24\n", - " 619\n", + " 38\n", + " 2\n", + " 6\n", + " 15\n", + " 17\n", + " 7\n", " 24\n", + " 74\n", " ...\n", " 0\n", " 0\n", @@ -275,16 +285,16 @@ " \n", " \n", " 8\n", - " 24\n", - " 406\n", - " 6\n", + " 65\n", + " 65\n", + " 4\n", " 2\n", - " 56\n", - " 57\n", - " 58\n", - " 41\n", - " 58\n", + " 6\n", + " 7\n", + " 8\n", " 4\n", + " 24\n", + " 11\n", " ...\n", " 0\n", " 0\n", @@ -299,16 +309,16 @@ " \n", " \n", " 9\n", - " 24\n", - " 44\n", - " 4\n", + " 7\n", + " 7\n", + " 13\n", " 2\n", - " 5\n", - " 43\n", + " 15\n", + " 17\n", + " 20\n", + " 8\n", + " 8\n", " 24\n", - " 4\n", - " 24\n", - " 10\n", " ...\n", " 0\n", " 0\n", @@ -327,62 +337,82 @@ "" ], "text/plain": [ - " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n", - "0 24 24 22 2 236 24 229 \n", - "1 24 426 25 2 752 24 603 \n", - "2 24 97 3 2 460 6 4 \n", - "3 24 47 7 2 56 57 58 \n", - "4 24 57 18 2 52 24 37 \n", - "5 24 24 22 2 5 43 24 \n", - "6 24 24 32 2 37 24 80 \n", - "7 24 113 3 2 112 24 4 \n", - "8 24 406 6 2 56 57 58 \n", - "9 24 44 4 2 5 43 24 \n", + " masked_lm_predictions label_ids masked_lm_positions 0 1 2 3 \\\n", + "0 414 377 14 2 11 8 3130 \n", + "1 102 1814 16 2 11 22 3597 \n", + "2 8 8 7 2 25 20 22 \n", + "3 8 8 2 2 11 4 3618 \n", + "4 22 22 6 2 399 24 24 \n", + "5 60 60 6 2 20 22 43 \n", + "6 8 8 21 2 31 32 33 \n", + "7 24 24 38 2 6 15 17 \n", + "8 65 65 4 2 6 7 8 \n", + "9 7 7 13 2 15 17 20 \n", "\n", - " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n", - "0 37 24 241 ... 0 0 0 0 0 0 0 0 0 0 \n", - "1 564 24 199 ... 1142 52 1142 769 24 24 24 654 24 3 \n", - "2 318 52 24 ... 24 236 29 1142 135 24 256 24 37 3 \n", - "3 106 236 24 ... 0 0 0 0 0 0 0 0 0 0 \n", - "4 52 24 52 ... 0 0 0 0 0 0 0 0 0 0 \n", - "5 152 318 10 ... 0 0 0 0 0 0 0 0 0 0 \n", - "6 37 318 75 ... 0 0 0 0 0 0 0 0 0 0 \n", - "7 24 619 24 ... 0 0 0 0 0 0 0 0 0 0 \n", - "8 41 58 4 ... 0 0 0 0 0 0 0 0 0 0 \n", - "9 4 24 10 ... 0 0 0 0 0 0 0 0 0 0 \n", + " 4 5 6 ... 54 55 56 57 58 59 60 61 62 63 \n", + "0 156 654 262 ... 0 0 0 0 0 0 0 0 0 0 \n", + "1 102 212 8 ... 0 0 0 0 0 0 0 0 0 0 \n", + "2 26 8 20 ... 0 0 0 0 0 0 0 0 0 0 \n", + "3 2928 415 8 ... 0 0 0 0 0 0 0 0 0 0 \n", + "4 24 24 22 ... 0 0 0 0 0 0 0 0 0 0 \n", + "5 8 27 4 ... 0 0 0 0 0 0 0 0 0 0 \n", + "6 414 37 24 ... 0 0 0 0 0 0 0 0 0 0 \n", + "7 7 24 74 ... 0 0 0 0 0 0 0 0 0 0 \n", + "8 4 24 11 ... 0 0 0 0 0 0 0 0 0 0 \n", + "9 8 8 24 ... 0 0 0 0 0 0 0 0 0 0 \n", "\n", "[10 rows x 67 columns]" ] }, - "execution_count": 4, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results_df = pd.read_csv(path+'pretraining_output-100k-2/eval_results_masked_lm.txt')\n", + "results_df = pd.read_csv(path+'pretraining_output-100k/eval_results_masked_lm.txt')\n", "results_df.head(10)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(34985, 67)" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1156, 1)" + "(9769, 1)" ] }, - "execution_count": 19, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_file = \"global_vocab.csv\"\n", - "vocab_file = \"sparse_tmp_vocab-code.txt\"\n", + "#vocab_file = \"sparse_tmp_vocab-code.txt\"\n", "#vocab_file = \"vocab-code.txt\"\n", "\n", "vocab_df = pd.read_csv(path+vocab_file, header=None)\n", @@ -391,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -400,7 +430,7 @@ "(1851, 1)" ] }, - "execution_count": 20, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -412,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 109, "metadata": { "scrolled": true }, @@ -420,10 +450,1010 @@ { "data": { "text/plain": [ - "set()" + "{nan,\n", + " 'smoothing',\n", + " 'memoryallocation',\n", + " 'rb',\n", + " 'qr',\n", + " 'lrc',\n", + " 'invaliddatastore',\n", + " 'yamlerror',\n", + " 'news',\n", + " 'can',\n", + " 'added',\n", + " 'powerstate',\n", + " 'filetype',\n", + " 'updateassignedlicense',\n", + " 'stickiness',\n", + " 'varg',\n", + " 'dscp',\n", + " 'lmost',\n", + " 'breadth',\n", + " 'reconfigure',\n", + " 'd1',\n", + " 'quiescedsnapshotssupported',\n", + " 'replicate',\n", + " 'memmap',\n", + " 'arraysetops',\n", + " 'privatenetwork',\n", + " 'entitlement',\n", + " 'keydata',\n", + " 'os',\n", + " 'tupleified',\n", + " 'unification',\n", + " 'ellipses',\n", + " 'poller',\n", + " 'brc',\n", + " 'camera',\n", + " 'ic',\n", + " 'skipifsdactive',\n", + " 'memset',\n", + " 'isclose',\n", + " 'initiatefiletransfertoguest',\n", + " 'novelty',\n", + " 'mi',\n", + " 'argv',\n", + " 'purpose',\n", + " 'shallow',\n", + " 'cli',\n", + " 'transformed',\n", + " 'flatpage',\n", + " 'consistent',\n", + " 'fromhex',\n", + " '4classes',\n", + " 'quote',\n", + " 'rcv',\n", + " 'passthruenabled',\n", + " 'esx',\n", + " 'attrname',\n", + " 'del',\n", + " 'bplayer',\n", + " 'base64',\n", + " 'replay',\n", + " 'dist1',\n", + " 'flatpages',\n", + " 'renditions',\n", + " 'multiendpoint',\n", + " 'have',\n", + " 'contextmanager',\n", + " 'virtualcdrom',\n", + " 'grp',\n", + " 'pls',\n", + " 'unlabelled',\n", + " 'vcid',\n", + " 'searcher',\n", + " 'invocations',\n", + " 'viewclip',\n", + " 'tape',\n", + " 'junos',\n", + " 'usm',\n", + " 'vxp',\n", + " 'ljust',\n", + " 'functionapp',\n", + " 'atomic',\n", + " 'visibles',\n", + " 'gettempdir',\n", + " 'invscale',\n", + " 'disco',\n", + " 'singer',\n", + " 'digits',\n", + " 'unrecognized',\n", + " 'pnic',\n", + " 'storagepoollookupbyname',\n", + " 'servers',\n", + " 'ib',\n", + " 'parentloop',\n", + " 'statistic',\n", + " 'elementtree',\n", + " 'scandir',\n", + " 'dens',\n", + " 'swap',\n", + " '404',\n", + " 'triforce',\n", + " 'html1',\n", + " 'playerpage',\n", + " 'qname',\n", + " 'autoconnect',\n", + " 'esrch',\n", + " 'passthruactive',\n", + " 'ifaces',\n", + " 'pad1d',\n", + " 'downloading',\n", + " 'imrd',\n", + " 'typ',\n", + " 'fid',\n", + " 'embedcode',\n", + " 'method',\n", + " 'unix',\n", + " 'adjacency',\n", + " 'monthrange',\n", + " 'createprojectuser',\n", + " 'datatype',\n", + " 'kservice',\n", + " 'accessmode',\n", + " 'prnt',\n", + " 'putheader',\n", + " 'smin',\n", + " 'absexp',\n", + " 'covariances',\n", + " 'ou',\n", + " 'modifier',\n", + " 'downloader',\n", + " 'capacityinkb',\n", + " 'date',\n", + " 'kick',\n", + " 'bpdu',\n", + " 'fld',\n", + " 'levels',\n", + " 'keystr',\n", + " 'spweights',\n", + " 'gdalexception',\n", + " 'software',\n", + " 'logical',\n", + " 'readlink',\n", + " 'terminator',\n", + " 'gettext',\n", + " 'cnga',\n", + " 'erule',\n", + " 'kd',\n", + " 'multipointfield',\n", + " 'subnet',\n", + " 'people',\n", + " 'intensity',\n", + " 'collate',\n", + " 'fx',\n", + " 'inotify',\n", + " 'i18n',\n", + " 'camel',\n", + " 'scsi',\n", + " 'offer',\n", + " 'logaddexp',\n", + " 'songnr',\n", + " 'centroid',\n", + " 'fact',\n", + " 'reg2',\n", + " 'escapes',\n", + " 'ovirt',\n", + " 'invalidation',\n", + " 'cal',\n", + " 'aggregation',\n", + " 'camelkey',\n", + " 'enhanced',\n", + " 'cum',\n", + " 'contour',\n", + " 'exterior',\n", + " 'deadline',\n", + " 'score3',\n", + " 'calledprocesserror',\n", + " 'half',\n", + " 'resourceversion',\n", + " 'py34',\n", + " 'choicefield',\n", + " 'percentile',\n", + " 'deployment',\n", + " 'mdhd',\n", + " 'checks',\n", + " 'statementtype',\n", + " 'executable',\n", + " 'prefetch',\n", + " 'mday',\n", + " 'probabilitiy',\n", + " 'pull',\n", + " 'mapped',\n", + " 'jsonp',\n", + " 'warnings',\n", + " 'g1',\n", + " 'projected',\n", + " 'dicts',\n", + " 'getrulestring',\n", + " 'ess',\n", + " 'waituntilcomplete',\n", + " 'drama',\n", + " 'yielding',\n", + " 'nccl',\n", + " 'finished',\n", + " 'preface',\n", + " 'startupscipts',\n", + " 'jsonresponse',\n", + " 'fielddoesnotexist',\n", + " 'efds',\n", + " 'numcorespersocket',\n", + " 'ctr',\n", + " 'dirobj',\n", + " 'routeros',\n", + " 'characters',\n", + " 'getattr',\n", + " 'resolved',\n", + " 'sips',\n", + " 'resilient',\n", + " 'redistribute',\n", + " 'encap',\n", + " 'anisotropic',\n", + " 'closable',\n", + " 'reconfiguredvs',\n", + " 'calculated',\n", + " 'hn',\n", + " 'edges',\n", + " 'quotamodetype',\n", + " 'rootpage',\n", + " 'pval',\n", + " 'kwonly',\n", + " 'aggregators',\n", + " 'rendered',\n", + " 'called',\n", + " 'resb',\n", + " 'ensemble',\n", + " 'xmldictnode',\n", + " 'irule',\n", + " 'objclass',\n", + " 'adminform',\n", + " 'setconsoletitlew',\n", + " 'exts',\n", + " 'out2',\n", + " 'oneexception',\n", + " 'unhealthy',\n", + " 'nfc',\n", + " 'eol',\n", + " 'acceleration',\n", + " 'logdirectory',\n", + " 'authstr',\n", + " 'rabbitmqctl',\n", + " 'param2',\n", + " 'getlogger',\n", + " 'gateways',\n", + " 'levelname',\n", + " 'nrk',\n", + " 'networkusage',\n", + " 'description',\n", + " 'virtualvmxnet3',\n", + " 'importorskip',\n", + " 'consumer',\n", + " 'consent',\n", + " 'quiet',\n", + " 'join2infos',\n", + " 'endresult',\n", + " 'episodes',\n", + " 'suff',\n", + " 'lf',\n", + " 'dead',\n", + " 'setbootorder',\n", + " 'val1',\n", + " 'nhp',\n", + " 'hostmask',\n", + " 'kps',\n", + " 'template',\n", + " 'blueprints',\n", + " 'ngrams',\n", + " 'affinity',\n", + " 'bootable',\n", + " '64bit',\n", + " 'findusers',\n", + " 'llconst',\n", + " 'unknowntimezoneerror',\n", + " 'itemsize',\n", + " 'deletedirectoryinguest',\n", + " 'found1',\n", + " 'actually',\n", + " 'hr',\n", + " 'coef0',\n", + " 'p2top',\n", + " 'outfile',\n", + " 'rurl',\n", + " 'linemerge',\n", + " 'drupal',\n", + " 'penalties',\n", + " 'sriovactive',\n", + " '7k',\n", + " 'rebootguest',\n", + " 'getmoid',\n", + " 'linkstatus',\n", + " 'byt',\n", + " 'visibledeprecationwarning',\n", + " 'sensitive',\n", + " 'getslice',\n", + " 'monitoringpolicy',\n", + " 'remote',\n", + " 'nextafter',\n", + " 'vrouter',\n", + " 'even',\n", + " 'migrations',\n", + " 'ipc',\n", + " 'opsworks',\n", + " 'suppress',\n", + " 'isidentifier',\n", + " 'thinprovisioned',\n", + " 'querier',\n", + " 'skipif',\n", + " 'returnables',\n", + " 'below',\n", + " 'polling',\n", + " 'sendgrid',\n", + " 'shortlist',\n", + " 'approx',\n", + " 'quantile',\n", + " 'cmp',\n", + " 'authenticated',\n", + " 'gitdir',\n", + " 'tmean',\n", + " 'agreements',\n", + " 'migrating',\n", + " 'getpos',\n", + " 'ovr1',\n", + " 'firstparams',\n", + " 'variableid',\n", + " 'isvalidreason',\n", + " 'hostname',\n", + " 'startindex',\n", + " 'lnotab',\n", + " 'bv',\n", + " 'whiten',\n", + " 'plain',\n", + " 'tenant',\n", + " 'bond',\n", + " 'geotransform',\n", + " 'bf',\n", + " 'filled',\n", + " 'cred',\n", + " 'clusters',\n", + " 'patches',\n", + " 'y5',\n", + " 'deletemodel',\n", + " 'noncebit',\n", + " 'memoryovercommit',\n", + " 'detect',\n", + " 'hdd',\n", + " 'iso',\n", + " 'bt1',\n", + " 'affinitylabelmapping',\n", + " 'kwstring',\n", + " 'housing',\n", + " 'reordered',\n", + " 'power',\n", + " 'submodules',\n", + " 'year',\n", + " 'reconstructed',\n", + " 'tup',\n", + " 'getsourcefile',\n", + " 'mgt',\n", + " 'getmembers',\n", + " 'sha256hash',\n", + " 'transfer',\n", + " 'maintenance',\n", + " 'leading',\n", + " 'uadd',\n", + " 'cacheable',\n", + " 'xcli',\n", + " 'ulimits',\n", + " 'argcount',\n", + " 'prop',\n", + " 'im',\n", + " 'gbrt',\n", + " 'diskmovetype',\n", + " 'polynomial',\n", + " 'newitems',\n", + " 'cdroms',\n", + " 'pool1d',\n", + " 'orig',\n", + " 'geta1',\n", + " 'acs',\n", + " 'linear1d',\n", + " 'logicaldevices',\n", + " 'passwords',\n", + " 'whitelist',\n", + " 'networkinterfacereference',\n", + " 'dir1',\n", + " 'registrationclustermapping',\n", + " 'pulsembed',\n", + " 'groupobject',\n", + " 'xboston',\n", + " 'rowgrpkey',\n", + " 'filtering',\n", + " 'meraki',\n", + " 'oldest',\n", + " 'rewrite',\n", + " 'operator',\n", + " 'methodname',\n", + " 'unproc',\n", + " 'mimi',\n", + " 'stderr',\n", + " 'marked',\n", + " 'vmcores',\n", + " 'qt',\n", + " 'hosted',\n", + " 'removeallsnapshots',\n", + " 'failures',\n", + " 'incluster',\n", + " 'accountkey',\n", + " 'statement',\n", + " 'punctuation',\n", + " 'spnprofile',\n", + " 'numerichost',\n", + " 'unclosed',\n", + " 'data2',\n", + " 'sqlite',\n", + " 'editproject',\n", + " 'fieldset',\n", + " 'paras',\n", + " 'portgroup',\n", + " 'queries',\n", + " 'cfoai',\n", + " 'getstdhandle',\n", + " 'pools',\n", + " 'memused',\n", + " 'leave',\n", + " 'sc',\n", + " 'grouped',\n", + " 'pofile',\n", + " 'residuals',\n", + " 'quotient',\n", + " 'marketplace',\n", + " 'op2',\n", + " 'buff',\n", + " 'dual',\n", + " 'ssbn',\n", + " 'region',\n", + " 'hellotime',\n", + " 'warns',\n", + " 'ma',\n", + " 'native',\n", + " 'semaphore',\n", + " 'cr',\n", + " 'nlocals',\n", + " 'rfc850',\n", + " 'interpolate',\n", + " 'preparation',\n", + " 'publisher',\n", + " 'writelines',\n", + " 'status',\n", + " 'gwtx',\n", + " 'omapi',\n", + " 'ix',\n", + " 'pred1',\n", + " 'cert',\n", + " 'node1',\n", + " 'profileclientssl',\n", + " 'fips',\n", + " 'inlier',\n", + " 'squash',\n", + " 'autoscale',\n", + " 'monitorname',\n", + " 'closefp',\n", + " 'p2',\n", + " 'panxapierror',\n", + " 'chassis',\n", + " 'followers',\n", + " 'attention',\n", + " 'vars',\n", + " 'supervised',\n", + " 'eai',\n", + " 'jwk',\n", + " 'except',\n", + " 'grpkey',\n", + " 'repeated',\n", + " 'lchown',\n", + " 'remainder',\n", + " 'authorization',\n", + " 'peertube',\n", + " 'hsrp',\n", + " 'argsort',\n", + " 'comp',\n", + " 'fixup',\n", + " 'argnames',\n", + " 'noregionerror',\n", + " 'component',\n", + " 'picklingerror',\n", + " 'kaiming',\n", + " 'chan',\n", + " 'closed',\n", + " 'lim',\n", + " 'palette',\n", + " 'distr',\n", + " 'opennebulaexception',\n", + " 'tostring',\n", + " 'customvalues',\n", + " 'blurb',\n", + " 'tmf',\n", + " 'security',\n", + " 'cursor',\n", + " 'sl',\n", + " 'scorer',\n", + " 'creds',\n", + " 'alertpolicies',\n", + " 'uiconf',\n", + " 'behaviors',\n", + " 'cursorwrapper',\n", + " 'nmcli',\n", + " 'lxml',\n", + " 'facility',\n", + " 'videoid',\n", + " 'restartpolicy',\n", + " 'covariance',\n", + " 'spheroid',\n", + " 'einval',\n", + " 'aggregates',\n", + " 'life',\n", + " 'bootdevice',\n", + " 'bincount',\n", + " 'subformat',\n", + " 'sim',\n", + " 'ogr',\n", + " 'gram',\n", + " 'iterated',\n", + " 'rpipes',\n", + " 'getfieldasinteger64',\n", + " 'classdoc',\n", + " 'virtualethernetcard',\n", + " 'nuevo',\n", + " 'ftp',\n", + " 'disksize',\n", + " 'fasthttps',\n", + " 'guestfamily',\n", + " 'bigsuds',\n", + " 'domainrecordnotunique',\n", + " 'numofvolumes',\n", + " 'wants',\n", + " 'cur',\n", + " 'renderer',\n", + " 'vnic',\n", + " 'l2vpn',\n", + " 'spatialreference',\n", + " 'rkf',\n", + " 'tl',\n", + " 'virtualserver',\n", + " 'interprets',\n", + " 'longmessage',\n", + " 'replication',\n", + " 'permalink',\n", + " 'hhmm',\n", + " 'openstackvolumeprovider',\n", + " 'symdifference',\n", + " 'localize',\n", + " 'versions',\n", + " 'tagging',\n", + " 'tracker',\n", + " 'refcnt',\n", + " 'convtranspose2d',\n", + " 'vepa',\n", + " 'agreement',\n", + " 'request',\n", + " 'information',\n", + " 'rst',\n", + " 'docutils',\n", + " 'cron',\n", + " 'certkeys',\n", + " 'perp',\n", + " 'openstacknetworkprovider',\n", + " 'poweruphostfromstandby',\n", + " 'distro',\n", + " 'priv',\n", + " 'nooptionerror',\n", + " 'cut',\n", + " 't2',\n", + " 'fvar',\n", + " 'remotepassthroughbackinginfo',\n", + " 'lst',\n", + " 'loose',\n", + " 'tpuclusterresolver',\n", + " 'cached',\n", + " 'fileinfo',\n", + " 'winexe',\n", + " 'unverified',\n", + " 'socks',\n", + " 'digiteka',\n", + " 'loadbalancersku',\n", + " 'ss1',\n", + " 'kazoo',\n", + " 'separate',\n", + " 'invalidrequest',\n", + " 'irules',\n", + " 'relatedobjectdoesnotexist',\n", + " 'unchanged',\n", + " 'msec',\n", + " 'today',\n", + " 'builtinfunctiontype',\n", + " 'rhs',\n", + " 'urljoin',\n", + " 'axis1',\n", + " 'kddcup',\n", + " 'issu',\n", + " 'delimeter1',\n", + " 'getcmd',\n", + " 'imagereference',\n", + " 'deletes',\n", + " 'snapshotfile',\n", + " 'getcwd',\n", + " 'permutations',\n", + " 'wkb',\n", + " 'ini',\n", + " 'addtests',\n", + " 'person',\n", + " 'scores1',\n", + " 'absfilepath',\n", + " 'subnets',\n", + " 'polygon',\n", + " 'background',\n", + " 'relinker',\n", + " 'manual',\n", + " 'unquote',\n", + " 'og',\n", + " 'mcc',\n", + " 'indexrange',\n", + " 'granttypes',\n", + " 'fileshares',\n", + " 'nbor',\n", + " 'cd',\n", + " 'ws',\n", + " 'bootablefloppydevice',\n", + " 'ninf',\n", + " 'unused',\n", + " 'i64',\n", + " 'lastgroup',\n", + " 'org',\n", + " 'multivariate',\n", + " 'cookies',\n", + " 'nonzeros',\n", + " 'alter',\n", + " 'ratios',\n", + " 'mcd',\n", + " 'union1d',\n", + " 'urlr',\n", + " 'processevent',\n", + " 'openauth',\n", + " 'distfunc',\n", + " 'upd',\n", + " 'dep',\n", + " 'portforwarding',\n", + " 'tasktemplate',\n", + " 'isgeographic',\n", + " 'trilinear3d',\n", + " 'symlinked',\n", + " 'getformat',\n", + " 'etree',\n", + " 'color1',\n", + " 'nesterovs',\n", + " 'flipped',\n", + " 'domainadminpassword',\n", + " 'durationfield',\n", + " 'matlab',\n", + " 'exception',\n", + " 'c3',\n", + " 'yi',\n", + " 'checkdim',\n", + " 'virtualmachinepowerstate',\n", + " 'moid',\n", + " 'dirs',\n", + " 'exo',\n", + " 'drsconfiginfo',\n", + " 'xframe',\n", + " 'phi',\n", + " 'utctimetuple',\n", + " 'samme',\n", + " 'selfip',\n", + " 'quantity',\n", + " 'ks',\n", + " 'rf',\n", + " '6432',\n", + " 'surrogateescape',\n", + " 'sys',\n", + " 'virtualnetwork',\n", + " 'suggestion',\n", + " 'etag',\n", + " 'asgd',\n", + " 'arctan',\n", + " 'dec',\n", + " 'addvirtualswitch',\n", + " 'endport',\n", + " 'getusers',\n", + " 'copyright',\n", + " 'toks',\n", + " 'ds',\n", + " 'virtualmachinescalesetstorageprofile',\n", + " 'containerregistry',\n", + " 'encoded',\n", + " 'isocalendar',\n", + " 'nulls',\n", + " 'pagefunc',\n", + " 'flat',\n", + " 'norootsoa',\n", + " 'memfree',\n", + " 'buffer',\n", + " 'dynamictype',\n", + " 'subscriptions',\n", + " 'intersects',\n", + " 'networkresourcepooloverrideallowed',\n", + " 'pw',\n", + " 'alldata',\n", + " 'duplicatehandle',\n", + " 'using',\n", + " 'listener',\n", + " 'submod',\n", + " 'thisobj',\n", + " 'subcontext',\n", + " 'density',\n", + " 'softshrink',\n", + " 'huber',\n", + " 'rd',\n", + " 'overlaps',\n", + " 'pretty',\n", + " 'china',\n", + " 'authenticationfailed',\n", + " 'reselect',\n", + " 'usermodel',\n", + " 'colnames',\n", + " 'disableruleset',\n", + " 'getmember',\n", + " 'repository',\n", + " 'saga',\n", + " 'mgmt',\n", + " 'privilege',\n", + " 'starttest',\n", + " 'explicit',\n", + " 'prctl',\n", + " 'altermodeltable',\n", + " 'oses',\n", + " 'selectfeatures',\n", + " 'achieved',\n", + " 'umask',\n", + " 'sec',\n", + " 'identities',\n", + " 'dated',\n", + " 'contains',\n", + " 'sids',\n", + " 'reloader',\n", + " 'flavors',\n", + " 'spanner',\n", + " 'transactions',\n", + " 'router',\n", + " 'configobj',\n", + " 'timestr',\n", + " 'tol',\n", + " 'resample',\n", + " 'a1',\n", + " 'vertical',\n", + " 'testcases',\n", + " 'license',\n", + " 'connecting',\n", + " 'wkt',\n", + " 'flv',\n", + " 'protectederror',\n", + " 'probb',\n", + " 'permanent',\n", + " 'mat',\n", + " 'distances',\n", + " 'subgroups',\n", + " 'popup',\n", + " 'calculate',\n", + " 'mtu',\n", + " 'reassignment',\n", + " 'environment',\n", + " 'pkl',\n", + " 'hiddens',\n", + " 'route',\n", + " 'rts',\n", + " 'filesystemvolumeinfo',\n", + " 'conditiontuple',\n", + " 'debugundefined',\n", + " 'poll',\n", + " 'bytesio',\n", + " 'cfacter',\n", + " 'timezone',\n", + " 'nick',\n", + " 'staticfiles',\n", + " 'bodies',\n", + " 'right',\n", + " 'ag',\n", + " 'authz',\n", + " 'related',\n", + " 'denominator',\n", + " 'nowait',\n", + " 'systemnotfoundexception',\n", + " 'ewkb',\n", + " 'templar',\n", + " 'rtt',\n", + " 'bw',\n", + " 'identical',\n", + " 'translations',\n", + " 'certfile',\n", + " 'silence',\n", + " 'fragment',\n", + " 'qas',\n", + " 'checksum',\n", + " 'ascontiguousarray',\n", + " 'timecode',\n", + " 'helm',\n", + " 'refer',\n", + " 'catalog',\n", + " '20',\n", + " 'asn',\n", + " 'spfintervaltype',\n", + " 'iosxr',\n", + " 'addvirtualnic',\n", + " 'obj1',\n", + " 'recover',\n", + " 'virtualmachine',\n", + " 'system',\n", + " 'stages',\n", + " 't00',\n", + " 'supernet',\n", + " 'hw',\n", + " 'lwgeom',\n", + " 'slogdet',\n", + " 'getgrgid',\n", + " 'isvalidlsastartarrivalinterval',\n", + " 'subitem',\n", + " 'nicdevice',\n", + " 'cloning',\n", + " 'way',\n", + " 'masked',\n", + " 'cv',\n", + " 'mas',\n", + " 'deep',\n", + " 'machines',\n", + " 'ptype',\n", + " 'readfile',\n", + " 'healthy',\n", + " 'googleapi',\n", + " 'fromfile',\n", + " 'tapes',\n", + " 'dc',\n", + " 'checksanityofvariable',\n", + " 'spweights2',\n", + " 'ap',\n", + " 'dr',\n", + " 'setordinate',\n", + " 'booloption',\n", + " 'reader',\n", + " 'pol',\n", + " 'supported',\n", + " 'war',\n", + " 'spdiags',\n", + " 'act',\n", + " 'expr2',\n", + " 'autoselect',\n", + " 'binarizer',\n", + " 'pickling',\n", + " 'unmatched',\n", + " 'maskstr',\n", + " 'consumed',\n", + " 'reversepolicy',\n", + " 'ulonglong',\n", + " 'cid',\n", + " 'xsupi',\n", + " 'sslrootcert',\n", + " 'configlet',\n", + " 'minimal',\n", + " 'parts',\n", + " 'abspath',\n", + " 'preview',\n", + " 'incomplete',\n", + " 'jinja',\n", + " 'dvswitches',\n", + " 'y2',\n", + " 'regex',\n", + " 'cplerr',\n", + " 'positives',\n", + " 'actions',\n", + " 'plflag0',\n", + " 'shutstate',\n", + " 'ipad',\n", + " 'nat64',\n", + " 'clustering2',\n", + " 'adminpassword',\n", + " 'autodetector',\n", + " 'wms',\n", + " 'proj',\n", + " 'clouds',\n", + " 'dom',\n", + " 'adminstatus',\n", + " 'nocolor',\n", + " 'lbs',\n", + " 'highavailability',\n", + " 'networking',\n", + " 'inertia',\n", + " 'ovf',\n", + " 'linkspeed',\n", + " 'saveme',\n", + " 'ora',\n", + " 'namespace',\n", + " 'crt',\n", + " 'notification',\n", + " 'percentage',\n", + " 'flasharray',\n", + " 'tk',\n", + " 'setfield',\n", + " 'db',\n", + " '4xx',\n", + " 'getheaders',\n", + " 'configurepowerpolicy',\n", + " 'configlets',\n", + " 'suffixes',\n", + " 'alarms',\n", + " 'tagdict',\n", + " 'dummy',\n", + " 'ad',\n", + " 'authorizaion',\n", + " 'linuxconfiguration',\n", + " 'saml',\n", + " 'colaprocessor',\n", + " 'exiting',\n", + " 'cleanup',\n", + " 'vap',\n", + " 'associable',\n", + " 'jw',\n", + " 'treeclassifier',\n", + " 'join1infos',\n", + " 'records',\n", + " 'klass',\n", + " 'tenants',\n", + " 'frac',\n", + " 'testsrun',\n", + " 'nics',\n", + " 'lookupbyid',\n", + " 'pcx',\n", + " 'dm',\n", + " 'endtime',\n", + " 'protected',\n", + " 'oca',\n", + " 'openshift',\n", + " 'publicdnsname',\n", + " 'opath',\n", + " 'serviceobject',\n", + " 'fkey',\n", + " 'req',\n", + " 'dirn',\n", + " 'accounting',\n", + " 'maxmksconnections',\n", + " 'disassociated',\n", + " 'newelbs',\n", + " 'disks',\n", + " 'sns',\n", + " 'protected64',\n", + " 'bom',\n", + " 'computed',\n", + " 'svcs',\n", + " 'adjusted',\n", + " 'ipaddresses',\n", + " 'loginautherror',\n", + " 'removelicense',\n", + " 'updatehostimageacceptancelevel',\n", + " 'distinct',\n", + " 'estimator7',\n", + " 'caller',\n", + " 'datastorefolder',\n", + " 'deleteprotection',\n", + " 'typographic',\n", + " 'hostimageconfiggetacceptance',\n", + " 'lsaostartinterval',\n", + " 'reservation',\n", + " 'tier',\n", + " 'dstr',\n", + " 'statistics',\n", + " 'orgname',\n", + " 'parsedate',\n", + " 'cookiejar',\n", + " 'ge',\n", + " 'clean',\n", + " 'networklabelnotunique',\n", + " 'hexlify',\n", + " 'stringified',\n", + " 'salt',\n", + " 'unbounded',\n", + " 'configured',\n", + " 'suspend',\n", + " 'err1',\n", + " 'evalue',\n", + " 'dbsnapshot',\n", + " 'computeresource',\n", + " 'floatlist',\n", + " 'xapi',\n", + " 'll',\n", + " 'vals2',\n", + " 'numbertypes',\n", + " 'calc',\n", + " ...}" ] }, - "execution_count": 21, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -434,7 +1464,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 110, "metadata": { "scrolled": true }, @@ -494,293 +1524,293 @@ " \n", " \n", " 7\n", - " unaryop\n", + " compare\n", " \n", " \n", " 8\n", - " not\n", + " name\n", " \n", " \n", " 9\n", - " call\n", + " is\n", " \n", " \n", " 10\n", - " name\n", + " nameconstant\n", " \n", " \n", " 11\n", - " raise\n", + " assign\n", " \n", " \n", " 12\n", - " str\n", + " negative\n", " \n", " \n", " 13\n", - " for\n", + " slope\n", " \n", " \n", " 14\n", - " layer\n", + " num\n", " \n", " \n", " 15\n", - " attribute\n", + " boolop\n", " \n", " \n", " 16\n", - " input\n", + " or\n", " \n", " \n", " 17\n", - " layers\n", + " and\n", " \n", " \n", " 18\n", - " assign\n", + " unaryop\n", " \n", " \n", " 19\n", - " tensor\n", + " not\n", " \n", " \n", " 20\n", - " keyword\n", + " call\n", " \n", " \n", " 21\n", - " batch\n", + " raise\n", " \n", " \n", " 22\n", - " shape\n", + " attribute\n", " \n", " \n", " 23\n", - " dtype\n", + " format\n", " \n", " \n", " 24\n", - " sparse\n", + " str\n", " \n", " \n", " 25\n", - " name\n", + " return\n", " \n", " \n", " 26\n", - " expr\n", + " sqrt\n", " \n", " \n", " 27\n", - " append\n", + " binop\n", " \n", " \n", " 28\n", - " newly\n", + " div\n", " \n", " \n", " 29\n", - " created\n", + " add\n", " \n", " \n", " ...\n", " ...\n", " \n", " \n", - " 1126\n", - " deserialize\n", + " 9739\n", + " tfrecordwriter\n", " \n", " \n", - " 1127\n", - " deserialized\n", + " 9740\n", + " assertallclose\n", " \n", " \n", - " 1128\n", - " opened\n", + " 9741\n", + " pooled\n", " \n", " \n", - " 1129\n", - " yaml\n", + " 9742\n", + " assertallequal\n", " \n", " \n", - " 1130\n", - " nbytes\n", + " 9743\n", + " tester\n", " \n", " \n", - " 1131\n", - " dset\n", + " 9744\n", + " bertmodeltester\n", " \n", " \n", - " 1132\n", - " sublayer\n", + " 9745\n", + " bertconfig\n", " \n", " \n", - " 1133\n", - " kernels\n", + " 9746\n", + " unreachable\n", " \n", " \n", - " 1134\n", - " gates\n", + " 9747\n", + " bert\n", " \n", " \n", - " 1135\n", - " hsplit\n", + " 9748\n", + " colaprocessor\n", " \n", " \n", - " 1136\n", - " t\n", + " 9749\n", + " mnliprocessor\n", " \n", " \n", - " 1137\n", - " correlation\n", + " 9750\n", + " mrpcprocessor\n", " \n", " \n", - " 1138\n", - " gen\n", + " 9751\n", + " polynomial\n", " \n", " \n", - " 1139\n", - " enqueuer\n", + " 9752\n", + " lm\n", " \n", " \n", - " 1140\n", - " kwarg\n", + " 9753\n", + " sentence\n", " \n", " \n", - " 1141\n", - " lp\n", + " 9754\n", + " act\n", " \n", " \n", - " 1142\n", - " attribute\n", + " 9755\n", + " interleave\n", " \n", " \n", - " 1143\n", - " intermediate\n", + " 9756\n", + " tfrecorddataset\n", " \n", " \n", - " 1144\n", - " insecure\n", + " 9757\n", + " opposite\n", " \n", " \n", - " 1145\n", - " weighted\n", + " 9758\n", + " wordpiece\n", " \n", " \n", - " 1146\n", - " modes\n", + " 9759\n", + " punc\n", " \n", " \n", - " 1147\n", - " accuracy\n", + " 9760\n", + " unk\n", " \n", " \n", - " 1148\n", - " categorical\n", + " 9761\n", + " basictokenizer\n", " \n", " \n", - " 1149\n", - " suffix\n", + " 9762\n", + " floatlist\n", " \n", " \n", - " 1150\n", - " lengths\n", + " 9763\n", + " lms\n", " \n", " \n", - " 1151\n", - " ref\n", + " 9764\n", + " dupe\n", " \n", " \n", - " 1152\n", - " cw\n", + " 9765\n", + " getwriter\n", " \n", " \n", - " 1153\n", - " batches\n", + " 9766\n", + " attention\n", " \n", " \n", - " 1154\n", - " score\n", + " 9767\n", + " ndims\n", " \n", " \n", - " 1155\n", - " existing\n", + " 9768\n", + " adder\n", " \n", " \n", "\n", - "

1156 rows × 1 columns

\n", + "

9769 rows × 1 columns

\n", "" ], "text/plain": [ - " 0\n", - "0 [PAD]\n", - "1 [UNK]\n", - "2 [CLS]\n", - "3 [SEP]\n", - "4 [MASK]\n", - "5 [cls]\n", - "6 if\n", - "7 unaryop\n", - "8 not\n", - "9 call\n", - "10 name\n", - "11 raise\n", - "12 str\n", - "13 for\n", - "14 layer\n", - "15 attribute\n", - "16 input\n", - "17 layers\n", - "18 assign\n", - "19 tensor\n", - "20 keyword\n", - "21 batch\n", - "22 shape\n", - "23 dtype\n", - "24 sparse\n", - "25 name\n", - "26 expr\n", - "27 append\n", - "28 newly\n", - "29 created\n", - "... ...\n", - "1126 deserialize\n", - "1127 deserialized\n", - "1128 opened\n", - "1129 yaml\n", - "1130 nbytes\n", - "1131 dset\n", - "1132 sublayer\n", - "1133 kernels\n", - "1134 gates\n", - "1135 hsplit\n", - "1136 t\n", - "1137 correlation\n", - "1138 gen\n", - "1139 enqueuer\n", - "1140 kwarg\n", - "1141 lp\n", - "1142 attribute\n", - "1143 intermediate\n", - "1144 insecure\n", - "1145 weighted\n", - "1146 modes\n", - "1147 accuracy\n", - "1148 categorical\n", - "1149 suffix\n", - "1150 lengths\n", - "1151 ref\n", - "1152 cw\n", - "1153 batches\n", - "1154 score\n", - "1155 existing\n", + " 0\n", + "0 [PAD]\n", + "1 [UNK]\n", + "2 [CLS]\n", + "3 [SEP]\n", + "4 [MASK]\n", + "5 [cls]\n", + "6 if\n", + "7 compare\n", + "8 name\n", + "9 is\n", + "10 nameconstant\n", + "11 assign\n", + "12 negative\n", + "13 slope\n", + "14 num\n", + "15 boolop\n", + "16 or\n", + "17 and\n", + "18 unaryop\n", + "19 not\n", + "20 call\n", + "21 raise\n", + "22 attribute\n", + "23 format\n", + "24 str\n", + "25 return\n", + "26 sqrt\n", + "27 binop\n", + "28 div\n", + "29 add\n", + "... ...\n", + "9739 tfrecordwriter\n", + "9740 assertallclose\n", + "9741 pooled\n", + "9742 assertallequal\n", + "9743 tester\n", + "9744 bertmodeltester\n", + "9745 bertconfig\n", + "9746 unreachable\n", + "9747 bert\n", + "9748 colaprocessor\n", + "9749 mnliprocessor\n", + "9750 mrpcprocessor\n", + "9751 polynomial\n", + "9752 lm\n", + "9753 sentence\n", + "9754 act\n", + "9755 interleave\n", + "9756 tfrecorddataset\n", + "9757 opposite\n", + "9758 wordpiece\n", + "9759 punc\n", + "9760 unk\n", + "9761 basictokenizer\n", + "9762 floatlist\n", + "9763 lms\n", + "9764 dupe\n", + "9765 getwriter\n", + "9766 attention\n", + "9767 ndims\n", + "9768 adder\n", "\n", - "[1156 rows x 1 columns]" + "[9769 rows x 1 columns]" ] }, - "execution_count": 22, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -791,16 +1821,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1156" + "9769" ] }, - "execution_count": 23, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -811,7 +1841,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ @@ -820,7 +1850,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -829,26 +1859,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 114, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0msnippet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mresults_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmsk_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'masked_lm_positions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmasked_tk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msnippet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmsk_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvocab_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mresults_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'masked_lm_predictions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1499\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1500\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1502\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 2230\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2234\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_setter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_loc\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_ixs\u001b[0;34m(self, i, axis)\u001b[0m\n\u001b[1;32m 2849\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2850\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2851\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2852\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2853\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mfast_xs\u001b[0;34m(self, loc)\u001b[0m\n\u001b[1;32m 874\u001b[0m \u001b[0msingle\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m \"\"\"\n\u001b[0;32m--> 876\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 877\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "for i in range(len(results_df)):\n", " snippet = [results_df[str(_)][i] for _ in range(64)]\n", @@ -869,7 +1882,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ @@ -883,7 +1896,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": {}, "outputs": [], "source": [ @@ -892,7 +1905,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ @@ -902,726 +1915,597 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('keyword', 1.0),\n", - " ('float16', 0.0),\n", - " ('gradients', 0.0),\n", - " ('decode', 0.0),\n", - " ('yaml', 0.0),\n", - " ('list', 0.0),\n", - " ('batches', 0.0),\n", - " ('filewriter', 0.0),\n", - " ('upsampling1d', 0.0),\n", - " ('indexedslices', 0.0),\n", - " ('check', 0.0),\n", - " ('fieldnames', 0.0),\n", + "[('like', 1.0),\n", + " ('usub', 1.0),\n", + " ('lambda', 1.0),\n", + " ('scale', 1.0),\n", + " ('withitem', 1.0),\n", + " ('2', 1.0),\n", + " ('replace', 1.0),\n", + " ('comprehension', 1.0),\n", + " ('classdef', 1.0),\n", + " ('boolop', 1.0),\n", + " ('not', 1.0),\n", + " ('try', 1.0),\n", + " ('reduce', 1.0),\n", + " ('unaryop', 1.0),\n", + " ('learning', 1.0),\n", + " ('negative', 1.0),\n", + " ('functiondef', 1.0),\n", + " ('items', 1.0),\n", + " ('arange', 1.0),\n", + " ('path', 1.0),\n", + " ('yield', 1.0),\n", + " ('randint', 1.0),\n", + " ('join', 1.0),\n", + " ('with', 1.0),\n", + " ('compare', 1.0),\n", + " ('newaxis', 1.0),\n", + " ('importfrom', 1.0),\n", + " ('pred', 1.0),\n", + " ('extslice', 1.0),\n", + " ('alias', 1.0),\n", + " ('binop', 0.9992614475627769),\n", + " ('subscript', 0.9988304093567252),\n", + " ('if', 0.994059405940594),\n", + " ('keyword', 0.9891411648568608),\n", + " ('arg', 0.9882075471698113),\n", + " ('self', 0.9857142857142858),\n", + " ('assign', 0.9757343550446999),\n", + " ('call', 0.9751243781094527),\n", + " ('attribute', 0.9609347750261598),\n", + " ('name', 0.9574687725818106),\n", + " ('and', 0.9523809523809523),\n", + " ('arguments', 0.9488372093023256),\n", + " ('index', 0.9410898379970545),\n", + " ('expr', 0.9367088607594937),\n", + " ('excepthandler', 0.9333333333333333),\n", + " ('sum', 0.9166666666666666),\n", + " ('augassign', 0.9090909090909091),\n", + " ('expand', 0.9),\n", + " ('dict', 0.875),\n", + " ('listcomp', 0.8507462686567164),\n", + " ('raise', 0.8410596026490066),\n", + " ('names', 0.8333333333333334),\n", + " ('size', 0.8289473684210527),\n", + " ('append', 0.8255813953488372),\n", + " ('str', 0.8058181818181818),\n", + " ('for', 0.8028169014084507),\n", + " ('num', 0.8007042253521127),\n", + " ('kernel', 0.8),\n", + " ('eq', 0.7975460122699386),\n", + " ('mod', 0.7901234567901234),\n", + " ('get', 0.7818181818181819),\n", + " ('rate', 0.7777777777777778),\n", + " ('format', 0.7733333333333333),\n", + " ('assert', 0.75),\n", + " ('mult', 0.7388535031847133),\n", + " ('nameconstant', 0.7342995169082126),\n", + " ('add', 0.7305389221556886),\n", + " ('init', 0.7241379310344828),\n", + " ('random', 0.7142857142857143),\n", + " ('tuple', 0.6821515892420538),\n", + " ('isnot', 0.6792452830188679),\n", + " ('fit', 0.6666666666666666),\n", + " ('beta', 0.6666666666666666),\n", + " ('sample', 0.6666666666666666),\n", + " ('data', 0.6438356164383562),\n", + " ('i', 0.64),\n", + " ('return', 0.6122448979591837),\n", + " ('stop', 0.6),\n", + " ('batch', 0.5925925925925926),\n", + " ('generatorexp', 0.5882352941176471),\n", + " ('div', 0.5833333333333334),\n", + " ('shape', 0.5785123966942148),\n", + " ('list', 0.578125),\n", + " ('slice', 0.5601659751037344),\n", + " ('ifexp', 0.559322033898305),\n", + " ('weight', 0.5555555555555556),\n", + " ('target', 0.5384615384615384),\n", + " ('in', 0.5365853658536586),\n", + " ('asarray', 0.5),\n", + " ('or', 0.5),\n", + " ('dot', 0.5),\n", + " ('base', 0.5),\n", + " ('split', 0.5),\n", + " ('to', 0.5),\n", + " ('inf', 0.5),\n", + " ('hstack', 0.5),\n", + " ('metadata', 0.5),\n", + " ('normal', 0.45454545454545453),\n", + " ('is', 0.43902439024390244),\n", + " ('alpha', 0.42857142857142855),\n", + " ('y', 0.41935483870967744),\n", + " ('decode', 0.36363636363636365),\n", + " ('info', 0.35714285714285715),\n", + " ('version', 0.35714285714285715),\n", + " ('warn', 0.35294117647058826),\n", + " ('state', 0.3392857142857143),\n", + " ('a', 0.3333333333333333),\n", + " ('u', 0.3333333333333333),\n", + " ('writer', 0.3333333333333333),\n", + " ('class', 0.3333333333333333),\n", + " ('left', 0.3333333333333333),\n", + " ('matrix', 0.3333333333333333),\n", + " ('round', 0.3333333333333333),\n", + " ('x', 0.3247863247863248),\n", + " ('array', 0.3157894736842105),\n", + " ('sub', 0.3130434782608696),\n", + " ('filters', 0.3076923076923077),\n", + " ('mode', 0.3),\n", + " ('o', 0.2857142857142857),\n", + " ('length', 0.2857142857142857),\n", + " ('predict', 0.2857142857142857),\n", + " ('reshape', 0.2765957446808511),\n", + " ('out', 0.2727272727272727),\n", + " ('gt', 0.2549019607843137),\n", + " ('by', 0.25),\n", + " ('result', 0.25),\n", + " ('prefix', 0.25),\n", + " ('no', 0.25),\n", + " ('dtype', 0.2459016393442623),\n", + " ('args', 0.23076923076923078),\n", + " ('param', 0.2222222222222222),\n", + " ('file', 0.2222222222222222),\n", + " ('update', 0.2222222222222222),\n", + " ('train', 0.2222222222222222),\n", + " ('layer', 0.21052631578947367),\n", + " ('noteq', 0.203125),\n", + " ('legacy', 0.2),\n", + " ('r', 0.2),\n", + " ('copy', 0.2),\n", + " ('s', 0.2),\n", + " ('equal', 0.2),\n", + " ('support', 0.2),\n", + " ('grad', 0.2),\n", + " ('clip', 0.19047619047619047),\n", + " ('forward', 0.1875),\n", + " ('new', 0.1875),\n", + " ('int', 0.18181818181818182),\n", + " ('output', 0.18181818181818182),\n", + " ('dim', 0.18181818181818182),\n", + " ('parameter', 0.16666666666666666),\n", + " ('l', 0.16666666666666666),\n", + " ('abs', 0.16666666666666666),\n", + " ('updates', 0.16666666666666666),\n", + " ('order', 0.16666666666666666),\n", + " ('kwargs', 0.16071428571428573),\n", + " ('uniform', 0.15384615384615385),\n", + " ('mask', 0.15151515151515152),\n", + " ('concatenate', 0.14814814814814814),\n", + " ('gradient', 0.14285714285714285),\n", + " ('floordiv', 0.14285714285714285),\n", + " ('transpose', 0.14285714285714285),\n", + " ('indices', 0.14285714285714285),\n", + " ('all', 0.14285714285714285),\n", + " ('max', 0.14285714285714285),\n", + " ('set', 0.14285714285714285),\n", + " ('ndim', 0.14285714285714285),\n", + " ('predictions', 0.14285714285714285),\n", + " ('h', 0.13333333333333333),\n", + " ('config', 0.13333333333333333),\n", + " ('f', 0.13333333333333333),\n", + " ('notin', 0.13157894736842105),\n", + " ('log', 0.13043478260869565),\n", + " ('lt', 0.12903225806451613),\n", + " ('dense', 0.125),\n", + " ('float32', 0.125),\n", + " ('encode', 0.125),\n", + " ('extend', 0.1111111111111111),\n", + " ('function', 0.1111111111111111),\n", + " ('ndarray', 0.1111111111111111),\n", + " ('gte', 0.1111111111111111),\n", + " ('starred', 0.1111111111111111),\n", + " ('mean', 0.10256410256410256),\n", + " ('bias', 0.10144927536231885),\n", + " ('loss', 0.1),\n", + " ('v', 0.1),\n", + " ('zeros', 0.1),\n", + " ('type', 0.1),\n", + " ('outputs', 0.09375),\n", + " ('verbose', 0.09090909090909091),\n", + " ('test', 0.09090909090909091),\n", + " ('device', 0.08333333333333333),\n", + " ('t', 0.08333333333333333),\n", + " ('val', 0.08333333333333333),\n", + " ('cell', 0.08),\n", + " ('input', 0.0759493670886076),\n", + " ('w', 0.07407407407407407),\n", + " ('weights', 0.07407407407407407),\n", + " ('pop', 0.07142857142857142),\n", + " ('axis', 0.06818181818181818),\n", + " ('pow', 0.06666666666666667),\n", + " ('values', 0.06666666666666667),\n", + " ('lte', 0.0625),\n", + " ('dims', 0.0625),\n", + " ('pool', 0.0625),\n", + " ('metrics', 0.05555555555555555),\n", + " ('sqrt', 0.05555555555555555),\n", + " ('value', 0.05263157894736842),\n", + " ('spec', 0.047619047619047616),\n", + " ('tensor', 0.047619047619047616),\n", + " ('padding', 0.034482758620689655),\n", + " ('maxval', 0.0),\n", + " ('attrs', 0.0),\n", + " ('dump', 0.0),\n", + " ('score', 0.0),\n", + " ('schedule', 0.0),\n", + " ('inbound', 0.0),\n", + " ('around', 0.0),\n", + " ('key', 0.0),\n", + " ('block', 0.0),\n", + " ('shapes', 0.0),\n", + " ('skip', 0.0),\n", + " ('keepdims', 0.0),\n", + " ('relu', 0.0),\n", + " ('height', 0.0),\n", + " ('results', 0.0),\n", + " ('cls', 0.0),\n", + " ('total', 0.0),\n", + " ('dictwriter', 0.0),\n", + " ('bn', 0.0),\n", + " ('units', 0.0),\n", + " ('axes', 0.0),\n", + " ('stddev', 0.0),\n", " ('last', 0.0),\n", - " ('tuple', 0.0),\n", - " ('b', 0.0),\n", - " ('devices', 0.0),\n", - " ('chunk', 0.0),\n", - " ('when', 0.0),\n", - " ('msg', 0.0),\n", - " ('extslice', 0.0),\n", - " ('conv1d', 0.0),\n", - " ('states', 0.0),\n", - " ('pass', 0.0),\n", - " ('elements', 0.0),\n", - " ('gt', 0.0),\n", + " ('bic', 0.0),\n", " ('ordering', 0.0),\n", - " ('add', 0.0),\n", - " ('attribute', 0.0),\n", - " ('expand', 0.0),\n", - " ('eq', 0.0),\n", - " ('[PAD]', 0.0),\n", - " ('nw', 0.0),\n", - " ('tensorsharedvariable', 0.0),\n", + " ('load', 0.0),\n", + " ('epochs', 0.0),\n", + " ('hot', 0.0),\n", + " ('floatx', 0.0),\n", + " ('module', 0.0),\n", + " ('readline', 0.0),\n", + " ('center', 0.0),\n", + " ('attrname', 0.0),\n", + " ('greater', 0.0),\n", + " ('timeseriesgenerator', 0.0),\n", + " ('foldr', 0.0),\n", + " ('pool2d', 0.0),\n", + " ('global', 0.0),\n", + " ('targets', 0.0),\n", " ('seen', 0.0),\n", - " ('history', 0.0),\n", - " ('phases', 0.0),\n", - " ('dictcomp', 0.0),\n", + " ('float64', 0.0),\n", + " ('arrays', 0.0),\n", " ('next', 0.0),\n", - " ('step', 0.0),\n", - " ('t', 0.0),\n", - " ('nodes', 0.0),\n", - " ('mult', 0.0),\n", - " ('old', 0.0),\n", - " ('dict', 0.0),\n", - " ('import', 0.0),\n", - " ('devs', 0.0),\n", - " ('preprocess', 0.0),\n", - " ('graph', 0.0),\n", - " ('self', 0.0),\n", - " ('at', 0.0),\n", - " ('rnn', 0.0),\n", - " ('stack', 0.0),\n", - " ('select', 0.0),\n", - " ('preprocessor', 0.0),\n", - " ('model', 0.0),\n", - " ('tmp', 0.0),\n", - " ('minval', 0.0),\n", - " ('to', 0.0),\n", - " ('where', 0.0),\n", - " ('writer', 0.0),\n", - " ('collected', 0.0),\n", - " ('constructor', 0.0),\n", - " ('zeros', 0.0),\n", - " ('metrics', 0.0),\n", - " ('rate', 0.0),\n", - " ('dim', 0.0),\n", - " ('constraints', 0.0),\n", - " ('minimum', 0.0),\n", - " ('constants', 0.0),\n", - " ('prelu', 0.0),\n", - " ('prime', 0.0),\n", - " ('placeholder', 0.0),\n", - " ('gain', 0.0),\n", - " ('pooling3d', 0.0),\n", - " ('running', 0.0),\n", - " ('reduce', 0.0),\n", - " ('sqrt', 0.0),\n", - " ('gens', 0.0),\n", - " ('argument', 0.0),\n", - " ('regularizer', 0.0),\n", - " ('required', 0.0),\n", - " ('metadata', 0.0),\n", - " ('threshold', 0.0),\n", - " ('uniform', 0.0),\n", - " ('amsgrad', 0.0),\n", - " ('true', 0.0),\n", - " ('training', 0.0),\n", - " ('filepath', 0.0),\n", - " ('session', 0.0),\n", - " ('pooling1d', 0.0),\n", - " ('neg', 0.0),\n", - " ('listcomp', 0.0),\n", + " ('tolist', 0.0),\n", + " ('permute', 0.0),\n", + " ('tiled', 0.0),\n", + " ('expected', 0.0),\n", + " ('modules', 0.0),\n", + " ('n', 0.0),\n", + " ('strides', 0.0),\n", " ('cache', 0.0),\n", - " ('1', 0.0),\n", - " ('cell', 0.0),\n", - " ('total', 0.0),\n", - " ('bool', 0.0),\n", - " ('converted', 0.0),\n", - " ('slice', 0.0),\n", - " ('return', 0.0),\n", - " ('support', 0.0),\n", - " ('iterations', 0.0),\n", - " ('decay', 0.0),\n", - " ('sub', 0.0),\n", - " ('order', 0.0),\n", - " ('abs', 0.0),\n", - " ('kernel', 0.0),\n", - " ('functiondef', 0.0),\n", - " ('cpu', 0.0),\n", - " ('probs', 0.0),\n", - " ('setattr', 0.0),\n", - " ('close', 0.0),\n", - " ('open', 0.0),\n", - " ('ident', 0.0),\n", - " ('count', 0.0),\n", - " ('update', 0.0),\n", - " ('constraint', 0.0),\n", - " ('densenet169', 0.0),\n", - " ('input', 0.0),\n", - " ('cells', 0.0),\n", + " ('plscanonical', 0.0),\n", + " ('c', 0.0),\n", + " ('model', 0.0),\n", + " ('table', 0.0),\n", + " ('variables', 0.0),\n", + " ('astype', 0.0),\n", + " ('ravel', 0.0),\n", + " ('backwards', 0.0),\n", + " ('enqueuer', 0.0),\n", + " ('tanh', 0.0),\n", + " ('scoring', 0.0),\n", + " ('coordinates', 0.0),\n", + " ('backward', 0.0),\n", + " ('keys', 0.0),\n", + " ('ins', 0.0),\n", + " ('sparse', 0.0),\n", " ('params', 0.0),\n", - " ('generatorexp', 0.0),\n", - " ('dropout', 0.0),\n", - " ('noteq', 0.0),\n", - " ('continue', 0.0),\n", - " ('inferreddimension', 0.0),\n", - " ('flush', 0.0),\n", - " ('target', 0.0),\n", - " ('gradient', 0.0),\n", - " ('spatialdropout1d', 0.0),\n", - " ('func', 0.0),\n", - " ('classdef', 0.0),\n", - " ('train', 0.0),\n", - " ('signature', 0.0),\n", - " ('uid', 0.0),\n", - " ('2', 0.0),\n", - " ('condition', 0.0),\n", - " ('child', 0.0),\n", - " ('arguments', 0.0),\n", - " ('file', 0.0),\n", - " ('sigmoid', 0.0),\n", - " ('truncated', 0.0),\n", + " ('select', 0.0),\n", + " ('trainable', 0.0),\n", + " ('reverse', 0.0),\n", + " ('sampling', 0.0),\n", + " ('pipeline', 0.0),\n", + " ('epsilon', 0.0),\n", + " ('dynamic', 0.0),\n", + " ('lr', 0.0),\n", + " ('progbar', 0.0),\n", + " ('method', 0.0),\n", + " ('cuda', 0.0),\n", + " ('oov', 0.0),\n", + " ('initial', 0.0),\n", + " ('history', 0.0),\n", " ('inputs', 0.0),\n", - " ('pool2d', 0.0),\n", - " ('identity', 0.0),\n", - " ('setdefault', 0.0),\n", - " ('channels', 0.0),\n", - " ('id', 0.0),\n", - " ('desired', 0.0),\n", - " ('conv2d', 0.0),\n", - " ('resnet50', 0.0),\n", - " ('learning', 0.0),\n", - " ('exp', 0.0),\n", - " ('deconv2d', 0.0),\n", - " ('spatial', 0.0),\n", - " ('bar', 0.0),\n", - " ('message', 0.0),\n", - " ('tolist', 0.0),\n", - " ('cloned', 0.0),\n", - " ('cooldown', 0.0),\n", - " ('ctype', 0.0),\n", - " ('svd', 0.0),\n", - " ('validation', 0.0),\n", - " ('arange', 0.0),\n", - " ('scope', 0.0),\n", - " ('weight', 0.0),\n", - " ('feature', 0.0),\n", - " ('tuples', 0.0),\n", - " ('init', 0.0),\n", - " ('arg', 0.0),\n", - " ('restore', 0.0),\n", - " ('nesterov', 0.0),\n", - " ('hasher', 0.0),\n", - " ('permute', 0.0),\n", - " ('1d', 0.0),\n", - " ('argmax', 0.0),\n", - " ('default', 0.0),\n", - " ('exc', 0.0),\n", - " ('dimensions', 0.0),\n", + " ('placeholder', 0.0),\n", + " ('flip', 0.0),\n", + " ('required', 0.0),\n", + " ('concat', 0.0),\n", + " ('freq', 0.0),\n", " ('separable', 0.0),\n", - " ('reshape', 0.0),\n", - " ('moving', 0.0),\n", - " ('name', 0.0),\n", - " ('obj', 0.0),\n", - " ('module', 0.0),\n", - " ('new', 0.0),\n", - " ('far', 0.0),\n", - " ('expects', 0.0),\n", - " ('accumulators', 0.0),\n", - " ('inceptionv3', 0.0),\n", - " ('ndim', 0.0),\n", - " ('function', 0.0),\n", - " ('densenet121', 0.0),\n", - " ('callbacks', 0.0),\n", - " ('assert', 0.0),\n", - " ('l1l2', 0.0),\n", - " ('std', 0.0),\n", - " ('types', 0.0),\n", - " ('as', 0.0),\n", - " ('pool', 0.0),\n", - " ('subclassed', 0.0),\n", - " ('astype', 0.0),\n", - " ('upper', 0.0),\n", - " ('range', 0.0),\n", - " ('hash', 0.0),\n", - " ('dump', 0.0),\n", - " ('dims', 0.0),\n", - " ('info', 0.0),\n", - " ('use', 0.0),\n", - " ('increment', 0.0),\n", - " ('for', 0.0),\n", - " ('inceptionresnetv2', 0.0),\n", - " ('th', 0.0),\n", - " ('outputs', 0.0),\n", - " ('shared', 0.0),\n", - " ('phase', 0.0),\n", - " ('stopped', 0.0),\n", - " ('binop', 0.0),\n", - " ('axes', 0.0),\n", - " ('sharedvar', 0.0),\n", - " ('cls', 0.0),\n", - " ('dtype', 0.0),\n", - " ('setter', 0.0),\n", - " ('or', 0.0),\n", - " ('eye', 0.0),\n", - " ('scale', 0.0),\n", - " ('vgg16', 0.0),\n", - " ('workers', 0.0),\n", - " ('pattern', 0.0),\n", - " ('logits', 0.0),\n", - " ('softsign', 0.0),\n", - " ('filename', 0.0),\n", - " ('alloc', 0.0),\n", - " ('hot', 0.0),\n", - " ('done', 0.0),\n", - " ('ins', 0.0),\n", - " ('withitem', 0.0),\n", - " ('end', 0.0),\n", - " ('on', 0.0),\n", - " ('reverse', 0.0),\n", - " ('recurrent', 0.0),\n", - " ('level', 0.0),\n", - " ('float64', 0.0),\n", - " ('second', 0.0),\n", - " ('logs', 0.0),\n", - " ('notin', 0.0),\n", - " ('items', 0.0),\n", - " ('exists', 0.0),\n", - " ('sparsetype', 0.0),\n", - " ('startswith', 0.0),\n", - " ('keepdims', 0.0),\n", - " ('lte', 0.0),\n", - " ('predict', 0.0),\n", - " ('isnot', 0.0),\n", - " ('lt', 0.0),\n", - " ('trainable', 0.0),\n", - " ('flags', 0.0),\n", - " ('max', 0.0),\n", - " ('squared', 0.0),\n", - " ('forward', 0.0),\n", - " ('split', 0.0),\n", - " ('stop', 0.0),\n", - " ('flatten', 0.0),\n", - " ('assign', 0.0),\n", - " ('theta', 0.0),\n", - " ('dynamic', 0.0),\n", - " ('full', 0.0),\n", - " ('layers', 0.0),\n", - " ('lr', 0.0),\n", - " ('seed', 0.0),\n", - " ('compute', 0.0),\n", - " ('times', 0.0),\n", - " ('totals', 0.0),\n", - " ('process', 0.0),\n", - " ('biases', 0.0),\n", - " ('bias', 0.0),\n", - " ('weights', 0.0),\n", - " ('vhats', 0.0),\n", - " ('ms', 0.0),\n", - " ('dimshuffle', 0.0),\n", - " ('k', 0.0),\n", - " ('img', 0.0),\n", - " ('x', 0.0),\n", - " ('fill', 0.0),\n", - " ('normal', 0.0),\n", - " ('make', 0.0),\n", - " ('expr', 0.0),\n", - " ('task', 0.0),\n", - " ('repeat', 0.0),\n", - " ('unfinished', 0.0),\n", - " ('binary', 0.0),\n", - " ('schedule', 0.0),\n", - " ('device', 0.0),\n", - " ('type', 0.0),\n", - " ('in', 0.0),\n", - " ('time', 0.0),\n", - " ('create', 0.0),\n", - " ('targets', 0.0),\n", - " ('isinf', 0.0),\n", - " ('fpath', 0.0),\n", - " ('str', 0.0),\n", - " ('classes', 0.0),\n", - " ('class', 0.0),\n", - " ('freq', 0.0),\n", - " ('foldl', 0.0),\n", - " ('nn', 0.0),\n", - " ('elems', 0.0),\n", - " ('group', 0.0),\n", - " ('equal', 0.0),\n", - " ('readline', 0.0),\n", - " ('built', 0.0),\n", - " ('proba', 0.0),\n", - " ('period', 0.0),\n", - " ('alt', 0.0),\n", - " ('distribution', 0.0),\n", - " ('binomial', 0.0),\n", - " ('moves', 0.0),\n", - " ('available', 0.0),\n", - " ('user', 0.0),\n", - " ('foldr', 0.0),\n", - " ('methods', 0.0),\n", - " ('prefix', 0.0),\n", + " ('begin', 0.0),\n", + " ('edge', 0.0),\n", " ('tf', 0.0),\n", - " ('squeeze', 0.0),\n", - " ('var', 0.0),\n", - " ('conv', 0.0),\n", - " ('hstack', 0.0),\n", - " ('initializer', 0.0),\n", - " ('lambda', 0.0),\n", - " ('rng', 0.0),\n", - " ('zeropadding3d', 0.0),\n", - " ('state', 0.0),\n", - " ('sample', 0.0),\n", - " ('randint', 0.0),\n", - " ('wait', 0.0),\n", - " ('predictions', 0.0),\n", - " ('theano', 0.0),\n", + " ('supports', 0.0),\n", + " ('conflict', 0.0),\n", + " ('gamma', 0.0),\n", + " ('accuracy', 0.0),\n", + " ('skipgrams', 0.0),\n", + " ('generator', 0.0),\n", + " ('int32', 0.0),\n", " ('idx', 0.0),\n", - " ('deepcopy', 0.0),\n", - " ('sequences', 0.0),\n", - " ('csv', 0.0),\n", - " ('int', 0.0),\n", - " ('append', 0.0),\n", - " ('lower', 0.0),\n", - " ('include', 0.0),\n", - " ('legacy', 0.0),\n", - " ('verbose', 0.0),\n", - " ('steps', 0.0),\n", - " ('images', 0.0),\n", - " ('epoch', 0.0),\n", - " ('fn', 0.0),\n", - " ('pow', 0.0),\n", - " ('compare', 0.0),\n", - " ('simple', 0.0),\n", - " ('receptive', 0.0),\n", - " ('keys', 0.0),\n", - " ('iterable', 0.0),\n", - " ('beta', 0.0),\n", - " ('classification', 0.0),\n", - " ('gpus', 0.0),\n", - " ('tensorlike', 0.0),\n", - " ('dir', 0.0),\n", - " ('prod', 0.0),\n", - " ('so', 0.0),\n", - " ('by', 0.0),\n", - " ('custom', 0.0),\n", - " ('cols', 0.0),\n", - " ('all', 0.0),\n", - " ('splice', 0.0),\n", - " ('subtensor', 0.0),\n", - " ('any', 0.0),\n", - " ('inf', 0.0),\n", - " ('baseline', 0.0),\n", - " ('save', 0.0),\n", - " ('summary', 0.0),\n", - " ('noise', 0.0),\n", - " ('value', 0.0),\n", - " ('nameconstant', 0.0),\n", - " ('usub', 0.0),\n", " ('nnet', 0.0),\n", - " ('greater', 0.0),\n", - " ('op', 0.0),\n", - " ('pooling', 0.0),\n", - " ('dense', 0.0),\n", - " ('gte', 0.0),\n", - " ('vgg19', 0.0),\n", - " ('line', 0.0),\n", - " ('args', 0.0),\n", - " ('write', 0.0),\n", - " ('nasnetmobile', 0.0),\n", - " ('high', 0.0),\n", - " ('f', 0.0),\n", - " ('load', 0.0),\n", - " ('subscript', 0.0),\n", - " ('decrement', 0.0),\n", - " ('ndarray', 0.0),\n", - " ('begin', 0.0),\n", - " ('m', 0.0),\n", - " ('sess', 0.0),\n", - " ('norm', 0.0),\n", - " ('clipvalue', 0.0),\n", - " ('p', 0.0),\n", - " ('loss', 0.0),\n", - " ('copy', 0.0),\n", - " ('pad', 0.0),\n", - " ('reference', 0.0),\n", - " ('pop', 0.0),\n", - " ('a', 0.0),\n", - " ('filter', 0.0),\n", - " ('current', 0.0),\n", - " ('maximum', 0.0),\n", - " ('optimizer', 0.0),\n", - " ('v', 0.0),\n", - " ('length', 0.0),\n", - " ('rank', 0.0),\n", - " ('post', 0.0),\n", - " ('random', 0.0),\n", - " ('epochs', 0.0),\n", - " ('regularization', 0.0),\n", - " ('requestexception', 0.0),\n", - " ('build', 0.0),\n", - " ('g', 0.0),\n", - " ('root', 0.0),\n", - " ('string', 0.0),\n", - " ('activation', 0.0),\n", - " ('raise', 0.0),\n", - " ('softmax', 0.0),\n", - " ('slope', 0.0),\n", - " ('stateful', 0.0),\n", - " ('pv', 0.0),\n", - " ('argmin', 0.0),\n", - " ('cast', 0.0),\n", - " ('loads', 0.0),\n", - " ('first', 0.0),\n", - " ('d', 0.0),\n", - " ('positions', 0.0),\n", - " ('moments', 0.0),\n", - " ('intersection', 0.0),\n", - " ('crossentropy', 0.0),\n", - " ('object', 0.0),\n", + " ('xs', 0.0),\n", " ('square', 0.0),\n", - " ('closure', 0.0),\n", - " ('[MASK]', 0.0),\n", - " ('s', 0.0),\n", - " ('extra', 0.0),\n", - " ('objects', 0.0),\n", - " ('relu', 0.0),\n", - " ('like', 0.0),\n", - " ('py', 0.0),\n", - " ('reraise', 0.0),\n", - " ('nonzero', 0.0),\n", - " ('floordiv', 0.0),\n", - " ('clip', 0.0),\n", - " ('embedding', 0.0),\n", - " ('gather', 0.0),\n", - " ('low', 0.0),\n", - " ('momentum', 0.0),\n", - " ('float32', 0.0),\n", - " ('is', 0.0),\n", - " ('retain', 0.0),\n", - " ('units', 0.0),\n", - " ('axis', 0.0),\n", - " ('functions', 0.0),\n", " ('acc', 0.0),\n", - " ('extend', 0.0),\n", - " ('mobilenet', 0.0),\n", - " ('carry', 0.0),\n", - " ('unaryop', 0.0),\n", - " ('clear', 0.0),\n", - " ('if', 0.0),\n", - " ('2d', 0.0),\n", - " ('repeats', 0.0),\n", - " ('non', 0.0),\n", - " ('upsampling3d', 0.0),\n", - " ('pool3d', 0.0),\n", - " ('with', 0.0),\n", - " ('[SEP]', 0.0),\n", - " ('go', 0.0),\n", - " ('run', 0.0),\n", - " ('factor', 0.0),\n", - " ('mobilenetv2', 0.0),\n", - " ('updates', 0.0),\n", - " ('idxs', 0.0),\n", - " ('best', 0.0),\n", - " ('switch', 0.0),\n", - " ('keras', 0.0),\n", - " ('kwd', 0.0),\n", - " ('log', 0.0),\n", - " ('rows', 0.0),\n", - " ('delete', 0.0),\n", - " ('w', 0.0),\n", - " ('prob', 0.0),\n", - " ('indices', 0.0),\n", - " ('element', 0.0),\n", - " ('e', 0.0),\n", - " ('conversions', 0.0),\n", - " ('grad', 0.0),\n", - " ('symbolic', 0.0),\n", + " ('extra', 0.0),\n", + " ('normed', 0.0),\n", + " ('contents', 0.0),\n", + " ('only', 0.0),\n", + " ('depthwise', 0.0),\n", + " ('uses', 0.0),\n", " ('l2', 0.0),\n", - " ('standardize', 0.0),\n", - " ('batch', 0.0),\n", - " ('lookup', 0.0),\n", - " ('normalization', 0.0),\n", - " ('map', 0.0),\n", - " ('format', 0.0),\n", - " ('z', 0.0),\n", - " ('cntk', 0.0),\n", - " ('hsplit', 0.0),\n", - " ('vs', 0.0),\n", - " ('epsilon', 0.0),\n", - " ('floatx', 0.0),\n", - " ('histogram', 0.0),\n", " ('seq', 0.0),\n", - " ('spec', 0.0),\n", - " ('result', 0.0),\n", - " ('constant', 0.0),\n", + " ('flat', 0.0),\n", " ('l1', 0.0),\n", - " ('normalized', 0.0),\n", - " ('progbar', 0.0),\n", - " ('warn', 0.0),\n", - " ('shuffle', 0.0),\n", - " ('matrix', 0.0),\n", - " ('mask', 0.0),\n", - " ('allowed', 0.0),\n", - " ('origin', 0.0),\n", - " ('maxval', 0.0),\n", - " ('nb', 0.0),\n", - " ('updated', 0.0),\n", - " ('apply', 0.0),\n", - " ('join', 0.0),\n", + " ('logits', 0.0),\n", + " ('pattern', 0.0),\n", + " ('swapaxes', 0.0),\n", + " ('future', 0.0),\n", + " ('percentile', 0.0),\n", + " ('cells', 0.0),\n", + " ('interpolation', 0.0),\n", + " ('algorithm', 0.0),\n", + " ('parameters', 0.0),\n", + " ('nodes', 0.0),\n", " ('softplus', 0.0),\n", - " ('comprehension', 0.0),\n", - " ('asarray', 0.0),\n", - " ('multiprocessing', 0.0),\n", - " ('round', 0.0),\n", - " ('item', 0.0),\n", - " ('backend', 0.0),\n", - " ('inbound', 0.0),\n", - " ('py2', 0.0),\n", + " ('conv', 0.0),\n", + " ('multiplier', 0.0),\n", + " ('probs', 0.0),\n", + " ('at', 0.0),\n", + " ('compile', 0.0),\n", + " ('conv3d', 0.0),\n", + " ('patternbroadcast', 0.0),\n", + " ('densenet121', 0.0),\n", + " ('active', 0.0),\n", + " ('keras', 0.0),\n", + " ('vals', 0.0),\n", + " ('crossentropy', 0.0),\n", + " ('float16', 0.0),\n", + " ('process', 0.0),\n", + " ('x1', 0.0),\n", + " ('remove', 0.0),\n", + " ('cols', 0.0),\n", + " ('elu', 0.0),\n", + " ('layers', 0.0),\n", + " ('neg', 0.0),\n", + " ('attributes', 0.0),\n", + " ('tmp', 0.0),\n", + " ('pass', 0.0),\n", + " ('reset', 0.0),\n", + " ('elems', 0.0),\n", + " ('per', 0.0),\n", " ('from', 0.0),\n", - " ('global', 0.0),\n", - " ('neq', 0.0),\n", - " ('gates', 0.0),\n", - " ('tensorvariable', 0.0),\n", - " ('algorithm', 0.0),\n", - " ('nasnetlarge', 0.0),\n", - " ('intermediate', 0.0),\n", - " ('queue', 0.0),\n", - " ('timesteps', 0.0),\n", - " ('tensor', 0.0),\n", - " ('alpha', 0.0),\n", - " ('serialize', 0.0),\n", - " ('fields', 0.0),\n", - " ('top', 0.0),\n", - " ('negative', 0.0),\n", - " ('grads', 0.0),\n", - " ('transpose', 0.0),\n", - " ('gaussiandropout', 0.0),\n", - " ('disconnected', 0.0),\n", - " ('delta', 0.0),\n", - " ('[UNK]', 0.0),\n", - " ('explicitly', 0.0),\n", - " ('ones', 0.0),\n", + " ('end', 0.0),\n", + " ('exp', 0.0),\n", + " ('scan', 0.0),\n", + " ('dim2', 0.0),\n", + " ('nn', 0.0),\n", + " ('dset', 0.0),\n", + " ('time', 0.0),\n", " ('network', 0.0),\n", - " ('volumes', 0.0),\n", - " ('callback', 0.0),\n", - " ('seqs', 0.0),\n", - " ('eval', 0.0),\n", - " ('call', 0.0),\n", - " ('convert', 0.0),\n", - " ('[cls]', 0.0),\n", - " ('dot', 0.0),\n", - " ('opt', 0.0),\n", - " ('key', 0.0),\n", - " ('json', 0.0),\n", - " ('async', 0.0),\n", - " ('embeddings', 0.0),\n", - " ('starred', 0.0),\n", - " ('num', 0.0),\n", - " ('print', 0.0),\n", - " ('y', 0.0),\n", - " ('preds', 0.0),\n", - " ('data', 0.0),\n", - " ('cudnn', 0.0),\n", - " ('upsampling2d', 0.0),\n", - " ('config', 0.0),\n", - " ('image', 0.0),\n", - " ('mode', 0.0),\n", - " ('uses', 0.0),\n", - " ('min', 0.0),\n", - " ('val', 0.0),\n", + " ('steps', 0.0),\n", + " ('closure', 0.0),\n", + " ('func', 0.0),\n", + " ('var', 0.0),\n", + " ('symbols', 0.0),\n", + " ('isinf', 0.0),\n", + " ('permutation', 0.0),\n", + " ('current', 0.0),\n", + " ('minval', 0.0),\n", + " ('wait', 0.0),\n", + " ('filter', 0.0),\n", + " ('range', 0.0),\n", + " ('as', 0.0),\n", + " ('phase', 0.0),\n", + " ('unknown', 0.0),\n", + " ('metric', 0.0),\n", + " ('dnn', 0.0),\n", + " ('long', 0.0),\n", + " ('optimizer', 0.0),\n", + " ('gen', 0.0),\n", + " ('flush', 0.0),\n", + " ('chunk', 0.0),\n", + " ('start', 0.0),\n", + " ('uid', 0.0),\n", + " ('while', 0.0),\n", + " ('fix', 0.0),\n", + " ('elements', 0.0),\n", + " ('callbacks', 0.0),\n", + " ('kshape', 0.0),\n", + " ('this', 0.0),\n", + " ('monitor', 0.0),\n", + " ('fn', 0.0),\n", + " ('item', 0.0),\n", + " ('variance', 0.0),\n", + " ('width', 0.0),\n", + " ('best', 0.0),\n", + " ('ref', 0.0),\n", + " ('shifted', 0.0),\n", + " ('stateful', 0.0),\n", + " ('startswith', 0.0),\n", + " ('spatial', 0.0),\n", + " ('high', 0.0),\n", + " ('conf', 0.0),\n", + " ('csv', 0.0),\n", + " ('conv1d', 0.0),\n", + " ('exists', 0.0),\n", + " ('delta', 0.0),\n", + " ('dropout', 0.0),\n", + " ('rows', 0.0),\n", + " ('cw', 0.0),\n", + " ('d', 0.0),\n", + " ('depth', 0.0),\n", + " ('sigmoid', 0.0),\n", + " ('ops', 0.0),\n", + " ('binary', 0.0),\n", + " ('cropping', 0.0),\n", + " ('eps', 0.0),\n", + " ('shift', 0.0),\n", + " ('loop', 0.0),\n", + " ('regularization', 0.0),\n", + " ('average', 0.0),\n", + " ('maximum', 0.0),\n", + " ('bool', 0.0),\n", + " ('uint8', 0.0),\n", + " ('row', 0.0),\n", + " ('1', 0.0),\n", + " ('activity', 0.0),\n", + " ('sparsetensor', 0.0),\n", + " ('classes', 0.0),\n", + " ('shared', 0.0),\n", + " ('subtensor', 0.0),\n", + " ('gaussiannoise', 0.0),\n", + " ('stack', 0.0),\n", + " ('vs', 0.0),\n", + " ('go', 0.0),\n", + " ('local', 0.0),\n", + " ('momentum', 0.0),\n", " ('masking', 0.0),\n", - " ('normalize', 0.0),\n", - " ('send', 0.0),\n", - " ('shapes', 0.0),\n", - " ('merged', 0.0),\n", - " ('output', 0.0),\n", - " ('padding', 0.0),\n", - " ('spatialdropoutnd', 0.0),\n", - " ('backwards', 0.0),\n", - " ('const', 0.0),\n", - " ('filters', 0.0),\n", - " ('strip', 0.0),\n", - " ('parameter', 0.0),\n", - " ('dilation', 0.0),\n", - " ('patience', 0.0),\n", - " ('cropping2d', 0.0),\n", - " ('mean', 0.0),\n", - " ('pooling2d', 0.0),\n", - " ('unpack', 0.0),\n", + " ('initializer', 0.0),\n", + " ('old', 0.0),\n", + " ('biases', 0.0),\n", + " ('maxlen', 0.0),\n", + " ('decay', 0.0),\n", + " ('upper', 0.0),\n", + " ('compute', 0.0),\n", + " ('built', 0.0),\n", + " ('rho', 0.0),\n", + " ('hdf5', 0.0),\n", " ('losses', 0.0),\n", - " ('generator', 0.0),\n", - " ('fan', 0.0),\n", - " ('size', 0.0),\n", - " ('backup', 0.0),\n", - " ('sparse', 0.0),\n", - " ('batchnorm', 0.0),\n", - " ('elu', 0.0),\n", - " ('[CLS]', 0.0),\n", - " ('counter', 0.0),\n", - " ('less', 0.0),\n", - " ('isnan', 0.0),\n", - " ('evaluate', 0.0),\n", - " ('tasks', 0.0),\n", - " ('cumprod', 0.0),\n", + " ('repeat', 0.0),\n", + " ('sw', 0.0),\n", + " ('convolution', 0.0),\n", " ('ts', 0.0),\n", - " ('and', 0.0),\n", - " ('limit', 0.0),\n", - " ('put', 0.0),\n", - " ('feed', 0.0),\n", - " ('override', 0.0),\n", - " ('rho', 0.0),\n", - " ('shape', 0.0),\n", - " ('header', 0.0),\n", - " ('alias', 0.0),\n", - " ('3d', 0.0),\n", - " ('field', 0.0),\n", - " ('cropping3d', 0.0),\n", - " ('norms', 0.0),\n", - " ('div', 0.0),\n", - " ('supports', 0.0),\n", - " ('reset', 0.0),\n", - " ('path', 0.0),\n", - " ('error', 0.0),\n", - " ('atleast', 0.0),\n", - " ('normed', 0.0),\n", - " ('try', 0.0),\n", - " ('strides', 0.0),\n", - " ('importfrom', 0.0),\n", - " ('execute', 0.0),\n", - " ('ifexp', 0.0),\n", - " ('names', 0.0),\n", - " ('boolop', 0.0),\n", - " ('original', 0.0),\n", - " ('generic', 0.0),\n", - " ('pred', 0.0),\n", - " ('logsumexp', 0.0),\n", - " ('yield', 0.0),\n", - " ('index', 0.0),\n", - " ('created', 0.0),\n", - " ('tensors', 0.0),\n", - " ('gamma', 0.0),\n", - " ('xception', 0.0),\n", - " ('concatenate', 0.0),\n", - " ('unroll', 0.0),\n", - " ('diff', 0.0),\n", - " ('c', 0.0),\n", - " ('slices', 0.0),\n", - " ('insecure', 0.0),\n", - " ('variable', 0.0),\n", + " ('argmax', 0.0),\n", + " ('regularizer', 0.0),\n", + " ('nonzero', 0.0),\n", + " ('normalization', 0.0),\n", + " ('prod', 0.0),\n", + " ('constant', 0.0),\n", + " ('splice', 0.0),\n", + " ('id', 0.0),\n", + " ('tile', 0.0),\n", + " ('build', 0.0),\n", + " ('queue', 0.0),\n", " ('sequence', 0.0),\n", - " ('initial', 0.0),\n", - " ('concat', 0.0),\n", - " ('allow', 0.0),\n", - " ('monitor', 0.0),\n", - " ('not', 0.0),\n", - " ('get', 0.0),\n", - " ('sum', 0.0),\n", - " ('excepthandler', 0.0),\n", - " ('densenet201', 0.0),\n", - " ('backward', 0.0),\n", - " ('label', 0.0),\n", - " ('u', 0.0),\n", - " ('stddev', 0.0),\n", - " ('while', 0.0),\n", - " ('linalg', 0.0),\n", - " ('out', 0.0),\n", - " ('temporal', 0.0),\n", + " ('bad', 0.0),\n", + " ('m', 0.0),\n", + " ('callback', 0.0),\n", + " ('recurrent', 0.0),\n", + " ('one', 0.0),\n", + " ('group', 0.0),\n", + " ('count', 0.0),\n", + " ('inferreddimension', 0.0),\n", + " ('epoch', 0.0),\n", + " ('scope', 0.0),\n", + " ('proceed', 0.0),\n", + " ('fixed', 0.0),\n", + " ('read', 0.0),\n", + " ('create', 0.0),\n", + " ('on', 0.0),\n", + " ('source', 0.0),\n", + " ('unique', 0.0),\n", + " ('open', 0.0),\n", + " ('dimshuffle', 0.0),\n", + " ('merge', 0.0),\n", + " ('iterations', 0.0),\n", + " ('cumprod', 0.0),\n", + " ('b', 0.0),\n", + " ('save', 0.0),\n", + " ('truncated', 0.0),\n", + " ('low', 0.0),\n", + " ('z', 0.0),\n", + " ('img', 0.0),\n", + " ('logs', 0.0),\n", + " ('cast', 0.0),\n", + " ('pointwise', 0.0),\n", + " ('node', 0.0),\n", + " ('upsampling1d', 0.0),\n", + " ('bernoulli', 0.0),\n", + " ('dilation', 0.0),\n", + " ('dimension', 0.0),\n", + " ('outs', 0.0),\n", + " ('filename', 0.0),\n", + " ('conv2d', 0.0),\n", + " ('states', 0.0),\n", + " ('combine', 0.0),\n", + " ('freedimension', 0.0),\n", + " ('normalizer', 0.0),\n", + " ('successful', 0.0),\n", + " ('use', 0.0),\n", + " ('part', 0.0),\n", + " ('categorical', 0.0),\n", + " ('col', 0.0),\n", + " ('embeddings', 0.0),\n", + " ('sandbox', 0.0),\n", + " ('callable', 0.0),\n", + " ('lbpath', 0.0),\n", + " ('dictcomp', 0.0),\n", + " ('rank', 0.0),\n", + " ('preprocess', 0.0),\n", + " ('words', 0.0),\n", + " ('histogram', 0.0),\n", + " ('isfile', 0.0),\n", + " ('moving', 0.0),\n", + " ('modes', 0.0),\n", + " ('pad', 0.0),\n", + " ('training', 0.0),\n", + " ('solver', 0.0),\n", + " ('seed', 0.0),\n", + " ('3d', 0.0),\n", + " ('default', 0.0),\n", + " ('ident', 0.0),\n", + " ('deconv', 0.0),\n", " ('sk', 0.0),\n", - " ('zero', 0.0),\n", - " ('clipnorm', 0.0),\n", - " ('set', 0.0),\n", - " ('pos', 0.0),\n", - " ('ops', 0.0),\n", - " ('gaussiannoise', 0.0),\n", - " ('sparsetensor', 0.0),\n", - " ('attrs', 0.0),\n", - " ('headers', 0.0),\n", - " ('mod', 0.0),\n", - " ('conv3d', 0.0),\n", - " ('dumps', 0.0),\n", - " ('kwargs', 0.0),\n", - " ('augassign', 0.0),\n", - " ('instance', 0.0),\n", - " ('overwrite', 0.0),\n", - " ('layer', 0.0),\n", - " ('only', 0.0),\n", - " ('res', 0.0),\n", + " ('char', 0.0),\n", + " ('header', 0.0),\n", + " ('dimensions', 0.0),\n", + " ('deserialize', 0.0),\n", + " ('backend', 0.0),\n", + " ('non', 0.0),\n", + " ('dilated', 0.0),\n", + " ('p', 0.0),\n", + " ('frombuffer', 0.0),\n", + " ('theta', 0.0),\n", + " ('ones', 0.0),\n", + " ('setter', 0.0),\n", + " ('rep', 0.0),\n", + " ('elemwise', 0.0),\n", + " ('diag', 0.0),\n", + " ('full', 0.0),\n", + " ('step', 0.0),\n", + " ('make', 0.0),\n", + " ('generic', 0.0),\n", + " ('session', 0.0),\n", + " ('feature', 0.0),\n", + " ('constraint', 0.0),\n", + " ('functions', 0.0),\n", + " ('element', 0.0),\n", + " ('eval', 0.0),\n", + " ('oh', 0.0),\n", + " ('hash', 0.0),\n", + " ('pooling', 0.0),\n", + " ('updated', 0.0),\n", + " ('normalize', 0.0),\n", " ('saver', 0.0),\n", - " ('values', 0.0),\n", - " ('cumsum', 0.0)]" + " ('variable', 0.0),\n", + " ('xception', 0.0),\n", + " ('user', 0.0),\n", + " ('image', 0.0),\n", + " ('identity', 0.0),\n", + " ('sequences', 0.0),\n", + " ('feed', 0.0)]" ] }, - "execution_count": 16, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -1633,14 +2517,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -1650,8 +2534,8 @@ } ], "source": [ - "plt.figure(figsize=(30,10))\n", - "labels, values = zip(*c.most_common(100))\n", + "plt.figure(figsize=(80,15))\n", + "labels, values = zip(*c.most_common(300))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", @@ -1664,10 +2548,10 @@ "plt.bar(indexes, values, width, label='Accuracy')\n", "plt.bar(indexes, freqs, width, label='Frequency')\n", "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('BERT (100k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.title('MARGARET (300k epochs) - Corpus-Sm - mean_freq = {:.3f} / max_freq = {:.2f} / F1-Macro = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", "plt.legend()\n", "plt.tight_layout()\n", - "plt.savefig('BERT-100k2_epochs_top100.pdf')\n", + "#plt.savefig('MARGARET-400k_epochs_top200.pdf')\n", "plt.show()" ] }, @@ -1680,7 +2564,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 119, "metadata": { "scrolled": true }, @@ -1689,43 +2573,43 @@ "data": { "text/plain": [ "[2,\n", - " 2,\n", - " 237,\n", - " 25,\n", - " 230,\n", - " 53,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 95,\n", + " 33,\n", + " 14,\n", + " 510,\n", + " 37,\n", + " 24,\n", " 25,\n", - " 25,\n", - " 603,\n", - " 25,\n", - " 25,\n", - " 7,\n", - " 98,\n", - " 319,\n", - " 25,\n", - " 3,\n", - " 2,\n", - " 398,\n", - " 44,\n", - " 1142,\n", - " 653,\n", - " 25,\n", - " 104,\n", - " 603,\n", - " 1142,\n", - " 657,\n", + " 20,\n", + " 22,\n", + " 1381,\n", " 4,\n", - " 655,\n", - " 1142,\n", - " 871,\n", - " 25,\n", - " 1142,\n", - " 655,\n", - " 659,\n", - " 1142,\n", - " 871,\n", - " 25,\n", - " 3,\n", + " 8,\n", + " 8,\n", + " 54,\n", + " 8,\n", + " 54,\n", + " 18,\n", + " 71,\n", + " 14,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", " 0,\n", " 0,\n", " 0,\n", @@ -1754,7 +2638,7 @@ " 0]" ] }, - "execution_count": 72, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -1766,7 +2650,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -1775,7 +2659,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 121, "metadata": { "scrolled": true }, @@ -1784,43 +2668,43 @@ "data": { "text/plain": [ "['[CLS]',\n", - " '[CLS]',\n", - " 'slice',\n", - " 'keyword',\n", - " 'noteq',\n", - " 'equal',\n", - " 'keyword',\n", - " 'keyword',\n", - " 'print',\n", - " 'keyword',\n", - " 'keyword',\n", + " 'functiondef',\n", " 'arguments',\n", - " 'training',\n", - " 'instance',\n", - " 'keyword',\n", - " '[SEP]',\n", - " '[CLS]',\n", - " 'biases',\n", - " 'string',\n", - " 'batchnorm',\n", - " 'subclassed',\n", - " 'keyword',\n", - " 'target',\n", - " 'print',\n", - " 'batchnorm',\n", - " 'setattr',\n", + " 'arg',\n", + " 'indices',\n", + " 'arg',\n", + " 'num',\n", + " 'classes',\n", + " 'expr',\n", + " 'str',\n", + " 'return',\n", + " 'call',\n", + " 'attribute',\n", + " 'one',\n", " '[MASK]',\n", - " 'expects',\n", - " 'batchnorm',\n", - " 'stopped',\n", + " 'name',\n", + " 'name',\n", " 'keyword',\n", - " 'batchnorm',\n", - " 'expects',\n", - " 'inbound',\n", - " 'batchnorm',\n", - " 'stopped',\n", + " 'name',\n", " 'keyword',\n", - " '[SEP]',\n", + " 'unaryop',\n", + " 'usub',\n", + " 'num',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", + " '[PAD]',\n", " '[PAD]',\n", " '[PAD]',\n", " '[PAD]',\n", @@ -1849,7 +2733,7 @@ " '[PAD]']" ] }, - "execution_count": 74, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -1860,7 +2744,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 122, "metadata": { "scrolled": true }, @@ -1868,109 +2752,109 @@ { "data": { "text/plain": [ - "[('keyword', 0.26686460438364684),\n", - " ('batchnorm', 0.08261153427638737),\n", - " ('equal', 0.08151406808642934),\n", - " ('instance', 0.04415047411782994),\n", - " ('name', 0.043056116897248566),\n", - " ('filters', 0.03942484066531945),\n", - " ('expects', 0.026326752681486088),\n", - " ('assign', 0.0256831960205192),\n", - " ('slice', 0.02468210788123737),\n", - " ('excepthandler', 0.021517177055806),\n", - " ('string', 0.015712731229597387),\n", - " ('functiondef', 0.015134462925540184),\n", - " ('overwrite', 0.014801803202238457),\n", - " ('floatx', 0.014245297683817814),\n", - " ('print', 0.012267993160267372),\n", - " ('is', 0.011898025804445825),\n", - " ('biases', 0.009127934089849216),\n", - " ('expr', 0.008235659878750193),\n", - " ('arguments', 0.007903000155448469),\n", - " ('extend', 0.0066967200373076324),\n", - " ('saver', 0.006395150007772424),\n", - " ('any', 0.005561946214829784),\n", - " ('argmax', 0.005207523705891497),\n", - " ('on', 0.004651018187470854),\n", - " ('low', 0.004514223534898182),\n", - " ('call', 0.004315249494792476),\n", - " ('progbar', 0.004249961137882792),\n", - " ('queue', 0.004047878128400436),\n", - " ('items', 0.003764961915125136),\n", - " ('cast', 0.0036965645888388),\n", - " ('training', 0.003491372609979792),\n", - " ('noteq', 0.003370122804290378),\n", - " ('init', 0.0033639048655370743),\n", - " ('v', 0.003283071661744132),\n", - " ('pooling1d', 0.002962847815949013),\n", - " ('lambda', 0.0029566298771957094),\n", - " ('metrics', 0.002944193999689103),\n", - " ('gt', 0.002860251826519509),\n", - " ('only', 0.002720348204570185),\n", - " ('alpha', 0.002524483133841132),\n", - " ('best', 0.002462303746308099),\n", - " ('reset', 0.002269547644955697),\n", - " ('in', 0.0022166951655526192),\n", - " ('with', 0.002048810819213431),\n", - " ('cell', 0.002005285247940308),\n", - " ('tf', 0.0019555417379138814),\n", - " ('str', 0.0018591636872376807),\n", - " ('default', 0.0018156381159645577),\n", - " ('item', 0.001796984299704648),\n", - " ('callback', 0.0017254780040416601),\n", - " ('pool', 0.0017130421265350536),\n", - " ('true', 0.0016881703715218405),\n", - " ('new', 0.001598010259598943),\n", - " ('nameconstant', 0.0015855743820923365),\n", - " ('lr', 0.0015233949945593036),\n", - " ('target', 0.001520286025182652),\n", - " ('k', 0.0015078501476760455),\n", - " ('pool2d', 0.001486087362039484),\n", - " ('config', 0.0014643245764029225),\n", - " ('schedule', 0.001442561790766361),\n", - " ('concat', 0.0013866003419866315),\n", - " ('d', 0.0013803824032333281),\n", - " ('hsplit', 0.0013368568319602051),\n", - " ('compare', 0.0013368568319602051),\n", - " ('list', 0.001330638893206902),\n", - " ('scale', 0.0013057671381936887),\n", - " ('explicitly', 0.0013057671381936887),\n", - " ('units', 0.001277786413803824),\n", - " ('far', 0.001268459505673869),\n", - " ('norms', 0.0011378827918545002),\n", - " ('stddev', 0.0010725944349448157),\n", - " ('sum', 0.001053940618684906),\n", - " ('algorithm', 0.0010477226799316026),\n", - " ('round', 0.0010383957718016477),\n", - " ('raise', 0.0009793253536452666),\n", - " ('for', 0.0009513446292554018),\n", - " ('support', 0.0009202549354888855),\n", - " ('l1', 0.0008922742110990207),\n", - " ('grad', 0.0008798383335924141),\n", - " ('isinf', 0.0008736203948391108),\n", - " ('prime', 0.0008642934867091559),\n", - " ('sub', 0.0008611845173325043),\n", - " ('types', 0.0008580755479558526),\n", - " ('legacy', 0.0008580755479558526),\n", - " ('unaryop', 0.0008487486398258977),\n", - " ('states', 0.0008394217316959428),\n", - " ('nw', 0.0008300948235659878),\n", - " ('non', 0.0008052230685527748),\n", - " ('done', 0.0007399347116430903),\n", - " ('hstack', 0.0007306078035131353),\n", - " ('verbose', 0.0007243898647598321),\n", - " ('required', 0.0007181719260065288),\n", - " ('task', 0.0006933001709933157),\n", - " ('multiprocessing', 0.0006870822322400125),\n", - " ('1d', 0.0006870822322400125),\n", - " ('chunk', 0.0006746463547334058),\n", - " ('atleast', 0.0006715373853567543),\n", - " ('params', 0.0006622104772267993),\n", - " ('update', 0.0006591015078501477),\n", - " ('strides', 0.0006591015078501477)]" + "[('name', 0.2768900957553237),\n", + " ('attribute', 0.08194940688866657),\n", + " ('call', 0.08043447191653566),\n", + " ('num', 0.040588823781620695),\n", + " ('str', 0.03930255823924539),\n", + " ('binop', 0.03870230098613692),\n", + " ('keyword', 0.02895526654280406),\n", + " ('subscript', 0.02443904530513077),\n", + " ('assign', 0.022381020437330285),\n", + " ('index', 0.01940831785050736),\n", + " ('compare', 0.01583535801057596),\n", + " ('if', 0.014434757753322853),\n", + " ('add', 0.014320423038445048),\n", + " ('arg', 0.012119479777047306),\n", + " ('nameconstant', 0.011833642989852794),\n", + " ('tuple', 0.011690724596255538),\n", + " ('return', 0.00980420180077176),\n", + " ('unaryop', 0.007231670716021152),\n", + " ('shape', 0.006917250250107189),\n", + " ('slice', 0.006888666571387737),\n", + " ('expr', 0.006774331856509933),\n", + " ('arguments', 0.006145490924682006),\n", + " ('list', 0.0054880663141346295),\n", + " ('functiondef', 0.005287980563098471),\n", + " ('comprehension', 0.005173645848220666),\n", + " ('boolop', 0.004973560097184508),\n", + " ('eq', 0.004659139631270544),\n", + " ('mult', 0.004487637558953837),\n", + " ('raise', 0.00431613548663713),\n", + " ('kernel', 0.003858796627125911),\n", + " ('listcomp', 0.00383021294840646),\n", + " ('x', 0.00334429041017579),\n", + " ('sub', 0.003287123052736887),\n", + " ('output', 0.003144204659139631),\n", + " ('usub', 0.003087037301700729),\n", + " ('and', 0.003001286265542375),\n", + " ('append', 0.0024581963698728027),\n", + " ('is', 0.002343861654994998),\n", + " ('mod', 0.0023152779762755466),\n", + " ('not', 0.002258110618836644),\n", + " ('input', 0.002258110618836644),\n", + " ('items', 0.002229526940117193),\n", + " ('size', 0.0021723595826782906),\n", + " ('format', 0.0021437759039588393),\n", + " ('data', 0.002086608546519937),\n", + " ('for', 0.0020294411890810346),\n", + " ('self', 0.0020008575103615837),\n", + " ('bias', 0.0019722738316421324),\n", + " ('noteq', 0.0018293554380448764),\n", + " ('dtype', 0.0017436044018865227),\n", + " ('ifexp', 0.0016864370444476203),\n", + " ('padding', 0.0016578533657281693),\n", + " ('layer', 0.001629269687008718),\n", + " ('extslice', 0.001629269687008718),\n", + " ('state', 0.0016006860082892669),\n", + " ('kwargs', 0.0016006860082892669),\n", + " ('augassign', 0.0015721023295698156),\n", + " ('get', 0.0015721023295698156),\n", + " ('batch', 0.0015435186508503645),\n", + " ('isnot', 0.0015149349721309132),\n", + " ('gt', 0.0014577676146920108),\n", + " ('i', 0.0014291839359725598),\n", + " ('units', 0.0014006002572531085),\n", + " ('dict', 0.0013720165785336574),\n", + " ('inputs', 0.001343432899814206),\n", + " ('reshape', 0.001343432899814206),\n", + " ('axis', 0.0012576818636558524),\n", + " ('strides', 0.001171930827497499),\n", + " ('in', 0.001171930827497499),\n", + " ('or', 0.0011433471487780477),\n", + " ('mean', 0.0011147634700585966),\n", + " ('value', 0.0010861797913391453),\n", + " ('notin', 0.0010861797913391453),\n", + " ('stateful', 0.001029012433900243),\n", + " ('states', 0.001029012433900243),\n", + " ('warn', 0.0009718450764613406),\n", + " ('mask', 0.0009432613977418894),\n", + " ('constraint', 0.0009432613977418894),\n", + " ('outputs', 0.0009146777190224382),\n", + " ('new', 0.0009146777190224382),\n", + " ('pool', 0.0009146777190224382),\n", + " ('regularizer', 0.000886094040302987),\n", + " ('y', 0.000886094040302987),\n", + " ('recurrent', 0.000886094040302987),\n", + " ('lt', 0.000886094040302987),\n", + " ('config', 0.0008575103615835358),\n", + " ('values', 0.0008575103615835358),\n", + " ('init', 0.0008289266828640846),\n", + " ('cast', 0.0008289266828640846),\n", + " ('initializer', 0.0008003430041446334),\n", + " ('weight', 0.0007717593254251823),\n", + " ('w', 0.0007717593254251823),\n", + " ('weights', 0.0007717593254251823),\n", + " ('concatenate', 0.0007717593254251823),\n", + " ('starred', 0.0007717593254251823),\n", + " ('args', 0.0007431756467057311),\n", + " ('cell', 0.0007145919679862799),\n", + " ('sum', 0.0006860082892668287),\n", + " ('axes', 0.0006860082892668287),\n", + " ('div', 0.0006860082892668287)]" ] }, - "execution_count": 75, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -1982,12 +2866,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2000,7 +2884,7 @@ ], "source": [ "plt.figure(figsize=(20,10))\n", - "labels, values = zip(*d.most_common(100))\n", + "labels, values = zip(*d.most_common(200))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", @@ -2010,10 +2894,75 @@ "plt.bar(indexes, accuracies, width, label='Accuracy')\n", "plt.bar(indexes, values, width, label='Frequency')\n", "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('BERT (50k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.title('MARGARET (400k epochs) - Corpus-Lg - mean_freq = {:.3f} / max_freq = {:.2f} / F1-Macro = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.savefig('MARGARET-freq-400k_epochs_top200.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABX4AAAGOCAYAAAAgkRtnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xm4JFV9P+DPV0Z21KiDqCCLoIlL1AgGoxEi5OcSNRKXuCUuMYlAUBMjYCSJMaJAVhUhcUWjxIi7cYmiouIWEBNXIqiIG4iiyKaDcH5/VLW3abp77r1zZy7UvO/z1FNdVedUnaqu7nnmc0+fqtZaAAAAAAAYjhutdgMAAAAAAFhZgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AbCJV9aSqamPTmxZZ75MT9fafUuZ5Y9t3W2K7TpvY/4MXUeeOE3XOW8ox2bjG3pcnrXZbNqaq2ruqTqmq71TVVWPnvf1qt40bpqrac+w+uu+U7d/qtx21Gu1brKp6Qd/Oc6dsO3DsHHdejfYtxQ2tvQBwfSL4BYDV89Cquum8AlW1Z5J9N1F7Rn5vhcqsV1Wd1P9n/rSV2N9Kq6r9lxuoT9nXKJw/b0Uat5mrqn2SfDzJI5PcOsma1W0RLE9Vnd5/N7xytduyKVXV6/vzPnW12wIAQyX4BYDV8eMkWyd51HrK/f5Y+Y1tdIzfrqodZhWqqkryhIk6sKk9O8mWSb6b5P5JbplkhyQ7tNYuW82GAQDA9YHgFwBWx5v7+cyesxMB6ykbvUXJZ5N8Nck26XpRznK/JLsmuTTJ+zZBu2Cau/Xzk1trH26t/aC1dpnQl42ptbZza61aay9Y7bbM01o7qm/nnqvdlg3VWju1P5dqrX1rtdsDADckgl8AWB2vT9KS/HpV7TqjzH2T7J7ksiRv24TtSuYP5TDqhfyWJFdu3ObATNv28x+taisAAOB6SvALAKvj60lOT1KZHbKO1r81yeWbolFJ/q2f719Vu0xurKqts9Ab+HXLPcjoQXdJntiv2m/iYXGtqk6aUfeAqjq5qr5RVT+pqkuq6oyqOrKqtptzzO2q6vCq+kRVXdw/DOyiqvpS/4CwPxyv37fvw2O7+PpkGxd5rvv3Zf+6X7XrlHM9bUq9G1XV71XV+6rqe1W1rp+/r6oe3/cIX5aquntVfbc/9ueq6jZTymxbVX9aVR/tr9O6vs7bquqBc/Z9rYdKVdUeVfWv/fv106q6sL/ed5u1jzn7/vmDt5KMHvL0txPX8gl92TXj66pqi6r6k+oelviDfv2fTDnGHavq+Kr6clVdWlVXVNXZVfXiaZ+JKfV/u6pOraofVtVlVfX5qjqqqrapOQ+pmrdtotzMh3ZNlPvNqvr3sc/Jj6rqv6vqiKradkadaz3YrKpu3N8DZ/XX4tKq+lRVPXUx919/77+uqr5aVZdX91n9YlW9qaoeW1Vr+nK7VdU1/XGfup59bt1f21ZVL1pfG2bs435V9c7+vr6yqr5SVcdW1c0XUXfuw93663ZyVX293/cV/Xvwiap6UVXtPVb2Bf29fJ9+1R/Udb8bjposP/bZuktVvaqqzus/W9+fVXY957RjVf1jVZ3bt/l71X3O7z2nzqLG5512vfr7pyV5fL/qgCnn/cqx8ov9bOzX3/Pf7K/HD/v79fCa/2/DtcZY7vfzjqq6oN/P16rqJVW147xzBYDrIw/BAIDV829Jfj1dwHutnw1XF7A+aqzcJtFa+2pVfSLJr6UbZmIyWHl4kpsk+WaS07IQ3G50/TV5dZLHTmzaKsne/fSHVfXA1to5E3VvneQjSfaaqHvLfvqldIH2Z5OcufKtX5qqulmSd6QbVmPc2iQP6KenVNVBrbUljbNcVfsleWe69/HjSR7aWvvhRJm792Umg86d0t0DD+9Dkqe11q6ec6z7p+utfpOx1Tumu9YPqaoHt9Y+PLXyyto6yYdy3et5LVV1eJIXJtliYtMd++kPqurRrbX3zKj/4iRPn1h9l356ZJK/XXrTl6aqtklyUpJHT2zaKsk+/fTUqnpAa+1rc3a1Q7rPzGT496v9tHeSp81ow7bpPqu/O2XznfrpUUk+n+QLrbXzquqDSQ5M8qQk8x5ydlCSm/WvXzOn3FRV9awkf5fuj24jeyU5vG/vHyx1n2P7PjLX/c5Mktv1073Tfdc8fLnHGDvW7yR5Q7p7e0Psme6XHrcdW7d1ujY+rKqe3lp72QYeY6Oqqhsl+eckh01s2jIL9+sh/b8NZ69nX89OcmyufX/s3u/7oVV179baBSvWeADYyPT4BYDVc0qSnya5Q1Xda2LbQ9OFG99OF1htSqOgeVpP5NG6N7TWFtXjdYbXpwuW3tAvn94vj09/PKVdj02yLsnfpwuebpEunHxiujB6jyTvmtK767h04c4V6QKeu6QLfHdMcs8kh6YLucbPaYckDx5bvvOUNi7Gx/qyo0Do/Cn7edCocN+T8o1ZCClfmeRX+vb+SrpALekeaPbvi2zDaN8PTzcu802SvDvJb04JfXdL19N5lyTnJHlKktsnuXmSX07yT0muSfLUJH8553A3TzeW9dfShUg7Jbl1kicnuSRduPTqqpoMWef5ahau2Xf6dX+ba1/LN06p97x0vSqPS3LXdPfNPZN8dFSgqp6RLvDZom/3AUlulS5sf1CSTyXZLskpVXWnyQNU1dOyEPp+Kl2IuTbJHfo23ild4LixnZwu9F2X7nzvmYXPyZPSfafsmeQ/a0bP397x6a7VkenO4Rbp/iD08X77H/fB/rWM3b+j0Pe96a7fbbJwD/9Zkv+dqPqqfn6fqpr8A824J/fzj7fWvjKn3HVU11P979OFev+X7r68VZLd+jbdIskrlrLPsX3fKcnR/eIH0v1xZtckv5Due+khSV6a5OKxas9Pd89+sl9+ba773XDslMPdsi97brog/NbpesAvJ7R+TZLt0wWbu6a7Ho/o932jJC+tqv2Xsd95Tkp3bv/RL5+W6573oUvY31FZCH0/ku67cW267/yjkvwk3bn9V/9HtVnun+56vzXd98Ut031Wjk73b8Nu6T5TAHDD0VozmUwmk8m0CaZ0oUvrp936daf0yy+dKPuufv1x/fL+Y3X3n7Lv503uewntOq2vd1q//AvpAumW5J5j5W6V5Kp+/Z36dSf1y+ct85qcNH7sOeV+py93TZKHzSizc5Lv9eX+fGLbD/r1z1hi+8av+5Ku65z3aO61ShdGjY75whlljhsrc53rMbbtSWPr/iDJz/r1r0uyZsa+39OX+UqSm84oc0hf5idJdprY9oKx438mybZT6j96rMyBy7ye3+rrHzVj+5qxY7QkfzhnXzuP3fPHzihz43R/oGhJ3j6xbZt0gd7onLeZUv8pE+3ZeWL7gbO2zbi+5865rtck+a0Z9W+X5Pt9uWdObNtzrA1XJbnPlPo7JLmgL/P6KdufMLaPv1/Pe7jF2Outxj6nR88ov0uSq/syT1nGPfPlvu75SW45ZfuB/bUbtf++i73vkvxpv/47SW68xHaN7qtXrqfc+Gfry0lussz7ZPxe+1mSX5tS5lZJvtuX+eyU7aNx6k9dT5tnfk6XsI+Zn410f1BY12/7YKZ8ryV52Fj94+Zc/5bkxBltOCEL33nbLfXeM5lMJpNptSY9fgFgdY3GyX1MVd04SapqbZIHTmzfZFrXA/Q/+8XxXr+PTRemfaa19qVN3Kxn9PM3tdbeOa1A6572fny/+PiJzaPhrb69Edq20kZjnF6QLiye5i/Thdzj5Weqquek6zm8RbqfRD+xtfazKeX2ykLv40Naa5fM2OWJSb6RLqx7xJxDP7u1dsWU9W9JMhqiYp/1tX8FfL61Nq8n58Hpfhb+jSR/Ma1Aa+2qJH/VLz6kqsaHr/jtdH8wSZIjWmvXeehha+3VSf5nqQ1fotHn5OTW2runFWitnZ8uxEqu+zkZ9++ttY9PrmytXZru/Uumv3ejNnwlyRHzGtvGhglprf00Yw+X7H++P+n30/VCvTzJm+bte1JV7ZvkF/vF57fWvj9ZprV2arohVpZj9B3zvf5e2diOaksc5mWGN7bWPjG5srV2YbphT5Lk7v3wL9dHv5/ujzJJcti077X+34zR8CxP6XulT3NZZt+zr+nnW6X75QMA3CAIfgFgdb0vXe+7W2Yh7B0FrP/TWvvCKrVrNNzDzx/AlO4/2OPbNon+5+ijcUY/XFXbz5qSjK7XL1fVlmO7+Ww/f1H/4J5lPxhtY+rbNXrQ0ztba+umletDsnf1i/ddzy7/KQsBzlGttT9trbUZ5Q/s51cm+cyc67xdks/1ZfeeuqduWI2PzGj/1emGbUi6ISA2tqlj8o4ZnfdpSbaZc95f7sttkeQeY/VH78GPM39olrcusd2LVlU7pBvLNFn85+Tuc4baeO+cw/1fP7/We9f/jP5X+sXXtznjP88wGu5hl3RDbUx6Uj8/pbV22RL3Pf45efuccst9j0bfMXerqqNrEQ+K2wDXpPu3YyW8bZHb7jOz1Ooava9fWs8fJE/p57dIN+zKNJ+YE6b/39jrTfGdBQArQvALAKuo7xk2GpN0FKyOetlu0oB1wnvS/ex6xyQPqKo7pwu6fpYljiu7AvbIQo+uf0ly6Zxp1BPxRunGmB05PN3PgfdMF+59t6reVFWH9ed2fXGTLDy4an29qr/Yz39hovfpuOcleWa6oOiPW2tHzyg3csd+Phq6YN61fmhfdu2MfX1vPcHfqCfwvHFmV8q8h5glC+f9xMw/5++M1Rk/7936+Vdaa9fMOc6X52zbULfPwkPpXpn55zEaW3VNFnoqT/rOjPXJ7Pdu9yz8/2LJvZtba5/LwsMVnzy+rap+Pd3nN1nGQ92y8B59f1pv3zHLeo/63sKjX0r8RZILq+qTVfV3VfXQKeOOb4gLW2uXr9C+Zj7srP8VxShg33WFjrfSRu1a7PfleJ1Ji7nnk03znQUAK0LwCwCrbzScw0Or6t7pelBene4hTaui72k6Cod+Lwuh9H+11r43vdZGc9Nl1vv50+5ba/+d5F7perCtSzd+5aOSvCTJF6rqf/oHP6228QfGra9H46Uz6o0bhXrXpAvy12c513rrGesX29tzU/S+njbcxLhZwfk84+e9fT9f33u21F6qS7HBn5MJi3n/Jt+78et4aZZn1Ov34VU1fk5P6ufnttY+mqXbFO/RI9INFfD1dKH6vkn+PMk7k3yvql7c98zeUOu7n5difec7CphXot0bw6hdK/F9eX36zgKAFSH4BYBV1lo7I93PSLfKwhiXH2itXbB6rUqy0OP4YVndXsjj/6F/cGutFjmdN76T1tr/ttZ+J10Yev90T3v/SLoH9twtyXuq6qBNdE6zjIcT288sdd3ts0K256Q7xzVJ3lhV88bjTRau9XeWcJ0PnLvHG4ZRuPXCJZz368fqj67bUt6zSbOG35i0Zsb68c/J/1vCeXxrkcddjMWEa+tzcrpgc5skj0mSvrfso/vtJy1zvyvxHs3VWlvXWjuutbZHkjuk60H+qiQXpusl+vQkH5gzvMZqWN/5jnoqT37HbOj9ulJG7VqJ70sAGBzBLwBcP4wC1T0mlldNa+1TSc5JF8DcOt34pct98NGGOC9dj9Wk+zn7BmmtXdFa+3Br7ejW2v5J7prkonS9uP56Q/e/gX6c5If961njUI6Mhqi4eM64lJcn+a1cO/z9nTn7HA2JsGM/FuzmYnTey72/zuvnd5jxULKRX5qz7Sdjr7eZU+42M9Z/PQth3AZ/Tpbpa1n4rC7rYWD9vfzmfvFJ/fyR6YK7a5K8dpltO6+f37Kqbjmn3Lz3aNFaa+e01l7XWntqujGLX9Zv+tUsPEDx+uAXZ22oqp2zEJh+Y2Lz6H6dea9W1Va59pA7G8N5/Xyx35fjdQBg8AS/AHD98PoshDaXZv4Ddzal8QD6lNbaT2aWXJ6r+vnMHnCttUuS/He/+LsrfPy01r6YhXGLJ0Ofq8Zeb2gvvcWca0vy8X7xoVV142nl+kBlNMbux6eVGdvnZPj7H3PC3/f38zXpfra+uRid9wPnjJc8z+n9/CbpepPPMi90/+7Y6ztOK9DfD9MeepbW2sVJPtMvrvjnZDFaaz/Kwhi9j9+Anq2j4R72rao7ZmG83w9sQA/l08deP3xOuXnv0bL0Y7n/zdiqWd8zq9ETeN6vHMav0+T3zOh+3WvOwzIPyML47NOsxHmP3tc7VdW80P6R/fwH2bhjbQPA9YrgFwCuB1pr30j30+BfSnLX1tqVq9ykkX9M16ZfSjdW5UobjTs7qxfjyD/08/tW1Z/NK1hVW1TVnmPL21XVLuvZ/6iH5OQ4uOPL62vj+oz2tbaq5v38eRR63TrJX80o87x04xQnySvWd+DFhr99CP5f/eJx49dxmqq61cQ4rDdUL0039vNNk7xiPe9P+jBy3Duy0FP72Kq6Ti/IqnpK5veC/Xq6nudJN0TANH+R+ffh6HOyf1U9fU65VNWaqtoYPYNf0s/vmOSF62nD1MCvH8P3nH7x+Unu179ezkPdRvv8VBYeZPZX03r9VtUBSX57OfuvqvX19h6/1rO+Zzb0O2Y5HtOPLX8tVXWrdPdbkvxPa23yYX2f7udrkzx4Sv3tkxyznmOvxHn/WxYC5BdP++xW1W8leUi/+Kr+D2wAsFkQ/ALA9URr7dzW2tl9CLwh7lFV+65n2mmRbbq8b9PZfW++lTbqobhHVR1aVTv2gdSa8RCltfbmJG/sF/+hqt5WVQ+uqttU1c2qateqemBVHZvu5+bPHDvG2iRfq6p3VNWTq+quVXXLPrS8d1W9Jl0omiz0/B05N93wC0lyZB/ubD1q4zLPdaskz+/bfuN+X+MB2Duy0AP1qKr616q6W1XdvKruXlWvSHJkv/09rbV3LebgU8LfWcM+PC3J95PsmOTMqvrrqvqVqrpFf93uUlW/V1VvSvfz792XcA2ul1pr5yd5Vr/46CSfrqonVNXuVXXT/r26b1UdXlWfycKDD0f1r8xCSPYrST5UVffvr9meVfU3Sf4lXbg7qw0tC8HmI6vqJVW1V1X9QlXtXVWvShf4f3XOPt6YhWESXlxVb6mqB419TnbrPyfHpfucHLb4q7RoJycZ3ZOHV9W7quoBVbVTfw/fraoOq6qzMn9YhdEfQB6dbhiWHyZ5+wa27U/7+S5JPlZVD6uqtVV1u6p6Zr//85a5779Kck5VHV1VB1bVLv17t2cf+o/el0uT/OdE3dF3w35V9Yj+Oo2+Bzf2g8S+meS9/ffvLv138EHpetLeOt0vUab9se39SUa9r19bVY/r6+7U1/9kFoYImmV03neoqqf178V1vv/naa19J8kL+sXfTPL+qtqv/+zdvqr+IskpY+f6osXsFwAGo7VmMplMJpNpE0zpxqts/bTbEuvuP1Z3/ynbnze2fTHTM8fqntavO20Z53RSX/e8ZV6TbdIFWdPaeNJE2S3ThWeLOb9/HKu32yLrfCDJdlPa+MJZdZZxvh+fsa/TJsrdLAsPnps1fSjJTWccZ1TmSVO2bTf2nq9LctCUMndK1ztyfdfsmiR3nqj7gn7bueu5Fqf35V65zHvnW339o2ZsXzPWzicscp+HpBu7dH3n/d8z6r94Tp3/TTd8xmh55yn1t0/yP3P28Xfru77p/rDwikXe88dN1N1zbNt951ynp/ZlfjZj+7bpgs71Hf8uc46xU7qenKOyxy/nPpmy32f19+209nwj3fAEM6/BrPsuC8P1zJsuT/LQKfu8Tbpge1qdo8bKLeqztb6ySQ4c2//+Sb4z49hXJzl0zjEOzOzPy8VJ7jvreo3dJ1+fUf+VM9o77XNzo3S99udd+/OS/OJyv4uyjO8Tk8lkMpmuD5MevwDAqmldT8lfTxfonptrP+Bqsuy61trTktwzyb+mG6fx0iQ/SxcynJHuP/8PyLWHpTg/yX3SPbjtg+mC5svThZ7fSvLOdGOi/r/W9YqddFSSP0nyqXS919ryzjZJ95Pov0vypSQzh/NoXe/q30jy++l61l2U7jwv6pd/L8kBrRv/eEnatXv+3jjdsA8HTZT5UrqH3j05Xe/E76S7Xj9J12vufel6He/ZuuEhBqG1dkK6n+S/MN399MN04delSb6Y7mflj08Xlk2r/4x046J+KMklSa5I917/TZJ79+vmHf+ydMMavDDJV5L8NN3P4T+QLjB89iLO4aettT9Msk+Sl6cL8Mc/J/+dbjiG38xCz/EV1boHKD4yyQPT9Y7+Zrpz+VG66/i6dD+9P3vOPi5I8p6xVa9eobb9Q7r37z/TXdufpPvu+cd03y3L/cXFn6f7vL42XXh/Ybpr/uMkZyU5Nskd25Qe+q3rtfprSd6Q7vtq3TLbsBznpuul/tJ0vcB/mq7H/zvSBd8vm1WxtXZquvv6Lem+m9alu37/kuQerbXTZ9Xt61+R7vv/X9N9L/90OSfQWrumtXZYuvf1P9J9r69L93n7dLr7/M6ttZn3GwAMVbW2If93AQAAFqOqDkwX4ibJLm35DyrbLFTVG9P9UeZzrbW7rXZ7AABuaPT4BQAArleq6hey8KC1FentCwCwuRH8AgAA1zd/nGTrdEOivG6V2wIAcIO01KdRAwAArLiqWpPuIY4HJnluv/oVrbUfrl6rAABuuAS/AADAqupD36smVn8n3YPxAABYhsE/3O2Wt7xl22233Va7GQAAbOZ+/OMf55xzzkmS3PWud82WW265yi26/mit5ayzzkqSrFmzJjvssENue9vbZquttlrllgEAXP985jOf+X5rbe36yg2+x+9uu+2WM888c7WbAQAAAACwwarqG4sp5+FuAAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgVmz2g1g5e125Ls3+jHOO+a3NvoxAAAAAIDl0eMXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAOz6OC3qk6qqjZnOntGvRtV1aFVdWZVXVZVl1TVx6rqsYs45uP6spf0dc/s9yWwBgAAAACYYc0y6nw8yblT1n93ckVVbZHkrUkeluTHSd6fZKskByQ5uar2ba09Y9pBquplSQ5J8pMkH0xyVV/v+CQHVNUjW2vXLKP9AAAAAACDtpzg95WttZMWWfaZ6ULfLyW5f2vtwiSpqr2SfCzJ06vqQ621d4xXqqpHpAt9L0hyv9baOf36WyX5cJKDkhyW5MXLaD8AAAAAwKBttCET+t6+h/eLB49C3yTpg9wj+sXnTqn+nH5+xCj07etdmOTgfvFIQz4AAAAAAFzXxgxO751kxyTfaq19dMr2U9IN37BPVd12tLKqdk5yzyTr+jLX0lr7SJJvJ9kpyb4bod0AAAAAADdoyxnq4Teq6peTbJ/kwiSnJ/nAlPF279HPz5i2k9baFVX1xSR376dvT9T7YmvtyhltOCPJbfuyn1jGOQAAAAAADNZygt/fn7LuS1X1mNba58fW7d7PvzFnX+enC313H1u32HrjZQEAAAAA6C1lqIf/SfL0JHdK19v3NkkekuR/+3Wnjg/Z0JdJksvn7POyfr7DCtT7uar6o6o6s6rOvOiii+bsBgAAAABgeBYd/LbW/rm19tLW2pdba5e31r7bWnt3knsl+VS68XyfM38vm0Zr7eWttb1ba3uvXbt2tZsDAAAAALBJbfDD3Vpr65K8qF988NimUa/c7eZUH/XuvXQF6gEAAAAAkBUIfntn9/PxoR7O6+e7zqm3y0TZDakHAAAAAEBWLvi9RT+/bGzdWf18n2kVqmrbJHfpFz87tmn0+s5Vtc2M4+0zURYAAAAAgN5KBb+P7udnjK37ZJKLkuxcVfebUudRSW6c5IzW2rdHK1tr30wXGm/Zl7mWqtovyc5JLuiPAQAAAADAmEUFv1V196p6SFVtMbF+TVU9K8nT+1X/NNrWWrs6yXH94olVteNYvb2SHNMvHj3lkKMxg4+tqj3H6u2Y5IR+8ZjW2jWLaT8AAAAAwOZkzSLL7ZbkbUkurqqzknwv3fAOd01ymyTXJDm8tfZfE/X+Kcn9kjw0yTlV9cF0vXwPTLJ1kpe21t4xebDW2pur6sQkByf5fFWdmuSqJAckuUmStyc5fgnnCQAAAACw2Vhs8Pu/SV6c5F5J7pTk15O0JN9K8pokL2utfWayUmvt6qp6eJJDkjw5yQOSXJ3kM0lOaK2dPOuArbVDqur0JIcm2S/JFukeIvfqJCfq7QsAAAAAMN2igt/W2teTPHM5B+gD2uOzjB66fTA8MxwGAAAAAOC6VurhbgAAAAAAXE8IfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAOz7OC3ql5YVa2f/nw4y2ufAAAgAElEQVROucdV1ceq6pKquqyqzqyqQ6tq7rGr6oFV9f6quriqrqiqL1TVc6tqq+W2GQAAAABgc7Cs4Leq9klyeJK2nnIvS/KGJHsn+ViSDyS5Q5Ljk7x5VvhbVYcneW+S+yc5K8m7k+yY5AVJTquqbZfTbgAAAACAzcGSg9++x+1rk1yY5B1zyj0iySFJLkjyy621h7TWDkqyV5IvJzkoyWFT6u2d5JgkVyS5T2vtwNbao5LskeSjSfZNcvRS2w0AAAAAsLlYTo/f5yf5pSRPS3LJnHLP6edHtNbOGa1srV2Y5OB+8cgpvX6PTFJJjm2tfXqs3mVJnpzkmiSHVNXNltF2AAAAAIDBW1LwW1W/muRZSU5urb1rTrmdk9wzybokp0xub619JMm3k+yUrgfvqN6WSR7UL75hSr2vJflkki2TPHgpbQcAAAAA2FwsOvitqq3TDfFwcZJnrKf4Pfr5F1trV84oc8ZE2SS5Y5Jtk1zcWvvqEuoBAAAAANBbs4SyR6cLZh/TWvv+esru3s+/MafM+RNlx1+fn9mm1QMAAAAAoLeoHr9V9WtJnpnk7a21/1hEle37+eVzylzWz3dYgXrXUlV/VFVnVtWZF1100dyGAgAAAAAMzXqD36raJslJSX6c5JCN3aCV0Fp7eWtt79ba3mvXrl3t5gAAAAAAbFKLGerhhUn2SvKU1tp3F7nfUa/c7eaUGfXuvXQF6gEAAAAA0FtM8HtQkmuSPLGqnjix7Rf7+cFV9ZAk57bWnprkvH79rnP2u0s/P29s3ej17ZZYDwAAAACA3mIf7najJPvN2b5HP92sX/5sP79zVW3TWrtySp19JsomydlJrkxy86q6fWvtq1Pq3WtKPQAAAAAAeusd47e1tltrraZNSV7bF3t2v+7ufZ1vJjkryZZJHjW5z6raL8nOSS5I8smxY61L8t5+8fFT6u2R5N5J1iV59xLOEwAAAABgs7He4HcDvKifH1tVe45WVtWOSU7oF49prV0zUe+YJC3JEVV1r7F62yd5dbo2n9Ba+9FGazkAAAAAwA3YRgt+W2tvTnJikp2SfL6q3lVVb01yTpI7JXl7kuOn1DsjyZFJtk3yiap6f1W9KclX0w038ekkz91Y7QYAAAAAuKFb7Bi/y9JaO6SqTk9yaLrQdot04/i+OsmJU3r7juodV1WfS/KsdGMBb53ka0lekuTvW2s/3ZjtBgAAAAC4Idug4Le19qQkT1pPmZOTnLyMfb8vyfuW1TAAAAAAgM3YxhzjFwAAAACAVSD4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYBYd/FbVYVX1pqr6clX9oKquqqqLqurUqnpCVdWMejeqqkOr6syquqyqLqmqj1XVYxdxzMf1ZS/p657Z70tgDQAAAAAww5ollD0iyY5JvpDkE0kuT7JrkvsnOSDJI6vqd1pr14wqVNUWSd6a5GFJfpzk/Um26sufXFX7ttaeMe1gVfWyJIck+UmSDya5qq93fJIDquqR48cCAAAAAKCzlOD3MUk+21q7fHxlVd05XTD720memOQ1Y5ufmS70/VKS+7fWLuzr7JXkY0meXlUfaq29Y2Kfj0gX+l6Q5H6ttXP69bdK8uEkByU5LMmLl9B+AAAAAIDNwqKHTGitnT4Z+vbrv5jkZf3ib47W9719D+8XDx6Fvn2dc9L1IE6S50453HP6+RGj0Levd2GSg/vFIw35AAAAAABwXSsVnP6sn/90bN290w0N8a3W2ken1Dkl3fAN+1TVbUcrq2rnJPdMsq4vcy2ttY8k+XaSnZLsuyKtBwAAAAAYkA0Ofqtq9yRP6xffObbpHv38jGn1WmtXJPliv3j3KfW+2Fq7csZhz5goCwAAAABAbylj/CZJqurJSfZLcuMkOyf5tXQB8gtba28bK7p7P//GnN2dny703X1s3WLrjZcFAAAAAKC35OA3yX3SPcRt5GdJ/jLJP06U276fX2dc4DGX9fMdVqDez1XVHyX5oyS53e1uN2c3AAAAAADDs+ShHlprT22tVZJtk9w5yT8neV6ST1XVbVa2ecvTWnt5a23v1trea9euXe3mAAAAAABsUsse47e1dmVr7UuttWcneU6SuyU5fqzIqFfudnN2M+rde+kK1AMAAAAAICvwcLfeSf38oVV14/71ef181zn1dpkouyH1AAAAAADIygW/P0w31u+aJDfv153Vz/eZVqGqtk1yl37xs2ObRq/vXFXbzDjePhNlAQAAAADorVTwe790oe+Pkny/X/fJJBcl2bmq7jelzqOS3DjJGa21b49Wtta+mS403rIvcy1VtV+SnZNc0B8DAAAAAIAxiwp+q+q+VfWQqlozZdt9kryqX3xVa+3qJOnnx/XrT6yqHcfq7JXkmH7x6CmHfFE/P7aq9hyrt2OSE/rFY1pr1yym/QAAAAAAm5PrBLkz7JnkNUl+VFVnpettu0OS2ye5U1/m3Un+cqLeP6XrDfzQJOdU1QfT9fI9MMnWSV7aWnvH5MFaa2+uqhOTHJzk81V1apKrkhyQ5CZJ3p5rP0gOAAAAAIDeYoPfjyT52yS/nmSvJL+WpNIFwG9J8vrW2tsnK7XWrq6qhyc5JMmTkzwgydVJPpPkhNbaybMO2Fo7pKpOT3Jokv2SbJHk7CSvTnKi3r4AAAAAANMtKvhtrX09yV8t5wB9QHt8ltFDtw+GZ4bDAAAAAABc10o93A0AAAAAgOsJwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgFhX8VtWNq+qAqvqHqjqzqn5cVeuq6ttV9eaq2n899R9XVR+rqkuq6rJ+H4dW1dzjV9UDq+r9VXVxVV1RVV+oqudW1VZLOEcAAAAAgM3KYnv87pfk1CR/luS2ST6a5G1JLk7yiCQfrqrnT6tYVS9L8oYkeyf5WJIPJLlDkuOTvHlW+FtVhyd5b5L7JzkrybuT7JjkBUlOq6ptF9l2AAAAAIDNymKD32uSvCXJ/Vprt26tPaS19ruttbsmeUySq5P8ZVX9xnilqnpEkkOSXJDkl/t6ByXZK8mXkxyU5LDJg1XV3kmOSXJFkvu01g5srT0qyR7pQud9kxy99NMFAAAAABi+RQW/rbUPtdYe2Vr72JRt/5HkpH7xCRObn9PPj2itnTNW58IkB/eLR07p9XtkkkpybGvt02P1Lkvy5HRB9CFVdbPFtB8AAAAAYHOyUg93+2w/33m0oqp2TnLPJOuSnDJZobX2kSTfTrJTuh68o3pbJnlQv/iGKfW+luSTSbZM8uCVaT4AAAAAwHCsVPC7Vz//7ti6e/TzL7bWrpxR74yJsklyxyTbJrm4tfbVJdQDAAAAACArEPxW1U5JntQvvmVs0+79/Btzqp8/UXb89fmZbVo9AAAAAACygcFvVa1J8vokN03ywdbau8Y2b9/PL5+zi8v6+Q4rUG+8XX9UVWdW1ZkXXXTRnN0AAAAAAAzPhvb4/ZckByT5Zq77YLdV01p7eWtt79ba3mvXrl3t5gAAAAAAbFLLDn6r6sVJ/iDJBUkOaK1dMFFk1Ct3uzm7GfXuvXQF6gEAAAAAkGUGv1X1D0menuSidKHvOVOKndfPd52zq10myo6/vt0S6wEAAAAAkGUEv1V1XJI/S/KDJAe21r40o+hn+/mdq2qbGWX2mSibJGcnuTLJzavq9jPq3WtKPQAAAAAAssTgt6qOSfLsJD9M8puttc/NKtta+2aSs5JsmeRRU/a1X5Kd0w0V8cmxeuuSvLdffPyUenskuXeSdUnevZT2AwAAAABsDhYd/FbVC5IckeRH6ULfxfS2fVE/P7aq9hzb145JTugXj2mtXTNR75gkLckRVXWvsXrbJ3l13+4TWms/Wmz7AQAAAAA2F2sWU6iqHpbkuf3iuUkOq6ppRc9urR0zWmitvbmqTkxycJLPV9WpSa5KckCSmyR5e5LjJ3fSWjujqo5McmyST1TVh9IFzvsl2THJp8faAwAAAADAmEUFv0luPvZ6736a5iPpeuv+XGvtkKo6Pcmh6YLbLdKN4/vqJCdO6e07qndcVX0uybPSjQW8dZKvJXlJkr9vrf10kW0HAAAAANisLCr4ba2dlOSk5R6ktXZykpOXUe99Sd633OMCAAAAAGyOlvRwNwAAAAAArv8EvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwCAAAAAAyM4BcAAAAAYGAEvwAAAAAAAyP4BQAAAAAYGMEvAAAAAMDACH4BAAAAAAZG8AsAAAAAMDCCXwAAAACAgRH8AgAAAAAMjOAXAAAAAGBgBL8AAAAAAAMj+AUAAAAAGBjBLwAAAADAwAh+AQAAAAAGRvALAAAAADAwgl8AAAAAgIFZdPBbVXesqmdU1eur6uyquqaqWlU9chF1H1dVH6uqS6rqsqo6s6oOraq5x6+qB1bV+6vq4qq6oqq+UFXPraqtFttuAAAAAIDNzZollD04yTOWeoCqelmSQ5L8JMkHk1yV5IAkxyc5oKoe2Vq7Zkq9w5Mcm+TqJKcl+WGS/ZK8IMlDquqA1toVS20PAAAAAMDQLWWohy8k+bskv5tkzyQfWV+FqnpEutD3giS/3Fp7SGvtoCR7JflykoOSHDal3t5JjklyRZL7tNYObK09KskeST6aZN8kRy+h7QAAAAAAm41FB7+ttVe21g5vrb2ptfbVRVZ7Tj8/orV2zti+LkzXgzhJjpwy5MORSSrJsa21T4/VuyzJk5Nck+SQqrrZYtsPAAAAALC52GgPd6uqnZPcM8m6JKdMbm+tfSTJt5PslK4H76jelkke1C++YUq9ryX5ZJItkzx4xRsOAAAAAHADt9GC3yT36OdfbK1dOaPMGRNlk+SOSbZNcvGcnsXT6gEAAAAAkI0b/O7ez78xp8z5E2XHX5+f2abVAwAAAAAgGzf43b6fXz6nzGX9fIcVqPdzVfVHVXVmVZ150UUXrbehAAAAAABDsjGD31XTWnt5a23v1trea9euXe3mAAAAAABsUhsz+B31yt1uTplR795LV6AeAAAAAADZuMHvef181zlldpkoO/76dkusBwAAAABANm7w+9l+fueq2mZGmX0myibJ2UmuTHLzqrr9jHr3mlIPAAAAAIBsxOC3tfbNJGcl2TLJoya3V9V+SXZOckGST47VW5fkvf3i46fU2yPJvZOsS/LuFW84AAAAAMAN3MZ+uNuL+vmxVbXnaGVV7ZjkhH7xmNbaNRP1jknSkhxRVfcaq7d9klena/cJrbUfbbSWAwAAAADcQK1ZbMGq+pUshLVJcqd+/sKq+vPRytbavmOv31xVJyY5OMnnq+rUJFclOSDJTZK8Pcnxk8dqrZ1RVUcmOTbJJ6rqQ0l+lGS/JDsm+XSS5y627QAAAAAAm5NFB7/pgtpfnbJ+r3mVWmuHVNXpSQ5NF9xukW4c31cnOXFKb99RveOq6nNJnpVuLOCtk3wtyUuS/H1r7adLaDsAAAAAwGZj0cFva+20JLWcg7TWTk5y8jLqvS/J+5ZzTAAAAACAzdXGHuMXAAAAAIBNTPALAAAAADAwgl8AAAAAgIER/AIAAAAADIzgFwAAAABgYAS/AAAAAAADI/gFAAAAABgYwS8AAAAAwMAIfgEAAAAABkbwCwAAAAAwMIJfAAAAAICBEfwC8P/bu/cY28r6jOPfRygUBKStpTQCyq1qSawXbi1/0EgapVLlFGoqatAmJQWkNZYKpGlKGlrPsRpMC5jS1ChEWyMVaaOtLVUJtJZKoTUFMQg5YJFLDRc5IBfh1z/WmjBs58bMnlnvXvP9JCvvmbXetebdc+aZd+a31363JEmSJEkaGQu/kiRJkiRJkjQyFn4lSZIkSZIkaWQs/EqSJEmSJEnSyFj4lSRJkiRJkqSRsfArSZIkSZIkSSNj4VeSJEmSJEmSRsbCryRJkiRJkiSNjIVfSZIkSZIkSRoZC7+SJEmSJEmSNDIWfiVJkiRJkiRpZCz8SpIkSZIkSdLIWPiVJEmSJEmSpJGx8CtJkiRJkiRJI2PhV5IkSZIkSZJGxsKvJEmSJEmSJI2MhV9JkiRJkiRJGhkLv5IkSZIkSZI0MhZ+JUmSJEmSJGlkLPxKkiRJkiRJ0shY+JUkSZIkSZKkkbHwK0mSJEmSJEkjY+FXkiRJkiRJkkbGwq8kSZIkSZIkjYyFX0mSJEmSJEkaGQu/kiRJkiRJkjQyFn4lSZIkSZIkaWQs/EqSJEmSJEnSyFj4lSRJkiRJkqSRab7wm+SUJNcmeTjJjiQ3JDkzSfNjlyRJkiRJkqQhNF08TXIx8EngcOBa4J+BnwEuAq6w+CtJkiRJkiRJP6zZwmmSk4AzgHuBV1XVCVW1BTgU+AawBThrwCFKkiRJkiRJUpN2HnoASzivb8+pqtvmdlbVfUlOB74CnJvkz6vqmSEGuJm97NzPDz2ENdm+9U1DD0GSJEmSJElaN03e8ZtkP+B1wJPAZyaPV9U1wN3AvsDRGzs6SZIkSZIkSWpbk4Vf4DV9e3NVfX+RPl+b6CtJkiRJkiRJot2lHg7s2zuX6HPXRF9pxWZ9qQqtjEt6aBo24ueF36uSJEmSxmq9/6by76nFtVr43aNvH12iz46+3XPyQJLTgNPm+iX55hTHNgteDHx36EFIQ8u2Dfk05k1rtkHfq2Ng3qSNY96kjWPepI1j3kZok/499dKVdGq18LsmVXUpcOnQ4xhKkhuq6vChxyFtBuZN2jjmTdo45k3aOOZN2jjmTZtNq2v8zt3N+8Il+szdFfzIOo9FkiRJkiRJkmZKq4Xf7X271G3L+0/0lSRJkiRJkiTRbuH3pr49LMlui/Q5YqKvnrVpl7mQBmDepI1j3qSNY96kjWPepI1j3rSppKqGHsOCkvwn8Frg1Kq6bOLYscBXgHuBl1TVMxs/QkmSJEmSJElqU6t3/AJ8oG+3JTlkbmeSfYBL+g+3WvSVJEmSJEmSpOdq9o5fgCSXAKcDjwNXA08BxwF7AZ8DTq6qp4cboSRJkiRJkiS1p+U7fqmqM4C3AzcCxwJvAL4FvAc4aSxF3ySnJLk2ycNJdiS5IcmZSVb1/5PkjUn+KckDSR5L8j9Jfj/Jrsucd1SSK5Pcn+TxJLcl+WCSF63ukUntGTpvSd6VpJbZ9l3bo5TaMK28Jdk/yelJ/irJ15P8oM/K2Ss8f1XzojRLhs5bkvOXmdseX9sjlNoxjbwleUGSX0hyQZJ/S/JgkqeS3JfkC0lOXME1nN80ekPnzflNs27noQewnKr6FPCpocexXpJcDJxBd1fzv/DsXc0XAcclOfn5LGeR5P3ANuBpunWQH6Qrml8AnJDkuKp6bIHz3gZcDuwE/CtwN3A08HvAliTHVNX9q32cUgtayVvvduC6RY59f6VjkFo15bydBFy4ynGsJafSTGglb73/Bv5rgf1PreGaUjOmmLeD6P7uAngA+A+6Oeog4Hjg+CQfB36jFniZrvObNoNW8tZzftNsqiq3gTa6X6wLuAc4dN7+nwJu6Y/9zvO43uHAM8CjwFHz9u8BXNNf78IFztsPeIzul4a3zNu/M/A3/XlXDv31cnNby9ZQ3t7VH/v40F8TN7f12tYhb28BPgK8E3glcFl/jbOXOW9VOXVzm6Wtobyd3/c7f+iviZvbem3TzBtwMF0h643AThPHjgV29Nd79wLnOr+5jX5rKG/Ob24zvTW91MMmcF7fnlNVt83trKr76NY2Bjj3ebyE4VwgwLaqun7e9XYA76b75eCMJHtPnPdeYDfgE1V11bzzfgCcBnwPODHJz674kUntaSVv0mYw1bxV1VVV9d6quryqvkGXr5Uwp9oMWsmbtBlMLW9VdXtVHVdV/1gTSxhW1TXA1v7DdyxwuvObNoNW8ibNNAu/A0myH/A64EngM5PH+x8+dwP70i25sNz1dqF7iQLAJxe43h3AV4FdgF+eODy3ns1C530P+PuJftJMaSxv0qhNO29rGIc51ei1kjdpMxggbzf17X4T43B+0+i1kjdpDCz8Duc1fXtzVS22nufXJvou5eXA7sADVXX7Sq+XZC+6lz3MP76WcUgtaiJvEw7p31zg0iQf6t+0YI8VfG6pddPO22pNI6dS61rJ23yvTbKtn9+2JtnSF6qkWbfReTu0b++Z2O/8ps2glbzN5/ymmdT8m7uN2IF9e+cSfe6a6LuS6921RJ+Frveyvn2ov7t3reOQWtRK3uY7pt/mezDJaVV1xQrGILVq2nlb6zjWklOpda3kbb5f6bf5/jfJO/o7tKRZtWF5S7I78Nv9h3+7yDic3zRmreRtPuc3zSTv+B3O3J19jy7RZ0ff7rmO15v2OKQWtZI36J5FvgA4EngxsDfw88CVwI8Bn07yhhWMQWpVK/NKK+OQ1lNL3+e3063H+GrgRcBPAq+ne6Op/YAvJHnVOo9BWk8bmbdL6IpZtwCXDjgOaSit5A2c3zTjvONXkjZQVX0R+OLE7n8HfjXJh4H3AR9eoI8kSc2qqssX2P1l4MtJrqB7d/Y/AU7Y0IFJMybJHwCnAg8Db62qJwYekjRaK8mb85tmnXf8Dmfu2akXLtFn7lmuR9bxetMeh9SiVvK2nAuAp4HDkhzwPM6TWtLKvNLKOKT1NCvf53/Ut7+U5EcGHIe0FuuetyTvo8vLDuD4qrp5iHFIDWglb8txflPzLPwOZ3vfvnSJPvtP9F3J9ZYqFi10vbk1c/bu3+htreOQWrS9b4fO25Kq6kHg/v7Dl6z0PKkx2/t2Wnlb6zimmlOpMdv7dui8LefWvt2FbpkjaRZt79t1yVuSs+he9fV94ISq+uoy43B+05ht79uh87Yc5zc1z8LvcG7q28OS7LZInyMm+i7lVrofWj+e5OBF+hw5eb2qephuzZr5n2/Z86QZ00TelpNkJ7p1o+DZZ7mlWTPtvK3WuuRUakwreVvOT8z7t/ObZtW65S3JmcCfAY8Db17mjaKc37QZtJK35Ti/qXkWfgdSVd8GbqR7ZujXJo8nOZZuofB7gWWffaqqJ4F/6D98+wLXO4juDaSeBD4/cfiqJc7bi2ffufLK5cYhtaixvC3lBGB3upcr3bpMX6lJ087bGsaxXjmVmtFK3lbgrX37zarypeeaSeuVtyS/BVwEPAGcWFVXLzMO5zeNXit5WwHnNzXPwu+wPtC325IcMrczyT507ywJsLWqnpl37D1Jbk1y2QLX2woUcE6SI+edswfwMbr/70uq6qGJ8z5C96zxqUnePO+8nYG/APYCPldVt6zycUotGDxvSXZPcnrf5zmSvAn4y/7Di6vqqVU9SqkN087baq12XpRmyeB5S3JAklOS7DqxP0neOW+MF07j80kDmmrekvxmf94TwJb+TYBXwvlNm8HgeXN+0xikqoYew6aW5BLgdLqXGVwNPAUcR19sBU6uqqfn9T8f+EPgmqr6xQWu935gG90bRH0JeAg4FtgHuB54fVU9tsB5bwMup/sl4TrgO8DRdGvqfAs4pqrunzxPmiVD5y3J3sCD/ee/Efg23bPYrwRe0Xf7LPDrFn4166aZtyQ/zXNfdXIw3TpqdwH3zNu/parumTh3VfOiNEuGzluSV9O91PYRuvntO8CewGHAgX3/i6rqrLU/WmlY08pbn5sbgdC90uv6RT7ld6vq7AXG4fym0Rs6b85vGoOdhx7AZldVZyS5DjiTbqLeie4H0ceAj85/9mqF1/tgkq8Dv0u35s2PAnfQrWHzoap6YpHz/jrJHcB5wDHAUXRFqT8F/rhfC1iaaQ3k7THgArp1114O/Bxd4ff/gL8DPlFVn13lw5OaMuW87Uo3L006gOe+uc2ukx1WOy9Ks6SBvM39zngEcAjdPPcCupfgfhq4tKq+9DzGIDVrinnbm64IBd0NAK9YpN+dwA8Vfp3ftBk0kDfnN8087/iVJEmSJEmSpJFxjV9JkiRJkiRJGhkLv5IkSZIkSZI0MgDCY8MAAABUSURBVBZ+JUmSJEmSJGlkLPxKkiRJkiRJ0shY+JUkSZIkSZKkkbHwK0mSJEmSJEkjY+FXkiRJkiRJkkbGwq8kSZIkSZIkjYyFX0mSJEmSJEkamf8HVHVoaQKCz/EAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labels, values = zip(*d.most_common())\n", + "\n", + "plt.figure(figsize=(20,6))\n", + "plt.title(\"MLM test token frequency distribution\")\n", + "plt.hist(values,bins=50);\n", + "width = 1\n", + "plt.tight_layout()\n", + "#plt.xticks([i + width * 0.5 for i in range(16)], [str(i) for i in range(16)]);\n", + "plt.savefig(\"mlm-test-lg-freq.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABXAAAAGOCAYAAAA+WCvUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xm8XdPdx/HvL4mEDEQSkchsFsMTRKqliCnUUEOoKuVRbYloVT1EaR+tDmi1aoxQVFWfkiJS1FRzTaHVmKpEYqghkRCJRKbf88daO2fn3H3OPdO959ybz/v12q9zz91rrb3OPns6v732WubuAgAAAAAAAAA0ng71rgAAAAAAAAAAIBsBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwgTows6Fm5lVOQ+v9OdoSMxuRWncj6l2fejKzBXE9jM+Y9+c4b3I96lYqMxub+j67583rk5q3f73qWKq2Vt/WZGb9zOx/zOweM5tlZp/E6Q0zm2pmp5hZ33rXs73L20Z/Ue/6oG0zs53NbIqZvWtmS+N2taze9WrPzGyUmf3ezN40s8Vm9raZ/cnMRtdwGQPM7EIzeyUep+eY2UNmdpyZWUvW08y6m9nhZnaVmf3dzD6K29b7Zna/mZ1kZl1r80lXLvObcdt9spblru7M7IB4zp9jZsvjOn613vVqz8xsLzO7zcz+E/e7N8zsejPbrspyzcx2NLMfmdlfzewdM1tiZvPN7J9mdpGZbVJiWUPM7Odx/54f9+8PzOwRMzvdzNappq5AW0EAF0DZYtDEzezDGpR1WyzrtlrUDY3HzC6N3/G0etelNZnZafFzz6l3XdoaM+tgZudIek3SBZL2kjRY0lpxGiRpf0m/kvSGmf3CzDrWqboAShQDcQ9KOlDS+pI61bVCqwEz+7akv0k6UtJASV0kbSDpEEn3m9mPa7CMXST9U9KpkjZROE73lrSLpN9IutvM1myJeppZT0nvS/qjpOMljZC0tsK2tZ6k3SVdKukfZja8qg+6qgPj6+01LHO1ZmZfVVifeylsP8QqWli8KXuPpC9K6q+w3w2SdLSkJ83shCqK/72kxyV9X9JoSf0krSGph6StJX1b0vNmdlIzdTxc0ouSTlPYv3so7N+9JO0s6XxJL5nZtlXUFWgTOCgC9TFL4eSTNX0hle6EIulmtWJ9AaBVmFlnSbdI+l9JXSW9Lel7kkYp/JhfX+EC/hRJ0xR+bHxXIWAAoLGdKamjpBkKP7x7K1zT0HqqBcSnOn6lsM6fkrSbQlBzR0n3STJJZ5nZcVUsY5CkWxWCKe9JOkIhULO5pEtisr0kTWqhenZSOP4vknS9pMMkbRjrMyLWwRUCy/fGgG9VzKybQmBYIoBbS2fH16clbS9pXYXjwzZ1q1E7ZmbjFK6fJOkuheus9STtIelZhX3rMjPbs8JFrB1fn5D0LYX9cT1JQxRutrwjqbOkS2OQNquOWyoEgrtKmifpO5K2lNRX0g6SrlTYv/tLut3ynsoD2hvuegN14O4uaUHWPDNblHr7qbtnpgNagru3iUf43X2ywg+6Ns/d56idfJYauUChJYgk3SzpGHdflJfmfUnPSfp1vOi/shXrB6By/xVfr3X3x+pak3YuPpVwocL55TVJu7v7wjh7jpntq9A6bqSkn5nZH1Pzy3GOQrB0iaS93H16/P97kr5lZssVbrgdbWaXuPvTNa7nMkk/l3Seu8/Nq9u8WIdXJf1a4SbgqZJ+UMHnTNtb0pqSXk99XlTBzNZSCLJL0sXu/mw969PexS4Hklbtj0o6wN2Xx/d/jU9LTFd4+umXZjbC3VeUuZiHJZ3r7lndjPzGzO6T9HeFQP3Pzezm+Bs57WTlYlaHuPuDqXmzJU0zs48VWucOVLh+/H2Z9QTaDFrgAgCAhmBmOyg8UieFHxRHZARvV+HuNym0wvi0hasHoHpJP6RVd8GEZu0ladP494/yg7PuvkzSWfFtX0mHlrsAM1tb0lHx7e8LBDN/qNA6VpJOrHU93f1Ddz89I3ibdqnC0xyStG+RdKVKuk+YWoOyEKT7KOb40PK+rBA4laSzUsFbSZK7z5f0k/h2a4UnJsri7hcUCN4m82dJmhjfDpaU1cVJMm7J23nB27Tfpf7evNx6Am0JAVygHYj9RR5pYVCfpIP4D2KH8ccV6xvSzPqb2flm9o/YKfySWMY/zewaMzsilXaEmbnCY26StI41HVztHyXW+ZRYVtLS7osZZV2UkW8tM/uumf3NzOaa2adm9paZ3WRme5S+1jLrdJCZLYrLnhpbA+Sn6WtmPzazZ8xsnoXO/mea2W+tyOBoljc4mJl9xsxutjBgwKcWBmiaaGYDqvkMsexNY33ettxgBFeb2cYl5C06iJmZbWZml5vZi2a2MNb9bTN71kJft/uk0o6N33HSt9X2Gd/x5Pz0cepuYQCrC83s5bgsN7OBWWmb+Uxrmtn34ja9IH5vD5hZwR+rVmL/tVnry8xGxs/98/iv3hmfe1oqfUmDmJnZ5nEb+beFAWI+NrPpcf/tVyTfKn0Qm9kWZnathUFiPrWwv//ezBrhovd/Un+fVGprD3d/1d2X5v/fgi+Z2R1m9p6F49tsM7vXzP7bih8bVw72F8s53swejvlXmNnZMd0q319rb29580ea2XVm9qqFY9miuP8/aWHwj88VK78lmdlwM/ud5QZJmWVhwKGN4/yCgyu2Yh3z95URcd94O67LV8zsh+ljjpmtbWZnxX1xgYXz0lQrYbBMM+thZhMsnM/mWO54erOZ7dpM3u0tDAzzNwvn+6VxW3vSzM62Io+J529vZjbQzC4xsxnxu5ltoX/6z5S+9orWdWSyj0hK1t0ltuoxcf+YNn9/6mzhnP9U/HxuZkdlLGMbM7syfkcL4/SCmV1gZuuXUMcjLAy49aGFY+s/LAyguIaVcb5pMAfE12UK3dJkuU+hlaqUC0qWY4zCI9CSdFNWAnf/UKGPTUk6wKzJgGYtXs94Lnkpvt2g3PxpZtZB0n7xbVndJ+RvSxauKS+0cF5fZOG8fK2lBiq24KsWBmv6IG7bj5vZYc0sq6JjhJntarnBw36VlSamOyf1WSoOilvuWjF9/puad3zYKqYdmf5fPP7+0ML5dn78f5NAo5l93sL55/W4nudbuG79voWbEMXq18HMTozHoAXxGPG4mX09fjdtdbyDZL97190fLpBmskL3BFJlx4dSvJD6O2vfXBxf81vmpqWDz+9XXSOgkbk7ExNTA00K/X55nI4tIf36Co+WeZHpIUk9M/KOVLggLpbXJXWK6UeUkPYfJX7OU0oo66K8PBtLerWZPFdK6pixvHTdR2TM/5rCjweX9NvkM+elOUjSx0WWvVzSKQU+759jmsmSvplaVv70jqRhVWw/X1Bo6ZJV9keSPq/QfYdLGl+snhnzDlRo5Vhs/c9MpR9bwnc8uUD6nRUuwvLTD8xI2z2vnn1S845Q6Ce10PKvkGQZn/W0OH9OM+u7yfpS2K+a+9zTCtR3/wLLOVHS0iLlzZe0d4G8lybLVLhgX1ikjB3qeOxbQ9InsS5P1qC87pL+0sz38JikXgXyJ/vJKZLuzMh7diNsb3nbyIpmPu+DFa7L9Gf8RQX5D1LhY8dHCvt7weNSK26D6X3lMIUfjll1fljh8emBkp4vkOYTSTsWWdbnJL3bzPd1YYG8ezSTzxX6yd+sQP6V25ukz8TXrDKWKDxWW+16LeWYuH/GtnZkgf3pqFTZpvAY8PIiZc+TtEuBunWQdG2RvH9TGMwned+92vXRitvzE7HORa/LlDtOvl7BMs5LrZsm15mpdBNS6Ya0dj1j/n/G/C9WuV53Sm1Xa5SZN33tMlKhVXDWdveOpI0Uzos3Fdk+v1VgOdUeI34S06yQNCZj/ueUu479dZXrs5Rrxa1i2vSxZB+FLjfy0+6cKnsNhUH0ipX9hqQtC9RtTUl3F8n7J0mnx7+Lnr8bbVLu/HNbM+lejukeaKF6nJpan6My5v8yNT/znCrpR3H+0kLbNBNTe5nqXgEmJqZVJ5URwFUYtCG5IJ2r8KNsuMIjMRvFk2IScJyakX96nPeWpOMUAqTrKgw+8TmFC+7nlQvgdlAIiiQXKx/G9+lprRI/5xox/dSkfhlldU6l7566UPtU4ZG8TZUb5fje1Hr7acbyCgZwFQZVSeZdqOwAy+7K/UB8VCEgMUCh37cdFfrqTMo4KCN/EniZqXDRe6ekXRV+rA5RGKQpuRi+vcJtZ0PlgnOzFQYIGBC/zyPj9/y2coGUkgO4cVv7IM6brtwgIT3jMkZLOlepAJHCYCTdlRtg4NmM73jNVPr0RfxbCj9gjld4rKqvwsV6j4y0xQK4rytc0P1E0mZx3m6SHkml+XbGeqgmgJvsJ99Lysj43GsVqG+TAK5C4DyZ/y+FEbnXVwgeHa9coHuRpOEZ+ZOg1LsKQbLHFR4h7Ru/uxNS280/63js2zH1OX9eg/L+lCrvdwo//HorDIZyeWreA5I6ZORPAopvKfyIvVjhOJKUMbIRtrf4/0HK7dePKLQOG6Kwfw5R2HculHRTheuy4gBuXA9JIPRdSf8dt7v1JX1J4Zj4loocl1pxG0zvKwsVbn7uHj//xnEbSNbDqQqB3PclfSN+B33iZ0qOlZn7k8J5Otnnnpf0lfg9rRu3sYmp5TS5KRi3qTsUbgbupHAs7i1pK4UnHl5Plq/sG5rJ9rZQ4Tj7ksIxvb/CceHLyh1X3lPqOF3hek2Oid2V269O1arHxI4Z21qyXfxQ0hbxM+4gafNU2f+bSn+9wvXAevFzHKDQv6IrBNuGZtQtHVj8a8zfW+Ex3AsUzvszUmnKDuAqd71T8VThek9u0N/STLorYrrl5X7XCoOXuaS5zaT7Umod7l2Hem6aWv7VVW7P58dy/lBB3vS1ywyF49+Rcd/rp3AjLjle3qpwLlkWX7dQuObcWdI/lDvvD2iBY0QnSU/GNO9IWi81b51U/unlfhcZy0quFYek1s3YvH2gQ0ybDuC+pXAD8FSF3zx9FPbfgamyr41plyn0gTwqrocNFI5zye+K15Xd0CV9LL5Z4fjTW+E4nQSGk+NDRQFchRbs1RwfulWwzHVSn+uXzaS9K6b7TzXfc5HyH4vlL8jaluJ3NT+1LR6ncP27psL1xfnK/X6a0BJ1ZGJqpKnuFWBiYlp1UnkB3B+lTnpNAjcxzWdTJ7Y9U/8flFrOrmXWMWk9+2ENPu9tsazm7gCfk6rv4RnzOyrXSm6Z8lqxKiOAq9By51ep/2ee+BUuZGfFNHcpI9gT002KaV5T3gWxcoEXV+hcPytI/FPlfpz0rWBd/l/Mv1jS1hnzhylc7Cb1KCeAOzqVb2CZ9VrZqq2ZdOkfNh9J2qjEtMUCuC7pGxn5uyjXcn2+YmA4Nb8WLSJLLaNgADdu18m297qk3hn5t1AuKHJPkfXvkh5U6sZIKs03UmlGlrvt1WJS+DGV1OFrVZa1Z6qsiwuk+UEqzZEZ8xek5v9Pid9fXbY3haBoUnZJN9HKXJ/VBHCTQPonkrbImD9IuYBn5nGpFbfB9L7yV2W0rlOuFeBShQBo1k2TI1LlbJ8x/29x3j8KfV/K3QD6SNLaZX6O3sq1rjowY/5pqfq9LGmdjDR7p9KMreE6LtrSOmN/OqxIWVsod2P1zAJpuioX7Loub966yrX6f6jA931qXn0qCeCelldGJVNZy1U4dyR5JzaT9oeptP3LXE5yc+rlZtKlryG+XId63l5snyyzrJfyP0cZedPXLu9J2iAjTXJTYYXC9eyxGWkGKbSQd0nfraAeRY8RMc1GyjUC+XPq/zfG/y1SxrVmFeu1lKeR0gHcZZI+W6S89DGsyTk+pumrEAh2SefkzdtCuadafl8gf/qmXqUB3PR5p5Kp7OXG7zbJXzToqfBUoisMrF2T7zpV9iGpelxSJN22Kv4E5v2FthkmpvY20Qcu0EaZWSdJ4+LbH7v7i1np3P1x5fro+kpqVqfU32+r8R0fXx/wMGjRKjx0vn+ywom8o0JAo6C4/q5XCEYvl/R1dz+vQPKDFVqBrlC4kC7UL+dZMc2GCq0JsyyVdKq7e8a8a+NrB0nbF6t/vtiH18Hx7dWeMZCIu7+u8ChSJZLtZYlap3+pi939tRqU87y7T8r/p7t/qtCSXJJ6qILBW1rJXgrbnhQGmfggP4G7v6TQskSS9jKzIUXKO9Xdl2T8/waFH0NSaGFSD71Sf1c7gElyvPhQ4cdwlp8qPDqZTp9llkLr1VLUa3tL9s+53sygb60p9rGY9Jt3ZdxWV+Hub6r09duaTvWMfpUVbpRJYZ1PKnDuvUXhWCmFLgpWsjBQ32fj2+OLfF8/Vwhsr61cX4UliceJO+PbvZpJ/j13/yijjHsk/Se+rdcx4VF3v7nI/PEK58vpCo/yN+Hunyjc7Jakw8xsjdTsLyk8XSKFIFjW932RQgu7tqZb6u/FBVMF6W2w3D5+k+VUuowWr6eZfVu5fegad3+m1LwZZW2s0Dp7mcIN/Wr8zN3/k/H/5Bhjkqa7+3X5CeJx82/xbdl9VZdyjIjXYEmf5PtZ6A/+aIWbrZJ0Rta1Ziv6Q/yNU0gyIOod7n5jVgJ3f1+56+Kv5M0+RuE7WCbpuwWW8T2Fm1JtTSX7Xee842dVzGyYpKvi2/eUO0434e5/VxgzpdC+O1DSRhn9awPtDgFcoO3aVuEOuiQ9FAdDyJwUHpGSwp3rxCzlBoS4ysy2bKV6ly0O5pAM7lXwx1y82Hw2vv18kSK7KgS1j1J4PPMwd7+6SPo94+tzkhYWWc+LlAsIjcwsSfq7u79XYN6rynXEX3BQqgJGKTeQyK1F0hUaIKQ50xXq1lnS9c0ECWvhjhqVc1uhGe7+iHKDZuxUo+XVWjIYxzIV+Sxadb8oNFLw++7+bNaMGOR4M74td9trRMk6uDN+tiY8jGye7Cs7xps6Wf5S5KZNvnptb3+Pr0PM7Ndm1rfG5VdqB+WCy8UG+5nSCnUpx/vuXmhAzvSNpbuzEsSbJG/Ft/n7U3I+mSPpX0XOJ12UG9ylyfnEzDrFgY2mWhioLhmAMxksLLmJuWmRz7mi0GeI/lXgM7SW5s4Dybp8QFK3IusyCbJ31aqjnCfHibfcfZoyxH2/2LG3We7+C3e3Kqe2GCSqOzPbS9Iv4tuXlQvqVSoZePdhD4OzVaPQ8WOmcteCxfbP5FiUuX/W4hjh7r+V9Mf49ueSLot/3yXpkiJ1aw0Fjw/xfL5rfPvXZn4jJcfZjc1snVQxyfHhCXd/N2s5cb+8J2teqdx9fJXHhj7VLL8eYqOTKQo37pdL+qq7zy6Q1szsXIXuhjZReCpik5h3K4XuRTZUuNk2uci1HNAusIEDbddmqb//VjDVqtZL/nD3FWZ2qkKrz90kPW9mryn06feIpPviHf5GkA4WZrY0TnlBofVqsQDjHxRaNX4s6Yvu/kAzZSbretuYpxTrFfh/VmsLSZK7u5ktVrgz3rXE5SSGpv5+uUi6YvMKcvd3zexnks5WaLX0JTN7XmFbeUTSve5eyxF4a9XiqbnP+y/l+iFuREm9ZhYKREbpUXwLfZaC216UlF/WtmdmnZW7eZDv0wKt2rLMTf2dOTp2ifXppNwNn1KOF1JohddX2euonG2xLtubu08zsxsUbkp9S9J4M3tGoW+5hyXd7+7z8/OZWTeFFkZZZdYiYDQ09fe/CiVqZl5RNdz+0ortK+lWgO+UkG6tvP8n55M+Cl1elGKV84mZ9VboyqHQjcK0dYrMm+/uC4vMr+iYUEMF9z0z66DQL7EUtvlvlVhmel0Oja/N7bdNWo63Aenvdc1m0qa30XL3+2Q5lS6jxeppZiMVblp3UnjSbJ8aHNeSJwqK3ZAqVbHjTHItWMkxppbHCCn0o7ujwnlrTYWnsP67wJNkranYuXmgcq1ML1TpT3msp9BtjcTxIZFsX0sqPJ+uwsy6KARvt47/OiE+8VHIdxV+eyyTtEfezbZ5ks42s+kKLdcPUbhJ04hP9QA1QQtcoO1q7oIryyon6fhY1j4KP/BdoU+k/5Z0jaRZZnavmW1TZT1roUfq7+YuvpMAa48iadaNr0tV2qPaVa/rlOUF/p+v3MeA0o8TFlxH8eLr0zLLTvJ+X2E07qS131YKA27cKOkdM7vJzAZVUnaGYsHKcjS3vSTzi20v9ZTUq+jnyPteC32Wltr2fqmw32VN3yyjnNdTf29eZh3SStoXovQNmULrrZxtsZ7b27EKP1xeUbi+20Ghi5hbJL1nZpPMrFdenvdU4LuLLZOqlX5Ms2CgMLaGbu4xzkJqtf2llbqvlJIuf3+qxfnkaoXAzHKFFnF7K/Rx3kth2+qh3KOpxRprtNQxoVaK7Xs9VNnvmPS6TLbxUvfbipjZGsVaAJYylbvM2K1Ucn3TXIv8ZP4K5Z7MKlVy47bUZUihaxBJLVdPM9tCoZVod4VBXfd091nNlF9UPH4mT0/UIoBbyv5XyTFGqt0xQrGLlVdS/7qvyJNkranY8aGS46xUn+ND5yqPD92aX0oT6QYXpe53TbrwKle8wf5HhYZDknRasScg4426pPupW4o8KfFHhScFJenr1dYTaGQEcIG2K33BsEGJj9k0adXm7ne7+64KrYEOUOhH7h8KF4R7SvpbA3SvkA6yNPdDJplfrKXsUQp37ntJus/Mtm2mzGRd31nGI02nNVNmraW3h4LryEL/VV0qXYi73+Du2ymMCnuYQt+rryr8ADhM0pNmVqj1cT1Uur2U2rKkpZ9kSepV9HPkfa+lthJvNM8q16JolyrKKWlfyJhfi/VWt+3N3Ze7+8XuvpnC44RHSbpSoeXZmgo/ah6OrV9aSzpoW/BHZvxR11wroPYi2T5fLON8MjbJbGb9lXuM+4z4+O297j7T3ee5+4LYyrBerWZby0Ll9pvTy1iXf06VkXwX5RwnKvFtFb7JUNJU4Q2VpGX7hs2kGxZf33D3cm+kJMtY10Kf180tI50n/31N6mmhb817Fa5rP5I0xt0revooz34KYyw872FMgYZU62OEmX1Lq/aTe6SZ7Vfjatda+jrg8DKOD89nlNHSx4diNyFLmcq+MRGD8kkQvtT9ruInZaSVwdjrlNs2z3X35lrKbqTcUxOZwduUp+PrpnSjgPaMAC7QdqUfHdqo2sLcfa67/9ndz3T3bSXtr9BCtZuk/6m2/CrNTP09vFCiKAk2zyyS5g2FEZHTQdwRRdIn67rq9dyCZqb+LtZ6sZqWjSu5+zvuPtndT3H3TZQbUK+/Km/11hKa+7zJ48z5F8DJj8Mmjybm2aDsGpVnZnwdambF6pLeL2YWStQSvHj/bZeWUc4SSUlwZZSZbV0sfZFylinX/2ipx4tFqs3gfA2xvbn76+7+e3c/QeGx15/FWVsq3GhJ0nUv8t3VoguF9Ocs1hdrsXlF1Wr7a0XJ+WRwhT8yt1auxd3/NZOu3Yr7edLNU6Xn5pnxtbn9dosKy6+3ZMCfLQu10jMzU+hDP52+kmVIxQfTSgZ2naPcWAH5ZVRdTzPbQGFE+gEKLTT38zAAUi3UsvuEllSzY4SZbSXp/Pj2D5KejH9fY2brV1zDlve2ck8lcXzIluxHBfdbM1tXufNzxYP/RZcpN1Dcxe7+gxLypG/sNvckSBLXcpV+UxxocwjgAm3Xk8r1n/elWhfu7ndISkZ3zb84SfpA6liDRTVbVnzsLQnIFBy9Pba62D6+fbTYQt09P4h7f5EgbtI302Zm9l/Fyq2jp5Qb9fzgIukOaYmFu/sVyvXn1pLbS7kOKjTDzHZWaKEjhb5C05J+57qa2cAC+QcpFwDMUovPnWzHnZRrtZBlbOrv/M/Slvwi9fflscVGs8xsY1t1dORkve1rZpmtjMyso3L7yhMxIFStem5vmTw8ovxDhceOpdb9sfmUQr91Ui74kaXYtt3eJOeT7got+sqVbkGdeWyJQZdG6P6opSXr8otmVkkL7uQ4MTD2l9pEPAZVtX16/QYxmxpfO6nwdcGeynUrVUlg8m7lrj0Oy0oQB4ZKWnBOdW/Sd2pN6mlmfSTdp9BicImkg929JufD2Nf2mGLLbyA1OUbEpzVuVAiizZR0gkIAboHCY/XX1qCuLSK20H4kvj28wmKS48OOZlZooLjuWrV1ctmauQnZkoOYJftdv3h9kmWscoHTird7M7tAYfuRwnZzSolZ031Ab18wVZAcw9+O1z1Au0QAF2ijYmu1ZATYb5rZmGLpzaxbOjBgZv1s1dFW89OvoTDQl9S036PkfXczq7Yvx6Ss5lqW/Sa+7mFmTYKQ8UfWJQoXGssV+vEtqowg7s3KtfS5ppnHBGVmNWnlWg4PAxTdGt8en9V6MQa4v1NJ+WY2pNgP5LhOese3hbaX/pUsu0pbmdk38v8Zf5j8PL79WNKf8pI8pdwd/GMy8ndQGPG22Hk0+dzrVNhHmRR+jCatlX4SW0Pk12Uz5S6G7/Eq+/mrJ3d/Srnj2s6S/tBMy2OZ2WEKj86lf7Qmx4t1FUYozjJBucHEriqQplx12d5iALvYjYJhqbxV92NXKg+jtCc/Er8Zt9VVmNkAhUFKVgsxoJS0Yru4UMA+YWYD844f6Ue3D8hIv5akSVVXtG24SOHGRD9JlzR3wydj+7tJuW5bLsy7CZQ4RY399E0x9ynXd+kP8m9mxRbgP45v31fT41Kz4rXHDfHt0QW63PqBco/rX9ES9YzXoncp3KBaLunLXnxgpHKNVug39l2F43Ujq9Ux4gKFVrrLJR3l7vPd/TVJ4+P8fc3s5Gor24KSx/O3NbPvF0toZh3MbJO8f/9W4bzcSYUHxfqpGncMheb8Qbm+pH+af/yM+9RZ8e10NdMwphAz+55yT3JOlvT1jJs4mTwMjvxcfHuwmW1XYBlHKIzLIYXB+4D2y92ZmJgaaFLo2D15/OPYZtKupdBvpCu0crpC0ucV7oyvK2kThdYMVyqM8H58Ku9BCoGE3yrcnd405hmoMNjBval6HJ233E1S8y6OedZQuMjpWObn/XosZ7lC4KJPLKeTpA6pdN0lvRbTLlb4QbCJQuD18woFGPB7AAAgAElEQVStQJI6/TRjOSNS80fkzRucKntO/vzU97I0ppkl6WSFHwo9Ja2vcGf4xLjeVmTk/3PMO7mZ9bEgphtfwbazoXL9Ar4v6WsKgfF+kr6sEIROHivLXEaheko6TWEgkMsUWtJtGD/7kLiNJdvhCkk75eXdN7Xu/yeur6zveGwqXfdmPmvBtHEbSua9Hr+3H8ftpXf8Lh9Opfl2gWUk29QShQDTwFj2aIWWX0vjtpD5vcbtI1nGrxQe6Wyyn+TVd/+Mcg5MzX9JYd/tG8v7msIPSlcIRAzPyH9pnD+tmXX6fEz3i5Y4tpWxHXdObYeu0Pp+gkLrivUV+kP7L4VR558ush3ckpp3naTtFI4XW8d1siLOeyC9HZa7LzbC9hY/zxsKQeIxCse0nsr1hZs+dg6r4DtJf8bfKzwOXWwakcq7uXLHnHcUjvMbxO/ysLjO3lKR41IrbnvN7itxO0zWxVaV7E8KXXvMV+6cc2bcpnvF7XsbhQFFb43f+9BUXpP0Qsy7QLkAY19JX1DoJ3CFwrEi87MoHM9d0pxm1kdJ560y13HR/UrNHA8z0p+dSv83hSeShigMYjRA0q4KQYjpkh7MyD8hlf+vCtcTvRW6O7lA4dpkRipN0XNTo00K3WElx7rHFfoX76PQHcE9qc91XAnbe+a5QdIghRtDyT5+eNweN1EIsif5r2+Jeiq0EH0gleYkhWvGglMF6/GyWPZVVX4fJV3nNLefxDSZxyvV5hixT+r7+FHG/D8qd91R8DhY5rppdt9XicffVPqrUunvVAhoD1Q4PgxWaD37E4Vz5HUZ+Sem8t8Ul99L4Xj9m/j/5PhQ9HjaiJNC92fJ57sjfr4+Ctct05T7fbZXCdtz1m+KE1LzH4zrrti+uUZGGQenyvhQoU/xjRSucYYrXG8tSW3vG9V7vTIxteRU9wowMTGtOqmMAG5M31urXtwWm76ayndQiXkuK7DcOwuk/0eZn3cdhUfvs8q6KC/txgoDZhWr75XKCCKrSAA3zs8P4v5XRpq947zm1tmSjLwtHsCN+b+gcEGdVa/5Cj+KCi6jUD2V+8FfbFquMKJsfpmdlPsxkT9NTqVriQDulxX67SpU54mSrMAyNlYuONrkO5Z0bHPfqwrvm9MK1LfQj5YTlbuBUOi73btA3jYVwI116SjpXIU+DJvb7hYptILpkFdGd616YydrekxS72r2xUbY3lLfcbFpsUKrtEq+jz4llJ+eXs3YX5cUSPuRQmvr5ObTCXXc7lolgBvnb6cQvC7luDowL++Oqe0zf1qhELAs+FnUjgK4Mc/pKn58TKZ7MvImA+sUyvO4pKNT79tUADd+xm8r3OQvtL38uMTtveC5QeHaYm6xdS9pzZaoZ14dS53K+h4VbpC5pAOq/C5aPIAb51V8jFC4iZScix6X1Cmj/J6pdfJPSV1qsJ22RAC3k8IN9BVqfpuYlJF/TRW/jrhV4fjT7PG0USeF1sWFPt9SFTknq/kA7vNFys6aCp0Xvqvmj/FzVeA6mImpPU10oQC0ce7+gbvvrRC4u1Ghn6pFCie69xRafv1U0ih3vz6V9S8Kd9h/rhDEmKXQAmqxQiDz95JGu/tJBRZ9mMJdz+kKQZZK6/+RpM8pdHnwunKDDmSlfVWh9dxpCheVHyoEBd5WeCxnT3f/plfQ95Gv2p1Cb4XuFP4rL809Ci3aTlO4kzxb4cfGJwqB5VsU7mYPKHf5teLudyq0DPidQkuYJQqt234raQd3f7jCoicp3AW/TKHV49sK29hChVYcV0razt1/kZ/RQ9+ioxUejX9FuQGbWsPHCsGh/1UIIn+iEDB6UNJYdz/B3T0rY9zedlBowfGmwud9V6FLjc+5+3UlLP8Qhf3veVW3n1yh0CpvksK2tkhh3T+vsA9v6rV9XLSu3H25u39foZXFGQqD0rylsO0sjn/fofCDf5C7f8/dV+SVsUDhGPdlhUdr31f4Dj9QeGT3OEm7uHstuxSo1/Z2jqQjFVoE/T3mWxbr85zCD9jh7v6Hqj5dhdx9sqRtFc5R7yocl95U6AtvB4WWk0lXGR/Xo46tzd2fVWidfILC+fhdhe98scL5+A6FH61D3f2tvLxPKKy3Pyi3Xb8jaYrCD9hC3Ya0S+5+gcJTRD9X2P4/VAh8z1e4RrlWoVVok36Y3X2Fux+rcJx4JOZZqBCUOlOhBe+i/Hxtibv/WuE66w8K5+4lCtvLrZL2cPeza7CMhxWuz34l6d8K2/FchXX6NUljPPRLWtd6VsLMtlVoZfyJwrmj4VV5jLhW4QmJBQpdJzTpH95D9zhHKQRGt1ZuoLOG4u7L3P07CtfFlymcl+crHB/mKdxwvVyhP/JxGfkXKzxFNk6hRerCmP/p+L9DlOtfvk1y9+8qPLlzu1Y9P98g6TPuPrGO1ZMkufuFCt/hJQrH5o+V+w6fVOjnf3h7ug4GCrECvyMAAADQjDhwzuz49gB3/3M969MWxb4Hkz4wd3P3h+pZHyDNzMYq3EiRpB5e2YBiaKPM7H8VbpLd7u6r04CLKIGZnaZw8+gDr3xAMQAoCS1wAQAAUE/JQDvLJf2jnhUBgDxJy+3b61oLAMBqr1O9KwAAAID2y8x6ufvcAvMGS/pefHtP7FYHABqCu29f7zoAACARwAUAAEDLmmhmHRX6Vn9GoV/gvgoDQ56l0O/4coXHlNGg4ne4VrMJm/rU3ZfWuj4AGoeZrSGpSwVZF1UydgUArI4I4AIAAKAldVAY7OWQAvOXSfq6uz/VelVCBQ5Wri/YcpysMOI9gPbr2wp9wZbrAEn0HQ8AJWgXg5j16dPHhw4dWu9qAACA1cyyZcv03HPPSZI22mgj9ezZs841ajwLFy7UvHnztGDBAi1ZskTLli2Tmalz587q0aOH+vbtqzXXXLPe1UQz5s2bpxkzZpSdb9CgQerbt28L1Kh1pD/3iBEj1LFjxzrXCGg87777rt5+++2y87X182byuTt27KgRI0bUuzoA2qhnnnlmjruv11y6dtECd+jQoZo2bVq9qwEAAAAAAAAAJTGzWaWk69DSFQEAAAAAAAAAVIYALgAAAAAAAAA0KAK4AAAAAAAAANCgCOACAAAAAAAAQIMigAsAAAAAAAAADYoALgAAAAAAAAA0KAK4AAAAAAAAANCgOtW7AqjM0Al3VJV/5nn71agmAAAAAAAAAFoKLXABAAAAAAAAoEHRAhcAAAAAAABVW7ZsmebOnauPPvpIy5Ytq3d1gFbTqVMnrbPOOurVq5c6dap9uJUALgAAAAAAAKqyYsUKvfnmm+rSpYsGDx6szp07y8zqXS2gxbm7lixZog8++EBvvvmmhgwZog4datvpAV0oAAAAAAAAoCrz5s1Tp06d1L9/f3Xp0oXgLVYbZqYuXbqof//+6tSpk+bNm1fzZRDABQAAAAAAQFUWLFignj17ErjFasvM1LNnTy1cuLDmZRPABQAAAAAAQFUWL16srl271rsaQF117dpVixYtqnm5BHABAAAAAABQlRUrVtS830+grenQoYNWrFhR+3JrXiIAAAAAAABWO3SfgNVdS+0DBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaVKd6VwAAAAAAAADt39AJd9S7CmWZed5+NS1v6NChmjVrVtE0t956qw466KCaLhdtHwFcAAAAAAAAoJWMGTNG/fr1y5w3ePDgVq4N2gICuAAAAAAAAEArmTBhgnbbbbd6VwNtCH3gAgAAAAAAAECDIoALAAAAAAAANJCrr75aZqbjjz9ec+bM0fjx4zVs2DB17txZY8eOXSXtG2+8oW9961vadNNNtdZaa2nttdfWzjvvrOuvv75g+bNnz9a4ceM0cOBArbnmmtp444111llnadGiRdp5551lZnr00UdXyTNw4ECZmd56663MMgvlkyR314033qi99tpLvXv3VufOnTVkyBB985vf1BtvvNEk/X333Scz05577qklS5bo3HPP1WabbaY111xTffv21dFHH12wHpI0a9YsnXLKKdpiiy3UrVs3rb322ho+fLhOOukkvfjii5KkBx54QGamrbfeumA57733nrp06aJu3brpww8/LJiupdGFAgAAAAAAANCA3n//fW2//fZauHChPv/5z2vkyJHq27fvyvn333+/DjnkEM2fP1+bbLKJ9tlnH3388cd64okndMwxx+jBBx/UNddcs0qZ//nPf7TTTjtp5syZ6tu3rw444AAtXrxYF110kR566CEtXbq0pp9h6dKlOuywwzRlyhR17dpV22+/vdZff31Nnz5dkyZN0uTJk3Xfffdp2223bZJ3yZIlGjNmjJ555hntsssuGj58uB5//HHdcMMNeuSRR/Tcc89pnXXWWSXPXXfdpcMPP1wLFizQgAEDtM8++0iSZsyYoYkTJ6p///4aPny4Ro8era222krPP/+8Hn74Ye2yyy5Nln/VVVdpyZIl+upXv6qePXvWdL2UgwAuAAAAAAAA0ICmTp2qfffdVzfddJO6d+++yry33npLhx56qD755BP97ne/01FHHbVy3htvvKH9999f1157rXbfffdV5p144omaOXOmxowZoz/96U/q1q2bJOnNN9/U7rvvrldffbWmn+HMM8/UlClTNHr0aN1www3aYIMNVs676KKL9J3vfEdHHHGEXnzxRXXs2HGVvI888ohGjRqlGTNmqE+fPpKkDz/8ULvttpuee+45TZw4UWecccbK9K+//vrK4O1Pf/pTnX766auUOWvWLH3wwQcr348fP14nnHCCLr/88iYB3OXLl2vSpEmSpJNOOql2K6QCdKEAAAAAAAAAtJLRo0fLzJpMxx57bJO0Xbp00ZVXXtkkeCtJv/rVr/TRRx/p9NNPXyVAK0mDBw9eGXy85JJLVv5/xowZmjp1qjp16qSJEyeuDN5K0qBBg3TBBRfU6FMGs2fP1qWXXqp11llHN9100yrBW0k65ZRTNGbMGL3yyiu65557muTv0KGDrr322pXBW0nq2bOnTj/9dEmhBXLahRdeqAULFugrX/mKzjzzzCYB4SFDhmi77bZb+f6oo45Sz549dcstt+i9995bJe3tt9+uN998U5/97Gc1YsSIylZAjdACFwAAAAAAAGglY8aMUb9+/Zr8f+edd27yv5EjR2rQoEGZ5dx5552SpMMOOyxz/qhRo7TWWmvpmWee0dKlS7XGGmvo4Ycflrtrp5120tChQ5vkOeigg9S9e3ctWLCgjE9U2F//+ld9+umn2meffVYJwqbtuuuuuvvuu/X4449r3333XWXesGHDNHz48CZ5Nt98c0mhO4i0v/zlL5Kk448/vqT6devWTccdd5x++ctf6qqrrtLZZ5+9ct7ll18uqf6tbyUCuAAAAAAAAECrmTBhgnbbbbeS0g4ZMqTgvNdff12SMvuOzTd37lytv/76Kwf+GjZsWGY6M9OQIUP0wgsvlFS/5syYMUOSNGXKFJlZ0bSzZ89u8r/Bgwdnpl177bUlSYsXL17l/8mAaEmAtxTjx4/XRRddpEmTJq1stfvKK6/o/vvvV9++fQsGyFsTAVwAAAAAAACgAa211loF5y1fvlySdMQRR6hLly5Fy+ncuXNN65VlxYoVTf6X1HHzzTfXZz7zmaL5R40a1eR/HTq0fO+vw4YN03777aepU6dq6tSpOuigg3T55ZfL3fW1r32tVdZdcwjgAgAAAAAAAG3MwIEDNXPmTJ1zzjnabLPNSsozYMAASdLMmTMz57u7Zs2alTkvCWQW6l4hK1/S/cOIESN03XXXlVTHagwePFivvfaa/vWvf2V2U1HIySefrKlTp+ryyy/X3nvvrd/+9rfq2LGjTjjhhBasbekYxAwAAAAAAABoY5L+Ym+++eaS8+yyyy6SpEcffTQz4Hr77bcXDNAmwd+XX365ybznnnuuSX+0krTXXnupU6dOuueeezR//vyS61mpMWPGSJKuvvrqsvLtueee2nzzzXXffffphz/8oT788EPtv//+BbtwaG0EcAEAAAAAAIA25vTTT1ePHj107rnnauLEiVq2bFmTNNOnT9dtt9228v1GG22k/fbbT8uWLdOJJ56oTz75ZOW8t956S6effnrB5e2xxx6SpPPPP18ff/zxyv/PmjVLxx57bGaeDTbYQCeccILmzp2rAw88UK+88kqTNAsXLtQNN9yQ2Qduub773e+qW7duuuGGG3TBBRes7MIhXddnn322ST4z0/jx4+XuuuCCCyRJ48aNq7o+tUIAFwAAAAAAAGhjhg4dqltuuUVdu3bViSeeqMGDB2vvvffWUUcdpS984QsaNGiQttlmG02ePHmVfBMnTtTgwYN11113adiwYTr88MN14IEHavPNN1efPn0y+6KVQjcDAwYM0BNPPKHNNttMhxxyiEaPHq3hw4erT58+Bfu4vfDCC3XooYfqoYce0pZbbqkddthBhx9+uA4//HCNGjVKvXr10tFHH62PPvqo6nWy4YYb6v/+7//UrVs3nXHGGRo6dKjGjh2rQw89VNttt5023HBD3XnnnZl5jznmmJWDo22yySbaa6+9qq5PrdAHLgAAAAAAAFrczPP2q3cV2p0999xTL774oi6++GLdddddevzxx7V06VL169dPG2+8sU4++WSNHTt2lTwDBw7UU089pXPOOUdTpkzRlClTNGDAAI0fP14/+MEPtPfee2cuq3fv3nrsscd05pln6t5779Udd9yhoUOHasKECZowYYJGjx6dma9z586aPHmypkyZomuuuUZPPfWUnnvuOa299trq37+/vvKVr+iLX/yihg4dWpN1sv/+++uf//ynLrzwQt19993685//rC5dumjgwIEaN25ck/WR6N69uz772c/q7rvv1rhx42RmNalPLZi717sOVRs5cqRPmzat3tVoVUMn3FFVfg6aAAAAAACgVl566SVtscUW9a4GamDnnXfWY489pkceeUQ777xzvavTat555x0NGTJEa6yxht5++2317NmzonLK2RfM7Bl3H9lcOrpQAAAAAAAAALBa+/GPf6ylS5fquOOOqzh421LoQgEAAAAAAADAaufRRx/Vddddp1dffVUPPfSQ1l13XZ111ln1rlYTtMAFAAAAAAAAsNp5+eWX9Zvf/EZPP/20Pv/5z+svf/mL+vXrV+9qNUELXAAAAAAAAACSQqvU1cXxxx+v448/vt7VaBYtcAEAAAAAAACgQRHABQAAAAAAAIAGRQAXAAAAAAAAABoUAVwAAAAAAABUzd3rXQWgrlpqHyCACwAAAAAAgKp07NhRS5curXc1gLpaunSpOnbsWPNyCeACAAAAAACgKj169ND8+fPrXQ2grubPn68ePXrUvFwCuAAAAAAAAKhKr169NG/ePM2ZM0dLliyhOwWsNtxdS5Ys0Zw5czRv3jz16tWr5svoVPMSAQAAAAAAsFrp0qWLBg8erLlz52rmzJlavnx5vasEtJqOHTuqR48eGjx4sLp06VLz8ssO4JrZkZJOlLSNpI6SXpZ0raQr3H1FiWV0kLSjpC9I2l3SFpK6S5or6RlJk9z9tnLrBgAAAAAAgPro0qWL+vfvr/79+9e7KkC7UlYA18wukzRO0mJJ90taKmkPSZdK2sPMxpYYxN1Q0mPx77mSnpI0L/5/X0n7mtl1ko5z2twDAAAAAAAAWE2V3AeumR2qELx9V9I27r6/ux8saRNJL0k6WNLJJRbnkv6qEKzt6+5j3P0Idx8laTdJCyUdGycAAAAAAAAAWC2VM4jZmfH1DHf/d/JPd39PoUsFSZoQu0coyt1fc/c93P0v7r48b95Dks6Lb48qo34AAAAAAAAA0K6UFMA1s4GStpe0RNLN+fNj0PVtSf0U+rat1t/j68AalAUAAAAAAAAAbVKpLXC3ja8vuPuiAmmezktbjU3i6zs1KAsAAAAAAAAA2qRSA7jD4uusImneyEtbETPrKulb8e2fqikLAAAAAAAAANqyUgO43ePrwiJpFsTXHpVXR5J0uUIQ+EVJkwolMrNvmNk0M5s2e/bsKhcJAAAAAAAAAI2nnEHMWpyZfV/SMZI+knS4u39aKK27T3L3ke4+cr311mu1OgIAAAAAAABAayk1gJu0ru1WJE3SSvfjSipiZqdK+lFc1r7u/kIl5QAAAAAAAABAe1FqAHdmfB1SJM2gvLQlM7OTJV0oaZGk/d398XLLAAAAAAAAAID2ptQA7t/j65ZmtlaBNDvkpS2JmZ0k6WJJiyUd6O4PlZMfAAAAAAAAANqrkgK47v6mpGcldZZ0WP58M9tV0kBJ70oqufWsmZ0g6VJJn0o6yN3vKzUvAAAAAAAAALR35Qxi9rP4er6ZbZz808z6Sro8vj3P3Vek5o03s5fN7Pr8wszs6zHfp5IOdve7y649AAAAAAAAALRjnUpN6O6TzewKSSdKmm5m90laKmkPSWtLuk2hNW1aH0mbKbTMXcnMRki6UpJJel3Sl8zsSxmLnePup5VaRwAAAAAAAABoT0oO4EqSu48zs0clnSRpV0kdJb0s6RpJV6Rb3zajp0LwVpI2j1OWWZII4AIAAAAAAABYLZUVwJUkd79R0o0lpj1H0jkZ/39QuQAuAAAAAAAAACBDOX3gAgAAAAAAAABaEQFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaVNkBXDM70sweMbOPzGyBmU0zs5PMrKyyzGyQmZ1oZr8xs3+a2TIzczM7rdw6AQAAAAAAAEB71KmcxGZ2maRxkhZLul/SUkl7SLpU0h5mNtbdV5RY3KGSflXO8gEAAAAAAABgdVJyq1kzO1QhePuupG3cfX93P1jSJpJeknSwpJPLWPbrkn4t6auShkv6XRl5AQAAAAAAAKDdK6fbgzPj6xnu/u/kn+7+nqQT49sJpXal4O5T3P0Ud/+du78kqdSWuwAAAAAAAACwWigp2GpmAyVtL2mJpJvz57v7Q5LeltRP0o61rCAAAAAAAAAArK5KbYG7bXx9wd0XFUjzdF5aAAAAAAAAAEAVSg3gDouvs4qkeSMvLQAAAAAAAACgCqUGcLvH14VF0iyIrz0qr07pzOwbZjbNzKbNnj27NRYJAAAAAAAAAK2qnEHMGoq7T3L3ke4+cr311qt3dQAAAAAAAACg5koN4Cata7sVSZO00v248uoAAAAAAAAAABKlBnBnxtchRdIMyksLAAAAAAAAAKhCqQHcv8fXLc1srQJpdshLCwAAAAAAAACoQkkBXHd/U9KzkjpLOix/vpntKmmgpHclPV7LCgIAAAAAAADA6qqcQcx+Fl/PN7ONk3+aWV9Jl8e357n7itS88Wb2spldX31VAQAAAAAAAGD10qnUhO4+2cyukHSipOlmdp+kpZL2kLS2pNskXZqXrY+kzRRa5q7CzPpLujX1r43i68lmNjb1/4Pd/Z1S6wkAAAAAAAAA7UXJAVxJcvdxZvaopJMk7Sqpo6SXJV0j6Yp069sSdJH0mYz/D45TOh0AAAAAAAAArHbKCuBKkrvfKOnGEtOeI+mcAvNmSrJylw8AAAAAAAAAq4ty+sAFAAAAAAAAALSislvgon0YOuGOqvLPPG+/GtUEAAAAAAAAQCEEcFERAsAAAAAAAABAy6MLBQAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGhQBXAAAAAAAAABoUARwAQAAAAAAAKBBEcAFAAAAAAAAgAZFABcAAAAAAAAAGlSnelcAq6ehE+6ouoyZ5+1Xg5oAAAAAAAAAjYsALtqsWgSBq0EAGQAAAAAAAC2NLhQAAAAAAAAAoEERwAUAAAAAAACABkUAFwAAAAAAAAAaFAFcAAAAAAAAAGhQBHABAAAAAAAAoEF1qncFgLZq6IQ7qso/87z9alQTAAAAAAAAtFe0wAUAAAAAAACABkUAFwAAAAAAAAAaFF0oAHVCFwwAAAAAAABoDgFcoI0iAAwAAAAAAND+0YUCAAAAAAAAADQoWuACq6lqW/BKtOIFAAAAAABoabTABQAAAAAAAIAGRQAXAAAAAAAAABoUXSgAqFgtumGoBl04AJispisAACAASURBVAAAAACA9o4ALoA2q9oAMgFgAAAAAADQ6MoO4JrZkZJOlLSNpI6SXpZ0raQr3H1FBeXtI+lUSSMlrSlphqQ/SPqFu39abnkAUKp6tyCuFgFoAAAAAADav7ICuGZ2maRxkhZLul/SUkl7SLpU0h5mNracIK6ZnS7pfEnLJT0oaZ6kXSX9WNL+ZraHu39STh0BYHVRiwA0QWAAAAAAABpbyYOYmdmhCsHbdyVt4+77u/vBkjaR9JKkgyWdXEZ5IyWdJ+kTSTu5+57ufpikDSU9LGlHST8ptTwAAAAAAAAAaG/KaYF7Znw9w93/nfzT3d8zsxMVWtBOMLNLSmyFO0GSSTrf3Z9MlbfAzP5b0r8ljTOzH7r7h2XUEwBQorbejUS1aIEMAAAAAGh0JQVwzWygpO0lLZF0c/58d3/IzN6WNECh5ezfmimvs6R949vfZ5Q3w8wel7STpC9IurGUegIAUI7VPYBdCwTBAQAAAKBlldoCd9v4+oK7LyqQ5mmFAO62aiaAK2kzSV0lzXX314qUt1MsjwAuAAANiCA4COIDAAAALavUAO6w+DqrSJo38tKWUt4bRdKUUx4AAADqgCA+AKARcEMRQHtWagC3e3xdWCTNgvjaozXKM7NvSPpGktbM/lXCclcnfSTNacP5G6EO5Oc7bOv5G6EO5Oc7bOv5G6EO5Oc7bOv5G6EO5Oc7bOv5G6EODZ3fzq/v8lupDPLzHa7u+dujIaUkKmcQs4bi7pMkTap3PRqVmU1z95FtNX8j1IH8fIdtPX8j1IH8fIdtPX8j1IH8fIdtPX8j1IH8fIdtPX8j1IH8fIere/5GqMPqnn911qHEdAvia7ciaZJWtR/XoTwAAAAAAAAAaHdKDeDOjK/FmvUOyktbSnmDa1QeAAAAAAAAALQ7pQZw/x5ftzSztQqk2SEvbTEvS1okqZeZbVQgzagyykNT1XYvUe/8jVAH8lev3nVY3fM3Qh3IX71612F1z98IdSB/9epdh9U9fyPUgfzVq3cdVvf8jVAH8lev3nUgf/XqXYfVPf9qy9y9tIRmz0jaTtIx7n593rxdJT0o6V1JA9x9RQnl/UnSIZL+191/lDdvQ0n/lrRM0vru/mFJlQQAAAAAAACAdqTUFriS9LP4er6ZbZz808z6Sro8vj0vHbw1s/Fm9rKZrRLwTdJKcklnmNmoVJ7ukq6Jdbuc4C0AAAAAAACA1VXJAVx3nyzpCkn9JE03s/9n77zD7aiqv/9ZJITeO0gIvSpdkCCELhaKKIJSQhGpNoqFXl4VBH4ogigSAtIjVYoiJQkQmhTpSEsIvRl6Se5d7x/fPTlz5uyZM+fMubkhnO/zzHPvmdlt9uy99tprrb3W383sCmQpuwpwFfCHTLYFgRWJ+Lp193uBnwOzA+PM7EYzuwx4BtgYuBs4vOU36qKLLrrooosuuuiiiy666KKLLrrooosuuphBMLCVxO6+v5ndDhyAhKwDkD/bEcAfy7hOyJR3kpk9BByMfOjOCjwL/B442d0/bqW8Lrrooosuuuiiiy666KKLLrrooosuuuiiixkJpX3gdtFFF1100UUXXXTR/zCzOd39vf5uRxdddNFFF1100UUXXXQxbdCKD9wuuuiiiy666KIDMLO3zOz01O/dzGyD/mxTF/0LM7vazOYpkW5t4IFp0KQuupjmKEsLzWx9M9utD+rfxsy2bjHPD81s8063pYsuuuiiiy666CKNrgC3i089zOwLZrZaf7ejXZjZCDPbs0S64WY2Ylq0aVqgu+HJh5ktZ2ZfMrMV+rstnxaY2almdlR/t6MFzAvMkfo9Eti7f5rSxXSCbwAPmtn6eQnM7CDgdmCZvmiAmW1Uhu6Y2fJmtlFftKGLzy5fEDCScrRwL+DcPqj/SuDHLeY5Dfhu8sPMnjWzEzvaqi66mMaYEeiQmQ02s/lLpJvPzBpi9vQ3zOwoM9umRLpvfMp44E8Nprf9hZkNMrPFyozrLmZMdAW4MyCqCn8+hYThQRoD6LUNM1vCzHY2s0PD9V0z+1ynyo9gOLBhiXRDgd37sB2VYWYbm9koM3vBzD42s3NSz7Yws1+Z2aLh1gy14TGzW8zssBLpDjGzWyL3BwZG7VXgSSSk+Xnq+ffMbNy0UFaY2cJm9gsz+6eZPRquG8O9RUrkn8fMNg/zqGWrUjNb1cz2DvVtk7o/k5kNysl2ELB6q3W10KbNzexgM/uWmQ0oSFdqswB8DCzcuRZ2Hv1ACyvPo9TzeczsADO7IIzjw1LPVjCzLc1stk61u0M4H1gKGGNmh6QfhPe5AtFNB/YrU2BYy9cN1+IlsowGflYi3WHArX3Uhi4q8gXWz1asqfL7ch5ah5qZxVvAGy3m6UVxQRIMARbqVINiMLMFitaiDtbTCl83XcHasKaOlDGTmX3NzE4wsz9ZSqBpZguFcVz4HaqUEfZkC5vZrJn7c4by/m5mp5vZklXeMwfD6ef9SQdo2XPAb0tUdRKKwzPNEL7h2mZWxAseA2xXorhtgKM70rAc9BUtqELLTMrkHcxsnXbyl0Sf7i/KIsyFe4H3gReAk1PPtjezi8xs6X5rYAVMq/VsRkFLQcy6mH5hZgOBX6IAcwuG2+cBe4bn3wvP9nH3R3LK2A0RqTWQcD+df3vg28DhwNMVmurunjvuAgOyMbA4CmqXV8bxqd+TECGrBDNbCDgd2IFG5YaHzfOB7v6amVVZ5N3dl20j38xokxCFmc2NvvFmNO+/hvrN7KfufmqzRoR6/uLuO2buHwMcSf2mKv3/JCQYeBE4gwobHqumCc2OH6y85cAnaGN3H3B9JtDiMGB8iTJWRGM8Xf9A4Hr07aYAjwOrZPLdAfwVjc/oHE6VtwSwCa3NoyTvDsA5wFzUf7+VQ/t+bmZ7u/uoSN55gP8DvkdtfTkPGBee7w0cB3zT3e+K5B+MrK/S/XMecE34f2/gj2a2pbvfnMn+Cuq7tmFm3wd+gujk7an7ZxNoYcDY0IbJkWKeC++wV5PqPgS+ZmabUqOpc1pJCxB3fz7vWZXvH/KXpoWZfJ2Yl8Nocx6l2vEV4EJk5WxI4PliJu9VSIF0qZn1tN3q5mvaIGBtIBF8vwjcFwvS6u7DzWw0UkieaGbDgN2A5YFLkHD3CWDHvHU8Ve++aCwvl7n/NPA7dz+zKHtR2WVRtg1Vxw3a4Ladv+j7ZWFmMyFasDowAfiTu7/bTzwB5PMFI8M1rkn+vdD7nN9KpWa2ADDJ3XPnTqvzsJX6Az4HFPqBDgqDbYEVgLmJj2139zS9vgdoVVH6FnqfjsHM1gC2BK5x9ydS97dEa/TiwNtm9jN3PzuSv4pFpLv7Xm3wdYUws8VI0UJ3f6lJ+lwlXQZp3uxid38l3L8SuAm4oWQ52frXQrR3WWpjeGYUvBtgc+ACJGD7ex+VcSTa320I3BnKnAkYi+hQ8j22N7PV3f3NSBtmBdahmCfA3VuiAykU7k8qtmEk1WiZUX5Ns9DWhA970d17yvJlCdL8mZltgvbPZ7v7A6n7w9GcmRXoNbMT3f2IVurJYAAaWy3BzOZEtGtilqfLpDuGNmlBB2jZNxH/f6y73526fwRa/5PvdrG779L8rVtG5f1Fgnb5czMbCeyK3vU9YM5MnieBnZB7rVyFRcn6twX+U/QeBciup0m9lcZAFxm4e/f6lF9IUHIj0IOsuh5BC+mIVJoh4d7ROWWMDPl7gXci+VcJ9w4Nf9u+Ct7hLEQge1JtSV/JvZ5M3luAOyv24fxoY9wDTAZuQ8Kyv4b/J4e6nwxp894v1u5m7a/r64I23gu8nvNsSST0KKq/N1Z/ph1XA/MVtGFtJGzKvsM3Qv4JSOizUOy90CJ4Q/j/NeCuVvshlTZvjBRd0feP5M8rN/37ZWCrNr7j+cAnmXs/DvlvBBbLKw/4L3BHQdkG/C6M15bmUcg/lNpYH40Y4s3CtReyuOtFm6WhmbxzIMahN3zna7PvACwa6v9NpO4FwxjuRYzDHyL55wvt+30k/7lIkTOwAh24AW3EZ07d+1Jox9vh2z0d3mH3grFZZhzcg5jturFZ8prSF98/lNESLcyZl2XmYbQ9LfRfwzwK91cDPghj9HS0ccqOo5mBd4GLMvO/k2varMBv0KYm+/5vI2uf2XLyroLW8YTOfBzqGgnM3qRfBgCXp/p1CjAxXFNSfX4lMKDC+L0aeK8TbciMg5bpeR99v5+HcTQsQiPSdT+CaF/HeIIWv0OUL2gh/19y6l8DWVmvlLm/ZfiOPYhWfj+n3JbmIVJUJFcvEk7tlnPtiTaok4FxBe/2Y+CjyJjJrulZfmajME73atZ/mfnQE9o9IpT73/B/s+ucnDLPCe1YPHVvEbR5T+ZV8nfdnDHQCl+TfdYyX1fQP/uiNSM7h58E9i/I18o8Sv7/ANgz5H8duLDsd8zUvRTwZij378AhkTE8O1LG5n3DTpQxDng+c28HarzSnoje9gKHR/L/BPhfpO8brk7ToWnchjxaVjb/KOCDVJ4pwAqp323xZ0iJ9RGpvRWwNKKNvcDz1Hi2zSq0fwzwVs6zTYAzgTUz94cja86E3zshJ38lWkB1WnZFSDt76t5qIc8niO6+Gd7jm036aVakDNmR/DVmt0yeTuwv2ubPkXV7L3A/sFYoK9b/E4BbOlC/R56VvfL2FpXGQPeqv7oWuDMGDkQa3JuQUOFlM+tNJ3D38cHqZUvg2PQzM9sdEawHkYbrATSJ0/kfM7OJwNbu3heuN44B9kET93rgKZpYVqTwe+AKM/uKu/+jQv0rADcD+7r7M+mHZrYM8EfUz0ejxTeLHwI/QpvSv1KzIhsC7AJsj6wTT49YRmxYYC0xEFk/rgVcl5PmV8BgRNxPRAKYd3LS5uFxtEg/YGY7ecZC0sx+hAQSsyBrgjR+iIQMX3H3x0P6WB0PUrPGuhP4upmNpWaBWNQP2XKuztxbGo3jD5EgdHy4PwTYApgNWXOOpxF7IMuAA9Ai/Te0EPaG/Dug/j0TCVQ2ATYFrjSzdd390RJtTqwm1qbxeOauiPnY0d0nFRTxOLBmwfNDkRV9L/APWh8HRyGLy/3c/U+R5+eY2T5I2XIk8JXUs0OQNcgFaA59EKFDr5jZY6jvsvgF6uMTgV+6u5vZ/pn8/zOzh4gf6TsaHSE7y8x+5O7vl3jfLFYBHvF6y9qdEDOzs7tfHyzPxqMxc14bdSSYgIQk45ACZmm08Wz16G4aVb8/tE4Lf5R6fByaL+3Ow1IomEcgS6VZgO3d/ZqQvs66z90nm9kDhCNxnV7TTEfCbwLWR0zzi9T3wxLAwcCXzWxTd/8w077HgvXiw4jBdcSol/EL+iO01ryI5uhF7v5JaNfMyNrxeDRXfgScao2+bBeN3EuQrEdbInrUiTYcGymjND1394b8ZnYK8ANEq2Lr8b7IevaQbN6ArdDcGZMqc8tw/wUkTN8C+CISoFTiCUL5neYLyiDPivUgtGm8ILlhcp9zBRI49SLL2j+a2YPufm8mf6vzMKGzCYaGKw/JBvbk6EOzrYBT0Tc8GVn2fwmNieXQmr402tA+GCniLODPZvYt9P0moLHYAHcfG953dbQ2JevTcmSsz3PgxE9sbAA85PVWqruh/j8NCdi/jr7JQeFZGnug8bkf8BISUI0Pz4YA30K06EwkgMuiHb6uDuFI7GXIujT5Zi+Hx4uh0wWnm9kWwLe80aJ7E0QnfgLcDVxMPW+2M6KzpyHr202RUOpPZvY47VlTJzgcKY0P9NppgZPSCQKf8x9g3T4sYwiQ5TG3ReNmF3d/OFjnTUQ05f8liUyuGk4JPx+nBE/QaTrUThvaxFRaFrGYLTrdlF7Tngv3nkf9Oznzux18EfiPu/8vdW/XUO/P3P23puP/dwH7AzdHTqWsUXBSJWn/hsjwIoa9Ec07PLlhOmr/55D/BTQff2Fmt3rjCbeqtKAqLVsT9eEHqXu7oG+yt7ufH3jTx4Dvh3IaYGY/QfucuWPPM0hbcndif1GFP/8+UnZ+w91fhNz+f5jG05vt1L86jXvsqqg6BrpIo78lyN2r+oWYlteBeVP3YpqZq8loccP925FF0BJN8l8LPNtH7zABEacvtJF3MBLifowEC1ui4yCDY1dOGc8DrwJzFNQzR0gT68PtkND72wX5vxXSbE95C5309RLw+ZyyXw3P56rwDWZDm9Le0JeHhvvzomOOPUhTu3ck71vArZl7sTH0V+Dd8P+q1CwuK2n4wrd9HW0UFoq0b8Hw7HVgqcjz1RDjdyIRDSticE4M7//5cO8IxDy8hKzAe1P/x66x4XkPOuKXLv894NoS/XcB8GHBN3w8fLsN2xwDk9Dx7jI0Z1Lm3iOICZylyTtcjo6lZcv8L/AMYE3yjwJeieQ/KvRPD7Luvgj4dbifvY7Mea/3I9/mAeCNzL3r0XGz9PhLrt4w1qL0BwWf+hqiuY8WvWsb36/S9w9ltE0LaXMeZuZJ2/MolPUS8O8Sc+licqxVql5IONmLrKOGRZ5vjDY6PcBxkedfQmtiD9pQJtYSp9DEAiTMw/eBZQvSLIuUBY+m+idr1VrG+vUHnWpDJ8ZR6vleaOP95YL6Nwxp8ixIJ5KxZAH+FN59g/B7NqREaDgBRIs8Qeo7tM0X0EErVkRL7s/cS05gnYrWxO3C7/OrzkPEe5wbrl60Hpybc/0J0fHVC/r2utCH64bf55LiG4BBSHjxDrB8pJ0xa91Cazu0ER2W6f/dy1w57/AmcHXm3j8RjZ8nde8e4MlI/s+jOXYaMCjyfGakQHifCO9NG3xdpIyfhjwTkWB1UKb+3dGa0wP8NGeefoIEoHnf+gBS8x0JrnuRocFGtGhNnSp3Ahn6lPP+l5B/Oq4TZXwEXJC59zSZ/RgSfLyWuZesM99t4b07vT9ppw2VaBn187aVNe2XrY6TEu8yCbg8c280mpuzpe6NBZ7OtL+VfdF7wMY5bXgKuDtz76iQL9nrrRPmyuWR/JVoAdVp2TvAZZl7dyI+emDq3k1IqRvrgz1TffUo2ovkrTHnRvqq6v6ibf48vOc/S/T/BQQr8k7W36F5UGkMdK/6q2uBO2NgRWC0F1vugQSkMR+jn0dH2V+MPEtjEjoC3RdYGLjZ3R9qI+9z4a8hK959CtI6cd/PCyO/LLlaNXd/38zGICvVLA4B7vGIX9BU/r+Z2T0h7R6pNo9AQvRzcrJ+giyZ7vJgxRTB3Mgn67t59TeDywpsuJndivwX/SZYRSyPjoE9jixEY9ams6HNdDNMDe7k7o+a2SpIOz0YbeCK+qEIJ6BFYJdYH7n7G2a2CwpQ8P+Q5jaNY5FQMRq8x92nmNnP0Yb1WOCbyBr5eKS1XhSNrUVpPkceoDFIkKPFuBkWR8x8HpYGbvOU/9YWMRP5VnVpPIHGRRrLIAajwbdnBh8BC0TuL4mE2N4k/xRk0ZLFMagfDQl4doqkSZ47+nZZzISsxgAws9mRcP/6TLo3qfkaByki0u3eIVxFMHS0LsEY1K9VUPX7QzVa2O48HJZORvvzCDS2xjbJCxLg9FUQs53RhmNzd2+gi+4+JtDWp5A16lHJMzP7GbJknhk4G1m+bIw2Rj8GhprZd9x9Qk7dyyLB4zM5z3H3ZwKdTyzhx1IbvxujDUreWEzWoyvdPerzsc02pFGVnu+P5sFtBfXfbma3IevEmL+1BWkcRxsi5dG4UMaHZjaOuOVcqzzBlVTnC0bSOSvWhUP9aWyOhCTHuvsU4Coz+zewXiR/S/PQ3YdPbZROhd3u7nvm5mqOdZEAOWZZirt/YmYHAF9F1lXpMZSeD6Xhsg4bDVP9FT7t7lVOacxFo3X0F5Fg/e3UvWeI86XHoTHyk9i66rKA/imyejqOxkBJLfN1EeyJLJeHZemB66TLeWZ2O7Ic2wspB9I4EglAcwMVu/sZJv/6RyC3VucG35hDkXVxq9bUCRZBVpHNYOhbxdCJMiYD80xNqIBXy5Cyjg/4gEa/mCsioeZFJdqQoNP7k3baMJJqtCxtMTuY4tNNU9c0OhgQO4XZqVnyJieI1kHrQ3ocTkQni0DzMeFXjyJ+6jBB0v5/uvurOWkWArL7600RP/4HAHf/d1jPYsG6qtKCqrRsFlL+dk2xBdYAxoS1KMEr5I+TH6I+3bXFsQid2V9U4c9nptyp5IVJjbUO1t8JVB0DXaTQFeDOGKgq/OkEYaiK59GGrR1MpP2jLQleRBuJZhiENM1ZfIFaoKUiPAt8Pc3UB8fwd1Vk9Mej71gZ7n6eKcrlHcj3KYix2SXDbKTxMrBSieJXQcxzUlenNjxbICVGHgOZbNhuR5vQLL4M/KuoAnf3sFndMvyeYmZ3o6M9WyHrwH8gS90YPkFC4ljwqeeA1c1sJnePzuVwLPsLFAtYJyHhS7t4GG0MmmFpGgOpTaYgMEUKSxKnNx8ia+9mGILeM4uE4a2CFxBTmGAL5KPzjky6eZE/twSVNwvuvknbra6h6veHarSw3XmYvLtRbR6Bvsvncp6lsSyyIs6FKSjfLsgidiGkZDwpPFsBjcXbInRxCeAfMeFtAnd/PQgwp7ohMbMbEH15Hxju7heHRzea2erI6mMYcnPzfXe/PFL025Q7lvduSIu7D0u1oRf5sKsiPGu5DRlUpecrUu7438toAxFDL7I0B6aOhZWQ1U4abxOnWy3xBKC1N1XfMbTOF5xPjQ7tjjZCWdqVIKFDV7t7LFhJ1c1WlXm4dKTuVjEP9RHlExcecyTKqSDAvIMa/SHcH1axbkKZrzRNVYz/IeU5MDUIzDw0ftOZiPPmGwI3FilFA19zD+JhsmiLr8ugqjJnXRoVqDE8ioTxCR4j0BFqgpWtCPxbXlOo3xe/iwSwzbAM+et9J8r4L1LczeruHyHlsNOoYFmMxvX/fcSflEYf7E9abgMVaZm7D0kehjVtVMU1rQpeo96twPpIqJt9n1kIigV3Pya5GVwnPOgRV0EtoB0hchpVaUFVWvYy9a4BNkL9lc0/J/m8RzuKhASd2F9U4c+fp4krmOCuZlU0VzpdfydQdQx0kUJXgDtjoKrwpyXCYOWjwsbg7r5Z5P4lwAFmNqe7t8S4pxfqChgF7G9mi3otem0dzGxRxGCeFWsG5Ra3hijFHWr/BcBhZraARyLQtgIzmw8JT+ahxvhujDbKeRZXtyLr3S3d/caccr+DiPfvcsqosuGZl3zrhTTmJGXJkLkfs07PYiFSm3o091Z199HBInG0u4+JZy3ENcgH7MHkRw89DFmeFgkmbiHfj1oZnAqMMrMdcoRDSTTY9YHvZB49CaxpZrPkWeGGsbU68tWcxSPA2mY2T0ZAkM6/RMjf0MdphrcC/gnsZ2ZnhP9PRHPg2ky6NUhtSDq5WQiM9dbUhIZ3u/uI8GwhNAae8Xj096rfH6rRwrbmYXrOVJxHoONXW5nZ8u7+VCyBma2L1sOLY89Dmq8gC+l5qVlVpE+prIhcy3wXuDST/Q3KRSyeQv2mfSvkdmHHbNtd/qM3Q9aCR4Q6YzzcTcDGZjYoTwAarFeGovGSRScET1XbUJWef0yxr/AEa5KvOH4OWC/FV30djYOs0GQh4oKXtnkCaI8v6LAVa9XNVtvzsMC6vBW8Qb2fw7fC3yHU+xOdlfiJjkqoQL/S+DewpZmt54q8/hM0rrJzZnlqfmXTmAMZXjTDwkjAk0Un+LqqypyZkVK0GQZTb8TwIZrbd9G+4OUBYAMzW8zdY/2Lma2I+IE83rgTZYxCcS7GBqXV3khoeVWqjAHID+19mbzjaN8HcKf2Jy23ocO0bA9qcTb6A3cC3zSzHZFy+nA0JrNGIysTMRDyzvjob1mInEFVWlCVlo0BdjGzw1AfHh/yZ+PerIYMMWJoR5EAdGx/UYU//ydwoJnt4u5Zy/sEP0BKnBGdrt/y/S/H4O4es0CuOga6SMOnAz8O3avahY4b9hD82IR7db5R0KavBzlMz+b/XXi2S0H+/cO9xLdfu1dedMJEkzaGEPVzGvfh7GiRfRwFass+/wpi+u8kEjkcLcQ95PjTC2n2Dn1wY5O2LIaI7LqkojU2yTMwtOFuYJUK/TAULXC96MjOyuiI6pTwfv9H3EfsSsi6+23kwmKBZAyFvt0Naf/eA5YOeQaHa0Dmd6krU/+jaHFeuuDdkiBRMZ+LDyBmv8in3uqIab4/dW8sOf6WWuz3+ZFwqAdZ2X0z9N81SJg3gpo/zFw/x8jaZRI5PphKtGMwEuJORgKibZCLlc8jK6tLwrNTIt/kV6HNp6XKy9KRP4b32C9S977U/NYNyuZHgoIksv33qvZ5wdx7mXr/Y3/NpFkz3D81p4zdgaFt1r8WsrZJ+21L99/O4d43+uL7hzLapoVV52GHvuFW1PzPrhgZR8sgS/MecnyBoU3AB2G+nw58OzKWZ0ZCh4si+f+IjhsWzdW5Q5o/ZfLNUuIdNyHiRzo8G4KEV5cCC0aezx/m2OvAkD76BpXaUHUcIev2HsSvWOS5IYuaXuQKIlZ+Qs+uRkcvX0K0b6lMOa8CYyP5O8YThLSLIYupdYDFSqRfCligwje8LrzveuH3eeF9vppJdz/wWCR/5XkY0s2B1t4vI6urhisn3x0oIGXy+zuh/uNT9xZGPMvjHRjzLfEv2SunzC2prQNvhv+fIvBMIc2C4TvF6ND9iIatXdDutUOaBt/3tMHXRcq4AFn1NfjgTaUZFNLE3mEc4j+3KMi/eeijO1L37iT4E63wTXcK73tLMpcyY3hutGfpAb7Wh2XMgoKKJvuoycABmTRbh2dHRb7vx+T4WW6xP+YJfb0zwQ94yXyV2kBFWtbfFzq58DH1vnaz/sE/R4bH6HAbRoW6dwxjLvERPiyT7lEUaCqbvxItoDotWyHUne7DGyNpeoEzc/rgGhQIrRP92dJ6HPK0zZ+H8TEJ0epfob1CL9ovrozcbHyEeKqF+6D+9J6oQa6T+S55cp5KgmXahgAAIABJREFUY6B7ZfqzvxvQvTrwESsKf1olDMgas+2r4D3mQD56JiNt6WjiQWxuzuQbAexZop+Gk7M4hnJvzxCX+8L1Zoo43R5rD9pcJELOm5Hfr03CtQeySOoJ75a34dgXWTFmHes/Cezf5N1uAW4L7Z+Cjg6W6r9UGb8IY6AXBQlJB6PaAm1Ue5BlTWyB3imMk55UX3ySeo+PkWVZkj5p6wqp32UCDdQFDgl5Dwn5nydEd009G4iOQY8no+hIpdkn5H8z9MMQdHR+JsQ8/hwJJHqAfUKe2RCzckWH5vHnw3eL9UMvOpa0WpMydkNC9h60gTkKjfvdYlckf5mgD3nPpiDmL5knSfCSW5CfyVvCsweJB1QZSG0MP4MCE/aG8XYimgdJeQ1CmUh5LStCQr5Fkd+9P4R+sszzXZGAqC0hbUG9S1Fjav6eGtNpoeHsyDrinL74/h2ghU9RYR52sC9/l2r/Q+Hv80jBldC4kwvyXxTybJOhV9mAEWOJCxAXQGvYGCIKNbSujg791dbGlBDci3gQjZGhve+iwDanhOvycK8npGlg5GlD8NQHbahKz1dDAuAepBA5Dq3DeyAf5sk6OzUoZaSMeRA9S29Ufp1J8+Vw/1eR/JV5glDO94nzBU8QCSjawTlUebNFhXmIrMUSIXJpXiCV/7jwPBmjc1KjX5eGsZiMoV/nlLEK4oWeRGv9e+H/s9DJm3TaVviXUu8Qyh2O+IL3EI1dKfP8J6HufSJ59wrP/ofWtGUQXzMAKUCOQJbJPeQE+aJFvi6SfwjVlDk7hHf4KHyLYaHtQ9Ce4iy0JvYAO6Tm7kfAhR2YB5eH+t9Ge6pe5J7hUmrzoiGYZh+UYYimfAtYJvJ8E+BHZHhzpOT4NbVxv1vow41iV07d86B9VloImeZL9kYKrvVTdWavSm2o+A3LzsOPkPXmVcB2HW7DFmj+PhL6cpHM84PRPN05kvfA0L6vF5T/9ZAmL7BoZSEy1WnBcNqkZeH5aqHvrkU8RtaAYD9kjPPVnPyVlRnkr8dP0mQ9pvr+bBNq9Dq2L5tEsYyl7fqREWDsOhbxceNDG/4CHN1XY6B7pfqqvxvQvTr0ISsKf6oShg60f8FAeJP6iq6eTN7cBSeT7uxs3kwZ7V49oYwkcE3eN3iXiOUgYqYvT6WbgqwRJob/k/KuJLV5qtD+oj54G9gp5/ki1KwA/lcwDi8N4yWp731kxbR2Ju14pFRYOvO71JUpayASeiV9NRmN+QnUNoC9aOHP68M/Z77dZOo3j73A2an0q6DAQlun7g0I4+AvyG/bLTlXnhB9VsSEXIMYvceBG9GiNkeJMd5L/RwqZFgj+Vv6BpFrCWQxk25Huv/uBZYoaP9caDOXN3avoMCqMZTRtiKkQ7RsAcQsL5i5vwSyRno4jNU1c8bf/ql7DbQNHQltsJDoxPfvBC2kxXlIXPhX9sq1JAjj4KVIG18HftjkG75E4+Ym9i0uBt6K5B+B6HVCz+9HNP5yJAhPNj9XhrTpKyqcb2HOx36XXgvKjJvUNaWP2tAJer4x2oznrccvAps06dvZkAD5ECL8Dwr69H/AF3Lyt8UTpPKPTKXtocYXpO+dm5N3o1aunDKGU3GzRRvzEAkSXgvpXkAuPXqRVe2rqXe/nUxk9FQZKyOe78upe9sifiTdjvuIrK1I+JkIK2Lj9iNSQk8qrp2tzPnIGJ2H/HlwZmb8xfiaPzapozRfF8l7FOWVOVH6DvySGs2MzaMe4IhUncsj6/svRdozCCl35y/ZvwNRwNrsuOlFwqBTiZxK63QZFcZHmhaXoueZ/HOgvVkvmofXklkLkdK7B/hNps68b9VSG1L1lFaoRPqg1SuXtk7rCyn7XqbAcAEZm7yCApnlpWlbiNwJWlDiPQtpWQf6cSMqKBKosB5H5mLTuZBTxqLIoOWhMP4/RErq3wGfa/L+lesvKHvW0D8vkGMBPD2MgRnpstBhXcwAMLNZkWXH1tQ07ROBG4A/e0FU8ZB/UcSQx/L/1t3z/Mp0ou1/QRYqyWL8NAVBLLzeZ2IvMNKb+EcKQbK+5+4Nwb7MbOP2Wl7fHjNbDGmjN6IWwONFZIl1jrs3+DcKUYBPDumORNYsScCNmZGPxeORAOhQd89G6W25/R7xz2ZmDwDfdvdcX1FmlkREPSLWj5l0C6Ax9IbH/XV2FKHOA1Gk9qUzj59DFp2ne46f6FDGdui47JfQsTWQhvnOkPeKgrzzIWHrWqSipebA3X1AkzQtI4zx0kTd3fdonqqtdnwFBRTJ0pGrvMSiY2YrE6FD7v5AQZ4BwGVIqJJEJE78KC2GGFxHwvFvxcakmY1AvtZGNGnfcMTgNdAcMzsZ0dE13f2hcG8WZDE3mNrYeBsJfiaGNBOA99x91VRZDbTNzC4BNnP3Bp/Nnfj+VWkhskotPQ/DOzrN50waSfrCeRT8Ca9B/Ti6x+ujFsfyfYyO1u+Uuhf7Fpcja4/ZMvnbeacELdEGMzu6jTrSldUFRjGz8cTH0ExoHiV+dyeE/Et3ug2hHZ2g57Mii7WNaVyP/+b5QTk7hnZ4gpBvZ+SD+TVk6TLSg2/xQE+Go8jYCyO+5pJM/mQMloG7e8sxMUJshUGIbuWu8a3OQzP7A3Lbdby7H21m5yKLoAHh+RbI3cjzwFbuXjrgSfCj/nVk+fkEcE227Wa2HjXfkKOQsOPZ8HsZxKt+G/Xvhi5/ftMtzOwbaB5tQI2v+RgpW3/v7mUC/rXF17VAC9NpGui7ma2F6MFGiBcGKQbGAme4+7+btGM34CA0DmcCzktouZltj77n4e7+XE7++ZChS3oM3+TupYMCdaKMVmFmo2mNJ6gL6Bdo+9FI+byvu3+QsxY+DHzo7l9stc5mbQjl7wWcgVwXxcbSJ8itxDmxMs3sJKRMOhOdsJmAeMQhaI+1P1L4nIa+0W+Rf/Ndvb2gV+m6d0PuPMY1Sbc+OpF4fub+C8jNyxZN8v8LuasZXKW9ZdAfe7yqyNCiZuOzbk2suh6HdCNL1JtuQEf3Z31df+C3ngP+7u77tNa6LlpFV4DbxXQBM0v8Tq7iOQGMCvKWFeDei45olQlWNc1gZo+gDeoXPCdSr5ktiyz3nksLeDrcjtzgU5G0G7n72NTvU4FJ7n5cX7StVYRN2tTNcqvKhyAMXDD8fLOZwCfkORMxiBPR8fsnKAjeEROid9E+OqQIKUtLzkZuWxoEbWZ2HzC3uy+furc7cC6yfvgV8i38Q6QY+1lI8xGKovydVL7YRulSYFt3n7V5r/QvyszDvhD+VYWZvYI2XBum7sW+xYPAvJ4J9BK+d9vwahG/+wxmNhD5Nv09UnRUes8W6q1Ez0vWkWx6X3T3ntTvUnD3toKjFLTnFiRwW8vdH8tJswqyjrvD3TfNPBtNviB+KWDJ8PtOYHJMaNJfMLOnkGB4aXfvzQpwQ5plkYuL4939/3W4/lHIFdnO7n5ZTppvIyuuv7n7jp2sv6+Q4WumlWL9GKoJ8yrT9yC42BUJbt5D7jSm0vIwjx5BMULygsj2G4IAsAw+Qe4qHvCKwYxTdT+CgkoumxJY5Skz13f3JeIlVWpDJYWKme2BLHc3cve7Cuq4DcVnOCcIU8chAfuWFdtfia8MCuVR7r5Lk/wXIjciDbxhVSHy9AIzWw4F60qC/F7t7oeFZ+shf+mXufukSN7RtKnMqLoef1ZgZsnpws8VpKkSqLmLgJY17l3MeDCzjYBX3P2/TdItj5x1j815vjg6orYCcpIe05K6u+8VuT8XsrArJbwNlnJpbBi5l2AgOk63FvKp1nGY2f3As+7+rTayLwvckie8BXD3Z8zsVhT5vU9QVngb0mbHwEHIsnG6gLu/SH3E+EJkBdBh4Xi1xWq3QceP1nP3qlHc24KZbYM24zf0R/39jD3RcaJh2bkULLTOM0Vwfhgdj20Q4LaAmZHCKYYlkJ/fNL6GGMfvBwufW8zs6ygg2M9CmneRm5JmWIZ41PvpCmY2D6K7CwET8oRufSGALWjTnMCKwMQmVk/3AFuZ2fLu/lROWesCX0BuFOowvQpgqyIosq4LVrr3mdk97n7GNKi3JXreJsYTlMjoSOJ4WrBgpfP89BrA6LzNIoC7Pxb4gi9Gng0rKtzMVkNKpY8QHeoogsCgBwkePilItwGwXEZg8DkUoCahsYm1/syBlic80RjkpqJBgGvVTlNsCNybJ7wN9Y8ys4ORX9I+RaA136I5b71ZUTlt8jVJGwYhH5JpK/L7mvGN3pno7W0jKNN2Q2vy3tRctU1FmEcTkVChowLcIPj5h7uf1CTdIeg0R4zHH0lrQnA3s2uBgzyc8KmAZdCx/Gb7g4+QRWZf4BA05mMKlWeAf5nZFUihcjAK1JXGAcBtecJbAHe/28xuQ5a457j7XaZTiWt26iVKIM9KfRI6vdUMnyP/5OrIcBUKcBFvvCeQK8AtQwsC/XXgl+7+asH+PIaonCBlhT0oSUdNIQWKEfFH5CLm3Eihw1poQxaV1uPPEAZS/03qYDpJcQmSeySW0DMjpQwoSOIF6CTl3/u0pZ9ydAW4XYCCqZyLCHcRDkOEPWZ19mPk4yl9rD59HCr57Tn1PI6EuGUxPPW/o4AXyzXJ8wpweLOCg7VP+pjWiyjKdNEGciXk/qEdvE2BpWYK74a0fYo2tWOvIB9ln1Z0QgC9IGJ0mwpvA1PfLoo2alcif1mVBLhmtiBy1j+M+nlwK2JuXy/IOwgFHhlGPYM3Grg8byNgZlsDhyKLqltz0myKAq/82t3/lXk8LRUhqyKmOob5aBSwfgl40uuPZz4AbJb5vYGZLebuLxOBma2IGMlSjE3qmBvIV2vucfOc/C3TwiC4/T/ge9R4jPMIGwcz2xsFGPpm0YaqXZjZJsga52xPudwIVjh/QL66es3sRHc/IqeYM9Ax67+Z2Y7uXkfbzWwZxHA62jB8puDuj4YTLfugvppuEawOF0DfPYpgPfs8+p7Jcfzkdyfa8CUaaelod7+zINvsKC5BM7yF/Ma1BHd/xMy+iYIp/Rw4IZvGdOR7f3SkeHHy+9DdfdnMvZGo/35gZtu6+/9y8n4fCdjSAoOP0BH/BIlQYmHqBflvIWFrDMPD32aCg6HA7oi3TTA/8vffDE/TRMBTxWIspDkN8SdZ9wJkfheOVTNbNdWGR939mnB/JuR/NSpkNx2LPQadLsry6O+Z2Z9Q0Jo+d0fSJr6PeOdvJOuWlsUGPIyUN1NhZt9F82I/d/9nLJPJXdSZyHp3VCTJMKQMaoYVkauXGM5HfiG3Rd/5P9S7AFgdjYG/I5+1ayKDgjXMbO2K1riTKaCdKSxJgdu7iqiqUFkJ+WhthleA9VK/n0WBs6YV8gSw9wObNVEoL4/md9WTfbmuTlqkBcPRWD0RKY2Gt9CGBjmBmQ1FVtTvoX38WBQMM40xaI+8DREBbkX06XrcDC3uGZsq8/oCZrYCmn/R/YGZLQX8C+2RrkPfK6vYuhqdJOgKcJugK8CdQRCYxJ9RY7RnyUnqHvd11o6vvqTurZA12zvoCPMwtJD8AAlVd0AuAn5Ho2VagjOAs8xshWaWwAGJbxZDDPrtQNT3ESIGLwJ3NbEEmTe0Y0d0zDCNXtPR5QNzGO0J6FhWO7gJ2NjMBhUw0YPQRiNKxFsg7skRq/tQ1Ns6YWMF7dhNwBZmNtBLuBuoCjM7qkJ2d/fjM/c6IYB+qYUyhlWop2ij9hYVrTODIPVCtGFI04VV0Pc/1Mx2iVn5BmuqixAzn6UpewG/NrPvufvtkar3ANZB1o95uAdYFzGDWQFuW4qQPrDm/5CUBtp0FHsJGunTJ9QsCUBzbHPgQjP7dnbTZWZzo0BnM0XKIpN2C2S1siG1zddHwcLklIjwO5u/LVpoZnMgQf3qyFfYv5Ev5DSuRYz4diggW6exN1pzpirrzGzpUOdAFGRhMeAXZnaruzcIatz9n2Z2OhKcPGZmj6J5t7mZ3Y02yAOBU3PGcr+hRdoYo4Vl8QqaB33WhiD43IzmwsOYtc56SFHwZfL5IQjWs55xg5H93Q7MbAiipesnt1J1YmZ3Aru4+/hI9heBL5qZucd9nQUFzbpo7WkZ7j7RzO4BdiEjwA085RgUNKWM/9IYehENGmdmX/Uc/6IRvEi9xVnil/9LwN9C+wzNw6pK7dhpirdobhAA4pNyN/VVLcZMfhd/iNwyHY+scLdAbkyWQ0qyDZCQ5B85bRiMhOlp4eB51BTWewN/NLMts7TQ5OP4JjR+DX2X8eHxELSuHQx82cw2jQlxzWw9L+kj2Mz2d/czI/fnRlaUZWhBVpHwecT7N7Pgn4TGeho7Iz4oqlAOuBW5GPgeOt7fLmYhYxmcwk+QsGo08vP6ePqhma2Extkq6FtNQd98W+D/TDFGmuET5FYjGwPjSWBNK3CxFhQ9qyNBY+x5VUF4VYXKx0jx3QxrUK84GoT4xZZhjW4vlovcS5DwlZuhQL9ZnIvm/NVm9k13fyJT14ooOOAAqgsuo0LkVmkBtT16YoxQ1Z/rYYh+bp0oPrOKGJe7nQdQXya0DzrjFqmj67EpzkfRiQoyp1KGlWkyKWWe6XQ1yN/8R6nfpeD1bhKL3LjMiZQkuyLhdYP/34DDkfD2wITOm3xTp+v8wMz+g/qxiyL4dBBJrXtVu5DQ4x1KRn2O5O8lE107p56rUbCK7P3rQt3rht/nkopgiBbBP4c2Ll9Q/m8Q4duLJtEUM/nGAydV7MPZEPPRg5ifO4C/husOahFw7wNmi+Q/AW0kFmyj7iFI6HZpLD9iXi5BUZuH5JSRjiqZ9+2z0Sc/QP6WkjKWAt4Mz/+OBEB1YwNtOD4kEykdbbbeBP5CJKJzH4z5vHdtFuG2l0h0zTBmX6BCJGB09O712PiIpN24ylVQ7nXAfyq8w0phXPQii8nvI6Zys/D/OGpRZ7MRyVdFjF8vYqSPR3N5r/D/U+HZe0QiBqOjcLeVaONtyJdX9v4FaKM7qCDvoJDmosjcaTZ/0tdLwOdz6hgX3nHB8PsXodxdM+nGIkv29L3LQ/lvow12L7KQu5Ta3Ly4Sf8cm3mPKeFKv+MxBfnbpoUouEMvshiaPdW/IzLpHkZMZaz+3Vq5IvmfQqcG0veOCu04NPxeJ7zH5U36ct/wrbPf/3Xgh03yDkSb+r8A1yPlW+y6ud35mlNv0ThuSgtL1jEzsk56sy/agIQZV2XS5a5rkfxD0TqVpHkTBdeIXp3s/1Qb5g/l9yLe50JEB48P/78Tnj0LzBfJf2Z495OJRGRGipWTQpozKrTzchR8KHv/6tC+MUjZsiriEaJXzhg4HylOegnWbZF0dfxiuDci9M+s4ffy1KJ+b42Eckn/XFMwBsvwtfcCr0f6pAedEsjLt12o4285z4ciGjMJCTbWzbYpfMO3UMDEWBm3IMHasgV9dRQSOq0Tyb8gNdcg/0EnELJtmA8JkH8fyX98Ku+wyPONkVFGD3Bczjt8DPy0yTeYGwnmY3N5yfAOZdbmWP4PyND52NhAAWjfztybANxaYgzdSg4dKTMOwzh4FHgp5/npSCE6Z0EZc4U0fwi/Fwhjz2nOF6ev/6FAXnOFcg4L73Ba3jshJUQPEtDG2vZ3RIOLeLNZwly4KvLsZSSEb/Yd7gRejty/OrTvqIK8R4T3uip17yHg4Wb1Fnz3unWuyZXwalGaQ40eT0a845/DNSbc60XBo9J50rxSb8iXx0vtifYwk4Fxkfor04IqVxjbdzSbW2htfTv1fAry6Zv9Js2uKZlyO7IeI4XbIyXGQpbO5+0HN0EGLaNCvl8T9oip9+3E+zfLm9Dga8iZ54iePlriG15CZk3uXo1XN4jZDIBgfTkMbfJPBJ5y9/eb5Nko9XM00t7/Jid5oh08GUXCrLO6MbPXEPOyXvh9Lo0BJ2ZGm5nRHnHEbmZ5mucY3EtGTA7HSr6AfDDmRqk1s18gP2rjkJ/KrIZ7ZbQRGYp8+pyYeT4b6seBwP7eQlTiYK20DFpE30eWhc+Fx0OALZHg9K/UHPcncHc/3hQ5fhtqmvqLqT9itTPSnJ6GBC+bIqLfi/y/3Wlmf0bWGGntWC+NwQruQsKZL2TeYYVQz5tIUzsBbaKzcG/f6iup7+jI7aVRH36ImPHx4f4QZLUyG9pQjvfGyOuD0RH2K4EfNZs/OW2aEwm4JgB7ex9GFi5ow0Zo0/cDz4nG2yT/SNSHh7r7KTlpkmBh53kqSqkpiMX2iIE40jPH9cNRzeOAXwJXeMZftJl9gJjn7zZp40XANu4+Z+b+EGTxeTOyUnkj83x+xIRthpRN48P93ZMkdMCa38ySSMfPI0Ho19CYHOzu74Q0syKlzRh3/1oq70CkDDqIxmNYk5GVzWGeH739K0hY+AHa9I2gfh7siSJ5z4787TVYw1ShhdaBgCdWixTcDHVRylP5JyGh6A6pe6ORX7IFPFiJmdlYYHF3L7S0C+N2Deojh9+T9w1CnkUQHV+VEtaL2XeoghzaCLXgVcOQwm0E8gVc2gexmc2OlDxHAd9AwrPtO90GM/s1OlH0Hlr3mgWErPM5bGY3oTXubESL+oMWJ+/wNyTYyFrUzw+chawqf+Puv8w8H4w2xPOgOXwR4gscjcWd0Zo3CVjD2/B1aWZzIQu7Ae6+SObZ22gtXymP1jUpe+q8DzTlBOQaYRd3vzKVLsYv7oA2cTu7e2Jxm/AnCW0wRI/XdfeHQ5oRqSYMR4rEPAv5utMU7r5Nqv4NkLDDES91HvV9vxvq/5mAL3vEFYaZXY1OH2zkNYuxGC28GVjC3VeKlPEm2vBuVNBXhpRWD2fnopmdgnjCExGt9pw23AdYhLd/GgmBl/cct0kmt1pPIevNBlpqZu8ji9nrgN0940rDzNZB33oZtHdZMfP8r0gRdn94j2a0YEIm/xPh3VZM3avrA5ObleeBV9N9YAoserm7fy+vvpDuQmB7d589/L4l9XgYUl48EckKGofLIf/3l7n7zpHyn0dKyW83accopCQZHH7fik4g3IUERyAB7fOI918KKZo8pFkY8QkzISVrkudeRPfvRJaeJ6P9zijkrmjjkP6LMVphZhNQjJDCQImhvUPcfenM/cuRwuTb7n5FTt7tQttivOXqoe2zIP/mlyI+3UMf7Bje72NgA3d/MNDf8cCZ7n5gUbtz2jOSGq3aHdGiO3KSJ3zl1e7+n5zyZkYC1n2pP7kF4g3PQnz7J6k8aV6qqZuVkKYX2DHbz52gBVVgCuR2lTcP8ns1sLm7z2Hy1e/Apu7+XOp3KaTHYSfW42Ap/2/Ef49DFv9LI/q3HLIeH4CE9W+n91dlYGb7o9PQQ939PqsFbdvV3V+wakHcRhbkTcbvze6eN8YTejpDBWruV5SV9Hav6fdCQr9HW8zTqnYwSfeDSFkfk7IKQ5v7HjKWmGjRfLGgPaWvTN7tkdBivcz9I6lZi/UAFxT0xwNoszJPQZp5Q5oHIs9uoWad2IOstu6khMVVKk9T64LI/z2hjA0RET2woP0HoIX+y+H3HqGMS8LvtrVjJd+hktVXk/E8GFnFXQYsFHm+YHj2OnFroaOQBWcP0vRehASRR0WuI3PaMIJ6C8pb0DG2EZHrnE69e6YNGyErmx7kB3cfdPRqo9gVyf888FCJeh4Cns/cewMpeJrlfRwxeNn7/0Mb6Wb5ryNjKZP6hiND/7+LmPlTwnV5uNcT0kS/KZ2x5p8p1Y5kLOyQSbNjeHZYThnzoSjohyAh0HeBhUv2zeTYt82MkSnAtTnP26aFSHB8ZeZenpXERzllj0SWZtnrPLRpTE6bXAGcG8n/CYGmpb7He0h5mG3D+1XnXM47/DW89xPAj5EQP8+CYuO+aENB22YNffxCbExRnheYBKzSR214NszXFdss/x1a5In6oJ8fQ5uaWQrSzBLSPJbzfH1qwpbYN5hAxKo15B1ccK0S6MtdoazzI/knpedRG+9fN++BnZAiawrw49T9cynBE6CN7cGIr3oSbXLXj9QZ45eKruhpCmC/QEvyxv8n5FgchvwtW4xFyviI+tMiZ4X658qkuxgJH7P5/4tOtliTNoxCgYyz+T8kxzo4k+5KIlbc4dkqyLq0F62vX0o9+0l4x17EfzWc3kL+M1/KvnML4/B3oc92KRib+4d7x2fyvgn8q0QdNwL/qzgO70NK3rxxcEWJdlyR/g6Ij/0QKTEeAb4SybMV4ufuQHR5CDrl1AP8PKRZgvr9TXb/eC9SQuS16yPgwhLtvxD4IHJ/A0Q3JiMjjM2Q0Gzp8P95aD5OSY+vTBmbUjtNE5vPLyPBX5J+oVB27nu1MAYb5lyFshYCvoMsow8N/zfseULakdT4p15ED2K81blo334UsHpOWZVoATnrVE4Z+0fuJYrzwn5FPNd/O9HXkTa0vR6nvkcvMoyAxtPKKyM3cY/QPr17gowl9vRyof336BLf8F7ghf5u7/R+dX3gzhj4EB1raAWJdQFoE/ka+RriRLtypbvHnEq/gY5AJUh8gg1BjFuCWZFgogHunvWz2Ap2RUKJh5MbpgjLx6IF/S5kCbWzmV3hcQ3u8ihSbK4/NXefFDTEsYjNw1L/G9KsZf1pTS0q8/u4yL1WcSTasP4hL4G7n2EKIHQEsJW7n2tmRyBLOpAFQBmflEajA/tOvEMVnIAUCbt4xALA3d8ws12QYOD/IZ9/aRxDzX/Qgmiz2VAMNS12zIJ4OLU+mItin0VOTtBAazPac8DoVDu3QtbbRW3IrgGLINrQDA8jP6NpJEfvm+F+5Jsti8eR/9l58uahyRfehogRzeIYav0/B7LYiGFXqPNFOfWbegf8Xrosj4cHq/SFgSfcPetT7L9I8RSdby4rpailSRN8EQkNcr+hu481+cJdLydJFVpYOeCJuw8vymhmC6NN3HLULITSeI16/5XrI4uHrGXALMRPCHQCX0VWV+t7TnCi/oLLF9q+yHrkBKTkSaNa2hU3AAAgAElEQVTIYngywdICBRJ8to/asDg6utxuYFBDQon+xBBkoZxLt9394zAXt8l5flc4RZRYuaWDoI0BRhWUP55yFlcTkZuXLB4kn4dpGe5+iZm9iFxjnGIKBPijFvL3UFPI5aGt2AjASmb2OXd/IVXfH83sjtDGjWjs+9+7e9EYmwcpKJphTvLjkbxGLQglaAMMom8PpO7PQzwGw5JIUddsHEwhzpu/QTm//lPI8b3visy+DjqVsjswxsyOQy4lvoGUfvt7CJQbwdzA9e7eli9SZLW4OzDCzFYh+FAGZg2nSb6NTgW9iU6tpPEQMNTMFnH3V2OFm9miiCe5L3U7sVwzpMj/B7IejuETZNjyfM5z0JjbxMzm85xggMGifxPq/W8uhGj255HlZMNJBJe/9wcRT3KUu//S5LP2ScTj/cblP3iDcMLnq9SfRrkBWUYWjbH3ES/UDAtR74M2aeM4MzsICeO/F640DI3BgzwnMKS732Jmy6ITD2la+hLieUe5+wep9K9Tzu9uGSxNhwK8hXZdWjLt8OT/cNLsdk9ZOraIqrRgrJn9wt1PzcsY+PsRiDfO+sK+A/iWma3jOadpTbEfVkBuqzqOiusxaE/4lLufnVP+42b2dWStfSQS0reKh6kepBkzewspXQ4Kv3dDruvGVSi244GaP8voCnBnDNyDFtTScPdhyf/BhP2GCoR9PDqGkuBBtKDuhIhQsukehjRUncaayO/nB6l7u6DNy97ufn7YLDyG/Hi2IxhphsKjQUVw92M6UP+6yAq5GR6lPqjQY8i9AMjiaZGGHI1YhswC3aF3qIItkGYv96inu39iZrejQFFZdEIAXclJv3Um2nNaMdMO3qHGlBRhcRqDOzyJgkM1w2LomFUWVyBh2wgz+26WEQqC7RFoo3p5JH9/KxHq4LXI9rFnD5If0LEK5qKc0OAlFBCo06gc8KQZ3P21sMF8Cs2XQzJJ7gS+aWY7oo3z4WhcZAO3rUyTYBNmtgS1wKBFgXOyCp1BwC3Tm/A2QRCgxgLMVVWmdqQNSFBVJiBhHh6mg8LHNjEZKQ6aYbaQNoowjy4IVyt4nhJHHpG/vtg4PRm4ysw2qLhpmwp3v80UmO56dCJoKSICmwrlT3WlYWbHIFc35+XnmJr2AWQdtVf4PQIJO0aQo2gtgdeQ4KYZViQnajfayKfLuBfx1vuiIMHJhncT4mvqh+i0RDMMQRbXWVyLhCZz5QlQg9BlGAV8deBZ9ghKvzORcQXI0mwnd3+soG3jkc/ttuA6Orw94hl+Fi5HlovfQf35DvCtiIDzYiSo+ZuZbevudQHrgtD0MqQMvDhV55hUmjGINx1D+7gstPufZvajrJDSFLDx90jYfVa4Z8BqaJ9/Y0x4m2rvq+Hb7IhcbUw0s/uR4Ded7h/kBMtrgnYF4em6qypUknGY+POfZvCMW49+QlUhclVaMAX4rZkNo4QrlUj+/0OC0yuCIdJNmfwbof3BFBoVMR1DhfUYxJOkgx/3AKT55cDfjkFC7HYEuIvS6H6NUE96XcuFmQ1HCr05UrdHhqsKLzCCDgVq7oKuC4UZ4UIWlB9TEHChSf6NafOoYsh/HCJEg8PvOZE2uwdpCk+hFoTg133w/u8g31Hpe3eio8sDU/duQv5PY2U8iISSuccWEHP0BvBgf3/zSNveBcaWSDcWeDf1+zJqDt9vRAv8Yqnn2aNmK6LNZtPjXNP4/T8k50h4Jt215Bz16+f2z4Y0zMlRnInoGNtt4f/kyM6dlAiSVqEdN4TvO7QgTXKc7frM/X3C/aK8SWCXfSPPZkenAHrQsc/jkOuA76IN3zPh2ZP0caA8JNw8Am02Yy4wKrvBIMetRdkrp8yyLjD+g3yPxp61TQvpQMCTFvrvH0QCxyAr5I+pP9r270yaz2XblXluyNpncqac2NHYWOCce4B/9uUY7cAYv4EcNxb93QZ0nHMibQaVRIKZKcgXXX+9211oXV60IM2iIU3TAD399A4HIdc2xyPhyhBy3DJE8hbNrwXRWjb1KGo/v2eWRlU+8oyEEVNIBReL1LNFuPfnnDKSAJgrh9+zhHnRE2jM5eH79BACNGbyj0Un4uYpaMMSyAr2hkj+BZAQeQwRdylICTYaCVwWKNEnhyLlQUJD7wSWbJLncMTLNy2/STmLIivYhxCf+yGyOv0dOUGTkfAzcR0wCZ38+GW4zgv3esNcn7kPx+ccSLCZzJfx4ZuMRqcYkrXoPgJvhHw7Twz9fWmJOi6l3v3CxXRofUC8YS/iZ+ePPJ8/jNUeIsfnP20XNbo4IPO71aut/H30TpVoARVdqYR0B6fmQEL33kJuVpI58ONOvG8f9eEbpAIqIn+1PWSCk6N9eYMrkRLl7xTKuz/neal1DcUOcGRR3FLeEmVXDtTcvUJf9ncDuleHPqS0NW8gzeIuSAtWeuNfse6Vw4T/curetujYTHrDO5W56HD9H2cIzSDEnP0jk+4C8v10HR7aeCuwXOT5cshapQdpqPv9m2faNw5tFrYoSLN5aP8dqXt3omMRCfHvRUe+Fgj3phJtJLQZE8r4Wn+/c+bdHg3jbemCNEujjUq/+kbMaVu/RnhN1fO10I53kAB1WbSJGRD+PzYsvD0oCFY2/6lIIHEiCh44V7g+j4IkvgOcUlD/YGSZOXVjT70A7X4yzE6H338WdMQ3T2hXJ8ALeXYL11yZ382utCCw1WtKTvvPD89/VPCOB4V6z8t53jYtREL4R8Oz24GfUqMp+4W/PWEs50akLvmtriHfj+4Woa5HkLB9kczzg9EGYOec/IkgegqymDgFODrviuTfDW1I2laM9uWFjhm+BzwzPbYBHbediKzJcn3INin/WGTJux99tKltUv+BYQw9goKoZJ9vgoRJPSjoYlFZA0KfTOtN+4bUlGot0SOabPiQRfvfyFeCzBdo0U1og/dszlV5DKM17YaybS9Z5nqhb55Hroxmop6f2oiagK3BB29IMzj0wdqpe19C7lnSa9E1RJQdyFK3FwmTB2XfLbTp8tDO70Xyj0A+LRNaeH9Ifzni55MYE1dSoOBEArprQ9p3QrvGhHLfQAFr8vpxIDo9cTdt+tyu+B3nRTxBdr1Ofl8NzDcN2jEnCkL8Lo28yHvIArdB6RrG2MsUKP6RAcHLpPxOIgXpK5TnST5Cp3+uAraLfMN+F4RPwzGTzJcVUr9L83ZV8+e0KSoPyLsi+SvTgjDOzqXmQ/yIMH+SMbxnib7dGtGC7Bz4Dwpu3O/fv6DtDwL3pn7vE/psj9S9mdFJ5QmR/s+7LqPGS/QAwwvGZRkB7kgkwH0LuWMYHPJeRkUlAqIFv6FRPtSLZDmn0qbi/rN2WejQLj7lMLPvIF9PzY4/u7vXuc4I0RVLw4t9NaXLXQL4OmLenkD+4Hpy0kbv5zeh9g4hsuT77r5q+L05siY92lNHW83sKqT1a3ATYIqufRc6ctQT/n+OWoTJ9dEm6uFQxgfZMlJlLUHjEZ+xLj9SiS8ZkE/hd1O/y778+ZE6d0CBKD5BDNHF1EdZ3Rn5ARuEIoxebmbzIM3l1Ci7pmiv2yMmcQz6fk+E994cbaou9Uik3JB/VrQxXQEJfGP+FN0bjxxXgpkdApyEGMjDkQZvSng2EAmnT0A+4X7u7r+t0H7c/bgOt79fI7xm6kmipyeLQ2/4mxytNuJR01uZw1lMndPh6N82yL/qUqEdzwP/RBFMo4uWma3n7neXqczM9nf3rI+t9Lu/h5RhzSJen2e1SL8ru/t/rT7ybxGS/hydujeImmuDSdRcziyFNpKOaNMnHonoHHz83RfKuR1tlNJ0bDcklPkYWYY1HF2tSgsD/RsV0jn10Y8ttG+7hB62g3Dc8lFkpbBku+UUlP84etfN3D0vin2zMk5G1uNHImvcMq4tKqPJejInira9K6Jvv3H3w3PKWRgdHx9GvT/uW9EmIHoUthNtCP6jByO3NBORMD4JHpJFw3pShZ/oFMK68y+kfHPkriOZR0sj/sBQf24Z443C0ejjUCT5WQqq6/g7hKOu/6AW8fxNCo7geiZyfAv1bIgszdLHzpdD/MeiFPtkDlX7gILyy6zpe1Gz0HwabWBvp6QvxRhPFuo+GPHljtaRuZGweDJa7w34qbufVqaeVLmzIR5zfuRj/YGcdAPR+BqKxt51SLHw73B/O+TzfDSidZ7Jn6xlzb5BDO7uA8L3vQjRkIeAb7v7U2Y2E1Ky/CKU/wfgEHevcydiZrcgocZQNP8T10R5tGCzTP6ZXH7pK8HMVifCk7jcIZXJvzgybGnGGxe67Ajffm3qfbj+23Nca5nZmUhgfh2ybp2Yef45dNLoa8Cf3H3/cH98KHv9Mu+XfQ8UGHGqWzEzmxfNq21SaaDWD39Hgqeoj99PE0LfOVLcPZf6XbqIKvljtLgFvjQU0SAnqEwLUmXthsZcctT/UZq7UqmDmS2A1tEB6DRZoTus6QFmdgbiaRZ193cCr/wcMiz6Bdq/fh/NxUuSfXnIW4aGvYsMfKJ+4kMZI72Ju0wzuxcZ5s1O/Txte/xE6pgPrctpf9o3eYGrly7q0RXgzgAIwrtLkUDgTXQ8oYjRrtv4VyXsnUBJ4pRuxFQ/fWZ2HrI6/gXacPwJHaNd393vTaV7GrkLWDunDQug47070LhIOdI07ucZvy2p/PMCZyA/Ulk/gr3oGx2ItFrtCHzUkJzNipn9Em32YgtsQnyPdvcTQvrlkUDneg8+tQLDfwKy0sv60Zkc3u+wRDiaqX8HZDE1f0HzjSYbrnYQ2n0lWvgc9XeyoC+Ovoch33vb5myWO9L+dhh1M0ssxrcvqBszuxJFEs7zcbRRUf5II6LBrszsq8hKcQNqgoOPkZuHU929wd9yq3M40pZKvjfN7GOgdJCE2Dc0s2dRII11vGQAJTMbicbcz12+5JLfpZBscsLG7GY0/g5x92sz9XwNCQMmoc123oZtGyR8nivSDkNM3q7ufk3BO1WihaGMtgKeNBnDifDvAHSc+4/ufmBB+rZgZh8hX2Exf9lly1gO+YJbtUnSjq6pJdaT5Htei/w+NvgND7TwHDSGYt//PeRfflRftKHkZnGqciA7l6vwE52Emc2CTlfsS2OQqffQenOkR/xFm9lQZH2a0N//UaxMigpQTQFdDyIuiD/Dc/xGmoKrDUWK0d/4NPTnbGZXowBXtyHfh09RzNNGfUy2sqYjZdWAzL1SaMIPbI18da+befQw+va5dLgTMLO50Am5HXOSXIV8Ujb4tTQFPmobQcE5GfXrn9HJkKxv+82AC9G6+4C7r5N53spcjtGCiWhPcHaR0qkvYWY/RlZnaV++6UCqye8obxnW9MnufkMbdS+E3G0shXj4O6k37vgSUtJMAL7o7q+b2VpIyH+Cux9lZichGnYmEsZPQDz2EKSk3B+NsdOQUOa36Hvu6u4XZdrTliA88G4HAJvR3Cf9smX65rMEMxtNnKbNhL5Fogi/E421rJygMi1IlXUoCiad8D13I8OiibG8MwoCrTsLODih+2Z2LFLyp+nAJGDN9LrWpP8Tn/b3ZvcFJr+3CYYjBWWeUcJAJLhdCyl8bkRB/5ZEwvL3yQlWmUW7Ct0uyqMrwJ0BYGb3oah9ByBfWi1tXgq0ezOhgEMJkZ0AjRMzMKiHoijut+bUsSk6LvFrd88Gkylqm6HF5WtIW3+Gux+dSbMCCu6QbJAMaXK2zKR5Ajgr0TAX1DkYWbykLWhv8wLL4yB8uQMF53G0ID0bHi+DjtMZOkLxGDpuUkngk9OOtZCQOG0BnERZPcNzondGymlJOxYshW5HTN0oZL2XHJtfDh1pngcJz15w92Nj5VRBGCsHAj+GhuAhz6EjZqfH5ken2t8uox42GXe5+7ebvOMopJiIWh12WhljZgOoRcF+Myb4nl5gZu8jpv46SgRJcPcVI2V8BNzq7ltPgyZn6/4VGr8ren6E1sWRD+A/uHsscnySbhF0PCt7EmAMLWxk26GFVVFyDBuKaLtZXwiWzOwVFITsu23mXx1ZteVa8afRSQFik/VkavAqd78jJ/9Q1PYBaN34K1IKgzbsuyCr0inAJrFyOtCGo2P389AX60knYbICTVvNvQjc5+4fFeS5CR1fPBsJ+lq2TAnr0UnoW8bGYcKHNCi9zOxdZN2ZFTz2OczsbWSMsFJMwVCyjJbWdHSUdztk+T0cbXSj4zOLIp4s1Z5+tRgzs5XR8eM6hZrnWO92sN63gX3c/dKCNIsgIe4mEQHsxq3U55lgYan1ZDJS8p/hbZ6qaAdmthVSXL6DrIyHIaHpvmgc7oDGxe+RT/mGoHumEwU3uftWbbZhMaSQ/QZxhdx1SCH7YirPAHfvMbM9kAB8I3e/K6f89ZCyZT93P8fM1kcuE+r2Ye3CzJYM5S8ZaX8WHTcQ+SwgKPrORScEvhIz0ulAHfOjU2FbI2HgYeh06JeRYdNe7n51p+ud3hEUjd+idlr5NHd/rkNlp/e7ZS2oXwG2cveHM+U0td5t0pZbkKHSSU3SHYJc9G3abl2fBXQFuDMAguDiPndvyfquZNkDga0Qc3G7uzdogczsMqRRXczd388pZ07kY+kaTx0LaLEtGyKrkV3d/ZLMs9WQv8WFkbb5t2lNlJnthwQah8esB6vCzH6BNIrjgO+7++OZ5ysjJmgo8ht5Yqfb0J8IgsVvIh9E15nZucBuCSNlZgsi5mAtYK2+toQwHU2Zam3kTY4vd6L9VRh1M/sjWsCXiVnChDRzo0BeV7j7D3LSjKaClv3TDJP7gFFIg/w88m+aWJb/BPg1sjS5CPhBjFYFQfo4d//ONGt4re5ngEfcfdsm6a4GVptRrUwKxjCkhH8ocOXkwBQ6Etq/EH6XhXvmyG1ow0XAuu6+fGutn5r/H8jv5cVIgPZ03to4vcHM/onc5ezv7n/KSbMPsiS50d2/Mi3b91mBmb2DBH3NLLjz8n8DCSWnIJoXE8R/DwnztnP3v2fyv4YEMKWUGMHSxxF/82rG8qcZ3OtPpExCG72dWigj25621/RObFabtG155CN+QlqpbjV3Zi8G4VmfuDebljCz5dz96RLpDPHnJ3S4/tWQYvS7yMjDkV/qM4EL+poum9l1aH+0vrvfGxmHgxC/uBPydfxUpIzXEa1ta++UKmcp6hWyLyGF7PiCPP9GJxcb1slMupuBeT2ccAyGRYPdfaFAC25390KaYGbDkaB4z8z9vyJadT9yc9LMtVXUIr+LYgRB+WPAiX0wD1typRJ+twv3Drvp+7QiZblrSFl5OzpdFUPCX9+VVZya2a1I4VcofG3SlrIuHM5G/pC7ipgi+HTgiLd7VbvQInxRH9exKnJS3xBsAwmVbitRxm2EgFkV2nE302HEZmQN9iapaL+RNPOGNA9k7n8BCWT6/T0qvP+LwH9Sv88lE5gEHcd9A1lB93ubO91+ZMXQgwQ/DWUg4eGfEeO5fCZvR6M9F7znasha/SbiQU/uQhv7SgGmSrbl2UA7lk79LntFA9dQMUgCUrJMjPVNQZ5STv3zrlQ5H1EuWvRl5ARj/Cxe1AJ8pAN+lL0agieFMpZFx9iObLNNk5g+gyUujxRJ6zRp+30lyroPmNTf7zSjXsjVSdvRmBG/VRhwFLk46UU++rPPLie1Jpaor2PzEK11oyv2X9trOgpOWCkgDhIeXw+sl7l/BLWAPz1IiJjuw44FL0Ib9jKBgYYTCW7DNOQH+voK3/ogJKBK+nUS8Dtk6U3Sj6n+70Rg0deAu5uMw5mRFfgFOWVc18pc7HC/vQdcWCLdhcB7qd+jgI9T47hM8KSzs30T7r+K9rkNQdo+bRclAlK2OO5KjcMW2nczOnnR6feeHMZBNDApco2RBM37d2qONqwVTa5cvu6zfiEF7kn9WH9ZOnA+ivPR7302PV8d92XaRb/gRmComZmH0d9puPujJsfW+yA/qGkshgSrzTARWLNiUyag4xd9gnDUcR2KfSzhjUErlkcWI28X5JkUtFhZi6UH0WarpaNiVWEt+kvNwuv9py5I/XHDJIDYbB4soV0B28bS99+vnSBqnWj/uiiYxL2xh+7+iZkdgDbNR6ONUYLfIp942wIPmdlDyO0DyFpq9fAu1wC/lbFK3fsUBr5IJXzEzL6JNjE/R/6O0/giCoJ3qpmdgza2fWXNMARZw8yc+l0WUToXvtUeYZ6diTT7IIubMkESjkTf5w9m1uCvLwfj89pTAk7NRc3rwMZmNofnn2SYHdGJUn6oyqBVK68svP+tvhIr8uczv6tgKNpoH2PyB30D+YFzYutBL4qKPM0R5vfewLGeCupnZkciumPh98XuvkukiJmAxyP3s3gCrXutti9qfZiTtlJQSZMf4h+gkxALoSCIh4Vn6yG6epn3oX/XYNW0Mc39NmbXpIdRYK12sQY6TXBdXgJ3v97M7iDOlx0J3BPo4O9K1Je4EXg587sdnAxcZWYbuPu4Nstoe033zrjk2AW5sEkfQ10NxSmYgoSjqwI7m9kV7n4FojHJcX9Sv9vF8PC3mTX0UBTkNmsZ1VF+wBQ4d100FydU+LYtw3Wy6XTgdDPbBLmc2wYJdQ8MJz+MehrTSsCmvLTzUHOnBlIsk17nXSdJ/j975x0uR1X+8c8bQg+CVBGICV1C700IIAiCSO8lglSVogg/ehMBsaGIFIXQQSQUCaBASBCpQkB6D0QgSpMaEpK8vz++Z7Kzc2d2Z3dm725u9vs889y7M+fMOdPOec9bvu8/yJ67zgZGmdn+7p7lPdcqTEJjST2sGspGmA0ZoRrBrKTPsV9AuToaPV/HwBpISElj712PpgrUBRk11s88efPJ+D6lBpWKu99top66Cn0HaWPwYJS3ZSLSe4wL+wchSpw50Xg1rmfVzkEYB/eiIpfc7cGz1cyWQ9GSeyKFdDNIXRO6+6AmzzcdwWN6Kyp9f8iDZ72Jb/uLyMGmqb6H869BiWucvoquArdv4HjkDfNzMzvGW8BdEzABhZslMQkJKfUwL80PSBGGkLGILooQZn0SmpTqITXrcJP4H1Ju50ZJ4YqjKUfxBEqwEhdKogXx4shrNF5v4SbbrAlrLGFJcrFcRv+LCOrDqDyLfkgYThOa08LrHWXRzgV3H29mDyMBIqnA/TZa2GwOHAMcZWa3Aee7+1/rnTuEAx6O6CAiAS+1GzCdpzjiXSuT9H4RtICIhMtPyLeYOBj4K8oEu2UIx6+V8fp0ii+yI9yMkoHcYGYHeyKs0cwGIR67BcPfiFOqKH3AOMobB3odnuA8TP5uEsOpLKTWQYqMWkjOBw9T7vvcCLIUR6eSrTiK40nEk1kPg5FhpAdqKJFPQAmd6imRGx3PeyhwzWx/ZGyeLexy9O1EmAt9R58jZX2pMNFPnYfuQzQOpfFPZs1J5wJXmdmqnjPTfQKfE/IW1MHrKDIjiTXRffmlme2ExsV/k23EuKzW70bg7rcGeWykmZ2Xo+00I1K7ZZLVkNfkp7F9e4X2vuvul5vZksiYegCiRhoUP0EZC+6cyFKcFZIHIgSFxa+QUiKaLy5DlGOY2XfRN7wDchIpKtvWK3QPcI+JG/b/qCT5c+Tpua2Z/c7L4SZ/h2o56L3wdxDwdGz/HEj5kYULgIvCt3gj+rbTEplGNHJl0XDcB2xjZifVMJSdgKLE4gn5BlMx5uTFECrfaRzjqM4rMUPBSkhIaWa/QMbIC0inwzkYuNDdjyrQz3mQYi61b9ZAjg96rknW8DpUKuF735wUKpXwHj8K/BlFAr+dOL4gctrYBs1dHQlTct+rUERudK/eiBVZFiWWLKKIz7UmDOPfdF5+z8i9ESu/OsojshSVvs9KxUD4deBKxCX/l1AnuSbZMmVfhP6IbnARFGnYRQ10Fbh9A/sjD6EjgO2C91mWsJvm6VEXZjYrsoqkJd14FtjQzObN8kA18XduCLzQaNuh/gJoAbo8CvEoFWa2H/CL8PNZ6nAspeAlYKiZzZNlJQ73YGgoG8fjaEBsBMPQ4Hk2Ci8a1kDdaHC/l56Km9nQBA4SpKIF4FfQhONIAZBMLDIehf5EeAoN8NsgwR0zmxu9A29QMoJ1+1r0zl9DdsKSP6JvI4ky+l9EUC/irdQMUq3sLh7Ev5jZUkiZuC9KfLGNiaP198ClaR5rwVvuHqTsqid8WNKTpwxPX6udJGFs8GCplSThFCpKlSiZTRJVSpcSF9knh35vAbxgZg9S7YW9HpqzXw1lobLonCv2Oy+ib78sBXSpKOC5WAYup9g9OR0YbWbbu/uNJfUpLxpWHCXq/xK43sx2dPcb0hoICtp1gSyu6Ga8D+PnLzSehwXzhSj893g01yWjhMaghC3b0gIFLhpLDkTXextSGn6ct7K7X2fi9b7TzE4CRjbo7f4Y6YrZJIaghXESw6mMdRtQwysrILdRO6cX9lgk2xwXtixkGZHaKpMgWqRkNM7G6B24GsDdXzGz+5Dyq51IVZwVkQcihHs8Gnm7/xeFR38zUexW9L1uRzmybV2Y2WpIOR3xLE9Dcv+Q0PbBZraVp3DSNohxSH6O8Dh6D3dDXu6Y2cJo7s6SgUZT+Ra/gWSELPRDjjIroPXWOBpIbEvPb+kkNN6ebGa7A9eFfjq6rl3QuuwzNOZFyraVgGdjCvgNayjj+6NvYHVEF5HElcDRZraAu7+b81o6Caci5W1TCSmDMfIwYFN3/3vi8BPAE6bcCPeY2fPufnHKOWop8gegZ3g0Up5dlVL/G0g2+BBFSAxFMulBVOf4OBe941Wop7yNlXN6OpYQ9k0C9vKUxJbu/o6Z7YWcaM6gOsKxIxBkoBHofT8fySVJj+Q7kLfyU0hZ34p+HIB4hpdO7H8R+Lm7/yGlzleAO9HadSSSn5J8uDcj3cB0BS7VaxJHUUX1IovGIoNhFzXQVeD2DZxCZXIfTLrnTy1Pj0yYQnaXR5P4V6i2sEYYgRZzl5jZHsmw4+CVd9WtkdQAACAASURBVAmaJLIWhK+k7Q8YgIRhQ4PDKXn73wAOQ/dmb3e/uon616P7eouZHZCcrEyhnBeiwS+Z8fk3wAgz29Ld78jZXuFwRXcfmujjnEg5/gJwlLvfmji+NQr1N3oK4KOBw81soWAZvRVNQmea2ZfQInsf5AGVVBiUgaOQ4LqdVxKWrOTux4e+RwlLvkm6F3kZ/R9Hk4J6EW+lRlHPyh768zLwIzM7HilAD0UGnJ8DPzGza5AXzmOxaj9E3oq3Iy/cE4C9kfJtaSRQHQn8wt1PbMF1ZSVJuIhKkoQRwaPrKHf/POU0bctm7+7vmtn6SLDbDikWNowXIXjpxhYxhekDetHLKxdK8FwsDHcfVvAUs6CFzPXhW6nnQXhv2v4mUVRx9Ajwa+BaMxuBFnNxQ8KeaLH2K+Ch5MIwKBmLKpGLjudHh7a28koiw6oC7j7NzMZm3IMysDcyIm3g7v9qtLIp83yE8xCtS1Zxd/ekPH8GUv4e6e6/ymjjCKTkTVMIFTJi1PDCrkvlYWZD0UI28p5+lwaU3wGjaa9MMjuxcSvIwasCYxJRchOQgrwUpCjJiijOgKblgQhHIeXtlcDB7v6pVWdGx90nmNkzwKaUS8VRhfAMdgv9Xws9n3eRDH5+iGJZBRmavoXGuG0KNns3cLyZDQxj40jkgXmcmS2L3sMd0TrnpoxzpDlbZGFd9E6VQsPh7k+Y2Tbo+S1HkGVjMKRo3zsWKTAxtLl82BzJgEtTGxOQwS2Js5HcfJuZfcfrU2F1GtYGnvWM5MM5cCjKM5NU3k6Hu99nZn8HDkGK4iTGUf89MGT4SksgFq2RN/dKMr71ImVxGNfPQwaUNTIbaJ7mbnPEi95DeRurNDnINV/PKtNmHIfmhe3d/RYAM6tS4LqiNMeiXCelrwvNbDiSTSL5+c1w6MvomVxoZhu4e3LcPR7pL77v7ueHc1UpcMPY/gQaWyNEaxIDRqF5PSuJ+2TkCdxuWrYZAuatoUztohdhZqfQwATtCX6vxEIhsxmk8Fk/OXkGJe9jiA9vHFrwPRcOL4cWboOQ5+nqnp79vR4twmTEE3tStCArE2Y2EfGXfq3J+nMhr6IVkfU78p5zFI66LlrUP4kmvU9jdQciIfcgpOiuFR7VMs5JM/spyta7XFYohYn/6HngPHc/NrZ/bbRgPMfd/xb2HYSUUdOLIeFgDXcvld/GzN4A3nH3VcLvS4ll+g375kHP5M/ufnCifuH+m9lpaJIb7O6vm9kA9BznQ2E/kaC+BCKSL5JlNRUNWNnXQokp9mng3GshIW5PKuPNQ8Av3f3PZvYYetcHuvuHGc9gS7SA2dPdr23g0vL073P0jV0E9OCvNbPN0Ni0EEok2MlhVksgD8bFw643kADfKj7ijoGZ/QQJurk8F5PzWew8zXK1YWbbAp+7++0NXwDT57O4krkW0pRvTcPMJgG3uPvO4fdsyNN0jLtvGSt3JbCju8+ZqB/JA7X6nnXM3b2/mX2IOOF3iZ33AeQVtkCkwDKzu4Clk0aEEsbz/wIvuvsGsX3TSGRANrOrgG3cPQ8FVEMIMsXd7t6UAiiHTFQFT4R7mzjud0Dh4Y8g5X1SEb8WUl718BIvalQIyv8tgIUjeSd4IP2Lai/s+ZCxbUSs7t+RUvNnwFm1PDxrtN9umWQc8Im7Dwm/v454G0+OKyfM7CYkEy5S41yzIdlhKNVzwmjghvhcl3hv8vJpTgC+4e5P1i1ZaaemPBAr9xR6xktF/cz4Fm8A1nX3KKS3NJjZYKTY+g6iZDHk5fVblCiwB9e9mT2EZOH5Crb9VWTcvjxSwJnZt9H3GB97xwIbpa2POgHBwWMnZAyMntGbSLl8fcJYh5ntG/2L1jX3oYiJNExG7/ODaQo6U8j1rGhMmIaU0rWorTbLf2Wth5l9BNzq7rs3Wf9jxN++Z51yVwHfdvcBKcfGkT2fR/f/buB3aeNtmFNfdfd1wu+0OXlWNMeMThrlwvHctEjx84a6ueZTM7sV2Cwp13QCzOxN4M342iNjLLwGjce17lMz7e+O1kD/RUbU4bExeXYU7XAKomGpWqOZ2WsoSeGQOn2/Ft3/hVLavwe43QPfbxfF0PXA7QNw91MKnqKWgPc5lYH9THfv4SkbrC5bIOvxqvS0oBryRtyhhnBSiy9wMvC2t47bF+Qp07RiNNyDTVBI2Y5I0Ih7VThS4h2SFHSoLKoMhVweWKspWvfd7grck6W8BXD3N4MwtQsxK627P4wspPGyF5rZo+h+zI+U+pc2sxjLgUJJyErq/zUood9XgNfd/WMTNcfVwM6xcmNJDxEqA+MoZmVPr2C2CArd2zh2jg+RYeI6U7j/ssA/3D3y7PVQdxYPhPbufocpGeL3w5jRLNIUb40mSehYuPt4UsLYZhIU8lyE6Z6FzXK1gRRadyFv8mbQiMdU2XgLKUojbIS8Pv6RKDeAdC/88RTve1Hvw6JJJeclnSoniQG0bj59neqkPg0hqZBtAqOpKPDWptorBirP57CwVTVP8ftSxAt7VeBRd/+/ZhvvAJlkDLCXmR2NvI5OR9eejLJakRrvqikq42pk+E3K6vsjj+I93f2+sC/ynCqsOKvRpzzywA7u/h9k1P1rmpI0gc9Q9ECpMHH2boE8+qegaLnfeEoCNavmZlwCmNfy88qnKg7d/Vn0fsf33Ry8b7eh8h7e4k0m/ukNhHH3irDlKT/dezA4GT3ozXsUDo393w8ZoAZlNd1kG61E0YSUk8iXAHw1MuacpJG0CRRKxmfFae5eATYxs8Hu/mrK8chQs2min52EBZBsWA+zUW3cKQsHoOe2qScc8cL4fGEwno5Feoi4k80iyOhaDwbMk3bA3Tt63TWjoavA7aKMhQIuj8M1EJ/clkiJ5WgR81dkPcycWL39nmX3k48vLhOusOZdghfk14iRgyPvuSwFcRkL5kxY/qzfiyF+snqYhMIt6iK0l+ecRdGShCWN9L+IoG7lJeqoFS5X18qeRPDiOhSF9M+KFkDXIq+th1GY4YmIkmEaCkeMEHmQz5fY/zKwNfU5FWuhcJKEeg1YGzNmtwLBA2wV5BV+Y73FYvCm2AktnuJj2Wjk9ZhGQVEGFkaeFs0qbwtxtQW8R4EsuJ6gp+llFFIclbDQg+JK5KLj+X+pbRSOsByt4T8FjZPfM7MB7t5o+H8ZKNWIYKKBWgh4193z5DIoQuUxkernXBp6USY5A82bZ4bNgLvcffo9CbLBkmRwHZrZEOS1OxdSSlxDdfKi3VD+hDvMbB13f7pkxVmyP43IA78C9kBOIFkc5nEsQeM0GXmwJRoPLgIucPc3a5QdmvjtKfuy0NC35u5vIFq1Po8S5pQZXfFTNCHlvSix3ukoCrXqXTMzo5IjplaOhyIomoyvKC3SpSgiY4yJyuUar0Ty9Edj4U+Q3DC8ievrDbxPJYKiFpZCtCQ9YM1TUIAMo6OTyttExWeCp2wyce9HSIlbD0tSQHbuIj+6CtwuSkOYVG6mdRNIK3EqcL+Z7VtU2A2K2tzec2UsmK2ErN/A28DGcYtqSjtzoUVYpw3Q7U5YkomcgvqweqcJf2t5DZb1Lg1A3ICHICVMxHF2IVoATYgVvzmELD2BFuFxxX6kHFoZJTeLMChcQ6mJ2+LK21rK1zBOZXpAWwMZs909j0W6IQQBbU1qJ+/C3XskDTIlJzgSeSLfF9t/MbBfrOi9ZrZFlhI2GOOuR4a4pID4XcR7uLOncx4WRSHPRcrhanuYgga9NqKw4qgEFPU+LDqe/wPYyczWzDJaBkPOskCPhB0l4aeIi2+kiRe/qQSuzaIMI0JYGB+Hkj0tGHZfRhhLzGzPcOxAd38qUb2IF/bfEb3CDAt3f8GUTO+HyMjwMMohEMdmaO68lXSchpS3Z6LkR1Uh42Z2cihzHJJhd0r0YVCxqygkD0QRNs8Dq5nZ7FleuGb2RWRcfMyUsK9ZpCku9gH+lNPDuHRFYTDO3+fuNY30ZjYMUSjsV6fc3MgQmaW8qaI/MSWAXgp4xWM0IWa2GOKiXAUZBU5y97E5LqnX4e5j2t2HIvDiCSlPRN/TccCuIUw9ToezG3onJqJ8Na3AOIol41sfeMrdU7m2XUnI9kDXdSpwcKLIr9Hac2ukoL0kUBKAZOV+oT+30TPPTKfgYeAbZraMZyRHNFHTrIyMdcljuSkoSM8NMRcVxXstvEdPD+CxwPpmtqhnUywuh+b4v6Qdj5Vrmt6sixjcvbv1kQ0t9rdCCYRORAN5cjux3f3McR2zIIF3YNbWgjY3QkLyVJQVch80EW2UtqXUX7XN92wE8l6YK7ZvReQVORlZcN8N17dDxjnOC+XvAAalHB+EQoqnIg7ctHPMhpJcXIi4Tkciz4c9gNlbeP3nhOtcKPxeINyPz5CQGvEATgV+36I+XALsl6PcMOCSxL59M7bvIAX8Q+HZ/ArYt4X38XzElzk1tPdgeHaz5rh2B96K7Yv4ysYA84R9u4d9/2hR/+cNfZkUrmFq/F4j5eObiGsvrf7cSFCZhhQLt4b/4+f4UjjvWS3o/5HISj+13pZR/3YkfM0a2xd5R3+AEhO9FM6R+h4hD4F3Qp1xSCG4f9jOCPumIYPPYi24B6eG9gc0Wf+/wEOx35cm7xfyHvs3cGXGOTZC3mX7t+I9bfWGxv5Lwvt7EjBn4vgh4T3/Zkrdpsex2LFlY+NINJb8LaXMNJQ8KFm/0HiOkilORcaAKHx6+nccnu/40MZKLXwOcyPO18/DdzcaJfJIbne3+51J6Xt/5P05FY2nT9FzLBwU9p2cUn8c8HTs99dD2RMT5W4C/pPYt0J43oc30N+6Y2aNbUq773fGNb2Dkh/VK/cs4oyuV25RZBxcE1g0R/ki8sDU8P/Roe6vY8eT79HvQxuHhGNRe/Gt3jOcFrXZSVvyWmuUu7hW/5GCbmQYS3K/yygKZSqwcmzf7EhRFr/P7wNLtPhe9Ik1ahPXXXgsQsrLKBFq2rv/BrBJjr6siNZnzyOqqk9Q4uoL4+9ISr3TQlsDw+8BVNaU1wG/QGP+VES3mKw/CRlSqt53esomI5DTRVofDM39L6eMDy+H96pfu593jXv4jdDXJxC/dtX4gIzqT4b7smGi7jrh25+EEgo+HsqdEe7/e+H3xaTMx+EcLyM5xGr00VD0y8uJ/buFvo5CeQySff8CWutNBbaucf4jkByXfH+nJX533FjeaVvbO9DdSnqQCgt9u87EUPOjQErTYxHlwdNh+1vYt0gvXMM6oe1P61xH6cI21UJjwxNsqPefMJAeCCzZy8//VRJKMcQtNBURzUeTw2eIRDztHAtQmRgjpe9lYRsT9kUT5QIp9denMoGnCd+vkZiUSrz+tYE7gS1i+w5Kef9fAxZsUR9KEdRr1PspCiVeukaZUcDROc51FDAq4xo+Q4q+tRro2zDk9TYNGBrbH+2bTEXYm4qysDZy7cugMW7NGmUKK18Rsf+0cP1zZT1XJGQ9XPL7s1/se3kauAEpH1O3jHO8Btyb2HduuOZvht8LoHCo0RnniAw5vyZloY4UO78KZX5b5j0I549C7ccAyzZRfxIKr4t+Xxiuf+5EuetQxtu0c2wU7sNUpBQ/EAnfuQx6sfOsQGWx9HHYnkdeFEPKvncl3f+yFA5FlMiFx3PgR1Tmnsgo8h6ap6P6R7TwPi4Yri9tPuwxP7b7uaf0/4jQt78RlH1p7wZa/PcwyCG5YSpS4K0MPBB+r5Uo9xLiu43v2wclmJqKvHFPQHPMPmlbrG9Nb+2+3xnP4BOUbLReuatQgpms4weEcScpyz6H+Iiz6hWRBy4N/8+F5rOpiI/3h1QUAYeEv1ORQmI2NAcnt+GhzieIn/xXYbsRjalT0bx4crufWcY9zDOeDkeJM9OOLY4Mk9OQEm9C+P8faDyLxrn7UB6LeN1HUULH+L59Q527EGfor8Pvs1t4HwqvUVPO2Q8Z5X+LZNp52v28a7wDhccipADfC829t4ftDyhvwJw5+nEEksWz5qTJwA8z6n41tPu12L5vh28yfo5HSchaoewERKUY/T4n9GOZRLkbgIk5rmUxpDNYB1i83c+4gXfh3Nj3+q/w93XkpBOtsX+eUu96YspREo4JSN74C1Lkp+prkEFuKjLqzJJyvB+iqZiKaPaSx2+g4gxyS/j/GSRLvxt+X1Pj2iMF9v9QFOR9oa0DkGE+ci5pqaNSX9na3oHuVsJDLMcys2P4qLKUbx+gTMGtuoYNUPhH1Oa7SCmZurWg/dEozDvXllL/4fAM4grgV9HkuhvKxJzV9lZIiM20niIhaxQKC047/iEx62bY90B4bv1j++4CxtVoZ5EwSGe9BzeS4rmBwh0/DuVeQuEbkdfe6ciiNy2U6TXFBfI0ORMpUY4E5mthW4UF9Tr1+iEFea0JspDyBS2UF2ry+geE73ix2L6FkVAxJfZdZwmIO6Dwp3US+08M9aPvKstrsrDyFXmZ/ZuYt3jGOW4gQ/lX4P2Jxu09Cpzjk+T7gZRI7yT23QaMzzjHS9S30vcLZV5utq91rqNpz0UkwI6M/Y4iK4Ykyt0MfJrRfjTexce+Rj1m9qfiaZC2WPqMDvTwbWAMGU4T41jBvjU0nqO59aGUe/8EsG2L+/qH0NazyDNoa+RFlbq1sB9zIKqJXchQgBKUoIl6jyKFy3yxfWlj4c0oaWeyftNe2A1+f7WMCL9AMsfPUaj4vGFbBSkQPiJlsdwpG/AYKYbWlHKjgLEZx4bH7uVU5Hk+PrHv0oy6J1BDdm3gOhZDFETTEu1G/z9CRjQHirp7G/gTKbIJUlz8KZT5So0+rIWUBDeiPAC55pMa5/s6MhLtRIoypNY3k1HuEZSsOe1YZFQ9NfxOKm82R/PkKBJGV6Q4uyOx70/h3g+O7XsJJR1sxXtcaI0K/B9y7Bma2B9FBEbv01OkKA+7m4P4qSMl7XBE37JU2DYL71Sk3P1WA+ddDBlXjwW2z/oWwvs9NvY7MiIcGds3NzJIvNTu+9XiZ3EwigRMyiVvA4dl1Hkj/n0mx4Cwbx4UtXFBxjkGxr61l9HafD8U6Xk6FQXqu6R44yPnjbPoqbSfFr7tXxLTN6TUH0nMiJsyjs2GInY/JKHY724p97PdHehuJTzEgpYZpHSJlI+j0cJzs7Dtj5SW0cC/QYuu4a7QxoWUIDC26TnMiyySv0EeB8nFx7/CALd1ot6fwoCVKXgg5dhHZHhjhMHz+tjv2ZBCPCm4XUk+6+YSiAP0mLDtRW3hOLLMnUFKCAtS+PwklPlzu59Vi55/YUE9R90RJMJNm+zD5cDklP2/RFxoZd+buVD4ZmZ4EwVpQChB+YoWCTfWu6fI4+mzku/RRJTssOg5RiTu++fEPB/C/iuyxoFwjqtztHV1nrGkiWso5LmIvJKeiv3eNZQ9PbZvYaRcSg1PprhBbx1kdJiCuMw2p7JY2jzsmxKezTrN3qtWbL0xjrXhmhZAyt91gC/3UptvIZlr3jZed9OULGEsvrXeu0ENmYImvbCRguHSvFtG2/uH7+tracdDmQ1DmQPa/Y5m9O/AME5kyt1Ifp8CHJxyLKIsmoCULPG5cfaw763wDuyWUn8gMH+Ofn6RHNRmKKHYb8L7cDtarG9PbWPh5When61GmdmoTYnza3oaBJLOCT3mE+QZ9gw9w5mj8O9ou4dq2qJLYts05KV+ScZ2ORU6mFsy+v8iijboF36nKW+WQkbB4xP7JyXvC1LgP5PYdz3wXove46Jr1HvCO2yxfVuEe/s6Cu+PPPx/0MpvckbdUCTD9GeQUeab4Z7e24L2205z10kbWhOvjgxAu6II1lrKz8IUFOH4uuGbSTOMTgvjTE2ZFI33OyCv92MQrU5dvQ0l0Jt1t8rWTWLWN1CUHPwkNJgc4u5pyZb+aGYHorDPE5EQVjbWRovpg1pw7l6Bu39ALImbmX2JiiJ8M7SYGYKS7MS/vTWQZS01cVg498dm9jhagKahaNbvZHvjaSARG/Iiet5DRtGU800DTggk7EMbOG9HIySoiGPDlH0R+qMwpNWRJbIZfIHqTLANw8z6oXcuLRHdD1BoTKlw90+RcrQWVkPfQbzcXohb97vufrmZLYkWVAcgQSWOJYG/ekailBg+Q8JjGtqZMfsTJFgVwb9REoEImyNO8eQ4MB9S7KRhIrWTJESYP5QtG2chD7mIauAlGrvXdwPHm9lAV6KQkehajwvJu/6NIk4GIP7NHvDiCaCOQlxiu7v7nxLHXkbJTEYg76MfIe/ItqHscczMpjbQvLt7S2VRd38XGX96E/MguqIPerldAMxsP+SBCvICfo4cc38MjhZ09fBlNKb2PIESm2UmZXL33yP+0+T+Yfm6WBOHIoPY32u0f5+Z/R0pki8uoc1S4e4XmdnywB1mdj6SyV4NhwchI/uhwLnunpaQ8ACkNNnUE5nHwzx5Ybj+sUhZfG2i/qtImV4vmczPkBdXfzM7DCkH70q5njvomciwHjZHdD+ZScjcfbKZ3Ye8YqtgZrsjmXs88jLbKZzzG4hXdk+0hjo7pW87INqleGLg9dD9+AjJ+usjeXsPRBsC1UlpPbSzdJ3rnACkys+IQuFvXkliNy30ZVYPiUjd/WUzG4OU9mfE6k6kkoAQMxuIvCb/mGhjMlKEtwJF16hLo3fKY/t2RPd2N3e/38zORM94D0Sp0EU1VgXuz3oGAO5+m5n9A8niZeP60IfV0Lv8rpn9CIX1HxXKGHqGJ5rZPmHfje7+Uex3LnhKkt9OQviWHwtbHryP1vQR/hf+Lo4MPNNPjRwUstp90MyWAXZG6/bFwqE3EG3Z9fXWUO7+Pj3XX3kwL/BK7PdkUGLGSP/h7p+Hd7D0hJJ9DV0Fbt/AglQv0KcAmNmc7j4RIAyA96KQwiTWAR7PUN4S6l9kZgch600rYMhDtc/A3ScE4XgO5Am3EOnKoUWJCYg1MJ7sibVo1u+imJN8E9FjyEu5r2BY7P8yBPVMmNn6wNeQAii+f1Si6JYp+yL0D/1bBHl+p/VtSsr+3sACyAIfx8ZIeXc1gLu/EhZqX02pX4bytaGM2TnaagT3o++zCP4KHGJmvwv/n43ey2SW81XJVhb/CxhqZsu7+3NpBUK22aEoqU3Z2BoZpNZtUvl1DRpTv4JCuz8OyqyrkdAaYSyKCmgFNgQeSVHeToe7Xx8WMF9rUR8awbDY/2WMY6nZ0Uso2xTMbFG00AF5379Zq3xJeBYpcduFw9Cz3Nvdr26i/qvAKmbWL6Y4qoKZzYn4bZ9tvpup590W0XPcXuA0yxGM6XXwFnIgaDvqGD6OoqLoSOIIMzs8xRCyKlJ+PpNWCcDdnzGze0i/B0b+7zMq92uk9L0LwMxeQUqBY3KeJ4n5yPcdDUAKgiQOQDLNpkHJuQGAu9+JeLZ/b2YnobHshkTdFZDi8fPYvt3Qd7V7UHgtgKitvkNFgfud8NeQl+199FSYRpiMlCcP1lBSf4Y88CJE8svCoW6E99DcE8czyCC3oLu/gxTWjiKa4lgCha+3AkXXqAvSs78bAhPc/f5Qf6KZ3Y+oMjoKHWLQ/Bx5V9bD69SRQ81scWS4y5S33f3exO+HkeEkvu9CM3sUKePnR0bGS939f2Y2HL2nDyJjSfQ7LzpagdsExqOIiAhPofFlG8QZi5nNjb6LN3rUjiGsba4MWy6ENeUd7v6zOuWOQhE1m6YcfodqB6T3wt9BKGo5whzIy7eLGugqcPsGilpm+pFPAH8OJRNqBZ5Elu5eQbBCgxZzU2O/cyF4dqWd94uIr/brYVsydvhJ5B2W9EyYRLrgmcS8KMwhDWcA2yF+wDPRwH6Xu09XiAXvsyWRV1smzGwOFG5ab4KOT5DPI6VJPSxK9Ts5o6MUQT0sILIwAFgeeb7PEtqJY2jsf0ffUb1vaSwKfUniLmBzM+vv7k0pcoOHymbUfn/c3ZNePbMTWyya2WxoATom0ZcJKGw0iTKUr39GHqBno4QPafgpeiaZyrkmcSpwv5nt6+6X1S2djjOQMHwI8mIxRLsyfQFvZqshq/v1Gef4I/IoGmVmJ6BQpshSPivyij4dhTq1wmutkOeiuz+LFu3xfTeH8W8bKguFW9w918IqvIsLAJPc/b165UMbd+co9xKt8XZpFKUqHNy9X9p+MzOkWN8ave+/c/eTC/S7JszsYEQjsHRi/0vIa/H8VrUN/A64wMyWdfcXWthOFpZDHlfNKG9BkRjHIg/xczLKHI0WWnkUpY3gRjQXFVHgTiLft7Ua1cqxdqKIMSOt7lxUFsm18B4ywjeL+ajcw2lITokwCDkvNItXgE3MbLC7v5pWwMwGI7n7lZTDq6Cx6uWUYxFOR1zQxyNKhwgLIsNqHBsB77v7bSDv/uCosVJUID5/m9kpof1m53TQeBtfo7wU/q6HZJZobF0NUQPFcTnycvynmT2Gxt7Iezjq4xwoomJMgT7WQtE16jTEjwqAmc2LZOKkwv0D9C52GjrBoPkY+RwEhiD+8x4wsx3Q+rKecdfJqV9y938C/0w5dHk4zweJ3zM8zGwJ5JxSb410euz3aOBwM1vI3d9GThmfAmeGaN9/ozFsQZrzjq2HochQVQ/LoWtLwzgk/0V4HL3vu6Hobsxs4dBWHmPDzA3vAB6H7lZsoyA5OLKM3p+jnfvzlGvyGnZFVtlVe+meTQvtLRv7XZcnjoykNWhSe4QKl/A0JExeHK4tMzFUuK8fUIMrD1mtPkBeXVllms76HSvTFGceBbna+sKGJqefFXgf63F+TgF+m1J347ANDeVuIztZznrU4KpDi4R3UQKehpJBIAH9Jqr5lBrhLh0HPB37/fVQ9sREuZtI4QFGyoRpwK8T9/WS2O/fh74dknENDWXMLvn92YhKwq3rkDA2NOzvsdU4z5eQMHReOIclju+NFCS1vtWrYu/k58gr47Xwf/RsW8JRhcbROwrU/wIlZaMOr3n73AAAIABJREFU9++R2HXH36XtkVfv4JR6b6FFe73zPwC81Yr7WOCax9HkONZgOxH/aA/uzRLOPQvVyTinUEneFCVUnBq+g8wERCX04yyUrGR/ejlTNvJ2SeXMz1l/fqQ4mhre8x3CfbsFecldQiVZa6nZ31Eyl6b7Hs5xY+jf6ckxMBw3xJ05jQTveV/ZULROvYSUhpRoL4ffA2PbNGSoHJixLYkUgh8Q5m7Ec/hg7Py5OLVr9O8oKlyne1OdlLc/MiiOC8/6xyn1PyPG6Y4cGKYm31kUufGfxL7CnPJ1rm0ZZHBds065SxD9yRyxelFCuq2Q8jjKMH9Lom4/5L0YyV4fADsmyuwSjh3dovew6Br1KTSnRhzAe4b6hyXK3UFGctZO3MK3Nwj4HhqvT21hW5ugue/IGmWOoOKtnjz2LSpz5/tIyXtP1tbue9uJWxivLqA6KXOPtREpayQUIXEnsEVs30FUr8sjDtsFc/RlFmQsyRrbBybK5xrHycivEo6dFvo5MPweQCWvyXWI8mlc+H1mu59Xp28WbmIXMzDM7ByU5Xgxd387hPS8hgaLc6lYZlYHLnL3QxL1d0TeWDu7e9KiGZXZAVl6d3X3LM+totdxKuLzOgllES/KB1mrrXHImrepu78a+50L7j44cb5pof6TyPPmbndP8wZI68tRiEPsRpSBflLi+GxoAbU9SlBwVt5+NoIQZvyH8LMuZ567fyf+28x+iTzfanG1XezuPyq1430AwUsj6/2LPN7ucXET1zrPPch7sWaYS436J6HM4LujifUuNJakcZ26xyzEgYPsGBTedwX1358qjxQzuwwtxo5FgviFSGhZ16s9yV8CPnD3NRL150ILheWRYmwEyj4+mjC+ISX2k8DanuE9aGaRd+q66JkYlWdjSHDdzt1rhik1itgYEm8vC+4t5g01s0OR993gxKFXgF96i7wXzWwYEnJX9iY8F8N9fMTds/jC855nOFIYGHqnBwDD3X2/cHwFtLA8xt3PSdS9AUVE7Ozuqd4QZrYdekdHuPtORfo6o8LMHkLvcqnUTGb2Q/Ttv4GMGVd7tRf5HkixtxhS+vyyzPZDO20NmzWzW1Di0VUKnGMl5Kk3iJ5jUsRXuLWL67Y0mNlIpPAu0vcVETXVHEiReS3VMsluyJPsMzTHPFmkz50IE2/uQSjE9hhPRByY+PDPQuP8Be7+vdg8BPnmoqjcCe7+UzO7GUU6/AMpj4eFv/flOI97IjLHzPoj2XhrKrzMEQXKl5GC0pDh+tsp1/g6yq/xjfD7dOA4pDQdGyt3G7Cxu8c9PV9EBp4lw+9vh778X1zGMrO/AGu4+5d73Biz7ZFcfKq7x7l0TwROpuJxeY2775V2U8Ia7VpE2xB53F4EfJfqZzUZZXjv8S6HKMOFgefc/ePEsVWRZ9yD7l46jUIJa9SfAv+HEp3dHf5fCFja3V8LZQxFZz3v7huVfQ2thpltiJSfe7t7kou6jPNvhIxwUbKwq+m5RlsLJRm8MeUUZyN5/ETgHK+mFWk5zGxlYFrZc01vwsx+gsaeKWi8epEa+R3c/dQc51yTFAqKGuXXQYrUr1HtFZ/SfEUmCfPCdPk349z90Prqixlj4VeRU8zlHrjpw5h6NdURIGORk0pmXqAu6Cpw+wLMbG0UOnuOu/8t7DsIKdKmF0PC9hrIipzEEWhgH0G68m1HNLCf2wrFarsXO0UR+h8Ju28ixdddwCh3f6tO3blQeMsyyPp0FRqIQeEIe6Hn8BKweqsGNVOStJVogjOvweeXRMc9z7JhIo1fGWUH7REuFITrj71OeHagAJinVcaNhBIxC9OVjO4+PVTSxHW3EFoYPd9E28siwXJAtAvRgGyRKPMcWmwemnKO0pSvZrYlysq7JLJWj0chvTd5CyZOMxtNY0akXiH5D/d0eqKDshXXGW2ehRZ0J6LEdLl5u83sA+AvWYvhnOfYF2XIfRwtkscir4AqAdbMXkOea5sm6q+POPsceXZdhuZUR+/TPshI0g/4mrs/0GxfZ2SY2Z+Ardy9VK5YM3sKGR5W9ozQaTNbCi02XnX3IWW2H86fyhubBc+gnSjQ/hoouufApLGswfPMgSg2tqLnWHhRK+SRoGwYBRzk7llUHnnOszGSp75MugL6LWAvd7+n2TY6GUGueBzRb42jorSJxqHd0XfyPxT9Nj7hzDAQhemmJTyFinH5RuA8d3czG4KSGzZESxZQJVPErsOA76N1StKg+Cpam/zWU7iaTdyNi7v7suH3tiiK52IPSZNNnO6PAy+6+8qxuuehyJsLEKf8z5CcvpJX0xKNR3NjD0OUKVnlFihL+6dh34qIa34K4vgcgkL/Mw1+KeedJdyPnagob85091bw0hdCo2tUF1dvvP68aCyL5z44292PjZX5GqKAOMvdj2vJhbQYrTJohnMnZfu08TBtf7RvEjKErFl23/Ig9P/v7r5xO9ovA0FenB9FvzWU88fMVvAaXOY5z7EB0ktEitv3qZ3Y9NXY/0ORgSQ1LwaJ/CruvnsD/VqMJunNZmZ0Fbh9GFmWmYSFvapKxv74sZYo29q92CmKIGBsgrg/N0NegKB79hwV7tvR7t5jwAyC9k2I8zNtYn0c2MHdx2W0vwCwFPBKXPgJA+PZiAdsHHCyu6fyf5rZROCf7t5wUp1Gn18SnfY8m4HJS/279PS0OAE4hRqeFkEBPjzpfZLSxsXAd/J8g2EhtR5Sqj7t7reE/f1QGGIPD9Q6nsA9ELcQm9lnyEs4LQlFLoSFzQ+Rp8jDSOCfGDt+CKLrON4DB13GeXpV+dqJsCYSTXQCihrzzOxB4DN3H1qgD/chY9YKkcI6zQPBzG4NZZZMOcchyLuoh0ICjQVTgMPd/ffN9rMVsNp83Em4V/O0NdrW00i5kocDvpHzTkTG063rlBuJonCK8H92JIISdCtELfNnpFR7nZDBPolOGgtC33dByrO/IQVhViRIzb4HBfROKPpieiI7pOz5c3x+6Ysws3URDcLiZHtR7xKXWWJ163pdZbQ5F/LWG4jC92txalehnrEhyLTxhIQ1jXtmdixKVrmiuz9rZrMjZ4gvI4PueMSf+wXkWXtOrO6iyLliESrKr6vcfe9YmdXCeX7t7j9Maf9V4E133yC27yzgx0iWu9zMlkTJxgrJT42inmNBL7Sf23vQlDBxJ/QsHnH3MYnj26Fv/NJGlWOdglYZNMO5R1OMQ3Y1FBm7Zzk9agxm9h5wWxHDfLsR5JK73X2bJupORVzBw9EaMtPLtsY57kJj3cWImu6/dcrHZYV6jj0RxgLbt8rJqIsKugrcmRDWIF1AEp6gD+iiJ4Lg93WkzN2UivA8FSlJ10+pY8C2KFnVV0L515Hl/+Zaiicz+znir10tEl6CoPocEqKjgfcD5JXUIxTfzN5B3m5tmaBndBTxtMi7UAoK3P3SvFRiZaJFU9xSfZlXQr8PRFywW7h7nkRLuRC8UO53913LOmcXjcMaSDSR0xCwKNUeuDUjCoqiqDHPzPZH9BvruHtqMo4cffgAhZN+I9GvpAL3SmRYS4tqwRT2dzjiLZ5+D5Hy6DeduNCs44Ufn4N6eOE30MYCKInZIWhBs0WdKo2efwJShtT0AjGza4Gh7t5rCVR7C1aQksXM9kF8lMkkTsl21kW5BErL+p3DWyyOljgV9CUEWTCiEEqOQ9d7dtLPfdE78I8CbTelBC4LQR7aG/GqPxr2rYeMAvGEWbeisXxKov6XEAXCIsiofEVcFjezvVFo+s/T7pOZfRja3iW27wFgBWCBqL2gXFna3QcVvujq9pt2LOiid9Eqg2YZMLO7gbncfb02tT8K5XVpS/tlwMyeB55y9x2bqPsWFUPSZMRFPxyt2XPJzGEsGu85I45MESygMWIUorY7O6P4ZLQ+yFTcmtklwH3ufkmddochCoW2zBkzCrpCz0yIsgWEvgQT3+yOKFwg7q0xGrghS9BNIig5rgCuMGXIPRQR1c8BpHIzBqHwZprL6LwJ8r6NKwR2Q4rgUcBPkXL4MBSKdkzKOe4nX5bSLtKxGvBEpLwN2AtNuN9NeFocQHOZQuPZnnvAzBZEodsDUXjw39G7F8f1iKf528gzvCzcBnzTzPonF0EzIkKI4gLU9mDtKCuzmX0LeVv1Q8aaV6gdIlXrXAegBDJLJ/a/iBarf0itWBBFvfHd/Y9mtgpwp5mdTfDeyzt2B8xKDW6yGBZGSW2y+vIvlMBqRkIW71o/NJ8MRePLJch7rQdMdCpZGIC+q4iz8ZQm+1kLdwEbm9lsaZEGMH2u3wDNj30REYVHsxgetpoKXPR+74eSl5SFon3vIoYw9l0ZtkbqNU29EcMmKPS2LQhz9BmJfQ8EuXwjgvenx/hwE2UnIL7srPNfgWT9LMxOzBgWxp1VgTEJOWkCGo96wMxuQgmMRrn7szXaSsNe6Dqn8+IGx4LTqHYs2N3MRnhOCodWIijdQQqhqbHfudBpclk9xAyay1OuTF4mzgLuMLPN3f3ONrT/G2CEmW3p7ne0of0ycC3wPTMb4Ake6hxYHPgG4hT/FjLI7QRMCI4El3l9igVDDkW5EPdyN7MxKIJ4TI0q9TAs/K2pwEXj4L5IrugiA10P3D6GGVHp0Ckw8RZeDSxBT+8jR0T7e7p7zWQMYTLelIoHbuSxHHnCjPWSeYSCx9Hj7r5lbN+fkDJ6aXd/Nex7CfjEU5KDWEmceTMrGvW0QEJ1hHEozPWojNP3R/xfVwP/zrKgmtkvkCf22cBx7u4ZnoOPovF/9WauNaPthVEo4UgUGt6IwqxjYEomcTKwITBbjaIt8fwyJcc6HCnK4t5S9yCev6dr1H2AEhJNWHUCr4jXGxR2Gu273BOJDDsBVgKfupk9h76P5WL7qr6jMNe+jjKXl/YddTpCSPoFaH5bPS0ML4cX9WRkXDrJW8D/a2aDULjh3cD3vCen4vyIf3EzlPRnXNl9mNHRQFTIH1AoeMOe2F10FhocO5Nwd+9fVPlGZZHfbB+apnRJogyPsRDx+Ekks5nZ1xEtyMlenQT2JmA9d18k5Rxx2rsJaFy7G0Uv1KOQ6BgKh7wOMuF6pyF6ohcsm/YvDR3nkd+gQXPTelEPvYGM7/a76L35DfUpeUrVM4T+HIWSMl5CfVqdjtNzhEiIUchwcoA3kaQ3nGc+xF++L5L3Qd/Hoyh3QyrFgpndD0zyXsqfkdJ+XpliONK1zNorHZtB0VXg9hFYk5kFuxBMfKEPoQRvr6DEM+PC4UHIm3UplNBhnaQSxcy+QUVhuzKajCMl8EtUOHDv8USiKsvPX3tSlpeAmU1CoXB7xfaNBz5y9xVi+64HNnP3+VPOUQpn3sxqRAjP4BZ33zn8ng15QY5JKNavRELs7BTI9pzRhxcQ5+bSUZhfhgL3epQ8KTV0OChpNgGWRdxwaeHUG6MQzDgGooQ345GgkvX+lLrQKgtmtgUKpYzGyHepnSW2VDoZU/j/75AHaNo9n4wUUql8gmb2CQUTTZjZ7ijxz3+RInt4bGE1O1pgn4K8T/f0FmRMLoIcysMqpHn8mtm5KFJhX3e/MnbeuAL3UOA84Ax3PzFRfwn0/TzkGQn9TIlz1kFeVbmTtHUCwvjwKkoWd2DK8a/UqD4ZeLuVXvomHt8oWdwnyHstnph1CzTXX4Hm+zg6cmzqbTSw2LoDKZ46Luy3iwryyGVUZN6m4O79iirfkKd/GoVLvXNEMlShOTkumzbwDWRSW5nZZcgL9lgUgnwhUrqs6+6PxMq9BHzg7muknGMrKvk1ovVFdD9epKLQHZVU3DTqWOAtitBsxEHGKjR/m7r7q9Yg7V/ZcllRtNugmejLHMCa1MmPgKIvGs2VE6F0PUPMuNSW9suCmc0NPIAccl5D733WGmmzHOdbFq259kIOH46UtD1ovcxsVyTbr+nujzd9EU2igfH0EWCQuy/UOz2bMdGRL3gXjcEazyzYRU+chhZ0ZyJy76oB1cxODmWOQ6EuOyXq305F6JyAlFd3A3d5Ct9sAscS+GsJ2X6DouQ+Kvy1Q4ANzSyVvxZZIReM9XcgGsyTip7JZHsVjo5dw04p1xiHkxg/GvFcTNbtI3gLCcURNkLfZJIXbQD6PifSZLbnGn1YArg1Ut7WwBTgi2kHzGxH5GHXQ8kfLxa2oVQL49H7M5B0T5o4J2MnKklOR+/mz1FG5/d7q+FghLsw/PwT8jKIlEtLonCinYELzOwpT0k6g8L5UxWGDeAAKp4gVSFZQZF7oZn9HSUrOBCFhXUM0hSyTeAc5N1wSfCI/nPYP4eZfRU9h+OQgv+3KfUPQ8n4Vkg5FsdwZKQ7tk65joK7f2Zm/0SJAtOOv9bLXUriFCpj69zAdhnlIi9z6PyxqSbKCDs28d7GsXTKvghRVMhmwCMZZbpoMxp07kgzZv0Ceb1dgAwe48KhQUhpcDBwobtH0UOvo2/o88TvvBiesm8wMsZMRN6r8T5sDswJXBb2j2uwvTialU1nJcPRAdE3bIfWFmeiMeauhPJ2WTTHX5DaKffb0RojLcJv2bAdDEwzs7HuvnasemEKh6IIDjJ/o76DzB1mtk5SidwqpXIvopZCueUGzQhmdiRwEnLKqIdGv9tWYzyd1Z+GYaK4uxOt5w198z0S4AbkutbgxXtsMFqfg2TP1HHe3a8L8uydofzIRp2pzOzLiH6vlnOPe0jIHaIY4tgwZV+ESKZYHTmQdVEDfVGJMjPiVPTB5s0sOBUNDpGFvHDIaR/AxsDz7n582sGg0D0hKLeGphS5FSnR70565+ZAGfy1z6CBccHgxbsnesZJL9klgP9k9KNp3rlGPRf7KMYAe5nZ0cjT4nR0P5N8TSsiGoTpnhbBMnl9PctkDkxEPLn1MAhIC7FZBynkpiEhe0VgJcR/tTRaLM2LDANL0dMDd0bHSsCj7n50G9o+CglDu7v7nxLHXkZC1wjgOuBHKEt7Eo+SLRDmxaqI6yqTT8vdnzGze6iEb/UpuPu/zWx74AY03h6DvuVdw2bICLNTxny7BfB0lvdtaON5U9KSbzCDKXAD+hMzGnYYTmMGX+w1gXEEz0fgBRpTZEWKq+GJOhtQW6ljoc2fN9TTnLACdDJdFHfuCBEhhyFj3t8Th58AnjCzm4F7zOx5d7+4bOVbMEQ8ioxo33P3txPHF0R0KNsgz8Lv0Pvf/hBS5CmQgiU8hx+iqJWHkaIljs3Q/by1XkPu/i7KY3A9TL8/3w/bHEDSg7dRx4JWoKiDzAyNDjBoYmb7Ab8IP59FCa4zn7d3GD1WH1Dig9ZRqyAniwtQdG6hdXIwjgxDa/6IfiWVViKh6zkPOM8sTf8KpCc2PQJdQ5zaIJloNDKCR7kfhsXPidaR9RIsTwBSdTFdVNClUOgDsMYzC0aT5/KxEKfcKMnDqaMQQo9vcvc965S7Cvi2uw8ose0y+GsPRkLs68BjwNZoEB/o7h+GMnMgD88x7r51Wf0P534IWIs2eC52CoIXxSNIEIaKp8UWiTLPARe4+6Gx/YWzPYfz3IuUroPd/YOwLxn6vRgKuxvjCb6zQK2wA7Ctu480s0uBfaLQwLBYuhRZSFd39yxjwAyJ8C2Ocvc92tD2WyjZ1rp1yj0IfMXdF005tjkyGGzpTSaaMLPPgBH17oGZXQ1s7+5zNtPOjABTBvIjEbXMkoieZDzyhjoni/rAzN5DSvAd6pz/RkRl0qmK0FSEcewxxP+7VLv70wWUEXZs4p6L6uyLFphZc1IUFXKzuz/RZLczYQXpZLqYHha/KTmdO1LqP4rC+jetU24UMJ+3gAvczC5H17Ck105I+Aoac/dKK9NAe3HvsGHoG8jKe1HlMebu2xZpOy/MbBHkgRt54S5GxZjyz7gMUQaFQwn9fQd5mX61TrlngYVmtPlwRoCZPY4cFPZ296vb3Z+ZEUHGj+hlPihwni8ihe2+aOyJ5sf7kRH2Onf/KKVe07oeE03k7Ujpfx4yqq6HPP+XRvqKwYgf+XEPeXTC2pbQx0vQWJo1Z0cyxYNZY30XFfRFT8qZEY1mFuxX6/dMiueBHgqRFCyKlF9VCArUK9z9kyba/iI9Q+fXQx7Br8b2jUXCWhouAtZFYWYDgY+A/SPlbcC2yAreCq/JdnoudgSKeFp4eUnjrkaK/AvNbJ/kJGhm/dAEOzvpGanXB55y99TwFXd/x8z2QHySp6LJuy7MbBnE3faau/8z78W0AZECvB2Yn3wZiF9CdCtpeB6FbN5iZs0mmngDWNvMLIuKw2S2X4tKcrM+CVcG8sgDtxHMgYTRepiMQvw7BjVC5kHGqeUR9cCcdBh9xsyMMjwf3X1Y9H9YeN1XQlRIwyiJTqYLKeqedfeDmqy/HHBzjnJv0bpojM2RYjZzPHX3yWZ2H1JoFsWw+KnpAI8xE2/mUCpK2xWoKG2eB35PJcdGUjFUmMKhBMyJDH718BgKz+6ifCwH3D+jKm+thISCHYB5gNubUd6aOMy/iZS2WyOaQkMculcgJ50euok4Cup6DkPj4ebu/khw7lnP3S8O/TsRKXb3JxYFEF/bmtkpSDnbTZJeAroK3L6BJ4HUZERd5MYFwPlmtkGWF2RQzm2EQpWSOB84M3gL/N7dn2ug7cL8tSEkaVjgtVkYeM7dk6EZLwDbAw/G2oECnHmxnx+Sotie2eDuT6HFZdbx3yNhu1X4A7LM7gKsZWaRInZFMzsbCfLLIL7jNEFuQao9rqIEF3O6+0QAd/8oePomvXd3QFlqT40vqMPEfjJhwWFm1xT1kmkhTgUeMLMj3P3Xvdz2e9RfKIKoK97LODaOCpfnUWHLQhbf318R5+E5ZnaMu1dR7AQjwFm0dsHXVpjZ/J5INlmj7DopCqQ36BnKmobV0eK/kzCc2p6bkeLgVvS9dDzMbFFiWc/dvU8bHkrCYNpHg1QGnUwXDTp3pGAS2cbCOFYLZVuB+ZDiox4GIHqnoohCxzvJY+w9KnP1BJSIKKJse6NWxbIpHJpEIQeZLkrBJ8iY3xTMbDAy2j8Up4Qws1VQpESUbPtoF2dz2RgW/tZU4CLKn32psQ5rI54l31iWhjeAhdC49Bma+y5FxpjeCKVfC3n3p/LdByPa95CS+WTk9Z8sMyjr5DOQk0/HoKvA7Rs4F7jKzFb1JjILmtljiIO1T/EONQJ3v8jMlkck+ucjASmetXpP4FDgXHdPU1rcCHwL+AHw/cAP+TsUXlgvbKEM/troOl4nY5IO70b8/RhHcc68CO30XOwiwN2nmNk3UcjkLlSMDWuGDeAmYN+MSf99qgnwI163xakWrB0tBuLYCxk4nox2mNmKSMkzBRkOhgC7m9kIdx/R2NW1Hu7+tInP+Roz2wmFHGZlicXdLy+x+fuB7cxsh6x7Y2bbAesAWfeujMQTZyEO7iOB7QNVwqvhvEsCuyPlzv9C2b6IW8xsM1fStkyY2WoorCyZ8O8eYH8zG+buwzPq7ouU8ZeW0N8ycTnZ71CksLi7KN1LbyBExhxJwjASwoXPdffz29KxDkTSoIvegbmD919dZHjzN4sNgUdSlLfx9q43sx+h5FxdpKOoc8e9wLZmdjpwUlJmCJEYpyKv/Dyeus3gFWATMxuciEiL92Mwoll4Je14I+hQj7FZ0ff4JPJyu8vdx+Wt3AGOBUUdZLoojvsptkb7EXAISl4FgJl9ASXlihyQhgA3Bl1EI05MZaJWQsF243coamRZV/KxRrAw8BCSF69NRNf2BualenydDIoOiCKP3f1zM/sHyuvTA6a8EgcwYzv5dA7cvbv1gQ0JUW+jAXZgg3U/Ba5p9zX08v2aWmCbknHOLyMi/jfRBDIV8SWeACxSoy8Hh/LjkGJmElKOfCFWZg7kDTOyznXNgTwjDwdORBlHk9uJoew4pJgZnPida0u0OwR54R7R7mfbCRviylwY0Vmkbr3Qh68ir4vzkHB+HLBanTqPAGNjv/cN7+aRsX1zI0PCS4m6rwL/SOw7K3wH+4TfSyLr8e3tfkY17sGPw7tcdywoud31kaL7c6RE2yzcr8Hh/8uQ0DQFhS618h6sS4V6IXnd04DXgHXa/axaeP1RUsFaZYagObfHfIAUGp+F53UG4m+Mjg0O+yaFbUi7r7evbWH8vSH2vk5Bc/H48H/0Xt8IzNLu/nbCFrtPy8Z+F5KJCvRlEnBljnJXApPafe86dUMJF6cAqzZZf0XkuTcVGflPQx6q30FrjufDsU+AlVp0DUeFd/F1RN3SP3asPzIcjwv9+HG773mL7sEvkfNF/Jt7CdGM7Aws0O4+5ryGj4CzkafdPGGLkuR+CPyi3f3sqxuKCJqEnDeaqf8vRK8W33do+DavRrLqEeH3+S3o/zTgkhzlHkF8y22/5xn9OwvpCPYHFm+g3nJt7vcbxPQPiI5lalJ+RYa8TzPOMQLpMeaK7VsxPNvJyGD4bjjvDu1+Vp2+dZOY9QFYdWbBenDvmVnwWaSM+Va5PetcNErmnYTX4JIxs/6I0PtQ5B3iSClzI5rY/p4o3w+FhUTcgx8B+7n7DbEyuyC+wf9z959ltLsjsnQnvcGqiqn7SkpVJsxsXeAaNND3pudixyBw952GnvvsNYr2+A5Lav8L4dw9COxz1j8HKf8Xc/e3zWwBpKzrjzz9/43e09WBi9z9kFjdD4E73H2X2L4HkIf3Au4e0THchZLzDWqmj62EmR1ExRPlCepkifWSM/Wa2SHoPqd9n4YW44e7PGZaCjObHS0ON6Y6A/wYpNxsVchs2xG8jncFfuPuR6YcXxbdh0VQIqUezyNwyf6ByrOcEv5G3/004ADP8NDtFAT6genP393famd/8sDMfogSar6BDJlXewhxNrNZgT2A09F1/djdf9muvnYKrIQkaCX2JW9CxweAQZ6S0LELwcxORbLoSWgB3pCntJltjCLSvkzP98EQ/+1e7n5PCd1Na78/kp23Du1Po8K9/mWgX+hRvprKAAAgAElEQVTHbSjBcCProXptp9HjZJU91Fvs0W9KIrtZ2DZFSjPQPfkX4tC/y93/2sp+1EODa9IkWiIbz+wws42Qc8/RwJ+pnx+hKgLUzN5G9AnbxPbdjL7LxTwkNDazJ4F+njOpep0+d3xCwUZQVFfTTgTP2nndfcXwe1e03j/D3U8M+xZGkZpvekrCQjN7NRzbILbvLOQ08x13v9zMlkRRyfd4Isl2Fwm0W4Pc3YpvaADOvaXU/wnwAbBgu6+lr23IunR+uL+R5fwJ4EBgzkTZgSjMfUDKeVZF5P6pnrworPpzgucKFWv9GYgr573w+2Lg5BZda1s8FztlQ9xLE2Pf2rvk9GAusQ/TkJDVbP21UUjUFrF9ByWeX+SBuWCi7iRiXouIr3kiUurGy10JTGz388q4/mfCdWzZxj6sjDj3XkTREZ+G//8ArNzuezQzbOHdHR3e9yMSxwYjT85pwI/qnGdNRFnycWxc+DjsW6vd11mn7wdQ8bCLb88B3213/+r0/SnkFbhUjTJLhW/r6Xb3t7v1eDaR93SmFw7ic58G/Lnd/e3ULY8sFtsyvahRZNdeQX68PWx/ICQz7IXrMERP9jI91zQvI6Nzvxa0Own4YZ0yX0AKsV6Xa4GvIGqEq8JYlvocw3z2YxSC/X4z70CD/WpoTZrcevs+zgwblYiKPJEVae/QZBJRESgS77HEvuuA/7XgPZqa9q6kbG/SomiAkq9nhvoOkGPSVEL0KOIcj7xlrwN+QSUS4syMc3wI/Cmx7wGkH4lHVtwFjGv3NXf61vXA7QIzmxMtVvsDh3o3o2+pCNxyRyMviAiOBr8T3f3C1IqNtXE9sAOwrbuPDBki9/HgaRss95ci6+TqHqylZaHdnoudgOBZuila5Jzo7v9tQx8+AP7iJfMHmdmayKt8fqTAudTd/5coMw74xIPl3cy+DvwNGQxOj5W7CVEALFJmH8uAmX2KaCA2b3dfiiB4TX0fWA8lPrjS3fcPxzZHHFW/cfdOS6DVMTCzeRFv3HLAru5+g5ktjsK8BgGnuPtpOc/VD/HEOfCu1+dFbyvMbDhSzhjqc9zjLdp3eaeO42Y2ERjl7lvXKTcSeZzO2Ts96yIPzGx99J058vK5jGoe7n0QF3c/4Gvu/kCbutrRaDTSzItlKe8VmNliVCck/HcL2/oEKa9HotDz9xPH10SRcUsCL7r7cq3qS0rfFkWeuF8Pf6ePzR6LsDOzORAn+9pUElBmYkZ4B7poHGY2msYiKqp4TM3sXeAJd980/F4OJeU6392/Hyt3DbC1u3+hhD7vG/1L5yQUnClhZhEl3+UeoojN7NuIPiMuP40FNvLAi5s4xyTgFnffOfyeDSlvx7j7lrFyVwI7duWy2ugY9+wu2oqRyGqyFnC/mf0HedhNTCnr7r5Zb3ZuRoWZbYmUtluhhcanyPvwTuTN8C1E7D+Xu/8qVq+ZjNnrI36ikWkH3f0dM9sDLYJORby7ZeJw5AH8bXe/o+RzzyhYG3jW3Q9qYx+epfLulAZXVtB6mUHHAHuZ2dGIQuN0JDAm34cVERVDJ+JtZFiZYRESr5xI9WIt/v//gGOQsPu7GueZDSnthxIbj5Cx7wbvwxQKAO7+QUgI+ABwRUjY8xOkvP1ZLeVtMNp97O7vhXNNA3oYdMzsi8A8Xm4CqEIws92Rguy/KLHE8OhZB1qNYcApwD5m9ld3v7ZNXa2FD5C3Rz18FMp20UFw9/vN7AeITmbPsMUR0cn8oKu8zUZfVMa5+xtoHuoNrAVcD2wDjDWz3aP3zcyORDyQsyElRkvlvkCPNZSKwnb56FD4+zSiULg7UfWHKELvdiSnn4CMc3Og5I57oUSPv/AQCt1F34O7Dy14iieA9c1sKXd/GUXoOJIH4xiMaFUKwzszoeBMCXd/Fj3z+L6bA53YNlSce27xbBqbtxClXoSNENVgMrHhAPLJbzM32u0C3N3av9GYW3+fDH8v8V5+ESVdeJFKyMfLKIPnfImya6DF40vh98Gkh6w+jzyja7U7iVhoAvICnUpPmoYRiFuu7Ov+FLiz3fe/zc/+I9qcDBAR408B1mhD28tSTRUyDfhbSpmWJDko6Rp+g5TLs7W7L032/1tUKC52RN63PZI/ABOokUgOGYTGkR62NjWcf8N2X28v3dPVwrcd3Yvf5qgzFfhjjnIXU3ICqBKudxRKwLZCjTIrhDlnVLv7m9G/KxHNReZ3jBQv4xE/btv7PDNviDamR0IXunQy3a3NG/Iuu5RKop0TUKKeiA5nv17owwOh7fh8/Fr4NvagdpLkx4glRQ7XMjVRZstw7t3afb+7W2duwG7hvfsgvFNTkUJujliZeYLsMKKX+7ZMkHfXbPd96m41n9Nl4b05OsztD4TfayXKvQQ82u7+dvrW9cDtAhRO20UBmNnayNt2Z2TZNsTj8lvgVg+jUhzu/qiZ3QbsZGY3IE43Q5NkZMFcFE1Ovw2hzzt5unXrfaqTZkXh7YujBc/0ZoGFm7rI2pjhPRdLwJPAl9rZAXf/o5mtAtxpZmej5B+veRPekmY2C7AAep+z2ns99v8LZrYB8vhYGHgYOCdRZTNkyb+10f70Ek4CNgcuD0lJ3mtVQyFBgwPHuft/Egkb6sE9UCIkcBgVDt9nQztp9R9H3jdp/RqCqC/mAl5BIczjwuFBSJBfCrgjJHl5uoF+z3Bw97FmthPwF+Ayd/9BjmpGjnDVWNlOwqrAaHd/JquAuz9jZlFYbifiBBQxcIWZfc/d34kfNLP5ETf9HMBxbehfF9UYCwxHBshobLzP3S+J9nUxcyNQAWyCjMBfIH3cdI/RNZUBd58IfCeMd+ejCDYQz/ZutcbJErEOymNxD1pX3O3uL+Wsuwxwv7tHHm0iFTabJVpLuPsdZvYIol3qxIiKLhpEiAICRXFOjf3OBU9EBbn7tWa2POJSXhXJhPu4+2exYrtQyR9QKsxse+QBeqrHaB7N7EQUKWTh9zVeMoVcFz3m5FrlhiEKhf1SDp+B9Bxnhs1Q0sVHYvWXRZQ0F5TU9T6LrgK3C9x9TLv70AfwYPj7CRp4zosUKHXwCcpSvj31M2Zvi0Kg0jJmj0dJ0CI8hQbHbYBfhXPNDWxIa8LPbgZ2MLPZfOblHzoXuMrMVnX3x9vRgUSW05+GLUuJ556S5dTM1kGE9V+j2ijQoz6JOcTdn0JJNdIruP+eCldyJ+KXiIZiZ2BLM/sn8shN4xLMUqLmxTB0D89GySCGNVDXSVdsrIHCzOqNPW+jpHtpOA0pb89EXM5V125mJ4cyx6HF7E4N9Lsj0UB24P3MLPl+p35HOTEfUrh3EuZCyoJ6eI9q7rNOwj7ISLQP8E0zuxPRB4GMEFug67wCUUHE65auBOqiLpIGj2HhbyNGrS76KMxsRyRXz1+rGJoXW/XtLoKUU9F7+gmKzOgNrAE8nuYIkgP9qHauiKjx5kvsfxmoyRnexQyFcUhuXQF4IfzO+/70kO0B3P0UM/sp8uZ+p2c17kQRSy830d962BuF3D8Z7TCzFZEMOgWtwYcAu5vZCHcf0YI+zMwYFv7Wm5M3APYlZR3YR5x8OgZdBW4XmNlGwAR3f6FOuWWARd393t7p2QyFl4HzUHKn3Nwt7v5dM1sX8QYNdXELxY9/DlxmZvehiWt/0hW4o4HDzWwhd38bDX6fAmea2ZeQEmoflEinFRNbr3kudirc/TozWwF5v54EjExasXsBjXjz9SgbJte7qChu32fm4iIahoRXQ14+m9Yom6VEzYsoAdRbid9FMCdSztZDrYXwxsDz7n582sGg0D0hLKqHNtzDzkQRL9jI8yPp4TKghtdLf+CrSJH4akaZduENYG0zsyyFQeADXotKcrNOwylUFqtzI6+PNESJ2qDy3bdSCdRFOj5C0UZddFGFYFC+FimjrkEc+isBZ6Eoks2BeRGdQOnc+sFb/3KUy+ITFP67OzJwjzWz/d395rLbTeC1vMrbEBUTT0T9JkpwFiG6Rysjj94Ig2ggyVUXHY/X0fP8PPG7EIKDTpryNvLabdWaZzWURO3T2L690DV9190vN7MlgWeQp25XgdsezEq6wwvQJ5x8OgZdBW4XIOXfpdRXRhyNPrxZ6pSb6eDuyxSovhTiEsy0Wrr7yyGEK0uhdD0Ka1kN8Y6+a2Y/QiFfR4Uyhjx1W5GooDc9FzsSCS++84DzMjxfoZjXXia8eNKSU5Hy9mLkfdkj8VIfRxlK1FzwRDKG5O8m8RaV5Ca1sALi0EvDnIjjrB4eA76ds18djRK+G+jp4bJj2GrBgKtKaLtM/BUl5DnHzI5JUvaYWT+kPOnkMLfT6CojZiQ8BWxqZqch/juApc1snzyV3f3ylvWsi3bjKORFup27jzSzS4GVIgOjmS2I1i/fBFYvs2Ez2xAlKFsc+Bews7u/aGYXIVnpWGCEmZ0HHBUcLlqBW8xss3pUWGa2GkpWFjfQPgWsG/t9L5p3TjGzf7r7RyFx5XqIk7KLPgB3H1TrdxEEmq31UI6Fp939lrC/H9C/RVGYCwCPJPZtjHiorwZw91eCs9NXW9B+F/kwhAqFYxcthDUXkdFFX4KZTUOZpjOtIqHcxYiwv6vArYOQwX0BYFI9b1QzmwDc4+671yl3LfLSzc2zamZrIiVClCHyUncvfXAN71DkwVQP3hffoXAPcqMkpVGpMLMPgfHuPqTdfemicZjZH5EX8Vbu/rewr2p8N7NdkSfTue5+ZMo5HgP+5+61vI8xs1HAF919tXKvYsaEmY2jojQciCIgUj1VUEKaN/j/9u497tK53v/46zPjfJocNw1jSFKIQamMnCIUyohC45CUw85uK9lsMkrS9FPYyeHn1CNUDjkWklPkOCFiqDAOGzk3DqUx7/3H51pmzZq17uO11rXu+34/H495mLWu772uz8zc1n2tz/X5fj7Zo/p/Brg1ti2KquF7yKq2x8gPR4+Sf7ZVyeqzVciL9HUlPVFNpDZcRMQO5E3o2nVBrRK6T4bj9YSliHgKeF7SOsXjs8jem6Pr1ixOvkddKOnLJZ77X+T35GnAQY0J1IjYgrwBtyxwt6QNyjp3w3lmAxdJ+kwPa9Yki3GWrC8OiIh9yRttm0u6oXjuFjIBN4usfn9HsXwnSb9sx5/Bhr7i2uBsMnFac07dteW+ZPXkVpJ+W/K5/wlcVvt/oPiM/Qpwo6St69b9FJgkqVvbOw0ZDXM59iRvrt7cYnltV9l65O7T7dsbnbkC1/pjOeb0T7ImioqRfyerYUeRUxdrP9w+TVaoHi6pftvstcAmPfWPLX5YbUROCO8zSXeRw1zarWOVi92qGxOyAxBkpYl1WFHBfXZv1enFjbS9WlRwTwV2Ay6IiK8DF9V93SJkv9oTyeTiiS1OcQpwckRsJOmWFjFsRPYjO7DnP9XIUV/hUnzgvqC3m6LdSNLjEbEt8AsyUds45Ku2k2NnJ2+tDJIujRwE+yny5seeZFuqpu8/NqIsw9zfB7MAImJh5YAxiirSm8g2B2V6HdhX0s+bHZT028ihsefS3mHQPwN2iYgftLjpujr5OWJp4ICGw+cBf2LOIFLImRtnkH9fS5Ktso5x8tZaKSrdbyLfn+8DfkcO7q53AfAjcmdWqQlccnfZ++oef5TcLdj4M2IxRlbbt3bas+73IlvWNB1+XOcZoGn7NSuXE7gjVNH3tt7yTZ6rqe/X15fBXCNSRJzNnJ56r5I/SOo9RE5wv5u5G3cP+YnZJW3/turdB/S5wttK1TjIp7e185A0vZgCezZZCXEyeeG1OzlYAPID8OcbbiLVv8ZpxbThqyLiZPLDaf0AqN3IC/cTJHXrFvqq7cWcreBDjqTbip73nyGrbcYWh54CbiST0902fG0e0bfJ9Ug6upNx2byUgz/vgbcnWd88FG+AWOleYu5hqrUdZCsCf657XmSRSZnWl9Tj+7ikZyNiS9qbtNiT7GP7lYiYIemHtQMRsQqZLPs34OtFD8n6+F6lIclVtMbarripOwZ4Vg3DSs0a/BeZvD0OOEySImKuBK6klyLij+Sw7LLdCOweEYcAV5F96lX8vt5atKEX9ghVK8wKcnjZzeSNn2Zqu8pua1MLDWvgFgojVN2Wd+j7drUA9pN0atsCG6IiYg+yD9c9wD5kkrZWUbd33boZtecbXmJVcsjYa+Qkz1YTs/8qT8i2Nim2158LbFB8oB5RGrYM9abUXs79aGVzPvBpSQv1sGZt8sbQx8nEFeTuiWuBoyVNq1v71ryv0Gdt6eVsNlj9mVzvLfjdJSK+SW5Jv6zqWKxaEXEn2VdzQvG4dq19sKQfFM8tCjwCzJTUW4XYQOMYQw5uXJYcKvb7dpynl/P/HngPsIukiyJiRbIqcjxwlG9EWbtExMNkO5HVai2fml2zRsQFwMb9afXXx/OvTvbArRVGBXCtpK0a1kwHTpHUWB1sg1C0CPuFpEOqjsWSP3iNXDcxJ2m7CfA38o2vmbf79Um6vAOxDUVfJHtJbSfpKYAWA6zuAz4BfJO5q4H6MjF7MkNgQnbR7mF95q7amjZS7spFxPzkVvVNmfvv4AayR1u7Bl0MmqSfR8T7gN9ExJFkL6N2TZXtRnv2crzxplfHhvEVAyLeSw4y7LHCQNJ95JbLILdVjib7CDZL1va16rdpWIP42mErIpYmh1M+Ur+jIiLGkhUs65BbWo+UdHclQQ5jVU+ut8GRNKXqGKxr3AAcFBHLSnoOuIJsbXBsRCxP/v87mWy1UPrk+SJx+gNy50ntM/M5ZDKViNiHHJq4o6Tbyj5/jaRXitY2t5K79QL4Npm8/Z6Tt9ZmKwFX9KFf/yyyLUepJD1ctO76T7LS/g7m3skKsAVwL/keYSXqaQhesVvr/eSNrU60bDRcgWv0vfLLWouIV8itAx+ve67Z3cmfAjsDxwz0XN364aZIXB5F9uBavOHwq8BJwJRuTmAOVkSsT/aBWpl5k1sikzafkfSHDofWJ/2sxhx21ZdFdU8zo8h/023IKpwTgHsG2zqk4e+7P4N7jpf09cGc29onIr4PfBWYIOmPxXMLkjdJxzHnveEV4P3uJVuuogpoR2B7zZlc//bgo5gzuX49YD1Jz1YXrZm1UvRGPgaYWjeY80tke6C3l5F9uddvbEE2yHMvSm4bXocscrkL2Ja5h4IuT96gnyrp0LLO3UNME8gCnEXIP/ePJP17D+uP7ONLv0kO3Zzmm4rWKCJeJL83tqx7rtln3FuBVSX9WwVhWpsUM3y+SH6Gv73u+SOYuyDtfEm7VxDiiDOsPnzbgG1GNgi3gZufTFL2ZjngjW5Nwg5URIwm73p+jHwjf5rc0gbZHmIFsofSByJi2xaVgENasZ3tanLL7uNkK4L6v4PdyKFAV0fEurVK7S7Tn4rKYVd92YeE7FER8R2yB2wZE6cbq/B7+jv9F8VOCOCIEs5t7bMZWX1bPxDws+RNgOuA7wDbA18hB8F9o+MRDm8fAe6XdGWzg5Kej4hdyVZFU4DSJtebWXkk3UFWzNc/d2pETAMmkddb04GzJL3c5CUG42tk8vanwJclvV4krepjeSYiHiB3xrSdpLsjYifgcuCcnpK3haPoe4u82tb4P5KDUkdcGy1r6X5g/YgYI+mVZguKHUbrkP1qbXj5PDk47r7aExGxFnn9NAu4DVgT+FxEXCyp9N0QNjcncA1yuM3NwMM9LSoGS3zUlbpNPU5u02ypSHKuSU5XHm72JS+yHwYOknR1/cGI+DjwQzLB+0WyN+Fwcyj5YeJEcpjEXJXGRV+/qcBBxdreLrw7TtKoqmMYAv4b2JVsZfK5wbxQ/d93WTshiveZncntZO8khx+2OL22GMy5rKWxFAOZ6nyC/ID8xWKA3HUR8Ulga5zALVuVk+vNrM2Krbrt3q77GeB/yffsnoY2Pgx8qKyT9mMn1N4R0Xi90Lgz6mhy18eezJmxMYNsLzOevG5flGwLMYscQLUOcG1ErDfCWmhZa+eRVe+nRsTkxpZ4RYuvE8mBgz+tID5rrwnAvZJer3tud/Kadh9JP4mIVYEHyM/4TuC2mRO4BnP6PvY2wGcjMtnrBO68rgYOjIjdJbX64fUlshK1P4OShoraALYtmlWWSro6Ij5GVkrswfBM4G5NVtx+tVmfKEmzIuJgYDtyG17XJXCtd5JmR8QfKL/iZgrzJv36JSKWBK4ht4b3ViHt/kntsyS5HbXeh4GHiuRtzd1kot3KVeXkejMbHlYFru4leQvwD7LXfFnK7Et/BjCN7AX+FUkvzLU4rxlOIm8wbkDu8jmJ3JXwNXKXiNn/J3cR7kzupKztblkrIo4jZ7e8m+xZfV4lEVo7LU0Okau3Cbnz+DwASY9ExM3krA5rMydwrT/mJ+/a2rymkonJM4shUBcWzy8UEe8l7+QfBrxAXhy9rR89qiDvrnfjELP3Adf31BZA0lMRcT35pj8cjSUH/bVMjBXJvzuAT3cuLGuDJYpfpSmprcox5ADBJ4D/IW+Y/L2E17X+eYOsAgUgIsaR7w9nNKx7E1igg3GNFE+QVWc195OJjU+SA4lq/S0nkgkLM+tixXDcSeRw2BWLp2vDYS/qQ5J1IP5F6x0s9Vaiby3U+qTknVDfJhPMezabPyHppYjYi9wZ+G1JkyPia+Qg3q1KjMOGsKIAZVvgdDKJe2BxaAPmtBO7BNijD4PObOhZkLqbQ8X78brAjZJm1a17hiz2szZzAtf6Y03mVLJYHUlPFk2+LyK3w36DrO7ZpfgVZCJlJ0l/a/jyo2jd/7L+B2GtR1U3JnDnJycD9+b1Yu1w9AbZQqE3SxVru85IH2LWFxHxEWBjSm6FUtz42Qm4vNUQkYhYj0xC/ULS9CZLtierDzeU9EyZ8Vm/PABMjIhliqE6u5Hv3Tc1rFsJ8ACt8t1AhZPrzaw8xc/c88j3y8br5C+Q/1/vJunmkk/9EDAhIhZslSAuKljXAbpyMC2ZhL2hp+HBkv4VEb+n6DVc9Pq9l+wlbgZk2yHgsxExhWw9tCowmrxh+msPvxvWniYLtWo+SiZ1b2lYtxguGumIEffh21JENG7jn9jkuZr5yJL49YCmQ0EMJF1fJGG+SpMfbuSU2iebfGmryrtR5NCbTclqojOL1+pGM4CNI2KBxt5INcUdu42LtcPRH4FNI2KNFsk1IuI95L/nbZ0MrB9G9BCzXqrhFwPWIFtljKb8VigHkL2ke3rdvwFHktuZDmpyfBlyy6eTt9X6Cdkv7q6i3cYngJnApbUFEbEQ+TPVAz/KdwFZHTIBuEbSC0X7mpPJbcEwZ3K9BwKadamIWJNsC7QI2aLqfOCx4vB4cjjku4CrImJDSX8q8fQXAt8FjgP+o8Wa75DXBr8o8bxlegcZX28WLdbWPNeecGwoioglyKKNmZIeBB6sOibrqBuB3SPiEOAqspBMxe/rrUXeILc2C1e6j0wNk1R7m35e8wzwcUn39brSSlN80D+FHAC2XpMK3soVPZC+Dvwc2K9xGnBEjAF+RA59mirp0M5H2V4R8XlyEMQz5KCrn9aS2RExP9nw/VtkH+TJks6tKtb+ioggbyZ8grzh8CNJ36w2qvIV74u9vR/OBn7ch+nP/T33A8DrkjboZd1dwIKS1m5y7BHgbkmTyozN+qcY6HEmWeUJmbzdW9JFdWt2Bn4GHCrpe52PcuSJiA1o/+R6MytJRFxEtpw6FjhC0uyG46PIQV2HARdL2qnEcy9C9n1cA7iVrNb/PlnhfwHZGm0TcjL7B1sVL1SpuK4YB7yv1UCyosXPg8AMSe8rnrseeJekcc2+xkaW4tr4TkkbVh2LdV5ErE6+F9ZuBgVwraStGtZMB06RtH/noxxZnMAdoSJij9pvyQ+aNzNvf76aN8leU7d14wVKN4iIpSS92Me1G0q6vZ+vvxDwKLm9et+BxNhOEbE0OZBnLJmsuJyMV2Ql8nbA4uSduQl9/bsaaiLiXDJJLTLR93Tx+3eSFdUBnCdp98qCHKSImAhcD3xe0s+qjqdMEdFTUrr2Pni9pNIr4SPiFbJ6dude1l0AfEzSkk2OTSWHUo6T1JVtOkaS4oPxcsB0Sa82HFuXvClymyS3UTAzaxARzwPPSepxME5EPAgsK2mZntYN4PxjyWTth5hzc7f2wTnIAWGf6mn+Q5Ui4htk8vtRsrDgF5LeKo6NJpPQx5DVzIdJOi4i5iN3+9wmadtKAreuUlyfXj6UP7vY4ETEWsB/kte0d5DFWG/UHd+P3EV4uKRfVRPlyOEErhERj5E/1A+pOpahqpi8uEVvgxQiYgLwW0l96ZXa+LWXk8nPFXtdXIGIWI3sU1arIKy/yIW8e7erpFJ7h3abiNgfOBhYpeHQI8Dxkk7ufFTliojbye1UH6o6luEiIl4FruqtgigiLgS2lbRIk2OLkT2pZgD7dGO1vpmZWV9ExGvAJZJ262XducAOkvrSLmAgcWwNbMu8rdEu6eahTUUy9nLg4+Q1+VvMXVgwmrxGvxrYrhhWtQ45BPVUST+tJHDrKhFxG/APSZtWHYuZOYFrVopie8lFkj7Tw5o1ya1XSw5k+FNE/BrYTFJfpuJWpqjQ3ISsxoWsWryxDQMmulpRufH230G3VmgMRET8AthG0uJVxzJcRMSfgCWAlRu3idatGUUmZ1+X9J4WfcvfAXyKrISfBjxOVoM3kqQvlBK8mZlZyYoe4i9L2ryXddeR19YTOhPZ0FFcN3yl+DW+4fAM4CTghFplrlmjiPgCcCo5IHda1fGYjXRO4Nrbim36mwGrk4mEZn0gJelbHQ1sCIiI84BdgBMlfbXJ8dXJJuD/Bhwg6cf9fP3VySm3z0p6Vwkhmw1YkWxcUdKYqmMZLiLieHIw2X9LOrbFmkPJoSn/I+krDb3M+0uSRg/i660QEbVet7+UNLPucZ9I+kkbwjIzG9IiYl9y+OAmkhonntfWbEReXx8o6ZROxjfURL65WdEAABftSURBVMSKzCks+N92tIOy4SkiTiRneRwH/JLsmdzjrlMzaw8ncA2AiJhEDsrqaWt/4A/9TUXEAuSk3I2BgyX9sO7YKsBN5EXT1yX9v4av7enD/mLkAIXPk0n170o6vOTwBy0itiGHmH1L0vUt1mxO9uA6VtJvOhlfJ0TEgmSC/iVJM1usWRxYEnhmKPaTLnodTwH2I1uBbNXLlwwpLSpam3kTeJ6scP1VGRexxQer+8le0T8je5JPLw6/B9iHnLj9KvB+STPqepkPiKRzBvP1luqG371X0sN1j/vEP1PNzJorbm5+kUzknkv2c4WsJt0N2B84XdLBlQRoNsxFRH+qszWQXaZm1ndO4BoRsSE5xGw22ax/LWBt4LvAasCWwBhy2NmTkqZUFGpXi4gxwO/JZMsuki4qkjI3kReaR0k6usnX9fZhv1YJfQWwUzcm/oot9VsDK0h6rcWaxcjeW5f11s9sKIqIQ8hhEVtIuqHFmk2B35KJ/OM7F13fRMQjPRxeDFia/H58E9hc0u87EliH1FW0NvZvpsXzIod97Cnp6hLOvxVwIfl33fieEGTydmdJVw32XFaeiDib/Pc6VNKzdY/7RNJebQrNzGzI6mfiqJETSWYl6O9uL0mj2hWLmTmBa7w91XxHYHtJV0bEWcDkWlVQRCwDnAWsB6znidmtRcTKwK1kH8rJwLfJlhTfk3Roi685m9Yf9t8ke8j+ttX2sW4QEX8lt2Nt3Mu635FJ3tU6E1nnFIPsVpQ0vpd1M4DHJG3SkcD6oQ8XaW8CvwOOlHRrB0LqqKKidQPgAOBJMpk6g7y5NR6YBIwjK4GeJlvObA78A/iApD+VEMNK5BC8jwMrk+8Nj5NDRo6X9Phgz2FmZtbtBtkmaMQnkiLiyH4sd4s8M7MhwAlcIyKeAp6XtE7xeK4EbvHc4uS2pQslfbmaSIeGiJhAVt0uQlbN/UjSv1cbVXtFxOvkNN5de1l3HnmjoC2TgqsUEU8D90jappd1vya3wI/taV0VihsQrbwJPCdpVqfi6bSIWAu4DfgRcHjjn7WY6HwMcCDwIUn3RcR/A0cDZ0vau9Mxt1IMTfwwsCzwJ0mXFc+PAubrxkp+MzMzK0fdDr+mM03ql+IWeWZmQ4K3lhjAMkB9decsgIhYWNIbAMVglpuAHpNTBpLujoidgMuBcwaTvI2IdwPvJ5vF31VWjG3wT7LNRm/GAMN10u1SwIt9WPci2Yqg60iaUXUMFZsCPCXpG80OSppVDBL7VLF2R7LVzJeBTTsVZE8iYhxwNlBf4X0OcFnx+32AH0fEVpJ+2+HwhqXi73zAXFVtZmZt0Krl3Shyh8+m5K6iMwEPNDMzGwKcwDWAl4AF6x6/XPx3ReDPdc8LWK5TQXWzfvTl2jsiGqvy5urLFRE7kkmVKZJur3v+COCbFHfOI+J8SbsPLvK2eRCYGBFjJL3SbEFELAFMBB7uaGSd8zzZM7o3qzHn/zHrLhsDPQ7Yk6SIuAvYqng8KyLuowsSuEW7m5vID2T3ke0u9m9YdgFZYbwD2Y/ZBu8x+tHztoHwtZiZmZWst5klEbEQOcB6a7JNnpmZdTl/aDDIu671FUT3k0nDTwI/AIiIRcnk21Mdj647NduONNCv3R34KJlwyQW5lXsKWQ19G7Am8LmIuFjSxYM4d7tcDHwIODMidpX0z/qDEbEAeYd/MeCiCuLrhNuBHSLiA5LubLYgIj5A9li9sqORWV8tRrYc6M2ywKJ1j1+m2LkwGMVNjgOALYB3Agu1WCpJ72ry/H+R7+XHAYcVyeb9G77wpYj4I/l+buV4nIEncM3MzDpO0j8i4stki7xvA/tWHJKZmfXCCVwDuAE4KCKWlfQccAXwOnBsRCxPDvOZTLZa6MbkYceVPBhhAnCvpNfrntudTAjsI+knEbEq8ADwRbrz3+Bksor4U8ADEXEuML049h7yzzMe+AtwUhUBdsCp5J//kojYU9JclZwRsSU5DBCy4sG6z0PAJhGxjqR7my2IiHXIatv7654eC7wwmBMXw8t+B6xE7zeIWiULtyM/iB2mnhvcP0JWG1sJehtcaGZm1o2KJO5dwLZVx2JmZr1zAtcgt9SuSyYSr5H0QkQcTCblvlasCbJS94hqQhzWlgYaKzY3AV4FzgOQ9EhE3Ay8t8Ox9Ymk1yNiK+AS8nvp8IYlAdwD7CjptU7H1wmSro6IU4EvAVdFxJNkQhAyib0i+fdwuqRfVRSm9ezHZHL9uoj4PnA++b4nMrH6OfI9cXSxjohYmNx6eM0gz/0dsnr2D2QF7XTg7/18jZWAK3pJ3kJWCy/Z7wjNzMxsuJmPLNIxM7Mu5wSuIekOYMuG506NiGnAJHI403TgLEnu3Vm+BamruCvaDawL3Cipflv2M8BGHY6tzyQ9HhHrA9uT/bRWJhNfjwNXA5f2IbE0pEnaLyIeIhPYKxW/ap4HjpX0g0qCs15JOi0iNiCryb9d/JpdHK5V3QdwhqTTiserAL+kuNkyCFuR/49vJmnmAF/jDeAdfVg3HvdhNjMzG9EiYnVyR45b5JmZDQFO4FpLku4C7qo6jhHgaeB9dY8/SiZ1b2lYtxj9r8jrqCJBe2nxa0SS9MOIOInsdVufxJ7WkJC3LiRp34j4FfAV4MPMGfD4JnArcFJ9H2pJDwCfL+HUSwC/GkTyFrKtw/q9DBMcC6wD3DiI85iZmVkXi4jJPRxeDFiDvH5ZGPhZR4IyM7NBcQLXrHo3ArtHxCHAVcC3yKTfVQ3r1iL7EVuXk/QWOdTs9qpjsf6TdAnZy3g0c7YVvtDmBPxjwPyDfI3zyNY3p0bEZElv1h+MiFHAiWRS+qeDPJeZmZl1r7PpecBmbfffFeTgZDMz63IxzHc0m3W9YvvSneTdcMgLqmslbdWwZjpwiqT9530V6zYRsRqwLJn4e7jqeKx3EXE88LKkoys49+HAIcCqkgY0EC0i5gOuJ1utPApcCRxI7qS4nhyy925ycOUWw72liZmZ2UgVEWfTOoH7Jtk24beSGnf8mZlZl3IC16wLRMRawH8CywF3AFMlvVF3fD9gX+BwD8DqXkUC7TDgAOZUbp4jae/i+G7FsX0l3V9NlNZKRPwLuEzSpArOPR/wa7KVwl5Fa4aBvM7iwOnAzi2WXALsMchWDWZmZmZmZtZBTuCamZWgSMD9CtgCmAX8mextfHZdAnc88AgwRZK3q3WZiHgC+L2kXSo493VkC4WNyMFpjxe/ZjdZLklb9PJ67wW2AVYFRgNPAL+WdHeZcZuZmZmZmVn7OYFrZlaCiPgP4HjgWrLC8emImE1dArdY9zDwnKSNKgrVWoiIs4AtgfGdHjhXfK/0lSSNbvIaSxTHXF1rZmZmRMRCwGbA6uQun2iyTJK+1dHAzMys35zANTMrQURMA8YB75b0cvFcswTupcAESeOqidRaiYhxwN3AL4GDJL3WwXNv0p/1km5s8hqzgTslbVhaYGZmZjYkRcQk4BRgqZ6W0eLGsJmZdZf5qg7AzGyYeA9wQy1524OZ5HAz6z57kn1o9wK2j4hrgRnAG03Wllqt0iwhOwAzydYdZmZmNoJFxIbAz8hWTOcDawFrA98FViN3HI0BzgCerChMMzPrBydwzczKIZr3K230TuAfbY7FBuYo8t8xyCF0n22ypnZcQLdtN3wQWLHqIMzMzKxyXwNGAZ+SdGXRJmptSYcDRMQywFnAtsB61YVpZmZ95QSumVk5HgXWiYhRkpomciNiYeD9ZKLNus/RZGJ2qDodODUi1pc0repgzMzMrDIfAe6XdGWzg5Kej4hdyevXKcCXOxmcmZn1nxO4ZmbluAz4L+BgYGqLNYcASwKXdioo6ztJR1V17oi4rh/LJWmLJk+eERHrAL+JiOPIXr4zJP2zrDjNzMxsSFgGuKXu8SzIYgJJbwBImhkRNwHbVBCfmZn1kxO4ZmblOJ7snfrdiJgAXFg8v0xEbAN8BtgDeBw4uZoQrYtt2oc19e0b5hERb9U9/E7xi4hmA6eRJF8DmJmZDU8vAQvWPa7NaFiRufvlC1iuU0GZmdnA+cObmVkJJL0YEVuT1bWfBXYhL4o/UfwK4AlgO0kzKwvUehURCwGbAasDS5D/dvOQdHSJp92sxfOjgJXJ76FJwHHAVS3WNo2zhLVmZmY2tDwBjKt7fD/5s/+TwA8AImJRYCLwVMejMzOzfgtpKLf7MzPrLkXyby9yO9qqwGjyIvrXwGmSXqswPOtFREwCTgGW6mkZWcE6ujNRFSeN2J+s9N7IPW7NzMyslYiYChwEjJX0XEQsDcwgC7hOAJ4EJpMDzE6TtF9lwZqZWZ84gWtmZgZExIbAzcBs4AJgLWBt4LvAasCWwBjgTOBJSVMqiHE68GdJ23X63GZmZjY0RMQHgWOAqZKuKZ77EnO38artDltf0vOdj9LMzPrDCVwzMzMgIi4AdgS2l3RlRJwFTK5V2kbEMsBZZLXKepKerSjGzSUt3elzm5mZ2dAWERuQLZmWAqYDZ0l6ueevMjOzbuAErpmZGRARTwHPS1qneDxXArd4bnHgUeBCSV+uIMbfkZUyi/SwZgHyw9mm5LASyP52NwAXSfpnm8M0MzMzMzOzEnmImZlZSSJiNeAb5ECqdzL39N96kuT33+6zDHBL3eNZABGxsKQ3ACTNjIibyB7HHRURnwU+Atzbw5qPAOcBKzHvoLIvAMdGxG6Sbm5boGZmZmZmZlYqJxDMzEpQbEm7DliUeRNn8yxvf0Q2AC8xd9K9tqVwReDPdc8LWK7ME0fEmT0cXgxYA1izeHxii9dYE7gGWAR4BDgfeKw4PB74LPAu4KqI2FDSnwYduJmZmZmZmbWdE7hmZuX4Hplo+zlwHDlo6rVqQ7J+egIYV/f4fjLZ/kngBwARsSgwkWxJUKY9+7BmJnC0pLNbHD+aTN4eCxwhaXb9wYj4ZrHmMGAKsNNAgzUzMzMzM7POcQ9cM7MSRMRrwGOS1ux1sXWliJgKHASMlfRcRCwNzCBvdp4APAlMJoeYnSZpv0Gc6/3Ai5KeLB7v0cPyN8mE8Z21Vg4tXvN54DlJ7+3l3A8Cy0papv+Rm5mZmZmZWae5AtfMrBxv0ENvUhsSLgDWBSYA10h6ISIOBk4GvlasCbJS94hBnutu4GyyLy3AJsDNknpqpdCbhYE/9GHdH4AdBnEeMzMzMzMz6yAncM3MynEHsGrVQdjASboD2LLhuVMjYhowCVgKmA6cJenlJi/RH8HcvZD3LP47mATuQ8AKfVi3AnP39DUzMzMzM7Mu5gSumVk5jgGui4gdJV1cdTBWHkl3AXeV/LIz6VuytT9OAU6OiI0k3dJsQURsBHwUOLDkc5uZmZmZmVmbOIFrZlYCSbdExGeB0yPi08DVZM/U2S3W39TJ+Kzr3A9sHhFHA38pnlstIib35Ysl/aTJc6dFxBrAVRFxMnAu8GhxeDywG7A/cIKkUwYZv5mZmZmZmXWIh5iZmZUkInYBpgJje1kqSb6BNoJFxA5kz93RtaeAPv9AljS68bmIeGsQIfl70szMzMzMrEv5w5qZWQkiYhJZ8TgKeAF4DHi1ypise0m6NCI+CHwKGEf2wP0r0LT1QR9F70va8rVmZmZmZmbWRq7ANTMrQTHoal3gAOA0SU1bJ5g1ExGzgbMl7V11LGZmZmZmZtZdXIFrZlaONYBb3FvUBmgKcHfVQZiZmZmZmVn3cQLXzKwcr5BDy8z6TdKUqmMwMzMzMzOz7uQErplZOa4BNoqIkHvTWMUiYjSwNLBQqzWSHu9cRGZmZmZmZjZQ7oFrZlaCiBgLTCMHmX1D0qyKQ7IRKCImAt8EJgIL9LBUknwT18zMzMzMbAhwAtfMrAQRcSSwCjAZeAy4nmyp0GyYmSR9q3PR2UgQEVsBVzBnd80LwKut1ktapRNxmZmZmZmZ2eA4gWtmVoKImA0IiB6W1Y5L0uiOBGYjRkTcDnwA+D5wrKSXKg7JzMzMzMzMSuDtk2Zm5TiaTNCaVWVtYJqkQ6oOxMzMzMzMzMrjBK6ZWQkkHVV1DDbi/R34c9VBmJmZmZmZWblGVR2AmZmZleImYK2qgzAzMzMzM7NyOYFrZmY2PEwBxkfEf1QdiJmZmZmZmZXHQ8zMzEoUEQsBmwGrA0vQfKiZJH2ro4HZiBARHwLOB54CrgKeBGY3WyvpJx0MzczMzMzMzAbICVwzs5JExCTgFGCpnpaRCdzRnYnKRpKI+DpwBLBob2v9PWhmZmZmZjY0eIiZmVkJImJD4GdkteP5ZC/StYHvAqsBWwJjgDPIqkizUkXEl4Djiof3An8BXq0uIjMzMzMzMyuDK3DNzEoQERcAOwLbS7oyIs4CJteqHCNiGeAsYD1gPUnPVhetDUcR8QDwLmAHSVdVHY+ZmZmZmZmVw0PMzMzK8RHgfklXNjso6XlgV2BBctiUWdnGAzc5eWtmZmZmZja8OIFrZlaOZYCH6h7PAoiIhWtPSJoJ3ARs09nQbIR4Dnih6iDMzMzMzMysXE7gmpmV4yWyurbm5eK/KzasE7BcRyKykeZSYGJELFB1IGZmZmZmZlYeJ3DNzMrxBDCu7vH9QACfrD0REYsCE4GnOhuajRBHAjOBn0TEUlUHY2ZmZmZmZuWYr+oAzMyGiRuAgyJiWUnPAVcArwPHRsTywJPAZLLVwsWVRWnD2fHAg8BngK0j4i7y+252k7WS9IVOBmdmZmZmZmYDE5KqjsHMbMiLiA8CxwBTJV1TPPcl4OT6ZWSl7vrFUDOz0kTEbLJFR/RhuSSNbnNIZmZmZmZmVgIncM3M2igiNgAmAUsB04GzJL3c81eZ9V9E7NGf9ZLOaVcsZmZmZmZmVh4ncM3MzMzMzMzMzMy6lIeYmZmZmZmZmZmZmXUpDzEzMytZRIwGlgYWarVG0uOdi8hGmohYAFgfGFs89RQwTdKb1UVlZmZmZmZmA+EErplZSSJiQ+BoYGNgwR6WCr//WhtExPzAUcABwOINh1+NiJOAKZL+1enYzMzMzMzMbGDcA9fMrAQRsRFwLXMSty8Bf2+1XtIqnYjLRo6i8vtXwMeAAJ4GHikOrwqsQN48uBbYVtJbVcRpZmZmZmZm/eMKMDOzckwhk7enA0dI+lvF8djIsy+wJfAwcJCkq+sPRsTHgR+SCd4vAqd0PEIzMzMzMzPrN1fgmpmVICL+Djwhac2qY7GRKSJuBdYC1pD0VIs1Y4HpwP2SPtzJ+MzMzMzMzGxgRlUdgJnZMBHAH6sOwka09wHXt0reAhTHri/WmpmZmZmZ2RDgBK6ZWTnuA5avOggb0eYHXu/DuteLtWZmZmZmZjYEOIFrZlaOE4CNI2LdqgOxEWsG+T24QKsFxbGNi7VmZmZmZmY2BDiBa2ZWAkk/B44BfhMR+0XEuKpjshHnMmAF4JyIeEfjwYgYA5xJVopf2uHYzMzMzMzMbIA8xMzMrAQR8VY/lkvSfG0LxkakiFgauBsYC8wELgceBQSsCmwHLA48CUyQ9GJFoZqZmZmZmVk/OIFrZlaCiJjdn/WSvAPCShcRqwHnARsUT9V+yEfx3zuBXSX9tdOxmZmZmZmZ2cA4gWtmZjbMRMREYBOyGhfgKeBGSTdXF5WZmZmZmZkNhBO4ZmZmZmZmZmZmZl3KW3jNzMyGgYjYJiKui4jNelizebFmy07GZmZmZmZmZgPnBK6ZmdnwsBfZ+/aOHtbcAXwA2LMTAZmZmZmZmdngOYFrZmY2PKwP3CvptVYLJL0K3ANs2LGozMzMzMzMbFCcwDUzMxseVgCe6MO6J4Dl2xyLmZmZmZmZlcQJXDMzs+Hhn8CYPqwbA7zV5ljMzMzMzMysJE7gmpmZDQ8PAhMjomUSNyKWACYCD3csKjMzMzMzMxsUJ3DNzMyGh4uBxYEzI2LBxoMRsQBwJrAYcFGHYzMzMzMzM7MBCklVx2BmZmaDFBGLAH8A3g08BpwLTC8OvwfYHRgP/AVYr6dhZ2ZmZmZmZtY9nMA1MzMbJiJiHHAJsC7Q+AM+gHuAHSU91uHQzMzMzMzMbICcwDUzMxtGIiKA7YGtgZXJRO7jwNXApfIPfjMzMzMzsyHFCVwzMzMzMzMzMzOzLuUhZmZmZmZmZmZmZmZdyglcMzMzMzMzMzMzsy7lBK6ZmZmZmZmZmZlZl3IC18zMzMzMzMzMzKxL/R8xJLvNGSQrJQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,6))\n", + "labels, values = zip(*d.most_common(75))\n", + "\n", + "indexes = np.arange(len(labels))\n", + "width = 1\n", + "\n", + "#plt.bar(indexes, accuracies, width, label='Accuracy')\n", + "plt.bar(indexes, values, width, label='Frequency')\n", + "plt.xticks(indexes , labels, rotation=90)\n", + "plt.title('Test token distribution - Corpus-Lg - mean_freq = {:.3f} / max_freq = {:.2f}'.format(mean_freq, np.max(values)))\n", "plt.legend()\n", "plt.tight_layout()\n", - "plt.savefig('BERT-freq-50k_epochs_top100.png')\n", + "plt.savefig('test-freq-epochs_top75.pdf')\n", "plt.show()" ] }, @@ -2026,7 +2975,14 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2035,7 +2991,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2053,7 +3009,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2062,2885 +3018,18 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'1': Counter({'keyword': 12}),\n", - " '1d': Counter({'keyword': 221}),\n", - " '2': Counter({'keyword': 12}),\n", - " '2d': Counter({'keyword': 78}),\n", - " '3d': Counter({'keyword': 24}),\n", - " '[CLS]': Counter({'keyword': 9}),\n", - " '[MASK]': Counter({'keyword': 6}),\n", - " '[PAD]': Counter({'keyword': 1}),\n", - " '[SEP]': Counter({'keyword': 19}),\n", - " '[UNK]': Counter({'keyword': 3}),\n", - " '[cls]': Counter({'keyword': 5}),\n", - " 'a': Counter({'keyword': 13}),\n", - " 'abs': Counter({'keyword': 112}),\n", - " 'acc': Counter({'keyword': 4}),\n", - " 'accumulators': Counter({'keyword': 7}),\n", - " 'activation': Counter({'keyword': 2}),\n", - " 'add': Counter({'keyword': 12}),\n", - " 'algorithm': Counter({'keyword': 337}),\n", - " 'alias': Counter({'keyword': 3}),\n", - " 'all': Counter({'keyword': 18}),\n", - " 'alloc': Counter({'keyword': 1}),\n", - " 'allow': Counter({'keyword': 32}),\n", - " 'allowed': Counter({'keyword': 83}),\n", - " 'alpha': Counter({'keyword': 812}),\n", - " 'alt': Counter({'keyword': 7}),\n", - " 'amsgrad': Counter({'keyword': 18}),\n", - " 'and': Counter({'keyword': 54}),\n", - " 'any': Counter({'keyword': 1789}),\n", - " 'append': Counter({'keyword': 36}),\n", - " 'apply': Counter({'keyword': 2}),\n", - " 'arange': Counter({'keyword': 95}),\n", - " 'arg': Counter({'keyword': 2}),\n", - " 'argmax': Counter({'keyword': 1675}),\n", - " 'argmin': Counter({'keyword': 11}),\n", - " 'args': Counter({'keyword': 80}),\n", - " 'argument': Counter({'keyword': 24}),\n", - " 'arguments': Counter({'keyword': 2542}),\n", - " 'as': Counter({'keyword': 65}),\n", - " 'asarray': Counter({'keyword': 48}),\n", - " 'assert': Counter({'keyword': 96}),\n", - " 'assign': Counter({'keyword': 8261}),\n", - " 'astype': Counter({'keyword': 5}),\n", - " 'async': Counter({'keyword': 78}),\n", - " 'at': Counter({'keyword': 26}),\n", - " 'atleast': Counter({'keyword': 216}),\n", - " 'attribute': Counter({'keyword': 9}),\n", - " 'attrs': Counter({'keyword': 111}),\n", - " 'augassign': Counter({'keyword': 11}),\n", - " 'available': Counter({'keyword': 16}),\n", - " 'axes': Counter({'keyword': 1}),\n", - " 'axis': Counter({'keyword': 73}),\n", - " 'b': Counter({'keyword': 56}),\n", - " 'backend': Counter({'keyword': 200}),\n", - " 'backup': Counter({'keyword': 7}),\n", - " 'backward': Counter({'keyword': 187}),\n", - " 'backwards': Counter({'keyword': 41}),\n", - " 'bar': Counter({'keyword': 7}),\n", - " 'baseline': Counter({'keyword': 27}),\n", - " 'batch': Counter({'keyword': 149}),\n", - " 'batches': Counter({'keyword': 20}),\n", - " 'batchnorm': Counter({'keyword': 26572}),\n", - " 'begin': Counter({'keyword': 126}),\n", - " 'best': Counter({'keyword': 792}),\n", - " 'beta': Counter({'keyword': 48}),\n", - " 'bias': Counter({'keyword': 79}),\n", - " 'biases': Counter({'keyword': 2936}),\n", - " 'binary': Counter({'keyword': 174}),\n", - " 'binomial': Counter({'keyword': 6}),\n", - " 'binop': Counter({'keyword': 106}),\n", - " 'bool': Counter({'keyword': 20}),\n", - " 'boolop': Counter({'keyword': 18}),\n", - " 'build': Counter({'keyword': 9}),\n", - " 'built': Counter({'keyword': 211}),\n", - " 'by': Counter({'keyword': 5}),\n", - " 'c': Counter({'keyword': 104}),\n", - " 'cache': Counter({'keyword': 5}),\n", - " 'call': Counter({'keyword': 1388}),\n", - " 'callback': Counter({'keyword': 555}),\n", - " 'callbacks': Counter({'keyword': 87}),\n", - " 'carry': Counter({'keyword': 7}),\n", - " 'cast': Counter({'keyword': 1189}),\n", - " 'cell': Counter({'keyword': 645}),\n", - " 'cells': Counter({'keyword': 2}),\n", - " 'channels': Counter({'keyword': 9}),\n", - " 'check': Counter({'keyword': 29}),\n", - " 'child': Counter({'keyword': 2}),\n", - " 'chunk': Counter({'keyword': 217}),\n", - " 'class': Counter({'keyword': 29}),\n", - " 'classdef': Counter({'keyword': 3}),\n", - " 'classes': Counter({'keyword': 34}),\n", - " 'classification': Counter({'keyword': 4}),\n", - " 'clear': Counter({'keyword': 9}),\n", - " 'clip': Counter({'keyword': 7}),\n", - " 'clipnorm': Counter({'keyword': 22}),\n", - " 'clipvalue': Counter({'keyword': 42}),\n", - " 'cloned': Counter({'keyword': 3}),\n", - " 'close': Counter({'keyword': 69}),\n", - " 'closure': Counter({'keyword': 17}),\n", - " 'cls': Counter({'keyword': 30}),\n", - " 'cntk': Counter({'keyword': 99}),\n", - " 'collected': Counter({'keyword': 17}),\n", - " 'cols': Counter({'keyword': 5}),\n", - " 'compare': Counter({'keyword': 430}),\n", - " 'comprehension': Counter({'keyword': 6}),\n", - " 'compute': Counter({'keyword': 19}),\n", - " 'concat': Counter({'keyword': 446}),\n", - " 'concatenate': Counter({'keyword': 69}),\n", - " 'condition': Counter({'keyword': 1}),\n", - " 'config': Counter({'keyword': 471}),\n", - " 'const': Counter({'keyword': 2}),\n", - " 'constant': Counter({'keyword': 64}),\n", - " 'constants': Counter({'keyword': 7}),\n", - " 'constraint': Counter({'keyword': 7}),\n", - " 'constraints': Counter({'keyword': 9}),\n", - " 'constructor': Counter({'keyword': 5}),\n", - " 'continue': Counter({'keyword': 17}),\n", - " 'conv': Counter({'keyword': 70}),\n", - " 'conv1d': Counter({'keyword': 12}),\n", - " 'conv2d': Counter({'keyword': 66}),\n", - " 'conv3d': Counter({'keyword': 3}),\n", - " 'conversions': Counter({'keyword': 62}),\n", - " 'convert': Counter({'keyword': 12}),\n", - " 'converted': Counter({'keyword': 27}),\n", - " 'cooldown': Counter({'keyword': 6}),\n", - " 'copy': Counter({'keyword': 2}),\n", - " 'count': Counter({'keyword': 43}),\n", - " 'counter': Counter({'keyword': 3}),\n", - " 'cpu': Counter({'keyword': 7}),\n", - " 'create': Counter({'keyword': 30}),\n", - " 'created': Counter({'keyword': 4}),\n", - " 'cropping2d': Counter({'keyword': 8}),\n", - " 'cropping3d': Counter({'keyword': 3}),\n", - " 'crossentropy': Counter({'keyword': 4}),\n", - " 'csv': Counter({'keyword': 18}),\n", - " 'ctype': Counter({'keyword': 91}),\n", - " 'cudnn': Counter({'keyword': 35}),\n", - " 'cumprod': Counter({'keyword': 3}),\n", - " 'cumsum': Counter({'keyword': 20}),\n", - " 'current': Counter({'keyword': 108}),\n", - " 'custom': Counter({'keyword': 187}),\n", - " 'd': Counter({'keyword': 444}),\n", - " 'data': Counter({'keyword': 84}),\n", - " 'decay': Counter({'keyword': 2}),\n", - " 'decode': Counter({'keyword': 34}),\n", - " 'deconv2d': Counter({'keyword': 28}),\n", - " 'decrement': Counter({'keyword': 12}),\n", - " 'deepcopy': Counter({'keyword': 4}),\n", - " 'default': Counter({'keyword': 584}),\n", - " 'delete': Counter({'keyword': 1}),\n", - " 'delta': Counter({'keyword': 42}),\n", - " 'dense': Counter({'keyword': 20}),\n", - " 'densenet121': Counter({'keyword': 3}),\n", - " 'densenet169': Counter({'keyword': 121}),\n", - " 'densenet201': Counter({'keyword': 44}),\n", - " 'desired': Counter({'keyword': 3}),\n", - " 'device': Counter({'keyword': 4}),\n", - " 'devices': Counter({'keyword': 26}),\n", - " 'devs': Counter({'keyword': 23}),\n", - " 'dict': Counter({'keyword': 112}),\n", - " 'dictcomp': Counter({'keyword': 12}),\n", - " 'diff': Counter({'keyword': 74}),\n", - " 'dilation': Counter({'keyword': 1}),\n", - " 'dim': Counter({'keyword': 55}),\n", - " 'dimensions': Counter({'keyword': 36}),\n", - " 'dims': Counter({'keyword': 37}),\n", - " 'dimshuffle': Counter({'keyword': 16}),\n", - " 'dir': Counter({'keyword': 34}),\n", - " 'disconnected': Counter({'keyword': 15}),\n", - " 'distribution': Counter({'keyword': 81}),\n", - " 'div': Counter({'keyword': 85}),\n", - " 'done': Counter({'keyword': 238}),\n", - " 'dot': Counter({'keyword': 204}),\n", - " 'dropout': Counter({'keyword': 46}),\n", - " 'dtype': Counter({'keyword': 15}),\n", - " 'dump': Counter({'keyword': 18}),\n", - " 'dumps': Counter({'keyword': 8}),\n", - " 'dynamic': Counter({'keyword': 31}),\n", - " 'e': Counter({'keyword': 18}),\n", - " 'element': Counter({'keyword': 7}),\n", - " 'elements': Counter({'keyword': 4}),\n", - " 'elems': Counter({'keyword': 17}),\n", - " 'elu': Counter({'keyword': 57}),\n", - " 'embedding': Counter({'keyword': 7}),\n", - " 'embeddings': Counter({'keyword': 9}),\n", - " 'end': Counter({'keyword': 33}),\n", - " 'epoch': Counter({'keyword': 10}),\n", - " 'epochs': Counter({'keyword': 28}),\n", - " 'epsilon': Counter({'keyword': 63}),\n", - " 'eq': Counter({'keyword': 55}),\n", - " 'equal': Counter({'keyword': 26219}),\n", - " 'error': Counter({'keyword': 3}),\n", - " 'eval': Counter({'keyword': 26}),\n", - " 'evaluate': Counter({'keyword': 31}),\n", - " 'exc': Counter({'keyword': 2}),\n", - " 'excepthandler': Counter({'keyword': 6921}),\n", - " 'execute': Counter({'keyword': 1}),\n", - " 'exists': Counter({'keyword': 6}),\n", - " 'exp': Counter({'keyword': 73}),\n", - " 'expand': Counter({'keyword': 15}),\n", - " 'expects': Counter({'keyword': 8468}),\n", - " 'explicitly': Counter({'keyword': 420}),\n", - " 'expr': Counter({'keyword': 2649}),\n", - " 'extend': Counter({'keyword': 2154}),\n", - " 'extra': Counter({'keyword': 1}),\n", - " 'extslice': Counter({'keyword': 54}),\n", - " 'eye': Counter({'keyword': 52}),\n", - " 'f': Counter({'keyword': 10}),\n", - " 'factor': Counter({'keyword': 23}),\n", - " 'fan': Counter({'keyword': 7}),\n", - " 'far': Counter({'keyword': 408}),\n", - " 'feature': Counter({'keyword': 1}),\n", - " 'feed': Counter({'keyword': 4}),\n", - " 'field': Counter({'keyword': 171}),\n", - " 'fieldnames': Counter({'keyword': 26}),\n", - " 'fields': Counter({'keyword': 27}),\n", - " 'file': Counter({'keyword': 178}),\n", - " 'filename': Counter({'keyword': 38}),\n", - " 'filepath': Counter({'keyword': 13}),\n", - " 'filewriter': Counter({'keyword': 3}),\n", - " 'fill': Counter({'keyword': 52}),\n", - " 'filter': Counter({'keyword': 13}),\n", - " 'filters': Counter({'keyword': 12681}),\n", - " 'first': Counter({'keyword': 16}),\n", - " 'flags': Counter({'keyword': 2}),\n", - " 'flatten': Counter({'keyword': 40}),\n", - " 'float16': Counter({'keyword': 87}),\n", - " 'float32': Counter({'keyword': 116}),\n", - " 'float64': Counter({'keyword': 23}),\n", - " 'floatx': Counter({'keyword': 4582}),\n", - " 'floordiv': Counter({'keyword': 44}),\n", - " 'flush': Counter({'keyword': 38}),\n", - " 'fn': Counter({'keyword': 3}),\n", - " 'foldl': Counter({'keyword': 3}),\n", - " 'foldr': Counter({'keyword': 5}),\n", - " 'for': Counter({'keyword': 306}),\n", - " 'format': Counter({'keyword': 59}),\n", - " 'forward': Counter({'keyword': 6}),\n", - " 'fpath': Counter({'keyword': 4}),\n", - " 'freq': Counter({'keyword': 5}),\n", - " 'from': Counter({'keyword': 36}),\n", - " 'full': Counter({'keyword': 6}),\n", - " 'func': Counter({'keyword': 17}),\n", - " 'function': Counter({'keyword': 56}),\n", - " 'functiondef': Counter({'keyword': 4868}),\n", - " 'functions': Counter({'keyword': 8}),\n", - " 'g': Counter({'keyword': 3}),\n", - " 'gain': Counter({'keyword': 12}),\n", - " 'gamma': Counter({'keyword': 1}),\n", - " 'gates': Counter({'keyword': 5}),\n", - " 'gather': Counter({'keyword': 3}),\n", - " 'gaussiandropout': Counter({'keyword': 5}),\n", - " 'gaussiannoise': Counter({'keyword': 2}),\n", - " 'generator': Counter({'keyword': 1}),\n", - " 'generatorexp': Counter({'keyword': 53}),\n", - " 'generic': Counter({'keyword': 26}),\n", - " 'gens': Counter({'keyword': 87}),\n", - " 'get': Counter({'keyword': 197}),\n", - " 'global': Counter({'keyword': 68}),\n", - " 'go': Counter({'keyword': 45}),\n", - " 'gpus': Counter({'keyword': 23}),\n", - " 'grad': Counter({'keyword': 283}),\n", - " 'gradient': Counter({'keyword': 15}),\n", - " 'gradients': Counter({'keyword': 22}),\n", - " 'grads': Counter({'keyword': 4}),\n", - " 'graph': Counter({'keyword': 34}),\n", - " 'greater': Counter({'keyword': 158}),\n", - " 'group': Counter({'keyword': 49}),\n", - " 'gt': Counter({'keyword': 920}),\n", - " 'gte': Counter({'keyword': 120}),\n", - " 'hash': Counter({'keyword': 1}),\n", - " 'hasher': Counter({'keyword': 152}),\n", - " 'header': Counter({'keyword': 3}),\n", - " 'headers': Counter({'keyword': 137}),\n", - " 'high': Counter({'keyword': 15}),\n", - " 'histogram': Counter({'keyword': 2}),\n", - " 'history': Counter({'keyword': 32}),\n", - " 'hot': Counter({'keyword': 5}),\n", - " 'hsplit': Counter({'keyword': 430}),\n", - " 'hstack': Counter({'keyword': 235}),\n", - " 'id': Counter({'keyword': 6}),\n", - " 'ident': Counter({'keyword': 57}),\n", - " 'identity': Counter({'keyword': 39}),\n", - " 'idx': Counter({'keyword': 11}),\n", - " 'idxs': Counter({'keyword': 4}),\n", - " 'if': Counter({'keyword': 74}),\n", - " 'ifexp': Counter({'keyword': 111}),\n", - " 'image': Counter({'keyword': 8}),\n", - " 'images': Counter({'keyword': 29}),\n", - " 'img': Counter({'keyword': 57}),\n", - " 'import': Counter({'keyword': 42}),\n", - " 'importfrom': Counter({'keyword': 16}),\n", - " 'in': Counter({'keyword': 713}),\n", - " 'inbound': Counter({'keyword': 17}),\n", - " 'inceptionresnetv2': Counter({'keyword': 4}),\n", - " 'inceptionv3': Counter({'keyword': 7}),\n", - " 'include': Counter({'keyword': 8}),\n", - " 'increment': Counter({'keyword': 26}),\n", - " 'index': Counter({'keyword': 40}),\n", - " 'indexedslices': Counter({'keyword': 1}),\n", - " 'indices': Counter({'keyword': 98}),\n", - " 'inf': Counter({'keyword': 52}),\n", - " 'inferreddimension': Counter({'keyword': 6}),\n", - " 'info': Counter({'keyword': 178}),\n", - " 'init': Counter({'keyword': 1082}),\n", - " 'initial': Counter({'keyword': 3}),\n", - " 'initializer': Counter({'keyword': 11}),\n", - " 'input': Counter({'keyword': 6}),\n", - " 'inputs': Counter({'keyword': 48}),\n", - " 'ins': Counter({'keyword': 1}),\n", - " 'insecure': Counter({'keyword': 4}),\n", - " 'instance': Counter({'keyword': 14201}),\n", - " 'int': Counter({'keyword': 23}),\n", - " 'intermediate': Counter({'keyword': 34}),\n", - " 'intersection': Counter({'keyword': 15}),\n", - " 'is': Counter({'keyword': 3827}),\n", - " 'isinf': Counter({'keyword': 281}),\n", - " 'isnan': Counter({'keyword': 46}),\n", - " 'isnot': Counter({'keyword': 46}),\n", - " 'item': Counter({'keyword': 578}),\n", - " 'items': Counter({'keyword': 1211}),\n", - " 'iterable': Counter({'keyword': 2}),\n", - " 'iterations': Counter({'keyword': 30}),\n", - " 'join': Counter({'keyword': 52}),\n", - " 'json': Counter({'keyword': 113}),\n", - " 'k': Counter({'keyword': 485}),\n", - " 'keepdims': Counter({'keyword': 12}),\n", - " 'keras': Counter({'keyword': 31}),\n", - " 'kernel': Counter({'keyword': 34}),\n", - " 'key': Counter({'keyword': 4}),\n", - " 'keys': Counter({'keyword': 138}),\n", - " 'keyword': Counter(),\n", - " 'kwargs': Counter({'keyword': 66}),\n", - " 'kwd': Counter({'keyword': 85}),\n", - " 'l1': Counter({'keyword': 287}),\n", - " 'l1l2': Counter({'keyword': 93}),\n", - " 'l2': Counter({'keyword': 199}),\n", - " 'label': Counter({'keyword': 6}),\n", - " 'lambda': Counter({'keyword': 951}),\n", - " 'last': Counter({'keyword': 9}),\n", - " 'layer': Counter({'keyword': 21}),\n", - " 'layers': Counter({'keyword': 37}),\n", - " 'learning': Counter({'keyword': 104}),\n", - " 'legacy': Counter({'keyword': 276}),\n", - " 'length': Counter({'keyword': 28}),\n", - " 'less': Counter({'keyword': 36}),\n", - " 'level': Counter({'keyword': 3}),\n", - " 'like': Counter({'keyword': 1}),\n", - " 'limit': Counter({'keyword': 1}),\n", - " 'linalg': Counter({'keyword': 29}),\n", - " 'line': Counter({'keyword': 2}),\n", - " 'list': Counter({'keyword': 428}),\n", - " 'listcomp': Counter({'keyword': 3}),\n", - " 'load': Counter({'keyword': 206}),\n", - " 'loads': Counter({'keyword': 12}),\n", - " 'log': Counter({'keyword': 24}),\n", - " 'logits': Counter({'keyword': 143}),\n", - " 'logs': Counter({'keyword': 44}),\n", - " 'logsumexp': Counter({'keyword': 1}),\n", - " 'lookup': Counter({'keyword': 20}),\n", - " 'loss': Counter({'keyword': 1}),\n", - " 'losses': Counter({'keyword': 26}),\n", - " 'low': Counter({'keyword': 1452}),\n", - " 'lower': Counter({'keyword': 141}),\n", - " 'lr': Counter({'keyword': 490}),\n", - " 'lt': Counter({'keyword': 115}),\n", - " 'lte': Counter({'keyword': 1}),\n", - " 'm': Counter({'keyword': 102}),\n", - " 'make': Counter({'keyword': 2}),\n", - " 'map': Counter({'keyword': 3}),\n", - " 'mask': Counter({'keyword': 33}),\n", - " 'masking': Counter({'keyword': 106}),\n", - " 'matrix': Counter({'keyword': 25}),\n", - " 'max': Counter({'keyword': 15}),\n", - " 'maximum': Counter({'keyword': 6}),\n", - " 'maxval': Counter({'keyword': 93}),\n", - " 'mean': Counter({'keyword': 53}),\n", - " 'merged': Counter({'keyword': 5}),\n", - " 'message': Counter({'keyword': 3}),\n", - " 'metadata': Counter({'keyword': 8}),\n", - " 'methods': Counter({'keyword': 52}),\n", - " 'metrics': Counter({'keyword': 947}),\n", - " 'min': Counter({'keyword': 31}),\n", - " 'minimum': Counter({'keyword': 79}),\n", - " 'minval': Counter({'keyword': 35}),\n", - " 'mobilenet': Counter({'keyword': 7}),\n", - " 'mobilenetv2': Counter({'keyword': 182}),\n", - " 'mod': Counter({'keyword': 91}),\n", - " 'mode': Counter({'keyword': 126}),\n", - " 'model': Counter({'keyword': 115}),\n", - " 'module': Counter({'keyword': 4}),\n", - " 'moments': Counter({'keyword': 14}),\n", - " 'momentum': Counter({'keyword': 89}),\n", - " 'monitor': Counter({'keyword': 154}),\n", - " 'moves': Counter({'keyword': 97}),\n", - " 'moving': Counter({'keyword': 10}),\n", - " 'ms': Counter({'keyword': 2}),\n", - " 'msg': Counter({'keyword': 8}),\n", - " 'mult': Counter({'keyword': 157}),\n", - " 'multiprocessing': Counter({'keyword': 221}),\n", - " 'name': Counter({'keyword': 13849}),\n", - " 'nameconstant': Counter({'keyword': 510}),\n", - " 'names': Counter({'keyword': 27}),\n", - " 'nasnetlarge': Counter({'keyword': 41}),\n", - " 'nasnetmobile': Counter({'keyword': 10}),\n", - " 'nb': Counter({'keyword': 13}),\n", - " 'ndarray': Counter({'keyword': 103}),\n", - " 'ndim': Counter({'keyword': 107}),\n", - " 'neg': Counter({'keyword': 26}),\n", - " 'negative': Counter({'keyword': 52}),\n", - " 'neq': Counter({'keyword': 67}),\n", - " 'nesterov': Counter({'keyword': 3}),\n", - " 'network': Counter({'keyword': 6}),\n", - " 'new': Counter({'keyword': 514}),\n", - " 'next': Counter({'keyword': 60}),\n", - " 'nn': Counter({'keyword': 28}),\n", - " 'nnet': Counter({'keyword': 12}),\n", - " 'nodes': Counter({'keyword': 28}),\n", - " 'noise': Counter({'keyword': 4}),\n", - " 'non': Counter({'keyword': 259}),\n", - " 'nonzero': Counter({'keyword': 3}),\n", - " 'norm': Counter({'keyword': 137}),\n", - " 'normal': Counter({'keyword': 136}),\n", - " 'normalization': Counter({'keyword': 2}),\n", - " 'normalize': Counter({'keyword': 5}),\n", - " 'normalized': Counter({'keyword': 3}),\n", - " 'normed': Counter({'keyword': 21}),\n", - " 'norms': Counter({'keyword': 366}),\n", - " 'not': Counter({'keyword': 112}),\n", - " 'noteq': Counter({'keyword': 1084}),\n", - " 'notin': Counter({'keyword': 95}),\n", - " 'num': Counter({'keyword': 108}),\n", - " 'nw': Counter({'keyword': 267}),\n", - " 'obj': Counter({'keyword': 81}),\n", - " 'object': Counter({'keyword': 171}),\n", - " 'objects': Counter({'keyword': 6}),\n", - " 'old': Counter({'keyword': 131}),\n", - " 'on': Counter({'keyword': 1496}),\n", - " 'ones': Counter({'keyword': 16}),\n", - " 'only': Counter({'keyword': 875}),\n", - " 'op': Counter({'keyword': 58}),\n", - " 'open': Counter({'keyword': 189}),\n", - " 'ops': Counter({'keyword': 10}),\n", - " 'opt': Counter({'keyword': 6}),\n", - " 'optimizer': Counter({'keyword': 33}),\n", - " 'or': Counter({'keyword': 103}),\n", - " 'order': Counter({'keyword': 63}),\n", - " 'ordering': Counter({'keyword': 3}),\n", - " 'origin': Counter({'keyword': 76}),\n", - " 'original': Counter({'keyword': 1}),\n", - " 'out': Counter({'keyword': 175}),\n", - " 'output': Counter({'keyword': 156}),\n", - " 'outputs': Counter({'keyword': 9}),\n", - " 'override': Counter({'keyword': 10}),\n", - " 'overwrite': Counter({'keyword': 4761}),\n", - " 'p': Counter({'keyword': 27}),\n", - " 'pad': Counter({'keyword': 2}),\n", - " 'padding': Counter({'keyword': 174}),\n", - " 'parameter': Counter({'keyword': 13}),\n", - " 'params': Counter({'keyword': 213}),\n", - " 'pass': Counter({'keyword': 1}),\n", - " 'path': Counter({'keyword': 5}),\n", - " 'patience': Counter({'keyword': 37}),\n", - " 'pattern': Counter({'keyword': 15}),\n", - " 'period': Counter({'keyword': 148}),\n", - " 'permute': Counter({'keyword': 1}),\n", - " 'phase': Counter({'keyword': 141}),\n", - " 'phases': Counter({'keyword': 2}),\n", - " 'placeholder': Counter({'keyword': 94}),\n", - " 'pool': Counter({'keyword': 551}),\n", - " 'pool2d': Counter({'keyword': 478}),\n", - " 'pool3d': Counter({'keyword': 5}),\n", - " 'pooling': Counter({'keyword': 135}),\n", - " 'pooling1d': Counter({'keyword': 953}),\n", - " 'pooling2d': Counter({'keyword': 166}),\n", - " 'pooling3d': Counter({'keyword': 2}),\n", - " 'pop': Counter({'keyword': 11}),\n", - " 'pos': Counter({'keyword': 28}),\n", - " 'positions': Counter({'keyword': 13}),\n", - " 'post': Counter({'keyword': 2}),\n", - " 'pow': Counter({'keyword': 19}),\n", - " 'pred': Counter({'keyword': 7}),\n", - " 'predict': Counter({'keyword': 15}),\n", - " 'predictions': Counter({'keyword': 21}),\n", - " 'preds': Counter({'keyword': 2}),\n", - " 'prefix': Counter({'keyword': 3}),\n", - " 'prelu': Counter({'keyword': 2}),\n", - " 'preprocess': Counter({'keyword': 20}),\n", - " 'preprocessor': Counter({'keyword': 10}),\n", - " 'prime': Counter({'keyword': 278}),\n", - " 'print': Counter({'keyword': 3946}),\n", - " 'prob': Counter({'keyword': 41}),\n", - " 'proba': Counter({'keyword': 28}),\n", - " 'probs': Counter({'keyword': 63}),\n", - " 'process': Counter({'keyword': 27}),\n", - " 'prod': Counter({'keyword': 20}),\n", - " 'progbar': Counter({'keyword': 1367}),\n", - " 'put': Counter({'keyword': 124}),\n", - " 'pv': Counter({'keyword': 19}),\n", - " 'py': Counter({'keyword': 7}),\n", - " 'py2': Counter({'keyword': 13}),\n", - " 'queue': Counter({'keyword': 1302}),\n", - " 'raise': Counter({'keyword': 315}),\n", - " 'randint': Counter({'keyword': 1}),\n", - " 'random': Counter({'keyword': 138}),\n", - " 'range': Counter({'keyword': 98}),\n", - " 'rank': Counter({'keyword': 4}),\n", - " 'rate': Counter({'keyword': 91}),\n", - " 'readline': Counter({'keyword': 1}),\n", - " 'receptive': Counter({'keyword': 10}),\n", - " 'recurrent': Counter({'keyword': 5}),\n", - " 'reduce': Counter({'keyword': 11}),\n", - " 'reference': Counter({'keyword': 8}),\n", - " 'regularization': Counter({'keyword': 93}),\n", - " 'regularizer': Counter({'keyword': 4}),\n", - " 'relu': Counter({'keyword': 153}),\n", - " 'repeat': Counter({'keyword': 3}),\n", - " 'repeats': Counter({'keyword': 7}),\n", - " 'requestexception': Counter({'keyword': 61}),\n", - " 'required': Counter({'keyword': 231}),\n", - " 'reraise': Counter({'keyword': 4}),\n", - " 'res': Counter({'keyword': 51}),\n", - " 'reset': Counter({'keyword': 730}),\n", - " 'reshape': Counter({'keyword': 188}),\n", - " 'resnet50': Counter({'keyword': 23}),\n", - " 'restore': Counter({'keyword': 156}),\n", - " 'result': Counter({'keyword': 9}),\n", - " 'retain': Counter({'keyword': 2}),\n", - " 'return': Counter({'keyword': 40}),\n", - " 'reverse': Counter({'keyword': 1}),\n", - " 'rho': Counter({'keyword': 10}),\n", - " 'rng': Counter({'keyword': 1}),\n", - " 'rnn': Counter({'keyword': 199}),\n", - " 'root': Counter({'keyword': 97}),\n", - " 'round': Counter({'keyword': 334}),\n", - " 'rows': Counter({'keyword': 4}),\n", - " 'run': Counter({'keyword': 10}),\n", - " 'running': Counter({'keyword': 13}),\n", - " 's': Counter({'keyword': 43}),\n", - " 'sample': Counter({'keyword': 3}),\n", - " 'save': Counter({'keyword': 36}),\n", - " 'saver': Counter({'keyword': 2057}),\n", - " 'scale': Counter({'keyword': 420}),\n", - " 'schedule': Counter({'keyword': 464}),\n", - " 'scope': Counter({'keyword': 1}),\n", - " 'second': Counter({'keyword': 10}),\n", - " 'seed': Counter({'keyword': 85}),\n", - " 'seen': Counter({'keyword': 47}),\n", - " 'select': Counter({'keyword': 124}),\n", - " 'self': Counter({'keyword': 184}),\n", - " 'send': Counter({'keyword': 47}),\n", - " 'separable': Counter({'keyword': 6}),\n", - " 'seq': Counter({'keyword': 21}),\n", - " 'seqs': Counter({'keyword': 104}),\n", - " 'sequence': Counter({'keyword': 16}),\n", - " 'sequences': Counter({'keyword': 31}),\n", - " 'serialize': Counter({'keyword': 3}),\n", - " 'sess': Counter({'keyword': 4}),\n", - " 'session': Counter({'keyword': 23}),\n", - " 'set': Counter({'keyword': 160}),\n", - " 'setattr': Counter({'keyword': 15}),\n", - " 'setdefault': Counter({'keyword': 86}),\n", - " 'setter': Counter({'keyword': 4}),\n", - " 'shape': Counter({'keyword': 13}),\n", - " 'shapes': Counter({'keyword': 16}),\n", - " 'shared': Counter({'keyword': 36}),\n", - " 'sharedvar': Counter({'keyword': 1}),\n", - " 'shuffle': Counter({'keyword': 22}),\n", - " 'sigmoid': Counter({'keyword': 10}),\n", - " 'signature': Counter({'keyword': 18}),\n", - " 'simple': Counter({'keyword': 2}),\n", - " 'size': Counter({'keyword': 50}),\n", - " 'sk': Counter({'keyword': 15}),\n", - " 'slice': Counter({'keyword': 7939}),\n", - " 'slices': Counter({'keyword': 15}),\n", - " 'slope': Counter({'keyword': 57}),\n", - " 'so': Counter({'keyword': 4}),\n", - " 'softmax': Counter({'keyword': 188}),\n", - " 'softplus': Counter({'keyword': 20}),\n", - " 'softsign': Counter({'keyword': 1}),\n", - " 'sparse': Counter({'keyword': 5}),\n", - " 'sparsetensor': Counter({'keyword': 5}),\n", - " 'sparsetype': Counter({'keyword': 62}),\n", - " 'spatial': Counter({'keyword': 13}),\n", - " 'spatialdropout1d': Counter({'keyword': 29}),\n", - " 'spatialdropoutnd': Counter({'keyword': 15}),\n", - " 'spec': Counter({'keyword': 4}),\n", - " 'splice': Counter({'keyword': 99}),\n", - " 'split': Counter({'keyword': 10}),\n", - " 'sqrt': Counter({'keyword': 97}),\n", - " 'square': Counter({'keyword': 40}),\n", - " 'squared': Counter({'keyword': 163}),\n", - " 'squeeze': Counter({'keyword': 49}),\n", - " 'stack': Counter({'keyword': 3}),\n", - " 'standardize': Counter({'keyword': 9}),\n", - " 'starred': Counter({'keyword': 6}),\n", - " 'startswith': Counter({'keyword': 109}),\n", - " 'state': Counter({'keyword': 24}),\n", - " 'stateful': Counter({'keyword': 24}),\n", - " 'states': Counter({'keyword': 270}),\n", - " 'std': Counter({'keyword': 3}),\n", - " 'stddev': Counter({'keyword': 345}),\n", - " 'step': Counter({'keyword': 49}),\n", - " 'steps': Counter({'keyword': 2}),\n", - " 'stop': Counter({'keyword': 15}),\n", - " 'stopped': Counter({'keyword': 105}),\n", - " 'str': Counter({'keyword': 598}),\n", - " 'strides': Counter({'keyword': 212}),\n", - " 'string': Counter({'keyword': 5054}),\n", - " 'strip': Counter({'keyword': 24}),\n", - " 'sub': Counter({'keyword': 277}),\n", - " 'subclassed': Counter({'keyword': 11}),\n", - " 'subscript': Counter({'keyword': 54}),\n", - " 'subtensor': Counter({'keyword': 27}),\n", - " 'sum': Counter({'keyword': 339}),\n", - " 'summary': Counter({'keyword': 79}),\n", - " 'support': Counter({'keyword': 296}),\n", - " 'supports': Counter({'keyword': 35}),\n", - " 'svd': Counter({'keyword': 8}),\n", - " 'switch': Counter({'keyword': 17}),\n", - " 'symbolic': Counter({'keyword': 80}),\n", - " 't': Counter({'keyword': 19}),\n", - " 'target': Counter({'keyword': 489}),\n", - " 'targets': Counter({'keyword': 10}),\n", - " 'task': Counter({'keyword': 223}),\n", - " 'tasks': Counter({'keyword': 5}),\n", - " 'temporal': Counter({'keyword': 18}),\n", - " 'tensor': Counter({'keyword': 12}),\n", - " 'tensorlike': Counter({'keyword': 5}),\n", - " 'tensors': Counter({'keyword': 11}),\n", - " 'tensorsharedvariable': Counter({'keyword': 97}),\n", - " 'tensorvariable': Counter({'keyword': 103}),\n", - " 'tf': Counter({'keyword': 629}),\n", - " 'th': Counter({'keyword': 34}),\n", - " 'theano': Counter({'keyword': 69}),\n", - " 'theta': Counter({'keyword': 33}),\n", - " 'threshold': Counter({'keyword': 22}),\n", - " 'time': Counter({'keyword': 160}),\n", - " 'times': Counter({'keyword': 134}),\n", - " 'timesteps': Counter({'keyword': 25}),\n", - " 'tmp': Counter({'keyword': 2}),\n", - " 'to': Counter({'keyword': 143}),\n", - " 'tolist': Counter({'keyword': 5}),\n", - " 'top': Counter({'keyword': 95}),\n", - " 'total': Counter({'keyword': 136}),\n", - " 'totals': Counter({'keyword': 51}),\n", - " 'train': Counter({'keyword': 63}),\n", - " 'trainable': Counter({'keyword': 3}),\n", - " 'training': Counter({'keyword': 1123}),\n", - " 'transpose': Counter({'keyword': 7}),\n", - " 'true': Counter({'keyword': 543}),\n", - " 'truncated': Counter({'keyword': 20}),\n", - " 'try': Counter({'keyword': 67}),\n", - " 'ts': Counter({'keyword': 1}),\n", - " 'tuple': Counter({'keyword': 189}),\n", - " 'tuples': Counter({'keyword': 7}),\n", - " 'type': Counter({'keyword': 67}),\n", - " 'types': Counter({'keyword': 276}),\n", - " 'u': Counter({'keyword': 10}),\n", - " 'uid': Counter({'keyword': 5}),\n", - " 'unaryop': Counter({'keyword': 273}),\n", - " 'unfinished': Counter({'keyword': 9}),\n", - " 'uniform': Counter({'keyword': 21}),\n", - " 'units': Counter({'keyword': 411}),\n", - " 'unpack': Counter({'keyword': 2}),\n", - " 'unroll': Counter({'keyword': 5}),\n", - " 'update': Counter({'keyword': 212}),\n", - " 'updated': Counter({'keyword': 4}),\n", - " 'updates': Counter({'keyword': 34}),\n", - " 'upper': Counter({'keyword': 53}),\n", - " 'upsampling1d': Counter({'keyword': 3}),\n", - " 'upsampling2d': Counter({'keyword': 14}),\n", - " 'upsampling3d': Counter({'keyword': 10}),\n", - " 'use': Counter({'keyword': 32}),\n", - " 'user': Counter({'keyword': 5}),\n", - " 'uses': Counter({'keyword': 49}),\n", - " 'usub': Counter({'keyword': 80}),\n", - " 'v': Counter({'keyword': 1056}),\n", - " 'val': Counter({'keyword': 41}),\n", - " 'validation': Counter({'keyword': 50}),\n", - " 'value': Counter({'keyword': 4}),\n", - " 'values': Counter({'keyword': 6}),\n", - " 'var': Counter({'keyword': 48}),\n", - " 'variable': Counter({'keyword': 16}),\n", - " 'verbose': Counter({'keyword': 233}),\n", - " 'vgg16': Counter({'keyword': 19}),\n", - " 'vgg19': Counter({'keyword': 17}),\n", - " 'vhats': Counter({'keyword': 12}),\n", - " 'volumes': Counter({'keyword': 2}),\n", - " 'vs': Counter({'keyword': 6}),\n", - " 'w': Counter({'keyword': 44}),\n", - " 'wait': Counter({'keyword': 199}),\n", - " 'warn': Counter({'keyword': 154}),\n", - " 'weight': Counter({'keyword': 17}),\n", - " 'weights': Counter({'keyword': 23}),\n", - " 'when': Counter({'keyword': 10}),\n", - " 'where': Counter({'keyword': 7}),\n", - " 'while': Counter({'keyword': 103}),\n", - " 'with': Counter({'keyword': 659}),\n", - " 'withitem': Counter({'keyword': 26}),\n", - " 'workers': Counter({'keyword': 15}),\n", - " 'write': Counter({'keyword': 4}),\n", - " 'writer': Counter({'keyword': 50}),\n", - " 'x': Counter({'keyword': 16}),\n", - " 'xception': Counter({'keyword': 97}),\n", - " 'y': Counter({'keyword': 209}),\n", - " 'yaml': Counter({'keyword': 2}),\n", - " 'yield': Counter({'keyword': 8}),\n", - " 'z': Counter({'keyword': 12}),\n", - " 'zero': Counter({'keyword': 18}),\n", - " 'zeropadding3d': Counter({'keyword': 3}),\n", - " 'zeros': Counter({'keyword': 38})}" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "confusion_counter" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label -- a\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- grads\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- y\n", - "Preds -- keyword (209)\n", - "\n", - "Label -- expects\n", - "Preds -- keyword (8468)\n", - "\n", - "Label -- names\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- algorithm\n", - "Preds -- keyword (337)\n", - "\n", - "Label -- get\n", - "Preds -- keyword (197)\n", - "\n", - "Label -- network\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- constructor\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- argmin\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- constraint\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- identity\n", - "Preds -- keyword (39)\n", - "\n", - "Label -- ones\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- channels\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- upsampling1d\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- fill\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- parameter\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- separable\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- nesterov\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- attribute\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- indexedslices\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- lte\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- mean\n", - "Preds -- keyword (53)\n", - "\n", - "Label -- chunk\n", - "Preds -- keyword (217)\n", - "\n", - "Label -- upsampling3d\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- tasks\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- 2d\n", - "Preds -- keyword (78)\n", - "\n", - "Label -- baseline\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- clear\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- best\n", - "Preds -- keyword (792)\n", - "\n", - "Label -- ifexp\n", - "Preds -- keyword (111)\n", - "\n", - "Label -- zero\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- g\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- subclassed\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- inceptionv3\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- seq\n", - "Preds -- keyword (21)\n", - "\n", - "Label -- pool\n", - "Preds -- keyword (551)\n", - "\n", - "Label -- kernel\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- stddev\n", - "Preds -- keyword (345)\n", - "\n", - "Label -- readline\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- alias\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- loss\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- argument\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- p\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- on\n", - "Preds -- keyword (1496)\n", - "\n", - "Label -- gaussiandropout\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- extslice\n", - "Preds -- keyword (54)\n", - "\n", - "Label -- excepthandler\n", - "Preds -- keyword (6921)\n", - "\n", - "Label -- states\n", - "Preds -- keyword (270)\n", - "\n", - "Label -- error\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- custom\n", - "Preds -- keyword (187)\n", - "\n", - "Label -- stateful\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- steps\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- f\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- layers\n", - "Preds -- keyword (37)\n", - "\n", - "Label -- squared\n", - "Preds -- keyword (163)\n", - "\n", - "Label -- embeddings\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- volumes\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- norm\n", - "Preds -- keyword (137)\n", - "\n", - "Label -- gte\n", - "Preds -- keyword (120)\n", - "\n", - "Label -- v\n", - "Preds -- keyword (1056)\n", - "\n", - "Label -- m\n", - "Preds -- keyword (102)\n", - "\n", - "Label -- svd\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- starred\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- e\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- prime\n", - "Preds -- keyword (278)\n", - "\n", - "Label -- cloned\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- notin\n", - "Preds -- keyword (95)\n", - "\n", - "Label -- isnot\n", - "Preds -- keyword (46)\n", - "\n", - "Label -- only\n", - "Preds -- keyword (875)\n", - "\n", - "Label -- output\n", - "Preds -- keyword (156)\n", - "\n", - "Label -- softmax\n", - "Preds -- keyword (188)\n", - "\n", - "Label -- data\n", - "Preds -- keyword (84)\n", - "\n", - "Label -- begin\n", - "Preds -- keyword (126)\n", - "\n", - "Label -- normalization\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- equal\n", - "Preds -- keyword (26219)\n", - "\n", - "Label -- time\n", - "Preds -- keyword (160)\n", - "\n", - "Label -- name\n", - "Preds -- keyword (13849)\n", - "\n", - "Label -- iterations\n", - "Preds -- keyword (30)\n", - "\n", - "Label -- repeat\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- dictcomp\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- at\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- feed\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- metrics\n", - "Preds -- keyword (947)\n", - "\n", - "Label -- uid\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- override\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- sub\n", - "Preds -- keyword (277)\n", - "\n", - "Label -- gradients\n", - "Preds -- keyword (22)\n", - "\n", - "Label -- list\n", - "Preds -- keyword (428)\n", - "\n", - "Label -- totals\n", - "Preds -- keyword (51)\n", - "\n", - "Label -- bias\n", - "Preds -- keyword (79)\n", - "\n", - "Label -- opt\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- fpath\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- vgg19\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- rng\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- updates\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- classdef\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- supports\n", - "Preds -- keyword (35)\n", - "\n", - "Label -- writer\n", - "Preds -- keyword (50)\n", - "\n", - "Label -- wait\n", - "Preds -- keyword (199)\n", - "\n", - "Label -- z\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- result\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- when\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- pooling\n", - "Preds -- keyword (135)\n", - "\n", - "Label -- keepdims\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- ins\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- saver\n", - "Preds -- keyword (2057)\n", - "\n", - "Label -- yaml\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- float64\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- [MASK]\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- nnet\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- extra\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- moments\n", - "Preds -- keyword (14)\n", - "\n", - "Label -- serialize\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- flush\n", - "Preds -- keyword (38)\n", - "\n", - "Label -- subscript\n", - "Preds -- keyword (54)\n", - "\n", - "Label -- const\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- pool2d\n", - "Preds -- keyword (478)\n", - "\n", - "Label -- phase\n", - "Preds -- keyword (141)\n", - "\n", - "Label -- mode\n", - "Preds -- keyword (126)\n", - "\n", - "Label -- try\n", - "Preds -- keyword (67)\n", - "\n", - "Label -- ndarray\n", - "Preds -- keyword (103)\n", - "\n", - "Label -- metadata\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- argmax\n", - "Preds -- keyword (1675)\n", - "\n", - "Label -- check\n", - "Preds -- keyword (29)\n", - "\n", - "Label -- binop\n", - "Preds -- keyword (106)\n", - "\n", - "Label -- condition\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- cast\n", - "Preds -- keyword (1189)\n", - "\n", - "Label -- setattr\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- diff\n", - "Preds -- keyword (74)\n", - "\n", - "Label -- tuples\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- gain\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- rate\n", - "Preds -- keyword (91)\n", - "\n", - "Label -- img\n", - "Preds -- keyword (57)\n", - "\n", - "Label -- tensorsharedvariable\n", - "Preds -- keyword (97)\n", - "\n", - "Label -- standardize\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- decrement\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- device\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- evaluate\n", - "Preds -- keyword (31)\n", - "\n", - "Label -- x\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- ts\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- lower\n", - "Preds -- keyword (141)\n", - "\n", - "Label -- convert\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- matrix\n", - "Preds -- keyword (25)\n", - "\n", - "Label -- setter\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- switch\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- dimshuffle\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- listcomp\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- self\n", - "Preds -- keyword (184)\n", - "\n", - "Label -- not\n", - "Preds -- keyword (112)\n", - "\n", - "Label -- collected\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- sigmoid\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- gens\n", - "Preds -- keyword (87)\n", - "\n", - "Label -- maximum\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- dense\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- uses\n", - "Preds -- keyword (49)\n", - "\n", - "Label -- split\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- info\n", - "Preds -- keyword (178)\n", - "\n", - "Label -- kwargs\n", - "Preds -- keyword (66)\n", - "\n", - "Label -- preprocess\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- summary\n", - "Preds -- keyword (79)\n", - "\n", - "Label -- format\n", - "Preds -- keyword (59)\n", - "\n", - "Label -- extend\n", - "Preds -- keyword (2154)\n", - "\n", - "Label -- pv\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- deconv2d\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- add\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- msg\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- non\n", - "Preds -- keyword (259)\n", - "\n", - "Label -- inf\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- s\n", - "Preds -- keyword (43)\n", - "\n", - "Label -- batch\n", - "Preds -- keyword (149)\n", - "\n", - "Label -- 1\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- py2\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- make\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- dims\n", - "Preds -- keyword (37)\n", - "\n", - "Label -- isinf\n", - "Preds -- keyword (281)\n", - "\n", - "Label -- range\n", - "Preds -- keyword (98)\n", - "\n", - "Label -- concat\n", - "Preds -- keyword (446)\n", - "\n", - "Label -- minimum\n", - "Preds -- keyword (79)\n", - "\n", - "Label -- unroll\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- weights\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- apply\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- pooling1d\n", - "Preds -- keyword (953)\n", - "\n", - "Label -- random\n", - "Preds -- keyword (138)\n", - "\n", - "Label -- std\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- schedule\n", - "Preds -- keyword (464)\n", - "\n", - "Label -- tf\n", - "Preds -- keyword (629)\n", - "\n", - "Label -- accumulators\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- sk\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- sum\n", - "Preds -- keyword (339)\n", - "\n", - "Label -- dot\n", - "Preds -- keyword (204)\n", - "\n", - "Label -- gates\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- hsplit\n", - "Preds -- keyword (430)\n", - "\n", - "Label -- dict\n", - "Preds -- keyword (112)\n", - "\n", - "Label -- predictions\n", - "Preds -- keyword (21)\n", - "\n", - "Label -- min\n", - "Preds -- keyword (31)\n", - "\n", - "Label -- alloc\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- minval\n", - "Preds -- keyword (35)\n", - "\n", - "Label -- allow\n", - "Preds -- keyword (32)\n", - "\n", - "Label -- moving\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- losses\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- pool3d\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- densenet201\n", - "Preds -- keyword (44)\n", - "\n", - "Label -- linalg\n", - "Preds -- keyword (29)\n", - "\n", - "Label -- execute\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- temporal\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- densenet121\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- filter\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- cropping3d\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- queue\n", - "Preds -- keyword (1302)\n", - "\n", - "Label -- iterable\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- inbound\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- disconnected\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- headers\n", - "Preds -- keyword (137)\n", - "\n", - "Label -- end\n", - "Preds -- keyword (33)\n", - "\n", - "Label -- hasher\n", - "Preds -- keyword (152)\n", - "\n", - "Label -- original\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- params\n", - "Preds -- keyword (213)\n", - "\n", - "Label -- images\n", - "Preds -- keyword (29)\n", - "\n", - "Label -- str\n", - "Preds -- keyword (598)\n", - "\n", - "Label -- epsilon\n", - "Preds -- keyword (63)\n", - "\n", - "Label -- simple\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- norms\n", - "Preds -- keyword (366)\n", - "\n", - "Label -- save\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- return\n", - "Preds -- keyword (40)\n", - "\n", - "Label -- fields\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- rows\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- op\n", - "Preds -- keyword (58)\n", - "\n", - "Label -- [UNK]\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- tmp\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- kwd\n", - "Preds -- keyword (85)\n", - "\n", - "Label -- units\n", - "Preds -- keyword (411)\n", - "\n", - "Label -- binary\n", - "Preds -- keyword (174)\n", - "\n", - "Label -- top\n", - "Preds -- keyword (95)\n", - "\n", - "Label -- overwrite\n", - "Preds -- keyword (4761)\n", - "\n", - "Label -- objects\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- zeros\n", - "Preds -- keyword (38)\n", - "\n", - "Label -- boolop\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- u\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- function\n", - "Preds -- keyword (56)\n", - "\n", - "Label -- spec\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- layer\n", - "Preds -- keyword (21)\n", - "\n", - "Label -- cooldown\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- clipnorm\n", - "Preds -- keyword (22)\n", - "\n", - "Label -- py\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- neq\n", - "Preds -- keyword (67)\n", - "\n", - "Label -- support\n", - "Preds -- keyword (296)\n", - "\n", - "Label -- attrs\n", - "Preds -- keyword (111)\n", - "\n", - "Label -- initializer\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- normalized\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- methods\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- constants\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- build\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- id\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- loads\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- scope\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- step\n", - "Preds -- keyword (49)\n", - "\n", - "Label -- preds\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- update\n", - "Preds -- keyword (212)\n", - "\n", - "Label -- forward\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- high\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- nameconstant\n", - "Preds -- keyword (510)\n", - "\n", - "Label -- patience\n", - "Preds -- keyword (37)\n", - "\n", - "Label -- flags\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- message\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- elu\n", - "Preds -- keyword (57)\n", - "\n", - "Label -- square\n", - "Preds -- keyword (40)\n", - "\n", - "Label -- types\n", - "Preds -- keyword (276)\n", - "\n", - "Label -- ndim\n", - "Preds -- keyword (107)\n", - "\n", - "Label -- clipvalue\n", - "Preds -- keyword (42)\n", - "\n", - "Label -- intersection\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- constraints\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- create\n", - "Preds -- keyword (30)\n", - "\n", - "Label -- unfinished\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- prod\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- select\n", - "Preds -- keyword (124)\n", - "\n", - "Label -- ctype\n", - "Preds -- keyword (91)\n", - "\n", - "Label -- desired\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- validation\n", - "Preds -- keyword (50)\n", - "\n", - "Label -- csv\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- values\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- label\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- config\n", - "Preds -- keyword (471)\n", - "\n", - "Label -- instance\n", - "Preds -- keyword (14201)\n", - "\n", - "Label -- prob\n", - "Preds -- keyword (41)\n", - "\n", - "Label -- reference\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- 2\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- mask\n", - "Preds -- keyword (33)\n", - "\n", - "Label -- use\n", - "Preds -- keyword (32)\n", - "\n", - "Label -- regularization\n", - "Preds -- keyword (93)\n", - "\n", - "Label -- scale\n", - "Preds -- keyword (420)\n", - "\n", - "Label -- abs\n", - "Preds -- keyword (112)\n", - "\n", - "Label -- normalize\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- index\n", - "Preds -- keyword (40)\n", - "\n", - "Label -- nodes\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- bool\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- pow\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- global\n", - "Preds -- keyword (68)\n", - "\n", - "Label -- indices\n", - "Preds -- keyword (98)\n", - "\n", - "Label -- densenet169\n", - "Preds -- keyword (121)\n", - "\n", - "Label -- learning\n", - "Preds -- keyword (104)\n", - "\n", - "Label -- workers\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- timesteps\n", - "Preds -- keyword (25)\n", - "\n", - "Label -- string\n", - "Preds -- keyword (5054)\n", - "\n", - "Label -- import\n", - "Preds -- keyword (42)\n", - "\n", - "Label -- pass\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- spatialdropoutnd\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- eval\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- [SEP]\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- join\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- backwards\n", - "Preds -- keyword (41)\n", - "\n", - "Label -- nb\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- generator\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- item\n", - "Preds -- keyword (578)\n", - "\n", - "Label -- xception\n", - "Preds -- keyword (97)\n", - "\n", - "Label -- gradient\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- and\n", - "Preds -- keyword (54)\n", - "\n", - "Label -- max\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- value\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- isnan\n", - "Preds -- keyword (46)\n", - "\n", - "Label -- noteq\n", - "Preds -- keyword (1084)\n", - "\n", - "Label -- momentum\n", - "Preds -- keyword (89)\n", - "\n", - "Label -- prelu\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- input\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- unaryop\n", - "Preds -- keyword (273)\n", - "\n", - "Label -- stop\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- reshape\n", - "Preds -- keyword (188)\n", - "\n", - "Label -- image\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- dimensions\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- axes\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- seqs\n", - "Preds -- keyword (104)\n", - "\n", - "Label -- out\n", - "Preds -- keyword (175)\n", - "\n", - "Label -- histogram\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- setdefault\n", - "Preds -- keyword (86)\n", - "\n", - "Label -- l1\n", - "Preds -- keyword (287)\n", - "\n", - "Label -- less\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- nasnetmobile\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- strip\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- placeholder\n", - "Preds -- keyword (94)\n", - "\n", - "Label -- nn\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- usub\n", - "Preds -- keyword (80)\n", - "\n", - "Label -- positions\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- reraise\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- level\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- repeats\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- arg\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- while\n", - "Preds -- keyword (103)\n", - "\n", - "Label -- all\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- alpha\n", - "Preds -- keyword (812)\n", - "\n", - "Label -- distribution\n", - "Preds -- keyword (81)\n", - "\n", - "Label -- class\n", - "Preds -- keyword (29)\n", - "\n", - "Label -- merged\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- order\n", - "Preds -- keyword (63)\n", - "\n", - "Label -- module\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- go\n", - "Preds -- keyword (45)\n", - "\n", - "Label -- arange\n", - "Preds -- keyword (95)\n", - "\n", - "Label -- expand\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- training\n", - "Preds -- keyword (1123)\n", - "\n", - "Label -- send\n", - "Preds -- keyword (47)\n", - "\n", - "Label -- ops\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- relu\n", - "Preds -- keyword (153)\n", - "\n", - "Label -- keras\n", - "Preds -- keyword (31)\n", - "\n", - "Label -- filewriter\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- low\n", - "Preds -- keyword (1452)\n", - "\n", - "Label -- object\n", - "Preds -- keyword (171)\n", - "\n", - "Label -- truncated\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- round\n", - "Preds -- keyword (334)\n", - "\n", - "Label -- keyword\n", - "Preds -- \n", - "\n", - "Label -- assert\n", - "Preds -- keyword (96)\n", - "\n", - "Label -- items\n", - "Preds -- keyword (1211)\n", - "\n", - "Label -- biases\n", - "Preds -- keyword (2936)\n", - "\n", - "Label -- trainable\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- dtype\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- conv\n", - "Preds -- keyword (70)\n", - "\n", - "Label -- cumprod\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- withitem\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- pred\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- proba\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- moves\n", - "Preds -- keyword (97)\n", - "\n", - "Label -- tuple\n", - "Preds -- keyword (189)\n", - "\n", - "Label -- next\n", - "Preds -- keyword (60)\n", - "\n", - "Label -- float16\n", - "Preds -- keyword (87)\n", - "\n", - "Label -- graph\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- uniform\n", - "Preds -- keyword (21)\n", - "\n", - "Label -- first\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- cropping2d\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- delta\n", - "Preds -- keyword (42)\n", - "\n", - "Label -- asarray\n", - "Preds -- keyword (48)\n", - "\n", - "Label -- length\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- decay\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- created\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- so\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- child\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- beta\n", - "Preds -- keyword (48)\n", - "\n", - "Label -- mult\n", - "Preds -- keyword (157)\n", - "\n", - "Label -- hstack\n", - "Preds -- keyword (235)\n", - "\n", - "Label -- receptive\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- exc\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- conv3d\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- new\n", - "Preds -- keyword (514)\n", - "\n", - "Label -- expr\n", - "Preds -- keyword (2649)\n", - "\n", - "Label -- sequence\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- tolist\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- put\n", - "Preds -- keyword (124)\n", - "\n", - "Label -- current\n", - "Preds -- keyword (108)\n", - "\n", - "Label -- negative\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- gt\n", - "Preds -- keyword (920)\n", - "\n", - "Label -- args\n", - "Preds -- keyword (80)\n", - "\n", - "Label -- post\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- elements\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- multiprocessing\n", - "Preds -- keyword (221)\n", - "\n", - "Label -- bar\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- w\n", - "Preds -- keyword (44)\n", - "\n", - "Label -- epoch\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- b\n", - "Preds -- keyword (56)\n", - "\n", - "Label -- mobilenetv2\n", - "Preds -- keyword (182)\n", - "\n", - "Label -- res\n", - "Preds -- keyword (51)\n", - "\n", - "Label -- grad\n", - "Preds -- keyword (283)\n", - "\n", - "Label -- variable\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- crossentropy\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- json\n", - "Preds -- keyword (113)\n", - "\n", - "Label -- fieldnames\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- progbar\n", - "Preds -- keyword (1367)\n", - "\n", - "Label -- shuffle\n", - "Preds -- keyword (22)\n", - "\n", - "Label -- path\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- requestexception\n", - "Preds -- keyword (61)\n", - "\n", - "Label -- built\n", - "Preds -- keyword (211)\n", - "\n", - "Label -- 3d\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- stack\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- last\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- is\n", - "Preds -- keyword (3827)\n", - "\n", - "Label -- running\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- load\n", - "Preds -- keyword (206)\n", - "\n", - "Label -- lt\n", - "Preds -- keyword (115)\n", - "\n", - "Label -- k\n", - "Preds -- keyword (485)\n", - "\n", - "Label -- if\n", - "Preds -- keyword (74)\n", - "\n", - "Label -- close\n", - "Preds -- keyword (69)\n", - "\n", - "Label -- init\n", - "Preds -- keyword (1082)\n", - "\n", - "Label -- gamma\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- batchnorm\n", - "Preds -- keyword (26572)\n", - "\n", - "Label -- symbolic\n", - "Preds -- keyword (80)\n", - "\n", - "Label -- default\n", - "Preds -- keyword (584)\n", - "\n", - "Label -- append\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- normal\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preds -- keyword (136)\n", - "\n", - "Label -- vgg16\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- origin\n", - "Preds -- keyword (76)\n", - "\n", - "Label -- allowed\n", - "Preds -- keyword (83)\n", - "\n", - "Label -- padding\n", - "Preds -- keyword (174)\n", - "\n", - "Label -- amsgrad\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- from\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- targets\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- callbacks\n", - "Preds -- keyword (87)\n", - "\n", - "Label -- filename\n", - "Preds -- keyword (38)\n", - "\n", - "Label -- devs\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- tensors\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- period\n", - "Preds -- keyword (148)\n", - "\n", - "Label -- weight\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- shapes\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- dir\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- cumsum\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- fan\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- float32\n", - "Preds -- keyword (116)\n", - "\n", - "Label -- cells\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- tensor\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- insecure\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- history\n", - "Preds -- keyword (32)\n", - "\n", - "Label -- lr\n", - "Preds -- keyword (490)\n", - "\n", - "Label -- sharedvar\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- devices\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- slices\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- generic\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- cls\n", - "Preds -- keyword (30)\n", - "\n", - "Label -- dumps\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- conversions\n", - "Preds -- keyword (62)\n", - "\n", - "Label -- neg\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- full\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- pad\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- floordiv\n", - "Preds -- keyword (44)\n", - "\n", - "Label -- callback\n", - "Preds -- keyword (555)\n", - "\n", - "Label -- squeeze\n", - "Preds -- keyword (49)\n", - "\n", - "Label -- model\n", - "Preds -- keyword (115)\n", - "\n", - "Label -- closure\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- upsampling2d\n", - "Preds -- keyword (14)\n", - "\n", - "Label -- var\n", - "Preds -- keyword (48)\n", - "\n", - "Label -- log\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- deepcopy\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- restore\n", - "Preds -- keyword (156)\n", - "\n", - "Label -- dropout\n", - "Preds -- keyword (46)\n", - "\n", - "Label -- atleast\n", - "Preds -- keyword (216)\n", - "\n", - "Label -- spatial\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- c\n", - "Preds -- keyword (104)\n", - "\n", - "Label -- filepath\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- classification\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- func\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- embedding\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- softplus\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- reset\n", - "Preds -- keyword (730)\n", - "\n", - "Label -- spatialdropout1d\n", - "Preds -- keyword (29)\n", - "\n", - "Label -- hot\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- stopped\n", - "Preds -- keyword (105)\n", - "\n", - "Label -- with\n", - "Preds -- keyword (659)\n", - "\n", - "Label -- axis\n", - "Preds -- keyword (73)\n", - "\n", - "Label -- alt\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- div\n", - "Preds -- keyword (85)\n", - "\n", - "Label -- task\n", - "Preds -- keyword (223)\n", - "\n", - "Label -- factor\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- functions\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- [CLS]\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- cols\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- concatenate\n", - "Preds -- keyword (69)\n", - "\n", - "Label -- foldr\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- file\n", - "Preds -- keyword (178)\n", - "\n", - "Label -- seed\n", - "Preds -- keyword (85)\n", - "\n", - "Label -- root\n", - "Preds -- keyword (97)\n", - "\n", - "Label -- pop\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- epochs\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- gpus\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- gather\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- threshold\n", - "Preds -- keyword (22)\n", - "\n", - "Label -- intermediate\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- assign\n", - "Preds -- keyword (8261)\n", - "\n", - "Label -- strides\n", - "Preds -- keyword (212)\n", - "\n", - "Label -- to\n", - "Preds -- keyword (143)\n", - "\n", - "Label -- dilation\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- sample\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- slice\n", - "Preds -- keyword (7939)\n", - "\n", - "Label -- binomial\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- rnn\n", - "Preds -- keyword (199)\n", - "\n", - "Label -- logsumexp\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- continue\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- [cls]\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- total\n", - "Preds -- keyword (136)\n", - "\n", - "Label -- cudnn\n", - "Preds -- keyword (35)\n", - "\n", - "Label -- outputs\n", - "Preds -- keyword (9)\n", - "\n", - "Label -- second\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- sess\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- rho\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- eye\n", - "Preds -- keyword (52)\n", - "\n", - "Label -- permute\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- map\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- retain\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- sparsetype\n", - "Preds -- keyword (62)\n", - "\n", - "Label -- shape\n", - "Preds -- keyword (13)\n", - "\n", - "Label -- importfrom\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- seen\n", - "Preds -- keyword (47)\n", - "\n", - "Label -- predict\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- pos\n", - "Preds -- keyword (28)\n", - "\n", - "Label -- functiondef\n", - "Preds -- keyword (4868)\n", - "\n", - "Label -- splice\n", - "Preds -- keyword (99)\n", - "\n", - "Label -- size\n", - "Preds -- keyword (50)\n", - "\n", - "Label -- activation\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- cache\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- acc\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- augassign\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- counter\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- vs\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- lookup\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- l1l2\n", - "Preds -- keyword (93)\n", - "\n", - "Label -- any\n", - "Preds -- keyword (1789)\n", - "\n", - "Label -- keys\n", - "Preds -- keyword (138)\n", - "\n", - "Label -- run\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- vhats\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- mod\n", - "Preds -- keyword (91)\n", - "\n", - "Label -- converted\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- generatorexp\n", - "Preds -- keyword (53)\n", - "\n", - "Label -- transpose\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- unpack\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- done\n", - "Preds -- keyword (238)\n", - "\n", - "Label -- elems\n", - "Preds -- keyword (17)\n", - "\n", - "Label -- include\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- train\n", - "Preds -- keyword (63)\n", - "\n", - "Label -- prefix\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- signature\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- conv1d\n", - "Preds -- keyword (12)\n", - "\n", - "Label -- pooling2d\n", - "Preds -- keyword (166)\n", - "\n", - "Label -- limit\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- available\n", - "Preds -- keyword (16)\n", - "\n", - "Label -- maxval\n", - "Preds -- keyword (93)\n", - "\n", - "Label -- type\n", - "Preds -- keyword (67)\n", - "\n", - "Label -- conv2d\n", - "Preds -- keyword (66)\n", - "\n", - "Label -- greater\n", - "Preds -- keyword (158)\n", - "\n", - "Label -- idxs\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- times\n", - "Preds -- keyword (134)\n", - "\n", - "Label -- sequences\n", - "Preds -- keyword (31)\n", - "\n", - "Label -- session\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- sparsetensor\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- resnet50\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- field\n", - "Preds -- keyword (171)\n", - "\n", - "Label -- open\n", - "Preds -- keyword (189)\n", - "\n", - "Label -- ident\n", - "Preds -- keyword (57)\n", - "\n", - "Label -- true\n", - "Preds -- keyword (543)\n", - "\n", - "Label -- updated\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- group\n", - "Preds -- keyword (49)\n", - "\n", - "Label -- t\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- process\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- d\n", - "Preds -- keyword (444)\n", - "\n", - "Label -- nasnetlarge\n", - "Preds -- keyword (41)\n", - "\n", - "Label -- [PAD]\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- legacy\n", - "Preds -- keyword (276)\n", - "\n", - "Label -- optimizer\n", - "Preds -- keyword (33)\n", - "\n", - "Label -- in\n", - "Preds -- keyword (713)\n", - "\n", - "Label -- logits\n", - "Preds -- keyword (143)\n", - "\n", - "Label -- comprehension\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- logs\n", - "Preds -- keyword (44)\n", - "\n", - "Label -- set\n", - "Preds -- keyword (160)\n", - "\n", - "Label -- upper\n", - "Preds -- keyword (53)\n", - "\n", - "Label -- softsign\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- foldl\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- batches\n", - "Preds -- keyword (20)\n", - "\n", - "Label -- startswith\n", - "Preds -- keyword (109)\n", - "\n", - "Label -- async\n", - "Preds -- keyword (78)\n", - "\n", - "Label -- nonzero\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- compute\n", - "Preds -- keyword (19)\n", - "\n", - "Label -- filters\n", - "Preds -- keyword (12681)\n", - "\n", - "Label -- inferreddimension\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- ms\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- required\n", - "Preds -- keyword (231)\n", - "\n", - "Label -- write\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- increment\n", - "Preds -- keyword (26)\n", - "\n", - "Label -- cell\n", - "Preds -- keyword (645)\n", - "\n", - "Label -- exp\n", - "Preds -- keyword (73)\n", - "\n", - "Label -- initial\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- freq\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- floatx\n", - "Preds -- keyword (4582)\n", - "\n", - "Label -- slope\n", - "Preds -- keyword (57)\n", - "\n", - "Label -- regularizer\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- backend\n", - "Preds -- keyword (200)\n", - "\n", - "Label -- compare\n", - "Preds -- keyword (430)\n", - "\n", - "Label -- astype\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- element\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- eq\n", - "Preds -- keyword (55)\n", - "\n", - "Label -- backward\n", - "Preds -- keyword (187)\n", - "\n", - "Label -- backup\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- zeropadding3d\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- target\n", - "Preds -- keyword (489)\n", - "\n", - "Label -- count\n", - "Preds -- keyword (43)\n", - "\n", - "Label -- where\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- delete\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- obj\n", - "Preds -- keyword (81)\n", - "\n", - "Label -- 1d\n", - "Preds -- keyword (221)\n", - "\n", - "Label -- randint\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- feature\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- gaussiannoise\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- call\n", - "Preds -- keyword (1388)\n", - "\n", - "Label -- like\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- verbose\n", - "Preds -- keyword (233)\n", - "\n", - "Label -- l2\n", - "Preds -- keyword (199)\n", - "\n", - "Label -- flatten\n", - "Preds -- keyword (40)\n", - "\n", - "Label -- clip\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- theta\n", - "Preds -- keyword (33)\n", - "\n", - "Label -- exists\n", - "Preds -- keyword (6)\n", - "\n", - "Label -- as\n", - "Preds -- keyword (65)\n", - "\n", - "Label -- masking\n", - "Preds -- keyword (106)\n", - "\n", - "Label -- decode\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- int\n", - "Preds -- keyword (23)\n", - "\n", - "Label -- sqrt\n", - "Preds -- keyword (97)\n", - "\n", - "Label -- raise\n", - "Preds -- keyword (315)\n", - "\n", - "Label -- header\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- mobilenet\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- user\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- reduce\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- explicitly\n", - "Preds -- keyword (420)\n", - "\n", - "Label -- normed\n", - "Preds -- keyword (21)\n", - "\n", - "Label -- noise\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- hash\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- val\n", - "Preds -- keyword (41)\n", - "\n", - "Label -- constant\n", - "Preds -- keyword (64)\n", - "\n", - "Label -- cntk\n", - "Preds -- keyword (99)\n", - "\n", - "Label -- probs\n", - "Preds -- keyword (63)\n", - "\n", - "Label -- arguments\n", - "Preds -- keyword (2542)\n", - "\n", - "Label -- copy\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- monitor\n", - "Preds -- keyword (154)\n", - "\n", - "Label -- theano\n", - "Preds -- keyword (69)\n", - "\n", - "Label -- phases\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- tensorvariable\n", - "Preds -- keyword (103)\n", - "\n", - "Label -- reverse\n", - "Preds -- keyword (1)\n", - "\n", - "Label -- lambda\n", - "Preds -- keyword (951)\n", - "\n", - "Label -- ordering\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- cpu\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- classes\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- nw\n", - "Preds -- keyword (267)\n", - "\n", - "Label -- rank\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- dump\n", - "Preds -- keyword (18)\n", - "\n", - "Label -- key\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- warn\n", - "Preds -- keyword (154)\n", - "\n", - "Label -- state\n", - "Preds -- keyword (24)\n", - "\n", - "Label -- or\n", - "Preds -- keyword (103)\n", - "\n", - "Label -- pooling3d\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- old\n", - "Preds -- keyword (131)\n", - "\n", - "Label -- num\n", - "Preds -- keyword (108)\n", - "\n", - "Label -- subtensor\n", - "Preds -- keyword (27)\n", - "\n", - "Label -- dim\n", - "Preds -- keyword (55)\n", - "\n", - "Label -- yield\n", - "Preds -- keyword (8)\n", - "\n", - "Label -- dynamic\n", - "Preds -- keyword (31)\n", - "\n", - "Label -- tensorlike\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- inceptionresnetv2\n", - "Preds -- keyword (4)\n", - "\n", - "Label -- print\n", - "Preds -- keyword (3946)\n", - "\n", - "Label -- for\n", - "Preds -- keyword (306)\n", - "\n", - "Label -- shared\n", - "Preds -- keyword (36)\n", - "\n", - "Label -- line\n", - "Preds -- keyword (2)\n", - "\n", - "Label -- by\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- idx\n", - "Preds -- keyword (11)\n", - "\n", - "Label -- carry\n", - "Preds -- keyword (7)\n", - "\n", - "Label -- pattern\n", - "Preds -- keyword (15)\n", - "\n", - "Label -- sparse\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- th\n", - "Preds -- keyword (34)\n", - "\n", - "Label -- preprocessor\n", - "Preds -- keyword (10)\n", - "\n", - "Label -- fn\n", - "Preds -- keyword (3)\n", - "\n", - "Label -- far\n", - "Preds -- keyword (408)\n", - "\n", - "Label -- recurrent\n", - "Preds -- keyword (5)\n", - "\n", - "Label -- inputs\n", - "Preds -- keyword (48)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "for key, c in confusion_counter.items():\n", " print(\"Label -- \", key)\n", @@ -4957,7 +3046,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -4967,7 +3056,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -4977,7 +3066,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -5002,16 +3091,16 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.4620767915436033, 0.5379232084563967)" + "(0.9932113341204251, 0.00678866587957497)" ] }, - "execution_count": 85, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -5036,7 +3125,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -5065,7 +3154,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -5075,7 +3164,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -5103,7 +3192,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -5126,7 +3215,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -5136,7 +3225,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -5161,16 +3250,16 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.2747738224778486, 0.7252261775221515)" + "(0.9867178276269185, 0.013282172373081463)" ] }, - "execution_count": 92, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -5188,7 +3277,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -5197,7 +3286,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -5217,57 +3306,57 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [4.00000e+00, 1.75970e+04, 1.94400e+04, 2.06000e+02, 8.03300e+03,\n", - " 1.57000e+02, 6.85000e+02, 1.92000e+02, 6.97600e+03, 8.83810e+04,\n", - " 6.92100e+03, 6.00000e+00, 2.90000e+01, 1.73023e+05],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00],\n", - " [0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n", - " 0.00000e+00, 0.00000e+00, 0.00000e+00, 0.00000e+00]])" + "array([[0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 2.280e+02, 3.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.000e+00],\n", + " [0.000e+00, 1.500e+01, 2.157e+03, 1.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.400e+01],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.010e+02, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 8.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 7.800e+01, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 1.700e+01, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 6.500e+01, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 2.000e+00],\n", + " [0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 1.620e+02, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.800e+01,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 0.000e+00],\n", + " [0.000e+00, 0.000e+00, 3.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n", + " 2.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n", + " 0.000e+00, 5.080e+02]])" ] }, - "execution_count": 95, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -5278,29 +3367,29 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'0': 1.243587750660656e-05,\n", - " '1': 0.05470853412093891,\n", - " '10': 0.021517177055806,\n", - " '11': 1.865381625990984e-05,\n", - " '12': 9.016011192289756e-05,\n", - " '13': 0.5379232084563967,\n", - " '2': 0.06043836468210788,\n", - " '3': 0.0006404476915902378,\n", - " '4': 0.024974351002642625,\n", - " '5': 0.0004881081921343075,\n", - " '6': 0.0021296440230063733,\n", - " '7': 0.0005969221203171148,\n", - " '8': 0.02168817037152184,\n", - " '9': 0.2747738224778486}" + "{'0': 0.0,\n", + " '1': 0.07201889020070838,\n", + " '10': 0.00029515938606847696,\n", + " '11': 0.005312868949232586,\n", + " '12': 0.0,\n", + " '13': 0.15495867768595042,\n", + " '2': 0.6384297520661157,\n", + " '3': 0.0005903187721369539,\n", + " '4': 0.029811097992916175,\n", + " '5': 0.0023612750885478157,\n", + " '6': 0.023612750885478158,\n", + " '7': 0.005017709563164109,\n", + " '8': 0.019185360094451005,\n", + " '9': 0.048406139315230225}" ] }, - "execution_count": 96, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -5319,18 +3408,18 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([4.00000e+00, 1.75970e+04, 1.94400e+04, 2.06000e+02, 8.03300e+03,\n", - " 1.57000e+02, 6.85000e+02, 1.92000e+02, 6.97600e+03, 8.83810e+04,\n", - " 6.92100e+03, 6.00000e+00, 2.90000e+01, 1.73023e+05])" + "array([0.000e+00, 2.440e+02, 2.163e+03, 2.000e+00, 1.010e+02, 8.000e+00,\n", + " 8.000e+01, 1.700e+01, 6.500e+01, 1.640e+02, 1.000e+00, 1.800e+01,\n", + " 0.000e+00, 5.250e+02])" ] }, - "execution_count": 97, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -5341,22 +3430,30 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 48, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 98, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5378,32 +3475,40 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 49, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in true_divide\n", + " \n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "MOD 0.0 4 0.0\n", - "STMT 0.0 17597 0.055\n", - "EXPR 0.0 19440 0.06\n", - "EXPR_CONT 0.0 206 0.001\n", - "SLICE 0.0 8033 0.025\n", - "BOOLOP 0.0 157 0.0\n", - "OPERATOR 0.0 685 0.002\n", - "UNARY 0.0 192 0.001\n", - "CMPOP 0.0 6976 0.022\n", - "COMPR 1.0 88381 0.275\n", - "EXCEPT 0.0 6921 0.022\n", - "ARG 0.0 6 0.0\n", - "IMPORT 0.0 29 0.0\n", - "VAR 0.0 173023 0.538\n" + "MOD nan 0 0.0\n", + "STMT 0.934 244 0.072\n", + "EXPR 0.997 2163 0.638\n", + "EXPR_CONT 0.0 2 0.001\n", + "SLICE 1.0 101 0.03\n", + "BOOLOP 1.0 8 0.002\n", + "OPERATOR 0.975 80 0.024\n", + "UNARY 1.0 17 0.005\n", + "CMPOP 1.0 65 0.019\n", + "COMPR 0.988 164 0.048\n", + "EXCEPT 1.0 1 0.0\n", + "ARG 1.0 18 0.005\n", + "IMPORT nan 0 0.0\n", + "VAR 0.968 525 0.155\n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -5440,29 +3545,29 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0.0,\n", - " 0.0,\n", + "[nan,\n", + " 0.06729634002361275,\n", + " 0.6366587957497049,\n", " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.2747738224778486,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0]" + " 0.029811097992916175,\n", + " 0.0023612750885478157,\n", + " 0.023022432113341203,\n", + " 0.005017709563164109,\n", + " 0.019185360094451005,\n", + " 0.04781582054309327,\n", + " 0.00029515938606847696,\n", + " 0.005312868949232586,\n", + " nan,\n", + " 0.14994096812278632]" ] }, - "execution_count": 100, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -5473,7 +3578,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -5489,7 +3594,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -5508,16 +3613,16 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.26686460438364684" + "0.9158795749704841" ] }, - "execution_count": 103, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -5528,16 +3633,26 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 54, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n", + "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1145: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.\n", + " 'recall', 'true', average, warn_for)\n" + ] + }, { "data": { "text/plain": [ - "0.0005925447676435586" + "0.5054794553485005" ] }, - "execution_count": 104, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -5548,16 +3663,16 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.1124298789727235" + "0.9094296595935276" ] }, - "execution_count": 105, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -5573,6 +3688,69 @@ "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Naturalness 2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "keras_z = {0:0, 1:0, 2:-7.15, 3:0, 4:0, 5:0, 6:7.90, 7:0, 8:0, 9:0, 10:0, 11:0, 12:0}" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "requests_z = {0:-0.09, 1:-3.95, 2:0.08, 3:0, 4:-0.42, 5:0, 6:3.95, 7:-0.25, 8:0, 9:0, 10:0, 11:0, 12:0}" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [], + "source": [ + "flask_z = {0:-0.17, 1:-2.52, 2:0.27, 3:0, 4:-0.56, 5:0, 6:2.52, 7:-0.25, 8:0, 9:0, 10:0, 11:0, 12:0}" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,10))\n", + "plt.axis('off')\n", + "plt.plot([0]*12, '.', c='k', linewidth=5, markersize=12)\n", + "plt.plot(keras_z.values(), linewidth=3, label=\"Keras\")\n", + "plt.plot(requests_z.values(), linewidth=3, label=\"Requests\")\n", + "plt.plot(flask_z.values(), linewidth=3, label=\"Flask\")\n", + "plt.legend();\n", + "plt.savefig(\"n2.pdf\")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebook/Inspect Predictions - Method Naming.ipynb b/notebook/Inspect Predictions - Method Naming.ipynb index 64995dd..d23edb0 100644 --- a/notebook/Inspect Predictions - Method Naming.ipynb +++ b/notebook/Inspect Predictions - Method Naming.ipynb @@ -32,17 +32,19 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#path = '../../bert-cmp/bert/'\n", - "path = '../large-corpus/'" + "path = '../large-corpus/'\n", + "#path = '../mid-corpus/'\n", + "#path = '../sparse/'" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -92,243 +94,243 @@ " \n", " \n", " 0\n", - " 4.185846e-06\n", - " 1.072607e-05\n", - " 2.678383e-05\n", - " 5.868325e-05\n", - " 5.790507e-05\n", - " 5.501922e-06\n", - " 1.831420e-08\n", - " 1.441312e-05\n", - " 5.782143e-06\n", - " 2.314050e-08\n", + " 1.893087e-04\n", + " 3.720649e-05\n", + " 4.334738e-05\n", + " 6.313464e-05\n", + " 7.198097e-05\n", + " 4.255775e-06\n", + " 5.436607e-07\n", + " 3.448942e-04\n", + " 1.935019e-05\n", + " 5.596717e-07\n", " ...\n", - " 2.607210e-08\n", - " 1.714628e-06\n", - " 7.930739e-07\n", - " 1.207565e-05\n", - " 2.242353e-08\n", - " 1.116518e-05\n", - " 6.553058e-06\n", - " 4.554523e-05\n", - " 3.510054e-05\n", - " 8.818220e-06\n", + " 6.176365e-07\n", + " 8.141250e-05\n", + " 3.762231e-05\n", + " 8.451996e-06\n", + " 6.509721e-07\n", + " 1.122268e-05\n", + " 4.968296e-05\n", + " 9.619240e-05\n", + " 3.641405e-05\n", + " 6.911501e-06\n", " \n", " \n", " 1\n", - " 5.863501e-08\n", - " 5.862878e-07\n", - " 2.793636e-07\n", - " 6.325499e-08\n", - " 1.527967e-07\n", - " 1.050582e-06\n", - " 5.837550e-10\n", - " 2.072408e-07\n", - " 1.121828e-06\n", - " 7.275072e-10\n", + " 1.186375e-06\n", + " 2.120317e-05\n", + " 1.604127e-05\n", + " 3.292936e-07\n", + " 1.815985e-06\n", + " 5.827188e-07\n", + " 1.906651e-08\n", + " 4.147374e-07\n", + " 1.152003e-06\n", + " 1.376997e-08\n", " ...\n", - " 8.214197e-10\n", - " 1.467926e-06\n", - " 8.688337e-07\n", - " 4.756328e-07\n", - " 8.600707e-10\n", - " 1.622881e-06\n", - " 2.258160e-07\n", - " 3.646840e-08\n", - " 1.392962e-07\n", - " 1.779033e-07\n", + " 1.648270e-08\n", + " 8.983916e-07\n", + " 3.274619e-09\n", + " 4.169344e-07\n", + " 1.511837e-08\n", + " 8.178460e-07\n", + " 1.085395e-05\n", + " 1.431082e-06\n", + " 2.633090e-07\n", + " 1.769684e-05\n", " \n", " \n", " 2\n", - " 8.035826e-12\n", - " 8.520677e-12\n", - " 1.845136e-11\n", - " 2.835352e-11\n", - " 6.616994e-11\n", - " 2.340380e-11\n", - " 1.256565e-12\n", - " 8.290610e-12\n", - " 1.351190e-11\n", - " 1.341703e-12\n", + " 1.708866e-11\n", + " 3.150337e-11\n", + " 4.289185e-12\n", + " 2.428148e-12\n", + " 2.044481e-11\n", + " 1.012115e-10\n", + " 2.984188e-10\n", + " 8.895008e-11\n", + " 1.456473e-10\n", + " 3.260175e-10\n", " ...\n", - " 1.138942e-12\n", - " 2.038623e-11\n", - " 2.836039e-11\n", - " 3.386069e-11\n", - " 1.292096e-12\n", - " 8.172017e-11\n", - " 3.077739e-11\n", - " 2.709178e-11\n", - " 7.781061e-12\n", - " 4.475734e-12\n", + " 4.075223e-10\n", + " 1.365983e-12\n", + " 8.831996e-12\n", + " 9.239480e-13\n", + " 3.785499e-10\n", + " 9.423760e-13\n", + " 5.571290e-12\n", + " 2.251778e-12\n", + " 4.904751e-12\n", + " 2.022519e-12\n", " \n", " \n", " 3\n", - " 1.629240e-09\n", - " 1.657102e-09\n", - " 3.991607e-09\n", - " 3.730912e-09\n", - " 1.109841e-08\n", - " 3.196105e-08\n", - " 2.281141e-11\n", - " 4.942599e-09\n", - " 3.330944e-08\n", - " 2.364193e-11\n", + " 1.197860e-09\n", + " 6.094972e-10\n", + " 7.371137e-09\n", + " 1.632908e-09\n", + " 1.371966e-08\n", + " 3.758911e-09\n", + " 4.874247e-12\n", + " 4.862137e-09\n", + " 8.776032e-08\n", + " 4.664997e-12\n", " ...\n", - " 2.342191e-11\n", - " 3.134998e-08\n", - " 1.240997e-07\n", - " 1.207544e-07\n", - " 2.330592e-11\n", - " 6.678697e-08\n", - " 3.448734e-07\n", - " 2.266654e-08\n", - " 5.745768e-08\n", - " 5.050016e-08\n", + " 4.321220e-12\n", + " 8.964186e-09\n", + " 2.868924e-07\n", + " 4.754775e-08\n", + " 5.756340e-12\n", + " 3.998879e-07\n", + " 7.743447e-08\n", + " 1.691233e-08\n", + " 5.171584e-09\n", + " 3.737280e-08\n", " \n", " \n", " 4\n", - " 1.361640e-10\n", - " 1.136607e-09\n", - " 1.075225e-09\n", - " 3.519405e-10\n", - " 6.087009e-10\n", - " 2.253288e-09\n", - " 5.446154e-12\n", - " 3.097352e-09\n", - " 3.180823e-09\n", - " 4.855120e-12\n", + " 1.194712e-09\n", + " 4.821380e-09\n", + " 1.665609e-08\n", + " 1.874733e-10\n", + " 3.145001e-09\n", + " 4.896519e-10\n", + " 1.219395e-11\n", + " 2.773377e-10\n", + " 3.074961e-09\n", + " 9.464363e-12\n", " ...\n", - " 4.808974e-12\n", - " 7.125919e-09\n", - " 7.132718e-09\n", - " 1.443698e-09\n", - " 4.461857e-12\n", - " 1.002815e-08\n", - " 1.321813e-09\n", - " 6.019674e-10\n", - " 2.569878e-09\n", - " 4.361596e-10\n", + " 6.550847e-12\n", + " 7.729197e-09\n", + " 2.296581e-10\n", + " 3.196337e-09\n", + " 1.109901e-11\n", + " 1.188689e-08\n", + " 7.914286e-09\n", + " 2.946413e-09\n", + " 8.031492e-11\n", + " 5.985605e-09\n", " \n", " \n", " 5\n", - " 2.701245e-06\n", - " 4.310450e-07\n", - " 1.089020e-05\n", - " 3.724303e-06\n", - " 2.525652e-05\n", - " 1.133505e-05\n", - " 2.727089e-07\n", - " 1.112282e-06\n", - " 3.154634e-05\n", - " 3.732426e-07\n", + " 8.290472e-07\n", + " 3.643275e-07\n", + " 2.237437e-06\n", + " 6.347396e-07\n", + " 6.702496e-06\n", + " 1.004328e-05\n", + " 1.137207e-06\n", + " 3.239460e-05\n", + " 1.338376e-04\n", + " 1.217226e-06\n", " ...\n", - " 3.667135e-07\n", - " 1.896600e-06\n", - " 5.428673e-06\n", - " 1.023166e-06\n", - " 3.757380e-07\n", - " 4.105170e-06\n", - " 1.751427e-06\n", - " 4.579334e-06\n", - " 8.206015e-06\n", - " 3.169529e-06\n", + " 1.149302e-06\n", + " 1.953034e-06\n", + " 2.918235e-06\n", + " 1.167601e-06\n", + " 1.001415e-06\n", + " 1.025094e-06\n", + " 5.521478e-06\n", + " 9.062952e-06\n", + " 3.059311e-06\n", + " 3.977366e-06\n", " \n", " \n", " 6\n", - " 1.695260e-08\n", - " 1.195565e-07\n", - " 3.549296e-07\n", - " 9.854104e-08\n", - " 1.579093e-07\n", - " 1.599512e-08\n", - " 1.479867e-09\n", - " 7.495963e-08\n", - " 8.016224e-07\n", - " 1.795321e-09\n", + " 2.150679e-08\n", + " 2.688416e-09\n", + " 7.824313e-08\n", + " 7.275691e-09\n", + " 4.525153e-08\n", + " 8.773259e-10\n", + " 1.818674e-10\n", + " 1.125958e-07\n", + " 1.678673e-08\n", + " 1.672551e-10\n", " ...\n", - " 2.049684e-09\n", - " 3.846296e-09\n", - " 7.721437e-09\n", - " 5.957828e-09\n", - " 2.226605e-09\n", - " 4.003595e-07\n", - " 9.653867e-09\n", - " 1.345950e-07\n", - " 1.921127e-07\n", - " 2.055729e-07\n", + " 1.697925e-10\n", + " 4.981665e-09\n", + " 3.887653e-08\n", + " 1.969246e-08\n", + " 2.114279e-10\n", + " 1.219196e-06\n", + " 3.016035e-08\n", + " 3.099242e-07\n", + " 1.922896e-07\n", + " 9.369855e-08\n", " \n", " \n", " 7\n", - " 2.466879e-05\n", - " 3.460404e-06\n", - " 1.159605e-05\n", - " 1.466012e-05\n", - " 2.050074e-05\n", - " 8.891102e-05\n", - " 6.953192e-07\n", - " 4.678439e-07\n", - " 3.123330e-06\n", - " 7.668597e-07\n", + " 2.178705e-05\n", + " 2.138039e-05\n", + " 1.282018e-05\n", + " 1.876779e-05\n", + " 1.038614e-05\n", + " 1.163241e-05\n", + " 2.549153e-06\n", + " 5.077348e-06\n", + " 1.125266e-05\n", + " 2.513376e-06\n", " ...\n", - " 6.962269e-07\n", - " 5.100338e-06\n", - " 3.355947e-05\n", - " 7.287473e-07\n", - " 7.831615e-07\n", - " 1.708988e-07\n", - " 3.960274e-06\n", - " 1.827873e-06\n", - " 1.429972e-06\n", - " 2.114608e-05\n", + " 2.546031e-06\n", + " 4.021757e-06\n", + " 2.615195e-06\n", + " 6.160058e-05\n", + " 3.374086e-06\n", + " 3.037136e-06\n", + " 3.080697e-05\n", + " 1.918841e-05\n", + " 6.851284e-04\n", + " 4.261412e-04\n", " \n", " \n", " 8\n", - " 1.051640e-04\n", - " 3.420593e-05\n", - " 2.547091e-05\n", - " 9.280176e-05\n", - " 1.873575e-04\n", - " 3.183725e-04\n", - " 1.016280e-06\n", - " 1.573967e-05\n", - " 5.953427e-05\n", - " 9.584609e-07\n", + " 2.194299e-05\n", + " 3.835131e-06\n", + " 2.914791e-06\n", + " 1.572850e-05\n", + " 2.549096e-05\n", + " 9.452924e-06\n", + " 1.630521e-06\n", + " 4.264576e-04\n", + " 5.658377e-05\n", + " 1.910584e-06\n", " ...\n", - " 1.229316e-06\n", - " 9.896682e-06\n", - " 1.189033e-05\n", - " 3.031065e-06\n", - " 1.001922e-06\n", - " 1.676208e-05\n", - " 9.383207e-06\n", - " 8.991116e-06\n", - " 1.964020e-05\n", - " 1.475222e-04\n", + " 1.926853e-06\n", + " 7.096163e-06\n", + " 2.541951e-04\n", + " 1.662770e-05\n", + " 1.384810e-06\n", + " 4.382142e-06\n", + " 5.448551e-05\n", + " 1.536754e-05\n", + " 1.325632e-04\n", + " 3.411226e-04\n", " \n", " \n", " 9\n", - " 5.281368e-06\n", - " 2.373689e-06\n", - " 4.936921e-05\n", - " 1.568270e-05\n", - " 3.264111e-05\n", - " 1.276078e-04\n", - " 8.125390e-07\n", - " 9.331784e-07\n", - " 1.343735e-05\n", - " 6.495602e-07\n", + " 1.691479e-04\n", + " 5.059341e-06\n", + " 1.000065e-04\n", + " 5.766935e-05\n", + " 2.320377e-05\n", + " 9.631544e-05\n", + " 1.381983e-07\n", + " 6.306406e-05\n", + " 3.058194e-05\n", + " 1.598655e-07\n", " ...\n", - " 7.573024e-07\n", - " 1.871267e-05\n", - " 1.719145e-05\n", - " 1.415784e-04\n", - " 6.460597e-07\n", - " 6.544438e-05\n", - " 1.676144e-03\n", - " 1.460053e-05\n", - " 1.618803e-05\n", - " 1.788506e-05\n", + " 1.737135e-07\n", + " 3.781930e-05\n", + " 1.692495e-05\n", + " 4.778548e-06\n", + " 1.863484e-07\n", + " 1.370581e-05\n", + " 2.134034e-04\n", + " 5.654188e-06\n", + " 4.860560e-05\n", + " 3.064503e-05\n", " \n", " \n", "\n", @@ -337,69 +339,69 @@ ], "text/plain": [ " 0 1 2 3 4 \\\n", - "0 4.185846e-06 1.072607e-05 2.678383e-05 5.868325e-05 5.790507e-05 \n", - "1 5.863501e-08 5.862878e-07 2.793636e-07 6.325499e-08 1.527967e-07 \n", - "2 8.035826e-12 8.520677e-12 1.845136e-11 2.835352e-11 6.616994e-11 \n", - "3 1.629240e-09 1.657102e-09 3.991607e-09 3.730912e-09 1.109841e-08 \n", - "4 1.361640e-10 1.136607e-09 1.075225e-09 3.519405e-10 6.087009e-10 \n", - "5 2.701245e-06 4.310450e-07 1.089020e-05 3.724303e-06 2.525652e-05 \n", - "6 1.695260e-08 1.195565e-07 3.549296e-07 9.854104e-08 1.579093e-07 \n", - "7 2.466879e-05 3.460404e-06 1.159605e-05 1.466012e-05 2.050074e-05 \n", - "8 1.051640e-04 3.420593e-05 2.547091e-05 9.280176e-05 1.873575e-04 \n", - "9 5.281368e-06 2.373689e-06 4.936921e-05 1.568270e-05 3.264111e-05 \n", + "0 1.893087e-04 3.720649e-05 4.334738e-05 6.313464e-05 7.198097e-05 \n", + "1 1.186375e-06 2.120317e-05 1.604127e-05 3.292936e-07 1.815985e-06 \n", + "2 1.708866e-11 3.150337e-11 4.289185e-12 2.428148e-12 2.044481e-11 \n", + "3 1.197860e-09 6.094972e-10 7.371137e-09 1.632908e-09 1.371966e-08 \n", + "4 1.194712e-09 4.821380e-09 1.665609e-08 1.874733e-10 3.145001e-09 \n", + "5 8.290472e-07 3.643275e-07 2.237437e-06 6.347396e-07 6.702496e-06 \n", + "6 2.150679e-08 2.688416e-09 7.824313e-08 7.275691e-09 4.525153e-08 \n", + "7 2.178705e-05 2.138039e-05 1.282018e-05 1.876779e-05 1.038614e-05 \n", + "8 2.194299e-05 3.835131e-06 2.914791e-06 1.572850e-05 2.549096e-05 \n", + "9 1.691479e-04 5.059341e-06 1.000065e-04 5.766935e-05 2.320377e-05 \n", "\n", " 5 6 7 8 9 ... \\\n", - "0 5.501922e-06 1.831420e-08 1.441312e-05 5.782143e-06 2.314050e-08 ... \n", - "1 1.050582e-06 5.837550e-10 2.072408e-07 1.121828e-06 7.275072e-10 ... \n", - "2 2.340380e-11 1.256565e-12 8.290610e-12 1.351190e-11 1.341703e-12 ... \n", - "3 3.196105e-08 2.281141e-11 4.942599e-09 3.330944e-08 2.364193e-11 ... \n", - "4 2.253288e-09 5.446154e-12 3.097352e-09 3.180823e-09 4.855120e-12 ... \n", - "5 1.133505e-05 2.727089e-07 1.112282e-06 3.154634e-05 3.732426e-07 ... \n", - "6 1.599512e-08 1.479867e-09 7.495963e-08 8.016224e-07 1.795321e-09 ... \n", - "7 8.891102e-05 6.953192e-07 4.678439e-07 3.123330e-06 7.668597e-07 ... \n", - "8 3.183725e-04 1.016280e-06 1.573967e-05 5.953427e-05 9.584609e-07 ... \n", - "9 1.276078e-04 8.125390e-07 9.331784e-07 1.343735e-05 6.495602e-07 ... \n", + "0 4.255775e-06 5.436607e-07 3.448942e-04 1.935019e-05 5.596717e-07 ... \n", + "1 5.827188e-07 1.906651e-08 4.147374e-07 1.152003e-06 1.376997e-08 ... \n", + "2 1.012115e-10 2.984188e-10 8.895008e-11 1.456473e-10 3.260175e-10 ... \n", + "3 3.758911e-09 4.874247e-12 4.862137e-09 8.776032e-08 4.664997e-12 ... \n", + "4 4.896519e-10 1.219395e-11 2.773377e-10 3.074961e-09 9.464363e-12 ... \n", + "5 1.004328e-05 1.137207e-06 3.239460e-05 1.338376e-04 1.217226e-06 ... \n", + "6 8.773259e-10 1.818674e-10 1.125958e-07 1.678673e-08 1.672551e-10 ... \n", + "7 1.163241e-05 2.549153e-06 5.077348e-06 1.125266e-05 2.513376e-06 ... \n", + "8 9.452924e-06 1.630521e-06 4.264576e-04 5.658377e-05 1.910584e-06 ... \n", + "9 9.631544e-05 1.381983e-07 6.306406e-05 3.058194e-05 1.598655e-07 ... \n", "\n", " 10646 10647 10648 10649 10650 \\\n", - "0 2.607210e-08 1.714628e-06 7.930739e-07 1.207565e-05 2.242353e-08 \n", - "1 8.214197e-10 1.467926e-06 8.688337e-07 4.756328e-07 8.600707e-10 \n", - "2 1.138942e-12 2.038623e-11 2.836039e-11 3.386069e-11 1.292096e-12 \n", - "3 2.342191e-11 3.134998e-08 1.240997e-07 1.207544e-07 2.330592e-11 \n", - "4 4.808974e-12 7.125919e-09 7.132718e-09 1.443698e-09 4.461857e-12 \n", - "5 3.667135e-07 1.896600e-06 5.428673e-06 1.023166e-06 3.757380e-07 \n", - "6 2.049684e-09 3.846296e-09 7.721437e-09 5.957828e-09 2.226605e-09 \n", - "7 6.962269e-07 5.100338e-06 3.355947e-05 7.287473e-07 7.831615e-07 \n", - "8 1.229316e-06 9.896682e-06 1.189033e-05 3.031065e-06 1.001922e-06 \n", - "9 7.573024e-07 1.871267e-05 1.719145e-05 1.415784e-04 6.460597e-07 \n", + "0 6.176365e-07 8.141250e-05 3.762231e-05 8.451996e-06 6.509721e-07 \n", + "1 1.648270e-08 8.983916e-07 3.274619e-09 4.169344e-07 1.511837e-08 \n", + "2 4.075223e-10 1.365983e-12 8.831996e-12 9.239480e-13 3.785499e-10 \n", + "3 4.321220e-12 8.964186e-09 2.868924e-07 4.754775e-08 5.756340e-12 \n", + "4 6.550847e-12 7.729197e-09 2.296581e-10 3.196337e-09 1.109901e-11 \n", + "5 1.149302e-06 1.953034e-06 2.918235e-06 1.167601e-06 1.001415e-06 \n", + "6 1.697925e-10 4.981665e-09 3.887653e-08 1.969246e-08 2.114279e-10 \n", + "7 2.546031e-06 4.021757e-06 2.615195e-06 6.160058e-05 3.374086e-06 \n", + "8 1.926853e-06 7.096163e-06 2.541951e-04 1.662770e-05 1.384810e-06 \n", + "9 1.737135e-07 3.781930e-05 1.692495e-05 4.778548e-06 1.863484e-07 \n", "\n", " 10651 10652 10653 10654 10655 \n", - "0 1.116518e-05 6.553058e-06 4.554523e-05 3.510054e-05 8.818220e-06 \n", - "1 1.622881e-06 2.258160e-07 3.646840e-08 1.392962e-07 1.779033e-07 \n", - "2 8.172017e-11 3.077739e-11 2.709178e-11 7.781061e-12 4.475734e-12 \n", - "3 6.678697e-08 3.448734e-07 2.266654e-08 5.745768e-08 5.050016e-08 \n", - "4 1.002815e-08 1.321813e-09 6.019674e-10 2.569878e-09 4.361596e-10 \n", - "5 4.105170e-06 1.751427e-06 4.579334e-06 8.206015e-06 3.169529e-06 \n", - "6 4.003595e-07 9.653867e-09 1.345950e-07 1.921127e-07 2.055729e-07 \n", - "7 1.708988e-07 3.960274e-06 1.827873e-06 1.429972e-06 2.114608e-05 \n", - "8 1.676208e-05 9.383207e-06 8.991116e-06 1.964020e-05 1.475222e-04 \n", - "9 6.544438e-05 1.676144e-03 1.460053e-05 1.618803e-05 1.788506e-05 \n", + "0 1.122268e-05 4.968296e-05 9.619240e-05 3.641405e-05 6.911501e-06 \n", + "1 8.178460e-07 1.085395e-05 1.431082e-06 2.633090e-07 1.769684e-05 \n", + "2 9.423760e-13 5.571290e-12 2.251778e-12 4.904751e-12 2.022519e-12 \n", + "3 3.998879e-07 7.743447e-08 1.691233e-08 5.171584e-09 3.737280e-08 \n", + "4 1.188689e-08 7.914286e-09 2.946413e-09 8.031492e-11 5.985605e-09 \n", + "5 1.025094e-06 5.521478e-06 9.062952e-06 3.059311e-06 3.977366e-06 \n", + "6 1.219196e-06 3.016035e-08 3.099242e-07 1.922896e-07 9.369855e-08 \n", + "7 3.037136e-06 3.080697e-05 1.918841e-05 6.851284e-04 4.261412e-04 \n", + "8 4.382142e-06 5.448551e-05 1.536754e-05 1.325632e-04 3.411226e-04 \n", + "9 1.370581e-05 2.134034e-04 5.654188e-06 4.860560e-05 3.064503e-05 \n", "\n", "[10 rows x 10656 columns]" ] }, - "execution_count": 143, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "results_df = pd.read_csv(path+'cls_output-methodname/test_results.tsv', header=None, sep='\\t')\n", + "results_df = pd.read_csv(path+'cls_output-methodname-lg/test_results.tsv', header=None, sep='\\t')\n", "results_df.head(10)" ] }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -408,7 +410,7 @@ "(1903, 10656)" ] }, - "execution_count": 144, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -419,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -428,7 +430,7 @@ "(10656, 1)" ] }, - "execution_count": 145, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -441,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -455,1016 +457,1016 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[[37, 42, 264, 2388, 2387, 2323, 52, 2108, 41, 36],\n", - " [677, 7957, 159, 3535, 387, 2716, 675, 624, 167, 2715],\n", - " [79, 160, 9626, 119, 8040, 2260, 1203, 246, 206, 4011],\n", - " [2157, 2175, 7635, 2236, 7678, 4141, 2197, 679, 677, 2896],\n", - " [677, 2236, 159, 2502, 2503, 870, 2504, 679, 7957, 2716],\n", - " [79, 119, 2194, 2260, 2276, 1046, 2684, 2168, 9037, 2215],\n", - " [2200, 2197, 3991, 2274, 171, 2194, 2276, 2394, 2169, 6824],\n", - " [2693, 2628, 7487, 3100, 1940, 10495, 9473, 7444, 7509, 478],\n", - " [79, 5263, 206, 4011, 2215, 2896, 6533, 119, 1815, 6726],\n", - " [387, 1917, 2258, 2253, 2255, 838, 2244, 2252, 2249, 60],\n", - " [2246, 7894, 146, 2355, 2053, 2254, 3337, 2173, 84, 387],\n", - " [2246, 7894, 146, 2355, 2053, 2254, 3337, 2173, 84, 387],\n", - " [2246, 7894, 146, 2355, 2053, 2254, 3337, 2173, 84, 387],\n", - " [2247, 7894, 387, 7627, 2406, 679, 2157, 2252, 2348, 901],\n", - " [2258, 2255, 2249, 2250, 2253, 2244, 7236, 2254, 2251, 7237],\n", - " [2197, 2173, 108, 84, 2157, 8319, 2259, 8021, 901, 2504],\n", - " [2261, 2264, 2263, 2262, 2236, 3325, 2348, 387, 2326, 3497],\n", - " [2334, 2367, 2386, 2264, 2329, 2385, 2396, 2266, 2317, 2382],\n", - " [79, 160, 9626, 8040, 119, 2260, 246, 206, 4011, 205],\n", - " [2196, 2293, 108, 124, 87, 2337, 128, 667, 901, 8335],\n", - " [79, 160, 119, 9626, 246, 2260, 8040, 206, 4011, 4073],\n", - " [2283, 2197, 2157, 2295, 2173, 788, 2359, 883, 901, 2266],\n", - " [2283, 2197, 2173, 2157, 2359, 883, 2295, 10504, 788, 2408],\n", - " [2173, 192, 2197, 3991, 677, 8507, 870, 2437, 2155, 3165],\n", - " [128, 2293, 2427, 2292, 901, 246, 31, 108, 2408, 156],\n", - " [79, 160, 9626, 119, 2260, 246, 206, 8040, 690, 2197],\n", - " [2197, 108, 2260, 129, 3991, 2157, 2173, 690, 2283, 156],\n", - " [2273, 2272, 2197, 2271, 2173, 675, 5534, 901, 2259, 192],\n", - " [2197, 2200, 2259, 3991, 1977, 2194, 2271, 8155, 2236, 2369],\n", - " [2293, 2196, 2259, 108, 124, 8173, 5114, 3002, 2190, 1894],\n", - " [2173, 8507, 192, 2197, 2437, 2547, 3991, 2155, 9302, 2344],\n", - " [2197, 1977, 204, 2273, 8482, 128, 205, 79, 239, 2259],\n", - " [2173, 192, 2197, 677, 3991, 8507, 2155, 2437, 870, 5534],\n", - " [2301, 2305, 2350, 2412, 2313, 637, 2433, 2297, 2304, 2434],\n", - " [2306, 2328, 2380, 3158, 2327, 2451, 2398, 2325, 669, 2339],\n", - " [2321, 2323, 2383, 660, 2379, 670, 2322, 2320, 2374, 2331],\n", - " [2325, 2324, 2331, 2327, 2328, 2326, 2330, 2344, 2333, 2332],\n", - " [2326, 2327, 2324, 2325, 2186, 2331, 2182, 2328, 2190, 2330],\n", - " [2330, 2324, 2331, 2326, 2325, 2329, 2328, 2327, 2264, 52],\n", - " [697, 2311, 2351, 2350, 3210, 870, 2341, 2323, 2108, 10610],\n", - " [2380, 2309, 899, 2328, 706, 6712, 7330, 2306, 9886, 2410],\n", - " [2326, 2325, 2328, 2324, 2327, 2331, 2330, 2333, 2344, 2329],\n", - " [2331, 2324, 2333, 2327, 2325, 2332, 2326, 2328, 2330, 2379],\n", - " [2390, 2387, 1078, 2388, 1753, 2385, 2395, 2386, 1897, 1029],\n", - " [318, 358, 1901, 2386, 2385, 1264, 1902, 2357, 934, 2264],\n", - " [207, 2392, 124, 246, 2313, 356, 52, 2312, 2342, 2408],\n", - " [2401, 55, 2405, 52, 2386, 2387, 2312, 2398, 2365, 41],\n", - " [2359, 2349, 2229, 2402, 848, 2299, 2393, 2295, 2408, 1892],\n", - " [2320, 2317, 2292, 2318, 2379, 2351, 2108, 51, 2319, 2316],\n", - " [2383, 42, 2345, 2384, 2387, 264, 37, 1203, 2388, 410],\n", - " [2326, 2330, 52, 2328, 2333, 741, 2324, 2190, 922, 1057],\n", - " [7291, 669, 897, 7935, 10228, 2380, 7516, 1345, 7934, 910],\n", - " [2344, 2186, 2327, 2326, 2182, 2264, 52, 2250, 2215, 2324],\n", - " [2361, 2360, 2357, 2363, 2362, 2358, 2349, 2346, 2396, 2345],\n", - " [2417, 9849, 7903, 2580, 2237, 3892, 2376, 3005, 8761, 2608],\n", - " [2316, 2318, 2317, 2292, 108, 2315, 2294, 883, 639, 2175],\n", - " [2405, 2406, 2375, 2404, 2252, 739, 881, 880, 2258, 10652],\n", - " [2348, 2412, 1115, 2478, 2428, 1046, 6799, 748, 6836, 911],\n", - " [2276, 2289, 2431, 2287, 2432, 6470, 787, 2476, 2166, 119],\n", - " [2157, 2175, 2452, 2441, 7678, 3192, 2896, 4141, 2155, 5450],\n", - " [2277, 2278, 2270, 2476, 87, 2269, 8507, 4088, 2901, 640],\n", - " [2465, 2463, 2461, 2462, 2464, 7933, 2469, 9002, 10541, 1379],\n", - " [2469, 2466, 2467, 2305, 2372, 7920, 695, 8804, 688, 774],\n", - " [2278, 2277, 2194, 2476, 156, 2197, 87, 494, 1977, 128],\n", - " [2108, 1805, 1959, 517, 757, 2322, 317, 1676, 2003, 1921],\n", - " [210, 2322, 2323, 2374, 2073, 1348, 2108, 1959, 2320, 1382],\n", - " [118, 697, 640, 2155, 207, 159, 8319, 86, 4088, 677],\n", - " [8453, 758, 216, 8972, 8335, 494, 2278, 1464, 1250, 9529],\n", - " [221, 220, 1936, 944, 224, 982, 480, 1938, 485, 228],\n", - " [204, 517, 504, 520, 1465, 205, 206, 513, 501, 521],\n", - " [228, 1936, 242, 244, 493, 501, 243, 2129, 10146, 248],\n", - " [79, 160, 708, 9626, 4011, 704, 1203, 8040, 119, 206],\n", - " [79, 160, 206, 119, 246, 9626, 4011, 1203, 8040, 4073],\n", - " [2275, 866, 733, 863, 8992, 494, 8714, 510, 952, 119],\n", - " [208, 214, 1711, 1295, 206, 519, 211, 257, 233, 356],\n", - " [211, 204, 207, 9508, 247, 520, 823, 701, 1711, 1607],\n", - " [207, 247, 211, 246, 205, 204, 9508, 8521, 2337, 264],\n", - " [79, 4011, 1203, 119, 8040, 160, 206, 2260, 9626, 4073],\n", - " [239, 240, 698, 2348, 838, 79, 2504, 641, 901, 204],\n", - " [208, 211, 1711, 214, 257, 206, 233, 519, 1295, 207],\n", - " [119, 208, 246, 3991, 635, 165, 679, 519, 214, 211],\n", - " [233, 519, 1711, 211, 208, 257, 1082, 214, 668, 246],\n", - " [213, 211, 207, 246, 1046, 517, 519, 1295, 1082, 233],\n", - " [356, 223, 1921, 2322, 2296, 1107, 1891, 1082, 1662, 2108],\n", - " [246, 667, 1917, 2613, 1713, 10306, 694, 1255, 277, 102],\n", - " [246, 207, 205, 206, 1046, 211, 1255, 2342, 667, 2119],\n", - " [207, 246, 205, 1082, 204, 1295, 1203, 517, 264, 211],\n", - " [1847, 1642, 1161, 3122, 292, 1609, 380, 1099, 473, 377],\n", - " [304, 303, 305, 285, 291, 301, 377, 347, 1204, 381],\n", - " [303, 304, 305, 301, 285, 381, 1326, 291, 533, 379],\n", - " [305, 1193, 1078, 1273, 306, 308, 963, 962, 236, 285],\n", - " [318, 358, 2385, 2386, 1264, 1946, 1901, 1997, 2162, 934],\n", - " [334, 330, 1020, 329, 964, 1092, 553, 7228, 541, 335],\n", - " [2052, 76, 1718, 70, 2675, 1642, 1604, 1602, 3275, 740],\n", - " [360, 1670, 1159, 550, 1845, 1583, 1646, 1627, 1020, 392],\n", - " [594, 533, 591, 568, 588, 577, 595, 601, 582, 590],\n", - " [1136, 591, 533, 2320, 1651, 1239, 1167, 1232, 1228, 1340],\n", - " [1263, 1153, 1996, 1995, 1929, 1309, 206, 223, 495, 251],\n", - " [246, 206, 207, 1046, 214, 204, 1203, 79, 517, 1295],\n", - " [118, 640, 2173, 86, 2155, 4693, 8319, 4172, 3991, 677],\n", - " [430, 434, 432, 433, 1104, 2486, 1032, 330, 1029, 2364],\n", - " [430, 434, 432, 433, 1104, 1032, 330, 1000, 336, 2486],\n", - " [562, 342, 612, 203, 348, 2139, 563, 340, 495, 1875],\n", - " [532, 457, 1273, 1577, 460, 550, 316, 549, 1890, 1035],\n", - " [79, 698, 240, 629, 2352, 208, 3099, 206, 870, 4067],\n", - " [205, 207, 206, 246, 204, 2119, 1471, 214, 245, 2342],\n", - " [208, 211, 207, 118, 246, 165, 205, 206, 214, 519],\n", - " [213, 206, 79, 204, 1063, 513, 1509, 521, 944, 667],\n", - " [513, 206, 204, 512, 517, 52, 510, 1063, 223, 1086],\n", - " [533, 535, 1166, 1167, 1271, 336, 1092, 1535, 1238, 1554],\n", - " [541, 407, 960, 330, 289, 403, 422, 401, 2083, 554],\n", - " [208, 233, 257, 214, 1711, 519, 211, 206, 1295, 635],\n", - " [603, 543, 594, 587, 533, 590, 598, 574, 597, 601],\n", - " [576, 1651, 2320, 7860, 372, 1063, 591, 593, 426, 10375],\n", - " [579, 1490, 561, 544, 7319, 10436, 543, 1397, 593, 9552],\n", - " [594, 589, 577, 568, 530, 595, 583, 588, 582, 1546],\n", - " [1622, 1646, 1648, 1403, 1488, 1927, 1647, 1593, 1922, 1642],\n", - " [612, 350, 562, 407, 563, 614, 564, 417, 547, 471],\n", - " [631, 632, 10499, 167, 10500, 7957, 9775, 9482, 10163, 7312],\n", - " [8812, 8323, 5597, 10103, 4655, 81, 1115, 9370, 8248, 1515],\n", - " [79, 8021, 9713, 1514, 174, 3039, 4075, 2230, 2196, 1506],\n", - " [651, 2172, 4165, 3993, 2309, 136, 3992, 638, 697, 2371],\n", - " [8077, 7227, 721, 635, 732, 7428, 7426, 7429, 10350, 10491],\n", - " [71, 3298, 854, 759, 977, 3361, 9128, 3005, 3378, 711],\n", - " [1910, 7237, 7363, 688, 720, 685, 7993, 7642, 904, 10584],\n", - " [86, 1471, 640, 677, 205, 206, 246, 1255, 2236, 3991],\n", - " [701, 698, 697, 708, 715, 8155, 683, 2514, 4088, 2155],\n", - " [727, 724, 723, 730, 1378, 692, 729, 752, 722, 691],\n", - " [86, 1471, 640, 9288, 4172, 3991, 8335, 1255, 9039, 205],\n", - " [2051, 2238, 6064, 2355, 5057, 571, 1736, 273, 5059, 4001],\n", - " [6447, 758, 248, 4526, 2647, 2654, 8453, 8335, 260, 2646],\n", - " [79, 5437, 660, 10250, 2684, 7915, 119, 8460, 8374, 9626],\n", - " [771, 2041, 721, 591, 1377, 352, 1657, 2456, 593, 349],\n", - " [801, 9517, 136, 697, 9854, 621, 8058, 827, 800, 799],\n", - " [801, 800, 621, 633, 810, 635, 7050, 875, 809, 8059],\n", - " [79, 160, 9626, 119, 2260, 8040, 206, 690, 246, 4073],\n", - " [801, 800, 799, 8806, 804, 810, 8413, 806, 8341, 9775],\n", - " [720, 721, 678, 2452, 774, 5655, 7678, 2440, 7983, 7683],\n", - " [825, 824, 823, 827, 840, 2901, 2197, 682, 839, 121],\n", - " [86, 838, 690, 677, 1917, 2197, 691, 7626, 9891, 640],\n", - " [2240, 8317, 737, 9952, 1443, 9522, 197, 620, 7261, 9523],\n", - " [2224, 8262, 7497, 3619, 755, 3064, 6052, 705, 907, 844],\n", - " [860, 859, 2278, 10132, 767, 2277, 916, 2257, 758, 10201],\n", - " [79, 160, 9626, 4073, 1203, 641, 8040, 690, 2260, 206],\n", - " [118, 119, 677, 690, 3991, 2173, 2236, 2155, 679, 870],\n", - " [119, 118, 79, 208, 2236, 675, 3991, 690, 677, 2260],\n", - " [878, 898, 899, 2373, 7975, 2167, 841, 2689, 882, 855],\n", - " [7894, 2254, 2255, 7975, 881, 2250, 626, 683, 2258, 7353],\n", - " [854, 2291, 3485, 7304, 2407, 782, 2227, 2240, 697, 2350],\n", - " [677, 679, 2502, 2503, 5826, 734, 1805, 2716, 2504, 2715],\n", - " [10317, 10593, 9063, 10527, 10048, 10464, 9046, 4708, 10435, 9837],\n", - " [1171, 1173, 930, 1815, 1353, 1784, 1619, 9584, 1204, 1137],\n", - " [678, 680, 10528, 7608, 887, 5655, 2274, 7955, 10581, 2410],\n", - " [1282, 1262, 1001, 1071, 511, 1027, 1266, 1715, 251, 1516],\n", - " [0, 277, 1929, 1, 2149, 2231, 2150, 10, 8113, 1122],\n", - " [966, 968, 336, 329, 1848, 1543, 1674, 1689, 9982, 1636],\n", - " [1022, 987, 1675, 370, 1848, 1674, 1548, 10190, 1835, 1638],\n", - " [994, 998, 508, 921, 1015, 989, 983, 2189, 1513, 2183],\n", - " [1005, 1841, 1549, 1523, 1933, 1543, 9525, 1508, 508, 1473],\n", - " [1198, 484, 1490, 292, 593, 485, 1550, 1007, 543, 603],\n", - " [1032, 1104, 1671, 1636, 2105, 1267, 355, 1390, 1022, 2010],\n", - " [1035, 1486, 1033, 1999, 462, 1717, 1946, 1390, 1628, 434],\n", - " [2108, 10322, 7724, 7510, 3401, 7304, 1927, 1676, 1958, 6044],\n", - " [206, 246, 205, 1046, 203, 214, 204, 263, 52, 1295],\n", - " [1991, 2296, 1293, 2108, 599, 1670, 416, 519, 1778, 757],\n", - " [8802, 120, 186, 7514, 826, 7405, 1251, 911, 552, 2896],\n", - " [206, 203, 204, 205, 513, 213, 495, 512, 79, 246],\n", - " [1092, 1335, 1271, 1099, 1331, 410, 1100, 1695, 1267, 1102],\n", - " [1120, 1122, 2224, 1993, 8339, 15, 2773, 1049, 10041, 9131],\n", - " [1122, 2149, 1120, 277, 940, 7586, 1498, 261, 934, 933],\n", - " [108, 2600, 2601, 9877, 1614, 3323, 8765, 2931, 3447, 870],\n", - " [1471, 2236, 8334, 9039, 268, 9719, 3421, 753, 6024, 2337],\n", - " [204, 1046, 205, 517, 206, 520, 213, 1465, 207, 1295],\n", - " [204, 1046, 205, 517, 206, 520, 213, 1465, 207, 1295],\n", - " [517, 246, 1046, 206, 214, 519, 204, 1295, 233, 207],\n", - " [517, 1113, 207, 246, 367, 1046, 204, 206, 519, 1713],\n", - " [1576, 356, 207, 2337, 206, 5110, 2046, 1662, 670, 1255],\n", - " [1137, 371, 1145, 1399, 1044, 1815, 1402, 1259, 1451, 1799],\n", - " [539, 1161, 425, 1651, 439, 1558, 1259, 294, 533, 979],\n", - " [1099, 2008, 604, 2007, 707, 406, 1161, 1733, 1535, 381],\n", - " [533, 1944, 1542, 2100, 1711, 1577, 535, 1654, 2102, 1652],\n", - " [1239, 1213, 1229, 1207, 1228, 1231, 1226, 1236, 1227, 1237],\n", - " [1239, 1228, 347, 1236, 1229, 1237, 1232, 1231, 1326, 1219],\n", - " [1064, 7335, 2186, 859, 5566, 2182, 1727, 279, 511, 7301],\n", - " [533, 1326, 295, 614, 285, 472, 347, 1102, 1777, 380],\n", - " [317, 261, 2332, 1593, 3333, 2061, 1650, 581, 1348, 8271],\n", - " [6533, 6532, 10336, 7383, 3543, 7973, 2899, 7460, 8284, 2935],\n", - " [8285, 287, 8510, 338, 3592, 964, 2067, 610, 3996, 1836],\n", - " [1306, 1305, 1301, 580, 1057, 1135, 76, 9599, 917, 3169],\n", - " [1300, 1003, 1002, 1028, 1556, 543, 999, 547, 603, 545],\n", - " [580, 1306, 1305, 1301, 1325, 882, 9370, 700, 1126, 194],\n", - " [1597, 681, 1093, 403, 794, 1454, 1443, 1090, 1325, 1389],\n", - " [10581, 2224, 1093, 10229, 1604, 815, 9568, 882, 8341, 7228],\n", - " [8202, 6777, 8198, 1982, 8991, 2821, 8388, 183, 2617, 294],\n", - " [8202, 6777, 8198, 1982, 8991, 2821, 8388, 183, 2617, 294],\n", - " [1338, 370, 1848, 539, 981, 1689, 1271, 353, 1733, 461],\n", - " [239, 240, 698, 2348, 838, 79, 2504, 641, 901, 204],\n", - " [1, 1264, 1309, 1997, 1263, 1353, 1702, 7, 2675, 936],\n", - " [75, 971, 531, 4911, 1798, 3145, 702, 1573, 1242, 3137],\n", - " [551, 1991, 294, 1635, 8108, 2104, 1670, 1658, 4093, 1778],\n", - " [8113, 277, 2149, 276, 273, 7724, 7227, 3401, 1929, 218],\n", - " [1422, 1412, 1418, 1427, 1428, 1429, 1425, 1426, 1020, 1258],\n", - " [1425, 1429, 1428, 1427, 1426, 1430, 1412, 1418, 1422, 1020],\n", - " [1372, 1373, 2455, 727, 1365, 722, 1369, 1368, 723, 367],\n", - " [1449, 1446, 1454, 1448, 1457, 1459, 1456, 1458, 1750, 2619],\n", - " [1451, 1159, 1399, 1457, 1396, 1243, 1151, 1454, 1799, 1137],\n", - " [964, 1396, 291, 740, 2727, 1251, 1573, 336, 552, 578],\n", - " [2300, 10456, 7195, 319, 10617, 379, 8025, 7406, 1558, 3454],\n", - " [500, 10621, 3806, 4017, 5549, 2111, 5146, 1611, 3545, 2809],\n", - " [8477, 8476, 10377, 5695, 1802, 10411, 9602, 832, 6960, 2912],\n", - " [1695, 1326, 982, 1169, 563, 344, 565, 578, 999, 533],\n", - " [1290, 549, 1269, 604, 1291, 1280, 1188, 952, 1521, 1213],\n", - " [1672, 1694, 1162, 1213, 1280, 1338, 1145, 1133, 1165, 1151],\n", - " [1509, 208, 1510, 213, 1506, 214, 1295, 519, 211, 2386],\n", - " [1509, 1510, 213, 1506, 1508, 211, 9572, 208, 672, 207],\n", - " [79, 119, 160, 246, 206, 4011, 9626, 4073, 2260, 8040],\n", - " [1509, 1506, 1510, 519, 213, 211, 9572, 1508, 1712, 819],\n", - " [79, 119, 160, 4011, 641, 4073, 9626, 206, 8040, 1506],\n", - " [207, 1736, 367, 1084, 264, 1347, 1928, 1925, 1895, 1927],\n", - " [205, 206, 204, 2119, 207, 246, 180, 245, 86, 1046],\n", - " [204, 205, 1046, 517, 206, 207, 1465, 501, 2119, 520],\n", - " [79, 4011, 160, 9626, 206, 8040, 119, 4073, 205, 246],\n", - " [1525, 1157, 1903, 1526, 1077, 1200, 1472, 269, 2394, 1115],\n", - " [1488, 2058, 1778, 2445, 512, 1584, 2069, 1133, 1165, 1876],\n", - " [1548, 1551, 1546, 1160, 1656, 456, 1655, 2062, 539, 476],\n", - " [213, 108, 204, 691, 1295, 1509, 1046, 1711, 517, 257],\n", - " [1551, 1160, 1548, 1546, 476, 456, 1167, 539, 319, 1394],\n", - " [205, 204, 206, 207, 246, 640, 2119, 214, 1046, 245],\n", - " [1733, 971, 336, 1390, 1335, 2343, 1331, 984, 1674, 1636],\n", - " [901, 31, 7456, 186, 2273, 1574, 2562, 751, 3175, 184],\n", - " [1580, 1589, 1590, 1578, 1586, 1579, 1583, 1581, 1592, 1585],\n", - " [1846, 1840, 1598, 1581, 1329, 1822, 1335, 1578, 1730, 964],\n", - " [79, 160, 4011, 246, 2260, 3687, 9626, 3637, 1506, 119],\n", - " [246, 207, 1046, 206, 205, 204, 86, 1203, 214, 517],\n", - " [207, 205, 211, 204, 247, 52, 9522, 246, 9572, 3404],\n", - " [246, 108, 213, 208, 233, 206, 1046, 691, 214, 211],\n", - " [246, 1203, 206, 691, 204, 1046, 2337, 207, 213, 247],\n", - " [108, 654, 672, 697, 691, 213, 208, 207, 9526, 621],\n", - " [208, 211, 214, 206, 207, 246, 119, 519, 1711, 1295],\n", - " [1637, 1390, 1267, 1032, 1598, 1695, 1038, 1104, 1000, 336],\n", - " [1151, 1526, 282, 1671, 3411, 1558, 2335, 2159, 338, 1799],\n", - " [213, 208, 214, 1295, 257, 108, 206, 233, 1711, 211],\n", - " [1606, 1688, 511, 2149, 1059, 1107, 1122, 1715, 1686, 664],\n", - " [1860, 1866, 457, 1691, 1592, 460, 323, 340, 462, 1722],\n", - " [1700, 1701, 1753, 1620, 522, 90, 10653, 1491, 899, 2361],\n", - " [3149, 1057, 3010, 3148, 1114, 1064, 510, 276, 1309, 5855],\n", - " [2083, 1890, 1720, 1183, 537, 554, 1542, 1165, 1290, 1592],\n", - " [1733, 336, 1213, 2003, 518, 1694, 1718, 1696, 1203, 1335],\n", - " [2230, 9568, 9194, 9477, 7193, 7724, 10322, 10229, 657, 8855],\n", - " [1776, 1771, 7196, 782, 9584, 1767, 2141, 5549, 10129, 1775],\n", - " [8032, 1776, 8635, 8250, 8252, 8251, 9555, 9556, 9525, 527],\n", - " [8146, 1471, 5918, 10198, 2215, 10456, 9989, 5847, 5871, 10483],\n", - " [2119, 1748, 1749, 8483, 4951, 1062, 1047, 1995, 1806, 248],\n", - " [1776, 1771, 1767, 1775, 9552, 9556, 9555, 7196, 2070, 9584],\n", - " [1757, 561, 1457, 1964, 1454, 1459, 1458, 6726, 1456, 1673],\n", - " [1761, 1792, 4984, 996, 997, 1772, 394, 2984, 1096, 6966],\n", - " [1775, 1769, 1767, 1771, 1776, 2087, 2146, 1654, 564, 1102],\n", - " [1793, 1791, 1400, 1399, 1792, 2084, 1405, 1250, 353, 1435],\n", - " [1791, 1405, 1793, 336, 6991, 1743, 8610, 1523, 523, 1524],\n", - " [1636, 1641, 1000, 355, 1032, 1848, 2105, 1104, 462, 353],\n", - " [1799, 1673, 1165, 1584, 2102, 2013, 1400, 1839, 1622, 440],\n", - " [1889, 1474, 180, 1030, 889, 95, 1464, 1051, 1382, 9797],\n", - " [205, 207, 246, 1807, 1046, 245, 2119, 214, 206, 1082],\n", - " [1806, 2162, 2161, 6580, 6587, 1716, 3305, 10654, 3496, 10342],\n", - " [3115, 3238, 3061, 3062, 1069, 8484, 2778, 2946, 1071, 1072],\n", - " [1813, 1814, 146, 10115, 9822, 10497, 2249, 1917, 1395, 8186],\n", - " [214, 1295, 206, 499, 246, 1711, 263, 208, 213, 1046],\n", - " [211, 208, 1711, 246, 207, 519, 214, 233, 635, 204],\n", - " [2342, 1820, 3435, 1486, 92, 1035, 387, 72, 274, 8500],\n", - " [1821, 1761, 1598, 1661, 1669, 6568, 1824, 2088, 1550, 1671],\n", - " [1821, 7024, 1669, 1732, 6262, 439, 1761, 292, 1822, 222],\n", - " [7391, 213, 684, 87, 108, 271, 644, 1953, 93, 691],\n", - " [1879, 1884, 1858, 1857, 1870, 1862, 1531, 1861, 1866, 456],\n", - " [1864, 1875, 1871, 1872, 480, 593, 1867, 1861, 1629, 1870],\n", - " [1871, 1864, 1875, 1861, 1863, 537, 1867, 1862, 1077, 536],\n", - " [1897, 1895, 1318, 2046, 207, 593, 1903, 1033, 1107, 1035],\n", - " [1897, 1895, 1903, 593, 1318, 1113, 933, 2390, 1752, 367],\n", - " [1745, 1894, 1461, 8032, 10546, 6119, 8591, 10655, 2422, 10375],\n", - " [275, 2290, 508, 2334, 1620, 2488, 1473, 2243, 2396, 90],\n", - " [387, 8847, 1917, 9114, 2900, 10306, 7894, 1906, 667, 2241],\n", - " [79, 9626, 119, 160, 8040, 246, 2260, 206, 690, 641],\n", - " [108, 654, 691, 129, 136, 697, 159, 87, 635, 675],\n", - " [932, 1946, 1936, 1938, 228, 1003, 221, 222, 1029, 1027],\n", - " [252, 1931, 1948, 1349, 1657, 2088, 251, 1673, 1777, 255],\n", - " [1953, 8240, 3196, 2931, 3195, 8273, 2708, 684, 10600, 7893],\n", - " [2310, 2238, 1781, 9906, 1736, 2343, 7866, 2734, 2291, 6874],\n", - " [641, 2115, 690, 2514, 3046, 2236, 3047, 4172, 677, 8381],\n", - " [957, 2439, 7516, 1186, 2401, 507, 55, 8131, 2355, 103],\n", - " [1385, 2280, 2085, 7852, 1617, 1970, 931, 2275, 8138, 1133],\n", - " [7187, 10434, 2735, 5700, 8727, 10326, 9941, 10436, 5288, 7239],\n", - " [1607, 1925, 1738, 1310, 1650, 1922, 1921, 1891, 1606, 1954],\n", - " [1975, 2335, 362, 2159, 1977, 1677, 1752, 2331, 1903, 1922],\n", - " [660, 9473, 581, 404, 2449, 10412, 2233, 7894, 7972, 915],\n", - " [79, 2546, 1871, 9626, 2166, 8040, 1875, 1203, 1049, 41],\n", - " [2630, 2060, 2490, 10129, 3333, 7526, 8049, 1833, 7973, 1532],\n", - " [206, 246, 208, 205, 1046, 214, 207, 86, 204, 1295],\n", - " [1576, 2046, 356, 664, 1318, 1107, 665, 5110, 1713, 8108],\n", - " [987, 1022, 1674, 984, 1187, 1689, 1000, 319, 1141, 1531],\n", - " [8841, 3256, 259, 8800, 2119, 2111, 640, 2498, 248, 2113],\n", - " [206, 205, 203, 204, 246, 214, 2337, 86, 1086, 1203],\n", - " [2088, 2087, 1652, 2062, 1654, 1655, 1656, 456, 1629, 1872],\n", - " [338, 550, 436, 1635, 1558, 439, 535, 336, 586, 533],\n", - " [2108, 207, 2351, 205, 2331, 870, 2313, 1805, 246, 2323],\n", - " [10482, 7724, 9192, 2578, 8045, 102, 9528, 10326, 9526, 8427],\n", - " [2112, 2114, 8532, 1486, 2456, 2415, 101, 2125, 8469, 8404],\n", - " [2119, 2114, 2113, 180, 2046, 154, 206, 52, 248, 2342],\n", - " [2114, 2124, 2125, 2128, 2113, 2122, 2121, 2126, 2112, 2129],\n", - " [2119, 205, 180, 207, 52, 2113, 206, 2120, 1046, 248],\n", - " [641, 2115, 690, 3046, 1255, 2514, 640, 2118, 2117, 2116],\n", - " [79, 160, 9626, 246, 119, 206, 8040, 2684, 9991, 1203],\n", - " [205, 207, 246, 2108, 8335, 2337, 2119, 86, 8695, 640],\n", - " [86, 1471, 156, 640, 2337, 2119, 205, 9288, 9992, 9039],\n", - " [79, 160, 9626, 8040, 1203, 119, 2260, 246, 206, 205],\n", - " [86, 1471, 156, 640, 2337, 246, 2197, 205, 1255, 8335],\n", - " [2146, 2141, 2148, 350, 1769, 1479, 1143, 593, 2139, 2140],\n", - " [79, 9626, 160, 246, 119, 8040, 206, 1203, 2260, 4011],\n", - " [211, 1711, 208, 246, 207, 519, 214, 635, 204, 9566],\n", - " [31, 35, 34, 33, 2290, 63, 2391, 213, 36, 57],\n", - " [57, 31, 52, 129, 59, 63, 51, 47, 44, 38],\n", - " [41, 42, 52, 2386, 2344, 2352, 37, 31, 2391, 2312],\n", - " [63, 129, 57, 62, 59, 70, 47, 53, 31, 52],\n", - " [45, 53, 47, 76, 128, 1027, 52, 38, 59, 1282],\n", - " [6865, 8281, 158, 71, 954, 8172, 3476, 3439, 3110, 6142],\n", - " [85, 2115, 2514, 3046, 8319, 2118, 2117, 8402, 641, 2116],\n", - " [98, 97, 100, 99, 101, 96, 95, 2784, 9583, 102],\n", - " [108, 2197, 129, 2504, 753, 901, 690, 2236, 213, 668],\n", - " [119, 79, 208, 8040, 3991, 118, 9991, 2236, 2260, 690],\n", - " [128, 200, 184, 53, 127, 3042, 186, 60, 9435, 129],\n", - " [124, 657, 2196, 128, 2348, 1255, 51, 237, 2613, 233],\n", - " [129, 108, 2197, 128, 698, 8418, 3166, 174, 2660, 81],\n", - " [59, 57, 45, 60, 70, 47, 1604, 61, 3042, 76],\n", - " [79, 160, 9626, 8040, 119, 2260, 4011, 246, 206, 4073],\n", - " [79, 160, 9626, 8040, 119, 2260, 4011, 246, 206, 4073],\n", - " [129, 108, 2197, 156, 237, 213, 211, 698, 654, 3404],\n", - " [156, 2197, 2278, 1977, 505, 2076, 2283, 246, 86, 1046],\n", - " [79, 160, 9626, 119, 8040, 2260, 1203, 641, 246, 206],\n", - " [129, 640, 154, 156, 108, 1046, 2197, 246, 107, 205],\n", - " [156, 128, 2197, 129, 246, 2119, 2278, 86, 3054, 2194],\n", - " [159, 160, 9138, 161, 753, 3319, 8650, 5740, 2214, 2236],\n", - " [79, 160, 119, 9626, 206, 8040, 4011, 246, 4073, 208],\n", - " [79, 160, 119, 9626, 246, 8040, 206, 4011, 4073, 1506],\n", - " [79, 160, 119, 2260, 9626, 8040, 246, 1203, 641, 206],\n", - " [156, 153, 10430, 8238, 1514, 196, 1517, 8622, 7475, 1472],\n", - " [86, 156, 2197, 1471, 2173, 640, 2157, 108, 4088, 2337],\n", - " [79, 160, 9626, 119, 246, 4011, 8040, 206, 2260, 4073],\n", - " [176, 1736, 1895, 264, 1903, 1602, 1752, 1115, 2397, 2365],\n", - " [129, 108, 2197, 8418, 3210, 213, 174, 8731, 136, 698],\n", - " [129, 108, 2197, 2196, 128, 174, 156, 8418, 3991, 136],\n", - " [180, 1688, 2119, 94, 96, 9748, 205, 3010, 245, 4093],\n", - " [191, 186, 190, 184, 213, 98, 2917, 9846, 187, 1117],\n", - " [193, 196, 7896, 8317, 8204, 837, 8973, 3016, 7464, 8920],\n", - " [197, 620, 164, 198, 8417, 10542, 2869, 2235, 9747, 9778],\n", - " [10128, 7903, 10133, 2298, 2528, 9908, 8130, 8129, 1739, 10129],\n", - " [2822, 2746, 2750, 2565, 2554, 3249, 2806, 2766, 476, 2751],\n", - " [2498, 4683, 9982, 4709, 4535, 831, 5900, 3059, 8058, 494],\n", - " [2406, 10404, 2166, 898, 6895, 5515, 6808, 10652, 2432, 6922],\n", - " [3119, 2633, 2557, 7654, 7527, 6686, 8226, 7526, 7555, 2630],\n", - " [2759, 5077, 3305, 6458, 6802, 3197, 2689, 3394, 6450, 3191],\n", - " [3475, 6022, 2508, 2509, 7957, 6739, 7724, 7783, 677, 3172],\n", - " [2548, 2546, 2549, 138, 826, 801, 2545, 2547, 856, 8278],\n", - " [2546, 2549, 2545, 2548, 620, 8919, 2547, 2111, 2476, 1135],\n", - " [387, 8847, 7894, 7974, 1906, 9114, 2414, 9178, 2900, 3337],\n", - " [3178, 2601, 3157, 3323, 2214, 2603, 7957, 657, 1471, 9047],\n", - " [2604, 824, 2846, 8298, 2868, 7391, 867, 2603, 891, 8036],\n", - " [2613, 2337, 6152, 10136, 2868, 8254, 7585, 9326, 9079, 657],\n", - " [2613, 2337, 6152, 10136, 2868, 8254, 7585, 9326, 9079, 657],\n", - " [2228, 7992, 2221, 710, 2373, 10402, 7236, 7680, 7955, 7228],\n", - " [2647, 2640, 2654, 2638, 2637, 2642, 2653, 2646, 2649, 2650],\n", - " [2655, 2512, 2862, 824, 2932, 10165, 2611, 2931, 7527, 668],\n", - " [2679, 2680, 4094, 6029, 4700, 4550, 665, 5889, 4526, 252],\n", - " [2685, 8810, 3361, 2051, 6058, 6602, 4442, 6064, 3244, 2823],\n", - " [9379, 1471, 9531, 4172, 733, 9530, 2260, 3756, 8038, 7630],\n", - " [9104, 7179, 8454, 9227, 9033, 7724, 8876, 8585, 9021, 8808],\n", - " [6922, 6937, 6005, 6895, 6730, 8210, 2165, 6260, 2536, 6930],\n", - " [2503, 677, 2716, 679, 2502, 2715, 2504, 8266, 2567, 2236],\n", - " [2720, 2718, 2719, 6555, 5094, 8201, 528, 9693, 5535, 105],\n", - " [3249, 2766, 2746, 2822, 2750, 2806, 2561, 2558, 2565, 319],\n", - " [5703, 6952, 8325, 10227, 5742, 7277, 2776, 7986, 6815, 8441],\n", - " [10617, 6041, 2300, 10008, 7136, 7336, 3250, 8949, 7438, 7386],\n", - " [2660, 7016, 8418, 5231, 2919, 2862, 2630, 3197, 7609, 667],\n", - " [2794, 2795, 2817, 6638, 3009, 6678, 6493, 6122, 7107, 5629],\n", - " [3561, 5385, 3445, 3562, 5164, 3903, 2808, 10604, 7518, 3560],\n", - " [3249, 2766, 2822, 2746, 2750, 2806, 2561, 2558, 2751, 2565],\n", - " [79, 9626, 8040, 160, 119, 192, 1203, 1805, 4011, 206],\n", - " [2848, 2912, 2850, 6469, 2903, 5685, 6810, 2552, 6302, 759],\n", - " [2848, 2837, 194, 7354, 6175, 2834, 2974, 2847, 6113, 5687],\n", - " [2844, 2829, 3619, 908, 2833, 5521, 2912, 2830, 5741, 4010],\n", - " [2832, 5623, 2837, 2847, 2850, 2834, 5723, 5727, 2833, 5708],\n", - " [7442, 7332, 7769, 7449, 10598, 10582, 7821, 10527, 7236, 7690],\n", - " [3245, 3186, 7277, 5937, 2534, 3346, 3193, 3083, 8159, 3605],\n", - " [7401, 8045, 2930, 8810, 2929, 8130, 7229, 6603, 6064, 6543],\n", - " [79, 160, 9626, 119, 8040, 4011, 2260, 4073, 206, 246],\n", - " [10134, 10533, 7323, 10135, 7633, 10247, 10246, 10142, 9718, 10522],\n", - " [4088, 9823, 246, 387, 3039, 691, 1917, 2241, 870, 3344],\n", - " [2862, 2630, 2660, 6588, 667, 3500, 2932, 2512, 2611, 2608],\n", - " [2881, 2882, 2880, 2878, 2945, 8172, 2645, 2647, 3394, 7456],\n", - " [2899, 2898, 6497, 6537, 6498, 2525, 2981, 6984, 5530, 3325],\n", - " [2903, 2905, 5698, 2910, 7096, 8547, 6333, 6139, 3521, 2519],\n", - " [6288, 6291, 6664, 6752, 6290, 7025, 8117, 7466, 6777, 5770],\n", - " [3100, 2693, 2866, 5933, 3035, 8981, 10071, 8706, 2005, 2622],\n", - " [2921, 2917, 2897, 2933, 8407, 2968, 2823, 3243, 2916, 2731],\n", - " [79, 2534, 9626, 8040, 2166, 10369, 246, 5263, 7050, 9038],\n", - " [3200, 9285, 7475, 9303, 9298, 9278, 9344, 6837, 9364, 10159],\n", - " [3192, 5779, 675, 5953, 7410, 7409, 2776, 7924, 2350, 2838],\n", - " [2946, 2950, 3583, 151, 2823, 3075, 152, 2944, 3601, 3519],\n", - " [4515, 239, 3148, 3149, 1576, 8724, 4517, 5453, 240, 1662],\n", - " [2941, 2992, 2940, 79, 2990, 2991, 2955, 2956, 2165, 3836],\n", - " [2963, 2961, 2962, 2959, 2960, 2964, 3135, 2980, 9918, 2726],\n", - " [3301, 2815, 2169, 2977, 2759, 2992, 6802, 2165, 2957, 2993],\n", - " [2993, 2973, 2970, 2978, 3546, 3493, 31, 3967, 3166, 3164],\n", - " [8081, 2991, 2990, 9840, 2993, 2956, 2941, 2992, 2955, 8142],\n", - " [2956, 2941, 2990, 2992, 2797, 2955, 7602, 5235, 6893, 2991],\n", - " [9279, 3244, 7112, 3602, 4001, 2543, 2739, 3324, 240, 2801],\n", - " [7359, 7848, 3162, 10382, 6608, 3218, 9943, 6115, 2303, 3892],\n", - " [7271, 10248, 10246, 10247, 10316, 8037, 10420, 669, 10581, 10533],\n", - " [3030, 3032, 10604, 5012, 3075, 1740, 7526, 3005, 3043, 10307],\n", - " [2115, 3047, 2116, 3046, 2514, 2117, 641, 2214, 9475, 2118],\n", - " [641, 2115, 690, 3046, 2214, 2236, 2514, 4172, 640, 3991],\n", - " [3058, 4155, 4088, 4138, 4006, 4137, 4172, 3053, 4149, 9970],\n", - " [3066, 2497, 3065, 2906, 3064, 3068, 8795, 829, 3759, 873],\n", - " [10130, 10434, 10375, 9711, 9948, 10248, 8051, 7322, 2693, 7291],\n", - " [3054, 3052, 3050, 9039, 1471, 2622, 3042, 128, 8790, 8457],\n", - " [3063, 3065, 2583, 3038, 8093, 8325, 6390, 5769, 6608, 2215],\n", - " [3064, 3076, 3067, 3063, 3068, 8439, 873, 3055, 3059, 3621],\n", - " [1471, 86, 3052, 640, 1515, 2236, 9039, 2500, 4172, 2622],\n", - " [640, 1515, 1471, 86, 4172, 205, 8381, 758, 1514, 4006],\n", - " [2980, 9350, 2617, 7689, 7264, 7332, 2505, 2821, 3515, 7343],\n", - " [2503, 753, 2236, 690, 5549, 2582, 667, 2512, 677, 2534],\n", - " [3106, 3087, 5952, 6140, 5950, 5955, 5860, 5839, 5951, 5966],\n", - " [1805, 5848, 3086, 6146, 3091, 5966, 3340, 5950, 7071, 5849],\n", - " [3112, 3115, 3113, 871, 3084, 8360, 3016, 1685, 8396, 8422],\n", - " [3118, 3117, 3122, 3123, 6015, 6016, 6102, 3121, 6238, 3143],\n", - " [3119, 6148, 6147, 3394, 6237, 7734, 6664, 3111, 3393, 945],\n", - " [6272, 5883, 5807, 3142, 6159, 3141, 5944, 6264, 5987, 6077],\n", - " [3117, 3118, 6015, 3122, 3123, 6016, 6102, 3130, 3470, 6216],\n", - " [3135, 5925, 3137, 6042, 3136, 6043, 3142, 3141, 3145, 6004],\n", - " [2660, 2512, 2608, 713, 387, 8274, 7016, 2611, 901, 7609],\n", - " [4109, 4222, 6109, 10340, 4715, 9063, 782, 8923, 8919, 3192],\n", - " [1536, 6016, 5947, 7888, 6245, 2877, 755, 2227, 6146, 7923],\n", - " [2104, 2733, 1635, 319, 2625, 2806, 7526, 436, 2762, 3918],\n", - " [3161, 3974, 3160, 3667, 8142, 7986, 3034, 3033, 7280, 2677],\n", - " [5514, 8407, 7632, 8506, 2911, 9088, 9038, 7582, 9349, 10504],\n", - " [79, 9626, 119, 160, 8040, 2260, 246, 206, 4073, 4011],\n", - " [79, 160, 9626, 1203, 119, 206, 246, 8040, 205, 86],\n", - " [3180, 3182, 3181, 3184, 136, 108, 3210, 2931, 2118, 3046],\n", - " [79, 160, 9626, 8040, 119, 4011, 1203, 2260, 206, 246],\n", - " [79, 9626, 160, 8040, 119, 4011, 4073, 206, 690, 1203],\n", - " [3212, 3367, 3203, 2935, 7097, 3209, 3197, 3202, 2536, 3342],\n", - " [3207, 3204, 3206, 2351, 3098, 3209, 3203, 3202, 3213, 233],\n", - " [870, 3210, 2504, 2351, 1805, 3209, 5454, 679, 108, 2337],\n", - " [86, 1471, 640, 8335, 206, 7630, 4172, 1255, 3991, 2337],\n", - " [79, 160, 8040, 2534, 9626, 1203, 1805, 246, 704, 3687],\n", - " [79, 160, 9626, 119, 690, 641, 192, 246, 2684, 1506],\n", - " [2503, 2236, 1805, 107, 1471, 3098, 753, 1514, 2502, 6066],\n", - " [2237, 9991, 2236, 9254, 1805, 79, 2230, 6608, 4073, 3098],\n", - " [2796, 3192, 3278, 3195, 3261, 3165, 3621, 3166, 672, 3196],\n", - " [3193, 3192, 5831, 3346, 3186, 8731, 2534, 3195, 7277, 8919],\n", - " [2173, 3165, 3422, 2796, 2547, 5263, 3430, 3194, 8507, 84],\n", - " [2502, 2503, 677, 679, 2504, 1805, 2716, 2715, 2236, 7609],\n", - " [3192, 3165, 3166, 2796, 3278, 3194, 3347, 3261, 3277, 3323],\n", - " [2796, 3278, 3277, 6313, 3192, 3195, 3166, 3165, 3196, 3261],\n", - " [3173, 3165, 3325, 2348, 2660, 3637, 108, 230, 4135, 3200],\n", - " [3173, 3165, 4135, 230, 3325, 2611, 3337, 2348, 2660, 3588],\n", - " [6617, 6616, 6590, 3629, 6252, 6417, 6115, 10536, 7268, 5777],\n", - " [7398, 10051, 8505, 5081, 10103, 5598, 8695, 10333, 9900, 5809],\n", - " [3314, 5785, 6112, 2889, 6554, 3777, 5777, 50, 5786, 7124],\n", - " [3192, 2796, 3261, 3278, 677, 3166, 3165, 3195, 3193, 3184],\n", - " [3173, 3165, 3325, 2348, 2660, 3637, 108, 230, 4135, 3200],\n", - " [3165, 3347, 2534, 2611, 3422, 3192, 3171, 901, 3546, 2173],\n", - " [2873, 3098, 2236, 206, 2715, 2503, 5549, 679, 3207, 677],\n", - " [3192, 3195, 883, 677, 3422, 2173, 3196, 8508, 3165, 2776],\n", - " [3173, 3165, 3588, 3325, 3172, 230, 2681, 3546, 3200, 3476],\n", - " [3192, 2796, 3165, 3278, 3261, 677, 3166, 2236, 3348, 3638],\n", - " [3260, 3583, 3537, 3275, 657, 3515, 3774, 3265, 3904, 3327],\n", - " [3173, 3165, 230, 3325, 2681, 108, 1663, 2348, 2660, 3337],\n", - " [3325, 3637, 2236, 7023, 3687, 7017, 7016, 230, 2503, 3263],\n", - " [8032, 8673, 8483, 7741, 8516, 6893, 8591, 8672, 8432, 9094],\n", - " [79, 9626, 8040, 160, 119, 2260, 1805, 1203, 4073, 246],\n", - " [3403, 3412, 3417, 3420, 3419, 3418, 3404, 3409, 3458, 3405],\n", - " [3411, 3454, 3559, 3401, 10327, 5440, 3444, 8828, 3572, 3489],\n", - " [3402, 3413, 3405, 3404, 3449, 3416, 3457, 3417, 3966, 3403],\n", - " [3154, 4126, 7114, 2236, 7115, 2630, 677, 3602, 3325, 3606],\n", - " [3436, 3437, 3035, 5252, 3887, 3667, 3756, 5739, 3891, 5202],\n", - " [3421, 5337, 5649, 2236, 3887, 3285, 3500, 5650, 5769, 3273],\n", - " [79, 160, 9626, 8040, 2260, 4011, 119, 1203, 205, 206],\n", - " [3470, 3435, 901, 1663, 3497, 156, 3175, 128, 246, 3172],\n", - " [1514, 8384, 3447, 8521, 165, 5917, 3216, 160, 504, 211],\n", - " [3450, 3958, 3403, 3889, 3572, 5256, 3890, 3420, 3607, 3888],\n", - " [3451, 3978, 3563, 3890, 3409, 6851, 7859, 3412, 2784, 3426],\n", - " [3464, 8025, 6250, 10082, 3463, 3462, 6132, 5148, 6216, 2958],\n", - " [5100, 3972, 3433, 3539, 8074, 3667, 3245, 3260, 8334, 3276],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3916, 3885, 3429, 3884, 3403, 5439, 2173, 3498, 5384, 870],\n", - " [3470, 3493, 3548, 3511, 3549, 3492, 3530, 3544, 3539, 3542],\n", - " [3470, 3497, 3544, 3493, 901, 3476, 3171, 3548, 7106, 2197],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3599, 870, 2173, 3498, 8699, 3429, 3430, 3507, 8562, 3165],\n", - " [3980, 3353, 7141, 2682, 5935, 5771, 6839, 7173, 7135, 4098],\n", - " [3470, 3544, 3497, 3476, 3493, 3171, 3671, 3546, 901, 2197],\n", - " [5470, 3529, 7253, 10175, 3527, 7556, 2843, 7770, 3658, 2303],\n", - " [7770, 7596, 5868, 7699, 7549, 6235, 7731, 6762, 7794, 7612],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3519, 3516, 3517, 3515, 2946, 3342, 3583, 3774, 3590, 3752],\n", - " [3470, 3497, 901, 3493, 3171, 3544, 7106, 3476, 3501, 3172],\n", - " [3916, 5439, 3429, 3498, 3885, 3403, 3884, 3430, 3599, 8696],\n", - " [3474, 6022, 3478, 6062, 8087, 2508, 3298, 677, 6012, 3157],\n", - " [3470, 3497, 3493, 7106, 3544, 901, 3991, 3501, 3171, 156],\n", - " [3476, 3546, 3534, 3165, 3593, 3482, 3598, 3172, 3528, 3526],\n", - " [2173, 3165, 3476, 3422, 3648, 5263, 870, 3545, 84, 2301],\n", - " [7360, 9728, 7201, 631, 7202, 6067, 934, 6012, 232, 7312],\n", - " [3403, 3884, 3885, 3916, 5439, 3498, 3412, 3566, 3886, 3418],\n", - " [5463, 3292, 3571, 5462, 5439, 5472, 5505, 3566, 5441, 5458],\n", - " [8141, 5414, 6035, 7429, 5678, 5971, 7511, 6430, 6296, 6628],\n", - " [5427, 3470, 3513, 4116, 3586, 3175, 3548, 171, 3482, 6584],\n", - " [3470, 3497, 3476, 3548, 3544, 3546, 3172, 3493, 3171, 901],\n", - " [3476, 3165, 2236, 3172, 3546, 230, 901, 3422, 2776, 3497],\n", - " [3535, 7609, 677, 387, 7894, 679, 8274, 870, 7489, 2213],\n", - " [3474, 6022, 3478, 6062, 8087, 2508, 3298, 677, 6012, 3157],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3435, 2342, 1663, 2236, 3173, 3497, 3172, 387, 3546, 230],\n", - " [3415, 3416, 3413, 3402, 3405, 5258, 3449, 3572, 3457, 3417],\n", - " [3634, 3627, 3630, 3636, 3625, 556, 3238, 3521, 3776, 3236],\n", - " [3638, 3803, 3671, 3166, 2236, 2780, 4141, 3348, 3807, 3670],\n", - " [3648, 3647, 3545, 3806, 3991, 3543, 3690, 230, 5270, 3640],\n", - " [3587, 3658, 3991, 2236, 3990, 4709, 4203, 2435, 5769, 119],\n", - " [3667, 3779, 3974, 3973, 3242, 3368, 3891, 3865, 3864, 5625],\n", - " [3638, 3803, 3671, 3166, 3348, 3670, 3807, 2236, 2780, 3887],\n", - " [3782, 3426, 3832, 3686, 3762, 4111, 5917, 5246, 3872, 5245],\n", - " [3691, 3671, 3787, 3762, 3647, 3663, 5294, 3591, 230, 3652],\n", - " [3637, 3687, 2236, 690, 753, 3165, 205, 3263, 2351, 4203],\n", - " [3695, 4720, 5802, 4149, 4736, 3937, 3046, 4015, 6105, 2214],\n", - " [3702, 3701, 3699, 3697, 8122, 5646, 5811, 3707, 5644, 3816],\n", - " [3713, 3725, 3711, 3721, 3726, 3717, 3712, 3723, 3729, 3724],\n", - " [3743, 3744, 3733, 3738, 3748, 3734, 3735, 3737, 3845, 3739],\n", - " [3743, 3744, 3733, 3738, 3748, 3734, 3735, 3737, 3845, 3739],\n", - " [3644, 2236, 3165, 3543, 3637, 3690, 3687, 205, 5263, 2681],\n", - " [3638, 3803, 3671, 3166, 3348, 3670, 3807, 2236, 2780, 3887],\n", - " [3644, 2236, 3165, 3543, 3637, 3690, 3687, 205, 5263, 2681],\n", - " [3762, 3766, 3759, 3761, 3767, 3882, 3654, 3652, 3763, 3663],\n", - " [3325, 3637, 7023, 7017, 2236, 3687, 690, 6984, 7016, 2768],\n", - " [3637, 3687, 2236, 690, 753, 3165, 205, 3263, 2351, 4203],\n", - " [3647, 3545, 3648, 3991, 3543, 3687, 3759, 79, 3690, 2896],\n", - " [3775, 3776, 3358, 3774, 3037, 4122, 3590, 6162, 2778, 5114],\n", - " [3671, 3670, 2780, 2236, 3638, 870, 677, 3497, 4067, 8274],\n", - " [3671, 3670, 2780, 2236, 3638, 870, 677, 3497, 4067, 8274],\n", - " [3686, 3782, 3425, 3904, 2237, 3858, 3906, 5917, 3671, 3892],\n", - " [3545, 3647, 6546, 3658, 3648, 2896, 3991, 5826, 842, 5270],\n", - " [3637, 3687, 2236, 753, 690, 3325, 3263, 79, 4203, 160],\n", - " [3638, 3803, 3671, 2780, 3670, 3759, 870, 2236, 3762, 677],\n", - " [3638, 3803, 3671, 3166, 2236, 2780, 4141, 3348, 3807, 3670],\n", - " [3691, 3671, 3787, 3762, 3647, 3663, 5294, 3591, 230, 3652],\n", - " [3671, 3670, 2780, 2236, 3638, 870, 677, 3497, 4067, 8274],\n", - " [3649, 3832, 3801, 3534, 2681, 3754, 3808, 205, 3426, 4078],\n", - " [3667, 3779, 3974, 3973, 3242, 3368, 3891, 3865, 3864, 5625],\n", - " [107, 3874, 3835, 3763, 4720, 3762, 5252, 1471, 3654, 3754],\n", - " [3587, 3647, 3545, 3991, 3990, 3690, 3659, 3648, 3658, 4203],\n", - " [3643, 3348, 3836, 9567, 6893, 3347, 6905, 7174, 3690, 3987],\n", - " [3670, 3671, 2236, 2780, 3638, 870, 3497, 8274, 677, 3803],\n", - " [3847, 3849, 3848, 3851, 3853, 3862, 3758, 3863, 3861, 3345],\n", - " [3847, 3849, 3848, 3851, 3853, 3862, 3758, 3863, 3861, 3345],\n", - " [3637, 3687, 2236, 690, 753, 3165, 205, 3263, 2351, 4203],\n", - " [3644, 2236, 3165, 3543, 3637, 3690, 3687, 205, 5263, 2681],\n", - " [3667, 3779, 3974, 3864, 3242, 2214, 3865, 3685, 3509, 3233],\n", - " [3637, 3687, 2236, 690, 753, 3165, 205, 3263, 2351, 4203],\n", - " [3403, 3884, 3885, 3916, 5439, 3498, 3412, 3566, 3886, 3418],\n", - " [3885, 3886, 3861, 3884, 3450, 3958, 3605, 3758, 4107, 3665],\n", - " [3436, 3437, 3035, 5252, 3887, 3667, 3756, 5739, 3891, 5202],\n", - " [3427, 3426, 5245, 3412, 2236, 3454, 3405, 3402, 3916, 5246],\n", - " [3424, 5174, 3104, 3551, 5700, 2340, 2236, 677, 3428, 5428],\n", - " [3425, 3322, 4192, 3796, 5666, 3550, 3551, 3664, 3659, 3665],\n", - " [3320, 3606, 3319, 3353, 2630, 677, 2236, 3154, 3157, 7678],\n", - " [3909, 3324, 3893, 3913, 3920, 3914, 3912, 3917, 5266, 3898],\n", - " [3925, 3932, 3951, 3953, 3787, 3924, 3933, 3952, 3955, 3943],\n", - " [3925, 3933, 3924, 3953, 3947, 3946, 3955, 5244, 3951, 3932],\n", - " [3934, 3938, 3941, 3931, 3924, 3949, 3946, 3947, 2987, 6496],\n", - " [3955, 3953, 3951, 3952, 3949, 3933, 3925, 3947, 3932, 3787],\n", - " [3953, 3955, 3952, 3951, 3949, 3933, 3947, 3925, 3932, 3787],\n", - " [2955, 3901, 3173, 5235, 5259, 4131, 79, 9843, 3637, 2629],\n", - " [3422, 5384, 2173, 3165, 5379, 3423, 3430, 3315, 3194, 883],\n", - " [2917, 6883, 6830, 2591, 2611, 2737, 2592, 6973, 5762, 2885],\n", - " [79, 9626, 160, 119, 8040, 1203, 2955, 2260, 205, 192],\n", - " [3402, 3413, 3405, 3404, 3449, 3416, 3457, 3417, 3966, 3403],\n", - " [3403, 3412, 3417, 3420, 3419, 3418, 3404, 3409, 3458, 3405],\n", - " [3402, 3413, 3405, 3404, 3449, 3416, 3457, 3417, 3966, 3403],\n", - " [3403, 3412, 3417, 3420, 3419, 3418, 3404, 3409, 3458, 3405],\n", - " [3403, 3412, 3417, 3420, 3419, 3418, 3404, 3409, 3458, 3405],\n", - " [3154, 4126, 7114, 2236, 7115, 2630, 677, 3602, 3325, 3606],\n", - " [2955, 3901, 3173, 5235, 5259, 4131, 79, 9843, 3637, 2629],\n", - " [3969, 3035, 6035, 5657, 3869, 5648, 5387, 3036, 5414, 3864],\n", - " [6025, 3887, 5275, 6024, 6483, 3323, 5739, 2623, 3602, 2630],\n", - " [3402, 3413, 3405, 3404, 3449, 3416, 3457, 3417, 3966, 3403],\n", - " [3402, 3413, 3405, 3404, 3449, 3416, 3457, 3417, 3966, 3403],\n", - " [2955, 5235, 3901, 5259, 4158, 4131, 2629, 9840, 2956, 8021],\n", - " [4968, 3157, 3084, 3101, 3320, 3226, 698, 624, 8437, 3215],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 667],\n", - " [4001, 5057, 4393, 4002, 5058, 4554, 5059, 4555, 4607, 6064],\n", - " [4011, 2662, 4073, 4155, 4012, 4013, 4003, 4533, 4172, 4203],\n", - " [4008, 4007, 4009, 5030, 4251, 4014, 4748, 4995, 4029, 4977],\n", - " [4001, 5057, 5058, 4002, 4003, 4653, 4607, 4554, 5769, 3353],\n", - " [3989, 3990, 4038, 2367, 4028, 5041, 4027, 4738, 4177, 4178],\n", - " [4019, 4018, 4154, 4376, 4016, 4101, 4432, 4511, 4607, 4210],\n", - " [4076, 4136, 4748, 4214, 4617, 4703, 4035, 4747, 4746, 4204],\n", - " [4019, 4018, 4154, 4376, 4016, 4101, 4432, 4511, 4607, 4210],\n", - " [4030, 4015, 4348, 4149, 5130, 4736, 4208, 4298, 4190, 5007],\n", - " [4011, 4012, 4073, 4202, 4013, 205, 4138, 4155, 181, 206],\n", - " [4043, 5156, 4015, 4720, 4736, 4298, 4251, 5006, 4611, 4071],\n", - " [79, 160, 4011, 9626, 119, 8040, 206, 1506, 2260, 4073],\n", - " [4052, 4055, 4011, 3307, 4205, 4048, 4203, 4719, 4066, 241],\n", - " [4029, 4165, 5132, 4426, 4055, 517, 2003, 5131, 4159, 4009],\n", - " [3992, 677, 2738, 697, 7414, 5950, 7957, 6091, 8644, 3993],\n", - " [3999, 3998, 2629, 4542, 3325, 677, 2236, 690, 4540, 3997],\n", - " [3998, 4232, 3210, 3999, 2611, 4000, 4714, 4586, 690, 4600],\n", - " [3997, 3210, 3999, 3998, 677, 4067, 4540, 2504, 136, 3165],\n", - " [4086, 4085, 5058, 5154, 4648, 3050, 4029, 6529, 4227, 4207],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [4087, 4711, 4426, 5095, 4644, 4751, 4054, 4791, 3816, 10528],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3997, 3210, 3999, 3998, 4067, 4540, 2173, 677, 2504, 129],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 690, 4073],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 154, 3320, 79, 4968],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3996, 8381, 4710, 3574, 1515, 9394, 2435, 9354, 4989, 9416],\n", - " [4097, 5077, 3999, 4000, 4232, 6588, 2660, 753, 3998, 5080],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4987, 5032, 4155, 4138, 2884, 4073, 4012, 4742, 4202, 4011],\n", - " [4102, 4107, 4021, 4207, 4752, 4762, 4811, 4089, 4777, 4776],\n", - " [4102, 4107, 4021, 4207, 4752, 4762, 4811, 4089, 4777, 4776],\n", - " [79, 160, 9626, 8040, 206, 1203, 119, 4011, 2260, 4073],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [3997, 3210, 3998, 3999, 4067, 2173, 129, 2504, 677, 4540],\n", - " [4000, 3210, 2582, 205, 3998, 4135, 2236, 3637, 4714, 4681],\n", - " [4013, 5058, 5059, 4002, 4107, 4612, 4393, 3990, 4285, 4648],\n", - " [5053, 4438, 2926, 4155, 2693, 5013, 9905, 3100, 6161, 3508],\n", - " [4645, 7871, 4016, 10008, 3248, 4015, 8383, 8566, 4030, 4611],\n", - " [1471, 9992, 667, 3584, 2236, 2342, 9039, 2802, 8021, 3046],\n", - " [4149, 4266, 4270, 4273, 4545, 4694, 4289, 4267, 4271, 4287],\n", - " [4150, 4151, 4230, 3990, 4158, 4177, 4210, 3989, 4202, 4212],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [3991, 4172, 4011, 4149, 4006, 5149, 4614, 8021, 2715, 86],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 667],\n", - " [4184, 4164, 4158, 4183, 4431, 3990, 4018, 4101, 4212, 4016],\n", - " [4724, 4298, 4666, 4665, 3605, 4548, 5025, 2735, 5279, 9672],\n", - " [4173, 830, 828, 5133, 4054, 4582, 5131, 4029, 8058, 8498],\n", - " [5135, 5134, 4738, 4739, 4099, 5158, 3989, 4182, 4032, 4181],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [4073, 4203, 4012, 4011, 2503, 4138, 4155, 1805, 4013, 4208],\n", - " [4012, 4202, 4298, 4138, 4156, 4073, 5046, 4155, 668, 4013],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3992, 677, 2738, 697, 5950, 7414, 7957, 6091, 3993, 7472],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [4108, 4180, 4179, 5154, 5155, 4525, 4807, 4029, 4026, 3984],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [4184, 4183, 4164, 3990, 4431, 4158, 3989, 4038, 4212, 4210],\n", - " [3210, 5144, 5120, 4540, 3997, 4632, 5826, 9482, 5809, 677],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [4000, 2503, 10178, 4994, 205, 5866, 753, 2582, 4681, 5850],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [4011, 4203, 4073, 4202, 4055, 4052, 4003, 4159, 4048, 4049],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [3098, 1805, 2503, 2873, 6929, 4710, 2236, 8509, 9680, 8653],\n", - " [3997, 3210, 3999, 3998, 4067, 677, 4540, 2504, 129, 2173],\n", - " [3988, 4004, 4074, 1539, 3993, 4535, 3424, 3320, 5527, 4968],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [107, 4709, 4708, 4716, 5143, 4141, 3574, 2435, 4683, 4088],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 690, 4073],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [79, 4011, 9626, 160, 4073, 8040, 119, 206, 2260, 246],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [4000, 3210, 4135, 2582, 4714, 2236, 3637, 205, 4681, 4097],\n", - " [4181, 4212, 4182, 4210, 4169, 4177, 4178, 4738, 4170, 5134],\n", - " [3990, 3989, 4149, 4038, 2367, 181, 4738, 4739, 4183, 4184],\n", - " [2629, 2236, 4098, 3999, 2503, 7106, 3671, 4067, 642, 3670],\n", - " [4227, 5058, 4001, 3340, 4015, 4648, 4607, 4554, 3600, 4612],\n", - " [3067, 9188, 4720, 4234, 5071, 1515, 8894, 8440, 9183, 9979],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [4026, 4650, 4241, 4580, 4031, 4607, 4020, 4136, 4076, 4617],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [3098, 6929, 2873, 8509, 5054, 1805, 2236, 2503, 3207, 4710],\n", - " [4156, 4298, 4012, 4255, 4258, 4317, 4256, 4316, 4315, 4399],\n", - " [4189, 5014, 4532, 4531, 4528, 4529, 5063, 4259, 4527, 4261],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [4189, 5014, 4532, 4531, 4528, 4529, 5063, 4259, 4527, 4261],\n", - " [4189, 5014, 4532, 4531, 4528, 4529, 5063, 4259, 4527, 4261],\n", - " [4280, 4502, 4501, 4745, 4541, 4706, 4990, 205, 4537, 7114],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4253, 2629, 4067, 677, 230, 3098, 1781, 5159, 7260, 6560],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4399, 4320, 4318, 4323, 4322, 4321, 5128, 4012, 4072, 4570],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4254, 4741, 2571, 5057, 4248, 4735, 3343, 4705, 4521, 4502],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [4254, 4741, 2571, 5057, 4248, 4735, 3343, 4705, 4521, 4502],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [4149, 4266, 4270, 4273, 4545, 4694, 4289, 4267, 4271, 4287],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4376, 4377, 4211, 4367, 4374, 4364, 4368, 4363, 4366, 4370],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [4149, 4266, 4270, 4273, 4545, 4694, 4289, 4267, 4271, 4287],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4280, 4745, 4502, 205, 4706, 4990, 4501, 214, 4537, 4632],\n", - " [4149, 3990, 3989, 4162, 4208, 181, 4738, 4739, 5135, 5134],\n", - " [4753, 4400, 5086, 4169, 4181, 4189, 4182, 5066, 4099, 5064],\n", - " [4254, 4741, 2571, 5057, 3098, 4280, 4248, 181, 4735, 7107],\n", - " [4149, 4266, 4270, 4273, 4545, 4694, 4289, 4267, 4271, 4287],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [4280, 4745, 4706, 4502, 4990, 4232, 4537, 5017, 4501, 4993],\n", - " [4254, 4741, 2571, 5057, 4248, 4735, 3343, 4705, 4521, 4502],\n", - " [4442, 4393, 4001, 2685, 4555, 4075, 4013, 4607, 4140, 4002],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4149, 4266, 4270, 4273, 4545, 4694, 4289, 4267, 4271, 4287],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [4205, 4024, 4202, 4012, 4206, 3929, 5011, 5574, 4095, 4298],\n", - " [4298, 4015, 4156, 4611, 4043, 4399, 4012, 4317, 5046, 4258],\n", - " [5064, 5066, 4259, 5065, 5063, 4189, 4239, 4238, 5014, 4454],\n", - " [4002, 4016, 4393, 4648, 5058, 4555, 4554, 4207, 4612, 4607],\n", - " [4377, 4384, 4509, 4376, 4512, 4511, 4397, 4396, 4398, 4394],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 690, 4073],\n", - " [79, 9626, 160, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [3992, 677, 2738, 697, 7414, 5950, 7957, 6091, 8644, 3993],\n", - " [4011, 4073, 79, 4155, 4138, 4203, 2684, 4013, 4202, 4012],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 2955],\n", - " [107, 2503, 6066, 2236, 4716, 4141, 3574, 2567, 4708, 5185],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [79, 160, 9626, 8040, 206, 4011, 119, 1203, 2260, 4073],\n", - " [5058, 4002, 4003, 4648, 4001, 4393, 5057, 4015, 5059, 4013],\n", - " [5057, 4001, 5058, 5059, 4002, 4393, 4074, 4165, 2236, 5199],\n", - " [3210, 4540, 2504, 4000, 4632, 3998, 205, 4165, 677, 5120],\n", - " [3098, 1805, 6929, 2503, 2236, 8509, 2873, 677, 6066, 2715],\n", - " [3999, 4542, 2629, 3998, 3325, 677, 2236, 4540, 3997, 690],\n", - " [79, 160, 9626, 8040, 206, 4011, 119, 1203, 2260, 4073],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [2629, 4253, 2236, 2955, 2503, 7106, 5833, 5540, 4067, 3098],\n", - " [2629, 2236, 4067, 3671, 3999, 4098, 642, 677, 3670, 7124],\n", - " [4253, 2629, 4067, 677, 230, 3098, 1781, 5159, 7260, 6560],\n", - " [4563, 4560, 4565, 4561, 4566, 4567, 4564, 4562, 4377, 4376],\n", - " [79, 160, 9626, 119, 4011, 8040, 206, 2260, 205, 246],\n", - " [4438, 4001, 3039, 4155, 268, 10198, 6839, 4206, 4205, 4620],\n", - " [4439, 4441, 4440, 4158, 4503, 4195, 4194, 4196, 4384, 4369],\n", - " [4177, 4178, 2367, 4494, 4401, 4646, 4572, 4524, 4571, 4576],\n", - " [4177, 4178, 2367, 4494, 4401, 4646, 4572, 4524, 4571, 4576],\n", - " [4614, 4618, 4579, 4385, 4006, 4149, 5150, 4031, 4172, 4021],\n", - " [3098, 2503, 1805, 2873, 2236, 6929, 2715, 677, 2502, 3207],\n", - " [3999, 2629, 4542, 3998, 3325, 677, 4540, 3997, 2236, 690],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [3992, 677, 2738, 697, 5950, 7414, 7957, 6091, 3993, 7472],\n", - " [3990, 3991, 3989, 4149, 4002, 4029, 4178, 4170, 5058, 4169],\n", - " [4585, 4632, 5016, 4929, 5004, 5001, 471, 4933, 4600, 4998],\n", - " [4142, 4588, 4148, 4631, 4629, 4637, 4146, 4684, 4682, 4636],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [4073, 4203, 3990, 4138, 4011, 4155, 4012, 4208, 4202, 1805],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4191, 4720, 5044, 4547, 5550, 2214, 4721, 3298, 3511, 5732],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [3210, 5120, 4704, 4540, 5144, 4165, 4632, 3998, 4541, 4543],\n", - " [4092, 4088, 4013, 4193, 4387, 181, 5104, 4074, 4155, 697],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [4259, 4454, 5064, 5066, 5065, 5063, 4238, 4239, 4178, 4189],\n", - " [3996, 8381, 4710, 3574, 1515, 9394, 2435, 9354, 4989, 9416],\n", - " [4615, 4106, 4616, 5013, 6341, 2214, 9544, 2876, 3289, 6021],\n", - " [4155, 5032, 4138, 4533, 4987, 4073, 2884, 4719, 4015, 4011],\n", - " [4298, 4156, 4230, 5046, 4012, 4258, 4255, 4317, 4256, 4316],\n", - " [3990, 3989, 4738, 4739, 4038, 5135, 4177, 5134, 4169, 4042],\n", - " [4102, 4107, 4021, 4207, 4752, 4762, 4811, 4089, 4777, 4776],\n", - " [4626, 3866, 3627, 5186, 3634, 10143, 4740, 5525, 5739, 9512],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [4633, 4142, 4148, 4629, 4603, 2189, 4146, 4114, 4631, 4636],\n", - " [4588, 4631, 4591, 4633, 4148, 4684, 4142, 4717, 4636, 4637],\n", - " [4638, 4107, 4193, 4089, 4639, 4677, 4393, 4207, 4209, 5117],\n", - " [4026, 4650, 4241, 4580, 4031, 4607, 4020, 4136, 4076, 4617],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [4653, 7141, 7186, 4098, 4001, 7124, 4620, 7135, 5668, 6199],\n", - " [3992, 677, 2738, 697, 7414, 5950, 7957, 6091, 8644, 3993],\n", - " [4177, 4178, 2367, 4494, 4401, 4646, 4572, 4524, 4571, 4576],\n", - " [4177, 4178, 2367, 4494, 4401, 4646, 4572, 4524, 4571, 4576],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [3997, 3210, 3998, 3999, 4067, 677, 2504, 108, 129, 2173],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3997, 3210, 3999, 3998, 677, 4067, 2504, 4540, 2173, 3264],\n", - " [3210, 4000, 5120, 4632, 5121, 5144, 205, 3998, 3997, 4704],\n", - " [79, 9626, 160, 8040, 119, 4011, 2260, 246, 205, 206],\n", - " [107, 2503, 1805, 6066, 2236, 3098, 4141, 4716, 4708, 679],\n", - " [79, 160, 9626, 8040, 206, 4011, 119, 1203, 2260, 4073],\n", - " [4675, 4674, 4672, 3961, 4430, 5279, 3322, 5171, 3770, 4061],\n", - " [4675, 4674, 4672, 3961, 4430, 5279, 3322, 5171, 3770, 4061],\n", - " [4675, 4674, 4672, 3961, 4430, 5279, 3322, 5171, 3770, 4061],\n", - " [3098, 6929, 1805, 2503, 2236, 2873, 677, 8509, 2715, 6066],\n", - " [4688, 4080, 9441, 6066, 4948, 4822, 9443, 4979, 4869, 5185],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 2955],\n", - " [4696, 4697, 4695, 4698, 4702, 4030, 4659, 4424, 4660, 5049],\n", - " [4700, 4658, 4525, 4526, 4026, 4607, 2679, 599, 4020, 4650],\n", - " [4026, 4172, 5058, 4607, 4088, 4074, 3039, 5104, 3990, 208],\n", - " [4165, 4526, 5132, 4525, 4001, 4607, 4094, 4580, 2679, 4031],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3210, 5809, 3574, 2590, 4092, 4165, 3296, 9421, 4090, 2567],\n", - " [2629, 2236, 3999, 4098, 4067, 7106, 3671, 642, 3670, 7124],\n", - " [4614, 4006, 4385, 4618, 4149, 4579, 5150, 4031, 4011, 4021],\n", - " [4720, 5156, 4736, 4298, 4043, 5006, 4071, 4547, 4015, 4202],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4337, 4336, 4377, 4376, 4563, 4567, 4565, 4564, 4561, 4560],\n", - " [4254, 4741, 2571, 3098, 5057, 4280, 4248, 4735, 181, 208],\n", - " [4280, 205, 4706, 4502, 4745, 4232, 4501, 214, 5017, 7106],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [5104, 4088, 4149, 181, 4678, 2684, 4165, 4092, 4172, 4736],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3996, 8381, 3574, 4710, 1515, 2435, 181, 4172, 3615, 9394],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4012, 4202, 4298, 4138, 4156, 4073, 5046, 4155, 668, 4013],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [4000, 3210, 2582, 2236, 4714, 205, 3637, 4681, 4135, 8435],\n", - " [4317, 4449, 4447, 4298, 4448, 4450, 4451, 4015, 4230, 4399],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [3210, 3997, 4540, 5144, 3998, 5120, 4632, 677, 2504, 4165],\n", - " [3999, 2629, 3998, 4542, 3997, 677, 3325, 2236, 690, 4540],\n", - " [4015, 4043, 4298, 4611, 4016, 4448, 4449, 4317, 4447, 4450],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [3999, 4542, 2629, 3998, 3325, 4540, 677, 5057, 2236, 3997],\n", - " [4177, 4178, 2367, 4494, 4401, 4646, 4572, 4524, 4571, 4576],\n", - " [4177, 4494, 4575, 4571, 4573, 4576, 4572, 4401, 4646, 4578],\n", - " [4753, 4400, 4169, 4101, 5086, 4181, 4182, 4614, 4491, 4189],\n", - " [4686, 4080, 4026, 4012, 4152, 206, 5017, 615, 205, 214],\n", - " [4085, 4554, 4086, 5057, 5011, 5058, 4078, 5117, 5255, 5154],\n", - " [3996, 8381, 3574, 4710, 1515, 2435, 181, 4172, 3615, 9394],\n", - " [4087, 4711, 4426, 5095, 4644, 4751, 4054, 4791, 3816, 10528],\n", - " [4807, 4777, 4809, 4818, 5645, 4923, 4763, 4921, 4829, 4766],\n", - " [3322, 4762, 4809, 5562, 4776, 8415, 4812, 4640, 4847, 4903],\n", - " [4770, 4769, 4774, 4756, 4767, 4806, 4796, 4905, 4771, 4772],\n", - " [4782, 4781, 4777, 4779, 4786, 4761, 4809, 4796, 4810, 4924],\n", - " [4795, 4799, 4807, 4764, 4781, 4798, 4794, 4811, 4809, 6002],\n", - " [4771, 4794, 4795, 4767, 4768, 4797, 4792, 4773, 4781, 4769],\n", - " [4782, 4781, 4779, 4777, 4796, 4786, 4795, 4794, 4771, 4792],\n", - " [79, 2260, 4011, 5263, 704, 708, 9626, 2684, 405, 8040],\n", - " [4804, 4856, 4812, 4910, 4777, 4781, 4776, 4864, 4786, 4810],\n", - " [4804, 4856, 4910, 4791, 4908, 4909, 4786, 4812, 4975, 5044],\n", - " [4809, 4777, 4776, 4762, 4810, 3322, 4807, 4377, 4812, 4804],\n", - " [4813, 8296, 6727, 1471, 3647, 2965, 7606, 2622, 3258, 6447],\n", - " [4800, 4906, 4911, 4905, 4881, 4842, 4792, 4786, 4838, 4796],\n", - " [4814, 5977, 4944, 4785, 4789, 5978, 4949, 4880, 4856, 4807],\n", - " [4787, 4831, 4835, 4860, 4842, 4851, 4861, 3757, 4848, 4858],\n", - " [4823, 4815, 4829, 4852, 4916, 4863, 4840, 4831, 2701, 4825],\n", - " [4835, 4817, 4829, 4837, 4840, 4815, 2701, 4830, 4821, 4785],\n", - " [4838, 4842, 4863, 4814, 4962, 4825, 4930, 4817, 4829, 4916],\n", - " [4842, 4843, 4787, 4924, 4833, 4840, 4845, 4923, 4870, 4786],\n", - " [4789, 4785, 4915, 4925, 4965, 4934, 4956, 4916, 4835, 4830],\n", - " [4807, 4858, 4863, 4838, 4829, 4861, 4860, 4916, 4842, 4818],\n", - " [4838, 4842, 4905, 4926, 4818, 4792, 5546, 4863, 4820, 4914],\n", - " [4860, 4861, 4858, 4833, 3757, 4818, 4787, 4848, 4842, 4807],\n", - " [4842, 4851, 4867, 4870, 4815, 4830, 4866, 4843, 4787, 4849],\n", - " [4886, 4892, 4885, 4875, 4872, 4878, 4873, 4882, 4880, 4889],\n", - " [4892, 4885, 4886, 4875, 4872, 4878, 4880, 4876, 4873, 4896],\n", - " [4985, 4892, 4880, 4807, 4965, 4941, 4900, 4896, 4944, 4873],\n", - " [4842, 4915, 4830, 4911, 4835, 4837, 4823, 4838, 4905, 4916],\n", - " [4924, 4823, 4851, 4923, 4917, 4870, 4831, 4928, 4842, 4945],\n", - " [4759, 4149, 4761, 4172, 2701, 4088, 4776, 4847, 4829, 4774],\n", - " [4933, 4929, 4932, 4965, 4950, 4925, 4913, 4963, 4935, 4822],\n", - " [4933, 4929, 4932, 4950, 4956, 4965, 4935, 4930, 4913, 4953],\n", - " [4953, 4952, 4958, 4934, 4826, 4956, 4941, 4957, 4964, 4951],\n", - " [4956, 4953, 4965, 4932, 4933, 4957, 4950, 4952, 4930, 4943],\n", - " [4971, 4975, 4976, 4984, 4986, 4978, 4969, 4972, 4977, 4974],\n", - " [4969, 4971, 4974, 4979, 4972, 4984, 4981, 4978, 4926, 4967],\n", - " [4975, 4977, 4976, 524, 4978, 4974, 4784, 2523, 4985, 4971],\n", - " [4752, 5061, 4207, 4405, 4078, 2715, 5038, 4102, 4017, 5127],\n", - " [4000, 2654, 4165, 1242, 4526, 3191, 509, 7999, 2638, 4004],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [5007, 4208, 4348, 5008, 5157, 5009, 4162, 4190, 5130, 4149],\n", - " [5007, 4208, 4348, 5008, 5157, 5009, 4162, 4190, 5130, 4149],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [4897, 4887, 4806, 4900, 9547, 4766, 4873, 4811, 4880, 4975],\n", - " [5022, 5021, 5023, 4338, 4021, 4102, 3991, 4761, 4207, 4172],\n", - " [5022, 5021, 5023, 4338, 4021, 4102, 3991, 4761, 4207, 4172],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 154, 3320, 79, 4968],\n", - " [2629, 901, 2236, 3999, 642, 2955, 4067, 7124, 2503, 677],\n", - " [4666, 4665, 4548, 5025, 4109, 4715, 6070, 6128, 4724, 4525],\n", - " [1805, 3098, 2503, 679, 2504, 2502, 2236, 870, 677, 206],\n", - " [5030, 5087, 5088, 5104, 4165, 5132, 5131, 5133, 5130, 4031],\n", - " [5030, 5087, 5088, 5104, 4165, 5132, 5131, 5133, 5130, 4031],\n", - " [4149, 4736, 3990, 4172, 4208, 3989, 4006, 5130, 181, 4015],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [5064, 5066, 4259, 5065, 5063, 4189, 4239, 4238, 5014, 4454],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4002, 4535, 4612, 4607, 5058, 5059, 4074, 5060, 4648, 4526],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [5123, 2884, 4155, 4158, 4987, 5032, 4073, 4164, 4196, 4194],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [4076, 4136, 4748, 4214, 4617, 4703, 4035, 4747, 4746, 4204],\n", - " [4013, 4074, 4092, 4155, 181, 4393, 5059, 4107, 4612, 3990],\n", - " [4988, 4138, 4137, 4006, 3052, 4533, 3510, 4205, 3042, 4155],\n", - " [3993, 3992, 4000, 4130, 3428, 2376, 5527, 3574, 188, 642],\n", - " [3997, 3999, 3210, 3998, 4067, 4540, 677, 3165, 2504, 7472],\n", - " [3210, 4632, 4544, 5120, 4657, 4543, 4656, 5144, 4704, 4540],\n", - " [3210, 5144, 5120, 4540, 677, 4165, 4632, 5132, 2504, 3998],\n", - " [4696, 4697, 4695, 4698, 4702, 4030, 4659, 4424, 4660, 5049],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [4643, 4095, 4092, 4192, 4193, 4105, 3629, 4642, 4678, 3940],\n", - " [79, 160, 9626, 119, 4011, 8040, 2260, 206, 1203, 205],\n", - " [5059, 5060, 4648, 5058, 4393, 4555, 4535, 4013, 5057, 4002],\n", - " [5058, 4002, 4003, 4648, 4001, 4393, 5057, 4015, 5059, 4013],\n", - " [5058, 4002, 5057, 4029, 4078, 4001, 4607, 4612, 5131, 5059],\n", - " [5059, 5060, 4013, 4393, 5057, 4648, 4026, 4001, 4555, 5058],\n", - " [3999, 2629, 4542, 3998, 3325, 4540, 677, 2236, 690, 3997],\n", - " [3998, 4232, 3999, 3210, 2611, 2660, 4586, 4600, 690, 6588],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4074, 4013, 5129, 4607, 4165, 4088, 5059, 207, 4092, 4001],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [4074, 4013, 5129, 4607, 4026, 4658, 4031, 181, 4580, 4535],\n", - " [3098, 1805, 6929, 2503, 2236, 8509, 2873, 677, 6066, 2715],\n", - " [4719, 5053, 4438, 4228, 4230, 4155, 4015, 4206, 4596, 4616],\n", - " [2629, 2236, 7106, 4067, 4098, 2503, 7124, 3671, 3999, 7871],\n", - " [4091, 641, 12, 3991, 10250, 146, 2662, 2236, 4011, 6912],\n", - " [4000, 4097, 7021, 5866, 5077, 2567, 2715, 10178, 2348, 753],\n", - " [5082, 8755, 4604, 4095, 136, 9373, 8664, 5567, 1940, 9827],\n", - " [3992, 677, 2738, 697, 5950, 7414, 7957, 6091, 3993, 7472],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 667],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [3292, 6617, 5362, 6877, 8126, 6430, 5935, 5124, 2834, 6096],\n", - " [5059, 5060, 5057, 4074, 4393, 4013, 4001, 4648, 4026, 4555],\n", - " [79, 160, 9626, 8040, 4011, 206, 192, 119, 1805, 2260],\n", - " [3994, 4127, 2582, 4000, 2662, 5549, 5945, 5919, 7107, 2955],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [3989, 3990, 4038, 2367, 4028, 5041, 4027, 4738, 4177, 4178],\n", - " [4098, 7141, 7114, 2663, 7148, 2666, 7135, 7115, 3501, 4685],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [1805, 3098, 2503, 679, 2504, 677, 2502, 107, 4141, 2435],\n", - " [107, 2503, 2236, 4141, 6066, 1805, 4716, 3574, 4708, 2567],\n", - " [3574, 181, 4172, 2503, 107, 8381, 4847, 1515, 205, 4783],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 154, 3320, 79, 4968],\n", - " [4535, 5059, 5060, 4648, 4555, 4393, 4013, 4612, 4074, 5058],\n", - " [3996, 8381, 4710, 3574, 1515, 9394, 2435, 9354, 4989, 9416],\n", - " [4189, 4178, 3990, 4324, 4293, 4530, 3989, 4529, 4528, 5068],\n", - " [5064, 5066, 4259, 5065, 5063, 4189, 4239, 4238, 5014, 4454],\n", - " [654, 5110, 657, 1615, 5590, 1255, 9416, 9493, 653, 9454],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4091, 641, 12, 3991, 10250, 146, 2662, 2236, 4011, 6912],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 3320, 154, 4968, 79],\n", - " [3992, 677, 2738, 697, 7414, 5950, 7957, 6091, 8644, 3993],\n", - " [79, 119, 9626, 160, 246, 206, 4073, 8040, 4011, 690],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [5126, 5044, 6426, 5550, 10521, 4191, 4721, 2535, 5253, 638],\n", - " [3999, 4000, 3998, 690, 3210, 753, 3637, 3165, 2236, 2629],\n", - " [3995, 3098, 4231, 883, 3759, 9254, 2737, 3990, 3195, 3196],\n", - " [4012, 4138, 5046, 4202, 4156, 4155, 4298, 4073, 4570, 7106],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 154, 3320, 79, 4968],\n", - " [79, 160, 9626, 8040, 4011, 206, 119, 2260, 192, 1203],\n", - " [4004, 2629, 3999, 3422, 79, 3988, 119, 3637, 4011, 3998],\n", - " [4000, 3210, 4135, 2582, 4714, 2236, 3637, 205, 4681, 4097],\n", - " [4072, 4186, 4176, 4012, 4163, 4641, 181, 4329, 4349, 4350],\n", - " [3988, 4004, 4074, 1539, 4535, 3993, 154, 3320, 79, 4968],\n", - " [79, 119, 9626, 246, 160, 4073, 206, 8040, 4011, 690],\n", + "[[37, 44, 2387, 52, 2388, 73, 51, 74, 2108, 537],\n", + " [677, 883, 136, 8156, 668, 2507, 7957, 2314, 2522, 159],\n", + " [79, 208, 1805, 7106, 517, 119, 2503, 4098, 4000, 2546],\n", + " [2157, 2175, 2236, 7678, 2802, 2452, 5760, 7470, 870, 2780],\n", + " [677, 870, 159, 883, 2236, 2716, 2356, 4512, 2175, 7957],\n", + " [125, 8374, 836, 119, 2476, 2166, 2432, 240, 2168, 2428],\n", + " [2200, 2197, 31, 2290, 2324, 2289, 2327, 2278, 2274, 2412],\n", + " [10495, 7715, 10538, 7629, 773, 7444, 7499, 7306, 7222, 7818],\n", + " [1019, 9178, 1137, 2276, 8453, 2428, 8373, 1145, 1402, 1399],\n", + " [93, 146, 2244, 2249, 1395, 1917, 849, 2250, 2252, 2255],\n", + " [2246, 387, 672, 2355, 1917, 41, 7265, 8274, 10115, 6218],\n", + " [2246, 387, 672, 2355, 1917, 41, 7265, 8274, 10115, 6218],\n", + " [2246, 387, 672, 2355, 1917, 41, 7265, 8274, 10115, 6218],\n", + " [2247, 2197, 93, 3186, 672, 10115, 10306, 2277, 870, 2230],\n", + " [93, 146, 2250, 2252, 2546, 644, 2251, 1917, 2253, 849],\n", + " [2197, 2369, 108, 679, 2344, 2715, 6066, 246, 672, 2504],\n", + " [2262, 2263, 2261, 2264, 2236, 2266, 2213, 679, 2197, 2327],\n", + " [2264, 2367, 2263, 2261, 2262, 2334, 2327, 2197, 2236, 2331],\n", + " [79, 208, 1805, 119, 7106, 517, 4098, 2503, 4000, 2629],\n", + " [2196, 129, 128, 1401, 3073, 2260, 2293, 156, 124, 2421],\n", + " [79, 208, 517, 119, 4098, 7106, 1805, 2546, 4000, 7141],\n", + " [2283, 2197, 84, 122, 2173, 129, 2213, 2260, 9454, 2428],\n", + " [2283, 2197, 9454, 672, 122, 8750, 2213, 635, 84, 2348],\n", + " [2173, 2547, 8507, 1471, 2437, 2511, 5320, 3318, 2260, 3605],\n", + " [2368, 52, 30, 2398, 150, 27, 2344, 44, 53, 2266],\n", + " [79, 208, 1805, 7106, 4098, 517, 119, 2629, 2503, 2546],\n", + " [2197, 2173, 2369, 2236, 206, 2283, 2260, 2294, 2155, 108],\n", + " [2272, 2273, 2164, 660, 122, 2773, 2173, 2269, 2277, 2547],\n", + " [2197, 2200, 2266, 2327, 2278, 2293, 2215, 2259, 2274, 2230],\n", + " [2293, 123, 86, 1401, 930, 1001, 645, 42, 2196, 1984],\n", + " [2173, 1471, 8507, 2547, 2437, 2260, 5320, 2511, 124, 2175],\n", + " [2197, 108, 2236, 2277, 2344, 86, 246, 206, 204, 1805],\n", + " [2173, 8507, 2437, 1471, 2547, 5320, 2511, 118, 3318, 2260],\n", + " [2301, 627, 198, 675, 3432, 814, 2433, 623, 3248, 2350],\n", + " [2306, 279, 2325, 632, 2326, 2332, 1538, 2330, 2111, 7871],\n", + " [2321, 2374, 2323, 628, 627, 2328, 2348, 2331, 2356, 2367],\n", + " [2324, 2325, 2327, 2331, 2326, 2328, 2330, 7470, 2367, 2344],\n", + " [2344, 2368, 2388, 2398, 2186, 52, 2266, 2230, 2326, 2387],\n", + " [2330, 2324, 2331, 2328, 2325, 2393, 2326, 246, 2333, 214],\n", + " [697, 2311, 2351, 677, 870, 2350, 734, 4512, 386, 2236],\n", + " [2380, 2388, 176, 706, 147, 2393, 0, 7904, 2390, 251],\n", + " [2326, 2324, 2325, 2331, 2327, 2330, 2328, 2333, 2332, 2329],\n", + " [2333, 2331, 2324, 2325, 2328, 2327, 2326, 2330, 2329, 2367],\n", + " [2390, 55, 2388, 52, 1027, 2387, 1171, 51, 53, 819],\n", + " [318, 358, 1901, 1928, 2183, 44, 934, 936, 27, 1264],\n", + " [207, 2355, 1135, 52, 205, 3470, 942, 1893, 2162, 279],\n", + " [52, 51, 2401, 53, 146, 150, 30, 1135, 55, 44],\n", + " [2359, 2402, 2349, 2229, 2984, 5140, 2312, 2341, 2451, 3691],\n", + " [4012, 2377, 2318, 2317, 2322, 2108, 236, 2264, 2400, 2184],\n", + " [2383, 2345, 52, 2338, 205, 204, 1922, 2408, 42, 2388],\n", + " [2326, 1263, 2333, 1540, 1995, 2332, 922, 2325, 2328, 358],\n", + " [7291, 2372, 4741, 4029, 593, 9579, 2306, 1345, 9611, 415],\n", + " [2344, 10115, 2186, 108, 2388, 2197, 10409, 2160, 2264, 10408],\n", + " [2361, 2357, 2360, 2358, 2363, 2362, 283, 2388, 2393, 2395],\n", + " [2417, 2597, 734, 3332, 3330, 3331, 628, 3184, 7903, 3892],\n", + " [2316, 2317, 2318, 2377, 108, 4135, 1617, 236, 31, 2358],\n", + " [2405, 2406, 2404, 1910, 2175, 2341, 2545, 777, 2805, 1911],\n", + " [128, 206, 55, 2312, 1515, 124, 829, 107, 519, 248],\n", + " [2276, 125, 2432, 2168, 2476, 2428, 2278, 2166, 836, 1453],\n", + " [2175, 2157, 2452, 3192, 5760, 2451, 2780, 192, 3501, 679],\n", + " [2277, 2269, 2270, 2278, 2194, 822, 2476, 2287, 10100, 2454],\n", + " [2463, 2462, 2465, 2464, 2461, 2469, 2459, 2467, 2164, 2442],\n", + " [2469, 2466, 2467, 2459, 2465, 2461, 2442, 2463, 1467, 2462],\n", + " [2278, 2277, 9382, 173, 2476, 8631, 8501, 2365, 172, 2276],\n", + " [1985, 210, 1891, 1959, 1615, 2108, 439, 492, 1885, 10251],\n", + " [210, 2319, 1959, 1348, 1643, 1540, 7, 2073, 1400, 37],\n", + " [118, 679, 758, 7106, 2173, 119, 690, 677, 2715, 246],\n", + " [9525, 8638, 758, 2311, 9507, 8413, 9524, 9307, 2444, 493],\n", + " [221, 220, 1936, 224, 1559, 244, 2362, 1938, 1173, 228],\n", + " [204, 205, 1046, 517, 206, 518, 1465, 504, 497, 521],\n", + " [228, 242, 221, 244, 1936, 220, 2129, 248, 4857, 243],\n", + " [79, 208, 1805, 517, 119, 7106, 2503, 4098, 2546, 2166],\n", + " [79, 208, 119, 4098, 1805, 517, 7106, 8418, 7141, 3098],\n", + " [434, 1740, 1089, 79, 1335, 362, 1705, 2042, 1488, 1521],\n", + " [208, 1711, 1295, 214, 213, 233, 1805, 211, 257, 2503],\n", + " [207, 246, 264, 211, 204, 1203, 205, 367, 1255, 251],\n", + " [207, 247, 246, 205, 204, 8521, 211, 1046, 1203, 251],\n", + " [79, 208, 1805, 517, 119, 4000, 2166, 2428, 7106, 2503],\n", + " [239, 240, 1805, 679, 870, 2236, 698, 677, 2715, 8509],\n", + " [208, 213, 233, 1805, 211, 1295, 519, 517, 1711, 9729],\n", + " [208, 119, 206, 517, 519, 233, 257, 203, 207, 213],\n", + " [233, 1711, 208, 2074, 211, 1082, 1509, 519, 1576, 203],\n", + " [213, 208, 233, 129, 206, 519, 207, 517, 211, 1295],\n", + " [213, 356, 1263, 1576, 52, 1309, 1946, 1995, 1203, 2182],\n", + " [246, 2351, 2236, 108, 635, 1713, 107, 630, 247, 883],\n", + " [246, 207, 206, 205, 204, 203, 517, 211, 214, 180],\n", + " [207, 211, 205, 206, 246, 519, 214, 213, 1295, 208],\n", + " [1642, 2104, 2348, 292, 7, 10, 1199, 1646, 1609, 1876],\n", + " [304, 303, 305, 291, 381, 301, 4708, 377, 335, 610],\n", + " [303, 304, 305, 301, 1862, 294, 291, 292, 533, 1870],\n", + " [1273, 1193, 305, 308, 1577, 1861, 460, 1136, 306, 963],\n", + " [318, 358, 1901, 1928, 1266, 1540, 2183, 1195, 1978, 27],\n", + " [334, 329, 330, 965, 292, 904, 335, 2024, 425, 1143],\n", + " [536, 609, 2981, 334, 610, 292, 1143, 329, 552, 1890],\n", + " [360, 1267, 1158, 378, 1772, 1694, 1161, 1661, 2241, 520],\n", + " [305, 1273, 962, 1033, 295, 308, 533, 1078, 1866, 316],\n", + " [1847, 1635, 1881, 2001, 1769, 1792, 1592, 1200, 1775, 1571],\n", + " [1263, 1540, 1995, 2188, 922, 933, 1702, 1893, 1309, 2159],\n", + " [246, 204, 207, 206, 205, 108, 203, 247, 2236, 213],\n", + " [118, 7106, 7114, 3997, 3157, 2780, 4693, 239, 690, 622],\n", + " [430, 434, 432, 433, 2483, 2397, 981, 982, 1610, 1335],\n", + " [430, 434, 432, 433, 981, 2483, 2003, 1631, 10623, 982],\n", + " [342, 495, 562, 563, 417, 521, 550, 554, 533, 401],\n", + " [989, 532, 1021, 980, 323, 454, 988, 1204, 531, 981],\n", + " [3992, 79, 233, 5771, 4098, 8371, 9136, 6577, 517, 3264],\n", + " [205, 204, 207, 206, 2119, 504, 246, 519, 1515, 1046],\n", + " [208, 233, 517, 206, 207, 1805, 213, 79, 9729, 519],\n", + " [513, 517, 1063, 1062, 944, 108, 1086, 206, 691, 819],\n", + " [513, 206, 1063, 213, 944, 517, 52, 501, 1062, 819],\n", + " [535, 533, 422, 565, 1733, 1655, 1100, 1656, 336, 1236],\n", + " [541, 532, 1183, 1601, 1886, 1721, 2024, 1592, 1172, 565],\n", + " [208, 79, 1805, 1711, 1295, 211, 214, 257, 519, 517],\n", + " [603, 1503, 1497, 594, 1496, 1493, 596, 978, 1481, 1483],\n", + " [576, 1457, 1454, 1159, 381, 1137, 957, 537, 1456, 539],\n", + " [579, 10655, 8131, 295, 955, 1426, 2395, 1793, 1044, 3154],\n", + " [594, 589, 588, 583, 582, 595, 568, 577, 530, 1546],\n", + " [593, 1643, 2041, 533, 51, 416, 1329, 329, 1023, 609],\n", + " [612, 614, 561, 1167, 4148, 204, 4026, 1166, 1162, 992],\n", + " [631, 7187, 2678, 2393, 2735, 3122, 9720, 7792, 7904, 4512],\n", + " [6580, 4813, 5814, 5433, 6923, 6931, 4842, 8161, 4838, 8609],\n", + " [79, 119, 208, 9991, 160, 268, 165, 1120, 690, 1506],\n", + " [651, 5322, 870, 6774, 4654, 5482, 2375, 7833, 4152, 4901],\n", + " [10187, 1615, 291, 10522, 10533, 7227, 7755, 1894, 2058, 10492],\n", + " [10546, 8188, 170, 4719, 71, 9105, 1453, 2027, 1053, 10179],\n", + " [7237, 7642, 7363, 688, 2394, 2250, 710, 2253, 2427, 2203],\n", + " [86, 1471, 640, 690, 795, 1515, 2629, 8335, 641, 9039],\n", + " [701, 705, 108, 698, 708, 6066, 9302, 712, 9285, 8432],\n", + " [723, 3210, 8695, 4095, 2523, 724, 727, 730, 1377, 5598],\n", + " [86, 9039, 9288, 8453, 9950, 8319, 795, 2337, 2629, 4155],\n", + " [2238, 1576, 1698, 2533, 1029, 207, 3501, 2385, 211, 2046],\n", + " [162, 1514, 640, 1515, 8032, 7517, 829, 2155, 9384, 1061],\n", + " [79, 208, 7106, 1805, 517, 4098, 2503, 4000, 3637, 119],\n", + " [292, 1752, 2041, 1379, 2617, 294, 6536, 2348, 899, 323],\n", + " [801, 3216, 800, 9161, 2155, 386, 10029, 2115, 8276, 9475],\n", + " [676, 974, 801, 694, 799, 916, 682, 917, 138, 675],\n", + " [79, 208, 1805, 7106, 517, 4000, 119, 2503, 9991, 4098],\n", + " [770, 799, 8700, 8663, 801, 9231, 2155, 804, 800, 8417],\n", + " [10270, 660, 192, 6567, 231, 7918, 5917, 635, 7635, 9019],\n", + " [825, 823, 824, 165, 795, 4074, 8335, 826, 840, 1469],\n", + " [86, 1471, 2511, 156, 795, 204, 2197, 4155, 6066, 654],\n", + " [9723, 2240, 8579, 9166, 8407, 165, 825, 9656, 839, 2687],\n", + " [2844, 2617, 3275, 2228, 3123, 882, 2837, 841, 7981, 2838],\n", + " [712, 758, 860, 859, 903, 8453, 6924, 2546, 622, 387],\n", + " [79, 208, 1805, 7106, 517, 119, 2503, 4000, 2629, 690],\n", + " [118, 119, 1805, 758, 641, 7106, 2173, 2780, 1514, 207],\n", + " [119, 79, 118, 7106, 690, 9036, 240, 208, 1805, 2780],\n", + " [878, 898, 899, 192, 634, 1379, 1605, 2617, 8275, 882],\n", + " [2796, 2546, 881, 2534, 3192, 3195, 3193, 3346, 3599, 887],\n", + " [698, 2435, 2535, 8897, 7919, 1049, 5726, 2873, 9417, 8600],\n", + " [679, 677, 2503, 2502, 1805, 2504, 734, 8592, 2236, 5826],\n", + " [7398, 4709, 10317, 5782, 3868, 1979, 3642, 5770, 3702, 6496],\n", + " [1077, 1171, 1402, 1173, 1886, 10295, 512, 1075, 8812, 1780],\n", + " [678, 2175, 679, 660, 192, 231, 2236, 882, 5917, 3165],\n", + " [926, 1001, 61, 1522, 1562, 1996, 8, 1763, 1688, 1685],\n", + " [1, 2149, 277, 2, 8113, 942, 92, 2103, 3010, 1929],\n", + " [968, 966, 1907, 971, 1533, 1531, 368, 273, 987, 1543],\n", + " [1022, 987, 1000, 368, 2304, 1139, 2303, 3324, 7378, 954],\n", + " [994, 998, 991, 1021, 983, 927, 980, 999, 928, 993],\n", + " [1005, 1841, 164, 525, 1549, 1987, 1474, 1043, 1317, 1143],\n", + " [1006, 593, 484, 603, 978, 1864, 543, 1044, 1596, 2065],\n", + " [1032, 1104, 1022, 1636, 1029, 2105, 9730, 1203, 1276, 1639],\n", + " [1035, 989, 1078, 1033, 1683, 371, 1273, 1019, 532, 1632],\n", + " [2342, 6064, 2051, 2333, 2486, 2313, 2328, 1262, 6046, 3904],\n", + " [1045, 203, 1046, 205, 206, 2119, 1069, 1059, 1047, 214],\n", + " [236, 3584, 439, 107, 4001, 1991, 1873, 1752, 4554, 1864],\n", + " [2606, 6836, 1083, 1777, 1202, 1280, 10333, 10290, 6738, 1204],\n", + " [203, 206, 1086, 495, 512, 562, 501, 223, 521, 513],\n", + " [1102, 1695, 993, 336, 535, 1267, 1100, 1166, 461, 5046],\n", + " [1120, 1122, 79, 950, 8598, 888, 208, 1117, 1688, 517],\n", + " [1122, 126, 1120, 1688, 1713, 8677, 2119, 950, 2823, 1066],\n", + " [2236, 7957, 2305, 624, 2567, 677, 2301, 871, 8977, 2303],\n", + " [156, 1471, 3267, 517, 2222, 240, 267, 1066, 9626, 6826],\n", + " [204, 1046, 205, 206, 517, 2119, 505, 1465, 520, 524],\n", + " [204, 1046, 205, 206, 517, 2119, 505, 1465, 520, 524],\n", + " [208, 1711, 517, 211, 233, 1295, 1509, 129, 213, 79],\n", + " [1113, 207, 1897, 233, 1669, 4000, 1167, 517, 2340, 107],\n", + " [1576, 2046, 356, 1318, 922, 2345, 2191, 1255, 933, 276],\n", + " [1137, 1402, 1399, 1451, 1396, 1145, 371, 1173, 1035, 1401],\n", + " [537, 539, 2042, 1454, 1457, 2553, 1159, 1866, 473, 1434],\n", + " [1099, 425, 1500, 1627, 547, 4148, 329, 2087, 2008, 1864],\n", + " [1635, 533, 336, 1722, 1652, 2102, 1651, 416, 1592, 1886],\n", + " [1229, 1280, 79, 1207, 1711, 1228, 1239, 1226, 1213, 1231],\n", + " [1239, 1228, 1227, 1226, 1711, 1229, 1232, 1207, 1231, 1629],\n", + " [2950, 2946, 1569, 53, 924, 1107, 1923, 922, 2905, 933],\n", + " [1232, 1166, 993, 1167, 1650, 2008, 1326, 990, 1102, 1554],\n", + " [1272, 547, 1694, 1449, 1084, 4535, 1400, 1329, 1488, 790],\n", + " [6532, 1861, 2935, 1725, 3333, 7428, 2631, 1273, 1167, 2899],\n", + " [9880, 9881, 1631, 1852, 1632, 8570, 2085, 1249, 369, 430],\n", + " [1306, 1305, 1301, 580, 4074, 1300, 52, 2826, 1752, 1677],\n", + " [1300, 1003, 1002, 1305, 1301, 1306, 91, 1597, 8846, 955],\n", + " [580, 4074, 4026, 3604, 1306, 2067, 3325, 759, 7025, 363],\n", + " [1597, 1775, 1536, 100, 7292, 1889, 6188, 1925, 2041, 1749],\n", + " [1324, 2024, 2042, 1473, 1844, 3963, 2080, 2055, 1851, 1325],\n", + " [2948, 1866, 2054, 9083, 9113, 8565, 2905, 1932, 2842, 9104],\n", + " [2948, 1866, 2054, 9083, 9113, 8565, 2905, 1932, 2842, 9104],\n", + " [1338, 552, 554, 473, 988, 1554, 3135, 981, 1631, 609],\n", + " [239, 240, 1805, 679, 870, 2236, 698, 677, 2715, 8509],\n", + " [1264, 1997, 1842, 1538, 287, 1540, 2161, 1, 936, 1711],\n", + " [867, 399, 524, 482, 1246, 6124, 8581, 1909, 503, 1174],\n", + " [373, 442, 1991, 1637, 2307, 6373, 2224, 2041, 551, 1753],\n", + " [1929, 2149, 2706, 1841, 3010, 1993, 2576, 2150, 1, 1905],\n", + " [1418, 1412, 1422, 1428, 1429, 1427, 1425, 1426, 1430, 1439],\n", + " [1425, 1429, 1428, 1427, 1430, 1426, 1418, 1422, 1412, 1583],\n", + " [1373, 1372, 1365, 7009, 8311, 742, 8256, 724, 730, 192],\n", + " [1446, 1448, 1449, 1456, 1458, 1442, 553, 1443, 1151, 1459],\n", + " [1451, 1457, 1399, 1396, 174, 336, 12, 1280, 1454, 29],\n", + " [2552, 759, 1914, 2042, 561, 2067, 1500, 8, 1490, 450],\n", + " [954, 1103, 7195, 782, 5709, 573, 971, 7485, 5917, 7635],\n", + " [1464, 500, 1610, 1463, 180, 1605, 5841, 1964, 3090, 226],\n", + " [2912, 8476, 637, 1470, 196, 710, 909, 882, 639, 860],\n", + " [1326, 565, 1331, 1695, 563, 1655, 344, 1656, 1237, 2062],\n", + " [554, 1089, 1023, 1256, 1101, 920, 1162, 1074, 1502, 553],\n", + " [1672, 1771, 1583, 1655, 350, 1454, 1652, 1457, 1694, 1653],\n", + " [1509, 208, 1510, 1506, 1711, 257, 211, 9729, 233, 213],\n", + " [1509, 1510, 208, 1506, 1711, 233, 211, 9729, 257, 213],\n", + " [79, 208, 119, 1805, 517, 4098, 7106, 2546, 2503, 2166],\n", + " [1509, 1506, 1510, 257, 1711, 3415, 213, 519, 245, 160],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4098, 2629, 4000],\n", + " [207, 2392, 3424, 10386, 1897, 630, 2385, 54, 2296, 264],\n", + " [205, 206, 204, 504, 207, 246, 248, 515, 1046, 1515],\n", + " [204, 206, 1046, 205, 517, 246, 223, 86, 505, 520],\n", + " [79, 208, 1805, 119, 7106, 517, 160, 2503, 2546, 4000],\n", + " [363, 953, 264, 1525, 517, 52, 362, 1115, 180, 1805],\n", + " [1558, 1402, 1488, 1632, 2013, 1023, 1631, 1403, 1399, 1203],\n", + " [1548, 1160, 1551, 1546, 1169, 1652, 2087, 1441, 2005, 1554],\n", + " [213, 208, 206, 129, 1805, 2504, 108, 233, 204, 501],\n", + " [1551, 1160, 1394, 1548, 1546, 2087, 1573, 1535, 2899, 963],\n", + " [205, 206, 204, 1515, 207, 2119, 640, 1046, 1295, 519],\n", + " [1805, 3544, 208, 4298, 3687, 1187, 1032, 2236, 3637, 1636],\n", + " [2635, 9948, 3036, 848, 2316, 8156, 3150, 7905, 5931, 2190],\n", + " [1580, 1589, 1590, 1583, 1586, 1579, 1578, 1585, 1582, 1592],\n", + " [473, 1581, 1672, 1578, 1076, 1579, 1583, 1582, 1590, 1426],\n", + " [79, 208, 1805, 517, 7106, 119, 2503, 4098, 4000, 2546],\n", + " [246, 207, 247, 204, 205, 2236, 108, 206, 630, 2351],\n", + " [211, 1805, 246, 207, 107, 264, 206, 208, 214, 679],\n", + " [213, 246, 108, 1805, 208, 214, 211, 206, 207, 257],\n", + " [205, 1606, 1711, 246, 207, 1228, 2119, 1629, 1059, 203],\n", + " [1576, 7609, 107, 2597, 1736, 246, 2502, 2291, 2236, 233],\n", + " [208, 1711, 79, 211, 519, 517, 268, 1295, 214, 233],\n", + " [1636, 1672, 8947, 369, 1486, 2015, 370, 4043, 1689, 1638],\n", + " [1927, 1673, 2148, 1653, 318, 1654, 3890, 27, 1506, 1554],\n", + " [213, 208, 214, 1295, 519, 257, 2503, 206, 205, 1711],\n", + " [208, 1711, 1688, 9039, 268, 9034, 2119, 1047, 9205, 233],\n", + " [1860, 1691, 1866, 2102, 1847, 1635, 1792, 2069, 1844, 10655],\n", + " [1700, 1701, 10175, 3529, 3593, 7854, 10176, 3496, 2380, 8045],\n", + " [1715, 1276, 226, 517, 1895, 448, 1903, 1727, 1111, 1245],\n", + " [1886, 1256, 1172, 1592, 554, 1165, 1655, 533, 1583, 1656],\n", + " [441, 535, 1733, 533, 457, 1871, 1243, 342, 564, 340],\n", + " [2230, 8160, 657, 9775, 6942, 5626, 8402, 779, 3292, 8147],\n", + " [1776, 1767, 1771, 8576, 9656, 1634, 1232, 1550, 8849, 2631],\n", + " [1776, 8767, 1754, 9087, 1514, 1343, 1767, 9358, 1515, 8845],\n", + " [10406, 9531, 10456, 10457, 10140, 7596, 6660, 4172, 797, 4152],\n", + " [1748, 2119, 1749, 2132, 1775, 1771, 1769, 1806, 129, 654],\n", + " [1771, 1769, 1776, 1775, 1749, 1748, 1767, 1151, 2001, 9790],\n", + " [1757, 5592, 5443, 2619, 3515, 8549, 8296, 1076, 1771, 8558],\n", + " [1761, 5974, 3088, 10630, 10621, 2004, 3092, 6193, 5973, 1824],\n", + " [1769, 1771, 1775, 79, 2001, 1749, 1433, 1748, 1635, 1721],\n", + " [1791, 1793, 1999, 4012, 1784, 37, 306, 1847, 2576, 2410],\n", + " [1791, 1784, 2024, 10376, 373, 7231, 5917, 1841, 90, 1999],\n", + " [2105, 1636, 1641, 1390, 1104, 1799, 2100, 1022, 1638, 2003],\n", + " [1199, 207, 1897, 1893, 586, 1816, 1622, 376, 37, 1166],\n", + " [1889, 205, 1464, 81, 401, 3616, 4535, 3574, 180, 1225],\n", + " [205, 245, 1807, 641, 2119, 2330, 1856, 4535, 1607, 214],\n", + " [1806, 2, 6721, 6793, 3, 6676, 10196, 1929, 1863, 3042],\n", + " [8508, 1712, 3334, 6793, 1711, 1688, 6721, 2685, 3099, 1983],\n", + " [1814, 1813, 10115, 8274, 691, 8186, 1395, 1698, 8190, 146],\n", + " [214, 1295, 211, 3210, 107, 1711, 2503, 205, 208, 519],\n", + " [211, 1711, 214, 207, 519, 208, 1295, 492, 257, 246],\n", + " [2342, 1820, 387, 197, 3435, 8697, 1683, 2333, 1486, 1035],\n", + " [1772, 1627, 1550, 1355, 1661, 1670, 1694, 1821, 1161, 1864],\n", + " [1821, 379, 1212, 1627, 4026, 614, 2060, 1670, 1773, 380],\n", + " [7391, 108, 87, 8335, 1407, 8375, 2660, 705, 2194, 8573],\n", + " [1884, 1858, 1857, 1882, 1824, 5026, 1852, 1879, 1873, 1860],\n", + " [1864, 1875, 1872, 2131, 1867, 2141, 2004, 8154, 1161, 1821],\n", + " [1871, 1875, 1862, 1870, 1863, 355, 1861, 1733, 1864, 79],\n", + " [1897, 1895, 1873, 207, 1199, 1622, 1677, 2004, 1113, 1639],\n", + " [1897, 1895, 1873, 2004, 1622, 1639, 1199, 1525, 1903, 1752],\n", + " [1894, 1745, 335, 1791, 1107, 6721, 10237, 1682, 2135, 2139],\n", + " [275, 218, 2055, 1901, 2424, 2678, 358, 2410, 2220, 1957],\n", + " [387, 7894, 8274, 637, 621, 2250, 675, 9132, 386, 84],\n", + " [79, 208, 1805, 7106, 119, 517, 4000, 2503, 4098, 2546],\n", + " [108, 246, 213, 677, 2236, 697, 691, 2504, 635, 2108],\n", + " [220, 932, 221, 1936, 944, 797, 91, 151, 76, 920],\n", + " [1650, 1629, 252, 1778, 1642, 593, 251, 414, 1677, 1646],\n", + " [1953, 668, 9136, 3196, 684, 9869, 2655, 2931, 8155, 9954],\n", + " [206, 1781, 797, 1347, 207, 2310, 2309, 4338, 1612, 264],\n", + " [641, 2503, 8381, 3047, 3210, 640, 205, 2236, 3235, 3991],\n", + " [957, 2439, 593, 2319, 2346, 1186, 2345, 3523, 2376, 1982],\n", + " [1718, 2112, 2991, 4867, 216, 1716, 1970, 154, 75, 1139],\n", + " [10631, 7187, 672, 10434, 2392, 2427, 630, 5279, 3435, 4114],\n", + " [1607, 10229, 1650, 1921, 1606, 277, 9464, 4017, 1807, 933],\n", + " [1975, 1977, 2335, 362, 37, 1895, 1197, 1111, 1903, 1372],\n", + " [6682, 8371, 6798, 5708, 6738, 1203, 6560, 694, 8954, 3086],\n", + " [79, 208, 4098, 2546, 517, 7141, 119, 2534, 1805, 1871],\n", + " [2060, 2630, 707, 2025, 4026, 2490, 1103, 380, 668, 2512],\n", + " [206, 204, 207, 1060, 517, 205, 246, 264, 4338, 269],\n", + " [1576, 2046, 1318, 356, 207, 1255, 1135, 55, 1263, 2809],\n", + " [987, 1689, 1187, 1022, 971, 1636, 2015, 1674, 1141, 1531],\n", + " [8841, 1515, 8638, 1514, 9098, 9094, 9254, 8381, 2196, 9989],\n", + " [206, 203, 205, 204, 207, 180, 2896, 223, 264, 213],\n", + " [2088, 1839, 1870, 533, 1655, 1656, 535, 377, 1862, 1652],\n", + " [1647, 1635, 1646, 2146, 1645, 1730, 2102, 1658, 2104, 1848],\n", + " [2108, 2351, 2236, 207, 870, 2684, 2296, 246, 2392, 264],\n", + " [2578, 3396, 1713, 3260, 3754, 4140, 277, 2124, 3010, 4093],\n", + " [2112, 3248, 2114, 2127, 2124, 2137, 2944, 1405, 518, 1716],\n", + " [2119, 2114, 205, 206, 2113, 497, 226, 520, 10149, 1606],\n", + " [2114, 2113, 2125, 2127, 2112, 2121, 2137, 136, 2124, 2128],\n", + " [2119, 205, 1046, 2132, 156, 640, 2113, 1047, 1606, 504],\n", + " [641, 1514, 1515, 8381, 3047, 2117, 8566, 2115, 2514, 2118],\n", + " [79, 208, 7106, 517, 119, 1805, 2166, 8418, 9991, 2546],\n", + " [108, 246, 205, 206, 204, 1082, 203, 213, 1515, 691],\n", + " [86, 9039, 640, 9288, 1471, 2629, 8453, 2337, 4693, 8155],\n", + " [79, 208, 517, 7106, 1805, 119, 4098, 4000, 2503, 2546],\n", + " [86, 2629, 1471, 4155, 795, 204, 156, 640, 690, 8155],\n", + " [2146, 2141, 2140, 2150, 2139, 1876, 2132, 2148, 1875, 1661],\n", + " [79, 208, 4098, 119, 517, 1805, 7106, 2546, 8418, 3098],\n", + " [211, 1711, 214, 207, 519, 208, 1295, 492, 1635, 2411],\n", + " [35, 33, 34, 51, 53, 31, 2391, 73, 2368, 76],\n", + " [29, 25, 44, 30, 27, 52, 51, 38, 34, 35],\n", + " [41, 52, 42, 51, 2406, 2313, 264, 2387, 2344, 2391],\n", + " [40, 25, 43, 35, 33, 47, 34, 14, 29, 70],\n", + " [4, 55, 10, 53, 129, 62, 2146, 23, 141, 140],\n", + " [10497, 8049, 668, 9534, 8226, 5113, 10546, 6865, 9442, 3043],\n", + " [85, 2715, 86, 8319, 2130, 3216, 672, 8155, 6066, 198],\n", + " [98, 102, 95, 103, 105, 101, 94, 1135, 2111, 2113],\n", + " [2197, 108, 2236, 246, 2260, 213, 206, 2344, 2277, 2504],\n", + " [119, 9036, 2534, 9626, 79, 240, 125, 2260, 3543, 702],\n", + " [128, 62, 59, 29, 45, 184, 23, 40, 141, 60],\n", + " [124, 2313, 55, 2260, 870, 2155, 2173, 1471, 180, 129],\n", + " [129, 2197, 128, 2260, 156, 213, 108, 3404, 3098, 206],\n", + " [50, 3375, 15, 7664, 51, 10, 2607, 60, 53, 18],\n", + " [79, 208, 1805, 517, 7106, 119, 4098, 4000, 2503, 2546],\n", + " [79, 208, 1805, 517, 7106, 119, 4098, 4000, 2503, 2546],\n", + " [129, 2197, 86, 42, 211, 3402, 4254, 3404, 206, 156],\n", + " [156, 128, 129, 112, 86, 2196, 184, 2278, 190, 4155],\n", + " [79, 208, 1805, 4098, 517, 7106, 119, 2546, 7141, 2503],\n", + " [108, 640, 205, 129, 1515, 2236, 3048, 3049, 206, 203],\n", + " [129, 156, 128, 2197, 86, 3404, 3402, 1471, 204, 2337],\n", + " [160, 165, 167, 3179, 9856, 3992, 2115, 136, 2509, 9958],\n", + " [79, 208, 119, 1805, 517, 4098, 7106, 2546, 7141, 8418],\n", + " [79, 208, 119, 4098, 517, 1805, 7141, 8418, 7106, 2546],\n", + " [79, 208, 1805, 4098, 517, 119, 7106, 8418, 7141, 2546],\n", + " [2749, 2166, 2167, 2610, 2683, 2977, 8596, 2466, 2164, 822],\n", + " [156, 1471, 870, 2337, 86, 2277, 3166, 2281, 654, 2260],\n", + " [79, 208, 1805, 7106, 119, 517, 4098, 2546, 4000, 2503],\n", + " [176, 1203, 9599, 1773, 251, 146, 1576, 2380, 36, 9881],\n", + " [129, 2197, 213, 3404, 203, 206, 3402, 4202, 86, 52],\n", + " [129, 2197, 213, 4202, 206, 203, 3404, 3402, 211, 154],\n", + " [94, 3369, 95, 191, 186, 8431, 2872, 2110, 168, 1691],\n", + " [191, 186, 184, 190, 94, 62, 47, 1748, 102, 129],\n", + " [196, 3265, 193, 2120, 871, 1888, 94, 3094, 3188, 153],\n", + " [197, 620, 8278, 198, 3263, 8619, 759, 2342, 1468, 8297],\n", + " [7903, 2802, 5718, 2298, 10478, 10006, 10048, 9949, 5719, 9937],\n", + " [2746, 2822, 2750, 2766, 2565, 3249, 2554, 542, 2806, 984],\n", + " [2498, 2497, 5965, 4683, 2499, 4708, 4088, 86, 4709, 7610],\n", + " [819, 233, 87, 2759, 128, 2312, 147, 243, 829, 2803],\n", + " [2909, 6766, 2792, 2642, 6768, 6654, 5222, 5549, 6628, 2640],\n", + " [2759, 3211, 3305, 2703, 203, 2236, 206, 2341, 5077, 3754],\n", + " [883, 159, 677, 2715, 9047, 6074, 8977, 2429, 10002, 6022],\n", + " [2548, 8276, 3700, 2531, 8414, 892, 851, 120, 811, 2546],\n", + " [2546, 2545, 79, 2549, 4098, 2969, 1910, 2552, 112, 180],\n", + " [387, 637, 7894, 3535, 386, 2213, 8274, 8847, 9132, 9114],\n", + " [159, 9102, 2601, 2600, 8130, 8373, 9138, 136, 8699, 9192],\n", + " [2604, 2619, 129, 82, 3197, 2896, 2616, 5646, 3269, 4864],\n", + " [2613, 669, 2337, 10136, 729, 2379, 7458, 9760, 771, 2451],\n", + " [2613, 669, 2337, 10136, 729, 2379, 7458, 9760, 771, 2451],\n", + " [5814, 7470, 10206, 2624, 7891, 7721, 2610, 1443, 7615, 5813],\n", + " [2647, 2649, 2653, 2640, 2654, 2638, 2127, 4706, 2637, 9395],\n", + " [2655, 2862, 2512, 9447, 1953, 79, 2582, 713, 8172, 4000],\n", + " [2679, 4094, 1940, 7444, 4165, 8099, 3081, 9473, 9827, 3289],\n", + " [2685, 8810, 6602, 2930, 6064, 7229, 6832, 6989, 10008, 6543],\n", + " [1471, 4172, 9379, 7470, 8695, 159, 8335, 787, 870, 6900],\n", + " [2475, 3357, 2821, 183, 3546, 2954, 2620, 10528, 3672, 3425],\n", + " [6930, 5860, 6893, 3621, 203, 6117, 6116, 4783, 6902, 6066],\n", + " [2503, 677, 2716, 2715, 679, 2502, 2504, 2236, 7106, 8274],\n", + " [2719, 6555, 6589, 2720, 7142, 6455, 6887, 6449, 6907, 2718],\n", + " [3249, 2766, 2768, 7023, 2746, 2561, 2750, 2822, 2762, 1187],\n", + " [5703, 8325, 6952, 5656, 3281, 6301, 7883, 10227, 2887, 5640],\n", + " [7814, 10507, 7802, 7840, 2784, 10442, 10530, 7476, 4432, 3437],\n", + " [2660, 3098, 1805, 2512, 2862, 7016, 5231, 2608, 2502, 2629],\n", + " [6122, 2795, 6004, 2794, 5446, 2792, 3009, 2817, 6638, 6058],\n", + " [3210, 5809, 6913, 5769, 6025, 7518, 6565, 2523, 7105, 8696],\n", + " [2766, 3249, 2750, 2822, 2746, 2554, 2768, 2806, 2751, 2565],\n", + " [79, 208, 1805, 517, 7106, 2503, 119, 2546, 4000, 4098],\n", + " [6469, 4755, 7982, 6931, 5432, 2829, 5635, 4899, 2831, 7000],\n", + " [3192, 3346, 2971, 3194, 8348, 4975, 2620, 3169, 2837, 2728],\n", + " [2844, 4755, 2829, 4899, 3604, 2757, 2683, 2838, 2837, 2831],\n", + " [2832, 6532, 8696, 8039, 5307, 2630, 8293, 10142, 5723, 9969],\n", + " [7442, 9663, 5274, 7232, 9896, 7322, 23, 8141, 2727, 7669],\n", + " [3192, 3605, 3245, 901, 3186, 3337, 3346, 3349, 3470, 3344],\n", + " [6064, 8045, 6120, 7229, 7401, 5110, 8810, 7312, 5764, 2929],\n", + " [79, 208, 1805, 7106, 119, 517, 4098, 2503, 2546, 4000],\n", + " [10134, 7322, 7235, 7651, 7623, 10135, 10246, 10112, 5274, 10233],\n", + " [669, 8696, 672, 635, 192, 8463, 729, 725, 2427, 771],\n", + " [2862, 2655, 2630, 2512, 2660, 5231, 6588, 8172, 9613, 5318],\n", + " [2880, 2878, 2882, 2881, 3213, 3214, 3393, 3209, 3314, 3131],\n", + " [2899, 2935, 5864, 7371, 4000, 5463, 5618, 2896, 5673, 9417],\n", + " [7708, 2906, 5535, 5698, 6306, 6348, 7663, 2905, 6110, 7732],\n", + " [6291, 6736, 6873, 6611, 5605, 6290, 6664, 9265, 7025, 2474],\n", + " [2693, 2622, 3100, 2967, 3021, 3035, 2865, 9473, 4813, 10333],\n", + " [2917, 2921, 2278, 3227, 2916, 2950, 7579, 50, 2814, 2887],\n", + " [79, 2534, 2166, 119, 8040, 6814, 708, 9626, 875, 2546],\n", + " [3200, 5550, 7475, 9303, 9364, 7803, 2441, 9285, 2935, 9298],\n", + " [882, 1741, 2312, 2826, 2809, 2451, 3318, 8163, 2175, 6911],\n", + " [2950, 2916, 2946, 2921, 92, 3238, 2917, 6883, 2576, 1702],\n", + " [208, 213, 5071, 3101, 3211, 870, 4515, 7124, 3551, 2503],\n", + " [2941, 2955, 3173, 2503, 679, 5235, 3460, 2504, 2940, 2992],\n", + " [2963, 2961, 6999, 2962, 8138, 2959, 2960, 5900, 2967, 3078],\n", + " [2977, 4978, 2466, 2467, 4974, 2837, 5149, 4984, 256, 194],\n", + " [2978, 3173, 3206, 7686, 3924, 3211, 2993, 3314, 3204, 3245],\n", + " [2992, 2956, 2955, 2990, 5808, 5548, 3238, 1376, 882, 4253],\n", + " [2956, 2955, 2992, 5233, 3671, 3670, 2941, 4073, 4253, 2940],\n", + " [3002, 240, 2797, 2816, 3998, 2632, 2236, 2629, 239, 5057],\n", + " [2237, 3162, 7848, 3324, 3893, 3156, 2303, 2730, 2507, 7333],\n", + " [7664, 3022, 3276, 7715, 7740, 7320, 7321, 7286, 1741, 15],\n", + " [2319, 2861, 3343, 3617, 10604, 2156, 3610, 1657, 10635, 10651],\n", + " [2115, 3235, 3047, 2116, 3027, 8491, 3028, 8402, 9475, 2117],\n", + " [641, 3574, 3210, 205, 4149, 2214, 2503, 3991, 6805, 640],\n", + " [640, 205, 9508, 4172, 8319, 3067, 1817, 225, 4285, 2111],\n", + " [3066, 3065, 3064, 3067, 2906, 31, 3669, 203, 3068, 3668],\n", + " [7740, 2622, 7715, 7480, 7584, 7569, 8051, 7481, 7742, 7696],\n", + " [3054, 3068, 3067, 4066, 3050, 2622, 3052, 2500, 1471, 5277],\n", + " [3065, 3063, 5740, 3038, 3067, 3068, 2916, 6950, 6625, 7043],\n", + " [3064, 3076, 3068, 3066, 2865, 2847, 3253, 2583, 3055, 6901],\n", + " [1471, 86, 9039, 787, 3052, 3210, 640, 8155, 3053, 3991],\n", + " [640, 1471, 758, 8319, 1515, 8155, 86, 9508, 3210, 3991],\n", + " [2905, 8160, 2842, 2054, 3024, 3169, 3022, 5626, 3610, 2826],\n", + " [2236, 2503, 3991, 3094, 3998, 5539, 5540, 753, 167, 871],\n", + " [3093, 3106, 6140, 3087, 6043, 5950, 6008, 10174, 6042, 6034],\n", + " [3192, 1805, 5385, 3101, 2608, 2503, 678, 3349, 5760, 3348],\n", + " [3115, 3112, 3113, 3376, 2872, 3020, 3329, 3169, 190, 62],\n", + " [3117, 3118, 3122, 6016, 6015, 3123, 380, 3143, 5869, 379],\n", + " [3119, 6148, 6147, 6008, 3123, 6642, 3118, 6279, 2580, 6034],\n", + " [2792, 6080, 6159, 6253, 6279, 5885, 6146, 6265, 5884, 3122],\n", + " [3117, 6015, 3122, 3118, 7871, 3130, 6010, 6016, 3586, 3123],\n", + " [6732, 6042, 6155, 6610, 6043, 7123, 6156, 6247, 5915, 6004],\n", + " [713, 2660, 2502, 679, 160, 2608, 2236, 753, 677, 901],\n", + " [6109, 4642, 4644, 4109, 892, 10435, 712, 5025, 2497, 3226],\n", + " [5859, 2810, 3141, 2877, 6253, 6282, 3142, 7648, 6153, 7924],\n", + " [7017, 6198, 4098, 2780, 2666, 2682, 6199, 7135, 3464, 7095],\n", + " [3161, 3974, 3667, 4683, 7986, 3309, 3362, 4666, 3160, 3868],\n", + " [2035, 79, 5514, 8968, 690, 9958, 7999, 3286, 8919, 2534],\n", + " [79, 208, 1805, 517, 119, 7106, 4098, 4000, 2503, 2546],\n", + " [79, 208, 1805, 119, 7106, 517, 4098, 4000, 2503, 233],\n", + " [3181, 3184, 668, 3180, 8021, 2214, 8514, 3182, 5142, 8686],\n", + " [79, 208, 1805, 7106, 517, 119, 4098, 2546, 2503, 4000],\n", + " [79, 208, 7106, 1805, 517, 4098, 2546, 119, 2503, 8418],\n", + " [2567, 3200, 5468, 5611, 9317, 3367, 678, 7475, 2935, 3202],\n", + " [3207, 3204, 3206, 3202, 3203, 3213, 3214, 2351, 3209, 3098],\n", + " [3202, 870, 3209, 901, 3204, 3207, 10115, 2351, 3206, 3165],\n", + " [86, 1471, 9039, 640, 2260, 3991, 4155, 8335, 795, 1515],\n", + " [79, 208, 119, 1805, 8418, 7106, 3637, 3098, 7141, 517],\n", + " [79, 208, 119, 517, 7106, 1805, 4000, 2546, 3637, 3449],\n", + " [3098, 2503, 3216, 2236, 3325, 690, 1805, 4141, 753, 3999],\n", + " [2236, 4248, 2503, 5207, 107, 3326, 4000, 4001, 3972, 5448],\n", + " [2796, 3192, 3278, 3193, 3277, 3195, 3348, 6313, 3261, 2534],\n", + " [3193, 3192, 2534, 6889, 3195, 2796, 8826, 3200, 8922, 8298],\n", + " [2173, 2547, 118, 2437, 2175, 2780, 8507, 2260, 3422, 2511],\n", + " [2503, 2502, 679, 677, 2504, 1805, 2716, 2236, 2715, 753],\n", + " [2796, 3192, 3278, 3622, 5937, 3261, 3346, 5500, 3193, 6313],\n", + " [2796, 6313, 3278, 3277, 3195, 3193, 3192, 3046, 3261, 2534],\n", + " [3173, 3165, 901, 870, 2236, 4135, 3497, 3245, 871, 2611],\n", + " [3173, 3165, 870, 901, 2236, 4135, 3245, 3497, 2681, 2611],\n", + " [6617, 9933, 4643, 2856, 6616, 7822, 6041, 3178, 9528, 9930],\n", + " [10330, 1374, 4678, 7909, 2654, 4096, 419, 7980, 723, 10325],\n", + " [3314, 5172, 5184, 5174, 3207, 5176, 2889, 3206, 5385, 2197],\n", + " [2796, 3192, 2534, 6313, 3261, 2780, 3195, 3605, 3278, 3348],\n", + " [3173, 3165, 901, 870, 2236, 4135, 3497, 3245, 871, 2611],\n", + " [3165, 3347, 5260, 678, 2935, 2452, 3422, 2547, 679, 2611],\n", + " [2873, 4141, 753, 4133, 2435, 635, 4134, 8444, 2236, 3999],\n", + " [3192, 3195, 2534, 3346, 3186, 3278, 3605, 3194, 3193, 3337],\n", + " [3173, 3165, 3972, 3497, 901, 870, 2955, 3435, 2236, 2681],\n", + " [2796, 3192, 6313, 2534, 3605, 3261, 3195, 3193, 2780, 3278],\n", + " [3260, 3350, 1066, 2886, 8808, 7388, 1663, 2337, 5624, 8443],\n", + " [3173, 3165, 4135, 870, 901, 3497, 2236, 3972, 871, 2681],\n", + " [3325, 3637, 2502, 7017, 1805, 3098, 2768, 2236, 3687, 679],\n", + " [6896, 7741, 6325, 5612, 4647, 7253, 6898, 6335, 5614, 7663],\n", + " [79, 208, 1805, 7106, 517, 119, 2503, 4000, 3637, 2546],\n", + " [3403, 3417, 3412, 3419, 3420, 3418, 3404, 3458, 3457, 3409],\n", + " [3411, 8263, 8265, 1542, 5738, 7545, 7299, 8149, 6262, 3420],\n", + " [3413, 3402, 3405, 3449, 3404, 3966, 3403, 3458, 3416, 3457],\n", + " [3154, 4126, 6532, 387, 8284, 5307, 3605, 3325, 629, 8112],\n", + " [3436, 3437, 3566, 3887, 7610, 3973, 3035, 6281, 3891, 3111],\n", + " [3421, 4127, 4015, 6814, 6024, 3090, 3887, 5337, 2780, 4026],\n", + " [79, 208, 1805, 517, 7106, 119, 2503, 4000, 4098, 3637],\n", + " [2236, 3435, 2342, 3584, 3210, 870, 5458, 5450, 2993, 5463],\n", + " [3464, 3447, 8521, 207, 3122, 8880, 1714, 2512, 211, 2932],\n", + " [3450, 3958, 3403, 3412, 3607, 4623, 4622, 5246, 3885, 3420],\n", + " [3451, 3978, 9046, 3409, 10573, 3420, 6288, 537, 10069, 2784],\n", + " [6099, 6132, 6201, 8744, 7123, 3464, 7570, 3501, 6577, 6112],\n", + " [3964, 183, 3409, 3419, 3420, 3632, 3633, 5917, 3937, 3733],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3429, 3498, 3605, 3430, 3885, 3403, 3884, 3599, 6919, 2173],\n", + " [3493, 3605, 3511, 3492, 3425, 3470, 3245, 2987, 2971, 3530],\n", + " [3470, 3497, 3493, 3671, 3548, 3476, 3544, 3546, 870, 901],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3599, 870, 3498, 3916, 3315, 2173, 3403, 698, 3195, 3429],\n", + " [3980, 4098, 5771, 7141, 6839, 3353, 7173, 5935, 2682, 2734],\n", + " [3470, 3497, 3544, 3493, 3671, 3546, 3476, 870, 2236, 3548],\n", + " [3529, 3593, 1701, 2795, 2794, 1700, 7854, 3496, 3658, 8127],\n", + " [9166, 3157, 2333, 5578, 387, 7112, 8171, 7770, 7115, 7114],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3516, 3517, 3519, 3515, 7499, 2654, 2136, 3508, 3887, 2633],\n", + " [3470, 3497, 870, 3548, 3493, 2236, 3671, 3476, 901, 3546],\n", + " [3916, 5439, 3429, 3430, 3403, 3498, 3425, 3885, 3884, 3599],\n", + " [3474, 8583, 2508, 6022, 3165, 3478, 3298, 3192, 8681, 3226],\n", + " [3470, 3497, 3493, 2236, 3548, 870, 3671, 901, 3476, 3544],\n", + " [3476, 3546, 870, 3648, 5265, 3497, 3528, 2236, 5505, 3526],\n", + " [870, 3507, 3470, 3598, 3462, 3422, 3157, 901, 3347, 3497],\n", + " [9728, 9745, 9525, 2589, 9727, 1262, 9730, 2333, 6064, 2795],\n", + " [3403, 3885, 3884, 5439, 3412, 3498, 3916, 3607, 3886, 5245],\n", + " [5439, 5124, 3571, 3566, 6667, 5441, 9859, 2622, 6772, 5708],\n", + " [8141, 7739, 9896, 7002, 8262, 6189, 6934, 6857, 6943, 6390],\n", + " [5427, 3470, 3548, 3175, 3586, 2352, 870, 2312, 5463, 3426],\n", + " [3470, 3497, 870, 3546, 3544, 3476, 3596, 3493, 3671, 2236],\n", + " [3476, 2236, 870, 3497, 3482, 3546, 901, 3470, 5427, 5531],\n", + " [3535, 677, 8274, 387, 7894, 2911, 870, 2502, 2237, 2356],\n", + " [3474, 8583, 2508, 6022, 3165, 3478, 3298, 3192, 8681, 3226],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3435, 2342, 2236, 3497, 3470, 1683, 274, 2338, 1820, 85],\n", + " [3415, 3416, 3413, 3405, 3637, 3572, 3404, 3402, 3916, 3449],\n", + " [3634, 3627, 3630, 5027, 6047, 3776, 6222, 7167, 3625, 6050],\n", + " [3638, 3803, 2236, 3671, 7302, 9567, 3759, 3807, 677, 7017],\n", + " [3648, 3647, 2896, 3545, 3587, 3806, 3645, 5270, 3534, 1514],\n", + " [3587, 2896, 3658, 3545, 3659, 3806, 863, 4716, 3648, 2260],\n", + " [3667, 3779, 3974, 3864, 3776, 3866, 3891, 3513, 3973, 4666],\n", + " [3638, 3803, 2236, 677, 3807, 2503, 3671, 3426, 5528, 9567],\n", + " [3782, 3426, 4109, 4222, 4715, 5268, 4777, 3758, 4142, 3861],\n", + " [3691, 2359, 3657, 124, 3873, 3275, 3787, 95, 3543, 2312],\n", + " [3637, 3687, 2780, 3416, 2236, 3415, 2260, 211, 4073, 4013],\n", + " [3695, 4149, 7082, 3091, 5964, 5769, 7753, 5802, 5760, 7040],\n", + " [3698, 3702, 3777, 4787, 3703, 5547, 4849, 8291, 4842, 3705],\n", + " [3721, 3714, 3729, 3711, 3724, 3722, 3713, 3730, 3723, 3728],\n", + " [3738, 3743, 3733, 3739, 3737, 3744, 3734, 3748, 3841, 3735],\n", + " [3738, 3743, 3733, 3739, 3737, 3744, 3734, 3748, 3841, 3735],\n", + " [3644, 2236, 3690, 3687, 2260, 2780, 107, 753, 3543, 2503],\n", + " [3638, 3803, 2236, 677, 3807, 2503, 3671, 3426, 5528, 9567],\n", + " [3644, 2236, 3690, 3687, 2260, 2780, 107, 753, 3543, 2503],\n", + " [3762, 3761, 3766, 3882, 3763, 3759, 3767, 3671, 3807, 3638],\n", + " [3325, 3637, 7017, 2768, 2703, 3090, 1315, 389, 759, 3543],\n", + " [3637, 3687, 2780, 3416, 2236, 3415, 2260, 211, 4073, 4013],\n", + " [3647, 3648, 2780, 4011, 2896, 2210, 3755, 506, 4202, 206],\n", + " [3775, 3776, 7713, 3634, 3667, 3253, 3815, 3779, 3589, 3169],\n", + " [3671, 3670, 2503, 208, 2236, 3638, 1805, 3470, 3403, 3759],\n", + " [3671, 3670, 2503, 208, 2236, 3638, 1805, 3470, 3403, 3759],\n", + " [3686, 3782, 2385, 3649, 201, 3781, 7748, 5230, 4776, 755],\n", + " [3545, 6546, 7107, 3587, 207, 3658, 7106, 95, 5826, 2955],\n", + " [3637, 3687, 4073, 7017, 4158, 3671, 2236, 3542, 3320, 690],\n", + " [3638, 3803, 3759, 2236, 3671, 3762, 9567, 3882, 3807, 7302],\n", + " [3638, 3803, 2236, 3671, 7302, 9567, 3759, 3807, 677, 7017],\n", + " [3691, 2359, 3657, 124, 3873, 3275, 3787, 95, 3543, 2312],\n", + " [3671, 3670, 2503, 208, 2236, 3638, 1805, 3470, 3403, 3759],\n", + " [3649, 3546, 3754, 3832, 3688, 3476, 3753, 3671, 3657, 3801],\n", + " [3667, 3779, 3974, 3864, 3776, 3866, 3891, 3513, 3973, 4666],\n", + " [3835, 206, 3763, 2236, 5007, 2260, 230, 5008, 4202, 3991],\n", + " [3647, 3587, 3659, 3648, 2896, 3545, 3806, 3690, 3759, 3476],\n", + " [9567, 3638, 3759, 3671, 3803, 2236, 3670, 3187, 211, 9552],\n", + " [3670, 3671, 3470, 2503, 2236, 4073, 1805, 208, 3638, 3403],\n", + " [3851, 3862, 3849, 3853, 3847, 3848, 3863, 3758, 3861, 3665],\n", + " [3851, 3862, 3849, 3853, 3847, 3848, 3863, 3758, 3861, 3665],\n", + " [3637, 3687, 2780, 3416, 2236, 3415, 2260, 211, 4073, 4013],\n", + " [3644, 2236, 3690, 3687, 2260, 2780, 107, 753, 3543, 2503],\n", + " [3667, 3779, 3974, 3866, 3864, 3869, 3868, 3776, 5470, 3924],\n", + " [3637, 3687, 2780, 3416, 2236, 3415, 2260, 211, 4073, 4013],\n", + " [3403, 3885, 3884, 5439, 3412, 3498, 3916, 3607, 3886, 5245],\n", + " [3605, 3885, 3886, 3884, 3403, 3916, 3498, 3861, 3551, 5334],\n", + " [3436, 3437, 3566, 3887, 7610, 3973, 3035, 6281, 3891, 3111],\n", + " [3427, 5245, 870, 3412, 3403, 3572, 3916, 5246, 3426, 3454],\n", + " [3424, 9526, 207, 2576, 2392, 8466, 2312, 1784, 7238, 2737],\n", + " [3425, 3543, 3430, 3916, 3861, 3758, 1805, 2547, 3796, 3605],\n", + " [3320, 3606, 3319, 2236, 3323, 160, 2509, 814, 5527, 3159],\n", + " [3909, 3893, 3324, 3914, 3920, 3913, 3917, 3912, 3911, 3910],\n", + " [3925, 3924, 5286, 3933, 3787, 4079, 5297, 3654, 5347, 4121],\n", + " [3925, 3924, 5297, 4093, 3933, 2821, 4520, 3477, 3432, 5286],\n", + " [3931, 3938, 3934, 3941, 3947, 3949, 3946, 3924, 3942, 3939],\n", + " [3953, 3955, 3951, 3952, 3949, 2547, 3896, 3943, 3945, 3787],\n", + " [3953, 3955, 3952, 3951, 3949, 3896, 3787, 3542, 3943, 3939],\n", + " [2955, 3901, 3173, 7106, 4131, 79, 5235, 3449, 5259, 2503],\n", + " [3422, 5384, 3423, 3192, 3605, 2173, 5379, 3599, 3165, 2780],\n", + " [5979, 3269, 10026, 4754, 5611, 8183, 6594, 5813, 5648, 6305],\n", + " [79, 208, 1805, 7106, 517, 2503, 2629, 119, 2955, 4098],\n", + " [3413, 3402, 3405, 3449, 3404, 3966, 3403, 3458, 3416, 3457],\n", + " [3403, 3417, 3412, 3419, 3420, 3418, 3404, 3458, 3457, 3409],\n", + " [3413, 3402, 3405, 3449, 3404, 3966, 3403, 3458, 3416, 3457],\n", + " [3403, 3417, 3412, 3419, 3420, 3418, 3404, 3458, 3457, 3409],\n", + " [3403, 3417, 3412, 3419, 3420, 3418, 3404, 3458, 3457, 3409],\n", + " [3154, 4126, 6532, 387, 8284, 5307, 3605, 3325, 629, 8112],\n", + " [2955, 3901, 3173, 7106, 4131, 79, 5235, 3449, 5259, 2503],\n", + " [3871, 3969, 7698, 7568, 7299, 7696, 7664, 7697, 3266, 7534],\n", + " [5318, 6989, 6029, 2630, 6758, 3887, 2623, 7095, 6024, 8281],\n", + " [3413, 3402, 3405, 3449, 3404, 3966, 3403, 3458, 3416, 3457],\n", + " [3413, 3402, 3405, 3449, 3404, 3966, 3403, 3458, 3416, 3457],\n", + " [2955, 3901, 5917, 5259, 5235, 7106, 3449, 2503, 4158, 4131],\n", + " [677, 4968, 3099, 3157, 3474, 3478, 4982, 6065, 5148, 8437],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 207, 4393, 2875, 2236],\n", + " [4001, 5057, 4554, 4029, 4648, 4015, 4078, 4207, 4102, 4150],\n", + " [4011, 4073, 4138, 2236, 4155, 3991, 4208, 4001, 3319, 3323],\n", + " [4008, 4014, 4009, 4964, 4648, 4017, 4393, 4002, 5059, 4937],\n", + " [4001, 5057, 4020, 4026, 4094, 4658, 5058, 4620, 4607, 4003],\n", + " [3989, 3990, 2367, 4178, 4177, 181, 4739, 4738, 4027, 4038],\n", + " [4019, 4018, 4020, 4101, 4154, 4432, 4016, 4170, 4089, 4151],\n", + " [4076, 4136, 4747, 4746, 5037, 4748, 4035, 4703, 5102, 4204],\n", + " [4019, 4018, 4020, 4101, 4154, 4432, 4016, 4170, 4089, 4151],\n", + " [4015, 4030, 4348, 4208, 4149, 5130, 4190, 5007, 4162, 5157],\n", + " [4011, 4153, 4155, 4073, 4012, 4138, 4013, 4074, 4165, 5011],\n", + " [4043, 5156, 4015, 4016, 4150, 4071, 4251, 4019, 5769, 4227],\n", + " [79, 208, 1805, 7106, 517, 119, 4098, 2503, 8418, 7141],\n", + " [4052, 6029, 6024, 4029, 4026, 5133, 2525, 4002, 4066, 4722],\n", + " [4026, 4014, 4017, 205, 4752, 4007, 3809, 204, 4829, 4193],\n", + " [3992, 2051, 7414, 2738, 706, 5578, 207, 677, 1714, 6844],\n", + " [3999, 3998, 4000, 4542, 1805, 4535, 4074, 2503, 3098, 1514],\n", + " [3998, 4000, 3999, 4232, 2629, 5057, 3094, 3997, 4600, 4586],\n", + " [3997, 3998, 7106, 640, 4280, 3210, 4000, 2236, 7107, 2408],\n", + " [4086, 5058, 4085, 4393, 4648, 4002, 5057, 3176, 4667, 5757],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [4087, 4170, 4089, 7981, 9660, 4647, 4889, 4761, 5155, 4891],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3997, 7106, 640, 3998, 2408, 7107, 5110, 4280, 3210, 86],\n", + " [79, 208, 1805, 7106, 119, 2503, 4098, 517, 2546, 4074],\n", + " [3988, 4004, 1805, 3474, 1539, 2353, 4135, 2508, 3320, 3243],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3996, 5792, 2514, 8381, 86, 6387, 4710, 4759, 3574, 107],\n", + " [4097, 2567, 4000, 753, 667, 2512, 108, 3301, 2896, 5077],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4987, 4155, 5032, 4073, 4138, 2884, 5123, 4012, 4614, 4006],\n", + " [4102, 4107, 4021, 4155, 4013, 4026, 5057, 4207, 4011, 4752],\n", + " [4102, 4107, 4021, 4155, 4013, 4026, 5057, 4207, 4011, 4752],\n", + " [79, 208, 7106, 1805, 4098, 2503, 517, 2546, 119, 8418],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [3997, 7106, 640, 3998, 2408, 4280, 7107, 3210, 5110, 629],\n", + " [4000, 2503, 3999, 107, 79, 4714, 863, 6766, 5121, 4097],\n", + " [4021, 4081, 4016, 4102, 5058, 5057, 5056, 4107, 4013, 4336],\n", + " [4438, 5053, 10543, 4274, 4719, 4094, 4115, 8755, 2337, 3508],\n", + " [9492, 4608, 4607, 4101, 4393, 4512, 3990, 3250, 4441, 4667],\n", + " [4693, 108, 2236, 4001, 2342, 3435, 2196, 3172, 667, 274],\n", + " [4149, 4545, 4266, 4270, 4273, 4694, 4408, 4410, 4411, 4271],\n", + " [4150, 4151, 4001, 160, 4285, 4239, 519, 4019, 4015, 4238],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [3991, 4172, 4011, 1471, 4169, 5127, 4155, 5150, 5149, 4031],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 207, 4393, 2875, 2236],\n", + " [4184, 4101, 4183, 4431, 3990, 4164, 4149, 4210, 4738, 4739],\n", + " [4614, 4006, 4579, 4618, 4102, 4640, 4031, 4013, 5149, 5150],\n", + " [4173, 3675, 3676, 7986, 5006, 6544, 4724, 1597, 2818, 4854],\n", + " [5135, 5134, 4739, 4738, 4178, 3989, 3990, 2367, 4177, 4182],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [4073, 4155, 4012, 4138, 4011, 4149, 2236, 4208, 3637, 2503],\n", + " [4012, 4073, 4138, 4298, 4155, 4402, 5046, 4156, 4013, 4202],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3992, 2051, 7414, 2738, 207, 706, 5578, 1714, 677, 755],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [4108, 4179, 4180, 4807, 5362, 6716, 842, 4799, 3984, 4606],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [4184, 4183, 4164, 4431, 3990, 4038, 4210, 4212, 3989, 4158],\n", + " [3210, 5144, 4540, 1471, 5120, 214, 2776, 4541, 3574, 4736],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [4000, 2503, 863, 4994, 6766, 4248, 107, 5207, 6598, 214],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [4011, 4055, 4153, 3991, 2955, 6814, 2166, 4155, 3811, 3689],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [79, 208, 1805, 119, 7106, 2503, 517, 4098, 2546, 4074],\n", + " [3098, 107, 6929, 1805, 2435, 2236, 2873, 5054, 5668, 9429],\n", + " [3997, 7106, 3998, 640, 2408, 4280, 7107, 3210, 2236, 2351],\n", + " [3988, 4004, 1805, 3474, 3243, 4135, 2796, 4759, 2508, 2353],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [829, 4708, 4711, 4054, 4709, 5829, 6834, 4644, 4815, 1805],\n", + " [79, 208, 1805, 7106, 119, 2503, 4098, 517, 2546, 4074],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [79, 208, 1805, 119, 7106, 517, 9991, 3449, 3637, 8627],\n", + " [79, 208, 1805, 119, 7106, 2503, 517, 4098, 2546, 4074],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [4000, 4097, 6766, 107, 863, 4714, 2503, 6626, 6598, 79],\n", + " [4181, 4182, 4212, 4210, 4169, 5134, 5135, 4239, 4238, 4211],\n", + " [3990, 3989, 4162, 181, 4015, 5157, 4030, 2367, 4178, 4149],\n", + " [2629, 86, 690, 4067, 4091, 3996, 3998, 5962, 8508, 154],\n", + " [4227, 4648, 5058, 4612, 5117, 3098, 4554, 7029, 4019, 4393],\n", + " [10435, 7974, 4234, 9943, 5156, 9964, 9093, 7336, 4336, 4377],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [4650, 4241, 4026, 4580, 5030, 4031, 4029, 5088, 5087, 4020],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [3098, 5668, 5696, 6929, 5682, 5666, 5054, 4141, 7029, 9429],\n", + " [4156, 4012, 4298, 4258, 4256, 4255, 4317, 4316, 4315, 5046],\n", + " [4530, 4527, 4265, 4529, 4259, 4531, 4276, 4189, 4261, 4278],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [4530, 4527, 4265, 4529, 4259, 4531, 4276, 4189, 4261, 4278],\n", + " [4530, 4527, 4265, 4529, 4259, 4531, 4276, 4189, 4261, 4278],\n", + " [4280, 3997, 4502, 4745, 4706, 5017, 4254, 5099, 246, 4693],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4253, 2629, 107, 3002, 2955, 233, 4132, 2632, 2797, 4085],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4399, 4318, 4323, 4320, 4322, 4321, 5128, 4336, 4337, 4376],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4254, 2571, 4280, 4502, 8376, 5099, 211, 2348, 4735, 4741],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [4254, 2571, 4280, 4502, 8376, 5099, 211, 2348, 4735, 4741],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [4149, 4545, 4266, 4270, 4273, 4694, 4408, 4410, 4411, 4271],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4368, 4361, 4364, 4363, 4374, 4366, 4362, 4367, 4371, 4370],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [4149, 4545, 4266, 4270, 4273, 4694, 4408, 4410, 4411, 4271],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4280, 4502, 4990, 4745, 5099, 4706, 4934, 4254, 246, 4741],\n", + " [4149, 181, 4162, 3990, 3989, 4184, 4230, 4736, 4183, 4101],\n", + " [5065, 4753, 5063, 5064, 5066, 4259, 4400, 5014, 5086, 5106],\n", + " [4254, 4280, 2571, 4502, 5099, 211, 4741, 4735, 8376, 4693],\n", + " [4149, 4545, 4266, 4270, 4273, 4694, 4408, 4410, 4411, 4271],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [4280, 4254, 4502, 5099, 4706, 4990, 4745, 4934, 4967, 4010],\n", + " [4254, 2571, 4280, 4502, 8376, 5099, 211, 2348, 4735, 4741],\n", + " [4442, 4393, 4555, 4648, 4001, 5057, 4554, 5059, 4140, 2051],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4149, 4545, 4266, 4270, 4273, 4694, 4408, 4410, 4411, 4271],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [4205, 4206, 4138, 4155, 4013, 3566, 3973, 3510, 3891, 4092],\n", + " [4012, 4156, 4298, 4258, 4256, 4138, 4255, 4015, 4150, 4155],\n", + " [5064, 5063, 5066, 5065, 4259, 4189, 5014, 4454, 5106, 5069],\n", + " [4016, 4612, 4393, 4002, 3600, 4608, 4680, 4336, 4337, 2533],\n", + " [4377, 4509, 4384, 4376, 4441, 4608, 4439, 4440, 4511, 4607],\n", + " [79, 208, 1805, 7106, 119, 2503, 4098, 517, 2546, 4074],\n", + " [79, 208, 1805, 7106, 119, 2503, 4098, 517, 2546, 4074],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [3992, 2051, 7414, 2738, 706, 5578, 207, 677, 1714, 6844],\n", + " [4011, 4155, 4138, 4073, 2684, 3991, 3068, 4165, 4153, 3067],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 4393, 207, 2875, 4001],\n", + " [107, 3574, 1805, 2590, 2236, 3996, 214, 3098, 4000, 206],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [79, 208, 7106, 1805, 4098, 2503, 517, 2546, 8418, 7141],\n", + " [5058, 4002, 4003, 4030, 4015, 4648, 5059, 4207, 4013, 5154],\n", + " [5057, 4001, 4015, 4554, 4102, 4074, 4165, 4078, 4026, 4393],\n", + " [3210, 214, 2236, 2503, 5144, 3574, 1471, 4540, 3999, 2351],\n", + " [3098, 6929, 1805, 5668, 107, 5054, 2236, 2503, 2435, 8418],\n", + " [3999, 3998, 3098, 4542, 2503, 4074, 1805, 4000, 2236, 4535],\n", + " [79, 208, 7106, 1805, 4098, 2503, 517, 2546, 8418, 7141],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [2629, 86, 2503, 4253, 107, 7106, 4073, 3996, 1805, 79],\n", + " [2629, 86, 4067, 2503, 4073, 3998, 4253, 2236, 679, 3002],\n", + " [4253, 2629, 107, 3002, 2955, 233, 4132, 2632, 2797, 4085],\n", + " [4561, 4564, 4563, 4565, 4567, 4566, 4560, 4562, 4377, 4376],\n", + " [79, 208, 1805, 7106, 119, 4098, 2546, 517, 2503, 8418],\n", + " [4438, 5053, 4155, 4137, 3508, 3039, 668, 10543, 9039, 9857],\n", + " [4441, 4439, 4440, 4384, 4376, 4608, 4607, 4158, 4377, 4509],\n", + " [4177, 4178, 2367, 4494, 4646, 4401, 4575, 4491, 4572, 4571],\n", + " [4177, 4178, 2367, 4494, 4646, 4401, 4575, 4491, 4572, 4571],\n", + " [4614, 4579, 4618, 4006, 4012, 4385, 4753, 5015, 4607, 5150],\n", + " [3098, 107, 6929, 5668, 1805, 9429, 5054, 2435, 4013, 6888],\n", + " [3999, 4542, 3998, 4535, 1805, 3098, 5848, 4074, 4000, 3285],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [3992, 2051, 7414, 2738, 207, 706, 5578, 1714, 677, 755],\n", + " [3991, 3990, 3989, 4172, 4169, 4149, 2236, 5760, 5007, 4011],\n", + " [4585, 5999, 4521, 4994, 3998, 4998, 4600, 5001, 3094, 4543],\n", + " [4142, 4148, 4631, 4588, 4591, 4629, 4799, 4637, 4633, 4636],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [4073, 4138, 4203, 4155, 4012, 4149, 2236, 2503, 4158, 4987],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4191, 5044, 8822, 4720, 4721, 5732, 5680, 5550, 9329, 4581],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [3210, 5144, 4704, 4540, 4090, 4543, 4762, 5120, 4886, 8381],\n", + " [4092, 4088, 2497, 4172, 3039, 3800, 3572, 4095, 4708, 4387],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [4259, 4454, 5064, 4238, 5063, 5065, 4239, 5066, 5127, 5152],\n", + " [3996, 5792, 2514, 8381, 86, 6387, 4710, 4759, 3574, 107],\n", + " [4615, 4106, 4616, 4377, 4435, 4101, 4376, 4445, 4446, 4509],\n", + " [4073, 4138, 4987, 4012, 4203, 4155, 2884, 4719, 5032, 5046],\n", + " [4298, 4156, 4015, 4317, 4230, 4316, 4611, 4399, 4315, 4258],\n", + " [3990, 3989, 4739, 4738, 4178, 4164, 4169, 2367, 4177, 4183],\n", + " [4102, 4107, 4021, 4155, 4013, 4026, 5057, 4207, 4011, 4752],\n", + " [4626, 2497, 7907, 10328, 7911, 8078, 873, 8121, 3362, 3882],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [4633, 4148, 4142, 4588, 4631, 4603, 4714, 4717, 4684, 4591],\n", + " [4142, 4148, 4588, 4631, 4591, 4633, 4637, 4629, 4636, 4684],\n", + " [4193, 4107, 4638, 4639, 4677, 4089, 4738, 4393, 4207, 4209],\n", + " [4650, 4241, 4026, 4580, 5030, 4031, 4029, 5088, 5087, 4020],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [4653, 8758, 9685, 9631, 5934, 9645, 2873, 2876, 5868, 5897],\n", + " [3992, 2051, 7414, 2738, 706, 5578, 207, 677, 1714, 6844],\n", + " [4177, 4178, 2367, 4494, 4646, 4401, 4575, 4491, 4572, 4571],\n", + " [4177, 4178, 2367, 4494, 4646, 4401, 4575, 4491, 4572, 4571],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [3997, 3998, 7106, 640, 2408, 4280, 2236, 7107, 5110, 2351],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3997, 640, 3998, 7106, 4280, 3210, 2408, 2236, 7107, 2351],\n", + " [3210, 3999, 4714, 4000, 5144, 1471, 3997, 4102, 4704, 5104],\n", + " [79, 208, 1805, 7106, 119, 517, 2503, 4098, 8418, 2546],\n", + " [107, 1805, 3098, 2236, 3574, 2503, 3996, 214, 4000, 2590],\n", + " [79, 208, 7106, 1805, 4098, 2503, 517, 2546, 8418, 7141],\n", + " [4675, 4674, 4672, 9456, 6070, 5279, 3756, 9535, 10316, 9545],\n", + " [4675, 4674, 4672, 9456, 6070, 5279, 3756, 9535, 10316, 9545],\n", + " [4675, 4674, 4672, 9456, 6070, 5279, 3756, 9535, 10316, 9545],\n", + " [3098, 6929, 5668, 1805, 107, 5054, 8418, 2236, 2435, 2873],\n", + " [4688, 8444, 2500, 6315, 4835, 86, 9641, 4080, 4843, 6154],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 4393, 207, 2875, 4001],\n", + " [4696, 4698, 4695, 4697, 4702, 4660, 4647, 4424, 4030, 4659],\n", + " [4700, 4658, 4526, 4647, 4026, 4525, 5132, 4748, 4607, 4696],\n", + " [4693, 9684, 9386, 8021, 4659, 5934, 3039, 668, 4084, 4667],\n", + " [4165, 4658, 4011, 4526, 5057, 5104, 4700, 4074, 4026, 136],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3210, 5809, 2590, 3296, 9421, 6913, 5098, 4989, 3574, 5842],\n", + " [2629, 86, 4091, 4067, 690, 3998, 8508, 4073, 7106, 4155],\n", + " [4006, 4618, 4614, 4385, 4031, 4149, 5150, 4579, 4607, 5015],\n", + " [4720, 5156, 5006, 8058, 4013, 4043, 4298, 4742, 4235, 5054],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4337, 4336, 4376, 4377, 4393, 4320, 4612, 4323, 4322, 4321],\n", + " [4254, 2571, 211, 8376, 4502, 4735, 4280, 5099, 4741, 4248],\n", + " [4280, 3997, 4745, 4502, 5017, 4706, 5099, 4148, 5057, 4026],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [5104, 4165, 2684, 4088, 3289, 4155, 4021, 4026, 4031, 4001],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3996, 5792, 8381, 107, 2514, 3574, 6387, 86, 4710, 4759],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4012, 4073, 4138, 4298, 4155, 4402, 5046, 4156, 4013, 4202],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [4000, 2503, 3999, 863, 107, 6766, 4714, 3998, 79, 842],\n", + " [4298, 4317, 4399, 4156, 4316, 4315, 4449, 4450, 4447, 4451],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [3210, 5144, 3574, 1471, 3999, 3997, 214, 4090, 4540, 2236],\n", + " [3999, 3998, 1805, 4542, 3098, 4000, 4074, 4535, 2236, 2873],\n", + " [4298, 4432, 4043, 4317, 4449, 4447, 4451, 4015, 4448, 4450],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [3999, 4542, 3098, 4074, 5850, 6929, 4000, 2873, 4535, 1805],\n", + " [4177, 4178, 2367, 4494, 4646, 4401, 4575, 4491, 4572, 4571],\n", + " [4401, 4494, 4732, 4730, 4729, 4731, 4576, 4573, 4572, 4575],\n", + " [4753, 4400, 4614, 5065, 5063, 5066, 5064, 5014, 4101, 4259],\n", + " [4686, 4080, 8032, 4013, 5262, 4102, 4220, 3325, 4026, 1343],\n", + " [4085, 4086, 5057, 3002, 5757, 4393, 675, 3157, 3470, 5058],\n", + " [3996, 5792, 8381, 107, 2514, 3574, 6387, 86, 4710, 4759],\n", + " [4087, 4170, 4089, 7981, 9660, 4647, 4889, 4761, 5155, 4891],\n", + " [4846, 5155, 6557, 5154, 4766, 4845, 4852, 4869, 4830, 4847],\n", + " [3322, 4787, 4847, 4804, 4986, 4800, 4770, 4906, 4762, 9380],\n", + " [4770, 4774, 4769, 4798, 4771, 4796, 4786, 4804, 4782, 4756],\n", + " [4781, 4761, 4818, 4764, 4779, 4782, 4796, 4891, 4647, 3801],\n", + " [4807, 4812, 4872, 4842, 4811, 4802, 4800, 4756, 4799, 4804],\n", + " [4771, 4767, 4756, 4798, 4795, 4770, 4772, 4777, 4830, 4792],\n", + " [4781, 4782, 4779, 4796, 4818, 4891, 4761, 4772, 4764, 863],\n", + " [79, 208, 1805, 517, 119, 7106, 4000, 2503, 3449, 3637],\n", + " [4804, 4924, 4905, 4796, 4802, 4800, 4979, 4951, 4762, 4810],\n", + " [4804, 4802, 4797, 4721, 4856, 4786, 4951, 4796, 4775, 4769],\n", + " [4809, 4777, 4377, 4795, 4799, 4647, 4797, 4792, 4764, 4796],\n", + " [4813, 8296, 6921, 2622, 496, 10538, 2277, 561, 960, 2500],\n", + " [4800, 4923, 4924, 4845, 4846, 4779, 4905, 4921, 7663, 5679],\n", + " [4905, 4785, 4846, 4843, 4924, 4773, 4796, 4845, 4916, 4855],\n", + " [4787, 4866, 4814, 4779, 4815, 4847, 4845, 4764, 4905, 4786],\n", + " [4823, 4818, 4829, 4905, 4054, 4943, 4860, 4869, 4779, 4838],\n", + " [4847, 4840, 4835, 4783, 4916, 4824, 4830, 2701, 4845, 4965],\n", + " [4838, 4842, 4833, 4834, 4825, 4831, 4851, 4869, 4864, 4856],\n", + " [4843, 4842, 4846, 4845, 4947, 4962, 4869, 4834, 4830, 4874],\n", + " [4789, 4785, 4837, 5031, 4913, 4916, 4830, 4932, 4798, 4783],\n", + " [4858, 4799, 4866, 4807, 4825, 4830, 4764, 4869, 4861, 4756],\n", + " [4838, 4842, 4831, 4833, 4823, 4851, 4869, 4861, 4834, 4856],\n", + " [4861, 4860, 4858, 4856, 4838, 4869, 4855, 4831, 4764, 4833],\n", + " [4869, 4779, 4838, 4866, 4923, 4823, 4842, 4825, 4787, 4833],\n", + " [4875, 4885, 4886, 4872, 4873, 4892, 4900, 4876, 4896, 4880],\n", + " [4888, 4882, 4892, 4881, 4872, 4879, 4876, 4875, 4884, 4799],\n", + " [4872, 4985, 4887, 4896, 4876, 4757, 4804, 4875, 4892, 4960],\n", + " [4905, 4962, 4947, 4846, 4867, 4820, 4956, 4949, 4943, 4869],\n", + " [4783, 4955, 4855, 4811, 4869, 4845, 4795, 4837, 4857, 4924],\n", + " [4759, 4847, 4761, 205, 4762, 4790, 86, 4788, 8720, 4149],\n", + " [4929, 4960, 4962, 4933, 4949, 4932, 4938, 4959, 4935, 4950],\n", + " [4933, 4929, 4913, 4932, 4935, 4880, 4939, 4822, 2567, 4931],\n", + " [4956, 4953, 4952, 4945, 4950, 4957, 4943, 4942, 4905, 4947],\n", + " [4953, 4957, 4927, 4925, 4956, 4928, 4832, 4819, 4934, 10269],\n", + " [4969, 4986, 4984, 4971, 4978, 4979, 4975, 4804, 4977, 4985],\n", + " [4969, 4974, 4979, 4972, 4971, 4975, 4984, 7659, 4978, 6335],\n", + " [4977, 4971, 4979, 4982, 4976, 4986, 4981, 4975, 4856, 4905],\n", + " [4752, 5061, 4405, 2715, 4089, 4207, 5038, 4107, 5127, 4078],\n", + " [4714, 4006, 2644, 4526, 5131, 4029, 4525, 4533, 5059, 4988],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [5007, 4208, 4348, 5008, 5009, 4190, 5130, 4149, 4736, 3991],\n", + " [5007, 4208, 4348, 5008, 5009, 4190, 5130, 4149, 4736, 3991],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [4741, 5099, 4502, 4990, 4693, 4501, 4017, 4705, 4745, 4993],\n", + " [5023, 5021, 5022, 5281, 5280, 9989, 9999, 9992, 8671, 8670],\n", + " [5023, 5021, 5022, 5281, 5280, 9989, 9999, 9992, 8671, 8670],\n", + " [3988, 4004, 1805, 3474, 1539, 2353, 4135, 2508, 3320, 3243],\n", + " [2629, 4067, 2236, 86, 2197, 1805, 679, 3998, 2503, 2292],\n", + " [5025, 4548, 4666, 4665, 6070, 6128, 4109, 5490, 6109, 4724],\n", + " [1805, 3098, 3996, 5054, 4159, 107, 2503, 3999, 6929, 3370],\n", + " [5030, 5087, 5088, 5104, 5132, 5133, 5131, 4020, 4026, 4165],\n", + " [5030, 5087, 5088, 5104, 5132, 5133, 5131, 4020, 4026, 4165],\n", + " [4149, 4694, 181, 4736, 3990, 4208, 4196, 4738, 4172, 3989],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [5064, 5063, 5066, 5065, 4259, 4189, 5014, 4454, 5106, 5069],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [4002, 4023, 4172, 4612, 4088, 5058, 4535, 5059, 4054, 4648],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [2884, 4073, 4987, 4203, 4138, 4159, 4202, 107, 4155, 5123],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [4076, 4136, 4747, 4746, 5037, 4748, 4035, 4703, 5102, 4204],\n", + " [4013, 4074, 5059, 5057, 4239, 4238, 4607, 4107, 5129, 5060],\n", + " [4006, 4988, 4533, 4137, 4138, 10482, 4196, 4608, 6676, 5114],\n", + " [3993, 3992, 4004, 1902, 3320, 642, 4064, 1805, 4061, 4535],\n", + " [3997, 3998, 4280, 5057, 2236, 3210, 640, 870, 7106, 264],\n", + " [3997, 3210, 4714, 4543, 4599, 4745, 4600, 4544, 4000, 5001],\n", + " [3210, 5144, 2236, 3997, 1471, 3999, 3574, 214, 8381, 2503],\n", + " [4696, 4698, 4695, 4697, 4702, 4660, 4647, 4424, 4030, 4659],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [4643, 4095, 4092, 4734, 3629, 6617, 4898, 3292, 2856, 4642],\n", + " [79, 208, 1805, 119, 7106, 517, 2503, 4000, 8418, 4098],\n", + " [5059, 5060, 5058, 4555, 4393, 4013, 4535, 4648, 4002, 4069],\n", + " [5058, 4002, 4003, 4030, 4015, 4648, 5059, 4207, 4013, 5154],\n", + " [5058, 4002, 5059, 4013, 5060, 4030, 4648, 4003, 4019, 4015],\n", + " [5059, 5060, 5058, 4393, 4002, 4193, 4555, 4736, 4013, 4207],\n", + " [3999, 3998, 4542, 3098, 4000, 1805, 4074, 4535, 2503, 2236],\n", + " [3998, 4000, 4232, 3999, 5057, 2629, 3997, 3094, 4600, 4586],\n", + " [79, 208, 1805, 119, 7106, 2503, 517, 4098, 2546, 4074],\n", + " [4074, 5129, 4607, 4580, 1469, 4026, 581, 4013, 4658, 4204],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [4074, 4607, 4013, 5129, 4031, 4580, 5057, 4658, 4026, 5132],\n", + " [3098, 6929, 1805, 5668, 107, 5054, 2236, 2503, 2435, 8418],\n", + " [5053, 4719, 4438, 9039, 4736, 4274, 4015, 4298, 7242, 5030],\n", + " [2629, 4067, 3410, 86, 2665, 3998, 4073, 6539, 2797, 690],\n", + " [4091, 8224, 6912, 2629, 8418, 8274, 8186, 2715, 4067, 679],\n", + " [4000, 107, 5121, 6766, 6626, 3088, 3987, 6598, 4994, 5207],\n", + " [5082, 8755, 10002, 9827, 8485, 9638, 4604, 4654, 9829, 4095],\n", + " [3992, 2051, 7414, 2738, 207, 706, 5578, 1714, 677, 755],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 207, 4393, 2875, 2236],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [3292, 7081, 6617, 5738, 5439, 3403, 55, 8747, 2349, 8953],\n", + " [5059, 5060, 4013, 5058, 4193, 4736, 4393, 4555, 4535, 4002],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [3994, 4127, 2582, 4078, 6015, 3476, 4393, 207, 2875, 4001],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [3989, 3990, 2367, 4178, 4177, 181, 4739, 4738, 4027, 4038],\n", + " [4098, 7141, 7173, 8265, 7135, 8188, 8418, 6839, 7136, 6947],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [1805, 3098, 2503, 107, 679, 2236, 2166, 3999, 4159, 79],\n", + " [107, 1805, 3098, 3574, 2236, 3996, 2590, 214, 2503, 4000],\n", + " [3574, 107, 3210, 3996, 214, 4759, 2503, 205, 641, 5474],\n", + " [3988, 4004, 1805, 3474, 1539, 2353, 4135, 2508, 3320, 3243],\n", + " [4535, 4648, 5059, 4002, 4149, 5060, 4193, 1514, 1046, 4555],\n", + " [3996, 5792, 2514, 8381, 86, 6387, 4710, 4759, 3574, 107],\n", + " [4189, 4178, 3990, 4324, 4293, 5070, 4528, 3989, 4532, 5067],\n", + " [5064, 5063, 5066, 5065, 4259, 4189, 5014, 4454, 5106, 5069],\n", + " [5110, 657, 654, 2155, 1471, 640, 2337, 367, 7894, 4654],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [4091, 8224, 6912, 2629, 8418, 8274, 8186, 2715, 4067, 679],\n", + " [3988, 1805, 3474, 4004, 79, 4759, 4135, 3243, 2353, 1539],\n", + " [3992, 2051, 7414, 2738, 706, 5578, 207, 677, 1714, 6844],\n", + " [79, 208, 1805, 119, 7106, 3637, 517, 2503, 4098, 7141],\n", + " [79, 208, 1805, 119, 7106, 2503, 517, 4098, 2546, 4074],\n", + " [5126, 4721, 4581, 4720, 4109, 5487, 5130, 228, 5680, 4647],\n", + " [4000, 3999, 1805, 107, 2236, 2503, 3210, 4013, 4026, 204],\n", + " [3995, 9254, 4231, 1515, 3098, 3996, 205, 5553, 3615, 9499],\n", + " [4012, 4138, 4155, 4073, 4298, 4614, 4570, 4156, 4013, 4202],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [3988, 4004, 1805, 3474, 1539, 2353, 4135, 2508, 3320, 3243],\n", + " [79, 208, 1805, 7106, 119, 2503, 517, 4098, 2546, 4074],\n", + " [4004, 2629, 5056, 3320, 5428, 4232, 4685, 5149, 5263, 3988],\n", + " [4000, 4097, 6766, 107, 863, 4714, 2503, 6626, 6598, 79],\n", + " [4072, 4186, 4176, 4163, 4012, 181, 4641, 4358, 4328, 4357],\n", + " [3988, 4004, 1805, 3474, 1539, 2353, 4135, 2508, 3320, 3243],\n", + " [79, 208, 1805, 119, 3637, 7106, 517, 2503, 4098, 7141],\n", " ...]" ] }, - "execution_count": 147, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1475,10016 +1477,10016 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[37 0.772695\n", - " 42 0.033525\n", - " 264 0.011058\n", - " 2388 0.008950\n", - " 2387 0.007749\n", - " 2323 0.005315\n", - " 52 0.004629\n", - " 2108 0.004538\n", - " 41 0.003611\n", - " 36 0.002727\n", - " Name: 0, dtype: float64, 677 0.896984\n", - " 7957 0.017557\n", - " 159 0.010347\n", - " 3535 0.008073\n", - " 387 0.004320\n", - " 2716 0.002397\n", - " 675 0.001957\n", - " 624 0.001692\n", - " 167 0.000988\n", - " 2715 0.000980\n", - " Name: 1, dtype: float64, 79 9.999963e-01\n", - " 160 1.972942e-07\n", - " 9626 1.376567e-07\n", - " 119 1.152779e-07\n", - " 8040 9.365866e-08\n", - " 2260 9.172145e-08\n", - " 1203 8.231978e-08\n", - " 246 6.859354e-08\n", - " 206 6.340200e-08\n", - " 4011 5.775825e-08\n", - " Name: 2, dtype: float64, 2157 0.997329\n", - " 2175 0.000366\n", - " 7635 0.000122\n", - " 2236 0.000115\n", - " 7678 0.000070\n", - " 4141 0.000052\n", - " 2197 0.000047\n", - " 679 0.000042\n", - " 677 0.000041\n", - " 2896 0.000040\n", - " Name: 3, dtype: float64, 677 0.997978\n", - " 2236 0.000288\n", - " 159 0.000238\n", - " 2502 0.000118\n", - " 2503 0.000114\n", - " 870 0.000108\n", - " 2504 0.000106\n", - " 679 0.000105\n", - " 7957 0.000076\n", - " 2716 0.000065\n", - " Name: 4, dtype: float64, 79 0.425616\n", - " 119 0.268998\n", - " 2194 0.008453\n", - " 2260 0.007860\n", - " 2276 0.005782\n", - " 1046 0.005387\n", - " 2684 0.005005\n", - " 2168 0.004508\n", - " 9037 0.004197\n", - " 2215 0.003977\n", - " Name: 5, dtype: float64, 2200 0.998030\n", - " 2197 0.000339\n", - " 3991 0.000096\n", - " 2274 0.000049\n", - " 171 0.000042\n", - " 2194 0.000041\n", - " 2276 0.000035\n", - " 2394 0.000029\n", - " 2169 0.000023\n", - " 6824 0.000021\n", - " Name: 6, dtype: float64, 2693 0.167119\n", - " 2628 0.072360\n", - " 7487 0.040647\n", - " 3100 0.031182\n", - " 1940 0.017435\n", - " 10495 0.014096\n", - " 9473 0.013704\n", - " 7444 0.012889\n", - " 7509 0.012856\n", - " 478 0.009195\n", - " Name: 7, dtype: float64, 79 0.089228\n", - " 5263 0.062209\n", - " 206 0.021386\n", - " 4011 0.016789\n", - " 2215 0.010879\n", - " 2896 0.007021\n", - " 6533 0.006329\n", - " 119 0.005947\n", - " 1815 0.005443\n", - " 6726 0.005408\n", - " Name: 8, dtype: float64, 387 0.059774\n", - " 1917 0.046145\n", - " 2258 0.041636\n", - " 2253 0.033428\n", - " 2255 0.033331\n", - " 838 0.027562\n", - " 2244 0.024168\n", - " 2252 0.020303\n", - " 2249 0.017117\n", - " 60 0.015297\n", - " Name: 9, dtype: float64, 2246 0.999666\n", - " 7894 0.000041\n", - " 146 0.000022\n", - " 2355 0.000015\n", - " 2053 0.000011\n", - " 2254 0.000008\n", - " 3337 0.000007\n", - " 2173 0.000006\n", - " 84 0.000006\n", - " 387 0.000005\n", - " Name: 10, dtype: float64, 2246 0.999666\n", - " 7894 0.000041\n", - " 146 0.000022\n", - " 2355 0.000015\n", - " 2053 0.000011\n", - " 2254 0.000008\n", - " 3337 0.000007\n", - " 2173 0.000006\n", - " 84 0.000006\n", - " 387 0.000005\n", - " Name: 11, dtype: float64, 2246 0.999666\n", - " 7894 0.000041\n", - " 146 0.000022\n", - " 2355 0.000015\n", - " 2053 0.000011\n", - " 2254 0.000008\n", - " 3337 0.000007\n", - " 2173 0.000006\n", - " 84 0.000006\n", - " 387 0.000005\n", - " Name: 12, dtype: float64, 2247 0.999875\n", - " 7894 0.000004\n", - " 387 0.000003\n", - " 7627 0.000003\n", - " 2406 0.000003\n", - " 679 0.000002\n", - " 2157 0.000002\n", - " 2252 0.000002\n", - " 2348 0.000002\n", - " 901 0.000002\n", - " Name: 13, dtype: float64, 2258 0.154367\n", - " 2255 0.112764\n", - " 2249 0.065707\n", - " 2250 0.047743\n", - " 2253 0.047740\n", - " 2244 0.042815\n", - " 7236 0.031358\n", - " 2254 0.019452\n", - " 2251 0.015503\n", - " 7237 0.013642\n", - " Name: 14, dtype: float64, 2197 0.449157\n", - " 2173 0.351104\n", - " 108 0.044049\n", - " 84 0.005437\n", - " 2157 0.005424\n", - " 8319 0.005405\n", - " 2259 0.004214\n", - " 8021 0.003127\n", - " 901 0.003022\n", - " 2504 0.002815\n", - " Name: 15, dtype: float64, 2261 0.191372\n", - " 2264 0.188606\n", - " 2263 0.185669\n", - " 2262 0.178810\n", - " 2236 0.115727\n", - " 3325 0.002141\n", - " 2348 0.001660\n", - " 387 0.001628\n", - " 2326 0.001547\n", - " 3497 0.001218\n", - " Name: 16, dtype: float64, 2334 0.084376\n", - " 2367 0.060693\n", - " 2386 0.046324\n", - " 2264 0.037695\n", - " 2329 0.019085\n", - " 2385 0.016093\n", - " 2396 0.014883\n", - " 2266 0.014742\n", - " 2317 0.013987\n", - " 2382 0.011238\n", - " Name: 17, dtype: float64, 79 9.999964e-01\n", - " 160 2.104605e-07\n", - " 9626 1.256681e-07\n", - " 8040 1.094555e-07\n", - " 119 9.963641e-08\n", - " 2260 9.599977e-08\n", - " 246 7.308211e-08\n", - " 206 6.041248e-08\n", - " 4011 5.990132e-08\n", - " 205 5.536892e-08\n", - " Name: 18, dtype: float64, 2196 0.901146\n", - " 2293 0.050640\n", - " 108 0.005127\n", - " 124 0.002374\n", - " 87 0.001618\n", - " 2337 0.001065\n", - " 128 0.000801\n", - " 667 0.000798\n", - " 901 0.000700\n", - " 8335 0.000693\n", - " Name: 19, dtype: float64, 79 9.999949e-01\n", - " 160 2.290488e-07\n", - " 119 1.780061e-07\n", - " 9626 1.752191e-07\n", - " 246 1.444028e-07\n", - " 2260 1.342961e-07\n", - " 8040 1.322731e-07\n", - " 206 1.019088e-07\n", - " 4011 8.929871e-08\n", - " 4073 8.540582e-08\n", - " Name: 20, dtype: float64, 2283 0.998998\n", - " 2197 0.000089\n", - " 2157 0.000083\n", - " 2295 0.000042\n", - " 2173 0.000028\n", - " 788 0.000016\n", - " 2359 0.000012\n", - " 883 0.000011\n", - " 901 0.000010\n", - " 2266 0.000010\n", - " Name: 21, dtype: float64, 2283 0.998295\n", - " 2197 0.000102\n", - " 2173 0.000100\n", - " 2157 0.000060\n", - " 2359 0.000050\n", - " 883 0.000050\n", - " 2295 0.000047\n", - " 10504 0.000021\n", - " 788 0.000020\n", - " 2408 0.000018\n", - " Name: 22, dtype: float64, 2173 9.999443e-01\n", - " 192 2.708546e-06\n", - " 2197 2.447492e-06\n", - " 3991 1.503712e-06\n", - " 677 1.459782e-06\n", - " 8507 1.155046e-06\n", - " 870 1.035403e-06\n", - " 2437 1.002955e-06\n", - " 2155 7.911363e-07\n", - " 3165 7.655652e-07\n", - " Name: 23, dtype: float64, 128 0.029297\n", - " 2293 0.027201\n", - " 2427 0.014859\n", - " 2292 0.013262\n", - " 901 0.012706\n", - " 246 0.011609\n", - " 31 0.008204\n", - " 108 0.007827\n", - " 2408 0.007455\n", - " 156 0.006980\n", - " Name: 24, dtype: float64, 79 9.999946e-01\n", - " 160 3.020302e-07\n", - " 9626 1.824680e-07\n", - " 119 1.792496e-07\n", - " 2260 1.488201e-07\n", - " 246 1.361520e-07\n", - " 206 1.332261e-07\n", - " 8040 1.172149e-07\n", - " 690 9.828647e-08\n", - " 2197 9.738423e-08\n", - " Name: 25, dtype: float64, 2197 0.999595\n", - " 108 0.000103\n", - " 2260 0.000016\n", - " 129 0.000014\n", - " 3991 0.000012\n", - " 2157 0.000010\n", - " 2173 0.000009\n", - " 690 0.000009\n", - " 2283 0.000006\n", - " 156 0.000006\n", - " Name: 26, dtype: float64, 2273 0.996202\n", - " 2272 0.001829\n", - " 2197 0.000228\n", - " 2271 0.000133\n", - " 2173 0.000108\n", - " 675 0.000061\n", - " 5534 0.000035\n", - " 901 0.000027\n", - " 2259 0.000027\n", - " 192 0.000025\n", - " Name: 27, dtype: float64, 2197 0.988268\n", - " 2200 0.007275\n", - " 2259 0.000272\n", - " 3991 0.000226\n", - " 1977 0.000170\n", - " 2194 0.000149\n", - " 2271 0.000065\n", - " 8155 0.000065\n", - " 2236 0.000061\n", - " 2369 0.000061\n", - " Name: 28, dtype: float64, 2293 0.941258\n", - " 2196 0.008586\n", - " 2259 0.002153\n", - " 108 0.001952\n", - " 124 0.001506\n", - " 8173 0.000568\n", - " 5114 0.000544\n", - " 3002 0.000524\n", - " 2190 0.000493\n", - " 1894 0.000392\n", - " Name: 29, dtype: float64, 2173 0.999914\n", - " 8507 0.000006\n", - " 192 0.000005\n", - " 2197 0.000002\n", - " 2437 0.000002\n", - " 2547 0.000002\n", - " 3991 0.000002\n", - " 2155 0.000002\n", - " 9302 0.000001\n", - " 2344 0.000001\n", - " Name: 30, dtype: float64, 2197 0.891488\n", - " 1977 0.014944\n", - " 204 0.007745\n", - " 2273 0.005501\n", - " 8482 0.002321\n", - " 128 0.002273\n", - " 205 0.001992\n", - " 79 0.001896\n", - " 239 0.001634\n", - " 2259 0.001615\n", - " Name: 31, dtype: float64, 2173 0.999925\n", - " 192 0.000003\n", - " 2197 0.000002\n", - " 677 0.000002\n", - " 3991 0.000002\n", - " 8507 0.000002\n", - " 2155 0.000002\n", - " 2437 0.000001\n", - " 870 0.000001\n", - " 5534 0.000001\n", - " Name: 32, dtype: float64, 2301 0.989036\n", - " 2305 0.000311\n", - " 2350 0.000296\n", - " 2412 0.000223\n", - " 2313 0.000193\n", - " 637 0.000176\n", - " 2433 0.000121\n", - " 2297 0.000094\n", - " 2304 0.000091\n", - " 2434 0.000090\n", - " Name: 33, dtype: float64, 2306 0.995612\n", - " 2328 0.000259\n", - " 2380 0.000157\n", - " 3158 0.000151\n", - " 2327 0.000097\n", - " 2451 0.000090\n", - " 2398 0.000074\n", - " 2325 0.000063\n", - " 669 0.000055\n", - " 2339 0.000053\n", - " Name: 34, dtype: float64, 2321 0.991450\n", - " 2323 0.000422\n", - " 2383 0.000208\n", - " 660 0.000191\n", - " 2379 0.000181\n", - " 670 0.000152\n", - " 2322 0.000142\n", - " 2320 0.000102\n", - " 2374 0.000073\n", - " 2331 0.000073\n", - " Name: 35, dtype: float64, 2325 0.308579\n", - " 2324 0.247214\n", - " 2331 0.220912\n", - " 2327 0.151899\n", - " 2328 0.018009\n", - " 2326 0.010383\n", - " 2330 0.005298\n", - " 2344 0.001943\n", - " 2333 0.001179\n", - " 2332 0.001133\n", - " Name: 36, dtype: float64, 2326 0.074640\n", - " 2327 0.073644\n", - " 2324 0.065162\n", - " 2325 0.064430\n", - " 2186 0.051694\n", - " 2331 0.027020\n", - " 2182 0.023056\n", - " 2328 0.021761\n", - " 2190 0.012682\n", - " 2330 0.007343\n", - " Name: 37, dtype: float64, 2330 0.958287\n", - " 2324 0.007409\n", - " 2331 0.005934\n", - " 2326 0.003680\n", - " 2325 0.002571\n", - " 2329 0.001466\n", - " 2328 0.001205\n", - " 2327 0.001059\n", - " 2264 0.000749\n", - " 52 0.000538\n", - " Name: 38, dtype: float64, 697 0.332581\n", - " 2311 0.263304\n", - " 2351 0.119436\n", - " 2350 0.053761\n", - " 3210 0.018685\n", - " 870 0.013328\n", - " 2341 0.006491\n", - " 2323 0.004176\n", - " 2108 0.003785\n", - " 10610 0.003173\n", - " Name: 39, dtype: float64, 2380 0.817769\n", - " 2309 0.006215\n", - " 899 0.004607\n", - " 2328 0.002210\n", - " 706 0.002167\n", - " 6712 0.002054\n", - " 7330 0.001908\n", - " 2306 0.001843\n", - " 9886 0.001838\n", - " 2410 0.001737\n", - " Name: 40, dtype: float64, 2326 0.548070\n", - " 2325 0.113032\n", - " 2328 0.067470\n", - " 2324 0.067307\n", - " 2327 0.067114\n", - " 2331 0.020206\n", - " 2330 0.017298\n", - " 2333 0.004504\n", - " 2344 0.004353\n", - " 2329 0.002860\n", - " Name: 41, dtype: float64, 2331 0.512659\n", - " 2324 0.115401\n", - " 2333 0.074905\n", - " 2327 0.069224\n", - " 2325 0.046489\n", - " 2332 0.015900\n", - " 2326 0.014045\n", - " 2328 0.009875\n", - " 2330 0.008623\n", - " 2379 0.003849\n", - " Name: 42, dtype: float64, 2390 0.930227\n", - " 2387 0.001810\n", - " 1078 0.001637\n", - " 2388 0.001524\n", - " 1753 0.001357\n", - " 2385 0.001100\n", - " 2395 0.000915\n", - " 2386 0.000851\n", - " 1897 0.000846\n", - " 1029 0.000837\n", - " Name: 43, dtype: float64, 318 0.451773\n", - " 358 0.094746\n", - " 1901 0.048346\n", - " 2386 0.047094\n", - " 2385 0.031432\n", - " 1264 0.007408\n", - " 1902 0.006207\n", - " 2357 0.005521\n", - " 934 0.004758\n", - " 2264 0.004486\n", - " Name: 44, dtype: float64, 207 0.292094\n", - " 2392 0.071558\n", - " 124 0.057770\n", - " 246 0.026974\n", - " 2313 0.019331\n", - " 356 0.017019\n", - " 52 0.008402\n", - " 2312 0.007872\n", - " 2342 0.006377\n", - " 2408 0.006252\n", - " Name: 45, dtype: float64, 2401 0.070640\n", - " 55 0.034209\n", - " 2405 0.020526\n", - " 52 0.019050\n", - " 2386 0.015566\n", - " 2387 0.012729\n", - " 2312 0.012259\n", - " 2398 0.010041\n", - " 2365 0.009866\n", - " 41 0.008957\n", - " Name: 46, dtype: float64, 2359 0.947054\n", - " 2349 0.002667\n", - " 2229 0.001870\n", - " 2402 0.001293\n", - " 848 0.000643\n", - " 2299 0.000630\n", - " 2393 0.000432\n", - " 2295 0.000422\n", - " 2408 0.000393\n", - " 1892 0.000386\n", - " Name: 47, dtype: float64, 2320 0.191829\n", - " 2317 0.073341\n", - " 2292 0.025034\n", - " 2318 0.022201\n", - " 2379 0.019929\n", - " 2351 0.017972\n", - " 2108 0.013500\n", - " 51 0.013429\n", - " 2319 0.012662\n", - " 2316 0.010040\n", - " Name: 48, dtype: float64, 2383 0.216895\n", - " 42 0.048376\n", - " 2345 0.046919\n", - " 2384 0.029439\n", - " 2387 0.025779\n", - " 264 0.021741\n", - " 37 0.013127\n", - " 1203 0.011457\n", - " 2388 0.011172\n", - " 410 0.007959\n", - " Name: 49, dtype: float64, 2326 0.125890\n", - " 2330 0.099103\n", - " 52 0.038511\n", - " 2328 0.031572\n", - " 2333 0.025778\n", - " 741 0.023155\n", - " 2324 0.021724\n", - " 2190 0.017715\n", - " 922 0.016853\n", - " 1057 0.016427\n", - " Name: 50, dtype: float64, 7291 0.570088\n", - " 669 0.008496\n", - " 897 0.007473\n", - " 7935 0.006947\n", - " 10228 0.005330\n", - " 2380 0.004522\n", - " 7516 0.004380\n", - " 1345 0.004171\n", - " 7934 0.004153\n", - " 910 0.003700\n", - " Name: 51, dtype: float64, 2344 0.659427\n", - " 2186 0.030675\n", - " 2327 0.029189\n", - " 2326 0.014259\n", - " 2182 0.006841\n", - " 2264 0.005792\n", - " 52 0.004577\n", - " 2250 0.004189\n", - " 2215 0.003580\n", - " 2324 0.003363\n", - " Name: 52, dtype: float64, 2361 0.872484\n", - " 2360 0.029169\n", - " 2357 0.028497\n", - " 2363 0.006605\n", - " 2362 0.003311\n", - " 2358 0.002159\n", - " 2349 0.001373\n", - " 2346 0.001020\n", - " 2396 0.000993\n", - " 2345 0.000918\n", - " Name: 53, dtype: float64, 2417 0.998041\n", - " 9849 0.000103\n", - " 7903 0.000071\n", - " 2580 0.000051\n", - " 2237 0.000042\n", - " 3892 0.000027\n", - " 2376 0.000026\n", - " 3005 0.000023\n", - " 8761 0.000021\n", - " 2608 0.000020\n", - " Name: 54, dtype: float64, 2316 0.994166\n", - " 2318 0.000951\n", - " 2317 0.000562\n", - " 2292 0.000206\n", - " 108 0.000192\n", - " 2315 0.000075\n", - " 2294 0.000074\n", - " 883 0.000037\n", - " 639 0.000034\n", - " 2175 0.000027\n", - " Name: 55, dtype: float64, 2405 0.865024\n", - " 2406 0.028197\n", - " 2375 0.003814\n", - " 2404 0.002620\n", - " 2252 0.002074\n", - " 739 0.001884\n", - " 881 0.001614\n", - " 880 0.001604\n", - " 2258 0.001403\n", - " 10652 0.001402\n", - " Name: 56, dtype: float64, 2348 0.076503\n", - " 2412 0.037332\n", - " 1115 0.019507\n", - " 2478 0.015149\n", - " 2428 0.013930\n", - " 1046 0.008825\n", - " 6799 0.008556\n", - " 748 0.008099\n", - " 6836 0.007926\n", - " 911 0.007476\n", - " Name: 57, dtype: float64, 2276 0.308563\n", - " 2289 0.031055\n", - " 2431 0.023908\n", - " 2287 0.011294\n", - " 2432 0.007219\n", - " 6470 0.007203\n", - " 787 0.006883\n", - " 2476 0.006194\n", - " 2166 0.005983\n", - " 119 0.005823\n", - " Name: 58, dtype: float64, 2157 0.425048\n", - " 2175 0.078452\n", - " 2452 0.073947\n", - " 2441 0.011206\n", - " 7678 0.009591\n", - " 3192 0.008526\n", - " 2896 0.006459\n", - " 4141 0.006066\n", - " 2155 0.005144\n", - " 5450 0.004879\n", - " Name: 59, dtype: float64, 2277 0.986866\n", - " 2278 0.001866\n", - " 2270 0.000573\n", - " 2476 0.000460\n", - " 87 0.000407\n", - " 2269 0.000355\n", - " 8507 0.000260\n", - " 4088 0.000213\n", - " 2901 0.000190\n", - " 640 0.000185\n", - " Name: 60, dtype: float64, 2465 0.177074\n", - " 2463 0.175553\n", - " 2461 0.169521\n", - " 2462 0.165393\n", - " 2464 0.156811\n", - " 7933 0.003468\n", - " 2469 0.001852\n", - " 9002 0.001757\n", - " 10541 0.001593\n", - " 1379 0.001591\n", - " Name: 61, dtype: float64, 2469 0.853670\n", - " 2466 0.041806\n", - " 2467 0.040737\n", - " 2305 0.001423\n", - " 2372 0.001062\n", - " 7920 0.000613\n", - " 695 0.000572\n", - " 8804 0.000526\n", - " 688 0.000431\n", - " 774 0.000410\n", - " Name: 62, dtype: float64, 2278 0.981420\n", - " 2277 0.008321\n", - " 2194 0.000955\n", - " 2476 0.000598\n", - " 156 0.000563\n", - " 2197 0.000259\n", - " 87 0.000150\n", - " 494 0.000146\n", - " 1977 0.000145\n", - " 128 0.000138\n", - " Name: 63, dtype: float64, 2108 0.050179\n", - " 1805 0.030255\n", - " 1959 0.025854\n", - " 517 0.014161\n", - " 757 0.011231\n", - " 2322 0.011180\n", - " 317 0.008928\n", - " 1676 0.008882\n", - " 2003 0.008861\n", - " 1921 0.007196\n", - " Name: 64, dtype: float64, 210 0.113283\n", - " 2322 0.078679\n", - " 2323 0.043275\n", - " 2374 0.041585\n", - " 2073 0.027717\n", - " 1348 0.019366\n", - " 2108 0.018588\n", - " 1959 0.016429\n", - " 2320 0.015363\n", - " 1382 0.012442\n", - " Name: 65, dtype: float64, 118 0.992963\n", - " 697 0.001600\n", - " 640 0.000679\n", - " 2155 0.000302\n", - " 207 0.000289\n", - " 159 0.000282\n", - " 8319 0.000248\n", - " 86 0.000189\n", - " 4088 0.000187\n", - " 677 0.000178\n", - " Name: 66, dtype: float64, 8453 0.088549\n", - " 758 0.074865\n", - " 216 0.040246\n", - " 8972 0.013573\n", - " 8335 0.011955\n", - " 494 0.009681\n", - " 2278 0.008353\n", - " 1464 0.008099\n", - " 1250 0.008022\n", - " 9529 0.007755\n", - " Name: 67, dtype: float64, 221 0.442070\n", - " 220 0.435488\n", - " 1936 0.017164\n", - " 944 0.004377\n", - " 224 0.003410\n", - " 982 0.001784\n", - " 480 0.001608\n", - " 1938 0.000980\n", - " 485 0.000935\n", - " 228 0.000879\n", - " Name: 68, dtype: float64, 204 0.877381\n", - " 517 0.017317\n", - " 504 0.010703\n", - " 520 0.007994\n", - " 1465 0.007061\n", - " 205 0.005190\n", - " 206 0.003635\n", - " 513 0.002667\n", - " 501 0.002652\n", - " 521 0.002587\n", - " Name: 69, dtype: float64, 228 0.978303\n", - " 1936 0.002582\n", - " 242 0.001541\n", - " 244 0.001274\n", - " 493 0.001038\n", - " 501 0.000790\n", - " 243 0.000490\n", - " 2129 0.000442\n", - " 10146 0.000208\n", - " 248 0.000190\n", - " Name: 70, dtype: float64, 79 9.999934e-01\n", - " 160 1.872344e-07\n", - " 708 1.665198e-07\n", - " 9626 1.384728e-07\n", - " 4011 1.307981e-07\n", - " 704 1.302950e-07\n", - " 1203 1.283236e-07\n", - " 8040 1.213313e-07\n", - " 119 1.160591e-07\n", - " 206 8.025143e-08\n", - " Name: 71, dtype: float64, 79 9.999955e-01\n", - " 160 2.476770e-07\n", - " 206 1.150723e-07\n", - " 119 1.144574e-07\n", - " 246 1.069374e-07\n", - " 9626 1.056256e-07\n", - " 4011 8.640124e-08\n", - " 1203 7.729343e-08\n", - " 8040 7.293165e-08\n", - " 4073 7.265923e-08\n", - " Name: 72, dtype: float64, 2275 0.029738\n", - " 866 0.026889\n", - " 733 0.013326\n", - " 863 0.012733\n", - " 8992 0.009951\n", - " 494 0.009943\n", - " 8714 0.009449\n", - " 510 0.009261\n", - " 952 0.009037\n", - " 119 0.008294\n", - " Name: 73, dtype: float64, 208 0.640154\n", - " 214 0.292708\n", - " 1711 0.010576\n", - " 1295 0.008196\n", - " 206 0.006316\n", - " 519 0.002340\n", - " 211 0.001970\n", - " 257 0.001733\n", - " 233 0.001681\n", - " 356 0.001505\n", - " Name: 74, dtype: float64, 211 0.116149\n", - " 204 0.093373\n", - " 207 0.043366\n", - " 9508 0.023213\n", - " 247 0.020456\n", - " 520 0.014892\n", - " 823 0.008955\n", - " 701 0.007092\n", - " 1711 0.006976\n", - " 1607 0.006587\n", - " Name: 75, dtype: float64, 207 0.659713\n", - " 247 0.073698\n", - " 211 0.059186\n", - " 246 0.031756\n", - " 205 0.014736\n", - " 204 0.013953\n", - " 9508 0.008675\n", - " 8521 0.005698\n", - " 2337 0.003650\n", - " 264 0.003159\n", - " Name: 76, dtype: float64, 79 9.999889e-01\n", - " 4011 5.577231e-07\n", - " 1203 4.041923e-07\n", - " 119 3.760691e-07\n", - " 8040 3.223507e-07\n", - " 160 2.848734e-07\n", - " 206 2.608320e-07\n", - " 2260 2.371922e-07\n", - " 9626 2.063501e-07\n", - " 4073 1.843652e-07\n", - " Name: 77, dtype: float64, 239 0.999480\n", - " 240 0.000044\n", - " 698 0.000014\n", - " 2348 0.000010\n", - " 838 0.000007\n", - " 79 0.000006\n", - " 2504 0.000006\n", - " 641 0.000005\n", - " 901 0.000005\n", - " 204 0.000005\n", - " Name: 78, dtype: float64, 208 0.999445\n", - " 211 0.000102\n", - " 1711 0.000070\n", - " 214 0.000048\n", - " 257 0.000044\n", - " 206 0.000015\n", - " 233 0.000014\n", - " 519 0.000013\n", - " 1295 0.000011\n", - " 207 0.000010\n", - " Name: 79, dtype: float64, 119 0.716871\n", - " 208 0.268966\n", - " 246 0.000426\n", - " 3991 0.000397\n", - " 635 0.000371\n", - " 165 0.000311\n", - " 679 0.000298\n", - " 519 0.000298\n", - " 214 0.000285\n", - " 211 0.000283\n", - " Name: 80, dtype: float64, 233 0.972684\n", - " 519 0.008264\n", - " 1711 0.001930\n", - " 211 0.001532\n", - " 208 0.001354\n", - " 257 0.001322\n", - " 1082 0.000704\n", - " 214 0.000563\n", - " 668 0.000399\n", - " 246 0.000322\n", - " Name: 81, dtype: float64, 213 0.108444\n", - " 211 0.098731\n", - " 207 0.067583\n", - " 246 0.066349\n", - " 1046 0.050355\n", - " 517 0.033381\n", - " 519 0.032684\n", - " 1295 0.031065\n", - " 1082 0.030631\n", - " 233 0.028630\n", - " Name: 82, dtype: float64, 356 0.036498\n", - " 223 0.034429\n", - " 1921 0.028198\n", - " 2322 0.019808\n", - " 2296 0.014546\n", - " 1107 0.014519\n", - " 1891 0.013641\n", - " 1082 0.013559\n", - " 1662 0.011215\n", - " 2108 0.011096\n", - " Name: 83, dtype: float64, 246 0.101774\n", - " 667 0.051442\n", - " 1917 0.026461\n", - " 2613 0.008497\n", - " 1713 0.008193\n", - " 10306 0.008026\n", - " 694 0.008012\n", - " 1255 0.007410\n", - " 277 0.006412\n", - " 102 0.005708\n", - " Name: 84, dtype: float64, 246 0.562903\n", - " 207 0.382160\n", - " 205 0.008452\n", - " 206 0.004411\n", - " 1046 0.002671\n", - " 211 0.002071\n", - " 1255 0.001763\n", - " 2342 0.001689\n", - " 667 0.001149\n", - " 2119 0.001121\n", - " Name: 85, dtype: float64, 207 0.995817\n", - " 246 0.000894\n", - " 205 0.000408\n", - " 1082 0.000094\n", - " 204 0.000086\n", - " 1295 0.000075\n", - " 1203 0.000070\n", - " 517 0.000067\n", - " 264 0.000051\n", - " 211 0.000050\n", - " Name: 86, dtype: float64, 1847 0.024484\n", - " 1642 0.018234\n", - " 1161 0.014251\n", - " 3122 0.013231\n", - " 292 0.012605\n", - " 1609 0.011193\n", - " 380 0.009235\n", - " 1099 0.008538\n", - " 473 0.008464\n", - " 377 0.007648\n", - " Name: 87, dtype: float64, 304 0.456602\n", - " 303 0.411921\n", - " 305 0.014579\n", - " 285 0.004995\n", - " 291 0.004041\n", - " 301 0.002595\n", - " 377 0.001457\n", - " 347 0.001289\n", - " 1204 0.001178\n", - " 381 0.001057\n", - " Name: 88, dtype: float64, 303 0.248794\n", - " 304 0.168653\n", - " 305 0.117006\n", - " 301 0.023558\n", - " 285 0.019136\n", - " 381 0.010138\n", - " 1326 0.008392\n", - " 291 0.008118\n", - " 533 0.006751\n", - " 379 0.006070\n", - " Name: 89, dtype: float64, 305 0.151608\n", - " 1193 0.146455\n", - " 1078 0.107658\n", - " 1273 0.086727\n", - " 306 0.055794\n", - " 308 0.043135\n", - " 963 0.024867\n", - " 962 0.013426\n", - " 236 0.012772\n", - " 285 0.010465\n", - " Name: 90, dtype: float64, 318 0.927331\n", - " 358 0.011260\n", - " 2385 0.004159\n", - " 2386 0.002777\n", - " 1264 0.001852\n", - " 1946 0.001771\n", - " 1901 0.001507\n", - " 1997 0.001138\n", - " 2162 0.000976\n", - " 934 0.000923\n", - " Name: 91, dtype: float64, 334 0.157453\n", - " 330 0.127191\n", - " 1020 0.010668\n", - " 329 0.009564\n", - " 964 0.009068\n", - " 1092 0.007951\n", - " 553 0.007833\n", - " 7228 0.007247\n", - " 541 0.007097\n", - " 335 0.005866\n", - " Name: 92, dtype: float64, 2052 0.014521\n", - " 76 0.014119\n", - " 1718 0.013302\n", - " 70 0.011088\n", - " 2675 0.010440\n", - " 1642 0.009883\n", - " 1604 0.009345\n", - " 1602 0.009120\n", - " 3275 0.008484\n", - " 740 0.008197\n", - " Name: 93, dtype: float64, 360 0.377994\n", - " 1670 0.020213\n", - " 1159 0.007793\n", - " 550 0.005838\n", - " 1845 0.005810\n", - " 1583 0.005678\n", - " 1646 0.005655\n", - " 1627 0.005361\n", - " 1020 0.004862\n", - " 392 0.004352\n", - " Name: 94, dtype: float64, 594 0.041151\n", - " 533 0.031726\n", - " 591 0.027504\n", - " 568 0.022085\n", - " 588 0.019547\n", - " 577 0.018737\n", - " 595 0.015060\n", - " 601 0.014386\n", - " 582 0.013995\n", - " 590 0.012873\n", - " Name: 95, dtype: float64, 1136 0.033973\n", - " 591 0.021275\n", - " 533 0.016077\n", - " 2320 0.014372\n", - " 1651 0.012768\n", - " 1239 0.012753\n", - " 1167 0.011526\n", - " 1232 0.009570\n", - " 1228 0.008296\n", - " 1340 0.008109\n", - " Name: 96, dtype: float64, 1263 0.080696\n", - " 1153 0.028327\n", - " 1996 0.026887\n", - " 1995 0.026737\n", - " 1929 0.019269\n", - " 1309 0.018525\n", - " 206 0.014432\n", - " 223 0.013482\n", - " 495 0.012979\n", - " 251 0.012779\n", - " Name: 97, dtype: float64, 246 0.984251\n", - " 206 0.003466\n", - " 207 0.001713\n", - " 1046 0.001578\n", - " 214 0.000870\n", - " 204 0.000317\n", - " 1203 0.000220\n", - " 79 0.000217\n", - " 517 0.000207\n", - " 1295 0.000203\n", - " Name: 98, dtype: float64, 118 0.931614\n", - " 640 0.028095\n", - " 2173 0.011713\n", - " 86 0.009268\n", - " 2155 0.002249\n", - " 4693 0.001178\n", - " 8319 0.000952\n", - " 4172 0.000690\n", - " 3991 0.000523\n", - " 677 0.000460\n", - " Name: 99, dtype: float64, 430 0.692049\n", - " 434 0.062301\n", - " 432 0.033071\n", - " 433 0.027309\n", - " 1104 0.005367\n", - " 2486 0.003359\n", - " 1032 0.002863\n", - " 330 0.002119\n", - " 1029 0.001953\n", - " 2364 0.001833\n", - " Name: 100, dtype: float64, 430 0.771518\n", - " 434 0.061229\n", - " 432 0.028657\n", - " 433 0.025406\n", - " 1104 0.004579\n", - " 1032 0.002365\n", - " 330 0.001694\n", - " 1000 0.001631\n", - " 336 0.001238\n", - " 2486 0.001187\n", - " Name: 101, dtype: float64, 562 0.368620\n", - " 342 0.056324\n", - " 612 0.056257\n", - " 203 0.022011\n", - " 348 0.021765\n", - " 2139 0.013285\n", - " 563 0.011345\n", - " 340 0.009402\n", - " 495 0.007761\n", - " 1875 0.006857\n", - " Name: 102, dtype: float64, 532 0.036408\n", - " 457 0.024033\n", - " 1273 0.023867\n", - " 1577 0.017733\n", - " 460 0.017346\n", - " 550 0.014554\n", - " 316 0.013451\n", - " 549 0.012503\n", - " 1890 0.012394\n", - " 1035 0.009657\n", - " Name: 103, dtype: float64, 79 0.361675\n", - " 698 0.060659\n", - " 240 0.050368\n", - " 629 0.050227\n", - " 2352 0.023016\n", - " 208 0.019329\n", - " 3099 0.018458\n", - " 206 0.017587\n", - " 870 0.016732\n", - " 4067 0.012063\n", - " Name: 104, dtype: float64, 205 0.992580\n", - " 207 0.002293\n", - " 206 0.000361\n", - " 246 0.000272\n", - " 204 0.000222\n", - " 2119 0.000214\n", - " 1471 0.000174\n", - " 214 0.000138\n", - " 245 0.000120\n", - " 2342 0.000093\n", - " Name: 105, dtype: float64, 208 0.469079\n", - " 211 0.252588\n", - " 207 0.102501\n", - " 118 0.020261\n", - " 246 0.011681\n", - " 165 0.009884\n", - " 205 0.005951\n", - " 206 0.005428\n", - " 214 0.003712\n", - " 519 0.003464\n", - " Name: 106, dtype: float64, 213 0.126920\n", - " 206 0.058556\n", - " 79 0.048843\n", - " 204 0.029168\n", - " 1063 0.017733\n", - " 513 0.017448\n", - " 1509 0.013613\n", - " 521 0.012812\n", - " 944 0.012543\n", - " 667 0.012388\n", - " Name: 107, dtype: float64, 513 0.329863\n", - " 206 0.205559\n", - " 204 0.085304\n", - " 512 0.025753\n", - " 517 0.022708\n", - " 52 0.022165\n", - " 510 0.020951\n", - " 1063 0.009023\n", - " 223 0.008385\n", - " 1086 0.007619\n", - " Name: 108, dtype: float64, 533 0.115944\n", - " 535 0.085690\n", - " 1166 0.036592\n", - " 1167 0.031006\n", - " 1271 0.030333\n", - " 336 0.027689\n", - " 1092 0.025637\n", - " 1535 0.023730\n", - " 1238 0.017838\n", - " 1554 0.013302\n", - " Name: 109, dtype: float64, 541 0.792448\n", - " 407 0.007824\n", - " 960 0.005220\n", - " 330 0.003972\n", - " 289 0.003688\n", - " 403 0.003566\n", - " 422 0.003299\n", - " 401 0.003263\n", - " 2083 0.003190\n", - " 554 0.003095\n", - " Name: 110, dtype: float64, 208 0.999665\n", - " 233 0.000042\n", - " 257 0.000025\n", - " 214 0.000019\n", - " 1711 0.000015\n", - " 519 0.000015\n", - " 211 0.000008\n", - " 206 0.000007\n", - " 1295 0.000005\n", - " 635 0.000005\n", - " Name: 111, dtype: float64, 603 0.097104\n", - " 543 0.045658\n", - " 594 0.044519\n", - " 587 0.043125\n", - " 533 0.041861\n", - " 590 0.040634\n", - " 598 0.040376\n", - " 574 0.039606\n", - " 597 0.038954\n", - " 601 0.038394\n", - " Name: 112, dtype: float64, 576 0.677204\n", - " 1651 0.006286\n", - " 2320 0.005139\n", - " 7860 0.004338\n", - " 372 0.004208\n", - " 1063 0.003773\n", - " 591 0.003713\n", - " 593 0.002657\n", - " 426 0.002517\n", - " 10375 0.002329\n", - " Name: 113, dtype: float64, 579 0.168140\n", - " 1490 0.024397\n", - " 561 0.010751\n", - " 544 0.007149\n", - " 7319 0.006706\n", - " 10436 0.006702\n", - " 543 0.006182\n", - " 1397 0.005815\n", - " 593 0.005559\n", - " 9552 0.005392\n", - " Name: 114, dtype: float64, 594 0.090638\n", - " 589 0.081772\n", - " 577 0.080348\n", - " 568 0.079165\n", - " 530 0.076901\n", - " 595 0.074297\n", - " 583 0.073254\n", - " 588 0.072749\n", - " 582 0.069229\n", - " 1546 0.012398\n", - " Name: 115, dtype: float64, 1622 0.039355\n", - " 1646 0.021245\n", - " 1648 0.020089\n", - " 1403 0.014550\n", - " 1488 0.014266\n", - " 1927 0.013162\n", - " 1647 0.012945\n", - " 1593 0.010850\n", - " 1922 0.010136\n", - " 1642 0.009106\n", - " Name: 116, dtype: float64, 612 0.920349\n", - " 350 0.004230\n", - " 562 0.002201\n", - " 407 0.001572\n", - " 563 0.001434\n", - " 614 0.001268\n", - " 564 0.001196\n", - " 417 0.001141\n", - " 547 0.001061\n", - " 471 0.000862\n", - " Name: 117, dtype: float64, 631 0.985980\n", - " 632 0.000629\n", - " 10499 0.000409\n", - " 167 0.000401\n", - " 10500 0.000167\n", - " 7957 0.000151\n", - " 9775 0.000148\n", - " 9482 0.000133\n", - " 10163 0.000123\n", - " 7312 0.000116\n", - " Name: 118, dtype: float64, 8812 0.015799\n", - " 8323 0.011758\n", - " 5597 0.008784\n", - " 10103 0.007553\n", - " 4655 0.007543\n", - " 81 0.006715\n", - " 1115 0.006438\n", - " 9370 0.005540\n", - " 8248 0.004916\n", - " 1515 0.004757\n", - " Name: 119, dtype: float64, 79 0.057856\n", - " 8021 0.041914\n", - " 9713 0.033177\n", - " 1514 0.028771\n", - " 174 0.024617\n", - " 3039 0.019279\n", - " 4075 0.016915\n", - " 2230 0.014948\n", - " 2196 0.014650\n", - " 1506 0.014562\n", - " Name: 120, dtype: float64, 651 0.142680\n", - " 2172 0.020901\n", - " 4165 0.020263\n", - " 3993 0.018116\n", - " 2309 0.012772\n", - " 136 0.011801\n", - " 3992 0.011475\n", - " 638 0.009856\n", - " 697 0.009852\n", - " 2371 0.009298\n", - " Name: 121, dtype: float64, 8077 0.054438\n", - " 7227 0.051496\n", - " 721 0.047572\n", - " 635 0.035410\n", - " 732 0.020657\n", - " 7428 0.015657\n", - " 7426 0.015382\n", - " 7429 0.013582\n", - " 10350 0.013032\n", - " 10491 0.011567\n", - " Name: 122, dtype: float64, 71 0.065244\n", - " 3298 0.031502\n", - " 854 0.016505\n", - " 759 0.015771\n", - " 977 0.014144\n", - " 3361 0.010923\n", - " 9128 0.009730\n", - " 3005 0.009352\n", - " 3378 0.007771\n", - " 711 0.007669\n", - " Name: 123, dtype: float64, 1910 0.060905\n", - " 7237 0.036458\n", - " 7363 0.019293\n", - " 688 0.016262\n", - " 720 0.012807\n", - " 685 0.011747\n", - " 7993 0.011199\n", - " 7642 0.009016\n", - " 904 0.007704\n", - " 10584 0.006522\n", - " Name: 124, dtype: float64, 86 0.999547\n", - " 1471 0.000128\n", - " 640 0.000076\n", - " 677 0.000027\n", - " 205 0.000020\n", - " 206 0.000020\n", - " 246 0.000009\n", - " 1255 0.000007\n", - " 2236 0.000007\n", - " 3991 0.000006\n", - " Name: 125, dtype: float64, 701 0.480709\n", - " 698 0.175852\n", - " 697 0.033532\n", - " 708 0.027934\n", - " 715 0.009198\n", - " 8155 0.007162\n", - " 683 0.005976\n", - " 2514 0.005951\n", - " 4088 0.004844\n", - " 2155 0.004702\n", - " Name: 126, dtype: float64, 727 0.159643\n", - " 724 0.087402\n", - " 723 0.050028\n", - " 730 0.048833\n", - " 1378 0.017956\n", - " 692 0.017685\n", - " 729 0.014818\n", - " 752 0.014613\n", - " 722 0.011445\n", - " 691 0.010533\n", - " Name: 127, dtype: float64, 86 0.998172\n", - " 1471 0.000419\n", - " 640 0.000272\n", - " 9288 0.000132\n", - " 4172 0.000047\n", - " 3991 0.000041\n", - " 8335 0.000039\n", - " 1255 0.000039\n", - " 9039 0.000032\n", - " 205 0.000027\n", - " Name: 128, dtype: float64, 2051 0.110872\n", - " 2238 0.042853\n", - " 6064 0.027202\n", - " 2355 0.018270\n", - " 5057 0.017100\n", - " 571 0.014118\n", - " 1736 0.012778\n", - " 273 0.012225\n", - " 5059 0.011633\n", - " 4001 0.011139\n", - " Name: 129, dtype: float64, 6447 0.036266\n", - " 758 0.022486\n", - " 248 0.012053\n", - " 4526 0.008565\n", - " 2647 0.007478\n", - " 2654 0.007387\n", - " 8453 0.007137\n", - " 8335 0.007080\n", - " 260 0.006982\n", - " 2646 0.006867\n", - " Name: 130, dtype: float64, 79 0.897613\n", - " 5437 0.002767\n", - " 660 0.002448\n", - " 10250 0.002306\n", - " 2684 0.002114\n", - " 7915 0.002063\n", - " 119 0.001882\n", - " 8460 0.001679\n", - " 8374 0.001672\n", - " 9626 0.001490\n", - " Name: 131, dtype: float64, 771 0.071022\n", - " 2041 0.033034\n", - " 721 0.031805\n", - " 591 0.012839\n", - " 1377 0.009049\n", - " 352 0.008767\n", - " 1657 0.007739\n", - " 2456 0.007683\n", - " 593 0.007352\n", - " 349 0.006341\n", - " Name: 132, dtype: float64, 801 0.037089\n", - " 9517 0.027230\n", - " 136 0.022038\n", - " 697 0.021140\n", - " 9854 0.020309\n", - " 621 0.017539\n", - " 8058 0.011675\n", - " 827 0.011063\n", - " 800 0.009387\n", - " 799 0.008785\n", - " Name: 133, dtype: float64, 801 0.050267\n", - " 800 0.024989\n", - " 621 0.021191\n", - " 633 0.015861\n", - " 810 0.015709\n", - " 635 0.014497\n", - " 7050 0.011944\n", - " 875 0.010781\n", - " 809 0.010380\n", - " 8059 0.010041\n", - " Name: 134, dtype: float64, 79 9.999909e-01\n", - " 160 4.838061e-07\n", - " 9626 4.132084e-07\n", - " 119 4.100002e-07\n", - " 2260 2.112560e-07\n", - " 8040 1.923651e-07\n", - " 206 1.791437e-07\n", - " 690 1.768302e-07\n", - " 246 1.749308e-07\n", - " 4073 1.517458e-07\n", - " Name: 135, dtype: float64, 801 0.293351\n", - " 800 0.023361\n", - " 799 0.022492\n", - " 8806 0.009715\n", - " 804 0.009581\n", - " 810 0.008195\n", - " 8413 0.007302\n", - " 806 0.006608\n", - " 8341 0.006233\n", - " 9775 0.005966\n", - " Name: 136, dtype: float64, 720 0.067283\n", - " 721 0.027683\n", - " 678 0.018540\n", - " 2452 0.018176\n", - " 774 0.013549\n", - " 5655 0.011573\n", - " 7678 0.011453\n", - " 2440 0.010179\n", - " 7983 0.009882\n", - " 7683 0.009844\n", - " Name: 137, dtype: float64, 825 0.992336\n", - " 824 0.001533\n", - " 823 0.001066\n", - " 827 0.000183\n", - " 840 0.000170\n", - " 2901 0.000096\n", - " 2197 0.000095\n", - " 682 0.000081\n", - " 839 0.000068\n", - " 121 0.000059\n", - " Name: 138, dtype: float64, 86 0.734129\n", - " 838 0.021927\n", - " 690 0.004493\n", - " 677 0.004080\n", - " 1917 0.004061\n", - " 2197 0.003922\n", - " 691 0.003237\n", - " 7626 0.002464\n", - " 9891 0.002133\n", - " 640 0.001981\n", - " Name: 139, dtype: float64, 2240 0.039632\n", - " 8317 0.016593\n", - " 737 0.012212\n", - " 9952 0.009893\n", - " 1443 0.009217\n", - " 9522 0.009017\n", - " 197 0.008312\n", - " 620 0.007754\n", - " 7261 0.006815\n", - " 9523 0.006128\n", - " Name: 140, dtype: float64, 2224 0.018033\n", - " 8262 0.017066\n", - " 7497 0.012130\n", - " 3619 0.009428\n", - " 755 0.008798\n", - " 3064 0.008494\n", - " 6052 0.008132\n", - " 705 0.007883\n", - " 907 0.007753\n", - " 844 0.007657\n", - " Name: 141, dtype: float64, 860 0.177236\n", - " 859 0.073361\n", - " 2278 0.028247\n", - " 10132 0.012773\n", - " 767 0.010065\n", - " 2277 0.010061\n", - " 916 0.009350\n", - " 2257 0.009155\n", - " 758 0.008173\n", - " 10201 0.007238\n", - " Name: 142, dtype: float64, 79 9.999913e-01\n", - " 160 4.115435e-07\n", - " 9626 2.413104e-07\n", - " 4073 2.100123e-07\n", - " 1203 1.954840e-07\n", - " 641 1.802608e-07\n", - " 8040 1.685145e-07\n", - " 690 1.431650e-07\n", - " 2260 1.379233e-07\n", - " 206 1.302216e-07\n", - " Name: 143, dtype: float64, 118 0.975178\n", - " 119 0.014188\n", - " 677 0.002023\n", - " 690 0.000495\n", - " 3991 0.000448\n", - " 2173 0.000436\n", - " 2236 0.000321\n", - " 2155 0.000286\n", - " 679 0.000269\n", - " 870 0.000261\n", - " Name: 144, dtype: float64, 119 0.999590\n", - " 118 0.000096\n", - " 79 0.000025\n", - " 208 0.000025\n", - " 2236 0.000015\n", - " 675 0.000010\n", - " 3991 0.000009\n", - " 690 0.000009\n", - " 677 0.000007\n", - " 2260 0.000006\n", - " Name: 145, dtype: float64, 878 0.533409\n", - " 898 0.061302\n", - " 899 0.048839\n", - " 2373 0.011970\n", - " 7975 0.006938\n", - " 2167 0.006899\n", - " 841 0.005809\n", - " 2689 0.005473\n", - " 882 0.003433\n", - " 855 0.003052\n", - " Name: 146, dtype: float64, 7894 0.088643\n", - " 2254 0.032871\n", - " 2255 0.023649\n", - " 7975 0.014977\n", - " 881 0.014251\n", - " 2250 0.013879\n", - " 626 0.012446\n", - " 683 0.011924\n", - " 2258 0.011902\n", - " 7353 0.008335\n", - " Name: 147, dtype: float64, 854 0.141852\n", - " 2291 0.028108\n", - " 3485 0.019520\n", - " 7304 0.009591\n", - " 2407 0.009280\n", - " 782 0.008094\n", - " 2227 0.007669\n", - " 2240 0.006502\n", - " 697 0.006283\n", - " 2350 0.006070\n", - " Name: 148, dtype: float64, 677 0.511517\n", - " 679 0.089102\n", - " 2502 0.086908\n", - " 2503 0.051806\n", - " 5826 0.023227\n", - " 734 0.019565\n", - " 1805 0.013991\n", - " 2716 0.012620\n", - " 2504 0.012376\n", - " 2715 0.008159\n", - " Name: 149, dtype: float64, 10317 0.049320\n", - " 10593 0.045504\n", - " 9063 0.028464\n", - " 10527 0.018881\n", - " 10048 0.017686\n", - " 10464 0.016671\n", - " 9046 0.015869\n", - " 4708 0.014501\n", - " 10435 0.012112\n", - " 9837 0.011331\n", - " Name: 150, dtype: float64, 1171 0.148665\n", - " 1173 0.057704\n", - " 930 0.011385\n", - " 1815 0.010437\n", - " 1353 0.010096\n", - " 1784 0.009038\n", - " 1619 0.008359\n", - " 9584 0.007202\n", - " 1204 0.006651\n", - " 1137 0.006451\n", - " Name: 151, dtype: float64, 678 0.847926\n", - " 680 0.005136\n", - " 10528 0.003517\n", - " 7608 0.003300\n", - " 887 0.002253\n", - " 5655 0.001886\n", - " 2274 0.001703\n", - " 7955 0.001649\n", - " 10581 0.001526\n", - " 2410 0.001467\n", - " Name: 152, dtype: float64, 1282 0.076901\n", - " 1262 0.066983\n", - " 1001 0.033665\n", - " 1071 0.025900\n", - " 511 0.024599\n", - " 1027 0.020444\n", - " 1266 0.019416\n", - " 1715 0.017716\n", - " 251 0.015473\n", - " 1516 0.014301\n", - " Name: 153, dtype: float64, 0 0.131476\n", - " 277 0.054935\n", - " 1929 0.033900\n", - " 1 0.026993\n", - " 2149 0.019022\n", - " 2231 0.015355\n", - " 2150 0.013423\n", - " 10 0.011817\n", - " 8113 0.011704\n", - " 1122 0.010903\n", - " Name: 154, dtype: float64, 966 0.452977\n", - " 968 0.442174\n", - " 336 0.003982\n", - " 329 0.001699\n", - " 1848 0.001273\n", - " 1543 0.001168\n", - " 1674 0.001150\n", - " 1689 0.001123\n", - " 9982 0.001077\n", - " 1636 0.000942\n", - " Name: 155, dtype: float64, 1022 0.040413\n", - " 987 0.025208\n", - " 1675 0.024541\n", - " 370 0.015810\n", - " 1848 0.011250\n", - " 1674 0.009243\n", - " 1548 0.007806\n", - " 10190 0.007251\n", - " 1835 0.005780\n", - " 1638 0.005711\n", - " Name: 156, dtype: float64, 994 0.958392\n", - " 998 0.002463\n", - " 508 0.001115\n", - " 921 0.001053\n", - " 1015 0.000814\n", - " 989 0.000534\n", - " 983 0.000504\n", - " 2189 0.000502\n", - " 1513 0.000360\n", - " 2183 0.000340\n", - " Name: 157, dtype: float64, 1005 0.537610\n", - " 1841 0.021682\n", - " 1549 0.012747\n", - " 1523 0.006751\n", - " 1933 0.004504\n", - " 1543 0.004249\n", - " 9525 0.003508\n", - " 1508 0.003242\n", - " 508 0.003180\n", - " 1473 0.003168\n", - " Name: 158, dtype: float64, 1198 0.041551\n", - " 484 0.028707\n", - " 1490 0.024774\n", - " 292 0.020052\n", - " 593 0.018669\n", - " 485 0.018644\n", - " 1550 0.017592\n", - " 1007 0.016751\n", - " 543 0.012328\n", - " 603 0.011840\n", - " Name: 159, dtype: float64, 1032 0.825785\n", - " 1104 0.052462\n", - " 1671 0.005654\n", - " 1636 0.005137\n", - " 2105 0.004324\n", - " 1267 0.003588\n", - " 355 0.003212\n", - " 1390 0.002609\n", - " 1022 0.001867\n", - " 2010 0.001510\n", - " Name: 160, dtype: float64, 1035 0.891770\n", - " 1486 0.003516\n", - " 1033 0.003133\n", - " 1999 0.002969\n", - " 462 0.001627\n", - " 1717 0.001503\n", - " 1946 0.001263\n", - " 1390 0.001150\n", - " 1628 0.001074\n", - " 434 0.001008\n", - " Name: 161, dtype: float64, 2108 0.025456\n", - " 10322 0.021776\n", - " 7724 0.016462\n", - " 7510 0.016348\n", - " 3401 0.016299\n", - " 7304 0.016248\n", - " 1927 0.016182\n", - " 1676 0.014054\n", - " 1958 0.012520\n", - " 6044 0.008872\n", - " Name: 162, dtype: float64, 206 0.848725\n", - " 246 0.023117\n", - " 205 0.022944\n", - " 1046 0.013849\n", - " 203 0.013328\n", - " 214 0.010744\n", - " 204 0.005807\n", - " 263 0.003434\n", - " 52 0.002912\n", - " 1295 0.001658\n", - " Name: 163, dtype: float64, 1991 0.045255\n", - " 2296 0.029036\n", - " 1293 0.025626\n", - " 2108 0.014970\n", - " 599 0.013664\n", - " 1670 0.011130\n", - " 416 0.010479\n", - " 519 0.010064\n", - " 1778 0.009861\n", - " 757 0.009583\n", - " Name: 164, dtype: float64, 8802 0.027333\n", - " 120 0.025727\n", - " 186 0.012205\n", - " 7514 0.010766\n", - " 826 0.008797\n", - " 7405 0.008473\n", - " 1251 0.007941\n", - " 911 0.007781\n", - " 552 0.007735\n", - " 2896 0.007642\n", - " Name: 165, dtype: float64, 206 0.953730\n", - " 203 0.008069\n", - " 204 0.007765\n", - " 205 0.003559\n", - " 513 0.001785\n", - " 213 0.001005\n", - " 495 0.000995\n", - " 512 0.000812\n", - " 79 0.000774\n", - " 246 0.000721\n", - " Name: 166, dtype: float64, 1092 0.032146\n", - " 1335 0.030050\n", - " 1271 0.029128\n", - " 1099 0.028905\n", - " 1331 0.023577\n", - " 410 0.018832\n", - " 1100 0.018149\n", - " 1695 0.017203\n", - " 1267 0.016258\n", - " 1102 0.015101\n", - " Name: 167, dtype: float64, 1120 0.992408\n", - " 1122 0.000176\n", - " 2224 0.000137\n", - " 1993 0.000110\n", - " 8339 0.000102\n", - " 15 0.000097\n", - " 2773 0.000093\n", - " 1049 0.000086\n", - " 10041 0.000084\n", - " 9131 0.000084\n", - " Name: 168, dtype: float64, 1122 0.967886\n", - " 2149 0.001781\n", - " 1120 0.001082\n", - " 277 0.000613\n", - " 940 0.000478\n", - " 7586 0.000421\n", - " 1498 0.000420\n", - " 261 0.000412\n", - " 934 0.000362\n", - " 933 0.000359\n", - " Name: 169, dtype: float64, 108 0.032891\n", - " 2600 0.016169\n", - " 2601 0.014278\n", - " 9877 0.014071\n", - " 1614 0.013773\n", - " 3323 0.012587\n", - " 8765 0.011366\n", - " 2931 0.010698\n", - " 3447 0.008468\n", - " 870 0.008218\n", - " Name: 170, dtype: float64, 1471 0.584289\n", - " 2236 0.022462\n", - " 8334 0.017744\n", - " 9039 0.017672\n", - " 268 0.009902\n", - " 9719 0.008581\n", - " 3421 0.008493\n", - " 753 0.007519\n", - " 6024 0.005858\n", - " 2337 0.005735\n", - " Name: 171, dtype: float64, 204 0.975832\n", - " 1046 0.006904\n", - " 205 0.005530\n", - " 517 0.003168\n", - " 206 0.000629\n", - " 520 0.000415\n", - " 213 0.000376\n", - " 1465 0.000300\n", - " 207 0.000300\n", - " 1295 0.000252\n", - " Name: 172, dtype: float64, 204 0.975832\n", - " 1046 0.006904\n", - " 205 0.005530\n", - " 517 0.003168\n", - " 206 0.000629\n", - " 520 0.000415\n", - " 213 0.000376\n", - " 1465 0.000300\n", - " 207 0.000300\n", - " 1295 0.000252\n", - " Name: 173, dtype: float64, 517 0.470260\n", - " 246 0.060387\n", - " 1046 0.050858\n", - " 206 0.030713\n", - " 214 0.022693\n", - " 519 0.020940\n", - " 204 0.017745\n", - " 1295 0.017580\n", - " 233 0.015989\n", - " 207 0.013120\n", - " Name: 174, dtype: float64, 517 0.452996\n", - " 1113 0.088354\n", - " 207 0.036412\n", - " 246 0.023728\n", - " 367 0.020289\n", - " 1046 0.017684\n", - " 204 0.011670\n", - " 206 0.006522\n", - " 519 0.005466\n", - " 1713 0.005138\n", - " Name: 175, dtype: float64, 1576 0.398873\n", - " 356 0.060154\n", - " 207 0.025882\n", - " 2337 0.017153\n", - " 206 0.017021\n", - " 5110 0.015749\n", - " 2046 0.010212\n", - " 1662 0.009702\n", - " 670 0.008615\n", - " 1255 0.008169\n", - " Name: 176, dtype: float64, 1137 0.963313\n", - " 371 0.002710\n", - " 1145 0.002193\n", - " 1399 0.001068\n", - " 1044 0.000897\n", - " 1815 0.000710\n", - " 1402 0.000662\n", - " 1259 0.000660\n", - " 1451 0.000613\n", - " 1799 0.000478\n", - " Name: 177, dtype: float64, 539 0.021164\n", - " 1161 0.017440\n", - " 425 0.013157\n", - " 1651 0.013142\n", - " 439 0.012617\n", - " 1558 0.011702\n", - " 1259 0.010927\n", - " 294 0.009987\n", - " 533 0.009364\n", - " 979 0.009310\n", - " Name: 178, dtype: float64, 1099 0.083975\n", - " 2008 0.052540\n", - " 604 0.025144\n", - " 2007 0.024064\n", - " 707 0.018119\n", - " 406 0.014036\n", - " 1161 0.013956\n", - " 1733 0.010143\n", - " 1535 0.009776\n", - " 381 0.008075\n", - " Name: 179, dtype: float64, 533 0.041579\n", - " 1944 0.041265\n", - " 1542 0.027035\n", - " 2100 0.025142\n", - " 1711 0.024165\n", - " 1577 0.021257\n", - " 535 0.020231\n", - " 1654 0.017883\n", - " 2102 0.015166\n", - " 1652 0.014149\n", - " Name: 180, dtype: float64, 1239 0.084777\n", - " 1213 0.056079\n", - " 1229 0.048704\n", - " 1207 0.038293\n", - " 1228 0.036872\n", - " 1231 0.031915\n", - " 1226 0.030558\n", - " 1236 0.023446\n", - " 1227 0.021329\n", - " 1237 0.020138\n", - " Name: 181, dtype: float64, 1239 0.658822\n", - " 1228 0.063392\n", - " 347 0.025195\n", - " 1236 0.012638\n", - " 1229 0.008993\n", - " 1237 0.007293\n", - " 1232 0.006643\n", - " 1231 0.006136\n", - " 1326 0.005958\n", - " 1219 0.005477\n", - " Name: 182, dtype: float64, 1064 0.021855\n", - " 7335 0.013488\n", - " 2186 0.012765\n", - " 859 0.009964\n", - " 5566 0.009613\n", - " 2182 0.009439\n", - " 1727 0.008016\n", - " 279 0.007644\n", - " 511 0.007561\n", - " 7301 0.007251\n", - " Name: 183, dtype: float64, 533 0.064869\n", - " 1326 0.025413\n", - " 295 0.024713\n", - " 614 0.022171\n", - " 285 0.014184\n", - " 472 0.013850\n", - " 347 0.012700\n", - " 1102 0.012568\n", - " 1777 0.011965\n", - " 380 0.011518\n", - " Name: 184, dtype: float64, 317 0.160122\n", - " 261 0.015780\n", - " 2332 0.015105\n", - " 1593 0.009856\n", - " 3333 0.009711\n", - " 2061 0.009307\n", - " 1650 0.008538\n", - " 581 0.007815\n", - " 1348 0.006347\n", - " 8271 0.006079\n", - " Name: 185, dtype: float64, 6533 0.163833\n", - " 6532 0.118595\n", - " 10336 0.049796\n", - " 7383 0.016330\n", - " 3543 0.009221\n", - " 7973 0.008417\n", - " 2899 0.007437\n", - " 7460 0.007417\n", - " 8284 0.006952\n", - " 2935 0.006936\n", - " Name: 186, dtype: float64, 8285 0.036772\n", - " 287 0.025327\n", - " 8510 0.014226\n", - " 338 0.013275\n", - " 3592 0.008555\n", - " 964 0.008397\n", - " 2067 0.007575\n", - " 610 0.005476\n", - " 3996 0.004515\n", - " 1836 0.004486\n", - " Name: 187, dtype: float64, 1306 0.487654\n", - " 1305 0.190058\n", - " 1301 0.172042\n", - " 580 0.016769\n", - " 1057 0.001499\n", - " 1135 0.001440\n", - " 76 0.001437\n", - " 9599 0.001277\n", - " 917 0.001271\n", - " 3169 0.001216\n", - " Name: 188, dtype: float64, 1300 0.873189\n", - " 1003 0.012826\n", - " 1002 0.007784\n", - " 1028 0.004306\n", - " 1556 0.003820\n", - " 543 0.002166\n", - " 999 0.001716\n", - " 547 0.001632\n", - " 603 0.001219\n", - " 545 0.001147\n", - " Name: 189, dtype: float64, 580 0.903979\n", - " 1306 0.008743\n", - " 1305 0.005163\n", - " 1301 0.003915\n", - " 1325 0.002141\n", - " 882 0.001298\n", - " 9370 0.000979\n", - " 700 0.000937\n", - " 1126 0.000758\n", - " 194 0.000746\n", - " Name: 190, dtype: float64, 1597 0.061873\n", - " 681 0.025339\n", - " 1093 0.018385\n", - " 403 0.013378\n", - " 794 0.010960\n", - " 1454 0.009470\n", - " 1443 0.009055\n", - " 1090 0.008938\n", - " 1325 0.008253\n", - " 1389 0.008170\n", - " Name: 191, dtype: float64, 10581 0.022062\n", - " 2224 0.019532\n", - " 1093 0.013841\n", - " 10229 0.012120\n", - " 1604 0.010320\n", - " 815 0.007569\n", - " 9568 0.005948\n", - " 882 0.005743\n", - " 8341 0.005432\n", - " 7228 0.005149\n", - " Name: 192, dtype: float64, 8202 0.044438\n", - " 6777 0.023290\n", - " 8198 0.020535\n", - " 1982 0.018176\n", - " 8991 0.016389\n", - " 2821 0.015783\n", - " 8388 0.007725\n", - " 183 0.007607\n", - " 2617 0.006931\n", - " 294 0.006500\n", - " Name: 193, dtype: float64, 8202 0.044438\n", - " 6777 0.023290\n", - " 8198 0.020535\n", - " 1982 0.018176\n", - " 8991 0.016389\n", - " 2821 0.015783\n", - " 8388 0.007725\n", - " 183 0.007607\n", - " 2617 0.006931\n", - " 294 0.006500\n", - " Name: 194, dtype: float64, 1338 0.181217\n", - " 370 0.045845\n", - " 1848 0.019484\n", - " 539 0.014618\n", - " 981 0.012084\n", - " 1689 0.009642\n", - " 1271 0.009438\n", - " 353 0.009239\n", - " 1733 0.008776\n", - " 461 0.008411\n", - " Name: 195, dtype: float64, 239 0.999480\n", - " 240 0.000044\n", - " 698 0.000014\n", + "[37 0.764060\n", + " 44 0.020856\n", + " 2387 0.013921\n", + " 52 0.008853\n", + " 2388 0.007249\n", + " 73 0.004392\n", + " 51 0.003539\n", + " 74 0.003239\n", + " 2108 0.003081\n", + " 537 0.002911\n", + " Name: 0, dtype: float64, 677 0.777846\n", + " 883 0.034291\n", + " 136 0.021677\n", + " 8156 0.008344\n", + " 668 0.006883\n", + " 2507 0.004084\n", + " 7957 0.003573\n", + " 2314 0.003556\n", + " 2522 0.003083\n", + " 159 0.002819\n", + " Name: 1, dtype: float64, 79 9.999967e-01\n", + " 208 9.331006e-07\n", + " 1805 1.774457e-07\n", + " 7106 7.214614e-08\n", + " 517 7.207380e-08\n", + " 119 5.610887e-08\n", + " 2503 4.691927e-08\n", + " 4098 3.785669e-08\n", + " 4000 2.764636e-08\n", + " 2546 2.747263e-08\n", + " Name: 2, dtype: float64, 2157 0.997368\n", + " 2175 0.000368\n", + " 2236 0.000297\n", + " 7678 0.000171\n", + " 2802 0.000144\n", + " 2452 0.000132\n", + " 5760 0.000077\n", + " 7470 0.000061\n", + " 870 0.000039\n", + " 2780 0.000037\n", + " Name: 3, dtype: float64, 677 0.998174\n", + " 870 0.000394\n", + " 159 0.000224\n", + " 883 0.000190\n", + " 2236 0.000060\n", + " 2716 0.000038\n", + " 2356 0.000033\n", + " 4512 0.000027\n", + " 2175 0.000024\n", + " 7957 0.000019\n", + " Name: 4, dtype: float64, 125 0.079052\n", + " 8374 0.040892\n", + " 836 0.040432\n", + " 119 0.034676\n", + " 2476 0.029309\n", + " 2166 0.022977\n", + " 2432 0.022001\n", + " 240 0.021456\n", + " 2168 0.018810\n", + " 2428 0.018569\n", + " Name: 5, dtype: float64, 2200 0.998771\n", + " 2197 0.000281\n", + " 31 0.000057\n", + " 2290 0.000031\n", + " 2324 0.000030\n", + " 2289 0.000030\n", + " 2327 0.000028\n", + " 2278 0.000026\n", + " 2274 0.000022\n", + " 2412 0.000022\n", + " Name: 6, dtype: float64, 10495 0.127961\n", + " 7715 0.053914\n", + " 10538 0.030493\n", + " 7629 0.024844\n", + " 773 0.022013\n", + " 7444 0.021424\n", + " 7499 0.020819\n", + " 7306 0.020753\n", + " 7222 0.020674\n", + " 7818 0.017811\n", + " Name: 7, dtype: float64, 1019 0.041690\n", + " 9178 0.024669\n", + " 1137 0.024271\n", + " 2276 0.022310\n", + " 8453 0.021078\n", + " 2428 0.015916\n", + " 8373 0.012847\n", + " 1145 0.008866\n", + " 1402 0.008575\n", + " 1399 0.007910\n", + " Name: 8, dtype: float64, 93 0.062095\n", + " 146 0.045464\n", + " 2244 0.042401\n", + " 2249 0.038786\n", + " 1395 0.030527\n", + " 1917 0.023233\n", + " 849 0.022538\n", + " 2250 0.020041\n", + " 2252 0.018795\n", + " 2255 0.016955\n", + " Name: 9, dtype: float64, 2246 0.999737\n", + " 387 0.000035\n", + " 672 0.000019\n", + " 2355 0.000016\n", + " 1917 0.000011\n", + " 41 0.000009\n", + " 7265 0.000005\n", + " 8274 0.000005\n", + " 10115 0.000004\n", + " 6218 0.000004\n", + " Name: 10, dtype: float64, 2246 0.999737\n", + " 387 0.000035\n", + " 672 0.000019\n", + " 2355 0.000016\n", + " 1917 0.000011\n", + " 41 0.000009\n", + " 7265 0.000005\n", + " 8274 0.000005\n", + " 10115 0.000004\n", + " 6218 0.000004\n", + " Name: 11, dtype: float64, 2246 0.999737\n", + " 387 0.000035\n", + " 672 0.000019\n", + " 2355 0.000016\n", + " 1917 0.000011\n", + " 41 0.000009\n", + " 7265 0.000005\n", + " 8274 0.000005\n", + " 10115 0.000004\n", + " 6218 0.000004\n", + " Name: 12, dtype: float64, 2247 0.999604\n", + " 2197 0.000019\n", + " 93 0.000014\n", + " 3186 0.000010\n", + " 672 0.000009\n", + " 10115 0.000007\n", + " 10306 0.000007\n", + " 2277 0.000006\n", + " 870 0.000005\n", + " 2230 0.000005\n", + " Name: 13, dtype: float64, 93 0.133881\n", + " 146 0.047827\n", + " 2250 0.022772\n", + " 2252 0.020016\n", + " 2546 0.016834\n", + " 644 0.015353\n", + " 2251 0.014687\n", + " 1917 0.014165\n", + " 2253 0.013914\n", + " 849 0.013750\n", + " Name: 14, dtype: float64, 2197 0.985172\n", + " 2369 0.001876\n", + " 108 0.001474\n", + " 679 0.000993\n", + " 2344 0.000430\n", + " 2715 0.000401\n", + " 6066 0.000382\n", + " 246 0.000355\n", + " 672 0.000276\n", + " 2504 0.000275\n", + " Name: 15, dtype: float64, 2262 0.201740\n", + " 2263 0.191804\n", + " 2261 0.177610\n", + " 2264 0.172446\n", + " 2236 0.145739\n", + " 2266 0.003200\n", + " 2213 0.001739\n", + " 679 0.001670\n", + " 2197 0.001500\n", + " 2327 0.001398\n", + " Name: 16, dtype: float64, 2264 0.167130\n", + " 2367 0.086378\n", + " 2263 0.080205\n", + " 2261 0.053166\n", + " 2262 0.044024\n", + " 2334 0.043367\n", + " 2327 0.020992\n", + " 2197 0.018682\n", + " 2236 0.013524\n", + " 2331 0.013251\n", + " Name: 17, dtype: float64, 79 9.999965e-01\n", + " 208 7.511873e-07\n", + " 1805 1.626670e-07\n", + " 119 9.314365e-08\n", + " 7106 7.140128e-08\n", + " 517 5.434524e-08\n", + " 4098 5.412635e-08\n", + " 2503 4.311265e-08\n", + " 4000 3.233327e-08\n", + " 2629 2.902523e-08\n", + " Name: 18, dtype: float64, 2196 0.475720\n", + " 129 0.131521\n", + " 128 0.045926\n", + " 1401 0.010575\n", + " 3073 0.009532\n", + " 2260 0.006189\n", + " 2293 0.006062\n", + " 156 0.005990\n", + " 124 0.004490\n", + " 2421 0.004216\n", + " Name: 19, dtype: float64, 79 9.999953e-01\n", + " 208 1.268462e-06\n", + " 517 1.402350e-07\n", + " 119 1.206409e-07\n", + " 4098 1.039409e-07\n", + " 7106 9.989300e-08\n", + " 1805 9.836925e-08\n", + " 2546 4.598144e-08\n", + " 4000 3.572415e-08\n", + " 7141 3.272609e-08\n", + " Name: 20, dtype: float64, 2283 0.999483\n", + " 2197 0.000142\n", + " 84 0.000011\n", + " 122 0.000010\n", + " 2173 0.000008\n", + " 129 0.000007\n", + " 2213 0.000006\n", + " 2260 0.000005\n", + " 9454 0.000005\n", + " 2428 0.000004\n", + " Name: 21, dtype: float64, 2283 0.998950\n", + " 2197 0.000208\n", + " 9454 0.000017\n", + " 672 0.000016\n", + " 122 0.000015\n", + " 8750 0.000015\n", + " 2213 0.000013\n", + " 635 0.000012\n", + " 84 0.000011\n", " 2348 0.000010\n", - " 838 0.000007\n", - " 79 0.000006\n", - " 2504 0.000006\n", - " 641 0.000005\n", - " 901 0.000005\n", - " 204 0.000005\n", - " Name: 196, dtype: float64, 1 0.131941\n", - " 1264 0.110607\n", - " 1309 0.046634\n", - " 1997 0.044260\n", - " 1263 0.030820\n", - " 1353 0.015195\n", - " 1702 0.013189\n", - " 7 0.012697\n", - " 2675 0.011978\n", - " 936 0.006700\n", - " Name: 197, dtype: float64, 75 0.054756\n", - " 971 0.028462\n", - " 531 0.020165\n", - " 4911 0.009646\n", - " 1798 0.008890\n", - " 3145 0.008737\n", - " 702 0.008510\n", - " 1573 0.007476\n", - " 1242 0.006739\n", - " 3137 0.006734\n", - " Name: 198, dtype: float64, 551 0.049767\n", - " 1991 0.016412\n", - " 294 0.015606\n", - " 1635 0.009751\n", - " 8108 0.008932\n", - " 2104 0.008805\n", - " 1670 0.008663\n", - " 1658 0.008577\n", - " 4093 0.008171\n", - " 1778 0.008023\n", - " Name: 199, dtype: float64, 8113 0.106902\n", - " 277 0.039338\n", - " 2149 0.034164\n", - " 276 0.023534\n", - " 273 0.020239\n", - " 7724 0.019446\n", - " 7227 0.013493\n", - " 3401 0.011269\n", - " 1929 0.010983\n", - " 218 0.009185\n", - " Name: 200, dtype: float64, 1422 0.275982\n", - " 1412 0.257200\n", - " 1418 0.251509\n", - " 1427 0.011523\n", - " 1428 0.010316\n", - " 1429 0.009486\n", - " 1425 0.009273\n", - " 1426 0.005477\n", - " 1020 0.002930\n", - " 1258 0.002763\n", - " Name: 201, dtype: float64, 1425 0.167084\n", - " 1429 0.154257\n", - " 1428 0.151244\n", - " 1427 0.137098\n", - " 1426 0.090210\n", - " 1430 0.060616\n", - " 1412 0.018415\n", - " 1418 0.014896\n", - " 1422 0.014568\n", - " 1020 0.009018\n", - " Name: 202, dtype: float64, 1372 0.032351\n", - " 1373 0.030499\n", - " 2455 0.025586\n", - " 727 0.013536\n", - " 1365 0.013410\n", - " 722 0.011170\n", - " 1369 0.010591\n", - " 1368 0.010247\n", - " 723 0.010107\n", - " 367 0.008629\n", - " Name: 203, dtype: float64, 1449 0.332428\n", - " 1446 0.071180\n", - " 1454 0.035779\n", - " 1448 0.027697\n", - " 1457 0.022476\n", - " 1459 0.013097\n", - " 1456 0.012943\n", - " 1458 0.010689\n", - " 1750 0.005916\n", - " 2619 0.005752\n", - " Name: 204, dtype: float64, 1451 0.951803\n", - " 1159 0.003848\n", - " 1399 0.001063\n", - " 1457 0.000976\n", - " 1396 0.000957\n", - " 1243 0.000918\n", - " 1151 0.000917\n", - " 1454 0.000746\n", - " 1799 0.000661\n", - " 1137 0.000544\n", - " Name: 205, dtype: float64, 964 0.024745\n", - " 1396 0.024667\n", - " 291 0.023007\n", - " 740 0.018004\n", - " 2727 0.017606\n", - " 1251 0.014039\n", - " 1573 0.010120\n", - " 336 0.010083\n", - " 552 0.009604\n", - " 578 0.009599\n", - " Name: 206, dtype: float64, 2300 0.024335\n", - " 10456 0.015129\n", - " 7195 0.014856\n", - " 319 0.013952\n", - " 10617 0.011947\n", - " 379 0.010500\n", - " 8025 0.010135\n", - " 7406 0.007085\n", - " 1558 0.006674\n", - " 3454 0.006334\n", - " Name: 207, dtype: float64, 500 0.067727\n", - " 10621 0.017556\n", - " 3806 0.015034\n", - " 4017 0.011502\n", - " 5549 0.010438\n", - " 2111 0.010424\n", - " 5146 0.007674\n", - " 1611 0.007493\n", - " 3545 0.007241\n", - " 2809 0.007110\n", - " Name: 208, dtype: float64, 8477 0.082460\n", - " 8476 0.026924\n", - " 10377 0.020495\n", - " 5695 0.010767\n", - " 1802 0.008019\n", - " 10411 0.007801\n", - " 9602 0.007124\n", - " 832 0.006176\n", - " 6960 0.005975\n", - " 2912 0.005726\n", - " Name: 209, dtype: float64, 1695 0.072954\n", - " 1326 0.057711\n", - " 982 0.021030\n", - " 1169 0.016523\n", - " 563 0.015745\n", - " 344 0.015150\n", - " 565 0.014446\n", - " 578 0.009622\n", - " 999 0.008393\n", - " 533 0.008280\n", - " Name: 210, dtype: float64, 1290 0.058960\n", - " 549 0.028576\n", - " 1269 0.020705\n", - " 604 0.016176\n", - " 1291 0.010464\n", - " 1280 0.009075\n", - " 1188 0.008905\n", - " 952 0.008060\n", - " 1521 0.006811\n", - " 1213 0.006769\n", - " Name: 211, dtype: float64, 1672 0.150808\n", - " 1694 0.089905\n", - " 1162 0.084140\n", - " 1213 0.042754\n", - " 1280 0.041704\n", - " 1338 0.017140\n", - " 1145 0.015060\n", - " 1133 0.013134\n", - " 1165 0.011463\n", - " 1151 0.010793\n", - " Name: 212, dtype: float64, 1509 0.713789\n", - " 208 0.136612\n", - " 1510 0.080558\n", - " 213 0.008337\n", - " 1506 0.007072\n", - " 214 0.002322\n", - " 1295 0.001893\n", - " 519 0.001718\n", - " 211 0.001608\n", - " 2386 0.001101\n", - " Name: 213, dtype: float64, 1509 0.982513\n", - " 1510 0.002720\n", - " 213 0.002293\n", - " 1506 0.000488\n", - " 1508 0.000477\n", - " 211 0.000375\n", - " 9572 0.000166\n", - " 208 0.000165\n", - " 672 0.000161\n", - " 207 0.000157\n", - " Name: 214, dtype: float64, 79 9.999919e-01\n", - " 119 3.990866e-07\n", - " 160 3.042264e-07\n", - " 246 2.076826e-07\n", - " 206 2.007943e-07\n", - " 4011 1.895485e-07\n", - " 9626 1.849453e-07\n", - " 4073 1.780618e-07\n", - " 2260 1.481114e-07\n", - " 8040 1.443352e-07\n", - " Name: 215, dtype: float64, 1509 0.969794\n", - " 1506 0.009581\n", - " 1510 0.009086\n", - " 519 0.000340\n", - " 213 0.000186\n", - " 211 0.000179\n", - " 9572 0.000152\n", - " 1508 0.000145\n", - " 1712 0.000098\n", - " 819 0.000097\n", - " Name: 216, dtype: float64, 79 9.999908e-01\n", - " 119 5.876966e-07\n", - " 160 3.782697e-07\n", - " 4011 2.769307e-07\n", - " 641 2.406778e-07\n", - " 4073 2.391964e-07\n", - " 9626 2.344872e-07\n", - " 206 2.072344e-07\n", - " 8040 2.039332e-07\n", - " 1506 1.862114e-07\n", - " Name: 217, dtype: float64, 207 0.431697\n", - " 1736 0.044374\n", - " 367 0.041593\n", - " 1084 0.020080\n", - " 264 0.014384\n", - " 1347 0.009956\n", - " 1928 0.009662\n", - " 1925 0.009252\n", - " 1895 0.008716\n", - " 1927 0.005992\n", - " Name: 218, dtype: float64, 205 0.995245\n", - " 206 0.001943\n", - " 204 0.000461\n", - " 2119 0.000395\n", - " 207 0.000278\n", - " 246 0.000138\n", - " 180 0.000112\n", - " 245 0.000107\n", - " 86 0.000064\n", - " 1046 0.000051\n", - " Name: 219, dtype: float64, 204 0.929131\n", - " 205 0.024390\n", - " 1046 0.011095\n", - " 517 0.006912\n", - " 206 0.003808\n", - " 207 0.001866\n", - " 1465 0.001703\n", - " 501 0.001085\n", - " 2119 0.000809\n", - " 520 0.000707\n", - " Name: 220, dtype: float64, 79 9.999901e-01\n", - " 4011 3.945993e-07\n", - " 160 3.771061e-07\n", - " 9626 3.363487e-07\n", - " 206 3.191716e-07\n", - " 8040 2.284312e-07\n", - " 119 2.161263e-07\n", - " 4073 2.079509e-07\n", - " 205 1.952400e-07\n", - " 246 1.593184e-07\n", - " Name: 221, dtype: float64, 1525 0.064875\n", - " 1157 0.021238\n", - " 1903 0.020568\n", - " 1526 0.013805\n", - " 1077 0.009141\n", - " 1200 0.009015\n", - " 1472 0.008043\n", - " 269 0.007678\n", - " 2394 0.006967\n", - " 1115 0.006907\n", - " Name: 222, dtype: float64, 1488 0.173405\n", - " 2058 0.026697\n", - " 1778 0.024664\n", - " 2445 0.021544\n", - " 512 0.018194\n", - " 1584 0.016459\n", - " 2069 0.014863\n", - " 1133 0.011759\n", - " 1165 0.011463\n", - " 1876 0.008453\n", - " Name: 223, dtype: float64, 1548 0.754881\n", - " 1551 0.029050\n", - " 1546 0.014866\n", - " 1160 0.011615\n", - " 1656 0.010101\n", - " 456 0.008888\n", - " 1655 0.008274\n", - " 2062 0.004002\n", - " 539 0.003530\n", - " 476 0.002316\n", - " Name: 224, dtype: float64, 213 0.999505\n", - " 108 0.000115\n", - " 204 0.000018\n", - " 691 0.000014\n", - " 1295 0.000013\n", - " 1509 0.000013\n", - " 1046 0.000010\n", - " 1711 0.000010\n", - " 517 0.000009\n", - " 257 0.000009\n", - " Name: 225, dtype: float64, 1551 0.418132\n", - " 1160 0.138528\n", - " 1548 0.042366\n", - " 1546 0.023296\n", - " 476 0.014254\n", - " 456 0.008795\n", - " 1167 0.008069\n", - " 539 0.008027\n", - " 319 0.007108\n", - " 1394 0.006876\n", - " Name: 226, dtype: float64, 205 0.998109\n", - " 204 0.000638\n", - " 206 0.000606\n", - " 207 0.000075\n", - " 246 0.000030\n", - " 640 0.000029\n", - " 2119 0.000026\n", - " 214 0.000023\n", + " Name: 22, dtype: float64, 2173 9.999734e-01\n", + " 2547 3.690244e-06\n", + " 8507 1.878531e-06\n", + " 1471 1.371498e-06\n", + " 2437 1.355153e-06\n", + " 2511 1.090240e-06\n", + " 5320 9.424972e-07\n", + " 3318 6.432013e-07\n", + " 2260 5.688299e-07\n", + " 3605 5.123662e-07\n", + " Name: 23, dtype: float64, 2368 0.037304\n", + " 52 0.017858\n", + " 30 0.017397\n", + " 2398 0.016057\n", + " 150 0.013946\n", + " 27 0.013723\n", + " 2344 0.010657\n", + " 44 0.008913\n", + " 53 0.008144\n", + " 2266 0.008135\n", + " Name: 24, dtype: float64, 79 9.999909e-01\n", + " 208 2.848480e-06\n", + " 1805 3.175611e-07\n", + " 7106 2.983316e-07\n", + " 4098 2.189658e-07\n", + " 517 1.515249e-07\n", + " 119 1.156257e-07\n", + " 2629 8.884374e-08\n", + " 2503 7.583670e-08\n", + " 2546 6.768163e-08\n", + " Name: 25, dtype: float64, 2197 0.995380\n", + " 2173 0.000385\n", + " 2369 0.000246\n", + " 2236 0.000186\n", + " 206 0.000183\n", + " 2283 0.000145\n", + " 2260 0.000088\n", + " 2294 0.000084\n", + " 2155 0.000083\n", + " 108 0.000071\n", + " Name: 26, dtype: float64, 2272 0.947031\n", + " 2273 0.037152\n", + " 2164 0.000379\n", + " 660 0.000357\n", + " 122 0.000329\n", + " 2773 0.000315\n", + " 2173 0.000219\n", + " 2269 0.000188\n", + " 2277 0.000185\n", + " 2547 0.000176\n", + " Name: 27, dtype: float64, 2197 0.429914\n", + " 2200 0.215473\n", + " 2266 0.025642\n", + " 2327 0.015928\n", + " 2278 0.009238\n", + " 2293 0.006843\n", + " 2215 0.006474\n", + " 2259 0.006400\n", + " 2274 0.005857\n", + " 2230 0.005537\n", + " Name: 28, dtype: float64, 2293 0.969100\n", + " 123 0.003624\n", + " 86 0.000912\n", + " 1401 0.000473\n", + " 930 0.000466\n", + " 1001 0.000465\n", + " 645 0.000427\n", + " 42 0.000364\n", + " 2196 0.000353\n", + " 1984 0.000334\n", + " Name: 29, dtype: float64, 2173 9.999836e-01\n", + " 1471 1.430155e-06\n", + " 8507 1.128920e-06\n", + " 2547 1.059984e-06\n", + " 2437 8.049979e-07\n", + " 2260 5.854882e-07\n", + " 5320 5.101637e-07\n", + " 2511 4.368158e-07\n", + " 124 3.592182e-07\n", + " 2175 2.874264e-07\n", + " Name: 30, dtype: float64, 2197 0.998559\n", + " 108 0.000140\n", + " 2236 0.000108\n", + " 2277 0.000071\n", + " 2344 0.000070\n", + " 86 0.000063\n", + " 246 0.000045\n", + " 206 0.000037\n", + " 204 0.000036\n", + " 1805 0.000026\n", + " Name: 31, dtype: float64, 2173 9.999758e-01\n", + " 8507 2.183115e-06\n", + " 2437 2.083397e-06\n", + " 1471 1.888242e-06\n", + " 2547 1.799165e-06\n", + " 5320 1.055670e-06\n", + " 2511 9.351523e-07\n", + " 118 8.397527e-07\n", + " 3318 4.509135e-07\n", + " 2260 3.801641e-07\n", + " Name: 32, dtype: float64, 2301 0.982813\n", + " 627 0.000911\n", + " 198 0.000356\n", + " 675 0.000347\n", + " 3432 0.000301\n", + " 814 0.000228\n", + " 2433 0.000220\n", + " 623 0.000214\n", + " 3248 0.000207\n", + " 2350 0.000205\n", + " Name: 33, dtype: float64, 2306 0.992699\n", + " 279 0.000287\n", + " 2325 0.000151\n", + " 632 0.000145\n", + " 2326 0.000117\n", + " 2332 0.000113\n", + " 1538 0.000088\n", + " 2330 0.000083\n", + " 2111 0.000075\n", + " 7871 0.000070\n", + " Name: 34, dtype: float64, 2321 0.995668\n", + " 2374 0.000364\n", + " 2323 0.000187\n", + " 628 0.000113\n", + " 627 0.000092\n", + " 2328 0.000091\n", + " 2348 0.000083\n", + " 2331 0.000081\n", + " 2356 0.000063\n", + " 2367 0.000056\n", + " Name: 35, dtype: float64, 2324 0.298943\n", + " 2325 0.269564\n", + " 2327 0.207924\n", + " 2331 0.178539\n", + " 2326 0.007342\n", + " 2328 0.003995\n", + " 2330 0.002511\n", + " 7470 0.001992\n", + " 2367 0.001886\n", + " 2344 0.000984\n", + " Name: 36, dtype: float64, 2344 0.256932\n", + " 2368 0.065138\n", + " 2388 0.030748\n", + " 2398 0.029420\n", + " 2186 0.020112\n", + " 52 0.016212\n", + " 2266 0.013107\n", + " 2230 0.012838\n", + " 2326 0.011896\n", + " 2387 0.009642\n", + " Name: 37, dtype: float64, 2330 0.944324\n", + " 2324 0.013442\n", + " 2331 0.007996\n", + " 2328 0.004514\n", + " 2325 0.003621\n", + " 2393 0.001634\n", + " 2326 0.001332\n", + " 246 0.001057\n", + " 2333 0.000920\n", + " 214 0.000636\n", + " Name: 38, dtype: float64, 697 0.515875\n", + " 2311 0.179850\n", + " 2351 0.066718\n", + " 677 0.026814\n", + " 870 0.024585\n", + " 2350 0.021980\n", + " 734 0.017324\n", + " 4512 0.008080\n", + " 386 0.004298\n", + " 2236 0.004207\n", + " Name: 39, dtype: float64, 2380 0.891543\n", + " 2388 0.002313\n", + " 176 0.002118\n", + " 706 0.002066\n", + " 147 0.001601\n", + " 2393 0.001261\n", + " 0 0.000956\n", + " 7904 0.000948\n", + " 2390 0.000844\n", + " 251 0.000787\n", + " Name: 40, dtype: float64, 2326 0.769698\n", + " 2324 0.056645\n", + " 2325 0.046068\n", + " 2331 0.038628\n", + " 2327 0.020420\n", + " 2330 0.013571\n", + " 2328 0.011318\n", + " 2333 0.003757\n", + " 2332 0.002381\n", + " 2329 0.001693\n", + " Name: 41, dtype: float64, 2333 0.305736\n", + " 2331 0.168137\n", + " 2324 0.162330\n", + " 2325 0.142103\n", + " 2328 0.033590\n", + " 2327 0.028602\n", + " 2326 0.023017\n", + " 2330 0.011660\n", + " 2329 0.011570\n", + " 2367 0.007149\n", + " Name: 42, dtype: float64, 2390 0.504949\n", + " 55 0.042385\n", + " 2388 0.018485\n", + " 52 0.017706\n", + " 1027 0.013559\n", + " 2387 0.013181\n", + " 1171 0.008514\n", + " 51 0.008509\n", + " 53 0.007070\n", + " 819 0.006825\n", + " Name: 43, dtype: float64, 318 0.725706\n", + " 358 0.050276\n", + " 1901 0.034053\n", + " 1928 0.007080\n", + " 2183 0.005974\n", + " 44 0.005195\n", + " 934 0.005062\n", + " 936 0.003959\n", + " 27 0.002559\n", + " 1264 0.002309\n", + " Name: 44, dtype: float64, 207 0.245070\n", + " 2355 0.052588\n", + " 1135 0.033307\n", + " 52 0.032366\n", + " 205 0.020436\n", + " 3470 0.016542\n", + " 942 0.016268\n", + " 1893 0.015973\n", + " 2162 0.015646\n", + " 279 0.012666\n", + " Name: 45, dtype: float64, 52 0.588454\n", + " 51 0.034996\n", + " 2401 0.023672\n", + " 53 0.016197\n", + " 146 0.010968\n", + " 150 0.010368\n", + " 30 0.009675\n", + " 1135 0.007158\n", + " 55 0.006186\n", + " 44 0.005734\n", + " Name: 46, dtype: float64, 2359 0.806902\n", + " 2402 0.038394\n", + " 2349 0.006079\n", + " 2229 0.005854\n", + " 2984 0.003421\n", + " 5140 0.003334\n", + " 2312 0.002402\n", + " 2341 0.002090\n", + " 2451 0.001823\n", + " 3691 0.001455\n", + " Name: 47, dtype: float64, 4012 0.126310\n", + " 2377 0.094624\n", + " 2318 0.077090\n", + " 2317 0.050063\n", + " 2322 0.037048\n", + " 2108 0.031424\n", + " 236 0.028607\n", + " 2264 0.017067\n", + " 2400 0.015802\n", + " 2184 0.014089\n", + " Name: 48, dtype: float64, 2383 0.310750\n", + " 2345 0.066042\n", + " 52 0.041867\n", + " 2338 0.033581\n", + " 205 0.019306\n", + " 204 0.015803\n", + " 1922 0.015756\n", + " 2408 0.015717\n", + " 42 0.012655\n", + " 2388 0.010384\n", + " Name: 49, dtype: float64, 2326 0.302228\n", + " 1263 0.029058\n", + " 2333 0.024521\n", + " 1540 0.020775\n", + " 1995 0.020179\n", + " 2332 0.017628\n", + " 922 0.017128\n", + " 2325 0.016853\n", + " 2328 0.015867\n", + " 358 0.013445\n", + " Name: 50, dtype: float64, 7291 0.491464\n", + " 2372 0.010601\n", + " 4741 0.009119\n", + " 4029 0.007038\n", + " 593 0.005957\n", + " 9579 0.005495\n", + " 2306 0.005413\n", + " 1345 0.005341\n", + " 9611 0.004559\n", + " 415 0.004313\n", + " Name: 51, dtype: float64, 2344 0.601000\n", + " 10115 0.017847\n", + " 2186 0.012717\n", + " 108 0.011744\n", + " 2388 0.010093\n", + " 2197 0.008418\n", + " 10409 0.007439\n", + " 2160 0.007416\n", + " 2264 0.007198\n", + " 10408 0.006524\n", + " Name: 52, dtype: float64, 2361 0.858269\n", + " 2357 0.031221\n", + " 2360 0.029394\n", + " 2358 0.011844\n", + " 2363 0.008073\n", + " 2362 0.007473\n", + " 283 0.002836\n", + " 2388 0.001673\n", + " 2393 0.001010\n", + " 2395 0.000947\n", + " Name: 53, dtype: float64, 2417 0.992583\n", + " 2597 0.000564\n", + " 734 0.000318\n", + " 3332 0.000250\n", + " 3330 0.000215\n", + " 3331 0.000212\n", + " 628 0.000189\n", + " 3184 0.000186\n", + " 7903 0.000181\n", + " 3892 0.000122\n", + " Name: 54, dtype: float64, 2316 0.996275\n", + " 2317 0.000531\n", + " 2318 0.000306\n", + " 2377 0.000156\n", + " 108 0.000088\n", + " 4135 0.000066\n", + " 1617 0.000063\n", + " 236 0.000059\n", + " 31 0.000057\n", + " 2358 0.000051\n", + " Name: 55, dtype: float64, 2405 0.498270\n", + " 2406 0.271637\n", + " 2404 0.043722\n", + " 1910 0.004592\n", + " 2175 0.004402\n", + " 2341 0.004147\n", + " 2545 0.003747\n", + " 777 0.002903\n", + " 2805 0.002843\n", + " 1911 0.002402\n", + " Name: 56, dtype: float64, 128 0.219411\n", + " 206 0.044478\n", + " 55 0.035102\n", + " 2312 0.031635\n", + " 1515 0.026984\n", + " 124 0.021088\n", + " 829 0.018689\n", + " 107 0.012903\n", + " 519 0.011686\n", + " 248 0.010776\n", + " Name: 57, dtype: float64, 2276 0.701551\n", + " 125 0.016019\n", + " 2432 0.013911\n", + " 2168 0.011345\n", + " 2476 0.009364\n", + " 2428 0.008819\n", + " 2278 0.007514\n", + " 2166 0.005194\n", + " 836 0.002896\n", + " 1453 0.002867\n", + " Name: 58, dtype: float64, 2175 0.406410\n", + " 2157 0.270926\n", + " 2452 0.142096\n", + " 3192 0.015945\n", + " 5760 0.012114\n", + " 2451 0.010041\n", + " 2780 0.003932\n", + " 192 0.003781\n", + " 3501 0.003701\n", + " 679 0.003630\n", + " Name: 59, dtype: float64, 2277 0.994221\n", + " 2269 0.001226\n", + " 2270 0.000539\n", + " 2278 0.000415\n", + " 2194 0.000178\n", + " 822 0.000158\n", + " 2476 0.000121\n", + " 2287 0.000095\n", + " 10100 0.000090\n", + " 2454 0.000074\n", + " Name: 60, dtype: float64, 2463 0.181173\n", + " 2462 0.174944\n", + " 2465 0.165786\n", + " 2464 0.163222\n", + " 2461 0.135209\n", + " 2469 0.008580\n", + " 2459 0.005546\n", + " 2467 0.001458\n", + " 2164 0.001453\n", + " 2442 0.001410\n", + " Name: 61, dtype: float64, 2469 0.875131\n", + " 2466 0.040641\n", + " 2467 0.031944\n", + " 2459 0.005290\n", + " 2465 0.001212\n", + " 2461 0.001146\n", + " 2442 0.001067\n", + " 2463 0.001005\n", + " 1467 0.000878\n", + " 2462 0.000848\n", + " Name: 62, dtype: float64, 2278 0.996877\n", + " 2277 0.000233\n", + " 9382 0.000135\n", + " 173 0.000108\n", + " 2476 0.000092\n", + " 8631 0.000060\n", + " 8501 0.000051\n", + " 2365 0.000051\n", + " 172 0.000045\n", + " 2276 0.000038\n", + " Name: 63, dtype: float64, 1985 0.038299\n", + " 210 0.032405\n", + " 1891 0.027337\n", + " 1959 0.024235\n", + " 1615 0.015695\n", + " 2108 0.015284\n", + " 439 0.012626\n", + " 492 0.011261\n", + " 1885 0.009685\n", + " 10251 0.009277\n", + " Name: 64, dtype: float64, 210 0.210259\n", + " 2319 0.092678\n", + " 1959 0.030706\n", + " 1348 0.013433\n", + " 1643 0.012065\n", + " 1540 0.011862\n", + " 7 0.010356\n", + " 2073 0.009640\n", + " 1400 0.008747\n", + " 37 0.007608\n", + " Name: 65, dtype: float64, 118 0.997216\n", + " 679 0.000191\n", + " 758 0.000187\n", + " 7106 0.000148\n", + " 2173 0.000143\n", + " 119 0.000116\n", + " 690 0.000109\n", + " 677 0.000079\n", + " 2715 0.000062\n", + " 246 0.000054\n", + " Name: 66, dtype: float64, 9525 0.227147\n", + " 8638 0.030785\n", + " 758 0.017740\n", + " 2311 0.017076\n", + " 9507 0.016100\n", + " 8413 0.015601\n", + " 9524 0.015492\n", + " 9307 0.015325\n", + " 2444 0.015032\n", + " 493 0.015013\n", + " Name: 67, dtype: float64, 221 0.529166\n", + " 220 0.301179\n", + " 1936 0.039168\n", + " 224 0.008334\n", + " 1559 0.006584\n", + " 244 0.003024\n", + " 2362 0.002802\n", + " 1938 0.002550\n", + " 1173 0.002442\n", + " 228 0.002373\n", + " Name: 68, dtype: float64, 204 0.298722\n", + " 205 0.214626\n", + " 1046 0.074294\n", + " 517 0.066479\n", + " 206 0.044377\n", + " 518 0.024950\n", + " 1465 0.020148\n", + " 504 0.017449\n", + " 497 0.010321\n", + " 521 0.007970\n", + " Name: 69, dtype: float64, 228 0.980587\n", + " 242 0.000990\n", + " 221 0.000692\n", + " 244 0.000686\n", + " 1936 0.000441\n", + " 220 0.000344\n", + " 2129 0.000343\n", + " 248 0.000300\n", + " 4857 0.000297\n", + " 243 0.000267\n", + " Name: 70, dtype: float64, 79 9.999965e-01\n", + " 208 8.561599e-07\n", + " 1805 1.650654e-07\n", + " 517 8.969933e-08\n", + " 119 7.477041e-08\n", + " 7106 6.408884e-08\n", + " 2503 4.269278e-08\n", + " 4098 4.071497e-08\n", + " 2546 3.893581e-08\n", + " 2166 3.099359e-08\n", + " Name: 71, dtype: float64, 79 9.999924e-01\n", + " 208 1.657989e-06\n", + " 119 2.320573e-07\n", + " 4098 2.232035e-07\n", + " 1805 2.158030e-07\n", + " 517 1.571164e-07\n", + " 7106 1.065742e-07\n", + " 8418 7.699773e-08\n", + " 7141 6.547038e-08\n", + " 3098 6.388378e-08\n", + " Name: 72, dtype: float64, 434 0.022020\n", + " 1740 0.017667\n", + " 1089 0.015554\n", + " 79 0.012367\n", + " 1335 0.011579\n", + " 362 0.009520\n", + " 1705 0.008589\n", + " 2042 0.008475\n", + " 1488 0.007996\n", + " 1521 0.007640\n", + " Name: 73, dtype: float64, 208 0.998497\n", + " 1711 0.000341\n", + " 1295 0.000302\n", + " 214 0.000297\n", + " 213 0.000152\n", + " 233 0.000025\n", + " 1805 0.000023\n", + " 211 0.000021\n", + " 257 0.000017\n", + " 2503 0.000014\n", + " Name: 74, dtype: float64, 207 0.520027\n", + " 246 0.104048\n", + " 264 0.029618\n", + " 211 0.026883\n", + " 204 0.026673\n", + " 1203 0.020586\n", + " 205 0.009228\n", + " 367 0.007507\n", + " 1255 0.007250\n", + " 251 0.004789\n", + " Name: 75, dtype: float64, 207 0.918526\n", + " 247 0.020374\n", + " 246 0.011704\n", + " 205 0.008402\n", + " 204 0.003583\n", + " 8521 0.002788\n", + " 211 0.002047\n", + " 1046 0.001503\n", + " 1203 0.001270\n", + " 251 0.001064\n", + " Name: 76, dtype: float64, 79 9.999784e-01\n", + " 208 7.494582e-06\n", + " 1805 1.381540e-06\n", + " 517 3.584635e-07\n", + " 119 2.982363e-07\n", + " 4000 1.558235e-07\n", + " 2166 1.519241e-07\n", + " 2428 1.365352e-07\n", + " 7106 1.211621e-07\n", + " 2503 1.132057e-07\n", + " Name: 77, dtype: float64, 239 0.999551\n", + " 240 0.000055\n", + " 1805 0.000036\n", + " 679 0.000017\n", + " 870 0.000011\n", + " 2236 0.000009\n", + " 698 0.000008\n", + " 677 0.000008\n", + " 2715 0.000008\n", + " 8509 0.000006\n", + " Name: 78, dtype: float64, 208 0.999421\n", + " 213 0.000140\n", + " 233 0.000079\n", + " 1805 0.000053\n", + " 211 0.000040\n", + " 1295 0.000028\n", + " 519 0.000021\n", + " 517 0.000016\n", + " 1711 0.000016\n", + " 9729 0.000011\n", + " Name: 79, dtype: float64, 208 0.516256\n", + " 119 0.155734\n", + " 206 0.067800\n", + " 517 0.042493\n", + " 519 0.023410\n", + " 233 0.015400\n", + " 257 0.007474\n", + " 203 0.007440\n", + " 207 0.005141\n", + " 213 0.004930\n", + " Name: 80, dtype: float64, 233 0.986595\n", + " 1711 0.002576\n", + " 208 0.001878\n", + " 2074 0.001244\n", + " 211 0.000429\n", + " 1082 0.000340\n", + " 1509 0.000277\n", + " 519 0.000196\n", + " 1576 0.000175\n", + " 203 0.000102\n", + " Name: 81, dtype: float64, 213 0.975510\n", + " 208 0.007818\n", + " 233 0.003505\n", + " 129 0.001475\n", + " 206 0.000990\n", + " 519 0.000628\n", + " 207 0.000581\n", + " 517 0.000572\n", + " 211 0.000440\n", + " 1295 0.000364\n", + " Name: 82, dtype: float64, 213 0.134068\n", + " 356 0.063417\n", + " 1263 0.046778\n", + " 1576 0.031371\n", + " 52 0.024921\n", + " 1309 0.024617\n", + " 1946 0.020764\n", + " 1995 0.019944\n", + " 1203 0.017232\n", + " 2182 0.015564\n", + " Name: 83, dtype: float64, 246 0.682731\n", + " 2351 0.038957\n", + " 2236 0.019731\n", + " 108 0.015963\n", + " 635 0.015168\n", + " 1713 0.007103\n", + " 107 0.005023\n", + " 630 0.004157\n", + " 247 0.003894\n", + " 883 0.003460\n", + " Name: 84, dtype: float64, 246 0.639482\n", + " 207 0.117043\n", + " 206 0.090043\n", + " 205 0.042501\n", + " 204 0.032797\n", + " 203 0.007698\n", + " 517 0.004896\n", + " 211 0.003252\n", + " 214 0.003034\n", + " 180 0.001912\n", + " Name: 85, dtype: float64, 207 0.990486\n", + " 211 0.003299\n", + " 205 0.000842\n", + " 206 0.000229\n", + " 246 0.000198\n", + " 519 0.000177\n", + " 214 0.000157\n", + " 213 0.000130\n", + " 1295 0.000125\n", + " 208 0.000121\n", + " Name: 86, dtype: float64, 1642 0.162404\n", + " 2104 0.111215\n", + " 2348 0.044670\n", + " 292 0.033673\n", + " 7 0.027609\n", + " 10 0.020528\n", + " 1199 0.015910\n", + " 1646 0.014526\n", + " 1609 0.009326\n", + " 1876 0.009164\n", + " Name: 87, dtype: float64, 304 0.510765\n", + " 303 0.385292\n", + " 305 0.005937\n", + " 291 0.005853\n", + " 381 0.002080\n", + " 301 0.001976\n", + " 4708 0.001650\n", + " 377 0.000985\n", + " 335 0.000921\n", + " 610 0.000887\n", + " Name: 88, dtype: float64, 303 0.373407\n", + " 304 0.339649\n", + " 305 0.019988\n", + " 301 0.012792\n", + " 1862 0.005418\n", + " 294 0.005200\n", + " 291 0.004237\n", + " 292 0.003469\n", + " 533 0.002952\n", + " 1870 0.002124\n", + " Name: 89, dtype: float64, 1273 0.257247\n", + " 1193 0.257015\n", + " 305 0.101230\n", + " 308 0.031865\n", + " 1577 0.025663\n", + " 1861 0.016131\n", + " 460 0.009356\n", + " 1136 0.008588\n", + " 306 0.007729\n", + " 963 0.006494\n", + " Name: 90, dtype: float64, 318 0.971389\n", + " 358 0.006893\n", + " 1901 0.002807\n", + " 1928 0.000446\n", + " 1266 0.000427\n", + " 1540 0.000380\n", + " 2183 0.000352\n", + " 1195 0.000322\n", + " 1978 0.000303\n", + " 27 0.000301\n", + " Name: 91, dtype: float64, 334 0.056295\n", + " 329 0.029072\n", + " 330 0.028109\n", + " 965 0.027986\n", + " 292 0.012491\n", + " 904 0.012062\n", + " 335 0.011526\n", + " 2024 0.009224\n", + " 425 0.008491\n", + " 1143 0.007588\n", + " Name: 92, dtype: float64, 536 0.028862\n", + " 609 0.018990\n", + " 2981 0.017501\n", + " 334 0.013116\n", + " 610 0.012379\n", + " 292 0.010117\n", + " 1143 0.009758\n", + " 329 0.009408\n", + " 552 0.008044\n", + " 1890 0.007862\n", + " Name: 93, dtype: float64, 360 0.844449\n", + " 1267 0.004755\n", + " 1158 0.004179\n", + " 378 0.003596\n", + " 1772 0.002593\n", + " 1694 0.002340\n", + " 1161 0.002076\n", + " 1661 0.002007\n", + " 2241 0.001868\n", + " 520 0.001769\n", + " Name: 94, dtype: float64, 305 0.032930\n", + " 1273 0.028676\n", + " 962 0.026583\n", + " 1033 0.020592\n", + " 295 0.019969\n", + " 308 0.019268\n", + " 533 0.015436\n", + " 1078 0.013501\n", + " 1866 0.013256\n", + " 316 0.012511\n", + " Name: 95, dtype: float64, 1847 0.160653\n", + " 1635 0.044527\n", + " 1881 0.034268\n", + " 2001 0.027445\n", + " 1769 0.025365\n", + " 1792 0.022783\n", + " 1592 0.016882\n", + " 1200 0.016879\n", + " 1775 0.015137\n", + " 1571 0.014967\n", + " Name: 96, dtype: float64, 1263 0.210310\n", + " 1540 0.045490\n", + " 1995 0.035292\n", + " 2188 0.024255\n", + " 922 0.022347\n", + " 933 0.021900\n", + " 1702 0.021797\n", + " 1893 0.020288\n", + " 1309 0.018438\n", + " 2159 0.017527\n", + " Name: 97, dtype: float64, 246 0.991508\n", + " 204 0.001821\n", + " 207 0.001363\n", + " 206 0.001306\n", + " 205 0.000492\n", + " 108 0.000439\n", + " 203 0.000314\n", + " 247 0.000100\n", + " 2236 0.000089\n", + " 213 0.000085\n", + " Name: 98, dtype: float64, 118 0.914545\n", + " 7106 0.004295\n", + " 7114 0.003874\n", + " 3997 0.003237\n", + " 3157 0.003233\n", + " 2780 0.002931\n", + " 4693 0.002447\n", + " 239 0.002394\n", + " 690 0.002299\n", + " 622 0.001955\n", + " Name: 99, dtype: float64, 430 0.849920\n", + " 434 0.038826\n", + " 432 0.020087\n", + " 433 0.015715\n", + " 2483 0.001012\n", + " 2397 0.000838\n", + " 981 0.000829\n", + " 982 0.000814\n", + " 1610 0.000786\n", + " 1335 0.000719\n", + " Name: 100, dtype: float64, 430 0.895172\n", + " 434 0.032226\n", + " 432 0.014800\n", + " 433 0.011552\n", + " 981 0.001145\n", + " 2483 0.000687\n", + " 2003 0.000471\n", + " 1631 0.000409\n", + " 10623 0.000403\n", + " 982 0.000386\n", + " Name: 101, dtype: float64, 342 0.065717\n", + " 495 0.037746\n", + " 562 0.034736\n", + " 563 0.030358\n", + " 417 0.028362\n", + " 521 0.023759\n", + " 550 0.021975\n", + " 554 0.021155\n", + " 533 0.017722\n", + " 401 0.016575\n", + " Name: 102, dtype: float64, 989 0.052133\n", + " 532 0.052083\n", + " 1021 0.050325\n", + " 980 0.032019\n", + " 323 0.031262\n", + " 454 0.023445\n", + " 988 0.019566\n", + " 1204 0.017945\n", + " 531 0.017801\n", + " 981 0.008974\n", + " Name: 103, dtype: float64, 3992 0.211866\n", + " 79 0.104347\n", + " 233 0.047175\n", + " 5771 0.023028\n", + " 4098 0.012644\n", + " 8371 0.011375\n", + " 9136 0.011222\n", + " 6577 0.010919\n", + " 517 0.010062\n", + " 3264 0.009735\n", + " Name: 104, dtype: float64, 205 0.999186\n", + " 204 0.000137\n", + " 207 0.000087\n", + " 206 0.000063\n", + " 2119 0.000031\n", + " 504 0.000029\n", + " 246 0.000026\n", + " 519 0.000024\n", + " 1515 0.000022\n", " 1046 0.000020\n", - " 245 0.000020\n", - " Name: 227, dtype: float64, 1733 0.025795\n", - " 971 0.022451\n", - " 336 0.015081\n", - " 1390 0.011864\n", - " 1335 0.010759\n", - " 2343 0.010238\n", - " 1331 0.009999\n", - " 984 0.009536\n", - " 1674 0.009454\n", - " 1636 0.008722\n", - " Name: 228, dtype: float64, 901 0.051937\n", - " 31 0.024036\n", - " 7456 0.016520\n", - " 186 0.015032\n", - " 2273 0.012291\n", - " 1574 0.011120\n", - " 2562 0.008065\n", - " 751 0.008024\n", - " 3175 0.007875\n", - " 184 0.007536\n", - " Name: 229, dtype: float64, 1580 0.201317\n", - " 1589 0.193641\n", - " 1590 0.108456\n", - " 1578 0.088072\n", - " 1586 0.086360\n", - " 1579 0.080559\n", - " 1583 0.036156\n", - " 1581 0.008514\n", - " 1592 0.008389\n", - " 1585 0.003616\n", - " Name: 230, dtype: float64, 1846 0.105967\n", - " 1840 0.050184\n", - " 1598 0.036870\n", - " 1581 0.026687\n", - " 1329 0.011522\n", - " 1822 0.010556\n", - " 1335 0.009503\n", - " 1578 0.008880\n", - " 1730 0.008637\n", - " 964 0.008210\n", - " Name: 231, dtype: float64, 79 9.999822e-01\n", - " 160 1.918270e-06\n", - " 4011 4.742878e-07\n", - " 246 3.533742e-07\n", - " 2260 3.251077e-07\n", - " 3687 3.156571e-07\n", - " 9626 2.998196e-07\n", - " 3637 2.944852e-07\n", - " 1506 2.824536e-07\n", - " 119 2.773055e-07\n", - " Name: 232, dtype: float64, 246 0.986593\n", - " 207 0.004921\n", - " 1046 0.002132\n", - " 206 0.001309\n", - " 205 0.000284\n", - " 204 0.000213\n", - " 86 0.000212\n", - " 1203 0.000165\n", - " 214 0.000141\n", - " 517 0.000140\n", - " Name: 233, dtype: float64, 207 0.507721\n", - " 205 0.103045\n", - " 211 0.017389\n", - " 204 0.015210\n", - " 247 0.011473\n", - " 52 0.010188\n", - " 9522 0.008834\n", - " 246 0.008258\n", - " 9572 0.008094\n", - " 3404 0.007814\n", - " Name: 234, dtype: float64, 246 0.638690\n", - " 108 0.066879\n", - " 213 0.061120\n", - " 208 0.027049\n", - " 233 0.025068\n", - " 206 0.019650\n", - " 1046 0.016734\n", - " 691 0.014246\n", - " 214 0.007107\n", - " 211 0.006503\n", - " Name: 235, dtype: float64, 246 0.588314\n", - " 1203 0.077821\n", - " 206 0.076299\n", - " 691 0.028489\n", - " 204 0.026190\n", - " 1046 0.026061\n", - " 2337 0.009349\n", - " 207 0.008026\n", - " 213 0.003246\n", - " 247 0.003229\n", - " Name: 236, dtype: float64, 108 0.201136\n", - " 654 0.112585\n", - " 672 0.051498\n", - " 697 0.039885\n", - " 691 0.021720\n", - " 213 0.018232\n", - " 208 0.014834\n", - " 207 0.014012\n", - " 9526 0.012821\n", - " 621 0.011137\n", - " Name: 237, dtype: float64, 208 0.993165\n", - " 211 0.001492\n", - " 214 0.000817\n", - " 206 0.000593\n", - " 207 0.000537\n", - " 246 0.000432\n", - " 119 0.000233\n", - " 519 0.000216\n", - " 1711 0.000191\n", - " 1295 0.000121\n", - " Name: 238, dtype: float64, 1637 0.057197\n", - " 1390 0.041569\n", - " 1267 0.032519\n", - " 1032 0.017774\n", - " 1598 0.015130\n", - " 1695 0.014160\n", - " 1038 0.013808\n", - " 1104 0.013532\n", - " 1000 0.012309\n", - " 336 0.010846\n", - " Name: 239, dtype: float64, 1151 0.023714\n", - " 1526 0.023376\n", - " 282 0.016270\n", - " 1671 0.008754\n", - " 3411 0.007795\n", - " 1558 0.006874\n", - " 2335 0.005996\n", - " 2159 0.005701\n", - " 338 0.005592\n", - " 1799 0.005375\n", - " Name: 240, dtype: float64, 213 0.988964\n", - " 208 0.004120\n", - " 214 0.001244\n", - " 1295 0.000736\n", - " 257 0.000660\n", - " 108 0.000621\n", - " 206 0.000238\n", - " 233 0.000177\n", - " 1711 0.000172\n", - " 211 0.000156\n", - " Name: 241, dtype: float64, 1606 0.040941\n", - " 1688 0.039910\n", - " 511 0.035549\n", - " 2149 0.028893\n", - " 1059 0.021838\n", - " 1107 0.013548\n", - " 1122 0.011524\n", - " 1715 0.010532\n", - " 1686 0.009995\n", - " 664 0.008848\n", - " Name: 242, dtype: float64, 1860 0.708374\n", - " 1866 0.029061\n", - " 457 0.016403\n", - " 1691 0.005113\n", - " 1592 0.003332\n", - " 460 0.002885\n", - " 323 0.002636\n", - " 340 0.002293\n", - " 462 0.002248\n", - " 1722 0.002087\n", - " Name: 243, dtype: float64, 1700 0.823764\n", - " 1701 0.074587\n", - " 1753 0.001431\n", - " 1620 0.001191\n", - " 522 0.001105\n", - " 90 0.000883\n", - " 10653 0.000872\n", - " 1491 0.000860\n", - " 899 0.000856\n", - " 2361 0.000761\n", - " Name: 244, dtype: float64, 3149 0.020177\n", - " 1057 0.016563\n", - " 3010 0.015964\n", - " 3148 0.015246\n", - " 1114 0.015029\n", - " 1064 0.014010\n", - " 510 0.013010\n", - " 276 0.008828\n", - " 1309 0.008803\n", - " 5855 0.007159\n", - " Name: 245, dtype: float64, 2083 0.027864\n", - " 1890 0.019771\n", - " 1720 0.019066\n", - " 1183 0.018293\n", - " 537 0.017317\n", - " 554 0.015092\n", - " 1542 0.013789\n", - " 1165 0.013277\n", - " 1290 0.012844\n", - " 1592 0.012604\n", - " Name: 246, dtype: float64, 1733 0.074034\n", - " 336 0.018099\n", - " 1213 0.014940\n", - " 2003 0.011622\n", - " 518 0.010995\n", - " 1694 0.009576\n", - " 1718 0.008878\n", - " 1696 0.008199\n", - " 1203 0.007789\n", - " 1335 0.007751\n", - " Name: 247, dtype: float64, 2230 0.046230\n", - " 9568 0.020065\n", - " 9194 0.012588\n", - " 9477 0.010847\n", - " 7193 0.009952\n", - " 7724 0.008488\n", - " 10322 0.007690\n", - " 10229 0.007145\n", - " 657 0.007138\n", - " 8855 0.007028\n", - " Name: 248, dtype: float64, 1776 0.207007\n", - " 1771 0.026383\n", - " 7196 0.020737\n", - " 782 0.012618\n", - " 9584 0.011763\n", - " 1767 0.008811\n", - " 2141 0.007891\n", - " 5549 0.007050\n", - " 10129 0.006523\n", - " 1775 0.006171\n", - " Name: 249, dtype: float64, 8032 0.040239\n", - " 1776 0.033576\n", - " 8635 0.019037\n", - " 8250 0.015734\n", - " 8252 0.010610\n", - " 8251 0.010589\n", - " 9555 0.010067\n", - " 9556 0.009498\n", - " 9525 0.008015\n", - " 527 0.007316\n", - " Name: 250, dtype: float64, 8146 0.078178\n", - " 1471 0.019135\n", - " 5918 0.014090\n", - " 10198 0.012588\n", - " 2215 0.012279\n", - " 10456 0.008879\n", - " 9989 0.008092\n", - " 5847 0.007261\n", - " 5871 0.005986\n", - " 10483 0.004973\n", - " Name: 251, dtype: float64, 2119 0.059450\n", - " 1748 0.038807\n", - " 1749 0.020348\n", - " 8483 0.019724\n", - " 4951 0.015458\n", - " 1062 0.012279\n", - " 1047 0.011622\n", - " 1995 0.011075\n", - " 1806 0.009577\n", - " 248 0.008467\n", - " Name: 252, dtype: float64, 1776 0.089169\n", - " 1771 0.042264\n", - " 1767 0.021854\n", - " 1775 0.018795\n", - " 9552 0.010112\n", - " 9556 0.009665\n", - " 9555 0.008863\n", - " 7196 0.008295\n", - " 2070 0.007907\n", - " 9584 0.007488\n", - " Name: 253, dtype: float64, 1757 0.562950\n", - " 561 0.011033\n", - " 1457 0.009949\n", - " 1964 0.006236\n", - " 1454 0.005944\n", - " 1459 0.004287\n", - " 1458 0.003593\n", - " 6726 0.003579\n", - " 1456 0.003477\n", - " 1673 0.002742\n", - " Name: 254, dtype: float64, 1761 0.980487\n", - " 1792 0.000342\n", - " 4984 0.000298\n", - " 996 0.000285\n", - " 997 0.000250\n", - " 1772 0.000249\n", - " 394 0.000235\n", - " 2984 0.000210\n", - " 1096 0.000201\n", - " 6966 0.000180\n", - " Name: 255, dtype: float64, 1775 0.228822\n", - " 1769 0.114218\n", - " 1767 0.038397\n", - " 1771 0.034271\n", - " 1776 0.013136\n", - " 2087 0.010915\n", - " 2146 0.010654\n", - " 1654 0.007148\n", - " 564 0.007092\n", - " 1102 0.006747\n", - " Name: 256, dtype: float64, 1793 0.344795\n", - " 1791 0.038613\n", - " 1400 0.031536\n", - " 1399 0.015492\n", - " 1792 0.013145\n", - " 2084 0.012581\n", - " 1405 0.009924\n", - " 1250 0.007595\n", - " 353 0.006288\n", - " 1435 0.006245\n", - " Name: 257, dtype: float64, 1791 0.986215\n", - " 1405 0.000281\n", - " 1793 0.000269\n", - " 336 0.000178\n", - " 6991 0.000176\n", - " 1743 0.000140\n", - " 8610 0.000139\n", - " 1523 0.000138\n", - " 523 0.000130\n", - " 1524 0.000122\n", - " Name: 258, dtype: float64, 1636 0.213901\n", - " 1641 0.144282\n", - " 1000 0.035144\n", - " 355 0.025523\n", - " 1032 0.024998\n", - " 1848 0.021667\n", - " 2105 0.016776\n", - " 1104 0.016234\n", - " 462 0.013532\n", - " 353 0.011449\n", - " Name: 259, dtype: float64, 1799 0.091518\n", - " 1673 0.028219\n", - " 1165 0.023657\n", - " 1584 0.020470\n", - " 2102 0.019330\n", - " 2013 0.018976\n", - " 1400 0.016480\n", - " 1839 0.013398\n", - " 1622 0.011523\n", - " 440 0.010319\n", - " Name: 260, dtype: float64, 1889 0.179681\n", - " 1474 0.043489\n", - " 180 0.028179\n", - " 1030 0.023226\n", - " 889 0.012848\n", - " 95 0.011044\n", - " 1464 0.009410\n", - " 1051 0.008287\n", - " 1382 0.007608\n", - " 9797 0.007525\n", - " Name: 261, dtype: float64, 205 0.331906\n", - " 207 0.082348\n", - " 246 0.047998\n", - " 1807 0.042687\n", - " 1046 0.042063\n", - " 245 0.039193\n", - " 2119 0.021191\n", - " 214 0.020065\n", - " 206 0.015638\n", - " 1082 0.010408\n", - " Name: 262, dtype: float64, 1806 0.022721\n", - " 2162 0.018744\n", - " 2161 0.016539\n", - " 6580 0.011410\n", - " 6587 0.009133\n", - " 1716 0.008064\n", - " 3305 0.007966\n", - " 10654 0.006881\n", - " 3496 0.006843\n", - " 10342 0.006002\n", - " Name: 263, dtype: float64, 3115 0.012302\n", - " 3238 0.012057\n", - " 3061 0.011960\n", - " 3062 0.010342\n", - " 1069 0.008046\n", - " 8484 0.007969\n", - " 2778 0.007706\n", - " 2946 0.007450\n", - " 1071 0.007430\n", - " 1072 0.006894\n", - " Name: 264, dtype: float64, 1813 0.453610\n", - " 1814 0.430220\n", - " 146 0.009325\n", - " 10115 0.007844\n", - " 9822 0.006203\n", - " 10497 0.005295\n", - " 2249 0.001764\n", - " 1917 0.001599\n", - " 1395 0.001405\n", - " 8186 0.001367\n", - " Name: 265, dtype: float64, 214 0.984049\n", - " 1295 0.003270\n", - " 206 0.000926\n", - " 499 0.000581\n", - " 246 0.000560\n", - " 1711 0.000458\n", - " 263 0.000357\n", - " 208 0.000332\n", - " 213 0.000291\n", - " 1046 0.000252\n", - " Name: 266, dtype: float64, 211 0.995202\n", - " 208 0.001082\n", - " 1711 0.000688\n", - " 246 0.000320\n", - " 207 0.000277\n", - " 519 0.000210\n", - " 214 0.000185\n", - " 233 0.000129\n", - " 635 0.000098\n", - " 204 0.000096\n", - " Name: 267, dtype: float64, 2342 0.092553\n", - " 1820 0.072159\n", - " 3435 0.028577\n", - " 1486 0.015187\n", - " 92 0.009165\n", - " 1035 0.008602\n", - " 387 0.007746\n", - " 72 0.007340\n", - " 274 0.004915\n", - " 8500 0.004735\n", - " Name: 268, dtype: float64, 1821 0.040240\n", - " 1761 0.032633\n", - " 1598 0.021504\n", - " 1661 0.016615\n", - " 1669 0.014118\n", - " 6568 0.012530\n", - " 1824 0.011289\n", - " 2088 0.007936\n", - " 1550 0.007194\n", - " 1671 0.006872\n", - " Name: 269, dtype: float64, 1821 0.019547\n", - " 7024 0.016705\n", - " 1669 0.013308\n", - " 1732 0.011505\n", - " 6262 0.009689\n", - " 439 0.009594\n", - " 1761 0.008833\n", - " 292 0.008665\n", - " 1822 0.008574\n", - " 222 0.007903\n", - " Name: 270, dtype: float64, 7391 0.515723\n", - " 213 0.016248\n", - " 684 0.014199\n", - " 87 0.011505\n", - " 108 0.011449\n", - " 271 0.010090\n", - " 644 0.009233\n", - " 1953 0.007612\n", - " 93 0.007450\n", - " 691 0.007089\n", - " Name: 271, dtype: float64, 1879 0.090446\n", - " 1884 0.051959\n", - " 1858 0.020591\n", - " 1857 0.016734\n", - " 1870 0.013848\n", - " 1862 0.012522\n", - " 1531 0.011990\n", - " 1861 0.011937\n", - " 1866 0.010666\n", - " 456 0.010385\n", - " Name: 272, dtype: float64, 1864 0.685211\n", - " 1875 0.046418\n", - " 1871 0.015912\n", - " 1872 0.009935\n", - " 480 0.003399\n", - " 593 0.003086\n", - " 1867 0.002972\n", - " 1861 0.002865\n", - " 1629 0.002535\n", - " 1870 0.002146\n", - " Name: 273, dtype: float64, 1871 0.915225\n", - " 1864 0.014145\n", - " 1875 0.007596\n", - " 1861 0.001863\n", - " 1863 0.001116\n", - " 537 0.001076\n", - " 1867 0.001012\n", - " 1862 0.000641\n", - " 1077 0.000631\n", - " 536 0.000624\n", - " Name: 274, dtype: float64, 1897 0.964285\n", - " 1895 0.007678\n", - " 1318 0.000749\n", - " 2046 0.000521\n", - " 207 0.000476\n", - " 593 0.000465\n", - " 1903 0.000445\n", - " 1033 0.000396\n", - " 1107 0.000393\n", - " 1035 0.000319\n", - " Name: 275, dtype: float64, 1897 0.775420\n", - " 1895 0.062875\n", - " 1903 0.004825\n", - " 593 0.004251\n", - " 1318 0.002893\n", - " 1113 0.002603\n", - " 933 0.001808\n", - " 2390 0.001632\n", - " 1752 0.001446\n", - " 367 0.001399\n", - " Name: 276, dtype: float64, 1745 0.047272\n", - " 1894 0.042522\n", - " 1461 0.016925\n", - " 8032 0.015203\n", - " 10546 0.015060\n", - " 6119 0.010711\n", - " 8591 0.009153\n", - " 10655 0.008095\n", - " 2422 0.007129\n", - " 10375 0.006883\n", - " Name: 277, dtype: float64, 275 0.164227\n", - " 2290 0.020313\n", - " 508 0.016604\n", - " 2334 0.015532\n", - " 1620 0.013006\n", - " 2488 0.009846\n", - " 1473 0.009333\n", - " 2243 0.008618\n", - " 2396 0.008552\n", - " 90 0.007649\n", - " Name: 278, dtype: float64, 387 0.904137\n", - " 8847 0.010402\n", - " 1917 0.002240\n", - " 9114 0.001462\n", - " 2900 0.001273\n", - " 10306 0.001272\n", - " 7894 0.001272\n", - " 1906 0.001199\n", - " 667 0.001109\n", - " 2241 0.001099\n", - " Name: 279, dtype: float64, 79 9.999919e-01\n", - " 9626 4.675345e-07\n", - " 119 4.423599e-07\n", - " 160 4.020341e-07\n", - " 8040 2.384301e-07\n", - " 246 2.084564e-07\n", - " 2260 1.993315e-07\n", - " 206 1.888696e-07\n", - " 690 1.565272e-07\n", - " 641 1.296416e-07\n", - " Name: 280, dtype: float64, 108 0.999282\n", - " 654 0.000052\n", - " 691 0.000032\n", - " 129 0.000032\n", - " 136 0.000025\n", - " 697 0.000024\n", - " 159 0.000017\n", - " 87 0.000016\n", - " 635 0.000014\n", - " 675 0.000012\n", - " Name: 281, dtype: float64, 932 0.038175\n", - " 1946 0.031805\n", - " 1936 0.030655\n", - " 1938 0.027321\n", - " 228 0.012333\n", - " 1003 0.011867\n", - " 221 0.010695\n", - " 222 0.010152\n", - " 1029 0.009428\n", - " 1027 0.009175\n", - " Name: 282, dtype: float64, 252 0.359065\n", - " 1931 0.039187\n", - " 1948 0.033342\n", - " 1349 0.014340\n", - " 1657 0.012850\n", - " 2088 0.010348\n", - " 251 0.008772\n", - " 1673 0.008709\n", - " 1777 0.006714\n", - " 255 0.006564\n", - " Name: 283, dtype: float64, 1953 0.944224\n", - " 8240 0.003108\n", - " 3196 0.001884\n", - " 2931 0.001840\n", - " 3195 0.001640\n", - " 8273 0.001317\n", - " 2708 0.001089\n", - " 684 0.001019\n", - " 10600 0.000968\n", - " 7893 0.000702\n", - " Name: 284, dtype: float64, 2310 0.129786\n", - " 2238 0.037737\n", - " 1781 0.020343\n", - " 9906 0.018293\n", - " 1736 0.016405\n", - " 2343 0.012940\n", - " 7866 0.008431\n", - " 2734 0.008361\n", - " 2291 0.007520\n", - " 6874 0.006767\n", - " Name: 285, dtype: float64, 641 0.999572\n", - " 2115 0.000094\n", - " 690 0.000049\n", - " 2514 0.000022\n", - " 3046 0.000013\n", - " 2236 0.000008\n", - " 3047 0.000007\n", - " 4172 0.000006\n", - " 677 0.000006\n", - " 8381 0.000005\n", - " Name: 286, dtype: float64, 957 0.271464\n", - " 2439 0.066042\n", - " 7516 0.022814\n", - " 1186 0.018686\n", - " 2401 0.007757\n", - " 507 0.007361\n", - " 55 0.004041\n", - " 8131 0.003945\n", - " 2355 0.003809\n", - " 103 0.003778\n", - " Name: 287, dtype: float64, 1385 0.033478\n", - " 2280 0.024684\n", - " 2085 0.014824\n", - " 7852 0.012186\n", - " 1617 0.011266\n", - " 1970 0.009110\n", - " 931 0.008609\n", - " 2275 0.007968\n", - " 8138 0.007937\n", - " 1133 0.007453\n", - " Name: 288, dtype: float64, 7187 0.110011\n", - " 10434 0.048651\n", - " 2735 0.014391\n", - " 5700 0.013608\n", - " 8727 0.013324\n", - " 10326 0.011804\n", - " 9941 0.009748\n", - " 10436 0.007112\n", - " 5288 0.007044\n", - " 7239 0.006142\n", - " Name: 289, dtype: float64, 1607 0.448324\n", - " 1925 0.035244\n", - " 1738 0.021454\n", - " 1310 0.020097\n", - " 1650 0.017156\n", - " 1922 0.015233\n", - " 1921 0.006700\n", - " 1891 0.005780\n", - " 1606 0.003540\n", - " 1954 0.003369\n", - " Name: 290, dtype: float64, 1975 0.383107\n", - " 2335 0.130491\n", - " 362 0.018906\n", - " 2159 0.010330\n", - " 1977 0.009968\n", - " 1677 0.007605\n", - " 1752 0.005495\n", - " 2331 0.005382\n", - " 1903 0.005262\n", - " 1922 0.004987\n", - " Name: 291, dtype: float64, 660 0.032280\n", - " 9473 0.028766\n", - " 581 0.025215\n", - " 404 0.020317\n", - " 2449 0.017435\n", - " 10412 0.015305\n", - " 2233 0.014910\n", - " 7894 0.012069\n", - " 7972 0.010967\n", - " 915 0.010906\n", - " Name: 292, dtype: float64, 79 0.134637\n", - " 2546 0.104159\n", - " 1871 0.086752\n", - " 9626 0.075398\n", - " 2166 0.010419\n", - " 8040 0.009416\n", - " 1875 0.007998\n", - " 1203 0.007225\n", - " 1049 0.006970\n", - " 41 0.005917\n", - " Name: 293, dtype: float64, 2630 0.058430\n", - " 2060 0.050806\n", - " 2490 0.043716\n", - " 10129 0.017824\n", - " 3333 0.016951\n", - " 7526 0.012877\n", - " 8049 0.012490\n", - " 1833 0.010744\n", - " 7973 0.010339\n", - " 1532 0.008871\n", - " Name: 294, dtype: float64, 206 0.927531\n", - " 246 0.016733\n", - " 208 0.011047\n", - " 205 0.005340\n", - " 1046 0.004517\n", - " 214 0.002078\n", - " 207 0.001638\n", - " 86 0.001326\n", - " 204 0.001264\n", - " 1295 0.001065\n", - " Name: 295, dtype: float64, 1576 0.837524\n", - " 2046 0.046298\n", - " 356 0.010867\n", - " 664 0.005508\n", - " 1318 0.004155\n", - " 1107 0.001912\n", - " 665 0.001482\n", - " 5110 0.001359\n", - " 1713 0.001352\n", - " 8108 0.001324\n", - " Name: 296, dtype: float64, 987 0.320651\n", - " 1022 0.098866\n", - " 1674 0.078613\n", - " 984 0.053844\n", - " 1187 0.027104\n", - " 1689 0.018727\n", - " 1000 0.013956\n", - " 319 0.010342\n", - " 1141 0.007258\n", - " 1531 0.005895\n", - " Name: 297, dtype: float64, 8841 0.371092\n", - " 3256 0.057524\n", - " 259 0.019979\n", - " 8800 0.017471\n", - " 2119 0.009874\n", - " 2111 0.009677\n", - " 640 0.008230\n", - " 2498 0.007015\n", - " 248 0.006715\n", - " 2113 0.006148\n", - " Name: 298, dtype: float64, 206 0.989276\n", - " 205 0.004574\n", - " 203 0.002270\n", - " 204 0.001186\n", - " 246 0.000382\n", - " 214 0.000126\n", - " 2337 0.000105\n", - " 86 0.000083\n", - " 1086 0.000071\n", - " 1203 0.000065\n", - " Name: 299, dtype: float64, 2088 0.389242\n", - " 2087 0.025651\n", - " 1652 0.018844\n", - " 2062 0.012131\n", - " 1654 0.010764\n", - " 1655 0.009016\n", - " 1656 0.008983\n", - " 456 0.008767\n", - " 1629 0.008378\n", - " 1872 0.008325\n", - " Name: 300, dtype: float64, 338 0.051759\n", - " 550 0.024592\n", - " 436 0.024040\n", - " 1635 0.022129\n", - " 1558 0.015820\n", - " 439 0.015101\n", - " 535 0.012323\n", - " 336 0.010829\n", - " 586 0.010728\n", - " 533 0.009999\n", - " Name: 301, dtype: float64, 2108 0.961734\n", - " 207 0.004166\n", - " 2351 0.003016\n", - " 205 0.000980\n", - " 2331 0.000933\n", - " 870 0.000882\n", - " 2313 0.000876\n", - " 1805 0.000845\n", - " 246 0.000662\n", - " 2323 0.000637\n", - " Name: 302, dtype: float64, 10482 0.018177\n", - " 7724 0.015961\n", - " 9192 0.015135\n", - " 2578 0.014609\n", - " 8045 0.010677\n", - " 102 0.010375\n", - " 9528 0.008889\n", - " 10326 0.008269\n", - " 9526 0.007754\n", - " 8427 0.007191\n", - " Name: 303, dtype: float64, 2112 0.371783\n", - " 2114 0.031521\n", - " 8532 0.013123\n", - " 1486 0.012763\n", - " 2456 0.011266\n", - " 2415 0.007562\n", - " 101 0.006125\n", - " 2125 0.006049\n", - " 8469 0.005829\n", - " 8404 0.005589\n", - " Name: 304, dtype: float64, 2119 0.096724\n", - " 2114 0.049990\n", - " 2113 0.038511\n", - " 180 0.035023\n", - " 2046 0.032320\n", - " 154 0.022748\n", - " 206 0.021590\n", - " 52 0.020116\n", - " 248 0.018848\n", - " 2342 0.015784\n", - " Name: 305, dtype: float64, 2114 0.497792\n", - " 2124 0.167587\n", - " 2125 0.035335\n", - " 2128 0.020801\n", - " 2113 0.015664\n", - " 2122 0.012552\n", - " 2121 0.010358\n", - " 2126 0.009596\n", - " 2112 0.007172\n", - " 2129 0.005178\n", - " Name: 306, dtype: float64, 2119 0.992608\n", - " 205 0.001582\n", - " 180 0.000365\n", - " 207 0.000238\n", - " 52 0.000123\n", - " 2113 0.000122\n", - " 206 0.000111\n", - " 2120 0.000103\n", - " 1046 0.000102\n", - " 248 0.000079\n", - " Name: 307, dtype: float64, 641 0.991200\n", - " 2115 0.000791\n", - " 690 0.000511\n", - " 3046 0.000309\n", - " 1255 0.000292\n", - " 2514 0.000218\n", - " 640 0.000211\n", - " 2118 0.000206\n", - " 2117 0.000192\n", - " 2116 0.000183\n", - " Name: 308, dtype: float64, 79 9.999932e-01\n", - " 160 3.699960e-07\n", - " 9626 1.980646e-07\n", - " 246 1.609106e-07\n", - " 119 1.603979e-07\n", - " 206 1.365310e-07\n", - " 8040 1.133230e-07\n", - " 2684 1.108637e-07\n", - " 9991 1.083513e-07\n", - " 1203 1.080418e-07\n", - " Name: 309, dtype: float64, 205 0.571555\n", - " 207 0.183222\n", - " 246 0.023650\n", - " 2108 0.017125\n", - " 8335 0.014035\n", - " 2337 0.008224\n", - " 2119 0.004731\n", - " 86 0.004179\n", - " 8695 0.004062\n", - " 640 0.003701\n", - " Name: 310, dtype: float64, 86 0.994390\n", - " 1471 0.002820\n", - " 156 0.000825\n", - " 640 0.000440\n", - " 2337 0.000068\n", - " 2119 0.000066\n", - " 205 0.000063\n", - " 9288 0.000049\n", - " 9992 0.000045\n", - " 9039 0.000045\n", - " Name: 311, dtype: float64, 79 9.999963e-01\n", - " 160 2.010512e-07\n", - " 9626 1.282653e-07\n", - " 8040 9.203144e-08\n", - " 1203 8.949048e-08\n", - " 119 8.871400e-08\n", - " 2260 7.640408e-08\n", - " 246 6.885543e-08\n", - " 206 6.860793e-08\n", - " 205 5.844744e-08\n", - " Name: 312, dtype: float64, 86 0.999013\n", - " 1471 0.000340\n", - " 156 0.000144\n", - " 640 0.000065\n", - " 2337 0.000023\n", - " 246 0.000021\n", - " 2197 0.000021\n", - " 205 0.000019\n", - " 1255 0.000019\n", - " 8335 0.000017\n", - " Name: 313, dtype: float64, 2146 0.055556\n", - " 2141 0.029665\n", - " 2148 0.028799\n", - " 350 0.022723\n", - " 1769 0.016480\n", - " 1479 0.013331\n", - " 1143 0.013139\n", - " 593 0.011515\n", - " 2139 0.010261\n", - " 2140 0.009561\n", - " Name: 314, dtype: float64, 79 9.999958e-01\n", - " 9626 1.908145e-07\n", - " 160 1.473887e-07\n", - " 246 1.293773e-07\n", - " 119 1.262680e-07\n", - " 8040 1.155636e-07\n", - " 206 9.589393e-08\n", - " 1203 8.111230e-08\n", - " 2260 7.912990e-08\n", - " 4011 7.730688e-08\n", - " Name: 315, dtype: float64, 211 0.997524\n", - " 1711 0.000441\n", - " 208 0.000245\n", - " 246 0.000210\n", - " 207 0.000165\n", - " 519 0.000112\n", - " 214 0.000088\n", - " 635 0.000075\n", - " 204 0.000037\n", - " 9566 0.000035\n", - " Name: 316, dtype: float64, 31 0.425006\n", - " 35 0.414055\n", - " 34 0.009642\n", - " 33 0.009359\n", - " 2290 0.004230\n", - " 63 0.003318\n", - " 2391 0.002450\n", - " 213 0.002423\n", - " 36 0.002103\n", - " 57 0.001680\n", - " Name: 317, dtype: float64, 57 0.162362\n", - " 31 0.109339\n", - " 52 0.072598\n", - " 129 0.044709\n", - " 59 0.028436\n", - " 63 0.027291\n", - " 51 0.019022\n", - " 47 0.018288\n", - " 44 0.013045\n", - " 38 0.012384\n", - " Name: 318, dtype: float64, 41 0.909398\n", - " 42 0.046229\n", - " 52 0.003881\n", - " 2386 0.001588\n", - " 2344 0.001536\n", - " 2352 0.001364\n", - " 37 0.001198\n", - " 31 0.001022\n", - " 2391 0.001018\n", - " 2312 0.000965\n", - " Name: 319, dtype: float64, 63 0.158645\n", - " 129 0.044303\n", - " 57 0.042270\n", - " 62 0.041276\n", - " 59 0.039770\n", - " 70 0.033902\n", - " 47 0.021882\n", - " 53 0.017090\n", - " 31 0.015381\n", - " 52 0.013069\n", - " Name: 320, dtype: float64, 45 0.084044\n", - " 53 0.041375\n", - " 47 0.021876\n", - " 76 0.020604\n", - " 128 0.018515\n", - " 1027 0.015471\n", - " 52 0.015465\n", - " 38 0.015213\n", - " 59 0.013126\n", - " 1282 0.012345\n", - " Name: 321, dtype: float64, 6865 0.017152\n", - " 8281 0.017023\n", - " 158 0.015454\n", - " 71 0.013078\n", - " 954 0.012077\n", - " 8172 0.010934\n", - " 3476 0.007897\n", - " 3439 0.007868\n", - " 3110 0.006677\n", - " 6142 0.005926\n", - " Name: 322, dtype: float64, 85 0.992348\n", - " 2115 0.001625\n", - " 2514 0.000488\n", - " 3046 0.000271\n", - " 8319 0.000267\n", - " 2118 0.000219\n", - " 2117 0.000215\n", - " 8402 0.000207\n", - " 641 0.000183\n", - " 2116 0.000156\n", - " Name: 323, dtype: float64, 98 0.646830\n", - " 97 0.029787\n", - " 100 0.009503\n", - " 99 0.008522\n", - " 101 0.006518\n", - " 96 0.005834\n", - " 95 0.004944\n", - " 2784 0.004198\n", - " 9583 0.004124\n", - " 102 0.004052\n", - " Name: 324, dtype: float64, 108 0.986402\n", - " 2197 0.004534\n", - " 129 0.003411\n", - " 2504 0.000336\n", - " 753 0.000206\n", - " 901 0.000193\n", - " 690 0.000150\n", - " 2236 0.000146\n", - " 213 0.000143\n", - " 668 0.000135\n", - " Name: 325, dtype: float64, 119 0.998603\n", - " 79 0.000305\n", - " 208 0.000199\n", - " 8040 0.000053\n", - " 3991 0.000052\n", - " 118 0.000034\n", - " 9991 0.000026\n", - " 2236 0.000021\n", - " 2260 0.000019\n", - " 690 0.000019\n", - " Name: 326, dtype: float64, 128 0.081153\n", - " 200 0.023182\n", - " 184 0.022792\n", - " 53 0.014435\n", - " 127 0.012738\n", - " 3042 0.012330\n", - " 186 0.010282\n", - " 60 0.009797\n", - " 9435 0.009591\n", - " 129 0.008955\n", - " Name: 327, dtype: float64, 124 0.998031\n", - " 657 0.000081\n", - " 2196 0.000076\n", - " 128 0.000049\n", - " 2348 0.000047\n", - " 1255 0.000030\n", - " 51 0.000028\n", - " 237 0.000026\n", - " 2613 0.000023\n", - " 233 0.000023\n", - " Name: 328, dtype: float64, 129 0.999759\n", - " 108 0.000035\n", - " 2197 0.000016\n", - " 128 0.000011\n", + " Name: 105, dtype: float64, 208 0.996396\n", + " 233 0.000960\n", + " 517 0.000669\n", + " 206 0.000146\n", + " 207 0.000118\n", + " 1805 0.000088\n", + " 213 0.000067\n", + " 79 0.000065\n", + " 9729 0.000062\n", + " 519 0.000059\n", + " Name: 106, dtype: float64, 513 0.267914\n", + " 517 0.113474\n", + " 1063 0.077552\n", + " 1062 0.024667\n", + " 944 0.021719\n", + " 108 0.012413\n", + " 1086 0.009980\n", + " 206 0.007080\n", + " 691 0.006805\n", + " 819 0.006001\n", + " Name: 107, dtype: float64, 513 0.459035\n", + " 206 0.089394\n", + " 1063 0.061520\n", + " 213 0.042295\n", + " 944 0.026761\n", + " 517 0.024263\n", + " 52 0.016646\n", + " 501 0.014774\n", + " 1062 0.008047\n", + " 819 0.007149\n", + " Name: 108, dtype: float64, 535 0.238731\n", + " 533 0.111131\n", + " 422 0.037977\n", + " 565 0.023789\n", + " 1733 0.023296\n", + " 1655 0.022073\n", + " 1100 0.020727\n", + " 1656 0.019825\n", + " 336 0.019174\n", + " 1236 0.016141\n", + " Name: 109, dtype: float64, 541 0.944935\n", + " 532 0.002344\n", + " 1183 0.001934\n", + " 1601 0.001909\n", + " 1886 0.001252\n", + " 1721 0.001090\n", + " 2024 0.001068\n", + " 1592 0.001026\n", + " 1172 0.000760\n", + " 565 0.000577\n", + " Name: 110, dtype: float64, 208 0.999788\n", + " 79 0.000022\n", + " 1805 0.000021\n", + " 1711 0.000021\n", + " 1295 0.000018\n", + " 211 0.000009\n", + " 214 0.000007\n", + " 257 0.000006\n", + " 519 0.000006\n", + " 517 0.000004\n", + " Name: 111, dtype: float64, 603 0.138079\n", + " 1503 0.074244\n", + " 1497 0.071014\n", + " 594 0.068267\n", + " 1496 0.066459\n", + " 1493 0.064776\n", + " 596 0.061556\n", + " 978 0.042138\n", + " 1481 0.027173\n", + " 1483 0.024326\n", + " Name: 112, dtype: float64, 576 0.529108\n", + " 1457 0.018335\n", + " 1454 0.017660\n", + " 1159 0.016017\n", + " 381 0.009437\n", + " 1137 0.006715\n", + " 957 0.006253\n", + " 537 0.005638\n", + " 1456 0.005584\n", + " 539 0.004673\n", + " Name: 113, dtype: float64, 579 0.259720\n", + " 10655 0.021343\n", + " 8131 0.014120\n", + " 295 0.013882\n", + " 955 0.008344\n", + " 1426 0.006638\n", + " 2395 0.005892\n", + " 1793 0.005650\n", + " 1044 0.005442\n", + " 3154 0.005391\n", + " Name: 114, dtype: float64, 594 0.144498\n", + " 589 0.070240\n", + " 588 0.069165\n", + " 583 0.067322\n", + " 582 0.064092\n", + " 595 0.063571\n", + " 568 0.063564\n", + " 577 0.050726\n", + " 530 0.050696\n", + " 1546 0.026937\n", + " Name: 115, dtype: float64, 593 0.018347\n", + " 1643 0.013452\n", + " 2041 0.012169\n", + " 533 0.012122\n", + " 51 0.011793\n", + " 416 0.011748\n", + " 1329 0.009133\n", + " 329 0.008733\n", + " 1023 0.007799\n", + " 609 0.007384\n", + " Name: 116, dtype: float64, 612 0.963205\n", + " 614 0.001498\n", + " 561 0.000840\n", + " 1167 0.000787\n", + " 4148 0.000706\n", + " 204 0.000544\n", + " 4026 0.000525\n", + " 1166 0.000471\n", + " 1162 0.000414\n", + " 992 0.000355\n", + " Name: 117, dtype: float64, 631 0.921366\n", + " 7187 0.001284\n", + " 2678 0.001108\n", + " 2393 0.000922\n", + " 2735 0.000901\n", + " 3122 0.000864\n", + " 9720 0.000849\n", + " 7792 0.000747\n", + " 7904 0.000685\n", + " 4512 0.000656\n", + " Name: 118, dtype: float64, 6580 0.034802\n", + " 4813 0.020202\n", + " 5814 0.018847\n", + " 5433 0.018700\n", + " 6923 0.010927\n", + " 6931 0.009679\n", + " 4842 0.008631\n", + " 8161 0.008450\n", + " 4838 0.008000\n", + " 8609 0.007784\n", + " Name: 119, dtype: float64, 79 0.999667\n", + " 119 0.000025\n", + " 208 0.000016\n", + " 9991 0.000012\n", + " 160 0.000009\n", + " 268 0.000007\n", + " 165 0.000005\n", + " 1120 0.000005\n", + " 690 0.000003\n", + " 1506 0.000003\n", + " Name: 120, dtype: float64, 651 0.641227\n", + " 5322 0.009682\n", + " 870 0.004825\n", + " 6774 0.004670\n", + " 4654 0.004651\n", + " 5482 0.004490\n", + " 2375 0.003925\n", + " 7833 0.003779\n", + " 4152 0.002676\n", + " 4901 0.002318\n", + " Name: 121, dtype: float64, 10187 0.050966\n", + " 1615 0.032210\n", + " 291 0.022775\n", + " 10522 0.021005\n", + " 10533 0.020928\n", + " 7227 0.020790\n", + " 7755 0.019420\n", + " 1894 0.012968\n", + " 2058 0.011853\n", + " 10492 0.011498\n", + " Name: 122, dtype: float64, 10546 0.022190\n", + " 8188 0.018592\n", + " 170 0.018515\n", + " 4719 0.017904\n", + " 71 0.016695\n", + " 9105 0.014956\n", + " 1453 0.014440\n", + " 2027 0.013045\n", + " 1053 0.011941\n", + " 10179 0.009455\n", + " Name: 123, dtype: float64, 7237 0.055620\n", + " 7642 0.047365\n", + " 7363 0.041551\n", + " 688 0.015725\n", + " 2394 0.013098\n", + " 2250 0.012952\n", + " 710 0.011443\n", + " 2253 0.010910\n", + " 2427 0.010426\n", + " 2203 0.009078\n", + " Name: 124, dtype: float64, 86 0.999430\n", + " 1471 0.000135\n", + " 640 0.000050\n", + " 690 0.000023\n", + " 795 0.000022\n", + " 1515 0.000018\n", + " 2629 0.000018\n", + " 8335 0.000013\n", + " 641 0.000010\n", + " 9039 0.000010\n", + " Name: 125, dtype: float64, 701 0.551487\n", + " 705 0.031890\n", + " 108 0.022999\n", + " 698 0.015262\n", + " 708 0.015173\n", + " 6066 0.013024\n", + " 9302 0.011756\n", + " 712 0.006544\n", + " 9285 0.005415\n", + " 8432 0.005276\n", + " Name: 126, dtype: float64, 723 0.207650\n", + " 3210 0.102493\n", + " 8695 0.063156\n", + " 4095 0.029590\n", + " 2523 0.022837\n", + " 724 0.022686\n", + " 727 0.017273\n", + " 730 0.016389\n", + " 1377 0.012609\n", + " 5598 0.010944\n", + " Name: 127, dtype: float64, 86 0.966352\n", + " 9039 0.005028\n", + " 9288 0.004161\n", + " 8453 0.001622\n", + " 9950 0.001390\n", + " 8319 0.001194\n", + " 795 0.001067\n", + " 2337 0.001064\n", + " 2629 0.000635\n", + " 4155 0.000622\n", + " Name: 128, dtype: float64, 2238 0.075883\n", + " 1576 0.044115\n", + " 1698 0.030000\n", + " 2533 0.023467\n", + " 1029 0.019363\n", + " 207 0.018452\n", + " 3501 0.016523\n", + " 2385 0.014508\n", + " 211 0.014354\n", + " 2046 0.013812\n", + " Name: 129, dtype: float64, 162 0.056114\n", + " 1514 0.038651\n", + " 640 0.030145\n", + " 1515 0.024127\n", + " 8032 0.019266\n", + " 7517 0.018430\n", + " 829 0.016554\n", + " 2155 0.014646\n", + " 9384 0.014021\n", + " 1061 0.012779\n", + " Name: 130, dtype: float64, 79 9.999898e-01\n", + " 208 4.326652e-06\n", + " 7106 4.504298e-07\n", + " 1805 1.599282e-07\n", + " 517 1.451567e-07\n", + " 4098 8.848173e-08\n", + " 2503 7.134063e-08\n", + " 4000 5.362289e-08\n", + " 3637 5.029397e-08\n", + " 119 4.883956e-08\n", + " Name: 131, dtype: float64, 292 0.058776\n", + " 1752 0.045260\n", + " 2041 0.033254\n", + " 1379 0.015043\n", + " 2617 0.014821\n", + " 294 0.013547\n", + " 6536 0.013015\n", + " 2348 0.010849\n", + " 899 0.010650\n", + " 323 0.008846\n", + " Name: 132, dtype: float64, 801 0.126556\n", + " 3216 0.064267\n", + " 800 0.057019\n", + " 9161 0.054048\n", + " 2155 0.033339\n", + " 386 0.018517\n", + " 10029 0.018353\n", + " 2115 0.018141\n", + " 8276 0.012186\n", + " 9475 0.011726\n", + " Name: 133, dtype: float64, 676 0.140992\n", + " 974 0.108582\n", + " 801 0.087563\n", + " 694 0.034953\n", + " 799 0.017044\n", + " 916 0.016507\n", + " 682 0.012907\n", + " 917 0.010135\n", + " 138 0.008197\n", + " 675 0.008145\n", + " Name: 134, dtype: float64, 79 9.999919e-01\n", + " 208 3.571728e-06\n", + " 1805 2.067265e-07\n", + " 7106 1.930306e-07\n", + " 517 1.573812e-07\n", + " 4000 6.021239e-08\n", + " 119 5.906974e-08\n", + " 2503 4.589891e-08\n", + " 9991 4.319962e-08\n", + " 4098 4.295690e-08\n", + " Name: 135, dtype: float64, 770 0.029334\n", + " 799 0.029291\n", + " 8700 0.018333\n", + " 8663 0.014638\n", + " 801 0.014427\n", + " 9231 0.010370\n", + " 2155 0.009302\n", + " 804 0.007809\n", + " 800 0.006911\n", + " 8417 0.006865\n", + " Name: 136, dtype: float64, 10270 0.473641\n", + " 660 0.052614\n", + " 192 0.036568\n", + " 6567 0.030027\n", + " 231 0.012475\n", + " 7918 0.011534\n", + " 5917 0.009871\n", + " 635 0.008038\n", + " 7635 0.007441\n", + " 9019 0.007278\n", + " Name: 137, dtype: float64, 825 0.994204\n", + " 823 0.000463\n", + " 824 0.000242\n", + " 165 0.000220\n", + " 795 0.000140\n", + " 4074 0.000084\n", + " 8335 0.000083\n", + " 826 0.000079\n", + " 840 0.000066\n", + " 1469 0.000064\n", + " Name: 138, dtype: float64, 86 0.988829\n", + " 1471 0.004401\n", + " 2511 0.000574\n", + " 156 0.000244\n", + " 795 0.000219\n", + " 204 0.000201\n", + " 2197 0.000167\n", + " 4155 0.000125\n", + " 6066 0.000120\n", + " 654 0.000096\n", + " Name: 139, dtype: float64, 9723 0.059070\n", + " 2240 0.049333\n", + " 8579 0.036632\n", + " 9166 0.027156\n", + " 8407 0.026780\n", + " 165 0.018342\n", + " 825 0.016388\n", + " 9656 0.014632\n", + " 839 0.014131\n", + " 2687 0.013908\n", + " Name: 140, dtype: float64, 2844 0.066648\n", + " 2617 0.046492\n", + " 3275 0.011945\n", + " 2228 0.011267\n", + " 3123 0.010542\n", + " 882 0.010172\n", + " 2837 0.009790\n", + " 841 0.009667\n", + " 7981 0.008293\n", + " 2838 0.008031\n", + " Name: 141, dtype: float64, 712 0.079664\n", + " 758 0.067606\n", + " 860 0.034386\n", + " 859 0.030899\n", + " 903 0.017393\n", + " 8453 0.014826\n", + " 6924 0.013803\n", + " 2546 0.011856\n", + " 622 0.010132\n", + " 387 0.009716\n", + " Name: 142, dtype: float64, 79 9.999948e-01\n", + " 208 1.469158e-06\n", + " 1805 2.339879e-07\n", + " 7106 1.714057e-07\n", + " 517 1.122285e-07\n", + " 119 8.982005e-08\n", + " 2503 8.242664e-08\n", + " 4000 5.239167e-08\n", + " 2629 4.491531e-08\n", + " 690 4.208464e-08\n", + " Name: 143, dtype: float64, 118 0.940458\n", + " 119 0.052580\n", + " 1805 0.000570\n", + " 758 0.000404\n", + " 641 0.000259\n", + " 7106 0.000254\n", + " 2173 0.000197\n", + " 2780 0.000152\n", + " 1514 0.000130\n", + " 207 0.000104\n", + " Name: 144, dtype: float64, 119 0.997548\n", + " 79 0.000807\n", + " 118 0.000267\n", + " 7106 0.000121\n", + " 690 0.000093\n", + " 9036 0.000092\n", + " 240 0.000047\n", + " 208 0.000046\n", + " 1805 0.000045\n", + " 2780 0.000041\n", + " Name: 145, dtype: float64, 878 0.440062\n", + " 898 0.278301\n", + " 899 0.015783\n", + " 192 0.011643\n", + " 634 0.005211\n", + " 1379 0.004479\n", + " 1605 0.002908\n", + " 2617 0.002031\n", + " 8275 0.001787\n", + " 882 0.001668\n", + " Name: 146, dtype: float64, 2796 0.353037\n", + " 2546 0.046218\n", + " 881 0.025979\n", + " 2534 0.024337\n", + " 3192 0.023769\n", + " 3195 0.023397\n", + " 3193 0.014351\n", + " 3346 0.008938\n", + " 3599 0.008562\n", + " 887 0.007432\n", + " Name: 147, dtype: float64, 698 0.159380\n", + " 2435 0.032551\n", + " 2535 0.026697\n", + " 8897 0.018326\n", + " 7919 0.013794\n", + " 1049 0.011140\n", + " 5726 0.010934\n", + " 2873 0.010354\n", + " 9417 0.009914\n", + " 8600 0.007844\n", + " Name: 148, dtype: float64, 679 0.227499\n", + " 677 0.086444\n", + " 2503 0.066008\n", + " 2502 0.044682\n", + " 1805 0.031611\n", + " 2504 0.027518\n", + " 734 0.021350\n", + " 8592 0.015354\n", + " 2236 0.013389\n", + " 5826 0.013069\n", + " Name: 149, dtype: float64, 7398 0.020937\n", + " 4709 0.020863\n", + " 10317 0.017138\n", + " 5782 0.014221\n", + " 3868 0.013615\n", + " 1979 0.011810\n", + " 3642 0.010648\n", + " 5770 0.010475\n", + " 3702 0.010062\n", + " 6496 0.009582\n", + " Name: 150, dtype: float64, 1077 0.061032\n", + " 1171 0.042213\n", + " 1402 0.027064\n", + " 1173 0.021746\n", + " 1886 0.020524\n", + " 10295 0.017409\n", + " 512 0.015979\n", + " 1075 0.013620\n", + " 8812 0.012408\n", + " 1780 0.012022\n", + " Name: 151, dtype: float64, 678 0.788312\n", + " 2175 0.017131\n", + " 679 0.015345\n", + " 660 0.007482\n", + " 192 0.005724\n", + " 231 0.004667\n", + " 2236 0.004619\n", + " 882 0.004261\n", + " 5917 0.004142\n", + " 3165 0.003653\n", + " Name: 152, dtype: float64, 926 0.043774\n", + " 1001 0.034915\n", + " 61 0.021794\n", + " 1522 0.016311\n", + " 1562 0.014641\n", + " 1996 0.013459\n", + " 8 0.012873\n", + " 1763 0.011081\n", + " 1688 0.010366\n", + " 1685 0.009334\n", + " Name: 153, dtype: float64, 1 0.284890\n", + " 2149 0.026407\n", + " 277 0.020486\n", + " 2 0.016030\n", + " 8113 0.012982\n", + " 942 0.012320\n", + " 92 0.012145\n", + " 2103 0.011380\n", + " 3010 0.010176\n", + " 1929 0.009964\n", + " Name: 154, dtype: float64, 968 0.483438\n", + " 966 0.415077\n", + " 1907 0.002740\n", + " 971 0.001325\n", + " 1533 0.001074\n", + " 1531 0.000997\n", + " 368 0.000946\n", + " 273 0.000847\n", + " 987 0.000778\n", + " 1543 0.000762\n", + " Name: 155, dtype: float64, 1022 0.073063\n", + " 987 0.068225\n", + " 1000 0.028441\n", + " 368 0.024770\n", + " 2304 0.015314\n", + " 1139 0.011278\n", + " 2303 0.009240\n", + " 3324 0.008699\n", + " 7378 0.007109\n", + " 954 0.007048\n", + " Name: 156, dtype: float64, 994 0.954530\n", + " 998 0.009626\n", + " 991 0.003781\n", + " 1021 0.002678\n", + " 983 0.000773\n", + " 927 0.000747\n", + " 980 0.000731\n", + " 999 0.000558\n", + " 928 0.000453\n", + " 993 0.000431\n", + " Name: 157, dtype: float64, 1005 0.881711\n", + " 1841 0.008200\n", + " 164 0.002772\n", + " 525 0.001953\n", + " 1549 0.001544\n", + " 1987 0.001537\n", + " 1474 0.001402\n", + " 1043 0.001240\n", + " 1317 0.001086\n", + " 1143 0.001043\n", + " Name: 158, dtype: float64, 1006 0.037851\n", + " 593 0.028366\n", + " 484 0.028085\n", + " 603 0.024177\n", + " 978 0.019786\n", + " 1864 0.019651\n", + " 543 0.017539\n", + " 1044 0.016699\n", + " 1596 0.013756\n", + " 2065 0.012576\n", + " Name: 159, dtype: float64, 1032 0.938876\n", + " 1104 0.019565\n", + " 1022 0.001997\n", + " 1636 0.001828\n", + " 1029 0.001120\n", + " 2105 0.001104\n", + " 9730 0.001003\n", + " 1203 0.000889\n", + " 1276 0.000685\n", + " 1639 0.000681\n", + " Name: 160, dtype: float64, 1035 0.967528\n", + " 989 0.001164\n", + " 1078 0.000697\n", + " 1033 0.000640\n", + " 1683 0.000429\n", + " 371 0.000428\n", + " 1273 0.000338\n", + " 1019 0.000317\n", + " 532 0.000311\n", + " 1632 0.000310\n", + " Name: 161, dtype: float64, 2342 0.066632\n", + " 6064 0.064790\n", + " 2051 0.020318\n", + " 2333 0.018642\n", + " 2486 0.018544\n", + " 2313 0.018288\n", + " 2328 0.012278\n", + " 1262 0.010731\n", + " 6046 0.009461\n", + " 3904 0.008366\n", + " Name: 162, dtype: float64, 1045 0.170641\n", + " 203 0.147774\n", + " 1046 0.076776\n", + " 205 0.040517\n", + " 206 0.028725\n", + " 2119 0.023543\n", + " 1069 0.016307\n", + " 1059 0.014669\n", + " 1047 0.014646\n", + " 214 0.013653\n", + " Name: 163, dtype: float64, 236 0.070938\n", + " 3584 0.052750\n", + " 439 0.026978\n", + " 107 0.018924\n", + " 4001 0.013578\n", + " 1991 0.012817\n", + " 1873 0.012550\n", + " 1752 0.012452\n", + " 4554 0.011769\n", + " 1864 0.010947\n", + " Name: 164, dtype: float64, 2606 0.035023\n", + " 6836 0.019215\n", + " 1083 0.013556\n", + " 1777 0.010479\n", + " 1202 0.009100\n", + " 1280 0.008689\n", + " 10333 0.007950\n", + " 10290 0.007897\n", + " 6738 0.006564\n", + " 1204 0.006549\n", + " Name: 165, dtype: float64, 203 0.600803\n", + " 206 0.233360\n", + " 1086 0.018700\n", + " 495 0.017045\n", + " 512 0.011262\n", + " 562 0.007828\n", + " 501 0.006385\n", + " 223 0.005588\n", + " 521 0.003819\n", + " 513 0.003456\n", + " Name: 166, dtype: float64, 1102 0.039691\n", + " 1695 0.034523\n", + " 993 0.021338\n", + " 336 0.019779\n", + " 535 0.019560\n", + " 1267 0.016859\n", + " 1100 0.015051\n", + " 1166 0.013992\n", + " 461 0.013945\n", + " 5046 0.013920\n", + " Name: 167, dtype: float64, 1120 0.994239\n", + " 1122 0.000255\n", + " 79 0.000197\n", + " 950 0.000179\n", + " 8598 0.000154\n", + " 888 0.000119\n", + " 208 0.000096\n", + " 1117 0.000090\n", + " 1688 0.000065\n", + " 517 0.000064\n", + " Name: 168, dtype: float64, 1122 0.970072\n", + " 126 0.001487\n", + " 1120 0.000808\n", + " 1688 0.000686\n", + " 1713 0.000653\n", + " 8677 0.000532\n", + " 2119 0.000417\n", + " 950 0.000383\n", + " 2823 0.000371\n", + " 1066 0.000314\n", + " Name: 169, dtype: float64, 2236 0.044551\n", + " 7957 0.037371\n", + " 2305 0.035935\n", + " 624 0.028988\n", + " 2567 0.020251\n", + " 677 0.019524\n", + " 2301 0.016859\n", + " 871 0.014445\n", + " 8977 0.014156\n", + " 2303 0.013035\n", + " Name: 170, dtype: float64, 156 0.072802\n", + " 1471 0.040783\n", + " 3267 0.033882\n", + " 517 0.017963\n", + " 2222 0.014698\n", + " 240 0.014365\n", + " 267 0.012887\n", + " 1066 0.012280\n", + " 9626 0.011118\n", + " 6826 0.009683\n", + " Name: 171, dtype: float64, 204 0.944522\n", + " 1046 0.028378\n", + " 205 0.003705\n", + " 206 0.003060\n", + " 517 0.002728\n", + " 2119 0.000823\n", + " 505 0.000465\n", + " 1465 0.000434\n", + " 520 0.000390\n", + " 524 0.000253\n", + " Name: 172, dtype: float64, 204 0.944522\n", + " 1046 0.028378\n", + " 205 0.003705\n", + " 206 0.003060\n", + " 517 0.002728\n", + " 2119 0.000823\n", + " 505 0.000465\n", + " 1465 0.000434\n", + " 520 0.000390\n", + " 524 0.000253\n", + " Name: 173, dtype: float64, 208 0.949228\n", + " 1711 0.010487\n", + " 517 0.010435\n", + " 211 0.008078\n", + " 233 0.003870\n", + " 1295 0.000661\n", + " 1509 0.000613\n", + " 129 0.000569\n", + " 213 0.000558\n", + " 79 0.000530\n", + " Name: 174, dtype: float64, 1113 0.290652\n", + " 207 0.046416\n", + " 1897 0.023024\n", + " 233 0.018605\n", + " 1669 0.015564\n", + " 4000 0.014959\n", + " 1167 0.013481\n", + " 517 0.009782\n", + " 2340 0.009527\n", + " 107 0.007957\n", + " Name: 175, dtype: float64, 1576 0.439343\n", + " 2046 0.136436\n", + " 356 0.102038\n", + " 1318 0.019364\n", + " 922 0.010946\n", + " 2345 0.008690\n", + " 2191 0.007945\n", + " 1255 0.005500\n", + " 933 0.005208\n", + " 276 0.003904\n", + " Name: 176, dtype: float64, 1137 0.979638\n", + " 1402 0.004326\n", + " 1399 0.001228\n", + " 1451 0.000537\n", + " 1396 0.000503\n", + " 1145 0.000405\n", + " 371 0.000380\n", + " 1173 0.000265\n", + " 1035 0.000180\n", + " 1401 0.000160\n", + " Name: 177, dtype: float64, 537 0.029938\n", + " 539 0.018049\n", + " 2042 0.015233\n", + " 1454 0.010613\n", + " 1457 0.008850\n", + " 2553 0.008557\n", + " 1159 0.008450\n", + " 1866 0.007921\n", + " 473 0.007673\n", + " 1434 0.007603\n", + " Name: 178, dtype: float64, 1099 0.639196\n", + " 425 0.006645\n", + " 1500 0.004978\n", + " 1627 0.004451\n", + " 547 0.003871\n", + " 4148 0.003728\n", + " 329 0.003510\n", + " 2087 0.003302\n", + " 2008 0.003199\n", + " 1864 0.003157\n", + " Name: 179, dtype: float64, 1635 0.162927\n", + " 533 0.033986\n", + " 336 0.033932\n", + " 1722 0.032828\n", + " 1652 0.032323\n", + " 2102 0.016969\n", + " 1651 0.011669\n", + " 416 0.010780\n", + " 1592 0.010608\n", + " 1886 0.010590\n", + " Name: 180, dtype: float64, 1229 0.061804\n", + " 1280 0.057726\n", + " 79 0.033995\n", + " 1207 0.033258\n", + " 1711 0.025149\n", + " 1228 0.024002\n", + " 1239 0.012989\n", + " 1226 0.012888\n", + " 1213 0.012244\n", + " 1231 0.011569\n", + " Name: 181, dtype: float64, 1239 0.849456\n", + " 1228 0.059678\n", + " 1227 0.006516\n", + " 1226 0.005757\n", + " 1711 0.005652\n", + " 1229 0.004790\n", + " 1232 0.003381\n", + " 1207 0.002210\n", + " 1231 0.001808\n", + " 1629 0.001786\n", + " Name: 182, dtype: float64, 2950 0.124153\n", + " 2946 0.028564\n", + " 1569 0.027967\n", + " 53 0.016833\n", + " 924 0.010113\n", + " 1107 0.008857\n", + " 1923 0.008631\n", + " 922 0.007136\n", + " 2905 0.006516\n", + " 933 0.006359\n", + " Name: 183, dtype: float64, 1232 0.057415\n", + " 1166 0.047801\n", + " 993 0.039592\n", + " 1167 0.034359\n", + " 1650 0.015526\n", + " 2008 0.014279\n", + " 1326 0.014044\n", + " 990 0.012974\n", + " 1102 0.011602\n", + " 1554 0.011473\n", + " Name: 184, dtype: float64, 1272 0.064565\n", + " 547 0.027685\n", + " 1694 0.027150\n", + " 1449 0.015785\n", + " 1084 0.015083\n", + " 4535 0.014822\n", + " 1400 0.013090\n", + " 1329 0.012883\n", + " 1488 0.011746\n", + " 790 0.011262\n", + " Name: 185, dtype: float64, 6532 0.138548\n", + " 1861 0.045910\n", + " 2935 0.032761\n", + " 1725 0.022883\n", + " 3333 0.012915\n", + " 7428 0.012034\n", + " 2631 0.011462\n", + " 1273 0.011096\n", + " 1167 0.009320\n", + " 2899 0.008943\n", + " Name: 186, dtype: float64, 9880 0.077969\n", + " 9881 0.039636\n", + " 1631 0.021246\n", + " 1852 0.013224\n", + " 1632 0.013043\n", + " 8570 0.011523\n", + " 2085 0.011177\n", + " 1249 0.008522\n", + " 369 0.008341\n", + " 430 0.008196\n", + " Name: 187, dtype: float64, 1306 0.834451\n", + " 1305 0.052018\n", + " 1301 0.039867\n", + " 580 0.003216\n", + " 4074 0.002681\n", + " 1300 0.001870\n", + " 52 0.001212\n", + " 2826 0.000854\n", + " 1752 0.000765\n", + " 1677 0.000712\n", + " Name: 188, dtype: float64, 1300 0.963864\n", + " 1003 0.003818\n", + " 1002 0.002795\n", + " 1305 0.000689\n", + " 1301 0.000648\n", + " 1306 0.000611\n", + " 91 0.000351\n", + " 1597 0.000267\n", + " 8846 0.000244\n", + " 955 0.000238\n", + " Name: 189, dtype: float64, 580 0.923072\n", + " 4074 0.005009\n", + " 4026 0.003535\n", + " 3604 0.001439\n", + " 1306 0.001292\n", + " 2067 0.000777\n", + " 3325 0.000762\n", + " 759 0.000684\n", + " 7025 0.000608\n", + " 363 0.000566\n", + " Name: 190, dtype: float64, 1597 0.108018\n", + " 1775 0.075739\n", + " 1536 0.022786\n", + " 100 0.019096\n", + " 7292 0.018112\n", + " 1889 0.012259\n", + " 6188 0.009046\n", + " 1925 0.008637\n", + " 2041 0.008366\n", + " 1749 0.008361\n", + " Name: 191, dtype: float64, 1324 0.036462\n", + " 2024 0.017343\n", + " 2042 0.015254\n", + " 1473 0.014526\n", + " 1844 0.012841\n", + " 3963 0.012148\n", + " 2080 0.009949\n", + " 2055 0.009827\n", + " 1851 0.009403\n", + " 1325 0.008563\n", + " Name: 192, dtype: float64, 2948 0.026283\n", + " 1866 0.019634\n", + " 2054 0.016925\n", + " 9083 0.014700\n", + " 9113 0.014041\n", + " 8565 0.010925\n", + " 2905 0.009874\n", + " 1932 0.009689\n", + " 2842 0.008595\n", + " 9104 0.007996\n", + " Name: 193, dtype: float64, 2948 0.026283\n", + " 1866 0.019634\n", + " 2054 0.016925\n", + " 9083 0.014700\n", + " 9113 0.014041\n", + " 8565 0.010925\n", + " 2905 0.009874\n", + " 1932 0.009689\n", + " 2842 0.008595\n", + " 9104 0.007996\n", + " Name: 194, dtype: float64, 1338 0.068904\n", + " 552 0.048503\n", + " 554 0.037316\n", + " 473 0.022307\n", + " 988 0.013676\n", + " 1554 0.011079\n", + " 3135 0.009946\n", + " 981 0.009712\n", + " 1631 0.009169\n", + " 609 0.007935\n", + " Name: 195, dtype: float64, 239 0.999551\n", + " 240 0.000055\n", + " 1805 0.000036\n", + " 679 0.000017\n", + " 870 0.000011\n", + " 2236 0.000009\n", " 698 0.000008\n", - " 8418 0.000007\n", - " 3166 0.000005\n", - " 174 0.000003\n", - " 2660 0.000003\n", - " 81 0.000003\n", - " Name: 329, dtype: float64, 59 0.060523\n", - " 57 0.035873\n", - " 45 0.030456\n", - " 60 0.026766\n", - " 70 0.023483\n", - " 47 0.017459\n", - " 1604 0.014284\n", - " 61 0.013438\n", - " 3042 0.012724\n", - " 76 0.011808\n", - " Name: 330, dtype: float64, 79 9.999958e-01\n", - " 160 2.553083e-07\n", - " 9626 1.732382e-07\n", - " 8040 1.419923e-07\n", - " 119 1.342466e-07\n", - " 2260 1.111653e-07\n", - " 4011 8.127988e-08\n", - " 246 8.030384e-08\n", - " 206 7.473258e-08\n", - " 4073 6.546686e-08\n", - " Name: 331, dtype: float64, 79 9.999958e-01\n", - " 160 2.553083e-07\n", - " 9626 1.732382e-07\n", - " 8040 1.419923e-07\n", - " 119 1.342466e-07\n", - " 2260 1.111653e-07\n", - " 4011 8.127988e-08\n", - " 246 8.030384e-08\n", - " 206 7.473258e-08\n", - " 4073 6.546686e-08\n", - " Name: 332, dtype: float64, 129 0.998608\n", - " 108 0.000460\n", - " 2197 0.000055\n", - " 156 0.000025\n", - " 237 0.000020\n", - " 213 0.000019\n", - " 211 0.000015\n", - " 698 0.000013\n", - " 654 0.000012\n", - " 3404 0.000011\n", - " Name: 333, dtype: float64, 156 0.998365\n", - " 2197 0.000380\n", - " 2278 0.000076\n", - " 1977 0.000069\n", - " 505 0.000049\n", - " 2076 0.000046\n", - " 2283 0.000028\n", - " 246 0.000027\n", - " 86 0.000027\n", + " 677 0.000008\n", + " 2715 0.000008\n", + " 8509 0.000006\n", + " Name: 196, dtype: float64, 1264 0.361751\n", + " 1997 0.054461\n", + " 1842 0.029444\n", + " 1538 0.017479\n", + " 287 0.016579\n", + " 1540 0.014949\n", + " 2161 0.011106\n", + " 1 0.009190\n", + " 936 0.007517\n", + " 1711 0.006858\n", + " Name: 197, dtype: float64, 867 0.081716\n", + " 399 0.026503\n", + " 524 0.018762\n", + " 482 0.014755\n", + " 1246 0.009878\n", + " 6124 0.009853\n", + " 8581 0.009752\n", + " 1909 0.008819\n", + " 503 0.008692\n", + " 1174 0.008269\n", + " Name: 198, dtype: float64, 373 0.043802\n", + " 442 0.033748\n", + " 1991 0.028527\n", + " 1637 0.025652\n", + " 2307 0.017069\n", + " 6373 0.014998\n", + " 2224 0.014315\n", + " 2041 0.014183\n", + " 551 0.012984\n", + " 1753 0.009912\n", + " Name: 199, dtype: float64, 1929 0.236518\n", + " 2149 0.053929\n", + " 2706 0.015546\n", + " 1841 0.009681\n", + " 3010 0.009640\n", + " 1993 0.008894\n", + " 2576 0.008083\n", + " 2150 0.007180\n", + " 1 0.007110\n", + " 1905 0.007045\n", + " Name: 200, dtype: float64, 1418 0.315620\n", + " 1412 0.276721\n", + " 1422 0.263683\n", + " 1428 0.010472\n", + " 1429 0.009306\n", + " 1427 0.008116\n", + " 1425 0.005149\n", + " 1426 0.002391\n", + " 1430 0.001394\n", + " 1439 0.000822\n", + " Name: 201, dtype: float64, 1425 0.231546\n", + " 1429 0.184772\n", + " 1428 0.151052\n", + " 1427 0.136449\n", + " 1430 0.087260\n", + " 1426 0.046052\n", + " 1418 0.004135\n", + " 1422 0.003722\n", + " 1412 0.003683\n", + " 1583 0.003078\n", + " Name: 202, dtype: float64, 1373 0.369805\n", + " 1372 0.044781\n", + " 1365 0.040669\n", + " 7009 0.022499\n", + " 8311 0.014303\n", + " 742 0.010921\n", + " 8256 0.007810\n", + " 724 0.005714\n", + " 730 0.004948\n", + " 192 0.004768\n", + " Name: 203, dtype: float64, 1446 0.165765\n", + " 1448 0.083307\n", + " 1449 0.074044\n", + " 1456 0.039934\n", + " 1458 0.039801\n", + " 1442 0.025221\n", + " 553 0.018462\n", + " 1443 0.009239\n", + " 1151 0.008142\n", + " 1459 0.007844\n", + " Name: 204, dtype: float64, 1451 0.978094\n", + " 1457 0.001933\n", + " 1399 0.000543\n", + " 1396 0.000421\n", + " 174 0.000410\n", + " 336 0.000293\n", + " 12 0.000257\n", + " 1280 0.000239\n", + " 1454 0.000226\n", + " 29 0.000203\n", + " Name: 205, dtype: float64, 2552 0.025664\n", + " 759 0.020637\n", + " 1914 0.019283\n", + " 2042 0.016768\n", + " 561 0.016537\n", + " 2067 0.010645\n", + " 1500 0.010423\n", + " 8 0.009693\n", + " 1490 0.008244\n", + " 450 0.007738\n", + " Name: 206, dtype: float64, 954 0.047404\n", + " 1103 0.013177\n", + " 7195 0.011936\n", + " 782 0.011638\n", + " 5709 0.010254\n", + " 573 0.008624\n", + " 971 0.008060\n", + " 7485 0.007695\n", + " 5917 0.006712\n", + " 7635 0.005872\n", + " Name: 207, dtype: float64, 1464 0.184795\n", + " 500 0.168100\n", + " 1610 0.024507\n", + " 1463 0.023020\n", + " 180 0.008439\n", + " 1605 0.007263\n", + " 5841 0.007251\n", + " 1964 0.005603\n", + " 3090 0.005526\n", + " 226 0.005211\n", + " Name: 208, dtype: float64, 2912 0.067037\n", + " 8476 0.040653\n", + " 637 0.036912\n", + " 1470 0.019870\n", + " 196 0.015949\n", + " 710 0.011300\n", + " 909 0.011121\n", + " 882 0.010989\n", + " 639 0.008760\n", + " 860 0.008682\n", + " Name: 209, dtype: float64, 1326 0.207653\n", + " 565 0.034595\n", + " 1331 0.031038\n", + " 1695 0.030435\n", + " 563 0.023515\n", + " 1655 0.023410\n", + " 344 0.021199\n", + " 1656 0.018312\n", + " 1237 0.015190\n", + " 2062 0.011011\n", + " Name: 210, dtype: float64, 554 0.059886\n", + " 1089 0.027272\n", + " 1023 0.025657\n", + " 1256 0.020252\n", + " 1101 0.017471\n", + " 920 0.014762\n", + " 1162 0.014562\n", + " 1074 0.014480\n", + " 1502 0.013833\n", + " 553 0.012383\n", + " Name: 211, dtype: float64, 1672 0.033151\n", + " 1771 0.029444\n", + " 1583 0.016117\n", + " 1655 0.015157\n", + " 350 0.014398\n", + " 1454 0.011998\n", + " 1652 0.011692\n", + " 1457 0.011526\n", + " 1694 0.011153\n", + " 1653 0.010866\n", + " Name: 212, dtype: float64, 1509 0.938476\n", + " 208 0.026681\n", + " 1510 0.015666\n", + " 1506 0.007147\n", + " 1711 0.000880\n", + " 257 0.000879\n", + " 211 0.000715\n", + " 9729 0.000445\n", + " 233 0.000350\n", + " 213 0.000272\n", + " Name: 213, dtype: float64, 1509 0.913842\n", + " 1510 0.025944\n", + " 208 0.022510\n", + " 1506 0.011745\n", + " 1711 0.002553\n", + " 233 0.002044\n", + " 211 0.000928\n", + " 9729 0.000511\n", + " 257 0.000478\n", + " 213 0.000422\n", + " Name: 214, dtype: float64, 79 9.999964e-01\n", + " 208 8.555730e-07\n", + " 119 1.404812e-07\n", + " 1805 1.197013e-07\n", + " 517 1.113018e-07\n", + " 4098 8.094482e-08\n", + " 7106 7.097776e-08\n", + " 2546 3.934025e-08\n", + " 2503 3.293742e-08\n", + " 2166 3.004838e-08\n", + " Name: 215, dtype: float64, 1509 0.989224\n", + " 1506 0.002572\n", + " 1510 0.002520\n", + " 257 0.000172\n", + " 1711 0.000156\n", + " 3415 0.000113\n", + " 213 0.000110\n", + " 519 0.000088\n", + " 245 0.000059\n", + " 160 0.000056\n", + " Name: 216, dtype: float64, 79 9.999956e-01\n", + " 208 1.351330e-06\n", + " 1805 1.781336e-07\n", + " 119 8.680362e-08\n", + " 7106 7.220856e-08\n", + " 517 7.067712e-08\n", + " 2503 4.882988e-08\n", + " 4098 3.630681e-08\n", + " 2629 3.496810e-08\n", + " 4000 3.408589e-08\n", + " Name: 217, dtype: float64, 207 0.788328\n", + " 2392 0.016250\n", + " 3424 0.009030\n", + " 10386 0.005407\n", + " 1897 0.005236\n", + " 630 0.004033\n", + " 2385 0.003082\n", + " 54 0.002987\n", + " 2296 0.002961\n", + " 264 0.002735\n", + " Name: 218, dtype: float64, 205 0.991328\n", + " 206 0.003249\n", + " 204 0.001494\n", + " 504 0.000485\n", + " 207 0.000192\n", + " 246 0.000161\n", + " 248 0.000139\n", + " 515 0.000138\n", + " 1046 0.000129\n", + " 1515 0.000119\n", + " Name: 219, dtype: float64, 204 0.977181\n", + " 206 0.008888\n", + " 1046 0.003499\n", + " 205 0.001868\n", + " 517 0.001038\n", + " 246 0.000389\n", + " 223 0.000228\n", + " 86 0.000177\n", + " 505 0.000164\n", + " 520 0.000160\n", + " Name: 220, dtype: float64, 79 9.999929e-01\n", + " 208 2.018077e-06\n", + " 1805 2.734107e-07\n", + " 119 2.289929e-07\n", + " 7106 1.264246e-07\n", + " 517 1.040961e-07\n", + " 160 6.508245e-08\n", + " 2503 6.464573e-08\n", + " 2546 5.270136e-08\n", + " 4000 4.794506e-08\n", + " Name: 221, dtype: float64, 363 0.075942\n", + " 953 0.045326\n", + " 264 0.030177\n", + " 1525 0.023272\n", + " 517 0.021560\n", + " 52 0.012140\n", + " 362 0.010919\n", + " 1115 0.010349\n", + " 180 0.010159\n", + " 1805 0.009695\n", + " Name: 222, dtype: float64, 1558 0.068121\n", + " 1402 0.059947\n", + " 1488 0.030794\n", + " 1632 0.029509\n", + " 2013 0.023404\n", + " 1023 0.017141\n", + " 1631 0.014305\n", + " 1403 0.010816\n", + " 1399 0.010528\n", + " 1203 0.010460\n", + " Name: 223, dtype: float64, 1548 0.829294\n", + " 1160 0.011122\n", + " 1551 0.008447\n", + " 1546 0.007348\n", + " 1169 0.007046\n", + " 1652 0.001845\n", + " 2087 0.001796\n", + " 1441 0.001427\n", + " 2005 0.001342\n", + " 1554 0.001320\n", + " Name: 224, dtype: float64, 213 0.999796\n", + " 208 0.000027\n", + " 206 0.000013\n", + " 129 0.000010\n", + " 1805 0.000008\n", + " 2504 0.000007\n", + " 108 0.000007\n", + " 233 0.000005\n", + " 204 0.000004\n", + " 501 0.000004\n", + " Name: 225, dtype: float64, 1551 0.537200\n", + " 1160 0.175066\n", + " 1394 0.048932\n", + " 1548 0.009956\n", + " 1546 0.004007\n", + " 2087 0.003974\n", + " 1573 0.003726\n", + " 1535 0.003109\n", + " 2899 0.002373\n", + " 963 0.001813\n", + " Name: 226, dtype: float64, 205 0.999202\n", + " 206 0.000087\n", + " 204 0.000076\n", + " 1515 0.000041\n", + " 207 0.000026\n", + " 2119 0.000025\n", + " 640 0.000022\n", " 1046 0.000020\n", - " Name: 334, dtype: float64, 79 9.999965e-01\n", - " 160 1.825732e-07\n", - " 9626 1.258574e-07\n", - " 119 1.023975e-07\n", - " 8040 1.003601e-07\n", - " 2260 7.524338e-08\n", - " 1203 5.953626e-08\n", - " 641 5.945308e-08\n", - " 246 5.843062e-08\n", - " 206 5.528683e-08\n", - " Name: 335, dtype: float64, 129 0.133697\n", - " 640 0.107681\n", - " 154 0.041697\n", - " 156 0.033722\n", - " 108 0.023589\n", - " 1046 0.023401\n", - " 2197 0.023031\n", - " 246 0.019859\n", - " 107 0.017826\n", - " 205 0.016010\n", - " Name: 336, dtype: float64, 156 0.737351\n", - " 128 0.042816\n", - " 2197 0.013313\n", - " 129 0.006097\n", - " 246 0.005891\n", - " 2119 0.005298\n", - " 2278 0.005273\n", - " 86 0.005016\n", - " 3054 0.004220\n", - " 2194 0.003964\n", - " Name: 337, dtype: float64, 159 0.261469\n", - " 160 0.234296\n", - " 9138 0.053087\n", - " 161 0.022927\n", - " 753 0.016554\n", - " 3319 0.012515\n", - " 8650 0.011587\n", - " 5740 0.011433\n", - " 2214 0.007117\n", - " 2236 0.006575\n", - " Name: 338, dtype: float64, 79 9.999926e-01\n", - " 160 3.367379e-07\n", - " 119 2.410365e-07\n", - " 9626 2.299101e-07\n", - " 206 1.657837e-07\n", - " 8040 1.628066e-07\n", - " 4011 1.622737e-07\n", - " 246 1.371241e-07\n", - " 4073 1.313522e-07\n", - " 208 1.170091e-07\n", - " Name: 339, dtype: float64, 79 9.999955e-01\n", - " 160 2.338371e-07\n", - " 119 1.566375e-07\n", - " 9626 1.423809e-07\n", - " 246 1.041173e-07\n", - " 8040 9.167555e-08\n", - " 206 8.154461e-08\n", - " 4011 7.933221e-08\n", - " 4073 7.709716e-08\n", - " 1506 7.269708e-08\n", - " Name: 340, dtype: float64, 79 9.999956e-01\n", - " 160 2.655677e-07\n", - " 119 1.299602e-07\n", - " 2260 1.216764e-07\n", - " 9626 1.159159e-07\n", - " 8040 1.101066e-07\n", - " 246 8.041417e-08\n", - " 1203 7.476378e-08\n", - " 641 6.738452e-08\n", - " 206 6.482483e-08\n", - " Name: 341, dtype: float64, 156 0.042124\n", - " 153 0.020672\n", - " 10430 0.011473\n", - " 8238 0.008076\n", - " 1514 0.007103\n", - " 196 0.006228\n", - " 1517 0.005916\n", - " 8622 0.005910\n", - " 7475 0.005797\n", - " 1472 0.005606\n", - " Name: 342, dtype: float64, 86 0.301106\n", - " 156 0.211695\n", - " 2197 0.171286\n", - " 1471 0.058106\n", - " 2173 0.019589\n", - " 640 0.009183\n", - " 2157 0.007313\n", - " 108 0.007074\n", - " 4088 0.006240\n", - " 2337 0.005273\n", - " Name: 343, dtype: float64, 79 9.999962e-01\n", - " 160 1.939019e-07\n", - " 9626 1.494097e-07\n", - " 119 1.148368e-07\n", - " 246 9.924207e-08\n", - " 4011 9.249990e-08\n", - " 8040 9.202652e-08\n", - " 206 7.588427e-08\n", - " 2260 7.529547e-08\n", - " 4073 7.086818e-08\n", - " Name: 344, dtype: float64, 176 0.920517\n", - " 1736 0.003645\n", - " 1895 0.002118\n", - " 264 0.001922\n", - " 1903 0.001692\n", - " 1602 0.001160\n", - " 1752 0.000922\n", - " 1115 0.000872\n", - " 2397 0.000750\n", - " 2365 0.000677\n", - " Name: 345, dtype: float64, 129 9.999580e-01\n", - " 108 1.847192e-05\n", - " 2197 4.271443e-06\n", - " 8418 7.212153e-07\n", - " 3210 6.244383e-07\n", - " 213 5.818225e-07\n", - " 174 4.778348e-07\n", - " 8731 4.422808e-07\n", - " 136 4.262428e-07\n", - " 698 4.195577e-07\n", - " Name: 346, dtype: float64, 129 0.999876\n", - " 108 0.000031\n", - " 2197 0.000024\n", - " 2196 0.000004\n", - " 128 0.000003\n", - " 174 0.000003\n", - " 156 0.000002\n", - " 8418 0.000002\n", - " 3991 0.000001\n", - " 136 0.000001\n", - " Name: 347, dtype: float64, 180 0.051896\n", - " 1688 0.043879\n", - " 2119 0.040138\n", - " 94 0.027481\n", - " 96 0.026257\n", - " 9748 0.021514\n", - " 205 0.015723\n", - " 3010 0.014386\n", - " 245 0.012646\n", - " 4093 0.010146\n", - " Name: 348, dtype: float64, 191 0.612736\n", - " 186 0.063611\n", - " 190 0.038476\n", - " 184 0.005729\n", - " 213 0.003420\n", - " 98 0.002468\n", - " 2917 0.002366\n", - " 9846 0.002329\n", - " 187 0.001976\n", - " 1117 0.001853\n", - " Name: 349, dtype: float64, 193 0.189405\n", - " 196 0.037873\n", - " 7896 0.015304\n", - " 8317 0.008088\n", - " 8204 0.007175\n", - " 837 0.006662\n", - " 8973 0.006566\n", - " 3016 0.006053\n", - " 7464 0.006007\n", - " 8920 0.005394\n", - " Name: 350, dtype: float64, 197 0.213083\n", - " 620 0.074374\n", - " 164 0.041309\n", - " 198 0.017523\n", - " 8417 0.006642\n", - " 10542 0.006611\n", - " 2869 0.006542\n", - " 2235 0.006520\n", - " 9747 0.006422\n", - " 9778 0.005865\n", - " Name: 351, dtype: float64, 10128 0.039555\n", - " 7903 0.037828\n", - " 10133 0.027551\n", - " 2298 0.026884\n", - " 2528 0.025098\n", - " 9908 0.023164\n", - " 8130 0.021191\n", - " 8129 0.017696\n", - " 1739 0.012966\n", - " 10129 0.012556\n", - " Name: 352, dtype: float64, 2822 0.421771\n", - " 2746 0.239578\n", - " 2750 0.094413\n", - " 2565 0.033085\n", - " 2554 0.025503\n", - " 3249 0.020262\n", - " 2806 0.017782\n", - " 2766 0.010481\n", - " 476 0.003476\n", - " 2751 0.003205\n", - " Name: 353, dtype: float64, 2498 0.995826\n", - " 4683 0.000196\n", - " 9982 0.000105\n", - " 4709 0.000056\n", - " 4535 0.000051\n", - " 831 0.000049\n", - " 5900 0.000042\n", - " 3059 0.000041\n", - " 8058 0.000040\n", - " 494 0.000039\n", - " Name: 354, dtype: float64, 2406 0.041466\n", - " 10404 0.031225\n", - " 2166 0.025703\n", - " 898 0.014105\n", - " 6895 0.012023\n", - " 5515 0.010110\n", - " 6808 0.009397\n", - " 10652 0.008707\n", - " 2432 0.008568\n", - " 6922 0.008483\n", - " Name: 355, dtype: float64, 3119 0.020362\n", - " 2633 0.012381\n", - " 2557 0.012347\n", - " 7654 0.010966\n", - " 7527 0.009935\n", - " 6686 0.009090\n", - " 8226 0.008241\n", - " 7526 0.007666\n", - " 7555 0.007139\n", - " 2630 0.005687\n", - " Name: 356, dtype: float64, 2759 0.528763\n", - " 5077 0.015113\n", - " 3305 0.013767\n", - " 6458 0.005745\n", - " 6802 0.005592\n", - " 3197 0.005479\n", - " 2689 0.004701\n", - " 3394 0.004414\n", - " 6450 0.004324\n", - " 3191 0.004300\n", - " Name: 357, dtype: float64, 3475 0.106776\n", - " 6022 0.020124\n", - " 2508 0.019446\n", - " 2509 0.014550\n", - " 7957 0.012144\n", - " 6739 0.011683\n", - " 7724 0.011369\n", - " 7783 0.010972\n", - " 677 0.010017\n", - " 3172 0.009902\n", - " Name: 358, dtype: float64, 2548 0.923612\n", - " 2546 0.023524\n", - " 2549 0.002161\n", - " 138 0.001267\n", - " 826 0.000976\n", - " 801 0.000925\n", - " 2545 0.000771\n", - " 2547 0.000492\n", - " 856 0.000452\n", - " 8278 0.000368\n", - " Name: 359, dtype: float64, 2546 0.931295\n", - " 2549 0.008971\n", - " 2545 0.003841\n", - " 2548 0.001010\n", - " 620 0.000813\n", - " 8919 0.000611\n", - " 2547 0.000518\n", - " 2111 0.000455\n", - " 2476 0.000402\n", - " 1135 0.000384\n", - " Name: 360, dtype: float64, 387 0.859926\n", - " 8847 0.028654\n", - " 7894 0.004591\n", - " 7974 0.002416\n", - " 1906 0.002412\n", - " 9114 0.001976\n", - " 2414 0.001679\n", - " 9178 0.001345\n", - " 2900 0.001333\n", - " 3337 0.001311\n", - " Name: 361, dtype: float64, 3178 0.064385\n", - " 2601 0.039399\n", - " 3157 0.027954\n", - " 3323 0.024626\n", - " 2214 0.017484\n", - " 2603 0.017076\n", - " 7957 0.016948\n", - " 657 0.016124\n", - " 1471 0.014977\n", - " 9047 0.014970\n", - " Name: 362, dtype: float64, 2604 0.976940\n", - " 824 0.000566\n", - " 2846 0.000444\n", - " 8298 0.000387\n", - " 2868 0.000280\n", - " 7391 0.000245\n", - " 867 0.000220\n", - " 2603 0.000187\n", - " 891 0.000185\n", - " 8036 0.000181\n", - " Name: 363, dtype: float64, 2613 0.968530\n", - " 2337 0.011271\n", - " 6152 0.000794\n", - " 10136 0.000476\n", - " 2868 0.000406\n", - " 8254 0.000372\n", - " 7585 0.000299\n", - " 9326 0.000290\n", - " 9079 0.000236\n", - " 657 0.000223\n", - " Name: 364, dtype: float64, 2613 0.968530\n", - " 2337 0.011271\n", - " 6152 0.000794\n", - " 10136 0.000476\n", - " 2868 0.000406\n", - " 8254 0.000372\n", - " 7585 0.000299\n", - " 9326 0.000290\n", - " 9079 0.000236\n", - " 657 0.000223\n", - " Name: 365, dtype: float64, 2228 0.049355\n", - " 7992 0.035845\n", - " 2221 0.030201\n", - " 710 0.018361\n", - " 2373 0.012049\n", - " 10402 0.009805\n", - " 7236 0.009209\n", - " 7680 0.008954\n", - " 7955 0.007995\n", - " 7228 0.007526\n", - " Name: 366, dtype: float64, 2647 0.904184\n", - " 2640 0.016726\n", - " 2654 0.007374\n", - " 2638 0.005071\n", - " 2637 0.004828\n", - " 2642 0.003688\n", - " 2653 0.003597\n", - " 2646 0.003051\n", - " 2649 0.002231\n", - " 2650 0.001817\n", - " Name: 367, dtype: float64, 2655 0.625349\n", - " 2512 0.022397\n", - " 2862 0.017670\n", - " 824 0.011196\n", - " 2932 0.008268\n", - " 10165 0.007403\n", - " 2611 0.007207\n", - " 2931 0.006320\n", - " 7527 0.005168\n", - " 668 0.004093\n", - " Name: 368, dtype: float64, 2679 0.981321\n", - " 2680 0.000653\n", - " 4094 0.000615\n", - " 6029 0.000340\n", - " 4700 0.000267\n", - " 4550 0.000203\n", - " 665 0.000185\n", - " 5889 0.000143\n", - " 4526 0.000130\n", - " 252 0.000124\n", - " Name: 369, dtype: float64, 2685 0.947780\n", - " 8810 0.006233\n", - " 3361 0.003316\n", - " 2051 0.001425\n", - " 6058 0.001333\n", - " 6602 0.001077\n", - " 4442 0.000887\n", - " 6064 0.000799\n", - " 3244 0.000757\n", - " 2823 0.000622\n", - " Name: 370, dtype: float64, 9379 0.113023\n", - " 1471 0.034889\n", - " 9531 0.032596\n", - " 4172 0.026356\n", - " 733 0.022541\n", - " 9530 0.018357\n", - " 2260 0.014390\n", - " 3756 0.013621\n", - " 8038 0.007691\n", - " 7630 0.007205\n", - " Name: 371, dtype: float64, 9104 0.033938\n", - " 7179 0.021575\n", - " 8454 0.020800\n", - " 9227 0.016258\n", - " 9033 0.012970\n", - " 7724 0.010529\n", - " 8876 0.006979\n", - " 8585 0.006829\n", - " 9021 0.006610\n", - " 8808 0.006412\n", - " Name: 372, dtype: float64, 6922 0.029204\n", - " 6937 0.028576\n", - " 6005 0.017500\n", - " 6895 0.013870\n", - " 6730 0.010788\n", - " 8210 0.009889\n", - " 2165 0.008909\n", - " 6260 0.008147\n", - " 2536 0.008132\n", - " 6930 0.008091\n", - " Name: 373, dtype: float64, 2503 0.373849\n", - " 677 0.328473\n", - " 2716 0.207137\n", - " 679 0.019205\n", - " 2502 0.014459\n", - " 2715 0.011930\n", - " 2504 0.005256\n", - " 8266 0.002110\n", - " 2567 0.001654\n", - " 2236 0.001274\n", - " Name: 374, dtype: float64, 2720 0.152348\n", - " 2718 0.092671\n", - " 2719 0.054651\n", - " 6555 0.009847\n", - " 5094 0.009093\n", - " 8201 0.008491\n", - " 528 0.005892\n", - " 9693 0.005697\n", - " 5535 0.005460\n", - " 105 0.004862\n", - " Name: 375, dtype: float64, 3249 0.422802\n", - " 2766 0.191679\n", - " 2746 0.078680\n", - " 2822 0.041887\n", - " 2750 0.025335\n", - " 2806 0.014593\n", - " 2561 0.009362\n", - " 2558 0.008769\n", - " 2565 0.004991\n", - " 319 0.004688\n", - " Name: 376, dtype: float64, 5703 0.208476\n", - " 6952 0.083306\n", - " 8325 0.022128\n", - " 10227 0.018050\n", - " 5742 0.016719\n", - " 7277 0.011027\n", - " 2776 0.010142\n", - " 7986 0.009999\n", - " 6815 0.008242\n", - " 8441 0.006743\n", - " Name: 377, dtype: float64, 10617 0.094020\n", - " 6041 0.058288\n", - " 2300 0.015877\n", - " 10008 0.012854\n", - " 7136 0.011275\n", - " 7336 0.010477\n", - " 3250 0.009480\n", - " 8949 0.009479\n", - " 7438 0.009011\n", - " 7386 0.008296\n", - " Name: 378, dtype: float64, 2660 0.984194\n", - " 7016 0.004154\n", - " 8418 0.000656\n", - " 5231 0.000493\n", - " 2919 0.000399\n", - " 2862 0.000293\n", - " 2630 0.000271\n", - " 3197 0.000264\n", - " 7609 0.000243\n", - " 667 0.000221\n", - " Name: 379, dtype: float64, 2794 0.152707\n", - " 2795 0.129922\n", - " 2817 0.026659\n", - " 6638 0.023941\n", - " 3009 0.017544\n", - " 6678 0.013737\n", - " 6493 0.010725\n", - " 6122 0.007955\n", - " 7107 0.007003\n", - " 5629 0.006362\n", - " Name: 380, dtype: float64, 3561 0.054614\n", - " 5385 0.016281\n", - " 3445 0.014144\n", - " 3562 0.013726\n", - " 5164 0.011824\n", - " 3903 0.010067\n", - " 2808 0.009606\n", - " 10604 0.009123\n", - " 7518 0.008692\n", - " 3560 0.008228\n", - " Name: 381, dtype: float64, 3249 0.361993\n", - " 2766 0.187536\n", - " 2822 0.058857\n", - " 2746 0.053779\n", - " 2750 0.053339\n", - " 2806 0.021377\n", - " 2561 0.013834\n", - " 2558 0.013624\n", - " 2751 0.009874\n", - " 2565 0.006112\n", - " Name: 382, dtype: float64, 79 9.999920e-01\n", - " 9626 4.283625e-07\n", - " 8040 2.985172e-07\n", - " 160 2.614587e-07\n", - " 119 2.140198e-07\n", - " 192 1.682887e-07\n", - " 1203 1.604400e-07\n", - " 1805 1.541362e-07\n", - " 4011 1.483696e-07\n", - " 206 1.374380e-07\n", - " Name: 383, dtype: float64, 2848 0.039309\n", - " 2912 0.017763\n", - " 2850 0.015413\n", - " 6469 0.014310\n", - " 2903 0.013658\n", - " 5685 0.011742\n", - " 6810 0.011530\n", - " 2552 0.010741\n", - " 6302 0.009514\n", - " 759 0.007830\n", - " Name: 384, dtype: float64, 2848 0.017487\n", - " 2837 0.015994\n", - " 194 0.013968\n", - " 7354 0.010664\n", - " 6175 0.010362\n", - " 2834 0.009260\n", - " 2974 0.007800\n", - " 2847 0.007127\n", - " 6113 0.006735\n", - " 5687 0.006498\n", - " Name: 385, dtype: float64, 2844 0.363187\n", - " 2829 0.119782\n", - " 3619 0.018545\n", - " 908 0.008425\n", - " 2833 0.007570\n", - " 5521 0.006774\n", - " 2912 0.006296\n", - " 2830 0.005843\n", - " 5741 0.005228\n", - " 4010 0.004271\n", - " Name: 386, dtype: float64, 2832 0.803192\n", - " 5623 0.016129\n", - " 2837 0.009569\n", - " 2847 0.006547\n", - " 2850 0.003872\n", - " 2834 0.003376\n", - " 5723 0.002758\n", - " 5727 0.002709\n", - " 2833 0.001840\n", - " 5708 0.001708\n", - " Name: 387, dtype: float64, 7442 0.059154\n", - " 7332 0.033031\n", - " 7769 0.022765\n", - " 7449 0.018509\n", - " 10598 0.018013\n", - " 10582 0.013735\n", - " 7821 0.012158\n", - " 10527 0.011563\n", - " 7236 0.010913\n", - " 7690 0.009745\n", - " Name: 388, dtype: float64, 3245 0.101143\n", - " 3186 0.028138\n", - " 7277 0.026016\n", - " 5937 0.019788\n", - " 2534 0.015665\n", - " 3346 0.015318\n", - " 3193 0.013476\n", - " 3083 0.011530\n", - " 8159 0.011153\n", - " 3605 0.009328\n", - " Name: 389, dtype: float64, 7401 0.176180\n", - " 8045 0.126645\n", - " 2930 0.056520\n", - " 8810 0.056208\n", - " 2929 0.018874\n", - " 8130 0.016563\n", - " 7229 0.013340\n", - " 6603 0.012418\n", - " 6064 0.011672\n", - " 6543 0.009372\n", - " Name: 390, dtype: float64, 79 9.999963e-01\n", - " 160 1.825906e-07\n", - " 9626 1.550779e-07\n", - " 119 1.153782e-07\n", - " 8040 1.100112e-07\n", - " 4011 9.677373e-08\n", - " 2260 8.883523e-08\n", - " 4073 7.843914e-08\n", - " 206 7.368859e-08\n", - " 246 7.063297e-08\n", - " Name: 391, dtype: float64, 10134 0.806234\n", - " 10533 0.004735\n", - " 7323 0.004524\n", - " 10135 0.004037\n", - " 7633 0.003220\n", - " 10247 0.002407\n", - " 10246 0.002218\n", - " 10142 0.001840\n", - " 9718 0.001814\n", - " 10522 0.001778\n", - " Name: 392, dtype: float64, 4088 0.059481\n", - " 9823 0.028951\n", - " 246 0.028625\n", - " 387 0.028376\n", - " 3039 0.022459\n", - " 691 0.015846\n", - " 1917 0.015569\n", - " 2241 0.014190\n", - " 870 0.012810\n", - " 3344 0.008922\n", - " Name: 393, dtype: float64, 2862 0.938909\n", - " 2630 0.006700\n", - " 2660 0.003745\n", - " 6588 0.002680\n", - " 667 0.001489\n", - " 3500 0.001226\n", - " 2932 0.001081\n", - " 2512 0.000831\n", - " 2611 0.000795\n", - " 2608 0.000767\n", - " Name: 394, dtype: float64, 2881 0.220287\n", - " 2882 0.213323\n", - " 2880 0.208097\n", - " 2878 0.204920\n", - " 2945 0.002052\n", - " 8172 0.001880\n", - " 2645 0.001562\n", - " 2647 0.001457\n", - " 3394 0.001153\n", - " 7456 0.001041\n", - " Name: 395, dtype: float64, 2899 0.550971\n", - " 2898 0.053302\n", - " 6497 0.012291\n", - " 6537 0.010441\n", - " 6498 0.009527\n", - " 2525 0.008099\n", - " 2981 0.005459\n", - " 6984 0.005016\n", - " 5530 0.004639\n", - " 3325 0.003770\n", - " Name: 396, dtype: float64, 2903 0.147591\n", - " 2905 0.040542\n", - " 5698 0.021087\n", - " 2910 0.011576\n", - " 7096 0.009431\n", - " 8547 0.007402\n", - " 6333 0.006340\n", - " 6139 0.005255\n", - " 3521 0.005120\n", - " 2519 0.004735\n", - " Name: 397, dtype: float64, 6288 0.099399\n", - " 6291 0.062068\n", - " 6664 0.054621\n", - " 6752 0.025229\n", - " 6290 0.020600\n", - " 7025 0.016618\n", - " 8117 0.012872\n", - " 7466 0.011114\n", - " 6777 0.008823\n", - " 5770 0.007726\n", - " Name: 398, dtype: float64, 3100 0.301774\n", - " 2693 0.136954\n", - " 2866 0.020811\n", - " 5933 0.018814\n", - " 3035 0.007290\n", - " 8981 0.006045\n", - " 10071 0.005463\n", - " 8706 0.005445\n", - " 2005 0.005103\n", - " 2622 0.004602\n", - " Name: 399, dtype: float64, 2921 0.544514\n", - " 2917 0.039678\n", - " 2897 0.008514\n", - " 2933 0.006651\n", - " 8407 0.006599\n", - " 2968 0.006175\n", - " 2823 0.005594\n", - " 3243 0.005339\n", - " 2916 0.005224\n", - " 2731 0.004791\n", - " Name: 400, dtype: float64, 79 0.999216\n", - " 2534 0.000144\n", - " 9626 0.000101\n", - " 8040 0.000025\n", - " 2166 0.000011\n", - " 10369 0.000010\n", - " 246 0.000009\n", - " 5263 0.000008\n", - " 7050 0.000008\n", - " 9038 0.000006\n", - " Name: 401, dtype: float64, 3200 0.063240\n", - " 9285 0.048159\n", - " 7475 0.038126\n", - " 9303 0.024251\n", - " 9298 0.016587\n", - " 9278 0.014197\n", - " 9344 0.011692\n", - " 6837 0.010580\n", - " 9364 0.010534\n", - " 10159 0.009273\n", - " Name: 402, dtype: float64, 3192 0.026832\n", - " 5779 0.024443\n", - " 675 0.015129\n", - " 5953 0.011211\n", - " 7410 0.009442\n", - " 7409 0.008838\n", - " 2776 0.008291\n", - " 7924 0.008134\n", - " 2350 0.007418\n", - " 2838 0.006431\n", - " Name: 403, dtype: float64, 2946 0.098706\n", - " 2950 0.077236\n", - " 3583 0.021873\n", - " 151 0.018674\n", - " 2823 0.018534\n", - " 3075 0.011697\n", - " 152 0.011152\n", - " 2944 0.008255\n", - " 3601 0.008211\n", - " 3519 0.008003\n", - " Name: 404, dtype: float64, 4515 0.044339\n", - " 239 0.032440\n", - " 3148 0.023433\n", - " 3149 0.023346\n", - " 1576 0.016687\n", - " 8724 0.013264\n", - " 4517 0.011794\n", - " 5453 0.010393\n", - " 240 0.009870\n", - " 1662 0.009802\n", - " Name: 405, dtype: float64, 2941 0.982214\n", - " 2992 0.003302\n", - " 2940 0.001103\n", - " 79 0.001018\n", - " 2990 0.000840\n", - " 2991 0.000499\n", - " 2955 0.000363\n", - " 2956 0.000316\n", - " 2165 0.000208\n", - " 3836 0.000181\n", - " Name: 406, dtype: float64, 2963 0.371135\n", - " 2961 0.259533\n", - " 2962 0.140495\n", - " 2959 0.022981\n", - " 2960 0.019247\n", - " 2964 0.014248\n", - " 3135 0.004444\n", - " 2980 0.002354\n", - " 9918 0.001964\n", - " 2726 0.001798\n", - " Name: 407, dtype: float64, 3301 0.030151\n", - " 2815 0.023410\n", - " 2169 0.020556\n", - " 2977 0.014619\n", - " 2759 0.014474\n", - " 2992 0.012297\n", - " 6802 0.011617\n", - " 2165 0.008905\n", - " 2957 0.008486\n", - " 2993 0.007596\n", - " Name: 408, dtype: float64, 2993 0.063476\n", - " 2973 0.046138\n", - " 2970 0.043974\n", - " 2978 0.024294\n", - " 3546 0.012129\n", - " 3493 0.010667\n", - " 31 0.010628\n", - " 3967 0.009953\n", - " 3166 0.009665\n", - " 3164 0.009120\n", - " Name: 409, dtype: float64, 8081 0.023139\n", - " 2991 0.020227\n", - " 2990 0.019065\n", - " 9840 0.016338\n", - " 2993 0.014286\n", - " 2956 0.014270\n", - " 2941 0.012010\n", - " 2992 0.011070\n", - " 2955 0.009187\n", - " 8142 0.008984\n", - " Name: 410, dtype: float64, 2956 0.924919\n", - " 2941 0.008614\n", - " 2990 0.005486\n", - " 2992 0.002662\n", - " 2797 0.002491\n", - " 2955 0.001921\n", - " 7602 0.001406\n", - " 5235 0.001244\n", - " 6893 0.000951\n", - " 2991 0.000771\n", - " Name: 411, dtype: float64, 9279 0.027534\n", - " 3244 0.024295\n", - " 7112 0.023219\n", - " 3602 0.013011\n", - " 4001 0.011465\n", - " 2543 0.009690\n", - " 2739 0.008901\n", - " 3324 0.008702\n", - " 240 0.008602\n", - " 2801 0.008055\n", - " Name: 412, dtype: float64, 7359 0.015813\n", - " 7848 0.014521\n", - " 3162 0.013428\n", - " 10382 0.010869\n", - " 6608 0.008473\n", - " 3218 0.007562\n", - " 9943 0.007360\n", - " 6115 0.006207\n", - " 2303 0.005522\n", - " 3892 0.005083\n", - " Name: 413, dtype: float64, 7271 0.053124\n", - " 10248 0.035813\n", - " 10246 0.032274\n", - " 10247 0.026314\n", - " 10316 0.022865\n", - " 8037 0.013147\n", - " 10420 0.010382\n", - " 669 0.010236\n", - " 10581 0.009995\n", - " 10533 0.009589\n", - " Name: 414, dtype: float64, 3030 0.306358\n", - " 3032 0.122944\n", - " 10604 0.008289\n", - " 5012 0.007814\n", - " 3075 0.006564\n", - " 1740 0.004190\n", - " 7526 0.004102\n", - " 3005 0.003958\n", - " 3043 0.003851\n", - " 10307 0.003751\n", - " Name: 415, dtype: float64, 2115 0.636075\n", - " 3047 0.060258\n", - " 2116 0.048898\n", - " 3046 0.031740\n", - " 2514 0.022154\n", - " 2117 0.021340\n", - " 641 0.020678\n", - " 2214 0.014697\n", - " 9475 0.011886\n", - " 2118 0.009297\n", - " Name: 416, dtype: float64, 641 0.997175\n", - " 2115 0.000805\n", - " 690 0.000184\n", - " 3046 0.000160\n", - " 2214 0.000092\n", - " 2236 0.000070\n", - " 2514 0.000049\n", - " 4172 0.000044\n", - " 640 0.000038\n", - " 3991 0.000031\n", - " Name: 417, dtype: float64, 3058 0.123450\n", - " 4155 0.053385\n", - " 4088 0.044904\n", - " 4138 0.034281\n", - " 4006 0.028077\n", - " 4137 0.023502\n", - " 4172 0.018176\n", - " 3053 0.015243\n", - " 4149 0.014683\n", - " 9970 0.013327\n", - " Name: 418, dtype: float64, 3066 0.995531\n", - " 2497 0.000747\n", - " 3065 0.000329\n", - " 2906 0.000251\n", - " 3064 0.000231\n", - " 3068 0.000061\n", - " 8795 0.000043\n", - " 829 0.000040\n", - " 3759 0.000039\n", - " 873 0.000039\n", - " Name: 419, dtype: float64, 10130 0.066041\n", - " 10434 0.026793\n", - " 10375 0.022128\n", - " 9711 0.019050\n", - " 9948 0.011282\n", - " 10248 0.010949\n", - " 8051 0.009739\n", - " 7322 0.008988\n", - " 2693 0.008050\n", - " 7291 0.007392\n", - " Name: 420, dtype: float64, 3054 0.995949\n", - " 3052 0.000392\n", - " 3050 0.000185\n", - " 9039 0.000100\n", - " 1471 0.000083\n", + " 1295 0.000018\n", + " 519 0.000016\n", + " Name: 227, dtype: float64, 1805 0.043739\n", + " 3544 0.020567\n", + " 208 0.014774\n", + " 4298 0.014470\n", + " 3687 0.013725\n", + " 1187 0.010342\n", + " 1032 0.010069\n", + " 2236 0.009156\n", + " 3637 0.008584\n", + " 1636 0.008365\n", + " Name: 228, dtype: float64, 2635 0.066513\n", + " 9948 0.027419\n", + " 3036 0.022100\n", + " 848 0.017474\n", + " 2316 0.016741\n", + " 8156 0.016598\n", + " 3150 0.014347\n", + " 7905 0.013971\n", + " 5931 0.012532\n", + " 2190 0.010024\n", + " Name: 229, dtype: float64, 1580 0.297883\n", + " 1589 0.267163\n", + " 1590 0.230653\n", + " 1583 0.036667\n", + " 1586 0.032078\n", + " 1579 0.025611\n", + " 1578 0.024639\n", + " 1585 0.005260\n", + " 1582 0.003112\n", + " 1592 0.002297\n", + " Name: 230, dtype: float64, 473 0.029822\n", + " 1581 0.021666\n", + " 1672 0.019060\n", + " 1578 0.017822\n", + " 1076 0.017251\n", + " 1579 0.012454\n", + " 1583 0.011213\n", + " 1582 0.010621\n", + " 1590 0.009777\n", + " 1426 0.009630\n", + " Name: 231, dtype: float64, 79 9.999953e-01\n", + " 208 1.300548e-06\n", + " 1805 1.824631e-07\n", + " 517 1.047071e-07\n", + " 7106 9.244885e-08\n", + " 119 6.992087e-08\n", + " 2503 5.433015e-08\n", + " 4098 5.355266e-08\n", + " 4000 5.024823e-08\n", + " 2546 4.147798e-08\n", + " Name: 232, dtype: float64, 246 0.996720\n", + " 207 0.000687\n", + " 247 0.000206\n", + " 204 0.000158\n", + " 205 0.000120\n", + " 2236 0.000099\n", + " 108 0.000084\n", + " 206 0.000073\n", + " 630 0.000052\n", + " 2351 0.000046\n", + " Name: 233, dtype: float64, 211 0.251444\n", + " 1805 0.171541\n", + " 246 0.111656\n", + " 207 0.050219\n", + " 107 0.028838\n", + " 264 0.021818\n", + " 206 0.018562\n", + " 208 0.016018\n", + " 214 0.012787\n", + " 679 0.011898\n", + " Name: 234, dtype: float64, 213 0.869790\n", + " 246 0.038746\n", + " 108 0.007640\n", + " 1805 0.007362\n", + " 208 0.007195\n", + " 214 0.006787\n", + " 211 0.005599\n", + " 206 0.004407\n", + " 207 0.003070\n", + " 257 0.003043\n", + " Name: 235, dtype: float64, 205 0.225126\n", + " 1606 0.037357\n", + " 1711 0.034908\n", + " 246 0.031529\n", + " 207 0.030289\n", + " 1228 0.025258\n", + " 2119 0.020904\n", + " 1629 0.020241\n", + " 1059 0.018517\n", + " 203 0.016757\n", + " Name: 236, dtype: float64, 1576 0.024764\n", + " 7609 0.024217\n", + " 107 0.024066\n", + " 2597 0.022053\n", + " 1736 0.018461\n", + " 246 0.017495\n", + " 2502 0.015460\n", + " 2291 0.014209\n", + " 2236 0.012481\n", + " 233 0.012324\n", + " Name: 237, dtype: float64, 208 0.995705\n", + " 1711 0.000761\n", + " 79 0.000597\n", + " 211 0.000505\n", + " 519 0.000242\n", + " 517 0.000215\n", + " 268 0.000173\n", + " 1295 0.000165\n", + " 214 0.000054\n", + " 233 0.000032\n", + " Name: 238, dtype: float64, 1636 0.018365\n", + " 1672 0.018087\n", + " 8947 0.014816\n", + " 369 0.014528\n", + " 1486 0.012437\n", + " 2015 0.012405\n", + " 370 0.011897\n", + " 4043 0.010917\n", + " 1689 0.009896\n", + " 1638 0.008596\n", + " Name: 239, dtype: float64, 1927 0.035401\n", + " 1673 0.029080\n", + " 2148 0.027343\n", + " 1653 0.026019\n", + " 318 0.025352\n", + " 1654 0.021110\n", + " 3890 0.021033\n", + " 27 0.020495\n", + " 1506 0.014459\n", + " 1554 0.013313\n", + " Name: 240, dtype: float64, 213 0.985256\n", + " 208 0.006811\n", + " 214 0.002242\n", + " 1295 0.002148\n", + " 519 0.000384\n", + " 257 0.000283\n", + " 2503 0.000242\n", + " 206 0.000162\n", + " 205 0.000152\n", + " 1711 0.000150\n", + " Name: 241, dtype: float64, 208 0.077296\n", + " 1711 0.053600\n", + " 1688 0.050016\n", + " 9039 0.018279\n", + " 268 0.014321\n", + " 9034 0.012849\n", + " 2119 0.010744\n", + " 1047 0.010426\n", + " 9205 0.010152\n", + " 233 0.009425\n", + " Name: 242, dtype: float64, 1860 0.462464\n", + " 1691 0.090370\n", + " 1866 0.018554\n", + " 2102 0.018139\n", + " 1847 0.016878\n", + " 1635 0.016498\n", + " 1792 0.010056\n", + " 2069 0.009288\n", + " 1844 0.007337\n", + " 10655 0.007220\n", + " Name: 243, dtype: float64, 1700 0.925723\n", + " 1701 0.023444\n", + " 10175 0.003335\n", + " 3529 0.001672\n", + " 3593 0.001425\n", + " 7854 0.000806\n", + " 10176 0.000590\n", + " 3496 0.000575\n", + " 2380 0.000559\n", + " 8045 0.000449\n", + " Name: 244, dtype: float64, 1715 0.058361\n", + " 1276 0.058140\n", + " 226 0.033123\n", + " 517 0.032964\n", + " 1895 0.025955\n", + " 448 0.016269\n", + " 1903 0.010881\n", + " 1727 0.008280\n", + " 1111 0.006864\n", + " 1245 0.006759\n", + " Name: 245, dtype: float64, 1886 0.036554\n", + " 1256 0.021127\n", + " 1172 0.019991\n", + " 1592 0.019608\n", + " 554 0.019140\n", + " 1165 0.017203\n", + " 1655 0.016893\n", + " 533 0.015320\n", + " 1583 0.014214\n", + " 1656 0.012377\n", + " Name: 246, dtype: float64, 441 0.071461\n", + " 535 0.057808\n", + " 1733 0.045533\n", + " 533 0.030952\n", + " 457 0.025137\n", + " 1871 0.023423\n", + " 1243 0.022871\n", + " 342 0.013205\n", + " 564 0.010678\n", + " 340 0.009589\n", + " Name: 247, dtype: float64, 2230 0.742147\n", + " 8160 0.008117\n", + " 657 0.008063\n", + " 9775 0.006379\n", + " 6942 0.006032\n", + " 5626 0.003845\n", + " 8402 0.003454\n", + " 779 0.003435\n", + " 3292 0.003434\n", + " 8147 0.003095\n", + " Name: 248, dtype: float64, 1776 0.245509\n", + " 1767 0.022933\n", + " 1771 0.015366\n", + " 8576 0.014723\n", + " 9656 0.014256\n", + " 1634 0.010103\n", + " 1232 0.009497\n", + " 1550 0.008487\n", + " 8849 0.007749\n", + " 2631 0.007139\n", + " Name: 249, dtype: float64, 1776 0.079363\n", + " 8767 0.039429\n", + " 1754 0.027464\n", + " 9087 0.018288\n", + " 1514 0.007405\n", + " 1343 0.006937\n", + " 1767 0.006805\n", + " 9358 0.006774\n", + " 1515 0.006711\n", + " 8845 0.006399\n", + " Name: 250, dtype: float64, 10406 0.033153\n", + " 9531 0.017321\n", + " 10456 0.014492\n", + " 10457 0.014198\n", + " 10140 0.013380\n", + " 7596 0.012812\n", + " 6660 0.011727\n", + " 4172 0.011124\n", + " 797 0.009957\n", + " 4152 0.009605\n", + " Name: 251, dtype: float64, 1748 0.161727\n", + " 2119 0.055168\n", + " 1749 0.053234\n", + " 2132 0.049713\n", + " 1775 0.029262\n", + " 1771 0.024301\n", + " 1769 0.021434\n", + " 1806 0.016513\n", + " 129 0.016451\n", + " 654 0.016132\n", + " Name: 252, dtype: float64, 1771 0.228401\n", + " 1769 0.063560\n", + " 1776 0.052922\n", + " 1775 0.034188\n", + " 1749 0.031120\n", + " 1748 0.014920\n", + " 1767 0.014510\n", + " 1151 0.012108\n", + " 2001 0.010285\n", + " 9790 0.007302\n", + " Name: 253, dtype: float64, 1757 0.774969\n", + " 5592 0.006826\n", + " 5443 0.005503\n", + " 2619 0.002661\n", + " 3515 0.002285\n", + " 8549 0.002235\n", + " 8296 0.002233\n", + " 1076 0.002063\n", + " 1771 0.001972\n", + " 8558 0.001857\n", + " Name: 254, dtype: float64, 1761 0.984136\n", + " 5974 0.000384\n", + " 3088 0.000364\n", + " 10630 0.000350\n", + " 10621 0.000315\n", + " 2004 0.000246\n", + " 3092 0.000223\n", + " 6193 0.000218\n", + " 5973 0.000196\n", + " 1824 0.000184\n", + " Name: 255, dtype: float64, 1769 0.869733\n", + " 1771 0.025673\n", + " 1775 0.018690\n", + " 79 0.007337\n", + " 2001 0.002467\n", + " 1749 0.002407\n", + " 1433 0.001833\n", + " 1748 0.001652\n", + " 1635 0.001550\n", + " 1721 0.001290\n", + " Name: 256, dtype: float64, 1791 0.347776\n", + " 1793 0.185241\n", + " 1999 0.014708\n", + " 4012 0.011530\n", + " 1784 0.011058\n", + " 37 0.008284\n", + " 306 0.007546\n", + " 1847 0.006301\n", + " 2576 0.005377\n", + " 2410 0.004650\n", + " Name: 257, dtype: float64, 1791 0.992823\n", + " 1784 0.000547\n", + " 2024 0.000167\n", + " 10376 0.000166\n", + " 373 0.000141\n", + " 7231 0.000121\n", + " 5917 0.000093\n", + " 1841 0.000073\n", + " 90 0.000064\n", + " 1999 0.000063\n", + " Name: 258, dtype: float64, 2105 0.502767\n", + " 1636 0.088503\n", + " 1641 0.058204\n", + " 1390 0.053774\n", + " 1104 0.028171\n", + " 1799 0.013823\n", + " 2100 0.008782\n", + " 1022 0.008174\n", + " 1638 0.005698\n", + " 2003 0.005264\n", + " Name: 259, dtype: float64, 1199 0.054809\n", + " 207 0.035702\n", + " 1897 0.017107\n", + " 1893 0.013650\n", + " 586 0.013557\n", + " 1816 0.011780\n", + " 1622 0.010851\n", + " 376 0.010577\n", + " 37 0.009017\n", + " 1166 0.008883\n", + " Name: 260, dtype: float64, 1889 0.301891\n", + " 205 0.018678\n", + " 1464 0.017850\n", + " 81 0.014769\n", + " 401 0.012097\n", + " 3616 0.011585\n", + " 4535 0.009798\n", + " 3574 0.007384\n", + " 180 0.007222\n", + " 1225 0.007067\n", + " Name: 261, dtype: float64, 205 0.738931\n", + " 245 0.091586\n", + " 1807 0.035225\n", + " 641 0.006650\n", + " 2119 0.005380\n", + " 2330 0.003648\n", + " 1856 0.003080\n", + " 4535 0.002986\n", + " 1607 0.002914\n", + " 214 0.002846\n", + " Name: 262, dtype: float64, 1806 0.054858\n", + " 2 0.051376\n", + " 6721 0.018354\n", + " 6793 0.017476\n", + " 3 0.011332\n", + " 6676 0.010113\n", + " 10196 0.010033\n", + " 1929 0.009562\n", + " 1863 0.008820\n", + " 3042 0.008089\n", + " Name: 263, dtype: float64, 8508 0.023307\n", + " 1712 0.019481\n", + " 3334 0.015155\n", + " 6793 0.014923\n", + " 1711 0.012652\n", + " 1688 0.009276\n", + " 6721 0.008368\n", + " 2685 0.007490\n", + " 3099 0.007485\n", + " 1983 0.006250\n", + " Name: 264, dtype: float64, 1814 0.443009\n", + " 1813 0.442375\n", + " 10115 0.004808\n", + " 8274 0.004597\n", + " 691 0.003531\n", + " 8186 0.002763\n", + " 1395 0.002339\n", + " 1698 0.001690\n", + " 8190 0.001560\n", + " 146 0.001550\n", + " Name: 265, dtype: float64, 214 0.994738\n", + " 1295 0.001162\n", + " 211 0.000343\n", + " 3210 0.000336\n", + " 107 0.000312\n", + " 1711 0.000259\n", + " 2503 0.000198\n", + " 205 0.000135\n", + " 208 0.000071\n", + " 519 0.000071\n", + " Name: 266, dtype: float64, 211 0.998753\n", + " 1711 0.000222\n", + " 214 0.000175\n", + " 207 0.000110\n", + " 519 0.000096\n", + " 208 0.000062\n", + " 1295 0.000028\n", + " 492 0.000025\n", + " 257 0.000013\n", + " 246 0.000013\n", + " Name: 267, dtype: float64, 2342 0.709877\n", + " 1820 0.051485\n", + " 387 0.010057\n", + " 197 0.007945\n", + " 3435 0.006496\n", + " 8697 0.005863\n", + " 1683 0.004732\n", + " 2333 0.004562\n", + " 1486 0.003775\n", + " 1035 0.003583\n", + " Name: 268, dtype: float64, 1772 0.067391\n", + " 1627 0.047383\n", + " 1550 0.034263\n", + " 1355 0.033618\n", + " 1661 0.026658\n", + " 1670 0.026244\n", + " 1694 0.022211\n", + " 1821 0.017948\n", + " 1161 0.013344\n", + " 1864 0.012270\n", + " Name: 269, dtype: float64, 1821 0.062400\n", + " 379 0.052580\n", + " 1212 0.049114\n", + " 1627 0.041594\n", + " 4026 0.018853\n", + " 614 0.014272\n", + " 2060 0.013349\n", + " 1670 0.010855\n", + " 1773 0.010649\n", + " 380 0.010135\n", + " Name: 270, dtype: float64, 7391 0.120485\n", + " 108 0.070463\n", + " 87 0.047619\n", + " 8335 0.016212\n", + " 1407 0.015002\n", + " 8375 0.013540\n", + " 2660 0.013489\n", + " 705 0.011465\n", + " 2194 0.010442\n", + " 8573 0.010227\n", + " Name: 271, dtype: float64, 1884 0.409555\n", + " 1858 0.068222\n", + " 1857 0.067747\n", + " 1882 0.057949\n", + " 1824 0.020064\n", + " 5026 0.014776\n", + " 1852 0.013781\n", + " 1879 0.012710\n", + " 1873 0.009057\n", + " 1860 0.008083\n", + " Name: 272, dtype: float64, 1864 0.901734\n", + " 1875 0.013342\n", + " 1872 0.003303\n", + " 2131 0.002037\n", + " 1867 0.001892\n", + " 2141 0.001883\n", + " 2004 0.001170\n", + " 8154 0.001134\n", + " 1161 0.000743\n", + " 1821 0.000690\n", + " Name: 273, dtype: float64, 1871 0.961342\n", + " 1875 0.004328\n", + " 1862 0.001626\n", + " 1870 0.000873\n", + " 1863 0.000872\n", + " 355 0.000564\n", + " 1861 0.000500\n", + " 1733 0.000377\n", + " 1864 0.000365\n", + " 79 0.000363\n", + " Name: 274, dtype: float64, 1897 0.987455\n", + " 1895 0.001970\n", + " 1873 0.000695\n", + " 207 0.000649\n", + " 1199 0.000642\n", + " 1622 0.000422\n", + " 1677 0.000422\n", + " 2004 0.000135\n", + " 1113 0.000100\n", + " 1639 0.000098\n", + " Name: 275, dtype: float64, 1897 0.836220\n", + " 1895 0.024671\n", + " 1873 0.010445\n", + " 2004 0.006279\n", + " 1622 0.005206\n", + " 1639 0.004874\n", + " 1199 0.004311\n", + " 1525 0.002792\n", + " 1903 0.002433\n", + " 1752 0.002134\n", + " Name: 276, dtype: float64, 1894 0.217690\n", + " 1745 0.033228\n", + " 335 0.013109\n", + " 1791 0.012670\n", + " 1107 0.011384\n", + " 6721 0.009045\n", + " 10237 0.008986\n", + " 1682 0.007026\n", + " 2135 0.005806\n", + " 2139 0.005197\n", + " Name: 277, dtype: float64, 275 0.080446\n", + " 218 0.029511\n", + " 2055 0.026457\n", + " 1901 0.025905\n", + " 2424 0.022379\n", + " 2678 0.021284\n", + " 358 0.020680\n", + " 2410 0.016203\n", + " 2220 0.015187\n", + " 1957 0.012637\n", + " Name: 278, dtype: float64, 387 0.991663\n", + " 7894 0.000943\n", + " 8274 0.000615\n", + " 637 0.000473\n", + " 621 0.000225\n", + " 2250 0.000112\n", + " 675 0.000109\n", + " 9132 0.000104\n", + " 386 0.000098\n", + " 84 0.000097\n", + " Name: 279, dtype: float64, 79 9.999931e-01\n", + " 208 1.938405e-06\n", + " 1805 2.592717e-07\n", + " 7106 2.574209e-07\n", + " 119 2.040482e-07\n", + " 517 1.377028e-07\n", + " 4000 5.798046e-08\n", + " 2503 5.101488e-08\n", + " 4098 4.441338e-08\n", + " 2546 4.136042e-08\n", + " Name: 280, dtype: float64, 108 0.999652\n", + " 246 0.000037\n", + " 213 0.000033\n", + " 677 0.000026\n", + " 2236 0.000023\n", + " 697 0.000013\n", + " 691 0.000013\n", + " 2504 0.000011\n", + " 635 0.000009\n", + " 2108 0.000009\n", + " Name: 281, dtype: float64, 220 0.044627\n", + " 932 0.038646\n", + " 221 0.033110\n", + " 1936 0.030796\n", + " 944 0.018313\n", + " 797 0.015850\n", + " 91 0.015071\n", + " 151 0.014236\n", + " 76 0.014022\n", + " 920 0.011776\n", + " Name: 282, dtype: float64, 1650 0.118885\n", + " 1629 0.037207\n", + " 252 0.036656\n", + " 1778 0.027505\n", + " 1642 0.021485\n", + " 593 0.020504\n", + " 251 0.017154\n", + " 414 0.016945\n", + " 1677 0.015783\n", + " 1646 0.014843\n", + " Name: 283, dtype: float64, 1953 0.838142\n", + " 668 0.014641\n", + " 9136 0.011343\n", + " 3196 0.004884\n", + " 684 0.004660\n", + " 9869 0.004034\n", + " 2655 0.003534\n", + " 2931 0.003290\n", + " 8155 0.002844\n", + " 9954 0.002568\n", + " Name: 284, dtype: float64, 206 0.102712\n", + " 1781 0.063380\n", + " 797 0.057582\n", + " 1347 0.051951\n", + " 207 0.045490\n", + " 2310 0.037895\n", + " 2309 0.029115\n", + " 4338 0.026545\n", + " 1612 0.024769\n", + " 264 0.024655\n", + " Name: 285, dtype: float64, 641 0.999854\n", + " 2503 0.000039\n", + " 8381 0.000008\n", + " 3047 0.000006\n", + " 3210 0.000004\n", + " 640 0.000004\n", + " 205 0.000004\n", + " 2236 0.000003\n", + " 3235 0.000003\n", + " 3991 0.000003\n", + " Name: 286, dtype: float64, 957 0.465175\n", + " 2439 0.184018\n", + " 593 0.009901\n", + " 2319 0.008467\n", + " 2346 0.007596\n", + " 1186 0.007120\n", + " 2345 0.004939\n", + " 3523 0.004237\n", + " 2376 0.003342\n", + " 1982 0.003170\n", + " Name: 287, dtype: float64, 1718 0.045427\n", + " 2112 0.029631\n", + " 2991 0.014237\n", + " 4867 0.013146\n", + " 216 0.012023\n", + " 1716 0.010485\n", + " 1970 0.010234\n", + " 154 0.009974\n", + " 75 0.009897\n", + " 1139 0.009803\n", + " Name: 288, dtype: float64, 10631 0.043709\n", + " 7187 0.043692\n", + " 672 0.010501\n", + " 10434 0.009728\n", + " 2392 0.009639\n", + " 2427 0.008808\n", + " 630 0.008281\n", + " 5279 0.008214\n", + " 3435 0.008080\n", + " 4114 0.007944\n", + " Name: 289, dtype: float64, 1607 0.539018\n", + " 10229 0.015917\n", + " 1650 0.013400\n", + " 1921 0.010260\n", + " 1606 0.007059\n", + " 277 0.006398\n", + " 9464 0.005777\n", + " 4017 0.004335\n", + " 1807 0.004098\n", + " 933 0.004016\n", + " Name: 290, dtype: float64, 1975 0.775118\n", + " 1977 0.011819\n", + " 2335 0.011248\n", + " 362 0.007772\n", + " 37 0.006444\n", + " 1895 0.004685\n", + " 1197 0.003371\n", + " 1111 0.003215\n", + " 1903 0.003079\n", + " 1372 0.002865\n", + " Name: 291, dtype: float64, 6682 0.036834\n", + " 8371 0.034211\n", + " 6798 0.029567\n", + " 5708 0.024485\n", + " 6738 0.024263\n", + " 1203 0.022819\n", + " 6560 0.016245\n", + " 694 0.014993\n", + " 8954 0.013524\n", + " 3086 0.009305\n", + " Name: 292, dtype: float64, 79 0.999853\n", + " 208 0.000019\n", + " 4098 0.000011\n", + " 2546 0.000011\n", + " 517 0.000007\n", + " 7141 0.000002\n", + " 119 0.000002\n", + " 2534 0.000001\n", + " 1805 0.000001\n", + " 1871 0.000001\n", + " Name: 293, dtype: float64, 2060 0.155839\n", + " 2630 0.075774\n", + " 707 0.017881\n", + " 2025 0.012343\n", + " 4026 0.011630\n", + " 2490 0.010051\n", + " 1103 0.008933\n", + " 380 0.008203\n", + " 668 0.007998\n", + " 2512 0.007459\n", + " Name: 294, dtype: float64, 206 0.961985\n", + " 204 0.009603\n", + " 207 0.004451\n", + " 1060 0.001683\n", + " 517 0.001231\n", + " 205 0.001208\n", + " 246 0.001134\n", + " 264 0.000703\n", + " 4338 0.000358\n", + " 269 0.000323\n", + " Name: 295, dtype: float64, 1576 0.614686\n", + " 2046 0.081968\n", + " 1318 0.047681\n", + " 356 0.017443\n", + " 207 0.007446\n", + " 1255 0.006592\n", + " 1135 0.005300\n", + " 55 0.004046\n", + " 1263 0.003791\n", + " 2809 0.003745\n", + " Name: 296, dtype: float64, 987 0.311626\n", + " 1689 0.087117\n", + " 1187 0.059453\n", + " 1022 0.044499\n", + " 971 0.026546\n", + " 1636 0.018024\n", + " 2015 0.016904\n", + " 1674 0.016561\n", + " 1141 0.016054\n", + " 1531 0.014336\n", + " Name: 297, dtype: float64, 8841 0.087286\n", + " 1515 0.043796\n", + " 8638 0.029962\n", + " 1514 0.022756\n", + " 9098 0.021045\n", + " 9094 0.020420\n", + " 9254 0.018927\n", + " 8381 0.018081\n", + " 2196 0.013295\n", + " 9989 0.010022\n", + " Name: 298, dtype: float64, 206 0.992161\n", + " 203 0.003063\n", + " 205 0.002142\n", + " 204 0.001255\n", + " 207 0.000089\n", + " 180 0.000060\n", + " 2896 0.000045\n", + " 223 0.000043\n", + " 264 0.000036\n", + " 213 0.000034\n", + " Name: 299, dtype: float64, 2088 0.240984\n", + " 1839 0.026171\n", + " 1870 0.023990\n", + " 533 0.021905\n", + " 1655 0.020563\n", + " 1656 0.018110\n", + " 535 0.013458\n", + " 377 0.012690\n", + " 1862 0.011658\n", + " 1652 0.011145\n", + " Name: 300, dtype: float64, 1647 0.071415\n", + " 1635 0.066641\n", + " 1646 0.050308\n", + " 2146 0.049371\n", + " 1645 0.045633\n", + " 1730 0.030210\n", + " 2102 0.025241\n", + " 1658 0.012781\n", + " 2104 0.010757\n", + " 1848 0.009954\n", + " Name: 301, dtype: float64, 2108 0.898945\n", + " 2351 0.028204\n", + " 2236 0.006760\n", + " 207 0.004308\n", + " 870 0.002977\n", + " 2684 0.002336\n", + " 2296 0.002131\n", + " 246 0.001781\n", + " 2392 0.001456\n", + " 264 0.001410\n", + " Name: 302, dtype: float64, 2578 0.027694\n", + " 3396 0.026334\n", + " 1713 0.017610\n", + " 3260 0.016769\n", + " 3754 0.014286\n", + " 4140 0.013753\n", + " 277 0.010832\n", + " 2124 0.010399\n", + " 3010 0.009711\n", + " 4093 0.008470\n", + " Name: 303, dtype: float64, 2112 0.910662\n", + " 3248 0.002195\n", + " 2114 0.001819\n", + " 2127 0.001813\n", + " 2124 0.001382\n", + " 2137 0.001322\n", + " 2944 0.001307\n", + " 1405 0.001080\n", + " 518 0.000983\n", + " 1716 0.000685\n", + " Name: 304, dtype: float64, 2119 0.608734\n", + " 2114 0.080349\n", + " 205 0.022920\n", + " 206 0.013471\n", + " 2113 0.012010\n", + " 497 0.005365\n", + " 226 0.005106\n", + " 520 0.005094\n", + " 10149 0.004236\n", + " 1606 0.003432\n", + " Name: 305, dtype: float64, 2114 0.628372\n", + " 2113 0.035804\n", + " 2125 0.024677\n", + " 2127 0.024630\n", + " 2112 0.020870\n", + " 2121 0.017975\n", + " 2137 0.010932\n", + " 136 0.010637\n", + " 2124 0.005513\n", + " 2128 0.004491\n", + " Name: 306, dtype: float64, 2119 0.997275\n", + " 205 0.000217\n", + " 1046 0.000100\n", + " 2132 0.000048\n", + " 156 0.000043\n", + " 640 0.000039\n", + " 2113 0.000034\n", + " 1047 0.000031\n", + " 1606 0.000029\n", + " 504 0.000029\n", + " Name: 307, dtype: float64, 641 0.981540\n", + " 1514 0.001968\n", + " 1515 0.001363\n", + " 8381 0.000953\n", + " 3047 0.000779\n", + " 2117 0.000626\n", + " 8566 0.000548\n", + " 2115 0.000446\n", + " 2514 0.000347\n", + " 2118 0.000330\n", + " Name: 308, dtype: float64, 79 9.999912e-01\n", + " 208 1.361190e-06\n", + " 7106 2.131418e-07\n", + " 517 2.104794e-07\n", + " 119 2.077842e-07\n", + " 1805 1.233183e-07\n", + " 2166 1.108081e-07\n", + " 8418 8.934489e-08\n", + " 9991 8.708965e-08\n", + " 2546 8.495239e-08\n", + " Name: 309, dtype: float64, 108 0.420277\n", + " 246 0.087442\n", + " 205 0.053398\n", + " 206 0.041174\n", + " 204 0.030087\n", + " 1082 0.016200\n", + " 203 0.015047\n", + " 213 0.015024\n", + " 1515 0.014463\n", + " 691 0.012631\n", + " Name: 310, dtype: float64, 86 0.994909\n", + " 9039 0.000907\n", + " 640 0.000527\n", + " 9288 0.000372\n", + " 1471 0.000269\n", + " 2629 0.000191\n", + " 8453 0.000157\n", + " 2337 0.000081\n", + " 4693 0.000074\n", + " 8155 0.000072\n", + " Name: 311, dtype: float64, 79 9.999974e-01\n", + " 208 6.816567e-07\n", + " 517 6.093028e-08\n", + " 7106 6.053642e-08\n", + " 1805 5.661675e-08\n", + " 119 4.069366e-08\n", + " 4098 3.768195e-08\n", + " 4000 2.528834e-08\n", + " 2503 2.264351e-08\n", + " 2546 2.179309e-08\n", + " Name: 312, dtype: float64, 86 0.999563\n", + " 2629 0.000066\n", + " 1471 0.000065\n", + " 4155 0.000028\n", + " 795 0.000016\n", + " 204 0.000010\n", + " 156 0.000010\n", + " 640 0.000008\n", + " 690 0.000007\n", + " 8155 0.000006\n", + " Name: 313, dtype: float64, 2146 0.137515\n", + " 2141 0.118175\n", + " 2140 0.096601\n", + " 2150 0.044063\n", + " 2139 0.032472\n", + " 1876 0.024882\n", + " 2132 0.023787\n", + " 2148 0.015633\n", + " 1875 0.012689\n", + " 1661 0.009279\n", + " Name: 314, dtype: float64, 79 9.999929e-01\n", + " 208 1.666523e-06\n", + " 4098 2.205597e-07\n", + " 119 2.182528e-07\n", + " 517 2.087674e-07\n", + " 1805 1.332726e-07\n", + " 7106 1.081941e-07\n", + " 2546 8.545111e-08\n", + " 8418 7.604674e-08\n", + " 3098 6.114117e-08\n", + " Name: 315, dtype: float64, 211 0.998897\n", + " 1711 0.000199\n", + " 214 0.000123\n", + " 207 0.000118\n", + " 519 0.000047\n", + " 208 0.000042\n", + " 1295 0.000020\n", + " 492 0.000018\n", + " 1635 0.000014\n", + " 2411 0.000013\n", + " Name: 316, dtype: float64, 35 0.511655\n", + " 33 0.157979\n", + " 34 0.141780\n", + " 51 0.009324\n", + " 53 0.008403\n", + " 31 0.007067\n", + " 2391 0.004540\n", + " 73 0.003995\n", + " 2368 0.003738\n", + " 76 0.003496\n", + " Name: 317, dtype: float64, 29 0.094081\n", + " 25 0.075818\n", + " 44 0.056694\n", + " 30 0.055383\n", + " 27 0.043307\n", + " 52 0.038881\n", + " 51 0.028669\n", + " 38 0.026397\n", + " 34 0.025509\n", + " 35 0.024411\n", + " Name: 318, dtype: float64, 41 0.669901\n", + " 52 0.074578\n", + " 42 0.049874\n", + " 51 0.007930\n", + " 2406 0.005121\n", + " 2313 0.004616\n", + " 264 0.004513\n", + " 2387 0.004336\n", + " 2344 0.004206\n", + " 2391 0.004057\n", + " Name: 319, dtype: float64, 40 0.086148\n", + " 25 0.057782\n", + " 43 0.044790\n", + " 35 0.043612\n", + " 33 0.040645\n", + " 47 0.038988\n", + " 34 0.035543\n", + " 14 0.025236\n", + " 29 0.022969\n", + " 70 0.022943\n", + " Name: 320, dtype: float64, 4 0.059035\n", + " 55 0.054194\n", + " 10 0.047742\n", + " 53 0.041903\n", + " 129 0.023519\n", + " 62 0.022970\n", + " 2146 0.019396\n", + " 23 0.018003\n", + " 141 0.012389\n", + " 140 0.012096\n", + " Name: 321, dtype: float64, 10497 0.045303\n", + " 8049 0.024761\n", + " 668 0.018342\n", + " 9534 0.014372\n", + " 8226 0.013476\n", + " 5113 0.013062\n", + " 10546 0.012297\n", + " 6865 0.011423\n", + " 9442 0.011296\n", + " 3043 0.010943\n", + " Name: 322, dtype: float64, 85 0.999393\n", + " 2715 0.000141\n", + " 86 0.000033\n", + " 8319 0.000032\n", + " 2130 0.000021\n", + " 3216 0.000020\n", + " 672 0.000016\n", + " 8155 0.000015\n", + " 6066 0.000008\n", + " 198 0.000008\n", + " Name: 323, dtype: float64, 98 0.907573\n", + " 102 0.006566\n", + " 95 0.006356\n", + " 103 0.001947\n", + " 105 0.001895\n", + " 101 0.001456\n", + " 94 0.001047\n", + " 1135 0.000760\n", + " 2111 0.000691\n", + " 2113 0.000688\n", + " Name: 324, dtype: float64, 2197 0.566161\n", + " 108 0.423586\n", + " 2236 0.002244\n", + " 246 0.000857\n", + " 2260 0.000333\n", + " 213 0.000236\n", + " 206 0.000198\n", + " 2344 0.000190\n", + " 2277 0.000169\n", + " 2504 0.000140\n", + " Name: 325, dtype: float64, 119 0.996858\n", + " 9036 0.000348\n", + " 2534 0.000211\n", + " 9626 0.000173\n", + " 79 0.000157\n", + " 240 0.000112\n", + " 125 0.000078\n", + " 2260 0.000060\n", + " 3543 0.000054\n", + " 702 0.000049\n", + " Name: 326, dtype: float64, 128 0.548402\n", + " 62 0.030101\n", + " 59 0.012930\n", + " 29 0.011711\n", + " 45 0.008479\n", + " 184 0.007525\n", + " 23 0.006113\n", + " 40 0.005883\n", + " 141 0.005542\n", + " 60 0.005447\n", + " Name: 327, dtype: float64, 124 0.999446\n", + " 2313 0.000033\n", + " 55 0.000023\n", + " 2260 0.000020\n", + " 870 0.000017\n", + " 2155 0.000016\n", + " 2173 0.000014\n", + " 1471 0.000014\n", + " 180 0.000013\n", + " 129 0.000010\n", + " Name: 328, dtype: float64, 129 0.999712\n", + " 2197 0.000045\n", + " 128 0.000012\n", + " 2260 0.000007\n", + " 156 0.000006\n", + " 213 0.000005\n", + " 108 0.000005\n", + " 3404 0.000004\n", + " 3098 0.000003\n", + " 206 0.000003\n", + " Name: 329, dtype: float64, 50 0.018883\n", + " 3375 0.012351\n", + " 15 0.012310\n", + " 7664 0.010774\n", + " 51 0.009938\n", + " 10 0.009080\n", + " 2607 0.008906\n", + " 60 0.008384\n", + " 53 0.007924\n", + " 18 0.006769\n", + " Name: 330, dtype: float64, 79 9.999965e-01\n", + " 208 1.121107e-06\n", + " 1805 1.387413e-07\n", + " 517 8.294598e-08\n", + " 7106 5.981536e-08\n", + " 119 4.945398e-08\n", + " 4098 4.792110e-08\n", + " 4000 3.187342e-08\n", + " 2503 3.024463e-08\n", + " 2546 2.802099e-08\n", + " Name: 331, dtype: float64, 79 9.999965e-01\n", + " 208 1.121107e-06\n", + " 1805 1.387413e-07\n", + " 517 8.294598e-08\n", + " 7106 5.981536e-08\n", + " 119 4.945398e-08\n", + " 4098 4.792110e-08\n", + " 4000 3.187342e-08\n", + " 2503 3.024463e-08\n", + " 2546 2.802099e-08\n", + " Name: 332, dtype: float64, 129 0.998719\n", + " 2197 0.000152\n", + " 86 0.000102\n", + " 42 0.000059\n", + " 211 0.000052\n", + " 3402 0.000050\n", + " 4254 0.000024\n", + " 3404 0.000023\n", + " 206 0.000019\n", + " 156 0.000017\n", + " Name: 333, dtype: float64, 156 0.999223\n", + " 128 0.000305\n", + " 129 0.000075\n", + " 112 0.000009\n", + " 86 0.000009\n", + " 2196 0.000008\n", + " 184 0.000007\n", + " 2278 0.000006\n", + " 190 0.000006\n", + " 4155 0.000004\n", + " Name: 334, dtype: float64, 79 9.999956e-01\n", + " 208 9.947145e-07\n", + " 1805 1.347519e-07\n", + " 4098 1.256144e-07\n", + " 517 9.558294e-08\n", + " 7106 8.108135e-08\n", + " 119 7.397116e-08\n", + " 2546 5.984816e-08\n", + " 7141 4.387263e-08\n", + " 2503 3.778192e-08\n", + " Name: 335, dtype: float64, 108 0.134852\n", + " 640 0.062286\n", + " 205 0.042285\n", + " 129 0.034475\n", + " 1515 0.032808\n", + " 2236 0.025296\n", + " 3048 0.024580\n", + " 3049 0.024554\n", + " 206 0.023278\n", + " 203 0.016638\n", + " Name: 336, dtype: float64, 129 0.964849\n", + " 156 0.024233\n", + " 128 0.002330\n", + " 2197 0.000815\n", + " 86 0.000805\n", + " 3404 0.000391\n", + " 3402 0.000385\n", + " 1471 0.000258\n", + " 204 0.000197\n", + " 2337 0.000187\n", + " Name: 337, dtype: float64, 160 0.417558\n", + " 165 0.147952\n", + " 167 0.053069\n", + " 3179 0.009438\n", + " 9856 0.007239\n", + " 3992 0.007038\n", + " 2115 0.006297\n", + " 136 0.004912\n", + " 2509 0.004907\n", + " 9958 0.004883\n", + " Name: 338, dtype: float64, 79 9.999899e-01\n", + " 208 1.949036e-06\n", + " 119 5.423437e-07\n", + " 1805 4.074238e-07\n", + " 517 3.922474e-07\n", + " 4098 3.145317e-07\n", + " 7106 1.100841e-07\n", + " 2546 1.088724e-07\n", + " 7141 1.046608e-07\n", + " 8418 8.784779e-08\n", + " Name: 339, dtype: float64, 79 9.999906e-01\n", + " 208 1.976406e-06\n", + " 119 4.519637e-07\n", + " 4098 4.065577e-07\n", + " 517 3.040465e-07\n", + " 1805 2.400528e-07\n", + " 7141 1.299035e-07\n", + " 8418 1.291644e-07\n", + " 7106 1.262596e-07\n", + " 2546 1.078667e-07\n", + " Name: 340, dtype: float64, 79 9.999937e-01\n", + " 208 1.510998e-06\n", + " 1805 2.329450e-07\n", + " 4098 1.930856e-07\n", + " 517 1.615321e-07\n", + " 119 1.525200e-07\n", + " 7106 8.951552e-08\n", + " 8418 7.178137e-08\n", + " 7141 5.887885e-08\n", + " 2546 5.801800e-08\n", + " Name: 341, dtype: float64, 2749 0.026078\n", + " 2166 0.024599\n", + " 2167 0.019085\n", + " 2610 0.012930\n", + " 2683 0.012306\n", + " 2977 0.009161\n", + " 8596 0.009003\n", + " 2466 0.008078\n", + " 2164 0.008028\n", + " 822 0.007973\n", + " Name: 342, dtype: float64, 156 0.125358\n", + " 1471 0.124838\n", + " 870 0.067229\n", + " 2337 0.038005\n", + " 86 0.036195\n", + " 2277 0.024860\n", + " 3166 0.024732\n", + " 2281 0.021985\n", + " 654 0.020616\n", + " 2260 0.013095\n", + " Name: 343, dtype: float64, 79 9.999956e-01\n", + " 208 9.511695e-07\n", + " 1805 1.816614e-07\n", + " 7106 1.130414e-07\n", + " 119 1.118909e-07\n", + " 517 1.000454e-07\n", + " 4098 9.467372e-08\n", + " 2546 6.566569e-08\n", + " 4000 4.042059e-08\n", + " 2503 4.020408e-08\n", + " Name: 344, dtype: float64, 176 0.946065\n", + " 1203 0.001877\n", + " 9599 0.001566\n", + " 1773 0.000939\n", + " 251 0.000740\n", + " 146 0.000675\n", + " 1576 0.000565\n", + " 2380 0.000557\n", + " 36 0.000547\n", + " 9881 0.000508\n", + " Name: 345, dtype: float64, 129 9.999706e-01\n", + " 2197 2.841393e-06\n", + " 213 2.111489e-06\n", + " 3404 8.299926e-07\n", + " 203 7.720476e-07\n", + " 206 7.444008e-07\n", + " 3402 6.644590e-07\n", + " 4202 6.197415e-07\n", + " 86 5.359908e-07\n", + " 52 4.598866e-07\n", + " Name: 346, dtype: float64, 129 9.999671e-01\n", + " 2197 4.052586e-06\n", + " 213 1.214345e-06\n", + " 4202 1.066149e-06\n", + " 206 7.750326e-07\n", + " 203 6.606057e-07\n", + " 3404 6.026021e-07\n", + " 3402 4.842543e-07\n", + " 211 4.511208e-07\n", + " 154 3.967860e-07\n", + " Name: 347, dtype: float64, 94 0.056030\n", + " 3369 0.032356\n", + " 95 0.029160\n", + " 191 0.024369\n", + " 186 0.023843\n", + " 8431 0.018684\n", + " 2872 0.015183\n", + " 2110 0.013897\n", + " 168 0.010510\n", + " 1691 0.010335\n", + " Name: 348, dtype: float64, 191 0.547771\n", + " 186 0.196014\n", + " 184 0.024706\n", + " 190 0.022287\n", + " 94 0.016198\n", + " 62 0.005691\n", + " 47 0.004484\n", + " 1748 0.004155\n", + " 102 0.002397\n", + " 129 0.001886\n", + " Name: 349, dtype: float64, 196 0.191920\n", + " 3265 0.032617\n", + " 193 0.030637\n", + " 2120 0.012138\n", + " 871 0.011167\n", + " 1888 0.011119\n", + " 94 0.008943\n", + " 3094 0.008482\n", + " 3188 0.008159\n", + " 153 0.006741\n", + " Name: 350, dtype: float64, 197 0.856432\n", + " 620 0.019345\n", + " 8278 0.002528\n", + " 198 0.002078\n", + " 3263 0.001856\n", + " 8619 0.001566\n", + " 759 0.001482\n", + " 2342 0.001410\n", + " 1468 0.001244\n", + " 8297 0.001165\n", + " Name: 351, dtype: float64, 7903 0.120989\n", + " 2802 0.062934\n", + " 5718 0.032155\n", + " 2298 0.021681\n", + " 10478 0.017260\n", + " 10006 0.014391\n", + " 10048 0.014071\n", + " 9949 0.013136\n", + " 5719 0.012019\n", + " 9937 0.009449\n", + " Name: 352, dtype: float64, 2746 0.395424\n", + " 2822 0.313534\n", + " 2750 0.081729\n", + " 2766 0.022796\n", + " 2565 0.022117\n", + " 3249 0.017780\n", + " 2554 0.016488\n", + " 542 0.006228\n", + " 2806 0.005388\n", + " 984 0.003000\n", + " Name: 353, dtype: float64, 2498 0.997492\n", + " 2497 0.000145\n", + " 5965 0.000071\n", + " 4683 0.000051\n", + " 2499 0.000042\n", + " 4708 0.000038\n", + " 4088 0.000032\n", + " 86 0.000029\n", + " 4709 0.000028\n", + " 7610 0.000027\n", + " Name: 354, dtype: float64, 819 0.060464\n", + " 233 0.022113\n", + " 87 0.021653\n", + " 2759 0.017906\n", + " 128 0.015637\n", + " 2312 0.010515\n", + " 147 0.010034\n", + " 243 0.009834\n", + " 829 0.009766\n", + " 2803 0.008820\n", + " Name: 355, dtype: float64, 2909 0.232448\n", + " 6766 0.015248\n", + " 2792 0.011128\n", + " 2642 0.010443\n", + " 6768 0.009513\n", + " 6654 0.008006\n", + " 5222 0.007359\n", + " 5549 0.007158\n", + " 6628 0.007063\n", + " 2640 0.006781\n", + " Name: 356, dtype: float64, 2759 0.253117\n", + " 3211 0.055339\n", + " 3305 0.039260\n", + " 2703 0.038150\n", + " 203 0.020259\n", + " 2236 0.015866\n", + " 206 0.012871\n", + " 2341 0.006936\n", + " 5077 0.006696\n", + " 3754 0.006094\n", + " Name: 357, dtype: float64, 883 0.227149\n", + " 159 0.045673\n", + " 677 0.043397\n", + " 2715 0.017710\n", + " 9047 0.013867\n", + " 6074 0.008593\n", + " 8977 0.008130\n", + " 2429 0.008128\n", + " 10002 0.007399\n", + " 6022 0.007199\n", + " Name: 358, dtype: float64, 2548 0.961654\n", + " 8276 0.001142\n", + " 3700 0.000741\n", + " 2531 0.000662\n", + " 8414 0.000655\n", + " 892 0.000543\n", + " 851 0.000518\n", + " 120 0.000463\n", + " 811 0.000426\n", + " 2546 0.000421\n", + " Name: 359, dtype: float64, 2546 0.893694\n", + " 2545 0.014192\n", + " 79 0.013659\n", + " 2549 0.011314\n", + " 4098 0.002738\n", + " 2969 0.001450\n", + " 1910 0.001171\n", + " 2552 0.000752\n", + " 112 0.000685\n", + " 180 0.000616\n", + " Name: 360, dtype: float64, 387 0.996466\n", + " 637 0.000339\n", + " 7894 0.000270\n", + " 3535 0.000117\n", + " 386 0.000112\n", + " 2213 0.000107\n", + " 8274 0.000107\n", + " 8847 0.000095\n", + " 9132 0.000050\n", + " 9114 0.000035\n", + " Name: 361, dtype: float64, 159 0.071935\n", + " 9102 0.069921\n", + " 2601 0.062279\n", + " 2600 0.030790\n", + " 8130 0.030265\n", + " 8373 0.021923\n", + " 9138 0.021771\n", + " 136 0.013904\n", + " 8699 0.011541\n", + " 9192 0.009928\n", + " Name: 362, dtype: float64, 2604 0.853121\n", + " 2619 0.004358\n", + " 129 0.001734\n", + " 82 0.001730\n", + " 3197 0.001688\n", + " 2896 0.001444\n", + " 2616 0.001362\n", + " 5646 0.001329\n", + " 3269 0.001245\n", + " 4864 0.001217\n", + " Name: 363, dtype: float64, 2613 0.833851\n", + " 669 0.051884\n", + " 2337 0.016660\n", + " 10136 0.002618\n", + " 729 0.001852\n", + " 2379 0.001494\n", + " 7458 0.001406\n", + " 9760 0.001260\n", + " 771 0.001221\n", + " 2451 0.001114\n", + " Name: 364, dtype: float64, 2613 0.833851\n", + " 669 0.051884\n", + " 2337 0.016660\n", + " 10136 0.002618\n", + " 729 0.001852\n", + " 2379 0.001494\n", + " 7458 0.001406\n", + " 9760 0.001260\n", + " 771 0.001221\n", + " 2451 0.001114\n", + " Name: 365, dtype: float64, 5814 0.018146\n", + " 7470 0.016074\n", + " 10206 0.012653\n", + " 2624 0.011656\n", + " 7891 0.011444\n", + " 7721 0.011279\n", + " 2610 0.010106\n", + " 1443 0.009729\n", + " 7615 0.008719\n", + " 5813 0.008350\n", + " Name: 366, dtype: float64, 2647 0.964701\n", + " 2649 0.004250\n", + " 2653 0.002828\n", + " 2640 0.002035\n", + " 2654 0.000887\n", + " 2638 0.000713\n", + " 2127 0.000706\n", + " 4706 0.000625\n", + " 2637 0.000552\n", + " 9395 0.000548\n", + " Name: 367, dtype: float64, 2655 0.847982\n", + " 2862 0.011149\n", + " 2512 0.005375\n", + " 9447 0.004169\n", + " 1953 0.003141\n", + " 79 0.003086\n", + " 2582 0.002926\n", + " 713 0.002517\n", + " 8172 0.002499\n", + " 4000 0.002189\n", + " Name: 368, dtype: float64, 2679 0.895993\n", + " 4094 0.008410\n", + " 1940 0.003983\n", + " 7444 0.002132\n", + " 4165 0.001584\n", + " 8099 0.001372\n", + " 3081 0.001352\n", + " 9473 0.001205\n", + " 9827 0.001007\n", + " 3289 0.000954\n", + " Name: 369, dtype: float64, 2685 0.801629\n", + " 8810 0.084942\n", + " 6602 0.005326\n", + " 2930 0.003439\n", + " 6064 0.003248\n", + " 7229 0.002090\n", + " 6832 0.001667\n", + " 6989 0.001443\n", + " 10008 0.001428\n", + " 6543 0.001405\n", + " Name: 370, dtype: float64, 1471 0.787723\n", + " 4172 0.038657\n", + " 9379 0.017680\n", + " 7470 0.012296\n", + " 8695 0.005175\n", + " 159 0.004728\n", + " 8335 0.004126\n", + " 787 0.003937\n", + " 870 0.003445\n", + " 6900 0.003005\n", + " Name: 371, dtype: float64, 2475 0.020628\n", + " 3357 0.013929\n", + " 2821 0.011912\n", + " 183 0.011261\n", + " 3546 0.009964\n", + " 2954 0.008985\n", + " 2620 0.007894\n", + " 10528 0.007069\n", + " 3672 0.005874\n", + " 3425 0.005771\n", + " Name: 372, dtype: float64, 6930 0.043687\n", + " 5860 0.034774\n", + " 6893 0.032357\n", + " 3621 0.020797\n", + " 203 0.018184\n", + " 6117 0.017473\n", + " 6116 0.014575\n", + " 4783 0.014142\n", + " 6902 0.013375\n", + " 6066 0.011810\n", + " Name: 373, dtype: float64, 2503 0.363357\n", + " 677 0.299900\n", + " 2716 0.261199\n", + " 2715 0.038674\n", + " 679 0.011123\n", + " 2502 0.004036\n", + " 2504 0.002527\n", + " 2236 0.000692\n", + " 7106 0.000539\n", + " 8274 0.000521\n", + " Name: 374, dtype: float64, 2719 0.199241\n", + " 6555 0.082704\n", + " 6589 0.018561\n", + " 2720 0.015229\n", + " 7142 0.014840\n", + " 6455 0.014708\n", + " 6887 0.011457\n", + " 6449 0.008862\n", + " 6907 0.007802\n", + " 2718 0.007665\n", + " Name: 375, dtype: float64, 3249 0.440612\n", + " 2766 0.172661\n", + " 2768 0.040080\n", + " 7023 0.017905\n", + " 2746 0.013136\n", + " 2561 0.008903\n", + " 2750 0.007860\n", + " 2822 0.007732\n", + " 2762 0.007329\n", + " 1187 0.004281\n", + " Name: 376, dtype: float64, 5703 0.047301\n", + " 8325 0.042624\n", + " 6952 0.032766\n", + " 5656 0.007259\n", + " 3281 0.007231\n", + " 6301 0.007009\n", + " 7883 0.006720\n", + " 10227 0.006307\n", + " 2887 0.006172\n", + " 5640 0.005888\n", + " Name: 377, dtype: float64, 7814 0.105128\n", + " 10507 0.075226\n", + " 7802 0.034904\n", + " 7840 0.022502\n", + " 2784 0.019244\n", + " 10442 0.018879\n", + " 10530 0.014794\n", + " 7476 0.012481\n", + " 4432 0.011697\n", + " 3437 0.011210\n", + " Name: 378, dtype: float64, 2660 0.990917\n", + " 3098 0.001877\n", + " 1805 0.000490\n", + " 2512 0.000445\n", + " 2862 0.000377\n", + " 7016 0.000365\n", + " 5231 0.000201\n", + " 2608 0.000187\n", + " 2502 0.000181\n", + " 2629 0.000157\n", + " Name: 379, dtype: float64, 6122 0.128208\n", + " 2795 0.030996\n", + " 6004 0.025547\n", + " 2794 0.018491\n", + " 5446 0.017447\n", + " 2792 0.017081\n", + " 3009 0.016778\n", + " 2817 0.016296\n", + " 6638 0.013797\n", + " 6058 0.011713\n", + " Name: 380, dtype: float64, 3210 0.130186\n", + " 5809 0.081478\n", + " 6913 0.049874\n", + " 5769 0.016038\n", + " 6025 0.012080\n", + " 7518 0.011655\n", + " 6565 0.009134\n", + " 2523 0.008764\n", + " 7105 0.008080\n", + " 8696 0.007780\n", + " Name: 381, dtype: float64, 2766 0.350572\n", + " 3249 0.224684\n", + " 2750 0.083077\n", + " 2822 0.055632\n", + " 2746 0.040375\n", + " 2554 0.017310\n", + " 2768 0.016933\n", + " 2806 0.015261\n", + " 2751 0.008001\n", + " 2565 0.004959\n", + " Name: 382, dtype: float64, 79 9.999905e-01\n", + " 208 4.199806e-06\n", + " 1805 4.100787e-07\n", + " 517 2.433074e-07\n", + " 7106 2.063034e-07\n", + " 2503 1.401323e-07\n", + " 119 8.780462e-08\n", + " 2546 8.430408e-08\n", + " 4000 8.141210e-08\n", + " 4098 6.623038e-08\n", + " Name: 383, dtype: float64, 6469 0.033910\n", + " 4755 0.029289\n", + " 7982 0.016174\n", + " 6931 0.014281\n", + " 5432 0.013335\n", + " 2829 0.011540\n", + " 5635 0.011514\n", + " 4899 0.011438\n", + " 2831 0.011308\n", + " 7000 0.009871\n", + " Name: 384, dtype: float64, 3192 0.102849\n", + " 3346 0.066709\n", + " 2971 0.036575\n", + " 3194 0.021994\n", + " 8348 0.018866\n", + " 4975 0.018753\n", + " 2620 0.015940\n", + " 3169 0.015792\n", + " 2837 0.013346\n", + " 2728 0.009446\n", + " Name: 385, dtype: float64, 2844 0.042593\n", + " 4755 0.036772\n", + " 2829 0.011881\n", + " 4899 0.010793\n", + " 3604 0.010663\n", + " 2757 0.008701\n", + " 2683 0.007388\n", + " 2838 0.007324\n", + " 2837 0.007048\n", + " 2831 0.006883\n", + " Name: 386, dtype: float64, 2832 0.141483\n", + " 6532 0.039516\n", + " 8696 0.025749\n", + " 8039 0.018399\n", + " 5307 0.016544\n", + " 2630 0.016218\n", + " 8293 0.013249\n", + " 10142 0.011368\n", + " 5723 0.010755\n", + " 9969 0.010606\n", + " Name: 387, dtype: float64, 7442 0.033498\n", + " 9663 0.032210\n", + " 5274 0.023142\n", + " 7232 0.019183\n", + " 9896 0.018310\n", + " 7322 0.013579\n", + " 23 0.011991\n", + " 8141 0.011237\n", + " 2727 0.010915\n", + " 7669 0.009118\n", + " Name: 388, dtype: float64, 3192 0.164520\n", + " 3605 0.066398\n", + " 3245 0.047245\n", + " 901 0.044910\n", + " 3186 0.039336\n", + " 3337 0.038461\n", + " 3346 0.028445\n", + " 3349 0.026807\n", + " 3470 0.017108\n", + " 3344 0.014700\n", + " Name: 389, dtype: float64, 6064 0.548027\n", + " 8045 0.051211\n", + " 6120 0.032280\n", + " 7229 0.030671\n", + " 7401 0.028467\n", + " 5110 0.023554\n", + " 8810 0.019895\n", + " 7312 0.010957\n", + " 5764 0.008762\n", + " 2929 0.007244\n", + " Name: 390, dtype: float64, 79 9.999955e-01\n", + " 208 1.160017e-06\n", + " 1805 1.910435e-07\n", + " 7106 9.887301e-08\n", + " 119 8.893671e-08\n", + " 517 8.465158e-08\n", + " 4098 7.167027e-08\n", + " 2503 4.771711e-08\n", + " 2546 4.311943e-08\n", + " 4000 3.942946e-08\n", + " Name: 391, dtype: float64, 10134 0.937029\n", + " 7322 0.001547\n", + " 7235 0.001281\n", + " 7651 0.001133\n", + " 7623 0.000740\n", + " 10135 0.000713\n", + " 10246 0.000695\n", + " 10112 0.000662\n", + " 5274 0.000534\n", + " 10233 0.000497\n", + " Name: 392, dtype: float64, 669 0.098190\n", + " 8696 0.072743\n", + " 672 0.057592\n", + " 635 0.037382\n", + " 192 0.030294\n", + " 8463 0.023828\n", + " 729 0.016709\n", + " 725 0.016121\n", + " 2427 0.015530\n", + " 771 0.014954\n", + " Name: 393, dtype: float64, 2862 0.975381\n", + " 2655 0.002117\n", + " 2630 0.001807\n", + " 2512 0.001462\n", + " 2660 0.001314\n", + " 5231 0.001064\n", + " 6588 0.000964\n", + " 8172 0.000732\n", + " 9613 0.000406\n", + " 5318 0.000298\n", + " Name: 394, dtype: float64, 2880 0.349859\n", + " 2878 0.306506\n", + " 2882 0.117623\n", + " 2881 0.114443\n", + " 3213 0.001830\n", + " 3214 0.001787\n", + " 3393 0.001357\n", + " 3209 0.001079\n", + " 3314 0.001013\n", + " 3131 0.001000\n", + " Name: 395, dtype: float64, 2899 0.113180\n", + " 2935 0.051134\n", + " 5864 0.024092\n", + " 7371 0.018280\n", + " 4000 0.018095\n", + " 5463 0.015586\n", + " 5618 0.012450\n", + " 2896 0.012057\n", + " 5673 0.011711\n", + " 9417 0.010048\n", + " Name: 396, dtype: float64, 7708 0.027961\n", + " 2906 0.022185\n", + " 5535 0.017210\n", + " 5698 0.016981\n", + " 6306 0.014243\n", + " 6348 0.013580\n", + " 7663 0.012673\n", + " 2905 0.012283\n", + " 6110 0.011290\n", + " 7732 0.010798\n", + " Name: 397, dtype: float64, 6291 0.130042\n", + " 6736 0.066458\n", + " 6873 0.032488\n", + " 6611 0.019642\n", + " 5605 0.019133\n", + " 6290 0.018415\n", + " 6664 0.015912\n", + " 9265 0.014463\n", + " 7025 0.008514\n", + " 2474 0.008405\n", + " Name: 398, dtype: float64, 2693 0.083047\n", + " 2622 0.027995\n", + " 3100 0.026017\n", + " 2967 0.019616\n", + " 3021 0.018499\n", + " 3035 0.016501\n", + " 2865 0.015504\n", + " 9473 0.014996\n", + " 4813 0.011632\n", + " 10333 0.011546\n", + " Name: 399, dtype: float64, 2917 0.421925\n", + " 2921 0.268046\n", + " 2278 0.030128\n", + " 3227 0.016188\n", + " 2916 0.011209\n", + " 2950 0.008828\n", + " 7579 0.005945\n", + " 50 0.005403\n", + " 2814 0.003987\n", + " 2887 0.003942\n", + " Name: 400, dtype: float64, 79 0.976922\n", + " 2534 0.003268\n", + " 2166 0.001289\n", + " 119 0.000871\n", + " 8040 0.000707\n", + " 6814 0.000471\n", + " 708 0.000457\n", + " 9626 0.000426\n", + " 875 0.000386\n", + " 2546 0.000379\n", + " Name: 401, dtype: float64, 3200 0.116288\n", + " 5550 0.065154\n", + " 7475 0.047135\n", + " 9303 0.039204\n", + " 9364 0.029974\n", + " 7803 0.028748\n", + " 2441 0.027302\n", + " 9285 0.021983\n", + " 2935 0.021950\n", + " 9298 0.019940\n", + " Name: 402, dtype: float64, 882 0.025825\n", + " 1741 0.024830\n", + " 2312 0.019413\n", + " 2826 0.019278\n", + " 2809 0.013891\n", + " 2451 0.013450\n", + " 3318 0.013295\n", + " 8163 0.012766\n", + " 2175 0.012317\n", + " 6911 0.009159\n", + " Name: 403, dtype: float64, 2950 0.554904\n", + " 2916 0.035599\n", + " 2946 0.023758\n", + " 2921 0.014916\n", + " 92 0.009893\n", + " 3238 0.009415\n", + " 2917 0.005717\n", + " 6883 0.005306\n", + " 2576 0.004423\n", + " 1702 0.004134\n", + " Name: 404, dtype: float64, 208 0.112068\n", + " 213 0.018746\n", + " 5071 0.012958\n", + " 3101 0.010541\n", + " 3211 0.009600\n", + " 870 0.008796\n", + " 4515 0.007638\n", + " 7124 0.007365\n", + " 3551 0.006758\n", + " 2503 0.006683\n", + " Name: 405, dtype: float64, 2941 0.990501\n", + " 2955 0.005167\n", + " 3173 0.000519\n", + " 2503 0.000138\n", + " 679 0.000112\n", + " 5235 0.000092\n", + " 3460 0.000085\n", + " 2504 0.000077\n", + " 2940 0.000064\n", + " 2992 0.000060\n", + " Name: 406, dtype: float64, 2963 0.116218\n", + " 2961 0.112618\n", + " 6999 0.086564\n", + " 2962 0.039256\n", + " 8138 0.035337\n", + " 2959 0.033181\n", + " 2960 0.026478\n", + " 5900 0.016282\n", + " 2967 0.009877\n", + " 3078 0.008381\n", + " Name: 407, dtype: float64, 2977 0.070193\n", + " 4978 0.031890\n", + " 2466 0.013306\n", + " 2467 0.011448\n", + " 4974 0.010357\n", + " 2837 0.009959\n", + " 5149 0.007947\n", + " 4984 0.007944\n", + " 256 0.007521\n", + " 194 0.006890\n", + " Name: 408, dtype: float64, 2978 0.105087\n", + " 3173 0.038175\n", + " 3206 0.027233\n", + " 7686 0.020257\n", + " 3924 0.016578\n", + " 3211 0.014226\n", + " 2993 0.013704\n", + " 3314 0.012508\n", + " 3204 0.006806\n", + " 3245 0.005890\n", + " Name: 409, dtype: float64, 2992 0.104828\n", + " 2956 0.037763\n", + " 2955 0.035224\n", + " 2990 0.017023\n", + " 5808 0.016398\n", + " 5548 0.014862\n", + " 3238 0.013842\n", + " 1376 0.013536\n", + " 882 0.011928\n", + " 4253 0.010191\n", + " Name: 410, dtype: float64, 2956 0.248438\n", + " 2955 0.228967\n", + " 2992 0.090729\n", + " 5233 0.025939\n", + " 3671 0.015806\n", + " 3670 0.013295\n", + " 2941 0.008989\n", + " 4073 0.007279\n", + " 4253 0.007061\n", + " 2940 0.007042\n", + " Name: 411, dtype: float64, 3002 0.277977\n", + " 240 0.055347\n", + " 2797 0.030236\n", + " 2816 0.016985\n", + " 3998 0.015777\n", + " 2632 0.014262\n", + " 2236 0.012200\n", + " 2629 0.008892\n", + " 239 0.008812\n", + " 5057 0.008682\n", + " Name: 412, dtype: float64, 2237 0.153623\n", + " 3162 0.080719\n", + " 7848 0.048185\n", + " 3324 0.043771\n", + " 3893 0.023474\n", + " 3156 0.013736\n", + " 2303 0.011430\n", + " 2730 0.008228\n", + " 2507 0.007863\n", + " 7333 0.007774\n", + " Name: 413, dtype: float64, 7664 0.060364\n", + " 3022 0.038933\n", + " 3276 0.018690\n", + " 7715 0.015856\n", + " 7740 0.014656\n", + " 7320 0.014272\n", + " 7321 0.012888\n", + " 7286 0.011791\n", + " 1741 0.010860\n", + " 15 0.009372\n", + " Name: 414, dtype: float64, 2319 0.032741\n", + " 2861 0.030079\n", + " 3343 0.029269\n", + " 3617 0.015679\n", + " 10604 0.014642\n", + " 2156 0.010560\n", + " 3610 0.009925\n", + " 1657 0.008186\n", + " 10635 0.007202\n", + " 10651 0.006824\n", + " Name: 415, dtype: float64, 2115 0.255851\n", + " 3235 0.231438\n", + " 3047 0.034547\n", + " 2116 0.028231\n", + " 3027 0.023058\n", + " 8491 0.018843\n", + " 3028 0.017686\n", + " 8402 0.016772\n", + " 9475 0.016766\n", + " 2117 0.014142\n", + " Name: 416, dtype: float64, 641 0.974384\n", + " 3574 0.004747\n", + " 3210 0.002227\n", + " 205 0.001499\n", + " 4149 0.000984\n", + " 2214 0.000823\n", + " 2503 0.000372\n", + " 3991 0.000365\n", + " 6805 0.000349\n", + " 640 0.000313\n", + " Name: 417, dtype: float64, 640 0.496760\n", + " 205 0.062114\n", + " 9508 0.058706\n", + " 4172 0.018985\n", + " 8319 0.010818\n", + " 3067 0.010595\n", + " 1817 0.007124\n", + " 225 0.006159\n", + " 4285 0.005490\n", + " 2111 0.005260\n", + " Name: 418, dtype: float64, 3066 0.997892\n", + " 3065 0.000208\n", + " 3064 0.000138\n", + " 3067 0.000124\n", + " 2906 0.000034\n", + " 31 0.000029\n", + " 3669 0.000021\n", + " 203 0.000020\n", + " 3068 0.000019\n", + " 3668 0.000018\n", + " Name: 419, dtype: float64, 7740 0.064202\n", + " 2622 0.063790\n", + " 7715 0.035508\n", + " 7480 0.026981\n", + " 7584 0.019826\n", + " 7569 0.017939\n", + " 8051 0.014308\n", + " 7481 0.014052\n", + " 7742 0.014045\n", + " 7696 0.013648\n", + " Name: 420, dtype: float64, 3054 0.995343\n", + " 3068 0.000119\n", + " 3067 0.000117\n", + " 4066 0.000091\n", + " 3050 0.000088\n", " 2622 0.000082\n", - " 3042 0.000076\n", - " 128 0.000043\n", - " 8790 0.000040\n", - " 8457 0.000040\n", - " Name: 421, dtype: float64, 3063 0.653359\n", - " 3065 0.018783\n", - " 2583 0.006977\n", - " 3038 0.006224\n", - " 8093 0.005097\n", - " 8325 0.003819\n", - " 6390 0.003519\n", - " 5769 0.003141\n", - " 6608 0.003009\n", - " 2215 0.002768\n", - " Name: 422, dtype: float64, 3064 0.988541\n", - " 3076 0.001492\n", - " 3067 0.000766\n", - " 3063 0.000438\n", - " 3068 0.000340\n", - " 8439 0.000248\n", - " 873 0.000185\n", - " 3055 0.000174\n", - " 3059 0.000169\n", - " 3621 0.000127\n", - " Name: 423, dtype: float64, 1471 0.997414\n", - " 86 0.000680\n", - " 3052 0.000511\n", - " 640 0.000343\n", - " 1515 0.000114\n", - " 2236 0.000083\n", - " 9039 0.000060\n", - " 2500 0.000035\n", - " 4172 0.000023\n", - " 2622 0.000022\n", - " Name: 424, dtype: float64, 640 0.995348\n", - " 1515 0.001543\n", - " 1471 0.001074\n", - " 86 0.000223\n", - " 4172 0.000196\n", - " 205 0.000069\n", - " 8381 0.000062\n", - " 758 0.000060\n", - " 1514 0.000058\n", - " 4006 0.000046\n", - " Name: 425, dtype: float64, 2980 0.027549\n", - " 9350 0.015808\n", - " 2617 0.012434\n", - " 7689 0.011611\n", - " 7264 0.008708\n", - " 7332 0.008261\n", - " 2505 0.007416\n", - " 2821 0.006676\n", - " 3515 0.005656\n", - " 7343 0.005041\n", - " Name: 426, dtype: float64, 2503 0.221443\n", - " 753 0.066759\n", - " 2236 0.062327\n", - " 690 0.034256\n", - " 5549 0.029025\n", - " 2582 0.023808\n", - " 667 0.021582\n", - " 2512 0.019482\n", - " 677 0.019433\n", - " 2534 0.012125\n", - " Name: 427, dtype: float64, 3106 0.330348\n", - " 3087 0.057276\n", - " 5952 0.054957\n", - " 6140 0.047658\n", - " 5950 0.039502\n", - " 5955 0.014903\n", - " 5860 0.012879\n", - " 5839 0.009734\n", - " 5951 0.009717\n", - " 5966 0.006971\n", - " Name: 428, dtype: float64, 1805 0.050509\n", - " 5848 0.035095\n", - " 3086 0.024211\n", - " 6146 0.013020\n", - " 3091 0.012637\n", - " 5966 0.012378\n", - " 3340 0.012322\n", - " 5950 0.009374\n", - " 7071 0.009265\n", - " 5849 0.008634\n", - " Name: 429, dtype: float64, 3112 0.386578\n", - " 3115 0.380676\n", - " 3113 0.100275\n", - " 871 0.009707\n", - " 3084 0.002154\n", - " 8360 0.002012\n", - " 3016 0.001923\n", - " 1685 0.001575\n", - " 8396 0.001528\n", - " 8422 0.001036\n", - " Name: 430, dtype: float64, 3118 0.500998\n", - " 3117 0.200946\n", - " 3122 0.082677\n", - " 3123 0.050997\n", - " 6015 0.013157\n", - " 6016 0.009654\n", - " 6102 0.004259\n", - " 3121 0.002657\n", - " 6238 0.002185\n", - " 3143 0.001474\n", - " Name: 431, dtype: float64, 3119 0.931157\n", - " 6148 0.005223\n", - " 6147 0.001111\n", - " 3394 0.000943\n", - " 6237 0.000480\n", - " 7734 0.000403\n", - " 6664 0.000335\n", - " 3111 0.000331\n", - " 3393 0.000328\n", - " 945 0.000327\n", - " Name: 432, dtype: float64, 6272 0.092193\n", - " 5883 0.029510\n", - " 5807 0.025906\n", - " 3142 0.014599\n", - " 6159 0.012769\n", - " 3141 0.012397\n", - " 5944 0.011856\n", - " 6264 0.011158\n", - " 5987 0.010497\n", - " 6077 0.010481\n", - " Name: 433, dtype: float64, 3117 0.274355\n", - " 3118 0.240662\n", - " 6015 0.145969\n", - " 3122 0.061517\n", - " 3123 0.026383\n", - " 6016 0.024900\n", - " 6102 0.002674\n", - " 3130 0.002279\n", - " 3470 0.002117\n", - " 6216 0.002113\n", - " Name: 434, dtype: float64, 3135 0.124064\n", - " 5925 0.045957\n", - " 3137 0.039843\n", - " 6042 0.036639\n", - " 3136 0.031433\n", - " 6043 0.026939\n", - " 3142 0.022839\n", - " 3141 0.021941\n", - " 3145 0.019827\n", - " 6004 0.016965\n", - " Name: 435, dtype: float64, 2660 0.211923\n", - " 2512 0.102428\n", - " 2608 0.081723\n", - " 713 0.068072\n", - " 387 0.017300\n", - " 8274 0.016887\n", - " 7016 0.015771\n", - " 2611 0.011786\n", - " 901 0.008642\n", - " 7609 0.008197\n", - " Name: 436, dtype: float64, 4109 0.061983\n", - " 4222 0.029851\n", - " 6109 0.028411\n", - " 10340 0.022585\n", - " 4715 0.020054\n", - " 9063 0.013646\n", - " 782 0.012216\n", - " 8923 0.011293\n", - " 8919 0.009553\n", - " 3192 0.009297\n", - " Name: 437, dtype: float64, 1536 0.028816\n", - " 6016 0.012398\n", - " 5947 0.011818\n", - " 7888 0.010503\n", - " 6245 0.009765\n", - " 2877 0.009701\n", - " 755 0.009427\n", - " 2227 0.008998\n", - " 6146 0.008804\n", - " 7923 0.008726\n", - " Name: 438, dtype: float64, 2104 0.052501\n", - " 2733 0.016806\n", - " 1635 0.016688\n", - " 319 0.012458\n", - " 2625 0.012432\n", - " 2806 0.009745\n", - " 7526 0.008976\n", - " 436 0.008740\n", - " 2762 0.008668\n", - " 3918 0.008657\n", - " Name: 439, dtype: float64, 3161 0.974327\n", - " 3974 0.004576\n", - " 3160 0.001670\n", - " 3667 0.001452\n", - " 8142 0.001116\n", - " 7986 0.000778\n", - " 3034 0.000221\n", - " 3033 0.000179\n", - " 7280 0.000174\n", - " 2677 0.000160\n", - " Name: 440, dtype: float64, 5514 0.043853\n", - " 8407 0.029624\n", - " 7632 0.026385\n", - " 8506 0.024026\n", - " 2911 0.022387\n", - " 9088 0.018485\n", - " 9038 0.017090\n", - " 7582 0.009204\n", - " 9349 0.008880\n", - " 10504 0.008770\n", - " Name: 441, dtype: float64, 79 9.999931e-01\n", - " 9626 4.113480e-07\n", - " 119 3.298454e-07\n", - " 160 3.008987e-07\n", - " 8040 2.379674e-07\n", - " 2260 1.880317e-07\n", - " 246 1.854812e-07\n", - " 206 1.377126e-07\n", - " 4073 1.354654e-07\n", - " 4011 1.203253e-07\n", - " Name: 442, dtype: float64, 79 9.999938e-01\n", - " 160 1.768050e-07\n", - " 9626 1.729983e-07\n", - " 1203 1.683517e-07\n", - " 119 1.502667e-07\n", - " 206 1.452969e-07\n", - " 246 1.442199e-07\n", - " 8040 1.273355e-07\n", - " 205 1.264917e-07\n", - " 86 1.138937e-07\n", - " Name: 443, dtype: float64, 3180 0.479530\n", - " 3182 0.269785\n", - " 3181 0.061402\n", - " 3184 0.006913\n", - " 136 0.006904\n", - " 108 0.004292\n", - " 3210 0.003465\n", - " 2931 0.002796\n", - " 2118 0.002320\n", - " 3046 0.002236\n", - " Name: 444, dtype: float64, 79 9.999962e-01\n", - " 160 1.936317e-07\n", - " 9626 1.928617e-07\n", - " 8040 1.019725e-07\n", - " 119 9.165883e-08\n", - " 4011 9.155696e-08\n", - " 1203 8.538932e-08\n", - " 2260 8.061647e-08\n", - " 206 7.943584e-08\n", - " 246 7.597885e-08\n", - " Name: 445, dtype: float64, 79 9.999949e-01\n", - " 9626 3.829182e-07\n", - " 160 1.787164e-07\n", - " 8040 1.777972e-07\n", - " 119 1.283600e-07\n", - " 4011 1.253091e-07\n", - " 4073 1.069932e-07\n", - " 206 8.554113e-08\n", - " 690 8.411524e-08\n", - " 1203 7.642743e-08\n", - " Name: 446, dtype: float64, 3212 0.046595\n", - " 3367 0.033553\n", - " 3203 0.022417\n", - " 2935 0.021306\n", - " 7097 0.016279\n", - " 3209 0.013030\n", - " 3197 0.012985\n", - " 3202 0.009773\n", - " 2536 0.009189\n", - " 3342 0.008266\n", - " Name: 447, dtype: float64, 3207 0.391333\n", - " 3204 0.378181\n", - " 3206 0.141073\n", - " 2351 0.004028\n", - " 3098 0.003498\n", - " 3209 0.003013\n", - " 3203 0.001795\n", - " 3202 0.001549\n", - " 3213 0.001497\n", - " 233 0.001479\n", - " Name: 448, dtype: float64, 870 0.960793\n", - " 3210 0.002285\n", - " 2504 0.001552\n", - " 2351 0.001351\n", - " 1805 0.000994\n", - " 3209 0.000902\n", - " 5454 0.000650\n", - " 679 0.000589\n", - " 108 0.000455\n", - " 2337 0.000399\n", - " Name: 449, dtype: float64, 86 0.987085\n", - " 1471 0.009427\n", - " 640 0.000607\n", - " 8335 0.000461\n", - " 206 0.000122\n", - " 7630 0.000122\n", - " 4172 0.000072\n", - " 1255 0.000070\n", - " 3991 0.000068\n", - " 2337 0.000066\n", - " Name: 450, dtype: float64, 79 9.999676e-01\n", - " 160 2.643326e-06\n", - " 8040 8.356530e-07\n", - " 2534 6.299123e-07\n", - " 9626 6.140847e-07\n", - " 1203 4.721012e-07\n", - " 1805 4.692527e-07\n", - " 246 4.322802e-07\n", - " 704 4.123831e-07\n", - " 3687 4.047287e-07\n", - " Name: 451, dtype: float64, 79 9.999902e-01\n", - " 160 4.140721e-07\n", - " 9626 3.488207e-07\n", - " 119 2.081601e-07\n", - " 690 1.696395e-07\n", - " 641 1.606675e-07\n", - " 192 1.586334e-07\n", - " 246 1.577334e-07\n", - " 2684 1.548972e-07\n", - " 1506 1.523492e-07\n", - " Name: 452, dtype: float64, 2503 0.379380\n", - " 2236 0.243263\n", - " 1805 0.131119\n", - " 107 0.072808\n", - " 1471 0.016900\n", - " 3098 0.011589\n", - " 753 0.009309\n", - " 1514 0.007965\n", - " 2502 0.007917\n", - " 6066 0.006007\n", - " Name: 453, dtype: float64, 2237 0.134221\n", - " 9991 0.041678\n", - " 2236 0.023586\n", - " 9254 0.023292\n", - " 1805 0.022703\n", - " 79 0.021483\n", - " 2230 0.017044\n", - " 6608 0.015217\n", - " 4073 0.013278\n", - " 3098 0.012178\n", - " Name: 454, dtype: float64, 2796 0.952775\n", - " 3192 0.038326\n", - " 3278 0.000777\n", - " 3195 0.000619\n", - " 3261 0.000502\n", - " 3165 0.000389\n", - " 3621 0.000366\n", - " 3166 0.000339\n", - " 672 0.000202\n", - " 3196 0.000150\n", - " Name: 455, dtype: float64, 3193 0.976149\n", - " 3192 0.001343\n", - " 5831 0.001150\n", - " 3346 0.000792\n", - " 3186 0.000642\n", - " 8731 0.000590\n", - " 2534 0.000324\n", - " 3195 0.000260\n", - " 7277 0.000238\n", - " 8919 0.000231\n", - " Name: 456, dtype: float64, 2173 0.999035\n", - " 3165 0.000167\n", - " 3422 0.000064\n", - " 2796 0.000038\n", - " 2547 0.000028\n", - " 5263 0.000018\n", - " 3430 0.000015\n", - " 3194 0.000013\n", - " 8507 0.000011\n", - " 84 0.000011\n", - " Name: 457, dtype: float64, 2502 0.230479\n", - " 2503 0.219391\n", - " 677 0.215912\n", - " 679 0.191965\n", - " 2504 0.094473\n", - " 1805 0.030275\n", - " 2716 0.003072\n", - " 2715 0.002449\n", - " 2236 0.001342\n", - " 7609 0.000278\n", - " Name: 458, dtype: float64, 3192 0.273485\n", - " 3165 0.210611\n", - " 3166 0.126551\n", - " 2796 0.075301\n", - " 3278 0.070104\n", - " 3194 0.010006\n", - " 3347 0.005922\n", - " 3261 0.005813\n", - " 3277 0.005662\n", - " 3323 0.005315\n", - " Name: 459, dtype: float64, 2796 0.997158\n", - " 3278 0.000774\n", - " 3277 0.000165\n", - " 6313 0.000129\n", - " 3192 0.000075\n", - " 3195 0.000071\n", - " 3166 0.000042\n", - " 3165 0.000040\n", - " 3196 0.000039\n", - " 3261 0.000037\n", - " Name: 460, dtype: float64, 3173 0.998646\n", - " 3165 0.000509\n", - " 3325 0.000067\n", - " 2348 0.000037\n", - " 2660 0.000023\n", - " 3637 0.000020\n", - " 108 0.000020\n", - " 230 0.000018\n", - " 4135 0.000017\n", - " 3200 0.000013\n", - " Name: 461, dtype: float64, 3173 0.997641\n", - " 3165 0.001439\n", - " 4135 0.000034\n", - " 230 0.000030\n", - " 3325 0.000027\n", - " 2611 0.000025\n", - " 3337 0.000023\n", - " 2348 0.000023\n", - " 2660 0.000021\n", - " 3588 0.000021\n", - " Name: 462, dtype: float64, 6617 0.164048\n", - " 6616 0.039095\n", - " 6590 0.034601\n", - " 3629 0.013532\n", - " 6252 0.008738\n", - " 6417 0.008728\n", - " 6115 0.008588\n", - " 10536 0.007876\n", - " 7268 0.007811\n", - " 5777 0.007242\n", - " Name: 463, dtype: float64, 7398 0.032485\n", - " 10051 0.030159\n", - " 8505 0.019279\n", - " 5081 0.017430\n", - " 10103 0.015325\n", - " 5598 0.013350\n", - " 8695 0.011930\n", - " 10333 0.011602\n", - " 9900 0.011055\n", - " 5809 0.009785\n", - " Name: 464, dtype: float64, 3314 0.042260\n", - " 5785 0.029531\n", - " 6112 0.016753\n", - " 2889 0.014809\n", - " 6554 0.012541\n", - " 3777 0.012072\n", - " 5777 0.010732\n", - " 50 0.010617\n", - " 5786 0.008836\n", - " 7124 0.008564\n", - " Name: 465, dtype: float64, 3192 0.647762\n", - " 2796 0.312137\n", - " 3261 0.002577\n", - " 3278 0.002138\n", - " 677 0.001351\n", - " 3166 0.001276\n", - " 3165 0.001203\n", - " 3195 0.000915\n", - " 3193 0.000762\n", - " 3184 0.000616\n", - " Name: 466, dtype: float64, 3173 0.998646\n", - " 3165 0.000509\n", - " 3325 0.000067\n", - " 2348 0.000037\n", - " 2660 0.000023\n", - " 3637 0.000020\n", - " 108 0.000020\n", - " 230 0.000018\n", - " 4135 0.000017\n", - " 3200 0.000013\n", - " Name: 467, dtype: float64, 3165 0.999193\n", - " 3347 0.000070\n", - " 2534 0.000048\n", - " 2611 0.000028\n", - " 3422 0.000024\n", - " 3192 0.000021\n", - " 3171 0.000014\n", - " 901 0.000014\n", - " 3546 0.000011\n", - " 2173 0.000010\n", - " Name: 468, dtype: float64, 2873 0.409476\n", - " 3098 0.103369\n", - " 2236 0.076454\n", - " 206 0.041779\n", - " 2715 0.040199\n", - " 2503 0.014797\n", - " 5549 0.013365\n", - " 679 0.009677\n", - " 3207 0.007375\n", - " 677 0.006921\n", - " Name: 469, dtype: float64, 3192 0.239934\n", - " 3195 0.080471\n", - " 883 0.070686\n", - " 677 0.048353\n", - " 3422 0.030810\n", - " 2173 0.028254\n", - " 3196 0.024882\n", - " 8508 0.023586\n", - " 3165 0.016657\n", - " 2776 0.011898\n", - " Name: 470, dtype: float64, 3173 0.999290\n", - " 3165 0.000176\n", - " 3588 0.000043\n", - " 3325 0.000022\n", - " 3172 0.000016\n", - " 230 0.000016\n", - " 2681 0.000015\n", - " 3546 0.000015\n", - " 3200 0.000007\n", - " 3476 0.000007\n", - " Name: 471, dtype: float64, 3192 0.782810\n", - " 2796 0.150941\n", - " 3165 0.010274\n", - " 3278 0.008044\n", - " 3261 0.002907\n", - " 677 0.002176\n", - " 3166 0.001398\n", - " 2236 0.001299\n", - " 3348 0.000975\n", - " 3638 0.000662\n", - " Name: 472, dtype: float64, 3260 0.963986\n", - " 3583 0.001745\n", - " 3537 0.000682\n", - " 3275 0.000512\n", - " 657 0.000484\n", - " 3515 0.000473\n", - " 3774 0.000399\n", - " 3265 0.000363\n", - " 3904 0.000337\n", - " 3327 0.000331\n", - " Name: 473, dtype: float64, 3173 0.998109\n", - " 3165 0.000630\n", - " 230 0.000038\n", - " 3325 0.000030\n", - " 2681 0.000027\n", - " 108 0.000026\n", - " 1663 0.000026\n", - " 2348 0.000023\n", - " 2660 0.000022\n", - " 3337 0.000020\n", - " Name: 474, dtype: float64, 3325 0.999743\n", - " 3637 0.000129\n", - " 2236 0.000017\n", - " 7023 0.000009\n", - " 3687 0.000005\n", - " 7017 0.000005\n", - " 7016 0.000003\n", - " 230 0.000002\n", - " 2503 0.000002\n", - " 3263 0.000002\n", - " Name: 475, dtype: float64, 8032 0.032365\n", - " 8673 0.023658\n", - " 8483 0.019351\n", - " 7741 0.017243\n", - " 8516 0.016735\n", - " 6893 0.015026\n", - " 8591 0.014050\n", - " 8672 0.013483\n", - " 8432 0.012943\n", - " 9094 0.012425\n", - " Name: 476, dtype: float64, 79 9.999914e-01\n", - " 9626 5.389041e-07\n", - " 8040 3.627089e-07\n", - " 160 2.923950e-07\n", - " 119 2.534469e-07\n", - " 2260 1.884608e-07\n", - " 1805 1.625241e-07\n", - " 1203 1.601250e-07\n", - " 4073 1.588755e-07\n", - " 246 1.403763e-07\n", - " Name: 477, dtype: float64, 3403 0.111433\n", - " 3412 0.093800\n", - " 3417 0.088607\n", - " 3420 0.079798\n", - " 3419 0.079730\n", - " 3418 0.071304\n", - " 3404 0.066055\n", - " 3409 0.044713\n", - " 3458 0.041255\n", - " 3405 0.036888\n", - " Name: 478, dtype: float64, 3411 0.957222\n", - " 3454 0.001301\n", - " 3559 0.000800\n", - " 3401 0.000756\n", - " 10327 0.000609\n", - " 5440 0.000571\n", - " 3444 0.000561\n", - " 8828 0.000522\n", - " 3572 0.000518\n", - " 3489 0.000470\n", - " Name: 479, dtype: float64, 3402 0.260238\n", - " 3413 0.192305\n", - " 3405 0.181223\n", - " 3404 0.076306\n", - " 3449 0.068973\n", - " 3416 0.048213\n", - " 3457 0.019626\n", - " 3417 0.018788\n", - " 3966 0.018106\n", - " 3403 0.016213\n", - " Name: 480, dtype: float64, 3154 0.999371\n", - " 4126 0.000080\n", - " 7114 0.000038\n", + " 3052 0.000080\n", + " 2500 0.000071\n", + " 1471 0.000065\n", + " 5277 0.000057\n", + " Name: 421, dtype: float64, 3065 0.159519\n", + " 3063 0.059063\n", + " 5740 0.039214\n", + " 3038 0.020909\n", + " 3067 0.015573\n", + " 3068 0.012558\n", + " 2916 0.010425\n", + " 6950 0.010037\n", + " 6625 0.009309\n", + " 7043 0.007671\n", + " Name: 422, dtype: float64, 3064 0.993340\n", + " 3076 0.000350\n", + " 3068 0.000252\n", + " 3066 0.000180\n", + " 2865 0.000151\n", + " 2847 0.000134\n", + " 3253 0.000104\n", + " 2583 0.000102\n", + " 3055 0.000090\n", + " 6901 0.000086\n", + " Name: 423, dtype: float64, 1471 0.999140\n", + " 86 0.000071\n", + " 9039 0.000043\n", + " 787 0.000036\n", + " 3052 0.000031\n", + " 3210 0.000027\n", + " 640 0.000022\n", + " 8155 0.000020\n", + " 3053 0.000018\n", + " 3991 0.000016\n", + " Name: 424, dtype: float64, 640 0.998727\n", + " 1471 0.000420\n", + " 758 0.000066\n", + " 8319 0.000042\n", + " 1515 0.000036\n", + " 8155 0.000031\n", + " 86 0.000022\n", + " 9508 0.000020\n", + " 3210 0.000017\n", + " 3991 0.000015\n", + " Name: 425, dtype: float64, 2905 0.021200\n", + " 8160 0.019101\n", + " 2842 0.017993\n", + " 2054 0.015835\n", + " 3024 0.015073\n", + " 3169 0.013904\n", + " 3022 0.013019\n", + " 5626 0.012196\n", + " 3610 0.010232\n", + " 2826 0.009581\n", + " Name: 426, dtype: float64, 2236 0.776826\n", + " 2503 0.053595\n", + " 3991 0.037356\n", + " 3094 0.013582\n", + " 3998 0.006368\n", + " 5539 0.004706\n", + " 5540 0.004296\n", + " 753 0.002909\n", + " 167 0.002849\n", + " 871 0.001961\n", + " Name: 427, dtype: float64, 3093 0.270554\n", + " 3106 0.176412\n", + " 6140 0.059287\n", + " 3087 0.023585\n", + " 6043 0.023203\n", + " 5950 0.019873\n", + " 6008 0.016958\n", + " 10174 0.014267\n", + " 6042 0.009160\n", + " 6034 0.008995\n", + " Name: 428, dtype: float64, 3192 0.204637\n", + " 1805 0.115800\n", + " 5385 0.082018\n", + " 3101 0.033724\n", + " 2608 0.031120\n", + " 2503 0.025961\n", + " 678 0.022791\n", + " 3349 0.017949\n", + " 5760 0.016326\n", + " 3348 0.015881\n", + " Name: 429, dtype: float64, 3115 0.468839\n", + " 3112 0.164017\n", + " 3113 0.123894\n", + " 3376 0.007998\n", + " 2872 0.004425\n", + " 3020 0.004397\n", + " 3329 0.003521\n", + " 3169 0.003376\n", + " 190 0.002762\n", + " 62 0.002588\n", + " Name: 430, dtype: float64, 3117 0.569922\n", + " 3118 0.239811\n", + " 3122 0.107354\n", + " 6016 0.007529\n", + " 6015 0.007192\n", + " 3123 0.006291\n", + " 380 0.001634\n", + " 3143 0.000939\n", + " 5869 0.000928\n", + " 379 0.000647\n", + " Name: 431, dtype: float64, 3119 0.776089\n", + " 6148 0.120266\n", + " 6147 0.006710\n", + " 6008 0.002016\n", + " 3123 0.001967\n", + " 6642 0.001917\n", + " 3118 0.001423\n", + " 6279 0.001365\n", + " 2580 0.001318\n", + " 6034 0.001151\n", + " Name: 432, dtype: float64, 2792 0.086997\n", + " 6080 0.054746\n", + " 6159 0.053880\n", + " 6253 0.053137\n", + " 6279 0.028083\n", + " 5885 0.015258\n", + " 6146 0.014568\n", + " 6265 0.014294\n", + " 5884 0.014128\n", + " 3122 0.012831\n", + " Name: 433, dtype: float64, 3117 0.240034\n", + " 6015 0.163391\n", + " 3122 0.128086\n", + " 3118 0.088576\n", + " 7871 0.024610\n", + " 3130 0.010492\n", + " 6010 0.006523\n", + " 6016 0.005900\n", + " 3586 0.005864\n", + " 3123 0.005744\n", + " Name: 434, dtype: float64, 6732 0.050869\n", + " 6042 0.050191\n", + " 6155 0.044484\n", + " 6610 0.039016\n", + " 6043 0.037348\n", + " 7123 0.019733\n", + " 6156 0.018384\n", + " 6247 0.016640\n", + " 5915 0.012464\n", + " 6004 0.010005\n", + " Name: 435, dtype: float64, 713 0.218678\n", + " 2660 0.209056\n", + " 2502 0.099860\n", + " 679 0.076318\n", + " 160 0.024266\n", + " 2608 0.020567\n", + " 2236 0.015801\n", + " 753 0.013674\n", + " 677 0.011661\n", + " 901 0.011454\n", + " Name: 436, dtype: float64, 6109 0.329939\n", + " 4642 0.019601\n", + " 4644 0.019380\n", + " 4109 0.015369\n", + " 892 0.010999\n", + " 10435 0.010269\n", + " 712 0.007231\n", + " 5025 0.006399\n", + " 2497 0.006256\n", + " 3226 0.005915\n", + " Name: 437, dtype: float64, 5859 0.068070\n", + " 2810 0.053583\n", + " 3141 0.022549\n", + " 2877 0.019285\n", + " 6253 0.018729\n", + " 6282 0.015252\n", + " 3142 0.013995\n", + " 7648 0.012319\n", + " 6153 0.012246\n", + " 7924 0.010264\n", + " Name: 438, dtype: float64, 7017 0.035511\n", + " 6198 0.027257\n", + " 4098 0.021225\n", + " 2780 0.014306\n", + " 2666 0.013723\n", + " 2682 0.011138\n", + " 6199 0.010976\n", + " 7135 0.009242\n", + " 3464 0.008628\n", + " 7095 0.008498\n", + " Name: 439, dtype: float64, 3161 0.961067\n", + " 3974 0.003284\n", + " 3667 0.002580\n", + " 4683 0.001193\n", + " 7986 0.000931\n", + " 3309 0.000617\n", + " 3362 0.000504\n", + " 4666 0.000467\n", + " 3160 0.000464\n", + " 3868 0.000460\n", + " Name: 440, dtype: float64, 2035 0.034823\n", + " 79 0.021098\n", + " 5514 0.019397\n", + " 8968 0.019281\n", + " 690 0.018869\n", + " 9958 0.018524\n", + " 7999 0.016354\n", + " 3286 0.011755\n", + " 8919 0.011601\n", + " 2534 0.010797\n", + " Name: 441, dtype: float64, 79 9.999959e-01\n", + " 208 1.160182e-06\n", + " 1805 1.907190e-07\n", + " 517 8.895389e-08\n", + " 119 7.597173e-08\n", + " 7106 7.390420e-08\n", + " 4098 5.567091e-08\n", + " 4000 4.440622e-08\n", + " 2503 3.572049e-08\n", + " 2546 3.316687e-08\n", + " Name: 442, dtype: float64, 79 9.999856e-01\n", + " 208 5.987029e-06\n", + " 1805 9.297264e-07\n", + " 119 3.026641e-07\n", + " 7106 2.670754e-07\n", + " 517 2.219607e-07\n", + " 4098 1.847976e-07\n", + " 4000 1.315960e-07\n", + " 2503 9.772013e-08\n", + " 233 7.980356e-08\n", + " Name: 443, dtype: float64, 3181 0.210483\n", + " 3184 0.038226\n", + " 668 0.033139\n", + " 3180 0.032545\n", + " 8021 0.017887\n", + " 2214 0.016814\n", + " 8514 0.011319\n", + " 3182 0.008865\n", + " 5142 0.008426\n", + " 8686 0.008147\n", + " Name: 444, dtype: float64, 79 9.999971e-01\n", + " 208 7.426572e-07\n", + " 1805 8.862122e-08\n", + " 7106 7.145213e-08\n", + " 517 6.269723e-08\n", + " 119 5.312324e-08\n", + " 4098 5.033196e-08\n", + " 2546 3.413096e-08\n", + " 2503 3.322738e-08\n", + " 4000 2.730724e-08\n", + " Name: 445, dtype: float64, 79 9.999944e-01\n", + " 208 1.293490e-06\n", + " 7106 1.702181e-07\n", + " 1805 1.521280e-07\n", + " 517 1.006819e-07\n", + " 4098 6.781833e-08\n", + " 2546 4.502079e-08\n", + " 119 4.484202e-08\n", + " 2503 4.010515e-08\n", + " 8418 3.329587e-08\n", + " Name: 446, dtype: float64, 2567 0.059591\n", + " 3200 0.027395\n", + " 5468 0.026723\n", + " 5611 0.021431\n", + " 9317 0.019890\n", + " 3367 0.016712\n", + " 678 0.016104\n", + " 7475 0.015710\n", + " 2935 0.012790\n", + " 3202 0.011434\n", + " Name: 447, dtype: float64, 3207 0.491090\n", + " 3204 0.380616\n", + " 3206 0.046226\n", + " 3202 0.007471\n", + " 3203 0.006106\n", + " 3213 0.004145\n", + " 3214 0.003658\n", + " 2351 0.002652\n", + " 3209 0.002332\n", + " 3098 0.001209\n", + " Name: 448, dtype: float64, 3202 0.463169\n", + " 870 0.190928\n", + " 3209 0.055873\n", + " 901 0.035507\n", + " 3204 0.026666\n", + " 3207 0.010880\n", + " 10115 0.008334\n", + " 2351 0.005390\n", + " 3206 0.005251\n", + " 3165 0.004707\n", + " Name: 449, dtype: float64, 86 0.931990\n", + " 1471 0.065655\n", + " 9039 0.000406\n", + " 640 0.000095\n", + " 2260 0.000079\n", + " 3991 0.000075\n", + " 4155 0.000062\n", + " 8335 0.000056\n", + " 795 0.000048\n", + " 1515 0.000047\n", + " Name: 450, dtype: float64, 79 9.999820e-01\n", + " 208 1.905272e-06\n", + " 119 1.248767e-06\n", + " 1805 6.189654e-07\n", + " 8418 2.742460e-07\n", + " 7106 2.641934e-07\n", + " 3637 2.241024e-07\n", + " 3098 1.736520e-07\n", + " 7141 1.568193e-07\n", + " 517 1.553926e-07\n", + " Name: 451, dtype: float64, 79 9.999868e-01\n", + " 208 2.427395e-06\n", + " 119 6.083629e-07\n", + " 517 4.496564e-07\n", + " 7106 3.625298e-07\n", + " 1805 2.320441e-07\n", + " 4000 2.218930e-07\n", + " 2546 1.830044e-07\n", + " 3637 1.826849e-07\n", + " 3449 1.222982e-07\n", + " Name: 452, dtype: float64, 3098 0.264227\n", + " 2503 0.128023\n", + " 3216 0.067119\n", + " 2236 0.034071\n", + " 3325 0.032776\n", + " 690 0.030942\n", + " 1805 0.017554\n", + " 4141 0.014395\n", + " 753 0.009466\n", + " 3999 0.008109\n", + " Name: 453, dtype: float64, 2236 0.080577\n", + " 4248 0.045786\n", + " 2503 0.044777\n", + " 5207 0.027096\n", + " 107 0.023874\n", + " 3326 0.018232\n", + " 4000 0.012277\n", + " 4001 0.011651\n", + " 3972 0.011031\n", + " 5448 0.010796\n", + " Name: 454, dtype: float64, 2796 0.733467\n", + " 3192 0.218526\n", + " 3278 0.008891\n", + " 3193 0.003089\n", + " 3277 0.002090\n", + " 3195 0.001474\n", + " 3348 0.001408\n", + " 6313 0.001308\n", + " 3261 0.001016\n", + " 2534 0.000636\n", + " Name: 455, dtype: float64, 3193 0.983978\n", + " 3192 0.002239\n", + " 2534 0.000773\n", + " 6889 0.000543\n", + " 3195 0.000466\n", + " 2796 0.000312\n", + " 8826 0.000280\n", + " 3200 0.000176\n", + " 8922 0.000168\n", + " 8298 0.000146\n", + " Name: 456, dtype: float64, 2173 0.999540\n", + " 2547 0.000093\n", + " 118 0.000025\n", + " 2437 0.000021\n", + " 2175 0.000017\n", + " 2780 0.000013\n", + " 8507 0.000012\n", + " 2260 0.000009\n", + " 3422 0.000008\n", + " 2511 0.000007\n", + " Name: 457, dtype: float64, 2503 0.228944\n", + " 2502 0.217831\n", + " 679 0.211337\n", + " 677 0.207785\n", + " 2504 0.082678\n", + " 1805 0.035442\n", + " 2716 0.002609\n", + " 2236 0.002184\n", + " 2715 0.000880\n", + " 753 0.000421\n", + " Name: 458, dtype: float64, 2796 0.244089\n", + " 3192 0.166404\n", + " 3278 0.034862\n", + " 3622 0.028377\n", + " 5937 0.020297\n", + " 3261 0.019355\n", + " 3346 0.014566\n", + " 5500 0.014089\n", + " 3193 0.012152\n", + " 6313 0.010854\n", + " Name: 459, dtype: float64, 2796 0.991613\n", + " 6313 0.002367\n", + " 3278 0.001372\n", + " 3277 0.000557\n", + " 3195 0.000396\n", + " 3193 0.000379\n", + " 3192 0.000238\n", + " 3046 0.000157\n", + " 3261 0.000129\n", + " 2534 0.000051\n", + " Name: 460, dtype: float64, 3173 0.999444\n", + " 3165 0.000100\n", + " 901 0.000033\n", + " 870 0.000031\n", + " 2236 0.000020\n", + " 4135 0.000016\n", + " 3497 0.000012\n", + " 3245 0.000010\n", + " 871 0.000008\n", + " 2611 0.000008\n", + " Name: 461, dtype: float64, 3173 0.998994\n", + " 3165 0.000186\n", + " 870 0.000103\n", + " 901 0.000060\n", + " 2236 0.000045\n", + " 4135 0.000026\n", + " 3245 0.000020\n", + " 3497 0.000016\n", + " 2681 0.000016\n", + " 2611 0.000011\n", + " Name: 462, dtype: float64, 6617 0.152012\n", + " 9933 0.029239\n", + " 4643 0.025417\n", + " 2856 0.022989\n", + " 6616 0.017242\n", + " 7822 0.012166\n", + " 6041 0.010767\n", + " 3178 0.008128\n", + " 9528 0.007193\n", + " 9930 0.007189\n", + " Name: 463, dtype: float64, 10330 0.071200\n", + " 1374 0.025846\n", + " 4678 0.019860\n", + " 7909 0.017158\n", + " 2654 0.012649\n", + " 4096 0.012007\n", + " 419 0.011308\n", + " 7980 0.010523\n", + " 723 0.008998\n", + " 10325 0.008255\n", + " Name: 464, dtype: float64, 3314 0.119847\n", + " 5172 0.070927\n", + " 5184 0.050059\n", + " 5174 0.027050\n", + " 3207 0.025693\n", + " 5176 0.024911\n", + " 2889 0.013224\n", + " 3206 0.010809\n", + " 5385 0.009200\n", + " 2197 0.006469\n", + " Name: 465, dtype: float64, 2796 0.938262\n", + " 3192 0.012638\n", + " 2534 0.003391\n", + " 6313 0.002828\n", + " 3261 0.002522\n", + " 2780 0.002299\n", + " 3195 0.002092\n", + " 3605 0.001636\n", + " 3278 0.000788\n", + " 3348 0.000758\n", + " Name: 466, dtype: float64, 3173 0.999444\n", + " 3165 0.000100\n", + " 901 0.000033\n", + " 870 0.000031\n", + " 2236 0.000020\n", + " 4135 0.000016\n", + " 3497 0.000012\n", + " 3245 0.000010\n", + " 871 0.000008\n", + " 2611 0.000008\n", + " Name: 467, dtype: float64, 3165 0.999432\n", + " 3347 0.000090\n", + " 5260 0.000024\n", + " 678 0.000019\n", + " 2935 0.000019\n", + " 2452 0.000012\n", + " 3422 0.000011\n", + " 2547 0.000011\n", + " 679 0.000011\n", + " 2611 0.000010\n", + " Name: 468, dtype: float64, 2873 0.941945\n", + " 4141 0.007321\n", + " 753 0.004625\n", + " 4133 0.004341\n", + " 2435 0.002031\n", + " 635 0.001881\n", + " 4134 0.001383\n", + " 8444 0.001202\n", + " 2236 0.001105\n", + " 3999 0.000985\n", + " Name: 469, dtype: float64, 3192 0.910558\n", + " 3195 0.004853\n", + " 2534 0.004701\n", + " 3346 0.004451\n", + " 3186 0.004215\n", + " 3278 0.001954\n", + " 3605 0.001943\n", + " 3194 0.001699\n", + " 3193 0.001533\n", + " 3337 0.001140\n", + " Name: 470, dtype: float64, 3173 0.997202\n", + " 3165 0.000502\n", + " 3972 0.000366\n", + " 3497 0.000140\n", + " 901 0.000067\n", + " 870 0.000058\n", + " 2955 0.000057\n", + " 3435 0.000051\n", " 2236 0.000035\n", - " 7115 0.000022\n", - " 2630 0.000013\n", - " 677 0.000011\n", - " 3602 0.000011\n", - " 3325 0.000009\n", - " 3606 0.000008\n", - " Name: 481, dtype: float64, 3436 0.789582\n", - " 3437 0.088831\n", - " 3035 0.003165\n", - " 5252 0.001679\n", - " 3887 0.001666\n", - " 3667 0.001399\n", - " 3756 0.001376\n", - " 5739 0.001349\n", - " 3891 0.001299\n", - " 5202 0.001206\n", - " Name: 482, dtype: float64, 3421 0.995362\n", - " 5337 0.000435\n", - " 5649 0.000140\n", - " 2236 0.000131\n", - " 3887 0.000104\n", - " 3285 0.000091\n", - " 3500 0.000070\n", - " 5650 0.000061\n", - " 5769 0.000060\n", - " 3273 0.000053\n", - " Name: 483, dtype: float64, 79 9.999946e-01\n", - " 160 1.906391e-07\n", - " 9626 1.851299e-07\n", - " 8040 1.537844e-07\n", - " 2260 1.240213e-07\n", - " 4011 1.191370e-07\n", - " 119 1.045229e-07\n", - " 1203 1.000680e-07\n", - " 205 9.773593e-08\n", - " 206 9.551395e-08\n", - " Name: 484, dtype: float64, 3470 0.250180\n", - " 3435 0.099780\n", - " 901 0.062903\n", - " 1663 0.041508\n", - " 3497 0.035744\n", - " 156 0.018912\n", - " 3175 0.016778\n", - " 128 0.015497\n", - " 246 0.014857\n", - " 3172 0.013687\n", - " Name: 485, dtype: float64, 1514 0.117824\n", - " 8384 0.040610\n", - " 3447 0.040436\n", - " 8521 0.035602\n", - " 165 0.027248\n", - " 5917 0.019625\n", - " 3216 0.018036\n", - " 160 0.009119\n", - " 504 0.008062\n", - " 211 0.007549\n", - " Name: 486, dtype: float64, 3450 0.793209\n", - " 3958 0.145575\n", - " 3403 0.002075\n", - " 3889 0.001306\n", - " 3572 0.001212\n", - " 5256 0.001130\n", - " 3890 0.001052\n", - " 3420 0.000942\n", - " 3607 0.000931\n", - " 3888 0.000841\n", - " Name: 487, dtype: float64, 3451 0.022345\n", - " 3978 0.021380\n", - " 3563 0.012842\n", - " 3890 0.011515\n", - " 3409 0.009572\n", - " 6851 0.009364\n", - " 7859 0.008296\n", - " 3412 0.007872\n", - " 2784 0.007542\n", - " 3426 0.005623\n", - " Name: 488, dtype: float64, 3464 0.315270\n", - " 8025 0.065208\n", - " 6250 0.024594\n", - " 10082 0.007601\n", - " 3463 0.007179\n", - " 3462 0.006749\n", - " 6132 0.004988\n", - " 5148 0.004795\n", - " 6216 0.004653\n", - " 2958 0.004581\n", - " Name: 489, dtype: float64, 5100 0.033510\n", - " 3972 0.022266\n", - " 3433 0.020344\n", - " 3539 0.018434\n", - " 8074 0.016872\n", - " 3667 0.015675\n", - " 3245 0.013595\n", - " 3260 0.012738\n", - " 8334 0.012221\n", - " 3276 0.010835\n", - " Name: 490, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 491, dtype: float64, 3916 0.083817\n", - " 3885 0.074500\n", - " 3429 0.069990\n", - " 3884 0.050496\n", - " 3403 0.043954\n", - " 5439 0.037682\n", - " 2173 0.029921\n", - " 3498 0.024709\n", - " 5384 0.015778\n", - " 870 0.010454\n", - " Name: 492, dtype: float64, 3470 0.488541\n", - " 3493 0.275276\n", - " 3548 0.019950\n", - " 3511 0.009058\n", - " 3549 0.007092\n", - " 3492 0.006777\n", - " 3530 0.006406\n", - " 3544 0.004399\n", - " 3539 0.003927\n", - " 3542 0.003417\n", - " Name: 493, dtype: float64, 3470 0.999430\n", - " 3497 0.000152\n", - " 3544 0.000042\n", - " 3493 0.000027\n", - " 901 0.000019\n", - " 3476 0.000019\n", - " 3171 0.000012\n", - " 3548 0.000010\n", - " 7106 0.000009\n", - " 2197 0.000007\n", - " Name: 494, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 495, dtype: float64, 3599 0.822330\n", - " 870 0.101501\n", - " 2173 0.007290\n", - " 3498 0.004134\n", - " 8699 0.003480\n", - " 3429 0.002422\n", - " 3430 0.002342\n", - " 3507 0.001765\n", - " 8562 0.001040\n", - " 3165 0.000849\n", - " Name: 496, dtype: float64, 3980 0.848813\n", - " 3353 0.015777\n", - " 7141 0.009003\n", - " 2682 0.007808\n", - " 5935 0.005943\n", - " 5771 0.003961\n", - " 6839 0.003736\n", - " 7173 0.003639\n", - " 7135 0.002554\n", - " 4098 0.002408\n", - " Name: 497, dtype: float64, 3470 0.985153\n", - " 3544 0.004653\n", - " 3497 0.002972\n", - " 3476 0.000691\n", - " 3493 0.000261\n", - " 3171 0.000165\n", - " 3671 0.000127\n", - " 3546 0.000123\n", - " 901 0.000101\n", - " 2197 0.000099\n", - " Name: 498, dtype: float64, 5470 0.029195\n", - " 3529 0.024891\n", - " 7253 0.023419\n", - " 10175 0.022085\n", - " 3527 0.020619\n", - " 7556 0.015183\n", - " 2843 0.011568\n", - " 7770 0.008566\n", - " 3658 0.008029\n", - " 2303 0.007604\n", - " Name: 499, dtype: float64, 7770 0.063420\n", - " 7596 0.044961\n", - " 5868 0.026285\n", - " 7699 0.017520\n", - " 7549 0.015292\n", - " 6235 0.014723\n", - " 7731 0.013457\n", - " 6762 0.011117\n", - " 7794 0.011040\n", - " 7612 0.009143\n", - " Name: 500, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 501, dtype: float64, 3519 0.289881\n", - " 3516 0.280300\n", - " 3517 0.245442\n", - " 3515 0.032941\n", - " 2946 0.002806\n", - " 3342 0.002654\n", - " 3583 0.002472\n", - " 3774 0.002037\n", - " 3590 0.001980\n", - " 3752 0.001957\n", - " Name: 502, dtype: float64, 3470 0.999313\n", - " 3497 0.000109\n", - " 901 0.000034\n", - " 3493 0.000026\n", - " 3171 0.000026\n", - " 3544 0.000024\n", - " 7106 0.000014\n", - " 3476 0.000013\n", - " 3501 0.000012\n", - " 3172 0.000011\n", - " Name: 503, dtype: float64, 3916 0.277061\n", - " 5439 0.159990\n", - " 3429 0.065193\n", - " 3498 0.052380\n", - " 3885 0.027146\n", - " 3403 0.027018\n", - " 3884 0.023570\n", - " 3430 0.008707\n", - " 3599 0.006719\n", - " 8696 0.005947\n", - " Name: 504, dtype: float64, 3474 0.999573\n", - " 6022 0.000081\n", - " 3478 0.000060\n", - " 6062 0.000027\n", - " 8087 0.000010\n", - " 2508 0.000007\n", - " 3298 0.000006\n", - " 677 0.000004\n", - " 6012 0.000003\n", - " 3157 0.000003\n", - " Name: 505, dtype: float64, 3470 0.997996\n", - " 3497 0.000197\n", - " 3493 0.000074\n", - " 7106 0.000062\n", - " 3544 0.000060\n", - " 901 0.000046\n", - " 3991 0.000041\n", - " 3501 0.000041\n", - " 3171 0.000033\n", - " 156 0.000027\n", - " Name: 506, dtype: float64, 3476 0.903820\n", - " 3546 0.054957\n", - " 3534 0.003662\n", - " 3165 0.002767\n", - " 3593 0.002036\n", - " 3482 0.001473\n", - " 3598 0.001331\n", - " 3172 0.001098\n", - " 3528 0.000959\n", - " 3526 0.000752\n", - " Name: 507, dtype: float64, 2173 0.369654\n", - " 3165 0.171805\n", - " 3476 0.026672\n", - " 3422 0.026215\n", - " 3648 0.018512\n", - " 5263 0.016571\n", - " 870 0.015662\n", - " 3545 0.010182\n", - " 84 0.009099\n", - " 2301 0.007344\n", - " Name: 508, dtype: float64, 7360 0.068518\n", - " 9728 0.048455\n", - " 7201 0.026616\n", - " 631 0.024263\n", - " 7202 0.021055\n", - " 6067 0.018083\n", - " 934 0.016472\n", - " 6012 0.012992\n", - " 232 0.011339\n", - " 7312 0.010978\n", - " Name: 509, dtype: float64, 3403 0.351766\n", - " 3884 0.287114\n", - " 3885 0.236090\n", - " 3916 0.011572\n", - " 5439 0.011427\n", - " 3498 0.006474\n", - " 3412 0.005404\n", - " 3566 0.002894\n", - " 3886 0.002433\n", - " 3418 0.002229\n", - " Name: 510, dtype: float64, 5463 0.051611\n", - " 3292 0.036710\n", - " 3571 0.027637\n", - " 5462 0.021414\n", - " 5439 0.020380\n", - " 5472 0.018287\n", - " 5505 0.016280\n", - " 3566 0.013511\n", - " 5441 0.012197\n", - " 5458 0.011876\n", - " Name: 511, dtype: float64, 8141 0.025037\n", - " 5414 0.018495\n", - " 6035 0.015780\n", - " 7429 0.014381\n", - " 5678 0.011384\n", - " 5971 0.008473\n", - " 7511 0.007960\n", - " 6430 0.007482\n", - " 6296 0.006885\n", - " 6628 0.006810\n", - " Name: 512, dtype: float64, 5427 0.988391\n", - " 3470 0.003670\n", - " 3513 0.000569\n", - " 4116 0.000376\n", - " 3586 0.000331\n", - " 3175 0.000286\n", - " 3548 0.000198\n", - " 171 0.000136\n", - " 3482 0.000126\n", - " 6584 0.000095\n", - " Name: 513, dtype: float64, 3470 0.630993\n", - " 3497 0.165418\n", - " 3476 0.033591\n", - " 3548 0.009397\n", - " 3544 0.007424\n", - " 3546 0.007294\n", - " 3172 0.004515\n", - " 3493 0.003940\n", - " 3171 0.003177\n", - " 901 0.003049\n", - " Name: 514, dtype: float64, 3476 0.560534\n", - " 3165 0.061977\n", - " 2236 0.034279\n", - " 3172 0.021689\n", - " 3546 0.016018\n", - " 230 0.012558\n", - " 901 0.012384\n", - " 3422 0.011263\n", - " 2776 0.010522\n", - " 3497 0.009684\n", - " Name: 515, dtype: float64, 3535 0.991376\n", - " 7609 0.001194\n", - " 677 0.001047\n", - " 387 0.000516\n", - " 7894 0.000293\n", - " 679 0.000203\n", - " 8274 0.000153\n", - " 870 0.000146\n", - " 7489 0.000120\n", - " 2213 0.000114\n", - " Name: 516, dtype: float64, 3474 0.999573\n", - " 6022 0.000081\n", - " 3478 0.000060\n", - " 6062 0.000027\n", - " 8087 0.000010\n", - " 2508 0.000007\n", - " 3298 0.000006\n", - " 677 0.000004\n", - " 6012 0.000003\n", - " 3157 0.000003\n", - " Name: 517, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 518, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 519, dtype: float64, 3435 9.999139e-01\n", - " 2342 3.968682e-05\n", - " 1663 5.422433e-06\n", - " 2236 2.685723e-06\n", - " 3173 1.795915e-06\n", - " 3497 1.723633e-06\n", - " 3172 1.242794e-06\n", - " 387 6.868481e-07\n", - " 3546 6.590905e-07\n", - " 230 5.480355e-07\n", - " Name: 520, dtype: float64, 3415 0.712612\n", - " 3416 0.242450\n", - " 3413 0.005020\n", - " 3402 0.002591\n", - " 3405 0.002497\n", - " 5258 0.002031\n", - " 3449 0.001485\n", - " 3572 0.001227\n", - " 3457 0.001003\n", - " 3417 0.000756\n", - " Name: 521, dtype: float64, 3634 0.828222\n", - " 3627 0.030843\n", - " 3630 0.020751\n", - " 3636 0.005504\n", - " 3625 0.002066\n", - " 556 0.001967\n", - " 3238 0.001285\n", - " 3521 0.001225\n", - " 3776 0.001133\n", - " 3236 0.001072\n", - " Name: 522, dtype: float64, 3638 0.976067\n", - " 3803 0.018929\n", - " 3671 0.000424\n", - " 3166 0.000117\n", - " 2236 0.000110\n", - " 2780 0.000097\n", - " 4141 0.000093\n", - " 3348 0.000084\n", - " 3807 0.000082\n", - " 3670 0.000081\n", - " Name: 523, dtype: float64, 3648 0.996511\n", - " 3647 0.000595\n", - " 3545 0.000145\n", - " 3806 0.000094\n", - " 3991 0.000088\n", - " 3543 0.000081\n", - " 3690 0.000050\n", - " 230 0.000044\n", - " 5270 0.000042\n", - " 3640 0.000039\n", - " Name: 524, dtype: float64, 3587 0.987866\n", - " 3658 0.002435\n", - " 3991 0.000324\n", - " 2236 0.000305\n", - " 3990 0.000304\n", - " 4709 0.000284\n", - " 4203 0.000220\n", - " 2435 0.000211\n", - " 5769 0.000192\n", - " 119 0.000177\n", - " Name: 525, dtype: float64, 3667 0.985226\n", - " 3779 0.006646\n", - " 3974 0.000751\n", - " 3973 0.000142\n", - " 3242 0.000093\n", - " 3368 0.000086\n", - " 3891 0.000084\n", - " 3865 0.000081\n", - " 3864 0.000081\n", - " 5625 0.000075\n", - " Name: 526, dtype: float64, 3638 0.973197\n", - " 3803 0.019992\n", - " 3671 0.000678\n", - " 3166 0.000165\n", - " 3348 0.000158\n", - " 3670 0.000157\n", - " 3807 0.000133\n", + " 2681 0.000034\n", + " Name: 471, dtype: float64, 2796 0.862134\n", + " 3192 0.050387\n", + " 6313 0.005528\n", + " 2534 0.004508\n", + " 3605 0.004210\n", + " 3261 0.003831\n", + " 3195 0.003740\n", + " 3193 0.002671\n", + " 2780 0.002651\n", + " 3278 0.001691\n", + " Name: 472, dtype: float64, 3260 0.960332\n", + " 3350 0.001195\n", + " 1066 0.000637\n", + " 2886 0.000418\n", + " 8808 0.000396\n", + " 7388 0.000393\n", + " 1663 0.000376\n", + " 2337 0.000375\n", + " 5624 0.000353\n", + " 8443 0.000332\n", + " Name: 473, dtype: float64, 3173 0.999306\n", + " 3165 0.000129\n", + " 4135 0.000045\n", + " 870 0.000029\n", + " 901 0.000022\n", + " 3497 0.000020\n", + " 2236 0.000020\n", + " 3972 0.000009\n", + " 871 0.000009\n", + " 2681 0.000009\n", + " Name: 474, dtype: float64, 3325 0.999759\n", + " 3637 0.000095\n", + " 2502 0.000016\n", + " 7017 0.000011\n", + " 1805 0.000010\n", + " 3098 0.000007\n", + " 2768 0.000003\n", + " 2236 0.000003\n", + " 3687 0.000003\n", + " 679 0.000003\n", + " Name: 475, dtype: float64, 6896 0.155170\n", + " 7741 0.018850\n", + " 6325 0.016312\n", + " 5612 0.011902\n", + " 4647 0.011693\n", + " 7253 0.009973\n", + " 6898 0.009349\n", + " 6335 0.008728\n", + " 5614 0.008718\n", + " 7663 0.008037\n", + " Name: 476, dtype: float64, 79 9.999967e-01\n", + " 208 8.025027e-07\n", + " 1805 2.094937e-07\n", + " 7106 9.753835e-08\n", + " 517 7.567863e-08\n", + " 119 7.116866e-08\n", + " 2503 5.665149e-08\n", + " 4000 3.351583e-08\n", + " 3637 3.157524e-08\n", + " 2546 2.564377e-08\n", + " Name: 477, dtype: float64, 3403 0.108197\n", + " 3417 0.100100\n", + " 3412 0.085804\n", + " 3419 0.083108\n", + " 3420 0.075425\n", + " 3418 0.073976\n", + " 3404 0.056561\n", + " 3458 0.051310\n", + " 3457 0.043490\n", + " 3409 0.041522\n", + " Name: 478, dtype: float64, 3411 0.929707\n", + " 8263 0.001820\n", + " 8265 0.000991\n", + " 1542 0.000789\n", + " 5738 0.000700\n", + " 7545 0.000524\n", + " 7299 0.000517\n", + " 8149 0.000461\n", + " 6262 0.000420\n", + " 3420 0.000411\n", + " Name: 479, dtype: float64, 3413 0.295553\n", + " 3402 0.243903\n", + " 3405 0.127821\n", + " 3449 0.112072\n", + " 3404 0.035591\n", + " 3966 0.025003\n", + " 3403 0.021826\n", + " 3458 0.021378\n", + " 3416 0.020364\n", + " 3457 0.019638\n", + " Name: 480, dtype: float64, 3154 0.999583\n", + " 4126 0.000068\n", + " 6532 0.000028\n", + " 387 0.000008\n", + " 8284 0.000007\n", + " 5307 0.000005\n", + " 3605 0.000005\n", + " 3325 0.000003\n", + " 629 0.000003\n", + " 8112 0.000003\n", + " Name: 481, dtype: float64, 3436 0.949393\n", + " 3437 0.013584\n", + " 3566 0.001156\n", + " 3887 0.000831\n", + " 7610 0.000442\n", + " 3973 0.000382\n", + " 3035 0.000374\n", + " 6281 0.000305\n", + " 3891 0.000292\n", + " 3111 0.000287\n", + " Name: 482, dtype: float64, 3421 0.998788\n", + " 4127 0.000156\n", + " 4015 0.000050\n", + " 6814 0.000030\n", + " 6024 0.000029\n", + " 3090 0.000024\n", + " 3887 0.000021\n", + " 5337 0.000014\n", + " 2780 0.000013\n", + " 4026 0.000011\n", + " Name: 483, dtype: float64, 79 9.999951e-01\n", + " 208 1.422083e-06\n", + " 1805 3.256943e-07\n", + " 517 1.018937e-07\n", + " 7106 9.656453e-08\n", + " 119 8.481155e-08\n", + " 2503 7.454474e-08\n", + " 4000 3.552525e-08\n", + " 4098 3.261380e-08\n", + " 3637 2.943659e-08\n", + " Name: 484, dtype: float64, 2236 0.163524\n", + " 3435 0.152993\n", + " 2342 0.057950\n", + " 3584 0.055546\n", + " 3210 0.020911\n", + " 870 0.012554\n", + " 5458 0.011922\n", + " 5450 0.011145\n", + " 2993 0.009807\n", + " 5463 0.009306\n", + " Name: 485, dtype: float64, 3464 0.060458\n", + " 3447 0.040127\n", + " 8521 0.034835\n", + " 207 0.027980\n", + " 3122 0.021898\n", + " 8880 0.017189\n", + " 1714 0.015963\n", + " 2512 0.015540\n", + " 211 0.013861\n", + " 2932 0.012348\n", + " Name: 486, dtype: float64, 3450 0.886135\n", + " 3958 0.075513\n", + " 3403 0.002950\n", + " 3412 0.001625\n", + " 3607 0.001239\n", + " 4623 0.001122\n", + " 4622 0.001108\n", + " 5246 0.000937\n", + " 3885 0.000882\n", + " 3420 0.000862\n", + " Name: 487, dtype: float64, 3451 0.114086\n", + " 3978 0.010475\n", + " 9046 0.009937\n", + " 3409 0.009556\n", + " 10573 0.009287\n", + " 3420 0.008880\n", + " 6288 0.007507\n", + " 537 0.006939\n", + " 10069 0.006406\n", + " 2784 0.006238\n", + " Name: 488, dtype: float64, 6099 0.187080\n", + " 6132 0.021369\n", + " 6201 0.020847\n", + " 8744 0.017424\n", + " 7123 0.016027\n", + " 3464 0.014345\n", + " 7570 0.012286\n", + " 3501 0.007267\n", + " 6577 0.007099\n", + " 6112 0.006655\n", + " Name: 489, dtype: float64, 3964 0.088176\n", + " 183 0.026677\n", + " 3409 0.013804\n", + " 3419 0.012599\n", + " 3420 0.011639\n", + " 3632 0.011050\n", + " 3633 0.010760\n", + " 5917 0.009716\n", + " 3937 0.007913\n", + " 3733 0.007677\n", + " Name: 490, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 491, dtype: float64, 3429 0.181585\n", + " 3498 0.086993\n", + " 3605 0.081543\n", + " 3430 0.056018\n", + " 3885 0.041129\n", + " 3403 0.039290\n", + " 3884 0.019326\n", + " 3599 0.017795\n", + " 6919 0.012101\n", + " 2173 0.011850\n", + " Name: 492, dtype: float64, 3493 0.401846\n", + " 3605 0.095207\n", + " 3511 0.065939\n", + " 3492 0.042224\n", + " 3425 0.025886\n", + " 3470 0.015407\n", + " 3245 0.015002\n", + " 2987 0.013481\n", + " 2971 0.008595\n", + " 3530 0.006324\n", + " Name: 493, dtype: float64, 3470 0.999364\n", + " 3497 0.000107\n", + " 3493 0.000053\n", + " 3671 0.000032\n", + " 3548 0.000030\n", + " 3476 0.000024\n", + " 3544 0.000021\n", + " 3546 0.000020\n", + " 870 0.000019\n", + " 901 0.000015\n", + " Name: 494, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 495, dtype: float64, 3599 0.624954\n", + " 870 0.086789\n", + " 3498 0.019242\n", + " 3916 0.012351\n", + " 3315 0.006066\n", + " 2173 0.005950\n", + " 3403 0.005420\n", + " 698 0.004945\n", + " 3195 0.004563\n", + " 3429 0.004417\n", + " Name: 496, dtype: float64, 3980 0.796465\n", + " 4098 0.013153\n", + " 5771 0.012235\n", + " 7141 0.010397\n", + " 6839 0.010112\n", + " 3353 0.009011\n", + " 7173 0.008677\n", + " 5935 0.006725\n", + " 2682 0.005993\n", + " 2734 0.005177\n", + " Name: 497, dtype: float64, 3470 0.998667\n", + " 3497 0.000284\n", + " 3544 0.000104\n", + " 3493 0.000067\n", + " 3671 0.000062\n", + " 3546 0.000050\n", + " 3476 0.000048\n", + " 870 0.000035\n", + " 2236 0.000034\n", + " 3548 0.000021\n", + " Name: 498, dtype: float64, 3529 0.265424\n", + " 3593 0.127173\n", + " 1701 0.050445\n", + " 2795 0.025467\n", + " 2794 0.019195\n", + " 1700 0.012323\n", + " 7854 0.011147\n", + " 3496 0.011100\n", + " 3658 0.010837\n", + " 8127 0.009932\n", + " Name: 499, dtype: float64, 9166 0.182814\n", + " 3157 0.048745\n", + " 2333 0.031649\n", + " 5578 0.015531\n", + " 387 0.012220\n", + " 7112 0.010413\n", + " 8171 0.009644\n", + " 7770 0.008294\n", + " 7115 0.007855\n", + " 7114 0.007332\n", + " Name: 500, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 501, dtype: float64, 3516 0.204041\n", + " 3517 0.168334\n", + " 3519 0.154747\n", + " 3515 0.069644\n", + " 7499 0.004842\n", + " 2654 0.003461\n", + " 2136 0.003406\n", + " 3508 0.003245\n", + " 3887 0.003179\n", + " 2633 0.002960\n", + " Name: 502, dtype: float64, 3470 0.998884\n", + " 3497 0.000153\n", + " 870 0.000059\n", + " 3548 0.000057\n", + " 3493 0.000051\n", + " 2236 0.000045\n", + " 3671 0.000030\n", + " 3476 0.000026\n", + " 901 0.000024\n", + " 3546 0.000017\n", + " Name: 503, dtype: float64, 3916 0.244169\n", + " 5439 0.190263\n", + " 3429 0.094272\n", + " 3430 0.076488\n", + " 3403 0.026223\n", + " 3498 0.016216\n", + " 3425 0.014896\n", + " 3885 0.013071\n", + " 3884 0.012037\n", + " 3599 0.012021\n", + " Name: 504, dtype: float64, 3474 0.998995\n", + " 8583 0.000197\n", + " 2508 0.000059\n", + " 6022 0.000049\n", + " 3165 0.000030\n", + " 3478 0.000014\n", + " 3298 0.000013\n", + " 3192 0.000012\n", + " 8681 0.000012\n", + " 3226 0.000010\n", + " Name: 505, dtype: float64, 3470 0.997989\n", + " 3497 0.000355\n", + " 3493 0.000097\n", + " 2236 0.000089\n", + " 3548 0.000070\n", + " 870 0.000052\n", + " 3671 0.000043\n", + " 901 0.000043\n", + " 3476 0.000041\n", + " 3544 0.000036\n", + " Name: 506, dtype: float64, 3476 0.851129\n", + " 3546 0.074851\n", + " 870 0.004069\n", + " 3648 0.003052\n", + " 5265 0.002853\n", + " 3497 0.001307\n", + " 3528 0.001153\n", + " 2236 0.001080\n", + " 5505 0.001020\n", + " 3526 0.000999\n", + " Name: 507, dtype: float64, 870 0.237570\n", + " 3507 0.114023\n", + " 3470 0.026194\n", + " 3598 0.017414\n", + " 3462 0.016237\n", + " 3422 0.015974\n", + " 3157 0.015113\n", + " 901 0.012432\n", + " 3347 0.012007\n", + " 3497 0.011141\n", + " Name: 508, dtype: float64, 9728 0.386590\n", + " 9745 0.027260\n", + " 9525 0.012915\n", + " 2589 0.012117\n", + " 9727 0.010161\n", + " 1262 0.009710\n", + " 9730 0.008721\n", + " 2333 0.007512\n", + " 6064 0.006426\n", + " 2795 0.006159\n", + " Name: 509, dtype: float64, 3403 0.383631\n", + " 3885 0.270070\n", + " 3884 0.269275\n", + " 5439 0.007227\n", + " 3412 0.006488\n", + " 3498 0.003935\n", + " 3916 0.002350\n", + " 3607 0.002168\n", + " 3886 0.001997\n", + " 5245 0.001758\n", + " Name: 510, dtype: float64, 5439 0.185462\n", + " 5124 0.026155\n", + " 3571 0.025116\n", + " 3566 0.021264\n", + " 6667 0.020322\n", + " 5441 0.017204\n", + " 9859 0.007262\n", + " 2622 0.007127\n", + " 6772 0.006749\n", + " 5708 0.006713\n", + " Name: 511, dtype: float64, 8141 0.114720\n", + " 7739 0.023863\n", + " 9896 0.021597\n", + " 7002 0.017530\n", + " 8262 0.014389\n", + " 6189 0.013331\n", + " 6934 0.012626\n", + " 6857 0.010416\n", + " 6943 0.009597\n", + " 6390 0.009366\n", + " Name: 512, dtype: float64, 5427 0.930445\n", + " 3470 0.011089\n", + " 3548 0.010504\n", + " 3175 0.003280\n", + " 3586 0.003163\n", + " 2352 0.000962\n", + " 870 0.000820\n", + " 2312 0.000798\n", + " 5463 0.000796\n", + " 3426 0.000650\n", + " Name: 513, dtype: float64, 3470 0.975989\n", + " 3497 0.007597\n", + " 870 0.001201\n", + " 3546 0.000973\n", + " 3544 0.000796\n", + " 3476 0.000753\n", + " 3596 0.000724\n", + " 3493 0.000618\n", + " 3671 0.000478\n", + " 2236 0.000367\n", + " Name: 514, dtype: float64, 3476 0.797335\n", + " 2236 0.028735\n", + " 870 0.020354\n", + " 3497 0.008011\n", + " 3482 0.007899\n", + " 3546 0.006665\n", + " 901 0.004968\n", + " 3470 0.004560\n", + " 5427 0.004124\n", + " 5531 0.002739\n", + " Name: 515, dtype: float64, 3535 0.968180\n", + " 677 0.017623\n", + " 8274 0.002714\n", + " 387 0.002006\n", + " 7894 0.000436\n", + " 2911 0.000261\n", + " 870 0.000244\n", + " 2502 0.000218\n", + " 2237 0.000136\n", + " 2356 0.000130\n", + " Name: 516, dtype: float64, 3474 0.998995\n", + " 8583 0.000197\n", + " 2508 0.000059\n", + " 6022 0.000049\n", + " 3165 0.000030\n", + " 3478 0.000014\n", + " 3298 0.000013\n", + " 3192 0.000012\n", + " 8681 0.000012\n", + " 3226 0.000010\n", + " Name: 517, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 518, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 519, dtype: float64, 3435 9.998645e-01\n", + " 2342 8.269207e-05\n", + " 2236 4.286006e-06\n", + " 3497 2.770052e-06\n", + " 3470 1.648902e-06\n", + " 1683 7.623727e-07\n", + " 274 7.118318e-07\n", + " 2338 6.377010e-07\n", + " 1820 6.126409e-07\n", + " 85 5.841174e-07\n", + " Name: 520, dtype: float64, 3415 0.662004\n", + " 3416 0.320406\n", + " 3413 0.001249\n", + " 3405 0.000910\n", + " 3637 0.000908\n", + " 3572 0.000813\n", + " 3404 0.000800\n", + " 3402 0.000742\n", + " 3916 0.000727\n", + " 3449 0.000356\n", + " Name: 521, dtype: float64, 3634 0.515873\n", + " 3627 0.012647\n", + " 3630 0.010047\n", + " 5027 0.008620\n", + " 6047 0.006933\n", + " 3776 0.006248\n", + " 6222 0.004770\n", + " 7167 0.004609\n", + " 3625 0.004533\n", + " 6050 0.004044\n", + " Name: 522, dtype: float64, 3638 0.959243\n", + " 3803 0.031225\n", + " 2236 0.000982\n", + " 3671 0.000309\n", + " 7302 0.000224\n", + " 9567 0.000187\n", + " 3759 0.000154\n", + " 3807 0.000152\n", + " 677 0.000145\n", + " 7017 0.000131\n", + " Name: 523, dtype: float64, 3648 0.997046\n", + " 3647 0.000390\n", + " 2896 0.000210\n", + " 3545 0.000184\n", + " 3587 0.000060\n", + " 3806 0.000052\n", + " 3645 0.000044\n", + " 5270 0.000043\n", + " 3534 0.000035\n", + " 1514 0.000034\n", + " Name: 524, dtype: float64, 3587 0.931514\n", + " 2896 0.009853\n", + " 3658 0.006707\n", + " 3545 0.006481\n", + " 3659 0.005514\n", + " 3806 0.002670\n", + " 863 0.001437\n", + " 4716 0.001297\n", + " 3648 0.001230\n", + " 2260 0.000934\n", + " Name: 525, dtype: float64, 3667 0.956431\n", + " 3779 0.019410\n", + " 3974 0.004067\n", + " 3864 0.000593\n", + " 3776 0.000576\n", + " 3866 0.000536\n", + " 3891 0.000364\n", + " 3513 0.000270\n", + " 3973 0.000268\n", + " 4666 0.000213\n", + " Name: 526, dtype: float64, 3638 0.976620\n", + " 3803 0.016565\n", + " 2236 0.000440\n", + " 677 0.000140\n", + " 3807 0.000111\n", + " 2503 0.000108\n", + " 3671 0.000103\n", + " 3426 0.000091\n", + " 5528 0.000081\n", + " 9567 0.000081\n", + " Name: 527, dtype: float64, 3782 0.033994\n", + " 3426 0.022157\n", + " 4109 0.018588\n", + " 4222 0.014060\n", + " 4715 0.013412\n", + " 5268 0.012044\n", + " 4777 0.010677\n", + " 3758 0.009745\n", + " 4142 0.009700\n", + " 3861 0.009052\n", + " Name: 528, dtype: float64, 3691 0.980684\n", + " 2359 0.000737\n", + " 3657 0.000311\n", + " 124 0.000301\n", + " 3873 0.000300\n", + " 3275 0.000299\n", + " 3787 0.000243\n", + " 95 0.000234\n", + " 3543 0.000200\n", + " 2312 0.000185\n", + " Name: 529, dtype: float64, 3637 0.836648\n", + " 3687 0.160961\n", + " 2780 0.000150\n", + " 3416 0.000141\n", " 2236 0.000120\n", - " 2780 0.000090\n", - " 3887 0.000080\n", - " Name: 527, dtype: float64, 3782 0.331756\n", - " 3426 0.021280\n", - " 3832 0.016425\n", - " 3686 0.012724\n", - " 3762 0.010772\n", - " 4111 0.008550\n", - " 5917 0.008027\n", - " 5246 0.007028\n", - " 3872 0.006330\n", - " 5245 0.006235\n", - " Name: 528, dtype: float64, 3691 0.998198\n", - " 3671 0.000057\n", - " 3787 0.000042\n", - " 3762 0.000020\n", - " 3647 0.000018\n", - " 3663 0.000017\n", - " 5294 0.000017\n", - " 3591 0.000014\n", - " 230 0.000014\n", - " 3652 0.000014\n", - " Name: 529, dtype: float64, 3637 0.833382\n", - " 3687 0.162733\n", - " 2236 0.000261\n", - " 690 0.000134\n", - " 753 0.000106\n", - " 3165 0.000083\n", - " 205 0.000081\n", - " 3263 0.000071\n", - " 2351 0.000060\n", - " 4203 0.000058\n", - " Name: 530, dtype: float64, 3695 0.669020\n", - " 4720 0.017231\n", - " 5802 0.006403\n", - " 4149 0.004277\n", - " 4736 0.003511\n", - " 3937 0.003119\n", - " 3046 0.002731\n", - " 4015 0.002589\n", - " 6105 0.002482\n", - " 2214 0.002128\n", - " Name: 531, dtype: float64, 3702 0.046937\n", - " 3701 0.034536\n", - " 3699 0.028261\n", - " 3697 0.019143\n", - " 8122 0.017442\n", - " 5646 0.011959\n", - " 5811 0.009755\n", - " 3707 0.009444\n", - " 5644 0.008186\n", - " 3816 0.008129\n", - " Name: 532, dtype: float64, 3713 0.042107\n", - " 3725 0.041416\n", - " 3711 0.039709\n", - " 3721 0.039682\n", - " 3726 0.039332\n", - " 3717 0.039292\n", - " 3712 0.039292\n", - " 3723 0.039090\n", - " 3729 0.038852\n", - " 3724 0.038831\n", - " Name: 533, dtype: float64, 3743 0.071922\n", - " 3744 0.071494\n", - " 3733 0.069278\n", - " 3738 0.069098\n", - " 3748 0.067403\n", - " 3734 0.066643\n", - " 3735 0.066396\n", - " 3737 0.066142\n", - " 3845 0.064045\n", - " 3739 0.063521\n", - " Name: 534, dtype: float64, 3743 0.071922\n", - " 3744 0.071494\n", - " 3733 0.069278\n", - " 3738 0.069098\n", - " 3748 0.067403\n", - " 3734 0.066643\n", - " 3735 0.066396\n", - " 3737 0.066142\n", - " 3845 0.064045\n", - " 3739 0.063521\n", - " Name: 535, dtype: float64, 3644 0.999906\n", - " 2236 0.000009\n", - " 3165 0.000004\n", - " 3543 0.000004\n", - " 3637 0.000003\n", - " 3690 0.000002\n", - " 3687 0.000002\n", - " 205 0.000002\n", - " 5263 0.000002\n", - " 2681 0.000002\n", - " Name: 536, dtype: float64, 3638 0.973197\n", - " 3803 0.019992\n", - " 3671 0.000678\n", - " 3166 0.000165\n", - " 3348 0.000158\n", - " 3670 0.000157\n", - " 3807 0.000133\n", + " 3415 0.000073\n", + " 2260 0.000056\n", + " 211 0.000040\n", + " 4073 0.000037\n", + " 4013 0.000031\n", + " Name: 530, dtype: float64, 3695 0.288664\n", + " 4149 0.162826\n", + " 7082 0.016004\n", + " 3091 0.014389\n", + " 5964 0.013142\n", + " 5769 0.012771\n", + " 7753 0.010126\n", + " 5802 0.008033\n", + " 5760 0.006997\n", + " 7040 0.006316\n", + " Name: 531, dtype: float64, 3698 0.117286\n", + " 3702 0.029081\n", + " 3777 0.028689\n", + " 4787 0.016402\n", + " 3703 0.016018\n", + " 5547 0.014849\n", + " 4849 0.009405\n", + " 8291 0.007468\n", + " 4842 0.007147\n", + " 3705 0.006817\n", + " Name: 532, dtype: float64, 3721 0.046462\n", + " 3714 0.046143\n", + " 3729 0.043894\n", + " 3711 0.042981\n", + " 3724 0.042976\n", + " 3722 0.042679\n", + " 3713 0.041902\n", + " 3730 0.041489\n", + " 3723 0.041012\n", + " 3728 0.040603\n", + " Name: 533, dtype: float64, 3738 0.079329\n", + " 3743 0.076253\n", + " 3733 0.075995\n", + " 3739 0.074190\n", + " 3737 0.072988\n", + " 3744 0.070689\n", + " 3734 0.069051\n", + " 3748 0.064559\n", + " 3841 0.061518\n", + " 3735 0.061292\n", + " Name: 534, dtype: float64, 3738 0.079329\n", + " 3743 0.076253\n", + " 3733 0.075995\n", + " 3739 0.074190\n", + " 3737 0.072988\n", + " 3744 0.070689\n", + " 3734 0.069051\n", + " 3748 0.064559\n", + " 3841 0.061518\n", + " 3735 0.061292\n", + " Name: 535, dtype: float64, 3644 0.999800\n", + " 2236 0.000060\n", + " 3690 0.000020\n", + " 3687 0.000007\n", + " 2260 0.000007\n", + " 2780 0.000005\n", + " 107 0.000005\n", + " 753 0.000003\n", + " 3543 0.000003\n", + " 2503 0.000003\n", + " Name: 536, dtype: float64, 3638 0.976620\n", + " 3803 0.016565\n", + " 2236 0.000440\n", + " 677 0.000140\n", + " 3807 0.000111\n", + " 2503 0.000108\n", + " 3671 0.000103\n", + " 3426 0.000091\n", + " 5528 0.000081\n", + " 9567 0.000081\n", + " Name: 537, dtype: float64, 3644 0.999800\n", + " 2236 0.000060\n", + " 3690 0.000020\n", + " 3687 0.000007\n", + " 2260 0.000007\n", + " 2780 0.000005\n", + " 107 0.000005\n", + " 753 0.000003\n", + " 3543 0.000003\n", + " 2503 0.000003\n", + " Name: 538, dtype: float64, 3762 0.769795\n", + " 3761 0.029048\n", + " 3766 0.025648\n", + " 3882 0.014919\n", + " 3763 0.010348\n", + " 3759 0.009808\n", + " 3767 0.006665\n", + " 3671 0.002372\n", + " 3807 0.001878\n", + " 3638 0.001726\n", + " Name: 539, dtype: float64, 3325 0.989724\n", + " 3637 0.001130\n", + " 7017 0.000384\n", + " 2768 0.000311\n", + " 2703 0.000208\n", + " 3090 0.000189\n", + " 1315 0.000169\n", + " 389 0.000155\n", + " 759 0.000144\n", + " 3543 0.000128\n", + " Name: 540, dtype: float64, 3637 0.836648\n", + " 3687 0.160961\n", + " 2780 0.000150\n", + " 3416 0.000141\n", " 2236 0.000120\n", - " 2780 0.000090\n", - " 3887 0.000080\n", - " Name: 537, dtype: float64, 3644 0.999906\n", - " 2236 0.000009\n", - " 3165 0.000004\n", - " 3543 0.000004\n", - " 3637 0.000003\n", - " 3690 0.000002\n", - " 3687 0.000002\n", - " 205 0.000002\n", - " 5263 0.000002\n", - " 2681 0.000002\n", - " Name: 538, dtype: float64, 3762 0.305716\n", - " 3766 0.146966\n", - " 3759 0.119630\n", - " 3761 0.100528\n", - " 3767 0.028966\n", - " 3882 0.027467\n", - " 3654 0.014470\n", - " 3652 0.014263\n", - " 3763 0.011127\n", - " 3663 0.010478\n", - " Name: 539, dtype: float64, 3325 0.993878\n", - " 3637 0.001350\n", - " 7023 0.000732\n", - " 7017 0.000533\n", - " 2236 0.000147\n", - " 3687 0.000101\n", - " 690 0.000089\n", - " 6984 0.000076\n", - " 7016 0.000072\n", - " 2768 0.000060\n", - " Name: 540, dtype: float64, 3637 0.833382\n", - " 3687 0.162733\n", - " 2236 0.000261\n", - " 690 0.000134\n", - " 753 0.000106\n", - " 3165 0.000083\n", - " 205 0.000081\n", - " 3263 0.000071\n", - " 2351 0.000060\n", - " 4203 0.000058\n", - " Name: 541, dtype: float64, 3647 0.999798\n", - " 3545 0.000027\n", - " 3648 0.000019\n", - " 3991 0.000009\n", - " 3543 0.000007\n", - " 3687 0.000005\n", - " 3759 0.000004\n", - " 79 0.000004\n", - " 3690 0.000004\n", - " 2896 0.000004\n", - " Name: 542, dtype: float64, 3775 0.894031\n", - " 3776 0.003338\n", - " 3358 0.003156\n", - " 3774 0.002797\n", - " 3037 0.002348\n", - " 4122 0.001583\n", - " 3590 0.001165\n", - " 6162 0.001143\n", - " 2778 0.001143\n", - " 5114 0.001106\n", - " Name: 543, dtype: float64, 3671 0.594616\n", - " 3670 0.393114\n", - " 2780 0.000886\n", - " 2236 0.000441\n", - " 3638 0.000269\n", - " 870 0.000238\n", - " 677 0.000202\n", - " 3497 0.000170\n", - " 4067 0.000156\n", - " 8274 0.000131\n", - " Name: 544, dtype: float64, 3671 0.594616\n", - " 3670 0.393114\n", - " 2780 0.000886\n", - " 2236 0.000441\n", - " 3638 0.000269\n", - " 870 0.000238\n", - " 677 0.000202\n", - " 3497 0.000170\n", - " 4067 0.000156\n", - " 8274 0.000131\n", - " Name: 545, dtype: float64, 3686 0.998685\n", - " 3782 0.000135\n", - " 3425 0.000075\n", - " 3904 0.000058\n", - " 2237 0.000031\n", - " 3858 0.000024\n", - " 3906 0.000020\n", - " 5917 0.000014\n", - " 3671 0.000013\n", - " 3892 0.000010\n", - " Name: 546, dtype: float64, 3545 0.999894\n", - " 3647 0.000011\n", - " 6546 0.000008\n", - " 3658 0.000003\n", - " 3648 0.000003\n", - " 2896 0.000003\n", - " 3991 0.000002\n", - " 5826 0.000001\n", - " 842 0.000001\n", - " 5270 0.000001\n", - " Name: 547, dtype: float64, 3637 0.581869\n", - " 3687 0.403762\n", - " 2236 0.000815\n", - " 753 0.000614\n", - " 690 0.000334\n", - " 3325 0.000308\n", - " 3263 0.000286\n", - " 79 0.000272\n", - " 4203 0.000258\n", - " 160 0.000228\n", - " Name: 548, dtype: float64, 3638 0.836823\n", - " 3803 0.051689\n", - " 3671 0.037359\n", - " 2780 0.003870\n", - " 3670 0.003810\n", - " 3759 0.003431\n", - " 870 0.002626\n", - " 2236 0.002217\n", - " 3762 0.001357\n", - " 677 0.000975\n", - " Name: 549, dtype: float64, 3638 0.976067\n", - " 3803 0.018929\n", - " 3671 0.000424\n", - " 3166 0.000117\n", - " 2236 0.000110\n", - " 2780 0.000097\n", - " 4141 0.000093\n", - " 3348 0.000084\n", - " 3807 0.000082\n", - " 3670 0.000081\n", - " Name: 550, dtype: float64, 3691 0.998198\n", - " 3671 0.000057\n", - " 3787 0.000042\n", - " 3762 0.000020\n", - " 3647 0.000018\n", - " 3663 0.000017\n", - " 5294 0.000017\n", - " 3591 0.000014\n", - " 230 0.000014\n", - " 3652 0.000014\n", - " Name: 551, dtype: float64, 3671 0.594616\n", - " 3670 0.393114\n", - " 2780 0.000886\n", - " 2236 0.000441\n", - " 3638 0.000269\n", - " 870 0.000238\n", - " 677 0.000202\n", - " 3497 0.000170\n", - " 4067 0.000156\n", - " 8274 0.000131\n", - " Name: 552, dtype: float64, 3649 0.997563\n", - " 3832 0.000560\n", - " 3801 0.000081\n", - " 3534 0.000054\n", - " 2681 0.000045\n", - " 3754 0.000040\n", - " 3808 0.000034\n", - " 205 0.000027\n", - " 3426 0.000020\n", - " 4078 0.000019\n", - " Name: 553, dtype: float64, 3667 0.985226\n", - " 3779 0.006646\n", - " 3974 0.000751\n", - " 3973 0.000142\n", - " 3242 0.000093\n", - " 3368 0.000086\n", - " 3891 0.000084\n", - " 3865 0.000081\n", - " 3864 0.000081\n", - " 5625 0.000075\n", - " Name: 554, dtype: float64, 107 0.118531\n", - " 3874 0.062072\n", - " 3835 0.027990\n", - " 3763 0.023080\n", - " 4720 0.021291\n", - " 3762 0.013538\n", - " 5252 0.013374\n", - " 1471 0.010847\n", - " 3654 0.010366\n", - " 3754 0.009663\n", - " Name: 555, dtype: float64, 3587 0.611058\n", - " 3647 0.067186\n", - " 3545 0.025934\n", - " 3991 0.018561\n", - " 3990 0.009897\n", - " 3690 0.009873\n", - " 3659 0.006594\n", - " 3648 0.006039\n", - " 3658 0.004586\n", - " 4203 0.003632\n", - " Name: 556, dtype: float64, 3643 0.049198\n", - " 3348 0.043938\n", - " 3836 0.036324\n", - " 9567 0.028099\n", - " 6893 0.022415\n", - " 3347 0.015729\n", - " 6905 0.009785\n", - " 7174 0.009259\n", - " 3690 0.009214\n", - " 3987 0.007123\n", - " Name: 557, dtype: float64, 3670 0.524091\n", - " 3671 0.465189\n", - " 2236 0.000419\n", - " 2780 0.000270\n", - " 3638 0.000244\n", - " 870 0.000212\n", - " 3497 0.000165\n", - " 8274 0.000150\n", - " 677 0.000138\n", - " 3803 0.000129\n", - " Name: 558, dtype: float64, 3847 0.123992\n", - " 3849 0.121657\n", - " 3848 0.120513\n", - " 3851 0.119501\n", - " 3853 0.117106\n", - " 3862 0.115745\n", - " 3758 0.037467\n", - " 3863 0.035135\n", - " 3861 0.015356\n", - " 3345 0.012776\n", - " Name: 559, dtype: float64, 3847 0.123992\n", - " 3849 0.121657\n", - " 3848 0.120513\n", - " 3851 0.119501\n", - " 3853 0.117106\n", - " 3862 0.115745\n", - " 3758 0.037467\n", - " 3863 0.035135\n", - " 3861 0.015356\n", - " 3345 0.012776\n", - " Name: 560, dtype: float64, 3637 0.833382\n", - " 3687 0.162733\n", - " 2236 0.000261\n", - " 690 0.000134\n", - " 753 0.000106\n", - " 3165 0.000083\n", - " 205 0.000081\n", - " 3263 0.000071\n", - " 2351 0.000060\n", - " 4203 0.000058\n", - " Name: 561, dtype: float64, 3644 0.999906\n", - " 2236 0.000009\n", - " 3165 0.000004\n", - " 3543 0.000004\n", - " 3637 0.000003\n", - " 3690 0.000002\n", - " 3687 0.000002\n", - " 205 0.000002\n", - " 5263 0.000002\n", - " 2681 0.000002\n", - " Name: 562, dtype: float64, 3667 0.868315\n", - " 3779 0.089990\n", - " 3974 0.001372\n", - " 3864 0.000922\n", - " 3242 0.000469\n", - " 2214 0.000415\n", - " 3865 0.000415\n", - " 3685 0.000317\n", - " 3509 0.000297\n", - " 3233 0.000278\n", - " Name: 563, dtype: float64, 3637 0.833382\n", - " 3687 0.162733\n", - " 2236 0.000261\n", - " 690 0.000134\n", - " 753 0.000106\n", - " 3165 0.000083\n", - " 205 0.000081\n", - " 3263 0.000071\n", - " 2351 0.000060\n", - " 4203 0.000058\n", - " Name: 564, dtype: float64, 3403 0.351766\n", - " 3884 0.287114\n", - " 3885 0.236090\n", - " 3916 0.011572\n", - " 5439 0.011427\n", - " 3498 0.006474\n", - " 3412 0.005404\n", - " 3566 0.002894\n", - " 3886 0.002433\n", - " 3418 0.002229\n", - " Name: 565, dtype: float64, 3885 0.135234\n", - " 3886 0.066520\n", - " 3861 0.050021\n", - " 3884 0.049455\n", - " 3450 0.033257\n", - " 3958 0.014614\n", - " 3605 0.012150\n", - " 3758 0.009556\n", - " 4107 0.009046\n", - " 3665 0.008900\n", - " Name: 566, dtype: float64, 3436 0.789582\n", - " 3437 0.088831\n", - " 3035 0.003165\n", - " 5252 0.001679\n", - " 3887 0.001666\n", - " 3667 0.001399\n", - " 3756 0.001376\n", - " 5739 0.001349\n", - " 3891 0.001299\n", - " 5202 0.001206\n", - " Name: 567, dtype: float64, 3427 0.995437\n", - " 3426 0.000407\n", - " 5245 0.000343\n", - " 3412 0.000231\n", - " 2236 0.000097\n", - " 3454 0.000076\n", - " 3405 0.000055\n", - " 3402 0.000050\n", - " 3916 0.000045\n", - " 5246 0.000038\n", - " Name: 568, dtype: float64, 3424 9.999202e-01\n", - " 5174 5.096279e-06\n", - " 3104 2.156154e-06\n", - " 3551 1.854505e-06\n", - " 5700 1.771546e-06\n", - " 2340 1.029167e-06\n", - " 2236 9.585967e-07\n", - " 677 9.444391e-07\n", - " 3428 8.891836e-07\n", - " 5428 8.228814e-07\n", - " Name: 569, dtype: float64, 3425 0.862407\n", - " 3322 0.001563\n", - " 4192 0.001504\n", - " 3796 0.001298\n", - " 5666 0.001126\n", - " 3550 0.001102\n", - " 3551 0.000943\n", - " 3664 0.000861\n", - " 3659 0.000793\n", - " 3665 0.000772\n", - " Name: 570, dtype: float64, 3320 0.991123\n", - " 3606 0.001463\n", - " 3319 0.000607\n", - " 3353 0.000548\n", - " 2630 0.000404\n", - " 677 0.000247\n", - " 2236 0.000247\n", - " 3154 0.000195\n", - " 3157 0.000116\n", - " 7678 0.000099\n", - " Name: 571, dtype: float64, 3909 0.252934\n", - " 3324 0.144875\n", - " 3893 0.128592\n", - " 3913 0.089508\n", - " 3920 0.087421\n", - " 3914 0.087298\n", - " 3912 0.010596\n", - " 3917 0.010021\n", - " 5266 0.009821\n", - " 3898 0.009810\n", - " Name: 572, dtype: float64, 3925 0.688179\n", - " 3932 0.017122\n", - " 3951 0.012725\n", - " 3953 0.012052\n", - " 3787 0.011069\n", - " 3924 0.010670\n", - " 3933 0.010254\n", - " 3952 0.009862\n", - " 3955 0.008305\n", - " 3943 0.008138\n", - " Name: 573, dtype: float64, 3925 0.441482\n", - " 3933 0.077708\n", - " 3924 0.022945\n", - " 3953 0.022178\n", - " 3947 0.017279\n", - " 3946 0.011756\n", - " 3955 0.010433\n", - " 5244 0.009887\n", - " 3951 0.009027\n", - " 3932 0.008977\n", - " Name: 574, dtype: float64, 3934 0.213083\n", - " 3938 0.210345\n", - " 3941 0.208105\n", - " 3931 0.164705\n", - " 3924 0.023429\n", - " 3949 0.014446\n", - " 3946 0.006341\n", - " 3947 0.005854\n", - " 2987 0.003367\n", - " 6496 0.003319\n", - " Name: 575, dtype: float64, 3955 0.358884\n", - " 3953 0.297605\n", - " 3951 0.080665\n", - " 3952 0.079490\n", - " 3949 0.015278\n", - " 3933 0.006420\n", - " 3925 0.006163\n", - " 3947 0.005105\n", - " 3932 0.004918\n", - " 3787 0.003428\n", - " Name: 576, dtype: float64, 3953 0.354797\n", - " 3955 0.301627\n", - " 3952 0.080219\n", - " 3951 0.077642\n", - " 3949 0.011260\n", - " 3933 0.008855\n", - " 3947 0.006480\n", - " 3925 0.006442\n", - " 3932 0.005758\n", - " 3787 0.004477\n", - " Name: 577, dtype: float64, 2955 0.999806\n", - " 3901 0.000013\n", - " 3173 0.000007\n", - " 5235 0.000006\n", - " 5259 0.000006\n", - " 4131 0.000006\n", - " 79 0.000005\n", - " 9843 0.000003\n", - " 3637 0.000003\n", - " 2629 0.000002\n", - " Name: 578, dtype: float64, 3422 0.999582\n", - " 5384 0.000054\n", - " 2173 0.000037\n", - " 3165 0.000028\n", - " 5379 0.000023\n", - " 3423 0.000019\n", - " 3430 0.000018\n", - " 3315 0.000009\n", - " 3194 0.000008\n", - " 883 0.000007\n", - " Name: 579, dtype: float64, 2917 0.016832\n", - " 6883 0.013719\n", - " 6830 0.012923\n", - " 2591 0.011526\n", - " 2611 0.008688\n", - " 2737 0.007383\n", - " 2592 0.006371\n", - " 6973 0.006023\n", - " 5762 0.005559\n", - " 2885 0.005252\n", - " Name: 580, dtype: float64, 79 9.999953e-01\n", - " 9626 2.077114e-07\n", - " 160 1.583164e-07\n", - " 119 1.290514e-07\n", - " 8040 1.281136e-07\n", - " 1203 1.097907e-07\n", - " 2955 1.089247e-07\n", - " 2260 9.837526e-08\n", - " 205 8.386442e-08\n", - " 192 7.217826e-08\n", - " Name: 581, dtype: float64, 3402 0.260238\n", - " 3413 0.192305\n", - " 3405 0.181223\n", - " 3404 0.076306\n", - " 3449 0.068973\n", - " 3416 0.048213\n", - " 3457 0.019626\n", - " 3417 0.018788\n", - " 3966 0.018106\n", - " 3403 0.016213\n", - " Name: 582, dtype: float64, 3403 0.111433\n", - " 3412 0.093800\n", - " 3417 0.088607\n", - " 3420 0.079798\n", - " 3419 0.079730\n", - " 3418 0.071304\n", - " 3404 0.066055\n", - " 3409 0.044713\n", - " 3458 0.041255\n", - " 3405 0.036888\n", - " Name: 583, dtype: float64, 3402 0.260238\n", - " 3413 0.192305\n", - " 3405 0.181223\n", - " 3404 0.076306\n", - " 3449 0.068973\n", - " 3416 0.048213\n", - " 3457 0.019626\n", - " 3417 0.018788\n", - " 3966 0.018106\n", - " 3403 0.016213\n", - " Name: 584, dtype: float64, 3403 0.111433\n", - " 3412 0.093800\n", - " 3417 0.088607\n", - " 3420 0.079798\n", - " 3419 0.079730\n", - " 3418 0.071304\n", - " 3404 0.066055\n", - " 3409 0.044713\n", - " 3458 0.041255\n", - " 3405 0.036888\n", - " Name: 585, dtype: float64, 3403 0.111433\n", - " 3412 0.093800\n", - " 3417 0.088607\n", - " 3420 0.079798\n", - " 3419 0.079730\n", - " 3418 0.071304\n", - " 3404 0.066055\n", - " 3409 0.044713\n", - " 3458 0.041255\n", - " 3405 0.036888\n", - " Name: 586, dtype: float64, 3154 0.999371\n", - " 4126 0.000080\n", - " 7114 0.000038\n", - " 2236 0.000035\n", - " 7115 0.000022\n", - " 2630 0.000013\n", - " 677 0.000011\n", - " 3602 0.000011\n", - " 3325 0.000009\n", - " 3606 0.000008\n", - " Name: 587, dtype: float64, 2955 0.999806\n", - " 3901 0.000013\n", - " 3173 0.000007\n", - " 5235 0.000006\n", - " 5259 0.000006\n", - " 4131 0.000006\n", - " 79 0.000005\n", - " 9843 0.000003\n", - " 3637 0.000003\n", - " 2629 0.000002\n", - " Name: 588, dtype: float64, 3969 0.568728\n", - " 3035 0.008166\n", - " 6035 0.005056\n", - " 5657 0.004889\n", - " 3869 0.004709\n", - " 5648 0.004665\n", - " 5387 0.004165\n", - " 3036 0.003973\n", - " 5414 0.003310\n", - " 3864 0.002788\n", - " Name: 589, dtype: float64, 6025 0.396022\n", - " 3887 0.085066\n", - " 5275 0.043525\n", - " 6024 0.031422\n", - " 6483 0.030897\n", - " 3323 0.023567\n", - " 5739 0.018070\n", - " 2623 0.012996\n", - " 3602 0.010578\n", - " 2630 0.008474\n", - " Name: 590, dtype: float64, 3402 0.260238\n", - " 3413 0.192305\n", - " 3405 0.181223\n", - " 3404 0.076306\n", - " 3449 0.068973\n", - " 3416 0.048213\n", - " 3457 0.019626\n", - " 3417 0.018788\n", - " 3966 0.018106\n", - " 3403 0.016213\n", - " Name: 591, dtype: float64, 3402 0.260238\n", - " 3413 0.192305\n", - " 3405 0.181223\n", - " 3404 0.076306\n", - " 3449 0.068973\n", - " 3416 0.048213\n", - " 3457 0.019626\n", - " 3417 0.018788\n", - " 3966 0.018106\n", - " 3403 0.016213\n", - " Name: 592, dtype: float64, 2955 0.999564\n", - " 5235 0.000069\n", - " 3901 0.000033\n", - " 5259 0.000028\n", - " 4158 0.000011\n", - " 4131 0.000009\n", + " 3415 0.000073\n", + " 2260 0.000056\n", + " 211 0.000040\n", + " 4073 0.000037\n", + " 4013 0.000031\n", + " Name: 541, dtype: float64, 3647 0.999864\n", + " 3648 0.000021\n", + " 2780 0.000006\n", + " 4011 0.000006\n", + " 2896 0.000002\n", + " 2210 0.000002\n", + " 3755 0.000002\n", + " 506 0.000002\n", + " 4202 0.000001\n", + " 206 0.000001\n", + " Name: 542, dtype: float64, 3775 0.718495\n", + " 3776 0.018148\n", + " 7713 0.008618\n", + " 3634 0.004036\n", + " 3667 0.002717\n", + " 3253 0.002450\n", + " 3815 0.002443\n", + " 3779 0.002016\n", + " 3589 0.001987\n", + " 3169 0.001870\n", + " Name: 543, dtype: float64, 3671 0.578483\n", + " 3670 0.413012\n", + " 2503 0.000325\n", + " 208 0.000182\n", + " 2236 0.000158\n", + " 3638 0.000150\n", + " 1805 0.000149\n", + " 3470 0.000143\n", + " 3403 0.000123\n", + " 3759 0.000116\n", + " Name: 544, dtype: float64, 3671 0.578483\n", + " 3670 0.413012\n", + " 2503 0.000325\n", + " 208 0.000182\n", + " 2236 0.000158\n", + " 3638 0.000150\n", + " 1805 0.000149\n", + " 3470 0.000143\n", + " 3403 0.000123\n", + " 3759 0.000116\n", + " Name: 545, dtype: float64, 3686 0.997968\n", + " 3782 0.000199\n", + " 2385 0.000045\n", + " 3649 0.000043\n", + " 201 0.000039\n", + " 3781 0.000029\n", + " 7748 0.000025\n", + " 5230 0.000024\n", + " 4776 0.000023\n", + " 755 0.000023\n", + " Name: 546, dtype: float64, 3545 9.999288e-01\n", + " 6546 5.286768e-06\n", + " 7107 4.502076e-06\n", + " 3587 2.021436e-06\n", + " 207 1.727519e-06\n", + " 3658 1.558033e-06\n", + " 7106 1.333394e-06\n", + " 95 1.073730e-06\n", + " 5826 1.000064e-06\n", + " 2955 8.807516e-07\n", + " Name: 547, dtype: float64, 3637 0.873903\n", + " 3687 0.107129\n", + " 4073 0.000674\n", + " 7017 0.000652\n", + " 4158 0.000526\n", + " 3671 0.000485\n", + " 2236 0.000468\n", + " 3542 0.000253\n", + " 3320 0.000220\n", + " 690 0.000191\n", + " Name: 548, dtype: float64, 3638 0.858867\n", + " 3803 0.057096\n", + " 3759 0.022049\n", + " 2236 0.010466\n", + " 3671 0.007634\n", + " 3762 0.001934\n", + " 9567 0.001690\n", + " 3882 0.001175\n", + " 3807 0.000933\n", + " 7302 0.000926\n", + " Name: 549, dtype: float64, 3638 0.959243\n", + " 3803 0.031225\n", + " 2236 0.000982\n", + " 3671 0.000309\n", + " 7302 0.000224\n", + " 9567 0.000187\n", + " 3759 0.000154\n", + " 3807 0.000152\n", + " 677 0.000145\n", + " 7017 0.000131\n", + " Name: 550, dtype: float64, 3691 0.980684\n", + " 2359 0.000737\n", + " 3657 0.000311\n", + " 124 0.000301\n", + " 3873 0.000300\n", + " 3275 0.000299\n", + " 3787 0.000243\n", + " 95 0.000234\n", + " 3543 0.000200\n", + " 2312 0.000185\n", + " Name: 551, dtype: float64, 3671 0.578483\n", + " 3670 0.413012\n", + " 2503 0.000325\n", + " 208 0.000182\n", + " 2236 0.000158\n", + " 3638 0.000150\n", + " 1805 0.000149\n", + " 3470 0.000143\n", + " 3403 0.000123\n", + " 3759 0.000116\n", + " Name: 552, dtype: float64, 3649 0.998756\n", + " 3546 0.000108\n", + " 3754 0.000088\n", + " 3832 0.000042\n", + " 3688 0.000038\n", + " 3476 0.000034\n", + " 3753 0.000033\n", + " 3671 0.000030\n", + " 3657 0.000027\n", + " 3801 0.000016\n", + " Name: 553, dtype: float64, 3667 0.956431\n", + " 3779 0.019410\n", + " 3974 0.004067\n", + " 3864 0.000593\n", + " 3776 0.000576\n", + " 3866 0.000536\n", + " 3891 0.000364\n", + " 3513 0.000270\n", + " 3973 0.000268\n", + " 4666 0.000213\n", + " Name: 554, dtype: float64, 3835 0.084999\n", + " 206 0.048948\n", + " 3763 0.034433\n", + " 2236 0.023997\n", + " 5007 0.023343\n", + " 2260 0.021738\n", + " 230 0.015374\n", + " 5008 0.012864\n", + " 4202 0.012712\n", + " 3991 0.012700\n", + " Name: 555, dtype: float64, 3647 0.430380\n", + " 3587 0.164428\n", + " 3659 0.063825\n", + " 3648 0.019474\n", + " 2896 0.016457\n", + " 3545 0.014887\n", + " 3806 0.013909\n", + " 3690 0.013045\n", + " 3759 0.008949\n", + " 3476 0.006917\n", + " Name: 556, dtype: float64, 9567 0.357333\n", + " 3638 0.110305\n", + " 3759 0.041456\n", + " 3671 0.038316\n", + " 3803 0.012437\n", + " 2236 0.011972\n", + " 3670 0.011218\n", + " 3187 0.009586\n", + " 211 0.009121\n", + " 9552 0.008090\n", + " Name: 557, dtype: float64, 3670 0.546640\n", + " 3671 0.446454\n", + " 3470 0.000295\n", + " 2503 0.000240\n", + " 2236 0.000209\n", + " 4073 0.000195\n", + " 1805 0.000145\n", + " 208 0.000129\n", + " 3638 0.000093\n", + " 3403 0.000090\n", + " Name: 558, dtype: float64, 3851 0.153277\n", + " 3862 0.141504\n", + " 3849 0.135750\n", + " 3853 0.132234\n", + " 3847 0.128353\n", + " 3848 0.110145\n", + " 3863 0.026152\n", + " 3758 0.021334\n", + " 3861 0.015264\n", + " 3665 0.002910\n", + " Name: 559, dtype: float64, 3851 0.153277\n", + " 3862 0.141504\n", + " 3849 0.135750\n", + " 3853 0.132234\n", + " 3847 0.128353\n", + " 3848 0.110145\n", + " 3863 0.026152\n", + " 3758 0.021334\n", + " 3861 0.015264\n", + " 3665 0.002910\n", + " Name: 560, dtype: float64, 3637 0.836648\n", + " 3687 0.160961\n", + " 2780 0.000150\n", + " 3416 0.000141\n", + " 2236 0.000120\n", + " 3415 0.000073\n", + " 2260 0.000056\n", + " 211 0.000040\n", + " 4073 0.000037\n", + " 4013 0.000031\n", + " Name: 561, dtype: float64, 3644 0.999800\n", + " 2236 0.000060\n", + " 3690 0.000020\n", + " 3687 0.000007\n", + " 2260 0.000007\n", + " 2780 0.000005\n", + " 107 0.000005\n", + " 753 0.000003\n", + " 3543 0.000003\n", + " 2503 0.000003\n", + " Name: 562, dtype: float64, 3667 0.707248\n", + " 3779 0.208161\n", + " 3974 0.003739\n", + " 3866 0.003643\n", + " 3864 0.003384\n", + " 3869 0.002002\n", + " 3868 0.001828\n", + " 3776 0.000945\n", + " 5470 0.000899\n", + " 3924 0.000569\n", + " Name: 563, dtype: float64, 3637 0.836648\n", + " 3687 0.160961\n", + " 2780 0.000150\n", + " 3416 0.000141\n", + " 2236 0.000120\n", + " 3415 0.000073\n", + " 2260 0.000056\n", + " 211 0.000040\n", + " 4073 0.000037\n", + " 4013 0.000031\n", + " Name: 564, dtype: float64, 3403 0.383631\n", + " 3885 0.270070\n", + " 3884 0.269275\n", + " 5439 0.007227\n", + " 3412 0.006488\n", + " 3498 0.003935\n", + " 3916 0.002350\n", + " 3607 0.002168\n", + " 3886 0.001997\n", + " 5245 0.001758\n", + " Name: 565, dtype: float64, 3605 0.359683\n", + " 3885 0.093531\n", + " 3886 0.087342\n", + " 3884 0.063662\n", + " 3403 0.039395\n", + " 3916 0.016730\n", + " 3498 0.012905\n", + " 3861 0.010607\n", + " 3551 0.007853\n", + " 5334 0.007294\n", + " Name: 566, dtype: float64, 3436 0.949393\n", + " 3437 0.013584\n", + " 3566 0.001156\n", + " 3887 0.000831\n", + " 7610 0.000442\n", + " 3973 0.000382\n", + " 3035 0.000374\n", + " 6281 0.000305\n", + " 3891 0.000292\n", + " 3111 0.000287\n", + " Name: 567, dtype: float64, 3427 0.986301\n", + " 5245 0.001854\n", + " 870 0.001592\n", + " 3412 0.001176\n", + " 3403 0.000438\n", + " 3572 0.000348\n", + " 3916 0.000290\n", + " 5246 0.000166\n", + " 3426 0.000143\n", + " 3454 0.000119\n", + " Name: 568, dtype: float64, 3424 0.999357\n", + " 9526 0.000031\n", + " 207 0.000028\n", + " 2576 0.000020\n", + " 2392 0.000014\n", + " 8466 0.000009\n", + " 2312 0.000008\n", + " 1784 0.000007\n", + " 7238 0.000006\n", + " 2737 0.000006\n", + " Name: 569, dtype: float64, 3425 0.972337\n", + " 3543 0.001042\n", + " 3430 0.000633\n", + " 3916 0.000510\n", + " 3861 0.000505\n", + " 3758 0.000504\n", + " 1805 0.000485\n", + " 2547 0.000468\n", + " 3796 0.000447\n", + " 3605 0.000419\n", + " Name: 570, dtype: float64, 3320 0.557377\n", + " 3606 0.267764\n", + " 3319 0.021276\n", + " 2236 0.016705\n", + " 3323 0.007841\n", + " 160 0.007338\n", + " 2509 0.005437\n", + " 814 0.004389\n", + " 5527 0.004034\n", + " 3159 0.003846\n", + " Name: 571, dtype: float64, 3909 0.278146\n", + " 3893 0.100345\n", + " 3324 0.069824\n", + " 3914 0.060138\n", + " 3920 0.057409\n", + " 3913 0.054409\n", + " 3917 0.044685\n", + " 3912 0.017060\n", + " 3911 0.015623\n", + " 3910 0.015586\n", + " Name: 572, dtype: float64, 3925 0.749143\n", + " 3924 0.042850\n", + " 5286 0.005087\n", + " 3933 0.003239\n", + " 3787 0.003051\n", + " 4079 0.002707\n", + " 5297 0.002342\n", + " 3654 0.002254\n", + " 5347 0.002242\n", + " 4121 0.001838\n", + " Name: 573, dtype: float64, 3925 0.509761\n", + " 3924 0.060944\n", + " 5297 0.008966\n", + " 4093 0.007618\n", + " 3933 0.006713\n", + " 2821 0.005002\n", + " 4520 0.004270\n", + " 3477 0.004096\n", + " 3432 0.003936\n", + " 5286 0.003884\n", + " Name: 574, dtype: float64, 3931 0.228809\n", + " 3938 0.213935\n", + " 3934 0.210808\n", + " 3941 0.208012\n", + " 3947 0.007157\n", + " 3949 0.006620\n", + " 3946 0.006394\n", + " 3924 0.005243\n", + " 3942 0.002630\n", + " 3939 0.001971\n", + " Name: 575, dtype: float64, 3953 0.365146\n", + " 3955 0.355672\n", + " 3951 0.049996\n", + " 3952 0.047195\n", + " 3949 0.017128\n", + " 2547 0.002368\n", + " 3896 0.002100\n", + " 3943 0.001799\n", + " 3945 0.001749\n", + " 3787 0.001674\n", + " Name: 576, dtype: float64, 3953 0.469782\n", + " 3955 0.268214\n", + " 3952 0.044411\n", + " 3951 0.042974\n", + " 3949 0.017428\n", + " 3896 0.002371\n", + " 3787 0.002186\n", + " 3542 0.002120\n", + " 3943 0.001994\n", + " 3939 0.001972\n", + " Name: 577, dtype: float64, 2955 0.999902\n", + " 3901 0.000005\n", + " 3173 0.000005\n", + " 7106 0.000004\n", + " 4131 0.000004\n", + " 79 0.000003\n", + " 5235 0.000003\n", + " 3449 0.000002\n", + " 5259 0.000002\n", + " 2503 0.000002\n", + " Name: 578, dtype: float64, 3422 0.999668\n", + " 5384 0.000081\n", + " 3423 0.000033\n", + " 3192 0.000021\n", + " 3605 0.000014\n", + " 2173 0.000014\n", + " 5379 0.000012\n", + " 3599 0.000010\n", + " 3165 0.000009\n", + " 2780 0.000006\n", + " Name: 579, dtype: float64, 5979 0.017972\n", + " 3269 0.012575\n", + " 10026 0.009164\n", + " 4754 0.008606\n", + " 5611 0.008507\n", + " 8183 0.008127\n", + " 6594 0.008057\n", + " 5813 0.007671\n", + " 5648 0.007180\n", + " 6305 0.007112\n", + " Name: 580, dtype: float64, 79 9.999937e-01\n", + " 208 1.156526e-06\n", + " 1805 2.760640e-07\n", + " 7106 1.934470e-07\n", + " 517 1.163563e-07\n", + " 2503 7.768985e-08\n", + " 2629 7.060976e-08\n", + " 119 6.407607e-08\n", + " 2955 6.111814e-08\n", + " 4098 5.151053e-08\n", + " Name: 581, dtype: float64, 3413 0.295553\n", + " 3402 0.243903\n", + " 3405 0.127821\n", + " 3449 0.112072\n", + " 3404 0.035591\n", + " 3966 0.025003\n", + " 3403 0.021826\n", + " 3458 0.021378\n", + " 3416 0.020364\n", + " 3457 0.019638\n", + " Name: 582, dtype: float64, 3403 0.108197\n", + " 3417 0.100100\n", + " 3412 0.085804\n", + " 3419 0.083108\n", + " 3420 0.075425\n", + " 3418 0.073976\n", + " 3404 0.056561\n", + " 3458 0.051310\n", + " 3457 0.043490\n", + " 3409 0.041522\n", + " Name: 583, dtype: float64, 3413 0.295553\n", + " 3402 0.243903\n", + " 3405 0.127821\n", + " 3449 0.112072\n", + " 3404 0.035591\n", + " 3966 0.025003\n", + " 3403 0.021826\n", + " 3458 0.021378\n", + " 3416 0.020364\n", + " 3457 0.019638\n", + " Name: 584, dtype: float64, 3403 0.108197\n", + " 3417 0.100100\n", + " 3412 0.085804\n", + " 3419 0.083108\n", + " 3420 0.075425\n", + " 3418 0.073976\n", + " 3404 0.056561\n", + " 3458 0.051310\n", + " 3457 0.043490\n", + " 3409 0.041522\n", + " Name: 585, dtype: float64, 3403 0.108197\n", + " 3417 0.100100\n", + " 3412 0.085804\n", + " 3419 0.083108\n", + " 3420 0.075425\n", + " 3418 0.073976\n", + " 3404 0.056561\n", + " 3458 0.051310\n", + " 3457 0.043490\n", + " 3409 0.041522\n", + " Name: 586, dtype: float64, 3154 0.999583\n", + " 4126 0.000068\n", + " 6532 0.000028\n", + " 387 0.000008\n", + " 8284 0.000007\n", + " 5307 0.000005\n", + " 3605 0.000005\n", + " 3325 0.000003\n", + " 629 0.000003\n", + " 8112 0.000003\n", + " Name: 587, dtype: float64, 2955 0.999902\n", + " 3901 0.000005\n", + " 3173 0.000005\n", + " 7106 0.000004\n", + " 4131 0.000004\n", + " 79 0.000003\n", + " 5235 0.000003\n", + " 3449 0.000002\n", + " 5259 0.000002\n", + " 2503 0.000002\n", + " Name: 588, dtype: float64, 3871 0.023304\n", + " 3969 0.019482\n", + " 7698 0.012904\n", + " 7568 0.009969\n", + " 7299 0.009631\n", + " 7696 0.008219\n", + " 7664 0.006803\n", + " 7697 0.005799\n", + " 3266 0.005373\n", + " 7534 0.004984\n", + " Name: 589, dtype: float64, 5318 0.190690\n", + " 6989 0.060512\n", + " 6029 0.039519\n", + " 2630 0.035391\n", + " 6758 0.025414\n", + " 3887 0.020436\n", + " 2623 0.020081\n", + " 7095 0.018072\n", + " 6024 0.012747\n", + " 8281 0.011774\n", + " Name: 590, dtype: float64, 3413 0.295553\n", + " 3402 0.243903\n", + " 3405 0.127821\n", + " 3449 0.112072\n", + " 3404 0.035591\n", + " 3966 0.025003\n", + " 3403 0.021826\n", + " 3458 0.021378\n", + " 3416 0.020364\n", + " 3457 0.019638\n", + " Name: 591, dtype: float64, 3413 0.295553\n", + " 3402 0.243903\n", + " 3405 0.127821\n", + " 3449 0.112072\n", + " 3404 0.035591\n", + " 3966 0.025003\n", + " 3403 0.021826\n", + " 3458 0.021378\n", + " 3416 0.020364\n", + " 3457 0.019638\n", + " Name: 592, dtype: float64, 2955 0.999241\n", + " 3901 0.000083\n", + " 5917 0.000066\n", + " 5259 0.000033\n", + " 5235 0.000018\n", + " 7106 0.000017\n", + " 3449 0.000016\n", + " 2503 0.000015\n", + " 4158 0.000014\n", + " 4131 0.000011\n", + " Name: 593, dtype: float64, 677 0.044107\n", + " 4968 0.033216\n", + " 3099 0.024964\n", + " 3157 0.024625\n", + " 3474 0.016609\n", + " 3478 0.015031\n", + " 4982 0.014692\n", + " 6065 0.013679\n", + " 5148 0.012147\n", + " 8437 0.011553\n", + " Name: 594, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 595, dtype: float64, 3994 9.999875e-01\n", + " 4127 2.374518e-06\n", + " 2582 1.509753e-06\n", + " 4078 2.940485e-07\n", + " 6015 1.691750e-07\n", + " 3476 1.581487e-07\n", + " 207 1.215147e-07\n", + " 4393 1.194028e-07\n", + " 2875 1.170854e-07\n", + " 2236 1.122649e-07\n", + " Name: 596, dtype: float64, 4001 0.841748\n", + " 5057 0.136641\n", + " 4554 0.001742\n", + " 4029 0.001375\n", + " 4648 0.001061\n", + " 4015 0.000955\n", + " 4078 0.000590\n", + " 4207 0.000438\n", + " 4102 0.000316\n", + " 4150 0.000254\n", + " Name: 597, dtype: float64, 4011 0.786516\n", + " 4073 0.080307\n", + " 4138 0.005204\n", + " 2236 0.004590\n", + " 4155 0.004313\n", + " 3991 0.004231\n", + " 4208 0.003683\n", + " 4001 0.003676\n", + " 3319 0.003466\n", + " 3323 0.003420\n", + " Name: 598, dtype: float64, 4008 0.984653\n", + " 4014 0.001097\n", + " 4009 0.000285\n", + " 4964 0.000243\n", + " 4648 0.000212\n", + " 4017 0.000212\n", + " 4393 0.000200\n", + " 4002 0.000187\n", + " 5059 0.000181\n", + " 4937 0.000155\n", + " Name: 599, dtype: float64, 4001 0.336105\n", + " 5057 0.067103\n", + " 4020 0.053781\n", + " 4026 0.017665\n", + " 4094 0.014868\n", + " 4658 0.010236\n", + " 5058 0.007885\n", + " 4620 0.007737\n", + " 4607 0.006417\n", + " 4003 0.005538\n", + " Name: 600, dtype: float64, 3989 0.650860\n", + " 3990 0.326077\n", + " 2367 0.002372\n", + " 4178 0.001767\n", + " 4177 0.001308\n", + " 181 0.000826\n", + " 4739 0.000779\n", + " 4738 0.000727\n", + " 4027 0.000537\n", + " 4038 0.000525\n", + " Name: 601, dtype: float64, 4019 0.771174\n", + " 4018 0.113849\n", + " 4020 0.004739\n", + " 4101 0.003173\n", + " 4154 0.002977\n", + " 4432 0.002346\n", + " 4016 0.001784\n", + " 4170 0.001345\n", + " 4089 0.001142\n", + " 4151 0.001121\n", + " Name: 602, dtype: float64, 4076 0.150968\n", + " 4136 0.145497\n", + " 4747 0.070567\n", + " 4746 0.058309\n", + " 5037 0.056887\n", + " 4748 0.056014\n", + " 4035 0.054280\n", + " 4703 0.051784\n", + " 5102 0.049885\n", + " 4204 0.045936\n", + " Name: 603, dtype: float64, 4019 0.771174\n", + " 4018 0.113849\n", + " 4020 0.004739\n", + " 4101 0.003173\n", + " 4154 0.002977\n", + " 4432 0.002346\n", + " 4016 0.001784\n", + " 4170 0.001345\n", + " 4089 0.001142\n", + " 4151 0.001121\n", + " Name: 604, dtype: float64, 4015 0.337328\n", + " 4030 0.293186\n", + " 4348 0.112010\n", + " 4208 0.030651\n", + " 4149 0.025081\n", + " 5130 0.018322\n", + " 4190 0.015547\n", + " 5007 0.011532\n", + " 4162 0.011355\n", + " 5157 0.010902\n", + " Name: 605, dtype: float64, 4011 0.937665\n", + " 4153 0.016426\n", + " 4155 0.007553\n", + " 4073 0.005760\n", + " 4012 0.005448\n", + " 4138 0.003363\n", + " 4013 0.002812\n", + " 4074 0.001138\n", + " 4165 0.000626\n", + " 5011 0.000604\n", + " Name: 606, dtype: float64, 4043 0.843214\n", + " 5156 0.012672\n", + " 4015 0.009866\n", + " 4016 0.005962\n", + " 4150 0.004311\n", + " 4071 0.003369\n", + " 4251 0.003303\n", + " 4019 0.001972\n", + " 5769 0.001789\n", + " 4227 0.001382\n", + " Name: 607, dtype: float64, 79 9.999937e-01\n", + " 208 1.472266e-06\n", + " 1805 3.778828e-07\n", + " 7106 1.436983e-07\n", + " 517 1.243571e-07\n", + " 119 1.191672e-07\n", + " 4098 9.024273e-08\n", + " 2503 8.094059e-08\n", + " 8418 5.832835e-08\n", + " 7141 5.157020e-08\n", + " Name: 608, dtype: float64, 4052 0.983838\n", + " 6029 0.000330\n", + " 6024 0.000290\n", + " 4029 0.000263\n", + " 4026 0.000223\n", + " 5133 0.000183\n", + " 2525 0.000162\n", + " 4002 0.000158\n", + " 4066 0.000155\n", + " 4722 0.000130\n", + " Name: 609, dtype: float64, 4026 0.084311\n", + " 4014 0.079219\n", + " 4017 0.032500\n", + " 205 0.018290\n", + " 4752 0.017184\n", + " 4007 0.016881\n", + " 3809 0.013711\n", + " 204 0.012045\n", + " 4829 0.011348\n", + " 4193 0.010947\n", + " Name: 610, dtype: float64, 3992 0.999888\n", + " 2051 0.000017\n", + " 7414 0.000008\n", + " 2738 0.000004\n", + " 706 0.000002\n", + " 5578 0.000002\n", + " 207 0.000002\n", + " 677 0.000001\n", + " 1714 0.000001\n", + " 6844 0.000001\n", + " Name: 611, dtype: float64, 3999 0.999714\n", + " 3998 0.000080\n", + " 4000 0.000017\n", + " 4542 0.000016\n", + " 1805 0.000014\n", + " 4535 0.000006\n", + " 4074 0.000006\n", + " 2503 0.000005\n", + " 3098 0.000004\n", + " 1514 0.000004\n", + " Name: 612, dtype: float64, 3998 0.999708\n", + " 4000 0.000056\n", + " 3999 0.000045\n", + " 4232 0.000033\n", " 2629 0.000007\n", - " 9840 0.000006\n", - " 2956 0.000006\n", - " 8021 0.000004\n", - " Name: 593, dtype: float64, 4968 0.195737\n", - " 3157 0.092092\n", - " 3084 0.044899\n", - " 3101 0.037946\n", - " 3320 0.024526\n", - " 3226 0.015814\n", - " 698 0.009425\n", - " 624 0.009308\n", - " 8437 0.008737\n", - " 3215 0.007466\n", - " Name: 594, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 595, dtype: float64, 3994 9.999853e-01\n", - " 4127 2.312993e-06\n", - " 2582 2.130482e-06\n", - " 4000 5.700608e-07\n", - " 2662 5.318103e-07\n", - " 5549 2.956811e-07\n", - " 5945 2.446522e-07\n", - " 5919 2.233565e-07\n", - " 7107 1.757616e-07\n", - " 667 1.607860e-07\n", - " Name: 596, dtype: float64, 4001 0.933095\n", - " 5057 0.047354\n", - " 4393 0.001019\n", - " 4002 0.000891\n", - " 5058 0.000884\n", - " 4554 0.000845\n", - " 5059 0.000754\n", - " 4555 0.000389\n", - " 4607 0.000310\n", - " 6064 0.000306\n", - " Name: 597, dtype: float64, 4011 0.763732\n", - " 2662 0.044247\n", - " 4073 0.023233\n", - " 4155 0.018542\n", - " 4012 0.005337\n", - " 4013 0.005269\n", - " 4003 0.004185\n", - " 4533 0.003624\n", - " 4172 0.003415\n", - " 4203 0.003414\n", - " Name: 598, dtype: float64, 4008 0.956254\n", - " 4007 0.002005\n", - " 4009 0.001863\n", - " 5030 0.000798\n", - " 4251 0.000440\n", - " 4014 0.000379\n", - " 4748 0.000343\n", - " 4995 0.000335\n", - " 4029 0.000297\n", - " 4977 0.000294\n", - " Name: 599, dtype: float64, 4001 0.459864\n", - " 5057 0.146301\n", - " 5058 0.044266\n", - " 4002 0.030269\n", - " 4003 0.020141\n", - " 4653 0.010839\n", - " 4607 0.006086\n", - " 4554 0.005511\n", - " 5769 0.005364\n", - " 3353 0.004958\n", - " Name: 600, dtype: float64, 3989 0.576012\n", - " 3990 0.371863\n", - " 4038 0.004579\n", - " 2367 0.003033\n", - " 4028 0.002103\n", - " 5041 0.001818\n", - " 4027 0.001650\n", - " 4738 0.001622\n", - " 4177 0.001566\n", - " 4178 0.001507\n", - " Name: 601, dtype: float64, 4019 0.715758\n", - " 4018 0.045516\n", - " 4154 0.010784\n", - " 4376 0.009763\n", - " 4016 0.008912\n", - " 4101 0.008889\n", - " 4432 0.006343\n", - " 4511 0.005775\n", - " 4607 0.004589\n", - " 4210 0.004025\n", - " Name: 602, dtype: float64, 4076 0.153459\n", - " 4136 0.137009\n", - " 4748 0.053385\n", - " 4214 0.049273\n", - " 4617 0.048607\n", - " 4703 0.048297\n", - " 4035 0.047356\n", - " 4747 0.046762\n", - " 4746 0.045811\n", - " 4204 0.044179\n", - " Name: 603, dtype: float64, 4019 0.715758\n", - " 4018 0.045516\n", - " 4154 0.010784\n", - " 4376 0.009763\n", - " 4016 0.008912\n", - " 4101 0.008889\n", - " 4432 0.006343\n", - " 4511 0.005775\n", - " 4607 0.004589\n", - " 4210 0.004025\n", - " Name: 604, dtype: float64, 4030 0.239539\n", - " 4015 0.133507\n", - " 4348 0.100346\n", - " 4149 0.069422\n", - " 5130 0.041462\n", - " 4736 0.020358\n", - " 4208 0.017745\n", - " 4298 0.011650\n", - " 4190 0.011114\n", - " 5007 0.007625\n", - " Name: 605, dtype: float64, 4011 0.947892\n", - " 4012 0.014686\n", - " 4073 0.007964\n", - " 4202 0.001470\n", - " 4013 0.001209\n", - " 205 0.001063\n", - " 4138 0.000939\n", - " 4155 0.000852\n", - " 181 0.000813\n", - " 206 0.000662\n", - " Name: 606, dtype: float64, 4043 0.319760\n", - " 5156 0.157835\n", - " 4015 0.085520\n", - " 4720 0.044255\n", - " 4736 0.020752\n", - " 4298 0.019232\n", - " 4251 0.017942\n", - " 5006 0.013505\n", - " 4611 0.006012\n", - " 4071 0.005916\n", - " Name: 607, dtype: float64, 79 9.999946e-01\n", - " 160 2.136951e-07\n", - " 4011 1.595397e-07\n", - " 9626 1.455223e-07\n", - " 119 1.431853e-07\n", - " 8040 1.345697e-07\n", - " 206 1.068390e-07\n", - " 1506 9.905753e-08\n", - " 2260 9.482870e-08\n", - " 4073 8.528225e-08\n", - " Name: 608, dtype: float64, 4052 0.979435\n", - " 4055 0.000816\n", - " 4011 0.000385\n", - " 3307 0.000267\n", - " 4205 0.000237\n", - " 4048 0.000223\n", - " 4203 0.000154\n", - " 4719 0.000142\n", - " 4066 0.000130\n", - " 241 0.000128\n", - " Name: 609, dtype: float64, 4029 0.087796\n", - " 4165 0.073478\n", - " 5132 0.016314\n", - " 4426 0.014607\n", - " 4055 0.011641\n", - " 517 0.010528\n", - " 2003 0.010520\n", - " 5131 0.010199\n", - " 4159 0.009010\n", - " 4009 0.008687\n", - " Name: 610, dtype: float64, 3992 9.999642e-01\n", - " 677 1.108366e-05\n", - " 2738 1.801027e-06\n", - " 697 1.225081e-06\n", - " 7414 1.139922e-06\n", - " 5950 6.983145e-07\n", - " 7957 5.626734e-07\n", - " 6091 3.793423e-07\n", - " 8644 3.617332e-07\n", - " 3993 3.494245e-07\n", - " Name: 611, dtype: float64, 3999 0.999644\n", - " 3998 0.000045\n", - " 2629 0.000028\n", - " 4542 0.000019\n", - " 3325 0.000015\n", - " 677 0.000013\n", - " 2236 0.000011\n", - " 690 0.000009\n", - " 4540 0.000009\n", - " 3997 0.000007\n", - " Name: 612, dtype: float64, 3998 0.999504\n", - " 4232 0.000036\n", - " 3210 0.000034\n", - " 3999 0.000029\n", - " 2611 0.000026\n", - " 4000 0.000022\n", - " 4714 0.000012\n", - " 4586 0.000009\n", - " 690 0.000008\n", - " 4600 0.000008\n", - " Name: 613, dtype: float64, 3997 0.999859\n", - " 3210 0.000046\n", - " 3999 0.000013\n", - " 3998 0.000006\n", - " 677 0.000005\n", - " 4067 0.000003\n", - " 4540 0.000003\n", - " 2504 0.000002\n", - " 136 0.000001\n", - " 3165 0.000001\n", - " Name: 614, dtype: float64, 4086 0.999260\n", - " 4085 0.000072\n", - " 5058 0.000050\n", - " 5154 0.000018\n", - " 4648 0.000017\n", - " 3050 0.000008\n", - " 4029 0.000008\n", - " 6529 0.000007\n", - " 4227 0.000007\n", - " 4207 0.000006\n", - " Name: 615, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 616, dtype: float64, 4087 0.994704\n", - " 4711 0.000291\n", - " 4426 0.000272\n", - " 5095 0.000153\n", - " 4644 0.000103\n", - " 4751 0.000069\n", - " 4054 0.000068\n", - " 4791 0.000064\n", - " 3816 0.000062\n", - " 10528 0.000055\n", - " Name: 617, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 618, dtype: float64, 3997 9.999495e-01\n", - " 3210 1.073969e-05\n", - " 3999 3.712599e-06\n", - " 3998 2.690494e-06\n", - " 4067 1.216748e-06\n", - " 4540 1.041435e-06\n", - " 2173 9.570372e-07\n", - " 677 8.544646e-07\n", - " 2504 7.994163e-07\n", - " 129 6.829038e-07\n", - " Name: 619, dtype: float64, 79 9.999925e-01\n", - " 160 2.698233e-07\n", - " 9626 2.653256e-07\n", - " 8040 2.240738e-07\n", - " 4011 1.899876e-07\n", - " 206 1.763377e-07\n", - " 119 1.716841e-07\n", - " 2260 1.452654e-07\n", - " 690 1.206203e-07\n", - " 4073 1.204217e-07\n", - " Name: 620, dtype: float64, 3988 9.999981e-01\n", - " 4004 7.825608e-08\n", - " 4074 2.904527e-08\n", - " 1539 2.572120e-08\n", - " 4535 1.159967e-08\n", - " 3993 1.078722e-08\n", - " 154 1.068691e-08\n", - " 3320 1.046666e-08\n", - " 79 9.245329e-09\n", - " 4968 9.133679e-09\n", - " Name: 621, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 622, dtype: float64, 3996 9.999737e-01\n", - " 8381 1.741967e-06\n", - " 4710 9.066468e-07\n", - " 3574 6.314740e-07\n", - " 1515 5.614004e-07\n", - " 9394 4.338572e-07\n", - " 2435 4.066889e-07\n", - " 9354 3.671372e-07\n", - " 4989 3.391952e-07\n", - " 9416 3.216211e-07\n", - " Name: 623, dtype: float64, 4097 0.992598\n", - " 5077 0.000745\n", - " 3999 0.000183\n", - " 4000 0.000168\n", - " 4232 0.000135\n", - " 6588 0.000107\n", - " 2660 0.000094\n", - " 753 0.000067\n", - " 3998 0.000066\n", - " 5080 0.000063\n", - " Name: 624, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 625, dtype: float64, 4987 0.351187\n", - " 5032 0.120272\n", - " 4155 0.106452\n", - " 4138 0.054803\n", - " 2884 0.031477\n", - " 4073 0.025018\n", - " 4012 0.016597\n", - " 4742 0.013425\n", - " 4202 0.012355\n", - " 4011 0.009277\n", - " Name: 626, dtype: float64, 4102 0.957250\n", - " 4107 0.010824\n", - " 4021 0.002582\n", - " 4207 0.000958\n", - " 4752 0.000527\n", - " 4762 0.000474\n", - " 4811 0.000456\n", - " 4089 0.000440\n", - " 4777 0.000383\n", - " 4776 0.000361\n", - " Name: 627, dtype: float64, 4102 0.957250\n", - " 4107 0.010824\n", - " 4021 0.002582\n", - " 4207 0.000958\n", - " 4752 0.000527\n", - " 4762 0.000474\n", - " 4811 0.000456\n", - " 4089 0.000440\n", - " 4777 0.000383\n", - " 4776 0.000361\n", - " Name: 628, dtype: float64, 79 9.999933e-01\n", - " 160 3.109944e-07\n", - " 9626 1.971066e-07\n", - " 8040 1.890472e-07\n", - " 206 1.721924e-07\n", - " 1203 1.481838e-07\n", - " 119 1.258704e-07\n", - " 4011 1.167760e-07\n", - " 2260 1.140654e-07\n", - " 4073 1.015685e-07\n", - " Name: 629, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 630, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 631, dtype: float64, 3997 9.999398e-01\n", - " 3210 1.387922e-05\n", - " 3998 3.942795e-06\n", - " 3999 3.223768e-06\n", - " 4067 1.455328e-06\n", - " 2173 1.417393e-06\n", - " 129 1.014452e-06\n", - " 2504 9.700688e-07\n", - " 677 9.398196e-07\n", - " 4540 9.236233e-07\n", - " Name: 632, dtype: float64, 4000 0.999860\n", - " 3210 0.000009\n", - " 2582 0.000007\n", - " 205 0.000003\n", - " 3998 0.000003\n", - " 4135 0.000002\n", + " 5057 0.000007\n", + " 3094 0.000005\n", + " 3997 0.000005\n", + " 4600 0.000004\n", + " 4586 0.000003\n", + " Name: 613, dtype: float64, 3997 0.999909\n", + " 3998 0.000007\n", + " 7106 0.000006\n", + " 640 0.000006\n", + " 4280 0.000004\n", + " 3210 0.000003\n", + " 4000 0.000003\n", " 2236 0.000002\n", - " 3637 0.000002\n", - " 4714 0.000002\n", - " 4681 0.000002\n", - " Name: 633, dtype: float64, 4013 0.283664\n", - " 5058 0.052440\n", - " 5059 0.025677\n", - " 4002 0.019704\n", - " 4107 0.019379\n", - " 4612 0.019211\n", - " 4393 0.016868\n", - " 3990 0.016565\n", - " 4285 0.015233\n", - " 4648 0.014144\n", - " Name: 634, dtype: float64, 5053 0.044858\n", - " 4438 0.032504\n", - " 2926 0.015450\n", - " 4155 0.015092\n", - " 2693 0.014669\n", - " 5013 0.008194\n", - " 9905 0.007892\n", - " 3100 0.007737\n", - " 6161 0.007178\n", - " 3508 0.006717\n", - " Name: 635, dtype: float64, 4645 0.017003\n", - " 7871 0.009056\n", - " 4016 0.008598\n", - " 10008 0.008585\n", - " 3248 0.007786\n", - " 4015 0.006266\n", - " 8383 0.006234\n", - " 8566 0.005271\n", - " 4030 0.005116\n", - " 4611 0.005034\n", - " Name: 636, dtype: float64, 1471 0.131694\n", - " 9992 0.037996\n", - " 667 0.036884\n", - " 3584 0.032911\n", - " 2236 0.025505\n", - " 2342 0.023161\n", - " 9039 0.019286\n", - " 2802 0.019046\n", - " 8021 0.018704\n", - " 3046 0.012301\n", - " Name: 637, dtype: float64, 4149 0.469764\n", - " 4266 0.038747\n", - " 4270 0.038445\n", - " 4273 0.036484\n", - " 4545 0.036443\n", - " 4694 0.016874\n", - " 4289 0.016655\n", - " 4267 0.016116\n", - " 4271 0.016114\n", - " 4287 0.016030\n", - " Name: 638, dtype: float64, 4150 0.776015\n", - " 4151 0.122013\n", - " 4230 0.005020\n", - " 3990 0.001828\n", - " 4158 0.001766\n", - " 4177 0.001557\n", - " 4210 0.001454\n", - " 3989 0.001339\n", - " 4202 0.001005\n", - " 4212 0.000836\n", - " Name: 639, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 640, dtype: float64, 3991 0.921986\n", - " 4172 0.054838\n", - " 4011 0.001180\n", - " 4149 0.000895\n", - " 4006 0.000819\n", - " 5149 0.000761\n", - " 4614 0.000654\n", - " 8021 0.000526\n", - " 2715 0.000524\n", - " 86 0.000511\n", - " Name: 641, dtype: float64, 3994 9.999853e-01\n", - " 4127 2.312993e-06\n", - " 2582 2.130482e-06\n", - " 4000 5.700608e-07\n", - " 2662 5.318103e-07\n", - " 5549 2.956811e-07\n", - " 5945 2.446522e-07\n", - " 5919 2.233565e-07\n", - " 7107 1.757616e-07\n", - " 667 1.607860e-07\n", - " Name: 642, dtype: float64, 4184 0.072827\n", - " 4164 0.072448\n", - " 4158 0.071890\n", - " 4183 0.041961\n", - " 4431 0.037693\n", - " 3990 0.014728\n", - " 4018 0.010965\n", - " 4101 0.010885\n", - " 4212 0.010258\n", - " 4016 0.009762\n", - " Name: 643, dtype: float64, 4724 0.035578\n", - " 4298 0.019255\n", - " 4666 0.018695\n", - " 4665 0.014535\n", - " 3605 0.013344\n", - " 4548 0.013054\n", - " 5025 0.011095\n", - " 2735 0.008383\n", - " 5279 0.007864\n", - " 9672 0.007772\n", - " Name: 644, dtype: float64, 4173 0.474143\n", - " 830 0.008067\n", - " 828 0.007562\n", - " 5133 0.006188\n", - " 4054 0.005505\n", - " 4582 0.005309\n", - " 5131 0.004388\n", - " 4029 0.004346\n", - " 8058 0.003921\n", - " 8498 0.003749\n", - " Name: 645, dtype: float64, 5135 0.102754\n", - " 5134 0.102118\n", - " 4738 0.074903\n", - " 4739 0.061442\n", - " 4099 0.034374\n", - " 5158 0.031519\n", - " 3989 0.022424\n", - " 4182 0.021274\n", - " 4032 0.017253\n", - " 4181 0.016364\n", - " Name: 646, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 647, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 648, dtype: float64, 4073 0.996838\n", - " 4203 0.000291\n", - " 4012 0.000256\n", - " 4011 0.000146\n", - " 2503 0.000122\n", - " 4138 0.000120\n", - " 4155 0.000107\n", - " 1805 0.000101\n", - " 4013 0.000080\n", - " 4208 0.000078\n", - " Name: 649, dtype: float64, 4012 0.993211\n", - " 4202 0.000489\n", - " 4298 0.000349\n", - " 4138 0.000343\n", - " 4156 0.000277\n", - " 4073 0.000198\n", - " 5046 0.000163\n", - " 4155 0.000120\n", - " 668 0.000082\n", - " 4013 0.000079\n", - " Name: 650, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 651, dtype: float64, 3992 9.999424e-01\n", - " 677 1.476421e-05\n", - " 2738 2.154386e-06\n", - " 697 1.492650e-06\n", - " 5950 1.385210e-06\n", - " 7414 1.365843e-06\n", - " 7957 1.297597e-06\n", - " 6091 9.492531e-07\n", - " 3993 8.500680e-07\n", - " 7472 5.800820e-07\n", - " Name: 652, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 653, dtype: float64, 4108 0.083921\n", - " 4180 0.030207\n", - " 4179 0.019898\n", - " 5154 0.018219\n", - " 5155 0.017358\n", - " 4525 0.016525\n", - " 4807 0.012731\n", - " 4029 0.010004\n", - " 4026 0.008780\n", - " 3984 0.006963\n", - " Name: 654, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 655, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 656, dtype: float64, 4184 0.596019\n", - " 4183 0.154499\n", - " 4164 0.036487\n", - " 3990 0.028976\n", - " 4431 0.020947\n", - " 4158 0.016552\n", - " 3989 0.012752\n", - " 4038 0.012100\n", - " 4212 0.002934\n", - " 4210 0.002420\n", - " Name: 657, dtype: float64, 3210 0.912682\n", - " 5144 0.055427\n", - " 5120 0.002131\n", - " 4540 0.001436\n", - " 3997 0.001361\n", - " 4632 0.000565\n", - " 5826 0.000518\n", - " 9482 0.000323\n", - " 5809 0.000311\n", - " 677 0.000271\n", - " Name: 658, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 659, dtype: float64, 4000 0.986175\n", - " 2503 0.000699\n", - " 10178 0.000625\n", - " 4994 0.000441\n", - " 205 0.000369\n", - " 5866 0.000277\n", - " 753 0.000268\n", - " 2582 0.000220\n", - " 4681 0.000180\n", - " 5850 0.000177\n", - " Name: 660, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 661, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 662, dtype: float64, 4011 0.338188\n", - " 4203 0.083913\n", - " 4073 0.035569\n", - " 4202 0.022854\n", - " 4055 0.017273\n", - " 4052 0.015057\n", - " 4003 0.013942\n", - " 4159 0.013146\n", - " 4048 0.009019\n", - " 4049 0.008406\n", - " Name: 663, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 664, dtype: float64, 79 9.999923e-01\n", - " 160 2.718998e-07\n", - " 9626 2.579705e-07\n", - " 8040 2.230048e-07\n", - " 4011 1.745024e-07\n", - " 206 1.651428e-07\n", - " 119 1.595027e-07\n", - " 2260 1.378042e-07\n", - " 192 1.302861e-07\n", - " 1203 1.236871e-07\n", - " Name: 665, dtype: float64, 3098 0.999024\n", - " 1805 0.000127\n", - " 2503 0.000053\n", - " 2873 0.000043\n", - " 6929 0.000037\n", - " 4710 0.000030\n", - " 2236 0.000025\n", - " 8509 0.000017\n", - " 9680 0.000015\n", - " 8653 0.000014\n", - " Name: 666, dtype: float64, 3997 9.999480e-01\n", - " 3210 1.333998e-05\n", - " 3999 3.803329e-06\n", - " 3998 2.361577e-06\n", - " 4067 1.215325e-06\n", - " 677 1.051945e-06\n", - " 4540 9.444004e-07\n", - " 2504 8.992500e-07\n", - " 129 7.323942e-07\n", - " 2173 5.199950e-07\n", - " Name: 667, dtype: float64, 3988 9.999978e-01\n", - " 4004 1.525704e-07\n", - " 4074 4.827068e-08\n", - " 1539 2.604783e-08\n", - " 3993 2.164657e-08\n", - " 4535 1.967476e-08\n", - " 3424 1.427935e-08\n", - " 3320 1.074848e-08\n", - " 5527 1.063733e-08\n", - " 4968 1.051336e-08\n", - " Name: 668, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 669, dtype: float64, 107 0.293466\n", - " 4709 0.170814\n", - " 4708 0.083659\n", - " 4716 0.011408\n", - " 5143 0.009108\n", - " 4141 0.008854\n", - " 3574 0.008832\n", - " 2435 0.006554\n", - " 4683 0.005751\n", - " 4088 0.004284\n", - " Name: 670, dtype: float64, 79 9.999925e-01\n", - " 160 2.698233e-07\n", - " 9626 2.653256e-07\n", - " 8040 2.240738e-07\n", - " 4011 1.899876e-07\n", - " 206 1.763377e-07\n", - " 119 1.716841e-07\n", - " 2260 1.452654e-07\n", - " 690 1.206203e-07\n", - " 4073 1.204217e-07\n", - " Name: 671, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 672, dtype: float64, 79 9.999881e-01\n", - " 4011 7.933901e-07\n", - " 9626 4.716590e-07\n", - " 160 4.152631e-07\n", - " 4073 3.697401e-07\n", - " 8040 3.692511e-07\n", - " 119 3.668166e-07\n", - " 206 3.607514e-07\n", - " 2260 2.281216e-07\n", - " 246 1.973419e-07\n", - " Name: 673, dtype: float64, 79 9.999923e-01\n", - " 160 2.718998e-07\n", - " 9626 2.579705e-07\n", - " 8040 2.230048e-07\n", - " 4011 1.745024e-07\n", - " 206 1.651428e-07\n", - " 119 1.595027e-07\n", - " 2260 1.378042e-07\n", - " 192 1.302861e-07\n", - " 1203 1.236871e-07\n", - " Name: 674, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 675, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 676, dtype: float64, 4000 0.999917\n", - " 3210 0.000011\n", - " 4135 0.000002\n", - " 2582 0.000002\n", + " 7107 0.000002\n", + " 2408 0.000002\n", + " Name: 614, dtype: float64, 4086 0.998956\n", + " 5058 0.000104\n", + " 4085 0.000077\n", + " 4393 0.000067\n", + " 4648 0.000022\n", + " 4002 0.000021\n", + " 5057 0.000013\n", + " 3176 0.000013\n", + " 4667 0.000013\n", + " 5757 0.000012\n", + " Name: 615, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 616, dtype: float64, 4087 0.997296\n", + " 4170 0.000094\n", + " 4089 0.000040\n", + " 7981 0.000034\n", + " 9660 0.000032\n", + " 4647 0.000030\n", + " 4889 0.000027\n", + " 4761 0.000026\n", + " 5155 0.000026\n", + " 4891 0.000025\n", + " Name: 617, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 618, dtype: float64, 3997 9.999479e-01\n", + " 7106 4.501720e-06\n", + " 640 3.742593e-06\n", + " 3998 3.025686e-06\n", + " 2408 1.340486e-06\n", + " 7107 1.262201e-06\n", + " 5110 9.914878e-07\n", + " 4280 9.095332e-07\n", + " 3210 7.989304e-07\n", + " 86 7.604002e-07\n", + " Name: 619, dtype: float64, 79 9.999934e-01\n", + " 208 1.653049e-06\n", + " 1805 3.937443e-07\n", + " 7106 1.964412e-07\n", + " 119 1.852119e-07\n", + " 2503 1.166403e-07\n", + " 4098 1.079652e-07\n", + " 517 9.870586e-08\n", + " 2546 7.405401e-08\n", + " 4074 6.706089e-08\n", + " Name: 620, dtype: float64, 3988 9.999988e-01\n", + " 4004 2.624934e-08\n", + " 1805 2.143999e-08\n", + " 3474 1.529523e-08\n", + " 1539 1.119369e-08\n", + " 2353 9.991578e-09\n", + " 4135 9.488947e-09\n", + " 2508 8.878290e-09\n", + " 3320 8.648589e-09\n", + " 3243 8.234005e-09\n", + " Name: 621, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 622, dtype: float64, 3996 9.999572e-01\n", + " 5792 7.607747e-06\n", + " 2514 2.427629e-06\n", + " 8381 2.024190e-06\n", + " 86 1.782123e-06\n", + " 6387 1.176251e-06\n", + " 4710 1.124004e-06\n", + " 4759 8.360333e-07\n", + " 3574 7.973029e-07\n", + " 107 7.692284e-07\n", + " Name: 623, dtype: float64, 4097 0.992311\n", + " 2567 0.000880\n", + " 4000 0.000835\n", + " 753 0.000276\n", + " 667 0.000162\n", + " 2512 0.000152\n", + " 108 0.000144\n", + " 3301 0.000136\n", + " 2896 0.000120\n", + " 5077 0.000119\n", + " Name: 624, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 625, dtype: float64, 4987 0.375552\n", + " 4155 0.225133\n", + " 5032 0.080171\n", + " 4073 0.069508\n", + " 4138 0.051855\n", + " 2884 0.016010\n", + " 5123 0.014335\n", + " 4012 0.012999\n", + " 4614 0.010527\n", + " 4006 0.003612\n", + " Name: 626, dtype: float64, 4102 0.972277\n", + " 4107 0.007309\n", + " 4021 0.006121\n", + " 4155 0.000869\n", + " 4013 0.000844\n", + " 4026 0.000383\n", + " 5057 0.000353\n", + " 4207 0.000281\n", + " 4011 0.000222\n", + " 4752 0.000210\n", + " Name: 627, dtype: float64, 4102 0.972277\n", + " 4107 0.007309\n", + " 4021 0.006121\n", + " 4155 0.000869\n", + " 4013 0.000844\n", + " 4026 0.000383\n", + " 5057 0.000353\n", + " 4207 0.000281\n", + " 4011 0.000222\n", + " 4752 0.000210\n", + " Name: 628, dtype: float64, 79 9.999950e-01\n", + " 208 1.122966e-06\n", + " 7106 2.370715e-07\n", + " 1805 1.831005e-07\n", + " 4098 1.014163e-07\n", + " 2503 7.240575e-08\n", + " 517 7.001107e-08\n", + " 2546 4.622482e-08\n", + " 119 4.189932e-08\n", + " 8418 4.154392e-08\n", + " Name: 629, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 630, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 631, dtype: float64, 3997 9.999338e-01\n", + " 7106 5.554517e-06\n", + " 640 3.678205e-06\n", + " 3998 3.056216e-06\n", + " 2408 2.362787e-06\n", + " 4280 1.704668e-06\n", + " 7107 1.360919e-06\n", + " 3210 1.199475e-06\n", + " 5110 9.154632e-07\n", + " 629 9.067145e-07\n", + " Name: 632, dtype: float64, 4000 0.999912\n", + " 2503 0.000006\n", + " 3999 0.000006\n", + " 107 0.000003\n", + " 79 0.000003\n", " 4714 0.000002\n", - " 2236 0.000002\n", - " 3637 0.000001\n", - " 205 0.000001\n", - " 4681 0.000001\n", + " 863 0.000002\n", + " 6766 0.000002\n", + " 5121 0.000001\n", " 4097 0.000001\n", - " Name: 677, dtype: float64, 4181 0.078017\n", - " 4212 0.070490\n", - " 4182 0.065528\n", - " 4210 0.056893\n", - " 4169 0.051909\n", - " 4177 0.051575\n", - " 4178 0.036122\n", - " 4738 0.021991\n", - " 4170 0.019154\n", - " 5134 0.017312\n", - " Name: 678, dtype: float64, 3990 0.705323\n", - " 3989 0.213915\n", - " 4149 0.006328\n", - " 4038 0.003984\n", - " 2367 0.002895\n", - " 181 0.002398\n", - " 4738 0.002128\n", - " 4739 0.001943\n", - " 4183 0.001723\n", - " 4184 0.001695\n", - " Name: 679, dtype: float64, 2629 0.999863\n", - " 2236 0.000012\n", - " 4098 0.000004\n", - " 3999 0.000003\n", + " Name: 633, dtype: float64, 4021 0.149609\n", + " 4081 0.075527\n", + " 4016 0.068859\n", + " 4102 0.058205\n", + " 5058 0.036875\n", + " 5057 0.026063\n", + " 5056 0.022346\n", + " 4107 0.019798\n", + " 4013 0.017958\n", + " 4336 0.016391\n", + " Name: 634, dtype: float64, 4438 0.376976\n", + " 5053 0.209321\n", + " 10543 0.011216\n", + " 4274 0.008102\n", + " 4719 0.007897\n", + " 4094 0.004701\n", + " 4115 0.004149\n", + " 8755 0.003692\n", + " 2337 0.003208\n", + " 3508 0.003193\n", + " Name: 635, dtype: float64, 9492 0.013114\n", + " 4608 0.012931\n", + " 4607 0.009171\n", + " 4101 0.007426\n", + " 4393 0.006567\n", + " 4512 0.006395\n", + " 3990 0.006354\n", + " 3250 0.006225\n", + " 4441 0.006164\n", + " 4667 0.005345\n", + " Name: 636, dtype: float64, 4693 0.062551\n", + " 108 0.059431\n", + " 2236 0.049190\n", + " 4001 0.027108\n", + " 2342 0.024762\n", + " 3435 0.019052\n", + " 2196 0.018986\n", + " 3172 0.015507\n", + " 667 0.010912\n", + " 274 0.010869\n", + " Name: 637, dtype: float64, 4149 0.478275\n", + " 4545 0.052350\n", + " 4266 0.040709\n", + " 4270 0.040258\n", + " 4273 0.039107\n", + " 4694 0.018334\n", + " 4408 0.018120\n", + " 4410 0.017849\n", + " 4411 0.017023\n", + " 4271 0.016805\n", + " Name: 638, dtype: float64, 4150 0.929626\n", + " 4151 0.028695\n", + " 4001 0.001118\n", + " 160 0.000945\n", + " 4285 0.000616\n", + " 4239 0.000507\n", + " 519 0.000485\n", + " 4019 0.000458\n", + " 4015 0.000458\n", + " 4238 0.000347\n", + " Name: 639, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 640, dtype: float64, 3991 0.856706\n", + " 4172 0.114434\n", + " 4011 0.007980\n", + " 1471 0.001916\n", + " 4169 0.001147\n", + " 5127 0.000885\n", + " 4155 0.000823\n", + " 5150 0.000675\n", + " 5149 0.000422\n", + " 4031 0.000418\n", + " Name: 641, dtype: float64, 3994 9.999875e-01\n", + " 4127 2.374518e-06\n", + " 2582 1.509753e-06\n", + " 4078 2.940485e-07\n", + " 6015 1.691750e-07\n", + " 3476 1.581487e-07\n", + " 207 1.215147e-07\n", + " 4393 1.194028e-07\n", + " 2875 1.170854e-07\n", + " 2236 1.122649e-07\n", + " Name: 642, dtype: float64, 4184 0.163257\n", + " 4101 0.121480\n", + " 4183 0.059793\n", + " 4431 0.054715\n", + " 3990 0.037869\n", + " 4164 0.036864\n", + " 4149 0.024612\n", + " 4210 0.016154\n", + " 4738 0.014717\n", + " 4739 0.014540\n", + " Name: 643, dtype: float64, 4614 0.242467\n", + " 4006 0.032113\n", + " 4579 0.023200\n", + " 4618 0.018613\n", + " 4102 0.017892\n", + " 4640 0.010465\n", + " 4031 0.008786\n", + " 4013 0.008657\n", + " 5149 0.008440\n", + " 5150 0.008069\n", + " Name: 644, dtype: float64, 4173 0.538755\n", + " 3675 0.010556\n", + " 3676 0.010060\n", + " 7986 0.005745\n", + " 5006 0.005311\n", + " 6544 0.004463\n", + " 4724 0.003524\n", + " 1597 0.002888\n", + " 2818 0.002763\n", + " 4854 0.002662\n", + " Name: 645, dtype: float64, 5135 0.152008\n", + " 5134 0.144479\n", + " 4739 0.083513\n", + " 4738 0.051438\n", + " 4178 0.050495\n", + " 3989 0.050470\n", + " 3990 0.043059\n", + " 2367 0.031281\n", + " 4177 0.030506\n", + " 4182 0.020247\n", + " Name: 646, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 647, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", " 2503 0.000002\n", - " 7106 0.000002\n", - " 3671 0.000002\n", - " 4067 0.000002\n", - " 642 0.000002\n", - " 3670 0.000002\n", - " Name: 680, dtype: float64, 4227 0.944388\n", - " 5058 0.003164\n", - " 4001 0.001615\n", - " 3340 0.001574\n", - " 4015 0.001457\n", - " 4648 0.000721\n", - " 4607 0.000640\n", - " 4554 0.000601\n", - " 3600 0.000587\n", - " 4612 0.000577\n", - " Name: 681, dtype: float64, 3067 0.018229\n", - " 9188 0.014531\n", - " 4720 0.012431\n", - " 4234 0.011400\n", - " 5071 0.010168\n", - " 1515 0.009527\n", - " 8894 0.009146\n", - " 8440 0.008136\n", - " 9183 0.007587\n", - " 9979 0.005772\n", - " Name: 682, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 683, dtype: float64, 4026 0.249484\n", - " 4650 0.220685\n", - " 4241 0.158473\n", - " 4580 0.022034\n", - " 4031 0.018632\n", - " 4607 0.014632\n", - " 4020 0.011107\n", - " 4136 0.008140\n", - " 4076 0.008114\n", - " 4617 0.006595\n", - " Name: 684, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 685, dtype: float64, 3098 0.998820\n", - " 6929 0.000119\n", - " 2873 0.000062\n", - " 8509 0.000048\n", - " 5054 0.000036\n", - " 1805 0.000032\n", + " 4000 0.000002\n", + " Name: 648, dtype: float64, 4073 0.997052\n", + " 4155 0.000414\n", + " 4012 0.000342\n", + " 4138 0.000238\n", + " 4011 0.000169\n", + " 4149 0.000106\n", + " 2236 0.000098\n", + " 4208 0.000096\n", + " 3637 0.000086\n", + " 2503 0.000073\n", + " Name: 649, dtype: float64, 4012 0.996505\n", + " 4073 0.000354\n", + " 4138 0.000336\n", + " 4298 0.000159\n", + " 4155 0.000123\n", + " 4402 0.000095\n", + " 5046 0.000085\n", + " 4156 0.000072\n", + " 4013 0.000046\n", + " 4202 0.000041\n", + " Name: 650, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 651, dtype: float64, 3992 9.999071e-01\n", + " 2051 1.064764e-05\n", + " 7414 7.064210e-06\n", + " 2738 4.026061e-06\n", + " 207 2.244178e-06\n", + " 706 1.591229e-06\n", + " 5578 1.389246e-06\n", + " 1714 1.168669e-06\n", + " 677 1.134416e-06\n", + " 755 8.309394e-07\n", + " Name: 652, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 653, dtype: float64, 4108 0.447951\n", + " 4179 0.412340\n", + " 4180 0.011833\n", + " 4807 0.011614\n", + " 5362 0.003920\n", + " 6716 0.002656\n", + " 842 0.002346\n", + " 4799 0.002206\n", + " 3984 0.001357\n", + " 4606 0.001219\n", + " Name: 654, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 655, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 656, dtype: float64, 4184 0.558098\n", + " 4183 0.195252\n", + " 4164 0.044346\n", + " 4431 0.021831\n", + " 3990 0.017014\n", + " 4038 0.015782\n", + " 4210 0.009116\n", + " 4212 0.008808\n", + " 3989 0.005651\n", + " 4158 0.005123\n", + " Name: 657, dtype: float64, 3210 0.997598\n", + " 5144 0.001616\n", + " 4540 0.000039\n", + " 1471 0.000025\n", + " 5120 0.000019\n", + " 214 0.000016\n", + " 2776 0.000015\n", + " 4541 0.000013\n", + " 3574 0.000010\n", + " 4736 0.000010\n", + " Name: 658, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 659, dtype: float64, 4000 0.996630\n", + " 2503 0.000222\n", + " 863 0.000138\n", + " 4994 0.000108\n", + " 6766 0.000104\n", + " 4248 0.000098\n", + " 107 0.000083\n", + " 5207 0.000052\n", + " 6598 0.000049\n", + " 214 0.000049\n", + " Name: 660, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 661, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 662, dtype: float64, 4011 0.496976\n", + " 4055 0.070337\n", + " 4153 0.017481\n", + " 3991 0.011895\n", + " 2955 0.011140\n", + " 6814 0.008325\n", + " 2166 0.007347\n", + " 4155 0.006493\n", + " 3811 0.006427\n", + " 3689 0.005793\n", + " Name: 663, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 664, dtype: float64, 79 9.999949e-01\n", + " 208 1.292303e-06\n", + " 1805 2.670109e-07\n", + " 119 1.656839e-07\n", + " 7106 1.474233e-07\n", + " 2503 8.628919e-08\n", + " 517 7.540819e-08\n", + " 4098 7.278041e-08\n", + " 2546 5.599010e-08\n", + " 4074 4.734322e-08\n", + " Name: 665, dtype: float64, 3098 0.999456\n", + " 107 0.000077\n", + " 6929 0.000052\n", + " 1805 0.000035\n", + " 2435 0.000022\n", + " 2236 0.000016\n", + " 2873 0.000012\n", + " 5054 0.000012\n", + " 5668 0.000010\n", + " 9429 0.000008\n", + " Name: 666, dtype: float64, 3997 0.999926\n", + " 7106 0.000005\n", + " 3998 0.000005\n", + " 640 0.000004\n", + " 2408 0.000002\n", + " 4280 0.000002\n", + " 7107 0.000001\n", + " 3210 0.000001\n", + " 2236 0.000001\n", + " 2351 0.000001\n", + " Name: 667, dtype: float64, 3988 9.999988e-01\n", + " 4004 4.426539e-08\n", + " 1805 1.737529e-08\n", + " 3474 1.106467e-08\n", + " 3243 8.333496e-09\n", + " 4135 8.036409e-09\n", + " 2796 7.852463e-09\n", + " 4759 7.773127e-09\n", + " 2508 7.462479e-09\n", + " 2353 7.378746e-09\n", + " Name: 668, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 669, dtype: float64, 829 0.116168\n", + " 4708 0.032694\n", + " 4711 0.030310\n", + " 4054 0.028077\n", + " 4709 0.024109\n", + " 5829 0.018813\n", + " 6834 0.018649\n", + " 4644 0.018012\n", + " 4815 0.016966\n", + " 1805 0.016889\n", + " Name: 670, dtype: float64, 79 9.999934e-01\n", + " 208 1.653049e-06\n", + " 1805 3.937443e-07\n", + " 7106 1.964412e-07\n", + " 119 1.852119e-07\n", + " 2503 1.166403e-07\n", + " 4098 1.079652e-07\n", + " 517 9.870586e-08\n", + " 2546 7.405401e-08\n", + " 4074 6.706089e-08\n", + " Name: 671, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 672, dtype: float64, 79 9.999932e-01\n", + " 208 1.309259e-06\n", + " 1805 1.955515e-07\n", + " 119 1.233184e-07\n", + " 7106 1.169870e-07\n", + " 517 1.148788e-07\n", + " 9991 6.369829e-08\n", + " 3449 5.206038e-08\n", + " 3637 4.893708e-08\n", + " 8627 4.576584e-08\n", + " Name: 673, dtype: float64, 79 9.999949e-01\n", + " 208 1.292303e-06\n", + " 1805 2.670109e-07\n", + " 119 1.656839e-07\n", + " 7106 1.474233e-07\n", + " 2503 8.628919e-08\n", + " 517 7.540819e-08\n", + " 4098 7.278041e-08\n", + " 2546 5.599010e-08\n", + " 4074 4.734322e-08\n", + " Name: 674, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 675, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 676, dtype: float64, 4000 9.999533e-01\n", + " 4097 1.627581e-06\n", + " 6766 1.548405e-06\n", + " 107 1.221485e-06\n", + " 863 1.011378e-06\n", + " 4714 9.951327e-07\n", + " 2503 9.053660e-07\n", + " 6626 8.570247e-07\n", + " 6598 7.949969e-07\n", + " 79 7.401295e-07\n", + " Name: 677, dtype: float64, 4181 0.165300\n", + " 4182 0.152450\n", + " 4212 0.101888\n", + " 4210 0.086602\n", + " 4169 0.054155\n", + " 5134 0.045094\n", + " 5135 0.036011\n", + " 4239 0.028447\n", + " 4238 0.021682\n", + " 4211 0.016283\n", + " Name: 678, dtype: float64, 3990 0.830574\n", + " 3989 0.113026\n", + " 4162 0.009131\n", + " 181 0.001816\n", + " 4015 0.001574\n", + " 5157 0.001446\n", + " 4030 0.001392\n", + " 2367 0.001378\n", + " 4178 0.001278\n", + " 4149 0.001057\n", + " Name: 679, dtype: float64, 2629 9.999162e-01\n", + " 86 2.195793e-05\n", + " 690 3.686020e-06\n", + " 4067 2.581584e-06\n", + " 4091 1.823202e-06\n", + " 3996 1.494085e-06\n", + " 3998 1.029177e-06\n", + " 5962 8.786422e-07\n", + " 8508 6.890165e-07\n", + " 154 6.420809e-07\n", + " Name: 680, dtype: float64, 4227 0.958397\n", + " 4648 0.002552\n", + " 5058 0.002104\n", + " 4612 0.001689\n", + " 5117 0.001112\n", + " 3098 0.001046\n", + " 4554 0.001000\n", + " 7029 0.000858\n", + " 4019 0.000711\n", + " 4393 0.000546\n", + " Name: 681, dtype: float64, 10435 0.028213\n", + " 7974 0.011674\n", + " 4234 0.011194\n", + " 9943 0.009864\n", + " 5156 0.008047\n", + " 9964 0.007803\n", + " 9093 0.007763\n", + " 7336 0.007205\n", + " 4336 0.007033\n", + " 4377 0.006552\n", + " Name: 682, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 683, dtype: float64, 4650 0.282205\n", + " 4241 0.240690\n", + " 4026 0.212188\n", + " 4580 0.026057\n", + " 5030 0.011508\n", + " 4031 0.010437\n", + " 4029 0.008190\n", + " 5088 0.007643\n", + " 5087 0.006613\n", + " 4020 0.006010\n", + " Name: 684, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 685, dtype: float64, 3098 0.943841\n", + " 5668 0.003254\n", + " 5696 0.002632\n", + " 6929 0.001667\n", + " 5682 0.001266\n", + " 5666 0.000836\n", + " 5054 0.000695\n", + " 4141 0.000668\n", + " 7029 0.000587\n", + " 9429 0.000564\n", + " Name: 686, dtype: float64, 4156 0.371225\n", + " 4012 0.129858\n", + " 4298 0.118793\n", + " 4258 0.074276\n", + " 4256 0.070212\n", + " 4255 0.063506\n", + " 4317 0.021413\n", + " 4316 0.009684\n", + " 4315 0.008602\n", + " 5046 0.006383\n", + " Name: 687, dtype: float64, 4530 0.031517\n", + " 4527 0.030239\n", + " 4265 0.030200\n", + " 4529 0.028489\n", + " 4259 0.027769\n", + " 4531 0.026677\n", + " 4276 0.026035\n", + " 4189 0.025390\n", + " 4261 0.024516\n", + " 4278 0.023475\n", + " Name: 688, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 689, dtype: float64, 4530 0.031517\n", + " 4527 0.030239\n", + " 4265 0.030200\n", + " 4529 0.028489\n", + " 4259 0.027769\n", + " 4531 0.026677\n", + " 4276 0.026035\n", + " 4189 0.025390\n", + " 4261 0.024516\n", + " 4278 0.023475\n", + " Name: 690, dtype: float64, 4530 0.031517\n", + " 4527 0.030239\n", + " 4265 0.030200\n", + " 4529 0.028489\n", + " 4259 0.027769\n", + " 4531 0.026677\n", + " 4276 0.026035\n", + " 4189 0.025390\n", + " 4261 0.024516\n", + " 4278 0.023475\n", + " Name: 691, dtype: float64, 4280 0.999527\n", + " 3997 0.000059\n", + " 4502 0.000031\n", + " 4745 0.000027\n", + " 4706 0.000022\n", + " 5017 0.000013\n", + " 4254 0.000009\n", + " 5099 0.000009\n", + " 246 0.000008\n", + " 4693 0.000008\n", + " Name: 692, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 693, dtype: float64, 4253 0.999926\n", + " 2629 0.000005\n", + " 107 0.000004\n", + " 3002 0.000003\n", + " 2955 0.000002\n", + " 233 0.000002\n", + " 4132 0.000001\n", + " 2632 0.000001\n", + " 2797 0.000001\n", + " 4085 0.000001\n", + " Name: 694, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 695, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 696, dtype: float64, 4399 0.263258\n", + " 4318 0.106509\n", + " 4323 0.099493\n", + " 4320 0.098723\n", + " 4322 0.085048\n", + " 4321 0.084559\n", + " 5128 0.076328\n", + " 4336 0.007389\n", + " 4337 0.006887\n", + " 4376 0.004165\n", + " Name: 697, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 698, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 699, dtype: float64, 4254 9.999762e-01\n", + " 2571 2.440461e-06\n", + " 4280 1.586211e-06\n", + " 4502 7.736662e-07\n", + " 8376 7.349540e-07\n", + " 5099 5.877843e-07\n", + " 211 5.860749e-07\n", + " 2348 4.481344e-07\n", + " 4735 4.459124e-07\n", + " 4741 4.263066e-07\n", + " Name: 700, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 701, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 702, dtype: float64, 4254 9.999762e-01\n", + " 2571 2.440461e-06\n", + " 4280 1.586211e-06\n", + " 4502 7.736662e-07\n", + " 8376 7.349540e-07\n", + " 5099 5.877843e-07\n", + " 211 5.860749e-07\n", + " 2348 4.481344e-07\n", + " 4735 4.459124e-07\n", + " 4741 4.263066e-07\n", + " Name: 703, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 704, dtype: float64, 4149 0.478275\n", + " 4545 0.052350\n", + " 4266 0.040709\n", + " 4270 0.040258\n", + " 4273 0.039107\n", + " 4694 0.018334\n", + " 4408 0.018120\n", + " 4410 0.017849\n", + " 4411 0.017023\n", + " 4271 0.016805\n", + " Name: 705, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 706, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 707, dtype: float64, 4368 0.073492\n", + " 4361 0.070812\n", + " 4364 0.069913\n", + " 4363 0.068326\n", + " 4374 0.067422\n", + " 4366 0.066348\n", + " 4362 0.064429\n", + " 4367 0.064081\n", + " 4371 0.063371\n", + " 4370 0.060158\n", + " Name: 708, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 709, dtype: float64, 4149 0.478275\n", + " 4545 0.052350\n", + " 4266 0.040709\n", + " 4270 0.040258\n", + " 4273 0.039107\n", + " 4694 0.018334\n", + " 4408 0.018120\n", + " 4410 0.017849\n", + " 4411 0.017023\n", + " 4271 0.016805\n", + " Name: 710, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 711, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 712, dtype: float64, 4280 0.998331\n", + " 4502 0.000215\n", + " 4990 0.000119\n", + " 4745 0.000105\n", + " 5099 0.000104\n", + " 4706 0.000061\n", + " 4934 0.000032\n", + " 4254 0.000028\n", + " 246 0.000026\n", + " 4741 0.000026\n", + " Name: 713, dtype: float64, 4149 0.434093\n", + " 181 0.053421\n", + " 4162 0.033340\n", + " 3990 0.027463\n", + " 3989 0.019231\n", + " 4184 0.019143\n", + " 4230 0.017189\n", + " 4736 0.016061\n", + " 4183 0.014329\n", + " 4101 0.012814\n", + " Name: 714, dtype: float64, 5065 0.058712\n", + " 4753 0.058140\n", + " 5063 0.052259\n", + " 5064 0.049743\n", + " 5066 0.047499\n", + " 4259 0.029698\n", + " 4400 0.027559\n", + " 5014 0.025052\n", + " 5086 0.023774\n", + " 5106 0.023206\n", + " Name: 715, dtype: float64, 4254 9.999392e-01\n", + " 4280 5.084195e-06\n", + " 2571 3.877296e-06\n", + " 4502 2.835776e-06\n", + " 5099 2.604653e-06\n", + " 211 2.551494e-06\n", + " 4741 2.445543e-06\n", + " 4735 1.228585e-06\n", + " 8376 8.927037e-07\n", + " 4693 7.141085e-07\n", + " Name: 716, dtype: float64, 4149 0.478275\n", + " 4545 0.052350\n", + " 4266 0.040709\n", + " 4270 0.040258\n", + " 4273 0.039107\n", + " 4694 0.018334\n", + " 4408 0.018120\n", + " 4410 0.017849\n", + " 4411 0.017023\n", + " 4271 0.016805\n", + " Name: 717, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 718, dtype: float64, 4280 0.997164\n", + " 4254 0.000763\n", + " 4502 0.000242\n", + " 5099 0.000128\n", + " 4706 0.000104\n", + " 4990 0.000065\n", + " 4745 0.000063\n", + " 4934 0.000056\n", + " 4967 0.000027\n", + " 4010 0.000024\n", + " Name: 719, dtype: float64, 4254 9.999762e-01\n", + " 2571 2.440461e-06\n", + " 4280 1.586211e-06\n", + " 4502 7.736662e-07\n", + " 8376 7.349540e-07\n", + " 5099 5.877843e-07\n", + " 211 5.860749e-07\n", + " 2348 4.481344e-07\n", + " 4735 4.459124e-07\n", + " 4741 4.263066e-07\n", + " Name: 720, dtype: float64, 4442 0.970761\n", + " 4393 0.010046\n", + " 4555 0.000619\n", + " 4648 0.000605\n", + " 4001 0.000487\n", + " 5057 0.000480\n", + " 4554 0.000265\n", + " 5059 0.000248\n", + " 4140 0.000229\n", + " 2051 0.000206\n", + " Name: 721, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 722, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 723, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 724, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 725, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 726, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 727, dtype: float64, 4149 0.478275\n", + " 4545 0.052350\n", + " 4266 0.040709\n", + " 4270 0.040258\n", + " 4273 0.039107\n", + " 4694 0.018334\n", + " 4408 0.018120\n", + " 4410 0.017849\n", + " 4411 0.017023\n", + " 4271 0.016805\n", + " Name: 728, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 729, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 730, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 731, dtype: float64, 4205 0.880939\n", + " 4206 0.013268\n", + " 4138 0.006090\n", + " 4155 0.003848\n", + " 4013 0.002048\n", + " 3566 0.001859\n", + " 3973 0.001747\n", + " 3510 0.001604\n", + " 3891 0.001578\n", + " 4092 0.001402\n", + " Name: 732, dtype: float64, 4012 0.171456\n", + " 4156 0.074931\n", + " 4298 0.044759\n", + " 4258 0.021401\n", + " 4256 0.017437\n", + " 4138 0.017001\n", + " 4255 0.014469\n", + " 4015 0.013328\n", + " 4150 0.012063\n", + " 4155 0.011892\n", + " Name: 733, dtype: float64, 5064 0.082679\n", + " 5063 0.076061\n", + " 5066 0.074298\n", + " 5065 0.069763\n", + " 4259 0.045076\n", + " 4189 0.033644\n", + " 5014 0.027812\n", + " 4454 0.027490\n", + " 5106 0.026329\n", + " 5069 0.022840\n", + " Name: 734, dtype: float64, 4016 0.388863\n", + " 4612 0.051203\n", + " 4393 0.013849\n", + " 4002 0.013798\n", + " 3600 0.007048\n", + " 4608 0.006957\n", + " 4680 0.006804\n", + " 4336 0.006775\n", + " 4337 0.006320\n", + " 2533 0.005990\n", + " Name: 735, dtype: float64, 4377 0.254931\n", + " 4509 0.211708\n", + " 4384 0.200875\n", + " 4376 0.160079\n", + " 4441 0.007631\n", + " 4608 0.007376\n", + " 4439 0.005282\n", + " 4440 0.005248\n", + " 4511 0.005238\n", + " 4607 0.004853\n", + " Name: 736, dtype: float64, 79 9.999934e-01\n", + " 208 1.653049e-06\n", + " 1805 3.937443e-07\n", + " 7106 1.964412e-07\n", + " 119 1.852119e-07\n", + " 2503 1.166403e-07\n", + " 4098 1.079652e-07\n", + " 517 9.870586e-08\n", + " 2546 7.405401e-08\n", + " 4074 6.706089e-08\n", + " Name: 737, dtype: float64, 79 9.999933e-01\n", + " 208 1.761542e-06\n", + " 1805 4.035150e-07\n", + " 7106 1.886260e-07\n", + " 119 1.875589e-07\n", + " 2503 1.189337e-07\n", + " 4098 9.993738e-08\n", + " 517 9.786881e-08\n", + " 2546 6.950588e-08\n", + " 4074 6.163120e-08\n", + " Name: 738, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 739, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 740, dtype: float64, 3992 0.999888\n", + " 2051 0.000017\n", + " 7414 0.000008\n", + " 2738 0.000004\n", + " 706 0.000002\n", + " 5578 0.000002\n", + " 207 0.000002\n", + " 677 0.000001\n", + " 1714 0.000001\n", + " 6844 0.000001\n", + " Name: 741, dtype: float64, 4011 0.999568\n", + " 4155 0.000075\n", + " 4138 0.000043\n", + " 4073 0.000032\n", + " 2684 0.000031\n", + " 3991 0.000012\n", + " 3068 0.000010\n", + " 4165 0.000010\n", + " 4153 0.000009\n", + " 3067 0.000008\n", + " Name: 742, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 743, dtype: float64, 3994 9.999876e-01\n", + " 4127 2.356943e-06\n", + " 2582 1.413928e-06\n", + " 4078 3.059756e-07\n", + " 6015 1.825343e-07\n", + " 3476 1.593517e-07\n", + " 4393 1.271551e-07\n", + " 207 1.131661e-07\n", + " 2875 1.108352e-07\n", + " 4001 1.086125e-07\n", + " Name: 744, dtype: float64, 107 0.998816\n", + " 3574 0.000591\n", + " 1805 0.000109\n", + " 2590 0.000063\n", + " 2236 0.000037\n", + " 3996 0.000024\n", + " 214 0.000017\n", + " 3098 0.000013\n", + " 4000 0.000009\n", + " 206 0.000008\n", + " Name: 745, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 746, dtype: float64, 79 9.999945e-01\n", + " 208 1.034801e-06\n", + " 7106 3.072564e-07\n", + " 1805 1.717218e-07\n", + " 4098 1.354914e-07\n", + " 2503 7.208519e-08\n", + " 517 7.146340e-08\n", + " 2546 5.161177e-08\n", + " 8418 4.965747e-08\n", + " 7141 4.927611e-08\n", + " Name: 747, dtype: float64, 5058 0.495713\n", + " 4002 0.451588\n", + " 4003 0.011259\n", + " 4030 0.003140\n", + " 4015 0.002897\n", + " 4648 0.001932\n", + " 5059 0.001824\n", + " 4207 0.000833\n", + " 4013 0.000816\n", + " 5154 0.000806\n", + " Name: 748, dtype: float64, 5057 0.790310\n", + " 4001 0.193346\n", + " 4015 0.000794\n", + " 4554 0.000589\n", + " 4102 0.000512\n", + " 4074 0.000448\n", + " 4165 0.000436\n", + " 4078 0.000321\n", + " 4026 0.000318\n", + " 4393 0.000297\n", + " Name: 749, dtype: float64, 3210 0.999645\n", + " 214 0.000036\n", " 2236 0.000031\n", - " 2503 0.000030\n", - " 3207 0.000028\n", - " 4710 0.000025\n", - " Name: 686, dtype: float64, 4156 0.279031\n", - " 4298 0.190396\n", - " 4012 0.081343\n", - " 4255 0.044702\n", - " 4258 0.044600\n", - " 4317 0.044406\n", - " 4256 0.043887\n", - " 4316 0.029787\n", - " 4315 0.027217\n", - " 4399 0.025127\n", - " Name: 687, dtype: float64, 4189 0.033476\n", - " 5014 0.029083\n", - " 4532 0.020998\n", - " 4531 0.020367\n", - " 4528 0.020290\n", - " 4529 0.019632\n", - " 5063 0.019294\n", - " 4259 0.018996\n", - " 4527 0.018829\n", - " 4261 0.018381\n", - " Name: 688, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 689, dtype: float64, 4189 0.033476\n", - " 5014 0.029083\n", - " 4532 0.020998\n", - " 4531 0.020367\n", - " 4528 0.020290\n", - " 4529 0.019632\n", - " 5063 0.019294\n", - " 4259 0.018996\n", - " 4527 0.018829\n", - " 4261 0.018381\n", - " Name: 690, dtype: float64, 4189 0.033476\n", - " 5014 0.029083\n", - " 4532 0.020998\n", - " 4531 0.020367\n", - " 4528 0.020290\n", - " 4529 0.019632\n", - " 5063 0.019294\n", - " 4259 0.018996\n", - " 4527 0.018829\n", - " 4261 0.018381\n", - " Name: 691, dtype: float64, 4280 0.998827\n", - " 4502 0.000272\n", - " 4501 0.000052\n", - " 4745 0.000049\n", - " 4541 0.000048\n", - " 4706 0.000044\n", - " 4990 0.000026\n", - " 205 0.000025\n", - " 4537 0.000020\n", - " 7114 0.000018\n", - " Name: 692, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 693, dtype: float64, 4253 9.999147e-01\n", - " 2629 6.732313e-06\n", - " 4067 2.122914e-06\n", - " 677 1.215444e-06\n", - " 230 1.164094e-06\n", - " 3098 1.136982e-06\n", - " 1781 9.990117e-07\n", - " 5159 8.093609e-07\n", - " 7260 8.001970e-07\n", - " 6560 7.586785e-07\n", - " Name: 694, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 695, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 696, dtype: float64, 4399 0.352149\n", - " 4320 0.074040\n", - " 4318 0.074039\n", - " 4323 0.071507\n", - " 4322 0.070247\n", - " 4321 0.069418\n", - " 5128 0.066023\n", - " 4012 0.002916\n", - " 4072 0.001915\n", - " 4570 0.001909\n", - " Name: 697, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 698, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 699, dtype: float64, 4254 9.999751e-01\n", - " 4741 3.004782e-06\n", - " 2571 9.751080e-07\n", - " 5057 6.670442e-07\n", - " 4248 4.890608e-07\n", - " 4735 4.791796e-07\n", - " 3343 3.714640e-07\n", - " 4705 3.201825e-07\n", - " 4521 3.112848e-07\n", - " 4502 3.014402e-07\n", - " Name: 700, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 701, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 702, dtype: float64, 4254 9.999751e-01\n", - " 4741 3.004782e-06\n", - " 2571 9.751080e-07\n", - " 5057 6.670442e-07\n", - " 4248 4.890608e-07\n", - " 4735 4.791796e-07\n", - " 3343 3.714640e-07\n", - " 4705 3.201825e-07\n", - " 4521 3.112848e-07\n", - " 4502 3.014402e-07\n", - " Name: 703, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 704, dtype: float64, 4149 0.469764\n", - " 4266 0.038747\n", - " 4270 0.038445\n", - " 4273 0.036484\n", - " 4545 0.036443\n", - " 4694 0.016874\n", - " 4289 0.016655\n", - " 4267 0.016116\n", - " 4271 0.016114\n", - " 4287 0.016030\n", - " Name: 705, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 706, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 707, dtype: float64, 4376 0.055951\n", - " 4377 0.048811\n", - " 4211 0.044904\n", - " 4367 0.043463\n", - " 4374 0.042066\n", - " 4364 0.040867\n", - " 4368 0.039966\n", - " 4363 0.039806\n", - " 4366 0.039801\n", - " 4370 0.039478\n", - " Name: 708, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 709, dtype: float64, 4149 0.469764\n", - " 4266 0.038747\n", - " 4270 0.038445\n", - " 4273 0.036484\n", - " 4545 0.036443\n", - " 4694 0.016874\n", - " 4289 0.016655\n", - " 4267 0.016116\n", - " 4271 0.016114\n", - " 4287 0.016030\n", - " Name: 710, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 711, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 712, dtype: float64, 4280 0.997209\n", - " 4745 0.000432\n", - " 4502 0.000199\n", - " 205 0.000179\n", - " 4706 0.000170\n", - " 4990 0.000079\n", - " 4501 0.000063\n", - " 214 0.000047\n", - " 4537 0.000039\n", - " 4632 0.000038\n", - " Name: 713, dtype: float64, 4149 0.116108\n", - " 3990 0.079415\n", - " 3989 0.064472\n", - " 4162 0.050605\n", - " 4208 0.043580\n", - " 181 0.018861\n", - " 4738 0.015326\n", - " 4739 0.015200\n", - " 5135 0.014448\n", - " 5134 0.013796\n", - " Name: 714, dtype: float64, 4753 0.067375\n", - " 4400 0.043297\n", - " 5086 0.025588\n", - " 4169 0.023916\n", - " 4181 0.021976\n", - " 4189 0.021822\n", - " 4182 0.016215\n", - " 5066 0.013297\n", - " 4099 0.011589\n", - " 5064 0.011138\n", - " Name: 715, dtype: float64, 4254 9.999719e-01\n", - " 4741 1.565915e-06\n", - " 2571 7.162803e-07\n", - " 5057 6.963552e-07\n", - " 3098 6.159360e-07\n", - " 4280 5.667886e-07\n", - " 4248 5.051792e-07\n", - " 181 4.143544e-07\n", - " 4735 3.317609e-07\n", - " 7107 3.023236e-07\n", - " Name: 716, dtype: float64, 4149 0.469764\n", - " 4266 0.038747\n", - " 4270 0.038445\n", - " 4273 0.036484\n", - " 4545 0.036443\n", - " 4694 0.016874\n", - " 4289 0.016655\n", - " 4267 0.016116\n", - " 4271 0.016114\n", - " 4287 0.016030\n", - " Name: 717, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 718, dtype: float64, 4280 0.989089\n", - " 4745 0.001336\n", - " 4706 0.001064\n", - " 4502 0.000686\n", - " 4990 0.000572\n", - " 4232 0.000219\n", - " 4537 0.000201\n", - " 5017 0.000194\n", - " 4501 0.000186\n", - " 4993 0.000117\n", - " Name: 719, dtype: float64, 4254 9.999751e-01\n", - " 4741 3.004782e-06\n", - " 2571 9.751080e-07\n", - " 5057 6.670442e-07\n", - " 4248 4.890608e-07\n", - " 4735 4.791796e-07\n", - " 3343 3.714640e-07\n", - " 4705 3.201825e-07\n", - " 4521 3.112848e-07\n", - " 4502 3.014402e-07\n", - " Name: 720, dtype: float64, 4442 0.947029\n", - " 4393 0.006545\n", - " 4001 0.001571\n", - " 2685 0.000774\n", - " 4555 0.000709\n", - " 4075 0.000705\n", - " 4013 0.000687\n", - " 4607 0.000645\n", - " 4140 0.000574\n", - " 4002 0.000573\n", - " Name: 721, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 722, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 723, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 724, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 725, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 726, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 727, dtype: float64, 4149 0.469764\n", - " 4266 0.038747\n", - " 4270 0.038445\n", - " 4273 0.036484\n", - " 4545 0.036443\n", - " 4694 0.016874\n", - " 4289 0.016655\n", - " 4267 0.016116\n", - " 4271 0.016114\n", - " 4287 0.016030\n", - " Name: 728, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 729, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 730, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 731, dtype: float64, 4205 0.511840\n", - " 4024 0.043167\n", - " 4202 0.032090\n", - " 4012 0.031087\n", - " 4206 0.026135\n", - " 3929 0.012154\n", - " 5011 0.005559\n", - " 5574 0.005532\n", - " 4095 0.004050\n", - " 4298 0.003672\n", - " Name: 732, dtype: float64, 4298 0.122487\n", - " 4015 0.085428\n", - " 4156 0.044778\n", - " 4611 0.038001\n", - " 4043 0.033824\n", - " 4399 0.028401\n", - " 4012 0.027908\n", - " 4317 0.025683\n", - " 5046 0.015596\n", - " 4258 0.015540\n", - " Name: 733, dtype: float64, 5064 0.058679\n", - " 5066 0.055554\n", - " 4259 0.049729\n", - " 5065 0.048020\n", - " 5063 0.047816\n", - " 4189 0.034839\n", - " 4239 0.025767\n", - " 4238 0.023884\n", - " 5014 0.020850\n", - " 4454 0.020136\n", - " Name: 734, dtype: float64, 4002 0.156293\n", - " 4016 0.119603\n", - " 4393 0.062372\n", - " 4648 0.052438\n", - " 5058 0.031888\n", - " 4555 0.016686\n", - " 4554 0.015200\n", - " 4207 0.011654\n", - " 4612 0.009855\n", - " 4607 0.009355\n", - " Name: 735, dtype: float64, 4377 0.282729\n", - " 4384 0.208891\n", - " 4509 0.174786\n", - " 4376 0.154189\n", - " 4512 0.008976\n", - " 4511 0.007600\n", - " 4397 0.004816\n", - " 4396 0.003744\n", - " 4398 0.003698\n", - " 4394 0.003538\n", - " Name: 736, dtype: float64, 79 9.999925e-01\n", - " 160 2.698233e-07\n", - " 9626 2.653256e-07\n", - " 8040 2.240738e-07\n", - " 4011 1.899876e-07\n", - " 206 1.763377e-07\n", - " 119 1.716841e-07\n", - " 2260 1.452654e-07\n", - " 690 1.206203e-07\n", - " 4073 1.204217e-07\n", - " Name: 737, dtype: float64, 79 9.999915e-01\n", - " 9626 2.843017e-07\n", - " 160 2.819658e-07\n", - " 8040 2.433469e-07\n", - " 4011 1.992678e-07\n", - " 206 1.982015e-07\n", - " 119 1.820135e-07\n", - " 2260 1.564248e-07\n", - " 192 1.447671e-07\n", - " 1203 1.376575e-07\n", - " Name: 738, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 739, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 740, dtype: float64, 3992 9.999642e-01\n", - " 677 1.108366e-05\n", - " 2738 1.801027e-06\n", - " 697 1.225081e-06\n", - " 7414 1.139922e-06\n", - " 5950 6.983145e-07\n", - " 7957 5.626734e-07\n", - " 6091 3.793423e-07\n", - " 8644 3.617332e-07\n", - " 3993 3.494245e-07\n", - " Name: 741, dtype: float64, 4011 0.998725\n", - " 4073 0.000275\n", - " 79 0.000099\n", - " 4155 0.000052\n", - " 4138 0.000047\n", - " 4203 0.000045\n", - " 2684 0.000037\n", - " 4013 0.000034\n", - " 4202 0.000031\n", - " 4012 0.000024\n", - " Name: 742, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 743, dtype: float64, 3994 9.999862e-01\n", - " 4127 2.097253e-06\n", - " 2582 2.080553e-06\n", - " 4000 5.963194e-07\n", - " 2662 5.416661e-07\n", - " 5549 2.588756e-07\n", - " 5945 2.192588e-07\n", - " 5919 2.003120e-07\n", - " 7107 1.688453e-07\n", - " 2955 1.628869e-07\n", - " Name: 744, dtype: float64, 107 0.999188\n", - " 2503 0.000103\n", - " 6066 0.000048\n", - " 2236 0.000043\n", - " 4716 0.000036\n", - " 4141 0.000031\n", - " 3574 0.000029\n", - " 2567 0.000016\n", - " 4708 0.000013\n", - " 5185 0.000008\n", - " Name: 745, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 746, dtype: float64, 79 9.999940e-01\n", - " 160 2.940790e-07\n", - " 9626 1.791401e-07\n", - " 8040 1.641702e-07\n", - " 206 1.405118e-07\n", - " 4011 1.186987e-07\n", - " 119 1.176985e-07\n", - " 1203 1.154319e-07\n", - " 2260 1.048017e-07\n", - " 4073 8.942595e-08\n", - " Name: 747, dtype: float64, 5058 0.486399\n", - " 4002 0.410043\n", - " 4003 0.019145\n", - " 4648 0.004598\n", - " 4001 0.004142\n", - " 4393 0.003616\n", - " 5057 0.003442\n", - " 4015 0.002853\n", - " 5059 0.002657\n", - " 4013 0.002562\n", - " Name: 748, dtype: float64, 5057 0.772762\n", - " 4001 0.189304\n", - " 5058 0.003425\n", - " 5059 0.002115\n", - " 4002 0.001723\n", - " 4393 0.001241\n", - " 4074 0.001137\n", - " 4165 0.001055\n", - " 2236 0.000997\n", - " 5199 0.000735\n", - " Name: 749, dtype: float64, 3210 0.999696\n", - " 4540 0.000012\n", - " 2504 0.000009\n", - " 4000 0.000008\n", - " 4632 0.000007\n", - " 3998 0.000007\n", - " 205 0.000007\n", - " 4165 0.000007\n", - " 677 0.000006\n", - " 5120 0.000006\n", - " Name: 750, dtype: float64, 3098 0.999746\n", - " 1805 0.000018\n", - " 6929 0.000016\n", - " 2503 0.000010\n", - " 2236 0.000010\n", - " 8509 0.000009\n", - " 2873 0.000008\n", - " 677 0.000008\n", - " 6066 0.000005\n", - " 2715 0.000005\n", - " Name: 751, dtype: float64, 3999 0.999323\n", - " 4542 0.000063\n", - " 2629 0.000060\n", - " 3998 0.000058\n", - " 3325 0.000028\n", - " 677 0.000021\n", - " 2236 0.000018\n", - " 4540 0.000018\n", - " 3997 0.000014\n", - " 690 0.000013\n", - " Name: 752, dtype: float64, 79 9.999940e-01\n", - " 160 2.940790e-07\n", - " 9626 1.791401e-07\n", - " 8040 1.641702e-07\n", - " 206 1.405118e-07\n", - " 4011 1.186987e-07\n", - " 119 1.176985e-07\n", - " 1203 1.154319e-07\n", - " 2260 1.048017e-07\n", - " 4073 8.942595e-08\n", - " Name: 753, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", + " 2503 0.000017\n", + " 5144 0.000011\n", + " 3574 0.000008\n", + " 1471 0.000008\n", + " 4540 0.000006\n", + " 3999 0.000005\n", + " 2351 0.000005\n", + " Name: 750, dtype: float64, 3098 0.999804\n", + " 6929 0.000021\n", + " 1805 0.000016\n", + " 5668 0.000011\n", + " 107 0.000009\n", + " 5054 0.000005\n", + " 2236 0.000004\n", + " 2503 0.000003\n", " 2435 0.000003\n", - " Name: 754, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 755, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 756, dtype: float64, 2629 0.996723\n", - " 4253 0.001034\n", - " 2236 0.000203\n", - " 2955 0.000122\n", - " 2503 0.000070\n", - " 7106 0.000060\n", - " 5833 0.000036\n", - " 5540 0.000031\n", - " 4067 0.000029\n", - " 3098 0.000027\n", - " Name: 757, dtype: float64, 2629 0.999827\n", - " 2236 0.000012\n", - " 4067 0.000007\n", - " 3671 0.000005\n", - " 3999 0.000004\n", - " 4098 0.000004\n", - " 642 0.000003\n", - " 677 0.000003\n", - " 3670 0.000003\n", - " 7124 0.000003\n", - " Name: 758, dtype: float64, 4253 9.999147e-01\n", - " 2629 6.732313e-06\n", - " 4067 2.122914e-06\n", - " 677 1.215444e-06\n", - " 230 1.164094e-06\n", - " 3098 1.136982e-06\n", - " 1781 9.990117e-07\n", - " 5159 8.093609e-07\n", - " 7260 8.001970e-07\n", - " 6560 7.586785e-07\n", - " Name: 759, dtype: float64, 4563 0.121481\n", - " 4560 0.120778\n", - " 4565 0.118106\n", - " 4561 0.116999\n", - " 4566 0.116290\n", - " 4567 0.112875\n", - " 4564 0.112543\n", - " 4562 0.046196\n", - " 4377 0.008619\n", - " 4376 0.007728\n", - " Name: 760, dtype: float64, 79 9.999963e-01\n", - " 160 1.616382e-07\n", - " 9626 1.425669e-07\n", - " 119 1.115760e-07\n", - " 4011 9.668887e-08\n", - " 8040 9.005193e-08\n", - " 206 8.351380e-08\n", - " 2260 8.039444e-08\n", - " 205 7.063284e-08\n", - " 246 6.807299e-08\n", - " Name: 761, dtype: float64, 4438 0.909106\n", - " 4001 0.003499\n", - " 3039 0.001570\n", - " 4155 0.001474\n", - " 268 0.001461\n", - " 10198 0.001388\n", - " 6839 0.001356\n", - " 4206 0.001242\n", - " 4205 0.000905\n", - " 4620 0.000848\n", - " Name: 762, dtype: float64, 4439 0.359932\n", - " 4441 0.351330\n", - " 4440 0.046393\n", - " 4158 0.017083\n", - " 4503 0.013006\n", - " 4195 0.007975\n", - " 4194 0.007585\n", - " 4196 0.006154\n", - " 4384 0.005745\n", - " 4369 0.004746\n", - " Name: 763, dtype: float64, 4177 0.249011\n", - " 4178 0.120479\n", - " 2367 0.092162\n", - " 4494 0.055341\n", - " 4401 0.030263\n", - " 4646 0.027796\n", - " 4572 0.024785\n", - " 4524 0.024203\n", - " 4571 0.024080\n", - " 4576 0.021877\n", - " Name: 764, dtype: float64, 4177 0.249011\n", - " 4178 0.120479\n", - " 2367 0.092162\n", - " 4494 0.055341\n", - " 4401 0.030263\n", - " 4646 0.027796\n", - " 4572 0.024785\n", - " 4524 0.024203\n", - " 4571 0.024080\n", - " 4576 0.021877\n", - " Name: 765, dtype: float64, 4614 0.495259\n", - " 4618 0.062629\n", - " 4579 0.060515\n", - " 4385 0.048580\n", - " 4006 0.044454\n", - " 4149 0.023953\n", - " 5150 0.022891\n", - " 4031 0.012099\n", - " 4172 0.006130\n", - " 4021 0.005099\n", - " Name: 766, dtype: float64, 3098 0.999363\n", - " 2503 0.000046\n", + " 8418 0.000003\n", + " Name: 751, dtype: float64, 3999 0.999794\n", + " 3998 0.000025\n", + " 3098 0.000012\n", + " 4542 0.000009\n", + " 2503 0.000007\n", + " 4074 0.000007\n", + " 1805 0.000007\n", + " 4000 0.000006\n", + " 2236 0.000005\n", + " 4535 0.000004\n", + " Name: 752, dtype: float64, 79 9.999945e-01\n", + " 208 1.034801e-06\n", + " 7106 3.072564e-07\n", + " 1805 1.717218e-07\n", + " 4098 1.354914e-07\n", + " 2503 7.208519e-08\n", + " 517 7.146340e-08\n", + " 2546 5.161177e-08\n", + " 8418 4.965747e-08\n", + " 7141 4.927611e-08\n", + " Name: 753, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 754, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 755, dtype: float64, 107 0.999825\n", " 1805 0.000038\n", - " 2873 0.000036\n", - " 2236 0.000034\n", - " 6929 0.000030\n", - " 2715 0.000021\n", - " 677 0.000014\n", - " 2502 0.000012\n", - " 3207 0.000011\n", - " Name: 767, dtype: float64, 3999 0.999287\n", - " 2629 0.000098\n", - " 4542 0.000079\n", - " 3998 0.000029\n", - " 3325 0.000025\n", - " 677 0.000022\n", - " 4540 0.000021\n", - " 3997 0.000016\n", - " 2236 0.000014\n", - " 690 0.000014\n", - " Name: 768, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 769, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 770, dtype: float64, 3992 9.999424e-01\n", - " 677 1.476421e-05\n", - " 2738 2.154386e-06\n", - " 697 1.492650e-06\n", - " 5950 1.385210e-06\n", - " 7414 1.365843e-06\n", - " 7957 1.297597e-06\n", - " 6091 9.492531e-07\n", - " 3993 8.500680e-07\n", - " 7472 5.800820e-07\n", - " Name: 771, dtype: float64, 3990 0.549638\n", - " 3991 0.305791\n", - " 3989 0.012023\n", - " 4149 0.005815\n", - " 4002 0.004300\n", - " 4029 0.002978\n", - " 4178 0.002712\n", - " 4170 0.002652\n", - " 5058 0.002645\n", - " 4169 0.002450\n", - " Name: 772, dtype: float64, 4585 0.959334\n", - " 4632 0.001303\n", - " 5016 0.001295\n", - " 4929 0.001060\n", - " 5004 0.000796\n", - " 5001 0.000700\n", - " 471 0.000593\n", - " 4933 0.000512\n", - " 4600 0.000483\n", - " 4998 0.000450\n", - " Name: 773, dtype: float64, 4142 0.700569\n", - " 4588 0.035356\n", - " 4148 0.026158\n", - " 4631 0.025371\n", - " 4629 0.014120\n", - " 4637 0.009133\n", - " 4146 0.008534\n", - " 4684 0.008422\n", - " 4682 0.008262\n", - " 4636 0.007265\n", - " Name: 774, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 775, dtype: float64, 4073 0.994609\n", - " 4203 0.001439\n", - " 3990 0.000228\n", - " 4138 0.000165\n", - " 4011 0.000145\n", - " 4155 0.000144\n", - " 4012 0.000140\n", - " 4208 0.000112\n", - " 4202 0.000093\n", - " 1805 0.000092\n", - " Name: 776, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 777, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 778, dtype: float64, 4191 0.998056\n", - " 4720 0.000447\n", - " 5044 0.000269\n", - " 4547 0.000113\n", - " 5550 0.000065\n", - " 2214 0.000057\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 756, dtype: float64, 2629 0.997620\n", + " 86 0.000186\n", + " 2503 0.000128\n", + " 4253 0.000108\n", + " 107 0.000084\n", + " 7106 0.000084\n", + " 4073 0.000079\n", + " 3996 0.000067\n", + " 1805 0.000045\n", + " 79 0.000039\n", + " Name: 757, dtype: float64, 2629 9.999424e-01\n", + " 86 5.596320e-06\n", + " 4067 4.048708e-06\n", + " 2503 1.317995e-06\n", + " 4073 1.034116e-06\n", + " 3998 7.073113e-07\n", + " 4253 6.770485e-07\n", + " 2236 6.482413e-07\n", + " 679 6.421597e-07\n", + " 3002 6.107694e-07\n", + " Name: 758, dtype: float64, 4253 0.999926\n", + " 2629 0.000005\n", + " 107 0.000004\n", + " 3002 0.000003\n", + " 2955 0.000002\n", + " 233 0.000002\n", + " 4132 0.000001\n", + " 2632 0.000001\n", + " 2797 0.000001\n", + " 4085 0.000001\n", + " Name: 759, dtype: float64, 4561 0.133687\n", + " 4564 0.130497\n", + " 4563 0.123944\n", + " 4565 0.121622\n", + " 4567 0.119963\n", + " 4566 0.115346\n", + " 4560 0.112216\n", + " 4562 0.049209\n", + " 4377 0.008370\n", + " 4376 0.006609\n", + " Name: 760, dtype: float64, 79 9.999959e-01\n", + " 208 7.045417e-07\n", + " 1805 1.341237e-07\n", + " 7106 1.039031e-07\n", + " 119 1.022602e-07\n", + " 4098 7.598029e-08\n", + " 2546 6.512450e-08\n", + " 517 5.719911e-08\n", + " 2503 4.948452e-08\n", + " 8418 3.878140e-08\n", + " Name: 761, dtype: float64, 4438 0.940571\n", + " 5053 0.004403\n", + " 4155 0.001191\n", + " 4137 0.000917\n", + " 3508 0.000539\n", + " 3039 0.000463\n", + " 668 0.000448\n", + " 10543 0.000428\n", + " 9039 0.000422\n", + " 9857 0.000417\n", + " Name: 762, dtype: float64, 4441 0.421091\n", + " 4439 0.370592\n", + " 4440 0.065730\n", + " 4384 0.019425\n", + " 4376 0.007927\n", + " 4608 0.004951\n", + " 4607 0.004896\n", + " 4158 0.004593\n", + " 4377 0.004417\n", + " 4509 0.004077\n", + " Name: 763, dtype: float64, 4177 0.219184\n", + " 4178 0.134928\n", + " 2367 0.086307\n", + " 4494 0.074992\n", + " 4646 0.039247\n", + " 4401 0.033840\n", + " 4575 0.030946\n", + " 4491 0.030800\n", + " 4572 0.030286\n", + " 4571 0.030132\n", + " Name: 764, dtype: float64, 4177 0.219184\n", + " 4178 0.134928\n", + " 2367 0.086307\n", + " 4494 0.074992\n", + " 4646 0.039247\n", + " 4401 0.033840\n", + " 4575 0.030946\n", + " 4491 0.030800\n", + " 4572 0.030286\n", + " 4571 0.030132\n", + " Name: 765, dtype: float64, 4614 0.791527\n", + " 4579 0.053965\n", + " 4618 0.020122\n", + " 4006 0.009453\n", + " 4012 0.009021\n", + " 4385 0.006111\n", + " 4753 0.004110\n", + " 5015 0.003371\n", + " 4607 0.002456\n", + " 5150 0.002330\n", + " Name: 766, dtype: float64, 3098 0.999298\n", + " 107 0.000068\n", + " 6929 0.000041\n", + " 5668 0.000039\n", + " 1805 0.000032\n", + " 9429 0.000015\n", + " 5054 0.000015\n", + " 2435 0.000011\n", + " 4013 0.000009\n", + " 6888 0.000008\n", + " Name: 767, dtype: float64, 3999 0.999720\n", + " 4542 0.000027\n", + " 3998 0.000025\n", + " 4535 0.000019\n", + " 1805 0.000011\n", + " 3098 0.000010\n", + " 5848 0.000010\n", + " 4074 0.000010\n", + " 4000 0.000009\n", + " 3285 0.000005\n", + " Name: 768, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 769, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 770, dtype: float64, 3992 9.999071e-01\n", + " 2051 1.064764e-05\n", + " 7414 7.064210e-06\n", + " 2738 4.026061e-06\n", + " 207 2.244178e-06\n", + " 706 1.591229e-06\n", + " 5578 1.389246e-06\n", + " 1714 1.168669e-06\n", + " 677 1.134416e-06\n", + " 755 8.309394e-07\n", + " Name: 771, dtype: float64, 3991 0.665893\n", + " 3990 0.282945\n", + " 3989 0.006362\n", + " 4172 0.002600\n", + " 4169 0.002472\n", + " 4149 0.001674\n", + " 2236 0.001158\n", + " 5760 0.001141\n", + " 5007 0.000985\n", + " 4011 0.000919\n", + " Name: 772, dtype: float64, 4585 0.983808\n", + " 5999 0.000562\n", + " 4521 0.000502\n", + " 4994 0.000348\n", + " 3998 0.000275\n", + " 4998 0.000262\n", + " 4600 0.000248\n", + " 5001 0.000246\n", + " 3094 0.000210\n", + " 4543 0.000195\n", + " Name: 773, dtype: float64, 4142 0.782559\n", + " 4148 0.055210\n", + " 4631 0.028986\n", + " 4588 0.025303\n", + " 4591 0.018834\n", + " 4629 0.008351\n", + " 4799 0.006641\n", + " 4637 0.001540\n", + " 4633 0.001473\n", + " 4636 0.001133\n", + " Name: 774, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 775, dtype: float64, 4073 0.996942\n", + " 4138 0.000499\n", + " 4203 0.000366\n", + " 4155 0.000214\n", + " 4012 0.000147\n", + " 4149 0.000132\n", + " 2236 0.000086\n", + " 2503 0.000079\n", + " 4158 0.000058\n", + " 4987 0.000055\n", + " Name: 776, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 777, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 778, dtype: float64, 4191 0.998762\n", + " 5044 0.000075\n", + " 8822 0.000044\n", + " 4720 0.000041\n", " 4721 0.000028\n", - " 3298 0.000026\n", - " 3511 0.000014\n", - " 5732 0.000014\n", - " Name: 779, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 780, dtype: float64, 3210 0.998131\n", - " 5120 0.000120\n", - " 4704 0.000101\n", - " 4540 0.000094\n", - " 5144 0.000084\n", - " 4165 0.000041\n", - " 4632 0.000034\n", - " 3998 0.000034\n", - " 4541 0.000029\n", - " 4543 0.000025\n", - " Name: 781, dtype: float64, 4092 0.971542\n", - " 4088 0.002313\n", - " 4013 0.001672\n", - " 4193 0.000934\n", - " 4387 0.000794\n", - " 181 0.000723\n", - " 5104 0.000525\n", - " 4074 0.000400\n", - " 4155 0.000388\n", - " 697 0.000315\n", - " Name: 782, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 783, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 784, dtype: float64, 4259 0.068210\n", - " 4454 0.060286\n", - " 5064 0.059740\n", - " 5066 0.056862\n", - " 5065 0.056159\n", - " 5063 0.054816\n", - " 4238 0.053423\n", - " 4239 0.050981\n", - " 4178 0.024656\n", - " 4189 0.022040\n", - " Name: 785, dtype: float64, 3996 9.999737e-01\n", - " 8381 1.741967e-06\n", - " 4710 9.066468e-07\n", - " 3574 6.314740e-07\n", - " 1515 5.614004e-07\n", - " 9394 4.338572e-07\n", - " 2435 4.066889e-07\n", - " 9354 3.671372e-07\n", - " 4989 3.391952e-07\n", - " 9416 3.216211e-07\n", - " Name: 786, dtype: float64, 4615 0.680463\n", - " 4106 0.241570\n", - " 4616 0.006567\n", - " 5013 0.000892\n", - " 6341 0.000615\n", - " 2214 0.000554\n", - " 9544 0.000548\n", - " 2876 0.000515\n", - " 3289 0.000476\n", - " 6021 0.000432\n", - " Name: 787, dtype: float64, 4155 0.500846\n", - " 5032 0.044010\n", - " 4138 0.040680\n", - " 4533 0.039698\n", - " 4987 0.022412\n", - " 4073 0.019027\n", - " 2884 0.014685\n", - " 4719 0.011652\n", - " 4015 0.010208\n", - " 4011 0.007280\n", - " Name: 788, dtype: float64, 4298 0.508352\n", - " 4156 0.056014\n", - " 4230 0.053201\n", - " 5046 0.039486\n", - " 4012 0.019214\n", - " 4258 0.015455\n", - " 4255 0.013946\n", - " 4317 0.013522\n", - " 4256 0.011529\n", - " 4316 0.011106\n", - " Name: 789, dtype: float64, 3990 0.507305\n", - " 3989 0.291765\n", - " 4738 0.019721\n", - " 4739 0.017848\n", - " 4038 0.011147\n", - " 5135 0.005965\n", - " 4177 0.004831\n", - " 5134 0.004521\n", - " 4169 0.004450\n", - " 4042 0.004054\n", - " Name: 790, dtype: float64, 4102 0.957250\n", - " 4107 0.010824\n", - " 4021 0.002582\n", - " 4207 0.000958\n", - " 4752 0.000527\n", - " 4762 0.000474\n", - " 4811 0.000456\n", - " 4089 0.000440\n", - " 4777 0.000383\n", - " 4776 0.000361\n", - " Name: 791, dtype: float64, 4626 0.983672\n", - " 3866 0.000938\n", - " 3627 0.000195\n", - " 5186 0.000169\n", - " 3634 0.000126\n", - " 10143 0.000115\n", - " 4740 0.000113\n", - " 5525 0.000098\n", - " 5739 0.000087\n", - " 9512 0.000086\n", - " Name: 792, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 793, dtype: float64, 4633 0.217679\n", - " 4142 0.049477\n", - " 4148 0.031397\n", - " 4629 0.024766\n", - " 4603 0.024312\n", - " 2189 0.015720\n", - " 4146 0.013461\n", - " 4114 0.012565\n", - " 4631 0.012520\n", - " 4636 0.010972\n", - " Name: 794, dtype: float64, 4588 0.179534\n", - " 4631 0.178686\n", - " 4591 0.110927\n", - " 4633 0.056377\n", - " 4148 0.044725\n", - " 4684 0.037265\n", - " 4142 0.030847\n", - " 4717 0.026398\n", - " 4636 0.022802\n", - " 4637 0.021582\n", - " Name: 795, dtype: float64, 4638 0.105135\n", - " 4107 0.102400\n", - " 4193 0.088390\n", - " 4089 0.051661\n", - " 4639 0.047194\n", - " 4677 0.034444\n", - " 4393 0.033665\n", - " 4207 0.033285\n", - " 4209 0.032939\n", - " 5117 0.031885\n", - " Name: 796, dtype: float64, 4026 0.249484\n", - " 4650 0.220685\n", - " 4241 0.158473\n", - " 4580 0.022034\n", - " 4031 0.018632\n", - " 4607 0.014632\n", - " 4020 0.011107\n", - " 4136 0.008140\n", - " 4076 0.008114\n", - " 4617 0.006595\n", - " Name: 797, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 798, dtype: float64, 4653 0.684836\n", - " 7141 0.011392\n", - " 7186 0.010862\n", - " 4098 0.010518\n", - " 4001 0.006344\n", - " 7124 0.006157\n", - " 4620 0.004278\n", - " 7135 0.003915\n", - " 5668 0.003686\n", - " 6199 0.003651\n", - " Name: 799, dtype: float64, 3992 9.999642e-01\n", - " 677 1.108366e-05\n", - " 2738 1.801027e-06\n", - " 697 1.225081e-06\n", - " 7414 1.139922e-06\n", - " 5950 6.983145e-07\n", - " 7957 5.626734e-07\n", - " 6091 3.793423e-07\n", - " 8644 3.617332e-07\n", - " 3993 3.494245e-07\n", - " Name: 800, dtype: float64, 4177 0.249011\n", - " 4178 0.120479\n", - " 2367 0.092162\n", - " 4494 0.055341\n", - " 4401 0.030263\n", - " 4646 0.027796\n", - " 4572 0.024785\n", - " 4524 0.024203\n", - " 4571 0.024080\n", - " 4576 0.021877\n", - " Name: 801, dtype: float64, 4177 0.249011\n", - " 4178 0.120479\n", - " 2367 0.092162\n", - " 4494 0.055341\n", - " 4401 0.030263\n", - " 4646 0.027796\n", - " 4572 0.024785\n", - " 4524 0.024203\n", - " 4571 0.024080\n", - " 4576 0.021877\n", - " Name: 802, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 803, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 804, dtype: float64, 3997 0.999934\n", - " 3210 0.000016\n", - " 3998 0.000004\n", - " 3999 0.000003\n", - " 4067 0.000001\n", + " 5732 0.000026\n", + " 5680 0.000019\n", + " 5550 0.000018\n", + " 9329 0.000017\n", + " 4581 0.000014\n", + " Name: 779, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 780, dtype: float64, 3210 0.998597\n", + " 5144 0.000101\n", + " 4704 0.000070\n", + " 4540 0.000037\n", + " 4090 0.000037\n", + " 4543 0.000030\n", + " 4762 0.000026\n", + " 5120 0.000020\n", + " 4886 0.000018\n", + " 8381 0.000018\n", + " Name: 781, dtype: float64, 4092 0.987618\n", + " 4088 0.000613\n", + " 2497 0.000582\n", + " 4172 0.000567\n", + " 3039 0.000404\n", + " 3800 0.000253\n", + " 3572 0.000226\n", + " 4095 0.000214\n", + " 4708 0.000206\n", + " 4387 0.000149\n", + " Name: 782, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 783, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 784, dtype: float64, 4259 0.109424\n", + " 4454 0.074042\n", + " 5064 0.073586\n", + " 4238 0.070322\n", + " 5063 0.070058\n", + " 5065 0.069890\n", + " 4239 0.061968\n", + " 5066 0.059785\n", + " 5127 0.018070\n", + " 5152 0.016291\n", + " Name: 785, dtype: float64, 3996 9.999572e-01\n", + " 5792 7.607747e-06\n", + " 2514 2.427629e-06\n", + " 8381 2.024190e-06\n", + " 86 1.782123e-06\n", + " 6387 1.176251e-06\n", + " 4710 1.124004e-06\n", + " 4759 8.360333e-07\n", + " 3574 7.973029e-07\n", + " 107 7.692284e-07\n", + " Name: 786, dtype: float64, 4615 0.638132\n", + " 4106 0.294775\n", + " 4616 0.007607\n", + " 4377 0.002105\n", + " 4435 0.001664\n", + " 4101 0.001402\n", + " 4376 0.001153\n", + " 4445 0.001058\n", + " 4446 0.001052\n", + " 4509 0.000626\n", + " Name: 787, dtype: float64, 4073 0.376407\n", + " 4138 0.175707\n", + " 4987 0.065934\n", + " 4012 0.056073\n", + " 4203 0.026748\n", + " 4155 0.024624\n", + " 2884 0.012732\n", + " 4719 0.011748\n", + " 5032 0.007431\n", + " 5046 0.005379\n", + " Name: 788, dtype: float64, 4298 0.782543\n", + " 4156 0.025366\n", + " 4015 0.013771\n", + " 4317 0.010863\n", + " 4230 0.009759\n", + " 4316 0.007073\n", + " 4611 0.006428\n", + " 4399 0.005330\n", + " 4315 0.004626\n", + " 4258 0.003470\n", + " Name: 789, dtype: float64, 3990 0.525522\n", + " 3989 0.311635\n", + " 4739 0.039089\n", + " 4738 0.029292\n", + " 4178 0.006083\n", + " 4164 0.005785\n", + " 4169 0.003845\n", + " 2367 0.003403\n", + " 4177 0.003123\n", + " 4183 0.003100\n", + " Name: 790, dtype: float64, 4102 0.972277\n", + " 4107 0.007309\n", + " 4021 0.006121\n", + " 4155 0.000869\n", + " 4013 0.000844\n", + " 4026 0.000383\n", + " 5057 0.000353\n", + " 4207 0.000281\n", + " 4011 0.000222\n", + " 4752 0.000210\n", + " Name: 791, dtype: float64, 4626 0.959277\n", + " 2497 0.001395\n", + " 7907 0.000676\n", + " 10328 0.000459\n", + " 7911 0.000418\n", + " 8078 0.000354\n", + " 873 0.000353\n", + " 8121 0.000346\n", + " 3362 0.000277\n", + " 3882 0.000269\n", + " Name: 792, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 793, dtype: float64, 4633 0.372509\n", + " 4148 0.204891\n", + " 4142 0.051542\n", + " 4588 0.023562\n", + " 4631 0.017427\n", + " 4603 0.014167\n", + " 4714 0.007601\n", + " 4717 0.005643\n", + " 4684 0.004281\n", + " 4591 0.004231\n", + " Name: 794, dtype: float64, 4142 0.233025\n", + " 4148 0.214434\n", + " 4588 0.200413\n", + " 4631 0.126509\n", + " 4591 0.098035\n", + " 4633 0.008773\n", + " 4637 0.004468\n", + " 4629 0.004047\n", + " 4636 0.003864\n", + " 4684 0.003632\n", + " Name: 795, dtype: float64, 4193 0.132135\n", + " 4107 0.128689\n", + " 4638 0.117842\n", + " 4639 0.053147\n", + " 4677 0.042508\n", + " 4089 0.038843\n", + " 4738 0.038788\n", + " 4393 0.032670\n", + " 4207 0.030852\n", + " 4209 0.029920\n", + " Name: 796, dtype: float64, 4650 0.282205\n", + " 4241 0.240690\n", + " 4026 0.212188\n", + " 4580 0.026057\n", + " 5030 0.011508\n", + " 4031 0.010437\n", + " 4029 0.008190\n", + " 5088 0.007643\n", + " 5087 0.006613\n", + " 4020 0.006010\n", + " Name: 797, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 798, dtype: float64, 4653 0.179749\n", + " 8758 0.085064\n", + " 9685 0.018733\n", + " 9631 0.012905\n", + " 5934 0.012421\n", + " 9645 0.010555\n", + " 2873 0.009564\n", + " 2876 0.008241\n", + " 5868 0.007926\n", + " 5897 0.007621\n", + " Name: 799, dtype: float64, 3992 0.999888\n", + " 2051 0.000017\n", + " 7414 0.000008\n", + " 2738 0.000004\n", + " 706 0.000002\n", + " 5578 0.000002\n", + " 207 0.000002\n", " 677 0.000001\n", - " 2504 0.000001\n", - " 108 0.000001\n", - " 129 0.000001\n", - " 2173 0.000001\n", - " Name: 805, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 806, dtype: float64, 3997 9.999328e-01\n", - " 3210 1.469682e-05\n", - " 3999 8.461382e-06\n", - " 3998 2.854749e-06\n", - " 677 1.841118e-06\n", - " 4067 1.696612e-06\n", - " 2504 1.354737e-06\n", - " 4540 1.191843e-06\n", - " 2173 8.205266e-07\n", - " 3264 6.219146e-07\n", - " Name: 807, dtype: float64, 3210 0.987788\n", - " 4000 0.005266\n", - " 5120 0.000600\n", - " 4632 0.000364\n", - " 5121 0.000171\n", - " 5144 0.000166\n", - " 205 0.000164\n", - " 3998 0.000151\n", - " 3997 0.000132\n", - " 4704 0.000118\n", - " Name: 808, dtype: float64, 79 9.999967e-01\n", - " 9626 1.426945e-07\n", - " 160 1.270777e-07\n", - " 8040 9.058478e-08\n", - " 119 8.809941e-08\n", - " 4011 7.303056e-08\n", - " 2260 7.268815e-08\n", - " 246 6.472124e-08\n", - " 205 6.076507e-08\n", - " 206 6.057947e-08\n", - " Name: 809, dtype: float64, 107 0.998198\n", - " 2503 0.000368\n", - " 1805 0.000298\n", - " 6066 0.000136\n", - " 2236 0.000125\n", - " 3098 0.000102\n", - " 4141 0.000078\n", - " 4716 0.000063\n", - " 4708 0.000013\n", - " 679 0.000013\n", - " Name: 810, dtype: float64, 79 9.999940e-01\n", - " 160 2.940790e-07\n", - " 9626 1.791401e-07\n", - " 8040 1.641702e-07\n", - " 206 1.405118e-07\n", - " 4011 1.186987e-07\n", - " 119 1.176985e-07\n", - " 1203 1.154319e-07\n", - " 2260 1.048017e-07\n", - " 4073 8.942595e-08\n", - " Name: 811, dtype: float64, 4675 0.305930\n", - " 4674 0.298158\n", - " 4672 0.283679\n", - " 3961 0.000914\n", - " 4430 0.000877\n", - " 5279 0.000863\n", - " 3322 0.000798\n", - " 5171 0.000711\n", - " 3770 0.000643\n", - " 4061 0.000621\n", - " Name: 812, dtype: float64, 4675 0.305930\n", - " 4674 0.298158\n", - " 4672 0.283679\n", - " 3961 0.000914\n", - " 4430 0.000877\n", - " 5279 0.000863\n", - " 3322 0.000798\n", - " 5171 0.000711\n", - " 3770 0.000643\n", - " 4061 0.000621\n", - " Name: 813, dtype: float64, 4675 0.305930\n", - " 4674 0.298158\n", - " 4672 0.283679\n", - " 3961 0.000914\n", - " 4430 0.000877\n", - " 5279 0.000863\n", - " 3322 0.000798\n", - " 5171 0.000711\n", - " 3770 0.000643\n", - " 4061 0.000621\n", - " Name: 814, dtype: float64, 3098 0.999728\n", - " 6929 0.000021\n", - " 1805 0.000013\n", - " 2503 0.000012\n", - " 2236 0.000012\n", - " 2873 0.000010\n", - " 677 0.000008\n", - " 8509 0.000008\n", - " 2715 0.000007\n", - " 6066 0.000005\n", - " Name: 815, dtype: float64, 4688 0.983849\n", - " 4080 0.000374\n", - " 9441 0.000314\n", - " 6066 0.000226\n", - " 4948 0.000216\n", - " 4822 0.000207\n", - " 9443 0.000150\n", - " 4979 0.000111\n", - " 4869 0.000098\n", - " 5185 0.000090\n", - " Name: 816, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 817, dtype: float64, 3994 9.999862e-01\n", - " 4127 2.097253e-06\n", - " 2582 2.080553e-06\n", - " 4000 5.963194e-07\n", - " 2662 5.416661e-07\n", - " 5549 2.588756e-07\n", - " 5945 2.192588e-07\n", - " 5919 2.003120e-07\n", - " 7107 1.688453e-07\n", - " 2955 1.628869e-07\n", - " Name: 818, dtype: float64, 4696 0.198883\n", - " 4697 0.196484\n", - " 4695 0.185195\n", - " 4698 0.178392\n", - " 4702 0.030324\n", - " 4030 0.015619\n", - " 4659 0.006394\n", - " 4424 0.004824\n", - " 4660 0.003367\n", - " 5049 0.003035\n", - " Name: 819, dtype: float64, 4700 0.923577\n", - " 4658 0.005297\n", - " 4525 0.004696\n", - " 4526 0.003469\n", - " 4026 0.002808\n", - " 4607 0.001488\n", - " 2679 0.001176\n", - " 599 0.000786\n", - " 4020 0.000713\n", - " 4650 0.000658\n", - " Name: 820, dtype: float64, 4026 0.086201\n", - " 4172 0.082936\n", - " 5058 0.030022\n", - " 4607 0.026826\n", - " 4088 0.025108\n", - " 4074 0.015245\n", - " 3039 0.013599\n", - " 5104 0.010969\n", - " 3990 0.010846\n", - " 208 0.010594\n", - " Name: 821, dtype: float64, 4165 0.810496\n", - " 4526 0.026909\n", - " 5132 0.008568\n", - " 4525 0.006891\n", - " 4001 0.005506\n", - " 4607 0.004233\n", - " 4094 0.003419\n", - " 4580 0.002955\n", - " 2679 0.002643\n", - " 4031 0.002363\n", - " Name: 822, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 823, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 824, dtype: float64, 3210 0.074525\n", - " 5809 0.065095\n", - " 3574 0.043014\n", - " 2590 0.024503\n", - " 4092 0.020773\n", - " 4165 0.017372\n", - " 3296 0.016194\n", - " 9421 0.014992\n", - " 4090 0.013256\n", - " 2567 0.010681\n", - " Name: 825, dtype: float64, 2629 0.999876\n", - " 2236 0.000009\n", - " 3999 0.000004\n", - " 4098 0.000003\n", - " 4067 0.000003\n", - " 7106 0.000003\n", - " 3671 0.000002\n", - " 642 0.000002\n", - " 3670 0.000002\n", - " 7124 0.000002\n", - " Name: 826, dtype: float64, 4614 0.219073\n", - " 4006 0.151327\n", - " 4385 0.070547\n", - " 4618 0.065304\n", - " 4149 0.051290\n", - " 4579 0.049664\n", - " 5150 0.040799\n", - " 4031 0.024867\n", - " 4011 0.010824\n", - " 4021 0.008363\n", - " Name: 827, dtype: float64, 4720 0.914342\n", - " 5156 0.012784\n", - " 4736 0.002690\n", - " 4298 0.002424\n", - " 4043 0.002086\n", - " 5006 0.001579\n", - " 4071 0.001556\n", - " 4547 0.001216\n", - " 4015 0.001205\n", - " 4202 0.001108\n", - " Name: 828, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 829, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 830, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 831, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 832, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 833, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 834, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 835, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 836, dtype: float64, 4337 0.666008\n", - " 4336 0.199727\n", - " 4377 0.004935\n", - " 4376 0.004286\n", - " 4563 0.002807\n", - " 4567 0.002755\n", - " 4565 0.002669\n", - " 4564 0.002513\n", - " 4561 0.002487\n", - " 4560 0.002237\n", - " Name: 837, dtype: float64, 4254 9.999642e-01\n", - " 4741 1.353084e-06\n", - " 2571 1.012791e-06\n", - " 3098 8.311343e-07\n", - " 5057 7.830243e-07\n", - " 4280 7.705216e-07\n", - " 4248 6.490473e-07\n", - " 4735 5.510526e-07\n", - " 181 5.440579e-07\n", - " 208 3.940769e-07\n", - " Name: 838, dtype: float64, 4280 0.999635\n", - " 205 0.000031\n", - " 4706 0.000024\n", - " 4502 0.000018\n", + " 1714 0.000001\n", + " 6844 0.000001\n", + " Name: 800, dtype: float64, 4177 0.219184\n", + " 4178 0.134928\n", + " 2367 0.086307\n", + " 4494 0.074992\n", + " 4646 0.039247\n", + " 4401 0.033840\n", + " 4575 0.030946\n", + " 4491 0.030800\n", + " 4572 0.030286\n", + " 4571 0.030132\n", + " Name: 801, dtype: float64, 4177 0.219184\n", + " 4178 0.134928\n", + " 2367 0.086307\n", + " 4494 0.074992\n", + " 4646 0.039247\n", + " 4401 0.033840\n", + " 4575 0.030946\n", + " 4491 0.030800\n", + " 4572 0.030286\n", + " 4571 0.030132\n", + " Name: 802, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 803, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 804, dtype: float64, 3997 9.999473e-01\n", + " 3998 4.974102e-06\n", + " 7106 2.744417e-06\n", + " 640 1.935426e-06\n", + " 2408 1.703823e-06\n", + " 4280 1.283216e-06\n", + " 2236 9.930136e-07\n", + " 7107 9.097912e-07\n", + " 5110 7.222434e-07\n", + " 2351 6.979245e-07\n", + " Name: 805, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 806, dtype: float64, 3997 9.999582e-01\n", + " 640 2.310994e-06\n", + " 3998 2.167029e-06\n", + " 7106 1.881034e-06\n", + " 4280 1.592374e-06\n", + " 3210 1.489015e-06\n", + " 2408 1.134831e-06\n", + " 2236 8.764329e-07\n", + " 7107 6.177365e-07\n", + " 2351 6.172860e-07\n", + " Name: 807, dtype: float64, 3210 0.983275\n", + " 3999 0.001990\n", + " 4714 0.001803\n", + " 4000 0.000847\n", + " 5144 0.000504\n", + " 1471 0.000357\n", + " 3997 0.000308\n", + " 4102 0.000243\n", + " 4704 0.000179\n", + " 5104 0.000178\n", + " Name: 808, dtype: float64, 79 9.999964e-01\n", + " 208 1.028192e-06\n", + " 1805 1.520217e-07\n", + " 7106 8.654299e-08\n", + " 119 7.401441e-08\n", + " 517 6.015630e-08\n", + " 2503 4.786437e-08\n", + " 4098 4.617616e-08\n", + " 8418 3.501705e-08\n", + " 2546 3.199072e-08\n", + " Name: 809, dtype: float64, 107 0.998939\n", + " 1805 0.000483\n", + " 3098 0.000256\n", + " 2236 0.000033\n", + " 3574 0.000019\n", + " 2503 0.000015\n", + " 3996 0.000011\n", + " 214 0.000010\n", + " 4000 0.000006\n", + " 2590 0.000004\n", + " Name: 810, dtype: float64, 79 9.999945e-01\n", + " 208 1.034801e-06\n", + " 7106 3.072564e-07\n", + " 1805 1.717218e-07\n", + " 4098 1.354914e-07\n", + " 2503 7.208519e-08\n", + " 517 7.146340e-08\n", + " 2546 5.161177e-08\n", + " 8418 4.965747e-08\n", + " 7141 4.927611e-08\n", + " Name: 811, dtype: float64, 4675 0.304578\n", + " 4674 0.248536\n", + " 4672 0.225150\n", + " 9456 0.008431\n", + " 6070 0.003281\n", + " 5279 0.003043\n", + " 3756 0.002972\n", + " 9535 0.002418\n", + " 10316 0.002096\n", + " 9545 0.002077\n", + " Name: 812, dtype: float64, 4675 0.304578\n", + " 4674 0.248536\n", + " 4672 0.225150\n", + " 9456 0.008431\n", + " 6070 0.003281\n", + " 5279 0.003043\n", + " 3756 0.002972\n", + " 9535 0.002418\n", + " 10316 0.002096\n", + " 9545 0.002077\n", + " Name: 813, dtype: float64, 4675 0.304578\n", + " 4674 0.248536\n", + " 4672 0.225150\n", + " 9456 0.008431\n", + " 6070 0.003281\n", + " 5279 0.003043\n", + " 3756 0.002972\n", + " 9535 0.002418\n", + " 10316 0.002096\n", + " 9545 0.002077\n", + " Name: 814, dtype: float64, 3098 0.999716\n", + " 6929 0.000029\n", + " 5668 0.000022\n", + " 1805 0.000016\n", + " 107 0.000012\n", + " 5054 0.000006\n", + " 8418 0.000005\n", + " 2236 0.000004\n", + " 2435 0.000004\n", + " 2873 0.000004\n", + " Name: 815, dtype: float64, 4688 0.976858\n", + " 8444 0.000937\n", + " 2500 0.000745\n", + " 6315 0.000593\n", + " 4835 0.000490\n", + " 86 0.000339\n", + " 9641 0.000292\n", + " 4080 0.000278\n", + " 4843 0.000276\n", + " 6154 0.000262\n", + " Name: 816, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 817, dtype: float64, 3994 9.999876e-01\n", + " 4127 2.356943e-06\n", + " 2582 1.413928e-06\n", + " 4078 3.059756e-07\n", + " 6015 1.825343e-07\n", + " 3476 1.593517e-07\n", + " 4393 1.271551e-07\n", + " 207 1.131661e-07\n", + " 2875 1.108352e-07\n", + " 4001 1.086125e-07\n", + " Name: 818, dtype: float64, 4696 0.208615\n", + " 4698 0.203460\n", + " 4695 0.195960\n", + " 4697 0.168551\n", + " 4702 0.039970\n", + " 4660 0.014974\n", + " 4647 0.011278\n", + " 4424 0.008691\n", + " 4030 0.007286\n", + " 4659 0.003905\n", + " Name: 819, dtype: float64, 4700 0.917378\n", + " 4658 0.006948\n", + " 4526 0.006756\n", + " 4647 0.002667\n", + " 4026 0.002272\n", + " 4525 0.002202\n", + " 5132 0.001382\n", + " 4748 0.001048\n", + " 4607 0.000826\n", + " 4696 0.000792\n", + " Name: 820, dtype: float64, 4693 0.039658\n", + " 9684 0.028849\n", + " 9386 0.024560\n", + " 8021 0.021494\n", + " 4659 0.013811\n", + " 5934 0.013066\n", + " 3039 0.012899\n", + " 668 0.012271\n", + " 4084 0.011992\n", + " 4667 0.011960\n", + " Name: 821, dtype: float64, 4165 0.965945\n", + " 4658 0.001343\n", + " 4011 0.001192\n", + " 4526 0.001171\n", + " 5057 0.001147\n", + " 5104 0.001129\n", + " 4700 0.000865\n", + " 4074 0.000786\n", + " 4026 0.000651\n", + " 136 0.000620\n", + " Name: 822, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 823, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 824, dtype: float64, 3210 0.376132\n", + " 5809 0.040560\n", + " 2590 0.034400\n", + " 3296 0.030012\n", + " 9421 0.023672\n", + " 6913 0.019590\n", + " 5098 0.016499\n", + " 4989 0.014832\n", + " 3574 0.011039\n", + " 5842 0.010489\n", + " Name: 825, dtype: float64, 2629 9.999150e-01\n", + " 86 1.853769e-05\n", + " 4091 9.000495e-06\n", + " 4067 5.554359e-06\n", + " 690 1.248380e-06\n", + " 3998 1.112019e-06\n", + " 8508 8.163830e-07\n", + " 4073 6.926954e-07\n", + " 7106 6.231824e-07\n", + " 4155 6.010441e-07\n", + " Name: 826, dtype: float64, 4006 0.287672\n", + " 4618 0.114167\n", + " 4614 0.108871\n", + " 4385 0.056356\n", + " 4031 0.056329\n", + " 4149 0.052922\n", + " 5150 0.045425\n", + " 4579 0.027772\n", + " 4607 0.007625\n", + " 5015 0.007034\n", + " Name: 827, dtype: float64, 4720 0.987013\n", + " 5156 0.001692\n", + " 5006 0.000447\n", + " 8058 0.000252\n", + " 4013 0.000233\n", + " 4043 0.000176\n", + " 4298 0.000166\n", + " 4742 0.000140\n", + " 4235 0.000120\n", + " 5054 0.000094\n", + " Name: 828, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 829, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 830, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 831, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 832, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 833, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 834, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 835, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 836, dtype: float64, 4337 0.712319\n", + " 4336 0.186567\n", + " 4376 0.004980\n", + " 4377 0.004271\n", + " 4393 0.003787\n", + " 4320 0.002900\n", + " 4612 0.002794\n", + " 4323 0.002705\n", + " 4322 0.002646\n", + " 4321 0.002330\n", + " Name: 837, dtype: float64, 4254 9.999460e-01\n", + " 2571 4.867645e-06\n", + " 211 2.578979e-06\n", + " 8376 1.903476e-06\n", + " 4502 1.634383e-06\n", + " 4735 1.551995e-06\n", + " 4280 1.248777e-06\n", + " 5099 1.129892e-06\n", + " 4741 1.114732e-06\n", + " 4248 7.229494e-07\n", + " Name: 838, dtype: float64, 4280 0.999817\n", + " 3997 0.000036\n", " 4745 0.000016\n", - " 4232 0.000008\n", - " 4501 0.000008\n", - " 214 0.000006\n", - " 5017 0.000006\n", - " 7106 0.000006\n", - " Name: 839, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 840, dtype: float64, 5104 0.710661\n", - " 4088 0.022895\n", - " 4149 0.019145\n", - " 181 0.010375\n", - " 4678 0.008137\n", - " 2684 0.007724\n", - " 4165 0.007655\n", - " 4092 0.006462\n", - " 4172 0.005118\n", - " 4736 0.004465\n", - " Name: 841, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 842, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 843, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 844, dtype: float64, 3996 9.999545e-01\n", - " 8381 3.087773e-06\n", - " 3574 2.508891e-06\n", - " 4710 1.323210e-06\n", - " 1515 1.134222e-06\n", - " 2435 6.829907e-07\n", - " 181 6.125558e-07\n", - " 4172 5.594303e-07\n", - " 3615 5.405926e-07\n", - " 9394 5.315109e-07\n", - " Name: 845, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 846, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 847, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 848, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 849, dtype: float64, 4012 0.993211\n", - " 4202 0.000489\n", - " 4298 0.000349\n", - " 4138 0.000343\n", - " 4156 0.000277\n", - " 4073 0.000198\n", - " 5046 0.000163\n", - " 4155 0.000120\n", - " 668 0.000082\n", - " 4013 0.000079\n", - " Name: 850, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 851, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 852, dtype: float64, 4000 9.999425e-01\n", - " 3210 3.570432e-06\n", - " 2582 1.726347e-06\n", - " 2236 1.401032e-06\n", - " 4714 9.748197e-07\n", - " 205 8.024509e-07\n", - " 3637 7.739589e-07\n", - " 4681 7.617680e-07\n", - " 4135 7.544772e-07\n", - " 8435 5.030468e-07\n", - " Name: 853, dtype: float64, 4317 0.084226\n", - " 4449 0.066794\n", - " 4447 0.066790\n", - " 4298 0.062527\n", - " 4448 0.059056\n", - " 4450 0.056876\n", - " 4451 0.053130\n", - " 4015 0.019788\n", - " 4230 0.016775\n", - " 4399 0.014061\n", - " Name: 854, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 855, dtype: float64, 3210 0.999624\n", - " 3997 0.000050\n", - " 4540 0.000014\n", - " 5144 0.000012\n", - " 3998 0.000012\n", - " 5120 0.000011\n", - " 4632 0.000010\n", - " 677 0.000009\n", - " 2504 0.000006\n", - " 4165 0.000006\n", - " Name: 856, dtype: float64, 3999 0.999528\n", - " 2629 0.000049\n", - " 3998 0.000038\n", - " 4542 0.000028\n", - " 3997 0.000019\n", - " 677 0.000015\n", - " 3325 0.000015\n", + " 4502 0.000008\n", + " 5017 0.000007\n", + " 4706 0.000004\n", + " 5099 0.000002\n", + " 4148 0.000002\n", + " 5057 0.000002\n", + " 4026 0.000002\n", + " Name: 839, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 840, dtype: float64, 5104 0.902403\n", + " 4165 0.009247\n", + " 2684 0.002735\n", + " 4088 0.002477\n", + " 3289 0.002353\n", + " 4155 0.001721\n", + " 4021 0.001575\n", + " 4026 0.001563\n", + " 4031 0.001323\n", + " 4001 0.001250\n", + " Name: 841, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 842, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 843, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 844, dtype: float64, 3996 9.999452e-01\n", + " 5792 7.362532e-06\n", + " 8381 5.031404e-06\n", + " 107 3.384116e-06\n", + " 2514 3.240801e-06\n", + " 3574 2.653306e-06\n", + " 6387 1.712372e-06\n", + " 86 1.423426e-06\n", + " 4710 1.223891e-06\n", + " 4759 8.492820e-07\n", + " Name: 845, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 846, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 847, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 848, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 849, dtype: float64, 4012 0.996505\n", + " 4073 0.000354\n", + " 4138 0.000336\n", + " 4298 0.000159\n", + " 4155 0.000123\n", + " 4402 0.000095\n", + " 5046 0.000085\n", + " 4156 0.000072\n", + " 4013 0.000046\n", + " 4202 0.000041\n", + " Name: 850, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 851, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 852, dtype: float64, 4000 0.999912\n", + " 2503 0.000007\n", + " 3999 0.000003\n", + " 863 0.000003\n", + " 107 0.000003\n", + " 6766 0.000003\n", + " 4714 0.000002\n", + " 3998 0.000002\n", + " 79 0.000002\n", + " 842 0.000001\n", + " Name: 853, dtype: float64, 4298 0.212832\n", + " 4317 0.153269\n", + " 4399 0.105393\n", + " 4156 0.057125\n", + " 4316 0.041192\n", + " 4315 0.036657\n", + " 4449 0.029571\n", + " 4450 0.028200\n", + " 4447 0.027368\n", + " 4451 0.026957\n", + " Name: 854, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 855, dtype: float64, 3210 0.999889\n", + " 5144 0.000015\n", + " 3574 0.000003\n", + " 1471 0.000003\n", + " 3999 0.000003\n", + " 3997 0.000003\n", + " 214 0.000003\n", + " 4090 0.000002\n", + " 4540 0.000002\n", + " 2236 0.000002\n", + " Name: 856, dtype: float64, 3999 0.999883\n", + " 3998 0.000019\n", + " 1805 0.000012\n", + " 4542 0.000006\n", + " 3098 0.000006\n", + " 4000 0.000004\n", + " 4074 0.000003\n", + " 4535 0.000003\n", + " 2236 0.000002\n", + " 2873 0.000002\n", + " Name: 857, dtype: float64, 4298 0.128811\n", + " 4432 0.046069\n", + " 4043 0.039225\n", + " 4317 0.036243\n", + " 4449 0.033328\n", + " 4447 0.031685\n", + " 4451 0.027785\n", + " 4015 0.026454\n", + " 4448 0.025537\n", + " 4450 0.024435\n", + " Name: 858, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 859, dtype: float64, 3999 0.992225\n", + " 4542 0.001430\n", + " 3098 0.000830\n", + " 4074 0.000269\n", + " 5850 0.000246\n", + " 6929 0.000186\n", + " 4000 0.000150\n", + " 2873 0.000137\n", + " 4535 0.000116\n", + " 1805 0.000114\n", + " Name: 860, dtype: float64, 4177 0.219184\n", + " 4178 0.134928\n", + " 2367 0.086307\n", + " 4494 0.074992\n", + " 4646 0.039247\n", + " 4401 0.033840\n", + " 4575 0.030946\n", + " 4491 0.030800\n", + " 4572 0.030286\n", + " 4571 0.030132\n", + " Name: 861, dtype: float64, 4401 0.078286\n", + " 4494 0.073743\n", + " 4732 0.045451\n", + " 4730 0.045259\n", + " 4729 0.041998\n", + " 4731 0.040654\n", + " 4576 0.040418\n", + " 4573 0.038977\n", + " 4572 0.037643\n", + " 4575 0.037633\n", + " Name: 862, dtype: float64, 4753 0.130137\n", + " 4400 0.042213\n", + " 4614 0.038990\n", + " 5065 0.031440\n", + " 5063 0.027500\n", + " 5066 0.026746\n", + " 5064 0.025431\n", + " 5014 0.019071\n", + " 4101 0.016449\n", + " 4259 0.015989\n", + " Name: 863, dtype: float64, 4686 0.969224\n", + " 4080 0.003475\n", + " 8032 0.000830\n", + " 4013 0.000681\n", + " 5262 0.000614\n", + " 4102 0.000470\n", + " 4220 0.000429\n", + " 3325 0.000392\n", + " 4026 0.000343\n", + " 1343 0.000341\n", + " Name: 864, dtype: float64, 4085 0.999442\n", + " 4086 0.000041\n", + " 5057 0.000037\n", + " 3002 0.000024\n", + " 5757 0.000012\n", + " 4393 0.000011\n", + " 675 0.000009\n", + " 3157 0.000008\n", + " 3470 0.000007\n", + " 5058 0.000006\n", + " Name: 865, dtype: float64, 3996 9.999452e-01\n", + " 5792 7.362532e-06\n", + " 8381 5.031404e-06\n", + " 107 3.384116e-06\n", + " 2514 3.240801e-06\n", + " 3574 2.653306e-06\n", + " 6387 1.712372e-06\n", + " 86 1.423426e-06\n", + " 4710 1.223891e-06\n", + " 4759 8.492820e-07\n", + " Name: 866, dtype: float64, 4087 0.997296\n", + " 4170 0.000094\n", + " 4089 0.000040\n", + " 7981 0.000034\n", + " 9660 0.000032\n", + " 4647 0.000030\n", + " 4889 0.000027\n", + " 4761 0.000026\n", + " 5155 0.000026\n", + " 4891 0.000025\n", + " Name: 867, dtype: float64, 4846 0.044515\n", + " 5155 0.044134\n", + " 6557 0.032674\n", + " 5154 0.029654\n", + " 4766 0.024312\n", + " 4845 0.023843\n", + " 4852 0.022814\n", + " 4869 0.017524\n", + " 4830 0.012444\n", + " 4847 0.011913\n", + " Name: 868, dtype: float64, 3322 0.979072\n", + " 4787 0.000632\n", + " 4847 0.000610\n", + " 4804 0.000579\n", + " 4986 0.000447\n", + " 4800 0.000357\n", + " 4770 0.000294\n", + " 4906 0.000240\n", + " 4762 0.000237\n", + " 9380 0.000217\n", + " Name: 869, dtype: float64, 4770 0.885974\n", + " 4774 0.025993\n", + " 4769 0.019129\n", + " 4798 0.004485\n", + " 4771 0.002472\n", + " 4796 0.002348\n", + " 4786 0.001468\n", + " 4804 0.001287\n", + " 4782 0.001270\n", + " 4756 0.001224\n", + " Name: 870, dtype: float64, 4781 0.770402\n", + " 4761 0.023038\n", + " 4818 0.018852\n", + " 4764 0.011363\n", + " 4779 0.006168\n", + " 4782 0.005433\n", + " 4796 0.004599\n", + " 4891 0.004509\n", + " 4647 0.004031\n", + " 3801 0.002825\n", + " Name: 871, dtype: float64, 4807 0.114371\n", + " 4812 0.046236\n", + " 4872 0.017782\n", + " 4842 0.017289\n", + " 4811 0.017153\n", + " 4802 0.016563\n", + " 4800 0.015767\n", + " 4756 0.014521\n", + " 4799 0.014494\n", + " 4804 0.013735\n", + " Name: 872, dtype: float64, 4771 0.895188\n", + " 4767 0.008791\n", + " 4756 0.006012\n", + " 4798 0.005125\n", + " 4795 0.005076\n", + " 4770 0.003798\n", + " 4772 0.002873\n", + " 4777 0.002667\n", + " 4830 0.002653\n", + " 4792 0.001586\n", + " Name: 873, dtype: float64, 4781 0.792289\n", + " 4782 0.027923\n", + " 4779 0.009448\n", + " 4796 0.008291\n", + " 4818 0.007967\n", + " 4891 0.006613\n", + " 4761 0.006527\n", + " 4772 0.003531\n", + " 4764 0.003306\n", + " 863 0.003113\n", + " Name: 874, dtype: float64, 79 9.999923e-01\n", + " 208 2.181556e-06\n", + " 1805 3.084156e-07\n", + " 517 1.410595e-07\n", + " 119 1.303583e-07\n", + " 7106 1.249102e-07\n", + " 4000 1.122967e-07\n", + " 2503 8.984792e-08\n", + " 3449 6.361287e-08\n", + " 3637 4.506382e-08\n", + " Name: 875, dtype: float64, 4804 0.554876\n", + " 4924 0.027117\n", + " 4905 0.022252\n", + " 4796 0.017372\n", + " 4802 0.012997\n", + " 4800 0.009478\n", + " 4979 0.009464\n", + " 4951 0.009368\n", + " 4762 0.006666\n", + " 4810 0.006424\n", + " Name: 876, dtype: float64, 4804 0.762809\n", + " 4802 0.050945\n", + " 4797 0.015198\n", + " 4721 0.008073\n", + " 4856 0.005401\n", + " 4786 0.005353\n", + " 4951 0.004041\n", + " 4796 0.003473\n", + " 4775 0.003322\n", + " 4769 0.003053\n", + " Name: 877, dtype: float64, 4809 0.965942\n", + " 4777 0.001876\n", + " 4377 0.001811\n", + " 4795 0.001069\n", + " 4799 0.000931\n", + " 4647 0.000808\n", + " 4797 0.000683\n", + " 4792 0.000652\n", + " 4764 0.000530\n", + " 4796 0.000480\n", + " Name: 878, dtype: float64, 4813 0.873444\n", + " 8296 0.042928\n", + " 6921 0.002053\n", + " 2622 0.001545\n", + " 496 0.001149\n", + " 10538 0.001115\n", + " 2277 0.001083\n", + " 561 0.001047\n", + " 960 0.001027\n", + " 2500 0.000978\n", + " Name: 879, dtype: float64, 4800 0.502692\n", + " 4923 0.013936\n", + " 4924 0.012567\n", + " 4845 0.011664\n", + " 4846 0.008016\n", + " 4779 0.006607\n", + " 4905 0.006177\n", + " 4921 0.006025\n", + " 7663 0.005574\n", + " 5679 0.005278\n", + " Name: 880, dtype: float64, 4905 0.126057\n", + " 4785 0.040603\n", + " 4846 0.033719\n", + " 4843 0.022899\n", + " 4924 0.021390\n", + " 4773 0.016239\n", + " 4796 0.015690\n", + " 4845 0.014465\n", + " 4916 0.012659\n", + " 4855 0.011169\n", + " Name: 881, dtype: float64, 4787 0.808339\n", + " 4866 0.010681\n", + " 4814 0.006094\n", + " 4779 0.005004\n", + " 4815 0.004723\n", + " 4847 0.003140\n", + " 4845 0.002704\n", + " 4764 0.002693\n", + " 4905 0.002529\n", + " 4786 0.002384\n", + " Name: 882, dtype: float64, 4823 0.740951\n", + " 4818 0.010939\n", + " 4829 0.009091\n", + " 4905 0.008682\n", + " 4054 0.008116\n", + " 4943 0.007800\n", + " 4860 0.004219\n", + " 4869 0.004180\n", + " 4779 0.003898\n", + " 4838 0.003214\n", + " Name: 883, dtype: float64, 4847 0.284138\n", + " 4840 0.097280\n", + " 4835 0.090154\n", + " 4783 0.075973\n", + " 4916 0.042893\n", + " 4824 0.033994\n", + " 4830 0.028713\n", + " 2701 0.021307\n", + " 4845 0.020811\n", + " 4965 0.018530\n", + " Name: 884, dtype: float64, 4838 0.817649\n", + " 4842 0.077947\n", + " 4833 0.005802\n", + " 4834 0.003077\n", + " 4825 0.002814\n", + " 4831 0.002283\n", + " 4851 0.002278\n", + " 4869 0.002159\n", + " 4864 0.001596\n", + " 4856 0.001486\n", + " Name: 885, dtype: float64, 4843 0.691711\n", + " 4842 0.110073\n", + " 4846 0.026748\n", + " 4845 0.017132\n", + " 4947 0.006751\n", + " 4962 0.005977\n", + " 4869 0.004889\n", + " 4834 0.003920\n", + " 4830 0.003013\n", + " 4874 0.002663\n", + " Name: 886, dtype: float64, 4789 0.837556\n", + " 4785 0.010101\n", + " 4837 0.007723\n", + " 5031 0.003766\n", + " 4913 0.003697\n", + " 4916 0.003452\n", + " 4830 0.003021\n", + " 4932 0.002678\n", + " 4798 0.002001\n", + " 4783 0.001630\n", + " Name: 887, dtype: float64, 4858 0.731198\n", + " 4799 0.030060\n", + " 4866 0.014452\n", + " 4807 0.014306\n", + " 4825 0.010593\n", + " 4830 0.006086\n", + " 4764 0.005428\n", + " 4869 0.004238\n", + " 4861 0.003830\n", + " 4756 0.003476\n", + " Name: 888, dtype: float64, 4838 0.789371\n", + " 4842 0.040286\n", + " 4831 0.006936\n", + " 4833 0.006364\n", + " 4823 0.005819\n", + " 4851 0.004584\n", + " 4869 0.004401\n", + " 4861 0.003548\n", + " 4834 0.003359\n", + " 4856 0.003201\n", + " Name: 889, dtype: float64, 4861 0.569586\n", + " 4860 0.221400\n", + " 4858 0.095899\n", + " 4856 0.004214\n", + " 4838 0.002475\n", + " 4869 0.002437\n", + " 4855 0.002264\n", + " 4831 0.002254\n", + " 4764 0.001786\n", + " 4833 0.001774\n", + " Name: 890, dtype: float64, 4869 0.102706\n", + " 4779 0.035849\n", + " 4838 0.034166\n", + " 4866 0.030885\n", + " 4923 0.026677\n", + " 4823 0.021238\n", + " 4842 0.020929\n", + " 4825 0.019350\n", + " 4787 0.019108\n", + " 4833 0.017281\n", + " Name: 891, dtype: float64, 4875 0.219743\n", + " 4885 0.121528\n", + " 4886 0.099034\n", + " 4872 0.070010\n", + " 4873 0.054778\n", + " 4892 0.034659\n", + " 4900 0.034473\n", + " 4876 0.028761\n", + " 4896 0.019238\n", + " 4880 0.014233\n", + " Name: 892, dtype: float64, 4888 0.080290\n", + " 4882 0.063853\n", + " 4892 0.046307\n", + " 4881 0.045324\n", + " 4872 0.042833\n", + " 4879 0.042556\n", + " 4876 0.041453\n", + " 4875 0.031134\n", + " 4884 0.029693\n", + " 4799 0.026986\n", + " Name: 893, dtype: float64, 4872 0.227924\n", + " 4985 0.185010\n", + " 4887 0.042268\n", + " 4896 0.028874\n", + " 4876 0.020066\n", + " 4757 0.018152\n", + " 4804 0.012930\n", + " 4875 0.012549\n", + " 4892 0.010640\n", + " 4960 0.009321\n", + " Name: 894, dtype: float64, 4905 0.118930\n", + " 4962 0.046028\n", + " 4947 0.023886\n", + " 4846 0.021883\n", + " 4867 0.019636\n", + " 4820 0.017230\n", + " 4956 0.016972\n", + " 4949 0.016227\n", + " 4943 0.015740\n", + " 4869 0.015365\n", + " Name: 895, dtype: float64, 4783 0.055025\n", + " 4955 0.024149\n", + " 4855 0.022846\n", + " 4811 0.022516\n", + " 4869 0.020836\n", + " 4845 0.020767\n", + " 4795 0.020228\n", + " 4837 0.018566\n", + " 4857 0.016687\n", + " 4924 0.016516\n", + " Name: 896, dtype: float64, 4759 0.999580\n", + " 4847 0.000046\n", + " 4761 0.000042\n", + " 205 0.000037\n", + " 4762 0.000028\n", + " 4790 0.000026\n", + " 86 0.000019\n", + " 4788 0.000016\n", + " 8720 0.000011\n", + " 4149 0.000010\n", + " Name: 897, dtype: float64, 4929 0.149689\n", + " 4960 0.138299\n", + " 4962 0.093089\n", + " 4933 0.080667\n", + " 4949 0.041402\n", + " 4932 0.027646\n", + " 4938 0.021812\n", + " 4959 0.018047\n", + " 4935 0.014879\n", + " 4950 0.013900\n", + " Name: 898, dtype: float64, 4933 0.656140\n", + " 4929 0.083499\n", + " 4913 0.049683\n", + " 4932 0.021652\n", + " 4935 0.014120\n", + " 4880 0.012099\n", + " 4939 0.006228\n", + " 4822 0.003918\n", + " 2567 0.003801\n", + " 4931 0.003429\n", + " Name: 899, dtype: float64, 4956 0.364663\n", + " 4953 0.317636\n", + " 4952 0.025038\n", + " 4945 0.020389\n", + " 4950 0.012920\n", + " 4957 0.012098\n", + " 4943 0.009536\n", + " 4942 0.007711\n", + " 4905 0.007589\n", + " 4947 0.006799\n", + " Name: 900, dtype: float64, 4953 0.073677\n", + " 4957 0.073452\n", + " 4927 0.040050\n", + " 4925 0.039956\n", + " 4956 0.031019\n", + " 4928 0.021972\n", + " 4832 0.020427\n", + " 4819 0.016782\n", + " 4934 0.015076\n", + " 10269 0.013567\n", + " Name: 901, dtype: float64, 4969 0.220927\n", + " 4986 0.114273\n", + " 4984 0.044612\n", + " 4971 0.033471\n", + " 4978 0.032506\n", + " 4979 0.029180\n", + " 4975 0.022735\n", + " 4804 0.013327\n", + " 4977 0.008955\n", + " 4985 0.008899\n", + " Name: 902, dtype: float64, 4969 0.660250\n", + " 4974 0.047993\n", + " 4979 0.043381\n", + " 4972 0.041480\n", + " 4971 0.015597\n", + " 4975 0.014235\n", + " 4984 0.009363\n", + " 7659 0.006387\n", + " 4978 0.003873\n", + " 6335 0.003676\n", + " Name: 903, dtype: float64, 4977 0.187741\n", + " 4971 0.076970\n", + " 4979 0.047473\n", + " 4982 0.032482\n", + " 4976 0.030613\n", + " 4986 0.023414\n", + " 4981 0.017928\n", + " 4975 0.017548\n", + " 4856 0.015062\n", + " 4905 0.011265\n", + " Name: 904, dtype: float64, 4752 0.476621\n", + " 5061 0.372546\n", + " 4405 0.011335\n", + " 2715 0.011285\n", + " 4089 0.009570\n", + " 4207 0.009566\n", + " 5038 0.005133\n", + " 4107 0.004955\n", + " 5127 0.003217\n", + " 4078 0.002970\n", + " Name: 905, dtype: float64, 4714 0.030987\n", + " 4006 0.024151\n", + " 2644 0.019570\n", + " 4526 0.016104\n", + " 5131 0.014477\n", + " 4029 0.014230\n", + " 4525 0.012224\n", + " 4533 0.009970\n", + " 5059 0.009435\n", + " 4988 0.009093\n", + " Name: 906, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 907, dtype: float64, 5007 0.373767\n", + " 4208 0.196072\n", + " 4348 0.064277\n", + " 5008 0.043485\n", + " 5009 0.027287\n", + " 4190 0.026423\n", + " 5130 0.021574\n", + " 4149 0.019877\n", + " 4736 0.018063\n", + " 3991 0.017497\n", + " Name: 908, dtype: float64, 5007 0.373767\n", + " 4208 0.196072\n", + " 4348 0.064277\n", + " 5008 0.043485\n", + " 5009 0.027287\n", + " 4190 0.026423\n", + " 5130 0.021574\n", + " 4149 0.019877\n", + " 4736 0.018063\n", + " 3991 0.017497\n", + " Name: 909, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 910, dtype: float64, 4741 0.139707\n", + " 5099 0.097442\n", + " 4502 0.079950\n", + " 4990 0.030311\n", + " 4693 0.018996\n", + " 4501 0.015508\n", + " 4017 0.013573\n", + " 4705 0.013172\n", + " 4745 0.012867\n", + " 4993 0.012814\n", + " Name: 911, dtype: float64, 5023 0.339303\n", + " 5021 0.301302\n", + " 5022 0.219953\n", + " 5281 0.002280\n", + " 5280 0.002258\n", + " 9989 0.002042\n", + " 9999 0.001759\n", + " 9992 0.001475\n", + " 8671 0.001454\n", + " 8670 0.001217\n", + " Name: 912, dtype: float64, 5023 0.339303\n", + " 5021 0.301302\n", + " 5022 0.219953\n", + " 5281 0.002280\n", + " 5280 0.002258\n", + " 9989 0.002042\n", + " 9999 0.001759\n", + " 9992 0.001475\n", + " 8671 0.001454\n", + " 8670 0.001217\n", + " Name: 913, dtype: float64, 3988 9.999988e-01\n", + " 4004 2.624934e-08\n", + " 1805 2.143999e-08\n", + " 3474 1.529523e-08\n", + " 1539 1.119369e-08\n", + " 2353 9.991578e-09\n", + " 4135 9.488947e-09\n", + " 2508 8.878290e-09\n", + " 3320 8.648589e-09\n", + " 3243 8.234005e-09\n", + " Name: 914, dtype: float64, 2629 0.998821\n", + " 4067 0.000138\n", + " 2236 0.000045\n", + " 86 0.000033\n", + " 2197 0.000025\n", + " 1805 0.000017\n", + " 679 0.000017\n", + " 3998 0.000017\n", + " 2503 0.000015\n", + " 2292 0.000014\n", + " Name: 915, dtype: float64, 5025 0.298726\n", + " 4548 0.188132\n", + " 4666 0.147087\n", + " 4665 0.145706\n", + " 6070 0.012711\n", + " 6128 0.012174\n", + " 4109 0.003313\n", + " 5490 0.002982\n", + " 6109 0.002739\n", + " 4724 0.002546\n", + " Name: 916, dtype: float64, 1805 0.968008\n", + " 3098 0.028827\n", + " 3996 0.000219\n", + " 5054 0.000166\n", + " 4159 0.000119\n", + " 107 0.000118\n", + " 2503 0.000079\n", + " 3999 0.000077\n", + " 6929 0.000069\n", + " 3370 0.000053\n", + " Name: 917, dtype: float64, 5030 0.315848\n", + " 5087 0.209545\n", + " 5088 0.194594\n", + " 5104 0.029200\n", + " 5132 0.013672\n", + " 5133 0.012088\n", + " 5131 0.011920\n", + " 4020 0.010979\n", + " 4026 0.010497\n", + " 4165 0.006716\n", + " Name: 918, dtype: float64, 5030 0.315848\n", + " 5087 0.209545\n", + " 5088 0.194594\n", + " 5104 0.029200\n", + " 5132 0.013672\n", + " 5133 0.012088\n", + " 5131 0.011920\n", + " 4020 0.010979\n", + " 4026 0.010497\n", + " 4165 0.006716\n", + " Name: 919, dtype: float64, 4149 0.991756\n", + " 4694 0.000503\n", + " 181 0.000410\n", + " 4736 0.000361\n", + " 3990 0.000256\n", + " 4208 0.000249\n", + " 4196 0.000171\n", + " 4738 0.000144\n", + " 4172 0.000136\n", + " 3989 0.000132\n", + " Name: 920, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 921, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 922, dtype: float64, 5064 0.082679\n", + " 5063 0.076061\n", + " 5066 0.074298\n", + " 5065 0.069763\n", + " 4259 0.045076\n", + " 4189 0.033644\n", + " 5014 0.027812\n", + " 4454 0.027490\n", + " 5106 0.026329\n", + " 5069 0.022840\n", + " Name: 923, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 924, dtype: float64, 4002 0.132665\n", + " 4023 0.030362\n", + " 4172 0.029135\n", + " 4612 0.024803\n", + " 4088 0.023142\n", + " 5058 0.020312\n", + " 4535 0.019481\n", + " 5059 0.015968\n", + " 4054 0.015140\n", + " 4648 0.014387\n", + " Name: 925, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 926, dtype: float64, 2884 0.130325\n", + " 4073 0.038395\n", + " 4987 0.035984\n", + " 4203 0.034455\n", + " 4138 0.021658\n", + " 4159 0.017874\n", + " 4202 0.017774\n", + " 107 0.012886\n", + " 4155 0.012474\n", + " 5123 0.011847\n", + " Name: 927, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 928, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", + " 3999 0.000002\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 929, dtype: float64, 4076 0.150968\n", + " 4136 0.145497\n", + " 4747 0.070567\n", + " 4746 0.058309\n", + " 5037 0.056887\n", + " 4748 0.056014\n", + " 4035 0.054280\n", + " 4703 0.051784\n", + " 5102 0.049885\n", + " 4204 0.045936\n", + " Name: 930, dtype: float64, 4013 0.923347\n", + " 4074 0.016239\n", + " 5059 0.003394\n", + " 5057 0.002370\n", + " 4239 0.001777\n", + " 4238 0.001700\n", + " 4607 0.001683\n", + " 4107 0.001360\n", + " 5129 0.001265\n", + " 5060 0.001224\n", + " Name: 931, dtype: float64, 4006 0.412437\n", + " 4988 0.368313\n", + " 4533 0.024560\n", + " 4137 0.011808\n", + " 4138 0.004867\n", + " 10482 0.002845\n", + " 4196 0.002588\n", + " 4608 0.002463\n", + " 6676 0.002324\n", + " 5114 0.002174\n", + " Name: 932, dtype: float64, 3993 9.999945e-01\n", + " 3992 2.456390e-07\n", + " 4004 1.933616e-07\n", + " 1902 1.527744e-07\n", + " 3320 1.067714e-07\n", + " 642 9.040956e-08\n", + " 4064 6.325320e-08\n", + " 1805 5.080657e-08\n", + " 4061 4.313567e-08\n", + " 4535 3.473819e-08\n", + " Name: 933, dtype: float64, 3997 0.999499\n", + " 3998 0.000108\n", + " 4280 0.000053\n", + " 5057 0.000017\n", + " 2236 0.000015\n", + " 3210 0.000010\n", + " 640 0.000009\n", + " 870 0.000007\n", + " 7106 0.000007\n", + " 264 0.000006\n", + " Name: 934, dtype: float64, 3997 0.144904\n", + " 3210 0.052221\n", + " 4714 0.038247\n", + " 4543 0.032747\n", + " 4599 0.028157\n", + " 4745 0.021408\n", + " 4600 0.020642\n", + " 4544 0.016226\n", + " 4000 0.015517\n", + " 5001 0.013326\n", + " Name: 935, dtype: float64, 3210 0.999927\n", + " 5144 0.000005\n", + " 2236 0.000004\n", + " 3997 0.000003\n", + " 1471 0.000003\n", + " 3999 0.000002\n", + " 3574 0.000002\n", + " 214 0.000002\n", + " 8381 0.000001\n", + " 2503 0.000001\n", + " Name: 936, dtype: float64, 4696 0.208615\n", + " 4698 0.203460\n", + " 4695 0.195960\n", + " 4697 0.168551\n", + " 4702 0.039970\n", + " 4660 0.014974\n", + " 4647 0.011278\n", + " 4424 0.008691\n", + " 4030 0.007286\n", + " 4659 0.003905\n", + " Name: 937, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 938, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 939, dtype: float64, 4643 0.963596\n", + " 4095 0.004472\n", + " 4092 0.004316\n", + " 4734 0.001482\n", + " 3629 0.000726\n", + " 6617 0.000707\n", + " 4898 0.000692\n", + " 3292 0.000563\n", + " 2856 0.000409\n", + " 4642 0.000350\n", + " Name: 940, dtype: float64, 79 9.999936e-01\n", + " 208 1.811400e-06\n", + " 1805 5.096231e-07\n", + " 119 1.497345e-07\n", + " 7106 1.199157e-07\n", + " 517 1.024898e-07\n", + " 2503 8.929246e-08\n", + " 4000 5.537193e-08\n", + " 8418 5.154729e-08\n", + " 4098 4.789730e-08\n", + " Name: 941, dtype: float64, 5059 0.749016\n", + " 5060 0.197185\n", + " 5058 0.008447\n", + " 4555 0.005420\n", + " 4393 0.003660\n", + " 4013 0.002906\n", + " 4535 0.001727\n", + " 4648 0.001387\n", + " 4002 0.001114\n", + " 4069 0.000963\n", + " Name: 942, dtype: float64, 5058 0.495713\n", + " 4002 0.451588\n", + " 4003 0.011259\n", + " 4030 0.003140\n", + " 4015 0.002897\n", + " 4648 0.001932\n", + " 5059 0.001824\n", + " 4207 0.000833\n", + " 4013 0.000816\n", + " 5154 0.000806\n", + " Name: 943, dtype: float64, 5058 0.976316\n", + " 4002 0.006574\n", + " 5059 0.001616\n", + " 4013 0.000733\n", + " 5060 0.000730\n", + " 4030 0.000546\n", + " 4648 0.000535\n", + " 4003 0.000494\n", + " 4019 0.000410\n", + " 4015 0.000396\n", + " Name: 944, dtype: float64, 5059 0.980844\n", + " 5060 0.006192\n", + " 5058 0.001060\n", + " 4393 0.000753\n", + " 4002 0.000539\n", + " 4193 0.000481\n", + " 4555 0.000390\n", + " 4736 0.000306\n", + " 4013 0.000304\n", + " 4207 0.000272\n", + " Name: 945, dtype: float64, 3999 0.999843\n", + " 3998 0.000034\n", + " 4542 0.000009\n", + " 3098 0.000008\n", + " 4000 0.000006\n", + " 1805 0.000006\n", + " 4074 0.000004\n", + " 4535 0.000004\n", + " 2503 0.000003\n", + " 2236 0.000002\n", + " Name: 946, dtype: float64, 3998 0.999844\n", + " 4000 0.000026\n", + " 4232 0.000015\n", + " 3999 0.000013\n", + " 5057 0.000006\n", + " 2629 0.000005\n", + " 3997 0.000004\n", + " 3094 0.000004\n", + " 4600 0.000004\n", + " 4586 0.000003\n", + " Name: 947, dtype: float64, 79 9.999949e-01\n", + " 208 1.292303e-06\n", + " 1805 2.670109e-07\n", + " 119 1.656839e-07\n", + " 7106 1.474233e-07\n", + " 2503 8.628919e-08\n", + " 517 7.540819e-08\n", + " 4098 7.278041e-08\n", + " 2546 5.599010e-08\n", + " 4074 4.734322e-08\n", + " Name: 948, dtype: float64, 4074 0.999040\n", + " 5129 0.000094\n", + " 4607 0.000092\n", + " 4580 0.000037\n", + " 1469 0.000035\n", + " 4026 0.000019\n", + " 581 0.000016\n", + " 4013 0.000016\n", + " 4658 0.000014\n", + " 4204 0.000013\n", + " Name: 949, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", " 2236 0.000013\n", - " 690 0.000013\n", - " 4540 0.000011\n", - " Name: 857, dtype: float64, 4015 0.142290\n", - " 4043 0.060287\n", - " 4298 0.056797\n", - " 4611 0.037576\n", - " 4016 0.029750\n", - " 4448 0.025260\n", - " 4449 0.024924\n", - " 4317 0.021825\n", - " 4447 0.021402\n", - " 4450 0.021275\n", - " Name: 858, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 859, dtype: float64, 3999 0.999315\n", - " 4542 0.000102\n", - " 2629 0.000048\n", - " 3998 0.000040\n", - " 3325 0.000026\n", - " 4540 0.000024\n", - " 677 0.000017\n", - " 5057 0.000014\n", - " 2236 0.000012\n", - " 3997 0.000012\n", - " Name: 860, dtype: float64, 4177 0.249011\n", - " 4178 0.120479\n", - " 2367 0.092162\n", - " 4494 0.055341\n", - " 4401 0.030263\n", - " 4646 0.027796\n", - " 4572 0.024785\n", - " 4524 0.024203\n", - " 4571 0.024080\n", - " 4576 0.021877\n", - " Name: 861, dtype: float64, 4177 0.049441\n", - " 4494 0.042737\n", - " 4575 0.038259\n", - " 4571 0.037294\n", - " 4573 0.037154\n", - " 4576 0.037091\n", - " 4572 0.035780\n", - " 4401 0.035299\n", - " 4646 0.032192\n", - " 4578 0.029168\n", - " Name: 862, dtype: float64, 4753 0.164995\n", - " 4400 0.045446\n", - " 4169 0.021428\n", - " 4101 0.019944\n", - " 5086 0.014815\n", - " 4181 0.014720\n", - " 4182 0.011105\n", - " 4614 0.010409\n", - " 4491 0.010105\n", - " 4189 0.009022\n", - " Name: 863, dtype: float64, 4686 0.905748\n", - " 4080 0.024432\n", - " 4026 0.004672\n", - " 4012 0.004202\n", - " 4152 0.001302\n", - " 206 0.000841\n", - " 5017 0.000808\n", - " 615 0.000604\n", - " 205 0.000583\n", - " 214 0.000541\n", - " Name: 864, dtype: float64, 4085 0.999250\n", - " 4554 0.000048\n", - " 4086 0.000027\n", - " 5057 0.000018\n", - " 5011 0.000016\n", - " 5058 0.000014\n", - " 4078 0.000012\n", - " 5117 0.000010\n", - " 5255 0.000007\n", - " 5154 0.000007\n", - " Name: 865, dtype: float64, 3996 9.999545e-01\n", - " 8381 3.087773e-06\n", - " 3574 2.508891e-06\n", - " 4710 1.323210e-06\n", - " 1515 1.134222e-06\n", - " 2435 6.829907e-07\n", - " 181 6.125558e-07\n", - " 4172 5.594303e-07\n", - " 3615 5.405926e-07\n", - " 9394 5.315109e-07\n", - " Name: 866, dtype: float64, 4087 0.994704\n", - " 4711 0.000291\n", - " 4426 0.000272\n", - " 5095 0.000153\n", - " 4644 0.000103\n", - " 4751 0.000069\n", - " 4054 0.000068\n", - " 4791 0.000064\n", - " 3816 0.000062\n", - " 10528 0.000055\n", - " Name: 867, dtype: float64, 4807 0.127083\n", - " 4777 0.029347\n", - " 4809 0.026217\n", - " 4818 0.019415\n", - " 5645 0.013769\n", - " 4923 0.013036\n", - " 4763 0.012983\n", - " 4921 0.012308\n", - " 4829 0.011323\n", - " 4766 0.010921\n", - " Name: 868, dtype: float64, 3322 0.982613\n", - " 4762 0.000667\n", - " 4809 0.000544\n", - " 5562 0.000388\n", - " 4776 0.000252\n", - " 8415 0.000191\n", - " 4812 0.000179\n", - " 4640 0.000135\n", - " 4847 0.000132\n", - " 4903 0.000108\n", - " Name: 869, dtype: float64, 4770 0.586438\n", - " 4769 0.098873\n", - " 4774 0.018284\n", - " 4756 0.017156\n", - " 4767 0.017102\n", - " 4806 0.008640\n", - " 4796 0.008241\n", - " 4905 0.007440\n", - " 4771 0.007080\n", - " 4772 0.006522\n", - " Name: 870, dtype: float64, 4782 0.564379\n", - " 4781 0.102428\n", - " 4777 0.027436\n", - " 4779 0.017034\n", - " 4786 0.009617\n", - " 4761 0.006816\n", - " 4809 0.006506\n", - " 4796 0.006305\n", - " 4810 0.005570\n", - " 4924 0.004453\n", - " Name: 871, dtype: float64, 4795 0.218349\n", - " 4799 0.088750\n", - " 4807 0.032306\n", - " 4764 0.023197\n", - " 4781 0.020616\n", - " 4798 0.019114\n", - " 4794 0.013687\n", - " 4811 0.012636\n", - " 4809 0.011543\n", - " 6002 0.008932\n", - " Name: 872, dtype: float64, 4771 0.454323\n", - " 4794 0.052139\n", - " 4795 0.039097\n", - " 4767 0.018008\n", - " 4768 0.014668\n", - " 4797 0.012488\n", - " 4792 0.011157\n", - " 4773 0.010154\n", - " 4781 0.008652\n", - " 4769 0.008602\n", - " Name: 873, dtype: float64, 4782 0.368919\n", - " 4781 0.175211\n", - " 4779 0.029689\n", - " 4777 0.018501\n", - " 4796 0.013448\n", - " 4786 0.013147\n", - " 4795 0.009916\n", - " 4794 0.009822\n", - " 4771 0.007912\n", - " 4792 0.006979\n", - " Name: 874, dtype: float64, 79 0.999804\n", - " 2260 0.000006\n", - " 4011 0.000004\n", - " 5263 0.000003\n", - " 704 0.000003\n", - " 708 0.000003\n", - " 9626 0.000003\n", - " 2684 0.000002\n", - " 405 0.000002\n", - " 8040 0.000002\n", - " Name: 875, dtype: float64, 4804 0.849395\n", - " 4856 0.008218\n", - " 4812 0.003975\n", - " 4910 0.003460\n", - " 4777 0.002811\n", - " 4781 0.002809\n", - " 4776 0.002607\n", - " 4864 0.002509\n", - " 4786 0.001935\n", - " 4810 0.001647\n", - " Name: 876, dtype: float64, 4804 0.615004\n", - " 4856 0.022560\n", - " 4910 0.011565\n", - " 4791 0.008674\n", - " 4908 0.007845\n", - " 4909 0.006179\n", - " 4786 0.006021\n", - " 4812 0.004835\n", - " 4975 0.004339\n", - " 5044 0.004327\n", - " Name: 877, dtype: float64, 4809 0.766887\n", - " 4777 0.029979\n", - " 4776 0.016237\n", - " 4762 0.011995\n", - " 4810 0.011153\n", - " 3322 0.006211\n", - " 4807 0.006001\n", - " 4377 0.004599\n", - " 4812 0.003283\n", - " 4804 0.003049\n", - " Name: 878, dtype: float64, 4813 0.989201\n", - " 8296 0.000695\n", - " 6727 0.000233\n", - " 1471 0.000187\n", - " 3647 0.000173\n", - " 2965 0.000157\n", - " 7606 0.000148\n", - " 2622 0.000112\n", - " 3258 0.000091\n", - " 6447 0.000086\n", - " Name: 879, dtype: float64, 4800 0.445033\n", - " 4906 0.033125\n", - " 4911 0.030150\n", - " 4905 0.017999\n", - " 4881 0.015217\n", - " 4842 0.012220\n", - " 4792 0.007707\n", - " 4786 0.007625\n", - " 4838 0.005110\n", - " 4796 0.005059\n", - " Name: 880, dtype: float64, 4814 0.096531\n", - " 5977 0.046500\n", - " 4944 0.021173\n", - " 4785 0.017749\n", - " 4789 0.016317\n", - " 5978 0.013346\n", - " 4949 0.011687\n", - " 4880 0.011109\n", - " 4856 0.010555\n", - " 4807 0.009898\n", - " Name: 881, dtype: float64, 4787 0.421028\n", - " 4831 0.023177\n", - " 4835 0.019904\n", - " 4860 0.014856\n", - " 4842 0.013397\n", - " 4851 0.013203\n", - " 4861 0.011275\n", - " 3757 0.010725\n", - " 4848 0.010546\n", - " 4858 0.010323\n", - " Name: 882, dtype: float64, 4823 0.200772\n", - " 4815 0.056901\n", - " 4829 0.056587\n", - " 4852 0.033732\n", - " 4916 0.030920\n", - " 4863 0.027391\n", - " 4840 0.023612\n", - " 4831 0.020289\n", - " 2701 0.019432\n", - " 4825 0.019382\n", - " Name: 883, dtype: float64, 4835 0.215043\n", - " 4817 0.054483\n", - " 4829 0.047697\n", - " 4837 0.040064\n", - " 4840 0.034853\n", - " 4815 0.028015\n", - " 2701 0.026590\n", - " 4830 0.021554\n", - " 4821 0.019264\n", - " 4785 0.017325\n", - " Name: 884, dtype: float64, 4838 0.450781\n", - " 4842 0.021417\n", - " 4863 0.017101\n", - " 4814 0.013424\n", - " 4962 0.009688\n", - " 4825 0.007831\n", - " 4930 0.007500\n", - " 4817 0.007379\n", - " 4829 0.006647\n", - " 4916 0.006567\n", - " Name: 885, dtype: float64, 4842 0.367837\n", - " 4843 0.147843\n", - " 4787 0.038766\n", - " 4924 0.009763\n", - " 4833 0.008760\n", - " 4840 0.006492\n", - " 4845 0.006164\n", - " 4923 0.006105\n", - " 4870 0.005829\n", - " 4786 0.005781\n", - " Name: 886, dtype: float64, 4789 0.423635\n", - " 4785 0.083567\n", - " 4915 0.011150\n", - " 4925 0.010233\n", - " 4965 0.009745\n", - " 4934 0.008349\n", - " 4956 0.007731\n", - " 4916 0.006403\n", - " 4835 0.006250\n", - " 4830 0.006242\n", - " Name: 887, dtype: float64, 4807 0.492786\n", - " 4858 0.095533\n", - " 4863 0.018889\n", - " 4838 0.014006\n", - " 4829 0.012026\n", - " 4861 0.011076\n", - " 4860 0.007251\n", - " 4916 0.006595\n", - " 4842 0.006281\n", - " 4818 0.006201\n", - " Name: 888, dtype: float64, 4838 0.334243\n", - " 4842 0.022849\n", - " 4905 0.022512\n", - " 4926 0.014233\n", - " 4818 0.013127\n", - " 4792 0.011426\n", - " 5546 0.010720\n", - " 4863 0.009529\n", - " 4820 0.008845\n", - " 4914 0.008670\n", - " Name: 889, dtype: float64, 4860 0.577925\n", - " 4861 0.154026\n", - " 4858 0.088087\n", - " 4833 0.006122\n", - " 3757 0.005270\n", - " 4818 0.003512\n", - " 4787 0.003390\n", - " 4848 0.002820\n", - " 4842 0.002575\n", - " 4807 0.002564\n", - " Name: 890, dtype: float64, 4842 0.062370\n", - " 4851 0.022917\n", - " 4867 0.016667\n", - " 4870 0.016464\n", - " 4815 0.015695\n", - " 4830 0.014872\n", - " 4866 0.013920\n", - " 4843 0.010690\n", - " 4787 0.009704\n", - " 4849 0.009076\n", - " Name: 891, dtype: float64, 4886 0.309614\n", - " 4892 0.121548\n", - " 4885 0.077931\n", - " 4875 0.069432\n", - " 4872 0.053703\n", - " 4878 0.030020\n", - " 4873 0.028610\n", - " 4882 0.013126\n", - " 4880 0.012867\n", - " 4889 0.010942\n", - " Name: 892, dtype: float64, 4892 0.149497\n", - " 4885 0.147482\n", - " 4886 0.079164\n", - " 4875 0.060693\n", - " 4872 0.044581\n", - " 4878 0.041033\n", - " 4880 0.023326\n", - " 4876 0.018237\n", - " 4873 0.016583\n", - " 4896 0.014187\n", - " Name: 893, dtype: float64, 4985 0.049975\n", - " 4892 0.029991\n", - " 4880 0.028166\n", - " 4807 0.019647\n", - " 4965 0.015451\n", - " 4941 0.013550\n", - " 4900 0.013300\n", - " 4896 0.012356\n", - " 4944 0.011880\n", - " 4873 0.010039\n", - " Name: 894, dtype: float64, 4842 0.071968\n", - " 4915 0.042362\n", - " 4830 0.020971\n", - " 4911 0.018562\n", - " 4835 0.017634\n", - " 4837 0.015646\n", - " 4823 0.015534\n", - " 4838 0.014889\n", - " 4905 0.014192\n", - " 4916 0.013436\n", - " Name: 895, dtype: float64, 4924 0.108021\n", - " 4823 0.030602\n", - " 4851 0.024152\n", - " 4923 0.023939\n", - " 4917 0.023078\n", - " 4870 0.020022\n", - " 4831 0.018969\n", - " 4928 0.018056\n", - " 4842 0.017686\n", - " 4945 0.014746\n", - " Name: 896, dtype: float64, 4759 0.998992\n", - " 4149 0.000118\n", - " 4761 0.000069\n", - " 4172 0.000045\n", - " 2701 0.000033\n", - " 4088 0.000019\n", - " 4776 0.000016\n", - " 4847 0.000012\n", - " 4829 0.000011\n", - " 4774 0.000011\n", - " Name: 897, dtype: float64, 4933 0.177590\n", - " 4929 0.076472\n", - " 4932 0.041137\n", - " 4965 0.026067\n", - " 4950 0.019661\n", - " 4925 0.018682\n", - " 4913 0.017809\n", - " 4963 0.015708\n", - " 4935 0.015434\n", - " 4822 0.014320\n", - " Name: 898, dtype: float64, 4933 0.519493\n", - " 4929 0.063703\n", - " 4932 0.043469\n", - " 4950 0.020771\n", - " 4956 0.016771\n", - " 4965 0.014385\n", - " 4935 0.012873\n", - " 4930 0.012584\n", - " 4913 0.011547\n", - " 4953 0.010578\n", - " Name: 899, dtype: float64, 4953 0.112509\n", - " 4952 0.106194\n", - " 4958 0.039008\n", - " 4934 0.024925\n", - " 4826 0.020031\n", - " 4956 0.018158\n", - " 4941 0.017008\n", - " 4957 0.015010\n", - " 4964 0.014432\n", - " 4951 0.013931\n", - " Name: 900, dtype: float64, 4956 0.079707\n", - " 4953 0.036886\n", - " 4965 0.035066\n", - " 4932 0.033895\n", - " 4933 0.024826\n", - " 4957 0.023869\n", - " 4950 0.022762\n", - " 4952 0.019714\n", - " 4930 0.019174\n", - " 4943 0.018799\n", - " Name: 901, dtype: float64, 4971 0.206718\n", - " 4975 0.126914\n", - " 4976 0.111232\n", - " 4984 0.046926\n", - " 4986 0.042886\n", - " 4978 0.038423\n", - " 4969 0.033649\n", - " 4972 0.031119\n", - " 4977 0.016975\n", - " 4974 0.012339\n", - " Name: 902, dtype: float64, 4969 0.111862\n", - " 4971 0.083372\n", - " 4974 0.043199\n", - " 4979 0.027594\n", - " 4972 0.020251\n", - " 4984 0.015755\n", - " 4981 0.009278\n", - " 4978 0.007992\n", - " 4926 0.007115\n", - " 4967 0.006352\n", - " Name: 903, dtype: float64, 4975 0.055352\n", - " 4977 0.052664\n", - " 4976 0.045542\n", - " 524 0.026702\n", - " 4978 0.021184\n", - " 4974 0.018417\n", - " 4784 0.015019\n", - " 2523 0.014269\n", - " 4985 0.010303\n", - " 4971 0.007913\n", - " Name: 904, dtype: float64, 4752 0.530214\n", - " 5061 0.307833\n", - " 4207 0.015727\n", - " 4405 0.008584\n", - " 4078 0.005677\n", - " 2715 0.004823\n", - " 5038 0.003597\n", - " 4102 0.001952\n", - " 4017 0.001349\n", - " 5127 0.001327\n", - " Name: 905, dtype: float64, 4000 0.025535\n", - " 2654 0.014678\n", - " 4165 0.014462\n", - " 1242 0.008389\n", - " 4526 0.008175\n", - " 3191 0.005721\n", - " 509 0.005630\n", - " 7999 0.005428\n", - " 2638 0.005228\n", - " 4004 0.005052\n", - " Name: 906, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 907, dtype: float64, 5007 0.276833\n", - " 4208 0.186461\n", - " 4348 0.050185\n", - " 5008 0.048041\n", - " 5157 0.046517\n", - " 5009 0.045553\n", - " 4162 0.031705\n", - " 4190 0.020626\n", - " 5130 0.018278\n", - " 4149 0.014453\n", - " Name: 908, dtype: float64, 5007 0.276833\n", - " 4208 0.186461\n", - " 4348 0.050185\n", - " 5008 0.048041\n", - " 5157 0.046517\n", - " 5009 0.045553\n", - " 4162 0.031705\n", - " 4190 0.020626\n", - " 5130 0.018278\n", - " 4149 0.014453\n", - " Name: 909, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 910, dtype: float64, 4897 0.025246\n", - " 4887 0.021874\n", - " 4806 0.021167\n", - " 4900 0.018562\n", - " 9547 0.012715\n", - " 4766 0.012658\n", - " 4873 0.012148\n", - " 4811 0.011105\n", - " 4880 0.010137\n", - " 4975 0.009156\n", - " Name: 911, dtype: float64, 5022 0.277198\n", - " 5021 0.272195\n", - " 5023 0.270725\n", - " 4338 0.003070\n", - " 4021 0.003049\n", - " 4102 0.002867\n", - " 3991 0.002237\n", - " 4761 0.001964\n", - " 4207 0.001819\n", - " 4172 0.001581\n", - " Name: 912, dtype: float64, 5022 0.277198\n", - " 5021 0.272195\n", - " 5023 0.270725\n", - " 4338 0.003070\n", - " 4021 0.003049\n", - " 4102 0.002867\n", - " 3991 0.002237\n", - " 4761 0.001964\n", - " 4207 0.001819\n", - " 4172 0.001581\n", - " Name: 913, dtype: float64, 3988 9.999981e-01\n", - " 4004 7.825608e-08\n", - " 4074 2.904527e-08\n", - " 1539 2.572120e-08\n", - " 4535 1.159967e-08\n", - " 3993 1.078722e-08\n", - " 154 1.068691e-08\n", - " 3320 1.046666e-08\n", - " 79 9.245329e-09\n", - " 4968 9.133679e-09\n", - " Name: 914, dtype: float64, 2629 0.997830\n", - " 901 0.000175\n", - " 2236 0.000123\n", - " 3999 0.000108\n", - " 642 0.000104\n", - " 2955 0.000054\n", - " 4067 0.000052\n", - " 7124 0.000040\n", - " 2503 0.000029\n", - " 677 0.000025\n", - " Name: 915, dtype: float64, 4666 0.221173\n", - " 4665 0.208705\n", - " 4548 0.196862\n", - " 5025 0.168590\n", - " 4109 0.009444\n", - " 4715 0.008491\n", - " 6070 0.006199\n", - " 6128 0.004005\n", - " 4724 0.002451\n", - " 4525 0.002145\n", - " Name: 916, dtype: float64, 1805 0.998194\n", - " 3098 0.000978\n", - " 2503 0.000094\n", - " 679 0.000047\n", - " 2504 0.000036\n", - " 2502 0.000024\n", - " 2236 0.000022\n", - " 870 0.000018\n", - " 677 0.000018\n", - " 206 0.000013\n", - " Name: 917, dtype: float64, 5030 0.241153\n", - " 5087 0.192136\n", - " 5088 0.178305\n", - " 5104 0.079500\n", - " 4165 0.029501\n", - " 5132 0.015121\n", - " 5131 0.013921\n", - " 5133 0.012014\n", - " 5130 0.009608\n", - " 4031 0.007358\n", - " Name: 918, dtype: float64, 5030 0.241153\n", - " 5087 0.192136\n", - " 5088 0.178305\n", - " 5104 0.079500\n", - " 4165 0.029501\n", - " 5132 0.015121\n", - " 5131 0.013921\n", - " 5133 0.012014\n", - " 5130 0.009608\n", - " 4031 0.007358\n", - " Name: 919, dtype: float64, 4149 0.977301\n", - " 4736 0.001875\n", - " 3990 0.000917\n", - " 4172 0.000790\n", - " 4208 0.000543\n", - " 3989 0.000493\n", - " 4006 0.000455\n", - " 5130 0.000450\n", - " 181 0.000439\n", - " 4015 0.000371\n", - " Name: 920, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 921, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 922, dtype: float64, 5064 0.058679\n", - " 5066 0.055554\n", - " 4259 0.049729\n", - " 5065 0.048020\n", - " 5063 0.047816\n", - " 4189 0.034839\n", - " 4239 0.025767\n", - " 4238 0.023884\n", - " 5014 0.020850\n", - " 4454 0.020136\n", - " Name: 923, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 924, dtype: float64, 4002 0.148577\n", - " 4535 0.063250\n", - " 4612 0.032712\n", - " 4607 0.032139\n", - " 5058 0.028707\n", - " 5059 0.022341\n", - " 4074 0.016919\n", - " 5060 0.012546\n", - " 4648 0.010714\n", - " 4526 0.008732\n", - " Name: 925, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 926, dtype: float64, 5123 0.044871\n", - " 2884 0.035139\n", - " 4155 0.024539\n", - " 4158 0.024240\n", - " 4987 0.017547\n", - " 5032 0.017284\n", - " 4073 0.016419\n", - " 4164 0.012205\n", - " 4196 0.008092\n", - " 4194 0.007626\n", - " Name: 927, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 928, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 950, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 951, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 952, dtype: float64, 4074 0.921859\n", + " 4607 0.022128\n", + " 4013 0.021582\n", + " 5129 0.017516\n", + " 4031 0.002267\n", + " 4580 0.001478\n", + " 5057 0.000680\n", + " 4658 0.000679\n", + " 4026 0.000618\n", + " 5132 0.000590\n", + " Name: 953, dtype: float64, 3098 0.999804\n", + " 6929 0.000021\n", + " 1805 0.000016\n", + " 5668 0.000011\n", + " 107 0.000009\n", + " 5054 0.000005\n", + " 2236 0.000004\n", + " 2503 0.000003\n", " 2435 0.000003\n", - " Name: 929, dtype: float64, 4076 0.153459\n", - " 4136 0.137009\n", - " 4748 0.053385\n", - " 4214 0.049273\n", - " 4617 0.048607\n", - " 4703 0.048297\n", - " 4035 0.047356\n", - " 4747 0.046762\n", - " 4746 0.045811\n", - " 4204 0.044179\n", - " Name: 930, dtype: float64, 4013 0.886861\n", - " 4074 0.017691\n", - " 4092 0.004773\n", - " 4155 0.004015\n", - " 181 0.003069\n", - " 4393 0.001918\n", - " 5059 0.001901\n", - " 4107 0.001673\n", - " 4612 0.001641\n", - " 3990 0.001618\n", - " Name: 931, dtype: float64, 4988 0.288534\n", - " 4138 0.170436\n", - " 4137 0.138507\n", - " 4006 0.122932\n", - " 3052 0.016985\n", - " 4533 0.009353\n", - " 3510 0.007740\n", - " 4205 0.007543\n", - " 3042 0.007065\n", - " 4155 0.006712\n", - " Name: 932, dtype: float64, 3993 9.999905e-01\n", - " 3992 3.863385e-07\n", - " 4000 1.547760e-07\n", - " 4130 1.170060e-07\n", - " 3428 9.657679e-08\n", - " 2376 8.408841e-08\n", - " 5527 7.393424e-08\n", - " 3574 7.303527e-08\n", - " 188 7.296314e-08\n", - " 642 6.800445e-08\n", - " Name: 933, dtype: float64, 3997 0.999726\n", - " 3999 0.000081\n", - " 3210 0.000032\n", - " 3998 0.000024\n", - " 4067 0.000008\n", - " 4540 0.000008\n", - " 677 0.000004\n", - " 3165 0.000003\n", - " 2504 0.000003\n", - " 7472 0.000001\n", - " Name: 934, dtype: float64, 3210 0.196913\n", - " 4632 0.130721\n", - " 4544 0.041371\n", - " 5120 0.033157\n", - " 4657 0.025959\n", - " 4543 0.024822\n", - " 4656 0.021745\n", - " 5144 0.014939\n", - " 4704 0.012859\n", - " 4540 0.011094\n", - " Name: 935, dtype: float64, 3210 0.999845\n", - " 5144 0.000005\n", - " 5120 0.000004\n", - " 4540 0.000004\n", - " 677 0.000004\n", - " 4165 0.000004\n", - " 4632 0.000004\n", - " 5132 0.000003\n", - " 2504 0.000003\n", - " 3998 0.000003\n", - " Name: 936, dtype: float64, 4696 0.198883\n", - " 4697 0.196484\n", - " 4695 0.185195\n", - " 4698 0.178392\n", - " 4702 0.030324\n", - " 4030 0.015619\n", - " 4659 0.006394\n", - " 4424 0.004824\n", - " 4660 0.003367\n", - " 5049 0.003035\n", - " Name: 937, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 938, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 939, dtype: float64, 4643 0.959021\n", - " 4095 0.002620\n", - " 4092 0.002263\n", - " 4192 0.000903\n", - " 4193 0.000788\n", - " 4105 0.000665\n", - " 3629 0.000614\n", - " 4642 0.000604\n", - " 4678 0.000543\n", - " 3940 0.000536\n", - " Name: 940, dtype: float64, 79 9.999956e-01\n", - " 160 1.907815e-07\n", - " 9626 1.361053e-07\n", - " 119 1.262249e-07\n", - " 4011 1.234924e-07\n", - " 8040 1.135801e-07\n", - " 2260 8.443985e-08\n", - " 206 7.990824e-08\n", - " 1203 7.861013e-08\n", - " 205 7.079329e-08\n", - " Name: 941, dtype: float64, 5059 0.750494\n", - " 5060 0.099689\n", - " 4648 0.023653\n", - " 5058 0.013191\n", - " 4393 0.010110\n", - " 4555 0.008766\n", - " 4535 0.008355\n", - " 4013 0.005068\n", - " 5057 0.004279\n", - " 4002 0.003773\n", - " Name: 942, dtype: float64, 5058 0.486399\n", - " 4002 0.410043\n", - " 4003 0.019145\n", - " 4648 0.004598\n", - " 4001 0.004142\n", - " 4393 0.003616\n", - " 5057 0.003442\n", - " 4015 0.002853\n", - " 5059 0.002657\n", - " 4013 0.002562\n", - " Name: 943, dtype: float64, 5058 0.943620\n", - " 4002 0.005247\n", - " 5057 0.002348\n", - " 4029 0.001906\n", - " 4078 0.001793\n", - " 4001 0.001697\n", - " 4607 0.001324\n", - " 4612 0.001087\n", - " 5131 0.001048\n", - " 5059 0.000921\n", - " Name: 944, dtype: float64, 5059 0.914047\n", - " 5060 0.013113\n", - " 4013 0.003625\n", - " 4393 0.003403\n", - " 5057 0.003261\n", - " 4648 0.003243\n", - " 4026 0.003098\n", - " 4001 0.003081\n", - " 4555 0.002094\n", - " 5058 0.001983\n", - " Name: 945, dtype: float64, 3999 0.999297\n", - " 2629 0.000089\n", - " 4542 0.000065\n", - " 3998 0.000053\n", - " 3325 0.000023\n", - " 4540 0.000023\n", - " 677 0.000023\n", - " 2236 0.000015\n", - " 690 0.000014\n", - " 3997 0.000013\n", - " Name: 946, dtype: float64, 3998 0.999652\n", - " 4232 0.000027\n", - " 3999 0.000016\n", - " 3210 0.000015\n", - " 2611 0.000010\n", - " 2660 0.000008\n", - " 4586 0.000008\n", - " 4600 0.000008\n", - " 690 0.000008\n", - " 6588 0.000007\n", - " Name: 947, dtype: float64, 79 9.999923e-01\n", - " 160 2.718998e-07\n", - " 9626 2.579705e-07\n", - " 8040 2.230048e-07\n", - " 4011 1.745024e-07\n", - " 206 1.651428e-07\n", - " 119 1.595027e-07\n", - " 2260 1.378042e-07\n", - " 192 1.302861e-07\n", - " 1203 1.236871e-07\n", - " Name: 948, dtype: float64, 4074 0.998773\n", - " 4013 0.000243\n", - " 5129 0.000148\n", - " 4607 0.000084\n", - " 4165 0.000063\n", - " 4088 0.000048\n", - " 5059 0.000021\n", - " 207 0.000020\n", - " 4092 0.000019\n", - " 4001 0.000019\n", - " Name: 949, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 950, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 951, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 952, dtype: float64, 4074 0.931280\n", - " 4013 0.022082\n", - " 5129 0.014144\n", - " 4607 0.011951\n", - " 4026 0.000926\n", - " 4658 0.000832\n", - " 4031 0.000729\n", - " 181 0.000478\n", - " 4580 0.000474\n", - " 4535 0.000436\n", - " Name: 953, dtype: float64, 3098 0.999746\n", - " 1805 0.000018\n", - " 6929 0.000016\n", - " 2503 0.000010\n", - " 2236 0.000010\n", - " 8509 0.000009\n", - " 2873 0.000008\n", - " 677 0.000008\n", - " 6066 0.000005\n", - " 2715 0.000005\n", - " Name: 954, dtype: float64, 4719 0.084126\n", - " 5053 0.060995\n", - " 4438 0.037402\n", - " 4228 0.027910\n", - " 4230 0.011130\n", - " 4155 0.010982\n", - " 4015 0.008294\n", - " 4206 0.007709\n", - " 4596 0.007277\n", - " 4616 0.005799\n", - " Name: 955, dtype: float64, 2629 0.999895\n", - " 2236 0.000010\n", - " 7106 0.000003\n", + " 8418 0.000003\n", + " Name: 954, dtype: float64, 5053 0.512545\n", + " 4719 0.156746\n", + " 4438 0.026143\n", + " 9039 0.011452\n", + " 4736 0.009493\n", + " 4274 0.009262\n", + " 4015 0.002514\n", + " 4298 0.002487\n", + " 7242 0.002457\n", + " 5030 0.002407\n", + " Name: 955, dtype: float64, 2629 0.999884\n", + " 4067 0.000003\n", + " 3410 0.000003\n", + " 86 0.000003\n", + " 2665 0.000002\n", + " 3998 0.000002\n", + " 4073 0.000002\n", + " 6539 0.000002\n", + " 2797 0.000002\n", + " 690 0.000001\n", + " Name: 956, dtype: float64, 4091 0.999642\n", + " 8224 0.000039\n", + " 6912 0.000034\n", + " 2629 0.000014\n", + " 8418 0.000008\n", + " 8274 0.000005\n", + " 8186 0.000004\n", + " 2715 0.000003\n", " 4067 0.000003\n", - " 4098 0.000002\n", + " 679 0.000003\n", + " Name: 957, dtype: float64, 4000 0.998325\n", + " 107 0.000087\n", + " 5121 0.000068\n", + " 6766 0.000066\n", + " 6626 0.000058\n", + " 3088 0.000046\n", + " 3987 0.000045\n", + " 6598 0.000044\n", + " 4994 0.000041\n", + " 5207 0.000035\n", + " Name: 958, dtype: float64, 5082 0.396577\n", + " 8755 0.147415\n", + " 10002 0.060300\n", + " 9827 0.023975\n", + " 8485 0.022998\n", + " 9638 0.009572\n", + " 4604 0.009377\n", + " 4654 0.009035\n", + " 9829 0.007929\n", + " 4095 0.007896\n", + " Name: 959, dtype: float64, 3992 9.999071e-01\n", + " 2051 1.064764e-05\n", + " 7414 7.064210e-06\n", + " 2738 4.026061e-06\n", + " 207 2.244178e-06\n", + " 706 1.591229e-06\n", + " 5578 1.389246e-06\n", + " 1714 1.168669e-06\n", + " 677 1.134416e-06\n", + " 755 8.309394e-07\n", + " Name: 960, dtype: float64, 3994 9.999875e-01\n", + " 4127 2.374518e-06\n", + " 2582 1.509753e-06\n", + " 4078 2.940485e-07\n", + " 6015 1.691750e-07\n", + " 3476 1.581487e-07\n", + " 207 1.215147e-07\n", + " 4393 1.194028e-07\n", + " 2875 1.170854e-07\n", + " 2236 1.122649e-07\n", + " Name: 961, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 962, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", " 2503 0.000002\n", - " 7124 0.000002\n", - " 3671 0.000002\n", + " 4000 0.000002\n", + " Name: 963, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 964, dtype: float64, 3292 0.996541\n", + " 7081 0.000207\n", + " 6617 0.000207\n", + " 5738 0.000053\n", + " 5439 0.000051\n", + " 3403 0.000036\n", + " 55 0.000032\n", + " 8747 0.000031\n", + " 2349 0.000027\n", + " 8953 0.000026\n", + " Name: 965, dtype: float64, 5059 0.970611\n", + " 5060 0.006975\n", + " 4013 0.002221\n", + " 5058 0.001286\n", + " 4193 0.000919\n", + " 4736 0.000752\n", + " 4393 0.000704\n", + " 4555 0.000542\n", + " 4535 0.000508\n", + " 4002 0.000495\n", + " Name: 966, dtype: float64, 79 9.999942e-01\n", + " 208 1.510120e-06\n", + " 1805 3.109878e-07\n", + " 7106 1.637218e-07\n", + " 119 1.600656e-07\n", + " 2503 1.015973e-07\n", + " 517 8.257593e-08\n", + " 4098 8.048617e-08\n", + " 2546 6.081803e-08\n", + " 4074 4.946094e-08\n", + " Name: 967, dtype: float64, 3994 9.999876e-01\n", + " 4127 2.356943e-06\n", + " 2582 1.413928e-06\n", + " 4078 3.059756e-07\n", + " 6015 1.825343e-07\n", + " 3476 1.593517e-07\n", + " 4393 1.271551e-07\n", + " 207 1.131661e-07\n", + " 2875 1.108352e-07\n", + " 4001 1.086125e-07\n", + " Name: 968, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 969, dtype: float64, 3989 0.650860\n", + " 3990 0.326077\n", + " 2367 0.002372\n", + " 4178 0.001767\n", + " 4177 0.001308\n", + " 181 0.000826\n", + " 4739 0.000779\n", + " 4738 0.000727\n", + " 4027 0.000537\n", + " 4038 0.000525\n", + " Name: 970, dtype: float64, 4098 0.974279\n", + " 7141 0.010991\n", + " 7173 0.000374\n", + " 8265 0.000278\n", + " 7135 0.000237\n", + " 8188 0.000234\n", + " 8418 0.000232\n", + " 6839 0.000182\n", + " 7136 0.000164\n", + " 6947 0.000164\n", + " Name: 971, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 972, dtype: float64, 1805 0.999797\n", + " 3098 0.000080\n", + " 2503 0.000014\n", + " 107 0.000008\n", + " 679 0.000005\n", + " 2236 0.000004\n", + " 2166 0.000003\n", " 3999 0.000002\n", - " 7871 0.000001\n", - " Name: 956, dtype: float64, 4091 0.999893\n", - " 641 0.000004\n", - " 12 0.000003\n", - " 3991 0.000002\n", - " 10250 0.000002\n", - " 146 0.000002\n", - " 2662 0.000002\n", - " 2236 0.000001\n", - " 4011 0.000001\n", - " 6912 0.000001\n", - " Name: 957, dtype: float64, 4000 0.660199\n", - " 4097 0.065695\n", - " 7021 0.011941\n", - " 5866 0.006324\n", - " 5077 0.004577\n", - " 2567 0.003198\n", - " 2715 0.003151\n", - " 10178 0.003027\n", - " 2348 0.003021\n", - " 753 0.002650\n", - " Name: 958, dtype: float64, 5082 0.967709\n", - " 8755 0.004857\n", - " 4604 0.002484\n", - " 4095 0.001626\n", - " 136 0.000653\n", - " 9373 0.000301\n", - " 8664 0.000240\n", - " 5567 0.000206\n", - " 1940 0.000198\n", - " 9827 0.000178\n", - " Name: 959, dtype: float64, 3992 9.999424e-01\n", - " 677 1.476421e-05\n", - " 2738 2.154386e-06\n", - " 697 1.492650e-06\n", - " 5950 1.385210e-06\n", - " 7414 1.365843e-06\n", - " 7957 1.297597e-06\n", - " 6091 9.492531e-07\n", - " 3993 8.500680e-07\n", - " 7472 5.800820e-07\n", - " Name: 960, dtype: float64, 3994 9.999853e-01\n", - " 4127 2.312993e-06\n", - " 2582 2.130482e-06\n", - " 4000 5.700608e-07\n", - " 2662 5.318103e-07\n", - " 5549 2.956811e-07\n", - " 5945 2.446522e-07\n", - " 5919 2.233565e-07\n", - " 7107 1.757616e-07\n", - " 667 1.607860e-07\n", - " Name: 961, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 962, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 963, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 964, dtype: float64, 3292 0.998400\n", - " 6617 0.000188\n", - " 5362 0.000023\n", - " 6877 0.000021\n", - " 8126 0.000019\n", - " 6430 0.000017\n", - " 5935 0.000016\n", - " 5124 0.000015\n", - " 2834 0.000013\n", - " 6096 0.000013\n", - " Name: 965, dtype: float64, 5059 0.890790\n", - " 5060 0.016095\n", - " 5057 0.004481\n", - " 4074 0.004416\n", - " 4393 0.004022\n", - " 4013 0.003656\n", - " 4001 0.003567\n", - " 4648 0.002995\n", - " 4026 0.002955\n", - " 4555 0.002463\n", - " Name: 966, dtype: float64, 79 9.999906e-01\n", - " 160 3.210088e-07\n", - " 9626 3.183109e-07\n", - " 8040 2.583267e-07\n", - " 4011 2.210667e-07\n", - " 206 2.068686e-07\n", - " 192 1.801180e-07\n", - " 119 1.734916e-07\n", - " 1805 1.678033e-07\n", - " 2260 1.658263e-07\n", - " Name: 967, dtype: float64, 3994 9.999862e-01\n", - " 4127 2.097253e-06\n", - " 2582 2.080553e-06\n", - " 4000 5.963194e-07\n", - " 2662 5.416661e-07\n", - " 5549 2.588756e-07\n", - " 5945 2.192588e-07\n", - " 5919 2.003120e-07\n", - " 7107 1.688453e-07\n", - " 2955 1.628869e-07\n", - " Name: 968, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 969, dtype: float64, 3989 0.576012\n", - " 3990 0.371863\n", - " 4038 0.004579\n", - " 2367 0.003033\n", - " 4028 0.002103\n", - " 5041 0.001818\n", - " 4027 0.001650\n", - " 4738 0.001622\n", - " 4177 0.001566\n", - " 4178 0.001507\n", - " Name: 970, dtype: float64, 4098 0.977112\n", - " 7141 0.006995\n", - " 7114 0.000699\n", - " 2663 0.000375\n", - " 7148 0.000315\n", - " 2666 0.000310\n", - " 7135 0.000293\n", - " 7115 0.000194\n", - " 3501 0.000172\n", - " 4685 0.000159\n", - " Name: 971, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 972, dtype: float64, 1805 0.999716\n", - " 3098 0.000039\n", - " 2503 0.000033\n", - " 679 0.000031\n", - " 2504 0.000017\n", - " 677 0.000011\n", - " 2502 0.000010\n", - " 107 0.000007\n", - " 4141 0.000004\n", - " 2435 0.000003\n", - " Name: 973, dtype: float64, 107 0.999740\n", - " 2503 0.000046\n", - " 2236 0.000021\n", - " 4141 0.000014\n", - " 6066 0.000014\n", - " 1805 0.000005\n", - " 4716 0.000005\n", - " 3574 0.000004\n", - " 4708 0.000004\n", - " 2567 0.000003\n", - " Name: 974, dtype: float64, 3574 9.999471e-01\n", - " 181 3.523423e-06\n", - " 4172 2.315429e-06\n", - " 2503 1.441230e-06\n", - " 107 1.197314e-06\n", - " 8381 8.878144e-07\n", - " 4847 7.268104e-07\n", - " 1515 7.229171e-07\n", - " 205 6.328848e-07\n", - " 4783 6.254656e-07\n", - " Name: 975, dtype: float64, 3988 9.999981e-01\n", - " 4004 7.825608e-08\n", - " 4074 2.904527e-08\n", - " 1539 2.572120e-08\n", - " 4535 1.159967e-08\n", - " 3993 1.078722e-08\n", - " 154 1.068691e-08\n", - " 3320 1.046666e-08\n", - " 79 9.245329e-09\n", - " 4968 9.133679e-09\n", - " Name: 976, dtype: float64, 4535 0.963715\n", - " 5059 0.006737\n", - " 5060 0.004152\n", - " 4648 0.002280\n", - " 4555 0.001193\n", - " 4393 0.001150\n", - " 4013 0.001092\n", - " 4612 0.000872\n", - " 4074 0.000568\n", - " 5058 0.000285\n", - " Name: 977, dtype: float64, 3996 9.999737e-01\n", - " 8381 1.741967e-06\n", - " 4710 9.066468e-07\n", - " 3574 6.314740e-07\n", - " 1515 5.614004e-07\n", - " 9394 4.338572e-07\n", - " 2435 4.066889e-07\n", - " 9354 3.671372e-07\n", - " 4989 3.391952e-07\n", - " 9416 3.216211e-07\n", - " Name: 978, dtype: float64, 4189 0.049858\n", - " 4178 0.026137\n", - " 3990 0.021080\n", - " 4324 0.021016\n", - " 4293 0.014241\n", - " 4530 0.010648\n", - " 3989 0.010442\n", - " 4529 0.010266\n", - " 4528 0.010017\n", - " 5068 0.009925\n", - " Name: 979, dtype: float64, 5064 0.058679\n", - " 5066 0.055554\n", - " 4259 0.049729\n", - " 5065 0.048020\n", - " 5063 0.047816\n", - " 4189 0.034839\n", - " 4239 0.025767\n", - " 4238 0.023884\n", - " 5014 0.020850\n", - " 4454 0.020136\n", - " Name: 980, dtype: float64, 654 0.527219\n", - " 5110 0.229516\n", - " 657 0.152388\n", - " 1615 0.004411\n", - " 5590 0.001751\n", - " 1255 0.001677\n", - " 9416 0.001471\n", - " 9493 0.001190\n", - " 653 0.001057\n", - " 9454 0.001040\n", - " Name: 981, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 982, dtype: float64, 4091 0.999893\n", - " 641 0.000004\n", - " 12 0.000003\n", - " 3991 0.000002\n", - " 10250 0.000002\n", - " 146 0.000002\n", - " 2662 0.000002\n", - " 2236 0.000001\n", - " 4011 0.000001\n", - " 6912 0.000001\n", - " Name: 983, dtype: float64, 3988 9.999982e-01\n", - " 4004 5.630643e-08\n", - " 4074 2.440987e-08\n", - " 1539 2.045204e-08\n", - " 4535 9.588295e-09\n", - " 3993 9.557183e-09\n", - " 3320 9.376461e-09\n", - " 154 9.286030e-09\n", - " 4968 7.542653e-09\n", - " 79 6.765060e-09\n", - " Name: 984, dtype: float64, 3992 9.999642e-01\n", - " 677 1.108366e-05\n", - " 2738 1.801027e-06\n", - " 697 1.225081e-06\n", - " 7414 1.139922e-06\n", - " 5950 6.983145e-07\n", - " 7957 5.626734e-07\n", - " 6091 3.793423e-07\n", - " 8644 3.617332e-07\n", - " 3993 3.494245e-07\n", - " Name: 985, dtype: float64, 79 9.999934e-01\n", - " 119 2.548180e-07\n", - " 9626 2.389225e-07\n", - " 160 2.279519e-07\n", - " 246 2.115094e-07\n", - " 206 1.718150e-07\n", - " 4073 1.706412e-07\n", - " 8040 1.641951e-07\n", - " 4011 1.532191e-07\n", - " 690 1.421142e-07\n", - " Name: 986, dtype: float64, 79 9.999923e-01\n", - " 160 2.718998e-07\n", - " 9626 2.579705e-07\n", - " 8040 2.230048e-07\n", - " 4011 1.745024e-07\n", - " 206 1.651428e-07\n", - " 119 1.595027e-07\n", - " 2260 1.378042e-07\n", - " 192 1.302861e-07\n", - " 1203 1.236871e-07\n", - " Name: 987, dtype: float64, 5126 0.431879\n", - " 5044 0.017556\n", - " 6426 0.010869\n", - " 5550 0.009268\n", - " 10521 0.006966\n", - " 4191 0.006797\n", - " 4721 0.005692\n", - " 2535 0.005496\n", - " 5253 0.004312\n", - " 638 0.004043\n", - " Name: 988, dtype: float64, 3999 0.839818\n", - " 4000 0.044969\n", - " 3998 0.022513\n", - " 690 0.012262\n", - " 3210 0.006234\n", - " 753 0.005938\n", - " 3637 0.004489\n", - " 3165 0.002298\n", - " 2236 0.002258\n", - " 2629 0.002077\n", - " Name: 989, dtype: float64, 3995 9.999751e-01\n", - " 3098 2.001216e-06\n", - " 4231 1.139706e-06\n", - " 883 9.789246e-07\n", - " 3759 7.006241e-07\n", - " 9254 6.651733e-07\n", - " 2737 4.293474e-07\n", - " 3990 4.099840e-07\n", - " 3195 4.072367e-07\n", - " 3196 3.101813e-07\n", - " Name: 990, dtype: float64, 4012 0.992264\n", - " 4138 0.000426\n", - " 5046 0.000285\n", - " 4202 0.000280\n", - " 4156 0.000259\n", - " 4155 0.000257\n", - " 4298 0.000246\n", - " 4073 0.000171\n", - " 4570 0.000143\n", - " 7106 0.000110\n", - " Name: 991, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 992, dtype: float64, 3988 9.999981e-01\n", - " 4004 7.825608e-08\n", - " 4074 2.904527e-08\n", - " 1539 2.572120e-08\n", - " 4535 1.159967e-08\n", - " 3993 1.078722e-08\n", - " 154 1.068691e-08\n", - " 3320 1.046666e-08\n", - " 79 9.245329e-09\n", - " 4968 9.133679e-09\n", - " Name: 993, dtype: float64, 79 9.999921e-01\n", - " 160 2.859192e-07\n", - " 9626 2.750193e-07\n", - " 8040 2.333611e-07\n", - " 4011 1.922597e-07\n", - " 206 1.820115e-07\n", - " 119 1.742540e-07\n", - " 2260 1.514265e-07\n", - " 192 1.370257e-07\n", - " 1203 1.298195e-07\n", - " Name: 994, dtype: float64, 4004 9.999945e-01\n", - " 2629 5.912505e-07\n", - " 3999 2.944664e-07\n", - " 3422 2.287082e-07\n", - " 79 1.669099e-07\n", - " 3988 1.464238e-07\n", - " 119 7.704138e-08\n", - " 3637 5.260574e-08\n", - " 4011 4.787834e-08\n", - " 3998 4.623336e-08\n", - " Name: 995, dtype: float64, 4000 0.999917\n", - " 3210 0.000011\n", - " 4135 0.000002\n", - " 2582 0.000002\n", - " 4714 0.000002\n", - " 2236 0.000002\n", - " 3637 0.000001\n", - " 205 0.000001\n", - " 4681 0.000001\n", - " 4097 0.000001\n", - " Name: 996, dtype: float64, 4072 0.188184\n", - " 4186 0.077492\n", - " 4176 0.041751\n", - " 4012 0.027855\n", - " 4163 0.027495\n", - " 4641 0.017215\n", - " 181 0.017114\n", - " 4329 0.016864\n", - " 4349 0.016394\n", - " 4350 0.016387\n", - " Name: 997, dtype: float64, 3988 9.999981e-01\n", - " 4004 7.825608e-08\n", - " 4074 2.904527e-08\n", - " 1539 2.572120e-08\n", - " 4535 1.159967e-08\n", - " 3993 1.078722e-08\n", - " 154 1.068691e-08\n", - " 3320 1.046666e-08\n", - " 79 9.245329e-09\n", - " 4968 9.133679e-09\n", - " Name: 998, dtype: float64, 79 9.999930e-01\n", - " 119 2.660983e-07\n", - " 9626 2.548752e-07\n", - " 246 2.300676e-07\n", - " 160 2.276728e-07\n", - " 4073 1.877542e-07\n", - " 206 1.842580e-07\n", - " 8040 1.714034e-07\n", - " 4011 1.684251e-07\n", - " 690 1.537333e-07\n", + " 4159 0.000002\n", + " 79 0.000001\n", + " Name: 973, dtype: float64, 107 0.999825\n", + " 1805 0.000038\n", + " 3098 0.000017\n", + " 3574 0.000014\n", + " 2236 0.000013\n", + " 3996 0.000003\n", + " 2590 0.000003\n", + " 214 0.000003\n", + " 2503 0.000002\n", + " 4000 0.000002\n", + " Name: 974, dtype: float64, 3574 9.999634e-01\n", + " 107 1.105101e-05\n", + " 3210 7.330075e-06\n", + " 3996 1.415818e-06\n", + " 214 1.111840e-06\n", + " 4759 9.559996e-07\n", + " 2503 8.507703e-07\n", + " 205 4.796709e-07\n", + " 641 3.308011e-07\n", + " 5474 3.047268e-07\n", + " Name: 975, dtype: float64, 3988 9.999988e-01\n", + " 4004 2.624934e-08\n", + " 1805 2.143999e-08\n", + " 3474 1.529523e-08\n", + " 1539 1.119369e-08\n", + " 2353 9.991578e-09\n", + " 4135 9.488947e-09\n", + " 2508 8.878290e-09\n", + " 3320 8.648589e-09\n", + " 3243 8.234005e-09\n", + " Name: 976, dtype: float64, 4535 0.986002\n", + " 4648 0.001923\n", + " 5059 0.001240\n", + " 4002 0.000455\n", + " 4149 0.000413\n", + " 5060 0.000368\n", + " 4193 0.000324\n", + " 1514 0.000210\n", + " 1046 0.000205\n", + " 4555 0.000204\n", + " Name: 977, dtype: float64, 3996 9.999572e-01\n", + " 5792 7.607747e-06\n", + " 2514 2.427629e-06\n", + " 8381 2.024190e-06\n", + " 86 1.782123e-06\n", + " 6387 1.176251e-06\n", + " 4710 1.124004e-06\n", + " 4759 8.360333e-07\n", + " 3574 7.973029e-07\n", + " 107 7.692284e-07\n", + " Name: 978, dtype: float64, 4189 0.047640\n", + " 4178 0.028141\n", + " 3990 0.025144\n", + " 4324 0.021541\n", + " 4293 0.015288\n", + " 5070 0.012317\n", + " 4528 0.011317\n", + " 3989 0.010907\n", + " 4532 0.010891\n", + " 5067 0.010778\n", + " Name: 979, dtype: float64, 5064 0.082679\n", + " 5063 0.076061\n", + " 5066 0.074298\n", + " 5065 0.069763\n", + " 4259 0.045076\n", + " 4189 0.033644\n", + " 5014 0.027812\n", + " 4454 0.027490\n", + " 5106 0.026329\n", + " 5069 0.022840\n", + " Name: 980, dtype: float64, 5110 0.512285\n", + " 657 0.329510\n", + " 654 0.081513\n", + " 2155 0.005114\n", + " 1471 0.002791\n", + " 640 0.002439\n", + " 2337 0.001651\n", + " 367 0.001448\n", + " 7894 0.001415\n", + " 4654 0.001332\n", + " Name: 981, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 982, dtype: float64, 4091 0.999642\n", + " 8224 0.000039\n", + " 6912 0.000034\n", + " 2629 0.000014\n", + " 8418 0.000008\n", + " 8274 0.000005\n", + " 8186 0.000004\n", + " 2715 0.000003\n", + " 4067 0.000003\n", + " 679 0.000003\n", + " Name: 983, dtype: float64, 3988 9.999988e-01\n", + " 1805 2.957542e-08\n", + " 3474 2.036665e-08\n", + " 4004 1.379507e-08\n", + " 79 1.233308e-08\n", + " 4759 1.081896e-08\n", + " 4135 9.951998e-09\n", + " 3243 9.705360e-09\n", + " 2353 8.982327e-09\n", + " 1539 8.960835e-09\n", + " Name: 984, dtype: float64, 3992 0.999888\n", + " 2051 0.000017\n", + " 7414 0.000008\n", + " 2738 0.000004\n", + " 706 0.000002\n", + " 5578 0.000002\n", + " 207 0.000002\n", + " 677 0.000001\n", + " 1714 0.000001\n", + " 6844 0.000001\n", + " Name: 985, dtype: float64, 79 9.999813e-01\n", + " 208 6.142720e-06\n", + " 1805 1.995668e-06\n", + " 119 7.029428e-07\n", + " 7106 2.888712e-07\n", + " 3637 2.628414e-07\n", + " 517 2.130425e-07\n", + " 2503 1.935210e-07\n", + " 4098 1.162905e-07\n", + " 7141 9.548045e-08\n", + " Name: 986, dtype: float64, 79 9.999949e-01\n", + " 208 1.292303e-06\n", + " 1805 2.670109e-07\n", + " 119 1.656839e-07\n", + " 7106 1.474233e-07\n", + " 2503 8.628919e-08\n", + " 517 7.540819e-08\n", + " 4098 7.278041e-08\n", + " 2546 5.599010e-08\n", + " 4074 4.734322e-08\n", + " Name: 987, dtype: float64, 5126 0.920353\n", + " 4721 0.003425\n", + " 4581 0.001741\n", + " 4720 0.001481\n", + " 4109 0.001388\n", + " 5487 0.001113\n", + " 5130 0.000919\n", + " 228 0.000826\n", + " 5680 0.000710\n", + " 4647 0.000704\n", + " Name: 988, dtype: float64, 4000 0.353511\n", + " 3999 0.277767\n", + " 1805 0.098900\n", + " 107 0.046172\n", + " 2236 0.027056\n", + " 2503 0.024352\n", + " 3210 0.007577\n", + " 4013 0.004679\n", + " 4026 0.003865\n", + " 204 0.003566\n", + " Name: 989, dtype: float64, 3995 9.999560e-01\n", + " 9254 1.941869e-06\n", + " 4231 1.732496e-06\n", + " 1515 1.456612e-06\n", + " 3098 1.388244e-06\n", + " 3996 1.106082e-06\n", + " 205 9.118161e-07\n", + " 5553 8.320109e-07\n", + " 3615 8.111144e-07\n", + " 9499 8.015110e-07\n", + " Name: 990, dtype: float64, 4012 0.995717\n", + " 4138 0.000309\n", + " 4155 0.000173\n", + " 4073 0.000159\n", + " 4298 0.000144\n", + " 4614 0.000130\n", + " 4570 0.000095\n", + " 4156 0.000092\n", + " 4013 0.000080\n", + " 4202 0.000062\n", + " Name: 991, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 992, dtype: float64, 3988 9.999988e-01\n", + " 4004 2.624934e-08\n", + " 1805 2.143999e-08\n", + " 3474 1.529523e-08\n", + " 1539 1.119369e-08\n", + " 2353 9.991578e-09\n", + " 4135 9.488947e-09\n", + " 2508 8.878290e-09\n", + " 3320 8.648589e-09\n", + " 3243 8.234005e-09\n", + " Name: 993, dtype: float64, 79 9.999936e-01\n", + " 208 1.743155e-06\n", + " 1805 3.630250e-07\n", + " 7106 1.757779e-07\n", + " 119 1.698333e-07\n", + " 2503 1.115021e-07\n", + " 517 9.029936e-08\n", + " 4098 8.636746e-08\n", + " 2546 6.248283e-08\n", + " 4074 5.397917e-08\n", + " Name: 994, dtype: float64, 4004 9.999988e-01\n", + " 2629 7.159181e-08\n", + " 5056 2.046064e-08\n", + " 3320 2.024743e-08\n", + " 5428 1.952450e-08\n", + " 4232 1.772522e-08\n", + " 4685 1.753065e-08\n", + " 5149 1.683958e-08\n", + " 5263 1.626949e-08\n", + " 3988 1.549836e-08\n", + " Name: 995, dtype: float64, 4000 9.999533e-01\n", + " 4097 1.627581e-06\n", + " 6766 1.548405e-06\n", + " 107 1.221485e-06\n", + " 863 1.011378e-06\n", + " 4714 9.951327e-07\n", + " 2503 9.053660e-07\n", + " 6626 8.570247e-07\n", + " 6598 7.949969e-07\n", + " 79 7.401295e-07\n", + " Name: 996, dtype: float64, 4072 0.199254\n", + " 4186 0.073234\n", + " 4176 0.041968\n", + " 4163 0.033675\n", + " 4012 0.032862\n", + " 181 0.023385\n", + " 4641 0.020311\n", + " 4358 0.018194\n", + " 4328 0.017937\n", + " 4357 0.017838\n", + " Name: 997, dtype: float64, 3988 9.999988e-01\n", + " 4004 2.624934e-08\n", + " 1805 2.143999e-08\n", + " 3474 1.529523e-08\n", + " 1539 1.119369e-08\n", + " 2353 9.991578e-09\n", + " 4135 9.488947e-09\n", + " 2508 8.878290e-09\n", + " 3320 8.648589e-09\n", + " 3243 8.234005e-09\n", + " Name: 998, dtype: float64, 79 9.999752e-01\n", + " 208 8.534266e-06\n", + " 1805 2.165273e-06\n", + " 119 1.063809e-06\n", + " 3637 4.358510e-07\n", + " 7106 4.019400e-07\n", + " 517 2.489450e-07\n", + " 2503 2.246322e-07\n", + " 4098 1.624638e-07\n", + " 7141 1.241254e-07\n", " Name: 999, dtype: float64, ...]" ] }, - "execution_count": 148, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -11495,17 +11497,19 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ - "fs = ['keras', 'pytorch', 'sklearn', 'ansible', 'requests', 'django', 'youtube-dl', 'bert', 'httpie', 'flask']\n", + "fs = ['keras', 'sklearn', 'pytorch', 'ansible', 'requests', 'django', 'httpie', 'youtube-dl', 'flask', 'bert']\n", + "#fs = ['keras', 'sklearn', 'pytorch']\n", + "#fs = ['keras', 'sklearn']\n", "#fs = ['sparse']" ] }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -11515,27 +11519,40 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "labels= []; labels_str =[]\n", "for label_df in label_dfs:\n", " for idx, row in label_df.iterrows():\n", - " labels.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n", + " #labels.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n", " labels_str.append(row[0])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels" + ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 85, "metadata": { "scrolled": true }, @@ -11543,1028 +11560,38 @@ { "data": { "text/plain": [ - "['relu',\n", - " 'get',\n", - " '__init__',\n", - " 'deserialize',\n", - " 'get',\n", - " '__init__',\n", - " 'compute_mask',\n", - " '_normalize_device_name',\n", - " 'print_layer_summary',\n", - " 'NASNetLarge',\n", - " 'decode_predictions',\n", - " 'decode_predictions',\n", - " 'decode_predictions',\n", - " 'preprocess_input',\n", - " 'DenseNet201',\n", - " '_merge_function',\n", - " 'minimum',\n", - " 'dot',\n", - " '__init__',\n", - " 'compute_output_shape',\n", - " '__init__',\n", - " '_pooling_function',\n", - " '_pooling_function',\n", - " 'get_config',\n", - " 'get_tuple_shape',\n", - " '__init__',\n", - " 'call',\n", - " 'get_losses_for',\n", - " 'compute_mask',\n", - " '_get_noise_shape',\n", - " 'get_config',\n", - " 'call',\n", - " 'get_config',\n", - " 'name_scope',\n", - " 'in_test_phase',\n", - " 'eye',\n", - " 'sum',\n", - " 'var',\n", - " 'std',\n", - " 'get_value',\n", - " 'update_sub',\n", - " 'sum',\n", - " 'all',\n", - " 'less',\n", - " 'minimum',\n", - " 'one_hot',\n", - " 'softsign',\n", - " '_preprocess_padding',\n", - " 'random_normal_variable',\n", - " 'sqrt',\n", - " 'logsumexp',\n", - " 'batch_get_value',\n", - " 'l2_normalize',\n", - " '_preprocess_conv2d_input',\n", - " 'int_or_none',\n", - " 'random_uniform',\n", - " 'foldl',\n", - " 'uses_learning_phase',\n", - " 'reset_states',\n", - " 'model_from_config',\n", - " 'trainable_weights',\n", - " 'get_input_shape_at',\n", - " 'input_mask',\n", - " 'weights',\n", - " 'alpha_dropout',\n", - " 'hardtanh',\n", - " 'elu',\n", - " 'rrelu',\n", - " '_no_grad_embedding_renorm_',\n", - " 'assert_int_or_pair',\n", - " '__deepcopy__',\n", - " 'half',\n", - " '__call__',\n", - " '__setstate__',\n", - " 'backward',\n", - " 'gather',\n", - " 'forward',\n", - " '_renorm',\n", - " '__init__',\n", - " '__init__',\n", - " 'forward',\n", - " 'extra_repr',\n", - " '__init__',\n", - " 'reset_parameters',\n", - " 'extra_repr',\n", - " '__setitem__',\n", - " '__init__',\n", - " '__init__',\n", - " '__init__',\n", - " 'reset_parameters',\n", - " 'extra_repr',\n", - " '__init__',\n", - " 'parse',\n", - " 'forward',\n", - " 'forward',\n", - " 'type',\n", - " 'register_forward_pre_hook',\n", - " 'buffers',\n", - " 'modules',\n", - " '_check_inputs_dtype',\n", - " '_get_mask',\n", - " '__getstate__',\n", - " 'biclusters_',\n", - " 'is_classifier',\n", - " 'decision_function',\n", - " '_pairwise',\n", - " '__init__',\n", - " '__init__',\n", - " '_iter',\n", - " 'fit_predict',\n", - " 'transform',\n", - " '_transform',\n", - " '__init__',\n", - " 'get_params',\n", - " 'fit',\n", - " '__setstate__',\n", - " 'fit',\n", - " '_count',\n", - " 'trace_dot',\n", - " 'inverse_transform',\n", - " 'transform',\n", - " 'score',\n", - " 'test_inverse_transform',\n", - " 'test_dict_learning_overcomplete',\n", - " 'test_dict_learning_unknown_fit_algorithm',\n", - " 'test_unknown_method',\n", - " 'histogram',\n", - " 'test_perplexity_input_format',\n", - " 'test_check_update_with_no_data',\n", - " 'test_imputation_deletion_warning',\n", - " 'test_basic_property_of_random_matrix',\n", - " 'test_basic_property_of_sparse_random_matrix',\n", - " 'softmax',\n", - " 'transform',\n", - " '__getstate__',\n", - " 'test_check_increasing_small_number_of_samples',\n", - " 'test_check_increasing_up_extreme',\n", - " 'test_classifier_exceptions',\n", - " 'test_mean_strategy_regressor',\n", - " 'set_params',\n", - " 'predict',\n", - " 'predict',\n", - " '_score_to_proba',\n", - " '_score_to_proba',\n", - " 'test_warm_start_with_oob_score_fails',\n", - " 'test_base_zero_n_estimators',\n", - " 'fit',\n", - " 'test_probability',\n", - " 'test_oob_score_classifiers',\n", - " 'test_pickle',\n", - " 'test_class_weight_errors',\n", - " 'test_quantile_loss_function',\n", - " 'test_notfitted',\n", - " 'indexbytes',\n", - " 'parameters',\n", - " '__init__',\n", - " '_get_data_object_for_decoding',\n", - " 'write_zfile',\n", - " 'register_store_backend',\n", - " '_get_func_fullname',\n", - " '__repr__',\n", - " '_effective_n_jobs',\n", - " 'contains_path',\n", - " '__repr__',\n", - " '_funcname',\n", - " 'get_nested_backend',\n", - " '__init__',\n", - " 'make_memmap',\n", - " 'save_builtin_function',\n", - " 'save_classmethod',\n", - " '__init__',\n", - " 'save_weakset',\n", - " 'dump',\n", - " 'add_cancelled',\n", - " '__repr__',\n", - " '_chain_from_iterable_of_lists',\n", - " '_get_next_executor_id',\n", - " '__reduce__',\n", - " '__init__',\n", - " '__getstate__',\n", - " '__setstate__',\n", - " 'reduce_pipe_connection',\n", - " 'fromfd',\n", - " 'set_start_method',\n", - " '_check_alive',\n", - " 'is_forking',\n", - " 'set_cause',\n", - " 'dump',\n", - " '_binary_uninterpolated_average_precision',\n", - " '_generalized_average',\n", - " 'test_complete_but_not_homogeneous_labeling',\n", - " 'test_expected_mutual_info_overflow',\n", - " 'test_classification_report_multiclass_with_long_string_label',\n", - " 'check_single_sample',\n", - " 'test_label_ranking_avp',\n", - " 'test_paired_euclidean_distances',\n", - " 'test_rbf_kernel',\n", - " 'tuplify',\n", - " 'predict_proba',\n", - " '_check_means',\n", - " '_check_proba',\n", - " '_predict_proba',\n", - " 'test_linear_svx_uppercase_loss_penality_raises_error',\n", - " '_errors',\n", - " '_errors_svd',\n", - " '_get_learning_rate_type',\n", - " '_check_proba',\n", - " 'predict',\n", - " 'predict',\n", - " '_set_intercept',\n", - " 'sparsify',\n", - " 'f',\n", - " 'test_correct_shapes_gram',\n", - " 'test_nan',\n", - " 'test_logreg_intercept_scaling',\n", - " 'test_ridge_classifier_no_support_multilabel',\n", - " 'test_set_intercept_to_intercept',\n", - " 'test_fit_then_partial_fit',\n", - " 'get_pobj',\n", - " 'test_precompute_invalid_argument',\n", - " 'test_enet_copy_X_False_check_input_False',\n", - " 'no_stdout_stderr',\n", - " 'test_calc_breakdown_point',\n", - " 'check_cdist_bool',\n", - " 'test_pdist_bool_metrics',\n", - " 'test_pickle_bool_metrics',\n", - " 'test_not_fitted_error_gets_raised',\n", - " 'check_two_point',\n", - " 'test_node_heap',\n", - " 'test_node_heap',\n", - " 'test_fit_transduction',\n", - " 'get_params',\n", - " '_check_standard_scaled',\n", - " 'test_column_transformer_no_estimators_set_params',\n", - " '_inverse_permutation',\n", - " '_find_permutation',\n", - " 'test_decode_anneal',\n", - " 'test_fetch_openml_raises_missing_values_target',\n", - " 'test_data_home',\n", - " 'test_default_empty_load_files',\n", - " 'test_load_digits',\n", - " 'test_load_sample_image',\n", - " 'test_bunch_dir',\n", - " 'feature_importances_',\n", - " 'ancestor',\n", - " 'test_importances_raises',\n", - " 'test_max_leaf_nodes_max_depth',\n", - " 'test_decision_path_hardcoded',\n", - " '_iter_test_indices',\n", - " '_iter_test_indices',\n", - " '__init__',\n", - " 'get_n_splits',\n", - " '__init__',\n", - " '_index_param_value',\n", - " 'predict',\n", - " 'decision_function',\n", - " '__init__',\n", - " 'train_test_split_mock_pandas',\n", - " 'test_train_test_split_allow_nans',\n", - " 'test_cross_val_predict_method_checking',\n", - " 'score',\n", - " 'test_random_search_with_fit_params',\n", - " 'predict',\n", - " '_run_search',\n", - " '__call__',\n", - " 'test_method_not_available',\n", - " 'test_angle_out_of_range_checks',\n", - " '__init__',\n", - " 'transform',\n", - " 'transform',\n", - " 'fit',\n", - " 'inverse_transform',\n", - " '_transform',\n", - " 'fit',\n", - " 'test_robust_scaler_invalid_range',\n", - " 'test_np_log',\n", - " 'score',\n", - " 'score',\n", - " 'test_mcd_issue1127',\n", - " 'inplace_logistic_derivative',\n", - " '_pack',\n", - " 'test_multioutput_regression',\n", - " 'test_adaptive_learning_rate',\n", - " 'strip_accents_unicode',\n", - " '_check_vocabulary',\n", - " 'get_feature_names',\n", - " '_make_int_array',\n", - " 'idf_',\n", - " 'idf_',\n", - " 'test_unicode_decode_error',\n", - " 'test_countvectorizer_custom_vocabulary_repeated_indices',\n", - " 'test_tfidf_vectorizer_setter',\n", - " 'test_extract_patch_same_size_image',\n", - " 'test_reconstruct_patches_perfect',\n", - " 'test_width_patch',\n", - " 'test_hash_empty_input',\n", - " '_get_leaves',\n", - " 'transform',\n", - " 'make_piecewise',\n", - " '_project_and_cluster',\n", - " '_complete_linkage',\n", - " 'fit_predict',\n", - " 'fit_transform',\n", - " 'all_equal_preferences',\n", - " 'test_min_cluster_size_invalid',\n", - " 'test_min_cluster_size_invalid2',\n", - " 'increment',\n", - " 'test_k_means_plus_plus_init_2_jobs',\n", - " 'test_k_means_init',\n", - " 'test_minibatch_default_init_size',\n", - " 'inplace_column_scale',\n", - " 'inplace_swap_row',\n", - " '_minor_reduce',\n", - " '_get_elem_at_rank',\n", - " '__call__',\n", - " '__init__',\n", - " '__call__',\n", - " '_is_pairwise_metric',\n", - " 'check_transformers_unfitted',\n", - " 'assertRaises',\n", - " '_is_arraylike',\n", - " '__eq__',\n", - " 'linear_assignment',\n", - " '_step3',\n", - " '_parse_version',\n", - " 'boxcox',\n", - " '_argmax',\n", - " 'test_delegated_docstring',\n", - " 'test_is_deprecated',\n", - " 'test_assert_raise_message',\n", - " 'score',\n", - " 'hess',\n", - " 'test_resample',\n", - " '_get_support_mask',\n", - " 'predict_proba',\n", - " 'test_calling_fit_reinitializes',\n", - " 'test_select_kbest_all',\n", - " 'linear',\n", - " 'hyperparameters',\n", - " 'bounds',\n", - " 'diag',\n", - " 'bounds',\n", - " 'diag',\n", - " '__eq__',\n", - " '__init__',\n", - " 'diag',\n", - " '__repr__',\n", - " '__init__',\n", - " '__repr__',\n", - " 'test_gpr_interpolation',\n", - " '__init__',\n", - " 'fit_transform',\n", - " 'mk_boolean',\n", - " 'online_argument_spec',\n", - " 'ok',\n", - " 'session',\n", - " 'resource_absent',\n", - " 'gcdns_connect',\n", - " '_singleton',\n", - " 'construct',\n", - " 'module_by_name',\n", - " 'xcli_wrapper',\n", - " '_check_type_list',\n", - " '_check_type_bits',\n", - " 'sha1',\n", - " 'sha256',\n", - " '_restore_signal_handlers',\n", - " 'sql_client',\n", - " 'fail',\n", - " 'fq_name',\n", - " '_filter_params',\n", - " 'get_mcp_version',\n", - " 'required_together',\n", - " 'https_open',\n", - " 'options',\n", - " 'volume_exists',\n", - " 'na_ontap_host_argument_spec',\n", - " 'get_host_by_name',\n", - " 'is_quoted',\n", - " 'fail_json',\n", - " 'get_tags',\n", - " 'check_zone_domain',\n", - " 'aws_common_argument_spec',\n", - " '__init__',\n", - " 'find_cluster_by_name_datacenter',\n", - " 'find_object_by_name',\n", - " 'find_cluster_by_name',\n", - " 'find_datastore_by_name',\n", - " 'send_data',\n", - " 'exec_command',\n", - " 'sanitize_result',\n", - " '__init__',\n", - " 'nopad_b64',\n", - " 'read_file',\n", - " 'do_fail',\n", - " 'delete_user',\n", - " 'unregister',\n", - " 'get_vpn',\n", - " 'get_public_ip',\n", - " 'camel',\n", - " 'get_client_template_id',\n", - " '__init__',\n", - " 'logout',\n", - " 'platform_match',\n", - " 'resolve_requires',\n", - " 'get_all_facts',\n", - " 'collect',\n", - " 'parse_media_line',\n", - " 'get_device_facts',\n", - " 'get_uptime_facts',\n", - " 'get_distribution_NetBSD',\n", - " 'get_distribution_SMGL',\n", - " 'remove_aliases',\n", - " 'is_masklen',\n", - " '_compat_bytes_to_byte_vals',\n", - " 'ip_network',\n", - " '__sub__',\n", - " '__eq__',\n", - " 'hostmask',\n", - " 'is_private',\n", - " '_string_from_ip_int',\n", - " 'with_hostmask',\n", - " 'is_reserved',\n", - " 'is_link_local',\n", - " '__str__',\n", - " '__hash__',\n", - " 'get_signature_key',\n", - " 'modify',\n", - " '_get_elb_listener_rules',\n", - " 'get_elb',\n", - " 'eks_model',\n", - " 'get_distribution',\n", - " 'get_origin_access_identity_config',\n", - " 'list_origin_access_identities',\n", - " 'get_aliases_from_distribution_id',\n", - " 'get_rule_with_backoff',\n", - " 'exit_json',\n", - " '_gather_versions',\n", - " 'is_boto3_error_code',\n", - " 'transform_commands',\n", - " 'validate_ip_v6_address',\n", - " '__init__',\n", - " '__init__',\n", - " '__init__',\n", - " 'le',\n", - " '__init__',\n", - " '__init__',\n", - " 'login',\n", - " 'get_trans_changes',\n", - " 'get_schema',\n", - " '__str__',\n", - " '__init__',\n", - " '__init__',\n", - " 'add',\n", - " 'complete_missing_attributes',\n", - " 'get_capabilities',\n", - " '_get_connection',\n", - " 'get_config',\n", - " 'put',\n", - " 'get_connection',\n", - " 'get_device_capabilities',\n", - " 'run_commands',\n", - " 'run_commands',\n", - " 'is_uuid',\n", - " 'get_id_of_provider_name',\n", - " 'get_net_id',\n", - " 'get_connection',\n", - " 'run_commands',\n", - " 'load_config',\n", - " 'execute',\n", - " 'get_connection',\n", - " 'run_commands',\n", - " 'get_connection',\n", - " 'sanitize',\n", - " 'run_commands',\n", - " 'main',\n", - " 'main',\n", - " '__init__',\n", - " 'parse_memtotal',\n", - " 'parse_interfaces',\n", - " 'parse_macaddress',\n", - " 'to_lines',\n", - " 'validate_ipv4',\n", - " 'check_args',\n", - " '__init__',\n", - " 'run',\n", - " 'transform_iterable',\n", - " 'parse_filesystems',\n", - " 'parse_memfree_mb',\n", - " 'parse_structured_power_supply_info',\n", - " 'parse_vlans',\n", - " 'flatten_list',\n", - " 'parse_mode',\n", - " 'get_system_mode',\n", - " 'execute_show_command',\n", - " 'flatten_list',\n", - " 'get_value',\n", - " 'get_desired',\n", - " 'execute_show_command',\n", - " 'fix_delta',\n", - " 'get_pim_interface_defaults',\n", - " 'flatten_list',\n", - " 'deactivate_operation',\n", - " 'execute_show_command',\n", - " 'parse_mode',\n", - " 'apply_key_map',\n", - " 'execute_show_command',\n", - " 'state_absent',\n", - " 'get_existing',\n", - " 'difference',\n", - " 'parse_system_mtu',\n", - " 'parse_remote_server',\n", - " 'normalize_area',\n", - " 'get_admin_state',\n", - " 'execute_show_command',\n", - " 'state_present',\n", - " 'invoke',\n", - " 'apply_key_map',\n", - " 'flatten_list',\n", - " 'flatten_list',\n", - " 'flatten_list',\n", - " 'parse_bandwidth',\n", - " 'nat_rule_exists',\n", - " 'check_response',\n", - " 'work',\n", - " 'get_proposed',\n", - " 'is_valid_address',\n", - " 'check_response',\n", - " 'is_config_exist',\n", - " 'build_config_xml',\n", - " 'init_module',\n", - " 'is_valid_description',\n", - " 'isvalidlsaoholdinterval',\n", - " 'get_exist_lsa_a_hold_interval',\n", - " 'get_authorization_domain',\n", - " 'get_local_user_group',\n", - " 'cli_load_config',\n", - " 'check_response',\n", - " 'cli_load_config',\n", - " 'rollback_last',\n", - " 'main',\n", - " 'init_module',\n", - " 'get_end_state',\n", - " 'is_vlan_bitmap_empty',\n", - " 'netconf_get_config',\n", - " 'netconf_set_config',\n", - " 'is_config_exist',\n", - " 'get_existing',\n", - " '__init_module__',\n", - " 'netconf_set_config',\n", - " 'check_response',\n", - " 'build_config_xml',\n", - " 'netconf_get_config',\n", - " 'cli_get_connect_port',\n", - " 'is_valid_address',\n", - " 'get_update_cmd',\n", - " 'get_proposed',\n", - " 'netconf_set_action',\n", - " 'netconf_get_config',\n", - " 'get_ip_vpn_vni',\n", - " 'get_vbdif_mac',\n", - " 'init_module',\n", - " 'cli_load_config',\n", - " 'is_valid_v4addr',\n", - " 'init_module',\n", - " 'parse_hostname',\n", - " 'map_config_to_obj',\n", - " 'validate_ipv4',\n", - " 'parse_config_argument',\n", - " 'search_obj_in_list',\n", - " 'has_lldp',\n", - " 'validate_param_values',\n", - " 'validate_rotate_frequency',\n", - " 'reset_property',\n", - " '_query_iptun_props',\n", - " 'interface_exists',\n", - " 'delete_addr',\n", - " 'disable_addr',\n", - " 'populate',\n", - " 'get_running_config',\n", - " 'response_type',\n", - " '__init__',\n", - " 'parse_version',\n", - " 'parse_model',\n", - " 'parse_image',\n", - " 'parse_lldp_intf',\n", - " 'parse_lldp_host',\n", - " 'to_lines',\n", - " 'populate',\n", - " 'set_ipv6_interfaces',\n", - " 'validate_level',\n", - " 'parse_version',\n", - " 'parse_serialnum',\n", - " 'populate',\n", - " 'api_params',\n", - " '__default',\n", - " '_announce_deprecations',\n", - " 'port_lists',\n", - " '_format_port_for_destination',\n", - " 'has_fastl4_profiles',\n", - " 'policies',\n", - " 'enabled',\n", - " 'sec_nat_policy',\n", - " 'firewall_enforced_policy',\n", - " 'sec_nat_use_device_policy',\n", - " 'irules',\n", - " 'port',\n", - " 'disabled_vlans',\n", - " '__init__',\n", - " '_verify_fallback_persistence_profile_for_type',\n", - " '_verify_minimum_profile',\n", - " 'compare',\n", - " 'create_on_device',\n", - " 'update_on_device',\n", - " 'read_current_from_device',\n", - " 'node_addresses',\n", - " '__default',\n", - " 'has_no_service_environment',\n", - " '_set_changed_options',\n", - " 'read_current_from_device',\n", - " '__init__',\n", - " 'to_return',\n", - " '__default',\n", - " 'present',\n", - " 'upload_file_to_device',\n", - " 'type',\n", - " 'fallback_ip',\n", - " 'monitors_list',\n", - " 'monitors_list',\n", - " '__init__',\n", - " '_set_changed_options',\n", - " 'should_update',\n", - " 'read_current_from_device',\n", - " 'remove_from_device',\n", - " 'raw_commands',\n", - " 'chdir',\n", - " 'wait_for',\n", - " 'notify_non_idempotent_commands',\n", - " 'description',\n", - " 'gzip_window_size',\n", - " 'update',\n", - " 'state',\n", - " '_announce_deprecations',\n", - " 'use_route_advertisement',\n", - " 'route_advertisement',\n", - " 'arp',\n", - " 'arp',\n", - " '__default',\n", - " 'remove',\n", - " 'ip',\n", - " 'port',\n", - " '__default',\n", - " 'compare',\n", - " 'should_update',\n", - " 'members',\n", - " '__init__',\n", - " 'update',\n", - " 'save_on_auto_sync',\n", - " 'exists',\n", - " 'update',\n", - " 'remove_members_in_group_from_device',\n", - " 'mandatory_attributes',\n", - " '_set_changed_options',\n", - " '_set_default_creation_values',\n", - " 'update',\n", - " '__init__',\n", - " 'create',\n", - " 'read_current_from_device',\n", - " 'to_return',\n", - " 'should_update',\n", - " '_wait_for_fqdn_checks',\n", - " '__init__',\n", - " 'absent',\n", - " '__init__',\n", - " '__init__',\n", - " '_set_changed_options',\n", - " 'update',\n", - " 'remove_from_device',\n", - " 'iquery_allow_service_check',\n", - " 'enabled',\n", - " 'exec_module',\n", - " 'search',\n", - " 'frame_size',\n", - " 'remove',\n", - " 'port_misuse_policy',\n", - " 'remove',\n", - " 'create',\n", - " 'renegotiation_period',\n", - " 'session_ticket',\n", - " '__init__',\n", - " 'network_failover_enabled',\n", - " 'autosync_enabled',\n", - " 'read_collection_from_device',\n", - " 'server_timestamp',\n", - " '_exec_module',\n", - " 'server_timestamp',\n", - " 'reassemble_fragments',\n", - " 'ip_tos_to_server',\n", - " 'manual_resume',\n", - " 'qos_topology',\n", - " 'read_facts',\n", - " 'exec_module',\n", - " '__init__',\n", - " 'read_facts',\n", - " '__init__',\n", - " 'description',\n", - " 'transparent',\n", - " 'adaptive',\n", - " 'max_header_size',\n", - " 'exec_module',\n", - " 'mac_address',\n", - " 'exec_module',\n", - " 'queue_on_connection_limit',\n", - " 'read_collection_from_device',\n", - " 'traffic_group_inherited',\n", - " 'floating',\n", - " 'read_facts',\n", - " 'ocsp',\n", - " 'exec_module',\n", - " 'read_collection_from_device',\n", - " 'read_facts',\n", - " 'hardware_information',\n", - " 'manual_resume',\n", - " 'reverse',\n", - " '__init__',\n", - " 'mptcp_make_after_break',\n", - " 'multipath_tcp',\n", - " 'enhanced_loss_recovery',\n", - " 'mac_masquerade_address',\n", - " '__init__',\n", - " '__init__',\n", - " 'exec_module',\n", - " 'destination_port',\n", - " 'rate_limit',\n", - " 'nat64_enabled',\n", - " 'persistence_profile',\n", - " 'sflow_poll_interval',\n", - " '__init__',\n", - " '__init__',\n", - " 'update',\n", - " 'net_reboot',\n", - " 'compare',\n", - " 'destination',\n", - " '__init__',\n", - " '_announce_deprecations',\n", - " 'remove',\n", - " '__init__',\n", - " '__init__',\n", - " 'ports',\n", - " 'port_lists',\n", - " 'exists',\n", - " 'create',\n", - " 'create_on_device',\n", - " '__init__',\n", - " 'update',\n", - " '__default',\n", - " 'remove',\n", - " 'exec_module',\n", - " 'exec_module',\n", - " '_exec_module',\n", - " 'license_end_date_time',\n", - " '__init__',\n", - " 'package_version',\n", - " 'product_changelist',\n", - " 'enable_gtm',\n", - " 'enable_zone_transfer',\n", - " 'unhandled_query_action',\n", - " 'create',\n", - " 'create_on_device',\n", - " '__default',\n", - " 'should_update',\n", - " 'compare',\n", - " 'cgnat',\n", - " 'deprovision_cgnat_on_device',\n", - " '_get_last_reboot',\n", - " '__default',\n", - " 'ip',\n", - " 'probe_timeout',\n", - " 'concurrency_limit',\n", - " 'target_password',\n", - " 'remove',\n", - " 'exists',\n", - " 'key_checksum',\n", - " '__default',\n", - " 'remove',\n", - " 'insert_xforwarded_for',\n", - " 'present',\n", - " '_get_availability_value',\n", - " 'address',\n", - " 'translation_port',\n", - " 'disabled',\n", - " 'monitors_list',\n", - " 'fault_number',\n", - " '__init__',\n", - " 'upload_eula_to_device',\n", - " 'reload_license',\n", - " 'device_username',\n", - " 'irule',\n", - " 'absent',\n", - " '_handle_http_uri_condition',\n", - " 'compare',\n", - " 'transparent',\n", - " 'reverse',\n", - " '_set_changed_options',\n", - " 'should_update',\n", - " 'read_current_from_device',\n", - " '__default',\n", - " 'read_current_from_device',\n", - " 'exists',\n", - " '__init__',\n", - " 'remove',\n", - " '__init__',\n", - " 'cpu_coefficient',\n", - " 'cpu_threshold',\n", - " 'memory_threshold',\n", - " 'create',\n", - " 'redirect_virtual_server',\n", - " '__init__',\n", - " '_announce_deprecations',\n", - " 'param_description',\n", - " 'param_device_group',\n", - " 'metadata',\n", - " 'traffic_group',\n", - " 'should_update',\n", - " '__default',\n", - " '_file_is_missing',\n", - " 'exec_module',\n", - " 'source',\n", - " 'snmp_privacy_password',\n", - " '__init__',\n", - " '_set_changed_options',\n", - " 'update',\n", - " 'remove',\n", - " '__init__',\n", - " 'max_answers_returned',\n", - " 'qos_packet_rate',\n", - " 'qos_rtt',\n", - " 'availability_state',\n", - " 'read_facts',\n", - " 'read_collection_from_device',\n", - " 'should_update',\n", - " 'content',\n", - " '__init__',\n", - " '_set_changed_options',\n", - " '_set_changed_options',\n", - " 'present',\n", - " '__default',\n", - " '_set_changed_options',\n", - " 'absent',\n", - " 'timeout',\n", - " 'port',\n", - " '__default',\n", - " 'absent',\n", - " 'remove_from_device',\n", - " 'source_mask',\n", - " 'should_update',\n", - " 'exists',\n", - " 'create_on_device',\n", - " 'routing_protocol',\n", - " 'update',\n", - " 'create_on_device',\n", - " 'lacp_enabled',\n", - " 'lacp_enabled',\n", - " 'frame_distribution_hash',\n", - " 'inbound_virtual_server',\n", - " 'nodes',\n", - " 'present',\n", - " 'has_no_service_environment',\n", - " 'get_bundle_state',\n", - " 'get_mtu',\n", - " 'get_sfp_media_state',\n", - " 'get_stp_enabled_state',\n", - " 'get_configured_member_count',\n", - " 'get_media_speed',\n", - " 'get_stp_enabled_state',\n", - " '__init__',\n", - " 'get_failsafe_action',\n", - " 'get_failsafe_timeout',\n", - " 'get_sflow_sampling_rate',\n", - " 'get_name',\n", - " 'get_auto_lasthop',\n", - " 'get_bw_controller_policy',\n", - " 'get_fw_rule',\n", - " 'get_persistence_profile',\n", - " 'get_rule',\n", - " 'get_snat_type',\n", - " 'get_source_address_translation_lsn_pool',\n", - " 'get_staged_firewall_policy',\n", - " 'get_active_member_count',\n", - " 'get_allow_snat_state',\n", - " 'get_minimum_up_member',\n", - " 'get_slow_ramp_time',\n", - " 'get_hostname',\n", - " 'get_platform_id',\n", - " 'get_software_version',\n", - " 'get_ignore_vertification',\n", - " 'get_arp_state',\n", - " 'get_description',\n", - " 'get_crl_file',\n", - " 'get_key_file',\n", - " 'get_renegotiation_throughput',\n", - " 'get_ssl_option',\n", - " 'generate_self_ip_dict',\n", - " 'generate_pool_dict',\n", - " 'generate_address_class_dict',\n", - " 'allowed_slots',\n", - " 'mgmt_address',\n", - " 'absent',\n", - " 'mirror_secondary_address',\n", - " 'mirror_primary_address',\n", - " 'should_update',\n", - " 'basename',\n", - " 'reset_trust',\n", - " 'include_chassis_level_config',\n", - " 'to_return',\n", - " 'exec_module',\n", - " 'is_version_v1',\n", - " 'create',\n", - " 'service_policy',\n", - " 'staged_policy',\n", - " 'description',\n", - " 'timeout',\n", - " 'time_until_up',\n", - " 'reverse',\n", - " '__init__',\n", - " 'profile',\n", - " 'should_update',\n", - " 'name',\n", - " '__default',\n", - " 'update',\n", - " 'traffic_group',\n", - " 'route_domain',\n", - " 'destination_ip',\n", - " '__default',\n", - " 'read_current_from_device',\n", - " 'read_partition_default_route_domain_from_device',\n", - " 'exists',\n", - " 'param_persist',\n", - " '__init__',\n", - " 'should_update',\n", - " 'device_is_id',\n", - " '__init__',\n", - " 'geo_locations',\n", - " 'addresses',\n", - " 'addresses',\n", - " 'fqdns',\n", - " 'create_on_device',\n", - " 'update_on_device',\n", - " '__init__',\n", - " 'parent',\n", - " 'remove',\n", - " 'absent',\n", - " 'http_only',\n", - " 'parent',\n", - " 'create',\n", - " 'max_file_size',\n", - " 'exec_module',\n", - " 'get_manager',\n", - " '_remove_temporary_cli_script_from_device',\n", - " '_cert_filename',\n", - " 'compare',\n", - " '_announce_deprecations',\n", - " 'absent',\n", - " 'remove',\n", - " '_set_changed_options',\n", - " 'is_valid_hostname',\n", - " 'fqdns',\n", - " '__init__',\n", - " '_announce_deprecations',\n", - " 'ratio',\n", - " 'disabled',\n", - " 'collection',\n", - " '__init__',\n", - " 'update',\n", - " 'remove',\n", - " 'absent',\n", - " 'to_return',\n", - " 'rules',\n", - " 'present',\n", - " 'iso_date',\n", - " 'console_log',\n", - " 'encode_string',\n", - " '__init__',\n", - " 'get_manager',\n", - " 'to_return',\n", - " 'compare',\n", - " '__init__',\n", - " '__init__',\n", - " 'provided_password',\n", - " 'remove_from_device',\n", - " 'should_update',\n", - " 'port',\n", - " 'time_until_up',\n", - " 'to_return',\n", - " '__init__',\n", - " '_set_changed_options',\n", - " 'remove_from_device',\n", - " 'synchronization_group_name',\n", - " 'to_return',\n", - " '__init__',\n", - " ...]" + "1903" ] }, - "execution_count": 153, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "labels_str" + "len(labels_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams.update({'font.size': 22})" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -12575,42 +11602,77 @@ ], "source": [ "c = Counter(labels_str)\n", + "n = 50\n", "\n", - "plt.figure(figsize=(15,10))\n", - "labels, values = zip(*c.most_common(50))\n", + "plt.figure(figsize=(20,10))\n", + "labels, values = zip(*c.most_common(n))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", "\n", + "plt.title(\"MethodNaming test label frequency distribution (top-{})\".format(n))\n", "plt.bar(indexes, values, width)\n", "plt.xticks(indexes, labels, rotation=90)\n", + "plt.tight_layout()\n", + "plt.savefig(\"methodname-lg-freq-top50-test.pdf\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labels, values = zip(*c.most_common())\n", + "\n", + "plt.figure(figsize=(20,6))\n", + "plt.title(\"MethodNaming test label frequency distribution. (Total = {})\".format(len(labels_str)))\n", + "plt.hist(values[1:],bins=16);\n", + "width = 1\n", + "plt.xticks([i + width * 0.5 for i in range(16)], [str(i) for i in range(16)]);\n", + "plt.tight_layout()\n", + "plt.savefig(\"methodname-test-lg-freq.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ + "#l_dfs = [pd.read_csv(path+f+'_fname2_split_magret_label.txt', header=None) for f in fs]\n", "l_dfs = [pd.read_csv(path+f+'_methodname_split_magret_label.txt', header=None) for f in fs]\n", "labels_train = []; labels_train_str =[]\n", "for l_df in l_dfs:\n", " for idx, row in l_df.iterrows():\n", - " labels_train.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n", + " #labels_train.append(vocab_label_df.index[vocab_label_df[0]==str(row[0])][0])\n", " labels_train_str.append(row[0])" ] }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -12621,21 +11683,55 @@ ], "source": [ "c = Counter(labels_train_str)\n", + "n = 50\n", "\n", - "plt.figure(figsize=(15,10))\n", - "labels, values = zip(*c.most_common(50))\n", + "plt.figure(figsize=(20,10))\n", + "labels, values = zip(*c.most_common(n))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", "\n", + "plt.title(\"MethodNaming train label frequency distribution (top-{})\".format(n))\n", "plt.bar(indexes, values, width)\n", "plt.xticks(indexes, labels, rotation=90)\n", + "plt.tight_layout()\n", + "plt.savefig(\"methodname-lg-freq-top50-train.pdf\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labels, values = zip(*c.most_common(2000))\n", + "\n", + "plt.figure(figsize=(20,6))\n", + "plt.title(\"MethodNaming train label frequency distribution. (Total = {})\".format(len(labels_train_str)))\n", + "plt.hist(values[1:],bins=30);\n", + "width = 1\n", + "plt.tight_layout()\n", + "#plt.xticks([i + width * 0.5 for i in range(16)], [str(i) for i in range(16)]);\n", + "plt.savefig(\"methodname-train-lg-freq.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -12651,7 +11747,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -12660,7 +11756,7 @@ "'uniform_'" ] }, - "execution_count": 158, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -12671,7 +11767,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -12681,7 +11777,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -12690,19207 +11786,7663 @@ "text": [ "Label = relu\n", "Pred =\n", - "---- 0. relu (0.773)\n", - " 1. selu (0.034)\n", - " 2. norm (0.011)\n", - " 3. greater_equal (0.009)\n", - " 4. greater (0.008)\n", - " 5. ones_like (0.005)\n", - " 6. softmax (0.005)\n", + "---- 0. relu (0.764)\n", "\n", "Label = get\n", "Pred =\n", - "---- 0. get (0.897)\n", - " 1. setdefault (0.018)\n", - " 2. __getitem__ (0.01)\n", - " 3. invoke (0.008)\n", - " 4. wrapper (0.004)\n", - " 5. head (0.002)\n", - " 6. register (0.002)\n", + "---- 0. get (0.778)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = deserialize\n", "Pred =\n", "---- 0. deserialize (0.997)\n", - " 1. from_config (0.0)\n", - " 2. _download_webpage_handle (0.0)\n", - " 3. add (0.0)\n", - " 4. extract (0.0)\n", - " 5. reset (0.0)\n", - " 6. call (0.0)\n", "\n", "Label = get\n", "Pred =\n", "---- 0. get (0.998)\n", - " 1. add (0.0)\n", - " 2. __getitem__ (0.0)\n", - " 3. post (0.0)\n", - " 4. delete (0.0)\n", - " 5. get_value (0.0)\n", - " 6. patch (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.426)\n", - " 1. __setstate__ (0.269)\n", - " 2. get_weights (0.008)\n", - " 3. build (0.008)\n", - " 4. reset_states (0.006)\n", - " 5. score_samples (0.005)\n", - " 6. partition (0.005)\n", + " 0. train (0.079)\n", "\n", "Label = compute_mask\n", "Pred =\n", - "---- 0. compute_mask (0.998)\n", - " 1. call (0.0)\n", - " 2. state (0.0)\n", - " 3. step (0.0)\n", - " 4. permute_hidden (0.0)\n", - " 5. get_weights (0.0)\n", - " 6. reset_states (0.0)\n", + "---- 0. compute_mask (0.999)\n", "\n", "Label = _normalize_device_name\n", "Pred =\n", - " 0. underscore_to_camel (0.167)\n", - " 1. normalize_location_name (0.072)\n", - " 2. url_repl (0.041)\n", - " 3. cc (0.031)\n", - " 4. is_public_parameter (0.017)\n", - " 5. unescape_string_literal (0.014)\n", - " 6. pretty_name (0.014)\n", + " 0. unescape_string_literal (0.128)\n", "\n", "Label = print_layer_summary\n", "Pred =\n", - " 0. __init__ (0.089)\n", - " 1. get_required_config (0.062)\n", - " 2. predict_proba (0.021)\n", - " 3. destination (0.017)\n", - " 4. count_params (0.011)\n", - " 5. enable (0.007)\n", - " 6. stderr_redirector (0.006)\n", + " 0. test_auc_errors (0.042)\n", "\n", "Label = NASNetLarge\n", "Pred =\n", - " 0. wrapper (0.06)\n", - " 1. wrapped (0.046)\n", - " 2. InceptionResNetV2 (0.042)\n", - " 3. VGG16 (0.033)\n", - " 4. DenseNet169 (0.033)\n", - " 5. submit (0.028)\n", - " 6. NASNetMobile (0.024)\n", + " 0. bind (0.062)\n", "\n", "Label = decode_predictions\n", "Pred =\n", "---- 0. decode_predictions (1.0)\n", - " 1. decorator (0.0)\n", - " 2. symbolic (0.0)\n", - " 3. in_top_k (0.0)\n", - " 4. check_warning (0.0)\n", - " 5. DenseNet121 (0.0)\n", - " 6. command (0.0)\n", "\n", "Label = decode_predictions\n", "Pred =\n", "---- 0. decode_predictions (1.0)\n", - " 1. decorator (0.0)\n", - " 2. symbolic (0.0)\n", - " 3. in_top_k (0.0)\n", - " 4. check_warning (0.0)\n", - " 5. DenseNet121 (0.0)\n", - " 6. command (0.0)\n", "\n", "Label = decode_predictions\n", "Pred =\n", "---- 0. decode_predictions (1.0)\n", - " 1. decorator (0.0)\n", - " 2. symbolic (0.0)\n", - " 3. in_top_k (0.0)\n", - " 4. check_warning (0.0)\n", - " 5. DenseNet121 (0.0)\n", - " 6. command (0.0)\n", "\n", "Label = preprocess_input\n", "Pred =\n", "---- 0. preprocess_input (1.0)\n", - " 1. decorator (0.0)\n", - " 2. wrapper (0.0)\n", - " 3. _extract_playlist (0.0)\n", - " 4. foldr (0.0)\n", - " 5. put (0.0)\n", - " 6. deserialize (0.0)\n", "\n", "Label = DenseNet201\n", "Pred =\n", - " 0. InceptionResNetV2 (0.154)\n", - " 1. DenseNet169 (0.113)\n", - " 2. MobileNet (0.066)\n", - " 3. Xception (0.048)\n", - " 4. VGG16 (0.048)\n", - " 5. NASNetMobile (0.043)\n", - " 6. compat_ctypes_WINFUNCTYPE (0.031)\n", + " 0. bind (0.134)\n", "\n", "Label = _merge_function\n", "Pred =\n", - " 0. call (0.449)\n", - " 1. get_config (0.351)\n", - " 2. __call__ (0.044)\n", - " 3. __new__ (0.005)\n", - " 4. deserialize (0.005)\n", - " 5. __copy__ (0.005)\n", - "---- 6. _merge_function (0.004)\n", + " 0. call (0.985)\n", "\n", "Label = minimum\n", "Pred =\n", - " 0. subtract (0.191)\n", - " 1. maximum (0.189)\n", - " 2. average (0.186)\n", - " 3. multiply (0.179)\n", - " 4. add (0.116)\n", - " 5. main (0.002)\n", - " 6. function (0.002)\n", + " 0. multiply (0.202)\n", "\n", "Label = dot\n", "Pred =\n", - " 0. classification_error (0.084)\n", - " 1. reverse (0.061)\n", - " 2. not_equal (0.046)\n", - " 3. maximum (0.038)\n", - " 4. var (0.019)\n", - " 5. equal (0.016)\n", - " 6. expand_dims (0.015)\n", + " 0. maximum (0.167)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = compute_output_shape\n", "Pred =\n", - "---- 0. compute_output_shape (0.901)\n", - " 1. _get_noise_shape (0.051)\n", - " 2. __call__ (0.005)\n", - " 3. gather (0.002)\n", - " 4. __reduce_ex__ (0.002)\n", - " 5. sign (0.001)\n", - " 6. backward (0.001)\n", + "---- 0. compute_output_shape (0.476)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. build (0.0)\n", - " 6. init_poolmanager (0.0)\n", "\n", "Label = _pooling_function\n", "Pred =\n", "---- 0. _pooling_function (0.999)\n", - " 1. call (0.0)\n", - " 2. deserialize (0.0)\n", - " 3. set_floatx (0.0)\n", - " 4. get_config (0.0)\n", - " 5. _check_not_closed (0.0)\n", - " 6. _preprocess_border_mode (0.0)\n", "\n", "Label = _pooling_function\n", "Pred =\n", - "---- 0. _pooling_function (0.998)\n", - " 1. call (0.0)\n", - " 2. get_config (0.0)\n", - " 3. deserialize (0.0)\n", - " 4. _preprocess_border_mode (0.0)\n", - " 5. get_context (0.0)\n", - " 6. set_floatx (0.0)\n", + "---- 0. _pooling_function (0.999)\n", "\n", "Label = get_config\n", "Pred =\n", "---- 0. get_config (1.0)\n", - " 1. load (0.0)\n", - " 2. call (0.0)\n", - " 3. state (0.0)\n", - " 4. get (0.0)\n", - " 5. __invert__ (0.0)\n", - " 6. get_value (0.0)\n", "\n", "Label = get_tuple_shape\n", "Pred =\n", - " 0. backward (0.029)\n", - " 1. _get_noise_shape (0.027)\n", - " 2. save_img (0.015)\n", - " 3. noised (0.013)\n", - " 4. _send (0.013)\n", - " 5. inverse_transform (0.012)\n", - " 6. dropout (0.008)\n", + " 0. _reshape_batch (0.037)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. build (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = call\n", "Pred =\n", - "---- 0. call (1.0)\n", - " 1. __call__ (0.0)\n", - " 2. build (0.0)\n", - " 3. forward (0.0)\n", - " 4. state (0.0)\n", - " 5. deserialize (0.0)\n", - " 6. get_config (0.0)\n", + "---- 0. call (0.995)\n", "\n", "Label = get_losses_for\n", "Pred =\n", - "---- 0. get_losses_for (0.996)\n", - " 1. losses (0.002)\n", - " 2. call (0.0)\n", - " 3. get_updates_for (0.0)\n", - " 4. get_config (0.0)\n", - " 5. register (0.0)\n", - " 6. url (0.0)\n", + " 0. losses (0.947)\n", "\n", "Label = compute_mask\n", "Pred =\n", - " 0. call (0.988)\n", - "---- 1. compute_mask (0.007)\n", - " 2. _merge_function (0.0)\n", - " 3. state (0.0)\n", - " 4. _sparse_argmax (0.0)\n", - " 5. get_weights (0.0)\n", - " 6. get_updates_for (0.0)\n", + " 0. call (0.43)\n", "\n", "Label = _get_noise_shape\n", "Pred =\n", - "---- 0. _get_noise_shape (0.941)\n", - " 1. compute_output_shape (0.009)\n", - " 2. _merge_function (0.002)\n", - " 3. __call__ (0.002)\n", - " 4. gather (0.002)\n", - " 5. format_position (0.001)\n", - " 6. encode_network (0.001)\n", + "---- 0. _get_noise_shape (0.969)\n", "\n", "Label = get_config\n", "Pred =\n", "---- 0. get_config (1.0)\n", - " 1. __invert__ (0.0)\n", - " 2. load (0.0)\n", - " 3. call (0.0)\n", - " 4. _updated_config (0.0)\n", - " 5. config (0.0)\n", - " 6. state (0.0)\n", "\n", "Label = call\n", "Pred =\n", - "---- 0. call (0.891)\n", - " 1. _sparse_argmax (0.015)\n", - " 2. decision_function (0.008)\n", - " 3. get_losses_for (0.006)\n", - " 4. _combine (0.002)\n", - " 5. backward (0.002)\n", - " 6. predict (0.002)\n", + "---- 0. call (0.999)\n", "\n", "Label = get_config\n", "Pred =\n", "---- 0. get_config (1.0)\n", - " 1. load (0.0)\n", - " 2. call (0.0)\n", - " 3. get (0.0)\n", - " 4. state (0.0)\n", - " 5. __invert__ (0.0)\n", - " 6. clone (0.0)\n", "\n", "Label = name_scope\n", "Pred =\n", - "---- 0. name_scope (0.989)\n", - " 1. clear_session (0.0)\n", - " 2. batch_get_value (0.0)\n", - " 3. _step (0.0)\n", - " 4. ndim (0.0)\n", - " 5. add_metaclass (0.0)\n", - " 6. add_unprocessed_node (0.0)\n", + "---- 0. name_scope (0.983)\n", "\n", "Label = in_test_phase\n", "Pred =\n", - "---- 0. in_test_phase (0.996)\n", - " 1. logsumexp (0.0)\n", - " 2. update_sub (0.0)\n", - " 3. ternary (0.0)\n", - " 4. prod (0.0)\n", - " 5. model_from_yaml (0.0)\n", - " 6. stack (0.0)\n", + "---- 0. in_test_phase (0.993)\n", "\n", "Label = eye\n", "Pred =\n", - "---- 0. eye (0.991)\n", - " 1. ones_like (0.0)\n", - " 2. sqrt (0.0)\n", - " 3. dumps (0.0)\n", - " 4. cast (0.0)\n", - " 5. asbytes (0.0)\n", - " 6. zeros_like (0.0)\n", + "---- 0. eye (0.996)\n", "\n", "Label = sum\n", "Pred =\n", - " 0. min (0.309)\n", - " 1. max (0.247)\n", - " 2. mean (0.221)\n", - " 3. prod (0.152)\n", - " 4. logsumexp (0.018)\n", - "---- 5. sum (0.01)\n", - " 6. std (0.005)\n", + " 0. max (0.299)\n", "\n", "Label = var\n", "Pred =\n", - " 0. sum (0.075)\n", - " 1. prod (0.074)\n", - " 2. max (0.065)\n", - " 3. min (0.064)\n", - " 4. squared_hinge (0.052)\n", - " 5. mean (0.027)\n", - " 6. mean_squared_error (0.023)\n", + " 0. l2_normalize (0.257)\n", "\n", "Label = std\n", "Pred =\n", - "---- 0. std (0.958)\n", - " 1. max (0.007)\n", - " 2. mean (0.006)\n", - " 3. sum (0.004)\n", - " 4. min (0.003)\n", - " 5. var (0.001)\n", - " 6. logsumexp (0.001)\n", + "---- 0. std (0.944)\n", "\n", "Label = get_value\n", "Pred =\n", - " 0. eval (0.333)\n", - " 1. is_tensor (0.263)\n", - " 2. set_value (0.119)\n", - " 3. batch_get_value (0.054)\n", - " 4. exists (0.019)\n", - "---- 5. get_value (0.013)\n", - " 6. gradients (0.006)\n", + " 0. eval (0.516)\n", "\n", "Label = update_sub\n", "Pred =\n", - "---- 0. update_sub (0.818)\n", - " 1. is_placeholder (0.006)\n", - " 2. rebuild_connection (0.005)\n", - " 3. logsumexp (0.002)\n", - " 4. wrap_future_result (0.002)\n", - " 5. route_to_dict (0.002)\n", - " 6. merge_dicts (0.002)\n", + "---- 0. update_sub (0.892)\n", "\n", "Label = sum\n", "Pred =\n", - "---- 0. sum (0.548)\n", - " 1. min (0.113)\n", - " 2. logsumexp (0.067)\n", - " 3. max (0.067)\n", - " 4. prod (0.067)\n", - " 5. mean (0.02)\n", - " 6. std (0.017)\n", + "---- 0. sum (0.77)\n", "\n", "Label = all\n", "Pred =\n", - " 0. mean (0.513)\n", - " 1. max (0.115)\n", - "---- 2. all (0.075)\n", - " 3. prod (0.069)\n", - " 4. min (0.046)\n", - " 5. any (0.016)\n", - " 6. sum (0.014)\n", + "---- 0. all (0.306)\n", "\n", "Label = less\n", "Pred =\n", - " 0. less_equal (0.93)\n", - " 1. greater (0.002)\n", - " 2. test_gaussian_mixture_n_iter (0.002)\n", - " 3. greater_equal (0.002)\n", - " 4. grid_to_graph (0.001)\n", - " 5. equal (0.001)\n", - " 6. tile (0.001)\n", + " 0. less_equal (0.505)\n", "\n", "Label = minimum\n", "Pred =\n", - " 0. histogram (0.452)\n", - " 1. logging_histogram_kernel (0.095)\n", - " 2. _sparse_min_max (0.048)\n", - " 3. not_equal (0.047)\n", - " 4. equal (0.031)\n", - " 5. squared_loss (0.007)\n", - " 6. _sparse_nan_min_max (0.006)\n", + " 0. histogram (0.726)\n", "\n", "Label = one_hot\n", "Pred =\n", - " 0. transform (0.292)\n", - " 1. reshape (0.072)\n", - " 2. gather (0.058)\n", - " 3. inverse_transform (0.027)\n", - " 4. ndim (0.019)\n", - " 5. linear_kernel (0.017)\n", - " 6. softmax (0.008)\n", + " 0. transform (0.245)\n", "\n", "Label = softsign\n", "Pred =\n", - " 0. softplus (0.071)\n", - " 1. sigmoid (0.034)\n", - " 2. foldl (0.021)\n", - " 3. softmax (0.019)\n", - " 4. not_equal (0.016)\n", - " 5. greater (0.013)\n", - " 6. int_shape (0.012)\n", + " 0. softmax (0.588)\n", "\n", "Label = _preprocess_padding\n", "Pred =\n", - " 0. _preprocess_border_mode (0.947)\n", - " 1. temporal_padding (0.003)\n", - " 2. normalize_padding (0.002)\n", - "---- 3. _preprocess_padding (0.001)\n", - " 4. _get_chunks (0.001)\n", - " 5. set_image_dim_ordering (0.001)\n", - " 6. permute_dimensions (0.0)\n", + " 0. _preprocess_border_mode (0.807)\n", "\n", "Label = random_normal_variable\n", "Pred =\n", - " 0. ones (0.192)\n", - " 1. random_normal (0.073)\n", - " 2. noised (0.025)\n", - " 3. truncated_normal (0.022)\n", - " 4. cast (0.02)\n", - " 5. set_value (0.018)\n", - " 6. constant (0.013)\n", + " 0. port (0.126)\n", "\n", "Label = sqrt\n", "Pred =\n", - "---- 0. sqrt (0.217)\n", - " 1. selu (0.048)\n", - " 2. hard_sigmoid (0.047)\n", - " 3. pow (0.029)\n", - " 4. greater (0.026)\n", - " 5. norm (0.022)\n", - " 6. relu (0.013)\n", + "---- 0. sqrt (0.311)\n", "\n", "Label = logsumexp\n", "Pred =\n", - " 0. sum (0.126)\n", - " 1. std (0.099)\n", - " 2. softmax (0.039)\n", - "---- 3. logsumexp (0.032)\n", - " 4. all (0.026)\n", - " 5. _is_raw_file (0.023)\n", - " 6. max (0.022)\n", + " 0. sum (0.302)\n", "\n", "Label = batch_get_value\n", "Pred =\n", - " 0. intlist_to_bytes (0.57)\n", - " 1. format (0.008)\n", - " 2. _reduce_socket (0.007)\n", - " 3. after_request (0.007)\n", - " 4. _fd (0.005)\n", - " 5. update_sub (0.005)\n", - " 6. ie_key (0.004)\n", + " 0. intlist_to_bytes (0.491)\n", "\n", "Label = l2_normalize\n", "Pred =\n", - "---- 0. l2_normalize (0.659)\n", - " 1. squared_hinge (0.031)\n", - " 2. prod (0.029)\n", - " 3. sum (0.014)\n", - " 4. mean_squared_error (0.007)\n", - " 5. maximum (0.006)\n", - " 6. softmax (0.005)\n", + "---- 0. l2_normalize (0.601)\n", "\n", "Label = _preprocess_conv2d_input\n", "Pred =\n", - " 0. _preprocess_conv3d_input (0.872)\n", - " 1. _postprocess_conv2d_output (0.029)\n", - "---- 2. _preprocess_conv2d_input (0.028)\n", - " 3. _postprocess_conv3d_output (0.007)\n", - " 4. _preprocess_conv3d_kernel (0.003)\n", - " 5. _preprocess_conv2d_kernel (0.002)\n", - " 6. temporal_padding (0.001)\n", + " 0. _preprocess_conv3d_input (0.858)\n", "\n", "Label = int_or_none\n", "Pred =\n", - "---- 0. int_or_none (0.998)\n", - " 1. clear_script_prefix (0.0)\n", - " 2. _make_timedelta (0.0)\n", - " 3. jsonify (0.0)\n", - " 4. to_list (0.0)\n", - " 5. is_hop (0.0)\n", - " 6. to_dense (0.0)\n", + "---- 0. int_or_none (0.993)\n", "\n", "Label = random_uniform\n", "Pred =\n", - "---- 0. random_uniform (0.994)\n", - " 1. truncated_normal (0.001)\n", - " 2. random_normal (0.001)\n", - " 3. noised (0.0)\n", - " 4. __call__ (0.0)\n", - " 5. random_binomial (0.0)\n", - " 6. dropped_inputs (0.0)\n", + "---- 0. random_uniform (0.996)\n", "\n", "Label = foldl\n", "Pred =\n", - "---- 0. foldl (0.865)\n", - " 1. foldr (0.028)\n", - " 2. is_sparse (0.004)\n", - " 3. map_fn (0.003)\n", - " 4. VGG19 (0.002)\n", - " 5. showwarning (0.002)\n", - " 6. _rebuild_socket (0.002)\n", + "---- 0. foldl (0.498)\n", "\n", "Label = uses_learning_phase\n", "Pred =\n", - " 0. function (0.077)\n", - " 1. _step (0.037)\n", - " 2. _mv (0.02)\n", - " 3. _uses_dynamic_learning_phase (0.015)\n", - " 4. _init_subclassed_network (0.014)\n", - " 5. score_samples (0.009)\n", - " 6. create_sdk_records (0.009)\n", + " 0. backward (0.219)\n", "\n", "Label = reset_states\n", "Pred =\n", - "---- 0. reset_states (0.309)\n", - " 1. states (0.031)\n", - " 2. stateful (0.024)\n", - " 3. state_size (0.011)\n", - " 4. state_updates (0.007)\n", - " 5. get_vms_by_ids (0.007)\n", - " 6. writable (0.007)\n", + "---- 0. reset_states (0.702)\n", "\n", "Label = model_from_config\n", "Pred =\n", - " 0. deserialize (0.425)\n", - " 1. from_config (0.078)\n", - " 2. model_from_json (0.074)\n", - " 3. summary (0.011)\n", - " 4. extract (0.01)\n", - " 5. get_connection (0.009)\n", - " 6. enable (0.006)\n", + " 0. from_config (0.406)\n", "\n", "Label = trainable_weights\n", "Pred =\n", - "---- 0. trainable_weights (0.987)\n", - " 1. non_trainable_weights (0.002)\n", - " 2. updates (0.001)\n", - " 3. _check_trainable_weights_consistency (0.0)\n", - " 4. __reduce_ex__ (0.0)\n", - " 5. trainable (0.0)\n", - " 6. __invert__ (0.0)\n", + "---- 0. trainable_weights (0.994)\n", "\n", "Label = get_input_shape_at\n", "Pred =\n", - " 0. get_output_mask_at (0.177)\n", - " 1. get_output_at (0.176)\n", - " 2. get_output_shape_at (0.17)\n", - " 3. get_input_at (0.165)\n", - " 4. get_input_mask_at (0.157)\n", - " 5. before_request (0.003)\n", - " 6. output_mask (0.002)\n", + " 0. get_output_at (0.181)\n", "\n", "Label = input_mask\n", "Pred =\n", - " 0. output_mask (0.854)\n", - " 1. input (0.042)\n", - " 2. output (0.041)\n", - " 3. clear_session (0.001)\n", - " 4. _get_available_gpus (0.001)\n", - " 5. make_null_session (0.001)\n", - " 6. cachedir (0.001)\n", + " 0. output_mask (0.875)\n", "\n", "Label = weights\n", "Pred =\n", - " 0. non_trainable_weights (0.981)\n", - " 1. trainable_weights (0.008)\n", - " 2. get_weights (0.001)\n", - " 3. _check_trainable_weights_consistency (0.001)\n", - " 4. extra_repr (0.001)\n", - " 5. call (0.0)\n", - " 6. __reduce_ex__ (0.0)\n", + " 0. non_trainable_weights (0.997)\n", "\n", - "Label = alpha_dropout\n", + "Label = _check_inputs_dtype\n", "Pred =\n", - " 0. constant (0.05)\n", - " 1. update (0.03)\n", - " 2. _ensure_no_complex_data (0.026)\n", - " 3. _decision_function (0.014)\n", - " 4. reduce_memmap (0.011)\n", - " 5. zeros_like (0.011)\n", - " 6. _assert_nmf_no_nan (0.009)\n", + " 0. _is_integral_float (0.038)\n", "\n", - "Label = hardtanh\n", + "Label = _get_mask\n", "Pred =\n", - " 0. _get_mask (0.113)\n", - " 1. zeros_like (0.079)\n", - " 2. ones_like (0.043)\n", - " 3. _to_tensor (0.042)\n", - " 4. _clean_nans (0.028)\n", - " 5. _is_empty_column_selection (0.019)\n", - " 6. constant (0.019)\n", + "---- 0. _get_mask (0.21)\n", "\n", - "Label = elu\n", + "Label = __getstate__\n", "Pred =\n", - " 0. __getstate__ (0.993)\n", - " 1. eval (0.002)\n", - " 2. __hash__ (0.001)\n", - " 3. clone (0.0)\n", - " 4. transform (0.0)\n", - " 5. __getitem__ (0.0)\n", - " 6. __copy__ (0.0)\n", + "---- 0. __getstate__ (0.997)\n", "\n", - "Label = rrelu\n", + "Label = biclusters_\n", "Pred =\n", - " 0. deconstruct (0.089)\n", - " 1. __reduce__ (0.075)\n", - " 2. get_indices (0.04)\n", - " 3. import_models (0.014)\n", - " 4. serialize (0.012)\n", - " 5. _weights_not_none (0.01)\n", - " 6. non_trainable_weights (0.008)\n", + " 0. tuple (0.227)\n", "\n", - "Label = _no_grad_embedding_renorm_\n", + "Label = is_classifier\n", "Pred =\n", - " 0. is_outlier_detector (0.442)\n", - " 1. is_regressor (0.435)\n", - " 2. _is_pairwise (0.017)\n", - " 3. _passthrough_scorer (0.004)\n", - " 4. _check_estimator (0.003)\n", - " 5. test_average_precision_score_tied_values (0.002)\n", - " 6. test_parameters_default_constructible (0.002)\n", + " 0. is_outlier_detector (0.529)\n", "\n", - "Label = assert_int_or_pair\n", + "Label = decision_function\n", "Pred =\n", - " 0. decision_function (0.877)\n", - " 1. _decision_function (0.017)\n", - " 2. staged_predict (0.011)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 3. staged_decision_function (0.008)\n", - " 4. decision_path (0.007)\n", - " 5. predict (0.005)\n", - " 6. predict_proba (0.004)\n", + "---- 0. decision_function (0.299)\n", "\n", - "Label = __deepcopy__\n", + "Label = _pairwise\n", "Pred =\n", - " 0. _pairwise (0.978)\n", - " 1. _is_pairwise (0.003)\n", - " 2. _estimator_type (0.002)\n", - " 3. _final_estimator (0.001)\n", - " 4. named_estimators (0.001)\n", - " 5. _validate_X_predict (0.001)\n", - " 6. named_steps (0.0)\n", + "---- 0. _pairwise (0.981)\n", "\n", - "Label = half\n", + "Label = __init__\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. unregister (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. destination (0.0)\n", - " 5. reset_batch_stats (0.0)\n", - " 6. func (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = __call__\n", + "Label = __init__\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. predict_proba (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. start_serialization (0.0)\n", - " 6. destination (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = __setstate__\n", + "Label = _iter\n", "Pred =\n", - " 0. set_weights (0.03)\n", - " 1. _rebuild (0.027)\n", - " 2. clear_location (0.013)\n", - " 3. __del__ (0.013)\n", - " 4. optimize (0.01)\n", - " 5. _weights_not_none (0.01)\n", - " 6. rename_column_references (0.009)\n", + " 0. test_check_ci_warn (0.022)\n", "\n", - "Label = backward\n", + "Label = fit_predict\n", "Pred =\n", - " 0. fit (0.64)\n", - " 1. fit_predict (0.293)\n", - " 2. _partial_fit (0.011)\n", - " 3. _fit_predict (0.008)\n", - " 4. predict_proba (0.006)\n", - " 5. _validate_y (0.002)\n", - " 6. fit_transform (0.002)\n", + " 0. fit (0.998)\n", "\n", - "Label = gather\n", + "Label = transform\n", "Pred =\n", - " 0. fit_transform (0.116)\n", - " 1. decision_function (0.093)\n", - " 2. transform (0.043)\n", - " 3. wkt (0.023)\n", - " 4. _inverse_transform (0.02)\n", - " 5. staged_decision_function (0.015)\n", - " 6. add_result (0.009)\n", + "---- 0. transform (0.52)\n", "\n", - "Label = forward\n", + "Label = _transform\n", "Pred =\n", - " 0. transform (0.66)\n", - " 1. _inverse_transform (0.074)\n", - " 2. fit_transform (0.059)\n", - " 3. inverse_transform (0.032)\n", - " 4. predict (0.015)\n", - " 5. decision_function (0.014)\n", - " 6. wkt (0.009)\n", + " 0. transform (0.919)\n", "\n", - "Label = _renorm\n", + "Label = __init__\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. destination (0.0)\n", - " 2. func (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. __setitem__ (0.0)\n", - " 6. predict_proba (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = __init__\n", + "Label = get_params\n", "Pred =\n", - " 0. get_params (0.999)\n", - " 1. set_params (0.0)\n", - " 2. configure (0.0)\n", - " 3. function (0.0)\n", - " 4. submit (0.0)\n", - "---- 5. __init__ (0.0)\n", - " 6. patch (0.0)\n", + "---- 0. get_params (1.0)\n", "\n", - "Label = __init__\n", + "Label = fit\n", "Pred =\n", - " 0. fit (0.999)\n", - " 1. fit_transform (0.0)\n", - " 2. _partial_fit (0.0)\n", - " 3. fit_predict (0.0)\n", - " 4. _check_fit_data (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. partial_fit (0.0)\n", + "---- 0. fit (0.999)\n", "\n", - "Label = forward\n", + "Label = __setstate__\n", "Pred =\n", - " 0. __setstate__ (0.717)\n", - " 1. fit (0.269)\n", - " 2. inverse_transform (0.0)\n", - " 3. state (0.0)\n", - " 4. write (0.0)\n", - " 5. append (0.0)\n", - " 6. put (0.0)\n", + " 0. fit (0.516)\n", "\n", - "Label = extra_repr\n", + "Label = fit\n", "Pred =\n", - " 0. partial_fit (0.973)\n", - " 1. _validate_y (0.008)\n", - " 2. _partial_fit (0.002)\n", - " 3. fit_transform (0.002)\n", - " 4. fit (0.001)\n", - " 5. _check_fit_data (0.001)\n", - " 6. _validate_targets (0.001)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. score (0.108)\n", - " 1. fit_transform (0.099)\n", - " 2. transform (0.068)\n", - " 3. inverse_transform (0.066)\n", - " 4. score_samples (0.05)\n", - " 5. _decision_function (0.033)\n", - " 6. _validate_y (0.033)\n", - "\n", - "Label = reset_parameters\n", - "Pred =\n", - " 0. linear_kernel (0.036)\n", - " 1. _predict_binary (0.034)\n", - " 2. squared_norm (0.028)\n", - " 3. zeros_like (0.02)\n", - " 4. cast_to_floatx (0.015)\n", - " 5. update_coeff (0.015)\n", - " 6. _raise_typeerror (0.014)\n", - "\n", - "Label = extra_repr\n", - "Pred =\n", - " 0. inverse_transform (0.102)\n", - " 1. warn (0.051)\n", - " 2. wrapped (0.026)\n", - " 3. md5 (0.008)\n", - " 4. _mean_hiddens (0.008)\n", - " 5. no_translations (0.008)\n", - " 6. _hash_func (0.008)\n", - "\n", - "Label = __setitem__\n", - "Pred =\n", - " 0. inverse_transform (0.563)\n", - " 1. transform (0.382)\n", - " 2. predict (0.008)\n", - " 3. predict_proba (0.004)\n", - " 4. score_samples (0.003)\n", - " 5. fit_transform (0.002)\n", - " 6. project (0.002)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. transform (0.996)\n", - " 1. inverse_transform (0.001)\n", - " 2. predict (0.0)\n", - " 3. _validate_targets (0.0)\n", - " 4. decision_function (0.0)\n", - " 5. _fit_predict (0.0)\n", - " 6. func (0.0)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. test_agglomerative_clustering_wrong_arg_memory (0.024)\n", - " 1. test_power_transformer_notfitted (0.018)\n", - " 2. test_multinomial_validation (0.014)\n", - " 3. get_streaming_distribution (0.013)\n", - " 4. test_too_many_components (0.013)\n", - " 5. power_transform (0.011)\n", - " 6. test_too_many_samples_to_find_a_safe_embedding (0.009)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. test_dict_learning_online_overcomplete (0.457)\n", - " 1. test_dict_learning_online_estimator_shapes (0.412)\n", - " 2. test_dict_learning_online_initialization (0.015)\n", - " 3. test_no_empty_slice_warning (0.005)\n", - " 4. test_attributes (0.004)\n", - " 5. test_dict_learning_split (0.003)\n", - " 6. test_sparse_random_projection_transformer_invalid_density (0.001)\n", - "\n", - "Label = reset_parameters\n", - "Pred =\n", - " 0. test_dict_learning_online_estimator_shapes (0.249)\n", - " 1. test_dict_learning_online_overcomplete (0.169)\n", - " 2. test_dict_learning_online_initialization (0.117)\n", - " 3. test_dict_learning_split (0.024)\n", - " 4. test_no_empty_slice_warning (0.019)\n", - " 5. test_warning_n_components_greater_than_n_features (0.01)\n", - " 6. test_precomputed (0.008)\n", - "\n", - "Label = extra_repr\n", - "Pred =\n", - " 0. test_dict_learning_online_initialization (0.152)\n", - " 1. test_ridge_regression_convergence_fail (0.146)\n", - " 2. test_gaussian_mixture_n_iter (0.108)\n", - " 3. test_multioutput_enetcv_error (0.087)\n", - " 4. test_dict_learning_online_readonly_initialization (0.056)\n", - " 5. test_sparse_encode_error_default_sparsity (0.043)\n", - " 6. assert_raises_on_all_points_same_cluster (0.025)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. histogram (0.927)\n", - " 1. logging_histogram_kernel (0.011)\n", - " 2. equal (0.004)\n", - " 3. not_equal (0.003)\n", - " 4. squared_loss (0.002)\n", - " 5. multioutput_estimator_convert_y_2d (0.002)\n", - " 6. _sparse_min_max (0.002)\n", - "\n", - "Label = parse\n", - "Pred =\n", - " 0. test_lda_fit_perplexity (0.157)\n", - " 1. test_lda_no_component_error (0.127)\n", - " 2. test_deprecated_auc_reorder (0.011)\n", - " 3. test_lda_negative_input (0.01)\n", - " 4. test_deprecated_calinski_harabaz_score (0.009)\n", - " 5. test_crammer_singer_binary (0.008)\n", - " 6. test_oob_improvement_raise (0.008)\n", - "\n", - "Label = forward\n", - "Pred =\n", - " 0. test_gen_even_slices (0.015)\n", - " 1. pairwise_distance (0.014)\n", - " 2. test_base_optimizer (0.013)\n", - " 3. upsample (0.011)\n", - " 4. _full_jitter_backoff (0.01)\n", - " 5. test_power_transformer_notfitted (0.01)\n", - " 6. _combinations (0.009)\n", - "\n", - "Label = forward\n", - "Pred =\n", - " 0. test_imputation_error_invalid_strategy (0.378)\n", - " 1. test_label_encoder_str_bad_shape (0.02)\n", - " 2. test_lr_liblinear_warning (0.008)\n", - " 3. test_float_class_labels (0.006)\n", - " 4. test_log_normalize (0.006)\n", - " 5. test_init_not_available (0.006)\n", - " 6. test_power_transformer_lambda_zero (0.006)\n", - "\n", - "Label = type\n", - "Pred =\n", - " 0. test_class_weights (0.041)\n", - " 1. test_warm_start_smaller_n_estimators (0.032)\n", - " 2. test_memory_layout (0.028)\n", - " 3. test_regressor_attributes (0.022)\n", - " 4. test_min_samples_split (0.02)\n", - " 5. test_oob_score_raise_error (0.019)\n", - " 6. test_class_weight_balanced_and_bootstrap_multi_output (0.015)\n", - "\n", - "Label = register_forward_pre_hook\n", - "Pred =\n", - " 0. f_noise (0.034)\n", - " 1. test_memory_layout (0.021)\n", - " 2. test_warm_start_smaller_n_estimators (0.016)\n", - " 3. ones (0.014)\n", - " 4. test_valid_n_bins (0.013)\n", - " 5. test_partial_fit_exception (0.013)\n", - " 6. test_saga_sparse (0.012)\n", - "\n", - "Label = buffers\n", - "Pred =\n", - " 0. log_loss (0.081)\n", - " 1. objective_function (0.028)\n", - " 2. one_pass_var (0.027)\n", - " 3. naive_log_logistic (0.027)\n", - " 4. _deterministic_vector_sign_flip (0.019)\n", - " 5. custom_metric (0.019)\n", - " 6. predict_proba (0.014)\n", - "\n", - "Label = modules\n", - "Pred =\n", - " 0. inverse_transform (0.984)\n", - " 1. predict_proba (0.003)\n", - " 2. transform (0.002)\n", - " 3. score_samples (0.002)\n", - " 4. fit_predict (0.001)\n", - " 5. decision_function (0.0)\n", - " 6. func (0.0)\n", - "\n", - "Label = _check_inputs_dtype\n", - "Pred =\n", - " 0. __getstate__ (0.932)\n", - " 1. __hash__ (0.028)\n", - " 2. get_config (0.012)\n", - " 3. __repr__ (0.009)\n", - " 4. clone (0.002)\n", - " 5. lists (0.001)\n", - " 6. __copy__ (0.001)\n", - "\n", - "Label = _get_mask\n", - "Pred =\n", - " 0. test_check_increasing_up (0.692)\n", - " 1. test_check_ci_warn (0.062)\n", - " 2. test_check_increasing_down (0.033)\n", - " 3. test_check_increasing_down_extreme (0.027)\n", - " 4. test_ill_posed_min_c (0.005)\n", - " 5. set_of_lengths (0.003)\n", - " 6. test_paired_manhattan_distances (0.003)\n", - "\n", - "Label = __getstate__\n", - "Pred =\n", - " 0. test_check_increasing_up (0.772)\n", - " 1. test_check_ci_warn (0.061)\n", - " 2. test_check_increasing_down (0.029)\n", - " 3. test_check_increasing_down_extreme (0.025)\n", - " 4. test_ill_posed_min_c (0.005)\n", - " 5. test_paired_manhattan_distances (0.002)\n", - " 6. test_lda_no_component_error (0.002)\n", - "\n", - "Label = biclusters_\n", - "Pred =\n", - " 0. test_oneclass_adaboost_proba (0.369)\n", - " 1. test_gnb_priors (0.056)\n", - " 2. test_predictproba_hardvoting (0.056)\n", - " 3. predict_log_proba (0.022)\n", - " 4. test_discrete_prior (0.022)\n", - " 5. test_predict_consistent (0.013)\n", - " 6. test_regression_toy (0.011)\n", - "\n", - "Label = is_classifier\n", - "Pred =\n", - " 0. test_bagging_sample_weight_unsupported_but_passed (0.036)\n", - " 1. test_y_mean_attribute_regressor (0.024)\n", - " 2. test_multioutput_enetcv_error (0.024)\n", - " 3. test_trustworthiness_not_euclidean_metric (0.018)\n", - " 4. test_constant_size_multioutput_regressor (0.017)\n", - " 5. test_float_class_labels (0.015)\n", - " 6. test_n_components_greater_n_features (0.013)\n", - "\n", - "Label = decision_function\n", - "Pred =\n", - " 0. __init__ (0.362)\n", - " 1. configure (0.061)\n", - " 2. set_params (0.05)\n", - " 3. iteritems (0.05)\n", - " 4. print_tensor (0.023)\n", - " 5. fit (0.019)\n", - " 6. client (0.018)\n", - "\n", - "Label = _pairwise\n", - "Pred =\n", - " 0. predict (0.993)\n", - " 1. transform (0.002)\n", - " 2. predict_proba (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. decision_function (0.0)\n", - " 5. diag (0.0)\n", - " 6. __str__ (0.0)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. fit (0.469)\n", - " 1. fit_transform (0.253)\n", - " 2. transform (0.103)\n", - " 3. __getstate__ (0.02)\n", - " 4. inverse_transform (0.012)\n", - " 5. append (0.01)\n", - " 6. predict (0.006)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. score (0.127)\n", - " 1. predict_proba (0.059)\n", - "---- 2. __init__ (0.049)\n", - " 3. decision_function (0.029)\n", - " 4. aic (0.018)\n", - " 5. _score_to_decision (0.017)\n", - " 6. get_n_splits (0.014)\n", - "\n", - "Label = _iter\n", - "Pred =\n", - " 0. _score_to_decision (0.33)\n", - " 1. predict_proba (0.206)\n", - " 2. decision_function (0.085)\n", - " 3. _score_to_proba (0.026)\n", - " 4. _decision_function (0.023)\n", - " 5. softmax (0.022)\n", - " 6. negative_gradient (0.021)\n", - "\n", - "Label = fit_predict\n", - "Pred =\n", - " 0. test_warm_start_smaller_n_estimators (0.116)\n", - " 1. test_oob_score_removed_on_warm_start (0.086)\n", - " 2. test_liblinear_logregcv_sparse (0.037)\n", - " 3. test_saga_sparse (0.031)\n", - " 4. test_lasso_alpha_warning (0.03)\n", - " 5. test_verbosity (0.028)\n", - " 6. test_crammer_singer_binary (0.026)\n", - "\n", - "Label = transform\n", - "Pred =\n", - " 0. test_base_not_int_n_estimators (0.792)\n", - " 1. test_ovr_fit_predict_svc (0.008)\n", - " 2. test_non_encoded_labels (0.005)\n", - " 3. test_lda_no_component_error (0.004)\n", - " 4. test_pca_bad_solver (0.004)\n", - " 5. test_pickle_version_no_warning_is_issued_with_non_sklearn_estimator (0.004)\n", - " 6. test_multi_target_regression_one_target (0.003)\n", - "\n", - "Label = _transform\n", - "Pred =\n", - " 0. fit (1.0)\n", - " 1. partial_fit (0.0)\n", - " 2. _check_fit_data (0.0)\n", - " 3. fit_predict (0.0)\n", - " 4. _partial_fit (0.0)\n", - " 5. _validate_y (0.0)\n", - " 6. fit_transform (0.0)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. test_decision_path (0.097)\n", - " 1. test_classification_toy (0.046)\n", - " 2. test_class_weights (0.045)\n", - " 3. test_max_leaf_nodes_max_depth (0.043)\n", - " 4. test_warm_start_smaller_n_estimators (0.042)\n", - " 5. test_min_weight_fraction_leaf (0.041)\n", - " 6. test_warm_start_clear (0.04)\n", - "\n", - "Label = get_params\n", - "Pred =\n", - " 0. test_oob_score_regressors (0.677)\n", - " 1. test_valid_n_bins (0.006)\n", - " 2. ones (0.005)\n", - " 3. m_r_get_ruin (0.004)\n", - " 4. check_zero_mean_and_unit_norm (0.004)\n", - " 5. aic (0.004)\n", - " 6. test_memory_layout (0.004)\n", - "\n", - "Label = fit\n", - "Pred =\n", - " 0. test_parallel (0.168)\n", - " 1. test_sparse (0.024)\n", - " 2. test_sparse_input (0.011)\n", - " 3. test_classification_synthetic (0.007)\n", - " 4. detect_exe_version (0.007)\n", - " 5. get_docs_version (0.007)\n", - " 6. test_classification_toy (0.006)\n", - "\n", - "Label = __setstate__\n", - "Pred =\n", - " 0. test_class_weights (0.091)\n", - " 1. test_min_samples_leaf (0.082)\n", - " 2. test_oob_score_raise_error (0.08)\n", - " 3. test_regressor_attributes (0.079)\n", - " 4. test_gridsearch (0.077)\n", - " 5. test_class_weight_balanced_and_bootstrap_multi_output (0.074)\n", - " 6. test_classes_shape (0.073)\n", - "\n", - "Label = fit\n", - "Pred =\n", - " 0. test_scale_1d (0.039)\n", - " 1. test_power_transformer_lambda_zero (0.021)\n", - " 2. test_yeo_johnson_darwin_example (0.02)\n", - " 3. test_make_spd_matrix (0.015)\n", - " 4. test_big_input (0.014)\n", - " 5. safe_min (0.013)\n", - " 6. test_power_transformer_lambda_one (0.013)\n", + " 0. partial_fit (0.987)\n", "\n", "Label = _count\n", "Pred =\n", - " 0. test_predictproba_hardvoting (0.92)\n", - " 1. test_input_check_fit (0.004)\n", - " 2. test_oneclass_adaboost_proba (0.002)\n", - " 3. test_ovr_fit_predict_svc (0.002)\n", - " 4. test_regression_toy (0.001)\n", - " 5. test_multilabel (0.001)\n", - " 6. test_error (0.001)\n", + " 0. score (0.976)\n", "\n", "Label = trace_dot\n", "Pred =\n", - " 0. indexbytes (0.986)\n", - " 1. iterbytes (0.001)\n", - " 2. getlist (0.0)\n", - " 3. pop (0.0)\n", - " 4. dict (0.0)\n", - " 5. setdefault (0.0)\n", - " 6. interpolate_normalized (0.0)\n", + " 0. score (0.134)\n", "\n", "Label = inverse_transform\n", "Pred =\n", - " 0. arrayvar (0.016)\n", - " 1. popitem (0.012)\n", - " 2. getContactListByName (0.009)\n", - " 3. _allowed_methods (0.008)\n", - " 4. conditions (0.008)\n", - " 5. _apply (0.007)\n", - " 6. _mv (0.006)\n", + "---- 0. inverse_transform (0.683)\n", "\n", "Label = transform\n", "Pred =\n", - " 0. __init__ (0.058)\n", - " 1. to_python (0.042)\n", - " 2. _set_single (0.033)\n", - " 3. __iter__ (0.029)\n", - " 4. check_forward_input (0.025)\n", - " 5. version (0.019)\n", - " 6. variables (0.017)\n", + " 0. inverse_transform (0.639)\n", "\n", "Label = score\n", "Pred =\n", - " 0. _get_data_object_for_encoding (0.143)\n", - " 1. handle_value (0.021)\n", - " 2. traffic_group (0.02)\n", - " 3. __default (0.018)\n", - " 4. is_placeholder (0.013)\n", - " 5. __getattr__ (0.012)\n", - " 6. compare (0.011)\n", + " 0. transform (0.99)\n", "\n", "Label = test_inverse_transform\n", "Pred =\n", - " 0. write_stream_with_colors_win_py3 (0.054)\n", - " 1. compat_print (0.051)\n", - " 2. write_func (0.048)\n", - " 3. write (0.035)\n", - " 4. _concurrency_safe_write (0.021)\n", - " 5. write_flv_header (0.016)\n", - " 6. write_unsigned_int (0.015)\n", + " 0. test_power_transformer_notfitted (0.162)\n", "\n", "Label = test_dict_learning_overcomplete\n", "Pred =\n", - " 0. _check_size_scale_factor (0.065)\n", - " 1. validate_params (0.032)\n", - " 2. _check_max_depth (0.017)\n", - " 3. _cleanup (0.016)\n", - " 4. check_scorer_memmap (0.014)\n", - " 5. match_filters (0.011)\n", - " 6. add_action (0.01)\n", + " 0. test_dict_learning_online_overcomplete (0.511)\n", "\n", "Label = test_dict_learning_unknown_fit_algorithm\n", "Pred =\n", - " 0. if_matplotlib (0.061)\n", - " 1. resf (0.036)\n", - " 2. call_function (0.019)\n", - " 3. _build_func_identifier (0.016)\n", - " 4. concurrency_safe_write (0.013)\n", - " 5. extract_first_line (0.012)\n", - " 6. record_once (0.011)\n", + " 0. test_dict_learning_online_estimator_shapes (0.373)\n", "\n", "Label = test_unknown_method\n", "Pred =\n", - " 0. __repr__ (1.0)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. get (0.0)\n", - " 4. predict (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. inverse_transform (0.0)\n", + " 0. test_multioutput_enetcv_error (0.257)\n", "\n", "Label = histogram\n", "Pred =\n", - " 0. effective_n_jobs (0.481)\n", - " 1. configure (0.176)\n", - " 2. eval (0.034)\n", - " 3. unregister (0.028)\n", - " 4. _terminate_backend (0.009)\n", - " 5. render (0.007)\n", - " 6. map (0.006)\n", + "---- 0. histogram (0.971)\n", "\n", "Label = test_perplexity_input_format\n", "Pred =\n", - " 0. clear_path (0.16)\n", - " 1. get_item_info (0.087)\n", - " 2. contains_item (0.05)\n", - " 3. get_cached_func_info (0.049)\n", - " 4. download_20newsgroups (0.018)\n", - " 5. _get_argument_hash (0.018)\n", - " 6. get_cached_func_code (0.015)\n", + " 0. test_lda_fit_perplexity (0.056)\n", "\n", "Label = test_check_update_with_no_data\n", "Pred =\n", - " 0. __repr__ (0.998)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. test_func (0.0)\n", - " 4. name (0.0)\n", - " 5. state (0.0)\n", - " 6. serialize (0.0)\n", + " 0. test_oob_score_consistency (0.029)\n", "\n", "Label = test_imputation_deletion_warning\n", "Pred =\n", - " 0. to_tuple (0.111)\n", - " 1. unpack_singleton (0.043)\n", - " 2. ordered_obj (0.027)\n", - " 3. in_top_k (0.018)\n", - " 4. address_lists (0.017)\n", - " 5. binomial (0.014)\n", - " 6. _tosequence (0.013)\n", + " 0. test_imputation_error_invalid_strategy (0.844)\n", "\n", "Label = test_basic_property_of_random_matrix\n", "Pred =\n", - " 0. get_image (0.036)\n", - " 1. __reduce__ (0.022)\n", - " 2. classes_ (0.012)\n", - " 3. client_key (0.009)\n", - " 4. sql_client (0.007)\n", - " 5. monitor_client (0.007)\n", - " 6. deconstruct (0.007)\n", + " 0. test_dict_learning_online_initialization (0.033)\n", "\n", "Label = test_basic_property_of_sparse_random_matrix\n", "Pred =\n", - " 0. __init__ (0.898)\n", - " 1. validate (0.003)\n", - " 2. dumps (0.002)\n", - " 3. new_file (0.002)\n", - " 4. partition (0.002)\n", - " 5. _set_debug (0.002)\n", - " 6. __setstate__ (0.002)\n", + " 0. test_agglomerative_clustering_wrong_arg_memory (0.161)\n", "\n", "Label = softmax\n", "Pred =\n", - " 0. make_memmap (0.071)\n", - " 1. check_memmap (0.033)\n", - " 2. write_func (0.032)\n", - " 3. test_memory_layout (0.013)\n", - " 4. _fetch_remote (0.009)\n", - " 5. test_sample_weight_multiclass (0.009)\n", - " 6. _run_one_hot (0.008)\n", + " 0. log_loss (0.21)\n", "\n", "Label = transform\n", "Pred =\n", - " 0. save_module (0.037)\n", - " 1. _geomgen (0.027)\n", - " 2. __getattr__ (0.022)\n", - " 3. eval (0.021)\n", - " 4. resolve_template (0.02)\n", - " 5. __get__ (0.018)\n", - " 6. interrupted (0.012)\n", + " 0. inverse_transform (0.992)\n", "\n", "Label = __getstate__\n", "Pred =\n", - " 0. save_module (0.05)\n", - " 1. save_buffer (0.025)\n", - " 2. __get__ (0.021)\n", - " 3. reraise (0.016)\n", - " 4. save_root_logger (0.016)\n", - " 5. write (0.014)\n", - " 6. process_request (0.012)\n", + "---- 0. __getstate__ (0.915)\n", "\n", "Label = test_check_increasing_small_number_of_samples\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. build (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", + " 0. test_check_increasing_up (0.85)\n", "\n", "Label = test_check_increasing_up_extreme\n", "Pred =\n", - " 0. save_module (0.293)\n", - " 1. save_buffer (0.023)\n", - " 2. save_memoryview (0.022)\n", - " 3. _savepoint_commit (0.01)\n", - " 4. save_property (0.01)\n", - " 5. save_root_logger (0.008)\n", - " 6. _clone (0.007)\n", + " 0. test_check_increasing_up (0.895)\n", "\n", "Label = test_classifier_exceptions\n", "Pred =\n", - " 0. concurrency_safe_write (0.067)\n", - " 1. write_func (0.028)\n", - " 2. send (0.019)\n", - " 3. model_from_json (0.018)\n", - " 4. open_py_source (0.014)\n", - " 5. add_permission (0.012)\n", - " 6. extract (0.011)\n", + " 0. test_gnb_priors (0.066)\n", "\n", "Label = test_mean_strategy_regressor\n", "Pred =\n", - " 0. add_cancelled (0.992)\n", - " 1. add_exception (0.002)\n", - " 2. add_result (0.001)\n", - " 3. cancel (0.0)\n", - " 4. _invoke_callbacks (0.0)\n", - " 5. filter (0.0)\n", - " 6. call (0.0)\n", + " 0. random_ys (0.052)\n", "\n", "Label = set_params\n", "Pred =\n", - " 0. __repr__ (0.734)\n", - " 1. submit (0.022)\n", - " 2. clear (0.004)\n", - " 3. get (0.004)\n", - " 4. wrapped (0.004)\n", - " 5. call (0.004)\n", - " 6. call_and_shelve (0.003)\n", + " 0. compare (0.212)\n", "\n", "Label = predict\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. is_all_none (0.04)\n", - " 1. fromkeys (0.017)\n", - " 2. retrieval_context (0.012)\n", - " 3. _reset_dicts (0.01)\n", - " 4. test_category_dir_2 (0.009)\n", - " 5. x (0.009)\n", - " 6. children (0.008)\n", + "Pred =\n", + "---- 0. predict (0.999)\n", "\n", "Label = predict\n", "Pred =\n", - " 0. _get_executor_init (0.018)\n", - " 1. is_file (0.017)\n", - " 2. is_logged (0.012)\n", - " 3. delete_address_from_mapping (0.009)\n", - " 4. has_shareable_memory (0.009)\n", - " 5. is_link_local (0.008)\n", - " 6. add_new_permissions (0.008)\n", + " 0. fit (0.996)\n", "\n", "Label = _score_to_proba\n", "Pred =\n", - " 0. _reconstruct_wrapper (0.177)\n", - " 1. _wrap_non_picklable_objects (0.073)\n", - " 2. non_trainable_weights (0.028)\n", - " 3. get_available_image_extensions (0.013)\n", - " 4. hash (0.01)\n", - " 5. trainable_weights (0.01)\n", - " 6. _reduce_partial (0.009)\n", + " 0. _score_to_decision (0.268)\n", "\n", "Label = _score_to_proba\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. ip (0.0)\n", - " 4. func (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. init_poolmanager (0.0)\n", + " 0. _score_to_decision (0.459)\n", "\n", "Label = test_warm_start_with_oob_score_fails\n", "Pred =\n", - " 0. __getstate__ (0.975)\n", - " 1. __setstate__ (0.014)\n", - " 2. get (0.002)\n", - " 3. clear (0.0)\n", - " 4. state (0.0)\n", - " 5. get_config (0.0)\n", - " 6. add (0.0)\n", + " 0. test_oob_score_removed_on_warm_start (0.239)\n", "\n", "Label = test_base_zero_n_estimators\n", "Pred =\n", - " 0. __setstate__ (1.0)\n", - " 1. __getstate__ (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. fit (0.0)\n", - " 4. add (0.0)\n", - " 5. register (0.0)\n", - " 6. state (0.0)\n", + " 0. test_base_not_int_n_estimators (0.945)\n", "\n", "Label = fit\n", "Pred =\n", - " 0. rebuild_pipe_connection (0.533)\n", - " 1. reduce_connection (0.061)\n", - " 2. rebuild_connection (0.049)\n", - " 3. _has_nchw_support (0.012)\n", - " 4. get_template_attribute (0.007)\n", - " 5. on_train_end (0.007)\n", - " 6. _get_memory_usage (0.006)\n", + "---- 0. fit (1.0)\n", "\n", "Label = test_probability\n", "Pred =\n", - " 0. decorator (0.089)\n", - " 1. DenseNet121 (0.033)\n", - " 2. DenseNet169 (0.024)\n", - " 3. get_template_attribute (0.015)\n", - " 4. _rebuild_socket (0.014)\n", - " 5. Xception (0.014)\n", - " 6. create_bound_method (0.012)\n", + " 0. test_decision_path (0.138)\n", "\n", "Label = test_oob_score_classifiers\n", "Pred =\n", - " 0. _check_max_depth (0.142)\n", - " 1. to_list_or_none (0.028)\n", - " 2. needs_update (0.02)\n", - " 3. remove_start (0.01)\n", - " 4. _is_explicit_shape (0.009)\n", - " 5. _check_versions (0.008)\n", - " 6. next_sample (0.008)\n", + " 0. test_oob_score_regressors (0.529)\n", "\n", "Label = test_pickle\n", "Pred =\n", - " 0. get (0.512)\n", - " 1. put (0.089)\n", - " 2. post (0.087)\n", - " 3. delete (0.052)\n", - " 4. get_info (0.023)\n", - " 5. is_weakrefable (0.02)\n", - " 6. update (0.014)\n", + " 0. test_parallel (0.26)\n", "\n", "Label = test_class_weight_errors\n", "Pred =\n", - " 0. get_command_line_option (0.049)\n", - " 1. get_language (0.046)\n", - " 2. localtime (0.028)\n", - " 3. cookie_date (0.019)\n", - " 4. from_current_timezone (0.018)\n", - " 5. make_naive (0.017)\n", - " 6. get_language_info (0.016)\n", + " 0. test_class_weights (0.144)\n", "\n", "Label = test_quantile_loss_function\n", "Pred =\n", - " 0. is_data_valid (0.149)\n", - " 1. is_model_valid (0.058)\n", - " 2. check_paired_arrays (0.011)\n", - " 3. _validate_center_shape (0.01)\n", - " 4. _check_shifted_by_one (0.01)\n", - " 5. _make_images (0.009)\n", - " 6. _check_dim_1axis (0.008)\n", + " 0. test_1d_input (0.018)\n", "\n", "Label = test_notfitted\n", "Pred =\n", - " 0. send (0.848)\n", - " 1. _setup_queues (0.005)\n", - " 2. http_date (0.004)\n", - " 3. _extract_videos (0.003)\n", - " 4. SimpleQueue (0.002)\n", - " 5. add_permission (0.002)\n", - " 6. step (0.002)\n", + " 0. test_predictproba_hardvoting (0.963)\n", "\n", "Label = indexbytes\n", "Pred =\n", - " 0. cost_func (0.077)\n", - " 1. log_dloss (0.067)\n", - " 2. precision_recall_curve_padded_thresholds (0.034)\n", - " 3. _estimate_means (0.026)\n", - " 4. update_terminal_regions (0.025)\n", - " 5. callable_rbf_kernel (0.02)\n", - " 6. get_step_size (0.019)\n", + "---- 0. indexbytes (0.921)\n", "\n", "Label = parameters\n", "Pred =\n", - " 0. uniform_ (0.131)\n", - " 1. _sym_decorrelation (0.055)\n", - " 2. _deterministic_vector_sign_flip (0.034)\n", - " 3. normal_ (0.027)\n", - " 4. _svd_cross_product (0.019)\n", - " 5. _check_pydot (0.015)\n", - " 6. check_ortho (0.013)\n", + " 0. _get_image_binds (0.035)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. test_homogeneous_but_not_complete_labeling (0.453)\n", - " 1. test_not_complete_and_not_homogeneous_labeling (0.442)\n", - " 2. test_verbosity (0.004)\n", - " 3. test_lda_negative_input (0.002)\n", - " 4. test_connectivity_fixing_non_lil (0.001)\n", - " 5. test_learning_curve_remove_duplicate_sample_sizes (0.001)\n", - " 6. test_invalid_input_label_binarize (0.001)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = _get_data_object_for_decoding\n", "Pred =\n", - " 0. test_precision_recall_curve_errors (0.04)\n", - " 1. test_matthews_corrcoef_nan (0.025)\n", - " 2. test_inverse_binarize_multiclass (0.025)\n", - " 3. check_input_size_random_matrix (0.016)\n", - " 4. test_connectivity_fixing_non_lil (0.011)\n", - " 5. test_invalid_input_label_binarize (0.009)\n", - " 6. test_gridsearchcv_cross_val_predict_with_method (0.008)\n", + " 0. _get_data_object_for_encoding (0.641)\n", "\n", "Label = write_zfile\n", "Pred =\n", - " 0. test_classification_report_multiclass_with_unicode_label (0.958)\n", - " 1. test_multilabel_classification_report (0.002)\n", - " 2. _accumulate_prediction (0.001)\n", - " 3. max_error (0.001)\n", - " 4. test_roc_curve_multi (0.001)\n", - " 5. random_ys (0.001)\n", - " 6. test_precision_recall_fscore_support_errors (0.001)\n", + " 0. _write_content (0.051)\n", "\n", "Label = register_store_backend\n", "Pred =\n", - " 0. check_single_sample_multioutput (0.538)\n", - " 1. _test_shape_indices (0.022)\n", - " 2. assert_grid_iter_equals_getitem (0.013)\n", - " 3. test_get_n_splits_for_repeated_kfold (0.007)\n", - " 4. _yield_outliers_checks (0.005)\n", - " 5. test_learning_curve_remove_duplicate_sample_sizes (0.004)\n", - " 6. tuple (0.004)\n", + " 0. import_string (0.022)\n", "\n", "Label = _get_func_fullname\n", "Pred =\n", - " 0. test_dense_sparse (0.042)\n", - " 1. test_non_meta_estimators (0.029)\n", - " 2. test_sparse (0.025)\n", - " 3. test_too_many_components (0.02)\n", - " 4. test_1d_input (0.019)\n", - " 5. test_no_attributes_set_in_init (0.019)\n", - " 6. test_validate_parameter_grid_input (0.018)\n", + " 0. resf (0.056)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. test_paired_manhattan_distances (0.826)\n", - " 1. test_ill_posed_min_c (0.052)\n", - " 2. test_multilabel_binarizer_empty_sample (0.006)\n", - " 3. test_add_dummy_feature (0.005)\n", - " 4. test_chi2_negative (0.004)\n", - " 5. test_classifier_single_class (0.004)\n", - " 6. test_input_validation (0.003)\n", + "---- 0. __repr__ (0.999)\n", "\n", "Label = _effective_n_jobs\n", "Pred =\n", - " 0. test_linear_kernel (0.892)\n", - " 1. test_arrays_persist (0.004)\n", - " 2. test_kernel_symmetry (0.003)\n", - " 3. test_softmax (0.003)\n", - " 4. test_classification_sample_weight (0.002)\n", - " 5. gibbs (0.002)\n", - " 6. multioutput_estimator_convert_y_2d (0.001)\n", + " 0. effective_n_jobs (0.551)\n", "\n", "Label = contains_path\n", "Pred =\n", - " 0. constant (0.025)\n", - " 1. all_items_equal (0.022)\n", - " 2. cut (0.016)\n", - " 3. calc_hash (0.016)\n", - " 4. escape_quotes (0.016)\n", - " 5. remove_start (0.016)\n", - " 6. safe_min (0.016)\n", + " 0. contains_item (0.208)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. predict_proba (0.849)\n", - " 1. inverse_transform (0.023)\n", - " 2. predict (0.023)\n", - " 3. score_samples (0.014)\n", - " 4. predict_log_proba (0.013)\n", - " 5. fit_predict (0.011)\n", - " 6. decision_function (0.006)\n", + "---- 0. __repr__ (0.966)\n", "\n", "Label = _funcname\n", "Pred =\n", - " 0. test_randomized_svd_low_rank_all_dtypes (0.045)\n", - " 1. cast_to_floatx (0.029)\n", - " 2. _query_include_self (0.026)\n", - " 3. constant (0.015)\n", - " 4. check_warm_start_smaller_n_estimators (0.014)\n", - " 5. test_label_encoder_str_bad_shape (0.011)\n", - " 6. test_ovo_float_y (0.01)\n", + " 0. unpack_singleton (0.076)\n", "\n", "Label = get_nested_backend\n", "Pred =\n", - " 0. wrap_oracle_errors (0.027)\n", - " 1. _check_default_group (0.026)\n", - " 2. register_forward_hook (0.012)\n", - " 3. initialize (0.011)\n", - " 4. _decrement_pending_calls (0.009)\n", - " 5. python_implementation (0.008)\n", - " 6. test_gradient_squared_epsilon_insensitive (0.008)\n", + " 0. __dir__ (0.056)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. predict_proba (0.954)\n", - " 1. predict_log_proba (0.008)\n", - " 2. decision_function (0.008)\n", - " 3. predict (0.004)\n", - " 4. _score_to_decision (0.002)\n", - " 5. score (0.001)\n", - " 6. _collect_probas (0.001)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = make_memmap\n", "Pred =\n", - " 0. test_crammer_singer_binary (0.032)\n", - " 1. test_contamination_future_warning (0.03)\n", - " 2. test_lasso_alpha_warning (0.029)\n", - " 3. test_linear_svc_intercept_scaling (0.029)\n", - " 4. test_contamination (0.024)\n", - " 5. test_ovr_coef_exceptions (0.019)\n", - " 6. test_lsvc_intercept_scaling_zero (0.018)\n", + " 0. test_too_many_components (0.059)\n", "\n", "Label = save_builtin_function\n", "Pred =\n", - " 0. _values (0.992)\n", - " 1. _values_svd (0.0)\n", - " 2. _get_executor_init (0.0)\n", - " 3. max_loading_is_positive (0.0)\n", - " 4. set_autocommit (0.0)\n", - " 5. max_pool1d_with_indices (0.0)\n", - " 6. get_parameter (0.0)\n", + " 0. save_module (0.127)\n", "\n", "Label = save_classmethod\n", "Pred =\n", - " 0. _values_svd (0.968)\n", - " 1. _svd_cross_product (0.002)\n", - " 2. _values (0.001)\n", - " 3. _sym_decorrelation (0.001)\n", - " 4. laplacian_kernel (0.0)\n", - " 5. _get_n_results (0.0)\n", - " 6. check_presort_sparse (0.0)\n", + " 0. dispatcher (0.141)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. __call__ (0.033)\n", - " 1. _check_type_int (0.016)\n", - " 2. _check_type_float (0.014)\n", - " 3. import_library (0.014)\n", - " 4. _check_inverse_transform (0.014)\n", - " 5. _validate_range (0.013)\n", - " 6. utc_tzinfo_factory (0.011)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = save_weakset\n", "Pred =\n", - " 0. __str__ (0.584)\n", - " 1. add (0.022)\n", - " 2. writelines (0.018)\n", - " 3. describe (0.018)\n", - " 4. _check_params (0.01)\n", - " 5. _checkindex (0.009)\n", - " 6. check_args (0.008)\n", + " 0. save_set (0.029)\n", "\n", "Label = dump\n", "Pred =\n", - " 0. decision_function (0.976)\n", - " 1. score_samples (0.007)\n", - " 2. predict (0.006)\n", - " 3. _decision_function (0.003)\n", - " 4. predict_proba (0.001)\n", - " 5. staged_decision_function (0.0)\n", - " 6. score (0.0)\n", + " 0. end_object (0.474)\n", "\n", "Label = add_cancelled\n", "Pred =\n", - " 0. decision_function (0.976)\n", - " 1. score_samples (0.007)\n", - " 2. predict (0.006)\n", - " 3. _decision_function (0.003)\n", - " 4. predict_proba (0.001)\n", - " 5. staged_decision_function (0.0)\n", - " 6. score (0.0)\n", + "---- 0. add_cancelled (0.994)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. _decision_function (0.47)\n", - " 1. inverse_transform (0.06)\n", - " 2. score_samples (0.051)\n", - " 3. predict_proba (0.031)\n", - " 4. fit_predict (0.023)\n", - " 5. _validate_y (0.021)\n", - " 6. decision_function (0.018)\n", + "---- 0. __repr__ (0.989)\n", "\n", "Label = _chain_from_iterable_of_lists\n", "Pred =\n", - " 0. _decision_function (0.453)\n", - " 1. sparse_coef_ (0.088)\n", - " 2. transform (0.036)\n", - " 3. inverse_transform (0.024)\n", - " 4. densify (0.02)\n", - " 5. score_samples (0.018)\n", - " 6. decision_function (0.012)\n", + " 0. _set_x (0.059)\n", "\n", "Label = _get_next_executor_id\n", "Pred =\n", - " 0. grad (0.399)\n", - " 1. linear_kernel (0.06)\n", - " 2. transform (0.026)\n", - " 3. sign (0.017)\n", - " 4. predict_proba (0.017)\n", - " 5. encode_dict (0.016)\n", - " 6. grad_hess (0.01)\n", + " 0. get_all_objs (0.067)\n", "\n", "Label = __reduce__\n", "Pred =\n", - " 0. test_correct_shapes (0.963)\n", - " 1. check_size_generated (0.003)\n", - " 2. test_omp_return_path_prop_with_gram (0.002)\n", - " 3. test_make_hastie_10_2 (0.001)\n", - " 4. _check_shape (0.001)\n", - " 5. _validate_center_shape (0.001)\n", - " 6. test_make_sparse_uncorrelated (0.001)\n", + " 0. __enter__ (0.08)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. test_bagging_small_max_features (0.021)\n", - " 1. test_multinomial_validation (0.017)\n", - " 2. test_multi_output_classification_partial_fit_no_first_classes_exception (0.013)\n", - " 3. test_valid_n_bins (0.013)\n", - " 4. test_isotonic_regression_oob_bad (0.013)\n", - " 5. test_refit (0.012)\n", - " 6. test_classifier_refit (0.011)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = __getstate__\n", "Pred =\n", - " 0. test_linear_svc_intercept_scaling (0.084)\n", - " 1. test_check_array_large_indices_non_supported_scipy_version (0.053)\n", - " 2. test_nestimators_future_warning (0.025)\n", - " 3. test_check_array_accept_large_sparse_raise_exception (0.024)\n", - " 4. _register_dask (0.018)\n", - " 5. test_check_classification_targets (0.014)\n", - " 6. test_multinomial_validation (0.014)\n", + "---- 0. __getstate__ (0.94)\n", "\n", "Label = __setstate__\n", "Pred =\n", - " 0. test_warm_start_smaller_n_estimators (0.042)\n", - " 1. check_regressor_data_not_an_array (0.041)\n", - " 2. test_learning_curve_incremental_learning_not_possible (0.027)\n", - " 3. test_f_classif_constant_feature (0.025)\n", - " 4. _partial_fit (0.024)\n", - " 5. test_trustworthiness_not_euclidean_metric (0.021)\n", - " 6. test_oob_score_removed_on_warm_start (0.02)\n", + "---- 0. __setstate__ (0.998)\n", "\n", "Label = reduce_pipe_connection\n", "Pred =\n", - " 0. test_partial_fit_exception (0.085)\n", - " 1. test_sgd (0.056)\n", - " 2. test_set_intercept_binary (0.049)\n", - " 3. test_input_format (0.038)\n", - " 4. test_sgd_early_stopping_with_partial_fit (0.037)\n", - " 5. test_sgd_at_least_two_labels (0.032)\n", - " 6. test_provide_coef (0.031)\n", + " 0. rebuild_pipe_connection (0.44)\n", "\n", "Label = fromfd\n", "Pred =\n", - " 0. test_partial_fit_exception (0.659)\n", - " 1. test_sgd_early_stopping_with_partial_fit (0.063)\n", - " 2. test_gnb_pfit_wrong_nb_features (0.025)\n", - " 3. test_wrong_class_weight_label (0.013)\n", - " 4. test_set_intercept_binary (0.009)\n", - " 5. test_wrong_class_weight_format (0.007)\n", - " 6. test_partial_fit_weight_class_balanced (0.007)\n", + " 0. get_capabilities (0.353)\n", "\n", "Label = set_start_method\n", "Pred =\n", - " 0. _log_dirichlet_norm (0.022)\n", - " 1. is_outdated_version (0.013)\n", - " 2. squared_hinge (0.013)\n", - " 3. _wrap_non_picklable_objects (0.01)\n", - " 4. decode_hex (0.01)\n", - " 5. mean_squared_error (0.009)\n", - " 6. loss_grad_fun (0.008)\n", + " 0. configure (0.159)\n", "\n", "Label = _check_alive\n", "Pred =\n", - " 0. test_warm_start_smaller_n_estimators (0.065)\n", - " 1. test_precomputed (0.025)\n", - " 2. test_spca_deprecation_warning (0.025)\n", - " 3. test_multilabel (0.022)\n", - " 4. test_no_empty_slice_warning (0.014)\n", - " 5. test_make_union (0.014)\n", - " 6. test_gnb_pfit_wrong_nb_features (0.013)\n", + " 0. put (0.227)\n", "\n", "Label = is_forking\n", "Pred =\n", - " 0. _assert_nmf_no_nan (0.16)\n", - " 1. _count (0.016)\n", - " 2. any (0.015)\n", - " 3. test_reduction_to_one_component (0.01)\n", - " 4. debugOutput (0.01)\n", - " 5. test_check_no_attributes_set_in_init (0.009)\n", - " 6. test_power_transform_default_method (0.009)\n", + " 0. ensure_dir_exists (0.021)\n", "\n", "Label = set_cause\n", "Pred =\n", - " 0. stderr_redirector (0.164)\n", - " 1. stdout_redirector (0.119)\n", - " 2. _validate_app_names (0.05)\n", - " 3. report_download (0.016)\n", - " 4. rollback (0.009)\n", - " 5. generator (0.008)\n", - " 6. unsubscribe (0.007)\n", + " 0. test_bayesian_mixture_check_is_fitted (0.061)\n", "\n", "Label = dump\n", "Pred =\n", - " 0. freeze_time (0.037)\n", - " 1. test_randomized_pca_check_list (0.025)\n", - " 2. reverse_ordering (0.014)\n", - " 3. test_incremental_pca_against_pca_iris (0.013)\n", - " 4. validate_config (0.009)\n", - " 5. test_deprecated_calinski_harabaz_score (0.008)\n", - " 6. test_masked_array_obj_dtype_pickleable (0.008)\n", + " 0. send (0.788)\n", "\n", "Label = _binary_uninterpolated_average_precision\n", "Pred =\n", - " 0. check_pdist_bool (0.488)\n", - " 1. check_pdist (0.19)\n", - " 2. check_cdist (0.172)\n", - " 3. check_pickle (0.017)\n", - " 4. _estimate_gaussian_covariances_spherical (0.001)\n", - " 5. f (0.001)\n", - " 6. pairwise_distance (0.001)\n", + " 0. fbeta_score (0.044)\n", "\n", "Label = _generalized_average\n", "Pred =\n", - " 0. test_cdist_bool_metric (0.873)\n", - " 1. test_classification_inf_nan_input (0.013)\n", - " 2. test_regression_thresholded_inf_nan_input (0.008)\n", - " 3. test_pairwise_parallel (0.004)\n", - " 4. f1_loss (0.004)\n", - " 5. test_classification_toy (0.002)\n", - " 6. test__check_targets_multiclass_with_both_y_true_and_y_pred_binary (0.002)\n", + " 0. normal_ (0.285)\n", "\n", "Label = test_complete_but_not_homogeneous_labeling\n", "Pred =\n", - " 0. check_pickle (0.904)\n", - " 1. check_pdist_bool (0.009)\n", - " 2. check_pdist (0.005)\n", - " 3. check_cdist (0.004)\n", - " 4. check_pickle_protocol (0.002)\n", - " 5. fork_exec (0.001)\n", - " 6. _get_pass (0.001)\n", + " 0. test_not_complete_and_not_homogeneous_labeling (0.483)\n", "\n", "Label = test_expected_mutual_info_overflow\n", "Pred =\n", - " 0. test_isomap_clone_bug (0.062)\n", - " 1. terminate (0.025)\n", - " 2. test_linearsvc_verbose (0.018)\n", - " 3. test_pickle_version_no_warning_is_issued_with_non_sklearn_estimator (0.013)\n", - " 4. _rewind (0.011)\n", - " 5. test_load_diabetes (0.009)\n", - " 6. test_category_dir_2 (0.009)\n", + " 0. test_precision_recall_curve_errors (0.073)\n", "\n", "Label = test_classification_report_multiclass_with_long_string_label\n", "Pred =\n", - " 0. constant_time_compare (0.022)\n", - " 1. _get_executor_init (0.02)\n", - " 2. test_linearsvc_verbose (0.014)\n", - " 3. lock (0.012)\n", - " 4. _combinations (0.01)\n", - " 5. print_exec (0.008)\n", - " 6. pretty_wkt (0.006)\n", + " 0. test_classification_report_multiclass_with_unicode_label (0.955)\n", "\n", "Label = check_single_sample\n", "Pred =\n", - " 0. check_subtest_picklable (0.044)\n", - " 1. _to_bytes (0.023)\n", - " 2. addSubTest (0.021)\n", - " 3. _object_dtype_isnan (0.018)\n", - " 4. stable_topological_sort (0.016)\n", - " 5. value_is_list (0.016)\n", - " 6. earliest (0.008)\n", + " 0. check_single_sample_multioutput (0.882)\n", "\n", "Label = test_label_ranking_avp\n", "Pred =\n", - " 0. check_subtest_picklable (0.044)\n", - " 1. _to_bytes (0.023)\n", - " 2. addSubTest (0.021)\n", - " 3. _object_dtype_isnan (0.018)\n", - " 4. stable_topological_sort (0.016)\n", - " 5. value_is_list (0.016)\n", - " 6. earliest (0.008)\n", + " 0. test_single_sample (0.038)\n", "\n", "Label = test_paired_euclidean_distances\n", "Pred =\n", - " 0. test_predict (0.181)\n", - " 1. check_input_size_random_matrix (0.046)\n", - " 2. test_connectivity_fixing_non_lil (0.019)\n", - " 3. test_bagging_small_max_features (0.015)\n", - " 4. test_average_precision_score_duplicate_values (0.012)\n", - " 5. test_mcd (0.01)\n", - " 6. test_lasso_alpha_warning (0.009)\n", + " 0. test_paired_manhattan_distances (0.939)\n", "\n", "Label = test_rbf_kernel\n", "Pred =\n", - " 0. get_params (0.999)\n", - " 1. set_params (0.0)\n", - " 2. configure (0.0)\n", - " 3. function (0.0)\n", - " 4. submit (0.0)\n", - " 5. __init__ (0.0)\n", - " 6. patch (0.0)\n", + " 0. test_linear_kernel (0.968)\n", "\n", "Label = tuplify\n", "Pred =\n", - " 0. normal_ (0.132)\n", - " 1. squared_loss (0.111)\n", - " 2. custom_metric (0.047)\n", - " 3. two_pass_var (0.044)\n", - " 4. log_loss (0.031)\n", - " 5. _check_shifted_by_one (0.015)\n", - " 6. binary_log_loss (0.013)\n", + " 0. flatten (0.067)\n", "\n", "Label = predict_proba\n", "Pred =\n", - " 0. affine_grid (0.055)\n", - " 1. test_entropy (0.028)\n", - " 2. test_bagging_with_pipeline (0.02)\n", - " 3. get_unit_id (0.01)\n", - " 4. test_hasher_set_params (0.009)\n", - " 5. run_func_with_change_token_backoff (0.009)\n", - " 6. abort_everything (0.009)\n", + " 0. _e_step (0.171)\n", "\n", "Label = _check_means\n", "Pred =\n", - " 0. test_shape_y (0.05)\n", - " 1. test_randomized_svd_low_rank_all_dtypes (0.016)\n", - " 2. test_integers (0.016)\n", - " 3. test_fit_transform (0.01)\n", - " 4. sha_utf8 (0.009)\n", - " 5. test_chi2_coo (0.009)\n", - " 6. test_label_encoder_str_bad_shape (0.009)\n", + " 0. gaussian_random_matrix (0.071)\n", "\n", "Label = _check_proba\n", "Pred =\n", - " 0. unicode_is_ascii (0.107)\n", - " 1. _sym_decorrelation (0.039)\n", - " 2. _svd_cross_product (0.034)\n", - " 3. _gs_decorrelation (0.024)\n", - " 4. my_svd (0.02)\n", - " 5. cut (0.019)\n", - " 6. compat_print (0.013)\n", + " 0. _log_to_syslog (0.035)\n", "\n", "Label = _predict_proba\n", "Pred =\n", - " 0. test_decode_emotions (0.276)\n", - " 1. test_decode_iris (0.257)\n", - " 2. test_decode_cpu (0.252)\n", - " 3. test_string_attribute (0.012)\n", - " 4. test_dataset_with_openml_error (0.01)\n", - " 5. test_dataset_with_openml_warning (0.009)\n", - " 6. test_fetch_nonexiting (0.009)\n", + " 0. predict_log_proba (0.601)\n", "\n", "Label = test_linear_svx_uppercase_loss_penality_raises_error\n", "Pred =\n", - " 0. test_fetch_nonexiting (0.167)\n", - " 1. test_dataset_with_openml_warning (0.154)\n", - " 2. test_dataset_with_openml_error (0.151)\n", - " 3. test_string_attribute (0.137)\n", - " 4. test_raises_illegal_multitarget (0.09)\n", - " 5. test_illegal_column (0.061)\n", - " 6. test_decode_iris (0.018)\n", + " 0. test_gamma_auto (0.04)\n", "\n", "Label = _errors\n", "Pred =\n", - " 0. get_data_home (0.032)\n", - " 1. clear_data_home (0.03)\n", - " 2. transform_kernels (0.026)\n", - " 3. clear_path (0.014)\n", - " 4. _get_local_path (0.013)\n", - " 5. clear_item (0.011)\n", - " 6. _get_data_features (0.011)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " 0. _values (0.994)\n", + "\n", "Label = _errors_svd\n", "Pred =\n", - " 0. test_load_sample_images (0.332)\n", - " 1. test_default_load_files (0.071)\n", - " 2. test_load_diabetes (0.036)\n", - " 3. test_load_files_wo_load_content (0.028)\n", - " 4. test_load_wine (0.022)\n", - " 5. test_load_boston (0.013)\n", - " 6. test_load_iris (0.013)\n", + " 0. _values_svd (0.97)\n", "\n", "Label = _get_learning_rate_type\n", "Pred =\n", - " 0. test_load_digits_n_class_lt_10 (0.952)\n", - " 1. test_lr_liblinear_warning (0.004)\n", - " 2. test_make_hastie_10_2 (0.001)\n", - " 3. test_load_wine (0.001)\n", - " 4. test_fetch (0.001)\n", - " 5. test_numerical_stability_large_gradient (0.001)\n", - " 6. test_singular_matrix (0.001)\n", + " 0. add (0.045)\n", "\n", "Label = _check_proba\n", "Pred =\n", - " 0. test_deprecated_calinski_harabaz_score (0.025)\n", - " 1. test_fetch (0.025)\n", - " 2. test_attributes (0.023)\n", - " 3. kill_process (0.018)\n", - " 4. engine_version (0.018)\n", - " 5. test_gradient_squared_epsilon_insensitive (0.014)\n", - " 6. test_integer_input (0.01)\n", + " 0. extra_repr (0.073)\n", "\n", "Label = predict\n", "Pred =\n", - " 0. image_dim_ordering (0.024)\n", - " 1. get_current_timezone (0.015)\n", - " 2. _len_and_data (0.015)\n", - " 3. test_kernel_pca_invalid_parameters (0.014)\n", - " 4. is_whitespace (0.012)\n", - " 5. test_try_to_transform_before_fit (0.011)\n", - " 6. auth (0.01)\n", + " 0. decision_function (0.945)\n", "\n", "Label = predict\n", "Pred =\n", - " 0. feature_importances_ (0.068)\n", - " 1. append_feature (0.018)\n", - " 2. config_evnp_bd (0.015)\n", - " 3. security_log_profiles (0.012)\n", - " 4. disable (0.01)\n", - " 5. n_dims (0.01)\n", - " 6. _get_details_from_resource (0.008)\n", + " 0. decision_function (0.945)\n", "\n", "Label = _set_intercept\n", "Pred =\n", - " 0. get_ancestor_link (0.082)\n", - " 1. get_parent_list (0.027)\n", - " 2. get_func_args (0.02)\n", - " 3. find_authorization_role (0.011)\n", - " 4. _get_leaves (0.008)\n", - " 5. wraps (0.008)\n", - " 6. int64_output (0.007)\n", + " 0. fit (0.949)\n", "\n", "Label = sparsify\n", "Pred =\n", - " 0. test_threshold_deprecation (0.073)\n", - " 1. test_precomputed (0.058)\n", - " 2. test_average_precision_score_tied_values (0.021)\n", - " 3. test_logreg_intercept_scaling_zero (0.017)\n", - " 4. test_regression_toy (0.016)\n", - " 5. test_gnb_prior_greater_one (0.015)\n", - " 6. test_sample_weight_missing (0.014)\n", + " 0. sparse_coef_ (0.291)\n", "\n", "Label = f\n", "Pred =\n", - " 0. test_subpopulation (0.059)\n", - " 1. test_symbol_labels (0.029)\n", - " 2. test_lasso_cv_with_some_model_selection (0.021)\n", - " 3. test_nestimators_future_warning (0.016)\n", - " 4. test_subsamples (0.01)\n", - " 5. test_coef_shape_not_zero (0.009)\n", - " 6. test_huber_equals_lr_for_high_epsilon (0.009)\n", + " 0. grad (0.439)\n", "\n", "Label = test_correct_shapes_gram\n", "Pred =\n", - " 0. test_multilabel_binarizer_non_unique (0.151)\n", - " 1. test_raw_values_deprecation (0.09)\n", - " 2. test_write_parameters (0.084)\n", - " 3. test_sgd (0.043)\n", - " 4. test_coef_shape_not_zero (0.042)\n", - " 5. test_predict (0.017)\n", - " 6. test_omp_return_path_prop_with_gram (0.015)\n", + " 0. test_correct_shapes (0.98)\n", "\n", "Label = test_nan\n", "Pred =\n", - " 0. get_n_splits (0.714)\n", - " 1. fit (0.137)\n", - " 2. _iter_indices (0.081)\n", - " 3. score (0.008)\n", - " 4. split (0.007)\n", - " 5. fit_predict (0.002)\n", - " 6. _fit_predict (0.002)\n", + " 0. test_max_samples_consistency (0.03)\n", "\n", "Label = test_logreg_intercept_scaling\n", "Pred =\n", - " 0. get_n_splits (0.983)\n", - " 1. _iter_indices (0.003)\n", - " 2. score (0.002)\n", - " 3. split (0.0)\n", - " 4. _iter_test_indices (0.0)\n", - " 5. fit_transform (0.0)\n", - " 6. ur (0.0)\n", + " 0. test_linear_svc_intercept_scaling (0.639)\n", "\n", "Label = test_ridge_classifier_no_support_multilabel\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. destination (0.0)\n", - " 6. start_serialization (0.0)\n", + " 0. test_fit_transform (0.163)\n", "\n", "Label = test_set_intercept_to_intercept\n", "Pred =\n", - " 0. get_n_splits (0.97)\n", - " 1. split (0.01)\n", - " 2. _iter_indices (0.009)\n", - " 3. _validate_y (0.0)\n", - " 4. score (0.0)\n", - " 5. fit_transform (0.0)\n", - " 6. ur (0.0)\n", + " 0. test_set_intercept_binary (0.062)\n", "\n", "Label = test_fit_then_partial_fit\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. destination (0.0)\n", - " 4. __eq__ (0.0)\n", - " 5. ip (0.0)\n", - " 6. start_serialization (0.0)\n", + " 0. test_partial_fit_exception (0.849)\n", "\n", "Label = get_pobj\n", "Pred =\n", - " 0. transform (0.432)\n", - " 1. _tosequence (0.044)\n", - " 2. densify (0.042)\n", - " 3. _compute_kernel (0.02)\n", - " 4. norm (0.014)\n", - " 5. _check_X (0.01)\n", - " 6. make_nonnegative (0.01)\n", + " 0. _solve_deps (0.124)\n", "\n", "Label = test_precompute_invalid_argument\n", "Pred =\n", - " 0. predict (0.995)\n", - " 1. predict_proba (0.002)\n", - " 2. decision_function (0.0)\n", - " 3. diag (0.0)\n", - " 4. transform (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. apply (0.0)\n", + " 0. test_partial_fit_weight_class_balanced (0.057)\n", "\n", "Label = test_enet_copy_X_False_check_input_False\n", "Pred =\n", - " 0. decision_function (0.929)\n", - " 1. predict (0.024)\n", - " 2. score_samples (0.011)\n", - " 3. _decision_function (0.007)\n", - " 4. predict_proba (0.004)\n", - " 5. transform (0.002)\n", - " 6. decision_path (0.002)\n", + " 0. test_enet_positive_constraint (0.065)\n", "\n", "Label = no_stdout_stderr\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. destination (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. __setstate__ (0.0)\n", + " 0. stdout_redirector (0.139)\n", "\n", "Label = test_calc_breakdown_point\n", "Pred =\n", - " 0. train_test_split_pandas (0.065)\n", - " 1. test_lars_path_readonly_data (0.021)\n", - " 2. min_max_axis (0.021)\n", - " 3. train_test_split_sparse (0.014)\n", - " 4. test_bayesian_mixture_check_is_fitted (0.009)\n", - " 5. test_ridge_sparse_svd (0.009)\n", - " 6. max_extents (0.008)\n", + " 0. infix (0.078)\n", "\n", "Label = check_cdist_bool\n", "Pred =\n", - " 0. test_big_input (0.173)\n", - " 1. test_int_float_dict_argmin (0.027)\n", - " 2. test_tfidf_transformer_type (0.025)\n", - " 3. predict_classes (0.022)\n", - " 4. _score_to_proba (0.018)\n", - " 5. test_init_ndarray (0.016)\n", - " 6. test_compute_sample_weight_more_than_32 (0.015)\n", + " 0. check_pdist_bool (0.834)\n", "\n", "Label = test_pdist_bool_metrics\n", "Pred =\n", - " 0. test_gridsearchcv_cross_val_predict_with_method (0.755)\n", - " 1. test_grid_search_with_fit_params (0.029)\n", - " 2. test_cross_val_predict_with_method (0.015)\n", - " 3. test_predict_3_classes (0.012)\n", - " 4. test_invalid_strategy_option (0.01)\n", - " 5. test_quantile_strategy_empty_train (0.009)\n", - " 6. test_invalid_encode_option (0.008)\n", + " 0. test_cdist_bool_metric (0.964)\n", "\n", "Label = test_pickle_bool_metrics\n", "Pred =\n", - " 0. score (1.0)\n", - " 1. __call__ (0.0)\n", - " 2. decision_function (0.0)\n", - " 3. call_and_shelve (0.0)\n", - " 4. _fit_predict (0.0)\n", - " 5. get_n_splits (0.0)\n", - " 6. score_samples (0.0)\n", + " 0. check_pickle (0.923)\n", "\n", "Label = test_not_fitted_error_gets_raised\n", "Pred =\n", - " 0. test_grid_search_with_fit_params (0.418)\n", - " 1. test_predict_3_classes (0.139)\n", - " 2. test_gridsearchcv_cross_val_predict_with_method (0.042)\n", - " 3. test_cross_val_predict_with_method (0.023)\n", - " 4. test_set_params_nested_pipeline (0.014)\n", - " 5. test_quantile_strategy_empty_train (0.009)\n", - " 6. test_saga_sparse (0.008)\n", + " 0. test_isomap_clone_bug (0.108)\n", "\n", "Label = check_two_point\n", "Pred =\n", - " 0. predict (0.998)\n", - " 1. decision_function (0.001)\n", - " 2. predict_proba (0.001)\n", - " 3. transform (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. __hash__ (0.0)\n", - " 6. diag (0.0)\n", + " 0. check_results (0.036)\n", "\n", "Label = test_node_heap\n", "Pred =\n", - " 0. test_tolerance (0.026)\n", - " 1. test_entropy (0.022)\n", - " 2. test_verbosity (0.015)\n", - " 3. test_load_svmlight_file_multilabel (0.012)\n", - " 4. test_contamination_future_warning (0.011)\n", - " 5. has_seq_axis (0.01)\n", - " 6. test_contamination (0.01)\n", + " 0. build_dep_data (0.026)\n", "\n", "Label = test_node_heap\n", "Pred =\n", - " 0. _send (0.052)\n", - " 1. dropout (0.024)\n", - " 2. report_warning (0.017)\n", - " 3. register_forward_hook (0.015)\n", - " 4. get_losses_for (0.012)\n", - " 5. flat_function (0.011)\n", - " 6. execute_pyxcli_command (0.008)\n", + " 0. build_dep_data (0.026)\n", "\n", "Label = test_fit_transduction\n", "Pred =\n", - " 0. test_non_square_precomputed_distances (0.201)\n", - " 1. test_pca_initialization_not_compatible_with_precomputed_kernel (0.194)\n", - " 2. test_n_components_range (0.108)\n", - " 3. test_early_exaggeration_too_small (0.088)\n", - " 4. test_distance_not_available (0.086)\n", - " 5. test_too_few_iterations (0.081)\n", - " 6. test_init_not_available (0.036)\n", + " 0. test_predict (0.069)\n", "\n", "Label = get_params\n", "Pred =\n", - " 0. test_fit_best_piecewise (0.106)\n", - " 1. test_meanshift_all_orphans (0.05)\n", - " 2. test_MDS (0.037)\n", - " 3. test_non_positive_precomputed_distances (0.027)\n", - " 4. test_manhattan_metric (0.012)\n", - " 5. test_empty_extract (0.011)\n", - " 6. test_contamination_future_warning (0.01)\n", + "---- 0. get_params (1.0)\n", "\n", "Label = _check_standard_scaled\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. build (0.0)\n", - " 5. __init_module__ (0.0)\n", - " 6. start_serialization (0.0)\n", + " 0. squared_loss (0.362)\n", "\n", "Label = test_column_transformer_no_estimators_set_params\n", "Pred =\n", - " 0. inverse_transform (0.987)\n", - " 1. transform (0.005)\n", - " 2. score_samples (0.002)\n", - " 3. predict_proba (0.001)\n", - " 4. predict (0.0)\n", - " 5. decision_function (0.0)\n", - " 6. __repr__ (0.0)\n", + " 0. raiseFromErrno (0.082)\n", "\n", "Label = _inverse_permutation\n", "Pred =\n", - " 0. transform (0.508)\n", - " 1. predict (0.103)\n", - " 2. fit_transform (0.017)\n", - " 3. decision_function (0.015)\n", - " 4. _inverse_transform (0.011)\n", - " 5. softmax (0.01)\n", - " 6. x (0.009)\n", + " 0. check_input_with_sparse_random_matrix (0.044)\n", "\n", "Label = _find_permutation\n", "Pred =\n", - " 0. inverse_transform (0.639)\n", - " 1. __call__ (0.067)\n", - " 2. score (0.061)\n", - " 3. fit (0.027)\n", - " 4. partial_fit (0.025)\n", - " 5. predict_proba (0.02)\n", - " 6. score_samples (0.017)\n", + " 0. _deterministic_vector_sign_flip (0.237)\n", "\n", "Label = test_decode_anneal\n", "Pred =\n", - " 0. inverse_transform (0.588)\n", - " 1. func (0.078)\n", - " 2. predict_proba (0.076)\n", - " 3. call_and_shelve (0.028)\n", - " 4. decision_function (0.026)\n", - " 5. score_samples (0.026)\n", - " 6. sign (0.009)\n", + " 0. test_decode_cpu (0.316)\n", "\n", "Label = test_fetch_openml_raises_missing_values_target\n", "Pred =\n", - " 0. __call__ (0.201)\n", - " 1. decode (0.113)\n", - " 2. read (0.051)\n", - " 3. eval (0.04)\n", - " 4. call_and_shelve (0.022)\n", - " 5. score (0.018)\n", - " 6. fit (0.015)\n", + " 0. test_fetch_nonexiting (0.232)\n", "\n", "Label = test_data_home\n", "Pred =\n", - " 0. fit (0.993)\n", - " 1. fit_transform (0.001)\n", - " 2. fit_predict (0.001)\n", - " 3. predict_proba (0.001)\n", - " 4. transform (0.001)\n", - " 5. inverse_transform (0.0)\n", - " 6. __setstate__ (0.0)\n", + " 0. clear_data_home (0.37)\n", "\n", "Label = test_default_empty_load_files\n", "Pred =\n", - " 0. test_add_dummy_feature_coo (0.057)\n", - " 1. test_load_svmlight_file_multilabel (0.042)\n", - " 2. test_classifier_single_class (0.033)\n", - " 3. test_paired_manhattan_distances (0.018)\n", - " 4. test_MDS (0.015)\n", - " 5. test_threshold_deprecation (0.014)\n", - " 6. test_check_dense_matrices (0.014)\n", + " 0. test_default_load_files (0.166)\n", "\n", "Label = test_load_digits\n", "Pred =\n", - " 0. test_singular_matrix (0.024)\n", - " 1. train_test_split_sparse (0.023)\n", - " 2. center_and_norm (0.016)\n", - " 3. test_multilabel_binarizer_empty_sample (0.009)\n", - " 4. parse_interfaces (0.008)\n", - " 5. test_refit (0.007)\n", - " 6. argmax (0.006)\n", + " 0. test_load_digits_n_class_lt_10 (0.978)\n", "\n", "Label = test_load_sample_image\n", "Pred =\n", - " 0. score (0.989)\n", - " 1. fit (0.004)\n", - " 2. fit_predict (0.001)\n", - " 3. _fit_predict (0.001)\n", - " 4. _check_fit_data (0.001)\n", - " 5. __call__ (0.001)\n", - " 6. predict_proba (0.0)\n", + " 0. umc_module_for_add (0.026)\n", "\n", "Label = test_bunch_dir\n", "Pred =\n", - " 0. _box_cox_inverse_tranform (0.041)\n", - " 1. mahalanobis (0.04)\n", - " 2. update_terminal_regions (0.036)\n", - " 3. _svd_cross_product (0.029)\n", - " 4. _estimate_log_prob (0.022)\n", - " 5. update_coeff (0.014)\n", - " 6. _values_svd (0.012)\n", + " 0. test_symmetric_non_symmetric_union (0.047)\n", "\n", "Label = feature_importances_\n", "Pred =\n", - " 0. test_k_means_n_init (0.708)\n", - " 1. test_minibatch_with_many_reassignments (0.029)\n", - " 2. test_y_mean_attribute_regressor (0.016)\n", - " 3. test_mcd_class_on_invalid_input (0.005)\n", - " 4. test_chebyshev_metric (0.003)\n", - " 5. test_constant_size_multioutput_regressor (0.003)\n", - " 6. test_kernel_pca_invalid_kernel (0.003)\n", + " 0. get_n_leaves (0.185)\n", "\n", "Label = ancestor\n", "Pred =\n", - " 0. inplace_tanh_derivative (0.824)\n", - " 1. inplace_relu_derivative (0.075)\n", - " 2. grid_to_graph (0.001)\n", - " 3. assert_correct_incr (0.001)\n", - " 4. _generate_indices (0.001)\n", - " 5. legacy_get_enum (0.001)\n", - " 6. transpose_for_scores (0.001)\n", + " 0. get_role (0.067)\n", "\n", "Label = test_importances_raises\n", "Pred =\n", - " 0. botocore_at_least (0.02)\n", - " 1. _estimate_gaussian_covariances_spherical (0.017)\n", - " 2. is_string (0.016)\n", - " 3. boto3_at_least (0.015)\n", - " 4. create_mv (0.015)\n", - " 5. _log_dirichlet_norm (0.014)\n", - " 6. negative_gradient (0.013)\n", + " 0. test_precomputed (0.208)\n", "\n", "Label = test_max_leaf_nodes_max_depth\n", "Pred =\n", - " 0. test_rfe_estimator_tags (0.028)\n", - " 1. test_threshold (0.02)\n", - " 2. test_fit (0.019)\n", - " 3. test_ransac_none_estimator (0.018)\n", - " 4. test_max_samples_consistency (0.017)\n", - " 5. test_oob_multilcass_iris (0.015)\n", - " 6. test_learning_curve_incremental_learning_not_possible (0.014)\n", + " 0. test_oob_multilcass_iris (0.06)\n", "\n", "Label = test_decision_path_hardcoded\n", "Pred =\n", - " 0. test_tolerance (0.074)\n", - " 1. test_verbosity (0.018)\n", - " 2. test_sgd (0.015)\n", - " 3. test_check_array_dtype_stability (0.012)\n", - " 4. _staged_decision_function (0.011)\n", - " 5. test_raw_values_deprecation (0.01)\n", - " 6. test_base_optimizer (0.009)\n", + " 0. test_multilabel_binarizer_non_unique (0.033)\n", "\n", "Label = _iter_test_indices\n", "Pred =\n", - " 0. normalize (0.046)\n", - " 1. pretty_wkt (0.02)\n", - " 2. value_omitted_from_data (0.013)\n", - " 3. __imul__ (0.011)\n", - " 4. recvall (0.01)\n", - " 5. cut (0.008)\n", - " 6. all_items_equal (0.008)\n", + " 0. get_n_splits (0.938)\n", "\n", "Label = _iter_test_indices\n", "Pred =\n", - " 0. test_vectorizer_vocab_clone (0.207)\n", - " 1. test_tfidfvectorizer_invalid_idf_attr (0.026)\n", - " 2. _check_response_version (0.021)\n", - " 3. _check_versions (0.013)\n", - " 4. _flush (0.012)\n", - " 5. test_tfidf_vectorizer_with_fixed_vocabulary (0.009)\n", - " 6. test_lml_precomputed (0.008)\n", + " 0. get_n_splits (0.914)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. items (0.04)\n", - " 1. test_vectorizer_vocab_clone (0.034)\n", - " 2. target_field (0.019)\n", - " 3. skipIfDBFeature (0.016)\n", - " 4. skipUnlessAnyDBFeature (0.011)\n", - " 5. skipUnlessDBFeature (0.011)\n", - " 6. angular_name (0.01)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = get_n_splits\n", "Pred =\n", - " 0. default_headers (0.078)\n", - " 1. __str__ (0.019)\n", - " 2. get_container_property_params (0.014)\n", - " 3. E006 (0.013)\n", - " 4. count_params (0.012)\n", - " 5. get_current_timezone (0.009)\n", - " 6. as_ul (0.008)\n", + "---- 0. get_n_splits (0.989)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. diag (0.059)\n", - " 1. idf_ (0.039)\n", - " 2. use_idf (0.02)\n", - " 3. __neg__ (0.02)\n", - " 4. get_renegotiation_maximum_record_delay (0.015)\n", - " 5. bic (0.012)\n", - " 6. _estimate_weighted_log_prob (0.012)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = _index_param_value\n", "Pred =\n", - " 0. test_vectorizer_vocab_clone (0.089)\n", - " 1. test_tfidfvectorizer_invalid_idf_attr (0.042)\n", - " 2. test_tfidf_vectorizer_with_fixed_vocabulary (0.022)\n", - " 3. test_tfidfvectorizer_export_idf (0.019)\n", - " 4. srid (0.01)\n", - " 5. angular_units (0.01)\n", - " 6. angular_name (0.009)\n", + " 0. transform (0.788)\n", "\n", "Label = predict\n", "Pred =\n", - " 0. test_word_analyzer_unigrams_and_bigrams (0.563)\n", - " 1. test_sparse_input (0.011)\n", - " 2. test_load_wine (0.01)\n", - " 3. _get_sys_info (0.006)\n", - " 4. test_load_diabetes (0.006)\n", - " 5. test_load_boston (0.004)\n", - " 6. test_load_breast_cancer (0.004)\n", + "---- 0. predict (0.991)\n", "\n", "Label = decision_function\n", "Pred =\n", - " 0. test_countvectorizer_custom_vocabulary_gap_index (0.98)\n", - " 1. test_patch_extractor_fit (0.0)\n", - " 2. generate_system_info_dict (0.0)\n", - " 3. test_classification_report_labels_target_names_unequal_length (0.0)\n", - " 4. test_classification_report_no_labels_target_names_unequal_length (0.0)\n", - " 5. test_non_unique_vocab (0.0)\n", - " 6. test_repr (0.0)\n", + "---- 0. decision_function (0.977)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. test_tfidfvectorizer_export_idf (0.229)\n", - " 1. test_transformer_idf_setter (0.114)\n", - " 2. test_tfidf_vectorizer_with_fixed_vocabulary (0.038)\n", - " 3. test_tfidfvectorizer_invalid_idf_attr (0.034)\n", - " 4. test_vectorizer_vocab_clone (0.013)\n", - " 5. test_invalid_input (0.011)\n", - " 6. test_no_optimizer (0.011)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = train_test_split_mock_pandas\n", "Pred =\n", - " 0. test_patch_extractor_max_patches_default (0.345)\n", - " 1. test_reconstruct_patches_perfect_color (0.039)\n", - " 2. test_make_blobs_n_samples_list (0.032)\n", - " 3. test_make_hastie_10_2 (0.015)\n", - " 4. test_patch_extractor_fit (0.013)\n", - " 5. formula1 (0.013)\n", - " 6. test_make_moons (0.01)\n", + " 0. safe_mean (0.076)\n", "\n", "Label = test_train_test_split_allow_nans\n", "Pred =\n", - " 0. test_reconstruct_patches_perfect_color (0.986)\n", - " 1. test_make_moons (0.0)\n", - " 2. test_patch_extractor_max_patches_default (0.0)\n", - " 3. test_verbosity (0.0)\n", - " 4. associate_floating_ips (0.0)\n", - " 5. _validate_params (0.0)\n", - " 6. set_extra_mask (0.0)\n", + " 0. test_refit (0.068)\n", "\n", "Label = test_cross_val_predict_method_checking\n", "Pred =\n", - " 0. test_add_dummy_feature (0.214)\n", - " 1. test_quantile_transform_valid_axis (0.144)\n", - " 2. test_balanced_accuracy_score_unseen (0.035)\n", - " 3. test_input_validation (0.026)\n", - " 4. test_paired_manhattan_distances (0.025)\n", - " 5. test_connectivity_fixing_non_lil (0.022)\n", - " 6. test_chi2_negative (0.017)\n", + " 0. test_gridsearchcv_cross_val_predict_with_method (0.829)\n", "\n", "Label = score\n", "Pred =\n", - " 0. test_hasher_zeros (0.092)\n", - " 1. test_label_binarize_multiclass (0.028)\n", - " 2. test_liblinear_decision_function_zero (0.024)\n", - " 3. test_init_ndarray (0.02)\n", - " 4. test_variance_threshold (0.019)\n", - " 5. test_check_X_y_informative_error (0.019)\n", - " 6. test_make_blobs_n_samples_list (0.016)\n", + "---- 0. score (1.0)\n", "\n", "Label = test_random_search_with_fit_params\n", "Pred =\n", - " 0. check_threshold (0.18)\n", - " 1. third_walk (0.043)\n", - " 2. apply (0.028)\n", - " 3. _reduce_func (0.023)\n", - " 4. Manager (0.013)\n", - " 5. cpu (0.011)\n", - " 6. get_n_leaves (0.009)\n", + " 0. test_grid_search_with_fit_params (0.537)\n", "\n", "Label = predict\n", "Pred =\n", - "---- 0. predict (0.332)\n", - " 1. transform (0.082)\n", - " 2. inverse_transform (0.048)\n", - " 3. _check_fit (0.043)\n", - " 4. score_samples (0.042)\n", - " 5. _transform (0.039)\n", - " 6. diag (0.021)\n", + "---- 0. predict (0.999)\n", "\n", "Label = _run_search\n", "Pred =\n", - " 0. radius (0.023)\n", - " 1. sparse_top_k_categorical_accuracy (0.019)\n", - " 2. top_k_categorical_accuracy (0.017)\n", - " 3. _get_image_binds (0.011)\n", - " 4. _get_expected_healthcheck (0.009)\n", - " 5. _free_energy (0.008)\n", - " 6. get_name_of_provider_id (0.008)\n", + " 0. update (0.044)\n", "\n", "Label = __call__\n", "Pred =\n", - " 0. rds_model (0.012)\n", - " 1. get_immutables_intersection (0.012)\n", - " 2. subnet_of (0.012)\n", - " 3. supernet_of (0.01)\n", - " 4. _initialize (0.008)\n", - " 5. __truediv__ (0.008)\n", - " 6. requires_template_update (0.008)\n", + " 0. get_poller_result (0.067)\n", "\n", "Label = test_method_not_available\n", "Pred =\n", - " 0. _average_linkage (0.454)\n", - " 1. _single_linkage (0.43)\n", - " 2. symbolic (0.009)\n", - " 3. wrapped_view (0.008)\n", - " 4. x_robots_tag (0.006)\n", - " 5. _format_lazy (0.005)\n", - " 6. MobileNet (0.002)\n", + " 0. test_non_square_precomputed_distances (0.298)\n", "\n", "Label = test_angle_out_of_range_checks\n", "Pred =\n", - " 0. fit_predict (0.984)\n", - " 1. _fit_predict (0.003)\n", - " 2. predict_proba (0.001)\n", - " 3. staged_score (0.001)\n", - " 4. inverse_transform (0.001)\n", - " 5. _partial_fit (0.0)\n", - " 6. _joint_log_likelihood (0.0)\n", + " 0. test_make_union_kwargs (0.03)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. fit_transform (0.995)\n", - " 1. fit (0.001)\n", - " 2. _partial_fit (0.001)\n", - " 3. inverse_transform (0.0)\n", - " 4. transform (0.0)\n", - " 5. _validate_y (0.0)\n", - " 6. fit_predict (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = transform\n", "Pred =\n", - " 0. flatten (0.093)\n", - " 1. all_equal_similarities (0.072)\n", - " 2. flatten_list (0.029)\n", - " 3. test_arrays_persist (0.015)\n", - " 4. clip_grad_value_ (0.009)\n", - " 5. test_linear_kernel (0.009)\n", - " 6. wrapper (0.008)\n", + " 0. inverse_transform (0.997)\n", "\n", "Label = transform\n", "Pred =\n", - " 0. test_minimum_number_of_sample_check (0.04)\n", - " 1. test_countvectorizer_custom_vocabulary_gap_index (0.033)\n", - " 2. test_MDS (0.022)\n", - " 3. test_one_hot_encoder_warning (0.017)\n", - " 4. test_function_transformer_frame (0.014)\n", - " 5. is_volume_permissions (0.013)\n", - " 6. test_bad_reachability (0.011)\n", + " 0. fit_transform (0.251)\n", "\n", "Label = fit\n", "Pred =\n", - " 0. test_minimum_number_of_sample_check (0.02)\n", - " 1. _add_publicip_to_server (0.017)\n", - " 2. test_function_transformer_frame (0.013)\n", - " 3. test_partial_fit_errors (0.012)\n", - " 4. create_aws_list (0.01)\n", - " 5. test_isotonic_regression_oob_bad (0.01)\n", - " 6. test_countvectorizer_custom_vocabulary_gap_index (0.009)\n", + " 0. score (0.87)\n", "\n", "Label = inverse_transform\n", "Pred =\n", - " 0. __next__ (0.516)\n", - " 1. score (0.016)\n", - " 2. unwrap (0.014)\n", - " 3. __reduce_ex__ (0.012)\n", - " 4. __call__ (0.011)\n", - " 5. perplexity (0.01)\n", - " 6. bind_partial (0.009)\n", + " 0. predict (0.225)\n", "\n", "Label = _transform\n", "Pred =\n", - " 0. test_max_iter_error (0.09)\n", - " 1. test_sample_weight_length (0.052)\n", - " 2. test_k_means_random_init_not_precomputed (0.021)\n", - " 3. test_k_means_plus_plus_init_not_precomputed (0.017)\n", - " 4. test_minibatch_tol (0.014)\n", - " 5. test_minibatch_init_with_large_k (0.013)\n", - " 6. test_train_test_default_warning (0.012)\n", + " 0. grad (0.025)\n", "\n", "Label = fit\n", "Pred =\n", - " 0. test_minibatch_k_means_init (0.685)\n", - " 1. test_predict_minibatch_dense_sparse (0.046)\n", - " 2. test_minibatch_set_init_size (0.016)\n", - " 3. test_k_means_invalid_init (0.01)\n", - " 4. test_parameters_default_constructible (0.003)\n", - " 5. test_1d_input (0.003)\n", - " 6. test_sparse_mb_k_means_callable_init (0.003)\n", + "---- 0. fit (0.996)\n", "\n", "Label = test_robust_scaler_invalid_range\n", "Pred =\n", - " 0. test_minibatch_set_init_size (0.915)\n", - " 1. test_minibatch_k_means_init (0.014)\n", - " 2. test_predict_minibatch_dense_sparse (0.008)\n", - " 3. test_mb_kmeans_verbose (0.002)\n", - " 4. test_minibatch_k_means_init_multiple_runs_with_explicit_centers (0.001)\n", - " 5. test_max_samples_consistency (0.001)\n", - " 6. test_sparse_mb_k_means_callable_init (0.001)\n", + " 0. test_add_dummy_feature (0.018)\n", "\n", "Label = test_np_log\n", "Pred =\n", - " 0. inplace_row_scale (0.964)\n", - " 1. mean_variance_axis (0.008)\n", - " 2. sparse_metric (0.001)\n", - " 3. grad_hess (0.001)\n", - " 4. transform (0.0)\n", - " 5. test_1d_input (0.0)\n", - " 6. min_max_axis (0.0)\n", + " 0. safe_min (0.035)\n", "\n", "Label = score\n", "Pred =\n", - " 0. inplace_row_scale (0.775)\n", - " 1. mean_variance_axis (0.063)\n", - " 2. min_max_axis (0.005)\n", - " 3. test_1d_input (0.004)\n", - " 4. sparse_metric (0.003)\n", - " 5. sparse_coef_ (0.003)\n", - " 6. cosine_distances (0.002)\n", + "---- 0. score (0.985)\n", "\n", "Label = score\n", "Pred =\n", - " 0. _document_frequency (0.047)\n", - " 1. inplace_csr_row_scale (0.043)\n", - " 2. test_bunch_pickle_generated_with_0_16_and_read_with_0_17 (0.017)\n", - " 3. items (0.015)\n", - " 4. import_string (0.015)\n", - " 5. build_unique_id (0.011)\n", - " 6. set_empty (0.009)\n", + " 0. fit (0.077)\n", "\n", "Label = test_mcd_issue1127\n", "Pred =\n", - " 0. _infer_dimension_ (0.164)\n", - " 1. _generate_dropout_mask (0.02)\n", - " 2. _accumulate_prediction (0.017)\n", - " 3. classification_error (0.016)\n", - " 4. assert_correct_incr (0.013)\n", - " 5. make_batches (0.01)\n", - " 6. buchheim (0.009)\n", + " 0. test_k_means_n_init (0.462)\n", "\n", "Label = inplace_logistic_derivative\n", "Pred =\n", - " 0. wrapper (0.904)\n", - " 1. none_guard (0.01)\n", - " 2. wrapped (0.002)\n", - " 3. _model_admin_wrapper (0.001)\n", - " 4. subscribe (0.001)\n", - " 5. no_translations (0.001)\n", - " 6. decorator (0.001)\n", + " 0. inplace_tanh_derivative (0.926)\n", "\n", "Label = _pack\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. build (0.0)\n", + " 0. _sample_visibles (0.058)\n", "\n", "Label = test_multioutput_regression\n", "Pred =\n", - " 0. __call__ (0.999)\n", - " 1. decode (0.0)\n", - " 2. call_and_shelve (0.0)\n", - " 3. forward (0.0)\n", - " 4. __getattr__ (0.0)\n", - " 5. eval (0.0)\n", - " 6. __getitem__ (0.0)\n", + " 0. test_iter_attribute (0.037)\n", "\n", "Label = test_adaptive_learning_rate\n", "Pred =\n", - " 0. pairwise_distances_argmin (0.038)\n", - " 1. multioutput_estimator_convert_y_2d (0.032)\n", - " 2. _is_pairwise (0.031)\n", - " 3. pairwise_estimator_convert_X (0.027)\n", - " 4. _pairwise (0.012)\n", - " 5. test_classification_inf_nan_input (0.012)\n", - " 6. is_outlier_detector (0.011)\n", + " 0. test_isotonic_duplicate_min_entry (0.071)\n", "\n", "Label = strip_accents_unicode\n", - "Pred =\n", - " 0. _fit_transform_one (0.359)\n", - " 1. _yield_transformer_checks (0.039)\n", - " 2. check_classifiers_regression_target (0.033)\n", - " 3. _get_transformer_list (0.014)\n", - " 4. _run_one_hot (0.013)\n", - " 5. test_input_estimator_unchanged (0.01)\n", - " 6. _transform_one (0.009)\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0. normalize (0.742)\n", "\n", "Label = _check_vocabulary\n", "Pred =\n", - " 0. assertRaisesRegex (0.944)\n", - " 1. assertNumQueries (0.003)\n", - " 2. save_module_context (0.002)\n", - " 3. _invoke_method (0.002)\n", - " 4. read_module_context (0.002)\n", - " 5. decorate_callable (0.001)\n", - " 6. _build_https_connection (0.001)\n", + " 0. test_vectorizer_vocab_clone (0.246)\n", "\n", "Label = get_feature_names\n", "Pred =\n", - " 0. is_keras_tensor (0.13)\n", - " 1. unpack_singleton (0.038)\n", - " 2. build_preprocessor (0.02)\n", - " 3. capfirst (0.018)\n", - " 4. _tosequence (0.016)\n", - " 5. has_seq_axis (0.013)\n", - " 6. get_event (0.008)\n", + " 0. test_vectorizer_vocab_clone (0.079)\n", "\n", "Label = _make_int_array\n", "Pred =\n", - " 0. __eq__ (1.0)\n", - " 1. __add__ (0.0)\n", - " 2. clear (0.0)\n", - " 3. difference (0.0)\n", - " 4. __lt__ (0.0)\n", - " 5. add (0.0)\n", - " 6. __sub__ (0.0)\n", + " 0. create_unbound_method (0.033)\n", "\n", "Label = idf_\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. generate_formats (0.271)\n", - " 1. get_json_type (0.066)\n", - " 2. ie_key (0.023)\n", - " 3. test_sparse_coef (0.019)\n", - " 4. softplus (0.008)\n", - " 5. _generate_unsampled_indices (0.007)\n", - " 6. sigmoid (0.004)\n", + "Pred =\n", + "---- 0. idf_ (0.162)\n", "\n", "Label = idf_\n", "Pred =\n", - " 0. _shuffle (0.033)\n", - " 1. _canonical_to_params (0.025)\n", - " 2. formula2 (0.015)\n", - " 3. handle_input8 (0.012)\n", - " 4. sparse_random (0.011)\n", - " 5. _clear_covers (0.009)\n", - " 6. _argmin_min_reduce (0.009)\n", + " 0. test_tfidfvectorizer_invalid_idf_attr (0.228)\n", "\n", "Label = test_unicode_decode_error\n", "Pred =\n", - " 0. version_tuple (0.11)\n", - " 1. get_main_version (0.049)\n", - " 2. engine_supported (0.014)\n", - " 3. _find_path_in_tree (0.014)\n", - " 4. psycopg2_version (0.013)\n", - " 5. normalize_eols (0.012)\n", - " 6. slice_filter (0.01)\n", + " 0. test_word_analyzer_unigrams_and_bigrams (0.775)\n", "\n", "Label = test_countvectorizer_custom_vocabulary_repeated_indices\n", "Pred =\n", - " 0. _box_cox_optimize (0.448)\n", - " 1. safe_sparse_dot (0.035)\n", - " 2. strip_accents_ascii (0.021)\n", - " 3. _weight_func (0.02)\n", - " 4. test_power_transform_default_method (0.017)\n", - " 5. row_norms (0.015)\n", - " 6. squared_norm (0.007)\n", + " 0. test_countvectorizer_custom_vocabulary_gap_index (0.984)\n", "\n", "Label = test_tfidf_vectorizer_setter\n", "Pred =\n", - " 0. _argmax (0.383)\n", - " 1. argmax (0.13)\n", - " 2. safe_median (0.019)\n", - " 3. categorical_accuracy (0.01)\n", - " 4. _sparse_argmax (0.01)\n", - " 5. _check_transform_selected (0.008)\n", - " 6. img_to_graph (0.005)\n", + " 0. test_transformer_idf_setter (0.87)\n", "\n", "Label = test_extract_patch_same_size_image\n", "Pred =\n", - " 0. dumps (0.032)\n", - " 1. pretty_name (0.029)\n", - " 2. test_pickle (0.025)\n", - " 3. test_pickling_when_getstate_is_overwritten_by_mixin (0.02)\n", - " 4. unpickle_model (0.017)\n", - " 5. python_2_unicode_compatible (0.015)\n", - " 6. serialize_keras_object (0.015)\n", + " 0. test_reconstruct_patches_perfect_color (0.348)\n", "\n", "Label = test_reconstruct_patches_perfect\n", "Pred =\n", - " 0. __init__ (0.135)\n", - " 1. construct (0.104)\n", - " 2. test_minibatch_set_init_size (0.087)\n", - " 3. start_serialization (0.075)\n", - " 4. on_train_begin (0.01)\n", - " 5. init_poolmanager (0.009)\n", - " 6. test_predict_minibatch_dense_sparse (0.008)\n", + " 0. test_reconstruct_patches_perfect_color (0.993)\n", "\n", "Label = test_width_patch\n", "Pred =\n", - " 0. fail (0.058)\n", - " 1. test_check_estimators_unfitted (0.051)\n", - " 2. _warning (0.044)\n", - " 3. validate_ipv46_address (0.018)\n", - " 4. debugOutput (0.017)\n", - " 5. raise_login_required (0.013)\n", - " 6. log_error (0.012)\n", + " 0. test_chi2_negative (0.503)\n", "\n", "Label = test_hash_empty_input\n", "Pred =\n", - " 0. predict_proba (0.928)\n", - " 1. inverse_transform (0.017)\n", - " 2. fit (0.011)\n", - " 3. predict (0.005)\n", - " 4. score_samples (0.005)\n", - " 5. fit_predict (0.002)\n", - " 6. transform (0.002)\n", + " 0. test_ridge_cv_sparse_svd (0.055)\n", "\n", "Label = _get_leaves\n", "Pred =\n", - " 0. grad (0.838)\n", - " 1. grad_hess (0.046)\n", - " 2. linear_kernel (0.011)\n", - " 3. format_time (0.006)\n", - " 4. sparse_metric (0.004)\n", - " 5. update_coeff (0.002)\n", - " 6. short_format_time (0.001)\n", + " 0. check_threshold (0.302)\n", "\n", "Label = transform\n", "Pred =\n", - " 0. test_matthews_corrcoef_nan (0.321)\n", - " 1. test_precision_recall_curve_errors (0.099)\n", - " 2. test_invalid_input_label_binarize (0.079)\n", - " 3. test_precision_recall_f_unused_pos_label (0.054)\n", - " 4. test_sparse_enet_not_as_toy_dataset (0.027)\n", - " 5. test_mcd (0.019)\n", - " 6. test_balanced_accuracy_score_unseen (0.014)\n", + " 0. predict (0.739)\n", "\n", "Label = make_piecewise\n", "Pred =\n", - " 0. supports_stddev (0.371)\n", - " 1. supports_sessions (0.058)\n", - " 2. _get_coef (0.02)\n", - " 3. compiler_module (0.017)\n", - " 4. diag (0.01)\n", - " 5. n_dims (0.01)\n", - " 6. __hash__ (0.008)\n", + " 0. radius (0.055)\n", "\n", "Label = _project_and_cluster\n", "Pred =\n", - " 0. predict_proba (0.989)\n", - " 1. predict (0.005)\n", - " 2. predict_log_proba (0.002)\n", - " 3. decision_function (0.001)\n", - " 4. inverse_transform (0.0)\n", - " 5. fit_predict (0.0)\n", - " 6. sign (0.0)\n", + " 0. as_oracle (0.023)\n", "\n", "Label = _complete_linkage\n", "Pred =\n", - " 0. test_input_estimator_unchanged (0.389)\n", - " 1. test_invalid_input (0.026)\n", - " 2. test_invalid_n_bins (0.019)\n", - " 3. test_check_estimator_pairwise (0.012)\n", - " 4. test_transform_1d_behavior (0.011)\n", - " 5. test_invalid_encode_option (0.009)\n", - " 6. test_invalid_strategy_option (0.009)\n", + " 0. _single_linkage (0.443)\n", "\n", "Label = fit_predict\n", "Pred =\n", - " 0. test_incremental_pca_against_pca_iris (0.052)\n", - " 1. test_float_class_labels (0.025)\n", - " 2. test_isotonic_min_max_boundaries (0.024)\n", - " 3. test_fit_transform (0.022)\n", - " 4. test_refit (0.016)\n", - " 5. test_isotonic_regression_oob_bad (0.015)\n", - " 6. test_oob_score_removed_on_warm_start (0.012)\n", + "---- 0. fit_predict (0.995)\n", "\n", "Label = fit_transform\n", "Pred =\n", - " 0. constant (0.962)\n", - " 1. transform (0.004)\n", - " 2. set_value (0.003)\n", - " 3. predict (0.001)\n", - " 4. mean (0.001)\n", - " 5. get_value (0.001)\n", - " 6. ndim (0.001)\n", + "---- 0. fit_transform (0.999)\n", "\n", "Label = all_equal_preferences\n", "Pred =\n", - " 0. is_email_simple (0.018)\n", - " 1. cut (0.016)\n", - " 2. format_value (0.015)\n", - " 3. get_flags_from_attributes (0.015)\n", - " 4. prepare_dict (0.011)\n", - " 5. char (0.01)\n", - " 6. django (0.009)\n", + " 0. flatten (0.71)\n", "\n", "Label = test_min_cluster_size_invalid\n", "Pred =\n", - " 0. hyperparameters (0.372)\n", - " 1. bounds (0.032)\n", - " 2. get_deferred_fields (0.013)\n", - " 3. test_arrays_persist (0.013)\n", - " 4. transpose_input (0.011)\n", - " 5. _preprocess_conv2d_depthwise_kernel (0.008)\n", - " 6. short (0.006)\n", + " 0. test_non_unique_vocab (0.067)\n", "\n", "Label = test_min_cluster_size_invalid2\n", "Pred =\n", - " 0. diag (0.097)\n", - " 1. bounds (0.05)\n", - " 2. theta (0.039)\n", - " 3. apply (0.035)\n", - " 4. grad_hess (0.032)\n", - " 5. reset_parameters (0.023)\n", - " 6. predict_proba (0.022)\n", + " 0. test_minimum_number_of_sample_check (0.062)\n", "\n", "Label = increment\n", "Pred =\n", - " 0. bounds (0.498)\n", - " 1. hyperparameter_length_scale (0.168)\n", - " 2. hyperparameter_alpha (0.035)\n", - " 3. hyperparameter_gamma (0.021)\n", - " 4. theta (0.016)\n", - " 5. hyperparameter_noise_level (0.013)\n", - " 6. hyperparameter_constant_value (0.01)\n", + " 0. __next__ (0.12)\n", "\n", "Label = test_k_means_plus_plus_init_2_jobs\n", "Pred =\n", - " 0. diag (0.993)\n", - " 1. predict (0.002)\n", - " 2. apply (0.0)\n", - " 3. transform (0.0)\n", - " 4. softmax (0.0)\n", - " 5. theta (0.0)\n", - " 6. predict_proba (0.0)\n", + " 0. test_sample_weight_length (0.41)\n", "\n", "Label = test_k_means_init\n", "Pred =\n", - " 0. __eq__ (0.991)\n", - " 1. __add__ (0.001)\n", - " 2. clear (0.001)\n", - " 3. __lt__ (0.0)\n", - " 4. project (0.0)\n", - " 5. difference (0.0)\n", - " 6. __hash__ (0.0)\n", + " 0. test_minibatch_k_means_init (0.902)\n", "\n", "Label = test_minibatch_default_init_size\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. init_poolmanager (0.0)\n", + " 0. test_minibatch_set_init_size (0.961)\n", "\n", "Label = inplace_column_scale\n", "Pred =\n", - " 0. predict (0.572)\n", - " 1. transform (0.183)\n", - " 2. inverse_transform (0.024)\n", - " 3. constant (0.017)\n", - " 4. serialize (0.014)\n", - " 5. sign (0.008)\n", - " 6. diag (0.005)\n", + " 0. inplace_row_scale (0.987)\n", "\n", "Label = inplace_swap_row\n", "Pred =\n", - " 0. __repr__ (0.994)\n", - " 1. __str__ (0.003)\n", - " 2. extra_repr (0.001)\n", - " 3. __hash__ (0.0)\n", - " 4. sign (0.0)\n", - " 5. diag (0.0)\n", - " 6. predict (0.0)\n", + " 0. inplace_row_scale (0.836)\n", "\n", "Label = _minor_reduce\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. func (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. build (0.0)\n", + " 0. inplace_csr_row_scale (0.218)\n", "\n", "Label = _get_elem_at_rank\n", "Pred =\n", - " 0. __repr__ (0.999)\n", - " 1. __str__ (0.0)\n", - " 2. extra_repr (0.0)\n", - " 3. __hash__ (0.0)\n", - " 4. sign (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. call (0.0)\n", + " 0. _infer_dimension_ (0.08)\n", "\n", "Label = __call__\n", "Pred =\n", - " 0. test_no_optimizer (0.056)\n", - " 1. test_lml_precomputed (0.03)\n", - " 2. test_gpr_correct_error_message (0.029)\n", - " 3. test_input_check_fit (0.023)\n", - " 4. test_transformer_idf_setter (0.016)\n", - " 5. test_arrayrepr (0.013)\n", - " 6. test_identical_regressors (0.013)\n", + " 0. wrapper (0.992)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = __call__\n", "Pred =\n", - " 0. fit_transform (0.998)\n", - " 1. _partial_fit (0.0)\n", - " 2. fit (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. transform (0.0)\n", - " 5. _validate_y (0.0)\n", - " 6. fit_predict (0.0)\n", + "---- 0. __call__ (1.0)\n", "\n", "Label = _is_pairwise_metric\n", "Pred =\n", - " 0. dropout (0.425)\n", - " 1. feature_alpha_dropout (0.414)\n", - " 2. dropout3d (0.01)\n", - " 3. dropout2d (0.009)\n", - " 4. _generate_dropout_mask (0.004)\n", - " 5. cross_entropy (0.003)\n", - " 6. _regular_normalize_batch_in_training (0.002)\n", + " 0. is_regressor (0.045)\n", "\n", "Label = check_transformers_unfitted\n", "Pred =\n", - " 0. bilinear (0.162)\n", - " 1. dropout (0.109)\n", - " 2. softmax (0.073)\n", - " 3. forward (0.045)\n", - " 4. instance_norm (0.028)\n", - " 5. cross_entropy (0.027)\n", - " 6. softmin (0.019)\n", + " 0. test_power_transform_default_method (0.119)\n", "\n", "Label = assertRaises\n", "Pred =\n", - " 0. elu (0.909)\n", - " 1. selu (0.046)\n", - " 2. softmax (0.004)\n", - " 3. not_equal (0.002)\n", - " 4. l2_normalize (0.002)\n", - " 5. print_tensor (0.001)\n", - " 6. relu (0.001)\n", + " 0. assertRaisesRegex (0.838)\n", "\n", "Label = _is_arraylike\n", "Pred =\n", - " 0. cross_entropy (0.159)\n", - " 1. forward (0.044)\n", - " 2. bilinear (0.042)\n", - " 3. ctc_loss (0.041)\n", - " 4. instance_norm (0.04)\n", - " 5. upsample (0.034)\n", - " 6. hardshrink (0.022)\n", + " 0. predict_proba (0.103)\n", "\n", "Label = __eq__\n", "Pred =\n", - " 0. prelu (0.084)\n", - " 1. log_softmax (0.041)\n", - " 2. hardshrink (0.022)\n", - " 3. pairwise_distance (0.021)\n", - " 4. backward (0.019)\n", - " 5. callable_rbf_kernel (0.015)\n", - " 6. softmax (0.015)\n", + "---- 0. __eq__ (1.0)\n", "\n", "Label = linear_assignment\n", "Pred =\n", - " 0. check_options (0.017)\n", - " 1. replacement (0.017)\n", - " 2. _get_item_by_idx (0.015)\n", - " 3. _check_size_scale_factor (0.013)\n", - " 4. test_symmetric_non_symmetric_union (0.012)\n", - " 5. error (0.011)\n", - " 6. state_absent (0.008)\n", + " 0. generate_formats (0.465)\n", "\n", "Label = _step3\n", "Pred =\n", - " 0. __deepcopy__ (0.992)\n", - " 1. __add__ (0.002)\n", - " 2. difference (0.0)\n", - " 3. __lt__ (0.0)\n", - " 4. __copy__ (0.0)\n", - " 5. __rmul__ (0.0)\n", - " 6. __mul__ (0.0)\n", + " 0. test_base_optimizer (0.045)\n", "\n", "Label = _parse_version\n", "Pred =\n", - " 0. half (0.647)\n", - " 1. float (0.03)\n", - " 2. int (0.01)\n", - " 3. long (0.009)\n", - " 4. short (0.007)\n", - " 5. double (0.006)\n", - " 6. cpu (0.005)\n", + " 0. whitespace_tokenize (0.044)\n", "\n", "Label = boxcox\n", "Pred =\n", - " 0. __call__ (0.986)\n", - " 1. call (0.005)\n", - " 2. forward (0.003)\n", - " 3. patch (0.0)\n", - " 4. set (0.0)\n", - " 5. _send (0.0)\n", - " 6. clear (0.0)\n", + " 0. _box_cox_optimize (0.539)\n", "\n", "Label = _argmax\n", "Pred =\n", - " 0. __setstate__ (0.999)\n", - " 1. __init__ (0.0)\n", - " 2. fit (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. state (0.0)\n", - " 5. __getstate__ (0.0)\n", - " 6. subwidgets (0.0)\n", + "---- 0. _argmax (0.775)\n", "\n", "Label = test_delegated_docstring\n", "Pred =\n", - " 0. backward (0.081)\n", - " 1. zero_grad (0.023)\n", - " 2. register_backward_hook (0.023)\n", - " 3. log_softmax (0.014)\n", - " 4. _queue_reduction (0.013)\n", - " 5. _ip_int_from_prefix (0.012)\n", - " 6. register_forward_hook (0.01)\n", + " 0. resource_group_to_dict (0.037)\n", "\n", "Label = test_is_deprecated\n", "Pred =\n", - " 0. gather (0.998)\n", - " 1. encode (0.0)\n", - " 2. compute_output_shape (0.0)\n", - " 3. backward (0.0)\n", - " 4. function (0.0)\n", - " 5. project (0.0)\n", - " 6. softmin (0.0)\n", + " 0. __init__ (1.0)\n", "\n", "Label = test_assert_raise_message\n", "Pred =\n", - " 0. forward (1.0)\n", - " 1. __call__ (0.0)\n", - " 2. call (0.0)\n", - " 3. backward (0.0)\n", - " 4. configure (0.0)\n", - " 5. resolve_expression (0.0)\n", - " 6. cli (0.0)\n", + " 0. test_check_estimators_unfitted (0.156)\n", "\n", "Label = score\n", "Pred =\n", - " 0. instance_norm (0.061)\n", - " 1. bilinear (0.036)\n", - " 2. prelu (0.03)\n", - " 3. layer_norm (0.027)\n", - " 4. upsample (0.023)\n", - " 5. hardshrink (0.017)\n", - " 6. _combinations (0.014)\n", + " 0. predict_proba (0.962)\n", "\n", "Label = hess\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. destination (0.0)\n", + " 0. grad (0.615)\n", "\n", "Label = test_resample\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. destination (0.0)\n", + " 0. test_matthews_corrcoef_nan (0.312)\n", "\n", "Label = _get_support_mask\n", "Pred =\n", - " 0. forward (0.999)\n", - " 1. __call__ (0.0)\n", - " 2. call (0.0)\n", - " 3. extra_repr (0.0)\n", - " 4. _make_random_matrix (0.0)\n", - " 5. score (0.0)\n", - " 6. fit_transform (0.0)\n", + " 0. supports_stddev (0.087)\n", "\n", "Label = predict_proba\n", "Pred =\n", - " 0. extra_repr (0.998)\n", - " 1. call (0.0)\n", - " 2. non_trainable_weights (0.0)\n", - " 3. _sparse_argmax (0.0)\n", - " 4. threshold_ (0.0)\n", - " 5. _get_support_mask (0.0)\n", - " 6. _pooling_function (0.0)\n", + "---- 0. predict_proba (0.992)\n", "\n", "Label = test_calling_fit_reinitializes\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. func (0.0)\n", + " 0. test_input_estimator_unchanged (0.241)\n", "\n", "Label = test_select_kbest_all\n", "Pred =\n", - " 0. forward (0.134)\n", - " 1. __hash__ (0.108)\n", - " 2. reset_parameters (0.042)\n", - " 3. extra_repr (0.034)\n", - " 4. __call__ (0.024)\n", - " 5. score_samples (0.023)\n", - " 6. call (0.023)\n", + " 0. test_power_transformer_lambda_one (0.071)\n", "\n", "Label = linear\n", "Pred =\n", - " 0. extra_repr (0.737)\n", - " 1. backward (0.043)\n", - " 2. call (0.013)\n", - " 3. forward (0.006)\n", - " 4. inverse_transform (0.006)\n", - " 5. diag (0.005)\n", - " 6. non_trainable_weights (0.005)\n", + " 0. constant (0.899)\n", "\n", "Label = hyperparameters\n", "Pred =\n", - " 0. __getitem__ (0.261)\n", - " 1. __setitem__ (0.234)\n", - " 2. index (0.053)\n", - " 3. __delitem__ (0.023)\n", - " 4. set (0.017)\n", - " 5. _validate_key (0.013)\n", - " 6. __set__ (0.012)\n", + " 0. get_flags_from_attributes (0.028)\n", "\n", "Label = bounds\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. destination (0.0)\n", + " 0. hyperparameters (0.911)\n", "\n", "Label = diag\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", + "---- 0. diag (0.609)\n", "\n", "Label = bounds\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. build (0.0)\n", - " 4. start_serialization (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. inverse_transform (0.0)\n", + "---- 0. bounds (0.628)\n", "\n", "Label = diag\n", "Pred =\n", - " 0. extra_repr (0.042)\n", - " 1. reset_running_stats (0.021)\n", - " 2. _get_duration_components (0.011)\n", - " 3. _fixture_setup (0.008)\n", - " 4. __iter__ (0.007)\n", - " 5. named_buffers (0.006)\n", - " 6. _run_search (0.006)\n", + "---- 0. diag (0.997)\n", "\n", "Label = __eq__\n", "Pred =\n", - " 0. __repr__ (0.301)\n", - " 1. extra_repr (0.212)\n", - " 2. call (0.171)\n", - " 3. __str__ (0.058)\n", - " 4. get_config (0.02)\n", - " 5. __hash__ (0.009)\n", - " 6. deserialize (0.007)\n", + "---- 0. __eq__ (0.982)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. destination (0.0)\n", - " 6. init_poolmanager (0.0)\n", "\n", "Label = diag\n", "Pred =\n", - " 0. _ntuple (0.921)\n", - " 1. _tosequence (0.004)\n", - " 2. mean_variance_axis (0.002)\n", - " 3. norm (0.002)\n", - " 4. min_max_axis (0.002)\n", - " 5. _handle_zeros_in_scale (0.001)\n", - " 6. img_to_graph (0.001)\n", + " 0. __call__ (0.42)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. forward (1.0)\n", - " 1. __call__ (0.0)\n", - " 2. call (0.0)\n", - " 3. resolve_expression (0.0)\n", - " 4. exists (0.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 5. score (0.0)\n", - " 6. check_forward_input (0.0)\n", + "---- 0. __repr__ (0.995)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. forward (1.0)\n", - " 1. __call__ (0.0)\n", - " 2. call (0.0)\n", - " 3. compute_output_shape (0.0)\n", - " 4. backward (0.0)\n", - " 5. check_forward_input (0.0)\n", - " 6. extra_repr (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. apply (0.052)\n", - " 1. mahalanobis (0.044)\n", - " 2. diag (0.04)\n", - " 3. cuda (0.027)\n", - " 4. double (0.026)\n", - " 5. _create_collection (0.022)\n", - " 6. predict (0.016)\n", + "---- 0. __repr__ (1.0)\n", "\n", "Label = test_gpr_interpolation\n", "Pred =\n", - " 0. _register_load_state_dict_pre_hook (0.613)\n", - " 1. register_forward_hook (0.064)\n", - " 2. _register_state_dict_hook (0.038)\n", - " 3. register_backward_hook (0.006)\n", - " 4. score (0.003)\n", - " 5. half (0.002)\n", - " 6. get_client_id (0.002)\n", + " 0. test_no_optimizer (0.138)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. parameters (0.189)\n", - " 1. named_buffers (0.038)\n", - " 2. _load_plugin_commands (0.015)\n", - " 3. fromkeys (0.008)\n", - " 4. addSkip (0.007)\n", - " 5. set_exception (0.007)\n", - " 6. bulk_update (0.007)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = fit_transform\n", "Pred =\n", - " 0. children (0.213)\n", - " 1. _import_module (0.074)\n", - " 2. insert (0.041)\n", - " 3. named_children (0.018)\n", - " 4. _merge_known_related_objects (0.007)\n", - " 5. get_format_modules (0.007)\n", - " 6. parse_key (0.007)\n", + "---- 0. fit_transform (0.999)\n", "\n", - "Label = mk_boolean\n", + "Label = alpha_dropout\n", "Pred =\n", - " 0. validate_ipv6_address (0.04)\n", - " 1. _make_timedelta (0.038)\n", - " 2. validate_image_file_extension (0.028)\n", - " 3. normalize_data_format (0.027)\n", - " 4. _str_sorted (0.025)\n", - " 5. json_script (0.023)\n", - " 6. to_key_val_list (0.021)\n", + " 0. feature_alpha_dropout (0.512)\n", "\n", - "Label = online_argument_spec\n", + "Label = hardtanh\n", "Pred =\n", - " 0. scaleway_argument_spec (0.422)\n", - " 1. digital_ocean_argument_spec (0.24)\n", - " 2. acs_common_argument_spec (0.094)\n", - " 3. cloudscale_argument_spec (0.033)\n", - " 4. heroku_argument_spec (0.026)\n", - " 5. a10_argument_spec (0.02)\n", - " 6. postgres_common_argument_spec (0.018)\n", + " 0. adaptive_avg_pool2d (0.094)\n", "\n", - "Label = ok\n", + "Label = elu\n", "Pred =\n", - "---- 0. ok (0.996)\n", - " 1. _wait_for_task (0.0)\n", - " 2. compare_ordering_key (0.0)\n", - " 3. wait_for_task (0.0)\n", - " 4. rules (0.0)\n", - " 5. __get_result (0.0)\n", - " 6. icmp_present (0.0)\n", + "---- 0. elu (0.67)\n", "\n", - "Label = session\n", + "Label = rrelu\n", "Pred =\n", - " 0. foldr (0.041)\n", - " 1. is_package (0.031)\n", - " 2. on_train_begin (0.026)\n", - " 3. reduce_connection (0.014)\n", - " 4. get_volume_names (0.012)\n", - " 5. shutdown (0.01)\n", - " 6. _get_export_domain_service (0.009)\n", + " 0. relu6 (0.086)\n", "\n", - "Label = resource_absent\n", + "Label = _no_grad_embedding_renorm_\n", "Pred =\n", - " 0. get_origin_access_identity (0.02)\n", - " 1. get_resource_group (0.012)\n", - " 2. __wrapper (0.012)\n", - " 3. decrypt_sig (0.011)\n", - " 4. raise_geo_restricted (0.01)\n", - " 5. name_exists (0.009)\n", - " 6. assertNotContains (0.008)\n", + " 0. zeros_ (0.059)\n", "\n", - "Label = gcdns_connect\n", + "Label = assert_int_or_pair\n", "Pred =\n", - " 0. gce_connect (0.529)\n", - " 1. _update_temporary_cli_script_on_device (0.015)\n", - " 2. get_name_of_provider_id (0.014)\n", - " 3. get_service_by_id (0.006)\n", - " 4. provider_module_params (0.006)\n", - " 5. _error (0.005)\n", - " 6. get_google_api_client (0.005)\n", + " 0. _format_lazy (0.045)\n", "\n", - "Label = _singleton\n", + "Label = __deepcopy__\n", "Pred =\n", - " 0. fix_proposed (0.107)\n", - " 1. add_key_else_change_dict_key (0.02)\n", - " 2. remove_nones_from_dict (0.019)\n", - " 3. replace_resource_dict (0.015)\n", - " 4. setdefault (0.012)\n", - " 5. is_property_changed (0.012)\n", - " 6. cut (0.011)\n", + "---- 0. __deepcopy__ (0.999)\n", "\n", - "Label = construct\n", + "Label = half\n", "Pred =\n", - " 0. init_modules (0.924)\n", - "---- 1. construct (0.024)\n", - " 2. position_base_dn (0.002)\n", - " 3. _initialize_backend (0.001)\n", - " 4. _decrement_pending_calls (0.001)\n", - " 5. save_module (0.001)\n", - " 6. config_registry (0.001)\n", + "---- 0. half (0.908)\n", "\n", - "Label = module_by_name\n", + "Label = __call__\n", "Pred =\n", - " 0. construct (0.931)\n", - " 1. position_base_dn (0.009)\n", - " 2. config_registry (0.004)\n", - " 3. init_modules (0.001)\n", - " 4. _import_module (0.001)\n", - " 5. ensure_connection (0.001)\n", - " 6. config (0.001)\n", + " 0. call (0.566)\n", "\n", - "Label = xcli_wrapper\n", + "Label = __setstate__\n", "Pred =\n", - " 0. wrapper (0.86)\n", - " 1. none_guard (0.029)\n", - " 2. decorator (0.005)\n", - " 3. make_response (0.002)\n", - " 4. ignore_warnings (0.002)\n", - " 5. _model_admin_wrapper (0.002)\n", - " 6. _assert_has_capability (0.002)\n", + "---- 0. __setstate__ (0.997)\n", "\n", - "Label = _check_type_list\n", + "Label = backward\n", "Pred =\n", - " 0. _cast_value (0.064)\n", - " 1. _check_type_float (0.039)\n", - " 2. to_dict (0.028)\n", - " 3. _validate_range (0.025)\n", - " 4. __contains__ (0.017)\n", - " 5. _check_type_jsonarg (0.017)\n", - " 6. setdefault (0.017)\n", + "---- 0. backward (0.548)\n", "\n", - "Label = _check_type_bits\n", + "Label = gather\n", "Pred =\n", - " 0. _check_type_bytes (0.977)\n", - " 1. add_exception (0.001)\n", - " 2. is_vcenter (0.0)\n", - " 3. unget (0.0)\n", - " 4. get_keyauthorization (0.0)\n", - " 5. __next__ (0.0)\n", - " 6. raiseFromErrno (0.0)\n", + "---- 0. gather (0.999)\n", "\n", - "Label = sha1\n", + "Label = forward\n", "Pred =\n", - " 0. md5 (0.969)\n", - " 1. sign (0.011)\n", - " 2. sha256sum (0.001)\n", - " 3. base64_hmac (0.0)\n", - " 4. get_keyauthorization (0.0)\n", - " 5. file_path (0.0)\n", - " 6. _generic_title (0.0)\n", + "---- 0. forward (1.0)\n", "\n", - "Label = sha256\n", + "Label = _renorm\n", "Pred =\n", - " 0. md5 (0.969)\n", - " 1. sign (0.011)\n", - " 2. sha256sum (0.001)\n", - " 3. base64_hmac (0.0)\n", - " 4. get_keyauthorization (0.0)\n", - " 5. file_path (0.0)\n", - " 6. _generic_title (0.0)\n", + " 0. _get_softmax_dim (0.019)\n", "\n", - "Label = _restore_signal_handlers\n", + "Label = __init__\n", "Pred =\n", - " 0. _run (0.049)\n", - " 1. record (0.036)\n", - " 2. is_running (0.03)\n", - " 3. delayed (0.018)\n", - " 4. _has_nchw_support (0.012)\n", - " 5. __get_module (0.01)\n", - " 6. compat_ctypes_WINFUNCTYPE (0.009)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = sql_client\n", + "Label = __init__\n", "Pred =\n", - "---- 0. sql_client (0.904)\n", - " 1. rm_client (0.017)\n", - " 2. monitor_client (0.007)\n", - " 3. network_client (0.005)\n", - " 4. storage_client (0.005)\n", - " 5. compute_client (0.004)\n", - " 6. traffic_manager_management_client (0.004)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = fail\n", + "Label = forward\n", "Pred =\n", - " 0. _default_fail_impl (0.625)\n", - " 1. raise_for_status (0.022)\n", - " 2. do_fail (0.018)\n", - " 3. add_exception (0.011)\n", - " 4. handle_exception (0.008)\n", - " 5. process_exception_by_middleware (0.007)\n", - " 6. exit_json (0.007)\n", + "---- 0. forward (0.999)\n", "\n", - "Label = fq_name\n", + "Label = extra_repr\n", "Pred =\n", - "---- 0. fq_name (0.981)\n", - " 1. fq_list_names (0.001)\n", - " 2. issuer_cert (0.001)\n", - " 3. validate_attribute_with_allowed_values (0.0)\n", - " 4. param_traffic_group (0.0)\n", - " 5. validate_shell_parameter (0.0)\n", - " 6. short_format_time (0.0)\n", + "---- 0. extra_repr (0.999)\n", "\n", - "Label = _filter_params\n", + "Label = __init__\n", "Pred =\n", - "---- 0. _filter_params (0.948)\n", - " 1. _format_params (0.006)\n", - " 2. match_filters (0.003)\n", - " 3. to_tuple (0.001)\n", - " 4. tags_match (0.001)\n", - " 5. payload_from_security_group (0.001)\n", - " 6. hardware_information (0.001)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = get_mcp_version\n", + "Label = reset_parameters\n", "Pred =\n", - " 0. id_for_label (0.113)\n", - " 1. __str__ (0.035)\n", - " 2. driver_count (0.033)\n", - " 3. name (0.026)\n", - " 4. clear_location (0.023)\n", - " 5. ensure_registered (0.018)\n", - " 6. build (0.014)\n", + " 0. __call__ (0.135)\n", "\n", - "Label = required_together\n", + "Label = extra_repr\n", "Pred =\n", - " 0. flatten_fieldsets (0.034)\n", - " 1. add_deletions (0.022)\n", - " 2. normalize_together (0.021)\n", - " 3. get_action_choices (0.016)\n", - " 4. _check_for_duplicates (0.013)\n", - " 5. cut (0.011)\n", - " 6. for_update_sql (0.007)\n", + " 0. forward (0.965)\n", "\n", - "Label = https_open\n", + "Label = __setitem__\n", "Pred =\n", - " 0. get_maxVcpus (0.029)\n", - " 1. vm_status (0.029)\n", - " 2. is_active (0.018)\n", - " 3. get_volume_names (0.014)\n", - " 4. delete_containerinstance (0.011)\n", - " 5. setup_test_environment (0.01)\n", - " 6. on_epoch_end (0.009)\n", + "---- 0. __setitem__ (0.418)\n", "\n", - "Label = options\n", + "Label = __init__\n", "Pred =\n", - " 0. delete (0.374)\n", - " 1. get (0.328)\n", - " 2. head (0.207)\n", - " 3. put (0.019)\n", - " 4. post (0.014)\n", - "---- 5. options (0.012)\n", - " 6. patch (0.005)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = volume_exists\n", + "Label = __init__\n", "Pred =\n", - " 0. volume_id_exists (0.152)\n", - " 1. get_volume (0.093)\n", - " 2. get_volume_id (0.055)\n", - " 3. get_existing_volume (0.01)\n", - " 4. wait_for_device_reboot (0.009)\n", - " 5. _print_unpicklable_subtest (0.008)\n", - " 6. convert_feature (0.006)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = na_ontap_host_argument_spec\n", + "Label = __init__\n", "Pred =\n", - " 0. a10_argument_spec (0.423)\n", - " 1. ontap_sf_host_argument_spec (0.192)\n", - " 2. digital_ocean_argument_spec (0.079)\n", - " 3. scaleway_argument_spec (0.042)\n", - " 4. acs_common_argument_spec (0.025)\n", - " 5. postgres_common_argument_spec (0.015)\n", - " 6. spectrum_accelerate_spec (0.009)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = get_host_by_name\n", + "Label = reset_parameters\n", "Pred =\n", - " 0. gather_host_portgroup_facts (0.208)\n", - " 1. get_Host (0.083)\n", - " 2. validate_host (0.022)\n", - " 3. check_allowed_hosts (0.018)\n", - " 4. get_esx_host (0.017)\n", - " 5. _hc_connect (0.011)\n", - " 6. get_template_by_name (0.01)\n", + " 0. update_fw_settings (0.026)\n", "\n", - "Label = is_quoted\n", + "Label = extra_repr\n", "Pred =\n", - " 0. is_whitespace (0.094)\n", - " 1. ports_from_permission (0.058)\n", - " 2. image_dim_ordering (0.016)\n", - " 3. boolean_check (0.013)\n", - " 4. decode_response (0.011)\n", - " 5. ytdl_is_updateable (0.01)\n", - " 6. axapi_failure (0.009)\n", + "---- 0. extra_repr (0.125)\n", "\n", - "Label = fail_json\n", + "Label = __init__\n", "Pred =\n", - "---- 0. fail_json (0.984)\n", - " 1. failure (0.004)\n", - " 2. resolve_expression (0.001)\n", - " 3. fail_module (0.0)\n", - " 4. create_client (0.0)\n", - " 5. do_fail (0.0)\n", - " 6. fail (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", - "Label = get_tags\n", + "Label = parse\n", "Pred =\n", - " 0. _tags_that_should_exist_or_be_updated (0.153)\n", - " 1. _tags_that_should_not_exist (0.13)\n", - " 2. ansible_dict_to_boto3_tag_list (0.027)\n", - " 3. format_item (0.024)\n", - " 4. diff_objects (0.018)\n", - " 5. serialize_vmss (0.014)\n", - " 6. expand_list (0.011)\n", + " 0. _ntuple (0.946)\n", "\n", - "Label = check_zone_domain\n", + "Label = forward\n", "Pred =\n", - " 0. parse_event_status (0.055)\n", - " 1. ensure_absent (0.016)\n", - " 2. valid_template (0.014)\n", - " 3. parse_event (0.014)\n", - " 4. is_network_bound (0.012)\n", - " 5. status_needs_update (0.01)\n", - " 6. check_zone (0.01)\n", + "---- 0. forward (1.0)\n", "\n", - "Label = aws_common_argument_spec\n", + "Label = forward\n", "Pred =\n", - " 0. a10_argument_spec (0.362)\n", - " 1. ontap_sf_host_argument_spec (0.188)\n", - " 2. scaleway_argument_spec (0.059)\n", - " 3. digital_ocean_argument_spec (0.054)\n", - " 4. acs_common_argument_spec (0.053)\n", - " 5. postgres_common_argument_spec (0.021)\n", - " 6. spectrum_accelerate_spec (0.014)\n", + "---- 0. forward (1.0)\n", "\n", - "Label = __init__\n", + "Label = type\n", "Pred =\n", - "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. load (0.0)\n", - " 6. func (0.0)\n", + " 0. cuda (0.056)\n", "\n", - "Label = find_cluster_by_name_datacenter\n", + "Label = register_forward_pre_hook\n", "Pred =\n", - " 0. get_all_hosts_by_cluster (0.039)\n", - " 1. get_role (0.018)\n", - " 2. find_datastore_cluster_by_name (0.015)\n", - " 3. get_vm_by_id (0.014)\n", - " 4. get_datacenter (0.014)\n", - " 5. get_rule_key_by_name (0.012)\n", - " 6. check_host (0.012)\n", + " 0. _register_load_state_dict_pre_hook (0.548)\n", "\n", - "Label = find_object_by_name\n", + "Label = buffers\n", "Pred =\n", - " 0. get_all_hosts_by_cluster (0.017)\n", - " 1. find_vm_by_name (0.016)\n", - " 2. named_parameters (0.014)\n", - " 3. long2short (0.011)\n", - " 4. get_template (0.01)\n", - " 5. find_hostsystem_by_name (0.009)\n", - " 6. get_facter_output (0.008)\n", + " 0. named_buffers (0.192)\n", "\n", - "Label = find_cluster_by_name\n", + "Label = modules\n", "Pred =\n", - " 0. get_all_objs (0.363)\n", - "---- 1. find_cluster_by_name (0.12)\n", - " 2. delete_address_from_mapping (0.019)\n", - " 3. _recursive_terminate_with_psutil (0.008)\n", - " 4. find_dvs_by_name (0.008)\n", - " 5. remove_check (0.007)\n", - " 6. get_role (0.006)\n", + " 0. children (0.856)\n", "\n", - "Label = find_datastore_by_name\n", + "Label = mk_boolean\n", "Pred =\n", - "---- 0. find_datastore_by_name (0.803)\n", - " 1. lookup_datastore_by_cluster (0.016)\n", - " 2. find_vm_by_name (0.01)\n", - " 3. gather_facts (0.007)\n", - " 4. find_datastore_cluster_by_name (0.004)\n", - " 5. find_hostsystem_by_name (0.003)\n", - " 6. mount_datastore_host (0.003)\n", + " 0. _make_timedelta (0.121)\n", "\n", - "Label = send_data\n", + "Label = online_argument_spec\n", + "Pred =\n", + " 0. digital_ocean_argument_spec (0.395)\n", + "\n", + "Label = ok\n", + "Pred =\n", + "---- 0. ok (0.997)\n", + "\n", + "Label = session\n", + "Pred =\n", + " 0. _make_empty_cell (0.06)\n", + "\n", + "Label = resource_absent\n", + "Pred =\n", + " 0. get_load_balancer (0.232)\n", + "\n", + "Label = gcdns_connect\n", + "Pred =\n", + " 0. gce_connect (0.253)\n", + "\n", + "Label = _singleton\n", + "Pred =\n", + " 0. get_context (0.227)\n", + "\n", + "Label = construct\n", + "Pred =\n", + " 0. init_modules (0.962)\n", + "\n", + "Label = module_by_name\n", + "Pred =\n", + " 0. construct (0.894)\n", + "\n", + "Label = xcli_wrapper\n", + "Pred =\n", + " 0. wrapper (0.996)\n", + "\n", + "Label = _check_type_list\n", + "Pred =\n", + " 0. __getitem__ (0.072)\n", + "\n", + "Label = _check_type_bits\n", + "Pred =\n", + " 0. _check_type_bytes (0.853)\n", + "\n", + "Label = sha1\n", + "Pred =\n", + " 0. md5 (0.834)\n", + "\n", + "Label = sha256\n", + "Pred =\n", + " 0. md5 (0.834)\n", + "\n", + "Label = _restore_signal_handlers\n", + "Pred =\n", + " 0. format_for_deletion (0.018)\n", + "\n", + "Label = sql_client\n", + "Pred =\n", + "---- 0. sql_client (0.965)\n", + "\n", + "Label = fail\n", + "Pred =\n", + " 0. _default_fail_impl (0.848)\n", + "\n", + "Label = fq_name\n", + "Pred =\n", + "---- 0. fq_name (0.896)\n", + "\n", + "Label = _filter_params\n", + "Pred =\n", + "---- 0. _filter_params (0.802)\n", + "\n", + "Label = get_mcp_version\n", + "Pred =\n", + " 0. __str__ (0.788)\n", + "\n", + "Label = required_together\n", + "Pred =\n", + " 0. _collect_input_shape (0.021)\n", + "\n", + "Label = https_open\n", + "Pred =\n", + " 0. status (0.044)\n", + "\n", + "Label = options\n", "Pred =\n", - " 0. box (0.059)\n", - " 1. qualities (0.033)\n", - " 2. _make_valid_url (0.023)\n", - " 3. format_eta (0.019)\n", - " 4. do_ntranslate (0.018)\n", - " 5. pbkdf2 (0.014)\n", - " 6. check_video (0.012)\n", + " 0. delete (0.363)\n", + "\n", + "Label = volume_exists\n", + "Pred =\n", + " 0. get_volume_id (0.199)\n", + "\n", + "Label = na_ontap_host_argument_spec\n", + "Pred =\n", + " 0. a10_argument_spec (0.441)\n", + "\n", + "Label = get_host_by_name\n", + "Pred =\n", + " 0. gather_host_portgroup_facts (0.047)\n", + "\n", + "Label = is_quoted\n", + "Pred =\n", + " 0. p0 (0.105)\n", + "\n", + "Label = fail_json\n", + "Pred =\n", + "---- 0. fail_json (0.991)\n", + "\n", + "Label = get_tags\n", + "Pred =\n", + " 0. get_tag_list (0.128)\n", + "\n", + "Label = check_zone_domain\n", + "Pred =\n", + " 0. exists (0.13)\n", + "\n", + "Label = aws_common_argument_spec\n", + "Pred =\n", + " 0. ontap_sf_host_argument_spec (0.351)\n", + "\n", + "Label = __init__\n", + "Pred =\n", + "---- 0. __init__ (1.0)\n", + "\n", + "Label = find_cluster_by_name_datacenter\n", + "Pred =\n", + " 0. get_vm_by_id (0.034)\n", + "\n", + "Label = find_object_by_name\n", + "Pred =\n", + " 0. get_connection (0.103)\n", + "\n", + "Label = find_cluster_by_name\n", + "Pred =\n", + " 0. get_all_objs (0.043)\n", + "\n", + "Label = find_datastore_by_name\n", + "Pred =\n", + "---- 0. find_datastore_by_name (0.141)\n", + "\n", + "Label = send_data\n", + "Pred =\n", + " 0. box (0.033)\n", "\n", "Label = exec_command\n", "Pred =\n", - " 0. get_defaults_flag (0.101)\n", - " 1. exec_rpc (0.028)\n", - " 2. _hc_connect (0.026)\n", - " 3. setup_client (0.02)\n", - " 4. connect (0.016)\n", - " 5. get_nc_connection (0.015)\n", - " 6. _get_connection (0.013)\n", + " 0. get_connection (0.165)\n", "\n", "Label = sanitize_result\n", "Pred =\n", - " 0. filter_requested_info (0.176)\n", - " 1. prepare_dict (0.127)\n", - " 2. flatten_extattrs (0.057)\n", - " 3. _format_params (0.056)\n", - " 4. normalize_extattrs (0.019)\n", - " 5. to_key_val_list (0.017)\n", - " 6. compat_kwargs (0.013)\n", + " 0. ordered_obj (0.548)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = nopad_b64\n", "Pred =\n", - " 0. b64_encode (0.806)\n", - " 1. urlsafe_base64_decode (0.005)\n", - " 2. escape_url (0.005)\n", - " 3. b64_decode (0.004)\n", - " 4. _set_language (0.003)\n", - " 5. endswith_lf (0.002)\n", - " 6. endswith_cr (0.002)\n", + " 0. b64_encode (0.937)\n", "\n", "Label = read_file\n", "Pred =\n", - " 0. content (0.059)\n", - " 1. get_view_name (0.029)\n", - " 2. inverse_transform (0.029)\n", - " 3. wrapper (0.028)\n", - " 4. version (0.022)\n", - " 5. call_and_shelve (0.016)\n", - " 6. wrapped (0.016)\n", + " 0. format (0.098)\n", "\n", "Label = do_fail\n", "Pred =\n", - "---- 0. do_fail (0.939)\n", - " 1. fail (0.007)\n", - " 2. fail_json (0.004)\n", - " 3. _output_logs (0.003)\n", - " 4. warn (0.001)\n", - " 5. validate_vrf (0.001)\n", - " 6. handle_exception (0.001)\n", + "---- 0. do_fail (0.975)\n", "\n", "Label = delete_user\n", "Pred =\n", - " 0. update_user_role (0.22)\n", - " 1. update_user_password (0.213)\n", - " 2. disable_user (0.208)\n", - " 3. enable_user (0.205)\n", - " 4. _get_requires_by_collector_name (0.002)\n", - " 5. error (0.002)\n", - " 6. web_client (0.002)\n", + " 0. disable_user (0.35)\n", "\n", "Label = unregister\n", "Pred =\n", - " 0. unsubscribe (0.551)\n", - " 1. is_registered (0.053)\n", - " 2. do_uninstall (0.012)\n", - " 3. get_failure_info (0.01)\n", - " 4. do_rollback (0.01)\n", - " 5. fail_if_missing (0.008)\n", - " 6. _run_lsblk (0.005)\n", + " 0. unsubscribe (0.113)\n", "\n", "Label = get_vpn\n", "Pred =\n", - " 0. get_datacenter (0.148)\n", - " 1. get_appliance (0.041)\n", - " 2. vmdisk_id (0.021)\n", - " 3. get_server (0.012)\n", - " 4. all_detailed_servers (0.009)\n", - " 5. _check_indexes (0.007)\n", - " 6. get_nic_from_result (0.006)\n", + " 0. _fix_subtitles (0.028)\n", "\n", "Label = get_public_ip\n", "Pred =\n", - " 0. _find_address_by_ip (0.099)\n", - " 1. address_is_associated_with_device (0.062)\n", - " 2. get_public_ip_address (0.055)\n", - " 3. ssl_certificate_id (0.025)\n", - " 4. find_address (0.021)\n", - " 5. _remove_publicip_from_server (0.017)\n", - " 6. _get_idna_encoded_host (0.013)\n", + " 0. address_is_associated_with_device (0.13)\n", "\n", "Label = camel\n", "Pred =\n", - " 0. cc (0.302)\n", - " 1. underscore_to_camel (0.137)\n", - " 2. _convert_int_to_bytes (0.021)\n", - " 3. pc (0.019)\n", - " 4. _split_optional_netmask (0.007)\n", - " 5. swappable_dependency (0.006)\n", - " 6. get_plural (0.005)\n", + " 0. underscore_to_camel (0.083)\n", "\n", "Label = get_client_template_id\n", "Pred =\n", - " 0. get_client_template_by_name (0.545)\n", - " 1. get_client_id (0.04)\n", - " 2. get_option_default (0.009)\n", - " 3. issubset (0.007)\n", - " 4. extra (0.007)\n", - " 5. get_interfaces_info (0.006)\n", - " 6. payload_from_object (0.006)\n", + " 0. get_client_id (0.422)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.999)\n", - " 1. connect (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. on_train_begin (0.0)\n", - " 5. unset_installed_apps (0.0)\n", - " 6. inverse_transform (0.0)\n", + "---- 0. __init__ (0.977)\n", "\n", "Label = logout\n", "Pred =\n", - " 0. login (0.063)\n", - " 1. get_login_url (0.048)\n", - " 2. _real_initialize (0.038)\n", - " 3. logout_then_login (0.024)\n", - " 4. get_success_url (0.017)\n", - " 5. has_perm (0.014)\n", - " 6. _remove_invalid_user (0.012)\n", + " 0. login (0.116)\n", "\n", "Label = platform_match\n", "Pred =\n", - " 0. get_connection (0.027)\n", - " 1. _get_default_project_id (0.024)\n", - " 2. register (0.015)\n", - " 3. reboot_db_instance (0.011)\n", - " 4. can_download (0.009)\n", - " 5. supports (0.009)\n", - " 6. get_template_by_name (0.008)\n", + " 0. fork_exec (0.026)\n", "\n", "Label = resolve_requires\n", "Pred =\n", - " 0. find_unresolved_requires (0.099)\n", - " 1. _solve_deps (0.077)\n", - " 2. remove_ntp_auth_key (0.022)\n", - " 3. make_default_double_backwards_fn (0.019)\n", - " 4. payload_from_object (0.019)\n", - " 5. _parse_hextet (0.012)\n", - " 6. default_double_backwards_fn (0.011)\n", + " 0. _solve_deps (0.555)\n", "\n", "Label = get_all_facts\n", "Pred =\n", - " 0. handle_all_keyword (0.044)\n", - " 1. get_params (0.032)\n", - " 2. boto3_at_least (0.023)\n", - " 3. botocore_at_least (0.023)\n", - " 4. grad (0.017)\n", - " 5. get_key_columns (0.013)\n", - " 6. handle_monitors_keyword (0.012)\n", + " 0. fit (0.112)\n", "\n", "Label = collect\n", "Pred =\n", - "---- 0. collect (0.982)\n", - " 1. parse_distribution_file_Alpine (0.003)\n", - " 2. collect_with_namespace (0.001)\n", - " 3. __init__ (0.001)\n", - " 4. parse_distribution_file_Slackware (0.001)\n", - " 5. parse_distribution_file_Amazon (0.0)\n", - " 6. populate (0.0)\n", + "---- 0. collect (0.991)\n", "\n", "Label = parse_media_line\n", "Pred =\n", - " 0. parse_lladdr_line (0.371)\n", - " 1. parse_ether_line (0.26)\n", - " 2. parse_status_line (0.14)\n", - " 3. parse_options_line (0.023)\n", - " 4. parse_nd6_line (0.019)\n", - " 5. parse_tunnel_line (0.014)\n", - " 6. get_byte_match_set_with_backoff (0.004)\n", + " 0. parse_lladdr_line (0.116)\n", "\n", "Label = get_device_facts\n", "Pred =\n", - " 0. merge_proxy_to_param (0.03)\n", - " 1. boto_fix_security_token_in_profile (0.023)\n", - " 2. on_batch_begin (0.021)\n", - " 3. get_dmi_facts (0.015)\n", - " 4. gce_connect (0.014)\n", - " 5. parse_distribution_file_Alpine (0.012)\n", - " 6. provider_module_params (0.012)\n", + " 0. get_dmi_facts (0.07)\n", "\n", "Label = get_uptime_facts\n", "Pred =\n", - " 0. get_distribution_AIX (0.063)\n", - " 1. run_facter (0.046)\n", - " 2. run_ohai (0.044)\n", - "---- 3. get_uptime_facts (0.024)\n", - " 4. state_present (0.012)\n", - " 5. get_reset_reasons (0.011)\n", - " 6. dropout (0.011)\n", + "---- 0. get_uptime_facts (0.105)\n", "\n", "Label = get_distribution_NetBSD\n", "Pred =\n", - " 0. get_style_class (0.023)\n", - " 1. parse_distribution_file_Amazon (0.02)\n", - " 2. parse_distribution_file_Slackware (0.019)\n", - " 3. reverse_dict (0.016)\n", - " 4. get_distribution_AIX (0.014)\n", - " 5. get_virtual_facts (0.014)\n", - " 6. collect (0.012)\n", + " 0. parse_distribution_file_Alpine (0.105)\n", "\n", "Label = get_distribution_SMGL\n", "Pred =\n", - " 0. get_virtual_facts (0.925)\n", - " 1. collect (0.009)\n", - " 2. parse_distribution_file_Slackware (0.005)\n", - " 3. parse_distribution_file_Alpine (0.003)\n", - " 4. get_result_and_facts (0.002)\n", - " 5. populate (0.002)\n", - " 6. _vimeo_sort_formats (0.001)\n", + " 0. get_virtual_facts (0.248)\n", "\n", "Label = remove_aliases\n", "Pred =\n", - " 0. get_filters_params (0.028)\n", - " 1. new_resource_to_string_convert (0.024)\n", - " 2. resource_to_request (0.023)\n", - " 3. validate_roles (0.013)\n", - " 4. port_lists (0.011)\n", - " 5. openstack_module_kwargs (0.01)\n", - " 6. diff_update (0.009)\n", + " 0. argspec (0.278)\n", "\n", "Label = is_masklen\n", "Pred =\n", - " 0. urshift (0.016)\n", - " 1. digit_sum (0.015)\n", - " 2. validate_prefix (0.013)\n", - " 3. make_hashable (0.011)\n", - " 4. find (0.008)\n", - " 5. is_cycle (0.008)\n", - " 6. get_digit (0.007)\n", + " 0. to_list (0.154)\n", "\n", "Label = _compat_bytes_to_byte_vals\n", "Pred =\n", - " 0. decodeFilename (0.053)\n", - " 1. equals_lf (0.036)\n", - " 2. endswith_cr (0.032)\n", - " 3. endswith_lf (0.026)\n", - " 4. get_random_secret_key (0.023)\n", - " 5. iter_lines (0.013)\n", - " 6. uses_server_time (0.01)\n", + " 0. _msectotimecode (0.06)\n", "\n", "Label = ip_network\n", "Pred =\n", - " 0. ip_address (0.306)\n", - " 1. ip_interface (0.123)\n", - " 2. get_language_from_path (0.008)\n", - " 3. _validate_unicast_failover_address (0.008)\n", - " 4. _parse_hextet (0.007)\n", - " 5. _check_stop_list (0.004)\n", - " 6. raise_login_required (0.004)\n", + " 0. zeros (0.033)\n", "\n", "Label = __sub__\n", "Pred =\n", - " 0. __add__ (0.636)\n", - "---- 1. __sub__ (0.06)\n", - " 2. __radd__ (0.049)\n", - " 3. __lt__ (0.032)\n", - " 4. difference (0.022)\n", - " 5. __mul__ (0.021)\n", - " 6. __eq__ (0.021)\n", + " 0. __add__ (0.256)\n", "\n", "Label = __eq__\n", "Pred =\n", - "---- 0. __eq__ (0.997)\n", - " 1. __add__ (0.001)\n", - " 2. clear (0.0)\n", - " 3. __lt__ (0.0)\n", - " 4. __contains__ (0.0)\n", - " 5. add (0.0)\n", - " 6. difference (0.0)\n", + "---- 0. __eq__ (0.974)\n", "\n", "Label = hostmask\n", "Pred =\n", - " 0. _get_networks_key (0.123)\n", - " 1. address (0.053)\n", - " 2. content (0.045)\n", - " 3. netmask (0.034)\n", - " 4. source (0.028)\n", - " 5. destination_ip (0.024)\n", - " 6. name (0.018)\n", + " 0. __hash__ (0.497)\n", "\n", "Label = is_private\n", "Pred =\n", - " 0. is_global (0.996)\n", - " 1. json (0.001)\n", - "---- 2. is_private (0.0)\n", - " 3. get_private_network (0.0)\n", - " 4. is_link_local (0.0)\n", - " 5. is_loopback (0.0)\n", - " 6. adapt_datetimefield_value (0.0)\n", + " 0. is_global (0.998)\n", "\n", "Label = _string_from_ip_int\n", "Pred =\n", - " 0. ip_address_validators (0.066)\n", - " 1. get_main_version (0.027)\n", - " 2. paragraph (0.022)\n", - " 3. spatialite_version_tuple (0.019)\n", - " 4. filesize_number_format (0.011)\n", - " 5. equals_lf (0.011)\n", - " 6. filename_from_content_disposition (0.01)\n", + " 0. _extract_items (0.064)\n", "\n", "Label = with_hostmask\n", "Pred =\n", - "---- 0. with_hostmask (0.996)\n", - " 1. with_prefixlen (0.0)\n", - " 2. broadcast_address (0.0)\n", - " 3. describe (0.0)\n", - " 4. __str__ (0.0)\n", - " 5. get_base_url (0.0)\n", - " 6. _ip_int_from_prefix (0.0)\n", + "---- 0. with_hostmask (0.995)\n", "\n", "Label = is_reserved\n", "Pred =\n", - "---- 0. is_reserved (0.653)\n", - " 1. is_private (0.019)\n", - " 2. selinux_mls_enabled (0.007)\n", - " 3. get_mixed_type_key (0.006)\n", - " 4. multiple_domains (0.005)\n", - " 5. validate_host (0.004)\n", - " 6. present_static_nat (0.004)\n", + " 0. is_private (0.16)\n", "\n", "Label = is_link_local\n", "Pred =\n", - "---- 0. is_link_local (0.989)\n", - " 1. is_site_local (0.001)\n", - " 2. is_unspecified (0.001)\n", - " 3. is_reserved (0.0)\n", - " 4. is_loopback (0.0)\n", - " 5. contains_aggregate (0.0)\n", - " 6. is_set (0.0)\n", + "---- 0. is_link_local (0.993)\n", "\n", "Label = __str__\n", "Pred =\n", - "---- 0. __str__ (0.997)\n", - " 1. __repr__ (0.001)\n", - " 2. with_prefixlen (0.001)\n", - " 3. __hash__ (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. add (0.0)\n", - " 6. describe (0.0)\n", + "---- 0. __str__ (0.999)\n", "\n", "Label = __hash__\n", "Pred =\n", - "---- 0. __hash__ (0.995)\n", - " 1. __len__ (0.002)\n", - " 2. __str__ (0.001)\n", - " 3. __repr__ (0.0)\n", - " 4. name (0.0)\n", - " 5. predict (0.0)\n", - " 6. __bool__ (0.0)\n", + "---- 0. __hash__ (0.999)\n", "\n", "Label = get_signature_key\n", "Pred =\n", - " 0. get_partition_uuid (0.028)\n", - " 1. get_hasher (0.016)\n", - " 2. _read_from_pipes (0.012)\n", - " 3. ror (0.012)\n", - " 4. sanitize_url (0.009)\n", - " 5. qualities (0.008)\n", - " 6. pg_quote_identifier (0.007)\n", + " 0. get_appliance (0.021)\n", "\n", "Label = modify\n", "Pred =\n", - " 0. delete (0.221)\n", - " 1. set (0.067)\n", - " 2. add (0.062)\n", - " 3. clear (0.034)\n", - " 4. disable (0.029)\n", - " 5. deprecate (0.024)\n", - " 6. warn (0.022)\n", + " 0. add (0.777)\n", "\n", "Label = _get_elb_listener_rules\n", "Pred =\n", - " 0. get_elb_listener_rules (0.33)\n", - " 1. get_elb_tags (0.057)\n", - " 2. modify_db_instance (0.055)\n", - " 3. get_role_with_backoff (0.048)\n", - " 4. delete_db_instance (0.04)\n", - " 5. create_db_snapshot (0.015)\n", - " 6. describe_subnets_with_backoff (0.013)\n", + " 0. _get_elb_listeners (0.271)\n", "\n", "Label = get_elb\n", "Pred =\n", - " 0. update (0.051)\n", - " 1. _create_policy (0.035)\n", - " 2. update_elb_attributes (0.024)\n", - " 3. _find_instance_info (0.013)\n", - " 4. compare_security_groups (0.013)\n", - " 5. _get_instance (0.012)\n", - " 6. to_param_list (0.012)\n", + " 0. get_connection (0.205)\n", "\n", "Label = eks_model\n", "Pred =\n", - " 0. ec2_model (0.387)\n", - " 1. rds_model (0.381)\n", - " 2. waf_model (0.1)\n", - " 3. wait (0.01)\n", - " 4. wait_for_status (0.002)\n", - " 5. CASCADE (0.002)\n", - " 6. lock_file (0.002)\n", + " 0. rds_model (0.469)\n", "\n", "Label = get_distribution\n", "Pred =\n", - " 0. get_distribution_config (0.501)\n", - "---- 1. get_distribution (0.201)\n", - " 2. get_streaming_distribution (0.083)\n", - " 3. get_streaming_distribution_config (0.051)\n", - " 4. delete_distribution (0.013)\n", - " 5. update_distribution (0.01)\n", - " 6. get_bucket_list (0.004)\n", + "---- 0. get_distribution (0.57)\n", "\n", "Label = get_origin_access_identity_config\n", "Pred =\n", - " 0. get_origin_access_identity (0.931)\n", - " 1. delete_origin_access_identity (0.005)\n", - " 2. create_origin_access_identity (0.001)\n", - " 3. send_message (0.001)\n", - " 4. create_empty_api (0.0)\n", - " 5. _server_time (0.0)\n", - " 6. get_public_ip_address (0.0)\n", + " 0. get_origin_access_identity (0.776)\n", "\n", "Label = list_origin_access_identities\n", "Pred =\n", - " 0. describe_volumes_with_backoff (0.092)\n", - " 1. list_iam_roles_with_backoff (0.03)\n", - " 2. paginated_list (0.026)\n", - " 3. list_web_acls_with_backoff (0.015)\n", - " 4. list_file_systems (0.013)\n", - " 5. list_rules_with_backoff (0.012)\n", - " 6. create_resource (0.012)\n", + " 0. get_tags (0.087)\n", "\n", "Label = get_aliases_from_distribution_id\n", "Pred =\n", - " 0. get_distribution (0.274)\n", - " 1. get_distribution_config (0.241)\n", - " 2. delete_distribution (0.146)\n", - " 3. get_streaming_distribution (0.062)\n", - " 4. get_streaming_distribution_config (0.026)\n", - " 5. update_distribution (0.025)\n", - " 6. get_bucket_list (0.003)\n", + " 0. get_distribution (0.24)\n", "\n", "Label = get_rule_with_backoff\n", "Pred =\n", - " 0. get_byte_match_set_with_backoff (0.124)\n", - " 1. get_kms_tags_with_backoff (0.046)\n", - " 2. get_size_constraint_set_with_backoff (0.04)\n", - " 3. get_security_groups_with_backoff (0.037)\n", - " 4. get_ip_set_with_backoff (0.031)\n", - " 5. sg_exists_with_backoff (0.027)\n", - " 6. list_web_acls_with_backoff (0.023)\n", + " 0. create_service_principal_profile_instance (0.051)\n", "\n", "Label = exit_json\n", "Pred =\n", - " 0. fail_json (0.212)\n", - " 1. raise_for_status (0.102)\n", - " 2. boolean (0.082)\n", - " 3. __exit__ (0.068)\n", - " 4. wrapper (0.017)\n", - " 5. inner (0.017)\n", - " 6. failure (0.016)\n", + " 0. __exit__ (0.219)\n", "\n", "Label = _gather_versions\n", "Pred =\n", - " 0. version_is_less_than_12 (0.062)\n", - " 1. version_is_less_than (0.03)\n", - " 2. ecs_api_handles_network_configuration (0.028)\n", - " 3. is_writable (0.023)\n", - " 4. version_is_less_than_13 (0.02)\n", - " 5. localtime (0.014)\n", - " 6. _check_versions (0.012)\n", + " 0. ecs_api_handles_network_configuration (0.33)\n", "\n", "Label = is_boto3_error_code\n", "Pred =\n", - " 0. assert_fit_params (0.029)\n", - " 1. update_distribution (0.012)\n", - " 2. get_db_snapshot (0.012)\n", - " 3. _called_with_wrong_args (0.011)\n", - " 4. create_rule_lookup (0.01)\n", - " 5. get_exception (0.01)\n", - " 6. has_shareable_memory (0.009)\n", + " 0. get_attached_policy_list (0.068)\n", "\n", "Label = transform_commands\n", "Pred =\n", - " 0. test_chi2_coo (0.053)\n", - " 1. ovirt_full_argument_spec (0.017)\n", - " 2. test_fit_transform (0.017)\n", - " 3. test_kernel_pca_invalid_parameters (0.012)\n", - " 4. ping (0.012)\n", - " 5. postgres_common_argument_spec (0.01)\n", - " 6. raise_login_required (0.009)\n", + " 0. define_argument_spec (0.036)\n", "\n", "Label = validate_ip_v6_address\n", "Pred =\n", - "---- 0. validate_ip_v6_address (0.974)\n", - " 1. is_ipv6 (0.005)\n", - " 2. validate_ip_address (0.002)\n", - " 3. is_valid_address (0.001)\n", - " 4. is_ipv4_address (0.001)\n", - " 5. is_ip (0.001)\n", - " 6. v6_int_to_packed (0.0)\n", + "---- 0. validate_ip_v6_address (0.961)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. list (0.044)\n", - " 1. extra (0.03)\n", - " 2. _handle_errors (0.026)\n", - " 3. _prepare (0.024)\n", - " 4. get_user (0.022)\n", - " 5. get_handler (0.018)\n", - " 6. database_backwards (0.017)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " 0. test_warn_wrong_warning (0.035)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. func (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = le\n", "Pred =\n", - " 0. gt (0.48)\n", - " 1. lt (0.27)\n", - " 2. ge (0.061)\n", - " 3. contains (0.007)\n", - " 4. __getattr__ (0.007)\n", - " 5. __call__ (0.004)\n", - " 6. exists (0.003)\n", + " 0. ge (0.21)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. ip (0.0)\n", "\n", "Label = login\n", "Pred =\n", - " 0. query (0.047)\n", - " 1. raise_for_failure (0.034)\n", - " 2. delete_trans (0.022)\n", - " 3. logout (0.021)\n", - " 4. update_fields (0.016)\n", - " 5. get_module_prefix_map (0.013)\n", - " 6. _error (0.013)\n", + " 0. _delete (0.06)\n", "\n", "Label = get_trans_changes\n", "Pred =\n", - " 0. commit (0.391)\n", - " 1. validate_trans (0.378)\n", - " 2. validate_commit (0.141)\n", - " 3. set_value (0.004)\n", - " 4. create (0.003)\n", - " 5. get_module_prefix_map (0.003)\n", - " 6. delete_trans (0.002)\n", + " 0. commit (0.491)\n", "\n", "Label = get_schema\n", "Pred =\n", - " 0. get_value (0.961)\n", - " 1. exists (0.002)\n", - " 2. patch (0.002)\n", - " 3. set_value (0.001)\n", - " 4. update (0.001)\n", - " 5. get_module_prefix_map (0.001)\n", - " 6. _list (0.001)\n", + " 0. new_trans (0.463)\n", "\n", "Label = __str__\n", "Pred =\n", - " 0. __repr__ (0.987)\n", - "---- 1. __str__ (0.009)\n", - " 2. __hash__ (0.001)\n", - " 3. serialize (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. get_url (0.0)\n", - " 6. name (0.0)\n", + " 0. __repr__ (0.932)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. connect (0.0)\n", - " 4. start_serialization (0.0)\n", - " 5. func (0.0)\n", - " 6. update (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. clear (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. load (0.0)\n", "\n", "Label = add\n", "Pred =\n", - " 0. delete (0.379)\n", - "---- 1. add (0.243)\n", - " 2. update (0.131)\n", - " 3. remove (0.073)\n", - " 4. __str__ (0.017)\n", - " 5. create (0.012)\n", - " 6. set (0.009)\n", + " 0. create (0.264)\n", "\n", "Label = complete_missing_attributes\n", "Pred =\n", - " 0. to_list (0.134)\n", - " 1. subwidgets (0.042)\n", - " 2. add (0.024)\n", - " 3. has_changed (0.023)\n", - " 4. update (0.023)\n", - " 5. __init__ (0.021)\n", - " 6. normalize (0.017)\n", + " 0. add (0.081)\n", "\n", "Label = get_capabilities\n", "Pred =\n", - "---- 0. get_capabilities (0.953)\n", - " 1. get_connection (0.038)\n", - " 2. is_netconf (0.001)\n", - " 3. read_module_context (0.001)\n", - " 4. lock_configuration (0.001)\n", - " 5. load_config (0.0)\n", - " 6. _commit (0.0)\n", + "---- 0. get_capabilities (0.733)\n", "\n", "Label = _get_connection\n", "Pred =\n", - "---- 0. _get_connection (0.976)\n", - " 1. get_connection (0.001)\n", - " 2. _get_elb_connection (0.001)\n", - " 3. get_nc_connection (0.001)\n", - " 4. exec_rpc (0.001)\n", - " 5. create_cursor (0.001)\n", - " 6. connect (0.0)\n", + "---- 0. _get_connection (0.984)\n", "\n", "Label = get_config\n", "Pred =\n", - "---- 0. get_config (0.999)\n", - " 1. load_config (0.0)\n", - " 2. get_running_config (0.0)\n", - " 3. get_capabilities (0.0)\n", - " 4. config (0.0)\n", - " 5. get_required_config (0.0)\n", - " 6. interface_is_portchannel (0.0)\n", + "---- 0. get_config (1.0)\n", "\n", "Label = put\n", "Pred =\n", - " 0. post (0.23)\n", - " 1. delete (0.219)\n", - " 2. get (0.216)\n", - "---- 3. put (0.192)\n", - " 4. patch (0.094)\n", - " 5. update (0.03)\n", - " 6. head (0.003)\n", + " 0. delete (0.229)\n", "\n", "Label = get_connection\n", "Pred =\n", - "---- 0. get_connection (0.273)\n", - " 1. load_config (0.211)\n", - " 2. cli (0.127)\n", - " 3. get_capabilities (0.075)\n", - " 4. is_netconf (0.07)\n", - " 5. get_diff (0.01)\n", - " 6. set_nc_config (0.006)\n", + " 0. get_capabilities (0.244)\n", "\n", "Label = get_device_capabilities\n", "Pred =\n", - " 0. get_capabilities (0.997)\n", - " 1. is_netconf (0.001)\n", - " 2. is_cliconf (0.0)\n", - " 3. _cloudstack_ver (0.0)\n", - " 4. get_connection (0.0)\n", - " 5. read_module_context (0.0)\n", - " 6. cli (0.0)\n", + " 0. get_capabilities (0.992)\n", "\n", "Label = run_commands\n", "Pred =\n", "---- 0. run_commands (0.999)\n", - " 1. load_config (0.001)\n", - " 2. main (0.0)\n", - " 3. function (0.0)\n", - " 4. fail_json (0.0)\n", - " 5. init_module (0.0)\n", - " 6. __call__ (0.0)\n", "\n", "Label = run_commands\n", "Pred =\n", - "---- 0. run_commands (0.998)\n", - " 1. load_config (0.001)\n", - " 2. execute_on_device (0.0)\n", - " 3. set_config (0.0)\n", - " 4. main (0.0)\n", - " 5. exit_json (0.0)\n", - " 6. command (0.0)\n", + "---- 0. run_commands (0.999)\n", "\n", "Label = is_uuid\n", "Pred =\n", - " 0. is_valid_uuid (0.164)\n", - " 1. vm_state_transition (0.039)\n", - " 2. _image_is_different (0.035)\n", - " 3. get_tag (0.014)\n", - " 4. compare_group_members (0.009)\n", - " 5. _type_security_group_match (0.009)\n", - " 6. check_dp_status (0.009)\n", + " 0. is_valid_uuid (0.152)\n", "\n", "Label = get_id_of_provider_name\n", "Pred =\n", - " 0. ensure_dir_exists (0.032)\n", - " 1. engine (0.03)\n", - " 2. resolve (0.019)\n", - " 3. _download_file (0.017)\n", - " 4. _allowed_methods (0.015)\n", - " 5. url_filename (0.013)\n", - " 6. path (0.012)\n", + " 0. is_ignored (0.071)\n", "\n", "Label = get_net_id\n", "Pred =\n", - " 0. get_config_templates (0.042)\n", - " 1. _get_glue_job (0.03)\n", - " 2. pipeline_field (0.017)\n", - " 3. login_vchs (0.015)\n", - " 4. docker_stack_inspect (0.013)\n", - " 5. is_vlan_valid (0.012)\n", - " 6. get_security_group_id (0.011)\n", + " 0. get_config_templates (0.12)\n", "\n", "Label = get_connection\n", "Pred =\n", - "---- 0. get_connection (0.648)\n", - " 1. get_capabilities (0.312)\n", - " 2. lock_configuration (0.003)\n", - " 3. is_netconf (0.002)\n", - " 4. get (0.001)\n", - " 5. cli (0.001)\n", - " 6. load_config (0.001)\n", + " 0. get_capabilities (0.938)\n", "\n", "Label = run_commands\n", "Pred =\n", "---- 0. run_commands (0.999)\n", - " 1. load_config (0.001)\n", - " 2. main (0.0)\n", - " 3. function (0.0)\n", - " 4. fail_json (0.0)\n", - " 5. init_module (0.0)\n", - " 6. __call__ (0.0)\n", "\n", "Label = load_config\n", "Pred =\n", "---- 0. load_config (0.999)\n", - " 1. set_nc_config (0.0)\n", - " 2. connect (0.0)\n", - " 3. exit_json (0.0)\n", - " 4. get_running_config (0.0)\n", - " 5. get_connection (0.0)\n", - " 6. to_command (0.0)\n", "\n", "Label = execute\n", "Pred =\n", - "---- 0. execute (0.409)\n", - " 1. create (0.103)\n", - " 2. add (0.076)\n", - " 3. predict_proba (0.042)\n", - " 4. options (0.04)\n", - " 5. delete (0.015)\n", - " 6. disable (0.013)\n", + "---- 0. execute (0.942)\n", "\n", "Label = get_connection\n", "Pred =\n", - "---- 0. get_connection (0.24)\n", - " 1. read_module_context (0.08)\n", - " 2. get_context (0.071)\n", - " 3. get (0.048)\n", - " 4. get_running_config (0.031)\n", - " 5. get_config (0.028)\n", - " 6. save_module_context (0.025)\n", + "---- 0. get_connection (0.911)\n", "\n", "Label = run_commands\n", "Pred =\n", - "---- 0. run_commands (0.999)\n", - " 1. load_config (0.0)\n", - " 2. apply_patch (0.0)\n", - " 3. main (0.0)\n", - " 4. add_commands (0.0)\n", - " 5. set_config (0.0)\n", - " 6. to_commands (0.0)\n", + "---- 0. run_commands (0.997)\n", "\n", "Label = get_connection\n", "Pred =\n", - "---- 0. get_connection (0.783)\n", - " 1. get_capabilities (0.151)\n", - " 2. load_config (0.01)\n", - " 3. is_netconf (0.008)\n", - " 4. lock_configuration (0.003)\n", - " 5. get (0.002)\n", - " 6. cli (0.001)\n", + " 0. get_capabilities (0.862)\n", "\n", "Label = sanitize\n", "Pred =\n", - "---- 0. sanitize (0.964)\n", - " 1. remove_ntp_auth_key (0.002)\n", - " 2. verify_remote_file_exists (0.001)\n", - " 3. etree_find (0.001)\n", - " 4. encode (0.0)\n", - " 5. add_operation (0.0)\n", - " 6. vlan_vid_to_bitmap (0.0)\n", + "---- 0. sanitize (0.96)\n", "\n", "Label = run_commands\n", "Pred =\n", - "---- 0. run_commands (0.998)\n", - " 1. load_config (0.001)\n", - " 2. set_config (0.0)\n", - " 3. main (0.0)\n", - " 4. to_commands (0.0)\n", - " 5. __call__ (0.0)\n", - " 6. _func (0.0)\n", + "---- 0. run_commands (0.999)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. _define_module_argument_spec (0.0)\n", - " 4. __init_module__ (0.0)\n", - " 5. define_argument_spec (0.0)\n", - " 6. failure (0.0)\n", "\n", "Label = main\n", "Pred =\n", - " 0. items (0.032)\n", - " 1. get_foreign_related_value (0.024)\n", - " 2. __neg__ (0.019)\n", - " 3. _findall_ns (0.017)\n", - " 4. subclasses (0.017)\n", - " 5. get_xml (0.015)\n", - " 6. set_empty (0.014)\n", + " 0. get_devices (0.155)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. update (0.0)\n", "\n", "Label = parse_memtotal\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = parse_interfaces\n", "Pred =\n", - "---- 0. parse_interfaces (0.957)\n", - " 1. parse_state (0.001)\n", - " 2. split_interface (0.001)\n", - " 3. escape_quotes (0.001)\n", - " 4. gettext_version (0.001)\n", - " 5. parse_hostnameprefix (0.001)\n", - " 6. port_configurable (0.001)\n", + "---- 0. parse_interfaces (0.93)\n", "\n", "Label = parse_macaddress\n", "Pred =\n", - " 0. parse_version (0.26)\n", - "---- 1. parse_macaddress (0.192)\n", - " 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n", - " 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + "---- 0. parse_macaddress (0.296)\n", "\n", "Label = to_lines\n", "Pred =\n", - "---- 0. to_lines (0.999)\n", - " 1. _to_lines (0.0)\n", - " 2. _request_for_item (0.0)\n", - " 3. add (0.0)\n", - " 4. _response_from_item (0.0)\n", - " 5. fail (0.0)\n", - " 6. get (0.0)\n", + "---- 0. to_lines (1.0)\n", "\n", "Label = validate_ipv4\n", "Pred =\n", - "---- 0. validate_ipv4 (0.79)\n", - " 1. validate_ipv6 (0.089)\n", - " 2. _split_optional_netmask (0.003)\n", - " 3. requires_vrf (0.002)\n", - " 4. validate_privilege (0.002)\n", - " 5. is_valid_address (0.001)\n", - " 6. get_ip_version (0.001)\n", + "---- 0. validate_ipv4 (0.949)\n", "\n", "Label = check_args\n", "Pred =\n", - "---- 0. check_args (0.995)\n", - " 1. _validate_param_values (0.0)\n", - " 2. enter_maintenance (0.0)\n", - " 3. add (0.0)\n", - " 4. validate_privilege (0.0)\n", - " 5. prepare_method (0.0)\n", - " 6. validate_vrf (0.0)\n", + "---- 0. check_args (0.999)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. build (0.0)\n", - " 5. destination (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = run\n", "Pred =\n", - " 0. execute_show_command (0.25)\n", - " 1. flatten_list (0.1)\n", - " 2. _send (0.063)\n", - " 3. _func (0.042)\n", - " 4. execute_show_commands (0.036)\n", - " 5. extra_repr (0.019)\n", - " 6. get_command (0.017)\n", + " 0. add (0.164)\n", "\n", "Label = transform_iterable\n", "Pred =\n", - " 0. __iter__ (0.118)\n", - " 1. _iterator (0.041)\n", - " 2. transform_dict (0.04)\n", - " 3. get_transform (0.036)\n", - " 4. append (0.027)\n", - " 5. chunks (0.02)\n", - " 6. values (0.018)\n", + " 0. parse_structured_module (0.06)\n", "\n", "Label = parse_filesystems\n", "Pred =\n", - "---- 0. parse_filesystems (0.793)\n", - " 1. parse_memory (0.146)\n", - " 2. parse_hostname (0.002)\n", - " 3. parse_sshkey (0.001)\n", - " 4. parse_interface (0.001)\n", - " 5. parse_view (0.001)\n", - " 6. parse_privilege (0.001)\n", + "---- 0. parse_filesystems (0.886)\n", "\n", "Label = parse_memfree_mb\n", "Pred =\n", - " 0. parse_memtotal_mb (0.022)\n", - " 1. parse_full_name (0.021)\n", - " 2. parse_message (0.013)\n", - " 3. parse_privilege (0.012)\n", - " 4. parse_memfree (0.01)\n", - " 5. __get_minor (0.009)\n", - " 6. _rsa_fun (0.008)\n", + " 0. parse_memtotal_mb (0.114)\n", "\n", "Label = parse_structured_power_supply_info\n", "Pred =\n", - " 0. parse_structured_module (0.315)\n", - " 1. auth (0.065)\n", - " 2. fix_invalid_varnames (0.025)\n", - " 3. get_day (0.008)\n", - " 4. parse_structured_vlans (0.007)\n", - " 5. parse_structured_interfaces (0.007)\n", - " 6. _get_port (0.005)\n", + " 0. pretty_instance (0.187)\n", "\n", "Label = parse_vlans\n", "Pred =\n", - " 0. _read_ucs_files_from_output (0.034)\n", - " 1. sanitize_config (0.022)\n", - " 2. vlan_range_to_list (0.02)\n", - " 3. get_flash_size (0.018)\n", - " 4. format_headers (0.017)\n", - " 5. is_valid_address (0.016)\n", - " 6. get_defaults_flag (0.014)\n", + " 0. get_protocol_list (0.088)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = parse_mode\n", "Pred =\n", - " 0. parse_port (0.084)\n", - " 1. parse_lookup_source (0.074)\n", - " 2. is_switchport (0.07)\n", - " 3. parse_domain_name (0.05)\n", - " 4. parse_hostname (0.044)\n", - " 5. parse_vrf (0.038)\n", - " 6. get_config (0.03)\n", + " 0. is_switchport (0.182)\n", "\n", "Label = get_system_mode\n", "Pred =\n", - " 0. execute_show_command (0.489)\n", - " 1. get_reset_reasons (0.275)\n", - " 2. send_show_command (0.02)\n", - " 3. get_vtp_password (0.009)\n", - " 4. get_dot1q_id (0.007)\n", - " 5. get_maintenance_timeout (0.007)\n", - " 6. is_default (0.006)\n", + " 0. get_reset_reasons (0.402)\n", "\n", "Label = execute_show_command\n", "Pred =\n", "---- 0. execute_show_command (0.999)\n", - " 1. execute_show_commands (0.0)\n", - " 2. reboot (0.0)\n", - " 3. get_reset_reasons (0.0)\n", - " 4. _send (0.0)\n", - " 5. state_absent (0.0)\n", - " 6. to_command (0.0)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = get_value\n", "Pred =\n", - " 0. get_custom_value (0.822)\n", - "---- 1. get_value (0.102)\n", - " 2. get_config (0.007)\n", - " 3. get_group_timeout (0.004)\n", - " 4. get_prep_value (0.003)\n", - " 5. is_switchport (0.002)\n", - " 6. interface_is_portchannel (0.002)\n", + " 0. get_custom_value (0.625)\n", "\n", "Label = get_desired\n", "Pred =\n", - " 0. map_param_to_obj (0.849)\n", - " 1. map_params_to_obj (0.016)\n", - " 2. self_link (0.009)\n", - " 3. _get_connect_params (0.008)\n", - " 4. get_qualifier (0.006)\n", - " 5. _choose_id_value (0.004)\n", - " 6. username (0.004)\n", + " 0. map_param_to_obj (0.796)\n", "\n", "Label = execute_show_command\n", - "Pred =\n", - "---- 0. execute_show_command (0.985)\n", - " 1. reboot (0.005)\n", - " 2. execute_show_commands (0.003)\n", - " 3. state_absent (0.001)\n", - " 4. get_reset_reasons (0.0)\n", - " 5. to_command (0.0)\n", - " 6. netconf_set_config (0.0)\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---- 0. execute_show_command (0.999)\n", "\n", "Label = fix_delta\n", "Pred =\n", - " 0. _are_equivalent (0.029)\n", - " 1. config_default_igmp_interface (0.025)\n", - " 2. find_xpath_attr (0.023)\n", - " 3. incr (0.022)\n", - " 4. remove_filter (0.021)\n", - " 5. build_stream_name (0.015)\n", - " 6. get_vnc_extraconfig (0.012)\n", + " 0. config_default_igmp_interface (0.265)\n", "\n", "Label = get_pim_interface_defaults\n", "Pred =\n", - " 0. get_attribute (0.063)\n", - " 1. _mrss_url (0.045)\n", - " 2. load_tags (0.026)\n", - " 3. _build_playlist (0.018)\n", - " 4. _check_formats (0.015)\n", - " 5. to_health_check (0.015)\n", - " 6. _extract_track_entries (0.013)\n", + " 0. _check_list_filter (0.183)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = deactivate_operation\n", "Pred =\n", - " 0. remove_operation (0.29)\n", - " 1. activate_operation (0.28)\n", - " 2. commit_operation (0.245)\n", - " 3. add_operation (0.033)\n", - " 4. find_unresolved_requires (0.003)\n", - " 5. get_aos_session (0.003)\n", - " 6. remove_ntp_auth_key (0.002)\n", + " 0. activate_operation (0.204)\n", "\n", "Label = execute_show_command\n", "Pred =\n", "---- 0. execute_show_command (0.999)\n", - " 1. execute_show_commands (0.0)\n", - " 2. _send (0.0)\n", - " 3. get_reset_reasons (0.0)\n", - " 4. to_command (0.0)\n", - " 5. reboot (0.0)\n", - " 6. to_request (0.0)\n", "\n", "Label = parse_mode\n", "Pred =\n", - " 0. parse_port (0.277)\n", - " 1. parse_vrf (0.16)\n", - " 2. is_switchport (0.065)\n", - " 3. get_group_timeout (0.052)\n", - " 4. parse_lookup_source (0.027)\n", - " 5. parse_hostname (0.027)\n", - " 6. parse_domain_name (0.024)\n", + " 0. parse_port (0.244)\n", "\n", "Label = apply_key_map\n", "Pred =\n", - "---- 0. apply_key_map (1.0)\n", - " 1. add_key_else_change_dict_key (0.0)\n", - " 2. apply_value_map (0.0)\n", - " 3. rename_key (0.0)\n", - " 4. add_unredirected_header (0.0)\n", - " 5. remove_nones_from_dict (0.0)\n", - " 6. validate_params (0.0)\n", + "---- 0. apply_key_map (0.999)\n", "\n", "Label = execute_show_command\n", "Pred =\n", "---- 0. execute_show_command (0.998)\n", - " 1. execute_show_commands (0.0)\n", - " 2. get_reset_reasons (0.0)\n", - " 3. to_request (0.0)\n", - " 4. reboot (0.0)\n", - " 5. _send (0.0)\n", - " 6. state (0.0)\n", "\n", "Label = state_absent\n", "Pred =\n", - "---- 0. state_absent (0.904)\n", - " 1. state_present (0.055)\n", - " 2. action_delete_all (0.004)\n", - " 3. load_config (0.003)\n", - " 4. get_commands_config_udld_interface1 (0.002)\n", - " 5. get_commands_remove_udld_global (0.001)\n", - " 6. get_default_table_map_filter (0.001)\n", + "---- 0. state_absent (0.851)\n", "\n", "Label = get_existing\n", "Pred =\n", - " 0. get_config (0.37)\n", - " 1. load_config (0.172)\n", - " 2. state_absent (0.027)\n", - " 3. get_running_config (0.026)\n", - " 4. work (0.019)\n", - " 5. get_required_config (0.017)\n", - " 6. get_value (0.016)\n", + " 0. get_value (0.238)\n", "\n", "Label = difference\n", "Pred =\n", - " 0. _get_pixel (0.069)\n", - " 1. _get_point_3d (0.048)\n", - " 2. sub_bytes (0.027)\n", - " 3. indexbytes (0.024)\n", - " 4. sub_bytes_inv (0.021)\n", - " 5. _set_none_to_blank (0.018)\n", - " 6. paired_euclidean_distances (0.016)\n", + " 0. _get_point_3d (0.387)\n", "\n", "Label = parse_system_mtu\n", "Pred =\n", - " 0. parse_hostname (0.352)\n", - " 1. parse_domain_name (0.287)\n", - " 2. parse_lookup_source (0.236)\n", - " 3. parse_port (0.012)\n", - " 4. parse_vrf (0.011)\n", - " 5. get_group_timeout (0.006)\n", - " 6. parse_description (0.005)\n", + " 0. parse_hostname (0.384)\n", "\n", "Label = parse_remote_server\n", "Pred =\n", - " 0. get_remote_url (0.052)\n", - " 1. is_valid_hostname (0.037)\n", - " 2. parse_use_vrf (0.028)\n", - " 3. is_not_a_branch (0.021)\n", - " 4. parse_vrf (0.02)\n", - " 5. get_revision (0.018)\n", - " 6. load_acl_with_token (0.016)\n", + " 0. parse_vrf (0.185)\n", "\n", "Label = normalize_area\n", "Pred =\n", - " 0. dotted_netmask (0.025)\n", - " 1. system_central_management (0.018)\n", - " 2. ensure_cgw_present (0.016)\n", - " 3. write_metadata_tag (0.014)\n", - " 4. check_vswitch_configuration (0.011)\n", - " 5. bucket_exists (0.008)\n", - " 6. _extract_embed_url (0.008)\n", + " 0. dotted_netmask (0.115)\n", "\n", "Label = get_admin_state\n", "Pred =\n", - " 0. get_command_from_state (0.988)\n", - " 1. execute_show_command (0.004)\n", - " 2. config_cmd_operation (0.001)\n", - " 3. merge_command_dict_cli (0.0)\n", - " 4. build_command (0.0)\n", - " 5. get_command (0.0)\n", - " 6. send_show_command (0.0)\n", + " 0. get_command_from_state (0.93)\n", "\n", "Label = execute_show_command\n", "Pred =\n", - "---- 0. execute_show_command (0.631)\n", - " 1. execute_show_commands (0.165)\n", - " 2. state_absent (0.034)\n", - " 3. send_show_command (0.009)\n", - " 4. reboot (0.007)\n", - " 5. state_present (0.007)\n", - " 6. add_commands (0.005)\n", + "---- 0. execute_show_command (0.976)\n", "\n", "Label = state_present\n", "Pred =\n", - " 0. state_absent (0.561)\n", - " 1. load_config (0.062)\n", - " 2. add (0.034)\n", - " 3. add_commands (0.022)\n", - "---- 4. state_present (0.016)\n", - " 5. set_config (0.013)\n", - " 6. _send (0.012)\n", + " 0. state_absent (0.797)\n", "\n", "Label = invoke\n", "Pred =\n", - "---- 0. invoke (0.991)\n", - " 1. _get_page (0.001)\n", - " 2. get (0.001)\n", - " 3. wrapper (0.001)\n", - " 4. decorator (0.0)\n", - " 5. put (0.0)\n", - " 6. inner (0.0)\n", + "---- 0. invoke (0.968)\n", "\n", "Label = apply_key_map\n", "Pred =\n", - "---- 0. apply_key_map (1.0)\n", - " 1. add_key_else_change_dict_key (0.0)\n", - " 2. apply_value_map (0.0)\n", - " 3. rename_key (0.0)\n", - " 4. add_unredirected_header (0.0)\n", - " 5. remove_nones_from_dict (0.0)\n", - " 6. validate_params (0.0)\n", + "---- 0. apply_key_map (0.999)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = flatten_list\n", "Pred =\n", "---- 0. flatten_list (1.0)\n", - " 1. flatten (0.0)\n", - " 2. _func (0.0)\n", - " 3. add (0.0)\n", - " 4. run_commands (0.0)\n", - " 5. execute_show_commands (0.0)\n", - " 6. add_commands (0.0)\n", "\n", "Label = parse_bandwidth\n", "Pred =\n", - " 0. parse_mtu (0.713)\n", - "---- 1. parse_bandwidth (0.242)\n", - " 2. parse_macaddress (0.005)\n", - " 3. parse_version (0.003)\n", - " 4. parse_serialnum (0.002)\n", - " 5. parse_iostype (0.002)\n", - " 6. parse_image (0.001)\n", + " 0. parse_mtu (0.662)\n", "\n", "Label = nat_rule_exists\n", "Pred =\n", - " 0. addressgroup_exists (0.828)\n", - " 1. admin_exists (0.031)\n", - " 2. pg_exists (0.021)\n", - " 3. if_exists (0.006)\n", - " 4. load_cfgfile (0.002)\n", - " 5. early_stopping_monitor (0.002)\n", - " 6. get_immutables_intersection (0.001)\n", + " 0. addressgroup_exists (0.516)\n", "\n", "Label = check_response\n", "Pred =\n", - "---- 0. check_response (0.976)\n", - " 1. __check_response__ (0.019)\n", - " 2. netconf_set_config (0.0)\n", - " 3. cli (0.0)\n", - " 4. add (0.0)\n", - " 5. run (0.0)\n", - " 6. reset (0.0)\n", + "---- 0. check_response (0.959)\n", "\n", "Label = work\n", "Pred =\n", "---- 0. work (0.997)\n", - " 1. get_end_state (0.001)\n", - " 2. get_existing (0.0)\n", - " 3. config_evnp_bd (0.0)\n", - " 4. state (0.0)\n", - " 5. rollback (0.0)\n", - " 6. config_netstream_export (0.0)\n", "\n", "Label = get_proposed\n", "Pred =\n", - "---- 0. get_proposed (0.988)\n", - " 1. judge_if_config_exist (0.002)\n", - " 2. state (0.0)\n", - " 3. add (0.0)\n", - " 4. enabled (0.0)\n", - " 5. wait_for_task (0.0)\n", - " 6. local_ip (0.0)\n", + "---- 0. get_proposed (0.932)\n", "\n", "Label = is_valid_address\n", "Pred =\n", - "---- 0. is_valid_address (0.985)\n", - " 1. is_valid_v4addr (0.007)\n", - " 2. is_ipv6 (0.001)\n", - " 3. is_ipv4 (0.0)\n", - " 4. get_ns_version (0.0)\n", - " 5. _get_model_name_from_url (0.0)\n", - " 6. is_address (0.0)\n", + "---- 0. is_valid_address (0.956)\n", "\n", "Label = check_response\n", "Pred =\n", - "---- 0. check_response (0.973)\n", - " 1. __check_response__ (0.02)\n", - " 2. netconf_set_config (0.001)\n", - " 3. cli (0.0)\n", - " 4. get_nc_config (0.0)\n", - " 5. netconf_get_config (0.0)\n", - " 6. check_vni_bd (0.0)\n", + "---- 0. check_response (0.977)\n", "\n", "Label = is_config_exist\n", "Pred =\n", - " 0. get_nvo3_gw_enhanced (0.332)\n", - " 1. parse_shutdown (0.021)\n", - " 2. get_snmp_local_engine (0.016)\n", - "---- 3. is_config_exist (0.013)\n", - " 4. rollback_label (0.011)\n", - " 5. find_no_change (0.009)\n", - " 6. chunks (0.008)\n", + " 0. get_nvo3_gw_enhanced (0.034)\n", "\n", "Label = build_config_xml\n", "Pred =\n", - "---- 0. build_config_xml (0.998)\n", - " 1. netconf_set_config (0.0)\n", - " 2. delete_interface (0.0)\n", - " 3. rollback_label (0.0)\n", - " 4. get_end_state (0.0)\n", - " 5. undo_config_vlan (0.0)\n", - " 6. get_access_vlan (0.0)\n", + "---- 0. build_config_xml (0.981)\n", "\n", "Label = init_module\n", "Pred =\n", - "---- 0. init_module (0.833)\n", - " 1. __init_module__ (0.163)\n", - " 2. add (0.0)\n", - " 3. clear (0.0)\n", - " 4. set (0.0)\n", - " 5. load_config (0.0)\n", - " 6. predict (0.0)\n", + "---- 0. init_module (0.837)\n", "\n", "Label = is_valid_description\n", "Pred =\n", - "---- 0. is_valid_description (0.669)\n", - " 1. snmp_auth_password (0.017)\n", - " 2. _remote_state (0.006)\n", - " 3. description (0.004)\n", - " 4. include (0.004)\n", - " 5. is_valid_vlan_id (0.003)\n", - " 6. __lt__ (0.003)\n", + "---- 0. is_valid_description (0.289)\n", "\n", "Label = isvalidlsaoholdinterval\n", "Pred =\n", - " 0. isvalidlsaoriginatemaxinterval (0.047)\n", - " 1. is_valid_lsa_originate_interval (0.035)\n", - " 2. isvalidlsastartarrivalinterval (0.028)\n", - " 3. is_valid_lsa_arrival_interval (0.019)\n", - " 4. is_permanent_redirect (0.017)\n", - " 5. configure_vsan (0.012)\n", - " 6. is_fakes3 (0.01)\n", + " 0. isvalidlsamaxarrivalinterval (0.117)\n", "\n", "Label = get_exist_lsa_a_hold_interval\n", "Pred =\n", - " 0. get_exist_vrf (0.042)\n", - " 1. get_exist_spf_interval (0.041)\n", - " 2. get_exist_ospf_id (0.04)\n", - " 3. getexistlsaointerval_flag (0.04)\n", - " 4. get_exist_spf_milli_interval (0.039)\n", - " 5. get_exist_lsa_a_max_interval (0.039)\n", - " 6. get_exist_route (0.039)\n", + " 0. getexistlsaointerval_flag (0.046)\n", "\n", "Label = get_authorization_domain\n", "Pred =\n", - " 0. get_radius_client (0.072)\n", - " 1. get_hwtacacs_template (0.071)\n", - " 2. get_authentication_scheme (0.069)\n", - " 3. get_accounting_domain (0.069)\n", - " 4. get_hwtacacs_global_cfg (0.067)\n", - " 5. get_authentication_domain (0.067)\n", - " 6. get_authorization_scheme (0.066)\n", + " 0. get_accounting_domain (0.079)\n", "\n", - "Label = get_local_user_group\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Label = get_local_user_group\n", "Pred =\n", - " 0. get_radius_client (0.072)\n", - " 1. get_hwtacacs_template (0.071)\n", - " 2. get_authentication_scheme (0.069)\n", - " 3. get_accounting_domain (0.069)\n", - " 4. get_hwtacacs_global_cfg (0.067)\n", - " 5. get_authentication_domain (0.067)\n", - " 6. get_authorization_scheme (0.066)\n", + " 0. get_accounting_domain (0.079)\n", "\n", "Label = cli_load_config\n", "Pred =\n", "---- 0. cli_load_config (1.0)\n", - " 1. add (0.0)\n", - " 2. load_config (0.0)\n", - " 3. rollback (0.0)\n", - " 4. init_module (0.0)\n", - " 5. config_netstream_export (0.0)\n", - " 6. __init_module__ (0.0)\n", "\n", "Label = check_response\n", "Pred =\n", - "---- 0. check_response (0.973)\n", - " 1. __check_response__ (0.02)\n", - " 2. netconf_set_config (0.001)\n", - " 3. cli (0.0)\n", - " 4. get_nc_config (0.0)\n", - " 5. netconf_get_config (0.0)\n", - " 6. check_vni_bd (0.0)\n", + "---- 0. check_response (0.977)\n", "\n", "Label = cli_load_config\n", "Pred =\n", "---- 0. cli_load_config (1.0)\n", - " 1. add (0.0)\n", - " 2. load_config (0.0)\n", - " 3. rollback (0.0)\n", - " 4. init_module (0.0)\n", - " 5. config_netstream_export (0.0)\n", - " 6. __init_module__ (0.0)\n", "\n", "Label = rollback_last\n", "Pred =\n", - " 0. rollback_label (0.306)\n", - " 1. clear_commitid_label (0.147)\n", - " 2. netconf_load_config (0.12)\n", - " 3. rollback_commit_id (0.101)\n", - " 4. clear_oldest (0.029)\n", - " 5. delete_process (0.027)\n", - " 6. set_trap_source_port (0.014)\n", + " 0. rollback_label (0.77)\n", "\n", "Label = main\n", "Pred =\n", - "---- 0. main (0.994)\n", - " 1. init_module (0.001)\n", - " 2. _define_module_argument_spec (0.001)\n", - " 3. define_argument_spec (0.001)\n", - " 4. add (0.0)\n", - " 5. __init_module__ (0.0)\n", - " 6. clear (0.0)\n", + "---- 0. main (0.99)\n", "\n", "Label = init_module\n", "Pred =\n", - "---- 0. init_module (0.833)\n", - " 1. __init_module__ (0.163)\n", - " 2. add (0.0)\n", - " 3. clear (0.0)\n", - " 4. set (0.0)\n", - " 5. load_config (0.0)\n", - " 6. predict (0.0)\n", + "---- 0. init_module (0.837)\n", "\n", "Label = get_end_state\n", "Pred =\n", "---- 0. get_end_state (1.0)\n", - " 1. get_existing (0.0)\n", - " 2. work (0.0)\n", - " 3. state (0.0)\n", - " 4. rollback (0.0)\n", - " 5. __init_module__ (0.0)\n", - " 6. netconf_load_config (0.0)\n", "\n", "Label = is_vlan_bitmap_empty\n", "Pred =\n", - "---- 0. is_vlan_bitmap_empty (0.894)\n", - " 1. is_vlan_in_bitmap (0.003)\n", - " 2. equal_values (0.003)\n", - " 3. vlan_vid_to_bitmap (0.003)\n", - " 4. _count_righthand_zero_bits (0.002)\n", - " 5. wait_for (0.002)\n", - " 6. check_mode (0.001)\n", + "---- 0. is_vlan_bitmap_empty (0.718)\n", "\n", "Label = netconf_get_config\n", "Pred =\n", - " 0. netconf_set_config (0.595)\n", - "---- 1. netconf_get_config (0.393)\n", - " 2. run (0.001)\n", - " 3. add (0.0)\n", - " 4. check_response (0.0)\n", - " 5. get_value (0.0)\n", - " 6. get (0.0)\n", + " 0. netconf_set_config (0.578)\n", "\n", "Label = netconf_set_config\n", "Pred =\n", - "---- 0. netconf_set_config (0.595)\n", - " 1. netconf_get_config (0.393)\n", - " 2. run (0.001)\n", - " 3. add (0.0)\n", - " 4. check_response (0.0)\n", - " 5. get_value (0.0)\n", - " 6. get (0.0)\n", + "---- 0. netconf_set_config (0.578)\n", "\n", "Label = is_config_exist\n", "Pred =\n", - "---- 0. is_config_exist (0.999)\n", - " 1. get_nvo3_gw_enhanced (0.0)\n", - " 2. has_lldp (0.0)\n", - " 3. _external_ids_to_dict (0.0)\n", - " 4. to_list (0.0)\n", - " 5. is_exist_channel_id_name (0.0)\n", - " 6. _fail_mode_to_str (0.0)\n", + "---- 0. is_config_exist (0.998)\n", "\n", "Label = get_existing\n", "Pred =\n", "---- 0. get_existing (1.0)\n", - " 1. get_end_state (0.0)\n", - " 2. is_container_connected (0.0)\n", - " 3. judge_if_config_exist (0.0)\n", - " 4. work (0.0)\n", - " 5. enable (0.0)\n", - " 6. state (0.0)\n", "\n", "Label = __init_module__\n", "Pred =\n", - " 0. init_module (0.582)\n", - "---- 1. __init_module__ (0.404)\n", - " 2. add (0.001)\n", - " 3. set (0.001)\n", - " 4. clear (0.0)\n", - " 5. main (0.0)\n", - " 6. locked_config (0.0)\n", + " 0. init_module (0.874)\n", "\n", "Label = netconf_set_config\n", "Pred =\n", - " 0. check_response (0.837)\n", - " 1. __check_response__ (0.052)\n", - "---- 2. netconf_set_config (0.037)\n", - " 3. run (0.004)\n", - " 4. netconf_get_config (0.004)\n", - " 5. netconf_load_config (0.003)\n", - " 6. get_value (0.003)\n", + " 0. check_response (0.859)\n", "\n", "Label = check_response\n", "Pred =\n", - "---- 0. check_response (0.976)\n", - " 1. __check_response__ (0.019)\n", - " 2. netconf_set_config (0.0)\n", - " 3. cli (0.0)\n", - " 4. add (0.0)\n", - " 5. run (0.0)\n", - " 6. reset (0.0)\n", + "---- 0. check_response (0.959)\n", "\n", "Label = build_config_xml\n", "Pred =\n", - "---- 0. build_config_xml (0.998)\n", - " 1. netconf_set_config (0.0)\n", - " 2. delete_interface (0.0)\n", - " 3. rollback_label (0.0)\n", - " 4. get_end_state (0.0)\n", - " 5. undo_config_vlan (0.0)\n", - " 6. get_access_vlan (0.0)\n", + "---- 0. build_config_xml (0.981)\n", "\n", "Label = netconf_get_config\n", "Pred =\n", - " 0. netconf_set_config (0.595)\n", - "---- 1. netconf_get_config (0.393)\n", - " 2. run (0.001)\n", - " 3. add (0.0)\n", - " 4. check_response (0.0)\n", - " 5. get_value (0.0)\n", - " 6. get (0.0)\n", + " 0. netconf_set_config (0.578)\n", "\n", "Label = cli_get_connect_port\n", "Pred =\n", - " 0. cli_get_config (0.998)\n", - " 1. get_snmp_local_engine (0.001)\n", - " 2. cli_get_stp_config (0.0)\n", - " 3. action_delete_all (0.0)\n", - " 4. to_commands (0.0)\n", - " 5. get_current_config (0.0)\n", - " 6. cli_get_netstream_config (0.0)\n", + " 0. cli_get_config (0.999)\n", "\n", "Label = is_valid_address\n", "Pred =\n", - "---- 0. is_valid_address (0.985)\n", - " 1. is_valid_v4addr (0.007)\n", - " 2. is_ipv6 (0.001)\n", - " 3. is_ipv4 (0.0)\n", - " 4. get_ns_version (0.0)\n", - " 5. _get_model_name_from_url (0.0)\n", - " 6. is_address (0.0)\n", + "---- 0. is_valid_address (0.956)\n", "\n", "Label = get_update_cmd\n", "Pred =\n", - " 0. remove (0.119)\n", - " 1. is_reflect_client_exist (0.062)\n", - " 2. is_vrf_exist (0.028)\n", - " 3. rollback_filename (0.023)\n", - " 4. snmp_auth_password (0.021)\n", - " 5. rollback_label (0.014)\n", - " 6. requires_vrf (0.013)\n", + " 0. is_vrf_exist (0.085)\n", "\n", "Label = get_proposed\n", "Pred =\n", - "---- 0. get_proposed (0.611)\n", - " 1. get_end_state (0.067)\n", - " 2. get_existing (0.026)\n", - " 3. state (0.019)\n", - " 4. enabled (0.01)\n", - " 5. config_netstream_export (0.01)\n", - " 6. config_evnp_global (0.007)\n", + " 0. get_end_state (0.43)\n", "\n", "Label = netconf_set_action\n", "Pred =\n", - " 0. undo_config_ntp_auth_keyid (0.049)\n", - " 1. get_nc_config (0.044)\n", - " 2. init_network_module (0.036)\n", - " 3. import_xml (0.028)\n", - " 4. get_xml (0.022)\n", - " 5. set_nc_config (0.016)\n", - " 6. define_from_xml (0.01)\n", + " 0. import_xml (0.357)\n", "\n", "Label = netconf_get_config\n", "Pred =\n", - "---- 0. netconf_get_config (0.524)\n", - " 1. netconf_set_config (0.465)\n", - " 2. add (0.0)\n", - " 3. run (0.0)\n", - " 4. check_response (0.0)\n", - " 5. get_value (0.0)\n", - " 6. execute_show_commands (0.0)\n", + "---- 0. netconf_get_config (0.547)\n", "\n", "Label = get_ip_vpn_vni\n", "Pred =\n", - " 0. get_dfs_source_ip (0.124)\n", - " 1. get_dfs_udp_port (0.122)\n", - " 2. get_dfs_source_vpn (0.121)\n", - " 3. get_ip_vpn (0.12)\n", - " 4. get_vbdif_vpn (0.117)\n", - " 5. get_evn_srouce (0.116)\n", - " 6. get_forward_enp (0.037)\n", + " 0. get_ip_vpn (0.153)\n", "\n", "Label = get_vbdif_mac\n", "Pred =\n", - " 0. get_dfs_source_ip (0.124)\n", - " 1. get_dfs_udp_port (0.122)\n", - " 2. get_dfs_source_vpn (0.121)\n", - " 3. get_ip_vpn (0.12)\n", - " 4. get_vbdif_vpn (0.117)\n", - " 5. get_evn_srouce (0.116)\n", - " 6. get_forward_enp (0.037)\n", + " 0. get_ip_vpn (0.153)\n", "\n", "Label = init_module\n", "Pred =\n", - "---- 0. init_module (0.833)\n", - " 1. __init_module__ (0.163)\n", - " 2. add (0.0)\n", - " 3. clear (0.0)\n", - " 4. set (0.0)\n", - " 5. load_config (0.0)\n", - " 6. predict (0.0)\n", + "---- 0. init_module (0.837)\n", "\n", "Label = cli_load_config\n", "Pred =\n", "---- 0. cli_load_config (1.0)\n", - " 1. add (0.0)\n", - " 2. load_config (0.0)\n", - " 3. rollback (0.0)\n", - " 4. init_module (0.0)\n", - " 5. config_netstream_export (0.0)\n", - " 6. __init_module__ (0.0)\n", "\n", "Label = is_valid_v4addr\n", "Pred =\n", - " 0. is_valid_address (0.868)\n", - "---- 1. is_valid_v4addr (0.09)\n", - " 2. is_ipv6 (0.001)\n", - " 3. is_valid_v6addr (0.001)\n", - " 4. get_ns_version (0.0)\n", - " 5. __contains__ (0.0)\n", - " 6. is_valid_tag (0.0)\n", + " 0. is_valid_address (0.707)\n", "\n", "Label = init_module\n", "Pred =\n", - "---- 0. init_module (0.833)\n", - " 1. __init_module__ (0.163)\n", - " 2. add (0.0)\n", - " 3. clear (0.0)\n", - " 4. set (0.0)\n", - " 5. load_config (0.0)\n", - " 6. predict (0.0)\n", + "---- 0. init_module (0.837)\n", "\n", "Label = parse_hostname\n", "Pred =\n", - "---- 0. parse_hostname (0.352)\n", - " 1. parse_domain_name (0.287)\n", - " 2. parse_lookup_source (0.236)\n", - " 3. parse_port (0.012)\n", - " 4. parse_vrf (0.011)\n", - " 5. get_group_timeout (0.006)\n", - " 6. parse_description (0.005)\n", + "---- 0. parse_hostname (0.384)\n", "\n", "Label = map_config_to_obj\n", "Pred =\n", - " 0. parse_lookup_source (0.135)\n", - " 1. parse_name_servers (0.067)\n", - " 2. get_evn_peers (0.05)\n", - " 3. parse_domain_name (0.049)\n", - " 4. parse_filesystems (0.033)\n", - " 5. parse_memory (0.015)\n", - "---- 6. map_config_to_obj (0.012)\n", + "---- 0. map_config_to_obj (0.36)\n", "\n", "Label = validate_ipv4\n", "Pred =\n", - "---- 0. validate_ipv4 (0.79)\n", - " 1. validate_ipv6 (0.089)\n", - " 2. _split_optional_netmask (0.003)\n", - " 3. requires_vrf (0.002)\n", - " 4. validate_privilege (0.002)\n", - " 5. is_valid_address (0.001)\n", - " 6. get_ip_version (0.001)\n", + "---- 0. validate_ipv4 (0.949)\n", "\n", "Label = parse_config_argument\n", "Pred =\n", - "---- 0. parse_config_argument (0.995)\n", - " 1. parse_shutdown (0.0)\n", - " 2. parse_rd (0.0)\n", - " 3. parse_description (0.0)\n", - " 4. add (0.0)\n", - " 5. parse_state (0.0)\n", - " 6. parse_serialnum (0.0)\n", + "---- 0. parse_config_argument (0.986)\n", "\n", "Label = search_obj_in_list\n", "Pred =\n", - "---- 0. search_obj_in_list (1.0)\n", - " 1. get_org (0.0)\n", - " 2. _get_elb (0.0)\n", - " 3. has_vrf (0.0)\n", - " 4. _find_path_in_tree (0.0)\n", - " 5. update_add (0.0)\n", - " 6. add (0.0)\n", + "---- 0. search_obj_in_list (0.999)\n", "\n", "Label = has_lldp\n", "Pred =\n", - "---- 0. has_lldp (0.862)\n", - " 1. get_mtu (0.002)\n", - " 2. quorum (0.002)\n", - " 3. mode_xml_to_cli_str (0.001)\n", - " 4. state_destroy_dvspg (0.001)\n", - " 5. get_ip_interface (0.001)\n", - " 6. has_vrf (0.001)\n", + "---- 0. has_lldp (0.972)\n", "\n", "Label = validate_param_values\n", "Pred =\n", - "---- 0. validate_param_values (0.991)\n", - " 1. get_param_value (0.001)\n", - " 2. _validate_key (0.001)\n", - " 3. map_params_to_obj (0.001)\n", - " 4. fail (0.0)\n", - " 5. get (0.0)\n", - " 6. add (0.0)\n", + "---- 0. validate_param_values (0.557)\n", "\n", "Label = validate_rotate_frequency\n", "Pred =\n", - " 0. validate_vlan_id (0.253)\n", - " 1. validate_mtu (0.145)\n", - " 2. validate_size (0.129)\n", - " 3. validate_device_count (0.09)\n", - " 4. validate_files (0.087)\n", - " 5. validate_min_links (0.087)\n", - " 6. validate_transmit_delay (0.011)\n", + " 0. validate_vlan_id (0.278)\n", "\n", "Label = reset_property\n", "Pred =\n", - " 0. delete_iptun (0.688)\n", - " 1. delete_flow (0.017)\n", - " 2. up_addr (0.013)\n", - " 3. enable_addr (0.012)\n", - " 4. delete_interface (0.011)\n", - " 5. iptun_exists (0.011)\n", - " 6. _query_flow_props (0.01)\n", + " 0. delete_iptun (0.749)\n", "\n", "Label = _query_iptun_props\n", "Pred =\n", - " 0. delete_iptun (0.441)\n", - " 1. _query_flow_props (0.078)\n", - " 2. iptun_exists (0.023)\n", - " 3. enable_addr (0.022)\n", - " 4. disable_interface (0.017)\n", - " 5. enable_interface (0.012)\n", - " 6. refresh_addr (0.01)\n", + " 0. delete_iptun (0.51)\n", "\n", "Label = interface_exists\n", "Pred =\n", - " 0. vlan_exists (0.213)\n", - " 1. vnic_exists (0.21)\n", - " 2. etherstub_exists (0.208)\n", - " 3. flow_exists (0.165)\n", - " 4. iptun_exists (0.023)\n", - " 5. addrobj_exists (0.014)\n", - " 6. enable_interface (0.006)\n", + " 0. flow_exists (0.229)\n", "\n", "Label = delete_addr\n", "Pred =\n", - " 0. refresh_addr (0.359)\n", - " 1. enable_addr (0.298)\n", - " 2. up_addr (0.081)\n", - " 3. down_addr (0.079)\n", - " 4. addrobj_exists (0.015)\n", - " 5. _query_flow_props (0.006)\n", - " 6. delete_iptun (0.006)\n", + " 0. enable_addr (0.365)\n", "\n", "Label = disable_addr\n", "Pred =\n", - " 0. enable_addr (0.355)\n", - " 1. refresh_addr (0.302)\n", - " 2. down_addr (0.08)\n", - " 3. up_addr (0.078)\n", - " 4. addrobj_exists (0.011)\n", - " 5. _query_flow_props (0.009)\n", - " 6. disable_interface (0.006)\n", + " 0. enable_addr (0.47)\n", "\n", "Label = populate\n", "Pred =\n", "---- 0. populate (1.0)\n", - " 1. populate_memory (0.0)\n", - " 2. run_commands (0.0)\n", - " 3. populate_interfaces (0.0)\n", - " 4. parse_stacks (0.0)\n", - " 5. _check_known_errors (0.0)\n", - " 6. __init__ (0.0)\n", "\n", "Label = get_running_config\n", "Pred =\n", "---- 0. get_running_config (1.0)\n", - " 1. get_device_config (0.0)\n", - " 2. get_config (0.0)\n", - " 3. load_config (0.0)\n", - " 4. get_active_config (0.0)\n", - " 5. get_candidate (0.0)\n", - " 6. interface_is_portchannel (0.0)\n", "\n", "Label = response_type\n", "Pred =\n", - " 0. get_client_id (0.017)\n", - " 1. rax_cdb_database (0.014)\n", - " 2. _action_save_configuration (0.013)\n", - " 3. set_directory_attributes_if_different (0.012)\n", - " 4. exit_json (0.009)\n", - " 5. changed (0.007)\n", - " 6. set_file_attributes_if_different (0.006)\n", + " 0. put_bucket_versioning (0.018)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. populate (0.0)\n", "\n", "Label = parse_version\n", "Pred =\n", - "---- 0. parse_version (0.26)\n", - " 1. parse_macaddress (0.192)\n", - " 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n", - " 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + " 0. parse_macaddress (0.296)\n", "\n", "Label = parse_model\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - "---- 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = parse_image\n", "Pred =\n", - " 0. parse_version (0.26)\n", - " 1. parse_macaddress (0.192)\n", - " 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---- 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + " 0. parse_macaddress (0.296)\n", "\n", "Label = parse_lldp_intf\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = parse_lldp_host\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = to_lines\n", "Pred =\n", - "---- 0. to_lines (0.999)\n", - " 1. _to_lines (0.0)\n", - " 2. _request_for_item (0.0)\n", - " 3. add (0.0)\n", - " 4. _response_from_item (0.0)\n", - " 5. fail (0.0)\n", - " 6. get (0.0)\n", + "---- 0. to_lines (1.0)\n", "\n", "Label = populate\n", "Pred =\n", "---- 0. populate (1.0)\n", - " 1. populate_memory (0.0)\n", - " 2. run_commands (0.0)\n", - " 3. populate_interfaces (0.0)\n", - " 4. parse_stacks (0.0)\n", - " 5. _check_known_errors (0.0)\n", - " 6. __init__ (0.0)\n", "\n", "Label = set_ipv6_interfaces\n", "Pred =\n", - " 0. set_ipv4_interfaces (0.569)\n", - " 1. _split_optional_netmask (0.008)\n", - " 2. ensure_cgw_present (0.005)\n", - " 3. serialize_pnics (0.005)\n", - " 4. get_exist_rd (0.005)\n", - " 5. reset_configuration (0.005)\n", - " 6. get_list_of_subnets (0.004)\n", + " 0. get_vrf (0.023)\n", "\n", "Label = validate_level\n", "Pred =\n", - " 0. validate_required_key (0.396)\n", - " 1. validate_privilege (0.085)\n", - " 2. _validate_attr_is_not_none (0.044)\n", - " 3. validate_is_list (0.031)\n", - " 4. check_attributes (0.031)\n", - " 5. _validate_range (0.024)\n", - " 6. integer_value (0.018)\n", + " 0. validate_duplex (0.191)\n", "\n", "Label = parse_version\n", "Pred =\n", - "---- 0. parse_version (0.26)\n", - " 1. parse_macaddress (0.192)\n", - " 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n", - " 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + " 0. parse_macaddress (0.296)\n", "\n", "Label = parse_serialnum\n", "Pred =\n", - " 0. parse_version (0.26)\n", - " 1. parse_macaddress (0.192)\n", - "---- 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n", - " 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + " 0. parse_macaddress (0.296)\n", "\n", "Label = populate\n", "Pred =\n", - "---- 0. populate (1.0)\n", - " 1. populate_interfaces (0.0)\n", - " 2. populate_memory (0.0)\n", - " 3. parse_stacks (0.0)\n", - " 4. fqdn_auto_populate (0.0)\n", - " 5. _check_known_errors (0.0)\n", - " 6. exec_module (0.0)\n", + "---- 0. populate (0.999)\n", "\n", "Label = api_params\n", "Pred =\n", - " 0. generate_simple_dict (0.196)\n", - " 1. to_dict (0.092)\n", - " 2. wait_for_status (0.045)\n", - " 3. set_api_params (0.038)\n", - " 4. validate_param_values (0.025)\n", - " 5. _get_module_prefix_map (0.016)\n", - " 6. configure (0.009)\n", + " 0. get (0.044)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = port_lists\n", "Pred =\n", - "---- 0. port_lists (0.933)\n", - " 1. address_lists (0.047)\n", - " 2. metadata (0.001)\n", - " 3. ports (0.001)\n", - " 4. addresses (0.001)\n", - " 5. slots (0.001)\n", - " 6. fqdns (0.001)\n", + "---- 0. port_lists (0.842)\n", "\n", "Label = _format_port_for_destination\n", "Pred =\n", - " 0. destination (0.764)\n", - " 1. get_result (0.044)\n", - " 2. ip (0.023)\n", - " 3. address (0.019)\n", - " 4. port (0.005)\n", - " 5. route_domain (0.005)\n", - " 6. port_ranges (0.004)\n", + " 0. destination (0.787)\n", "\n", "Label = has_fastl4_profiles\n", "Pred =\n", - "---- 0. has_fastl4_profiles (0.956)\n", - " 1. has_message_routing_profiles (0.002)\n", - " 2. has_fasthttp_profiles (0.002)\n", - " 3. enforced_policy (0.001)\n", - " 4. policies_attached (0.0)\n", - " 5. profile_types (0.0)\n", - " 6. service_policy (0.0)\n", + "---- 0. has_fastl4_profiles (0.985)\n", "\n", "Label = policies\n", "Pred =\n", - " 0. port_lists (0.46)\n", - " 1. address_lists (0.146)\n", - " 2. addresses (0.044)\n", - " 3. ports (0.03)\n", - " 4. port_ranges (0.02)\n", - " 5. _handle_enable_action (0.011)\n", - " 6. dns_resolver (0.006)\n", + " 0. port_lists (0.336)\n", "\n", "Label = enabled\n", "Pred =\n", - " 0. disabled (0.576)\n", - "---- 1. enabled (0.372)\n", - " 2. reject (0.005)\n", - " 3. reverse (0.003)\n", - " 4. vlans_disabled (0.002)\n", - " 5. authentication_enabled (0.002)\n", - " 6. vlans_enabled (0.002)\n", + " 0. disabled (0.651)\n", "\n", "Label = sec_nat_policy\n", "Pred =\n", - " 0. sec_nat_use_rd_policy (0.716)\n", - " 1. sec_nat_use_device_policy (0.046)\n", - " 2. fqdn (0.011)\n", - " 3. sflow_poll_interval (0.01)\n", - " 4. default_persistence_profile (0.009)\n", - " 5. availability_requirement_type (0.009)\n", - " 6. chassis_serial (0.006)\n", + " 0. sec_nat_use_rd_policy (0.771)\n", "\n", "Label = firewall_enforced_policy\n", "Pred =\n", - " 0. external_program (0.153)\n", - " 1. vlan (0.137)\n", - " 2. service_policy (0.053)\n", - " 3. datacenter (0.049)\n", - " 4. link (0.049)\n", - " 5. template (0.048)\n", - " 6. firewall_staged_policy (0.047)\n", + " 0. external_program (0.151)\n", "\n", "Label = sec_nat_use_device_policy\n", "Pred =\n", - " 0. sec_nat_use_rd_policy (0.716)\n", - "---- 1. sec_nat_use_device_policy (0.046)\n", - " 2. fqdn (0.011)\n", - " 3. sflow_poll_interval (0.01)\n", - " 4. default_persistence_profile (0.009)\n", - " 5. availability_requirement_type (0.009)\n", - " 6. chassis_serial (0.006)\n", + " 0. sec_nat_use_rd_policy (0.771)\n", "\n", "Label = irules\n", "Pred =\n", - " 0. snat (0.24)\n", - " 1. policies (0.134)\n", - " 2. product (0.1)\n", - " 3. description (0.069)\n", - " 4. responder_url (0.041)\n", - " 5. include (0.02)\n", - " 6. server_type (0.018)\n", + " 0. policies (0.337)\n", "\n", "Label = port\n", "Pred =\n", - " 0. destination (0.948)\n", - "---- 1. port (0.015)\n", - " 2. ip (0.008)\n", - " 3. remote_port (0.001)\n", - " 4. route_domain (0.001)\n", - " 5. predict (0.001)\n", - " 6. netmask (0.001)\n", + " 0. destination (0.938)\n", "\n", "Label = disabled_vlans\n", "Pred =\n", - " 0. enabled_vlans (0.32)\n", - " 1. mtu (0.158)\n", - " 2. policies (0.086)\n", - " 3. snmp_auth_password (0.044)\n", - " 4. include (0.021)\n", - " 5. idle_timeout (0.019)\n", - " 6. policies_attached (0.018)\n", + " 0. enabled_vlans (0.843)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = _verify_fallback_persistence_profile_for_type\n", "Pred =\n", - " 0. _verify_default_persistence_profile_for_type (0.979)\n", - " 1. _set_default_ip_protocol (0.001)\n", - " 2. destination (0.0)\n", - " 3. is_valid_ip (0.0)\n", - " 4. destination_address (0.0)\n", - " 5. _override_port_by_type (0.0)\n", - " 6. local_ip (0.0)\n", + " 0. _verify_default_persistence_profile_for_type (0.984)\n", "\n", "Label = _verify_minimum_profile\n", "Pred =\n", - " 0. vlans (0.088)\n", - " 1. traffic_group (0.073)\n", - " 2. key (0.016)\n", - " 3. active (0.015)\n", - " 4. _set_default_ip_protocol (0.012)\n", - " 5. _decision_function (0.011)\n", - " 6. test_check_array_dtype_stability (0.011)\n", + " 0. pool (0.084)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. _configuration_args (0.0)\n", - " 5. delete_db_instance (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", "---- 0. create_on_device (1.0)\n", - " 1. update_on_device (0.0)\n", - " 2. exec_module (0.0)\n", - " 3. _create_new_policy_draft (0.0)\n", - " 4. main (0.0)\n", - " 5. get (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = update_on_device\n", "Pred =\n", "---- 0. update_on_device (1.0)\n", - " 1. absent_on_device (0.0)\n", - " 2. exists (0.0)\n", - " 3. create_on_device (0.0)\n", - " 4. exit_json (0.0)\n", - " 5. remove_from_device (0.0)\n", - " 6. create_from_template_on_device (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. get (0.0)\n", - " 5. get_reportable_changes (0.0)\n", - " 6. policy_exists (0.0)\n", "\n", "Label = node_addresses\n", "Pred =\n", "---- 0. node_addresses (0.999)\n", - " 1. nodes (0.0)\n", - " 2. addresses (0.0)\n", - " 3. tagged_interfaces (0.0)\n", - " 4. icmp_message (0.0)\n", - " 5. broadcast_address (0.0)\n", - " 6. vlans (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = has_no_service_environment\n", "Pred =\n", - "---- 0. has_no_service_environment (0.995)\n", - " 1. is_activated (0.0)\n", - " 2. active (0.0)\n", - " 3. _get_backup_file (0.0)\n", - " 4. device_is_name (0.0)\n", - " 5. _verify_quorum_type (0.0)\n", - " 6. _update_persistence_profile (0.0)\n", + "---- 0. has_no_service_environment (0.997)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. policy_exists (0.0)\n", - " 6. get_config (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. reset_parameters (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = present\n", "Pred =\n", "---- 0. present (1.0)\n", - " 1. __bool__ (0.0)\n", - " 2. create_blank (0.0)\n", - " 3. absent (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. initial_form_count (0.0)\n", - " 6. save (0.0)\n", "\n", "Label = upload_file_to_device\n", "Pred =\n", - "---- 0. upload_file_to_device (0.993)\n", - " 1. _update_temporary_cli_script_on_device (0.001)\n", - " 2. create_on_device (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. absent_on_device (0.0)\n", - " 5. _output_logs (0.0)\n", - " 6. fail_json (0.0)\n", + "---- 0. upload_file_to_device (0.992)\n", "\n", "Label = type\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - "---- 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = fallback_ip\n", "Pred =\n", - " 0. mgmt_route (0.351)\n", - " 1. receive (0.12)\n", - " 2. address (0.106)\n", - " 3. netmask (0.055)\n", - " 4. gateway (0.031)\n", - " 5. ip (0.025)\n", - " 6. port (0.017)\n", + " 0. mgmt_route (0.376)\n", "\n", "Label = monitors_list\n", "Pred =\n", - "---- 0. monitors_list (0.957)\n", - " 1. monitors (0.011)\n", - " 2. irules (0.003)\n", - " 3. devices (0.001)\n", - " 4. interfaces (0.001)\n", - " 5. get_if_index (0.0)\n", - " 6. get_true_mac_address (0.0)\n", + "---- 0. monitors_list (0.972)\n", "\n", "Label = monitors_list\n", "Pred =\n", - "---- 0. monitors_list (0.957)\n", - " 1. monitors (0.011)\n", - " 2. irules (0.003)\n", - " 3. devices (0.001)\n", - " 4. interfaces (0.001)\n", - " 5. get_if_index (0.0)\n", - " 6. get_true_mac_address (0.0)\n", + "---- 0. monitors_list (0.972)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. update_on_device (0.0)\n", - " 3. create_on_device (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. get_config (0.0)\n", - " 6. forward (0.0)\n", "\n", "Label = remove_from_device\n", "Pred =\n", "---- 0. remove_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. predict (0.0)\n", - " 4. update_on_device (0.0)\n", - " 5. execute_on_device (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = raw_commands\n", "Pred =\n", - " 0. route_domain (0.284)\n", - " 1. addresses (0.052)\n", - " 2. fqdns (0.026)\n", - " 3. ports (0.02)\n", - " 4. monitors (0.019)\n", - " 5. explicit_proxy (0.019)\n", - " 6. metadata (0.017)\n", + " 0. irules (0.15)\n", "\n", "Label = chdir\n", "Pred =\n", - " 0. tenants (0.045)\n", - " 1. package_version (0.033)\n", - " 2. rax_slugify (0.015)\n", - " 3. address (0.015)\n", - " 4. underscore_to_camel (0.015)\n", - " 5. _get_validated_ip_address (0.008)\n", - " 6. addslashes (0.008)\n", + " 0. package_version (0.377)\n", "\n", "Label = wait_for\n", "Pred =\n", - " 0. device_reference (0.017)\n", - " 1. get_video_info (0.009)\n", - " 2. default_persistence_profile (0.009)\n", - " 3. boolean_check (0.009)\n", - " 4. _diff_line (0.008)\n", - " 5. policies (0.006)\n", - " 6. __or__ (0.006)\n", + " 0. point_count (0.013)\n", "\n", "Label = notify_non_idempotent_commands\n", "Pred =\n", - " 0. __str__ (0.132)\n", - " 1. as_text (0.038)\n", - " 2. warn (0.037)\n", - " 3. parse_commands (0.033)\n", - " 4. add (0.026)\n", - " 5. flatten (0.023)\n", - " 6. describe (0.019)\n", + " 0. lists (0.063)\n", "\n", "Label = description\n", "Pred =\n", - "---- 0. description (0.47)\n", - " 1. server_name (0.039)\n", - " 2. ca_file (0.038)\n", - " 3. crl_file (0.036)\n", - " 4. shell (0.036)\n", - " 5. device_group (0.017)\n", - " 6. secondary_mirror_address (0.017)\n", + "---- 0. description (0.478)\n", "\n", "Label = gzip_window_size\n", "Pred =\n", - " 0. gzip_memory_level (0.776)\n", - "---- 1. gzip_window_size (0.122)\n", - " 2. ip_version (0.005)\n", - " 3. enabled (0.002)\n", - " 4. fqdn_auto_populate (0.002)\n", - " 5. ignore_down_response (0.002)\n", - " 6. iquery_allow_path (0.001)\n", + " 0. gzip_memory_level (0.93)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = state\n", "Pred =\n", - "---- 0. state (0.922)\n", - " 1. name (0.055)\n", - " 2. destination (0.001)\n", - " 3. description (0.001)\n", - " 4. source (0.001)\n", - " 5. terminal_access (0.001)\n", - " 6. encrypt_algorithm (0.001)\n", + "---- 0. state (0.857)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = use_route_advertisement\n", "Pred =\n", - " 0. full_sync (0.073)\n", - " 1. auto_delete (0.072)\n", - " 2. fqdn_auto_populate (0.072)\n", - " 3. save_on_auto_sync (0.042)\n", - " 4. is_bundle (0.038)\n", - " 5. enabled (0.015)\n", - " 6. sec_nat_use_device_policy (0.011)\n", + " 0. full_sync (0.163)\n", "\n", "Label = route_advertisement\n", "Pred =\n", - " 0. version_is_less_than_3 (0.036)\n", - " 1. idle_timeout (0.019)\n", - " 2. is_version_with_default_network (0.019)\n", - " 3. is_version_without_network (0.015)\n", - " 4. map_config_to_obj (0.013)\n", - " 5. is_version_less_than_13 (0.013)\n", - " 6. is_version_v1 (0.011)\n", + " 0. encrypt_algorithm (0.242)\n", "\n", "Label = arp\n", "Pred =\n", - " 0. arp_state (0.474)\n", - " 1. done (0.008)\n", - " 2. cancelled (0.008)\n", - " 3. trusted_responders (0.006)\n", - " 4. _update_persistence_profile (0.006)\n", - " 5. level (0.005)\n", - " 6. certificate (0.004)\n", + " 0. arp_state (0.539)\n", "\n", "Label = arp\n", "Pred =\n", - " 0. synchronize_zone_files (0.103)\n", - " 1. synchronization (0.102)\n", - " 2. auto_check (0.075)\n", - " 3. auto_phone_home (0.061)\n", - " 4. verify_member_availability (0.034)\n", - " 5. dag_round_robin (0.032)\n", - " 6. disabled (0.022)\n", + " 0. synchronize_zone_files (0.152)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = ip\n", "Pred =\n", "---- 0. ip (0.997)\n", - " 1. local_ip (0.0)\n", - " 2. port (0.0)\n", - " 3. destination (0.0)\n", - " 4. delete (0.0)\n", - " 5. netmask (0.0)\n", - " 6. address (0.0)\n", "\n", "Label = port\n", "Pred =\n", - "---- 0. port (0.993)\n", - " 1. remote_port (0.0)\n", - " 2. idle_timeout (0.0)\n", - " 3. netmask (0.0)\n", - " 4. rate_limit (0.0)\n", - " 5. ip (0.0)\n", - " 6. allow_service (0.0)\n", + "---- 0. port (0.997)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. delete_db_instance (0.0)\n", - " 5. _configuration_args (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = members\n", "Pred =\n", - "---- 0. members (0.084)\n", - " 1. _format_member_address (0.03)\n", - " 2. _clear_member_prefix (0.02)\n", - " 3. tagged_interfaces (0.018)\n", - " 4. untagged_interfaces (0.017)\n", - " 5. client_cert (0.017)\n", - " 6. get_member (0.013)\n", + "---- 0. members (0.448)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = save_on_auto_sync\n", "Pred =\n", - " 0. full_sync (0.596)\n", - "---- 1. save_on_auto_sync (0.154)\n", - " 2. auto_delete (0.036)\n", - " 3. enabled (0.029)\n", - " 4. is_bundle (0.021)\n", - " 5. fqdn_auto_populate (0.017)\n", - " 6. disabled (0.013)\n", + " 0. full_sync (0.558)\n", "\n", "Label = exists\n", "Pred =\n", - "---- 0. exists (0.913)\n", - " 1. _device_group_exists (0.055)\n", - " 2. external_file_exists (0.002)\n", - " 3. policy_exists (0.001)\n", - " 4. read_current_from_device (0.001)\n", - " 5. any_license_exists (0.001)\n", - " 6. get_info (0.001)\n", + "---- 0. exists (0.998)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = remove_members_in_group_from_device\n", "Pred =\n", - " 0. remove_from_device (0.986)\n", - " 1. delete (0.001)\n", - " 2. delete_many (0.001)\n", - " 3. remove_virtual_disk_from_device (0.0)\n", - " 4. predict (0.0)\n", - " 5. delete_vgw (0.0)\n", - " 6. set (0.0)\n", + " 0. remove_from_device (0.997)\n", "\n", "Label = mandatory_attributes\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_default_creation_values\n", "Pred =\n", - " 0. destination (0.338)\n", - " 1. local_ip (0.084)\n", - " 2. ip (0.036)\n", - " 3. remote_port (0.023)\n", - " 4. _set_default_ip_protocol (0.017)\n", - " 5. _verify_default_persistence_profile_for_type (0.015)\n", - " 6. port_ranges (0.014)\n", + " 0. destination (0.497)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (0.999)\n", - " 1. update (0.0)\n", - " 2. delete (0.0)\n", - " 3. execute (0.0)\n", - " 4. start (0.0)\n", - " 5. create_blank (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. get (0.0)\n", - " 6. policy_exists (0.0)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. __default (0.0)\n", - " 5. rules (0.0)\n", - " 6. search_obj_in_list (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = _wait_for_fqdn_checks\n", "Pred =\n", - " 0. remove (0.293)\n", - " 1. wait_for_task (0.171)\n", - " 2. activate (0.084)\n", - " 3. create_from_file (0.011)\n", - " 4. _sync_to_group_required (0.009)\n", - " 5. reset (0.009)\n", - " 6. absent (0.009)\n", + " 0. running (0.116)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. destination (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. ip (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. __setstate__ (0.0)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = remove_from_device\n", "Pred =\n", "---- 0. remove_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. execute_on_device (0.0)\n", - " 3. deprecate (0.0)\n", - " 4. create_from_template_on_device (0.0)\n", - " 5. add (0.0)\n", - " 6. init_module (0.0)\n", "\n", "Label = iquery_allow_service_check\n", "Pred =\n", - " 0. network_failover (0.078)\n", - " 1. iquery_allow_snmp (0.07)\n", - " 2. auto_sync (0.066)\n", - " 3. iquery_allow_path (0.057)\n", - " 4. arp (0.052)\n", - " 5. ignore_down_response (0.052)\n", - " 6. transparent (0.036)\n", + " 0. network_failover (0.165)\n", "\n", "Label = enabled\n", "Pred =\n", - "---- 0. enabled (0.705)\n", - " 1. disabled (0.214)\n", - " 2. description (0.006)\n", - " 3. reject (0.004)\n", - " 4. reverse (0.003)\n", - " 5. type (0.002)\n", - " 6. auto_check (0.002)\n", + "---- 0. enabled (0.831)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. collection (0.0)\n", - " 3. create_on_device (0.0)\n", - " 4. delete (0.0)\n", - " 5. to_request (0.0)\n", - " 6. netconf_set_config (0.0)\n", "\n", "Label = search\n", "Pred =\n", - " 0. name_servers (0.944)\n", - " 1. addresses (0.003)\n", - " 2. port_lists (0.002)\n", - " 3. to_param_list (0.002)\n", - " 4. policies (0.001)\n", - " 5. icmp_message (0.001)\n", - " 6. dns_resolver (0.001)\n", + " 0. name_servers (0.958)\n", "\n", "Label = frame_size\n", "Pred =\n", - " 0. is_unspecified (0.018)\n", - " 1. expected_parameters (0.015)\n", - " 2. snmp_auth_password (0.012)\n", - " 3. receive_window (0.011)\n", - " 4. exclude (0.01)\n", - " 5. __len__ (0.01)\n", - " 6. window_frame_rows_start_end (0.009)\n", + " 0. get_complete_version (0.028)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = port_misuse_policy\n", "Pred =\n", - " 0. pool (0.249)\n", - " 1. schedule (0.221)\n", - " 2. timer_policy (0.158)\n", - " 3. cache_name (0.022)\n", - " 4. fallback_persistence_profile (0.019)\n", - " 5. dns_resolver (0.015)\n", - " 6. sec_nat_policy (0.011)\n", + " 0. schedule (0.282)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = create\n", "Pred =\n", - "---- 0. create (0.999)\n", - " 1. start (0.0)\n", - " 2. execute (0.0)\n", - " 3. as_mysql (0.0)\n", - " 4. upsert (0.0)\n", - " 5. update (0.0)\n", - " 6. add (0.0)\n", + "---- 0. create (0.944)\n", "\n", "Label = renegotiation_period\n", "Pred =\n", - " 0. rate_limit (0.279)\n", - " 1. idle_timeout (0.19)\n", - " 2. port (0.081)\n", - " 3. alert_timeout (0.045)\n", - " 4. handshake_timeout (0.045)\n", - " 5. keep_alive_interval (0.044)\n", - " 6. renegotiation_maximum_record_delay (0.044)\n", + " 0. rate_limit (0.371)\n", "\n", "Label = session_ticket\n", "Pred =\n", - " 0. manual_resume (0.033)\n", - " 1. cluster_mirroring (0.029)\n", - " 2. quiet_boot (0.021)\n", - " 3. net_reboot (0.02)\n", - " 4. gui_setup (0.02)\n", - " 5. lcd_display (0.02)\n", - " 6. match_across_pools (0.019)\n", + " 0. mgmt_dhcp (0.032)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = network_failover_enabled\n", "Pred =\n", - " 0. manual_resume (0.033)\n", - " 1. cluster_mirroring (0.029)\n", - " 2. quiet_boot (0.021)\n", - " 3. net_reboot (0.02)\n", - " 4. gui_setup (0.02)\n", - " 5. lcd_display (0.02)\n", - " 6. match_across_pools (0.019)\n", + " 0. mgmt_dhcp (0.032)\n", "\n", "Label = autosync_enabled\n", "Pred =\n", - " 0. manual_resume (0.033)\n", - " 1. cluster_mirroring (0.029)\n", - " 2. quiet_boot (0.021)\n", - " 3. net_reboot (0.02)\n", - " 4. gui_setup (0.02)\n", - " 5. lcd_display (0.02)\n", - " 6. match_across_pools (0.019)\n", + " 0. mgmt_dhcp (0.032)\n", "\n", "Label = read_collection_from_device\n", "Pred =\n", - "---- 0. read_collection_from_device (0.999)\n", - " 1. _read_current_fasthttp_profiles_from_device (0.0)\n", - " 2. _read_current_fastl4_profiles_from_device (0.0)\n", - " 3. read_domains_from_device (0.0)\n", - " 4. draft_exists (0.0)\n", - " 5. _templates_from_device (0.0)\n", - " 6. initial_image_exists (0.0)\n", + "---- 0. read_collection_from_device (1.0)\n", "\n", "Label = server_timestamp\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = _exec_module\n", "Pred =\n", "---- 0. _exec_module (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. get (0.0)\n", - " 4. set_config (0.0)\n", - " 5. create (0.0)\n", - " 6. build_preprocessor (0.0)\n", "\n", "Label = server_timestamp\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = reassemble_fragments\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = ip_tos_to_server\n", "Pred =\n", - " 0. idle_timeout_override (0.352)\n", - " 1. link_qos_to_client (0.074)\n", - " 2. ip_tos_to_client (0.074)\n", - " 3. priority_to_server (0.072)\n", - " 4. priority_to_client (0.07)\n", - " 5. link_qos_to_server (0.069)\n", - " 6. cache_timeout (0.066)\n", + " 0. idle_timeout_override (0.263)\n", "\n", "Label = manual_resume\n", "Pred =\n", - "---- 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + "---- 0. manual_resume (0.048)\n", "\n", "Label = qos_topology\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = read_facts\n", "Pred =\n", "---- 0. read_facts (1.0)\n", - " 1. uuid (0.0)\n", - " 2. find_collection_resource_or_fail (0.0)\n", - " 3. address_lists (0.0)\n", - " 4. _remove_internal_keywords (0.0)\n", - " 5. get_facts_from_collection (0.0)\n", - " 6. find_collection_item (0.0)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = read_facts\n", "Pred =\n", "---- 0. read_facts (1.0)\n", - " 1. uuid (0.0)\n", - " 2. find_collection_resource_or_fail (0.0)\n", - " 3. address_lists (0.0)\n", - " 4. _remove_internal_keywords (0.0)\n", - " 5. get_facts_from_collection (0.0)\n", - " 6. find_collection_item (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = description\n", "Pred =\n", - "---- 0. description (0.47)\n", - " 1. server_name (0.039)\n", - " 2. ca_file (0.038)\n", - " 3. crl_file (0.036)\n", - " 4. shell (0.036)\n", - " 5. device_group (0.017)\n", - " 6. secondary_mirror_address (0.017)\n", + "---- 0. description (0.478)\n", "\n", "Label = transparent\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - "---- 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = adaptive\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - "---- 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = max_header_size\n", "Pred =\n", - " 0. sflow_poll_interval (0.056)\n", - " 1. sflow_sampling_rate (0.049)\n", - " 2. iquery_allow_service_check (0.045)\n", - " 3. oversize_client_headers (0.043)\n", - " 4. hsts_max_age (0.042)\n", - " 5. max_header_count (0.041)\n", - " 6. oversize_server_headers (0.04)\n", + " 0. oversize_server_headers (0.073)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = mac_address\n", "Pred =\n", - " 0. description (0.47)\n", - " 1. server_name (0.039)\n", - " 2. ca_file (0.038)\n", - " 3. crl_file (0.036)\n", - " 4. shell (0.036)\n", - " 5. device_group (0.017)\n", - " 6. secondary_mirror_address (0.017)\n", + " 0. description (0.478)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = queue_on_connection_limit\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = read_collection_from_device\n", "Pred =\n", - "---- 0. read_collection_from_device (0.997)\n", - " 1. read_domains_from_device (0.0)\n", - " 2. _read_current_fasthttp_profiles_from_device (0.0)\n", - " 3. predict (0.0)\n", - " 4. _templates_from_device (0.0)\n", - " 5. initial_image_exists (0.0)\n", - " 6. _read_current_fastl4_profiles_from_device (0.0)\n", + "---- 0. read_collection_from_device (0.998)\n", "\n", "Label = traffic_group_inherited\n", "Pred =\n", - " 0. description (0.116)\n", - " 1. enabled (0.079)\n", - " 2. disabled (0.064)\n", - " 3. availability_calculation (0.051)\n", - " 4. server_type (0.044)\n", - " 5. type (0.019)\n", - " 6. auto_check (0.015)\n", + " 0. description (0.434)\n", "\n", "Label = floating\n", "Pred =\n", - " 0. frame_distribution_hash (0.067)\n", - " 1. share_pools (0.043)\n", - " 2. phase1_verify_peer_cert (0.026)\n", - " 3. arp (0.024)\n", - " 4. network_failover (0.022)\n", - " 5. manual_resume (0.022)\n", - " 6. auto_sync (0.016)\n", + " 0. match_across_virtuals (0.059)\n", "\n", "Label = read_facts\n", "Pred =\n", "---- 0. read_facts (1.0)\n", - " 1. uuid (0.0)\n", - " 2. find_collection_resource_or_fail (0.0)\n", - " 3. address_lists (0.0)\n", - " 4. create (0.0)\n", - " 5. read_collection_from_device (0.0)\n", - " 6. _remove_internal_keywords (0.0)\n", "\n", "Label = ocsp\n", "Pred =\n", - " 0. description (0.47)\n", - " 1. server_name (0.039)\n", - " 2. ca_file (0.038)\n", - " 3. crl_file (0.036)\n", - " 4. shell (0.036)\n", - " 5. device_group (0.017)\n", - " 6. secondary_mirror_address (0.017)\n", + " 0. description (0.478)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = read_collection_from_device\n", "Pred =\n", - "---- 0. read_collection_from_device (0.989)\n", - " 1. read_domains_from_device (0.001)\n", - " 2. _templates_from_device (0.001)\n", - " 3. _read_current_fasthttp_profiles_from_device (0.001)\n", - " 4. initial_image_exists (0.001)\n", - " 5. absent_on_device (0.0)\n", - " 6. _get_rule_names (0.0)\n", + "---- 0. read_collection_from_device (0.997)\n", "\n", "Label = read_facts\n", "Pred =\n", - "---- 0. read_facts (1.0)\n", - " 1. uuid (0.0)\n", - " 2. find_collection_resource_or_fail (0.0)\n", - " 3. address_lists (0.0)\n", - " 4. _remove_internal_keywords (0.0)\n", - " 5. get_facts_from_collection (0.0)\n", - " 6. find_collection_item (0.0)\n", + "---- 0. read_facts (1.0)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Label = hardware_information\n", "Pred =\n", - "---- 0. hardware_information (0.947)\n", - " 1. metadata (0.007)\n", - " 2. port_lists (0.002)\n", - " 3. _filter_params (0.001)\n", - " 4. tags (0.001)\n", - " 5. variables (0.001)\n", - " 6. route_domain (0.001)\n", + "---- 0. hardware_information (0.971)\n", "\n", "Label = manual_resume\n", "Pred =\n", - "---- 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + "---- 0. manual_resume (0.048)\n", "\n", "Label = reverse\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = mptcp_make_after_break\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = multipath_tcp\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = enhanced_loss_recovery\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = mac_masquerade_address\n", "Pred =\n", - " 0. description (0.47)\n", - " 1. server_name (0.039)\n", - " 2. ca_file (0.038)\n", - " 3. crl_file (0.036)\n", - " 4. shell (0.036)\n", - " 5. device_group (0.017)\n", - " 6. secondary_mirror_address (0.017)\n", + " 0. description (0.478)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = destination_port\n", "Pred =\n", - " 0. destination_address (0.512)\n", - " 1. _check_port (0.043)\n", - " 2. remote_port (0.032)\n", - " 3. port (0.031)\n", - " 4. source_address (0.026)\n", - " 5. is_valid_port (0.012)\n", - " 6. _validate_unicast_failover_port (0.006)\n", + " 0. destination_address (0.881)\n", "\n", "Label = rate_limit\n", "Pred =\n", - " 0. idle_timeout (0.122)\n", - " 1. policies (0.085)\n", - "---- 2. rate_limit (0.045)\n", - " 3. encrypt_cookies (0.038)\n", - " 4. enabled_vlans (0.034)\n", - " 5. idle_timeout_override (0.028)\n", - " 6. port (0.028)\n", + " 0. port (0.171)\n", "\n", "Label = nat64_enabled\n", "Pred =\n", - " 0. match_across_services (0.059)\n", - " 1. override_connection_limit (0.056)\n", - " 2. allow_non_ssl (0.05)\n", - " 3. match_across_virtuals (0.048)\n", - " 4. match_across_pools (0.048)\n", - " 5. manual_resume (0.035)\n", - " 6. insert_header (0.026)\n", + " 0. match_across_services (0.083)\n", "\n", "Label = persistence_profile\n", "Pred =\n", - " 0. ports (0.156)\n", - " 1. default_persistence_profile (0.12)\n", - " 2. metadata (0.062)\n", - " 3. icmp_message (0.052)\n", - " 4. addresses (0.032)\n", - " 5. tags (0.017)\n", - " 6. slots (0.015)\n", + " 0. default_persistence_profile (0.389)\n", "\n", "Label = sflow_poll_interval\n", "Pred =\n", - " 0. sflow_sampling_rate (0.283)\n", - " 1. sflow_poll_interval_global (0.209)\n", - " 2. sflow_sampling_rate_global (0.175)\n", - "---- 3. sflow_poll_interval (0.154)\n", - " 4. tag (0.009)\n", - " 5. true_mac_address (0.008)\n", - " 6. status_reason (0.005)\n", + " 0. sflow_sampling_rate (0.255)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = net_reboot\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. _configuration_args (0.0)\n", - " 5. delete_db_instance (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = destination\n", "Pred =\n", - "---- 0. destination (0.999)\n", - " 1. ip (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. address (0.0)\n", - " 4. netmask (0.0)\n", - " 5. local_ip (0.0)\n", - " 6. partition (0.0)\n", + "---- 0. destination (1.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (0.999)\n", - " 1. delete (0.0)\n", - " 2. copy (0.0)\n", - " 3. add (0.0)\n", - " 4. create_from_file (0.0)\n", - " 5. reset (0.0)\n", - " 6. absent (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. destination (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = ports\n", "Pred =\n", - " 0. addresses (0.486)\n", - "---- 1. ports (0.41)\n", - " 2. port_ranges (0.019)\n", - " 3. icmp_message (0.005)\n", - " 4. port_lists (0.004)\n", - " 5. metadata (0.004)\n", - " 6. address_lists (0.003)\n", + " 0. addresses (0.496)\n", "\n", "Label = port_lists\n", "Pred =\n", - " 0. address_lists (0.773)\n", - "---- 1. port_lists (0.189)\n", - " 2. addresses (0.003)\n", - " 3. fqdns (0.002)\n", - " 4. ports (0.002)\n", - " 5. metadata (0.001)\n", - " 6. parent (0.001)\n", + " 0. address_lists (0.79)\n", "\n", "Label = exists\n", "Pred =\n", "---- 0. exists (1.0)\n", - " 1. policy_exists (0.0)\n", - " 2. patch (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. any_license_exists (0.0)\n", - " 5. update_on_device (0.0)\n", - " 6. predict (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. update (0.0)\n", - " 2. start (0.0)\n", - " 3. delete (0.0)\n", - " 4. add (0.0)\n", - " 5. as_mysql (0.0)\n", - " 6. execute (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", - "---- 0. create_on_device (0.999)\n", - " 1. _create_new_policy_draft (0.0)\n", - " 2. exec_module (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. main (0.0)\n", - " 5. get (0.0)\n", - " 6. add (0.0)\n", + "---- 0. create_on_device (1.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. destination (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = exec_module\n", "Pred =\n", - "---- 0. exec_module (0.997)\n", - " 1. _exec_module (0.001)\n", - " 2. add (0.0)\n", - " 3. populate (0.0)\n", - " 4. delete (0.0)\n", - " 5. to_request (0.0)\n", - " 6. _delete_elb (0.0)\n", + "---- 0. exec_module (0.998)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. netconf_set_config (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. collection (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = _exec_module\n", "Pred =\n", "---- 0. _exec_module (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. get_reportable_changes (0.0)\n", - " 3. get (0.0)\n", - " 4. set_config (0.0)\n", - " 5. create (0.0)\n", - " 6. build_preprocessor (0.0)\n", "\n", "Label = license_end_date_time\n", "Pred =\n", - " 0. evaluation_end_date_time (0.121)\n", - " 1. registration_key (0.121)\n", - " 2. licensed_version (0.118)\n", - " 3. license_start_date_time (0.117)\n", - " 4. licensed_date_time (0.116)\n", - " 5. vendor (0.113)\n", - " 6. evaluation_start_date_time (0.113)\n", + " 0. license_start_date_time (0.134)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = package_version\n", "Pred =\n", - "---- 0. package_version (0.909)\n", - " 1. port_lists (0.003)\n", - " 2. version (0.002)\n", - " 3. address (0.001)\n", - " 4. _check_params (0.001)\n", - " 5. E006 (0.001)\n", - " 6. username (0.001)\n", + "---- 0. package_version (0.941)\n", "\n", "Label = product_changelist\n", "Pred =\n", - " 0. product_built (0.36)\n", - " 1. product_jobid (0.351)\n", - "---- 2. product_changelist (0.046)\n", - " 3. fqdn_auto_populate (0.017)\n", - " 4. snat_type (0.013)\n", - " 5. fqdn_down_interval (0.008)\n", - " 6. fqdn_up_interval (0.008)\n", + " 0. product_jobid (0.421)\n", "\n", "Label = enable_gtm\n", "Pred =\n", - " 0. ignore_down_response (0.249)\n", - " 1. transparent (0.12)\n", - " 2. reverse (0.092)\n", - " 3. datagram_load_balancing (0.055)\n", - " 4. strict (0.03)\n", - " 5. logging (0.028)\n", - " 6. use_local_bind (0.025)\n", + " 0. ignore_down_response (0.219)\n", "\n", "Label = enable_zone_transfer\n", "Pred =\n", - " 0. ignore_down_response (0.249)\n", - " 1. transparent (0.12)\n", - " 2. reverse (0.092)\n", - " 3. datagram_load_balancing (0.055)\n", - " 4. strict (0.03)\n", - " 5. logging (0.028)\n", - " 6. use_local_bind (0.025)\n", + " 0. ignore_down_response (0.219)\n", "\n", "Label = unhandled_query_action\n", "Pred =\n", - " 0. encrypt_algorithm (0.495)\n", - " 1. translation_address (0.063)\n", - "---- 2. unhandled_query_action (0.061)\n", - " 3. mac_address (0.049)\n", - " 4. source (0.044)\n", - " 5. description (0.024)\n", - " 6. partition_access (0.023)\n", + " 0. encrypt_algorithm (0.792)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (0.999)\n", - " 1. delete (0.0)\n", - " 2. update (0.0)\n", - " 3. execute (0.0)\n", - " 4. add (0.0)\n", - " 5. start (0.0)\n", - " 6. options (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", - "---- 0. create_on_device (0.999)\n", - " 1. exec_module (0.0)\n", - " 2. _create_new_policy_draft (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. main (0.0)\n", - " 5. get (0.0)\n", - " 6. policy_exists (0.0)\n", + "---- 0. create_on_device (1.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. delete_db_instance (0.0)\n", - " 5. _configuration_args (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = cgnat\n", "Pred =\n", - " 0. enabled (0.55)\n", - " 1. state (0.306)\n", - " 2. disabled (0.012)\n", - " 3. description (0.006)\n", - " 4. ports (0.004)\n", - " 5. vlans (0.003)\n", - " 6. transparent (0.003)\n", + " 0. state (0.666)\n", "\n", "Label = deprovision_cgnat_on_device\n", "Pred =\n", - " 0. provision_cgnat_on_device (0.959)\n", - " 1. any_license_exists (0.001)\n", - " 2. update_cluster_mirroring_on_device (0.001)\n", - " 3. get_ca_file (0.001)\n", - " 4. deploy_on_device (0.001)\n", - " 5. provision_on_device (0.001)\n", - " 6. test_predict_with_predict_params (0.001)\n", + " 0. provision_cgnat_on_device (0.984)\n", "\n", "Label = _get_last_reboot\n", "Pred =\n", - " 0. reset_device (0.701)\n", - " 1. _is_mprov_running_on_device (0.035)\n", - " 2. save_on_device (0.026)\n", - " 3. _is_mcpd_ready_on_device (0.025)\n", - " 4. read_dossier_from_device (0.014)\n", - " 5. remove_eula_from_device (0.009)\n", - " 6. move_on_device (0.009)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " 0. reset_device (0.783)\n", + "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = ip\n", "Pred =\n", - "---- 0. ip (0.995)\n", - " 1. local_ip (0.001)\n", - " 2. enabled (0.0)\n", - " 3. netmask (0.0)\n", - " 4. destination (0.0)\n", - " 5. address (0.0)\n", - " 6. port (0.0)\n", + "---- 0. ip (0.997)\n", "\n", "Label = probe_timeout\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - "---- 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = concurrency_limit\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = target_password\n", "Pred =\n", - "---- 0. target_password (0.998)\n", - " 1. snmp_auth_password (0.0)\n", - " 2. smtp_server_password (0.0)\n", - " 3. password_credential (0.0)\n", - " 4. check_password (0.0)\n", - " 5. __contains__ (0.0)\n", - " 6. snmp_privacy_password (0.0)\n", + "---- 0. target_password (0.999)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = exists\n", "Pred =\n", - "---- 0. exists (0.998)\n", - " 1. external_file_exists (0.0)\n", - " 2. template_exists (0.0)\n", - " 3. policy_exists (0.0)\n", - " 4. _device_group_exists (0.0)\n", - " 5. traffic_group (0.0)\n", - " 6. any_license_exists (0.0)\n", + "---- 0. exists (0.999)\n", "\n", "Label = key_checksum\n", "Pred =\n", - " 0. checksum (0.972)\n", - " 1. content (0.002)\n", - " 2. route_domain (0.002)\n", - " 3. monitor_type (0.001)\n", - " 4. signature (0.001)\n", - " 5. type (0.001)\n", - " 6. monitor (0.001)\n", + " 0. checksum (0.988)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = insert_xforwarded_for\n", "Pred =\n", - " 0. allow_non_ssl (0.068)\n", - " 1. time_wait_recycle (0.06)\n", - " 2. match_across_services (0.06)\n", - " 3. override_connection_limit (0.057)\n", - " 4. match_across_virtuals (0.056)\n", - " 5. match_across_pools (0.055)\n", - " 6. enforce_tls_requirements (0.053)\n", + " 0. allow_non_ssl (0.109)\n", "\n", "Label = present\n", "Pred =\n", "---- 0. present (1.0)\n", - " 1. __bool__ (0.0)\n", - " 2. create_blank (0.0)\n", - " 3. absent (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. initial_form_count (0.0)\n", - " 6. save (0.0)\n", "\n", "Label = _get_availability_value\n", "Pred =\n", - " 0. _get_limit_value (0.68)\n", - "---- 1. _get_availability_value (0.242)\n", - " 2. _get_limit_status (0.007)\n", - " 3. _get_validated_ip_address (0.001)\n", - " 4. _transform_ip_list (0.001)\n", - " 5. __contains__ (0.001)\n", - " 6. type_name (0.001)\n", + " 0. _get_limit_value (0.638)\n", "\n", "Label = address\n", "Pred =\n", - "---- 0. address (0.501)\n", - " 1. receive (0.044)\n", - " 2. netmask (0.041)\n", - " 3. network (0.04)\n", - " 4. mgmt_route (0.022)\n", - " 5. ip (0.019)\n", - " 6. gateway (0.015)\n", + " 0. ip (0.376)\n", "\n", "Label = translation_port\n", "Pred =\n", - " 0. idle_timeout (0.508)\n", - " 1. rate_limit (0.056)\n", - " 2. ip_version (0.053)\n", - " 3. allow_service (0.039)\n", - " 4. port (0.019)\n", - " 5. handshake_timeout (0.015)\n", - " 6. alert_timeout (0.014)\n", + " 0. idle_timeout (0.783)\n", "\n", "Label = disabled\n", "Pred =\n", - " 0. enabled (0.507)\n", - "---- 1. disabled (0.292)\n", - " 2. auto_check (0.02)\n", - " 3. auto_phone_home (0.018)\n", - " 4. reject (0.011)\n", - " 5. synchronize_zone_files (0.006)\n", - " 6. ignore_down_response (0.005)\n", + " 0. enabled (0.526)\n", "\n", "Label = monitors_list\n", "Pred =\n", - "---- 0. monitors_list (0.957)\n", - " 1. monitors (0.011)\n", - " 2. irules (0.003)\n", - " 3. devices (0.001)\n", - " 4. interfaces (0.001)\n", - " 5. get_if_index (0.0)\n", - " 6. get_true_mac_address (0.0)\n", + "---- 0. monitors_list (0.972)\n", "\n", "Label = fault_number\n", "Pred =\n", - " 0. fault_text (0.984)\n", - " 1. is_valid_preference (0.001)\n", - " 2. admin_exists (0.0)\n", - " 3. make_request (0.0)\n", - " 4. addressgroup_exists (0.0)\n", - " 5. get_page (0.0)\n", - " 6. wait_for_removal (0.0)\n", + " 0. fault_text (0.959)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = upload_eula_to_device\n", "Pred =\n", - " 0. upload_license_to_device (0.218)\n", - " 1. reset_device (0.049)\n", - " 2. save_on_device (0.031)\n", - " 3. read_dossier_from_device (0.025)\n", - " 4. _generate_template_checksum_on_device (0.024)\n", - " 5. logcosh (0.016)\n", - " 6. move_on_device (0.013)\n", + " 0. upload_license_to_device (0.373)\n", "\n", "Label = reload_license\n", "Pred =\n", - " 0. _is_mprov_running_on_device (0.18)\n", - " 1. _is_mcpd_ready_on_device (0.179)\n", - " 2. _restart_asm (0.111)\n", - " 3. upload_license_to_device (0.056)\n", - " 4. save_on_device (0.045)\n", - " 5. enable_iapplx_on_device (0.037)\n", - " 6. reset_device (0.031)\n", + " 0. reset_device (0.233)\n", "\n", "Label = device_username\n", "Pred =\n", - " 0. device_password (0.105)\n", - " 1. monitors (0.102)\n", - " 2. monitor_type (0.088)\n", - " 3. src (0.052)\n", - "---- 4. device_username (0.047)\n", - " 5. package (0.034)\n", - " 6. metadata (0.034)\n", + " 0. monitor_type (0.132)\n", "\n", "Label = irule\n", "Pred =\n", - " 0. pool (0.249)\n", - " 1. schedule (0.221)\n", - " 2. timer_policy (0.158)\n", - " 3. cache_name (0.022)\n", - " 4. fallback_persistence_profile (0.019)\n", - " 5. dns_resolver (0.015)\n", - " 6. sec_nat_policy (0.011)\n", + " 0. schedule (0.282)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = _handle_http_uri_condition\n", "Pred =\n", - " 0. _handle_enable_action (0.685)\n", - " 1. self_link (0.011)\n", - " 2. metadata_decoder (0.011)\n", - " 3. collection (0.011)\n", - " 4. port_lists (0.006)\n", - " 5. encode_request (0.006)\n", - " 6. virtual_server_dependencies (0.004)\n", + " 0. _handle_enable_action (0.18)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. _configuration_args (0.0)\n", - " 5. delete_db_instance (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = transparent\n", "Pred =\n", - " 0. ignore_down_response (0.249)\n", - "---- 1. transparent (0.12)\n", - " 2. reverse (0.092)\n", - " 3. datagram_load_balancing (0.055)\n", - " 4. strict (0.03)\n", - " 5. logging (0.028)\n", - " 6. use_local_bind (0.025)\n", + " 0. ignore_down_response (0.219)\n", "\n", "Label = reverse\n", "Pred =\n", - " 0. ignore_down_response (0.249)\n", - " 1. transparent (0.12)\n", - "---- 2. reverse (0.092)\n", - " 3. datagram_load_balancing (0.055)\n", - " 4. strict (0.03)\n", - " 5. logging (0.028)\n", - " 6. use_local_bind (0.025)\n", + " 0. ignore_down_response (0.219)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. update_on_device (0.0)\n", - " 3. create_on_device (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. get (0.0)\n", - " 6. patch (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", "---- 0. read_current_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. get (0.0)\n", - " 5. get_reportable_changes (0.0)\n", - " 6. patch (0.0)\n", "\n", "Label = exists\n", "Pred =\n", - "---- 0. exists (0.988)\n", - " 1. remove_from_device (0.005)\n", - " 2. external_file_exists (0.001)\n", - " 3. any_license_exists (0.0)\n", - " 4. remove_data_group_file_from_device (0.0)\n", - " 5. _device_group_exists (0.0)\n", - " 6. predict (0.0)\n", + "---- 0. exists (0.983)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = remove\n", "Pred =\n", - "---- 0. remove (0.998)\n", - " 1. delete (0.0)\n", - " 2. update (0.0)\n", - " 3. copy (0.0)\n", - " 4. add (0.0)\n", - " 5. create (0.0)\n", - " 6. reset (0.0)\n", + "---- 0. remove (0.999)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. destination (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = cpu_coefficient\n", "Pred =\n", - " 0. disk_threshold (0.306)\n", - " 1. disk_coefficient (0.298)\n", - " 2. memory_coefficient (0.284)\n", - " 3. parse_secondary_ipv4 (0.001)\n", - " 4. sha1_checksum (0.001)\n", - " 5. _get_curr_version (0.001)\n", - " 6. get_mtu (0.001)\n", + " 0. disk_threshold (0.305)\n", "\n", "Label = cpu_threshold\n", "Pred =\n", - " 0. disk_threshold (0.306)\n", - " 1. disk_coefficient (0.298)\n", - " 2. memory_coefficient (0.284)\n", - " 3. parse_secondary_ipv4 (0.001)\n", - " 4. sha1_checksum (0.001)\n", - " 5. _get_curr_version (0.001)\n", - " 6. get_mtu (0.001)\n", + " 0. disk_threshold (0.305)\n", "\n", "Label = memory_threshold\n", "Pred =\n", - " 0. disk_threshold (0.306)\n", - " 1. disk_coefficient (0.298)\n", - " 2. memory_coefficient (0.284)\n", - " 3. parse_secondary_ipv4 (0.001)\n", - " 4. sha1_checksum (0.001)\n", - " 5. _get_curr_version (0.001)\n", - " 6. get_mtu (0.001)\n", + " 0. disk_threshold (0.305)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. start (0.0)\n", - " 2. update (0.0)\n", - " 3. delete (0.0)\n", - " 4. add (0.0)\n", - " 5. execute (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = redirect_virtual_server\n", "Pred =\n", - "---- 0. redirect_virtual_server (0.984)\n", - " 1. virtual (0.0)\n", - " 2. messages (0.0)\n", - " 3. copy (0.0)\n", - " 4. get_passphrase (0.0)\n", - " 5. get_destination (0.0)\n", - " 6. get_messages (0.0)\n", + "---- 0. redirect_virtual_server (0.977)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = param_description\n", "Pred =\n", - " 0. param_tables (0.199)\n", - " 1. param_variables (0.196)\n", - " 2. param_lists (0.185)\n", - " 3. param_metadata (0.178)\n", - " 4. param_strict_updates (0.03)\n", - " 5. snat (0.016)\n", - " 6. syslog_format (0.006)\n", + " 0. param_tables (0.209)\n", "\n", "Label = param_device_group\n", "Pred =\n", - " 0. param_traffic_group (0.924)\n", - " 1. forward_to (0.005)\n", - " 2. client_cert (0.005)\n", - " 3. client_key (0.003)\n", - " 4. pool (0.003)\n", - " 5. dns_resolver (0.001)\n", - " 6. fq_name (0.001)\n", + " 0. param_traffic_group (0.917)\n", "\n", "Label = metadata\n", "Pred =\n", - " 0. pool (0.086)\n", - " 1. name (0.083)\n", - " 2. addresses (0.03)\n", - " 3. dns_resolver (0.027)\n", - " 4. content (0.025)\n", - " 5. parent (0.015)\n", - " 6. version (0.014)\n", + " 0. lists (0.04)\n", "\n", "Label = traffic_group\n", "Pred =\n", - "---- 0. traffic_group (0.81)\n", - " 1. client_key (0.027)\n", - " 2. key (0.009)\n", - " 3. client_cert (0.007)\n", - " 4. port_lists (0.006)\n", - " 5. dns_resolver (0.004)\n", - " 6. issuer_cert (0.003)\n", + "---- 0. traffic_group (0.966)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = _file_is_missing\n", "Pred =\n", - " 0. exists (0.075)\n", - " 1. path_check (0.065)\n", - " 2. absent (0.043)\n", - " 3. check_file_absent_if_check_mode (0.025)\n", - " 4. checksum (0.021)\n", - " 5. traffic_group (0.017)\n", - " 6. on_bigip (0.016)\n", + " 0. exists (0.376)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. collection (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. to_request (0.0)\n", - " 6. netconf_set_config (0.0)\n", "\n", "Label = source\n", "Pred =\n", - " 0. encrypt_algorithm (0.219)\n", - "---- 1. source (0.151)\n", - " 2. mac_address (0.071)\n", - " 3. translation_address (0.065)\n", - " 4. description (0.051)\n", - " 5. unhandled_query_action (0.05)\n", - " 6. partition_access (0.041)\n", + "---- 0. source (0.288)\n", "\n", "Label = snmp_privacy_password\n", "Pred =\n", - " 0. snmp_auth_password (0.914)\n", - " 1. mtu (0.013)\n", - " 2. include (0.003)\n", - " 3. idle_timeout (0.002)\n", - " 4. enabled_vlans (0.002)\n", - " 5. multicast_port (0.002)\n", - " 6. interval (0.002)\n", + " 0. snmp_auth_password (0.987)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = max_answers_returned\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = qos_packet_rate\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = qos_rtt\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = availability_state\n", "Pred =\n", - " 0. enabled_state (0.666)\n", - "---- 1. availability_state (0.2)\n", - " 2. sflow_sampling_rate (0.005)\n", - " 3. sflow_poll_interval (0.004)\n", - " 4. evaluation_end_date_time (0.003)\n", - " 5. vendor (0.003)\n", - " 6. licensed_version (0.003)\n", + " 0. enabled_state (0.712)\n", "\n", "Label = read_facts\n", "Pred =\n", "---- 0. read_facts (1.0)\n", - " 1. uuid (0.0)\n", - " 2. find_collection_resource_or_fail (0.0)\n", - " 3. create (0.0)\n", - " 4. address_lists (0.0)\n", - " 5. read_collection_from_device (0.0)\n", - " 6. _remove_internal_keywords (0.0)\n", "\n", "Label = read_collection_from_device\n", "Pred =\n", "---- 0. read_collection_from_device (1.0)\n", - " 1. predict (0.0)\n", - " 2. _templates_from_device (0.0)\n", - " 3. _read_current_fasthttp_profiles_from_device (0.0)\n", - " 4. read_domains_from_device (0.0)\n", - " 5. absent_on_device (0.0)\n", - " 6. _read_current_fastl4_profiles_from_device (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = content\n", "Pred =\n", - " 0. monitor (0.711)\n", - "---- 1. content (0.023)\n", - " 2. description (0.019)\n", - " 3. type (0.01)\n", - " 4. package_file (0.008)\n", - " 5. partition (0.008)\n", - " 6. traffic_group (0.008)\n", + " 0. monitor (0.902)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = present\n", "Pred =\n", "---- 0. present (1.0)\n", - " 1. __bool__ (0.0)\n", - " 2. absent (0.0)\n", - " 3. create_blank (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. save (0.0)\n", - " 6. type (0.0)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = timeout\n", "Pred =\n", - "---- 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + "---- 0. timeout (0.199)\n", "\n", "Label = port\n", "Pred =\n", - "---- 0. port (0.993)\n", - " 1. remote_port (0.0)\n", - " 2. idle_timeout (0.0)\n", - " 3. netmask (0.0)\n", - " 4. rate_limit (0.0)\n", - " 5. ip (0.0)\n", - " 6. allow_service (0.0)\n", + "---- 0. port (0.997)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = remove_from_device\n", "Pred =\n", "---- 0. remove_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. add (0.0)\n", - " 4. create_from_template_on_device (0.0)\n", - " 5. predict (0.0)\n", - " 6. init_module (0.0)\n", "\n", "Label = source_mask\n", "Pred =\n", - " 0. keep_alive_interval (0.084)\n", - " 1. fin_wait_1 (0.067)\n", - " 2. time_wait (0.067)\n", - " 3. idle_timeout (0.063)\n", - " 4. close_wait (0.059)\n", - " 5. fin_wait_2 (0.057)\n", - " 6. zero_window_timeout (0.053)\n", + " 0. idle_timeout (0.213)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = exists\n", "Pred =\n", "---- 0. exists (1.0)\n", - " 1. read_current_from_device (0.0)\n", - " 2. policy_exists (0.0)\n", - " 3. _device_group_exists (0.0)\n", - " 4. update_on_device (0.0)\n", - " 5. external_file_exists (0.0)\n", - " 6. any_license_exists (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", "---- 0. create_on_device (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. update_on_device (0.0)\n", - " 3. _create_new_policy_draft (0.0)\n", - " 4. read_current_from_device (0.0)\n", - " 5. get (0.0)\n", - " 6. main (0.0)\n", "\n", "Label = routing_protocol\n", "Pred =\n", - " 0. policies (0.142)\n", - " 1. enabled_vlans (0.06)\n", - " 2. idle_timeout (0.057)\n", - " 3. encrypt_cookies (0.038)\n", - " 4. default_persistence_profile (0.03)\n", - " 5. close_wait (0.025)\n", - " 6. fin_wait_1 (0.025)\n", + " 0. idle_timeout (0.129)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", - "---- 0. create_on_device (0.999)\n", - " 1. _create_new_policy_draft (0.0)\n", - " 2. exec_module (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. main (0.0)\n", - " 5. policy_exists (0.0)\n", - " 6. get (0.0)\n", + "---- 0. create_on_device (0.992)\n", "\n", "Label = lacp_enabled\n", "Pred =\n", - " 0. ignore_down_response (0.249)\n", - " 1. transparent (0.12)\n", - " 2. reverse (0.092)\n", - " 3. datagram_load_balancing (0.055)\n", - " 4. strict (0.03)\n", - " 5. logging (0.028)\n", - " 6. use_local_bind (0.025)\n", + " 0. ignore_down_response (0.219)\n", "\n", "Label = lacp_enabled\n", "Pred =\n", - " 0. ignore_down_response (0.049)\n", - " 1. datagram_load_balancing (0.043)\n", - " 2. enable_cache (0.038)\n", - " 3. enable_dns_firewall (0.037)\n", - " 4. process_recursion_desired (0.037)\n", - " 5. enable_dnssec (0.037)\n", - " 6. use_local_bind (0.036)\n", + " 0. strict (0.078)\n", "\n", "Label = frame_distribution_hash\n", "Pred =\n", - "---- 0. frame_distribution_hash (0.165)\n", - " 1. share_pools (0.045)\n", - " 2. arp (0.021)\n", - " 3. availability_requirement_type (0.02)\n", - " 4. phase1_verify_peer_cert (0.015)\n", - " 5. network_failover (0.015)\n", - " 6. auto_sync (0.011)\n", + "---- 0. frame_distribution_hash (0.13)\n", "\n", "Label = inbound_virtual_server\n", "Pred =\n", - "---- 0. inbound_virtual_server (0.906)\n", - " 1. virtual (0.024)\n", - " 2. pool (0.005)\n", - " 3. port (0.004)\n", - " 4. full_name (0.001)\n", - " 5. predict_proba (0.001)\n", - " 6. read_cluster_mirroring_from_device (0.001)\n", + "---- 0. inbound_virtual_server (0.969)\n", "\n", "Label = nodes\n", "Pred =\n", "---- 0. nodes (0.999)\n", - " 1. slots (0.0)\n", - " 2. node_addresses (0.0)\n", - " 3. address_lists (0.0)\n", - " 4. _validate_unicast_failover_port (0.0)\n", - " 5. addresses (0.0)\n", - " 6. destinations (0.0)\n", "\n", "Label = present\n", "Pred =\n", "---- 0. present (1.0)\n", - " 1. __bool__ (0.0)\n", - " 2. absent (0.0)\n", - " 3. create_blank (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. save (0.0)\n", - " 6. type (0.0)\n", "\n", "Label = has_no_service_environment\n", "Pred =\n", - "---- 0. has_no_service_environment (0.995)\n", - " 1. is_activated (0.0)\n", - " 2. active (0.0)\n", - " 3. _get_backup_file (0.0)\n", - " 4. device_is_name (0.0)\n", - " 5. _verify_quorum_type (0.0)\n", - " 6. _update_persistence_profile (0.0)\n", + "---- 0. has_no_service_environment (0.997)\n", "\n", "Label = get_bundle_state\n", "Pred =\n", - " 0. get_member (0.127)\n", - " 1. get_sflow_poll_interval_global (0.029)\n", - " 2. get_sflow_sampling_rate_global (0.026)\n", - " 3. get_cmp_enable_mode (0.019)\n", - " 4. configure_drs (0.014)\n", - " 5. get_is_floating_state (0.013)\n", - " 6. get_learning_mode (0.013)\n", + " 0. get_translate_port_state (0.045)\n", "\n", "Label = get_mtu\n", "Pred =\n", - "---- 0. get_mtu (0.983)\n", - " 1. get_if_index (0.001)\n", - " 2. get_sflow_sampling_rate_global (0.001)\n", - " 3. modify_connection_team_slave (0.0)\n", - " 4. get_sflow_poll_interval (0.0)\n", - " 5. _next_is_sticky (0.0)\n", - " 6. get_vlan_id (0.0)\n", + "---- 0. get_mtu (0.979)\n", "\n", "Label = get_sfp_media_state\n", "Pred =\n", - " 0. get_media_sfp (0.586)\n", - " 1. get_media_option_sfp (0.099)\n", - " 2. get_prefer_sfp_state (0.018)\n", - " 3. get_active_media (0.017)\n", - " 4. get_media (0.017)\n", - " 5. get_mac_masquerade_address (0.009)\n", - " 6. get_lacp_enabled_state (0.008)\n", + " 0. get_media_sfp (0.886)\n", "\n", "Label = get_stp_enabled_state\n", "Pred =\n", - " 0. get_stp_protocol_detection_reset_state (0.564)\n", - " 1. get_stp_link_type (0.102)\n", - " 2. get_sflow_poll_interval_global (0.027)\n", - " 3. get_stp_active_edge_port_state (0.017)\n", - " 4. get_floating_state (0.01)\n", - " 5. get_enabled_state (0.007)\n", - " 6. get_sflow_sampling_rate_global (0.007)\n", + " 0. get_stp_link_type (0.77)\n", "\n", "Label = get_configured_member_count\n", "Pred =\n", - " 0. get_interface (0.218)\n", - " 1. get_operational_member_count (0.089)\n", - " 2. get_member (0.032)\n", - " 3. get_lldp_admin_status (0.023)\n", - " 4. get_stp_link_type (0.021)\n", - " 5. get_link_selection_policy (0.019)\n", - " 6. get_distribution_hash_option (0.014)\n", + " 0. get_member (0.114)\n", "\n", "Label = get_media_speed\n", "Pred =\n", - "---- 0. get_media_speed (0.454)\n", - " 1. get_distribution_hash_option (0.052)\n", - " 2. get_interface (0.039)\n", - " 3. get_media (0.018)\n", - " 4. get_media_option (0.015)\n", - " 5. get_lacp_timeout_option (0.012)\n", - " 6. get_active_lacp_state (0.011)\n", + "---- 0. get_media_speed (0.895)\n", "\n", "Label = get_stp_enabled_state\n", "Pred =\n", - " 0. get_stp_protocol_detection_reset_state (0.369)\n", - " 1. get_stp_link_type (0.175)\n", - " 2. get_stp_active_edge_port_state (0.03)\n", - " 3. get_sflow_poll_interval_global (0.019)\n", - " 4. get_lacp_enabled_state (0.013)\n", - " 5. get_floating_state (0.013)\n", - " 6. get_interface (0.01)\n", + " 0. get_stp_link_type (0.792)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. build (0.0)\n", - " 2. destination (0.0)\n", - " 3. get_required_config (0.0)\n", - " 4. reset_batch_stats (0.0)\n", - " 5. unregister (0.0)\n", - " 6. start_serialization (0.0)\n", "\n", "Label = get_failsafe_action\n", "Pred =\n", - " 0. get_failsafe_state (0.849)\n", - " 1. get_gateway_failsafe_device (0.008)\n", - " 2. get_vlan_id (0.004)\n", - " 3. get_is_floating (0.003)\n", - " 4. get_sflow_poll_interval_global (0.003)\n", - " 5. get_stp_link_type (0.003)\n", - " 6. get_sflow_poll_interval (0.003)\n", + " 0. get_failsafe_state (0.555)\n", "\n", "Label = get_failsafe_timeout\n", "Pred =\n", - " 0. get_failsafe_state (0.615)\n", - " 1. get_gateway_failsafe_device (0.023)\n", - " 2. get_is_floating (0.012)\n", - " 3. get_is_traffic_group_inherited (0.009)\n", - " 4. get_ha_load_factor (0.008)\n", - " 5. get_ha_order (0.006)\n", - " 6. get_floating_state (0.006)\n", + " 0. get_failsafe_state (0.763)\n", "\n", "Label = get_sflow_sampling_rate\n", "Pred =\n", - " 0. get_sflow_sampling_rate_global (0.767)\n", - " 1. get_sflow_poll_interval_global (0.03)\n", - " 2. get_sflow_poll_interval (0.016)\n", - " 3. get_if_index (0.012)\n", - " 4. get_source_check_state (0.011)\n", - " 5. get_mtu (0.006)\n", - " 6. get_member (0.006)\n", + " 0. get_sflow_sampling_rate_global (0.966)\n", "\n", "Label = get_name\n", "Pred =\n", - "---- 0. get_name (0.989)\n", - " 1. IE_sanitize (0.001)\n", - " 2. get_disk (0.0)\n", - " 3. __str__ (0.0)\n", - " 4. get_end_state (0.0)\n", - " 5. get_options (0.0)\n", - " 6. _page_url (0.0)\n", + "---- 0. get_name (0.873)\n", "\n", "Label = get_auto_lasthop\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---- 0. get_auto_lasthop (0.445)\n", - " 1. get_auto_failback_time (0.033)\n", - " 2. get_unit_id (0.03)\n", - " 3. get_auto_failback_enabled_state (0.018)\n", - " 4. get_edition (0.015)\n", - " 5. get_source_address_translation_snat_pool (0.012)\n", - " 6. get_active_lacp_state (0.008)\n", + "Pred =\n", + "---- 0. get_auto_lasthop (0.503)\n", "\n", "Label = get_bw_controller_policy\n", "Pred =\n", - " 0. get_actual_hardware_acceleration (0.097)\n", - " 1. get_bucket_request_payment (0.047)\n", - " 2. get_forward_proxy_lookup_by_ipaddr_port_state (0.021)\n", - " 3. get_enforced_firewall_policy (0.018)\n", - " 4. get_staged_firewall_policy (0.016)\n", - " 5. get_bucket_versioning (0.013)\n", - " 6. get_peer_certification_mode (0.012)\n", + " 0. get_auto_failback_enabled_state (0.126)\n", "\n", "Label = get_fw_rule\n", "Pred =\n", - "---- 0. get_fw_rule (0.421)\n", - " 1. get_rate_class (0.023)\n", - " 2. get_related_rule (0.02)\n", - " 3. get_minimum_up_member_action (0.015)\n", - " 4. get_source_address_translation_snat_pool (0.013)\n", - " 5. get_aggregate_dynamic_ratio (0.013)\n", - " 6. get_minimum_up_member_enabled_state (0.011)\n", + "---- 0. get_fw_rule (0.808)\n", "\n", "Label = get_persistence_profile\n", "Pred =\n", - " 0. get_fallback_persistence_profile (0.201)\n", - " 1. get_authentication_profile (0.057)\n", - " 2. get_profile (0.057)\n", - " 3. get_allow_nat_state (0.034)\n", - " 4. get_monitor_rule (0.031)\n", - " 5. get_monitor_instance (0.027)\n", - " 6. get_source_address (0.024)\n", + " 0. get_fallback_persistence_profile (0.741)\n", "\n", "Label = get_rule\n", "Pred =\n", - " 0. get_related_rule (0.215)\n", - " 1. get_clone_pool (0.054)\n", - " 2. get_profile (0.048)\n", - " 3. get_security_log_profile (0.04)\n", - " 4. get_source_address (0.035)\n", - " 5. get_authentication_profile (0.028)\n", - " 6. get_vlan (0.027)\n", + " 0. get_type (0.284)\n", "\n", "Label = get_snat_type\n", "Pred =\n", - " 0. get_snat_pool (0.451)\n", - " 1. get_source_address_translation_snat_pool (0.021)\n", - " 2. get_monitor_instance (0.017)\n", - " 3. get_actual_hardware_acceleration (0.013)\n", - " 4. get_strict_resume_state (0.01)\n", - " 5. get_last_hop_pool (0.008)\n", - " 6. get_cache_size (0.007)\n", + " 0. get_snat_pool (0.818)\n", "\n", "Label = get_source_address_translation_lsn_pool\n", "Pred =\n", - " 0. get_source_address_translation_snat_pool (0.368)\n", - " 1. get_source_address_translation_type (0.148)\n", - " 2. get_fw_rule (0.039)\n", - " 3. get_route_advertisement_state (0.01)\n", - " 4. get_rate_limit_mode (0.009)\n", - " 5. get_source_address (0.006)\n", - " 6. get_translate_address_state (0.006)\n", + " 0. get_source_address_translation_type (0.692)\n", "\n", "Label = get_staged_firewall_policy\n", "Pred =\n", - "---- 0. get_staged_firewall_policy (0.424)\n", - " 1. get_enforced_firewall_policy (0.084)\n", - " 2. get_dynamic_ratio (0.011)\n", - " 3. get_alert_timeout (0.01)\n", - " 4. get_is_system_profile (0.01)\n", - " 5. get_cipher_list (0.008)\n", - " 6. get_secure_renegotiation_mode (0.008)\n", + "---- 0. get_staged_firewall_policy (0.838)\n", "\n", "Label = get_active_member_count\n", "Pred =\n", - " 0. get_member (0.493)\n", - " 1. get_minimum_active_member (0.096)\n", - " 2. get_monitor_instance (0.019)\n", - " 3. get_snat_pool (0.014)\n", - " 4. get_profile (0.012)\n", - " 5. get_minimum_up_member_enabled_state (0.011)\n", - " 6. get_minimum_up_member_action (0.007)\n", + " 0. get_minimum_active_member (0.731)\n", "\n", "Label = get_allow_snat_state\n", "Pred =\n", - " 0. get_snat_pool (0.334)\n", - " 1. get_source_address_translation_snat_pool (0.023)\n", - " 2. get_auto_failback_enabled_state (0.023)\n", - " 3. get_allow_nonssl_state (0.014)\n", - " 4. get_cmp_enable_mode (0.013)\n", - " 5. get_active_lacp_state (0.011)\n", - " 6. action_reply_is_correct (0.011)\n", + " 0. get_snat_pool (0.789)\n", "\n", "Label = get_minimum_up_member\n", "Pred =\n", - " 0. get_minimum_up_member_action (0.578)\n", - " 1. get_minimum_up_member_enabled_state (0.154)\n", - " 2. get_minimum_active_member (0.088)\n", - " 3. get_rate_limit_mode (0.006)\n", - " 4. get_rate_limit (0.005)\n", - " 5. get_cmp_enable_mode (0.004)\n", - " 6. get_fw_rule (0.003)\n", + " 0. get_minimum_up_member_enabled_state (0.57)\n", "\n", "Label = get_slow_ramp_time\n", "Pred =\n", - " 0. get_source_address_translation_snat_pool (0.062)\n", - " 1. get_aggregate_dynamic_ratio (0.023)\n", - " 2. get_reselect_tries (0.017)\n", - " 3. get_simple_timeout (0.016)\n", - " 4. get_authentication_profile (0.016)\n", - " 5. get_protocol (0.015)\n", - " 6. get_queue_time_limit (0.014)\n", + " 0. get_server_link_qos (0.103)\n", "\n", "Label = get_hostname\n", "Pred =\n", - " 0. get_management_address (0.31)\n", - " 1. get_product (0.122)\n", - " 2. get_location (0.078)\n", - " 3. get_build (0.069)\n", - " 4. get_active_modules (0.054)\n", - " 5. get_comment (0.03)\n", - " 6. get_base_mac_address (0.029)\n", + " 0. get_build (0.22)\n", "\n", "Label = get_platform_id\n", "Pred =\n", - " 0. get_product (0.149)\n", - " 1. get_location (0.147)\n", - " 2. get_management_address (0.079)\n", - " 3. get_build (0.061)\n", - " 4. get_active_modules (0.045)\n", - " 5. get_comment (0.041)\n", - " 6. get_contact (0.023)\n", + " 0. get_multicast_address (0.08)\n", "\n", "Label = get_software_version\n", "Pred =\n", - " 0. generate_software_list (0.05)\n", - " 1. get_product (0.03)\n", - " 2. get_contact (0.028)\n", - " 3. get_member (0.02)\n", - " 4. get_is_system_profile (0.015)\n", - " 5. get_forward_proxy_certificate_extension_include (0.014)\n", - " 6. get_device (0.013)\n", + " 0. get_active_modules (0.228)\n", "\n", "Label = get_ignore_vertification\n", "Pred =\n", - " 0. get_source_address_translation_snat_pool (0.072)\n", - " 1. get_dynamic_ratio (0.042)\n", - " 2. get_protocol (0.021)\n", - " 3. get_unit_id (0.019)\n", - " 4. get_related_rule (0.018)\n", - " 5. get_security_log_profile (0.016)\n", - " 6. get_fallback_persistence_profile (0.016)\n", + " 0. get_auto_failback_enabled_state (0.119)\n", "\n", "Label = get_arp_state\n", "Pred =\n", - " 0. get_route_advertisement_state (0.108)\n", - " 1. get_fallback_persistence_profile (0.031)\n", - " 2. get_aggregate_dynamic_ratio (0.024)\n", - " 3. get_is_floating_state (0.024)\n", - " 4. get_monitor_status (0.023)\n", - " 5. get_simple_timeout (0.02)\n", - " 6. get_rate_class (0.019)\n", + " 0. get_address (0.055)\n", "\n", "Label = get_description\n", "Pred =\n", - "---- 0. get_description (0.999)\n", - " 1. description (0.0)\n", - " 2. get_enabled_state (0.0)\n", - " 3. name (0.0)\n", - " 4. get_vlan (0.0)\n", - " 5. content (0.0)\n", - " 6. get_sflow_poll_interval (0.0)\n", + "---- 0. get_description (1.0)\n", "\n", "Label = get_crl_file\n", "Pred =\n", - " 0. get_chain_file (0.178)\n", - " 1. get_ca_file (0.076)\n", - " 2. get_certificate_file (0.041)\n", - " 3. get_is_system_profile (0.026)\n", - " 4. get_profile_mode (0.02)\n", - " 5. get_alert_timeout (0.019)\n", - " 6. get_verification_status (0.018)\n", + " 0. get_ca_file (0.15)\n", "\n", "Label = get_key_file\n", "Pred =\n", - " 0. get_chain_file (0.519)\n", - " 1. get_ca_file (0.064)\n", - " 2. get_certificate_file (0.043)\n", - " 3. get_profile_mode (0.021)\n", - " 4. get_secure_renegotiation_mode (0.017)\n", - " 5. get_is_system_profile (0.014)\n", - " 6. get_client_certificate_ca_file (0.013)\n", + " 0. get_chain_file (0.656)\n", "\n", "Label = get_renegotiation_throughput\n", "Pred =\n", - " 0. get_renegotiation_state (0.113)\n", - " 1. get_renegotiation_period (0.106)\n", - " 2. get_session_ticket_state (0.039)\n", - " 3. get_cipher_list (0.025)\n", - " 4. get_nat64_state (0.02)\n", - " 5. get_secure_renegotiation_mode (0.018)\n", - " 6. get_forward_proxy_certificate_extension_include (0.017)\n", + " 0. get_secure_renegotiation_mode (0.365)\n", "\n", "Label = get_ssl_option\n", "Pred =\n", - " 0. get_secure_renegotiation_mode (0.08)\n", - " 1. get_renegotiation_state (0.037)\n", - " 2. get_is_system_profile (0.035)\n", - " 3. get_certificate_file (0.034)\n", - " 4. get_chain_file (0.025)\n", - " 5. get_server_name (0.024)\n", - " 6. get_profile_mode (0.023)\n", + " 0. get_renegotiation_state (0.074)\n", "\n", "Label = generate_self_ip_dict\n", "Pred =\n", - " 0. generate_trunk_dict (0.207)\n", - " 1. generate_device_group_dict (0.127)\n", - " 2. generate_traffic_group_dict (0.111)\n", - " 3. generate_system_info_dict (0.047)\n", - " 4. generate_provision_dict (0.043)\n", - " 5. generate_node_dict (0.038)\n", - " 6. generate_interface_dict (0.034)\n", + " 0. generate_interface_dict (0.221)\n", "\n", "Label = generate_pool_dict\n", "Pred =\n", - " 0. generate_interface_dict (0.112)\n", - " 1. generate_trunk_dict (0.083)\n", - " 2. generate_device_dict (0.043)\n", - " 3. generate_virtual_address_dict (0.028)\n", - " 4. generate_vlan_dict (0.02)\n", - " 5. generate_system_info_dict (0.016)\n", - " 6. generate_certificate_dict (0.009)\n", + " 0. generate_interface_dict (0.66)\n", "\n", "Label = generate_address_class_dict\n", "Pred =\n", - " 0. generate_device_group_dict (0.055)\n", - " 1. generate_rule_dict (0.053)\n", - " 2. generate_traffic_group_dict (0.046)\n", - " 3. _parallel_decision_function (0.027)\n", - " 4. generate_node_dict (0.021)\n", - " 5. generate_device_dict (0.018)\n", - " 6. get_allow_access_list (0.015)\n", + " 0. generate_rule_dict (0.188)\n", "\n", "Label = allowed_slots\n", "Pred =\n", - " 0. interfaces (0.53)\n", - " 1. allowed_addresses (0.308)\n", - " 2. devices (0.016)\n", - " 3. c3d_cert_extension_includes (0.009)\n", - " 4. destinations (0.006)\n", - " 5. options (0.005)\n", - " 6. aliases (0.004)\n", + " 0. interfaces (0.477)\n", "\n", "Label = mgmt_address\n", "Pred =\n", - " 0. remove_from_device (0.026)\n", - " 1. monitor_client (0.015)\n", - " 2. traffic_group (0.014)\n", - " 3. test_loss_function_epsilon (0.008)\n", - " 4. client_key (0.008)\n", - " 5. load_params (0.006)\n", - " 6. _update_terminal_region (0.006)\n", + " 0. create_from_template_on_device (0.031)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = mirror_secondary_address\n", "Pred =\n", - " 0. multicast_address (0.277)\n", - " 1. server_type (0.186)\n", - " 2. product (0.05)\n", - " 3. mirror_primary_address (0.048)\n", - " 4. cmp_hash (0.047)\n", - "---- 5. mirror_secondary_address (0.046)\n", - " 6. availability_calculation (0.032)\n", + " 0. multicast_address (0.374)\n", "\n", "Label = mirror_primary_address\n", "Pred =\n", - " 0. multicast_address (0.277)\n", - " 1. server_type (0.186)\n", - " 2. product (0.05)\n", - "---- 3. mirror_primary_address (0.048)\n", - " 4. cmp_hash (0.047)\n", - " 5. mirror_secondary_address (0.046)\n", - " 6. availability_calculation (0.032)\n", + " 0. multicast_address (0.374)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = basename\n", "Pred =\n", - " 0. get_unicast_addresses (0.025)\n", - " 1. get_marketing_name (0.022)\n", - " 2. get_mac_masquerade_address (0.021)\n", - " 3. get_device (0.019)\n", - " 4. auth_name (0.013)\n", - " 5. get_mac_address (0.013)\n", - " 6. get_base_mac_address (0.012)\n", + " 0. uuid (0.14)\n", "\n", "Label = reset_trust\n", "Pred =\n", - " 0. no_platform_check (0.277)\n", - " 1. passphrase (0.272)\n", - " 2. no_license (0.271)\n", - " 3. availability_status (0.003)\n", - " 4. irules (0.003)\n", - " 5. monitors_list (0.003)\n", - " 6. state (0.002)\n", + " 0. no_license (0.339)\n", "\n", "Label = include_chassis_level_config\n", "Pred =\n", - " 0. no_platform_check (0.277)\n", - " 1. passphrase (0.272)\n", - " 2. no_license (0.271)\n", - " 3. availability_status (0.003)\n", - " 4. irules (0.003)\n", - " 5. monitors_list (0.003)\n", - " 6. state (0.002)\n", + " 0. no_license (0.339)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. reset_parameters (0.0)\n", "\n", "Label = exec_module\n", "Pred =\n", - "---- 0. exec_module (0.998)\n", - " 1. _send (0.0)\n", - " 2. add (0.0)\n", - " 3. create_on_device (0.0)\n", - " 4. __ne__ (0.0)\n", - " 5. populate (0.0)\n", - " 6. get_reportable_changes (0.0)\n", + "---- 0. exec_module (0.999)\n", "\n", "Label = is_version_v1\n", "Pred =\n", - " 0. is_version_with_default_network (0.221)\n", - " 1. is_version_without_network (0.209)\n", - " 2. is_version_less_than_13 (0.197)\n", - "---- 3. is_version_v1 (0.169)\n", - " 4. version_is_less_than_12 (0.009)\n", - " 5. version_is_less_than_13 (0.008)\n", - " 6. boto_supports_volume_encryption (0.006)\n", + "---- 0. is_version_v1 (0.299)\n", "\n", "Label = create\n", "Pred =\n", - " 0. update (0.998)\n", - "---- 1. create (0.001)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. post (0.0)\n", - " 6. add (0.0)\n", + " 0. update (0.968)\n", "\n", "Label = service_policy\n", "Pred =\n", - " 0. enforced_policy (0.241)\n", - " 1. phase1_cert (0.192)\n", - " 2. phase1_key (0.178)\n", - " 3. monitor (0.08)\n", - " 4. traffic_group (0.03)\n", - " 5. key (0.015)\n", - " 6. certificate (0.014)\n", + " 0. enforced_policy (0.316)\n", "\n", "Label = staged_policy\n", "Pred =\n", - " 0. enforced_policy (0.241)\n", - " 1. phase1_cert (0.192)\n", - " 2. phase1_key (0.178)\n", - " 3. monitor (0.08)\n", - " 4. traffic_group (0.03)\n", - " 5. key (0.015)\n", - " 6. certificate (0.014)\n", + " 0. enforced_policy (0.316)\n", "\n", "Label = description\n", "Pred =\n", - "---- 0. description (0.977)\n", - " 1. include (0.002)\n", - " 2. enabled (0.001)\n", - " 3. name (0.001)\n", - " 4. server_type (0.001)\n", - " 5. disabled (0.0)\n", - " 6. source (0.0)\n", + "---- 0. description (0.992)\n", "\n", "Label = timeout\n", "Pred =\n", - "---- 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + "---- 0. timeout (0.199)\n", "\n", "Label = time_until_up\n", "Pred =\n", - " 0. timeout (0.188)\n", - "---- 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = reverse\n", "Pred =\n", - " 0. match_across_services (0.059)\n", - " 1. override_connection_limit (0.056)\n", - " 2. allow_non_ssl (0.05)\n", - " 3. match_across_virtuals (0.048)\n", - " 4. match_across_pools (0.048)\n", - " 5. manual_resume (0.035)\n", - " 6. insert_header (0.026)\n", + " 0. match_across_services (0.083)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = profile\n", "Pred =\n", - " 0. ports (0.149)\n", - " 1. rules (0.063)\n", - " 2. explicit_proxy (0.033)\n", - " 3. dns_resolver (0.032)\n", - " 4. addresses (0.029)\n", - " 5. fqdns (0.022)\n", - " 6. parent (0.017)\n", + " 0. ports (0.133)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = name\n", "Pred =\n", - " 0. peer_server (0.045)\n", - " 1. gateway (0.035)\n", - " 2. address (0.025)\n", - " 3. fqdn_auto_populate (0.024)\n", - " 4. mgmt_route (0.018)\n", - " 5. receive (0.017)\n", - " 6. ip (0.016)\n", + " 0. gateway (0.13)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = traffic_group\n", "Pred =\n", - " 0. external_program (0.153)\n", - " 1. vlan (0.137)\n", - " 2. service_policy (0.053)\n", - " 3. datacenter (0.049)\n", - " 4. link (0.049)\n", - " 5. template (0.048)\n", - " 6. firewall_staged_policy (0.047)\n", + " 0. external_program (0.151)\n", "\n", "Label = route_domain\n", "Pred =\n", - "---- 0. route_domain (0.887)\n", - " 1. parent (0.018)\n", - " 2. checksum (0.005)\n", - " 3. address (0.004)\n", - " 4. type (0.003)\n", - " 5. metadata (0.002)\n", - " 6. fqdns (0.002)\n", + "---- 0. route_domain (0.923)\n", "\n", "Label = destination_ip\n", "Pred =\n", - " 0. mgmt_address (0.289)\n", - " 1. netmask (0.17)\n", - "---- 2. destination_ip (0.139)\n", - " 3. source (0.123)\n", - " 4. with_prefixlen (0.017)\n", - " 5. network (0.009)\n", - " 6. get_network_start (0.008)\n", + " 0. source (0.412)\n", "\n", "Label = __default\n", "Pred =\n", "---- 0. __default (1.0)\n", - " 1. compare (0.0)\n", - " 2. remove_from_device (0.0)\n", - " 3. determine_change (0.0)\n", - " 4. add_command_to_interface (0.0)\n", - " 5. to_dense (0.0)\n", - " 6. _add (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", - "---- 0. read_current_from_device (1.0)\n", - " 1. create_on_device (0.0)\n", - " 2. exists (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. get_reportable_changes (0.0)\n", - " 5. policy_exists (0.0)\n", - " 6. get (0.0)\n", + "---- 0. read_current_from_device (0.999)\n", "\n", "Label = read_partition_default_route_domain_from_device\n", "Pred =\n", - " 0. exists (0.197)\n", - " 1. any_license_exists (0.131)\n", - " 2. publish (0.041)\n", - " 3. external_file_exists (0.033)\n", - " 4. publish_on_device (0.026)\n", - " 5. _create_existing_policy_draft (0.025)\n", - " 6. _create_existing_policy_draft_on_device (0.022)\n", + " 0. read_current_from_device (0.145)\n", "\n", "Label = exists\n", "Pred =\n", "---- 0. exists (1.0)\n", - " 1. _device_group_exists (0.0)\n", - " 2. external_file_exists (0.0)\n", - " 3. policy_exists (0.0)\n", - " 4. get (0.0)\n", - " 5. traffic_group (0.0)\n", - " 6. any_license_exists (0.0)\n", "\n", "Label = param_persist\n", "Pred =\n", - " 0. param_tables (0.199)\n", - " 1. param_variables (0.196)\n", - " 2. param_lists (0.185)\n", - " 3. param_metadata (0.178)\n", - " 4. param_strict_updates (0.03)\n", - " 5. snat (0.016)\n", - " 6. syslog_format (0.006)\n", + " 0. param_tables (0.209)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = device_is_id\n", "Pred =\n", - "---- 0. device_is_id (0.959)\n", - " 1. filename (0.003)\n", - " 2. checksum (0.002)\n", - " 3. quorum (0.001)\n", - " 4. monitor_type (0.001)\n", - " 5. at_least (0.001)\n", - " 6. get_tag (0.001)\n", + "---- 0. device_is_id (0.964)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n" + "\n", + "Label = geo_locations\n", + "Pred =\n", + " 0. fqdns (0.749)\n", + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", - "\n", - "Label = geo_locations\n", - "Pred =\n", - " 0. fqdns (0.75)\n", - "---- 1. geo_locations (0.1)\n", - " 2. icmp_message (0.024)\n", - " 3. addresses (0.013)\n", - " 4. metadata (0.01)\n", - " 5. tags (0.009)\n", - " 6. rules (0.008)\n", - "\n", "Label = addresses\n", "Pred =\n", - "---- 0. addresses (0.486)\n", - " 1. ports (0.41)\n", - " 2. port_ranges (0.019)\n", - " 3. icmp_message (0.005)\n", - " 4. port_lists (0.004)\n", - " 5. metadata (0.004)\n", - " 6. address_lists (0.003)\n", + "---- 0. addresses (0.496)\n", "\n", "Label = addresses\n", "Pred =\n", - "---- 0. addresses (0.944)\n", - " 1. ports (0.005)\n", - " 2. address_lists (0.002)\n", - " 3. vlans (0.002)\n", - " 4. destinations (0.002)\n", - " 5. port_lists (0.002)\n", - " 6. dns_resolver (0.001)\n", + "---- 0. addresses (0.976)\n", "\n", "Label = fqdns\n", "Pred =\n", - "---- 0. fqdns (0.914)\n", - " 1. geo_locations (0.013)\n", - " 2. route_domain (0.004)\n", - " 3. metadata (0.003)\n", - " 4. address_lists (0.003)\n", - " 5. icmp_message (0.003)\n", - " 6. pool (0.003)\n", + "---- 0. fqdns (0.981)\n", "\n", "Label = create_on_device\n", "Pred =\n", - "---- 0. create_on_device (0.999)\n", - " 1. exec_module (0.0)\n", - " 2. _create_new_policy_draft (0.0)\n", - " 3. update_on_device (0.0)\n", - " 4. main (0.0)\n", - " 5. policy_exists (0.0)\n", - " 6. get (0.0)\n", + "---- 0. create_on_device (1.0)\n", "\n", "Label = update_on_device\n", "Pred =\n", "---- 0. update_on_device (1.0)\n", - " 1. absent_on_device (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. exists (0.0)\n", - " 4. exit_json (0.0)\n", - " 5. fail_json (0.0)\n", - " 6. provision_non_dedicated_on_device (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = parent\n", "Pred =\n", "---- 0. parent (0.999)\n", - " 1. route_domain (0.0)\n", - " 2. proxy_server_pool (0.0)\n", - " 3. dns_resolver (0.0)\n", - " 4. traffic_group (0.0)\n", - " 5. content (0.0)\n", - " 6. fqdns (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = http_only\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = parent\n", "Pred =\n", - "---- 0. parent (0.931)\n", - " 1. route_domain (0.022)\n", - " 2. proxy_server_pool (0.014)\n", - " 3. dns_resolver (0.012)\n", - " 4. pool (0.001)\n", - " 5. forward_to (0.001)\n", - " 6. fallback_persistence_profile (0.001)\n", + "---- 0. parent (0.922)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. update (0.0)\n", - " 2. start (0.0)\n", - " 3. delete (0.0)\n", - " 4. add (0.0)\n", - " 5. as_mysql (0.0)\n", - " 6. execute (0.0)\n", "\n", "Label = max_file_size\n", "Pred =\n", - " 0. access (0.084)\n", - " 1. tenants (0.061)\n", - " 2. package_version (0.037)\n", - " 3. cache (0.028)\n", - " 4. ip_version (0.011)\n", - " 5. address (0.011)\n", - " 6. policies (0.008)\n", + " 0. tenants (0.513)\n", "\n", "Label = exec_module\n", "Pred =\n", "---- 0. exec_module (1.0)\n", - " 1. add (0.0)\n", - " 2. to_request (0.0)\n", - " 3. get_reportable_changes (0.0)\n", - " 4. collection (0.0)\n", - " 5. delete (0.0)\n", - " 6. encode_request (0.0)\n", "\n", "Label = get_manager\n", "Pred =\n", "---- 0. get_manager (1.0)\n", - " 1. __eq__ (0.0)\n", - " 2. deprecated_init (0.0)\n", - " 3. state (0.0)\n", - " 4. new_file (0.0)\n", - " 5. symbolic (0.0)\n", - " 6. get_result (0.0)\n", "\n", "Label = _remove_temporary_cli_script_from_device\n", "Pred =\n", - " 0. remove_from_device (0.66)\n", - " 1. upload_file_to_device (0.066)\n", - " 2. _get_servers_from_clc (0.012)\n", - " 3. delete_vgw (0.006)\n", - " 4. _update_temporary_cli_script_on_device (0.005)\n", - " 5. _delete (0.003)\n", - " 6. options (0.003)\n", + " 0. remove_from_device (0.998)\n", "\n", "Label = _cert_filename\n", "Pred =\n", - " 0. _key_filename (0.968)\n", - " 1. quote_name (0.005)\n", - " 2. key_filename (0.002)\n", - " 3. filename (0.002)\n", - " 4. __getattr__ (0.001)\n", - " 5. max_name_length (0.0)\n", - " 6. get_limit_choices_to (0.0)\n", + " 0. _key_filename (0.397)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. delete_db_instance (0.0)\n", - " 5. _configuration_args (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = is_valid_hostname\n", "Pred =\n", - "---- 0. is_valid_hostname (0.998)\n", - " 1. is_valid_uuid (0.0)\n", - " 2. _extract_if_name (0.0)\n", - " 3. get_specified_device_identifiers (0.0)\n", - " 4. proxy_bypass (0.0)\n", - " 5. _poll_for_maintenance (0.0)\n", - " 6. get_qualifier (0.0)\n", + "---- 0. is_valid_hostname (0.997)\n", "\n", "Label = fqdns\n", "Pred =\n", - "---- 0. fqdns (0.891)\n", - " 1. geo_locations (0.016)\n", - " 2. address_lists (0.004)\n", - " 3. parent (0.004)\n", - " 4. metadata (0.004)\n", - " 5. route_domain (0.004)\n", - " 6. port_lists (0.004)\n", + "---- 0. fqdns (0.971)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. load (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = ratio\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = disabled\n", "Pred =\n", - "---- 0. disabled (0.576)\n", - " 1. enabled (0.372)\n", - " 2. reject (0.005)\n", - " 3. reverse (0.003)\n", - " 4. vlans_disabled (0.002)\n", - " 5. authentication_enabled (0.002)\n", - " 6. vlans_enabled (0.002)\n", + "---- 0. disabled (0.651)\n", "\n", "Label = collection\n", "Pred =\n", - "---- 0. collection (0.977)\n", - " 1. self_link (0.007)\n", - " 2. _request_for_item (0.001)\n", - " 3. get_plan (0.0)\n", - " 4. url_map_update (0.0)\n", - " 5. get_region (0.0)\n", - " 6. resource_to_update (0.0)\n", + "---- 0. collection (0.974)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = remove\n", "Pred =\n", "---- 0. remove (1.0)\n", - " 1. delete (0.0)\n", - " 2. add (0.0)\n", - " 3. reset (0.0)\n", - " 4. copy (0.0)\n", - " 5. update (0.0)\n", - " 6. create_from_file (0.0)\n", "\n", "Label = absent\n", "Pred =\n", "---- 0. absent (1.0)\n", - " 1. type (0.0)\n", - " 2. name (0.0)\n", - " 3. delete (0.0)\n", - " 4. remove (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. get_type (0.0)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. reset_parameters (0.0)\n", "\n", "Label = rules\n", "Pred =\n", - "---- 0. rules (0.964)\n", - " 1. fqdns (0.007)\n", - " 2. geo_locations (0.004)\n", - " 3. icmp_message (0.002)\n", - " 4. tags (0.001)\n", - " 5. metadata (0.001)\n", - " 6. route_domain (0.001)\n", + "---- 0. rules (0.986)\n", "\n", "Label = present\n", "Pred =\n", "---- 0. present (1.0)\n", - " 1. __bool__ (0.0)\n", - " 2. create_blank (0.0)\n", - " 3. absent (0.0)\n", - " 4. __len__ (0.0)\n", - " 5. initial_form_count (0.0)\n", - " 6. save (0.0)\n", "\n", "Label = iso_date\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = console_log\n", "Pred =\n", - " 0. match_across_services (0.059)\n", - " 1. override_connection_limit (0.056)\n", - " 2. allow_non_ssl (0.05)\n", - " 3. match_across_virtuals (0.048)\n", - " 4. match_across_pools (0.048)\n", - " 5. manual_resume (0.035)\n", - " 6. insert_header (0.026)\n", + " 0. match_across_services (0.083)\n", "\n", "Label = encode_string\n", "Pred =\n", - " 0. decode (0.527)\n", - " 1. encode_dict (0.23)\n", - " 2. encode (0.152)\n", - " 3. _encode (0.004)\n", - " 4. updateRecord (0.002)\n", - " 5. project (0.002)\n", - " 6. get_decoded (0.001)\n", + " 0. encode_dict (0.512)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = get_manager\n", "Pred =\n", "---- 0. get_manager (1.0)\n", - " 1. __eq__ (0.0)\n", - " 2. deprecated_init (0.0)\n", - " 3. state (0.0)\n", - " 4. new_file (0.0)\n", - " 5. symbolic (0.0)\n", - " 6. get_result (0.0)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. validate_param_values (0.0)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. _configuration_args (0.0)\n", - " 5. delete_db_instance (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. ip (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = provided_password\n", "Pred =\n", - " 0. provided_username (0.432)\n", - " 1. smtp_server_password (0.018)\n", - " 2. _get_url (0.011)\n", - " 3. check_password (0.009)\n", - " 4. _i18n_cache_key_suffix (0.007)\n", - " 5. target_password (0.007)\n", - " 6. snmp_privacy_password (0.006)\n", + " 0. provided_username (0.92)\n", "\n", "Label = remove_from_device\n", "Pred =\n", - " 0. create_on_device (0.84)\n", - "---- 1. remove_from_device (0.045)\n", - " 2. update_on_device (0.023)\n", - " 3. clear (0.012)\n", - " 4. exists (0.006)\n", - " 5. set (0.006)\n", - " 6. init_module (0.004)\n", + "---- 0. remove_from_device (0.354)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = port\n", "Pred =\n", - "---- 0. port (0.992)\n", - " 1. netmask (0.0)\n", - " 2. allow_service (0.0)\n", - " 3. remote_port (0.0)\n", - " 4. rate_limit (0.0)\n", - " 5. address (0.0)\n", - " 6. idle_timeout (0.0)\n", + "---- 0. port (0.996)\n", "\n", "Label = time_until_up\n", "Pred =\n", - " 0. timeout (0.188)\n", - "---- 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. reset_parameters (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = remove_from_device\n", "Pred =\n", "---- 0. remove_from_device (1.0)\n", - " 1. exists (0.0)\n", - " 2. execute_on_device (0.0)\n", - " 3. deprecate (0.0)\n", - " 4. create_from_template_on_device (0.0)\n", - " 5. add (0.0)\n", - " 6. init_module (0.0)\n", "\n", "Label = synchronization_group_name\n", "Pred =\n", - " 0. timeout (0.188)\n", - " 1. time_until_up (0.077)\n", - " 2. probe_timeout (0.042)\n", - " 3. port (0.028)\n", - " 4. connection_limit (0.027)\n", - " 5. device_port (0.017)\n", - " 6. type (0.017)\n", + " 0. timeout (0.199)\n", "\n", "Label = to_return\n", "Pred =\n", "---- 0. to_return (1.0)\n", - " 1. _set_changed_options (0.0)\n", - " 2. parent (0.0)\n", - " 3. test_cross_val_score_errors (0.0)\n", - " 4. rules (0.0)\n", - " 5. __default (0.0)\n", - " 6. reset_parameters (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setitem__ (0.0)\n", - " 5. ip (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = read_current_from_device\n", "Pred =\n", - " 0. exists (0.621)\n", - " 1. remove_from_device (0.092)\n", - " 2. execute_on_device (0.02)\n", - " 3. get_session_status (0.009)\n", - " 4. create_from_template_on_device (0.009)\n", - " 5. any_license_exists (0.007)\n", - " 6. _sync_to_group_required (0.007)\n", + " 0. _sync_to_group_required (0.289)\n", "\n", "Label = _get_status_from_resource\n", "Pred =\n", - " 0. _get_record (0.018)\n", - " 1. get_data (0.013)\n", - " 2. get_arn_from_role_name (0.012)\n", - " 3. disconnect_missing (0.012)\n", - " 4. get_snapshots_by_name_recursively (0.008)\n", - " 5. get_snapshot (0.008)\n", - " 6. admin_list_filter (0.007)\n", + " 0. parse_resource_to_dict (0.071)\n", "\n", "Label = interval\n", "Pred =\n", - "---- 0. interval (0.997)\n", - " 1. port (0.001)\n", - " 2. remote_port (0.0)\n", - " 3. idle_timeout (0.0)\n", - " 4. mtu (0.0)\n", - " 5. route_domain (0.0)\n", - " 6. snmp_auth_password (0.0)\n", + "---- 0. interval (0.999)\n", "\n", "Label = ip\n", "Pred =\n", - "---- 0. ip (0.995)\n", - " 1. local_ip (0.001)\n", - " 2. enabled (0.0)\n", - " 3. netmask (0.0)\n", - " 4. destination (0.0)\n", - " 5. address (0.0)\n", - " 6. port (0.0)\n", + "---- 0. ip (0.997)\n", "\n", "Label = parent\n", "Pred =\n", - "---- 0. parent (0.931)\n", - " 1. route_domain (0.022)\n", - " 2. proxy_server_pool (0.014)\n", - " 3. dns_resolver (0.012)\n", - " 4. pool (0.001)\n", - " 5. forward_to (0.001)\n", - " 6. fallback_persistence_profile (0.001)\n", + "---- 0. parent (0.922)\n", "\n", "Label = _set_changed_options\n", "Pred =\n", "---- 0. _set_changed_options (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. create_on_device (0.0)\n", - " 3. get_running_config (0.0)\n", - " 4. __init__ (0.0)\n", - " 5. to_return (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = should_update\n", "Pred =\n", "---- 0. should_update (1.0)\n", - " 1. create (0.0)\n", - " 2. should_absent (0.0)\n", - " 3. get_context (0.0)\n", - " 4. netconf_load_config (0.0)\n", - " 5. has_changed (0.0)\n", - " 6. changed (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (1.0)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. get (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = parent\n", "Pred =\n", - "---- 0. parent (0.931)\n", - " 1. route_domain (0.022)\n", - " 2. proxy_server_pool (0.014)\n", - " 3. dns_resolver (0.012)\n", - " 4. pool (0.001)\n", - " 5. forward_to (0.001)\n", - " 6. fallback_persistence_profile (0.001)\n", + "---- 0. parent (0.922)\n", "\n", "Label = compare\n", "Pred =\n", "---- 0. compare (1.0)\n", - " 1. get (0.0)\n", - " 2. param (0.0)\n", - " 3. eval (0.0)\n", - " 4. _configuration_args (0.0)\n", - " 5. delete_db_instance (0.0)\n", - " 6. setdefault (0.0)\n", "\n", "Label = create_on_device\n", "Pred =\n", "---- 0. create_on_device (1.0)\n", - " 1. exec_module (0.0)\n", - " 2. update_on_device (0.0)\n", - " 3. _create_new_policy_draft (0.0)\n", - " 4. main (0.0)\n", - " 5. get (0.0)\n", - " 6. read_current_from_device (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = untagged_interfaces\n", "Pred =\n", - " 0. tagged_interfaces (0.735)\n", - "---- 1. untagged_interfaces (0.218)\n", - " 2. interfaces (0.002)\n", - " 3. vlans (0.002)\n", - " 4. devices (0.001)\n", - " 5. icmp_message (0.001)\n", - " 6. addresses (0.001)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " 0. tagged_interfaces (0.72)\n", "\n", "Label = fail_safe\n", "Pred =\n", - " 0. manual_resume (0.05)\n", - " 1. transparent (0.026)\n", - " 2. enabled (0.021)\n", - " 3. adaptive (0.021)\n", - " 4. reset_on_timeout (0.014)\n", - " 5. mgmt_dhcp (0.011)\n", - " 6. disabled (0.01)\n", + " 0. manual_resume (0.048)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = _announce_deprecations\n", "Pred =\n", "---- 0. _announce_deprecations (1.0)\n", - " 1. _announce_warnings (0.0)\n", - " 2. deprecate (0.0)\n", - " 3. remove_from_device (0.0)\n", - " 4. get_result (0.0)\n", - " 5. disable (0.0)\n", - " 6. delete_resource (0.0)\n", "\n", "Label = convert_cps_raw_data\n", "Pred =\n", - " 0. get_auth_plugin_mapping (0.009)\n", - " 1. get_option_default (0.009)\n", - " 2. openstack_module_kwargs (0.008)\n", - " 3. set_constant (0.008)\n", - " 4. find_param (0.007)\n", - " 5. _add_publicip_to_server (0.007)\n", - " 6. check_change_service_owner (0.007)\n", + " 0. get_oper_state (0.031)\n", "\n", "Label = cps_get\n", "Pred =\n", - " 0. to_list (0.014)\n", - " 1. netconf_set_config (0.012)\n", - " 2. remove_src (0.011)\n", - " 3. get_all (0.011)\n", - " 4. diff_update (0.011)\n", - " 5. cli_bool_option (0.01)\n", - " 6. _has_value_changed (0.009)\n", + " 0. _get_expected_sysctls (0.024)\n", "\n", "Label = get_available_number\n", "Pred =\n", - " 0. extract_key_data (0.072)\n", - " 1. parse_structured_vlans (0.037)\n", - " 2. parse_state (0.029)\n", - " 3. parse_structured_interfaces (0.021)\n", - " 4. get_enum_value (0.02)\n", - " 5. _get_port (0.019)\n", - " 6. get_from_attributes (0.018)\n", + " 0. _read_gateway_fileshare_response (0.029)\n", "\n", "Label = delete_admin\n", "Pred =\n", - " 0. get_snmp (0.879)\n", - " 1. temp_get_nets (0.023)\n", - " 2. get_admins (0.008)\n", - " 3. get_org_devices (0.007)\n", - " 4. get_config_templates (0.006)\n", - " 5. get_rules (0.004)\n", - " 6. get_org (0.002)\n", + " 0. get_snmp (0.79)\n", "\n", "Label = parse_version\n", "Pred =\n", - "---- 0. parse_version (0.26)\n", - " 1. parse_macaddress (0.192)\n", - " 2. parse_serialnum (0.181)\n", - " 3. parse_model (0.076)\n", - " 4. parse_image (0.069)\n", - " 5. parse_bandwidth (0.048)\n", - " 6. parse_lldp_intf (0.02)\n", + " 0. parse_macaddress (0.296)\n", "\n", "Label = run\n", "Pred =\n", - "---- 0. run (0.824)\n", - " 1. _show_cmd (0.083)\n", - " 2. _show_mlag_cmd (0.053)\n", - " 3. get (0.004)\n", - " 4. get_bgp_summary (0.001)\n", - " 5. get_value (0.001)\n", - " 6. _show_igmp (0.001)\n", + "---- 0. run (0.417)\n", "\n", "Label = parse_model\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - "---- 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = diff\n", "Pred =\n", - " 0. diff_list (0.414)\n", - " 1. servicegroup_identical (0.077)\n", - " 2. cs_vserver_identical (0.06)\n", - "---- 3. diff (0.053)\n", - " 4. gslb_vserver_identical (0.034)\n", - " 5. gslb_site_identical (0.017)\n", - " 6. lb_vserver_diff (0.016)\n", + " 0. servicegroup_identical (0.053)\n", "\n", "Label = lbmonitor_exists\n", "Pred =\n", - " 0. policy_exists (0.248)\n", - " 1. server_exists (0.188)\n", - " 2. lb_vserver_exists (0.175)\n", - " 3. action_exists (0.038)\n", - " 4. service_exists (0.03)\n", - " 5. gslb_vserver_exists (0.029)\n", - " 6. gslb_site_exists (0.028)\n", + " 0. server_exists (0.117)\n", "\n", "Label = service_identical\n", "Pred =\n", - " 0. diff (0.343)\n", - " 1. gslb_vserver_identical (0.072)\n", - " 2. cs_vserver_identical (0.058)\n", - " 3. gslb_site_identical (0.042)\n", - " 4. servicegroup_identical (0.011)\n", - " 5. action_identical (0.007)\n", - " 6. pipeline_field (0.007)\n", + " 0. diff (0.18)\n", "\n", "Label = diff_list\n", "Pred =\n", - " 0. diff (0.082)\n", - " 1. cs_vserver_identical (0.026)\n", - " 2. get_tags (0.016)\n", - " 3. gslb_vserver_identical (0.01)\n", - " 4. lb_vserver_diff (0.01)\n", - " 5. find_key_pair (0.007)\n", - " 6. provider_module_params (0.007)\n", + " 0. lb_vserver_diff (0.067)\n", "\n", "Label = do_state_change\n", "Pred =\n", - "---- 0. do_state_change (0.999)\n", - " 1. enable (0.0)\n", - " 2. exec_module (0.0)\n", - " 3. from_config (0.0)\n", - " 4. update_check (0.0)\n", - " 5. tagged_interfaces (0.0)\n", - " 6. options (0.0)\n", + "---- 0. do_state_change (0.993)\n", "\n", "Label = parse_serialnum\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = populate\n", "Pred =\n", - "---- 0. populate (0.912)\n", - " 1. populate_memory (0.01)\n", - " 2. get_end_state (0.005)\n", - " 3. _check_known_errors (0.004)\n", - " 4. fqdn_auto_populate (0.002)\n", - " 5. reverse_dict (0.002)\n", - " 6. populate_ipv6_interfaces (0.001)\n", + "---- 0. populate (0.984)\n", "\n", "Label = parse_filesystem_info\n", "Pred =\n", - " 0. parse_memory_info (0.979)\n", - " 1. parse_stacks (0.001)\n", - " 2. _find_group_recursive (0.0)\n", - " 3. collect_with_namespace (0.0)\n", - " 4. existsGroup (0.0)\n", - " 5. parse_distribution_file_Slackware (0.0)\n", - " 6. get_firewall_group (0.0)\n", + " 0. parse_memory_info (0.994)\n", "\n", "Label = to_lines\n", "Pred =\n", - "---- 0. to_lines (0.999)\n", - " 1. _to_lines (0.0)\n", - " 2. _request_for_item (0.0)\n", - " 3. add (0.0)\n", - " 4. _response_from_item (0.0)\n", - " 5. fail (0.0)\n", - " 6. get (0.0)\n", + "---- 0. to_lines (1.0)\n", "\n", "Label = search_obj_in_list\n", "Pred =\n", "---- 0. search_obj_in_list (1.0)\n", - " 1. get_org (0.0)\n", - " 2. get (0.0)\n", - " 3. diff_banners (0.0)\n", - " 4. get_template_id (0.0)\n", - " 5. getGroupId (0.0)\n", - " 6. _find_path_in_tree (0.0)\n", "\n", "Label = has_lldp\n", "Pred =\n", - "---- 0. has_lldp (0.959)\n", - " 1. is_config_exist (0.001)\n", - " 2. run_commands (0.001)\n", - " 3. summary (0.001)\n", - " 4. mode_xml_to_cli_str (0.0)\n", - " 5. wait_for (0.0)\n", - " 6. config_nets_export_vxlan_ver (0.0)\n", + "---- 0. has_lldp (0.987)\n", "\n", "Label = search_obj_in_list\n", "Pred =\n", - "---- 0. search_obj_in_list (1.0)\n", - " 1. get_org (0.0)\n", - " 2. has_vrf (0.0)\n", - " 3. get (0.0)\n", - " 4. _find_path_in_tree (0.0)\n", - " 5. _get_elb (0.0)\n", - " 6. add (0.0)\n", + "---- 0. search_obj_in_list (0.999)\n", "\n", "Label = parse_vlan_brief\n", "Pred =\n", - " 0. deduplicate_rules_args (0.038)\n", - " 1. unquote (0.02)\n", - " 2. is_valid_vlan_id (0.012)\n", - " 3. reduce_memmap (0.01)\n", - " 4. _rsa_fun (0.009)\n", - " 5. escape_quotes (0.007)\n", - " 6. get_evn_peers (0.006)\n", + " 0. unquote_if_non_empty (0.039)\n", "\n", "Label = diff_list\n", "Pred =\n", - " 0. gslb_site_identical (0.123)\n", - " 1. gslb_vserver_identical (0.077)\n", - "---- 2. diff_list (0.073)\n", - " 3. diff (0.037)\n", - " 4. cs_vserver_identical (0.012)\n", - " 5. subnets_removed (0.01)\n", - " 6. subnets_added (0.008)\n", + "---- 0. diff_list (0.94)\n", "\n", "Label = parse_domain_search\n", "Pred =\n", - " 0. parse_domain_name (0.619)\n", - " 1. parse_lookup_source (0.156)\n", - " 2. parse_name_servers (0.087)\n", - " 3. parse_hostname (0.021)\n", - " 4. parse_vrf (0.016)\n", - " 5. parse_use_vrf (0.003)\n", - " 6. parse_description (0.002)\n", + " 0. parse_domain_name (0.678)\n", "\n", "Label = add_command_to_interface\n", "Pred =\n", - "---- 0. add_command_to_interface (0.997)\n", - " 1. add_command_to_vrf (0.0)\n", - " 2. add (0.0)\n", - " 3. state_present (0.0)\n", - " 4. _add_interface_commands (0.0)\n", - " 5. get_interface_type_removed_cmds (0.0)\n", - " 6. _generate_no_switchport_commands (0.0)\n", + "---- 0. add_command_to_interface (0.993)\n", "\n", "Label = default_switchport_config\n", "Pred =\n", - "---- 0. default_switchport_config (0.996)\n", - " 1. add_command_to_vrf (0.0)\n", - " 2. _execute (0.0)\n", - " 3. add_ssh (0.0)\n", - " 4. _query_flow_props (0.0)\n", - " 5. _add_igmp_vlan_commands (0.0)\n", - " 6. set_rollback_location (0.0)\n", + "---- 0. default_switchport_config (0.986)\n", "\n", "Label = parse_hostname\n", "Pred =\n", - "---- 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + "---- 0. parse_hostname (0.108)\n", "\n", "Label = parse_model\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - "---- 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = parse_mediatype\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = check_args\n", "Pred =\n", - "---- 0. check_args (0.995)\n", - " 1. _validate_param_values (0.001)\n", - " 2. add (0.0)\n", - " 3. enter_maintenance (0.0)\n", - " 4. validate_privilege (0.0)\n", - " 5. prepare_method (0.0)\n", - " 6. fail (0.0)\n", + "---- 0. check_args (0.998)\n", "\n", "Label = _get_ip_routing\n", "Pred =\n", - " 0. zeroize (0.074)\n", - " 1. ipv4addrs (0.062)\n", - " 2. supports_provisioned_mode (0.058)\n", - " 3. run (0.04)\n", - " 4. map_config_to_obj (0.034)\n", - " 5. _show_mlag_cmd (0.03)\n", - " 6. version_is_less_than_12 (0.012)\n", + " 0. ipv4addrs (0.088)\n", "\n", "Label = _generate_igmp_vlan_cmds\n", "Pred =\n", - " 0. _gen_ptp_commands (0.401)\n", - " 1. _gen_pfc_commands (0.15)\n", - " 2. _generate_no_ipl_commands (0.064)\n", - " 3. _generate_no_ospf_commands (0.049)\n", - " 4. _enable_ntp (0.034)\n", - " 5. _disable_ntp (0.022)\n", - " 6. _generate_igmp_mrouter_cmds (0.02)\n", + " 0. _gen_ptp_commands (0.654)\n", "\n", "Label = _generate_igmp_querier_cmds\n", "Pred =\n", - " 0. _gen_querier_attr_commands (0.442)\n", - " 1. _add_querier_commands (0.032)\n", - " 2. _generate_igmp_version_cmds (0.012)\n", - " 3. _gen_ptp_commands (0.012)\n", - " 4. have_vlan_list (0.011)\n", - " 5. _enable_ntp (0.011)\n", - " 6. _disable_ntp (0.011)\n", + " 0. _gen_querier_attr_commands (0.676)\n", "\n", "Label = validate_access_vlan\n", "Pred =\n", - " 0. validate_secondary_priority (0.136)\n", - " 1. validate_vlan_id (0.127)\n", - " 2. validate_magp_id (0.125)\n", - " 3. validate_ospf (0.119)\n", - " 4. validate_primary_priority (0.117)\n", - " 5. validate_domain (0.11)\n", - " 6. validate_mtu (0.037)\n", + " 0. validate_domain (0.158)\n", "\n", "Label = generate_commands\n", "Pred =\n", - "---- 0. generate_commands (0.29)\n", - " 1. _generate_no_ipl_commands (0.046)\n", - " 2. _add_interface_commands (0.02)\n", - " 3. normalize_commands (0.009)\n", - " 4. _gen_querier_attr_commands (0.009)\n", - " 5. _gen_ptp_commands (0.008)\n", - " 6. has_lldp (0.007)\n", + "---- 0. generate_commands (0.796)\n", "\n", "Label = get_required_config\n", "Pred =\n", "---- 0. get_required_config (0.999)\n", - " 1. get_config (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. execute (0.0)\n", - " 4. rollback (0.0)\n", - " 5. name (0.0)\n", - " 6. destination (0.0)\n", "\n", "Label = _update_magp_data\n", "Pred =\n", - " 0. load_current_config (0.082)\n", - " 1. read_current (0.034)\n", - " 2. _detach_ip_list (0.019)\n", - " 3. ensure_state (0.015)\n", - " 4. _prepare_instance_info_patch (0.015)\n", - " 5. _set_igmp_config (0.009)\n", - " 6. copy (0.008)\n", + " 0. load_current_config (0.346)\n", "\n", "Label = validate_purge\n", "Pred =\n", - " 0. validate_duplex (0.882)\n", - " 1. _validate_rollback_id (0.004)\n", - " 2. set_urlconf (0.003)\n", - " 3. check_lib (0.002)\n", - " 4. validate_ipv46_address (0.002)\n", - " 5. validate_ipv6_address (0.002)\n", - " 6. validate_vlan_id (0.002)\n", + " 0. validate_duplex (0.634)\n", "\n", "Label = _get_interface_type\n", "Pred =\n", - "---- 0. _get_interface_type (0.881)\n", - " 1. _set_if_type (0.004)\n", - " 2. parse_interface (0.003)\n", - " 3. get_intf_param_type (0.002)\n", - " 4. split_interface (0.002)\n", - " 5. parse_level (0.002)\n", - " 6. is_switchport (0.001)\n", + "---- 0. _get_interface_type (0.937)\n", "\n", "Label = get_admin_state\n", "Pred =\n", - " 0. get_if_name (0.186)\n", - " 1. from_config (0.148)\n", - " 2. get_config (0.025)\n", - " 3. get_value (0.024)\n", - " 4. register (0.022)\n", - " 5. serialize_keras_object (0.013)\n", - " 6. __get__ (0.013)\n", + " 0. get_if_name (0.341)\n", "\n", "Label = _get_interfaces_status\n", "Pred =\n", - " 0. _get_interfaces_rates (0.959)\n", - " 1. _get_port_channels (0.005)\n", - " 2. _show_ptp_interface_config (0.002)\n", - " 3. get_access_vlan (0.001)\n", - " 4. get_magp_id (0.001)\n", - " 5. parse_interface (0.0)\n", - " 6. get_stp_link_type (0.0)\n", + " 0. _get_interfaces_rates (0.975)\n", "\n", "Label = _get_interface_type\n", "Pred =\n", - "---- 0. _get_interface_type (0.881)\n", - " 1. _set_if_type (0.004)\n", - " 2. parse_interface (0.003)\n", - " 3. get_intf_param_type (0.002)\n", - " 4. split_interface (0.002)\n", - " 5. parse_level (0.002)\n", - " 6. is_switchport (0.001)\n", + "---- 0. _get_interface_type (0.937)\n", "\n", "Label = _generate_no_bgp_cmds\n", "Pred =\n", - " 0. _generate_igmp_version_cmds (0.401)\n", - " 1. _generate_no_ospf_commands (0.155)\n", - " 2. _generate_igmp_interface_cmds (0.032)\n", - " 3. _gen_querier_attr_commands (0.022)\n", - " 4. _generate_no_igmp_cmds (0.02)\n", - " 5. _disable_ntp (0.017)\n", - " 6. _enable_ntp (0.017)\n", + " 0. _generate_igmp_version_cmds (0.295)\n", "\n", "Label = _set_ntp_config\n", "Pred =\n", - " 0. _sync_state (0.048)\n", - " 1. get_end_state (0.045)\n", - " 2. _set_igmp_config (0.04)\n", - " 3. get_existing (0.037)\n", - " 4. get_ntp_auth (0.021)\n", - " 5. load_current_config (0.018)\n", - " 6. _validate_interface_type (0.018)\n", + " 0. get_end_state (0.134)\n", "\n", "Label = generate_commands\n", "Pred =\n", - " 0. _generate_igmp_interface_cmds (0.233)\n", - " 1. _generate_igmp_version_cmds (0.133)\n", - " 2. _generate_no_ospf_commands (0.124)\n", - " 3. _generate_no_igmp_cmds (0.118)\n", - " 4. _disable_ntp (0.021)\n", - " 5. _add_commands_to_interface (0.021)\n", - " 6. _gen_querier_attr_commands (0.016)\n", + " 0. _generate_no_igmp_cmds (0.224)\n", "\n", "Label = _show_mlag\n", "Pred =\n", - " 0. _show_mlag_vip (0.95)\n", - " 1. _generate_port_channel_command (0.002)\n", - " 2. _generate_vlan_if_command (0.002)\n", - " 3. _show_igmp (0.001)\n", - " 4. is_json (0.001)\n", - " 5. _show_igmp_interfaces (0.001)\n", - " 6. _show_ptp_config (0.001)\n", + " 0. _show_mlag_vip (0.987)\n", "\n", "Label = _generate_no_igmp_cmds\n", "Pred =\n", - " 0. _enable_ntp (0.377)\n", - " 1. _disable_ntp (0.184)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2. _enable_ptp (0.058)\n", - " 3. _disable_ptp (0.053)\n", - " 4. _generate_igmp_interface_cmds (0.05)\n", - "---- 5. _generate_no_igmp_cmds (0.03)\n", - " 6. _generate_no_ospf_commands (0.023)\n", + " 0. _enable_ntp (0.239)\n", "\n", "Label = _get_aggregate_spec\n", "Pred =\n", "---- 0. _get_aggregate_spec (0.999)\n", - " 1. map_config_to_obj (0.0)\n", - " 2. to_dict (0.0)\n", - " 3. from_dict (0.0)\n", - " 4. _get_element_spec (0.0)\n", - " 5. append (0.0)\n", - " 6. pop (0.0)\n", "\n", "Label = _get_lag_type\n", "Pred =\n", - " 0. _extract_lag_name (0.312)\n", - " 1. _split_optional_netmask (0.017)\n", - " 2. _get_interface_cmd_name (0.014)\n", - " 3. is_local_branch (0.011)\n", - " 4. docker_stack_services (0.01)\n", - " 5. needs_modification (0.008)\n", - " 6. is_switchport (0.006)\n", + " 0. parse_hostnameprefix (0.036)\n", "\n", "Label = prov_template_exists\n", "Pred =\n", - " 0. install_pp (0.214)\n", - " 1. fos_request (0.139)\n", - " 2. delete_script (0.087)\n", - " 3. assign_provision_template (0.063)\n", - " 4. assign_dev_grp (0.053)\n", - " 5. dev_group_exists (0.028)\n", - " 6. set_script (0.022)\n", + " 0. assign_dev_grp (0.38)\n", "\n", "Label = update_install_target\n", "Pred =\n", - " 0. install_pp (0.876)\n", - " 1. assign_provision_template (0.018)\n", - " 2. assign_dev_grp (0.014)\n", - " 3. fos_request (0.006)\n", - " 4. delete_script (0.006)\n", - " 5. update_flags (0.005)\n", - " 6. calculate_local_etag (0.003)\n", + " 0. install_pp (0.963)\n", "\n", "Label = run\n", "Pred =\n", - "---- 0. run (0.824)\n", - " 1. _show_cmd (0.083)\n", - " 2. _show_mlag_cmd (0.053)\n", - " 3. get (0.004)\n", - " 4. get_bgp_summary (0.001)\n", - " 5. get_value (0.001)\n", - " 6. _show_igmp (0.001)\n", + "---- 0. run (0.417)\n", "\n", "Label = parse_memtotal\n", "Pred =\n", - " 0. parse_memfree (0.505)\n", - "---- 1. parse_memtotal (0.142)\n", - " 2. parse_image (0.042)\n", - " 3. parse_model (0.029)\n", - " 4. parse_version (0.028)\n", - " 5. parse_serialnum (0.026)\n", - " 6. parse_description (0.017)\n", + " 0. parse_memfree (0.607)\n", "\n", "Label = parse_model\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - "---- 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = parse_sysmac\n", "Pred =\n", - " 0. parse_hostname (0.111)\n", - " 1. parse_description (0.094)\n", - " 2. parse_duplex (0.089)\n", - " 3. parse_operstatus (0.08)\n", - " 4. parse_lineprotocol (0.08)\n", - " 5. parse_type (0.071)\n", - " 6. parse_model (0.066)\n", + " 0. parse_hostname (0.108)\n", "\n", "Label = save_config\n", "Pred =\n", "---- 0. save_config (0.999)\n", - " 1. fail_module (0.0)\n", - " 2. warn (0.0)\n", - " 3. fail_json (0.0)\n", - " 4. get_defaults_flag (0.0)\n", - " 5. _announce_warnings (0.0)\n", - " 6. __init__ (0.0)\n", "\n", "Label = get_command_from_state\n", "Pred =\n", "---- 0. get_command_from_state (0.999)\n", - " 1. execute_show_command (0.0)\n", - " 2. _system_state_change (0.0)\n", - " 3. config_cmd_operation (0.0)\n", - " 4. merge_command_dict_cli (0.0)\n", - " 5. permute_hidden (0.0)\n", - " 6. check_exists (0.0)\n", "\n", "Label = get_command_from_state\n", "Pred =\n", "---- 0. get_command_from_state (0.999)\n", - " 1. execute_show_command (0.0)\n", - " 2. _system_state_change (0.0)\n", - " 3. config_cmd_operation (0.0)\n", - " 4. merge_command_dict_cli (0.0)\n", - " 5. permute_hidden (0.0)\n", - " 6. check_exists (0.0)\n", "\n", "Label = get_command_from_state\n", "Pred =\n", - "---- 0. get_command_from_state (0.999)\n", - " 1. execute_show_command (0.0)\n", - " 2. merge_command_dict_cli (0.0)\n", - " 3. _system_state_change (0.0)\n", - " 4. config_cmd_operation (0.0)\n", - " 5. permute_hidden (0.0)\n", - " 6. send_show_command (0.0)\n", + "---- 0. get_command_from_state (1.0)\n", "\n", "Label = get_command_from_state\n", "Pred =\n", - "---- 0. get_command_from_state (0.999)\n", - " 1. execute_show_command (0.0)\n", - " 2. merge_command_dict_cli (0.0)\n", - " 3. _system_state_change (0.0)\n", - " 4. config_cmd_operation (0.0)\n", - " 5. permute_hidden (0.0)\n", - " 6. send_show_command (0.0)\n", + "---- 0. get_command_from_state (1.0)\n", "\n", "Label = populate\n", "Pred =\n", - "---- 0. populate (0.988)\n", - " 1. collect (0.002)\n", - " 2. get_virtual_facts (0.001)\n", - " 3. __init__ (0.001)\n", - " 4. populate_interfaces (0.0)\n", - " 5. parse_stacks (0.0)\n", - " 6. get_result_and_facts (0.0)\n", + "---- 0. populate (0.997)\n", "\n", "Label = is_download_operation\n", "Pred =\n", - " 0. is_upload_operation (0.899)\n", - " 1. is_post_request (0.003)\n", - " 2. parse_spec (0.002)\n", - " 3. _get_model_name (0.002)\n", - " 4. _filter (0.002)\n", - " 5. is_put_request (0.001)\n", - " 6. policy_equal (0.001)\n", + " 0. is_upload_operation (0.702)\n", "\n", "Label = validate\n", "Pred =\n", - " 0. validate_params (0.569)\n", - " 1. has_tags (0.02)\n", - " 2. dict_selection (0.02)\n", - " 3. get (0.009)\n", - " 4. _validate_range (0.008)\n", - " 5. get_or_fallback (0.007)\n", - " 6. _fallback (0.006)\n", + " 0. validate_params (0.311)\n", "\n", "Label = is_find_by_filter_operation\n", "Pred =\n", - " 0. is_add_operation (0.111)\n", - " 1. is_edit_operation (0.108)\n", - " 2. is_delete_operation (0.014)\n", - " 3. get_port_group_by_name (0.013)\n", - " 4. find_in_array (0.012)\n", - " 5. validate_query_params (0.012)\n", - " 6. is_same_model_operation (0.012)\n", + " 0. is_post_request (0.277)\n", "\n", "Label = search_obj_in_list\n", "Pred =\n", - " 0. __setattr__ (0.042)\n", - " 1. _get_vm_prop (0.037)\n", - " 2. _group_exists (0.034)\n", - " 3. __getattr__ (0.031)\n", - " 4. diff_banners (0.025)\n", - " 5. parent (0.015)\n", - " 6. try_get (0.011)\n", + " 0. parent (0.031)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.558)\n", - " 1. populate (0.428)\n", - " 2. start_serialization (0.001)\n", - " 3. delete (0.0)\n", - " 4. ip (0.0)\n", - " 5. init_module (0.0)\n", - " 6. load (0.0)\n", + "---- 0. __init__ (0.936)\n", "\n", "Label = parse_filesystems\n", "Pred =\n", - "---- 0. parse_filesystems (0.793)\n", - " 1. parse_memory (0.146)\n", - " 2. parse_hostname (0.002)\n", - " 3. parse_sshkey (0.001)\n", - " 4. parse_interface (0.001)\n", - " 5. parse_view (0.001)\n", - " 6. parse_privilege (0.001)\n", + "---- 0. parse_filesystems (0.886)\n", "\n", "Label = run\n", "Pred =\n", - "---- 0. run (0.997)\n", - " 1. get (0.0)\n", - " 2. configure (0.0)\n", - " 3. _show_cmd (0.0)\n", - " 4. put (0.0)\n", - " 5. invoke (0.0)\n", - " 6. netconf_set_config (0.0)\n", + "---- 0. run (0.999)\n", "\n", "Label = links\n", "Pred =\n", - " 0. get_vrf (0.063)\n", - " 1. get_all_keys (0.024)\n", - "---- 2. links (0.019)\n", - " 3. populate_ipv6_interfaces (0.012)\n", - " 4. parse_m3u8_attributes (0.01)\n", - " 5. _process_page (0.008)\n", - " 6. get_vlans_list (0.007)\n", + " 0. get_all_keys (0.073)\n", "\n", "Label = _clean504\n", "Pred =\n", - " 0. _cleanall (0.974)\n", - " 1. is_network_bound (0.001)\n", - " 2. _external_ids_to_dict (0.0)\n", - " 3. decode (0.0)\n", - " 4. _list (0.0)\n", - " 5. parse_name (0.0)\n", - " 6. add_new_permissions (0.0)\n", + " 0. _cleanall (0.981)\n", "\n", "Label = clone\n", "Pred =\n", - " 0. update (0.269)\n", - " 1. checkout (0.122)\n", - " 2. export (0.089)\n", - " 3. has_local_mods (0.06)\n", - " 4. pull (0.051)\n", - " 5. reset (0.029)\n", - " 6. switch (0.028)\n", + " 0. update (0.188)\n", "\n", "Label = _get\n", "Pred =\n", " 0. _delete (0.987)\n", - " 1. add (0.001)\n", - " 2. patch (0.001)\n", - " 3. get (0.0)\n", - " 4. get_value (0.0)\n", - " 5. _list (0.0)\n", - " 6. put (0.0)\n", "\n", "Label = register_runner\n", "Pred =\n", - " 0. update_runner (0.103)\n", - " 1. recvall (0.061)\n", - " 2. _make_hash_value (0.014)\n", - " 3. update_last_login (0.014)\n", - " 4. make_token (0.011)\n", - " 5. calc_speed (0.01)\n", - " 6. permission_required (0.01)\n", + " 0. update_runner (0.138)\n", "\n", "Label = get_runner_list\n", "Pred =\n", - " 0. find_vswitch_by_name (0.011)\n", - " 1. dict_selection (0.009)\n", - " 2. run_suite (0.008)\n", - " 3. addSubTest (0.007)\n", - " 4. _router_internal_interfaces (0.007)\n", - " 5. update_runner (0.007)\n", - " 6. get_domain_byid (0.007)\n", + " 0. merge_secrets (0.033)\n", "\n", "Label = get_runner_details\n", "Pred =\n", - " 0. id_for_label (0.939)\n", - " 1. delete_runner (0.004)\n", - " 2. _api_prefix (0.002)\n", - " 3. build_format_id (0.001)\n", - " 4. driver_count (0.001)\n", - " 5. get_internal_type (0.001)\n", - " 6. deleteRecord (0.001)\n", + " 0. id_for_label (0.99)\n", "\n", "Label = deleteUser\n", "Pred =\n", - " 0. remove_permission (0.036)\n", - " 1. run (0.022)\n", - " 2. extract (0.016)\n", - " 3. add_permission (0.014)\n", - " 4. send (0.013)\n", - " 5. get_version (0.013)\n", - " 6. _enable_zones (0.009)\n", + " 0. _real_initialize (0.138)\n", "\n", "Label = get_consul_api\n", "Pred =\n", - " 0. get_value (0.408)\n", - " 1. build_entity (0.014)\n", - " 2. set_params (0.011)\n", - " 3. batch_get_value (0.009)\n", - " 4. get_vsan_facts (0.009)\n", - " 5. name (0.009)\n", - " 6. get (0.008)\n", + " 0. get_value (0.647)\n", "\n", "Label = encode_rules_as_hcl_string\n", "Pred =\n", - " 0. decode_rules_as_hcl_string (0.584)\n", - " 1. dns_resolver_address (0.017)\n", - " 2. decode_acl_as_json (0.01)\n", - " 3. deduplicate_rules_args (0.008)\n", - " 4. rules_to_permissions (0.006)\n", - " 5. max_loading_is_positive (0.004)\n", - " 6. has_fasthttp_profiles (0.004)\n", + " 0. decode_rules_as_hcl_string (0.879)\n", "\n", "Label = get_consul_client\n", "Pred =\n", - " 0. _create_server (0.086)\n", - " 1. live_server_url (0.037)\n", - " 2. static (0.03)\n", - " 3. base_url (0.025)\n", - " 4. get_secret (0.02)\n", - " 5. _create_server_thread (0.019)\n", - " 6. deregister (0.011)\n", + " 0. _extract_urls (0.034)\n", "\n", "Label = shutdown\n", "Pred =\n", - " 0. list (0.816)\n", - " 1. _close_files (0.012)\n", - " 2. on_train_end (0.012)\n", - " 3. clear (0.011)\n", - " 4. register (0.009)\n", - " 5. __enter__ (0.007)\n", - " 6. unregister (0.004)\n", + " 0. list (0.483)\n", "\n", "Label = wait\n", "Pred =\n", - " 0. _get_connect_params (0.135)\n", - " 1. _choose_id_value (0.068)\n", - " 2. build_entity (0.041)\n", - " 3. set_params (0.035)\n", - " 4. get_region (0.02)\n", - " 5. get_connection_params (0.016)\n", - " 6. map_param_to_obj (0.015)\n", + " 0. run (0.085)\n", "\n", "Label = register_with_consul\n", "Pred =\n", - " 0. execute (0.953)\n", - " 1. state (0.003)\n", - " 2. validate_results (0.001)\n", - " 3. create (0.001)\n", - " 4. add (0.001)\n", - " 5. process_state (0.001)\n", - " 6. options (0.001)\n", + " 0. execute (0.965)\n", "\n", "Label = get_consul_api\n", "Pred =\n", - " 0. set_params (0.05)\n", - " 1. build_entity (0.045)\n", - " 2. set_cookie (0.024)\n", - " 3. _get_common_args (0.016)\n", - " 4. get_value (0.014)\n", - " 5. get_env (0.009)\n", - " 6. _create_server (0.008)\n", + " 0. get_value (0.207)\n", "\n", "Label = change_required\n", "Pred =\n", - " 0. _is_value_absent (0.092)\n", - " 1. present (0.061)\n", - " 2. _ensure_policy_is_absent (0.043)\n", - " 3. check_should_throw_fail (0.035)\n", - " 4. ensure_present (0.034)\n", - " 5. end_transaction_sql (0.017)\n", - " 6. _ensure_policy_is_present (0.015)\n", + " 0. present (0.906)\n", "\n", "Label = is_present\n", "Pred =\n", - " 0. check_reply_is_correct (0.636)\n", - " 1. action_reply_is_correct (0.032)\n", - " 2. get_object_status (0.01)\n", - " 3. get_current_ttl_state (0.006)\n", - " 4. get_minimum_up_member_enabled_state (0.005)\n", - " 5. absent_user (0.005)\n", - " 6. get_connection_mirror_state (0.004)\n", + " 0. check_reply_is_correct (0.966)\n", "\n", "Label = list\n", "Pred =\n", - " 0. _alert_policy_exists (0.139)\n", - " 1. _policy_exists (0.076)\n", - " 2. find_key_pair (0.052)\n", - " 3. get_security_group_id (0.025)\n", - " 4. _ensure_policy_is_present (0.018)\n", - " 5. _get_policy_id_from_response (0.017)\n", - " 6. find (0.014)\n", + " 0. _ensure_policy_is_present (0.242)\n", "\n", "Label = enable\n", "Pred =\n", - " 0. disable (0.961)\n", - " 1. delete (0.015)\n", - " 2. add (0.001)\n", - " 3. delete_tags (0.001)\n", - " 4. _disable_tracing (0.001)\n", - " 5. _enable_tracing (0.001)\n", - " 6. clear (0.0)\n", + " 0. disable (0.957)\n", "\n", "Label = set_tags\n", "Pred =\n", - " 0. add (0.423)\n", - " 1. delete (0.197)\n", - " 2. clear (0.158)\n", - " 3. put (0.021)\n", - " 4. set (0.015)\n", - " 5. disable (0.011)\n", - " 6. change_password (0.008)\n", + " 0. add (0.099)\n", "\n", "Label = connection_to_string\n", "Pred =\n", - " 0. get_config (0.98)\n", - " 1. register (0.002)\n", - " 2. call (0.001)\n", - " 3. __new__ (0.001)\n", - " 4. clone (0.001)\n", - " 5. get (0.001)\n", - " 6. from_config (0.0)\n", + " 0. get_config (0.725)\n", "\n", "Label = main\n", "Pred =\n", - " 0. enable (0.047)\n", - " 1. match_request (0.015)\n", - " 2. construct (0.014)\n", - " 3. _flag_current_thread_clean_exit (0.014)\n", - " 4. test_oob_improvement (0.012)\n", - "---- 5. main (0.011)\n", - " 6. download_json (0.008)\n", + "---- 0. main (0.261)\n", "\n", "Label = getDomainByName\n", "Pred =\n", - " 0. getDomain (0.553)\n", - " 1. getContactListByName (0.035)\n", - " 2. get_auth_plugin_mapping (0.018)\n", - " 3. _get_info_for_comm (0.009)\n", - " 4. sanitize_url (0.009)\n", - " 5. get_condition_by_id_with_backoff (0.006)\n", - " 6. _get_module_prefix_map (0.005)\n", + " 0. getDomain (0.396)\n", "\n", "Label = getRecord\n", "Pred =\n", - " 0. getContactList (0.036)\n", - " 1. get_full_load_on_sync_state (0.018)\n", - " 2. find_authorization_role (0.014)\n", - " 3. get_vm_ide_device (0.013)\n", - " 4. template_link (0.008)\n", - " 5. get_aks_kubeconfig (0.008)\n", - " 6. _server_type_changed (0.007)\n", + " 0. deregister (0.029)\n", "\n", "Label = getContactlists\n", "Pred =\n", - " 0. getContactListByName (0.058)\n", - " 1. _smuggle_referrer (0.035)\n", - " 2. get_source_address_translation_type (0.018)\n", - " 3. decode_url (0.016)\n", - " 4. decrypt_url (0.015)\n", - " 5. getContactList (0.011)\n", - " 6. _fetch_upload_date (0.011)\n", + " 0. getContactList (0.222)\n", "\n", "Label = ipv6addrs\n", "Pred =\n", - " 0. ipv4addrs (0.976)\n", - " 1. _get_element_spec (0.003)\n", - " 2. map_config_to_obj (0.0)\n", - " 3. zeroize (0.0)\n", - " 4. ec2_argument_spec (0.0)\n", - " 5. switch_version (0.0)\n", - " 6. password_credential (0.0)\n", + " 0. ipv4addrs (0.972)\n", "\n", "Label = _is_value_present\n", "Pred =\n", - " 0. _is_entry_present (0.974)\n", - " 1. extract_filename_from_headers (0.001)\n", - " 2. get_autostart2 (0.001)\n", - " 3. check_should_throw_fail (0.0)\n", - " 4. _get_backend_policies (0.0)\n", - " 5. compare_assume_role_policy_doc (0.0)\n", - " 6. json (0.0)\n", + " 0. _is_entry_present (0.9)\n", "\n", "Label = present_record\n", "Pred =\n", "---- 0. present_record (0.998)\n", - " 1. is_global (0.0)\n", - " 2. _get_record (0.0)\n", - " 3. prepare_value (0.0)\n", - " 4. check_exists (0.0)\n", - " 5. exclude (0.0)\n", - " 6. __new__ (0.0)\n", "\n", "Label = lookup_datastore\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. find_datastore_cluster_by_name (0.114)\n", - " 1. find_datastore_by_name (0.049)\n", - " 2. mount_datastore_host (0.018)\n", - " 3. get_datastore_facts (0.011)\n", - " 4. find_host_portgroup_by_name (0.01)\n", - " 5. lookup_datastore_by_cluster (0.01)\n", - " 6. migrate_vm (0.009)\n", + "Pred =\n", + " 0. mount_datastore_host (0.046)\n", "\n", "Label = gather_host_facts\n", "Pred =\n", - " 0. gather_host_portgroup_facts (0.372)\n", - " 1. get_Host (0.041)\n", - " 2. disconnect_all_containers (0.021)\n", - " 3. get_esx_host (0.019)\n", - " 4. vmdisk_id (0.016)\n", - " 5. get_datastore_facts (0.013)\n", - " 6. whitespace_tokenize (0.01)\n", + " 0. gather_host_portgroup_facts (0.078)\n", "\n", "Label = get_snapshots_by_name_recursively\n", "Pred =\n", - "---- 0. get_snapshots_by_name_recursively (0.989)\n", - " 1. get_current_snap_obj (0.001)\n", - " 2. _get_datacenter_id (0.0)\n", - " 3. add_object (0.0)\n", - " 4. list_snapshots_recursively (0.0)\n", - " 5. describe_target_health (0.0)\n", - " 6. _parse_meta (0.0)\n", + "---- 0. get_snapshots_by_name_recursively (0.992)\n", "\n", "Label = __exit__\n", "Pred =\n", "---- 0. __exit__ (1.0)\n", - " 1. reraise (0.0)\n", - " 2. add_exception (0.0)\n", - " 3. raise_for_status (0.0)\n", - " 4. handle_exception (0.0)\n", - " 5. clear (0.0)\n", - " 6. __setitem__ (0.0)\n", "\n", "Label = bytes_read\n", "Pred =\n", - " 0. read (0.088)\n", - " 1. read_unsigned_int (0.032)\n", - " 2. read_string (0.025)\n", - " 3. read_bytes (0.025)\n", - " 4. read_unsigned_char (0.022)\n", - " 5. equals_lf (0.018)\n", - " 6. __cast (0.015)\n", + " 0. hex (0.084)\n", "\n", "Label = normalize_rule_spec\n", "Pred =\n", - " 0. normalize_vm_vm_rule_spec (0.959)\n", - " 1. check_vswitch_configuration (0.001)\n", - " 2. get_port_group_by_name (0.001)\n", - " 3. set_ttl_state (0.001)\n", - " 4. check_dvs_configuration (0.001)\n", - " 5. get_vm_cdrom_device (0.0)\n", - " 6. ensure_condition_absent (0.0)\n", + " 0. normalize_vm_vm_rule_spec (0.952)\n", "\n", "Label = select_resource_pool\n", "Pred =\n", - " 0. find_resource_pool_by_name (0.294)\n", - " 1. check_rp_state (0.089)\n", - " 2. fetch_authcode (0.02)\n", - " 3. apply_authcode (0.018)\n", - " 4. delete_address_from_mapping (0.015)\n", - " 5. configure (0.012)\n", - " 6. get_all_hosts_by_cluster (0.007)\n", + " 0. find_resource_pool_by_name (0.3)\n", "\n", "Label = check_category_status\n", "Pred =\n", - " 0. revert (0.019)\n", - " 1. device_is_id (0.012)\n", - " 2. switch (0.009)\n", - " 3. _cloudstack_ver (0.009)\n", - " 4. _check_versions (0.008)\n", - " 5. is_l2vpn_family_evpn_exist (0.007)\n", - " 6. check_tag_status (0.006)\n", + " 0. get_allowed_vlans (0.027)\n", "\n", "Label = find_dvs_by_uuid\n", "Pred =\n", - " 0. check_dvs_configuration (0.07)\n", - " 1. get_port_group_by_name (0.038)\n", - " 2. is_local_branch (0.017)\n", - " 3. find_dvs_uplink_pg (0.015)\n", - " 4. _gen_querier_attr_commands (0.015)\n", - " 5. vmdisk_id (0.013)\n", - " 6. gather_host_portgroup_facts (0.012)\n", + " 0. node_check (0.048)\n", "\n", "Label = gather_guest_snapshot_facts\n", "Pred =\n", - " 0. gather_facts (0.053)\n", - " 1. get_rule_key_by_name (0.04)\n", - " 2. normalize_vm_vm_rule_spec (0.03)\n", - " 3. get_status2 (0.021)\n", - " 4. find_cluster_by_name (0.019)\n", - " 5. get_all_permissions (0.018)\n", - " 6. is_valid_uuid (0.013)\n", + " 0. gather_facts (0.033)\n", "\n", "Label = _delete_volume\n", "Pred =\n", - " 0. get_volume_id (0.042)\n", - " 1. associate_connection_and_lag (0.038)\n", - " 2. delete_trail (0.038)\n", - " 3. get_volume (0.015)\n", - " 4. volume_id_exists (0.011)\n", - " 5. find_image_by_id (0.01)\n", - " 6. _print_unpicklable_subtest (0.009)\n", + " 0. rax_cdb (0.058)\n", "\n", "Label = _remove_datacenter\n", "Pred =\n", - " 0. delete_connection (0.057)\n", - " 1. delete_tags (0.025)\n", - " 2. del_notification_config (0.024)\n", - " 3. delete_placement_group (0.021)\n", - " 4. delete_model (0.014)\n", - " 5. delete_trail (0.014)\n", - " 6. associate_connection_and_lag (0.012)\n", + " 0. _delete_policy (0.034)\n", "\n", "Label = _parse_labels\n", "Pred =\n", - " 0. _external_ids_to_dict (0.496)\n", - " 1. diff_update (0.012)\n", - " 2. list_to_csv (0.009)\n", - " 3. _parse_meta (0.008)\n", - " 4. _has_value_changed (0.008)\n", - " 5. filter_empty (0.006)\n", - " 6. fortios_system (0.005)\n", + " 0. _external_ids_to_dict (0.745)\n", "\n", "Label = _is_true\n", "Pred =\n", - " 0. _is_false (0.024)\n", - " 1. parse_password (0.012)\n", - " 2. fqdn_auto_populate (0.012)\n", - " 3. is_valid_ipv6_address (0.009)\n", - " 4. map_ports_str_to_list (0.009)\n", - " 5. test__check_targets_multiclass_with_both_y_true_and_y_pred_binary (0.008)\n", - " 6. test_average_precision_score_tied_values (0.006)\n", + " 0. _is_false (0.017)\n", "\n", "Label = _needs_update\n", "Pred =\n", - " 0. _system_state_change (0.841)\n", - " 1. ensure_absent (0.017)\n", - "---- 2. _needs_update (0.014)\n", - " 3. _ah_esp_gre_match (0.003)\n", - " 4. undefine (0.003)\n", - " 5. update_instance_state (0.002)\n", - " 6. _allocation_state_enabled_disabled_changed (0.002)\n", + " 0. _system_state_change (0.996)\n", "\n", "Label = get_current_compute_environment\n", "Pred =\n", - " 0. remove_role_perm (0.03)\n", - " 1. absent_role (0.023)\n", - " 2. check_change_role_cardinality (0.023)\n", - " 3. get_attached_policy_list (0.021)\n", - " 4. _get_role_perm (0.021)\n", - " 5. _get_affinity_group_mappings (0.021)\n", - " 6. stack_set_facts (0.018)\n", + " 0. _get_affinity_label_mappings (0.094)\n", "\n", "Label = ensure_disabled\n", "Pred =\n", - " 0. ensure_present (0.036)\n", - " 1. check_should_throw_fail (0.019)\n", - " 2. ensure (0.017)\n", - " 3. pre_save (0.014)\n", - " 4. record_applied (0.013)\n", - " 5. get_autocommit (0.012)\n", - " 6. _user_get_all_permissions (0.01)\n", + " 0. present (0.116)\n", "\n", "Label = changed_properties\n", "Pred =\n", - " 0. create_from_template (0.033)\n", - " 1. status_needs_update (0.013)\n", - " 2. compare_bucket_logging (0.013)\n", - " 3. _sync_to_group_required (0.011)\n", - " 4. deregister_hook (0.009)\n", - " 5. rds_connect (0.009)\n", - " 6. reboot_db_instance (0.009)\n", + " 0. test_estimate_bandwidth (0.189)\n", "\n", "Label = is_fakes3\n", "Pred =\n", "---- 0. is_fakes3 (0.982)\n", - " 1. sanitize_url (0.0)\n", - " 2. is_permanent_redirect (0.0)\n", - " 3. parse_ping (0.0)\n", - " 4. _get_image_url (0.0)\n", - " 5. brightcove_url_result (0.0)\n", - " 6. extract_tag_box (0.0)\n", "\n", "Label = describe_subnets_with_backoff\n", "Pred =\n", - "---- 0. describe_subnets_with_backoff (0.921)\n", - " 1. get_elasticache_tags_with_backoff (0.009)\n", - " 2. describe_vpc_attr_with_backoff (0.003)\n", - " 3. list_regex_patterns_with_backoff (0.003)\n", - " 4. get_key_policy_with_backoff (0.002)\n", - " 5. get_role_with_backoff (0.002)\n", - " 6. get_default_vpc (0.001)\n", + "---- 0. describe_subnets_with_backoff (0.934)\n", "\n", "Label = create_tags\n", "Pred =\n", - "---- 0. create_tags (0.35)\n", - " 1. find_tags (0.074)\n", - " 2. delete_vgw (0.057)\n", - " 3. get_tags (0.045)\n", - " 4. add_tags (0.026)\n", - " 5. tag_resource (0.021)\n", - " 6. create_network_acl (0.016)\n", + "---- 0. create_tags (0.303)\n", "\n", "Label = add_routes\n", "Pred =\n", - " 0. remove_routes (0.445)\n", - " 1. add_tags (0.051)\n", - " 2. remove_tags (0.022)\n", - " 3. lb_instance_health (0.009)\n", - " 4. describe_instance_health (0.009)\n", - " 5. delete_network_acl (0.007)\n", - " 6. delete_repository_policy (0.007)\n", + " 0. remove_routes (0.761)\n", "\n", "Label = get_target_group_tags\n", "Pred =\n", - " 0. get_load_balancer_tags (0.923)\n", - " 1. get_tags (0.023)\n", - "---- 2. get_target_group_tags (0.011)\n", - " 3. get_elb_listeners (0.003)\n", - " 4. get_elb_tags (0.002)\n", - " 5. find_tags (0.001)\n", - " 6. describe_route_tables_with_backoff (0.001)\n", + " 0. get_load_balancer_tags (0.835)\n", "\n", "Label = restore_default_associations\n", "Pred =\n", - " 0. subnets_removed (0.067)\n", - " 1. subnets_added (0.061)\n", - " 2. validate_roles (0.012)\n", - " 3. get_web_acl_by_name (0.01)\n", - " 4. gslb_vserver_identical (0.01)\n", - " 5. diff_banners (0.009)\n", - " 6. match_asg_tags (0.009)\n", + " 0. subnets_added (0.048)\n", "\n", "Label = get_kms_metadata_with_backoff\n", "Pred =\n", - " 0. get_kms_tags_with_backoff (0.269)\n", - " 1. get_key_policy_with_backoff (0.239)\n", - " 2. get_security_groups_with_backoff (0.143)\n", - " 3. sg_exists_with_backoff (0.034)\n", - " 4. connection_status (0.022)\n", - " 5. get_condition_by_id_with_backoff (0.014)\n", - " 6. get_bucket_versioning (0.013)\n", + " 0. get_security_groups_with_backoff (0.268)\n", "\n", "Label = get_listener_rules\n", "Pred =\n", - " 0. get_elb_listener_rules (0.187)\n", - " 1. get_load_balancer_tags (0.107)\n", - " 2. get_elb_listeners (0.094)\n", - " 3. get_role_with_backoff (0.036)\n", - " 4. parse_scale (0.035)\n", - " 5. create_db_snapshot (0.027)\n", - " 6. get_tags (0.013)\n", + " 0. get_elb_listener_rules (0.324)\n", "\n", "Label = modify_db_instance\n", "Pred =\n", - "---- 0. modify_db_instance (0.813)\n", - " 1. delete_db_instance (0.07)\n", - " 2. reboot_db_instance (0.016)\n", - " 3. create_db_snapshot (0.013)\n", - " 4. _disable_zones (0.004)\n", - " 5. create_db_instance_read_replica (0.003)\n", - " 6. get_elb_listener_rules (0.002)\n", + "---- 0. modify_db_instance (0.977)\n", "\n", "Label = restore_db_instance_from_db_snapshot\n", "Pred =\n", - "---- 0. restore_db_instance_from_db_snapshot (0.937)\n", - " 1. reboot_db_instance (0.005)\n", - " 2. create_db_snapshot (0.005)\n", - " 3. delete_db_instance (0.003)\n", - " 4. promote_read_replica (0.002)\n", - " 5. create_db_instance_read_replica (0.002)\n", - " 6. create_db_instance (0.002)\n", + "---- 0. restore_db_instance_from_db_snapshot (0.964)\n", "\n", "Label = __init__\n", - "Pred =\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = list_apps\n", "Pred =\n", - " 0. describe_app (0.084)\n", - " 1. import_key_pair (0.048)\n", - " 2. describe_target_health (0.02)\n", - " 3. boto_supports_profile_name_arg (0.017)\n", - " 4. get_provisioned_throughput_in_mibps (0.017)\n", - " 5. boto_supports_param_in_spot_request (0.013)\n", - " 6. pipeline_description (0.012)\n", + " 0. pipeline_description (0.127)\n", "\n", "Label = put_bucket_tagging\n", "Pred =\n", - " 0. delete_bucket_tagging (0.4)\n", - " 1. get_bucket_request_payment (0.087)\n", - " 2. put_bucket_policy (0.076)\n", - " 3. put_bucket_request_payment (0.026)\n", - " 4. get_current_bucket_tags_dict (0.026)\n", - " 5. delete_bucket_policy (0.025)\n", - " 6. get_bucket_versioning (0.013)\n", + " 0. delete_bucket_tagging (0.205)\n", "\n", "Label = wait_for_eni\n", "Pred =\n", - " 0. detach_volume (0.085)\n", - " 1. address_is_associated_with_device (0.011)\n", - " 2. is_valid_lsa_arrival_interval (0.009)\n", - " 3. test_fit_best_piecewise (0.008)\n", - " 4. deregister_hook (0.007)\n", - " 5. makedirs (0.007)\n", - " 6. _ensure_executor_running (0.006)\n", + " 0. detach_volume (0.125)\n", "\n", "Label = match_tags\n", "Pred =\n", - " 0. match_asg_tags (0.38)\n", - " 1. tags_match (0.025)\n", - " 2. _needs_update (0.016)\n", - " 3. _get_tags (0.014)\n", - " 4. match_filters (0.012)\n", - " 5. _transform_ip_list (0.011)\n", - " 6. get_tag_list (0.011)\n", + " 0. match_asg_tags (0.472)\n", "\n", "Label = validate_field_level_encryption_id\n", "Pred =\n", - " 0. is_placeholder (0.011)\n", - " 1. get_source (0.011)\n", - " 2. _gen_pfc_commands (0.01)\n", - " 3. _generate_igmp_interface_cmds (0.01)\n", - " 4. get_source_port_behavior (0.009)\n", - " 5. min_max_axis (0.009)\n", - " 6. _gen_ptp_commands (0.007)\n", + " 0. get_source_port_behavior (0.05)\n", "\n", "Label = validate_comment\n", "Pred =\n", - " 0. _create_if_lldp_data (0.035)\n", - " 1. unpack_facts (0.022)\n", - " 2. _get_os_version (0.019)\n", - " 3. get_form_kwargs (0.013)\n", - " 4. _generate_no_mlag_vip_cmds (0.012)\n", - " 5. decode_cert_data (0.012)\n", - " 6. prepare_value (0.01)\n", + " 0. _create_if_lldp_data (0.045)\n", "\n", "Label = update_parameter\n", "Pred =\n", - " 0. copy (0.069)\n", - " 1. checkFail (0.034)\n", - " 2. ensure_absent (0.027)\n", - " 3. create (0.023)\n", - " 4. state_create_dvspg (0.019)\n", - " 5. put (0.016)\n", - " 6. inner (0.013)\n", + " 0. _sendback_result (0.115)\n", "\n", "Label = _flatten\n", "Pred =\n", - " 0. values (0.051)\n", - " 1. rule_to_string (0.012)\n", - " 2. assert_grid_iter_equals_getitem (0.011)\n", - " 3. group_list_of_dict (0.011)\n", - " 4. __iter__ (0.01)\n", - " 5. _assert_raises_or_warns_cm (0.01)\n", - " 6. _targets_to_put (0.01)\n", + " 0. name_scope (0.04)\n", "\n", "Label = warn_if_public_ip_assignment_changed\n", "Pred =\n", - " 0. clc_install_package (0.057)\n", - " 1. address_is_associated_with_device (0.027)\n", - " 2. get_public_ip_address (0.018)\n", - " 3. _delete_server_snapshot (0.017)\n", - " 4. vm_size_is_valid (0.017)\n", - " 5. create_or_update_pip (0.014)\n", - " 6. find_address (0.013)\n", + " 0. get_public_ip_address (0.057)\n", "\n", "Label = diff\n", "Pred =\n", - " 0. to_dict (0.226)\n", - " 1. root_attributes (0.173)\n", - " 2. widget_attrs (0.067)\n", - " 3. to_python (0.037)\n", - " 4. _validate_attrs (0.017)\n", - " 5. get_shape (0.011)\n", - " 6. build_attrs (0.011)\n", + " 0. get_flags_from_attributes (0.171)\n", "\n", "Label = targets_equal\n", "Pred =\n", - " 0. validate_params (0.308)\n", - " 1. has_tags (0.03)\n", - " 2. rijndael_mul (0.024)\n", - " 3. add_missing_key (0.023)\n", - " 4. is_object_ref (0.012)\n", - " 5. _transform_ip_list (0.012)\n", - " 6. match_filters (0.011)\n", + " 0. _is_object_changed (0.262)\n", "\n", "Label = resource_exists\n", "Pred =\n", - "---- 0. resource_exists (0.264)\n", - " 1. get_rule (0.091)\n", - " 2. delete_resource (0.068)\n", - " 3. rule_exists (0.063)\n", - " 4. _create_repository (0.032)\n", - " 5. _list_topics (0.028)\n", - " 6. create_resource (0.019)\n", + "---- 0. resource_exists (0.833)\n", "\n", "Label = get_distribution_config\n", "Pred =\n", - " 0. get_distribution (0.47)\n", - "---- 1. get_distribution_config (0.212)\n", - " 2. get_streaming_distribution_config (0.059)\n", - " 3. update_distribution (0.041)\n", - " 4. delete_distribution (0.03)\n", - " 5. get_streaming_distribution (0.028)\n", - " 6. get_bucket_list (0.005)\n", + " 0. get_distribution (0.479)\n", "\n", "Label = calculate_s3_path\n", "Pred =\n", - " 0. calculate_local_etag (0.965)\n", - " 1. serialize_pnics (0.001)\n", - " 2. get_current_bucket_tags_dict (0.001)\n", - " 3. webfilter_fortiguard (0.001)\n", - " 4. install_pp (0.001)\n", - " 5. ensure_cgw_present (0.001)\n", - " 6. rule_to_string (0.001)\n", + " 0. calculate_local_etag (0.968)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. split (0.0)\n", "\n", "Label = _get_instance_health\n", "Pred =\n", - "---- 0. _get_instance_health (0.984)\n", - " 1. _get_instance (0.0)\n", - " 2. _get_vpc_id (0.0)\n", - " 3. get_db_instance (0.0)\n", - " 4. delete_user (0.0)\n", - " 5. get_attached_policy_list (0.0)\n", - " 6. _disable_zones (0.0)\n", + "---- 0. _get_instance_health (0.991)\n", "\n", "Label = validate_comment\n", "Pred =\n", - " 0. _live_title (0.597)\n", - " 1. _datetime_from_timestamp (0.016)\n", - " 2. __mod__ (0.014)\n", - " 3. _make_date_lookup_arg (0.011)\n", - " 4. strptime (0.009)\n", - " 5. now (0.006)\n", - " 6. get_lexer_for_body (0.005)\n", + " 0. _live_title (0.492)\n", "\n", "Label = get_current_function\n", "Pred =\n", - " 0. __getattr__ (0.041)\n", - " 1. get (0.037)\n", - " 2. get_connection (0.029)\n", - " 3. invoke (0.016)\n", - " 4. converter (0.012)\n", - " 5. create_db_instance (0.012)\n", - " 6. delete_db_instance (0.01)\n", + " 0. get_attached_policy_list (0.028)\n", "\n", "Label = _get_vpc_connection\n", "Pred =\n", - "---- 0. _get_vpc_connection (0.956)\n", - " 1. _get_ec2_connection (0.01)\n", - " 2. _get_elb_connection (0.007)\n", - " 3. _get_vpc_id (0.001)\n", - " 4. setup_client (0.001)\n", - " 5. _get_instance (0.001)\n", - " 6. get_bucket_list (0.0)\n", + "---- 0. _get_vpc_connection (0.97)\n", "\n", "Label = _delete_elb\n", "Pred =\n", - "---- 0. _delete_elb (0.979)\n", - " 1. delete (0.002)\n", - " 2. delete_elb (0.002)\n", - " 3. ensure_absent (0.002)\n", - " 4. delete_network_acl_entry (0.0)\n", - " 5. get_info (0.0)\n", - " 6. _delete_elb_listeners (0.0)\n", + "---- 0. _delete_elb (0.989)\n", "\n", "Label = _update_policy\n", "Pred =\n", - "---- 0. _update_policy (0.977)\n", - " 1. _create_policy (0.004)\n", - " 2. _delete_policy (0.003)\n", - " 3. _policy_exists (0.001)\n", - " 4. delete_tags (0.0)\n", - " 5. copy (0.0)\n", - " 6. _alert_policy_exists (0.0)\n", + "---- 0. _update_policy (0.989)\n", "\n", "Label = _diff_list\n", "Pred =\n", "---- 0. _diff_list (0.99)\n", - " 1. format_number (0.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2. remove_all_targets (0.0)\n", - " 3. is_string (0.0)\n", - " 4. _compare (0.0)\n", - " 5. hash_type_xml_to_cli_str (0.0)\n", - " 6. is_empty_list (0.0)\n", "\n", "Label = _delete_repository\n", "Pred =\n", - " 0. _create_repository (0.392)\n", - " 1. delete_resource (0.288)\n", - " 2. get_rule (0.029)\n", - " 3. create_resource (0.02)\n", - " 4. ensure_assign_ipv6_on_create (0.019)\n", - " 5. list_rules (0.008)\n", - " 6. list_web_acls (0.006)\n", + " 0. _create_repository (0.8)\n", "\n", "Label = format_for_deletion\n", "Pred =\n", - " 0. format_for_insertion (0.984)\n", - " 1. _get_affinity_label_mappings (0.001)\n", - " 2. format_for_update (0.0)\n", - " 3. _read_gateway_fileshare_response (0.0)\n", - " 4. create_linux_profile_instance (0.0)\n", - " 5. get_optional (0.0)\n", - " 6. ensure_condition_absent (0.0)\n", + " 0. format_for_insertion (0.983)\n", "\n", "Label = asg_exists\n", "Pred =\n", - " 0. get_snmp_groups (0.031)\n", - " 1. get_key_policy_with_backoff (0.016)\n", - " 2. converter (0.012)\n", - " 3. set_cookie (0.011)\n", - " 4. describe_target_health (0.01)\n", - " 5. build_volume_spec (0.01)\n", - " 6. set_signed_cookie (0.009)\n", + " 0. as_sql (0.048)\n", "\n", "Label = list_all_groups\n", "Pred =\n", - " 0. list_all_instance_profiles (0.218)\n", - " 1. list_all_roles (0.052)\n", - " 2. _content_of_file_at_path (0.026)\n", - " 3. is_logged (0.021)\n", - " 4. present_firewall_group (0.01)\n", - " 5. serialize_sshkey (0.009)\n", - " 6. get_arn_from_role_name (0.006)\n", + " 0. list_all_instance_profiles (0.622)\n", "\n", "Label = list_all_users\n", "Pred =\n", - " 0. get_user (0.021)\n", - " 1. all_domains (0.018)\n", - " 2. present_user (0.015)\n", - " 3. state_create_dvs (0.014)\n", - " 4. get_arn_from_role_name (0.011)\n", - " 5. list_all_instance_profiles (0.009)\n", - " 6. check_change_service_owner (0.008)\n", + " 0. get_user (0.186)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. inverse_transform (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. clear (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. init_poolmanager (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. split (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. create_client (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. init_poolmanager (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = delete_lag\n", "Pred =\n", - " 0. associate_connection_and_lag (0.47)\n", - " 1. delete_virtual_interface (0.145)\n", - " 2. delete_connection (0.017)\n", - " 3. delete_trail (0.008)\n", - " 4. disassociate_connection_and_lag (0.008)\n", - " 5. disassociate_vis (0.006)\n", - " 6. describe_virtual_interfaces (0.006)\n", + " 0. untag_resource (0.135)\n", "\n", "Label = lag_changed\n", "Pred =\n", - " 0. _system_state_change (0.045)\n", - " 1. _allocation_state_enabled_disabled_changed (0.045)\n", - " 2. cmp_simple_list (0.028)\n", - " 3. _get_rule (0.021)\n", - " 4. is_updatable_property_changed (0.018)\n", - " 5. cmp_str_with_none (0.018)\n", - " 6. link_in_col (0.018)\n", + " 0. kernel_persist_check (0.037)\n", "\n", "Label = get_current_job_queue\n", "Pred =\n", - " 0. describe_route_tables_with_backoff (0.037)\n", - " 1. get_classic_link_with_backoff (0.03)\n", - " 2. stack_set_facts (0.027)\n", - " 3. get_attached_policy_list (0.026)\n", - " 4. peer_status (0.018)\n", - " 5. _get_role_perm (0.017)\n", - " 6. bucket_exists (0.014)\n", + " 0. _enable_maintenance (0.062)\n", "\n", "Label = list_tasks\n", "Pred =\n", - " 0. get_vm_by_id (0.034)\n", - " 1. try_except_ClientError (0.019)\n", - " 2. get_throughput_mode (0.017)\n", - " 3. is_site_config_changed (0.014)\n", - " 4. get_tags (0.014)\n", - " 5. get_attached_policy_list (0.013)\n", - " 6. describe_app (0.012)\n", + " 0. ecs_connect (0.024)\n", "\n", "Label = stop_task\n", "Pred =\n", - " 0. deregister_task (0.178)\n", - " 1. create_cluster (0.062)\n", - " 2. delete_service (0.05)\n", - " 3. delete_cluster (0.023)\n", - " 4. describe_task (0.023)\n", - " 5. setHost (0.017)\n", - " 6. update_check (0.015)\n", + " 0. describe_task (0.05)\n", "\n", "Label = build_changeset_name\n", "Pred =\n", - " 0. avoid_wrapping (0.022)\n", - " 1. normalize_data_format (0.017)\n", - " 2. get_debug_flag (0.016)\n", - " 3. addslashes (0.013)\n", - " 4. E006 (0.011)\n", - " 5. item_enclosures (0.01)\n", - " 6. handle_youtubedl_headers (0.01)\n", + " 0. rax_slugify (0.027)\n", "\n", "Label = create_or_update_role_perm\n", "Pred =\n", - " 0. _get_rule_order (0.14)\n", - " 1. _get_rule (0.083)\n", - " 2. present_role (0.076)\n", - " 3. check_local_role_manager_state (0.029)\n", - " 4. find_authorization_role (0.017)\n", - " 5. _get_role_perm (0.015)\n", - " 6. assigned_role (0.01)\n", + " 0. _get_rule (0.193)\n", "\n", "Label = present_region\n", "Pred =\n", - " 0. _get_region_name (0.22)\n", - " 1. absent_region (0.131)\n", - " 2. get_region (0.058)\n", - " 3. get_cloudwatchevents_client (0.033)\n", - " 4. absent_snapshot_policy (0.021)\n", - " 5. _get_elasticache_connection (0.019)\n", - " 6. describe_instance_health (0.014)\n", + " 0. _get_region_name (0.445)\n", "\n", "Label = _egress_all_match\n", "Pred =\n", - " 0. _ah_esp_gre_match (0.134)\n", - " 1. icmp_present (0.032)\n", - " 2. _canonicalize_endpoint (0.022)\n", - " 3. _system_state_change (0.015)\n", - " 4. ensure_libs (0.011)\n", - " 5. update_fw_settings (0.011)\n", - " 6. check_exists (0.01)\n", + " 0. wait_for_poweroff (0.181)\n", "\n", "Label = get_rule\n", "Pred =\n", - " 0. get_tags (0.041)\n", - " 1. get_db_snapshot (0.028)\n", - " 2. remove_role_perm (0.025)\n", - " 3. get_elb_listener_rules (0.013)\n", - " 4. get_rule_by_name (0.01)\n", - " 5. get_definition (0.01)\n", - " 6. get_target_group_tags (0.01)\n", + " 0. absent_snapshot_policy (0.03)\n", "\n", "Label = get_zone\n", "Pred =\n", - " 0. absent_zone (0.074)\n", - " 1. get_region (0.05)\n", - " 2. get_plan (0.039)\n", - " 3. get_ssh_key (0.038)\n", - " 4. present_zone (0.024)\n", - " 5. _check_filter_item (0.016)\n", - " 6. _request_opts (0.015)\n", + " 0. absent_zone (0.1)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = _get_members_of_rule\n", "Pred =\n", - " 0. get_current (0.045)\n", - " 1. from_response (0.02)\n", - " 2. recover_instance (0.017)\n", - " 3. absent_user (0.016)\n", - " 4. get_server_user_data (0.015)\n", - " 5. absent_zone (0.014)\n", - " 6. format_for_update (0.013)\n", + " 0. remove_role_perm (0.076)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = get_result\n", "Pred =\n", - " 0. client (0.154)\n", - "---- 1. get_result (0.148)\n", - " 2. __init__ (0.065)\n", - " 3. get_vm (0.062)\n", - " 4. get_manager (0.025)\n", - " 5. get_startup_script (0.013)\n", - " 6. get_firewall_group (0.013)\n", + "---- 0. get_result (0.551)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = get_host_tags\n", "Pred =\n", - " 0. present_host (0.052)\n", - " 1. _poll_for_maintenance (0.035)\n", - " 2. get_storage_tags (0.029)\n", - " 3. _matches_entity (0.025)\n", - " 4. proxy_bypass (0.025)\n", - " 5. get_all_hosts_by_cluster (0.017)\n", - " 6. get_network_acl (0.015)\n", + " 0. get_storage_tags (0.048)\n", "\n", "Label = get_result\n", "Pred =\n", - "---- 0. get_result (0.719)\n", - " 1. from_response (0.011)\n", - " 2. resource_to_request (0.005)\n", - " 3. __setattr__ (0.005)\n", - " 4. to_request (0.004)\n", - " 5. remove_from_device (0.004)\n", - " 6. decode_request (0.004)\n", + "---- 0. get_result (0.363)\n", "\n", "Label = _get_user_data_json\n", "Pred =\n", - " 0. decode (0.101)\n", - " 1. encode (0.023)\n", - " 2. info (0.017)\n", - " 3. __getitem__ (0.016)\n", - " 4. loads (0.015)\n", - " 5. get_json_data (0.013)\n", - " 6. deserialize_messages (0.013)\n", + " 0. patch (0.026)\n", "\n", "Label = _get_dhcp_lease_file\n", "Pred =\n", - " 0. _flag_current_thread_clean_exit (0.024)\n", - " 1. _remove_invalid_user (0.011)\n", - " 2. get_style_class (0.01)\n", - " 3. login_vchs (0.009)\n", - " 4. ess_connect (0.007)\n", - " 5. _get_duration_components (0.007)\n", - " 6. is_eapi (0.007)\n", + " 0. get_config_templates (0.066)\n", "\n", "Label = get_all_images\n", "Pred =\n", - " 0. get_image (0.89)\n", - " 1. get_managed_disk (0.006)\n", - " 2. delete_containerinstance (0.004)\n", - " 3. get_aks_kubeconfig (0.002)\n", - " 4. create_service_principal_profile_dict (0.001)\n", - " 5. create_cluster (0.001)\n", - " 6. get_client_link_qos (0.001)\n", + " 0. get_image (0.989)\n", "\n", "Label = present_script\n", "Pred =\n", - "---- 0. present_script (0.989)\n", - " 1. get_script (0.001)\n", - " 2. set_script (0.0)\n", - " 3. queryset (0.0)\n", - " 4. test_pls_errors (0.0)\n", - " 5. addon_chdir (0.0)\n", - " 6. create_empty_api (0.0)\n", + "---- 0. present_script (0.988)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = present_domain\n", "Pred =\n", - "---- 0. present_domain (0.998)\n", - " 1. _get_domain_id (0.0)\n", - " 2. get_domain (0.0)\n", - " 3. full_address (0.0)\n", - " 4. list_domains (0.0)\n", - " 5. multiple_domains (0.0)\n", - " 6. _get_loadbalancer_id (0.0)\n", + "---- 0. present_domain (0.997)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = main\n", "Pred =\n", - "---- 0. main (0.996)\n", - " 1. init_module (0.001)\n", - " 2. add (0.0)\n", - " 3. failure (0.0)\n", - " 4. _define_module_argument_spec (0.0)\n", - " 5. core (0.0)\n", - " 6. __init_module__ (0.0)\n", + "---- 0. main (1.0)\n", "\n", "Label = get_os\n", "Pred =\n", - " 0. get_region (0.305)\n", - " 1. get_plan (0.296)\n", - " 2. get_ssh_key (0.209)\n", - "---- 3. get_os (0.09)\n", - " 4. get_startup_script (0.019)\n", - " 5. get_firewall_group (0.01)\n", - " 6. _get_region_name (0.001)\n", + " 0. get_plan (0.294)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = parse_network_list\n", "Pred =\n", - " 0. container_names_in_network (0.131)\n", - " 1. find_network_by_name (0.02)\n", - " 2. get_virtual_network (0.016)\n", - " 3. validate_host (0.015)\n", - " 4. network (0.013)\n", - " 5. get_security_group (0.01)\n", - " 6. parse_servers_list (0.01)\n", + " 0. get_mixed_type_key (0.036)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = present_firewall_rule\n", "Pred =\n", - "---- 0. present_firewall_rule (0.977)\n", - " 1. present_firewall_group (0.003)\n", - " 2. get_rule (0.001)\n", - " 3. _rule_matches_aws (0.0)\n", - " 4. create_tags (0.0)\n", - " 5. _find_instance_info (0.0)\n", - " 6. create_network_acl_entry (0.0)\n", + "---- 0. present_firewall_rule (0.992)\n", "\n", "Label = parse_startupscript_list\n", "Pred =\n", - " 0. parse_fw_group_list (0.028)\n", - " 1. parse_oses_list (0.013)\n", - " 2. get_notes (0.011)\n", - " 3. pipeline_description (0.009)\n", - " 4. _container_exists (0.009)\n", - " 5. get_tags_for_object (0.007)\n", - " 6. recreate_instances_in_mig (0.006)\n", + " 0. find_dvspg_by_name (0.037)\n", "\n", "Label = get_block_storage_volumes\n", "Pred =\n", - " 0. get_vm (0.066)\n", - " 1. get_existing_volume (0.059)\n", - " 2. _get_affinity_label_mappings (0.019)\n", - " 3. get_snapshot_policy (0.015)\n", - " 4. get_item (0.015)\n", - " 5. parse_network_interface (0.013)\n", - " 6. get_hosted_zone (0.01)\n", + " 0. get_existing_volume (0.045)\n", "\n", "Label = present_block_storage_volume\n", "Pred =\n", - " 0. volume_id_exists (0.038)\n", - " 1. get_volume (0.027)\n", - " 2. generate_added_constraints (0.013)\n", - " 3. payload_from_security_group (0.012)\n", - " 4. stable_topological_sort (0.01)\n", - " 5. is_uuid (0.009)\n", - " 6. _image_is_different (0.009)\n", + " 0. volume_id_exists (0.043)\n", "\n", "Label = parse_keys_list\n", "Pred =\n", - " 0. _transform_ip_list (0.165)\n", - " 1. parse_fw_group_list (0.069)\n", - " 2. is_object_ref (0.024)\n", - " 3. _index_by_key (0.024)\n", - " 4. find (0.019)\n", - " 5. group_list_of_dict (0.016)\n", - " 6. parse_regions_list (0.009)\n", + " 0. make_hashable (0.128)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. destination (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. __init_module__ (0.0)\n", - " 4. failure (0.0)\n", - " 5. _define_module_argument_spec (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = get_networks_names_ids\n", "Pred =\n", - " 0. get_network (0.453)\n", - " 1. _find_network_id (0.037)\n", - " 2. get_network_byid (0.011)\n", - " 3. _get_loadbalancer_id (0.01)\n", - " 4. get_indices (0.008)\n", - " 5. get_security_group (0.007)\n", - " 6. get_network_interface (0.006)\n", + " 0. get_network (0.908)\n", "\n", "Label = disconnect_container\n", "Pred =\n", - " 0. disconnect (0.357)\n", - " 1. fail_json (0.019)\n", - " 2. closing_iterator_wrapper (0.019)\n", - " 3. raise_for_status (0.014)\n", - " 4. disable (0.011)\n", - " 5. get_related_url (0.01)\n", - " 6. lock_configuration (0.009)\n", + " 0. state_forwards (0.051)\n", "\n", "Label = main\n", "Pred =\n", - "---- 0. main (0.989)\n", - " 1. init_module (0.001)\n", - " 2. _define_module_argument_spec (0.001)\n", - " 3. add (0.0)\n", - " 4. core (0.0)\n", - " 5. failure (0.0)\n", - " 6. fail (0.0)\n", + "---- 0. main (1.0)\n", "\n", "Label = __call__\n", "Pred =\n", "---- 0. __call__ (1.0)\n", - " 1. eval (0.0)\n", - " 2. patch (0.0)\n", - " 3. wrapper (0.0)\n", - " 4. decode (0.0)\n", - " 5. call_and_shelve (0.0)\n", - " 6. __next__ (0.0)\n", "\n", "Label = __call__\n", "Pred =\n", - " 0. _container_data (0.071)\n", - " 1. construct (0.038)\n", - " 2. delete_containerinstance (0.019)\n", - " 3. get_available_image_extensions (0.014)\n", - " 4. vm_status (0.014)\n", - " 5. inspect_swarm (0.013)\n", - " 6. setCD (0.013)\n", + " 0. inspect_swarm (0.087)\n", "\n", "Label = _get_network_id\n", "Pred =\n", - " 0. _find_network_id (0.314)\n", - " 1. get_network (0.233)\n", - " 2. get_network_byid (0.023)\n", - " 3. get_security_group (0.02)\n", - " 4. get_virtual_network (0.016)\n", - " 5. delete_virtual_network (0.016)\n", - " 6. get_vms_by_ids (0.014)\n", + " 0. _find_network_id (0.466)\n", "\n", "Label = paused\n", "Pred =\n", - " 0. running (0.958)\n", - " 1. _get_state (0.003)\n", - " 2. _get_container (0.003)\n", - " 3. wait_for_task (0.001)\n", - " 4. _image_is_different (0.001)\n", - " 5. is_site_local (0.001)\n", - " 6. cpu_count (0.0)\n", + " 0. running (0.986)\n", "\n", "Label = _convert_simple_dict_to_list\n", "Pred =\n", - " 0. getkeyordie (0.035)\n", - " 1. payload_from_security_group (0.031)\n", - " 2. searchable_attributes (0.026)\n", - " 3. _parse_meta (0.015)\n", - " 4. get_snmp_groups (0.014)\n", - " 5. load_tags (0.014)\n", - " 6. tags_match (0.014)\n", + " 0. distinct_sql (0.084)\n", "\n", "Label = detect_ipvX_address_usage\n", "Pred =\n", - " 0. get_nic_from_result (0.046)\n", - " 1. container_names_in_network (0.044)\n", - " 2. _find_network_id (0.031)\n", - " 3. decode_response (0.027)\n", - " 4. prefer_best (0.023)\n", - " 5. _find_description (0.02)\n", - " 6. get_vms_by_ids (0.017)\n", + " 0. network (0.083)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. _define_module_argument_spec (0.0)\n", - " 4. define_argument_spec (0.0)\n", - " 5. fail (0.0)\n", - " 6. failure (0.0)\n", "\n", "Label = release_floating_ip\n", "Pred =\n", - " 0. delete (0.271)\n", - " 1. delete_server (0.149)\n", - " 2. _delete_policy (0.041)\n", - " 3. remove (0.028)\n", - " 4. delete_model (0.022)\n", - " 5. delete_elb (0.022)\n", - " 6. _delete_elb (0.019)\n", + " 0. __init__ (0.998)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = extract_names_from_blob_uri\n", "Pred =\n", - " 0. _make_valid_url (0.024)\n", - " 1. mkdirp (0.016)\n", - " 2. quality (0.012)\n", - " 3. _build_brighcove_url_from_js (0.012)\n", - " 4. sanitize_url (0.009)\n", - " 5. tmpdata (0.009)\n", - " 6. hex_to_bytes (0.008)\n", + " 0. add_operation (0.017)\n", "\n", "Label = account_has_blob_containers\n", "Pred =\n", - " 0. container_has_blobs (0.033)\n", - " 1. delete_containerinstance (0.031)\n", - " 2. get_vmss (0.03)\n", - " 3. get_aks_kubeconfig (0.029)\n", - " 4. delete_record_set (0.026)\n", - " 5. vm_size_is_valid (0.024)\n", - " 6. get_healthcheck (0.021)\n", + " 0. container_has_blobs (0.05)\n", "\n", "Label = shorten_traffic_manager_dict\n", "Pred =\n", - " 0. create_service_principal_profile_instance (0.041)\n", - " 1. _get_datacenter_id (0.018)\n", - " 2. delete_repository (0.014)\n", - " 3. get_cluster_byid (0.013)\n", - " 4. get_attached_policy_list (0.012)\n", - " 5. vmdisk_id (0.008)\n", - " 6. _enable_maintenance (0.008)\n", + " 0. get_datacenter (0.045)\n", "\n", "Label = format_item\n", "Pred =\n", - "---- 0. format_item (0.995)\n", - " 1. parse_resource_to_dict (0.0)\n", - " 2. query_tags (0.0)\n", - " 3. get_source_vm (0.0)\n", - " 4. _response_from_item (0.0)\n", - " 5. _mrss_url (0.0)\n", - " 6. get_item (0.0)\n", + "---- 0. format_item (0.998)\n", "\n", "Label = vm_size_is_valid\n", "Pred =\n", - "---- 0. vm_size_is_valid (0.976)\n", - " 1. get_virtual_network (0.001)\n", - " 2. rax_clb_node_to_dict (0.0)\n", - " 3. create_or_update_vnet (0.0)\n", - " 4. get_subnet (0.0)\n", - " 5. delete_mysqlserver (0.0)\n", - " 6. create_stack_set (0.0)\n", + "---- 0. vm_size_is_valid (0.994)\n", "\n", "Label = create_or_update_resource_group\n", "Pred =\n", - " 0. get_resource_group (0.358)\n", - " 1. name_exists (0.061)\n", - " 2. client (0.024)\n", - " 3. build_resource_from_name (0.018)\n", - " 4. create_or_update_auto_scale (0.008)\n", - " 5. test_all_scorers_repr (0.007)\n", - " 6. create_on_device (0.006)\n", + " 0. get_resource_group (0.247)\n", "\n", "Label = delete_resource_group\n", "Pred =\n", - " 0. delete_nic (0.236)\n", - " 1. delete_table (0.225)\n", - " 2. delete_route (0.061)\n", - " 3. create_or_update_table (0.05)\n", - " 4. create_or_update_load_balancer (0.038)\n", - " 5. create_or_update_route (0.037)\n", - " 6. delete_managed_disks (0.017)\n", + " 0. delete_nic (0.461)\n", "\n", "Label = delete_zone\n", "Pred =\n", - " 0. delete_route (0.246)\n", - " 1. delete_nic (0.115)\n", - " 2. delete_table (0.079)\n", - " 3. delete_virtual_network (0.025)\n", - " 4. create_or_update_route (0.024)\n", - " 5. delete_mysqlserver (0.016)\n", - " 6. delete_tags (0.013)\n", + " 0. delete_nic (0.381)\n", "\n", "Label = aggregated_app_settings\n", "Pred =\n", - " 0. update_app_settings (0.073)\n", - " 1. prepare_test_settings (0.037)\n", - " 2. _nodb_connection (0.035)\n", - " 3. test_db_signature (0.035)\n", - " 4. create_or_update_auto_scale (0.027)\n", - " 5. check_session_cookie_httponly (0.024)\n", - " 6. _test_settings_get (0.021)\n", + " 0. update_app_settings (0.061)\n", "\n", "Label = storage_connection_string\n", "Pred =\n", - " 0. storage_key (0.237)\n", - " 1. name (0.087)\n", - " 2. path (0.056)\n", - " 3. local (0.026)\n", - " 4. height (0.009)\n", - " 5. _current_scheme_host (0.008)\n", - " 6. label_lower (0.007)\n", + " 0. path (0.498)\n", "\n", "Label = instance_to_dict\n", "Pred =\n", - " 0. create_agent_pool_profiles_dict (0.04)\n", - " 1. appserviceplan_to_dict (0.029)\n", - " 2. _get_instance_ids (0.019)\n", - " 3. cdnprofile_to_dict (0.012)\n", - " 4. subnet_to_dict (0.011)\n", - " 5. zone_to_dict (0.009)\n", - " 6. get_aks_kubeconfig (0.009)\n", + " 0. inspect_swarm (0.111)\n", "\n", "Label = create_agent_pool_profile_instance\n", "Pred =\n", - " 0. getOvhClient (0.081)\n", - " 1. _add_publicip_to_server (0.023)\n", - " 2. subnet_to_dict (0.018)\n", - " 3. get_attribute (0.014)\n", - " 4. _program_guid (0.013)\n", - " 5. _get_firewallgroup_name (0.012)\n", - " 6. _get_affinity_group_mappings (0.011)\n", + " 0. extract_video_data (0.057)\n", "\n", "Label = format_item\n", "Pred =\n", - "---- 0. format_item (0.996)\n", - " 1. parse_resource_to_dict (0.0)\n", - " 2. get_source_vm (0.0)\n", - " 3. query_tags (0.0)\n", - " 4. _mrss_url (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. get_item (0.0)\n", + "---- 0. format_item (0.998)\n", "\n", "Label = format_item\n", "Pred =\n", - "---- 0. format_item (0.995)\n", - " 1. parse_resource_to_dict (0.0)\n", - " 2. query_tags (0.0)\n", - " 3. get_source_vm (0.0)\n", - " 4. _response_from_item (0.0)\n", - " 5. _mrss_url (0.0)\n", - " 6. get_item (0.0)\n", + "---- 0. format_item (0.998)\n", "\n", "Label = public_ip_id\n", "Pred =\n", - " 0. backend_address_pool_id (0.193)\n", - " 1. probe_id (0.192)\n", - " 2. frontend_ip_configuration_id (0.097)\n", - " 3. redirect_configuration_id (0.077)\n", - " 4. http_listener_id (0.065)\n", - " 5. backend_http_settings_id (0.06)\n", - " 6. frontend_port_id (0.055)\n", + " 0. backend_address_pool_id (0.226)\n", "\n", "Label = frontend_ip_configuration_id\n", "Pred =\n", - " 0. backend_address_pool_id (0.188)\n", - " 1. probe_id (0.182)\n", - " 2. redirect_configuration_id (0.119)\n", - " 3. backend_http_settings_id (0.113)\n", - " 4. http_listener_id (0.113)\n", - " 5. frontend_port_id (0.104)\n", - "---- 6. frontend_ip_configuration_id (0.041)\n", + " 0. backend_address_pool_id (0.189)\n", "\n", "Label = create_agent_pool_profile_instance\n", "Pred =\n", - " 0. create_service_principal_profile_instance (0.066)\n", - " 1. _get_affinity_group_mappings (0.025)\n", - " 2. mmc_url (0.022)\n", - " 3. await_stack_set_exists (0.02)\n", - " 4. _extract_info_helper (0.016)\n", - " 5. get_igw_info (0.013)\n", - " 6. list_changesets (0.013)\n", + " 0. create_service_principal_profile_instance (0.076)\n", "\n", "Label = create_master_profile_instance\n", "Pred =\n", - " 0. get_package_libraries (0.018)\n", - " 1. bucket_exists (0.013)\n", - " 2. bytes_to_intlist (0.013)\n", - " 3. __get_major (0.01)\n", - " 4. _get_affinity_group_mappings (0.01)\n", - " 5. import_string (0.009)\n", - " 6. create_monitor_config_instance (0.009)\n", + " 0. create_master_profile_dict (0.067)\n", "\n", "Label = create_diagnotstics_profile_dict\n", "Pred =\n", - " 0. normalize_vm_vm_rule_spec (0.021)\n", - " 1. suspend (0.018)\n", - " 2. is_active (0.017)\n", - " 3. get_maxMemory (0.016)\n", - " 4. _raw_delete (0.014)\n", - " 5. is_pending (0.01)\n", - " 6. resume (0.01)\n", + " 0. get_vm_by_id (0.027)\n", "\n", "Label = check_plural\n", "Pred =\n", - " 0. copy_identity_properties (0.14)\n", - " 1. cut (0.014)\n", - " 2. _cleanall (0.011)\n", - " 3. _are_equivalent (0.01)\n", - " 4. get_snapshots_by_name_recursively (0.008)\n", - " 5. diff_banners (0.007)\n", - " 6. __getattr__ (0.007)\n", + " 0. configure (0.058)\n", "\n", "Label = exec_module\n", "Pred =\n", - "---- 0. exec_module (0.999)\n", - " 1. encode_request (0.0)\n", - " 2. show_result (0.0)\n", - " 3. collection (0.0)\n", - " 4. _send (0.0)\n", - " 5. add (0.0)\n", - " 6. create_on_device (0.0)\n", + "---- 0. exec_module (1.0)\n", "\n", "Label = get_public_ip_address_instance\n", "Pred =\n", - " 0. list_items (0.056)\n", - " 1. get_source_vm (0.05)\n", - " 2. get_virtual_network (0.03)\n", - " 3. create_or_update_route (0.029)\n", - " 4. get_vmss (0.028)\n", - " 5. get_item (0.027)\n", - " 6. vm_size_is_valid (0.025)\n", + " 0. backend_address_pool_id (0.056)\n", "\n", "Label = get_cdn_client\n", "Pred =\n", - "---- 0. get_cdn_client (0.969)\n", - " 1. rm_client (0.002)\n", - " 2. containerregistry_client (0.001)\n", - " 3. compute_client (0.001)\n", - " 4. storage_client (0.001)\n", - " 5. monitor_client (0.001)\n", - " 6. _create_repository (0.001)\n", + "---- 0. get_cdn_client (0.989)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. destination (0.0)\n", "\n", "Label = post_export_action\n", "Pred =\n", - " 0. post_import_action (0.099)\n", - " 1. get_vm (0.07)\n", - " 2. get_domain (0.033)\n", - " 3. _get_export_domain_service (0.018)\n", - " 4. _post_start_action (0.018)\n", - " 5. _action_save_configuration (0.016)\n", - " 6. on_train_begin (0.016)\n", + " 0. post_import_action (0.331)\n", "\n", "Label = _wait_for_UP\n", "Pred =\n", - " 0. post_reinstall (0.102)\n", - " 1. _post_start_action (0.037)\n", - " 2. pre_remove (0.026)\n", - " 3. __suspend_shutdown_common (0.025)\n", - " 4. login (0.014)\n", - " 5. post_import_action (0.011)\n", - " 6. login_vca (0.011)\n", + " 0. __suspend_shutdown_common (0.499)\n", "\n", "Label = get_type_name\n", "Pred =\n", - " 0. _calculate_correct_fan (0.03)\n", - " 1. get_complete_version (0.018)\n", - " 2. _list_with_default (0.015)\n", - " 3. _is_whitespace (0.014)\n", - " 4. is_registered (0.012)\n", - " 5. _workspace_cmd (0.01)\n", - " 6. docker_stack_services (0.008)\n", + " 0. ip_version (0.024)\n", "\n", "Label = post_export_action\n", "Pred =\n", - " 0. post_import_action (0.124)\n", - " 1. pre_remove (0.099)\n", - " 2. _post_start_action (0.052)\n", - " 3. pre_create (0.045)\n", - " 4. get_domain (0.026)\n", - " 5. _action_save_configuration (0.023)\n", - " 6. update_check (0.018)\n", + " 0. post_import_action (0.691)\n", "\n", "Label = _objects_service\n", "Pred =\n", - " 0. __get_network_filter_id (0.347)\n", - " 1. get_data_center (0.05)\n", - " 2. get_file_system_state (0.029)\n", - " 3. build_googleapi_url (0.017)\n", - " 4. get_existing_devices (0.013)\n", - " 5. generate_system_info_dict (0.009)\n", - " 6. _get_target_groups (0.006)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " 0. describe_autoscaling_groups (0.088)\n", + "\n", "Label = selector\n", "Pred =\n", - " 0. filter_requested_info (0.038)\n", - " 1. format_attributes (0.03)\n", - " 2. _format_params (0.025)\n", - " 3. get_event (0.017)\n", - " 4. ordered_obj (0.013)\n", - " 5. tags_match (0.012)\n", - " 6. account_data (0.01)\n", + " 0. endpoint (0.057)\n", "\n", "Label = is_valid_uuid\n", "Pred =\n", - "---- 0. is_valid_uuid (0.994)\n", - " 1. parse_number (0.0)\n", - " 2. vm_state_transition (0.0)\n", - " 3. get_device_by_type (0.0)\n", - " 4. check_dp_exists (0.0)\n", - " 5. is_valid_hostname (0.0)\n", - " 6. build_entity (0.0)\n", + "---- 0. is_valid_uuid (0.998)\n", "\n", "Label = listify_string_name_or_id\n", "Pred =\n", - " 0. get_valid_filename (0.126)\n", - " 1. compat_shlex_quote (0.058)\n", - " 2. u (0.04)\n", - " 3. typecast_date (0.02)\n", - " 4. compat_shlex_split (0.018)\n", - " 5. is_drm_protected (0.017)\n", - " 6. encodeArgument (0.013)\n", + " 0. escape_quotes (0.301)\n", "\n", "Label = all_have_public_ip\n", "Pred =\n", - " 0. address_is_associated_with_device (0.075)\n", - " 1. has_public_ip (0.051)\n", - " 2. _normalize_port (0.041)\n", - " 3. present_static_nat (0.039)\n", - " 4. _find_address_by_device_id (0.03)\n", - " 5. present_ip_address (0.018)\n", - " 6. get_internet_gateway_info (0.017)\n", + " 0. __contains__ (0.047)\n", "\n", "Label = _activate_virtualenv\n", "Pred =\n", - " 0. write_func (0.077)\n", - " 1. get_word_index (0.035)\n", - " 2. _decorator (0.023)\n", - " 3. get_metadata (0.02)\n", - " 4. wrapped (0.019)\n", - " 5. get_cached_func_code (0.012)\n", - " 6. get_view_name (0.011)\n", + " 0. wrapped (0.172)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. _define_module_argument_spec (0.0)\n", - " 4. create_on_device (0.0)\n", - " 5. failure (0.0)\n", - " 6. __init_module__ (0.0)\n", "\n", "Label = build\n", "Pred =\n", - "---- 0. build (0.832)\n", - " 1. __init__ (0.004)\n", - " 2. name (0.004)\n", - " 3. get_uuid (0.003)\n", - " 4. count_params (0.003)\n", - " 5. call (0.003)\n", - " 6. get_address (0.002)\n", + "---- 0. build (0.413)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = autostart\n", "Pred =\n", - "---- 0. autostart (0.059)\n", - " 1. set_autostart (0.037)\n", - " 2. get_autostart2 (0.036)\n", - " 3. setCPU (0.022)\n", - " 4. find_datastore_cluster_by_name (0.016)\n", - " 5. get_vm (0.015)\n", - " 6. vm_stop (0.012)\n", + "---- 0. autostart (0.844)\n", "\n", "Label = freemem\n", "Pred =\n", - " 0. __get_conn (0.258)\n", - " 1. get_maxVcpus (0.118)\n", - " 2. get_cluster_byid (0.031)\n", - " 3. status (0.021)\n", - " 4. _get_region_name (0.019)\n", - " 5. get_new_connection (0.016)\n", - " 6. pause (0.014)\n", + " 0. __get_conn (0.086)\n", "\n", "Label = shutdown\n", "Pred =\n", - "---- 0. shutdown (0.435)\n", - " 1. pause (0.045)\n", - " 2. status (0.04)\n", - " 3. unpause (0.029)\n", - " 4. get_maxVcpus (0.024)\n", - " 5. get_code (0.02)\n", - " 6. setCPUShare (0.016)\n", + "---- 0. shutdown (0.786)\n", "\n", "Label = undefine\n", "Pred =\n", - "---- 0. undefine (0.944)\n", - " 1. createVM (0.003)\n", - " 2. get_vm (0.002)\n", - " 3. find (0.002)\n", - " 4. get_domain_byid (0.001)\n", - " 5. remove_VM (0.001)\n", - " 6. setUpClass (0.001)\n", + "---- 0. undefine (0.992)\n", "\n", "Label = create_vm_template\n", "Pred =\n", - " 0. remove_CD (0.086)\n", - " 1. get_vm (0.061)\n", - " 2. clc_install_package (0.038)\n", - " 3. delete_containerinstance (0.031)\n", - " 4. subnet_to_dict (0.019)\n", - " 5. remove_VM (0.017)\n", - " 6. disconnect_all_containers (0.017)\n", + " 0. vm_remove (0.088)\n", "\n", "Label = get_cluster\n", "Pred =\n", - " 0. get_cluster_byid (0.793)\n", - " 1. setHost (0.015)\n", - " 2. unpause (0.004)\n", - " 3. get_VM (0.003)\n", - " 4. vm_status (0.003)\n", - " 5. delete_containerinstance (0.003)\n", - " 6. get_new_connection (0.003)\n", + " 0. get_cluster_byid (0.97)\n", "\n", "Label = get_NIC\n", "Pred =\n", - " 0. get_domain (0.125)\n", - " 1. _parse_nics (0.085)\n", - " 2. get_nic (0.059)\n", - " 3. absent_nic (0.047)\n", - " 4. del_NIC (0.035)\n", - " 5. get_network (0.02)\n", - " 6. get_vm (0.019)\n", + " 0. get_vm (0.278)\n", "\n", "Label = get_Host_byid\n", "Pred =\n", - " 0. get_Host (0.95)\n", - " 1. get_domain (0.003)\n", - " 2. gather_host_portgroup_facts (0.003)\n", - " 3. get_network_byid (0.002)\n", - " 4. _get_image_url (0.001)\n", - " 5. validate_host (0.001)\n", - " 6. check_allowed_hosts (0.001)\n", + " 0. get_Host (0.978)\n", "\n", "Label = set_DeleteProtection\n", "Pred =\n", - " 0. setDeleteProtection (0.864)\n", - " 1. _find_ttl (0.005)\n", - " 2. set_CPU (0.004)\n", - " 3. list_nets (0.004)\n", - " 4. addSubTest (0.003)\n", - " 5. list_pools (0.002)\n", - " 6. find_instance (0.002)\n", + " 0. setDeleteProtection (0.923)\n", "\n", "Label = stop_VM\n", "Pred =\n", - " 0. start_VM (0.738)\n", - " 1. remove_CD (0.035)\n", - " 2. vm_stop (0.027)\n", - " 3. vm_restart (0.024)\n", - " 4. remove_VM (0.01)\n", - " 5. assigned_vms (0.004)\n", - " 6. _remove_publicip_from_server (0.003)\n", + " 0. start_VM (0.694)\n", "\n", "Label = get_autostart2\n", "Pred =\n", - "---- 0. get_autostart2 (0.963)\n", - " 1. get_autostart (0.006)\n", - " 2. autostart (0.004)\n", - " 3. refresh (0.001)\n", - " 4. set_autostart (0.001)\n", - " 5. is_link_local (0.001)\n", - " 6. is_file (0.0)\n", + "---- 0. get_autostart2 (0.988)\n", "\n", "Label = get_dhcp_leases\n", "Pred =\n", - " 0. autostart (0.07)\n", - " 1. get_bridge (0.069)\n", - " 2. get_maxMemory (0.067)\n", - " 3. get_volume_count (0.049)\n", - " 4. get_volume_names (0.043)\n", - " 5. suspend (0.04)\n", - " 6. get_uuid (0.037)\n", + " 0. get_bridge (0.212)\n", "\n", "Label = get_key_or_fail\n", "Pred =\n", - " 0. getkeyordie (0.812)\n", - " 1. get_count (0.01)\n", - " 2. validate_required_key (0.007)\n", - " 3. get_prep_value (0.004)\n", - " 4. _needs_update (0.003)\n", - " 5. _validate_param_values (0.003)\n", - " 6. parse_config_argument (0.002)\n", + " 0. getkeyordie (0.673)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. _define_module_argument_spec (0.0)\n", - " 4. failure (0.0)\n", - " 5. __init_module__ (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = add\n", "Pred =\n", - " 0. list_all (0.495)\n", - " 1. present_ssh_key (0.013)\n", - " 2. _list (0.011)\n", - " 3. delete_key (0.01)\n", - " 4. create_key (0.009)\n", - " 5. create_cluster (0.006)\n", - " 6. iterkeys (0.005)\n", + " 0. ssh_key_fingerprint (0.119)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. build (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _wait_for_requests\n", "Pred =\n", - "---- 0. _wait_for_requests (0.97)\n", - " 1. _wait_for_requests_to_complete (0.001)\n", - " 2. rax_cdb_user (0.001)\n", - " 3. get_vmid (0.0)\n", - " 4. assert_and_parse_html (0.0)\n", - " 5. describe_stack_events (0.0)\n", - " 6. update_trail (0.0)\n", + "---- 0. _wait_for_requests (0.984)\n", "\n", "Label = main\n", "Pred =\n", "---- 0. main (1.0)\n", - " 1. init_module (0.0)\n", - " 2. add (0.0)\n", - " 3. _define_module_argument_spec (0.0)\n", - " 4. failure (0.0)\n", - " 5. __init_module__ (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = _create_server_snapshot\n", "Pred =\n", - " 0. _attached_sd_service (0.023)\n", - " 1. disassociate_vis (0.015)\n", - " 2. disconnect_all_containers (0.015)\n", - " 3. bucket_exists (0.012)\n", - " 4. clc_install_package (0.011)\n", - " 5. describe_virtual_interfaces (0.01)\n", - " 6. find_virtual_interface_by_connection_id (0.01)\n", + " 0. _restore_server_snapshot (0.051)\n", "\n", "Label = _insert_network_data\n", "Pred =\n", - " 0. _get_server_id (0.078)\n", - " 1. _transform_state (0.062)\n", - " 2. has_public_ip (0.056)\n", - " 3. _find_address_by_device_id (0.052)\n", - " 4. _resp2info (0.022)\n", - " 5. get_virtual_gateway_info (0.015)\n", - " 6. vmdisk_id (0.013)\n", + " 0. _transform_state (0.12)\n", "\n", "Label = main\n", "Pred =\n", - "---- 0. main (0.758)\n", - " 1. fail (0.053)\n", - " 2. add (0.013)\n", - " 3. __init_module__ (0.006)\n", - " 4. init_module (0.006)\n", - " 5. enable (0.006)\n", - " 6. set (0.005)\n", + "---- 0. main (0.999)\n", "\n", "Label = label_fingerprint_update\n", "Pred =\n", - "---- 0. label_fingerprint_update (0.305)\n", - " 1. size_gb_update (0.184)\n", - " 2. labels_update (0.077)\n", - " 3. quic_override_update (0.067)\n", - " 4. private_ip_google_access_update (0.064)\n", - " 5. ip_cidr_range_update (0.058)\n", - " 6. machine_type_update (0.042)\n", + "---- 0. label_fingerprint_update (0.41)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = fetch_list\n", "Pred =\n", "---- 0. fetch_list (1.0)\n", - " 1. __init__ (0.0)\n", - " 2. delete (0.0)\n", - " 3. create (0.0)\n", - " 4. copy (0.0)\n", - " 5. get_info (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = fetch_resource\n", "Pred =\n", "---- 0. fetch_resource (1.0)\n", - " 1. delete (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. update (0.0)\n", - " 4. create (0.0)\n", - " 5. fetch_list (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.991)\n", - " 1. from_response (0.005)\n", - " 2. options (0.0)\n", - " 3. port (0.0)\n", - " 4. clear (0.0)\n", - " 5. state (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.996)\n", "\n", "Label = _request_for_item\n", "Pred =\n", - " 0. _response_from_item (0.644)\n", - "---- 1. _request_for_item (0.349)\n", - " 2. from_response (0.001)\n", - " 3. get_firewall_group (0.0)\n", - " 4. _mrss_url (0.0)\n", - " 5. to_request (0.0)\n", - " 6. get (0.0)\n", + " 0. _response_from_item (0.607)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. add_instances (0.067)\n", - " 1. _get_instance_ids (0.036)\n", - " 2. instances_with_model (0.025)\n", - " 3. _attach_subnets (0.025)\n", - " 4. delete_elb (0.013)\n", - " 5. modify_security_groups (0.013)\n", - " 6. remove_instances (0.012)\n", + " 0. add_instances (0.429)\n", "\n", "Label = list_instances\n", "Pred =\n", - " 0. _build_request (0.484)\n", - " 1. add_instances (0.2)\n", - " 2. serialize_instances (0.024)\n", - " 3. _login (0.023)\n", - " 4. remove_instances (0.02)\n", - " 5. _get_instance_ids (0.007)\n", - " 6. get_instances_info (0.006)\n", + " 0. _build_request (0.439)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.991)\n", - " 1. from_response (0.005)\n", - " 2. options (0.0)\n", - " 3. port (0.0)\n", - " 4. clear (0.0)\n", - " 5. state (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.996)\n", "\n", "Label = response_to_hash\n", "Pred =\n", - "---- 0. response_to_hash (0.994)\n", - " 1. _get_common_args (0.0)\n", - " 2. _response_from_item (0.0)\n", - " 3. from_response (0.0)\n", - " 4. state (0.0)\n", - " 5. to_health_check (0.0)\n", - " 6. to_request (0.0)\n", + "---- 0. response_to_hash (0.988)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.991)\n", - " 1. from_response (0.005)\n", - " 2. options (0.0)\n", - " 3. port (0.0)\n", - " 4. clear (0.0)\n", - " 5. state (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.996)\n", "\n", "Label = fetch_list\n", "Pred =\n", "---- 0. fetch_list (1.0)\n", - " 1. __init__ (0.0)\n", - " 2. delete (0.0)\n", - " 3. create (0.0)\n", - " 4. copy (0.0)\n", - " 5. get_info (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = fetch_resource\n", "Pred =\n", "---- 0. fetch_resource (1.0)\n", - " 1. delete (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. update (0.0)\n", - " 4. create (0.0)\n", - " 5. fetch_list (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = list_func\n", "Pred =\n", - " 0. _format_member_address (0.027)\n", - " 1. compat_conf (0.022)\n", - " 2. _is_member (0.017)\n", - " 3. catalog (0.012)\n", - " 4. switch (0.01)\n", - " 5. is_nexthop_change (0.009)\n", - " 6. tagged_interfaces (0.008)\n", + " 0. _clear_member_prefix (0.115)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (0.999)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. get (0.0)\n", - " 5. post (0.0)\n", - " 6. patch (0.0)\n", "\n", "Label = wait_for_operation\n", "Pred =\n", "---- 0. wait_for_operation (1.0)\n", - " 1. get (0.0)\n", - " 2. ok (0.0)\n", - " 3. modify_db_instance (0.0)\n", - " 4. get_connection (0.0)\n", - " 5. main (0.0)\n", - " 6. __repr__ (0.0)\n", "\n", "Label = async_op_url\n", "Pred =\n", "---- 0. async_op_url (1.0)\n", - " 1. get_context_data (0.0)\n", - " 2. _download_chinese_webpage (0.0)\n", - " 3. options (0.0)\n", - " 4. form_valid (0.0)\n", - " 5. head (0.0)\n", - " 6. get_vrf_list (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. start (0.0)\n", - " 2. update (0.0)\n", - " 3. execute (0.0)\n", - " 4. add (0.0)\n", - " 5. delete (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = delete\n", "Pred =\n", "---- 0. delete (0.999)\n", - " 1. post (0.0)\n", - " 2. get (0.0)\n", - " 3. put (0.0)\n", - " 4. clear (0.0)\n", - " 5. set (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = resource_to_create\n", "Pred =\n", - " 0. resource_to_update (0.939)\n", - " 1. self_link (0.012)\n", - " 2. map_params_to_obj (0.002)\n", - " 3. collection (0.002)\n", - " 4. add (0.002)\n", - " 5. username (0.001)\n", - " 6. validate_tags (0.001)\n", + " 0. resource_to_update (0.972)\n", "\n", "Label = wait_for_operation\n", "Pred =\n", "---- 0. wait_for_operation (1.0)\n", - " 1. get (0.0)\n", - " 2. modify_db_instance (0.0)\n", - " 3. ok (0.0)\n", - " 4. exists (0.0)\n", - " 5. get_connection (0.0)\n", - " 6. delete_db_instance (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = __init__\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = _get_zone\n", "Pred =\n", - "---- 0. _get_zone (0.982)\n", - " 1. create_or_update_zone (0.0)\n", - " 2. delete_repository_policy (0.0)\n", - " 3. get_zones (0.0)\n", - " 4. recreate_instances_in_mig (0.0)\n", - " 5. find_in_app (0.0)\n", - " 6. find_instance (0.0)\n", + "---- 0. _get_zone (0.988)\n", "\n", "Label = from_response\n", "Pred =\n", "---- 0. from_response (0.996)\n", - " 1. to_request (0.002)\n", - " 2. _response_from_item (0.0)\n", - " 3. get (0.0)\n", - " 4. get_current (0.0)\n", - " 5. _request_for_item (0.0)\n", - " 6. warn (0.0)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.502)\n", - " 1. from_response (0.492)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.509)\n", "\n", "Label = wait_for_operation\n", "Pred =\n", "---- 0. wait_for_operation (1.0)\n", - " 1. get (0.0)\n", - " 2. ok (0.0)\n", - " 3. modify_db_instance (0.0)\n", - " 4. get_connection (0.0)\n", - " 5. main (0.0)\n", - " 6. __repr__ (0.0)\n", "\n", "Label = fetch_resource\n", "Pred =\n", "---- 0. fetch_resource (1.0)\n", - " 1. delete (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. update (0.0)\n", - " 4. create (0.0)\n", - " 5. fetch_list (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = async_op_url\n", "Pred =\n", "---- 0. async_op_url (1.0)\n", - " 1. get_context_data (0.0)\n", - " 2. _download_chinese_webpage (0.0)\n", - " 3. options (0.0)\n", - " 4. form_valid (0.0)\n", - " 5. head (0.0)\n", - " 6. get_vrf_list (0.0)\n", "\n", "Label = update\n", "Pred =\n", "---- 0. update (0.999)\n", - " 1. create (0.0)\n", - " 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. get (0.0)\n", - " 5. post (0.0)\n", - " 6. patch (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", "---- 0. from_response (0.996)\n", - " 1. to_request (0.002)\n", - " 2. _response_from_item (0.0)\n", - " 3. get (0.0)\n", - " 4. get_current (0.0)\n", - " 5. _request_for_item (0.0)\n", - " 6. warn (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. update (0.0)\n", - " 2. start (0.0)\n", - " 3. add (0.0)\n", - " 4. get (0.0)\n", - " 5. delete (0.0)\n", - " 6. copy (0.0)\n", "\n", "Label = wait_for_operation\n", "Pred =\n", "---- 0. wait_for_operation (1.0)\n", - " 1. get (0.0)\n", - " 2. ok (0.0)\n", - " 3. modify_db_instance (0.0)\n", - " 4. get_connection (0.0)\n", - " 5. main (0.0)\n", - " 6. __repr__ (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.539)\n", - "---- 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + " 0. to_request (0.532)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.539)\n", - " 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.532)\n", "\n", "Label = _request_for_item\n", "Pred =\n", - "---- 0. _request_for_item (0.769)\n", - " 1. to_request (0.079)\n", - " 2. _response_from_item (0.041)\n", - " 3. from_response (0.008)\n", - " 4. options (0.005)\n", - " 5. _disable_tracing (0.003)\n", - " 6. ip (0.002)\n", + "---- 0. _request_for_item (0.783)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.539)\n", - "---- 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + " 0. to_request (0.532)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.561)\n", - "---- 1. from_response (0.433)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. get_current (0.0)\n", + " 0. to_request (0.548)\n", "\n", "Label = delete_url_map\n", "Pred =\n", - " 0. get_url_map (0.413)\n", - " 1. get_global_forwarding_rule (0.081)\n", - " 2. delete_global_forwarding_rule (0.079)\n", - " 3. delete_target_http_proxy (0.033)\n", - " 4. get_healthcheck (0.031)\n", - " 5. get_target_http_proxy (0.02)\n", - " 6. get_ssh_key (0.007)\n", + " 0. get_url_map (0.955)\n", "\n", "Label = raise_if_errors\n", "Pred =\n", "---- 0. raise_if_errors (1.0)\n", - " 1. fail_json (0.0)\n", - " 2. checkFail (0.0)\n", - " 3. add (0.0)\n", - " 4. delete_network_acl_entry (0.0)\n", - " 5. failure (0.0)\n", - " 6. find (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. update (0.0)\n", - " 2. start (0.0)\n", - " 3. add (0.0)\n", - " 4. get (0.0)\n", - " 5. delete (0.0)\n", - " 6. copy (0.0)\n", "\n", "Label = fetch_list\n", "Pred =\n", "---- 0. fetch_list (1.0)\n", - " 1. __init__ (0.0)\n", - " 2. delete (0.0)\n", - " 3. fetch_resource (0.0)\n", - " 4. execute (0.0)\n", - " 5. copy (0.0)\n", - " 6. create (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", "---- 0. from_response (0.996)\n", - " 1. to_request (0.002)\n", - " 2. _response_from_item (0.0)\n", - " 3. get (0.0)\n", - " 4. get_current (0.0)\n", - " 5. _request_for_item (0.0)\n", - " 6. warn (0.0)\n", "\n", "Label = delete\n", "Pred =\n", " 0. update (0.999)\n", - " 1. create (0.0)\n", - "---- 2. delete (0.0)\n", - " 3. put (0.0)\n", - " 4. post (0.0)\n", - " 5. copy (0.0)\n", - " 6. get (0.0)\n", "\n", "Label = response_to_hash\n", "Pred =\n", "---- 0. response_to_hash (1.0)\n", - " 1. _get_common_args (0.0)\n", - " 2. _response_from_item (0.0)\n", - " 3. to_health_check (0.0)\n", - " 4. _request_for_item (0.0)\n", - " 5. collection (0.0)\n", - " 6. from_response (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. destination (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict (0.0)\n", "\n", "Label = add_additions\n", "Pred =\n", - " 0. resource_to_change_request (0.369)\n", - " 1. add_deletions (0.038)\n", - " 2. create_change (0.032)\n", - " 3. execute_wrapper (0.022)\n", - " 4. uniform_ (0.008)\n", - " 5. commit (0.006)\n", - " 6. expand_scaling_policies (0.006)\n", + " 0. resource_to_change_request (0.823)\n", "\n", "Label = update\n", "Pred =\n", - "---- 0. update (0.999)\n", - " 1. create (0.0)\n", - " 2. put (0.0)\n", - " 3. delete (0.0)\n", - " 4. patch (0.0)\n", - " 5. post (0.0)\n", - " 6. get (0.0)\n", + "---- 0. update (0.998)\n", "\n", "Label = fetch_list\n", "Pred =\n", "---- 0. fetch_list (1.0)\n", - " 1. __init__ (0.0)\n", - " 2. delete (0.0)\n", - " 3. create (0.0)\n", - " 4. copy (0.0)\n", - " 5. get_info (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.539)\n", - "---- 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + " 0. to_request (0.532)\n", "\n", "Label = from_response\n", "Pred =\n", "---- 0. from_response (0.996)\n", - " 1. to_request (0.002)\n", - " 2. _response_from_item (0.0)\n", - " 3. get (0.0)\n", - " 4. get_current (0.0)\n", - " 5. _request_for_item (0.0)\n", - " 6. warn (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.527)\n", - "---- 1. from_response (0.467)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + " 0. to_request (0.521)\n", "\n", "Label = _request_for_item\n", "Pred =\n", - " 0. _response_from_item (0.644)\n", - "---- 1. _request_for_item (0.349)\n", - " 2. from_response (0.001)\n", - " 3. get_firewall_group (0.0)\n", - " 4. _mrss_url (0.0)\n", - " 5. to_request (0.0)\n", - " 6. get (0.0)\n", + " 0. _response_from_item (0.607)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = from_response\n", "Pred =\n", - " 0. to_request (0.539)\n", - "---- 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + " 0. to_request (0.532)\n", "\n", "Label = delete\n", "Pred =\n", "---- 0. delete (0.999)\n", - " 1. post (0.0)\n", - " 2. get (0.0)\n", - " 3. put (0.0)\n", - " 4. clear (0.0)\n", - " 5. set (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = resource_to_request\n", "Pred =\n", - "---- 0. resource_to_request (0.996)\n", - " 1. openstack_module_kwargs (0.0)\n", - " 2. collection (0.0)\n", - " 3. resource_to_update (0.0)\n", - " 4. encode_request (0.0)\n", - " 5. from_response (0.0)\n", - " 6. get_vm (0.0)\n", + "---- 0. resource_to_request (0.999)\n", "\n", "Label = self_link\n", "Pred =\n", - "---- 0. self_link (0.707)\n", - " 1. collection (0.261)\n", - " 2. resource_to_update (0.002)\n", - " 3. username (0.002)\n", - " 4. map_param_to_obj (0.002)\n", - " 5. updated_record (0.001)\n", - " 6. get_region (0.0)\n", + "---- 0. self_link (0.494)\n", "\n", "Label = update\n", "Pred =\n", - "---- 0. update (0.999)\n", - " 1. create (0.0)\n", - " 2. put (0.0)\n", - " 3. delete (0.0)\n", - " 4. patch (0.0)\n", - " 5. post (0.0)\n", - " 6. get (0.0)\n", + "---- 0. update (0.998)\n", "\n", "Label = async_op_url\n", "Pred =\n", "---- 0. async_op_url (1.0)\n", - " 1. get_context_data (0.0)\n", - " 2. _download_chinese_webpage (0.0)\n", - " 3. options (0.0)\n", - " 4. form_valid (0.0)\n", - " 5. head (0.0)\n", - " 6. get_vrf_list (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.539)\n", - " 1. from_response (0.456)\n", - " 2. get (0.001)\n", - " 3. _request_for_item (0.0)\n", - " 4. options (0.0)\n", - " 5. _response_from_item (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.532)\n", "\n", "Label = to_request\n", "Pred =\n", - "---- 0. to_request (0.991)\n", - " 1. from_response (0.005)\n", - " 2. options (0.0)\n", - " 3. port (0.0)\n", - " 4. clear (0.0)\n", - " 5. state (0.0)\n", - " 6. messages (0.0)\n", + "---- 0. to_request (0.996)\n", "\n", "Label = from_response\n", "Pred =\n", "---- 0. from_response (0.996)\n", - " 1. to_request (0.002)\n", - " 2. _response_from_item (0.0)\n", - " 3. get (0.0)\n", - " 4. get_current (0.0)\n", - " 5. _request_for_item (0.0)\n", - " 6. warn (0.0)\n", "\n", "Label = create\n", "Pred =\n", "---- 0. create (1.0)\n", - " 1. update (0.0)\n", - " 2. start (0.0)\n", - " 3. add (0.0)\n", - " 4. get (0.0)\n", - " 5. delete (0.0)\n", - " 6. copy (0.0)\n", "\n", "Label = fetch_resource\n", "Pred =\n", "---- 0. fetch_resource (1.0)\n", - " 1. delete (0.0)\n", - " 2. __init__ (0.0)\n", - " 3. update (0.0)\n", - " 4. create (0.0)\n", - " 5. fetch_list (0.0)\n", - " 6. __ne__ (0.0)\n", "\n", "Label = wait_for_operation\n", "Pred =\n", "---- 0. wait_for_operation (1.0)\n", - " 1. get (0.0)\n", - " 2. ok (0.0)\n", - " 3. modify_db_instance (0.0)\n", - " 4. get_connection (0.0)\n", - " 5. main (0.0)\n", - " 6. __repr__ (0.0)\n", "\n", "Label = __getitem__\n", "Pred =\n", - "---- 0. __getitem__ (0.978)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1. get (0.003)\n", - " 2. encode (0.001)\n", - " 3. to_python (0.001)\n", - " 4. __call__ (0.001)\n", - " 5. __str__ (0.001)\n", - " 6. __len__ (0.001)\n", + "---- 0. __getitem__ (0.977)\n", "\n", "Label = __delitem__\n", "Pred =\n", - "---- 0. __delitem__ (0.673)\n", - " 1. __setitem__ (0.169)\n", - " 2. __getitem__ (0.034)\n", - " 3. _delete (0.008)\n", - " 4. setdefault (0.007)\n", - " 5. set (0.006)\n", - " 6. pop (0.004)\n", + "---- 0. __delitem__ (0.954)\n", "\n", "Label = get_dict\n", "Pred =\n", - " 0. get_key_func (0.067)\n", - " 1. change_dict_key_name (0.024)\n", - " 2. _set_none_to_blank (0.021)\n", - " 3. add_key_else_change_dict_key (0.019)\n", - " 4. id_for_label (0.016)\n", - " 5. try_get (0.013)\n", - " 6. get_prep_value (0.012)\n", + " 0. change_dict_key_name (0.126)\n", "\n", "Label = __delitem__\n", "Pred =\n", - " 0. append (0.6)\n", - " 1. create_cursor (0.021)\n", - " 2. fit_transform (0.017)\n", - " 3. insert (0.008)\n", - " 4. __delattr__ (0.007)\n", - " 5. reverse (0.006)\n", - " 6. save_form_data (0.005)\n", + " 0. __init__ (0.141)\n", "\n", "Label = merge_hooks\n", "Pred =\n", - " 0. remove_weight_norm (0.02)\n", - " 1. prepare_hooks (0.018)\n", - " 2. remove_spectral_norm (0.016)\n", - " 3. _get_server_id (0.009)\n", - " 4. get_child (0.008)\n", - " 5. check_err (0.008)\n", - " 6. add_field_update (0.007)\n", + " 0. double_output (0.017)\n", "\n", "Label = sha512_utf8\n", "Pred =\n", - " 0. sha256_utf8 (0.206)\n", - " 1. md5_utf8 (0.194)\n", - " 2. sha_utf8 (0.049)\n", - " 3. hexdigest (0.033)\n", - " 4. aws_hash (0.025)\n", - " 5. set_response_etag (0.019)\n", - " 6. test_ovo_one_class (0.017)\n", + " 0. sha_utf8 (0.046)\n", "\n", "Label = __ne__\n", "Pred =\n", "---- 0. __ne__ (0.999)\n", - " 1. has_equal_attributes (0.0)\n", - " 2. exec_module (0.0)\n", - " 3. __add__ (0.0)\n", - " 4. __lt__ (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. __radd__ (0.0)\n", "\n", "Label = patch\n", "Pred =\n", - " 0. put (0.861)\n", - "---- 1. patch (0.058)\n", - " 2. post (0.027)\n", - " 3. _get_page (0.007)\n", - " 4. head (0.007)\n", - " 5. update (0.004)\n", - " 6. delete (0.004)\n", + " 0. put (0.891)\n", "\n", "Label = prepare_content_length\n", "Pred =\n", - " 0. prepare_headers (0.743)\n", - " 1. has_header (0.045)\n", - " 2. add_unredirected_header (0.014)\n", - " 3. get_header (0.012)\n", - " 4. __delitem__ (0.005)\n", - " 5. get_encoding_from_headers (0.004)\n", - " 6. scheme (0.004)\n", + " 0. prepare_headers (0.167)\n", "\n", "Label = apparent_encoding\n", "Pred =\n", - " 0. check_local_role_manager_state (0.029)\n", - " 1. get_autostart (0.021)\n", - " 2. _get_pass (0.018)\n", - " 3. autostart (0.016)\n", - " 4. _getpass (0.016)\n", - " 5. centroid (0.014)\n", - " 6. ellipsoid (0.013)\n", + " 0. create_service_principal_profile_instance (0.026)\n", "\n", "Label = __getstate__\n", "Pred =\n", - "---- 0. __getstate__ (0.951)\n", - " 1. __copy__ (0.014)\n", - " 2. __hash__ (0.009)\n", - " 3. clone (0.004)\n", - " 4. __getitem__ (0.004)\n", - " 5. get (0.003)\n", - " 6. __reduce__ (0.001)\n", + "---- 0. __getstate__ (0.957)\n", "\n", "Label = file_storage_changed\n", "Pred =\n", - " 0. static_storage_changed (0.954)\n", - " 1. clear_serializers_cache (0.005)\n", - " 2. __setattr__ (0.002)\n", - " 3. localize_settings_changed (0.001)\n", - " 4. __init__ (0.001)\n", - " 5. clear_cache_handlers (0.001)\n", - " 6. __delattr__ (0.001)\n", + " 0. static_storage_changed (0.921)\n", "\n", "Label = auth_password_validators_changed\n", "Pred =\n", - " 0. static_finders_changed (0.843)\n", - " 1. reset_hashers (0.061)\n", - " 2. root_urlconf_changed (0.022)\n", - " 3. reset_cache (0.005)\n", - " 4. update_installed_apps (0.005)\n", - " 5. reset_template_engines (0.002)\n", - " 6. localize_settings_changed (0.002)\n", + " 0. static_finders_changed (0.723)\n", "\n", "Label = addError\n", "Pred =\n", - " 0. _run_checks (0.052)\n", - " 1. _clean_form (0.016)\n", - " 2. clear (0.012)\n", - " 3. get_auth (0.012)\n", - " 4. stopTest (0.01)\n", - " 5. log (0.009)\n", - " 6. push (0.009)\n", + " 0. _validate_app_names (0.176)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.158)\n", - " 1. __setstate__ (0.09)\n", - " 2. init_poolmanager (0.059)\n", - " 3. disable (0.016)\n", - " 4. rollback (0.015)\n", - " 5. set (0.015)\n", - " 6. failed_state (0.011)\n", + " 0. connect (0.133)\n", "\n", "Label = addError\n", "Pred =\n", " 0. addFailure (0.991)\n", - " 1. addUnexpectedSuccess (0.002)\n", - " 2. addSubTest (0.001)\n", - " 3. stopTest (0.001)\n", - " 4. addSkip (0.0)\n", - " 5. login_vchs (0.0)\n", - " 6. _og_regexes (0.0)\n", "\n", "Label = addSuccess\n", "Pred =\n", - " 0. stopTest (0.985)\n", - " 1. addUnexpectedSuccess (0.003)\n", - " 2. addSkip (0.002)\n", - " 3. startTest (0.002)\n", - " 4. addFailure (0.001)\n", - " 5. _setup_query (0.0)\n", - " 6. addSubTest (0.0)\n", + " 0. stopTest (0.983)\n", "\n", "Label = addExpectedFailure\n", "Pred =\n", - " 0. addFailure (0.7)\n", - " 1. stopTest (0.036)\n", - " 2. addSubTest (0.024)\n", - " 3. addSkip (0.011)\n", - " 4. addUnexpectedSuccess (0.008)\n", - " 5. choices (0.008)\n", - " 6. chassis_serial (0.002)\n", + " 0. addFailure (0.955)\n", "\n", "Label = _should_check_constraints\n", "Pred =\n", - " 0. _executemany (0.038)\n", - " 1. enable_constraint_checking (0.035)\n", - " 2. get_rollback (0.03)\n", - " 3. CASCADE (0.027)\n", - " 4. execute_sql_flush (0.013)\n", - " 5. _non_atomic_requests (0.012)\n", - " 6. mark_for_rollback_on_error (0.011)\n", + " 0. city (0.04)\n", "\n", "Label = skip_wrapper\n", "Pred =\n", - " 0. compat_ctypes_WINFUNCTYPE (0.326)\n", - " 1. resf (0.032)\n", - " 2. _lazy_proxy_unpickle (0.027)\n", - " 3. setupmethod (0.025)\n", - " 4. wrapped (0.017)\n", - " 5. no_translations (0.013)\n", - " 6. cache_control (0.008)\n", + " 0. wrapped (0.031)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = post\n", "Pred =\n", - " 0. put (0.562)\n", - " 1. delete (0.147)\n", - "---- 2. post (0.09)\n", - " 3. patch (0.033)\n", - " 4. head (0.011)\n", - " 5. get (0.007)\n", - " 6. _get_page (0.007)\n", + " 0. put (0.406)\n", "\n", "Label = patch\n", "Pred =\n", - " 0. delete (0.454)\n", - " 1. put (0.395)\n", - " 2. post (0.054)\n", - "---- 3. patch (0.02)\n", - " 4. update (0.02)\n", - " 5. options (0.011)\n", - " 6. get (0.008)\n", + " 0. delete (0.476)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. func (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = setUp\n", "Pred =\n", - " 0. tearDown (0.925)\n", - " 1. log_exception (0.02)\n", - " 2. stopTest (0.002)\n", - " 3. startTest (0.001)\n", - " 4. _add_ns (0.001)\n", - " 5. _meta_regex (0.001)\n", - " 6. has_level_handler (0.001)\n", + " 0. tearDown (0.449)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. func (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. reset_batch_stats (0.0)\n", "\n", "Label = _get_full_path\n", "Pred =\n", - " 0. get_full_path_info (0.028)\n", - " 1. check_query_object_type (0.021)\n", - " 2. check_session_cookie_secure (0.016)\n", - " 3. list_paths (0.015)\n", - " 4. should_redirect_with_slash (0.015)\n", - " 5. get_path_info (0.014)\n", - " 6. handle_app_config (0.012)\n", + " 0. generate_filename (0.089)\n", "\n", "Label = parse_file_upload\n", "Pred =\n", - " 0. upload_handlers (0.941)\n", - " 1. _initialize_handlers (0.008)\n", - " 2. setup_test_environment (0.002)\n", - " 3. teardown_test_environment (0.001)\n", - " 4. _key_to_file (0.001)\n", - " 5. _close_files (0.001)\n", - " 6. copy (0.001)\n", + " 0. upload_handlers (0.352)\n", "\n", "Label = charset\n", "Pred =\n", - " 0. _encode_data (0.014)\n", - " 1. create_or_update_pip (0.014)\n", - " 2. make_bytes (0.012)\n", - " 3. get_hasher (0.01)\n", - " 4. get_arn_from_role_name (0.01)\n", - " 5. normalize_email (0.009)\n", - " 6. ask_auto_now_add_addition (0.008)\n", + " 0. get_autoscaler (0.064)\n", "\n", "Label = __setitem__\n", "Pred =\n", - "---- 0. __setitem__ (0.34)\n", - " 1. __set__ (0.103)\n", - " 2. set (0.07)\n", - " 3. __delitem__ (0.04)\n", - " 4. __call__ (0.019)\n", - " 5. name (0.012)\n", - " 6. to_python (0.012)\n", + " 0. appendlist (0.133)\n", "\n", "Label = get\n", "Pred =\n", - " 0. __getitem__ (0.168)\n", - " 1. __delitem__ (0.155)\n", - " 2. has_header (0.047)\n", - " 3. get_environ (0.016)\n", - " 4. prepare_headers (0.016)\n", - " 5. __call__ (0.014)\n", - " 6. format_headers (0.013)\n", + " 0. _get_hasher (0.227)\n", "\n", "Label = _set_streaming_content\n", "Pred =\n", - " 0. close (0.965)\n", - " 1. dumps (0.003)\n", - " 2. end_object (0.002)\n", - " 3. end_serialization (0.002)\n", - " 4. __del__ (0.001)\n", - " 5. write (0.001)\n", - " 6. close_rings (0.001)\n", + " 0. __del__ (0.248)\n", "\n", "Label = PROTECT\n", "Pred =\n", - " 0. set_on_delete (0.075)\n", - " 1. related_objects (0.037)\n", - " 2. SET_NULL (0.037)\n", - " 3. get_obj (0.032)\n", - " 4. add_field_update (0.012)\n", - " 5. lookups (0.01)\n", - " 6. select_collector_classes (0.01)\n", + " 0. _matching_loader_thinks_module_is_package (0.03)\n", "\n", "Label = set_on_delete\n", "Pred =\n", - "---- 0. set_on_delete (0.68)\n", - " 1. SET_NULL (0.206)\n", - " 2. SET_DEFAULT (0.031)\n", - " 3. get_obj (0.003)\n", - " 4. select_collector_classes (0.002)\n", - " 5. bulk_batch_size (0.002)\n", - " 6. related_objects (0.002)\n", + "---- 0. set_on_delete (0.828)\n", "\n", "Label = __eq__\n", "Pred =\n", "---- 0. __eq__ (1.0)\n", - " 1. __add__ (0.0)\n", - " 2. __lt__ (0.0)\n", - " 3. clone (0.0)\n", - " 4. difference (0.0)\n", - " 5. __sub__ (0.0)\n", - " 6. clear (0.0)\n", "\n", "Label = __deepcopy__\n", "Pred =\n", - "---- 0. __deepcopy__ (0.999)\n", - " 1. to_dict (0.0)\n", - " 2. pop (0.0)\n", - " 3. __copy__ (0.0)\n", - " 4. __add__ (0.0)\n", - " 5. named_children (0.0)\n", - " 6. insert (0.0)\n", + "---- 0. __deepcopy__ (1.0)\n", "\n", "Label = filter\n", "Pred =\n", - " 0. exclude (0.987)\n", - " 1. get_exclude (0.003)\n", - " 2. update (0.001)\n", - " 3. complex_filter (0.0)\n", - " 4. __iter__ (0.0)\n", - " 5. __bool__ (0.0)\n", - " 6. copy (0.0)\n", + " 0. exclude (0.998)\n", "\n", "Label = defer\n", "Pred =\n", - " 0. add_deferred_loading (0.166)\n", - " 1. many_to_many (0.043)\n", - " 2. bulk_batch_size (0.029)\n", - " 3. clear_deferred_loading (0.024)\n", - " 4. related_objects (0.024)\n", - " 5. distinct (0.019)\n", - " 6. _fixture_teardown (0.015)\n", + " 0. add_deferred_loading (0.093)\n", "\n", "Label = using\n", "Pred =\n", - " 0. db_manager (0.151)\n", - " 1. none (0.086)\n", - " 2. get_initial_alias (0.049)\n", - " 3. test_db_signature (0.025)\n", - " 4. extra (0.021)\n", - " 5. _apply_rel_filters (0.02)\n", - " 6. resolve_expression (0.012)\n", + " 0. _set_point_3d (0.043)\n", "\n", "Label = __instancecheck__\n", "Pred =\n", - " 0. save_form_data (0.071)\n", - " 1. _is_instance_state_pending (0.023)\n", - " 2. has_changed (0.022)\n", - " 3. tag (0.022)\n", - " 4. __get__ (0.015)\n", - " 5. db (0.015)\n", - " 6. get_count (0.014)\n", + " 0. pre_save_val (0.081)\n", "\n", "Label = __eq__\n", "Pred =\n", "---- 0. __eq__ (1.0)\n", - " 1. __add__ (0.0)\n", - " 2. __lt__ (0.0)\n", - " 3. __contains__ (0.0)\n", - " 4. difference (0.0)\n", - " 5. equals (0.0)\n", - " 6. __sub__ (0.0)\n", "\n", "Label = get_prep_lookup\n", "Pred =\n", - " 0. value (0.049)\n", - " 1. process_rhs (0.031)\n", - " 2. as_sql (0.022)\n", - " 3. prepare (0.021)\n", - " 4. prepare_value (0.02)\n", - "---- 5. get_prep_lookup (0.019)\n", - " 6. convert_value (0.018)\n", + "---- 0. get_prep_lookup (0.433)\n", "\n", "Label = get_group_by_cols\n", "Pred =\n", - "---- 0. get_group_by_cols (0.936)\n", - " 1. get_source_expressions (0.016)\n", - " 2. clone (0.007)\n", - " 3. set_select (0.004)\n", - " 4. get_vm (0.002)\n", - " 5. copy (0.002)\n", - " 6. save_manifest (0.001)\n", + "---- 0. get_group_by_cols (0.996)\n", "\n", "Label = process_rhs\n", "Pred =\n", - "---- 0. process_rhs (0.695)\n", - " 1. as_sql (0.067)\n", - " 2. get_rhs_op (0.023)\n", - " 3. clone (0.01)\n", - " 4. equals (0.008)\n", - " 5. units (0.008)\n", - " 6. union (0.005)\n", + "---- 0. process_rhs (0.1)\n", "\n", "Label = swapped\n", "Pred =\n", - " 0. get_models (0.031)\n", - " 1. _get_edited_object_pks (0.017)\n", - " 2. contains_column_references (0.014)\n", - " 3. find_migration (0.009)\n", - " 4. check_for_language (0.009)\n", - " 5. model_fields (0.008)\n", - " 6. _user_has_module_perms (0.007)\n", + " 0. has_tags (0.118)\n", "\n", "Label = __radd__\n", "Pred =\n", - " 0. __add__ (0.942)\n", - " 1. __eq__ (0.009)\n", - " 2. __contains__ (0.006)\n", - "---- 3. __radd__ (0.003)\n", - " 4. __sub__ (0.003)\n", - " 5. __or__ (0.003)\n", - " 6. difference (0.003)\n", + " 0. __add__ (0.973)\n", "\n", "Label = __getstate__\n", "Pred =\n", "---- 0. __getstate__ (1.0)\n", - " 1. clone (0.0)\n", - " 2. __copy__ (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. __hash__ (0.0)\n", - " 5. get (0.0)\n", - " 6. transform (0.0)\n", "\n", "Label = _parse_expressions\n", "Pred =\n", - " 0. _get_ordering_expressions_index (0.053)\n", - " 1. _listarr (0.051)\n", - " 2. adapt_timefield_value (0.021)\n", - " 3. _resolve_output_field (0.02)\n", - " 4. visible_fields (0.015)\n", - " 5. format_value (0.014)\n", - " 6. get_prep_value (0.013)\n", + " 0. coalesce (0.023)\n", "\n", "Label = desc\n", "Pred =\n", - " 0. fetch (0.69)\n", - " 1. configure (0.037)\n", - " 2. call_and_shelve (0.025)\n", - " 3. score (0.011)\n", - " 4. resolve_expression (0.007)\n", - " 5. settings (0.007)\n", - " 6. symbolic (0.007)\n", + " 0. modify_settings (0.304)\n", "\n", "Label = desc\n", "Pred =\n", - " 0. fetch (0.69)\n", - " 1. configure (0.037)\n", - " 2. call_and_shelve (0.025)\n", - " 3. score (0.011)\n", - " 4. resolve_expression (0.007)\n", - " 5. settings (0.007)\n", - " 6. symbolic (0.007)\n", + " 0. modify_settings (0.304)\n", "\n", "Label = as_sql\n", "Pred =\n", - "---- 0. as_sql (0.993)\n", - " 1. as_mysql (0.001)\n", - " 2. as_sqlite (0.001)\n", - " 3. sql_with_params (0.0)\n", - " 4. resolve_expression_parameter (0.0)\n", - " 5. process_lhs (0.0)\n", - " 6. compiler_module (0.0)\n", + "---- 0. as_sql (0.74)\n", "\n", "Label = __repr__\n", "Pred =\n", "---- 0. __repr__ (1.0)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. get (0.0)\n", - " 4. predict (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. state (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. inverse_transform (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = __str__\n", "Pred =\n", - "---- 0. __str__ (0.965)\n", - " 1. get_source_expressions (0.007)\n", - " 2. __len__ (0.005)\n", - " 3. __repr__ (0.003)\n", - " 4. copy (0.003)\n", - " 5. describe (0.003)\n", - " 6. __hash__ (0.002)\n", + "---- 0. __str__ (0.997)\n", "\n", "Label = __invert__\n", "Pred =\n", - " 0. clone (0.214)\n", - " 1. get_source_expressions (0.045)\n", - " 2. _resolve_output_field (0.035)\n", - " 3. pk_field (0.034)\n", - " 4. get_queryset (0.027)\n", - " 5. output_field (0.012)\n", - " 6. union (0.012)\n", + " 0. __iter__ (0.129)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. reverse_ordering (0.2)\n", - " 1. clear_routers_cache (0.056)\n", - " 2. disable_implicit_wait (0.013)\n", - " 3. full_clean (0.013)\n", - " 4. teardown_test_environment (0.006)\n", - " 5. ensure_registered (0.006)\n", - " 6. update_last_login (0.005)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = as_mysql\n", "Pred =\n", - " 0. as_sqlite (0.988)\n", - "---- 1. as_mysql (0.003)\n", - " 2. as_oracle (0.002)\n", - " 3. as_postgresql (0.001)\n", - " 4. as_sql (0.001)\n", - " 5. render (0.0)\n", - " 6. get_compiler (0.0)\n", + " 0. as_sqlite (0.991)\n", "\n", "Label = __repr__\n", "Pred =\n", "---- 0. __repr__ (1.0)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. get (0.0)\n", - " 4. predict (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. add (0.0)\n", "\n", "Label = set_source_expressions\n", "Pred =\n", - "---- 0. set_source_expressions (0.999)\n", - " 1. get_source_expressions (0.0)\n", - " 2. clone (0.0)\n", - " 3. __invert__ (0.0)\n", - " 4. on_epoch_end (0.0)\n", - " 5. __init__ (0.0)\n", - " 6. __eq__ (0.0)\n", + "---- 0. set_source_expressions (0.998)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. ip (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. set (0.0)\n", - " 6. subwidgets (0.0)\n", "\n", "Label = check\n", "Pred =\n", - " 0. _get_opts (0.246)\n", - " 1. get_content_type (0.063)\n", - " 2. get_field_names_from_opts (0.019)\n", - " 3. get_deferred_fields (0.019)\n", - " 4. get_default_table_map_filter (0.017)\n", - " 5. _check_single_primary_key (0.016)\n", - " 6. get_base_chain (0.016)\n", + " 0. is_reverse_o2o (0.12)\n", "\n", "Label = prepare_database_save\n", "Pred =\n", - " 0. related_objects (0.094)\n", - " 1. value_from_object (0.059)\n", - " 2. get_forward_related_filter (0.056)\n", - " 3. has_related_field_in_list_display (0.05)\n", - " 4. serializable_value (0.029)\n", - " 5. get_default (0.029)\n", - " 6. contribute_to_related_class (0.021)\n", + " 0. get_forward_related_filter (0.06)\n", "\n", "Label = model_unpickle\n", "Pred =\n", - " 0. ask_rename_model (0.044)\n", - " 1. ask_rename (0.037)\n", - " 2. _is_object (0.024)\n", - " 3. expand_list (0.019)\n", - " 4. _set_field_new_type_null_status (0.018)\n", - " 5. model_class (0.017)\n", - " 6. raise_duplicate_arg_error (0.016)\n", + " 0. _related_non_m2m_objects (0.198)\n", "\n", "Label = __str__\n", "Pred =\n", - " 0. time_trunc_sql (0.063)\n", - " 1. last_executed_query (0.05)\n", - " 2. sql_with_params (0.032)\n", - " 3. distinct_sql (0.03)\n", - " 4. time_extract_sql (0.028)\n", - " 5. _create_spatial_index_name (0.028)\n", - " 6. datetime_trunc_sql (0.024)\n", + " 0. time_trunc_sql (0.129)\n", "\n", "Label = unref_alias\n", "Pred =\n", - " 0. reset_refcounts (0.408)\n", - " 1. ref_alias (0.071)\n", - " 2. _execute_create_test_db (0.011)\n", - " 3. _vchs_login (0.01)\n", - " 4. _clone_test_db (0.01)\n", - " 5. disable_action (0.009)\n", - " 6. nested (0.008)\n", + " 0. reset_refcounts (0.618)\n", "\n", "Label = is_empty\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. __str__ (0.415)\n", - " 1. get_source_expressions (0.356)\n", - " 2. __len__ (0.043)\n", - " 3. to_python (0.021)\n", - " 4. get_indices (0.011)\n", - " 5. lists (0.01)\n", - " 6. __invert__ (0.006)\n", + "Pred =\n", + " 0. get_source_expressions (0.823)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. _set_standard (0.0)\n", - " 2. reset_batch_stats (0.0)\n", - " 3. __eq__ (0.0)\n", - " 4. __setitem__ (0.0)\n", - " 5. start_serialization (0.0)\n", - " 6. set_source_expressions (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = add_update_fields\n", "Pred =\n", - " 0. hosts (0.037)\n", - " 1. to_list (0.025)\n", - " 2. subwidgets (0.024)\n", - " 3. flatten (0.02)\n", - " 4. __iter__ (0.019)\n", - " 5. overlaps (0.012)\n", - " 6. append (0.011)\n", + " 0. resolve_expression (0.523)\n", "\n", "Label = add_related_update\n", "Pred =\n", - " 0. add_field_update (0.064)\n", - " 1. check_field_type (0.026)\n", - " 2. make_generic_foreign_order_accessors (0.014)\n", - " 3. _check_fields (0.012)\n", - " 4. add_index (0.012)\n", - " 5. local_setter (0.01)\n", - " 6. _check_list_select_related (0.01)\n", + " 0. add_field_update (0.478)\n", "\n", "Label = add_subquery\n", "Pred =\n", - " 0. as_sql (0.945)\n", - " 1. resolve_expression_parameter (0.003)\n", - " 2. as_mysql (0.003)\n", - " 3. add_field (0.002)\n", - " 4. sql_with_params (0.002)\n", - " 5. run_geometry_sql (0.001)\n", - " 6. as_sqlite (0.001)\n", + " 0. as_sql (0.992)\n", "\n", "Label = get_tzname\n", "Pred =\n", - " 0. T (0.396)\n", - " 1. e (0.239)\n", - " 2. _rollback (0.019)\n", - " 3. localdate (0.01)\n", - " 4. adapt_datetimefield_value (0.009)\n", - " 5. localtime (0.008)\n", - " 6. contains (0.008)\n", + " 0. datetime_cast_time_sql (0.112)\n", "\n", "Label = as_sql\n", "Pred =\n", - "---- 0. as_sql (0.999)\n", - " 1. process_lhs (0.0)\n", - " 2. as_mysql (0.0)\n", - " 3. as_sqlite (0.0)\n", - " 4. get_compiler (0.0)\n", - " 5. process_rhs (0.0)\n", - " 6. sql_with_params (0.0)\n", + "---- 0. as_sql (0.998)\n", "\n", "Label = as_mysql\n", "Pred =\n", - " 0. as_oracle (0.529)\n", - " 1. as_sqlite (0.267)\n", - "---- 2. as_mysql (0.186)\n", - " 3. as_postgresql (0.008)\n", - " 4. as_sql (0.001)\n", - " 5. render (0.0)\n", - " 6. convert_timefield_value (0.0)\n", + " 0. as_oracle (0.379)\n", "\n", "Label = as_postgresql\n", "Pred =\n", - " 0. as_oracle (0.529)\n", - " 1. as_sqlite (0.267)\n", - " 2. as_mysql (0.186)\n", - "---- 3. as_postgresql (0.008)\n", - " 4. as_sql (0.001)\n", - " 5. render (0.0)\n", - " 6. convert_timefield_value (0.0)\n", + " 0. as_oracle (0.379)\n", "\n", "Label = _resolve_output_field\n", "Pred =\n", - "---- 0. _resolve_output_field (0.998)\n", - " 1. output_field (0.0)\n", - " 2. get_source_fields (0.0)\n", - " 3. get_substr (0.0)\n", - " 4. is_stationary (0.0)\n", - " 5. flatten (0.0)\n", - " 6. tuple (0.0)\n", + "---- 0. _resolve_output_field (0.999)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. predict_proba (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. destination (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = get_related_field\n", "Pred =\n", - " 0. _check_object_id_field (0.072)\n", - " 1. get_forward_related_filter (0.045)\n", - " 2. url_for_result (0.032)\n", - " 3. _get_field_name (0.03)\n", - " 4. set_field_name (0.028)\n", - " 5. resolve_related_fields (0.023)\n", - " 6. contribute_to_related_class (0.014)\n", + " 0. RelatedObjectDoesNotExist (0.062)\n", "\n", "Label = __reduce__\n", "Pred =\n", - " 0. deconstruct (0.179)\n", - "---- 1. __reduce__ (0.093)\n", - " 2. resolve_related_fields (0.027)\n", - " 3. serialize (0.027)\n", - " 4. add_model (0.026)\n", - " 5. model_class (0.025)\n", - " 6. choice (0.019)\n", + " 0. resolve_related_fields (0.083)\n", "\n", "Label = related_model\n", "Pred =\n", - " 0. check_models_ready (0.425)\n", - " 1. mysql_client (0.022)\n", - " 2. _resolve_address (0.015)\n", - " 3. related_query_name (0.009)\n", - " 4. get_app_configs (0.009)\n", - " 5. related_objects (0.007)\n", - " 6. compute_models (0.007)\n", + " 0. get_app_configs (0.177)\n", "\n", "Label = swappable_setting\n", "Pred =\n", - " 0. resolve_related_fields (0.027)\n", - " 1. url_for_result (0.015)\n", - " 2. _dsn (0.015)\n", - " 3. should_skip_detecting_model (0.013)\n", - " 4. timezone_name (0.011)\n", - " 5. _get_pk_val (0.011)\n", - " 6. has_table (0.01)\n", + " 0. get_default (0.038)\n", "\n", "Label = target_field\n", "Pred =\n", - "---- 0. target_field (0.984)\n", - " 1. get_package_libraries (0.0)\n", - " 2. _check_single_primary_key (0.0)\n", - " 3. _check_indexes (0.0)\n", - " 4. get_path_info (0.0)\n", - " 5. is_discoverable (0.0)\n", - " 6. _get_field_choices (0.0)\n", + "---- 0. target_field (0.993)\n", "\n", "Label = related_fields\n", "Pred =\n", - " 0. related_objects (0.144)\n", - " 1. is_hidden (0.14)\n", - " 2. local_related_fields (0.045)\n", - " 3. get_deferred_fields (0.037)\n", - " 4. foreign_related_fields (0.034)\n", - " 5. related_query_name (0.031)\n", - " 6. geo_field (0.029)\n", + " 0. _resolve_output_field (0.386)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. func (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = deconstruct\n", "Pred =\n", "---- 0. deconstruct (1.0)\n", - " 1. clone (0.0)\n", - " 2. name (0.0)\n", - " 3. __reduce__ (0.0)\n", - " 4. serialize (0.0)\n", - " 5. model (0.0)\n", - " 6. state (0.0)\n", "\n", "Label = url\n", "Pred =\n", - " 0. path (0.989)\n", - " 1. _get_file (0.001)\n", - "---- 2. url (0.001)\n", - " 3. filename (0.0)\n", - " 4. name (0.0)\n", - " 5. __str__ (0.0)\n", - " 6. work_path (0.0)\n", + " 0. path (0.949)\n", "\n", "Label = closed\n", "Pred =\n", - "---- 0. closed (0.943)\n", - " 1. readable (0.02)\n", - " 2. writable (0.014)\n", - " 3. _get_image_dimensions (0.001)\n", - " 4. srid (0.0)\n", - " 5. vsi_buffer (0.0)\n", - " 6. width (0.0)\n", + "---- 0. closed (0.995)\n", "\n", "Label = __set__\n", "Pred =\n", - " 0. __setitem__ (0.103)\n", - " 1. set (0.042)\n", - " 2. _set_single (0.04)\n", - "---- 3. __set__ (0.028)\n", - " 4. __setstate__ (0.016)\n", - " 5. _set_single_rebuild (0.014)\n", - " 6. _set_byteorder (0.014)\n", + " 0. __setitem__ (0.058)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. build (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = contribute_to_class\n", "Pred =\n", - "---- 0. contribute_to_class (0.778)\n", - " 1. init_poolmanager (0.028)\n", - " 2. state_forwards (0.018)\n", - " 3. runshell (0.01)\n", - " 4. start_serialization (0.01)\n", - " 5. database_forwards (0.009)\n", - " 6. database_backwards (0.007)\n", + "---- 0. contribute_to_class (0.677)\n", "\n", "Label = formfield\n", "Pred =\n", - "---- 0. formfield (0.982)\n", - " 1. formfield_for_dbfield (0.003)\n", - " 2. get_form_kwargs (0.001)\n", - " 3. as_hidden (0.001)\n", - " 4. get_changelist_form (0.0)\n", - " 5. bind_parameter (0.0)\n", - " 6. covers (0.0)\n", + "---- 0. formfield (0.937)\n", "\n", "Label = __init__\n", - "Pred =\n", + "Pred =\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. func (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = _convert_field_to_tz\n", "Pred =\n", - "---- 0. _convert_field_to_tz (0.962)\n", - " 1. datetime_cast_time_sql (0.007)\n", - " 2. _convert_tzname_to_sql (0.005)\n", - " 3. datetime_cast_date_sql (0.004)\n", - " 4. datetime_trunc_sql (0.003)\n", - " 5. datetime_extract_sql (0.0)\n", - " 6. handle_app_config (0.0)\n", + "---- 0. _convert_field_to_tz (0.888)\n", "\n", "Label = window_frame_range_start_end\n", "Pred =\n", - "---- 0. window_frame_range_start_end (0.66)\n", - " 1. window_frame_start_end (0.033)\n", - " 2. _tcp_udp_match (0.008)\n", - " 3. remove_start (0.007)\n", - " 4. shape (0.007)\n", - " 5. get_nodes_by_type (0.007)\n", - " 6. setproxy (0.007)\n", + " 0. window_frame_start_end (0.377)\n", "\n", "Label = convert_binaryfield_value\n", "Pred =\n", - " 0. convert_textfield_value (0.981)\n", - " 1. convert_value (0.002)\n", - " 2. _convert_field_to_tz (0.001)\n", - " 3. _split_lines (0.0)\n", - " 4. convert_datefield_value (0.0)\n", - " 5. convert_empty_bytes (0.0)\n", - " 6. window_frame_start_end (0.0)\n", + " 0. convert_textfield_value (0.988)\n", "\n", "Label = convert_empty_string\n", "Pred =\n", - " 0. converter (0.968)\n", - " 1. convert_value (0.012)\n", - " 2. convert_empty_strings (0.004)\n", - " 3. convert_empty_bytes (0.003)\n", - " 4. convert_datefield_value (0.001)\n", - " 5. convert_timefield_value (0.001)\n", - " 6. convert_uuidfield_value (0.0)\n", + " 0. converter (0.989)\n", "\n", "Label = _savepoint_commit\n", "Pred =\n", - " 0. _prepare_cursor (0.043)\n", - " 1. hyperparameter_periodicity (0.016)\n", - " 2. validate_thread_sharing (0.009)\n", - " 3. queries (0.009)\n", - " 4. clean_password2 (0.009)\n", - " 5. test_log_dirichlet_norm (0.008)\n", - " 6. clean_new_password2 (0.007)\n", + " 0. _prepare_cursor (0.141)\n", "\n", "Label = oracle_version\n", "Pred =\n", - " 0. pg_version (0.164)\n", - " 1. supports_area_geodetic (0.066)\n", - " 2. supports_make_line_aggr (0.025)\n", - " 3. spatial_version (0.012)\n", - " 4. supports_empty_geometry_collection (0.012)\n", - " 5. _get_expected_entrypoint (0.01)\n", - " 6. mysql_server_info (0.01)\n", + " 0. pg_version (0.032)\n", "\n", "Label = execute\n", "Pred =\n", - " 0. get_object_for_this_type (0.067)\n", - " 1. related_manager_cls (0.053)\n", - " 2. get_all_objects_for_this_type (0.05)\n", - " 3. related_objects (0.043)\n", - "---- 4. execute (0.028)\n", - " 5. clone (0.028)\n", - " 6. _clone (0.025)\n", + " 0. compiler (0.111)\n", "\n", "Label = runshell\n", "Pred =\n", - " 0. _update_method_wrapper (0.042)\n", - " 1. create_client (0.038)\n", - " 2. resource (0.014)\n", - " 3. exec_rpc (0.013)\n", - " 4. _execute_with_wrappers (0.011)\n", - " 5. wraps (0.009)\n", - " 6. add_node (0.007)\n", + " 0. set_model (0.146)\n", "\n", "Label = _execute_test_db_destruction\n", "Pred =\n", - " 0. _destroy_test_user (0.952)\n", - " 1. _execute_test_db_creation (0.016)\n", - " 2. alter_db_tablespace (0.001)\n", - " 3. _execute_create_test_db (0.0)\n", - " 4. reload_model (0.0)\n", - " 5. related_objects (0.0)\n", - " 6. distinct (0.0)\n", + " 0. _destroy_test_user (0.97)\n", "\n", "Label = date_trunc_sql\n", "Pred =\n", - " 0. time_trunc_sql (0.556)\n", - " 1. time_extract_sql (0.241)\n", - "---- 2. date_trunc_sql (0.109)\n", - " 3. date_extract_sql (0.032)\n", - " 4. datetime_extract_sql (0.008)\n", - " 5. datetime_trunc_sql (0.005)\n", - " 6. as_text (0.003)\n", + " 0. time_extract_sql (0.451)\n", "\n", "Label = adapt_datetimefield_value\n", "Pred =\n", - "---- 0. adapt_datetimefield_value (0.97)\n", - " 1. to_current_timezone (0.002)\n", - " 2. from_current_timezone (0.002)\n", - " 3. adapt_timefield_value (0.002)\n", - " 4. adapt_datefield_value (0.001)\n", - " 5. T (0.001)\n", - " 6. process_clob (0.001)\n", + "---- 0. adapt_datetimefield_value (0.977)\n", "\n", "Label = convert_datefield_value\n", "Pred =\n", - "---- 0. convert_datefield_value (0.39)\n", - " 1. convert_empty_strings (0.258)\n", - " 2. convert_value (0.054)\n", - " 3. converter (0.05)\n", - " 4. convert_durationfield_value (0.028)\n", - " 5. convert_empty_bytes (0.017)\n", - " 6. convert_timefield_value (0.015)\n", + "---- 0. convert_datefield_value (0.432)\n", "\n", "Label = combine_expression\n", "Pred =\n", - " 0. combine_duration_expression (0.945)\n", - "---- 1. combine_expression (0.024)\n", - " 2. date_interval_sql (0.001)\n", - " 3. format_value (0.001)\n", - " 4. tablespace_sql (0.001)\n", - " 5. _generate_temp_name (0.001)\n", - " 6. kml (0.0)\n", + " 0. combine_duration_expression (0.941)\n", "\n", "Label = is_self_referential\n", "Pred =\n", - " 0. is_not_a_generic_foreign_key (0.587)\n", - " 1. is_not_a_generic_relation (0.082)\n", - " 2. is_not_an_m2m_field (0.053)\n", - " 3. _related_non_m2m_objects (0.011)\n", - " 4. modelform_defines_fields (0.011)\n", - " 5. related_query_name (0.008)\n", - " 6. get_fieldsets (0.006)\n", + " 0. is_not_a_generic_foreign_key (0.5)\n", "\n", "Label = executemany\n", "Pred =\n", - "---- 0. executemany (0.663)\n", - " 1. _executemany (0.09)\n", - " 2. _clone (0.018)\n", - " 3. setup_databases (0.005)\n", - " 4. raw (0.004)\n", - " 5. covers (0.004)\n", - " 6. get_template_sources (0.003)\n", + "---- 0. executemany (0.975)\n", "\n", "Label = _sqlite_datetime_cast_date\n", "Pred =\n", - " 0. _sqlite_datetime_cast_time (0.969)\n", - " 1. _sqlite_time_extract (0.003)\n", - " 2. _num_days (0.002)\n", - " 3. _sqlite_datetime_parse (0.001)\n", - " 4. iso8601_format (0.001)\n", - " 5. verify (0.001)\n", - " 6. datetime_extract_sql (0.0)\n", + " 0. _sqlite_datetime_cast_time (0.913)\n", "\n", "Label = _sqlite_rpad\n", "Pred =\n", - " 0. _sqlite_lpad (0.974)\n", - " 1. limit_length (0.001)\n", - " 2. _get_no_autofield_sequence_name (0.001)\n", - " 3. range_less_than (0.0)\n", - " 4. _reduce_func (0.0)\n", - " 5. _get_mask (0.0)\n", - " 6. _get_valid_samples_by_column (0.0)\n", + " 0. _sqlite_lpad (0.752)\n", "\n", "Label = _get_test_db_name\n", "Pred =\n", - " 0. is_in_memory_db (0.23)\n", - " 1. test_db_signature (0.096)\n", - " 2. _test_database_tblspace_tmp (0.063)\n", - "---- 3. _get_test_db_name (0.051)\n", - " 4. get_connection_params (0.035)\n", - " 5. runshell (0.019)\n", - " 6. db_type (0.014)\n", + " 0. test_db_signature (0.65)\n", "\n", "Label = deserialize_db_from_string\n", "Pred =\n", - " 0. _get (0.42)\n", - " 1. prepare (0.041)\n", - " 2. setup_test_environment (0.013)\n", - " 3. save_form_data (0.012)\n", - " 4. contribute_to_class (0.011)\n", - " 5. delete_model (0.01)\n", - " 6. teardown_test_environment (0.009)\n", + " 0. _get (0.14)\n", "\n", "Label = get_names\n", "Pred =\n", - " 0. _normalize_distance_lookup_arg (0.063)\n", - " 1. _program_guid (0.03)\n", - " 2. _get_sequence_name (0.023)\n", - " 3. month_by_name (0.014)\n", - " 4. match_str (0.012)\n", - " 5. new_datetime (0.011)\n", - " 6. model_installed (0.01)\n", + " 0. get_citext_oids (0.045)\n", "\n", "Label = time_trunc_sql\n", "Pred =\n", - " 0. date_extract_sql (0.588)\n", - " 1. date_trunc_sql (0.336)\n", - "---- 2. time_trunc_sql (0.018)\n", - " 3. time_extract_sql (0.013)\n", - " 4. datetime_trunc_sql (0.004)\n", - " 5. datetime_extract_sql (0.004)\n", - " 6. get_distance (0.003)\n", + " 0. date_trunc_sql (0.564)\n", "\n", "Label = to_string\n", "Pred =\n", - " 0. extract_count (0.098)\n", - " 1. get_height (0.053)\n", - " 2. compat_print (0.015)\n", - " 3. extract_unavailable_message (0.012)\n", - " 4. _len_and_data (0.012)\n", - " 5. extract_view_count (0.011)\n", - " 6. base36_to_int (0.009)\n", + " 0. unquote_if_non_empty (0.054)\n", "\n", "Label = integer_field_range\n", "Pred =\n", - " 0. _standardize_value (0.027)\n", - " 1. column_name_converter (0.022)\n", - " 2. get_n_splits (0.011)\n", - " 3. __conform__ (0.01)\n", - " 4. is_email_simple (0.009)\n", - " 5. _is_false (0.008)\n", - " 6. get_current_to_attr (0.008)\n", + " 0. q (0.319)\n", "\n", "Label = window_frame_start\n", "Pred =\n", - " 0. window_frame_end (0.095)\n", - " 1. _paired (0.011)\n", - " 2. spatial_version (0.01)\n", - " 3. window_frame_rows_start_end (0.01)\n", - " 4. wait_until_invisible (0.009)\n", - " 5. check_perms (0.008)\n", - " 6. expected_parameters (0.007)\n", + " 0. window_frame_end (0.151)\n", "\n", "Label = remove_index\n", "Pred =\n", - " 0. remove_constraint (0.381)\n", - " 1. add_index (0.071)\n", - " 2. remove_field (0.034)\n", - " 3. _destroy_test_db (0.021)\n", - " 4. execute_sql_flush (0.02)\n", - " 5. delete_model (0.015)\n", - " 6. add_constraint (0.007)\n", + " 0. remove_constraint (0.835)\n", "\n", "Label = _savepoint_rollback\n", "Pred =\n", - " 0. _savepoint (0.397)\n", - " 1. _savepoint_commit (0.257)\n", - " 2. savepoint_rollback (0.1)\n", - " 3. _database_exists (0.013)\n", - " 4. execute_sql_flush (0.011)\n", - " 5. savepoint_commit (0.008)\n", - " 6. _execute_create_test_db (0.006)\n", + " 0. _savepoint_commit (0.557)\n", "\n", "Label = copy\n", "Pred =\n", - " 0. _generate_plan (0.043)\n", - " 1. to_list (0.028)\n", - " 2. get_nc_config (0.02)\n", - " 3. __contains__ (0.013)\n", - " 4. database_backwards (0.013)\n", - " 5. get_connection (0.01)\n", - " 6. set_source_expressions (0.01)\n", + " 0. savepoint_rollback (0.035)\n", "\n", "Label = validate_autopk_value\n", "Pred =\n", - " 0. get_prep_value (0.581)\n", - " 1. __call__ (0.223)\n", - " 2. get_value (0.026)\n", - " 3. _check_type_str (0.015)\n", - " 4. compute_output_shape (0.005)\n", - " 5. create_cursor (0.005)\n", - " 6. prepare_value (0.004)\n", + " 0. get_prep_value (0.792)\n", "\n", "Label = _is_limited_data_type\n", "Pred =\n", - " 0. db_type (0.353)\n", - " 1. check_field_type (0.073)\n", - " 2. _field_should_be_indexed (0.049)\n", - " 3. get_placeholder (0.038)\n", - " 4. field_cast_sql (0.014)\n", - " 5. convert_value (0.011)\n", - " 6. _from_db_value (0.009)\n", + " 0. _field_should_be_indexed (0.168)\n", "\n", "Label = __new__\n", "Pred =\n", - "---- 0. __new__ (0.989)\n", - " 1. load (0.001)\n", - " 2. __get__ (0.001)\n", - " 3. name (0.001)\n", - " 4. register (0.0)\n", - " 5. open (0.0)\n", - " 6. get_template (0.0)\n", + "---- 0. __new__ (0.999)\n", "\n", "Label = _boolean_input\n", "Pred =\n", - " 0. get_option_default (0.029)\n", - " 1. _get_info_for_comm (0.023)\n", - " 2. __conform__ (0.019)\n", - " 3. cli_configuration_args (0.018)\n", - " 4. _make_archive_id (0.014)\n", - " 5. axapi_failure (0.014)\n", - " 6. boolean_check (0.012)\n", + " 0. make_entry (0.098)\n", "\n", "Label = ask_merge\n", "Pred =\n", - " 0. sql_with_params (0.025)\n", - " 1. _num_days (0.024)\n", - " 2. __xor__ (0.02)\n", - " 3. changeform_view (0.017)\n", - " 4. is_same_model_operation (0.016)\n", - " 5. _is_enum (0.013)\n", - " 6. get_placeholder (0.012)\n", + " 0. _from_pickle_wkb (0.058)\n", "\n", "Label = feed\n", "Pred =\n", - " 0. debug (0.057)\n", - " 1. __call__ (0.023)\n", - " 2. clear (0.022)\n", - " 3. encode (0.018)\n", - " 4. _get_output_identifiers (0.018)\n", - " 5. _send (0.014)\n", - " 6. get_keyauthorization (0.01)\n", + " 0. tell (0.04)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. func (0.0)\n", - " 5. build (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = validate_consistency\n", "Pred =\n", - " 0. add_dummy_node (0.03)\n", - " 1. _nodes_and_edges (0.021)\n", - " 2. add_node (0.018)\n", - " 3. relabel_aliases (0.016)\n", - " 4. extra (0.01)\n", - " 5. clear_select_fields (0.01)\n", - " 6. get_template_libraries (0.009)\n", + " 0. test_recalculate_max_depth (0.028)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = record_migration\n", "Pred =\n", - " 0. get_models (0.063)\n", - " 1. has_module_perms (0.026)\n", - " 2. contains_column_references (0.019)\n", - " 3. cookies (0.009)\n", - " 4. _should_reload_connections (0.009)\n", - " 5. get_storage_engine (0.007)\n", - " 6. tuple (0.007)\n", + " 0. handle_field (0.03)\n", "\n", "Label = migration_qs\n", "Pred =\n", - " 0. supports_provisioned_mode (0.298)\n", - " 1. has_table (0.016)\n", - " 2. _get_test_db_name (0.013)\n", - " 3. e (0.012)\n", - " 4. semi_major (0.01)\n", - " 5. needs_explain_extended (0.01)\n", - " 6. supports_empty_geometry_collection (0.01)\n", + " 0. output_field (0.206)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. process_request (0.0)\n", - " 4. start_serialization (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. _set_list (0.0)\n", + "---- 0. __init__ (0.636)\n", "\n", "Label = changes\n", "Pred =\n", - " 0. _lazy_method (0.031)\n", - " 1. runshell (0.012)\n", - " 2. connect (0.011)\n", - " 3. _generate_plan (0.011)\n", - " 4. test_db_signature (0.01)\n", - " 5. resolve_expression (0.01)\n", - " 6. database_backwards (0.01)\n", + " 0. allow_migrate_model (0.06)\n", "\n", "Label = state_forwards\n", "Pred =\n", - "---- 0. state_forwards (0.5)\n", - " 1. __setstate__ (0.139)\n", - " 2. database_forwards (0.094)\n", - " 3. clone (0.012)\n", - " 4. contribute_to_class (0.012)\n", - " 5. reset (0.009)\n", - " 6. deconstruct (0.007)\n", + "---- 0. state_forwards (0.552)\n", "\n", "Label = database_forwards\n", "Pred =\n", - " 0. database_backwards (0.957)\n", - "---- 1. database_forwards (0.034)\n", - " 2. remove_model (0.0)\n", - " 3. ensure_schema (0.0)\n", - " 4. remove_sql (0.0)\n", - " 5. execute (0.0)\n", - " 6. add_legacy_name (0.0)\n", + " 0. database_backwards (0.979)\n", "\n", "Label = database_backwards\n", "Pred =\n", - "---- 0. database_backwards (0.981)\n", - " 1. database_forwards (0.008)\n", - " 2. _get_child_type (0.0)\n", - " 3. ensure_schema (0.0)\n", - " 4. remove_model (0.0)\n", - " 5. _get_schema (0.0)\n", - " 6. remove_sql (0.0)\n", + "---- 0. database_backwards (0.987)\n", "\n", "Label = reduce\n", "Pred =\n", - "---- 0. reduce (0.998)\n", - " 1. get_url (0.0)\n", - " 2. references_model (0.0)\n", - " 3. __reduce_ex__ (0.0)\n", - " 4. is_same_model_operation (0.0)\n", - " 5. references_field (0.0)\n", - " 6. _get_limit_offset_params (0.0)\n", + "---- 0. reduce (0.989)\n", "\n", "Label = database_backwards\n", "Pred =\n", - "---- 0. database_backwards (0.507)\n", - " 1. database_forwards (0.482)\n", - " 2. remove_model (0.001)\n", - " 3. list (0.0)\n", - " 4. state_forwards (0.0)\n", - " 5. unset_installed_apps (0.0)\n", - " 6. add_prefix (0.0)\n", + "---- 0. database_backwards (0.606)\n", "\n", "Label = deconstruct\n", "Pred =\n", "---- 0. deconstruct (1.0)\n", - " 1. __reduce__ (0.0)\n", - " 2. serialize (0.0)\n", - " 3. RelatedObjectDoesNotExist (0.0)\n", - " 4. model (0.0)\n", - " 5. contribute_to_class (0.0)\n", - " 6. clone (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = database_forwards\n", "Pred =\n", - " 0. database_backwards (0.916)\n", - "---- 1. database_forwards (0.072)\n", - " 2. remove_model (0.001)\n", - " 3. _get_child_type (0.0)\n", - " 4. ensure_schema (0.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 5. references_field (0.0)\n", - " 6. remove_sql (0.0)\n", + " 0. database_backwards (0.867)\n", "\n", "Label = state_forwards\n", "Pred =\n", - "---- 0. state_forwards (0.18)\n", - " 1. mutate_state (0.037)\n", - " 2. check_and_update_obj (0.03)\n", - " 3. __setstate__ (0.02)\n", - " 4. add_model (0.019)\n", - " 5. handle_app_config (0.011)\n", - " 6. register_endpoint (0.01)\n", + "---- 0. state_forwards (0.585)\n", "\n", "Label = database_forwards\n", "Pred =\n", - " 0. database_backwards (0.959)\n", - "---- 1. database_forwards (0.019)\n", - " 2. ensure_schema (0.001)\n", - " 3. _get_child_type (0.0)\n", - " 4. remove_model (0.0)\n", - " 5. _is_empty_leaf (0.0)\n", - " 6. unset_installed_apps (0.0)\n", + " 0. database_backwards (0.966)\n", "\n", "Label = database_backwards\n", "Pred =\n", - "---- 0. database_backwards (0.959)\n", - " 1. database_forwards (0.019)\n", - " 2. ensure_schema (0.001)\n", - " 3. _get_child_type (0.0)\n", - " 4. remove_model (0.0)\n", - " 5. _is_empty_leaf (0.0)\n", - " 6. unset_installed_apps (0.0)\n", + "---- 0. database_backwards (0.966)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. describe (0.627)\n", - "---- 1. __repr__ (0.173)\n", - " 2. __str__ (0.172)\n", - " 3. as_text (0.001)\n", - " 4. serialize (0.001)\n", - " 5. add (0.001)\n", - " 6. name (0.001)\n", + "---- 0. __repr__ (0.779)\n", "\n", "Label = state_forwards\n", "Pred =\n", - "---- 0. state_forwards (0.995)\n", - " 1. database_forwards (0.001)\n", - " 2. add_model (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. process_state (0.0)\n", - " 5. mutate_state (0.0)\n", - " 6. database_backwards (0.0)\n", + "---- 0. state_forwards (0.997)\n", "\n", "Label = database_forwards\n", "Pred =\n", - " 0. database_backwards (0.894)\n", - "---- 1. database_forwards (0.089)\n", - " 2. remove_sql (0.001)\n", - " 3. ensure_schema (0.0)\n", - " 4. remove_model (0.0)\n", - " 5. allow_migrate_model (0.0)\n", - " 6. _get_child_type (0.0)\n", + " 0. database_backwards (0.736)\n", "\n", "Label = database_backwards\n", "Pred =\n", - "---- 0. database_backwards (0.898)\n", - " 1. database_forwards (0.036)\n", - " 2. allow_migrate_model (0.002)\n", - " 3. ensure_schema (0.001)\n", - " 4. remove_sql (0.001)\n", - " 5. _get_child_type (0.001)\n", - " 6. _ensure_schema_cached (0.001)\n", + "---- 0. database_backwards (0.78)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. __setstate__ (0.0)\n", "\n", "Label = deconstruct\n", "Pred =\n", - "---- 0. deconstruct (0.999)\n", - " 1. contribute_to_class (0.0)\n", - " 2. __reduce__ (0.0)\n", - " 3. model (0.0)\n", - " 4. name (0.0)\n", - " 5. import_models (0.0)\n", - " 6. clone (0.0)\n", + "---- 0. deconstruct (1.0)\n", "\n", "Label = describe\n", "Pred =\n", "---- 0. describe (0.999)\n", - " 1. __str__ (0.001)\n", - " 2. add_prefix (0.0)\n", - " 3. label_lower (0.0)\n", - " 4. get_url (0.0)\n", - " 5. time_trunc_sql (0.0)\n", - " 6. as_text (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = list\n", "Pred =\n", - "---- 0. list (0.761)\n", - " 1. name (0.022)\n", - " 2. register (0.008)\n", - " 3. _setup (0.006)\n", - " 4. process_request (0.006)\n", - " 5. _prepare (0.006)\n", - " 6. url (0.005)\n", + "---- 0. list (0.062)\n", "\n", "Label = list\n", "Pred =\n", - " 0. local (0.053)\n", - " 1. references_table (0.019)\n", - "---- 2. list (0.014)\n", - " 3. _mark_post_parse_error (0.013)\n", - " 4. get_login_url (0.012)\n", - " 5. get_level (0.012)\n", - " 6. FILES (0.01)\n", + "---- 0. list (0.038)\n", "\n", "Label = _should_handle\n", "Pred =\n", - "---- 0. _should_handle (0.978)\n", - " 1. url_or_none (0.001)\n", - " 2. url (0.001)\n", - " 3. escape_leading_slashes (0.001)\n", - " 4. combine_url (0.0)\n", - " 5. file_path (0.0)\n", - " 6. prepend_host (0.0)\n", + "---- 0. _should_handle (0.938)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. init_poolmanager (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. __setitem__ (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = get_admin_url\n", "Pred =\n", - " 0. label (0.128)\n", - " 1. get_url (0.078)\n", - " 2. label_lower (0.055)\n", - " 3. id_for_label (0.033)\n", - " 4. __str__ (0.026)\n", - " 5. getMonitor (0.013)\n", - " 6. model_class (0.01)\n", + " 0. get_url (0.084)\n", "\n", "Label = media\n", "Pred =\n", - "---- 0. media (0.97)\n", - " 1. debug (0.001)\n", - " 2. wkt (0.001)\n", - " 3. x (0.001)\n", - " 4. base_location (0.001)\n", - " 5. as_ul (0.001)\n", - " 6. fit_transform (0.0)\n", + "---- 0. media (0.988)\n", "\n", "Label = quote\n", "Pred =\n", - " 0. escape (0.43)\n", - " 1. escapejs (0.06)\n", - " 2. mark_safe (0.058)\n", - " 3. is_drm_protected (0.016)\n", - " 4. unescapeHTML (0.014)\n", - " 5. unquote_if_non_empty (0.012)\n", - " 6. smart_bytes (0.012)\n", + " 0. escapejs (0.246)\n", "\n", "Label = has_css_class\n", "Pred =\n", - " 0. get_css_value (0.141)\n", - " 1. child_text (0.009)\n", - " 2. normalize_email (0.009)\n", - " 3. simplify (0.009)\n", - " 4. generate_removed_constraints (0.009)\n", - " 5. get_storage_engine (0.008)\n", - " 6. supports_transactions (0.007)\n", + " 0. cookie_date (0.035)\n", "\n", "Label = unregister\n", "Pred =\n", - " 0. relabel_aliases (0.037)\n", - " 1. get_field_by_name (0.019)\n", - " 2. close_all (0.013)\n", - " 3. remove_move (0.011)\n", - " 4. _check_parent_chain (0.011)\n", - " 5. add_field_update (0.008)\n", - " 6. wait_for_status (0.008)\n", + " 0. add_blocks (0.032)\n", "\n", "Label = is_registered\n", "Pred =\n", - " 0. build_kwargs (0.133)\n", - " 1. is_container_connected (0.02)\n", - " 2. __reduce__ (0.012)\n", - " 3. related_objects (0.011)\n", - " 4. _should_reload_connections (0.008)\n", - " 5. units (0.007)\n", - " 6. __get__ (0.006)\n", + " 0. exists (0.447)\n", "\n", "Label = _check_field_spec\n", "Pred =\n", - " 0. serializable_value (0.033)\n", - " 1. __contains__ (0.018)\n", - " 2. _check_list_filter (0.015)\n", - " 3. _field_indexes_sql (0.014)\n", - " 4. index (0.011)\n", - " 5. prepare_sql_script (0.009)\n", - " 6. format_value (0.009)\n", + " 0. time_trunc_sql (0.055)\n", "\n", "Label = must_inherit_from\n", "Pred =\n", - " 0. must_be (0.787)\n", - " 1. _check_relation (0.038)\n", - " 2. refer_to_missing_field (0.032)\n", - " 3. _check_list_display_links_item (0.009)\n", - " 4. _check_radio_fields_value (0.006)\n", - " 5. get_url (0.004)\n", - " 6. make_response (0.003)\n", + " 0. must_be (0.381)\n", "\n", "Label = has_output\n", "Pred =\n", - "---- 0. has_output (0.524)\n", - " 1. _get_choices (0.021)\n", - " 2. expected_parameters (0.009)\n", - " 3. dimension (0.009)\n", - " 4. dims (0.008)\n", - " 5. quote_name (0.007)\n", - " 6. local_related_fields (0.007)\n", + " 0. get_limit_choices_to (0.157)\n", "\n", "Label = field_choices\n", "Pred =\n", - "---- 0. field_choices (0.161)\n", - " 1. pre_save (0.037)\n", - " 2. ask_rename (0.028)\n", - " 3. get_action_choices (0.025)\n", - " 4. _check_save_on_top (0.018)\n", - " 5. _coerce_field_name (0.017)\n", - " 6. _populate_pk_values (0.016)\n", + "---- 0. field_choices (0.069)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setitem__ (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. ip (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. init_poolmanager (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = base_url_parameters\n", "Pred =\n", - " 0. get_limit_choices_to (0.832)\n", - " 1. apply_limit_choices_to_to_formfield (0.026)\n", - " 2. read_manifest (0.009)\n", - " 3. formfield (0.002)\n", - " 4. _set_queryset (0.002)\n", - " 5. _check_max_num (0.002)\n", - " 6. trim_url (0.001)\n", + " 0. get_limit_choices_to (0.984)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setstate__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setitem__ (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. ip (0.0)\n", "\n", "Label = confirm_login_allowed\n", "Pred =\n", - "---- 0. confirm_login_allowed (0.399)\n", - " 1. get_invalid_login_error (0.02)\n", - " 2. validate (0.015)\n", - " 3. process_request (0.015)\n", - " 4. _prepare (0.014)\n", - " 5. _apply_rel_filters (0.014)\n", - " 6. get_user (0.013)\n", + "---- 0. confirm_login_allowed (0.035)\n", "\n", "Label = check\n", "Pred =\n", - "---- 0. check (0.244)\n", - " 1. _download_webpage_handle (0.093)\n", - " 2. clone (0.048)\n", - " 3. _run_checks (0.037)\n", - " 4. debug (0.014)\n", - " 5. copy (0.012)\n", - " 6. clear (0.009)\n", + "---- 0. check (0.74)\n", "\n", "Label = get_fieldsets\n", "Pred =\n", - "---- 0. get_fieldsets (0.286)\n", - " 1. _set_queryset (0.03)\n", - " 2. modelform_defines_fields (0.016)\n", - " 3. get_fields (0.015)\n", - " 4. get_readonly_fields (0.011)\n", - " 5. _check_search_fields (0.011)\n", - " 6. deletion_field (0.009)\n", + "---- 0. get_fieldsets (0.948)\n", "\n", "Label = log_addition\n", "Pred =\n", - " 0. log_change (0.97)\n", - " 1. log_deletion (0.003)\n", - " 2. _do_insert (0.001)\n", - " 3. get_or_create (0.0)\n", - " 4. get_reverse_path_info (0.0)\n", - " 5. delete_model (0.0)\n", - " 6. get_by_natural_key (0.0)\n", + " 0. log_change (0.984)\n", "\n", "Label = get_queryset\n", "Pred =\n", - " 0. queryset (0.204)\n", - "---- 1. get_queryset (0.153)\n", - " 2. get_object (0.034)\n", - " 3. related_objects (0.032)\n", - " 4. delete_queryset (0.021)\n", - " 5. get_dated_items (0.02)\n", - " 6. field_choices (0.02)\n", + "---- 0. get_queryset (0.105)\n", "\n", "Label = admin_urlname\n", "Pred =\n", - " 0. must_be (0.067)\n", - " 1. label_lower (0.058)\n", - " 2. get_registered_model (0.035)\n", - " 3. refer_to_missing_field (0.023)\n", - " 4. gfk_key (0.018)\n", - " 5. _is_object (0.018)\n", - " 6. _get_app_label_and_model_name (0.016)\n", + " 0. date_format (0.024)\n", "\n", "Label = static\n", "Pred =\n", - " 0. do_static (0.019)\n", - " 1. get_storage_account (0.016)\n", - " 2. reset_format_cache (0.013)\n", - " 3. _current_scheme_host (0.013)\n", - " 4. dumps (0.012)\n", - " 5. get_template (0.011)\n", - " 6. _get_static_folder (0.01)\n", + " 0. do_static (0.083)\n", "\n", "Label = _boolean_icon\n", "Pred =\n", - " 0. decrypt_url (0.022)\n", - " 1. _ids_to_results (0.018)\n", - " 2. sanitize_url (0.014)\n", - " 3. _parse_smil_subtitles (0.013)\n", - " 4. get_expiration_time (0.013)\n", - " 5. permission_required (0.013)\n", - " 6. _fetch_page (0.011)\n", + " 0. extract_url (0.121)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = result_list_tag\n", "Pred =\n", - " 0. date_hierarchy_tag (0.193)\n", - " 1. pagination_tag (0.183)\n", - " 2. search_form_tag (0.178)\n", - " 3. submit_row_tag (0.074)\n", - " 4. prepopulated_fields_js_tag (0.073)\n", - " 5. admin_actions_tag (0.062)\n", - " 6. do_static (0.012)\n", + " 0. search_form_tag (0.225)\n", "\n", "Label = has_perm\n", "Pred =\n", - " 0. has_view_or_change_permission (0.103)\n", - " 1. get_model_perms (0.08)\n", - " 2. _has_add_permission (0.063)\n", - " 3. lookups (0.019)\n", - " 4. has_delete_permission (0.016)\n", - " 5. _check_save_on_top (0.014)\n", - " 6. ask_rename (0.013)\n", + " 0. has_view_or_change_permission (0.413)\n", "\n", "Label = has_permission\n", "Pred =\n", - " 0. get_users (0.055)\n", - " 1. get_permission_required (0.032)\n", - " 2. get_urls (0.026)\n", - " 3. has_module_permission (0.022)\n", - " 4. handle_no_permission (0.021)\n", - " 5. check_perms (0.018)\n", - " 6. get_state (0.015)\n", + " 0. get_permission_required (0.153)\n", "\n", "Label = formfield_for_manytomany\n", "Pred =\n", - " 0. get_changelist_form (0.103)\n", - " 1. formfield (0.078)\n", - " 2. formfield_for_dbfield (0.072)\n", - " 3. url_parameters (0.021)\n", - " 4. get_key_columns (0.019)\n", - " 5. _get_set_deprecation_msg_params (0.018)\n", - " 6. _delete_constraint_sql (0.017)\n", + " 0. formfield_for_dbfield (0.136)\n", "\n", "Label = lookup_allowed\n", "Pred =\n", - " 0. time_trunc_sql (0.157)\n", - " 1. get_distance (0.066)\n", - " 2. date_extract_sql (0.057)\n", - " 3. date_trunc_sql (0.042)\n", - " 4. time_extract_sql (0.037)\n", - " 5. get_lookup (0.017)\n", - " 6. as_text (0.015)\n", + " 0. date_extract_sql (0.444)\n", "\n", "Label = response_add\n", "Pred =\n", - " 0. get_post_parameters (0.288)\n", - " 1. put (0.023)\n", - " 2. route (0.021)\n", - " 3. request (0.016)\n", - " 4. ping (0.015)\n", - " 5. get (0.014)\n", - " 6. head (0.013)\n", + " 0. response_post_save_add (0.119)\n", "\n", "Label = has_perms\n", "Pred =\n", - "---- 0. has_perms (0.969)\n", - " 1. has_perm (0.006)\n", - " 2. __contains__ (0.001)\n", - " 3. has_module_perms (0.001)\n", - " 4. _user_has_perm (0.001)\n", - " 5. changed_data (0.001)\n", - " 6. ordering_field (0.0)\n", + "---- 0. has_perms (0.987)\n", "\n", "Label = get_full_name\n", "Pred =\n", - " 0. __repr__ (0.927)\n", - " 1. __str__ (0.043)\n", - " 2. id_for_label (0.003)\n", - " 3. describe (0.002)\n", - " 4. version (0.002)\n", - " 5. sign (0.001)\n", - " 6. name (0.001)\n", + " 0. __repr__ (0.83)\n", "\n", "Label = set_password\n", "Pred =\n", " 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. destination (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.989)\n", - " 1. subwidgets (0.0)\n", - " 2. import_models (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. add_model (0.0)\n", - " 5. _write_string (0.0)\n", - " 6. contribute_to_class (0.0)\n", + " 0. process_request (0.437)\n", "\n", "Label = __getitem__\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. __contains__ (0.218)\n", - " 1. has_perms (0.101)\n", - " 2. __str__ (0.059)\n", - " 3. count (0.039)\n", - " 4. has_perm (0.028)\n", - " 5. has_module_perms (0.027)\n", - " 6. is_cidr_notation (0.026)\n", + "Pred =\n", + " 0. __contains__ (0.62)\n", "\n", "Label = get_help_text\n", "Pred =\n", - " 0. as_ul (0.079)\n", - " 1. as_text (0.038)\n", - " 2. as_table (0.031)\n", - " 3. quote_name (0.024)\n", - " 4. inline_formset_data (0.023)\n", - " 5. RelatedObjectDoesNotExist (0.02)\n", - " 6. get_date_error_message (0.019)\n", + " 0. __str__ (0.219)\n", "\n", "Label = _post_clean\n", "Pred =\n", - " 0. validate (0.79)\n", - " 1. process_request (0.016)\n", - " 2. run_validators (0.015)\n", - " 3. confirm_login_allowed (0.01)\n", - " 4. _check_error (0.009)\n", - " 5. clean_password2 (0.008)\n", - " 6. _real_initialize (0.008)\n", + " 0. _clean_form (0.204)\n", "\n", "Label = clean_old_password\n", "Pred =\n", - " 0. confirm_login_allowed (0.157)\n", - " 1. get_urls (0.067)\n", - " 2. validate (0.056)\n", - " 3. clean_new_password2 (0.039)\n", - " 4. create_user (0.033)\n", - " 5. get_username (0.027)\n", - " 6. get_invalid_login_error (0.026)\n", + " 0. clean (0.494)\n", "\n", "Label = get_hashers\n", "Pred =\n", - " 0. add_operation (0.029)\n", - " 1. get_hasher (0.025)\n", - " 2. buffer_with_style (0.019)\n", - " 3. get_hashers_by_algorithm (0.019)\n", - " 4. _sqlite_timestamp_diff (0.015)\n", - " 5. print_help (0.015)\n", - " 6. _execute_test_db_creation (0.013)\n", + " 0. get_hasher (0.308)\n", "\n", "Label = must_update\n", "Pred =\n", - "---- 0. must_update (0.032)\n", - " 1. _geos_ptr (0.019)\n", - " 2. get_session_auth_hash (0.019)\n", - " 3. make_token (0.018)\n", - " 4. _num_days (0.017)\n", - " 5. keys (0.016)\n", - " 6. wait_for_popup (0.015)\n", + " 0. salt (0.072)\n", "\n", "Label = encode\n", "Pred =\n", - "---- 0. encode (0.867)\n", - " 1. get_session_auth_hash (0.028)\n", - " 2. generate_hash (0.02)\n", - " 3. hash_key (0.012)\n", - " 4. _hash (0.006)\n", - " 5. base64_hmac (0.004)\n", - " 6. _key_to_file (0.003)\n", + "---- 0. encode (0.922)\n", "\n", "Label = _get_input_message\n", "Pred =\n", - " 0. _get_model_tuple (0.074)\n", - " 1. _get_field_name (0.068)\n", - " 2. _check_prepopulated_fields_value_item (0.048)\n", - " 3. add_prefix (0.024)\n", - " 4. check_query_object_type (0.024)\n", - " 5. _check_filter_item (0.021)\n", - " 6. refer_to_missing_field (0.018)\n", + " 0. _page_url (0.073)\n", "\n", "Label = _combine\n", "Pred =\n", - "---- 0. _combine (0.997)\n", - " 1. __call__ (0.0)\n", - " 2. intersection (0.0)\n", - " 3. nud (0.0)\n", - " 4. __and__ (0.0)\n", - " 5. __sub__ (0.0)\n", - " 6. _get_support_mask (0.0)\n", + "---- 0. _combine (0.999)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. build (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. ip (0.0)\n", - " 3. destination (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. __eq__ (0.0)\n", - " 6. init_poolmanager (0.0)\n", "\n", "Label = check_supported\n", "Pred =\n", - " 0. source_is_geography (0.027)\n", - " 1. ewkb_w (0.014)\n", - " 2. _tracing_name (0.014)\n", - " 3. schema_editor (0.012)\n", - " 4. right (0.012)\n", - " 5. check_sts_preload (0.012)\n", - " 6. _get_schema (0.012)\n", + " 0. execute_sql_flush (0.049)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. build (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = prepare_value\n", "Pred =\n", - "---- 0. prepare_value (0.991)\n", - " 1. to_python (0.001)\n", - " 2. format_value (0.001)\n", - " 3. _check_type_str (0.001)\n", - " 4. number (0.0)\n", - " 5. clean (0.0)\n", - " 6. get_prep_value (0.0)\n", + "---- 0. prepare_value (0.994)\n", "\n", "Label = decompress\n", "Pred =\n", - " 0. serialize (0.424)\n", - " 1. exists (0.062)\n", - " 2. __call__ (0.046)\n", - " 3. deconstruct (0.021)\n", - " 4. get_value (0.019)\n", - " 5. __reduce__ (0.017)\n", - " 6. project (0.013)\n", + " 0. compare (0.178)\n", "\n", "Label = as_sql\n", "Pred =\n", - "---- 0. as_sql (0.455)\n", - " 1. as_mysql (0.241)\n", - " 2. as_sqlite (0.048)\n", - " 3. as_oracle (0.041)\n", - " 4. resolve_expression_parameter (0.029)\n", - " 5. select_format (0.027)\n", - " 6. time_trunc_sql (0.006)\n", + "---- 0. as_sql (0.999)\n", "\n", "Label = output_field\n", "Pred =\n", - "---- 0. output_field (0.544)\n", - " 1. _resolve_output_field (0.232)\n", - " 2. get_substr (0.024)\n", - " 3. _output_field_or_none (0.013)\n", - " 4. get_source_fields (0.013)\n", - " 5. lhs (0.008)\n", - " 6. geo_field (0.007)\n", + "---- 0. output_field (0.496)\n", "\n", "Label = __call__\n", "Pred =\n", "---- 0. __call__ (1.0)\n", - " 1. patch (0.0)\n", - " 2. eval (0.0)\n", - " 3. call_and_shelve (0.0)\n", - " 4. put (0.0)\n", - " 5. write (0.0)\n", - " 6. post (0.0)\n", "\n", "Label = set_attributes_from_name\n", "Pred =\n", - "---- 0. set_attributes_from_name (0.987)\n", - " 1. local_setter (0.001)\n", - " 2. __setattr__ (0.001)\n", - " 3. add_blocks (0.0)\n", - " 4. add_select_related (0.0)\n", - " 5. register (0.0)\n", - " 6. get_loaded_field_names_cb (0.0)\n", + "---- 0. set_attributes_from_name (0.983)\n", "\n", "Label = get_context_data\n", "Pred =\n", - " 0. options (0.066)\n", - " 1. to_request (0.05)\n", - " 2. collection (0.043)\n", - " 3. from_response (0.032)\n", - " 4. _request_for_item (0.029)\n", - " 5. response_to_hash (0.029)\n", - " 6. head (0.016)\n", + " 0. to_request (0.097)\n", "\n", "Label = get_filter_kwargs_for_object\n", "Pred =\n", - " 0. get_forward_related_filter (0.937)\n", - " 1. _is_matching_generic_foreign_key (0.006)\n", - " 2. _check_object_id_field (0.002)\n", - " 3. content_type (0.001)\n", - " 4. related_objects (0.001)\n", - " 5. test_2D_transformer_output (0.001)\n", - " 6. Country (0.001)\n", + " 0. get_forward_related_filter (0.689)\n", "\n", "Label = __call__\n", "Pred =\n", "---- 0. __call__ (0.999)\n", - " 1. eval (0.0)\n", - " 2. write (0.0)\n", - " 3. patch (0.0)\n", - " 4. clone (0.0)\n", - " 5. __getattr__ (0.0)\n", - " 6. __new__ (0.0)\n", "\n", "Label = _remove_prefetched_objects\n", "Pred =\n", - "---- 0. _remove_prefetched_objects (0.991)\n", - " 1. delete_model (0.0)\n", - " 2. related_objects (0.0)\n", - " 3. delete_cached_value (0.0)\n", - " 4. get_queryset (0.0)\n", - " 5. clear (0.0)\n", - " 6. get_object (0.0)\n", + "---- 0. _remove_prefetched_objects (0.997)\n", "\n", "Label = dumps\n", "Pred =\n", - " 0. has_header (0.033)\n", - " 1. loads (0.02)\n", - " 2. _pickle_copy (0.019)\n", - " 3. pull (0.018)\n", - " 4. model_from_json (0.018)\n", - " 5. _str_sorted (0.017)\n", - " 6. decode (0.017)\n", + " 0. _str_sorted (0.061)\n", "\n", "Label = clear\n", "Pred =\n", - " 0. __init__ (0.106)\n", - " 1. on_update (0.082)\n", - " 2. process_default (0.05)\n", - " 3. _set_session_key (0.032)\n", - " 4. cycle_key (0.032)\n", - " 5. flush (0.025)\n", - "---- 6. clear (0.023)\n", + " 0. on_update (0.686)\n", "\n", "Label = flush\n", "Pred =\n", - "---- 0. flush (0.989)\n", - " 1. delete (0.001)\n", - " 2. __delitem__ (0.001)\n", - " 3. delete_model (0.001)\n", - " 4. remove (0.001)\n", - " 5. clear (0.0)\n", - " 6. cycle_key (0.0)\n", + "---- 0. flush (0.994)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. predict_proba (0.0)\n", - " 5. build (0.0)\n", - " 6. func (0.0)\n", "\n", "Label = _get_storage_path\n", "Pred =\n", - " 0. get_storage_class (0.045)\n", - " 1. local (0.045)\n", - " 2. ensure_dir_exists (0.028)\n", - " 3. is_ignored (0.019)\n", - " 4. _XML (0.014)\n", - " 5. get_version_tuple (0.013)\n", - " 6. should_redirect_with_slash (0.012)\n", + " 0. get_storage_class (0.048)\n", "\n", "Label = create\n", "Pred =\n", - "---- 0. create (0.998)\n", - " 1. delete (0.0)\n", - " 2. update (0.0)\n", - " 3. get (0.0)\n", - " 4. add (0.0)\n", - " 5. start (0.0)\n", - " 6. copy (0.0)\n", + "---- 0. create (0.983)\n", "\n", "Label = _check_for_i18n\n", "Pred =\n", - " 0. _format_arg (0.045)\n", - " 1. check_envelope (0.017)\n", - " 2. get_notes (0.015)\n", - " 3. filter (0.012)\n", - " 4. _get_chunks (0.011)\n", - " 5. has_level_handler (0.01)\n", - " 6. __do_ytdl_file (0.009)\n", + " 0. _rebuild (0.064)\n", "\n", "Label = _hash\n", "Pred =\n", - "---- 0. _hash (0.249)\n", - " 1. get_session_auth_hash (0.215)\n", - " 2. hash_key (0.07)\n", - " 3. _make_token_with_timestamp (0.029)\n", - " 4. generate_hash (0.025)\n", - " 5. _encode (0.014)\n", - " 6. _coerce (0.009)\n", + "---- 0. _hash (0.562)\n", "\n", "Label = tags\n", "Pred =\n", - " 0. check_tag_status (0.067)\n", - " 1. get_storage_tags (0.054)\n", - " 2. construct_tags (0.025)\n", - " 3. encode_dict (0.024)\n", - " 4. tag (0.02)\n", - " 5. ansible_dict_to_boto3_tag_list (0.017)\n", - " 6. has_tags_modifications (0.016)\n", + " 0. check_tag_status (0.192)\n", "\n", "Label = _store\n", "Pred =\n", - " 0. _get (0.393)\n", - " 1. process_response (0.05)\n", - " 2. post (0.038)\n", - " 3. as_sqlite (0.016)\n", - " 4. resolve_expression (0.014)\n", - " 5. inner (0.012)\n", - " 6. contribute_to_class (0.011)\n", + " 0. _get (0.281)\n", "\n", "Label = ptr\n", "Pred =\n", - " 0. srid (0.109)\n", - " 1. outdim (0.105)\n", - " 2. content (0.058)\n", - " 3. rename_column_references (0.055)\n", - " 4. geom_type (0.042)\n", - " 5. partition (0.026)\n", - " 6. type (0.024)\n", + " 0. content (0.037)\n", "\n", "Label = __add__\n", "Pred =\n", - " 0. __truediv__ (0.31)\n", - " 1. __sub__ (0.223)\n", - " 2. __mul__ (0.211)\n", - " 3. __iadd__ (0.03)\n", - " 4. __rmul__ (0.023)\n", - "---- 5. __add__ (0.023)\n", - " 6. __rtruediv__ (0.015)\n", + " 0. __mul__ (0.274)\n", "\n", "Label = layer_count\n", "Pred =\n", - " 0. count (0.15)\n", - " 1. geom_count (0.106)\n", - " 2. point_count (0.056)\n", - " 3. width (0.042)\n", - " 4. size (0.029)\n", - " 5. num_fields (0.025)\n", - " 6. layer_name (0.024)\n", + " 0. is_unspecified (0.08)\n", "\n", "Label = __eq__\n", "Pred =\n", - "---- 0. __eq__ (0.997)\n", - " 1. equals (0.001)\n", - " 2. __add__ (0.0)\n", - " 3. __lt__ (0.0)\n", - " 4. clone (0.0)\n", - " 5. __contains__ (0.0)\n", - " 6. difference (0.0)\n", + "---- 0. __eq__ (0.999)\n", "\n", "Label = kml\n", "Pred =\n", - "---- 0. kml (0.807)\n", - " 1. nodata_value (0.012)\n", - " 2. format_value (0.005)\n", - " 3. dims (0.005)\n", - " 4. tuple (0.004)\n", - " 5. contains (0.003)\n", - " 6. point_count (0.003)\n", + "---- 0. kml (0.375)\n", "\n", "Label = _topology\n", "Pred =\n", - " 0. _geomgen (0.099)\n", - " 1. eval (0.087)\n", - " 2. __call__ (0.071)\n", - " 3. __getattr__ (0.03)\n", - " 4. url (0.024)\n", - " 5. __radd__ (0.022)\n", - " 6. update (0.016)\n", + " 0. distance (0.085)\n", "\n", "Label = y\n", "Pred =\n", - "---- 0. y (0.365)\n", - " 1. _get_y (0.202)\n", - " 2. dims (0.063)\n", - " 3. size (0.015)\n", - " 4. x (0.013)\n", - " 5. _geos_ptr (0.007)\n", - " 6. getOrdinate (0.007)\n", + "---- 0. y (0.836)\n", "\n", "Label = tuple\n", "Pred =\n", - " 0. _set_coord_dim (0.022)\n", - " 1. srid (0.019)\n", - " 2. _get_coord_dim (0.015)\n", - "---- 3. tuple (0.014)\n", - " 4. ur (0.013)\n", - " 5. num_feat (0.011)\n", - " 6. _validate_y (0.01)\n", + " 0. ll (0.173)\n", "\n", "Label = shell\n", "Pred =\n", - " 0. first (0.158)\n", - " 1. last (0.028)\n", - " 2. format_value (0.026)\n", - " 3. chassis_serial (0.019)\n", - " 4. default_persistence_profile (0.018)\n", - " 5. date (0.012)\n", - " 6. as_ul (0.011)\n", + " 0. default_persistence_profile (0.038)\n", "\n", "Label = point_count\n", "Pred =\n", - "---- 0. point_count (0.988)\n", - " 1. tuple (0.002)\n", - " 2. geom_count (0.001)\n", - " 3. count (0.0)\n", - " 4. _geos_ptr (0.0)\n", - " 5. __len__ (0.0)\n", - " 6. field_types (0.0)\n", + "---- 0. point_count (0.994)\n", "\n", "Label = centroid\n", "Pred =\n", - "---- 0. centroid (0.445)\n", - " 1. semi_major (0.005)\n", - " 2. is_reserved (0.005)\n", - " 3. height (0.005)\n", - " 4. allow_relation (0.005)\n", - " 5. cpu (0.004)\n", - " 6. vm_status (0.004)\n", + "---- 0. centroid (0.476)\n", "\n", "Label = std_call\n", "Pred =\n", - " 0. get_key_func (0.104)\n", - " 1. account_data (0.054)\n", - " 2. get_uid (0.026)\n", - " 3. cut (0.018)\n", - " 4. decorator (0.013)\n", - " 5. _sanitize_token (0.012)\n", - " 6. none_guard (0.01)\n", + " 0. keep_lazy_text (0.042)\n", "\n", "Label = __getitem__\n", "Pred =\n", - " 0. _checkindex (0.239)\n", - " 1. tuple (0.074)\n", - " 2. index (0.061)\n", - " 3. insert (0.038)\n", - " 4. get_indices (0.023)\n", - " 5. get_shape (0.021)\n", - " 6. __iter__ (0.019)\n", + " 0. index (0.8)\n", "\n", "Label = _from_sequence\n", "Pred =\n", - " 0. envelope (0.111)\n", - " 1. add_georss_point (0.024)\n", - " 2. _set_standard (0.018)\n", - " 3. identify_epsg (0.015)\n", - " 4. retrieval_context (0.012)\n", - " 5. _add_to_cache (0.011)\n", - " 6. inverse_flattening (0.009)\n", + " 0. envelope (0.161)\n", "\n", "Label = num_fields\n", "Pred =\n", - "---- 0. num_fields (0.115)\n", - " 1. geom_count (0.08)\n", - " 2. count (0.052)\n", - " 3. point_count (0.048)\n", - " 4. field_types (0.047)\n", - " 5. width (0.038)\n", - " 6. field_widths (0.02)\n", + " 0. field_types (0.056)\n", "\n", "Label = name\n", "Pred =\n", - " 0. description (0.48)\n", - "---- 1. name (0.198)\n", - " 2. type (0.022)\n", - " 3. state (0.019)\n", - " 4. content (0.01)\n", - " 5. enabled (0.008)\n", - " 6. get_description (0.008)\n", + " 0. description (0.82)\n", "\n", "Label = driver\n", "Pred =\n", - " 0. name (0.957)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1. __str__ (0.007)\n", - " 2. build (0.002)\n", - " 3. width (0.002)\n", - " 4. driver_count (0.002)\n", - " 5. partition (0.001)\n", - " 6. version (0.001)\n", + " 0. name (0.982)\n", "\n", "Label = srid\n", "Pred =\n", - "---- 0. srid (0.14)\n", - " 1. srs (0.122)\n", - " 2. partition (0.1)\n", - " 3. content (0.016)\n", - " 4. permanent (0.01)\n", - " 5. array (0.009)\n", - " 6. validate (0.009)\n", + "---- 0. srid (0.15)\n", "\n", "Label = height\n", "Pred =\n", - "---- 0. height (0.913)\n", - " 1. width (0.016)\n", - " 2. dims (0.004)\n", - " 3. num_interior_rings (0.003)\n", - " 4. num_fields (0.002)\n", - " 5. geom_count (0.001)\n", - " 6. point_count (0.001)\n", + "---- 0. height (0.932)\n", "\n", "Label = min\n", "Pred =\n", - " 0. max (0.332)\n", - " 1. mean (0.324)\n", - " 2. std (0.275)\n", - "---- 3. min (0.017)\n", - " 4. sum (0.004)\n", - " 5. var (0.002)\n", - " 6. prod (0.002)\n", + " 0. mean (0.372)\n", "\n", "Label = void_output\n", "Pred =\n", - " 0. int_output (0.135)\n", - " 1. voidptr_output (0.126)\n", - " 2. chararray_output (0.12)\n", - " 3. double_output (0.106)\n", - " 4. geom_output (0.062)\n", - " 5. srs_output (0.045)\n", - " 6. env_func (0.034)\n", + " 0. double_output (0.332)\n", "\n", "Label = check_arg_errcode\n", "Pred =\n", - " 0. check_errcode (0.9)\n", - " 1. check_cs_get (0.017)\n", - " 2. check_const_string (0.011)\n", - " 3. geomerrcheck (0.003)\n", - " 4. check_dbl (0.002)\n", - " 5. check_sized_string (0.001)\n", - " 6. check_err (0.001)\n", + " 0. check_errcode (0.334)\n", "\n", "Label = check_pointer\n", "Pred =\n", - " 0. check_geom (0.258)\n", - " 1. check_srs (0.201)\n", - " 2. errcheck (0.06)\n", - " 3. check_string (0.044)\n", - " 4. check_minus_one (0.037)\n", - " 5. check_sized_string (0.032)\n", - " 6. check_predicate (0.023)\n", + " 0. check_geom (0.383)\n", "\n", "Label = make_multi\n", "Pred =\n", - " 0. _is_matching_generic_foreign_key (0.06)\n", - " 1. set_field_name (0.017)\n", - " 2. _field_became_primary_key (0.015)\n", - " 3. ptr (0.013)\n", - " 4. get_joining_columns (0.012)\n", - " 5. references_model (0.012)\n", - " 6. _rename_field_sql (0.011)\n", + " 0. geom_type (0.047)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.353)\n", - " 1. as_oracle (0.018)\n", - " 2. set_model (0.015)\n", - " 3. external_entity_ref_handler (0.014)\n", - " 4. submit (0.013)\n", - " 5. clear (0.012)\n", - " 6. executemany (0.011)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = get_urls\n", "Pred =\n", - " 0. _tracks_page_func (0.065)\n", - " 1. _handle_page (0.036)\n", - " 2. decrypt_url (0.02)\n", - " 3. decorator (0.013)\n", - " 4. _extract_url (0.012)\n", - " 5. playlist_from_matches (0.011)\n", - " 6. ohdave_rsa_encrypt (0.007)\n", + " 0. _find_jwplayer_data (0.1)\n", "\n", "Label = output_field\n", "Pred =\n", - "---- 0. output_field (0.742)\n", - " 1. geo_field (0.037)\n", - " 2. _resolve_output_field (0.027)\n", - " 3. is_hidden (0.014)\n", - " 4. get_date_field (0.009)\n", - " 5. location (0.005)\n", - " 6. ewkt (0.004)\n", + "---- 0. output_field (0.9)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = as_sql\n", "Pred =\n", - "---- 0. as_sql (0.748)\n", - " 1. as_sqlite (0.114)\n", - " 2. as_mysql (0.097)\n", - " 3. as_oracle (0.016)\n", - " 4. get_compiler (0.003)\n", - " 5. as_postgresql (0.002)\n", - " 6. resolve_expression_parameter (0.001)\n", + "---- 0. as_sql (0.628)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. predict_proba (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. func (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. build (0.0)\n", + "---- 0. __init__ (1.0)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Label = as_sql\n", "Pred =\n", - "---- 0. as_sql (0.986)\n", - " 1. as_sqlite (0.005)\n", - " 2. as_mysql (0.001)\n", - " 3. process_lhs (0.001)\n", - " 4. db_type (0.001)\n", - " 5. get_compiler (0.001)\n", - " 6. process_rhs (0.001)\n", + "---- 0. as_sql (1.0)\n", "\n", "Label = process_distance\n", "Pred =\n", - " 0. as_sql (0.707)\n", - " 1. process_rhs (0.241)\n", - " 2. get_rhs_op (0.007)\n", - " 3. process_lhs (0.004)\n", - " 4. get_compiler (0.004)\n", - " 5. resolve_expression_parameter (0.003)\n", - " 6. get_db_converters (0.002)\n", + " 0. as_sql (0.542)\n", "\n", "Label = deconstruct\n", "Pred =\n", - "---- 0. deconstruct (0.999)\n", - " 1. serialize (0.0)\n", - " 2. clone (0.0)\n", - " 3. __reduce__ (0.0)\n", - " 4. name (0.0)\n", - " 5. RelatedObjectDoesNotExist (0.0)\n", - " 6. model (0.0)\n", + "---- 0. deconstruct (1.0)\n", "\n", "Label = get_raster_prep_value\n", "Pred =\n", - " 0. dumps (0.07)\n", - " 1. get_prep_value (0.061)\n", - " 2. contribute_to_class (0.051)\n", - " 3. transform (0.028)\n", - " 4. __setstate__ (0.023)\n", - " 5. update (0.02)\n", - " 6. name (0.017)\n", + " 0. __call__ (0.354)\n", "\n", "Label = select_format\n", "Pred =\n", - "---- 0. select_format (0.518)\n", - " 1. sql_with_params (0.117)\n", - " 2. as_sql (0.113)\n", - " 3. resolve_expression_parameter (0.034)\n", - " 4. get_placeholder (0.02)\n", - " 5. as_mysql (0.013)\n", - " 6. last_executed_query (0.01)\n", + "---- 0. select_format (0.912)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. load (0.0)\n", - " 6. register (0.0)\n", "\n", "Label = unpack\n", "Pred =\n", - " 0. pack (0.353)\n", - " 1. get_random_secret_key (0.037)\n", - " 2. avoid_wrapping (0.018)\n", - " 3. version_tuple (0.013)\n", - " 4. combine_duration_expression (0.012)\n", - " 5. addslashes (0.009)\n", - " 6. hex_to_bytes (0.007)\n", + " 0. u (0.27)\n", "\n", "Label = get_geometry_converter\n", "Pred =\n", - " 0. converter (0.855)\n", - " 1. convert_value (0.006)\n", - " 2. handle_field (0.006)\n", - " 3. from_db_value (0.005)\n", - " 4. _from_db_value (0.004)\n", - " 5. convert_datefield_value (0.003)\n", - " 6. convert_timefield_value (0.003)\n", + " 0. converter (0.536)\n", "\n", "Label = check_relate_argument\n", "Pred =\n", - " 0. build (0.032)\n", - " 1. call (0.031)\n", - " 2. _iter_test_masks (0.027)\n", - " 3. get_ipa_version (0.012)\n", - " 4. _dist_broadcast_coalesced (0.012)\n", - " 5. insert (0.011)\n", - " 6. writelines (0.011)\n", + " 0. has_local_mods (0.044)\n", "\n", "Label = supports_spatial_index\n", "Pred =\n", - " 0. get_storage_engine (0.139)\n", - " 1. window_frame_end (0.031)\n", - " 2. regex_lookup (0.025)\n", - " 3. supports_over_clause (0.021)\n", - " 4. has_select_for_update_skip_locked (0.017)\n", - " 5. needs_explain_extended (0.017)\n", - " 6. generate_removed_constraints (0.01)\n", + " 0. get_storage_engine (0.37)\n", "\n", "Label = spheroid\n", "Pred =\n", - " 0. datum (0.974)\n", - " 1. _get_srs (0.001)\n", - " 2. srs (0.001)\n", - " 3. _get_srid (0.001)\n", - " 4. availability_status (0.0)\n", - " 5. permanent (0.0)\n", - " 6. srid (0.0)\n", + " 0. datum (0.989)\n", "\n", "Label = check_expression_support\n", "Pred =\n", - " 0. supports_stddev (0.048)\n", - " 1. _check_connection (0.023)\n", - " 2. get_area_att_for_field (0.02)\n", - " 3. _require_file (0.018)\n", - " 4. check_settings (0.018)\n", - " 5. execute_sql_flush (0.015)\n", - " 6. _set_coord_dim (0.015)\n", + " 0. _destroy_test_user (0.101)\n", "\n", "Label = converter\n", "Pred =\n", "---- 0. converter (0.997)\n", - " 1. convert_empty_strings (0.001)\n", - " 2. convert_value (0.0)\n", - " 3. convert_empty_bytes (0.0)\n", - " 4. convert_datefield_value (0.0)\n", - " 5. convert_timefield_value (0.0)\n", - " 6. transform_value (0.0)\n", "\n", "Label = _set_z\n", "Pred =\n", - " 0. _set_single (0.567)\n", - " 1. _set_x (0.05)\n", - " 2. _set_y (0.038)\n", - " 3. _get_x (0.026)\n", - " 4. __setitem__ (0.023)\n", - " 5. _get_y (0.02)\n", - " 6. setOrdinate (0.016)\n", + " 0. _set_single (0.221)\n", "\n", "Label = contains_properly\n", "Pred =\n", - " 0. contains (0.365)\n", - " 1. relate_pattern (0.054)\n", - " 2. distance (0.028)\n", - " 3. within (0.017)\n", - " 4. __iadd__ (0.015)\n", - " 5. intersection (0.015)\n", - " 6. union (0.013)\n", + " 0. contains (0.161)\n", "\n", "Label = _set_list\n", "Pred =\n", - "---- 0. _set_list (0.382)\n", - " 1. __init__ (0.321)\n", - " 2. start_serialization (0.036)\n", - " 3. _set_vimeo_cookie (0.025)\n", - " 4. init_poolmanager (0.014)\n", - " 5. reraise (0.008)\n", - " 6. info (0.005)\n", + " 0. __init__ (1.0)\n", "\n", "Label = __or__\n", "Pred =\n", - "---- 0. __or__ (0.899)\n", - " 1. __and__ (0.02)\n", - " 2. __sub__ (0.014)\n", - " 3. union (0.004)\n", - " 4. __ror__ (0.003)\n", - " 5. __rsub__ (0.003)\n", - " 6. __add__ (0.003)\n", + "---- 0. __or__ (0.96)\n", "\n", "Label = num_geom\n", "Pred =\n", - " 0. geom_typeid (0.06)\n", - " 1. proj (0.036)\n", - " 2. empty (0.034)\n", - " 3. valid (0.02)\n", - " 4. geographic (0.017)\n", - " 5. point_on_surface (0.016)\n", - " 6. width (0.014)\n", + " 0. y (0.123)\n", "\n", "Label = num_coords\n", "Pred =\n", - " 0. num_interior_rings (0.062)\n", - " 1. lon_lat (0.025)\n", - " 2. _reader (0.025)\n", - " 3. width (0.021)\n", - " 4. _country_or_city (0.017)\n", - " 5. valid (0.016)\n", - " 6. y (0.015)\n", + " 0. _reader (0.188)\n", "\n", "Label = ring\n", "Pred =\n", - " 0. empty (0.247)\n", - " 1. valid_reason (0.047)\n", - " 2. geom_typeid (0.035)\n", - " 3. hasz (0.031)\n", - " 4. simple (0.025)\n", - " 5. geos (0.022)\n", - " 6. merged (0.019)\n", + " 0. convex_hull (0.211)\n", "\n", "Label = contains\n", "Pred =\n", - "---- 0. contains (0.83)\n", - " 1. __contains__ (0.134)\n", - " 2. overlaps (0.007)\n", - " 3. __add__ (0.002)\n", - " 4. value (0.001)\n", - " 5. difference (0.001)\n", - " 6. update (0.0)\n", + "---- 0. contains (0.993)\n", "\n", "Label = disjoint\n", "Pred =\n", - "---- 0. disjoint (0.985)\n", - " 1. overlaps (0.001)\n", - " 2. union (0.001)\n", - " 3. __and__ (0.0)\n", - " 4. relate_pattern (0.0)\n", - " 5. intersection (0.0)\n", - " 6. relate (0.0)\n", + "---- 0. disjoint (0.993)\n", "\n", "Label = equals\n", "Pred =\n", - "---- 0. equals (0.989)\n", - " 1. __eq__ (0.003)\n", - " 2. __lt__ (0.001)\n", - " 3. union (0.0)\n", - " 4. __iadd__ (0.0)\n", - " 5. has_equal_attributes (0.0)\n", - " 6. overlaps (0.0)\n", + "---- 0. equals (0.994)\n", "\n", "Label = equals_exact\n", "Pred =\n", - " 0. distance (0.083)\n", - " 1. union (0.082)\n", - " 2. intersection (0.038)\n", - " 3. difference (0.024)\n", - " 4. relate_pattern (0.022)\n", - " 5. use_substr (0.021)\n", - " 6. contains (0.019)\n", + " 0. distance (0.069)\n", "\n", "Label = overlaps\n", "Pred =\n", - "---- 0. overlaps (0.996)\n", - " 1. __contains__ (0.0)\n", - " 2. contains (0.0)\n", - " 3. disjoint (0.0)\n", - " 4. __lt__ (0.0)\n", - " 5. has_equal_attributes (0.0)\n", - " 6. relate_pattern (0.0)\n", + "---- 0. overlaps (0.997)\n", "\n", "Label = kml\n", "Pred =\n", - "---- 0. kml (0.194)\n", - " 1. check_fid_range (0.017)\n", - " 2. height (0.017)\n", - " 3. hexewkb (0.016)\n", - " 4. valid_reason (0.015)\n", - " 5. hex (0.014)\n", - " 6. filename (0.014)\n", + "---- 0. kml (0.504)\n", "\n", "Label = ogr\n", "Pred =\n", - " 0. gml (0.206)\n", - " 1. geom_name (0.053)\n", - " 2. as_string (0.021)\n", - " 3. srs (0.021)\n", - " 4. y (0.02)\n", - " 5. hex (0.017)\n", - " 6. geom (0.017)\n", + " 0. from_gml (0.175)\n", "\n", "Label = extend\n", "Pred =\n", - " 0. format_value (0.051)\n", - " 1. int (0.029)\n", - " 2. get_prep_lookup (0.027)\n", - " 3. selector_function (0.016)\n", - " 4. to_python (0.015)\n", - " 5. _check_for_duplicates (0.012)\n", - " 6. items (0.012)\n", + " 0. test_pickle (0.116)\n", "\n", "Label = _ogr_ptr\n", "Pred =\n", - " 0. __len__ (0.354)\n", - " 1. _geos_ptr (0.298)\n", - " 2. __bool__ (0.024)\n", - " 3. wkb (0.01)\n", - " 4. contains (0.009)\n", - " 5. __iter__ (0.009)\n", - " 6. hasz (0.008)\n", + " 0. clone (0.16)\n", "\n", "Label = __iter__\n", "Pred =\n", - "---- 0. __iter__ (0.998)\n", - " 1. values (0.001)\n", - " 2. __len__ (0.0)\n", - " 3. __bool__ (0.0)\n", - " 4. tuple (0.0)\n", - " 5. __contains__ (0.0)\n", - " 6. append (0.0)\n", + "---- 0. __iter__ (0.999)\n", "\n", "Label = check_cs_op\n", "Pred =\n", - " 0. check_cs_get (0.225)\n", - " 1. check_dbl (0.187)\n", - " 2. geomerrcheck (0.072)\n", - " 3. check_minus_one (0.068)\n", - " 4. check_geom (0.053)\n", - " 5. check_predicate (0.052)\n", - " 6. errcheck (0.023)\n", + " 0. check_minus_one (0.214)\n", "\n", "Label = country\n", "Pred =\n", - " 0. city (0.485)\n", - " 1. country_code (0.202)\n", - " 2. country_name (0.163)\n", - " 3. lon_lat (0.006)\n", - " 4. lat_lon (0.004)\n", - " 5. center (0.003)\n", - " 6. _get_FIELD_display (0.002)\n", + " 0. city (0.785)\n", "\n", "Label = info\n", "Pred =\n", - " 0. __repr__ (0.819)\n", - " 1. _url_builder (0.025)\n", - " 2. __str__ (0.016)\n", - " 3. extra_repr (0.015)\n", - " 4. with_netmask (0.009)\n", - " 5. version (0.008)\n", - " 6. with_prefixlen (0.006)\n", + " 0. __repr__ (0.893)\n", "\n", "Label = _get_site_by_id\n", "Pred =\n", - " 0. get_current (0.141)\n", - " 1. get (0.132)\n", - " 2. setdefault (0.067)\n", - " 3. add_output_format_meta (0.023)\n", - " 4. render_value (0.022)\n", - " 5. try_get (0.016)\n", - " 6. from_response (0.012)\n", + " 0. __getitem__ (0.07)\n", "\n", "Label = __str__\n", "Pred =\n", - " 0. describe (0.839)\n", - "---- 1. __str__ (0.036)\n", - " 2. _url_builder (0.011)\n", - " 3. label_lower (0.008)\n", - " 4. enable (0.004)\n", - " 5. __repr__ (0.004)\n", - " 6. add_prefix (0.003)\n", + " 0. describe (0.956)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = dispatch\n", "Pred =\n", - "---- 0. dispatch (0.343)\n", - " 1. get (0.268)\n", - " 2. as_oracle (0.052)\n", - " 3. push (0.018)\n", - " 4. render (0.016)\n", - " 5. as_sqlite (0.009)\n", - " 6. __add__ (0.009)\n", + "---- 0. dispatch (0.923)\n", "\n", "Label = check\n", "Pred =\n", - "---- 0. check (0.128)\n", - " 1. check_database_backends (0.022)\n", - " 2. _trailing_slash_required (0.011)\n", - " 3. _check_list_max_show_all (0.01)\n", - " 4. pack (0.008)\n", - " 5. _check_pattern_startswith_slash (0.008)\n", - " 6. for_update_sql (0.008)\n", + "---- 0. check (0.915)\n", "\n", "Label = _compile\n", "Pred =\n", - " 0. _sdk4_error_maybe (0.091)\n", - " 1. is_usable (0.055)\n", - " 2. get_json_data (0.023)\n", - " 3. get_exception (0.013)\n", - " 4. _raise_error (0.01)\n", - "---- 5. _compile (0.01)\n", - " 6. split_url (0.009)\n", + " 0. _extract_count (0.036)\n", "\n", "Label = check\n", "Pred =\n", - "---- 0. check (0.874)\n", - " 1. copy (0.032)\n", - " 2. __str__ (0.016)\n", - " 3. clone (0.006)\n", - " 4. __hash__ (0.004)\n", - " 5. __repr__ (0.004)\n", - " 6. as_text (0.004)\n", + "---- 0. check (0.972)\n", "\n", "Label = get_urlconf\n", "Pred =\n", - " 0. first_or_default (0.176)\n", - " 1. bool_or_none (0.082)\n", - " 2. _configuration_args (0.077)\n", - " 3. _getlist (0.029)\n", - " 4. get (0.029)\n", - " 5. str_or_none (0.024)\n", - " 6. getlist (0.016)\n", + " 0. _configuration_args (0.758)\n", "\n", "Label = rendered_content\n", "Pred =\n", - " 0. render (0.92)\n", - " 1. eval (0.03)\n", - " 2. content (0.005)\n", - " 3. __enter__ (0.003)\n", - " 4. reset (0.002)\n", - " 5. _get_context_stack_frame (0.001)\n", - " 6. render_annotated (0.001)\n", + " 0. render (0.773)\n", "\n", "Label = content\n", "Pred =\n", - " 0. save_property (0.105)\n", - " 1. partition (0.018)\n", - " 2. _set_choices (0.017)\n", - " 3. n_values_ (0.013)\n", - " 4. style_func (0.013)\n", - " 5. _set_queryset (0.012)\n", - " 6. reverse (0.009)\n", + " 0. save_property (0.31)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. inverse_transform (0.0)\n", "\n", "Label = __str__\n", "Pred =\n", - " 0. cc (0.099)\n", - " 1. pc (0.037)\n", - " 2. underscore_to_camel (0.028)\n", - " 3. pretty_name (0.019)\n", - " 4. get_current_prefetch_to (0.018)\n", - " 5. get_base_url (0.015)\n", - " 6. get_name (0.014)\n", + " 0. pc (0.135)\n", "\n", "Label = skip_past\n", "Pred =\n", - " 0. incr (0.027)\n", - " 1. add_prefix (0.02)\n", - " 2. get_current_to_attr (0.017)\n", - " 3. _parse_filter (0.015)\n", - " 4. is_same_field_operation (0.012)\n", - " 5. _get_model_tuple (0.011)\n", - " 6. refs_expression (0.009)\n", + " 0. _check_pattern_startswith_slash (0.06)\n", "\n", "Label = add_library\n", "Pred =\n", - " 0. disable (0.07)\n", - " 1. state_forwards (0.048)\n", - " 2. database_backwards (0.025)\n", - " 3. clear (0.021)\n", - " 4. predict_proba (0.021)\n", - " 5. runshell (0.021)\n", - " 6. modify_tags (0.014)\n", + " 0. __iter__ (0.018)\n", "\n", "Label = led\n", "Pred =\n", - "---- 0. led (0.515)\n", - " 1. _combine (0.032)\n", - " 2. buffer_with_style (0.015)\n", - " 3. __instancecheck__ (0.008)\n", - " 4. max_name_length (0.008)\n", - " 5. popitem (0.006)\n", - " 6. new_file (0.006)\n", + "---- 0. led (0.792)\n", "\n", "Label = find_template_loader\n", "Pred =\n", - " 0. find_migration (0.107)\n", - " 1. _raise_error (0.02)\n", - " 2. to_screen (0.018)\n", - " 3. _check_type_float (0.017)\n", - " 4. index (0.011)\n", - " 5. _report_error (0.01)\n", - " 6. _sleep (0.01)\n", + " 0. find_migration (0.067)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.831)\n", - " 1. smooth_idf (0.005)\n", - " 2. started (0.003)\n", - " 3. _read_from_pipes (0.002)\n", - " 4. add_distinct_fields (0.001)\n", - " 5. _setup (0.001)\n", - " 6. use_idf (0.001)\n", + "---- 0. __init__ (0.371)\n", "\n", "Label = eval\n", "Pred =\n", - " 0. render (0.628)\n", - "---- 1. eval (0.059)\n", - " 2. __copy__ (0.027)\n", - " 3. contains (0.018)\n", - " 4. new (0.008)\n", - " 5. __add__ (0.008)\n", - " 6. url (0.008)\n", + " 0. resolve_expression (0.917)\n", "\n", "Label = capfirst\n", "Pred =\n", - "---- 0. capfirst (0.181)\n", - " 1. first (0.114)\n", - " 2. last_arg_byref (0.045)\n", - " 3. last (0.036)\n", - " 4. readable_file_arg (0.015)\n", - " 5. upper (0.013)\n", - " 6. DupFd (0.01)\n", + "---- 0. capfirst (0.253)\n", "\n", "Label = truncatewords\n", "Pred =\n", - " 0. truncatewords_html (0.975)\n", - " 1. length (0.001)\n", - " 2. wordcount (0.0)\n", - " 3. first (0.0)\n", - " 4. _text_words (0.0)\n", - " 5. phone2numeric_filter (0.0)\n", - " 6. parse_interface_line (0.0)\n", + " 0. truncatewords_html (0.899)\n", "\n", "Label = urlize\n", "Pred =\n", - " 0. linebreaks_filter (0.251)\n", - " 1. join (0.133)\n", - " 2. nodata_value (0.014)\n", - " 3. urlizetrunc (0.013)\n", - " 4. dictsortreversed (0.012)\n", - " 5. localize (0.012)\n", - " 6. unlocalize (0.009)\n", + " 0. linebreaks_filter (0.323)\n", "\n", "Label = wordwrap\n", "Pred =\n", - " 0. dictsort (0.147)\n", - " 1. dictsortreversed (0.141)\n", - " 2. length_is (0.06)\n", - " 3. safeseq (0.056)\n", - " 4. default_if_none (0.039)\n", - " 5. iriencode (0.035)\n", - " 6. make_list (0.031)\n", + " 0. default_if_none (0.123)\n", "\n", "Label = linebreaksbr\n", "Pred =\n", - " 0. linebreaks_filter (0.757)\n", - " 1. join (0.066)\n", - " 2. nodata_value (0.005)\n", - " 3. _normalize_distance_lookup_arg (0.003)\n", - " 4. is_nullable (0.002)\n", - " 5. length_is (0.002)\n", - " 6. render_value_in_context (0.002)\n", + " 0. linebreaks_filter (0.702)\n", "\n", "Label = random\n", "Pred =\n", - " 0. _choice_has_empty_value (0.061)\n", - " 1. strip_spaces_between_tags (0.05)\n", - " 2. escapejs (0.03)\n", - " 3. m2m_value (0.027)\n", - " 4. json_script (0.019)\n", - " 5. camel_case_to_spaces (0.017)\n", - " 6. localize (0.016)\n", + " 0. _choice_has_empty_value (0.057)\n", "\n", "Label = add\n", "Pred =\n", - " 0. length_is (0.285)\n", - " 1. timesince_filter (0.088)\n", - " 2. stringformat (0.085)\n", - " 3. timeuntil_filter (0.075)\n", - " 4. dictsort (0.071)\n", - " 5. dictsortreversed (0.055)\n", - " 6. get_digit (0.023)\n", + " 0. dictsort (0.288)\n", "\n", "Label = time\n", "Pred =\n", - " 0. date (0.082)\n", - " 1. stringformat (0.05)\n", - " 2. dictsort (0.043)\n", - " 3. dictsortreversed (0.036)\n", - " 4. urlencode (0.026)\n", - " 5. join (0.024)\n", - " 6. length_is (0.024)\n", + " 0. strip_tags (0.029)\n", "\n", "Label = set_upward\n", "Pred =\n", - " 0. _set_single (0.222)\n", - " 1. __init__ (0.12)\n", - " 2. subwidgets (0.098)\n", - " 3. new (0.034)\n", - " 4. external_entity_ref_handler (0.029)\n", - " 5. __setitem__ (0.017)\n", - " 6. push (0.011)\n", + " 0. __setitem__ (0.712)\n", "\n", "Label = push_state\n", "Pred =\n", - " 0. new (0.428)\n", - " 1. render (0.089)\n", - " 2. __init__ (0.037)\n", - " 3. find_template (0.036)\n", - " 4. subwidgets (0.029)\n", - " 5. bind_template (0.025)\n", - " 6. as_sqlite (0.024)\n", + " 0. __init__ (0.319)\n", "\n", "Label = push\n", "Pred =\n", - " 0. add_blocks (0.369)\n", - " 1. get_block (0.096)\n", - " 2. __setattr__ (0.035)\n", - " 3. insert (0.026)\n", - " 4. compat_setenv (0.016)\n", - " 5. add_item_elements (0.013)\n", - " 6. setdefault (0.009)\n", + " 0. add_blocks (0.745)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. destination (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = get_contents\n", "Pred =\n", - " 0. get_template (0.334)\n", - " 1. add_dummy_node (0.024)\n", - " 2. client (0.012)\n", - " 3. get_template_by_name (0.009)\n", - " 4. get_basename (0.008)\n", - " 5. connection_exists (0.007)\n", - " 6. _get_from_cache (0.006)\n", + " 0. supports (0.042)\n", "\n", "Label = get_template_sources\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. bind_template (0.118)\n", - " 1. as_oracle (0.048)\n", - " 2. get_template (0.024)\n", - " 3. on_template_render (0.024)\n", - " 4. as_sqlite (0.024)\n", - "---- 5. get_template_sources (0.022)\n", - " 6. __iter__ (0.017)\n", + "Pred =\n", + " 0. __iter__ (0.535)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. load (0.0)\n", - " 5. destination (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = empty_form\n", "Pred =\n", - " 0. errors (0.221)\n", - " 1. as_table (0.055)\n", - " 2. total_error_count (0.044)\n", - " 3. auto_id (0.031)\n", - " 4. as_ul (0.026)\n", - " 5. non_form_errors (0.021)\n", - " 6. forms (0.019)\n", + " 0. forms (0.049)\n", "\n", "Label = add_prefix\n", "Pred =\n", - " 0. __str__ (0.532)\n", - " 1. describe (0.22)\n", - "---- 2. add_prefix (0.046)\n", - " 3. id_for_label (0.029)\n", - " 4. __getitem__ (0.012)\n", - " 5. __repr__ (0.008)\n", - " 6. add_initial_prefix (0.007)\n", + "---- 0. add_prefix (0.129)\n", "\n", "Label = as_p\n", "Pred =\n", - " 0. as_table (0.855)\n", - " 1. as_ul (0.065)\n", - "---- 2. as_p (0.011)\n", - " 3. total_error_count (0.002)\n", - " 4. serialize_messages (0.002)\n", - " 5. extra_forms (0.002)\n", - " 6. as_text (0.002)\n", + " 0. as_table (0.825)\n", "\n", "Label = data\n", "Pred =\n", - " 0. value_from_datadict (0.331)\n", - " 1. reverse_dict (0.016)\n", - " 2. readable (0.015)\n", - " 3. _mark_post_parse_error (0.013)\n", - " 4. FILES (0.012)\n", - " 5. __or__ (0.011)\n", - " 6. M (0.009)\n", + " 0. value_from_datadict (0.913)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.834)\n", - " 1. __setstate__ (0.041)\n", - " 2. subwidgets (0.024)\n", - " 3. handle_starttag (0.015)\n", - " 4. __getstate__ (0.01)\n", - " 5. get_context (0.007)\n", - " 6. partition (0.004)\n", + "---- 0. __init__ (0.998)\n", "\n", "Label = _render\n", "Pred =\n", - " 0. render (0.992)\n", - " 1. as_sqlite (0.001)\n", - " 2. eval (0.0)\n", - " 3. __call__ (0.0)\n", - " 4. tag (0.0)\n", - " 5. _get_context_stack_frame (0.0)\n", - " 6. _from_pickle_wkb (0.0)\n", + " 0. render (0.901)\n", "\n", "Label = format_value\n", "Pred =\n", - "---- 0. format_value (0.147)\n", - " 1. adapt_datefield_value (0.106)\n", - " 2. adapt_ipaddressfield_value (0.094)\n", - " 3. get_prep_value (0.068)\n", - " 4. to_python (0.041)\n", - " 5. is_initial (0.035)\n", - " 6. adapt_datetimefield_value (0.03)\n", + " 0. is_initial (0.191)\n", "\n", "Label = clear_checkbox_id\n", "Pred =\n", - " 0. clear_checkbox_name (0.979)\n", - " 1. format_value (0.001)\n", - " 2. _encode_comment (0.001)\n", - " 3. is_initial (0.0)\n", - " 4. h (0.0)\n", - " 5. adapt_datetimefield_value (0.0)\n", - " 6. mark_safe (0.0)\n", + " 0. clear_checkbox_name (0.956)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. ip (0.0)\n", "\n", "Label = value_from_datadict\n", "Pred =\n", - "---- 0. value_from_datadict (0.995)\n", - " 1. value_omitted_from_data (0.002)\n", - " 2. _mark_post_parse_error (0.0)\n", - " 3. load (0.0)\n", - " 4. use_required_attribute (0.0)\n", - " 5. setlistdefault (0.0)\n", - " 6. info (0.0)\n", + "---- 0. value_from_datadict (0.994)\n", "\n", "Label = _get_media\n", "Pred =\n", - " 0. media (0.995)\n", - " 1. total_error_count (0.0)\n", - " 2. use_required_attribute (0.0)\n", - " 3. has_changed (0.0)\n", - " 4. x (0.0)\n", - " 5. base_location (0.0)\n", - " 6. value_omitted_from_data (0.0)\n", + " 0. media (0.933)\n", "\n", "Label = validate\n", "Pred =\n", - "---- 0. validate (0.994)\n", - " 1. to_python (0.001)\n", - " 2. process_request (0.0)\n", - " 3. __setitem__ (0.0)\n", - " 4. __set__ (0.0)\n", - " 5. _coerce (0.0)\n", - " 6. add_item_elements (0.0)\n", + "---- 0. validate (0.995)\n", "\n", "Label = __deepcopy__\n", "Pred =\n", "---- 0. __deepcopy__ (1.0)\n", - " 1. __copy__ (0.0)\n", - " 2. difference (0.0)\n", - " 3. __add__ (0.0)\n", - " 4. argspec (0.0)\n", - " 5. to_dict (0.0)\n", - " 6. intersection (0.0)\n", "\n", "Label = to_python\n", "Pred =\n", - " 0. strptime (0.143)\n", - " 1. decompress (0.118)\n", - " 2. as_json (0.036)\n", - " 3. u (0.02)\n", - " 4. format_value (0.015)\n", - " 5. _coerce (0.011)\n", - " 6. as_ul (0.01)\n", + " 0. strptime (0.507)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. inverse_transform (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. init_poolmanager (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. destination (0.0)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. func (0.0)\n", - " 6. build (0.0)\n", "\n", "Label = clean\n", "Pred =\n", - "---- 0. clean (0.989)\n", - " 1. check (0.002)\n", - " 2. validate (0.0)\n", - " 3. __set__ (0.0)\n", - " 4. describe (0.0)\n", - " 5. errors (0.0)\n", - " 6. RelatedObjectDoesNotExist (0.0)\n", + "---- 0. clean (0.998)\n", "\n", "Label = to_python\n", "Pred =\n", - " 0. format_value (0.305)\n", - "---- 1. to_python (0.196)\n", - " 2. prepare_value (0.104)\n", - " 3. unpack (0.04)\n", - " 4. decompress (0.031)\n", - " 5. __str__ (0.008)\n", - " 6. __invert__ (0.008)\n", + " 0. _split_lines (0.024)\n", "\n", "Label = to_python\n", "Pred =\n", - "---- 0. to_python (0.918)\n", - " 1. prepare_value (0.027)\n", - " 2. clean (0.012)\n", - " 3. check (0.005)\n", - " 4. name (0.004)\n", - " 5. tag (0.003)\n", - " 6. validate (0.003)\n", + "---- 0. to_python (0.991)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. clean (0.103)\n", - " 1. errors (0.069)\n", - " 2. describe (0.035)\n", - " 3. is_valid (0.023)\n", - " 4. as_text (0.02)\n", - " 5. _hash (0.018)\n", - " 6. initial_form_count (0.016)\n", + " 0. describe (0.549)\n", "\n", "Label = is_multipart\n", "Pred =\n", - " 0. needs_multipart_form (0.657)\n", - " 1. pk_field (0.036)\n", - "---- 2. is_multipart (0.022)\n", - " 3. deletion_field (0.012)\n", - " 4. ordering_field (0.011)\n", - " 5. non_form_errors (0.011)\n", - " 6. total_error_count (0.008)\n", + " 0. needs_multipart_form (0.573)\n", "\n", "Label = hidden_fields\n", "Pred =\n", - " 0. visible_fields (0.966)\n", - " 1. is_hidden (0.008)\n", - " 2. get_fields (0.002)\n", - " 3. related_objects (0.001)\n", - " 4. field_references_model (0.001)\n", - " 5. ask_rename (0.0)\n", - " 6. _check_list_select_related (0.0)\n", + " 0. visible_fields (0.971)\n", "\n", "Label = get_initial_for_field\n", "Pred =\n", - " 0. format_value (0.226)\n", - " 1. use_required_attribute (0.167)\n", - " 2. has_changed (0.04)\n", - " 3. to_python (0.033)\n", - " 4. _value_from_field (0.025)\n", - " 5. bound_data (0.019)\n", - " 6. prepare_value (0.016)\n", + " 0. format_value (0.729)\n", "\n", "Label = _get_to_python\n", "Pred =\n", - " 0. save_form_data (0.036)\n", - " 1. _get_field_name (0.034)\n", - " 2. _check_formset (0.03)\n", - " 3. has_changed (0.022)\n", - " 4. m2m_convert (0.021)\n", - " 5. get_object (0.014)\n", - " 6. result_hidden_fields (0.014)\n", + " 0. has_changed (0.216)\n", "\n", "Label = delete_existing\n", "Pred =\n", - " 0. delete (0.798)\n", - " 1. save (0.018)\n", - " 2. delete_model (0.015)\n", - " 3. modify (0.013)\n", - " 4. delete_queryset (0.011)\n", - " 5. flush (0.007)\n", - " 6. delete_asg (0.007)\n", + " 0. flush (0.134)\n", "\n", "Label = get_traceback_html\n", "Pred =\n", - " 0. get_traceback_text (0.965)\n", - " 1. hex (0.003)\n", - " 2. default_urlconf (0.001)\n", - " 3. hexewkb (0.001)\n", - " 4. check_str_arg (0.001)\n", - " 5. action_checkbox (0.0)\n", - " 6. I (0.0)\n", + " 0. get_traceback_text (0.985)\n", "\n", "Label = get_next_year\n", "Pred =\n", - " 0. get_next_week (0.124)\n", - " 1. get_previous_week (0.124)\n", - " 2. get_previous_year (0.117)\n", - " 3. get_previous_month (0.117)\n", - " 4. get_next_month (0.113)\n", - " 5. get_next_day (0.109)\n", - " 6. get_previous_day (0.108)\n", + " 0. get_previous_year (0.137)\n", "\n", "Label = _get_current_year\n", "Pred =\n", - " 0. _get_next_day (0.134)\n", - " 1. decompress (0.032)\n", - " 2. get_next_week (0.03)\n", - " 3. get_previous_week (0.027)\n", - " 4. get_previous_month (0.025)\n", - " 5. get_next_day (0.025)\n", - " 6. get_previous_day (0.021)\n", + " 0. _get_next_day (0.177)\n", "\n", "Label = _get_current_month\n", "Pred =\n", - " 0. _get_next_day (0.203)\n", - " 1. decompress (0.033)\n", - " 2. get_next_week (0.024)\n", - " 3. get_previous_week (0.022)\n", - " 4. get_previous_month (0.02)\n", - " 5. get_next_month (0.019)\n", - " 6. get_next_day (0.018)\n", + " 0. _get_next_day (0.154)\n", "\n", "Label = _get_next_week\n", "Pred =\n", - " 0. _get_current_week (0.853)\n", - " 1. _get_weekday (0.021)\n", - " 2. _get_next_day (0.01)\n", - " 3. date_interval_sql (0.003)\n", - " 4. D (0.003)\n", - " 5. l (0.003)\n", - " 6. w (0.003)\n", + " 0. _get_current_week (0.672)\n", "\n", "Label = get_success_url\n", "Pred =\n", - "---- 0. get_success_url (0.831)\n", - " 1. logout_then_login (0.018)\n", - " 2. get_login_url (0.012)\n", - " 3. end_transaction_sql (0.01)\n", - " 4. _make_hash_value (0.008)\n", - " 5. _proto_relative_url (0.003)\n", - " 6. check_password (0.003)\n", + "---- 0. get_success_url (0.866)\n", "\n", "Label = get_form_kwargs\n", "Pred =\n", - "---- 0. get_form_kwargs (0.993)\n", - " 1. options (0.001)\n", - " 2. get_form (0.001)\n", - " 3. form_valid (0.0)\n", - " 4. state (0.0)\n", - " 5. to_request (0.0)\n", - " 6. tag (0.0)\n", + "---- 0. get_form_kwargs (0.996)\n", "\n", "Label = get_success_url\n", "Pred =\n", - " 0. item_link (0.481)\n", - "---- 1. get_success_url (0.064)\n", - " 2. get_login_url (0.026)\n", - " 3. has_add_permission (0.018)\n", - " 4. _get_obj_does_not_exist_redirect (0.014)\n", - " 5. handle_no_permission (0.009)\n", - " 6. get_urls (0.007)\n", + "---- 0. get_success_url (0.438)\n", "\n", "Label = get\n", "Pred =\n", - "---- 0. get (0.996)\n", - " 1. put (0.001)\n", - " 2. post (0.0)\n", - " 3. delete (0.0)\n", - " 4. patch (0.0)\n", - " 5. add (0.0)\n", - " 6. _real_extract (0.0)\n", + "---- 0. get (0.998)\n", "\n", "Label = http_method_not_allowed\n", "Pred =\n", - " 0. options (0.807)\n", - " 1. get (0.047)\n", - " 2. head (0.029)\n", - " 3. post (0.016)\n", - " 4. put (0.01)\n", - " 5. patch (0.005)\n", - " 6. delete (0.004)\n", + " 0. options (0.91)\n", "\n", "Label = render_to_response\n", "Pred =\n", - " 0. get_context_data (0.564)\n", - " 1. from_string (0.045)\n", - " 2. _prepare (0.022)\n", - " 3. find_template (0.015)\n", - " 4. get_template (0.009)\n", - " 5. options (0.008)\n", - " 6. get_success_url (0.007)\n", + " 0. get_context_data (0.402)\n", "\n", "Label = sensitive_variables\n", "Pred =\n", - " 0. sensitive_variables_wrapper (0.473)\n", - " 1. decorator (0.157)\n", - " 2. sensitive_post_parameters (0.014)\n", - " 3. vary_on_headers (0.013)\n", - " 4. with_appcontext (0.013)\n", - " 5. no_translations (0.011)\n", - " 6. vary_on_cookie (0.007)\n", + " 0. decorator (0.67)\n", "\n", "Label = inner_func\n", "Pred =\n", - " 0. _wrapped_view_func (0.413)\n", - " 1. inner (0.331)\n", - " 2. wrapper (0.045)\n", - "---- 3. inner_func (0.045)\n", - " 4. x_robots_tag (0.014)\n", - " 5. decorator (0.014)\n", - " 6. wrapped (0.008)\n", + "---- 0. inner_func (0.415)\n", "\n", "Label = process_view\n", "Pred =\n", - " 0. filter (0.1)\n", - " 1. dispatch (0.095)\n", - " 2. submit (0.043)\n", - " 3. __call__ (0.034)\n", - " 4. to_command (0.031)\n", - " 5. apply_async (0.022)\n", - " 6. call (0.02)\n", + " 0. apply_async (0.331)\n", "\n", "Label = signature\n", "Pred =\n", - " 0. _hash (0.16)\n", - " 1. __str__ (0.116)\n", - " 2. encode (0.065)\n", - " 3. sign (0.054)\n", - " 4. generate_hash (0.04)\n", - " 5. as_text (0.035)\n", - " 6. to_python (0.024)\n", + " 0. encode (0.89)\n", "\n", "Label = inner\n", "Pred =\n", - " 0. convert_exception_to_response (0.834)\n", - " 1. _wrapped_view_func (0.004)\n", - " 2. process_response (0.003)\n", - " 3. get_xframe_options_value (0.003)\n", - " 4. _handle_error (0.002)\n", - " 5. _get_requires_by_collector_name (0.002)\n", - " 6. get_messages (0.002)\n", + " 0. convert_exception_to_response (0.891)\n", "\n", "Label = __init__\n", "Pred =\n", "---- 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. func (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = make_view_atomic\n", "Pred =\n", - " 0. _non_atomic_requests (0.078)\n", - " 1. window_frame_rows_start_end (0.028)\n", - " 2. action_checkbox (0.017)\n", - " 3. get_nodes_by_type (0.017)\n", - " 4. admin_list_filter (0.016)\n", - " 5. record_applied (0.008)\n", - " 6. record_unapplied (0.008)\n", + " 0. _non_atomic_requests (0.068)\n", "\n", "Label = touch\n", "Pred =\n", - " 0. add (0.618)\n", - " 1. set (0.314)\n", - "---- 2. touch (0.021)\n", - " 3. delete (0.006)\n", - " 4. clear (0.003)\n", - " 5. set_many (0.002)\n", - " 6. handle (0.002)\n", + " 0. add (0.38)\n", "\n", "Label = delete_many\n", "Pred =\n", - "---- 0. delete_many (0.838)\n", - " 1. flush (0.033)\n", - " 2. delete (0.019)\n", - " 3. __delitem__ (0.015)\n", - " 4. set (0.008)\n", - " 5. delete_model (0.002)\n", - " 6. disable_action (0.001)\n", + "---- 0. delete_many (0.994)\n", "\n", "Label = touch\n", "Pred =\n", - "---- 0. touch (0.858)\n", - " 1. add (0.073)\n", - " 2. set (0.016)\n", - " 3. get (0.005)\n", - " 4. has_key (0.005)\n", - " 5. set_many (0.005)\n", - " 6. _delete (0.003)\n", + " 0. delete (0.367)\n", "\n", "Label = touch\n", "Pred =\n", - " 0. add (0.921)\n", - " 1. get (0.029)\n", - "---- 2. touch (0.016)\n", - " 3. set (0.006)\n", - " 4. has_key (0.004)\n", - " 5. set_many (0.004)\n", - " 6. delete (0.002)\n", + " 0. add (0.838)\n", "\n", "Label = _has_expired\n", "Pred =\n", - " 0. _delete (0.04)\n", - " 1. even_odd (0.039)\n", - " 2. encode (0.031)\n", - " 3. sign (0.013)\n", - " 4. intercept_ (0.012)\n", - " 5. _wrapper (0.011)\n", - " 6. _get_hash (0.01)\n", + " 0. touch (0.162)\n", "\n", "Label = __repr__\n", "Pred =\n", - "---- 0. __repr__ (1.0)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. get (0.0)\n", - " 4. state (0.0)\n", - " 5. name (0.0)\n", - " 6. add (0.0)\n", + "---- 0. __repr__ (0.705)\n", "\n", "Label = check_setting_app_dirs_loaders\n", "Pred =\n", - " 0. check_allowed_hosts (0.081)\n", - " 1. get_base_chain (0.024)\n", - " 2. check_session_cookie_secure (0.019)\n", - " 3. _extract_smil_info (0.014)\n", - " 4. check_url_settings (0.013)\n", - " 5. register_socks_protocols (0.008)\n", - " 6. prepend_scheme_if_needed (0.008)\n", + " 0. _make_archive_id (0.036)\n", "\n", "Label = inner\n", "Pred =\n", - " 0. get_checks (0.646)\n", - " 1. disable (0.028)\n", - " 2. tags_available (0.015)\n", - " 3. compat_print (0.009)\n", - " 4. has_checks (0.008)\n", - " 5. create (0.008)\n", - " 6. _run_checks (0.008)\n", + " 0. get_checks (0.188)\n", "\n", "Label = app_model_error\n", "Pred =\n", - " 0. is_ip (0.064)\n", - " 1. readable_file_arg (0.023)\n", - " 2. boto_exception (0.022)\n", - " 3. make_response (0.017)\n", - " 4. errorhandler (0.015)\n", - " 5. is_wellformed (0.015)\n", - " 6. unexpected_error_msg (0.014)\n", + " 0. get_distribution_AIX (0.044)\n", "\n", "Label = check_xss_filter\n", "Pred =\n", - " 0. check_ssl_redirect (0.035)\n", - " 1. check_sts_include_subdomains (0.029)\n", - " 2. get_finder (0.022)\n", - " 3. permission_required (0.022)\n", - " 4. check_sts (0.02)\n", - " 5. check_for_language (0.019)\n", - " 6. get_models (0.017)\n", + " 0. is_expired (0.061)\n", "\n", "Label = _value_or_setting\n", "Pred =\n", - " 0. __new__ (0.168)\n", - " 1. __getattr__ (0.059)\n", - " 2. __call__ (0.038)\n", - " 3. get_prep_value (0.034)\n", - " 4. inner (0.018)\n", - " 5. resolve_expression (0.018)\n", - " 6. _check_type_str (0.016)\n", + " 0. __new__ (0.205)\n", "\n", "Label = open\n", "Pred =\n", - "---- 0. open (0.996)\n", - " 1. write (0.001)\n", - " 2. __call__ (0.0)\n", - " 3. __enter__ (0.0)\n", - " 4. reset (0.0)\n", - " 5. load (0.0)\n", - " 6. lines_exceeded (0.0)\n", + "---- 0. open (0.991)\n", "\n", "Label = handle_raw_input\n", "Pred =\n", - " 0. __init__ (0.03)\n", - " 1. _write_content (0.028)\n", - " 2. new_file (0.016)\n", - " 3. iter_body (0.012)\n", - " 4. __next__ (0.011)\n", - " 5. _body_from_file (0.011)\n", - " 6. init_poolmanager (0.01)\n", + " 0. chunks (0.027)\n", "\n", "Label = new_file\n", "Pred =\n", " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. destination (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. start_serialization (0.0)\n", - " 5. __setstate__ (0.0)\n", - " 6. predict_proba (0.0)\n", "\n", "Label = file_complete\n", "Pred =\n", - " 0. open (0.194)\n", - " 1. readable (0.111)\n", - " 2. chunks (0.031)\n", - " 3. _get_image_dimensions (0.018)\n", - " 4. _get_file (0.017)\n", - " 5. layer_name (0.013)\n", - " 6. words (0.011)\n", + " 0. open (0.839)\n", "\n", "Label = handle_raw_input\n", "Pred =\n", - " 0. __init__ (0.992)\n", - " 1. new_file (0.0)\n", - " 2. process_request (0.0)\n", - " 3. __setitem__ (0.0)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 4. _set_list (0.0)\n", - " 5. started (0.0)\n", - " 6. _body_from_file (0.0)\n", + " 0. __init__ (0.999)\n", "\n", "Label = receive_data_chunk\n", "Pred =\n", - " 0. file_complete (0.136)\n", - " 1. write_stream_with_colors_win_py3 (0.034)\n", - " 2. write (0.022)\n", - " 3. _is_empty_column_selection (0.017)\n", - " 4. render (0.017)\n", - " 5. read_manifest (0.016)\n", - " 6. area (0.015)\n", + "---- 0. receive_data_chunk (0.679)\n", "\n", "Label = attach_file\n", "Pred =\n", - " 0. update (0.032)\n", - " 1. _body_from_file (0.028)\n", - " 2. extract (0.02)\n", - " 3. is_valid_path (0.017)\n", - " 4. find (0.017)\n", - " 5. match_request (0.013)\n", - " 6. ensure_connection (0.009)\n", + " 0. smooth_idf (0.027)\n", "\n", "Label = _set_list_header_if_not_empty\n", "Pred =\n", - " 0. prepare_headers (0.173)\n", - " 1. setdefault (0.113)\n", - " 2. handle_youtubedl_headers (0.042)\n", - " 3. add_unredirected_header (0.042)\n", - " 4. compat_setenv (0.022)\n", - " 5. _is_object_changed (0.014)\n", - " 6. _check_type_jsonarg (0.012)\n", + " 0. setdefault (0.518)\n", "\n", "Label = send_messages\n", "Pred =\n", - " 0. to_screen (0.063)\n", - " 1. report_error (0.055)\n", - " 2. _real_initialize (0.034)\n", - " 3. unclosed_block_tag (0.012)\n", - " 4. report_warning (0.011)\n", - " 5. validate (0.011)\n", - " 6. error (0.01)\n", + " 0. _check_extra (0.021)\n", "\n", "Label = open\n", "Pred =\n", - " 0. _get_file (0.039)\n", - " 1. local (0.018)\n", - " 2. readable (0.016)\n", - " 3. auto_id (0.014)\n", - " 4. _get_level (0.014)\n", - " 5. as_ul (0.014)\n", - " 6. e (0.013)\n", + " 0. getvalue (0.047)\n", "\n", "Label = close\n", "Pred =\n", - " 0. _flush_bg_loading_exception (0.018)\n", - " 1. ensure_connection (0.017)\n", - " 2. get_autocommit (0.012)\n", - " 3. _country_or_city (0.012)\n", - " 4. finish (0.011)\n", - " 5. supports_explaining_query_execution (0.009)\n", - " 6. get_requests_session (0.009)\n", + " 0. close_all (0.028)\n", "\n", "Label = get_dump_object\n", "Pred =\n", - " 0. _unique_should_be_added (0.131)\n", - " 1. _field_became_primary_key (0.035)\n", - " 2. generate_added_fields (0.022)\n", - " 3. _get_field_name (0.019)\n", - " 4. auth (0.014)\n", - " 5. generate_removed_fields (0.014)\n", - " 6. _check_single_primary_key (0.014)\n", + " 0. handle_m2m (0.131)\n", "\n", "Label = handle_field\n", "Pred =\n", - "---- 0. handle_field (0.332)\n", - " 1. save_form_data (0.102)\n", - " 2. __set__ (0.072)\n", - " 3. delete_model (0.042)\n", - " 4. set_cached_value (0.021)\n", - " 5. add_field (0.019)\n", - " 6. local_setter (0.015)\n", + "---- 0. handle_field (0.59)\n", "\n", "Label = handle_field\n", "Pred =\n", - " 0. handle_m2m_field (0.494)\n", - " 1. handle_fk_field (0.412)\n", - " 2. _value_from_field (0.006)\n", - " 3. serializable_value (0.003)\n", - "---- 4. handle_field (0.002)\n", - " 5. save_form_data (0.002)\n", - " 6. _coerce_field_name (0.001)\n", + " 0. handle_fk_field (0.459)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. __str__ (0.983)\n", - "---- 1. __repr__ (0.015)\n", - " 2. describe (0.0)\n", - " 3. __hash__ (0.0)\n", - " 4. label (0.0)\n", - " 5. name (0.0)\n", - " 6. __len__ (0.0)\n", + " 0. __str__ (0.996)\n", "\n", "Label = entity_decl\n", "Pred =\n", - " 0. unparsed_entity_decl (0.783)\n", - " 1. attach_alternative (0.008)\n", - " 2. start_doctype_decl (0.006)\n", - " 3. as_sqlite (0.006)\n", - " 4. external_entity_ref_handler (0.004)\n", - " 5. debug (0.003)\n", - " 6. simplify (0.002)\n", + " 0. unparsed_entity_decl (0.941)\n", "\n", "Label = get_internal_wsgi_application\n", "Pred =\n", - " 0. find_migration (0.09)\n", - " 1. get_language_from_path (0.041)\n", - " 2. _fetch (0.014)\n", - " 3. handle_simple (0.013)\n", - " 4. migrations_module (0.012)\n", - " 5. find_in_app (0.011)\n", - " 6. record_download_archive (0.011)\n", + " 0. make_union (0.099)\n", "\n", "Label = error\n", "Pred =\n", - " 0. __call__ (0.232)\n", - " 1. report_error (0.046)\n", - " 2. validate (0.039)\n", - " 3. deserialize (0.037)\n", - " 4. handle_error (0.024)\n", - " 5. _download_json (0.023)\n", - "---- 6. error (0.019)\n", + " 0. raise_for_status (0.077)\n", "\n", "Label = handle_default_options\n", "Pred =\n", - " 0. ipython (0.067)\n", - " 1. handle_app_config (0.034)\n", - " 2. sql_table_creation_suffix (0.03)\n", - " 3. explain (0.023)\n", - " 4. runshell (0.015)\n", - " 5. get_new_connection (0.014)\n", - " 6. add_legacy_name (0.014)\n", + " 0. handle (0.023)\n", "\n", "Label = add_usage\n", "Pred =\n", - " 0. disconnect (0.022)\n", - " 1. add_exception (0.02)\n", - " 2. boolean (0.02)\n", - " 3. debug (0.018)\n", - " 4. receiver (0.017)\n", - " 5. add_arguments (0.016)\n", - " 6. calc_time_key (0.015)\n", + " 0. _generate_removed_field (0.043)\n", "\n", "Label = check_programs\n", "Pred =\n", - " 0. match_str (0.022)\n", - " 1. simplify (0.015)\n", - " 2. get_hasher (0.015)\n", - " 3. get_partition_uuid (0.014)\n", - " 4. check_perms (0.013)\n", - " 5. ror (0.009)\n", - " 6. _sqlite_datetime_parse (0.007)\n", + " 0. CASCADE (0.027)\n", "\n", "Label = add_arguments\n", "Pred =\n", "---- 0. add_arguments (1.0)\n", - " 1. debug (0.0)\n", - " 2. _call_api (0.0)\n", - " 3. handle (0.0)\n", - " 4. clear (0.0)\n", - " 5. add (0.0)\n", - " 6. execute (0.0)\n", "\n", "Label = add_arguments\n", "Pred =\n", "---- 0. add_arguments (1.0)\n", - " 1. debug (0.0)\n", - " 2. _call_api (0.0)\n", - " 3. handle (0.0)\n", - " 4. clear (0.0)\n", - " 5. add (0.0)\n", - " 6. setup_test_environment (0.0)\n", "\n", "Label = table2model\n", "Pred =\n", - " 0. _convert_int_to_bytes (0.032)\n", - " 1. _get_no_autofield_sequence_name (0.025)\n", - " 2. title (0.02)\n", - " 3. get_valid_filename (0.015)\n", - " 4. extract_title (0.013)\n", - " 5. is_ad_fragment (0.012)\n", - " 6. _clean_win_chars (0.012)\n", + " 0. manifest_url (0.042)\n", "\n", "Label = run_from_argv\n", "Pred =\n", - " 0. _prepare_and_start_frag_download (0.056)\n", - " 1. clean_savepoints (0.042)\n", - " 2. commit (0.021)\n", - " 3. initialize (0.014)\n", - " 4. add_exception (0.014)\n", - " 5. get_requests_session (0.011)\n", - " 6. get_command_line_option (0.011)\n", + " 0. logout (0.043)\n", "\n", "Label = handle\n", "Pred =\n", - "---- 0. handle (0.998)\n", - " 1. check (0.0)\n", - " 2. options (0.0)\n", - " 3. add (0.0)\n", - " 4. post (0.0)\n", - " 5. __set__ (0.0)\n", - " 6. reset (0.0)\n", + "---- 0. handle (0.956)\n", "\n", "Label = unset_available_apps\n", "Pred =\n", - " 0. unset_installed_apps (0.285)\n", - " 1. clear_cache (0.08)\n", - " 2. start_serialization (0.051)\n", - " 3. list (0.021)\n", - " 4. runshell (0.016)\n", - " 5. get_app_configs (0.015)\n", - " 6. match_request (0.015)\n", + " 0. unset_installed_apps (0.473)\n", "\n", "Label = _curried\n", "Pred =\n", - " 0. curry (0.801)\n", - " 1. format_html_join (0.01)\n", - " 2. timezone_today (0.003)\n", - " 3. get_hashers_by_algorithm (0.003)\n", - " 4. _cache_controller (0.002)\n", - " 5. interpolate (0.002)\n", - " 6. buffer_with_style (0.002)\n", + " 0. curry (0.869)\n", "\n", "Label = __promise__\n", "Pred =\n", - " 0. __wrapper__ (0.547)\n", - " 1. iterlists (0.057)\n", - " 2. decorator (0.056)\n", - " 3. check_warning (0.026)\n", - " 4. _cache_controlled (0.018)\n", - " 5. __new__ (0.014)\n", - " 6. iteritems (0.012)\n", + " 0. __wrapper__ (0.49)\n", "\n", "Label = __bytes_cast_encoded\n", "Pred =\n", - " 0. encode (0.063)\n", - " 1. timestamp (0.045)\n", - " 2. dumps (0.045)\n", - " 3. loads (0.04)\n", - " 4. __text_cast (0.034)\n", - " 5. sign (0.028)\n", - " 6. __bytes_cast (0.023)\n", + " 0. __call__ (0.236)\n", "\n", "Label = assertRaisesRegex\n", "Pred =\n", - " 0. assertRegex (0.441)\n", - " 1. assertCountEqual (0.399)\n", - " 2. _get_page (0.018)\n", - " 3. wrapped_view (0.015)\n", - " 4. inner (0.006)\n", - " 5. call_and_shelve (0.005)\n", - " 6. put (0.005)\n", + " 0. assertRegex (0.483)\n", "\n", "Label = wrapper\n", "Pred =\n", - "---- 0. wrapper (0.29)\n", - " 1. new_method_proxy (0.069)\n", - " 2. decorator (0.05)\n", - " 3. get_word_index (0.02)\n", - " 4. __new__ (0.019)\n", - " 5. __copy__ (0.017)\n", - " 6. inner (0.014)\n", + " 0. decorator (0.572)\n", "\n", "Label = is_iterable\n", "Pred =\n", - " 0. _tosequence (0.25)\n", - " 1. norm (0.029)\n", - " 2. to_list_or_none (0.02)\n", - " 3. has_shareable_memory (0.019)\n", - " 4. logistic (0.019)\n", - " 5. laplacian_kernel (0.017)\n", - " 6. _decision_function (0.014)\n", + " 0. to_list_or_none (0.135)\n", "\n", "Label = get_tag_uri\n", "Pred =\n", - " 0. normalize (0.085)\n", - " 1. is_password_usable (0.047)\n", - " 2. literals (0.025)\n", - " 3. shift_rows (0.014)\n", - " 4. get_last_modified (0.013)\n", - " 5. shift_rows_inv (0.013)\n", - " 6. all_items_equal (0.009)\n", + " 0. sign (0.069)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (0.852)\n", - " 1. harden_runtime (0.013)\n", - " 2. validate (0.007)\n", - " 3. process_request (0.005)\n", - " 4. rename_column_references (0.003)\n", - " 5. _set_debug (0.002)\n", - " 6. prepare_headers (0.002)\n", + "---- 0. __init__ (0.813)\n", "\n", "Label = duration_string\n", "Pred =\n", - " 0. _get_duration_components (0.446)\n", - " 1. test_repr (0.017)\n", - " 2. duration_microseconds (0.009)\n", - " 3. test_basic_tokenizer_lower (0.009)\n", - " 4. _parse_date_fmt (0.008)\n", - " 5. split_time_odd_even (0.008)\n", - " 6. report_resuming_byte (0.007)\n", + " 0. _get_duration_components (0.745)\n", "\n", "Label = get_fixed_timezone\n", "Pred =\n", - " 0. srt_subtitles_timecode (0.053)\n", - " 1. _shape_repr (0.026)\n", - " 2. utc_tzinfo_factory (0.022)\n", - " 3. read_bootstrap_info (0.019)\n", - " 4. parse_count (0.016)\n", - " 5. cookie_date (0.013)\n", - " 6. check_geom_offset (0.012)\n", + " 0. copy_current_request_context (0.018)\n", "\n", "Label = get_default_timezone\n", "Pred =\n", - " 0. get_default_password_validators (0.051)\n", - " 1. tz (0.047)\n", - " 2. new_datetime (0.044)\n", - " 3. no_style (0.026)\n", - " 4. make_naive (0.025)\n", - " 5. get_languages (0.023)\n", - " 6. now (0.018)\n", + " 0. no_style (0.621)\n", "\n", "Label = __exit__\n", "Pred =\n", - "---- 0. __exit__ (0.999)\n", - " 1. reraise (0.0)\n", - " 2. debug (0.0)\n", - " 3. raise_for_status (0.0)\n", - " 4. info (0.0)\n", - " 5. handle_exception (0.0)\n", - " 6. fit (0.0)\n", + "---- 0. __exit__ (0.998)\n", "\n", "Label = _safety_decorator\n", "Pred =\n", - " 0. MobileNet (0.057)\n", - " 1. MobileNetV2 (0.038)\n", - " 2. ResNet50 (0.024)\n", - " 3. decode_url (0.022)\n", - " 4. create_unbound_method (0.011)\n", - " 5. box (0.01)\n", - " 6. _paginate (0.008)\n", + " 0. find_resource_pool_by_name (0.099)\n", "\n", "Label = format_html\n", "Pred =\n", - " 0. ogrinspect (0.216)\n", - " 1. args_to_str (0.041)\n", - " 2. _format_lazy (0.023)\n", - " 3. replacement (0.021)\n", - " 4. as_text (0.019)\n", - " 5. compat_kwargs (0.011)\n", - " 6. inner_func (0.011)\n", + " 0. cut (0.11)\n", "\n", "Label = _strip_once\n", "Pred =\n", - " 0. extract_attributes (0.183)\n", - " 1. close (0.094)\n", - " 2. end_object (0.019)\n", - " 3. date (0.016)\n", - " 4. norm (0.016)\n", - " 5. writeString (0.015)\n", - " 6. dumps (0.011)\n", + " 0. extract_attributes (0.085)\n", "\n", "Label = read\n", "Pred =\n", - " 0. _get_image_dimensions (0.094)\n", - " 1. as_string (0.075)\n", - " 2. readable (0.071)\n", - " 3. open (0.038)\n", - " 4. words (0.031)\n", - " 5. as_bytes (0.027)\n", - " 6. _get_file (0.018)\n", + " 0. chunks (0.103)\n", "\n", "Label = __setitem__\n", "Pred =\n", - "---- 0. __setitem__ (0.992)\n", - " 1. set (0.002)\n", - " 2. setlist (0.002)\n", - " 3. __delitem__ (0.001)\n", - " 4. appendlist (0.0)\n", - " 5. _set_single (0.0)\n", - " 6. clear (0.0)\n", + "---- 0. __setitem__ (0.986)\n", "\n", "Label = startElement\n", "Pred =\n", - " 0. widget_attrs (0.16)\n", - " 1. addQuickElement (0.075)\n", - " 2. build_attrs (0.053)\n", - " 3. as_textarea (0.02)\n", - " 4. handle_starttag (0.017)\n", - " 5. root_attributes (0.016)\n", - " 6. label_tag (0.015)\n", + " 0. addQuickElement (0.109)\n", "\n", "Label = dictvalue\n", "Pred =\n", - " 0. make_qs_param (0.054)\n", - " 1. first (0.046)\n", - " 2. get_prep_value (0.027)\n", - " 3. format_time (0.021)\n", - " 4. grad (0.012)\n", - " 5. _squeeze_time (0.011)\n", - " 6. _to_tuple (0.011)\n", + " 0. compare_ordering_key (0.066)\n", "\n", "Label = parse_http_date_safe\n", "Pred =\n", - " 0. date (0.037)\n", - " 1. get_filter_type (0.016)\n", - " 2. get_next_week (0.014)\n", - " 3. get_previous_week (0.014)\n", - " 4. get_next_month (0.013)\n", - " 5. get_previous_year (0.013)\n", - " 6. get_next_day (0.012)\n", + " 0. parse_structured_interfaces (0.023)\n", "\n", "Label = urlsafe_base64_encode\n", "Pred =\n", - " 0. b64_encode (0.911)\n", - " 1. endswith_lf (0.002)\n", - " 2. urlsafe_base64_decode (0.002)\n", - " 3. cookie_jar_to_list (0.002)\n", - " 4. b64_decode (0.002)\n", - " 5. _set_language (0.002)\n", - " 6. endswith_cr (0.002)\n", + " 0. b64_encode (0.94)\n", "\n", "Label = quote_etag\n", "Pred =\n", - " 0. parse_etags (0.103)\n", - " 1. _if_none_match_passes (0.036)\n", - " 2. _if_match_passes (0.015)\n", - " 3. extract_tag_box (0.013)\n", - " 4. get_element_by_id (0.011)\n", - " 5. add_truncation_text (0.01)\n", - " 6. spaceless (0.007)\n", + " 0. parse_etags (0.333)\n", "\n", "Label = module_dir\n", "Pred =\n", - " 0. auto_find_instance_path (0.046)\n", - " 1. should_redirect_with_slash (0.026)\n", - " 2. ensure_dir_exists (0.021)\n", - " 3. can_merge (0.021)\n", - " 4. find_library (0.02)\n", - " 5. humanize (0.019)\n", - " 6. path_exists (0.019)\n", + " 0. list_paths (0.043)\n", "\n", "Label = a\n", "Pred =\n", - " 0. A (0.913)\n", - " 1. g (0.026)\n", - " 2. P (0.004)\n", - " 3. new_datetime (0.002)\n", - " 4. i18n (0.001)\n", - " 5. _get_user_description (0.001)\n", - " 6. b (0.001)\n", + " 0. A (0.973)\n", "\n", "Label = i\n", "Pred =\n", - " 0. f (0.263)\n", - " 1. commit (0.058)\n", - " 2. _joint_log_likelihood (0.014)\n", - " 3. P (0.014)\n", - " 4. predict_log_proba (0.013)\n", - " 5. predict_proba (0.01)\n", - " 6. _predict_log_proba (0.008)\n", + " 0. f (0.646)\n", "\n", "Label = O\n", "Pred =\n", - " 0. srt_subtitles_timecode (0.135)\n", - " 1. Z (0.055)\n", - " 2. __next__ (0.038)\n", - " 3. d (0.023)\n", - " 4. iter_body (0.015)\n", - " 5. process_body (0.013)\n", - " 6. s (0.012)\n", + " 0. srt_subtitles_timecode (0.626)\n", "\n", "Label = __init__\n", "Pred =\n", - "---- 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. destination (0.0)\n", - " 5. predict_proba (0.0)\n", - " 6. build (0.0)\n", + "---- 0. __init__ (0.995)\n", "\n", "Label = __call__\n", - "Pred =\n", - " 0. convert_exception_to_response (0.358)\n", - " 1. process_response (0.061)\n", - " 2. _handle_error (0.054)\n", - " 3. get_xframe_options_value (0.018)\n", - " 4. lookups (0.009)\n", - " 5. _prepare (0.007)\n", - " 6. _model_admin_wrapper (0.007)\n", - "\n", - "Label = _new_gnu_trans\n", - "Pred =\n", - " 0. _add_local_translations (0.092)\n", - " 1. _reordered_actions (0.039)\n", - " 2. change_view (0.036)\n", - " 3. _num_days (0.03)\n", - " 4. box (0.016)\n", - " 5. get_expiration_time (0.015)\n" + "Pred =\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - " 6. make_token (0.012)\n", + " 0. convert_exception_to_response (0.327)\n", + "\n", + "Label = _new_gnu_trans\n", + "Pred =\n", + " 0. _add_local_translations (0.057)\n", "\n", "Label = translation\n", "Pred =\n", - " 0. get_uid (0.106)\n", - " 1. cut (0.029)\n", - " 2. all (0.021)\n", - " 3. get_language_info (0.017)\n", - " 4. _sanitize_token (0.017)\n", - " 5. get_supported_language_variant (0.015)\n", - " 6. activate (0.015)\n", + " 0. get_uid (0.013)\n", + "\n", + "Label = __init__\n", + "Pred =\n", + "---- 0. __init__ (1.0)\n", + "\n", + "Label = __eq__\n", + "Pred =\n", + "---- 0. __eq__ (1.0)\n", + "\n", + "Label = items\n", + "Pred =\n", + " 0. __iter__ (0.526)\n", + "\n", + "Label = dump_request\n", + "Pred =\n", + " 0. _warning (0.179)\n", + "\n", + "Label = finalize_headers\n", + "Pred =\n", + " 0. serialize_headers (0.057)\n", + "\n", + "Label = get_auth_plugin\n", + "Pred =\n", + " 0. __iter__ (0.06)\n", + "\n", + "Label = get_auth\n", + "Pred =\n", + " 0. check_password (0.541)\n", + "\n", + "Label = write_stream\n", + "Pred =\n", + " 0. write_stream_with_colors_win_py3 (0.626)\n", "\n", "Label = _get_cache_fn\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. start_serialization (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. inverse_transform (0.0)\n", - " 6. build (0.0)\n", + " 0. get (0.782)\n", "\n", "Label = _setup_socks4a\n", "Pred =\n", - " 0. __eq__ (0.999)\n", - " 1. __add__ (0.0)\n", - " 2. clear (0.0)\n", - " 3. add (0.0)\n", - " 4. __lt__ (0.0)\n", - " 5. difference (0.0)\n", - " 6. clone (0.0)\n", + " 0. setproxy (0.113)\n", "\n", "Label = connect_ex\n", "Pred =\n", - " 0. __iter__ (0.166)\n", - " 1. __init__ (0.139)\n", - " 2. to_dict (0.057)\n", - " 3. __init_module__ (0.031)\n", - " 4. init_module (0.03)\n", - " 5. ip (0.016)\n", - " 6. values (0.015)\n", + " 0. connect (0.582)\n", "\n", "Label = next_value\n", "Pred =\n", - " 0. log_error (0.178)\n", - " 1. log (0.045)\n", - " 2. _warning (0.033)\n", - " 3. api_validation (0.029)\n", - " 4. _print (0.021)\n", - " 5. stop_server (0.013)\n", - " 6. start_server (0.012)\n", + " 0. _check_type_str (0.057)\n", "\n", "Label = _etree_iter\n", "Pred =\n", - " 0. add_unredirected_header (0.126)\n", - " 1. serialize_headers (0.083)\n", - " 2. compat_setenv (0.054)\n", - " 3. format_headers (0.045)\n", - " 4. get_encoding_from_headers (0.04)\n", - " 5. normalize_data_format (0.025)\n", - " 6. handle_youtubedl_headers (0.025)\n", + " 0. random_birthday (0.048)\n", "\n", "Label = compat_xpath\n", "Pred =\n", - " 0. __contains__ (0.545)\n", - " 1. __getitem__ (0.147)\n", - " 2. as_sql (0.021)\n", - " 3. count (0.017)\n", - " 4. __iter__ (0.007)\n", - " 5. _create_spatial_index_name (0.006)\n", - " 6. get (0.005)\n", + " 0. get_devices (0.146)\n", "\n", "Label = compat_getenv\n", "Pred =\n", - " 0. check_password (0.037)\n", - " 1. connection (0.029)\n", - " 2. create_user (0.014)\n", - " 3. user_del_cmd (0.01)\n", - " 4. has_perm (0.01)\n", - " 5. change_view (0.01)\n", - " 6. smtp_server_password (0.01)\n", + " 0. navigate_hash (0.099)\n", "\n", "Label = __repr__\n", "Pred =\n", - " 0. write_stream_with_colors_win_py3 (0.95)\n", - " 1. write (0.003)\n", - " 2. write_unsigned_int (0.002)\n", - " 3. write_unsigned_int_24 (0.001)\n", - " 4. _concurrency_safe_write (0.001)\n", - " 5. write_metadata_tag (0.001)\n", - " 6. chunks (0.001)\n", + "---- 0. __repr__ (1.0)\n", "\n", "Label = find_xpath_attr\n", "Pred =\n", - " 0. get (0.164)\n", - " 1. _delete (0.08)\n", - " 2. extract (0.067)\n", - " 3. encode (0.031)\n", - " 4. _make_url_result (0.025)\n", - " 5. __str__ (0.021)\n", - " 6. __getitem__ (0.02)\n", + " 0. if_exists (0.045)\n", "\n", "Label = timeconvert\n", "Pred =\n", - " 0. add_info_extractor (0.041)\n", - " 1. try_utime (0.017)\n", - " 2. enable (0.013)\n", - " 3. _set_debug (0.013)\n", - " 4. unset_installed_apps (0.012)\n", - " 5. setproxy (0.011)\n", - " 6. reset_translations (0.011)\n", + " 0. create_logger (0.015)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. connect (0.124)\n", - " 1. _get_connection (0.072)\n", - " 2. get_template (0.035)\n", - " 3. _get_elb_connection (0.027)\n", - " 4. lock_configuration (0.024)\n", - " 5. exec_rpc (0.023)\n", - " 6. get_template_by_name (0.021)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. __enter__ (0.196)\n", - " 1. get_config (0.106)\n", - " 2. enable (0.031)\n", - " 3. get_capabilities (0.03)\n", - " 4. __invert__ (0.027)\n", - " 5. json (0.025)\n", - " 6. get_value (0.024)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = hyphenate_date\n", "Pred =\n", - " 0. find_xpath_attr (0.019)\n", - " 1. get_max_age (0.016)\n", - " 2. extract (0.015)\n", - " 3. temp_get_nets (0.014)\n", - " 4. generate_vlan_dict (0.014)\n", - " 5. parse_m3u8_attributes (0.012)\n", - " 6. is_vlan_valid (0.01)\n", + " 0. escapejs (0.043)\n", "\n", "Label = next_nonbmp_pos\n", "Pred =\n", - " 0. get_host (0.026)\n", - " 1. get_devices (0.021)\n", - " 2. if_exists (0.021)\n", - " 3. _meta_regex (0.018)\n", - " 4. check_subtest_picklable (0.018)\n", - " 5. _get_plan_name (0.016)\n", - " 6. get_format (0.015)\n", + " 0. ordered_obj (0.067)\n", "\n", "Label = unsmuggle_url\n", "Pred =\n", - " 0. setdefault (0.19)\n", - " 1. compat_setenv (0.076)\n", - " 2. setlistdefault (0.015)\n", - " 3. get_action (0.014)\n", - " 4. save_itemgetter (0.013)\n", - " 5. format (0.01)\n", - " 6. sanitize_numeric_fields (0.008)\n", + " 0. make_iframe_entry (0.028)\n", "\n", "Label = prepend_extension\n", "Pred =\n", - " 0. __repr__ (1.0)\n", - " 1. __str__ (0.0)\n", - " 2. __hash__ (0.0)\n", - " 3. describe (0.0)\n", - " 4. extra_repr (0.0)\n", - " 5. sign (0.0)\n", - " 6. predict_proba (0.0)\n", + " 0. splitext (0.101)\n", "\n", "Label = replace_extension\n", "Pred =\n", - " 0. find_xpath_attr (0.049)\n", - " 1. get_current_to_attr (0.025)\n", - " 2. parse_m3u8_attributes (0.017)\n", - " 3. addSubTest (0.016)\n", - " 4. propagate_exceptions (0.012)\n", - " 5. _roundup (0.011)\n", - " 6. is_netmask (0.01)\n", + " 0. splitext (0.093)\n", "\n", "Label = update_url_query\n", "Pred =\n", - " 0. _unlock_file (0.021)\n", - " 1. qualities (0.012)\n", - " 2. check_sts_include_subdomains (0.011)\n", - " 3. has_leading_dir (0.011)\n", - " 4. protocol_to_tuple (0.011)\n", - " 5. _non_atomic_requests (0.009)\n", - " 6. connection_class (0.009)\n", + " 0. _search_dimensions_in_video_url (0.07)\n", "\n", "Label = encode_data_uri\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. __setstate__ (0.0)\n", - " 4. inverse_transform (0.0)\n", - " 5. init_poolmanager (0.0)\n", - " 6. destination (0.0)\n", + " 0. is_valid_mime (0.255)\n", "\n", "Label = age_restricted\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. start_serialization (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. destination (0.0)\n", + " 0. _fetch_thumbnail (0.053)\n", "\n", "Label = resf\n", "Pred =\n", - " 0. get_valid_filename (0.023)\n", - " 1. is_image_name_id (0.022)\n", - " 2. _fix_subtitle (0.02)\n", - " 3. escape_quotes (0.019)\n", - " 4. get_from_attributes (0.017)\n", - " 5. compat_shlex_quote (0.017)\n", - " 6. strip_tags (0.014)\n", + " 0. build_function (0.966)\n", "\n", "Label = add_progress_hook\n", "Pred =\n", - " 0. tuple (0.165)\n", - " 1. mark_safe (0.043)\n", - " 2. to_int (0.029)\n", - " 3. get_digit (0.018)\n", - " 4. compat_ord (0.015)\n", - " 5. get_rule_by_name (0.014)\n", - " 6. deduplicate_rules_args (0.013)\n", + " 0. _hook_progress (0.614)\n", "\n", "Label = _filter\n", "Pred =\n", - " 0. decrypt_url (0.025)\n", - " 1. parse_js_value (0.017)\n", - " 2. _ids_to_results (0.013)\n", - " 3. resolve_permalink_url (0.011)\n", - " 4. _parse_smil_subtitles (0.011)\n", - " 5. recvall (0.01)\n", - " 6. _render (0.01)\n", + " 0. get_from_attributes (0.016)\n", "\n", "Label = _valueless_option\n", "Pred =\n", - " 0. splitext (0.118)\n", - " 1. _m3u8_meta_format (0.083)\n", - " 2. extract_subtitle (0.027)\n", - " 3. cleanup_url (0.022)\n", - " 4. fixup (0.018)\n", - " 5. url_basename (0.016)\n", - " 6. get_unique_filename (0.016)\n", + " 0. _option (0.379)\n", "\n", "Label = supports\n", "Pred =\n", - " 0. splitext (0.093)\n", - " 1. _m3u8_meta_format (0.06)\n", - " 2. fixup (0.027)\n", - " 3. get_unique_filename (0.023)\n", - " 4. extract_subtitle (0.02)\n", - " 5. cleanup_url (0.02)\n", - " 6. url_basename (0.018)\n", + "---- 0. supports (0.899)\n", "\n", "Label = read_unsigned_long_long\n", "Pred =\n", - " 0. escape_url (0.042)\n", - " 1. get_signing_serializer (0.022)\n", - " 2. check_secret_key (0.018)\n", - " 3. get_auth_from_url (0.018)\n", - " 4. check_video (0.013)\n", - " 5. url_basename (0.012)\n", - " 6. check_sts (0.009)\n", + " 0. read_unsigned_char (0.45)\n", "\n", "Label = full_box\n", "Pred =\n", - " 0. make_header (0.036)\n", - " 1. is_valid_mime (0.032)\n", - " 2. basic_auth_header (0.03)\n", - " 3. resolve_dash_template (0.029)\n", - " 4. make_password (0.017)\n", - " 5. _convert_int_to_bytes (0.015)\n", - " 6. get_lexer_for_body (0.014)\n", + " 0. box (0.851)\n", "\n", "Label = temp_name\n", "Pred =\n", - " 0. trim_url (0.058)\n", - " 1. is_outdated_version (0.026)\n", - " 2. same_permission (0.022)\n", - " 3. human_to_bytes (0.015)\n", - " 4. get_available_image_extensions (0.011)\n", - " 5. group_norm (0.008)\n", - " 6. _cube (0.008)\n", + " 0. local_file_exists (0.495)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. build_function (0.97)\n", - " 1. compat_ctypes_WINFUNCTYPE (0.001)\n", - " 2. _get_source_fast (0.001)\n", - " 3. render_template_string (0.001)\n", - " 4. _lazy_proxy_unpickle (0.0)\n", - " 5. record_once (0.0)\n", - " 6. resf (0.0)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _decrypt\n", "Pred =\n", - " 0. _hook_progress (0.78)\n", - " 1. print_help (0.012)\n", - " 2. report_resuming_byte (0.004)\n", - " 3. test_default (0.002)\n", - " 4. _live_title (0.002)\n", - " 5. _meta_regex (0.002)\n", - " 6. _check_can_read (0.002)\n", + " 0. decrypt_url (0.47)\n", "\n", "Label = suitable\n", "Pred =\n", - " 0. get_from_attributes (0.082)\n", - " 1. permanent (0.02)\n", - " 2. get_height (0.017)\n", - " 3. json_script (0.016)\n", - " 4. _make_timedelta (0.015)\n", - " 5. xml_text (0.015)\n", - " 6. get_attribute (0.015)\n", + "---- 0. suitable (1.0)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. _option (0.675)\n", - " 1. cli_valueless_option (0.093)\n", - " 2. _bool_option (0.037)\n", - " 3. cli_option (0.01)\n", - " 4. process_response (0.007)\n", - " 5. wrapped_view (0.005)\n", - " 6. get_context (0.003)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. supports (0.977)\n", - " 1. from_dict (0.002)\n", - " 2. parse_number (0.001)\n", - " 3. needs_modification (0.0)\n", - " 4. normalize_username (0.0)\n", - " 5. serialize_keras_object (0.0)\n", - " 6. resource_group_to_dict (0.0)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _ts\n", "Pred =\n", - " 0. read_unsigned_int (0.477)\n", - " 1. read_unsigned_char (0.434)\n", - " 2. read_bytes (0.002)\n", - " 3. endswith_cr (0.002)\n", - " 4. d (0.001)\n", - " 5. uses_server_time (0.001)\n", - " 6. S (0.001)\n", + " 0. _set_language (0.089)\n", "\n", "Label = _raise_error\n", "Pred =\n", - " 0. box (0.692)\n", - " 1. do_ntranslate (0.006)\n", - " 2. decode_args (0.005)\n", - " 3. format_eta (0.004)\n", - " 4. compat_ctypes_WINFUNCTYPE (0.004)\n", - " 5. curry (0.004)\n", - " 6. _lazy_proxy_unpickle (0.003)\n", + "---- 0. _raise_error (0.083)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. local_file_exists (0.485)\n", - " 1. src_is_valid (0.028)\n", - " 2. url_filename (0.014)\n", - " 3. _get_ssh_fingerprint (0.013)\n", - " 4. filename_from_content_disposition (0.008)\n", - " 5. _open (0.007)\n", - " 6. subclass_exception (0.006)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = get_count\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. _api_request (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. _extract_video_url (0.0)\n", - " 4. get (0.0)\n", - " 5. _process_page (0.0)\n", - " 6. _get_page (0.0)\n", + " 0. _file_name (0.051)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. decrypt_url (0.024)\n", - " 1. _ids_to_results (0.021)\n", - " 2. parse_js_value (0.017)\n", - " 3. _extract_entries (0.017)\n", - " 4. recvall (0.015)\n", - " 5. _recv_bytes (0.015)\n", - " 6. ror (0.013)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _parse_json\n", "Pred =\n", - " 0. suitable (1.0)\n", - " 1. _match_id (0.0)\n", - " 2. get (0.0)\n", - " 3. _resolv_url (0.0)\n", - " 4. deserialize (0.0)\n", - " 5. decorator (0.0)\n", - " 6. _delete (0.0)\n", + " 0. get_aos_session (0.056)\n", "\n", "Label = _og_search_description\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _api_request (0.0)\n", - " 3. _make_url_result (0.0)\n", - " 4. _get_page (0.0)\n", - " 5. _process_page (0.0)\n", - " 6. extract_id (0.0)\n", + " 0. _og_search_thumbnail (0.317)\n", "\n", "Label = _xpath_ns\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. put (0.0)\n", - " 4. _process_page (0.0)\n", - " 5. _api_request (0.0)\n", - " 6. extract (0.0)\n", + " 0. open_resource (0.043)\n", "\n", "Label = extract_Initialization\n", "Pred =\n", - " 0. _set_language (0.079)\n", - " 1. _convert_int_to_bytes (0.025)\n", - " 2. b64_encode (0.022)\n", - " 3. u (0.013)\n", - " 4. prepend_underscore_and_lower (0.013)\n", - " 5. get_internal_type (0.01)\n", - " 6. hexdigest (0.009)\n", + " 0. extract_artist (0.029)\n", "\n", "Label = location_key\n", "Pred =\n", - " 0. _raise_error (0.064)\n", - " 1. _report_error (0.016)\n", - " 2. _raise_extractor_error (0.012)\n", - " 3. _check_error (0.01)\n", - " 4. get_related_url (0.01)\n", - " 5. report_error (0.01)\n", - " 6. _download_theplatform_metadata (0.009)\n", + " 0. strip_tags (0.049)\n", "\n", "Label = add_segment_url\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. _api_request (0.0)\n", - " 4. put (0.0)\n", - " 5. _process_page (0.0)\n", - " 6. extract (0.0)\n", + " 0. video_url (0.05)\n", "\n", "Label = _get_cookies\n", "Pred =\n", - " 0. url_repl (0.023)\n", - " 1. is_placeholder (0.019)\n", - " 2. _file_name (0.018)\n", - " 3. label_tag (0.011)\n", - " 4. get_video_info (0.008)\n", - " 5. _element_factory (0.008)\n", - " 6. add_source_format (0.008)\n", + " 0. _real_extract (0.046)\n", "\n", "Label = _real_extract\n", "Pred =\n", - "---- 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. put (0.0)\n", - " 3. _make_url_result (0.0)\n", - " 4. patch (0.0)\n", - " 5. _download_webpage_handle_no_ff (0.0)\n", - " 6. _process_page (0.0)\n", + "---- 0. _real_extract (0.999)\n", "\n", "Label = _extract_url\n", "Pred =\n", - " 0. command_helper (0.037)\n", - " 1. parse_event_status (0.017)\n", - " 2. get_dot1q_id (0.012)\n", - " 3. do_ntranslate (0.011)\n", - " 4. to_command (0.011)\n", - " 5. retry_not_found (0.01)\n", - " 6. _validate_range (0.009)\n", + "---- 0. _extract_url (0.972)\n", "\n", "Label = _call_api\n", "Pred =\n", - " 0. _og_search_thumbnail (0.48)\n", - " 1. _og_search_url (0.195)\n", - " 2. _og_search_title (0.181)\n", - " 3. _extract_embed (0.004)\n", - " 4. _find_video_id (0.004)\n", - " 5. _fetch_description (0.002)\n", - " 6. _og_search_video_url (0.002)\n", + " 0. _extract_regular (0.223)\n", "\n", "Label = _call_api\n", "Pred =\n", - " 0. split_identifier (0.06)\n", - " 1. compat_etree_register_namespace (0.041)\n", - " 2. calc_hash (0.016)\n", - " 3. validate_attribute_with_allowed_values (0.012)\n", - " 4. linebreak_iter (0.01)\n", - " 5. migrations_module (0.009)\n", - " 6. prepend_host (0.009)\n", + "---- 0. _call_api (0.62)\n", "\n", "Label = _real_initialize\n", "Pred =\n", - " 0. find_param (0.154)\n", - " 1. setDeleteProtection (0.035)\n", - " 2. match_str (0.012)\n", - " 3. get_current_snap_obj (0.01)\n", - " 4. _parse_smil_subtitles (0.009)\n", - " 5. test_countvectorizer_custom_vocabulary_gap_index (0.007)\n", - " 6. is_vlan_valid (0.006)\n", + " 0. get_login_url (0.155)\n", "\n", "Label = _build_template_url\n", "Pred =\n", - " 0. strip_tags (0.022)\n", - " 1. _function_called_str (0.017)\n", - " 2. prepend_host (0.012)\n", - " 3. is_expired (0.012)\n", - " 4. combine_url (0.01)\n", - " 5. argument_hash (0.01)\n", - " 6. prepend_underscore_and_lower (0.01)\n", + " 0. _real_extract (0.995)\n", "\n", "Label = get_ysuid\n", "Pred =\n", - " 0. extract_m3u8 (0.019)\n", - " 1. temp_get_nets (0.017)\n", - " 2. get_video_info (0.014)\n", - " 3. _smuggle_referrer (0.011)\n", - " 4. _url_res (0.01)\n", - " 5. _search_dimensions_in_video_url (0.01)\n", - " 6. add_source_format (0.01)\n", + " 0. subtitles_filename (0.059)\n", "\n", "Label = _extract_data_config\n", "Pred =\n", - " 0. _extract_videos (0.039)\n", - " 1. query_api (0.037)\n", - " 2. _brightcove_new_url_result (0.026)\n", - " 3. needs_etag (0.016)\n", - " 4. send (0.015)\n", - " 5. theplatform_url_result (0.015)\n", - " 6. _calc_cookies (0.015)\n", + " 0. _extract_json (0.248)\n", "\n", "Label = _real_extract\n", "Pred =\n", "---- 0. _real_extract (1.0)\n", - " 1. _process_page (0.0)\n", - " 2. get (0.0)\n", - " 3. _extract_video_url (0.0)\n", - " 4. _make_url_result (0.0)\n", - " 5. put (0.0)\n", - " 6. extract (0.0)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. _extract_url (0.976)\n", - " 1. _search_iframe_url (0.015)\n", - " 2. _extract_domain_id (0.003)\n", - " 3. _extract_mrss_url (0.0)\n", - " 4. _extract_peertube_url (0.0)\n", - " 5. _extract_brightcove_url (0.0)\n", - " 6. _extract_info (0.0)\n", + "---- 0. _real_extract (0.999)\n", "\n", "Label = suitable\n", "Pred =\n", - " 0. _extract_regular (0.117)\n", - " 1. _get_video_url (0.052)\n", - " 2. _extract_annotations (0.036)\n", - " 3. _process_legacy_playlist (0.034)\n", - " 4. _extract_token_url (0.024)\n", - " 5. _get_token (0.024)\n", - " 6. _extract_video_url (0.023)\n", + "---- 0. suitable (1.0)\n", "\n", "Label = extract_file_url\n", "Pred =\n", - " 0. _call_api (0.844)\n", - " 1. _get_video_url (0.037)\n", - " 2. query_api (0.019)\n", - " 3. _call_rpc_api (0.007)\n", - " 4. _download_theplatform_metadata (0.004)\n", - " 5. _extract_annotations (0.004)\n", - " 6. _download_legacy_playlist_url (0.003)\n", + " 0. extract_title (0.221)\n", "\n", "Label = _extract_entries\n", "Pred =\n", - " 0. login (0.058)\n", - " 1. is_usable (0.026)\n", - " 2. get_login_url (0.024)\n", - " 3. _real_initialize (0.017)\n", - " 4. get_source_expressions (0.013)\n", - " 5. finish (0.009)\n", - " 6. contains_aggregate (0.008)\n", + " 0. _extract_urls (0.687)\n", "\n", "Label = _real_extract\n", "Pred =\n", - "---- 0. _real_extract (0.937)\n", - " 1. _make_url_result (0.02)\n", - " 2. extract_id (0.007)\n", - " 3. _extract_url (0.003)\n", - " 4. _api_request (0.002)\n", - " 5. _fetch_upload_date (0.002)\n", - " 6. _build_template_url (0.001)\n", + "---- 0. _real_extract (0.351)\n", "\n", "Label = _extract_urls\n", "Pred =\n", - " 0. literals (0.026)\n", - " 1. valid_reason (0.014)\n", - " 2. format_seconds (0.013)\n", - " 3. rfc3339_date (0.012)\n", - " 4. is_ignored (0.011)\n", - " 5. unescape_string_literal (0.01)\n", - " 6. _reordered_actions (0.009)\n", + "---- 0. _extract_urls (1.0)\n", "\n", "Label = _extract_url\n", "Pred =\n", - " 0. _extract_json (0.252)\n", - " 1. _extract_video_url (0.057)\n", - " 2. _extract_mrss_url (0.056)\n", - " 3. _extract_info (0.047)\n", - " 4. _extract_mgid (0.044)\n", - "---- 5. _extract_url (0.038)\n", - " 6. _extract_regular (0.035)\n", + "---- 0. _extract_url (0.966)\n", "\n", "Label = _add_skip_wall\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. put (0.0)\n", - " 4. _api_request (0.0)\n", - " 5. _process_page (0.0)\n", - " 6. patch (0.0)\n", + " 0. _search_dimensions_in_video_url (0.077)\n", "\n", "Label = _extract_urls\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. _make_url_result (0.0)\n", - " 2. get (0.0)\n", - " 3. _api_request (0.0)\n", - " 4. _process_page (0.0)\n", - " 5. _download_webpage_handle_no_ff (0.0)\n", - " 6. _fetch_upload_date (0.0)\n", + "---- 0. _extract_urls (1.0)\n", "\n", "Label = _get_api_key\n", "Pred =\n", - " 0. suitable (1.0)\n", - " 1. _match_id (0.0)\n", - " 2. get (0.0)\n", - " 3. _resolv_url (0.0)\n", - " 4. deserialize (0.0)\n", - " 5. _delete (0.0)\n", - " 6. decorator (0.0)\n", + " 0. extract_subtitle (0.038)\n", "\n", "Label = _get_real_id\n", "Pred =\n", - " 0. find_iframe_url (0.578)\n", - " 1. extract_data_val (0.041)\n", - " 2. _fetch_thumbnail (0.019)\n", - " 3. _search_mvp_id (0.018)\n", - " 4. extract_meta (0.017)\n", - " 5. get_height (0.013)\n", - " 6. extract_data (0.012)\n", + " 0. _extract_playlist (0.055)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. _extract_urls (0.942)\n", - " 1. extract (0.006)\n", - " 2. _extract_video_url (0.005)\n", - " 3. get (0.004)\n", - " 4. _process_page (0.003)\n", - " 5. _extract_folder (0.003)\n", - " 6. _extract_url (0.002)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = report_resolve\n", "Pred =\n", - " 0. _real_extract (0.493)\n", - " 1. _extract_folder (0.079)\n", - " 2. _extract_rtmp_video (0.033)\n", - " 3. _api_request (0.015)\n", - " 4. _process_page (0.013)\n", - " 5. _extract_formats_from_vmap_url (0.01)\n", - " 6. make_iframe_entry (0.006)\n", + " 0. report_information_extraction (0.317)\n", "\n", "Label = _get_n_results\n", "Pred =\n", - " 0. _extract_urls (1.0)\n", - " 1. _extract_url (0.0)\n", - " 2. smart_split (0.0)\n", - " 3. extract (0.0)\n", - " 4. base_url (0.0)\n", - " 5. _match_id (0.0)\n", - " 6. get (0.0)\n", + " 0. get_tags (0.042)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. _extract_url (0.946)\n", - " 1. _search_iframe_url (0.021)\n", - " 2. _extract_domain_id (0.011)\n", - " 3. _extract_mrss_url (0.002)\n", - " 4. _extract_peertube_url (0.001)\n", - " 5. _extract_json (0.001)\n", - " 6. _search_mvp_id (0.001)\n", + "---- 0. _real_extract (0.999)\n", "\n", "Label = md5_text\n", "Pred =\n", - " 0. escape_url (0.031)\n", - " 1. pbkdf2 (0.029)\n", - " 2. get_signing_serializer (0.026)\n", - " 3. smart_split (0.023)\n", - " 4. url_basename (0.019)\n", - " 5. check_video (0.014)\n", - " 6. _build_brighcove_url_from_js (0.012)\n", + " 0. md5_utf8 (0.14)\n", "\n", "Label = __init__\n", "Pred =\n", - " 0. _extract_urls (1.0)\n", - " 1. _extract_url (0.0)\n", - " 2. smart_split (0.0)\n", - " 3. extract (0.0)\n", - " 4. base_url (0.0)\n", - " 5. _match_id (0.0)\n", - " 6. get (0.0)\n", + "---- 0. __init__ (1.0)\n", "\n", "Label = get_programme_id\n", "Pred =\n", - " 0. sha256sum (0.088)\n", - " 1. names_digest (0.042)\n", - " 2. create_secret (0.035)\n", - " 3. build_unique_id (0.028)\n", - " 4. _generate_cache_header_key (0.026)\n", - " 5. set_response_etag (0.019)\n", - " 6. ssh_key_fingerprint (0.019)\n", + " 0. refs_expression (0.065)\n", "\n", "Label = aws_hmac_digest\n", "Pred =\n", - " 0. _next_page_url (0.066)\n", - " 1. _extract_video_url (0.044)\n", - " 2. _extract_track_entries (0.039)\n", - " 3. _tracks_page_func (0.039)\n", - " 4. _process_legacy_playlist (0.025)\n", - " 5. _make_url_result (0.02)\n", - " 6. _extract_count (0.018)\n", + " 0. sign (0.634)\n", "\n", "Label = aws_hmac_hexdigest\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. put (0.0)\n", - " 3. _make_url_result (0.0)\n", - " 4. patch (0.0)\n", - " 5. delete (0.0)\n", - " 6. _process_page (0.0)\n", + " 0. urlencode_postdata (0.08)\n", "\n", "Label = _real_initialize\n", "Pred =\n", - " 0. report_download_webpage (0.295)\n", - " 1. report_information_extraction (0.277)\n", - " 2. report_video_info_webpage_download (0.258)\n", - " 3. report_unavailable_format (0.027)\n", - " 4. report_extraction (0.009)\n", - " 5. report_following_redirect (0.006)\n", - " 6. report_file_already_downloaded (0.006)\n", + " 0. _login (0.121)\n", "\n", "Label = make_id\n", "Pred =\n", - " 0. find_collection_resource_or_fail (0.214)\n", - " 1. get_facts_from_collection (0.083)\n", - " 2. _read_current_fasthttp_profiles_from_device (0.033)\n", - " 3. read_domains_from_device (0.031)\n", - " 4. from_response (0.017)\n", - " 5. find_collection_item (0.015)\n", - " 6. _read_current_fastl4_profiles_from_device (0.014)\n", + " 0. encode_host (0.187)\n", "\n", "Label = _real_extract\n", "Pred =\n", "---- 0. _real_extract (1.0)\n", - " 1. _make_url_result (0.0)\n", - " 2. _fetch_upload_date (0.0)\n", - " 3. get (0.0)\n", - " 4. _api_request (0.0)\n", - " 5. _download_webpage_handle_no_ff (0.0)\n", - " 6. put (0.0)\n", "\n", "Label = extract_redirect_url\n", "Pred =\n", - " 0. names_digest (0.23)\n", - " 1. md5_utf8 (0.219)\n", - " 2. aws_hash (0.036)\n", - " 3. set_response_etag (0.034)\n", - " 4. get_session_auth_hash (0.031)\n", - " 5. sha_utf8 (0.022)\n", - " 6. hexdigest (0.021)\n", + " 0. search_field (0.112)\n", "\n", "Label = extract_count\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. start_serialization (0.0)\n", - " 2. __setitem__ (0.0)\n", - " 3. init_poolmanager (0.0)\n", - " 4. __setstate__ (0.0)\n", - " 5. build (0.0)\n", - " 6. func (0.0)\n", + " 0. extract_data (0.304)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. parse_content_type (0.027)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1. _extract_medias (0.026)\n", - " 2. get_node (0.02)\n", - " 3. _strip_spaces (0.018)\n", - " 4. get_from_attributes (0.017)\n", - " 5. _findall (0.013)\n", - " 6. xml_text (0.01)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _get_cms_resource\n", "Pred =\n", - " 0. sign (0.537)\n", - " 1. md5 (0.17)\n", - " 2. base64_hmac (0.064)\n", - " 3. sha256sum (0.007)\n", - " 4. gather (0.004)\n", - " 5. softsign (0.004)\n", - " 6. function (0.003)\n", + " 0. parse_resource_to_dict (0.097)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. md5_utf8 (0.047)\n", - " 1. hash_key (0.041)\n", - " 2. sha_utf8 (0.03)\n", - " 3. names_digest (0.029)\n", - " 4. _hash (0.028)\n", - " 5. get_session_auth_hash (0.028)\n", - " 6. build_unique_id (0.022)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = suitable\n", "Pred =\n", - " 0. _call_api (0.025)\n", - " 1. process_request (0.02)\n", - " 2. _get_consumer_secret (0.018)\n", - " 3. _real_initialize (0.018)\n", - " 4. get_raw_uri (0.014)\n", - " 5. get_path_info (0.012)\n", - " 6. extract (0.009)\n", + "---- 0. suitable (1.0)\n", "\n", "Label = _real_extract\n", "Pred =\n", - " 0. _create_path_to_field (0.04)\n", - " 1. subtitles_filename (0.02)\n", - " 2. encode_host (0.016)\n", - " 3. hash_type_xml_to_cli_str (0.011)\n", - " 4. decode_from_string (0.01)\n", - " 5. get_network_start (0.009)\n", - " 6. _count_righthand_zero_bits (0.009)\n", + "---- 0. _real_extract (1.0)\n", "\n", "Label = _extract_url\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _process_page (0.0)\n", - " 3. _api_request (0.0)\n", - " 4. _make_url_result (0.0)\n", - " 5. _download_webpage_handle_no_ff (0.0)\n", - " 6. put (0.0)\n", + "---- 0. _extract_url (0.98)\n", "\n", "Label = extract_json\n", "Pred =\n", - " 0. url_result (0.069)\n", - " 1. extract_url (0.063)\n", - " 2. make_iframe_entry (0.026)\n", - " 3. _extract_smil_info (0.026)\n", - " 4. make_video_entry (0.023)\n", - " 5. _m3u8_meta_format (0.02)\n", - " 6. make_entry (0.018)\n", + " 0. get_json_value (0.286)\n", "\n", "Label = _prefer_source\n", "Pred =\n", - " 0. _search_kane (0.226)\n", - " 1. extract (0.128)\n", - " 2. extract_count (0.067)\n", - " 3. find_iframe_url (0.059)\n", - " 4. extract_data (0.033)\n", - " 5. _extract_count (0.031)\n", - " 6. get (0.028)\n", + " 0. _vimeo_sort_formats (0.251)\n", "\n", "Label = _raise_unavailable\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. _process_page (0.0)\n", - " 4. _api_request (0.0)\n", - " 5. _fetch_upload_date (0.0)\n", - " 6. extract (0.0)\n", + " 0. _check_existence (0.08)\n", "\n", "Label = md5_text\n", "Pred =\n", - " 0. _download_info (0.081)\n", - " 1. _graphql_call (0.018)\n", - " 2. theplatform_url_result (0.015)\n", - " 3. _extract_track_entries (0.014)\n", - " 4. open_instance_resource (0.013)\n", - " 5. query_tags (0.011)\n", - " 6. _brightcove_new_url_result (0.011)\n", + " 0. md5_utf8 (0.141)\n", "\n", "Label = split_sum\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. put (0.0)\n", - " 4. _process_page (0.0)\n", - " 5. _api_request (0.0)\n", - " 6. patch (0.0)\n", + " 0. _botocore_exception_maybe (0.045)\n", "\n", "Label = mod\n", "Pred =\n", - " 0. suitable (1.0)\n", - " 1. _match_id (0.0)\n", - " 2. get (0.0)\n", - " 3. _resolv_url (0.0)\n", - " 4. deserialize (0.0)\n", - " 5. _delete (0.0)\n", - " 6. decorator (0.0)\n", + " 0. split_ip_time_sum (0.081)\n", "\n", "Label = handleSum\n", "Pred =\n", - " 0. _real_extract (1.0)\n", - " 1. get (0.0)\n", - " 2. _make_url_result (0.0)\n", - " 3. put (0.0)\n", - " 4. patch (0.0)\n", - " 5. delete (0.0)\n", - " 6. _download_webpage_handle_no_ff (0.0)\n", + " 0. handle_input16 (0.824)\n", "\n", "Label = probe_executable\n", "Pred =\n", - " 0. _extract_url (0.964)\n", - " 1. _extract_urls (0.007)\n", - " 2. _search_iframe_url (0.005)\n", - " 3. _extract_domain_id (0.002)\n", - " 4. _extract_brightcove_url (0.002)\n", - " 5. extract (0.001)\n", - " 6. get (0.001)\n", + " 0. executable (0.395)\n", "\n", "Label = _ffmpeg_filename_argument\n", "Pred =\n", - " 0. extract_data (0.729)\n", - " 1. search_field (0.021)\n", - " 2. extract_data_val (0.017)\n", - " 3. _extract_video_data (0.016)\n", - " 4. _extract_player (0.015)\n", - " 5. get_json_value (0.009)\n", - " 6. search_data (0.007)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. _vimeo_sort_formats (0.105)\n", - " 1. selector_function (0.057)\n", - " 2. _remove_duplicate_formats (0.056)\n", - " 3. flatten_fieldsets (0.018)\n", - " 4. diff_objects (0.015)\n", - " 5. final_selector (0.012)\n", - " 6. is_ipv4_address (0.01)\n", - "\n", - "Label = get_train_examples\n", - "Pred =\n", - " 0. to_screen (0.043)\n", - " 1. _raise_error (0.031)\n", - " 2. _check_errors (0.03)\n", - " 3. _handle_error (0.021)\n", - " 4. login_vca (0.019)\n", - " 5. _download_theplatform_metadata (0.018)\n", - " 6. _login (0.014)\n", - "\n", - "Label = compute_loss\n", - "Pred =\n", - " 0. md5_utf8 (0.292)\n", - " 1. set_response_etag (0.089)\n", - " 2. aws_hash (0.057)\n", - " 3. names_digest (0.044)\n", - " 4. _generate_cache_header_key (0.023)\n", - " 5. hash_key (0.019)\n", - " 6. str_to_int (0.018)\n", - "\n", - "Label = _tokenize_chinese_chars\n", - "Pred =\n", - " 0. _rsa_fun (0.184)\n", - " 1. unquote (0.034)\n", - " 2. kv_list (0.025)\n", - " 3. _len_and_data (0.017)\n", - " 4. deduplicate_rules_args (0.017)\n", - " 5. has_shareable_memory (0.008)\n", - " 6. _botocore_exception_maybe (0.008)\n", - "\n", - "Label = test_chinese\n", - "Pred =\n", - " 0. split_ip_time_sum (0.033)\n", - " 1. split_time_ip_sum (0.028)\n", - " 2. get_package_libraries (0.015)\n", - " 3. handle_input8 (0.014)\n", - " 4. ohdave_rsa_encrypt (0.013)\n", - " 5. _og_regexes (0.013)\n", - " 6. _recv_bytes (0.011)\n", - "\n", - "Label = test_is_control\n", - "Pred =\n", - " 0. handle_input16 (0.571)\n", - " 1. __init__ (0.273)\n", - " 2. set (0.006)\n", - " 3. __setitem__ (0.003)\n", - " 4. _score_to_decision (0.003)\n", - " 5. ip (0.003)\n", - " 6. destination (0.002)\n", - "\n", - "Label = __init__\n", - "Pred =\n", - " 0. executable (0.231)\n", - " 1. get_modified_time (0.016)\n", - " 2. is_silenced (0.01)\n", - " 3. timezone_today (0.009)\n", - " 4. empty (0.008)\n", - " 5. simple (0.006)\n", - " 6. now (0.006)\n", - "\n", - "Label = __eq__\n", - "Pred =\n", - " 0. spatial_function_name (0.101)\n", - " 1. submit (0.024)\n", - " 2. has_argument (0.021)\n", - " 3. encode (0.016)\n", - " 4. arrayvar (0.013)\n", - " 5. txt_helper (0.013)\n", - " 6. __getattr__ (0.01)\n", - "\n", - "Label = items\n", - "Pred =\n", - " 0. _load_in_background (0.942)\n", - " 1. _flush_bg_loading_exception (0.01)\n", - " 2. _called_with_wrong_args (0.004)\n", - " 3. _load_unlocked (0.003)\n", - " 4. _enable_zones (0.001)\n", - " 5. _disable_zones (0.001)\n", - " 6. errorhandler (0.001)\n", - "\n", - "Label = dump_request\n", - "Pred =\n", - " 0. get_config (0.376)\n", - " 1. __setattr__ (0.049)\n", - " 2. load (0.023)\n", - " 3. load_config (0.019)\n", - " 4. get_required_config (0.018)\n", - " 5. get_custom_value (0.014)\n", - " 6. populate (0.013)\n", - "\n", - "Label = finalize_headers\n", - "Pred =\n", - " 0. add_app_template_filter (0.347)\n", - " 1. add_template_global (0.128)\n", - " 2. add_template_test (0.093)\n", - " 3. add_app_template_global (0.044)\n", - " 4. do_teardown_appcontext (0.036)\n", - " 5. add_app_template_test (0.024)\n", - " 6. _set_debug (0.016)\n", - "\n", - "Label = get_auth_plugin\n", - "Pred =\n", - " 0. teardown_app_request (0.379)\n", - " 1. teardown_request (0.314)\n", - " 2. teardown_appcontext (0.058)\n", - " 3. after_app_request (0.027)\n", - " 4. after_request (0.018)\n", - " 5. app_url_defaults (0.005)\n", - " 6. url_defaults (0.005)\n", - "\n", - "Label = get_auth\n", - "Pred =\n", - " 0. shell_context_processor (0.533)\n", - " 1. context_processor (0.086)\n", - " 2. app_context_processor (0.075)\n", - " 3. template_context_processors (0.057)\n", - " 4. make_shell_context (0.008)\n", - " 5. render_template (0.006)\n", - " 6. admin_actions (0.004)\n", - "\n", - "Label = write_stream\n", - "Pred =\n", - " 0. login (0.029)\n", - " 1. reset_translations (0.024)\n", - " 2. match_request (0.02)\n", - " 3. add_info_extractor (0.015)\n", - " 4. _wait_queue (0.013)\n", - " 5. _load_in_background (0.011)\n", - " 6. try_utime (0.01)\n", + " 0. submit (0.053)\n", "\n", "Label = _load_app\n", "Pred =\n", - " 0. _prepare_name (0.076)\n", - " 1. get_debug_flag (0.053)\n", - " 2. get_str_from_wsgi (0.034)\n", - " 3. _get_gcp_environ_var (0.021)\n", - " 4. get_env (0.013)\n", - " 5. _configuration_args (0.013)\n", - " 6. get_bytes_from_wsgi (0.012)\n", + " 0. _load_in_background (0.879)\n", "\n", "Label = _set_templates_auto_reload\n", "Pred =\n", - " 0. template_global (0.248)\n", - " 1. app_template_filter (0.235)\n", - " 2. app_template_test (0.128)\n", - " 3. decorator (0.039)\n", - " 4. app_errorhandler (0.038)\n", - " 5. url_defaults (0.022)\n", - " 6. template_filter (0.016)\n", + " 0. get_config (0.337)\n", "\n", "Label = add_template_filter\n", "Pred =\n", - " 0. before_first_request (0.38)\n", - " 1. before_request (0.231)\n", - " 2. before_app_request (0.202)\n", - " 3. after_app_request (0.022)\n", - " 4. after_request (0.012)\n", - " 5. try_trigger_before_first_request_functions (0.005)\n", - " 6. template_global (0.004)\n", + " 0. add_app_template_filter (0.608)\n", "\n", "Label = teardown_request\n", "Pred =\n", - " 0. url_value_preprocessor (0.987)\n", - " 1. app_url_defaults (0.001)\n", - " 2. url_defaults (0.001)\n", - " 3. report_following_redirect (0.0)\n", - "---- 4. teardown_request (0.0)\n", - " 5. add_app_template_test (0.0)\n", - " 6. after_app_request (0.0)\n", + " 0. teardown_app_request (0.418)\n", "\n", "Label = context_processor\n", "Pred =\n", - " 0. _if_match_passes (0.03)\n", - " 1. attach_alternative (0.029)\n", - " 2. get_filename_max_length (0.013)\n", - " 3. format_percent (0.012)\n", - " 4. month_by_abbreviation (0.01)\n", - " 5. deactivate_all (0.009)\n", - " 6. _if_none_match_passes (0.008)\n", + " 0. shell_context_processor (0.5)\n", "\n", "Label = raise_routing_exception\n", "Pred =\n", - " 0. set_test_cookie (0.026)\n", - " 1. tag (0.025)\n", - " 2. cookies (0.024)\n", - " 3. itervalues (0.024)\n", - " 4. extract_view_count (0.019)\n", - " 5. iteritems (0.019)\n", - " 6. test_cookie_worked (0.014)\n", + " 0. error (0.077)\n", "\n", "Label = get_load_dotenv\n", "Pred =\n", - " 0. dumps (0.394)\n", - " 1. strip_tags (0.14)\n", - " 2. decorator (0.032)\n", - " 3. get_value (0.013)\n", - " 4. python_2_unicode_compatible (0.011)\n", - " 5. get_url (0.01)\n", - " 6. iteritems (0.008)\n", + " 0. get_str_from_wsgi (0.363)\n", "\n", "Label = app_template_global\n", "Pred =\n", - " 0. __exit__ (0.82)\n", - " 1. debug (0.059)\n", - " 2. clear (0.01)\n", - " 3. set (0.005)\n", - " 4. handle (0.005)\n", - " 5. do_fail (0.004)\n", - " 6. handle_exception (0.003)\n", + " 0. app_template_filter (0.491)\n", "\n", "Label = before_app_first_request\n", "Pred =\n", - " 0. loads (0.243)\n", - " 1. tag (0.041)\n", - " 2. decode (0.036)\n", - " 3. strptime (0.029)\n", - " 4. decompress (0.029)\n", - " 5. unpack (0.024)\n", - " 6. geos_version_tuple (0.02)\n", + " 0. before_first_request (0.747)\n", "\n", "Label = app_url_value_preprocessor\n", "Pred =\n", - " 0. to_python (0.933)\n", - " 1. tag (0.017)\n", - " 2. prepare_value (0.004)\n", - " 3. check (0.002)\n", - " 4. format_value (0.002)\n", - " 5. has_changed (0.002)\n", - " 6. get_prep_value (0.002)\n", + " 0. url_value_preprocessor (0.988)\n", "\n", "Label = is_json\n", "Pred =\n", - " 0. __init__ (1.0)\n", - " 1. __setitem__ (0.0)\n", - " 2. __setstate__ (0.0)\n", - " 3. split (0.0)\n", - " 4. init_poolmanager (0.0)\n", - " 5. ip (0.0)\n", - " 6. start_serialization (0.0)\n", + " 0. _msectotimecode (0.022)\n", "\n", "Label = max_cookie_size\n", "Pred =\n", - " 0. get_dev_examples (0.382)\n", - " 1. get_test_examples (0.36)\n", - " 2. get_train_examples (0.205)\n", - " 3. sign (0.001)\n", - " 4. aws_hmac (0.001)\n", - " 5. set_response_etag (0.001)\n", - " 6. hash_key (0.001)\n", + " 0. force_full_push (0.025)\n", "\n", "Label = implements_to_string\n", "Pred =\n", - " 0. function (0.351)\n", - " 1. binary_crossentropy (0.074)\n", - " 2. foldr (0.024)\n", - " 3. softmax (0.024)\n", - " 4. gather (0.021)\n", - " 5. call (0.02)\n", - " 6. __call__ (0.012)\n", + " 0. python_2_unicode_compatible (0.18)\n", "\n", "Label = __exit__\n", "Pred =\n", - " 0. _clean_text (0.781)\n", - " 1. _run_strip_accents (0.019)\n", - " 2. encode (0.01)\n", - " 3. _encode_relation (0.01)\n", - " 4. can_merge (0.003)\n", - " 5. verbatim (0.003)\n", - " 6. validate_key (0.003)\n", + "---- 0. __exit__ (0.999)\n", "\n", "Label = to_json\n", "Pred =\n", - " 0. test_basic_tokenizer_no_lower (0.59)\n", - " 1. test_basic_tokenizer_lower (0.275)\n", - " 2. _check_can_write (0.005)\n", - " 3. _check_can_read (0.004)\n", - " 4. as_bytes (0.003)\n", - " 5. _load_form_data (0.001)\n", - " 6. deactivate_all (0.001)\n", + " 0. loads (0.428)\n", "\n", "Label = untag\n", "Pred =\n", - " 0. disable_implicit_wait (0.032)\n", - " 1. add_votes (0.029)\n", - " 2. validate_ipv4_address (0.018)\n", - " 3. _loaded_messages (0.017)\n", - " 4. create_webdriver (0.014)\n", - " 5. from_current_timezone (0.013)\n", - " 6. get_expire_at_browser_close (0.011)\n", + " 0. to_python (0.993)\n", + "\n", + "Label = __init__\n", + "Pred =\n", + "---- 0. __init__ (1.0)\n", + "\n", + "Label = get_train_examples\n", + "Pred =\n", + " 0. get_dev_examples (0.392)\n", + "\n", + "Label = compute_loss\n", + "Pred =\n", + " 0. softmax (0.08)\n", + "\n", + "Label = _tokenize_chinese_chars\n", + "Pred =\n", + " 0. _clean_text (0.261)\n", + "\n", + "Label = test_chinese\n", + "Pred =\n", + " 0. test_basic_tokenizer_no_lower (0.489)\n", + "\n", + "Label = test_is_control\n", + "Pred =\n", + " 0. teardown_test_environment (0.038)\n", "\n" ] } @@ -31904,7 +19456,7 @@ " print(\"Pred =\")\n", " correct = False\n", " \n", - " for i in range(7):\n", + " for i in range(1):\n", " p = vocab_label_df.loc[r[i]][0]\n", " if p==labels_str[idx]:\n", " score +=1\n", @@ -31922,16 +19474,16 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.43825538623226484" + "0.41986337362059906" ] }, - "execution_count": 161, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -31942,16 +19494,16 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.2515199577055247" + "2.381727158948686" ] }, - "execution_count": 162, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -31962,7 +19514,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 98, "metadata": {}, "outputs": [], "source": [ @@ -31971,7 +19523,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -31988,7 +19540,1027 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'detect_ipvX_address_usage': 0,\n", + " '_render': 0,\n", + " 'get_admin_state': 0,\n", + " 'restore_db_instance_from_db_snapshot': 1,\n", + " 'one_hot': 0,\n", + " '_savepoint_commit': 0,\n", + " 'check_cs_op': 0,\n", + " 'check_arg_errcode': 0,\n", + " 'get_desired': 0,\n", + " 'create_vm_template': 0,\n", + " 'next_nonbmp_pos': 0,\n", + " 'get_distribution': 1,\n", + " 'test_random_search_with_fit_params': 0,\n", + " 'content': 0,\n", + " 'post': 0,\n", + " 'undefine': 1,\n", + " 'listify_string_name_or_id': 0,\n", + " 'get_stp_enabled_state': 0,\n", + " 'frame_size': 0,\n", + " 'test_classifier_exceptions': 0,\n", + " '_pooling_function': 2,\n", + " 'port_misuse_policy': 0,\n", + " 'create_diagnotstics_profile_dict': 0,\n", + " 'combine_expression': 0,\n", + " '_remove_prefetched_objects': 1,\n", + " 'get_names': 0,\n", + " 'apparent_encoding': 0,\n", + " '_get_test_db_name': 0,\n", + " 'get_failsafe_timeout': 0,\n", + " 'get_update_cmd': 0,\n", + " '_insert_network_data': 0,\n", + " 'test_base_zero_n_estimators': 0,\n", + " '__promise__': 0,\n", + " 'check_programs': 0,\n", + " 'timeout': 2,\n", + " 'isvalidlsaoholdinterval': 0,\n", + " 'location_key': 0,\n", + " 'NASNetLarge': 0,\n", + " 'get_command_from_state': 4,\n", + " 'main': 11,\n", + " 'minimum': 0,\n", + " 'void_output': 0,\n", + " 'remove_index': 0,\n", + " 'bounds': 1,\n", + " 'upload_file_to_device': 1,\n", + " '_convert_field_to_tz': 1,\n", + " '__setstate__': 2,\n", + " 'get_dhcp_leases': 0,\n", + " 'is_reserved': 0,\n", + " 'firewall_enforced_policy': 0,\n", + " 'should_update': 13,\n", + " 'hess': 0,\n", + " '_check_standard_scaled': 0,\n", + " 'delete_existing': 0,\n", + " 'get_admin_url': 0,\n", + " 'time_trunc_sql': 0,\n", + " 'members': 1,\n", + " '_generate_no_bgp_cmds': 0,\n", + " 'shutdown': 1,\n", + " '_get_zone': 1,\n", + " 'list': 2,\n", + " 'get_result': 2,\n", + " '_convert_simple_dict_to_list': 0,\n", + " 'quote': 0,\n", + " 'get_feature_names': 0,\n", + " 'fetch_list': 4,\n", + " 'runshell': 0,\n", + " 'get_all_facts': 0,\n", + " 'collect': 1,\n", + " '_activate_virtualenv': 0,\n", + " 'getRecord': 0,\n", + " 'num_coords': 0,\n", + " 'create_agent_pool_profile_instance': 0,\n", + " 'get_runner_details': 0,\n", + " '_get_to_python': 0,\n", + " 'prepare_value': 1,\n", + " 'normalize_rule_spec': 0,\n", + " 'get_contents': 0,\n", + " '__new__': 1,\n", + " '_set_z': 0,\n", + " 'resolve_requires': 0,\n", + " 'load_config': 1,\n", + " 'geo_locations': 0,\n", + " 'test_cross_val_predict_method_checking': 0,\n", + " 'var': 0,\n", + " 'paused': 0,\n", + " 'timeconvert': 0,\n", + " 'destination_ip': 0,\n", + " 'is_vlan_bitmap_empty': 1,\n", + " 'asg_exists': 0,\n", + " '_generate_no_igmp_cmds': 0,\n", + " 'parse_memtotal': 0,\n", + " 'feature_importances_': 0,\n", + " '__str__': 2,\n", + " 'check_args': 2,\n", + " 'get_zone': 0,\n", + " 'staged_policy': 0,\n", + " 'present_record': 1,\n", + " 'get_system_mode': 0,\n", + " 'save_classmethod': 0,\n", + " '_effective_n_jobs': 0,\n", + " 'test_np_log': 0,\n", + " '_get_storage_path': 0,\n", + " 'save_weakset': 0,\n", + " 'extract_names_from_blob_uri': 0,\n", + " 'supports_spatial_index': 0,\n", + " 'reload_license': 0,\n", + " 'compat_xpath': 0,\n", + " 'test_tfidf_vectorizer_setter': 0,\n", + " 'app_template_global': 0,\n", + " 'fit_predict': 1,\n", + " 'service_policy': 0,\n", + " 'decompress': 0,\n", + " 'add_related_update': 0,\n", + " '_sqlite_rpad': 0,\n", + " 'delete_addr': 0,\n", + " 'led': 1,\n", + " 'resource_exists': 1,\n", + " 'resource_to_create': 0,\n", + " 'read_current_from_device': 7,\n", + " 'time': 0,\n", + " 'process_rhs': 1,\n", + " 'get_autostart2': 1,\n", + " 'get_connection': 1,\n", + " 'get_load_dotenv': 0,\n", + " 'time_until_up': 0,\n", + " 'search_obj_in_list': 3,\n", + " 'sflow_poll_interval': 0,\n", + " 'autosync_enabled': 0,\n", + " 'extra_repr': 2,\n", + " 'self_link': 1,\n", + " 'get_block_storage_volumes': 0,\n", + " '_get_full_path': 0,\n", + " '_is_true': 0,\n", + " 'le': 0,\n", + " '_curried': 0,\n", + " 'get_distribution_SMGL': 0,\n", + " 'get_traceback_html': 0,\n", + " 'find_dvs_by_uuid': 0,\n", + " 'get_nested_backend': 0,\n", + " 'fqdns': 2,\n", + " 'read_unsigned_long_long': 0,\n", + " 'camel': 0,\n", + " 'test_robust_scaler_invalid_range': 0,\n", + " 'check_transformers_unfitted': 0,\n", + " 'compute_mask': 1,\n", + " 'destination': 1,\n", + " 'is_find_by_filter_operation': 0,\n", + " 'xcli_wrapper': 0,\n", + " 'execute_show_command': 5,\n", + " 'model_from_config': 0,\n", + " 'param_persist': 0,\n", + " '_get_interfaces_status': 0,\n", + " 'list_instances': 0,\n", + " '_boolean_input': 0,\n", + " '_gather_versions': 0,\n", + " 'frontend_ip_configuration_id': 0,\n", + " '_setup_socks4a': 0,\n", + " 'test_extract_patch_same_size_image': 0,\n", + " 'set_ipv6_interfaces': 0,\n", + " 'contains_properly': 0,\n", + " 'tuplify': 0,\n", + " 'check_response': 4,\n", + " 'test_load_digits': 0,\n", + " 'port_lists': 1,\n", + " 'register_forward_pre_hook': 0,\n", + " '__reduce__': 0,\n", + " 'test_logreg_intercept_scaling': 0,\n", + " 'test_assert_raise_message': 0,\n", + " 'hyperparameters': 0,\n", + " 'attach_file': 0,\n", + " 'get_consul_client': 0,\n", + " 'check_relate_argument': 0,\n", + " 'get_NIC': 0,\n", + " 'get_fixed_timezone': 0,\n", + " 'generate_commands': 1,\n", + " 'record_migration': 0,\n", + " 'release_floating_ip': 0,\n", + " '_iter_test_indices': 0,\n", + " 'cli_get_connect_port': 0,\n", + " 'render_to_response': 0,\n", + " 'get_uptime_facts': 1,\n", + " 'select_format': 1,\n", + " 'sha512_utf8': 0,\n", + " 'test_importances_raises': 0,\n", + " 'routing_protocol': 0,\n", + " 'check_two_point': 0,\n", + " 'is_multipart': 0,\n", + " '_pack': 0,\n", + " 'unpack': 0,\n", + " 'execute': 1,\n", + " 'qos_topology': 0,\n", + " 'interval': 1,\n", + " 'buffers': 0,\n", + " 'get_current_job_queue': 0,\n", + " 'reverse': 0,\n", + " 'session': 0,\n", + " '_get_input_message': 0,\n", + " '_get_leaves': 0,\n", + " 'availability_state': 0,\n", + " '_parse_labels': 0,\n", + " 'validate_access_vlan': 0,\n", + " 'duration_string': 0,\n", + " 'is_present': 0,\n", + " 'fail': 0,\n", + " '_get_ip_routing': 0,\n", + " '_get_network_id': 0,\n", + " 'test_max_leaf_nodes_max_depth': 0,\n", + " 'get_capabilities': 1,\n", + " 'getDomainByName': 0,\n", + " 'parse_structured_power_supply_info': 0,\n", + " 'test_pdist_bool_metrics': 0,\n", + " 'console_log': 0,\n", + " 'present': 5,\n", + " 'allowed_slots': 0,\n", + " 'get_runner_list': 0,\n", + " 'mk_boolean': 0,\n", + " 'instance_to_dict': 0,\n", + " '_extract_url': 3,\n", + " 'set_cause': 0,\n", + " '_filter': 0,\n", + " 'field_choices': 1,\n", + " 'test_min_cluster_size_invalid': 0,\n", + " '_egress_all_match': 0,\n", + " 'max_answers_returned': 0,\n", + " 'update_parameter': 0,\n", + " 'media': 1,\n", + " 'all_have_public_ip': 0,\n", + " 'traffic_group_inherited': 0,\n", + " 'addresses': 2,\n", + " 'model_unpickle': 0,\n", + " 'get_net_id': 0,\n", + " 'test_linear_svx_uppercase_loss_penality_raises_error': 0,\n", + " 'get_tuple_shape': 0,\n", + " 'netconf_set_action': 0,\n", + " '_make_int_array': 0,\n", + " 'validate_level': 0,\n", + " 'hyphenate_date': 0,\n", + " 'get_value': 0,\n", + " 'mptcp_make_after_break': 0,\n", + " 'build_config_xml': 2,\n", + " 'send_data': 0,\n", + " 'encode_string': 0,\n", + " 'list_all_users': 0,\n", + " 'max_header_size': 0,\n", + " '_minor_reduce': 0,\n", + " 'fallback_ip': 0,\n", + " 'format_html': 0,\n", + " 'relu': 1,\n", + " 'handle': 1,\n", + " 'test_calling_fit_reinitializes': 0,\n", + " 'get_running_config': 1,\n", + " 'add_segment_url': 0,\n", + " 'deconstruct': 4,\n", + " 'public_ip_id': 0,\n", + " '_raise_error': 1,\n", + " 'is_json': 0,\n", + " '_generate_igmp_querier_cmds': 0,\n", + " 'mod': 0,\n", + " 'data': 0,\n", + " 'set_start_method': 0,\n", + " 'y': 1,\n", + " 'has_permission': 0,\n", + " 'handle_raw_input': 0,\n", + " 'traffic_group': 1,\n", + " 'has_css_class': 0,\n", + " '__exit__': 3,\n", + " '_project_and_cluster': 0,\n", + " 'search': 0,\n", + " 'complete_missing_attributes': 0,\n", + " 'lookup_allowed': 0,\n", + " 'write_zfile': 0,\n", + " 'inner': 0,\n", + " '_singleton': 0,\n", + " 'test_dict_learning_unknown_fit_algorithm': 0,\n", + " 'layer_count': 0,\n", + " 'unsmuggle_url': 0,\n", + " 'platform_match': 0,\n", + " '_pairwise': 1,\n", + " '_find_permutation': 0,\n", + " 'update_sub': 1,\n", + " 'parse_macaddress': 1,\n", + " 'get_cdn_client': 1,\n", + " 'get_kms_metadata_with_backoff': 0,\n", + " 'split_sum': 0,\n", + " 'add_command_to_interface': 1,\n", + " 'normalize_area': 0,\n", + " 'date_trunc_sql': 0,\n", + " 'items': 0,\n", + " 'test_check_increasing_up_extreme': 0,\n", + " 'parse_http_date_safe': 0,\n", + " 'test_imputation_deletion_warning': 0,\n", + " 'parse_vlan_brief': 0,\n", + " 'make_piecewise': 0,\n", + " 'disconnect_container': 0,\n", + " 'prov_template_exists': 0,\n", + " 'get_hostname': 0,\n", + " 'mgmt_address': 0,\n", + " 'get_form_kwargs': 1,\n", + " 'test_label_ranking_avp': 0,\n", + " 'probe_executable': 0,\n", + " 'find_cluster_by_name_datacenter': 0,\n", + " 'inner_func': 1,\n", + " '_real_initialize': 0,\n", + " 'clean': 1,\n", + " 'format_value': 0,\n", + " 'invoke': 1,\n", + " 'linear': 0,\n", + " 'parse_startupscript_list': 0,\n", + " 'strip_accents_unicode': 0,\n", + " '_transform': 0,\n", + " 'encode_data_uri': 0,\n", + " 'result_list_tag': 0,\n", + " 'parse_lldp_intf': 0,\n", + " 'test_rbf_kernel': 0,\n", + " '__instancecheck__': 0,\n", + " 'state': 1,\n", + " 'check_cdist_bool': 0,\n", + " 'skip_wrapper': 0,\n", + " 'irules': 0,\n", + " 'present_firewall_rule': 1,\n", + " 'test_reconstruct_patches_perfect': 0,\n", + " 'warn_if_public_ip_assignment_changed': 0,\n", + " 'test_countvectorizer_custom_vocabulary_repeated_indices': 0,\n", + " 'difference': 0,\n", + " 'target_field': 1,\n", + " 'validate_field_level_encryption_id': 0,\n", + " 'quote_etag': 0,\n", + " 'age_restricted': 0,\n", + " 'get_media_speed': 1,\n", + " 'add_usage': 0,\n", + " 'handleSum': 0,\n", + " 'get_authorization_domain': 0,\n", + " '_check_type_bits': 0,\n", + " 'test_mcd_issue1127': 0,\n", + " 'is_link_local': 1,\n", + " 'wait_for_eni': 0,\n", + " 'app_url_value_preprocessor': 0,\n", + " '_check_type_list': 0,\n", + " 'reset_trust': 0,\n", + " 'linear_assignment': 0,\n", + " 'dumps': 0,\n", + " 'as_p': 0,\n", + " 'next_value': 0,\n", + " 'get_name': 1,\n", + " 'nodes': 1,\n", + " 'netconf_get_config': 1,\n", + " 'remove': 12,\n", + " '_cert_filename': 0,\n", + " 'to_json': 0,\n", + " 'deleteUser': 0,\n", + " 'extract_count': 0,\n", + " 'predict_proba': 1,\n", + " 'get_ignore_vertification': 0,\n", + " '_exec_module': 2,\n", + " 'gcdns_connect': 0,\n", + " 'num_fields': 0,\n", + " 'raise_if_errors': 1,\n", + " '_load_app': 0,\n", + " 'monitors_list': 3,\n", + " 'manual_resume': 2,\n", + " 'diff': 0,\n", + " '_get_api_key': 0,\n", + " '_get_real_id': 0,\n", + " 'set_params': 0,\n", + " 'get_aliases_from_distribution_id': 0,\n", + " 'route_advertisement': 0,\n", + " 'test_notfitted': 0,\n", + " 'table2model': 0,\n", + " 'random': 0,\n", + " 'validate_purge': 0,\n", + " 'get_proposed': 1,\n", + " 'has_lldp': 2,\n", + " 'test_gpr_interpolation': 0,\n", + " 'rendered_content': 0,\n", + " 'probe_timeout': 0,\n", + " 'test_method_not_available': 0,\n", + " 'check': 3,\n", + " 'max_file_size': 0,\n", + " 'required_together': 0,\n", + " '_is_limited_data_type': 0,\n", + " 'suitable': 3,\n", + " '_remove_temporary_cli_script_from_device': 0,\n", + " 'test_chinese': 0,\n", + " 'is_fakes3': 1,\n", + " '_decrypt': 0,\n", + " 'concurrency_limit': 0,\n", + " 'license_end_date_time': 0,\n", + " 'capfirst': 1,\n", + " 'find_object_by_name': 0,\n", + " 'reset_states': 1,\n", + " '_query_iptun_props': 0,\n", + " 'prepare_database_save': 0,\n", + " '_get_current_month': 0,\n", + " 'parse_model': 0,\n", + " 'get_dict': 0,\n", + " 'test_basic_property_of_random_matrix': 0,\n", + " 'empty_form': 0,\n", + " 'get_software_version': 0,\n", + " '_format_port_for_destination': 0,\n", + " 'reduce_pipe_connection': 0,\n", + " 'test_probability': 0,\n", + " 'unset_available_apps': 0,\n", + " 'get_current_function': 0,\n", + " '_strip_once': 0,\n", + " '__deepcopy__': 3,\n", + " 'check_expression_support': 0,\n", + " '__radd__': 0,\n", + " 'enhanced_loss_recovery': 0,\n", + " 'post_export_action': 0,\n", + " 'check_xss_filter': 0,\n", + " 'snmp_privacy_password': 0,\n", + " 'unref_alias': 0,\n", + " '__set__': 0,\n", + " 'test_ridge_classifier_no_support_multilabel': 0,\n", + " 'raise_routing_exception': 0,\n", + " 'get_platform_id': 0,\n", + " 'options': 0,\n", + " 'disable_addr': 0,\n", + " 'login': 0,\n", + " 'read_facts': 5,\n", + " 'has_perm': 0,\n", + " 'get_client_template_id': 0,\n", + " '_preprocess_padding': 0,\n", + " 'implements_to_string': 0,\n", + " '_wait_for_UP': 0,\n", + " 'gather': 1,\n", + " 'as_mysql': 0,\n", + " 'description': 3,\n", + " 'get_host_by_name': 0,\n", + " 'delete_resource_group': 0,\n", + " '_is_value_present': 0,\n", + " 'get_geometry_converter': 0,\n", + " 'test_unknown_method': 0,\n", + " '__call__': 4,\n", + " 'gzip_window_size': 0,\n", + " 'sql_client': 1,\n", + " 'remove_aliases': 0,\n", + " 'add_routes': 0,\n", + " 'int_or_none': 1,\n", + " 'is_boto3_error_code': 0,\n", + " 'process_view': 0,\n", + " 'register_runner': 0,\n", + " '_errors_svd': 0,\n", + " 'has_perms': 1,\n", + " 'test_class_weight_errors': 0,\n", + " 'mirror_secondary_address': 0,\n", + " 'validate_comment': 0,\n", + " 'get_active_member_count': 0,\n", + " 'eye': 1,\n", + " 'service_identical': 0,\n", + " 'get_success_url': 2,\n", + " 'async_op_url': 3,\n", + " 'untag': 0,\n", + " 'test_k_means_plus_plus_init_2_jobs': 0,\n", + " 'get_tzname': 0,\n", + " 'read': 0,\n", + " 'get_help_text': 0,\n", + " 'max_cookie_size': 0,\n", + " '_errors': 0,\n", + " '_chain_from_iterable_of_lists': 0,\n", + " 'response_add': 0,\n", + " 'get_ysuid': 0,\n", + " 'include_chassis_level_config': 0,\n", + " 'qos_rtt': 0,\n", + " 'is_uuid': 0,\n", + " '_resolve_output_field': 1,\n", + " 'run_from_argv': 0,\n", + " 'state_present': 0,\n", + " 'fit': 3,\n", + " 'connect_ex': 0,\n", + " '_wait_for_fqdn_checks': 0,\n", + " 'chdir': 0,\n", + " '_get_current_year': 0,\n", + " '_store': 0,\n", + " 'present_block_storage_volume': 0,\n", + " 'get_available_number': 0,\n", + " 'renegotiation_period': 0,\n", + " 'get_elb': 0,\n", + " 'na_ontap_host_argument_spec': 0,\n", + " 'database_forwards': 0,\n", + " '_clean504': 0,\n", + " 'parse_vlans': 0,\n", + " 'get_auth_plugin': 0,\n", + " 'wrapper': 0,\n", + " 'cpu_threshold': 0,\n", + " 'nopad_b64': 0,\n", + " 'delete_admin': 0,\n", + " 'is_config_exist': 1,\n", + " 'validate_rotate_frequency': 0,\n", + " 'get_description': 1,\n", + " 'truncatewords': 0,\n", + " 'changed_properties': 0,\n", + " 'policies': 0,\n", + " 'cgnat': 0,\n", + " 'get_schema': 0,\n", + " 'test_decision_path_hardcoded': 0,\n", + " '_generate_igmp_vlan_cmds': 0,\n", + " 'format_for_deletion': 0,\n", + " 'fetch_resource': 4,\n", + " 'account_has_blob_containers': 0,\n", + " 'disabled': 1,\n", + " 'create': 11,\n", + " 'add_arguments': 2,\n", + " 'test_perplexity_input_format': 0,\n", + " 'mirror_primary_address': 0,\n", + " 'validate_consistency': 0,\n", + " 'parse_keys_list': 0,\n", + " 'parse_filesystem_info': 0,\n", + " 'from_response': 5,\n", + " 'source_mask': 0,\n", + " 'test_check_increasing_small_number_of_samples': 0,\n", + " 'read_collection_from_device': 4,\n", + " 'iquery_allow_service_check': 0,\n", + " 'test_train_test_split_allow_nans': 0,\n", + " '_safety_decorator': 0,\n", + " 'boxcox': 0,\n", + " 'transform_commands': 0,\n", + " '_extract_data_config': 0,\n", + " 'multipath_tcp': 0,\n", + " 'get_next_year': 0,\n", + " 'ok': 1,\n", + " '_get': 0,\n", + " 'validate_ip_v6_address': 1,\n", + " 'migration_qs': 0,\n", + " 'open': 1,\n", + " 'formfield': 1,\n", + " '_xpath_ns': 0,\n", + " 'get_vbdif_mac': 0,\n", + " '_get_aggregate_spec': 1,\n", + " 'get_urlconf': 0,\n", + " '_ts': 0,\n", + " 'formfield_for_manytomany': 0,\n", + " 'tags': 0,\n", + " 'signature': 0,\n", + " '_get_dhcp_lease_file': 0,\n", + " 'set_attributes_from_name': 1,\n", + " '_set_templates_auto_reload': 0,\n", + " 'ancestor': 0,\n", + " 'network_failover_enabled': 0,\n", + " 'is_masklen': 0,\n", + " 'centroid': 1,\n", + " '_get_cache_fn': 0,\n", + " 'save_config': 1,\n", + " 'parse_remote_server': 0,\n", + " 'use_route_advertisement': 0,\n", + " 'is_empty': 0,\n", + " 'untagged_interfaces': 0,\n", + " 'test_is_deprecated': 0,\n", + " 'parse_hostname': 2,\n", + " 'mac_address': 0,\n", + " 'urlsafe_base64_encode': 0,\n", + " '_filter_params': 1,\n", + " 'disabled_vlans': 0,\n", + " 'encode': 1,\n", + " 'mandatory_attributes': 0,\n", + " 'test_calc_breakdown_point': 0,\n", + " 'reset_property': 0,\n", + " 'skip_past': 0,\n", + " 'is_valid_uuid': 1,\n", + " 'selector': 0,\n", + " 'encode_rules_as_hcl_string': 0,\n", + " 'push_state': 0,\n", + " '_check_for_i18n': 0,\n", + " '_parse_json': 0,\n", + " '_get_next_executor_id': 0,\n", + " 'update_install_target': 0,\n", + " 'create_on_device': 7,\n", + " 'flatten_list': 6,\n", + " 'num_geom': 0,\n", + " 'uses_learning_phase': 0,\n", + " 'get_rule': 0,\n", + " 'test_column_transformer_no_estimators_set_params': 0,\n", + " 'f': 0,\n", + " 'get_host_tags': 0,\n", + " 'enable_gtm': 0,\n", + " 'param_description': 0,\n", + " '_set_intercept': 0,\n", + " 'get_public_ip': 0,\n", + " 'get_slow_ramp_time': 0,\n", + " 'ensure_disabled': 0,\n", + " '_index_param_value': 0,\n", + " 'defer': 0,\n", + " 'get_programme_id': 0,\n", + " '_combine': 1,\n", + " 'rules': 1,\n", + " '_should_handle': 1,\n", + " 'histogram': 1,\n", + " '_get_instance_health': 1,\n", + " 'decode_predictions': 3,\n", + " 'receive_data_chunk': 1,\n", + " 'ocsp': 0,\n", + " '_ffmpeg_filename_argument': 0,\n", + " 'info': 0,\n", + " 'present_region': 0,\n", + " '_get_mask': 1,\n", + " 'test_k_means_init': 0,\n", + " '__bytes_cast_encoded': 0,\n", + " 'test_check_update_with_no_data': 0,\n", + " 'make_multi': 0,\n", + " 'i': 0,\n", + " 'ipv6addrs': 0,\n", + " 'parse_sysmac': 0,\n", + " 'get_mcp_version': 0,\n", + " '_add_skip_wall': 0,\n", + " 'resf': 0,\n", + " 'srid': 1,\n", + " 'get_configured_member_count': 0,\n", + " 'app_model_error': 0,\n", + " '__repr__': 10,\n", + " 'stop_VM': 0,\n", + " 'is_iterable': 0,\n", + " 'get_staged_firewall_policy': 1,\n", + " '_merge_function': 0,\n", + " 'parameters': 0,\n", + " 'rate_limit': 0,\n", + " 'changes': 0,\n", + " 'prepend_extension': 0,\n", + " 'has_fastl4_profiles': 1,\n", + " 'push': 0,\n", + " 'destination_port': 0,\n", + " 'window_frame_start': 0,\n", + " 'to_return': 7,\n", + " 'volume_exists': 0,\n", + " 'is_download_operation': 0,\n", + " 'prepare_content_length': 0,\n", + " 'iso_date': 0,\n", + " 'reduce': 1,\n", + " '_get_cookies': 0,\n", + " 'targets_equal': 0,\n", + " 'increment': 0,\n", + " 'logout': 0,\n", + " 'find_datastore_by_name': 1,\n", + " 'confirm_login_allowed': 1,\n", + " 'get': 3,\n", + " 'init_module': 4,\n", + " 'work': 1,\n", + " 'test_default_empty_load_files': 0,\n", + " 'floating': 0,\n", + " 'test_warm_start_with_oob_score_fails': 0,\n", + " 'parse_image': 0,\n", + " 'related_fields': 0,\n", + " 'assertRaisesRegex': 0,\n", + " 'test_fit_transduction': 0,\n", + " 'report_resolve': 0,\n", + " 'get_failsafe_action': 0,\n", + " '__ne__': 1,\n", + " 'hostmask': 0,\n", + " 'alpha_dropout': 0,\n", + " 'set_tags': 0,\n", + " 'O': 0,\n", + " 'unregister': 0,\n", + " 'oracle_version': 0,\n", + " 'charset': 0,\n", + " 'source': 1,\n", + " 'device_username': 0,\n", + " '_diff_list': 1,\n", + " '_argmax': 1,\n", + " 'translation_port': 0,\n", + " 'clone': 0,\n", + " 'swappable_setting': 0,\n", + " 'check_category_status': 0,\n", + " 'diag': 2,\n", + " 'get_distribution_NetBSD': 0,\n", + " 'aws_hmac_digest': 0,\n", + " 'a': 0,\n", + " '__eq__': 7,\n", + " 'compat_getenv': 0,\n", + " 'present_script': 1,\n", + " 'desc': 0,\n", + " 'delete_lag': 0,\n", + " 'parse_mediatype': 0,\n", + " 'get_related_field': 0,\n", + " 'add': 0,\n", + " 'get_existing': 1,\n", + " 'lag_changed': 0,\n", + " 'get_all_images': 0,\n", + " 'get_losses_for': 0,\n", + " 'raw_commands': 0,\n", + " 'addExpectedFailure': 0,\n", + " 'deactivate_operation': 0,\n", + " 'test_width_patch': 0,\n", + " 'inplace_logistic_derivative': 0,\n", + " 'transparent': 0,\n", + " 'parse': 0,\n", + " 'parse_media_line': 0,\n", + " 'min': 0,\n", + " 'absent': 8,\n", + " 'lbmonitor_exists': 0,\n", + " 'check_single_sample': 0,\n", + " '_check_vocabulary': 0,\n", + " 'preprocess_input': 1,\n", + " 'test_basic_property_of_sparse_random_matrix': 0,\n", + " 'no_stdout_stderr': 0,\n", + " 'parse_file_upload': 0,\n", + " 'driver': 0,\n", + " 'vm_size_is_valid': 1,\n", + " 'test_node_heap': 0,\n", + " 'get_urls': 0,\n", + " 'update_url_query': 0,\n", + " 'interface_exists': 0,\n", + " 'bytes_read': 0,\n", + " 'upload_eula_to_device': 0,\n", + " '_get_cms_resource': 0,\n", + " 'write_stream': 0,\n", + " '_run_search': 0,\n", + " 'fit_transform': 2,\n", + " 'print_layer_summary': 0,\n", + " 'get_persistence_profile': 0,\n", + " '_request_for_item': 1,\n", + " 'sum': 1,\n", + " 'sanitize': 1,\n", + " 'equals': 1,\n", + " 'http_only': 0,\n", + " '_show_mlag': 0,\n", + " 'parse_lldp_host': 0,\n", + " 'getContactlists': 0,\n", + " 'inverse_transform': 1,\n", + " 'make_id': 0,\n", + " 'get_pim_interface_defaults': 0,\n", + " 'check_setting_app_dirs_loaders': 0,\n", + " 'spheroid': 0,\n", + " 'tuple': 0,\n", + " 'is_valid_description': 1,\n", + " 'describe': 1,\n", + " 'run': 3,\n", + " 'extract_redirect_url': 0,\n", + " 'validate_autopk_value': 0,\n", + " 'fail_json': 1,\n", + " '__init_module__': 0,\n", + " 'get_public_ip_address_instance': 0,\n", + " 'save_on_auto_sync': 0,\n", + " 'fq_name': 1,\n", + " 'modules': 0,\n", + " 'net_reboot': 0,\n", + " 'get_context_data': 0,\n", + " 'test_precompute_invalid_argument': 0,\n", + " 'redirect_virtual_server': 1,\n", + " 'ip': 3,\n", + " '_update_policy': 1,\n", + " 'update': 16,\n", + " 'put': 0,\n", + " 'calculate_s3_path': 0,\n", + " '_step3': 0,\n", + " 'get_current_compute_environment': 0,\n", + " 'extract_Initialization': 0,\n", + " 'test_mean_strategy_regressor': 0,\n", + " 'is_valid_v4addr': 0,\n", + " '_objects_service': 0,\n", + " 'convert_empty_string': 0,\n", + " 'call': 2,\n", + " 'idf_': 1,\n", + " 'connection_to_string': 0,\n", + " 'get_input_shape_at': 0,\n", + " 'test_inverse_transform': 0,\n", + " 'parse_domain_search': 0,\n", + " 'test_dict_learning_overcomplete': 0,\n", + " 'dispatch': 1,\n", + " 'reset_parameters': 0,\n", + " 'executemany': 1,\n", + " 'indexbytes': 1,\n", + " 'converter': 1,\n", + " 'generate_pool_dict': 0,\n", + " 'param_device_group': 0,\n", + " '_new_gnu_trans': 0,\n", + " 'test_pickle_bool_metrics': 0,\n", + " 'hardtanh': 0,\n", + " 'fail_safe': 0,\n", + " 'before_app_first_request': 0,\n", + " 'get_template_sources': 0,\n", + " '_set_list': 0,\n", + " 'get_listener_rules': 0,\n", + " '_normalize_device_name': 0,\n", + " 'create_or_update_role_perm': 0,\n", + " 'apply_key_map': 2,\n", + " 'route_domain': 1,\n", + " '_get_availability_value': 0,\n", + " 'test_bunch_dir': 0,\n", + " 'assert_int_or_pair': 0,\n", + " 'extract_json': 0,\n", + " '_compile': 0,\n", + " 'inplace_swap_row': 0,\n", + " 'replace_extension': 0,\n", + " 'notify_non_idempotent_commands': 0,\n", + " 'setUp': 0,\n", + " 'sparsify': 0,\n", + " '_flatten': 0,\n", + " 'aws_common_argument_spec': 0,\n", + " 'rollback_last': 0,\n", + " 'register_store_backend': 0,\n", + " 'wait_for': 0,\n", + " 'input_mask': 0,\n", + " 'is_classifier': 0,\n", + " 'get_key_or_fail': 0,\n", + " 'find_xpath_attr': 0,\n", + " '_etree_iter': 0,\n", + " 'create_master_profile_instance': 0,\n", + " 'port': 3,\n", + " 'set_upward': 0,\n", + " 'eks_model': 0,\n", + " 'test_not_fitted_error_gets_raised': 0,\n", + " '_iter': 0,\n", + " 'get_target_group_tags': 0,\n", + " 'restore_default_associations': 0,\n", + " 'sec_nat_policy': 0,\n", + " 'all_equal_preferences': 0,\n", + " '_binary_uninterpolated_average_precision': 0,\n", + " 'parse_bandwidth': 0,\n", + " 'is_version_v1': 1,\n", + " 'cps_get': 0,\n", + " 'url': 0,\n", + " 'inplace_column_scale': 0,\n", + " 'server_timestamp': 0,\n", + " 'test_fit_then_partial_fit': 0,\n", + " 'modify': 0,\n", + " 'netconf_set_config': 1,\n", + " 'exit_json': 0,\n", + " 'test_fetch_openml_raises_missing_values_target': 0,\n", + " 'ptr': 0,\n", + " '_inverse_permutation': 0,\n", + " 'get_hashers': 0,\n", + " 'get_internal_wsgi_application': 0,\n", + " 'clear_checkbox_id': 0,\n", + " '_check_field_spec': 0,\n", + " 'convert_cps_raw_data': 0,\n", + " 'list_tasks': 0,\n", + " 'dump': 0,\n", + " 'is_registered': 0,\n", + " 'label_fingerprint_update': 1,\n", + " 'inbound_virtual_server': 1,\n", + " '_get_media': 0,\n", + " '_has_expired': 0,\n", + " 'is_self_referential': 0,\n", + " 'aggregated_app_settings': 0,\n", + " 'new_file': 0,\n", + " '_get_last_reboot': 0,\n", + " 'resource_to_request': 1,\n", + " '_prefer_source': 0,\n", + " 'static': 0,\n", + " '_savepoint_rollback': 0,\n", + " 'run_commands': 5,\n", + " 'point_count': 1,\n", + " 'add_library': 0,\n", + " 'test_adaptive_learning_rate': 0,\n", + " 'online_argument_spec': 0,\n", + " 'cli_load_config': 3,\n", + " 'make_view_atomic': 0,\n", + " 'full_box': 0,\n", + " 'test_delegated_docstring': 0,\n", + " 'test_load_sample_image': 0,\n", + " 'forward': 4,\n", + " 'teardown_request': 0,\n", + " 'test_paired_euclidean_distances': 0,\n", + " 'trace_dot': 0,\n", + " 'test_nan': 0,\n", + " 'module_dir': 0,\n", + " 'module_by_name': 0,\n", + " 'get_snapshots_by_name_recursively': 1,\n", + " 'transform_iterable': 0,\n", + " 'get_os': 0,\n", + " 'must_inherit_from': 0,\n", + " 'get_type_name': 0,\n", + " 'all': 1,\n", + " 'batch_get_value': 0,\n", + " 'test_set_intercept_to_intercept': 0,\n", + " 'get_pobj': 0,\n", + " 'file_storage_changed': 0,\n", + " 'rrelu': 0,\n", + " 'ratio': 0,\n", + " 'get_tags': 0,\n", + " 'get_params': 2,\n", + " '_get_status_from_resource': 0,\n", + " 'file_complete': 0,\n", + " 'elu': 1,\n", + " 'test_enet_copy_X_False_check_input_False': 0,\n", + " 'startElement': 0,\n", + " '_string_from_ip_int': 0,\n", + " 'get_manager': 2,\n", + " 'extract_file_url': 0,\n", + " 'fromfd': 0,\n", + " 'get_config': 4,\n", + " 'add_additions': 0,\n", + " '_set_default_creation_values': 0,\n", + " '_get_func_fullname': 0,\n", + " 'test_unicode_decode_error': 0,\n", + " 'exists': 6,\n", + " 'irule': 0,\n", + " 'country': 0,\n", + " 'get_minimum_up_member': 0,\n", + " '__delitem__': 1,\n", + " '_check_alive': 0,\n", + " 'adaptive': 0,\n", + " 'device_is_id': 1,\n", + " 'context_processor': 0,\n", + " 'parse_mode': 0,\n", + " '_predict_proba': 0,\n", + " 'get_filter_kwargs_for_object': 0,\n", + " 'map_config_to_obj': 1,\n", + " 'feed': 0,\n", + " '_get_elem_at_rank': 0,\n", + " '_check_proba': 0,\n", + " 'find_cluster_by_name': 0,\n", + " 'get_bundle_state': 0,\n", + " 'get_allow_snat_state': 0,\n", + " 'weights': 0,\n", + " '_get_noise_shape': 1,\n", + " 'persistence_profile': 0,\n", + " 'test_is_control': 0,\n", + " 'hardware_information': 1,\n", + " 'ask_merge': 0,\n", + " '_score_to_proba': 0,\n", + " 'find_template_loader': 0,\n", + " 'save_builtin_function': 0,\n", + " 'parse_system_mtu': 0,\n", + " 'compute_output_shape': 1,\n", + " 'process_distance': 0,\n", + " 'get_networks_names_ids': 0,\n", + " 'softmax': 0,\n", + " '_execute_test_db_destruction': 0,\n", + " 'score': 2,\n", + " '_no_grad_embedding_renorm_': 0,\n", + " 'state_forwards': 3,\n", + " 'get_auth': 0,\n", + " 'update_on_device': 2,\n", + " 'get_cluster': 0,\n", + " 'response_type': 0,\n", + " '_get_lag_type': 0,\n", + " 'md5_text': 0,\n", + " 'get_consul_api': 0,\n", + " 'ip_network': 0,\n", + " 'fix_delta': 0,\n", + " '_get_support_mask': 0,\n", + " 'list_apps': 0,\n", + " 'exec_module': 12,\n", + " 'validate_ipv4': 2,\n", + " 'std': 1,\n", + " '_remove_datacenter': 0,\n", + " 'get_renegotiation_throughput': 0,\n", + " '_set_changed_options': 12,\n", + " 'transform': 1,\n", + " 'get_raster_prep_value': 0,\n", + " 'parse_network_list': 0,\n", + " 'test_correct_shapes_gram': 0,\n", + " 'is_forking': 0,\n", + " 'default_switchport_config': 1,\n", + " 'target_password': 1,\n", + " '_set_ntp_config': 0,\n", + " 'enabled': 1,\n", + " 'sha256': 0,\n", + " 'test_pickle': 0,\n", + " 'integer_field_range': 0,\n", + " 'validate': 1,\n", + " 'to_lines': 3,\n", + " 'merge_hooks': 0,\n", + " 'get_dump_object': 0,\n", + " 'get_snat_type': 0,\n", + " '_file_is_missing': 0,\n", + " 'wait': 0,\n", + " 'contribute_to_class': 1,\n", + " 'filter': 0,\n", + " 'logsumexp': 0,\n", + " 'diff_list': 1,\n", + " 'list_all_groups': 0,\n", + " 'unhandled_query_action': 0,\n", + " 'get_id_of_provider_name': 0,\n", + " 'close': 0,\n", + " 'set_on_delete': 1,\n", + " 'exec_command': 0,\n", + " 'get_sflow_sampling_rate': 0,\n", + " 'disjoint': 1,\n", + " 'test_multioutput_regression': 0,\n", + " '_generalized_average': 0,\n", + " '_count': 0,\n", + " '_value_or_setting': 0,\n", + " 'generate_address_class_dict': 0,\n", + " 'to_python': 1,\n", + " 'delete_user': 0,\n", + " 'insert_xforwarded_for': 0,\n", + " 'patch': 0,\n", + " 'convert_datefield_value': 1,\n", + " '_get_vpc_connection': 1,\n", + " 'add_template_filter': 0,\n", + " 'name_scope': 1,\n", + " 'parse_version': 0,\n", + " 'get_device_facts': 0,\n", + " 'height': 1,\n", + " 'metadata': 0,\n", + " 'parse_filesystems': 2,\n", + " 'remove_members_in_group_from_device': 0,\n", + " 'add_prefix': 1,\n", + " 'cpu_coefficient': 0,\n", + " 'create_or_update_resource_group': 0,\n", + " 'match_tags': 0,\n", + " 'get_fieldsets': 1,\n", + " 'set_DeleteProtection': 0,\n", + " 'test_hash_empty_input': 0,\n", + " 'trainable_weights': 1,\n", + " 'create_tags': 1,\n", + " 'get_queryset': 1,\n", + " '_get_data_object_for_decoding': 0,\n", + " ...}" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "per_token_acc" + ] + }, + { + "cell_type": "code", + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -32002,1015 +20574,1015 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('sql_client', 1.0),\n", - " ('handle', 1.0),\n", - " ('has_lldp', 1.0),\n", - " ('dispatch', 1.0),\n", - " ('do_fail', 1.0),\n", - " ('present_firewall_rule', 1.0),\n", - " ('node_addresses', 1.0),\n", - " ('ip', 1.0),\n", - " ('_combine', 1.0),\n", - " ('device_is_id', 1.0),\n", - " ('l2_normalize', 1.0),\n", - " ('manual_resume', 1.0),\n", - " ('std', 1.0),\n", - " ('to_return', 1.0),\n", - " ('media', 1.0),\n", - " ('update_sub', 1.0),\n", - " ('validate_ip_v6_address', 1.0),\n", - " ('to_lines', 1.0),\n", - " ('contains', 1.0),\n", - " ('describe_subnets_with_backoff', 1.0),\n", - " ('self_link', 1.0),\n", - " ('get_losses_for', 1.0),\n", - " ('call', 1.0),\n", - " ('centroid', 1.0),\n", + "[('restore_db_instance_from_db_snapshot', 1.0),\n", + " ('get_distribution', 1.0),\n", " ('_remove_prefetched_objects', 1.0),\n", - " ('parse_interfaces', 1.0),\n", - " ('frame_distribution_hash', 1.0),\n", - " ('_resolve_output_field', 1.0),\n", - " ('address', 1.0),\n", - " ('kml', 1.0),\n", + " ('upload_file_to_device', 1.0),\n", + " ('_convert_field_to_tz', 1.0),\n", + " ('has_perms', 1.0),\n", + " ('members', 1.0),\n", + " ('_get_zone', 1.0),\n", + " ('fetch_list', 1.0),\n", + " ('relu', 1.0),\n", + " ('flush', 1.0),\n", + " ('load_config', 1.0),\n", + " ('add_arguments', 1.0),\n", + " ('led', 1.0),\n", + " ('receive_data_chunk', 1.0),\n", + " ('execute_show_command', 1.0),\n", + " ('get_uptime_facts', 1.0),\n", " ('interval', 1.0),\n", - " ('converter', 1.0),\n", - " ('_announce_deprecations', 1.0),\n", - " ('eye', 1.0),\n", + " ('get_capabilities', 1.0),\n", + " ('present', 1.0),\n", " ('field_choices', 1.0),\n", - " ('get_required_config', 1.0),\n", - " ('hardware_information', 1.0),\n", + " ('addresses', 1.0),\n", + " ('map_config_to_obj', 1.0),\n", + " ('self_link', 1.0),\n", + " ('handle', 1.0),\n", + " ('get_running_config', 1.0),\n", + " ('y', 1.0),\n", + " ('get_media_speed', 1.0),\n", + " ('_pairwise', 1.0),\n", + " ('update_sub', 1.0),\n", + " ('parse_macaddress', 1.0),\n", + " ('add_command_to_interface', 1.0),\n", + " ('formfield', 1.0),\n", + " ('present_firewall_rule', 1.0),\n", + " ('is_link_local', 1.0),\n", + " ('remove', 1.0),\n", " ('monitors_list', 1.0),\n", - " ('contribute_to_class', 1.0),\n", - " ('run_commands', 1.0),\n", - " ('_get_connection', 1.0),\n", - " ('get_capabilities', 1.0),\n", - " ('format_item', 1.0),\n", - " ('__default', 1.0),\n", - " ('should_update', 1.0),\n", - " ('in_test_phase', 1.0),\n", - " ('delete_many', 1.0),\n", + " ('manual_resume', 1.0),\n", + " ('has_lldp', 1.0),\n", + " ('reset_states', 1.0),\n", + " ('gather', 1.0),\n", + " ('eye', 1.0),\n", + " ('async_op_url', 1.0),\n", + " ('_extract_url', 1.0),\n", " ('get_description', 1.0),\n", - " ('save_config', 1.0),\n", - " ('get_auto_lasthop', 1.0),\n", - " ('get_form_kwargs', 1.0),\n", - " ('_delete_elb', 1.0),\n", - " ('redirect_virtual_server', 1.0),\n", - " ('adapt_datetimefield_value', 1.0),\n", - " ('is_valid_uuid', 1.0),\n", - " ('target_field', 1.0),\n", - " ('get_media_speed', 1.0),\n", - " ('get_staged_firewall_policy', 1.0),\n", - " ('parse_version', 1.0),\n", - " ('response_to_hash', 1.0),\n", - " ('has_perms', 1.0),\n", - " ('modify_db_instance', 1.0),\n", - " ('_get_noise_shape', 1.0),\n", + " ('_raise_error', 1.0),\n", + " ('validate_ip_v6_address', 1.0),\n", + " ('overlaps', 1.0),\n", + " ('read_collection_from_device', 1.0),\n", + " ('build_config_xml', 1.0),\n", + " ('srid', 1.0),\n", + " ('_get_aggregate_spec', 1.0),\n", " ('set_attributes_from_name', 1.0),\n", - " ('apply_key_map', 1.0),\n", - " ('formfield', 1.0),\n", - " ('work', 1.0),\n", - " ('fqdns', 1.0),\n", - " ('capfirst', 1.0),\n", - " ('build', 1.0),\n", - " ('trainable_weights', 1.0),\n", - " ('format_value', 1.0),\n", - " ('inbound_virtual_server', 1.0),\n", - " ('__ne__', 1.0),\n", - " ('wait_for_operation', 1.0),\n", - " ('led', 1.0),\n", - " ('parse_filesystems', 1.0),\n", - " ('value_from_datadict', 1.0),\n", - " ('ok', 1.0),\n", - " ('get_mtu', 1.0),\n", - " ('is_reserved', 1.0),\n", - " ('sanitize', 1.0),\n", - " ('autostart', 1.0),\n", - " ('__iter__', 1.0),\n", - " ('reduce', 1.0),\n", + " ('_filter_params', 1.0),\n", + " ('create_on_device', 1.0),\n", + " ('flatten_list', 1.0),\n", " ('get_autostart2', 1.0),\n", - " ('get_cdn_client', 1.0),\n", - " ('_get_zone', 1.0),\n", - " ('get_fieldsets', 1.0),\n", + " ('histogram', 1.0),\n", + " ('reduce', 1.0),\n", + " ('init_module', 1.0),\n", + " ('source', 1.0),\n", + " ('_diff_list', 1.0),\n", + " ('absent', 1.0),\n", " ('as_sql', 1.0),\n", - " ('compute_output_shape', 1.0),\n", - " ('build_config_xml', 1.0),\n", + " ('vm_size_is_valid', 1.0),\n", + " ('clean', 1.0),\n", + " ('name_scope', 1.0),\n", + " ('fail_json', 1.0),\n", + " ('redirect_virtual_server', 1.0),\n", + " ('ip', 1.0),\n", + " ('fit_transform', 1.0),\n", + " ('converter', 1.0),\n", + " ('route_domain', 1.0),\n", + " ('output_field', 1.0),\n", + " ('_resolve_output_field', 1.0),\n", " ('to_request', 1.0),\n", - " ('must_update', 1.0),\n", - " ('present_domain', 1.0),\n", - " ('has_output', 1.0),\n", - " ('__or__', 1.0),\n", - " ('get_command_from_state', 1.0),\n", - " ('raise_if_errors', 1.0),\n", + " ('resource_to_request', 1.0),\n", " ('rules', 1.0),\n", - " ('exists', 1.0),\n", - " ('name_scope', 1.0),\n", - " ('restore_db_instance_from_db_snapshot', 1.0),\n", - " ('get_proposed', 1.0),\n", - " ('get_end_state', 1.0),\n", - " ('disjoint', 1.0),\n", - " ('overlaps', 1.0),\n", - " ('get_group_by_cols', 1.0),\n", - " ('find_datastore_by_name', 1.0),\n", - " ('is_vlan_bitmap_empty', 1.0),\n", - " ('read_collection_from_device', 1.0),\n", - " ('get_config', 1.0),\n", - " ('create_tags', 1.0),\n", - " ('add_arguments', 1.0),\n", + " ('build', 1.0),\n", " ('__hash__', 1.0),\n", - " ('do_state_change', 1.0),\n", - " ('get_name', 1.0),\n", - " ('_get_aggregate_spec', 1.0),\n", - " ('_hash', 1.0),\n", - " ('num_fields', 1.0),\n", - " ('route_domain', 1.0),\n", + " ('exists', 1.0),\n", + " ('get_result', 1.0),\n", + " ('get_auto_lasthop', 1.0),\n", + " ('__exit__', 1.0),\n", + " ('compute_output_shape', 1.0),\n", + " ('state_forwards', 1.0),\n", + " ('update_on_device', 1.0),\n", + " ('sql_client', 1.0),\n", + " ('validate_ipv4', 1.0),\n", + " ('_set_changed_options', 1.0),\n", + " ('to_lines', 1.0),\n", + " ('contribute_to_class', 1.0),\n", + " ('convert_datefield_value', 1.0),\n", + " ('parse_filesystems', 1.0),\n", + " ('cli_load_config', 1.0),\n", + " ('elu', 1.0),\n", " ('read_facts', 1.0),\n", - " ('addresses', 1.0),\n", - " ('check_args', 1.0),\n", - " ('get_running_config', 1.0),\n", - " ('get_manager', 1.0),\n", - " ('database_backwards', 1.0),\n", - " ('parse_config_argument', 1.0),\n", - " ('create_on_device', 1.0),\n", - " ('closed', 1.0),\n", - " ('fetch_resource', 1.0),\n", + " ('resource_exists', 1.0),\n", + " ('get_prep_lookup', 1.0),\n", + " ('__iter__', 1.0),\n", + " ('autostart', 1.0),\n", + " ('get_end_state', 1.0),\n", + " ('adapt_datetimefield_value', 1.0),\n", + " ('_get_connection', 1.0),\n", + " ('remove_from_device', 1.0),\n", " ('sqrt', 1.0),\n", - " ('get_fw_rule', 1.0),\n", - " ('foldl', 1.0),\n", - " ('undefine', 1.0),\n", - " ('validate_ipv4', 1.0),\n", - " ('_get_instance_health', 1.0),\n", - " ('_update_policy', 1.0),\n", - " ('_set_list', 1.0),\n", - " ('label_fingerprint_update', 1.0),\n", - " ('present_record', 1.0),\n", - " ('int_or_none', 1.0),\n", - " ('equals', 1.0),\n", - " ('height', 1.0),\n", - " ('clean', 1.0),\n", - " ('_pooling_function', 1.0),\n", - " ('upload_file_to_device', 1.0),\n", - " ('is_valid_description', 1.0),\n", - " ('description', 1.0),\n", - " ('state', 1.0),\n", - " ('with_hostmask', 1.0),\n", - " ('remove', 1.0),\n", + " ('_should_handle', 1.0),\n", + " ('in_test_phase', 1.0),\n", + " ('do_fail', 1.0),\n", " ('package_version', 1.0),\n", - " ('present_script', 1.0),\n", - " ('point_count', 1.0),\n", + " ('__ne__', 1.0),\n", " ('populate', 1.0),\n", + " ('wait_for_operation', 1.0),\n", " ('preprocess_input', 1.0),\n", + " ('closed', 1.0),\n", + " ('__default', 1.0),\n", " ('compare', 1.0),\n", - " ('process_rhs', 1.0),\n", - " ('vm_size_is_valid', 1.0),\n", - " ('exec_module', 1.0),\n", - " ('update_on_device', 1.0),\n", - " ('wrapper', 1.0),\n", - " ('load_config', 1.0),\n", - " ('nodes', 1.0),\n", - " ('is_valid_address', 1.0),\n", - " ('convert_datefield_value', 1.0),\n", - " ('resource_exists', 1.0),\n", - " ('present', 1.0),\n", - " ('is_link_local', 1.0),\n", - " ('init_module', 1.0),\n", - " ('decode_predictions', 1.0),\n", - " ('invoke', 1.0),\n", + " ('parse_interfaces', 1.0),\n", + " ('decision_function', 1.0),\n", + " ('format_item', 1.0),\n", + " ('_real_extract', 1.0),\n", + " ('trainable_weights', 1.0),\n", + " ('_extract_urls', 1.0),\n", + " ('parse_config_argument', 1.0),\n", + " ('l2_normalize', 1.0),\n", + " ('kml', 1.0),\n", + " ('undefine', 1.0),\n", + " ('_pooling_function', 1.0),\n", + " ('timeout', 1.0),\n", + " ('should_update', 1.0),\n", " ('collect', 1.0),\n", - " ('cli_load_config', 1.0),\n", - " ('output_field', 1.0),\n", - " ('fq_name', 1.0),\n", - " ('y', 1.0),\n", + " ('prepare_value', 1.0),\n", " ('__new__', 1.0),\n", - " ('random_uniform', 1.0),\n", - " ('get_snapshots_by_name_recursively', 1.0),\n", - " ('window_frame_range_start_end', 1.0),\n", - " ('_exec_module', 1.0),\n", - " ('resource_to_request', 1.0),\n", - " ('collection', 1.0),\n", - " ('destination', 1.0),\n", - " ('flush', 1.0),\n", - " ('parent', 1.0),\n", - " ('relu', 1.0),\n", - " ('async_op_url', 1.0),\n", - " ('execute_show_command', 1.0),\n", - " ('absent', 1.0),\n", - " ('timeout', 1.0),\n", - " ('fail_json', 1.0),\n", - " ('describe', 1.0),\n", - " ('_set_changed_options', 1.0),\n", - " ('reset_states', 1.0),\n", - " ('update', 1.0),\n", - " ('confirm_login_allowed', 1.0),\n", - " ('_should_handle', 1.0),\n", - " ('set_on_delete', 1.0),\n", - " ('state_forwards', 1.0),\n", - " ('flatten_list', 1.0),\n", - " ('srid', 1.0),\n", - " ('set_source_expressions', 1.0),\n", - " ('_diff_list', 1.0),\n", - " ('target_password', 1.0),\n", + " ('is_vlan_bitmap_empty', 1.0),\n", + " ('check_args', 1.0),\n", + " ('present_record', 1.0),\n", + " ('fqdns', 1.0),\n", " ('check_response', 1.0),\n", - " ('parse_hostname', 1.0),\n", - " ('_get_interface_type', 1.0),\n", + " ('_hash', 1.0),\n", + " ('select_format', 1.0),\n", + " ('media', 1.0),\n", + " ('do_state_change', 1.0),\n", " ('deconstruct', 1.0),\n", - " ('fetch_list', 1.0),\n", - " ('_convert_field_to_tz', 1.0),\n", - " ('state_absent', 1.0),\n", - " ('executemany', 1.0),\n", + " ('get_form_kwargs', 1.0),\n", + " ('get_name', 1.0),\n", + " ('inner_func', 1.0),\n", + " ('invoke', 1.0),\n", + " ('create_tags', 1.0),\n", + " ('target_field', 1.0),\n", + " ('collection', 1.0),\n", + " ('nodes', 1.0),\n", + " ('_exec_module', 1.0),\n", + " ('raise_if_errors', 1.0),\n", + " ('suitable', 1.0),\n", " ('is_fakes3', 1.0),\n", - " ('get_connection', 1.0),\n", - " ('_get_vpc_connection', 1.0),\n", + " ('capfirst', 1.0),\n", + " ('__deepcopy__', 1.0),\n", + " ('description', 1.0),\n", + " ('response_to_hash', 1.0),\n", + " ('int_or_none', 1.0),\n", + " ('get_success_url', 1.0),\n", + " ('update', 1.0),\n", + " ('disjoint', 1.0),\n", + " ('fetch_resource', 1.0),\n", + " ('parse_hostname', 1.0),\n", + " ('encode', 1.0),\n", + " ('backward', 1.0),\n", + " ('is_valid_uuid', 1.0),\n", + " ('state', 1.0),\n", + " ('_combine', 1.0),\n", + " ('_get_instance_health', 1.0),\n", + " ('decode_predictions', 1.0),\n", + " ('get_manager', 1.0),\n", + " ('_get_mask', 1.0),\n", + " ('get_staged_firewall_policy', 1.0),\n", + " ('has_fastl4_profiles', 1.0),\n", + " ('to_return', 1.0),\n", + " ('find_datastore_by_name', 1.0),\n", + " ('confirm_login_allowed', 1.0),\n", + " ('work', 1.0),\n", + " ('_argmax', 1.0),\n", + " ('__eq__', 1.0),\n", + " ('present_script', 1.0),\n", + " ('get_cdn_client', 1.0),\n", + " ('sanitize', 1.0),\n", + " ('equals', 1.0),\n", + " ('is_valid_description', 1.0),\n", + " ('fq_name', 1.0),\n", + " ('_update_policy', 1.0),\n", + " ('call', 1.0),\n", + " ('dispatch', 1.0),\n", + " ('executemany', 1.0),\n", + " ('indexbytes', 1.0),\n", + " ('apply_key_map', 1.0),\n", + " ('is_version_v1', 1.0),\n", + " ('label_fingerprint_update', 1.0),\n", + " ('inbound_virtual_server', 1.0),\n", + " ('run_commands', 1.0),\n", + " ('process_rhs', 1.0),\n", + " ('all', 1.0),\n", + " ('get_command_from_state', 1.0),\n", + " ('get_params', 1.0),\n", + " ('get_config', 1.0),\n", + " ('destination', 1.0),\n", + " ('node_addresses', 1.0),\n", + " ('device_is_id', 1.0),\n", + " ('_get_noise_shape', 1.0),\n", + " ('hardware_information', 1.0),\n", + " ('exec_module', 1.0),\n", + " ('std', 1.0),\n", + " ('add_cancelled', 1.0),\n", + " ('point_count', 1.0),\n", + " ('default_switchport_config', 1.0),\n", + " ('target_password', 1.0),\n", + " ('set_on_delete', 1.0),\n", + " ('ok', 1.0),\n", + " ('add_prefix', 1.0),\n", + " ('get_fieldsets', 1.0),\n", + " ('get_queryset', 1.0),\n", + " ('is_valid_hostname', 1.0),\n", " ('deserialize', 1.0),\n", - " ('members', 1.0),\n", + " ('state_absent', 1.0),\n", + " ('get_group_by_cols', 1.0),\n", + " ('half', 1.0),\n", + " ('get_n_splits', 1.0),\n", + " ('_wait_for_requests', 1.0),\n", + " ('_delete_elb', 1.0),\n", + " ('foldl', 1.0),\n", + " ('height', 1.0),\n", + " ('get_fw_rule', 1.0),\n", + " ('save_config', 1.0),\n", + " ('present_domain', 1.0),\n", + " ('set_source_expressions', 1.0),\n", + " ('contains', 1.0),\n", + " ('_announce_deprecations', 1.0),\n", + " ('supports', 1.0),\n", + " ('get_mtu', 1.0),\n", + " ('frame_distribution_hash', 1.0),\n", + " ('describe_subnets_with_backoff', 1.0),\n", + " ('parent', 1.0),\n", " ('validate_param_values', 1.0),\n", - " ('prepare_value', 1.0),\n", - " ('default_switchport_config', 1.0),\n", - " ('select_format', 1.0),\n", + " ('with_hostmask', 1.0),\n", + " ('__or__', 1.0),\n", + " ('delete_many', 1.0),\n", + " ('describe', 1.0),\n", + " ('_get_interface_type', 1.0),\n", + " ('__getstate__', 1.0),\n", + " ('centroid', 1.0),\n", + " ('random_uniform', 1.0),\n", + " ('forward', 1.0),\n", + " ('value_from_datadict', 1.0),\n", " ('has_no_service_environment', 1.0),\n", - " ('_wait_for_requests', 1.0),\n", - " ('is_valid_hostname', 1.0),\n", - " ('encode', 1.0),\n", - " ('has_fastl4_profiles', 1.0),\n", - " ('_filter_params', 1.0),\n", - " ('add_command_to_interface', 1.0),\n", + " ('get_required_config', 1.0),\n", + " ('modify_db_instance', 1.0),\n", + " ('database_backwards', 1.0),\n", + " ('get_snapshots_by_name_recursively', 1.0),\n", + " ('_get_vpc_connection', 1.0),\n", + " ('is_valid_address', 1.0),\n", + " ('__init__', 0.9693251533742331),\n", + " ('main', 0.9166666666666666),\n", " ('create', 0.9166666666666666),\n", " ('read_current_from_device', 0.875),\n", - " ('main', 0.8333333333333334),\n", - " ('__init__', 0.8282208588957055),\n", - " ('remove_from_device', 0.8),\n", - " ('search_obj_in_list', 0.75),\n", + " ('__repr__', 0.8333333333333334),\n", + " ('check', 0.75),\n", " ('run', 0.75),\n", " ('port', 0.75),\n", + " ('search_obj_in_list', 0.75),\n", " ('get', 0.75),\n", - " ('check', 0.75),\n", - " ('__deepcopy__', 0.6666666666666666),\n", - " ('__setitem__', 0.6666666666666666),\n", - " ('__exit__', 0.6666666666666666),\n", + " ('extra_repr', 0.6666666666666666),\n", + " ('diag', 0.6666666666666666),\n", + " ('list', 0.6666666666666666),\n", " ('delete', 0.6666666666666666),\n", - " ('__eq__', 0.5714285714285714),\n", - " ('execute', 0.5),\n", - " ('handle_field', 0.5),\n", - " ('enabled', 0.5),\n", - " ('port_lists', 0.5),\n", + " ('__setstate__', 0.6666666666666666),\n", + " ('__setitem__', 0.6666666666666666),\n", + " ('fit', 0.6),\n", + " ('shutdown', 0.5),\n", + " ('fit_predict', 0.5),\n", " ('generate_commands', 0.5),\n", - " ('is_config_exist', 0.5),\n", + " ('open', 0.5),\n", + " ('idf_', 0.5),\n", + " ('inverse_transform', 0.5),\n", + " ('netconf_set_config', 0.5),\n", + " ('enabled', 0.5),\n", + " ('sum', 0.5),\n", " ('compute_mask', 0.5),\n", + " ('predict', 0.5),\n", + " ('bounds', 0.5),\n", + " ('is_config_exist', 0.5),\n", + " ('port_lists', 0.5),\n", + " ('execute', 0.5),\n", " ('traffic_group', 0.5),\n", - " ('open', 0.5),\n", - " ('__delitem__', 0.5),\n", + " ('get_proposed', 0.5),\n", + " ('handle_field', 0.5),\n", " ('disabled', 0.5),\n", - " ('sum', 0.5),\n", " ('get_existing', 0.5),\n", - " ('get_success_url', 0.5),\n", - " ('get_result', 0.5),\n", - " ('shutdown', 0.5),\n", + " ('__delitem__', 0.5),\n", + " ('predict_proba', 0.5),\n", " ('validate', 0.5),\n", - " ('netconf_set_config', 0.5),\n", + " ('diff_list', 0.5),\n", + " ('_call_api', 0.5),\n", " ('from_response', 0.45454545454545453),\n", - " ('__getstate__', 0.4),\n", + " ('__call__', 0.4444444444444444),\n", + " ('score', 0.4),\n", + " ('__str__', 0.3333333333333333),\n", " ('netconf_get_config', 0.3333333333333333),\n", " ('_request_for_item', 0.3333333333333333),\n", - " ('__call__', 0.3333333333333333),\n", " ('to_python', 0.3333333333333333),\n", " ('__getitem__', 0.3333333333333333),\n", - " ('touch', 0.3333333333333333),\n", - " ('list', 0.3333333333333333),\n", - " ('__str__', 0.3333333333333333),\n", - " ('_real_extract', 0.26666666666666666),\n", - " ('__repr__', 0.25),\n", - " ('predict', 0.16666666666666666),\n", - " ('content', 0.0),\n", - " ('check_zone_domain', 0.0),\n", - " ('_egress_all_match', 0.0),\n", - " ('_remove_temporary_cli_script_from_device', 0.0),\n", - " ('validate_comment', 0.0),\n", - " ('test_class_weight_errors', 0.0),\n", - " ('test_logreg_intercept_scaling', 0.0),\n", + " ('get_connection', 0.25),\n", + " ('transform', 0.16666666666666666),\n", + " ('_savepoint_commit', 0.0),\n", + " ('check_cs_op', 0.0),\n", + " ('read', 0.0),\n", + " ('get_desired', 0.0),\n", + " ('frame_size', 0.0),\n", + " ('get_initial_for_field', 0.0),\n", + " ('_clean504', 0.0),\n", + " ('get_block_storage_volumes', 0.0),\n", + " ('combine_expression', 0.0),\n", " ('apparent_encoding', 0.0),\n", + " ('get_failsafe_timeout', 0.0),\n", + " ('get_update_cmd', 0.0),\n", + " ('_insert_network_data', 0.0),\n", + " ('test_base_zero_n_estimators', 0.0),\n", + " ('check_programs', 0.0),\n", + " ('minimum', 0.0),\n", + " ('void_output', 0.0),\n", + " ('search', 0.0),\n", + " ('get_dhcp_leases', 0.0),\n", + " ('parse_vlan_brief', 0.0),\n", + " ('format_value', 0.0),\n", + " ('get_admin_url', 0.0),\n", + " ('quote', 0.0),\n", + " ('parse_model', 0.0),\n", + " ('__radd__', 0.0),\n", + " ('num_coords', 0.0),\n", + " ('normalize_rule_spec', 0.0),\n", + " ('resolve_requires', 0.0),\n", + " ('get_key_file', 0.0),\n", + " ('test_cross_val_predict_method_checking', 0.0),\n", + " ('parse_memtotal', 0.0),\n", + " ('test_pdist_bool_metrics', 0.0),\n", + " ('get_contents', 0.0),\n", + " ('get_system_mode', 0.0),\n", + " ('save_classmethod', 0.0),\n", " ('_effective_n_jobs', 0.0),\n", - " ('get_schema', 0.0),\n", - " ('desc', 0.0),\n", - " ('ocsp', 0.0),\n", + " ('test_np_log', 0.0),\n", + " ('_og_search_description', 0.0),\n", + " ('supports_spatial_index', 0.0),\n", + " ('reload_license', 0.0),\n", " ('test_tfidf_vectorizer_setter', 0.0),\n", - " ('test_warm_start_with_oob_score_fails', 0.0),\n", - " ('get_dump_object', 0.0),\n", - " ('get_id_of_provider_name', 0.0),\n", - " ('sanitize_result', 0.0),\n", - " ('test_bunch_dir', 0.0),\n", - " ('md5_text', 0.0),\n", - " ('extract_redirect_url', 0.0),\n", - " ('stop_task', 0.0),\n", - " ('parse_macaddress', 0.0),\n", - " ('allowed_slots', 0.0),\n", - " ('update_install_target', 0.0),\n", - " ('unhandled_query_action', 0.0),\n", - " ('max_cookie_size', 0.0),\n", - " ('save_classmethod', 0.0),\n", - " ('get_zone', 0.0),\n", - " ('inplace_swap_row', 0.0),\n", - " ('get_admin_state', 0.0),\n", - " ('gather_host_facts', 0.0),\n", - " ('get_pim_interface_defaults', 0.0),\n", - " ('test_select_kbest_all', 0.0),\n", - " ('dot', 0.0),\n", - " ('mod', 0.0),\n", - " ('get_contents', 0.0),\n", - " ('implements_to_string', 0.0),\n", - " ('destination_port', 0.0),\n", - " ('test_pickle', 0.0),\n", - " ('test_gpr_interpolation', 0.0),\n", - " ('mptcp_make_after_break', 0.0),\n", - " ('patch', 0.0),\n", - " ('filter', 0.0),\n", - " ('weights', 0.0),\n", - " ('_compile', 0.0),\n", - " ('test_reconstruct_patches_perfect', 0.0),\n", - " ('is_json', 0.0),\n", - " ('_delete_volume', 0.0),\n", - " ('get_feature_names', 0.0),\n", - " ('paused', 0.0),\n", - " ('_get_current_month', 0.0),\n", - " ('teardown_request', 0.0),\n", - " ('test_notfitted', 0.0),\n", - " ('_get_site_by_id', 0.0),\n", - " ('_prefer_source', 0.0),\n", - " ('__setstate__', 0.0),\n", - " ('push_state', 0.0),\n", - " ('check_pointer', 0.0),\n", - " ('warn_if_public_ip_assignment_changed', 0.0),\n", - " ('set_start_method', 0.0),\n", - " ('_check_alive', 0.0),\n", - " ('enable', 0.0),\n", - " ('difference', 0.0),\n", - " ('clone', 0.0),\n", - " ('get_software_version', 0.0),\n", - " ('_activate_virtualenv', 0.0),\n", - " ('provided_password', 0.0),\n", - " ('_remove_datacenter', 0.0),\n", + " ('service_policy', 0.0),\n", + " ('_sqlite_rpad', 0.0),\n", + " ('resource_to_create', 0.0),\n", + " ('related_model', 0.0),\n", + " ('get_count', 0.0),\n", + " ('time_until_up', 0.0),\n", + " ('autosync_enabled', 0.0),\n", + " ('__set__', 0.0),\n", + " ('_is_true', 0.0),\n", + " ('le', 0.0),\n", " ('_curried', 0.0),\n", - " ('frame_size', 0.0),\n", - " ('test_delegated_docstring', 0.0),\n", - " ('trace_dot', 0.0),\n", - " ('find_object_by_name', 0.0),\n", - " ('finalize_headers', 0.0),\n", - " ('half', 0.0),\n", - " ('delete_user', 0.0),\n", + " ('get_distribution_SMGL', 0.0),\n", + " ('test_robust_scaler_invalid_range', 0.0),\n", + " ('check_transformers_unfitted', 0.0),\n", + " ('is_find_by_filter_operation', 0.0),\n", + " ('param_persist', 0.0),\n", " ('_get_interfaces_status', 0.0),\n", - " ('parse_sysmac', 0.0),\n", - " ('hyperparameters', 0.0),\n", - " ('linear_assignment', 0.0),\n", - " ('ratio', 0.0),\n", - " ('create_master_profile_instance', 0.0),\n", - " ('http_method_not_allowed', 0.0),\n", - " ('cpu_threshold', 0.0),\n", - " ('all', 0.0),\n", - " ('firewall_enforced_policy', 0.0),\n", - " ('check_expression_support', 0.0),\n", - " ('swappable_setting', 0.0),\n", - " ('read', 0.0),\n", - " ('must_inherit_from', 0.0),\n", - " ('floating', 0.0),\n", - " ('get_vbdif_mac', 0.0),\n", - " ('_should_check_constraints', 0.0),\n", - " ('resource_to_create', 0.0),\n", - " ('fault_number', 0.0),\n", - " ('app_template_global', 0.0),\n", - " ('test_width_patch', 0.0),\n", - " ('to_json', 0.0),\n", - " ('find_cluster_by_name', 0.0),\n", - " ('getRecord', 0.0),\n", - " ('mac_address', 0.0),\n", - " ('validate_purge', 0.0),\n", - " ('get_names', 0.0),\n", - " ('ensure_disabled', 0.0),\n", - " ('max_answers_returned', 0.0),\n", - " ('test_min_cluster_size_invalid', 0.0),\n", - " ('_argmax', 0.0),\n", - " ('get_programme_id', 0.0),\n", - " ('is_iterable', 0.0),\n", - " ('_preprocess_conv2d_input', 0.0),\n", - " ('response_add', 0.0),\n", - " ('http_only', 0.0),\n", - " ('sha512_utf8', 0.0),\n", - " ('a', 0.0),\n", - " ('present_region', 0.0),\n", - " ('test_cross_val_predict_method_checking', 0.0),\n", - " ('fit', 0.0),\n", - " ('time_trunc_sql', 0.0),\n", - " ('is_forking', 0.0),\n", - " ('routing_protocol', 0.0),\n", - " ('record_migration', 0.0),\n", - " ('check_xss_filter', 0.0),\n", - " ('get_local_user_group', 0.0),\n", - " ('i', 0.0),\n", - " ('test_complete_but_not_homogeneous_labeling', 0.0),\n", - " ('skip_past', 0.0),\n", - " ('_generalized_average', 0.0),\n", - " ('interface_exists', 0.0),\n", - " ('enhanced_loss_recovery', 0.0),\n", + " ('integer_field_range', 0.0),\n", " ('frontend_ip_configuration_id', 0.0),\n", - " ('is_valid_v4addr', 0.0),\n", - " ('module_by_name', 0.0),\n", - " ('test_is_control', 0.0),\n", - " ('_get_input_message', 0.0),\n", - " ('score', 0.0),\n", - " ('queue_on_connection_limit', 0.0),\n", - " ('_complete_linkage', 0.0),\n", - " ('test_calling_fit_reinitializes', 0.0),\n", - " ('_get_storage_path', 0.0),\n", - " ('delete_addr', 0.0),\n", + " ('_setup_socks4a', 0.0),\n", + " ('push', 0.0),\n", + " ('register_forward_pre_hook', 0.0),\n", + " ('__reduce__', 0.0),\n", + " ('test_logreg_intercept_scaling', 0.0),\n", + " ('time', 0.0),\n", + " ('_iter_test_indices', 0.0),\n", + " ('cli_get_connect_port', 0.0),\n", + " ('render_to_response', 0.0),\n", + " ('online_argument_spec', 0.0),\n", + " ('sha512_utf8', 0.0),\n", + " ('routing_protocol', 0.0),\n", + " ('check_two_point', 0.0),\n", + " ('is_multipart', 0.0),\n", + " ('_pack', 0.0),\n", + " ('qos_topology', 0.0),\n", + " ('buffers', 0.0),\n", + " ('reverse', 0.0),\n", + " ('adaptive', 0.0),\n", + " ('availability_state', 0.0),\n", + " ('is_present', 0.0),\n", + " ('fail', 0.0),\n", + " ('_get_ip_routing', 0.0),\n", + " ('_get_cookies', 0.0),\n", + " ('getDomainByName', 0.0),\n", + " ('parse_structured_power_supply_info', 0.0),\n", + " ('console_log', 0.0),\n", + " ('_sqlite_datetime_cast_date', 0.0),\n", " ('_restore_signal_handlers', 0.0),\n", - " ('test_is_deprecated', 0.0),\n", - " ('random', 0.0),\n", - " ('parse_memtotal', 0.0),\n", - " ('wait_for', 0.0),\n", - " ('test_adaptive_learning_rate', 0.0),\n", - " ('get_persistence_profile', 0.0),\n", - " ('_ffmpeg_filename_argument', 0.0),\n", + " ('get_stp_enabled_state', 0.0),\n", + " ('_filter', 0.0),\n", + " ('max_answers_returned', 0.0),\n", + " ('_predict_proba', 0.0),\n", " ('dictvalue', 0.0),\n", - " ('ports', 0.0),\n", - " ('parse_structured_power_supply_info', 0.0),\n", - " ('get_networks_names_ids', 0.0),\n", - " ('normalize_rule_spec', 0.0),\n", - " ('_index_param_value', 0.0),\n", - " ('format_html', 0.0),\n", - " ('register_with_consul', 0.0),\n", - " ('translation_port', 0.0),\n", - " ('ask_merge', 0.0),\n", - " ('log_addition', 0.0),\n", - " ('_get_cms_resource', 0.0),\n", - " ('_check_type_bits', 0.0),\n", - " ('test_calc_breakdown_point', 0.0),\n", - " ('softsign', 0.0),\n", - " ('hardtanh', 0.0),\n", + " ('test_linear_svx_uppercase_loss_penality_raises_error', 0.0),\n", + " ('netconf_set_action', 0.0),\n", + " ('_get_to_python', 0.0),\n", + " ('hyphenate_date', 0.0),\n", + " ('_minor_reduce', 0.0),\n", + " ('ratio', 0.0),\n", + " ('add_segment_url', 0.0),\n", + " ('public_ip_id', 0.0),\n", + " ('is_json', 0.0),\n", + " ('mod', 0.0),\n", + " ('get_bundle_state', 0.0),\n", + " ('handle_raw_input', 0.0),\n", + " ('get_runner_details', 0.0),\n", + " ('_project_and_cluster', 0.0),\n", + " ('get_tzname', 0.0),\n", + " ('complete_missing_attributes', 0.0),\n", + " ('lookup_allowed', 0.0),\n", + " ('linear_assignment', 0.0),\n", + " ('layer_count', 0.0),\n", + " ('delete_admin', 0.0),\n", + " ('_find_permutation', 0.0),\n", + " ('get_kms_metadata_with_backoff', 0.0),\n", + " ('date_trunc_sql', 0.0),\n", + " ('items', 0.0),\n", + " ('test_imputation_deletion_warning', 0.0),\n", + " ('_strip_once', 0.0),\n", " ('prov_template_exists', 0.0),\n", - " ('set_DeleteProtection', 0.0),\n", - " ('test_classifier_exceptions', 0.0),\n", - " ('extract_json', 0.0),\n", - " ('related_model', 0.0),\n", - " ('cgnat', 0.0),\n", - " ('get_nested_backend', 0.0),\n", - " ('check_programs', 0.0),\n", - " ('mac_masquerade_address', 0.0),\n", - " ('as_p', 0.0),\n", - " ('test_decision_path_hardcoded', 0.0),\n", - " ('_renorm', 0.0),\n", - " ('has_css_class', 0.0),\n", - " ('_is_arraylike', 0.0),\n", - " ('as_mysql', 0.0),\n", - " ('gzip_window_size', 0.0),\n", - " ('to_string', 0.0),\n", - " ('get_slow_ramp_time', 0.0),\n", - " ('iso_date', 0.0),\n", - " ('targets_equal', 0.0),\n", - " ('deleteUser', 0.0),\n", - " ('get_NIC', 0.0),\n", - " ('_objects_service', 0.0),\n", - " ('unsmuggle_url', 0.0),\n", - " ('source_mask', 0.0),\n", - " ('get_context_data', 0.0),\n", - " ('idf_', 0.0),\n", - " ('_get_current_year', 0.0),\n", - " ('convert_cps_raw_data', 0.0),\n", - " ('get_train_examples', 0.0),\n", - " ('get_key_or_fail', 0.0),\n", - " ('fit_transform', 0.0),\n", - " ('list_origin_access_identities', 0.0),\n", - " ('reset_property', 0.0),\n", - " ('get_urlconf', 0.0),\n", + " ('wrapper', 0.0),\n", + " ('find_cluster_by_name_datacenter', 0.0),\n", + " ('_transform', 0.0),\n", + " ('parse_lldp_intf', 0.0),\n", " ('test_rbf_kernel', 0.0),\n", - " ('add_prefix', 0.0),\n", - " ('list_instances', 0.0),\n", - " ('test_k_means_plus_plus_init_2_jobs', 0.0),\n", - " ('process_distance', 0.0),\n", - " ('get_uptime_facts', 0.0),\n", - " ('test_classification_report_multiclass_with_long_string_label', 0.0),\n", - " ('_get_real_id', 0.0),\n", - " ('test_min_cluster_size_invalid2', 0.0),\n", - " ('_count', 0.0),\n", - " ('clear', 0.0),\n", - " ('test_pickle_bool_metrics', 0.0),\n", - " ('clean_old_password', 0.0),\n", - " ('profile', 0.0),\n", - " ('_get_n_results', 0.0),\n", - " ('_get_learning_rate_type', 0.0),\n", - " ('resource_absent', 0.0),\n", - " ('extract_count', 0.0),\n", - " ('_store', 0.0),\n", - " ('qos_packet_rate', 0.0),\n", - " ('get_failsafe_timeout', 0.0),\n", - " ('notify_non_idempotent_commands', 0.0),\n", - " ('response_type', 0.0),\n", - " ('check_relate_argument', 0.0),\n", - " ('qos_rtt', 0.0),\n", - " ('result_list_tag', 0.0),\n", + " ('check_cdist_bool', 0.0),\n", + " ('skip_wrapper', 0.0),\n", + " ('irules', 0.0),\n", " ('test_countvectorizer_custom_vocabulary_repeated_indices', 0.0),\n", + " ('age_restricted', 0.0),\n", + " ('add_usage', 0.0),\n", + " ('handleSum', 0.0),\n", + " ('disabled_vlans', 0.0),\n", + " ('test_mcd_issue1127', 0.0),\n", + " ('write_stream', 0.0),\n", + " ('app_url_value_preprocessor', 0.0),\n", + " ('_check_type_list', 0.0),\n", + " ('reset_trust', 0.0),\n", + " ('test_decision_path_hardcoded', 0.0),\n", + " ('to_json', 0.0),\n", + " ('extract_count', 0.0),\n", + " ('all_equal_preferences', 0.0),\n", + " ('get_local_user_group', 0.0),\n", + " ('get_aliases_from_distribution_id', 0.0),\n", + " ('test_notfitted', 0.0),\n", + " ('random', 0.0),\n", + " ('probe_timeout', 0.0),\n", + " ('test_method_not_available', 0.0),\n", + " ('required_together', 0.0),\n", + " ('_decrypt', 0.0),\n", " ('license_end_date_time', 0.0),\n", - " ('register_store_backend', 0.0),\n", - " ('time', 0.0),\n", - " ('_score_to_proba', 0.0),\n", - " ('sensitive_variables', 0.0),\n", - " ('test_base_zero_n_estimators', 0.0),\n", - " ('_get_next_week', 0.0),\n", - " ('_boolean_icon', 0.0),\n", - " ('ogr', 0.0),\n", - " ('get_internal_wsgi_application', 0.0),\n", - " ('create_or_update_role_perm', 0.0),\n", - " ('parse', 0.0),\n", - " ('_compat_bytes_to_byte_vals', 0.0),\n", - " ('adaptive', 0.0),\n", - " ('_sqlite_datetime_cast_date', 0.0),\n", - " ('copy', 0.0),\n", - " ('process_view', 0.0),\n", - " ('test_inverse_transform', 0.0),\n", + " ('find_object_by_name', 0.0),\n", + " ('_query_iptun_props', 0.0),\n", + " ('prepare_database_save', 0.0),\n", " ('_get_full_path', 0.0),\n", - " ('concurrency_limit', 0.0),\n", - " ('put', 0.0),\n", - " ('addExpectedFailure', 0.0),\n", - " ('test_probability', 0.0),\n", - " ('get_dhcp_leases', 0.0),\n", - " ('test_fetch_openml_raises_missing_values_target', 0.0),\n", - " ('get_current_function', 0.0),\n", - " ('parse_mode', 0.0),\n", - " ('test_dict_learning_overcomplete', 0.0),\n", - " ('arp', 0.0),\n", - " ('_file_is_missing', 0.0),\n", - " ('__invert__', 0.0),\n", - " ('batch_get_value', 0.0),\n", - " ('session_ticket', 0.0),\n", - " ('shell', 0.0),\n", - " ('_check_standard_scaled', 0.0),\n", - " ('traffic_group_inherited', 0.0),\n", - " ('make_view_atomic', 0.0),\n", - " ('listify_string_name_or_id', 0.0),\n", - " ('test_train_test_split_allow_nans', 0.0),\n", - " ('_check_inputs_dtype', 0.0),\n", - " ('test_data_home', 0.0),\n", - " ('_from_sequence', 0.0),\n", - " ('handle_default_options', 0.0),\n", - " ('read_file', 0.0),\n", - " ('combine_expression', 0.0),\n", - " ('make_multi', 0.0),\n", - " ('supports_spatial_index', 0.0),\n", + " ('get_dict', 0.0),\n", + " ('test_basic_property_of_random_matrix', 0.0),\n", + " ('empty_form', 0.0),\n", + " ('get_software_version', 0.0),\n", + " ('extract_redirect_url', 0.0),\n", + " ('unset_available_apps', 0.0),\n", + " ('links', 0.0),\n", + " ('_is_limited_data_type', 0.0),\n", + " ('addError', 0.0),\n", + " ('parse_filesystem_info', 0.0),\n", + " ('disable_addr', 0.0),\n", + " ('__init_module__', 0.0),\n", + " ('get_value', 0.0),\n", + " ('get_client_template_id', 0.0),\n", + " ('_preprocess_padding', 0.0),\n", + " ('implements_to_string', 0.0),\n", + " ('_wait_for_UP', 0.0),\n", + " ('as_mysql', 0.0),\n", + " ('delete_resource_group', 0.0),\n", + " ('test_unknown_method', 0.0),\n", + " ('_parse_labels', 0.0),\n", + " ('is_boto3_error_code', 0.0),\n", + " ('register_runner', 0.0),\n", " ('delete_existing', 0.0),\n", - " ('get_ignore_vertification', 0.0),\n", - " ('is_classifier', 0.0),\n", - " ('test_angle_out_of_range_checks', 0.0),\n", - " ('max_header_size', 0.0),\n", - " ('addSuccess', 0.0),\n", - " ('mirror_secondary_address', 0.0),\n", - " ('test_resample', 0.0),\n", - " ('get_arp_state', 0.0),\n", - " ('get_next_year', 0.0),\n", - " ('contains_path', 0.0),\n", - " ('extract_names_from_blob_uri', 0.0),\n", - " ('_decrypt', 0.0),\n", - " ('hidden_fields', 0.0),\n", - " ('rollback_last', 0.0),\n", - " ('parse_network_list', 0.0),\n", - " ('normalize_area', 0.0),\n", - " ('parse_file_upload', 0.0),\n", - " ('validate_consistency', 0.0),\n", - " ('parse_startupscript_list', 0.0),\n", - " ('fromfd', 0.0),\n", - " ('untag', 0.0),\n", - " ('disabled_vlans', 0.0),\n", - " ('check_cdist_bool', 0.0),\n", - " ('encode_data_uri', 0.0),\n", + " ('test_class_weight_errors', 0.0),\n", + " ('get_all_images', 0.0),\n", + " ('test_k_means_plus_plus_init_2_jobs', 0.0),\n", + " ('get_help_text', 0.0),\n", + " ('max_cookie_size', 0.0),\n", + " ('_errors', 0.0),\n", + " ('include_chassis_level_config', 0.0),\n", + " ('_wait_for_fqdn_checks', 0.0),\n", + " ('chdir', 0.0),\n", + " ('swapped', 0.0),\n", + " ('get_available_number', 0.0),\n", + " ('get_renegotiation_throughput', 0.0),\n", + " ('get_elb', 0.0),\n", + " ('quote_etag', 0.0),\n", + " ('add_update_fields', 0.0),\n", + " ('_check_inputs_dtype', 0.0),\n", + " ('PROTECT', 0.0),\n", " ('validate_rotate_frequency', 0.0),\n", - " ('post', 0.0),\n", - " ('lag_changed', 0.0),\n", - " ('get_urls', 0.0),\n", - " ('get_auth', 0.0),\n", - " ('set_upward', 0.0),\n", - " ('hyphenate_date', 0.0),\n", - " ('public_ip_id', 0.0),\n", - " ('_generate_igmp_querier_cmds', 0.0),\n", - " ('get_kms_metadata_with_backoff', 0.0),\n", - " ('timeconvert', 0.0),\n", - " ('extract_Initialization', 0.0),\n", - " ('data', 0.0),\n", - " ('volume_exists', 0.0),\n", - " ('_set_streaming_content', 0.0),\n", - " ('bytes_read', 0.0),\n", - " ('test_node_heap', 0.0),\n", + " ('cgnat', 0.0),\n", + " ('_generate_igmp_vlan_cmds', 0.0),\n", + " ('has_perm', 0.0),\n", + " ('test_check_increasing_small_number_of_samples', 0.0),\n", + " ('test_train_test_split_allow_nans', 0.0),\n", + " ('response_add', 0.0),\n", + " ('_extract_data_config', 0.0),\n", + " ('get_auth_plugin', 0.0),\n", + " ('changed_properties', 0.0),\n", + " ('migration_qs', 0.0),\n", + " ('check_xss_filter', 0.0),\n", + " ('_xpath_ns', 0.0),\n", + " ('get_vbdif_mac', 0.0),\n", + " ('tags', 0.0),\n", + " ('signature', 0.0),\n", + " ('_get_dhcp_lease_file', 0.0),\n", + " ('defer', 0.0),\n", + " ('changes', 0.0),\n", + " ('is_empty', 0.0),\n", + " ('untagged_interfaces', 0.0),\n", + " ('test_is_deprecated', 0.0),\n", + " ('test_default_empty_load_files', 0.0),\n", + " ('restore_default_associations', 0.0),\n", + " ('test_calc_breakdown_point', 0.0),\n", + " ('addExpectedFailure', 0.0),\n", + " ('push_state', 0.0),\n", + " ('_check_for_i18n', 0.0),\n", + " ('connection_to_string', 0.0),\n", + " ('uses_learning_phase', 0.0),\n", + " ('test_column_transformer_no_estimators_set_params', 0.0),\n", + " ('f', 0.0),\n", + " ('get_public_ip', 0.0),\n", + " ('get_slow_ramp_time', 0.0),\n", + " ('remove_index', 0.0),\n", + " ('_index_param_value', 0.0),\n", + " ('extract_file_url', 0.0),\n", + " ('get_programme_id', 0.0),\n", + " ('is_masklen', 0.0),\n", + " ('present_region', 0.0),\n", + " ('logsumexp', 0.0),\n", + " ('__bytes_cast_encoded', 0.0),\n", + " ('test_check_update_with_no_data', 0.0),\n", + " ('parse_sysmac', 0.0),\n", + " ('teardown_request', 0.0),\n", + " ('test_multioutput_regression', 0.0),\n", " ('app_model_error', 0.0),\n", - " ('get_queryset', 0.0),\n", - " ('__radd__', 0.0),\n", - " ('add', 0.0),\n", - " ('test_load_sample_image', 0.0),\n", - " ('get_prep_lookup', 0.0),\n", - " ('find_dvs_by_uuid', 0.0),\n", - " ('__instancecheck__', 0.0),\n", - " ('__sub__', 0.0),\n", - " ('add_template_filter', 0.0),\n", - " ('delete_lag', 0.0),\n", - " ('url', 0.0),\n", - " ('random_normal_variable', 0.0),\n", - " ('test_robust_scaler_invalid_range', 0.0),\n", - " ('num_coords', 0.0),\n", - " ('policies', 0.0),\n", - " ('search', 0.0),\n", - " ('lbmonitor_exists', 0.0),\n", - " ('test_correct_shapes_gram', 0.0),\n", - " ('has_permission', 0.0),\n", - " ('save_weakset', 0.0),\n", + " ('stop_VM', 0.0),\n", + " ('is_iterable', 0.0),\n", + " ('_merge_function', 0.0),\n", + " ('parameters', 0.0),\n", + " ('rate_limit', 0.0),\n", + " ('destination_port', 0.0),\n", + " ('volume_exists', 0.0),\n", + " ('prepare_content_length', 0.0),\n", + " ('test_quantile_loss_function', 0.0),\n", + " ('targets_equal', 0.0),\n", " ('read_unsigned_long_long', 0.0),\n", - " ('report_resolve', 0.0),\n", + " ('logout', 0.0),\n", + " ('make_memmap', 0.0),\n", + " ('test_warm_start_with_oob_score_fails', 0.0),\n", + " ('delete_url_map', 0.0),\n", + " ('related_fields', 0.0),\n", + " ('assertRaisesRegex', 0.0),\n", + " ('get_current_function', 0.0),\n", + " ('get_failsafe_action', 0.0),\n", + " ('hostmask', 0.0),\n", + " ('alpha_dropout', 0.0),\n", + " ('unregister', 0.0),\n", + " ('merge_hooks', 0.0),\n", + " ('timeconvert', 0.0),\n", + " ('get_tag_uri', 0.0),\n", + " ('extract_names_from_blob_uri', 0.0),\n", + " ('get_related_field', 0.0),\n", + " ('add_related_update', 0.0),\n", + " ('must_update', 0.0),\n", + " ('deactivate_operation', 0.0),\n", + " ('test_width_patch', 0.0),\n", + " ('parse', 0.0),\n", + " ('check_single_sample', 0.0),\n", + " ('_check_vocabulary', 0.0),\n", " ('test_basic_property_of_sparse_random_matrix', 0.0),\n", - " ('is_registered', 0.0),\n", - " ('_generate_igmp_vlan_cmds', 0.0),\n", - " ('parse_serialnum', 0.0),\n", + " ('no_stdout_stderr', 0.0),\n", + " ('parse_file_upload', 0.0),\n", + " ('interface_exists', 0.0),\n", + " ('_run_search', 0.0),\n", + " ('_parse_expressions', 0.0),\n", + " ('get_persistence_profile', 0.0),\n", + " ('http_only', 0.0),\n", + " ('_show_mlag', 0.0),\n", + " ('getContactlists', 0.0),\n", + " ('get_pim_interface_defaults', 0.0),\n", + " ('spheroid', 0.0),\n", + " ('validate_autopk_value', 0.0),\n", + " ('_get_current_year', 0.0),\n", + " ('mac_address', 0.0),\n", + " ('get_public_ip_address_instance', 0.0),\n", + " ('instance_to_dict', 0.0),\n", + " ('put', 0.0),\n", + " ('_step3', 0.0),\n", + " ('paused', 0.0),\n", + " ('_objects_service', 0.0),\n", + " ('get_input_shape_at', 0.0),\n", + " ('test_inverse_transform', 0.0),\n", " ('parse_domain_search', 0.0),\n", - " ('get_Host_byid', 0.0),\n", - " ('cpu_coefficient', 0.0),\n", - " ('forward', 0.0),\n", " ('generate_pool_dict', 0.0),\n", - " ('is_self_referential', 0.0),\n", - " ('_set_z', 0.0),\n", - " ('feature_importances_', 0.0),\n", - " ('test_extract_patch_same_size_image', 0.0),\n", - " ('_wait_for_fqdn_checks', 0.0),\n", - " ('alpha_dropout', 0.0),\n", - " ('_is_pairwise_metric', 0.0),\n", - " ('type', 0.0),\n", - " ('_get_data_object_for_decoding', 0.0),\n", - " ('https_open', 0.0),\n", - " ('_safety_decorator', 0.0),\n", - " ('full_box', 0.0),\n", - " ('_get_cookies', 0.0),\n", - " ('test_fit_then_partial_fit', 0.0),\n", - " ('write_stream', 0.0),\n", - " ('_get_dhcp_lease_file', 0.0),\n", - " ('generate_self_ip_dict', 0.0),\n", - " ('test_method_not_available', 0.0),\n", - " ('get_device_capabilities', 0.0),\n", - " ('snmp_privacy_password', 0.0),\n", - " ('handle_raw_input', 0.0),\n", - " ('changed_properties', 0.0),\n", - " ('platform_match', 0.0),\n", - " ('oracle_version', 0.0),\n", - " ('_parse_version', 0.0),\n", - " ('get_runner_list', 0.0),\n", - " ('enable_zone_transfer', 0.0),\n", - " ('test_multioutput_regression', 0.0),\n", - " ('__init_module__', 0.0),\n", - " ('test_decode_anneal', 0.0),\n", - " ('online_argument_spec', 0.0),\n", - " ('network_failover_enabled', 0.0),\n", - " ('_iter_test_indices', 0.0),\n", - " ('irules', 0.0),\n", - " ('test_nan', 0.0),\n", - " ('get_count', 0.0),\n", - " ('_preprocess_padding', 0.0),\n", - " ('get_distribution_NetBSD', 0.0),\n", - " ('get_elb', 0.0),\n", - " ('get_authorization_domain', 0.0),\n", - " ('get_system_mode', 0.0),\n", - " ('_make_int_array', 0.0),\n", - " ('backward', 0.0),\n", - " ('test_pdist_bool_metrics', 0.0),\n", - " ('charset', 0.0),\n", + " ('test_pickle_bool_metrics', 0.0),\n", + " ('hardtanh', 0.0),\n", + " ('fail_safe', 0.0),\n", + " ('get_listener_rules', 0.0),\n", + " ('num_geom', 0.0),\n", + " ('_get_availability_value', 0.0),\n", + " ('test_bunch_dir', 0.0),\n", + " ('extract_json', 0.0),\n", + " ('test_hash_empty_input', 0.0),\n", + " ('inplace_swap_row', 0.0),\n", + " ('_get_last_reboot', 0.0),\n", + " ('setUp', 0.0),\n", + " ('sparsify', 0.0),\n", + " ('_flatten', 0.0),\n", " ('aws_common_argument_spec', 0.0),\n", - " ('get_dict', 0.0),\n", - " ('add_cancelled', 0.0),\n", - " ('get_allow_snat_state', 0.0),\n", - " ('defer', 0.0),\n", - " ('items', 0.0),\n", - " ('include_chassis_level_config', 0.0),\n", - " ('xcli_wrapper', 0.0),\n", - " ('required_together', 0.0),\n", - " ('sec_nat_policy', 0.0),\n", - " ('PROTECT', 0.0),\n", + " ('rollback_last', 0.0),\n", + " ('register_store_backend', 0.0),\n", + " ('input_mask', 0.0),\n", + " ('is_classifier', 0.0),\n", + " ('_etree_iter', 0.0),\n", + " ('eks_model', 0.0),\n", + " ('_iter', 0.0),\n", " ('get_target_group_tags', 0.0),\n", - " ('info', 0.0),\n", - " ('get_params', 0.0),\n", - " ('product_changelist', 0.0),\n", - " ('unpack', 0.0),\n", - " ('param_device_group', 0.0),\n", - " ('check_transformers_unfitted', 0.0),\n", - " ('_render', 0.0),\n", - " ('get_tzname', 0.0),\n", - " ('parse_vlan_brief', 0.0),\n", - " ('get_trans_changes', 0.0),\n", - " ('_tokenize_chinese_chars', 0.0),\n", - " ('get_exist_lsa_a_hold_interval', 0.0),\n", - " ('_chain_from_iterable_of_lists', 0.0),\n", - " ('_value_or_setting', 0.0),\n", - " ('get_tags', 0.0),\n", - " ('state_present', 0.0),\n", - " ('availability_state', 0.0),\n", - " ('nopad_b64', 0.0),\n", - " ('_find_permutation', 0.0),\n", - " ('login', 0.0),\n", - " ('driver', 0.0),\n", - " ('reassemble_fragments', 0.0),\n", - " ('signature', 0.0),\n", - " ('_get_elem_at_rank', 0.0),\n", - " ('key_checksum', 0.0),\n", - " ('table2model', 0.0),\n", - " ('getContactlists', 0.0),\n", - " ('_raise_unavailable', 0.0),\n", - " ('_topology', 0.0),\n", - " ('register_forward_pre_hook', 0.0),\n", - " ('getDomainByName', 0.0),\n", - " ('get_desired', 0.0),\n", - " ('is_quoted', 0.0),\n", - " ('_handle_http_uri_condition', 0.0),\n", - " ('test_basic_property_of_random_matrix', 0.0),\n", - " ('diff_list', 0.0),\n", - " ('_xpath_ns', 0.0),\n", - " ('ptr', 0.0),\n", - " ('change_required', 0.0),\n", - " ('_cert_filename', 0.0),\n", - " ('post_export_action', 0.0),\n", - " ('service_policy', 0.0),\n", + " ('get_template_sources', 0.0),\n", + " ('parse_bandwidth', 0.0),\n", + " ('url', 0.0),\n", + " ('inplace_column_scale', 0.0),\n", + " ('test_fit_then_partial_fit', 0.0),\n", + " ('test_resample', 0.0),\n", + " ('test_fetch_openml_raises_missing_values_target', 0.0),\n", + " ('convert_cps_raw_data', 0.0),\n", + " ('static', 0.0),\n", + " ('test_load_sample_image', 0.0),\n", + " ('test_nan', 0.0),\n", + " ('module_by_name', 0.0),\n", + " ('create_vm_template', 0.0),\n", + " ('create_diagnotstics_profile_dict', 0.0),\n", " ('get_type_name', 0.0),\n", - " ('get_ssl_option', 0.0),\n", - " ('error', 0.0),\n", - " ('deprovision_cgnat_on_device', 0.0),\n", - " ('add_update_fields', 0.0),\n", - " ('_raise_error', 0.0),\n", - " ('one_hot', 0.0),\n", - " ('_get_elb_listener_rules', 0.0),\n", - " ('find_template_loader', 0.0),\n", - " ('parse_lldp_intf', 0.0),\n", - " ('test_mcd_issue1127', 0.0),\n", - " ('split_sum', 0.0),\n", + " ('_safety_decorator', 0.0),\n", + " ('file_storage_changed', 0.0),\n", + " ('runshell', 0.0),\n", + " ('get_tags', 0.0),\n", + " ('test_enet_copy_X_False_check_input_False', 0.0),\n", + " ('_string_from_ip_int', 0.0),\n", + " ('ocsp', 0.0),\n", + " ('get_context_data', 0.0),\n", + " ('_get_func_fullname', 0.0),\n", + " ('test_unicode_decode_error', 0.0),\n", + " ('get_minimum_up_member', 0.0),\n", + " ('_check_alive', 0.0),\n", + " ('parse_mode', 0.0),\n", + " ('update_parameter', 0.0),\n", + " ('get_filter_kwargs_for_object', 0.0),\n", + " ('feed', 0.0),\n", + " ('send_data', 0.0),\n", + " ('_check_proba', 0.0),\n", + " ('set_start_method', 0.0),\n", + " ('_real_initialize', 0.0),\n", + " ('_score_to_proba', 0.0),\n", + " ('parse_system_mtu', 0.0),\n", + " ('list_all_users', 0.0),\n", + " ('_no_grad_embedding_renorm_', 0.0),\n", + " ('md5_text', 0.0),\n", + " ('list_apps', 0.0),\n", + " ('_remove_datacenter', 0.0),\n", + " ('renegotiation_period', 0.0),\n", + " ('test_correct_shapes_gram', 0.0),\n", + " ('is_forking', 0.0),\n", + " ('test_pickle', 0.0),\n", + " ('add_progress_hook', 0.0),\n", + " ('_file_is_missing', 0.0),\n", + " ('wait', 0.0),\n", + " ('filter', 0.0),\n", + " ('unhandled_query_action', 0.0),\n", + " ('_value_or_setting', 0.0),\n", + " ('_render', 0.0),\n", + " ('_generalized_average', 0.0),\n", + " ('generate_address_class_dict', 0.0),\n", + " ('asg_exists', 0.0),\n", + " ('patch', 0.0),\n", + " ('add_template_filter', 0.0),\n", + " ('parse_version', 0.0),\n", + " ('create_or_update_resource_group', 0.0),\n", + " ('_compile', 0.0),\n", + " ('match_tags', 0.0),\n", + " ('_egress_all_match', 0.0),\n", + " ('register_with_consul', 0.0),\n", + " ('_get_next_week', 0.0),\n", " ('synchronization_group_name', 0.0),\n", - " ('equals_exact', 0.0),\n", - " ('rendered_content', 0.0),\n", - " ('handleSum', 0.0),\n", - " ('_errors_svd', 0.0),\n", - " ('biclusters_', 0.0),\n", - " ('set_params', 0.0),\n", + " ('freemem', 0.0),\n", + " ('test_data_home', 0.0),\n", + " ('softmax', 0.0),\n", + " ('construct', 0.0),\n", " ('get_distribution_config', 0.0),\n", - " ('get_tag_uri', 0.0),\n", - " ('test_max_leaf_nodes_max_depth', 0.0),\n", - " ('_set_list_header_if_not_empty', 0.0),\n", - " ('run_from_argv', 0.0),\n", - " ('get_all_images', 0.0),\n", - " ('find_cluster_by_name_datacenter', 0.0),\n", - " ('attach_file', 0.0),\n", - " ('modify', 0.0),\n", - " ('get_pobj', 0.0),\n", + " ('generate_self_ip_dict', 0.0),\n", " ('ring', 0.0),\n", - " ('port_misuse_policy', 0.0),\n", - " ('validate_level', 0.0),\n", - " ('_sqlite_rpad', 0.0),\n", - " ('le', 0.0),\n", - " ('save_builtin_function', 0.0),\n", - " ('update_parameter', 0.0),\n", - " ('tuple', 0.0),\n", - " ('_is_value_present', 0.0),\n", + " ('untag', 0.0),\n", + " ('get_dump_object', 0.0),\n", + " ('mandatory_attributes', 0.0),\n", + " ('_verify_fallback_persistence_profile_for_type', 0.0),\n", + " ('_needs_update', 0.0),\n", + " ('copy', 0.0),\n", + " ('_check_field_spec', 0.0),\n", + " ('sensitive_variables', 0.0),\n", + " ('delete_zone', 0.0),\n", + " ('_boolean_icon', 0.0),\n", + " ('enable', 0.0),\n", + " ('_renorm', 0.0),\n", + " ('list_all_groups', 0.0),\n", + " ('set_password', 0.0),\n", + " ('gather_host_facts', 0.0),\n", + " ('read_file', 0.0),\n", + " ('auth_password_validators_changed', 0.0),\n", + " ('dump_request', 0.0),\n", + " ('module_dir', 0.0),\n", + " ('get_signature_key', 0.0),\n", + " ('get_device_capabilities', 0.0),\n", + " ('_build_template_url', 0.0),\n", + " ('assertRaises', 0.0),\n", + " ('_get_input_message', 0.0),\n", + " ('_get_elb_listener_rules', 0.0),\n", + " ('get_trans_changes', 0.0),\n", + " ('type', 0.0),\n", + " ('translation', 0.0),\n", + " ('list_func', 0.0),\n", + " ('test_complete_but_not_homogeneous_labeling', 0.0),\n", + " ('get_crl_file', 0.0),\n", + " ('_inverse_permutation', 0.0),\n", + " ('wordwrap', 0.0),\n", + " ('lookup_datastore', 0.0),\n", + " ('check_category_status', 0.0),\n", + " ('temp_name', 0.0),\n", + " ('_topology', 0.0),\n", + " ('get_rule_with_backoff', 0.0),\n", + " ('biclusters_', 0.0),\n", + " ('_get_n_results', 0.0),\n", + " ('test_minibatch_default_init_size', 0.0),\n", + " ('check_plural', 0.0),\n", + " ('put_bucket_tagging', 0.0),\n", + " ('_valueless_option', 0.0),\n", + " ('lacp_enabled', 0.0),\n", + " ('http_method_not_allowed', 0.0),\n", + " ('_is_arraylike', 0.0),\n", " ('extend', 0.0),\n", - " ('__reduce__', 0.0),\n", - " ('gather', 0.0),\n", - " ('max_file_size', 0.0),\n", - " ('get_distribution_SMGL', 0.0),\n", - " ('get_minimum_up_member', 0.0),\n", - " ('get_ip_vpn_vni', 0.0),\n", - " ('is_empty', 0.0),\n", - " ('get_source_address_translation_lsn_pool', 0.0),\n", + " ('error', 0.0),\n", + " ('product_changelist', 0.0),\n", + " ('get_origin_access_identity_config', 0.0),\n", + " ('get_arp_state', 0.0),\n", + " ('fault_number', 0.0),\n", + " ('get_urlconf', 0.0),\n", + " ('memory_threshold', 0.0),\n", + " ('_activate_virtualenv', 0.0),\n", + " ('qos_packet_rate', 0.0),\n", + " ('test_angle_out_of_range_checks', 0.0),\n", + " ('get_vpn', 0.0),\n", + " ('select_resource_pool', 0.0),\n", + " ('profile', 0.0),\n", + " ('find_xpath_attr', 0.0),\n", + " ('sec_nat_policy', 0.0),\n", + " ('aws_hmac_digest', 0.0),\n", + " ('_ffmpeg_filename_argument', 0.0),\n", + " ('delete_user', 0.0),\n", + " ('has_output', 0.0),\n", + " ('location_key', 0.0),\n", + " ('nat64_enabled', 0.0),\n", + " ('get_auth', 0.0),\n", + " ('_preprocess_conv2d_input', 0.0),\n", + " ('check_pointer', 0.0),\n", + " ('eval', 0.0),\n", + " ('_update_magp_data', 0.0),\n", " ('_set_templates_auto_reload', 0.0),\n", - " ('_string_from_ip_int', 0.0),\n", - " ('app_url_value_preprocessor', 0.0),\n", - " ('convert_empty_string', 0.0),\n", - " ('all_equal_preferences', 0.0),\n", - " ('indexbytes', 0.0),\n", - " ('sparsify', 0.0),\n", - " ('decision_function', 0.0),\n", - " ('parse_filesystem_info', 0.0),\n", - " ('diff', 0.0),\n", - " ('iquery_allow_service_check', 0.0),\n", - " ('assertRaisesRegex', 0.0),\n", - " ('assert_int_or_pair', 0.0),\n", - " ('no_stdout_stderr', 0.0),\n", - " ('_get_func_fullname', 0.0),\n", - " ('check_arg_errcode', 0.0),\n", - " ('_check_means', 0.0),\n", - " ('startElement', 0.0),\n", + " ('has_permission', 0.0),\n", + " ('basename', 0.0),\n", + " ('sha1', 0.0),\n", + " ('linebreaksbr', 0.0),\n", " ('convert_binaryfield_value', 0.0),\n", - " ('bounds', 0.0),\n", - " ('_extract_urls', 0.0),\n", - " ('check_single_sample', 0.0),\n", - " ('get_full_name', 0.0),\n", - " ('cps_get', 0.0),\n", - " ('parse_image', 0.0),\n", - " ('wait', 0.0),\n", - " ('create_agent_pool_profile_instance', 0.0),\n", - " ('truncatewords', 0.0),\n", - " ('create_diagnotstics_profile_dict', 0.0),\n", - " ('asg_exists', 0.0),\n", - " ('_ts', 0.0),\n", - " ('test_k_means_init', 0.0),\n", - " ('has_perm', 0.0),\n", - " ('parse_vlans', 0.0),\n", - " ('_ogr_ptr', 0.0),\n", - " ('get_geometry_converter', 0.0),\n", - " ('_needs_update', 0.0),\n", - " ('aws_hmac_hexdigest', 0.0),\n", - " ('_og_search_description', 0.0),\n", - " ('enable_gtm', 0.0),\n", - " ('_merge_function', 0.0),\n", - " ('parse_remote_server', 0.0),\n", - " ('server_timestamp', 0.0),\n", - " ('set_password', 0.0),\n", - " ('rrelu', 0.0),\n", - " ('_query_iptun_props', 0.0),\n", - " ('set_tags', 0.0),\n", - " ('test_perplexity_input_format', 0.0),\n", - " ('na_ontap_host_argument_spec', 0.0),\n", - " ('make_memmap', 0.0),\n", - " ('rate_limit', 0.0),\n", - " ('_get_to_python', 0.0),\n", - " ('get_device_facts', 0.0),\n", - " ('file_storage_changed', 0.0),\n", - " ('delete_admin', 0.0),\n", - " ('_set_default_creation_values', 0.0),\n", - " ('_errors', 0.0),\n", - " ('transparent', 0.0),\n", - " ('push', 0.0),\n", - " ('test_check_increasing_small_number_of_samples', 0.0),\n", - " ('_filter', 0.0),\n", - " ('_new_gnu_trans', 0.0),\n", + " ('test_expected_mutual_info_overflow', 0.0),\n", + " ('__invert__', 0.0),\n", + " ('check_supported', 0.0),\n", + " ('policies', 0.0),\n", + " ('to_string', 0.0),\n", + " ('_set_list_header_if_not_empty', 0.0),\n", + " ('test_decode_anneal', 0.0),\n", + " ('_get_learning_rate_type', 0.0),\n", + " ('_prefer_source', 0.0),\n", + " ('parse_http_date_safe', 0.0),\n", + " ('detect_ipvX_address_usage', 0.0),\n", + " ('get_admin_state', 0.0),\n", + " ('one_hot', 0.0),\n", " ('test_random_search_with_fit_params', 0.0),\n", - " ('changes', 0.0),\n", - " ('is_multipart', 0.0),\n", - " ('wait_for_eni', 0.0),\n", - " ('read_partition_default_route_domain_from_device', 0.0),\n", - " ('date_trunc_sql', 0.0),\n", - " ('test_enet_copy_X_False_check_input_False', 0.0),\n", - " ('tuplify', 0.0),\n", - " ('input_mask', 0.0),\n", - " ('reset_trust', 0.0),\n", - " ('sec_nat_use_device_policy', 0.0),\n", + " ('content', 0.0),\n", + " ('post', 0.0),\n", + " ('listify_string_name_or_id', 0.0),\n", + " ('test_classifier_exceptions', 0.0),\n", + " ('port_misuse_policy', 0.0),\n", + " ('get_names', 0.0),\n", + " ('_get_test_db_name', 0.0),\n", + " ('__promise__', 0.0),\n", + " ('isvalidlsaoholdinterval', 0.0),\n", + " ('traffic_group_inherited', 0.0),\n", + " ('fallback_ip', 0.0),\n", + " ('inner', 0.0),\n", + " ('is_reserved', 0.0),\n", + " ('firewall_enforced_policy', 0.0),\n", + " ('hess', 0.0),\n", + " ('_check_standard_scaled', 0.0),\n", + " ('time_trunc_sql', 0.0),\n", + " ('before_app_first_request', 0.0),\n", + " ('_generate_no_bgp_cmds', 0.0),\n", + " ('get_distribution_NetBSD', 0.0),\n", + " ('_convert_simple_dict_to_list', 0.0),\n", + " ('get_all_facts', 0.0),\n", + " ('getRecord', 0.0),\n", + " ('shell', 0.0),\n", + " ('geo_locations', 0.0),\n", + " ('var', 0.0),\n", + " ('destination_ip', 0.0),\n", + " ('_generate_no_igmp_cmds', 0.0),\n", + " ('startElement', 0.0),\n", + " ('feature_importances_', 0.0),\n", + " ('get_zone', 0.0),\n", " ('staged_policy', 0.0),\n", - " ('get_fixed_timezone', 0.0),\n", - " ('_extract_url', 0.0),\n", - " ('add_additions', 0.0),\n", - " ('test_column_transformer_no_estimators_set_params', 0.0),\n", - " ('skip_wrapper', 0.0),\n", - " ('_singleton', 0.0),\n", - " ('validate_field_level_encryption_id', 0.0),\n", - " ('is_download_operation', 0.0),\n", - " ('receive_data_chunk', 0.0),\n", + " ('delete_lag', 0.0),\n", + " ('_get_storage_path', 0.0),\n", + " ('save_weakset', 0.0),\n", + " ('compat_xpath', 0.0),\n", + " ('iso_date', 0.0),\n", + " ('decompress', 0.0),\n", + " ('delete_addr', 0.0),\n", + " ('sflow_poll_interval', 0.0),\n", + " ('validate_level', 0.0),\n", " ('get_traceback_html', 0.0),\n", - " ('deserialize_db_from_string', 0.0),\n", - " ('_project_and_cluster', 0.0),\n", - " ('elu', 0.0),\n", + " ('find_dvs_by_uuid', 0.0),\n", + " ('get_nested_backend', 0.0),\n", + " ('camel', 0.0),\n", + " ('_extract_entries', 0.0),\n", + " ('parse_network_list', 0.0),\n", + " ('xcli_wrapper', 0.0),\n", + " ('model_from_config', 0.0),\n", + " ('list_instances', 0.0),\n", + " ('_gather_versions', 0.0),\n", + " ('finalize_headers', 0.0),\n", + " ('contains_properly', 0.0),\n", + " ('tuplify', 0.0),\n", + " ('test_load_digits', 0.0),\n", + " ('test_assert_raise_message', 0.0),\n", + " ('hyperparameters', 0.0),\n", + " ('attach_file', 0.0),\n", + " ('get_consul_client', 0.0),\n", + " ('check_relate_argument', 0.0),\n", + " ('get_NIC', 0.0),\n", + " ('record_migration', 0.0),\n", + " ('release_floating_ip', 0.0),\n", + " ('test_importances_raises', 0.0),\n", + " ('_handle_http_uri_condition', 0.0),\n", + " ('add_library', 0.0),\n", + " ('next_value', 0.0),\n", + " ('get_current_job_queue', 0.0),\n", + " ('train_test_split_mock_pandas', 0.0),\n", + " ('_get_leaves', 0.0),\n", + " ('stop_task', 0.0),\n", + " ('validate_access_vlan', 0.0),\n", + " ('_set_z', 0.0),\n", + " ('test_max_leaf_nodes_max_depth', 0.0),\n", + " ('next_nonbmp_pos', 0.0),\n", + " ('platform_match', 0.0),\n", + " ('get_runner_list', 0.0),\n", + " ('mk_boolean', 0.0),\n", + " ('set_cause', 0.0),\n", + " ('_post_clean', 0.0),\n", + " ('all_have_public_ip', 0.0),\n", + " ('replace_extension', 0.0),\n", + " ('model_unpickle', 0.0),\n", + " ('get_net_id', 0.0),\n", + " ('_make_int_array', 0.0),\n", + " ('test_select_kbest_all', 0.0),\n", + " ('mptcp_make_after_break', 0.0),\n", + " ('max_header_size', 0.0),\n", + " ('format_html', 0.0),\n", + " ('_generate_igmp_querier_cmds', 0.0),\n", + " ('test_dict_learning_unknown_fit_algorithm', 0.0),\n", + " ('has_css_class', 0.0),\n", + " ('write_zfile', 0.0),\n", + " ('unsmuggle_url', 0.0),\n", + " ('process_view', 0.0),\n", + " ('normalize_area', 0.0),\n", + " ('test_check_increasing_up_extreme', 0.0),\n", " ('make_piecewise', 0.0),\n", - " ('extract_file_url', 0.0),\n", - " ('_verify_fallback_persistence_profile_for_type', 0.0),\n", - " ('_get_next_executor_id', 0.0),\n", - " ('uses_learning_phase', 0.0),\n", + " ('check_expression_support', 0.0),\n", + " ('get_hostname', 0.0),\n", + " ('report_resolve', 0.0),\n", " ('test_label_ranking_avp', 0.0),\n", - " ('get_platform_id', 0.0),\n", - " ('_minor_reduce', 0.0),\n", + " ('probe_executable', 0.0),\n", " ('linear', 0.0),\n", - " ('get_renegotiation_throughput', 0.0),\n", - " ('reload_license', 0.0),\n", - " ('_savepoint_rollback', 0.0),\n", - " ('all_have_public_ip', 0.0),\n", - " ('add_subquery', 0.0),\n", - " ('nat64_enabled', 0.0),\n", - " ('_get_media', 0.0),\n", - " ('__add__', 0.0),\n", - " ('transform_iterable', 0.0),\n", - " ('model_from_config', 0.0),\n", - " ('lookup_datastore', 0.0),\n", - " ('chdir', 0.0),\n", - " ('country', 0.0),\n", - " ('_is_limited_data_type', 0.0),\n", - " ('get_stp_enabled_state', 0.0),\n", - " ('put_bucket_tagging', 0.0),\n", + " ('parse_startupscript_list', 0.0),\n", + " ('strip_accents_unicode', 0.0),\n", + " ('encode_data_uri', 0.0),\n", + " ('result_list_tag', 0.0),\n", " ('param_description', 0.0),\n", - " ('get_rule_with_backoff', 0.0),\n", - " ('feed', 0.0),\n", - " ('train_test_split_mock_pandas', 0.0),\n", - " ('_extract_data_config', 0.0),\n", - " ('__bytes_cast_encoded', 0.0),\n", - " ('_parse_expressions', 0.0),\n", - " ('_update_magp_data', 0.0),\n", - " ('qos_topology', 0.0),\n", - " ('send_data', 0.0),\n", - " ('diag', 0.0),\n", - " ('_get_last_reboot', 0.0),\n", - " ('validate_autopk_value', 0.0),\n", - " ('lacp_enabled', 0.0),\n", - " ('_get_status_from_resource', 0.0),\n", - " ('_pairwise', 0.0),\n", - " ('update_url_query', 0.0),\n", - " ('connection_to_string', 0.0),\n", - " ('param_persist', 0.0),\n", - " ('use_route_advertisement', 0.0),\n", - " ('is_find_by_filter_operation', 0.0),\n", - " ('_check_type_list', 0.0),\n", - " ('_generate_no_bgp_cmds', 0.0),\n", - " ('_boolean_input', 0.0),\n", - " ('get_block_storage_volumes', 0.0),\n", - " ('get_os', 0.0),\n", - " ('calculate_s3_path', 0.0),\n", - " ('_parse_json', 0.0),\n", - " ('test_np_log', 0.0),\n", - " ('compute_loss', 0.0),\n", + " ('convert_empty_string', 0.0),\n", + " ('validate_purge', 0.0),\n", " ('resf', 0.0),\n", - " ('_step3', 0.0),\n", - " ('location_key', 0.0),\n", - " ('freemem', 0.0),\n", - " ('get_failsafe_action', 0.0),\n", - " ('get_origin_access_identity_config', 0.0),\n", - " ('replace_extension', 0.0),\n", - " ('test_unicode_decode_error', 0.0),\n", - " ('delete_url_map', 0.0),\n", - " ('add_progress_hook', 0.0),\n", - " ('send_messages', 0.0),\n", - " ('get_bundle_state', 0.0),\n", - " ('list_all_groups', 0.0),\n", - " ('using', 0.0),\n", - " ('boxcox', 0.0),\n", - " ('_check_field_spec', 0.0),\n", - " ('_insert_network_data', 0.0),\n", - " ('dump', 0.0),\n", - " ('_has_expired', 0.0),\n", - " ('sha1', 0.0),\n", - " ('test_check_update_with_no_data', 0.0),\n", + " ('warn_if_public_ip_assignment_changed', 0.0),\n", + " ('difference', 0.0),\n", + " ('validate_field_level_encryption_id', 0.0),\n", + " ('swappable_setting', 0.0),\n", + " ('_check_type_bits', 0.0),\n", + " ('_singleton', 0.0),\n", + " ('dumps', 0.0),\n", + " ('_cert_filename', 0.0),\n", + " ('make_view_atomic', 0.0),\n", " ('gcdns_connect', 0.0),\n", - " ('_verify_minimum_profile', 0.0),\n", - " ('get_vpn', 0.0),\n", - " ('connect_ex', 0.0),\n", - " ('_extract_entries', 0.0),\n", - " ('service_identical', 0.0),\n", - " ('disconnect_container', 0.0),\n", - " ('eval', 0.0),\n", - " ('_savepoint_commit', 0.0),\n", - " ('parse_mediatype', 0.0),\n", - " ('set_ipv6_interfaces', 0.0),\n", - " ('_set_ntp_config', 0.0),\n", - " ('get_signature_key', 0.0),\n", - " ('get_help_text', 0.0),\n", - " ('metadata', 0.0),\n", - " ('source', 0.0),\n", - " ('_create_server_snapshot', 0.0),\n", - " ('_convert_simple_dict_to_list', 0.0),\n", - " ('is_present', 0.0),\n", - " ('histogram', 0.0),\n", - " ('prepare_content_length', 0.0),\n", - " ('encode_string', 0.0),\n", - " ('eks_model', 0.0),\n", - " ('write_zfile', 0.0),\n", - " ('make_id', 0.0),\n", - " ('check_cs_op', 0.0),\n", - " ('_gather_versions', 0.0),\n", - " ('check_supported', 0.0),\n", - " ('check_category_status', 0.0),\n", - " ('test_assert_raise_message', 0.0),\n", - " ('get_template_sources', 0.0),\n", - " ('quote', 0.0),\n", - " ('test_default_empty_load_files', 0.0),\n", + " ('num_fields', 0.0),\n", + " ('diff', 0.0),\n", + " ('_get_real_id', 0.0),\n", + " ('set_params', 0.0),\n", + " ('lag_changed', 0.0),\n", + " ('route_advertisement', 0.0),\n", + " ('get_fixed_timezone', 0.0),\n", + " ('transform_iterable', 0.0),\n", + " ('test_gpr_interpolation', 0.0),\n", + " ('get_urls', 0.0),\n", + " ('max_file_size', 0.0),\n", + " ('_remove_temporary_cli_script_from_device', 0.0),\n", + " ('test_chinese', 0.0),\n", + " ('concurrency_limit', 0.0),\n", + " ('get_ignore_vertification', 0.0),\n", + " ('_get_current_month', 0.0),\n", + " ('get_Host_byid', 0.0),\n", + " ('_format_port_for_destination', 0.0),\n", + " ('reduce_pipe_connection', 0.0),\n", + " ('test_probability', 0.0),\n", + " ('data', 0.0),\n", + " ('enhanced_loss_recovery', 0.0),\n", + " ('post_export_action', 0.0),\n", + " ('_get_cms_resource', 0.0),\n", + " ('snmp_privacy_password', 0.0),\n", + " ('test_ridge_classifier_no_support_multilabel', 0.0),\n", + " ('get_platform_id', 0.0),\n", + " ('options', 0.0),\n", + " ('login', 0.0),\n", + " ('entity_decl', 0.0),\n", + " ('get_host_by_name', 0.0),\n", + " ('_from_sequence', 0.0),\n", + " ('gzip_window_size', 0.0),\n", + " ('remove_aliases', 0.0),\n", + " ('add_routes', 0.0),\n", + " ('_errors_svd', 0.0),\n", " ('_add_skip_wall', 0.0),\n", - " ('list_func', 0.0),\n", - " ('base_url_parameters', 0.0),\n", - " ('unregister', 0.0),\n", - " ('fit_predict', 0.0),\n", - " ('parse_model', 0.0),\n", + " ('get_feature_names', 0.0),\n", + " ('validate_comment', 0.0),\n", + " ('get_active_member_count', 0.0),\n", + " ('service_identical', 0.0),\n", + " ('_chain_from_iterable_of_lists', 0.0),\n", + " ('qos_rtt', 0.0),\n", + " ('is_uuid', 0.0),\n", + " ('run_from_argv', 0.0),\n", + " ('state_present', 0.0),\n", + " ('connect_ex', 0.0),\n", + " ('_store', 0.0),\n", + " ('na_ontap_host_argument_spec', 0.0),\n", + " ('parse_vlans', 0.0),\n", + " ('mgmt_address', 0.0),\n", + " ('cpu_threshold', 0.0),\n", + " ('nopad_b64', 0.0),\n", + " ('truncatewords', 0.0),\n", + " ('test_min_cluster_size_invalid', 0.0),\n", + " ('get_schema', 0.0),\n", + " ('as_p', 0.0),\n", + " ('format_for_deletion', 0.0),\n", + " ('account_has_blob_containers', 0.0),\n", + " ('test_perplexity_input_format', 0.0),\n", + " ('mirror_primary_address', 0.0),\n", + " ('validate_consistency', 0.0),\n", + " ('parse_keys_list', 0.0),\n", + " ('test_reconstruct_patches_perfect', 0.0),\n", + " ('source_mask', 0.0),\n", + " ('iquery_allow_service_check', 0.0),\n", + " ('boxcox', 0.0),\n", + " ('transform_commands', 0.0),\n", + " ('present_block_storage_volume', 0.0),\n", + " ('get_next_year', 0.0),\n", + " ('_new_gnu_trans', 0.0),\n", + " ('duration_string', 0.0),\n", + " ('network_failover_enabled', 0.0),\n", " ('NASNetLarge', 0.0),\n", - " ('autosync_enabled', 0.0),\n", - " ('test_paired_euclidean_distances', 0.0),\n", - " ('dumps', 0.0),\n", - " ('parse_memfree_mb', 0.0),\n", - " ('unset_available_apps', 0.0),\n", - " ('test_load_digits', 0.0),\n", - " ('admin_urlname', 0.0),\n", - " ('fail', 0.0),\n", - " ('name', 0.0),\n", - " ('mandatory_attributes', 0.0),\n", - " ('test_minibatch_default_init_size', 0.0),\n", - " ('persistence_profile', 0.0),\n", - " ('sha256', 0.0),\n", - " ('setUp', 0.0),\n", - " ('increment', 0.0),\n", - " ('clear_checkbox_id', 0.0),\n", - " ('instance_to_dict', 0.0),\n", - " ('compat_xpath', 0.0),\n", - " ('add_segment_url', 0.0),\n", - " ('parameters', 0.0),\n", - " ('get_auth_plugin', 0.0),\n", - " ('route_advertisement', 0.0),\n", - " ('print_layer_summary', 0.0),\n", - " ('_pack', 0.0),\n", - " ('memory_threshold', 0.0),\n", - " ('links', 0.0),\n", - " ('save_on_auto_sync', 0.0)]" + " ('a', 0.0)]" ] }, - "execution_count": 166, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -33022,16 +21594,343 @@ }, { "cell_type": "code", - "execution_count": 167, - "metadata": { - "scrolled": true - }, + "execution_count": 142, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(40,20))\n", + "labels, values = zip(*c.most_common(250))\n", + "\n", + "indexes = np.arange(len(labels))\n", + "width = 1\n", + "\n", + "freqs = [per_token_freq[l] for l in labels]\n", + "\n", + "mean_freq = np.mean(list(per_token_freq.values()))\n", + "mean_acc = np.mean(list(total_per_token_accuracy.values()))\n", + "\n", + "plt.bar(indexes, values, width, label='Accuracy')\n", + "plt.bar(indexes, freqs, width, label='Frequency')\n", + "plt.xticks(indexes , labels, rotation=90)\n", + "plt.title('MethodNaming accuracy distribution (top-250) - CORPUS-lg - mean_freq = {:.3f} / max_freq = {:.2f} / F1-macro = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "#plt.savefig('accuracy-dist-methodname-lg-top250.pdf')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,5))\n", + "plt.title('MethodNaming accuracy distribution - Corpus-lg')\n", + "plt.hist([c[k] for k in inter])\n", + "plt.savefig('methodname-acc-dist-lg.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('__init__', 0.08565423016290068),\n", + " ('update', 0.008407777193904361),\n", + " ('__default', 0.007882291119285338),\n", + " ('_real_extract', 0.007882291119285338),\n", + " ('should_update', 0.006831318970047294),\n", + " ('main', 0.006305832895428271),\n", + " ('remove', 0.006305832895428271),\n", + " ('__repr__', 0.006305832895428271),\n", + " ('_set_changed_options', 0.006305832895428271),\n", + " ('create', 0.006305832895428271),\n", + " ('exec_module', 0.006305832895428271),\n", + " ('from_response', 0.005780346820809248),\n", + " ('__call__', 0.004729374671571204),\n", + " ('_announce_deprecations', 0.004729374671571204),\n", + " ('absent', 0.004203888596952181),\n", + " ('compare', 0.004203888596952181),\n", + " ('read_current_from_device', 0.004203888596952181),\n", + " ('create_on_device', 0.0036784025223331584),\n", + " ('to_request', 0.0036784025223331584),\n", + " ('to_return', 0.0036784025223331584),\n", + " ('__eq__', 0.0036784025223331584),\n", + " ('flatten_list', 0.0031529164477141357),\n", + " ('exists', 0.0031529164477141357),\n", + " ('transform', 0.0031529164477141357),\n", + " ('predict', 0.0031529164477141357),\n", + " ('__str__', 0.0031529164477141357),\n", + " ('execute_show_command', 0.002627430373095113),\n", + " ('present', 0.002627430373095113),\n", + " ('as_sql', 0.002627430373095113),\n", + " ('read_facts', 0.002627430373095113),\n", + " ('remove_from_device', 0.002627430373095113),\n", + " ('populate', 0.002627430373095113),\n", + " ('wait_for_operation', 0.002627430373095113),\n", + " ('fit', 0.002627430373095113),\n", + " ('score', 0.002627430373095113),\n", + " ('run_commands', 0.002627430373095113),\n", + " ('__getstate__', 0.002627430373095113),\n", + " ('fetch_list', 0.0021019442984760903),\n", + " ('parse_model', 0.0021019442984760903),\n", + " ('check', 0.0021019442984760903),\n", + " ('read_collection_from_device', 0.0021019442984760903),\n", + " ('init_module', 0.0021019442984760903),\n", + " ('run', 0.0021019442984760903),\n", + " ('port', 0.0021019442984760903),\n", + " ('get_connection', 0.0021019442984760903),\n", + " ('search_obj_in_list', 0.0021019442984760903),\n", + " ('check_response', 0.0021019442984760903),\n", + " ('deconstruct', 0.0021019442984760903),\n", + " ('fetch_resource', 0.0021019442984760903),\n", + " ('get', 0.0021019442984760903),\n", + " ('get_command_from_state', 0.0021019442984760903),\n", + " ('get_config', 0.0021019442984760903),\n", + " ('database_forwards', 0.0021019442984760903),\n", + " ('parent', 0.0021019442984760903),\n", + " ('forward', 0.0021019442984760903),\n", + " ('database_backwards', 0.0021019442984760903),\n", + " ('extra_repr', 0.0015764582238570678),\n", + " ('reverse', 0.0015764582238570678),\n", + " ('monitors_list', 0.0015764582238570678),\n", + " ('async_op_url', 0.0015764582238570678),\n", + " ('_extract_url', 0.0015764582238570678),\n", + " ('diag', 0.0015764582238570678),\n", + " ('ip', 0.0015764582238570678),\n", + " ('list', 0.0015764582238570678),\n", + " ('__exit__', 0.0015764582238570678),\n", + " ('state_forwards', 0.0015764582238570678),\n", + " ('to_lines', 0.0015764582238570678),\n", + " ('parse_version', 0.0015764582238570678),\n", + " ('cli_load_config', 0.0015764582238570678),\n", + " ('format_item', 0.0015764582238570678),\n", + " ('delete', 0.0015764582238570678),\n", + " ('__setstate__', 0.0015764582238570678),\n", + " ('netconf_get_config', 0.0015764582238570678),\n", + " ('suitable', 0.0015764582238570678),\n", + " ('__deepcopy__', 0.0015764582238570678),\n", + " ('description', 0.0015764582238570678),\n", + " ('_request_for_item', 0.0015764582238570678),\n", + " ('decode_predictions', 0.0015764582238570678),\n", + " ('add', 0.0015764582238570678),\n", + " ('to_python', 0.0015764582238570678),\n", + " ('touch', 0.0015764582238570678),\n", + " ('__setitem__', 0.0015764582238570678),\n", + " ('__getitem__', 0.0015764582238570678),\n", + " ('minimum', 0.0010509721492380452),\n", + " ('shutdown', 0.0010509721492380452),\n", + " ('parse_memtotal', 0.0010509721492380452),\n", + " ('add_arguments', 0.0010509721492380452),\n", + " ('fit_predict', 0.0010509721492380452),\n", + " ('time_until_up', 0.0010509721492380452),\n", + " ('__reduce__', 0.0010509721492380452),\n", + " ('generate_commands', 0.0010509721492380452),\n", + " ('_iter_test_indices', 0.0010509721492380452),\n", + " ('get_stp_enabled_state', 0.0010509721492380452),\n", + " ('addresses', 0.0010509721492380452),\n", + " ('handle_raw_input', 0.0010509721492380452),\n", + " ('_transform', 0.0010509721492380452),\n", + " ('manual_resume', 0.0010509721492380452),\n", + " ('has_lldp', 0.0010509721492380452),\n", + " ('addError', 0.0010509721492380452),\n", + " ('get_value', 0.0010509721492380452)]" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = Counter(per_token_freq)\n", + "d.most_common(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('__init__', 1475),\n", + " ('_real_extract', 139),\n", + " ('to_return', 127),\n", + " ('update', 123),\n", + " ('present', 106),\n", + " ('absent', 103),\n", + " ('should_update', 102),\n", + " ('compare', 101),\n", + " ('create', 101),\n", + " ('remove', 96),\n", + " ('fit', 94),\n", + " ('_set_changed_options', 93),\n", + " ('__default', 92),\n", + " ('exec_module', 91),\n", + " ('_announce_deprecations', 89),\n", + " ('forward', 84),\n", + " ('__repr__', 82),\n", + " ('delete', 79),\n", + " ('main', 75),\n", + " ('remove_from_device', 66),\n", + " ('get', 60),\n", + " ('read_facts', 59),\n", + " ('_exec_module', 59),\n", + " ('to_request', 58),\n", + " ('exists', 58),\n", + " ('get_config', 57),\n", + " ('from_response', 56),\n", + " ('predict', 55),\n", + " ('__call__', 54),\n", + " ('__eq__', 53),\n", + " ('suitable', 52),\n", + " ('__str__', 46),\n", + " ('populate', 43),\n", + " ('parent', 43),\n", + " ('init_module', 36),\n", + " ('description', 35),\n", + " ('as_sql', 35),\n", + " ('destination', 35),\n", + " ('update_on_device', 34),\n", + " ('read_current_from_device', 34),\n", + " ('port', 32),\n", + " ('call', 32),\n", + " ('render', 32),\n", + " ('fetch_resource', 31),\n", + " ('__getitem__', 31),\n", + " ('__iter__', 30),\n", + " ('transform', 29),\n", + " ('create_on_device', 29),\n", + " ('raise_if_errors', 28),\n", + " ('ip', 27),\n", + " ('deconstruct', 27),\n", + " ('async_op_url', 26),\n", + " ('_extract_url', 25),\n", + " ('_extract_urls', 25),\n", + " ('fetch_list', 25),\n", + " ('__getstate__', 24),\n", + " ('enabled', 24),\n", + " ('run', 24),\n", + " ('add', 24),\n", + " ('wait_for_operation', 24),\n", + " ('flatten_list', 23),\n", + " ('score', 23),\n", + " ('execute_show_command', 22),\n", + " ('search_obj_in_list', 22),\n", + " ('decorator', 22),\n", + " ('_system_state_change', 21),\n", + " ('_response_from_item', 21),\n", + " ('read_collection_from_device', 20),\n", + " ('check_response', 20),\n", + " ('__setstate__', 19),\n", + " ('transparent', 19),\n", + " ('add_arguments', 19),\n", + " ('get_existing', 19),\n", + " ('cli_load_config', 18),\n", + " ('post', 18),\n", + " ('_request_for_item', 18),\n", + " ('__exit__', 17),\n", + " ('load_config', 17),\n", + " ('reverse', 17),\n", + " ('apply_key_map', 17),\n", + " ('predict_proba', 17),\n", + " ('validate_param_values', 17),\n", + " ('ignore_down_response', 16),\n", + " ('__deepcopy__', 16),\n", + " ('extra_repr', 16),\n", + " ('interval', 16),\n", + " ('predict_log_proba', 16),\n", + " ('fit_transform', 16),\n", + " ('timeout', 16),\n", + " ('put', 16),\n", + " ('disabled', 15),\n", + " ('to_lines', 15),\n", + " ('__setitem__', 15),\n", + " ('__hash__', 15),\n", + " ('check', 15),\n", + " ('parse_hostname', 15),\n", + " ('get_manager', 15),\n", + " ('get_running_config', 15),\n", + " ('__contains__', 15),\n", + " ('name', 15)]" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Counter(labels_train_str).most_common(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17165" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(labels_train_str)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -33041,152 +21940,48 @@ } ], "source": [ - "plt.figure(figsize=(30,10))\n", - "labels, values = zip(*c.most_common(250))\n", + "plt.figure(figsize=(20,10))\n", + "labels, values = zip(*Counter(labels_train_str).most_common(50))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", "\n", - "freqs = [per_token_freq[l] for l in labels]\n", - "\n", - "mean_freq = np.mean(list(per_token_freq.values()))\n", - "#mean_acc = (accuracy / len(preds))\n", + "accuracies = [c[tok] for tok in labels]\n", + "mean_acc = np.mean(accuracies)\n", "\n", - "plt.bar(indexes, values, width, label='Accuracy')\n", - "plt.bar(indexes, freqs, width, label='Frequency')\n", + "#plt.bar(indexes, accuracies, width, label='Accuracy')\n", + "plt.bar(indexes, values, width, label='Frequency')\n", "plt.xticks(indexes , labels, rotation=90)\n", - "#plt.title('BERT (100k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "#plt.title('BERT (50k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", "plt.legend()\n", "plt.tight_layout()\n", - "#plt.savefig('BERT-100k2_epochs_top100.pdf')\n", + "#plt.savefig('BERT-freq-50k_epochs_top100.png')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 168, - "metadata": { - "scrolled": true - }, + "execution_count": 108, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('__init__', 0.08565423016290068),\n", - " ('update', 0.008407777193904361),\n", - " ('_real_extract', 0.007882291119285338),\n", - " ('__default', 0.007882291119285338),\n", - " ('should_update', 0.006831318970047294),\n", - " ('remove', 0.006305832895428271),\n", - " ('exec_module', 0.006305832895428271),\n", - " ('_set_changed_options', 0.006305832895428271),\n", - " ('main', 0.006305832895428271),\n", - " ('create', 0.006305832895428271),\n", - " ('__repr__', 0.006305832895428271),\n", - " ('from_response', 0.005780346820809248),\n", - " ('_announce_deprecations', 0.004729374671571204),\n", - " ('__call__', 0.004729374671571204),\n", - " ('read_current_from_device', 0.004203888596952181),\n", - " ('compare', 0.004203888596952181),\n", - " ('absent', 0.004203888596952181),\n", - " ('to_return', 0.0036784025223331584),\n", - " ('to_request', 0.0036784025223331584),\n", - " ('create_on_device', 0.0036784025223331584),\n", - " ('__eq__', 0.0036784025223331584),\n", - " ('exists', 0.0031529164477141357),\n", - " ('predict', 0.0031529164477141357),\n", - " ('flatten_list', 0.0031529164477141357),\n", - " ('transform', 0.0031529164477141357),\n", - " ('__str__', 0.0031529164477141357),\n", - " ('fit', 0.002627430373095113),\n", - " ('score', 0.002627430373095113),\n", - " ('remove_from_device', 0.002627430373095113),\n", - " ('run_commands', 0.002627430373095113),\n", - " ('wait_for_operation', 0.002627430373095113),\n", - " ('__getstate__', 0.002627430373095113),\n", - " ('as_sql', 0.002627430373095113),\n", - " ('read_facts', 0.002627430373095113),\n", - " ('populate', 0.002627430373095113),\n", - " ('present', 0.002627430373095113),\n", - " ('execute_show_command', 0.002627430373095113),\n", - " ('search_obj_in_list', 0.0021019442984760903),\n", - " ('run', 0.0021019442984760903),\n", - " ('port', 0.0021019442984760903),\n", - " ('get', 0.0021019442984760903),\n", - " ('check', 0.0021019442984760903),\n", - " ('forward', 0.0021019442984760903),\n", - " ('get_command_from_state', 0.0021019442984760903),\n", - " ('read_collection_from_device', 0.0021019442984760903),\n", - " ('get_config', 0.0021019442984760903),\n", - " ('database_backwards', 0.0021019442984760903),\n", - " ('fetch_resource', 0.0021019442984760903),\n", - " ('parse_model', 0.0021019442984760903),\n", - " ('init_module', 0.0021019442984760903),\n", - " ('database_forwards', 0.0021019442984760903),\n", - " ('parent', 0.0021019442984760903),\n", - " ('check_response', 0.0021019442984760903),\n", - " ('deconstruct', 0.0021019442984760903),\n", - " ('fetch_list', 0.0021019442984760903),\n", - " ('get_connection', 0.0021019442984760903),\n", - " ('ip', 0.0015764582238570678),\n", - " ('netconf_get_config', 0.0015764582238570678),\n", - " ('__setstate__', 0.0015764582238570678),\n", - " ('to_lines', 0.0015764582238570678),\n", - " ('_request_for_item', 0.0015764582238570678),\n", - " ('__deepcopy__', 0.0015764582238570678),\n", - " ('monitors_list', 0.0015764582238570678),\n", - " ('__setitem__', 0.0015764582238570678),\n", - " ('format_item', 0.0015764582238570678),\n", - " ('to_python', 0.0015764582238570678),\n", - " ('parse_version', 0.0015764582238570678),\n", - " ('add', 0.0015764582238570678),\n", - " ('__exit__', 0.0015764582238570678),\n", - " ('_extract_url', 0.0015764582238570678),\n", - " ('diag', 0.0015764582238570678),\n", - " ('description', 0.0015764582238570678),\n", - " ('delete', 0.0015764582238570678),\n", - " ('suitable', 0.0015764582238570678),\n", - " ('decode_predictions', 0.0015764582238570678),\n", - " ('cli_load_config', 0.0015764582238570678),\n", - " ('__getitem__', 0.0015764582238570678),\n", - " ('async_op_url', 0.0015764582238570678),\n", - " ('state_forwards', 0.0015764582238570678),\n", - " ('touch', 0.0015764582238570678),\n", - " ('list', 0.0015764582238570678),\n", - " ('reverse', 0.0015764582238570678),\n", - " ('extra_repr', 0.0015764582238570678),\n", - " ('content', 0.0010509721492380452),\n", - " ('has_lldp', 0.0010509721492380452),\n", - " ('validate_comment', 0.0010509721492380452),\n", - " ('desc', 0.0010509721492380452),\n", - " ('md5_text', 0.0010509721492380452),\n", - " ('get_admin_state', 0.0010509721492380452),\n", - " ('patch', 0.0010509721492380452),\n", - " ('execute', 0.0010509721492380452),\n", - " ('manual_resume', 0.0010509721492380452),\n", - " ('call', 0.0010509721492380452),\n", - " ('handle_field', 0.0010509721492380452),\n", - " ('kml', 0.0010509721492380452),\n", - " ('enabled', 0.0010509721492380452),\n", - " ('parse_memtotal', 0.0010509721492380452),\n", - " ('port_lists', 0.0010509721492380452),\n", - " ('as_mysql', 0.0010509721492380452),\n", - " ('idf_', 0.0010509721492380452)]" + "0.870199634380616" ] }, - "execution_count": 168, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "d = Counter(per_token_freq)\n", - "d.most_common(100)" + "mean_acc" ] }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -33195,7 +21990,7 @@ "1278" ] }, - "execution_count": 169, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -33206,12 +22001,12 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABX4AAAKuCAYAAAAW1QTMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xe8FcXdx/HPj46AAtJFvNgw2JUYFVSwYe8ldlBilJKYZkvyhESjRp9o1Ah2UEiMsSIJ2EWxPSr2GLHgpQgKKB3p8/wxu5y95+7uKfdczuXyfb9e+zr33J3ZnW2zc2ZnZ8w5h4iIiIiIiIiIiIjUHw3KnQARERERERERERERKS1V/IqIiIiIiIiIiIjUM6r4FREREREREREREalnVPErIiIiIiIiIiIiUs+o4ldERERERERERESknlHFr4iIiIiIiIiIiEg9o4pfqTfMbICZucj0zzzjvZYVr28tJzUtLRV1IR35MLPhQTorY+ZVZO3Te3IsKxp+QG2lubaZ2ehgGyaVOy0iNWVmxwXn82vlTktNRa7NXNNf81jWrmZ2j5lNN7MVZjbHzP5lZsdsiG0Rz7wfmdmrZrbQzNYFx/DxcqdNSsPMKoNjOrwWlp1YhtlQouXWEi7zomCZj5RqmSKbOpXv6w4zO9vMnjezeWb2nZl9ama3mNk2JVp+ZzO7yszeDMoWq83s26C+4H/MrH1K3M3N7Ewzu9fM3jOzxWa2ysy+NrOnzexCM2uaZzqOM7NHzGxGUNb8zsy+MLN/mFn/UmyrbFpU8Sv12bFmtkVaADPbHti3thNSG4X7jcy5ZrZduRMhIvkxs0bAn4Kvw2Pmb5J5mpmdB7wFnA90A5oCnYCjgfFmNqKMyavTauGc+RNwJ7AfsAVgJVqu1IAqSMruXmAmcJKZ7VfuxIhIeZnZpCBPHl3utNSEmTU2syeAMUA/oB3QDNgeGAa8b2aH13Ad/YH/Ar8BeuHLFo2ANvj6gt8DH5vZgTFxuwBfA38DBgK7Aa2AxkAH4DDgDuDtoP4hKQ1NzewxYBxwErA1vqzZDKgATgeeNLO/m1njmmyvbFpU8Sv11WJ8BnlqjnDnRsJL7WkE/E+5EyEieRsE7AS85px7qtyJKaGX8QXxpOnnSRHNrDdwN9AE+BDojy/M7w2ErUwvNrNLayvx4plZS+CnwddH8efqFvhj+MNypUuk3Jxzq4Drgq83lDMtIiIldDNwbPD3XUBPoCNwMjAL2Bx4KK1SNY2ZdQMewZcllgK/BnYF2gN7ATcBa4C2wOMxLX+b4OseluEfwJ0EdAe2xJcT7wrC9QSeNrMWCUm5ATgh+HsyvqzZNVjWyfjyJ8AZwO+K2VbZNKniV+qrh4PPc5ICmJkBZwdfH6r1FG26pgWfZ5nZjmVNSS1zzg1wzplzrm+50yJSLDNrAFwRfL2lnGmpBWudc0tTplUpcW/EP8T6GujrnHvaOTfPOfc2voD/TBDut2mvAkpJfA//IwvgKufcVOfc4uAYrihnwkTqgFHAEqC3mfUrd2JERGrCzHYBfhx8vds5d6Fz7r/OubnOuUeBg4Hv8JW/Vxe5mguBsDJ2oHPuGufch865+c65d5xzP8e3BAbfAvjMrPirgD8CWznnLnDOPeacq3TOfeuce9s5dyG+Mhl8Je6QmO1sGaQD4B3g0KCs+WWwrEeBg4AvgzAXBWV2kZx0okh9NRZwwAEpff70wWe8S4HHNlTCNkG3AguBhsS8Mi4idc6R+G4MluBfNdvkmVkvYJ/g6/XOuW+i851zDrg8+NqSlIeOUhKbRf5eWLZUiNRBzrnvyJRrL0wLKyKyEbgIX2+1mkzl63rOuU/xb2QBnGJm7YpYxx7B53L8m0RxxkT+3ikrDbOdc79xzi1KWcf1wLfB30fGzN8J360DwD/jGiM4574FJgRft8S3SBbJSRW/Ul99gX+l10j+AR7+/1H8axk5mVlzM7vEzF4MOpVfZWZfmdnjZnZUTPiKoD/DUZH/ZQ8mVJmyvkZm9lMzm2JmS4Lp/4LO4VP7MzSzJmY2NEjrfDNbaWazzexRMzs6j20N1/22mS0LOrZ/OejjshAL8S3lAE43s54Fxg8H8fmBmf3RzF4P0hJ2tv+qmV1mZq1S4lcZxMXMepgfnCnsMP9zM7vBzNpE4jQ3s58H27/YzBaZ75h//5T1JPZtmN2/pZm1C9b5aZCGb8xsYj6tc8xsd/Od+88J4n5hZiPChxyRc2tArmUlLH9XM/uNmb0UnOerzQ9wMMXMrs63NaOZHRjsk8+Cc2ixmX1kZg+ZH/wgtm8qM2tmZoOD/f1VcO7OMbM3zOwvZrZXVvi+kW2uSElP2oCE2cdnazO72cw+MbPlwbzW5dpHQXrWBum4KMcymwbnkzOzYl71DSsKHgsqEKLLLjpPM7Njzewx8/nQSvP50qTgWCf2U2ZZAzyZ2UAze8X89b8s2Oc/Nd8vcW05NvJ37MChQcvfz4OvxxW6guzz2My2CM6lj4NzcHZw3e+UFe8Uywx0sjzYHwPzWF8rM7vC/D1lQZCXzDCzv5nv1iItbjvzg59MMZ83rjY/eMmHZjbG/OArjYKwNboPZq13eLCsSZF/fxFdViRsQdd0EMbM7OTgPP0yOE+/NbPJuc7TIP5m5gd/+Y/5gVjmmc/Hjg3mp90j8uobN/t6SAhTUFklEi/7XllhZiODda4MjvHDZrZnTNwBwb4OywgHxRzn0WnbVojgWBVdLohZ3u7BuT8r2NYZZnaHmW2dR9yi9ncey21hZpcG2xNu3zzL3CN+ZMmvC/8t+DzRiqsEKZnsc9bMTjOzF8zfpxabz4POyIqzo5ndZb58s8LMZprZrWbWNo/17RiE/ch8uXm5mU01PwBUt5R4zczs6OC4fxDEDY/lBPP35MTfzpbVl6qZHRCcA2E55osgXR3z3Xf5MF9e72dmN5kvs4Z58jzz94aLzaxJHstpaD7vHmeZ/G+emb1jZndayqDTZtbR/D3hjeC4rgiO+wvmy0pblWhbC74mLM+BHLOPX46wB0aO7QrzZbj/TTs/zTsjOJfmBOfWoiDuM8F2pZ2fBZ3XlrlfHhT86zyrnicPz7WtdURYBnvJOfd1QpiwbNYQKGaw3fBtobRxCNZG/p5b6Aqcc2uAT4KvXVLSkG861pCpSBZJ55zTpKleTMAAfCbp8J2f/yj4e2pM2GbAgmD+oUDfSNy+CcvfDaiMhIub7gUaRuJU5AjvgMqE8Efj+/ZJind3yr7YGt8HUNp6/w40SYjfAngpJe79+Na7VdKfsB0D8P0uzg++/zNX+Jj5x+exHz8Fuidsz/q0AofjW3nHLeMdfN9OWwKvJ4RZBfRPWM/oIMykHOdnT3x/VHHLXwecm3Jsz8I/8Y6L+w2+H6nEfZnHdbR7Hvv6a2DvlGU0x//ozLWcPRLW/0WOeO9mxekbmVeRkq7150GO47MvviCVvd7W5dxHwMTgf6/nOIanRuJ/r8Dj3yI4xx1wRo5rNWmqzIrTFF8gT4vzHv71uLg0VQZhhpN5myNuehFokc+1if9h0LCA/fKvIP6sHOHC9C0u4tqLnsd98JXIcdu5IDgHDT+4WdL++EPKunYlOQ8KpxsAi4nbMzi/c50H7Yo9Z/K4hhOnYq7pIHwb4Lkcy38T6JCQto7ARylxr8o+D9PO0ZR9UBmEG54wv+CySlweia8sWJgQfwX+FdSkPDRpGl3gNZG4rZS2XHAs/jXhuGUsAnqnpLEm+3v9PouZ1xlfQZBrG3slpKtxZJsGFpoflXKiah5+R8q2/C4IfwT+jZO4MB8CrVLW9QuSy0gO39DjmIS4N+Wxv/8NNE2IPyk8z4Gf4Stn4pYxnYT7XZH796d5pPv/gLYpy9gGXwZOW8bChLhnBPs1Le5fSrCdRV0TpJT9ko5fzLzRwbxJ+C4Hko7tl0CPmPgN8WMB5Er7L0t1XpPH/ZKE+0hdmvCDuIXpvTolXDN8RagDbi1iPVdG1pOUR/wkEmbfIrdnWhD/jZh5TfHjDjn879C4+8bmZPLUieU+Ppo2nqnsCdCkqVQT1St+W+N/nDhgn6ywYcXILHzL976RuH1jlt0NX6nmgM/wI7pvi/+huAvwv5FCwNWReIZ/7ffHkeW3zJo2i4SviIT7HF9BeQWwI5kRRaMVsofHpLUp8H4wfw1+5PPv4Ssz98O/uh3GH5GwL6OVK2PwndqHndOHlVXhjasyJn50OwYE/7s8+L4O2DVX+Kz5RwfpHgTsH4TfEl95cQl+BGsH/F/C9gwP5i/E//h/C//Don2wrKsj678qWNeiYNlhx/zH4Qt0DpgBNIpZz+hg/qQc5+fn+MrNc/Ad9rfDd+Q/I5i/BNgyZhm7kyn4zcL3Ud05mM4O0hetLKq2L/O4jnYDnsWPkHsAfrTctvjKngvwo906fKGjecIyHouk4Un860ydg/24R7Bf3yar4jfY12HlzHLgGmDPYP1d8CPi/gV4Pite38j6KlK2LTwP4s7Z6PGZFRyLc4Pj0xHfeqBZOfcRcEokzk4p2zkhCJNaQZwQ99DIOraLmV9QnhbEiVZOPobPh7YM9tcNZArq7xDzMIpMAfeL4HMsPi/aEp83jYksf0zCdo0O5n+Dz8PX4vOiecB4fB+91So5I/E/DeK/lGP/XRVJS5cC9330PJ4GfIW/13TFDyJ3LpkfBJPxFQvr8NfErsE5uA/wCpn8v2fMerYEZpO5zi4HtsPnQ4fg35YJ03FpTPzwgeRc/OuXPYJ1d8Tfo34JTCFT8VvwOZOyj5oE4Y+MLKtndFlFXtONgFeDsIvJDOrSBn+PGEImb3oRaBBzXYT35nX4CqSdg33dG3iKqudw3D1idNK8hOtheMy8osoqMXnkgmA57wEnAp2CfXYumYfmVe6DwT5sSab8MDnmOMdWmBW5raUqFywIpk+Cbe0Q7Mdh+HJAmG9Uq/Avwf4eEMxzMfPCfG0Z8KvI+RQONDQYXwmV9oDxtWAZowvZ76WeIscxLDfegr+PZudZa/H3+UXAG/hyWjt8pWQ0b70mYT1DImEewednHYNlHB5Zz3fALgnnxJ343wh748sdnYDvB8dyeRD/+oT1T4ps5zr8/a53cNy6A38I/u+Av5dw/16IL5ufE+zPrYPzZA/8wMrhOVqt4UUQv03k2KwBbsPn5e2C/Xcgvpz8n5i4J0b2+azgvNwhWGb3YF8+ANxQgu0s6pqgtBW/s/APx98IztV2+DLgb4GVQZhPCO4tkfgXRPbTCOAH+DJfW/zr/WcA/wCGleq8JnO/DO/ZY6meJ8c2AErZR81jllHIFFsmzrHO3pHtPz9H2DDff7qI9WxB5qH2t8BQfB7fDF9G+h8y9Qr/W+Q5vGdkW2IfhuB/94dh/o0vM28enCuHkmmYNBvYsVT5iKb6P5U9AZo0lWoiq+I3+N9Dwfdbs8KOJ1J4I3fF7xNkKuxaJ6z/wiDMSrJ+8JNSuM8KVxFJxxrgwJgwLcj8aP9HzPxLIsu4MGa+4QsXYZjdsub3isy7MyGd90bCVObYjgGRdM8N/vdYrvAFHvvOZH6UHxwzf3hk+VOIKXiQ+bG6OpiqPcmlaqVYtVa/5F/xO4v4H5F7RcJcFDM/rDxYREwrJnzhM9pKpuB9mce+bon/gRtbAAN+GFl/agsPsirP8RWgYeF1vwLi9Y2ssyIlXngexJ2z0eMzH+ha1/YRvhAfXkN/SgjfhUxFarXrP4+0/yGI+02OcOv3V45w0ULu3xLCDI6E+UnM/MrI/KQ86a5ImGoVIZFrM216GmiTsPywsuvhHNs7LLK8XdPCxsSNnseLgR1iwgyKhFlNTOsg/I+DsMLq2pj5fwnmrSM+H2tC5ofiCiJ5Ff7HR7j+4wvcvrzOmSL2VUWu9eW6poGfR7Y3qQXlbmR+9J2SNS/6UObKmLgN8YP/hWEmpZyj1eYlXA/DY+bVtKwyPJLGd4hpQY9/SBKGOaLY7cjzOCduax5xCykXJN2TDyBTcVutBVkJ9nfiNUGmsu6nNdh/4bX+RU2PRYmOowMui5nflsxDrdX4lvXNYsKF5bQ5Ccc7vD7/nJCOxvgHNw74VxHbcUQQdykxrY7JVBym3atuiZwPm2+g/b8LvlywjvgHureTuSeclLKc7LJXCzJv830Sdw0lxS1yO4q6Jihtxa8D3iXmYSX+jbwwzC+y5j1CzO+fPNJe4/M6bbuKOAbRc7yYaVIR64y+3RHbEjcSdkoQbkqR27cj8EFK+l8FTi9y2UbmraK1pLyRF5yzKxPSsBD4KwU2LtCkSX38Sn13f/D5Q8v0ldkeX3iLzk9kZtuS6StoiHMuaSCZu/BPzJvgn3DX1D+dcy9l/9M5twxfoQ2+FUK2QcHn2865O2PiO/xrYauzwocGBJ8rgMsS0vYr/A0pb0G6/xR8PcFi+ggslnNuDr4FJvgn8Gkuc1n9lgb+EXw2wleovx4T5jl8IRf80/pi/cE5V61vKOf7CH0/+Frl2JpZZzLbdotz7ouY+J/hB9OrNc65pWQGPYjb1z8NPj/Ht/xLW9aa8G8z6wH0D75e75x7LZ94teQG59ysYiPX1j5yfpCHcGCJs82sYUyUc/CVTN+ROacLEfbBPa2IuHEuCD5X4R9KVeOcG4FvWQjV86OotDzpUjJ9o8X1b/sVflCNg/EPmpriW/adiG+5A/5YPZ7Qh2PYZ+CKmHlR0bylZY6waW5xfrCSbGGXGeBb+N+YHcD5gT+eCb5WyaeCc2ZA8PVx59xTMfFX4V9nBL+fzo7Mjp5zX7LxyHVNh9v7F+fcW3EBnHPv47tIAv8DP2pA8Pkl/jzLjrsW30K71tRCWeWy4L6d7XEyA+rFlUHqhALLBVcn3JMnAw8HX8+J5rkboGwY9llek+ss7HO8Irvf0zKZgW85W0WQZz0dfG0EXO6ci8trw3tap5g+TS/C51ezSLhPOOdW41tmAhxlWX185+KcexL/lkgLfCvzJMvx96Q4o4LPJvi3uGqdc+5D/BtEhm/AsJ6ZbU4m/xrjnEsa1Cqu7HU2vsUtwI/jrqGUuMUoxTVRCpc755Zn/9M59zcy5Ynsckixaa/183ojEM278i2DFVX+cs59gn8D88WEIF2A7ay4MSWuxJdBAf7qnPtvStg/4hsSxG1vC2Ar/EMBkbyp4lfquyfxFXXtyFT2noG/Ab8bFIZyOQRfWFoJvGlmLeMmfEYcVl70KkHaJ6bMmxp8dor+0/zgZGHFzcMkcL5j/LBS+YCs2X2Cz0nOuQUJ8b8h+aaYZgS+AgZ8y8K8mVljM7vAzP5tfvCV76zqgD7hD6odUxazkuR0fx75u1plCKyvNA8rxDrFhclTwccW/9pdOKDfEynxxxWbqFAwAMWpZvZIMBjJ8qx9/asg6I5Z8VqRqQj4W4EF/UMif48uOvGl8e9cAcq0jwDuCT674F/xyzYg+HzEObe4wGVDZnTg2Gu/CNH8ZF5KuDC/2iXlR0tanrSAzLVdbWAy59zlzrnLnHMvOOemO+dWOefmOeceD8KHP3YPpGpFZ7kk5UGL8RUPAM8659YlxA/zs+x8ZFf864yQeYAYt553Iss4IPL/Bfj+KQFuK+UDvFqWeE2b2Q74V8kBXky6xwf3+Q+CcL0i8Y3MOTc+6ZoOyhufxM0rkVKWVVYCL8TNCM658KFETe6DNVaicgH4V/JzzdsC33IyVNtlw3eCz2vN7KDgPCtUdNCfujDy+7PBQ5A4YX6Tbzkt+9wLKzRfBJqlHI+wssXw3TlUYWbtzezX5gd0DAdujZ5T4X5MO6deT3kQMDXyd8mun2D7LjGzZy0zmFw03WG5IzvdffAVi1B42Ssst81wzsXmFyVWimuippaRebAaJ8wvemaVZcK0n29m55hZU/JTkvO6VJxzfZ1zVoOpb22lrRTM7Of4+/Q+wG/wXSWG3XH8Gt9g4I/ARDPbrIDlHk/md++7JDdiwPyAolPw/aFPwXdt1SGYjsR39XACMNnMCh5IWDZdtTkCtkjZOedWm9k/8P30nIvv4uGcYPaYxIhV9Qg+m5Jp7ZlLKQrYs1PmhU+as2863chUDn6UY/n/wRfatsn6f0Xw+XGO+P8lvuIpkXPuOzO7FrgZOMbMvu+cezNXPDPrhG8Nsmseq9kiZd684Ml4nGhLvTkpywjDNc8jLUmKObYVkb+nkizXcUsVFCCfAPrlETx7X1eQaRH4boGr3i74XBzXmnkDS23tWsZ9hHPuIzN7Hf8gYCCRhwhmth++cAqZVkWFCvOuUo0SHOYv+eRH4POvbmRaFEblOrc/xrcaz87TUjnn1pjZhfiHg5vhW3Nmvw2yDN9vfLMci4vmC0sLSUeWtDwizIOKyaei+yafY7Id1ffnz/Cvre4DvG1m0/EPEifjK3bKff3GSbume0T+npDn8qL3+C3w5wbkd9/MVQlZrFKWVdLulZB8n9pgSlguWOiSR4mHqsd0GzKVuLVdNrwUf01tj3+9+mszC6+z551z/0mJG8qu+K3Md+VBpUZSA6FlwYPwQuWTr81LeSAaLadl523h8TiL6i3yk1Q5HmZ2AL5Fe9s84qadU4nb6ZxbHqmvLMn1Y2bfwzd0yW4FHSc73dtF/i623FZwWaZIpbgmaurTlIcXkMkvsssyN+EfzG+DL1+MNLNX8H3qTwJeTVhujc/reiD65km+ZbCCy19mNgD4c/D1OOfcs5HZC4BrzOwNfMX/ofhW1lfksdw++H6uG+D7+T8q4Y0GggcCz+LLCZOAw7LywyfN7NkgDX2BsWa2XY6GFSKAWvzKpiH8AX9sUDHSC9+3zt+To1SRVrhLkuvGlI+0gkWSVpG/c930lsTEgczrMbniF1upcQf+lSXwg3XkYwz+x91qfOHpEHwFWlt8+luROZ5pD7Ty3af5hCu6pUGOQmPS8qOvOsW9fhuqSWUT+P0bVmiOAo7CF+63JLOvrwvmZ+/rzSN/L6EwYdxC45Vc3Ct8Wcq1j0Jhq9/jzLfyDw0IPitJaK1XBmH+km9+FI2TLd88KSl+ouAthleDr3GtWMOKnQ45FhWd/02h6YjIJ48oJh+p8T3COfcY/vx/NkjDNvgHqncC08zsJTPbJ4+0bTA5ruli7vHR1lrRV0pr676Zj1KWVfK9V5ajxV2oVOWCQo5Z9Fqo1bKhc+4N/MOVx/Bd5XTEt2C+BfjQzN41syNSFlFTH+HzgLipoIdrEaXK16D6uVej42FmW+Df/GiL70v/MvwD1i7BssNzamYQpRRlzRpfP+ZfOX8EX8m4FF+uPgA/wFtrMul+JYiy0Zbb6sA1AUXmF865Rfi034qvRGyBbzzzB/yD05lm9lOr3tVUuX6DxjKz5mlvxeQxFdNoJvpgLd8yWDHlr7AS9/msSt/1gv+H5etBuVqdm9kewL/wFdKzgUODboiSnETm4fD/xD0EC/73u+BrK+D0tDSIhNTiV+o959ybZjYV/9R0bPDvZ5xzX6VEiwpv4nOdcx1LnsDSiha+cvVvFM7PLrAtxRc08o1fEOfcSjO7Bt/tQ38z25+U1hFmth2ZV52GOefuSAhXF/qvq03Ryt4W+IFQ4hTdr2iwD8MW8dc552KfZKcU3PKpvEuS9CAiH/m2PKrxPa/M+yj0D3xFR0vgTPwr983JFP5GF9kaC3w3AuGrbaWwBP/js5D8JOlHZLF5Wr7C/gnjupqYim9ltG2OZXQP0+CcS2vdVi6luEfgnHsR3y3CFvgRp3vjW1t/H1/pMNnMDgh+qNd10R/qPXP0u5crfk3umzXNxzamskqNlLhcUGzeVOv72zn3HnBS0Pr2B/h+ZQ/Dd0mzOzDBzE4OHsbEiebjif2v1hNL8Xn39c65xNeoU5yC7xZuHdDPORf7RoT5PnHrkoPw92zwg07GdhMUvKkUJ7tMUkg3TzUptxWlyGuilGXEossyQR/IPzGznwF74O+d/fBvG3XGD8ZYQdX+4Gt6XpfaRPw5V6wX8S1VCxF9yzGxDBa0lg37vU17MzIubhsyFa6x/fxHvIk/bu3wratj81Yz2wn/VsoW+Mrrw5xzucbP2Dfyd1o6om/L9kwMJRKhFr+yqQi7ddg263s+wky6XfAjty6bQaaAk+tGsHPwWZn1//D7TqT7Xo75ae4h009krr5+o4NfpA1Wlc/rnhuz6ZG/014V7pEyL5ceZFqyFbOvvyDT0mWPAtf9WfC5uZl1Tw1ZXfSVqbTWBF0KXG6ccu4jYP3gcf8Mvg4IPk/CFy4dNesjOXxdrFQVv5XBZ775kcPnY3Fy5Unh/OmpoZKF/S3GdTMxJfjcysy2SllGWGh/u8g01LbKyN/F3iPWc84tcs496Zz7rXNuH/wPuu/wAxddWXwyN6joD7HtEkMlW0TmnKnJfTPMxxLzMPOD1LZLmL0xlVVqqpTlgtZmllZxGz2m0bxlg+1v59zyoH/yPwZ9ZO6Kz6uNTMuvONF8vKBXgZ1zFSn9dFYWug0bQHg8irmGIXNOvZ9S6bs1xbXArE1huhekVPo2Ibls+Fnk72LLbRtkkLqoAq+JnHlrIJ8y4g4WP7BuKMwvEssyzrm1zrkpzrm/OudOxrfOfjmYPczMtowEr+l5vdFzzs0nsy/TBtb+Ppmu1KakhIsTbSWdqyV+tP4s9qGCmVXgu2Noj2+oc0RSvlJkOnKmQSSbKn5lUzGWTMa4hPSBPLKFnfg3wLcIKMb6vvJyFBhqJBh4J+zj6uSkcGbWAf90HDKFDbK+97WEQZaCQknRT3ydHzX+6uDrITmWFX2lNnbfmdm+5G6Jt7F7jcw5nNaZ//E1WEc++7ormXOnCufcEuD/gq9nWmGj3kZfqzqvgHhQta/T2B83wXV3aNy8ApVzH0WF3T30MrOdyYwg/bxzrtiKT8jkH9vmeIUt3zwtzE8OMrOkCivI5K0fpgyKk5YntSGTj7wSFyZNkCeGo7THVdqOj/x9asx8zA90Fv44SxuAsZw+xFdUQvo9Ynd8C2eofo9IFLQEDu+Z2ZWcG+Q+WIQPyeQhBb8yGbSuD8+5Y5Ku6eA6TXtoF6Zhh5jXfUP9qJoHRZWirFIK4XGuzWNc6nLBiSnzTgg+F+HPlVDZ9nfQl+kDwde0hwlhflSZRxdGG7ung8/Dk+4TOYTnVNp5m28fqxtSPuk+ieQX5WPZAAAgAElEQVTX/18mUzFaaNkrvAa2MbO+BcYtqRzXRJi3ts/qHms9M+tB5o2dNC3wLYyThPnFRyllmSqcc98CNwZfG1L1PlHT8xpKmCeXcXC3sAx2UFBeixOWzdaSxyDNWeaT2U+5BscLB+lcSkyXEmbWGf+bpiv+Qfixzrl8K6Kjv2fS0hEdKLQmZX7ZhKjiVzYJQUXIjvjCwK7Oue9yRInG/ZjMDeQ6M0sdmMXMOsQULKI3hlK0OkwTVgjtbWYXJIT5C75FFsDdWfNGB5/NgOsT4t9A8o/PfI0mM0Lzb1PCRQcKOjZ7ZvD62ogapqXOC/qECitHfxI8Ta7CzLYFhtVgNZWRv+P2dSN8P55plZW3BJ/bA39KW1m08sc59wl+cBKAy9L6CM2uWHHOzQDCrluSfrhcjm9VUVOVkb836D6Kcs69SmYU599Rtc/hmpgcfLYBdkgJl2+eFuZHTcn8sKnCzH5MpqXRXSnLSsuT/kTmh22VfWBmndIqGoPWUHdH4o/NDuOcewsIuy241MyqtIgOKsmvDb4upbC3SjaYoH/x0cHXk8ys2sOQoFVpeI6uILItZtYuqzVSdtwGZH48Z/8g2pD3wbwFFbfhuXmmmaVW4plZMzPL7uN0dPDZFT8AUXachvjuWdKED4TaEJ+3tCD5/C9VWaUUwuNcm8e41OWC35hZtQGRzA/2FVYmjIn2z1+b+9vMWgStS9OElbppfVmGreNezGe9G7nbgJX4bgfuDvKxREFFX1R4Tu1kZtvHhN+JuvkWQ5juzc2sWiMK84MgpuUbi8nkX+eaWWLjgZiHWn8nc/7dHncNpcQtSA2viTBvNfxA33Fp+0sBybku6Goiezlnkrnmssshud4Gibbojaa/pud1dHl15r5bhNvx3bA0JmZ8mKD7n0HB14cLHezM+cFMXwq+9jOzg+PCmdlhZLqqeMY5ty5rflt8Zf12+H6oT3LOvUT+oo1ghseVXYPz9feRf8W29BepxjmnSVO9mPCvPLtgqigwbt9I3L4x87sBXwfzF+Ez3L3xgzm1w78SezbwIP6Hcq+s+B3xTyAdcC9+YIwm+MqhhpFwFWnpiNvWmHlNgfeD+auBa/CvHrXFv4b8WGQdIxKWPzYS5n78YEdtgb2AvwX/nxZ8VsbEj27HgJTtOC8SLjY8/gl1uK5FwGB8xUIHfMvX94N9+3EQZlLMeoYnpbWIfT8pCDM6Zt7olDQkHrMClrEnsIbMK2Rn4l9P74RviTITX5mec9+nrP+lIO5K4Nf4Bybt8C2zXwzmfZS2P7POsQn4/ss6BefQbsBQfN9Ve2TF644fhdzh+zS+Cl8h2Bbfb1c/4H+B52LW+cfIOm8L0t0Gf53eGfz/s5RzNq/jU+59lLWMX0SW4fCvmzcv9JhnLbMFvrDqgLNTwuWVpwVh74yk8RH8D6O2+Hzpusg5/Q7QJGZdlcH8L4LPMfi8qC3+mrg/svwxMfEvwV8XfwjOoa7413W3wV8370biPw80SNjm3vg81eHzncOC475HsF3hMi4tct/3jSyjIiVcuD+Gp4QZnnKub4nvV93hK6kvxbeM3BI4OHJ+V9uWII3L8T/4zyBzb+kS7NsnInGHFXvOlGJfUdg13Tiy3euC9PXDX5Nt8D/ijsMPzDMX+GVWfMuKfyOZvrL3x/8wi57Dk2LS0Ajfcsfh88Gz8fe5jvhWZO/hWyUtSDr+1LysknjeZIWbRPJ98NTIsRkSbEOjYIq9tgo91yltuWBBME0N9nN7/EPCocGyHb7ipEMt7O/YcxRfHlkNjMO/zbFrsLyO+H5BR0X28Z9TzunvgjADi8mTSjUlHcdCzz1ylNOAiyLz38H3x78tPr/vAvQBfoXvH/PDrLg7kMmfpgbnUafgGF+M70bgq+BcSLr+JpFwXWSFK7qMFrOs1vhXyR0+Xz8Lf4/rEvxdiS9PVSalDZ/HhdfTavyDvx8E53H4Nszw7H0WxD0xsj0zgn21fZCuCvw1NRbfR21NtrNG1wT+rQyHvw6HAlsF8Q/Dl9tW4AeeTtpHo4N5X+LLSG/g3yLbEn9/+C2+TOiAT4BmWfG/AF7Hl9v2I1Pe2xlfjlwRxJ1SyvM6iP8rMmXWU4P1FpUnl3PCP8wL98Pt+PJHh+AcDLs6XARsnxB/OOn5x0Fk8oBl+MHeegTncg/8g5/lwfxVwF5Z8VsExzgsBwzA9/mcOCWk87lIOl/Ej5/QPpj64xtohPMfLvdx0bTxTGVPgCZNpZqoxYrfIMxOZCpzck27x8T/e0LYykiYilzpyN7WhPlb419JTEvj34mpZAnit6Dqj//saSzpFQvR7RiQsh0N8QVslxYe/wP8u4S0rMUPhDCa3D/wqqW1iH0/idwFw7g0pB6zfJYRzD+XTEVZ9vQtvo+r8HtixV3K+ncmU/kaN92Ya3/i+1F7MMf554ip1MRXos3IEe/dhHP27ZQ4N+U4Z/M6PnVhH0WW0YFMJa0Dbi/0eCcsN6y8q1aJmhUuZ54WhGuK75M4bTvfA7ZKWE9lEGY4mQdPcdOLQIuY+JfksZ8d8DiwRY5tPo/Mj7u4aWQN9nvfyHIqUsKt3x8pYXKdf7uS+ZGbNP0vYClpTJvGEFOZm+85U4p9RQHXdBB+8+AcyGf7fhITvyPpZYQ/kjt/T7vXLcT/ME09/tSgrJLrvImEmxSEGx0zrzlVH0BGp2rhc6wncVtz7KuCygX4Sr4VCctaBPROSWNN9nfsOUrV8kja9AwxeV6wjEODMCuAdsXkSaWacp2z+Z575FFOAy5MOZbR6e2YuFekhF+I78Ip7ZxMvC6ywoXLHFCi/XsWmQqr7GkFcFqutAX79v2EZazfBwlxzyb5Wgynv9RwG2t0TeDLbd8kxMm5j4jkJfjK7aT9/SXQI+UaSJu+AHashfO6E8ll1sRrsq5N+AfFT6Rs+yLg8JT4wyNhk/KP8/PYz0uBU2Pi9s3zHF0/JaRhS+DVPOI/C7Qq93HRtPFM6upBJE/Ov9a3G/6H/3j8k/VV+EqAmfgWPVcAOzg/6my2gcBv8K3LluIz7dpK60x8i7hh+CeD3+KflM/B/7A91jl3pvN97cbFX4Zv+fUz/NPl5fgb6mvABc65s0uUzrX4G3GucC/gWys/TKYfptn4Vnb9nHO5XqGtN5xz9+Mrdx/Ctzxbha8ovQvf0ujjSPAl1RaQe/n/CZYzGn++rMa3aHoSOME59/M8lvGdc+504HD8wDsz8NfJQvwP5LH413M/jIn7Lv6H9M/wDx++IXO8/w/fN/QZMfGW4StFrsY/TFiJP++fA453zv0sO06xyr2PIsuYS9W+Z+8tYnPi3Bl8nhD3OmNEXnmac26lc+40fP/T4/Ctplbjj89L+NY333fOfZlH2s4GfoRvVbEInze9g6/cPSQ4D7I9hm9l8zC+e4x5+IcnS/DXy2jgYOfcCc65RTHxo9tyH75vtVH4Y7YKf+wn4PPVi/PYhrJzzn2Ab5H6a3wroUX4bZmJ7yexj3Pul8657GP6Kr5C6Vr8vaUS/4N/ZfD3g/hBTM5xkdfiIzbYfbBQzrnFzrkT8C3378NXXi7Dn6vz8Nt+A37f3BIT/2v8uTEcf56twOdfzwMnOud+nUcaove6aP5+J/4hUM5X9ktQVqkR57vSOgDfIuszqg6+Wcr1lKxc4Jx7At8K70Ey+2sm/r66q3Musd/wWtrfM/BvGPwOfw8Lz8VV+Ac2T+D7oz48Ic8Dn1cCPOb84EibBOfcnfjWkH/Et8r8Fl9Jt4TMvfVs/DmaHfdafOvBF4PwK/D7fiSwpyvsle0Nxjn3N/yDkIn4MkSYb9wP/MA598+U6OEyKvG/Gwbhz9m5+GtqLv6h+k0k9G3rnBuLb+V7Pf4h7mL8vqvEn78/IdMVUrFqdE0E5bbw3v0lmfzi78A++eyjyLJG4u+D/8Lvn5VBem7E5xdTY6L1x++Hx/Hn4QJ8OWQ+vhz0C2AX57s9i1tnTc7rr/D52xgy5ZaNjnNulXPuOHwDmEn4+2t4jf4V/2Dt6eQl5LWOe/EPCf4Xf94vxu/nxfgB464DejrnHqrJenKk4Rv8cTwLf17Pwp9jK4O/H8e33D7M+XFDRPJi1cv0IiKysQoGmAoHp+rl8h9QQDZCZjYWXzj8j3NulxItsyG+5cnWwFnOub+XYrk1SE8lvluA3zvnhpczLSI1YWaj8RWEL7riB7kRSWVmzfAVUq3wD7VeKHOSREREpIzU4ldEpH4JB7pZSUprUdn4mdkWZEajH1Wq5QYtNcPWOTUZLFBERDa8gfhK31dV6SsiIiKq+BUR2YgEI8YmzdsB/7oYwBPOuZUbJlVSJj8CNsNX8o8u8bLvwneZsa+ZHVHiZYuISC0wsyb4riXAD+okIiIim7hG5U6AiIgUZIyZLcH3w/k2vn+vTsCR+BFnN8f333VV2VIotcbMGuEHuOiHH0Ua4J6gT7CScc6tMbPL8H2J/Q7ff7GIiNRt5+O76XnMOfdquRMjuZlZU6BxEVGXxfTDXqeZWcsioq0N+g4XEZEiqeJXRGTj0hA/gMXpCfNXAucGgzdJ/bM66/tX+IrZknPOjQOsNpYtIiKl55y7HT+wnmw87sD3/V2o7vgB1DYmxQxG9SLQt8TpEBHZpNSLwd3atWvnKioqyp0MEZFat3TpUhYuXMiSJUtYvXo1a9aswcxo0qQJm2++OR06dKBp06blTqbUkilT/Fh9jRo1omXLlmy11VY0a9aszKmqXR988AGrVq2ic+fOdOnSpdzJESlaZWUl33zzDS1btqRHjx7lTo6I1AFhvlCoXXbZZaMr74VlmEIovxQRSTZlypT5zrn2ucLVixa/FRUVvPXWW+VOhoiIiIiIiIiIiEitMrPp+YTT4G4iIiIiIiIiIiIi9YwqfkVERERERERERETqGVX8ioiIiIiIiIiIiNQzqvgVERERERERERERqWdU8SsiIiIiIiIiIiJSz6jiV0RERERERERERKSeUcWviIiIiIiIiIiISD2Td8WvmfUws5+a2Vgz+9jM1pmZM7NTapIAMzvTzCab2SIzW2pmb5nZEDNTpbSIiIiIiIiIiIhIERoVEPZi4KelXLmZ3QYMBlYAzwGrgUOAvwKHmNkpzrl1pVyniIiIiIiIiIiISH1XSMXvh8ANwFvAFOAe4KBiV2xmJ+Mrfb8CDnTOfRr8vyPwAnAiMAy4udh1iIiIiIiIiIhI3eacY8mSJSxevJjly5ezdu3acidJZINo1KgRW2yxBW3btqVRo0KqafNcfr4BnXN3R7+bWU3XfUXweVlY6Rus52szuxiYBFxuZreq1a+IiIiIiIiISP3jnGPu3LksW7aMtm3b0qlTJxo2bFiKeieROs05x6pVq/jmm2+YOXMm22yzDQ0alLbn27L0o2tmXYG9gVXAQ9nznXMvAl8CnYB9N2zqRERERERERERkQ1iyZAnLli1jm222oXXr1jRq1EiVvrJJMDOaNm1K586dadSoEQsWLCj5Oso1gNqewed/nHPfJYR5MyusiIiIiIiIiIjUI4sXL6Zt27Y0bNiw3EkRKQszo3Xr1ixbtqzkyy5XxW/34HN6SpgZWWFFRERERERERKQeWb58OS1btix3MkTKarPNNuO775Laxhav9L0G5ye8otOqspcGn63iZprZhcCFAN26dStdyjYhFZf/u9xJKLvK646u8TJquh9rmoZSHMdyp6EUx6Gm6sP1UO7jKKVRF66HmtK5VD/UhXOxPpxL2o91Yx/UBeU+DqVQH8oa9WEbaqrc16T2oVfu31B16TjcdVxnvmu+pODuHXbr2rrG635/1sIaL6MmSrEN5VbufQj1Yz82aNCAdetKP8RZuVr81phz7k7nXC/nXK/27duXOzkiIiIiIiIiIlIE9ekrm7raugbKVfEbtuZtkRImbBW8pJbTIiIiIiIiIiIiIlKvlKvitzL43CYlzNZZYUVEREREREREREQkD+Wq+H0n+NzZzJonhPl+VlgRERERERERERERyUNZBndzzs00s7eBvYBTgfuj883sIKAr8BXw2oZPoYiIiIiIiIiI1AXH/fWVciehIE8M7V1ry163bh0VFRXMnDmTdu3aMXv2bBo3blxr65ONW622+DWza83sYzO7NmZ2+L8/mdn2kTgdgBHB1+ucc6Uf0k5ERERERERERGQj88wzzzBz5kwA5s+fzxNPPFHmFEldlnfFr5ntZWavhxO+tS7ANVn/j+oM9Ag+q3DOPQyMBDoBH5jZeDN7FPgU6Ak8Dvy18E0SERERERERERGpf+69914AttpqqyrfReIU0tXD5sAPYv6/Q7Erd84NNrOXgSHAQUBD4GPgXmCkWvuKiIiIiIiIiIjAt99+y7hx4zAz/vGPf9C3b1+eeuopZs+eTZcuXcqdPKmD8q74dc5NAqyQhTvnBgADcoT5O/D3QpYrXsXl/y53EkREREREREREZAP429/+xsqVK+nXrx99+vTh8MMPZ+LEidx3331cccUVsXGWLVvGyJEjeeSRR/jvf//LypUr6dy5M3vvvTcDBw7kqKOOqhJ+9erVjBo1igceeID33nuPZcuW0bFjR3bbbTfOOOMMzjrrrPVhKyoqmD59OhNefY+ttu5Wbd0XnHoMb73+Cnf/czzf369P7P8bmDFq5M188M4UFi1cwI13juHgI47m22/mM/Hxh3n5hWepnPYp8+d+TZMmTei+fQ+OOek0Tj3nfBo2bBi7zd988w0333wz48eP5/PPP2ft2rV06dKF3r17c+GFF7L//vtTWVnJdtttxxZbbMGXX35J8+bNqy1n9erVdOvWja+++ooPP/yQnXfeOa/jVJeUZXA3ERERERERERERyV/YrcOAAQMAGDhwIBMnTmTUqFGxFb/Tp0+nf//+TJ06lZYtW9KnTx+22GILZs6cycSJE5k3b16Vit8FCxZw9NFH89prr9G0aVN69+5Nhw4dmD17Nq+88goffvhhlYrfmnrmX4/z0NhRbLtDD/Y9oC8LF3xLo8a+qvLVF5/j+uFX0LFzF7p1347d9uzF/Hlzef/tN/ngnbd4ffIkbrp7LGZV26i+8847HH300cyZM4e2bdvSt29fmjVrxvTp03nggQcA2H///amoqODYY49l3LhxPPDAA5x//vnV0vfII4/w1Vdf0bdv342y0hdU8SsiIiIiIiIiIlKnvfPOO7z77ru0atWKU045BYDjjjuOtm3b8umnnzJ58mQOOOCA9eHXrVvHiSeeyNSpUzn++OMZNWoUbdq0WT9/yZIlvPHGG1XWMXDgQF577TX2228/Hn744SrdR6xYsYIXXnihpNv04P338NvrbuKUswZUm9dz1z0Y88Qz7LZnryr/n/f1Vww57zReeHoCT41/jCOOO2n9vKVLl3LccccxZ84cLrroIm688cYqLXnnzZvH1KlT138fNmwY48aNY+TIkbEVvyNGjABgyJAhNd3Ussl7cDcRERERERERERHZ8MLWvqeddhqbbbYZAE2bNl3fAjd7kLcnnniCd955h4qKCh544IEqlb4ArVq14pBDDln//d1332XcuHG0atWKcePGVeszuFmzZhx55JEl3aZ9D+gXW+kLsO0OPapV+gK079iJn135ewCenfBElXl33303s2bNYr/99mPEiBHVum9o3749ffpkupw45JBD6NmzJ2+99Va1SvAPPviAyZMn06VLF0444YRiNq9OUItfERERERERERGROmrlypX8/e9+eKyBAwdWmTdw4EBuvfVWHnroIW699VZatmwJwJNPPgnAWWedFdt/bbYw/HHHHUf79u1LmfxEhxx5TOr8NWvW8MYrL/H+228yf+7XrFy5Epxj2bKlAEyf9lmV8OE2nH/++dW6gEgydOhQBg8ezIgRI9hnn33W/z9s7XvhhRfSqNHGW3268aZcRERERERERESknnv88cf59ttv2WGHHejdu3eVeXvuuSe777477733Hg8++CAXXHAB4Pv3Bdhpp53yWkeh4UuhS9etE+dVTvuMnw06m2mfTk0Ms3Tpkirfi9mGc889lyuuuIIHH3yQG2+8kbZt27J48WLGjh1L48aNufDCC/NeVl2krh5ERERERERERETqqLAbh0WLFtGnT59q09y5c6uEA/Ju8Vps+HysW7cudX7TZsktkX/54/OY9ulU+h52JKMfnchL70/j7cr5vDdzAeNefNMHcq5KnGK2oUWLFpx//vmsWLFi/f67//77Wbp0KSeccAKdO3cueJl1iVr8ioiIiIiIiIiI1EEzZ87k2WefBWDu3LnrK3njvPrqq0ydOpUePXrQrVs3gCqDmaUpNDxAkyZNAFgedL2Qbc6XM/NeVtQXn33Cpx9/RNt27bnxrjE0bNiwyvyZldNi43Xr1o3//ve/TJ06tUpfvrkMGTKEm2++mdtvv52f//znjBw5cv3/N3Zq8SsiIiIiIiIiIlIHjR49mnXr1nHwwQfjnEucTjvtNCDT6rd///4AjB07lhUrVuRcTxh+3LhxzJ8/P6+0bbXVVgBUfv5ptXmffvwRX83+Mq/lZFu0cAHgB3LLrvQFmPDYQ7Hxwm249957cVmtgdNst912HHnkkXz++edceeWVfPTRR+y8884cdNBBRaS+blHFr4iIiIiIiIiISB3jnGP06NEAnHPOOalhw/ljxoxh7dq1HH/88eyxxx5UVlZy1llnsWjRoirhlyxZwnPPPbf++5577smxxx7LkiVLOPHEE5kzZ06V8CtWrGDixIlV/nfIIYcAMGrkLSxdsnj9/7+aPYv/+fmQgipfo7p1344GDRrw+dT/MuX1V6rMe/zBvzFx3COx8QYNGkSXLl149dVXGTZsWLUK73nz5vHyyy/Hxh02bBgAf/rTnwAYPHhwUWmva1TxKyIiIiIiIiIiUsdMmjSJadOm0bx5c04++eTUsEcccQTt27dnzpw5TJgwgQYNGvDoo4+y/fbb8+ijj7L11ltz1FFHccYZZ9CnTx86d+7MVVddVWUZo0eP5vvf/z4vv/wy2267LYcddhhnnnkmffv2pXPnzlx88cVVwg8ZMoROXbbiP++9zfF99+FnPzqHQacfxwn99qVFq1bs0Wufora77ZbtOO3cC1izZg2DTj+OQacfx+VDB3Hyofvzu18O5fzBl8TGa9WqFePGjaNDhw7cdtttdO3aleOPP57TTz+dfffdl65du3L33XfHxj388MPp0aPH+uXkqmjfWKiPXxERERERERERqbOeGNq72v9269q6xst9f9bCGi+jNoXdNpxwwgm0atUqNWyjRo344Q9/yK233sq9997LscceS/fu3Xn77be59dZbeeSRR5g8eTJr166lU6dOHHPMMQwcOLDKMtq2bcvkyZO56667eOCBB3jjjTdYuXIlHTt25IADDuDMM8+sEr5Nmzbc99iT3HzdH3jtxeeZ/PzTdN5qawZcNIwLhvyMi846qehtv+z317Hj93bmn2Pu5cN336ZR40b03HUPfv7rP1Cx/Y7cc9tNsfF69erFBx98wE033cT48eN55plnaNCgAV26dOHMM8/kxz/+cWw8M+PQQw9l6tSpnHvuuTn398ZCFb8iIiIiIiIiIiJ1zJgxYxgzZkze4W+55RZuueWWKv9r1aoVV155JVdeeWVey2jatClDhw5l6NCheYXv1KUr195yZ+y8ex76V0H/j2rQoAEnn3keJ595Xuz892YuSIzboUMHrr32Wq699tqc6wmtWrWKhx9+GKg/3TyAunoQERERERERERGRTdhtt93G119/zRFHHEHPnj3LnZySUYtfERERERERERER2aRMnTqVG264gdmzZ/PUU0/RuHFjrrvuunInq6RU8SsiIiIiIiIiIiKblDlz5nDPPffQtGlTdt99d66++mp23333cierpFTxKyIiIiIiIiIiIpuUvn374pwrdzJqlSp+ZZNWcfm/y52EOkH7QURERERERESkftHgbiIiIiIiIiIiIiL1jCp+RUREREREREREROoZVfyKiIiIiIiIiIiI1DOq+BURERERERERERGpZ1TxKyIiIiIiIiIiIlLPqOJXREREREREREREpJ5Rxa+IiIiIiIiIiIhIPaOKXxEREREREREREZF6RhW/IiIiIiIiIiIiIvVMo3InQEREREREREREJMlud29TO8utlaXC+4Oml3R5FRUVTJ+evszHHnuME044oaTrlY2fKn5FRERERERERETquP79+9OpU6fYed26ddvAqZGNgSp+RURERERERERE6rjLL7+cvn37ljsZshFRxa+ISD1Rcfm/y50EEREREREREakjNLibiIiIiIiIiIhIPXD33XdjZgwaNIj58+czdOhQunfvTpMmTTjllFOqhJ0xYwY/+clP2HHHHWnevDmbb745ffr04f77709c/rx58xg8eDBdu3alWbNmHNNnL27901Ws+O47zjvpCHbfug1vv/FalTiHfX9ndt+6DV/P+TJ2mUnxAJxzTHjsIX585okcuOu27L1tB47Yd1f+cPklzPlyZrXwzz77LGbGoYceyqpVq7jqqqvo0aMHzZo1o0OHDpxzzjnMmjUrcfumT5/OJZdcwve+9z1atGjB5ptvTs+ePRkyZAgfffQRAC+88AJmxq677pq4nK+//pqmTZvSokULFi5cmBiutqnFr4iIiIiIiIiISD0yd+5c9t57b5YtW8YBBxxAr1696NChw/r5zz33HCeddBKLFy9mhx124IgjjmDJkiW8/vrrnHfeeUyaNIl77723yjJnz55N7969qayspEOHDhx77LHMXbiEsffczlv/9wprVq8p6TasXr2aX100gBeenkCz5pvRc9fd2bJ9Bz79+CMe+dt9PPvvJ7jjgcfZreuB1eKuWrWK/v37M2XKFA488EB69uzJa6+9xtixY5k8eTLvvfceW2yxRZU4EydO5LTTTmPp0qVstdVWHHHEEQBMmzaN26jXPvIAACAASURBVG+/nc6dO9OzZ0/69evHLrvswocffshLL73EgQdWX/9dd93FqlWrOPfcc2ndunVJ90shVPErIiIiIiIiIiJSj4wfP54jjzySf/7zn7Rs2bLKvFmzZnHyySezfPlyxowZw9lnn71+3owZMzjmmGMYNWoUBx98cJV5F198MZWVlfTv359HHnmEFi1a8P6shXw1exY/Ov14ZlROK+k23HLdH3jh6Ql8f/8DuObmO+jQqfP6eWPvHskNv7+Sy4ZcwCmffEzDhg2rxJ08eTL77LMP06ZNo127dgAsXLiQvn378t5773H77bdz2WWXrQ//xRdfrK/0veaaa7j00kurLHP69Ol88803678PHTqUiy66iBEjRlSr+F27di133nknAEOGDCndDimCunoQERERERERERGp4/r164eZVZsGDBhQLWzTpk254447qlX6Atx0000sWrSISy+9tErFLkC3bt3WV1reeuut6/8/bdo0xo8fT6NGjbj99ttp0aLF+nmdunTlkit/X6Kt9L79Zj7/uO8uWm2+OTeMHFWl0hfg7EEXs/9BhzB92mc8/fTT1eI3aNCAUaNGra/0BWjdujWXXnop4Fs8R/35z39m6dKlnHXWWVxxxRXVKpK32WYb9tprr8z6zz6b1q1b8+ijj/L1119XCfvEE08wc+ZM9ttvP/bYY4/idkCJqMWviIiIiIiIiIhIHde/f386depU7f99+vSp9r9evXqx9dZbxy5nwoQJAJx66qmx8/fZZx+aN2/OlClTWL16NY0bN+all17COUfv3r2pqKioFufgI45msxYtWb5saQFblOyNV15i1cqV9D7oENq03TI2TK999+fVF5/jtdde48gjj6wyr3v37vTs2bNanJ122gnw3VZEPfnkkwAMGjQor/S1aNGC888/nxtvvJG77rqL3/zmN+vnjRgxAih/a19Qxa+IiIiIiIiIiEidd/nll9O3b9+8wm6zzTaJ87744gsA9txzz5zL+fbbb+nYseP6AdG6d+8eG87M6LxVVz7/5OO80pfLlzMqAXjh6QnsvnWb1LDz5s2r9r9u3brFht18880BWLFiRZX/z5gxA8hUDOdj6NCh/OUvf+HOO+9c30r4k08+4bnnnqNDhw6JFesbkip+RURERERERERE6pHmzZsnzlu7di0AP/zhD2natGnqcpo0aVLSdMVx69ZV+9/atf5/3bffkV332Ds1/j777FPtfw0a1H7vtt27d+foo49m/PjxjB8/nhNOOIERI0bgnOOCCy7YIPsuF1X8ioiIiIiIiIiIbCK6du1KZWUlw4cPp0ePHnnF2WqrrQCorKyMne+cY86Xs2LnNW7SGIDly5bFzp89q3q8Tl38+nr03IWrbhqRmrbdurZOnZ+Pbt268fnnnzN16tTY7jSSDBs2jPHjxzNixAgOP/xw7rvvPho2bMhFF11U4zSVggZ3ExERERERERER2USE/eE+9NBDecc58MADAXj55ZeZPn16tfmTnp6Y2L9vh45+YLYvPvu02rypH33AvK/nVPv/vgf0pVGjRrz20gssXbI473QWq3///gDcfffdBcU79NBD2WmnnXj22Wf5/e9/z8KFCznmmGMSu5rY0FTxKyIiIiIiIiIisom49NJLadWqFVdddRW33347a9asqRbmgw8+4PHHH1//fbvttuPoo49mzZo1XHzxxSxfvnz9vK/nfMlN1/wucX0/6HMQAKNG3syypUvW/3/2rBn89ufxA6B16NSZU84eyKKFC/jp+WdSOe2zamGWL1/Gvx59MLaP30L94he/oEWLFowdO5brr79+fXcYoenTp/P2229Xi2dmDB06FOcc119/PQCDBw+ucXpKRRW/IiIiIiIiIiIim4iKigoeffRRNttsMy6++GK6devG4Ycfztlnn81RRx3F1ltvzW677cbDDz9cJd7tt99Ot27dmDhxIt27d+e0007jJwPP4Pi+P6BN27bsktAX7xkDL6RDpy68//abHH/QPvzsR+dwwWnHcuLB+9Gm7Zbsumev2Hi//O3VHHrUcbz1+iucfMh+nHn0wfzq4oH86uKBnHnMIRy467b8+qcXsWjRohrvk2233ZZ//OMftGjRgssuu4yKigpOOeUUTj75ZPbaay+23XZbJkyYEBv3vPPOWz9o3A477MBhhx1W4/SUivr4FRERERERERGROuv9QdW7FihFv67vz1pY42VsrA499FA++ugjbrnlFiZOnMhrr73G6tWr6dSpE9tvvz3Dhg3jlFNOqRKna9euvPHGGwwfPpxx48Yxbtw42nfqzA8HDOLHl1zKRWedFLuu1m3act9jE7nluj/w+uRJTH7+abp07cb5gy/h/CGXMOj0Y2PjNW7ShD/fcR8vPDWBxx8cy4fvvc3U/35Iy5ataNehI0edcCr9Dj+KioqKkuyTY445hvfff58///nPPPXUU/zrX/+iadOmdO3alcGDB1fbH6GWLVuy33778dRTTzF48GDMrCTpKQVV/IqUWcXl/y53EkRERERERESkjkoaUC3OoEGDGDRoUF5hO3fuzLXXXsu1116b9/I7duzIyJEjGTlyJJB/5XmXrt247q/x/efe9+iTqXH79T+Kfv2PSpzfqFGmevPQQw/FOZcYdvvtt0+dv+2223LbbbelpifbnDlzeP7559lss80YMGBAQXFrm7p6EBERERERERERESnC1VdfzerVqzn//PNp3brmLdFLSS1+RURERERERERERPL08ssvM3r0aD777DNefPFF2rRpw69//etyJ6uaglv8mtmZZjbZzBaZ2VIze8vMhphZMctqY2bXmNkHZrbMzFaa2XQzG2NmexS6PBEREREREREREZHa9PHHH3PPPffw5ptvcsABB/Dkk0/SqVOnciermoJa/JrZbcBgYAXwHLAaOAT4K3CImZ3inFuX57K6AZOBbsB84IVguXsAZwM/NLMfOuceKSSNIiIiIiIiIiIismHl6qu3PimkL+VyyruVrpmdjK/0/QrYzTl3jHPuRGAH4L/AicCwAtZ9Hb7SdwKwTbC8U4Adgd/jK6XvMLPGBSxTREREREREREREZJNXSPcMVwSflznnPg3/6Zz7Grg4+Hp5AV0+9As+r3bOLY8sbx1wFfAdsCW+YllERERERERERERE8pRXJa2ZdQX2BlYBD2XPd869CHwJdAL2zXPdK3PMd8Hn/DyXJyIiIiIiIiIiIiLk3+J3z+DzP8657xLCvJkVNpew44/fmNlm4T/NzIDfApsBTzjn5ua5PBERERERERER2cg453IHEqnHausayHdwt+7B5/SUMDOywubyG3wl8VHAdDN7Hd8KeHdgG2Asvk9hERERERERERGphxavXEeHtWugkYZ4kk3X6tWradiwYcmXm2+L35bB57KUMEuDz1b5LNA5Nx84GLgPaAccA5wMbA9MA150zi1Jim9mF5rZW2b21rx58/JZpYiIiIiIiIiI1CEvTFvKimWL1epXNmmLFy+mVau8qlQLUsjgbiVlZjsB7wD9gXOAzkBr4BB8BfNdZnZvUnzn3J3OuV7OuV7t27ffEEkWEREREREREZESer5yOe9VzmPpt3NZt+o73Lq1qgSWTYJzjlWrVjF//nwWLFhA27ZtS76OfLt6CFvztkgJE7YKTmylGzKzRv/P3p3HS1KVBx//PTCCKODGopFlAJUYVEBZRAyriIgSEXdRQZBXEIlJfGXcEDUG0MREZYkYkRBFDaigoqjIIgguKCoqKIrDJiCC8rKDM8/7x6nm9tR09+3u6jv3Ts3v+/n0p++tPnX6VNd26jmnTgFfoPTu3T4zL+n6+NyI2A34JbB/RPxPZp43ZDklSZIkSZK0nLj7geSDF93K9hvcxTbrPZz5j3wIq6wc0853xR2rNf7um//U7zFWy8YklmG2zfZvCMv377jyyiuzxhprsMEGG7DqqqtOPP9hA78Lq/cNB6RZv5Z2kG2BvwGurgV9AcjM2yLi68B+wHMAA7+SJEmSJEkt9JeEC665lwuuuXfoeRYevWfj791jwVmN82hiEssw22b7N4R2/I4zZdihHi6r3jeLiH5h9K1raQfZoHq/fUCaP1fvk+/nLEmSJEmSJEktNlTgNzOvA34MrAK8tP55ROwIrAfcBCzVg7eH31fvfx0Rj+yT5pnV+++GKaMkSZIkSZIkqRjl4W5HVe/HRMQTOhMjYh3g+OrfozNzcddnh0bElRFxSi2vSyjB39WAT0bEml3zrBQR76IEfv9CGQtYkiRJkiRJkjSkYcf4JTNPj4gTgIOByyPiHOABYFdgTeAM4NjabGsBm1J6AnfndX9E7AecCbwY2DEifgjcA2wBbAQsBt6Smb8dY7kkSZIkSZIkaYU1dOAXIDMPiYiLgDcBOwIrA1cCJwEndPf2HSKvb0XE5sA/ArsAO1F6IN8MfA74SGZ+b5TySZIkSZIkSZJGDPwCZOapwKlDpj0SOHLA51dRehBLkiRJkiRJkiZklDF+JUmSJEmSJEnLAQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS0zb7YLIEmSJKm3+QvOmu0iNDYXlmHh0Xs2mn8uLMNc4O8gSdLyxR6/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1jIFfSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaxsCvJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKllDPxKkiRJkiRJUssY+JUkSZIkSZKkljHwK0mSJEmSJEktY+BXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQy82a7AJIkSZIkadmYv+CsRvMvPHrPCZVk+dX0N2xLGdTcXFiP7tPtZo9fSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaxsCvJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKllDPxKkiRJkiRJUsuMHPiNiFdFxIURcXtE3BkRl0bEmyJirCByRKwcEW+MiO9ExK0RcW9EXBcRX4mIF46TpyRJkiRJkiStyOaNkjgijgMOAe4Fvg08AOwKHAvsGhEvyczFI+T3GODrwNbAbcAlwF3A+sBzgJuBr4xSRkmSJEmSJEla0Q0d+I2IfShB35uAHTLzqmr6usB5wN7Am4GPDJnfSsCXKUHfjwALMvPers/XAOYPWz5JkiRJkiRJUjHK8Axvr94P7wR9ATLzZuDg6t8FIwz58AbgWcBXM/Mt3UHfKt87MvPyEconSZIkSZIkSWLIwG9ErAc8A7gfOK3+eWZeANwAPBZ45pDffWj1/uEh00uSJEmSJEmShjDsUA9bVu+/yMx7+qT5IfD4Ku3FgzKLiMcBTwEWAZdExJOAlwPrUcb6vQD4RmbmkOWTJEmSJEmSJFWGDfxuVL1fMyDNtbW0gzy1er+VMkzEB2tlWQBcHBF7Z+YfhiyjJEmSJEmSJInhA7+rV+93DUhzZ/W+xhD5Pbrr/cPAZ4H3A9cDWwHHUcb/PQ3YsVcGEXEQcBDABhtsMMRXSpIkaVjzF5zVOI+FR+85gZJIkiRJGscoD3ebie+dB1yUma/KzCuqB7qdBzwXuAfYISJ27pVBZp6YmVtl5lZrr732Miq2JEmSJEmSJM19wwZ+O715Hz4gTadX8B1D5Ned5hP1DzPzeqDTzaRn4FeSJEmSJEmS1Nuwgd+F1fuGA9KsX0s7yO/6/N0rzWOHyE+SJEmSJEmSVBk28HtZ9b5ZRKzWJ83WtbSD/Iqp8YIf0yfNWtX7nX0+lyRJkiRJkiT1MFTgNzOvA34MrAK8tP55ROwIrAfcBFwyRH4PAF+t/t21R34PAXao/r10mDJKkiRJkiRJkopRHu52VPV+TEQ8oTMxItYBjq/+PTozF3d9dmhEXBkRp/TJbzFwUETs3jXPysAxwCbADcCXRiijJEmSJEmSJK3w5g2bMDNPj4gTgIOByyPiHOABSo/dNYEzgGNrs60FbErpCVzP76cR8RbgI8DXI+IHwPXAlsDGwO3ASzPznpGXSpIkSZIkSZJWYKP0+CUzDwFeTRn2YUdgd+A3wKHAPpm5aMT8PgbsAnwNeAKwFyUYfSKwRWZOO2yEJEmSJEmSJGlJQ/f47cjMU4FTh0x7JHDkNGnOB84ftRySJEmSJEmSpN5G6vErSZIkSZIkSZr7DPxKkiRJkiRJUssY+JUkSZIkSZKkljHwK0mSJEmSJEktY+BXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1jIFfSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaZt5sF0CSJE3O/AVnNc5j4dF7TqAkkiRJkqTZZI9fSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaxsCvJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKllDPxKkiRJkiRJUssY+JUkSZIkSZKklpk32wWQpPkLzmqcx8Kj95xASSRJkiRJktrBHr+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLOMavpFaYxDjBkiRJkiRJbWGPX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWGTnwGxGviogLI+L2iLgzIi6NiDdFROMgckQcFBFZvY5tmp8kSZIkSZIkrYhGCtZGxHHAZ4CtgAuBbwFPAo4FTm8S/I2IDYF/BXLcPCRJkiRJkiRJIwR+I2If4BDgJuBpmfmCzNwbeCJwBbA38OZxChERAXyyKs8p4+QhSZIkSZIkSSpG6aH79ur98My8qjMxM28GDq7+XTBmr983ArtW37FwjPklSZIkSZIkSZWhgrQRsR7wDOB+4LT655l5AXAD8FjgmaMUICI2Aj4IXEQZMkKSJEmSJEmS1MCwvXO3rN5/kZn39Enzw1raaVVDPJwEzAMOyEzH95UkSZIkSZKkhuYNmW6j6v2aAWmuraUdxqHATsCCzPz1CPNJkiRJkiRJkvoYNvC7evV+14A0d1bvawyTYURsAhwNXAr865Dl6J7/IOAggA022GDU2SVJkjTD5i84a7aLIEmSJK2wxnkQW2NdQzw8hDLEw6JR88jMEzNzq8zcau211554GSVJkiRJkiRpeTVs4LfTm/fhA9J0egXfMUR+hwE7AEdl5s+GLIMkSZIkSZIkaQjDDvWwsHrfcECa9WtpB9m7et8tInasfTa/kyYingLcmZkvGCJPSZIkSZIkSRLDB34vq943i4jVMvOeHmm2rqUdxnYDPvur6nX7CPlJkiRJkiRJ0gpvqKEeMvM64MfAKsBL659XvXbXA24CLhkiv50yM3q9gPdWyY6rpj1y2IWRJEmSJEmSJI32cLejqvdjIuIJnYkRsQ5wfPXv0Zm5uOuzQyPiyog4pXlRJUmSJEmSJEnDGHaoBzLz9Ig4ATgYuDwizgEeAHYF1gTOAI6tzbYWsCmlJ7AkSZIkSZIkaRkYOvALkJmHRMRFwJuAHYGVgSuBk4ATunv7SpIkSZIkSZJmx0iBX4DMPBU4dci0RwJHjpj/yPNIkiRJkiRJkqaMMsavJEmSJEmSJGk5YOBXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1zLzZLoAkSZpb5i84a7aLIEmSJGkZsO7fbvb4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1jIFfSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaxsCvJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKllDPxKkiRJkiRJUssY+JUkSZIkSZKkljHwK0mSJEmSJEktY+BXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWmTfbBZAkSZKkmTR/wVmzXQRJkqRlzh6/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1zMiB34h4VURcGBG3R8SdEXFpRLwpIobOKyJWiohnRcQ/R8TFEfGniHggIm6OiK9FxItGLZckSZIkSZIkqZg3SuKIOA44BLgX+DbwALArcCywa0S8JDMXD5HVxsB3q79vA34A/KmavgewR0ScDLw+M3OUMkqSJEmSJEnSim6UXrr7UIK+NwFPy8wXZObewBOBK4C9gTcPmV0C51KCvOtk5u6Z+YrM3AbYCbgL2K96SZIkSZIkSZJGMMpQD2+v3g/PzKs6EzPzZuDg6t8Fwwz5kJm/zcxdM/PszFxU++wC4Ojq331HKJ8kSZIkSZIkiSEDvxGxHvAM4H7gtPrnVbD2BuCxwDMnUK7Lqvf1JpCXJEmSJEmSJK1Qhu3xu2X1/ovMvKdPmh/W0jbxxOr9xgnkJUmSJEmSJEkrlGEDvxtV79cMSHNtLe1YIuJhwGHVv19okpckSZIkSZIkrYiGDfyuXr3fNSDNndX7GuMXB4DjKcHjXwInNsxLkiRJkiRJklY4ozzcbcZFxLuB1wG3Ay/LzPsGpD0oIi6NiEtvueWWZVZGSZIkSZIkSZrrhg38dnrzPnxAmk6v4DvGKUhE/CPwvuq79sjMXwxKn5knZuZWmbnV2muvPc5XSpIkSZIkSVIrDRv4XVi9bzggzfq1tEOLiDcD/wbcA7wgMy8ZNQ9JkiRJkiRJUjFs4Pey6n2ziFitT5qta2mHEhFvAj4K3AvslZkXjDK/JEmSJEmSJGlJQwV+M/M64MfAKsBL659HxI7AesBNwNC9dSPijcCxwH3AizLznGHnlSRJkiRJkiT1NsrD3Y6q3o+JiCd0JkbEOsDx1b9HZ+birs8OjYgrI+KUemYR8YZqvvuAvTPzGyOXXpIkSZIkSZK0lHnDJszM0yPiBOBg4PKIOAd4ANgVWBM4g9J7t9tawKaUnsAPiogtgI8DAfwOeHlEvLzH1/4xM986bBklSZIkSZIkSSMEfgEy85CIuAh4E7AjsDJwJXAScEJ3b99pPJIS9AX46+rVyzWAgV9JkiRJkiRJGsFIgV+AzDwVOHXItEcCR/aYfj5TgV9JkiRJkiRJ0gSNMsavJEmSJEmSJGk5MHKPX0mSNHPmLzhrtosgSZIkSWoBe/xKkiRJkiRJUssY+JUkSZIkSZKkljHwK0mSJEmSJEktY+BXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1jIFfSZIkSZIkSWoZA7+SJEmSJEmS1DIGfiVJkiRJkiSpZQz8SpIkSZIkSVLLGPiVJEmSJEmSpJYx8CtJkiRJkiRJLWPgV5IkSZIkSZJaxsCvJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKll5s12ATS+hQ99VaP559976oRKIkmSJEmSJGkuscevJEmSJEmSJLWMgV9JkiRJkiRJahkDv5IkSZIkSZLUMgZ+JUmSJEmSJKllDPxKkiRJkiRJUssY+JUkSZIkSZKkljHwK0mSJEmSJEktY+BXkiRJkiRJklrGwK8kSZIkSZIktYyBX0mSJEmSJElqGQO/kiRJkiRJktQyBn4lSZIkSZIkqWUM/EqSJEmSJElSyxj4lSRJkiRJkqSWMfArSZIkSZIkSS1j4FeSJEmSJEmSWsbAryRJkiRJkiS1jIFfSZIkSZIkSWoZA7+SJEmSJEmS1DIjB34j4lURcWFE3B4Rd0bEpRHxpogYK4gcEc+LiG9GxG0RcXdE/Dwi3hkRq46TnyRJkiRJkiSt6EYK1kbEccBngK2AC4FvAU8CjgVOHzX4GxFvA74O7AL8GDgLWAf4Z+D8iHjYKPlJkiRJkiRJkkYI/EbEPsAhwE3A0zLzBZm5N/BE4Apgb+DNI+S3FXA0cDewfWY+JzNfCmwMfAd4JvCBYfOTJEmSJEmSJBWj9NB9e/V+eGZe1ZmYmTcDB1f/Lhih1+8CIIBjMvP7XfndCewPLAYOiYhHjlBGSZIkSZIkSVrhzRsmUUSsBzwDuB84rf55Zl4QETcAj6f01L14mkofRAAAIABJREFUmvxWAfao/v1Mj/yujohLgO2B5wOnDlNOjWbhQ18120VobP69bhqSJEmSJElS3VCBX2DL6v0XmXlPnzQ/pAR+t2SawC+wKfAw4LbM/O2A/Lav8jO6p57aELyW5gobUiRJkiRJao9hA78bVe/XDEhzbS3tMPldOyDNKPlJkhqyIUWSZsCRzWZf+NCJlEJSx5HNZnefxN9Qc8eRzbNwe2yL22e7AHPWsIHf1av3uwakubN6X2NZ5BcRBwEHddJGxK+G+N4VzVrAH5fj+edCGVyGuVEGl2FulMFlmBtlcBnmRhlchrlRhtmefy6UwWWYG2VwGeZGGVyGuVEGl2FulMFlmBtlcBmWxW/w3miY/XJpw2ESDRv4nXMy80TgxNkux1wWEZdm5lbL6/xzoQwuw9wog8swN8rgMsyNMrgMc6MMLsPcKMNszz8XyuAyzI0yuAxzowwuw9wog8swN8rgMsyNMrgMc+M3WJGtNGS6O6v3hw9I0+nFe8cs5CdJkiRJkiRJqgwb+F1YvQ/qRrx+Le0w+W0wofwkSZIkSZIkSZVhA7+XVe+bRcRqfdJsXUs7yJXAPcCjI2KTPmm2GSE/9dZ0KIzZnn8ulMFlmBtlcBnmRhlchrlRBpdhbpTBZZgbZZjt+edCGVyGuVEGl2FulMFlmBtlcBnmRhlchrlRBpdhbvwGK6zIzOESRvwIeDrwusw8pfbZjsD5wE3A4zNz8RD5fQF4MfCezHxf7bONgauAvwDrZuafhyqkJEmSJEmSJGnoHr8AR1Xvx0TEEzoTI2Id4Pjq36O7g74RcWhEXBkRSwSKO2mBBA6PiG265lkdOKkq2/EGfSVJkiRJkiRpNEMHfjPzdOAE4LHA5RHxlYj4IqVn7t8AZwDH1mZbC9iUHmP5ZuYPgQXAw4CLI+KbEfG/wG+BHYHvA+8ceYkkSZIkSZIkaQU3So9fMvMQ4NXAjynB2d2B3wCHAvtk5qIR8/sgsAdwHmWM4BcCfwTeBeyYmXePkp+GFxEfiojfzoFybBARjx4i3aMiYtDDAFdIEfF3EXHEbJdjeTdX9gfNroh4UkTsMNvlWN5FxKci4i+zXQ6pqYg4ICJOmu1ySJI0F0XEo71GX/61YT1GxLMi4rWzXY65aqTAL0BmnpqZ22fmmpn58Mx8RmYe12tc38w8MjMjM3cakN/ZmblbZj4qM1fLzM0y8wOZed+oZdNI1gLmN80kIq6OiGOGSHdUn8Da74APDfFVHwSuHrV8K4AXAe/p92FErBQRB0bExyLirRGxxjIs2/JkIvuDlntvpzREEhE7RMSTppshIp5osLinmO0CaHJW4EbaZwOvm+1CSJI0R/0bXqO3QRvW4xuAT812IeaqebNdAC335gNrD5GuX2AtGD5AYCChj4hYABwBPD8zz+/66CzguZTfLoH9ImLbzLxr2ZdyNBGxCvAY4L7MvG0Zfec2wNWZ+ceuaY8HjgE2BxYCR2TmZcuiPMujiDgXOLu6o2NQurdSttddlk3JRnY+pfJwwDTp3ga8Hlh5pgs0rioQd+d0+1FEPApYIzOvXTYl03Lkd8DJTL8/fBDYnxbWLyNiEXByZg78DSLiE8D+mTkjv0FERA75ZOaIWDszb5mJcqg9ZqO+1SZV4+9NmfnradI9EXhcZn5n2ZRsehGxF/BAZn59tsuyrDS8UzIz8/0TK0x7zOg1erXvPA24JjMvncnvWsEZa2mx1lXM1VxEbAZsRwno/iIzv1xNXwmYl5n3j5HtakCTW38fCUzbC7wq4x5Mlf/7mXlS9dnawKOA3446LMlyYHfg/wEXdCZExHOr6ddTLth3A7ahBKk+Nkym1YMc1wZuna5C22Pesbaj6haNNwNbUO5K+O+qzETE3sBLgXdm5u9GKc8wRQYuAbakDDlDRKwKXEQZpzyAzYBnR8TTMvO6vhmVW4Mv6mx7A9LtB+yQma+fyBIM/q6x1+WIdqIEyKezKWXIoL6qoPvOwF8BD+2TbCYr4TNWAaoeZLopcF1m/mGatNsz3O/QLyC13Aft5nKDwmxdkIyzT0fEdsCujL4tTayRdqbO06PsU2OaKw3VxwJvmrYApYf2tyjn0taZzWNCFTj6SadeMyDdC4EtM/N9k/rurrwb1zOa1rfmYl1nUkY8rp/PHGwoHnIZvgScA0w08DtqY0JEXA2clpmHT5PuKOBlmblJg+IdSekMM8pxupM+gTkb+F2eG/oj4sXAgcB7M/P7XdPfRVlnUf3/2czcd1YKOaQJ1Nvr+S2ra7gZMUOxJY1ozl3cafZUJ4uTWTIY899Ap2J7IHBCRDw3M789Qr6PALYHbur6nm6rD7g1dB7wZEqv1YGBvoh4OvA5YBOmTs4PAToV0ucAn6YMj/CVafJaj8EHawa12FcH6P/D1EHuzMx8W/XZtpTeo/+bmX8eVI4RPAH4Za0X0D6U3+AVmXlxVVm6DngVAwK/ETEPeAflwnKtanL3xcCrq88Oysyf95h/7O0oIk4GXkNZf3cCq9ey/xXwCuAyhhsiZBRBCTb8rGvaK4ANgXOBfwH2Ag6jjGs+qHK6X/U+3diQ21NuI+55MRQRO1bf1dmOPt2pJETEbpRKxUcz86Y+8zddlzO5Ha8K9AzsREQA/wEcwtSQRPUK+lyphK8D3NPrg4jYmXLh/InuXuLVRfBxlOPL4og4JjPf1WP+1YHTKMc/GHyRkvS/6JwrAasmdmL4BoWdGvTo6dmQMKkLkup8uC9T+9S3O4GramiR+cCFmdlvmxprn64asT5PeZYCjL8tTWdgI23T83TTfWoZWR14YAbzPzgirhkU8KyGdfoG8NQBaSZyfG96QdegvrUTDRoZI2Jl4GUM1xCya23akZR6zsDAL6XO8HqgZ+C34TrYr3ofq54xofpWozJ0lWWsuk4MP/72/ZQG/R8BX+sMJzjhQNOw586VqrsHxpH1OwkmsAy3UXV2mIQGjQnzGeEO0hHWfS8/Ac5sMH9PVWPUMLq3x8/2q8OPaVYb+iNiI8pzm76fmdd0Td+cco7u3D35th69zPcFdgAu75rvKZTj51+A71E64LwyIr6YmV+cpixjdR6J4Ydvux/4Y2b+pmveSdXbG1/DzQUzFVvSeAz8CoCIWAv4DqVn4+XAhZSgS7fTKAftL0bErV3TXxIRO/XJeh6wbvX+yWraQsrBrmOf6jWwiMBnBpR/Q0rPlkdRhje4gHJS63Ym5SDdN/BbVaCOogRSB0n67D8RcQDld1qlK+1aXUkeBpxAuTCc1Dg0nfXX7dmUW88uBsjMeyLiYsoJuafqJPM1yoXQX4ArgL+pJfsu8D+Udfbz2vyjbEd/B3y7a97XAa+lVMgOpFxsLFE5zsxfRsR1lN5ikw78AtTHod6Tsv7eUFVSz42IFwDPY3Dgd1gPAZYaHx0gIo4E3s2SlYbuv/9cleEGyu9Zn7/pupyx7bgKCDyD/hcb/5dy4bAYOBu4ktKjfaatW5WvU+l77IAKYHej1BV90hxI+W3f2ZlQVYpPrOa/Hngc8PaIOK9HpedoSq/92yjBsKsoF+gzZag7Kwapeu2sTbm4HXWssGzYiwdKg0JQxj6fZG+exhckEfE8ynnskV3fdUNXkk2BMyiNc5/vMX+TffpIShDqzurzafepSTfSTug83XSfmjHVce3JwC5VOTrTm9xhtFSgh7IN/ktEXJuZn+tRjodTjpvPoE+9aRLH96YXdJOobw1pqUbGqrfbN4GnM/1xYqhhNfpYud/8y7CuuFQ9YxbqWzNV19mvU9we8/WansAfImK/zPwGEw40DWGdPuUcVq/5mi7DD4CnjFmeJQu3bDpvdO4g3W/M+aEcV5/eYP5+durkT/913P3ZK4EPRMSh9V7z1Tp8c5XnetXkGyjPoTiu1klliVkHfHevtJP2T8DBwIPPyIiINSnn/s7xbTPgSxGxRWZe2TXvlsBPM/Purmn7Un6zAzPzlIjYGPglZSzXnvvjBDqPnM8Ix/2I+H+Uc9+7Kee0xvX2ptdwkzBC3eUByvXcpZThsM6o5h87JqCZYeBXHW+n7JjHAO/IzIyIJXbOzPxTRPyMUlHuPCQsKSf2+sm92/2Ui9lOoOxapg6oGwB30z8AdD/lRPclyu2N/byTcjF5aGYeDxARS1xQZubdEfFT+gQ+o9yS97+Uk8TtlAHORwo2Rbm14+OUg/w7KQe879eSXVDlvxeTC/wuBh7eVY5HAH8NfKGW7nZK0KGfQyk9rs4BXpeZN0bEEpX1zFwYEb+hXOC/tzb/KNvRs2vzvgG4A3hhZt5QLUevMl7O0ie/Salvh9sBv6r1TLiMciKehM0oFzVLqLbFIyg9tP+Rsh3d3J0mM38YEbcAL6BH4JcG63LU7TgiXlP77HkDej7Mo1zor0vZ33rZn1KR2DUzL+qTZibsUb2fV73vXr0GCcpv1cs2lErsn7qmvYbyGxyemR+KiK0oF2aHsHSlZx/gT8AWmXk9I5h00G4E85mqYM/vmj7MhVCTAEt3g8IdlIdUTFKjC5LqIu6LlN/6eMo+VQ/unk05H/5dj8+g2fH55cBdwNaZ+ashl3khk22kbXyepvk+NarXlWI+eAH0uipoNp1Tuv5ucnHda949KcMSfSoift/dGzYiHkoJvG5H2d6WKusk6ilNL+gmUd8axoBGxg9U06+j1C1nqnFxk175LuO6Yq96xrKub81UXWd/YCtKr7frgdOBayh14vmU49UGlGPujZTef7tQgk5b0+C43qNReNiG4svqQceI+DdKz+//pARyFlYfza/K80bg45n51h55Nw2WHUPp1HBAZn6SMS2LxoRY8g7SJuP01vOd1DjHO1P213+g7M+fZcnt8ZXAMylByR9RtsX9gI9HxBWZeUlVnrdQGkVXZslzwBOq1/4RsSAzP9ygrI0b+vvYAbgiM7s70uxLCfp+DngX5Tf6MOUOyu7zxmOAH9by25FynDwVIDOvjoiLKPtTP007j3yHss8+q/r/T5TYxWLKXaCPpuxf36M05sxnKki/LmPW22uaXo9PwrB1l1Uovar3Al4YEadk5v40iwloBhj4VccLKRf878gc+NCQq4H1KRdlUf1/OuUg28v9wC2Z+eD4vpk5v/N3dRA7LZuP+7U75URz/DTpFtI/aPcOyjK9C/hQZo5zq+bbKCeDPbpO4EskyMzFEXEZg09ao/odsG1ErJSZiykV5KCMT9ttbQbf0vUa4FbK+FmDbu+8glLZrBtlO/rb2rSnAt/rXIQM8GfgsdOkGdeDvW2qINnjmeqp3nE/Uz106Epfv+3s2T2mdXQuAp5O6flWdxilQva8zLyiyr9XPj+hf2+pJuty1O34Sd0fUdbPdOvoMvr3mt6Icrv7sgz6QrmYeCyl0rcj8AdKhbGXBxulMrPf0DFrA/VeGbsA91I1ZGXmpVF64m/eY/41gW+OWXlcyITvrBjSRpQLun2AjatphwF/T2nA63VRuzfw79SGoOnReDB0g0JmTroS3PSC5B2Unod759St8EsEdzPzgWqf6rUtQLN9+q+A80YI+sLkG2kncZ5uuk+NKrrepxsT8gGmfoN3dyZm5kp95xhDZl4fEc+nnN+/FBHPzswrIuIhlKDOzpSeQq+o6gN1k6inNL2gG6u+1eSYUPtsL8rF+bY55G3WsfTwMVv0mNb93U+mLPv5PT4fax1MsJ4xdn1rjtV1fkQJBn+IMnzAEs8SiYjDKUH+Q4FnZuYHogyB8D5Kz8Qmx/XzWfIcO1ZDcdXz+zBgl8y8sJb+p8BPI+JM4LyI+FVmfqKWZhLBsv8EToyIl1COX9fQZwir7D/M3ciNCbH0XUFD30Gamf/dJ904JjXO8SJKAPCwzOx1HvxYRLyJEvjdJTMPqNbLJyl1pEuqxpAPU3p5/g+960yvBj4UEVdl5ldmsaG/l8exdCPW7pSg6T9k5s3Af1TbfX0Ins4dWwBEGSd6C+CC2r59E6UBoJ+mnUeeR9kefgm8NTPP7v4wInZnquHiqZRj5P9QAsWLgLMaBn2h+fV4Y5m5UtU4/0ZK49mpLNmQ8SpK4P4TlG16Z8rv8tqI+BbNYgKaAQZ+1bE+8NVpdkwoJ6JHZDVuT0T8NyVAc83g2fraH/jNtKmmty6l5W06wVRv5bqnUVri/6VBObYDftCpyA9wE6WXwqR8GVhAuQj8dvX3IrrGsIpSA9uScqtVP5sC509zkoFSues1Ftco29GjatMewnC3w6zDzI2f+OyIWCsz/0ipWCVLD6GxPrUeKZX9uv5OplrmB7mJrluWuzyDclHWbwiBjlvoX/lpsi5H3Y53rv4PynjIZ1MCAr3cD9yQgx8o8WdK0HVZOxt4bWbuXDVKfb1ho9TD6NpWq95nW1F+2+6Lquso67zut4x/np6JOyumlZnXRMRdXX+/CHgLJQh1Wi35T4Ezq4vNz1NuW+s+l+zUnTXNGxSaaHpBshPl/DLdmKA30P+22yb79C2M2KNxBhppJ3GebrpPjepkyjFh5eo3OHkCDdWNZeblEbEPJZj2taqn4UcpF6znAvvUg2BdJlFPaXpBN259a6euv5scE9YCvjFs0LdyJEsG/7dg+gfn3U3v8X3HXQf7df3dpJ7RpL41qTJA87rOeyn1iZ7H/Mz8S0QsoAwd817gxZQhlN5I2ZaaHNe/w9Q5tklD8SGU66h60Ld7OS6KiAspt9DXA79Nz03nM7Vd787U2KQ9i0L/Osk4jQnza3mPegfpgyLiMODuzPyvab6/l0mNc/xuyjjnfetRmXlcRBxIafTaPTM/VTVGdNZNp1Fo78ysN5b8Fvh2RJwGfJXS6eorzF5Dfy+PYOne/c8EflYFfTt+ydINJTey5B0GO1C27+/W0q3O4PpM084j76Jsz0/MHg+KzcxvRMRPgF8DR2TmOyLiVUxdX08ivtb0eryxiNifUn/fITPrdbfLKUNqnUG54+eKzPxkRFwFXEw5TzSJCWgGGPhVxz0MHgKgYz5dB/QsXfnHNsEW2zuoxuecxsb0P7k/wOCg6DAeQdfYfgOszmT3v2Mot1O+kKmH9xxTC8g/m3JyGHQrV9JnHLaav6L0sKobazuqXMs044xFeRjLZiw9Fu8kJCWocGlE/JhyO+0dLBk8fyil58oFPebv7AtBedBJpxW/l85FwPey94NvVqNc6Ezn0QM+a7IuR9qOM/PB3yMiLqBUVnr9RsM6lwFjUS8jO1M9kLKBP7DkBfEzKdtYvRK7Kr1713wKeF9EPHbEAMVM3VkxjrdSAhz1oO+DMvP0iPhBlfZLXR9NukGhiaYXJI9h6UakXlah7P+9NNmnvwY8PyLmDQgIDjKJRtpJnKeb7lNNvJcSRJwTMvOciHgDJTh9BeV3uAT4u6weXtXHJOopTS/oxq1vTeqY8PuqbKN4H1MBsiMY/ICozjn+G7VgR8e462BS9Ywm9a25VNf5W8rYoX1VvdEvpQpoVsHgyymB37GP65m5U+fvhg3FmzLcg8ZupAx102t6k3NTdwC7iXEaEzaq3se6g7Tmw5Qeu+MEfic1zvHWlHPtdH4BPL/r/18Cu1V/bwFc3CPo+6DM/FpEfJepnp6z0tDfxx2UOggAEbEp5dqzXgdczNT4ux0XAPtGxNsox/b3U5br7Fq6pzD4+Nm088jLKXdI9c0jM2+OiPMoDwh9R2ZeV107PoPygOGR6+31r6DZ9fgkvIkSQO/bYJ+Z368apQ6h9MT/XnWnypY0iwloBhj4VcfPgWdExCMy8/ZeCaI8HXNzege9xhIR21FuD/qvrB5C1iPN9pSnXv5nZv6gT1aXAc+KiMdl5o198tmUckLtd1v2j5i6NXlcf2CqIjPIpiz5UJ9GMvP2KOMavoRyYf3DHoG3xwAfoYyx1M/vgM27hoxYSkSsRumt06uHRpPt6BvAoRGxb2Z+uk/5/g/lNqImT/PtJym36ryWUnG6AzggM7sryntRLq6X2ge6GzGiPKzkew0aNm6kjNE8nb9hyR6S3Zqsy7G348zcuU/aUbwb+FFEvDtrT9tdVhoGrjsuAV4cES+jVFzfSdnO6heqT6YEIur+g3Lr2LkR8Wbg3CECLb1M6s6KYXU/oOhpTD3saZCrKUPUPGhSDQpRnjI+tsw8heYXJH9i6gEtg2xC7zsKoNk+/W7KheaxEfH30wQGlzKhRtpJnKeb7lNjywkPHxJlvMp9Kb0/1wa+nZkfrD57EuVi6MJaT+Z6mU6JiPUp2+OPKLfM3zXNV0+intL0gm6s+tYEGxm/AOwXEasN+n1r331k13cfAfykwTYx1jqYYD1j7PrWHKvrrM5wvd3Wpus5GJRt8i9MLtDUpKH4Poa7VXtLeo/J2mgZugPYDY3cmNDdOWUCd5DeQqm3j2Mi4xxTgt/9hljotkGVtuMeptbtA/Sv13d78PeeQw39UO7kelZEbJJlnN83ULbH82vpNqLs/90+QOmdf1T1CuCczHxwKJPq3LgxZXiSfpp2HlkP+PEQ6e6jDAnYcR3lDo2zaV5vb3o9Pgl/zXCNUjcB23b9fzVl2/w+sxBb0gCZ6WsFfFEuQhd1/f9GSsvS54BVqmmLgZOqv1eiVJQXUw7eJ43x+mSPcnyKcsJ7zICyrkVpzfqvAWleUZXt3E5etfKvSTmoLAL27JPHbtXnuzX4XT9HqUxu1TXtwXJ0fc9i4MQJrMdbgKsnuF38c/Ub/N8B5X9PlebwHvMPux0tAl5dm3c9SmX8fuBfKD1rF1PGFHoypYfNvdUyrzNT+wOlQrYVsHqPdFtQelavO8nv7/E9n6x+o+cOWA8vr6b9+6TX5Uxtx5QHFfwTpYFi5QHpXksZ83URJdBzBOW2odf2ek3wd/9W13awEaUlf8Nams0pPZzuoNzqtMeA/LahVAwXVa/FwKU9tvslftva54+mjGm6qNr+F1IqVfXXb2dym2zwO95eX+Y+81wK3D5D5VnctQ5GflV5PKlalu51+c3a9zypmn58jzJ8udoWnlgrV/c+tXU17TN9lqPJPn0EpSfUomobOoly6/oRPV7vnqH1MInzdON9asQyH065cIPSG3sd4KG1NKtX6+YrlHGq1x8i3+dRxu/rLMOi2np8YTXt5dXvNd3rPsqFan36t3t8d+PjO6WX4G2Uob/65fF4Ss+zr/eYfxL1rX+n3GY7zryrUwIUX2bC9Ykhv3+Z1hV7fP+s1bdq5WhU16E0Jt0HbD7gOzavlvPHte13IQ2P6xP6Db5Ufff7gejxeVB6my+mDBVR/3zWl6H6jo9U37/vgHV5SDXt/TPw/acBV4057w6U3q+LKL2GD6IMQ7BDr9eAfC6u9uu+xzVKPXgR8N2uaZcAv6n+PofSqDRdmX9CCYrWp78O2H6m1nOfsnyWqbpS5zx/O+WctIgS4H1oV/o1quPLF3vk9RRK/eSr1XFotdrnB1f7/fMHlGcTyvFtrLoMJYB7Y/27a2lWq9Jc3zXtbEoQtHG9nYbX403XY/X/rZShS6ab7xfArV3/n0npcT52TKDBMiwR3/JV+31muwC+ZmnFl1sDu3fueZQxWhZTWmI/Wv39A0pL6K+q/7N6H+e11I5IGR/nkiHKewlw5TRpOoHp2ymV+cWU22c+Xx28FgOfHTD/BpTK1T3VMu9A6a2yQa9Xnzy2rQ5g11JuK1updpDbgXJCuR946qTX4wTyezSld8kiygXAi6vyf5nyFN6Tqs9+B6zRY/5ht6Nz6V3B3ZlyMdkrALOYciLfcab3h9l+UVpZ76225YMovbUXV7//wygBzz9TbqvbaNLrssl2TGnd/yXw7Nr0T9TW53nAQ/qUvRMIWdz198DA3IR+96908mPqImCTrs/XpPTU6j6u3Qv89YA8d6u2959Xv/m6tc//iVJReWWPeedX62ARYxxfu/LZrvruZw1Is32VZpsJ/I6f6vodv1WV/w0D0h9Ij4vVCa7XkynjAna2pcsoF9tfYurJ44uq/z9Vf3XlM/YFCVMPN/kpsGnXdt7ZpzamNCQsqu87E9qn6/tUz22o6/1qyjF8o2r+Xhct/V6DLmYanaeb7lMNt+X3V7/Ndl2fr8TUxW3nd7yewY3ZT6EERO+nBIpf2r0tVGkeQmlcOnWadTZOvatxPYWGF3RMpr71APCFMdfrSbVt8VzKceKkHq+lOi1Mk/fqlNt9+wZMJ7QOVq1+n6XqYl1p1qjSrNLjs4nWtyjDVzwHeCUDzjW1eRrVdap5FlOOHW+vtqGVq99zQ8rzLv5YLdNB1TyrVfl9sWt/HOu43meZ1qM0UO3Q79XjeHBXVcZfV/vF/tXrvZR686IqTb9tYaLLMOY+NSONCdW6PJByrHxrv+2dMibrPdVvttT1xTTfMUqd8y8D8tmHqXrhxynDiWxUbZc7Unqp3lPls0/XfnMvVYMvZb/8C+VBaP2+5y1Vml1man2O+PvVA4ZHVtvrYkqdoH49cED12WEzVJ7X0qDzCOVBZosp1wRLNeRW2/qXq/yP75q+kHI3yyTq7Y2uxye0Hs+svqNvAytlPOTFwBld035Gqc82igmMuQwnD/pdV/RXVD+SVjAR8SmqB5Z0TVuDEpx5WZ/ZzqDcHjb2WDJZux0sIu6kjBP3imnK+zlKhWXNAWnmUVrI3szSYyQ+QHny79uyz/hQ1e0xydTTu6dZlOw5VEpE/BPlqZZJGU9rTUql9gFK7+UA/jEz/2Oa75hWfT1WY9BuRRnz56EDCn/KgDyfSjnYz2fp3yEoFyN7ZubP+8w/zHb0uszseUtWRDwW+AfKiW1jSiX+Okor/Iey+ZNSe33nUvvDhPLdjvJ0+kHrIzPzgB7zvoJyAnsIU9vlIsrvAaXS95rMrD+tvDuPsdfluNtxRHydclG7blZPaq9+h+8yNWbysyiV4dfXjwlV+pN7lLevbDjWeNf3PrgdRHka/UqZ+ZSuzw+hBIQ/R6ns7EUZV+4/M/OQXnk2LM9plAuJCyjDPvyGAWPoZZ9bJKu6saD0AAAgAElEQVTlegWwXmbe2ifNWpSA1acz88CG5e7+Hf+WEuQPyt0in2HqSdLzKQ9R3JlS+ds1+z8xvHOb6MsYbp/atWu+R1NuObsZODgzL6/l+xRKRf9xwLaZedsoyzusiPgI5fyUlB4Sm1Eq9jdSbuOdB3w4M986II+x9umIeM+IxX1Plf+TM/PX1flxWNnvWNr0PL2s1bbliyn70AZdn+9D6Wl2OaXH257A3pSeRh/ok+eplF6Me2f1sL9eD46LiO9QAmGNji3ZYyiEpvWUaj2eR2kw+h3lIXOHUnrun0e5XfeJlH1+16xdbEyivhUR11HGwnz5NPP3Kn/3909nqe05InamBOw/kZmXdU3fj7INP5RyTDsmM9/VpwxN18HbKLdD75qZ5/dJsxPwbUqvsQ/3+LxxfasasuTfKcfyznr67862HOVBVu8DXpw9xopsWteJiBMpgcHOdtQ5VnXGDw1K8P4NVfq/oQSJT83Mr0+3fMOKiBdT1sd0D7pbanuOiB0p58a/ovdx/UZKT9rzJlTcpVTnwTdTgpWdYYluoOzPx2Xmz4bIY2dKg8ojen1M2c7/rs8xaQElQPf87u25qlM+l6ljxRWU8/RdtflfS3mWyQGUh+ydSRkyoedQLt3XQRFxPqPVOfsOaRYR76Bs772OLZ1leE9m/nOV/omUAOTXMvOSKA/qfDFlXfyQEvSr15m2pgTRvsTSfkHp8Xp1lgdVd8r1eEqwbXNKgPKI7mNXE31iCqsAa3aXoeuzDShjv/82M4cZF3rU8tSP7wPXbY/j+9qU4OSGlOPxJZRtKatp21HuALqG0lniloh4OuX8dyWlQatRvb0qR6Pr8VH1iClsTln2VSmNUp9nyd/hZZRlvY/S2PeTat0upATED20aE2i6DFqSgd8V1KAdIyKeTI9K4KROELXvup0ygPqLpkl3BvCczBz0tNdO2kdRAgnd5T8nBwzSXs23kNFO/H3HZ4uIPSgtnvUxhi6nXBAOM+bltGoXpf9AqTT1DY53THdArALI+9P7YuDEeoWrTx7LbDtqqvY7jh2s7cpvVcoJsvOgvUEXl4OCJE+levIvU+v1HsqtYO/LzB8NyLeTx9jrcpztOCKuAa7JzB26pn2EEhR4YZaHUjyGqmU8Jze+XGO17eAW4PuZ+YKuz8+kBHYen9UDe6I8JGalzNysR36vpdy+13P88q50zwSeVG+QiYg/UgIBT87eD8YZdrl+TbkNa7tp0l0CPCozhxlzcVA+9crjKym9X1and+X1LuCNmdn3CdPVcf2blF5E0wVrltinIuI4SqBt41xyzO7u/B9B6ZHwv72C+DE1pufAY3dEvAB4ema+r8/nb6Qcpx9b++hWyu2vHx2Uf5VH4+PzEN+xOeWYc3FmLoqIDUeZf9DFTJX/WOfpZa12TPg95bbH3bo+P4VyIb5FZl4eEStR3SaamVv1yfP3wO+7P+8T+P0s5anvgx7i2WTZGtVTmlzQTaK+Va2b3YD5ozYURMTrRkmfS3da+AylUe5xmfmnatpGlB5M8yiNaI+jBB+fm5nf7lOOsddBRFxEaYiYP6js1Tl5YWbuOCjdOCLi4ZShjzan3A1zKWUs8Qe35Sq4fAMlkLygTz6N6joR8SLgMEpAZtVq8v2UoMXHMvOL4y7jMCLihZQg3EqUc/bV9H6QGtA7cFgd119C6RnaHXi9ADg9hxyLehwR8Rbgg5Rjca/z61+ABb0aD3rkNVZjQpQHZT2Zsk9lNe25lNvnr6c0DuxG6U39lsz8WG3+RsG+SaqCgIdSenh3xoD9PWWIkeMy89IB8063HIOWLynBxn8AtuwE66trkispvf87898OPC0zrxt+yfqWeaLBtoh4AmWM8c7492dm5tuqz7alHG/+NzN7PhAsJtB5JCIeB5xAuY6r7xNJaew8ODNv6JpnZUoHg8b19q48Z7y+1/VdvQL4uwCfptRZe22LN1Ma5s6p0q9NGXf4ytpvs0xiAgZ+BzPwu4KaKztGlCdgrk+pvPZ82Ex1wrqeciH1tGVZvqaqANdGVAe5zJzow2Y665HqAXnV5CsoJ/hBlc6J9JBsi67f8Ss0DNZW+R1FGRvyTsoD46ZbHwMfjBIRQen5tTLwx8xcNCj9pI2yHUfEXcCXM/OVXdMuo9wutVbXtK9Rbltcv0ceewEP5AR74wyjFuS5n1Kx3Lfr85spT4l/ete0z1OCM0s96KhXMKfP936C0vu53uvgdkrFaOAdEUMs18TurBjy+3pVHh9H6ZW1A0tf1H5yumNjRBxPub38Okqv6+n2qe6HQF1LCeK/dJrvOI3Sk2iDHp81Wpe1NCtRxgvvrgD/YNTg1UyKiEWU5T2g+v8k4KLMPKlBnk8DFueEeqcsC7Vjwr2UAEz3MeE3lIafjbumfZFya+s6ffK8jzJW5yu6pvUK/H6Bsj/We0ZPVNN6ymw18kbpWdQZtuXvJ3kBPMR3XwXclpnbdk07ghLEPTwzPxTlgbvfowQt9pkmv5HXQUTcSGmM2mOadF+nBHgePyjdOKLcSfAeSmDgjZl5d59t+XLgnszcZpr8GtV1qsBLp55x67I6plaNpttQHqL5oazudloeVEHrMynB3VMpddaF1cfzKQ+gfDVlnbwoM/s9eLNpOa6jjNG7S9e0j1PqDX+bmRdHeZhVJ912tflPZhbuFJu0GLH3cQ9rUnraPrErz9dRhi06lzIMx16UhpIPZebhDb6rk//EYgoRcQDlrolVqknJkncQ7ExpEDowMz/V9PuGKM+GwN+yZAD/wsxc2Cf9ROrts6Hfeqz2u06jVL0h47TMvHuZFnSAuRLfmqt63qquFc+wF3VRbmPbYbqL3xF8ldLK/2+U1tFe/pUy1s2JA8p1LnB2Vk/EHpDurZQLqV0GpZuULLdV97y1esIOo5wcX5OZp46bSTTsoTgTqiDJ6yktvNcAH+/Vg2gSX0WpDA0VrJ3Gyym9GLfOzF+NXJCIDwN/zqrXYNX7YanbpZaVEbfjlZjqcUNEPIwy/tzXauluZeoCre5LlIrdMg381txB6fUNQERsSul5cFot3WKmbikdV79Ghp8C6zbMG8pts32HfunyUJovS0+ZeSNljNRx7UUZt3XbzBz16enrMHXb8CArM9wT4qfLY7qeRosp48IO89ToB03y+FwF4jsV+Buq9bNEEpbcLver3scO/FIeRvMdyq3EQ4mIqxt8X2bmJg3mr3uArluYI2IdSrDz07V0d1N6t/fzJ6YaPwbZhNKbZgkRsT6lt/T3+51fquPVtpQH0w28Xb9pPSUz/z975x1uR1W18d9KoSMIEaQnFCnSewkQOihVAekEkE96kWIFAiqICCJSRSAfgghBEAQlHy303nsJhI70TiDcrO+Pd889c+bMzJk5Myc36H2fZ557z8xuM7Nnl1Xe9QTdiyqeh5FojtgN2NzMriPbtdvdvcr4k8TXEI9hHOsiKrRTQ4X3muhBlmlXWIfvYDbE0dsO7yBhai9q7EPbIAHAnlnGGwFPA6um1DE/8JEHep2stY7JQ2Bmd38xpw6CoLjlm4mVU0Vp7p5B8YYs3B5w92PLFmpm7wCPesxLqk36uu/hcDRnbeXuVyeujQeuD0rRq4DDkIFENzAEzQ9xDAdej+Y8d/80fFNJC3ncfWSX2jVF4RW94MzsdTTXxvFt9I73dPfngRtM3kkbIyOVKvWdiyh/LPxfBu4x70kzWwN5h30E/Az1h7sSeW5CFrWbI2F2V+HyXsr1YEqglnX71LQfd3kb/Dkc/fgSo1/w248II8PfdoP2GihiaKR5O7JEHWkL75ORNndvk2vpeUjgBrBoqGd15EL2u5yyR9DQUOdhUaSx6grM7BJ0D2PD5n5KYVHkktux0DdgdDhyJxrEobU7UNtEYxn8XsidJs7vNdLMWvi96mgCWmx0JKxNYG5EYdJpOfsjAv+OYWbfRK6o/8iyvDK5o22KLFufjJ2fFi1c3s0SspvcfL+KFuVxd6aXkTVjhA2QMOy2RBGzIgFIGt6hDwXdAQ8Bq5vZQu4+HlnVO+KsjGMY4t+rgnlJ5wA7AbjczFZz9zsqlD8eWMPMps3anId3vgZyUZ0aMQSNq2WFvqA+uY6Zze75HMfrIiFGFSxE5wqjdhhNxfHZzPZEAXIWTpx/Bvitu0eeIx8iV/U68R56F2UwNON8Hj9rUe7YsngafUfTuftENL46cnWPYy60ZsnC3cBGZraIuz+TlsDMVkLCpItSLh8A/BBYok17RyNOx58kyq4yvk9NGEXjXQ9BPOZJxPtCquDXxEW5AjFFCKIhyrvvGZAiICpjAIqvcLc3u+S/FMouDBPn59KIMinTJRzNke34ZAlpki7RlfpQDAuicTlP6AsSiM+ecv75UEcmdVbAb5CAv+q+tQincyd5JyGaj04wDeondbSjk7zLov1DUujbCxdF122Ii75chcWNNyYDM8byzYL4Q/+WSPc+Wj/WBhOvbmF4TiyCqQBfpXX9vBrwVBD6RngA0dpVxUga/Wpk7Hw0/6bRJETnneZvP1JCbBKteeUEEMvsPjl4ES5etIExTwKQp0Y39+d1rdtH00f78X7856Jf8NuPshhMI3ACNC+8k4hvulIX3u7+jpl9Gwm51kBCXhL5XkXBAOoQBE2LrN+6ha3RZvB1M7sAuac83sX6InyMoohOKRjUbnmwERKYxF20Nwznk/xeu6MIv3Xj1hqEvqCoxVWEP68jt7sq2AdFvM5T5ryBhO2zAwfGzh9ICBpDq6AzwgqEoDEowFmEsUiRc1r4/3j07V+VyL8s2X32bmQl3Jf4I1Io3W9m42lwGPbeRxCOLEvMmjlo6eNYOOVchEFo8boeCuKRxEPIG+JaM/sdep4v0zwG9yLHGqoWz4o+xqt0/k1cjAQX15nZAe5+S/yimQ1HgblmRi6G0fmkYnPZHGVn9C6Hk/LNmNl+oY4t3D35LURpNkUut/u4+1kF7isLqYKB4Aq7M435OBJyzw18AzjLzNYILrCPAuua2TEoOAnk9+UmZFifPIgE42WQxqV/ABqjLifdLXkrpCiue44Yg9xkbzbxq34f8Yj+PUoQXM2XR5G9s3AaUrhdambbJuccM1sQjduOOAaT2BBxDWfOVe7+lJk9hubPpNCuyvheC4KlZ2FkjG3HUEG4b2aD0Rp2X/Ttx/GRmf0BODrDbf8NmoWuqyJhcFLBOS0pFsimQGDfD+XfFTt/BKJOiNZYF3mMWiSBu4AtzGwld0+bPyIFwopIgR5H1T4UYRLFvEnmI125mfQsyIPFxt9Tw/6hrPFJi0eLmZ2I+ETPJH082QsJKzODbqLvfcGc63l4lmzvpxZ04R4mUcyi8UVy1mU1GG88D6xiZgOCYG7TkCepWPsa9RsGjKP4WOK0kZ+E725rNK9+hYw9sruvFxsLX3Hx6VcdGz8l1p9CefMA5yTSfU6DTqEKdkPCx+Hhf9A+bW+0xhhDc3/cOrTndFrXvash5Vk7genraFzLhZltgBTdw2mMUxPN7BbgRHe/NqSr8x3UtW4viiqKoFrRRY+KftQFd+8//gsPZJXaE/s9GTi3QL57gDdjv4/KOI5GgroJoew/oSimWeXOCByE3PYeR1FJ/xXOzVSgXW3bj1yYH0NBVUAC4C+Qi0T0u+jxRUYd+4VnNDkcPWhxvjcwa7feIxKcP1RDeUX7wTVI6z65ypEo8yXkUhg/d1a4v9XD7+nRgu+OLjxHBy6uqbyzwv0MqtCelzvNH8p4HLi3QLp7gUcS525FwWDa5X0BuClxbi5kARv1/8nAnxNplgvnT8ood63wbe5R53su+Nzj4+IopFSZjCxhhyfS7xGuHRA7F913T+L/rGNyuNfvpLSnTDmpY1IoZza0AO8BbqHhSbE6WqjfEq69BgzpwnMcGr6JZ5EbfCf3cAJSqEzfQXtmQMqE6Dm+iBRMN4U+HD3je4AZUt5lmXHtI2DtlDZcF56v5bRzANrQjM24Xmp8TpzbPuR/HQkIpo1dmzacey3c73bAFmhjWKYP9h4Z7doylLNxhb61Zahjm5w0W4c0W9XZl8Nzuj72ricB+ybSbxKuHdmm3N/HnunDsX55V3juk5EFdlred4DLCrT9csSTmjxfenwv8+5TjpbvumR/yhwXKrzXgWhDHvXtV9A4eEv4P2rfWGBgSv4x4fq2SLBzdfg9IpHuMeDhlPyXobEiPt4sGer9HLk4vx3KbJkbQvqNYm3fIOX6Bmgd0YOEcbX1odj1u9GYEh9PmsYpZIEYBXJO6wdFxrQxaO6I3ss3YvnbjdG941dKuXug73jNnLqHhzR75qTZINTT8h4K3NuhSFg3rMO+XOke0Nz0YIF6HkQBOLOu3xj6gsXObRie/YtIUXNHeE77p+Q/NqS9Ain3Xg1tXiCWJgomdXNK/l3KHIm840L7k8dNaB8bjUW3pvXjRFknJ/pksn829Ucaa8Bkn+50bL0djS1Dwu+fhLQ7J9LdDIzvpM+l1BmfJ5dC3+rJwDQpaQcjxezHiHs8fu0zEvswUsaI0Ec+btOmoxPP/otwxN/DqLrfQex81XV7x+u9Cu/xQ7QXHhZ+P1fiGE/+OFxKJlBHX+w/Wo9+yfp/N5J8PMNz+Hkia6bliVkOuPvRbSqYDmmgNw55U+HS/J4cjkIIvL5xbJxyLsIgZJ0xJ3BJVATNmrIyWrPUtO5+KnCqKdjJbiggQmRxcZKZXYkE4nVTQRwN3G5mu3qbQGFJVLFQdPekhXYVVOL3qgGOXPsHefWAIEegyNanmtmB3t4NMomjEH/VmSF/J7QW86BNazs8D6yfOLcQrRxhaXgcuaT2wt1fM7PlEDXCnGhjmOSFWhIt3JIufHGcCfzRzLZGm88s7ka8S2537j7KzI5FgTLSLEyuRULs8bFz59OwHNk1XEtagUX4HG3ar3D3h1Kuv0QFi7YIPuU9KxoFi3LkVrKtXpLtyMLRaBN5sZl9393zXOmb4Ao4NAJ5nOyJqDXiHKsfI+Xkz705SEVkUWjIkulB1G/TEL3Lse6exjG5GOJxzHyfLhfGR4i5MNZoQb5naOO6nvBCCePTWcEK5gHgf9x9XTNbGQla50cunHl9uQjuR/ynV4S1RrvvOs0S5lBkEZTk2o7nu9TM7g5pL6/Q3mS5n5nZ+mhemhO4392fSySbiCKq51L1uPuBZvYE6leRFV3UL98GfuHup2Rknw69y3b4nJjrdAydjO91u5e/SPrYNgApD6P9SRluxTL4HySsexoFhmuaK81sI7QeXR99O2cm8p+A5uiIisNQfxgXK2Ne9D2OTql/OaSsj483O6Fn8n13Pz9Yfj8e6r8sWYC7jzUFv/oBcI2ZvUyDbmBR1JcMONvdkxz7VftQhEuBXyPPnoMy0hyLOK8vgVRr75lyLOyiMW1DtFYZg55RNE9VsvpGnlG3eMILJA53vzWMjXsDZ2ckewr4FXClmZ2C9kkvUszK73doTLkhWM3+veSaseo9/ApZJx7s7qmUemZ2EBqnNsxpx8LA44k5LqLD2c4VnO04tK7ZgVaPjOORwnEzGkGWj3dxrEYYjix+z0mpfzTF+kJkedzrleJteHXNbEkkUJqI9rNZ6bZHQuuX0HpjazTObISez45o/XU8EthBYyyclPjdKc5H1rT3mgKofxsJ9HrXLmFvvjwxD8sacQxaCx2ctt5x90lm9kNk0X0MWmNEeIN0L58kFg11pMLMNkb7sE9QPzuXZqvj3ZGh1hGmwIx1voOO1+01ewx2gig2weDwd2iJvO4p3gj9mMrQ15Ln/qNvDhoWjlkayazjVWCpknVNhyyJ/ljzPaRpUNsd9wHzT8HnPABN+BfRbK3wClrQ1PEee5CF5HHh/4uRRntEON9ypDzHMhrKSDOaaoVS4V4+Rgve6Pcsob5LEun+jKJD1/099KAJ+0xi1isdlnckEiT1oMXGuch69MiU44iM/BeE/G+gSMvHFc0fyvgIRaBv19ZLgU8S5z4DLiyQ90JgYhe+m+Q3PUWswahZU0xBjf2UOqjoWVGintHRc0Ru8JMR3cRKKEBPJ2WeixQFk5H12A2hnnNTjnNyypkOCb+/F47hFLAirvouwzd1QYF0Td8UNY3PyMLvmgL1X4M48GrvyyXvI8ur5oMSz7GyFUzdY0JK+QPQBnzr0B9Xp42nB/AMimrfruxngOcz+mKfje8F6h2EhBXjEV1W0XxzISX7isBcbdLegYQh8+SkmSekSfUwQgKdGxAtyrnAnInrhyAe++1T8n5A69rmjjC2DYqdu4421tlo/H6T1vXuG0j4Unsfil2fAc0jkTXkD0PdNyAh4w3h2oME6z/Kj2lRup92oa99VOJb+Cjn+uQS95O0EHwOCbXjeV8jw6qu7ntA+4LISvVOJLiMhK/7h3M9SECduZ9ACrwLE2U/htzn4+euBF7LaOP0iI7oUNI9Z7YM7Vg65dpoNF4nj/9FFr0fhPu4DDivg74yHxoPfp6T5gakLFko/G6ZP9C6/TNgxbr7cyh/QHgW0TjwPvDdRJptw7XDa6ozbvH7Zon++Fbi3F/R+mXF2LmmtQcadyeTI1NAipdJJPa7Kf3+C+CqbryHDp9jn+7HkXKuhzAHAQuUOfr6+SX7Yv/Relh4SP34EsIqRHw0Rc7cFVnvGFq03kq6FhUa1kx3egeBPszsH8By7l4kknXRMteO/kWT7TVIi5qGz9HiY0ry4DbBzL6CNna7os2du/tAMzsACd7+lFtAepk/QtrntSke0MY9xqMTeB+jPLsid+xOLRQ7hpk9irhm53FZvu2IhLwHeczyycyuAb7p7vPVWPcaaJP2DrLUfgn1qSyLDfecCOFmNpn84EPErru7D6wzfyjjMWRluYBnWJebgm68gPrforHzrwAvu/sqOfVjZneF8r+el64szGwcJTTm7r5OTfWORi6AtWitzWwBtNEqG609yv8V9H5TAzB1E1Yy0ngi76LA1939plDOe8Cins6VWbTMIt9EhNRvoi9hZv9GwVVyn6eZ3YTGtyHh92hqGJ/NbCJy7d6hTf1/QRQJ0yfOH4Wi1nccdNLMJlDuu26x/DGz95HAKpffz8zuBRZx91nKtjNRzh6I4mW3tomnEMzsj8i9ew93H52RZleC4MNjUdPDtT4d34sieAvcBxzi7qflpEsNWIiElvGAhfE87yMai83btOFKJICq1I9Syv0MuNLdtwm/p0ECmpvcfeNYuguQ0Gb69JJ60w1EAu8F0Df2IgpQl+q9VLUPJdLNgyxxV6V1HWroHW7p7q+E9BNi1+dHRhFZnibRmHY58ha4D41vR4ey1kJeYU9ntS8PZvY28G93zw1yZ2aPI8F+WoC6SmNbmNtKZG1ZL1a6h5S5NXkfWeej9gwK5XwMXOvuW4bfs6A19d/cfdtYfX8Gtm7Xp+uGmc2BrGHnRvRxaZzT7cq4Hu1RFsu4/jbizl4r/D4PrSkHxtIYGpsecfetyt9J4bbOD8wBPJm8VzNbFo0Vd3q6h1LZukYT1s5m9glwm7tv0CbPtcAa7j5D7NwqiKriFcSBfh0SbI52993D934h8rhZwd0fySj7TfQeRrRpw43Ikv0XyFr9uiL32y309X7czK4ANqtrD9QXMLPfojmziOX4fx36qR6+3BhNhxEf3f02YoOJmY1CE0ApmoASGERO8AIz24ZiRPgLxX7Eg4DdBIyLn5sKMRC5TyTJ9E9CVnelBb/ufjxwfFlBWaKMkdH/YaF/q7vv3klZFXEl8GMUDfX68H8Pze5Jhlwk6wjA1gt3vy24wUWL3/lpjk7bm5TGpiZT8Ivc0qugqvsiiObhQOBHyFo4DYcjq6ZTE+erBI2Jrq+NXKlWQ655F0SbR1PAhXWAU9z99WTedou1bsAUJOY5wjhl5YPGtPQHb3ZRTKtzduA9d+/JSPIecuHKFdB0CWUjjffCFTAo+kanRbQwHQt9A7oieDOzmZDb4Etegj6iA9wPrGdmi7j7MxltWQR9L73zWI3j8yvAymZmnqHxD+PrSjSCvvXC29A6FYG7D61aBqKOWdfM9nT3VLdrM/s+sqJt2cQFYWI0Jj0WCbKDEmxQXLEdNpnPoI1m9LswvHuR309CXj1/DH3mHA+UE2Y2DG2YD0UWT2mB2SqP71MC7v6Ymd2DaBlSBb9WLmBhHIORwLEdPqHh9lonXgPigrq10FiZ3OTPRIFAsWEOuSscRVC1D8XrfgXRZG2MKK4WROvdl9Da9u/xMSc+DgSh45iiY1oQWMVpSsYh4XSmYLoNbgY2N7NfIF7uprExjIlHI6qeLJqfqmNbVQFF1Xu4mRoopejb4Gxt4e5vmNkOaEwfhfp3WbxHK1VWHDMiXu0InwGY2cyRAt/dPYxr63ZQf2G4DJ1SjZ3c/UESdD8m+qVb3f3cvHLNbCSypI1/s4cgijqAJ4G1zWwFd08NcmpmKyCDpSbBrbvfZWaHIyqdf6Gxz4EtTXRlQ1Cf+mGW0DdgZprfQxZeReuBk5E85brQvufQuPSjAmXUhqlgP74pDTrMwn2iCMxsNeSJHBkAvoLkNi0yLDP7NXC6d2Cs5wpg2cm3/d8BnwrMjvuPzg6KE3//iT40e0eL749Id1EagNxu8qgael0e+vqZd3DvA5DL4hjkBhXdywuIww+0AfhLX7c1tGUBYPYK+dNc07KO8Ym8syC3sPi7Py6RZs1w/lhkkdvpcX1K248qc/T1uyrwLuZFi9QeJLhYF22G50ZC1wvDtfdJuOhQIWhMuD4q5ZuOu2qtFK7vW9f9xspucUUseERjTS1BY0IZyyLh+mKJ8xuiTXEPsohJDRoT3k1bt/Yu9Z8HyQgyVrKcByhAMdDle1kHcd4tlzi/G6KY6UECjl/mlLFfSLdpTppNQ5ofpFyLXCsfT/aHcH1RGi7TO2SU3/H4HO6/B/gt6cGqBgC/CWlO68v31eY+1kQWQD0o0Nru4f2uE97ndbH3GXdDnp+G28uDAp8AACAASURBVHl0xMek/wnn1oudyxoTihxfxMrZpcqR8Rx2oTn43mfhiH5PAkZm5K00vk/h9z2GjCA+lAxYmMj7RLj/luBDsTTThDRPdOG+/je063DEoxwFvVopke5ZZLnbjWfbcR+qsQ27Iqu/ouknAn+N/S60D8opb0kac8DTSOm+WziORgrMnpCmFM3dlDqmlnugZHA2NCbPT5iPYr8LHRXaeQ059CU5+WYO9/TvnDQvEls3IQORHlrXHv/MGtf6sB8VlSmcTc5+nEbQ43cRz26kCBqIlBw/R+veHjICOKMgqXfRuuZ+CNi8QBtfJCWoZkq6h9Ba/AtitEJVx5VQxtpo/no5jKvnxK5tEL6Xr+fkr7ofL11/8r5reg6L0KCLie+pot93IY/AZDsmhbFko774Hv5Tj36qhy8xgqZ8tLfRBplc41fzml3VQtm75FyeCWmYd0ZWvL92958l8u+DrA0fRAvgvYCtQr6FUbCL7ZDF4tnexoKuDnRifZxSxpLIYnRH5GpjSPB7OdIqXu/h4zOzMcCy7r5IfXfRNyjostZrNeutLmvTo2c/J7ISvClxfUs0mZ1HseA0mW1I1v2fCDPbEHH4zkS6+95HwLbufk1K3jPQxtnRwiEraMwPEvk2Q5P1S4jv72a00G8aq8zsdeQ6vknF20y2ezKdWa9E3/kQVzC0UWXK8RSLSDM7B21s53f3V8O5ORF35QxocTMg/F3NE9Z3ZnYn4tgcUepGFOTSgV3d/WXLDnqZcSu+npkdijYtS7j782XqT7RlHyRsXMLdJ3RaThWY2YUoyMxc7v5uODcM9elBqH/Phd7Fhu5+fUoZ1wHfBOaOxu6UNAPQxvAhd98o5foViDexBwl6ngyXFkVWRAOBq919s2Teqghunw8iBdsExBv+POonCyIh2jCkLFrW3Tuy9p4SMAXQOYvsce1jYC93jyx1hwD3IoHBI8AtKCBS75hkZl9FnKhnuPsB4dy4UP7O4TuKfheCBwqaimNS5lxlZiuiTfT6aDwBWaheB/wqOZ4k8nY0vsfyP1fiPnLXSzltHBzaNYunuNiHcW11YHlPBCyMpVkCKZ9uc/d1Y+ePBw5DMRH2dvf3EvlmQVbG2yPrs5WpOKYmyv8G8uaIgukYcJ27b5hI8yRwprvvk3JveevvtEacnzxXpQ/1BczsGWRVOdzdnyu6D2pT5tpIET436ePJa8BO7n5jp3V0G1PDPYRv5nZiwUlRLJOfxNKsiTxafo28+iajtcHTJcdJ9xhlXcl2Xonm+eli5+bPyRLtZQ9HRgsXunvqtxfGhXnd/Rvh9+YozkHvWGqiwnoQURYtnVZOyfuJ2nK5u3/Y6bhQQqYwGtjR3TM9IczsdLSfj95ntDeMKAQMOMvd925T1+xoXTIQeWW1eCNl5Dsf7b9/6O6/z0izP/B7RCm4CfCcu68arlUaV8L+4Qia5Qfx9cZKBD5tz6ExSpQ5DaJD/Mzd3+lG/SYKpNujfVkNz2E+5KU1J7Le/gfNQfY2RWvSN4CVPVj4mtmJSIbyVdSHxgNnINqhprm6H+XQL/j9kiExoI9GLjRZFAFRxMeDkBAtzz0lKn86ZDnTTuj5i5C+3UQd5b8KcTo18QObomkuAwxz939bOh/SbugeN3b3a9u0f24UFbZd+1vcwsKm/dKQP4tHMlNoGcrYHw1Wy8bKuAO9q4vdvcVlz8yWQgPjb4BRWUKFKQ0zmxEJ37OeI55wZTVxmqZhANJefhsFi/gN0j52LMi3BsdzR0gKlbuFsBheCbm3veBtOLm7UP98yA1rI5r5/8YCJ3mOK40pkvPP0GIjjreQNXZLBGgTb9dwtBl/IpxrWTwEhdRCaQoPq+BSHYQzg5H7FkiQFfWzBYBZ0TO4k5So5l4TX3BoyxMoEOHysXOHIS7yk9FGYlPk9XBBcjNh4hc9C1jFM1zmMuqNxuXFYxuqonAX9/hAFExtGbRBKxtpPN6e0ahP7I+sYcq0J628wu764fwzKGDZKrFzRyLL9B+5+wlBAHIn4kv7bkqdLyPLvyK8dYu6e8tGMgiyTkAboiTlzyQUWPKwZPsz6ulkfF4VufHNS7pw4CWkCCrqLt4RgiB2T+T2N084/QpwI5oX3ixQxlzIHX0tml0HbwplvBpLeyJwMPrufurunjEm3YfWxb3fax2wZt6+CF8FNg/nH6Z5MxQJA/4BvOtt+IVDvx8Synq76PfVyfgey1tJydumXTMgQcuRSFFypadwYZr4w+/2GCduRnnXoE3lbLFzsyOBcBTA7R80K0I2o+EuvBx6JpXG1JR2LYmUo3OgNeAJ7v5p7PreyBL9Z+7+z5T8RQVlbd9BhT6URVOUxCT0DO9F393fC+ZL1vdrNG9G910krkWETGFh2PtsjQwLkuPJpfH30qZ9a9MhxVVV1HgPhQVMKXnLGG9cid7duu7+vNXAAV+gfV9HnjWfeCxOSMFvKZojV/fAV51S/k+AXwJLuvsTZjYtstqfG/FTv4S8774C/NjdTyh7Dyl1pq33yjzHgbFyigh+7wGGuvvX2qTbDMkfVkceGCDL09vRN5BJnVIVQeF3H1pn3YqoLuPj+y5oTfoZojQ6Dq3Fb0Pva2T4m6QpSUOTTMFqNn4Jcp/9kVxhALJMjgS4WwHboDni+ar1m9lt4XkcH+5/NPlypuSDaFIuhrXPLki4foC7v5+4/hXglJDmfG+mupgO2AEp6ZdH724icBGigbi/SJv60Yx+we+XDIkBvciCx5CmbVt3v6xN2d9FG8/Z8pIRW0BmbGgiRMTj17s4hdPqfA+5sa0Xfp+LLOQGxQWgZvYwCuCwYVo5Ic1BSIsc10ImgxLkCW0rWx/HNgOvoIFutLcJOhEG9eHIPeZJNGC/gCyEW5AcWBNlLYF4XUfQuqH+g7s/lteWUMbCSAu6IQ3tbEZTymvcw8LvUiTI71Mi/TwEwULvM3T310rmnwVFH96RBp96fML+PnLJ+46735lRRilFTDdg5YPGvIMsHteJnUsTsvwZBXyZOaWMji0/wsbjejSOHeruVyXK/jYSvr2H3LoLbYY6gSnQx63uvkXs3Fj0fc4RLYLM7G5k1bZoShmnoLHneOQx8EI7AWxMKXKXu08sqyRxBWV7DvW36L2DtPJpz8s9w6LPGpaBQ0M5XyDrozTBQmY5oaz50UI0fj/xb+p/kFVAk9VumGeujwt0g4JgZeRK92k4dzOy6E0GicIUjGmMu++U1b6Q7kIUWGK6nDRfQ5u/+Dd1Q0GBZ6XxOWxEt0HPMD5H3ITuryPhflGY2SbIMm0WWsczR9/lTu7+rxrrfBpZDC0crSsyxqQxwJre5WBmZjYbcm/8N7I2fSRxfUlEzTEXUvqUEr6UbEup8T2Wr5KSt6DA0JCV0OqeYtFr1QMWLows36NAgfF1Isgidwd3H1/HmFomfRHkrL+jd7A8so69Ang/rkAws5MQv/wxFdvQiRLP0SZ/t1DGNGjdGve0y8o3I/CrkDYS3BUJ+KkCuhi0yCpa+VkN3oZVUFbAlJJ/aWCyuz/ajfa1g+UbDERWu/uitcgZ7r5fLO8ECuxlEQ1SpsVhWKPsjOit7gvnVkNrtzliSa9Ca//ccbYIYuPAj10GVNHvIliDhnBzJPnCzsiYbHnkmZQbGDPWvoE0Yvy85dkxLWqFydr6z0iBl6bo/hB581xpMia4GnkFlUWTTMFqMH6JpRlNg8P+I9SP42PKEsCjBAOGqvWb2RaIHiK6nzKKNbzVi/d1JKxdOGfPOAhZ9E6btfYys5XRt7sNMF1o0z1IZnOJFzCW6IfQL/j9ksG6FPHRFEnzVrQZH4M4o5ZCgtSFER/MLMC5KBp05UAvod5P0cJ9x/D7dOR+OHt8cg2b6Y09O6LuRjSI4E9FgpXVkPB2YeTmOwxplh70lCB2VoP1sZldhAQT13pxi4lIyJUXObcXaULrUM4eyDVxcKysOD5HnKrn5LRlXhSEaAhyWR6EFit3oOf4tdC+O4BJ3qF1pJk9gCyZWoIbBGHnikhDnik8yROAV4F1ECE8pYwZ0fe0DBKW3YuCnsQn7K+j7/MEd/9xShmlFTFTA8I3/Q9vjuKctui4GnFwpgl+x5G/oY02fC390MyORRY3i2YJ602eAU8Bp3rMDbFumNnnSJi2Y+zcuyjK8mqxcxehSLozJfKXWSB3pIjJQskNfWYfrLGcjtz1w/nP0TyzXfg9AM0V93qMRiPMM1u6+4wp9f8beMpDtO4smAKNftPdM4OZdoopNT53C2a2GGr/dEgAch7iewdZ4ewGrIqUCyu4+5Np5XRQ76fAVe6+Texc2ph0EdqMT5tSTG0ws9OA7wELeooXUEgzC9oMXeIpbv6xdKWs36ck8pS8bcaFSTQELcd5CDqWUv541N8XiQT6KWkM8Z4OyFFODSdFEeLuRay9plqY2RzI0m1uJDz/KHZtErKkbvFu6KCe36C19ulIkP4C2ksMpWGxdTbyclkHKV6/hgQalyHDhJUpIMBNCm7TvuOSbX8HeLTduF6gnCpWdpW8Deu4h04ETCllTAZucfdK3nidwopb7T6AlP5TzGXcZJCwFlrPP+nuD0ypuvOQGIfj+9A8vI54V1ODq9WhVArC8vXI3we6p3jvJsqZE3lNrEWrovtsd/93LO0MaByKDAxuBTL3y4mG9MoUrAbjl3B9Vxq0ht9H/bYnpZwXUMycdeuo3xRAc0saQc7z5EzJ59DknRTWXpcXUM5eBGzh7jO0STcbehY/QDIdB95GcpnTPMMSvx8N1LZJ7MeUgXcv4uOhSKiypbtfHYSeS3ng5A2b7vOQ8KpON8jXkEtQhMj9aTG0MYzwdfIjKx+ABoAN3P2e0P7VPET9NrMjkEB4D2CFjDIWB+6ITQSRZVBvFHR3P8/MDkbccC2CX3ffPqeNWTifEhq1NATB/Vnh5yVIQB/fUO+ONGVnmtmjnu3K+2MkVPiFux8VE36vEerZAFnUfY6oAzrFM8hiLXkfByP3ziyLjzhqF/xa5xHCkzgUCX0vQFyTnyQ3uu7+upk9Tkpk3/A+/4o2TxeRrYg5h2KRa6ckXkPfbzssQYOCoQnehtPWZBF3HtIkJ918vwfcmCX0DeW/auJi2xb4SSiz0sbPE271Ae8iQXXU7mXRe0suogYgYUcShS2ZSqYtgtJulF0u5ydoIRp3128SiLn7uybvkOGJvG/QrMhZFfFZJt/DtGR4WiCB5Xpmtoi7P5OWwMwWQUK4btHITKnxuVv4MdrEHebuJyauXQ+cbWY/RJzQP0KC4DrwKaJ4aYehyOK429gMjVGpQl8Ad3/fzG5E7qdp/K6p1u/IfRq0OTrDzFI5q6cE3P3vZvYI8FNCtPTYtTosL8eiDeAJZvYjT1iSBYHar9Ea6Mycdt5KMZfersBkETc7+YruTqKbv2FmO6D11iiaI52/jrwvKiEYQxyElLhJz6VHgJ+Y2d+Rou4Jdz/HRL1zOxIqDAVWQYYbByK+4Z3Rs4g87Q4GTnT3I1Ka8CKikOgU0yBhbVUcgNzGN/aGlV1augdpNSrYCwla2nkbHosE6ElUuoewl9yFVgFTL9z9cTN7CXGhZtETvFelHTXgZopZ7V7i7mnrrY5gspJcJk/Q6fIqGltXnTUimmMN7RvzhJ3RM7yzjUJxfxrzUCmYPJIuRnNk1K4sONrTZyfQfr6QR6S7fwKMC+0YDTzrKUZiBTA90NZ7i3yjHhAd1ofIKOSV0K60dI+g/VQt9bv7g4QYOmY2kmpypucRrVU7zELGfjDRtnfM7HYkxxmG+scQtLY82MxOBn6eXAv0o4F+we+XG8OQZrYOrI40xlenXXT3t8IC8nkUJXavmup9iuYB6w70IR9uZt8Nm/s10eYmL5jXSsh6KzUQhbt/bmb7IsH1UWghlcS0NATPIKESaECKbwYfoVXYlAorQBcQF+ZXwKHouW3v7pckro0HrjWzy9CEeggSeKVhI7RwS7XodvdrTdbVj6EF6q86bO+CJMYfM9sdiIQBTyDai8yNsYmjs1O4J+gRTAGDdkFCoqOQdvSzcG1atEkZBexiZmPd/a855W+DhMZ7er7r9NNICJVEaUWMiSbFkVDs3+F3UcyBXHLvdrmxdsyxi6x3RgaBw/+lpTez7yGBaGrQhQL1PWpm3wEeRxP+L2OX50GWoe3wGRLoRxhH5woYJ30+vRfY0MxWCcqWg0PaZGCgRZDAvLnQLrqltoPXFEizrnLQRuB5gtA3J91zwJqJc3cA3zGzbVFE75+h95BU3i1OQ9mTxHlofLzCzL7jCWtUU8CWy5CL3HnWCBbzirv3WH7wmBZkCHqm1PjcLayL1hlJoW8v3P2ksNlYLyuNmQ1FioDIIijLQtddVvCPAiuY2Sye4JiLlTkPUtbdFDtXZfMQ1Z2GOWi4UuZhILKMbEIY/2+m1fo9jjHIA2iLYN0J9YzvZZGl5P2KivYPK5T9ayQQOxjYykTpEOdxjAcs/HWFerqCoOA9Bo1XeVbmWfNLW4SN8j3I6y0u+L0O2MDMBnk1d/N9kZVnKl1VaMNdZhb10XPc/U6T19dyaNP+AVq7fmBmkZHFJLQO/FnIe7WZPZZcd7n70AptB1mz1eGdsQISiD3RJt2byL0+jp3RfmOTsHbbESAoGJ8B/mUKLvonND4l59Sq99CpgCmJB4Gu0FAUQTuDgS5iS7R3qESb0inKri0SuNEbAbVGoT7cibAzjipKpVGI+/4jRNOQuw/sItahWR5QBpWNXwKWQu+jnRXre8g4ru76QWvNKtbp5wNHmdmi7v5UWgKTJ9i65Hw/Ji/anYG9kSFUxLd9BlrH74SUVocjg6mfVWjzfzT6Bb9fYtS4oQYtGuLWT1+A3FOCphJXtNCbkca3CdY5F+k1wEZmtlIQ2t6ABvotgFfN7FUaH/kZOe2fhYaFK4SgTWY2o7t/HCqfZCIuz3J/rcv6OJMuIFg6FKILKInhKJBCUujbC3cfY2aH0CoUiWNe4P+8QVMxGcDMBkcachff3U1oU1VKsBAsWw5Bwso7Epcjq+2d3f0vBcpKUmQUQa+7HK1a4D1Rv1nXE3yCQXh7VtiAPIBch/IEvwuiIFbt+DIn0hpYBzpTxIwM93U8cjEc2abuOAx914sjYfQ4SnDs0jyXnIB4jceYApn9rbcSuVJtjShXPgl/O4K7v2Tixt2JZsHvm8Da8W8/idCOtWm2FMqzFukUv0fP9XYTz+xXkSKm1/IjCHGWQoKaWlBS6J+Eu/seJr6/Z71NMEJTwLBveJeoV2KYD7nrt3tHX9BqYXAC2kxcFH4bcL+7j4sSmGgUFkdWlC1w90vChnwz4BETNVAk/F0UfbMDEffdX8L4NBktrp9Gwbs6/aYilBmfjwrKtPVdgXNS3eWz6vfu8EjOib6zdngECapaYKI2uJWcoHbx5OHvX5Ab+llmtkvSWilYhp6ChG8XpOTvBHl5XwbWMbPZ3f3t1MwaF9YlXRFR1vp9P9Sn6hjfy6JFyRvwHuLoWyXlWiG4+4tm9i3k5TQMWRbHEQ9YWMkSsQuK5jWQ8DUS+L5L9wQcnyO+6DiOQmPimWZ2YNZcWQCLIYqDdnid5nf9HFrbL4KiyEf3HnnaDYysttz9miC83o/8dVcnuAD4hZkN8wzu2oKoYmVX1duw6j10KmBK4hTgMjPb2N2vaVdpyTkpiVrnKBNV3hplLRuD0HXGxO/CyFDwlsUEajBaqEGJEqGKUul7wMfASlmCwikBr8bJXpfxy2CKGffNQbPHYG3GN16d1vMERN04zsyOAS6MxnozmxntFY9EnNctylkTxcw+aJ83M5rTbwb+gAJOR8r5+0wUI/cgJUy/4DcD/YLfLxG6ZEEU4V2aLQ4iC9d5kca5txiaCeqxElyktArbLkTClw9CG3tMbjN/Q4vCOdHm9jTP4aYNZcTpAaJgKEOR9VOE6ch2O6jF+tgq0gWETWgkDCwaXXk25MLUDs8iK4ssTESWkBGiSWcO5OIT4R1a3akxuc9nYSZkDTAreqfHJa4vijYAbYW+AbXwTMewLDAuKfSNw+XuFvHR5WESOW6bMcxH+sTeiSIm6k+vJX4XwY/RxuyT8LtjIai7Pxks9kYjZc3poaydEC856H52rrjRAo1TqyfOXYEWCn8zs73cfUL8YrAWPAM9415lUjesRdz9/4Lw7Uj0DY0D9vFmN6SdkcBwXI1Vj8xqUvibFlQrOh+5z40OR67gN6TdnS5QryTQsbu+u99tZpsigdkcwN3h/zi+B7xPCoVPDFujhexeaPyLj4GTUF8/LPx+ET3LSYnfVVBmfB6MnkWkpBxaop5uBX/4gIYHTB7mRhZoafgVUvT+E80BTxawGv0T2mBsC6xk4hcHWNLMjkcWW4ugb7B3/vHuWdxfjPrfdWZ2gLvfEr9o4pz9PdrotASBorz1ezSeVx7fi6KNkhf0flMpU8ogWI8uQpuAhcF624El3P3pktbcjjxw6lQ0H43W3GcDR7j7GyXKLQxTLIE1aBVKjkT0CrsBmweL0hfIDt6Z5S79GVo7tcOyNI9d06A+MD3iaIwQ1T9r4vx4FDCwBaGvbUsxTtCkJ8Hv0Dh+g5n9GAkTOglwWcXKrqq3YdV76FTAlMT9iFLviqB8vpycQNWUm5OSqHv8Go4ERmVd2ieEvx77PaWUahHqWFvUiSpKpbmRFXIpoW/Yezqwq7u/3GYvmkTauBCV20nMmbqMX15EcpBMhLHvm2h8rLv+tLpyKYnI3sfMicaGU4MRDDSv51dA64GFQl3bon3cmmgO/RRRkPzBM7ilgzHQtcgorR8Z6Bf8frkwgfotiCK8RHM0y0fRx7YpWlREpvbDiW0wrSIXqbu/hYS/8XPPAEubXGdnA54J6fIwgRiXJhLMGnIDPCK0dQ4U9C3LUrqy9bFVoAsws42Ry+JwGgPrRDO7Ffi9u/8z5/7foZU3LA0L0RCKp+EVmvvBs+Hvaij4BGZmSHic5jI7okAbxgM/cferEuc/RhNdIdSgiUxiBvKfTYR30GYlD08By5nZtFkLcFMgqmXQYjmJ0ooYT7hnJX+3QTLviBJ5W+DufzWzxxBf30ZIKTMITd7XAcd4iHjcKYK2eDVaraSOQsLwDYGnzexOJCQBbTRWC215PqStHWa2Oopie767jybDijTgTMStlrnxMrPBaLE2gmbBxjjgUm/lq0sT+q+M3KReRdbFE8L5oaHseZDgMpUuJweFBCFWPWJ5R+76sYKvJUeo66IfaKEgMAWTmMndXwzP+SAz+xWyxlwAfYMvAje4+5ux8oYmym/63SHKjM9voHcezdd1cS1Xwb3A+kHxmRosJHw7awKpliooSMsEYKuUfp8Kd/8iWIaejQREUTT3FcMB8He0aezKBjrej5DwegMaljCv0DxGzYu+j/tI96opZf2eHM/rUHJVVPKC3PjnrdoO6PXIuYBma+0kjOYxpyx/et3rjZUR5+0POi3A8ik7ZkKCyH3Re7gocX0UDcH0ELRWTiJPcB3hVmBTMzvSMzhOzeznyKo1zvs5DAlLp6OZcinaJyyNLNciDCVlvxPWUf+HFAzt3mna9/JMyLcAQeljZm+QLQDPmp+qWNlV9Taseg+dCpiSiMYwQ15x/5OT1pk65qSqeBEJw2aI/Z6iQthO1xYmry7MbOZgSLJLyXqzlP0j6Vyp9CadeT6MQM99htjvokh9X9ZhzJkajV/GAvuZ2U7unjW3/QB5c/R6+dVtfGPlKInylOXR+JxmfBetpSNEspEXUPv/5O7vtmsrWu/2Jc/41A937z++JAfa7DwPDEv8LnS0KfsE5A72tfB7diSImIjcCfdHAoEeFDE9yjcmnPt2+H0e0BO7PgT4B/oY50ypd3PEa1X12RwT2jF/+D0TshboQdY1J4bn1YOiRKeVMQRpyRaNnVsEeBhtXiajwfKUnHbcEJ7ZEjlplkCWDzfEzp0c2hav54vY75429f4tpPlOTpotQ1mX5qQ5F02608XuvwcNpJsgof7p4dyVKfnXzjlWi95PRt1XokikffV9jUeCFMtJY2iRPb5NWRHP0Mmxc5OBc2O/zwjPce+U/PegyM/R711D/oNj52ZElA7P9tUzK/hco43lnMDAgnnmzzmWAL6DNkQ9wPkp+eeMfROTE0cPskSZq4v33DQOVixrBWS5l3Uv44Hl25SxFNLunwxMk3J9MFLwfQwsndZfc8q+Bng/5/oAxH2b1v74fUzOe2bIynYyWhBOk2xjqCd65zvW/C6/6PZ3UrAtlcbnvj6Qxd7kcA/HIOHgIGTxvhASrr0f2v+tjDI+Bi6u0IbFgR8i65MzED3AclPg3pv6EdqgnhieRfJ7+DB8jzNmlPUOcG3iXMv3iixt/92l+8n6luPHM8DWGfn3QGucFfq6X/bFEd7xRTW8g542x2SkQJg1kXcUUnwWOnLasAyaW3qQMH8UEviMDHkfC9c+AZYNeeYP7ToVzcWvxcpbI1y7CZg5nNs+nLstpf7Tw7UXkLfFZuSsQzvsx73zVM5zWAyt/d9HAs/Zo28Sfeu7IAX+R4Q9XCzvv4AXYr83CHkvI6xHkdDlCxTLpNZ7QILoHmCnRJnx9eo+4dwvcp7BBGrak/bB99jxmq1K3j6+52j8+Ebid6GjQLm5/TCtP6Ig5S8Bg0reS/SNT5f4XehIKW/3WFsfQ2vL87KOjDYtheQP78XK+hh5Jbad95Bi9D0kmzkWKbcmI+XO4kgoPREJy+eou/5QxhpIYB/lf7vNd71AlSNW73XI6G5AX38n/2lHnzeg/5g6DmR9cC2wYezcD2hdQL4ADImleYWYsI6UCRC5K74FnJlSbw/iQq3a/sWRNc+asXNbhEEuPtncR8Zmqk35iyLB5ZA26d4BrilQ3jXAO+H/kaFt76NF80JIEDM4/H9UGLh7gN0yylsdLQonIc3jeohbb1j4/3/D5PEFsFpOu74bytg6du6PNC8InBURUwAAIABJREFUJqPJZqma++AKSCC+ax99A5HA5LekCCiRYOk3Ic1pbcqagcaG51YkaJiMFAN7h789yDI9TRDXkSImUcYlSBhUeuJEQeP6bMKl2AI0Go/mySlnPqTM+VE4diK2uOhi+2vZCKCF31vhXicg6789wvErGl4gb7Z5DpcTrIJy0hiyNLoPbVInI5fwXTKO3UM/nYQoWrLKjTaM9wPrI8vUHiS03AR5fPQgnubMd4MEhLeEssYjF7XJiLbheGRlH31jmffZh+8yUxlYooyOx2dkKfuNAnUsAqzVxW/juER7J4Uj3v5jc/I/QIE5dmo7svoRsnhcA1GNfA95/Ezfpqyb0Vpjlti5pKBmHiRs+1dK/srjOxWUvLEyTgn38SPkCTBth22ZBgkHzwKuDscfgR06LbPLfWEPpLS9sWI545CladoxFgkddwQGd/l+Ii7qtHl7MrJoXT+W/mtoXToPEpJOBkbErt8Wzn1Ow4CjB1n5J+t+OaT5eodt70gwkVHWdmjs7UFr7Z5wD1H7P0N808l8B4b7XSn8HogC1/aEZ3dfyNsD7FH3PVBRwDQ1HFRU0PLfKfgdHdo+Z+J3oSOn3FF0qFRCHowvIU+4Phu70d6sB9ihhrJKG7/E8q6D5sisPdB7pAiua6z/ulDPWVPrt99/lDsiTWI/+pEKM1sRbTZnQ7QH57n7e7HrnwFXuPu24ffZSCAwkwcu0nD+MqRhWiBR/psoWM2OXWr/PIiuImr/ld7Mr1l3fROBy9x9hzbp/oIWsdOb2b3IrW0NF8VEWvqV0GL4IXdfKSPN3khznxYt3NBC9EB3zwuSl1buQOAg5KYdPcfjPCeKcycIboubIGvZS9Hm7UU06bTAcyKNm9ni5LuU4wk3pcCZ/SCiJpmAFr3PQ2qE8GW9TbCY0PfGAKvS7DIJDTfeLT0loIaZrYwEeyd4cBs0sx8g4TSxMl5C31ULFYo1gt+9jlxg/9dz+ItT8r6KhHKF89UFM5tAtrvc50jhdD0SwL+XkW6KI/DaORL8b0uCQqMN3N33SJR3KhKcngIc5gnXdjMbhISvB6JnsX9GuwqNsxYiqqNvLt5fM7OEtNu6+2UZZd6BrMKGuSKWnwfs4u4DY2l2QzysG7soGbLaNzMNd/00RO767ThfCyOjvTMiap288aVpfAqcoveizdVFdfXbouNz+KbPS/axlPLOBnaP32/dCLQLhyCFZeQ6+Bma407yHFojUxCz3yJB+oSC9Z0L3Oru57ZJNxIJvcvyPBZpQ0s/qlDWXmguuCSU+Xl4v6PdfXdTnIAxhGjz7n5hIn+fju+hDaU4dt09laosUIP8BSn50rjLX0YeALcm8u1CHwWvDH1hVzR2rujumTEjviwws+nRGLQ2DTqiV5GSYoy7f5KRbyY0P0yI1kKBlu0cguIaUV/9yt1PSsk/ERmPbFHvHXUGM1uKZooraENxZQrkuBGy5n0qnFuERqwTUF853d0P6FK71wn1zZJ2GXkmbOHVgl51DVXH1yr56xzb/9thCqI5P/IaeAkp8rP2ge7Z3ONV2/Ep+h7X7CDv/MBH7p5LHRhoamb2NgH+TDztB6PxcEG0x38JeQqc4O4vJ9LXVr+ZfQC85O7fzCur27DOYh/1IwX9gt9+VIKZvQ7cFS26zOwEZN24mIurN0r3N+S6OX0i/9XAvO6+zBRsdtdgZuPRZmMRz/i4Agfj08jiZiEz+wS5sG3QpuxrgdXdfcacNEsjIdBatAY5OcXdHy57T1MKMUFlEYFT6kYwbAL/iKwUMqsK+VsWaWGTdwmygEi2IR4h/K427YuXuTGysEpO2H/P6iM5ZeUqYhJp90ObyxXCKaeg4CkoI5Yvmy/kzRWuJNAi7KwbZjYNWjB81m4hVENdUR/+OxK6lAoElOyTZhZxuOaNJwPQeGKewT9YYox5FVlc/Bn1nWdpDjIYRySAv8LdH8op8z3gPg/BM0L/2BW58nks3cPA6+6+YV4bQ9rFSVkEu/sD7fKWRXxTZ2YLI+XahuRzmbWMT2YW8Tg6enZXom9q7JRYxMYFg23SdV3wG6srChYCWswXEgaaAqgOR54PbZ/f1HDvecKBsCaInsM7Be5nELLqXAMpJ69GvMX3hvPxYHXrJceOKuN7XQjvpDA8JdCemX0TuAsp2p5DPLYTwuWhyAJzIWT5vIq7PxbL22d9IuoLyMthH2RReXU7AUBKOZsDk9z9X3W1bWqCKRDRLIiuJLW/mNlziBbru1O0cW0Q+6YHAm8VHdtSyikT66QSygqYpib0leA3KLAi/vCy83imQuu/FYl9YBZ694kdCurXR8qmF4DL075NM3sLrS1KG6WFPjG6oJJ9t7r7QJ31m9mHKJ5AnwRMs4zYR8iTtl3so36koH/A+Q+BFYi2WHZRWRAdBYWL4XgUiXYPdz+nC+0rDTObD1kttIsOnKZpHIsoMk4wsx8lJ5QgpPk1WlSdGU5/jALxtMObZEfHjRr1MHIjnCIIi+5O4QlBVaVI42a2GAryMQNwOwqEMQxxgi6MAh4NREK51OBQXjBCeJl2ufs1iNqjMtz9XrRJL5I2iqC6ONKe7wishAIKnWRmmYInd1/RzJZANCSF8wWMbNe08DcS8HelvwaLrv1RNPEByPp293BtK/SOf+YFghuUQBRUbf3E704xD1qYZn4X7j7ZzO4Gtsop50lgbTNbIc3iCMDMVkDuYA+5+25mtiuykqxq/Vg1YnkL3P0JxCc5xWBm86JxZQiyZBuEhOR3oPHla6g/30F65PN5kVXXSMRDGQW7e93MSlnkdxlz0GaeqQthfiwy9/UiNucMBa4CvghC9SyLoKxgTGkYnFFOV2BmGwCH0hrQ9RbgRM+wfveKweoqju+1IE2Q2wGOQfP9ccARyfaa2VEhzU8Rf/TWHdRRRnlXBgOQZSg0op1npc0SEF2OLElLC36DZV1RdM2yrk2lnyChfR7+hoKqTe8xL8MpDTM7CXjPQ4C78N1VFtR6sACeEnD312nQYnUMM1sbjUmroXnxgkgIFca8dZAhyuvZpXxpEA8aWXas6NbY0hHMbNaiij8zW64binZqCqJpZnsigeH/eMzbwxpeyRFuNgVkTK7ZbqdNwMO86in+brvRB+qs/xG0l64E6yC4s5mdjPZwUdpofp8e8aCvb2aneZe8IP5j4VMB30T/0fkBrIKEjVFwhayjK8FpqMhFiixTTw3X/4X4vjYK51uOWL75qxwZ9zIICWMjbq6sYEqTyeBzCuVHfDzjUSTk3ZEA6BfIgq4HcZLNF/L8nYbFXtZzjqyEr5hC/WoACsTzS8Tts3vs2tfQ4D0w5fkkn1PetVo5sdBmdTKwZ/jdxLuFrIDvRsqJmfv6253SR3inGyGrqE9i7+IV4Pg2+TZGAvSWfCS4S5E1Z9qxG+L+uiuU8Tsy+JyRe9fhBe7pUGJBEhN9IepnUfCkOP/lEuHcYV161rVwvtEBZ3jG9T3C/b4LHEHDmmcgUo78PDZu7RHyLADMXsM9PAdcF/t9ZKhn1US664EPptT3UPZdonlqMnB02jtGC9FnQ9/N5dQEZkVc33cmxsW7w/lZa2p7fP6cDPyTjLkV8XTui4S+93fpWX411DV3Tpp5QprUZ0D2vJI618TyFAlUeA/wZjf7Uez30TTPk2kBXUcVKLdSsDoKju/hO+70yA2GWuGZvgU8USDdE8jqMtmPKgevrNAXvExfzijnTeDCDtuQt05L8kh2jb801HFOgXRnk7KPQYGcH0IeFB1zUCJF3MWIQ3x82X6MlH1/67DuLYnxdU/pe0BB5E6v6X2OSulX8bXXSuH6vjX3o0prrir5k3lR0M6PECXRMkjJPUv4/wQU2PG3dd5/Tc9wAvKMaJduf+DTAummQ9bjB6J155EpxxFdupd/oTXt4Ni51WjE0zmfxn5815T8HcecSfb5nHRjgE+6cO+11Y/iDnxBCMzZQVs6Cu5MDbGP+o/0o9/i90sMM1sDafsjjrx3kZCjSN4qPLfuDeuDMciibjnEIfm2mR2C+OcOjapDlsFHpJQ1jobbxkbIhTazXhpW6hPo3Do0Xk4co5Dg+Qu0QX4GTd7FC3Z/MVjiXIIEKj9NJInTBUQcsUchK7ETg5VwGo/n8chiLIvbsjaY2fJoE7gQDavMwShQCMia8QK0WB2WUsQBaKK/HLmMTwjnh6LgWlshod8fam76COQOd3baRXd/wsw2RZP9EYhLuCsws9nR83vOY+55Jt7f4wmcdsCRnqM1DzQF30X3Nm84/Qr6bv7mJayPXdZQY4GxZvYVNKHving2DyXDyiPkuwa4JuTbPuRbNeQ7hNj35O7teG1HmdmxyMV1xYw0I2j0mzwsiiyzexEsVXdBfM3fR5ugpvHO3R83s5fQovSEAvX0FR4GRpjZYu7+ZFqC4Ao6AgkRU+Hu5wSL3r3QODeKhvY8srwz4CwPnhfu/kJew0Iff8/bu7A+hQTtEe4IdR1uZt91dzezNdF7zOS6NLNhaNN4V7xtZrYMcBqNb+pw747r80Zo7E61SHH3a81sIxTU8XDE0Z0Kl1XNGcAZZvYNpBTZCX0PK6CN4wwlaVNaqgnlxufJjcKRB0PKvm4g2gCujKym0/B1RFdwFFI8JpE256ThN9BEPTM853kOQgLU5RFtQlcR3BePQILWP6C5dUK4PBQpi/cDjjCzO9x9bFZZXtH6vcT4PjSrCLKthnpdcjttXxtMj4JGtsP9wBbBCySOhVPORYj6xHpIIVA3Jnt1+oi76dwqLcuybgBS+o1AhgznonGvW6hqnXYKWtNtBTxjZveRzwma5NEfgGJKbJHTjiL9+HW0d+gElwE9ZnY/UoBeh6iZCq3variHbyMDlEows82QMO8lpIy6GQUxbDTC/R5TzIFN0bz9n4C3UZ/DzPZAe6B13f2WRLqHgIfM7ArgRjN7Kmu/0keYH1nA/tTdT0xeDPPDuehbm5i8nkj7XWRINVteMtQfu+FNsATwaGI/vV2ob3t3/2dYw05A66TkvmVG4CTg3LCnz405Q+t+ZSYT124aorllQ0TVBDStVTrBDDTvaUvXnwZ3vzh4B10bvETKUhLthWQFD4b27YX6z2LIU24n9F6ORcq9CPuh8XR9b419NB442sz+iajo9kHKl34UQV9LnvuPzg8qRFuknNVMIeuDRPkrIve7s5C7RZb1zjiyoxK3HLF8E9CAlTzi7Xw3HPFzzwPPZ7TlBaSJXbqGdzMtGtDORkLkf4b/dyIRqRQJqU5HwqkX0WSzfzhODO3qQYukXcJxUzj2C2WcW+LItK5AC/63w7P6B9r4JTX2MyCrsJZy0ADfA2yTU8fWZERorvjMJxKzuEDBonpSnvc/kIC4m9/mb0PdS8fOTRv6X1zz+S7B8juljNVDP8+yPH8BGN5h+76KJsu7o/I6yP/7TvKG/APCvV2Ucb2oxvp84PPEuVuRlnievPKQm/hzXXr/54V31NZ6IpZnn5RzO4e2v4qEQdPErg1GC9aXQ107FqhjM7Sx/DTWlz4N57ZIpF0WLdQWS5zfEG3sepBFxZ5t6qwUsTxWTuQZslDs3FcQVUD825iYbHNN7/JTYh4XKPhQDwnrXqRcebSDegYDJ8e/qZTvvszRQ/P8Ojk876z5dSyaHzbrxjcR7uce4KkC6Z4G7qxYV5pFSbvjVWCpLt17r1UY2kROIubFlJJ+LbTxuapb7yOn7pbxHa0NkseJoY2R0GmZcGyBjAK+QHPhAl1q5/2keHykpLsBKQCjvtCT+D/rmBzu4Tvd6gsVy4n6SOa4WaHs6ZDnzMt0MZo7xef6i4CJGfmLft8tzxytgyaHvrR+6Ms9iCN7ExQAsQcpoTL7cXinLyPu+rLP4BKa57EepBS6DvgxUnjmeQRWugdkCXxZDe/yWjRPLp73fpGiqdY1eNVvCu39iuxtjyPf8vu+EmNSVzxrKjyDkcjYqQftk2aLXVsJCdwmI0Vj5j4ZeSJPQuu6C5DQrwcpwy+m4V12NnBUl+7lYxL7CzQHJD0//omClyXzx8eVdvPEF5SfW6J0P03U2enhVevPeI5Fyul9Din570Bj2ZxZ3ynax/QAG8TOfQJcW+A9Xwt83Nffzpfp6Lf4/XJjZeTm9oOyGT09UMaJiJ/2TNItNfdCgqLjCpRfiIvU3UcUbHIy39D476DxvgQJJX8B/Nnd3w/XZkHt/3lo0/cyip0DuN5rCIDm0tRfEI52GE1DGz8vEpTEEWnw9woHNKz0hiOByMgyzSObV/VnaNO3n7ufDmBmv2nK7P6JmT2EFgJJHArc7e5jMit3vzRwkh6KrIKbELSLByKLkzjH7o3AHzwWoCWBpHV2ZP0+F83a2E+BeQJXpCON4vMl+Yrd87kj10FCxXhf2g5tlm9A2s3NkWXAfiSsbU1BayK+4ufIDlpzjZmtkvNM4mUOQJuAkcjaYhoaFuhtI5Zn5IcOrIFcvLT3I/fyjhDaswKtPHpLIaFRGqd4HO9RA3dVG9xsZj/xlGjkERJWFKfHr7n7n4N14PZokXxW4DN1xEE+AL3Dv7j7he0a4+7/AP4ROOGHhNNZgWf2R5Z/vWOYmc2JrJNmQIvGWZHV6oPeqpWPcCF6Rx+ENvSY2RY0IpbPGco6zfN53tdC89342Lmdwn38FY3vmyPF2QFoM1wnJqKNTIRovJmDZv76d9C4XAjhWx+JuFbnDKcjrspKHNEes743BU35l1fnbK6CoeRYpsfwFFrfVEH07Ax9X7ciYX0aokCFd7r75xXrLYKVkUXfzVkJ3P3mwPW7Sl5BgXs6Lx4BefXEyskd3z3hAWBmWwIHAdulzPcPAVeY2dZoo38bUlTWjTOB081sDXdPDUIZPOPWQvPsqjQsHndFgoxKwSunApwJ/DE868vRc07lui3SD2JpJ5rZXkhZ/UvkDTfFEfrl4mitkBZcrCqP/s5obN/E3f9tZjsCuIJTPwP8y8yuQ4YEN5Hdj49C88+ZZnagu39ctAHuvi30eq+sh4S3a6J7Xhf12ffNbByiTTo9UUTVe7gK2N7MZnDxKneKFdAY2s4D4U0UmHJqwowkrJMzMIRs7weQF9oVBcp5jepzXK1w99FhbzYGWYE/EPrSymjPMg1aD+7dpn8fitamW7r71abAeUu5+88AzGwIEgB+CzgueF1c7u4f5nhgZLU5a+8ygIY3dBQockkk6I3jbRpr4TjKxpwZFks/PxJcZnF8R3PL5Wj/HqHKWPZbGmvSTutPQxkO4rS0iwN3uHv0bTko+KW7JLfufp6ZHQwchgS5UGPso340o1/w++WGIVfg6gUVd0+5BQ2m14V8z6GAV5WCAdSAQ9BEtXxy0REEwKeZWWT1cRhyt0/iRZo39lMK51PeFTISOkYLuKqL3wgbIeFKcmGZxAS0QE1iacS11g7Poc1lE0I/PA1Zv8UnkUXCMdLM9s0QEL1Mc6DByDV+HYIbiJkNRhvpN9HizUNdkL+YS6Ld+5qHVrf1b4d8e7oCit0QqCc2ppVmobagNWa2JA3B0hzouX6KhMmjkbIj836CYGq3vPxZedvgK+GI6rkhcX3jlHMRBiE3oTmRwieOwRSjaJmD9CBcdeILFOhxBOIKezd+0cxWRELLBdEGrQXuvqOZ3YbGuGE0KD9A39FJBb7XZJk9tN/krA487O5xl/xdUL88GVkDb4oEwfuHa2l1vYWEv/FzzwBLW7mI5XMhfug4NkJC44PDwvLkMIas3aasTvAKzePLs+HvasjCKorkvhwZwSMjmNlX0fe0K6IXiMa629E3dTEUok0pg3VoDrLXF5gZWVa1w4eIE7FjJITeo5BAos7nWQUzky7ESuJV1L9aYGbfQfPDwm3KcHLW+hXG98pK3qpw9z+agrpeY2ano3Hm+XB5KLqnfVD07zNpBNSNKIHqCF5ZCWFNsjWtiu5xwKXeGnQojnF0RpVWCEH4ey8S0NQGa6Wa2zW8j3ZoEfLU8E13KphIYiTiFd0N2DwIWrOE8O4pwfKCguEhFGRxEFqrrheOVZFH3eYkFMQ13MMotD691Mx+4A0aurKYHq2t2yHP/b82mNlcxCjSEmuZNBShiJqefEqPz9AaoB2Wo2/2m7lw0aCtiPrYrkhRAOrHe7n76ALFrI68nlJpk9z9LTPbAY3Vf0BruDvRvD+acvvhLMHvy8hrLcIGyNssqeibFXleJts4okQbmhCU7GPKzi0Vx7Kkkr90/RltqhqEtdPgzrcBK8XHsCTCentFspW3/UhBv+D3y41aoi0G7APckiL07YW732pmjhYZEYaiYF8dIQh2rnH337RJdyjwLXfPshAcCYzL0zS7OF5vRJNZmuD3r8C+ZjaTu5fi9s1o80AU7C7PEudFdx9Zta4aN7RzUswiy9DmtaUpiLunHRZtKdBsFRrckpcgK63ICndB5Oq+DbKoeNTdk0Kg24DdzOwr7v4BcqftAX5nZtOhhcCeaCH4Vxr8y5G1XlHuyCL4Kq3a1tWQm/PzsXMPkC5AXzuk/Vla4UEQ/PPAozUiLY2Z7Y++i2VpCJbuIAiWwjNKhZnNRkMwtVzZ/O1gZqsji5a49eaI2P+OxrZ249sDtArNX6QN72H4Nr+ZqL9ORLyFKyHriU2R9cT27n5HaMPBSHAzDfAX5G2RiiDYPd3EEd0rHChg1dzaMAl6omjbj7n7leH8AOSmGlk8zoGsJONYHwnLj3b3L4C/B8FArlViFrxcxPLkQhG0GX44tuEF0Ui047Etg6jv3w1sbWbTuftE5K4KGl8+RuPL3khB1bLhCX3uW+ib+jYNi/uXkYfN6CAQ7wrc/ab2qbqO1ynGSfpNsq1VgHJRoj3hIdRHiLfvDaQkbYclSRGkmLg0L0FWTe+jebLweFzT+F5JyVsHEgLEQ2nElUjiIDM7KHFuMhKC9RlMvOtjkCdQsv9+H/ilmW3j7lk8xmWt0jrBINIt4qogfq9O+rcbYRIN67S0OCFV0algIolRNO5lCPLKSiK67rTnNTX07AejuSLOxZ9E1Xv4LeKm3xTxJN9PvtA6y2PwNYqt/5egOx4AAARL9YNJKMXM7FmkBCqlKI/lnwVZKucpUG9Ggv9foBgeTd9nEFYdjZ5TEcvgKQ53/9TM/kgIOoj665VonVIEQ2gWxn0BYGbTu/unoY4PzexmpKwaQ0NZ3okhVBrGAnub2Wnh/+NDuVcl0i1L4GeuEbvRMAzoC/R1/XG8RsOTDRrfzmI0yxq+TsMIC6ay2Ef/SegX/H658XvgQjNb1t0zg+IURFH3lM+RhqwujKDDIE4JDEOa8nZ4L6ecY5Fg42oz29Pdny5QXgvMbDgatIbTcJdMQ2kLjCmAD2kepLOwIOkb87uBdcPzSw1aYGbfR1Zu1yUuHYoWtdu7e9KKczwil78MWcMdQutgfxlaRIwArnT3V8zsOLRZiNxZDPWBnyZdV5O/K+JTYpslE8n+PLS6Gn9Oeh8pFbQm49rvw99XaAiWivbpV2lYXZfKbwoAkIWZ0IS/MdK+nxu7tk5UBKLDuIZ0BQ0EVyVPDzIwFtjPzHZy9yyqlR8gC9JzM65XQlDmjIReq95e6wkzOwYJhDdD7lj7uPu5Ie07KEr7/uH3LsCz7n57KPcVmmkFCiP0wdE0j3//S0N4831E27Chu1+PFDtJBdjKiJcubtE6PtxLt/EhcmkHiILafQ1tGuKYTGOTXAeOQx4DQ5BL7abIEu8ZMzsHPbdoM2Gob6YpbF4J7TW0Mb84lHtdnsV9txA2sVkCUzK+raq4DdjOzL7l7kmXy6hdmyC6luQcEF2vKxjTlEbUj0CWmjuaXMJ/n5Y4KO6WIn2z/VN0fz8HTmhjFZqGjsf3GDpW8taIMm6oLXnd/e3aWtIZxiLrxxeRtXJc0b0jWteODWv8lnG/ilVaEZgCT65Jh3NOFuJWZME6bXQd1mmmYLgr0Gw5fZ/n07d0KphI4hgqjjcmqof1wzEcedcYUupcTSPwWxJV72EkjbZPgxSqq2Y0M48q7kbklbehu/9fWgIz+x5SdKSOe1VhZn9DAkujwWsPWu8tAvzBzDZASsOkonXr4J2VhkHoGQ8imzIItN/YEI3R3zOzv9LshbAdEkh/igLhTXUws8ORYmIQmofXQRSJw8zsewXWBu8So1mgoXyYl+Zn7sBAd+/1WK3DECrgVygw9t6IItHQ2vrxKIGZLYfGikyvlU7QDc+isF5bCa0hX4j2BFOq/groNLjzMmi/ciD6Li+l+TvaGvWnM5H3YJMi3bMpQP7rMbUJnfpRAl492mIcRd1TJgHTBk1dpFHKi5YdR56muB2mRdabWfgAWN3MBgVLtBYEDdFqZFjGuPtnZrYhGpgeM7MXkDXW5PTk3mKpGfJfRePbeptibufxMhZGg/vbRTdiwfJldLvna2ZnA7u5e9a3/wB6jnO5+2tpCYLAZVlE/p/EMWiRcKaZbUe66+U6NAJNxDEcuCdF6NsLdx9jZoegzUjy2vVoYRc/d5SZPYwmidkQ/cPJaUJeM1sLeL3dMzezRYC5PJ8v73H0XQxxubDviBY5yTzzke5y/xRaqLbDXGRQBCDB0mhEkJ/Wh/PgaMF3XshfZkMzivZWPJOB0939t70VxiwSzewmZMHfiZXiCUjIem4YHy8N56czs8WR1fhP0bf5h3aFBWvxFWnPo5m60AgWDrsFb4PTaURTfxTxYz4eSz4r4pqLMDocmYu8IjBxqt2MqAoeQZQ9SQ7cMYhmZQu0wXwXbdCiMpZF1h9Jt6oBFKDMMLP50OIu7zm6p7jABjyExqaFXDy/e6J+Ni6RbhiNzV5U9+yIHuc5j1FKBAvq49EicwKy0Hkg0aCn0PcIrZvmvcO1+PhynLs/ktL+ORBVxXnAXzuxmG+jVEmi5VkGS89foA1RnqdOt5SSv0cb34uCF8/5HiLXm9m0iC7khFD/KRlldBolOvqW16G9lXDbKONFPXpi/8f70a/ROHSa9nWrAAAgAElEQVSSibLhfDRPOhL67YLmw4mkK7+WBh5w92PbtTOraXQ+vkeoouStBV7BDdXMdjBRle3t7mMz0myMxuwfeQ6lRQd4Eo1Rc6N+fliKRdNR6Fs4EAX42r/G+iOlYhYiBe3OSAn91zrrTuBotO7sGCa6jFHAvrR6on1kZn9AXipp81SngokmuPuoCu2/CHH5Dgl1fxbaEQl672mzhqt6D3VRxZ2A1rpjzOwwxOMPEHGsbo36+ydkj++d4k9on7gdEvgfgWIffB7qHwzsgOa/zdF3NTSW31G/nymnjv9n77zD7aiqNv5bIRAQEIQACoKhq3QBqRKKFFEpoggCAYkgogh8IkiRqgIigogUEQgdQTqhp9ARKUF6TwgBFAihJCSEe9f3x7snZ985M3PmzDknN5G7nmc/956Z3WbPnr3XXuVdHwHXUu9lVqvE/Qkz2xqdfZajXgls6NvfNYdP6DUKfNKFyDBjMrCHu19mZosjr7SNkOfaj9y9CLpnPD1hsZ5Az/0t4JTQ1rxoj2urUikhd389CHb3QgL7B6lXoq6MDN6uIoesesyZtlAQ+J6CvquEJ7uAcCYIe+yxKABpGW/dqv2oCkl0C7Clma3tigMyEu1/2wKvmdlr6D0YcGZUbhjNxz6KqU/wm0c+C0SY60vVEi1GW0zVdU3IdxzUR49FH9mxSGAzmYpRxjPqbRjRFwkWngReK8hzYej/+cD8GffnoxaF/YKcOgYiBrRMhODM6LHoYN8N/B74TBPvsj/S/v4nemfnRfd3QQv9yjnlG45jyHdOXt/D/Z1CXSOBhdN1o8PynaF/38ypY2ckXM+KLNqNLPd2ySg3Dbi4xDNcDEzrwPfUDZzb6hiGPPuE+sYiS+RpSOv96SjP3EgpMDyj/N7IPWqDgjY2CHn26cBYLNBC2aMK0qFIqLFku/uc6sMm1CIHZ83BScDgEvUciASgDdfYEnX9Eh0ckjXk/vQ4ICuQK1NzsuF3XaLtk0NdxxPW96y6UUTqR8L/w5FAd53w+4LwrFunyjwCPFXQdn+klf849Q56rKcUrKuhnmRteje02YUOUHNHeeZHwrKrU2X/EPKvGl0bgIRtcX/eSd4JEq59vo1zcsU21NFN9vil19q6sUTwM8+H+x+htacbMe9xJOiXgZc7+G0eGrU1FXgupA+jfhxRUL5qlOgdEGxCoyjXjdb2dZCl5pQGdTXiubYJczlvn3wX2Can7CRkvVT1HVRe36M6vkbtmx6BoJg2CemHSGDVhdaQjTo1n1ro/w1I+TdXQZ4BaB+5tgPtvxBSHb8d5ekX8ryYc38kcHCJtg4CRqauZc279BzsRh4huWPU2wl5Dt0a9XkCUmzeHf5PnvNWZF2YLr9/yLN2VN9T1PaXhxH/1gUM7dAzJH0cg5Ra8zRZvtefIerLTmhd74rWh4+ieTUN2DGn7FxIQTp36vp8yFDkBqSsz+QfkYBxMrBsQf+WRWv3k0ix/QUkAO5GyrAv5KTPISissuMwN1JCnoOwg29Gwundmn2/M/FbGh/GYQywfOpePyQDSN7pnwvqOSm880XC74URv5EoMvcD/hXqObNDz/JpMmQBTdYxNJrLWTzX1JDnJeT5tnQo91ITKXNtD/XMi2QS3ciK/0ZSfDuy4u8CTsj4ln6JZBJFZ5hCPiXUtWboax7v/iKKr5RVdiCSX6wYXVsexadK6vgYOC1Vbhji7Sql3v6WZuXUZ/E7e1Or0RZjasY9ZWO0ICWuw0XRsus70r4gTjEdgSJSDwG2NbMbU/3/FrKmm0i+e80JyPLrWSSoeIEmrXWRa+bD7n5w2QLBEvkmhPX6MfA0PbX3ICu7i9Dh9Ykm+xTTfBRY57n75SbsxO2Bl4LlJcC6ZvZ35IL2GYQBmAfcf5kp+vCPkIZ4RnAFJDQ+17ODLEykcaAaEOM2sUS+KtSK62hMf0WuckPQd/I+YrhjK79tkCtfnVWrNx+0ppCacRMK7RcGp2pQ9pjGucqTlcekjfswKmjqD0TrwjLoMDQeMeAnuXthcCUz2xMJTEHf5DM0gaMZ1bMQUkx9Ax1KDkbKka8h64mh7p7A7LwIbGZmm1LzqJgvwDQ0JM/3+Pg2mj+HeeCqcuglatb0fwp9vs/MJqHv/kV0gE6ebSBa84qs4Y6mpsi4CQkfm8ZQD2vTFxEzuzpSqgxx4e0mtCNieEenim+CrH3jYKg7oQPdSGQdug0KcPozZNHzKNrfhgIEr5Z7PMByVOh/M3jGeZT3bfVDz7IxWm/OQ3M9pkPQ2nkeOnidCezm7ksEa6xd0Djc4+67taGvmeTux5vZM0gRtCo91/x/I8u8ImuipgMZmfDjL0eHjMuQhckqaM9fDgV+WQDxMbnrgpltgASaiRvrO1RYE0Ifrw+u9HujfTK2orkTOMd7YlfH9DBa0ypRK+t7VMfdZrYbwuXfhHqseUPr3T5e7B3TW7QqwgfPhQFweYE9hvjCTDKz9RDv1siTIe2NtQSKZJ+7Hrt7tyk43vY5WTamOlRaEZZmEvV9hLt3NHBOgGdYEHgvXsvNbD5k6Zx4Y/zes4OO7Y2+3+eA/T1lvW1mW6JgpF9H1n9pfukSBFv2HijwqZlti6wAV0Znj27gL54dVLgdNBF5jKyK1qn7TcHhRqAAikXWvtCLz2BmJyFrw2VD25eb2ZPoTLYlEsD1R2fHO4Bj3f3hnOp+jc6gGyIFX8Ln3YXmQcKfb29mq3k9VMuySMHxYl5/3f3F4H21qUeef2Z2AYpz0xbItzCXLw5pdqEl0Hp+gAdPnITCHDwynO0uQeePPC+EKxGPtgZwm7u/HTw1z6CGw26IRzmrLH+bRQU87yQkXK4Uf8KaiDmDzhZO+wOFH4Tm/cVoH51igsapFXZ/w8yeQh4DSd/nRhbJX6Xxmbbwvpl9nhYgibxicGdvH+RHH6WptyXPfWnWSYgxTKANsrT/E4BNUmWatkgj29KrUXoYWKpBvV8O+dIWUMnvR4CVCsq/Hp6xFWvHN5B7UTNlDgj9uw1BCGSOK2Js7y0Y09z3gAQDKyGL4hca9Kc/OhBnWXZPA/5IE5rvJsbhqvC+vlOQZ7vQj39k3FsJCfXXKCj/lZDni82OYZTvOuCDks+0FIIJmC/j3urI3WWxjHtlrPgbanCRMOM8atYeXfTUFv8I4T2um9P/wYiJezXUcW50b3MkKPpsu+dCNHYjU88W933vcG2zDrU/JtT/gxbq2BAxSz2sKML3eBw164k/IabxV9SvXU2/94x+9LAkzpvvSCg2Lfq9B2LyPgjv4oup/AeGevYuaHscUnys2mi8So7pXMDAgjmzWvp7Q+vyLalrV4RxWzq69gLwWNb4ZI1Xxf4vhyxi7kFKxt9H99YJ83rBinXPjYTVrwKLpu49idb/AeH3+dRbyq4V5uS+7fyWCvq7GDqcrE3GOlgwly+Jfp8R3uOCqXyXILgk0BrWRfBSST87skq5Ae3/uf1AgotudCBctEx/OzRum5OyaK5YT8vrO7KE+zVwO1KQPR3G6dfA4r01RiWefSolrKbDPJqScX0AcvtO85mZ1vgZ5Sem16Sc9m8BJubcK8uzXAh81NtjntO348L4rRdd60fNqyMZw1cJXmip8vej/WWJgjaWCHnub7JvKyKlc+Z+k1NmbqQw3T98A0dmpF9nlFsdxa64OfQ12fsnIavrn1NwfmnnMzRZf90+Et1LAt0tRoa1dUb++4BXUtd2CGPxGBK2XRV+H55R/g3gshLtXI5g3Xp9/s9KCfh+yXyLAbdWqH8t5HV2NuIdF6Q5HrcZnvddSniQFpRPeIZM6/SQ53uh/zcixXv/cP0LzaSC+p8I696A6FoW334VinmS/D4s5BuO+M1h4VnmRIrz3yKr9+NKjMPpoa5TgTkz7vdHUBTdFFiB96VZJ/V6B/rSrJVo0j0FHRyacmENZQYja4VuZAU2OCetRwOBb0b9GyJcpTNDOgL4WolyH5ASjlQYvyuQFUkzZR5GLqgLRteyFvfriJii1AbYzOZ5Usl+fQb4DtI6HoLwsTp22AXWR0KH6eigshnSKC4d/r8AWaJ8THRIiMr/JZTNdc9G1scfI0tZkKVVkpK5uFFO2hRhyH1IcIfv4FiUUYbkplBHZTehcO/oaG51Z5RdO9z/aQeefyCy8kkY/tMz2v9MeN+ntbHd8wnMZHjPd7dY3/TQ77OImLfo/mbh3XQBD4VrP0MWqy+Gsu8T3O8bpYJ+TERYnuk5ll5j7gf+08TzzYOUC7mHujCON3byeynRzzoYGWRt8lTq2pUEIQs6ONxcNF4V+pG4DsbKyXhOJ/jnP2yhjbmREvOvqeuTiWBlkEKoK/3uwtx7uBfe0fLokL9Wg3wvoYB4ye8jw3Osm8o3AlkRggS6j0X36oQVCCbkLeCsgrbfQ14HM3tslspIx4Zv60S0Pw3KyZfJP9GL6/uskBDMw+0l8t0GvJNx/fgwZu8h3mM/hC2fmTLKjw5rQZ0SOsqzYsgzOud+wzWJElBpvfweWhX2vYuC+TZq53rg3Q4/S7vgZPojz5ujkbVrrLif0MlnqPDMuYLfCnW9lv4mqcH4rRJ+9wvr+UMZ5S9G+3oRfMtcIc+lqetLIg+93PNs+B6H0EYIqNk1UQBR02Q9YynJ32algnofyFs3S/brdeCBEvkeAF7v0BhPQV4h8bUsvv0SYGr0+xEiaMGsbxThOHehOCNFfWgJkgjxayeWeNbjs8rH8w2dCwcC/Tox3p+U1Af10Ec9yJt0T/FU8KXgtrUwshrLdMf3nkGc7qJ6EKe8Pt2DrKmapaepDwzRLB2D3LQOcPdTS5ZZEY3BpAb53qdnQJ7YRcMpdtmYjpila5AVQiaZImN2u/sT7v4OwqetRMEFcmNSQPDufn9Wfne/zxTJ/E/IfWSXdJVIaLtfTh2boMN9rquuu79qZmOoucWMpqerzZYhFZFRcwEqRWb2OSLIC8+Guoj7WTloTUSV3IRCf7+NBCrjgf9Dh4//pMr+y8zeRDAqf4nKntdCn93lEnsoElqcSIAnMLN9UxnfCYH7NmyhvSxKvqPJyFq3FZqCrGH/nnXT3UeYInlfguYv7n46EnQnEc+v9NYjnj8BrGlmC3iOm3cIdLYaGdAjeeQKXPdhRl3nIxiB/mgMp6XztEIV4D8+RAxjUn4ptC6l3V4/QgdD0JhtambHUoPdWK5BUKQZ5KlgfwEm4GykYDwcfVP/TBW7k4Dtipj1psndp5rZQ8DWqVtd9IQlmBz+DqTnt/0a+qbbTiGQ2Y8QnMM/o+tHICGHhd+XufuuOdVUCWQ0kJ5BCT8O7cwT5jDu/n7gR75R9AgIjmJm01iyXUINrfMHZdxLyEkF6mtlff8fon8DG5jZYp4DqWFmn0X7S5Zr+vfRN7S2V4NxORcJ7EeG+X+x9wxEtSuyhp2TKEhhh6DSEh5lBq/mOcF9O0CDkGA6pm3RvN3V3R83s2Form6PrNVimhPts41oCvXBOTGz7YBReftiWWoXnAyAK0D13Wb2ANoTtkWeIHMjZX1HnqFVCrAk5yOr20bnmSxaiPpgx+sjeLLHYQb8yT/J5vuOAB4CLjKzn3rKfTzAbp2BxvGwVNmfo7UwDbOXpmGILz204dPM5lR0nnf3rP2oaXL3Qe2oJ4POAc42szU9H1qkiBZCyuNG9AKCtGgLpXjn6RQEj41oSXrCpy0P3Oc1aEEPdc/h7l0A7n6Lmf0LGZoUBe9sFZJoEMWBhBMaSAZEhpltjvibDamNxVQzuxs42d1vL1F3H0XUJ/jto7ZQOAzvh9yV+iHrzD3Dve2RS8Th7v5yXM7dN565PS2kvyC8oRXc/bkqFbj7k2a2BYpa/l3kppfAZ2TlT3DWMu+naHFk/ZGUnSEcDAKiYW0QEI1Bh8CNq1ZgZoOQIGvd5FL4m2w+9yOGfmy6rLufaWb3Ije5jajHPTzNe+J0xrQEEf5oAb2M8N5Az5psaIOB/yIs1yxKcO+ucfcbSrSDme2DXJqWS11/AVkdn1Gmnor0PSTE2ctTeF0peo7au0ro50hYt5W7Pw1glqlXGEM9LvMe4W8yrumCedeTe0OphknbbroPHd5aoTXd/YWiDO7+n8DcpKM/g+Z83nxshi5FB56zzWxIWjAaBKanIffldmHSJe/3cuCnZjafuzeN7dujQglsh9ETr/ICZM0FEiqeaWZbuHvMtD8FbGhmA8NhcBc019LYo0tSO3j+HlkAx+9lg5DKUDqq8MGhzW8kiqv0NxUY6EeRO14r1J9I0B3oNfR8CY0Nf9dEng4JfYk2C+oj2hWt6zOimZvZytSCxjyAIHt2NrOr3T1L8VglSvQ71HB5QdYwIGXc89F1R8GF8uhxMgQvVcjMlkOeNJugvX1ATlZHypO2HLQDtbK+11GzSt5ZhC5D68g/zGzbtGAjCImuQO/lsozyiyNhWyXsbne/yMy2QnjviZDidfSeF0d8tCHLxBgjceO4GjQfG83JR9FcqyMz2wsdrNM8yvPAH9z9b2WfqSK1KuwbB3zNzObKUPgBMwRYXwt503Q10GVmjyBBzx0IUq3ZNfAg9M62c/fhQYCzirsfHvowEAlFt0aQY5lkZl9BnkBfR3vNPMkttI6N7uAztEprof3kj2Z2Pdqrb/XGGMUJTUfCcQDMbFHk9ZfmSaageCVpGoI824YAW5vZ7fSMjbEFiqtxETAktebtipTIud+zuz8b8Iu35H9Y8Fv1PB+VfxvtyyMRTnil83SFfq+AoInucvdzg1HF7WZ2IjJ6GtfEN9GbMWeSifkssIaZDcjrt5l9BhlsPBJd7oc8WhJKjDMWTF1/Efhmg758iNboRrQQGUYgTdA8BIV8QmZ2DFLmJOPRHeXdAtjczI5z96NbaPeTR0XmwH1p1kp0KHJkqo25ECN6NsKHGY4CVf2ADFflUGYYNRem96h3GfxyuPbLjLI7lOzXHERYiAX5FkUb8q3IiuBJ5Kp3KCUwBJF2/jUkfKrkzoMCyrxHCdiFkP/fiCHtF9WRHsN5kBvqfTltHkVOBPAm+z6R1nCRFkKMVjIXLkFWK8eF/5P58RLwmTZ/Hx+Qgf2bke8fZGP2tezKnZqvCWZxN9rQxof0MTU372sogX1WsQ+V3ISieTCqRNmLgPdT13ZHSpRupPQ4BQny9w//J5GDTyfHJZaKmLRtGLMZLlHoADONDDfdinUvgA5zOwPrd+KdF7TdH0U570Z7yGnh/weR9cqz4fdI2uDClxrHAcja8k5ghRbqrAz/AewT8o5Fh+RpRK5wIc/cYQ2J4RBWR5ao54Xyz1ExqjBSKt2bupb3PVZ2SQZWCM/xYur6pWgPmSN6tm60/3wJebscEq7dUbX9Bn17OWMMTkBr4ZDwexmk4Lw5p46mo0SjIC+PRr93D/kOjK7NSwMMfGTl+TGweovjsBY1HqEUdE+b30Pl9T11fxD6ttM4t8nve4BBnZhLbRiD/ki51x3WgguRFeBhSNAxKdx7gGxsw/EoyG2r/diXGqxPnF4gA2ubNkKl0ZN376LGo8TXzu/we3gfuCH6vWho+8JUvovJ5ttOpGZlW4eNjvbdi8mHtLoCrc3x3J2ChKe/QpAnDfdEWoCTAX6MlIxvpb6lyej8cgjiRzL70a5nqPj+4r3+W+E5Poz6MSG8oy+XqOthtDbNHX7/JNSxdyrfCGBsRvmkzUZranqt6kZKlKtL9PEa4K1OfhO9mWjhPB/lmR6NbRdSXJ6PhOsdiQmSMRdbwgqmxZgzbXqGg0P9p6bmePw+zgz9/El07XkimAsk/+iiPkbTvWTAGKXyjKYFSKJ0f3PKL4B4w3HRta1C2Q8QDMTyyGNjzvD/8Wjv6AK27M1vZnZLvd6BvtTEy6otpCtEv8umhhhMSMs+luyNswsJJzdMlUkOUI8gLbZlfeih7MicZzqNDMY6yrMUcudshIu1A2LW8/r/LvC9gvItg8ojBi5p81HEBBUKB4DfhDp/mRqXeHE/KuQ5pMNzbCRNBsBIlU9w764gOxBHYkXTDfyuzX1/Eh1acvF/kCZ0PPBsxr3BNIlXXdDO/4VnHI8sYOeK7s0ZvptXwjv9vwZ1zYEOQ0vlpZxyPTBKs+ZVuHYXKUYWMe5XlCg7nHrB7yro0HEqGVhr4flPQYeazIBfdAiTtsR7i5mujcJ87gL+jqxINiYHA7qgzpYC7BXM4x8Bf0aWRvOXKDM/NVfUrHR1mXqaHcfwe14kmJuOBBqjqVmExGlEQZ0nh34eTzjE5syJh0lhcIfxGhY967uklI7AjuHewTntN2RgG4zJNFKCopz+XwdMzqljSEHaF+2l74Q59ttU2d1Ce99MtRUf0JKUO59bnBfvUb+u3B/eR//o2h1kHOxL1J8ZyAgF0/sIWCT8XhgdKKYiocR+SDjcBZzZoI1jEI7nT2gy/kBUx0hqwqrVgXk7Md4F7Vde36N7vabkbeM4LIgCtKUF1snv6/L6jowjxtOmQLfIWvqrIeUGKkuVGZW3XpUou3N4xjcQ3xoHEBoQrr1OCRzIFp+7VWHfwtR4qUlIWXFs+E4vonYmGAcsVNCP1RDfdhO14GrJnJiI9sfcoJdofb8i+n1OKDtPKt/VRAKOcC1pazpaD3+DPAFysWo78QwV31+PvT5cWxDtR/9M9eGf4f1mBi+lFtz2QRRE+j20Vi0a5ZkD7XF1ykmkpD2qYvoIuLzE8/4d+LBT30NvJlo8z0f350dKgFPoqZBN1tcnEa+yDZHyvZ1zMWqzVMqoq6WYM216hk+FsUqUqMm5cmT4jkaGe2Poeca8hgh3GHkOdCPji/nDtWT9zwwYH5VN+MbXkNV3+iz7Q2Tg0wXsEq6/FKWEP3gpJyVQcF1EcSkQ7zGd4rPVRmH8ezWGyOyWer0DfamJl1WLAtnWyJGhrpXQQagbHcyPQ1avQ8P/z1PTvqwUlbsHHdqWiK5lbRQ3Ai9ltDsjsBGwTMb9bZFrQjdwV0H/N6CmZRwd+r1ZSEMRg9yNFuoNcupoaaMIdTxFzYWy7HtdCGnFu5BV1ndCG9cjvMHzwr2XKSfgWQ9p+P4S0mGUtDCkpsEs3f+M559AjnV4yDMg5HmqIM8SaGP6ZUg/oIEFNmIUu4BDC/IkjGXbAoLltPMEEmwuW5BnWSQgzQwWhNwab0eMbxUlxIPh+8qNCIssJN+l3vrrJVJBCnO+65eBJ1LXrkHrRVEwAAt5rs25fxc6qCxQ0PclwvhlWgZWfG9pxjG2fKryDloNsPer8Iwbp67fnOrbE5QUHiHrzv9DFrNnovVhjTbP/3gcB4YxKGWJU1Dnc8gyzqJrWXPySnIidiNFyVrAfBn3Vkf7TaZnCC14VSAr3P8CD5b4pp4Bnsupp9FcTMbxelJCA2ThuET87GF+/hkJeKYhS+pSXjgVx2EakSU/8jD6ELglle9i2ni4RsK024Etoms/zhi7cURC4wZjXWlNCPVOpoUgcYgvGEnKgieVZ9OQZ/OMe5XX9+heryl5OzAvV0NWlWcgnukQGlh1I2XseHICd87qKcyNqRRYYiLrvmkUCHna0I+WhH3h/nKhfJ4A/58U8GIZ9fVHZ4ojkbBkWqin6Jt+A7gu+n1S6MPyqXxXpdc2FNei3UKwpp+hYjsz9vqc+yuGtSKBvOsK77fOWh6dDUZE7206qeCSYe3rBo5s83M8DzxfMt/L7Wx7Vkm0eJ4vqHdRdJ77GzVhYPKNfjSz5mKF+n6CZAZ5vNZHRJa2nXgGxLPdF41Zen37FylFIcID7yY6NyDr3qTPb0d1bF+iT5dE7U5Hwtpx4f+kHxdH+Xvw9KnfWWlqWBc/E9XxJiWC8yHZzpu9+d3MbqkP43c2IncfV/S7RToWaZeOB37tKUwmMzsq5DkMadK/G26tgiJfTmhQ/ySycchWQ8LOTYBHzGwvd78yBLf4AwIed+B3iIHJoyORVddP3D0r8Na5ZrY3YtJ/jdwIepC3J6DWICSgvqVsAXefGLDergN2Qu6kjrB3vomEZOOBb7v7+3n1mNnyyMJh7eRS0kS4/xBypy3CpHsECYSuC0G6rkELfCZ2j7ung18NQhGWc3GU3H1aAGbfJuMZFkGCiB3Q++xR1MyuBn7m7v/NqPqPSCP5m4AbeS41fNQVkYXkTsgS4uS8/kV9WQD4NDlB8zKePaZl0WHpxbwM7v6imY0iFVgttL0FYq6SNfpteoL3l6F/IDfqE4EDcvL8DmGlpQO/jAL2CFipt2UVNLPvI6XSn1K3NgRu87ArZ5G7ewgGkBdIrzcwadMU4z9XpcoB9gJtiQ7CcUDMLcL1V5EV6+ZIsLUn+nbqyMw+rab8fRem59MtPlczdAIag2fR+vsCzc9lED7tjUXzKtDHSKFRR+Gbzfxu3X0MtWBgWfePKdnPLDoUCcAXMrO13P2hrEwB63kFdEDKogQTPosSDPIR7n5v+qYrYNCE1LXJyNp1vzIP0QZ6nZ7BczaiBgUS03z0DETXErn7g+g7ia+dbWYPo71mIbRXnO89gxIVBUxtREVlP0RC9qr0Q6TAeLAgz4OIF9gDCb1jamV9T2hb9D53y9rvA1+zGxI+bUd9MKVZhtz9MZp/H/sgSLG9qAVYewUdZDOa8OOKKrMmA8A2IjP7Olp3xyHIp65UltXRwfqpvDrc/anAo3y1lb40oFPQt7kJmtNdwAEpHm8L5DmTxmRP+vkC8FUz2xB5bvWIDeEK+NwMGeK95kTKqX7R9TwajxSLCT0R8ieWj5jZvIg/Sp+XRgDTvRaMqR1U5RnaTuG8caiZHYagroYi75rvZuSdFubthigg4SPu/lIq21QUN+P6dPkWaRQw1Mz2cPdhWRnMbHfE25/f5rZnFWr1PJ9J4Vu+DMW+WQ4JJn+G4LXmqNjXjpO3FnOmXX2YAKwfZARbI6vjOdB6czMynknzhJciS+Gx0bXt0Zn4G4g/fgd5hV1Tog+7hHH4BbJ4/nx0+yXgj94zXsbPZ0IAACAASURBVM3S4a+F+/9ABlxZ9BES3H6cuj4/DYJgBnoNGbv1UVnqbclzX+qdhDauj6PfbwFPlyj3NJFbOLJGuyqVJ0tDeBs5uIVocTgKHdi70KH3X6Ge14Gvl+jXJODhEvkeBiZ1cFzHUcJdKKfs3EjDeD1iGp8O43YgDaz5kFDk9TBmibtb4nZ5EVrkE6vDIsy3WJPYtEUT0hZfX+JZr0vPB2qH70SreHfo+0Xh/8Si+1lyXPbQAeG9nP4nLie51syhD3+hZonetDVXqOcNFNm40ThcToZ1IjUXud9T0U2Wim5CoewXEYP9LmLSFg5lzwv1Dgnz7AMC5nhUdgopmIac/t1OBmZfuDdTMWmjdtttMfAEYl5yra7DtavQYT9dfjwpayvkYtxFsOKnhv+dC9ES2vxnu56rmXFE69IEIuvtinW2DP+B1thvIEb+10hhmE6/7tR4hPQKWqf6xf1HB4vxiBFepQN9+CNttpKq0IcLwhgcDKwa3lUXsHYq3ws02M/RnrdrqCvrPXbkXbZpHG5CB+yq5V8E7i6R724yMItpYX2P6phCOdfoy8lZ52fnRDlrphm8VEE9+6D9LM1jPEsDt3wkdH6Kegi2c1J1jSIFpxbe/6UlnvNSOuzajs4AX0PCwCzPv03Cmp05F9vUh9WQYONmatiRCT99LVKOfamgfGU4mXDt1t5+hoptluKZkFfp7xEvkPlNIGOQb7Sxb59DyoS1gM81yJusiR8Bv43nIRJk/RZZTU8j8nr9X0q04TyfUedAZMx0DhICxuvSw8CJM3suzsqp08+A9vfPUQCJ2KB8U5BE4Xn2rNDOK6S8knLyPQaM7+33NjulXu9AX+qlF1/vTjAZuKREuUuAD6Lfz5DCS01vFEg7NYEU7mJG3RtTw+PqQlrwhgHZQtn3KBGULPT/vQ6O62lI0NMUNlcb2h0Wxv0CMgQsyHI1yTOsoJ6xyL2zVMoo/wBiOHMB/JGm+H1SB19qwr3byXDLQ5rOW8Pc+FNB/UsifNmnAyMzOfx/KsVC788gN64uxPwl0CcJDEdykMt89lRdFyMhTu48QFYY48k4fIV+/6sN86JpN6Go7E6IEe6ippSJ3Z6mATtmlHsk5FuzoF9rhjy5wh1mIiZt1Ga7Bb+VA+yF6x+SWpeRMH9C6tr1RJheGfW8SwtBG1sZx/AdXdmGOluC/0CWnW9CnYAlrRxqO9NNTfD7i+gbTLB4J6KgYkn7B3TonUwndaib2QlZM7+bGu/bMvJ0A2fk1NEfWY5/nKonV+CGhMANYTqAbzMThOPICnYaBYFjGpSfQnmh3Qc59yqt71H5ykreWSkhC9ErEd82DTg3urc58oqp42doEkM0o3zLAWCRgG8ikVAXWT91h/dzIVKidJEKUoqUBy9QDpKpVHDoXnh3Q2kxIC+yRIzX3w+RQvlwYB1KCkioCCcT8r1JifNXp5+hYtu5PBPiqX9KTeCdrM/3AEMz8rdLAL4X2cqUZ4AfFZQbQv0aGMdmmA7s0dvzvoPvsi3neeSRdhKC+ErWsu6wlpyJFDy5eNvtnIsIBuakMOeeJQoSH76NvcnBnO6ldzDbC6/bNA4Xhm9u/4I8+4V5dUFv93d2Sr3egb7USy++XvD7CCVwvAIzEUfH/lP4OHeNrqU3in3DteMK6p2TWpT2JL0AfKXk89wL3Fci331l8rUwrgsiIePlZTe2MPYtRQVFFqZjKQg0gg7M48jBv2zT8/8svLsngE0z7m+CwP67qMfuegUxr7nWzdQir7/Sgb6fEPr+N2RFOQx6gOzvhRj0i0rUNQhZYf6dFJMf7i8U5sibZEQ9D++z4cG+iWfbCgnWb0SHxb8i159Ca1nk+vV3ahHOu5Eg/TpyBLvoMNaNBFu/puaaNAeynDgCHVa7yGD+M+rrOCZt1NYN5B9i5kKa8tIMKy0E2AvXJxPhICN31y7qAzNdRIFVFlLIjO7EmOW0Fwt+/0UKw7VinfuEsbucoFCJxxJZ0CaClF1SZddBh7ZpSCkzJuT7bZjfyXw8hwwhTZvH4xv0DHqTpMdoEkM4zMe1Q1q8Qd7xZOAqzuwErIwsS29EAtl08KOfoAPj1jnlf0MNq+5adKgrFLhlfXM5dZ+T9/13YBy2R3vERchyeWNKBo5Ea+vwEm0Mp0DoSoX1PSpbWck7qyQUDCqtOIh517XJ4FXa1HbLAWARP3dX6lrCk28dfi8c3sHoVL4zQr4/kCFYRuvp70Oev/T2u8p5/vND/0ZSHe86EbCPQZAk87S5j2shCL2zkfdenZApfKePtdBGR5+hQdvbEu2ZYd58C7l3J/EpusNc/g2wXEFdLQnAQx3Doja7qClT4mvnN3hf11Iz/OgO/19LyjPlfy3RvvN8Ms6vI+XjnlQMhNpk/9NyjaFIuRkrhONn2SRc+2FGXUsiRUBuwG8EITiEBvFnmuh/MkedYgOFQi/U8PvcEu2dQ2Pv1QEIwibXyAYZ6SxFGw3eECRYsn7cGd7lpuGdDQ3XupASPBenvi9ljG1vd6Av9dKLr18g90aauczAZyHPBiHPPtG1z6NDw0fIMuIrYeG6FAlsjgwL75tEwRpS9S6HhJ/JJr0VOpx3h7K5Gp+ojh1C/tzANNSCpn2vg+N6HrJE7ArjcgdiRM7LSOeGMlMoAQvQoN0PKWcBdBkddLtEwuVR9GS67gqLdHKI6UbW3HOkyk4lJdDKaeMK2ux6GL4HR0LlAVnfSLi2VvgG9k1dPzIjDQvP+n6YEyeHdBU1N7xhZLgjh2ds6OYysxKy/BmIcNcyrY9S+c+I5kBiKTE9+t1Nyt0xVf7TtNmat4l5kH7nQ5DwMul/zDhuj9a6PHfoygH2wr0nEPPcL/zeJZT/eSrfLRS4OyFG6WMaCHPaOI4zBOhIqDEVWKHFOivDfyCLvi7gm1nvOcztG5AlSykvkzbMq4XDerIODYS2GfU17R4e+vAqBcrB2SEhYdf7wKpNlCkr+D2P4gBO64U8ucFSEZ90HvDVBm19n9qeWOpAF5W9L6wZufApaA19lxKeIzS5vocylZW8s0JC1t2JBeYOwCJZ8wSt320LIBrV244AsJNJ8Y5IafJW6tpNpPYHdFBPFF4vIliwPRF+9HHULIXfBpbs7feV8/znU4PwKjIYmC+sGXVCRaR8SQRDUxH/ejiwLh20lE31YSO0PzdUhOeU7/VnCP04GfErCY83BXkzbU4JSC5aF4DvHNp9A1lbx3zXgHAt6d9ODerqh4KSLTIzx7A3E204z4d6krn4GjrL70GbhKMN+j+Dz6Imr5iEPK3WTq/v4R1PJOWVF+4lwRkbCX67gePb1P9E6OvRGJZOqXraouhGUFpdpAJMp/JsHPL8H0G2QeCjyZZ95KVzU/Vug3iY+CwZnyHfpWLA5U9y6vUO9KVeevHZB9E/IuboRIS/N39IqyBryPeAkzPq2oQaA5n1cU4CBuf0Y6fow76ZnhG1hyLGtgtpW3OxThET+0cknPl7WDBWCenbyEpsOmJMlkqnNo5rskCVWaiTDepp4IYW232KEocTdADIxXKmxWjh4f4AZCnyXsYzvxfu1UXBRoePa0s8w7W02fWQmuB3eHTtvPAu0wLq0aQgCnLee1PzIFXfSmGsOuL2PTNS+O5GIKVE8qwfhmvbNijbzUzEpI3avT1+H/S0Hknmc8w4fjlc+2VOfQeH+6emni2u48zQRl10YMR8dyMLvJ8jRno68IUojyGFxV0Nnu00tE4fgtzpOxaJnnrB6gmh70Np4QBARfgPJNB9LK9/Ud1vAWd1ejxaqKeyezja695GHg2FmPGzcgpryI1Nlil7ELoTmNjgPX4ILFyQZyA6HP+tIM8O0ft6EymWRuWljPIHhbJXZX3HyDvhH2Eu/KpD76GykndWSGit/5AI8zRrniCl2vMdmsdlrbYzFd2hjquj359C+8N1qXyZHiFIMJgEpMvi3ccB6/T2uyoYm4Rvq4x3He6tTk9s3GQ8JiEYpZ/TQVxXJPg9PbR5MzLE2ZKSHgDtegYkHDsR7R8jEJ+fTiMKyif78H3hGT5dYRxaEYCPRGtvrgUg4tmmUcLD9ZOYaOE8H9XxbQSt93hqbXkWxVD5Dh2AV6Cn4Pe6sBauF93PWt9HAM9k1PUY5TBmH6cBjGXVZwi/T0YW539AGN4LhLQaEk6/D/whVUdZfucyMiDmUnnuAcaWqGsc2vuT971C1JeyKessvBjyHL0dyTmeCv8fQQeMND4JKYkY30efcDKzrujnQSFl0QFmdkDqmiNN4YFIcJiOOnmSu+dFZ7wULRKHufsJPSp1P9fMHkDWj9sgS4ZBOfW8nDwKwg+qixgb7h0QUrr/7foWflihzFXAfmY20N3fqtjuhcBRZraiK4puHZnZF5HQ9tiCelqNFo4rwvfBZnYkwnKNI6E+7O5Tc+q9EtjXzD7r7m/kPMNnwzOcVdC/ViiOqjw5/B2IBGsJvYZc2WI6pp2dcPcnzWwLFAX3u+jw+SraHLPyX9jO9ttB7n4DcIOZzYHGEGSJ1FVQLKH3ER7YzKYZ61SI4DwEuU/+CK0/Pfruino+Hq17J2XUdzpy2d3PzNZCgkmAQWb2E+B7CGPycaQlT9OJyJXy2yGBgmGMi/JsiKxSssonzxL3+3chYZYZ4NvdvdX1cEbU31Tbfy1ot2Hb7v4+sJOZHUPGXuPuj+YUHYjggBL6OPRjHnf/MKnbzO4K9c6qtD+yMp+AmOFL3f0jADObE/gBstbbJuT9Y1R2D7Qf/xDYxszuQMz6hxntuLsf16FnaJVeQQf3Qgr7T0yrZ1xLqD+yaNoQKfbyaANgjLu/nZfB3d8ys0dDXXl0GOJH9gX+6u6Z63oBnYHWpO2Ap8zsEoTPCLJC2hXxSi8Af26y7lLk7h+HSOPHIQv0Jajt9aDD6lnIo6XMmj+zaU0EQfF0g3xvAhuY2ZDw+5qwVgwpKpSmjD36XXryG3n0fsibRa8igV9Cm6P18N5UvgURPEi6Tw+Y2fLU9qF01PorAz83q9P4knnWyLrh7mPQPn+ymfVHlv2bIV5zS+CbAGb2hrsvkVVHQmY2F1LsbExtH5yA1pWrcsZzNDqHWGhvi4ImMs8rrT6DmZ2KMDOTzdmj/+PfXtC3E1EMkcwzSEk6C/hr4HuvIX+Pwt3vSl1aHUGaPJVXeeDZRiFM5j5KkbuPMrMvU+08n9RxA/KgwswWQ/MwmYs/IcB2mdkYFLD3sA48ynrAg+5+f4N8b6Azb5qWpJgXSOgFFJhyBoW94QV3v6+ooJmtiwSk8d7wt6RdMxuKFDabuvvdqeKPAY+Z2XXAKDN71t3PKdFfzKwf4nc2JTrz5NCyaF1pRE8hg8FEBvJ6+FtFJjKD3P0/iMfoozaRuRet4X30v0pmdj4wxN3nCL+bPXj0IHfvV7Ef44HvFy2QZjYPOujM6G9GnrEUMySF5O5LVy3bKoXnG42YuX3d/Z8V6pgDWTpvgAS7l7j7e+He/MhF/Eikid8x76BpZi8Cr7n717LuR/nuRlFyl2u2rwV1fgppXxdEmHY3p+5vhbSf76GNMJMZrNj2+Ugwcq+7bxiu/QJZJ3/b3W+K8j6KrBYXaVf7OX36JRLuzNsor7vPkRKwNd0csm6tQu7uQ1tou46Cwmequ2/cznpLtDtjXTSze5DHwJfdfUK4340ONntGZW4MeZbJqXMJpNRYl/rDk6HIxtslbWSUnwcpshZDbtt3pu5vhw7t57v7v3PqaGp9r7qeR+3F49iWts3s07rt71fozxvIgnzb8Psk5Jb2RXd/Psp3FcLGnKfZNjLajIWM26FD6dEli2cKXs3sCYSTvaq7v5jT7rJIkfCyu68UXe+m/jBf126473l7bW9TEPr/FGGjf1CQr8zzpmkKggO5M+ummX2ArI13atDHy9E8+nTO/clICbpRE31L17EU8n5ZnXrex9Bh7TvuPtbMzqvaDiXWdzObm+aUvL1OZvYh8rTaMbqWtb4PR5aI86Jx/pK7PxfNr1KU/p7M7GK0bi+bKG8y+jgX8oS6291/kHH/dCRIOQsFvv09sDywSiz8Cnz2BHdft2x/ZweK+Lab3P2bDfIOBzZ09wVK1j0nUt5si6xX56bBumhm6yNjliWpX3ccCVl2cfd7UuVG09xc2qRMvrLPYGY7I0iG8UjI8l2kRNgKQfHtAqyPBLu35K2PrVLGml00JnVKYjObiizg676VVL5Lge2z9nkzWxyN1woILidr/2g7z/tJITNbBq1ZP6XEN9Vk3cMQ39nPzKYhD9LvR/ez1vfrgK+7+7ypuqag4KWN9vq/I7iBeaJrde3klD0H2LNArvEwwujftEE9I5GldsJrN1LQxPRHd/9lQd3TUAyiXRr04RIEtTl3yXb7qJeoz+K3j4DWD/ot0OpF1jMAQcD3w2ChlJdnULs7NhNpOLIkXBu4z8z+Q7El1mYZ1xPhxWLIyvB0M5sUri0Y5VsTeCFlcefuvmz4/3Mo8FAjyrWeaIFuJGAqATeG/o8N9wZRe477geEZz5A1Ls2QA180szmChdIItIGeYGYvI6Z9X+RiM7LFtgrJzH6MmGyQZvcFZEFVWKyVJtEBKk2xgDLvuiMX/vpKzVZCmvdFEE7h9eF6P4Q1mnngRdhTZ5vZmu7+cNmHaDOtgizCMgWyEU1CQYwyKZRfPygutqbeguJaL9DAhvXvooL71yIBUC714vrezrYnIZf4dSqUHY+gDhJ6As3dbwGnAJjZvOiQ3Oh9l6WjqR1iLXWtiJJvKsvKYVnkopop9AVw9xeDRVP6sHBsibZnB/od8HW0B+zl7s/l5Eue15DScwxy/8yij9B7vzVYmORRFzqsNqK5EX5gHr1LY0ubQnL3V8xsTWTdvRXwBfS8ryAh4HXRurJHXjXhb6X1PerLVOqtTGd1eh34Yol8X0b82ENoLBLr2wtp7Xs6ItR5kZn91FPeXma2EDJ4mBtZiGfRb5F1aWJFZ0jpHwt910AC+Stb6OusThua2QLunmkZHZSGGwJ5a0WS7yvIMvHryIgiEeYYspgeXVB2JeA2BLfxEnKjHhtuD0KwdssCt5jZOu7+ZFK2nQruis+wF/KC2TTsHxuEft2OvPrODIrMw5GHYpl+VOH97qK1b2oC8FUzszyeynRwWBt57qXvHYBgqeaML4e/Ta2JnzQKSpjd0sL4cG9Rala/myFeLBnXlozOUvQLFNQV4L9ISd6IViSb55uAzsuN6CvIargKNTqzrUg+zxJTYl2bZ62fpuno+a5BxkVF9BZS/jSi5RCP3hYK68cOSDmb6ckX1rpvodhAz2Tl6aN66hP89lGvUiOhbyrvJZ3sS7upCcZn47gYEiLlCZLymKJBqTpAQaPS9IUGdU5D+EGNaAFSbu8xmdmSyJplcfIPymmrto3jKlD/s55h/ay6ijrbBC2EDtHD3X2Mmd2AXOyfSLVVBJcxg4Il1CYUWw/g7un69keb87bufkuZtrIEbGZ2MgpocRYSHo4NtwYhd+B9ULTpxzOq/Co6UL6GDo1x2e+iw+QZSBiXbncpZEE8OLp8AcKaA7kpn2lmW7j7iIxnOdfMVgNuN7MEc26cz1y30zlpLGwHBQCZ3ihTeI+l3mUfZVIr8B+jgf3NbBF3fxMpmaYAx5vgY15FsB4DqcFxtEox/Mt2SGF0YWjnQyQkGBvuD0IWVvOg72Qs2VTZPdzdjy7T6Vmd3H2aCQbnfuBJMxtHPgyOu/tmQWgxxt1bheR5Ebn9D8hbi8xsABK4vFRQz22hnlwBRRkKZa+j8eEwy92y8vr+P0KjgD3CHnRbVgYz+z7imf7k7gfG99x9jxbbH4LWoSHA1mZ2OzXIskHI3f9TaN8ekqHoPs7dXw+C3b2Q0v9B6pWEK6P50UNgZ2YLI0HkS7HQOXionIjWq7HAkXkH71mEHOGzn2dmP0h/l8Fq+jwU4K1OaBmU7F9HfFrCbxq1eAQjUJDmRxp8q8ei93U8gjfpsR6Z2VEhz2Fob8iCo6tEbXiG1ZCSO1ehiBSRQ5Dwd/uCvlTm/dogAL8V8bsnmdkhnoKYCWevE5Dy/azUvS0RNNJ7CE91Y3R++zESau2ABIl/opzr+yeRDGYo0TempoBYKb6PsH7vQPNyVGZFZgPRurYxPT1JRqEgYG+mywR5QiJTuBf4rpmt5e4P5bSxOTqb/S3j9ihgqJnt4e7DcsrvjtbQ87Pul6DPU3zGmEY5A6s1UGyChUO/Slkcl6R/Atua2drunskLmNnaCC5jeBvaS2hf5KVQ5K30X6TUXxidmfuoBPVBPXxCKQi0tm7WxSK4FFSlQqvMFqwDZynKY3ySRdjM9kYBnbZw9xFmNri+lnzKcrMysyyBbjN1jgv13Ic26aUaWE+MB55z97VT9/oji+MfUdvks6yJ6tyJmx2HjGeo7H4WtNVDkCb6XQ8uxIGBOQEx6QshHMVj3b2h1YOZ7YCYy4WKspHtejcFwU5sXuFxkjqGhvaz8KGSPBsiBmdfj/ChzGwVtOH/FTg4/e0FF8Lfo415PY8gBgLD9hAay8dRUJV9iRgRM/sM2rTPdPefZ/SrGdgKz7IyqELWE6LgGbRHrhjd78FQmSBWXgH+4+5faUcfcvo1N2KsihQpswzWs6WghNpUZ2X4DzP7KrKOOykR8oTD8hlxNrSurZm2vmuVLLggokPJKOCn6YNL+G7OQIf3tbwnjnOSp2X38NmdwjjdjvDkGlnM1K2tLbZ9LLLUPMPdf5aT589ovTvB3Q/PybMEgni5BDjE3T9uVx/LUCvre0ZdVZS8vU6mmAdj0OH6l0go+Cbi3X6G9vzTkIHMKu7+cnZNldreFikzy0CRpF3f2wLFYmZ/QDieayTvNygtnqGnRd67CFqmDI7uTKWIb3seQVyMRd9UHt71V9x9cqqOREDbhfiWRFB6bzNnDjN7C3jT3b/UIN/TwCLuPrAoXzPU6jNYCiLBzM5CQrcFPYJWMrPLED+5WE49LfN+rVA4e41BhiljEezGy+i7WQbYGQlvJyFv0/FR2eHI6GNdd/9XmocJ++rpyHJ7TY8govqojneehtbNZA15jZryYYS711lbp+r6BvqOF6B+fXT0/nb1FBxgqo51ELThBHQevQNZtQ9z9z3NbKPQxmLofT6eKp/sD/1Q/I5z3f2lcG/pUGcSC+kr9LQOHoaComUJlKEWU+AABN+WZcyEmV2DPHp+hxRwnrpvSIl0BPLw2T5cPwp4NJGhtEJBIXIzsirew+UFEN/fHAm+P0cKFjHcz4urkEUz+AQzewqY4u5Z+Mtx/Q+hALerNNHOJ5r6BL+fUMrY1MoKWhKrwvTEKeUymMWsNisozepUqKM0ufsrzeQvS73N+LRKZnYQOvBdA+RZT1yKNP6Heyogn5n9Blk0fAzchBjyXI1mGyyw2kLtFlQFpuMeZIV2JbK6WQUJkZdD1n0LIG3mq+lxCFZs93sDfKkGfWgGH2rBWHAZGI6VUeCBIpe555CSZrvo+snoQHkiCtroWRro0D/LEpjaTMakjdqNmdc/IQHA7u5+cdSv+FveFx0GfuvujVymqvbpQKTVzsQLjanR/LVqgWeaprzvKQjKF6ZYeJ25NgdFxtkoynxb4D9MAfd2oKbUOd/d2+auFrWzMBKyrQMs00Bo+xIKULNrxv1B1A72Re7hmwFru/vYnHYWQO6uiyBL+sIgJLMSmdnfgD2R1dBZNIDBaaQQNLP5kIBovLv/t0HehZD3x2LoUHk+PYVMeyKPlP8iYWGmAiEchpZGQquxSBlQZLXcVqFpK+t7dL+ykndWITPbCfGec1LraxeC4gHxMbu5+xVtbvd8FPSzMv/TKu8U9t9Pu/vy0bXd0ZweiYQN26DgQie5+yGttNcJigS/S1MS7zqjjj+h9XS0h7gYFfsyGcE2lcHC3Nbd58u492VkubYx9VaOf/YIHqKdz2BmrwBPu/uW4fdxiI9fyyNrbzO7CRjsKTzU6H7LvF+rZAqYdQXib7LmwngU5+SfqXL/Rbj464TfdTxMUIi9TM7+/EmmFO/8DpqzI4A7vIlgf0Hg+gjiER9A61HiPbMM8l5ZF1mzr+kFLv6mOC0noXnwHuKh30UeegPRfPg/dz81p/wQJLyN9wOoect3A3u5+zDrifleBmPXQvkd3T3Ty8zMVkYK2rmRMv9yenqF7ITOk1ORwiLLc7NlMrMzkeV7glOevM8V0XdmwDnu/uOMskWxFuIx6sEnmNm7CHprx4xycf1XIozmLO/gPsqgPqiHTy59PvW7LD5o8qHGwQW2Q8zKw8DF1LuTr4ncY+pwKIOg9C7qBaUxXQn8BQHuZwp+Q5tltRhO5+b+oehZYsanx/O4+ztm9m+Ko36XIivAVapIrUYL3w2YDGzgBVZCnwA6CClJtnP34eE9reLBAizM+/MR5msW83sd8B0zm6sZq5MUNYMPlY5wvCFwW55QALRDm9mDKGJ0TN9GzMlhReURM5cZRLBdgtwW6SR0OD8vHMj+Ea7PbWZfQpHQD0NWnFnfQstkZnuigIYAT6NvsdLh1IoDzwxFsAd1gWfaQSbL8qPQvJqrIGvu2uwdgP9wuQDmuQE2pUzMqPuV6P+3w/iPLvqe3f0jU1DBr+dkack9HFkwnoKC9STjfAESYmJmP0LuyN9x9wdKP+zMpW+iNWtdz/FKSZOZbYK+13NSgow9EG8xN4oyfqK7H5FXj7tPNLNvIrflDaiHHTJk3bRtntA30NHUDkNLh1TXHLUDZLutZVtZ3xM6GlkEl1Lyzork7peb2ZPIYmpLJBjoj4QKdyDvnk5hzHs7Fd9m9nkae4TcFf1cgnqX9W+i+baXy8J5pJl9C1lCznKC34S8ObzrNI0Aprci9A30LLJ6a0SfIwOyKCg2/4KUEPHCvXxIe5iwoM9Nl3X30m7OZjaP1wdGfoGea9C/Qh/2QQIfzGxFdO4rsnRtmfcLbVUSgAO4+wNmtjxa9YLXAgAAIABJREFU7wenyt8JXJnDMyxAT3iej0Jf5vVgJe7u083sXnqef/uonhZq8P6L6FdoDfulu5+cujcCOMfM/g/BcRxCNowRAO5+crAcPRopuqEWK+ZxBMmSaxXr7heG8kcgnuxT4dYUtD/81mvwBzHm++5IUJuHe5/EFLjO3R8raP8JM9saWSYvh2BWYjLEC+0aC32DAcGCwHseBVgNSu5fUYPx+b2X8ORw95+Y2bOh/SVDSugt4Hh3PyWneN4e1w+t0xsjmcl5SCmT0BwUx0lIyIABJfL1UULu3pc+gQkJnbpK5DN0oPwp+sCPSd3fCGnPflFQx4Ehz+CMeycjrdfx1CzQu4HzUvkeRvhUeW2MRQxHOo1Di2x3SC8jrW6nxvU5tOBbdC3rea4E3ujEe0RwGYcSgryF/9dros6lkMa1G1m/xKk73BuUU/ZDFPW8nWPaDwmj/4wEqvN34L3dEJ5vI2DxgnxLhDwLNqhvAvBYg/c0f/imzsoovyAS9F2OmKgqz/Q28FSJfE8Bb6euTQFuL1H2duSOk54DV6auZX0DlwHTOvAuVwA2qli2x3tCDP7EjO8g+RYmZa1rbXyWMaGtH7RYz0pIKNONDnnHIWHv0PD/8+HeB8BKbej3jHFEwsh4DX6T7LW6cG3OeQd56eM2PEPW+le5fUqujUiw+2GDPnU3SF0Z/3cBj4b/3wjt9PguEbZ8F4Ip6MicbsN7+SC9vpQocwmyivlMdG3paF6+gniULmCzEvXNi9w0b0br55Ph/wOA+UqUPxopQkqlDoxh5fU9ujcOYUmv2ttzok1jYsgKbDFgjg63VYr/LlnXd5DQsak1CUFcXJy6Np4Uz4B41Ym9/X5ynv0QYFQL5VcIY3NrG/qSKEE2KMizQcizT+r6OuH6x4gv2hxhhy4b/r8s3JuOPF7S9R5aso8DkAVmXfkwDl+K8o0P1x5EMCjvhN+/LKi/Zd4P8SRTyd/npgJDOzCX/oO87JLfx4c+rJTKd13emvhJTvTk+SYCd1Ws5xXg3yXy/Rt4pYl6F0ZwaetQcMYrKN8PxfNYBOjXIG/dnG9xbOdGRlfnID7jZmSJvBswT0b+48LcXS/V/0dS39WrwMJN9GOOMH47IsXKOgiGs9VnGxb6smh0/cmwBuWOdXim8cCz7Z7P/8upz+K3jwrJ9XWNBf5iZo8Bo8zsaXe/PGQ5AjGKac1cXMcpwbLmcKRxjaktGmJ3H5R3L7gkbomsne5x990L2mmVlkSH+0bazo/JDlxWmYIb8CXIDQZqVgMe7t+PNINji+rx1qwnXkEHiir9/xVyad/a3UdHt4Yj4VFi/bSHKSry5PpaKtNbof5RyPo1D4PqsyHPUcBvCuobSE9t78fQ09rC3d83s7uAb2SU/yMS/H4P2MqEY1TkCpwVYfguYJvgtleED/VF6i2DnwEGm9manmPxFObIYOoDw31ITateRINoYxTYiA5FFpFVXIvTEXRGBeuTA9F7WibUOx4xXye5+6utdbeQVgTuc/dLW6yn1wLPICa0P7LQON7d36lYT1mvlGbz5tErZHuRfCH6P7E4jQNi1mHzBnoJ2MTMlvYcvFATdtym5AcGOzanT2VoMLKuuBgJHqakIVXc/Y1g4VIID9PL9DRSmjVDX0WKuHju7Ybm5SHuflKA/XgAeRzleRYBEPaeU0Nqmrz3A+21sr4ntCjCa/yf8OwJ+2Nbsb2rUrBYXBV5NGR6JIR830Zu7f3QWvQS5T1CPkR8SlLXUkixnbYo/YhiL41eI3c/EXnXVaVD0di1/N7d/a/BTf0WMzsD8eLJOj8IeVnsiwIFnpUqfhDas3b2eliRF5GXy9XA34FfIMFLTL81s3FFfEI4B11NtrXqJWgcPhWeZZqZ7Yi8atYKCaQszLPsgxZ5PxNE2tnh5xXICjB2898T8cRnmdkTnoJraAOtG/0/Br2TnYBfh/4tivbQvD2+j0Rz0dN6sxlaDJ1dGtHjCKarFHnPwG9NU+CZC6GgIjoHnVnbQi6r3YtDKkObARPc/f7o2vYICudx5IH9zXBtHxT/okw/uhD0RKnvLngxbOAFQebcfaqZ7YPWyt8gBRpI1rA/Uu4dn1P8YLRnnV6mP30UqLclz32pdxIVLQ7QB/9A9HsiKauBnHIXA+9kXJ9p1oHI6m0qwkbs1LhOJGVJk/M896OAUG15jwij8uXQ1nuIkTsupEvCtW7ERH2m1XYL+nMMYqIbWj1llB2FLNFia+ktqFlkHRvGrQvYr839vh0JVBpqDpFV9wMN8ryBBOTJ75NCv5dP5buKDOs+ylv2ded9xwjDcXKo57kwfj8M6RhqVkKTEQxFXHZoqPsdxPQmAs85kKXcEdQsYYemyt4V7i2Q9w2gzXoKcHMH5mBlayokaB3cqe+jQn/eAi5pUz1Pl8j3NPBWG9obRs36YwoKYNHr49niM/VDcB//QdjP8fxeAHnFvI4s5OqsFNDhPlnLdiOylEACyF2RkrXQqqqF/j+BlEcDomtZe9NV6NDQ62Oe8xx7oH18hSbKTEIY1vG10WFuzhNduwt4oaCeIcD6JdpbF+EdduL5DwFGtlhH5fU9quPZ9Jj2pdLjn/BtN5Gy4Axj/zE1S91c/poaP3QYMGeTfbgPWc8PDL8Tq8/dUvnuAl6cCWOyQXj2M5DALyud24H34ETeWS3UVdU7pCv0oZCnDG08ALyecf2VsCZunFNujrCud1PC0j8qNw8ymtkZBQFslL8l3g/tnV0I9zSvje+FOq9o81wYE97DUuH3fEhQ2IUE7idT25+P7/T3MLslelr8jqGiFT3yCBtVIt8oFEwx7/4VyFij0Dq302PRS+/itfR3jqAoughnPcTPTgAemhXGAXndvhr9/jzi27qQDGNTBGW0OFJeXRLuvQt8obfGenZMvd6BvtRLL7664PcK4P3o9/soenijcnfH5aLrM1tQejdtYPIK6p+pQi9qB4jjE2aIDNcNJBi+IuT5XQeffwCydL2TJg7moex4UgdapP3vIhy2ESP6FpFLVpv6nRwAbiiR94ZGcxFhpD0a/d49jP2B0bV5kRCpTtAQ8pdOBf0YTM1SOAuqYAKwSU7ZM1LlplNzh07Kn5lRbp9w73JgrvQ3gBiOq0Idu3RgDraF6UKQHg3nMMLfqwQtUaLu69uxXiHhfkMBMmKmPmhDezME6EgJcmknxmdmJuCXSFH5pYI8X0IH8EMy7vUPa0fyTU1HlkPjou+qG1lVtd3VHO0516SuZe21lwBTe3u8GzzLCehwMxT4fIn8HwGXR7/7IaHX6Ixnn1xQTykXTmTx05GDXxvXt0rre1S+spJ3VklIILYzcpu9CQU1y0ojOvAOPczBT0XXVw7j/hHiJRPB03dy6plMxYM7tX16LLIEnYYO2p+O8swd+ji8g+9gPuQ9E8+7ppTcLb6HLiRobwk+oEG/GyWnvAFNnQEMMmqZhM4eX07ds7CudaPzT5Zr+KrAym0Yz5Z4P6Q4rSwAb7HvV4e+fi26tm34xuJ39TAwb6e+h9k10VPwexDilZauUM/NaB8qgkxZP3yzNxXkSfa2Ccgr4MvN9qVNYzEvwtT9GjpT1KUOtD81vZ4giLeXUteuBv47M8ah5Hufmrq2BTWjtazz63vAVjPrvf6vpD6ohz5qllaip7v5v4H1zWxrd78pq4CZfQMt1Pdn3H4CWNPMFvCcQC1mtgRaOAujc5ekN8gOptUuuhQdqM42syGeCuRjZv0Q5MQAyrttlKFtEdO0m2cELnAFpdkNWVVshyxE2k4uF7Et0Lt+0szGUQxRsFn0Own0F9OGCAv5vlDgQzO7jxpQf7vp/ZJ5FmiQZzSwv5kt4u5vImHOFBRA67NoTIagZ66L6OruFzTT6Txy9zvNbDnkuj+YWlDHJNDFP7w+0EdSdl8zS3Ar16cGoD8NWQud5u5ZweP+htwadwTWNrPh4frKISjXdkhYOhp9L7MqjUaMSxaMRkwHIxfETkStPwa4z8x2b3FOtBR4pllyRXFOIv/ehQQaszvtgQSFT+dlcPenzWwUUsicmLr3sZltg6yFD0CWlUtGWV5Ge8OfPQXFkUdm9jmi4DXu/npB9ukUBH6KaElm4SBdZtYV/fxruJaX3V2BT/+LgqMktC5ya04HXxmADqwtd7MNdXSUWljfE/odCngz3Mz2cvfnOtrhNpOZfQa4DfGDjd6Xd6gbj7n7lOj3rqGtH7kCCy2DMKT3IoNPQN/0sxnXy9BfCZbpKK7D+0j4GUNFbIO+k3bw3nl0ArIqnYh44t4IEngW8Fcz+y4haCg564D3DJAXX68ckNbMXqfn+pRHy6JxSrf9pJltT8D/NLP13D2BKzsHKTceQjBqWc81BgmFB1fpf0St8n4L0QBmJ9ALwBot9jVN76L94u7kgrtfZ2YrAN8KfXsGuN7l8t5HPWlg9P8p6Ow2MkD4XZt1Ls2h09F6cLOZnYqCz45D6+IgtF4dgNbsIhf/nyM+bE2ktD8oQOYNAy5z907AzPWgMP+3oDhImdP+YPPTic6oAaJkGeplDlOQ4q1XKXxjX0Pn0hnk7reZ2UoI3mZL6mEn/+hREOU+Kkd9gt8+KkVmtjA1PNB4Yz4JMaTXmNnFZONa7RZ+/yGj6pkmKDWzOdEmMLVR3haot4RegxBDkru5BqHs3YiZ7wiZ2UAEm7AS2piXCSmzS6nf3Ug7mtS1AJpvV6XyvUs5HLEqVEZAtRKNMeGuRHhKa6Do6W+b2S/QXD8o5DFk5fzrin0tRd48PlSyEX/W3W8AbjCzOagxdm8VMb5BwLU1OnDsiARd0BMr7lpkqdypA3W7qLcFOPMivOfzwpgOR0xPpmAw71CKDrZnmNkG7p4ZadjMNkAWCD/Lut8CHQPcb2YHuHslTNSYQsTiHRDWXqzIGI1czythjJegpYHcCMwRTSLnAB3m+5+BPweF5oz+exNY0Wa2F1pHlktdfx74g7v/LaPYs8AaZjYgb4yCMGw1FARkVqUqWM/3A98JuJW3oHgDjvaqmL5EPr57M/R5ZmHheUJV1veobCtK3lmBfov4wfFIgPAM5fFx20UTUr8Ho3lzKYC7v2Rm96B5mUUPk89fFVJQLu1hZkcivOZn3D09Z59DOJAPVGmjJO2AIEdWb2YNbCP1A36C1ootKcbm7ISQBqRo2c7MvuPuWQJ+zGw7FEwp874rHsFQ5NI93Mw2Qp6AeyJczy3dPc+wYRLVMVnjPrTK+02kBQF4izRn1kV3n0ANd7iP8ik+Ez2PvqcvENYyM/sv2coUd/dlox/Dw1n5ELRPH05tT0kEqAacmGdsFuo5HTjdzL6E4O12QQZDawF/NLPrkRD41rKK9iboU6Gv30D8RH+0xt6P5vciaC25Hwlp203PARuY2dzh/LdDaO+eVL7PUR63uCqZmQ3JuTcfOuPvhrx5L09ncPfxSNDfR22iPsFvHwFgZnnBZEAf58Josf0IRaQGwN2vDRq93yKLqD3SVaNF+3B3vzaj7o4LSs3sU2hxORJtRNdXqacM9aLQazohMEMDmofObDQJnYAEB88iYdMLlD8AvwysY2b9wkb8LTR/0pvVInQmCIsDK5WwXl8FwWbkV+T+IIrGHF8728weRptwYj1wfiPNcxB0rUlk2Qc8nFaStJl6BEgLgoD/lCloZich19RlzewYMoKiufujHel179CitMdKMItGo3lpyGq7KOha7qHUWws80xIFa6QtgMuCRdUt5AuIcPcL8+oys/XRPrAk9QLAociifhd3T68Z7aD3kHdLf3f/OKd//YH1KCFACgfKtOAnk8zsEHRw39TMhiFGOQl2mQgqF0dR6s8OAv4fpqr5B1qfTySfkf4d2u8L17fepIqWdSchhedl4bcBj3gURNTMPo8EbMPighmHluUKDjL9Qx2bIbif2YKqrO9ICFVVyTsr0DZI4LiOu7/RS32YsYaFfX514M7U+vIG8tTKohPQmr65u6eVGKUoWExlWk25+xhkDdpJ+jRSjveG0DehMsGkOkknI6+9v5vZZcjK8WX03SyDeLGd0Z5ZFEj7khCk77cIr/9zSAi0uRcHVR2DhKktUxAu71SR92tZAN4C7dLm+j5p9Pno/0HR/8kat1hOubq9wd0PDUZKv6DeG+VeZOmZK/RN1fU0cHCQU2yOZBTbIqzo7wJvmNnF7n5ImfpK0qrh73HufpSZnY8w/zcAMLPNgTORPGXLNrab0JWIl7srKA5/FNqaIYMJyt6vIOVhJ8mQ52TRfZBX7DEd7ksf0Sf47aMaDWpw/yPkCnSk94wUibv/3sxuA/ZDFmNpd/K/uHumBVG7BKUp98/cbOhAfniJvJWpRcanKj2NIsZ/Nu8QEyAGNgWe7ED7CX0TQU6s6znQHQV0PfArZD0+IvzfBcxwNzX59K5BdffGIkrm12VmdhBwYWIZZ2YDEPN9Ush3WqUGFKE7N0p3TMFC/WgUNCodxf4DM/szcIy7d1KQX4U+R1hPAtOV6xo/q1GwkonpsxnXEkqEPFvQuWe8izYITVLr40HUrM7TdICZpYWCibt8K/Q1pDxcCglGiyhT8Btcvm5DCq6XkBBvbLg9CEXfXhYJQtZx93avc7ehw+E5ZvbztPWUmc2HoiUvSXthfECKy8FmtjNah/4LHAUMS61Re6A1Y4iZ3erusQXF6cj1cT8zW4vawXmQmf0EHYQGI+uwc9vc/14ld3/QzL6FFFqLAg+G/2P6PvImSQvQhtHzG9yAfEEc1JTdWR5O/ws0EH1vrSh5ZwUaiKy9ekvoC/Dl6P+NqMVIiGk+8hVJzyIh3/VmdhqNPUIyBbzBu2ptpFQf5wFaaybRi/TuWbTb3TfpxfZx9/vMbD+0f+xCvRDSEKbpfunzV0Zdxwfh74+R8HhTd29k1XcacLWZbeXut1R6CMDMPq0u+PsVeb+2CMAr0nT6ZCKtUKy4WbpqJWHufhAEuzcFAeXC4fbbiTdK8E6av6yrfzAmuhW4NczT7yN+aH3ED7dT8Lt4+JspyHT3281sS3QWPxit4e2kU5CQexMkR+kCDkitA1sgOIhOK72cHJ4eyZUmIAz9TE9EmAH3mZYt3dXLysLZlvoWuU8upa2lihbqj1D0zEwrJ5hhGdAICzOvbDsEpUXun9MJiwuKxlpk3dwStYHxaZYS98yLEfN2RxBKjEz1axPEVH4KuKiD/ZkfvbNmhb4gS7RtgW+HBHLnGRfl2RAdTjolmDgCbcJnIXfshKlYEpgLzbMjO30wCszOjQhD0ZAwPZm3yyDh6qHISn7rMq65M5FmZ8uJ0fQU8mxJY4280SFXQHffuE1VtQJZ0RLchZn9mBre7WNUFxAdi9av44Ffp93zzOyokOcwxHAXWUdXoSPQ/jQE2NbMbqSn1fS3EATNRORd0gnaC+3Hm7r7U/GNIAA+O1jKPArsTeQ65+5TguX1leiwkwjgB4dkyPpjuw57E/QKBYvIXKtIdz+ZbGHChdTWhN2RoCrvkJIcZK5z9zKwILMztaLknRXoNSRM6w16G62BXzSzg5EXxHFonqUFbyvTU6gS01hqHiFFCj3I8AgJAt9T0J6d3LsAWV5iZj9Ca+p33L1TcA/nA8cWGS38r5GZrQpMjAUX7n6mmd0L7I+EHLGH150Ic/vfofx5DZqYA517XgZ+Yz0x0N3d02e1R5Bi8LpQdyOc4zxh2yTk6bBOg/5lUjsF4BVoEjpb9FGLlDqzNUsvI2Xr0FBXF9lwBL9HEA5V5FhzIGiPuap1sSHNCzOEzRAUcWY2Z2Ko4+4vmtmdSJHRVsGvC4bp6+i8vBjybkrLPaYCB6KAw50kz/A+K0VmtgiCRtuBepxkN7OrgZ+VUGz1UUR9gt9PLh1PZH7f4kJdiYL7w26JNVmzgtLg9rucu19Y0f2zE9QS41OBEsiDs9DiOBi43cxeo6YpXxoxkQaMCnk7RU9Tb51aitz93WCJ9l20Wf3L3dNBRRZGTGEdFlA7KFhLPIOs6ValJ97Yv5GF7TWdaDtFeyON7XPA/u5+a3wzaItPRYLhvejsO22WZmfLidjCdjBiOJ/JyZsIea5xYWXOstTL6+P+aE5s24o1EXofz7p7psdGYLKPMLME/7et5O6vmNlgpDhbg1ogJqgJx8egPa1T++nqKMDcU3kZ3P0pU4C5r2bcm4DgKrYCtialZEVBWGZFt/xMiqyBcoPWhfc2BHihkcLOzNYFVvAIbsTd94ju7w7c4+57ttr3/wFqRck7K9BVCON2Hs8JcNopcveDzOyviFc8PiQD7nD3GRAhJqz9Zcjf31+hokeImc2LFJ2roX3uIbQmxHQjUmpuR+dwfk9FiqiRQeg3cnZagyrSo0TCrSBsvcfdz6OcAc0eJdvZNOOaZ7SRKDAN8Z57F9RZhHP8Pi0Gh21GAN5meg5YxMzWdPdOu7/3UT4Z5Y0NShslmOIFfQN9O9+iZsgznnyL1KrURU9BZWLosCg94b0mIuFs2ymsoXcX3B+F5AG9Rma2PDpnjwvesPG9hVD/l0eC8/vo6eG3LpIV/D975x1uR1W18d+i9yAdkRA6iAhKE4IQOsJH7z00NRRBBATpoCIgIIKAICEgPfQiIC30ToAAoUsXqaGXJHd9f7x7cubMnVPumZk7596c93n2k9yZPTN75szssta73rWMKZll3prf/RZ9dXHeQUZ4ddb1MpGFTbYnYl9l6rTjBuQs5wnIPPHpIb4HkyQz1keskV+iydJ8sXqfo8XDEQWzQ/8GnG1mi3kLWb7DAqwmI9mlE52mFZ0bgmH3GjObm1gWUXdvSgMxJ+wMfAGsFYw1yTbeGjy6zyMWWjsZfvsscyLOsDWzLmTY6Bh5smEQCsvKYvQF6ZM3k3TsCRQ5kDuCwXU5M1sVGaKrZI08lhG8IMxAc0ltPkLPKxXht8j6e5SG8PyPQoumeqydyEgxIpRGkRq7o2RIteYCC9K35AyKRMtO3jbBMSjc9XIz26MRaygm+fOIu39dRwIoDY4yqL/k7p8CuPuLpoSaB1CRHzkpcdxaKErixtSTug/qQRuSOBAZfS8CfhkiAqqiKNz9XTN7jnQDYi5wdw9RIaOQnM54M3uX2kkCc9GiTcLMvo+MjUOoNjbeBZzu+UoHJY1bQ8O/jZi8EVpi0NXBm+Sjwz2Waq3XlhAMuy1FkGbAS8gBcZspv8w1yCBVVLLYyQJmtjLdv6lROTC2Z0Wav42u/wP0fe2A+llDTPZL0ZzgjgIcTV9SnYD85fDvyijXQly2MHfHqSln00hvoFtsZscDWxfVp8au8y9Emno4tu0INIez8Pel7r5j7LCjUc6KO9D49ErinAshneS1w3n2K/Ie+hM6ht8OOsjJgByQy8SnB4iHin2DROyPJD0Z2NdFN8bdR5iSSI0KHfutrerwmJKdzA580wvevG4OiGDo7U1jbxzfB+5KM/pGcPe3A7Nv9d5rVlMomznRE8ZAPayBQpnbAoGhtQhKhpN6f+5edpKaNLyPQpuz4gUkcdII81Kw882VPK6IBHKN8DawoplZrcVKWFCsQCXpW79CkKu4kcr8NQqbz+X09XYmmdy9PEa1GzI5edsAf0UL8s2Al0yJV2vp4zoytjnSdX+R7rJAzaDLzK4BdnfJgT2DHA2pcPez0OK2CGyF+og9Gxi3XkTsqkJgZoNQpE0UlTYN0oJPQ94GGlMTbHf0Pk9NdR+waChDzWxvd89LYuwzmhvLUuHuF+TUjuh8g3I61blIbqiluV9wprzbqD8JTMF5c57v7IJ++wEoMdYfw7XS6rpnz3vQrxG+64up9B3Rg/Sw/0FgR3d/Lej6xjFTyrYI8dwa/6lRhxA9MBRFSUXXfhAZey+PHHAF4X1gVjObLqy7Iyf7qWb2BVq3D0N9y001zpEFg2iOfBPp9ReJKRBzf0y0IRjjj0GyLQ+hBLHbmdnVXknquCl6jpu6+xfJk7r7q2a2OZJA3IyO4bdpdDquDjrIF5kmPnkgDDQ1hdKTCJPewXkwG606idQ5YVut6qmTpxCWuy8asKdAmnO7hX2boQXLYe5ec9BvAVXSJ1ZushPQAuTLJup9Geq2E8pmTvwGeYCzYhdk3Gu0CBkKrFYUM9jMFkHyJuvSXecqjnohmGXiOmBzM5vGs2nHng2caWaDvUYiiMCgW41KktD+hltR0p6TzOy3yeiNEM74J+qEhwdjZSSHEWcsjwKu6gMMp+PQe/5npNlfL1t9T/E9mjAilzRGtRXydPKWhKFUDIkzU18exqnIAEXjck8Tb86AkjRugRj5v+zBsUVgIfSbNfrev6aSXKkInIS+u7uR7EOPNOATMgn16g2l+zj9GzQ/uSr8fQVi3cbzKeyGvuezzeyZOGstA54B1jSzY6mwARcJ/UpD5BSdmDvc/TwzW4bW536j0Dy8Edv3YPS7TNlqW2sg0stuhDyIBf0WIUz/LhQx+TnSkY1/UxtRkXdZjopWeYQtQql7GWRYroXTwr9voyjSEb3ooLwaOAhJSlzp7i+Z2XnAHlSiNwzJxRWabL4BpqdYnfvn0fjxlLvH17ORTNoe7n5hYO8+h0h4keF3LuD6NKNvBHf/Iugkb1SrTgfd0Y6LxA466LPIYeJTBlZFjOc8DFc9mRB1q2tmI4Cdwr7PUUbrOF4AtkUaacmwyJYRSZ9YeyQ7ASXW+Gk9Y1kw4Pw01G0n5MacMLM50GRgCN3DL89z9/dTTvgh+TBMh4Z/G4VfDkb3nLvh18y+h969ORA7ayo0IXoQsX/nRBOoB5GObjviSKRXfaGZ7dUqM9LdzwmGplvM7Ew06Y8nV9sB2As4zd0Llz4JfUU99nVT2aZ7iD+h/u/XwGZmdgnVWc+3Q3IE40LdZJtXAS5BySqT7d4dON7MdgiM5nbF0iiC5eB6lVKMKPUMKxGLaC2ku1rvvCMoYYyKoS1+mzycvCWjR+HySZalt5B4M/QZY4CNCYbf4Nj7BQoDnhMlBjw47Fv0nuDWAAAgAElEQVQJyTFc4e7j6py3x+MkGi9qamPHMD/Fypusgfqw9Vp0DA4N//Z4nHb3D81sR/Qtb+fuVySOeQXN5a8GLkeG4q1baGMSJ6Ikm3Gjz+BQmkGV4dfMvoP6xZfdPTXSw8zmAxYGnk6+SxmN5/H98T6h1blfy0ZVM/sZMrgd59IwTauzJkrUerwr4WfUmHbJE9MfcBAy+l4JDAtz8kkIhuGzkUbrQVRrlQ9EzrUPSMek3BooIWEtXI7Yvbd5IhFw0XD3Q4BDEpuHobnBlsBsyCh6vLuPoQSEsWgwUDehZvimR3j3hJDJeucCu8a/aXc/wcwOo1rXGBSl+jmai0bs3fvQHCzC2zSXfG8a+ml0W1Fot4lYBx30aeQ08emzyDJ5MiXO2RklSNoDLZyrGG2uxEVvIpH+XBfV1j7JTgCuRxOiC8xsWMpEfQAKTZyHOprIJcES/zZTt/sOTeIvRgbkeL3vI12ng8xsR3e/udWG5oSpSQ8PzgOHIKPvce5+lCkh5s7uPhjAzNZBocDfAusV1IasOAVJ4GwFrG9mj6FQt1oajqkTzETfWi+D/f5mtn/KeTP3tWHBchxio9QLpSuEfe1KVLYBYqYtCPwu2USk17i1u79ZtcNsKaShOQNi31xKdbKMbZFh4BYzW8nz1bTME5/SnJTHCKpZRI0MK4beyT/XrFDAGBV+l8jo96y7Xx+2TwFMlTSGhXDzvELOsyCTk7ds5B0u3+Q1PzGzB9DiP4q2+huVBa6j/j7CDKh/H08sIimODOPkC8CPzGzaWsSEYFBchua01VvF1CiRb5ZokGavkzbmrBqunzT6ToK7jzSz3yBHe2a4+3VmtiKaRw5ExutX6EGkXgL7AUeghJ61jCDzIEfAUcDvE/uGhn+zOrl7q0+YC+m0JrErsDzSy66FR1A031Dgtjr1OmgdmyCZtJ3S+hZ3/8jMdkLv06ZxqRGTzvjIrBF07r5dT+qb2Z2xPwci4+yTzV/O12pQYSJwcii5w6TrG8eWZjakRvWpUAL1qWg8l8iabG/a+PZAWFoW5cSIs43fpXp+NhLYy8zmcfdU47SZzYP059spx03bo18ZnTrooA3QpxdDJWNf5OndyIO2bQ2D+Ri0qMkbbZHsJOBExN7bGviZmd1ANbNvIxSe+lao204YgYyTLYfhBWbnVYiR9BBa9MZDxXZF2mFXBlmV5zO1OBuWQgzLIrAeMuQdk7bT3W8zs/WAZ1H44x8KakcWDKUSQjkL9b+dtIzjEbL0l5n72mAEeRi9fxPRwnMGtMCZJ1zDEXulMLj7QyaNw60QcyKZ9XxkDUPOsaG9x6Mkn1V9m5kdFer8Dr1vWxZzB5lxD/CDJupdSMXwuwv1DSsRi+g6d3+qzjn3RPqcmccok4bhCKo12i9ATj+QYfksM1vX3e+od64y0GHItYyj0O86GDmRP0fMz3tQ/xLH3Sj5z8akGH4zjpNXoqiAE4CkoyzCHxGjvaZRNAc8hYwQRaPWOD0bSiDUCC+jZEy5wN2fJBiWApP2vgzGrg0R27emvJy7P25mr6Dw86Tht1nUdXK30idY9ySJ86RsixDXdx2bsn85FFZeLzz8czN7Elgp0Y4jgScjx1ud9m4E/Mjdj61XbzJEfCAchML0a0a6uvs3ZnYv6tvi2JWK/ElvYkjs/xbb5oltEeLb89YebwWDYv931G8no5Hi+BYlSq+bAK4HmIn0qMP/Uj0XWg0Zg5NzsZmQUz/CsWhudJeZHZB0XJqS2Z+M1j55SPtNNugYfjvoIEd0FkOZ8ENgaq+T0CxgHDK05I22SHYCk0IQ10ShMMujMPbkBORRYPtWQ+cbIK8EaT27qNli6LfdDS1mD3L3pIf8DuBcMzsAsfN+S05ZrkPIYxyrpmyLEC1CfkwxCRpA2of/jhnpugDMbGp3Hw/g7q8EnavtaE/Dby6/TRv0rb9FjNjhyEl1FmK0zGdmM6Bv9I9oAb9TkQ0J/dNFoTSL1YEX3D1VUy68Y4ebWaT/2644BnjQzPZ397/UquTuQ6P/B6ZuFsNKhKWBh7KOUSE0/x7ELBoD3ItkSuIYidigm9CcYarPIOrnvT2TUfYIZjYXclYNoVoz+y5guCtJ7CTEZKWuQ2P6zzxkuE86Edy9y8xGUx0CG8chtD5OnoEcIvua2fJUtBUHmdkwKo6lMRTLMD8JuMbMVo6eQyPkPE5/hGSTGmHhULcIHIOiB1rFIJqLQHsBsYJbRRFO7lFUG87Wo3H0kiGnSRLz0t15koY36W7EPxo54uoafpGhcjdkmOqggnielPHIydwI05MwFpYRiRGwRuz/v0Xv4F8Rm/5xNNd6LewfhHRql0Nawtf2ViPrYMHwryHn35UoajQN3wLvJxi3LSFEJi2JCB1pGv93Azua2cEoyd1x6Hu/JVHvB4njb0QEi8WBG81sHNXPf9bw/weBmxJjZ0MG9uSMjuG3gw46aBc0a9iZi2L0TNsl2QkA7v4ysKKZrUoKs69gHc4sCdKupTJA9xSHolDqt4FnUhazk+DupwSmTJ4D/ND4JdCCsNGi8F2KS9DwNRB/HyOtxbmo1s36CIWsth1KnMjnjY1QluG9A1tl0mLVlbji3GCkecjMHnT3M3O8dh7f+vQ0F7L9BDI2tiXc/VkzWxe41My2RAuIWtIhUSKkBclHp3TqJs/TaIw6FBl9TwB+5+5uZlWGX3f/2Myepv2+6yz9e4Son887OVOPYGZToqiatYDvUlvzNnUhGZwk56Hom/jKM9KLPsTM9nD3kSnnXBl4pAlj57vI+ZuGNWlxnAwRTesiB8MqoT2gucbq4X4eR6HYRcowPIWYW7eZ2akogWW97/kN8h2nHwA2NbPNvZJRvgpmtiliiKbuzwp3T43o6QFmRpEIjfAZkgRpJyd3PEni6khmrVYE1yR9V3e/IWX/N4T7a4ABJCR6eoApaQ+GZ1shcmiFP8cCazQZpt8WklLufnf0/9BfgpIEpznVAE4zs1+jiMtuht8QIbYXMig3GlsWztD06CSTcr2Y2QXAvfFtPUFCVg1gl+A8b4S0pJN/QJI2x4diwO3uPimXQnAEJxMSD4k3CfhOKEmskrKt833WQcfw20GZmKNxlQ4mI3xOg0lbWKgthcJ280a7JDupQjDwZjL8mJKE1Zt8EGdfeYYEae5+HXBdK8fGMDdaEDTCGBpn/+0JIkaUIWbnfdRmO0WLkIcKXBi/jQxEEaIQuJWRRx+Tq/tHKCS4g+IwCBgVcww5qE8K+m24+2MhScXuQG6G36Sua9BJi5i5cZbhKOCqGs6rFxAjqhHmpTkN3TLxU+R8G0jFYFULFyYXQOH5zQ5808OIiTdoIDPR5Bi1EZLu+Z2711ukvEpOuqJ5Iaf+vXSEhfm/kTGrUXRLt98oSDVchgxB9yCt/dfC7kGIEbY6cLGZvePuydDWAaQzpJKYidprtUzjZGCurxLCZjdAi+8pESPyZuDaBu9nHogSdBqSmUnqlscRaafnOU6fjBxdl5vZpUhuJS6rtTOKpumiIH3OHPAuzcnfLEUladbQ2PbSnNweS5IYpNVuzhCZMRYZsAe4e+p8yMxmQc60F1u8xsJUh6R30B0XIbbs7Wb2K3eP6+diZmsgpuwMtF+OkghT0NipdmowEh+GmK3ApISdd1ORAKuH3PtXd88aZRdvs1P/HsZTSbZ3REpbXgxj5QHIIf4I3XMfrIUcgDfGtq1BB4WgY/jtoEzUytrZQfPoTzrBbwMDQiKSWiHMv0CGiUZJKFpBuyQ7yZSdOFFnOxRG2Mij3C0ZVZgg701zbKjMHusEPqXCcK6H79Ic06UpxNmpZnY0WiyWyVh9BCVpmM7dv6YSHnWqmX2BDAfDgEUpTm4iNwSD23JUs9cfL5hRlhcmUr3gi3QE5wDi4dzvIB3FbjCzqYBtaI4FkspkN7NVkATM/HTv/3cHjjezHVIiAs4GzjSzwSlGqOjcg5EG2z412lU6zOwXiCkLWiy8TJOOODPbGcl0LIsWdhcQEhWZ2WYovP0wd/9PjVPcCuyTwxg1P3BjE0a1CaSzXDKjtxlJbYg/oL7oTSR78Dw9M+gcid6hYe6eFnZ+npn9HH13RwDrJ/a/RyU8tx4Wp3tW9Ai5jJPufgvdQ297C2/SQ+NHnuO0uz9gZvsiQ9QOocRh6Dvct1kpihJwP7CtmW3g7v9KqxDmlEtT0WtuNyc3qC9KZYg2iauRDNtwM9s+OY8P84/hyJlyVdD1jWPZlG0RItbzqsjB2kFtnI0cTasjJv87VJwpC6I+y5AcTjsn5hrTZJ0NE9tORnOAe4FTkSO918hCEYIMw8+oJI992N2Hh31zornFKxFxIUJcVi04Y0Zkkcly92eonRASdz8LSafFt91do3oHWeHundIppRSUUXVihuNHAF05tOP8LO0o+RkuDqzeH+4fuBxNDL5FWpk/RiyLS9CE60gU+v4+MFcB1z84XO8vsW1dSKcv+vssZAAaVvCzuAIt6masU2cmtJi7uMb+HUJbu5CT5VE00UotiWPnR+yl6Ph6Jbd3J3oXEdtoPDC4Tt1V0ILsX2W/uwW+B1uE57BlbNs50XOP/T5fA0uX3d469zE1MrSMi7U7Kp+gZDNTl93OBvfwPNKJjf7+TWj/Bol6o5F+WvL4uYGns3xTiLH1eajzMtJL2z2U49ACoyvUWSrl+FNCn3EC0lSfOZSlUaKnT4GTy37WDX6H51BI7/o9PG5E7Nl/Sve+/fth20F1zvG98A5nGqOQNMttiW1V7QnbHgT+V8AzXAQZcnq1f49dv/Q5B3KafYi0hls5fhxyWjWq9zgwLmX7ZWj8Wr7WOwCsE7adU+PcLY+T4R28p8zfoJ1K6A/PC33ol6G8BPwD+GHZ7WvQ9hWpjKV7AtPG9k0btkVj7yopx78GnFj2feTwHGZA4/REFHFxLLB9KMeEbRMRyWNGKvOoRn1gvHxOxjXX5FDCe3cilbE2Xj4N+6Ytu5012n4+Wove20Tde4HPEts+QdE605R4Dz9GrPbo/Z6YGFu2C9s2anCeo4CNy/5NOiXHd6PsBnTK5FvaYfIf2jGCHAzIfbW00e8QGf0+ortxKBq8xhU16QqTxmfDte5DoSldwJ2IVXln2Pdk0QN6mKA2O+l4uca+yMg0DJiyh9f/Z7j3xxALbmlggVqlgHdgw9gE8VjEWJ4KhaEuHCbxn5BieOvvJTyD3yCj0Aso7PonZberQXtvjX3Db4f39t7w/2hSemtP39Nevo9LkANlyvD3sqHtTyOj38woKUgX0jBLHh99U88D+4d3fPVapUYbrgrn+AMwRcr+KZARvQu4MrEvrU9ttkwo+/nH7uNLEkbTJo7ZJTyTJ6iE9qcZWl8H7mxwrjXIOEah8PyPgAGxbUmj33zhXm8u4BleF653N9LfW4pe6N9j1y99zoEM9NdlOP5T4KIm6l0MfJqyfaXwzrwBrBu+3UnvAGLev4mcDKlOPTKMk8iAleo07qsFyWesjYwa3Qycoc4Pge+V3dYC7v1QKmPp18jo8yLwVaxvOrzsdvbCcxgY+vm4czzePz8BDAp1j0aGraNj+46qUSJd8rnLvse+VFAkyWCkpb51+P90ZberQZsjw2/d9QVi004kRggI28cBl5XY/gWQU7MLuAE4kO7zixlC33Be2c+7wb1MEca536OEjrvF9s0JLEYbrxvasZTegE6ZvEoYlKNyReg0BzZbym5/fyy0wSIs3g6ki3QCMqh8HganF1EoXqETdrTYfiA2aUwyKx8F5uuFZ/ElcEkT9S4BPq+x7yuUBK6V6/8PhazPXMY7EP5/fOL5jw8l/nsc3wttmi5M8PZDIbtHppQjevM59aWCHA+RwXO9lP3rIW2+icAvy25vnfvYKdzHhrFtkQEtucBcLeX4D8M3NWuGNnwAjG2i3ljgg8S2nrCaupWyn3/sPl6nh4sq5Mj7JN53k274vRF4NfZ3qpEo6xgF/DJc/zKCEzHeHrTYuSq8SzsU8AxLZSTRBnOOcP9XZTj+fuCBJuo9UKsecuBF/cfHVBzf/4uNcfs3OH8z4+QfU457EiWzLe03yPG3HIBC+L+J3XfcyLFH6Ht/EvadF9s3nJgxoS8XYLPwuyb77yeBzeoct1IPrrFXju2diNjoi8X+zuyMRI69TVCE3r+QDNZZyMllNY7pNh50StO/4xPA0bG/V4t+075YqKxFu0Kfch5KRLdgKGugSICov9k0cfwolA+irPZHUYF7xbalzXceAp5u8pwDkPTfRYikcXBs32LIeTl9zveRC2u5UxLPtewGdMrkVRIT1C4qXrUeDfRhYGm5lP0c2qkgltpdbdCO0heDsbasj5IT3IjCKc9Bk+rUSWMB1/8YuKmJejcBn9TY9xZNGI9rHPsVGRbFeb0DKOHMHaE90SLmK+B2eoHpi6QW3m/QL3UV9d4izboze/t3yPkeHkTyAjUdJsjh8hnwYNntrdPGqUI7Z4ptmxE4HfgvWgQ8BWxR4/jPgJEZ2/AFTbD0EMsw1SHU10vol9+iB0ZLZOi8NbEtbSF0EfBl7O9CjEThXbo3tOGVcE9dSNP7BMTkj6JNch9zKJ+RVPpYjxLMvE+Li9UwNnTV+t5Dnc1Dna3q1PkZ8DDdjXVP0WSIbSvjJGKBfQUsWObvEGvPHIhVeSvwTCi3AocAc9Y5bkYkr9OF9GFvTH7byFEzEcnZJPf1O4MfkhVaEViBJhiqYew6oEGdWVBC2TylvaJ3dbHE333KGTm5lxrfVFszSRvcT2T4PZhqJ1py3j8BOCTl+P8L+1KjDnqh/a8Dz9b7jcK2y0iRJUs53/qIuFDLALtR2LZNjvfQb1jL7VY6yd066G28QSWRwxxIp/SNFs4zitazYTqdxIaT4O4nUEmWUybWoE2S1Xm5yU4gn+zEtwKrm5l5GCV7gNeQLmspMLOByHD1L+BfZjYlMHvY/aGHZAQhQdHM7t5KH9KoDSuhiVEXcCnKmh1poS6C9BcHIDZAM9nZW8GGwLUFnbu38H3kWKqVoAh3f9vM7kIyB20Jd59AIsmSu3+BkoXt28QpxqLFcxa8gJKGNMK8SJ+yP+JI9O1daGZ7uftHTRwzNc0lV5kLLfQiGNVj0tDw7/AmzlUT7j7BzDYAzkXhr1EyveVDAX33u7TQdzeDJ5ExbHLGMYildLmZ7eHu79WrHMakOB4F/gJcZmZXI2fLf8K+QUhjfwuU3OfhWud195uBm81sdsQmmxJ4093fafZGGo2TNXAqmj/caWaHANd6jaS2RSMkHrsYjafx7+37SLrhoJBQ8eaUww9ECXcvQhEjX4aERJPg7u+a2XOItfcZzfWhfRbu/j+qE442wgTgJDMbgvqcj+M7zWx5NBdaiBzHFY8lkUr7uzdhZgt67aSeybobunvbJ9PtRXyLjHBxtMVaLgvc/UQz+zea362GNP5B88C7gb+5e7dE3+5+o5n9GrjJzM5Aa7G30Foi7Tp5r1/mRmzeRjAkUVa7gtkPEAFlKuBMJFN1eaLaLShKdZOUfa3iMJR8bh93PzO05cR4hdDXP4UcXB00iY7xq4NehbsPiv5vZucDO7t7M5mNk7iH7obfaVD2ShCj5fXw/wWAWUP9h9Ag1adRJ/NsM3B3Py63xuSH+cu8uJl9BDzj7quV2Y6AHmUnrnGOo9Di9M9m9ttguGoWFwEHm9ns7v5hz5ufGf9B2tu7A4QFbNrC/ESUnbqIsexAFHK9qbvfFPqrpd39MAAzmwMxAzZAIUlF4G1KNMDnhKnRpLARvqTv32s9nAGcY2aLu/sLLZ7jbOBMMxvs7venVTCzwWiRsk/a/n6AU5ARfStgfTN7jNqLKnf33ZFz+Qf1ThqMZkshBm6EwoxE7v4ZsK2ZHYNYnwsRjH5I13d0EdcN+DNwrZmt4u4PFHiddsZfUYLEzYCXzOxx9J6kvkdonEkzwhuwZShp+/ZHMkFVY5SZ/Qqxy/8BEMbZTGNtnXEyDS+F9i2AJKMws/cQgyrl1L5wlrbVgpktgeYw06H5+flIhgP0TeyK5kJXmtly7v584hRbIRmHPRsYrl8M53kGWNPMjkW/P8AiZrZzM+119wubqdfHsAIwEjEVR5vZdu7+IEAwYB2P1leXAL8orZU9QMwJMl2tOgmD2xNmtru7X93gnCeib3rKvNraD/AmsJqZLeTurzas3f64L/qPuz9JWIf0EKOR8+V3odRCEUS0z5DxtxEWQvJh9fA7lKhvM3e/HsDMqoy77j7ezEYjB1xeWA/Jmp3ZoN5rwFo5Xrffo2P47aBPwt2HxP82s+lRqNuLwIHufmNi/4YotM+Qoaav42g0YCS9qo3YQRbqtKPh90tkyCwL06AJTDvgTKRLtynwnJldjDRSARYHdkSsopdRqHk3uPtbwQh0A7BpYFTWM5DE34kTgCGIRbSruz+X+Y56hiTTrlHdIrAKcgSkMjvc/QMz2x4ZqY9Bup1540ZgOzObwd2bMZ62I14Hfmpm07h7qtMtODJ+SsVZ19Yws6WQk3FOFFIXTYinAKZKu093v9DMfgjcZWZHIOmBHjHF3f2cYCi5xczOJJ1luBdwmrufXaPtUyMj1RAkXQFyMIxCCeHGpx3XRhhKZeybBbH4asHRou1WYJ/AGryoRt1fICNvnM1buJHI3cciQ3avoQ0YSe2AoVTmSzOj76EWnOpotTxwCpKR+kerJwgRL0ujBK+pDGEzmw8lenva3cfFdg2KVwv/1jIWFME6j3AIMs4d5O4nJ/bdAZxrZgcgZ8VvkSE4joVQX9qIrfw1MgTuhYych8X2DQ6lGbSt4dfMpkORc4uhvjFtbtSN+OHuzwVW75koEebdoc9bAYVxf4n0QjNFOvQGQqTWsWg+MW2dqkmD2yzAyDCuHpAcBwPj/3KUlPH9XBvd93EVkkV4yWzSK7eLme3SxLHu7oXaono6X3P381AkX6vXG4JYsNOETR/SXMRRXhgNrGJm87r7f9MqmNniKEHxDQ3ONQQYHT2zOnibBs71HiI31nIH1egYfjvoLzgCdTqLp3V0gbE3GoXKHom0xPoyjknZtiDKOvsV8G/kCQNN8NcBpgcuiG1vN1wKbFGiketlJD9SOkIIy7oo3HdZqhcpoMHuSWDzEG7eDWFS8xtkKJ4CvR/dLkW6M+DfiH25AvC0mb1BHTaUu+flce2JwRfE5C8qPHUOlMAnwgSQk8ndvwKx9szsHsTYKwJHI7mHK83sF+7eLo6JnuB64CDgAjMbljA+YGYDgL+h0PN/ltC+phEWfyOolqS4AN0jyFlzlpmt6+53pJzibBRefk44X61LpS6GzCweun1gKGnY38z2T54TLVpHIpZf8uJ7AL83s63SwhfbCEnDTzM4CRk0hpvZ95FWJcB0ZrYkYg3+Di3Q4o60E+mnRiLKZST1tJ8vAj16j9z9gpyv/z5iZmVBlHB0RcR6TcM8wF0oAuj3se2tRNoVgTWRgzVp9J0Edz/FzIaSzuwaTx1WZwzzI/mo68xsReRUH4gcAK9QPdb3OZjZFmh8ma1eNWoQP8KcZtdAEDiTyhrjGWDbEpz/PUYgOtxOxeD7MfBpk4evgxjNewErm9nWEXvVzDZDDprvoGjT7fNsdz/AUWiM2BJ9Z2mkpFoobBzIYb7WKo5DRt8TgT8l57y9gOFIIufiMJ+riiQxyQSeg9aFjQzcs6N3vhGmQTaGvJAna7mDGKwY+bAOOmiMmNRD5pAZM3sFTR43aVDvOuAHRYWtlYUwwD2OJvh7u/v7if1zoMncGsDy7l4Isy5otR0EHOfud9WosyZwOHC8u98W2z4bkiZ4Aeh1I5eZHYgG7O83q/VVNEyWoY2RuP4CVJhHtwLXeZ0O3MwOQ/czHmU2fpk6Xmd3n+RMSGrkNYDn9A0PRIbcGdEi7EpqG7amApZEE/W33H2prNdPac+7wMNRn2JmJwEHAEu4+0uxelehBDp5Tnqicw9HBuj/QxI1TyBWbK1w3FZC0gqFSbtyNJUEbjcglqqjSdtGyGP/FvAjb06ztdcR+tDHkLFgDErOtRcwwt13C3W+g0Ktz3L3XyWOXwaxamsxsargKXqHPfwu0/ARMgy8gdjC8ZDqHdC9fQgs63U0mfsizGwNxEwakLYbGQg2cfe7E8ctS7WR6GWaNBK5e6px0cwWRE61h+NjcXhH/oZCJl9DmbPTdE0zoaeMpDQ5rtA33deIBRgMdqtF30jYNjtKktgnGP5FwMxGou9s0QzneBSYxd0Xb1DvReAjd/9Jq9dKnG829PtlZoKb2Tco6eWODepdjBLpTZfY/gj6NheIWL+hn0z2y68BT7j7Gonjq+r2RQSW633IMT+S2vkIhqP5UhpxJDrXQcAfqDh7Hga27q35uJkNQsSctYDvUpu12805ama3I0fCucAR3kC3O+Xac6NxcU00HuyDnCp7o/nKH4Gj3T3rONyv0Q7fVNb5Wuw8KyCDdiMW/VqxYz4Dnnf30rRnw7pkMzTnvhutIZ5Hz2Jt5MS43N23a3Ced1FEyaqxbd1+XzN7EpjVY3KeGdv/bxR1uWhE5kvp1xdHjqkb3H3zPK47WcDbIMNcp0yeBXniJqLFx0FogvEx6Rk0JwIT6pzra9SJNbrmFcBXZd97Ac/yQhpkOg/P+S3gogLbcQWaMM1Yp85MaDC6OLF9OPLEdoXf8wHEAh6eUnLP4ok0u65FRqltgGnL/l0z3s8r4Tn/sIVjV+9JqXGOHmXpppItdmLi//VKF/C7gp7foyjEKfp7l3C9X8e2zYhYcy8X1IaeZLnOLdt2AfexCPBIvK2x368r9P0Ll93OBvdwcmjr8VSc5mmZkh9HBobk8beE+hcjw17NPrKg9p8Rrv8XYOqU/VOhhE9dwOllP++CnsE8SMbmaWTo/ArJQ50GfK+J47v93hl+i4nxdx4tKt9LfNNfI0dT3s/h3nD+P6HFWivnaOpZICNM2/ZNZRVkmPsKMTJ459QAACAASURBVCutxXO8jxa9jerdAPwvx7afT535eAv3cFcT9e4iJQM9CjHvAv4S25bM/n5W+N6GpRx/FLBxi21fDDk1yn6XRob72zD2+0yM7Z8jvANvA3PXOMdsSFpqIprD/xIZjLoQo26TXriPpaisARvOeVKO/xSF8mdpg4V3YgKVecrbwJpl/859pSAny4kltyHTfC3s+0viXUy+l5PWKonj3gMuKfn+pwrj+xcp3843SGpoqibOc32ov2hsW7J/XSFsuzjH9m8bznknMHvyumi+dHe83+uU5kqH8dtBaQjemoFIj2lFWmRBhXO9iULTF/baoe8zIKPet+5eaiKxvGFm/wVGeWPv3WXAEHcvJKN3YF6/4+4/bVDvXmBed18ktq2L5kOE3HNgmSba9Gq4dsSsBQ3gvZrsJC+Y2ZdoQbVhCdeulaUb9GzHAVVZus3sNSrPfSDSlasVwvMtmoxfA5zhBQxkgeG7HzCfu78fWGqvownVaciJsjNK7HaOuw8roA3NaKRNgucfjpwrzGxV5CyIa8ve7e731T6qPRBYc1MCi0TvWw3mw0jgp8k+1szGAW97Aez0ZmBmkUbtorW+lyAP8yJaKLV1/wZE2tDLUf0+Pe41tKRzuN5RNKd31+g8TwNTuPsPYtv2Qgbhy1BEzMZocXa2u++V5Xop18/MSGqW1WVmI4Ad3L1tEzdmeY8Cs2xPumtm34Uc1Kl6oCad6FWRBvXzwHXUjubAU/Sizexr4Gp3rxt6bmaXIFmoZiQRGiLnaL2bEQNtiNdOWLkKCjf+t7tvkNg3A3LSLgE8iBLj/hlFV4xEMi6rI6bbinn2DXk+h4zteBv4wN2XqdUuM5sZrX+udPdfJo5fFUVPfQ85xbZy95fCeHAMcuAb6p8O9IJ04M3sWtTv/Stc93lXEsxmj/8MuLHROqiJ82yFQuBnQnPSm4Ht3b1Z2YgOWkSYd2+edf6Rw3xtO7SGeRNFTm6JmPPrIyLDDoiRegJwi8cihQLbdpHoeywTgdW8BtXJY2/3JtnwZrYeev/HIOb/C/HnaGYLobHr+4gElNtcPi/WcgcJlG157pTJuyBtuS7gJtSZjkAenKlRKPcfkAHouAbnidhMtwCDUvYPQp3XRGQoKv3ec36OX6EJT6N6N1Ig4zn8Vg09nWiS+Xli2y49KQW0vV+wK2P38xKa5Pf2dZcI70EXYm3vicL21gr/fyDs+4IabDZyYtZlvI8VgduAdWPbfkF3xvHrwBwFtWEgMFsT9b4DDCz7nevPJfSxIxPb0hgklwLfpBz/UTN9Y8Htb7ZvbuuomDA/+ANyICWjAD5BWqbdWM3tUhDL8cbEtusQy2zu2LYxZGSw1bh+ZkZSs300Msp1Y2q2Q8n6HiFt949IZyhORBIaP6vz/JJsspqlxjleQ0nbGt3nUyjEP6/ndn6tNrVwrg3D/X+KSCALI+fqlOH/x4TfYiKSVEo7x3xU5hXJiKGu8A7OV8D7k9tzyNiOb4ArYn+fG+59+kS9q4HXU44fH57T2aREuqG527vhnI8VeB8fIfmhlvru8A7cleH60yBJvInhmfwJJd7sQhI/y5f9W/f3ktc3Rfb52p2IYLJwrXahfEHfJN8LZAT9HNiv7OeZ029yWqxPfTr8+waK1Ps27PtzAdfNhbXcKdWlk9ytg7KxJZrwbefun5qZA7g8ymOBwwI79CYze9bdL6txnqPQJHxd4EUze4jqbOcro07kP6Fuf8OrwBpmtqDX0KcNuoJrUtF1LALfkK6hmMQANHhMgpfPVmyXZCd54TJgmJnN5O4tZ5Q1ZQVfjWo20z1eWwM0a5ZuwraXU7b3Gtz9EeThj2/7u5k9DmyBQiOfB8734pI3/Ac5wxpp956InllnTC8OXyEN6kYYhAxJSTxCuX3MV9RP/BNhNmqwDtsBZjYlcmCujVho/6Vaq3hexFBbwcw2cPeJqScqFwPo/o78BBnx/hfb9hywXgHXvxeFVfcIQdc3jlVTtkWIdNh/jBz7bYWs75GZLYE0o6dD2cfPTxy/K/pNrzSz5dz9+UQTLqQS4dIq7ge2De37V1qFEH2zNJLhaju4Ei+fgOYCh4XSFXZHEX6GkiSl3mOYi6xiZusDG1DNbrsZuNaDJaGf4mOqtXCjvuV7iAAQwYG5Uo7/Evi5u1+ednJ3vyPoj1+M2INFYVrgUW+dUXwaSmi1rLs/2ZMDzWxR4HIkw/QOWpPeZ2bHIYP4DsB9Znaou5/aYvs66D1kna8tAzzk7q/UOfY4FPV3GGKmRlgejQenmNmWSOLuLdKTY+Mp0RxFIkQvjmt2buTu+5nZWGTojqKUvhfKh4iY99e82+nuE4BDwvjQMmu5gwTKtjx3yuRdEIX/1tjfw5FBcMpEvYdQIpF655obTcRrsS+uQfICpd93Ac/xwHCfbwA7EfOCoQXYjogdMhEZ5IpqxwOInTGgTp1ZQp1Hc7zuScArZf8O7VTQJPoeFPK4WAvHz4om+uPpzkAaD1xEij5keAebYSE9DbxR9nNq50I/0NFEDrk7gTXq1Fkz1Fmn7PbWaeM9iJE0ILYtqXU2H1pE35xy/ODw3WxWUvtH0UAzFlg81BlV9vOu08Zh4bk/D6yXsn895DSeCPwybLuzyXJL6Nd+DcxT4D18CNyZeO5dJKKREBvp0wKu3xIjiXR9w0blHWDpst+bPN6jxP4R4fjf1LnGAaHO+QXdw4pU2Ml7EmNrovF/Typs5lVyvG7uTFdksL0DGWyid+cr4HZqMH3LLkU8hxbbkSkfAQpLb+Y6Bhxe4H2MRmHzWc5xDIqoGEYPoqAQAakLOQq6RXABu4U+cyJwfdm/eX8teX1TZJ+vfU0sKgYZ/ycCMyfqXUpCPz1lfOxxNEfGe18WaZ8vkdi+LjKaTgzPZs8enncK5MjdEuXBWYUO47bPlQ47qIOyMQVaBEWImEazJra/gkLCasLFlNnCzOZHDMXvhV1vA/d6/84g/RekY7YhWpAMN7N3wr7voudsSDvrlALbcTViuQw3s+09ZFmOELT0hiPtrKtyvO4cyHPbQQU3o999VeBZM3ud2l5n9+qstNMjQ8gyiCXyENVsppWA7YAlzWxVd48zBOdGk65GGIOYsx1kx6yIbd+O2BUxIB6pU+cRlCBiKJLXaEdcgsJA/25mO3tCKzLoIf4VGVwuSjl+SsRIGmlml9KYBdLMN9QTnIfGxTvN7HCU5PPb0PapkXPwOBT+fm7O184TO6PQv7U8JerA3W81s7WRQW8XtGAbEu2mtoZ8fN92wB/MbB93r8VozYKnEENxYRejaM9w/VGJegsiJmreaJWRFEVnGBrH70PvVRoiHfaHkt9Km6CV9yiONYFnvHtUS/wcp5jZUBQqnxvMbHdgsEtj8XAkV3E2cLqZvRGqzY9C1w040t0fyLMNecPF5v1XYGLPHjZ/6O3J2G83jAL2M7M5XZrSNyKD1vFmNg+VfARzoDl6Fdx9UnSVmQ1AY/GcSBbigVg9R/InReFc4M9mNsjdX+vpwWYWf1fOAM4wq5kyxN09bgOZHjjU3U+oUXl4iCS9ggZr0Q7aAlnna+9R6YdAzgSQJOXo2PYBaD0bRx7RHFmwLxqzJt2Xmc2Nvv0Z0Dg/K3CWmT3p7o82c1J37wKeCKVXEWwHsyNZjo96+/r9CmVbnjtl8i4oDGlU7O9DkTdqjUS9+4GPy25vOxc0wd8XGcmTrJtXUKKqKQpuwwxooTQxXPNYYPtQjgnbJgIvkGNWe3JmXiBpkEOBv4XyO3JkzPTS+9CyZnG49y60sF8y5dxLIuPuROC3iX2ZsnRP7gXp+kalCy00BtYoC6FFyCcUoAWa0/28ghxvjerdSwobqV0Kipy4N9af/jX8/xGU4OMFKlmILeX4NP3JWmVCQfdwcez64xE7/3UqrP4uZBAu/XnXuYdPaIJxhbJRfxL+vzqVLN8PAr8CNgE2QmNmpA96CgrpPS/2jFYu4B6ijNWfoEXURGTgnS5WZ2bEOrq6gOvnpS9baub23n6PEtu/aeZbCd/c1zm3vWq+g8KMn6T7uP4kBUQYJK+f8VynIMN0q8c36kuj8jUygF4LbNpuzyFjOzLnI0AGrOHhvY6OibMj90Ds/Z8UfC8jkMzXz+jheiXl/a9bEscObvIa0wP/KPs3768lr2+K7PO1O4EXY39vHOr/PbZtcURWaxjh2MvPcCzwRGLbQVTmOFMBm4a/Lyy7vQ3uZWcU0RDNUeN90mbIwL9g2e3sS6XD+O2gbDyDGKIR7kEGzKPN7DF3/yxk11wZLdg6qAFXT3g6Yn3MR4zx7O5v9VIbvjSzddHkelmkfRSHocXI5u7+RW+0qScIOl//RIwHqDDAPOx/DGVLfqGE5vUUWbTYtka6cRu6+yfJne4+1sw2RhOqbdFEKsJjwNpmNtjrZ+n+KfDvDG3MFWb2auNaNeGeMQtxDK9RzRbYgsbMaEMGhnbEvCgJRCO8Cfyo4La0DHefYGYbIFbS1sA+YdfyoYD6vV1CX5zEPZTLAsHddzCz+4HfIDbp92K7XwVOcfczS2lc85gasdka4ctQF7Rg2Bf4lbufkVL3dDPbG0XOrOnuu5tZxGbdj5znHu5+WdCIPQiNk6+hceXrWLWtEWNzVJ7XDsjMSHL3Qfk0pTS08h7F8SkV3ft6+C6SNCsM7n4NcE1gdS2Afts3vFovul2xLzKut4qalM4EpkG/xcbARmZ2obun5Rfoc/CM+QjMbEbUzyyDmI6PIemNOG4E/o4MRg/lfAtRO6I52KBwvQlm9l9qR6ktnNgwRUq9plBrrppS7ytkBO+gjZHDfO024PdmtqS7j0VRMW8De5jZj9B8dU3Ur/wzfmBYG41395tzvq1mMRci7cSxNjKeHuPSzr02rGdXqnciM9u5yWt+C3yAJGc+bFS5GZjZCCRdaUhmJcmsfgGtP0cjuccOmoClv+8ddNA7MLOfoxC1Nd19VNh2PzL0TkAT5kigfcswwcXMBma5rru/0bhWB63CFF+1MbA+sYUIGjyvqzHQZrne+WjhPGWGc8yPvMFzo0XdDWhBDpqI/h9iRbwHrNif3yEz+xxprW3ZoN6VwPruPlNs24bo2X2ODCkXIKaJo+e4M7A/GsQ38hoJW3obZpYa5twkPMu7l2jHa1SMMgOR4eGDGtWjcOprkD5o2w3oZvYx8IC71w2PNLObgFXdvZnkkKXCzJZEjKSqJELuPrrugW2E4ByclLDRaydrbCuEJCOzIJZHqoRACAv8D9LHXdLMbgXmcve6jgUzGw285+7rhb9fAaZx9/lzvYnqds7i7t2+7zDH+Q7Srm85OWcH6WjlPUrsuxktpoc0cHDeA/zb3ZOGtCxtzzzfaZfrm9mbaHzYJsM5TgR+iUK7L0HzjS4039ge2AsZgP6CHOInISmDndz9kgzXLfV3yAtmdhRKen0R0rP+MsyHRrj7brF6Y4Cv3H3FgtrRkzlYbnOuGm1ZBL0jH7r7i0Vdp4NqFPFNtTJfC+PvTmgd9HjYtjKaa8cTJN6IiEwTYsdORMnHikjM2hBm9i0w0t13iG37GHje3VeObbsUrcGSBtX4ubromZPY0TPZ193f7HHjK9fdBbG/n0SOltHIgZ/sk15Hc6Q1W73W5IYO47eDsnEJ8CwVAxuIvn8e6qi/g5iHf4iMvgGv0TpjxenH7349ja7eQjBAXRdKX8FxyOj7T8QMq2K6mtksKFxoZyRhMbS3G9gX4M1n6T6hXYy+AT9ChoBSDfpxJl2YdI2MT3T6IMYCq5rZgDT2OEz6tlYF2naBFdro7v5ZYICMLbtNWRAMvU0Ze83stygBVjtMrq9HTNkLzGxYksEWxr+/AfNQYeKsgPTtG+FZqpluz5Fg0uUBMzsFZdU+lhpOneBYbBvnYoz5c02IxGqWCQT0fubyJtDKexTHGSgB3M1mVs/BaaFuoWiHeV+LuB1Yx8ymihtPmoWZ7Yqe82runmSijgEONbNrUdj3WHc/z8xeQvIuQ9EaZHLHVkjGYU9P5OVI4EWqIzTzxoJZDo6xM1s9fiok67Y30kMGfde7hf07hH0/d/dnsrS1g95DK/O1MP7+IbHtQTNbEOVKmA0ZUtOMxx9Rm6zRG/gYEa4AMLNlEWkp6aCcArGA6+HCcOwmaGx7imrH2jJojLsBJZD8ESJ9LWtmy2Vg/+6JiH8bRaSEGnrdY1Cy2g6aRL81fnXQNxCYLPcntr2HQrFmQB3O/1yi4nG8Qclhs+2GMPE/FWkURt/2BWiCi5ntgQyWm6dMkDsQO/kNYPe0BYi7fxqe4Rqhbp9BYPetRozhB9xTh+X3MjDEzGZ299Qw1WAIGxLqVsHdDzWze1FI+SoogQJIP+5+FFLeTkZfkNbmCHffHcDMhgP3eTHJnZrFrqQ83z6GshI+5o1xSGusbmhcP8USSCe3HXAiSr62NfAzM7sBsTIdMXo2Qvq4b4W6oFD9ZqKEBlId1v8VxSRNzBrenitChE6UyOajlPkWSHszSvT5WezvZtFuht9W3qNJ6IGD809FjnUlzfualVdoBkchQ8HZZraf91wCbG+kIV/z3tz94TAf2Qs4z90fCuz+tpUWagVhLN0Czcviya1HAVfVMeouBNzawOgL0kmevUGdluHZE3A/Y2Z3IYfNdTX6sVQEo++/UCLGCchQmDQo3Y+cQFsgmcIOJjMEqY9bG1R7BPhBLzSnFh4D1jWzldz9YeDXaFy7M1FvURonj/01kmobBeyddKwEyaq/oW/lJ+jbGYEMxQfQXe6xWSyNEsM2IieMQ87ZDppEx/DbQdvC3b+khgab9319uVzRLhpdsfYsgpJLrIwYKNe5+8Fh30qhnVckWTYlYwBwZz3WSdCNegANam0PM5sVDcpbU1mMRugys8uBfVJ+h5GIAX29me3psazP4byLoHfpOyhZQHzfQOBzb5Cl28y+A8zcRpIZRvWCdmj4tzTDr7tfUNa1c8SZKFRrU+A5M7sY6Q2CkmPsiJgDLyON8nbFZygZaSaY2feRbuwQqh0xdwGnu/uzWa/Rn+HuH5rZmoiptzwyeEUGyOj7fRTY3ivZn8cAq5jZOu5+W9p5zWxtYDDVY+P8VLJ554l30QKpVJjZOsCBiG0/Xdj8dTCSnZx4VpEu8CeJv/skWnyPkuco1cFZ4rzveBSGmweGAjcjJ+fGZnY7YpR9lVLX3f24xLYlaC6y7F2qnXavkt04k5wzlIYgK3IJ6rOSbdodON7MdnD3pPYniPU3Xcr2JOZHEl7tiveQ7uoawDtm9nfgXG9O63ofJN1yO9J9/W9SesLdXzOzl4F1UbLqDtoUgZm7AvBw3KFgZsugNdEyKHL4YM9fi/cE4E4z293dz8v53M3gNBQx/YCZjSNIRhEzWJvZHMi4OrLBuY5Fcps/TpOccvfnzWzTcP5j3X0fM9sTfYcb0brhd2qa62vmojFruYMYOobfDtoGfThUrR1wIBrIkhpdk+Du75rZc6hDLgxmtjsaWKeJLk0lbApgBuAs1FnntXjIA/9BA2QjDEALk7aGmU2PPLzLUGFqRckzFkKLoO2AJc1s1eDJjnAqsA1i+Y01s4eoZkP9BGlljUG6eXH8B3l8dwcIht73Upp4Ilrstcs49BlKRNZBjvA+nvAxhrFUJ0PrMWJ949RUL84XDWWome1d0mKhzyA4olY0s1VRHxU3oN+dYtw4GS1wbjCzC4BLqYTlL4D6wV1C3VNg0nzkRxTDQs8U3p4HzOwY4HAq72E0X5geGTbWMbPj3P1oAHcfGj8++XdfRAvvUdo56jo4C0Yu875Y22sa/+IOWldy27wS3B6NvkND88Rt0y4f9jtySMfxDRpXGmFZqtn705A96d5vEGO5VJjZUihR7gxojncp1fkptgUWBm4JLMCkc/EF4EdmNm0t1m9w1C+DIqMKR9BTHUL1NznK3esl2pwf2BIxu1dFxtnDzewa4G8NvuedgA+BrRsQUsbSz5jibYZrqZZ+bBW/AYYBi0UbQpTibVTWo0uhpJjLuvvz3U+RyVF/NnCOmW2JdIFrObNw93uav63GcPd/m9luwJHIMDoK2CsxJu2E1nCjGpxuEzQW1jTCBumnu1Hkxj7BqTqaShK9VvAGDRxzYdxaChmdO2gW7t4pnVJqQYa04WhSNjGU4bH9eyD9qZ+U3dZ2LSjs6C1g2ti2rvhzDNuuQol8imrHYMRkGocG3hWS7UDM04+QVmBe1z0fmJjxHIeggXnxOnWWQOFuvyv7N2/ifg4Nz/4+YMmU/UuixDMTgd+m7J8duCLs70qUiWHf7CnHdXvvarTv3Ky/Wc7P6/7QBx2L9Bm7wvPZuZlSdvvbvaCF+ybI6fMv4Kbw/00JiWbbuSBHxgRguRaPXykcPwEtzNdBi/GFw/8vDfvGAyuVfb+JtmfuX8suSLtxQmyOES9Rn3Z4rP6iyMi0cgFtGYiMDP8AZizhWawf7vlzxN5cFDkjpg7/Px4ZxSYibefSf7/+VFBysVcyHH9++G0yzftCn3QriqxL+y6iMqHAZ3E0Mp42VVKOvy608cg61zg8PJdrY9ueBsbE/l4dOYfeQvOA82L71gH+CMxT9rtT4/6uCvf3B2CKlP1TAL8Pda5M2X9w2PeXWu8RGqsnAsMKvpdBaC4W75u7Yn/fBwxq4jw/CG3+NHb80ygSsVufG/rCGxPb0r6li1CCu9J/93Yv4b3bMLx7fwd2i+2bExllpyzo2k8DzyS27RV+00sQgWX/8PeZNc6xO1rvpa2BusK+3VOO6yL93e3VvrXB85ke2V7qPv9wj1c3cb6r499FeMYtfyeItTwR2DHxXON9UvR7HlfGM+yrpfQGdMrkXZAY+Ojw8b6LwtKSH/c8oQP4U9ntbdeCJu7XJLalTVouBr4usB3XIcPFyg3acQcSxs/runkYfqcErkSaR8NQtvVo38woa/Q7oU63yXW7lfBdfQgMqFNn1lBndJ06A1EY7MGh7AAMrFO/WcPvSODLsp9TrD2bAN/S3RjUVCm7/f2xoIXBamW3I9aevyKn1W9D26btwbEjw7uydZ06W4X37oqy7zXRrj5v+A338WPkZH4ZOfm+QmyR84Hle7EdRyIDQhQNcQkyth6ZUo4o4Po3hXG65reFNOEnkDCGdEouzz/T90TF8NvyvA856b+iYpz4EEXrpJayn1mdZ7EMFcP1WGRI3hVJSByFEjZGz2rZcMzAcM9nhL+PprtxJ74GWSHs37vs+63xDD5Aiesa1RsLfJCyfYbYc7oPaXN2oYixYeHfiSgyZ5oC72O28L51IYPtxcj5dlz4f2TEfRX4TpPnnBnJODxLZU43DhmWlojV+wy4PnFs2rd0J/Bx2b95uxc01r5I9Vw6/k1tF7ZtVND130+OXWh9OgGYO7ZtDPBsyvEtO+oRi/auZkvZv1Wi7ecTM0aj+dHH9b638N1+TMyZiZjVLZPMUHTdOLQm+2N4nyKj/ZJobvR1+J3nKvu59aVSegM6ZfIuaGLWhfTiZgjb0gbbMcAjdc7TtIGGkjxsBT/HT4CbE9vSnuM9pEz8cmzHe8D9TbTjYuCTHK+7CSlskB6e49VQ4ga/D0OJvz//idWNSsvsnQJ/i89JYXek1LsSafJmudbAWOlCTKSBNcpCiAXwCSkTrpKf2bJoETg83MeLYSLUsJTd9v5YaCODY9YxBjmUHmriOg8B/y37ftv4d/gZWnyvUafOmqHOOmW3t0b7koygtDJpwVzA9d9HYdON6t0FvN+gznQorHpr+lBERJnvUdbviYrht+V5H5Ib6UJMvF5bOCPH2emxv3cGVsl4zjWRUz7NWdsV+t61Y/XnREm85kM6lF0oFHuLsC/tGb6bfNbtUoAvgIubqHcxNeZ64Vk8kOx7Yv9/FJiv4Ps4PlyrVjTZbGFfF/DHHp7bYueP97E3Inbw0+EdmCJ2TNIBMD0ysj9Q9m/ezgXJJ30Ynt8NSJIm+SxnQI6n8wpqw7fARYlt/wOeSGy7HBiXcnyfddRnfG5VY1Psm3mElOgnZCB/ODyr48M2C33unRnbskYYL2pFaY0DVi/7mfW10i7aih1MvtgKTdj29PoZZV9EuqK10JMEC22RjCFntItG1wAUKtcIM5Gjtqu7X0dzCT7qYVDs/9E7kqb5u0BaEzJeu6/jNaoT4yyFDOT1YGgh0jZw9ycRqwUzGwrc5+67ldqoDtoFWceY2VCkQyO8TEdDsB52Rdpxj9Sp8whi6Q1FzJOWYWbnAzu5e57z5WMpd8yYmebG6XdQgtZUmNmvEfNmlibOdWFzTes19Op7lAVmNqe7x5MMPo+MuTPS+rxvRcQS/UURba6DWVG7I4wIpeWcHu5+p5ktjPRdV6eixfkOek4jXcmio/rvE/piMxuBpB3W95Cx3iy1q38SWKTVNhaMF2guP8G81EhQ6u5vowSY66MEgQuhKLg3UfK9az1YYwrEJshgtFPa++zuH5nZToitvimS76mLkOB4NxSxt3DY/AJyfGyF7nVtFKq+DZKoO6nG6Q5Ga4Ksa43+jsPQc9rH3c8EMLMT4xVceuRPof61CHwGfDf6w8wWR06dZDKzLronvgY5Mx919ytqXcDdR5rZb4CfZm9u2+L3SPN/eeA+M3uT6vwIA9F8d3SoC5q/TiDjd+LudwWN5V8jR22yTzrJ3ZuZx3QQQ8fw20HZWAi4tYHRF0Tpn73WTndP67gxzeAWQAzDY5DA/1EttrWdcSXwJ5RNdP8adf6IDK41B7Ic8B6wYBP1FkcC+VUIi5S9kKfvu9RONuLuvnCNfa2imXb3JbwMDDGzmd09NYlJSHYwJNTNgjeoGDIWQBOBN2rU/Rb99tcAZ2S8bpE4Bk1mOuig5hjTA3xEc4aDhUPdDtKxHPCU10kE6O6fm9mTiI2SB3J1FntImFYi3gN+2ES9HyB2cDeE5DEnhz/HImPkp7m0rndQxnsUR1PvlJnNhozOkxKYufsJ7oCemQAAIABJREFUwAlmdjCtz/sMsRx7G98gx0OucCWn/WcoPcFyKBJjbIN67yODYzvibOBMMxvs7venVTCzwUi+ZZ96J3L3W4Bb8m9iUxiE5BZqrgfd/RszuxclkqoJM1serSW2QesIR7kFTnf3f4c6B6DncRLK3/Eu8Ccz+xFaUwHMYWY/Q0biXdC89sxWb3AywXrIqdToOb2GmPdF4CnkyFjY3V8B9kTvwKhEvQWRsyGJjqMecPcvzGx1ZNTdnUrkZoQvUYTkYdFY6u5PoESLeVz/XSSt9ts8ztdBx/DbQfkYT51MwjHMj0LXe4TgoX4N+FvwLt5lZmPd/bKenqvNcQaalOwbJjxXh+2DzGwYmrSsjiQziswYfz+wpZkt7+6PpVUws3WQPuY/EtsXAe5Gms6NFkW5Mw/c/fW8z1kyRiJttOvNbE9XBvNJCM/778gzf0qWC7n7oNh5XZu8TxvS3f2YstvQQb/CA8CmZra5u1+dVsHMNkVGptT9HQBirT3cRL036ccLsowYBexgZvu5+2lpFcxsX2BpahvSfoXG4Z3c/ZJCWlksyn6PzMwOdvcT61SYGSVfW7pGlSzzvjFortXbeAVYy8zWpOJwnsnMBtY5ZhLcvZZDuVVMTw3nRgKz5Xzd3ODu55jZEsAtZnYmiqSKIq4GobwMewGnufvZ5bSyKYxHEgCNMH2oWwUzmxZpxw5DDEVDcihnI9LPq/H67j4eONXM1kZkk5UQS3FbZDB2RBraMJzrTaRJm0qk6GAS5kaSVY1gFOAECjgHkVqeMLNXUNTDe0jaQxdX/7oscggkkdlRH9iq+4V2RFEIbyMJpdPd/dkmzl863P1zYH8zOxQ5yuIRFY8Fp1sHfQQdw28HZaPXJArc/T4zewIxI/qV4TeEzayLjH2rUAnPXD0UAx4HNnX3bwtsyqlosXG1me2BwqkmwcxWQ97BCcDpiWNPRouxe8N5XqIFY38Hk3AqmryuDow1s4fQYsAR0/4nKGxmDPCXHK97L+3LjOmgg7JwMgplvdzMLgUuoPp73BktWruoMCk76I5vkKRQIwxAWnAddMef0Dh9ipltjmQYku/iqijS6oQa51gcaV32RaMvtMd79EczeyONiGBmMyLm5XLUkETKOO87DbjYzJYNEke9hYsQCzkunbFFKI3g5L9u/S9iezbC91GIc+kws3rv44GhpGH/4Oxp17X/WGANM5snMP26wczmQZrOaUaztxGRwYDnkGPkwrjMRw38FyVqHROMdbuSHlp+TlqEQGDlz1SAU6Kv4jNk/G2EhZBmcu5w98uCM+QgZNx9DWnNfx2rtjUwDd1ZwJDRUW9muwN/A6ammsi0aChDzWxvdy+SiJUrgoH3vmbrh2cwuCOX115o186/g8kHvS1R8Doa0Psd2kGjy90fDuGHJ4Vrfoom65ua2YbAHGgQPMDdxyQOH4IG53UKNk7XhZkNAHZEi6g5gTsiVo6ZLYYYFPe2u5czLArXAM5Ci6rBVBtkHX1/w5qYGPcEr9Ax/HbQQRXc/YHAojwNMbB2SFQx5BDb190fzPv6ZnYKSmJybAuHNz3Z7wWMBVY1swHu/klahSBhsyrKDdC2MLPpENNsMaSTmxbp4u5+XJ7XdffnzGwbxOb9KXpWVU1Di/ed3P25Gqf5gtpyPn0BZb9HUdKx883sHXe/J3bd6YDr0RzkasTqTUWr8z53vzwYuW4zsyOBm3rDcOXufzKzz5Ee7/wo1PpLCjIANYG7kBFm3UgCIInwrSyA+u52QBbpmXbOcXIR8FfgdjP7lbvfGd8Z5rOnIVZwWiTCd9B3c3ry2AY4MTpfMAyeFUqzOBnYiY5NJcJo1CfN6+5pMgqR5u6yKPlbIXD3o83sj8As7p7Wv9yGojleSdnXsqPezFZC0ZQgu8VwlASccOxuyPF6tpk94+7NRJ70RayKnlNDw6+Z9eR7TcLdvSjJkP4Hb4MMc50y+RY0gD+LGBX3AQegjvROFK5zZ9j3JDBNDtd7Fvik7Pvu7wUZ1x+me6byp4CNaxwzDris5Havj7LRxrMaxzPRbhS2bVP2M+7hfQ1EhqaDQ9kBGFjQtTJlLO+UTolKf3yXkLbqeSii4ctQXkLSNz8s8LrjgavKvv8c7iPKEH4VYmkl90+DHFoTgUNyuF4h7yByxr1PesbqeObqwt5/xMo6Ai2AnwvlNuBwYO4Gx16PNHJLfyf6wnuU9k4hCYdPwpxjybBvahR63IXCkqfK6Zr13rNGZUKBv0NXfI5VwnuwBGK2fwL8HOUS6ULGmhmQ4WIcij5bsOz3tj8XZDi9Kzb3fhMl57sbOZmiPvEOYMqU4xcoqd39bp6S8XlsS2UdP3vYNuk7R07Ou8PvuWFBbTgFODLjOYahnCS1xuZvEXEmedzIUGfrOufeKpzjirJ/r0S7cnuXe3IuutsKohJ/3rW2d769HhQLD7yDDkqDmc2HOsqfIG+aUdFwjYeqdUsG1oNrzI6SNQ1DDM51MzW6g6YQnvuCBAaKu79Tp+4oAHcf0iuN6379H6As3lMhb+09wOXACA+hKmY2NdJzusHdty+jnc3CzH4IdLn7M7183fNRSNWUvXndDvofOu9SfgjZmB9w923KbksWmNkMSPZpURQhcjFKLAaSH9gRRWW8DPzY6yTvavJ6ub+DgRF0H1q0jERJ1JZG0U+LAOsgiYHhwFvehnrjZrYcCof9ubtfUHZ7eorefo8S1570TgV90ZuQXuJqiPG4CTKabOiNEx83e82uLMd79uSWqTCzu4CbvY7WcdEws22BEcjoHq1BJqJ5KygSYyd3LzIxcikIUWzzeIxxXiaCTu9xwC9RpGccnyO93iPy+i7yQGee0h1mdhWwGYocuRv4P9S/jgHWRuzsy919u4KuPx4lCmxGQqbeeX6IdHpXo1qn927gr+7eLUGmmf0XeN3df9Lg3A8hZ8W8WdqYJ/J8l3tyrpBALolN0bN/HEUDvBa2D0Lj83IoAuBad787a3snF3TCEjooHZ6DRIGZvVpn90zIi2/IQ3d0Ds3uoAm4+4eIzdIM/gxca2aruPsDBTarFn4HTAts5u7XA5jZ5fEK7j7ezEYjzel2x5PIeD2k5HZ00MFkDzO7GnjX3fcqqQm3A+uY2VTuPqGkNmSGV3RNr0Whooclqhjq+zbP01iXMw4EpkAO7ZvCAmlpdz8MwMzmQGyZDYAf533xkOvgVXffMsNpZkSsquFmtgEyXr6BjNnd0C6GpQjt8h65++1mticyPI5FLNMHgU2Sxq0G2q6N0OVtqO3q7mu0emz4bnbKel8uPdBnEdN9PcRInAr4CvWbx7r741mu0cY4FLGa28JoGd75g4P8SDyR1NvA416t0VqFQDRZGPVtH8S2z4fkBJdBxqMj3X10MXfQQcA2wO+BfZHRF8SuXwJFH/0FRR8WhXeRwyYTgmF39x4eNhtipTfCy7RfAto5yrho0nAb8gHtAxzk7mk5L04zs18jmZZre6GJ/QZtNwnoYPKFu9+Cklm0gkEN9n+Lkk4d6QXoJ5aNHiwIxiMttccQkzXXDtPMVvIm9YrMbC93PzP6291vDB35TWZ2Bspm/Ra1F5J569ENAUZHRt86eBsxtNod49Dz66CDDsrHhpQ7QT0K2Bjpyu3XxkbRhnD3NwLjdGMkz7MAYuq9gcaN6xo5i0vGKsAz7n5T2k53/8DMtkeagscg9lueWAIl1s2CUVTYkVuGUgtFJOXKjBLfo2upsJdw9wvNbH7EdHwcWL/G99lftV2zIJf7cuWc2MbMDBFFpgQ+cPdOgsgSEAy89/fwsEOBXyNj2gcwiUF8H5I7M2AppO39Q3d/M78WdxBHcC4fYmYnIB37OKHrdnd/r+AmlOno/ghF7jTCwqFuO6EsrfUkDgeeq2H0BcDdTzWzochp22H8Nom2m4h1MHnBzD5CC6DVMp5qwTr7vgX+n73zDJOsqtr2/QwZEVCSBHVIElRyEAbJWZIgSGYQBkVFfCUoSdKLiCIoHxJEouQk8JJGyUGCIEjOknMUAYnP92Ptmj5dXVVd3V25931ddXXXObvqrK6uc87eKzzrlW7OcqqDeie/UwJzEAud9SWdZnv7Btpxg6S9bB9RbUBqmHISUQZ0TNnuu4CXiOzbvWscpxkLyZmIDNnBmBKYpsHHbgZ3ExOLTKZbEb3jsHiOKCVuF+OJCprtgQ0kXUU0O63UpNJucEOxRpMcchenR10Ms6y5GRkwM9PfqfERgKRpnJqG2n5b0g00pxntUwwsox4qN9AnydW1tOp7VKl5TYVtHxLOkYvD/9jPzNUqyS1I+g3wXaIE/k8MLIf9HnC87d3rtXU0k74PneL8yAyNVYhs32L5/eZEQOcaolH4BsCPiGzCn7bcwlGG7TeIBpWtpp2B7r8RDc03tl3xb5e0EbAs7flsajFXuw1ILEVo3Q/GvURSRaZOsuM3026mJCKAI8L2Uw2wpWuxPUbSr4hJ/jHAmcTi7hNiAbAl8H3gBKLEZhXg18C2kv5q+8wGmfIR8GtJKwPbpZv+JCQtBZxNRH8fLdu3MpHxPWXa9Bqh6dUq3qC+m968hHO60zkKuFDS2imbvlW8Rnd3e880EEmfKb8ODIHdiAl8L3ApsIWkaW2/24bjH0BfhubMxIK4nKLGfkc7fofJcMqam+EEeoOQFSrxZvo5F/3viwZmbcLxLwB2kTSzK3c7H5R2afF3CHsRc6fPAD8AViMC6lNXGW9qJycUWazK6wcgaQfCibWq7RvLdv8T+Keki4FrJT1s+4TCa5cDJgB/rCatJWkcUeZ8nO3b67S/Z0j6y4sSc+k/5+zf1pCy31dikHOqQnByTiLhocg3iPNngu1/AddIWo/I8M+O3yaQAlpXDqbbLWl3YF3bqzbBjPEMIdBN/dfnSth2UQ7iN4RO+zmSzgJOJap3TKx9twW2INbnVTNa20SnVIlOQQRsBuOLZF/mkMgfVqbdPEabNGV6CUnbAz8GVrR9a9nue4G9JF1EyF08aPtESY8SkcnxhKO4ESxNNKtZD7hL0hYlaY0k43Ao4dg9k8hSKXJw2vcr4Je236S13A6sJWl+249WGiBpaWAR4KyWWjY8/gEcTWQPnQT8meoTn5rSGZLmA2YBXrP9SK2DpsyinF2UKfFK0sW+htA9u6GWTl+RIWqEdzoHEIvQ8yV9tw1lpgfRAxmabaAZGTDPEKXHJe4jHO7rAUcCSPoUsAKRKd5oDiEayE1Mkkt1yTNlBnAP8HkGr0owEWxvNN8Hbqzg9O07sH2TpBuJxsYnFHbtRAR/9qjx/g8TSQOfEPOjniPpK/8P0aTwpsL2E4DvFIbeIGlN2x+22sbRgqTJiTnrjvSdU+XnVq3g5GcYGKhbDng4OX1L3EUEazLNYWUKUjY1WIBw8DeDAxhaoHsklWWmoANs+2+SdiEaj22VHkVEJEnt0ovSkw3iHqL307q2K2b+SlqHkM3Kn+EQyI7fTLs5HThY0txlN+ZhIWkKQmtuZfo3BbgOOL+HJ20/IBYA5U7fSdi+LS0Avg+caPvW5JBpmLi87QdSVu8xwHbA9ZIOIhzC6wPvAt+3fVKFly9GNG/4WaPsGSK/Jxbe50vazHY/DURJ8xASFQaObYN9Q6V0PolY5O1UY+wA6Yw0Cd+b+G6VgjOnkhZDkrZK+3ayfV/jzM70GK8STVqWJAICH6RuxlcTOmy32x5Rx/ku4XDgfuIa82hqsFVLamGoDUVqYvuARr7fKKIZGTDXAbtKmsX2K0Q2+LvAoZI+l465LXHdbUYp6GXAx8R9+W+SXqL2d3E1SSVH9XO2Py48r4smaPK3GxHO+38QjaMeAv5dbXCTqtIWoD6JiheAZcq2jQPuTsG1iiSt6buIAESvsjHwOWBS8CNlQ+8AvE18vssDKxJO8FPbYONo4QBinvoRUeb9KEOr+nuPQiJRukbNCZxYNu4D+ioLM+1jKuI+1AyGGuh+cgTHmkvSXLYnzRVsHyvpZmBX4tpR9EdcDxxVJkmS6c+vibnPnyWdDpxB35p2LOFM3yY9P7zl1nUx2fGbaTdHEpPKayT9DLjIZZ2M6yU16TiPSP0vj97tCPyvpE1t/2MkBncoC1LfAuBFQleoxBM0uFFZ0ijcXtK1hAP4wLTrPmBz2w9Ueel7lMk/tBLbEyX9P6IL7QOpy7OB1SXdRjjIJweOKGaGdDDPMMwMv+T0vZzIiviI6Da+cNmwmwlNwU2I/20mMwDbn5P0FeK7tDoxCV4pPQ4ESlqmVwFX276/bcY2l/H0nY9TAl9Lj0r0yyDJ9BznEYHOxYG/2H5N0m7E/bJULSHiGr5fE46/cuF3EY6vz1UZW/rOPklkfi4MPJKe13t/6cjmbiNEhEN1Fdtvt8mG96kvcL94GltkDsJpPRhPEQ2xepWFiT4jxaSQzYnv7Ba2L5c0E/F9357s+G0m2wDvAOOG6RR7gGjcVpKw2Yr4P5ZrcX+e7pBr61kkjSGSAZqip93KQHdqrj4Pac6WKixvSglOeR43DGxflHxChxBz5/FlQ0TMR/Zxg5vU9zq9NhHLdB+PEifwF0lyA5JepnrmScVmVZLmIrowf5bQFz2DcGpCXJC3IjR8JkpazHYzyifbyftU1oYrZzH6LwCmJLIamsFs6f1LTvh3BjnWjbR5gWF7V0kPAj+nzyE+V3q8Bhxs+6h22TcUbI8dwct/SDjpriK0ml+Q1C8r0/aTkh4D1qTPuZ/JDCBlhN8H/E7SZESm4erAqkQp5jfo0+Lr1XlJI5tojhhJs1PIQrH9QjvtGU0kvdQ1yrYdL+lOIpD2WSKD9OQmSR4NR3bgaeL8/LDs+WjmluE6fZOO6SrAbeXVRYUxCxCB+muK2WQFbiD0Kw8Gfp4akxVfL+LeXCkx4GOq66cWmRoY0FSuh5iZkDwrsiLwRqnEOAVmbgS+2mrjRhmzEsHf4WZCnkYEz+5IFTXfoC9rGwBJUwNLEFmXjaJXGtAOmwqNKteu1NAyMTkwH7FGPLephrWG8ibE4wkN6UqVrZ3OlhT+FknbAo9V04EfhIeor2F6RWz/StJfiGSsFemT3SplTf++RxP5mkqvLrAy3cPYwu+li81sVcbWWmT8jFgsHQXsUS7pIGl/onRg1zR2l+EY28HcBKwn6ee2D6o0QNK+wELAJYXNcxNZKw1D0meJCdg6hLN3T0LI/uuE7u8OtitlJ+8H3J46sP6ukTYNBdvHSfoD4SSfh2gE9AxRkv5Ru+waDElHEDae3YC324ZwdG82iOPhQRooFZLpfVKDnFvTwuwWovPyBOpzQnQNkqYHPijpGdvuiEyxpGm5O7HwKm5/FDjc9h/bYlgG23cAdzT6fSUtArxech7aHrLTozyQOMLAYq8wxQhe+yPgJwyspCnnFEJKYq8K+/YjAq97A9+WdDb9y2E3J87z94hgdpHHgXGSpqpWZSdpKkIS4olK+3uEMRQaLUqalgj6l+tKvkbuR9JsnmZgZvpQ+ANRRbMtIcPyNrCD7aIEywbAtDTW8XsocHID368bWbnwu6ldRVLiLnqjwd7bwOyF56Uqmm5kSvr7Wk5JjyE7fm0fRty7ho3tu8lZ0w0lO34z7WbYnTSTg3G6pB23NjE5/Z/yrAcA2x+lUsr1gXXpPcfvz4kMov0lbQGcQ5Tomcim3ozI+vgvoaNV0r/6ChEhbwiSViAyt+cixNk3tf1ocqQeSCxeLpR0NLB7mYN+KWLydISkbxEZ3M8S5RwDsH1ao+yu8N6fEGWQ3RRN/DFxgz4bJpUfnTJMrdAFgOvqyDZ7m2j6lskMSpLjWT09liecvQLeIDowX9U+6xrOG8T5WCr/+zmhqXlJrRc1E0mnEEGdUkOT59OuOYAvAcdLGme7o7KTMyPmLvp/F4ulqJnhYWAlSTPV0smtwZrA/dWyfQFsP5wkp9aiguPX9n2S1iUq3OYD9ikbUpKj2Nr2vWX7LgX2JbrK/7CKCYcTCRV/GPzP6VqepX+13BpEsP/msnEzEtf0TPM4G/iBpOlsD0XbF5g0bx+f7rWzAg9VeJ9HgG8CVfuhDOO4DxONEEczpSoSEY18r6S60+8Dosqo6brvKcN7FWJ+Mz2Vs7Ntu7xR4FC4D1g19bN5rO/Q2raeFzdzLTsMPiauf12LpJOBbWxnH2cF8oeSaSsjbHjxG2IROzlRsvrnSk7fwrE+kXQ7cdPvKWz/U9J6RLO8BRioCyhC02qbFEGDyAJZgyjHaBTXEjeNPwC7ljJJ0oRsP0nXEYuUHxKZJEsVXnsKfd1VxxGOoVp00s2yE/iY/hlI5eVHQ8FUcbiXMQcRTMhkKiLpu4SjdxWi67aI78zNJF1foqljr5WNl59/BxDXuLY4flNAcFvgZWB/Iij0fto3FVGeeACwraSJDaocyNRA0pSEtMPK9C9jvA64YLj9DiodioGlqNCdpaidgoHbgcslbV+jd0E1Pk/8nwfjMaJaqrIR9vWS5iOaGq/EwHLY81PfhXJ+S/S+2FnSokTQvTQXXIBo4ro8cb04sg47u5WJxGfw+/T7YcT/9tKycYsRGam9xkjmiY3mF8Rc4TJJE2w/Mpw3SQ7Fiv+rtP65u9K+zPApVpFIup5IHGlkVvWQkbQJcBwRvKo6jDjfR+L4/RWh218eeKs3C7yT1rJvAzNKWpU+J/Z09TZz7aAmrp1yTes4suM30+2UTu73qH1xL/FZKusHdz22r5E0L30LgJJ+4/OEzs55tt8tjH+FcLo0kneBnWyfU8XGq9Mi4wwG6gyeRgs1A+uNxlajw6K0EAu0xSSpAU60fwGLShqTnPYDkDQNsAgh95DJVONY4rx+kAgIXQXc3ECnVqfyLvXdk1rFBCLTZtVyJ1X6XxyfdCzvIjqrZ8dvE5G0PFEd83kGLlJ2AA6VtFWDGomWl6JmhkiZXuWChETA1IRe+T2SSs6mSvdL216tbNvUxPk4GB8An6o1IMnJnJ4edWH7dUnfIAJRlQLtIuaOG6ZGWZ3IH6nPeV6LQ4jgy87A94i/+4ziNVLS4sR8+rwRHqsT2Y0IBLYd2+9LWpOQgLpf0lNUr/qrdE4BLcvyzFTB9nA05BuKpGWJOcwnwFlEdetXgV8S1RFrADMAJxLfsWFj+2JJywAbERIjpYqpTlsj1sMThAb2XwvbNkmPwejlHh09Q/4HZXqFe4CVJS1ou2IGa2qUsTINLPHpNFJmx5/Sox0safuxWgNsvyRpDcqio7bHN9OwCpzC8BzNpQhxp93UryO0lJ+QVNL5q9VgoUj5JPoSorR0N0IbuxJ7EhmclfSaM5kiAuYnGjR8ALwv6bZO1sxuAA8Ba0j6Dn2ZE5+TtGI9L7Y97KYYVViMyMKpmplo+wFJ1wLLNPjYmQKSvgz8hdCafIJYmD6Zdo8ltFnnBa6UtKzt+0d4yEqlqPN1aSlqu1i58HvJkTQu/RxD/N/GVnltpXnGc0RX+8FYAnixjnFDxvZdkhYkgkJrEbJgJhzYE4E/DqfkvlXYvpmBkgxDfY8XkmN3AtFf5HYGzp+/QsxzLhjJsZqNpJWIarrlCAmu00tSX2nOvQpwlO1J36ckUzIcqZKGI2lmwuH0ZeIcmyc9KlFx7t7CLM9MZ7M7cV3eyPZlqfz/q7b3gUnftZMJ6cclRnqwYia5pO1jU1dKZm1JBMEWI4LScxNJDJ0a/MsMkez4zfQKJxJOhWtSE7PTbX8AIGkKYGviJj8FcELbrOxxik5fSTMQ2TCzAE8Vu4KmjNT/bb2F/aiUYfwZovmDiWDCk2n7WCK7FcIp2olabz8lnGtLEQs4qK/BAgz8HI4gota/TIui89P2mSWtA2wKbEcsEBumEZ3pSRYFVkuPFYnMsv2Ad1KG6dXAVR5+J+9O5Tgiw7l4v1krPQajGZkT0wKv1zHudWCaBh+7UxhOWXMzSgYPIv4fhwL7lVdVpGa0BxFNuw4kqnhGQqVS1HH0OS4HIzt++1co/Yw4j0eS2XYtsIOk8bZPqTRA0nZEAKBpjaNsv0PIPvy2WccoImkkjeJse96GGdP3pi9Swwlou53JFHUh6QDivlq8XhV/f5OYIz4H/L51lg2JXxLzhYeJ++djQN2Bh1ZmeWYGR9IcwIYMnnndjOZdywP32b6s0k7br0rakqhuPJDI9m8Ud9O3Xuwqkl71j0vPJX1CVAt/p31WZRqJek9WLzNaSBG8bW1Plp6fQWQ8lvRJX0i/z0FE/gScaXvr9ljcGiRNBsxElBJWpJk6PMnheySwFX3Oi1NLNw5JOxKL2o1tV82+liTi74DoSF6P5uywSc0CbyO0kHcub4YiqdQIb3ZgWdv1OFFajqSxRLnRddRusNCPcj0uSV8lslzGMtAxLOAZ4Bu27xuJvZnRQ7o2LUPo+K1GdOAudRF+Fbja9pbts7CxJN31bxGZE6sQ15a6NNUbXS4p6XHic56/mhRMuuY+AoxphoOl3UiaiWgIW3dvgVQp9LlG6hVKehV4xfZCg4x7EJjF9swNOOZi9JWijiecKnVlS3Zp5lJN6m1wJ2k8sGJx4Tuc71GF912QcBCMIapqTrT9RNo3N6G/u3savkQDsr7Lj78t8FgxIF9l3NeALzUq6zs5EipR6u9Qa59L8/1MH5LWJ+ZqzwA/IaTdXiJ03Ivf2xeBu2yv0xZDB0HSC8TabWHbbw3j9ecBGwMbFLI8i2vEUpbnEsQ59VLjrM8UkfRjwuFe3ncE+tYTTTunJb0PXGx7s/T8BEK3fLqi5rmkC4lK1S9WfqdhHbvf966bSRVgV9j+VbttqZde+vybQc74zfQMtreSdDNRnj43fU0uIMopj7Dds9mJKdp9ENEIZKoaQ5umwyPpU4TDcVFCc/YOopSmyKXA8cQidIDjN5Wk7Q6sQJ/z+r8pO/A3tv9a/poGcTCR8buk7X+X73R00F4feJzIVv5+k+wYEbafBJ4MHw4vDtdhYfteSQsTmb/rECV3kxGLiyuAP6Rub4QaAAAgAElEQVRsoUymLmx/TOj33QIcnIItewE/ICoDvk2UmvUEti8lNQlKDo8r2pg5MRH4LvBrST9N/4tJSBpDLNTmIbKtOh5JcxGB3VpBzhsKvw+5rNnN6dg+DfCPOsb9g8iYGjFlpajjCafnaM7iGZ9+DtbgbhxR3TLps2pEebzthyTtROjU/gz4maSS9E1pfvYJMKHRTt/EKelR0/FL6E1/h8Zlfc9dYduPgF2BPxOZtU+m7WOJar1vEskE/69BNlQlJS5Uy07spOZFRX4EvA+sbftBgDT/K+duIvO1U/k0cY8cstM30c4sz0xC0lpE1eC/gcMJmZzliPnHfIRe7NzA72heo7036L8OfjP9nAt4tLDdwKxNsqHr6QS95kxjyY7fTE+RHLvHSJqTvuZmz9l+ro1mNR1J44imSaUb3RvETbfV7E44fU8Hvmf73fIMD9svSnoAWLX8xZIOBPalb9Jdeu00wJqEZubBtg9ogu3rA9dWcvqWsP1WioCuR4c6fgvMzRDK5IpI2gEYlxwDx6ZHJjMiUkbpUvRl/C5PXLNK53tHZtE3iFOBYTXqSg7y6UbodPgloR37P8A3JZ1JLIBNOHu3IK4Zb6axHYukjQmZhMGcGJ3abORh6mu2Njv9F6mN4kCiid+QkfQlIgO60RrUncoUVGguJamUxX9bCg4MIGWLLwtcY3tAabnt09JcaF/imjht2vUuMZ87xPbfG/JXDJ+GSp2UZ0lL2ogoLd7cdnnztH8CF0v6FnAOkaE+7CzraqTr68GEQ2qWGkM79XqyJHBryelbg1eoX96lHTxIOH+Hy8z0r2L4CKIRcSnL0/bbkm4gkhkyzeFHxLmyhu2/pwzM5WyfACBpP+BoIqhUj875cHiGqG4pcR9xLVuPCCKVEpVWIORPMplRQSfewDKZEZMcvaPpYn4g4UA5gdAMfLlNdmxKdIKe4OgUX41HiDLvSUham9Aoe5fI7DiJ/pkf3yEaV+wn6RbbExtqeUR96ykNmYzai4OOYCRlqESG0zgKWU6VaHQZaKb3SM6PkqN3ZUJjD2IS/i7R5OoqQuahWdkfbWeE5fK/AbZhBHM2209LWhc4l3Dw7l02pCTfspntZ4Z7nGaTqi7OJUrk3yKqedoR5BwJxxEB6nGOBlUDSMHcFYl7XkOxfeAIXr4XsC313St7gS/Tly1W5EdEWf3Cg7z+FEJuaa9KO23fAWyUMu5nJhwmrzVb2moIzMUwA8h1sjtwewWn7yRsny/p9jT2z408uKTPEBJf8wAfA+8RDvgXiP4IpUZgnZjpW2Iawqk7GLUannUCvweOk/Ql248M4/U5y7MzWBq4o1rQyvYHkn5AVIPuT2T1N5rrgF0lzWL7FaLy6l3gUEmfIzSetyWuuRc24fg9g6Tpiaq81ahdYdUUDfZMY8mO30ymN1gGeND2d9tsxzzAxEGcvgD/pU+/t8QuxMR73QrZRI8Ce0m6ArgmjW204/dZYBVJM6VSzgEkjbBVCed2L/N1qnRNLqPRZaCZ3uNB+jQaPyJkHq4mnL232v6wjbZ1EyPOvLN9q6T5iQDdShSqYoDriSYeg127283exGexL/Drbvz+2P5D0ni9UtIxwBlE9jVEkHMroqLkd7a7QnajG0i6vkVWqLCtxOTAQoQeaKXS8TWB+6tl+0LIhEi6n2gEV9HxWxj7CSGP1TSSrm+R+SpsK1H6+1cDmpl1vAjRMHcwniCy9RrNT4kGeicR88pjgW1szylpWuJc/AUhjbJNE47fCF4AFqxj3MI0IWO6Udg+JV0Xr0tZoRMrZcrXIGd5dgYzEOdriVKj9U+V5OFsf5ikGZslJXAesBiwOPAX269J2o3o01LSTi8Fu/dr8LGHVdnViaTKlhuJPhWDzUE7pWnYiHsi9DLZ8ZvpKSQtR31RqWZ0EW0nAu5ptxHAh9TQWyzweQZmkSwD3FyrhNT2DUnrd9nhm1iVc4jF2VWSfmT7xuJOSSsQmlSfpnO7IreaZnS8z/QW95EyeoHrbTczeyyTkPQj4AHbVxW3J8fu6enRjSxCNCj6RbsNGS6SivrKu9O3EC3nx6lJThHbznP34TG+8LsJqZDB5EJeBPapsP3zRFbZYDxGBFKrIunLhAbmLIQz+ZK0fQwwue0P6jhOPZxC/8X5OGqX/ouQuTi8QcevhKnPablAk46/PpEt+wPb70ua9PnYfhc4QdJdwK2p0qwT+4RcC4yXtKbtv1QaIOnbwBeJOWxHUnZd/EPaVm14pevgdeQsz07gVUInu0RJwmssUNQrn5roq9JwbN8OrFG27XhJdxKSLp8lGu2ebLtSRcdIjn0icGIj37ON/IIIpvyDqFx5iM6vsHq13QZ0MnnymOlmJs0IJE1FOO7WL99XAROZir3EvURZWrt5GFhc0lTVMsdSad2iDGxu82liYjYYzxOLpEZzCDFRWIrIOHiO/llYcxHfqzvT2Ezzy0AzXY7tRdptwyjlt4Sj5yoASU8QGb0/badRDeBDGt9srdWMJGCWg23DpyS3IiLD8yaqL9A/ILICb63ieJ06jRmMD4BPVdoh6QvEObpSYfOp9GXA7ggcmxx6V9dxrME4jT7H73ZEo9qKUiP0/f0X2/5nA45djduBVSVNKGmAliNpRyLz+qpK+0fIWOC6wnzV6ZiTlRpg2r5D0k3EuqETHb+/JjKTz5O0B3BBaUfKWv4WcBThBD2qLRbWx1CubZXGtjPLM9PHk0SQocTdxOe+OelzlzQrIf3V0gz0JK1zR/l2ST9Pvx5t+/XC8zrf1gc3xMDOY00i+LmK7bfbbUydzNVuAzqZ7PjNdDOHAien3w8ANiCcUH+iO6JSjeR3wBmSFmuzTub5RGOgw4iGHZX4BTAdodNY5GUim2swvkJ9emZDIjWiW5lo8jGBuHkUbyDvEB24902ZID1FhZJPdUAZaCaTGR6f0F+HdSxdoE1eB3cSkkJdi+0x7bZhNGL71NLvkg4gnLqnVn9FTZ6jvsZESxAL534k2agbiGyqe4ly2vKGsecR1UUbEhUTI8L2+MLxtyPkC2rq+LeAg4hy7+MkbU5l2ZNVCBmw/23C8T+m/1rhnfRzZuClwvbnaY7UxIix/ZCk8UQQ4VjC0WlCO3W7NOwjQsLiX5XeoxMY6XWxnVmemX5cDewj6QupIe1lhP7y3qk56LPE/2M64KL2mdmPA4hz5mwiQ7n0fLAkspIGeK86fqcHLu8ipy/Ul0A2asmO30zXkrTVSpk/3yYmbEvX0lzrVWyfI2lh4K8pUnnZCDvAD5ejiYnmLpKWoq+caqyknenTlryXgZk21wFbSdrVdsVyNEm7AF8lnPsNJzl0d5O0D7GoKzl+nwPuLHUG7lFOYaBG08kVxpVoRRlopkdIjo4JRJZHUVv2WuDEVJqZaRyv07zy6HbyS0Ibdw3bf223MZnuxPbYEb7FtcAOksbbPqXSgORcnZfK99G9CKfvYcDeti2pn+PX9huS7iE0SRvN3HRAtY7tGyVtAxxPOHhXLhsiYm7/vVoyYCPgeUK2o8ST6eeSwOWF7QsBHat/bvvspCe9L6EpPT2xxn+PyJQ+yPadbTSxbVTL8sw0jbOA2Yms36dt/0fSd4AziTVgibtoTjBnEpKmJJzMK9N/PXcdcEEh0/8gYv3zatnz0c6TwBTtNiLTOGTn73Wm+5H0X+Ba2+u025Z2UKaNNRhN1QiUNCeRqfI1+kdEoU8qYSPbz5W9buG0b0qiBPM0IvPDRIbXtsQC6H1gKdsPNOtvGCqSdgDGdUD2zLCRdAp9/6fx6ecpVYa3qgw00wNIWofI5JqBgRkUJrpvb237ilbb1g1IOhnY1vZkgw7ue83FRIbazYTO6Pj0s57GIx2rg5/K43cE9iDKli8DniaCUANoUwC0ZxnOd7HTkTQD0Yl+FuAp23+r4zULEiXMY4hS+xNtP5H2zU18R0vl5UvYvr/s9Y8QGfnzOS3EJH0CnFKcR0g6D/i67aZKeSUHyUzA+7ZfH2x8E44/O/GZrUh/B831xGfblIa6ks4kyplns/2xpMUIGbL7iISSZ4lM7EOBa2yv3gw7GolCGHcm4vv1akmyIpNpJ2ltuB59mdeXNPO7KWl5wtlcqTGZiXN7K9s904yt0aQkqD2Beao1Pe80enGO0kiy4zfTE0h6Bvib7W+325Z2kBYMddOKUlNJawPrEk7byQhdrSuAi1zlwiNpAyKb99MMjLYKeJsoV6unC3TL6LUbTWpw4lySnBkpyUHyD0IT81Yi+63U8XkeQnfza0Rm0pK2H2qHnZ3MMB2/Xyacol8YbGwF3KnXsnSvKw8oViM3QmswvXSvSw7fIwk5gdL35NSS4zVpyx4EbGz71gqv35aQfyp9Fh+ln6X3+gSYUCkjWNJ7wKW2Ny1sq+T4PSsdf6rh/p21SH/DLoQ26hj6//3fJDL09ulkiYCRkLKNTwXWt31Z2nYx0S+k/PqySpOyjjOZTANJ85/bgGmJ+eZZ9GXzjyX0hucldK+XLQ/MZQJJkxPr9umB7Tsp4aoavTRHaQZ5QpzpFS4H1pU0ue2PBh3dY3Sig872lcCVQ3zNJUkDaici86NYEn49cILtl6q9PtMwziPKozKZkfIzwum7h+3flO27muia/hNCMuSn9DVgyowA2/enKoplCOfvKdRuZtUtPE0uwcyMEEmfIsp9FyX6C9xBBKqLXEpIEGxEBK36Yfs0SQ8Q5fWrE04GCGfCVcAhtqtp4L8HzFiHqWOJioiGk6p8tiGCKP8hNDeLPEw4SO4ispp7kbOAa4C3Ctu2JCRlvkVfduJB3ej0lbQ68R1/Cvhzzv7NNBtJJxH64ScNMm48sGKTKiUPIq7HhwL72e6XHCVp/zRmb+BA4lzPDOQvhNTD0sA9kp6meoWVba/WSuMyQydn/GZ6gtQh9E4iw2nXgm5PJtN0ei3C2Gt/T6Z9pInim7ZrNm5MWpYz2h5OhmpP04jzsVI2YSYzVHrl3pAW/vsDpxP6se9Wybi9F3jP9jKDvN8YoiGYgdfKHQ0Vxt9ANKqd2/ZbaVu/46fS6EeB6xstY5b0h08m5Cp2JJy7HzPw738KeNz2qo08fqZxSJoA/A+wU7FsXdIJQPF6fwOwpu0PW2xiZhRR71yj9P1sxr1E0qvAK7YXGmTcg4TEz8YjOV43BoXqYYjVxB1RKdYrc5RmkTN+M11JamBWzkSiedDakq6hdlSqVztwdi2S/gE8YTtHXtvPQ8QioZQZNR9R6lOxw22vTnoyDWE20ndpEO4lZ5lXo1Zn6XpZBXhxWAeXNgQWtX1QA+zIdDeiMd/HdrMp0dhrwiCJAo8QUjT9kHQEEdA6CCA5el8ewvHPBI4Bjpe0re0Pyt5/DKFhPRXhnG40EwjprPVL/RZCGnYA9wILN+H4k5A0lmh2txowB/E3VyJLt1RmY+BzRGk7AJKWA3Yg/scXA8sTVXRbEtIWmUy7mYIq2vwNYBpCYmww/gFsSFR/DDcT0vSuP22VdhuQaSy9+kXN9D4H0KfzV6L0/Av0Naeiwn4D2fHbeSxIlBZmGkBq+HfKYE2aUtR9++KCyvZhki6QdBnR+KSWlEgvT3oyI+ff9Em21GIOYpGaGcihRHbesLF9/QhevhHRXDM7fjO7EZmy3c48wMQ6qsP+SzTKKmcXYCS9Bv5IaAtvBiyd7rUAX5F0GHHOzU84JM4cwXGq8VXg1vImuxV4k3AqNoWkxXkTNQLLxeHNsiPZMiWwJP2by93ZBRWECwP3lWXybk7MzbawfbmkmQiN0+3Jjt9MZ/BlmiRjQ6wlZ69j3OxEVcVbZAmpAYxw3pjpQPJiPdOtHNhuAzqNNGndldAq+hIxka5Ep2ZNPMVAjbnM8BlKZla/cZLmAv5GlK4+T9wrZgVuIbJ/ZyEmSbcAuWwwU4s7gNUljbN9c6UBqfvy1wk9sZ5G0nzAd4HliPPoYtt7pn3LEnqM59qetCCy/TA5KAaApCFJgdh+ulm29ALpWj8HocNdkWJFR+rs3RXdvQfhQ2r8zQU+T+jflvMifc3choztjyStC5xAOH9/mHYtlR4AFwHbVWuGO0KmoPLfVc6sNPcefwgwA9Gn40DgIdstDQBKmppIJvke0Vi4yH8kHQ/sb/u9Vto1BGYm5mtFVgTesH05xHkr6UbC4Z/JNJSk61tkhQrbSkwOLAQsQcgzNoPjgGMGmXeOI86TH9o+rkl2ZFpPL1QkNY1OdP5kMoNiOzt+C6SJ67VEI5+2Zk2MgAuAXSTNbPvVdhszipiOgQu7nxGLiYNt71/QTBoHIGkN4FjgA2CtVhqb6TqOJr4jV0j6LZFt9BQROBhLZJL+mLguHd0mG1uCpB2A3wNTpk0mzrMS0xLn1YeMMMO3h3mS+jNzcjVCFSRtTGSSzzfI0F79DB8GFpc0VbWMTkmfIQIxlUqGrwLWGElD4eTg3FzSgcA6RBbyZMAzwBW27xrO+9bJ04TGcFUkTUZk5T3eRDtWJM7pb7ZDe1bSNMT/8mvEPei5ZA/E/WlOIsv965JW7VDn7xgK8hiSpiX+t5eXjXuN/vebTKZRjC/8buK+Mti95UVgn2YYY/sPkhYErpR0DHAG8K+0eyxRbfF94HfZ6VsfSXO+vOn6DXVUjbSaEVfI9TK9OJnLZPohaX5gEeAp23e0254m8RNgWeAKIut3X6Jb89TEzXdrovnDb2zv1y4jB+EQYA1goqTv275tsBdkhk/SEFwIWBV4tmz3WsTis2KAxfZfJa0F3A/sSfzvMpkB2L4slS7/lJjk70OfrltJQkTAL0vZSb1Iyi45nsiy24fQPS6/xl1PlBxuQJ64VuNpKjt+xxBlm6V57VMts6jLkLQ+cC7xmb0FPEFIsowmzgd+CRxGBJ4q8QsiMHpuhX37E+fpcZJ2tf3OcA2x/SDw4HBfP0wmAj+UtLXtahrC3yXOqWqZe41gKuDvbWw4tjdRfXEv0Rj6uuJOSSsBvyOSKvYCKvUXaTfPAosVnq9BBBDKMx1nBN5olVGZUcX26aeI68VNwIlVxn5AOA1vLdc2bxRJ6q7E7ulRiR9LKr/+G1gf2INIfrm2yjFWJdbah9r+6whN7lgkzUgkLGzGQNm/TySdQ2RNN0u2Y0jkCrnaqDkVRJlMa0nZKzsCBxYdhpL2IybopSzXs2xv3QYTm0pqjDYP8AXb/67U1VLS2kRZzVa2z26TqVVJDfmmJjIvDLxELN4rZVjY9motNK8mndJFtGyyU9KzrocjbO9ReJ/3gL/Y3jA9P5GI6E9dXKBJmgjMabtm5lAmk8qadyOazJSyk94nFqdH9LLTF0DSxcC6wIq2b0nbBnS/lnQ1cU4t2B5LB9Ip17fBkDQ5EbQ6CrjJ9nZtNqkjkXQL4cjaD/h1G51ubSNlRf6d6C1wC3AhcDihqXse0fxtJcIhuEyF5ms/JyS1tiAyKa+i9nylo/pKJImP+4gqg8MJR/gdwNlED4xNCafo28CXbQ+lcd1Q7LgLeMn22s14/zqO/xiRBTu/7VeqjJmF0AF91fZgWYwtR9LRwM5EeftE4FeEPvRXbT9QGPcM8JztAc0KM5lGIelJQq5qzzbaMNKmcecDawOzVwvqSZoOeAG4xPZWIzxeR5IqIm4mKl9MJCs8kXbPQyScCbgbWKFDKyIyBbLjN9MTSLqQaEI1q+1307avAPcQOmy3EiVrMwKb2r6wXbY2A0lvA3+zvVZ6fhKwHTCl7Y8L424FPrK9Qnssrc4Qb9TuJCdEpzhGyj7D8uaH5XxIRN3/DOxj+7+F93kD+KvtzdLz3xEahF8olvVIOgvYwPanGvdXZHqZVD5capb0WvH61MtIehl4tCSXkrZVcvyeAaxne4Y2mFmRTrm+1UtqGHUnsJvt37fbnk5D0jvAg7aXGnRwD5NKV8+jL9hcDJaK+A5tVKmUNZ27g91jJ71nrXNnqDrLjULSKoTEVqVrjYgs8A2b2eBH0vcJx/PCtp9s1nFqHP894Erb3xxk3J+BtW1P0xrL6kfS7IQcyWz0fefOsL1NYczixPf5t7Z/0hZDM6OebqnAlfQ48Lztrw8y7kbCOdxxAaFGIGkvoqLzb8CEVJ1S3L8QUck2Dtjb9mGttzIzFLLUQ6ZXWBz4Z8npm9iamATtaPs0SfMADwATiOyOXmIM/RuulKJuM5Ztfxz4RquMqoWkRYDXbZdkBlZppz0j5CGidLut2J5UhlPJqTQEngOKTZQeSz+XIyLhSBJx3r01PGszo5Hk6G1K9liHMwMDJVUqMR15bjYibN8v6e/ATkSJYqY/H5JLIUkO3eVTNdS6lGnsAhfVaKx2ECPsAt9KneUK8y1sXytpYUIGbIDGMJENXs81a9jYPkbSMsBVknYBJtoeabbeUHiV+pr0fZTGdhy2X0iO3QmE8/d24E9lw74CXEw4+jOZpiHpm8R3sWYFrqROrsCdnYFSXJV4hlgH9SqbEfIw37A9YK1n+0FJGxC+hc0J6aRMB5MXF5leYSaibK/ISoSe4pkAtp+QdBOha9prPE9kjJQoTdYXIZq+lRjLCBcrDeQu4BRgh/R8O6I8t5l6cnUhaQZgaWAWIjJd3jG5HynK2Wk3vAOJz3g43A58S9LUKRP4yrT9yJQt9ixRWjg/zevKm8n0Ei8Dc9cxbgEi8JIZGS8SXcMzA7mTcPJlANtX0nePq/c1B4zkmG3QWe4330pVYaX51k/To+VIKpUNjwUuBT6S9AJ9OvBFbHveBptwKTHX+XRqtlfJxumBlenghBHbLxISHdX2/4mBzuBMphlsQzQBu7e0IVXgHkj/CtwtJF3YoRW471O5EqKcGYBerlqbn6iIqJrgY/tNSdcS0hiZDic7fjO9wlQUSu4kTUk0O7i+rOPyi0RJQq9xH1GuWOIG4vM4QNIdtt+WtAWRsXlLOwysgOhfJjk+/Wyb4zc5fI8kOr6Wro+nEmUuSNqRyPTZ2PatbTGyTmxXbMxWJ5cRk7f1gPNtP5p0fnckFkoQ/7sPaFJX3kzvkOQdNgNWo3ZJc0dpdzeYmwkHw1LVShwlrUHohv6xpZb1GJKmAJYE/jvY2FHKL4lu52v0clOaDmdv4h66L63RWe64+VZibOF3AVPQv9qoSDOSFvYlmqFdKmnnoiYuTCplPhZ4k/ifZTKZ2vRCBe6DwAqSZqjm9EwBoRWAR1pqWSYzArLjN9MrvAAsXHi+IuEMLu9qOx292b36CmBDSSvbvs72zamBy9eB15IG8IzEjffwdhpa4G2inKYjkPQporHLokR23h1E+WeRSwk9o42IqHVPYvsCYgFWZGeiPPhbwGcJeYtDbd9LJlMFSZ8B/kJkX9bSw4TOqUZoBkcSDZMuTAGkq4o7Ja1IOGE+Av5f683rflLDrgWBnwNfBC5pr0Udy8OEbt8lko4iAn1PUznLEttPt9C2llDWCLUWHxIl/ncQ0kkXNciERYC7bP+iQe83GB013ypQTxVEw0iZzuXcB2wA3CPpHuBfaftYYj4o4lryK/oq1DqWlMAwPVXut714Pmc6il6owL2QSKY6SdKWtt8v7kzJZScRPoVelk95DFi5zoqIxyrtz3QWublbpieQdCoRUdyLKNk7nuha/TXbfy+Mewx4y/aSbTG0SaTuoosCT5YakUiaFTiR0G4bQ+j0HGL7iLYZWkDSzcBShETCY0QZ4k3Ume1m+7QG27M/oT91OvA92+9Wab50L/Ce7WUaefxmIGkscU6UMi2nqjLUtnMgMNNwJB0DfI/QQjuaCBhUDb41s5FQu5G0G/BrwsH9b2Jx/hbhXJqZWKj/xPZv22ZkBSQdDmxiu6VOmiq21OOwKzWmWr48gy8zoDHZYIuAnrw3DLPru4HTbG9feJ+pif4EX6K6s822+5XhS3oTuKxV3eA7bb7VLob5fy/RUU2Fi0j6LCH1sAkhUVaNnjyfM52DpPeBS2xvmp5PScxzrre9dmHc6cS8ohMbJk5LNEycH3gSOIOYu0LIcW1NBIYeA5aw/U7rrWw+kvYhrivXE83dHivbPx/hb1kZ2K+FgczMMMmO30xPIOlLRIRxutIm4Crba5aNeQg4zvb3W29le0g3sBmAl1rcNKMmkjYkOmqXJtL1LEIn0egJuKT7iKzoeUvR3SqO3wuIgMKcjTx+o0md7W+iRuZHkWJjuEymUUh6FpgG+HLSIRzVSFoHOIDQEC9yLzFxbkqWatLSPM92TS1PSYcCmzVBS7MhDOK4+ZDQR76aqEZ4osbYUYukJxnavbbtDv9mIOlXRFDqGCIT7Ski63kssCXwfeAE4LeEc/fXhFNtG9tnStoEOI6ogKl6GCo4DCVdDUxre7lG/k1Vjeiw+Va7kLTdSF5v+9RG2dIoUlXN7YRu98eEBNe0RCXk5+j7Xz8NvXs+ZzqDdH95x/aX0/PViaqv/YsBMEkXAcvZnq0thg6CpC8AFxGykeXXSgF3E7J/T7bYtJaR/Ae3Es0hP06//4v4POYhsqInI+avy5XJe2Q6kBz1y/QEth+RNA74CTArMQn6ddmw1YB/0qdROipIF+KOuxjbvjh1c96I0HQbT3QGLZfnaBXzEB2l3x9k3H+JUqZO5xDC4X850VThoWqlOtWQNIbIGF+OWPDeVmq+J2kW4DPA47Z7ublBZmTMTJxXo97pC2D7CuAKSTMRZc6TAc/Yfr7Jhx5L7UywEjPTX3ezo8gBqpFje2y7bWg3krYHfgysWEGv/15gr+SYuBF40PaJkh4l9P7HS3ocOJtwFJ9FLIy/Sugnz0foxs5AVF09y0BaqrPcgfOtttCJjtsG8FNgXqL0fBdCk3gb23Mmx81WwC+IZn7btM/MzCjhemBrSXsSFbgHE47C8gaaX6HytbEjsP20pCUJGZi1CfmoUgBlInCxezx7MlW+rkJcUzYheiQV+yQZOB/YOTt9u4Oc8ZvJZDqCStm1LT7+W8DfbK9TyyZJNwAL2565DWbWjaTXiYYkCwyncVeqIioAACAASURBVIykJYiF7bz0ZYycWvosUrPA04GNbP9fwwzP9BQp0/Qu25u025bRTL3X11R+uantarIwbSXpyXmoQaxMpoikOwjZr5rNJFNm7owleTBJdxKO0+uAjYENbF8m6WRg21JmrKSZgZMJbfMlqCyztCOwB9ByneV2z7fKkbQp0T9gMMmMjqxEaCeS7icCdl+w/X75dzGNWYrI1vuR7WPaZGpmFJArcHuTlAH9daBU7foccGPWDO8ucsZvJtNDSFqOPj3XqasMs+1ObFBxIHBXG4//MLC4pKmqZf2mkrpFCe2nTmcq4O/DdPp+EfgrkdF7GRHB/1XZsIuJksKNgOz4zVTjAiJDbhrb77XbmHYh6VzCETSxkyR3iqSmQOOATs7OfpNYVC7bbkMyXc2CxD1sMF6k/3ftCSJTbXngPtuXVXqR7VclbUmUxR4I7ERlaQUBu6dHNUzj12vtnm8Bk6qKzgc2pLokVb161KOVscB1hXmrASRNVqrGsn1Haqa1AyFtksk0hVyB25skB+8Z7bYjMzKy4zeT6QEkTQWcA6xf2lRjuOnAzsS2D2yzCecT5ZeHESWglfgFEcU+t1VGjYBHiFLT4bAP4fT9YSk7JOkhTiKVAP2TgVqlmUyRA4E1gXMk7Wj75XYb1Ca+RZTKvZiyak9tduOxlG3dzwZJK1cZPjkwW/p5YjPtGiFvA4+224heIGWlTiAasxSzeK4FTrT9SptMawXvE9qNg7FYGltiSuI7ODP9ZRI+AigGuGy/nSqE1iGyeTvGcdkB860S3yOCx3cDe6bn3yQc8/MRTZQ2J+ZeJzTLCElzEM7nwTKOO27uTGhvFhumlhpNzQy8VNj+PLBeq4zKjF5s3wdUrSawfSwhH5DJZFpIdvxmMr3BAYQO0X+APxElNP+u9YLMAI4GtgN2SWVxF6btYyXtDGwKrETo/3WyY6TECcDhksYOo/nAWoSu4WCZIU8SkftMBgBJJ1XY/DixuH80lUpXK2nu1IV1I/gRcX1Zkijv3j2Vm58CnGX7zSYcc2zhdxNBq+kqDwUig/8iQjOyU3kQmKvdRnQ7qcngGURwsOjkWhhYHdhD0tZJk7oXuQlYT9LPbR9UaYCkfYGFgGLDxbmJplkf0V++oXT+zkX/wISBWTuxc32HsA3RN2Ed2y9J2grA9qPE53iFpKuAPxKVR0812gBJPyaC/lMUN6efLjzvyKQJwqH7+cLzJ9PPJYkeDyUWon8QI5PJZKqSqlb+l9DwnVhlzNpEFcFPbZ/XSvsyQydr/GYyPUDK7JoFWMr2w+22p1uRNCfR+fprDCwvFHAnoWn7XHssHBqSTgFWIBp+1F1iLum/ROOCbxe2VdI7PgfY0HY1WZHMKCN9T4aLe6V7fDUkLQRsTzTcmZ24vnxAOJdOoYFSEEmyBeLa9QRR1bBHleEfAK/Y/qgRx24WknYAjgeWtX1nu+3pRiQtSMgVTU3ofp5MfD8gmpxuT9wD3wOWtP1QO+xsJpIWBW4hnLePEBVTTxHn4xeBzYis0/eB5W3fnTQOnyQWucsCk9tePL3fdsTnuJvtI9O2TxGf69u252vdX9c9SHoTuLOktZwCh9sRn60L4+4BXizqhDbo+GsBVxCJEkcT2e/LEZnH8xFVGnMTOsx3d2JzOElnElU1s9n+WNJixPl9H/BtooHW94FDgWtsr942YzOZTNcg6f8IWaPZbX9QZcxURDD0BtsbtdK+zNDJjt9MpgdIjrpri43JMsMnRTDXJRbBkwHPEIuDi7qli2uhzHsssZj9iLg5V8u0nNQ0RdIrwP22Vy5sq+T4/TsxIcgZeBlgkgNk2HTiwroZJG3LNYDxRInx1MR5+iJwuu2GZt2mhj832q6Ukd1VSDqKKAE/DPgz8FQ1XfbMQFJAcFtgD9u/qTLmJ8DhhCTJ9i00r2VIWpVoUPo5BsowiCiT38b2VWn8LMAiREXVj4FdgTltvyJpJsJxPDnwO8LZti3R2O0PtncuO/Y6RBDmYNvX1rBvX+BQ238d+V/ceUh6D7jQ9lbp+THAd4GZilUQks4A1rY9U4OPfxmwNvA123+v0KRvSsIhvDkRBOk4mRlJ2wCnAuuXNKclXUxIv5V/r1exfUOLTcxkMl2IpKeAJ2yvMsi4a4GxtudujWWZ4ZIdv5lMDyDpGeBvxQzNzOhmiJmX/TItJf2FiPLOb/uFwvtNcvxKWoDIKPk/2xs3zvJMZnQhaXoiM2s74rzr+czn4SLp4yEMt+0saVaGpKeBN20vMsi4e4AZbX+hNZa1HknTEPrbK9Gnc/w8cANwnu13q7xuGeAQ4Ne2/5K2fZf+jbNEBI2XtP1q2evPJRyOs9t+hwpImo4I1l5Scoz2GilA/UQpC1XSz4H9gXG2by2MuxpY2vb0DT7+y8C/bC+bnvdz/KZtUxBN+q6zvXUjj98IJJX02d+y/Z+07VOEfMW3gM8SwYqDbF/QNkMzmUxXkZLKLhjs/pMCc9+0PW1rLMsMlzwhzmR6g8uBdSVN3umlupmWMZLI60mEzuMZkja1/VpxZ3JU/QEYQ3foHWcyncxkhL7klO02pAuo1bh0JGNHE7MRjs3BuJcode9ZUiO2P6XHUF53O5GxX9x2fNIw34Q+Z9vJVfS7lwT+Wc3pm97vP5LuJmQlepWHCV3pErcQ5+2ekjaxbUlfJxzzdzfh+DPQJ3MCIXmDpE+V/je2P5R0M1Az661dpDn/c2Xb3iEkvnZpi1GZTKYXeAeYtY5xs5D1w7uC7PjNZHqD/QhpgqMl7ZrLXoeOpOWIDud/tP23KmPGEc09jksLv47F9rCboNg+W9KmRHftJyRdn3Z9Len6rg58BjinVFqYyQwVSasDixIl0n+2PZRszq4mST2sQ0g9rEc4fUsZgqc14P1PIsp8905Nk4Yi8dCxTfZsj2m3DT3Av+nLbq3FHMDbTbalp7B9B3BHHUNnB26rY9wzwOIjMqqzuRJYS9LStv8OXEM4zDcEnpf0PPAV4tp4bBOO/ypQzCJ+Pf0cC9xf2D41MefJZDKZ0cI9wDhJs9l+qdIASZ8jesnkngtdQHb8ZjJdSCqHK2ci4bhcW9I1wNNU13M9uJn2dSk7ETpu1ZofQWSnbEl8rh3t+G0A3ya6ue5COKYgmt0sCHwI/BbYsz2mZboFSROA/wF2sn1TYfsJwHcKQ2+QtKbtD1ttYyuR9BXC2bsVkUkhoonWWURzt6sbpCM+nnD8HkZolY4fwms7tXt9pjHcAawuaZztmysNkLQ88HXgLy21bPTwPpFtOhgzAL0cEDuDcL7+GyA1J9sQuIBw+M5GzLd+b7sZ1UVPEs38StxNXJM3JxIqkDQr0fRt2MH0VpE0iZcESn0XniOa5+VkkEwmM1TOIqotzpe0oe3XizslfRY4l2iSelYb7MsMkazxm8l0IUlv1fQvZS1/Xk5pf9aPrICkR4DXbC83yLhbgM/YXrA1lo0MSTMQjZCWI8pxrrb9q7TvS0Rmy42p5LXS6z9DlDgWG91dZfvl5luf6XYkXUGUKs9Wcuqm7PqbiWzCiwld27mB7/RqczdJuxDO18Xou07fQjh7z7H97wYfr9Rk70Lbbw+16V6v/h8yIOkbwP8B/yECeKcSTi0T94NtieZl0xENoy5vj6Wdj6T5iGZkpfvrxbb3TPuWJSoazi2Xe5D0N+DLwBdsv1Xlvacn7reP2F66eX9FZ5L6CHwWeLRcI7mBxzgI2AeY2/bTSVf5KWBG4HyiSd8mwOeBX9neqxl2jBRJUwMHAN8DPl22+z/A8cD+1eZ5mUwmU07SD78B+BoRnLuEqMgAWICozJieSIT6eq8nbvQCOeM3k+lODmy3AT3IHMA/6hj3FLFg63gkrU1k1MxIcvrTXwtuAeAiIov5nMLrFgE+sX2f7TeAC1tmdKbXWBi4r2xCuDnxXdzC9uWSZiIyr7YnnFC9yO/Sz+cIPdFTbD/SrIOVO26zIzdTwvZlkg4Dfko4vfahrzqoJKUh4JfZ6VsdSTsAv6dPm9vAzIUh0xLyBB8CJ5e9/EJiMX2SpC3LMzJT5uZJhPN9VDbksv1wCw5zFiG78UXg6aSr/B3gTGDTwri7iAqojiM1KLyK+D6JuMc8mXaPJWRddgO+LmnV7PzNZDL1YPsjSesSCQobEElEpYzRUgLD/wHjs9O3O8gZv5lMJgNIegu41vZGg4y7CFjd9nStsWx4pJLy24kA3/FE1PYcwuH0nTRmCkLT7v9sb1l47SfADbZXbrXdmd5C0jtEV/otCtvuAj5ve+bCtsuBr9r+fBvMbDqSSlIOf7VdSYInk2kpaUG3G5FxP1Xa/D6RjX9EdvpWJ+n9X09kUx5M3F9vo//9dQwhY3C97W+WvX5aItA8P+GkO4P+mVRbE067x4AlajWByzQeSXMSElelJn2XdKoGvaSDieDNvcCutq8r278SEXj8KnCI7UpScZlMJlMVSYsCaxNBMhNykhNtN6PpZqZJ5IzfTCaTCR4nROynqqaHJmkqYBz9u0B3KnsTi/lv2r4EIDVmm0TqVn0XUY5a5E2ixDGTGSlj6HMqlRweXwHKnUqv0T9brqcoOr4zmU4gOXYvlzQZMFPa/FqnOrg6jD2Jxe86tm8BkPorbdn+JN1fFyp/se13Ja1JVNwsRjjuiojQm914NDh9JX2e0JKcg2ikVomW9aew/RwRMO8GtiDKsFe3/Ur5TtvXS1oDeJSo7sqO30wmMyRs/xP4Z7vtyIyM7PjNZHqA5JCcDXjDdsUu3JI+TXQlftH2B620r0u4FNgX+A3wwypjDicyQP7QKqNGwMrAXSWnbw2eIxxxRe4G5m2GUZlRx7OEY6PEGoRWdHlTqRmBN1plVLuRNDtRggvwnO0XWnTcqQnN7i8R2myVdOFzA9BRRHL0Zs32obEccHvJ6VuDF4GlKu1ImrJLEiW0AzKpCL3gni7LTBqSRwM70nctKr8mTepPQWRXN/L4JwE32T5pkHHjgRVL2dwdxpzAlZWcviVsvyLpWuJ7lslkMoPSI9fHTIHs+M1keoNdgUOB1YDrqoxZErga2AM4ojVmdRW/JRYfO6eSlpPpX3r5HaIk9mXgyLZYODRmIspPB2NKYJqybUcBF0pa2/aVDbcsM5qYSJxTv0+/H0Ys4C8tG7cY4fDoaSRNAHYH5ivb/ihwuO0/NvHYmwDHEcGrqsNogoMlk+kxZqC+qpjpqLHWSo7di9NjNHIAsBPwEVEF8ighn9EqxqefNR0bRKXXdsQ8sNN4lfj8BuOjNDaTyWTqYXz62c3Xx0yB7PjNZHqDDYBnyrW9iti+TtKzRBfO7Pgtw/brqdv5JcRNbPmyIQKeBzZsVofpBvMGMFcd4+YFXirb9g8iC+fiFPH9M9HUrmJTENs977DLDJtDiK7oOxMdxwWcYfuB0gBJixNZS+e1xcIWIekUYBv6nKvPp11zEBm4x0saZ3v7Jhx7WeBsoonXWUSW/1eBXxJO6DUIZ9aJZJmXnkJSqbT76HSfG0qpd87+rszLwNx1jFuA/g1VM/3ZBngHGGf7nnYbU4Mp6GuA2GlcCnxL0qdrVPxNT1SB5Ua9mUym0XTy9TFTIDt+M5neYF6iPH8wHgAWabItXYvtuyQtCEwA1mJg6eUfbbcyG2Uk3A6sJWl+249WGiBpaeL7cFbZrn+VhhDZODvVOI7J95JMFWy/kBy7Ewg5mtuBP5UN+wqR8daz3eslbQFsSziM9ieaQL2f9k1FZFYcAGwraaLtsxtswu6E3vJGti+TdDLRTG+fZMPMRJXDusASDT52pr0cQFynzyaaeZaeV5L5KNG08voe4WbC2baU7TsqDUi6ql8CmpbF3wPMClzd4U5fgC8TvQ86kX2JwN2lknYuBlUBJC0EHEvYv3cb7MtkMr1NJ18fMwXyYj2T6Q0+SyzoBuN1+pq4ZCqQGqn8Nj26md8TXanPl7SZ7YeLOyXNQ5TvmFgUFHkmbc9kRoztF6nhPLL9JwY6g5G0PDCf7dOaaF6rmAB8AKxavjBPDuDjJd0I3EUEWhrt+F0euM/2ZZV22n5V0pZE0OdAIjs70xscRFzPXy17nhk+RwKbEpJIOwJXFXdKWpG4v34E/L/Wm9c1PA1UbKbbLFIVU5EVKmwrMTnRnG8JoOK1s9VUsfU+ovLvHkn30Be8H0s07xVRzfYrYIcWmJnJZLqQbr8+ZmqjHu8bkMmMCiQ9Bzxre9lBxt0GfNH251pjWaadSPodsAuxyL+fiMo+B7wALE7ctI+wvXvbjMxkqpCyUre1PVm7bRkpkl4nmkHVbK4j6UpgGdu1dHiHc/z3iWZRm6XnJxB6bNPZfq8w7kJgSdtfbOTxM5leQ9JuwK+J++u/iWaJbwEfAjMTzraf2O72IHLTkHQg8ANgbKuqqSQVS5IHy3wv8SKwlu17m2NV/ZTZP1TcC/fTTCbTHLr9+pipTc74zWR6g9uADSUtbfvvlQaksv6lyFG5UYPtXSU9CPycKKeH0P2dC3gNONj2Ue2yL5MZRUxL/VUZ5c0WG8EbwFSF56WyvLmIhkolTJRfZzKZGtj+jaQHCOmMpdPmGdPPe4H9bF/SDtu6iF8AqwOXSZpg+5EWHLOkoS4iK/smQtu8Eh8QwfJbbX/QAtvqoeEa8JlMJpPo9utjpgY54zeT6QEkrQVcQWRyjrf917L9axD6jbMD69u+vPVWdj6SxgJ7AasRDZemqjLUtrsmcCZpDLAYMA8wGSHlcLvtip2gJa0D7EE4hq+tMmZVQlvu0PLvWybTCHos4/dxwqk6v6tMvCQJeAQYY3veBh//78DkthdPz7cj7gm72T4ybfsU8ATwtu35Gnn8TOcg6QngPNs/HWTcocBmjf4u9iKSZiKavU1GNNp9fpCXZBLpunMLUTL8FNFcslJWq22v1uBjPwmca3vPRr5vJpPJdDv5+th7dI3jIpPJVMf2REnHA98FrpT0LFDSdF2AyOoScEJ2+lZG0pf/f3t3HiZXWa1//3snYRCBMAsyhRkEBQwISJRJRmUQh1eRGRxA1HOOogcQFTmKyHEWBXNkEtEjqIDwQzxAQkCZJxkCogzBIILMEEiA3O8fz25SaXpK0lW7uur+XFddnd57NbUSuqt3rf08a1HubC7J4FtbhrL1pVaS9gBesn2J7dnAzdVjKA6irA6/foCY6ymrnA4EUviNGNillNfnkyR9wfYrjSermzPfoNycOaUJzz8Z+Iyk5W0/RpkEPwM4QdKKlGLL/pQt6pn83tnGAcsPIW65KjYGYftxyi4aJK0j6X3Ag/0NfouiGir5f5Q2VKK8/q3ZT/iwr1SyPW64/5sREZ0gr4+dJ4XfiA5h+zBJ9wDHAKtWjx7/oqzM/E4tyY0MXwPGAv+PMtzobtvP1pvSAvktZeDMJfPxteOB26pBd32y/ZykW4EB+0pHBFCKuh8C/h14r6RzKAN4TCl0fJiyYvCpKna4nUtZ9b8p8Afbj1c9Sn8E9PT4FmU3wLFNeP4YeV5HGU4WvUjaGzgUOM72dQ3Hv0hp/aDq81/Y3reWJEeGb1CGj91DueH1V6AlvX4jImJoJI2lLPZZnnJT8081pxTzIa0eIjqMpNGU1ZqrU4oK04Cb+tvWH0U1fOkpYD3bL9Wdz4KS9BilwPOR+fjaGcD5tvcZJO4cYA/bi89nmhH96qRWDwCStgR+RdmB0fviq6fo+sHGQlILctoMeB+wDHA3cLrtpwb+qhjJquEtZ9g+eICYscCtlLYjGfTXSzUEcSdgBdszqmMbAX+mFMuvpaxiXQr4gO2sou+DpH9Q2jq8yfbTNeaxKLAdsC797/qy7eNbmtg8kPRGYE8G/zsc0tLEImLEqq4FvgN8hDkLRs/suX6QdCjwVWBv29fWk2UMVVb8RnSYagvxddVjUJIOAbYe6E1gl1gEuKETir6V65kz0G1ezaSsfh7MWOCVQaMiAtvXSloH+ACwDbBydWo6cCWl7+rMFud0I5Dt6B2u6uvb6P2Stu0nfAzwhupjf0Ndut2mlF0xMxqO7Uu5oXOo7bMkrQncBXyUtE/pzxLAJTUXfd9HWW28zEBhlP+3bVn4lfRvlNXTCzUerj664XMDKfxGxKCq/uuTKbsyHqVcK+7WK+wi4FRgL8oNz2hjKfxGxARKb8duL/z+haEVO0eKE4ErJB1ie17fvE8FJkga298bMklLUr53WjGFO6IjVIXds6tHy0h6HLiielxuOz+33WVcw58NLF49+jMLOB8YcABcF1sWuKHXsW0obQrOAbB9n6SrKUPLom9TKcXfWkjaAvglZdXxLyg3y99MKaKuDexIuS78KaUPetuphjt/G3gG+G9gW2ArSk/5tSk7OtYAvkdZxR8RMRSfoxR9zwY+YXtGtWPoVbYfkXQXsH0dCca8SeE3IqKYCPy3pHG2H6g7mWFyCvATSe+n9Px9EHihr0DbUxo+/Q2wJXCapH16r0KUtDBwGqVw8OtmJB4Rw2pJSgFgbwBJ04HLq8dlth+pMbdovjWqjwLuA84DjuwndhbwWNpDDWgRGrbSV78TNwGu7PXv9giwdYtzG0lOBk6RtG5NN6M+B4wC9rJ9cdVe6M22j4FXh8+dTlnl9tYa8huKT1Nu5uxo+4bq77CV7YkAko4FfkhZ6Tu+vjQjYoT5APAw8NFBdqP9hfKeMdpcCr8REYDtH0l6G3CZpE8Bl9qePdjXtbHJlDcDAnam9CPsj5n798GPKINr9gLukvRzSv9PgPUoW1rHUQax/GA4k47odFUf9mWBRfuLsT1tmJ92GcqKxB2qx0bAAZTdHki6m6oIDEy2/cwwP3/UyPaDPX+WdCZwVeOxmGf/AN7U8Pk7KcXgP/aKW5yyEjP6YPsMSesDk6sC5aW2W7my9u3AHbYv7ie/f0nahzKI8zjgEy3Mbag2B2603XsFOgC2Z0n6JKV4/WXK9VtExGDWpLwmD9aC7EXKNW20uRR+IyKYqwfiOErPopcbBo/0ZttrtSq3+TSF1w6QGpJqO89OlK2+mwDH9AoRZcvg3rafX6AsI/on+h5QMyJJmkB54z0BWHiA0N43YhaY7Wcpr2sXVbmswJwi8PaU7ejrA5+k9O0eKL8YwWwfVHcOHeBKYF9Jnwd+T+n96urPjTaiTVsEtANJjTMCflId6y/ctof7fetyzF2sf7nK4XW2X6ie9FlJU4Bdh/m5h8tYyir+HrOg9OfsuT6z/ZKkP1IG2EVEDMVLDLBAocGqlDZH0eZS+I2IKMY1/FmUIRmr9RM7XwXVVrK97QJ+/TRJ44E9gF2A1Sl/72nApcAFttv+3yHaS7Ulellgpu0nBoq1fSBwYAvSarrqRspFzLnuepwaL5RtP0rpafkLSWsDHwOOoFzkj64rr2g+SatSCkDX2b6nn5j1gC2AK1q8AnOk+BplR8wJ1UOUlimvrrqUtC5lxdQptWQ4MszLjb1m3AR8krJSu8dT1cdVgHsbjhtYoQnPPxz+RWnl06Pn9+o44M6G44sCS7cop4gY+e4BNpW0SH+rfiUtTekDfHNLM4v5ksJvRESxxuAhI4ektwCzbd8xv/+NqrB7QfWImG+S9gc+RVlBPgo4k2qgpKT3UnqJHWP7/tqSbK7jKddc/w2cYPvJuhKp+lbuALyr+rh6w+lbKO0eonN9GvgP5m5V0JczKENCj2p2QiON7b9I2pry77gCcD1wUq+wHYDbqFbZx2vZHlVzCg8x9w3+OygF5vcA34FXJ9tPAKa3PLuheYC5X8NvpfwdPgQcC6/u8NiWMuchImIozqMMujwR+Ld+Yr5OaWn0q1YlFfNPWbAV0d2qQRD7284qrw5STV69yvY2decS3U3SGcB+lDejz1EuEs+w3VP4fRPlDfcXbPcunnQESTOAO21vXtPz70wp9L6LMrW+p+DyN0qh93LK6s4BV2HHyCfpNsr1/1sGibsdeMl2uw61ilggkk4CPgOsbPsxSctSiqNjgO9R2nTsTxns9hPbh9WWbD8kfZXSjmuNaqfW4pS/w1KUws3fKYM9VwW+aTs3ciJiUJIWA26gtAG7hjL4+78pM2TOpSzY2Aa4HXib7Vn1ZBpDlRW/ERGd6SnKapaI2kjqGSB2K2Vg4C2UHrKvsn2XpIcoPRQ7svBLGfB076BRzXMJZbvyo5SVGZdRtqYP9xC5aH+rUt64DeavwDuam0pErc6l7ELZFPiD7cclfZYy4PZzVYwo11LH1pPioH4BrERZ9TvN9nOSDgbOoRRmetwC/FcN+UXECNQw7+VcyiDMrapT21QPATcBe6XoOzKk8BsR0ZluBdp9AF10vo8CzwK7254O/Q7vuZ3Bt56PZFMog57qJErxd3bDI7rPolQDoAYxC3h9k3OJAEDSaEr/936HCQ33jSrb1wM79jp2qqSbKKtklwHuBk63/VQf/4na2Z5K+T3beOyCqsf0e5jzd7jQ9it9/CciIvpUXbe/XdIuwG6UvvWjKTfDLgHOz7yXkSOtHiK6XFo9FJLuGzzqVbbd1kVVSXtRtuXsZrv3pPGIlpD0NHCt7Z0bjs2modVDdexsYG/bi9WQZtNJ2pCyVe5Ltr9bw/PvTuk5ugOwIXMGVP6VuVs9tGVxI4aPpHsBbK8zhLgxtjuq/320F0kTgC9T+uguPECobWfBUkRExHzIL9CIuJuyGq3bjRtCjJmzaq7d3Qz8ELhA0mnAbyl9317oKzhbvqNJFqL09R3MCsBLTc6lNrbvrLbM/ULS+4HfU3ov9rnq1vZZw/z8vwN+ByDpDcwpAm8PHAZ8Apgt6Vbg/2wfPZzPH21lEnCIpANtn9FXQNWiZS3g9FYmFt2lek28iDnvRx9naL8volJd311t+7RB4g4E3tl4wzUiIrpHVvxGdABJr1BW0B0ySNxE4KCsmngtSav3c2oUpXfau4FPAd8Efmq7racjV98TMLRCdVbSRFNIuptyrbFew7G5VvxWW3ynAf/s5EFSko6kbb7lUAAAIABJREFU9IkcdPt8K3dgSFqTUvz9JGWbtbt9B0gnk7Q+pRXQKEpP7Z/avq86twalF3dPf9O32r6zlkSj40m6DticMjDoBNtPNvn5FqTVQVteJ/W1g6afuInAwXltj4h5VUcrnhh+bfcLLCLmi6rHUGOjl0EKufcDkyX9kTIleQpl9Ww7e4iRsTI5OtulwBGS9rV9dj8xH6cMpxlwxdJIJunjwInVp7dRWizUtrJN0grMWfW7A7Aac343pPdvB7N9t6SPAf8D/Cfwn5Jerk73vC+YDXw0Rd9osjcDN9n+fIueb0Guf0f6tfNC5LU9IuaBpC2Ar1IGvS4yQKhJXbHt5X9QRHdZnA7eTt1sts+XdDtwNKUvZtuyPa7uHCIoKwoPAE6T9CbKjROARSVtQJk6fjRli+8P6kmxJT5Dee3ds46e25JeD2xLKfK+i9LnF+YUM+5hTq/fSa3OL1rL9lmS7gK+SPl+6OmtPYPyffA12zfUlV90jWeAe1v1ZLZH9T4m6VuUm4+nAD8DHqhOjQP2pbTBOdX253p/7QizIZAe7hExJJK2plwP9BR8n6S8ZscIlVYPER1gsK1ekkYBGwBXAM/aXruV+XUSSb8CdrK9VN25RIwEkrYDfg2M7es05UJyT9tXtjSxFpI0A/ij7R0HDW7O88+k3OzvKfQ+TCnyXgZcbvvhOvKK+lXXB8tRVuw8bjurAqMlquup9W2/pabnP4RS8N3e9lX9xEyg3Aw73PbEVubXn6qvb48DKTtIru4nfAzl+v+twMW292hudhHRCSRdRpkDMRE41vajNacUCyiF34gRqlevsnkZOPZt20c2IaWuIOlGyhuVxevOJWKkkLQi8O/ArsCawGhKO5JLgJNs/73G9JpO0oPANbY/VNPzP0kpXlwOXGb7njryiIjoIWlD4BrgS7a/W8Pz3wQ8bXv7QeKuAJZqlx701WKPHj1DhwfzCLCz7dubk1VEdBJJzwAP2d5w0OAYEdLqIWLkarzQG+zC7yVgOvBbynChmEdVY/vPUlZNXFNzOoOStNq8xKcpfzST7UeAL1SPbnQBsLekhW3PquH5l81KzuitKrxtBSwP3Gn7wur4KGBMTd+r0SVs3ylpJ+AXkt4P/B74O/30orV91jCnsB7ltXkw/wDeNszPvSAOqj6K0hv/auCn/cTOolz/X5uf54iYBwL+XHcSMXyy4jeiAwx1qm/0r1rR0Z/FgbWApShF9r1sX9SSxOZT9T0x1Bf4tpxWHSOfpHcCj9j+yyBx6wAr2Z7SmsxaS9JSlBtGt1G2DD9Rc0rRxaobg2cA2zQcPrPnGqIa/vZjSlujy1ufYXQLSUdSFiS8frBY26OH+bkfB/5p+02DxN0FvMH2ssP5/MNB0gPAr1o4IC8iuoCkPwEzbW9Xdy4xPPJGP6IzHAfcUncSI9y2Q4j5G3BUuxd9K9Pou/A7CliJOa//D7Yso+hGk4HTgUMGifs8cDClBUQn+jYwlTLMbpeqZUx/K9tse7B/r4j5Imk5YAqwGnA7cBVweK+wc4GTgT0p7UEihp2kjwMnVp/eRulV+1wLU5gC7CHpeEq7ibmumSSJcn29PkNbGdxyGeQbEU3yPeDnkjaxfWvdycSCy4rfiAhA0jYDnJ4FTO+UdgiSxgA7A98HrrZ9QM0pRYca6m4ESROBg4d7RVe7aFiBP5RejO7Uf4eon6RvUfptnwgcbdt9/ZxW/U/VLn1No/NUK2nXogz3/H0Nz78RcB2wKOXG/i+B+6vT44APAWsDLwJbpj9uRHQTScdRbgx/iTIcsiPeB3errPiN6CCSxgL7Mqdn3+W2v1mdW5dyIXuV7RdqS7JN2b6y7hxaxfbLwMXVFsGbJF1v++Sa04rutgLQya9LBw0eEtESu1OKW0f3XuHYy33AO1qTUnSpccCUOoq+ALbvkLQb8HNKgfeYXiGi9Pfdt92LvpIWBbYD1gWWpO+bjLZ9fEsTi4gRqdcQ+R8CPyybIPqUloEjQP4HRXQISbtQLl6XolzwmTLQocd6wPnAPsD/tjzBaDvVYJUbgI9RtvVGLLCqr2+jFfs41mMMsAGwE6UVQkeyfWbdOURUVgUuGqToC/AysHQL8onu9RjweJ0J2L5S0trA+yk9r1epTk0HrgTOa/fFEpLeB5wCLDNQGOV9QQq/ETEUQ9mhNj+xUZMUfiM6QLVd7TeUn+kfUfqW9S7u/h6YQenZl8LvACRtRen5u3J1aDow2fY1tSXVPI8A2cobw2kyc/eX3rl6DETAqc1KKCJe9QLlBvFgxgFPNTeV6HIXAHtLWtj2rLqSsP0icHb1GFEkbUFpUTEb+AWwEfBm4BuUVcw7AmOBn1L6ykdEDMr2qLpziOGVwm9EZzgaWAR4r+0LASTNVdy1/ZKkW4CNa8hvRJA0jrJqesueQ9VHV+evoWz5e6DVuTWDpIWA8ZT+dRHDZQpzCr/bAI8Cd/cTO4tyY+W3tn/XgtxqJ2lD5rTjubPhNXsUMKbOAkh0hTuA8ZLG2n66rwBJK1OuFbqmBVLU4kuUwuRZkg63/UTdCY1An6MM7d3L9sWSTgfebPsYeHWY4+nAbuQmf0RE10rhN6IzbAvc0lNAGMB0ymqA6EXSMsAkYHXKVOnfUXocAqxJ6Yv4duAKSeNtP1lLosNA0mKUKdVfovx9B/u+iRgy29v2/LkaGnXJYMPduoGk1YAzKMXwHmcy5+fvUODHknayfXmL04vucQ5lZ9CpkvbvfaOhugHxfcrN5BG3AjJGlG9TWvx8ANhF0o2UVamz+4i17UNamdwI8XbgDtsX93XS9r8k7UPp630c8IlWJhcREe0hhd+IzrAsZZXdYBYGXtfkXEaqIylF0POAw2zP1XeuKgyfQukDdyRllXXb6tWUv98w4BleO9AkYrhsR2kn0tWqVVdTgNWA24GrKJOSG51L6bW9J5DCbzTL/wAfAT4IbC6pp2C0kaQTgb2AdSgtW86pJcPoFgdSdoeIMpBs+wFiDaTw+1rLAX9s+PxlAEmv6+lNbPtZSVOAXWvILyIi2kAKvxGd4UnmDKQYyFrAP5ucy0i1J2V68362Z/Y+afsJSfsBW1PeGLd14ZeBG+2/RFn9fTlwgu37BoiNmG+2s1W8OIpS9D0RONq2Jc1V+LX9pKQ/AxPqSDC6g+2XJe0GTKQUf4+oTm1WPaAMgj1gCAPgIhbEQXUn0AGepKzO79HTl3sV4N6G4wZWaFVSERHRXlL4jegM1wM7S1rH9r19BUjaHHgLZfhDvNY44MK+ir49bM+UdBWwR8uymk9pyh/toGpvMGS2pzUrl5rtTtlqe/QgxbT7gHe0JqXoVrafBT4k6TjKKsA1gdHAQ5TWLLfUmV90B9tn1p1DB3iIclOxxx2UG//vAb4DIOn1lBuK01ueXUREtIUUfiM6w8mUi7zzJH3Q9j2NJyWtCZxGueP/4xryGwleAhYbQtzrqtiIGNwDzBn0NhjTudclqwIXDWEF5cvA0i3IJwLbUyk9ViNiZJoMfEbS8rYfAy4CZgAnSFqR0jN5f0pLiN/UlmVERNSqU99gRXQV25dK+gHwKeAuSXdSiijvknQdsCnl5/3btq+uMdV2NhXYTtKKtvvsSVpdRG8P3NnSzCJGrmn0XfgdBazEnOuQB1uWUT1eAJYaQtw45mzVjYjoCpIWBsYDK1eHpgM39R4+GK9xLrAJ5Tr/D7Yfl/RZygDHz1UxoqwMPraeFCMiom7ZChzRIWx/hjIs6J/ARpQLvVWAzYGngX+z/bn+/wtd72zg9cBlkl4zYETSdsAfKKuCf9bi3OaZpF0lXVHl3V/M9lXMjq3MLbqH7XG21+jjsTrlZ6mnBcIU22vUm21T3QGMlzS2vwBJKwMbAze3LKuIiBpJWkjS14BHgauB/60eVwOPSfovSQvVmWM7s3297R1t/6Hh2KnAFsA3KcMcPwtsbPtfNaUZERE1U+Y2RHQWSaMod/8be/Zdb/vlWhNrc5LGAP8HbENZofgwpSBlYA3KKhQBk4CdbL9SU6pDIulXwC7ASraf7ydmccpAuwttf6SV+UX0kLQhcBPwWdsn151PM0j6BGUF1q+A/W3PkjQbOMP2wdXr9rmUwZH72/55jelGB5G0IL+rbDu7A6MpJI0G/h/wLsr11T8ofc6hXMOuRLkGuwzYrd2vuyIiItpVCr8RERVJiwDHA58AFu91+jngFODYgQbAtQtJfwMetj3goKhqWN1KttduTWYRr1V9Hy5pe+O6c2mG6sbSJGBryg2li4EjgBur43sB61D6Ne4whF7AEUNS3WCYbxkUGs0i6TDKjIq/AJ+xfWmv8zsD3wXWBT5p+5TWZxkRETHypfAb0eGqVZ3rAQ/ZfrTufEYCSYvSd6+5F+vLat5ImgGcb3ufQeLOAfaw3bvQHdEyks6lrOh6fd25NIukJYCJwAf7CTkfOMD2s63LKiKiHpKuobQmW9/29H5iVgbuBu6wvVUr82tHWcEfERHzIy/+ER2g6uP6AWCi7Vsajh8E/BBYFJgt6UTbX6wpzRGjKvD+se48FtBMoN9+og3GAtk+GbWp+jeOB0bMjZX5URV0PyTpOGBX5m7Hc0nja3dERBd4EzCpv6IvgO3pkiZR2nBFaYlRx9dGRMQIlu1bEZ3hUOBg4IGeA5LWAE4FXkdZsQpwlKQdWp5d1GEqMGGQYVJLAhMo2ywjWkrSYpLeSultuzowpeaUmkbSktWKX2xPtf1t20fYPsz211P0jYgutBAwYwhxM6rYrmd7VO8H8B3Kv9G3gU2BpavHpsC3gOeBb6dtS0RE98qK34jO8DbgNttPNhzbj/Iz/gXbJ0naDLgWOBy4vIYcR4RqW+F2wBspK6X7YtvHty6r+fIbYEvgNEn79O5LLGlh4DRKL+Nf15BfdIEhbksV8AxwTJPTqdNTwA2USesREQEPAu+QtLDtWX0FVNcq76hioxdJhwCfBra3fVWv07cBt0m6AJgk6R7bE1ueZERE1C49fiM6gKSngMttv6/h2GRKQXhZ2y9Ux6YAb8wgr9eSJMoQkcOZsxui97Y4V8dse3QL05tnkhYDbqYMjHoA+DmlTx6Uns/7AuOAvwJvtf1867OMTjfIYKmXKLsRLgdOsH3fALEjmqSngd/Z3rfuXKK7SLpiAb7ctrNLKJpC0onAkcD/AofZfqrX+bGU4W8fBk6y/Z+tz7K9SboJeNr29oPEXQEsZfutrcksIiLaSVb8RnSGxShFFAAkjQI2A67vKfpWHqL00ozXOhL4FDAb+D2lSPpMrRktANszJO1EGRi1Ca9dTSngVmDvFH2jWbK19FVTgVXqTiK60rYL8LVZHRLN9E1KUfeDwK6SfgfcT/m+WxPYHVgC+HsVG6+1HnDBEOL+QVkMEhERXSiF34jO8CjQuIp3S0oxuPeAskWAF4i+HEQpnu9g++q6kxkOtqdJGg/sAexC6aNqYBpwKXCBs+0johUmAqdKGm/7prqTia6yXd0JRPTF9uOStgfOoSxW+Ahzbjb07Li6AdjH9hM1pDgSzKT08h3MplVsRER0obR6iOgAks4F9qasnPg98AtKoW8H25Mb4u4EXrH9ljrybGeSXgSutv2uunOpm6R1gRVtd+ywrYhWk/R9SouVE4HfAg/27r0dEdGNJE0AtgFWrg5NB67slBvxzSLpt5Sb+18HvtT7Zn7Vxuw44IuUm/3vbX2WERFRtxR+IzqApLcBVzFnFb+Am21v1hCzCmWl5xm2D259lu1N0iPAFbb3qTuXukk6Hdi/3fsYR/uTtCuljcrxtif1E7M95U3pCbb/r5X5tcoQh9z1sO3syIqIiAFJ2gi4jjKM+G/ALyntMqDMcfgQZUfgi8CWtm+vIc2IiKhZ3lhEdADb10t6D3AUsAJwffXnRv8f8DQwV2FF0jLA4rantSLXNnYFsHndSUR0mIOo+o0PEHM95WfvQHq9PnWQ3oMihys2Yr5JWpjS979xleVNtmfVl1VEDJXtOyTtRhnguzZ9z3P4B7Bvir4REd0rK34july1unO/bl9hJmkt4CbgW7aPrzufOmXFbwwXSX8DHrb9jkHirgJWsr32QHERseAkLQR8BfgkZXhWo+eAHwDH2X6JiCbJjpDhI2lR4P2Udhk9g0SnA1cC5/Ua9BwREV2mqws9EfGqrDCDrYHTga9UqycuobTGmN1XsO2zWphbxEi1EmUb6mAeYmgDaiJiAUgaDVwEvIs5qwHvq06vSfmZPQrYXNJutuelTUnEvMiOkGFi+0Xg7OoRERExlxR+IyKKMyjTpAVsAbxtkPgUfiMGNxMYO4S4sUAKTBHN9zFgR+AvwGdsX9p4UtLOwHcpheGPAqe0PMPoFuOB22w/31+A7eck3Uq5LouIiIj5kMJvRERxFqXwGxHDZyowQdJY20/3FSBpSWACpRAVEc21P/A8sIPt6b1P2r5U0ruAu4EDSOE3mic7QiIiIloghd+ICMD2gXXnENGBfgNsCZwmaR/bMxtPVsOlTgMWB35dQ34R3eZNwKS+ir49bE+XNInSLzSiWbIjJCIiogVS+I2IiIhm+RFwKLAXcJekn1NWEgKsB+wLjAP+ShkoFRHNtRAwYwhxM6rYiGbJjpCIiIgWSOE3IiIimsL2DEk7AecDmwDH9AoRcCuw90B9HiNi2DwIvEPSwrZn9RVQrcR/RxUb0SzZERIREdECKfxGRDSQtDKwHfBGYNF+wmz7+NZlFTFy2Z4maTywB7ALsDqln/Y04FLgAtvprx3RGhcCRwJnSjrM9lONJyWNBU4GVgR+VkN+0T2yIyQiIqIFlPdaEd1N0unA/rZH151LnSSJMsn8cGBUz+FeYa6OuZP/vSSdQfmeGDVYbEREjBySlgVuAVYGngV+B9xP+f22JrA7sATwd2BT20/UlGp0AUmrMWdHSO83pY07Qh5ocWoREREdI4XfiC6Xwm8h6fPAN4DZlFWIdwPP9Bdv+7gWpdZyVWFgcdvZ5hsR0WEkrQ2cA2xWHep5M9Bzs/MGYB/bf2t1btF9qhvv2RESERHRJCn8RnS5anXnfin8aipltdMOtq+uO595Va2amW+2pw1XLhG9VYWmjwNbActT3sx/vjq3BbAx8Kve284jonkkTQC2oaz+BZgOXDkSfwdGd5G0LrCi7Sl15xIREdHu0uM3Ik4ATq87iTawBnDVCH7D+wCv3SY5VCa/D6JJJB1C6Rm6cHXIwHINIYsBPwZeIq9FES1T/b4bqb/zorsdBewPdPWihYiIiKHIG/2ILmf7HuCeuvNoA08Bj9adxAKYRt+F39Ub/vx09XFsw7G0c4imkbQ1cCrwHHAMMAW4rlfYlZTvzT1I4TciIiIiImLYZHBPRERxBbB53UnML9vjbK/R8wDWAm4CHgM+DSxte2nbSwNLA58C/gncWMVGNMPnKTckdrX9Lds39A6wPZsybGqDVicX0W0k7SrpCknbDRCzfRWzYytzi4iIiIjhl8JvRERxLLC8pGPrTmSYfBZ4N7Ct7R/a7lnti+2nbZ8MbE+Z4H5kTTlG59sKuN72NYPEPQKs1IJ8IrrdQZShbtcPEHM95Uboga1IKCIiIiKaJ60eIiKKrSnbzL8iaTfgEkr7hNl9Bds+q4W5zY8Dgcm2p/YXYHuqpEnAAcCJrUosuspY4O9DiFucXJNEtMJ44Dbbz/cXYPs5SbcCW7QurYiIiIhohrzJiogozqBsSRflze7bBolv98LvGsBtQ4h7ijLVPaIZHqV8Lw5mPWB6k3OJiLKyvnef7b48BGza5FwiIiIioslS+I2IKM6i7+FoI9UzwNsljbH9cl8BksZQtuI/09LMopv8EXi/pM1s39hXQNVHdF3gf1qaWUR3msncAz77MxZ4pcm5RERERESTpfAbEQHYPrDuHIbZH4CPABMlfdr2s40nJS0OfA9YFTi7hvyiO3wH+ADwG0mHApc1npT0TuA04GXgB61PL6LrTAUmSBrb2Pu9kaQlgQnAX1qaWUREREQMuxR+IyI60xeBXYH9gT0lXQTcX50bB7wHWAp4AvhSHQlG57N9naTPAydR+mY/Q1lZv5ekdwPLUdqr/Ift2+vLNKJr/AbYEjhN0j62ZzaelLQw5WbM4sCva8gvIiIiIoaR7E7a2RwRET0kvQn4GXP6NPa84Kv6eCuwn+07W51bdBdJuwJfATbvdep24FjbF7Y8qYguJGkx4GZgHeAB4OfA3dXp9YB9KTcH/wq8daAhcBF1kXQ6sL/t0XXnEhER0e5S+I2I6EXSBpSeo0syp0g6F9vtPtztVZImUAa4rVIdmg5cafuq+rKKbiRpWcqwt9HAQ7YfrjmliK4jaTXgfGATXtvbXpSbgnvbfqDFqUUMiaQzKIXfUXXnEhER0e5S+I2IqEh6O/ATYIOBwgBnlUlERIxUkgTsAewCrE4pAE8DLgUucN4gRJNJOg242vZpg8QdCLzT9sENx5YFFrf9YHOzjIiIGPlS+I2IACStD9wILAb8CViRsjLxl8DalHYJo4ELgKdtH1RTqhEjhqQtbF83xNjDbf+o2TlFxPyRtC6wou0pdecSI5+k2cAZjQXdfuImAgfnhntERMT8yfaYiIjiPylF34/bngBcBWD7I7a3ADYGbqK0gPh0bVnOJ0ljJa0qabW+HnXnFx1riqT/GChA0pKSzgN+0KKcImL+HAVMqjuJ6DoLAbPrTiIiImKkSuE3IqLYFrjX9sS+TtqeCrwHWA04toV5zTdJy0g6WdIjwBOUQT739/G4r7Yko9O9DJwk6UJJS/c+KWkzyqCpvSnDpCIiIhptCDxVdxIREREj1Zi6E4iIaBMrAhc3fP4KgKRFbM8EsP2opCuB9wKfb32KQ1cV2a4D1qT8XV6grGj+B+XvKub0dIxols2Bcyk3TW6R9GHb1wBI+nfgBGBh4Bzg47VlGRERTVf19W00oY9jPcZQZi68lbmvzyIiImIepPAbEVE81+vzZ6qPK1FWyvZ4AVi5FQktoC8AawGnAZ8CfgzsZ3tlSYsBHwG+Thmssl99aUYns31Xtar3R8ABwJWSvkopCO8OzAAOH2y4T0REdIQDG/5sygyFtQf5mkeAY5qVUERERKdL4Tciovg7pY1Dj7urj9sBpwNIWgjYAnistanNl90peX7S9kxJr07ytD0DmCjpFuBaSddkqFY0i+0XgIMkTaIUgI+rTt0BfMj2XbUlFxERrdQzGFeUG9NXAz/tJ3YWMB241vasFuQWERHRkVL4jYgo/kgpTi1p+xnKtsJXgO9IWpRSGP4osArwy/rSHLJxwOSeNhWUlTVIGm37FQDbN0q6GjiEUpCLaKY3UNo6qPr8eeDZ+tKJiIhWsn1mz58lfYVS1D2z/6+IiIiIBZXhbhERxW8oK0u2BbA9ndJ/dEngh8D5lD6lTwNH15PiPHmFOe0qoBTZAJbrFfcwsE5LMoquVA0ZvAj4BvAicDhwFWX1/C2S9qwzv4iIaD3b42y39byEiIiITpDCb0QEYPty2+vYvrDh2JeBDwC/Ai4DfgCMt/1g49dWha3VaC8PA6s2fP5A9XF8r7gNgJlENIGkCcCtwG7A7ZSfn1MoLVS+BiwF/EbS96pWKhER0WUkjZX0LkkflvT2uvOJiIjoJCn8RkQMwPavbX/Y9s62P2P7/j7CvgXc1+rcBnEzsL6k0dXnl1O22H9D0gaSlpD0BWBj4La6koyON4nSHuUnwBa27wWwPdv2scDOlF7URwDX1JZlRES0XFXwPQ14FLgUOBs4tOH8oZIelrRlXTlGRESMdCn8RkQMDw0e0lKXAMsAuwDYvhX4HbARZajWU8DXKb1/v1pTjtH5ZgAftv2Jhn7Tr7J9OeXmwyRg01YnFxER9ZD0emAycCDwJOW6pfe11EWU/vB7tTK3iIiITpLCb0REZ/oFpdXDlQ3H9gFOpqyseZlSAP6g7SmtTy+6xHjb/ztQgO1/AjsCX25NShExn0T73eSMketzlBt/ZwNr2n5P7wDbjwB3Adu3OLeIiIiOIdt15xARMaJJOh3Y3/boQYMjupSkscDmwPLAg7b/VHNKETEPJC0LLN67z33E/JB0B6XP+1o9O0IkzQbOsH1wQ9yvgS1tr1xPphERESPbmLoTiIiIiM5VFXy/A3yEOdcdZwJ/qs4fSmk3srfta2tJMqJDLejgUdvTGv78OPD4AicVUawJXNpXG6BeXgSWbUE+ERERHSmF34iIDidpQ2ArykrLO21fWB0fBYyxPavO/KJzNfRw3JjSYuRGYLdeYRcBp1J6OKbwGzG8HqD0cp8fJu8VonleAhYdQtyqwHNNziUiIqJj5WIuIqJDVSu9zgC2aTh8JnBh9edDgR9L2qkashUx3Bp7OH7C9oxqK++rbD8iKT0cI5pjGn0Xfldv+PPT1cexDcfSziGa7R5gU0mL9LfqV9LSlN8hN7c0s4iIiA6S4W4RER1I0nLAFGBbyhC3H/PaoTznArOBPVuaXHSTDwAPAx+1PWOAuL8A6d8YMcxsj7O9Rs8DWAu4CXgM+DSwtO2lbS8NLA18CvgnZXX+WnXlHV3hPGAF4MQBYr4OLA78qiUZRUREdKAUfiMiOtNRwGqUN1Sb2D6id4DtJ4E/AxNanFt0jzWBG9LDMaJtfBZ4N7Ct7R/a7lnti+2nbZ9MWX2/O3BkTTlGd/ghMBX4lKSrJf1HdXycpMMkXQF8DLgd+GldSUZERIx0KfxGRHSm3YH7gaNtD9Tf8T7gja1JKbpQejhGtJcDgcm2p/YXUJ2bBBzQqqSi+1S7QHYCrgPeDpxUndqGUhTeltLi4d2ZRRARETH/0uM3ImLB9W6h0A5WBS4apOgL8DJle29EM6SHY0R7WQO4bQhxTzF3f/iIYWd7OvB2SbtQBn+uCYwGHgIuAc4fwnVMREREDCCF34iIBXcCcHrdSfTyArDUEOLGUd7gRzTDecA3KC1H/q2fmPRwjGidZyiFtjH0q/7VAAAHBElEQVS2X+4rQNIYYKsqNqLpbP8e+H3deURERHSitHqIiFhAtu+xfWXdefRyBzBe0tj+AiStTFZaRnOlh2NEe/kDZUfIRElL9D4paXHg1Crm0hbnFhERERHDTNk9ExHReSR9AvgRZRXl/rZnSZoNnGH7YEmjgHOBvarzP68x3ehg1Q2Gc4EtAVNao/RcfAi4Cdir2vIbEU0kaTXKz9wywNPARZR+8FB2gLyHslvkCWAz2w/WkGZ0AUmvDDH0JeBfwI2Ua5jzm5dVRERE50nhNyKiA1VbdScBW1Pe1F8MHEF54zSJUvBdB5gM7JAeetFs6eEY0R4kvQn4GbBpdajxRgzArcB+tu9sdW7RPaqb0fPKwFm2DxrufCIiIjpVCr8RER2q2sY7EfhgPyHnAwfYfrZ1WUVERDuQNIEywG2V6tB04ErbV9WXVXQTSd8EenYonQM8CMymrD7fBzicch3zXWA74CRgecqNiXNqSDkiImLESeE3IqLDSdoA2JVeKy1t31JrYhEREdGVJB1E6Sf9TtvX9hOzBXAVcJjtn0raEvgTcJntnVqXbURExMiVwm9ERAeStCTgrOaNiIiIdiPpRuBp2zsMEnc5sJTt8dXnNwGr2V6+BWlGRESMeGPqTiAiIpriKeAGYIu6E4mIiPYkaSywJHP6+87F9rTWZhRdZH3ggiHEPcLc1zL3ARs1JaOIiIgOlMJvRERneha4t+4kIiKivUhaBjgeeB+lX2p/TN4rRPPMBDYZQtwmVWyPhSnXOBERETEEo+pOICIimmIqcwb2REREIGlp4DrKQK1lgBcoq30f6QmpPk6j9IOPaJargfUlfam/AElfBDag9PntsQbwjybnFhER0TFS+I2I6EwTgQmSxtedSEREtI0vAGsBpwNjgfMo/eBXBpYAPg48AVxte43asoxu8CXKSt4vS5oq6SuSDpJ0oKQvS7oTOA54EfgKgKTVKG0erqwr6YiIiJEmw90iIjqUpO8D+wInAr8FHrQ9c+CvioiITlUV05ajDMeaKel0YH/boxtiNgOuBT5t+0c1pRpdQNL2wNnAipTWInOdBv4J7Gf7sip+eeAtwN22p7cy14iIiJEqhd+IiA4k6ZV5CLft9HGMiOhwkp4HJtt+d/X5acABwMK2X2mImwwsYTu7RqKpJL0OeD+wDbBydfhhYApwru0ZdeUWERHRCfJGPyKiM/U5oX0YYiMiYuR6BXim4fPnq4/LUVZX9ngYeE+rkoruZfsF4GfVIyIiIoZZevxGRHQg26Pm5VF3vhER0RIPA6s2fP5A9bH3yt4NKP1XIyIiImIEy5v9iIiIiIjucDOwvqSenr6XU3Z9fEPSBpKWkPQFYGPgtrqSjIiIiIjhkR6/ERERERFdQNJ+wJnA7rYvro5dAOzOa4drbWd7SotTjIiIiIhhlMJvREREREQXkDQGeAPwtO3nqmOvB75BGbC1DHA38FXbv64t0YiIiIgYFin8RkRERERERERERHSY9PiNiIiIiIiIiIiI6DBj6k4gIiIiIiJaS9KGwFbA8sCdti+sjo8CxtieVWd+EREREbHgsuI3IiIiIqJLSFpN0hXAn4FTgf8C9moIORR4QdIOdeQXEREREcMnhd+IiIiIiC4gaTlgCrAtcAfwY0C9ws4FZgN7tjS5iIiIiBh2KfxGRERERHSHo4DVgBOBTWwf0TvA9pOU1cATWpxbRERERAyzFH4jIiIiIrrD7sD9wNG2PUDcfcAbW5NSRERERDRLCr8REREREd1hVeDmQYq+AC8DS7cgn4iIiIhoohR+IyIiIiK6wwvAUkOIGwc81dxUIiIiIqLZUviNiIiIiOgOdwDjJY3tL0DSysDGwM0tyyoiIiIimiKF34iIiIiI7nAOZcXvqZIW7n1S0ijg+8AiwNktzi0iIiIihpkGb/EVEREREREjnaQxwCRga8qQt4uBI4Abq+N7AesAk4EdhtALOCIiIiLaWAq/ERERERFdQtISwETgg/2EnA8cYPvZ1mUVEREREc2Qwm9ERERERJeRtAGwK7AmMBp4CLjE9i21JhYRERERwyaF34iIiIiILiBpScBZzRsRERHRHTLcLSIiIiKiOzwFXFZ3EhERERHRGin8RkRERER0h2eBe+tOIiIiIiJaI4XfiIiIiIjuMBVYpe4kIiIiIqI1UviNiIiIiOgOE4EJksbXnUhERERENF+Gu0VEREREdAlJ3wf2BU4Efgs8aHtmvVlFRERERDOk8BsRERER0QUkvTIP4bY9pmnJRERERETT5WIuIiIiIqI7qEmxEREREdGGsuI3IiIiIiIiIiIiosNkuFtEREREREREREREh0nhNyIiIiIiIiIiIqLDpPAbERERERERERER0WFS+I2IiIiIiIiIiIjoMP8/34gOeTAvQkMAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -33224,7 +22019,7 @@ ], "source": [ "plt.figure(figsize=(20,10))\n", - "labels, values = zip(*d.most_common(100))\n", + "labels, values = zip(*d.most_common(75))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", @@ -33235,16 +22030,16 @@ "plt.bar(indexes, accuracies, width, label='Accuracy')\n", "plt.bar(indexes, values, width, label='Frequency')\n", "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('BERT (50k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.title('MethodNaming accuracy (top50 most frequent labels) - mean_acc_subset = {:.3f}'.format(mean_acc))\n", "plt.legend()\n", "plt.tight_layout()\n", - "#plt.savefig('BERT-freq-50k_epochs_top100.png')\n", + "plt.savefig('top-freq-acc_top50.pdf')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 112, "metadata": {}, "outputs": [], "source": [ @@ -33254,7 +22049,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -33265,7 +22060,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 114, "metadata": {}, "outputs": [], "source": [ @@ -33276,11 +22071,12 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 115, "metadata": {}, "outputs": [], "source": [ - "inter_labels = [l for l in labels_str if l in inter]\n", + "trn_cnt = Counter(labels_train_str)\n", + "inter_labels = [l for l in labels_str if (l in inter) and (trn_cnt[l]>5)]\n", "inter_label_count = Counter(inter_labels)\n", "total = sum(inter_label_count.values(), 0.0)\n", "for key in inter_label_count:\n", @@ -33289,12 +22085,14 @@ }, { "cell_type": "code", - "execution_count": 178, - "metadata": {}, + "execution_count": 116, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -33313,12 +22111,15 @@ "width = 1\n", "\n", "accuracies = [c[tok] for tok in labels]\n", - "mean_acc = np.mean(accuracies)\n", + "all_acc = [c[tok] for tok in inter_label_count.keys()]\n", + "mean_acc = np.mean(all_acc)\n", + "inter_acc = np.sum([total_per_token_accuracy[key] * inter_label_count[key] for key in inter_label_count])\n", + "\n", "\n", "plt.bar(indexes, accuracies, width, label='Accuracy')\n", "plt.bar(indexes, values, width, label='Frequency')\n", "plt.xticks(indexes , labels, rotation=90)\n", - "plt.title('BERT (50k epochs) - mean_freq = {:.3f} / max_freq = {:.2f} / mean_acc = {:.3f}'.format(mean_freq, np.max(freqs), mean_acc))\n", + "plt.title('Accuracy sorted by frequency - mean_freq = {:.3f} / max_freq = {:.2f} / F1-Macro = {:.2f} / F1-Weighted = {:.2f}'.format(mean_freq, np.max(freqs), mean_acc, inter_acc))\n", "plt.legend()\n", "plt.tight_layout()\n", "#plt.savefig('BERT-freq-50k_epochs_top100.png')\n", @@ -33327,16 +22128,40 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6074847693646649" + "0.6805196149768681" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_acc = [c[tok] for tok in inter_label_count.keys()]\n", + "mean_acc = np.mean(all_acc)\n", + "mean_acc" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8164893617021276" ] }, - "execution_count": 185, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -33346,6 +22171,27 @@ "inter_acc" ] }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.41986337362059906" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weighted_acc = np.sum([total_per_token_accuracy[key] * label_count[key] for key in label_count])\n", + "weighted_acc" + ] + }, { "cell_type": "code", "execution_count": null, @@ -33355,7 +22201,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 120, "metadata": {}, "outputs": [], "source": [ @@ -33368,12 +22214,12 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 121, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -33386,7 +22232,7 @@ ], "source": [ "plt.figure(figsize=(20,10))\n", - "labels, values = zip(*novel_label_count.most_common(100))\n", + "labels, values = zip(*novel_label_count.most_common(1000))\n", "\n", "indexes = np.arange(len(labels))\n", "width = 1\n", @@ -33400,9 +22246,143 @@ "plt.legend()\n", "plt.tight_layout()\n", "#plt.savefig('BERT-freq-50k_epochs_top100.png')\n", + "\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "740" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(indexes)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [], + "source": [ + "labels, values = zip(*inter_label_count.most_common(2000))\n", + "accuracies = [c[tok] for tok in labels]" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title('MethodNaming accuracy@1 as a function of label frequency.')\n", + "plt.scatter(accuracies[1:], values[1:])\n", + "#plt.savefig('accuracy-freq-methodname.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "split_labels = [l.split('_') for l in list(vocab_label_df[0])]\n", + "#split_labels = [item for sublist in split_labels for item in sublist]" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "per_token_acc = {}; per_token_count = {}\n", + "for idx, r in enumerate(preds):\n", + " prediction = vocab_label_df.loc[r[0]][0]\n", + " pred_split = prediction.split('_')\n", + " label = labels_str[idx]\n", + " if per_token_acc.get(label, None) == None:\n", + " per_token_acc[label] = 0\n", + " per_token_count[label] = 0\n", + " split_label = label.split('_')\n", + " #commonalities = set(split_label).intersection(set(pred_split))\n", + " common = 0\n", + " for p in pred_split:\n", + " if p in split_label:\n", + " common += 1\n", + " per_token_acc[label] += common / len(pred_split)\n", + " per_token_count[label] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [], + "source": [ + "total_per_token_accuracy = {}\n", + "per_token_freq = {}\n", + "for k,v in per_token_acc.items():\n", + " if per_token_count[k] > 0:\n", + " total_per_token_accuracy[k] = v / per_token_count[k]\n", + " per_token_freq[k] = per_token_count[k] / len(preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8515514184397163" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inter_acc = np.sum([total_per_token_accuracy[key] * inter_label_count[key] for key in inter_label_count])\n", + "inter_acc" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebook/Inspect Predictions - Var Naming.ipynb b/notebook/Inspect Predictions - Var Naming.ipynb index e48ad15..994f6b7 100644 --- a/notebook/Inspect Predictions - Var Naming.ipynb +++ b/notebook/Inspect Predictions - Var Naming.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -41,17 +41,18 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "#path = '../../bert-cmp/bert/'\n", + "#path = '../large-corpus/'\n", "path = '../sparse/'" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -396,19 +397,20 @@ "[10 rows x 1156 columns]" ] }, - "execution_count": 142, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_df = pd.read_csv(path+'cls_output-reduce_mean-varname/test_results.tsv', header=None, sep='\\t')\n", + "#results_df = pd.read_csv(path+'cls_output-varname/test_results.tsv', header=None, sep='\\t')\n", "results_df.head(10)" ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -417,7 +419,7 @@ "(39552, 1156)" ] }, - "execution_count": 143, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -428,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -514,49 +516,60 @@ "9 call" ] }, - "execution_count": 144, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vocab_label_df = pd.read_csv(path+'sparse_tmp_vocab-code.txt', header=None)\n", + "#vocab_label_df = pd.read_csv(path+'global_vocab.csv', header=None)\n", "vocab_label_df.head(10)" ] }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(618, 5)" - ] - }, - "execution_count": 145, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "fs = ['keras', 'pytorch', 'sklearn', 'ansible', 'requests', 'django', 'youtube-dl', 'bert', 'httpie', 'flask']\n", + "#fs = ['sparse']" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "#label_dfs = [pd.read_csv(path+f+'_fname2_split_magret_label_val.txt', header=None) for f in fs]\n", + "label_dfs = [pd.read_csv(path+f+'_varnname_split_magret_label_val.txt', header=None) for f in fs]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], "source": [ - "label_df = pd.read_csv(path+'sparse_varname_split_magret_label_val.txt', header=None)\n", - "label_df.shape" + "#label_df = pd.read_csv(path+'sparse_varname_split_magret_label_val.txt', header=None)\n", + "#label_df = pd.read_csv(path+'sparse_varname_split_magret_label_val.txt', header=None)\n", + "#label_df.shape" ] }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "618" + "537" ] }, - "execution_count": 146, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -567,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -600,16 +613,16 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(618, 64, 7)" + "(537, 64, 7)" ] }, - "execution_count": 149, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -620,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 76, "metadata": { "scrolled": true }, @@ -628,16 +641,33 @@ { "data": { "text/plain": [ - "(618, 5)" + "10" ] }, - "execution_count": 150, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "label_df.shape" + "len(label_dfs)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "labels= []; labels_str =[]\n", + "for label_df in label_dfs:\n", + " for idx, row in label_df.iterrows():\n", + " l = vocab_label_df.index[vocab_label_df[0]==row[0]]\n", + " if len(l) > 0:\n", + " labels.append(l[0])\n", + " else:\n", + " labels.append(-1)\n", + " labels_str.append(list(row[:-1]))" ] }, { @@ -658,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 88, "metadata": { "scrolled": true }, @@ -666,638 +696,21 @@ { "data": { "text/plain": [ - "[['layer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['e', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['max', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['cast', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['y', 'true', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['stateful', 'metrics', '[PAD]', '[PAD]'],\n", - " ['use', 'steps', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['monitor', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['update', 'freq', '[PAD]', '[PAD]'],\n", - " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mode', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['logs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['on', 'train', 'begin', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sum', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['string', 'types', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mean', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mode', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['truncated', 'normal', '[PAD]', '[PAD]'],\n", - " ['dim', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['seed', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['receptive', 'field', 'size', '[PAD]'],\n", - " ['mean', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mean', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mean', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['y', 'true', '[PAD]', '[PAD]'],\n", - " ['maximum', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['mean', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['y', 'true', '[PAD]', '[PAD]'],\n", - " ['g', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['clipnorm', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sqrt', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['iterations', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['decay', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['new', 'p', '[PAD]', '[PAD]'],\n", - " ['update', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', 'value', '[PAD]', '[PAD]'],\n", - " ['iterations', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['lr', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['decay', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['t', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sqrt', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sqrt', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['decay', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['m', 't', '[PAD]', '[PAD]'],\n", - " ['epsilon', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['epsilon', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['beta', '1', '[PAD]', '[PAD]'],\n", - " ['beta', '1', '[PAD]', '[PAD]'],\n", - " ['v', 't', '[PAD]', '[PAD]'],\n", - " ['pow', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', 'value', '[PAD]', '[PAD]'],\n", - " ['lower', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['input', 'dim', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['states', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['config', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'output', 'length', '[PAD]'],\n", - " ['filters', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['str', 'val', '[PAD]', '[PAD]'],\n", - " ['signature', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['string', 'types', '[PAD]', '[PAD]'],\n", - " ['old', 'arg', '[PAD]', '[PAD]'],\n", - " ['legacy', 'dropout', 'support', '[PAD]'],\n", - " ['length', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', 'size', '[PAD]', '[PAD]'],\n", - " ['kernel', 'size', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', 'size', '[PAD]', '[PAD]'],\n", - " ['legacy', 'deconv2d', 'support', '[PAD]'],\n", - " ['value', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['opt', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['device', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['n', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['end', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['version', 'info', '[PAD]', '[PAD]'],\n", - " ['format', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sum', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['access', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['fpath', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sleep', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['backend', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['pad', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['y', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['y', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['built', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['inputlabels', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['fn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['bar', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['eta', 'format', '[PAD]', '[PAD]'],\n", - " ['total', 'width', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['open', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['version', 'info', '[PAD]', '[PAD]'],\n", - " ['num', 'words', '[PAD]', '[PAD]'],\n", - " ['path', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['xs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['xs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['w', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['build', 'fn', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['args', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['dims', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['batch', 'sizes', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['axes', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['inputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['max', 'value', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['inner', 'input', 'shape', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['get', 'shape', 'tuple', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', 'weights', '[PAD]', '[PAD]'],\n", - " ['merge', 'mode', '[PAD]', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['pivot', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'mask', '[PAD]', '[PAD]'],\n", - " ['get', 'updates', 'for', '[PAD]'],\n", - " ['forward', 'layer', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['state', 'spec', '[PAD]', '[PAD]'],\n", - " ['return', 'sequences', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['trainable', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'kernel', 'z', '[PAD]'],\n", - " ['kernel', 'r', '[PAD]', '[PAD]'],\n", - " ['bias', 'r', '[PAD]', '[PAD]'],\n", - " ['stateful', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['units', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['stateful', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['return', 'sequences', '[PAD]', '[PAD]'],\n", - " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'output', 'length', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'output', 'length', '[PAD]'],\n", - " ['len', 'dim3', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['pooling', 'function', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['format', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'output', 'length', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['states', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['inputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['return', 'sequences', '[PAD]', '[PAD]'],\n", - " ['kernel', 'size', '[PAD]', '[PAD]'],\n", - " ['kernel', 'i', '[PAD]', '[PAD]'],\n", - " ['kernel', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'kernel', 'o', '[PAD]'],\n", - " ['bias', 'c', '[PAD]', '[PAD]'],\n", - " ['bias', 'o', '[PAD]', '[PAD]'],\n", - " ['input', 'conv', '[PAD]', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['rank', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['strides', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['strides', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['bias', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['out', 'height', '[PAD]', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['deconv', 'length', '[PAD]', '[PAD]'],\n", - " ['deconv', 'length', '[PAD]', '[PAD]'],\n", - " ['deconv', 'length', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['channel', 'axis', '[PAD]', '[PAD]'],\n", - " ['bias', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['depthwise', 'conv2d', '[PAD]', '[PAD]'],\n", - " ['use', 'bias', '[PAD]', '[PAD]'],\n", - " ['spatial', 'axes', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['resize', 'images', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dim3', 'padding', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['normalized', 'cropping', '[PAD]', '[PAD]'],\n", - " ['input', 'length', '[PAD]', '[PAD]'],\n", - " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['padding', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'row', '[PAD]', '[PAD]'],\n", - " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['axis', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['scale', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['beta', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['epsilon', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['state', 'size', '[PAD]', '[PAD]'],\n", - " ['reverse', 'state', 'order', '[PAD]'],\n", - " ['constants', 'shape', '[PAD]', '[PAD]'],\n", - " ['cell', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['states', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['state', 'shape', '[PAD]', '[PAD]'],\n", - " ['input', 'shape', '[PAD]', '[PAD]'],\n", - " ['state', 'size', '[PAD]', '[PAD]'],\n", - " ['num', 'constants', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['num', 'constants', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['cell', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['bias', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', 'h', '[PAD]', '[PAD]'],\n", - " ['ones', 'like', '[PAD]', '[PAD]'],\n", - " ['matrix', 'inner', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'h', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'h', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['cls', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['concatenate', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', 'c', '[PAD]', '[PAD]'],\n", - " ['dropout', 'mask', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'dropout', '[PAD]', '[PAD]'],\n", - " ['ones', 'like', '[PAD]', '[PAD]'],\n", - " ['f', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'activation', '[PAD]', '[PAD]'],\n", - " ['z1', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['stddev', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['noise', 'shape', '[PAD]', '[PAD]'],\n", - " ['unknown', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['xs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['items', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['key', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['add', 'weight', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['Function', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['float16', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['Constant', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['seed', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['[PAD]', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['bernoulli', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['seed', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['p', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['p', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['normal', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['permutation', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['axes', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['new', 'shape', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['[PAD]', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['axis', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['any', 'matrix', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['beta', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['InferredDimension', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['InferredDimension', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['[PAD]', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['current', 'layout', '[PAD]', '[PAD]'],\n", - " ['step', 'function', '[PAD]', '[PAD]'],\n", - " ['slice', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['num', 'time', 'step', '[PAD]'],\n", - " ['c', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['new', 'output', '[PAD]', '[PAD]'],\n", - " ['new', 'states', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['convolution', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['depthwise', 'kernel', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['target', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['arguments', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['unrelated', 'updates', '[PAD]', '[PAD]'],\n", - " ['argument', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['prefix', 'shape', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['Parameter', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['condition', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dynamic', 'axes', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['result', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['init', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'variable', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['graph', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['a', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['adj', 'x', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['sqrt', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['fused', 'batch', 'norm', '[PAD]'],\n", - " ['beta', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['rank', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['fetches', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['axes', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', 'shape', '[PAD]', '[PAD]'],\n", - " ['reduce', 'sum', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['seed', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['padding', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['left', 'pad', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['spatial', 'start', 'dim', '[PAD]'],\n", - " ['strides', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['new', 'shape', '[PAD]', '[PAD]'],\n", - " ['seed', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['expand', 'dims', '[PAD]', '[PAD]'],\n", - " ['decoded', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dense', 'from', 'sparse', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['normal', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['reference', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dtype', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['inv', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['batch', 'normalization', 'test', '[PAD]'],\n", - " ['ndim', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dnn', 'batch', 'normalization', 'test'],\n", - " ['output', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['uses', 'learning', 'phase', '[PAD]'],\n", - " ['keras', 'shape', '[PAD]', '[PAD]'],\n", - " ['output', 'shape', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['keras', 'shape', '[PAD]', '[PAD]'],\n", - " ['indices', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['h', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['keras', 'shape', '[PAD]', '[PAD]'],\n", - " ['w', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['keras', 'shape', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['function', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['results', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['states', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['ndim', 'diff', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['target', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['zeros', 'like', '[PAD]', '[PAD]'],\n", - " ['arange', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['th', 'padding', '[PAD]', '[PAD]'],\n", - " ['value', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['filter', 'shape', '[PAD]', '[PAD]'],\n", - " ['filter', 'shape', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'out', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['conv', 'out', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['keras', 'shape', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['op', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['pointwise', 'kernel', 'shape', '[PAD]'],\n", - " ['w', 'pad', '[PAD]', '[PAD]'],\n", - " ['pool', '2d', '[PAD]', '[PAD]'],\n", - " ['pool', 'out', '[PAD]', '[PAD]'],\n", - " ['pool', 'out', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['arange', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['Y', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['p', 'prev', '[PAD]', '[PAD]'],\n", - " ['p', 'prev', '[PAD]', '[PAD]'],\n", - " ['mask', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['initializer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['foldl', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['path', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['warn', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['compute', 'previous', 'mask', '[PAD]'],\n", - " ['nodes', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['trainable', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['masks', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['input', 'layers', '[PAD]', '[PAD]'],\n", - " ['shape', 'key', '[PAD]', '[PAD]'],\n", - " ['call', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['activity', 'regularizer', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['layer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['node', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['x', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['count', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['count', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['callbacks', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['val', 'outs', '[PAD]', '[PAD]'],\n", - " ['output', 'tensors', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['config', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['encode', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['encode', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['split', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['deserialized', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['original', 'backend', '[PAD]', '[PAD]'],\n", - " ['layer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['format', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['config', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['join', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['attrs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['n', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['i', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['filters', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['data', 'format', '[PAD]', '[PAD]'],\n", - " ['concatenate', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['reshape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['transpose', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['transpose', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernels', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['kernel', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['tile', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['recurrent', 'kernels', '[PAD]', '[PAD]'],\n", - " ['source', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['attrs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['attrs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['asarray', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['callback', 'metrics', '[PAD]', '[PAD]'],\n", - " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['val', 'outs', '[PAD]', '[PAD]'],\n", - " ['l', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['steps', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['batch', 'size', '[PAD]', '[PAD]'],\n", - " ['average', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['enqueuer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['enqueuer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['progbar', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['prefix', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['layer', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', 'scope', '[PAD]', '[PAD]'],\n", - " ['max', 'ndim', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['activity', 'regularizer', '[PAD]', '[PAD]'],\n", - " ['supports', 'masking', '[PAD]', '[PAD]'],\n", - " ['inbound', 'nodes', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['input', 'shapes', '[PAD]', '[PAD]'],\n", - " ['output', 'shapes', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['batch', 'input', 'shape', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['dtype', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outbound', 'layer', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['output', 'names', '[PAD]', '[PAD]'],\n", - " ['outputs', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['target', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['placeholder', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['get', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['weight', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['append', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['loss', 'functions', '[PAD]', '[PAD]'],\n", - " ['all', 'inputs', '[PAD]', '[PAD]'],\n", - " ['is', 'tensor', '[PAD]', '[PAD]'],\n", - " ['is', 'graph', 'network', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['val', 'x', '[PAD]', '[PAD]'],\n", - " ['val', 'ins', '[PAD]', '[PAD]'],\n", - " ['y', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['self', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['data', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['is', 'tensor', '[PAD]', '[PAD]'],\n", - " ['dim', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['name', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]'],\n", - " ['shape', '[PAD]', '[PAD]', '[PAD]']]" + "16570" ] }, - "execution_count": 152, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "labels_str" + "len(labels_str)" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -1306,7 +719,7 @@ "'[PAD]'" ] }, - "execution_count": 153, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -1338,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -1346,5003 +759,33298 @@ "output_type": "stream", "text": [ "0\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute input layers Name Assign Name input tensor Call Name keyword Attribute batch input shape Name keyword Attribute dtype Name keyword Attribute sparse Name keyword Attribute name Name Expr Call Attribute append Name Name Assign Name newly created input layer Subscript Attribute keras history Name Index Num Assign Subscript Name Index Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['tensor', 'tensor', 'tensor', 'tensor']\n", - " 2. ['input', 'input', 'input', 'input']\n", - "\n", + "Label = ['softmax', '[PAD]', '[PAD]', '[PAD]']\n", "1\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name In Name Expr Call Attribute append Name Subscript Name Index Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['g', 'value', 'value', 'value']\n", - " 2. ['layer', 'data', 'data', 'data']\n", - "\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", "2\n", - "[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Name Sub Call Attribute max Name Name keyword Name keyword NameConstant Assign Name s Call Attribute sum Name Name keyword Name keyword NameConstant Return BinOp Name Div Name Raise Call Name BinOp Str Mod Name\n", - "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['p', 'log', 'log', 'log']\n", - " 2. ['v', 't', 't', 't']\n", - "\n", + "Label = ['in', 'top', 'k', '[PAD]']\n", "3\n", - "[CLS] BinOp Name Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword NameConstant\n", - "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['random', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['scan', 'normal', 'normal', 'normal']\n", - " 2. ['convolution', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "4\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name alpha Num Assign Name scale Num Return BinOp Name Mult Call Attribute elu Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'format', 'format', 'format']\n", - " 2. ['negative', 'true', 'true', 'true']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "5\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Return Call Name Name keyword Call Name keyword Name keyword Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'config', 'config', 'config']\n", - " 2. ['cls', 'string', 'string', 'string']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "6\n", - "[CLS] If Call Name Name If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name Return Name Raise Call Name Str Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pop', 'params', 'params', 'params']\n", - " 2. ['extend', 'function', 'function', 'function']\n", - "\n", + "Label = ['use', 'steps', '[PAD]', '[PAD]']\n", "7\n", - "[CLS] If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pop', 'params', 'params', 'params']\n", - " 2. ['append', 'config', 'config', 'config']\n", - "\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", "8\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute flatten Name Name Call Attribute cast Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute floatx Name Call Attribute floatx Name\n", - "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cast', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mean', 'true', 'true', 'true']\n", - " 2. ['equal', 'mean', 'mean', 'mean']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "9\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred arg k Num Return Call Attribute mean Name Call Attribute in top k Name Name Call Attribute argmax Name Name keyword UnaryOp USub Num Name keyword UnaryOp Num\n", - "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['y', 'true', 'true', 'true']\n", - " 1. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['true', 'train', 'train', 'train']\n", - "\n", - "10\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg model For Name callback Attribute callbacks Name Expr Call Attribute set model Name Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'model', 'model', 'model']\n", - " 2. ['layer', 'weights', 'weights', 'weights']\n", - "\n", + "10\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", "11\n", - "[CLS] If Name Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Assign Attribute stateful metrics Name Call Name\n", - "Label = ['stateful', 'metrics', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['stateful', 'metrics', 'metrics', 'metrics']\n", - " 1. ['metrics', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['append', 'stateful', 'stateful', 'stateful']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "12\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name target Subscript Attribute params Name Index Str Assign Name target Subscript Attribute params Name Index Str\n", - "Label = ['use', 'steps', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'sequences', 'sequences', 'sequences']\n", - " 2. ['stateful', 'function', 'function', 'function']\n", - "\n", + "Label = ['wait', '[PAD]', '[PAD]', '[PAD]']\n", "13\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute log values Name Tuple Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['extend', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['restore', 'best', 'weights', '[PAD]']\n", "14\n", - "[CLS] Call Name BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name Name Name\n", "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['float32', 'size', 'size', 'size']\n", - " 2. ['warn', 'weights', 'weights', 'weights']\n", - "\n", "15\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg logs NameConstant If BoolOp And Compare Attribute stopped epoch Name Gt Num Compare Attribute verbose Name Num Expr Call Name BinOp Str Mod BinOp Attribute stopped epoch Name Add Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'epoch', 'epoch', 'epoch']\n", - " 2. ['path', 'format', 'format', 'format']\n", - "\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", "16\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg root arg path arg field arg headers arg send as json Str Str Str NameConstant NameConstant Expr Call Attribute init Call Name Name Name Assign Attribute root Name Name Assign Attribute path Name Name Assign Attribute field Name Name Assign Attribute headers Name Name Assign Attribute send as json Name Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'name', 'name', 'name']\n", - " 2. ['path', 'fn', 'fn', 'fn']\n", - "\n", "17\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg schedule arg verbose Num Expr Call Attribute init Call Name Name Name Assign Attribute schedule Name Name Assign Attribute verbose Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'weights', 'weights', 'weights']\n", - " 2. ['path', 'format', 'format', 'format']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "18\n", - "[CLS] If Compare Name NotEq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name embeddings freq Num\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pop', 'format', 'format', 'format']\n", - " 2. ['update', 'data', 'data', 'data']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "19\n", - "[CLS] If Compare Name Eq Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Num Assign Attribute update freq Name Name\n", - "Label = ['update', 'freq', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['type', 'data', 'data', 'data']\n", - " 2. ['embeddings', 'format', 'format', 'format']\n", - "\n", + "Label = ['Variable', '[PAD]', '[PAD]', '[PAD]']\n", "20\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stack', 'kernel', 'kernel', 'kernel']\n", - " 2. ['transpose', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['embeddings', 'metadata', '[PAD]', '[PAD]']\n", "21\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotIn List Str Str Str Expr Call Attribute warn Name BinOp Str Mod Attribute mode Name Name Assign Attribute mode Name Str\n", - "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['merge', 'mode', 'mode', 'mode']\n", - " 2. ['verbose', 'format', 'format', 'format']\n", - "\n", + "Label = ['batch', 'val', '[PAD]', '[PAD]']\n", "22\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Mod Tuple Attribute monitor Name Call Attribute join Str Call Name Call Attribute keys Name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['update', 'function', 'function', 'function']\n", - " 2. ['post', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "23\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name k Attribute keys Name\n", - "Label = ['logs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'values', 'values', 'values']\n", - " 2. ['result', 'key', 'key', 'key']\n", - "\n", + "Label = ['save', '[PAD]', '[PAD]', '[PAD]']\n", "24\n", - "[CLS] If Compare Name IsNot NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Attribute on train begin Name Lambda arguments arg logs NameConstant\n", - "Label = ['on', 'train', 'begin', '[PAD]']\n", - "Pred =\n", - " 0. ['on', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['initial', 'begin', 'begin', 'begin']\n", - " 2. ['end', 'batch', 'batch', 'batch']\n", - "\n", + "Label = ['monitor', 'op', '[PAD]', '[PAD]']\n", "25\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x Assign Name regularization Num If Attribute l1 Name AugAssign Name regularization Add Call Attribute sum Name BinOp Attribute l1 Name Mult Call Attribute abs Name Name If Attribute l2 Name AugAssign Name regularization Call Attribute sum Name BinOp Attribute l2 Name Call Attribute square Name Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['l1', 'sum', 'sum', 'sum']\n", - " 2. ['xs', 't', 't', 't']\n", - "\n", + "Label = ['l1', '[PAD]', '[PAD]', '[PAD]']\n", "26\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Dict Str Str Attribute max value Name Attribute axis Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'value', 'value', 'value']\n", - " 2. ['model', 'config', 'config', 'config']\n", - "\n", + "Label = ['l1', '[PAD]', '[PAD]', '[PAD]']\n", "27\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg w Return BinOp Name Div BinOp Call Attribute epsilon Name Add Call Attribute sqrt Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'true', 'true', 'true']\n", - " 2. ['a', 'sum', 'sum', 'sum']\n", - "\n", "28\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", - "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reduce', 'sum', 'sum', 'sum']\n", - " 2. ['mean', 'function', 'function', 'function']\n", - "\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", "29\n", - "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name config Dict Str Str Call Name Name Dict Return Call Name Name If Call Name Name Return Name Raise Call Name BinOp Str Add Call Name Name\n", - "Label = ['string', 'types', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['string', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cell', 'types', 'types', 'types']\n", - " 2. ['data', 'tensor', 'tensor', 'tensor']\n", - "\n", - "30\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape arg dtype NameConstant Return Call Attribute constant Name Num keyword Name keyword Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'value', 'value', 'value']\n", - " 2. ['x', 'size', 'size', 'size']\n", - "\n", + "30\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", "31\n", - "[CLS] Return Dict Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute stddev Name Attribute seed Name\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['random', 'normal', 'normal', 'normal']\n", - " 2. ['truncated', 'uniform', 'uniform', 'uniform']\n", - "\n", + "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", "32\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str AugAssign Name scale Div Call Name Num Name If Compare Attribute mode Name Str AugAssign Name scale Call Name Num Name AugAssign Name scale Call Name Num BinOp Call Name BinOp Name Add Name Num\n", - "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['merge', 'mode', 'mode', 'mode']\n", - "\n", + "Label = ['gain', '[PAD]', '[PAD]', '[PAD]']\n", "33\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Num Name keyword Name keyword Attribute seed Name\n", - "Label = ['truncated', 'normal', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['truncated', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['random', 'normal', 'normal', 'normal']\n", - " 2. ['uniform', 'uniform', 'uniform', 'uniform']\n", - "\n", + "Label = ['identity', '[PAD]', '[PAD]', '[PAD]']\n", "34\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num AugAssign Name num rows Mult Name\n", - "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['a', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['s', 'out', 'out', 'out']\n", - " 2. ['i', 'size', 'size', 'size']\n", - "\n", - "35\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Expr Call Attribute seed Attribute random Name Attribute seed Name\n", "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['random', 'normal', 'normal', 'normal']\n", - " 2. ['activation', 'uniform', 'uniform', 'uniform']\n", - "\n", + "35\n", + "Label = ['identifier', '[PAD]', '[PAD]', '[PAD]']\n", "36\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num\n", - "Label = ['receptive', 'field', 'size', '[PAD]']\n", - "Pred =\n", - " 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'shape', 'shape', 'shape']\n", - " 2. ['batch', 'size', 'size', 'size']\n", - "\n", + "Label = ['second', 'log', '[PAD]', '[PAD]']\n", "37\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sum', 'sum', 'sum', 'sum']\n", - " 2. ['reduce', 'mean', 'mean', 'mean']\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "38\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name BinOp Name Sub Name keyword UnaryOp USub Num\n", "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sum', 'normal', 'normal', 'normal']\n", - " 2. ['max', 'function', 'function', 'function']\n", - "\n", "39\n", - "[CLS] BinOp Num Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword UnaryOp USub Num\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['max', 'normal', 'normal', 'normal']\n", - " 2. ['sum', 'sum', 'sum', 'sum']\n", - "\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", "40\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name pos Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp USub Num Assign Name neg Call Attribute max Name BinOp BinOp Num Sub Name Name keyword UnaryOp Num Return Call Attribute maximum Name Num BinOp BinOp Name Name Add Num\n", "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'true', 'true', 'true']\n", - " 2. ['self', 'train', 'train', 'train']\n", - "\n", "41\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num BinOp BinOp Name Sub Name Add Num\n", - "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['ndim', 'dims', 'dims', 'dims']\n", - " 2. ['clip', 'shape', 'shape', 'shape']\n", - "\n", - "42\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute binary crossentropy Name Name Name keyword UnaryOp USub Num\n", "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sum', 'mean', 'mean', 'mean']\n", - " 2. ['any', 'sum', 'sum', 'sum']\n", - "\n", + "42\n", + "Label = ['IndexedSlices', '[PAD]', '[PAD]', '[PAD]']\n", "43\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name y true Call Attribute l2 normalize Name Name keyword UnaryOp USub Num Assign Name y pred Call Attribute l2 normalize Name Name keyword UnaryOp Num Return UnaryOp Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp Num\n", - "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'true', 'true', 'true']\n", - " 2. ['u', 'train', 'train', 'train']\n", - "\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", "44\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute switch Name Call Attribute greater equal Name Name Name BinOp BinOp Name Mult Name Div Name Name\n", - "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['all', 'element', 'element', 'element']\n", - " 2. ['v', 't', 't', 't']\n", - "\n", - "45\n", - "[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num\n", "Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['clipvalue', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dynamic', 'axes', 'axes', 'axes']\n", - " 2. ['delta', 'data', 'data', 'data']\n", - "\n", + "45\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", "46\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name ListComp Call Attribute sum Name Call Attribute square Name Name comprehension Name g Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['items', 'sum', 'sum', 'sum']\n", - " 2. ['sum', 'list', 'list', 'list']\n", - "\n", + "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", "47\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'shape', 'shape', 'shape']\n", - " 2. ['name', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "48\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n", - "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cudnn', 'size', 'size', 'size']\n", - " 2. ['name', 'iterations', 'iterations', 'iterations']\n", - "\n", - "49\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['lr', 'decay', 'decay', 'decay']\n", - " 2. ['gain', 'size', 'size', 'size']\n", - "\n", + "49\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", "50\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Call Attribute sqrt Name Name Add Attribute epsilon Name\n", - "Label = ['new', 'p', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 't', 't', 't']\n", - " 2. ['t', 'p', 'p', 'p']\n", - "\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", "51\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Call Attribute sqrt Name BinOp Name Add Attribute epsilon Name Div Call Attribute sqrt Name BinOp Name Attribute epsilon Name\n", - "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 't', 't', 't']\n", - " 2. ['lr', 'p', 'p', 'p']\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "52\n", - "[CLS] Dict Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Attribute rho Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n", - "Label = ['get', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'value', 'value', 'value']\n", - " 2. ['cast', 'function', 'function', 'function']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "53\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n", - "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cudnn', 'size', 'size', 'size']\n", - " 2. ['name', 'iterations', 'iterations', 'iterations']\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "54\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", - "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['lr', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['decay', 'decay', 'decay', 'decay']\n", - " 2. ['t', 't', 't', 't']\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "55\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", - "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pred =\n", - " 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['lr', 'decay', 'decay', 'decay']\n", - " 2. ['gain', 'size', 'size', 'size']\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "56\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Attribute cast Name Attribute iterations Name Call Attribute floatx Name Add Num\n", - "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['t', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['lr', 't', 't', 't']\n", - " 2. ['decay', 'decay', 'decay', 'decay']\n", - "\n", + "Label = ['lr', 't', '[PAD]', '[PAD]']\n", "57\n", - "[CLS] BinOp Name Mult BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name Div BinOp Num Call Attribute pow Name Attribute beta 1 Name Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pow', 't', 't', 't']\n", - " 2. ['maximum', '1', '1', '1']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "58\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pow', 't', 't', 't']\n", - " 2. ['maximum', '1', '1', '1']\n", - "\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", "59\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg lr arg beta 1 arg beta 2 arg epsilon arg decay arg kwargs Num Num Num NameConstant Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'value', 'value', 'value']\n", - " 2. ['lr', 'format', 'format', 'format']\n", - "\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", "60\n", - "[CLS] BinOp Num Div BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", - "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['lr', 'decay', 'decay', 'decay']\n", - " 2. ['gain', 't', 't', 't']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "61\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 1 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", - "Label = ['m', 't', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['m', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['lr', 't', 't', 't']\n", - " 2. ['t', '1', '1', '1']\n", - "\n", + "Label = ['Optimizer', '[PAD]', '[PAD]', '[PAD]']\n", "62\n", - "[CLS] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'names', 'names', 'names']\n", - " 2. ['momentum', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "63\n", - "[CLS] BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['momentum', 'nodes', 'nodes', 'nodes']\n", - " 2. ['value', 'names', 'names', 'names']\n", - "\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", "64\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp BinOp Name Add Num Attribute schedule decay Name\n", - "Label = ['beta', '1', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['decay', '1', '1', '1']\n", - " 2. ['pow', 't', 't', 't']\n", - "\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", "65\n", - "[CLS] BinOp BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", - "Label = ['beta', '1', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['momentum', '1', '1', '1']\n", - " 2. ['lr', 't', 't', 't']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "66\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 2 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 2 Name Call Attribute square Name Name\n", - "Label = ['v', 't', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['v', 't', 't', 't']\n", - " 1. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['lr', '2', '2', '2']\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "67\n", - "[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 2 Name Name\n", - "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['pow', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sqrt', '1', '1', '1']\n", - " 2. ['maximum', 't', 't', 't']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "68\n", - "[CLS] Dict Str Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Call Name Call Attribute get value Name Attribute beta 1 Name Call Name Call Attribute get value Name Attribute beta 2 Name Attribute epsilon Name Attribute schedule decay Name\n", - "Label = ['get', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cast', 'value', 'value', 'value']\n", - " 2. ['output', 'function', 'function', 'function']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "69\n", - "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str\n", - "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['keys', 'shape', 'shape', 'shape']\n", - " 2. ['as', 'size', 'size', 'size']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "70\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Str Tuple Attribute input dim Name\n", - "Label = ['input', 'dim', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['return', 'dim', 'dim', 'dim']\n", - " 2. ['stateful', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "71\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Attribute nb feature Name Attribute output dim Name keyword Str keyword Str keyword Attribute b regularizer Name keyword Attribute b constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['bias', 'weight', 'weight', 'weight']\n", - " 2. ['b', 'bias', 'bias', 'bias']\n", - "\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "72\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg init arg activation arg weights arg W regularizer arg b regularizer arg activity regularizer arg W constraint arg b constraint arg bias arg input dim arg kwargs Str NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'format', 'format', 'format']\n", - " 2. ['model', 'bias', 'bias', 'bias']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "73\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name keyword Attribute init Name keyword Str keyword Attribute W regularizer Name keyword Attribute W constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['init', 'weight', 'weight', 'weight']\n", - " 2. ['pooling', 'function', 'function', 'function']\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "74\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs ImportFrom alias If Compare Str In Name Assign Name rate Call Attribute pop Name Str Assign Name rate Num Assign Subscript Name Index Str Name Expr Call Attribute warn Name Str Return Call Name Starred Name keyword Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'args', 'args', 'args']\n", - " 2. ['cls', 'format', 'format', 'format']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "75\n", - "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outputs', 'nodes', 'nodes', 'nodes']\n", - " 2. ['inputs', 'layers', 'layers', 'layers']\n", - "\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", "76\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'name', 'name', 'name']\n", - " 2. ['[PAD]', 'list', 'list', 'list']\n", - "\n", + "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", "77\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'name', 'name', 'name']\n", - " 2. ['[PAD]', 'list', 'list', 'list']\n", - "\n", + "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", "78\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Attribute units Name Str Call Name Attribute shape Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'shape', 'shape', 'shape']\n", - " 2. ['batch', 'size', 'size', 'size']\n", - "\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", "79\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Attribute return sequences Name Attribute return state Name Attribute go backwards Name Attribute stateful Name Attribute unroll Name Attribute implementation Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'sequences', 'sequences', 'sequences']\n", - " 2. ['last', 'config', 'config', 'config']\n", - "\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", "80\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute kernel size Name Index Num keyword Attribute padding Name keyword Subscript Attribute strides Name Index Num keyword Subscript Attribute dilation rate Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - " 0. ['conv', 'output', 'output', 'output']\n", - " 1. ['deconv', 'length', 'length', 'length']\n", - " 2. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", "81\n", - "[CLS] Tuple Subscript Name Index Num Subscript Name Index Num Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['units', 'dim', 'dim', 'dim']\n", - " 2. ['n', 'size', 'size', 'size']\n", - "\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", "82\n", - "[CLS] If Compare Call Name Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Name Slice Num Add Str\n", - "Label = ['str', 'val', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['str', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['line', 'val', 'val', 'val']\n", - " 2. ['data', 'state', 'state', 'state']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "83\n", - "[CLS] If BoolOp Or Compare Name Lt BinOp Call Name Subscript Name Slice Num Sub Num Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Str\n", - "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'shape', 'shape', 'shape']\n", - " 2. ['output', 'out', 'out', 'out']\n", - "\n", + "Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n", "84\n", - "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name signature Add BinOp BinOp Str Name Str If Call Name Name Attribute ndarray Name Assign Name str val Str Assign Name str val Call Name Name If Compare Call Name Name Gt Num Assign Name str val BinOp Subscript Name Slice Num Str AugAssign Name signature Name\n", - "Label = ['string', 'types', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['string', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['is', 'types', 'types', 'types']\n", - " 2. ['function', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "85\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg new arg Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Name Str Name Str\n", - "Label = ['old', 'arg', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['value', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'name', 'name', 'name']\n", - " 2. ['dim', 'names', 'names', 'names']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "86\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str\n", - "Label = ['legacy', 'dropout', 'support', '[PAD]']\n", - "Pred =\n", - " 0. ['legacy', 'support', 'support', 'support']\n", - " 1. ['support', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['x', 'conv2d', 'conv2d', 'conv2d']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "87\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str Assign Name length NameConstant\n", - "Label = ['length', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['length', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'length', 'length', 'length']\n", - " 2. ['[PAD]', 'size', 'size', 'size']\n", - "\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", "88\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['filter', 'shape', 'shape', 'shape']\n", - " 2. ['noise', 'size', 'size', 'size']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "89\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Call Attribute pop Name Str\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', 'size', 'size', 'size']\n", - " 1. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['filter', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "90\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List Subscript Name Index Num Subscript Name Index Num Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pattern', 'shape', 'shape', 'shape']\n", - " 2. ['data', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", "91\n", - "[CLS] If BoolOp And Compare Str In Name Compare Str Name Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Attribute pop Name Str Call Attribute pop Name Str Assign Subscript Name Index Str Name Expr Call Attribute append Name Tuple Str Str\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'size', 'size', 'size']\n", - " 2. ['new', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "92\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str keyword List Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant keyword Name\n", - "Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n", - "Pred =\n", - " 0. ['legacy', 'support', 'support', 'support']\n", - " 1. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['support', 'conv2d', 'conv2d', 'conv2d']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "93\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str If Compare Name Eq Str Assign Subscript Name Index Str NameConstant Expr Call Attribute append Name Tuple Str Str Expr Call Attribute warn Name Str keyword Num\n", - "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'size', 'size', 'size']\n", - " 2. ['init', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", "94\n", - "[CLS] If Call Name Subscript Name Index Num Tuple Name Name Assert Call Name Subscript Name Index Num Name Assert Compare Str In Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name params Name Name Assign Subscript Name Index Str Name Return Tuple List Name Name List\n", - "Label = ['opt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'size', 'size', 'size']\n", - " 2. ['n', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "95\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Mod Tuple Name Name Name\n", - "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'length', 'length', 'length']\n", - " 2. ['a', 'input', 'input', 'input']\n", - "\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", "96\n", - "[CLS] If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Subscript Name Index Name\n", - "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['idx', 'dim', 'dim', 'dim']\n", - " 2. ['i', 'data', 'data', 'data']\n", - "\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", "97\n", - "[CLS] Return BinOp Tuple BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Attribute start Name Add Attribute base shape Name\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['end', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['start', 'dim', 'dim', 'dim']\n", - " 2. ['max', 'end', 'end', 'end']\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "98\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Tuple Name Assign Subscript Name Slice Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inbound', 'shape', 'shape', 'shape']\n", - " 2. ['trainable', 'input', 'input', 'input']\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "99\n", - "[CLS] BoolOp And Name Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Num Call Name Subscript Name Index Num Name\n", - "Label = ['version', 'info', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['keras', 'size', 'size', 'size']\n", - " 2. ['args', 'format', 'format', 'format']\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "100\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Str Name In Attribute data Name Assign Name val Call Attribute loads Name Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['join', 'format', 'format', 'format']\n", - " 2. ['data', 'data', 'data', 'data']\n", - "\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", "101\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Attribute count params Name Name comprehension Name p Call Name Name\n", - "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['extend', 'sum', 'sum', 'sum']\n", - " 2. ['zeros', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "102\n", - "[CLS] If Compare Call Name Name Gt Num For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name Assign Name fields List Str Str Str Subscript Name Index Name Expr Call Name Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['key', 'i', 'i', 'i']\n", - " 2. ['o', 'list', 'list', 'list']\n", - "\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", "103\n", - "[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute W OK Name Assign Name datadir base Call Attribute join Attribute path Name Str Str\n", - "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['save', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['exists', 'scope', 'scope', 'scope']\n", - " 2. ['load', 'types', 'types', 'types']\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "104\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg file hash arg algorithm arg chunk size Str Num Expr Str If BoolOp Or Compare Name Is Str BoolOp And Compare Name Str Compare Call Name Name Num Assign Name hasher Str Assign Name hasher Str If Compare Call Name Call Name Name Name Name Eq Call Name Name Return NameConstant Return NameConstant\n", - "Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'fn', 'fn', 'fn']\n", - " 2. ['fname', 'id', 'id', 'id']\n", - "\n", + "Label = ['trainable', 'count', '[PAD]', '[PAD]']\n", "105\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return BoolOp And Compare Attribute stop signal Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stop', 'signal', 'signal', 'signal']\n", - " 2. ['variables', 'stop', 'stop', 'stop']\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "106\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg sequence arg use multiprocessing arg shuffle NameConstant NameConstant Expr Call Attribute init Call Name Name Name Name Name Assign Attribute shuffle Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'fn', 'fn', 'fn']\n", - " 2. ['index', 'array', 'array', 'array']\n", - "\n", + "Label = ['ki', '[PAD]', '[PAD]', '[PAD]']\n", "107\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['seqs', 'out', 'out', 'out']\n", - " 2. ['model', 'fn', 'fn', 'fn']\n", - "\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", "108\n", - "[CLS] While NameConstant Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num If BoolOp Or Compare Attribute unfinished tasks Attribute queue Name Eq Num Call Attribute is set Attribute stop signal Name Return\n", - "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'running', 'running', 'running']\n", - " 2. ['expand', 'tasks', 'tasks', 'tasks']\n", - "\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", "109\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name Attribute random seed Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['seqs', 'out', 'out', 'out']\n", - " 2. ['model', 'format', 'format', 'format']\n", - "\n", + "Label = ['extractall', '[PAD]', '[PAD]', '[PAD]']\n", "110\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Expr Call Attribute add Name Str\n", - "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['backend', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['image', 'size', 'size', 'size']\n", - " 2. ['device', 'format', 'format', 'format']\n", - "\n", + "Label = ['cache', 'dir', '[PAD]', '[PAD]']\n", "111\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num If Compare Name Str Assign Name pad BinOp Name Sub Num\n", - "Label = ['pad', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['pad', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'pad', 'pad', 'pad']\n", - " 2. ['axes', 'size', 'size', 'size']\n", - "\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", "112\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num Name keyword Tuple Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'shape', 'shape', 'shape']\n", - " 2. ['value', 'value', 'value', 'value']\n", - "\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", "113\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute zeros Name BinOp Tuple Name Add Name keyword Attribute float32 Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'train', 'train', 'train']\n", - " 2. ['dtype', 'placeholder', 'placeholder', 'placeholder']\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "114\n", - "[CLS] If Call Name Name Name If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute build Name\n", - "Label = ['built', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['built', 'inputs', 'inputs', 'inputs']\n", - " 2. ['layers', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", "115\n", - "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Str ListComp Call Name Name comprehension Name ishape Attribute input shapes Name Assign Name inputlabels Str\n", - "Label = ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cache', 'inputlabels', 'inputlabels', 'inputlabels']\n", - " 2. ['is', 'config', 'config', 'config']\n", - "\n", + "Label = ['stop', 'signal', '[PAD]', '[PAD]']\n", "116\n", - "[CLS] keyword Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", "117\n", - "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name fn Call Attribute get Name Name If Compare Name Is NameConstant Raise Call Name BinOp BinOp BinOp Str Add Name Str Name\n", - "Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['fn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['metric', 'fn', 'fn', 'fn']\n", - " 2. ['index', 'name', 'name', 'name']\n", - "\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", "118\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Attribute width Name Sub Name\n", - "Label = ['bar', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['info', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'size', 'size', 'size']\n", - " 2. ['bar', 't', 't', 't']\n", - "\n", + "Label = ['seqs', '[PAD]', '[PAD]', '[PAD]']\n", "119\n", - "[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Tuple BinOp Name FloorDiv Num BinOp BinOp Name Num Num BinOp Name Num If Compare Name Num Assign Name eta format BinOp Str Tuple BinOp Name Num BinOp Name Num Assign Name eta format BinOp Str Name\n", - "Label = ['eta', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['eta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dim', 'format', 'format', 'format']\n", - " 2. ['format', 'data', 'data', 'data']\n", - "\n", + "Label = ['executor', 'fn', '[PAD]', '[PAD]']\n", "120\n", - "[CLS] If Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name info Add BinOp Str Mult BinOp Name Sub Attribute total width Name\n", - "Label = ['total', 'width', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['min', 't', 't', 't']\n", - " 2. ['delta', 'updates', 'updates', 'updates']\n", - "\n", + "Label = ['is', 'set', '[PAD]', '[PAD]']\n", "121\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Num Assign Name fpath Call Attribute join Attribute path Name Name BinOp Str Add Call Name Name Assign Tuple Subscript Name ExtSlice Slice BinOp BinOp Name Sub Num Mult Num BinOp Name Num Slice Slice Slice Subscript Name Slice BinOp BinOp Name Num Num BinOp Name Num Call Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'out', 'out', 'out']\n", - " 2. ['i', 'dir', 'dir', 'dir']\n", - "\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", "122\n", - "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x train Call Attribute reshape Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num Call Name Name Num Num\n", - "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['open', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'test', 'test', 'test']\n", - " 2. ['device', 'train', 'train', 'train']\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "123\n", - "[CLS] Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['transpose', 'train', 'train', 'train']\n", - " 2. ['dimshuffle', 'test', 'test', 'test']\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "124\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Tuple Num Assign Name d Call Attribute load Name Name Assign Name d Call Attribute load Name Name keyword Str Assign Name d decoded Dict For Tuple Name k Name v Call Attribute items Name Assign Subscript Name Index Call Attribute decode Name Str Name Assign Name d Name\n", - "Label = ['version', 'info', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'spec', 'spec', 'spec']\n", - " 2. ['recurrent', 'updates', 'updates', 'updates']\n", - "\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", "125\n", - "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Call Name Name comprehension Name x Name\n", - "Label = ['num', 'words', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['gpus', 'words', 'words', 'words']\n", - " 2. ['words', 'tensors', 'tensors', 'tensors']\n", - "\n", + "Label = ['single', 'value', '[PAD]', '[PAD]']\n", "126\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Str Expr Str Assign Name path Call Name Name keyword Str keyword Str Assign Name f Call Name Name Assign Name data Call Attribute load Name Name Expr Call Attribute close Name Return Name\n", - "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['path', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'format', 'format', 'format']\n", - " 2. ['model', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "127\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name If Name Assign Name xs ListComp ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['padding', 'shape', 'shape', 'shape']\n", - " 2. ['w', 'val', 'val', 'val']\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "128\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['w', 'xs', 'xs', 'xs']\n", - " 2. ['bad', 'tensors', 'tensors', 'tensors']\n", - "\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", "129\n", - "[CLS] ListComp ListComp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Compare Name LtE Lt Name Name comprehension Name x Name\n", - "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['s', 'ndim', 'ndim', 'ndim']\n", - " 2. ['xs', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", "130\n", - "[CLS] If BoolOp And UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute FunctionType Name UnaryOp Call Name Attribute build fn Name Attribute MethodType Name Expr Call Attribute append Name Attribute call Attribute build fn Name Expr Call Attribute append Name Attribute build fn Name\n", - "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['build', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['fn', 'fn', 'fn', 'fn']\n", - " 2. ['is', 'build', 'build', 'build']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "131\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name output Call Name Attribute metrics names Attribute model Name Name If Compare Name Eq Str Return Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['i', 'name', 'name', 'name']\n", - " 2. ['n', 'names', 'names', 'names']\n", - "\n", + "Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n", "132\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute preprocess input Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'args', 'args', 'args']\n", - " 2. ['f', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['raw', 'code', '[PAD]', '[PAD]']\n", "133\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute VGG19 Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'args', 'args', 'args']\n", - " 2. ['f', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", "134\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'args', 'args', 'args']\n", - " 2. ['f', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", "135\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute DenseNet121 Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'args', 'args', 'args']\n", - " 2. ['f', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", "136\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'args', 'args', 'args']\n", - " 2. ['f', 'metadata', 'metadata', 'metadata']\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "137\n", - "[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name Num Expr Call Attribute append Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name Expr Call Attribute append Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['is', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", "138\n", - "[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name Expr Call Attribute append Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['is', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "139\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'shape', 'shape', 'shape']\n", - " 2. ['num', 'size', 'size', 'size']\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "140\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List BinOp Name Sub Num Add Call Name Call Name BinOp Name Num\n", - "Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dims', 'dims', 'dims', 'dims']\n", - " 2. ['ins', 'size', 'size', 'size']\n", - "\n", + "Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n", "141\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'shape', 'shape', 'shape']\n", - " 2. ['num', 'size', 'size', 'size']\n", - "\n", + "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", "142\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Subscript Name Index Num comprehension Name s Name Compare Name IsNot NameConstant\n", - "Label = ['batch', 'sizes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['batch', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'shape', 'shape', 'shape']\n", - " 2. ['filter', 'states', 'states', 'states']\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "143\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Call Name Name NotEq Num Raise Call Name Str Return BinOp Subscript Name Index Num Sub Subscript Name Index Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['variable', 'pad', 'pad', 'pad']\n", - " 2. ['x', 'nodes', 'nodes', 'nodes']\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "144\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Attribute axes Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['update', 'axes', 'axes', 'axes']\n", - " 2. ['axes', 'i', 'i', 'i']\n", - "\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", "145\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'axes', 'axes', 'axes']\n", - " 2. ['ndim', 'ndim', 'ndim', 'ndim']\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "146\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Expr Str Return Call Call Name keyword Name Name\n", - "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'inputs', 'inputs', 'inputs']\n", - " 2. ['f', 'list', 'list', 'list']\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "147\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Call Name Attribute alpha Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'config', 'config', 'config']\n", - " 2. ['layer', 'value', 'value', 'value']\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "148\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", "149\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute shared axes Name Assign Subscript Name Index BinOp Name Sub Num Num Assign Subscript Attribute param broadcast Name Index BinOp Name Num NameConstant\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['v', 'axes', 'axes', 'axes']\n", - " 2. ['axes', 'i', 'i', 'i']\n", - "\n", + "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", "150\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name If Compare Name NotIn Attribute shared axes Name Assign Subscript Name Index Name Subscript Name Index Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axes', 'axes', 'axes', 'axes']\n", - " 2. ['o', 'i', 'i', 'i']\n", - "\n", + "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", "151\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute axis Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'value', 'value', 'value']\n", - " 2. ['layer', 'config', 'config', 'config']\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "152\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast to floatx Name Name\n", - "Label = ['max', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['w', 't', 't', 't']\n", - " 2. ['result', 'dtype', 'dtype', 'dtype']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "153\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute layer Name Str Return Attribute updates Attribute layer Name Return List Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'layer', 'layer', 'layer']\n", - " 2. ['cls', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['probs', '[PAD]', '[PAD]', '[PAD]']\n", "154\n", - "[CLS] Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Attribute layer Name Call Attribute get config Attribute layer Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'scope', 'scope', 'scope']\n", - " 2. ['from', 'format', 'format', 'format']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "155\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Num\n", - "Label = ['inner', 'input', 'shape', '[PAD]']\n", - "Pred =\n", - " 0. ['inner', 'mask', 'mask', 'mask']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['depthwise', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "156\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', 'shape', 'shape', 'shape']\n", - " 1. ['inner', 'mask', 'mask', 'mask']\n", - " 2. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "157\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n", - "Label = ['get', 'shape', 'tuple', '[PAD]']\n", - "Pred =\n", - " 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'shape', 'shape', 'shape']\n", - " 2. ['expand', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['compute', 'elemwise', 'op', 'output']\n", "158\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg value Assign Attribute trainable Name Name Assign Attribute trainable Attribute forward layer Name Name Assign Attribute trainable Attribute backward layer Name Name Attribute setter Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'layer', 'layer', 'layer']\n", - " 2. ['model', 'value', 'value', 'value']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "159\n", - "[CLS] Return BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute forward layer Name Add Call Attribute get weights Attribute backward layer Name\n", - "Label = ['get', 'weights', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['initial', 'weights', 'weights', 'weights']\n", - " 2. ['state', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "160\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Return BinOp BinOp Name Add Name Call Attribute copy Name Name\n", - "Label = ['merge', 'mode', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['merge', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['return', 'mode', 'mode', 'mode']\n", - " 2. ['mode', 'state', 'state', 'state']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "161\n", - "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['get', 'for', 'for', 'for']\n", - " 2. ['init', 'list', 'list', 'list']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "162\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Name FloorDiv Num Add Num\n", - "Label = ['pivot', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'axes', 'axes', 'axes']\n", - " 2. ['y', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", "163\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp UnaryOp Not Attribute merge mode Name List NameConstant NameConstant NameConstant\n", - "Label = ['output', 'mask', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mode', 'mode', 'mode', 'mode']\n", - " 2. ['state', 'state', 'state', 'state']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "164\n", - "[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Add Name Name\n", - "Label = ['get', 'updates', 'for', '[PAD]']\n", - "Pred =\n", - " 0. ['get', 'for', 'for', 'for']\n", - " 1. ['init', 'losses', 'losses', 'losses']\n", - " 2. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "165\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute losses Attribute forward layer Name Add Attribute losses Attribute backward layer Name\n", - "Label = ['forward', 'layer', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['forward', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'layer', 'layer', 'layer']\n", - " 2. ['layer', 'losses', 'losses', 'losses']\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "166\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['mean', 'list', 'list', 'list']\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "167\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Name keyword Tuple NameConstant Name comprehension Name dim Name\n", - "Label = ['state', 'spec', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['states', 'spec', 'spec', 'spec']\n", - " 2. ['input', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", "168\n", - "[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Attribute go backwards Name Attribute stateful Name\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stateful', 'sequences', 'sequences', 'sequences']\n", - " 2. ['scale', 'size', 'size', 'size']\n", - "\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", "169\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['mean', 'list', 'list', 'list']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "170\n", - "[CLS] If BoolOp And Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute built Name Return List Attribute kernel Name Attribute recurrent kernel Name Attribute bias Name\n", - "Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reset', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stateful', 'after', 'after', 'after']\n", - " 2. ['use', 'bias', 'bias', 'bias']\n", - "\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", "171\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n", - "Label = ['recurrent', 'kernel', 'z', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', 'kernel', 'kernel', 'kernel']\n", - " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['reset', 'i', 'i', 'i']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "172\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name BinOp Attribute units Name Mult Num\n", - "Label = ['kernel', 'r', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'f', 'f', 'f']\n", - " 2. ['bias', 'r', 'r', 'r']\n", - "\n", + "Label = ['shared', 'axes', '[PAD]', '[PAD]']\n", "173\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['bias', 'r', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'c', 'c', 'c']\n", - " 2. ['input', 'i', 'i', 'i']\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "174\n", - "[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num\n", - "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['return', 'state', 'state', 'state']\n", - " 2. ['state', 'sequences', 'sequences', 'sequences']\n", - "\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", "175\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", "176\n", - "[CLS] ExtSlice Slice Slice BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['units', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['filters', 'kernel', 'kernel', 'kernel']\n", - " 2. ['gain', 'c', 'c', 'c']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "177\n", - "[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num Assign Name c Subscript Name Index Num\n", - "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reset', 'state', 'state', 'state']\n", - " 2. ['run', 'sequences', 'sequences', 'sequences']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "178\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name output Call Attribute transpose Name Name Tuple Num Num Num Assign Name output Subscript Name Index UnaryOp USub Num\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['use', 'sequences', 'sequences', 'sequences']\n", - " 2. ['reset', 'state', 'state', 'state']\n", - "\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", "179\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pooling function Name keyword Name keyword BinOp Attribute pool size Name Add Tuple Num keyword BinOp Attribute strides Name Tuple Num keyword Attribute padding Name keyword Attribute data format Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outputs', 'shape', 'shape', 'shape']\n", - " 2. ['x', 'size', 'size', 'size']\n", - "\n", + "Label = ['max', 'value', '[PAD]', '[PAD]']\n", "180\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - " 0. ['conv', 'output', 'output', 'output']\n", - " 1. ['deconv', 'length', 'length', 'length']\n", - " 2. ['resize', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['input', 'map', '[PAD]', '[PAD]']\n", "181\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Subscript Name Index Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', 'format', 'format', 'format']\n", - " 1. ['type', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['mode', 'data', 'data', 'data']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "182\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['get', 'shape', 'tuple', '[PAD]']\n", "183\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - " 0. ['conv', 'output', 'output', 'output']\n", - " 1. ['deconv', 'length', 'length', 'length']\n", - " 2. ['resize', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['nw', '[PAD]', '[PAD]', '[PAD]']\n", "184\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['len', 'dim3', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cols', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['rows', 'length', 'length', 'length']\n", - " 2. ['length', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['get', 'weights', '[PAD]', '[PAD]']\n", "185\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute pooling function Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'function', 'function', 'function']\n", - " 2. ['layer', 'size', 'size', 'size']\n", - "\n", + "Label = ['set', 'weights', '[PAD]', '[PAD]']\n", "186\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name\n", - "Label = ['pooling', 'function', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['pooling', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['add', 'function', 'function', 'function']\n", - " 2. ['constant', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", "187\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg kwargs Tuple Num Num Num NameConstant Str NameConstant Expr Call Attribute init Call Name Name Name Name Name Name Name keyword Name Attribute legacy pooling3d support Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'size', 'size', 'size']\n", - " 2. ['x', 'function', 'function', 'function']\n", - "\n", + "Label = ['int', 'shape', '[PAD]', '[PAD]']\n", "188\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs Str Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'format', 'format', 'format']\n", - " 2. ['model', 'size', 'size', 'size']\n", - "\n", + "Label = ['is', 'keras', 'tensor', '[PAD]']\n", "189\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs NameConstant Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute data format Name Call Attribute normalize data format Name Name Assign Attribute input spec Name Call Name keyword Num Attribute legacy global pooling support Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'format', 'format', 'format']\n", - " 2. ['layer', 'layer', 'layer', 'layer']\n", - "\n", "190\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute data format Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'config', 'config', 'config']\n", - " 2. ['cls', 'format', 'format', 'format']\n", - "\n", + "Label = ['get', 'updates', 'for', '[PAD]']\n", "191\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute mean Name Name keyword List Num Num Return Call Attribute mean Name Name keyword List Num Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'format', 'format', 'format']\n", - " 2. ['seqs', 'size', 'size', 'size']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "192\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute max Name Name keyword List Num Num Return Call Attribute max Name Name keyword List Num Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'format', 'format', 'format']\n", - " 2. ['x', 'data', 'data', 'data']\n", - "\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", "193\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['return', 'state', '[PAD]', '[PAD]']\n", "194\n", - "[CLS] Raise Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Attribute shape Name comprehension Name spec Attribute state spec Name Attribute state size Attribute cell Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['join', 'size', 'size', 'size']\n", - " 2. ['state', 'format', 'format', 'format']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "195\n", - "[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Subscript Name Index Name keyword Attribute padding Attribute cell Name keyword Subscript Attribute strides Attribute cell Name Index Name keyword Subscript Attribute dilation rate Attribute cell Name Index Name\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - " 0. ['conv', 'output', 'output', 'output']\n", - " 1. ['deconv', 'length', 'length', 'length']\n", - " 2. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "196\n", - "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['get', 'for', 'for', 'for']\n", - " 2. ['init', 'list', 'list', 'list']\n", - "\n", + "Label = ['kernel', 'r', '[PAD]', '[PAD]']\n", "197\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state arg constants NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['step', 'function', 'function', 'function']\n", - " 2. ['inputs', 'size', 'size', 'size']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "198\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outputs', 'layers', 'layers', 'layers']\n", - " 2. ['layers', 'uid', 'uid', 'uid']\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "199\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg states Assign Name constants Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name states Subscript Name Slice UnaryOp Attribute num constants Name Return Call Attribute call Attribute cell Name Name Name keyword Name keyword Name\n", - "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['num', 'inputs', 'inputs', 'inputs']\n", - " 2. ['constants', 'constants', 'constants', 'constants']\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "200\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name state shape BinOp Subscript Name Slice Num Add Subscript Name Slice Num\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['return', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stateful', 'state', 'state', 'state']\n", - " 2. ['reverse', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", "201\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Tuple Name BinOp Attribute filters Name Mult Num\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['bias', 'size', 'size', 'size']\n", - " 2. ['filters', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "202\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice Attribute filters Name\n", - "Label = ['kernel', 'i', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'i', 'i', 'i']\n", - " 2. ['bias', 'f', 'f', 'f']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "203\n", - "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n", - " 2. ['bias', 'size', 'size', 'size']\n", - "\n", + "Label = ['pool', 'size', '[PAD]', '[PAD]']\n", "204\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['recurrent', 'kernel', 'o', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', 'kernel', 'kernel', 'kernel']\n", - " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['bias', 'c', 'c', 'c']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "205\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", - "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'c', 'c', 'c']\n", - " 2. ['kernel', 'f', 'f', 'f']\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "206\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['bias', 'o', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'c', 'c', 'c']\n", - " 2. ['kernel', 'f', 'f', 'f']\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "207\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel i Name Attribute bias i Name keyword Attribute padding Name\n", - "Label = ['input', 'conv', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'conv', 'conv', 'conv']\n", - " 2. ['outputs', 'i', 'i', 'i']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "208\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['init', 'losses', 'losses', 'losses']\n", - " 2. ['get', 'list', 'list', 'list']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "209\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['mean', 'list', 'list', 'list']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "210\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Assign Name outputs Call Attribute conv1d Name Name Attribute kernel Name keyword Subscript Attribute strides Name Index Num keyword Attribute padding Name keyword Attribute data format Name keyword Subscript Attribute dilation rate Name Index Num\n", - "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['rank', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'transpose', 'transpose', 'transpose']\n", - " 2. ['ndim', 'format', 'format', 'format']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "211\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Assign Name new dim Call Attribute conv output length Name Subscript Name Index Name Subscript Attribute kernel size Name Index Name keyword Attribute padding Name keyword Subscript Attribute strides Name Index Name keyword Subscript Attribute dilation rate Name Index Name Expr Call Attribute append Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dim', 'dim', 'dim', 'dim']\n", - " 2. ['o', 'length', 'length', 'length']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "212\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", "213\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'generator', 'generator', 'generator']\n", - " 2. ['fit', 'loop', 'loop', 'loop']\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "214\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['rank', 'padding', 'padding', 'padding']\n", - " 2. ['pow', 'size', 'size', 'size']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "215\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['depthwise', 'kernel', 'kernel', 'kernel']\n", - " 2. ['[PAD]', 'weight', 'weight', 'weight']\n", - "\n", - "216\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Tuple Name h axis Name w axis Tuple Num Num Assign Tuple Name h axis Name w axis Tuple Num Num\n", "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mode', 'format', 'format', 'format']\n", - " 2. ['type', 'data', 'data', 'data']\n", - "\n", + "216\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "217\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Assign Name output shape Tuple Name Name Name Attribute filters Name\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mode', 'format', 'format', 'format']\n", - " 2. ['merge', 'data', 'data', 'data']\n", - "\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", "218\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d transpose Name Name Attribute kernel Name Name Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'out', 'out', 'out']\n", - " 2. ['cols', 'outputs', 'outputs', 'outputs']\n", - "\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", "219\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'bias', 'bias', 'bias']\n", - " 2. ['conv', 'out', 'out', 'out']\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "220\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['rank', 'padding', 'padding', 'padding']\n", - " 2. ['pow', 'size', 'size', 'size']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "221\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'bias', 'bias', 'bias']\n", - " 2. ['use', 'i', 'i', 'i']\n", - "\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", "222\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['bias', 'weight', 'weight', 'weight']\n", - " 2. ['parameter', 'bias', 'bias', 'bias']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "223\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute deconv length Name Name Name Name Attribute padding Name Name\n", - "Label = ['out', 'height', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['out', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['width', 'out', 'out', 'out']\n", - " 2. ['output', 'width', 'width', 'width']\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "224\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Name Assign Name output shape Tuple Name Name Name Name Attribute filters Name\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1. ['mode', 'format', 'format', 'format']\n", - " 2. ['merge', 'data', 'data', 'data']\n", - "\n", + "Label = ['recurrent', 'kernel', 'c', '[PAD]']\n", "225\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['deconv', 'length', 'length', 'length']\n", - " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['append', 'output', 'output', 'output']\n", - "\n", + "Label = ['bias', 'f', '[PAD]', '[PAD]']\n", "226\n", - "[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['deconv', 'length', 'length', 'length']\n", - " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['stateful', 'output', 'output', 'output']\n", - "\n", + "Label = ['x', 'f', '[PAD]', '[PAD]']\n", "227\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['deconv', 'length', 'length', 'length']\n", - " 1. ['conv', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['append', 'output', 'output', 'output']\n", - "\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", "228\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'name', 'name', 'name']\n", - " 2. ['encode', 'generator', 'generator', 'generator']\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "229\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Attribute data format Name Eq Str Num UnaryOp USub Num\n", - "Label = ['channel', 'axis', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['format', 'format', 'format', 'format']\n", - " 2. ['tf', 'data', 'data', 'data']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "230\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'bias', 'bias', 'bias']\n", - " 2. ['use', 'i', 'i', 'i']\n", - "\n", - "231\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'name', 'name', 'name']\n", - " 2. ['encode', 'generator', 'generator', 'generator']\n", - "\n", + "231\n", + "Label = ['out', 'height', '[PAD]', '[PAD]']\n", "232\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute depthwise kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute dilation rate Name keyword Attribute data format Name\n", - "Label = ['depthwise', 'conv2d', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['conv2d', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['separable', 'conv2d', 'conv2d', 'conv2d']\n", - " 2. ['conv3d', 'transpose', 'transpose', 'transpose']\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "233\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", - "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['use', 'bias', 'bias', 'bias']\n", - " 1. ['reset', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['outputs', 'after', 'after', 'after']\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "234\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Num BinOp Num Add Attribute rank Name\n", - "Label = ['spatial', 'axes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['spatial', 'axes', 'axes', 'axes']\n", - " 1. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['new', 'rank', 'rank', 'rank']\n", - "\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", "235\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute repeat elements Name Name Subscript Attribute size Name Index Num keyword Num Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'size', 'size', 'size']\n", - " 2. ['model', 'function', 'function', 'function']\n", - "\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", "236\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute resize images Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'size', 'size', 'size']\n", - " 2. ['seqs', 'format', 'format', 'format']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "237\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n", - "Label = ['resize', 'images', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['resize', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'length', 'length', 'length']\n", - " 2. ['deconv', 'output', 'output', 'output']\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "238\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Str keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'params', 'params', 'params']\n", - " 2. ['encode', 'list', 'list', 'list']\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "239\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n", - "Label = ['dim3', 'padding', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['width', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dim2', 'cropping', 'cropping', 'cropping']\n", - " 2. ['height', 'padding', 'padding', 'padding']\n", - "\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", "240\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num Tuple Num Num Tuple Num Num NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'format', 'format', 'format']\n", - " 2. ['args', 'size', 'size', 'size']\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "241\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Tuple Name Name Tuple Name Name Tuple Name Name\n", - "Label = ['normalized', 'cropping', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['args', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'shape', 'shape', 'shape']\n", - " 2. ['legacy', 'input', 'input', 'input']\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "242\n", - "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", - "Label = ['input', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', 'length', 'length', 'length']\n", - " 1. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['outputs', 'uid', 'uid', 'uid']\n", - "\n", + "Label = ['separable', 'conv2d', '[PAD]', '[PAD]']\n", "243\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv1d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outputs', 'out', 'out', 'out']\n", - " 2. ['conv', 'output', 'output', 'output']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "244\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Str Raise Call Name BinOp Str Add Name\n", - "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'names', 'names', 'names']\n", - " 2. ['shape', 'padding', 'padding', 'padding']\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "245\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute kernel size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['output', 'row', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cols', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['rows', 'length', 'length', 'length']\n", - " 2. ['output', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['size', 'all', 'dims', '[PAD]']\n", "246\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv2d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name Tuple Attribute output row Name Attribute output col Name Attribute data format Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'out', 'out', 'out']\n", - " 2. ['x', 'size', 'size', 'size']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "247\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'layers', 'layers', 'layers']\n", - " 2. ['name', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "248\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute gamma Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute gamma initializer Name keyword Attribute gamma regularizer Name keyword Attribute gamma constraint Name Assign Attribute gamma Name NameConstant\n", - "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['use', 'weight', 'weight', 'weight']\n", - " 2. ['center', 'gamma', 'gamma', 'gamma']\n", - "\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", "249\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute beta initializer Name keyword Attribute beta regularizer Name keyword Attribute beta constraint Name\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['depthwise', 'kernel', 'kernel', 'kernel']\n", - " 2. ['bias', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['slices', '[PAD]', '[PAD]', '[PAD]']\n", "250\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Str keyword Attribute moving mean initializer Name keyword NameConstant\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['parameter', 'weight', 'weight', 'weight']\n", - " 2. ['zeros', 'function', 'function', 'function']\n", - "\n", + "Label = ['cropping', 'all', 'dims', '[PAD]']\n", "251\n", - "[CLS] BinOp Name Div BinOp Name Sub BinOp Num Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['rank', 't', 't', 't']\n", - " 2. ['sqrt', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "252\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['mean', 'list', 'list', 'list']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "253\n", - "[CLS] Return Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute cells Name Index UnaryOp USub Num Index Num\n", - "Label = ['state', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'size', 'size', 'size']\n", - " 2. ['output', 'state', 'state', 'state']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "254\n", - "[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute cells Name Slice UnaryOp USub Num Attribute cells Name\n", - "Label = ['reverse', 'state', 'order', '[PAD]']\n", - "Pred =\n", - " 0. ['reverse', 'state', 'state', 'state']\n", - " 1. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['return', 'order', 'order', 'order']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "255\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice Num Assign Name input shape Subscript Name Index Num\n", - "Label = ['constants', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mask', 'shape', 'shape', 'shape']\n", - " 2. ['shape', 'input', 'input', 'input']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "256\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute cells Name If Call Name Name Name AugAssign Name weights Add Attribute non trainable weights Name\n", - "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'layer', 'layer', 'layer']\n", - " 2. ['state', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "257\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs NameConstant Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Call Attribute get losses for Name Name AugAssign Name losses Add Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cell', 'losses', 'losses', 'losses']\n", - " 2. ['cls', 'function', 'function', 'function']\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "258\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Attribute states Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name Return Attribute states Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cls', 'size', 'size', 'size']\n", - " 2. ['states', 'function', 'function', 'function']\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "259\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['states', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['state', 'size', 'size', 'size']\n", - " 2. ['tile', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "260\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Tuple Subscript Name Index Num Name comprehension Name dim Name\n", - "Label = ['state', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'shape', 'shape', 'shape']\n", - " 2. ['output', 'input', 'input', 'input']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "261\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n", - "Label = ['input', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['mask', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'shape', 'shape', 'shape']\n", - " 2. ['inputs', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "262\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Str Return ListComp Call Attribute tile Name Name List Num Name comprehension Name dim Attribute state size Attribute cell Name Return List Call Attribute tile Name Name List Num Attribute state size Attribute cell Name\n", - "Label = ['state', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'size', 'size', 'size']\n", - " 2. ['bn', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['center', '[PAD]', '[PAD]', '[PAD]']\n", "263\n", - "[CLS] If Call Name Name Name If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Assign Name initial state Subscript Name Slice Num Assign Name initial state Subscript Name Slice Num UnaryOp USub Attribute num constants Name If Compare Call Name Name Eq Num Assign Name initial state NameConstant Assign Name inputs Subscript Name Index Num\n", - "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['initial', 'constants', 'constants', 'constants']\n", - " 2. ['constants', 'function', 'function', 'function']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "264\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Name Str Call Name Attribute shape Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'shape', 'shape', 'shape']\n", - " 2. ['batch', 'size', 'size', 'size']\n", - "\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", "265\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'names', 'names', 'names']\n", - " 2. ['batch', 'nodes', 'nodes', 'nodes']\n", - "\n", + "Label = ['reverse', 'state', 'order', '[PAD]']\n", "266\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'names', 'names', 'names']\n", - " 2. ['shape', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['reverse', 'state', 'order', '[PAD]']\n", "267\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Subscript Name Index Str Attribute num constants Name\n", - "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['num', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['constants', 'constants', 'constants', 'constants']\n", - " 2. ['initial', 'function', 'function', 'function']\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "268\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", - "269\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name\n", "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['trainable', 'layer', 'layer', 'layer']\n", - " 2. ['forward', 'weights', 'weights', 'weights']\n", - "\n", + "269\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "270\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name h Call Attribute bias add Name Name Attribute bias Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'bias', 'bias', 'bias']\n", - " 2. ['use', 'i', 'i', 'i']\n", - "\n", + "Label = ['state', 'shape', '[PAD]', '[PAD]']\n", "271\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", - "Label = ['kernel', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'o', 'o', 'o']\n", - " 2. ['h', 'c', 'c', 'c']\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "272\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['ones', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'like', 'like', 'like']\n", - " 2. ['dropout', 'dropout', 'dropout', 'dropout']\n", - "\n", + "Label = ['build', '[PAD]', '[PAD]', '[PAD]']\n", "273\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", - "Label = ['matrix', 'inner', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'h', 'h', 'h']\n", - " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "274\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Subscript Name ExtSlice Slice Slice BinOp Num Attribute units Name\n", - "Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['h', 'kernel', 'kernel', 'kernel']\n", - " 2. ['kernel', 'h', 'h', 'h']\n", - "\n", + "Label = ['additional', 'inputs', '[PAD]', '[PAD]']\n", "275\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name BinOp Name Mult Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Attribute units Name\n", - "Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'h', 'h', 'h']\n", - " 2. ['h', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['state', 'spec', '[PAD]', '[PAD]']\n", "276\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'normal', 'normal', 'normal']\n", - " 2. ['fit', 'initializer', 'initializer', 'initializer']\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "277\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['init', 'losses', 'losses', 'losses']\n", - " 2. ['get', 'list', 'list', 'list']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "278\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['init', 'normal', 'normal', 'normal']\n", - " 2. ['get', 'initializer', 'initializer', 'initializer']\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "279\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg config If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Num Assign Subscript Name Index Str Num Return Call Name keyword Name Name\n", - "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cls', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'data', 'data', 'data']\n", - " 2. ['prefix', 'format', 'format', 'format']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "280\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute bias initializer Name Tuple Attribute units Name Starred Name keyword Name Call Call Attribute Ones Name Tuple Attribute units Name Starred Name keyword Name Call Attribute bias initializer Name Tuple BinOp Attribute units Name Mult Num Starred Name keyword Name\n", - "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['bias', 'initializer', 'initializer', 'initializer']\n", - " 2. ['stack', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", "281\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'o', 'o', 'o']\n", - " 2. ['h', 'c', 'c', 'c']\n", - "\n", + "Label = ['get', 'losses', 'for', '[PAD]']\n", "282\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Call Attribute ones like Name Name Attribute dropout Name keyword Name keyword Num\n", - "Label = ['dropout', 'mask', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'mask', 'mask', 'mask']\n", - " 2. ['mask', 'dropout', 'dropout', 'dropout']\n", - "\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", "283\n", - "[CLS] If BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant Assign Attribute recurrent dropout mask Name Call Name Call Attribute ones like Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pred =\n", - " 0. ['recurrent', 'dropout', 'dropout', 'dropout']\n", - " 1. ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['reset', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "284\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['ones', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'like', 'like', 'like']\n", - " 2. ['set', 'dropout', 'dropout', 'dropout']\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "285\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel f Name\n", - "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['c', 'i', 'i', 'i']\n", - " 2. ['o', 'c', 'c', 'c']\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "286\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", - "Label = ['recurrent', 'activation', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['activation', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['recurrent', 'activation', 'activation', 'activation']\n", - " 2. ['append', 'i', 'i', 'i']\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "287\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n", - "Label = ['z1', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'r', 'r', 'r']\n", - " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "288\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", "289\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'name', 'name', 'name']\n", - " 2. ['encode', 'losses', 'losses', 'losses']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "290\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n", - "Label = ['stddev', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['rate', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stddev', 't', 't', 't']\n", - " 2. ['y', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['recurrent', 'dropout', 'mask', '[PAD]']\n", "291\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "292\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name If Compare Attribute data format Name Eq Str Assign Name noise shape Tuple Subscript Name Index Num Subscript Name Index Num Num Num Assign Name noise shape Tuple Subscript Name Index Num Num Num Subscript Name Index Num Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'format', 'format', 'format']\n", - " 2. ['layer', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", "293\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Num Num Subscript Name Index Num\n", - "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['noise', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['filter', 'shape', 'shape', 'shape']\n", - " 2. ['new', 'size', 'size', 'size']\n", - "\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", "294\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str\n", - "Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['steps', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'epoch', 'epoch', 'epoch']\n", - " 2. ['do', 'per', 'per', 'per']\n", - "\n", - "295\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "295\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "296\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", "297\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n arg kwargs Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute n Name Name Assign Attribute input spec Name Call Name keyword Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'spec', 'spec', 'spec']\n", - " 2. ['layer', 'format', 'format', 'format']\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "298\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Return Tuple Subscript Name Index Num Attribute n Name Subscript Name Index Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'format', 'format', 'format']\n", - " 2. ['layer', 'size', 'size', 'size']\n", - "\n", + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "299\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute repeat Name Name Attribute n Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'nodes', 'nodes', 'nodes']\n", - " 2. ['y', 'format', 'format', 'format']\n", - "\n", + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "300\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Name comprehension Name shape Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['xs', 'placeholder', 'placeholder', 'placeholder']\n", - " 2. ['data', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['kept', 'idx', '[PAD]', '[PAD]']\n", "301\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['items', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'config', 'config', 'config']\n", - " 2. ['backend', 'list', 'list', 'list']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "302\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str If Call Name Subscript Subscript Name Index Str Index Name Name Assign Name arg dict Subscript Subscript Name Index Str Index Name If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Str Assign Subscript Subscript Name Index Str Index Name Call Attribute array Name Subscript Name Index Str\n", - "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['k', 'config', 'config', 'config']\n", - " 2. ['layer', 'data', 'data', 'data']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "303\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Name Attribute units Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', 'weight', 'weight', 'weight']\n", - " 1. ['compile', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['pooling', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "304\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['bias', 'weight', 'weight', 'weight']\n", - " 2. ['parameter', 'bias', 'bias', 'bias']\n", - "\n", + "Label = ['boolean', 'mask', '[PAD]', '[PAD]']\n", "305\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute asarray Name Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'value', 'value', 'value']\n", - " 2. ['tensor', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "306\n", - "[CLS] BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant\n", - "Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['function', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'function', 'function', 'function']\n", - " 2. ['parameter', 'value', 'value', 'value']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "307\n", - "[CLS] If Compare Name Eq Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Return Attribute float32 Name\n", - "Label = ['float16', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['float64', 'dtype', 'dtype', 'dtype']\n", - " 2. ['float32', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "308\n", - "[CLS] BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name\n", - "Label = ['Constant', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['constant', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['parameter', 'normal', 'normal', 'normal']\n", - " 2. ['variable', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "309\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Attribute shape Name Index Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['[PAD]', 'size', 'size', 'size']\n", - "\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", "310\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input Name keyword Name keyword Call Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outputs', 'input', 'input', 'input']\n", - " 2. ['output', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['fix', 'unknown', 'dimension', '[PAD]']\n", "311\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index BinOp Name Add Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['is', 'sparse', 'sparse', 'sparse']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "312\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'seed', 'seed', 'seed']\n", - " 2. ['[PAD]', 'value', 'value', 'value']\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "313\n", - "[CLS] For Name Name If Compare Name Is NameConstant Raise Call Name Str\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", "314\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['normal', 'normal', 'normal', 'normal']\n", - " 2. ['randint', 'uniform', 'uniform', 'uniform']\n", - "\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", "315\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'seed', 'seed', 'seed']\n", - " 2. ['[PAD]', 'value', 'value', 'value']\n", - "\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", "316\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name Name keyword Call Attribute uniform Attribute initializer Name Name keyword Name keyword Name keyword Name\n", - "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['v', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'out', 'out', 'out']\n", - " 2. ['out', 't', 't', 't']\n", - "\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", "317\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name keyword Name keyword Call Attribute normal Attribute initializer Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['v', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 't', 't', 't']\n", - " 2. ['parameter', 'out', 'out', 'out']\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "318\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['normal', 'normal', 'normal', 'normal']\n", - " 2. ['randint', 'uniform', 'uniform', 'uniform']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "319\n", - "[CLS] For Name Attribute [MASK] [MASK] [MASK] [MASK] Name If BoolOp Or Compare Name Eq Attribute InferredDimension Name Compare Name Attribute FreeDimension Name Raise Call Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['tensor', 'shape', 'shape', 'shape']\n", - " 2. ['value', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "320\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Call Name BinOp Call Name Name Sub Num\n", - "Label = ['permutation', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['permutation', 'shape', 'shape', 'shape']\n", - " 2. ['i', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "321\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Call Name Name Sub Num BinOp Call Name Name Num\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'axes', 'axes', 'axes']\n", - " 2. ['pattern', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['parameter', '[PAD]', '[PAD]', '[PAD]']\n", "322\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Is NameConstant Attribute InferredDimension Name Name comprehension Name Name\n", - "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'shape', 'shape', 'shape']\n", - " 2. ['result', 'value', 'value', 'value']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "323\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Compare Name GtE Num Name BinOp Name Add Call Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['update', 'shape', 'shape', 'shape']\n", - " 2. ['extend', 'value', 'value', 'value']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "324\n", - "[CLS] Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "325\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name rep Call Name Name If BoolOp And Compare Name GtE Name Compare Subscript Name Index Name IsNot NameConstant Assign Name tmp BinOp List Name Mult Name Assign Name x Call Attribute splice Name Starred Name keyword BinOp Name Sub Name AugAssign Name i Add Num\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['w', 't', 't', 't']\n", - " 2. ['a', 'size', 'size', 'size']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "326\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute all axes Attribute Axis Name\n", - "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'axes', 'axes', 'axes']\n", - " 2. ['a', 'axis', 'axis', 'axis']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "327\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute element select Name Name Call Name Name Call Name Name\n", - "Label = ['any', 'matrix', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['result', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['all', 'matrix', 'matrix', 'matrix']\n", - " 2. ['out', 'out', 'out', 'out']\n", - "\n", + "Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n", "328\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg increment Assign Name result BinOp Name Add Name Return Call Attribute assign Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'test', 'test', 'test']\n", - " 2. ['size', 'size', 'size', 'size']\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "329\n", - "[CLS] If BoolOp And Compare Call Name Name Eq Call Name Name Compare Subscript Call Name Name Index Num Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name List Num\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mean', 'shape', 'shape', 'shape']\n", - " 2. ['var', 'img', 'img', 'img']\n", - "\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", "330\n", - "[CLS] BoolOp Or Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name Call Name GeneratorExp Compare Name Attribute FreeDimension Name comprehension Name Attribute shape Name\n", - "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inferreddimension', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['freedimension', 'shape', 'shape', 'shape']\n", - " 2. ['ndim', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "331\n", - "[CLS] Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name\n", "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['freedimension', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inferreddimension', 'shape', 'shape', 'shape']\n", - " 2. ['ndim', 'spec', 'spec', 'spec']\n", - "\n", "332\n", - "[CLS] BinOp Call Name ListComp UnaryOp USub Num comprehension Name Call Name BinOp Name Sub Name Add Name\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "333\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Name comprehension Name i Call Name Name\n", - "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['result', 'list', 'list', 'list']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "334\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg initial states arg go backwards arg mask arg constants arg unroll arg input length NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['step', 'function', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['step', 'function', 'function', 'function']\n", - " 1. ['batch', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['function', 'step', 'step', 'step']\n", - "\n", + "Label = ['has', 'seq', '[PAD]', '[PAD]']\n", "335\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", - "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['slice', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dims', 'slice', 'slice', 'slice']\n", - " 2. ['activation', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "336\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute element select Attribute ops Name Name Name Name\n", "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['update', 'slice', 'slice', 'slice']\n", - " 2. ['set', 'shape', 'shape', 'shape']\n", - "\n", "337\n", - "[CLS] If BoolOp And Compare Name Is NameConstant UnaryOp Not Call Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Name Index Num\n", - "Label = ['num', 'time', 'step', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'length', 'length', 'length']\n", - " 2. ['mask', 'size', 'size', 'size']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "338\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Name Name Eq Num Expr Call Attribute append Name Call Attribute broadcast as Attribute sequence Name Name Name Expr Call Attribute append Name Name\n", - "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['s', 'i', 'i', 'i']\n", - " 2. ['o', 'o', 'o', 'o']\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "339\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name new states Call Name Name BinOp Call Name Name Add Call Name Name\n", - "Label = ['new', 'output', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'length', 'length', 'length']\n", - " 2. ['outputs', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", "340\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute element select Name Name Name Name comprehension Tuple Name n Name s Call Name Name Name\n", - "Label = ['new', 'states', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'states', 'states', 'states']\n", - " 2. ['initial', 'p', 'p', 'p']\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "341\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'shape', 'shape', 'shape']\n", - " 2. ['pool', 'out', 'out', 'out']\n", - "\n", + "Label = ['assign', '[PAD]', '[PAD]', '[PAD]']\n", "342\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['transpose', 'kernel', 'kernel', 'kernel']\n", - " 2. ['append', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", "343\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword List NameConstant Name Name keyword Subscript Attribute shape Name Index Num\n", - "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['scan', 'transpose', 'transpose', 'transpose']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2. ['rnn', 'normal', 'normal', 'normal']\n", - "\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", "344\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['depthwise', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n", - " 2. ['recurrent', 'img', 'img', 'img']\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "345\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format Tuple Num Num Num Str NameConstant\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'size', 'size', 'size']\n", - " 2. ['kernel', 'shape', 'shape', 'shape']\n", - "\n", "346\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute clip Name Name Call Name BinOp Num Sub Call Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'dimensions', 'dimensions', 'dimensions']\n", - " 2. ['out', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", "347\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute one hot Name Name Subscript Attribute shape Name Index Name keyword Name\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['targets', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['result', 'counter', 'counter', 'counter']\n", - " 2. ['feed', 'dict', 'dict', 'dict']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "348\n", - "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Expr Call Attribute append Name Name Expr Call Attribute append Name Name\n", - "Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'out', 'out', 'out']\n", - " 2. ['ndarray', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", "349\n", - "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Expr Call Attribute append Name Name Raise Call Name BinOp Str Mod Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['extend', 'params', 'params', 'params']\n", - "\n", + "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", "350\n", - "[CLS] If Compare Call Name Name Gt Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute combine Name ListComp Attribute output Name comprehension Name Name\n", - "Label = ['unrelated', 'updates', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['metrics', 'updates', 'updates', 'updates']\n", - " 2. ['unrelated', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", "351\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute arguments Attribute loss Name If Compare Name In Name Assign Subscript Name Index Name Subscript Name Index Name Raise Call Name BinOp Str Mod Attribute name Name\n", - "Label = ['argument', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['key', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'name', 'name', 'name']\n", - " 2. ['argument', 'config', 'config', 'config']\n", - "\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", "352\n", - "[CLS] If Compare Subscript Name Index Num Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Subscript Name Index Name Subscript Name Index Num Assign Name prefix shape Call Name Name Assign Name x Call Attribute splice Name Call Attribute constant Name keyword Num keyword Name Name keyword Name Assign Name base shape Attribute shape Name\n", - "Label = ['prefix', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['xs', 'shape', 'shape', 'shape']\n", - " 2. ['w', 'value', 'value', 'value']\n", - "\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", "353\n", - "[CLS] Assert Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Num Sub IfExp Compare Name Gt Num Num Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'shape', 'shape', 'shape']\n", - " 2. ['[PAD]', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "354\n", - "[CLS] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Expr Call Attribute append Name Attribute value Name Expr Call Attribute append Name Call Name Name\n", - "Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['parameter', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['constant', 'value', 'value', 'value']\n", - " 2. ['function', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "355\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg message Str Return Call Attribute user function Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x Call Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'function', 'function', 'function']\n", - " 2. ['pool', 'test', 'test', 'test']\n", - "\n", "356\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Subscript Name Index BinOp Name Add Name\n", - "Label = ['condition', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['slice', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['result', 'shape', 'shape', 'shape']\n", - " 2. ['output', 'length', 'length', 'length']\n", - "\n", + "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", "357\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format If Compare Name Eq Str Assign Name x Call Attribute transpose Name Name Tuple Num Num Num Return Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'format', 'format', 'format']\n", - " 2. ['a', 'data', 'data', 'data']\n", - "\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", "358\n", - "[CLS] If Call Name Name Str Return Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Return Num\n", - "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['in', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['tile', 'shape', 'shape', 'shape']\n", - " 2. ['get', 'like', 'like', 'like']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "359\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Slice Index Name Index Name Tuple UnaryOp USub Num Num Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['extend', 'kernel', 'kernel', 'kernel']\n", - " 2. ['set', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", "360\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Index Name Slice Tuple UnaryOp USub Num Num Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'shape', 'shape', 'shape']\n", - " 2. ['extend', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "361\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute as shape Call Attribute data Name BinOp Tuple Name Add Attribute target shape Name\n", - "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', 'shape', 'shape', 'shape']\n", - " 1. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['input', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "362\n", - "[CLS] BinOp Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Mult Call Attribute prod Name Call Attribute asarray Name Attribute target shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['arange', 'kernel', 'kernel', 'kernel']\n", - " 2. ['num', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "363\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name List Name keyword NameConstant keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['init', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['call', 'transpose', 'transpose', 'transpose']\n", - " 2. ['encode', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "364\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Subscript Attribute inputs Name Index Num Slice Num Attribute dtype Subscript Attribute inputs Name Index Num List Name\n", - "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', 'variable', 'variable', 'variable']\n", - " 1. ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['get', 'value', 'value', 'value']\n", - "\n", + "Label = ['axis', 'without', 'batch', '[PAD]']\n", "365\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg state arg root gradients Return Call Attribute Value Attribute cntk py Name Call Attribute data Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'value', 'value', 'value']\n", - " 2. ['layer', 'size', 'size', 'size']\n", - "\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", "366\n", - "[CLS] FunctionDef arguments Expr Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get default graph Name If Compare Name NotIn Name Assign Name phase Call Attribute placeholder with default Name NameConstant keyword Tuple keyword Str Assign Subscript Name Index Name Name Return Subscript Name Index Name\n", - "Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['phase', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['learning', 'graph', 'graph', 'graph']\n", - " 2. ['g', 'phase', 'phase', 'phase']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "367\n", - "[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword NameConstant Assign Name num thread Call Name Call Attribute get Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword Name keyword NameConstant\n", - "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['get', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['encode', 'fn', 'fn', 'fn']\n", - " 2. ['lower', 'size', 'size', 'size']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "368\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute eval Call Name Name keyword Call Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'shape', 'shape', 'shape']\n", - " 2. ['a', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['postfix', 'shape', '[PAD]', '[PAD]']\n", "369\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name keyword Name List Subscript Name Index UnaryOp USub Num UnaryOp Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['concatenate', 'format', 'format', 'format']\n", - " 2. ['stack', 'sum', 'sum', 'sum']\n", - "\n", + "Label = ['splice', '[PAD]', '[PAD]', '[PAD]']\n", "370\n", - "[CLS] If Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Str Str Call Name Name\n", - "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['a', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'input', 'input', 'input']\n", - " 2. ['m', 'list', 'list', 'list']\n", - "\n", + "Label = ['pattern', '[PAD]', '[PAD]', '[PAD]']\n", "371\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Subscript Name Index Num Eq BinOp Call Name Name Sub Num NameConstant NameConstant\n", - "Label = ['adj', 'x', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['adj', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['num', 'pad', 'pad', 'pad']\n", - " 2. ['new', 'size', 'size', 'size']\n", - "\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", "372\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute reduce max Name Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'devices', 'devices', 'devices']\n", - " 2. ['axis', 'list', 'list', 'list']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "373\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'shape', 'shape', 'shape']\n", - " 2. ['mean', 'function', 'function', 'function']\n", - "\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", "374\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute not equal Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'train', 'train', 'train']\n", - " 2. ['a', 'true', 'true', 'true']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "375\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute greater equal Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'train', 'train', 'train']\n", - " 2. ['a', 'true', 'true', 'true']\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "376\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name Name Name Name keyword Name keyword Name\n", - "Label = ['fused', 'batch', 'norm', '[PAD]']\n", - "Pred =\n", - " 0. ['max', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['avg', 'conv2d', 'conv2d', 'conv2d']\n", - " 2. ['separable', 'transpose', 'transpose', 'transpose']\n", - "\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", "377\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name If Compare Call Name Name Gt Num Assign Name beta Call Attribute reshape Name Name UnaryOp USub Num\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['gamma', 'beta', 'beta', 'beta']\n", - " 2. ['broadcast', 'gamma', 'gamma', 'gamma']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "378\n", - "[CLS] If Compare Name Lt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num If Name AugAssign Name axis Mod Name Assign Name axis Num\n", - "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axis', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'axis', 'axis', 'axis']\n", - " 2. ['gamma', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['num', 'element', '[PAD]', '[PAD]']\n", "379\n", - "[CLS] If Call Name ListComp Call Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return Call Attribute sparse concat Name Name Name Return Call Attribute concat Name ListComp Call Name Name comprehension Name x Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'shape', 'shape', 'shape']\n", - " 2. ['w', 'test', 'test', 'test']\n", - "\n", + "Label = ['Value', '[PAD]', '[PAD]', '[PAD]']\n", "380\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape Expr Str Return Call Attribute reshape Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'train', 'train', 'train']\n", - " 2. ['a', 'input', 'input', 'input']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "381\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pattern Expr Str Return Call Attribute transpose Name Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'true', 'true', 'true']\n", - " 2. ['a', 'img', 'img', 'img']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "382\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Subscript Name Index Name keyword Name\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['parameter', 'normal', 'normal', 'normal']\n", - " 2. ['add', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", "383\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name x Call Attribute reshape Name Name Call Attribute stack Name List UnaryOp USub Num Call Name Subscript Call Name Name Slice Num Return Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'train', 'train', 'train']\n", - " 2. ['y', 'true', 'true', 'true']\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "384\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num Tuple Num Num NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'size', 'size', 'size']\n", - " 2. ['padding', 'padding', 'padding', 'padding']\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "385\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute outputs Name Add List Attribute updates op Name Attribute fetches Name\n", - "Label = ['fetches', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'shape', 'shape', 'shape']\n", - " 2. ['size', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['run', '[PAD]', '[PAD]', '[PAD]']\n", "386\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List Num Num Add Call Name Call Name Num Name\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dims', 'axes', 'axes', 'axes']\n", - " 2. ['ins', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "387\n", - "[CLS] If Compare Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Name Sub Num Assign Name mask Call Name Name\n", - "Label = ['get', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['get', 'shape', 'shape', 'shape']\n", - " 1. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['mask', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "388\n", - "[CLS] UnaryOp USub Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Call Attribute log Name Name Name\n", - "Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['reduce', 'sum', 'sum', 'sum']\n", - " 1. ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['log', 'reduce', 'reduce', 'reduce']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "389\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg level arg noise shape arg seed NameConstant NameConstant Expr Str Assign Name retain prob BinOp Num Sub Name If Compare Name Is NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute dropout Attribute nn Name BinOp Name Mult Num Name Name keyword Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['seed', 'mask', 'mask', 'mask']\n", - " 2. ['kernel', 'true', 'true', 'true']\n", - "\n", "390\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'seed', 'seed', 'seed']\n", - " 2. ['[PAD]', 'value', 'value', 'value']\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "391\n", - "[CLS] If BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str Assign Name x Call Attribute cast Name Name Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['encode', 'size', 'size', 'size']\n", - " 2. ['dimshuffle', 'dtype', 'dtype', 'dtype']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "392\n", - "[CLS] BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['encode', 'list', 'list', 'list']\n", - " 2. ['dimshuffle', 'format', 'format', 'format']\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "393\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Name Eq Str Assign Name padding Str If Compare Name Str Assign Name padding Str Raise Call Name BinOp Str Add Call Name Name Return Name\n", - "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'padding', 'padding', 'padding']\n", - " 2. ['[PAD]', 'pad', 'pad', 'pad']\n", - "\n", + "Label = ['xt', '[PAD]', '[PAD]', '[PAD]']\n", "394\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num\n", - "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['h', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['left', 'pad', 'pad', 'pad']\n", - " 2. ['d', 'size', 'size', 'size']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "395\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute convolution Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pool', 'shape', 'shape', 'shape']\n", - " 2. ['kernel', 'x', 'x', 'x']\n", - "\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", "396\n", - "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute stack Name Name\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', 'shape', 'shape', 'shape']\n", - " 1. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['mask', 'input', 'input', 'input']\n", - "\n", + "Label = ['adj', 'x', '[PAD]', '[PAD]']\n", "397\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Call Attribute shape Name Name Index Num Add Call Name Subscript Name Slice Num Assign Name output shape Call Attribute stack Name Call Name Name\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', 'shape', 'shape', 'shape']\n", - " 1. ['size', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['state', 'size', 'size', 'size']\n", - "\n", - "398\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute atrous conv2d transpose Attribute nn Name Name Name Name Subscript Name Index Num Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['w', 'img', 'img', 'img']\n", - " 2. ['conv', 'out', 'out', 'out']\n", - "\n", + "398\n", + "Label = ['normed', '[PAD]', '[PAD]', '[PAD]']\n", "399\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name strides BinOp BinOp Tuple Num Add BinOp Name Mult Num Tuple Num Assign Name spatial start dim Num Assign Name strides BinOp Tuple Num Num BinOp Name Num\n", - "Label = ['spatial', 'start', 'dim', '[PAD]']\n", - "Pred =\n", - " 0. ['spatial', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'dim', 'dim', 'dim']\n", - " 2. ['num', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", "400\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Num Num Add BinOp Name Mult Num\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['padding', 'size', 'size', 'size']\n", - " 2. ['dims', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['concat', '[PAD]', '[PAD]', '[PAD]']\n", "401\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n", - " 2. ['image', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "402\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['volume', 'shape', 'shape', 'shape']\n", - " 2. ['filter', 'size', 'size', 'size']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "403\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['filter', 'shape', 'shape', 'shape']\n", - " 2. ['noise', 'size', 'size', 'size']\n", - "\n", + "Label = ['splits', '[PAD]', '[PAD]', '[PAD]']\n", "404\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv3d transpose Attribute nn Name Name Name Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 'img', 'img', 'img']\n", - " 2. ['recurrent', 'array', 'array', 'array']\n", - "\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", "405\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute max pool Attribute nn Name Name Name Name keyword Name keyword Name If Compare Name Str Assign Name x Call Attribute avg pool Attribute nn Name Name Name Name keyword Name keyword Name Raise Call Name BinOp Str Add Call Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pool', 'x', 'x', 'x']\n", - " 2. ['assign', 'out', 'out', 'out']\n", - "\n", "406\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n", - " 2. ['image', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", "407\n", - "[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Num Num Num Num Subscript Name Index Num Assign Name new shape BinOp Tuple Num Add Name\n", - "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'shape', 'shape', 'shape']\n", - " 2. ['filter', 'size', 'size', 'size']\n", - "\n", + "Label = ['assign', 'placeholder', '[PAD]', '[PAD]']\n", "408\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'seed', 'seed', 'seed']\n", - " 2. ['[PAD]', 'value', 'value', 'value']\n", - "\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", "409\n", - "[CLS] Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n", - "Label = ['expand', 'dims', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['expand', 'dims', 'dims', 'dims']\n", - " 1. ['float32', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['concatenate', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "410\n", - "[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name log prob Call Attribute ctc greedy decoder Name keyword Name keyword Name Assign Tuple Name decoded Name log prob Call Attribute ctc beam search decoder Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['decoded', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['decoded', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['l', 'fn', 'fn', 'fn']\n", - " 2. ['stop', 'out', 'out', 'out']\n", - "\n", + "Label = ['sparse', 'coo', '[PAD]', '[PAD]']\n", "411\n", - "[CLS] If Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return Name\n", - "Label = ['dense', 'from', 'sparse', '[PAD]']\n", - "Pred =\n", - " 0. ['sparse', 'tensor', 'tensor', 'tensor']\n", - " 1. ['ones', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['to', 'to', 'to', 'to']\n", - "\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", "412\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg ndim arg dtype arg sparse arg name NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'shape', 'shape', 'shape']\n", - " 2. ['value', 'ndim', 'ndim', 'ndim']\n", - "\n", - "413\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return BoolOp And Call Name Name Str Attribute theano placeholder Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['value', 'placeholder', 'placeholder', 'placeholder']\n", - " 2. ['self', 'function', 'function', 'function']\n", - "\n", + "413\n", + "Label = ['step', 'function', '[PAD]', '[PAD]']\n", "414\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute zeros Name Name Name Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'shape', 'shape', 'shape']\n", - " 2. ['size', 'size', 'size', 'size']\n", - "\n", + "Label = ['stack', '[PAD]', '[PAD]', '[PAD]']\n", "415\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Num keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['uniform', 'normal', 'normal', 'normal']\n", - " 2. ['randint', 'uniform', 'uniform', 'uniform']\n", - "\n", + "Label = ['stack', '[PAD]', '[PAD]', '[PAD]']\n", "416\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg indices Expr Str Assign Name y Subscript Name Index Name If BoolOp And Call Name Name Str Call Name Name Str Assign Attribute keras shape Name BinOp Attribute keras shape Name Add Subscript Attribute keras shape Name Slice Num Return Name\n", - "Label = ['reference', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'shape', 'shape', 'shape']\n", - " 2. ['a', 'train', 'train', 'train']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "417\n", - "[CLS] BoolOp Or Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute dtype Name Eq Str\n", - "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'dtype', 'dtype', 'dtype']\n", - " 2. ['monitor', 'format', 'format', 'format']\n", - "\n", + "Label = ['logits', '[PAD]', '[PAD]', '[PAD]']\n", "418\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Return Call Attribute var Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'dtype', 'dtype', 'dtype']\n", - " 2. ['inputs', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "419\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Return Call Attribute argmin Name Name keyword Name keyword NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'true', 'true', 'true']\n", - " 2. ['a', 'size', 'size', 'size']\n", - "\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", "420\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Assign Name z Call Attribute neq Name Name Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name Return Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'shape', 'shape', 'shape']\n", - " 2. ['y', 'train', 'train', 'train']\n", - "\n", "421\n", - "[CLS] Return Tuple Name Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Pow Num\n", - "Label = ['inv', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cast', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['arange', 'shape', 'shape', 'shape']\n", - " 2. ['pow', 'dims', 'dims', 'dims']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "422\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute bn Attribute nnet Name Name Name Name Name Name Name Name\n", - "Label = ['batch', 'normalization', 'test', '[PAD]']\n", - "Pred =\n", - " 0. ['batch', 'normalization', 'normalization', 'normalization']\n", - " 1. ['is', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['dnn', 'test', 'test', 'test']\n", - "\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", "423\n", - "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Attribute ndim Name Gt Num\n", - "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['max', 'ndim', 'ndim', 'ndim']\n", - " 2. ['data', 'format', 'format', 'format']\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "424\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute dnn Attribute cuda Attribute sandbox Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Str Name\n", - "Label = ['dnn', 'batch', 'normalization', 'test']\n", - "Pred =\n", - " 0. ['dnn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dimshuffle', 'batch', 'batch', 'batch']\n", - " 2. ['split', 'normalization', 'normalization', 'normalization']\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "425\n", - "[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute hstack Attribute basic Name Name keyword Str Raise Call Name Str Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['out', 'out', 'out', 'out']\n", - " 2. ['conv', 'output', 'output', 'output']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "426\n", - "[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute uses learning phase Name Assign Attribute uses learning phase Name NameConstant\n", - "Label = ['uses', 'learning', 'phase', '[PAD]']\n", - "Pred =\n", - " 0. ['uses', 'learning', 'learning', 'learning']\n", - " 1. ['learning', 'phase', 'phase', 'phase']\n", - " 2. ['phase', '[PAD]', '[PAD]', '[PAD]']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "427\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Subscript Attribute keras shape Name Index UnaryOp Num Mult Name\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['keras', 'shape', 'shape', 'shape']\n", - " 1. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['return', 'size', 'size', 'size']\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "428\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num BinOp BinOp Subscript Name Index Num Add Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['filter', 'shape', 'shape', 'shape']\n", - " 2. ['volume', 'size', 'size', 'size']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "429\n", - "[CLS] ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Slice\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'size', 'size', 'size']\n", - " 2. ['[PAD]', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "430\n", - "[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute keras shape Name Index Num BinOp Subscript Attribute keras shape Name Index Num Add Call Name Name Subscript Attribute keras shape Name Index Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['keras', 'shape', 'shape', 'shape']\n", - " 1. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['output', 'keras', 'keras', 'keras']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "431\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Name NameConstant Call Name Name BinOp Subscript Name Index Num Add Name Call Name Name BinOp Subscript Name Index Num Name Call Name NameConstant\n", - "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['new', 'shape', 'shape', 'shape']\n", - " 2. ['indices', 'size', 'size', 'size']\n", - "\n", + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "432\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n", - "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['h', 'shape', 'shape', 'shape']\n", - " 2. ['d', 'keras', 'keras', 'keras']\n", - "\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", "433\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num IsNot NameConstant Assign Name w BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num Assign Name w NameConstant\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['keras', 'shape', 'shape', 'shape']\n", - " 1. ['input', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['shape', 'size', 'size', 'size']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "434\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n", - "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['w', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['h', 'shape', 'shape', 'shape']\n", - " 2. ['d', 'keras', 'keras', 'keras']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "435\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Subscript Name Index Num Index Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['keras', 'shape', 'shape', 'shape']\n", - " 1. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['input', 'size', 'size', 'size']\n", - "\n", + "Label = ['batch', 'array', '[PAD]', '[PAD]']\n", "436\n", - "[CLS] GeneratorExp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name comprehension Name i Call Name Attribute ndim Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['keras', 'shape', 'shape', 'shape']\n", - " 2. ['ndim', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['SparseTensor', '[PAD]', '[PAD]', '[PAD]']\n", "437\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Attribute shape Call Attribute get value Name keyword NameConstant keyword NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'value', 'value', 'value']\n", - " 2. ['variable', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['slice', 'row', '[PAD]', '[PAD]']\n", "438\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute function Name Name Name keyword Name keyword NameConstant keyword Str keyword Name keyword Name\n", - "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['function', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pool', 'function', 'function', 'function']\n", - " 2. ['dtype', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "439\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assert Call Name Name Tuple Name Name Return Call Attribute function Name Starred Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'function', 'function', 'function']\n", - " 2. ['inputs', 'value', 'value', 'value']\n", - "\n", + "Label = ['const', '[PAD]', '[PAD]', '[PAD]']\n", "440\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute switch Name Subscript Name Index Name Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'value', 'value', 'value']\n", - " 2. ['update', 'scope', 'scope', 'scope']\n", - "\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", "441\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Call Attribute scan Name Name keyword List Name Name keyword BinOp List Name Add Name keyword Name keyword Name\n", - "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ret', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['results', 'mask', 'mask', 'mask']\n", - " 2. ['last', 'out', 'out', 'out']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "442\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name states Subscript Name Slice Num Assign Name outputs Name Assign Name states List\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inputs', 'state', 'state', 'state']\n", - " 2. ['initial', 'outputs', 'outputs', 'outputs']\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "443\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute stack Name Starred ListComp Subscript Name Index Name comprehension Name states at step Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sqrt', 'shape', 'shape', 'shape']\n", - " 2. ['extend', 'function', 'function', 'function']\n", - "\n", + "Label = ['bn', '[PAD]', '[PAD]', '[PAD]']\n", "444\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute squeeze Name Subscript Name Index UnaryOp USub Num comprehension Name state Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['state', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'state', 'state', 'state']\n", - " 2. ['constants', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "445\n", - "[CLS] If Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub Name For Name Call Name Name Assign Name condition Call Name Name\n", - "Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axes', 'shape', 'shape', 'shape']\n", - " 2. ['masks', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['shuffle', 'pattern', '[PAD]', '[PAD]']\n", "446\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'test', 'test', 'test']\n", - " 2. ['new', 'dtype', 'dtype', 'dtype']\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "447\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute to one hot Attribute extra ops Name Name keyword Subscript Attribute shape Name Index UnaryOp USub Num\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['targets', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'length', 'length', 'length']\n", - " 2. ['last', 'hot', 'hot', 'hot']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "448\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis NameConstant Assign Name square sum Call Attribute sum Name Call Attribute square Name Name keyword Name keyword NameConstant Assign Name norm Call Attribute sqrt Name Call Attribute maximum Name Name Call Name Return BinOp Name Div Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'sum', 'sum', 'sum']\n", - " 2. ['self', 'true', 'true', 'true']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "449\n", - "[CLS] If Compare Name Lt Num Try Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Str ExceptHandler Name Return Call Attribute zeros like Name Name keyword Str\n", - "Label = ['zeros', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ones', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['zeros', 'like', 'like', 'like']\n", - " 2. ['max', 'normal', 'normal', 'normal']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "450\n", - "[CLS] Index Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Name\n", - "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'subtensor', 'subtensor', 'subtensor']\n", - " 2. ['transpose', 'function', 'function', 'function']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "451\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Str Assign Name th padding Str Raise Call Name Str Call Name Name\n", - "Label = ['th', 'padding', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['padding', 'padding', 'padding', 'padding']\n", - " 1. ['th', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['tf', 'pad', 'pad', 'pad']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "452\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Return Call Name Name ExceptHandler Name Return NameConstant\n", - "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['value', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'value', 'value', 'value']\n", - " 2. ['seed', 'list', 'list', 'list']\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "453\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", - "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['filter', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['image', 'shape', 'shape', 'shape']\n", - " 2. ['volume', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "454\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", - "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['filter', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['image', 'shape', 'shape', 'shape']\n", - " 2. ['volume', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "455\n", - "[CLS] BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num\n", "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'size', 'size', 'size']\n", - " 2. ['kernel', 'pad', 'pad', 'pad']\n", - "\n", "456\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['keras', 'shape', 'shape', 'shape']\n", - " 2. ['[PAD]', 'size', 'size', 'size']\n", - "\n", + "Label = ['output', 'keras', 'shape', '[PAD]']\n", "457\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n", - "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['conv', 'out', 'out', 'out']\n", - " 1. ['pool', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['expected', 'width', 'width', 'width']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "458\n", - "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'size', 'size', 'size']\n", - " 2. ['kernel', 'format', 'format', 'format']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "459\n", - "[CLS] Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'size', 'size', 'size']\n", - " 2. ['kernel', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "460\n", - "[CLS] ExtSlice Slice Slice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'size', 'size', 'size']\n", - " 2. ['kernel', 'out', 'out', 'out']\n", - "\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", "461\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pool', 'out', 'out', 'out']\n", - " 2. ['conv', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "462\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'size', 'size', 'size']\n", - " 2. ['self', 'function', 'function', 'function']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "463\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['keras', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'shape', 'shape', 'shape']\n", - " 2. ['noise', 'size', 'size', 'size']\n", - "\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", "464\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'size', 'size', 'size']\n", - " 2. ['a', 'format', 'format', 'format']\n", - "\n", + "Label = ['scan', '[PAD]', '[PAD]', '[PAD]']\n", "465\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'size', 'size', 'size']\n", - " 2. ['kernel', 'length', 'length', 'length']\n", - "\n", + "Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n", "466\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute AbstractConv2d gradInputs Attribute abstract conv Attribute nnet Name keyword NameConstant keyword Name keyword Name keyword Name keyword UnaryOp Not Name keyword Name\n", - "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['op', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['result', 'updates', 'updates', 'updates']\n", - " 2. ['new', 'gradinputs', 'gradinputs', 'gradinputs']\n", - "\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", "467\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute shape Call Attribute eval Name\n", - "Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['depthwise', 'shape', 'shape', 'shape']\n", - " 2. ['recurrent', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", "468\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp BoolOp And Compare Subscript Name Index Num Gt Num Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", - "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['h', 'pad', 'pad', 'pad']\n", - " 1. ['w', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['d', 'out', 'out', 'out']\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "469\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str\n", - "Label = ['pool', '2d', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['pool', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['function', '3d', '3d', '3d']\n", - " 2. ['pooling', 'weight', 'weight', 'weight']\n", - "\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", "470\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pool 3d Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str Raise Call Name Str Name\n", - "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['pool', 'out', 'out', 'out']\n", - " 1. ['out', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['conv', 'pool', 'pool', 'pool']\n", - "\n", + "Label = ['to', 'one', 'hot', '[PAD]']\n", "471\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n", - "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pool', 'out', 'out', 'out']\n", - " 2. ['conv', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['random', 'tensor', '[PAD]', '[PAD]']\n", "472\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['kernel', 'input', 'input', 'input']\n", - "\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", "473\n", - "[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['kernel', 'x', 'x', 'x']\n", - "\n", + "Label = ['targets', 'values', '[PAD]', '[PAD]']\n", "474\n", - "[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'shape', 'shape', 'shape']\n", - " 2. ['a', 'out', 'out', 'out']\n", - "\n", - "475\n", - "[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Mult Num Add Num\n", "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'normal', 'normal', 'normal']\n", - " 2. ['reshape', 'subtensor', 'subtensor', 'subtensor']\n", - "\n", + "475\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "476\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name skip idxs BinOp BinOp Call Attribute arange Name BinOp BinOp Subscript Attribute shape Name Index Num Sub Num FloorDiv Num Mult Num Add Num Assign Name non repeats Call Attribute neq Name Subscript Name Index Name Subscript Name Index BinOp Name Num Return Subscript Name Index Call Attribute nonzero Name\n", - "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['y', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'train', 'train', 'train']\n", - " 2. ['path', 'true', 'true', 'true']\n", - "\n", + "Label = ['volume', 'shape', '[PAD]', '[PAD]']\n", "477\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Subscript Name Slice Name Sub Name\n", - "Label = ['p', 'prev', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['log', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['out', 'log', 'log', 'log']\n", - " 2. ['output', 't', 't', 't']\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "478\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute inc subtensor Name Subscript Name Index BinOp Name Add Num Subscript Name Index Name\n", - "Label = ['p', 'prev', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['p', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['result', 'p', 'p', 'p']\n", - " 2. ['i', 'values', 'values', 'values']\n", - "\n", + "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", "479\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Compare Name Lt Call Attribute dimshuffle Name Num Str BitAnd Subscript Compare Name Call Attribute dimshuffle Name Num Str ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num\n", - "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['strides', 'out', 'out', 'out']\n", - " 2. ['h', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", "480\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name elems Subscript Name Slice Num\n", - "Label = ['initializer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['fn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['initializer', 'fn', 'fn', 'fn']\n", - " 2. ['args', 'size', 'size', 'size']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "481\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name\n", - "Label = ['foldl', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['foldr', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['foldl', 'nodes', 'nodes', 'nodes']\n", - " 2. ['sort', 'foldr', 'foldr', 'foldr']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "482\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Subscript Name ExtSlice Index BinOp BinOp Name Mult Name Add Name Slice Slice\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['extend', 'params', 'params', 'params']\n", - " 2. ['set', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "483\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg data format arg file format arg scale arg kwargs NameConstant NameConstant NameConstant If Compare Name Is NameConstant Assign Name data format Call Attribute image data format Name Return Call Attribute save img Name Name Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'format', 'format', 'format']\n", - " 2. ['cls', 'data', 'data', 'data']\n", - "\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", "484\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['append', 'names', 'names', 'names']\n", - " 2. ['pop', 'params', 'params', 'params']\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "485\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['warn', 'names', 'names', 'names']\n", - " 2. ['keys', 'scope', 'scope', 'scope']\n", - "\n", + "Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n", "486\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BoolOp Or Call Name Attribute call Name Str Call Name Name Str\n", - "Label = ['compute', 'previous', 'mask', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['uses', 'learning', 'learning', 'learning']\n", - " 2. ['dynamic', 'phase', 'phase', 'phase']\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "487\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name nodes by depth Name layers Name layers by depth Call Name Attribute inputs Name Attribute outputs Name\n", - "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inbound', 'layer', 'layer', 'layer']\n", - " 2. ['node', 'index', 'index', 'index']\n", - "\n", + "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", "488\n", - "[CLS] If BoolOp And UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Attribute stateful Name Return List\n", - "Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reset', 'sequences', 'sequences', 'sequences']\n", - " 2. ['inputs', 'format', 'format', 'format']\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "489\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Name ListComp BoolOp And Call Name Name Str Attribute stateful Name comprehension Name layer Attribute layers Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'layer', 'layer', 'layer']\n", - " 2. ['cls', 'names', 'names', 'names']\n", - "\n", + "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", "490\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name Call Name Call Name Name Assign Name masks Call Name Name\n", - "Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['masks', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['output', 'masks', 'masks', 'masks']\n", - " 2. ['ndim', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "491\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Name Str Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['input', 'layers', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layers', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'layers', 'layers', 'layers']\n", - " 2. ['outputs', 'uid', 'uid', 'uid']\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "492\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute name Name Add BinOp Str Mod Tuple Name Name\n", - "Label = ['shape', 'key', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dim', 'key', 'key', 'key']\n", - " 2. ['eta', 'format', 'format', 'format']\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "493\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str If Compare Str NotIn Name Assign Subscript Name Index Str Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['call', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['function', 'axes', 'axes', 'axes']\n", - " 2. ['init', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "494\n", - "[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant\n", - "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['activity', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['attrs', 'regularizer', 'regularizer', 'regularizer']\n", - " 2. ['clipvalue', 'function', 'function', 'function']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "495\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Call Name Call Name Name Tuple Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['a', 'x', 'x', 'x']\n", - " 2. ['val', 'train', 'train', 'train']\n", - "\n", "496\n", - "[CLS] If BoolOp And Call Name Name Str Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute keras shape Name Expr Call Attribute append Name Name Assign Name output shapes NameConstant\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'shape', 'shape', 'shape']\n", - " 2. ['inputs', 'shapes', 'shapes', 'shapes']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "497\n", - "[CLS] BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute arguments Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'names', 'names', 'names']\n", - " 2. ['val', 'name', 'name', 'name']\n", - "\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", "498\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node data If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute append Subscript Name Index Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'data', 'data', 'data']\n", - " 2. ['self', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['non', 'repeats', '[PAD]', '[PAD]']\n", "499\n", - "[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Name Name Call Attribute backend Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'config', 'config', 'config']\n", - " 2. ['axis', 'scope', 'scope', 'scope']\n", - "\n", + "Label = ['active', 'skip', 'idxs', '[PAD]']\n", "500\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Eq Attribute name Name Return Attribute name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['module', 'format', 'format', 'format']\n", - " 2. ['mode', 'scope', 'scope', 'scope']\n", - "\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", "501\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name depth Call Attribute items Name If Compare Name NotIn Name Assign Subscript Name Index Name List Expr Call Attribute append Subscript Name Index Name Name\n", - "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['chunk', 'layer', 'layer', 'layer']\n", - " 2. ['inbound', 'dict', 'dict', 'dict']\n", - "\n", + "Label = ['minimum', '[PAD]', '[PAD]', '[PAD]']\n", "502\n", - "[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Attribute input tensors Name If Compare Name NotIn Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute name Name Str Call Name Name For Name x Attribute output tensors Name Expr Call Attribute append Name Name Expr Call Attribute append Name Attribute name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'x', 'x', 'x']\n", - " 2. ['a', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['float32', '[PAD]', '[PAD]', '[PAD]']\n", "503\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", - "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['count', 'nodes', 'nodes', 'nodes']\n", - " 2. ['get', 'names', 'names', 'names']\n", - "\n", + "Label = ['int32', '[PAD]', '[PAD]', '[PAD]']\n", "504\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", - "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['count', 'nodes', 'nodes', 'nodes']\n", - " 2. ['get', 'names', 'names', 'names']\n", - "\n", + "Label = ['dimshuffle', '[PAD]', '[PAD]', '[PAD]']\n", "505\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp BoolOp Or Name List List Attribute history Name\n", - "Label = ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['info', 'values', 'values', 'values']\n", - " 2. ['args', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "506\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Call Attribute toarray Subscript Name Index Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['i', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['o', 'i', 'i', 'i']\n", - " 2. ['k', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", "507\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['o', 'weights', 'weights', 'weights']\n", - " 2. ['w', 'o', 'o', 'o']\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "508\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num\n", - "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['y', 'outs', 'outs', 'outs']\n", - " 2. ['mask', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "509\n", - "[CLS] If Name If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute inbound nodes Name Index UnaryOp USub Num NotEq Num Raise Call Name Str Assign Attribute outputs Name List Subscript Attribute output tensors Subscript Attribute inbound nodes Name Index UnaryOp Num Index Num Assign Attribute inputs Name Call Attribute get source inputs Name Subscript Attribute outputs Name Index Num\n", - "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inbound', 'tensors', 'tensors', 'tensors']\n", - " 2. ['state', 'layers', 'layers', 'layers']\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "510\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Gt Num Return Call Attribute argmax Name keyword UnaryOp Num Return Call Attribute astype Compare Name Num Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['dynamic', 'size', 'size', 'size']\n", - " 2. ['kernel', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "511\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Attribute name Name Call Attribute deepcopy Name Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'config', 'config', 'config']\n", - " 2. ['metric', 'p', 'p', 'p']\n", - "\n", + "Label = ['is', 'graph', 'network', '[PAD]']\n", "512\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name build input shape Call Attribute get Name Str Assign Name layer configs Subscript Name Index Str Assign Name name Name build input shape NameConstant Assign Name layer configs Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['cls', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'layer', 'layer', 'layer']\n", - " 2. ['embeddings', 'names', 'names', 'names']\n", - "\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", "513\n", - "[CLS] Assign Subscript Name Index Str ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str comprehension Name layer Name\n", - "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['decode', 'name', 'name', 'name']\n", - " 2. ['pop', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", "514\n", - "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute backend Name Str\n", - "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['split', 'config', 'config', 'config']\n", - " 2. ['dimshuffle', 'list', 'list', 'list']\n", - "\n", + "Label = ['network', 'nodes', '[PAD]', '[PAD]']\n", "515\n", - "[CLS] If Call Name Name Str If Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str Assign Name name BinOp BinOp Call Name Attribute name Name Add Str Call Name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Call Name Name\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['encode', 'name', 'name', 'name']\n", - " 2. ['get', 'size', 'size', 'size']\n", - "\n", + "Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n", "516\n", - "[CLS] Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['split', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['encode', 'format', 'format', 'format']\n", - " 2. ['dimshuffle', 'size', 'size', 'size']\n", - "\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", "517\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Attribute name Name Add Str Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'input', 'input', 'input']\n", - " 2. ['batch', 'name', 'name', 'name']\n", - "\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", "518\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] List For Name value Name Expr Call Attribute append Name Call Name Name Return Name\n", - "Label = ['deserialized', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['axis', 'metrics', 'metrics', 'metrics']\n", - " 2. ['weights', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "519\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute decode Subscript Name Index Str Str Assign Name original backend NameConstant\n", - "Label = ['original', 'backend', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['original', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'fn', 'fn', 'fn']\n", - " 2. ['overwrite', 'names', 'names', 'names']\n", - "\n", - "520\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Name weights Attribute weights Name If Name Expr Call Attribute append Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['cell', 'weights', 'weights', 'weights']\n", - " 2. ['v', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n", + "520\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "521\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Name Name Call Name Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['format', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['join', 'size', 'size', 'size']\n", - " 2. ['[PAD]', 'list', 'list', 'list']\n", - "\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", "522\n", - "[CLS] If Compare Call Name Name NotEq Call Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'dim', 'dim', 'dim']\n", - " 2. ['ndarray', 'name', 'name', 'name']\n", - "\n", + "Label = ['layer', 'data', '[PAD]', '[PAD]']\n", "523\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Expr Str If Call Name Name Name Raise Call Name Str ImportFrom alias Return Call Name Name keyword Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['config', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['identifier', 'string', 'string', 'string']\n", - " 2. ['name', 'config', 'config', 'config']\n", - "\n", + "Label = ['load', 'weights', 'from', 'hdf5']\n", "524\n", - "[CLS] BinOp Str Mod Tuple Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Name comprehension Name x Name\n", - "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['join', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['format', 'weight', 'weight', 'weight']\n", - " 2. ['add', 'nodes', 'nodes', 'nodes']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "525\n", - "[CLS] While Compare BinOp Str Mod Tuple Name Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute extend Name ListComp Call Attribute decode Name Str comprehension Name n Subscript Attribute attrs Name Index BinOp Str Tuple Name Name AugAssign Name chunk id Add Num\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['args', 'size', 'size', 'size']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "526\n", - "[CLS] comprehension Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute attrs Name Index BinOp Str Mod Tuple Name Name\n", - "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['m', 'length', 'length', 'length']\n", - " 2. ['s', 'id', 'id', 'id']\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "527\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Tuple Name w Name val Call Name Call Name Name Name If BoolOp And Call Name Name Str Attribute name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Add Call Name Name Expr Call Attribute append Name Call Attribute encode Name Str\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'name', 'name', 'name']\n", - " 2. ['sw', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['num', 'train', 'samples', '[PAD]']\n", "528\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name In List Str Str Assign Name weights Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'format', 'format', 'format']\n", - " 2. ['mode', 'weights', 'weights', 'weights']\n", - "\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", "529\n", - "[CLS] Assert BoolOp And Compare Subscript Name Index Num Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Subscript Name Slice Num Tuple Subscript Attribute kernel size Name Index Num Num\n", - "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['kernel', 'kernel', 'kernel', 'kernel']\n", - " 2. ['output', 'format', 'format', 'format']\n", - "\n", + "Label = ['is', 'sparse', '[PAD]', '[PAD]']\n", "530\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str If Compare Attribute data format Name Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['[PAD]', 'data', 'data', 'data']\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "531\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['type', 'format', 'format', 'format']\n", - " 2. ['format', 'data', 'data', 'data']\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "532\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['type', 'format', 'format', 'format']\n", - " 2. ['format', 'data', 'data', 'data']\n", - "\n", + "Label = ['ins', 'batch', '[PAD]', '[PAD]']\n", "533\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num keyword UnaryOp USub Num\n", - "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['identity', 'weight', 'weight', 'weight']\n", - " 2. ['sum', 'kernel', 'kernel', 'kernel']\n", - "\n", + "Label = ['ins', 'batch', '[PAD]', '[PAD]']\n", "534\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str Call Name Call Attribute prod Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'size', 'size', 'size']\n", - " 2. ['start', 'dim', 'dim', 'dim']\n", - "\n", + "Label = ['batch', 'out', '[PAD]', '[PAD]']\n", "535\n", - "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Name\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['convert', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n", - " 2. ['unbroadcast', 'spec', 'spec', 'spec']\n", - "\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", "536\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['[PAD]', 'data', 'data', 'data']\n", - "\n", + "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", "537\n", - "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n", - " 2. ['arange', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", "538\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['reshape', 'kernel', 'kernel', 'kernel']\n", - " 2. ['arange', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "539\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func arg n gates Expr Str Return Call Attribute hstack Name ListComp Call Name Name comprehension Name k Call Attribute hsplit Name Name Name\n", - "Label = ['kernels', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['path', 'format', 'format', 'format']\n", - " 2. ['k', 'metrics', 'metrics', 'metrics']\n", - "\n", + "Label = ['init', 'graph', 'network', '[PAD]']\n", "540\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Attribute reshape Attribute T Name Attribute shape Name keyword Name\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['k', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 't', 't', 't']\n", - " 2. ['x', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "541\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Mult Subscript Name Index Num Num\n", - "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['expand', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['maximum', 'dims', 'dims', 'dims']\n", - " 2. ['float32', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", "542\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num Lambda arguments arg k Attribute T Name Name\n", - "Label = ['recurrent', 'kernels', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['recurrent', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['conv', 't', 't', 't']\n", - " 2. ['num', 'mask', 'mask', 'mask']\n", - "\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", "543\n", - "[CLS] If Compare Name Eq Tuple Num BinOp Name Mult Name Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple BinOp Name Name Assign Name source Str Raise Call Name BinOp Str Add Call Name Name\n", - "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['source', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'size', 'size', 'size']\n", - " 2. ['padding', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "544\n", - "[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original backend Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original backend NameConstant\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['args', 'size', 'size', 'size']\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "545\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str\n", "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'names', 'names', 'names']\n", - " 2. ['shape', 'dim', 'dim', 'dim']\n", - "\n", "546\n", - "[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original keras version Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original keras version Str\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'size', 'size', 'size']\n", - " 2. ['args', 'format', 'format', 'format']\n", - "\n", - "547\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Call Attribute setdefault Name Attribute name Name List Name\n", "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['stateful', 'dict', 'dict', 'dict']\n", - " 2. ['name', 'scope', 'scope', 'scope']\n", - "\n", + "547\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", "548\n", - "[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name comprehension Name weight name Name\n", - "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['concatenate', 'values', 'values', 'values']\n", - " 2. ['prod', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "549\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'dim', 'dim', 'dim']\n", - " 2. ['ndarray', 'scope', 'scope', 'scope']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "550\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Subscript Name Index Name Call Attribute format Str Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'name', 'name', 'name']\n", - " 2. ['[PAD]', 'format', 'format', 'format']\n", - "\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", "551\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['extend', 'value', 'value', 'value']\n", - " 2. ['update', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "552\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Add ListComp BinOp Str Name comprehension Name n Name\n", - "Label = ['callback', 'metrics', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'names', 'names', 'names']\n", - " 2. ['dim', 'metrics', 'metrics', 'metrics']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "553\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['o', 'weights', 'weights', 'weights']\n", - " 2. ['w', 'o', 'o', 'o']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "554\n", - "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute evaluate generator Name Name Name keyword Num Assign Name val outs Call Attribute evaluate Name Name Name keyword Name keyword Name keyword Num\n", - "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['outs', 'outs', 'outs', 'outs']\n", - " 2. ['y', 'val', 'val', 'val']\n", - "\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "555\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['l', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['w', 'weights', 'weights', 'weights']\n", - " 2. ['o', 'l', 'l', 'l']\n", - "\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", "556\n", - "[CLS] If Compare Name Is NameConstant If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n", - "Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['steps', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['epoch', 'epoch', 'epoch', 'epoch']\n", - " 2. ['do', 'per', 'per', 'per']\n", - "\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", "557\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Subscript Name Index Num Index Num If Call Name Name Name Assign Name batch size Subscript Attribute shape Subscript Call Name Call Attribute values Name Index Num Index Num Assign Name batch size Subscript Attribute shape Name Index Num\n", - "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['batch', 'size', 'size', 'size']\n", - " 1. ['size', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['mask', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "558\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n", - "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['sum', 'weight', 'weight', 'weight']\n", - " 2. ['extend', 'values', 'values', 'values']\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "559\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['pop', 'size', 'size', 'size']\n", - " 2. ['update', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['convert', 'kernel', '[PAD]', '[PAD]']\n", "560\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mean', 'size', 'size', 'size']\n", - " 2. ['keys', 'shape', 'shape', 'shape']\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "561\n", - "[CLS] If Compare Name Gt Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name Expr Call Attribute start Name keyword Name keyword Name Assign Name output generator Call Attribute get Name If Name Assign Name output generator Call Name Name Assign Name output generator Name\n", - "Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['val', 'enqueuer', 'enqueuer', 'enqueuer']\n", - " 2. ['progbar', 'metrics', 'metrics', 'metrics']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "562\n", - "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name\n", - "Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['val', 'enqueuer', 'enqueuer', 'enqueuer']\n", - " 2. ['progbar', 'outs', 'outs', 'outs']\n", - "\n", + "Label = ['biases', '[PAD]', '[PAD]', '[PAD]']\n", "563\n", - "[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name\n", - "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['mask', 'shape', 'shape', 'shape']\n", - " 2. ['input', 'out', 'out', 'out']\n", - "\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", "564\n", - "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name name BinOp BinOp Name Add Str Call Name Call Attribute get uid Name Name\n", - "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'name', 'name', 'name']\n", - " 2. ['fn', 'fn', 'fn', 'fn']\n", - "\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", "565\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return BinOp BinOp Attribute name Name Add Str Call Name Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'name', 'name', 'name']\n", - " 2. ['cls', 'layer', 'layer', 'layer']\n", - "\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", "566\n", - "[CLS] If Compare Name IsNot NameConstant With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute add loss Name Call Name Name\n", - "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['device', 'scope', 'scope', 'scope']\n", - " 2. ['backend', 'config', 'config', 'config']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "567\n", - "[CLS] BoolOp And Compare Name IsNot NameConstant Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['max', 'ndim', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['delta', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inferreddimension', 'batch', 'batch', 'batch']\n", - " 2. ['min', 't', 't', 't']\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "568\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute max ndim Name Str Call Name Call Attribute ndim Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'ndim', 'ndim', 'ndim']\n", - " 2. ['ndim', 'value', 'value', 'value']\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "569\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name Str Call Name Call Attribute ndim Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['ndim', 'ndim', 'ndim', 'ndim']\n", - " 2. ['batch', 'dtype', 'dtype', 'dtype']\n", - "\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", "570\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'ndim', 'ndim', 'ndim']\n", - " 2. ['start', 'axes', 'axes', 'axes']\n", - "\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", "571\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'names', 'names', 'names']\n", - " 2. ['batch', 'nodes', 'nodes', 'nodes']\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "572\n", - "[CLS] If BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant With withitem Call Attribute name scope Name Str Assign Name regularization losses ListComp Call Attribute activity regularizer Name Name comprehension Name x Call Name Name Expr Call Attribute add loss Name Name keyword Call Name Name\n", - "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['activity', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['activation', 'regularizer', 'regularizer', 'regularizer']\n", - " 2. ['run', 'losses', 'losses', 'losses']\n", - "\n", + "Label = ['get', 'uid', '[PAD]', '[PAD]']\n", "573\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Name IsNot NameConstant If Call Name Name Name If Call Name GeneratorExp Compare Name NameConstant comprehension Name m Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Name Raise Call Name BinOp BinOp BinOp Str Attribute name Name Str Call Name Name Return NameConstant\n", - "Label = ['supports', 'masking', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['built', 'nodes', 'nodes', 'nodes']\n", - " 2. ['trainable', 'tensor', 'tensor', 'tensor']\n", - "\n", + "Label = ['batch', 'input', 'shape', '[PAD]']\n", "574\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp Str Add Attribute name Name Str\n", - "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inbound', 'nodes', 'nodes', 'nodes']\n", - " 1. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['built', 'names', 'names', 'names']\n", - "\n", + "Label = ['is', 'placeholder', '[PAD]', '[PAD]']\n", "575\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Call Name Attribute inbound nodes Name NotEq Num Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Str Return Call Attribute get node attribute at index Name Num Str Str Name\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "576\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "577\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "578\n", + "Label = ['variable', '[PAD]', '[PAD]', '[PAD]']\n", + "579\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "580\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "581\n", + "Label = ['min', 'ndim', '[PAD]', '[PAD]']\n", + "582\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "583\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "584\n", + "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", + "585\n", + "Label = ['inbound', 'layer', '[PAD]', '[PAD]']\n", + "586\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "587\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "588\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "589\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "590\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "591\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", + "592\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "593\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "594\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "595\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "596\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", + "597\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", + "598\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", + "599\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", + "600\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "601\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", + "602\n", + "Label = ['train', 'function', '[PAD]', '[PAD]']\n", + "603\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "604\n", + "Label = ['test', 'function', '[PAD]', '[PAD]']\n", + "605\n", + "Label = ['predict', 'function', '[PAD]', '[PAD]']\n", + "606\n", + "Label = ['uses', 'dynamic', 'learning', 'phase']\n", + "607\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "608\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "609\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "610\n", + "Label = ['is', 'graph', 'network', '[PAD]']\n", + "611\n", + "Label = ['sample', 'weights', '[PAD]', '[PAD]']\n", + "612\n", + "Label = ['val', 'x', '[PAD]', '[PAD]']\n", + "613\n", + "Label = ['split', 'at', '[PAD]', '[PAD]']\n", + "614\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "615\n", + "Label = ['do', 'validation', '[PAD]', '[PAD]']\n", + "616\n", + "Label = ['ins', '[PAD]', '[PAD]', '[PAD]']\n", + "617\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "618\n", + "Label = ['test', 'loop', '[PAD]', '[PAD]']\n", + "619\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", + "620\n", + "Label = ['uses', 'dynamic', 'learning', 'phase']\n", + "621\n", + "Label = ['fit', 'generator', '[PAD]', '[PAD]']\n", + "622\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "623\n", + "Label = ['predict', 'generator', '[PAD]', '[PAD]']\n", + "624\n", + "Label = ['is', 'tensor', '[PAD]', '[PAD]']\n", + "625\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "626\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "627\n", + "Label = ['score', 'array', '[PAD]', '[PAD]']\n", + "628\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "629\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "630\n", + "Label = ['negative', 'slope', '[PAD]', '[PAD]']\n", + "631\n", + "Label = ['ndimension', '[PAD]', '[PAD]', '[PAD]']\n", + "632\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "633\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "634\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "635\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "636\n", + "Label = ['random', 'samples', '[PAD]', '[PAD]']\n", + "637\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "638\n", + "Label = ['stride', '[PAD]', '[PAD]', '[PAD]']\n", + "639\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "640\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "641\n", + "Label = ['stride', '[PAD]', '[PAD]', '[PAD]']\n", + "642\n", + "Label = ['mul', '[PAD]', '[PAD]', '[PAD]']\n", + "643\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "644\n", + "Label = ['boolean', 'dispatch', '[PAD]', '[PAD]']\n", + "645\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", + "646\n", + "Label = ['feature', 'alpha', 'dropout', '[PAD]']\n", + "647\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "648\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "649\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "650\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "651\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "652\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "653\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "654\n", + "Label = ['batch', 'norm', '[PAD]', '[PAD]']\n", + "655\n", + "Label = ['instance', 'norm', '[PAD]', '[PAD]']\n", + "656\n", + "Label = ['group', 'norm', '[PAD]', '[PAD]']\n", + "657\n", + "Label = ['view', '[PAD]', '[PAD]', '[PAD]']\n", + "658\n", + "Label = ['div', '[PAD]', '[PAD]', '[PAD]']\n", + "659\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "660\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "661\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "662\n", + "Label = ['reduction', 'enum', '[PAD]', '[PAD]']\n", + "663\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "664\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "665\n", + "Label = ['smooth', 'l1', 'loss', '[PAD]']\n", + "666\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "667\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "668\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "669\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "670\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "671\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "672\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "673\n", + "Label = ['upsample', 'linear1d', '[PAD]', '[PAD]']\n", + "674\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "675\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "676\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "677\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "678\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "679\n", + "Label = ['denom', '[PAD]', '[PAD]', '[PAD]']\n", + "680\n", + "Label = ['expand', 'as', '[PAD]', '[PAD]']\n", + "681\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "682\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "683\n", + "Label = ['grad', 'output', '[PAD]', '[PAD]']\n", + "684\n", + "Label = ['view', '[PAD]', '[PAD]', '[PAD]']\n", + "685\n", + "Label = ['narrow', '[PAD]', '[PAD]', '[PAD]']\n", + "686\n", + "Label = ['input', '[PAD]', '[PAD]', '[PAD]']\n", + "687\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "688\n", + "Label = ['view', '[PAD]', '[PAD]', '[PAD]']\n", + "689\n", + "Label = ['conv3d', '[PAD]', '[PAD]', '[PAD]']\n", + "690\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "691\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "692\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "693\n", + "Label = ['optional', '[PAD]', '[PAD]', '[PAD]']\n", + "694\n", + "Label = ['new', '[PAD]', '[PAD]', '[PAD]']\n", + "695\n", + "Label = ['cuda', '[PAD]', '[PAD]', '[PAD]']\n", + "696\n", + "Label = ['sorted', 'indices', '[PAD]', '[PAD]']\n", + "697\n", + "Label = ['cpu', '[PAD]', '[PAD]', '[PAD]']\n", + "698\n", + "Label = ['unsorted', 'indices', '[PAD]', '[PAD]']\n", + "699\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "700\n", + "Label = ['u', '[PAD]', '[PAD]', '[PAD]']\n", + "701\n", + "Label = ['no', 'grad', '[PAD]', '[PAD]']\n", + "702\n", + "Label = ['register', 'parameter', '[PAD]', '[PAD]']\n", + "703\n", + "Label = ['devices', '[PAD]', '[PAD]', '[PAD]']\n", + "704\n", + "Label = ['buffer', 'indices', '[PAD]', '[PAD]']\n", + "705\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "706\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", + "707\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", + "708\n", + "Label = ['process', 'group', '[PAD]', '[PAD]']\n", + "709\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "710\n", + "Label = ['dist', 'broadcast', 'coalesced', '[PAD]']\n", + "711\n", + "Label = ['module', 'copies', '[PAD]', '[PAD]']\n", + "712\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "713\n", + "Label = ['bucket', 'map', '[PAD]', '[PAD]']\n", + "714\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "715\n", + "Label = ['dist', 'broadcast', 'coalesced', '[PAD]']\n", + "716\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "717\n", + "Label = ['grad', 'acc', '[PAD]', '[PAD]']\n", + "718\n", + "Label = ['grad', '[PAD]', '[PAD]', '[PAD]']\n", + "719\n", + "Label = ['devs', 'ready', '[PAD]', '[PAD]']\n", + "720\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "721\n", + "Label = ['target', 'gpus', '[PAD]', '[PAD]']\n", + "722\n", + "Label = ['input', 'device', '[PAD]', '[PAD]']\n", + "723\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "724\n", + "Label = ['Thread', '[PAD]', '[PAD]', '[PAD]']\n", + "725\n", + "Label = ['max', 'pos', '[PAD]', '[PAD]']\n", + "726\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "727\n", + "Label = ['replicate', '[PAD]', '[PAD]', '[PAD]']\n", + "728\n", + "Label = ['device', 'ids', '[PAD]', '[PAD]']\n", + "729\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "730\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "731\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "732\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "733\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "734\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "735\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "736\n", + "Label = ['bucket', 'param', 'type', '[PAD]']\n", + "737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "738\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "739\n", + "Label = ['device', 'ids', '[PAD]', '[PAD]']\n", + "740\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "741\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "742\n", + "Label = ['buckets', '[PAD]', '[PAD]', '[PAD]']\n", + "743\n", + "Label = ['r', 'streams', '[PAD]', '[PAD]']\n", + "744\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "745\n", + "Label = ['reduction', 'queues', '[PAD]', '[PAD]']\n", + "746\n", + "Label = ['dev', 'id', '[PAD]', '[PAD]']\n", + "747\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "748\n", + "Label = ['SpatialCrossMapLRN', 'updateOutput', '[PAD]', '[PAD]']\n", + "749\n", + "Label = ['square', 'next', '[PAD]', '[PAD]']\n", + "750\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", + "751\n", + "Label = ['mul', '[PAD]', '[PAD]', '[PAD]']\n", + "752\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "753\n", + "Label = ['paddings', '[PAD]', '[PAD]', '[PAD]']\n", + "754\n", + "Label = ['symbolic', 'fns', '[PAD]', '[PAD]']\n", + "755\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "756\n", + "Label = ['bag', 'size', '[PAD]', '[PAD]']\n", + "757\n", + "Label = ['above', 'threshold', '[PAD]', '[PAD]']\n", + "758\n", + "Label = ['below', 'threshold', '[PAD]', '[PAD]']\n", + "759\n", + "Label = ['weights', 'maybe', 'resized', '[PAD]']\n", + "760\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "761\n", + "Label = ['scatter', '[PAD]', '[PAD]', '[PAD]']\n", + "762\n", + "Label = ['type', 'as', '[PAD]', '[PAD]']\n", + "763\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "764\n", + "Label = ['grad', 'output', 'expanded', '[PAD]']\n", + "765\n", + "Label = ['backward', 'cls', '[PAD]', '[PAD]']\n", + "766\n", + "Label = ['expected', 'params', '[PAD]', '[PAD]']\n", + "767\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "768\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "769\n", + "Label = ['apply', '[PAD]', '[PAD]', '[PAD]']\n", + "770\n", + "Label = ['classes', 'to', 'generate', '[PAD]']\n", + "771\n", + "Label = ['batch', 'norm', '[PAD]', '[PAD]']\n", + "772\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "773\n", + "Label = ['rrelu', '[PAD]', '[PAD]', '[PAD]']\n", + "774\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "775\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "776\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "777\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "778\n", + "Label = ['pairwise', 'distance', '[PAD]', '[PAD]']\n", + "779\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "780\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "781\n", + "Label = ['dropout3d', '[PAD]', '[PAD]', '[PAD]']\n", + "782\n", + "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", + "783\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "784\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "785\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "786\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "787\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "788\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "789\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "790\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "791\n", + "Label = ['register', 'parameter', '[PAD]', '[PAD]']\n", + "792\n", + "Label = ['is', 'cuda', '[PAD]', '[PAD]']\n", + "793\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "794\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "795\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "796\n", + "Label = ['CrossMapLRN2d', '[PAD]', '[PAD]', '[PAD]']\n", + "797\n", + "Label = ['elementwise', 'affine', '[PAD]', '[PAD]']\n", + "798\n", + "Label = ['layer', 'norm', '[PAD]', '[PAD]']\n", + "799\n", + "Label = ['affine', '[PAD]', '[PAD]', '[PAD]']\n", + "800\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "801\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "802\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "803\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "804\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "805\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "806\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "807\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "808\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "809\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "810\n", + "Label = ['margin', 'ranking', 'loss', '[PAD]']\n", + "811\n", + "Label = ['margin', 'ranking', 'loss', '[PAD]']\n", + "812\n", + "Label = ['multi', 'margin', 'loss', '[PAD]']\n", + "813\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "814\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "815\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", + "816\n", + "Label = ['in', 'features', '[PAD]', '[PAD]']\n", + "817\n", + "Label = ['argmax', '[PAD]', '[PAD]', '[PAD]']\n", + "818\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "819\n", + "Label = ['gate', 'size', '[PAD]', '[PAD]']\n", + "820\n", + "Label = ['layer', 'input', 'size', '[PAD]']\n", + "821\n", + "Label = ['is', 'cuda', '[PAD]', '[PAD]']\n", + "822\n", + "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", + "823\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "824\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "825\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "826\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "827\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "828\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "829\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "830\n", + "Label = ['hx', '[PAD]', '[PAD]', '[PAD]']\n", + "831\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "832\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "833\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "834\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "835\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "836\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "837\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "838\n", + "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", + "839\n", + "Label = ['Tensor', '[PAD]', '[PAD]', '[PAD]']\n", + "840\n", + "Label = ['Tensor', '[PAD]', '[PAD]', '[PAD]']\n", + "841\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "842\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "843\n", + "Label = ['conv1d', '[PAD]', '[PAD]', '[PAD]']\n", + "844\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "845\n", + "Label = ['conv3d', '[PAD]', '[PAD]', '[PAD]']\n", + "846\n", + "Label = ['dim', 'size', '[PAD]', '[PAD]']\n", + "847\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "848\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "849\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "850\n", + "Label = ['conv', 'transpose3d', '[PAD]', '[PAD]']\n", + "851\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "852\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "853\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "854\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "855\n", + "Label = ['max', 'unpool1d', '[PAD]', '[PAD]']\n", + "856\n", + "Label = ['fractional', 'max', 'pool2d', '[PAD]']\n", + "857\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "858\n", + "Label = ['fractional', 'max', 'pool3d', '[PAD]']\n", + "859\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "860\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "861\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "862\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "863\n", + "Label = ['apply', '[PAD]', '[PAD]', '[PAD]']\n", + "864\n", + "Label = ['apply', '[PAD]', '[PAD]', '[PAD]']\n", + "865\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "866\n", + "Label = ['hook', '[PAD]', '[PAD]', '[PAD]']\n", + "867\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "868\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "869\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "870\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "871\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "872\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "873\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "874\n", + "Label = ['gen', '[PAD]', '[PAD]', '[PAD]']\n", + "875\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "876\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "877\n", + "Label = ['means', '[PAD]', '[PAD]', '[PAD]']\n", + "878\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "879\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "880\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "881\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "882\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "883\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "884\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "885\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "886\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "887\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "888\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "889\n", + "Label = ['random', 'weights', '[PAD]', '[PAD]']\n", + "890\n", + "Label = ['random', 'offset', '[PAD]', '[PAD]']\n", + "891\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "892\n", + "Label = ['sin', '[PAD]', '[PAD]', '[PAD]']\n", + "893\n", + "Label = ['X', 'step', '[PAD]', '[PAD]']\n", + "894\n", + "Label = ['factor', 'nz', '[PAD]', '[PAD]']\n", + "895\n", + "Label = ['cosh', '[PAD]', '[PAD]', '[PAD]']\n", + "896\n", + "Label = ['csr', 'matrix', '[PAD]', '[PAD]']\n", + "897\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "898\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "899\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "900\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "901\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "902\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "903\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "904\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "905\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "906\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "907\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "908\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "909\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "910\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "911\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "912\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "913\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "914\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "915\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "916\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "917\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "918\n", + "Label = ['Xs', '[PAD]', '[PAD]', '[PAD]']\n", + "919\n", + "Label = ['Xs', '[PAD]', '[PAD]', '[PAD]']\n", + "920\n", + "Label = ['classes', 'index', '[PAD]', '[PAD]']\n", + "921\n", + "Label = ['code', 'book', '[PAD]', '[PAD]']\n", + "922\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "923\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "924\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "925\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "926\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "927\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "928\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "929\n", + "Label = ['chain', 'idx', '[PAD]', '[PAD]']\n", + "930\n", + "Label = ['Y', 'decision', 'chain', '[PAD]']\n", + "931\n", + "Label = ['decision', 'function', '[PAD]', '[PAD]']\n", + "932\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "933\n", + "Label = ['denominator', '[PAD]', '[PAD]', '[PAD]']\n", + "934\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "935\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "936\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "937\n", + "Label = ['binomial', '[PAD]', '[PAD]', '[PAD]']\n", + "938\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "939\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "940\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "941\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "942\n", + "Label = ['strategy', '[PAD]', '[PAD]', '[PAD]']\n", + "943\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "944\n", + "Label = ['strategy', '[PAD]', '[PAD]', '[PAD]']\n", + "945\n", + "Label = ['n', 'outputs', '[PAD]', '[PAD]']\n", + "946\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "947\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "948\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "949\n", + "Label = ['constant', '[PAD]', '[PAD]', '[PAD]']\n", + "950\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "951\n", + "Label = ['constant', '[PAD]', '[PAD]', '[PAD]']\n", + "952\n", + "Label = ['output', '2d', '[PAD]', '[PAD]']\n", + "953\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "954\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "955\n", + "Label = ['cachedir', '[PAD]', '[PAD]', '[PAD]']\n", + "956\n", + "Label = ['Xt', '[PAD]', '[PAD]', '[PAD]']\n", + "957\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "958\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "959\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "960\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "961\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "962\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "963\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "964\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "965\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "966\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "967\n", + "Label = ['base', 'estimator', '[PAD]', '[PAD]']\n", + "968\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "969\n", + "Label = ['calibrated', 'classifier', '[PAD]', '[PAD]']\n", + "970\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "971\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "972\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "973\n", + "Label = ['prob', 'pred', '[PAD]', '[PAD]']\n", + "974\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "975\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "976\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "977\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "978\n", + "Label = ['class', 'log', 'prior', '[PAD]']\n", + "979\n", + "Label = ['feature', 'count', '[PAD]', '[PAD]']\n", + "980\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "981\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "982\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "983\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "984\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "985\n", + "Label = ['feature', 'count', '[PAD]', '[PAD]']\n", + "986\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "987\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "988\n", + "Label = ['comp', 'count', '[PAD]', '[PAD]']\n", + "989\n", + "Label = ['neg', 'prob', '[PAD]', '[PAD]']\n", + "990\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "991\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "992\n", + "Label = ['W', '[PAD]', '[PAD]', '[PAD]']\n", + "993\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "994\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "995\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "996\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "997\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "998\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "999\n", + "Label = ['violation', '[PAD]', '[PAD]', '[PAD]']\n", + "1000\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "1001\n", + "Label = ['delta', 'W', '[PAD]', '[PAD]']\n", + "1002\n", + "Label = ['end', 'time', '[PAD]', '[PAD]']\n", + "1003\n", + "Label = ['reconstruction', 'err', '[PAD]', '[PAD]']\n", + "1004\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1005\n", + "Label = ['cov', '[PAD]', '[PAD]', '[PAD]']\n", + "1006\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1007\n", + "Label = ['whiten', '[PAD]', '[PAD]', '[PAD]']\n", + "1008\n", + "Label = ['cnts', '[PAD]', '[PAD]', '[PAD]']\n", + "1009\n", + "Label = ['learning', 'offset', '[PAD]', '[PAD]']\n", + "1010\n", + "Label = ['parallel', '[PAD]', '[PAD]', '[PAD]']\n", + "1011\n", + "Label = ['parallel', '[PAD]', '[PAD]', '[PAD]']\n", + "1012\n", + "Label = ['exp', 'dirichlet', 'component', '[PAD]']\n", + "1013\n", + "Label = ['init', 'latent', 'vars', '[PAD]']\n", + "1014\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1015\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1016\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1017\n", + "Label = ['X', 'data', '[PAD]', '[PAD]']\n", + "1018\n", + "Label = ['v0', '[PAD]', '[PAD]', '[PAD]']\n", + "1019\n", + "Label = ['K', '[PAD]', '[PAD]', '[PAD]']\n", + "1020\n", + "Label = ['W', '[PAD]', '[PAD]', '[PAD]']\n", + "1021\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1022\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1023\n", + "Label = ['pi', '[PAD]', '[PAD]', '[PAD]']\n", + "1024\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "1025\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "1026\n", + "Label = ['col', 'mean', '[PAD]', '[PAD]']\n", + "1027\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "1028\n", + "Label = ['pa', '[PAD]', '[PAD]', '[PAD]']\n", + "1029\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1030\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1031\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "1032\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "1033\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "1034\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1035\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1036\n", + "Label = ['whiten', '[PAD]', '[PAD]', '[PAD]']\n", + "1037\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1038\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "1039\n", + "Label = ['lasso', 'lars', '[PAD]', '[PAD]']\n", + "1040\n", + "Label = ['this', 'slice', '[PAD]', '[PAD]']\n", + "1041\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", + "1042\n", + "Label = ['dictionary', '[PAD]', '[PAD]', '[PAD]']\n", + "1043\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "1044\n", + "Label = ['dictionary', '[PAD]', '[PAD]', '[PAD]']\n", + "1045\n", + "Label = ['theta', '[PAD]', '[PAD]', '[PAD]']\n", + "1046\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "1047\n", + "Label = ['sin', '[PAD]', '[PAD]', '[PAD]']\n", + "1048\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1049\n", + "Label = ['s2', '[PAD]', '[PAD]', '[PAD]']\n", + "1050\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1051\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1052\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1053\n", + "Label = ['expected', '[PAD]', '[PAD]', '[PAD]']\n", + "1054\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1055\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1056\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1057\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1058\n", + "Label = ['assert', 'equal', '[PAD]', '[PAD]']\n", + "1059\n", + "Label = ['svd', 'solver', '[PAD]', '[PAD]']\n", + "1060\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1061\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "1062\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1063\n", + "Label = ['explained', 'variance', '[PAD]', '[PAD]']\n", + "1064\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "1065\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "1066\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1067\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "1068\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1069\n", + "Label = ['ll2', '[PAD]', '[PAD]', '[PAD]']\n", + "1070\n", + "Label = ['pca', '32', '[PAD]', '[PAD]']\n", + "1071\n", + "Label = ['pca', '32', '[PAD]', '[PAD]']\n", + "1072\n", + "Label = ['Xa', '[PAD]', '[PAD]', '[PAD]']\n", + "1073\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1074\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1075\n", + "Label = ['svd', '[PAD]', '[PAD]', '[PAD]']\n", + "1076\n", + "Label = ['apca', '[PAD]', '[PAD]', '[PAD]']\n", + "1077\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1078\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1079\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1080\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "1081\n", + "Label = ['components', '[PAD]', '[PAD]', '[PAD]']\n", + "1082\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1083\n", + "Label = ['spca', '[PAD]', '[PAD]', '[PAD]']\n", + "1084\n", + "Label = ['spca', 'lasso', '[PAD]', '[PAD]']\n", + "1085\n", + "Label = ['results', 'test', 'pca', '[PAD]']\n", + "1086\n", + "Label = ['X', 'read', 'only', '[PAD]']\n", + "1087\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1088\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "1089\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "1090\n", + "Label = ['V', '[PAD]', '[PAD]', '[PAD]']\n", + "1091\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "1092\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "1093\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "1094\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1095\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "1096\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "1097\n", + "Label = ['W', 'nmf', '[PAD]', '[PAD]']\n", + "1098\n", + "Label = ['W', 'nmf', '2', '[PAD]']\n", + "1099\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1100\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "1101\n", + "Label = ['eigen', 'solver', '[PAD]', '[PAD]']\n", + "1102\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "1103\n", + "Label = ['kpca', '[PAD]', '[PAD]', '[PAD]']\n", + "1104\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1105\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "1106\n", + "Label = ['correct', 'idx', 'grps', '[PAD]']\n", + "1107\n", + "Label = ['component', '[PAD]', '[PAD]', '[PAD]']\n", + "1108\n", + "Label = ['invalid', 'models', '[PAD]', '[PAD]']\n", + "1109\n", + "Label = ['correct', 'idx', 'grps', '[PAD]']\n", + "1110\n", + "Label = ['randint', '[PAD]', '[PAD]', '[PAD]']\n", + "1111\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "1112\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1113\n", + "Label = ['ipca', '[PAD]', '[PAD]', '[PAD]']\n", + "1114\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1115\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1116\n", + "Label = ['ipca', '[PAD]', '[PAD]', '[PAD]']\n", + "1117\n", + "Label = ['ipca', '[PAD]', '[PAD]', '[PAD]']\n", + "1118\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1119\n", + "Label = ['singular', 'values', '[PAD]', '[PAD]']\n", + "1120\n", + "Label = ['X', 'pca', '[PAD]', '[PAD]']\n", + "1121\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1122\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "1123\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1124\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1125\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1126\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1127\n", + "Label = ['clf', 'pf2', '[PAD]', '[PAD]']\n", + "1128\n", + "Label = ['clf2', '[PAD]', '[PAD]', '[PAD]']\n", + "1129\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1130\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1131\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1132\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1133\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "1134\n", + "Label = ['scores', '[PAD]', '[PAD]', '[PAD]']\n", + "1135\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1136\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1137\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1138\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1139\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1140\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1141\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1142\n", + "Label = ['alpha', '[PAD]', '[PAD]', '[PAD]']\n", + "1143\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "1144\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1145\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1146\n", + "Label = ['strategy', '[PAD]', '[PAD]', '[PAD]']\n", + "1147\n", + "Label = ['length', '[PAD]', '[PAD]', '[PAD]']\n", + "1148\n", + "Label = ['nb', 'missing', 'values', '[PAD]']\n", + "1149\n", + "Label = ['hstack', '[PAD]', '[PAD]', '[PAD]']\n", + "1150\n", + "Label = ['raises', '[PAD]', '[PAD]', '[PAD]']\n", + "1151\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1152\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "1153\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1154\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "1155\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1156\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1157\n", + "Label = ['randint', '[PAD]', '[PAD]', '[PAD]']\n", + "1158\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1159\n", + "Label = ['random', 'matrix', '[PAD]', '[PAD]']\n", + "1160\n", + "Label = ['var', '[PAD]', '[PAD]', '[PAD]']\n", + "1161\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1162\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1163\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "1164\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "1165\n", + "Label = ['var', '[PAD]', '[PAD]', '[PAD]']\n", + "1166\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1167\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1168\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "1169\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1170\n", + "Label = ['X', 'test', '[PAD]', '[PAD]']\n", + "1171\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1172\n", + "Label = ['uncalibrated', 'log', 'loss', '[PAD]']\n", + "1173\n", + "Label = ['X', 'test', '[PAD]', '[PAD]']\n", + "1174\n", + "Label = ['exF', '[PAD]', '[PAD]', '[PAD]']\n", + "1175\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1176\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "1177\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "1178\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1179\n", + "Label = ['this', 'incorrect', '[PAD]', '[PAD]']\n", + "1180\n", + "Label = ['this', 'incorrect', '[PAD]', '[PAD]']\n", + "1181\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "1182\n", + "Label = ['fit', 'args', '[PAD]', '[PAD]']\n", + "1183\n", + "Label = ['fit', 'args', '[PAD]', '[PAD]']\n", + "1184\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1185\n", + "Label = ['pred', '[PAD]', '[PAD]', '[PAD]']\n", + "1186\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1187\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1188\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1189\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1190\n", + "Label = ['pred', '[PAD]', '[PAD]', '[PAD]']\n", + "1191\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "1192\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "1193\n", + "Label = ['dec', '[PAD]', '[PAD]', '[PAD]']\n", + "1194\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1195\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1196\n", + "Label = ['estimators', '[PAD]', '[PAD]', '[PAD]']\n", + "1197\n", + "Label = ['message', 're', '[PAD]', '[PAD]']\n", + "1198\n", + "Label = ['argmax', '[PAD]', '[PAD]', '[PAD]']\n", + "1199\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1200\n", + "Label = ['multi', 'clf', '[PAD]', '[PAD]']\n", + "1201\n", + "Label = ['argmax', '[PAD]', '[PAD]', '[PAD]']\n", + "1202\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1203\n", + "Label = ['ecoc', '[PAD]', '[PAD]', '[PAD]']\n", + "1204\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1205\n", + "Label = ['estimators', '[PAD]', '[PAD]', '[PAD]']\n", + "1206\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1207\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1208\n", + "Label = ['y3', '[PAD]', '[PAD]', '[PAD]']\n", + "1209\n", + "Label = ['y6', '[PAD]', '[PAD]', '[PAD]']\n", + "1210\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1211\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1212\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1213\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "1214\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "1215\n", + "Label = ['vstack', '[PAD]', '[PAD]', '[PAD]']\n", + "1216\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "1217\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1218\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1219\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1220\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "1221\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1222\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "1223\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1224\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1225\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1226\n", + "Label = ['yyy', '[PAD]', '[PAD]', '[PAD]']\n", + "1227\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "1228\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1229\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1230\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1231\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1232\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1233\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1234\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1235\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "1236\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1237\n", + "Label = ['X', 'train', 'fit', '[PAD]']\n", + "1238\n", + "Label = ['pred', '[PAD]', '[PAD]', '[PAD]']\n", + "1239\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1240\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1241\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1242\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1243\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1244\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "1245\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1246\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1247\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1248\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1249\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1250\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1251\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "1252\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1253\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1254\n", + "Label = ['hstack', '[PAD]', '[PAD]', '[PAD]']\n", + "1255\n", + "Label = ['csc', 'matrix', '[PAD]', '[PAD]']\n", + "1256\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1257\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1258\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1259\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1260\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "1261\n", + "Label = ['scaler', '[PAD]', '[PAD]', '[PAD]']\n", + "1262\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "1263\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "1264\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "1265\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1266\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1267\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1268\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "1269\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1270\n", + "Label = ['feat', '[PAD]', '[PAD]', '[PAD]']\n", + "1271\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1272\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1273\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1274\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1275\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1276\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "1277\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1278\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1279\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1280\n", + "Label = ['abspath', '[PAD]', '[PAD]', '[PAD]']\n", + "1281\n", + "Label = ['catch', 'warnings', '[PAD]', '[PAD]']\n", + "1282\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1283\n", + "Label = ['lookup', '[PAD]', '[PAD]', '[PAD]']\n", + "1284\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "1285\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "1286\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "1287\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1288\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "1289\n", + "Label = ['maj', '[PAD]', '[PAD]', '[PAD]']\n", + "1290\n", + "Label = ['apply', 'along', 'axis', '[PAD]']\n", + "1291\n", + "Label = ['voting', '[PAD]', '[PAD]', '[PAD]']\n", + "1292\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1293\n", + "Label = ['feature', 'importances', '[PAD]', '[PAD]']\n", + "1294\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1295\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "1296\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1297\n", + "Label = ['n', 'estimators', '[PAD]', '[PAD]']\n", + "1298\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "1299\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1300\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1301\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "1302\n", + "Label = ['proba', '[PAD]', '[PAD]', '[PAD]']\n", + "1303\n", + "Label = ['learning', 'rate', '[PAD]', '[PAD]']\n", + "1304\n", + "Label = ['median', 'or', 'above', '[PAD]']\n", + "1305\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "1306\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1307\n", + "Label = ['contamination', '[PAD]', '[PAD]', '[PAD]']\n", + "1308\n", + "Label = ['n', 'samples', 'leaf', '[PAD]']\n", + "1309\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1310\n", + "Label = ['random', 'state', '[PAD]', '[PAD]']\n", + "1311\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "1312\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1313\n", + "Label = ['n', 'estimators', '[PAD]', '[PAD]']\n", + "1314\n", + "Label = ['n', 'estimators', '[PAD]', '[PAD]']\n", + "1315\n", + "Label = ['warm', 'start', '[PAD]', '[PAD]']\n", + "1316\n", + "Label = ['trees', '[PAD]', '[PAD]', '[PAD]']\n", + "1317\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1318\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1319\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "1320\n", + "Label = ['classes', 'k', '[PAD]', '[PAD]']\n", + "1321\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "1322\n", + "Label = ['n', 'outputs', '[PAD]', '[PAD]']\n", + "1323\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1324\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "1325\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1326\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1327\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1328\n", + "Label = ['quantile', '[PAD]', '[PAD]', '[PAD]']\n", + "1329\n", + "Label = ['prior', '[PAD]', '[PAD]', '[PAD]']\n", + "1330\n", + "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", + "1331\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1332\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "1333\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1334\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "1335\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "1336\n", + "Label = ['sq', 'loss', '[PAD]', '[PAD]']\n", + "1337\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "1338\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1339\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1340\n", + "Label = ['denominator', '[PAD]', '[PAD]', '[PAD]']\n", + "1341\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "1342\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1343\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1344\n", + "Label = ['terminal', 'region', '[PAD]', '[PAD]']\n", + "1345\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1346\n", + "Label = ['residual', '[PAD]', '[PAD]', '[PAD]']\n", + "1347\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1348\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "1349\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1350\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1351\n", + "Label = ['max', 'features', '[PAD]', '[PAD]']\n", + "1352\n", + "Label = ['subsample', '[PAD]', '[PAD]', '[PAD]']\n", + "1353\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1354\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1355\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1356\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1357\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1358\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1359\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", + "1360\n", + "Label = ['random', 'state', '[PAD]', '[PAD]']\n", + "1361\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1362\n", + "Label = ['decision', 'function', '[PAD]', '[PAD]']\n", + "1363\n", + "Label = ['max', 'samples', '[PAD]', '[PAD]']\n", + "1364\n", + "Label = ['warm', 'start', '[PAD]', '[PAD]']\n", + "1365\n", + "Label = ['n', 'estimators', '[PAD]', '[PAD]']\n", + "1366\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1367\n", + "Label = ['n', 'features', '[PAD]', '[PAD]']\n", + "1368\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "1369\n", + "Label = ['estimators', 'features', '[PAD]', '[PAD]']\n", + "1370\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1371\n", + "Label = ['n', 'features', '[PAD]', '[PAD]']\n", + "1372\n", + "Label = ['n', 'features', '[PAD]', '[PAD]']\n", + "1373\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "1374\n", + "Label = ['stage', '[PAD]', '[PAD]', '[PAD]']\n", + "1375\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "1376\n", + "Label = ['fx', '[PAD]', '[PAD]', '[PAD]']\n", + "1377\n", + "Label = ['fxs', '[PAD]', '[PAD]', '[PAD]']\n", + "1378\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1379\n", + "Label = ['ceil', '[PAD]', '[PAD]', '[PAD]']\n", + "1380\n", + "Label = ['XX', '[PAD]', '[PAD]', '[PAD]']\n", + "1381\n", + "Label = ['meshgrid', '[PAD]', '[PAD]', '[PAD]']\n", + "1382\n", + "Label = ['subplots', 'adjust', '[PAD]', '[PAD]']\n", + "1383\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "1384\n", + "Label = ['base', 'estimator', '[PAD]', '[PAD]']\n", + "1385\n", + "Label = ['sparse', 'classifier', '[PAD]', '[PAD]']\n", + "1386\n", + "Label = ['ensemble', '[PAD]', '[PAD]', '[PAD]']\n", + "1387\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1388\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1389\n", + "Label = ['ensemble', '[PAD]', '[PAD]', '[PAD]']\n", + "1390\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1391\n", + "Label = ['clf', 'ws', '[PAD]', '[PAD]']\n", + "1392\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1393\n", + "Label = ['bagging', '[PAD]', '[PAD]', '[PAD]']\n", + "1394\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "1395\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1396\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1397\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1398\n", + "Label = ['string', 'ensemble', '[PAD]', '[PAD]']\n", + "1399\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1400\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1401\n", + "Label = ['gbrt', '[PAD]', '[PAD]', '[PAD]']\n", + "1402\n", + "Label = ['presort', '[PAD]', '[PAD]', '[PAD]']\n", + "1403\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "1404\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1405\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1406\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1407\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1408\n", + "Label = ['reg', '[PAD]', '[PAD]', '[PAD]']\n", + "1409\n", + "Label = ['max', 'features', '[PAD]', '[PAD]']\n", + "1410\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1411\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1412\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "1413\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "1414\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "1415\n", + "Label = ['max', 'depth', '[PAD]', '[PAD]']\n", + "1416\n", + "Label = ['max', 'depth', '[PAD]', '[PAD]']\n", + "1417\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1418\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1419\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1420\n", + "Label = ['sparse', '[PAD]', '[PAD]', '[PAD]']\n", + "1421\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1422\n", + "Label = ['decision', 'function', '[PAD]', '[PAD]']\n", + "1423\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1424\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1425\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1426\n", + "Label = ['argmin', '[PAD]', '[PAD]', '[PAD]']\n", + "1427\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "1428\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1429\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "1430\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "1431\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "1432\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1433\n", + "Label = ['sprase', 'res', '[PAD]', '[PAD]']\n", + "1434\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1435\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1436\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1437\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", + "1438\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1439\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1440\n", + "Label = ['test', 'score', '[PAD]', '[PAD]']\n", + "1441\n", + "Label = ['oob', 'score', '[PAD]', '[PAD]']\n", + "1442\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1443\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "1444\n", + "Label = ['tree', '[PAD]', '[PAD]', '[PAD]']\n", + "1445\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "1446\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "1447\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1448\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1449\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1450\n", + "Label = ['X', '2d', '[PAD]', '[PAD]']\n", + "1451\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "1452\n", + "Label = ['random', 'state', '[PAD]', '[PAD]']\n", + "1453\n", + "Label = ['random', 'state', '[PAD]', '[PAD]']\n", + "1454\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1455\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1456\n", + "Label = ['ba', '[PAD]', '[PAD]', '[PAD]']\n", + "1457\n", + "Label = ['ba', '[PAD]', '[PAD]', '[PAD]']\n", + "1458\n", + "Label = ['grid', '[PAD]', '[PAD]', '[PAD]']\n", + "1459\n", + "Label = ['pdp', '2', '[PAD]', '[PAD]']\n", + "1460\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "1461\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1462\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1463\n", + "Label = ['logaddexp', '[PAD]', '[PAD]', '[PAD]']\n", + "1464\n", + "Label = ['datum', '[PAD]', '[PAD]', '[PAD]']\n", + "1465\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "1466\n", + "Label = ['is', 'multi', 'class', '[PAD]']\n", + "1467\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1468\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1469\n", + "Label = ['scores', '[PAD]', '[PAD]', '[PAD]']\n", + "1470\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1471\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1472\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1473\n", + "Label = ['clf1', 'res', '[PAD]', '[PAD]']\n", + "1474\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1475\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1476\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1477\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1478\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1479\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1480\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1481\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1482\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "1483\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1484\n", + "Label = ['eclf1', '[PAD]', '[PAD]', '[PAD]']\n", + "1485\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "1486\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "1487\n", + "Label = ['get', 'params', '[PAD]', '[PAD]']\n", + "1488\n", + "Label = ['eclf2', '[PAD]', '[PAD]', '[PAD]']\n", + "1489\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1490\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1491\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1492\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1493\n", + "Label = ['eclf2', '[PAD]', '[PAD]', '[PAD]']\n", + "1494\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1495\n", + "Label = ['eclf3', '[PAD]', '[PAD]', '[PAD]']\n", + "1496\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1497\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "1498\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1499\n", + "Label = ['grid', '[PAD]', '[PAD]', '[PAD]']\n", + "1500\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "1501\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1502\n", + "Label = ['warns', '[PAD]', '[PAD]', '[PAD]']\n", + "1503\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "1504\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "1505\n", + "Label = ['X', 'test', '[PAD]', '[PAD]']\n", + "1506\n", + "Label = ['y', 'test', '[PAD]', '[PAD]']\n", + "1507\n", + "Label = ['MAXSIZE', '[PAD]', '[PAD]', '[PAD]']\n", + "1508\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "1509\n", + "Label = ['buf', '[PAD]', '[PAD]', '[PAD]']\n", + "1510\n", + "Label = ['want', 'unicode', '[PAD]', '[PAD]']\n", + "1511\n", + "Label = ['meta', '[PAD]', '[PAD]', '[PAD]']\n", + "1512\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "1513\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "1514\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "1515\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1516\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1517\n", + "Label = ['co', 'flags', '[PAD]', '[PAD]']\n", + "1518\n", + "Label = ['return', 'annotation', '[PAD]', '[PAD]']\n", + "1519\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "1520\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1521\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1522\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "1523\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "1524\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1525\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "1526\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1527\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "1528\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1529\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1530\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1531\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "1532\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "1533\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "1534\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "1535\n", + "Label = ['pillow', 'installed', '[PAD]', '[PAD]']\n", + "1536\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1537\n", + "Label = ['full', 'msg', '[PAD]', '[PAD]']\n", + "1538\n", + "Label = ['logfile', '[PAD]', '[PAD]', '[PAD]']\n", + "1539\n", + "Label = ['new', 'array', '[PAD]', '[PAD]']\n", + "1540\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "1541\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1542\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "1543\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "1544\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1545\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "1546\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1547\n", + "Label = ['backend', 'key', '[PAD]', '[PAD]']\n", + "1548\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1549\n", + "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", + "1550\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1551\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1552\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "1553\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "1554\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "1555\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1556\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "1557\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "1558\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1559\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1560\n", + "Label = ['bytes', 'limit', '[PAD]', '[PAD]']\n", + "1561\n", + "Label = ['pool', '[PAD]', '[PAD]', '[PAD]']\n", + "1562\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1563\n", + "Label = ['nesting', 'level', '[PAD]', '[PAD]']\n", + "1564\n", + "Label = ['workers', '[PAD]', '[PAD]', '[PAD]']\n", + "1565\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "1566\n", + "Label = ['e', 'type', '[PAD]', '[PAD]']\n", + "1567\n", + "Label = ['sharedmem', 'backend', '[PAD]', '[PAD]']\n", + "1568\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", + "1569\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "1570\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "1571\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", + "1572\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1573\n", + "Label = ['print', '[PAD]', '[PAD]', '[PAD]']\n", + "1574\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "1575\n", + "Label = ['print', '[PAD]', '[PAD]', '[PAD]']\n", + "1576\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1577\n", + "Label = ['open', 'item', '[PAD]', '[PAD]']\n", + "1578\n", + "Label = ['last', 'access', '[PAD]', '[PAD]']\n", + "1579\n", + "Label = ['getsize', '[PAD]', '[PAD]', '[PAD]']\n", + "1580\n", + "Label = ['compress', '[PAD]', '[PAD]', '[PAD]']\n", + "1581\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "1582\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "1583\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1584\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "1585\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1586\n", + "Label = ['BZ2File', '[PAD]', '[PAD]', '[PAD]']\n", + "1587\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "1588\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "1589\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "1590\n", + "Label = ['hash', '[PAD]', '[PAD]', '[PAD]']\n", + "1591\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "1592\n", + "Label = ['arg', 'name', '[PAD]', '[PAD]']\n", + "1593\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "1594\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "1595\n", + "Label = ['FOLDER', 'PERMISSIONS', '[PAD]', '[PAD]']\n", + "1596\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "1597\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", + "1598\n", + "Label = ['basename', '[PAD]', '[PAD]', '[PAD]']\n", + "1599\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "1600\n", + "Label = ['stat', '[PAD]', '[PAD]', '[PAD]']\n", + "1601\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "1602\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "1603\n", + "Label = ['listdir', '[PAD]', '[PAD]', '[PAD]']\n", + "1604\n", + "Label = ['max', 'read', 'count', '[PAD]']\n", + "1605\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1606\n", + "Label = ['modules', '[PAD]', '[PAD]', '[PAD]']\n", + "1607\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "1608\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "1609\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1610\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1611\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "1612\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1613\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "1614\n", + "Label = ['basename', '[PAD]', '[PAD]', '[PAD]']\n", + "1615\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1616\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1617\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1618\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1619\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1620\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "1621\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "1622\n", + "Label = ['read', 'all', '[PAD]', '[PAD]']\n", + "1623\n", + "Label = ['read', 'all', '[PAD]', '[PAD]']\n", + "1624\n", + "Label = ['lock', '[PAD]', '[PAD]', '[PAD]']\n", + "1625\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1626\n", + "Label = ['save', 'reduce', '[PAD]', '[PAD]']\n", + "1627\n", + "Label = ['modname', '[PAD]', '[PAD]', '[PAD]']\n", + "1628\n", + "Label = ['MARK', '[PAD]', '[PAD]', '[PAD]']\n", + "1629\n", + "Label = ['qualname', '[PAD]', '[PAD]', '[PAD]']\n", + "1630\n", + "Label = ['save', 'reduce', '[PAD]', '[PAD]']\n", + "1631\n", + "Label = ['save', 'reduce', '[PAD]', '[PAD]']\n", + "1632\n", + "Label = ['INST', '[PAD]', '[PAD]', '[PAD]']\n", + "1633\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1634\n", + "Label = ['curloc', '[PAD]', '[PAD]', '[PAD]']\n", + "1635\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1636\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1637\n", + "Label = ['cp', '[PAD]', '[PAD]', '[PAD]']\n", + "1638\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1639\n", + "Label = ['mod', '[PAD]', '[PAD]', '[PAD]']\n", + "1640\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1641\n", + "Label = ['doc', '[PAD]', '[PAD]', '[PAD]']\n", + "1642\n", + "Label = ['base', 'globals', 'name', '[PAD]']\n", + "1643\n", + "Label = ['subclass', '[PAD]', '[PAD]', '[PAD]']\n", + "1644\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1645\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1646\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1647\n", + "Label = ['exception', '[PAD]', '[PAD]', '[PAD]']\n", + "1648\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1649\n", + "Label = ['slots', '[PAD]', '[PAD]', '[PAD]']\n", + "1650\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1651\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "1652\n", + "Label = ['critical', '[PAD]', '[PAD]', '[PAD]']\n", + "1653\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "1654\n", + "Label = ['broken', '[PAD]', '[PAD]', '[PAD]']\n", + "1655\n", + "Label = ['format', 'exception', '[PAD]', '[PAD]']\n", + "1656\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "1657\n", + "Label = ['queue', 'management', 'thread', '[PAD]']\n", + "1658\n", + "Label = ['max', 'workers', '[PAD]', '[PAD]']\n", + "1659\n", + "Label = ['nb', 'children', 'alive', '[PAD]']\n", + "1660\n", + "Label = ['func', 'code', '[PAD]', '[PAD]']\n", + "1661\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1662\n", + "Label = ['sem', 'unlink', '[PAD]', '[PAD]']\n", + "1663\n", + "Label = ['handle', '[PAD]', '[PAD]', '[PAD]']\n", + "1664\n", + "Label = ['deadline', '[PAD]', '[PAD]', '[PAD]']\n", + "1665\n", + "Label = ['sem', 'trywait', '[PAD]', '[PAD]']\n", + "1666\n", + "Label = ['sem', 'trywait', '[PAD]', '[PAD]']\n", + "1667\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1668\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1669\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1670\n", + "Label = ['get', 'value', '[PAD]', '[PAD]']\n", + "1671\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1672\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1673\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1674\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1675\n", + "Label = ['buffer', '[PAD]', '[PAD]', '[PAD]']\n", + "1676\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1677\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "1678\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "1679\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "1680\n", + "Label = ['handle', '[PAD]', '[PAD]', '[PAD]']\n", + "1681\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1682\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1683\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1684\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1685\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "1686\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1687\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "1688\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "1689\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "1690\n", + "Label = ['process', '[PAD]', '[PAD]', '[PAD]']\n", + "1691\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "1692\n", + "Label = ['returncode', '[PAD]', '[PAD]', '[PAD]']\n", + "1693\n", + "Label = ['check', 'output', '[PAD]', '[PAD]']\n", + "1694\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "1695\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1696\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1697\n", + "Label = ['fileno', '[PAD]', '[PAD]', '[PAD]']\n", + "1698\n", + "Label = ['deadline', '[PAD]', '[PAD]', '[PAD]']\n", + "1699\n", + "Label = ['deadline', '[PAD]', '[PAD]', '[PAD]']\n", + "1700\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "1701\n", + "Label = ['PY3', '[PAD]', '[PAD]', '[PAD]']\n", + "1702\n", + "Label = ['isabs', '[PAD]', '[PAD]', '[PAD]']\n", + "1703\n", + "Label = ['modules', '[PAD]', '[PAD]', '[PAD]']\n", + "1704\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "1705\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "1706\n", + "Label = ['DEFAULT', 'ENV', '[PAD]', '[PAD]']\n", + "1707\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "1708\n", + "Label = ['pid', '[PAD]', '[PAD]', '[PAD]']\n", + "1709\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "1710\n", + "Label = ['poll', '[PAD]', '[PAD]', '[PAD]']\n", + "1711\n", + "Label = ['cmd', 'python', '[PAD]', '[PAD]']\n", + "1712\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1713\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "1714\n", + "Label = ['process', '[PAD]', '[PAD]', '[PAD]']\n", + "1715\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "1716\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "1717\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1718\n", + "Label = ['recall', '[PAD]', '[PAD]', '[PAD]']\n", + "1719\n", + "Label = ['fps', '[PAD]', '[PAD]', '[PAD]']\n", + "1720\n", + "Label = ['thresholds', '[PAD]', '[PAD]', '[PAD]']\n", + "1721\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "1722\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "1723\n", + "Label = ['indptr', '[PAD]', '[PAD]', '[PAD]']\n", + "1724\n", + "Label = ['true', 'at', 'reversed', 'rank']\n", + "1725\n", + "Label = ['logical', 'or', '[PAD]', '[PAD]']\n", + "1726\n", + "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", + "1727\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1728\n", + "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", + "1729\n", + "Label = ['denominator', '[PAD]', '[PAD]', '[PAD]']\n", + "1730\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "1731\n", + "Label = ['unique', 'values', '[PAD]', '[PAD]']\n", + "1732\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1733\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "1734\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1735\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "1736\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "1737\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "1738\n", + "Label = ['w', 'mat', '[PAD]', '[PAD]']\n", + "1739\n", + "Label = ['cov', 'ytyp', '[PAD]', '[PAD]']\n", + "1740\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1741\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1742\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1743\n", + "Label = ['MCM', '[PAD]', '[PAD]', '[PAD]']\n", + "1744\n", + "Label = ['tp', 'sum', '[PAD]', '[PAD]']\n", + "1745\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "1746\n", + "Label = ['name', 'width', '[PAD]', '[PAD]']\n", + "1747\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "1748\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1749\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1750\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "1751\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1752\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "1753\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "1754\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1755\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "1756\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1757\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1758\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", + "1759\n", + "Label = ['iterator', '[PAD]', '[PAD]', '[PAD]']\n", + "1760\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "1761\n", + "Label = ['chunk', 'size', '[PAD]', '[PAD]']\n", + "1762\n", + "Label = ['squareform', '[PAD]', '[PAD]', '[PAD]']\n", + "1763\n", + "Label = ['squareform', '[PAD]', '[PAD]', '[PAD]']\n", + "1764\n", + "Label = ['kwds', '[PAD]', '[PAD]', '[PAD]']\n", + "1765\n", + "Label = ['kwds', '[PAD]', '[PAD]', '[PAD]']\n", + "1766\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1767\n", + "Label = ['average', 'weight', '[PAD]', '[PAD]']\n", + "1768\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "1769\n", + "Label = ['kwargs', 'string', '[PAD]', '[PAD]']\n", + "1770\n", + "Label = ['sign', '[PAD]', '[PAD]', '[PAD]']\n", + "1771\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1772\n", + "Label = ['sign', '[PAD]', '[PAD]', '[PAD]']\n", + "1773\n", + "Label = ['sign', '[PAD]', '[PAD]', '[PAD]']\n", + "1774\n", + "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", + "1775\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "1776\n", + "Label = ['n', 'c', '[PAD]', '[PAD]']\n", + "1777\n", + "Label = ['labels', 'true', '[PAD]', '[PAD]']\n", + "1778\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1779\n", + "Label = ['denominator', '[PAD]', '[PAD]', '[PAD]']\n", + "1780\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1781\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1782\n", + "Label = ['denom', '[PAD]', '[PAD]', '[PAD]']\n", + "1783\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1784\n", + "Label = ['centroids', '[PAD]', '[PAD]', '[PAD]']\n", + "1785\n", + "Label = ['metric', 'name', '[PAD]', '[PAD]']\n", + "1786\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1787\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1788\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1789\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1790\n", + "Label = ['score', 'dense', 'with', 'sampling']\n", + "1791\n", + "Label = ['labels1', '[PAD]', '[PAD]', '[PAD]']\n", + "1792\n", + "Label = ['approx', '[PAD]', '[PAD]', '[PAD]']\n", + "1793\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "1794\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "1795\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "1796\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "1797\n", + "Label = ['means', '[PAD]', '[PAD]', '[PAD]']\n", + "1798\n", + "Label = ['labels', 'a', '[PAD]', '[PAD]']\n", + "1799\n", + "Label = ['labels', 'a', '[PAD]', '[PAD]']\n", + "1800\n", + "Label = ['histogram2d', '[PAD]', '[PAD]', '[PAD]']\n", + "1801\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "1802\n", + "Label = ['score', 'both', '[PAD]', '[PAD]']\n", + "1803\n", + "Label = ['scoring', '[PAD]', '[PAD]', '[PAD]']\n", + "1804\n", + "Label = ['score2', '[PAD]', '[PAD]', '[PAD]']\n", + "1805\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "1806\n", + "Label = ['grid', 'search', '[PAD]', '[PAD]']\n", + "1807\n", + "Label = ['weighted', '[PAD]', '[PAD]', '[PAD]']\n", + "1808\n", + "Label = ['ignored', '[PAD]', '[PAD]', '[PAD]']\n", + "1809\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1810\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1811\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "1812\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1813\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1814\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "1815\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "1816\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "1817\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1818\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1819\n", + "Label = ['y2', '[PAD]', '[PAD]', '[PAD]']\n", + "1820\n", + "Label = ['corrcoef', '[PAD]', '[PAD]', '[PAD]']\n", + "1821\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1822\n", + "Label = ['mcc', 'jurman', '[PAD]', '[PAD]']\n", + "1823\n", + "Label = ['y', '1', '[PAD]', '[PAD]']\n", + "1824\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1825\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1826\n", + "Label = ['report', '[PAD]', '[PAD]', '[PAD]']\n", + "1827\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1828\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1829\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1830\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "1831\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1832\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "1833\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1834\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "1835\n", + "Label = ['fbeta', '[PAD]', '[PAD]', '[PAD]']\n", + "1836\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1837\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1838\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1839\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1840\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1841\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1842\n", + "Label = ['pred', 'decision', '[PAD]', '[PAD]']\n", + "1843\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1844\n", + "Label = ['TrueInputType', '[PAD]', '[PAD]', '[PAD]']\n", + "1845\n", + "Label = ['NOT', 'SYMMETRIC', 'METRICS', '[PAD]']\n", + "1846\n", + "Label = ['union', '[PAD]', '[PAD]', '[PAD]']\n", + "1847\n", + "Label = ['measure', 'with', 'strobj', '[PAD]']\n", + "1848\n", + "Label = ['y1', 'str', '[PAD]', '[PAD]']\n", + "1849\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1850\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1851\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1852\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1853\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "1854\n", + "Label = ['cm', '[PAD]', '[PAD]', '[PAD]']\n", + "1855\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1856\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1857\n", + "Label = ['y', 'true', 'multilabel', '[PAD]']\n", + "1858\n", + "Label = ['probas', 'pred', '[PAD]', '[PAD]']\n", + "1859\n", + "Label = ['prec', '[PAD]', '[PAD]', '[PAD]']\n", + "1860\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1861\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1862\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1863\n", + "Label = ['tpr', '[PAD]', '[PAD]', '[PAD]']\n", + "1864\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "1865\n", + "Label = ['y', 'score', '[PAD]', '[PAD]']\n", + "1866\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1867\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "1868\n", + "Label = ['y', 'score', '[PAD]', '[PAD]']\n", + "1869\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1870\n", + "Label = ['raises', '[PAD]', '[PAD]', '[PAD]']\n", + "1871\n", + "Label = ['X', 'tuples', '[PAD]', '[PAD]']\n", + "1872\n", + "Label = ['Z', '[PAD]', '[PAD]', '[PAD]']\n", + "1873\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "1874\n", + "Label = ['S', '[PAD]', '[PAD]', '[PAD]']\n", + "1875\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "1876\n", + "Label = ['D', '[PAD]', '[PAD]', '[PAD]']\n", + "1877\n", + "Label = ['tolist', '[PAD]', '[PAD]', '[PAD]']\n", + "1878\n", + "Label = ['Y', 'norm', 'sq', '[PAD]']\n", + "1879\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "1880\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "1881\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1882\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "1883\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1884\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "1885\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "1886\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "1887\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "1888\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1889\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1890\n", + "Label = ['init', 'params', '[PAD]', '[PAD]']\n", + "1891\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "1892\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "1893\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1894\n", + "Label = ['vstack', '[PAD]', '[PAD]', '[PAD]']\n", + "1895\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "1896\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1897\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "1898\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "1899\n", + "Label = ['precisions', '[PAD]', '[PAD]', '[PAD]']\n", + "1900\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1901\n", + "Label = ['nk', '[PAD]', '[PAD]', '[PAD]']\n", + "1902\n", + "Label = ['means', '[PAD]', '[PAD]', '[PAD]']\n", + "1903\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1904\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "1905\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1906\n", + "Label = ['log', 'det', 'chol', '[PAD]']\n", + "1907\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1908\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "1909\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1910\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1911\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1912\n", + "Label = ['precisions', 'init', '[PAD]', '[PAD]']\n", + "1913\n", + "Label = ['cov', 'params', '[PAD]', '[PAD]']\n", + "1914\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "1915\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1916\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "1917\n", + "Label = ['mean', 'precision', 'prior', '[PAD]']\n", + "1918\n", + "Label = ['mean', 'prior', '[PAD]', '[PAD]']\n", + "1919\n", + "Label = ['covariance', 'prior', '[PAD]', '[PAD]']\n", + "1920\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1921\n", + "Label = ['cumsum', '[PAD]', '[PAD]', '[PAD]']\n", + "1922\n", + "Label = ['estimate', 'wishart', 'full', '[PAD]']\n", + "1923\n", + "Label = ['mean', 'precision', 'prior', '[PAD]']\n", + "1924\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "1925\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1926\n", + "Label = ['nk', '[PAD]', '[PAD]', '[PAD]']\n", + "1927\n", + "Label = ['weight', 'concentration', '[PAD]', '[PAD]']\n", + "1928\n", + "Label = ['cumsum', '[PAD]', '[PAD]', '[PAD]']\n", + "1929\n", + "Label = ['weight', 'concentration', 'prior', 'type']\n", + "1930\n", + "Label = ['weight', 'dirichlet', 'sum', '[PAD]']\n", + "1931\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "1932\n", + "Label = ['bgmm', '[PAD]', '[PAD]', '[PAD]']\n", + "1933\n", + "Label = ['atleast', '2d', '[PAD]', '[PAD]']\n", + "1934\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1935\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1936\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1937\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1938\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1939\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "1940\n", + "Label = ['multivariate', 'normal', '[PAD]', '[PAD]']\n", + "1941\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1942\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1943\n", + "Label = ['not', 'positive', 'errors', '[PAD]']\n", + "1944\n", + "Label = ['error', 'norm', '[PAD]', '[PAD]']\n", + "1945\n", + "Label = ['precs', 'pred', '[PAD]', '[PAD]']\n", + "1946\n", + "Label = ['cov', 'full', '[PAD]', '[PAD]']\n", + "1947\n", + "Label = ['predected', 'det', '[PAD]', '[PAD]']\n", + "1948\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "1949\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "1950\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "1951\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "1952\n", + "Label = ['arg', 'idx1', '[PAD]', '[PAD]']\n", + "1953\n", + "Label = ['eye', '[PAD]', '[PAD]', '[PAD]']\n", + "1954\n", + "Label = ['arg', 'idx1', '[PAD]', '[PAD]']\n", + "1955\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1956\n", + "Label = ['g', 'best', '[PAD]', '[PAD]']\n", + "1957\n", + "Label = ['cv', 'type', '[PAD]', '[PAD]']\n", + "1958\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1959\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1960\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "1961\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "1962\n", + "Label = ['bic', '[PAD]', '[PAD]', '[PAD]']\n", + "1963\n", + "Label = ['covariance', 'type', '[PAD]', '[PAD]']\n", + "1964\n", + "Label = ['max', 'iter', '[PAD]', '[PAD]']\n", + "1965\n", + "Label = ['gmm', '[PAD]', '[PAD]', '[PAD]']\n", + "1966\n", + "Label = ['catch', 'warnings', '[PAD]', '[PAD]']\n", + "1967\n", + "Label = ['prec', '[PAD]', '[PAD]', '[PAD]']\n", + "1968\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1969\n", + "Label = ['covariances', '[PAD]', '[PAD]', '[PAD]']\n", + "1970\n", + "Label = ['cov', '[PAD]', '[PAD]', '[PAD]']\n", + "1971\n", + "Label = ['gmm1', '[PAD]', '[PAD]', '[PAD]']\n", + "1972\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "1973\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1974\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "1975\n", + "Label = ['isclose', '[PAD]', '[PAD]', '[PAD]']\n", + "1976\n", + "Label = ['support', '[PAD]', '[PAD]', '[PAD]']\n", + "1977\n", + "Label = ['n', 'class', '[PAD]', '[PAD]']\n", + "1978\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1979\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "1980\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1981\n", + "Label = ['take', '[PAD]', '[PAD]', '[PAD]']\n", + "1982\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "1983\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "1984\n", + "Label = ['C', '[PAD]', '[PAD]', '[PAD]']\n", + "1985\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "1986\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "1987\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1988\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "1989\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "1990\n", + "Label = ['cross', 'validation', '[PAD]', '[PAD]']\n", + "1991\n", + "Label = ['dual', 'coef', '[PAD]', '[PAD]']\n", + "1992\n", + "Label = ['kfunc', '[PAD]', '[PAD]', '[PAD]']\n", + "1993\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "1994\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "1995\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "1996\n", + "Label = ['lsvr', 'no', 'weight', '[PAD]']\n", + "1997\n", + "Label = ['dual', 'coef', '[PAD]', '[PAD]']\n", + "1998\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "1999\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2000\n", + "Label = ['SVC', '[PAD]', '[PAD]', '[PAD]']\n", + "2001\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "2002\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2003\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "2004\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2005\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2006\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2007\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2008\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2009\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2010\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2011\n", + "Label = ['ovr', 'clf', '[PAD]', '[PAD]']\n", + "2012\n", + "Label = ['lsvc', 'unflat', '[PAD]', '[PAD]']\n", + "2013\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2014\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2015\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2016\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "2017\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2018\n", + "Label = ['SVR', '[PAD]', '[PAD]', '[PAD]']\n", + "2019\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2020\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "2021\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2022\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2023\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "2024\n", + "Label = ['decision', 'function', '[PAD]', '[PAD]']\n", + "2025\n", + "Label = ['clf', 'lin', '[PAD]', '[PAD]']\n", + "2026\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "2027\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2028\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2029\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2030\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2031\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2032\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2033\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2034\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2035\n", + "Label = ['compute', 'score', '[PAD]', '[PAD]']\n", + "2036\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2037\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2038\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "2039\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2040\n", + "Label = ['sigma', '[PAD]', '[PAD]', '[PAD]']\n", + "2041\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2042\n", + "Label = ['eye', '[PAD]', '[PAD]', '[PAD]']\n", + "2043\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2044\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2045\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2046\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2047\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2048\n", + "Label = ['lambda', '[PAD]', '[PAD]', '[PAD]']\n", + "2049\n", + "Label = ['fmin', 'l', 'bfgs', 'b']\n", + "2050\n", + "Label = ['fit', 'intercept', '[PAD]', '[PAD]']\n", + "2051\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2052\n", + "Label = ['outliers', '[PAD]', '[PAD]', '[PAD]']\n", + "2053\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "2054\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2055\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2056\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "2057\n", + "Label = ['fit', 'intercept', '[PAD]', '[PAD]']\n", + "2058\n", + "Label = ['inter', 'terms', '[PAD]', '[PAD]']\n", + "2059\n", + "Label = ['r', 'yhat', '[PAD]', '[PAD]']\n", + "2060\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "2061\n", + "Label = ['w0', '[PAD]', '[PAD]', '[PAD]']\n", + "2062\n", + "Label = ['Y', 'multi', '[PAD]', '[PAD]']\n", + "2063\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "2064\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2065\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2066\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2067\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2068\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2069\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "2070\n", + "Label = ['multi', 'w0', '[PAD]', '[PAD]']\n", + "2071\n", + "Label = ['coefs', '[PAD]', '[PAD]', '[PAD]']\n", + "2072\n", + "Label = ['tol', '[PAD]', '[PAD]', '[PAD]']\n", + "2073\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "2074\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "2075\n", + "Label = ['predict', 'proba', 'lr', '[PAD]']\n", + "2076\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "2077\n", + "Label = ['Cs', '[PAD]', '[PAD]', '[PAD]']\n", + "2078\n", + "Label = ['n', 'iter', '[PAD]', '[PAD]']\n", + "2079\n", + "Label = ['L', '[PAD]', '[PAD]', '[PAD]']\n", + "2080\n", + "Label = ['mun', '[PAD]', '[PAD]', '[PAD]']\n", + "2081\n", + "Label = ['coefs', '[PAD]', '[PAD]', '[PAD]']\n", + "2082\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2083\n", + "Label = ['C', 'idx', '[PAD]', '[PAD]']\n", + "2084\n", + "Label = ['ss', '[PAD]', '[PAD]', '[PAD]']\n", + "2085\n", + "Label = ['solve', 'triangular', '[PAD]', '[PAD]']\n", + "2086\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2087\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2088\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "2089\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "2090\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2091\n", + "Label = ['temp', '[PAD]', '[PAD]', '[PAD]']\n", + "2092\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2093\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2094\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2095\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2096\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "2097\n", + "Label = ['stride', '[PAD]', '[PAD]', '[PAD]']\n", + "2098\n", + "Label = ['max', 'n', 'alphas', '[PAD]']\n", + "2099\n", + "Label = ['residues', '[PAD]', '[PAD]', '[PAD]']\n", + "2100\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "2101\n", + "Label = ['logspace', '[PAD]', '[PAD]', '[PAD]']\n", + "2102\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2103\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2104\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2105\n", + "Label = ['alphas', '[PAD]', '[PAD]', '[PAD]']\n", + "2106\n", + "Label = ['coefs', '[PAD]', '[PAD]', '[PAD]']\n", + "2107\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2108\n", + "Label = ['sparse', 'enet', 'coordinate', 'descent']\n", + "2109\n", + "Label = ['precompute', '[PAD]', '[PAD]', '[PAD]']\n", + "2110\n", + "Label = ['precompute', '[PAD]', '[PAD]', '[PAD]']\n", + "2111\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "2112\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2113\n", + "Label = ['warm', 'start', '[PAD]', '[PAD]']\n", + "2114\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2115\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "2116\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2117\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2118\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2119\n", + "Label = ['model', 'str', '[PAD]', '[PAD]']\n", + "2120\n", + "Label = ['may', 'share', 'memory', '[PAD]']\n", + "2121\n", + "Label = ['may', 'share', 'memory', '[PAD]']\n", + "2122\n", + "Label = ['l1', 'ratios', '[PAD]', '[PAD]']\n", + "2123\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "2124\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2125\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2126\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2127\n", + "Label = ['model', 'str', '[PAD]', '[PAD]']\n", + "2128\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2129\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2130\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2131\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "2132\n", + "Label = ['curr', 'alpha', '[PAD]', '[PAD]']\n", + "2133\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2134\n", + "Label = ['C', '[PAD]', '[PAD]', '[PAD]']\n", + "2135\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "2136\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2137\n", + "Label = ['LinAlgError', '[PAD]', '[PAD]', '[PAD]']\n", + "2138\n", + "Label = ['flat', '[PAD]', '[PAD]', '[PAD]']\n", + "2139\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "2140\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "2141\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "2142\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2143\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2144\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2145\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "2146\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2147\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2148\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2149\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2150\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "2151\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "2152\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2153\n", + "Label = ['G', 'diag', '[PAD]', '[PAD]']\n", + "2154\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2155\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2156\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2157\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "2158\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2159\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2160\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2161\n", + "Label = ['Lkk', '[PAD]', '[PAD]', '[PAD]']\n", + "2162\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2163\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2164\n", + "Label = ['max', 'features', '[PAD]', '[PAD]']\n", + "2165\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2166\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2167\n", + "Label = ['norms', 'squared', '[PAD]', '[PAD]']\n", + "2168\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "2169\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "2170\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2171\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2172\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2173\n", + "Label = ['norms', 'sq', '[PAD]', '[PAD]']\n", + "2174\n", + "Label = ['tol', '[PAD]', '[PAD]', '[PAD]']\n", + "2175\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2176\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2177\n", + "Label = ['argmin', '[PAD]', '[PAD]', '[PAD]']\n", + "2178\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2179\n", + "Label = ['ceil', '[PAD]', '[PAD]', '[PAD]']\n", + "2180\n", + "Label = ['ceil', '[PAD]', '[PAD]', '[PAD]']\n", + "2181\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2182\n", + "Label = ['residual', 'threshold', '[PAD]', '[PAD]']\n", + "2183\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2184\n", + "Label = ['is', 'data', 'valid', '[PAD]']\n", + "2185\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2186\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "2187\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2188\n", + "Label = ['n', 'iter', '[PAD]', '[PAD]']\n", + "2189\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2190\n", + "Label = ['plus', '1', '[PAD]', '[PAD]']\n", + "2191\n", + "Label = ['max', 'subpopulation', '[PAD]', '[PAD]']\n", + "2192\n", + "Label = ['n', 'iter', '[PAD]', '[PAD]']\n", + "2193\n", + "Label = ['learning', 'rate', '[PAD]', '[PAD]']\n", + "2194\n", + "Label = ['intercept', 'init', '[PAD]', '[PAD]']\n", + "2195\n", + "Label = ['intercept', '[PAD]', '[PAD]', '[PAD]']\n", + "2196\n", + "Label = ['coef', 'init', '[PAD]', '[PAD]']\n", + "2197\n", + "Label = ['average', 'coef', '[PAD]', '[PAD]']\n", + "2198\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2199\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2200\n", + "Label = ['validation', 'score', 'cb', '[PAD]']\n", + "2201\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", + "2202\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2203\n", + "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", + "2204\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "2205\n", + "Label = ['average', 'coef', '[PAD]', '[PAD]']\n", + "2206\n", + "Label = ['warm', 'start', '[PAD]', '[PAD]']\n", + "2207\n", + "Label = ['tol', '[PAD]', '[PAD]', '[PAD]']\n", + "2208\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2209\n", + "Label = ['tol', '[PAD]', '[PAD]', '[PAD]']\n", + "2210\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2211\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2212\n", + "Label = ['sw', 'matrix', '[PAD]', '[PAD]']\n", + "2213\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2214\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2215\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2216\n", + "Label = ['prob', '[PAD]', '[PAD]', '[PAD]']\n", + "2217\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2218\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2219\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "2220\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2221\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "2222\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "2223\n", + "Label = ['y', 'std', '[PAD]', '[PAD]']\n", + "2224\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2225\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2226\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2227\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2228\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2229\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "2230\n", + "Label = ['count', 'nonzero', '[PAD]', '[PAD]']\n", + "2231\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2232\n", + "Label = ['count', 'nonzero', '[PAD]', '[PAD]']\n", + "2233\n", + "Label = ['ocur', '[PAD]', '[PAD]', '[PAD]']\n", + "2234\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['lars', 'path', '[PAD]', '[PAD]']\n", + "2235\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2236\n", + "Label = ['alpha', '[PAD]', '[PAD]', '[PAD]']\n", + "2237\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "2238\n", + "Label = ['lasso', 'coef2', '[PAD]', '[PAD]']\n", + "2239\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "2240\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "2241\n", + "Label = ['coord', 'descent', '[PAD]', '[PAD]']\n", + "2242\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2243\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "2244\n", + "Label = ['alpha', '[PAD]', '[PAD]', '[PAD]']\n", + "2245\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "2246\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2247\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2248\n", + "Label = ['calls', '[PAD]', '[PAD]', '[PAD]']\n", + "2249\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2250\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2251\n", + "Label = ['expected', 'proba', 'class', '1']\n", + "2252\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2253\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2254\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", + "2255\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "2256\n", + "Label = ['inverse', 'transform', '[PAD]', '[PAD]']\n", + "2257\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "2258\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "2259\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "2260\n", + "Label = ['max', 'iter', '[PAD]', '[PAD]']\n", + "2261\n", + "Label = ['clf', 'sw', 'ones', '[PAD]']\n", + "2262\n", + "Label = ['clf', 'sw', 'lbfgs', '[PAD]']\n", + "2263\n", + "Label = ['clf', 'sw', 'n', '[PAD]']\n", + "2264\n", + "Label = ['clf', 'cw', '[PAD]', '[PAD]']\n", + "2265\n", + "Label = ['solver', '[PAD]', '[PAD]', '[PAD]']\n", + "2266\n", + "Label = ['clf', 'w', '[PAD]', '[PAD]']\n", + "2267\n", + "Label = ['argmax', '[PAD]', '[PAD]', '[PAD]']\n", + "2268\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2269\n", + "Label = ['lr', 'saga', '[PAD]', '[PAD]']\n", + "2270\n", + "Label = ['lr', 'cv', '[PAD]', '[PAD]']\n", + "2271\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2272\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2273\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "2274\n", + "Label = ['y', 'bin', '[PAD]', '[PAD]']\n", + "2275\n", + "Label = ['liblinear', '[PAD]', '[PAD]', '[PAD]']\n", + "2276\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2277\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2278\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2279\n", + "Label = ['raises', '[PAD]', '[PAD]', '[PAD]']\n", + "2280\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2281\n", + "Label = ['base', 'estimator', '[PAD]', '[PAD]']\n", + "2282\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2283\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2284\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2285\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2286\n", + "Label = ['ransac', 'estimator', '[PAD]', '[PAD]']\n", + "2287\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2288\n", + "Label = ['ransac', 'estimator', '[PAD]', '[PAD]']\n", + "2289\n", + "Label = ['ref', 'inlier', 'mask', '[PAD]']\n", + "2290\n", + "Label = ['ransac', 'estimator5', '[PAD]', '[PAD]']\n", + "2291\n", + "Label = ['ransac', 'estimator7', '[PAD]', '[PAD]']\n", + "2292\n", + "Label = ['ransac', 'estimator', '[PAD]', '[PAD]']\n", + "2293\n", + "Label = ['ransac', 'estimator0', '[PAD]', '[PAD]']\n", + "2294\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2295\n", + "Label = ['flatten', '[PAD]', '[PAD]', '[PAD]']\n", + "2296\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2297\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2298\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2299\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "2300\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2301\n", + "Label = ['huber', '[PAD]', '[PAD]', '[PAD]']\n", + "2302\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2303\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2304\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2305\n", + "Label = ['coefs2', '[PAD]', '[PAD]', '[PAD]']\n", + "2306\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2307\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2308\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2309\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2310\n", + "Label = ['sample', 'weights', '[PAD]', '[PAD]']\n", + "2311\n", + "Label = ['reg', '[PAD]', '[PAD]', '[PAD]']\n", + "2312\n", + "Label = ['max', 'iter', '[PAD]', '[PAD]']\n", + "2313\n", + "Label = ['assert', 'array', 'equal', '[PAD]']\n", + "2314\n", + "Label = ['G', 'diag', '[PAD]', '[PAD]']\n", + "2315\n", + "Label = ['assert', 'array', 'equal', '[PAD]']\n", + "2316\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2317\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2318\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2319\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2320\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2321\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2322\n", + "Label = ['Y4', '[PAD]', '[PAD]', '[PAD]']\n", + "2323\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", + "2324\n", + "Label = ['factory', '[PAD]', '[PAD]', '[PAD]']\n", + "2325\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2326\n", + "Label = ['clf3', '[PAD]', '[PAD]', '[PAD]']\n", + "2327\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2328\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2329\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2330\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2331\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2332\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2333\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2334\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2335\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2336\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2337\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2338\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2339\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2340\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "2341\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "2342\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2343\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2344\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2345\n", + "Label = ['X', 'imbalanced', '[PAD]', '[PAD]']\n", + "2346\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2347\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2348\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2349\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2350\n", + "Label = ['factory', '[PAD]', '[PAD]', '[PAD]']\n", + "2351\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2352\n", + "Label = ['factory', '[PAD]', '[PAD]', '[PAD]']\n", + "2353\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2354\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2355\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2356\n", + "Label = ['factory', '[PAD]', '[PAD]', '[PAD]']\n", + "2357\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2358\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2359\n", + "Label = ['factory', '[PAD]', '[PAD]', '[PAD]']\n", + "2360\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "2361\n", + "Label = ['partial', 'fit', '[PAD]', '[PAD]']\n", + "2362\n", + "Label = ['loss', 'functions', '[PAD]', '[PAD]']\n", + "2363\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2364\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "2365\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2366\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "2367\n", + "Label = ['solve', '[PAD]', '[PAD]', '[PAD]']\n", + "2368\n", + "Label = ['X3', '[PAD]', '[PAD]', '[PAD]']\n", + "2369\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2370\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2371\n", + "Label = ['may', 'share', 'memory', '[PAD]']\n", + "2372\n", + "Label = ['Xt', '32', '[PAD]', '[PAD]']\n", + "2373\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2374\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "2375\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "2376\n", + "Label = ['y2', '[PAD]', '[PAD]', '[PAD]']\n", + "2377\n", + "Label = ['reg1', '[PAD]', '[PAD]', '[PAD]']\n", + "2378\n", + "Label = ['reg2', '[PAD]', '[PAD]', '[PAD]']\n", + "2379\n", + "Label = ['reg', '[PAD]', '[PAD]', '[PAD]']\n", + "2380\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2381\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2382\n", + "Label = ['intercept', '[PAD]', '[PAD]', '[PAD]']\n", + "2383\n", + "Label = ['n', 'samples', '[PAD]', '[PAD]']\n", + "2384\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "2385\n", + "Label = ['weights2', '[PAD]', '[PAD]', '[PAD]']\n", + "2386\n", + "Label = ['clf3', '[PAD]', '[PAD]', '[PAD]']\n", + "2387\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2388\n", + "Label = ['step', 'size', 'sqr', '[PAD]']\n", + "2389\n", + "Label = ['step', 'size', 'log', '[PAD]']\n", + "2390\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2391\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2392\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2393\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "2394\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "2395\n", + "Label = ['loss', '2', '[PAD]', '[PAD]']\n", + "2396\n", + "Label = ['grad', 'gt', '[PAD]', '[PAD]']\n", + "2397\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2398\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2399\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "2400\n", + "Label = ['clf', 'unconstrained', '[PAD]', '[PAD]']\n", + "2401\n", + "Label = ['clf', 'constrained', '[PAD]', '[PAD]']\n", + "2402\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2403\n", + "Label = ['alphas', '[PAD]', '[PAD]', '[PAD]']\n", + "2404\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "2405\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2406\n", + "Label = ['high', 'reg', 'model', '[PAD]']\n", + "2407\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2408\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2409\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2410\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "2411\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "2412\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2413\n", + "Label = ['theil', 'sen', '[PAD]', '[PAD]']\n", + "2414\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2415\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2416\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2417\n", + "Label = ['variance', '[PAD]', '[PAD]', '[PAD]']\n", + "2418\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2419\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2420\n", + "Label = ['empty', 'obs', '[PAD]', '[PAD]']\n", + "2421\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2422\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2423\n", + "Label = ['contamination', '[PAD]', '[PAD]', '[PAD]']\n", + "2424\n", + "Label = ['distances', 'fit', 'X', '[PAD]']\n", + "2425\n", + "Label = ['errstate', '[PAD]', '[PAD]', '[PAD]']\n", + "2426\n", + "Label = ['issubdtype', '[PAD]', '[PAD]', '[PAD]']\n", + "2427\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "2428\n", + "Label = ['query', 'is', 'train', '[PAD]']\n", + "2429\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "2430\n", + "Label = ['A', 'data', '[PAD]', '[PAD]']\n", + "2431\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2432\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "2433\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2434\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2435\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2436\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2437\n", + "Label = ['atleast', '2d', '[PAD]', '[PAD]']\n", + "2438\n", + "Label = ['bandwidth', '[PAD]', '[PAD]', '[PAD]']\n", + "2439\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2440\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "2441\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2442\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2443\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2444\n", + "Label = ['outputs', '2d', '[PAD]', '[PAD]']\n", + "2445\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "2446\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2447\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2448\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2449\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2450\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2451\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2452\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2453\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2454\n", + "Label = ['vals', '[PAD]', '[PAD]', '[PAD]']\n", + "2455\n", + "Label = ['metric', '[PAD]', '[PAD]', '[PAD]']\n", + "2456\n", + "Label = ['sin', '[PAD]', '[PAD]', '[PAD]']\n", + "2457\n", + "Label = ['dist', 'to', 'rdist', '[PAD]']\n", + "2458\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2459\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "2460\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2461\n", + "Label = ['kneighbors', '[PAD]', '[PAD]', '[PAD]']\n", + "2462\n", + "Label = ['n', 'samples', '[PAD]', '[PAD]']\n", + "2463\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "2464\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "2465\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2466\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", + "2467\n", + "Label = ['y', 'prob', '[PAD]', '[PAD]']\n", + "2468\n", + "Label = ['n', 'samples', '[PAD]', '[PAD]']\n", + "2469\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2470\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2471\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2472\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", + "2473\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", + "2474\n", + "Label = ['z', '[PAD]', '[PAD]', '[PAD]']\n", + "2475\n", + "Label = ['rnn', '[PAD]', '[PAD]', '[PAD]']\n", + "2476\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2477\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", + "2478\n", + "Label = ['rand', '[PAD]', '[PAD]', '[PAD]']\n", + "2479\n", + "Label = ['y', 'pred', '[PAD]', '[PAD]']\n", + "2480\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2481\n", + "Label = ['n', 'samples', '[PAD]', '[PAD]']\n", + "2482\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2483\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "2484\n", + "Label = ['y', 'pred', 'idx', '[PAD]']\n", + "2485\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2486\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2487\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2488\n", + "Label = ['NearestNeighbors', '[PAD]', '[PAD]', '[PAD]']\n", + "2489\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2490\n", + "Label = ['nn', '[PAD]', '[PAD]', '[PAD]']\n", + "2491\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2492\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2493\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "2494\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "2495\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "2496\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "2497\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2498\n", + "Label = ['bt', '[PAD]', '[PAD]', '[PAD]']\n", + "2499\n", + "Label = ['round', '[PAD]', '[PAD]', '[PAD]']\n", + "2500\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "2501\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2502\n", + "Label = ['dens', '[PAD]', '[PAD]', '[PAD]']\n", + "2503\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "2504\n", + "Label = ['n', 'nodes', '[PAD]', '[PAD]']\n", + "2505\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2506\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2507\n", + "Label = ['pi', '[PAD]', '[PAD]', '[PAD]']\n", + "2508\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "2509\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2510\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2511\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2512\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2513\n", + "Label = ['cos', '[PAD]', '[PAD]', '[PAD]']\n", + "2514\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "2515\n", + "Label = ['n', 'nodes', '[PAD]', '[PAD]']\n", + "2516\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2517\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2518\n", + "Label = ['metric', '[PAD]', '[PAD]', '[PAD]']\n", + "2519\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2520\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2521\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2522\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2523\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2524\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2525\n", + "Label = ['negative', 'outlier', 'factor', '[PAD]']\n", + "2526\n", + "Label = ['score', 'samples', '[PAD]', '[PAD]']\n", + "2527\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2528\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2529\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2530\n", + "Label = ['valid', 'metrics', '[PAD]', '[PAD]']\n", + "2531\n", + "Label = ['nn', 'fit', '[PAD]', '[PAD]']\n", + "2532\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2533\n", + "Label = ['classes', '[PAD]', '[PAD]', '[PAD]']\n", + "2534\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2535\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2536\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "2537\n", + "Label = ['predict', 'proba', '[PAD]', '[PAD]']\n", + "2538\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2539\n", + "Label = ['unlabelled', 'idx', '[PAD]', '[PAD]']\n", + "2540\n", + "Label = ['nonzero', '[PAD]', '[PAD]', '[PAD]']\n", + "2541\n", + "Label = ['meshgrid', '[PAD]', '[PAD]', '[PAD]']\n", + "2542\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2543\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2544\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2545\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2546\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2547\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2548\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2549\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2550\n", + "Label = ['Xs', '[PAD]', '[PAD]', '[PAD]']\n", + "2551\n", + "Label = ['n', 'jobs', '[PAD]', '[PAD]']\n", + "2552\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "2553\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2554\n", + "Label = ['all', 'columns', '[PAD]', '[PAD]']\n", + "2555\n", + "Label = ['regr', '[PAD]', '[PAD]', '[PAD]']\n", + "2556\n", + "Label = ['inverse', 'transform', '[PAD]', '[PAD]']\n", + "2557\n", + "Label = ['inverse', 'func', '[PAD]', '[PAD]']\n", + "2558\n", + "Label = ['regr', '[PAD]', '[PAD]', '[PAD]']\n", + "2559\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2560\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2561\n", + "Label = ['tt', '[PAD]', '[PAD]', '[PAD]']\n", + "2562\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2563\n", + "Label = ['both', '[PAD]', '[PAD]', '[PAD]']\n", + "2564\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2565\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2566\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2567\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2568\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2569\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2570\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "2571\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "2572\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2573\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "2574\n", + "Label = ['X', 'array', '[PAD]', '[PAD]']\n", + "2575\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2576\n", + "Label = ['X', 'array', '[PAD]', '[PAD]']\n", + "2577\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2578\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2579\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2580\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2581\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2582\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2583\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2584\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2585\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2586\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2587\n", + "Label = ['X', 'array', '[PAD]', '[PAD]']\n", + "2588\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "2589\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2590\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2591\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2592\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "2593\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2594\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2595\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2596\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "2597\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "2598\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2599\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "2600\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2601\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2602\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2603\n", + "Label = ['transformers', '[PAD]', '[PAD]', '[PAD]']\n", + "2604\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2605\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2606\n", + "Label = ['makedirs', '[PAD]', '[PAD]', '[PAD]']\n", + "2607\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2608\n", + "Label = ['makedirs', '[PAD]', '[PAD]', '[PAD]']\n", + "2609\n", + "Label = ['fdst', '[PAD]', '[PAD]', '[PAD]']\n", + "2610\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2611\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2612\n", + "Label = ['col', 'idx', '[PAD]', '[PAD]']\n", + "2613\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2614\n", + "Label = ['TARGETS', '[PAD]', '[PAD]', '[PAD]']\n", + "2615\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "2616\n", + "Label = ['face', '[PAD]', '[PAD]', '[PAD]']\n", + "2617\n", + "Label = ['rst', 'file', '[PAD]', '[PAD]']\n", + "2618\n", + "Label = ['data', 'home', '[PAD]', '[PAD]']\n", + "2619\n", + "Label = ['data', 'home', '[PAD]', '[PAD]']\n", + "2620\n", + "Label = ['folders', '[PAD]', '[PAD]', '[PAD]']\n", + "2621\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "2622\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", + "2623\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2624\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "2625\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "2626\n", + "Label = ['image', 'name', '[PAD]', '[PAD]']\n", + "2627\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "2628\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "2629\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "2630\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "2631\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2632\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "2633\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "2634\n", + "Label = ['nz', 'labels', '[PAD]', '[PAD]']\n", + "2635\n", + "Label = ['b', '[PAD]', '[PAD]', '[PAD]']\n", + "2636\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "2637\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2638\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "2639\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2640\n", + "Label = ['faces', '[PAD]', '[PAD]', '[PAD]']\n", + "2641\n", + "Label = ['randint', '[PAD]', '[PAD]', '[PAD]']\n", + "2642\n", + "Label = ['randint', '[PAD]', '[PAD]', '[PAD]']\n", + "2643\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", + "2644\n", + "Label = ['shift', '[PAD]', '[PAD]', '[PAD]']\n", + "2645\n", + "Label = ['y', 'size', '[PAD]', '[PAD]']\n", + "2646\n", + "Label = ['n', 'words', '[PAD]', '[PAD]']\n", + "2647\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2648\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2649\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2650\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "2651\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "2652\n", + "Label = ['D', '[PAD]', '[PAD]', '[PAD]']\n", + "2653\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2654\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2655\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2656\n", + "Label = ['hstack', '[PAD]', '[PAD]', '[PAD]']\n", + "2657\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "2658\n", + "Label = ['col', 'labels', '[PAD]', '[PAD]']\n", + "2659\n", + "Label = ['n', 'row', 'clusters', '[PAD]']\n", + "2660\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "2661\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "2662\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "2663\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "2664\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2665\n", + "Label = ['rst', 'file', '[PAD]', '[PAD]']\n", + "2666\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "2667\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2668\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2669\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "2670\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "2671\n", + "Label = ['X1', '[PAD]', '[PAD]', '[PAD]']\n", + "2672\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2673\n", + "Label = ['query', 'id', '[PAD]', '[PAD]']\n", + "2674\n", + "Label = ['true', 'X', '[PAD]', '[PAD]']\n", + "2675\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "2676\n", + "Label = ['X', '2', '[PAD]', '[PAD]']\n", + "2677\n", + "Label = ['X', '0', '[PAD]', '[PAD]']\n", + "2678\n", + "Label = ['fetch', '20newsgroups', '[PAD]', '[PAD]']\n", + "2679\n", + "Label = ['target', 'names', '[PAD]', '[PAD]']\n", + "2680\n", + "Label = ['filenames', '[PAD]', '[PAD]', '[PAD]']\n", + "2681\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2682\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2683\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2684\n", + "Label = ['centroid', '[PAD]', '[PAD]', '[PAD]']\n", + "2685\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2686\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "2687\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2688\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "2689\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2690\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "2691\n", + "Label = ['wrong', 'std', 'msg', '[PAD]']\n", + "2692\n", + "Label = ['col', '[PAD]', '[PAD]', '[PAD]']\n", + "2693\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2694\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2695\n", + "Label = ['sign', '[PAD]', '[PAD]', '[PAD]']\n", + "2696\n", + "Label = ['errno', '[PAD]', '[PAD]', '[PAD]']\n", + "2697\n", + "Label = ['data2', '[PAD]', '[PAD]', '[PAD]']\n", + "2698\n", + "Label = ['sparse', '[PAD]', '[PAD]', '[PAD]']\n", + "2699\n", + "Label = ['data', 'downloaded', '[PAD]', '[PAD]']\n", + "2700\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2701\n", + "Label = ['assert', 'array', 'equal', '[PAD]']\n", + "2702\n", + "Label = ['count', 'nonzero', '[PAD]', '[PAD]']\n", + "2703\n", + "Label = ['count', 'nonzero', '[PAD]', '[PAD]']\n", + "2704\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "2705\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "2706\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2707\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2708\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "2709\n", + "Label = ['monkeypatch', '[PAD]', '[PAD]', '[PAD]']\n", + "2710\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2711\n", + "Label = ['urlopen', '[PAD]', '[PAD]', '[PAD]']\n", + "2712\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2713\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2714\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2715\n", + "Label = ['first', 'index', '[PAD]', '[PAD]']\n", + "2716\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "2717\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "2718\n", + "Label = ['data', 'home', '[PAD]', '[PAD]']\n", + "2719\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "2720\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "2721\n", + "Label = ['digits', '[PAD]', '[PAD]', '[PAD]']\n", + "2722\n", + "Label = ['min', 'samples', 'split', '[PAD]']\n", + "2723\n", + "Label = ['min', 'samples', 'split', '[PAD]']\n", + "2724\n", + "Label = ['max', 'features', '[PAD]', '[PAD]']\n", + "2725\n", + "Label = ['splitter', '[PAD]', '[PAD]', '[PAD]']\n", + "2726\n", + "Label = ['take', '[PAD]', '[PAD]', '[PAD]']\n", + "2727\n", + "Label = ['take', '[PAD]', '[PAD]', '[PAD]']\n", + "2728\n", + "Label = ['take', '[PAD]', '[PAD]', '[PAD]']\n", + "2729\n", + "Label = ['take', '[PAD]', '[PAD]', '[PAD]']\n", + "2730\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2731\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2732\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2733\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2734\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2735\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "2736\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2737\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2738\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2739\n", + "Label = ['color', '[PAD]', '[PAD]', '[PAD]']\n", + "2740\n", + "Label = ['feature', 'names', '[PAD]', '[PAD]']\n", + "2741\n", + "Label = ['string', 'types', '[PAD]', '[PAD]']\n", + "2742\n", + "Label = ['n', 'node', 'samples', '[PAD]']\n", + "2743\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "2744\n", + "Label = ['value', 'text', '[PAD]', '[PAD]']\n", + "2745\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2746\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "2747\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "2748\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "2749\n", + "Label = ['TREE', 'LEAF', '[PAD]', '[PAD]']\n", + "2750\n", + "Label = ['extents', '[PAD]', '[PAD]', '[PAD]']\n", + "2751\n", + "Label = ['fontsize', '[PAD]', '[PAD]', '[PAD]']\n", + "2752\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2753\n", + "Label = ['filled', '[PAD]', '[PAD]', '[PAD]']\n", + "2754\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2755\n", + "Label = ['lmost', 'sibling', '[PAD]', '[PAD]']\n", + "2756\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "2757\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2758\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2759\n", + "Label = ['contents1', '[PAD]', '[PAD]', '[PAD]']\n", + "2760\n", + "Label = ['finding', '[PAD]', '[PAD]', '[PAD]']\n", + "2761\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2762\n", + "Label = ['y', 'small', '[PAD]', '[PAD]']\n", + "2763\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "2764\n", + "Label = ['children', 'left', '[PAD]', '[PAD]']\n", + "2765\n", + "Label = ['feature', '[PAD]', '[PAD]', '[PAD]']\n", + "2766\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2767\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2768\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2769\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2770\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2771\n", + "Label = ['clf2', '[PAD]', '[PAD]', '[PAD]']\n", + "2772\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2773\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2774\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2775\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2776\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "2777\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "2778\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2779\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "2780\n", + "Label = ['impurity', '[PAD]', '[PAD]', '[PAD]']\n", + "2781\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2782\n", + "Label = ['fractional', 'node', 'weight', '[PAD]']\n", + "2783\n", + "Label = ['weighted', 'n', 'node', 'samples']\n", + "2784\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2785\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2786\n", + "Label = ['y', 'true', '[PAD]', '[PAD]']\n", + "2787\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2788\n", + "Label = ['vstack', '[PAD]', '[PAD]', '[PAD]']\n", + "2789\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2790\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2791\n", + "Label = ['duplicates', '[PAD]', '[PAD]', '[PAD]']\n", + "2792\n", + "Label = ['threshold', '[PAD]', '[PAD]', '[PAD]']\n", + "2793\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2794\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2795\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "2796\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2797\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "2798\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "2799\n", + "Label = ['tree', 'type', '[PAD]', '[PAD]']\n", + "2800\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2801\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "2802\n", + "Label = ['X', '2d', '[PAD]', '[PAD]']\n", + "2803\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "2804\n", + "Label = ['flat', '[PAD]', '[PAD]', '[PAD]']\n", + "2805\n", + "Label = ['left', 'leaf', '[PAD]', '[PAD]']\n", + "2806\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "2807\n", + "Label = ['child', '[PAD]', '[PAD]', '[PAD]']\n", + "2808\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2809\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2810\n", + "Label = ['n', 'splits', '[PAD]', '[PAD]']\n", + "2811\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2812\n", + "Label = ['n', 'splits', '[PAD]', '[PAD]']\n", + "2813\n", + "Label = ['per', 'cls', 'cvs', '[PAD]']\n", + "2814\n", + "Label = ['max', 'train', 'size', '[PAD]']\n", + "2815\n", + "Label = ['max', 'train', 'size', '[PAD]']\n", + "2816\n", + "Label = ['n', 'groups', '[PAD]', '[PAD]']\n", + "2817\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "2818\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2819\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2820\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2821\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "2822\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "2823\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "2824\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "2825\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "2826\n", + "Label = ['n', 'test', '[PAD]', '[PAD]']\n", + "2827\n", + "Label = ['test', 'size', '[PAD]', '[PAD]']\n", + "2828\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "2829\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "2830\n", + "Label = ['train', 'scores', '[PAD]', '[PAD]']\n", + "2831\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "2832\n", + "Label = ['fit', 'params', '[PAD]', '[PAD]']\n", + "2833\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "2834\n", + "Label = ['train', 'scores', '[PAD]', '[PAD]']\n", + "2835\n", + "Label = ['train', 'scores', '[PAD]', '[PAD]']\n", + "2836\n", + "Label = ['scorer', 'name', '[PAD]', '[PAD]']\n", + "2837\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "2838\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "2839\n", + "Label = ['predictions', 'for', 'all', 'classes']\n", + "2840\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "2841\n", + "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "2842\n", + "Label = ['pvalue', '[PAD]', '[PAD]', '[PAD]']\n", + "2843\n", + "Label = ['train', '[PAD]', '[PAD]', '[PAD]']\n", + "2844\n", + "Label = ['X', 'test', '[PAD]', '[PAD]']\n", + "2845\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "2846\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", + "2847\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2848\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "2849\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "2850\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "2851\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "2852\n", + "Label = ['scorer', '[PAD]', '[PAD]', '[PAD]']\n", + "2853\n", + "Label = ['refit', '[PAD]', '[PAD]', '[PAD]']\n", + "2854\n", + "Label = ['fit', 'and', 'score', 'kwargs']\n", + "2855\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "2856\n", + "Label = ['best', 'estimator', '[PAD]', '[PAD]']\n", + "2857\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2858\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "2859\n", + "Label = ['fmt', '[PAD]', '[PAD]', '[PAD]']\n", + "2860\n", + "Label = ['union', '[PAD]', '[PAD]', '[PAD]']\n", + "2861\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2862\n", + "Label = ['get', 'n', 'splits', '[PAD]']\n", + "2863\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2864\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2865\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2866\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2867\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2868\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "2869\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2870\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2871\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2872\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2873\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "2874\n", + "Label = ['n', 'train', '[PAD]', '[PAD]']\n", + "2875\n", + "Label = ['row', 'with', 'many', 'zeros']\n", + "2876\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2877\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "2878\n", + "Label = ['tolist', '[PAD]', '[PAD]', '[PAD]']\n", + "2879\n", + "Label = ['get', 'n', 'splits', '[PAD]']\n", + "2880\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2881\n", + "Label = ['cv', '[PAD]', '[PAD]', '[PAD]']\n", + "2882\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2883\n", + "Label = ['X', '4d', '[PAD]', '[PAD]']\n", + "2884\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2885\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2886\n", + "Label = ['types', '[PAD]', '[PAD]', '[PAD]']\n", + "2887\n", + "Label = ['X', 'train2', '[PAD]', '[PAD]']\n", + "2888\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "2889\n", + "Label = ['assert', 'equal', '[PAD]', '[PAD]']\n", + "2890\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2891\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2892\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "2893\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "2894\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "2895\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2896\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2897\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2898\n", + "Label = ['y2', '[PAD]', '[PAD]', '[PAD]']\n", + "2899\n", + "Label = ['cv', 'results', '[PAD]', '[PAD]']\n", + "2900\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "2901\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "2902\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "2903\n", + "Label = ['expected', 'neg', 'mse', '[PAD]']\n", + "2904\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "2905\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "2906\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2907\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "2908\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "2909\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2910\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2911\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2912\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2913\n", + "Label = ['predictions', '[PAD]', '[PAD]', '[PAD]']\n", + "2914\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "2915\n", + "Label = ['linspace', '[PAD]', '[PAD]', '[PAD]']\n", + "2916\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2917\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2918\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2919\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2920\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "2921\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2922\n", + "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "2923\n", + "Label = ['scores1', '[PAD]', '[PAD]', '[PAD]']\n", + "2924\n", + "Label = ['scores2', '[PAD]', '[PAD]', '[PAD]']\n", + "2925\n", + "Label = ['scores3', '[PAD]', '[PAD]', '[PAD]']\n", + "2926\n", + "Label = ['filterwarnings', '[PAD]', '[PAD]', '[PAD]']\n", + "2927\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2928\n", + "Label = ['exp', 'pred', 'test', '[PAD]']\n", + "2929\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "2930\n", + "Label = ['X', 'df', '[PAD]', '[PAD]']\n", + "2931\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2932\n", + "Label = ['grid', '[PAD]', '[PAD]', '[PAD]']\n", + "2933\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "2934\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2935\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "2936\n", + "Label = ['searcher', '[PAD]', '[PAD]', '[PAD]']\n", + "2937\n", + "Label = ['filterwarnings', '[PAD]', '[PAD]', '[PAD]']\n", + "2938\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2939\n", + "Label = ['search', 'auc', '[PAD]', '[PAD]']\n", + "2940\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2941\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2942\n", + "Label = ['grid', 'search', '[PAD]', '[PAD]']\n", + "2943\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "2944\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2945\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "2946\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2947\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2948\n", + "Label = ['X', '4d', '[PAD]', '[PAD]']\n", + "2949\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "2950\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "2951\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "2952\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2953\n", + "Label = ['score', 'keys', '[PAD]', '[PAD]']\n", + "2954\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "2955\n", + "Label = ['getmaskarray', '[PAD]', '[PAD]', '[PAD]']\n", + "2956\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "2957\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "2958\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "2959\n", + "Label = ['test', 'cv', 'scores', '[PAD]']\n", + "2960\n", + "Label = ['cv', 'results', '[PAD]', '[PAD]']\n", + "2961\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['cv', 'results', '[PAD]', '[PAD]']\n", + "2962\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "2963\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "2964\n", + "Label = ['assert', 'equal', '[PAD]', '[PAD]']\n", + "2965\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "2966\n", + "Label = ['cv', 'scores', '[PAD]', '[PAD]']\n", + "2967\n", + "Label = ['cv', 'results', '[PAD]', '[PAD]']\n", + "2968\n", + "Label = ['gs', '[PAD]', '[PAD]', '[PAD]']\n", + "2969\n", + "Label = ['gs4', '[PAD]', '[PAD]', '[PAD]']\n", + "2970\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2971\n", + "Label = ['grid', '[PAD]', '[PAD]', '[PAD]']\n", + "2972\n", + "Label = ['grid', '[PAD]', '[PAD]', '[PAD]']\n", + "2973\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2974\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "2975\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "2976\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "2977\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "2978\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "2979\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "2980\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "2981\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "2982\n", + "Label = ['X', 'embedded', '[PAD]', '[PAD]']\n", + "2983\n", + "Label = ['tsne', '[PAD]', '[PAD]', '[PAD]']\n", + "2984\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "2985\n", + "Label = ['flat', '[PAD]', '[PAD]', '[PAD]']\n", + "2986\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "2987\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "2988\n", + "Label = ['eigen', 'values', '[PAD]', '[PAD]']\n", + "2989\n", + "Label = ['flat', '[PAD]', '[PAD]', '[PAD]']\n", + "2990\n", + "Label = ['M', '[PAD]', '[PAD]', '[PAD]']\n", + "2991\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2992\n", + "Label = ['searchsorted', '[PAD]', '[PAD]', '[PAD]']\n", + "2993\n", + "Label = ['meshgrid', '[PAD]', '[PAD]', '[PAD]']\n", + "2994\n", + "Label = ['nbrs', '[PAD]', '[PAD]', '[PAD]']\n", + "2995\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "2996\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "2997\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "2998\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "2999\n", + "Label = ['seeds', '[PAD]', '[PAD]', '[PAD]']\n", + "3000\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3001\n", + "Label = ['adjacency', '[PAD]', '[PAD]', '[PAD]']\n", + "3002\n", + "Label = ['rand', '[PAD]', '[PAD]', '[PAD]']\n", + "3003\n", + "Label = ['nbrs', '[PAD]', '[PAD]', '[PAD]']\n", + "3004\n", + "Label = ['kernel', 'pca', '[PAD]', '[PAD]']\n", + "3005\n", + "Label = ['n', 'components', '[PAD]', '[PAD]']\n", + "3006\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "3007\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "3008\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3009\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", + "3010\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "3011\n", + "Label = ['X', '2d', 'grid', '[PAD]']\n", + "3012\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3013\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3014\n", + "Label = ['distances', 'nn', '[PAD]', '[PAD]']\n", + "3015\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3016\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3017\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "3018\n", + "Label = ['neighbors', 'nn', '[PAD]', '[PAD]']\n", + "3019\n", + "Label = ['P', '[PAD]', '[PAD]', '[PAD]']\n", + "3020\n", + "Label = ['tsne', '[PAD]', '[PAD]', '[PAD]']\n", + "3021\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3022\n", + "Label = ['bad', 'dist', '[PAD]', '[PAD]']\n", + "3023\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3024\n", + "Label = ['tsne', '[PAD]', '[PAD]', '[PAD]']\n", + "3025\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3026\n", + "Label = ['tsne', '[PAD]', '[PAD]', '[PAD]']\n", + "3027\n", + "Label = ['angle', '[PAD]', '[PAD]', '[PAD]']\n", + "3028\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3029\n", + "Label = ['pos', 'input', '[PAD]', '[PAD]']\n", + "3030\n", + "Label = ['random', 'state', '[PAD]', '[PAD]']\n", + "3031\n", + "Label = ['tsne', '[PAD]', '[PAD]', '[PAD]']\n", + "3032\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "3033\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3034\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3035\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3036\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "3037\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3038\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "3039\n", + "Label = ['sim', '[PAD]', '[PAD]', '[PAD]']\n", + "3040\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3041\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3042\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3043\n", + "Label = ['affinity', '[PAD]', '[PAD]', '[PAD]']\n", + "3044\n", + "Label = ['se', 'amg', '[PAD]', '[PAD]']\n", + "3045\n", + "Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n", + "3046\n", + "Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n", + "3047\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3048\n", + "Label = ['quantiles', '[PAD]', '[PAD]', '[PAD]']\n", + "3049\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "3050\n", + "Label = ['n', 'bins', '[PAD]', '[PAD]']\n", + "3051\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", + "3052\n", + "Label = ['eps', '[PAD]', '[PAD]', '[PAD]']\n", + "3053\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", + "3054\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "3055\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "3056\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3057\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3058\n", + "Label = ['n', 'samples', 'seen', '[PAD]']\n", + "3059\n", + "Label = ['sparse', 'constructor', '[PAD]', '[PAD]']\n", + "3060\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3061\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3062\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3063\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3064\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3065\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3066\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3067\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "3068\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "3069\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "3070\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "3071\n", + "Label = ['combinations', '[PAD]', '[PAD]', '[PAD]']\n", + "3072\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3073\n", + "Label = ['norms', '[PAD]', '[PAD]', '[PAD]']\n", + "3074\n", + "Label = ['norms', '[PAD]', '[PAD]', '[PAD]']\n", + "3075\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3076\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", + "3077\n", + "Label = ['n', 'quantiles', '[PAD]', '[PAD]']\n", + "3078\n", + "Label = ['clip', 'max', '[PAD]', '[PAD]']\n", + "3079\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3080\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3081\n", + "Label = ['power', '[PAD]', '[PAD]', '[PAD]']\n", + "3082\n", + "Label = ['spacing', '[PAD]', '[PAD]', '[PAD]']\n", + "3083\n", + "Label = ['spacing', '[PAD]', '[PAD]', '[PAD]']\n", + "3084\n", + "Label = ['power', '[PAD]', '[PAD]', '[PAD]']\n", + "3085\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3086\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "3087\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3088\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3089\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3090\n", + "Label = ['statistics', '[PAD]', '[PAD]', '[PAD]']\n", + "3091\n", + "Label = ['X', 'sel', '[PAD]', '[PAD]']\n", + "3092\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3093\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3094\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3095\n", + "Label = ['n', 'values', '[PAD]', '[PAD]']\n", + "3096\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3097\n", + "Label = ['row', 'indices', '[PAD]', '[PAD]']\n", + "3098\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "3099\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "3100\n", + "Label = ['column', 'indices', '[PAD]', '[PAD]']\n", + "3101\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "3102\n", + "Label = ['legacy', 'mode', '[PAD]', '[PAD]']\n", + "3103\n", + "Label = ['find', 'common', 'type', '[PAD]']\n", + "3104\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3105\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3106\n", + "Label = ['names', '[PAD]', '[PAD]', '[PAD]']\n", + "3107\n", + "Label = ['find', 'common', 'type', '[PAD]']\n", + "3108\n", + "Label = ['categories', '[PAD]', '[PAD]', '[PAD]']\n", + "3109\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3110\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3111\n", + "Label = ['uniques', '[PAD]', '[PAD]', '[PAD]']\n", + "3112\n", + "Label = ['table', '[PAD]', '[PAD]', '[PAD]']\n", + "3113\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3114\n", + "Label = ['valid', 'mask', '[PAD]', '[PAD]']\n", + "3115\n", + "Label = ['threshold', '[PAD]', '[PAD]', '[PAD]']\n", + "3116\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3117\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3118\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "3119\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "3120\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "3121\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3122\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3123\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3124\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3125\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3126\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "3127\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "3128\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3129\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3130\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "3131\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "3132\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "3133\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3134\n", + "Label = ['offsets', '[PAD]', '[PAD]', '[PAD]']\n", + "3135\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3136\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "3137\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3138\n", + "Label = ['n', 'samples', 'seen', '[PAD]']\n", + "3139\n", + "Label = ['trans', '1', '[PAD]', '[PAD]']\n", + "3140\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "3141\n", + "Label = ['catch', 'warnings', '[PAD]', '[PAD]']\n", + "3142\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "3143\n", + "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", + "3144\n", + "Label = ['std', '[PAD]', '[PAD]', '[PAD]']\n", + "3145\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3146\n", + "Label = ['q', '[PAD]', '[PAD]', '[PAD]']\n", + "3147\n", + "Label = ['q', 'range', '[PAD]', '[PAD]']\n", + "3148\n", + "Label = ['X', 'neg', '[PAD]', '[PAD]']\n", + "3149\n", + "Label = ['transformer', '[PAD]', '[PAD]', '[PAD]']\n", + "3150\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "3151\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3152\n", + "Label = ['X', 'expected', '[PAD]', '[PAD]']\n", + "3153\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "3154\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "3155\n", + "Label = ['X', 'trans', '[PAD]', '[PAD]']\n", + "3156\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "3157\n", + "Label = ['X', 'expected', '[PAD]', '[PAD]']\n", + "3158\n", + "Label = ['X', 'expected', 'new', '[PAD]']\n", + "3159\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3160\n", + "Label = ['X', 'expected', '[PAD]', '[PAD]']\n", + "3161\n", + "Label = ['abs', '[PAD]', '[PAD]', '[PAD]']\n", + "3162\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3163\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3164\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3165\n", + "Label = ['norms', '[PAD]', '[PAD]', '[PAD]']\n", + "3166\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3167\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3168\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3169\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3170\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3171\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "3172\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3173\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3174\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3175\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", + "3176\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3177\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3178\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3179\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3180\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "3181\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3182\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "3183\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3184\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3185\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3186\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3187\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3188\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3189\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3190\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3191\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3192\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "3193\n", + "Label = ['get', 'params', '[PAD]', '[PAD]']\n", + "3194\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3195\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3196\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3197\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3198\n", + "Label = ['tolist', '[PAD]', '[PAD]', '[PAD]']\n", + "3199\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3200\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "3201\n", + "Label = ['issubdtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3202\n", + "Label = ['parametrize', '[PAD]', '[PAD]', '[PAD]']\n", + "3203\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3204\n", + "Label = ['X', 'tr', '[PAD]', '[PAD]']\n", + "3205\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3206\n", + "Label = ['issubdtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3207\n", + "Label = ['DataFrame', '[PAD]', '[PAD]', '[PAD]']\n", + "3208\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3209\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "3210\n", + "Label = ['F', '[PAD]', '[PAD]', '[PAD]']\n", + "3211\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3212\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3213\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "3214\n", + "Label = ['F', '[PAD]', '[PAD]', '[PAD]']\n", + "3215\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3216\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3217\n", + "Label = ['expm1', '[PAD]', '[PAD]', '[PAD]']\n", + "3218\n", + "Label = ['trans', '[PAD]', '[PAD]', '[PAD]']\n", + "3219\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "3220\n", + "Label = ['expected', '[PAD]', '[PAD]', '[PAD]']\n", + "3221\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3222\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3223\n", + "Label = ['inverse', 'transform', '[PAD]', '[PAD]']\n", + "3224\n", + "Label = ['inverse', 'transform', '[PAD]', '[PAD]']\n", + "3225\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3226\n", + "Label = ['indicator', 'mat', '[PAD]', '[PAD]']\n", + "3227\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "3228\n", + "Label = ['matrix', '[PAD]', '[PAD]', '[PAD]']\n", + "3229\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3230\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3231\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "3232\n", + "Label = ['inversed', '[PAD]', '[PAD]', '[PAD]']\n", + "3233\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3234\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "3235\n", + "Label = ['sparse', 'matrix', '[PAD]', '[PAD]']\n", + "3236\n", + "Label = ['got', '[PAD]', '[PAD]', '[PAD]']\n", + "3237\n", + "Label = ['got', '[PAD]', '[PAD]', '[PAD]']\n", + "3238\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "3239\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3240\n", + "Label = ['isnan', '[PAD]', '[PAD]', '[PAD]']\n", + "3241\n", + "Label = ['isnan', '[PAD]', '[PAD]', '[PAD]']\n", + "3242\n", + "Label = ['warns', '[PAD]', '[PAD]', '[PAD]']\n", + "3243\n", + "Label = ['isnan', '[PAD]', '[PAD]', '[PAD]']\n", + "3244\n", + "Label = ['csr', 'matrix', '[PAD]', '[PAD]']\n", + "3245\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3246\n", + "Label = ['X', 'trans', '[PAD]', '[PAD]']\n", + "3247\n", + "Label = ['statistics', '[PAD]', '[PAD]', '[PAD]']\n", + "3248\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3249\n", + "Label = ['statistics', '[PAD]', '[PAD]', '[PAD]']\n", + "3250\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "3251\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3252\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3253\n", + "Label = ['z', '[PAD]', '[PAD]', '[PAD]']\n", + "3254\n", + "Label = ['nb', 'missing', 'values', '[PAD]']\n", + "3255\n", + "Label = ['nb', 'values', '[PAD]', '[PAD]']\n", + "3256\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", + "3257\n", + "Label = ['imputer', '[PAD]', '[PAD]', '[PAD]']\n", + "3258\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3259\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3260\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3261\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "3262\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "3263\n", + "Label = ['cols', '[PAD]', '[PAD]', '[PAD]']\n", + "3264\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3265\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3266\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "3267\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3268\n", + "Label = ['atleast', '2d', '[PAD]', '[PAD]']\n", + "3269\n", + "Label = ['assume', 'centered', '[PAD]', '[PAD]']\n", + "3270\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3271\n", + "Label = ['atleast', '2d', '[PAD]', '[PAD]']\n", + "3272\n", + "Label = ['atleast', '2d', '[PAD]', '[PAD]']\n", + "3273\n", + "Label = ['den', '[PAD]', '[PAD]', '[PAD]']\n", + "3274\n", + "Label = ['assume', 'centered', '[PAD]', '[PAD]']\n", + "3275\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3276\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3277\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "3278\n", + "Label = ['halves', 'start', '[PAD]', '[PAD]']\n", + "3279\n", + "Label = ['location', '[PAD]', '[PAD]', '[PAD]']\n", + "3280\n", + "Label = ['h', 'subset', '[PAD]', '[PAD]']\n", + "3281\n", + "Label = ['all', 'best', 'covariances', '[PAD]']\n", + "3282\n", + "Label = ['locations', 'best', '[PAD]', '[PAD]']\n", + "3283\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3284\n", + "Label = ['dist', '[PAD]', '[PAD]', '[PAD]']\n", + "3285\n", + "Label = ['gap', '[PAD]', '[PAD]', '[PAD]']\n", + "3286\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3287\n", + "Label = ['flat', '[PAD]', '[PAD]', '[PAD]']\n", + "3288\n", + "Label = ['inv', '[PAD]', '[PAD]', '[PAD]']\n", + "3289\n", + "Label = ['covariance', '[PAD]', '[PAD]', '[PAD]']\n", + "3290\n", + "Label = ['coefs', '[PAD]', '[PAD]', '[PAD]']\n", + "3291\n", + "Label = ['covariance', '[PAD]', '[PAD]', '[PAD]']\n", + "3292\n", + "Label = ['this', 'score', '[PAD]', '[PAD]']\n", + "3293\n", + "Label = ['alpha', '0', '[PAD]', '[PAD]']\n", + "3294\n", + "Label = ['percentile', '[PAD]', '[PAD]', '[PAD]']\n", + "3295\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3296\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3297\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3298\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3299\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "3300\n", + "Label = ['covariance', '[PAD]', '[PAD]', '[PAD]']\n", + "3301\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3302\n", + "Label = ['score', 'samples', '[PAD]', '[PAD]']\n", + "3303\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3304\n", + "Label = ['shrinkage', '[PAD]', '[PAD]', '[PAD]']\n", + "3305\n", + "Label = ['covariance', '[PAD]', '[PAD]', '[PAD]']\n", + "3306\n", + "Label = ['X', '1d', '[PAD]', '[PAD]']\n", + "3307\n", + "Label = ['covariance', '[PAD]', '[PAD]', '[PAD]']\n", + "3308\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3309\n", + "Label = ['icov', 'R', '[PAD]', '[PAD]']\n", + "3310\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "3311\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3312\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3313\n", + "Label = ['learning', 'rate', '[PAD]', '[PAD]']\n", + "3314\n", + "Label = ['momentum', '[PAD]', '[PAD]', '[PAD]']\n", + "3315\n", + "Label = ['updates', '[PAD]', '[PAD]', '[PAD]']\n", + "3316\n", + "Label = ['beta', '1', '[PAD]', '[PAD]']\n", + "3317\n", + "Label = ['beta', '2', '[PAD]', '[PAD]']\n", + "3318\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3319\n", + "Label = ['out', 'activation', '[PAD]', '[PAD]']\n", + "3320\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "3321\n", + "Label = ['intercept', 'grads', '[PAD]', '[PAD]']\n", + "3322\n", + "Label = ['fit', 'stochastic', '[PAD]', '[PAD]']\n", + "3323\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", + "3324\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "3325\n", + "Label = ['accumulated', 'loss', '[PAD]', '[PAD]']\n", + "3326\n", + "Label = ['loss', '[PAD]', '[PAD]', '[PAD]']\n", + "3327\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "3328\n", + "Label = ['layer', 'units', '[PAD]', '[PAD]']\n", + "3329\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3330\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3331\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3332\n", + "Label = ['logaddexp', '[PAD]', '[PAD]', '[PAD]']\n", + "3333\n", + "Label = ['components', '[PAD]', '[PAD]', '[PAD]']\n", + "3334\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3335\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3336\n", + "Label = ['components', '[PAD]', '[PAD]', '[PAD]']\n", + "3337\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3338\n", + "Label = ['shapes', '[PAD]', '[PAD]', '[PAD]']\n", + "3339\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3340\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "3341\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "3342\n", + "Label = ['velocity', '[PAD]', '[PAD]', '[PAD]']\n", + "3343\n", + "Label = ['updates', '[PAD]', '[PAD]', '[PAD]']\n", + "3344\n", + "Label = ['expected', '[PAD]', '[PAD]', '[PAD]']\n", + "3345\n", + "Label = ['learning', 'rate', '[PAD]', '[PAD]']\n", + "3346\n", + "Label = ['intercept', 'grads', '[PAD]', '[PAD]']\n", + "3347\n", + "Label = ['intercept', 'velocity', '[PAD]', '[PAD]']\n", + "3348\n", + "Label = ['intercepts', '[PAD]', '[PAD]', '[PAD]']\n", + "3349\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3350\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3351\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "3352\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3353\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "3354\n", + "Label = ['mlp', '[PAD]', '[PAD]', '[PAD]']\n", + "3355\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3356\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3357\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3358\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3359\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3360\n", + "Label = ['clf', '[PAD]', '[PAD]', '[PAD]']\n", + "3361\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3362\n", + "Label = ['y', '3classes', '[PAD]', '[PAD]']\n", + "3363\n", + "Label = ['n', 'iter', 'no', 'change']\n", + "3364\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3365\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "3366\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3367\n", + "Label = ['Xa', '[PAD]', '[PAD]', '[PAD]']\n", + "3368\n", + "Label = ['string', 'types', '[PAD]', '[PAD]']\n", + "3369\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3370\n", + "Label = ['token', '[PAD]', '[PAD]', '[PAD]']\n", + "3371\n", + "Label = ['doc', '[PAD]', '[PAD]', '[PAD]']\n", + "3372\n", + "Label = ['n', 'features', '[PAD]', '[PAD]']\n", + "3373\n", + "Label = ['Integral', '[PAD]', '[PAD]', '[PAD]']\n", + "3374\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "3375\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "3376\n", + "Label = ['term', '[PAD]', '[PAD]', '[PAD]']\n", + "3377\n", + "Label = ['Integral', '[PAD]', '[PAD]', '[PAD]']\n", + "3378\n", + "Label = ['validate', 'vocabulary', '[PAD]', '[PAD]']\n", + "3379\n", + "Label = ['nonzero', '[PAD]', '[PAD]', '[PAD]']\n", + "3380\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3381\n", + "Label = ['iteritems', '[PAD]', '[PAD]', '[PAD]']\n", + "3382\n", + "Label = ['spdiags', '[PAD]', '[PAD]', '[PAD]']\n", + "3383\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3384\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3385\n", + "Label = ['ravel', '[PAD]', '[PAD]', '[PAD]']\n", + "3386\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "3387\n", + "Label = ['st', '[PAD]', '[PAD]', '[PAD]']\n", + "3388\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", + "3389\n", + "Label = ['j', 's', '[PAD]', '[PAD]']\n", + "3390\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3391\n", + "Label = ['raw', 'X', '[PAD]', '[PAD]']\n", + "3392\n", + "Label = ['csr', 'matrix', '[PAD]', '[PAD]']\n", + "3393\n", + "Label = ['cnga', '[PAD]', '[PAD]', '[PAD]']\n", + "3394\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "3395\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3396\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3397\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "3398\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3399\n", + "Label = ['pipeline', '[PAD]', '[PAD]', '[PAD]']\n", + "3400\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3401\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3402\n", + "Label = ['toarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3403\n", + "Label = ['int32', '[PAD]', '[PAD]', '[PAD]']\n", + "3404\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "3405\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3406\n", + "Label = ['mgrid', '[PAD]', '[PAD]', '[PAD]']\n", + "3407\n", + "Label = ['assert', 'array', 'equal', '[PAD]']\n", + "3408\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "3409\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3410\n", + "Label = ['face', '[PAD]', '[PAD]', '[PAD]']\n", + "3411\n", + "Label = ['face', '[PAD]', '[PAD]', '[PAD]']\n", + "3412\n", + "Label = ['expected', 'n', 'patches', '[PAD]']\n", + "3413\n", + "Label = ['faces', '[PAD]', '[PAD]', '[PAD]']\n", + "3414\n", + "Label = ['image', 'shapes', '1D', '[PAD]']\n", + "3415\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3416\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3417\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3418\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "3419\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3420\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "3421\n", + "Label = ['min', '[PAD]', '[PAD]', '[PAD]']\n", + "3422\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3423\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3424\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3425\n", + "Label = ['Xt', '[PAD]', '[PAD]', '[PAD]']\n", + "3426\n", + "Label = ['fit', 'transform', '[PAD]', '[PAD]']\n", + "3427\n", + "Label = ['permutation', '[PAD]', '[PAD]', '[PAD]']\n", + "3428\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", + "3429\n", + "Label = ['min', 'cluster', 'size', '[PAD]']\n", + "3430\n", + "Label = ['radius', 'neighbors', '[PAD]', '[PAD]']\n", + "3431\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3432\n", + "Label = ['labels', '[PAD]', '[PAD]', '[PAD]']\n", + "3433\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3434\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3435\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3436\n", + "Label = ['avg', 'reach1', '[PAD]', '[PAD]']\n", + "3437\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3438\n", + "Label = ['append', 'subcluster', '[PAD]', '[PAD]']\n", + "3439\n", + "Label = ['squared', 'sum', '[PAD]', '[PAD]']\n", + "3440\n", + "Label = ['global', 'clustering', '[PAD]', '[PAD]']\n", + "3441\n", + "Label = ['clusterer', '[PAD]', '[PAD]', '[PAD]']\n", + "3442\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "3443\n", + "Label = ['labels', '[PAD]', '[PAD]', '[PAD]']\n", + "3444\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3445\n", + "Label = ['affinity', 'matrix', '[PAD]', '[PAD]']\n", + "3446\n", + "Label = ['affinity', 'matrix', '[PAD]', '[PAD]']\n", + "3447\n", + "Label = ['col', 'diag', '[PAD]', '[PAD]']\n", + "3448\n", + "Label = ['n', 'svd', 'vecs', '[PAD]']\n", + "3449\n", + "Label = ['u', '[PAD]', '[PAD]', '[PAD]']\n", + "3450\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "3451\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3452\n", + "Label = ['columns', '[PAD]', '[PAD]', '[PAD]']\n", + "3453\n", + "Label = ['vstack', '[PAD]', '[PAD]', '[PAD]']\n", + "3454\n", + "Label = ['row', 'labels', '[PAD]', '[PAD]']\n", + "3455\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "3456\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "3457\n", + "Label = ['neighborhoods', '[PAD]', '[PAD]', '[PAD]']\n", + "3458\n", + "Label = ['clust', '[PAD]', '[PAD]', '[PAD]']\n", + "3459\n", + "Label = ['isspmatrix', 'lil', '[PAD]', '[PAD]']\n", + "3460\n", + "Label = ['idx', 'i', '[PAD]', '[PAD]']\n", + "3461\n", + "Label = ['idx', 'j', '[PAD]', '[PAD]']\n", + "3462\n", + "Label = ['mst', 'array', '[PAD]', '[PAD]']\n", + "3463\n", + "Label = ['distances', '[PAD]', '[PAD]', '[PAD]']\n", + "3464\n", + "Label = ['distances', '[PAD]', '[PAD]', '[PAD]']\n", + "3465\n", + "Label = ['inertia', '[PAD]', '[PAD]', '[PAD]']\n", + "3466\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3467\n", + "Label = ['ini', '[PAD]', '[PAD]', '[PAD]']\n", + "3468\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3469\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3470\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "3471\n", + "Label = ['n', 'clusters', '[PAD]', '[PAD]']\n", + "3472\n", + "Label = ['linkage', '[PAD]', '[PAD]', '[PAD]']\n", + "3473\n", + "Label = ['linkage', '[PAD]', '[PAD]', '[PAD]']\n", + "3474\n", + "Label = ['labels', '[PAD]', '[PAD]', '[PAD]']\n", + "3475\n", + "Label = ['labels', '[PAD]', '[PAD]', '[PAD]']\n", + "3476\n", + "Label = ['trial', '[PAD]', '[PAD]', '[PAD]']\n", + "3477\n", + "Label = ['kmeans', 'single', '[PAD]', '[PAD]']\n", + "3478\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3479\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3480\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3481\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "3482\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3483\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3484\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3485\n", + "Label = ['new', 'centers', '[PAD]', '[PAD]']\n", + "3486\n", + "Label = ['choice', '[PAD]', '[PAD]', '[PAD]']\n", + "3487\n", + "Label = ['no', 'improvement', '[PAD]', '[PAD]']\n", + "3488\n", + "Label = ['labels', '[PAD]', '[PAD]', '[PAD]']\n", + "3489\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "3490\n", + "Label = ['counts', '[PAD]', '[PAD]', '[PAD]']\n", + "3491\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3492\n", + "Label = ['eps', '[PAD]', '[PAD]', '[PAD]']\n", + "3493\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3494\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "3495\n", + "Label = ['C5', '[PAD]', '[PAD]', '[PAD]']\n", + "3496\n", + "Label = ['randn', '[PAD]', '[PAD]', '[PAD]']\n", + "3497\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "3498\n", + "Label = ['clust', '[PAD]', '[PAD]', '[PAD]']\n", + "3499\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3500\n", + "Label = ['core', 'distances', '[PAD]', '[PAD]']\n", + "3501\n", + "Label = ['predecessor', '[PAD]', '[PAD]', '[PAD]']\n", + "3502\n", + "Label = ['core', 'distances', '[PAD]', '[PAD]']\n", + "3503\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3504\n", + "Label = ['cluster', 'center', 'indices', '[PAD]']\n", + "3505\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3506\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3507\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3508\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3509\n", + "Label = ['nnz', '[PAD]', '[PAD]', '[PAD]']\n", + "3510\n", + "Label = ['components', '[PAD]', '[PAD]', '[PAD]']\n", + "3511\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3512\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3513\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3514\n", + "Label = ['n', 'clusters', '2', '[PAD]']\n", + "3515\n", + "Label = ['core3', '[PAD]', '[PAD]', '[PAD]']\n", + "3516\n", + "Label = ['core', 'samples', '[PAD]', '[PAD]']\n", + "3517\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3518\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3519\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3520\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3521\n", + "Label = ['ground', 'truth', '[PAD]', '[PAD]']\n", + "3522\n", + "Label = ['S', '[PAD]', '[PAD]', '[PAD]']\n", + "3523\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3524\n", + "Label = ['row', 'sum', '[PAD]', '[PAD]']\n", + "3525\n", + "Label = ['mat', '[PAD]', '[PAD]', '[PAD]']\n", + "3526\n", + "Label = ['biclusters', '[PAD]', '[PAD]', '[PAD]']\n", + "3527\n", + "Label = ['biclusters', '[PAD]', '[PAD]', '[PAD]']\n", + "3528\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3529\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3530\n", + "Label = ['coo', 'matrix', '[PAD]', '[PAD]']\n", + "3531\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3532\n", + "Label = ['n', 'nodes', '[PAD]', '[PAD]']\n", + "3533\n", + "Label = ['lil', 'matrix', '[PAD]', '[PAD]']\n", + "3534\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "3535\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "3536\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3537\n", + "Label = ['true', 'labels', '[PAD]', '[PAD]']\n", + "3538\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3539\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", + "3540\n", + "Label = ['unstructured', 'items', '[PAD]', '[PAD]']\n", + "3541\n", + "Label = ['ones', '[PAD]', '[PAD]', '[PAD]']\n", + "3542\n", + "Label = ['rng', '[PAD]', '[PAD]', '[PAD]']\n", + "3543\n", + "Label = ['expected', 'centers', '[PAD]', '[PAD]']\n", + "3544\n", + "Label = ['dist', '[PAD]', '[PAD]', '[PAD]']\n", + "3545\n", + "Label = ['buffer', '[PAD]', '[PAD]', '[PAD]']\n", + "3546\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "3547\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3548\n", + "Label = ['rnd', '[PAD]', '[PAD]', '[PAD]']\n", + "3549\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3550\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3551\n", + "Label = ['mb', 'k', 'means', '[PAD]']\n", + "3552\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3553\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "3554\n", + "Label = ['array', 'init', '[PAD]', '[PAD]']\n", + "3555\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "3556\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3557\n", + "Label = ['c2', '[PAD]', '[PAD]', '[PAD]']\n", + "3558\n", + "Label = ['km2', '[PAD]', '[PAD]', '[PAD]']\n", + "3559\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "3560\n", + "Label = ['float32', '[PAD]', '[PAD]', '[PAD]']\n", + "3561\n", + "Label = ['float32', '[PAD]', '[PAD]', '[PAD]']\n", + "3562\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3563\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3564\n", + "Label = ['cluster', 'centers', '[PAD]', '[PAD]']\n", + "3565\n", + "Label = ['cluster', 'centers', '[PAD]', '[PAD]']\n", + "3566\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3567\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3568\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3569\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "3570\n", + "Label = ['indptr', '[PAD]', '[PAD]', '[PAD]']\n", + "3571\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "3572\n", + "Label = ['csc', 'matrix', '[PAD]', '[PAD]']\n", + "3573\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "3574\n", + "Label = ['f', 'ind', '[PAD]', '[PAD]']\n", + "3575\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "3576\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3577\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "3578\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "3579\n", + "Label = ['entered', '[PAD]', '[PAD]', '[PAD]']\n", + "3580\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3581\n", + "Label = ['ordering', '[PAD]', '[PAD]', '[PAD]']\n", + "3582\n", + "Label = ['estimators', '[PAD]', '[PAD]', '[PAD]']\n", + "3583\n", + "Label = ['estimators', '[PAD]', '[PAD]', '[PAD]']\n", + "3584\n", + "Label = ['estimators', '[PAD]', '[PAD]', '[PAD]']\n", + "3585\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "3586\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "3587\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "3588\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "3589\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "3590\n", + "Label = ['varargs', '[PAD]', '[PAD]', '[PAD]']\n", + "3591\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "3592\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "3593\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "3594\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "3595\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "3596\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3597\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "3598\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "3599\n", + "Label = ['prnt', '[PAD]', '[PAD]', '[PAD]']\n", + "3600\n", + "Label = ['inf', '[PAD]', '[PAD]', '[PAD]']\n", + "3601\n", + "Label = ['Q', '[PAD]', '[PAD]', '[PAD]']\n", + "3602\n", + "Label = ['Q', '[PAD]', '[PAD]', '[PAD]']\n", + "3603\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3604\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "3605\n", + "Label = ['sign', '[PAD]', '[PAD]', '[PAD]']\n", + "3606\n", + "Label = ['new', 'sum', '[PAD]', '[PAD]']\n", + "3607\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3608\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "3609\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "3610\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3611\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3612\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "3613\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "3614\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3615\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "3616\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "3617\n", + "Label = ['issparse', '[PAD]', '[PAD]', '[PAD]']\n", + "3618\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3619\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3620\n", + "Label = ['X', 'pred2', '[PAD]', '[PAD]']\n", + "3621\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3622\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "3623\n", + "Label = ['X', 'noise', '[PAD]', '[PAD]']\n", + "3624\n", + "Label = ['set', 'params', '[PAD]', '[PAD]']\n", + "3625\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3626\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "3627\n", + "Label = ['print', 'exc', '[PAD]', '[PAD]']\n", + "3628\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3629\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "3630\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "3631\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "3632\n", + "Label = ['decision', 'y', '[PAD]', '[PAD]']\n", + "3633\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "3634\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "3635\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "3636\n", + "Label = ['score', '[PAD]', '[PAD]', '[PAD]']\n", + "3637\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3638\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "3639\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3640\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3641\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3642\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "3643\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "3644\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "3645\n", + "Label = ['obj', 'name', '[PAD]', '[PAD]']\n", + "3646\n", + "Label = ['recip', 'freq', '[PAD]', '[PAD]']\n", + "3647\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "3648\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3649\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3650\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3651\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "3652\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3653\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3654\n", + "Label = ['may', 'share', 'memory', '[PAD]']\n", + "3655\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3656\n", + "Label = ['getformat', '[PAD]', '[PAD]', '[PAD]']\n", + "3657\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "3658\n", + "Label = ['random', '[PAD]', '[PAD]', '[PAD]']\n", + "3659\n", + "Label = ['modules', '[PAD]', '[PAD]', '[PAD]']\n", + "3660\n", + "Label = ['eps', '[PAD]', '[PAD]', '[PAD]']\n", + "3661\n", + "Label = ['alphak', '[PAD]', '[PAD]', '[PAD]']\n", + "3662\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3663\n", + "Label = ['percentile', 'idx', '[PAD]', '[PAD]']\n", + "3664\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "3665\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3666\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "3667\n", + "Label = ['ind', '[PAD]', '[PAD]', '[PAD]']\n", + "3668\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3669\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3670\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "3671\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3672\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "3673\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3674\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3675\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3676\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "3677\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "3678\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3679\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3680\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "3681\n", + "Label = ['classes', 'k', '[PAD]', '[PAD]']\n", + "3682\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "3683\n", + "Label = ['invalid', 'names', '[PAD]', '[PAD]']\n", + "3684\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "3685\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "3686\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "3687\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "3688\n", + "Label = ['fill', '[PAD]', '[PAD]', '[PAD]']\n", + "3689\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "3690\n", + "Label = ['doc', '[PAD]', '[PAD]', '[PAD]']\n", + "3691\n", + "Label = ['doc', '[PAD]', '[PAD]', '[PAD]']\n", + "3692\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3693\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3694\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3695\n", + "Label = ['sa', '[PAD]', '[PAD]', '[PAD]']\n", + "3696\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3697\n", + "Label = ['SparseEfficiencyWarning', '[PAD]', '[PAD]', '[PAD]']\n", + "3698\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3699\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3700\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3701\n", + "Label = ['u', 'based', '[PAD]', '[PAD]']\n", + "3702\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3703\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3704\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "3705\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "3706\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3707\n", + "Label = ['old', 'sample', 'count', '[PAD]']\n", + "3708\n", + "Label = ['final', 'means', '[PAD]', '[PAD]']\n", + "3709\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "3710\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "3711\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "3712\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "3713\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "3714\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", + "3715\n", + "Label = ['warns', '[PAD]', '[PAD]', '[PAD]']\n", + "3716\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3717\n", + "Label = ['X', 'checked', '[PAD]', '[PAD]']\n", + "3718\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3719\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3720\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3721\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3722\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3723\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3724\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3725\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3726\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3727\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3728\n", + "Label = ['astype', '[PAD]', '[PAD]', '[PAD]']\n", + "3729\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3730\n", + "Label = ['raises', '[PAD]', '[PAD]', '[PAD]']\n", + "3731\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "3732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3733\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3734\n", + "Label = ['expected', 'dtypes', '[PAD]', '[PAD]']\n", + "3735\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3736\n", + "Label = ['X', nan, '[PAD]', '[PAD]']\n", + "3737\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3738\n", + "Label = ['X', nan, 'means', '[PAD]']\n", + "3739\n", + "Label = ['csr', 'matrix', '[PAD]', '[PAD]']\n", + "3740\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3741\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "3742\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3743\n", + "Label = ['int64', '[PAD]', '[PAD]', '[PAD]']\n", + "3744\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "3745\n", + "Label = ['directed', '[PAD]', '[PAD]', '[PAD]']\n", + "3746\n", + "Label = ['dist', 'matrix', '[PAD]', '[PAD]']\n", + "3747\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3748\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "3749\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3750\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "3751\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3752\n", + "Label = ['A', '[PAD]', '[PAD]', '[PAD]']\n", + "3753\n", + "Label = ['C', '[PAD]', '[PAD]', '[PAD]']\n", + "3754\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3755\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "3756\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", + "3757\n", + "Label = ['actual', '[PAD]', '[PAD]', '[PAD]']\n", + "3758\n", + "Label = ['n', 'population', '[PAD]', '[PAD]']\n", + "3759\n", + "Label = ['n', 'samples', '[PAD]', '[PAD]']\n", + "3760\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3761\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3762\n", + "Label = ['class', 'probabilities', '[PAD]', '[PAD]']\n", + "3763\n", + "Label = ['bincount', '[PAD]', '[PAD]', '[PAD]']\n", + "3764\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3765\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3766\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3767\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3768\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "3769\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3770\n", + "Label = ['NON', 'ARRAY', 'LIKE', 'EXAMPLES']\n", + "3771\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3772\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3773\n", + "Label = ['example', '[PAD]', '[PAD]', '[PAD]']\n", + "3774\n", + "Label = ['example', '[PAD]', '[PAD]', '[PAD]']\n", + "3775\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3776\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3777\n", + "Label = ['xi', '[PAD]', '[PAD]', '[PAD]']\n", + "3778\n", + "Label = ['csr', 'matrix', '[PAD]', '[PAD]']\n", + "3779\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3780\n", + "Label = ['raises', '[PAD]', '[PAD]', '[PAD]']\n", + "3781\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3782\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3783\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3784\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3785\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3786\n", + "Label = ['sample', 'weight', '[PAD]', '[PAD]']\n", + "3787\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3788\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3789\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", + "3790\n", + "Label = ['sstot', '[PAD]', '[PAD]', '[PAD]']\n", + "3791\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "3792\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "3793\n", + "Label = ['any', '[PAD]', '[PAD]', '[PAD]']\n", + "3794\n", + "Label = ['float64', '[PAD]', '[PAD]', '[PAD]']\n", + "3795\n", + "Label = ['scores', '[PAD]', '[PAD]', '[PAD]']\n", + "3796\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", + "3797\n", + "Label = ['ties', '[PAD]', '[PAD]', '[PAD]']\n", + "3798\n", + "Label = ['kept', 'ties', '[PAD]', '[PAD]']\n", + "3799\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "3800\n", + "Label = ['argsort', '[PAD]', '[PAD]', '[PAD]']\n", + "3801\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3802\n", + "Label = ['pvalues', '[PAD]', '[PAD]', '[PAD]']\n", + "3803\n", + "Label = ['start', 'ptr', '[PAD]', '[PAD]']\n", + "3804\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3805\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "3806\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "3807\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3808\n", + "Label = ['coefs', '[PAD]', '[PAD]', '[PAD]']\n", + "3809\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3810\n", + "Label = ['scores', '[PAD]', '[PAD]', '[PAD]']\n", + "3811\n", + "Label = ['min', 'features', 'to', 'select']\n", + "3812\n", + "Label = ['estimator', '[PAD]', '[PAD]', '[PAD]']\n", + "3813\n", + "Label = ['grid', 'scores', '[PAD]', '[PAD]']\n", + "3814\n", + "Label = ['coef', '[PAD]', '[PAD]', '[PAD]']\n", + "3815\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3816\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3817\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3818\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "3819\n", + "Label = ['ranking', '[PAD]', '[PAD]', '[PAD]']\n", + "3820\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3821\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "3822\n", + "Label = ['ranking', '[PAD]', '[PAD]', '[PAD]']\n", + "3823\n", + "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", + "3824\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3825\n", + "Label = ['float32', '[PAD]', '[PAD]', '[PAD]']\n", + "3826\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3827\n", + "Label = ['inverse', 'transform', '[PAD]', '[PAD]']\n", + "3828\n", + "Label = ['n', 'neighbors', '[PAD]', '[PAD]']\n", + "3829\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "3830\n", + "Label = ['uniform', '[PAD]', '[PAD]', '[PAD]']\n", + "3831\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3832\n", + "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", + "3833\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3834\n", + "Label = ['transformer1', '[PAD]', '[PAD]', '[PAD]']\n", + "3835\n", + "Label = ['selected', 'indices', '[PAD]', '[PAD]']\n", + "3836\n", + "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", + "3837\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3838\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3839\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "3840\n", + "Label = ['transformer', '[PAD]', '[PAD]', '[PAD]']\n", + "3841\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3842\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "3843\n", + "Label = ['est', '[PAD]', '[PAD]', '[PAD]']\n", + "3844\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3845\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3846\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "3847\n", + "Label = ['X', 'r2', '[PAD]', '[PAD]']\n", + "3848\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3849\n", + "Label = ['get', 'support', '[PAD]', '[PAD]']\n", + "3850\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3851\n", + "Label = ['fit', '[PAD]', '[PAD]', '[PAD]']\n", + "3852\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "3853\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3854\n", + "Label = ['Xtrans2', '[PAD]', '[PAD]', '[PAD]']\n", + "3855\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3856\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "3857\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "3858\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3859\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3860\n", + "Label = ['repeat', '[PAD]', '[PAD]', '[PAD]']\n", + "3861\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3862\n", + "Label = ['td', '[PAD]', '[PAD]', '[PAD]']\n", + "3863\n", + "Label = ['hstack', '[PAD]', '[PAD]', '[PAD]']\n", + "3864\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "3865\n", + "Label = ['hstack', '[PAD]', '[PAD]', '[PAD]']\n", + "3866\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "3867\n", + "Label = ['iterable', '[PAD]', '[PAD]', '[PAD]']\n", + "3868\n", + "Label = ['X', 'train', '[PAD]', '[PAD]']\n", + "3869\n", + "Label = ['optima', '[PAD]', '[PAD]', '[PAD]']\n", + "3870\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3871\n", + "Label = ['y', 'var', '[PAD]', '[PAD]']\n", + "3872\n", + "Label = ['T', '[PAD]', '[PAD]', '[PAD]']\n", + "3873\n", + "Label = ['y', 'var', '[PAD]', '[PAD]']\n", + "3874\n", + "Label = ['multivariate', 'normal', '[PAD]', '[PAD]']\n", + "3875\n", + "Label = ['LinAlgError', '[PAD]', '[PAD]', '[PAD]']\n", + "3876\n", + "Label = ['log', 'likelihood', 'dims', '[PAD]']\n", + "3877\n", + "Label = ['theta', 'opt', '[PAD]', '[PAD]']\n", + "3878\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3879\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3880\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "3881\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3882\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3883\n", + "Label = ['theta', '[PAD]', '[PAD]', '[PAD]']\n", + "3884\n", + "Label = ['kernels', '[PAD]', '[PAD]', '[PAD]']\n", + "3885\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3886\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3887\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "3888\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3889\n", + "Label = ['vstack', '[PAD]', '[PAD]', '[PAD]']\n", + "3890\n", + "Label = ['is', 'stationary', '[PAD]', '[PAD]']\n", + "3891\n", + "Label = ['dstack', '[PAD]', '[PAD]', '[PAD]']\n", + "3892\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3893\n", + "Label = ['K1', '[PAD]', '[PAD]', '[PAD]']\n", + "3894\n", + "Label = ['newaxis', '[PAD]', '[PAD]', '[PAD]']\n", + "3895\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3896\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3897\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "3898\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", + "3899\n", + "Label = ['full', '[PAD]', '[PAD]', '[PAD]']\n", + "3900\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3901\n", + "Label = ['eye', '[PAD]', '[PAD]', '[PAD]']\n", + "3902\n", + "Label = ['anisotropic', '[PAD]', '[PAD]', '[PAD]']\n", + "3903\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3904\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3905\n", + "Label = ['K', 'gradient', '[PAD]', '[PAD]']\n", + "3906\n", + "Label = ['K', 'gradient', '[PAD]', '[PAD]']\n", + "3907\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3908\n", + "Label = ['length', 'scale', '[PAD]', '[PAD]']\n", + "3909\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3910\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3911\n", + "Label = ['einsum', '[PAD]', '[PAD]', '[PAD]']\n", + "3912\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "3913\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3914\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3915\n", + "Label = ['empty', '[PAD]', '[PAD]', '[PAD]']\n", + "3916\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "3917\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "3918\n", + "Label = ['where', '[PAD]', '[PAD]', '[PAD]']\n", + "3919\n", + "Label = ['L', '[PAD]', '[PAD]', '[PAD]']\n", + "3920\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "3921\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", + "3922\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", + "3923\n", + "Label = ['theta', 'opt', '[PAD]', '[PAD]']\n", + "3924\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3925\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "3926\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "3927\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "3928\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "3929\n", + "Label = ['nu', '[PAD]', '[PAD]', '[PAD]']\n", + "3930\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "3931\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "3932\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3933\n", + "Label = ['atleast', '1d', '[PAD]', '[PAD]']\n", + "3934\n", + "Label = ['log', 'marginal', 'likelihood', '[PAD]']\n", + "3935\n", + "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", + "3936\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "3937\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "3938\n", + "Label = ['X', '[PAD]', '[PAD]', '[PAD]']\n", + "3939\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "3940\n", + "Label = ['theta', 'opt', '[PAD]', '[PAD]']\n", + "3941\n", + "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", + "3942\n", + "Label = ['x', 'weights', '[PAD]', '[PAD]']\n", + "3943\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "3944\n", + "Label = ['deflation', 'mode', '[PAD]', '[PAD]']\n", + "3945\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "3946\n", + "Label = ['norm', 'y', 'weights', '[PAD]']\n", + "3947\n", + "Label = ['y', 'loadings', '[PAD]', '[PAD]']\n", + "3948\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3949\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3950\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", + "3951\n", + "Label = ['x', 'loadings', '[PAD]', '[PAD]']\n", + "3952\n", + "Label = ['diag', '[PAD]', '[PAD]', '[PAD]']\n", + "3953\n", + "Label = ['Xc', '[PAD]', '[PAD]', '[PAD]']\n", + "3954\n", + "Label = ['dot', '[PAD]', '[PAD]', '[PAD]']\n", + "3955\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3956\n", + "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", + "3957\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "3958\n", + "Label = ['y', 'loadings', '[PAD]', '[PAD]']\n", + "3959\n", + "Label = ['predict', '[PAD]', '[PAD]', '[PAD]']\n", + "3960\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", + "3961\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "3962\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "3963\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "3964\n", + "Label = ['BECOME', 'METHODS', '[PAD]', '[PAD]']\n", + "3965\n", + "Label = ['COMMON', 'CONNECTION', 'VARS', '[PAD]']\n", + "3966\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "3967\n", + "Label = ['SAFE', 'NODES', '[PAD]', '[PAD]']\n", + "3968\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "3969\n", + "Label = ['child', 'node', '[PAD]', '[PAD]']\n", + "3970\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "3971\n", + "Label = ['templar', '[PAD]', '[PAD]', '[PAD]']\n", + "3972\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3973\n", + "Label = ['identifier', '[PAD]', '[PAD]', '[PAD]']\n", + "3974\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "3975\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3976\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "3977\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "3978\n", + "Label = ['realpath', '[PAD]', '[PAD]', '[PAD]']\n", + "3979\n", + "Label = ['Credentials', '[PAD]', '[PAD]', '[PAD]']\n", + "3980\n", + "Label = ['mutual', '[PAD]', '[PAD]', '[PAD]']\n", + "3981\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "3982\n", + "Label = ['session', 'id', '[PAD]', '[PAD]']\n", + "3983\n", + "Label = ['glob', '[PAD]', '[PAD]', '[PAD]']\n", + "3984\n", + "Label = ['num', '[PAD]', '[PAD]', '[PAD]']\n", + "3985\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "3986\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "3987\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "3988\n", + "Label = ['OrderedDict', '[PAD]', '[PAD]', '[PAD]']\n", + "3989\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "3990\n", + "Label = ['string', 'types', '[PAD]', '[PAD]']\n", + "3991\n", + "Label = ['ONEVIEW', 'VALIDATE', 'ETAG', 'ARGS']\n", + "3992\n", + "Label = ['compare', '[PAD]', '[PAD]', '[PAD]']\n", + "3993\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3994\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "3995\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "3996\n", + "Label = ['user', 'agent', '[PAD]', '[PAD]']\n", + "3997\n", + "Label = ['HAS', 'REQUESTS', '[PAD]', '[PAD]']\n", + "3998\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "3999\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4000\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4001\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "4002\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4003\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4004\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "4005\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "4006\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4007\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4008\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4009\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4010\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4011\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4012\n", + "Label = ['algorithms', '[PAD]', '[PAD]', '[PAD]']\n", + "4013\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "4014\n", + "Label = ['subelement', '[PAD]', '[PAD]', '[PAD]']\n", + "4015\n", + "Label = ['sep', '[PAD]', '[PAD]', '[PAD]']\n", + "4016\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "4017\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "4018\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "4019\n", + "Label = ['limit', '[PAD]', '[PAD]', '[PAD]']\n", + "4020\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "4021\n", + "Label = ['datetime', '[PAD]', '[PAD]', '[PAD]']\n", + "4022\n", + "Label = ['encoding', '[PAD]', '[PAD]', '[PAD]']\n", + "4023\n", + "Label = ['legal', 'inputs', '[PAD]', '[PAD]']\n", + "4024\n", + "Label = ['uid', '[PAD]', '[PAD]', '[PAD]']\n", + "4025\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "4026\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4027\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4028\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4029\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "4030\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "4031\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "4032\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4033\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "4034\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "4035\n", + "Label = ['isdir', '[PAD]', '[PAD]', '[PAD]']\n", + "4036\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "4037\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4038\n", + "Label = ['term', '[PAD]', '[PAD]', '[PAD]']\n", + "4039\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4040\n", + "Label = ['non', 'zero', '[PAD]', '[PAD]']\n", + "4041\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4042\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4043\n", + "Label = ['options', 'context', '[PAD]', '[PAD]']\n", + "4044\n", + "Label = ['lowered', 'choices', '[PAD]', '[PAD]']\n", + "4045\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4046\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4047\n", + "Label = ['type', 'checker', '[PAD]', '[PAD]']\n", + "4048\n", + "Label = ['fallback', 'kwargs', '[PAD]', '[PAD]']\n", + "4049\n", + "Label = ['journal', 'msg', '[PAD]', '[PAD]']\n", + "4050\n", + "Label = ['facility', '[PAD]', '[PAD]', '[PAD]']\n", + "4051\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4052\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "4053\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4054\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4055\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4056\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4057\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4058\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "4059\n", + "Label = ['error', 'msg', '[PAD]', '[PAD]']\n", + "4060\n", + "Label = ['dirname', '[PAD]', '[PAD]', '[PAD]']\n", + "4061\n", + "Label = ['move', '[PAD]', '[PAD]', '[PAD]']\n", + "4062\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "4063\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "4064\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4065\n", + "Label = ['to', 'clean', 'args', '[PAD]']\n", + "4066\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4067\n", + "Label = ['prompt', 're', '[PAD]', '[PAD]']\n", + "4068\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4069\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4070\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "4071\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "4072\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "4073\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "4074\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4075\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "4076\n", + "Label = ['AZURE', 'CREDENTIAL', 'ENV', 'MAPPING']\n", + "4077\n", + "Label = ['AZURE', 'API', 'PROFILES', '[PAD]']\n", + "4078\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "4079\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4080\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "4081\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4082\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4083\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4084\n", + "Label = ['provisioning', 'state', '[PAD]', '[PAD]']\n", + "4085\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4086\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4087\n", + "Label = ['storage', 'endpoint', '[PAD]', '[PAD]']\n", + "4088\n", + "Label = ['SecurityRule', '[PAD]', '[PAD]', '[PAD]']\n", + "4089\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "4090\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4091\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4092\n", + "Label = ['get', 'mgmt', 'svc', 'client']\n", + "4093\n", + "Label = ['web', 'client', '[PAD]', '[PAD]']\n", + "4094\n", + "Label = ['get', 'mgmt', 'svc', 'client']\n", + "4095\n", + "Label = ['get', 'mgmt', 'svc', 'client']\n", + "4096\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4097\n", + "Label = ['credentials', '[PAD]', '[PAD]', '[PAD]']\n", + "4098\n", + "Label = ['acquire', 'token', 'with', 'username']\n", + "4099\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4100\n", + "Label = ['authority', 'uri', '[PAD]', '[PAD]']\n", + "4101\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4102\n", + "Label = ['api', 'cache', '[PAD]', '[PAD]']\n", + "4103\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4104\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "4105\n", + "Label = ['query', 'resource', 'by', 'key']\n", + "4106\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4107\n", + "Label = ['kn', 'salt', '[PAD]', '[PAD]']\n", + "4108\n", + "Label = ['hash', '[PAD]', '[PAD]', '[PAD]']\n", + "4109\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4110\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4111\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4112\n", + "Label = ['retry', '[PAD]', '[PAD]', '[PAD]']\n", + "4113\n", + "Label = ['retries', '[PAD]', '[PAD]', '[PAD]']\n", + "4114\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "4115\n", + "Label = ['api', '[PAD]', '[PAD]', '[PAD]']\n", + "4116\n", + "Label = ['api', '[PAD]', '[PAD]', '[PAD]']\n", + "4117\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "4118\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4119\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4120\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "4121\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4122\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4123\n", + "Label = ['project', 'id', '[PAD]', '[PAD]']\n", + "4124\n", + "Label = ['http', 'auth', '[PAD]', '[PAD]']\n", + "4125\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "4126\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4127\n", + "Label = ['txt', '[PAD]', '[PAD]', '[PAD]']\n", + "4128\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4129\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "4130\n", + "Label = ['excluded', 'fields', '[PAD]', '[PAD]']\n", + "4131\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4132\n", + "Label = ['matching', 'vlans', '[PAD]', '[PAD]']\n", + "4133\n", + "Label = ['vlan', '[PAD]', '[PAD]', '[PAD]']\n", + "4134\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4135\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4136\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "4137\n", + "Label = ['sock', '[PAD]', '[PAD]', '[PAD]']\n", + "4138\n", + "Label = ['HTTPSHandler', '[PAD]', '[PAD]', '[PAD]']\n", + "4139\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4140\n", + "Label = ['auth', '[PAD]', '[PAD]', '[PAD]']\n", + "4141\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4142\n", + "Label = ['redirect', 'request', '[PAD]', '[PAD]']\n", + "4143\n", + "Label = ['HTTPError', '[PAD]', '[PAD]', '[PAD]']\n", + "4144\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4145\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "4146\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4147\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "4148\n", + "Label = ['ssl', 's', '[PAD]', '[PAD]']\n", + "4149\n", + "Label = ['ssl', 's', '[PAD]', '[PAD]']\n", + "4150\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "4151\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4152\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "4153\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4154\n", + "Label = ['add', 'header', '[PAD]', '[PAD]']\n", + "4155\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4156\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4157\n", + "Label = ['URLError', '[PAD]', '[PAD]', '[PAD]']\n", + "4158\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4159\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4160\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4161\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "4162\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4163\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4164\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "4165\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4166\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "4167\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "4168\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "4169\n", + "Label = ['VERSION', '[PAD]', '[PAD]', '[PAD]']\n", + "4170\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4171\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4172\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "4173\n", + "Label = ['entity', '[PAD]', '[PAD]', '[PAD]']\n", + "4174\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "4175\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4176\n", + "Label = ['base', 'class', '[PAD]', '[PAD]']\n", + "4177\n", + "Label = ['fw', '[PAD]', '[PAD]', '[PAD]']\n", + "4178\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4179\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "4180\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "4181\n", + "Label = ['slb', '[PAD]', '[PAD]', '[PAD]']\n", + "4182\n", + "Label = ['ess', '[PAD]', '[PAD]', '[PAD]']\n", + "4183\n", + "Label = ['arg', 'spec', '[PAD]', '[PAD]']\n", + "4184\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "4185\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "4186\n", + "Label = ['retry', 'count', '[PAD]', '[PAD]']\n", + "4187\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "4188\n", + "Label = ['HAS', 'SF', 'SDK', '[PAD]']\n", + "4189\n", + "Label = ['query', 'details', '[PAD]', '[PAD]']\n", + "4190\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4191\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4192\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4193\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4194\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4195\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4196\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4197\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4198\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4199\n", + "Label = ['network', 'acl', '[PAD]', '[PAD]']\n", + "4200\n", + "Label = ['vpc', '[PAD]', '[PAD]', '[PAD]']\n", + "4201\n", + "Label = ['get', 'account', '[PAD]', '[PAD]']\n", + "4202\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "4203\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4204\n", + "Label = ['get', 'account', '[PAD]', '[PAD]']\n", + "4205\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4206\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4207\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4208\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "4209\n", + "Label = ['process', 'tags', '[PAD]', '[PAD]']\n", + "4210\n", + "Label = ['load', 'privatekey', '[PAD]', '[PAD]']\n", + "4211\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4212\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4213\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4214\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "4215\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4216\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4217\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4218\n", + "Label = ['region', '[PAD]', '[PAD]', '[PAD]']\n", + "4219\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "4220\n", + "Label = ['has', 'option', '[PAD]', '[PAD]']\n", + "4221\n", + "Label = ['filters', 'dict', '[PAD]', '[PAD]']\n", + "4222\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4223\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4224\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4225\n", + "Label = ['sg', '[PAD]', '[PAD]', '[PAD]']\n", + "4226\n", + "Label = ['each', '[PAD]', '[PAD]', '[PAD]']\n", + "4227\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "4228\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "4229\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4230\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4231\n", + "Label = ['relation', '[PAD]', '[PAD]', '[PAD]']\n", + "4232\n", + "Label = ['ok', '[PAD]', '[PAD]', '[PAD]']\n", + "4233\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4234\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4235\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4236\n", + "Label = ['folder', '[PAD]', '[PAD]', '[PAD]']\n", + "4237\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "4238\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "4239\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "4240\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "4241\n", + "Label = ['backing', '[PAD]', '[PAD]', '[PAD]']\n", + "4242\n", + "Label = ['optkeyname', '[PAD]', '[PAD]', '[PAD]']\n", + "4243\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4244\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4245\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4246\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4247\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4248\n", + "Label = ['TraversalSpec', '[PAD]', '[PAD]', '[PAD]']\n", + "4249\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4250\n", + "Label = ['temp', 'vm', 'object', 'property']\n", + "4251\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "4252\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "4253\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4254\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "4255\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4256\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4257\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4258\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "4259\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "4260\n", + "Label = ['HAS', 'DOCKER', 'SSLADAPTER', '[PAD]']\n", + "4261\n", + "Label = ['docker', 'version', '[PAD]', '[PAD]']\n", + "4262\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4263\n", + "Label = ['docker', 'py', 'version', '[PAD]']\n", + "4264\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4265\n", + "Label = ['docker', 'api', 'version', 'str']\n", + "4266\n", + "Label = ['tls', 'config', '[PAD]', '[PAD]']\n", + "4267\n", + "Label = ['get', 'tls', 'config', '[PAD]']\n", + "4268\n", + "Label = ['tls', 'config', '[PAD]', '[PAD]']\n", + "4269\n", + "Label = ['get', 'tls', 'config', '[PAD]']\n", + "4270\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4271\n", + "Label = ['inspection', '[PAD]', '[PAD]', '[PAD]']\n", + "4272\n", + "Label = ['inspection', '[PAD]', '[PAD]', '[PAD]']\n", + "4273\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4274\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "4275\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "4276\n", + "Label = ['bv', '[PAD]', '[PAD]', '[PAD]']\n", + "4277\n", + "Label = ['av', '[PAD]', '[PAD]', '[PAD]']\n", + "4278\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "4279\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "4280\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "4281\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "4282\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "4283\n", + "Label = ['account', 'key', 'type', '[PAD]']\n", + "4284\n", + "Label = ['groups', '[PAD]', '[PAD]', '[PAD]']\n", + "4285\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4286\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4287\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "4288\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "4289\n", + "Label = ['load', 'pem', 'private', 'key']\n", + "4290\n", + "Label = ['rr', '[PAD]', '[PAD]', '[PAD]']\n", + "4291\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4292\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4293\n", + "Label = ['payload64', '[PAD]', '[PAD]', '[PAD]']\n", + "4294\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4295\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4296\n", + "Label = ['get', 'account', 'data', '[PAD]']\n", + "4297\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4298\n", + "Label = ['HAS', 'CURRENT', 'CRYPTOGRAPHY', '[PAD]']\n", + "4299\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4300\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4301\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4302\n", + "Label = ['change', 'relevant', 'keys', '[PAD]']\n", + "4303\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4304\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4305\n", + "Label = ['exc', 'info', '[PAD]', '[PAD]']\n", + "4306\n", + "Label = ['UnarySub', '[PAD]', '[PAD]', '[PAD]']\n", + "4307\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4308\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4309\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4310\n", + "Label = ['post', 'request', '[PAD]', '[PAD]']\n", + "4311\n", + "Label = ['post', 'request', '[PAD]', '[PAD]']\n", + "4312\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4313\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4314\n", + "Label = ['get', 'request', '[PAD]', '[PAD]']\n", + "4315\n", + "Label = ['boot', 'seq', '[PAD]', '[PAD]']\n", + "4316\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4317\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "4318\n", + "Label = ['nic', '[PAD]', '[PAD]', '[PAD]']\n", + "4319\n", + "Label = ['properties', '[PAD]', '[PAD]', '[PAD]']\n", + "4320\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4321\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4322\n", + "Label = ['instance', 'list', '[PAD]', '[PAD]']\n", + "4323\n", + "Label = ['vca', '[PAD]', '[PAD]', '[PAD]']\n", + "4324\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4325\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4326\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4327\n", + "Label = ['oneandone', 'conn', '[PAD]', '[PAD]']\n", + "4328\n", + "Label = ['oneandone', 'conn', '[PAD]', '[PAD]']\n", + "4329\n", + "Label = ['words', '[PAD]', '[PAD]', '[PAD]']\n", + "4330\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "4331\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4332\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "4333\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "4334\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4335\n", + "Label = ['clientlist', 'url', '[PAD]', '[PAD]']\n", + "4336\n", + "Label = ['restheaders', '[PAD]', '[PAD]', '[PAD]']\n", + "4337\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4338\n", + "Label = ['restheaders', '[PAD]', '[PAD]', '[PAD]']\n", + "4339\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "4340\n", + "Label = ['restheaders', '[PAD]', '[PAD]', '[PAD]']\n", + "4341\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4342\n", + "Label = ['restheaders', '[PAD]', '[PAD]', '[PAD]']\n", + "4343\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "4344\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4345\n", + "Label = ['restheaders', '[PAD]', '[PAD]', '[PAD]']\n", + "4346\n", + "Label = ['NetworkNotFound', '[PAD]', '[PAD]', '[PAD]']\n", + "4347\n", + "Label = ['servers', '[PAD]', '[PAD]', '[PAD]']\n", + "4348\n", + "Label = ['set', 'setting', '[PAD]', '[PAD]']\n", + "4349\n", + "Label = ['set', 'setting', '[PAD]', '[PAD]']\n", + "4350\n", + "Label = ['api', 'key', '[PAD]', '[PAD]']\n", + "4351\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4352\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4353\n", + "Label = ['HAS', 'INFOBLOX', 'CLIENT', '[PAD]']\n", + "4354\n", + "Label = ['env', '[PAD]', '[PAD]', '[PAD]']\n", + "4355\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4356\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4357\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4358\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4359\n", + "Label = ['handle', 'exception', '[PAD]', '[PAD]']\n", + "4360\n", + "Label = ['current', 'object', '[PAD]', '[PAD]']\n", + "4361\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4362\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "4363\n", + "Label = ['subitem', '[PAD]', '[PAD]', '[PAD]']\n", + "4364\n", + "Label = ['ib', 'obj', '[PAD]', '[PAD]']\n", + "4365\n", + "Label = ['test', 'obj', 'filter', '[PAD]']\n", + "4366\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4367\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4368\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4369\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4370\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "4371\n", + "Label = ['collector', 'name', '[PAD]', '[PAD]']\n", + "4372\n", + "Label = ['unresolved', 'requires', '[PAD]', '[PAD]']\n", + "4373\n", + "Label = ['fact', 'collector', '[PAD]', '[PAD]']\n", + "4374\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4375\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "4376\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "4377\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4378\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4379\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4380\n", + "Label = ['virtual', 'vendor', 'facts', '[PAD]']\n", + "4381\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4382\n", + "Label = ['rstrip', '[PAD]', '[PAD]', '[PAD]']\n", + "4383\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4384\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4385\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4386\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4387\n", + "Label = ['get', 'options', '[PAD]', '[PAD]']\n", + "4388\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "4389\n", + "Label = ['netmask', 'bin', '[PAD]', '[PAD]']\n", + "4390\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "4391\n", + "Label = ['inet', 'ntoa', '[PAD]', '[PAD]']\n", + "4392\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4393\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "4394\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4395\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4396\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4397\n", + "Label = ['primary', '[PAD]', '[PAD]', '[PAD]']\n", + "4398\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4399\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4400\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4401\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4402\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "4403\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4404\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "4405\n", + "Label = ['parse', 'inet', 'line', '[PAD]']\n", + "4406\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4407\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "4408\n", + "Label = ['current', 'if', '[PAD]', '[PAD]']\n", + "4409\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4410\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "4411\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4412\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "4413\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4414\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4415\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "4416\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4417\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4418\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4419\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4420\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "4421\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4422\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4423\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4424\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4425\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4426\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4427\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4428\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4429\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4430\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4431\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4432\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4433\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4434\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "4435\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4436\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4437\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4438\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4439\n", + "Label = ['sysctl', 'to', 'dmi', '[PAD]']\n", + "4440\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4441\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4442\n", + "Label = ['xen', 'paravirt', '[PAD]', '[PAD]']\n", + "4443\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4444\n", + "Label = ['DMI', 'DICT', '[PAD]', '[PAD]']\n", + "4445\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4446\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4447\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "4448\n", + "Label = ['get', 'device', 'links', '[PAD]']\n", + "4449\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "4450\n", + "Label = ['serial', '[PAD]', '[PAD]', '[PAD]']\n", + "4451\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4452\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4453\n", + "Label = ['pv', 'line', '[PAD]', '[PAD]']\n", + "4454\n", + "Label = ['is', 'chroot', '[PAD]', '[PAD]']\n", + "4455\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "4456\n", + "Label = ['groups', '[PAD]', '[PAD]', '[PAD]']\n", + "4457\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4458\n", + "Label = ['distribution', '[PAD]', '[PAD]', '[PAD]']\n", + "4459\n", + "Label = ['distribution', 'files', '[PAD]', '[PAD]']\n", + "4460\n", + "Label = ['groups', '[PAD]', '[PAD]', '[PAD]']\n", + "4461\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4462\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4463\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "4464\n", + "Label = ['enforced', 'caps', '[PAD]', '[PAD]']\n", + "4465\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4466\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "4467\n", + "Label = ['devnull', '[PAD]', '[PAD]', '[PAD]']\n", + "4468\n", + "Label = ['pkg', 'mgr', 'name', '[PAD]']\n", + "4469\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "4470\n", + "Label = ['piece', '[PAD]', '[PAD]', '[PAD]']\n", + "4471\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4472\n", + "Label = ['get', 'bin', 'path', '[PAD]']\n", + "4473\n", + "Label = ['mac', 'ver', '[PAD]', '[PAD]']\n", + "4474\n", + "Label = ['mac', 'ver', '[PAD]', '[PAD]']\n", + "4475\n", + "Label = ['get', 'bin', 'path', '[PAD]']\n", + "4476\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4477\n", + "Label = ['uname', '[PAD]', '[PAD]', '[PAD]']\n", + "4478\n", + "Label = ['S', 'IXUSR', '[PAD]', '[PAD]']\n", + "4479\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "4480\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "4481\n", + "Label = ['resource', 'definitions', '[PAD]', '[PAD]']\n", + "4482\n", + "Label = ['namespaced', '[PAD]', '[PAD]', '[PAD]']\n", + "4483\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4484\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4485\n", + "Label = ['merge', 'type', '[PAD]', '[PAD]']\n", + "4486\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "4487\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4488\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4489\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4490\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4491\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4492\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4493\n", + "Label = ['HAVE', 'SELINUX', '[PAD]', '[PAD]']\n", + "4494\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "4495\n", + "Label = ['VALID', 'MASKS', '[PAD]', '[PAD]']\n", + "4496\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4497\n", + "Label = ['unpack', '[PAD]', '[PAD]', '[PAD]']\n", + "4498\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "4499\n", + "Label = ['eq', '[PAD]', '[PAD]', '[PAD]']\n", + "4500\n", + "Label = ['number', '[PAD]', '[PAD]', '[PAD]']\n", + "4501\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4502\n", + "Label = ['broadcast', 'address', '[PAD]', '[PAD]']\n", + "4503\n", + "Label = ['issuperset', '[PAD]', '[PAD]', '[PAD]']\n", + "4504\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4505\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4506\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4507\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4508\n", + "Label = ['prefixlen', '[PAD]', '[PAD]', '[PAD]']\n", + "4509\n", + "Label = ['step', '[PAD]', '[PAD]', '[PAD]']\n", + "4510\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4511\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4512\n", + "Label = ['string', 'from', 'ip', 'int']\n", + "4513\n", + "Label = ['string', 'from', 'ip', 'int']\n", + "4514\n", + "Label = ['network', 'address', '[PAD]', '[PAD]']\n", + "4515\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4516\n", + "Label = ['network', 'address', '[PAD]', '[PAD]']\n", + "4517\n", + "Label = ['HEXTET', 'COUNT', '[PAD]', '[PAD]']\n", + "4518\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4519\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4520\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "4521\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4522\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4523\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4524\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4525\n", + "Label = ['cannonical', 'headers', '[PAD]', '[PAD]']\n", + "4526\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4527\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4528\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4529\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "4530\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4531\n", + "Label = ['elb', '[PAD]', '[PAD]', '[PAD]']\n", + "4532\n", + "Label = ['elb', '[PAD]', '[PAD]', '[PAD]']\n", + "4533\n", + "Label = ['access', 'logs', 'enabled', '[PAD]']\n", + "4534\n", + "Label = ['access', 'logs', 's3', 'bucket']\n", + "4535\n", + "Label = ['jittered', 'backoff', '[PAD]', '[PAD]']\n", + "4536\n", + "Label = ['elb', '[PAD]', '[PAD]', '[PAD]']\n", + "4537\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4538\n", + "Label = ['new', 'load', 'balancer', '[PAD]']\n", + "4539\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "4540\n", + "Label = ['listener', '[PAD]', '[PAD]', '[PAD]']\n", + "4541\n", + "Label = ['jittered', 'backoff', '[PAD]', '[PAD]']\n", + "4542\n", + "Label = ['jittered', 'backoff', '[PAD]', '[PAD]']\n", + "4543\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "4544\n", + "Label = ['jittered', 'backoff', '[PAD]', '[PAD]']\n", + "4545\n", + "Label = ['jittered', 'backoff', '[PAD]', '[PAD]']\n", + "4546\n", + "Label = ['region', '[PAD]', '[PAD]', '[PAD]']\n", + "4547\n", + "Label = ['account', 'id', '[PAD]', '[PAD]']\n", + "4548\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4549\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "4550\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "4551\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "4552\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4553\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4554\n", + "Label = ['build', 'full', 'result', '[PAD]']\n", + "4555\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "4556\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4557\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4558\n", + "Label = ['waf', 'data', '[PAD]', '[PAD]']\n", + "4559\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "4560\n", + "Label = ['Waiter', '[PAD]', '[PAD]', '[PAD]']\n", + "4561\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "4562\n", + "Label = ['Waiter', '[PAD]', '[PAD]', '[PAD]']\n", + "4563\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4564\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "4565\n", + "Label = ['get', 'streaming', 'distribution', 'config']\n", + "4566\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4567\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "4568\n", + "Label = ['key', 'name', '[PAD]', '[PAD]']\n", + "4569\n", + "Label = ['invalidation', '[PAD]', '[PAD]', '[PAD]']\n", + "4570\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4571\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4572\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "4573\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "4574\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "4575\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4576\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4577\n", + "Label = ['wrapped', '[PAD]', '[PAD]', '[PAD]']\n", + "4578\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "4579\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "4580\n", + "Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n", + "4581\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4582\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4583\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4584\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4585\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4586\n", + "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", + "4587\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "4588\n", + "Label = ['inet', 'aton', '[PAD]', '[PAD]']\n", + "4589\n", + "Label = ['literal', 'eval', '[PAD]', '[PAD]']\n", + "4590\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4591\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "4592\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4593\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4594\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4595\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "4596\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4597\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "4598\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4599\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4600\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4601\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4602\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4603\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4604\n", + "Label = ['mkdir', '[PAD]', '[PAD]', '[PAD]']\n", + "4605\n", + "Label = ['load', 'config', '[PAD]', '[PAD]']\n", + "4606\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4607\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4608\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4609\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4610\n", + "Label = ['command', 'type', '[PAD]', '[PAD]']\n", + "4611\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4612\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "4613\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4614\n", + "Label = ['proper', 'interface', '[PAD]', '[PAD]']\n", + "4615\n", + "Label = ['SET', '[PAD]', '[PAD]', '[PAD]']\n", + "4616\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4617\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4618\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4619\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4620\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4621\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4622\n", + "Label = ['build', 'dict', '[PAD]', '[PAD]']\n", + "4623\n", + "Label = ['build', 'list', '[PAD]', '[PAD]']\n", + "4624\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "4625\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "4626\n", + "Label = ['entry', 'key', '[PAD]', '[PAD]']\n", + "4627\n", + "Label = ['add', 'value', '[PAD]', '[PAD]']\n", + "4628\n", + "Label = ['child', 'schema', '[PAD]', '[PAD]']\n", + "4629\n", + "Label = ['name', 'key', '[PAD]', '[PAD]']\n", + "4630\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4631\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4632\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4633\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "4634\n", + "Label = ['capabilities', '[PAD]', '[PAD]', '[PAD]']\n", + "4635\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4636\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4637\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "4638\n", + "Label = ['get', 'filtered', '[PAD]', '[PAD]']\n", + "4639\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "4640\n", + "Label = ['get', 'filtered', '[PAD]', '[PAD]']\n", + "4641\n", + "Label = ['enable', 'features', '[PAD]', '[PAD]']\n", + "4642\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4643\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4644\n", + "Label = ['add', 'child', '[PAD]', '[PAD]']\n", + "4645\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4646\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4647\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4648\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4649\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4650\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "4651\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "4652\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4653\n", + "Label = ['netconf', 'connection', '[PAD]', '[PAD]']\n", + "4654\n", + "Label = ['element', '[PAD]', '[PAD]', '[PAD]']\n", + "4655\n", + "Label = ['HAS', 'LXML', 'ETREE', '[PAD]']\n", + "4656\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4657\n", + "Label = ['strftime', '[PAD]', '[PAD]', '[PAD]']\n", + "4658\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4659\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4660\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4661\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4662\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4663\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "4664\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4665\n", + "Label = ['accepted', 'params', '[PAD]', '[PAD]']\n", + "4666\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4667\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "4668\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "4669\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4670\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4671\n", + "Label = ['update', 'qs', '[PAD]', '[PAD]']\n", + "4672\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4673\n", + "Label = ['update', 'qs', '[PAD]', '[PAD]']\n", + "4674\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4675\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4676\n", + "Label = ['cert', 'auth', '[PAD]', '[PAD]']\n", + "4677\n", + "Label = ['final', 'keys', '[PAD]', '[PAD]']\n", + "4678\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4679\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4680\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4681\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "4682\n", + "Label = ['rsplit', '[PAD]', '[PAD]', '[PAD]']\n", + "4683\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4684\n", + "Label = ['SubElement', '[PAD]', '[PAD]', '[PAD]']\n", + "4685\n", + "Label = ['root', '[PAD]', '[PAD]', '[PAD]']\n", + "4686\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4687\n", + "Label = ['candidate', 'data', '[PAD]', '[PAD]']\n", + "4688\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "4689\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4690\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4691\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4692\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4693\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4694\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "4695\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4696\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4697\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4698\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4699\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "4700\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "4701\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "4702\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4703\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4704\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "4705\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "4706\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "4707\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "4708\n", + "Label = ['validate', 'certs', '[PAD]', '[PAD]']\n", + "4709\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4710\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4711\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "4712\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "4713\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4714\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "4715\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "4716\n", + "Label = ['mark2', '[PAD]', '[PAD]', '[PAD]']\n", + "4717\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4718\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4719\n", + "Label = ['validate', 'params', '[PAD]', '[PAD]']\n", + "4720\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "4721\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "4722\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "4723\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4724\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "4725\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4726\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "4727\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "4728\n", + "Label = ['bit', 'masks', '[PAD]', '[PAD]']\n", + "4729\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4730\n", + "Label = ['api', 'client', '[PAD]', '[PAD]']\n", + "4731\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "4732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4733\n", + "Label = ['built', 'path', '[PAD]', '[PAD]']\n", + "4734\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4735\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "4736\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4737\n", + "Label = ['ios', 'connection', '[PAD]', '[PAD]']\n", + "4738\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4739\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "4740\n", + "Label = ['if', 'number', '[PAD]', '[PAD]']\n", + "4741\n", + "Label = ['command', 'obj', '[PAD]', '[PAD]']\n", + "4742\n", + "Label = ['HAS', 'PYFMGR', '[PAD]', '[PAD]']\n", + "4743\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4744\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "4745\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4746\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "4747\n", + "Label = ['HAS', 'PANDEVICE', '[PAD]', '[PAD]']\n", + "4748\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4749\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4750\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "4751\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4752\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "4753\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4754\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4755\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4756\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4757\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "4758\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "4759\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "4760\n", + "Label = ['retVal', '[PAD]', '[PAD]', '[PAD]']\n", + "4761\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4762\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4763\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4764\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4765\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4766\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4767\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4768\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4769\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4770\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4771\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4772\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "4773\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4774\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4775\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4776\n", + "Label = ['inet', 'pton', '[PAD]', '[PAD]']\n", + "4777\n", + "Label = ['remote', 'conn', '[PAD]', '[PAD]']\n", + "4778\n", + "Label = ['retVal', '[PAD]', '[PAD]', '[PAD]']\n", + "4779\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4780\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4781\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4782\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4783\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4784\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4785\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "4786\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "4787\n", + "Label = ['set', '[PAD]', '[PAD]', '[PAD]']\n", + "4788\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4789\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "4790\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "4791\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "4792\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "4793\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "4794\n", + "Label = ['aruba', 'provider', 'spec', '[PAD]']\n", + "4795\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4796\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4797\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4798\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "4799\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4800\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4801\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "4802\n", + "Label = ['elt', '[PAD]', '[PAD]', '[PAD]']\n", + "4803\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4804\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4805\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "4806\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "4807\n", + "Label = ['GET', '[PAD]', '[PAD]', '[PAD]']\n", + "4808\n", + "Label = ['fact', 'name', '[PAD]', '[PAD]']\n", + "4809\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4810\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "4811\n", + "Label = ['send', 'request', '[PAD]', '[PAD]']\n", + "4812\n", + "Label = ['send', 'request', '[PAD]', '[PAD]']\n", + "4813\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "4814\n", + "Label = ['send', 'request', '[PAD]', '[PAD]']\n", + "4815\n", + "Label = ['existing', 'obj', '[PAD]', '[PAD]']\n", + "4816\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4817\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "4818\n", + "Label = ['existing', 'object', '[PAD]', '[PAD]']\n", + "4819\n", + "Label = ['POST', '[PAD]', '[PAD]', '[PAD]']\n", + "4820\n", + "Label = ['POST', '[PAD]', '[PAD]', '[PAD]']\n", + "4821\n", + "Label = ['PROPERTIES', '[PAD]', '[PAD]', '[PAD]']\n", + "4822\n", + "Label = ['TYPE', '[PAD]', '[PAD]', '[PAD]']\n", + "4823\n", + "Label = ['REQUIRED', '[PAD]', '[PAD]', '[PAD]']\n", + "4824\n", + "Label = ['REQUIRED', '[PAD]', '[PAD]', '[PAD]']\n", + "4825\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4826\n", + "Label = ['is', 'string', 'type', '[PAD]']\n", + "4827\n", + "Label = ['model', 'prop', 'val', '[PAD]']\n", + "4828\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "4829\n", + "Label = ['REQUIRED', '[PAD]', '[PAD]', '[PAD]']\n", + "4830\n", + "Label = ['BOOLEANS', 'TRUE', '[PAD]', '[PAD]']\n", + "4831\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4832\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4833\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4834\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4835\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4836\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4837\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4838\n", + "Label = ['ANSIBLE', 'METADATA', '[PAD]', '[PAD]']\n", + "4839\n", + "Label = ['attachment', '[PAD]', '[PAD]', '[PAD]']\n", + "4840\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4841\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4842\n", + "Label = ['SSLError', '[PAD]', '[PAD]', '[PAD]']\n", + "4843\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4844\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4845\n", + "Label = ['SMTPAuthenticationError', '[PAD]', '[PAD]', '[PAD]']\n", + "4846\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "4847\n", + "Label = ['part', '[PAD]', '[PAD]', '[PAD]']\n", + "4848\n", + "Label = ['fp', '[PAD]', '[PAD]', '[PAD]']\n", + "4849\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "4850\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4851\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4852\n", + "Label = ['send', 'html', '[PAD]', '[PAD]']\n", + "4853\n", + "Label = ['secure', 'url', '[PAD]', '[PAD]']\n", + "4854\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "4855\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4856\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4857\n", + "Label = ['record', '[PAD]', '[PAD]', '[PAD]']\n", + "4858\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4859\n", + "Label = ['obscured', 'incoming', 'webhook', '[PAD]']\n", + "4860\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4861\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "4862\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "4863\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "4864\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "4865\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "4866\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4867\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4868\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4869\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4870\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4871\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4872\n", + "Label = ['devices', 'by', 'nickname', '[PAD]']\n", + "4873\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "4874\n", + "Label = ['push', 'link', '[PAD]', '[PAD]']\n", + "4875\n", + "Label = ['push', 'link', '[PAD]', '[PAD]']\n", + "4876\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "4877\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "4878\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "4879\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "4880\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4881\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4882\n", + "Label = ['nso', 'query', '[PAD]', '[PAD]']\n", + "4883\n", + "Label = ['REQUIRED', 'VERSIONS', '[PAD]', '[PAD]']\n", + "4884\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "4885\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "4886\n", + "Label = ['nso', 'config', '[PAD]', '[PAD]']\n", + "4887\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "4888\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "4889\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4890\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "4891\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "4892\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "4893\n", + "Label = ['parse', 'memtotal', '[PAD]', '[PAD]']\n", + "4894\n", + "Label = ['parse', 'memfree', '[PAD]', '[PAD]']\n", + "4895\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4896\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4897\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4898\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4899\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4900\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "4901\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "4902\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4903\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4904\n", + "Label = ['configobjs', '[PAD]', '[PAD]', '[PAD]']\n", + "4905\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "4906\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "4907\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "4908\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4909\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4910\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4911\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "4912\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4913\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4914\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "4915\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4916\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4917\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4918\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "4919\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4920\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "4921\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "4922\n", + "Label = ['av', 'check', '[PAD]', '[PAD]']\n", + "4923\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4924\n", + "Label = ['tv', 'check', '[PAD]', '[PAD]']\n", + "4925\n", + "Label = ['c2', '[PAD]', '[PAD]', '[PAD]']\n", + "4926\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "4927\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4928\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "4929\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4930\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4931\n", + "Label = ['ipv4', '[PAD]', '[PAD]', '[PAD]']\n", + "4932\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4933\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4934\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4935\n", + "Label = ['required', 'if', '[PAD]', '[PAD]']\n", + "4936\n", + "Label = ['sha1', '[PAD]', '[PAD]', '[PAD]']\n", + "4937\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "4938\n", + "Label = ['match', 'int', '[PAD]', '[PAD]']\n", + "4939\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "4940\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4941\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "4942\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4943\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "4944\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4945\n", + "Label = ['connect', '[PAD]', '[PAD]', '[PAD]']\n", + "4946\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "4947\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4948\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4949\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "4950\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "4951\n", + "Label = ['transform', 'dict', '[PAD]', '[PAD]']\n", + "4952\n", + "Label = ['parse', 'state', '[PAD]', '[PAD]']\n", + "4953\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4954\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4955\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "4956\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4957\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "4958\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "4959\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4960\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4961\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "4962\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "4963\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "4964\n", + "Label = ['PARAM', 'TO', 'COMMAND', 'KEYMAP']\n", + "4965\n", + "Label = ['arg', '[PAD]', '[PAD]', '[PAD]']\n", + "4966\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "4967\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "4968\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4969\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4970\n", + "Label = ['existing', 'value', '[PAD]', '[PAD]']\n", + "4971\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "4972\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "4973\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "4974\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "4975\n", + "Label = ['av', 'check', '[PAD]', '[PAD]']\n", + "4976\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4977\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "4978\n", + "Label = ['key', 'map', '[PAD]', '[PAD]']\n", + "4979\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "4980\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4981\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "4982\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4983\n", + "Label = ['channels', '[PAD]', '[PAD]', '[PAD]']\n", + "4984\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "4985\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4986\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "4987\n", + "Label = ['source', 'type', '[PAD]', '[PAD]']\n", + "4988\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "4989\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "4990\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4991\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4992\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "4993\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4994\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "4995\n", + "Label = ['http', 'port', '[PAD]', '[PAD]']\n", + "4996\n", + "Label = ['https', 'port', '[PAD]', '[PAD]']\n", + "4997\n", + "Label = ['tlsv1', '2', '[PAD]', '[PAD]']\n", + "4998\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "4999\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5000\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5001\n", + "Label = ['arg', '[PAD]', '[PAD]', '[PAD]']\n", + "5002\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5003\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5004\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5005\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5006\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5007\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5008\n", + "Label = ['candidate', '[PAD]', '[PAD]', '[PAD]']\n", + "5009\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "5010\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5011\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5012\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5013\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "5014\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "5015\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5016\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5017\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5018\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5019\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5020\n", + "Label = ['required', 'if', '[PAD]', '[PAD]']\n", + "5021\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5022\n", + "Label = ['get', 'diff', '[PAD]', '[PAD]']\n", + "5023\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5024\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "5025\n", + "Label = ['BOOL', 'PARAMS', '[PAD]', '[PAD]']\n", + "5026\n", + "Label = ['custom', '[PAD]', '[PAD]', '[PAD]']\n", + "5027\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5028\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5029\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5030\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5031\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5032\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5033\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5034\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5035\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5036\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5037\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5038\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "5039\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "5040\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "5041\n", + "Label = ['vrf', '[PAD]', '[PAD]', '[PAD]']\n", + "5042\n", + "Label = ['vrf', '[PAD]', '[PAD]', '[PAD]']\n", + "5043\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5044\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "5045\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5046\n", + "Label = ['intf', '[PAD]', '[PAD]', '[PAD]']\n", + "5047\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5048\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "5049\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "5050\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5051\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5052\n", + "Label = ['command', 'lists', '[PAD]', '[PAD]']\n", + "5053\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5054\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5055\n", + "Label = ['has', 'command', 'val', '[PAD]']\n", + "5056\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5057\n", + "Label = ['peer', '[PAD]', '[PAD]', '[PAD]']\n", + "5058\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5059\n", + "Label = ['commands', '[PAD]', '[PAD]', '[PAD]']\n", + "5060\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "5061\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "5062\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "5063\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5064\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "5065\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "5066\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5067\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5068\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5069\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5070\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "5071\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5072\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5073\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5074\n", + "Label = ['difference', '[PAD]', '[PAD]', '[PAD]']\n", + "5075\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5076\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5077\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5078\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "5079\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5080\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "5081\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5082\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5083\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5084\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "5085\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "5086\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5087\n", + "Label = ['intfs', 'match', '[PAD]', '[PAD]']\n", + "5088\n", + "Label = ['intfs', '[PAD]', '[PAD]', '[PAD]']\n", + "5089\n", + "Label = ['vlan', '[PAD]', '[PAD]', '[PAD]']\n", + "5090\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "5091\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5092\n", + "Label = ['mutually', 'exclusive', '[PAD]', '[PAD]']\n", + "5093\n", + "Label = ['has', 'size', '[PAD]', '[PAD]']\n", + "5094\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5095\n", + "Label = ['existing', 'value', '[PAD]', '[PAD]']\n", + "5096\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5097\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5098\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5099\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5100\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "5101\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5102\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5103\n", + "Label = ['ex', '[PAD]', '[PAD]', '[PAD]']\n", + "5104\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "5105\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "5106\n", + "Label = ['proposed', 'allowed', 'vlans', '[PAD]']\n", + "5107\n", + "Label = ['native', 'check', '[PAD]', '[PAD]']\n", + "5108\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "5109\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "5110\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5111\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5112\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5113\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5114\n", + "Label = ['command', 'lists', '[PAD]', '[PAD]']\n", + "5115\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "5116\n", + "Label = ['vrf', 'filter', '[PAD]', '[PAD]']\n", + "5117\n", + "Label = ['vrf', 'filter', '[PAD]', '[PAD]']\n", + "5118\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "5119\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5120\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5121\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5122\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5123\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5124\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "5125\n", + "Label = ['difference', '[PAD]', '[PAD]', '[PAD]']\n", + "5126\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5127\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5128\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5129\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5130\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "5131\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "5132\n", + "Label = ['cmds', '[PAD]', '[PAD]', '[PAD]']\n", + "5133\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5134\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "5135\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5136\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5137\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5138\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5139\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "5140\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5141\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5142\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5143\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5144\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "5145\n", + "Label = ['match', 'section', '[PAD]', '[PAD]']\n", + "5146\n", + "Label = ['groupdict', '[PAD]', '[PAD]', '[PAD]']\n", + "5147\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "5148\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "5149\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5150\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5151\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5152\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5153\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5154\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5155\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5156\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5157\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "5158\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5159\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5160\n", + "Label = ['feature', '[PAD]', '[PAD]', '[PAD]']\n", + "5161\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "5162\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5163\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "5164\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "5165\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5166\n", + "Label = ['no', 'source', 'interface', 'command']\n", + "5167\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "5168\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5169\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5170\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5171\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5172\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5173\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5174\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5175\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "5176\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5177\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5178\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "5179\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "5180\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5181\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5182\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5183\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5184\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5185\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "5186\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "5187\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5188\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5189\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5190\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5191\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5192\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5193\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5194\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5195\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5196\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5197\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5198\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5199\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5200\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5201\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5202\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5203\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5204\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "5205\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5206\n", + "Label = ['encryption', 'type', '[PAD]', '[PAD]']\n", + "5207\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5208\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "5209\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5210\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5211\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5212\n", + "Label = ['area', '[PAD]', '[PAD]', '[PAD]']\n", + "5213\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5214\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5215\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "5216\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "5217\n", + "Label = ['desc', '[PAD]', '[PAD]', '[PAD]']\n", + "5218\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "5219\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5220\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5221\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "5222\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "5223\n", + "Label = ['octect', '[PAD]', '[PAD]', '[PAD]']\n", + "5224\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5225\n", + "Label = ['candidate', '[PAD]', '[PAD]', '[PAD]']\n", + "5226\n", + "Label = ['key', 'id', '[PAD]', '[PAD]']\n", + "5227\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "5228\n", + "Label = ['proposed', '[PAD]', '[PAD]', '[PAD]']\n", + "5229\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5230\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5231\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5232\n", + "Label = ['difference', '[PAD]', '[PAD]', '[PAD]']\n", + "5233\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5234\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "5235\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5236\n", + "Label = ['cmd', 'str', '[PAD]', '[PAD]']\n", + "5237\n", + "Label = ['cmd', 'str', '[PAD]', '[PAD]']\n", + "5238\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5239\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5240\n", + "Label = ['pid', '[PAD]', '[PAD]', '[PAD]']\n", + "5241\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "5242\n", + "Label = ['result', 'data', '[PAD]', '[PAD]']\n", + "5243\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5244\n", + "Label = ['version', 'data', '[PAD]', '[PAD]']\n", + "5245\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "5246\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "5247\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5248\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5249\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5250\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "5251\n", + "Label = ['auth', 'enc', '[PAD]', '[PAD]']\n", + "5252\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5253\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5254\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "5255\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "5256\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "5257\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5258\n", + "Label = ['key', 'map', '[PAD]', '[PAD]']\n", + "5259\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5260\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5261\n", + "Label = ['each', '[PAD]', '[PAD]', '[PAD]']\n", + "5262\n", + "Label = ['pc', '[PAD]', '[PAD]', '[PAD]']\n", + "5263\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "5264\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "5265\n", + "Label = ['distance', 'group', '[PAD]', '[PAD]']\n", + "5266\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5267\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5268\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "5269\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5270\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5271\n", + "Label = ['network', '[PAD]', '[PAD]', '[PAD]']\n", + "5272\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5273\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5274\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5275\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "5276\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "5277\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5278\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5279\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5280\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5281\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "5282\n", + "Label = ['compile', '[PAD]', '[PAD]', '[PAD]']\n", + "5283\n", + "Label = ['pchannel', '[PAD]', '[PAD]', '[PAD]']\n", + "5284\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "5285\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5286\n", + "Label = ['existing', 'members', '[PAD]', '[PAD]']\n", + "5287\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5288\n", + "Label = ['member', '[PAD]', '[PAD]', '[PAD]']\n", + "5289\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5290\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "5291\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "5292\n", + "Label = ['dcore', '[PAD]', '[PAD]', '[PAD]']\n", + "5293\n", + "Label = ['doptions', '[PAD]', '[PAD]', '[PAD]']\n", + "5294\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "5295\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5296\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "5297\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5298\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5299\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "5300\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "5301\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "5302\n", + "Label = ['value', 'type', '[PAD]', '[PAD]']\n", + "5303\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "5304\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5305\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "5306\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5307\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5308\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "5309\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "5310\n", + "Label = ['memused', 'mb', '[PAD]', '[PAD]']\n", + "5311\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "5312\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "5313\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5314\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "5315\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5316\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5317\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "5318\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "5319\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5320\n", + "Label = ['metaclass', '[PAD]', '[PAD]', '[PAD]']\n", + "5321\n", + "Label = ['metaclass', '[PAD]', '[PAD]', '[PAD]']\n", + "5322\n", + "Label = ['xml', 'output', '[PAD]', '[PAD]']\n", + "5323\n", + "Label = ['Panorama', '[PAD]', '[PAD]', '[PAD]']\n", + "5324\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "5325\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "5326\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "5327\n", + "Label = ['MultipartEncoder', '[PAD]', '[PAD]', '[PAD]']\n", + "5328\n", + "Label = ['vulnerability', '[PAD]', '[PAD]', '[PAD]']\n", + "5329\n", + "Label = ['url', 'filtering', '[PAD]', '[PAD]']\n", + "5330\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "5331\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "5332\n", + "Label = ['Panorama', '[PAD]', '[PAD]', '[PAD]']\n", + "5333\n", + "Label = ['cert', 'cn', '[PAD]', '[PAD]']\n", + "5334\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5335\n", + "Label = ['vulnerability', '[PAD]', '[PAD]', '[PAD]']\n", + "5336\n", + "Label = ['rule', 'base', '[PAD]', '[PAD]']\n", + "5337\n", + "Label = ['sec', 'rule', '[PAD]', '[PAD]']\n", + "5338\n", + "Label = ['PanXapi', '[PAD]', '[PAD]', '[PAD]']\n", + "5339\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5340\n", + "Label = ['xapi', '[PAD]', '[PAD]', '[PAD]']\n", + "5341\n", + "Label = ['HAS', 'LIB', '[PAD]', '[PAD]']\n", + "5342\n", + "Label = ['HAS', 'LIB', '[PAD]', '[PAD]']\n", + "5343\n", + "Label = ['security', 'test', '[PAD]', '[PAD]']\n", + "5344\n", + "Label = ['nat', 'test', '[PAD]', '[PAD]']\n", + "5345\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "5346\n", + "Label = ['test', 'string', '[PAD]', '[PAD]']\n", + "5347\n", + "Label = ['rule', 'match', '[PAD]', '[PAD]']\n", + "5348\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "5349\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "5350\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "5351\n", + "Label = ['AddressObject', '[PAD]', '[PAD]', '[PAD]']\n", + "5352\n", + "Label = ['newobject', '[PAD]', '[PAD]', '[PAD]']\n", + "5353\n", + "Label = ['newobject', '[PAD]', '[PAD]', '[PAD]']\n", + "5354\n", + "Label = ['Tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5355\n", + "Label = ['new', 'object', '[PAD]', '[PAD]']\n", + "5356\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "5357\n", + "Label = ['set', '[PAD]', '[PAD]', '[PAD]']\n", + "5358\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5359\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "5360\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "5361\n", + "Label = ['AddressObject', '[PAD]', '[PAD]', '[PAD]']\n", + "5362\n", + "Label = ['member', 'string', '[PAD]', '[PAD]']\n", + "5363\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5364\n", + "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", + "5365\n", + "Label = ['HAS', 'LIB', '[PAD]', '[PAD]']\n", + "5366\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "5367\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5368\n", + "Label = ['new', 'rule', '[PAD]', '[PAD]']\n", + "5369\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5370\n", + "Label = ['auth', 'type', '[PAD]', '[PAD]']\n", + "5371\n", + "Label = ['ntp', 'auth', 'conf', '[PAD]']\n", + "5372\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5373\n", + "Label = ['key', 'id', '[PAD]', '[PAD]']\n", + "5374\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "5375\n", + "Label = ['port', 'number', '[PAD]', '[PAD]']\n", + "5376\n", + "Label = ['cur', 'cfg', '[PAD]', '[PAD]']\n", + "5377\n", + "Label = ['port', 'number', '[PAD]', '[PAD]']\n", + "5378\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5379\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5380\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5381\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5382\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5383\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "5384\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "5385\n", + "Label = ['vlan', 'id', '[PAD]', '[PAD]']\n", + "5386\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "5387\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5388\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5389\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5390\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5391\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5392\n", + "Label = ['reset', '[PAD]', '[PAD]', '[PAD]']\n", + "5393\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5394\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5395\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5396\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5397\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5398\n", + "Label = ['admin', 'interface', '[PAD]', '[PAD]']\n", + "5399\n", + "Label = ['preempt', 'timer', 'delay', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5400\n", + "Label = ['vrrp', 'global', 'info', '[PAD]']\n", + "5401\n", + "Label = ['vrrp', 'global', 'info', '[PAD]']\n", + "5402\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "5403\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5404\n", + "Label = ['admin', 'ignore', 'if', 'down']\n", + "5405\n", + "Label = ['vrrp', 'group', 'info', '[PAD]']\n", + "5406\n", + "Label = ['advertise', 'interval', '[PAD]', '[PAD]']\n", + "5407\n", + "Label = ['preempt', 'timer', 'delay', '[PAD]']\n", + "5408\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5409\n", + "Label = ['gratuitous', 'arp', 'interval', '[PAD]']\n", + "5410\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5411\n", + "Label = ['recover', 'delay', '[PAD]', '[PAD]']\n", + "5412\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5413\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5414\n", + "Label = ['priority', '[PAD]', '[PAD]', '[PAD]']\n", + "5415\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5416\n", + "Label = ['virtual', 'ip', '[PAD]', '[PAD]']\n", + "5417\n", + "Label = ['vrrp', 'type', '[PAD]', '[PAD]']\n", + "5418\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5419\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5420\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5421\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5422\n", + "Label = ['preempt', 'timer', 'delay', '[PAD]']\n", + "5423\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5424\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "5425\n", + "Label = ['admin', 'ignore', 'if', 'down']\n", + "5426\n", + "Label = ['admin', 'interface', '[PAD]', '[PAD]']\n", + "5427\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "5428\n", + "Label = ['CHANNEL', 'DEFAULT', 'TRAP', 'STATE']\n", + "5429\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5430\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5431\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "5432\n", + "Label = ['trap', 'time', 'stamp', '[PAD]']\n", + "5433\n", + "Label = ['module', 'name', '[PAD]', '[PAD]']\n", + "5434\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "5435\n", + "Label = ['module', 'name', '[PAD]', '[PAD]']\n", + "5436\n", + "Label = ['trap', 'enable', '[PAD]', '[PAD]']\n", + "5437\n", + "Label = ['trap', 'enable', '[PAD]', '[PAD]']\n", + "5438\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5439\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5440\n", + "Label = ['temp', 'data', '[PAD]', '[PAD]']\n", + "5441\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5442\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5443\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5444\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5445\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5446\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5447\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5448\n", + "Label = ['mlag', 'error', 'info', '[PAD]']\n", + "5449\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "5450\n", + "Label = ['mlag', 'trunk', 'attribute', 'info']\n", + "5451\n", + "Label = ['mlag', 'system', 'id', '[PAD]']\n", + "5452\n", + "Label = ['mlag', 'priority', 'id', '[PAD]']\n", + "5453\n", + "Label = ['mlag', 'global', 'info', '[PAD]']\n", + "5454\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5455\n", + "Label = ['mlag', 'priority', 'id', '[PAD]']\n", + "5456\n", + "Label = ['mlag', 'system', 'id', '[PAD]']\n", + "5457\n", + "Label = ['mlag', 'system', 'id', '[PAD]']\n", + "5458\n", + "Label = ['mlag', 'trunk', 'attribute', 'info']\n", + "5459\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "5460\n", + "Label = ['mlag', 'priority', 'id', '[PAD]']\n", + "5461\n", + "Label = ['mlag', 'system', 'id', '[PAD]']\n", + "5462\n", + "Label = ['mlag', 'error', 'down', 'info']\n", + "5463\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5464\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5465\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5466\n", + "Label = ['vpn', 'instance', 'name', '[PAD]']\n", + "5467\n", + "Label = ['pseudo', 'nickname', '[PAD]', '[PAD]']\n", + "5468\n", + "Label = ['pseudo', 'priority', '[PAD]', '[PAD]']\n", + "5469\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5470\n", + "Label = ['vpn', 'instance', 'name', '[PAD]']\n", + "5471\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "5472\n", + "Label = ['pseudo', 'nickname', '[PAD]', '[PAD]']\n", + "5473\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "5474\n", + "Label = ['nickname', '[PAD]', '[PAD]', '[PAD]']\n", + "5475\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5476\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "5477\n", + "Label = ['vpn', 'instance', 'name', '[PAD]']\n", + "5478\n", + "Label = ['peer', 'link', 'id', '[PAD]']\n", + "5479\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "5480\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5481\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5482\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5483\n", + "Label = ['host', 'ip', '[PAD]', '[PAD]']\n", + "5484\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5485\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5486\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "5487\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5488\n", + "Label = ['lsaostartinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5489\n", + "Label = ['spfintervalmi', '[PAD]', '[PAD]', '[PAD]']\n", + "5490\n", + "Label = ['spfmaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5491\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5492\n", + "Label = ['ospf', '[PAD]', '[PAD]', '[PAD]']\n", + "5493\n", + "Label = ['ospf', 'site', '[PAD]', '[PAD]']\n", + "5494\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5495\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5496\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5497\n", + "Label = ['route', 'id', '[PAD]', '[PAD]']\n", + "5498\n", + "Label = ['lsaamaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5499\n", + "Label = ['lsaastartinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5500\n", + "Label = ['isvalidlsastartarrivalinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5501\n", + "Label = ['lsaomaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5502\n", + "Label = ['lsaoholdinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5503\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5504\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5505\n", + "Label = ['route', 'id', '[PAD]', '[PAD]']\n", + "5506\n", + "Label = ['bandwidth', '[PAD]', '[PAD]', '[PAD]']\n", + "5507\n", + "Label = ['lsaamaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5508\n", + "Label = ['lsaamaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5509\n", + "Label = ['lsaastartinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5510\n", + "Label = ['lsaomaxinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5511\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5512\n", + "Label = ['spfintervalmi', '[PAD]', '[PAD]', '[PAD]']\n", + "5513\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5514\n", + "Label = ['spfstartinterval', '[PAD]', '[PAD]', '[PAD]']\n", + "5515\n", + "Label = ['description', 'changed', '[PAD]', '[PAD]']\n", + "5516\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5517\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5518\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5519\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5520\n", + "Label = ['spf', 'changed', '[PAD]', '[PAD]']\n", + "5521\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5522\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5523\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5524\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5525\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5526\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5527\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5528\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5529\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5530\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5531\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5532\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5533\n", + "Label = ['bgp', 'instance', '[PAD]', '[PAD]']\n", + "5534\n", + "Label = ['as', 'number', '[PAD]', '[PAD]']\n", + "5535\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5536\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5537\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5538\n", + "Label = ['l2vpn', 'evpn', 'exist', '[PAD]']\n", + "5539\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5540\n", + "Label = ['l2vpn', 'evpn', 'exist', '[PAD]']\n", + "5541\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "5542\n", + "Label = ['peer', 'enable', '[PAD]', '[PAD]']\n", + "5543\n", + "Label = ['peer', 'enable', '[PAD]', '[PAD]']\n", + "5544\n", + "Label = ['peer', 'enable', '[PAD]', '[PAD]']\n", + "5545\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "5546\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "5547\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5548\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5549\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5550\n", + "Label = ['collector', 'ip', 'vpn', '[PAD]']\n", + "5551\n", + "Label = ['collector', 'ip', 'vpn', '[PAD]']\n", + "5552\n", + "Label = ['collector', 'datagram', 'size', '[PAD]']\n", + "5553\n", + "Label = ['collector', 'version', '[PAD]', '[PAD]']\n", + "5554\n", + "Label = ['collector', 'version', '[PAD]', '[PAD]']\n", + "5555\n", + "Label = ['collector', 'udp', 'port', '[PAD]']\n", + "5556\n", + "Label = ['collector', 'datagram', 'size', '[PAD]']\n", + "5557\n", + "Label = ['collector', 'ip', 'vpn', '[PAD]']\n", + "5558\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5559\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5560\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "5561\n", + "Label = ['sample', 'collector', '[PAD]', '[PAD]']\n", + "5562\n", + "Label = ['exist', '[PAD]', '[PAD]', '[PAD]']\n", + "5563\n", + "Label = ['same', '[PAD]', '[PAD]', '[PAD]']\n", + "5564\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5565\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5566\n", + "Label = ['sflow', 'dict', '[PAD]', '[PAD]']\n", + "5567\n", + "Label = ['agent', 'ip', '[PAD]', '[PAD]']\n", + "5568\n", + "Label = ['source', 'ip', '[PAD]', '[PAD]']\n", + "5569\n", + "Label = ['collector', 'ip', '[PAD]', '[PAD]']\n", + "5570\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5571\n", + "Label = ['rate', 'limit', 'slot', '[PAD]']\n", + "5572\n", + "Label = ['agent', 'ip', '[PAD]', '[PAD]']\n", + "5573\n", + "Label = ['agent', 'ip', '[PAD]', '[PAD]']\n", + "5574\n", + "Label = ['sflow', 'interface', '[PAD]', '[PAD]']\n", + "5575\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "5576\n", + "Label = ['config', 'mem', '[PAD]', '[PAD]']\n", + "5577\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5578\n", + "Label = ['config', 'list', '[PAD]', '[PAD]']\n", + "5579\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5580\n", + "Label = ['sampler', 'interval', '[PAD]', '[PAD]']\n", + "5581\n", + "Label = ['sampler', 'direction', '[PAD]', '[PAD]']\n", + "5582\n", + "Label = ['statistics', 'record', '[PAD]', '[PAD]']\n", + "5583\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5584\n", + "Label = ['sampler', 'interval', '[PAD]', '[PAD]']\n", + "5585\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5586\n", + "Label = ['sampler', 'changed', '[PAD]', '[PAD]']\n", + "5587\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5588\n", + "Label = ['mem', '[PAD]', '[PAD]', '[PAD]']\n", + "5589\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5590\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5591\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5592\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "5593\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5594\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5595\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5596\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5597\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5598\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5599\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5600\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5601\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5602\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5603\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "5604\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5605\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5606\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5607\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5608\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5609\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5610\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5611\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5612\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5613\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5614\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "5615\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "5616\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5617\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5618\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5619\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5620\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5621\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5622\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5623\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5624\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5625\n", + "Label = ['intf', 'info', '[PAD]', '[PAD]']\n", + "5626\n", + "Label = ['mtu', '[PAD]', '[PAD]', '[PAD]']\n", + "5627\n", + "Label = ['jbf', 'max', '[PAD]', '[PAD]']\n", + "5628\n", + "Label = ['intf', 'info', '[PAD]', '[PAD]']\n", + "5629\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5630\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5631\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5632\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5633\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5634\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5635\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5636\n", + "Label = ['ele', '[PAD]', '[PAD]', '[PAD]']\n", + "5637\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5638\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5639\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5640\n", + "Label = ['ce', 'vid', '[PAD]', '[PAD]']\n", + "5641\n", + "Label = ['ce', 'vid', '[PAD]', '[PAD]']\n", + "5642\n", + "Label = ['ce', 'vid', '[PAD]', '[PAD]']\n", + "5643\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5644\n", + "Label = ['bridge', 'domain', 'id', '[PAD]']\n", + "5645\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5646\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5647\n", + "Label = ['encapsulation', '[PAD]', '[PAD]', '[PAD]']\n", + "5648\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5649\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5650\n", + "Label = ['bridge', 'domain', 'id', '[PAD]']\n", + "5651\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "5652\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5653\n", + "Label = ['neighbors', '[PAD]', '[PAD]', '[PAD]']\n", + "5654\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5655\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "5656\n", + "Label = ['each', 'num', '[PAD]', '[PAD]']\n", + "5657\n", + "Label = ['area', '[PAD]', '[PAD]', '[PAD]']\n", + "5658\n", + "Label = ['area', '[PAD]', '[PAD]', '[PAD]']\n", + "5659\n", + "Label = ['area', '[PAD]', '[PAD]', '[PAD]']\n", + "5660\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5661\n", + "Label = ['silent', 'interface', '[PAD]', '[PAD]']\n", + "5662\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5663\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5664\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5665\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5666\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5667\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5668\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5669\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "5670\n", + "Label = ['ospf', 'info', '[PAD]', '[PAD]']\n", + "5671\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5672\n", + "Label = ['updates', 'cmd', '[PAD]', '[PAD]']\n", + "5673\n", + "Label = ['tos', 'exp', 'dynamic', '[PAD]']\n", + "5674\n", + "Label = ['updates', 'cmd', '[PAD]', '[PAD]']\n", + "5675\n", + "Label = ['updates', 'cmd', '[PAD]', '[PAD]']\n", + "5676\n", + "Label = ['damp', 'init', 'wait', 'time']\n", + "5677\n", + "Label = ['damp', 'init', 'wait', 'time']\n", + "5678\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5679\n", + "Label = ['damp', 'init', 'wait', 'time']\n", + "5680\n", + "Label = ['damp', 'second', 'wait', 'time']\n", + "5681\n", + "Label = ['iftype', '[PAD]', '[PAD]', '[PAD]']\n", + "5682\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "5683\n", + "Label = ['intf', 'info', '[PAD]', '[PAD]']\n", + "5684\n", + "Label = ['vlan', 'list', '[PAD]', '[PAD]']\n", + "5685\n", + "Label = ['add', 'vlans', '[PAD]', '[PAD]']\n", + "5686\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5687\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5688\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5689\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5690\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5691\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5692\n", + "Label = ['acl', 'description', '[PAD]', '[PAD]']\n", + "5693\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "5694\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5695\n", + "Label = ['time', 'range', '[PAD]', '[PAD]']\n", + "5696\n", + "Label = ['rule', 'description', '[PAD]', '[PAD]']\n", + "5697\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5698\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5699\n", + "Label = ['rule', 'name', '[PAD]', '[PAD]']\n", + "5700\n", + "Label = ['rule', 'action', '[PAD]', '[PAD]']\n", + "5701\n", + "Label = ['src', 'wild', '[PAD]', '[PAD]']\n", + "5702\n", + "Label = ['time', 'range', '[PAD]', '[PAD]']\n", + "5703\n", + "Label = ['acl', 'step', '[PAD]', '[PAD]']\n", + "5704\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "5705\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5706\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5707\n", + "Label = ['frag', 'type', '[PAD]', '[PAD]']\n", + "5708\n", + "Label = ['rule', 'description', '[PAD]', '[PAD]']\n", + "5709\n", + "Label = ['source', 'ip', '[PAD]', '[PAD]']\n", + "5710\n", + "Label = ['cmp', 'cfg', '[PAD]', '[PAD]']\n", + "5711\n", + "Label = ['nvo3', 'eth', 'trunk', 'hash']\n", + "5712\n", + "Label = ['nvo3', 'acl', 'extend', '[PAD]']\n", + "5713\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5714\n", + "Label = ['manual', 'slot', '[PAD]', '[PAD]']\n", + "5715\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5716\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5717\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5718\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5719\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5720\n", + "Label = ['xmlstr', '[PAD]', '[PAD]', '[PAD]']\n", + "5721\n", + "Label = ['intf', 'type', '[PAD]', '[PAD]']\n", + "5722\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5723\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5724\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "5725\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "5726\n", + "Label = ['intf', 'type', '[PAD]', '[PAD]']\n", + "5727\n", + "Label = ['intf', 'type', '[PAD]', '[PAD]']\n", + "5728\n", + "Label = ['intf', 'type', '[PAD]', '[PAD]']\n", + "5729\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5730\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5731\n", + "Label = ['cfg', 'file', '[PAD]', '[PAD]']\n", + "5732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5733\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5734\n", + "Label = ['admin', 'down', '[PAD]', '[PAD]']\n", + "5735\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5736\n", + "Label = ['session', 'name', '[PAD]', '[PAD]']\n", + "5737\n", + "Label = ['remote', 'discr', '[PAD]', '[PAD]']\n", + "5738\n", + "Label = ['remote', 'discr', '[PAD]', '[PAD]']\n", + "5739\n", + "Label = ['detect', 'multi', '[PAD]', '[PAD]']\n", + "5740\n", + "Label = ['detect', 'multi', '[PAD]', '[PAD]']\n", + "5741\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "5742\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5743\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5744\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5745\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5746\n", + "Label = ['intf', 'info', '[PAD]', '[PAD]']\n", + "5747\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5748\n", + "Label = ['vpname', '[PAD]', '[PAD]', '[PAD]']\n", + "5749\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5750\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5751\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5752\n", + "Label = ['bfd', 'dict', '[PAD]', '[PAD]']\n", + "5753\n", + "Label = ['create', 'type', '[PAD]', '[PAD]']\n", + "5754\n", + "Label = ['addr', 'type', '[PAD]', '[PAD]']\n", + "5755\n", + "Label = ['dest', 'addr', '[PAD]', '[PAD]']\n", + "5756\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5757\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5758\n", + "Label = ['vrf', 'name', '[PAD]', '[PAD]']\n", + "5759\n", + "Label = ['src', 'addr', '[PAD]', '[PAD]']\n", + "5760\n", + "Label = ['vrf', 'name', '[PAD]', '[PAD]']\n", + "5761\n", + "Label = ['peer', '[PAD]', '[PAD]', '[PAD]']\n", + "5762\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5763\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "5764\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5765\n", + "Label = ['peer', 'type', '[PAD]', '[PAD]']\n", + "5766\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5767\n", + "Label = ['interface', '[PAD]', '[PAD]', '[PAD]']\n", + "5768\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5769\n", + "Label = ['ip', 'ver', '[PAD]', '[PAD]']\n", + "5770\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5771\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5772\n", + "Label = ['ip', 'addr', '[PAD]', '[PAD]']\n", + "5773\n", + "Label = ['vpn', 'name', '[PAD]', '[PAD]']\n", + "5774\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "5775\n", + "Label = ['peer', 'type', '[PAD]', '[PAD]']\n", + "5776\n", + "Label = ['proposed', '[PAD]', '[PAD]', '[PAD]']\n", + "5777\n", + "Label = ['ip', 'ver', '[PAD]', '[PAD]']\n", + "5778\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5779\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "5780\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5781\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "5782\n", + "Label = ['debug', 'enable', '[PAD]', '[PAD]']\n", + "5783\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5784\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5785\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5786\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5787\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5788\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5789\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5790\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5791\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5792\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5793\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5794\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5795\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5796\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5797\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5798\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5799\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5800\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5801\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5802\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5803\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5804\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5805\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5806\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5807\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5808\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5809\n", + "Label = ['HASH', 'XML2CLI', '[PAD]', '[PAD]']\n", + "5810\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5811\n", + "Label = ['mem', '[PAD]', '[PAD]', '[PAD]']\n", + "5812\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5813\n", + "Label = ['min', 'links', '[PAD]', '[PAD]']\n", + "5814\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "5815\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5816\n", + "Label = ['min', 'links', '[PAD]', '[PAD]']\n", + "5817\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "5818\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5819\n", + "Label = ['re', 'find', '[PAD]', '[PAD]']\n", + "5820\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5821\n", + "Label = ['re', 'find', '[PAD]', '[PAD]']\n", + "5822\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5823\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5824\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5825\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5826\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5827\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5828\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5829\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5830\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5831\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5832\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5833\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5834\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5835\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5836\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5837\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5838\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5839\n", + "Label = ['network', 'need', 'cfg', '[PAD]']\n", + "5840\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5841\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5842\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5843\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5844\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5845\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5846\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5847\n", + "Label = ['work', 'mode', '[PAD]', '[PAD]']\n", + "5848\n", + "Label = ['reset', '[PAD]', '[PAD]', '[PAD]']\n", + "5849\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "5850\n", + "Label = ['internal', '[PAD]', '[PAD]', '[PAD]']\n", + "5851\n", + "Label = ['reset', '[PAD]', '[PAD]', '[PAD]']\n", + "5852\n", + "Label = ['stp', 'mode', '[PAD]', '[PAD]']\n", + "5853\n", + "Label = ['cost', '[PAD]', '[PAD]', '[PAD]']\n", + "5854\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "5855\n", + "Label = ['stp', 'converge', '[PAD]', '[PAD]']\n", + "5856\n", + "Label = ['bpdu', 'protection', '[PAD]', '[PAD]']\n", + "5857\n", + "Label = ['stp', 'cfg', '[PAD]', '[PAD]']\n", + "5858\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5859\n", + "Label = ['stp', 'cfg', '[PAD]', '[PAD]']\n", + "5860\n", + "Label = ['stp', 'cfg', '[PAD]', '[PAD]']\n", + "5861\n", + "Label = ['tc', 'protection', '[PAD]', '[PAD]']\n", + "5862\n", + "Label = ['cost', '[PAD]', '[PAD]', '[PAD]']\n", + "5863\n", + "Label = ['stp', 'converge', '[PAD]', '[PAD]']\n", + "5864\n", + "Label = ['bpdu', 'filter', '[PAD]', '[PAD]']\n", + "5865\n", + "Label = ['bpdu', 'protection', '[PAD]', '[PAD]']\n", + "5866\n", + "Label = ['loop', 'protection', '[PAD]', '[PAD]']\n", + "5867\n", + "Label = ['tc', 'protection', '[PAD]', '[PAD]']\n", + "5868\n", + "Label = ['tc', 'protection', 'threshold', '[PAD]']\n", + "5869\n", + "Label = ['loop', 'protection', '[PAD]', '[PAD]']\n", + "5870\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5871\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5872\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5873\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5874\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5875\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5876\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5877\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5878\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5879\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5880\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5881\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5882\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5883\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5884\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5885\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5886\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5887\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5888\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5889\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5890\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5891\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5892\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "5893\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5894\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5895\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5896\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "5897\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "5898\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "5899\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5900\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "5901\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5902\n", + "Label = ['vpn', 'instance', '[PAD]', '[PAD]']\n", + "5903\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5904\n", + "Label = ['eles', '[PAD]', '[PAD]', '[PAD]']\n", + "5905\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5906\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5907\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "5908\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "5909\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "5910\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5911\n", + "Label = ['issubset', '[PAD]', '[PAD]', '[PAD]']\n", + "5912\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5913\n", + "Label = ['proposed', '[PAD]', '[PAD]', '[PAD]']\n", + "5914\n", + "Label = ['proposed', '[PAD]', '[PAD]', '[PAD]']\n", + "5915\n", + "Label = ['proposed', '[PAD]', '[PAD]', '[PAD]']\n", + "5916\n", + "Label = ['ele', '[PAD]', '[PAD]', '[PAD]']\n", + "5917\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5918\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "5919\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5920\n", + "Label = ['route', 'distinguisher', '[PAD]', '[PAD]']\n", + "5921\n", + "Label = ['ele', '[PAD]', '[PAD]', '[PAD]']\n", + "5922\n", + "Label = ['route', 'distinguisher', '[PAD]', '[PAD]']\n", + "5923\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "5924\n", + "Label = ['collect', 'interface', '[PAD]', '[PAD]']\n", + "5925\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "5926\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "5927\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "5928\n", + "Label = ['netstream', 'cfg', '[PAD]', '[PAD]']\n", + "5929\n", + "Label = ['netstream', 'cfg', '[PAD]', '[PAD]']\n", + "5930\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5931\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "5932\n", + "Label = ['dest', 'port', 'end', '[PAD]']\n", + "5933\n", + "Label = ['icmp', 'type', '[PAD]', '[PAD]']\n", + "5934\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "5935\n", + "Label = ['igmp', 'type', '[PAD]', '[PAD]']\n", + "5936\n", + "Label = ['acl', 'step', '[PAD]', '[PAD]']\n", + "5937\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "5938\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "5939\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "5940\n", + "Label = ['dest', 'mask', '[PAD]', '[PAD]']\n", + "5941\n", + "Label = ['dest', 'pool', 'name', '[PAD]']\n", + "5942\n", + "Label = ['src', 'port', 'begin', '[PAD]']\n", + "5943\n", + "Label = ['dest', 'port', 'begin', '[PAD]']\n", + "5944\n", + "Label = ['dest', 'port', 'pool', 'name']\n", + "5945\n", + "Label = ['tcp', 'flag', 'mask', '[PAD]']\n", + "5946\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "5947\n", + "Label = ['rule', 'action', '[PAD]', '[PAD]']\n", + "5948\n", + "Label = ['tmp', 'addr', '[PAD]', '[PAD]']\n", + "5949\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "5950\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "5951\n", + "Label = ['src', 'port', 'begin', '[PAD]']\n", + "5952\n", + "Label = ['dest', 'port', 'pool', 'name']\n", + "5953\n", + "Label = ['tcp', 'flag', 'mask', '[PAD]']\n", + "5954\n", + "Label = ['igmp', 'type', '[PAD]', '[PAD]']\n", + "5955\n", + "Label = ['dest', 'ip', '[PAD]', '[PAD]']\n", + "5956\n", + "Label = ['tos', '[PAD]', '[PAD]', '[PAD]']\n", + "5957\n", + "Label = ['syn', 'flag', '[PAD]', '[PAD]']\n", + "5958\n", + "Label = ['rule', 'description', '[PAD]', '[PAD]']\n", + "5959\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "5960\n", + "Label = ['acl', 'description', '[PAD]', '[PAD]']\n", + "5961\n", + "Label = ['acl', 'step', '[PAD]', '[PAD]']\n", + "5962\n", + "Label = ['precedence', '[PAD]', '[PAD]', '[PAD]']\n", + "5963\n", + "Label = ['rule', 'description', '[PAD]', '[PAD]']\n", + "5964\n", + "Label = ['src', 'pool', 'name', '[PAD]']\n", + "5965\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "5966\n", + "Label = ['rule', 'action', '[PAD]', '[PAD]']\n", + "5967\n", + "Label = ['src', 'wild', '[PAD]', '[PAD]']\n", + "5968\n", + "Label = ['dest', 'pool', 'name', '[PAD]']\n", + "5969\n", + "Label = ['src', 'port', 'op', '[PAD]']\n", + "5970\n", + "Label = ['frag', 'type', '[PAD]', '[PAD]']\n", + "5971\n", + "Label = ['vrf', 'name', '[PAD]', '[PAD]']\n", + "5972\n", + "Label = ['dest', 'port', 'pool', 'name']\n", + "5973\n", + "Label = ['connect', 'port', '[PAD]', '[PAD]']\n", + "5974\n", + "Label = ['recv', 'port', '[PAD]', '[PAD]']\n", + "5975\n", + "Label = ['security', 'name', '[PAD]', '[PAD]']\n", + "5976\n", + "Label = ['interface', 'name', '[PAD]', '[PAD]']\n", + "5977\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "5978\n", + "Label = ['interface', 'name', '[PAD]', '[PAD]']\n", + "5979\n", + "Label = ['temp', 'data', '[PAD]', '[PAD]']\n", + "5980\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "5981\n", + "Label = ['recv', 'port', '[PAD]', '[PAD]']\n", + "5982\n", + "Label = ['security', 'name', '[PAD]', '[PAD]']\n", + "5983\n", + "Label = ['security', 'level', '[PAD]', '[PAD]']\n", + "5984\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "5985\n", + "Label = ['member', '[PAD]', '[PAD]', '[PAD]']\n", + "5986\n", + "Label = ['nve', 'info', '[PAD]', '[PAD]']\n", + "5987\n", + "Label = ['nve', 'info', '[PAD]', '[PAD]']\n", + "5988\n", + "Label = ['nve', 'info', '[PAD]', '[PAD]']\n", + "5989\n", + "Label = ['member', '[PAD]', '[PAD]', '[PAD]']\n", + "5990\n", + "Label = ['peer', 'ip', '[PAD]', '[PAD]']\n", + "5991\n", + "Label = ['bridge', 'domain', 'id', '[PAD]']\n", + "5992\n", + "Label = ['source', 'ip', '[PAD]', '[PAD]']\n", + "5993\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "5994\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5995\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "5996\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "5997\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5998\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "5999\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "6000\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6001\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6002\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6003\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6004\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6005\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6006\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6007\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6008\n", + "Label = ['vpn', 'interface', '[PAD]', '[PAD]']\n", + "6009\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6010\n", + "Label = ['l3vpn', 'ifinfo', '[PAD]', '[PAD]']\n", + "6011\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6012\n", + "Label = ['acl', 'name', '[PAD]', '[PAD]']\n", + "6013\n", + "Label = ['direction', '[PAD]', '[PAD]', '[PAD]']\n", + "6014\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "6015\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6016\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6017\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6018\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6019\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6020\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6021\n", + "Label = ['re', 'find', '[PAD]', '[PAD]']\n", + "6022\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6023\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6024\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6025\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6026\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6027\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6028\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6029\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6030\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6031\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6032\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6033\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6034\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6035\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6036\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6037\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6038\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6039\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6040\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6041\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6042\n", + "Label = ['isalnum', '[PAD]', '[PAD]', '[PAD]']\n", + "6043\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6044\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6045\n", + "Label = ['cli', 'add', 'command', '[PAD]']\n", + "6046\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6047\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6048\n", + "Label = ['dfs', 'source', 'ip', '[PAD]']\n", + "6049\n", + "Label = ['dfs', 'source', 'vpn', '[PAD]']\n", + "6050\n", + "Label = ['dfs', 'peer', 'vpn', '[PAD]']\n", + "6051\n", + "Label = ['vpn', 'instance', '[PAD]', '[PAD]']\n", + "6052\n", + "Label = ['vbdif', 'name', '[PAD]', '[PAD]']\n", + "6053\n", + "Label = ['vpn', 'vni', '[PAD]', '[PAD]']\n", + "6054\n", + "Label = ['vpn', 'instance', '[PAD]', '[PAD]']\n", + "6055\n", + "Label = ['out', 'direct', '[PAD]', '[PAD]']\n", + "6056\n", + "Label = ['channel', 'out', 'direct', '[PAD]']\n", + "6057\n", + "Label = ['ip', 'type', '[PAD]', '[PAD]']\n", + "6058\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "6059\n", + "Label = ['id2name', '[PAD]', '[PAD]', '[PAD]']\n", + "6060\n", + "Label = ['id2name', '[PAD]', '[PAD]', '[PAD]']\n", + "6061\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6062\n", + "Label = ['change', 'flag', '[PAD]', '[PAD]']\n", + "6063\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6064\n", + "Label = ['ip', 'type', '[PAD]', '[PAD]']\n", + "6065\n", + "Label = ['conf', 'str', '[PAD]', '[PAD]']\n", + "6066\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['facility', '[PAD]', '[PAD]', '[PAD]']\n", + "6067\n", + "Label = ['transport', 'mode', '[PAD]', '[PAD]']\n", + "6068\n", + "Label = ['source', 'ip', '[PAD]', '[PAD]']\n", + "6069\n", + "Label = ['source', 'ip', '[PAD]', '[PAD]']\n", + "6070\n", + "Label = ['channel', 'id', '[PAD]', '[PAD]']\n", + "6071\n", + "Label = ['channel', 'name', '[PAD]', '[PAD]']\n", + "6072\n", + "Label = ['syslog', 'dns', '[PAD]', '[PAD]']\n", + "6073\n", + "Label = ['syslog', 'dns', '[PAD]', '[PAD]']\n", + "6074\n", + "Label = ['ssl', 'policy', 'name', '[PAD]']\n", + "6075\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6076\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "6077\n", + "Label = ['info', 'center', 'enable', '[PAD]']\n", + "6078\n", + "Label = ['packet', 'priority', '[PAD]', '[PAD]']\n", + "6079\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "6080\n", + "Label = ['logfile', 'max', 'size', '[PAD]']\n", + "6081\n", + "Label = ['cur', 'logfile', 'info', '[PAD]']\n", + "6082\n", + "Label = ['logfile', 'max', 'size', '[PAD]']\n", + "6083\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "6084\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "6085\n", + "Label = ['vrf', 'name', '[PAD]', '[PAD]']\n", + "6086\n", + "Label = ['ssl', 'policy', 'name', '[PAD]']\n", + "6087\n", + "Label = ['filter', 'feature', 'name', '[PAD]']\n", + "6088\n", + "Label = ['channel', 'name', '[PAD]', '[PAD]']\n", + "6089\n", + "Label = ['ssl', 'policy', 'name', '[PAD]']\n", + "6090\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6091\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "6092\n", + "Label = ['channel', 'id', '[PAD]', '[PAD]']\n", + "6093\n", + "Label = ['filter', 'feature', 'name', '[PAD]']\n", + "6094\n", + "Label = ['channel', 'info', '[PAD]', '[PAD]']\n", + "6095\n", + "Label = ['channel', 'direct', 'info', '[PAD]']\n", + "6096\n", + "Label = ['logfile', 'max', 'num', '[PAD]']\n", + "6097\n", + "Label = ['vrf', 'name', '[PAD]', '[PAD]']\n", + "6098\n", + "Label = ['channel', 'out', 'direct', '[PAD]']\n", + "6099\n", + "Label = ['cmp', 'cfg', '[PAD]', '[PAD]']\n", + "6100\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "6101\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6102\n", + "Label = ['cli', 'add', 'command', '[PAD]']\n", + "6103\n", + "Label = ['cli', 'add', 'command', '[PAD]']\n", + "6104\n", + "Label = ['cli', 'add', 'command', '[PAD]']\n", + "6105\n", + "Label = ['host', 'collect', 'protocol', '[PAD]']\n", + "6106\n", + "Label = ['evn', 'peer', 'ip', '[PAD]']\n", + "6107\n", + "Label = ['evn', 'reflect', 'client', '[PAD]']\n", + "6108\n", + "Label = ['evn', 'source', 'ip', '[PAD]']\n", + "6109\n", + "Label = ['vbdif', 'name', '[PAD]', '[PAD]']\n", + "6110\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "6111\n", + "Label = ['end', 'state', '[PAD]', '[PAD]']\n", + "6112\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "6113\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6114\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6115\n", + "Label = ['xml', 'str', '[PAD]', '[PAD]']\n", + "6116\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6117\n", + "Label = ['channel', 'id', '[PAD]', '[PAD]']\n", + "6118\n", + "Label = ['log', 'buff', 'size', '[PAD]']\n", + "6119\n", + "Label = ['module', 'name', '[PAD]', '[PAD]']\n", + "6120\n", + "Label = ['xmlstr', '[PAD]', '[PAD]', '[PAD]']\n", + "6121\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "6122\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "6123\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6124\n", + "Label = ['vrf', '[PAD]', '[PAD]', '[PAD]']\n", + "6125\n", + "Label = ['destvrf', '[PAD]', '[PAD]', '[PAD]']\n", + "6126\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "6127\n", + "Label = ['destvrf', '[PAD]', '[PAD]', '[PAD]']\n", + "6128\n", + "Label = ['configxmlstr', '[PAD]', '[PAD]', '[PAD]']\n", + "6129\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "6130\n", + "Label = ['next', 'hop', '[PAD]', '[PAD]']\n", + "6131\n", + "Label = ['next', 'hop', '[PAD]', '[PAD]']\n", + "6132\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "6133\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6134\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6135\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6136\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6137\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "6138\n", + "Label = ['pref', '[PAD]', '[PAD]', '[PAD]']\n", + "6139\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6140\n", + "Label = ['each', 'num', '[PAD]', '[PAD]']\n", + "6141\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6142\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6143\n", + "Label = ['vrf', 'aftype', '[PAD]', '[PAD]']\n", + "6144\n", + "Label = ['vpn', 'target', '[PAD]', '[PAD]']\n", + "6145\n", + "Label = ['vrf', 'aftype', '[PAD]', '[PAD]']\n", + "6146\n", + "Label = ['route', 'distinguisher', '[PAD]', '[PAD]']\n", + "6147\n", + "Label = ['vpn', 'target', 'value', '[PAD]']\n", + "6148\n", + "Label = ['evpn', '[PAD]', '[PAD]', '[PAD]']\n", + "6149\n", + "Label = ['evpn', '[PAD]', '[PAD]', '[PAD]']\n", + "6150\n", + "Label = ['evpn', '[PAD]', '[PAD]', '[PAD]']\n", + "6151\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6152\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6153\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "6154\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6155\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6156\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6157\n", + "Label = ['reflect', 'client', '[PAD]', '[PAD]']\n", + "6158\n", + "Label = ['as', 'number', '[PAD]', '[PAD]']\n", + "6159\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6160\n", + "Label = ['area', '[PAD]', '[PAD]', '[PAD]']\n", + "6161\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "6162\n", + "Label = ['eles', '[PAD]', '[PAD]', '[PAD]']\n", + "6163\n", + "Label = ['get', 'area', 'ip', '[PAD]']\n", + "6164\n", + "Label = ['network', '[PAD]', '[PAD]', '[PAD]']\n", + "6165\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "6166\n", + "Label = ['nexthop', '[PAD]', '[PAD]', '[PAD]']\n", + "6167\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6168\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6169\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6170\n", + "Label = ['xml', 'area', '[PAD]', '[PAD]']\n", + "6171\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6172\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "6173\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6174\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", + "6175\n", + "Label = ['auth', 'mode', '[PAD]', '[PAD]']\n", + "6176\n", + "Label = ['max', 'load', 'balance', '[PAD]']\n", + "6177\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6178\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6179\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6180\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "6181\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6182\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "6183\n", + "Label = ['need', 'cfg', '[PAD]', '[PAD]']\n", + "6184\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6185\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6186\n", + "Label = ['security', 'level', 'cli', '[PAD]']\n", + "6187\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6188\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "6189\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "6190\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "6191\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6192\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6193\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6194\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6195\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6196\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6197\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "6198\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "6199\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6200\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6201\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6202\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6203\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6204\n", + "Label = ['value', 'type', '[PAD]', '[PAD]']\n", + "6205\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6206\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6207\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6208\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6209\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6210\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6211\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "6212\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6213\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6214\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "6215\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6216\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6217\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6218\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "6219\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "6220\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "6221\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "6222\n", + "Label = ['required', 'if', '[PAD]', '[PAD]']\n", + "6223\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6224\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "6225\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6226\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "6227\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6228\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "6229\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6230\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "6231\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6232\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "6233\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "6234\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6235\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "6236\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6237\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "6238\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6239\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "6240\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6241\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6242\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6243\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6244\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6245\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6246\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "6247\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6248\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "6249\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6250\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6251\n", + "Label = ['COMMANDS', '[PAD]', '[PAD]', '[PAD]']\n", + "6252\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "6253\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6254\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6255\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6256\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6257\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6258\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "6259\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "6260\n", + "Label = ['candidate', '[PAD]', '[PAD]', '[PAD]']\n", + "6261\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6262\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6263\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6264\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6265\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6266\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "6267\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6268\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "6269\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6270\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6271\n", + "Label = ['def', 'server', '[PAD]', '[PAD]']\n", + "6272\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "6273\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6274\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "6275\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6276\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "6277\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6278\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6279\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6280\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6281\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6282\n", + "Label = ['get', 'text', '[PAD]', '[PAD]']\n", + "6283\n", + "Label = ['get', 'text', '[PAD]', '[PAD]']\n", + "6284\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "6285\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6286\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6287\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6288\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6289\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6290\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6291\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6292\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6293\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6294\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6295\n", + "Label = ['req', '[PAD]', '[PAD]', '[PAD]']\n", + "6296\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6297\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6298\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6299\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "6300\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6301\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6302\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "6303\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6304\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "6305\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6306\n", + "Label = ['reply', '[PAD]', '[PAD]', '[PAD]']\n", + "6307\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6308\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6309\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6310\n", + "Label = ['ParseError', '[PAD]', '[PAD]', '[PAD]']\n", + "6311\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "6312\n", + "Label = ['config', 'format', '[PAD]', '[PAD]']\n", + "6313\n", + "Label = ['candidate', '[PAD]', '[PAD]', '[PAD]']\n", + "6314\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6315\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "6316\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "6317\n", + "Label = ['field', 'top', '[PAD]', '[PAD]']\n", + "6318\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6319\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6320\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6321\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6322\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6323\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6324\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "6325\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6326\n", + "Label = ['property', 'is', 'set', '[PAD]']\n", + "6327\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6328\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6329\n", + "Label = ['local', 'address', '[PAD]', '[PAD]']\n", + "6330\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6331\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6332\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6333\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6334\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6335\n", + "Label = ['is', 'valid', 'vlan', 'id']\n", + "6336\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6337\n", + "Label = ['is', 'valid', 'vlan', 'id']\n", + "6338\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6339\n", + "Label = ['vnic', 'exists', '[PAD]', '[PAD]']\n", + "6340\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6341\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "6342\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6343\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6344\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6345\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6346\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6347\n", + "Label = ['xml', 'data', '[PAD]', '[PAD]']\n", + "6348\n", + "Label = ['xml', 'data', '[PAD]', '[PAD]']\n", + "6349\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6350\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6351\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "6352\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6353\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6354\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6355\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "6356\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6357\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6358\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6359\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6360\n", + "Label = ['VALID', 'ICMP6', 'TYPES', '[PAD]']\n", + "6361\n", + "Label = ['icmp', 'msg', 'type', '[PAD]']\n", + "6362\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6363\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6364\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6365\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6366\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6367\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6368\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6369\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6370\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6371\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6372\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6373\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6374\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6375\n", + "Label = ['qinq', '[PAD]', '[PAD]', '[PAD]']\n", + "6376\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6377\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6378\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6379\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6380\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6381\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6382\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6383\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6384\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6385\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6386\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6387\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6388\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6389\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6390\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6391\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6392\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6393\n", + "Label = ['aci', 'block', 'mo', '[PAD]']\n", + "6394\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6395\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6396\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6397\n", + "Label = ['expiration', '[PAD]', '[PAD]', '[PAD]']\n", + "6398\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6399\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6400\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6401\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6402\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6403\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6404\n", + "Label = ['encap', 'id', '[PAD]', '[PAD]']\n", + "6405\n", + "Label = ['construct', 'url', '[PAD]', '[PAD]']\n", + "6406\n", + "Label = ['HAS', 'XMLJSON', 'COBRA', '[PAD]']\n", + "6407\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6408\n", + "Label = ['config', 'object', '[PAD]', '[PAD]']\n", + "6409\n", + "Label = ['config', 'object', '[PAD]', '[PAD]']\n", + "6410\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6411\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6412\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6413\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6414\n", + "Label = ['VM', 'PROVIDER', 'MAPPING', '[PAD]']\n", + "6415\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6416\n", + "Label = ['sent', '[PAD]', '[PAD]', '[PAD]']\n", + "6417\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6418\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "6419\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6420\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6421\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "6422\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "6423\n", + "Label = ['interface', 'start', '[PAD]', '[PAD]']\n", + "6424\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "6425\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6426\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "6427\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6428\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6429\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "6430\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6431\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6432\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6433\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "6434\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "6435\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "6436\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "6437\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "6438\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "6439\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6440\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6441\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "6442\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6443\n", + "Label = ['new', 'obj', '[PAD]', '[PAD]']\n", + "6444\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6445\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "6446\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "6447\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "6448\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "6449\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6450\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6451\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "6452\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6453\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6454\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "6455\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "6456\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6457\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "6458\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "6459\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6460\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6461\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6462\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6463\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6464\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6465\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6466\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6467\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "6468\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6469\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6470\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "6471\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6472\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6473\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6474\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6475\n", + "Label = ['responses', '[PAD]', '[PAD]', '[PAD]']\n", + "6476\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "6477\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6478\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6479\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6480\n", + "Label = ['entry', '[PAD]', '[PAD]', '[PAD]']\n", + "6481\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "6482\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6483\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "6484\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "6485\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "6486\n", + "Label = ['HAS', 'BAMBOU', '[PAD]', '[PAD]']\n", + "6487\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6488\n", + "Label = ['children', '[PAD]', '[PAD]', '[PAD]']\n", + "6489\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "6490\n", + "Label = ['entity', '[PAD]', '[PAD]', '[PAD]']\n", + "6491\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "6492\n", + "Label = ['relationship', '[PAD]', '[PAD]', '[PAD]']\n", + "6493\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "6494\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6495\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "6496\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "6497\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6498\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "6499\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6500\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "6501\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6502\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6503\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6504\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6505\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6506\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6507\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6508\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6509\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6510\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6511\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6512\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6513\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6514\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6515\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6516\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6517\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6518\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "6519\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6520\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6521\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6522\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6523\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "6524\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6525\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "6526\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6527\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6528\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6529\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6530\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6531\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6532\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6533\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6534\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6535\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6536\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "6537\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6538\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6539\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6540\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6541\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6542\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6543\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6544\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6545\n", + "Label = ['ip', 'protocol', '[PAD]', '[PAD]']\n", + "6546\n", + "Label = ['ip', 'protocol', '[PAD]', '[PAD]']\n", + "6547\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6548\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6549\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6550\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6551\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6552\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6553\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "6554\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6555\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6556\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "6557\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "6558\n", + "Label = ['profiles', '[PAD]', '[PAD]', '[PAD]']\n", + "6559\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6560\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "6561\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6562\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6563\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6564\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "6565\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "6566\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "6567\n", + "Label = ['default', 'persistence', 'profile', '[PAD]']\n", + "6568\n", + "Label = ['pool', '[PAD]', '[PAD]', '[PAD]']\n", + "6569\n", + "Label = ['metadata', '[PAD]', '[PAD]', '[PAD]']\n", + "6570\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6571\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6572\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6573\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6574\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6575\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "6576\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6577\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6578\n", + "Label = ['choices', '[PAD]', '[PAD]', '[PAD]']\n", + "6579\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6580\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6581\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "6582\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", + "6583\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6584\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6585\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6586\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6587\n", + "Label = ['mm', '[PAD]', '[PAD]', '[PAD]']\n", + "6588\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6589\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6590\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6591\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6592\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6593\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6594\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6595\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6596\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6597\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6598\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6599\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6600\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6601\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6602\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6603\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6604\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "6605\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6606\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "6607\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "6608\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6609\n", + "Label = ['service', 'environment', '[PAD]', '[PAD]']\n", + "6610\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6611\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6612\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6613\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6614\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6615\n", + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "6616\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6617\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6618\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "6619\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6620\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6621\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6622\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6623\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6624\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6625\n", + "Label = ['source', 'path', '[PAD]', '[PAD]']\n", + "6626\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6627\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6628\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6629\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6630\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6631\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "6632\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6633\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "6634\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6635\n", + "Label = ['type', 'map', '[PAD]', '[PAD]']\n", + "6636\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6637\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6638\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6639\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6640\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6641\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6642\n", + "Label = ['monitors', '[PAD]', '[PAD]', '[PAD]']\n", + "6643\n", + "Label = ['availability', 'requirement', 'type', '[PAD]']\n", + "6644\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6645\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6646\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "6647\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6648\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6649\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "6650\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6651\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6652\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6653\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "6654\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6655\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "6656\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "6657\n", + "Label = ['preferred', 'lb', 'methods', '[PAD]']\n", + "6658\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "6659\n", + "Label = ['HAS', 'CLI', 'TRANSPORT', '[PAD]']\n", + "6660\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6661\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6662\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6663\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "6664\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6665\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "6666\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "6667\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6668\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6669\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6670\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6671\n", + "Label = ['route', 'domain', '[PAD]', '[PAD]']\n", + "6672\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "6673\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "6674\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "6675\n", + "Label = ['destination', 'ip', '[PAD]', '[PAD]']\n", + "6676\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6677\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6678\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "6679\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6680\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "6681\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6682\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6683\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6684\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6685\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6686\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6687\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6688\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6689\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6690\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6691\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6692\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6693\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6694\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6695\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6696\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6697\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6698\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "6699\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6700\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6701\n", + "Label = ['fqdn', 'auto', 'populate', '[PAD]']\n", + "6702\n", + "Label = ['fqdn', '[PAD]', '[PAD]', '[PAD]']\n", + "6703\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "6704\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6705\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6706\n", + "Label = ['route', 'advertisement', '[PAD]', '[PAD]']\n", + "6707\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6708\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6709\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6710\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "6711\n", + "Label = ['api', 'attributes', '[PAD]', '[PAD]']\n", + "6712\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6713\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "6714\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6715\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6716\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "6717\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "6718\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "6719\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "6720\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6721\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6722\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "6723\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6724\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "6725\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "6726\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6727\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6728\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "6729\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6730\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6731\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6733\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6734\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6735\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6736\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6738\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6739\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6740\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6741\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6742\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "6743\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6744\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6745\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6746\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6747\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6748\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6749\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "6750\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6751\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6752\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "6753\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6754\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6755\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6756\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6757\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "6758\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6759\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6760\n", + "Label = ['monitor', 'type', '[PAD]', '[PAD]']\n", + "6761\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6762\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6763\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6764\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6765\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6766\n", + "Label = ['monitors', 'list', '[PAD]', '[PAD]']\n", + "6767\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6768\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6769\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "6770\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "6771\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "6772\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "6773\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6774\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6775\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6776\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6777\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6778\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6779\n", + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "6780\n", + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "6781\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6782\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "6783\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "6784\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6785\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "6786\n", + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "6787\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6788\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "6789\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "6790\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6791\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6792\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "6793\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6794\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6795\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6796\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6797\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6798\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6799\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6800\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6801\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6802\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6803\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6804\n", + "Label = ['parent', '[PAD]', '[PAD]', '[PAD]']\n", + "6805\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "6806\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "6807\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "6808\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6809\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6810\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "6811\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6812\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6813\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6814\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6815\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "6816\n", + "Label = ['devices', '[PAD]', '[PAD]', '[PAD]']\n", + "6817\n", + "Label = ['raw', 'server', 'type', '[PAD]']\n", + "6818\n", + "Label = ['raw', 'server', 'type', '[PAD]']\n", + "6819\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "6820\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6821\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "6822\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6823\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6824\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "6825\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6826\n", + "Label = ['devices', '[PAD]', '[PAD]', '[PAD]']\n", + "6827\n", + "Label = ['virtual', 'server', 'discovery', '[PAD]']\n", + "6828\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6829\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6830\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6831\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6832\n", + "Label = ['parent', '[PAD]', '[PAD]', '[PAD]']\n", + "6833\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6834\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6835\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6836\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6837\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "6838\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "6839\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6840\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6841\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "6842\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6843\n", + "Label = ['port', 'misuse', 'policy', '[PAD]']\n", + "6844\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6845\n", + "Label = ['mm', '[PAD]', '[PAD]', '[PAD]']\n", + "6846\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6847\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", + "6848\n", + "Label = ['timedelta', '[PAD]', '[PAD]', '[PAD]']\n", + "6849\n", + "Label = ['is', 'mprov', 'running', 'on']\n", + "6850\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6851\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6852\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "6853\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "6854\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6855\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6856\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6857\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6858\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6859\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6860\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6861\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6862\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6863\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6864\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6865\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6866\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6867\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "6868\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6869\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6870\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6871\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "6872\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6873\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6874\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "6875\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6876\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "6877\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6878\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6879\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6880\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6881\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6882\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6883\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6884\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6885\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6886\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6887\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6888\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6889\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "6890\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6891\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "6892\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6893\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6894\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6895\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6896\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6897\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6898\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6899\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6900\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "6901\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6902\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6903\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6904\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6905\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6906\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6907\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6908\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6909\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6910\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6911\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6912\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6913\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "6914\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6915\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6916\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6917\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6918\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6919\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6920\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6921\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6922\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6923\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6924\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6925\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6926\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6927\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6928\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6929\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6930\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6931\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6932\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6933\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6934\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6935\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6936\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6937\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6938\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6939\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6940\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6941\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6942\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6943\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6944\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6945\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "6946\n", + "Label = ['findall', '[PAD]', '[PAD]', '[PAD]']\n", + "6947\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6948\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6949\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6950\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6951\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6952\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6953\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6954\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6955\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6956\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6957\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "6958\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6959\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6960\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6961\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6962\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6963\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6964\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6965\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6966\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6967\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6968\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6969\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6970\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6971\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "6972\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "6973\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6974\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "6975\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6976\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6977\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6978\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6979\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "6980\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "6981\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "6982\n", + "Label = ['day', '[PAD]', '[PAD]', '[PAD]']\n", + "6983\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6984\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "6985\n", + "Label = ['floor', '[PAD]', '[PAD]', '[PAD]']\n", + "6986\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6987\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6988\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "6989\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6990\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "6991\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "6992\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6993\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6994\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6995\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6996\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6997\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6998\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "6999\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7000\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7001\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7002\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7003\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7004\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7005\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7006\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7007\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "7008\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7009\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7010\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7011\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "7012\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7013\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7014\n", + "Label = ['collection', '[PAD]', '[PAD]', '[PAD]']\n", + "7015\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7016\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7017\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7018\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7019\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7020\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7021\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "7022\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7023\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7024\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7025\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7026\n", + "Label = ['managers', '[PAD]', '[PAD]', '[PAD]']\n", + "7027\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7028\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7029\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7030\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7031\n", + "Label = ['pool', 'members', '[PAD]', '[PAD]']\n", + "7032\n", + "Label = ['member', '[PAD]', '[PAD]', '[PAD]']\n", + "7033\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7034\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7035\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7036\n", + "Label = ['servers', '[PAD]', '[PAD]', '[PAD]']\n", + "7037\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7038\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "7039\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7040\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7041\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7042\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7043\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7044\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7045\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7046\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7047\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7048\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7049\n", + "Label = ['updatables', '[PAD]', '[PAD]', '[PAD]']\n", + "7050\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7051\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7052\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7053\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7054\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7055\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7056\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7057\n", + "Label = ['probe', 'timeout', '[PAD]', '[PAD]']\n", + "7058\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7059\n", + "Label = ['transparent', '[PAD]', '[PAD]', '[PAD]']\n", + "7060\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7061\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7062\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7063\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7064\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7065\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7066\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7067\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7068\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7069\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7070\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7071\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7072\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7073\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7074\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7075\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7076\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7077\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7078\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7079\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7080\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7081\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7082\n", + "Label = ['time', 'until', 'up', '[PAD]']\n", + "7083\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7084\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7085\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "7086\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7087\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7088\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7089\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7090\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7091\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7092\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7093\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "7094\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7095\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7096\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "7097\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7098\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "7099\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7100\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7101\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7102\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7103\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7104\n", + "Label = ['ports', '[PAD]', '[PAD]', '[PAD]']\n", + "7105\n", + "Label = ['ports', '[PAD]', '[PAD]', '[PAD]']\n", + "7106\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7107\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7108\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7109\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7110\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7111\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7112\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7113\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7114\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7115\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "7116\n", + "Label = ['partition', 'access', '[PAD]', '[PAD]']\n", + "7117\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "7118\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7119\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "7120\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7121\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7122\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7123\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7124\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7125\n", + "Label = ['collection', '[PAD]', '[PAD]', '[PAD]']\n", + "7126\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7127\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7128\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7129\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7130\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7131\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7132\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7133\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7134\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "7135\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7136\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7137\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7138\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "7139\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7140\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7141\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7142\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7143\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7144\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7145\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7146\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7147\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "7148\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7149\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7150\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7151\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7152\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7153\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7154\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7155\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7156\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7157\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7158\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7159\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7160\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7161\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7162\n", + "Label = ['parts', '[PAD]', '[PAD]', '[PAD]']\n", + "7163\n", + "Label = ['floor', '[PAD]', '[PAD]', '[PAD]']\n", + "7164\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7165\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7166\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "7167\n", + "Label = ['include', '[PAD]', '[PAD]', '[PAD]']\n", + "7168\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7169\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7170\n", + "Label = ['managers', '[PAD]', '[PAD]', '[PAD]']\n", + "7171\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7172\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7173\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7174\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7175\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7176\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7177\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7178\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7179\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7180\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7181\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7182\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7183\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7184\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7185\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7186\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "7187\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7188\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7189\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7190\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "7191\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7192\n", + "Label = ['level', '[PAD]', '[PAD]', '[PAD]']\n", + "7193\n", + "Label = ['resources', '[PAD]', '[PAD]', '[PAD]']\n", + "7194\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "7195\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7196\n", + "Label = ['exec', 'cmd', '[PAD]', '[PAD]']\n", + "7197\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "7198\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "7199\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7200\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7201\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7202\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7203\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7204\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7205\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7206\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7207\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7208\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7209\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7210\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7211\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7212\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7213\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7214\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7215\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7216\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7217\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7218\n", + "Label = ['cipher', 'list', '[PAD]', '[PAD]']\n", + "7219\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7220\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7221\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7222\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "7223\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7224\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7225\n", + "Label = ['exec', 'cmd', '[PAD]', '[PAD]']\n", + "7226\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7227\n", + "Label = ['matches', '[PAD]', '[PAD]', '[PAD]']\n", + "7228\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "7229\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7230\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7231\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7232\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7233\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7234\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7235\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7236\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7237\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7238\n", + "Label = ['encrypt', 'cookies', '[PAD]', '[PAD]']\n", + "7239\n", + "Label = ['encrypt', 'cookies', '[PAD]', '[PAD]']\n", + "7240\n", + "Label = ['encrypt', 'cookies', '[PAD]', '[PAD]']\n", + "7241\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7242\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7243\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7244\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7245\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7246\n", + "Label = ['api', 'attributes', '[PAD]', '[PAD]']\n", + "7247\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7248\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7249\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7250\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7251\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7252\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7253\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "7254\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7255\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7256\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "7257\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7258\n", + "Label = ['monitors', '[PAD]', '[PAD]', '[PAD]']\n", + "7259\n", + "Label = ['monitors', '[PAD]', '[PAD]', '[PAD]']\n", + "7260\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7261\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7262\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7263\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7264\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7265\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7266\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7267\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7268\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "7269\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "7270\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7271\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "7272\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7273\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7274\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7275\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7276\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7277\n", + "Label = ['exec', 'cmd', '[PAD]', '[PAD]']\n", + "7278\n", + "Label = ['exec', 'cmd', '[PAD]', '[PAD]']\n", + "7279\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7280\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7281\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7282\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7283\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7284\n", + "Label = ['device', 'is', 'id', '[PAD]']\n", + "7285\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7286\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7287\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7288\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7289\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7290\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7291\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7292\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7293\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7294\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7295\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "7296\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7297\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7298\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7299\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7300\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7301\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7302\n", + "Label = ['icmp', 'message', '[PAD]', '[PAD]']\n", + "7303\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7304\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7305\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7306\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7307\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "7308\n", + "Label = ['icmp', 'message', '[PAD]', '[PAD]']\n", + "7309\n", + "Label = ['icmp', 'message', '[PAD]', '[PAD]']\n", + "7310\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7311\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7312\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7313\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7314\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7315\n", + "Label = ['handle', 'enable', 'action', '[PAD]']\n", + "7316\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7317\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "7318\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "7319\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7320\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7321\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7322\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7323\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7324\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7325\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7326\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7327\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "7328\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7329\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7330\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7331\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "7332\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7333\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7334\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7335\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7336\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7337\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7338\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7339\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7340\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7341\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7342\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7343\n", + "Label = ['ignore', 'down', 'response', '[PAD]']\n", + "7344\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7345\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7346\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7347\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7348\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7349\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7350\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7351\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7352\n", + "Label = ['distribution', '[PAD]', '[PAD]', '[PAD]']\n", + "7353\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7354\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "7355\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7356\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7357\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7358\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7359\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7360\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7361\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7362\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "7363\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7364\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7365\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7366\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7367\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7368\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7369\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7370\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7371\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", + "7372\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", + "7373\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7374\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7375\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7376\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7377\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "7378\n", + "Label = ['exec', 'cmd', '[PAD]', '[PAD]']\n", + "7379\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7380\n", + "Label = ['updatables', '[PAD]', '[PAD]', '[PAD]']\n", + "7381\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7382\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7383\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7384\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7385\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7386\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7387\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7388\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7389\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7390\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7391\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7392\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7393\n", + "Label = ['package', 'name', '[PAD]', '[PAD]']\n", + "7394\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7395\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7396\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7397\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7398\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7399\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "7400\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7401\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7402\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7403\n", + "Label = ['cert', '[PAD]', '[PAD]', '[PAD]']\n", + "7404\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7405\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7406\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7407\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7408\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7409\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7410\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7411\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "7412\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7413\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7414\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "7415\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7416\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7417\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7418\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7419\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7420\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7421\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7422\n", + "Label = ['param', 'device', 'group', '[PAD]']\n", + "7423\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7424\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7425\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7426\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7427\n", + "Label = ['have', '[PAD]', '[PAD]', '[PAD]']\n", + "7428\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7429\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7430\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7431\n", + "Label = ['should', 'update', '[PAD]', '[PAD]']\n", + "7432\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7433\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7434\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7435\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "7436\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7437\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7438\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7439\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7440\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7441\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "7442\n", + "Label = ['api', 'attributes', '[PAD]', '[PAD]']\n", + "7443\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7444\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7445\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7446\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7447\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7448\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7449\n", + "Label = ['update', 'password', '[PAD]', '[PAD]']\n", + "7450\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7451\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7452\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7453\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7454\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7455\n", + "Label = ['mm', '[PAD]', '[PAD]', '[PAD]']\n", + "7456\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7457\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7458\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7459\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7460\n", + "Label = ['ciphers', '[PAD]', '[PAD]', '[PAD]']\n", + "7461\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7462\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7463\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7464\n", + "Label = ['allow', '[PAD]', '[PAD]', '[PAD]']\n", + "7465\n", + "Label = ['allow', '[PAD]', '[PAD]', '[PAD]']\n", + "7466\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7467\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7468\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7469\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7470\n", + "Label = ['valid', '[PAD]', '[PAD]', '[PAD]']\n", + "7471\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7472\n", + "Label = ['filtered', '[PAD]', '[PAD]', '[PAD]']\n", + "7473\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7474\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7475\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7476\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7477\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7478\n", + "Label = ['availability', 'state', '[PAD]', '[PAD]']\n", + "7479\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7480\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7481\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "7482\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "7483\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7484\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7485\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "7486\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7487\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7488\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7489\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7490\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7491\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7492\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7493\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7494\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7495\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7496\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7497\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7498\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7499\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7500\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7501\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7502\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7503\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7504\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7505\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7506\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7507\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "7508\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "7509\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7510\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7511\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7512\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7513\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7514\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7515\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7516\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7517\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7518\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7519\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7520\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7521\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7522\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7523\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7524\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7525\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7526\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7527\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7528\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7529\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7530\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7531\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7532\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7533\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7534\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7535\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7536\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7537\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7538\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7539\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7540\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7541\n", + "Label = ['routing', 'protocol', '[PAD]', '[PAD]']\n", + "7542\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7543\n", + "Label = ['parent', '[PAD]', '[PAD]', '[PAD]']\n", + "7544\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7545\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7546\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7547\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7548\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7549\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7550\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7551\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7552\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7553\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7554\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "7555\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7556\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7557\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7558\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7559\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7560\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7561\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "7562\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7563\n", + "Label = ['quorum', '[PAD]', '[PAD]', '[PAD]']\n", + "7564\n", + "Label = ['quorum', '[PAD]', '[PAD]', '[PAD]']\n", + "7565\n", + "Label = ['monitors', '[PAD]', '[PAD]', '[PAD]']\n", + "7566\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7567\n", + "Label = ['monitors', '[PAD]', '[PAD]', '[PAD]']\n", + "7568\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7569\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7570\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7571\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7572\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7573\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7574\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7575\n", + "Label = ['lacp', 'enabled', '[PAD]', '[PAD]']\n", + "7576\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7577\n", + "Label = ['mm', '[PAD]', '[PAD]', '[PAD]']\n", + "7578\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7579\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7580\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7581\n", + "Label = ['inbound', 'virtual', '[PAD]', '[PAD]']\n", + "7582\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7583\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7584\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7585\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7586\n", + "Label = ['cert', '[PAD]', '[PAD]', '[PAD]']\n", + "7587\n", + "Label = ['cert', '[PAD]', '[PAD]', '[PAD]']\n", + "7588\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7589\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7590\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7591\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7592\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7593\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7594\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7595\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7596\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7597\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7598\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7599\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7600\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7601\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7602\n", + "Label = ['rfind', '[PAD]', '[PAD]', '[PAD]']\n", + "7603\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7604\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7605\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7606\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7607\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7608\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7609\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7610\n", + "Label = ['rfind', '[PAD]', '[PAD]', '[PAD]']\n", + "7611\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7612\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7613\n", + "Label = ['api', 'response', '[PAD]', '[PAD]']\n", + "7614\n", + "Label = ['api', 'obj', '[PAD]', '[PAD]']\n", + "7615\n", + "Label = ['f5', '[PAD]', '[PAD]', '[PAD]']\n", + "7616\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "7617\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7618\n", + "Label = ['valid', 'includes', '[PAD]', '[PAD]']\n", + "7619\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7620\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7621\n", + "Label = ['cores', 'per', 'slot', '[PAD]']\n", + "7622\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7623\n", + "Label = ['stat', '[PAD]', '[PAD]', '[PAD]']\n", + "7624\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7625\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7626\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7627\n", + "Label = ['returnables', '[PAD]', '[PAD]', '[PAD]']\n", + "7628\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7629\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7630\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7631\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7632\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7633\n", + "Label = ['unicast', 'failover', '[PAD]', '[PAD]']\n", + "7634\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7635\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "7636\n", + "Label = ['is', 'config', 'reloading', 'failed']\n", + "7637\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "7638\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "7639\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7640\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7641\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7642\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "7643\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7644\n", + "Label = ['basename', '[PAD]', '[PAD]', '[PAD]']\n", + "7645\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7646\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7647\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7648\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7649\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7650\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7651\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7652\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7653\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7654\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7655\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7656\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7657\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7658\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7659\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7660\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7661\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7662\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7663\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7664\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7665\n", + "Label = ['api', 'attributes', '[PAD]', '[PAD]']\n", + "7666\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7667\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7668\n", + "Label = ['profile', '[PAD]', '[PAD]', '[PAD]']\n", + "7669\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7670\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7671\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7672\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7673\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7674\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7675\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7676\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7677\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7678\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7679\n", + "Label = ['irule', '[PAD]', '[PAD]', '[PAD]']\n", + "7680\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7681\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7682\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "7683\n", + "Label = ['aliases', '[PAD]', '[PAD]', '[PAD]']\n", + "7684\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7685\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7686\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7687\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7688\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7689\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7690\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7691\n", + "Label = ['authentication', 'enabled', '[PAD]', '[PAD]']\n", + "7692\n", + "Label = ['authentication', 'disabled', '[PAD]', '[PAD]']\n", + "7693\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7694\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7695\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7696\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7697\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7698\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7699\n", + "Label = ['attr2', '[PAD]', '[PAD]', '[PAD]']\n", + "7700\n", + "Label = ['have', '[PAD]', '[PAD]', '[PAD]']\n", + "7701\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7702\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7703\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7704\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7705\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7706\n", + "Label = ['address', '[PAD]', '[PAD]', '[PAD]']\n", + "7707\n", + "Label = ['updatables', '[PAD]', '[PAD]', '[PAD]']\n", + "7708\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7709\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7710\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7711\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "7712\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "7713\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7714\n", + "Label = ['ignore', 'changes', '[PAD]', '[PAD]']\n", + "7715\n", + "Label = ['ignore', 'changes', '[PAD]', '[PAD]']\n", + "7716\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7717\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7718\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7719\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7720\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7721\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7722\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7723\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7724\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", + "7725\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "7726\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7727\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "7728\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7729\n", + "Label = ['address', 'ranges', '[PAD]', '[PAD]']\n", + "7730\n", + "Label = ['fqdns', '[PAD]', '[PAD]', '[PAD]']\n", + "7731\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7732\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7733\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7734\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7735\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7736\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7738\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7739\n", + "Label = ['addresses', '[PAD]', '[PAD]', '[PAD]']\n", + "7740\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7741\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7742\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7743\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7744\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7745\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7746\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7747\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7748\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7749\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7750\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7751\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7752\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7753\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7754\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7755\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7756\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7757\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7758\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7759\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7760\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7761\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7762\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7763\n", + "Label = ['api', 'attribute', '[PAD]', '[PAD]']\n", + "7764\n", + "Label = ['is', 'version', 'less', 'than']\n", + "7765\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7766\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7767\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7768\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "7769\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7770\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7771\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "7772\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7773\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "7774\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7775\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7776\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7777\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7778\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7779\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7780\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7781\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7782\n", + "Label = ['phase1', 'auth', 'method', '[PAD]']\n", + "7783\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7784\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7785\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7786\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7787\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7788\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7789\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7790\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7791\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7792\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7793\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7794\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "7795\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7796\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", + "7797\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "7798\n", + "Label = ['addresses', '[PAD]', '[PAD]', '[PAD]']\n", + "7799\n", + "Label = ['address', 'lists', '[PAD]', '[PAD]']\n", + "7800\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7801\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7802\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7803\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7804\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7805\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7806\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7807\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7808\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "7809\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7810\n", + "Label = ['fail', 'on', 'missing', '[PAD]']\n", + "7811\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7812\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7813\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7814\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7815\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "7816\n", + "Label = ['api', 'map', '[PAD]', '[PAD]']\n", + "7817\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7818\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7819\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7820\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7821\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7822\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7823\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7824\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7825\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7826\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "7827\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "7828\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7829\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7830\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7831\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7832\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7833\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7834\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7835\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7836\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7837\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "7838\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7839\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7840\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7841\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7842\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "7843\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7844\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7845\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7846\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7847\n", + "Label = ['record', 'type', '[PAD]', '[PAD]']\n", + "7848\n", + "Label = ['prefixlen', '[PAD]', '[PAD]', '[PAD]']\n", + "7849\n", + "Label = ['encode', 'host', '[PAD]', '[PAD]']\n", + "7850\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7851\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7852\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7853\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7854\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7855\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7856\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7857\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7858\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7859\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7860\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7861\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7862\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7863\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", + "7864\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7865\n", + "Label = ['external', 'file', 'partition', '[PAD]']\n", + "7866\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7867\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7868\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7869\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "7870\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "7871\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7872\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7873\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7874\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7875\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7876\n", + "Label = ['responder', 'url', '[PAD]', '[PAD]']\n", + "7877\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7878\n", + "Label = ['trusted', 'responders', '[PAD]', '[PAD]']\n", + "7879\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7880\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7881\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7882\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7883\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7884\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7885\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7886\n", + "Label = ['destination', '[PAD]', '[PAD]', '[PAD]']\n", + "7887\n", + "Label = ['time', 'until', 'up', '[PAD]']\n", + "7888\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7889\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7890\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7891\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7892\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7893\n", + "Label = ['deprecate', '[PAD]', '[PAD]', '[PAD]']\n", + "7894\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7895\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "7896\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7897\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7898\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7899\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "7900\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7901\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7902\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7903\n", + "Label = ['interval', '[PAD]', '[PAD]', '[PAD]']\n", + "7904\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7905\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7906\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7907\n", + "Label = ['mm', '[PAD]', '[PAD]', '[PAD]']\n", + "7908\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7909\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7910\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7911\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7912\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7913\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7914\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7915\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7916\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "7917\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7918\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7919\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7920\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7921\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7922\n", + "Label = ['HAS', 'F5SDK', '[PAD]', '[PAD]']\n", + "7923\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7924\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7925\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7926\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "7927\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7928\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "7929\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7930\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "7931\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7932\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7933\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7934\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7935\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7936\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "7937\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7938\n", + "Label = ['changes', '[PAD]', '[PAD]', '[PAD]']\n", + "7939\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7940\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7941\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7942\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7943\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "7944\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7945\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7946\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7947\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7948\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7949\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "7950\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7951\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7952\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7953\n", + "Label = ['uri', '[PAD]', '[PAD]', '[PAD]']\n", + "7954\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "7955\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7956\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7957\n", + "Label = ['returnable', '[PAD]', '[PAD]', '[PAD]']\n", + "7958\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "7959\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "7960\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7961\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "7962\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "7963\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "7964\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7965\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7966\n", + "Label = ['isoformat', '[PAD]', '[PAD]', '[PAD]']\n", + "7967\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7968\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7969\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7970\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "7971\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7972\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "7973\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7974\n", + "Label = ['qual', 'from', 'key', '[PAD]']\n", + "7975\n", + "Label = ['module', 'name', '[PAD]', '[PAD]']\n", + "7976\n", + "Label = ['set', 'auto', 'commit', 'event']\n", + "7977\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "7978\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "7979\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7980\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "7981\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7982\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7983\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7984\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7985\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "7986\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "7987\n", + "Label = ['vlan', 'arg', 'spec', '[PAD]']\n", + "7988\n", + "Label = ['net', 'id', '[PAD]', '[PAD]']\n", + "7989\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "7990\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "7991\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7992\n", + "Label = ['construct', 'path', '[PAD]', '[PAD]']\n", + "7993\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "7994\n", + "Label = ['meraki', '[PAD]', '[PAD]', '[PAD]']\n", + "7995\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "7996\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "7997\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "7998\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "7999\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8000\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "8001\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8002\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "8003\n", + "Label = ['meraki', '[PAD]', '[PAD]', '[PAD]']\n", + "8004\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "8005\n", + "Label = ['fw', 'rules', '[PAD]', '[PAD]']\n", + "8006\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8007\n", + "Label = ['orgs', '[PAD]', '[PAD]', '[PAD]']\n", + "8008\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8009\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8010\n", + "Label = ['payload', '[PAD]', '[PAD]', '[PAD]']\n", + "8011\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8012\n", + "Label = ['meraki', '[PAD]', '[PAD]', '[PAD]']\n", + "8013\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8014\n", + "Label = ['get', 'net', 'id', '[PAD]']\n", + "8015\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8016\n", + "Label = ['networks', '[PAD]', '[PAD]', '[PAD]']\n", + "8017\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8018\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "8019\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8020\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8021\n", + "Label = ['net', 'id', '[PAD]', '[PAD]']\n", + "8022\n", + "Label = ['meraki', '[PAD]', '[PAD]', '[PAD]']\n", + "8023\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8024\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8025\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8026\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8027\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8028\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8029\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8030\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8031\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8032\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "8033\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8034\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "8035\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "8036\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8037\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "8038\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8039\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8040\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8041\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8042\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8043\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8044\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8045\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "8046\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8047\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "8048\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "8049\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "8050\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8051\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8052\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8053\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "8054\n", + "Label = ['after', '[PAD]', '[PAD]', '[PAD]']\n", + "8055\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "8056\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "8057\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "8058\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "8059\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "8060\n", + "Label = ['en', '[PAD]', '[PAD]', '[PAD]']\n", + "8061\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8062\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8063\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "8064\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8065\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8066\n", + "Label = ['get', 'filtered', '[PAD]', '[PAD]']\n", + "8067\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8068\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8069\n", + "Label = ['PYTHON', 'SDK', 'IMPORTED', '[PAD]']\n", + "8070\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "8071\n", + "Label = ['has', 'equal', 'attributes', '[PAD]']\n", + "8072\n", + "Label = ['actual', 'index', '[PAD]', '[PAD]']\n", + "8073\n", + "Label = ['configured', 'index', '[PAD]', '[PAD]']\n", + "8074\n", + "Label = ['monitorname', '[PAD]', '[PAD]', '[PAD]']\n", + "8075\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "8076\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "8077\n", + "Label = ['monitorname', '[PAD]', '[PAD]', '[PAD]']\n", + "8078\n", + "Label = ['monitorname', '[PAD]', '[PAD]', '[PAD]']\n", + "8079\n", + "Label = ['to', 'add', '[PAD]', '[PAD]']\n", + "8080\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8081\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8082\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8083\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8084\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8085\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8086\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8087\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8088\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8089\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "8090\n", + "Label = ['configured', 'monitor', 'bindings', '[PAD]']\n", + "8091\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8092\n", + "Label = ['diff', 'object', '[PAD]', '[PAD]']\n", + "8093\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8094\n", + "Label = ['get', 'filtered', '[PAD]', '[PAD]']\n", + "8095\n", + "Label = ['transforms', '[PAD]', '[PAD]', '[PAD]']\n", + "8096\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8097\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8098\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "8099\n", + "Label = ['monitor', 'name', '[PAD]', '[PAD]']\n", + "8100\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8101\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8102\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8103\n", + "Label = ['errorcode', '[PAD]', '[PAD]', '[PAD]']\n", + "8104\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8105\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8106\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8107\n", + "Label = ['binding', '[PAD]', '[PAD]', '[PAD]']\n", + "8108\n", + "Label = ['servicename', '[PAD]', '[PAD]', '[PAD]']\n", + "8109\n", + "Label = ['missing', 'service', 'bindings', '[PAD]']\n", + "8110\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8111\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8112\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8113\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8114\n", + "Label = ['lbvserver', 'list', '[PAD]', '[PAD]']\n", + "8115\n", + "Label = ['errorcode', '[PAD]', '[PAD]', '[PAD]']\n", + "8116\n", + "Label = ['errorcode', '[PAD]', '[PAD]', '[PAD]']\n", + "8117\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8118\n", + "Label = ['modify', 'keys', '[PAD]', '[PAD]']\n", + "8119\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8120\n", + "Label = ['hand', 'inserted', 'arguments', '[PAD]']\n", + "8121\n", + "Label = ['readonly', 'attrs', '[PAD]', '[PAD]']\n", + "8122\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8123\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8124\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8125\n", + "Label = ['option', '[PAD]', '[PAD]', '[PAD]']\n", + "8126\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8127\n", + "Label = ['diff', 'object', '[PAD]', '[PAD]']\n", + "8128\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8129\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8130\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8131\n", + "Label = ['csvserver', 'list', '[PAD]', '[PAD]']\n", + "8132\n", + "Label = ['binding', 'proxy', '[PAD]', '[PAD]']\n", + "8133\n", + "Label = ['configured', '[PAD]', '[PAD]', '[PAD]']\n", + "8134\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8135\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8136\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8137\n", + "Label = ['errorcode', '[PAD]', '[PAD]', '[PAD]']\n", + "8138\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8139\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8140\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8141\n", + "Label = ['count', 'filtered', '[PAD]', '[PAD]']\n", + "8142\n", + "Label = ['gslb', 'site', 'list', '[PAD]']\n", + "8143\n", + "Label = ['get', 'filtered', '[PAD]', '[PAD]']\n", + "8144\n", + "Label = ['readwrite', 'attrs', '[PAD]', '[PAD]']\n", + "8145\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8146\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8147\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8148\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8149\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8150\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8151\n", + "Label = ['filter', 'value', '[PAD]', '[PAD]']\n", + "8152\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "8153\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "8154\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "8155\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8156\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8157\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8158\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8159\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "8160\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8161\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8162\n", + "Label = ['parse', 'filesystem', 'info', '[PAD]']\n", + "8163\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8164\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "8165\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8166\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8167\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "8168\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8169\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "8170\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8171\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8172\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8173\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8174\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8175\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8176\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8177\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8178\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8179\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8180\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8181\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "8182\n", + "Label = ['subcfg', '[PAD]', '[PAD]', '[PAD]']\n", + "8183\n", + "Label = ['configobj', '[PAD]', '[PAD]', '[PAD]']\n", + "8184\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8185\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8186\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8187\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8188\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "8189\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8190\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8191\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8192\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8193\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8194\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8195\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8196\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "8197\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8198\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "8199\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8200\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8201\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8202\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8203\n", + "Label = ['present', '[PAD]', '[PAD]', '[PAD]']\n", + "8204\n", + "Label = ['dest', '[PAD]', '[PAD]', '[PAD]']\n", + "8205\n", + "Label = ['admin', 'distance', '[PAD]', '[PAD]']\n", + "8206\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8207\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "8208\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8209\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "8210\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8211\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8212\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8213\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8214\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8215\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8216\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "8217\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8218\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8219\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8220\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8221\n", + "Label = ['ipv4', '[PAD]', '[PAD]', '[PAD]']\n", + "8222\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8223\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "8224\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8225\n", + "Label = ['proposed', 'allowed', 'vlans', '[PAD]']\n", + "8226\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "8227\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "8228\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8229\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "8230\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "8231\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8232\n", + "Label = ['trunk', 'vlans', 'list', '[PAD]']\n", + "8233\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8234\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8235\n", + "Label = ['flags', '[PAD]', '[PAD]', '[PAD]']\n", + "8236\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8237\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8238\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8239\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "8240\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8241\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "8242\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8243\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "8244\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "8245\n", + "Label = ['if', 'name', '[PAD]', '[PAD]']\n", + "8246\n", + "Label = ['element', 'spec', '[PAD]', '[PAD]']\n", + "8247\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8248\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8249\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8250\n", + "Label = ['MIN', 'MULTICAST', 'IP', '[PAD]']\n", + "8251\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "8252\n", + "Label = ['required', 'config', '[PAD]', '[PAD]']\n", + "8253\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8254\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "8255\n", + "Label = ['required', 'config', '[PAD]', '[PAD]']\n", + "8256\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8257\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8258\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "8259\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8260\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8261\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8262\n", + "Label = ['COMMANDS', '[PAD]', '[PAD]', '[PAD]']\n", + "8263\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8264\n", + "Label = ['get', 'config', 'attr', '[PAD]']\n", + "8265\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8266\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8267\n", + "Label = ['generate', 'no', 'ipl', 'commands']\n", + "8268\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8269\n", + "Label = ['if', 'data', '[PAD]', '[PAD]']\n", + "8270\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8271\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8272\n", + "Label = ['curr', 'if', '[PAD]', '[PAD]']\n", + "8273\n", + "Label = ['if', 'name', '[PAD]', '[PAD]']\n", + "8274\n", + "Label = ['purge', '[PAD]', '[PAD]', '[PAD]']\n", + "8275\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8276\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8277\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8278\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8279\n", + "Label = ['req', 'state', '[PAD]', '[PAD]']\n", + "8280\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8281\n", + "Label = ['current', 'config', '[PAD]', '[PAD]']\n", + "8282\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8283\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "8284\n", + "Label = ['os', 'version', '[PAD]', '[PAD]']\n", + "8285\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8286\n", + "Label = ['current', 'config', '[PAD]', '[PAD]']\n", + "8287\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8288\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8289\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "8290\n", + "Label = ['current', 'config', '[PAD]', '[PAD]']\n", + "8291\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8292\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8293\n", + "Label = ['lag', 'summary', '[PAD]', '[PAD]']\n", + "8294\n", + "Label = ['member', '[PAD]', '[PAD]', '[PAD]']\n", + "8295\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8296\n", + "Label = ['current', 'config', '[PAD]', '[PAD]']\n", + "8297\n", + "Label = ['fmg', '[PAD]', '[PAD]', '[PAD]']\n", + "8298\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8299\n", + "Label = ['HAS', 'PYFMGR', '[PAD]', '[PAD]']\n", + "8300\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8301\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8302\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8303\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8304\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "8305\n", + "Label = ['required', 'if', '[PAD]', '[PAD]']\n", + "8306\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8307\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8308\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8309\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8310\n", + "Label = ['commands', '[PAD]', '[PAD]', '[PAD]']\n", + "8311\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8312\n", + "Label = ['commands', '[PAD]', '[PAD]', '[PAD]']\n", + "8313\n", + "Label = ['metaclass', '[PAD]', '[PAD]', '[PAD]']\n", + "8314\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8315\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "8316\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8317\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8318\n", + "Label = ['item', 'name', '[PAD]', '[PAD]']\n", + "8319\n", + "Label = ['Blueprints', '[PAD]', '[PAD]', '[PAD]']\n", + "8320\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8321\n", + "Label = ['my', 'new', 'ext', 'router']\n", + "8322\n", + "Label = ['loopback', '[PAD]', '[PAD]', '[PAD]']\n", + "8323\n", + "Label = ['item', 'name', '[PAD]', '[PAD]']\n", + "8324\n", + "Label = ['my', 'ext', 'router', '[PAD]']\n", + "8325\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8326\n", + "Label = ['ip', 'pool', '[PAD]', '[PAD]']\n", + "8327\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8328\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8329\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8330\n", + "Label = ['my', 'new', 'pool', '[PAD]']\n", + "8331\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8332\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8333\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8334\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8335\n", + "Label = ['range', '[PAD]', '[PAD]', '[PAD]']\n", + "8336\n", + "Label = ['my', 'new', 'pool', '[PAD]']\n", + "8337\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8338\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8339\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8340\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8341\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8342\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8343\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8344\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "8345\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8346\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8347\n", + "Label = ['aos', '[PAD]', '[PAD]', '[PAD]']\n", + "8348\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8349\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "8350\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8351\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "8352\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8353\n", + "Label = ['aos', '[PAD]', '[PAD]', '[PAD]']\n", + "8354\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8355\n", + "Label = ['wait', 'for', '[PAD]', '[PAD]']\n", + "8356\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "8357\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8358\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8359\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8360\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8361\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8362\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8363\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8364\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8365\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8366\n", + "Label = ['retVal', '[PAD]', '[PAD]', '[PAD]']\n", + "8367\n", + "Label = ['ftp', 'cmd', '[PAD]', '[PAD]']\n", + "8368\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8369\n", + "Label = ['retVal', '[PAD]', '[PAD]', '[PAD]']\n", + "8370\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8371\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "8372\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8373\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8374\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8375\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8376\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "8377\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8378\n", + "Label = ['serNums', '[PAD]', '[PAD]', '[PAD]']\n", + "8379\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8380\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8381\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8382\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "8383\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8384\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8385\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8386\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8387\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8388\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "8389\n", + "Label = ['https', '[PAD]', '[PAD]', '[PAD]']\n", + "8390\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8391\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8392\n", + "Label = ['https', '[PAD]', '[PAD]', '[PAD]']\n", + "8393\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8394\n", + "Label = ['option', 'list', '[PAD]', '[PAD]']\n", + "8395\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8396\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "8397\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8398\n", + "Label = ['json', '[PAD]', '[PAD]', '[PAD]']\n", + "8399\n", + "Label = ['attribute', '[PAD]', '[PAD]', '[PAD]']\n", + "8400\n", + "Label = ['set', '[PAD]', '[PAD]', '[PAD]']\n", + "8401\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8402\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8403\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8404\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8405\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8406\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8407\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8408\n", + "Label = ['new', 'addr', '[PAD]', '[PAD]']\n", + "8409\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8410\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "8411\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8412\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "8413\n", + "Label = ['elem', '[PAD]', '[PAD]', '[PAD]']\n", + "8414\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8415\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8416\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8417\n", + "Label = ['intf', '[PAD]', '[PAD]', '[PAD]']\n", + "8418\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8419\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "8420\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8421\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "8422\n", + "Label = ['failed', 'conditions', '[PAD]', '[PAD]']\n", + "8423\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8424\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8425\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8426\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8427\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8428\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "8429\n", + "Label = ['cli', '[PAD]', '[PAD]', '[PAD]']\n", + "8430\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8431\n", + "Label = ['LB', 'INTERFACE', 'EXISTS', '[PAD]']\n", + "8432\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8433\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8434\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8435\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8436\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8437\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8438\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8439\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8440\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8441\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8442\n", + "Label = ['NODE2', 'EXISTS', '[PAD]', '[PAD]']\n", + "8443\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8444\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8445\n", + "Label = ['VROUTER', 'EXISTS', '[PAD]', '[PAD]']\n", + "8446\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8447\n", + "Label = ['cli', '[PAD]', '[PAD]', '[PAD]']\n", + "8448\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8449\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8450\n", + "Label = ['cli', '[PAD]', '[PAD]', '[PAD]']\n", + "8451\n", + "Label = ['VROUTER', 'NAME', 'EXISTS', '[PAD]']\n", + "8452\n", + "Label = ['cli', '[PAD]', '[PAD]', '[PAD]']\n", + "8453\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8454\n", + "Label = ['argument', 'specs', '[PAD]', '[PAD]']\n", + "8455\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "8456\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "8457\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "8458\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "8459\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8460\n", + "Label = ['rsp', '[PAD]', '[PAD]', '[PAD]']\n", + "8461\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8462\n", + "Label = ['rsp', '[PAD]', '[PAD]', '[PAD]']\n", + "8463\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "8464\n", + "Label = ['existing', 'obj', '[PAD]', '[PAD]']\n", + "8465\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8466\n", + "Label = ['argument', 'specs', '[PAD]', '[PAD]']\n", + "8467\n", + "Label = ['get', 'session', '[PAD]', '[PAD]']\n", + "8468\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "8469\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8470\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8471\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8472\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "8473\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "8474\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8475\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "8476\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8477\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "8478\n", + "Label = ['METHOD', '[PAD]', '[PAD]', '[PAD]']\n", + "8479\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "8480\n", + "Label = ['download', 'file', '[PAD]', '[PAD]']\n", + "8481\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8482\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8483\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "8484\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8485\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8486\n", + "Label = ['conditionals', '[PAD]', '[PAD]', '[PAD]']\n", + "8487\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8488\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8489\n", + "Label = ['flag', '[PAD]', '[PAD]', '[PAD]']\n", + "8490\n", + "Label = ['validate', 'size', '[PAD]', '[PAD]']\n", + "8491\n", + "Label = ['have', 'console', 'level', '[PAD]']\n", + "8492\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8493\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "8494\n", + "Label = ['parse', 'size', '[PAD]', '[PAD]']\n", + "8495\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8496\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8497\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8498\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8499\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8500\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8501\n", + "Label = ['adds', '[PAD]', '[PAD]', '[PAD]']\n", + "8502\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8503\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8504\n", + "Label = ['sys', 'node', '[PAD]', '[PAD]']\n", + "8505\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "8506\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "8507\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8508\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8509\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8510\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8511\n", + "Label = ['commit', '[PAD]', '[PAD]', '[PAD]']\n", + "8512\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8513\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "8514\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8515\n", + "Label = ['intf', 'params', '[PAD]', '[PAD]']\n", + "8516\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8517\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8518\n", + "Label = ['line', 'state', 'filter', '[PAD]']\n", + "8519\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8520\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8521\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8522\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8523\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8524\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8525\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8526\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8527\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "8528\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8529\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8530\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8531\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8532\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8533\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8534\n", + "Label = ['HAS', 'B64', '[PAD]', '[PAD]']\n", + "8535\n", + "Label = ['aggregate', '[PAD]', '[PAD]', '[PAD]']\n", + "8536\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "8537\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8538\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8539\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8540\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8541\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8542\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8543\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8544\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8545\n", + "Label = ['running', 'base', 'diff', 'resp']\n", + "8546\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8547\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "8548\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8549\n", + "Label = ['session', '[PAD]', '[PAD]', '[PAD]']\n", + "8550\n", + "Label = ['strftime', '[PAD]', '[PAD]', '[PAD]']\n", + "8551\n", + "Label = ['json', '[PAD]', '[PAD]', '[PAD]']\n", + "8552\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8553\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "8554\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8555\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8556\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "8557\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8558\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8559\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8560\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8561\n", + "Label = ['dir', '[PAD]', '[PAD]', '[PAD]']\n", + "8562\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8563\n", + "Label = ['b64encode', '[PAD]', '[PAD]', '[PAD]']\n", + "8564\n", + "Label = ['current', 'hooks', '[PAD]', '[PAD]']\n", + "8565\n", + "Label = ['read', '[PAD]', '[PAD]', '[PAD]']\n", + "8566\n", + "Label = ['dot', 'git', 'file', '[PAD]']\n", + "8567\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8568\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8569\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "8570\n", + "Label = ['normpath', '[PAD]', '[PAD]', '[PAD]']\n", + "8571\n", + "Label = ['fd', '[PAD]', '[PAD]', '[PAD]']\n", + "8572\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8573\n", + "Label = ['needs', 'separate', 'git', 'dir']\n", + "8574\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "8575\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8576\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "8577\n", + "Label = ['git', 'path', '[PAD]', '[PAD]']\n", + "8578\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8579\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8580\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8581\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8582\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8583\n", + "Label = ['refspecs', '[PAD]', '[PAD]', '[PAD]']\n", + "8584\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8585\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8586\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8587\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8588\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8589\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8590\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8591\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8592\n", + "Label = ['umask', '[PAD]', '[PAD]', '[PAD]']\n", + "8593\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8594\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8595\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8596\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "8597\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8598\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8599\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8600\n", + "Label = ['groupObject', '[PAD]', '[PAD]', '[PAD]']\n", + "8601\n", + "Label = ['GitlabAuthenticationError', '[PAD]', '[PAD]', '[PAD]']\n", + "8602\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8603\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8604\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8605\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8606\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "8607\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8608\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8609\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8610\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8611\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8612\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8613\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "8614\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8615\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8616\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8617\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8618\n", + "Label = ['rev', '[PAD]', '[PAD]', '[PAD]']\n", + "8619\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "8620\n", + "Label = ['svn', 'path', '[PAD]', '[PAD]']\n", + "8621\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8622\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8623\n", + "Label = ['deleteproject', '[PAD]', '[PAD]', '[PAD]']\n", + "8624\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "8625\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8626\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8627\n", + "Label = ['level', '[PAD]', '[PAD]', '[PAD]']\n", + "8628\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8629\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8630\n", + "Label = ['user', 'changed', '[PAD]', '[PAD]']\n", + "8631\n", + "Label = ['git', '[PAD]', '[PAD]', '[PAD]']\n", + "8632\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8633\n", + "Label = ['existsUser', '[PAD]', '[PAD]', '[PAD]']\n", + "8634\n", + "Label = ['createOrUpdateUser', '[PAD]', '[PAD]', '[PAD]']\n", + "8635\n", + "Label = ['allowed', 'keys', '[PAD]', '[PAD]']\n", + "8636\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8637\n", + "Label = ['cluster', 'value', '[PAD]', '[PAD]']\n", + "8638\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8639\n", + "Label = ['destroy', '[PAD]', '[PAD]', '[PAD]']\n", + "8640\n", + "Label = ['token', '[PAD]', '[PAD]', '[PAD]']\n", + "8641\n", + "Label = ['token', '[PAD]', '[PAD]', '[PAD]']\n", + "8642\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8643\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "8644\n", + "Label = ['pattern', '[PAD]', '[PAD]', '[PAD]']\n", + "8645\n", + "Label = ['policy', '[PAD]', '[PAD]', '[PAD]']\n", + "8646\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "8647\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "8648\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8649\n", + "Label = ['rules', '[PAD]', '[PAD]', '[PAD]']\n", + "8650\n", + "Label = ['Consul', '[PAD]', '[PAD]', '[PAD]']\n", + "8651\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8652\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8653\n", + "Label = ['present', '[PAD]', '[PAD]', '[PAD]']\n", + "8654\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "8655\n", + "Label = ['check', 'id', '[PAD]', '[PAD]']\n", + "8656\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8657\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8658\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "8659\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "8660\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "8661\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8662\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8663\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8664\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8665\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "8666\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "8667\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8668\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8669\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8670\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "8671\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "8672\n", + "Label = ['apis', '[PAD]', '[PAD]', '[PAD]']\n", + "8673\n", + "Label = ['kinds', '[PAD]', '[PAD]', '[PAD]']\n", + "8674\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "8675\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "8676\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "8677\n", + "Label = ['set', 'definition', '[PAD]', '[PAD]']\n", + "8678\n", + "Label = ['json', 'body', '[PAD]', '[PAD]']\n", + "8679\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "8680\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "8681\n", + "Label = ['metaclass', '[PAD]', '[PAD]', '[PAD]']\n", + "8682\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8683\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8684\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8685\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8686\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "8687\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "8688\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "8689\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8690\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8691\n", + "Label = ['api', 'result', '[PAD]', '[PAD]']\n", + "8692\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8693\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "8694\n", + "Label = ['api', 'result', '[PAD]', '[PAD]']\n", + "8695\n", + "Label = ['component', '[PAD]', '[PAD]', '[PAD]']\n", + "8696\n", + "Label = ['apply', 'to', '[PAD]', '[PAD]']\n", + "8697\n", + "Label = ['bin', 'path', '[PAD]', '[PAD]']\n", + "8698\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8699\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8700\n", + "Label = ['plugin', '[PAD]', '[PAD]', '[PAD]']\n", + "8701\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8702\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8703\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "8704\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "8705\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n", + "8706\n", + "Label = ['permissions', '[PAD]', '[PAD]', '[PAD]']\n", + "8707\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8708\n", + "Label = ['rabbitmq', 'user', '[PAD]', '[PAD]']\n", + "8709\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "8710\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8711\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8712\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "8713\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8714\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8715\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8716\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8717\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8718\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8719\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8720\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8721\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8722\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8723\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8724\n", + "Label = ['require', 'version', '[PAD]', '[PAD]']\n", + "8725\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8726\n", + "Label = ['conn', 'name', '[PAD]', '[PAD]']\n", + "8727\n", + "Label = ['options', '[PAD]', '[PAD]', '[PAD]']\n", + "8728\n", + "Label = ['ifname', '[PAD]', '[PAD]', '[PAD]']\n", + "8729\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8730\n", + "Label = ['conn', 'name', '[PAD]', '[PAD]']\n", + "8731\n", + "Label = ['conn', 'name', '[PAD]', '[PAD]']\n", + "8732\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8733\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8734\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8735\n", + "Label = ['ifname', '[PAD]', '[PAD]', '[PAD]']\n", + "8736\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "8737\n", + "Label = ['conn', 'name', '[PAD]', '[PAD]']\n", + "8738\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8739\n", + "Label = ['ifname', '[PAD]', '[PAD]', '[PAD]']\n", + "8740\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8741\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8742\n", + "Label = ['conn', 'name', '[PAD]', '[PAD]']\n", + "8743\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8744\n", + "Label = ['operstatus', 'options', '[PAD]', '[PAD]']\n", + "8745\n", + "Label = ['rsplit', '[PAD]', '[PAD]', '[PAD]']\n", + "8746\n", + "Label = ['ifIndex', '[PAD]', '[PAD]', '[PAD]']\n", + "8747\n", + "Label = ['ifAlias', '[PAD]', '[PAD]', '[PAD]']\n", + "8748\n", + "Label = ['rsplit', '[PAD]', '[PAD]', '[PAD]']\n", + "8749\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8750\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8751\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8752\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8753\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8754\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8755\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8756\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8757\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "8758\n", + "Label = ['Update', '[PAD]', '[PAD]', '[PAD]']\n", + "8759\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8760\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8761\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8762\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8763\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8764\n", + "Label = ['version', 'values', '[PAD]', '[PAD]']\n", + "8765\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8766\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "8767\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8768\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8769\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8770\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8771\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8772\n", + "Label = ['domains', '[PAD]', '[PAD]', '[PAD]']\n", + "8773\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8774\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8775\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "8776\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "8777\n", + "Label = ['update', 'record', '[PAD]', '[PAD]']\n", + "8778\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8779\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8780\n", + "Label = ['unpack', '[PAD]', '[PAD]', '[PAD]']\n", + "8781\n", + "Label = ['stmt', 'join', '[PAD]', '[PAD]']\n", + "8782\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8783\n", + "Label = ['opcode', '[PAD]', '[PAD]', '[PAD]']\n", + "8784\n", + "Label = ['del', 'host', '[PAD]', '[PAD]']\n", + "8785\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8786\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8787\n", + "Label = ['proto', '[PAD]', '[PAD]', '[PAD]']\n", + "8788\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "8789\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "8790\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8791\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "8792\n", + "Label = ['error', 'msg', '[PAD]', '[PAD]']\n", + "8793\n", + "Label = ['error', 'msg', '[PAD]', '[PAD]']\n", + "8794\n", + "Label = ['error', 'msg', '[PAD]', '[PAD]']\n", + "8795\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "8796\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "8797\n", + "Label = ['records', '[PAD]', '[PAD]', '[PAD]']\n", + "8798\n", + "Label = ['get', 'dns', 'records', '[PAD]']\n", + "8799\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "8800\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8801\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "8802\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "8803\n", + "Label = ['search', 'value', '[PAD]', '[PAD]']\n", + "8804\n", + "Label = ['new', 'record', '[PAD]', '[PAD]']\n", + "8805\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "8806\n", + "Label = ['get', 'dns', 'records', '[PAD]']\n", + "8807\n", + "Label = ['do', 'update', '[PAD]', '[PAD]']\n", + "8808\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8809\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8810\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8811\n", + "Label = ['query', '[PAD]', '[PAD]', '[PAD]']\n", + "8812\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8813\n", + "Label = ['domain', 'records', '[PAD]', '[PAD]']\n", + "8814\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "8815\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8816\n", + "Label = ['updateMonitor', '[PAD]', '[PAD]', '[PAD]']\n", + "8817\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8818\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8819\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "8820\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8821\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "8822\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8823\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8824\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8825\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8826\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "8827\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8828\n", + "Label = ['request', 'url', '[PAD]', '[PAD]']\n", + "8829\n", + "Label = ['auth', 'user', '[PAD]', '[PAD]']\n", + "8830\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "8831\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "8832\n", + "Label = ['ip', 'item', '[PAD]', '[PAD]']\n", + "8833\n", + "Label = ['payload', 'data', '[PAD]', '[PAD]']\n", + "8834\n", + "Label = ['matched', 'network', 'id', '[PAD]']\n", + "8835\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8836\n", + "Label = ['payload', 'data', '[PAD]', '[PAD]']\n", + "8837\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8838\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "8839\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8840\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8841\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8842\n", + "Label = ['search', 's', '[PAD]', '[PAD]']\n", + "8843\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8844\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8845\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "8846\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8847\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "8848\n", + "Label = ['LDAPError', '[PAD]', '[PAD]', '[PAD]']\n", + "8849\n", + "Label = ['modify', 's', '[PAD]', '[PAD]']\n", + "8850\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8851\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8852\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "8853\n", + "Label = ['api', 'query', '[PAD]', '[PAD]']\n", + "8854\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8855\n", + "Label = ['record', 'type', '[PAD]', '[PAD]']\n", + "8856\n", + "Label = ['record', '[PAD]', '[PAD]', '[PAD]']\n", + "8857\n", + "Label = ['hosts', 'facts', '[PAD]', '[PAD]']\n", + "8858\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8859\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8860\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8861\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8862\n", + "Label = ['licenses', '[PAD]', '[PAD]', '[PAD]']\n", + "8863\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "8864\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8865\n", + "Label = ['cluster', '[PAD]', '[PAD]', '[PAD]']\n", + "8866\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8867\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "8868\n", + "Label = ['get', 'Tcp', '[PAD]', '[PAD]']\n", + "8869\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "8870\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "8871\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "8872\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8873\n", + "Label = ['check', 'tag', 'status', '[PAD]']\n", + "8874\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8875\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8876\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "8877\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "8878\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "8879\n", + "Label = ['desired', 'state', '[PAD]', '[PAD]']\n", + "8880\n", + "Label = ['enabled', '[PAD]', '[PAD]', '[PAD]']\n", + "8881\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "8882\n", + "Label = ['child', 'folder', 'obj', '[PAD]']\n", + "8883\n", + "Label = ['get', 'folder', '[PAD]', '[PAD]']\n", + "8884\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8885\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8886\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8887\n", + "Label = ['task', '[PAD]', '[PAD]', '[PAD]']\n", + "8888\n", + "Label = ['InvalidState', '[PAD]', '[PAD]', '[PAD]']\n", + "8889\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8890\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8891\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8892\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8893\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "8894\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "8895\n", + "Label = ['vm', 'boot', 'device', 'type']\n", + "8896\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8897\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8898\n", + "Label = ['datastore', 'object', '[PAD]', '[PAD]']\n", + "8899\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "8900\n", + "Label = ['datastore', 'object', '[PAD]', '[PAD]']\n", + "8901\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "8902\n", + "Label = ['temp', 'vm', 'object', '[PAD]']\n", + "8903\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8904\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8905\n", + "Label = ['dvs', 'uplink', 'pg', '[PAD]']\n", + "8906\n", + "Label = ['uplinkPortgroup', '[PAD]', '[PAD]', '[PAD]']\n", + "8907\n", + "Label = ['NotSupported', '[PAD]', '[PAD]', '[PAD]']\n", + "8908\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8909\n", + "Label = ['dvs', 'host', 'member', '[PAD]']\n", + "8910\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "8911\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8912\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8913\n", + "Label = ['InvalidName', '[PAD]', '[PAD]', '[PAD]']\n", + "8914\n", + "Label = ['NotSupported', '[PAD]', '[PAD]', '[PAD]']\n", + "8915\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8916\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8917\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "8918\n", + "Label = ['dest', '[PAD]', '[PAD]', '[PAD]']\n", + "8919\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8920\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8921\n", + "Label = ['perm', '[PAD]', '[PAD]', '[PAD]']\n", + "8922\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "8923\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8924\n", + "Label = ['current', 'obj', '[PAD]', '[PAD]']\n", + "8925\n", + "Label = ['vswitch', 'obj', '[PAD]', '[PAD]']\n", + "8926\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8927\n", + "Label = ['memorySize', '[PAD]', '[PAD]', '[PAD]']\n", + "8928\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "8929\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8930\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8931\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8932\n", + "Label = ['task', '[PAD]', '[PAD]', '[PAD]']\n", + "8933\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "8934\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "8935\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8936\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8937\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8938\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8939\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8940\n", + "Label = ['MakeDirectoryInGuest', '[PAD]', '[PAD]', '[PAD]']\n", + "8941\n", + "Label = ['FileAlreadyExists', '[PAD]', '[PAD]', '[PAD]']\n", + "8942\n", + "Label = ['DeleteDirectoryInGuest', '[PAD]', '[PAD]', '[PAD]']\n", + "8943\n", + "Label = ['InvalidGuestLogin', '[PAD]', '[PAD]', '[PAD]']\n", + "8944\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8945\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "8946\n", + "Label = ['CreateSnapshot', '[PAD]', '[PAD]', '[PAD]']\n", + "8947\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8948\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8949\n", + "Label = ['snap', 'obj', '[PAD]', '[PAD]']\n", + "8950\n", + "Label = ['RenameSnapshot', '[PAD]', '[PAD]', '[PAD]']\n", + "8951\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "8952\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8953\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "8954\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8955\n", + "Label = ['NumericRange', '[PAD]', '[PAD]', '[PAD]']\n", + "8956\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "8957\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8958\n", + "Label = ['vmdk', '[PAD]', '[PAD]', '[PAD]']\n", + "8959\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8960\n", + "Label = ['resource', 'pool', '[PAD]', '[PAD]']\n", + "8961\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "8962\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "8963\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "8964\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8965\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "8966\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "8967\n", + "Label = ['vmdk', 'tarinfo', '[PAD]', '[PAD]']\n", + "8968\n", + "Label = ['HttpNfcLeaseProgress', '[PAD]', '[PAD]', '[PAD]']\n", + "8969\n", + "Label = ['HttpNfcLeaseProgress', '[PAD]', '[PAD]', '[PAD]']\n", + "8970\n", + "Label = ['cluster', '[PAD]', '[PAD]', '[PAD]']\n", + "8971\n", + "Label = ['timeout', '[PAD]', '[PAD]', '[PAD]']\n", + "8972\n", + "Label = ['vswitch', 'states', '[PAD]', '[PAD]']\n", + "8973\n", + "Label = ['InvalidArgument', '[PAD]', '[PAD]', '[PAD]']\n", + "8974\n", + "Label = ['switch', '[PAD]', '[PAD]', '[PAD]']\n", + "8975\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8976\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8977\n", + "Label = ['switch', '[PAD]', '[PAD]', '[PAD]']\n", + "8978\n", + "Label = ['HostConfigFault', '[PAD]', '[PAD]', '[PAD]']\n", + "8979\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8980\n", + "Label = ['UpdateVirtualSwitch', '[PAD]', '[PAD]', '[PAD]']\n", + "8981\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8982\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8983\n", + "Label = ['mitigation', '[PAD]', '[PAD]', '[PAD]']\n", + "8984\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8985\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8986\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "8987\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8988\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8989\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8990\n", + "Label = ['NotSupported', '[PAD]', '[PAD]', '[PAD]']\n", + "8991\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "8992\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "8993\n", + "Label = ['state', 'remove', 'host', '[PAD]']\n", + "8994\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8995\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "8996\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "8997\n", + "Label = ['esxi', 'username', '[PAD]', '[PAD]']\n", + "8998\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "8999\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9000\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9001\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9002\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "9003\n", + "Label = ['port', 'group', 'obj', '[PAD]']\n", + "9004\n", + "Label = ['enable', 'vmotion', '[PAD]', '[PAD]']\n", + "9005\n", + "Label = ['enable', 'provisioning', '[PAD]', '[PAD]']\n", + "9006\n", + "Label = ['UpdateVirtualNic', '[PAD]', '[PAD]', '[PAD]']\n", + "9007\n", + "Label = ['enable', 'vmotion', '[PAD]', '[PAD]']\n", + "9008\n", + "Label = ['enable', 'vmotion', '[PAD]', '[PAD]']\n", + "9009\n", + "Label = ['set', 'service', 'type', '[PAD]']\n", + "9010\n", + "Label = ['set', 'service', 'type', '[PAD]']\n", + "9011\n", + "Label = ['set', 'service', 'type', '[PAD]']\n", + "9012\n", + "Label = ['set', 'service', 'type', '[PAD]']\n", + "9013\n", + "Label = ['enable', 'replication', '[PAD]', '[PAD]']\n", + "9014\n", + "Label = ['enable', 'replication', 'nfc', '[PAD]']\n", + "9015\n", + "Label = ['set', 'service', 'type', '[PAD]']\n", + "9016\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9017\n", + "Label = ['vmk', 'device', '[PAD]', '[PAD]']\n", + "9018\n", + "Label = ['AlreadyExists', '[PAD]', '[PAD]', '[PAD]']\n", + "9019\n", + "Label = ['enable', 'mgmt', '[PAD]', '[PAD]']\n", + "9020\n", + "Label = ['enable', 'replication', 'nfc', '[PAD]']\n", + "9021\n", + "Label = ['service', 'type', '[PAD]', '[PAD]']\n", + "9022\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9023\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9024\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9025\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9026\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9027\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9028\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9029\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9030\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9031\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9032\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9033\n", + "Label = ['InvalidName', '[PAD]', '[PAD]', '[PAD]']\n", + "9034\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9035\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9036\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "9037\n", + "Label = ['UpdateAuthorizationRole', '[PAD]', '[PAD]', '[PAD]']\n", + "9038\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9039\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9040\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9041\n", + "Label = ['dev', '[PAD]', '[PAD]', '[PAD]']\n", + "9042\n", + "Label = ['datastore', '[PAD]', '[PAD]', '[PAD]']\n", + "9043\n", + "Label = ['Name', '[PAD]', '[PAD]', '[PAD]']\n", + "9044\n", + "Label = ['cluster', '[PAD]', '[PAD]', '[PAD]']\n", + "9045\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "9046\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9047\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9048\n", + "Label = ['numCPU', '[PAD]', '[PAD]', '[PAD]']\n", + "9049\n", + "Label = ['numCPU', '[PAD]', '[PAD]', '[PAD]']\n", + "9050\n", + "Label = ['cdrom', 'type', '[PAD]', '[PAD]']\n", + "9051\n", + "Label = ['datastore', '[PAD]', '[PAD]', '[PAD]']\n", + "9052\n", + "Label = ['disconnect', '[PAD]', '[PAD]', '[PAD]']\n", + "9053\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9054\n", + "Label = ['dclist', '[PAD]', '[PAD]', '[PAD]']\n", + "9055\n", + "Label = ['dcmor', '[PAD]', '[PAD]', '[PAD]']\n", + "9056\n", + "Label = ['todvs', '[PAD]', '[PAD]', '[PAD]']\n", + "9057\n", + "Label = ['nic', 'backing', '[PAD]', '[PAD]']\n", + "9058\n", + "Label = ['node', 'list', '[PAD]', '[PAD]']\n", + "9059\n", + "Label = ['disconnect', '[PAD]', '[PAD]', '[PAD]']\n", + "9060\n", + "Label = ['Name', '[PAD]', '[PAD]', '[PAD]']\n", + "9061\n", + "Label = ['datastore', 'name', '[PAD]', '[PAD]']\n", + "9062\n", + "Label = ['disconnect', '[PAD]', '[PAD]', '[PAD]']\n", + "9063\n", + "Label = ['disk', 'ctrl', 'key', '[PAD]']\n", + "9064\n", + "Label = ['disconnect', '[PAD]', '[PAD]', '[PAD]']\n", + "9065\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9066\n", + "Label = ['shutdown', 'guest', '[PAD]', '[PAD]']\n", + "9067\n", + "Label = ['net', '[PAD]', '[PAD]', '[PAD]']\n", + "9068\n", + "Label = ['set', 'current', '[PAD]', '[PAD]']\n", + "9069\n", + "Label = ['check', 'dict', '[PAD]', '[PAD]']\n", + "9070\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9071\n", + "Label = ['proto', 'vm', 'disk', '[PAD]']\n", + "9072\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9073\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "9074\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9075\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9076\n", + "Label = ['pkgs', '[PAD]', '[PAD]', '[PAD]']\n", + "9077\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9078\n", + "Label = ['cluster', 'obj', 'list', '[PAD]']\n", + "9079\n", + "Label = ['rule', 'obj', '[PAD]', '[PAD]']\n", + "9080\n", + "Label = ['drs', 'rule', '[PAD]', '[PAD]']\n", + "9081\n", + "Label = ['VmHostRuleInfo', '[PAD]', '[PAD]', '[PAD]']\n", + "9082\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9083\n", + "Label = ['dc', 'obj', '[PAD]', '[PAD]']\n", + "9084\n", + "Label = ['promiscuous', 'mode', '[PAD]', '[PAD]']\n", + "9085\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9086\n", + "Label = ['UpdatePortGroup', '[PAD]', '[PAD]', '[PAD]']\n", + "9087\n", + "Label = ['UpdatePortGroup', '[PAD]', '[PAD]', '[PAD]']\n", + "9088\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9089\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9090\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "9091\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9092\n", + "Label = ['InvalidDeviceSpec', '[PAD]', '[PAD]', '[PAD]']\n", + "9093\n", + "Label = ['reconfigure', 'vm', '[PAD]', '[PAD]']\n", + "9094\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9095\n", + "Label = ['capacityInKB', '[PAD]', '[PAD]', '[PAD]']\n", + "9096\n", + "Label = ['reconfigure', 'vm', '[PAD]', '[PAD]']\n", + "9097\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9098\n", + "Label = ['expected', '[PAD]', '[PAD]', '[PAD]']\n", + "9099\n", + "Label = ['temp', 'disk', 'unit', 'number']\n", + "9100\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9101\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9102\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9103\n", + "Label = ['VirtualEthernetCard', '[PAD]', '[PAD]', '[PAD]']\n", + "9104\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9105\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9106\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9107\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9108\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9109\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "9110\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "9111\n", + "Label = ['lun', '[PAD]', '[PAD]', '[PAD]']\n", + "9112\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9113\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9114\n", + "Label = ['EnterMaintenanceMode', 'Task', '[PAD]', '[PAD]']\n", + "9115\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9116\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9117\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "9118\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9119\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9120\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "9121\n", + "Label = ['vendorName', '[PAD]', '[PAD]', '[PAD]']\n", + "9122\n", + "Label = ['directpath', 'io', '[PAD]', '[PAD]']\n", + "9123\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9124\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", + "9125\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9126\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9127\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9128\n", + "Label = ['NotFound', '[PAD]', '[PAD]', '[PAD]']\n", + "9129\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9130\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "9131\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9132\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9133\n", + "Label = ['dvs', 'states', '[PAD]', '[PAD]']\n", + "9134\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9135\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "9136\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9137\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9138\n", + "Label = ['ipV6Enabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9139\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9140\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9141\n", + "Label = ['NotSupported', '[PAD]', '[PAD]', '[PAD]']\n", + "9142\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9143\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9144\n", + "Label = ['AlreadyExists', '[PAD]', '[PAD]', '[PAD]']\n", + "9145\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9146\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9147\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9148\n", + "Label = ['RuntimeFault', '[PAD]', '[PAD]', '[PAD]']\n", + "9149\n", + "Label = ['user', 'account', '[PAD]', '[PAD]']\n", + "9150\n", + "Label = ['MethodFault', '[PAD]', '[PAD]', '[PAD]']\n", + "9151\n", + "Label = ['VirtualLsiLogicController', '[PAD]', '[PAD]', '[PAD]']\n", + "9152\n", + "Label = ['backing', '[PAD]', '[PAD]', '[PAD]']\n", + "9153\n", + "Label = ['backing', '[PAD]', '[PAD]', '[PAD]']\n", + "9154\n", + "Label = ['unitNumber', '[PAD]', '[PAD]', '[PAD]']\n", + "9155\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "9156\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9157\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "9158\n", + "Label = ['mem', 'limit', '[PAD]', '[PAD]']\n", + "9159\n", + "Label = ['num', 'cpus', '[PAD]', '[PAD]']\n", + "9160\n", + "Label = ['num', 'cpus', '[PAD]', '[PAD]']\n", + "9161\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9162\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9163\n", + "Label = ['memoryHotAddEnabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9164\n", + "Label = ['memoryHotAddEnabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9165\n", + "Label = ['cpuHotRemoveEnabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9166\n", + "Label = ['memory', 'reservation', 'mb', '[PAD]']\n", + "9167\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9168\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9169\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9170\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9171\n", + "Label = ['nestedHVEnabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9172\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "9173\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9174\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9175\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9176\n", + "Label = ['wakeOnLanEnabled', '[PAD]', '[PAD]', '[PAD]']\n", + "9177\n", + "Label = ['backing', '[PAD]', '[PAD]', '[PAD]']\n", + "9178\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9179\n", + "Label = ['distributedVirtualSwitch', '[PAD]', '[PAD]', '[PAD]']\n", + "9180\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "9181\n", + "Label = ['dnsDomain', '[PAD]', '[PAD]', '[PAD]']\n", + "9182\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9183\n", + "Label = ['dnsServerList', '[PAD]', '[PAD]', '[PAD]']\n", + "9184\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9185\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "9186\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "9187\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "9188\n", + "Label = ['unit', '[PAD]', '[PAD]', '[PAD]']\n", + "9189\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "9190\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9191\n", + "Label = ['thinProvisioned', '[PAD]', '[PAD]', '[PAD]']\n", + "9192\n", + "Label = ['eagerlyScrub', '[PAD]', '[PAD]', '[PAD]']\n", + "9193\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9194\n", + "Label = ['datastores', '[PAD]', '[PAD]', '[PAD]']\n", + "9195\n", + "Label = ['freeSpace', '[PAD]', '[PAD]', '[PAD]']\n", + "9196\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9197\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "9198\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9199\n", + "Label = ['paths', '[PAD]', '[PAD]', '[PAD]']\n", + "9200\n", + "Label = ['datacenter', '[PAD]', '[PAD]', '[PAD]']\n", + "9201\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9202\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9203\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9204\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9205\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9206\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "9207\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9208\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9209\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9210\n", + "Label = ['standbySupported', '[PAD]', '[PAD]', '[PAD]']\n", + "9211\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9212\n", + "Label = ['vendorName', '[PAD]', '[PAD]', '[PAD]']\n", + "9213\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9214\n", + "Label = ['operation', 'result', '[PAD]', '[PAD]']\n", + "9215\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "9216\n", + "Label = ['server', 'response', '[PAD]', '[PAD]']\n", + "9217\n", + "Label = ['start', 'server', '[PAD]', '[PAD]']\n", + "9218\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9219\n", + "Label = ['number', '[PAD]', '[PAD]', '[PAD]']\n", + "9220\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "9221\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9222\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9223\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9224\n", + "Label = ['servers', '[PAD]', '[PAD]', '[PAD]']\n", + "9225\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9226\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9227\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9228\n", + "Label = ['attach', 'volume', '[PAD]', '[PAD]']\n", + "9229\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9230\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9231\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9232\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "9233\n", + "Label = ['datacenter', '[PAD]', '[PAD]', '[PAD]']\n", + "9234\n", + "Label = ['server', 'found', '[PAD]', '[PAD]']\n", + "9235\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9236\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9237\n", + "Label = ['operation', 'result', '[PAD]', '[PAD]']\n", + "9238\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "9239\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "9240\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9241\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9242\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9243\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9244\n", + "Label = ['delete', 'volume', 'snapshot', '[PAD]']\n", + "9245\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "9246\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "9247\n", + "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", + "9248\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "9249\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "9250\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "9251\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9252\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9253\n", + "Label = ['get', 'image', 'id', '[PAD]']\n", + "9254\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9255\n", + "Label = ['add', 'ip', 'list', '[PAD]']\n", + "9256\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9257\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9258\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9259\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9260\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9261\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "9262\n", + "Label = ['aggregate', '[PAD]', '[PAD]', '[PAD]']\n", + "9263\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9264\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9265\n", + "Label = ['lb', '[PAD]', '[PAD]', '[PAD]']\n", + "9266\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9267\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9268\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9269\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "9270\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "9271\n", + "Label = ['OpenStackCloudException', '[PAD]', '[PAD]', '[PAD]']\n", + "9272\n", + "Label = ['search', 'networks', '[PAD]', '[PAD]']\n", + "9273\n", + "Label = ['OpenStackCloudException', '[PAD]', '[PAD]', '[PAD]']\n", + "9274\n", + "Label = ['unset', 'keys', '[PAD]', '[PAD]']\n", + "9275\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "9276\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "9277\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "9278\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9279\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "9280\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "9281\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9282\n", + "Label = ['zone', '[PAD]', '[PAD]', '[PAD]']\n", + "9283\n", + "Label = ['create', 'zone', '[PAD]', '[PAD]']\n", + "9284\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9285\n", + "Label = ['get', 'container', 'access', '[PAD]']\n", + "9286\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "9287\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9288\n", + "Label = ['metadata', '[PAD]', '[PAD]', '[PAD]']\n", + "9289\n", + "Label = ['keys', 'to', 'delete', '[PAD]']\n", + "9290\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9291\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "9292\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9293\n", + "Label = ['subnets', '[PAD]', '[PAD]', '[PAD]']\n", + "9294\n", + "Label = ['create', 'member', '[PAD]', '[PAD]']\n", + "9295\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9296\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9297\n", + "Label = ['endpoint', '[PAD]', '[PAD]', '[PAD]']\n", + "9298\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9299\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9300\n", + "Label = ['set', 'machine', 'maintenance', 'state']\n", + "9301\n", + "Label = ['remove', 'machine', 'from', 'maintenance']\n", + "9302\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9303\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9304\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9305\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9306\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9307\n", + "Label = ['create', 'stack', '[PAD]', '[PAD]']\n", + "9308\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9309\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9310\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9311\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9312\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9313\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9314\n", + "Label = ['fixed', 'ip', 'address', '[PAD]']\n", + "9315\n", + "Label = ['add', 'ips', 'to', 'server']\n", + "9316\n", + "Label = ['detach', 'ip', 'from', 'server']\n", + "9317\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9318\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9319\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9320\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "9321\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9322\n", + "Label = ['OpenStackCloudException', '[PAD]', '[PAD]', '[PAD]']\n", + "9323\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9324\n", + "Label = ['dom', '[PAD]', '[PAD]', '[PAD]']\n", + "9325\n", + "Label = ['project', '[PAD]', '[PAD]', '[PAD]']\n", + "9326\n", + "Label = ['project', '[PAD]', '[PAD]', '[PAD]']\n", + "9327\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9328\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9329\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "9330\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9331\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "9332\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9333\n", + "Label = ['ips', '[PAD]', '[PAD]', '[PAD]']\n", + "9334\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9335\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9336\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9337\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9338\n", + "Label = ['OpenStackCloudException', '[PAD]', '[PAD]', '[PAD]']\n", + "9339\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9340\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9341\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "9342\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9343\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9344\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "9345\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9346\n", + "Label = ['search', 'servers', '[PAD]', '[PAD]']\n", + "9347\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9348\n", + "Label = ['params', 'dict', '[PAD]', '[PAD]']\n", + "9349\n", + "Label = ['update', 'user', '[PAD]', '[PAD]']\n", + "9350\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9351\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9352\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9353\n", + "Label = ['secgroup', '[PAD]', '[PAD]', '[PAD]']\n", + "9354\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9355\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9356\n", + "Label = ['props', '[PAD]', '[PAD]', '[PAD]']\n", + "9357\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9358\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9359\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9360\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9361\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "9362\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "9363\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9364\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9365\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9366\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "9367\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9368\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9369\n", + "Label = ['deepcopy', '[PAD]', '[PAD]', '[PAD]']\n", + "9370\n", + "Label = ['glue', 'job', '[PAD]', '[PAD]']\n", + "9371\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9372\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9373\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9374\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9375\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "9376\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "9377\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9378\n", + "Label = ['error', 'code', '[PAD]', '[PAD]']\n", + "9379\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "9380\n", + "Label = ['rt', '[PAD]', '[PAD]', '[PAD]']\n", + "9381\n", + "Label = ['ensure', 'present', '[PAD]', '[PAD]']\n", + "9382\n", + "Label = ['ensure', 'absent', '[PAD]', '[PAD]']\n", + "9383\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9384\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9385\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9386\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9387\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9388\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9389\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9390\n", + "Label = ['ec2', 'connection', '[PAD]', '[PAD]']\n", + "9391\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9392\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9393\n", + "Label = ['delete', 'launch', 'configuration', '[PAD]']\n", + "9394\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9395\n", + "Label = ['retry', 'params', '[PAD]', '[PAD]']\n", + "9396\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9397\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9398\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9399\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9400\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9401\n", + "Label = ['key', 'head', '[PAD]', '[PAD]']\n", + "9402\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "9403\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "9404\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9405\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "9406\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9407\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9408\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9409\n", + "Label = ['merge', '[PAD]', '[PAD]', '[PAD]']\n", + "9410\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9411\n", + "Label = ['bucket', 'acl', '[PAD]', '[PAD]']\n", + "9412\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9413\n", + "Label = ['keyrtn', '[PAD]', '[PAD]', '[PAD]']\n", + "9414\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9415\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9416\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9417\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9418\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9419\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9420\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9421\n", + "Label = ['paginator', '[PAD]', '[PAD]', '[PAD]']\n", + "9422\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9423\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "9424\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9425\n", + "Label = ['update', 'hosted', 'zone', 'comment']\n", + "9426\n", + "Label = ['update', 'hosted', 'zone', 'comment']\n", + "9427\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9428\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9429\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9430\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9431\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "9432\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9433\n", + "Label = ['delete', 'hosted', 'zone', '[PAD]']\n", + "9434\n", + "Label = ['delete', 'hosted', 'zone', '[PAD]']\n", + "9435\n", + "Label = ['conditiontuple', '[PAD]', '[PAD]', '[PAD]']\n", + "9436\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "9437\n", + "Label = ['ensure', 'regex', 'pattern', 'present']\n", + "9438\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9439\n", + "Label = ['conditionsetid', '[PAD]', '[PAD]', '[PAD]']\n", + "9440\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9441\n", + "Label = ['regex', 'pattern', 'set', '[PAD]']\n", + "9442\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9443\n", + "Label = ['conditionsetid', '[PAD]', '[PAD]', '[PAD]']\n", + "9444\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9445\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "9446\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9447\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9448\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9449\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9450\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9451\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "9452\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "9453\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9454\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "9455\n", + "Label = ['listeners', '[PAD]', '[PAD]', '[PAD]']\n", + "9456\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9457\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9458\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "9459\n", + "Label = ['subnets', 'to', 'detach', '[PAD]']\n", + "9460\n", + "Label = ['subnets', '[PAD]', '[PAD]', '[PAD]']\n", + "9461\n", + "Label = ['zones', '[PAD]', '[PAD]', '[PAD]']\n", + "9462\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9463\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9464\n", + "Label = ['backends', '[PAD]', '[PAD]', '[PAD]']\n", + "9465\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9466\n", + "Label = ['purge', 'instance', 'ids', '[PAD]']\n", + "9467\n", + "Label = ['current', 'tags', '[PAD]', '[PAD]']\n", + "9468\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9469\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9470\n", + "Label = ['unquote', '[PAD]', '[PAD]', '[PAD]']\n", + "9471\n", + "Label = ['delete', 'group', 'policy', '[PAD]']\n", + "9472\n", + "Label = ['updated', 'policies', '[PAD]', '[PAD]']\n", + "9473\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9474\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9475\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "9476\n", + "Label = ['detach', 'user', 'policy', '[PAD]']\n", + "9477\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "9478\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9479\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9480\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9481\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9482\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9483\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9484\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "9485\n", + "Label = ['wait', '[PAD]', '[PAD]', '[PAD]']\n", + "9486\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "9487\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "9488\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9489\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "9490\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9491\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9492\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9493\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9494\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9495\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9496\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9497\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9498\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9499\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9500\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9501\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n", + "9502\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9503\n", + "Label = ['vpc', 'to', 'detach', '[PAD]']\n", + "9504\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9505\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9506\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9507\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "9508\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9509\n", + "Label = ['region', '[PAD]', '[PAD]', '[PAD]']\n", + "9510\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9511\n", + "Label = ['ParamValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "9512\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9513\n", + "Label = ['website', 'config', '[PAD]', '[PAD]']\n", + "9514\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9515\n", + "Label = ['delete', 'bucket', 'website', '[PAD]']\n", + "9516\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9517\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9518\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9519\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9520\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9521\n", + "Label = ['vpn', 'connection', 'id', '[PAD]']\n", + "9522\n", + "Label = ['delete', 'vpn', 'connection', 'route']\n", + "9523\n", + "Label = ['delete', 'vpn', 'connection', '[PAD]']\n", + "9524\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "9525\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9526\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "9527\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9528\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "9529\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9530\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "9531\n", + "Label = ['current', 'routes', '[PAD]', '[PAD]']\n", + "9532\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9533\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9534\n", + "Label = ['vpn', 'connection', '[PAD]', '[PAD]']\n", + "9535\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9536\n", + "Label = ['build', 'full', 'result', '[PAD]']\n", + "9537\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9538\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9539\n", + "Label = ['placement', 'group', '[PAD]', '[PAD]']\n", + "9540\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9541\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "9542\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "9543\n", + "Label = ['describe', '[PAD]', '[PAD]', '[PAD]']\n", + "9544\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "9545\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "9546\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "9547\n", + "Label = ['describe', '[PAD]', '[PAD]', '[PAD]']\n", + "9548\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "9549\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9550\n", + "Label = ['throughput', '[PAD]', '[PAD]', '[PAD]']\n", + "9551\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9552\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "9553\n", + "Label = ['throughput', '[PAD]', '[PAD]', '[PAD]']\n", + "9554\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9555\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9556\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9557\n", + "Label = ['HAS', 'BOTO3', '[PAD]', '[PAD]']\n", + "9558\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "9559\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9560\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9561\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9562\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9563\n", + "Label = ['feature', '[PAD]', '[PAD]', '[PAD]']\n", + "9564\n", + "Label = ['feature', '[PAD]', '[PAD]', '[PAD]']\n", + "9565\n", + "Label = ['feature', '[PAD]', '[PAD]', '[PAD]']\n", + "9566\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9567\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9568\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9569\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9570\n", + "Label = ['sg', '[PAD]', '[PAD]', '[PAD]']\n", + "9571\n", + "Label = ['difference', '[PAD]', '[PAD]', '[PAD]']\n", + "9572\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "9573\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9574\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9575\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9576\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9577\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9578\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9579\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9580\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "9581\n", + "Label = ['nacl', 'id', '[PAD]', '[PAD]']\n", + "9582\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9583\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9584\n", + "Label = ['associations', '[PAD]', '[PAD]', '[PAD]']\n", + "9585\n", + "Label = ['nacl', 'id', '[PAD]', '[PAD]']\n", + "9586\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9587\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9588\n", + "Label = ['association', '[PAD]', '[PAD]', '[PAD]']\n", + "9589\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9590\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9591\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "9592\n", + "Label = ['environments', '[PAD]', '[PAD]', '[PAD]']\n", + "9593\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "9594\n", + "Label = ['ceo', '[PAD]', '[PAD]', '[PAD]']\n", + "9595\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9596\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9597\n", + "Label = ['required', '[PAD]', '[PAD]', '[PAD]']\n", + "9598\n", + "Label = ['not', 'allowed', '[PAD]', '[PAD]']\n", + "9599\n", + "Label = ['error', 'code', '[PAD]', '[PAD]']\n", + "9600\n", + "Label = ['publicIp', '[PAD]', '[PAD]', '[PAD]']\n", + "9601\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "9602\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9603\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9604\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9605\n", + "Label = ['create', 'connection', '[PAD]', '[PAD]']\n", + "9606\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9607\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9608\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9609\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9610\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9611\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "9612\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9613\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9614\n", + "Label = ['policy', '[PAD]', '[PAD]', '[PAD]']\n", + "9615\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9616\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9617\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9618\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9619\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9620\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "9621\n", + "Label = ['describe', 'tags', '[PAD]', '[PAD]']\n", + "9622\n", + "Label = ['snaked', 'load', 'balancer', '[PAD]']\n", + "9623\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9624\n", + "Label = ['current', 'authorizations', '[PAD]', '[PAD]']\n", + "9625\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "9626\n", + "Label = ['put', 'aggregation', 'authorization', '[PAD]']\n", + "9627\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9628\n", + "Label = ['DEFAULT', 'PORTS', '[PAD]', '[PAD]']\n", + "9629\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9630\n", + "Label = ['get', 'all', 'dbsnapshots', '[PAD]']\n", + "9631\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "9632\n", + "Label = ['dbinstances', '[PAD]', '[PAD]', '[PAD]']\n", + "9633\n", + "Label = ['describe', 'db', 'instances', '[PAD]']\n", + "9634\n", + "Label = ['describe', 'db', 'snapshots', '[PAD]']\n", + "9635\n", + "Label = ['describe', 'db', 'snapshots', '[PAD]']\n", + "9636\n", + "Label = ['create', 'db', 'instance', 'read']\n", + "9637\n", + "Label = ['create', 'db', 'snapshot', '[PAD]']\n", + "9638\n", + "Label = ['endpoint', '[PAD]', '[PAD]', '[PAD]']\n", + "9639\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "9640\n", + "Label = ['ReadReplicaSourceDBInstanceIdentifier', '[PAD]', '[PAD]', '[PAD]']\n", + "9641\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9642\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "9643\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9644\n", + "Label = ['backoff', '[PAD]', '[PAD]', '[PAD]']\n", + "9645\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "9646\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9647\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "9648\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9649\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "9650\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9651\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9652\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "9653\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9654\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9655\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9656\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9657\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9658\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9659\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9660\n", + "Label = ['ctb', '[PAD]', '[PAD]', '[PAD]']\n", + "9661\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9662\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "9663\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "9664\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "9665\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9666\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9667\n", + "Label = ['lb', '[PAD]', '[PAD]', '[PAD]']\n", + "9668\n", + "Label = ['instance', 'id', '[PAD]', '[PAD]']\n", + "9669\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "9670\n", + "Label = ['existing', 'target', 'arn', '[PAD]']\n", + "9671\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9672\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9673\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9674\n", + "Label = ['delete', 'db', 'subnet', 'group']\n", + "9675\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9676\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9677\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9678\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9679\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9680\n", + "Label = ['current', 'policy', '[PAD]', '[PAD]']\n", + "9681\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9682\n", + "Label = ['dummy', '[PAD]', '[PAD]', '[PAD]']\n", + "9683\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "9684\n", + "Label = ['bucket', 'is', 'present', '[PAD]']\n", + "9685\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9686\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9687\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9688\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9689\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9690\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9691\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9692\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9693\n", + "Label = ['delete', 'key', 'pair', '[PAD]']\n", + "9694\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "9695\n", + "Label = ['attach', '[PAD]', '[PAD]', '[PAD]']\n", + "9696\n", + "Label = ['attach', '[PAD]', '[PAD]', '[PAD]']\n", + "9697\n", + "Label = ['assign', 'private', 'ip', 'addresses']\n", + "9698\n", + "Label = ['groups', '[PAD]', '[PAD]', '[PAD]']\n", + "9699\n", + "Label = ['assign', 'private', 'ip', 'addresses']\n", + "9700\n", + "Label = ['attachment', '[PAD]', '[PAD]', '[PAD]']\n", + "9701\n", + "Label = ['customer', 'gateway', '[PAD]', '[PAD]']\n", + "9702\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9703\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9704\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9705\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9706\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "9707\n", + "Label = ['create', 'success', '[PAD]', '[PAD]']\n", + "9708\n", + "Label = ['create', 'stream', '[PAD]', '[PAD]']\n", + "9709\n", + "Label = ['wait', 'success', '[PAD]', '[PAD]']\n", + "9710\n", + "Label = ['wait', 'success', '[PAD]', '[PAD]']\n", + "9711\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9712\n", + "Label = ['success', '[PAD]', '[PAD]', '[PAD]']\n", + "9713\n", + "Label = ['success', '[PAD]', '[PAD]', '[PAD]']\n", + "9714\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9715\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9716\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9717\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9718\n", + "Label = ['get', 'user', '[PAD]', '[PAD]']\n", + "9719\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9720\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9721\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9722\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9723\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9724\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9725\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "9726\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9727\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9728\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9729\n", + "Label = ['region', '[PAD]', '[PAD]', '[PAD]']\n", + "9730\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9731\n", + "Label = ['clusters', '[PAD]', '[PAD]', '[PAD]']\n", + "9732\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['arn', '[PAD]', '[PAD]', '[PAD]']\n", + "9733\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "9734\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9735\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9736\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9737\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9738\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9739\n", + "Label = ['describe', 'task', 'definition', '[PAD]']\n", + "9740\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9741\n", + "Label = ['register', 'task', '[PAD]', '[PAD]']\n", + "9742\n", + "Label = ['web', 'acls', '[PAD]', '[PAD]']\n", + "9743\n", + "Label = ['web', 'acl', '[PAD]', '[PAD]']\n", + "9744\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9745\n", + "Label = ['config', 'node', '[PAD]', '[PAD]']\n", + "9746\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9747\n", + "Label = ['create', 'distribution', '[PAD]', '[PAD]']\n", + "9748\n", + "Label = ['update', 'distribution', '[PAD]', '[PAD]']\n", + "9749\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9750\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9751\n", + "Label = ['issubset', '[PAD]', '[PAD]', '[PAD]']\n", + "9752\n", + "Label = ['issubset', '[PAD]', '[PAD]', '[PAD]']\n", + "9753\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9754\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "9755\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9756\n", + "Label = ['origin', '[PAD]', '[PAD]', '[PAD]']\n", + "9757\n", + "Label = ['add', 'missing', 'key', '[PAD]']\n", + "9758\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9759\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9760\n", + "Label = ['valid', 'cache', 'behavior', '[PAD]']\n", + "9761\n", + "Label = ['add', 'key', 'else', 'change']\n", + "9762\n", + "Label = ['cache', 'behavior', '[PAD]', '[PAD]']\n", + "9763\n", + "Label = ['validate', 'is', 'list', '[PAD]']\n", + "9764\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9765\n", + "Label = ['validate', 'attribute', 'with', nan]\n", + "9766\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9767\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9768\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9769\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9770\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9771\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9772\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "9773\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9774\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "9775\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9776\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9777\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9778\n", + "Label = ['not', 'allowed', '[PAD]', '[PAD]']\n", + "9779\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9780\n", + "Label = ['body', '[PAD]', '[PAD]', '[PAD]']\n", + "9781\n", + "Label = ['vpc', '[PAD]', '[PAD]', '[PAD]']\n", + "9782\n", + "Label = ['vpc', 'id', '[PAD]', '[PAD]']\n", + "9783\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9784\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9785\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9786\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9787\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "9788\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "9789\n", + "Label = ['listener', 'to', 'delete', '[PAD]']\n", + "9790\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9791\n", + "Label = ['Rule', '[PAD]', '[PAD]', '[PAD]']\n", + "9792\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "9793\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9794\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9795\n", + "Label = ['auto', 'group', '[PAD]', '[PAD]']\n", + "9796\n", + "Label = ['auto', 'group', '[PAD]', '[PAD]']\n", + "9797\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9798\n", + "Label = ['sources', '[PAD]', '[PAD]', '[PAD]']\n", + "9799\n", + "Label = ['source', 'types', '[PAD]', '[PAD]']\n", + "9800\n", + "Label = ['stype', '[PAD]', '[PAD]', '[PAD]']\n", + "9801\n", + "Label = ['revoke', 'security', 'group', 'ingress']\n", + "9802\n", + "Label = ['ip', '[PAD]', '[PAD]', '[PAD]']\n", + "9803\n", + "Label = ['create', 'tags', '[PAD]', '[PAD]']\n", + "9804\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9805\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9806\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "9807\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "9808\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "9809\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9810\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "9811\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9812\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9813\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9814\n", + "Label = ['rules', 'egress', '[PAD]', '[PAD]']\n", + "9815\n", + "Label = ['new', 'egress', 'permissions', '[PAD]']\n", + "9816\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9817\n", + "Label = ['security', 'group', '[PAD]', '[PAD]']\n", + "9818\n", + "Label = ['security', 'group', '[PAD]', '[PAD]']\n", + "9819\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9820\n", + "Label = ['matching', 'count', '[PAD]', '[PAD]']\n", + "9821\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9822\n", + "Label = ['delete', 'tags', '[PAD]', '[PAD]']\n", + "9823\n", + "Label = ['delete', 'tags', '[PAD]', '[PAD]']\n", + "9824\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9825\n", + "Label = ['route', '[PAD]', '[PAD]', '[PAD]']\n", + "9826\n", + "Label = ['delete', 'route', '[PAD]', '[PAD]']\n", + "9827\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9828\n", + "Label = ['current', 'association', 'ids', '[PAD]']\n", + "9829\n", + "Label = ['to', 'delete', '[PAD]', '[PAD]']\n", + "9830\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9831\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9832\n", + "Label = ['region', '[PAD]', '[PAD]', '[PAD]']\n", + "9833\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9834\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "9835\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "9836\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "9837\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "9838\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9839\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9840\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9841\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9842\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "9843\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9844\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9845\n", + "Label = ['reservations', '[PAD]', '[PAD]', '[PAD]']\n", + "9846\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "9847\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9848\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "9849\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "9850\n", + "Label = ['Version', '[PAD]', '[PAD]', '[PAD]']\n", + "9851\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9852\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9853\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "9854\n", + "Label = ['instances', '[PAD]', '[PAD]', '[PAD]']\n", + "9855\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "9856\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "9857\n", + "Label = ['volume', '[PAD]', '[PAD]', '[PAD]']\n", + "9858\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9859\n", + "Label = ['terminated', 'instances', '[PAD]', '[PAD]']\n", + "9860\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9861\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9862\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9863\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "9864\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9865\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9866\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9867\n", + "Label = ['vpc', 'id', '[PAD]', '[PAD]']\n", + "9868\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9869\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9870\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9871\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "9872\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "9873\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "9874\n", + "Label = ['ecs', 'instances', 'arns', '[PAD]']\n", + "9875\n", + "Label = ['delete', 'attributes', '[PAD]', '[PAD]']\n", + "9876\n", + "Label = ['delete', 'attributes', '[PAD]', '[PAD]']\n", + "9877\n", + "Label = ['attr', 'found', '[PAD]', '[PAD]']\n", + "9878\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9879\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "9880\n", + "Label = ['STATE', 'AVAILABLE', '[PAD]', '[PAD]']\n", + "9881\n", + "Label = ['get', 'tags', '[PAD]', '[PAD]']\n", + "9882\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", + "9883\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "9884\n", + "Label = ['sid', '[PAD]', '[PAD]', '[PAD]']\n", + "9885\n", + "Label = ['modify', 'mount', 'target', 'security']\n", + "9886\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "9887\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9888\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "9889\n", + "Label = ['loads', '[PAD]', '[PAD]', '[PAD]']\n", + "9890\n", + "Label = ['set', 'default', 'policy', 'version']\n", + "9891\n", + "Label = ['list', 'policy', 'versions', '[PAD]']\n", + "9892\n", + "Label = ['delete', 'policy', 'version', '[PAD]']\n", + "9893\n", + "Label = ['delete', 'policy', 'version', '[PAD]']\n", + "9894\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9895\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9896\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9897\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9898\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9899\n", + "Label = ['policy', '[PAD]', '[PAD]', '[PAD]']\n", + "9900\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9901\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9902\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9903\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9904\n", + "Label = ['delete', 'policy', '[PAD]', '[PAD]']\n", + "9905\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9906\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9907\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "9908\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9909\n", + "Label = ['strptime', '[PAD]', '[PAD]', '[PAD]']\n", + "9910\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "9911\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "9912\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "9913\n", + "Label = ['safe', 'substitute', '[PAD]', '[PAD]']\n", + "9914\n", + "Label = ['create', 'tags', '[PAD]', '[PAD]']\n", + "9915\n", + "Label = ['replace', 'iam', 'instance', 'profile']\n", + "9916\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "9917\n", + "Label = ['groups', '[PAD]', '[PAD]', '[PAD]']\n", + "9918\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9919\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "9920\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "9921\n", + "Label = ['parent', 'vpc', 'id', '[PAD]']\n", + "9922\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9923\n", + "Label = ['spec', '[PAD]', '[PAD]', '[PAD]']\n", + "9924\n", + "Label = ['state', 'opts', '[PAD]', '[PAD]']\n", + "9925\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9926\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9927\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "9928\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9929\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9930\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "9931\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "9932\n", + "Label = ['subs', 'by', 'az', '[PAD]']\n", + "9933\n", + "Label = ['to', 'change', '[PAD]', '[PAD]']\n", + "9934\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "9935\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "9936\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "9937\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "9938\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "9939\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "9940\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "9941\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "9942\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9943\n", + "Label = ['altered', '[PAD]', '[PAD]', '[PAD]']\n", + "9944\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "9945\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9946\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "9947\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "9948\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9949\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9950\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9951\n", + "Label = ['HAS', 'BOTO3', '[PAD]', '[PAD]']\n", + "9952\n", + "Label = ['server', 'certs', '[PAD]', '[PAD]']\n", + "9953\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9954\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "9955\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9956\n", + "Label = ['distribution', 'list', '[PAD]', '[PAD]']\n", + "9957\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9958\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "9959\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9960\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "9961\n", + "Label = ['facts', '[PAD]', '[PAD]', '[PAD]']\n", + "9962\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9963\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9964\n", + "Label = ['server', 'cert', '[PAD]', '[PAD]']\n", + "9965\n", + "Label = ['get', 'server', 'certificate', '[PAD]']\n", + "9966\n", + "Label = ['isoformat', '[PAD]', '[PAD]', '[PAD]']\n", + "9967\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "9968\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9969\n", + "Label = ['found', '[PAD]', '[PAD]', '[PAD]']\n", + "9970\n", + "Label = ['fileentry', '[PAD]', '[PAD]', '[PAD]']\n", + "9971\n", + "Label = ['head', 'object', '[PAD]', '[PAD]']\n", + "9972\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9973\n", + "Label = ['delta', '[PAD]', '[PAD]', '[PAD]']\n", + "9974\n", + "Label = ['current', 'keys', '[PAD]', '[PAD]']\n", + "9975\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9976\n", + "Label = ['restorable', 'by', 'user', 'ids']\n", + "9977\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "9978\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9979\n", + "Label = ['current', 'params', '[PAD]', '[PAD]']\n", + "9980\n", + "Label = ['describe', 'delivery', 'channels', '[PAD]']\n", + "9981\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "9982\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "9983\n", + "Label = ['existing', '[PAD]', '[PAD]', '[PAD]']\n", + "9984\n", + "Label = ['loadBalancer', '[PAD]', '[PAD]', '[PAD]']\n", + "9985\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "9986\n", + "Label = ['HAS', 'BOTO3', '[PAD]', '[PAD]']\n", + "9987\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9988\n", + "Label = ['dp', 'id', '[PAD]', '[PAD]']\n", + "9989\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "9990\n", + "Label = ['put', 'pipeline', 'definition', '[PAD]']\n", + "9991\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9992\n", + "Label = ['tags', 'list', '[PAD]', '[PAD]']\n", + "9993\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "9994\n", + "Label = ['delete', 'trail', '[PAD]', '[PAD]']\n", + "9995\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "9996\n", + "Label = ['required', 'if', '[PAD]', '[PAD]']\n", + "9997\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "9998\n", + "Label = ['rstrip', '[PAD]', '[PAD]', '[PAD]']\n", + "9999\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10000\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10001\n", + "Label = ['NoCredentialsError', '[PAD]', '[PAD]', '[PAD]']\n", + "10002\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10003\n", + "Label = ['metaclass', '[PAD]', '[PAD]', '[PAD]']\n", + "10004\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10005\n", + "Label = ['vols', '[PAD]', '[PAD]', '[PAD]']\n", + "10006\n", + "Label = ['delete', 'volume', '[PAD]', '[PAD]']\n", + "10007\n", + "Label = ['get', 'password', 'data', '[PAD]']\n", + "10008\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10009\n", + "Label = ['instance', 'id', '[PAD]', '[PAD]']\n", + "10010\n", + "Label = ['attachment', 'state', '[PAD]', '[PAD]']\n", + "10011\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10012\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10013\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10014\n", + "Label = ['attachment', 'state', '[PAD]', '[PAD]']\n", + "10015\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10016\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10017\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10018\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10019\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10020\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10021\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "10022\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "10023\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "10024\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10025\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "10026\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10027\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "10028\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "10029\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10030\n", + "Label = ['cn', '[PAD]', '[PAD]', '[PAD]']\n", + "10031\n", + "Label = ['ensure', 'present', '[PAD]', '[PAD]']\n", + "10032\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "10033\n", + "Label = ['reservations', '[PAD]', '[PAD]', '[PAD]']\n", + "10034\n", + "Label = ['get', 'unused', 'target', 'groups']\n", + "10035\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10036\n", + "Label = ['status', 'achieved', '[PAD]', '[PAD]']\n", + "10037\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10038\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10039\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "10040\n", + "Label = ['loads', '[PAD]', '[PAD]', '[PAD]']\n", + "10041\n", + "Label = ['update', 'assume', 'role', 'policy']\n", + "10042\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10043\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10044\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10045\n", + "Label = ['instance', 'profiles', '[PAD]', '[PAD]']\n", + "10046\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10047\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10048\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10049\n", + "Label = ['add', 'role', 'to', 'instance']\n", + "10050\n", + "Label = ['update', 'role', 'description', '[PAD]']\n", + "10051\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10052\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10053\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10054\n", + "Label = ['remove', 'role', 'from', 'instance']\n", + "10055\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10056\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10057\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10058\n", + "Label = ['list', 'tags', 'for', 'resource']\n", + "10059\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10060\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "10061\n", + "Label = ['error', 'code', '[PAD]', '[PAD]']\n", + "10062\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10063\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10064\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10065\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "10066\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10067\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10068\n", + "Label = ['reboot', 'instance', '[PAD]', '[PAD]']\n", + "10069\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10070\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "10071\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10072\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10073\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10074\n", + "Label = ['asg', 'elbs', '[PAD]', '[PAD]']\n", + "10075\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10076\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10077\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10078\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10079\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10080\n", + "Label = ['new', 'vpc', 'config', '[PAD]']\n", + "10081\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10082\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10083\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10084\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10085\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n", + "10086\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10087\n", + "Label = ['describe', 'images', '[PAD]', '[PAD]']\n", + "10088\n", + "Label = ['NoAuthHandlerFound', '[PAD]', '[PAD]', '[PAD]']\n", + "10089\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "10090\n", + "Label = ['NoAuthHandlerFound', '[PAD]', '[PAD]', '[PAD]']\n", + "10091\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "10092\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10093\n", + "Label = ['elb', '[PAD]', '[PAD]', '[PAD]']\n", + "10094\n", + "Label = ['subnets', '[PAD]', '[PAD]', '[PAD]']\n", + "10095\n", + "Label = ['subnets', '[PAD]', '[PAD]', '[PAD]']\n", + "10096\n", + "Label = ['access', 'logs', 'config', '[PAD]']\n", + "10097\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10098\n", + "Label = ['elb', 'info', '[PAD]', '[PAD]']\n", + "10099\n", + "Label = ['boolean', '[PAD]', '[PAD]', '[PAD]']\n", + "10100\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", + "10101\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10102\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10103\n", + "Label = ['set', 'lb', 'policies', 'of']\n", + "10104\n", + "Label = ['elb', '[PAD]', '[PAD]', '[PAD]']\n", + "10105\n", + "Label = ['tagdict', '[PAD]', '[PAD]', '[PAD]']\n", + "10106\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10107\n", + "Label = ['health', 'check', '[PAD]', '[PAD]']\n", + "10108\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10109\n", + "Label = ['elb', 'man', '[PAD]', '[PAD]']\n", + "10110\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "10111\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10112\n", + "Label = ['location', '[PAD]', '[PAD]', '[PAD]']\n", + "10113\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10114\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10115\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10116\n", + "Label = ['build', 'full', 'result', '[PAD]']\n", + "10117\n", + "Label = ['get', 'security', 'groups', '[PAD]']\n", + "10118\n", + "Label = ['available', '[PAD]', '[PAD]', '[PAD]']\n", + "10119\n", + "Label = ['file', 'systems', 'info', '[PAD]']\n", + "10120\n", + "Label = ['existing', 'value', '[PAD]', '[PAD]']\n", + "10121\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "10122\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10123\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10124\n", + "Label = ['pred', 'results', '[PAD]', '[PAD]']\n", + "10125\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10126\n", + "Label = ['condition', 'type', '[PAD]', '[PAD]']\n", + "10127\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "10128\n", + "Label = ['update', 'rule', '[PAD]', '[PAD]']\n", + "10129\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10130\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "10131\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10132\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10133\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "10134\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "10135\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10136\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10137\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10138\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10139\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "10140\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10141\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10142\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10143\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10144\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10145\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10146\n", + "Label = ['launch', 'object', '[PAD]', '[PAD]']\n", + "10147\n", + "Label = ['lt', '[PAD]', '[PAD]', '[PAD]']\n", + "10148\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "10149\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10150\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10151\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10152\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10153\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10154\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10155\n", + "Label = ['ag', '[PAD]', '[PAD]', '[PAD]']\n", + "10156\n", + "Label = ['launch', 'template', '[PAD]', '[PAD]']\n", + "10157\n", + "Label = ['enable', 'metrics', 'collection', '[PAD]']\n", + "10158\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10159\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10160\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10161\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "10162\n", + "Label = ['instances', '[PAD]', '[PAD]', '[PAD]']\n", + "10163\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10164\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10165\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10166\n", + "Label = ['BotoCoreError', '[PAD]', '[PAD]', '[PAD]']\n", + "10167\n", + "Label = ['wait', '[PAD]', '[PAD]', '[PAD]']\n", + "10168\n", + "Label = ['user', 'id', '[PAD]', '[PAD]']\n", + "10169\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "10170\n", + "Label = ['modify', 'image', 'attribute', '[PAD]']\n", + "10171\n", + "Label = ['delete', 'tags', '[PAD]', '[PAD]']\n", + "10172\n", + "Label = ['create', 'tags', '[PAD]', '[PAD]']\n", + "10173\n", + "Label = ['create', 'tags', '[PAD]', '[PAD]']\n", + "10174\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10175\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10176\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10177\n", + "Label = ['sorted', 'endpoint', 'rt', 'ids']\n", + "10178\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10179\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "10180\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10181\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10182\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "10183\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10184\n", + "Label = ['ec2', '[PAD]', '[PAD]', '[PAD]']\n", + "10185\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10186\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10187\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10188\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10189\n", + "Label = ['iam', '[PAD]', '[PAD]', '[PAD]']\n", + "10190\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10191\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10192\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10193\n", + "Label = ['current', 'keys', '[PAD]', '[PAD]']\n", + "10194\n", + "Label = ['ck', '[PAD]', '[PAD]', '[PAD]']\n", + "10195\n", + "Label = ['current', 'key', '[PAD]', '[PAD]']\n", + "10196\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10197\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10198\n", + "Label = ['update', 'group', '[PAD]', '[PAD]']\n", + "10199\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10200\n", + "Label = ['iam', '[PAD]', '[PAD]', '[PAD]']\n", + "10201\n", + "Label = ['current', 'path', '[PAD]', '[PAD]']\n", + "10202\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "10203\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10204\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10205\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10206\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10207\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10208\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10209\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "10210\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10211\n", + "Label = ['exponential', 'backoff', '[PAD]', '[PAD]']\n", + "10212\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10213\n", + "Label = ['ensure', 'tags', '[PAD]', '[PAD]']\n", + "10214\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10215\n", + "Label = ['format', 'exc', '[PAD]', '[PAD]']\n", + "10216\n", + "Label = ['virtual', 'interfaces', '[PAD]', '[PAD]']\n", + "10217\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10218\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "10219\n", + "Label = ['vi', '[PAD]', '[PAD]', '[PAD]']\n", + "10220\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10221\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10222\n", + "Label = ['JSONResponseError', '[PAD]', '[PAD]', '[PAD]']\n", + "10223\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "10224\n", + "Label = ['describe', 'clusters', '[PAD]', '[PAD]']\n", + "10225\n", + "Label = ['resource', '[PAD]', '[PAD]', '[PAD]']\n", + "10226\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10227\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "10228\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "10229\n", + "Label = ['HAS', 'BOTO3', '[PAD]', '[PAD]']\n", + "10230\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10231\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10232\n", + "Label = ['valid', 'entries', '[PAD]', '[PAD]']\n", + "10233\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "10234\n", + "Label = ['policy', 'json', 'string', '[PAD]']\n", + "10235\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10236\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10237\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10238\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10239\n", + "Label = ['gw', '[PAD]', '[PAD]', '[PAD]']\n", + "10240\n", + "Label = ['token', 'provided', '[PAD]', '[PAD]']\n", + "10241\n", + "Label = ['success', '[PAD]', '[PAD]', '[PAD]']\n", + "10242\n", + "Label = ['success', '[PAD]', '[PAD]', '[PAD]']\n", + "10243\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10244\n", + "Label = ['snap', '[PAD]', '[PAD]', '[PAD]']\n", + "10245\n", + "Label = ['total', 'seconds', '[PAD]', '[PAD]']\n", + "10246\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10247\n", + "Label = ['XMLStringMatchPart', '[PAD]', '[PAD]', '[PAD]']\n", + "10248\n", + "Label = ['check', 'id', '[PAD]', '[PAD]']\n", + "10249\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "10250\n", + "Label = ['del', 'res', '[PAD]', '[PAD]']\n", + "10251\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "10252\n", + "Label = ['apidata', '[PAD]', '[PAD]', '[PAD]']\n", + "10253\n", + "Label = ['awsret', '[PAD]', '[PAD]', '[PAD]']\n", + "10254\n", + "Label = ['describe', 'scaling', 'policies', '[PAD]']\n", + "10255\n", + "Label = ['scalable', 'targets', '[PAD]', '[PAD]']\n", + "10256\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10257\n", + "Label = ['put', 'scaling', 'policy', '[PAD]']\n", + "10258\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10259\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10260\n", + "Label = ['metric', 'name', '[PAD]', '[PAD]']\n", + "10261\n", + "Label = ['delete', 'web', 'acl', '[PAD]']\n", + "10262\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10263\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10264\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10265\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", + "10266\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10267\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10268\n", + "Label = ['arg', 'dict', '[PAD]', '[PAD]']\n", + "10269\n", + "Label = ['set', 'identity', 'headers', 'in']\n", + "10270\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10271\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10272\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "10273\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "10274\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10275\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "10276\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10277\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "10278\n", + "Label = ['newkey', '[PAD]', '[PAD]', '[PAD]']\n", + "10279\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10280\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10281\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "10282\n", + "Label = ['attach', 'group', 'policy', '[PAD]']\n", + "10283\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10284\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10285\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10286\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10287\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10288\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10289\n", + "Label = ['paginator', '[PAD]', '[PAD]', '[PAD]']\n", + "10290\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10291\n", + "Label = ['total', 'seconds', '[PAD]', '[PAD]']\n", + "10292\n", + "Label = ['stack', 'instances', '[PAD]', '[PAD]']\n", + "10293\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "10294\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10295\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10296\n", + "Label = ['describe', 'stack', 'set', 'operation']\n", + "10297\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10298\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10299\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10300\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "10301\n", + "Label = ['list', 'tags', 'for', 'resource']\n", + "10302\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "10303\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "10304\n", + "Label = ['modifier', '[PAD]', '[PAD]', '[PAD]']\n", + "10305\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10306\n", + "Label = ['list', 'tags', 'for', 'resource']\n", + "10307\n", + "Label = ['add', 'tags', 'to', 'resource']\n", + "10308\n", + "Label = ['remove', 'tags', 'from', 'resource']\n", + "10309\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10310\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10311\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10312\n", + "Label = ['NoCredentialsError', '[PAD]', '[PAD]', '[PAD]']\n", + "10313\n", + "Label = ['HAS', 'BOTO3', '[PAD]', '[PAD]']\n", + "10314\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "10315\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10316\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10317\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10318\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10319\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10320\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10321\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10322\n", + "Label = ['distribution', 'id', '[PAD]', '[PAD]']\n", + "10323\n", + "Label = ['topic', 'attributes', '[PAD]', '[PAD]']\n", + "10324\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10325\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10326\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10327\n", + "Label = ['display', 'name', '[PAD]', '[PAD]']\n", + "10328\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10329\n", + "Label = ['sns', 'topic', '[PAD]', '[PAD]']\n", + "10330\n", + "Label = ['sns', 'facts', '[PAD]', '[PAD]']\n", + "10331\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10332\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10333\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10334\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10335\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10336\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10337\n", + "Label = ['listener', 'dict', '[PAD]', '[PAD]']\n", + "10338\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10339\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10340\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10341\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10342\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10343\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10344\n", + "Label = ['current', 'status', '[PAD]', '[PAD]']\n", + "10345\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10346\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10347\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10348\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10349\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10350\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10351\n", + "Label = ['backoff', '[PAD]', '[PAD]', '[PAD]']\n", + "10352\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10353\n", + "Label = ['describe', 'tags', '[PAD]', '[PAD]']\n", + "10354\n", + "Label = ['backoff', '[PAD]', '[PAD]', '[PAD]']\n", + "10355\n", + "Label = ['ClientError', '[PAD]', '[PAD]', '[PAD]']\n", + "10356\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10357\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10358\n", + "Label = ['valid', 'cidr', '[PAD]', '[PAD]']\n", + "10359\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10360\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10361\n", + "Label = ['describe', 'job', 'queues', '[PAD]']\n", + "10362\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10363\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10364\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "10365\n", + "Label = ['retval', '[PAD]', '[PAD]', '[PAD]']\n", + "10366\n", + "Label = ['BotoServerError', '[PAD]', '[PAD]', '[PAD]']\n", + "10367\n", + "Label = ['errmsg', '[PAD]', '[PAD]', '[PAD]']\n", + "10368\n", + "Label = ['get', 'all', 'rrsets', '[PAD]']\n", + "10369\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "10370\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10371\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10372\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10373\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10374\n", + "Label = ['lc', '[PAD]', '[PAD]', '[PAD]']\n", + "10375\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "10376\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "10377\n", + "Label = ['associate', 'address', '[PAD]', '[PAD]']\n", + "10378\n", + "Label = ['get', 'all', 'addresses', '[PAD]']\n", + "10379\n", + "Label = ['addresses', '[PAD]', '[PAD]', '[PAD]']\n", + "10380\n", + "Label = ['instance', 'id', '[PAD]', '[PAD]']\n", + "10381\n", + "Label = ['network', 'interface', 'id', '[PAD]']\n", + "10382\n", + "Label = ['warnings', '[PAD]', '[PAD]', '[PAD]']\n", + "10383\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10384\n", + "Label = ['build', 'full', 'result', '[PAD]']\n", + "10385\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10386\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10387\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10388\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10389\n", + "Label = ['status', 'achieved', '[PAD]', '[PAD]']\n", + "10390\n", + "Label = ['add', 'tags', '[PAD]', '[PAD]']\n", + "10391\n", + "Label = ['add', 'tags', '[PAD]', '[PAD]']\n", + "10392\n", + "Label = ['repo', '[PAD]', '[PAD]', '[PAD]']\n", + "10393\n", + "Label = ['passed', '[PAD]', '[PAD]', '[PAD]']\n", + "10394\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10395\n", + "Label = ['gw', '[PAD]', '[PAD]', '[PAD]']\n", + "10396\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10397\n", + "Label = ['fail', 'json', 'aws', '[PAD]']\n", + "10398\n", + "Label = ['tapes', '[PAD]', '[PAD]', '[PAD]']\n", + "10399\n", + "Label = ['build', 'full', 'result', '[PAD]']\n", + "10400\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10401\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "10402\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10403\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10404\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10405\n", + "Label = ['stop', 'task', '[PAD]', '[PAD]']\n", + "10406\n", + "Label = ['reservation', '[PAD]', '[PAD]', '[PAD]']\n", + "10407\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "10408\n", + "Label = ['cs', '[PAD]', '[PAD]', '[PAD]']\n", + "10409\n", + "Label = ['update', 'termination', 'protection', '[PAD]']\n", + "10410\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10411\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10412\n", + "Label = ['read', '[PAD]', '[PAD]', '[PAD]']\n", + "10413\n", + "Label = ['read', '[PAD]', '[PAD]', '[PAD]']\n", + "10414\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "10415\n", + "Label = ['backoff', 'wrapper', '[PAD]', '[PAD]']\n", + "10416\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "10417\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10418\n", + "Label = ['HAS', 'FOOTMARK', '[PAD]', '[PAD]']\n", + "10419\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "10420\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10421\n", + "Label = ['instances', '[PAD]', '[PAD]', '[PAD]']\n", + "10422\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10423\n", + "Label = ['inst', '[PAD]', '[PAD]', '[PAD]']\n", + "10424\n", + "Label = ['modify', '[PAD]', '[PAD]', '[PAD]']\n", + "10425\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10426\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10427\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10428\n", + "Label = ['perm', 'order', '[PAD]', '[PAD]']\n", + "10429\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10430\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "10431\n", + "Label = ['get', 'by', 'key', '[PAD]']\n", + "10432\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10433\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10434\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10435\n", + "Label = ['async', 'result', '[PAD]', '[PAD]']\n", + "10436\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "10437\n", + "Label = ['nic', '[PAD]', '[PAD]', '[PAD]']\n", + "10438\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10439\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10440\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10441\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10442\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10443\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10444\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10445\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10446\n", + "Label = ['capitalize', '[PAD]', '[PAD]', '[PAD]']\n", + "10447\n", + "Label = ['pod', '[PAD]', '[PAD]', '[PAD]']\n", + "10448\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10449\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10450\n", + "Label = ['tcp', 'udp', 'match', '[PAD]']\n", + "10451\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "10452\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "10453\n", + "Label = ['firewall', 'rule', '[PAD]', '[PAD]']\n", + "10454\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10455\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10456\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "10457\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "10458\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10459\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10460\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10461\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10462\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10463\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "10464\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10465\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10466\n", + "Label = ['vpc', '[PAD]', '[PAD]', '[PAD]']\n", + "10467\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10468\n", + "Label = ['returns', '[PAD]', '[PAD]', '[PAD]']\n", + "10469\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10470\n", + "Label = ['volume', '[PAD]', '[PAD]', '[PAD]']\n", + "10471\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10472\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10473\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "10474\n", + "Label = ['returns', '[PAD]', '[PAD]', '[PAD]']\n", + "10475\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10476\n", + "Label = ['user', 'name', '[PAD]', '[PAD]']\n", + "10477\n", + "Label = ['u', '[PAD]', '[PAD]', '[PAD]']\n", + "10478\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "10479\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10480\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10481\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10482\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "10483\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "10484\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "10485\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10486\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10487\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10488\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10489\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10490\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10491\n", + "Label = ['no', '[PAD]', '[PAD]', '[PAD]']\n", + "10492\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10493\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "10494\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10495\n", + "Label = ['network', '[PAD]', '[PAD]', '[PAD]']\n", + "10496\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10497\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10498\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10499\n", + "Label = ['returns', 'to', 'int', '[PAD]']\n", + "10500\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10501\n", + "Label = ['search', 'key', '[PAD]', '[PAD]']\n", + "10502\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10503\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10504\n", + "Label = ['poll', 'job', '[PAD]', '[PAD]']\n", + "10505\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10506\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10507\n", + "Label = ['to', 'change', '[PAD]', '[PAD]']\n", + "10508\n", + "Label = ['query', 'api', '[PAD]', '[PAD]']\n", + "10509\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10510\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10511\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10512\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10513\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10514\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10515\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "10516\n", + "Label = ['get', 'vpc', '[PAD]', '[PAD]']\n", + "10517\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10518\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10519\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10520\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10521\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10522\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10523\n", + "Label = ['get', 'by', 'key', '[PAD]']\n", + "10524\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10525\n", + "Label = ['disk', 'offering', '[PAD]', '[PAD]']\n", + "10526\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10527\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10528\n", + "Label = ['get', 'account', '[PAD]', '[PAD]']\n", + "10529\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10530\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10531\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "10532\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10533\n", + "Label = ['policy', '[PAD]', '[PAD]', '[PAD]']\n", + "10534\n", + "Label = ['type', 'match', '[PAD]', '[PAD]']\n", + "10535\n", + "Label = ['security', 'group', 'name', '[PAD]']\n", + "10536\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10537\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10538\n", + "Label = ['sg', 'rule', '[PAD]', '[PAD]']\n", + "10539\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10540\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10541\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10542\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10543\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10544\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10545\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "10546\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10547\n", + "Label = ['instance', 'group', '[PAD]', '[PAD]']\n", + "10548\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "10549\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10550\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10551\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10552\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10553\n", + "Label = ['iso', '[PAD]', '[PAD]', '[PAD]']\n", + "10554\n", + "Label = ['returns', '[PAD]', '[PAD]', '[PAD]']\n", + "10555\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10556\n", + "Label = ['returns', '[PAD]', '[PAD]', '[PAD]']\n", + "10557\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10558\n", + "Label = ['get', 'by', 'key', '[PAD]']\n", + "10559\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10560\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10561\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10562\n", + "Label = ['cluster', '[PAD]', '[PAD]', '[PAD]']\n", + "10563\n", + "Label = ['network', 'offering', '[PAD]', '[PAD]']\n", + "10564\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10565\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10566\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10567\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "10568\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10569\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10570\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10571\n", + "Label = ['all', 'templates', '[PAD]', '[PAD]']\n", + "10572\n", + "Label = ['found', '[PAD]', '[PAD]', '[PAD]']\n", + "10573\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "10574\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "10575\n", + "Label = ['roles', 'status', '[PAD]', '[PAD]']\n", + "10576\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "10577\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10578\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10579\n", + "Label = ['other', 'octal', '[PAD]', '[PAD]']\n", + "10580\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10581\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10582\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10583\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10584\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10585\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10586\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10587\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "10588\n", + "Label = ['service', 'id', '[PAD]', '[PAD]']\n", + "10589\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "10590\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "10591\n", + "Label = ['vm', 'uptime', '[PAD]', '[PAD]']\n", + "10592\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10593\n", + "Label = ['attributes', 'str', '[PAD]', '[PAD]']\n", + "10594\n", + "Label = ['vm', 'filled', 'indexes', 'list']\n", + "10595\n", + "Label = ['counter', '[PAD]', '[PAD]', '[PAD]']\n", + "10596\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "10597\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10598\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10599\n", + "Label = ['old', 'vm', '[PAD]', '[PAD]']\n", + "10600\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10601\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10602\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "10603\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "10604\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "10605\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "10606\n", + "Label = ['template', 'id', '[PAD]', '[PAD]']\n", + "10607\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10608\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10609\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10610\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10611\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10612\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10613\n", + "Label = ['wait', 'for', 'state', '[PAD]']\n", + "10614\n", + "Label = ['MONITORED', '[PAD]', '[PAD]', '[PAD]']\n", + "10615\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "10616\n", + "Label = ['HAS', 'SPOTINST', 'SDK', '[PAD]']\n", + "10617\n", + "Label = ['action', 'fields', '[PAD]', '[PAD]']\n", + "10618\n", + "Label = ['device', 'name', '[PAD]', '[PAD]']\n", + "10619\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10620\n", + "Label = ['eg', 'scheduling', '[PAD]', '[PAD]']\n", + "10621\n", + "Label = ['elb', 'name', '[PAD]', '[PAD]']\n", + "10622\n", + "Label = ['target', 'arn', '[PAD]', '[PAD]']\n", + "10623\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "10624\n", + "Label = ['eni', '[PAD]', '[PAD]', '[PAD]']\n", + "10625\n", + "Label = ['up', '[PAD]', '[PAD]', '[PAD]']\n", + "10626\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10627\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "10628\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10629\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "10630\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "10631\n", + "Label = ['ssh', 'key', 'id', '[PAD]']\n", + "10632\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10633\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10634\n", + "Label = ['query', 'resource', 'by', 'key']\n", + "10635\n", + "Label = ['user', 'data', '[PAD]', '[PAD]']\n", + "10636\n", + "Label = ['plan', '[PAD]', '[PAD]', '[PAD]']\n", + "10637\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10638\n", + "Label = ['api', 'query', '[PAD]', '[PAD]']\n", + "10639\n", + "Label = ['api', 'query', '[PAD]', '[PAD]']\n", + "10640\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10641\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10642\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10643\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10644\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10645\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10646\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "10647\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10648\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10649\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10650\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10651\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10652\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10653\n", + "Label = ['script', 'id', '[PAD]', '[PAD]']\n", + "10654\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10655\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10656\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10657\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "10658\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10659\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10660\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10661\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "10662\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10663\n", + "Label = ['get', 'region', 'name', '[PAD]']\n", + "10664\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10665\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10666\n", + "Label = ['query', 'resource', 'by', 'key']\n", + "10667\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10668\n", + "Label = ['user', 'data', '[PAD]', '[PAD]']\n", + "10669\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10670\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10671\n", + "Label = ['server', 'power', 'state', '[PAD]']\n", + "10672\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10673\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10674\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "10675\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10676\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10677\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10678\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "10679\n", + "Label = ['query', 'resource', 'by', 'key']\n", + "10680\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10681\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10682\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10683\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10684\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10685\n", + "Label = ['get', 'firewall', 'group', '[PAD]']\n", + "10686\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "10687\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "10688\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "10689\n", + "Label = ['volume', '[PAD]', '[PAD]', '[PAD]']\n", + "10690\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10691\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10692\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10693\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10694\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10695\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10696\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10697\n", + "Label = ['fail', 'on', 'missing', 'params']\n", + "10698\n", + "Label = ['get', 'firewall', 'group', '[PAD]']\n", + "10699\n", + "Label = ['returns', '[PAD]', '[PAD]', '[PAD]']\n", + "10700\n", + "Label = ['Client', '[PAD]', '[PAD]', '[PAD]']\n", + "10701\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10702\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10703\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "10704\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "10705\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10706\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10707\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "10708\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10709\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10710\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10711\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "10712\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "10713\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10714\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10715\n", + "Label = ['archive', 'path', '[PAD]', '[PAD]']\n", + "10716\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10717\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10718\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "10719\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10720\n", + "Label = ['path', 'name', '[PAD]', '[PAD]']\n", + "10721\n", + "Label = ['warnings', '[PAD]', '[PAD]', '[PAD]']\n", + "10722\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "10723\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "10724\n", + "Label = ['fail', 'reason', '[PAD]', '[PAD]']\n", + "10725\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10726\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10727\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10728\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10729\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "10730\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "10731\n", + "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", + "10732\n", + "Label = ['networks', '[PAD]', '[PAD]', '[PAD]']\n", + "10733\n", + "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", + "10734\n", + "Label = ['mount', 'config', '[PAD]', '[PAD]']\n", + "10735\n", + "Label = ['restart', 'policy', '[PAD]', '[PAD]']\n", + "10736\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "10737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10738\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "10739\n", + "Label = ['tty', '[PAD]', '[PAD]', '[PAD]']\n", + "10740\n", + "Label = ['endpoint', 'mode', '[PAD]', '[PAD]']\n", + "10741\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "10742\n", + "Label = ['network', 'name', 'id', '[PAD]']\n", + "10743\n", + "Label = ['update', 'policy', '[PAD]', '[PAD]']\n", + "10744\n", + "Label = ['update', 'policy', '[PAD]', '[PAD]']\n", + "10745\n", + "Label = ['publish', 'def', '[PAD]', '[PAD]']\n", + "10746\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "10747\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10748\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10749\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10750\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10751\n", + "Label = ['driver', 'options', '[PAD]', '[PAD]']\n", + "10752\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10753\n", + "Label = ['ipam', 'driver', '[PAD]', '[PAD]']\n", + "10754\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10755\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "10756\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10757\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "10758\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "10759\n", + "Label = ['service', 'name', '[PAD]', '[PAD]']\n", + "10760\n", + "Label = ['fdopen', '[PAD]', '[PAD]', '[PAD]']\n", + "10761\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "10762\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "10763\n", + "Label = ['remove', 'volume', '[PAD]', '[PAD]']\n", + "10764\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "10765\n", + "Label = ['secrets', '[PAD]', '[PAD]', '[PAD]']\n", + "10766\n", + "Label = ['secrets', '[PAD]', '[PAD]', '[PAD]']\n", + "10767\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10768\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10769\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10770\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10771\n", + "Label = ['node', 'cert', 'expiry', '[PAD]']\n", + "10772\n", + "Label = ['task', 'history', 'retention', 'limit']\n", + "10773\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10774\n", + "Label = ['update', 'swarm', '[PAD]', '[PAD]']\n", + "10775\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10776\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10777\n", + "Label = ['isSwarmNode', '[PAD]', '[PAD]', '[PAD]']\n", + "10778\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10779\n", + "Label = ['registry', 'url', '[PAD]', '[PAD]']\n", + "10780\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "10781\n", + "Label = ['dump', '[PAD]', '[PAD]', '[PAD]']\n", + "10782\n", + "Label = ['dump', '[PAD]', '[PAD]', '[PAD]']\n", + "10783\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "10784\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10785\n", + "Label = ['entrypoint', '[PAD]', '[PAD]', '[PAD]']\n", + "10786\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "10787\n", + "Label = ['abspath', '[PAD]', '[PAD]', '[PAD]']\n", + "10788\n", + "Label = ['restart', 'policy', '[PAD]', '[PAD]']\n", + "10789\n", + "Label = ['container', 'port', '[PAD]', '[PAD]']\n", + "10790\n", + "Label = ['port', 'binds', '[PAD]', '[PAD]']\n", + "10791\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "10792\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "10793\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10794\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "10795\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10796\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "10797\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10798\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10799\n", + "Label = ['detach', '[PAD]', '[PAD]', '[PAD]']\n", + "10800\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10801\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10802\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10803\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10804\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10805\n", + "Label = ['extra', '[PAD]', '[PAD]', '[PAD]']\n", + "10806\n", + "Label = ['parts', '[PAD]', '[PAD]', '[PAD]']\n", + "10807\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10808\n", + "Label = ['container', 'port', '[PAD]', '[PAD]']\n", + "10809\n", + "Label = ['link', '[PAD]', '[PAD]', '[PAD]']\n", + "10810\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "10811\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10812\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10813\n", + "Label = ['image', 'ports', '[PAD]', '[PAD]']\n", + "10814\n", + "Label = ['normalize', 'port', '[PAD]', '[PAD]']\n", + "10815\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "10816\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "10817\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10818\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10819\n", + "Label = ['present', '[PAD]', '[PAD]', '[PAD]']\n", + "10820\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10821\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10822\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10823\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10824\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10825\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10826\n", + "Label = ['cleanup', '[PAD]', '[PAD]', '[PAD]']\n", + "10827\n", + "Label = ['kill', 'signal', '[PAD]', '[PAD]']\n", + "10828\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10829\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10830\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10831\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10832\n", + "Label = ['request', 'floating', 'ip', '[PAD]']\n", + "10833\n", + "Label = ['release', 'floating', 'ip', '[PAD]']\n", + "10834\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "10835\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10836\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10837\n", + "Label = ['start', 'server', '[PAD]', '[PAD]']\n", + "10838\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "10839\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10840\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10841\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10842\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "10843\n", + "Label = ['ip', 'lookup', '[PAD]', '[PAD]']\n", + "10844\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10845\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "10846\n", + "Label = ['patch', '[PAD]', '[PAD]', '[PAD]']\n", + "10847\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10848\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10849\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10850\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "10851\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10852\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10853\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10854\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10855\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "10856\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10857\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "10858\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10859\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "10860\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10861\n", + "Label = ['instances', '[PAD]', '[PAD]', '[PAD]']\n", + "10862\n", + "Label = ['cancel', 'instance', '[PAD]', '[PAD]']\n", + "10863\n", + "Label = ['cancel', 'instance', '[PAD]', '[PAD]']\n", + "10864\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10865\n", + "Label = ['has', 'failed', '[PAD]', '[PAD]']\n", + "10866\n", + "Label = ['counter', '[PAD]', '[PAD]', '[PAD]']\n", + "10867\n", + "Label = ['zone', '[PAD]', '[PAD]', '[PAD]']\n", + "10868\n", + "Label = ['has', 'failed', '[PAD]', '[PAD]']\n", + "10869\n", + "Label = ['has', 'failed', '[PAD]', '[PAD]']\n", + "10870\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10871\n", + "Label = ['domain', 'exists', '[PAD]', '[PAD]']\n", + "10872\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "10873\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10874\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10875\n", + "Label = ['zone', 'exists', '[PAD]', '[PAD]']\n", + "10876\n", + "Label = ['record', '[PAD]', '[PAD]', '[PAD]']\n", + "10877\n", + "Label = ['has', 'changed', '[PAD]', '[PAD]']\n", + "10878\n", + "Label = ['json', '[PAD]', '[PAD]', '[PAD]']\n", + "10879\n", + "Label = ['memset', 'api', '[PAD]', '[PAD]']\n", + "10880\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10881\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "10882\n", + "Label = ['fname', '[PAD]', '[PAD]', '[PAD]']\n", + "10883\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10884\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10885\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "10886\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "10887\n", + "Label = ['attrib', '[PAD]', '[PAD]', '[PAD]']\n", + "10888\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10889\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10890\n", + "Label = ['image', 'reference', '[PAD]', '[PAD]']\n", + "10891\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10892\n", + "Label = ['os', 'disk', 'size', 'gb']\n", + "10893\n", + "Label = ['update', 'tags', '[PAD]', '[PAD]']\n", + "10894\n", + "Label = ['short', 'hostname', '[PAD]', '[PAD]']\n", + "10895\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "10896\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10897\n", + "Label = ['availability', 'set', '[PAD]', '[PAD]']\n", + "10898\n", + "Label = ['NetworkInterfaceReference', '[PAD]', '[PAD]', '[PAD]']\n", + "10899\n", + "Label = ['OSProfile', '[PAD]', '[PAD]', '[PAD]']\n", + "10900\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10901\n", + "Label = ['data', 'disk', 'requested', 'vhd']\n", + "10902\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10903\n", + "Label = ['nics', '[PAD]', '[PAD]', '[PAD]']\n", + "10904\n", + "Label = ['NetworkInterfaceReference', '[PAD]', '[PAD]', '[PAD]']\n", + "10905\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10906\n", + "Label = ['ManagedDiskParameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10907\n", + "Label = ['SubResource', '[PAD]', '[PAD]', '[PAD]']\n", + "10908\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "10909\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10910\n", + "Label = ['SshPublicKey', '[PAD]', '[PAD]', '[PAD]']\n", + "10911\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10912\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "10913\n", + "Label = ['interface', 'dict', '[PAD]', '[PAD]']\n", + "10914\n", + "Label = ['pip', '[PAD]', '[PAD]', '[PAD]']\n", + "10915\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10916\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10917\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10918\n", + "Label = ['vhd', '[PAD]', '[PAD]', '[PAD]']\n", + "10919\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10920\n", + "Label = ['intersection', '[PAD]', '[PAD]', '[PAD]']\n", + "10921\n", + "Label = ['pip', 'dict', '[PAD]', '[PAD]']\n", + "10922\n", + "Label = ['delete', 'pip', '[PAD]', '[PAD]']\n", + "10923\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10924\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10925\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "10926\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "10927\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10928\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10929\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10930\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "10931\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "10932\n", + "Label = ['pip', '[PAD]', '[PAD]', '[PAD]']\n", + "10933\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10934\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "10935\n", + "Label = ['account', 'dict', '[PAD]', '[PAD]']\n", + "10936\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10937\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "10938\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "10939\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "10940\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10941\n", + "Label = ['StorageAccountCreateParameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10942\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "10943\n", + "Label = ['account', 'dict', '[PAD]', '[PAD]']\n", + "10944\n", + "Label = ['module', 'arg', 'spec', '[PAD]']\n", + "10945\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10946\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10947\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10948\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10949\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "10950\n", + "Label = ['list', 'by', 'server', '[PAD]']\n", + "10951\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10952\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "10953\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10954\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "10955\n", + "Label = ['capitalize', '[PAD]', '[PAD]', '[PAD]']\n", + "10956\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "10957\n", + "Label = ['plan', '[PAD]', '[PAD]', '[PAD]']\n", + "10958\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "10959\n", + "Label = ['site', 'config', '[PAD]', '[PAD]']\n", + "10960\n", + "Label = ['frameworks', '[PAD]', '[PAD]', '[PAD]']\n", + "10961\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "10962\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "10963\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "10964\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10965\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10966\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10967\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10968\n", + "Label = ['is', 'updatable', 'property', 'changed']\n", + "10969\n", + "Label = ['app', 'settings', '[PAD]', '[PAD]']\n", + "10970\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10971\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "10972\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10973\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "10974\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10975\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10976\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "10977\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10978\n", + "Label = ['create', 'or', 'update', 'source']\n", + "10979\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10980\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "10981\n", + "Label = ['is', 'different', '[PAD]', '[PAD]']\n", + "10982\n", + "Label = ['lun', '[PAD]', '[PAD]', '[PAD]']\n", + "10983\n", + "Label = ['data', 'disk', '[PAD]', '[PAD]']\n", + "10984\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "10985\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "10986\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "10987\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "10988\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "10989\n", + "Label = ['get', 'security', 'group', '[PAD]']\n", + "10990\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "10991\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10992\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "10993\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10994\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "10995\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "10996\n", + "Label = ['endpoint', '[PAD]', '[PAD]', '[PAD]']\n", + "10997\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "10998\n", + "Label = ['monitor', 'config', 'spec', '[PAD]']\n", + "10999\n", + "Label = ['endpoints', 'copy', '[PAD]', '[PAD]']\n", + "11000\n", + "Label = ['location', '[PAD]', '[PAD]', '[PAD]']\n", + "11001\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11002\n", + "Label = ['dns', 'config', '[PAD]', '[PAD]']\n", + "11003\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "11004\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11005\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11006\n", + "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", + "11007\n", + "Label = ['list', 'by', 'server', '[PAD]']\n", + "11008\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11009\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11010\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11011\n", + "Label = ['ssh', 'public', 'keys', '[PAD]']\n", + "11012\n", + "Label = ['image', 'reference', '[PAD]', '[PAD]']\n", + "11013\n", + "Label = ['vmss', '[PAD]', '[PAD]', '[PAD]']\n", + "11014\n", + "Label = ['upgrade', 'policy', '[PAD]', '[PAD]']\n", + "11015\n", + "Label = ['subnet', 'name', '[PAD]', '[PAD]']\n", + "11016\n", + "Label = ['backend', 'address', 'pools', '[PAD]']\n", + "11017\n", + "Label = ['VirtualMachineScaleSetStorageProfile', '[PAD]', '[PAD]', '[PAD]']\n", + "11018\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11019\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11020\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11021\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11022\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11023\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11024\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11025\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11026\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11027\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11028\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11029\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11030\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11031\n", + "Label = ['name', 'exists', '[PAD]', '[PAD]']\n", + "11032\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11033\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11034\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11035\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11036\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11037\n", + "Label = ['format', 'item', '[PAD]', '[PAD]']\n", + "11038\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "11039\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11040\n", + "Label = ['zone', '[PAD]', '[PAD]', '[PAD]']\n", + "11041\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11042\n", + "Label = ['zone', '[PAD]', '[PAD]', '[PAD]']\n", + "11043\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11044\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11045\n", + "Label = ['route', 'table', '[PAD]', '[PAD]']\n", + "11046\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11047\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "11048\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11049\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11050\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11051\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11052\n", + "Label = ['fx', '[PAD]', '[PAD]', '[PAD]']\n", + "11053\n", + "Label = ['settings', '[PAD]', '[PAD]', '[PAD]']\n", + "11054\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "11055\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11056\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11057\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11058\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11059\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11060\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11061\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11062\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11063\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11064\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11065\n", + "Label = ['keys', '[PAD]', '[PAD]', '[PAD]']\n", + "11066\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11067\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11068\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11069\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11070\n", + "Label = ['deployment', 'name', '[PAD]', '[PAD]']\n", + "11071\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11072\n", + "Label = ['parameters', 'link', '[PAD]', '[PAD]']\n", + "11073\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11074\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11075\n", + "Label = ['operations', '[PAD]', '[PAD]', '[PAD]']\n", + "11076\n", + "Label = ['target', 'resource', '[PAD]', '[PAD]']\n", + "11077\n", + "Label = ['Dependency', '[PAD]', '[PAD]', '[PAD]']\n", + "11078\n", + "Label = ['key1', '[PAD]', '[PAD]', '[PAD]']\n", + "11079\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11080\n", + "Label = ['routes', '[PAD]', '[PAD]', '[PAD]']\n", + "11081\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11082\n", + "Label = ['resource', 'group', '[PAD]', '[PAD]']\n", + "11083\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11084\n", + "Label = ['list', 'all', '[PAD]', '[PAD]']\n", + "11085\n", + "Label = ['dest', '[PAD]', '[PAD]', '[PAD]']\n", + "11086\n", + "Label = ['update', 'tags', '[PAD]', '[PAD]']\n", + "11087\n", + "Label = ['container', '[PAD]', '[PAD]', '[PAD]']\n", + "11088\n", + "Label = ['create', 'container', '[PAD]', '[PAD]']\n", + "11089\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11090\n", + "Label = ['get', 'blob', 'to', 'path']\n", + "11091\n", + "Label = ['fp', '[PAD]', '[PAD]', '[PAD]']\n", + "11092\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11093\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11094\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11095\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11096\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11097\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11098\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11099\n", + "Label = ['reason', '[PAD]', '[PAD]', '[PAD]']\n", + "11100\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11101\n", + "Label = ['purge', 'address', 'prefixes', '[PAD]']\n", + "11102\n", + "Label = ['vnet', '[PAD]', '[PAD]', '[PAD]']\n", + "11103\n", + "Label = ['dns', 'servers', '[PAD]', '[PAD]']\n", + "11104\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11105\n", + "Label = ['get', 'resource', '[PAD]', '[PAD]']\n", + "11106\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11107\n", + "Label = ['get', 'resource', '[PAD]', '[PAD]']\n", + "11108\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11109\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "11110\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11111\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11112\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11113\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11114\n", + "Label = ['module', 'arg', 'spec', '[PAD]']\n", + "11115\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11116\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11117\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11118\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "11119\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11120\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11121\n", + "Label = ['has', 'tags', '[PAD]', '[PAD]']\n", + "11122\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11123\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11124\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "11125\n", + "Label = ['image', '[PAD]', '[PAD]', '[PAD]']\n", + "11126\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11127\n", + "Label = ['ip', 'address', '[PAD]', '[PAD]']\n", + "11128\n", + "Label = ['vmss', '[PAD]', '[PAD]', '[PAD]']\n", + "11129\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11130\n", + "Label = ['subnet', 'id', '[PAD]', '[PAD]']\n", + "11131\n", + "Label = ['load', 'balancer', 'name', '[PAD]']\n", + "11132\n", + "Label = ['data', 'disks', '[PAD]', '[PAD]']\n", + "11133\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11134\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11135\n", + "Label = ['key', 'data', '[PAD]', '[PAD]']\n", + "11136\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "11137\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11138\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11139\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11140\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "11141\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11142\n", + "Label = ['location', '[PAD]', '[PAD]', '[PAD]']\n", + "11143\n", + "Label = ['access', 'profile', '[PAD]', '[PAD]']\n", + "11144\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11145\n", + "Label = ['profile', 'monitor', 'status', '[PAD]']\n", + "11146\n", + "Label = ['module', 'arg', 'spec', '[PAD]']\n", + "11147\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11148\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11149\n", + "Label = ['resource', 'group', '[PAD]', '[PAD]']\n", + "11150\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11151\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11152\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "11153\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11154\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "11155\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11156\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11157\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11158\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "11159\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11160\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11161\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11162\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11163\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11164\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "11165\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "11166\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "11167\n", + "Label = ['time', 'zone', '[PAD]', '[PAD]']\n", + "11168\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "11169\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "11170\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "11171\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "11172\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11173\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11174\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11175\n", + "Label = ['original', '[PAD]', '[PAD]', '[PAD]']\n", + "11176\n", + "Label = ['resource', 'group', '[PAD]', '[PAD]']\n", + "11177\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11178\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11179\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11180\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11181\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11182\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11183\n", + "Label = ['endpoint', 'status', '[PAD]', '[PAD]']\n", + "11184\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11185\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11186\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11187\n", + "Label = ['resource', 'ns', '[PAD]', '[PAD]']\n", + "11188\n", + "Label = ['serviceprincipalprofile', '[PAD]', '[PAD]', '[PAD]']\n", + "11189\n", + "Label = ['diagnosticsprofile', '[PAD]', '[PAD]', '[PAD]']\n", + "11190\n", + "Label = ['orchestratorprofile', '[PAD]', '[PAD]', '[PAD]']\n", + "11191\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11192\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11193\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "11194\n", + "Label = ['credentials', '[PAD]', '[PAD]', '[PAD]']\n", + "11195\n", + "Label = ['get', 'key', '[PAD]', '[PAD]']\n", + "11196\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11197\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "11198\n", + "Label = ['pem', 'password', '[PAD]', '[PAD]']\n", + "11199\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "11200\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11201\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11202\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "11203\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11204\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11205\n", + "Label = ['rule', '[PAD]', '[PAD]', '[PAD]']\n", + "11206\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11207\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11208\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11209\n", + "Label = ['rule', 'changed', '[PAD]', '[PAD]']\n", + "11210\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11211\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11212\n", + "Label = ['pip', '[PAD]', '[PAD]', '[PAD]']\n", + "11213\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "11214\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "11215\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "11216\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11217\n", + "Label = ['subscription', 'id', '[PAD]', '[PAD]']\n", + "11218\n", + "Label = ['SubResource', '[PAD]', '[PAD]', '[PAD]']\n", + "11219\n", + "Label = ['LoadBalancer', '[PAD]', '[PAD]', '[PAD]']\n", + "11220\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11221\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "11222\n", + "Label = ['subnet', '[PAD]', '[PAD]', '[PAD]']\n", + "11223\n", + "Label = ['public', 'ip', 'address', '[PAD]']\n", + "11224\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11225\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11226\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "11227\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "11228\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "11229\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "11230\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11231\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11232\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11233\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11234\n", + "Label = ['cdnprofile', '[PAD]', '[PAD]', '[PAD]']\n", + "11235\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11236\n", + "Label = ['poller', '[PAD]', '[PAD]', '[PAD]']\n", + "11237\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11238\n", + "Label = ['cdn', 'client', '[PAD]', '[PAD]']\n", + "11239\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11240\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11241\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11242\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11243\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11244\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11245\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11246\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11247\n", + "Label = ['record', 'type', '[PAD]', '[PAD]']\n", + "11248\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "11249\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11250\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11251\n", + "Label = ['record', 'subspec', '[PAD]', '[PAD]']\n", + "11252\n", + "Label = ['fail', '[PAD]', '[PAD]', '[PAD]']\n", + "11253\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11254\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11255\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11256\n", + "Label = ['snapshots', '[PAD]', '[PAD]', '[PAD]']\n", + "11257\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11258\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11259\n", + "Label = ['provider', 'params', '[PAD]', '[PAD]']\n", + "11260\n", + "Label = ['list', '[PAD]', '[PAD]', '[PAD]']\n", + "11261\n", + "Label = ['provider', 'type', '[PAD]', '[PAD]']\n", + "11262\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11263\n", + "Label = ['OpenStackVolumeProvider', '[PAD]', '[PAD]', '[PAD]']\n", + "11264\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11265\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "11266\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11267\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11268\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "11269\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "11270\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "11271\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11272\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "11273\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "11274\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11275\n", + "Label = ['DiskAttachment', '[PAD]', '[PAD]', '[PAD]']\n", + "11276\n", + "Label = ['Disk', '[PAD]', '[PAD]', '[PAD]']\n", + "11277\n", + "Label = ['HighAvailability', '[PAD]', '[PAD]', '[PAD]']\n", + "11278\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11279\n", + "Label = ['StorageDomain', '[PAD]', '[PAD]', '[PAD]']\n", + "11280\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11281\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11282\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11283\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11284\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11285\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11286\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11287\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11288\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11289\n", + "Label = ['cpu', 'set', '[PAD]', '[PAD]']\n", + "11290\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11291\n", + "Label = ['get', 'minor', '[PAD]', '[PAD]']\n", + "11292\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11293\n", + "Label = ['dev', '[PAD]', '[PAD]', '[PAD]']\n", + "11294\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11295\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11296\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "11297\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11298\n", + "Label = ['vm', '[PAD]', '[PAD]', '[PAD]']\n", + "11299\n", + "Label = ['snap', 'active', '[PAD]', '[PAD]']\n", + "11300\n", + "Label = ['snapshot', 'service', '[PAD]', '[PAD]']\n", + "11301\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11302\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11303\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11304\n", + "Label = ['Core', '[PAD]', '[PAD]', '[PAD]']\n", + "11305\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "11306\n", + "Label = ['Watchdog', '[PAD]', '[PAD]', '[PAD]']\n", + "11307\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11308\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11309\n", + "Label = ['Role', '[PAD]', '[PAD]', '[PAD]']\n", + "11310\n", + "Label = ['Role', '[PAD]', '[PAD]', '[PAD]']\n", + "11311\n", + "Label = ['AffinityGroup', '[PAD]', '[PAD]', '[PAD]']\n", + "11312\n", + "Label = ['RegistrationAffinityLabelMapping', '[PAD]', '[PAD]', '[PAD]']\n", + "11313\n", + "Label = ['Domain', '[PAD]', '[PAD]', '[PAD]']\n", + "11314\n", + "Label = ['Domain', '[PAD]', '[PAD]', '[PAD]']\n", + "11315\n", + "Label = ['StorageType', '[PAD]', '[PAD]', '[PAD]']\n", + "11316\n", + "Label = ['LogicalUnit', '[PAD]', '[PAD]', '[PAD]']\n", + "11317\n", + "Label = ['LogicalUnit', '[PAD]', '[PAD]', '[PAD]']\n", + "11318\n", + "Label = ['clusterMapping', '[PAD]', '[PAD]', '[PAD]']\n", + "11319\n", + "Label = ['tmp', '[PAD]', '[PAD]', '[PAD]']\n", + "11320\n", + "Label = ['event', '[PAD]', '[PAD]', '[PAD]']\n", + "11321\n", + "Label = ['vm', '[PAD]', '[PAD]', '[PAD]']\n", + "11322\n", + "Label = ['vm', '[PAD]', '[PAD]', '[PAD]']\n", + "11323\n", + "Label = ['vm', '[PAD]', '[PAD]', '[PAD]']\n", + "11324\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11325\n", + "Label = ['vm', '[PAD]', '[PAD]', '[PAD]']\n", + "11326\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "11327\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11328\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11329\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11330\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11331\n", + "Label = ['Option', '[PAD]', '[PAD]', '[PAD]']\n", + "11332\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11333\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11334\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11335\n", + "Label = ['NetworkLabel', '[PAD]', '[PAD]', '[PAD]']\n", + "11336\n", + "Label = ['IpAddressAssignment', '[PAD]', '[PAD]', '[PAD]']\n", + "11337\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11338\n", + "Label = ['unmanaged', 'networks', '[PAD]', '[PAD]']\n", + "11339\n", + "Label = ['HostNic', '[PAD]', '[PAD]', '[PAD]']\n", + "11340\n", + "Label = ['HostNic', '[PAD]', '[PAD]', '[PAD]']\n", + "11341\n", + "Label = ['NetworkLabel', '[PAD]', '[PAD]', '[PAD]']\n", + "11342\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "11343\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11344\n", + "Label = ['to', 'read', '[PAD]', '[PAD]']\n", + "11345\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "11346\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11347\n", + "Label = ['disk', '[PAD]', '[PAD]', '[PAD]']\n", + "11348\n", + "Label = ['update', 'storage', 'domains', '[PAD]']\n", + "11349\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "11350\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "11351\n", + "Label = ['vm', 'id', '[PAD]', '[PAD]']\n", + "11352\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11353\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11354\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11355\n", + "Label = ['hosts', '[PAD]', '[PAD]', '[PAD]']\n", + "11356\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11357\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11358\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11359\n", + "Label = ['vm', 'ids', '[PAD]', '[PAD]']\n", + "11360\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11361\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11362\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11363\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11364\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "11365\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "11366\n", + "Label = ['IscsiDetails', '[PAD]', '[PAD]', '[PAD]']\n", + "11367\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "11368\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11369\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11370\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11371\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11372\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11373\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11374\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11375\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11376\n", + "Label = ['snap', '[PAD]', '[PAD]', '[PAD]']\n", + "11377\n", + "Label = ['snap', '[PAD]', '[PAD]', '[PAD]']\n", + "11378\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11379\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11380\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11381\n", + "Label = ['quotas', '[PAD]', '[PAD]', '[PAD]']\n", + "11382\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11383\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11384\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11385\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11386\n", + "Label = ['roleMapping', '[PAD]', '[PAD]', '[PAD]']\n", + "11387\n", + "Label = ['RegistrationRoleMapping', '[PAD]', '[PAD]', '[PAD]']\n", + "11388\n", + "Label = ['Role', '[PAD]', '[PAD]', '[PAD]']\n", + "11389\n", + "Label = ['Domain', '[PAD]', '[PAD]', '[PAD]']\n", + "11390\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "11391\n", + "Label = ['Disk', '[PAD]', '[PAD]', '[PAD]']\n", + "11392\n", + "Label = ['template', 'name', '[PAD]', '[PAD]']\n", + "11393\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "11394\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "11395\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11396\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "11397\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11398\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11399\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11400\n", + "Label = ['list', '[PAD]', '[PAD]', '[PAD]']\n", + "11401\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11402\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11403\n", + "Label = ['quota', 'service', '[PAD]', '[PAD]']\n", + "11404\n", + "Label = ['users', 'service', '[PAD]', '[PAD]']\n", + "11405\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11406\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11407\n", + "Label = ['Role', '[PAD]', '[PAD]', '[PAD]']\n", + "11408\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11409\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11410\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11411\n", + "Label = ['cluster', 'network', 'entity', '[PAD]']\n", + "11412\n", + "Label = ['NetworkUsage', '[PAD]', '[PAD]', '[PAD]']\n", + "11413\n", + "Label = ['search', 'params', '[PAD]', '[PAD]']\n", + "11414\n", + "Label = ['vnic', 'id', '[PAD]', '[PAD]']\n", + "11415\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11416\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11417\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11418\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "11419\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "11420\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11421\n", + "Label = ['objs', 'service', '[PAD]', '[PAD]']\n", + "11422\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "11423\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11424\n", + "Label = ['vcpu', 'limit', '[PAD]', '[PAD]']\n", + "11425\n", + "Label = ['cl', 'limit', 'service', '[PAD]']\n", + "11426\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "11427\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11428\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "11429\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11430\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11431\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "11432\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11433\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "11434\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11435\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11436\n", + "Label = ['service', '[PAD]', '[PAD]', '[PAD]']\n", + "11437\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11438\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11439\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11440\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11441\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11442\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11443\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11444\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11445\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "11446\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "11447\n", + "Label = ['storage', 'domains', '[PAD]', '[PAD]']\n", + "11448\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11449\n", + "Label = ['clusters', '[PAD]', '[PAD]', '[PAD]']\n", + "11450\n", + "Label = ['Group', '[PAD]', '[PAD]', '[PAD]']\n", + "11451\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11452\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11453\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11454\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11455\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11456\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11457\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11458\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11459\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11460\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11461\n", + "Label = ['Property', '[PAD]', '[PAD]', '[PAD]']\n", + "11462\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11463\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11464\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11465\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11466\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11467\n", + "Label = ['mac', 'pool', '[PAD]', '[PAD]']\n", + "11468\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11469\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11470\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11471\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11472\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11473\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11474\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "11475\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "11476\n", + "Label = ['argument', 'spec', '[PAD]', '[PAD]']\n", + "11477\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "11478\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11479\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11480\n", + "Label = ['agent', '[PAD]', '[PAD]', '[PAD]']\n", + "11481\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11482\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "11483\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11484\n", + "Label = ['var', '[PAD]', '[PAD]', '[PAD]']\n", + "11485\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11486\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11487\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11488\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11489\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11490\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "11491\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "11492\n", + "Label = ['hostname', '[PAD]', '[PAD]', '[PAD]']\n", + "11493\n", + "Label = ['hostname', '[PAD]', '[PAD]', '[PAD]']\n", + "11494\n", + "Label = ['ip', 'addresses', '[PAD]', '[PAD]']\n", + "11495\n", + "Label = ['hn', '[PAD]', '[PAD]', '[PAD]']\n", + "11496\n", + "Label = ['reboot', '[PAD]', '[PAD]', '[PAD]']\n", + "11497\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "11498\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11499\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11500\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11501\n", + "Label = ['PyraxException', '[PAD]', '[PAD]', '[PAD]']\n", + "11502\n", + "Label = ['PyraxException', '[PAD]', '[PAD]', '[PAD]']\n", + "11503\n", + "Label = ['HAS', 'PYRAX', '[PAD]', '[PAD]']\n", + "11504\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11505\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "11506\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11507\n", + "Label = ['notification', 'plan', '[PAD]', '[PAD]']\n", + "11508\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "11509\n", + "Label = ['cont', 'obj', '[PAD]', '[PAD]']\n", + "11510\n", + "Label = ['download', 'object', '[PAD]', '[PAD]']\n", + "11511\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11512\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11513\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11514\n", + "Label = ['NoSuchContainer', '[PAD]', '[PAD]', '[PAD]']\n", + "11515\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11516\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "11517\n", + "Label = ['cont', 'web', 'index', '[PAD]']\n", + "11518\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11519\n", + "Label = ['notification', 'dict', '[PAD]', '[PAD]']\n", + "11520\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11521\n", + "Label = ['authenticated', '[PAD]', '[PAD]', '[PAD]']\n", + "11522\n", + "Label = ['attachments', '[PAD]', '[PAD]', '[PAD]']\n", + "11523\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11524\n", + "Label = ['queue', '[PAD]', '[PAD]', '[PAD]']\n", + "11525\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11526\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "11527\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11528\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "11529\n", + "Label = ['fileobj', '[PAD]', '[PAD]', '[PAD]']\n", + "11530\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "11531\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "11532\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11533\n", + "Label = ['arg', '[PAD]', '[PAD]', '[PAD]']\n", + "11534\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11535\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "11536\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "11537\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11538\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "11539\n", + "Label = ['names', '[PAD]', '[PAD]', '[PAD]']\n", + "11540\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11541\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11542\n", + "Label = ['cloudservers', '[PAD]', '[PAD]', '[PAD]']\n", + "11543\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11544\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11545\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11546\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "11547\n", + "Label = ['NotFound', '[PAD]', '[PAD]', '[PAD]']\n", + "11548\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11549\n", + "Label = ['keypair', '[PAD]', '[PAD]', '[PAD]']\n", + "11550\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11551\n", + "Label = ['new', 'dbs', '[PAD]', '[PAD]']\n", + "11552\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11553\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "11554\n", + "Label = ['retrieved', '[PAD]', '[PAD]', '[PAD]']\n", + "11555\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "11556\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11557\n", + "Label = ['HAS', 'PYRAX', '[PAD]', '[PAD]']\n", + "11558\n", + "Label = ['sg', '[PAD]', '[PAD]', '[PAD]']\n", + "11559\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "11560\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "11561\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11562\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11563\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11564\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11565\n", + "Label = ['ansible', 'facts', '[PAD]', '[PAD]']\n", + "11566\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "11567\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11568\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11569\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11570\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11571\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11572\n", + "Label = ['add', 'ssl', 'termination', '[PAD]']\n", + "11573\n", + "Label = ['delete', 'ssl', 'termination', '[PAD]']\n", + "11574\n", + "Label = ['PyraxException', '[PAD]', '[PAD]', '[PAD]']\n", + "11575\n", + "Label = ['new', 'ssl', '[PAD]', '[PAD]']\n", + "11576\n", + "Label = ['entity', '[PAD]', '[PAD]', '[PAD]']\n", + "11577\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11578\n", + "Label = ['details', '[PAD]', '[PAD]', '[PAD]']\n", + "11579\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11580\n", + "Label = ['session', 'id', '[PAD]', '[PAD]']\n", + "11581\n", + "Label = ['session', 'id', '[PAD]', '[PAD]']\n", + "11582\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11583\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11584\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11585\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11586\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11587\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "11588\n", + "Label = ['ENTRY', 'STATE', 'INFO', 'MAP']\n", + "11589\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11590\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11591\n", + "Label = ['xml', '[PAD]', '[PAD]', '[PAD]']\n", + "11592\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11593\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11594\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11595\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "11596\n", + "Label = ['proxmox', '[PAD]', '[PAD]', '[PAD]']\n", + "11597\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11598\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11599\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "11600\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11601\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11602\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11603\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11604\n", + "Label = ['taskid', '[PAD]', '[PAD]', '[PAD]']\n", + "11605\n", + "Label = ['taskid', '[PAD]', '[PAD]', '[PAD]']\n", + "11606\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11607\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11608\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11609\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11610\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11611\n", + "Label = ['vmid', '[PAD]', '[PAD]', '[PAD]']\n", + "11612\n", + "Label = ['vmid', '[PAD]', '[PAD]', '[PAD]']\n", + "11613\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11614\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11615\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11616\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11617\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11618\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11619\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11620\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "11621\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11622\n", + "Label = ['taskid', '[PAD]', '[PAD]', '[PAD]']\n", + "11623\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11624\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11625\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11626\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11627\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11628\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11629\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "11630\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11631\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "11632\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "11633\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11634\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11635\n", + "Label = ['proxmox', '[PAD]', '[PAD]', '[PAD]']\n", + "11636\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11637\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11638\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11639\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11640\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11641\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11642\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11643\n", + "Label = ['get', 'autostart', '[PAD]', '[PAD]']\n", + "11644\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11645\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11646\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11647\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11648\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11649\n", + "Label = ['nic', 'net1', '[PAD]', '[PAD]']\n", + "11650\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "11651\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "11652\n", + "Label = ['nd', '[PAD]', '[PAD]', '[PAD]']\n", + "11653\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11654\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11655\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11656\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11657\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11658\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11659\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11660\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "11661\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11662\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11663\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "11664\n", + "Label = ['api', 'password', '[PAD]', '[PAD]']\n", + "11665\n", + "Label = ['proxmox', '[PAD]', '[PAD]', '[PAD]']\n", + "11666\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11667\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11668\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "11669\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "11670\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "11671\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11672\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11673\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11674\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11675\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11676\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11677\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "11678\n", + "Label = ['tasks', '[PAD]', '[PAD]', '[PAD]']\n", + "11679\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11680\n", + "Label = ['session', '[PAD]', '[PAD]', '[PAD]']\n", + "11681\n", + "Label = ['session', '[PAD]', '[PAD]', '[PAD]']\n", + "11682\n", + "Label = ['newnic', '[PAD]', '[PAD]', '[PAD]']\n", + "11683\n", + "Label = ['guaranteed', '[PAD]', '[PAD]', '[PAD]']\n", + "11684\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11685\n", + "Label = ['tmpiface', '[PAD]', '[PAD]', '[PAD]']\n", + "11686\n", + "Label = ['HostNIC', '[PAD]', '[PAD]', '[PAD]']\n", + "11687\n", + "Label = ['tmpnetwork', '[PAD]', '[PAD]', '[PAD]']\n", + "11688\n", + "Label = ['IP', '[PAD]', '[PAD]', '[PAD]']\n", + "11689\n", + "Label = ['HOST', '[PAD]', '[PAD]', '[PAD]']\n", + "11690\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11691\n", + "Label = ['delete', '[PAD]', '[PAD]', '[PAD]']\n", + "11692\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11693\n", + "Label = ['boot', 'dev', '[PAD]', '[PAD]']\n", + "11694\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", + "11695\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11696\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11697\n", + "Label = ['setMemory', '[PAD]', '[PAD]', '[PAD]']\n", + "11698\n", + "Label = ['setCPUShare', '[PAD]', '[PAD]', '[PAD]']\n", + "11699\n", + "Label = ['setDisks', '[PAD]', '[PAD]', '[PAD]']\n", + "11700\n", + "Label = ['removeVM', '[PAD]', '[PAD]', '[PAD]']\n", + "11701\n", + "Label = ['rc', '[PAD]', '[PAD]', '[PAD]']\n", + "11702\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11703\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "11704\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11705\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11706\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "11707\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11708\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11709\n", + "Label = ['get', 'status2', '[PAD]', '[PAD]']\n", + "11710\n", + "Label = ['get', 'net', '[PAD]', '[PAD]']\n", + "11711\n", + "Label = ['get', 'net', '[PAD]', '[PAD]']\n", + "11712\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "11713\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11714\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "11715\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11716\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "11717\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11718\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "11719\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "11720\n", + "Label = ['container', '[PAD]', '[PAD]', '[PAD]']\n", + "11721\n", + "Label = ['old', 'password', '[PAD]', '[PAD]']\n", + "11722\n", + "Label = ['container', '[PAD]', '[PAD]', '[PAD]']\n", + "11723\n", + "Label = ['ou', '[PAD]', '[PAD]', '[PAD]']\n", + "11724\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11725\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11726\n", + "Label = ['cert', '[PAD]', '[PAD]', '[PAD]']\n", + "11727\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11728\n", + "Label = ['json', '[PAD]', '[PAD]', '[PAD]']\n", + "11729\n", + "Label = ['attach', 'detach', 'block', 'storage']\n", + "11730\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11731\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11732\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11733\n", + "Label = ['droplet', '[PAD]', '[PAD]', '[PAD]']\n", + "11734\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "11735\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11736\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11737\n", + "Label = ['found', '[PAD]', '[PAD]', '[PAD]']\n", + "11738\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "11739\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11740\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11741\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11742\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11743\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11744\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11745\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11746\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11747\n", + "Label = ['exit', 'json', '[PAD]', '[PAD]']\n", + "11748\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11749\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11750\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11751\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11752\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "11753\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11754\n", + "Label = ['at', 'record', '[PAD]', '[PAD]']\n", + "11755\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11756\n", + "Label = ['LXC', 'ANSIBLE', 'STATES', '[PAD]']\n", + "11757\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "11758\n", + "Label = ['communicate', '[PAD]', '[PAD]', '[PAD]']\n", + "11759\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11760\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "11761\n", + "Label = ['add', 'variables', '[PAD]', '[PAD]']\n", + "11762\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "11763\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "11764\n", + "Label = ['get', 'interfaces', '[PAD]', '[PAD]']\n", + "11765\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11766\n", + "Label = ['container', 'exists', '[PAD]', '[PAD]']\n", + "11767\n", + "Label = ['freeze', '[PAD]', '[PAD]', '[PAD]']\n", + "11768\n", + "Label = ['get', 'state', '[PAD]', '[PAD]']\n", + "11769\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11770\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11771\n", + "Label = ['lv', 'entry', '[PAD]', '[PAD]']\n", + "11772\n", + "Label = ['lv', 'info', '[PAD]', '[PAD]']\n", + "11773\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11774\n", + "Label = ['build', 'command', '[PAD]', '[PAD]']\n", + "11775\n", + "Label = ['realpath', '[PAD]', '[PAD]', '[PAD]']\n", + "11776\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11777\n", + "Label = ['failure', '[PAD]', '[PAD]', '[PAD]']\n", + "11778\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "11779\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11780\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "11781\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11782\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11783\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11784\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11785\n", + "Label = ['REQUESTS', 'FOUND', '[PAD]', '[PAD]']\n", + "11786\n", + "Label = ['servers', 'to', 'change', '[PAD]']\n", + "11787\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "11788\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11789\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11790\n", + "Label = ['server', 'params', '[PAD]', '[PAD]']\n", + "11791\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11792\n", + "Label = ['current', 'aa', 'policy', 'id']\n", + "11793\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11794\n", + "Label = ['Call', '[PAD]', '[PAD]', '[PAD]']\n", + "11795\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11796\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11797\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "11798\n", + "Label = ['Defaults', '[PAD]', '[PAD]', '[PAD]']\n", + "11799\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11800\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11801\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11802\n", + "Label = ['alert', 'policy', 'id', '[PAD]']\n", + "11803\n", + "Label = ['public', 'ips', '[PAD]', '[PAD]']\n", + "11804\n", + "Label = ['add', 'alert', 'policy', 'to']\n", + "11805\n", + "Label = ['Call', '[PAD]', '[PAD]', '[PAD]']\n", + "11806\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "11807\n", + "Label = ['alert', 'policy', 'id', '[PAD]']\n", + "11808\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "11809\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11810\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "11811\n", + "Label = ['aa', 'policy', '[PAD]', '[PAD]']\n", + "11812\n", + "Label = ['response', 'status', 'code', '[PAD]']\n", + "11813\n", + "Label = ['clc', '[PAD]', '[PAD]', '[PAD]']\n", + "11814\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11815\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11816\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11817\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11818\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11819\n", + "Label = ['t', 'email', 'list', '[PAD]']\n", + "11820\n", + "Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n", + "11821\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11822\n", + "Label = ['loadbalancerpool', 'exists', '[PAD]', '[PAD]']\n", + "11823\n", + "Label = ['remove', 'lbpool', 'nodes', '[PAD]']\n", + "11824\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11825\n", + "Label = ['Call', '[PAD]', '[PAD]', '[PAD]']\n", + "11826\n", + "Label = ['pool', 'list', '[PAD]', '[PAD]']\n", + "11827\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11828\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "11829\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11830\n", + "Label = ['clc', '[PAD]', '[PAD]', '[PAD]']\n", + "11831\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11832\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11833\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11834\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11835\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11836\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "11837\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11838\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11839\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "11840\n", + "Label = ['request', 'details', '[PAD]', '[PAD]']\n", + "11841\n", + "Label = ['agent', 'string', '[PAD]', '[PAD]']\n", + "11842\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11843\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11844\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "11845\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11846\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "11847\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11848\n", + "Label = ['request', 'details', '[PAD]', '[PAD]']\n", + "11849\n", + "Label = ['private', 'network', 'id', '[PAD]']\n", + "11850\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11851\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11852\n", + "Label = ['Hdd', '[PAD]', '[PAD]', '[PAD]']\n", + "11853\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "11854\n", + "Label = ['modify', 'server', 'status', '[PAD]']\n", + "11855\n", + "Label = ['oneandone', 'conn', '[PAD]', '[PAD]']\n", + "11856\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11857\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11858\n", + "Label = ['private', 'network', '[PAD]', '[PAD]']\n", + "11859\n", + "Label = ['OneAndOneService', '[PAD]', '[PAD]', '[PAD]']\n", + "11860\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11861\n", + "Label = ['firewall', 'policy', '[PAD]', '[PAD]']\n", + "11862\n", + "Label = ['rule', 'id', '[PAD]', '[PAD]']\n", + "11863\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11864\n", + "Label = ['oneandone', 'conn', '[PAD]', '[PAD]']\n", + "11865\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11866\n", + "Label = ['monitoring', 'policy', 'port', '[PAD]']\n", + "11867\n", + "Label = ['Process', '[PAD]', '[PAD]', '[PAD]']\n", + "11868\n", + "Label = ['monitoring', 'policy', '[PAD]', '[PAD]']\n", + "11869\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11870\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11871\n", + "Label = ['monitoring', 'policy', '[PAD]', '[PAD]']\n", + "11872\n", + "Label = ['server', 'id', '[PAD]', '[PAD]']\n", + "11873\n", + "Label = ['port', '[PAD]', '[PAD]', '[PAD]']\n", + "11874\n", + "Label = ['monitoring', 'policy', '[PAD]', '[PAD]']\n", + "11875\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11876\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11877\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11878\n", + "Label = ['attach', 'server', '[PAD]', '[PAD]']\n", + "11879\n", + "Label = ['check', 'mode', '[PAD]', '[PAD]']\n", + "11880\n", + "Label = ['load', 'balancer', 'rule', '[PAD]']\n", + "11881\n", + "Label = ['load', 'balancer', 'obj', '[PAD]']\n", + "11882\n", + "Label = ['load', 'balancer', '[PAD]', '[PAD]']\n", + "11883\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11884\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11885\n", + "Label = ['err', 'fmt', '[PAD]', '[PAD]']\n", + "11886\n", + "Label = ['exc', 'descr', '[PAD]', '[PAD]']\n", + "11887\n", + "Label = ['is', 'new', 'block', '[PAD]']\n", + "11888\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11889\n", + "Label = ['delete', 'event', 'handler', '[PAD]']\n", + "11890\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", + "11891\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "11892\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11893\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "11894\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11895\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11896\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11897\n", + "Label = ['message', 'list', '[PAD]', '[PAD]']\n", + "11898\n", + "Label = ['ack', 'ids', '[PAD]', '[PAD]']\n", + "11899\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11900\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11901\n", + "Label = ['req', '[PAD]', '[PAD]', '[PAD]']\n", + "11902\n", + "Label = ['setLabels', '[PAD]', '[PAD]', '[PAD]']\n", + "11903\n", + "Label = ['req', '[PAD]', '[PAD]', '[PAD]']\n", + "11904\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "11905\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11906\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11907\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "11908\n", + "Label = ['decoder', '[PAD]', '[PAD]', '[PAD]']\n", + "11909\n", + "Label = ['GCE', '[PAD]', '[PAD]', '[PAD]']\n", + "11910\n", + "Label = ['subnetname', '[PAD]', '[PAD]', '[PAD]']\n", + "11911\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "11912\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11913\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "11914\n", + "Label = ['pd', '[PAD]', '[PAD]', '[PAD]']\n", + "11915\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11916\n", + "Label = ['node', 'names', '[PAD]', '[PAD]']\n", + "11917\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11918\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "11919\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11920\n", + "Label = ['json', 'output', '[PAD]', '[PAD]']\n", + "11921\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "11922\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11923\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11924\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11925\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11926\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11927\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11928\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "11929\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "11930\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11931\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11932\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11933\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11934\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "11935\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11936\n", + "Label = ['decoder', '[PAD]', '[PAD]', '[PAD]']\n", + "11937\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11938\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11939\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11940\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11941\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11942\n", + "Label = ['module', 'instances', '[PAD]', '[PAD]']\n", + "11943\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11944\n", + "Label = ['decoder', '[PAD]', '[PAD]', '[PAD]']\n", + "11945\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11946\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11947\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11948\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "11949\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11950\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11951\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11952\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11953\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "11954\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "11955\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11956\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "11957\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11958\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "11959\n", + "Label = ['hc', '[PAD]', '[PAD]', '[PAD]']\n", + "11960\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11961\n", + "Label = ['hc', '[PAD]', '[PAD]', '[PAD]']\n", + "11962\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "11963\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11964\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11965\n", + "Label = ['json', 'data', '[PAD]', '[PAD]']\n", + "11966\n", + "Label = ['ex', 'get', 'instancetemplate', '[PAD]']\n", + "11967\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "11968\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "11969\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11970\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11971\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "11972\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "11973\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11974\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11975\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11976\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "11977\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11978\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11979\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11980\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11981\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "11982\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11983\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11984\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "11985\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11986\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "11987\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "11988\n", + "Label = ['forwarding', 'rule', '[PAD]', '[PAD]']\n", + "11989\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11990\n", + "Label = ['config', 'name', '[PAD]', '[PAD]']\n", + "11991\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "11992\n", + "Label = ['node', 'count', '[PAD]', '[PAD]']\n", + "11993\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "11994\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "11995\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11996\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "11997\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "11998\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "11999\n", + "Label = ['old', 'ttl', '[PAD]', '[PAD]']\n", + "12000\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12001\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12002\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12003\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12004\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "12005\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "12006\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12007\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "12008\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12009\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12010\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12011\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12012\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12013\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12014\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12015\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12016\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "12017\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12018\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12019\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12020\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12021\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "12022\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "12023\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "12024\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12025\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12026\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12027\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12028\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12029\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12030\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12031\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12032\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "12033\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12034\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12035\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12036\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12037\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12038\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "12039\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12040\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12041\n", + "Label = ['raise', 'for', 'status', '[PAD]']\n", + "12042\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12043\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12044\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12045\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12046\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12047\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12048\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "12049\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12050\n", + "Label = ['raise', 'for', 'status', '[PAD]']\n", + "12051\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12052\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12053\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12054\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12055\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12056\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12057\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12058\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12059\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12060\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12061\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12062\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12063\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12064\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12065\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12066\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12067\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12068\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12069\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12070\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12071\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12072\n", + "Label = ['as', 'policy', 'valid', '[PAD]']\n", + "12073\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "12074\n", + "Label = ['instance', 'group', '[PAD]', '[PAD]']\n", + "12075\n", + "Label = ['set', 'named', 'ports', '[PAD]']\n", + "12076\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12077\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12078\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12079\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12080\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12081\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12082\n", + "Label = ['pr', 'fields', '[PAD]', '[PAD]']\n", + "12083\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "12084\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12085\n", + "Label = ['changed', '[PAD]', '[PAD]', '[PAD]']\n", + "12086\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "12087\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12088\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12089\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12090\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12091\n", + "Label = ['patch', '[PAD]', '[PAD]', '[PAD]']\n", + "12092\n", + "Label = ['patch', '[PAD]', '[PAD]', '[PAD]']\n", + "12093\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12094\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12095\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12096\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12097\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12098\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12099\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "12100\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12101\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12102\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "12103\n", + "Label = ['bes', '[PAD]', '[PAD]', '[PAD]']\n", + "12104\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12105\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12106\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12107\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12108\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12109\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12110\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "12111\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12112\n", + "Label = ['original', '[PAD]', '[PAD]', '[PAD]']\n", + "12113\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12114\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12115\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12116\n", + "Label = ['original', 'record', '[PAD]', '[PAD]']\n", + "12117\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "12118\n", + "Label = ['isoformat', '[PAD]', '[PAD]', '[PAD]']\n", + "12119\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12120\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12121\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12122\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12123\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12124\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12125\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "12126\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "12127\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "12128\n", + "Label = ['zone', '[PAD]', '[PAD]', '[PAD]']\n", + "12129\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "12130\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", + "12131\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12132\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12133\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12134\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12135\n", + "Label = ['fail', 'json', '[PAD]', '[PAD]']\n", + "12136\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "12137\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "12138\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "12139\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12140\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12141\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12142\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12143\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12144\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12145\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12146\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12147\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12148\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12149\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12150\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12151\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12152\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12153\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12154\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "12155\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12156\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12157\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12158\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12159\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "12160\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12161\n", + "Label = ['raise', 'for', 'status', '[PAD]']\n", + "12162\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12163\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12164\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12165\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12166\n", + "Label = ['fetch', '[PAD]', '[PAD]', '[PAD]']\n", + "12167\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "12168\n", + "Label = ['decoder', '[PAD]', '[PAD]', '[PAD]']\n", + "12169\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12170\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12171\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12172\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12173\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12174\n", + "Label = ['from', 'response', '[PAD]', '[PAD]']\n", + "12175\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12176\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12177\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12178\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12179\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12180\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12181\n", + "Label = ['to', 'request', '[PAD]', '[PAD]']\n", + "12182\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12183\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12184\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12185\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12186\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12187\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12188\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12189\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12190\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12191\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12192\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "12193\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12194\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12195\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "12196\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12197\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12198\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12199\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12200\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "12201\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12202\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "12203\n", + "Label = ['jar', '[PAD]', '[PAD]', '[PAD]']\n", + "12204\n", + "Label = ['err', '[PAD]', '[PAD]', '[PAD]']\n", + "12205\n", + "Label = ['expires', '[PAD]', '[PAD]', '[PAD]']\n", + "12206\n", + "Label = ['expires', '[PAD]', '[PAD]', '[PAD]']\n", + "12207\n", + "Label = ['expires', '[PAD]', '[PAD]', '[PAD]']\n", + "12208\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12209\n", + "Label = ['preferred', 'clock', '[PAD]', '[PAD]']\n", + "12210\n", + "Label = ['none', 'keys', '[PAD]', '[PAD]']\n", + "12211\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "12212\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "12213\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "12214\n", + "Label = ['CookieJar', '[PAD]', '[PAD]', '[PAD]']\n", + "12215\n", + "Label = ['merged', 'cookies', '[PAD]', '[PAD]']\n", + "12216\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12217\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "12218\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12219\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "12220\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "12221\n", + "Label = ['base', '[PAD]', '[PAD]', '[PAD]']\n", + "12222\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12223\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "12224\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "12225\n", + "Label = ['fdata', '[PAD]', '[PAD]', '[PAD]']\n", + "12226\n", + "Label = ['rf', '[PAD]', '[PAD]', '[PAD]']\n", + "12227\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "12228\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12229\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "12230\n", + "Label = ['header', '[PAD]', '[PAD]', '[PAD]']\n", + "12231\n", + "Label = ['body', 'position', '[PAD]', '[PAD]']\n", + "12232\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12233\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "12234\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12235\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12236\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "12237\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "12238\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "12239\n", + "Label = ['encoding', '[PAD]', '[PAD]', '[PAD]']\n", + "12240\n", + "Label = ['reason', '[PAD]', '[PAD]', '[PAD]']\n", + "12241\n", + "Label = ['http', 'error', 'msg', '[PAD]']\n", + "12242\n", + "Label = ['implementation', 'version', '[PAD]', '[PAD]']\n", + "12243\n", + "Label = ['platform', 'info', '[PAD]', '[PAD]']\n", + "12244\n", + "Label = ['loc', '[PAD]', '[PAD]', '[PAD]']\n", + "12245\n", + "Label = ['bits', '[PAD]', '[PAD]', '[PAD]']\n", + "12246\n", + "Label = ['environ', '[PAD]', '[PAD]', '[PAD]']\n", + "12247\n", + "Label = ['no', 'proxy', '[PAD]', '[PAD]']\n", + "12248\n", + "Label = ['proxy', 'keys', '[PAD]', '[PAD]']\n", + "12249\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12250\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "12251\n", + "Label = ['header', '[PAD]', '[PAD]', '[PAD]']\n", + "12252\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "12253\n", + "Label = ['add', 'headers', '[PAD]', '[PAD]']\n", + "12254\n", + "Label = ['resp', '[PAD]', '[PAD]', '[PAD]']\n", + "12255\n", + "Label = ['reason', '[PAD]', '[PAD]', '[PAD]']\n", + "12256\n", + "Label = ['reason', '[PAD]', '[PAD]', '[PAD]']\n", + "12257\n", + "Label = ['DoesNotExist', '[PAD]', '[PAD]', '[PAD]']\n", + "12258\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12259\n", + "Label = ['SESSION', 'COOKIE', 'AGE', '[PAD]']\n", + "12260\n", + "Label = ['DATETIME', 'INPUT', 'FORMATS', '[PAD]']\n", + "12261\n", + "Label = ['DATETIME', 'INPUT', 'FORMATS', '[PAD]']\n", + "12262\n", + "Label = ['children', '[PAD]', '[PAD]', '[PAD]']\n", + "12263\n", + "Label = ['attributes', '[PAD]', '[PAD]', '[PAD]']\n", + "12264\n", + "Label = ['children', '[PAD]', '[PAD]', '[PAD]']\n", + "12265\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12266\n", + "Label = ['environ', '[PAD]', '[PAD]', '[PAD]']\n", + "12267\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "12268\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "12269\n", + "Label = ['cache', 'clear', '[PAD]', '[PAD]']\n", + "12270\n", + "Label = ['errors', '[PAD]', '[PAD]', '[PAD]']\n", + "12271\n", + "Label = ['seek', '[PAD]', '[PAD]', '[PAD]']\n", + "12272\n", + "Label = ['failureException', '[PAD]', '[PAD]', '[PAD]']\n", + "12273\n", + "Label = ['writeln', '[PAD]', '[PAD]', '[PAD]']\n", + "12274\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12275\n", + "Label = ['pool', '[PAD]', '[PAD]', '[PAD]']\n", + "12276\n", + "Label = ['failures', '[PAD]', '[PAD]', '[PAD]']\n", + "12277\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "12278\n", + "Label = ['tests', '[PAD]', '[PAD]', '[PAD]']\n", + "12279\n", + "Label = ['test', '[PAD]', '[PAD]', '[PAD]']\n", + "12280\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "12281\n", + "Label = ['dom', '[PAD]', '[PAD]', '[PAD]']\n", + "12282\n", + "Label = ['assertEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "12283\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12284\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "12285\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12286\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12287\n", + "Label = ['assertTrue', '[PAD]', '[PAD]', '[PAD]']\n", + "12288\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "12289\n", + "Label = ['assertTrue', '[PAD]', '[PAD]', '[PAD]']\n", + "12290\n", + "Label = ['assertEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "12291\n", + "Label = ['assertTrue', '[PAD]', '[PAD]', '[PAD]']\n", + "12292\n", + "Label = ['assertFalse', '[PAD]', '[PAD]', '[PAD]']\n", + "12293\n", + "Label = ['assertTrue', '[PAD]', '[PAD]', '[PAD]']\n", + "12294\n", + "Label = ['assertFalse', '[PAD]', '[PAD]', '[PAD]']\n", + "12295\n", + "Label = ['assertTrue', '[PAD]', '[PAD]', '[PAD]']\n", + "12296\n", + "Label = ['context', 'mgr', 'template', '[PAD]']\n", + "12297\n", + "Label = ['assertRaises', '[PAD]', '[PAD]', '[PAD]']\n", + "12298\n", + "Label = ['expected', 'data', '[PAD]', '[PAD]']\n", + "12299\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "12300\n", + "Label = ['standardMsg', '[PAD]', '[PAD]', '[PAD]']\n", + "12301\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "12302\n", + "Label = ['settings', 'dict', '[PAD]', '[PAD]']\n", + "12303\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "12304\n", + "Label = ['fixtures', '[PAD]', '[PAD]', '[PAD]']\n", + "12305\n", + "Label = ['should', 'reload', 'connections', '[PAD]']\n", + "12306\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12307\n", + "Label = ['setUpTestData', '[PAD]', '[PAD]', '[PAD]']\n", + "12308\n", + "Label = ['db', 'name', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12309\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12310\n", + "Label = ['features', '[PAD]', '[PAD]', '[PAD]']\n", + "12311\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12312\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "12313\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "12314\n", + "Label = ['conn', '[PAD]', '[PAD]', '[PAD]']\n", + "12315\n", + "Label = ['vendor', '[PAD]', '[PAD]', '[PAD]']\n", + "12316\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "12317\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12318\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "12319\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "12320\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "12321\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12322\n", + "Label = ['generic', '[PAD]', '[PAD]', '[PAD]']\n", + "12323\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12324\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12325\n", + "Label = ['resolver', 'match', '[PAD]', '[PAD]']\n", + "12326\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "12327\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "12328\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12329\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "12330\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", + "12331\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12332\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12333\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12334\n", + "Label = ['sig', '[PAD]', '[PAD]', '[PAD]']\n", + "12335\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12336\n", + "Label = ['test', 'module', '[PAD]', '[PAD]']\n", + "12337\n", + "Label = ['test', 'runner', '[PAD]', '[PAD]']\n", + "12338\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12339\n", + "Label = ['modified', 'settings', '[PAD]', '[PAD]']\n", + "12340\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12341\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12342\n", + "Label = ['want', '[PAD]', '[PAD]', '[PAD]']\n", + "12343\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "12344\n", + "Label = ['use', 'caching', '[PAD]', '[PAD]']\n", + "12345\n", + "Label = ['use', 'caching', '[PAD]', '[PAD]']\n", + "12346\n", + "Label = ['chunk', 'size', '[PAD]', '[PAD]']\n", + "12347\n", + "Label = ['transfer', 'encoding', '[PAD]', '[PAD]']\n", + "12348\n", + "Label = ['DATA', 'UPLOAD', 'MAX', 'NUMBER']\n", + "12349\n", + "Label = ['DATA', 'UPLOAD', 'MAX', 'NUMBER']\n", + "12350\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "12351\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12352\n", + "Label = ['handler', '[PAD]', '[PAD]', '[PAD]']\n", + "12353\n", + "Label = ['done', '[PAD]', '[PAD]', '[PAD]']\n", + "12354\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12355\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "12356\n", + "Label = ['encoding', '[PAD]', '[PAD]', '[PAD]']\n", + "12357\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "12358\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12359\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12360\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12361\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12362\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12363\n", + "Label = ['post', '[PAD]', '[PAD]', '[PAD]']\n", + "12364\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12365\n", + "Label = ['list', '[PAD]', '[PAD]', '[PAD]']\n", + "12366\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "12367\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "12368\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12369\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "12370\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12371\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12372\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12373\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "12374\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "12375\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12376\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "12377\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "12378\n", + "Label = ['rollback', '[PAD]', '[PAD]', '[PAD]']\n", + "12379\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12380\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12381\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "12382\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "12383\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "12384\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12385\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "12386\n", + "Label = ['parent', 'objs', '[PAD]', '[PAD]']\n", + "12387\n", + "Label = ['on', 'delete', '[PAD]', '[PAD]']\n", + "12388\n", + "Label = ['sub', 'objs', '[PAD]', '[PAD]']\n", + "12389\n", + "Label = ['filter', '[PAD]', '[PAD]', '[PAD]']\n", + "12390\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12391\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "12392\n", + "Label = ['can', 'fast', 'delete', '[PAD]']\n", + "12393\n", + "Label = ['auto', 'created', '[PAD]', '[PAD]']\n", + "12394\n", + "Label = ['instances', 'for', 'fieldvalues', '[PAD]']\n", + "12395\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "12396\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12397\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12398\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "12399\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "12400\n", + "Label = ['select', '[PAD]', '[PAD]', '[PAD]']\n", + "12401\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "12402\n", + "Label = ['results', 'iter', '[PAD]', '[PAD]']\n", + "12403\n", + "Label = ['names', '[PAD]', '[PAD]', '[PAD]']\n", + "12404\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "12405\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "12406\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", + "12407\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12408\n", + "Label = ['validate', 'values', 'are', 'expressions']\n", + "12409\n", + "Label = ['can', 'filter', '[PAD]', '[PAD]']\n", + "12410\n", + "Label = ['result', 'cache', '[PAD]', '[PAD]']\n", + "12411\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "12412\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12413\n", + "Label = ['pks', '[PAD]', '[PAD]', '[PAD]']\n", + "12414\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12415\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "12416\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "12417\n", + "Label = ['fset', '[PAD]', '[PAD]', '[PAD]']\n", + "12418\n", + "Label = ['fset', '[PAD]', '[PAD]', '[PAD]']\n", + "12419\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", + "12420\n", + "Label = ['order', 'by', '[PAD]', '[PAD]']\n", + "12421\n", + "Label = ['ordered', '[PAD]', '[PAD]', '[PAD]']\n", + "12422\n", + "Label = ['explain', '[PAD]', '[PAD]', '[PAD]']\n", + "12423\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12424\n", + "Label = ['filter', '[PAD]', '[PAD]', '[PAD]']\n", + "12425\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12426\n", + "Label = ['combined', 'queries', '[PAD]', '[PAD]']\n", + "12427\n", + "Label = ['qs', '[PAD]', '[PAD]', '[PAD]']\n", + "12428\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12429\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12430\n", + "Label = ['bulk', 'batch', 'size', '[PAD]']\n", + "12431\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12432\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12433\n", + "Label = ['is', 'empty', '[PAD]', '[PAD]']\n", + "12434\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "12435\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12436\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12437\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12438\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "12439\n", + "Label = ['get', 'col', '[PAD]', '[PAD]']\n", + "12440\n", + "Label = ['query', '[PAD]', '[PAD]', '[PAD]']\n", + "12441\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12442\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "12443\n", + "Label = ['iterable', 'class', '[PAD]', '[PAD]']\n", + "12444\n", + "Label = ['lookup', '[PAD]', '[PAD]', '[PAD]']\n", + "12445\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12446\n", + "Label = ['new', 'obj', '[PAD]', '[PAD]']\n", + "12447\n", + "Label = ['copy', '[PAD]', '[PAD]', '[PAD]']\n", + "12448\n", + "Label = ['fields', 'cache', '[PAD]', '[PAD]']\n", + "12449\n", + "Label = ['attname', 'indexes', '[PAD]', '[PAD]']\n", + "12450\n", + "Label = ['pk', 'idx', '[PAD]', '[PAD]']\n", + "12451\n", + "Label = ['klass', 'info', '[PAD]', '[PAD]']\n", + "12452\n", + "Label = ['lhs', '[PAD]', '[PAD]', '[PAD]']\n", + "12453\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12454\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "12455\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12456\n", + "Label = ['rhs', '[PAD]', '[PAD]', '[PAD]']\n", + "12457\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12458\n", + "Label = ['placeholder', '[PAD]', '[PAD]', '[PAD]']\n", + "12459\n", + "Label = ['pattern', '[PAD]', '[PAD]', '[PAD]']\n", + "12460\n", + "Label = ['rhs', 'is', 'direct', 'value']\n", + "12461\n", + "Label = ['param', 'pattern', '[PAD]', '[PAD]']\n", + "12462\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12463\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12464\n", + "Label = ['compiler', '[PAD]', '[PAD]', '[PAD]']\n", + "12465\n", + "Label = ['FORWARD', 'PROPERTIES', '[PAD]', '[PAD]']\n", + "12466\n", + "Label = ['ordering', 'clash', '[PAD]', '[PAD]']\n", + "12467\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12468\n", + "Label = ['order', 'with', 'respect', 'to']\n", + "12469\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12470\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12471\n", + "Label = ['features', '[PAD]', '[PAD]', '[PAD]']\n", + "12472\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12473\n", + "Label = ['is', 'relation', '[PAD]', '[PAD]']\n", + "12474\n", + "Label = ['is', 'relation', '[PAD]', '[PAD]']\n", + "12475\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12476\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12477\n", + "Label = ['parents', '[PAD]', '[PAD]', '[PAD]']\n", + "12478\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12479\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "12480\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12481\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12482\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12483\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12484\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12485\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12486\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12487\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "12488\n", + "Label = ['Field', '[PAD]', '[PAD]', '[PAD]']\n", + "12489\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12490\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12491\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12492\n", + "Label = ['setdefault', '[PAD]', '[PAD]', '[PAD]']\n", + "12493\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12494\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12495\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12496\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12497\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12498\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12499\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "12500\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12501\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12502\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "12503\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12504\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12505\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12506\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12507\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12508\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "12509\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12510\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12511\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "12512\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "12513\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "12514\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12515\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "12516\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12517\n", + "Label = ['compile', '[PAD]', '[PAD]', '[PAD]']\n", + "12518\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12519\n", + "Label = ['lazy', 'method', '[PAD]', '[PAD]']\n", + "12520\n", + "Label = ['post', 'migrate', '[PAD]', '[PAD]']\n", + "12521\n", + "Label = ['class', 'lookups', '[PAD]', '[PAD]']\n", + "12522\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "12523\n", + "Label = ['opts', '[PAD]', '[PAD]', '[PAD]']\n", + "12524\n", + "Label = ['meta', '[PAD]', '[PAD]', '[PAD]']\n", + "12525\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12526\n", + "Label = ['create', 'index', 'sql', '[PAD]']\n", + "12527\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12528\n", + "Label = ['column', 'name', '[PAD]', '[PAD]']\n", + "12529\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12530\n", + "Label = ['suffix', '[PAD]', '[PAD]', '[PAD]']\n", + "12531\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12532\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12533\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12534\n", + "Label = ['add', 'to', 'class', '[PAD]']\n", + "12535\n", + "Label = ['ordering', '[PAD]', '[PAD]', '[PAD]']\n", + "12536\n", + "Label = ['related', '[PAD]', '[PAD]', '[PAD]']\n", + "12537\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12538\n", + "Label = ['abstract', '[PAD]', '[PAD]', '[PAD]']\n", + "12539\n", + "Label = ['indexes', '[PAD]', '[PAD]', '[PAD]']\n", + "12540\n", + "Label = ['remote', 'field', '[PAD]', '[PAD]']\n", + "12541\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12542\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12543\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12544\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12545\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12546\n", + "Label = ['attname', '[PAD]', '[PAD]', '[PAD]']\n", + "12547\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12548\n", + "Label = ['primary', 'key', '[PAD]', '[PAD]']\n", + "12549\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12550\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12551\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12552\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "12553\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12554\n", + "Label = ['filter', '[PAD]', '[PAD]', '[PAD]']\n", + "12555\n", + "Label = ['unique', '[PAD]', '[PAD]', '[PAD]']\n", + "12556\n", + "Label = ['adding', '[PAD]', '[PAD]', '[PAD]']\n", + "12557\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12558\n", + "Label = ['check', 'swappable', '[PAD]', '[PAD]']\n", + "12559\n", + "Label = ['errors', '[PAD]', '[PAD]', '[PAD]']\n", + "12560\n", + "Label = ['clash', 'errors', '[PAD]', '[PAD]']\n", + "12561\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12562\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12563\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12564\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12565\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12566\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "12567\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "12568\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "12569\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "12570\n", + "Label = ['order', 'with', 'respect', 'to']\n", + "12571\n", + "Label = ['auto', 'created', '[PAD]', '[PAD]']\n", + "12572\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12573\n", + "Label = ['allowed', 'len', '[PAD]', '[PAD]']\n", + "12574\n", + "Label = ['db', 'column', '[PAD]', '[PAD]']\n", + "12575\n", + "Label = ['values', 'list', '[PAD]', '[PAD]']\n", + "12576\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12577\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "12578\n", + "Label = ['expr', '[PAD]', '[PAD]', '[PAD]']\n", + "12579\n", + "Label = ['expressions', '[PAD]', '[PAD]', '[PAD]']\n", + "12580\n", + "Label = ['expr', '[PAD]', '[PAD]', '[PAD]']\n", + "12581\n", + "Label = ['select', '[PAD]', '[PAD]', '[PAD]']\n", + "12582\n", + "Label = ['annotation', 'select', '[PAD]', '[PAD]']\n", + "12583\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12584\n", + "Label = ['quote', 'name', 'unless', 'alias']\n", + "12585\n", + "Label = ['extra', '[PAD]', '[PAD]', '[PAD]']\n", + "12586\n", + "Label = ['set', 'source', 'expressions', '[PAD]']\n", + "12587\n", + "Label = ['query', '[PAD]', '[PAD]', '[PAD]']\n", + "12588\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12589\n", + "Label = ['having', '[PAD]', '[PAD]', '[PAD]']\n", + "12590\n", + "Label = ['distinct', '[PAD]', '[PAD]', '[PAD]']\n", + "12591\n", + "Label = ['s', 'sql', '[PAD]', '[PAD]']\n", + "12592\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "12593\n", + "Label = ['has', 'select', 'for', 'update']\n", + "12594\n", + "Label = ['has', 'select', 'for', 'update']\n", + "12595\n", + "Label = ['explain', 'query', 'prefix', '[PAD]']\n", + "12596\n", + "Label = ['targets', '[PAD]', '[PAD]', '[PAD]']\n", + "12597\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "12598\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "12599\n", + "Label = ['alias', 'map', '[PAD]', '[PAD]']\n", + "12600\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "12601\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12602\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12603\n", + "Label = ['klass', 'info', '[PAD]', '[PAD]']\n", + "12604\n", + "Label = ['col', '[PAD]', '[PAD]', '[PAD]']\n", + "12605\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12606\n", + "Label = ['from', 'parent', '[PAD]', '[PAD]']\n", + "12607\n", + "Label = ['col', '[PAD]', '[PAD]', '[PAD]']\n", + "12608\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12609\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12610\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "12611\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "12612\n", + "Label = ['empty', 'fetchmany', 'value', '[PAD]']\n", + "12613\n", + "Label = ['insert', 'statement', '[PAD]', '[PAD]']\n", + "12614\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "12615\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12616\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12617\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12618\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12619\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "12620\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12621\n", + "Label = ['join', 'type', '[PAD]', '[PAD]']\n", + "12622\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "12623\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12624\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12625\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "12626\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "12627\n", + "Label = ['add', 'fields', '[PAD]', '[PAD]']\n", + "12628\n", + "Label = ['extra', '[PAD]', '[PAD]', '[PAD]']\n", + "12629\n", + "Label = ['relabeled', 'clone', '[PAD]', '[PAD]']\n", + "12630\n", + "Label = ['pos', '[PAD]', '[PAD]', '[PAD]']\n", + "12631\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", + "12632\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", + "12633\n", + "Label = ['table', 'alias', '[PAD]', '[PAD]']\n", + "12634\n", + "Label = ['table', 'alias', '[PAD]', '[PAD]']\n", + "12635\n", + "Label = ['annotations', '[PAD]', '[PAD]', '[PAD]']\n", + "12636\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12637\n", + "Label = ['is', 'relation', '[PAD]', '[PAD]']\n", + "12638\n", + "Label = ['rhs', '[PAD]', '[PAD]', '[PAD]']\n", + "12639\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12640\n", + "Label = ['add', 'q', '[PAD]', '[PAD]']\n", + "12641\n", + "Label = ['child', 'clause', '[PAD]', '[PAD]']\n", + "12642\n", + "Label = ['annotation', 'select', '[PAD]', '[PAD]']\n", + "12643\n", + "Label = ['annotation', 'select', '[PAD]', '[PAD]']\n", + "12644\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12645\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12646\n", + "Label = ['inner', 'pos', '[PAD]', '[PAD]']\n", + "12647\n", + "Label = ['targets', '[PAD]', '[PAD]', '[PAD]']\n", + "12648\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12649\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12650\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "12651\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12652\n", + "Label = ['attname', '[PAD]', '[PAD]', '[PAD]']\n", + "12653\n", + "Label = ['annotation', 'select', 'mask', '[PAD]']\n", + "12654\n", + "Label = ['add', 'fields', '[PAD]', '[PAD]']\n", + "12655\n", + "Label = ['negated', '[PAD]', '[PAD]', '[PAD]']\n", + "12656\n", + "Label = ['effective', 'connector', '[PAD]', '[PAD]']\n", + "12657\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12658\n", + "Label = ['negated', '[PAD]', '[PAD]', '[PAD]']\n", + "12659\n", + "Label = ['pos', '[PAD]', '[PAD]', '[PAD]']\n", + "12660\n", + "Label = ['clone', '[PAD]', '[PAD]', '[PAD]']\n", + "12661\n", + "Label = ['child', '[PAD]', '[PAD]', '[PAD]']\n", + "12662\n", + "Label = ['contains', 'aggregate', '[PAD]', '[PAD]']\n", + "12663\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12664\n", + "Label = ['ORDER', 'DIR', '[PAD]', '[PAD]']\n", + "12665\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "12666\n", + "Label = ['alias', 'map', '[PAD]', '[PAD]']\n", + "12667\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12668\n", + "Label = ['add', 'q', '[PAD]', '[PAD]']\n", + "12669\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12670\n", + "Label = ['compiler', '[PAD]', '[PAD]', '[PAD]']\n", + "12671\n", + "Label = ['USE', 'TZ', '[PAD]', '[PAD]']\n", + "12672\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12673\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12674\n", + "Label = ['as', 'sql', '[PAD]', '[PAD]']\n", + "12675\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12676\n", + "Label = ['source', 'expressions', '[PAD]', '[PAD]']\n", + "12677\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12678\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12679\n", + "Label = ['as', 'sqlite', '[PAD]', '[PAD]']\n", + "12680\n", + "Label = ['output', 'field', '[PAD]', '[PAD]']\n", + "12681\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12682\n", + "Label = ['ops', '[PAD]', '[PAD]', '[PAD]']\n", + "12683\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "12684\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "12685\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12686\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "12687\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12688\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12689\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12690\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12691\n", + "Label = ['raw', 'value', '[PAD]', '[PAD]']\n", + "12692\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "12693\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12694\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "12695\n", + "Label = ['queryset', '[PAD]', '[PAD]', '[PAD]']\n", + "12696\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "12697\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12698\n", + "Label = ['related', 'model', '[PAD]', '[PAD]']\n", + "12699\n", + "Label = ['reverse', '[PAD]', '[PAD]', '[PAD]']\n", + "12700\n", + "Label = ['lh', 'field', '[PAD]', '[PAD]']\n", + "12701\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12702\n", + "Label = ['column', '[PAD]', '[PAD]', '[PAD]']\n", + "12703\n", + "Label = ['attname', '[PAD]', '[PAD]', '[PAD]']\n", + "12704\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12705\n", + "Label = ['atomic', '[PAD]', '[PAD]', '[PAD]']\n", + "12706\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12707\n", + "Label = ['values', 'list', '[PAD]', '[PAD]']\n", + "12708\n", + "Label = ['db', '[PAD]', '[PAD]', '[PAD]']\n", + "12709\n", + "Label = ['get', 'or', 'create', '[PAD]']\n", + "12710\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "12711\n", + "Label = ['allow', 'relation', '[PAD]', '[PAD]']\n", + "12712\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", + "12713\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", + "12714\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12715\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "12716\n", + "Label = ['lazy', 'model', 'operation', '[PAD]']\n", + "12717\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12718\n", + "Label = ['model', 'name', '[PAD]', '[PAD]']\n", + "12719\n", + "Label = ['swapped', '[PAD]', '[PAD]', '[PAD]']\n", + "12720\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12721\n", + "Label = ['potential', 'clashes', '[PAD]', '[PAD]']\n", + "12722\n", + "Label = ['related', 'query', 'name', '[PAD]']\n", + "12723\n", + "Label = ['related', 'query', 'name', '[PAD]']\n", + "12724\n", + "Label = ['limit', 'choices', 'to', '[PAD]']\n", + "12725\n", + "Label = ['remote', 'field', '[PAD]', '[PAD]']\n", + "12726\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12727\n", + "Label = ['target', 'fields', '[PAD]', '[PAD]']\n", + "12728\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12729\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12730\n", + "Label = ['setting', 'name', '[PAD]', '[PAD]']\n", + "12731\n", + "Label = ['from', 'fields', '[PAD]', '[PAD]']\n", + "12732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12733\n", + "Label = ['foreign', 'related', 'fields', '[PAD]']\n", + "12734\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12735\n", + "Label = ['pk', '[PAD]', '[PAD]', '[PAD]']\n", + "12736\n", + "Label = ['pk', '[PAD]', '[PAD]', '[PAD]']\n", + "12737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12738\n", + "Label = ['ForeignKey', '[PAD]', '[PAD]', '[PAD]']\n", + "12739\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12740\n", + "Label = ['has', nan, 'arg', '[PAD]']\n", + "12741\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12742\n", + "Label = ['to', 'model', 'name', '[PAD]']\n", + "12743\n", + "Label = ['remote', 'field', '[PAD]', '[PAD]']\n", + "12744\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "12745\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12746\n", + "Label = ['through', '[PAD]', '[PAD]', '[PAD]']\n", + "12747\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "12748\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "12749\n", + "Label = ['auto', 'created', '[PAD]', '[PAD]']\n", + "12750\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12751\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12752\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12753\n", + "Label = ['is', 'relation', '[PAD]', '[PAD]']\n", + "12754\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12755\n", + "Label = ['related', 'name', '[PAD]', '[PAD]']\n", + "12756\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12757\n", + "Label = ['get', 'accessor', 'name', '[PAD]']\n", + "12758\n", + "Label = ['get', 'accessor', 'name', '[PAD]']\n", + "12759\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12760\n", + "Label = ['defaults', '[PAD]', '[PAD]', '[PAD]']\n", + "12761\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12762\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "12763\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "12764\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12765\n", + "Label = ['update', 'dimension', 'fields', '[PAD]']\n", + "12766\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12767\n", + "Label = ['height', 'field', '[PAD]', '[PAD]']\n", + "12768\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12769\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "12770\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12771\n", + "Label = ['target', 'fields', '[PAD]', '[PAD]']\n", + "12772\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "12773\n", + "Label = ['rhs', '[PAD]', '[PAD]', '[PAD]']\n", + "12774\n", + "Label = ['target', 'fields', '[PAD]', '[PAD]']\n", + "12775\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12776\n", + "Label = ['close', '[PAD]', '[PAD]', '[PAD]']\n", + "12777\n", + "Label = ['execute', 'with', 'wrappers', '[PAD]']\n", + "12778\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12779\n", + "Label = ['times', '[PAD]', '[PAD]', '[PAD]']\n", + "12780\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12781\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12782\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12783\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12784\n", + "Label = ['references', 'table', '[PAD]', '[PAD]']\n", + "12785\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12786\n", + "Label = ['references', 'column', '[PAD]', '[PAD]']\n", + "12787\n", + "Label = ['default', '[PAD]', '[PAD]', '[PAD]']\n", + "12788\n", + "Label = ['row', '[PAD]', '[PAD]', '[PAD]']\n", + "12789\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12790\n", + "Label = ['data', 'types', '[PAD]', '[PAD]']\n", + "12791\n", + "Label = ['get', 'autocommit', '[PAD]', '[PAD]']\n", + "12792\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "12793\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12794\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12795\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "12796\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "12797\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12798\n", + "Label = ['get', 'database', 'create', 'suffix']\n", + "12799\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "12800\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12801\n", + "Label = ['create', 'index', 'sql', '[PAD]']\n", + "12802\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12803\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12804\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12805\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12806\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12807\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "12808\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "12809\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12810\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12811\n", + "Label = ['SQL', 'KEYWORD', '[PAD]', '[PAD]']\n", + "12812\n", + "Label = ['SQL', 'KEYWORD', '[PAD]', '[PAD]']\n", + "12813\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12814\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "12815\n", + "Label = ['is', 'identity', 'column', '[PAD]']\n", + "12816\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "12817\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "12818\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12819\n", + "Label = ['nn', '[PAD]', '[PAD]', '[PAD]']\n", + "12820\n", + "Label = ['year', '[PAD]', '[PAD]', '[PAD]']\n", + "12821\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12822\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12823\n", + "Label = ['NUMBER', '[PAD]', '[PAD]', '[PAD]']\n", + "12824\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12825\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "12826\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "12827\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12828\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "12829\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "12830\n", + "Label = ['Timestamp', '[PAD]', '[PAD]', '[PAD]']\n", + "12831\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12832\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12833\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "12834\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "12835\n", + "Label = ['SQL', 'KEYWORD', '[PAD]', '[PAD]']\n", + "12836\n", + "Label = ['SQL', 'KEYWORD', '[PAD]', '[PAD]']\n", + "12837\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12838\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12839\n", + "Label = ['internal', 'type', '[PAD]', '[PAD]']\n", + "12840\n", + "Label = ['standard', 'pattern', 'ops', '[PAD]']\n", + "12841\n", + "Label = ['makedsn', '[PAD]', '[PAD]', '[PAD]']\n", + "12842\n", + "Label = ['connect', '[PAD]', '[PAD]', '[PAD]']\n", + "12843\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12844\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12845\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12846\n", + "Label = ['temporary', 'connection', '[PAD]', '[PAD]']\n", + "12847\n", + "Label = ['datetime', '[PAD]', '[PAD]', '[PAD]']\n", + "12848\n", + "Label = ['Decimal', '[PAD]', '[PAD]', '[PAD]']\n", + "12849\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "12850\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12851\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "12852\n", + "Label = ['execute', 'test', 'db', 'creation']\n", + "12853\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "12854\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "12855\n", + "Label = ['settings', 'dict', '[PAD]', '[PAD]']\n", + "12856\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12857\n", + "Label = ['allow', 'quiet', 'fail', '[PAD]']\n", + "12858\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "12859\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12860\n", + "Label = ['test', 'database', 'user', '[PAD]']\n", + "12861\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12862\n", + "Label = ['field', 'name', '[PAD]', '[PAD]']\n", + "12863\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12864\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "12865\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12866\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "12867\n", + "Label = ['fields', 'with', 'check', 'constraints']\n", + "12868\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12869\n", + "Label = ['orders', '[PAD]', '[PAD]', '[PAD]']\n", + "12870\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12871\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12872\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12873\n", + "Label = ['quantize', '[PAD]', '[PAD]', '[PAD]']\n", + "12874\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12875\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12876\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12877\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12878\n", + "Label = ['case', 'sql', '[PAD]', '[PAD]']\n", + "12879\n", + "Label = ['index', 'together', '[PAD]', '[PAD]']\n", + "12880\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12881\n", + "Label = ['remote', 'field', '[PAD]', '[PAD]']\n", + "12882\n", + "Label = ['executable', 'name', '[PAD]', '[PAD]']\n", + "12883\n", + "Label = ['conv', 'func', '[PAD]', '[PAD]']\n", + "12884\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "12885\n", + "Label = ['data', 'types', '[PAD]', '[PAD]']\n", + "12886\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "12887\n", + "Label = ['create', 'function', '[PAD]', '[PAD]']\n", + "12888\n", + "Label = ['is', 'in', 'memory', 'db']\n", + "12889\n", + "Label = ['month', '[PAD]', '[PAD]', '[PAD]']\n", + "12890\n", + "Label = ['isocalendar', '[PAD]', '[PAD]', '[PAD]']\n", + "12891\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12892\n", + "Label = ['is', 'in', 'memory', 'db']\n", + "12893\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "12894\n", + "Label = ['db', 'supports', 'all', 'required']\n", + "12895\n", + "Label = ['field', 'type', '[PAD]', '[PAD]']\n", + "12896\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12897\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "12898\n", + "Label = ['serialize', '[PAD]', '[PAD]', '[PAD]']\n", + "12899\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12900\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "12901\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12902\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "12903\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "12904\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "12905\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12906\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12907\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12908\n", + "Label = ['date', '[PAD]', '[PAD]', '[PAD]']\n", + "12909\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12910\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12911\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12912\n", + "Label = ['supports', 'temporal', 'subtraction', '[PAD]']\n", + "12913\n", + "Label = ['PRECEDING', '[PAD]', '[PAD]', '[PAD]']\n", + "12914\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "12915\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12916\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12917\n", + "Label = ['empty', 'strings', 'allowed', '[PAD]']\n", + "12918\n", + "Label = ['sql', 'create', 'inline', 'fk']\n", + "12919\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12920\n", + "Label = ['get', 'internal', 'type', '[PAD]']\n", + "12921\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12922\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12923\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "12924\n", + "Label = ['ignores', 'table', 'name', 'case']\n", + "12925\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12926\n", + "Label = ['many', 'to', 'many', '[PAD]']\n", + "12927\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12928\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12929\n", + "Label = ['through', '[PAD]', '[PAD]', '[PAD]']\n", + "12930\n", + "Label = ['constraint', 'names', '[PAD]', '[PAD]']\n", + "12931\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "12932\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12933\n", + "Label = ['rename', 'column', 'references', '[PAD]']\n", + "12934\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12935\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12936\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12937\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "12938\n", + "Label = ['db', 'tablespace', '[PAD]', '[PAD]']\n", + "12939\n", + "Label = ['tablespace', 'sql', '[PAD]', '[PAD]']\n", + "12940\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12941\n", + "Label = ['managed', '[PAD]', '[PAD]', '[PAD]']\n", + "12942\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "12943\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12944\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "12945\n", + "Label = ['sql', 'create', 'unique', '[PAD]']\n", + "12946\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "12947\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "12948\n", + "Label = ['supports', 'timezones', '[PAD]', '[PAD]']\n", + "12949\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "12950\n", + "Label = ['wrap', 'database', 'errors', '[PAD]']\n", + "12951\n", + "Label = ['in', 'atomic', 'block', '[PAD]']\n", + "12952\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12953\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "12954\n", + "Label = ['client', 'cert', '[PAD]', '[PAD]']\n", + "12955\n", + "Label = ['USE', 'TZ', '[PAD]', '[PAD]']\n", + "12956\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "12957\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12958\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "12959\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12960\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12961\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "12962\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12963\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12964\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12965\n", + "Label = ['data', 'types', '[PAD]', '[PAD]']\n", + "12966\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12967\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "12968\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "12969\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", + "12970\n", + "Label = ['Popen', '[PAD]', '[PAD]', '[PAD]']\n", + "12971\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12972\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "12973\n", + "Label = ['skip', 'default', '[PAD]', '[PAD]']\n", + "12974\n", + "Label = ['get', 'storage', 'engine', '[PAD]']\n", + "12975\n", + "Label = ['get', 'internal', 'type', '[PAD]']\n", + "12976\n", + "Label = ['get', 'internal', 'type', '[PAD]']\n", + "12977\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "12978\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "12979\n", + "Label = ['related', 'models', '[PAD]', '[PAD]']\n", + "12980\n", + "Label = ['rel', 'app', 'label', '[PAD]']\n", + "12981\n", + "Label = ['rel', 'app', 'label', '[PAD]']\n", + "12982\n", + "Label = ['model', 'state', '[PAD]', '[PAD]']\n", + "12983\n", + "Label = ['new', 'state', '[PAD]', '[PAD]']\n", + "12984\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "12985\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12986\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "12987\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "12988\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "12989\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "12990\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "12991\n", + "Label = ['abstract', '[PAD]', '[PAD]', '[PAD]']\n", + "12992\n", + "Label = ['label', 'lower', '[PAD]', '[PAD]']\n", + "12993\n", + "Label = ['Model', '[PAD]', '[PAD]', '[PAD]']\n", + "12994\n", + "Label = ['Model', '[PAD]', '[PAD]', '[PAD]']\n", + "12995\n", + "Label = ['get', 'model', '[PAD]', '[PAD]']\n", + "12996\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "12997\n", + "Label = ['reduces', 'to', 'sql', '[PAD]']\n", + "12998\n", + "Label = ['atomic', 'operation', '[PAD]', '[PAD]']\n", + "12999\n", + "Label = ['filenames', '[PAD]', '[PAD]', '[PAD]']\n", + "13000\n", + "Label = ['choice', 'input', '[PAD]', '[PAD]']\n", + "13001\n", + "Label = ['exit', '[PAD]', '[PAD]', '[PAD]']\n", + "13002\n", + "Label = ['exit', '[PAD]', '[PAD]', '[PAD]']\n", + "13003\n", + "Label = ['boolean', 'input', '[PAD]', '[PAD]']\n", + "13004\n", + "Label = ['feed', '[PAD]', '[PAD]', '[PAD]']\n", + "13005\n", + "Label = ['feed', '[PAD]', '[PAD]', '[PAD]']\n", + "13006\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13007\n", + "Label = ['discard', '[PAD]', '[PAD]', '[PAD]']\n", + "13008\n", + "Label = ['serialize', '[PAD]', '[PAD]', '[PAD]']\n", + "13009\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13010\n", + "Label = ['todo', '[PAD]', '[PAD]', '[PAD]']\n", + "13011\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13012\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13013\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "13014\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13015\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "13016\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13017\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "13018\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13019\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13020\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13021\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "13022\n", + "Label = ['state', '[PAD]', '[PAD]', '[PAD]']\n", + "13023\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "13024\n", + "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", + "13025\n", + "Label = ['migration', '[PAD]', '[PAD]', '[PAD]']\n", + "13026\n", + "Label = ['unapply', 'migration', '[PAD]', '[PAD]']\n", + "13027\n", + "Label = ['project', 'state', '[PAD]', '[PAD]']\n", + "13028\n", + "Label = ['many', 'to', 'many', '[PAD]']\n", + "13029\n", + "Label = ['column', 'names', '[PAD]', '[PAD]']\n", + "13030\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13031\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13032\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13033\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13034\n", + "Label = ['isnan', '[PAD]', '[PAD]', '[PAD]']\n", + "13035\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13036\n", + "Label = ['keywords', 'string', '[PAD]', '[PAD]']\n", + "13037\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13038\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13039\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13040\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "13041\n", + "Label = ['migration', 'names', '[PAD]', '[PAD]']\n", + "13042\n", + "Label = ['disk', 'migrations', '[PAD]', '[PAD]']\n", + "13043\n", + "Label = ['root', 'nodes', '[PAD]', '[PAD]']\n", + "13044\n", + "Label = ['remove', 'replaced', 'nodes', '[PAD]']\n", + "13045\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "13046\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13047\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "13048\n", + "Label = ['new', 'field', 'keys', '[PAD]']\n", + "13049\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13050\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13051\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "13052\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "13053\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "13054\n", + "Label = ['check', 'dependency', '[PAD]', '[PAD]']\n", + "13055\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13056\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "13057\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13058\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13059\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13060\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13061\n", + "Label = ['CreateModel', '[PAD]', '[PAD]', '[PAD]']\n", + "13062\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13063\n", + "Label = ['many', 'to', 'many', '[PAD]']\n", + "13064\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13065\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13066\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13067\n", + "Label = ['new', 'field', '[PAD]', '[PAD]']\n", + "13068\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13069\n", + "Label = ['constraint', '[PAD]', '[PAD]', '[PAD]']\n", + "13070\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13071\n", + "Label = ['dep', 'app', 'label', '[PAD]']\n", + "13072\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13073\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "13074\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13075\n", + "Label = ['models', 'to', 'check', '[PAD]']\n", + "13076\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13077\n", + "Label = ['add', 'operation', '[PAD]', '[PAD]']\n", + "13078\n", + "Label = ['ask', 'initial', '[PAD]', '[PAD]']\n", + "13079\n", + "Label = ['migration', '[PAD]', '[PAD]', '[PAD]']\n", + "13080\n", + "Label = ['suggest', 'name', '[PAD]', '[PAD]']\n", + "13081\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13082\n", + "Label = ['model', 'name', 'lower', '[PAD]']\n", + "13083\n", + "Label = ['model', 'name', 'lower', '[PAD]']\n", + "13084\n", + "Label = ['CreateModel', '[PAD]', '[PAD]', '[PAD]']\n", + "13085\n", + "Label = ['managers', '[PAD]', '[PAD]', '[PAD]']\n", + "13086\n", + "Label = ['managers', '[PAD]', '[PAD]', '[PAD]']\n", + "13087\n", + "Label = ['allow', 'migrate', 'model', '[PAD]']\n", + "13088\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13089\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13090\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "13091\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13092\n", + "Label = ['related', 'key', '[PAD]', '[PAD]']\n", + "13093\n", + "Label = ['new', 'name', '[PAD]', '[PAD]']\n", + "13094\n", + "Label = ['new', 'name', 'lower', '[PAD]']\n", + "13095\n", + "Label = ['reduce', '[PAD]', '[PAD]', '[PAD]']\n", + "13096\n", + "Label = ['auto', 'created', '[PAD]', '[PAD]']\n", + "13097\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13098\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13099\n", + "Label = ['option', 'value', '[PAD]', '[PAD]']\n", + "13100\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "13101\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13102\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13103\n", + "Label = ['qualname', '[PAD]', '[PAD]', '[PAD]']\n", + "13104\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13105\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13106\n", + "Label = ['allow', 'migrate', 'model', '[PAD]']\n", + "13107\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13108\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13109\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13110\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13111\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13112\n", + "Label = ['model', 'name', '[PAD]', '[PAD]']\n", + "13113\n", + "Label = ['database', 'operation', '[PAD]', '[PAD]']\n", + "13114\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13115\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13116\n", + "Label = ['allow', 'migrate', '[PAD]', '[PAD]']\n", + "13117\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13118\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13119\n", + "Label = ['model', 'name', '[PAD]', '[PAD]']\n", + "13120\n", + "Label = ['model', 'name', '[PAD]', '[PAD]']\n", + "13121\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13122\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "13123\n", + "Label = ['from', 'fields', '[PAD]', '[PAD]']\n", + "13124\n", + "Label = ['old', 'name', '[PAD]', '[PAD]']\n", + "13125\n", + "Label = ['to', 'fields', '[PAD]', '[PAD]']\n", + "13126\n", + "Label = ['old', 'name', '[PAD]', '[PAD]']\n", + "13127\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13128\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13129\n", + "Label = ['token', '[PAD]', '[PAD]', '[PAD]']\n", + "13130\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13131\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13132\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13133\n", + "Label = ['countervar', '[PAD]', '[PAD]', '[PAD]']\n", + "13134\n", + "Label = ['message', 'context', '[PAD]', '[PAD]']\n", + "13135\n", + "Label = ['varname', '[PAD]', '[PAD]', '[PAD]']\n", + "13136\n", + "Label = ['is', 'installed', '[PAD]', '[PAD]']\n", + "13137\n", + "Label = ['cache', 'name', '[PAD]', '[PAD]']\n", + "13138\n", + "Label = ['compile', 'filter', '[PAD]', '[PAD]']\n", + "13139\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13140\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13141\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "13142\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "13143\n", + "Label = ['path', 'info', '[PAD]', '[PAD]']\n", + "13144\n", + "Label = ['starts', 'with', '[PAD]', '[PAD]']\n", + "13145\n", + "Label = ['flatpages', '[PAD]', '[PAD]', '[PAD]']\n", + "13146\n", + "Label = ['TemplateSyntaxError', '[PAD]', '[PAD]', '[PAD]']\n", + "13147\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "13148\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "13149\n", + "Label = ['pattern', '[PAD]', '[PAD]', '[PAD]']\n", + "13150\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "13151\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13152\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "13153\n", + "Label = ['cache', 'name', '[PAD]', '[PAD]']\n", + "13154\n", + "Label = ['clean', 'name', '[PAD]', '[PAD]']\n", + "13155\n", + "Label = ['hashed', 'files', '[PAD]', '[PAD]']\n", + "13156\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "13157\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13158\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13159\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13160\n", + "Label = ['serve', '[PAD]', '[PAD]', '[PAD]']\n", + "13161\n", + "Label = ['MEDIA', 'ROOT', '[PAD]', '[PAD]']\n", + "13162\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "13163\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "13164\n", + "Label = ['normpath', '[PAD]', '[PAD]', '[PAD]']\n", + "13165\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "13166\n", + "Label = ['copied', 'files', '[PAD]', '[PAD]']\n", + "13167\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13168\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "13169\n", + "Label = ['makedirs', '[PAD]', '[PAD]', '[PAD]']\n", + "13170\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "13171\n", + "Label = ['copied', 'files', '[PAD]', '[PAD]']\n", + "13172\n", + "Label = ['create', '[PAD]', '[PAD]', '[PAD]']\n", + "13173\n", + "Label = ['is', 'addition', '[PAD]', '[PAD]']\n", + "13174\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13175\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13176\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "13177\n", + "Label = ['Form', '[PAD]', '[PAD]', '[PAD]']\n", + "13178\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "13179\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13180\n", + "Label = ['form', '[PAD]', '[PAD]', '[PAD]']\n", + "13181\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "13182\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13183\n", + "Label = ['is', 'first', '[PAD]', '[PAD]']\n", + "13184\n", + "Label = ['result', 'repr', '[PAD]', '[PAD]']\n", + "13185\n", + "Label = ['formset', '[PAD]', '[PAD]', '[PAD]']\n", + "13186\n", + "Label = ['formset', '[PAD]', '[PAD]', '[PAD]']\n", + "13187\n", + "Label = ['has', 'change', 'permission', '[PAD]']\n", + "13188\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13189\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "13190\n", + "Label = ['formset', '[PAD]', '[PAD]', '[PAD]']\n", + "13191\n", + "Label = ['errors', 'in', 'inline', 'form']\n", + "13192\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13193\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "13194\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13195\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13196\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13197\n", + "Label = ['is', 'relation', '[PAD]', '[PAD]']\n", + "13198\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13199\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13200\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13201\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13202\n", + "Label = ['swapped', '[PAD]', '[PAD]', '[PAD]']\n", + "13203\n", + "Label = ['admin', 'class', '[PAD]', '[PAD]']\n", + "13204\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "13205\n", + "Label = ['registry', '[PAD]', '[PAD]', '[PAD]']\n", + "13206\n", + "Label = ['password', 'change', 'done', '[PAD]']\n", + "13207\n", + "Label = ['urlpatterns', '[PAD]', '[PAD]', '[PAD]']\n", + "13208\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "13209\n", + "Label = ['each', 'context', '[PAD]', '[PAD]']\n", + "13210\n", + "Label = ['models', '[PAD]', '[PAD]', '[PAD]']\n", + "13211\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "13212\n", + "Label = ['app', 'dependencies', '[PAD]', '[PAD]']\n", + "13213\n", + "Label = ['app', 'name', '[PAD]', '[PAD]']\n", + "13214\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13215\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13216\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13217\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13218\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13219\n", + "Label = ['check', 'raw', 'id', 'fields']\n", + "13220\n", + "Label = ['many', 'to', 'many', '[PAD]']\n", + "13221\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13222\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13223\n", + "Label = ['check', 'field', 'spec', '[PAD]']\n", + "13224\n", + "Label = ['ManyToManyField', '[PAD]', '[PAD]', '[PAD]']\n", + "13225\n", + "Label = ['exclude', '[PAD]', '[PAD]', '[PAD]']\n", + "13226\n", + "Label = ['check', 'filter', 'item', '[PAD]']\n", + "13227\n", + "Label = ['check', 'filter', 'item', '[PAD]']\n", + "13228\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13229\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13230\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13231\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13232\n", + "Label = ['field', 'name', '[PAD]', '[PAD]']\n", + "13233\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13234\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13235\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13236\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13237\n", + "Label = ['check', 'list', 'display', 'item']\n", + "13238\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13239\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13240\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13241\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13242\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13243\n", + "Label = ['check', 'list', 'filter', 'item']\n", + "13244\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13245\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13246\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13247\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13248\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13249\n", + "Label = ['DateField', '[PAD]', '[PAD]', '[PAD]']\n", + "13250\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13251\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13252\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13253\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13254\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13255\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13256\n", + "Label = ['lookup', 'title', '[PAD]', '[PAD]']\n", + "13257\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "13258\n", + "Label = ['lookup', 'val', 'isnull', '[PAD]']\n", + "13259\n", + "Label = ['used', 'parameters', '[PAD]', '[PAD]']\n", + "13260\n", + "Label = ['lookup', 'val2', '[PAD]', '[PAD]']\n", + "13261\n", + "Label = ['lookup', 'val2', '[PAD]', '[PAD]']\n", + "13262\n", + "Label = ['lookup', 'val', '[PAD]', '[PAD]']\n", + "13263\n", + "Label = ['lookup', 'val', 'isnull', '[PAD]']\n", + "13264\n", + "Label = ['month', '[PAD]', '[PAD]', '[PAD]']\n", + "13265\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "13266\n", + "Label = [nan, '[PAD]', '[PAD]', '[PAD]']\n", + "13267\n", + "Label = ['links', '[PAD]', '[PAD]', '[PAD]']\n", + "13268\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13269\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "13270\n", + "Label = ['lookup', 'val', '[PAD]', '[PAD]']\n", + "13271\n", + "Label = ['lookup', 'val', '[PAD]', '[PAD]']\n", + "13272\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13273\n", + "Label = ['js', '[PAD]', '[PAD]', '[PAD]']\n", + "13274\n", + "Label = ['is', 'stacked', '[PAD]', '[PAD]']\n", + "13275\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "13276\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "13277\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "13278\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13279\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13280\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13281\n", + "Label = ['can', 'delete', 'related', '[PAD]']\n", + "13282\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13283\n", + "Label = ['param', '[PAD]', '[PAD]', '[PAD]']\n", + "13284\n", + "Label = ['can', 'add', 'related', '[PAD]']\n", + "13285\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13286\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "13287\n", + "Label = ['is', 'required', '[PAD]', '[PAD]']\n", + "13288\n", + "Label = ['selected', '[PAD]', '[PAD]', '[PAD]']\n", + "13289\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13290\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13291\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "13292\n", + "Label = ['radio', 'fields', '[PAD]', '[PAD]']\n", + "13293\n", + "Label = ['get', 'choices', '[PAD]', '[PAD]']\n", + "13294\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13295\n", + "Label = ['view', 'on', 'site', '[PAD]']\n", + "13296\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13297\n", + "Label = ['isdisjoint', '[PAD]', '[PAD]', '[PAD]']\n", + "13298\n", + "Label = ['isdisjoint', '[PAD]', '[PAD]', '[PAD]']\n", + "13299\n", + "Label = ['has', 'perm', '[PAD]', '[PAD]']\n", + "13300\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "13301\n", + "Label = ['as', 'view', '[PAD]', '[PAD]']\n", + "13302\n", + "Label = ['has', 'add', 'permission', '[PAD]']\n", + "13303\n", + "Label = ['exclude', '[PAD]', '[PAD]', '[PAD]']\n", + "13304\n", + "Label = ['has', 'change', 'permission', '[PAD]']\n", + "13305\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13306\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13307\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "13308\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13309\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13310\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13311\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13312\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13313\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13314\n", + "Label = ['opts', '[PAD]', '[PAD]', '[PAD]']\n", + "13315\n", + "Label = ['add', 'form', 'template', '[PAD]']\n", + "13316\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "13317\n", + "Label = ['popup', 'response', 'template', '[PAD]']\n", + "13318\n", + "Label = ['has', 'change', 'permission', '[PAD]']\n", + "13319\n", + "Label = ['popup', 'response', 'template', '[PAD]']\n", + "13320\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "13321\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "13322\n", + "Label = ['queryset', '[PAD]', '[PAD]', '[PAD]']\n", + "13323\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "13324\n", + "Label = ['message', 'user', '[PAD]', '[PAD]']\n", + "13325\n", + "Label = ['delete', 'confirmation', 'template', '[PAD]']\n", + "13326\n", + "Label = ['to', 'field', 'allowed', '[PAD]']\n", + "13327\n", + "Label = ['formsets', '[PAD]', '[PAD]', '[PAD]']\n", + "13328\n", + "Label = ['formsets', '[PAD]', '[PAD]', '[PAD]']\n", + "13329\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13330\n", + "Label = ['GET', '[PAD]', '[PAD]', '[PAD]']\n", + "13331\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "13332\n", + "Label = ['model', 'name', '[PAD]', '[PAD]']\n", + "13333\n", + "Label = ['to', 'field', 'allowed', '[PAD]']\n", + "13334\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "13335\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13336\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13337\n", + "Label = ['form', '[PAD]', '[PAD]', '[PAD]']\n", + "13338\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "13339\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13340\n", + "Label = ['remote', 'field', '[PAD]', '[PAD]']\n", + "13341\n", + "Label = ['PositiveSmallIntegerField', '[PAD]', '[PAD]', '[PAD]']\n", + "13342\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13343\n", + "Label = ['inline', 'admin', 'form', '[PAD]']\n", + "13344\n", + "Label = ['auto', 'id', '[PAD]', '[PAD]']\n", + "13345\n", + "Label = ['fieldset', '[PAD]', '[PAD]', '[PAD]']\n", + "13346\n", + "Label = ['get', 'query', 'string', '[PAD]']\n", + "13347\n", + "Label = ['page', 'range', '[PAD]', '[PAD]']\n", + "13348\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "13349\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13350\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13351\n", + "Label = ['month', '[PAD]', '[PAD]', '[PAD]']\n", + "13352\n", + "Label = ['year', '[PAD]', '[PAD]', '[PAD]']\n", + "13353\n", + "Label = ['u', '[PAD]', '[PAD]', '[PAD]']\n", + "13354\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13355\n", + "Label = ['from', 'date', '[PAD]', '[PAD]']\n", + "13356\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "13357\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "13358\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "13359\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "13360\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13361\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "13362\n", + "Label = ['get', 'full', 'path', '[PAD]']\n", + "13363\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "13364\n", + "Label = ['perm', '[PAD]', '[PAD]', '[PAD]']\n", + "13365\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13366\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13367\n", + "Label = ['POST', '[PAD]', '[PAD]', '[PAD]']\n", + "13368\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13369\n", + "Label = ['is', 'secure', '[PAD]', '[PAD]']\n", + "13370\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "13371\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13372\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13373\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", + "13374\n", + "Label = ['is', 'active', '[PAD]', '[PAD]']\n", + "13375\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13376\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13377\n", + "Label = ['has', 'perm', '[PAD]', '[PAD]']\n", + "13378\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13379\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13380\n", + "Label = ['normalize', '[PAD]', '[PAD]', '[PAD]']\n", + "13381\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "13382\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13383\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "13384\n", + "Label = ['validate', 'password', '[PAD]', '[PAD]']\n", + "13385\n", + "Label = ['max', 'length', '[PAD]', '[PAD]']\n", + "13386\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13387\n", + "Label = ['email', '[PAD]', '[PAD]', '[PAD]']\n", + "13388\n", + "Label = ['active', 'users', '[PAD]', '[PAD]']\n", + "13389\n", + "Label = ['new', 'password2', '[PAD]', '[PAD]']\n", + "13390\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13391\n", + "Label = ['CharField', '[PAD]', '[PAD]', '[PAD]']\n", + "13392\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13393\n", + "Label = ['MIDDLEWARE', '[PAD]', '[PAD]', '[PAD]']\n", + "13394\n", + "Label = ['username', '[PAD]', '[PAD]', '[PAD]']\n", + "13395\n", + "Label = ['get', 'username', '[PAD]', '[PAD]']\n", + "13396\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13397\n", + "Label = ['harden', 'runtime', '[PAD]', '[PAD]']\n", + "13398\n", + "Label = ['hashers', '[PAD]', '[PAD]', '[PAD]']\n", + "13399\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "13400\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "13401\n", + "Label = ['hashers', '[PAD]', '[PAD]', '[PAD]']\n", + "13402\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13403\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "13404\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "13405\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "13406\n", + "Label = ['hash', 'secret', '[PAD]', '[PAD]']\n", + "13407\n", + "Label = ['verify', 'secret', '[PAD]', '[PAD]']\n", + "13408\n", + "Label = ['digest', '[PAD]', '[PAD]', '[PAD]']\n", + "13409\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "13410\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13411\n", + "Label = ['diff', '[PAD]', '[PAD]', '[PAD]']\n", + "13412\n", + "Label = ['algorithm', '[PAD]', '[PAD]', '[PAD]']\n", + "13413\n", + "Label = ['num', 'days', '[PAD]', '[PAD]']\n", + "13414\n", + "Label = ['num', 'days', '[PAD]', '[PAD]']\n", + "13415\n", + "Label = ['pk', '[PAD]', '[PAD]', '[PAD]']\n", + "13416\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13417\n", + "Label = ['Critical', '[PAD]', '[PAD]', '[PAD]']\n", + "13418\n", + "Label = ['operations', '[PAD]', '[PAD]', '[PAD]']\n", + "13419\n", + "Label = ['CharField', '[PAD]', '[PAD]', '[PAD]']\n", + "13420\n", + "Label = ['operations', '[PAD]', '[PAD]', '[PAD]']\n", + "13421\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13422\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13423\n", + "Label = ['ManyToManyField', '[PAD]', '[PAD]', '[PAD]']\n", + "13424\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13425\n", + "Label = ['Migration', '[PAD]', '[PAD]', '[PAD]']\n", + "13426\n", + "Label = ['Migration', '[PAD]', '[PAD]', '[PAD]']\n", + "13427\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13428\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "13429\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13430\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "13431\n", + "Label = ['clean', '[PAD]', '[PAD]', '[PAD]']\n", + "13432\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "13433\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "13434\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "13435\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "13436\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "13437\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13438\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13439\n", + "Label = ['upper', '[PAD]', '[PAD]', '[PAD]']\n", + "13440\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13441\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13442\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13443\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13444\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "13445\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "13446\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13447\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13448\n", + "Label = ['empty', 'values', '[PAD]', '[PAD]']\n", + "13449\n", + "Label = ['final', 'attrs', '[PAD]', '[PAD]']\n", + "13450\n", + "Label = ['get', 'context', '[PAD]', '[PAD]']\n", + "13451\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13452\n", + "Label = ['remove', 'trailing', 'nulls', '[PAD]']\n", + "13453\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "13454\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13455\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13456\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", + "13457\n", + "Label = ['prep', 'value', '[PAD]', '[PAD]']\n", + "13458\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13459\n", + "Label = ['encoder', '[PAD]', '[PAD]', '[PAD]']\n", + "13460\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13461\n", + "Label = ['formfield', '[PAD]', '[PAD]', '[PAD]']\n", + "13462\n", + "Label = ['key', 'text', 'transform', '[PAD]']\n", + "13463\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13464\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13465\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13466\n", + "Label = ['base', 'field', '[PAD]', '[PAD]']\n", + "13467\n", + "Label = ['sql', '[PAD]', '[PAD]', '[PAD]']\n", + "13468\n", + "Label = ['Transform', '[PAD]', '[PAD]', '[PAD]']\n", + "13469\n", + "Label = ['Transform', '[PAD]', '[PAD]', '[PAD]']\n", + "13470\n", + "Label = ['from', 'db', 'value', '[PAD]']\n", + "13471\n", + "Label = ['from', 'db', 'value', '[PAD]']\n", + "13472\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13473\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13474\n", + "Label = ['enc', '[PAD]', '[PAD]', '[PAD]']\n", + "13475\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "13476\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "13477\n", + "Label = ['link', '[PAD]', '[PAD]', '[PAD]']\n", + "13478\n", + "Label = ['models', '[PAD]', '[PAD]', '[PAD]']\n", + "13479\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "13480\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", + "13481\n", + "Label = ['ct', 'id', '[PAD]', '[PAD]']\n", + "13482\n", + "Label = ['id', '[PAD]', '[PAD]', '[PAD]']\n", + "13483\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "13484\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13485\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "13486\n", + "Label = ['get', 'field', '[PAD]', '[PAD]']\n", + "13487\n", + "Label = ['meta', '[PAD]', '[PAD]', '[PAD]']\n", + "13488\n", + "Label = ['meta', '[PAD]', '[PAD]', '[PAD]']\n", + "13489\n", + "Label = ['lookup', '[PAD]', '[PAD]', '[PAD]']\n", + "13490\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13491\n", + "Label = ['adding', '[PAD]', '[PAD]', '[PAD]']\n", + "13492\n", + "Label = ['remove', '[PAD]', '[PAD]', '[PAD]']\n", + "13493\n", + "Label = ['add', '[PAD]', '[PAD]', '[PAD]']\n", + "13494\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13495\n", + "Label = ['filter', '[PAD]', '[PAD]', '[PAD]']\n", + "13496\n", + "Label = ['filter', '[PAD]', '[PAD]', '[PAD]']\n", + "13497\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13498\n", + "Label = ['object', 'domain', '[PAD]', '[PAD]']\n", + "13499\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "13500\n", + "Label = ['protocol', '[PAD]', '[PAD]', '[PAD]']\n", + "13501\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13502\n", + "Label = ['get', 'field', '[PAD]', '[PAD]']\n", + "13503\n", + "Label = ['ct', 'fk', 'field', '[PAD]']\n", + "13504\n", + "Label = ['ct', 'field', '[PAD]', '[PAD]']\n", + "13505\n", + "Label = ['Error', '[PAD]', '[PAD]', '[PAD]']\n", + "13506\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "13507\n", + "Label = ['can', 'delete', '[PAD]', '[PAD]']\n", + "13508\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13509\n", + "Label = ['ct', '[PAD]', '[PAD]', '[PAD]']\n", + "13510\n", + "Label = ['objects', '[PAD]', '[PAD]', '[PAD]']\n", + "13511\n", + "Label = ['abstract', '[PAD]', '[PAD]', '[PAD]']\n", + "13512\n", + "Label = ['help', '[PAD]', '[PAD]', '[PAD]']\n", + "13513\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13514\n", + "Label = ['session', '[PAD]', '[PAD]', '[PAD]']\n", + "13515\n", + "Label = ['b64encode', '[PAD]', '[PAD]', '[PAD]']\n", + "13516\n", + "Label = ['logger', '[PAD]', '[PAD]', '[PAD]']\n", + "13517\n", + "Label = ['session', 'key', '[PAD]', '[PAD]']\n", + "13518\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13519\n", + "Label = ['save', '[PAD]', '[PAD]', '[PAD]']\n", + "13520\n", + "Label = ['flags', '[PAD]', '[PAD]', '[PAD]']\n", + "13521\n", + "Label = ['session', 'file', '[PAD]', '[PAD]']\n", + "13522\n", + "Label = ['cache', 'key', 'prefix', '[PAD]']\n", + "13523\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13524\n", + "Label = ['cache', 'key', '[PAD]', '[PAD]']\n", + "13525\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13526\n", + "Label = ['templates', '[PAD]', '[PAD]', '[PAD]']\n", + "13527\n", + "Label = ['number', '[PAD]', '[PAD]', '[PAD]']\n", + "13528\n", + "Label = ['number', '[PAD]', '[PAD]', '[PAD]']\n", + "13529\n", + "Label = ['number', '[PAD]', '[PAD]', '[PAD]']\n", + "13530\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13531\n", + "Label = ['message', 'key', '[PAD]', '[PAD]']\n", + "13532\n", + "Label = ['process', 'messages', '[PAD]', '[PAD]']\n", + "13533\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "13534\n", + "Label = ['hash', '[PAD]', '[PAD]', '[PAD]']\n", + "13535\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13536\n", + "Label = ['extra', 'tags', '[PAD]', '[PAD]']\n", + "13537\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13538\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13539\n", + "Label = ['addQuickElement', '[PAD]', '[PAD]', '[PAD]']\n", + "13540\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13541\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13542\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "13543\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13544\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "13545\n", + "Label = ['standard', '[PAD]', '[PAD]', '[PAD]']\n", + "13546\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "13547\n", + "Label = ['ptr', 'type', '[PAD]', '[PAD]']\n", + "13548\n", + "Label = ['layer', 'count', '[PAD]', '[PAD]']\n", + "13549\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "13550\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13551\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13552\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13553\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13554\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13555\n", + "Label = ['klone', '[PAD]', '[PAD]', '[PAD]']\n", + "13556\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13557\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13558\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13559\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13560\n", + "Label = ['geom', 'count', '[PAD]', '[PAD]']\n", + "13561\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13562\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13563\n", + "Label = ['types', '[PAD]', '[PAD]', '[PAD]']\n", + "13564\n", + "Label = ['types', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13565\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13566\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13567\n", + "Label = ['alias', '[PAD]', '[PAD]', '[PAD]']\n", + "13568\n", + "Label = ['ensure', 'registered', '[PAD]', '[PAD]']\n", + "13569\n", + "Label = ['yy', '[PAD]', '[PAD]', '[PAD]']\n", + "13570\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13571\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13572\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13573\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13574\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "13575\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13576\n", + "Label = ['MinX', '[PAD]', '[PAD]', '[PAD]']\n", + "13577\n", + "Label = ['MaxX', '[PAD]', '[PAD]', '[PAD]']\n", + "13578\n", + "Label = ['expand', 'to', 'include', '[PAD]']\n", + "13579\n", + "Label = ['expand', 'to', 'include', '[PAD]']\n", + "13580\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13581\n", + "Label = ['get', 'field', 'name', '[PAD]']\n", + "13582\n", + "Label = ['get', 'field', 'precision', '[PAD]']\n", + "13583\n", + "Label = ['clone', 'geom', '[PAD]', '[PAD]']\n", + "13584\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", + "13585\n", + "Label = ['geotransform', '[PAD]', '[PAD]', '[PAD]']\n", + "13586\n", + "Label = ['geotransform', '[PAD]', '[PAD]', '[PAD]']\n", + "13587\n", + "Label = ['ptr', '[PAD]', '[PAD]', '[PAD]']\n", + "13588\n", + "Label = ['ptr', '[PAD]', '[PAD]', '[PAD]']\n", + "13589\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "13590\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "13591\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13592\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13593\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13594\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13595\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13596\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13597\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13598\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13599\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13600\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13601\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13602\n", + "Label = ['restype', '[PAD]', '[PAD]', '[PAD]']\n", + "13603\n", + "Label = ['get', 'field', 'as', 'integer64']\n", + "13604\n", + "Label = ['from', 'wkt', '[PAD]', '[PAD]']\n", + "13605\n", + "Label = ['free', 'dsl', '[PAD]', '[PAD]']\n", + "13606\n", + "Label = ['get', 'band', 'statistics', '[PAD]']\n", + "13607\n", + "Label = ['create', 'vsi', 'file', 'from']\n", + "13608\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "13609\n", + "Label = ['ptr', '[PAD]', '[PAD]', '[PAD]']\n", + "13610\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13611\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13612\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13613\n", + "Label = ['value', 'to', 'string', '[PAD]']\n", + "13614\n", + "Label = ['srid', '[PAD]', '[PAD]', '[PAD]']\n", + "13615\n", + "Label = ['cts', '[PAD]', '[PAD]', '[PAD]']\n", + "13616\n", + "Label = ['GDALException', '[PAD]', '[PAD]', '[PAD]']\n", + "13617\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "13618\n", + "Label = ['transform', '[PAD]', '[PAD]', '[PAD]']\n", + "13619\n", + "Label = ['ValidationError', '[PAD]', '[PAD]', '[PAD]']\n", + "13620\n", + "Label = ['encoding', '[PAD]', '[PAD]', '[PAD]']\n", + "13621\n", + "Label = ['transaction', 'decorator', '[PAD]', '[PAD]']\n", + "13622\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13623\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13624\n", + "Label = ['field', 'name', '[PAD]', '[PAD]']\n", + "13625\n", + "Label = ['target', 'srs', '[PAD]', '[PAD]']\n", + "13626\n", + "Label = ['progress', 'interval', '[PAD]', '[PAD]']\n", + "13627\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "13628\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13629\n", + "Label = ['srid', 'str', '[PAD]', '[PAD]']\n", + "13630\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13631\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13632\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13633\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13634\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "13635\n", + "Label = ['map', 'types', '[PAD]', '[PAD]']\n", + "13636\n", + "Label = ['media', '[PAD]', '[PAD]', '[PAD]']\n", + "13637\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13638\n", + "Label = ['source', 'expressions', '[PAD]', '[PAD]']\n", + "13639\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13640\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13641\n", + "Label = ['tolerance', '[PAD]', '[PAD]', '[PAD]']\n", + "13642\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13643\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13644\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13645\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13646\n", + "Label = ['handle', 'param', '[PAD]', '[PAD]']\n", + "13647\n", + "Label = ['handle', 'param', '[PAD]', '[PAD]']\n", + "13648\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13649\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13650\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13651\n", + "Label = ['rhs', 'params', '[PAD]', '[PAD]']\n", + "13652\n", + "Label = ['srid', '[PAD]', '[PAD]', '[PAD]']\n", + "13653\n", + "Label = ['semi', 'major', '[PAD]', '[PAD]']\n", + "13654\n", + "Label = ['spatial', 'index', '[PAD]', '[PAD]']\n", + "13655\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13656\n", + "Label = ['geography', '[PAD]', '[PAD]', '[PAD]']\n", + "13657\n", + "Label = ['GDALRaster', '[PAD]', '[PAD]', '[PAD]']\n", + "13658\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13659\n", + "Label = ['attname', '[PAD]', '[PAD]', '[PAD]']\n", + "13660\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13661\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13662\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13663\n", + "Label = ['ewkb', '[PAD]', '[PAD]', '[PAD]']\n", + "13664\n", + "Label = ['structure', '[PAD]', '[PAD]', '[PAD]']\n", + "13665\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13666\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "13667\n", + "Label = ['sql', 'create', 'index', '[PAD]']\n", + "13668\n", + "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", + "13669\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "13670\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "13671\n", + "Label = ['geography', '[PAD]', '[PAD]', '[PAD]']\n", + "13672\n", + "Label = ['source', 'expressions', '[PAD]', '[PAD]']\n", + "13673\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "13674\n", + "Label = ['spatial', 'version', '[PAD]', '[PAD]']\n", + "13675\n", + "Label = ['dist', 'param', '[PAD]', '[PAD]']\n", + "13676\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13677\n", + "Label = ['geom', 'type', '[PAD]', '[PAD]']\n", + "13678\n", + "Label = ['f', 'table', 'name', '[PAD]']\n", + "13679\n", + "Label = ['fix', 'polygon', '[PAD]', '[PAD]']\n", + "13680\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "13681\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "13682\n", + "Label = ['sql', 'clear', 'geometry', 'table']\n", + "13683\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "13684\n", + "Label = ['shell', '[PAD]', '[PAD]', '[PAD]']\n", + "13685\n", + "Label = ['ll', '[PAD]', '[PAD]', '[PAD]']\n", + "13686\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13687\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "13688\n", + "Label = ['SchemaEditorClass', '[PAD]', '[PAD]', '[PAD]']\n", + "13689\n", + "Label = ['dist', 'param', '[PAD]', '[PAD]']\n", + "13690\n", + "Label = ['unit', 'attname', '[PAD]', '[PAD]']\n", + "13691\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13692\n", + "Label = ['srs', '[PAD]', '[PAD]', '[PAD]']\n", + "13693\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13694\n", + "Label = ['srid', '[PAD]', '[PAD]', '[PAD]']\n", + "13695\n", + "Label = ['spatial', 'function', 'name', '[PAD]']\n", + "13696\n", + "Label = ['spatial', 'function', 'name', '[PAD]']\n", + "13697\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13698\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13699\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13700\n", + "Label = ['ogr', 'type', '[PAD]', '[PAD]']\n", + "13701\n", + "Label = ['quote', 'name', '[PAD]', '[PAD]']\n", + "13702\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13703\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "13704\n", + "Label = ['geom', 'table', '[PAD]', '[PAD]']\n", + "13705\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "13706\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "13707\n", + "Label = ['f', 'table', 'name', '[PAD]']\n", + "13708\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13709\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "13710\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13711\n", + "Label = ['ogr', 'fld', '[PAD]', '[PAD]']\n", + "13712\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13713\n", + "Label = ['coord', '[PAD]', '[PAD]', '[PAD]']\n", + "13714\n", + "Label = ['cs', '[PAD]', '[PAD]', '[PAD]']\n", + "13715\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13716\n", + "Label = ['lst', '[PAD]', '[PAD]', '[PAD]']\n", + "13717\n", + "Label = ['lib', 'name', '[PAD]', '[PAD]']\n", + "13718\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13719\n", + "Label = ['warn', 'msg', '[PAD]', '[PAD]']\n", + "13720\n", + "Label = ['fmt', '[PAD]', '[PAD]', '[PAD]']\n", + "13721\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13722\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "13723\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13724\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13725\n", + "Label = ['cs', 'gety', '[PAD]', '[PAD]']\n", + "13726\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13727\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "13728\n", + "Label = ['init', 'holes', '[PAD]', '[PAD]']\n", + "13729\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "13730\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13731\n", + "Label = ['holes', '[PAD]', '[PAD]', '[PAD]']\n", + "13732\n", + "Label = ['tuple', '[PAD]', '[PAD]', '[PAD]']\n", + "13733\n", + "Label = ['inner', 'kml', '[PAD]', '[PAD]']\n", + "13734\n", + "Label = ['geom', 'clone', '[PAD]', '[PAD]']\n", + "13735\n", + "Label = ['geom', 'clone', '[PAD]', '[PAD]']\n", + "13736\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13737\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13738\n", + "Label = ['srid', '[PAD]', '[PAD]', '[PAD]']\n", + "13739\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13740\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13741\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13742\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13743\n", + "Label = ['geos', 'equalsexact', '[PAD]', '[PAD]']\n", + "13744\n", + "Label = ['geos', 'relatepattern', '[PAD]', '[PAD]']\n", + "13745\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "13746\n", + "Label = ['write', 'hex', '[PAD]', '[PAD]']\n", + "13747\n", + "Label = ['write', 'hex', '[PAD]', '[PAD]']\n", + "13748\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13749\n", + "Label = ['clone', '[PAD]', '[PAD]', '[PAD]']\n", + "13750\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13751\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13752\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13753\n", + "Label = ['cache', '[PAD]', '[PAD]', '[PAD]']\n", + "13754\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13755\n", + "Label = ['cs', '[PAD]', '[PAD]', '[PAD]']\n", + "13756\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13757\n", + "Label = ['argtypes', '[PAD]', '[PAD]', '[PAD]']\n", + "13758\n", + "Label = ['restype', '[PAD]', '[PAD]', '[PAD]']\n", + "13759\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "13760\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13761\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13762\n", + "Label = ['wkb', 's', '[PAD]', '[PAD]']\n", + "13763\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13764\n", + "Label = ['ptr', '[PAD]', '[PAD]', '[PAD]']\n", + "13765\n", + "Label = ['wkb', '[PAD]', '[PAD]', '[PAD]']\n", + "13766\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13767\n", + "Label = ['wkt', 'w', '[PAD]', '[PAD]']\n", + "13768\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13769\n", + "Label = ['city', 'db', '[PAD]', '[PAD]']\n", + "13770\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13771\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13772\n", + "Label = ['country', '[PAD]', '[PAD]', '[PAD]']\n", + "13773\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "13774\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13775\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13776\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13777\n", + "Label = ['many', 'to', 'many', '[PAD]']\n", + "13778\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13779\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "13780\n", + "Label = ['save', '[PAD]', '[PAD]', '[PAD]']\n", + "13781\n", + "Label = ['sequence', 'reset', 'sql', '[PAD]']\n", + "13782\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13783\n", + "Label = ['new', 'path', '[PAD]', '[PAD]']\n", + "13784\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13785\n", + "Label = ['AutoField', '[PAD]', '[PAD]', '[PAD]']\n", + "13786\n", + "Label = ['page', '[PAD]', '[PAD]', '[PAD]']\n", + "13787\n", + "Label = ['maps', '[PAD]', '[PAD]', '[PAD]']\n", + "13788\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13789\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13790\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13791\n", + "Label = ['group', 'pattern', '[PAD]', '[PAD]']\n", + "13792\n", + "Label = ['unmatched', 'open', 'brackets', '[PAD]']\n", + "13793\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13794\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13795\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13796\n", + "Label = ['parse', 'rst', '[PAD]', '[PAD]']\n", + "13797\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13798\n", + "Label = ['func', '[PAD]', '[PAD]', '[PAD]']\n", + "13799\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13800\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13801\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13802\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13803\n", + "Label = ['exclude', '[PAD]', '[PAD]', '[PAD]']\n", + "13804\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "13805\n", + "Label = ['verbose', '[PAD]', '[PAD]', '[PAD]']\n", + "13806\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13807\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13808\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13809\n", + "Label = ['converter', '[PAD]', '[PAD]', '[PAD]']\n", + "13810\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "13811\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13812\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13813\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13814\n", + "Label = ['regex', '[PAD]', '[PAD]', '[PAD]']\n", + "13815\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "13816\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13817\n", + "Label = ['LANGUAGE', 'CODE', '[PAD]', '[PAD]']\n", + "13818\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "13819\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "13820\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13821\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13822\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13823\n", + "Label = ['sub', 'match', 'args', '[PAD]']\n", + "13824\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "13825\n", + "Label = ['ns', '[PAD]', '[PAD]', '[PAD]']\n", + "13826\n", + "Label = ['ns', '[PAD]', '[PAD]', '[PAD]']\n", + "13827\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13828\n", + "Label = ['urlconf', 'name', '[PAD]', '[PAD]']\n", + "13829\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13830\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "13831\n", + "Label = ['engine', '[PAD]', '[PAD]', '[PAD]']\n", + "13832\n", + "Label = ['parse', '[PAD]', '[PAD]', '[PAD]']\n", + "13833\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13834\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13835\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13836\n", + "Label = ['prepend', 'token', '[PAD]', '[PAD]']\n", + "13837\n", + "Label = ['lineno', '[PAD]', '[PAD]', '[PAD]']\n", + "13838\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13839\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", + "13840\n", + "Label = ['escape', '[PAD]', '[PAD]', '[PAD]']\n", + "13841\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13842\n", + "Label = ['lookup', '[PAD]', '[PAD]', '[PAD]']\n", + "13843\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13844\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "13845\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13846\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", + "13847\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", + "13848\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "13849\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13850\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "13851\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "13852\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "13853\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13854\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "13855\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13856\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "13857\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13858\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13859\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "13860\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13861\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13862\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13863\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13864\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13865\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13866\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", + "13867\n", + "Label = ['current', 'app', '[PAD]', '[PAD]']\n", + "13868\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13869\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13870\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "13871\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13872\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "13873\n", + "Label = ['parser', '[PAD]', '[PAD]', '[PAD]']\n", + "13874\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", + "13875\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "13876\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "13877\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "13878\n", + "Label = ['exp', '[PAD]', '[PAD]', '[PAD]']\n", + "13879\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", + "13880\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "13881\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13882\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13883\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "13884\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13885\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13886\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13887\n", + "Label = ['arg', '[PAD]', '[PAD]', '[PAD]']\n", + "13888\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "13889\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13890\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13891\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13892\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13893\n", + "Label = ['blocks', '[PAD]', '[PAD]', '[PAD]']\n", + "13894\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13895\n", + "Label = ['push', '[PAD]', '[PAD]', '[PAD]']\n", + "13896\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13897\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "13898\n", + "Label = ['compile', 'filter', '[PAD]', '[PAD]']\n", + "13899\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "13900\n", + "Label = ['DEBUG', '[PAD]', '[PAD]', '[PAD]']\n", + "13901\n", + "Label = ['from', 'iterable', '[PAD]', '[PAD]']\n", + "13902\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "13903\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13904\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13905\n", + "Label = ['dirs', '[PAD]', '[PAD]', '[PAD]']\n", + "13906\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13907\n", + "Label = ['get', 'template', '[PAD]', '[PAD]']\n", + "13908\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13909\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13910\n", + "Label = ['pkg', '[PAD]', '[PAD]', '[PAD]']\n", + "13911\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "13912\n", + "Label = ['template', 'file', '[PAD]', '[PAD]']\n", + "13913\n", + "Label = ['context', '[PAD]', '[PAD]', '[PAD]']\n", + "13914\n", + "Label = ['get', 'template', '[PAD]', '[PAD]']\n", + "13915\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "13916\n", + "Label = ['base', 'fields', '[PAD]', '[PAD]']\n", + "13917\n", + "Label = ['form', '[PAD]', '[PAD]', '[PAD]']\n", + "13918\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13919\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13920\n", + "Label = ['initial', 'form', 'count', '[PAD]']\n", + "13921\n", + "Label = ['is', 'valid', '[PAD]', '[PAD]']\n", + "13922\n", + "Label = ['max', 'num', '[PAD]', '[PAD]']\n", + "13923\n", + "Label = ['total', 'form', 'count', '[PAD]']\n", + "13924\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13925\n", + "Label = ['management', 'form', '[PAD]', '[PAD]']\n", + "13926\n", + "Label = ['form', '[PAD]', '[PAD]', '[PAD]']\n", + "13927\n", + "Label = ['formsets', '[PAD]', '[PAD]', '[PAD]']\n", + "13928\n", + "Label = ['contents', '[PAD]', '[PAD]', '[PAD]']\n", + "13929\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13930\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13931\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13932\n", + "Label = ['absolute', 'path', '[PAD]', '[PAD]']\n", + "13933\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13934\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13935\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13936\n", + "Label = ['template', 'name', '[PAD]', '[PAD]']\n", + "13937\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13938\n", + "Label = ['allow', 'multiple', 'selected', '[PAD]']\n", + "13939\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13940\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "13941\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13942\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13943\n", + "Label = ['year', 'choices', '[PAD]', '[PAD]']\n", + "13944\n", + "Label = ['get', 'context', '[PAD]', '[PAD]']\n", + "13945\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13946\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "13947\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "13948\n", + "Label = ['coerce', '[PAD]', '[PAD]', '[PAD]']\n", + "13949\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "13950\n", + "Label = ['min', 'value', '[PAD]', '[PAD]']\n", + "13951\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", + "13952\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13953\n", + "Label = ['step', '[PAD]', '[PAD]', '[PAD]']\n", + "13954\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13955\n", + "Label = ['strptime', '[PAD]', '[PAD]', '[PAD]']\n", + "13956\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13957\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13958\n", + "Label = ['error', 'messages', '[PAD]', '[PAD]']\n", + "13959\n", + "Label = ['max', 'length', '[PAD]', '[PAD]']\n", + "13960\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "13961\n", + "Label = ['url', 'fields', '[PAD]', '[PAD]']\n", + "13962\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "13963\n", + "Label = ['k2', '[PAD]', '[PAD]', '[PAD]']\n", + "13964\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13965\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13966\n", + "Label = ['initial', '[PAD]', '[PAD]', '[PAD]']\n", + "13967\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13968\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "13969\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "13970\n", + "Label = ['allow', 'files', '[PAD]', '[PAD]']\n", + "13971\n", + "Label = ['empty', 'values', '[PAD]', '[PAD]']\n", + "13972\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13973\n", + "Label = ['new', 'class', '[PAD]', '[PAD]']\n", + "13974\n", + "Label = ['default', 'renderer', '[PAD]', '[PAD]']\n", + "13975\n", + "Label = ['default', 'renderer', '[PAD]', '[PAD]']\n", + "13976\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13977\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13978\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13979\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13980\n", + "Label = ['bound', 'fields', 'cache', '[PAD]']\n", + "13981\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "13982\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "13983\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "13984\n", + "Label = ['help', 'text', '[PAD]', '[PAD]']\n", + "13985\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13986\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "13987\n", + "Label = ['all', '[PAD]', '[PAD]', '[PAD]']\n", + "13988\n", + "Label = ['has', 'default', '[PAD]', '[PAD]']\n", + "13989\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13990\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13991\n", + "Label = ['formfield', '[PAD]', '[PAD]', '[PAD]']\n", + "13992\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "13993\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "13994\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "13995\n", + "Label = ['instance', '[PAD]', '[PAD]', '[PAD]']\n", + "13996\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "13997\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "13998\n", + "Label = ['save', '[PAD]', '[PAD]', '[PAD]']\n", + "13999\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "14000\n", + "Label = ['is', 'bound', '[PAD]', '[PAD]']\n", + "14001\n", + "Label = ['object', 'dict', '[PAD]', '[PAD]']\n", + "14002\n", + "Label = ['save', 'existing', 'objects', '[PAD]']\n", + "14003\n", + "Label = ['unique', 'fields', '[PAD]', '[PAD]']\n", + "14004\n", + "Label = ['field', '[PAD]', '[PAD]', '[PAD]']\n", + "14005\n", + "Label = ['form', '[PAD]', '[PAD]', '[PAD]']\n", + "14006\n", + "Label = ['pk', 'value', '[PAD]', '[PAD]']\n", + "14007\n", + "Label = ['qs', '[PAD]', '[PAD]', '[PAD]']\n", + "14008\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "14009\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14010\n", + "Label = ['has', 'default', '[PAD]', '[PAD]']\n", + "14011\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "14012\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14013\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14014\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14015\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14016\n", + "Label = ['parent', 'instance', '[PAD]', '[PAD]']\n", + "14017\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14018\n", + "Label = ['to', 'field', 'name', '[PAD]']\n", + "14019\n", + "Label = ['error', 'messages', '[PAD]', '[PAD]']\n", + "14020\n", + "Label = ['val', '[PAD]', '[PAD]', '[PAD]']\n", + "14021\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14022\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14023\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "14024\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14025\n", + "Label = ['template', '[PAD]', '[PAD]', '[PAD]']\n", + "14026\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14027\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14028\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14029\n", + "Label = ['user', 'str', '[PAD]', '[PAD]']\n", + "14030\n", + "Label = ['pre', 'context', 'lineno', '[PAD]']\n", + "14031\n", + "Label = ['error', 'url', '[PAD]', '[PAD]']\n", + "14032\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "14033\n", + "Label = ['set', 'cookie', '[PAD]', '[PAD]']\n", + "14034\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "14035\n", + "Label = ['get', 'catalog', '[PAD]', '[PAD]']\n", + "14036\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14037\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14038\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14039\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14040\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14041\n", + "Label = ['day', '[PAD]', '[PAD]', '[PAD]']\n", + "14042\n", + "Label = ['get', 'context', 'data', '[PAD]']\n", + "14043\n", + "Label = ['is', 'empty', '[PAD]', '[PAD]']\n", + "14044\n", + "Label = ['verbose', 'name', 'plural', '[PAD]']\n", + "14045\n", + "Label = ['get', 'next', 'year', '[PAD]']\n", + "14046\n", + "Label = ['lookup', 'kwargs', '[PAD]', '[PAD]']\n", + "14047\n", + "Label = ['get', 'previous', 'day', '[PAD]']\n", + "14048\n", + "Label = ['verbose', 'name', 'plural', '[PAD]']\n", + "14049\n", + "Label = ['year', '[PAD]', '[PAD]', '[PAD]']\n", + "14050\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14051\n", + "Label = ['query', 'pk', 'and', 'slug']\n", + "14052\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "14053\n", + "Label = ['object', '[PAD]', '[PAD]', '[PAD]']\n", + "14054\n", + "Label = ['paginator', '[PAD]', '[PAD]', '[PAD]']\n", + "14055\n", + "Label = ['get', 'paginator', '[PAD]', '[PAD]']\n", + "14056\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14057\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "14058\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14059\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14060\n", + "Label = ['permanent', '[PAD]', '[PAD]', '[PAD]']\n", + "14061\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "14062\n", + "Label = ['parameters', '[PAD]', '[PAD]', '[PAD]']\n", + "14063\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14064\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "14065\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "14066\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "14067\n", + "Label = ['compile', '[PAD]', '[PAD]', '[PAD]']\n", + "14068\n", + "Label = ['hostname', 're', '[PAD]', '[PAD]']\n", + "14069\n", + "Label = ['scheme', '[PAD]', '[PAD]', '[PAD]']\n", + "14070\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "14071\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "14072\n", + "Label = ['domain', 'whitelist', '[PAD]', '[PAD]']\n", + "14073\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "14074\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "14075\n", + "Label = ['compressed', '[PAD]', '[PAD]', '[PAD]']\n", + "14076\n", + "Label = ['salt', '[PAD]', '[PAD]', '[PAD]']\n", + "14077\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "14078\n", + "Label = ['allow', 'empty', 'first', 'page']\n", + "14079\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14080\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14081\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14082\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "14083\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "14084\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "14085\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", + "14086\n", + "Label = ['rstrip', '[PAD]', '[PAD]', '[PAD]']\n", + "14087\n", + "Label = ['load', 'post', 'and', 'files']\n", + "14088\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "14089\n", + "Label = ['environ', '[PAD]', '[PAD]', '[PAD]']\n", + "14090\n", + "Label = ['wrapped', 'callback', '[PAD]', '[PAD]']\n", + "14091\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "14092\n", + "Label = ['fragment', 'name', '[PAD]', '[PAD]']\n", + "14093\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14094\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14095\n", + "Label = ['client', '[PAD]', '[PAD]', '[PAD]']\n", + "14096\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14097\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "14098\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14099\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "14100\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "14101\n", + "Label = ['execute', '[PAD]', '[PAD]', '[PAD]']\n", + "14102\n", + "Label = ['lock', '[PAD]', '[PAD]', '[PAD]']\n", + "14103\n", + "Label = ['lock', '[PAD]', '[PAD]', '[PAD]']\n", + "14104\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14105\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "14106\n", + "Label = ['lock', '[PAD]', '[PAD]', '[PAD]']\n", + "14107\n", + "Label = ['is', 'expired', '[PAD]', '[PAD]']\n", + "14108\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "14109\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14110\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "14111\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14112\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14113\n", + "Label = ['namespace', '[PAD]', '[PAD]', '[PAD]']\n", + "14114\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", + "14115\n", + "Label = ['passed', 'check', '[PAD]', '[PAD]']\n", + "14116\n", + "Label = ['passed', 'check', '[PAD]', '[PAD]']\n", + "14117\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "14118\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", + "14119\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14120\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14121\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14122\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14123\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14124\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14125\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14126\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14127\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "14128\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14129\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14130\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14131\n", + "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", + "14132\n", + "Label = ['current', 'chunk', '[PAD]', '[PAD]']\n", + "14133\n", + "Label = ['has', 'long', 'lines', '[PAD]']\n", + "14134\n", + "Label = ['attachment', '[PAD]', '[PAD]', '[PAD]']\n", + "14135\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14136\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "14137\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14138\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14139\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "14140\n", + "Label = ['SMTPException', '[PAD]', '[PAD]', '[PAD]']\n", + "14141\n", + "Label = ['use', 'natural', 'primary', 'keys']\n", + "14142\n", + "Label = ['value', 'from', 'field', '[PAD]']\n", + "14143\n", + "Label = ['use', 'natural', 'foreign', 'keys']\n", + "14144\n", + "Label = ['WithData', '[PAD]', '[PAD]', '[PAD]']\n", + "14145\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "14146\n", + "Label = ['DEFER', 'FIELD', '[PAD]', '[PAD]']\n", + "14147\n", + "Label = ['model', 'identifier', '[PAD]', '[PAD]']\n", + "14148\n", + "Label = ['DeserializationError', '[PAD]', '[PAD]', '[PAD]']\n", + "14149\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14150\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14151\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14152\n", + "Label = ['progress', 'width', '[PAD]', '[PAD]']\n", + "14153\n", + "Label = ['selected', 'fields', '[PAD]', '[PAD]']\n", + "14154\n", + "Label = ['stream', '[PAD]', '[PAD]', '[PAD]']\n", + "14155\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14156\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "14157\n", + "Label = ['WithData', '[PAD]', '[PAD]', '[PAD]']\n", + "14158\n", + "Label = ['pk', '[PAD]', '[PAD]', '[PAD]']\n", + "14159\n", + "Label = ['xml', '[PAD]', '[PAD]', '[PAD]']\n", + "14160\n", + "Label = ['SerializationError', '[PAD]', '[PAD]', '[PAD]']\n", + "14161\n", + "Label = ['startElement', '[PAD]', '[PAD]', '[PAD]']\n", + "14162\n", + "Label = ['hasAttribute', '[PAD]', '[PAD]', '[PAD]']\n", + "14163\n", + "Label = ['field', 'names', '[PAD]', '[PAD]']\n", + "14164\n", + "Label = ['obj', '[PAD]', '[PAD]', '[PAD]']\n", + "14165\n", + "Label = ['handle', 'forward', 'references', '[PAD]']\n", + "14166\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14167\n", + "Label = ['TimeField', '[PAD]', '[PAD]', '[PAD]']\n", + "14168\n", + "Label = ['dump', '[PAD]', '[PAD]', '[PAD]']\n", + "14169\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14170\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "14171\n", + "Label = ['send', '[PAD]', '[PAD]', '[PAD]']\n", + "14172\n", + "Label = ['lru', 'cache', '[PAD]', '[PAD]']\n", + "14173\n", + "Label = ['force', 'color', '[PAD]', '[PAD]']\n", + "14174\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14175\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "14176\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "14177\n", + "Label = ['is', 'serious', '[PAD]', '[PAD]']\n", + "14178\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14179\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14180\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "14181\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "14182\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14183\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "14184\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "14185\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "14186\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14187\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14188\n", + "Label = ['ArchiveException', '[PAD]', '[PAD]', '[PAD]']\n", + "14189\n", + "Label = ['scheme', '[PAD]', '[PAD]', '[PAD]']\n", + "14190\n", + "Label = ['settings', 'dict', '[PAD]', '[PAD]']\n", + "14191\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14192\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14193\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14194\n", + "Label = ['ancestry', '[PAD]', '[PAD]', '[PAD]']\n", + "14195\n", + "Label = ['migration', '[PAD]', '[PAD]', '[PAD]']\n", + "14196\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14197\n", + "Label = ['migration', 'name', '[PAD]', '[PAD]']\n", + "14198\n", + "Label = ['work', 'path', '[PAD]', '[PAD]']\n", + "14199\n", + "Label = ['fp', '[PAD]', '[PAD]', '[PAD]']\n", + "14200\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14201\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "14202\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14203\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "14204\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "14205\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "14206\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14207\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14208\n", + "Label = ['fnmatchcase', '[PAD]', '[PAD]', '[PAD]']\n", + "14209\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14210\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "14211\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "14212\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14213\n", + "Label = ['msgattrib', 'options', '[PAD]', '[PAD]']\n", + "14214\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "14215\n", + "Label = ['cursor', '[PAD]', '[PAD]', '[PAD]']\n", + "14216\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "14217\n", + "Label = ['candidate', '[PAD]', '[PAD]', '[PAD]']\n", + "14218\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "14219\n", + "Label = ['dirs', '[PAD]', '[PAD]', '[PAD]']\n", + "14220\n", + "Label = ['compression', 'formats', '[PAD]', '[PAD]']\n", + "14221\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "14222\n", + "Label = ['invalid', 'tag', '[PAD]', '[PAD]']\n", + "14223\n", + "Label = ['check', '[PAD]', '[PAD]', '[PAD]']\n", + "14224\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "14225\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "14226\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14227\n", + "Label = ['MIGRATE', 'LABEL', '[PAD]', '[PAD]']\n", + "14228\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14229\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14230\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14231\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14232\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14233\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14234\n", + "Label = ['db', 'table', '[PAD]', '[PAD]']\n", + "14235\n", + "Label = ['verbosity', '[PAD]', '[PAD]', '[PAD]']\n", + "14236\n", + "Label = ['primary', 'keys', '[PAD]', '[PAD]']\n", + "14237\n", + "Label = ['models', 'module', '[PAD]', '[PAD]']\n", + "14238\n", + "Label = ['models', 'module', '[PAD]', '[PAD]']\n", + "14239\n", + "Label = ['get', 'public', 'serializer', 'formats']\n", + "14240\n", + "Label = ['proxy', '[PAD]', '[PAD]', '[PAD]']\n", + "14241\n", + "Label = ['fields', '[PAD]', '[PAD]', '[PAD]']\n", + "14242\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14243\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "14244\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14245\n", + "Label = ['get', 'app', 'config', '[PAD]']\n", + "14246\n", + "Label = ['validate', 'app', 'names', '[PAD]']\n", + "14247\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "14248\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "14249\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14250\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "14251\n", + "Label = ['use', 'ipv6', '[PAD]', '[PAD]']\n", + "14252\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14253\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14254\n", + "Label = ['tup', '[PAD]', '[PAD]', '[PAD]']\n", + "14255\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14256\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "14257\n", + "Label = ['abspath', '[PAD]', '[PAD]', '[PAD]']\n", + "14258\n", + "Label = ['basedir', '[PAD]', '[PAD]', '[PAD]']\n", + "14259\n", + "Label = ['dirs', '[PAD]', '[PAD]', '[PAD]']\n", + "14260\n", + "Label = ['help', '[PAD]', '[PAD]', '[PAD]']\n", + "14261\n", + "Label = ['set', 'completer', '[PAD]', '[PAD]']\n", + "14262\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14263\n", + "Label = ['output', 'hash', '[PAD]', '[PAD]']\n", + "14264\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14265\n", + "Label = ['SUCCESS', '[PAD]', '[PAD]', '[PAD]']\n", + "14266\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14267\n", + "Label = ['migration', '[PAD]', '[PAD]', '[PAD]']\n", + "14268\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "14269\n", + "Label = ['get', 'app', 'config', '[PAD]']\n", + "14270\n", + "Label = ['app', 'label', '[PAD]', '[PAD]']\n", + "14271\n", + "Label = ['confirm', '[PAD]', '[PAD]', '[PAD]']\n", + "14272\n", + "Label = ['settings', 'dict', '[PAD]', '[PAD]']\n", + "14273\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14274\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14275\n", + "Label = ['streaming', '[PAD]', '[PAD]', '[PAD]']\n", + "14276\n", + "Label = ['streaming', '[PAD]', '[PAD]', '[PAD]']\n", + "14277\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "14278\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14279\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "14280\n", + "Label = ['secret', '[PAD]', '[PAD]', '[PAD]']\n", + "14281\n", + "Label = ['request', 'csrf', 'token', '[PAD]']\n", + "14282\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14283\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14284\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14285\n", + "Label = ['META', '[PAD]', '[PAD]', '[PAD]']\n", + "14286\n", + "Label = ['PREPEND', 'WWW', '[PAD]', '[PAD]']\n", + "14287\n", + "Label = ['should', 'redirect', 'with', 'slash']\n", + "14288\n", + "Label = ['DEBUG', '[PAD]', '[PAD]', '[PAD]']\n", + "14289\n", + "Label = ['should', 'redirect', 'with', 'slash']\n", + "14290\n", + "Label = ['is', 'internal', 'request', '[PAD]']\n", + "14291\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "14292\n", + "Label = ['app', 'configs', '[PAD]', '[PAD]']\n", + "14293\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "14294\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "14295\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14296\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "14297\n", + "Label = ['model', 'class', '[PAD]', '[PAD]']\n", + "14298\n", + "Label = ['now', '[PAD]', '[PAD]', '[PAD]']\n", + "14299\n", + "Label = ['isleap', '[PAD]', '[PAD]', '[PAD]']\n", + "14300\n", + "Label = ['since', '[PAD]', '[PAD]', '[PAD]']\n", + "14301\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "14302\n", + "Label = ['COMMON', 'WORDS', '[PAD]', '[PAD]']\n", + "14303\n", + "Label = ['sections', '[PAD]', '[PAD]', '[PAD]']\n", + "14304\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "14305\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14306\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "14307\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "14308\n", + "Label = ['MAXSIZE', '[PAD]', '[PAD]', '[PAD]']\n", + "14309\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14310\n", + "Label = ['fullname', '[PAD]', '[PAD]', '[PAD]']\n", + "14311\n", + "Label = ['known', 'modules', '[PAD]', '[PAD]']\n", + "14312\n", + "Label = ['mod', '[PAD]', '[PAD]', '[PAD]']\n", + "14313\n", + "Label = ['attr', '[PAD]', '[PAD]', '[PAD]']\n", + "14314\n", + "Label = ['add', 'module', '[PAD]', '[PAD]']\n", + "14315\n", + "Label = ['add', 'module', '[PAD]', '[PAD]']\n", + "14316\n", + "Label = ['dict', '[PAD]', '[PAD]', '[PAD]']\n", + "14317\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14318\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "14319\n", + "Label = ['klass', '[PAD]', '[PAD]', '[PAD]']\n", + "14320\n", + "Label = ['memoryview', '[PAD]', '[PAD]', '[PAD]']\n", + "14321\n", + "Label = ['kw', '[PAD]', '[PAD]', '[PAD]']\n", + "14322\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14323\n", + "Label = ['ljust', '[PAD]', '[PAD]', '[PAD]']\n", + "14324\n", + "Label = ['offset', 'mins', '[PAD]', '[PAD]']\n", + "14325\n", + "Label = ['offset', '[PAD]', '[PAD]', '[PAD]']\n", + "14326\n", + "Label = ['logging', 'config', 'func', '[PAD]']\n", + "14327\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14328\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "14329\n", + "Label = ['connector', '[PAD]', '[PAD]', '[PAD]']\n", + "14330\n", + "Label = ['class', '[PAD]', '[PAD]', '[PAD]']\n", + "14331\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "14332\n", + "Label = ['hostname', '[PAD]', '[PAD]', '[PAD]']\n", + "14333\n", + "Label = ['categories', '[PAD]', '[PAD]', '[PAD]']\n", + "14334\n", + "Label = ['addQuickElement', '[PAD]', '[PAD]', '[PAD]']\n", + "14335\n", + "Label = ['addQuickElement', '[PAD]', '[PAD]', '[PAD]']\n", + "14336\n", + "Label = ['enclosures', '[PAD]', '[PAD]', '[PAD]']\n", + "14337\n", + "Label = ['feed', '[PAD]', '[PAD]', '[PAD]']\n", + "14338\n", + "Label = ['cat', '[PAD]', '[PAD]', '[PAD]']\n", + "14339\n", + "Label = ['addQuickElement', '[PAD]', '[PAD]', '[PAD]']\n", + "14340\n", + "Label = ['addQuickElement', '[PAD]', '[PAD]', '[PAD]']\n", + "14341\n", + "Label = ['repo', 'dir', '[PAD]', '[PAD]']\n", + "14342\n", + "Label = ['USE', 'INOTIFY', '[PAD]', '[PAD]']\n", + "14343\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14344\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "14345\n", + "Label = ['signal', '[PAD]', '[PAD]', '[PAD]']\n", + "14346\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14347\n", + "Label = ['str', '[PAD]', '[PAD]', '[PAD]']\n", + "14348\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "14349\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "14350\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14351\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14352\n", + "Label = ['timezone', '[PAD]', '[PAD]', '[PAD]']\n", + "14353\n", + "Label = ['USE', 'TZ', '[PAD]', '[PAD]']\n", + "14354\n", + "Label = ['localize', '[PAD]', '[PAD]', '[PAD]']\n", + "14355\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14356\n", + "Label = ['neg', '[PAD]', '[PAD]', '[PAD]']\n", + "14357\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14358\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14359\n", + "Label = ['ch', '[PAD]', '[PAD]', '[PAD]']\n", + "14360\n", + "Label = ['ch', '[PAD]', '[PAD]', '[PAD]']\n", + "14361\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "14362\n", + "Label = ['z', '[PAD]', '[PAD]', '[PAD]']\n", + "14363\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14364\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14365\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "14366\n", + "Label = ['str', '[PAD]', '[PAD]', '[PAD]']\n", + "14367\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14368\n", + "Label = ['add', 'truncation', 'text', '[PAD]']\n", + "14369\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14370\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14371\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "14372\n", + "Label = ['bit', '[PAD]', '[PAD]', '[PAD]']\n", + "14373\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14374\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14375\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "14376\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14377\n", + "Label = ['warning', '[PAD]', '[PAD]', '[PAD]']\n", + "14378\n", + "Label = ['addr', '[PAD]', '[PAD]', '[PAD]']\n", + "14379\n", + "Label = ['ipv4', 'mapped', '[PAD]', '[PAD]']\n", + "14380\n", + "Label = ['ip', 'str', '[PAD]', '[PAD]']\n", + "14381\n", + "Label = ['color', 'names', '[PAD]', '[PAD]']\n", + "14382\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14383\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14384\n", + "Label = ['cc', '[PAD]', '[PAD]', '[PAD]']\n", + "14385\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "14386\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "14387\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "14388\n", + "Label = ['cache', 'key', '[PAD]', '[PAD]']\n", + "14389\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "14390\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "14391\n", + "Label = ['query', 'val', '[PAD]', '[PAD]']\n", + "14392\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "14393\n", + "Label = ['urlsafe', 'b64decode', '[PAD]', '[PAD]']\n", + "14394\n", + "Label = ['etag', 'matches', '[PAD]', '[PAD]']\n", + "14395\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14396\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "14397\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "14398\n", + "Label = ['netloc', '[PAD]', '[PAD]', '[PAD]']\n", + "14399\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "14400\n", + "Label = ['extension', 'map', '[PAD]', '[PAD]']\n", + "14401\n", + "Label = ['USE', 'L10N', '[PAD]', '[PAD]']\n", + "14402\n", + "Label = ['USE', 'L10N', '[PAD]', '[PAD]']\n", + "14403\n", + "Label = ['USE', 'L10N', '[PAD]', '[PAD]']\n", + "14404\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "14405\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "14406\n", + "Label = ['date', '[PAD]', '[PAD]', '[PAD]']\n", + "14407\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "14408\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14409\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14410\n", + "Label = ['offset', '[PAD]', '[PAD]', '[PAD]']\n", + "14411\n", + "Label = ['week', 'number', '[PAD]', '[PAD]']\n", + "14412\n", + "Label = ['doy', '[PAD]', '[PAD]', '[PAD]']\n", + "14413\n", + "Label = ['choices', '[PAD]', '[PAD]', '[PAD]']\n", + "14414\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14415\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "14416\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", + "14417\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14418\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14419\n", + "Label = ['token', 'type', '[PAD]', '[PAD]']\n", + "14420\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14421\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "14422\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14423\n", + "Label = ['localedir', '[PAD]', '[PAD]', '[PAD]']\n", + "14424\n", + "Label = ['language', '[PAD]', '[PAD]', '[PAD]']\n", + "14425\n", + "Label = ['find', '[PAD]', '[PAD]', '[PAD]']\n", + "14426\n", + "Label = ['supported', 'code', '[PAD]', '[PAD]']\n", + "14427\n", + "Label = ['outf', '[PAD]', '[PAD]', '[PAD]']\n", + "14428\n", + "Label = ['SOCKS4', '[PAD]', '[PAD]', '[PAD]']\n", + "14429\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14430\n", + "Label = ['remote', 'dns', '[PAD]', '[PAD]']\n", + "14431\n", + "Label = ['destport', '[PAD]', '[PAD]', '[PAD]']\n", + "14432\n", + "Label = ['setup', 'funcs', '[PAD]', '[PAD]']\n", + "14433\n", + "Label = ['temp', '[PAD]', '[PAD]', '[PAD]']\n", + "14434\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "14435\n", + "Label = ['mixed', '[PAD]', '[PAD]', '[PAD]']\n", + "14436\n", + "Label = ['compat', 'subprocess', 'get', 'DEVNULL']\n", + "14437\n", + "Label = ['asciire', '[PAD]', '[PAD]', '[PAD]']\n", + "14438\n", + "Label = ['list', 'e', '[PAD]', '[PAD]']\n", + "14439\n", + "Label = ['etree', 'iter', '[PAD]', '[PAD]']\n", + "14440\n", + "Label = ['namespace', 'map', '[PAD]', '[PAD]']\n", + "14441\n", + "Label = ['xpath', '[PAD]', '[PAD]', '[PAD]']\n", + "14442\n", + "Label = ['compat', 'os', 'name', '[PAD]']\n", + "14443\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "14444\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14445\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14446\n", + "Label = ['environ', '[PAD]', '[PAD]', '[PAD]']\n", + "14447\n", + "Label = ['environ', '[PAD]', '[PAD]', '[PAD]']\n", + "14448\n", + "Label = ['drive', '[PAD]', '[PAD]', '[PAD]']\n", + "14449\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "14450\n", + "Label = ['PRIVATE', 'OPTS', '[PAD]', '[PAD]']\n", + "14451\n", + "Label = ['eqre', '[PAD]', '[PAD]', '[PAD]']\n", + "14452\n", + "Label = ['userConfFile', '[PAD]', '[PAD]', '[PAD]']\n", + "14453\n", + "Label = ['takes', 'value', '[PAD]', '[PAD]']\n", + "14454\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "14455\n", + "Label = ['kw', '[PAD]', '[PAD]', '[PAD]']\n", + "14456\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14457\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14458\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14459\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14460\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14461\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14462\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14463\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14464\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14465\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14466\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14467\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14468\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14469\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14470\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14471\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14472\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14473\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14474\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14475\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14476\n", + "Label = ['add', 'option', '[PAD]', '[PAD]']\n", + "14477\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "14478\n", + "Label = ['framesize', 'len', '[PAD]', '[PAD]']\n", + "14479\n", + "Label = ['reader', '[PAD]', '[PAD]', '[PAD]']\n", + "14480\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14481\n", + "Label = ['patched', 'functions', '[PAD]', '[PAD]']\n", + "14482\n", + "Label = ['tag', 'code', '[PAD]', '[PAD]']\n", + "14483\n", + "Label = ['name', 'idx', '[PAD]', '[PAD]']\n", + "14484\n", + "Label = ['c2', '[PAD]', '[PAD]', '[PAD]']\n", + "14485\n", + "Label = ['metadata', 'count', '[PAD]', '[PAD]']\n", + "14486\n", + "Label = ['method', 'idxs', '[PAD]', '[PAD]']\n", + "14487\n", + "Label = ['c2', '[PAD]', '[PAD]', '[PAD]']\n", + "14488\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "14489\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", + "14490\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "14491\n", + "Label = ['KNOWN', 'EXTENSIONS', '[PAD]', '[PAD]']\n", + "14492\n", + "Label = ['chain', '[PAD]', '[PAD]', '[PAD]']\n", + "14493\n", + "Label = ['path', 'basename', '[PAD]', '[PAD]']\n", + "14494\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "14495\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14496\n", + "Label = ['class', 'name', '[PAD]', '[PAD]']\n", + "14497\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "14498\n", + "Label = ['html', 'element', '[PAD]', '[PAD]']\n", + "14499\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "14500\n", + "Label = ['el', '[PAD]', '[PAD]', '[PAD]']\n", + "14501\n", + "Label = ['update', 'cmd', '[PAD]', '[PAD]']\n", + "14502\n", + "Label = ['sock', '[PAD]', '[PAD]', '[PAD]']\n", + "14503\n", + "Label = ['filtered', 'headers', '[PAD]', '[PAD]']\n", + "14504\n", + "Label = ['do', 'open', '[PAD]', '[PAD]']\n", + "14505\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14506\n", + "Label = ['timedelta', '[PAD]', '[PAD]', '[PAD]']\n", + "14507\n", + "Label = ['upload', 'date', '[PAD]', '[PAD]']\n", + "14508\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "14509\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", + "14510\n", + "Label = ['WriteConsoleW', '[PAD]', '[PAD]', '[PAD]']\n", + "14511\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "14512\n", + "Label = ['Structure', '[PAD]', '[PAD]', '[PAD]']\n", + "14513\n", + "Label = ['lock', 'file', 'overlapped', 'p']\n", + "14514\n", + "Label = ['lock', 'file', 'overlapped', 'p']\n", + "14515\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14516\n", + "Label = ['units', 're', '[PAD]', '[PAD]']\n", + "14517\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14518\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14519\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "14520\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "14521\n", + "Label = ['communicate', '[PAD]', '[PAD]', '[PAD]']\n", + "14522\n", + "Label = ['startv', '[PAD]', '[PAD]', '[PAD]']\n", + "14523\n", + "Label = ['end', 'page', '[PAD]', '[PAD]']\n", + "14524\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "14525\n", + "Label = ['req', 'url', '[PAD]', '[PAD]']\n", + "14526\n", + "Label = ['boundary', '[PAD]', '[PAD]', '[PAD]']\n", + "14527\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14528\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "14529\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14530\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14531\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14532\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "14533\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "14534\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "14535\n", + "Label = ['str', '[PAD]', '[PAD]', '[PAD]']\n", + "14536\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14537\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "14538\n", + "Label = ['operator', 'rex', '[PAD]', '[PAD]']\n", + "14539\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14540\n", + "Label = ['operator', 'rex', '[PAD]', '[PAD]']\n", + "14541\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14542\n", + "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", + "14543\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "14544\n", + "Label = ['LEGACY', 'NAMESPACES', '[PAD]', '[PAD]']\n", + "14545\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", + "14546\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14547\n", + "Label = ['element', '[PAD]', '[PAD]', '[PAD]']\n", + "14548\n", + "Label = ['findall', '[PAD]', '[PAD]', '[PAD]']\n", + "14549\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "14550\n", + "Label = ['hexlify', '[PAD]', '[PAD]', '[PAD]']\n", + "14551\n", + "Label = ['pseudo', 'random', '[PAD]', '[PAD]']\n", + "14552\n", + "Label = ['errno', '[PAD]', '[PAD]', '[PAD]']\n", + "14553\n", + "Label = ['expr', '[PAD]', '[PAD]', '[PAD]']\n", + "14554\n", + "Label = ['right', 'val', '[PAD]', '[PAD]']\n", + "14555\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14556\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "14557\n", + "Label = ['NUMERIC', 'FIELDS', '[PAD]', '[PAD]']\n", + "14558\n", + "Label = ['screen', 'file', '[PAD]', '[PAD]']\n", + "14559\n", + "Label = ['output', 'process', '[PAD]', '[PAD]']\n", + "14560\n", + "Label = ['errno', '[PAD]', '[PAD]', '[PAD]']\n", + "14561\n", + "Label = ['platform', '[PAD]', '[PAD]', '[PAD]']\n", + "14562\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "14563\n", + "Label = ['ie', '[PAD]', '[PAD]', '[PAD]']\n", + "14564\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14565\n", + "Label = ['exc', 'info', '[PAD]', '[PAD]']\n", + "14566\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14567\n", + "Label = ['format', 'exception', '[PAD]', '[PAD]']\n", + "14568\n", + "Label = ['exc', 'info', '[PAD]', '[PAD]']\n", + "14569\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14570\n", + "Label = ['template', 'dict', '[PAD]', '[PAD]']\n", + "14571\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14572\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "14573\n", + "Label = ['ies', '[PAD]', '[PAD]', '[PAD]']\n", + "14574\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "14575\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14576\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14577\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "14578\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "14579\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14580\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14581\n", + "Label = ['current', 'selector', '[PAD]', '[PAD]']\n", + "14582\n", + "Label = ['ctx', '[PAD]', '[PAD]', '[PAD]']\n", + "14583\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "14584\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "14585\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14586\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "14587\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14588\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14589\n", + "Label = ['to', 'stdout', '[PAD]', '[PAD]']\n", + "14590\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14591\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", + "14592\n", + "Label = ['report', 'error', '[PAD]', '[PAD]']\n", + "14593\n", + "Label = ['report', 'error', '[PAD]', '[PAD]']\n", + "14594\n", + "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", + "14595\n", + "Label = ['sub', 'data', '[PAD]', '[PAD]']\n", + "14596\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "14597\n", + "Label = ['filter', 'requested', 'info', '[PAD]']\n", + "14598\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14599\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14600\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14601\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14602\n", + "Label = ['Popen', '[PAD]', '[PAD]', '[PAD]']\n", + "14603\n", + "Label = ['proxies', '[PAD]', '[PAD]', '[PAD]']\n", + "14604\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14605\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14606\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14607\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14608\n", + "Label = ['fsize', '[PAD]', '[PAD]', '[PAD]']\n", + "14609\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14610\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "14611\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "14612\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "14613\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14614\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14615\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "14616\n", + "Label = ['proc', '[PAD]', '[PAD]', '[PAD]']\n", + "14617\n", + "Label = ['speed', '[PAD]', '[PAD]', '[PAD]']\n", + "14618\n", + "Label = ['hook', 'progress', '[PAD]', '[PAD]']\n", + "14619\n", + "Label = ['basic', 'args', '[PAD]', '[PAD]']\n", + "14620\n", + "Label = ['report', 'error', '[PAD]', '[PAD]']\n", + "14621\n", + "Label = ['fragment', 'run', 'entry', 'table']\n", + "14622\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "14623\n", + "Label = ['fragments', 'list', '[PAD]', '[PAD]']\n", + "14624\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "14625\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "14626\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14627\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "14628\n", + "Label = ['loads', '[PAD]', '[PAD]', '[PAD]']\n", + "14629\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14630\n", + "Label = ['fragment', 'filename', '[PAD]', '[PAD]']\n", + "14631\n", + "Label = ['ydl', '[PAD]', '[PAD]', '[PAD]']\n", + "14632\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "14633\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "14634\n", + "Label = ['report', 'retry', 'fragment', '[PAD]']\n", + "14635\n", + "Label = ['pack', '[PAD]', '[PAD]', '[PAD]']\n", + "14636\n", + "Label = ['pack', '[PAD]', '[PAD]', '[PAD]']\n", + "14637\n", + "Label = ['unpack', '[PAD]', '[PAD]', '[PAD]']\n", + "14638\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14639\n", + "Label = ['ad', 'frags', '[PAD]', '[PAD]']\n", + "14640\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14641\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", + "14642\n", + "Label = ['fullmsg', '[PAD]', '[PAD]', '[PAD]']\n", + "14643\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14644\n", + "Label = ['format', 'percent', '[PAD]', '[PAD]']\n", + "14645\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14646\n", + "Label = ['max', 'sleep', 'interval', '[PAD]']\n", + "14647\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "14648\n", + "Label = ['chunk', 'size', '[PAD]', '[PAD]']\n", + "14649\n", + "Label = ['data', 'len', '[PAD]', '[PAD]']\n", + "14650\n", + "Label = ['data', 'block', '[PAD]', '[PAD]']\n", + "14651\n", + "Label = ['data', 'block', '[PAD]', '[PAD]']\n", + "14652\n", + "Label = ['slow', 'down', '[PAD]', '[PAD]']\n", + "14653\n", + "Label = ['data', 'len', '[PAD]', '[PAD]']\n", + "14654\n", + "Label = ['eta', '[PAD]', '[PAD]', '[PAD]']\n", + "14655\n", + "Label = ['calc', 'eta', '[PAD]', '[PAD]']\n", + "14656\n", + "Label = ['error', 'message', '[PAD]', '[PAD]']\n", + "14657\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "14658\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14659\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", + "14660\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14661\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14662\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "14663\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14664\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14665\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "14666\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14667\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "14668\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "14669\n", + "Label = ['bc', 'url', '[PAD]', '[PAD]']\n", + "14670\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "14671\n", + "Label = ['fmt', '[PAD]', '[PAD]', '[PAD]']\n", + "14672\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14673\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14674\n", + "Label = ['result', 'url', '[PAD]', '[PAD]']\n", + "14675\n", + "Label = ['quote', 'plus', '[PAD]', '[PAD]']\n", + "14676\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "14677\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14678\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14679\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14680\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14681\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14682\n", + "Label = ['search', 'url', '[PAD]', '[PAD]']\n", + "14683\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "14684\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14685\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14686\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14687\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "14688\n", + "Label = ['format', 'id', '[PAD]', '[PAD]']\n", + "14689\n", + "Label = ['unhexlify', '[PAD]', '[PAD]', '[PAD]']\n", + "14690\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "14691\n", + "Label = ['linesep', '[PAD]', '[PAD]', '[PAD]']\n", + "14692\n", + "Label = ['links', 'url', '[PAD]', '[PAD]']\n", + "14693\n", + "Label = ['sub', 'path', '[PAD]', '[PAD]']\n", + "14694\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14695\n", + "Label = ['caption', '[PAD]', '[PAD]', '[PAD]']\n", + "14696\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14697\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14698\n", + "Label = ['expiration', 'date', '[PAD]', '[PAD]']\n", + "14699\n", + "Label = ['hex', '[PAD]', '[PAD]', '[PAD]']\n", + "14700\n", + "Label = ['clear', 'text', '[PAD]', '[PAD]']\n", + "14701\n", + "Label = ['hexdigest', '[PAD]', '[PAD]', '[PAD]']\n", + "14702\n", + "Label = ['sig', '[PAD]', '[PAD]', '[PAD]']\n", + "14703\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14704\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14705\n", + "Label = ['smil', 'url', '[PAD]', '[PAD]']\n", + "14706\n", + "Label = ['smil', 'url', '[PAD]', '[PAD]']\n", + "14707\n", + "Label = ['partition', '[PAD]', '[PAD]', '[PAD]']\n", + "14708\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14709\n", + "Label = ['sources', 'url', '[PAD]', '[PAD]']\n", + "14710\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14711\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14712\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14713\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14714\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14715\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "14716\n", + "Label = ['set', 'cookie', '[PAD]', '[PAD]']\n", + "14717\n", + "Label = ['info', 'url', '[PAD]', '[PAD]']\n", + "14718\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "14719\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "14720\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "14721\n", + "Label = ['raise', 'geo', 'restricted', '[PAD]']\n", + "14722\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "14723\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14724\n", + "Label = ['creator', '[PAD]', '[PAD]', '[PAD]']\n", + "14725\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14726\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14727\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14728\n", + "Label = ['metadata', 'url', '[PAD]', '[PAD]']\n", + "14729\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14730\n", + "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", + "14731\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14732\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14733\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14734\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "14735\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14736\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14737\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14738\n", + "Label = ['sub', 'list', '[PAD]', '[PAD]']\n", + "14739\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14740\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14741\n", + "Label = ['edge', '[PAD]', '[PAD]', '[PAD]']\n", + "14742\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14743\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14744\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14745\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14746\n", + "Label = ['status', 'code', '[PAD]', '[PAD]']\n", + "14747\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "14748\n", + "Label = ['categories', '[PAD]', '[PAD]', '[PAD]']\n", + "14749\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14750\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14751\n", + "Label = ['extract', 'mpd', 'formats', '[PAD]']\n", + "14752\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14753\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14754\n", + "Label = ['call', 'api', '[PAD]', '[PAD]']\n", + "14755\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "14756\n", + "Label = ['season', '[PAD]', '[PAD]', '[PAD]']\n", + "14757\n", + "Label = ['season', '[PAD]', '[PAD]', '[PAD]']\n", + "14758\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14759\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14760\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "14761\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14762\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14763\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14764\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14765\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "14766\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "14767\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "14768\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "14769\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "14770\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "14771\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "14772\n", + "Label = ['m3u8', 'url', '[PAD]', '[PAD]']\n", + "14773\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14774\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "14775\n", + "Label = ['sc', '[PAD]', '[PAD]', '[PAD]']\n", + "14776\n", + "Label = ['tbr', '[PAD]', '[PAD]', '[PAD]']\n", + "14777\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14778\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14779\n", + "Label = ['params', '[PAD]', '[PAD]', '[PAD]']\n", + "14780\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "14781\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14782\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "14783\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14784\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14785\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14786\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14787\n", + "Label = ['categories', '[PAD]', '[PAD]', '[PAD]']\n", + "14788\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14789\n", + "Label = ['CONTENT', 'DOMAIN', '[PAD]', '[PAD]']\n", + "14790\n", + "Label = ['m3u8', 'formats', '[PAD]', '[PAD]']\n", + "14791\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14792\n", + "Label = ['cuts', '[PAD]', '[PAD]', '[PAD]']\n", + "14793\n", + "Label = ['thumbnail', 'data', '[PAD]', '[PAD]']\n", + "14794\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14795\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14796\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "14797\n", + "Label = ['categories', '[PAD]', '[PAD]', '[PAD]']\n", + "14798\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14799\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "14800\n", + "Label = ['query', 'api', '[PAD]', '[PAD]']\n", + "14801\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "14802\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14803\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14804\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "14805\n", + "Label = ['datestamp', '[PAD]', '[PAD]', '[PAD]']\n", + "14806\n", + "Label = ['strftime', '[PAD]', '[PAD]', '[PAD]']\n", + "14807\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "14808\n", + "Label = ['brs', '[PAD]', '[PAD]', '[PAD]']\n", + "14809\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14810\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14811\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14812\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14813\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14814\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14815\n", + "Label = ['widget', 'config', '[PAD]', '[PAD]']\n", + "14816\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "14817\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "14818\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", + "14819\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14820\n", + "Label = ['videos', '[PAD]', '[PAD]', '[PAD]']\n", + "14821\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14822\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14823\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "14824\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14825\n", + "Label = ['download', 'xml', '[PAD]', '[PAD]']\n", + "14826\n", + "Label = ['manifest', 'url', '[PAD]', '[PAD]']\n", + "14827\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14828\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "14829\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14830\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14831\n", + "Label = ['html', 'search', 'meta', '[PAD]']\n", + "14832\n", + "Label = ['attrib', '[PAD]', '[PAD]', '[PAD]']\n", + "14833\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "14834\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14835\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "14836\n", + "Label = ['preference', '[PAD]', '[PAD]', '[PAD]']\n", + "14837\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14838\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14839\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "14840\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14841\n", + "Label = ['download', 'xml', '[PAD]', '[PAD]']\n", + "14842\n", + "Label = ['store', '[PAD]', '[PAD]', '[PAD]']\n", + "14843\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "14844\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "14845\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14846\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "14847\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14848\n", + "Label = ['age', 'limit', '[PAD]', '[PAD]']\n", + "14849\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "14850\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14851\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "14852\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "14853\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14854\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14855\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "14856\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14857\n", + "Label = ['scheme', '[PAD]', '[PAD]', '[PAD]']\n", + "14858\n", + "Label = ['like', 'count', '[PAD]', '[PAD]']\n", + "14859\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14860\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "14861\n", + "Label = ['API', 'BASE', '[PAD]', '[PAD]']\n", + "14862\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14863\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "14864\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14865\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "14866\n", + "Label = ['last', 'id', '[PAD]', '[PAD]']\n", + "14867\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14868\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "14869\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14870\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14871\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "14872\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14873\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "14874\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14875\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "14876\n", + "Label = ['manifest', 'url', '[PAD]', '[PAD]']\n", + "14877\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "14878\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14879\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14880\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "14881\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "14882\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "14883\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "14884\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14885\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14886\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14887\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14888\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14889\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14890\n", + "Label = ['player', 'url', '[PAD]', '[PAD]']\n", + "14891\n", + "Label = ['ts', '[PAD]', '[PAD]', '[PAD]']\n", + "14892\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "14893\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14894\n", + "Label = ['episode', '[PAD]', '[PAD]', '[PAD]']\n", + "14895\n", + "Label = ['expires', '[PAD]', '[PAD]', '[PAD]']\n", + "14896\n", + "Label = ['exe', '[PAD]', '[PAD]', '[PAD]']\n", + "14897\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14898\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "14899\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "14900\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14901\n", + "Label = ['ns', 'map', '[PAD]', '[PAD]']\n", + "14902\n", + "Label = ['ns', '[PAD]', '[PAD]', '[PAD]']\n", + "14903\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14904\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "14905\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "14906\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14907\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "14908\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "14909\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14910\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14911\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14912\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "14913\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "14914\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "14915\n", + "Label = ['html', 'search', 'regex', '[PAD]']\n", + "14916\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "14917\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "14918\n", + "Label = ['signed', 'md5', '[PAD]', '[PAD]']\n", + "14919\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "14920\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14921\n", + "Label = ['m3u8', 'formats', '[PAD]', '[PAD]']\n", + "14922\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "14923\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14924\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14925\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "14926\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "14927\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14928\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "14929\n", + "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", + "14930\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", + "14931\n", + "Label = ['media', '[PAD]', '[PAD]', '[PAD]']\n", + "14932\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "14933\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14934\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "14935\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "14936\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14937\n", + "Label = ['GEO', 'BYPASS', '[PAD]', '[PAD]']\n", + "14938\n", + "Label = ['x', 'forwarded', 'for', 'ip']\n", + "14939\n", + "Label = ['initialize', '[PAD]', '[PAD]', '[PAD]']\n", + "14940\n", + "Label = ['x', 'forwarded', 'for', 'ip']\n", + "14941\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "14942\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "14943\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14944\n", + "Label = ['errmsg', '[PAD]', '[PAD]', '[PAD]']\n", + "14945\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "14946\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "14947\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14948\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14949\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "14950\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "14951\n", + "Label = ['username', '[PAD]', '[PAD]', '[PAD]']\n", + "14952\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14953\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14954\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14955\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14956\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14957\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14958\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14959\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14960\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14961\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14962\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14963\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14964\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "14965\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "14966\n", + "Label = ['mime', 'type', '[PAD]', '[PAD]']\n", + "14967\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "14968\n", + "Label = ['download', 'webpage', 'handle', '[PAD]']\n", + "14969\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14970\n", + "Label = ['stream', 'name', '[PAD]', '[PAD]']\n", + "14971\n", + "Label = ['smil', '[PAD]', '[PAD]', '[PAD]']\n", + "14972\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "14973\n", + "Label = ['upload', 'date', '[PAD]', '[PAD]']\n", + "14974\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "14975\n", + "Label = ['m3u8', 'formats', '[PAD]', '[PAD]']\n", + "14976\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "14977\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14978\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "14979\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "14980\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "14981\n", + "Label = ['ceil', '[PAD]', '[PAD]', '[PAD]']\n", + "14982\n", + "Label = ['res', '[PAD]', '[PAD]', '[PAD]']\n", + "14983\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14984\n", + "Label = ['fourcc', '[PAD]', '[PAD]', '[PAD]']\n", + "14985\n", + "Label = ['fragment', 'repeat', '[PAD]', '[PAD]']\n", + "14986\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14987\n", + "Label = ['lang', '[PAD]', '[PAD]', '[PAD]']\n", + "14988\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "14989\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "14990\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14991\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14992\n", + "Label = ['parse', 'jwplayer', 'formats', '[PAD]']\n", + "14993\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "14994\n", + "Label = ['a', 'format', '[PAD]', '[PAD]']\n", + "14995\n", + "Label = ['msg', '[PAD]', '[PAD]', '[PAD]']\n", + "14996\n", + "Label = ['merge', 'subtitle', 'items', '[PAD]']\n", + "14997\n", + "Label = ['merge', 'subtitle', 'items', '[PAD]']\n", + "14998\n", + "Label = ['splitext', '[PAD]', '[PAD]', '[PAD]']\n", + "14999\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15000\n", + "Label = ['splitext', '[PAD]', '[PAD]', '[PAD]']\n", + "15001\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15002\n", + "Label = ['brightcove', 'id', '[PAD]', '[PAD]']\n", + "15003\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15004\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15005\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15006\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15007\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15008\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15009\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15010\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15011\n", + "Label = ['media', '[PAD]', '[PAD]', '[PAD]']\n", + "15012\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15013\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15014\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15015\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15016\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "15017\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "15018\n", + "Label = ['capitalize', '[PAD]', '[PAD]', '[PAD]']\n", + "15019\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15020\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15021\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "15022\n", + "Label = ['perform', 'url', '[PAD]', '[PAD]']\n", + "15023\n", + "Label = ['tbr', '[PAD]', '[PAD]', '[PAD]']\n", + "15024\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "15025\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15026\n", + "Label = ['sort', 'formats', '[PAD]', '[PAD]']\n", + "15027\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "15028\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15029\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15030\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15031\n", + "Label = ['data', 'zone', '[PAD]', '[PAD]']\n", + "15032\n", + "Label = ['feed', 'url', '[PAD]', '[PAD]']\n", + "15033\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15034\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15035\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15036\n", + "Label = ['uploader', '[PAD]', '[PAD]', '[PAD]']\n", + "15037\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15038\n", + "Label = ['call', 'api', '[PAD]', '[PAD]']\n", + "15039\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "15040\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15041\n", + "Label = ['LOGIN', 'REQUIRED', '[PAD]', '[PAD]']\n", + "15042\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "15043\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15044\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "15045\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15046\n", + "Label = ['suffix', '[PAD]', '[PAD]', '[PAD]']\n", + "15047\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "15048\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15049\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15050\n", + "Label = ['video', 'description', '[PAD]', '[PAD]']\n", + "15051\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "15052\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "15053\n", + "Label = ['download', 'request', '[PAD]', '[PAD]']\n", + "15054\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15055\n", + "Label = ['cc', 'license', '[PAD]', '[PAD]']\n", + "15056\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15057\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15058\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15059\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15060\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15061\n", + "Label = ['flv', 'file', '[PAD]', '[PAD]']\n", + "15062\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15063\n", + "Label = ['groupdict', '[PAD]', '[PAD]', '[PAD]']\n", + "15064\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15065\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15066\n", + "Label = ['host', '[PAD]', '[PAD]', '[PAD]']\n", + "15067\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "15068\n", + "Label = ['mobj', '[PAD]', '[PAD]', '[PAD]']\n", + "15069\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15070\n", + "Label = ['src', '[PAD]', '[PAD]', '[PAD]']\n", + "15071\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "15072\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "15073\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15074\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15075\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15076\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15077\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15078\n", + "Label = ['call', 'api', '[PAD]', '[PAD]']\n", + "15079\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "15080\n", + "Label = ['ch', '[PAD]', '[PAD]', '[PAD]']\n", + "15081\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "15082\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15083\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15084\n", + "Label = ['tp', 'formats', '[PAD]', '[PAD]']\n", + "15085\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15086\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15087\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15088\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "15089\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15090\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15091\n", + "Label = ['age', 'limit', '[PAD]', '[PAD]']\n", + "15092\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15093\n", + "Label = ['page', '[PAD]', '[PAD]', '[PAD]']\n", + "15094\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "15095\n", + "Label = ['movie', '[PAD]', '[PAD]', '[PAD]']\n", + "15096\n", + "Label = ['partner', 'id', '[PAD]', '[PAD]']\n", + "15097\n", + "Label = ['m3u8', 'url', '[PAD]', '[PAD]']\n", + "15098\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15099\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15100\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15101\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15102\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15103\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15104\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15105\n", + "Label = ['brand', '[PAD]', '[PAD]', '[PAD]']\n", + "15106\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15107\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "15108\n", + "Label = ['hexdigest', '[PAD]', '[PAD]', '[PAD]']\n", + "15109\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15110\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15111\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15112\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15113\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "15114\n", + "Label = ['LOGIN', 'REQUIRED', '[PAD]', '[PAD]']\n", + "15115\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15116\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15117\n", + "Label = ['tfa', 'results', '[PAD]', '[PAD]']\n", + "15118\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15119\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15120\n", + "Label = ['playlist', 'id', '[PAD]', '[PAD]']\n", + "15121\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15122\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15123\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15124\n", + "Label = ['start', 'time', '[PAD]', '[PAD]']\n", + "15125\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15126\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15127\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15128\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15129\n", + "Label = ['raise', 'geo', 'restricted', '[PAD]']\n", + "15130\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15131\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15132\n", + "Label = ['to', 'screen', '[PAD]', '[PAD]']\n", + "15133\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15134\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15135\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15136\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15137\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15138\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15139\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15140\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15141\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15142\n", + "Label = ['video', 'duration', '[PAD]', '[PAD]']\n", + "15143\n", + "Label = ['mobj', '[PAD]', '[PAD]', '[PAD]']\n", + "15144\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15145\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15146\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15147\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15148\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15149\n", + "Label = ['alert', 'message', '[PAD]', '[PAD]']\n", + "15150\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "15151\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15152\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "15153\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15154\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15155\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15156\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15157\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15158\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15159\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15160\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "15161\n", + "Label = ['pref', '[PAD]', '[PAD]', '[PAD]']\n", + "15162\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15163\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15164\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15165\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15166\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15167\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15168\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15169\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15170\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15171\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15172\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15173\n", + "Label = ['embed', 'url', '[PAD]', '[PAD]']\n", + "15174\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15175\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "15176\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15177\n", + "Label = ['login', 'result', '[PAD]', '[PAD]']\n", + "15178\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15179\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15180\n", + "Label = ['download', 'webpage', 'handle', '[PAD]']\n", + "15181\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15182\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15183\n", + "Label = ['og', 'search', 'thumbnail', '[PAD]']\n", + "15184\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15185\n", + "Label = ['choice', '[PAD]', '[PAD]', '[PAD]']\n", + "15186\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15187\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15188\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15189\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15190\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15191\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15192\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15193\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "15194\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15195\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "15196\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15197\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15198\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15199\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15200\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "15201\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "15202\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15203\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15204\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15205\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15206\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15207\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "15208\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15209\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15210\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15211\n", + "Label = ['exts', '[PAD]', '[PAD]', '[PAD]']\n", + "15212\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15213\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15214\n", + "Label = ['findall', '[PAD]', '[PAD]', '[PAD]']\n", + "15215\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15216\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "15217\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "15218\n", + "Label = ['series', '[PAD]', '[PAD]', '[PAD]']\n", + "15219\n", + "Label = ['season', 'el', '[PAD]', '[PAD]']\n", + "15220\n", + "Label = ['episode', 'number', '[PAD]', '[PAD]']\n", + "15221\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15222\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", + "15223\n", + "Label = ['original', 'subtitles', '[PAD]', '[PAD]']\n", + "15224\n", + "Label = ['msec', '[PAD]', '[PAD]', '[PAD]']\n", + "15225\n", + "Label = ['data', 'url', '[PAD]', '[PAD]']\n", + "15226\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15227\n", + "Label = ['video', 'path', '[PAD]', '[PAD]']\n", + "15228\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15229\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "15230\n", + "Label = ['child', '[PAD]', '[PAD]', '[PAD]']\n", + "15231\n", + "Label = ['quality', '[PAD]', '[PAD]', '[PAD]']\n", + "15232\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15233\n", + "Label = ['theplatform', 'url', '[PAD]', '[PAD]']\n", + "15234\n", + "Label = ['og', 'search', 'video', 'url']\n", + "15235\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15236\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15237\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15238\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15239\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15240\n", + "Label = ['js', '[PAD]', '[PAD]', '[PAD]']\n", + "15241\n", + "Label = ['video', 'info', '[PAD]', '[PAD]']\n", + "15242\n", + "Label = ['timestamp', '[PAD]', '[PAD]', '[PAD]']\n", + "15243\n", + "Label = ['html', 'search', 'regex', '[PAD]']\n", + "15244\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15245\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15246\n", + "Label = ['bitrates', '[PAD]', '[PAD]', '[PAD]']\n", + "15247\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15248\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15249\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15250\n", + "Label = ['hexdigest', '[PAD]', '[PAD]', '[PAD]']\n", + "15251\n", + "Label = ['rss', '[PAD]', '[PAD]', '[PAD]']\n", + "15252\n", + "Label = ['http', 'format', 'info', '[PAD]']\n", + "15253\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15254\n", + "Label = ['sort', '[PAD]', '[PAD]', '[PAD]']\n", + "15255\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15256\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "15257\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15258\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15259\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15260\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15261\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15262\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "15263\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15264\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15265\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15266\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15267\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15268\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15269\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15270\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15271\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15272\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15273\n", + "Label = ['flashvars', '[PAD]', '[PAD]', '[PAD]']\n", + "15274\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15275\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15276\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15277\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "15278\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "15279\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15280\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15281\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15282\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15283\n", + "Label = ['extract', 'wowza', 'formats', '[PAD]']\n", + "15284\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15285\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15286\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15287\n", + "Label = ['flash', 'vars', 's', '[PAD]']\n", + "15288\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15289\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "15290\n", + "Label = ['object', 'doc', '[PAD]', '[PAD]']\n", + "15291\n", + "Label = ['flashvars', '[PAD]', '[PAD]', '[PAD]']\n", + "15292\n", + "Label = ['build', 'brighcove', 'url', 'from']\n", + "15293\n", + "Label = ['src', '[PAD]', '[PAD]', '[PAD]']\n", + "15294\n", + "Label = ['mobj', '[PAD]', '[PAD]', '[PAD]']\n", + "15295\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15296\n", + "Label = ['playlist', 'dto', '[PAD]', '[PAD]']\n", + "15297\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15298\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15299\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15300\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15301\n", + "Label = ['ie', '[PAD]', '[PAD]', '[PAD]']\n", + "15302\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15303\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15304\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15305\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15306\n", + "Label = ['playlist', 'no', '[PAD]', '[PAD]']\n", + "15307\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15308\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15309\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15310\n", + "Label = ['asset', 'id', '[PAD]', '[PAD]']\n", + "15311\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15312\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15313\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15314\n", + "Label = ['stream', 'access', 'url', '[PAD]']\n", + "15315\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15316\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "15317\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "15318\n", + "Label = ['raise', 'geo', 'restricted', '[PAD]']\n", + "15319\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "15320\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15321\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15322\n", + "Label = ['html', 'search', 'regex', '[PAD]']\n", + "15323\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15324\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15325\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "15326\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15327\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15328\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15329\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15330\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15331\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15332\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15333\n", + "Label = ['definition', '[PAD]', '[PAD]', '[PAD]']\n", + "15334\n", + "Label = ['assn', '[PAD]', '[PAD]', '[PAD]']\n", + "15335\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15336\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15337\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15338\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15339\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15340\n", + "Label = ['season', 'number', '[PAD]', '[PAD]']\n", + "15341\n", + "Label = ['findall', '[PAD]', '[PAD]', '[PAD]']\n", + "15342\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15343\n", + "Label = ['THEPLATFORM', 'KEY', '[PAD]', '[PAD]']\n", + "15344\n", + "Label = ['theplatform', 'url', 'result', '[PAD]']\n", + "15345\n", + "Label = ['og', 'search', 'property', '[PAD]']\n", + "15346\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15347\n", + "Label = ['artists', 'names', '[PAD]', '[PAD]']\n", + "15348\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15349\n", + "Label = ['track', '[PAD]', '[PAD]', '[PAD]']\n", + "15350\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15351\n", + "Label = ['auth', 'data', '[PAD]', '[PAD]']\n", + "15352\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15353\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15354\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15355\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15356\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15357\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15358\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15359\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15360\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15361\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15362\n", + "Label = ['download', 'xml', '[PAD]', '[PAD]']\n", + "15363\n", + "Label = ['query', '[PAD]', '[PAD]', '[PAD]']\n", + "15364\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15365\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15366\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15367\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15368\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", + "15369\n", + "Label = ['linesep', '[PAD]', '[PAD]', '[PAD]']\n", + "15370\n", + "Label = ['author', '[PAD]', '[PAD]', '[PAD]']\n", + "15371\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", + "15372\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15373\n", + "Label = ['req', 'ext', '[PAD]', '[PAD]']\n", + "15374\n", + "Label = ['viewclip', '[PAD]', '[PAD]', '[PAD]']\n", + "15375\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15376\n", + "Label = ['extract', 'subtitles', '[PAD]', '[PAD]']\n", + "15377\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15378\n", + "Label = ['QUALITIES', '[PAD]', '[PAD]', '[PAD]']\n", + "15379\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15380\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15381\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15382\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15383\n", + "Label = ['video', 'data', '[PAD]', '[PAD]']\n", + "15384\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15385\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15386\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "15387\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15388\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15389\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15390\n", + "Label = ['language', '[PAD]', '[PAD]', '[PAD]']\n", + "15391\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15392\n", + "Label = ['extract', 'playlist', 'entries', '[PAD]']\n", + "15393\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15394\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "15395\n", + "Label = ['translation', '[PAD]', '[PAD]', '[PAD]']\n", + "15396\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15397\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "15398\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15399\n", + "Label = ['mcp', 'id', '[PAD]', '[PAD]']\n", + "15400\n", + "Label = ['aws', 'execute', 'api', '[PAD]']\n", + "15401\n", + "Label = ['isnumeric', '[PAD]', '[PAD]', '[PAD]']\n", + "15402\n", + "Label = ['embed', 'url', '[PAD]', '[PAD]']\n", + "15403\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "15404\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "15405\n", + "Label = ['subs', '[PAD]', '[PAD]', '[PAD]']\n", + "15406\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15407\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15408\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15409\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "15410\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "15411\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15412\n", + "Label = ['og', 'search', 'thumbnail', '[PAD]']\n", + "15413\n", + "Label = ['faults', 'message', '[PAD]', '[PAD]']\n", + "15414\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "15415\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15416\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15417\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15418\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15419\n", + "Label = ['ceil', '[PAD]', '[PAD]', '[PAD]']\n", + "15420\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15421\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15422\n", + "Label = ['is', 'm3u8', '[PAD]', '[PAD]']\n", + "15423\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15424\n", + "Label = ['streaming', 'path', '[PAD]', '[PAD]']\n", + "15425\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "15426\n", + "Label = ['chapters', 'xml', '[PAD]', '[PAD]']\n", + "15427\n", + "Label = ['comment', 'count', '[PAD]', '[PAD]']\n", + "15428\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15429\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "15430\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "15431\n", + "Label = ['FORMAT', 'IDS', '[PAD]', '[PAD]']\n", + "15432\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15433\n", + "Label = ['num4', '[PAD]', '[PAD]', '[PAD]']\n", + "15434\n", + "Label = ['digest', '[PAD]', '[PAD]', '[PAD]']\n", + "15435\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "15436\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", + "15437\n", + "Label = ['convert', 'subtitles', 'to', 'srt']\n", + "15438\n", + "Label = ['language', '[PAD]', '[PAD]', '[PAD]']\n", + "15439\n", + "Label = ['video', 'description', '[PAD]', '[PAD]']\n", + "15440\n", + "Label = ['video', 'description', '[PAD]', '[PAD]']\n", + "15441\n", + "Label = ['video', 'upload', 'date', '[PAD]']\n", + "15442\n", + "Label = ['format', 'info', '[PAD]', '[PAD]']\n", + "15443\n", + "Label = ['direct', 'video', 'url', '[PAD]']\n", + "15444\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15445\n", + "Label = ['subtitles', '[PAD]', '[PAD]', '[PAD]']\n", + "15446\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15447\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", + "15448\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15449\n", + "Label = ['upload', 'date', '[PAD]', '[PAD]']\n", + "15450\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15451\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "15452\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15453\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15454\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "15455\n", + "Label = ['FILE', 'NOT', 'FOUND', '[PAD]']\n", + "15456\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15457\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15458\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "15459\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15460\n", + "Label = ['KNOWN', 'FORMATS', '[PAD]', '[PAD]']\n", + "15461\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15462\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15463\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "15464\n", + "Label = ['ims', 'video', '[PAD]', '[PAD]']\n", + "15465\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15466\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15467\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15468\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15469\n", + "Label = ['api', 'json', '[PAD]', '[PAD]']\n", + "15470\n", + "Label = ['next', 'url', '[PAD]', '[PAD]']\n", + "15471\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15472\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15473\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15474\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "15475\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15476\n", + "Label = ['qualities', '[PAD]', '[PAD]', '[PAD]']\n", + "15477\n", + "Label = ['sort', 'formats', '[PAD]', '[PAD]']\n", + "15478\n", + "Label = ['m3u8', 'formats', '[PAD]', '[PAD]']\n", + "15479\n", + "Label = ['onceux', 'url', '[PAD]', '[PAD]']\n", + "15480\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15481\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15482\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "15483\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15484\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15485\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15486\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15487\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15488\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "15489\n", + "Label = ['validation', 'url', '[PAD]', '[PAD]']\n", + "15490\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "15491\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15492\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15493\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15494\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15495\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15496\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15497\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15498\n", + "Label = ['real', 'url', '[PAD]', '[PAD]']\n", + "15499\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15500\n", + "Label = ['full', 'title', '[PAD]', '[PAD]']\n", + "15501\n", + "Label = ['candidates', '[PAD]', '[PAD]', '[PAD]']\n", + "15502\n", + "Label = ['extract', 'playlist', '[PAD]', '[PAD]']\n", + "15503\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15504\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15505\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15506\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "15507\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "15508\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15509\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15510\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15511\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15512\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "15513\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "15514\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15515\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", + "15516\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15517\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15518\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15519\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15520\n", + "Label = ['input', 'data', '[PAD]', '[PAD]']\n", + "15521\n", + "Label = ['video', 'data', 'url', '[PAD]']\n", + "15522\n", + "Label = ['time', '[PAD]', '[PAD]', '[PAD]']\n", + "15523\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15524\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15525\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15526\n", + "Label = ['get', 'anvato', 'videos', '[PAD]']\n", + "15527\n", + "Label = ['song', 'url', '[PAD]', '[PAD]']\n", + "15528\n", + "Label = ['album', 'intro', '[PAD]', '[PAD]']\n", + "15529\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15530\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15531\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15532\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15533\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15534\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15535\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15536\n", + "Label = ['mkd', '[PAD]', '[PAD]', '[PAD]']\n", + "15537\n", + "Label = ['req', '[PAD]', '[PAD]', '[PAD]']\n", + "15538\n", + "Label = ['content', 'path', '[PAD]', '[PAD]']\n", + "15539\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15540\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "15541\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "15542\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "15543\n", + "Label = ['extract', 'smil', 'formats', '[PAD]']\n", + "15544\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "15545\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15546\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15547\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15548\n", + "Label = ['connection', '[PAD]', '[PAD]', '[PAD]']\n", + "15549\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15550\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15551\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15552\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15553\n", + "Label = ['playlist', '[PAD]', '[PAD]', '[PAD]']\n", + "15554\n", + "Label = ['playlist', 'title', '[PAD]', '[PAD]']\n", + "15555\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15556\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15557\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15558\n", + "Label = ['ID', 'REGEX', '[PAD]', '[PAD]']\n", + "15559\n", + "Label = ['lead', 'media', '[PAD]', '[PAD]']\n", + "15560\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15561\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15562\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "15563\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15564\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15565\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15566\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15567\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15568\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15569\n", + "Label = ['episode', '[PAD]', '[PAD]', '[PAD]']\n", + "15570\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15571\n", + "Label = ['restapi', 'base', '[PAD]', '[PAD]']\n", + "15572\n", + "Label = ['server', '[PAD]', '[PAD]', '[PAD]']\n", + "15573\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15574\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "15575\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15576\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15577\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15578\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15579\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "15580\n", + "Label = ['AWS', 'PROXY', 'HOST', '[PAD]']\n", + "15581\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "15582\n", + "Label = ['speaker', 'id', '[PAD]', '[PAD]']\n", + "15583\n", + "Label = ['site', '[PAD]', '[PAD]', '[PAD]']\n", + "15584\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15585\n", + "Label = ['countdown', '[PAD]', '[PAD]', '[PAD]']\n", + "15586\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15587\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15588\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15589\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15590\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15591\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "15592\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15593\n", + "Label = ['fmt', '[PAD]', '[PAD]', '[PAD]']\n", + "15594\n", + "Label = ['video', 'data', '[PAD]', '[PAD]']\n", + "15595\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15596\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15597\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15598\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15599\n", + "Label = ['course', 'slug', '[PAD]', '[PAD]']\n", + "15600\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15601\n", + "Label = ['get', 'video', 'id', '[PAD]']\n", + "15602\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15603\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "15604\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15605\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "15606\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15607\n", + "Label = ['r', '[PAD]', '[PAD]', '[PAD]']\n", + "15608\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15609\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "15610\n", + "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", + "15611\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15612\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15613\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15614\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15615\n", + "Label = ['subs', '[PAD]', '[PAD]', '[PAD]']\n", + "15616\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "15617\n", + "Label = ['series', '[PAD]', '[PAD]', '[PAD]']\n", + "15618\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15619\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15620\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15621\n", + "Label = ['format', 'id', '[PAD]', '[PAD]']\n", + "15622\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15623\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15624\n", + "Label = ['player', 'url', '[PAD]', '[PAD]']\n", + "15625\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15626\n", + "Label = ['req', '[PAD]', '[PAD]', '[PAD]']\n", + "15627\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15628\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15629\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15630\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15631\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15632\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15633\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15634\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15635\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15636\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15637\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "15638\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15639\n", + "Label = ['video', 'data', '[PAD]', '[PAD]']\n", + "15640\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15641\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15642\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15643\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15644\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15645\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15646\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15647\n", + "Label = ['m3u8', 'url', '[PAD]', '[PAD]']\n", + "15648\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15649\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15650\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15651\n", + "Label = ['request', 'webpage', '[PAD]', '[PAD]']\n", + "15652\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15653\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "15654\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15655\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15656\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15657\n", + "Label = ['live', 'title', '[PAD]', '[PAD]']\n", + "15658\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15659\n", + "Label = ['connection', 'id', '[PAD]', '[PAD]']\n", + "15660\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15661\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15662\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15663\n", + "Label = ['content', 'video', 'ids', '[PAD]']\n", + "15664\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15665\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15666\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15667\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15668\n", + "Label = ['request', 'webpage', '[PAD]', '[PAD]']\n", + "15669\n", + "Label = ['request', 'webpage', '[PAD]', '[PAD]']\n", + "15670\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15671\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15672\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15673\n", + "Label = ['this', 'formats', '[PAD]', '[PAD]']\n", + "15674\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15675\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15676\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15677\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15678\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15679\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15680\n", + "Label = ['NETRC', 'MACHINE', '[PAD]', '[PAD]']\n", + "15681\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "15682\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15683\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15684\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15685\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15686\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15687\n", + "Label = ['hexdigest', '[PAD]', '[PAD]', '[PAD]']\n", + "15688\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15689\n", + "Label = ['content', '[PAD]', '[PAD]', '[PAD]']\n", + "15690\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15691\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15692\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "15693\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "15694\n", + "Label = ['token', '[PAD]', '[PAD]', '[PAD]']\n", + "15695\n", + "Label = ['episode', '[PAD]', '[PAD]', '[PAD]']\n", + "15696\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15697\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15698\n", + "Label = ['player', '[PAD]', '[PAD]', '[PAD]']\n", + "15699\n", + "Label = ['player', '[PAD]', '[PAD]', '[PAD]']\n", + "15700\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "15701\n", + "Label = ['upload', 'date', '[PAD]', '[PAD]']\n", + "15702\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15703\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15704\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15705\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "15706\n", + "Label = ['o', '[PAD]', '[PAD]', '[PAD]']\n", + "15707\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15708\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15709\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "15710\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15711\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", + "15712\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15713\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15714\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15715\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15716\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15717\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15718\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15719\n", + "Label = ['camera', 'name', '[PAD]', '[PAD]']\n", + "15720\n", + "Label = ['vid', '[PAD]', '[PAD]', '[PAD]']\n", + "15721\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15722\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15723\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15724\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15725\n", + "Label = ['video', 'data', '[PAD]', '[PAD]']\n", + "15726\n", + "Label = ['message', '[PAD]', '[PAD]', '[PAD]']\n", + "15727\n", + "Label = ['raise', 'geo', 'restricted', '[PAD]']\n", + "15728\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15729\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15730\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15731\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15732\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15733\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15734\n", + "Label = ['info', 'page', '[PAD]', '[PAD]']\n", + "15735\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15736\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15737\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15738\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15739\n", + "Label = ['media', '[PAD]', '[PAD]', '[PAD]']\n", + "15740\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15741\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15742\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15743\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15744\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15745\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15746\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15747\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15748\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15749\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "15750\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15751\n", + "Label = ['course', 'id', '[PAD]', '[PAD]']\n", + "15752\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15753\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "15754\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "15755\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "15756\n", + "Label = ['output', 'format', '[PAD]', '[PAD]']\n", + "15757\n", + "Label = ['text', 'tracks', '[PAD]', '[PAD]']\n", + "15758\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15759\n", + "Label = ['asset', 'type', '[PAD]', '[PAD]']\n", + "15760\n", + "Label = ['login', 'page', '[PAD]', '[PAD]']\n", + "15761\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "15762\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "15763\n", + "Label = ['redirect', 'url', '[PAD]', '[PAD]']\n", + "15764\n", + "Label = ['requestor', 'info', '[PAD]', '[PAD]']\n", + "15765\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15766\n", + "Label = ['store', '[PAD]', '[PAD]', '[PAD]']\n", + "15767\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15768\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15769\n", + "Label = ['season', '[PAD]', '[PAD]', '[PAD]']\n", + "15770\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15771\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "15772\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "15773\n", + "Label = ['html', 'search', 'meta', '[PAD]']\n", + "15774\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15775\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15776\n", + "Label = ['html', 'search', 'regex', '[PAD]']\n", + "15777\n", + "Label = ['format', 'url', '[PAD]', '[PAD]']\n", + "15778\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15779\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15780\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15781\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15782\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "15783\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "15784\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15785\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15786\n", + "Label = ['channel', '[PAD]', '[PAD]', '[PAD]']\n", + "15787\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15788\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15789\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "15790\n", + "Label = ['red', 'url', '[PAD]', '[PAD]']\n", + "15791\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15792\n", + "Label = ['is', 'valid', 'url', '[PAD]']\n", + "15793\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "15794\n", + "Label = ['episode', '[PAD]', '[PAD]', '[PAD]']\n", + "15795\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15796\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15797\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15798\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15799\n", + "Label = ['url', '[PAD]', '[PAD]', '[PAD]']\n", + "15800\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15801\n", + "Label = ['ERRORS', '[PAD]', '[PAD]', '[PAD]']\n", + "15802\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15803\n", + "Label = ['og', 'search', 'description', '[PAD]']\n", + "15804\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15805\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15806\n", + "Label = ['encoding', '[PAD]', '[PAD]', '[PAD]']\n", + "15807\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "15808\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15809\n", + "Label = ['bitrate', '[PAD]', '[PAD]', '[PAD]']\n", + "15810\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15811\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "15812\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15813\n", + "Label = ['confirm', 'url', '[PAD]', '[PAD]']\n", + "15814\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15815\n", + "Label = ['video', 'data', '[PAD]', '[PAD]']\n", + "15816\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15817\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15818\n", + "Label = ['height', '[PAD]', '[PAD]', '[PAD]']\n", + "15819\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15820\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "15821\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "15822\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15823\n", + "Label = ['dumps', '[PAD]', '[PAD]', '[PAD]']\n", + "15824\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15825\n", + "Label = ['thumbnail', 'w', '[PAD]', '[PAD]']\n", + "15826\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15827\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15828\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15829\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15830\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "15831\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15832\n", + "Label = ['text', '[PAD]', '[PAD]', '[PAD]']\n", + "15833\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15834\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "15835\n", + "Label = ['b', '[PAD]', '[PAD]', '[PAD]']\n", + "15836\n", + "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", + "15837\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15838\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15839\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15840\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15841\n", + "Label = ['width', '[PAD]', '[PAD]', '[PAD]']\n", + "15842\n", + "Label = ['playlist', 'info', '[PAD]', '[PAD]']\n", + "15843\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15844\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15845\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15846\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "15847\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15848\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15849\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15850\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15851\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "15852\n", + "Label = ['html', 'search', 'meta', '[PAD]']\n", + "15853\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15854\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15855\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "15856\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15857\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15858\n", + "Label = ['lang', '[PAD]', '[PAD]', '[PAD]']\n", + "15859\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15860\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "15861\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15862\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15863\n", + "Label = ['download', 'xml', '[PAD]', '[PAD]']\n", + "15864\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15865\n", + "Label = ['fmt', '[PAD]', '[PAD]', '[PAD]']\n", + "15866\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15867\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15868\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15869\n", + "Label = ['match', '[PAD]', '[PAD]', '[PAD]']\n", + "15870\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15871\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15872\n", + "Label = ['preference', '[PAD]', '[PAD]', '[PAD]']\n", + "15873\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15874\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15875\n", + "Label = ['image', 'id', '[PAD]', '[PAD]']\n", + "15876\n", + "Label = ['key1', '[PAD]', '[PAD]', '[PAD]']\n", + "15877\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15878\n", + "Label = ['CMS', 'SIGNING', '[PAD]', '[PAD]']\n", + "15879\n", + "Label = ['API', 'PARAMS', '[PAD]', '[PAD]']\n", + "15880\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15881\n", + "Label = ['content', 'package', '[PAD]', '[PAD]']\n", + "15882\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15883\n", + "Label = ['extract', 'mpd', 'formats', '[PAD]']\n", + "15884\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15885\n", + "Label = ['og', 'search', 'description', '[PAD]']\n", + "15886\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "15887\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", + "15888\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15889\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "15890\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15891\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15892\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15893\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15894\n", + "Label = ['m3u8', '[PAD]', '[PAD]', '[PAD]']\n", + "15895\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15896\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15897\n", + "Label = ['external', 'video', 'id', '[PAD]']\n", + "15898\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "15899\n", + "Label = ['manifest', '[PAD]', '[PAD]', '[PAD]']\n", + "15900\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15901\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15902\n", + "Label = ['stream', 'path', '[PAD]', '[PAD]']\n", + "15903\n", + "Label = ['stream', 'path', '[PAD]', '[PAD]']\n", + "15904\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15905\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15906\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "15907\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15908\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15909\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15910\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15911\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "15912\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "15913\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15914\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15915\n", + "Label = ['cc', 'file', '[PAD]', '[PAD]']\n", + "15916\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15917\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "15918\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15919\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15920\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15921\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15922\n", + "Label = ['m3u8', 'formats', '[PAD]', '[PAD]']\n", + "15923\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15924\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15925\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15926\n", + "Label = ['lecture', 'slug', '[PAD]', '[PAD]']\n", + "15927\n", + "Label = ['cfg', '[PAD]', '[PAD]', '[PAD]']\n", + "15928\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "15929\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15930\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "15931\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "15932\n", + "Label = ['proto', 'relative', 'url', '[PAD]']\n", + "15933\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15934\n", + "Label = ['Audio', '[PAD]', '[PAD]', '[PAD]']\n", + "15935\n", + "Label = ['user', 'id', '[PAD]', '[PAD]']\n", + "15936\n", + "Label = ['media', 'url', '[PAD]', '[PAD]']\n", + "15937\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "15938\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15939\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "15940\n", + "Label = ['models', '[PAD]', '[PAD]', '[PAD]']\n", + "15941\n", + "Label = ['videos', '[PAD]', '[PAD]', '[PAD]']\n", + "15942\n", + "Label = ['uploader', '[PAD]', '[PAD]', '[PAD]']\n", + "15943\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "15944\n", + "Label = ['track', '[PAD]', '[PAD]', '[PAD]']\n", + "15945\n", + "Label = ['extract', 'mvpd', 'auth', '[PAD]']\n", + "15946\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "15947\n", + "Label = ['msi', 'data', '[PAD]', '[PAD]']\n", + "15948\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15949\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15950\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "15951\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "15952\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15953\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "15954\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "15955\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15956\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "15957\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15958\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15959\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15960\n", + "Label = ['thumbnail', '[PAD]', '[PAD]', '[PAD]']\n", + "15961\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15962\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15963\n", + "Label = ['jwplayer', 'data', '[PAD]', '[PAD]']\n", + "15964\n", + "Label = ['player', 'url', '[PAD]', '[PAD]']\n", + "15965\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "15966\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15967\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15968\n", + "Label = ['service', 'path', '[PAD]', '[PAD]']\n", + "15969\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15970\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15971\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15972\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15973\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15974\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "15975\n", + "Label = ['status', '[PAD]', '[PAD]', '[PAD]']\n", + "15976\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "15977\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "15978\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", + "15979\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15980\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "15981\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15982\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15983\n", + "Label = ['embed', 'page', '[PAD]', '[PAD]']\n", + "15984\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "15985\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "15986\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15987\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15988\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15989\n", + "Label = ['metadata', '[PAD]', '[PAD]', '[PAD]']\n", + "15990\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "15991\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15992\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "15993\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "15994\n", + "Label = ['width', '[PAD]', '[PAD]', '[PAD]']\n", + "15995\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "15996\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "15997\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "15998\n", + "Label = ['og', 'search', 'description', '[PAD]']\n", + "15999\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16000\n", + "Label = ['ie', 'key', '[PAD]', '[PAD]']\n", + "16001\n", + "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", + "16002\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16003\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16004\n", + "Label = ['splitext', '[PAD]', '[PAD]', '[PAD]']\n", + "16005\n", + "Label = ['request', 'webpage', '[PAD]', '[PAD]']\n", + "16006\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "16007\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16008\n", + "Label = ['report', 'warning', '[PAD]', '[PAD]']\n", + "16009\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16010\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16011\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16012\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16013\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16014\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16015\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16016\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16017\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16018\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16019\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16020\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16021\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16022\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16023\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16024\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16025\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16026\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16027\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16028\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16029\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16030\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "16031\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16032\n", + "Label = ['playlist', 'from', 'matches', '[PAD]']\n", + "16033\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16034\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16035\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16036\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16037\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16038\n", + "Label = ['entry', 'info', 'dict', '[PAD]']\n", + "16039\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16040\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16041\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "16042\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "16043\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16044\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "16045\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16046\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "16047\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "16048\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16049\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16050\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16051\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "16052\n", + "Label = ['is', 'video', '[PAD]', '[PAD]']\n", + "16053\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16054\n", + "Label = ['og', 'search', 'thumbnail', '[PAD]']\n", + "16055\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16056\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "16057\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "16058\n", + "Label = ['extract', 'mpd', 'formats', '[PAD]']\n", + "16059\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16060\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16061\n", + "Label = ['tag', '[PAD]', '[PAD]', '[PAD]']\n", + "16062\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "16063\n", + "Label = ['tp', 'formats', '[PAD]', '[PAD]']\n", + "16064\n", + "Label = ['tp', 'f', '[PAD]', '[PAD]']\n", + "16065\n", + "Label = ['json', 'data', '[PAD]', '[PAD]']\n", + "16066\n", + "Label = ['domain', '[PAD]', '[PAD]', '[PAD]']\n", + "16067\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16068\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16069\n", + "Label = ['src', '[PAD]', '[PAD]', '[PAD]']\n", + "16070\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "16071\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "16072\n", + "Label = ['comment', 'count', '[PAD]', '[PAD]']\n", + "16073\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "16074\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16075\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16076\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "16077\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16078\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16079\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "16080\n", + "Label = ['playlist', 'id', '[PAD]', '[PAD]']\n", + "16081\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "16082\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "16083\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "16084\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16085\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16086\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16087\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16088\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16089\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", + "16090\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16091\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16092\n", + "Label = ['cause', '[PAD]', '[PAD]', '[PAD]']\n", + "16093\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16094\n", + "Label = ['is', 'valid', 'url', '[PAD]']\n", + "16095\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16096\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16097\n", + "Label = ['comment', 'count', '[PAD]', '[PAD]']\n", + "16098\n", + "Label = ['page', '[PAD]', '[PAD]', '[PAD]']\n", + "16099\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "16100\n", + "Label = ['vars', '[PAD]', '[PAD]', '[PAD]']\n", + "16101\n", + "Label = ['entry', 'id', '[PAD]', '[PAD]']\n", + "16102\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16103\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "16104\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16105\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16106\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "16107\n", + "Label = ['video', 'id', '[PAD]', '[PAD]']\n", + "16108\n", + "Label = ['sub', '[PAD]', '[PAD]', '[PAD]']\n", + "16109\n", + "Label = ['urls', 'info', '[PAD]', '[PAD]']\n", + "16110\n", + "Label = ['stream', '[PAD]', '[PAD]', '[PAD]']\n", + "16111\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16112\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16113\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "16114\n", + "Label = ['entries', '[PAD]', '[PAD]', '[PAD]']\n", + "16115\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16116\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16117\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16118\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16119\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16120\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16121\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16122\n", + "Label = ['tbr', '[PAD]', '[PAD]', '[PAD]']\n", + "16123\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16124\n", + "Label = ['IE', 'NAME', '[PAD]', '[PAD]']\n", + "16125\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16126\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "16127\n", + "Label = ['json', 'string', '[PAD]', '[PAD]']\n", + "16128\n", + "Label = ['login', 'step', '[PAD]', '[PAD]']\n", + "16129\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "16130\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16131\n", + "Label = ['search', 'regex', '[PAD]', '[PAD]']\n", + "16132\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "16133\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "16134\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16135\n", + "Label = ['html', 'search', 'meta', '[PAD]']\n", + "16136\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n", + "16137\n", + "Label = ['http', 'url', '[PAD]', '[PAD]']\n", + "16138\n", + "Label = ['http', 'url', '[PAD]', '[PAD]']\n", + "16139\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16140\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "16141\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "16142\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16143\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "16144\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16145\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16146\n", + "Label = ['ext', '[PAD]', '[PAD]', '[PAD]']\n", + "16147\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16148\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16149\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16150\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16151\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16152\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16153\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16154\n", + "Label = ['user', '[PAD]', '[PAD]', '[PAD]']\n", + "16155\n", + "Label = ['DOMAINS', '[PAD]', '[PAD]', '[PAD]']\n", + "16156\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16157\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16158\n", + "Label = ['media', '[PAD]', '[PAD]', '[PAD]']\n", + "16159\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16160\n", + "Label = ['webpage', '[PAD]', '[PAD]', '[PAD]']\n", + "16161\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16162\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "16163\n", + "Label = ['TOKEN', '[PAD]', '[PAD]', '[PAD]']\n", + "16164\n", + "Label = ['hexdigest', '[PAD]', '[PAD]', '[PAD]']\n", + "16165\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16166\n", + "Label = ['TOKEN', '[PAD]', '[PAD]', '[PAD]']\n", + "16167\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16168\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "16169\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "16170\n", + "Label = ['replace', '[PAD]', '[PAD]', '[PAD]']\n", + "16171\n", + "Label = ['width', '[PAD]', '[PAD]', '[PAD]']\n", + "16172\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16173\n", + "Label = ['TEST', '[PAD]', '[PAD]', '[PAD]']\n", + "16174\n", + "Label = ['audio', 'description', '[PAD]', '[PAD]']\n", + "16175\n", + "Label = ['audio', 'description', 'file', '[PAD]']\n", + "16176\n", + "Label = ['scheme', '[PAD]', '[PAD]', '[PAD]']\n", + "16177\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "16178\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "16179\n", + "Label = ['next', 'url', '[PAD]', '[PAD]']\n", + "16180\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16181\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16182\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "16183\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", + "16184\n", + "Label = ['strings', '[PAD]', '[PAD]', '[PAD]']\n", + "16185\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16186\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", + "16187\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16188\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16189\n", + "Label = ['digit', 'sum', '[PAD]', '[PAD]']\n", + "16190\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16191\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "16192\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16193\n", + "Label = ['curMs', '[PAD]', '[PAD]', '[PAD]']\n", + "16194\n", + "Label = ['thumbnail', 'url', '[PAD]', '[PAD]']\n", + "16195\n", + "Label = ['thumbnail', 'url', '[PAD]', '[PAD]']\n", + "16196\n", + "Label = ['line', '[PAD]', '[PAD]', '[PAD]']\n", + "16197\n", + "Label = ['info', 'dict', '[PAD]', '[PAD]']\n", + "16198\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16199\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16200\n", + "Label = ['url', 'result', '[PAD]', '[PAD]']\n", + "16201\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16202\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "16203\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16204\n", + "Label = ['token', 'doc', '[PAD]', '[PAD]']\n", + "16205\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16206\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16207\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16208\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "16209\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16210\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16211\n", + "Label = ['playlist', 'result', '[PAD]', '[PAD]']\n", + "16212\n", + "Label = ['cdn', 'data', '[PAD]', '[PAD]']\n", + "16213\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16214\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16215\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16216\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16217\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16218\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16219\n", + "Label = ['video', 'url', '[PAD]', '[PAD]']\n", + "16220\n", + "Label = ['isdigit', '[PAD]', '[PAD]', '[PAD]']\n", + "16221\n", + "Label = ['lang', '[PAD]', '[PAD]', '[PAD]']\n", + "16222\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16223\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16224\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16225\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16226\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16227\n", + "Label = ['qualities', '[PAD]', '[PAD]', '[PAD]']\n", + "16228\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", + "16229\n", + "Label = ['download', 'webpage', '[PAD]', '[PAD]']\n", + "16230\n", + "Label = ['eurl', '[PAD]', '[PAD]', '[PAD]']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16231\n", + "Label = ['eurl', '[PAD]', '[PAD]', '[PAD]']\n", + "16232\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16233\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16234\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "16235\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", + "16236\n", + "Label = ['element', 'name', '[PAD]', '[PAD]']\n", + "16237\n", + "Label = ['sig', '[PAD]', '[PAD]', '[PAD]']\n", + "16238\n", + "Label = ['dict', 'selection', '[PAD]', '[PAD]']\n", + "16239\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16240\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16241\n", + "Label = ['prepare', 'call', '[PAD]', '[PAD]']\n", + "16242\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", + "16243\n", + "Label = ['channel', '[PAD]', '[PAD]', '[PAD]']\n", + "16244\n", + "Label = ['random', '[PAD]', '[PAD]', '[PAD]']\n", + "16245\n", + "Label = ['query', '[PAD]', '[PAD]', '[PAD]']\n", + "16246\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", + "16247\n", + "Label = ['video', 'info', '[PAD]', '[PAD]']\n", + "16248\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16249\n", + "Label = ['session', 'api', 'data', '[PAD]']\n", + "16250\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "16251\n", + "Label = ['video', 'real', 'url', '[PAD]']\n", + "16252\n", + "Label = ['view', 'count', '[PAD]', '[PAD]']\n", + "16253\n", + "Label = ['uploader', 'id', '[PAD]', '[PAD]']\n", + "16254\n", + "Label = ['description', '[PAD]', '[PAD]', '[PAD]']\n", + "16255\n", + "Label = ['images', '[PAD]', '[PAD]', '[PAD]']\n", + "16256\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16257\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16258\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16259\n", + "Label = ['duration', '[PAD]', '[PAD]', '[PAD]']\n", + "16260\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16261\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16262\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16263\n", + "Label = ['endswith', '[PAD]', '[PAD]', '[PAD]']\n", + "16264\n", + "Label = ['video', '[PAD]', '[PAD]', '[PAD]']\n", + "16265\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16266\n", + "Label = ['og', 'search', 'title', '[PAD]']\n", + "16267\n", + "Label = ['uploader', 'id', '[PAD]', '[PAD]']\n", + "16268\n", + "Label = ['urljoin', '[PAD]', '[PAD]', '[PAD]']\n", + "16269\n", + "Label = ['parse', 'json', '[PAD]', '[PAD]']\n", + "16270\n", + "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", + "16271\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", + "16272\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "16273\n", + "Label = ['vcodec', '[PAD]', '[PAD]', '[PAD]']\n", + "16274\n", + "Label = ['suitable', '[PAD]', '[PAD]', '[PAD]']\n", + "16275\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16276\n", + "Label = ['title', '[PAD]', '[PAD]', '[PAD]']\n", + "16277\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16278\n", + "Label = ['download', 'json', '[PAD]', '[PAD]']\n", + "16279\n", + "Label = ['age', 'limit', '[PAD]', '[PAD]']\n", + "16280\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "16281\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16282\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16283\n", + "Label = ['og', 'search', 'description', '[PAD]']\n", + "16284\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "16285\n", + "Label = ['strip', '[PAD]', '[PAD]', '[PAD]']\n", + "16286\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "16287\n", + "Label = ['player', 'json', 'url', '[PAD]']\n", + "16288\n", + "Label = ['formats', '[PAD]', '[PAD]', '[PAD]']\n", + "16289\n", + "Label = ['TESTS', '[PAD]', '[PAD]', '[PAD]']\n", + "16290\n", + "Label = ['extract', 'm3u8', 'formats', '[PAD]']\n", + "16291\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16292\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", + "16293\n", + "Label = ['extract', 'f4m', 'formats', '[PAD]']\n", + "16294\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16295\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16296\n", + "Label = ['thumbnail', 'filename', '[PAD]', '[PAD]']\n", + "16297\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "16298\n", + "Label = ['regex', '[PAD]', '[PAD]', '[PAD]']\n", + "16299\n", + "Label = ['paths', '[PAD]', '[PAD]', '[PAD]']\n", + "16300\n", + "Label = ['paths', '[PAD]', '[PAD]', '[PAD]']\n", + "16301\n", + "Label = ['cmd', '[PAD]', '[PAD]', '[PAD]']\n", + "16302\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "16303\n", + "Label = ['st', 'mtime', '[PAD]', '[PAD]']\n", + "16304\n", + "Label = ['st', 'mtime', '[PAD]', '[PAD]']\n", + "16305\n", + "Label = ['Popen', '[PAD]', '[PAD]', '[PAD]']\n", + "16306\n", + "Label = ['preferredquality', '[PAD]', '[PAD]', '[PAD]']\n", + "16307\n", + "Label = ['preferredcodec', '[PAD]', '[PAD]', '[PAD]']\n", + "16308\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16309\n", + "Label = ['preferedformat', '[PAD]', '[PAD]', '[PAD]']\n", + "16310\n", + "Label = ['opts', '[PAD]', '[PAD]', '[PAD]']\n", + "16311\n", + "Label = ['reason', '[PAD]', '[PAD]', '[PAD]']\n", + "16312\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16313\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16314\n", + "Label = ['label', '[PAD]', '[PAD]', '[PAD]']\n", + "16315\n", + "Label = ['create', 'examples', '[PAD]', '[PAD]']\n", + "16316\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16317\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16318\n", + "Label = ['is', 'real', 'example', '[PAD]']\n", + "16319\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16320\n", + "Label = ['constant', '[PAD]', '[PAD]', '[PAD]']\n", + "16321\n", + "Label = ['run', 'config', '[PAD]', '[PAD]']\n", + "16322\n", + "Label = ['max', 'seq', 'length', '[PAD]']\n", + "16323\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16324\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16325\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16326\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16327\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16328\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16329\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16330\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16331\n", + "Label = ['assignment', 'map', '[PAD]', '[PAD]']\n", + "16332\n", + "Label = ['init', 'from', 'checkpoint', '[PAD]']\n", + "16333\n", + "Label = ['name', 'to', 'features', '[PAD]']\n", + "16334\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "16335\n", + "Label = ['start', 'logits', '[PAD]', '[PAD]']\n", + "16336\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16337\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16338\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16339\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", + "16340\n", + "Label = ['GFile', '[PAD]', '[PAD]', '[PAD]']\n", + "16341\n", + "Label = ['version', '2', 'with', 'negative']\n", + "16342\n", + "Label = ['verbose', 'logging', '[PAD]', '[PAD]']\n", + "16343\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", + "16344\n", + "Label = ['do', 'train', '[PAD]', '[PAD]']\n", + "16345\n", + "Label = ['max', 'seq', 'length', '[PAD]']\n", + "16346\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "16347\n", + "Label = ['n', 'best', 'size', '[PAD]']\n", + "16348\n", + "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", + "16349\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", + "16350\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16351\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16352\n", + "Label = ['assertEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "16353\n", + "Label = ['output', 'weights', '[PAD]', '[PAD]']\n", + "16354\n", + "Label = ['output', 'bias', '[PAD]', '[PAD]']\n", + "16355\n", + "Label = ['TPUEstimatorSpec', '[PAD]', '[PAD]', '[PAD]']\n", + "16356\n", + "Label = ['per', 'example', 'loss', '[PAD]']\n", + "16357\n", + "Label = ['TPUEstimatorSpec', '[PAD]', '[PAD]', '[PAD]']\n", + "16358\n", + "Label = ['run', '[PAD]', '[PAD]', '[PAD]']\n", + "16359\n", + "Label = ['TPUConfig', '[PAD]', '[PAD]', '[PAD]']\n", + "16360\n", + "Label = ['input', 'fn', 'builder', '[PAD]']\n", + "16361\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16362\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "16363\n", + "Label = ['learning', 'rate', '[PAD]', '[PAD]']\n", + "16364\n", + "Label = ['train', 'op', '[PAD]', '[PAD]']\n", + "16365\n", + "Label = ['search', '[PAD]', '[PAD]', '[PAD]']\n", + "16366\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", + "16367\n", + "Label = ['per', 'example', 'loss', '[PAD]']\n", + "16368\n", + "Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n", + "16369\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "16370\n", + "Label = ['d', '[PAD]', '[PAD]', '[PAD]']\n", + "16371\n", + "Label = ['train', 'input', 'fn', '[PAD]']\n", + "16372\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "16373\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "16374\n", + "Label = ['GFile', '[PAD]', '[PAD]', '[PAD]']\n", + "16375\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16376\n", + "Label = ['char', '[PAD]', '[PAD]', '[PAD]']\n", + "16377\n", + "Label = ['is', 'chinese', 'char', '[PAD]']\n", + "16378\n", + "Label = ['char', '[PAD]', '[PAD]', '[PAD]']\n", + "16379\n", + "Label = ['vocab', 'tokens', '[PAD]', '[PAD]']\n", + "16380\n", + "Label = ['assertAllEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "16381\n", + "Label = ['assertAllEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "16382\n", + "Label = ['assertAllEqual', '[PAD]', '[PAD]', '[PAD]']\n", + "16383\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16384\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16385\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16386\n", + "Label = ['random', '[PAD]', '[PAD]', '[PAD]']\n", + "16387\n", + "Label = ['a', 'end', '[PAD]', '[PAD]']\n", + "16388\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", + "16389\n", + "Label = ['randint', '[PAD]', '[PAD]', '[PAD]']\n", + "16390\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16391\n", + "Label = ['constant', '[PAD]', '[PAD]', '[PAD]']\n", + "16392\n", + "Label = ['output', 'spec', '[PAD]', '[PAD]']\n", + "16393\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16394\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16395\n", + "Label = ['unique', 'id', '[PAD]', '[PAD]']\n", + "16396\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16397\n", + "Label = ['features', '[PAD]', '[PAD]', '[PAD]']\n", + "16398\n", + "Label = ['model', 'fn', '[PAD]', '[PAD]']\n", + "16399\n", + "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", + "16400\n", + "Label = ['query', 'layer', '[PAD]', '[PAD]']\n", + "16401\n", + "Label = ['dense', '[PAD]', '[PAD]', '[PAD]']\n", + "16402\n", + "Label = ['attention', 'scores', '[PAD]', '[PAD]']\n", + "16403\n", + "Label = ['value', 'layer', '[PAD]', '[PAD]']\n", + "16404\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", + "16405\n", + "Label = ['context', 'layer', '[PAD]', '[PAD]']\n", + "16406\n", + "Label = ['index', '[PAD]', '[PAD]', '[PAD]']\n", + "16407\n", + "Label = ['auth', '[PAD]', '[PAD]', '[PAD]']\n", + "16408\n", + "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", + "16409\n", + "Label = ['file', '[PAD]', '[PAD]', '[PAD]']\n", + "16410\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16411\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16412\n", + "Label = ['errno', '[PAD]', '[PAD]', '[PAD]']\n", + "16413\n", + "Label = ['action', '[PAD]', '[PAD]', '[PAD]']\n", + "16414\n", + "Label = ['insert', '[PAD]', '[PAD]', '[PAD]']\n", + "16415\n", + "Label = ['ignore', 'stdin', '[PAD]', '[PAD]']\n", + "16416\n", + "Label = ['sep', '[PAD]', '[PAD]', '[PAD]']\n", + "16417\n", + "Label = ['error', '[PAD]', '[PAD]', '[PAD]']\n", + "16418\n", + "Label = ['output', 'options', '[PAD]', '[PAD]']\n", + "16419\n", + "Label = ['prettify', '[PAD]', '[PAD]', '[PAD]']\n", + "16420\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16421\n", + "Label = ['sep', '[PAD]', '[PAD]', '[PAD]']\n", + "16422\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", + "16423\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16424\n", + "Label = ['values', '[PAD]', '[PAD]', '[PAD]']\n", + "16425\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "16426\n", + "Label = ['basename', '[PAD]', '[PAD]', '[PAD]']\n", + "16427\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16428\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "16429\n", + "Label = ['headers', '[PAD]', '[PAD]', '[PAD]']\n", + "16430\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", + "16431\n", + "Label = ['write', '[PAD]', '[PAD]', '[PAD]']\n", + "16432\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", + "16433\n", + "Label = ['json', '[PAD]', '[PAD]', '[PAD]']\n", + "16434\n", + "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", + "16435\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", + "16436\n", + "Label = ['setupterm', '[PAD]', '[PAD]', '[PAD]']\n", + "16437\n", + "Label = ['wrap', 'stream', '[PAD]', '[PAD]']\n", + "16438\n", + "Label = ['stdin', 'encoding', '[PAD]', '[PAD]']\n", + "16439\n", + "Label = ['stdout', '[PAD]', '[PAD]', '[PAD]']\n", + "16440\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "16441\n", + "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", + "16442\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", + "16443\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "16444\n", + "Label = ['decode', '[PAD]', '[PAD]', '[PAD]']\n", + "16445\n", + "Label = ['stdout', 'isatty', '[PAD]', '[PAD]']\n", + "16446\n", + "Label = ['is', 'windows', '[PAD]', '[PAD]']\n", + "16447\n", + "Label = ['positional', '[PAD]', '[PAD]', '[PAD]']\n", + "16448\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16449\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "16450\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16451\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16452\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16453\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16454\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16455\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16456\n", + "Label = ['package', 'name', '[PAD]', '[PAD]']\n", + "16457\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16458\n", + "Label = ['add', 'argument', '[PAD]', '[PAD]']\n", + "16459\n", + "Label = ['instance', 'length', '[PAD]', '[PAD]']\n", + "16460\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "16461\n", + "Label = ['max', 'len', '[PAD]', '[PAD]']\n", + "16462\n", + "Label = ['resume', '[PAD]', '[PAD]', '[PAD]']\n", + "16463\n", + "Label = ['resume', '[PAD]', '[PAD]', '[PAD]']\n", + "16464\n", + "Label = ['stream', '[PAD]', '[PAD]', '[PAD]']\n", + "16465\n", + "Label = ['chunk', 'downloaded', '[PAD]', '[PAD]']\n", + "16466\n", + "Label = ['speed', '[PAD]', '[PAD]', '[PAD]']\n", + "16467\n", + "Label = ['spinner', 'pos', '[PAD]', '[PAD]']\n", + "16468\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16469\n", + "Label = ['group', '[PAD]', '[PAD]', '[PAD]']\n", + "16470\n", + "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", + "16471\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16472\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16473\n", + "Label = ['mime', '[PAD]', '[PAD]', '[PAD]']\n", + "16474\n", + "Label = ['app', 'context', '[PAD]', '[PAD]']\n", + "16475\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", + "16476\n", + "Label = ['environ', 'base', '[PAD]', '[PAD]']\n", + "16477\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16478\n", + "Label = ['request', '[PAD]', '[PAD]', '[PAD]']\n", + "16479\n", + "Label = ['app', '[PAD]', '[PAD]', '[PAD]']\n", + "16480\n", + "Label = ['get', 'or', 'select', 'template']\n", + "16481\n", + "Label = ['from', 'string', '[PAD]', '[PAD]']\n", + "16482\n", + "Label = ['attr', 'name', '[PAD]', '[PAD]']\n", + "16483\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "16484\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "16485\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", + "16486\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16487\n", + "Label = ['bg', 'loading', 'exc', 'info']\n", + "16488\n", + "Label = ['app', '[PAD]', '[PAD]', '[PAD]']\n", + "16489\n", + "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", + "16490\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16491\n", + "Label = ['command', '[PAD]', '[PAD]', '[PAD]']\n", + "16492\n", + "Label = ['add', 'command', '[PAD]', '[PAD]']\n", + "16493\n", + "Label = ['ep', '[PAD]', '[PAD]', '[PAD]']\n", + "16494\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16495\n", + "Label = ['isfile', '[PAD]', '[PAD]', '[PAD]']\n", + "16496\n", + "Label = ['new', 'dir', '[PAD]', '[PAD]']\n", + "16497\n", + "Label = ['getcwd', '[PAD]', '[PAD]', '[PAD]']\n", + "16498\n", + "Label = ['secho', '[PAD]', '[PAD]', '[PAD]']\n", + "16499\n", + "Label = ['echo', '[PAD]', '[PAD]', '[PAD]']\n", + "16500\n", + "Label = ['version', 'info', '[PAD]', '[PAD]']\n", + "16501\n", + "Label = ['echo', '[PAD]', '[PAD]', '[PAD]']\n", + "16502\n", + "Label = ['echo', '[PAD]', '[PAD]', '[PAD]']\n", + "16503\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "16504\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16505\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16506\n", + "Label = ['exc', 'class', '[PAD]', '[PAD]']\n", + "16507\n", + "Label = ['code', '[PAD]', '[PAD]', '[PAD]']\n", + "16508\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layer', 'nodes', 'nodes', 'nodes']\n", - " 2. ['model', 'data', 'data', 'data']\n", - "\n", - "576\n", - "[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n", - "Label = ['input', 'shapes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inbound', 'nodes', 'nodes', 'nodes']\n", - " 2. ['input', 'keras', 'keras', 'keras']\n", - "\n", - "577\n", - "[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n", - "Label = ['output', 'shapes', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['inbound', 'nodes', 'nodes', 'nodes']\n", - " 2. ['input', 'keras', 'keras', 'keras']\n", - "\n", - "578\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['batch', 'name', 'name', 'name']\n", - " 2. ['[PAD]', 'list', 'list', 'list']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "579\n", - "[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['batch', 'input', 'shape', '[PAD]']\n", - "Pred =\n", - " 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['function', 'shape', 'shape', 'shape']\n", - " 2. ['keras', 'function', 'function', 'function']\n", - "\n", - "580\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg shape arg ndim arg max ndim arg min ndim arg axes NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "16509\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", + "16510\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'value', 'value', 'value']\n", - " 2. ['max', 'ndim', 'ndim', 'ndim']\n", - "\n", - "581\n", - "[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute dtype Name Str\n", - "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'dtype', 'dtype', 'dtype']\n", - " 2. ['decay', 'size', 'size', 'size']\n", - "\n", - "582\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outbound layer Attribute name Attribute outbound layer Name Assign Name outbound layer NameConstant\n", - "Label = ['outbound', 'layer', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['arguments', 'layer', 'layer', 'layer']\n", - " 2. ['inputs', 'weights', 'weights', 'weights']\n", - "\n", - "583\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg optimizer arg loss arg metrics arg loss weights arg sample weight mode arg weighted metrics arg target tensors arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", + "16511\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'weights', 'weights', 'weights']\n", - " 2. ['args', 'format', 'format', 'format']\n", - "\n", - "584\n", - "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['states', 'layers', 'layers', 'layers']\n", - " 2. ['layers', 'uid', 'uid', 'uid']\n", - "\n", - "585\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layers', 'layers', 'layers', 'layers']\n", - " 2. ['states', 'tensor', 'tensor', 'tensor']\n", - "\n", - "586\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['states', 'layers', 'layers', 'layers']\n", - " 2. ['layers', 'uid', 'uid', 'uid']\n", - "\n", - "587\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['layers', 'layers', 'layers', 'layers']\n", - " 2. ['states', 'tensor', 'tensor', 'tensor']\n", - "\n", - "588\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['output', 'names', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['output', 'names', 'names', 'names']\n", - " 1. ['count', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['inbound', 'nodes', 'nodes', 'nodes']\n", - "\n", - "589\n", - "[CLS] Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['states', 'layers', 'layers', 'layers']\n", - " 2. ['layers', 'uid', 'uid', 'uid']\n", - "\n", - "590\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name target NameConstant\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['target', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['idx', 'axes', 'axes', 'axes']\n", - " 2. ['axes', 'shape', 'shape', 'shape']\n", - "\n", - "591\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Call Name Name keyword BinOp Name Add Str keyword Call Attribute is sparse Name Subscript Attribute outputs Name Index Name keyword Call Attribute dtype Name Subscript Attribute outputs Name Index Name\n", - "Label = ['placeholder', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['add', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['placeholder', 'weight', 'weight', 'weight']\n", - " 2. ['variable', 'target', 'target', 'target']\n", - "\n", - "592\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Eq Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", - "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['inbound', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'nodes', 'nodes', 'nodes']\n", - " 2. ['backend', 'placeholder', 'placeholder', 'placeholder']\n", - "\n", - "593\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", - "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['weight', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['target', 'placeholder', 'placeholder', 'placeholder']\n", - " 2. ['g', 'weight', 'weight', 'weight']\n", - "\n", - "594\n", - "[CLS] If Compare Name Eq Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Expr Call Attribute append Name Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'weight', 'weight', 'weight']\n", - " 2. ['extend', 'placeholder', 'placeholder', 'placeholder']\n", - "\n", - "595\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['append', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['set', 'weight', 'weight', 'weight']\n", - " 2. ['user', 'placeholder', 'placeholder', 'placeholder']\n", - "\n", - "596\n", - "[CLS] BoolOp Or Compare Subscript Name Index UnaryOp USub Num Eq Num Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Attribute binary crossentropy Name\n", - "Label = ['loss', 'functions', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['values', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['shape', 'fn', 'fn', 'fn']\n", - " 2. ['fn', 'i', 'i', 'i']\n", - "\n", - "597\n", - "[CLS] If Compare Name IsNot NameConstant AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name keyword NameConstant\n", - "Label = ['all', 'inputs', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['new', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'shape', 'shape', 'shape']\n", - " 2. ['additional', 'value', 'value', 'value']\n", - "\n", - "598\n", - "[CLS] If Call Name GeneratorExp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name v Name If UnaryOp Not Call Name GeneratorExp Call Attribute is tensor Name Name comprehension Name v Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name\n", - "Label = ['is', 'tensor', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['ndarray', 'tensor', 'tensor', 'tensor']\n", - " 2. ['run', 'keras', 'keras', 'keras']\n", - "\n", - "599\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes NameConstant Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes Attribute feed input shapes Name\n", - "Label = ['is', 'graph', 'network', '[PAD]']\n", - "Pred =\n", - " 0. ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'updates', 'updates', 'updates']\n", - " 2. ['input', 'names', 'names', 'names']\n", - "\n", - "600\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg y arg batch size arg epochs arg verbose arg callbacks arg validation split arg validation data arg shuffle arg class weight arg sample weight arg initial epoch arg steps per epoch arg validation steps arg kwargs NameConstant NameConstant NameConstant Num Num NameConstant Num NameConstant NameConstant NameConstant NameConstant Num NameConstant NameConstant\n", + "16512\n", + "Label = ['handler', 'map', '[PAD]', '[PAD]']\n", + "16513\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "16514\n", + "Label = ['method', '[PAD]', '[PAD]', '[PAD]']\n", + "16515\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['self', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['model', 'format', 'format', 'format']\n", - " 2. ['path', 'data', 'data', 'data']\n", - "\n", - "601\n", - "[CLS] If Compare Call Name Name Eq Num Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Name val sample weight Name Raise Call Name BinOp Str Mod Call Name Name\n", - "Label = ['val', 'x', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['val', 'x', 'x', 'x']\n", - " 1. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 2. ['sample', 'val', 'val', 'val']\n", - "\n", - "602\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Name Add Name Name List Num\n", - "Label = ['val', 'ins', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['ins', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['val', 'ins', 'ins', 'ins']\n", - " 2. ['pattern', 'size', 'size', 'size']\n", - "\n", - "603\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Tuple Call Name Name Num Name Call Name Name Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['val', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['x', 'x', 'x', 'x']\n", - " 2. ['y', 'test', 'test', 'test']\n", - "\n", - "604\n", - "[CLS] If BoolOp And Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Gt Name Compare BinOp Subscript Attribute shape Subscript Name Index Num Index Num Mod Name NotEq Num Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute shape Subscript Name Index Num Index Num Str Call Name Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'size', 'size', 'size']\n", - " 2. ['keras', 'layers', 'layers', 'layers']\n", - "\n", - "605\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Str Call Name Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['args', 'size', 'size', 'size']\n", - " 2. ['[PAD]', 'shape', 'shape', 'shape']\n", - "\n", - "606\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps arg max queue size arg workers arg use multiprocessing arg verbose NameConstant Num Num NameConstant Num\n", + "16516\n", + "Label = ['rv', '[PAD]', '[PAD]', '[PAD]']\n", + "16517\n", + "Label = ['rv', '[PAD]', '[PAD]', '[PAD]']\n", + "16518\n", + "Label = ['bind', '[PAD]', '[PAD]', '[PAD]']\n", + "16519\n", + "Label = ['bp', '[PAD]', '[PAD]', '[PAD]']\n", + "16520\n", + "Label = ['funcs', '[PAD]', '[PAD]', '[PAD]']\n", + "16521\n", + "Label = ['handler', '[PAD]', '[PAD]', '[PAD]']\n", + "16522\n", + "Label = ['before', 'request', 'funcs', '[PAD]']\n", + "16523\n", + "Label = ['teardown', 'request', 'funcs', '[PAD]']\n", + "16524\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['model', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['self', 'format', 'format', 'format']\n", - " 2. ['path', 'model', 'model', 'model']\n", - "\n", - "607\n", - "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Attribute name Attribute class Subscript Name Index Name Eq Str Attribute values Subscript Name Index Name Subscript Name Index Name comprehension Name x Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp Str Add Subscript Attribute args Name Index Num Str Call Name Name\n", - "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['data', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['config', 'data', 'data', 'data']\n", - " 2. ['new', 'layer', 'layer', 'layer']\n", - "\n", - "608\n", - "[CLS] ListComp IfExp Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Attribute values Name Name comprehension Name x Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['name', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['data', 'format', 'format', 'format']\n", - " 2. ['[PAD]', 'data', 'data', 'data']\n", - "\n", - "609\n", - "[CLS] BoolOp And Compare Subscript Name Index Name IsNot NameConstant UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name\n", - "Label = ['is', 'tensor', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['is', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['get', 'sparse', 'sparse', 'sparse']\n", - " 2. ['array', 'tensor', 'tensor', 'tensor']\n", - "\n", - "610\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name ref dim Call Name Name Name If BoolOp And Compare Name NotEq Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Subscript Name Index Name Str Call Name Name Str Call Name Name\n", - "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['dim', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['chunk', 'dim', 'dim', 'dim']\n", - " 2. ['w', 'layer', 'layer', 'layer']\n", - "\n", - "611\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Name Call Attribute get Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['x', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'config', 'config', 'config']\n", - " 2. ['o', 'scope', 'scope', 'scope']\n", - "\n", - "612\n", - "[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name y Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'shape', 'shape', 'shape']\n", - " 2. ['name', 'spec', 'spec', 'spec']\n", - "\n", - "613\n", - "[CLS] Call Name BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'spec', 'spec', 'spec']\n", - " 2. ['name', 'shape', 'shape', 'shape']\n", - "\n", - "614\n", - "[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['[PAD]', 'format', 'format', 'format']\n", - " 2. ['name', 'spec', 'spec', 'spec']\n", - "\n", - "615\n", - "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Call Name Attribute shape Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute shape Name Str Call Name Call Name Attribute shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['input', 'shape', 'shape', 'shape']\n", - " 2. ['times', 'layers', 'layers', 'layers']\n", - "\n", - "616\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['name', 'shape', 'shape', 'shape']\n", - " 2. ['axis', 'size', 'size', 'size']\n", - "\n", - "617\n", - "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute shape Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - " 0. ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - " 1. ['keras', 'size', 'size', 'size']\n", - " 2. ['kernel', 'shape', 'shape', 'shape']\n", - "\n" + "16525\n", + "Label = ['rv', '[PAD]', '[PAD]', '[PAD]']\n", + "16526\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16527\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16528\n", + "Label = ['permanent', '[PAD]', '[PAD]', '[PAD]']\n", + "16529\n", + "Label = ['modified', '[PAD]', '[PAD]', '[PAD]']\n", + "16530\n", + "Label = ['logger', '[PAD]', '[PAD]', '[PAD]']\n", + "16531\n", + "Label = ['app', '[PAD]', '[PAD]', '[PAD]']\n", + "16532\n", + "Label = ['set', 'etag', '[PAD]', '[PAD]']\n", + "16533\n", + "Label = ['getmtime', '[PAD]', '[PAD]', '[PAD]']\n", + "16534\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", + "16535\n", + "Label = ['sep', '[PAD]', '[PAD]', '[PAD]']\n", + "16536\n", + "Label = ['filename', '[PAD]', '[PAD]', '[PAD]']\n", + "16537\n", + "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", + "16538\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16539\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16540\n", + "Label = ['days', '[PAD]', '[PAD]', '[PAD]']\n", + "16541\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16542\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16543\n", + "Label = ['refcnt', '[PAD]', '[PAD]', '[PAD]']\n", + "16544\n", + "Label = ['refcnt', '[PAD]', '[PAD]', '[PAD]']\n", + "16545\n", + "Label = ['preserved', '[PAD]', '[PAD]', '[PAD]']\n", + "16546\n", + "Label = ['session', '[PAD]', '[PAD]', '[PAD]']\n", + "16547\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16548\n", + "Label = ['tests', '[PAD]', '[PAD]', '[PAD]']\n", + "16549\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16550\n", + "Label = ['record', 'once', '[PAD]', '[PAD]']\n", + "16551\n", + "Label = ['record', 'once', '[PAD]', '[PAD]']\n", + "16552\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16553\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16554\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "16555\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", + "16556\n", + "Label = ['record', 'once', '[PAD]', '[PAD]']\n", + "16557\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", + "16558\n", + "Label = ['startswith', '[PAD]', '[PAD]', '[PAD]']\n", + "16559\n", + "Label = ['cached', 'json', '[PAD]', '[PAD]']\n", + "16560\n", + "Label = ['rv', '[PAD]', '[PAD]', '[PAD]']\n", + "16561\n", + "Label = ['debug', '[PAD]', '[PAD]', '[PAD]']\n", + "16562\n", + "Label = ['tp', '[PAD]', '[PAD]', '[PAD]']\n", + "16563\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", + "16564\n", + "Label = ['blueprint', '[PAD]', '[PAD]', '[PAD]']\n", + "16565\n", + "Label = ['detail', '[PAD]', '[PAD]', '[PAD]']\n", + "16566\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", + "16567\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n", + "16568\n", + "Label = ['slots', '[PAD]', '[PAD]', '[PAD]']\n", + "16569\n", + "Label = ['tags', '[PAD]', '[PAD]', '[PAD]']\n" ] } ], "source": [ "n=3\n", "pred_str = []; score = 0; score_no_pad=0; rank =[]; skipped = 0\n", - "for idx in range(618):\n", + "for idx in range(16570):\n", " print(idx)\n", - " print(snippet.loc[idx][0])\n", + " #print(snippet.loc[idx][0])\n", " print(\"Label =\", labels_str[idx])\n", " try:\n", " msk_idx = snippet.loc[idx][0].split(\" \").index('[MASK]')\n", @@ -6371,16 +34079,16 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3" + "16570" ] }, - "execution_count": 156, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -6391,16 +34099,16 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2299" + "0" ] }, - "execution_count": 157, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -6411,7 +34119,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 87, "metadata": { "scrolled": true }, @@ -6421,3125 +34129,1247 @@ "output_type": "stream", "text": [ "0\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute input layers Name Assign Name input tensor Call Name keyword Attribute batch input shape Name keyword Attribute dtype Name keyword Attribute sparse Name keyword Attribute name Name Expr Call Attribute append Name Name Assign Name newly created input layer Subscript Attribute keras history Name Index Num Assign Subscript Name Index Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['softmax', '[PAD]', '[PAD]', '[PAD]']\n", "1\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name In Name Expr Call Attribute append Name Subscript Name Index Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", "2\n", - "[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Name Sub Call Attribute max Name Name keyword Name keyword NameConstant Assign Name s Call Attribute sum Name Name keyword Name keyword NameConstant Return BinOp Name Div Name Raise Call Name BinOp Str Mod Name\n", - "Label = ['e', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['in', 'top', 'k', '[PAD]']\n", "3\n", - "[CLS] BinOp Name Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword NameConstant\n", - "Label = ['max', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "4\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name alpha Num Assign Name scale Num Return BinOp Name Mult Call Attribute elu Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "5\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Return Call Name Name keyword Call Name keyword Name keyword Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "6\n", - "[CLS] If Call Name Name If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name Return Name Raise Call Name Str Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['use', 'steps', '[PAD]', '[PAD]']\n", "7\n", - "[CLS] If Call Name Name Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute format Str keyword Attribute name Attribute class Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", "8\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute equal Name Call Attribute flatten Name Name Call Attribute cast Name Call Attribute argmax Name Name keyword UnaryOp USub Num Call Attribute floatx Name Call Attribute floatx Name\n", - "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "9\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred arg k Num Return Call Attribute mean Name Call Attribute in top k Name Name Call Attribute argmax Name Name keyword UnaryOp USub Num Name keyword UnaryOp Num\n", - "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "10\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg model For Name callback Attribute callbacks Name Expr Call Attribute set model Name Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "10\n", + "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", "11\n", - "[CLS] If Name Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Assign Attribute stateful metrics Name Call Name\n", - "Label = ['stateful', 'metrics', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "12\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name target Subscript Attribute params Name Index Str Assign Name target Subscript Attribute params Name Index Str\n", - "Label = ['use', 'steps', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['wait', '[PAD]', '[PAD]', '[PAD]']\n", "13\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute log values Name Tuple Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['restore', 'best', 'weights', '[PAD]']\n", "14\n", - "[CLS] Call Name BinOp Str Mod Tuple BinOp Name Add Num Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute best Name Name Name\n", "Label = ['monitor', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "15\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg logs NameConstant If BoolOp And Compare Attribute stopped epoch Name Gt Num Compare Attribute verbose Name Num Expr Call Name BinOp Str Mod BinOp Attribute stopped epoch Name Add Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", "16\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg root arg path arg field arg headers arg send as json Str Str Str NameConstant NameConstant Expr Call Attribute init Call Name Name Name Assign Attribute root Name Name Assign Attribute path Name Name Assign Attribute field Name Name Assign Attribute headers Name Name Assign Attribute send as json Name Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "17\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg schedule arg verbose Num Expr Call Attribute init Call Name Name Name Assign Attribute schedule Name Name Assign Attribute verbose Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "18\n", - "[CLS] If Compare Name NotEq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Assign Name embeddings freq Num\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "19\n", - "[CLS] If Compare Name Eq Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Num Assign Attribute update freq Name Name\n", - "Label = ['update', 'freq', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['Variable', '[PAD]', '[PAD]', '[PAD]']\n", "20\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['embeddings', 'metadata', '[PAD]', '[PAD]']\n", "21\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotIn List Str Str Str Expr Call Attribute warn Name BinOp Str Mod Attribute mode Name Name Assign Attribute mode Name Str\n", - "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['batch', 'val', '[PAD]', '[PAD]']\n", "22\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Mod Tuple Attribute monitor Name Call Attribute join Str Call Name Call Attribute keys Name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "23\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Tuple Name IfExp Compare Name In Name Subscript Name Index Name Str comprehension Name k Attribute keys Name\n", - "Label = ['logs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['save', '[PAD]', '[PAD]', '[PAD]']\n", "24\n", - "[CLS] If Compare Name IsNot NameConstant Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Name Assign Attribute on train begin Name Lambda arguments arg logs NameConstant\n", - "Label = ['on', 'train', 'begin', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['monitor', 'op', '[PAD]', '[PAD]']\n", "25\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x Assign Name regularization Num If Attribute l1 Name AugAssign Name regularization Add Call Attribute sum Name BinOp Attribute l1 Name Mult Call Attribute abs Name Name If Attribute l2 Name AugAssign Name regularization Call Attribute sum Name BinOp Attribute l2 Name Call Attribute square Name Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['l1', '[PAD]', '[PAD]', '[PAD]']\n", "26\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Dict Str Str Attribute max value Name Attribute axis Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['l1', '[PAD]', '[PAD]', '[PAD]']\n", "27\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg w Return BinOp Name Div BinOp Call Attribute epsilon Name Add Call Attribute sqrt Name Call Attribute sum Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "28\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name Name keyword Attribute axis Name keyword NameConstant\n", - "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", "29\n", - "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name config Dict Str Str Call Name Name Dict Return Call Name Name If Call Name Name Return Name Raise Call Name BinOp Str Add Call Name Name\n", - "Label = ['string', 'types', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "30\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape arg dtype NameConstant Return Call Attribute constant Name Num keyword Name keyword Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "30\n", + "Label = ['random', 'uniform', '[PAD]', '[PAD]']\n", "31\n", - "[CLS] Return Dict Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute stddev Name Attribute seed Name\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", "32\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str AugAssign Name scale Div Call Name Num Name If Compare Attribute mode Name Str AugAssign Name scale Call Name Num Name AugAssign Name scale Call Name Num BinOp Call Name BinOp Name Add Name Num\n", - "Label = ['mode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['gain', '[PAD]', '[PAD]', '[PAD]']\n", "33\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Num Name keyword Name keyword Attribute seed Name\n", - "Label = ['truncated', 'normal', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['identity', '[PAD]', '[PAD]', '[PAD]']\n", "34\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice UnaryOp USub Num AugAssign Name num rows Mult Name\n", - "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "35\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Expr Call Attribute seed Attribute random Name Attribute seed Name\n", "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "35\n", + "Label = ['identifier', '[PAD]', '[PAD]', '[PAD]']\n", "36\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute prod Name Subscript Name Slice UnaryOp USub Num\n", - "Label = ['receptive', 'field', 'size', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['second', 'log', '[PAD]', '[PAD]']\n", "37\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute square Name BinOp Name Sub Name keyword UnaryOp USub Num\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "38\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute abs Name BinOp Name Sub Name keyword UnaryOp USub Num\n", "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "39\n", - "[CLS] BinOp Num Mult Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword UnaryOp USub Num\n", - "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", "40\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name pos Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp USub Num Assign Name neg Call Attribute max Name BinOp BinOp Num Sub Name Name keyword UnaryOp Num Return Call Attribute maximum Name Num BinOp BinOp Name Name Add Num\n", "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "41\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num BinOp BinOp Name Sub Name Add Num\n", - "Label = ['maximum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "42\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute binary crossentropy Name Name Name keyword UnaryOp USub Num\n", "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "42\n", + "Label = ['IndexedSlices', '[PAD]', '[PAD]', '[PAD]']\n", "43\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y pred Assign Name y true Call Attribute l2 normalize Name Name keyword UnaryOp USub Num Assign Name y pred Call Attribute l2 normalize Name Name keyword UnaryOp Num Return UnaryOp Call Attribute sum Name BinOp Name Mult Name keyword UnaryOp Num\n", - "Label = ['y', 'true', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", "44\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute switch Name Call Attribute greater equal Name Name Name BinOp BinOp Name Mult Name Div Name Name\n", - "Label = ['g', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "45\n", - "[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Num\n", "Label = ['clipnorm', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "45\n", + "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", "46\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name ListComp Call Attribute sum Name Call Attribute square Name Name comprehension Name g Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", "47\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "48\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n", - "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "49\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "49\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", "50\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Call Attribute sqrt Name Name Add Attribute epsilon Name\n", - "Label = ['new', 'p', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", "51\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Name Mult Call Attribute sqrt Name BinOp Name Add Attribute epsilon Name Div Call Attribute sqrt Name BinOp Name Attribute epsilon Name\n", - "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "52\n", - "[CLS] Dict Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Attribute rho Name Call Name Call Attribute get value Name Attribute decay Name Attribute epsilon Name\n", - "Label = ['get', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "53\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute variable Name Num keyword Str keyword Str\n", - "Label = ['iterations', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", "54\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Num Div BinOp Num Add BinOp Attribute decay Name Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", - "Label = ['lr', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "55\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "56\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Call Attribute cast Name Attribute iterations Name Call Attribute floatx Name Add Num\n", - "Label = ['t', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['lr', 't', '[PAD]', '[PAD]']\n", "57\n", - "[CLS] BinOp Name Mult BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name Div BinOp Num Call Attribute pow Name Attribute beta 1 Name Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "58\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Sub Call Attribute pow Name Attribute beta 2 Name Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", "59\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg lr arg beta 1 arg beta 2 arg epsilon arg decay arg kwargs Num Num Num NameConstant Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", "60\n", - "[CLS] BinOp Num Div BinOp Num Add BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Call Attribute cast Name Attribute iterations Name Call Attribute dtype Name Attribute decay Name\n", - "Label = ['decay', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "61\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 1 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", - "Label = ['m', 't', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['Optimizer', '[PAD]', '[PAD]', '[PAD]']\n", "62\n", - "[CLS] BinOp Name Sub BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "63\n", - "[CLS] BinOp BinOp Name Mult Name Div BinOp Name Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", "64\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult BinOp Num Sub BinOp Num Call Attribute pow Name Call Attribute cast to floatx Name Num BinOp BinOp Name Add Num Attribute schedule decay Name\n", - "Label = ['beta', '1', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", "65\n", - "[CLS] BinOp BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Name Add BinOp BinOp Num Sub Attribute beta 1 Name Name\n", - "Label = ['beta', '1', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "66\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute beta 2 Name Mult Name Add BinOp BinOp Num Sub Attribute beta 2 Name Call Attribute square Name Name\n", - "Label = ['v', 't', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "67\n", - "[CLS] BinOp Name Div BinOp Num Sub Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute beta 2 Name Name\n", - "Label = ['pow', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "68\n", - "[CLS] Dict Str Str Str Str Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute lr Name Call Name Call Attribute get value Name Attribute beta 1 Name Call Name Call Attribute get value Name Attribute beta 2 Name Attribute epsilon Name Attribute schedule decay Name\n", - "Label = ['get', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "69\n", - "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str\n", - "Label = ['lower', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "70\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Str Tuple Attribute input dim Name\n", - "Label = ['input', 'dim', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "71\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Attribute nb feature Name Attribute output dim Name keyword Str keyword Str keyword Attribute b regularizer Name keyword Attribute b constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "72\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg init arg activation arg weights arg W regularizer arg b regularizer arg activity regularizer arg W constraint arg b constraint arg bias arg input dim arg kwargs Str NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "73\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Name Name keyword Attribute init Name keyword Str keyword Attribute W regularizer Name keyword Attribute W constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "74\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs ImportFrom alias If Compare Str In Name Assign Name rate Call Attribute pop Name Str Assign Name rate Num Assign Subscript Name Index Str Name Expr Call Attribute warn Name Str Return Call Name Starred Name keyword Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "75\n", - "[CLS] If Compare Call Name Name NotEq Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['object', 'name', '[PAD]', '[PAD]']\n", "76\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", "77\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Attribute states Name Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", "78\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Attribute units Name Str Call Name Attribute shape Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pop', '[PAD]', '[PAD]', '[PAD]']\n", "79\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Str Str Str Str Attribute return sequences Name Attribute return state Name Attribute go backwards Name Attribute stateful Name Attribute unroll Name Attribute implementation Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'length', '[PAD]', '[PAD]']\n", "80\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute kernel size Name Index Num keyword Attribute padding Name keyword Subscript Attribute strides Name Index Num keyword Subscript Attribute dilation rate Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", "81\n", - "[CLS] Tuple Subscript Name Index Num Subscript Name Index Num Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", "82\n", - "[CLS] If Compare Call Name Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Subscript Name Slice Num Add Str\n", - "Label = ['str', 'val', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "83\n", - "[CLS] If BoolOp Or Compare Name Lt BinOp Call Name Subscript Name Slice Num Sub Num Name AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Str\n", - "Label = ['signature', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n", "84\n", - "[CLS] If Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name signature Add BinOp BinOp Str Name Str If Call Name Name Attribute ndarray Name Assign Name str val Str Assign Name str val Call Name Name If Compare Call Name Name Gt Num Assign Name str val BinOp Subscript Name Slice Num Str AugAssign Name signature Name\n", - "Label = ['string', 'types', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "85\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg new arg Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Name Str Name Str\n", - "Label = ['old', 'arg', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "86\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str Str keyword List Tuple Str Str\n", - "Label = ['legacy', 'dropout', 'support', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "87\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str Assign Name length NameConstant\n", - "Label = ['length', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", "88\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "89\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Call Attribute pop Name Str\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "90\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] List Subscript Name Index Num Subscript Name Index Num Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", "91\n", - "[CLS] If BoolOp And Compare Str In Name Compare Str Name Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Attribute pop Name Str Call Attribute pop Name Str Assign Subscript Name Index Str Name Expr Call Attribute append Name Tuple Str Str\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "92\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword List Str Str keyword List Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str Tuple Str Str keyword Dict Str Dict Str Str Str Str Str NameConstant keyword Name\n", - "Label = ['legacy', 'deconv2d', 'support', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "93\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pop Name Str If Compare Name Eq Str Assign Subscript Name Index Str NameConstant Expr Call Attribute append Name Tuple Str Str Expr Call Attribute warn Name Str keyword Num\n", - "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", "94\n", - "[CLS] If Call Name Subscript Name Index Num Tuple Name Name Assert Call Name Subscript Name Index Num Name Assert Compare Str In Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name params Name Name Assign Subscript Name Index Str Name Return Tuple List Name Name List\n", - "Label = ['opt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "95\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Name NotIn Name Raise Call Name BinOp Str Mod Tuple Name Name Name\n", - "Label = ['device', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['start', '[PAD]', '[PAD]', '[PAD]']\n", "96\n", - "[CLS] If Compare Name In Name AugAssign Subscript Name Index Name Add Num AugAssign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Subscript Name Index Name\n", - "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", "97\n", - "[CLS] Return BinOp Tuple BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Sub Attribute start Name Add Attribute base shape Name\n", - "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "98\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Tuple Name Assign Subscript Name Slice Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "99\n", - "[CLS] BoolOp And Name Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Eq Num Call Name Subscript Name Index Num Name\n", - "Label = ['version', 'info', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "100\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Str Name In Attribute data Name Assign Name val Call Attribute loads Name Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", "101\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Attribute count params Name Name comprehension Name p Call Name Name\n", - "Label = ['sum', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "102\n", - "[CLS] If Compare Call Name Name Gt Num For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name Assign Name fields List Str Str Str Subscript Name Index Name Expr Call Name Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['v', '[PAD]', '[PAD]', '[PAD]']\n", "103\n", - "[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute W OK Name Assign Name datadir base Call Attribute join Attribute path Name Str Str\n", - "Label = ['access', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "104\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg file hash arg algorithm arg chunk size Str Num Expr Str If BoolOp Or Compare Name Is Str BoolOp And Compare Name Str Compare Call Name Name Num Assign Name hasher Str Assign Name hasher Str If Compare Call Name Call Name Name Name Name Eq Call Name Name Return NameConstant Return NameConstant\n", - "Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['trainable', 'count', '[PAD]', '[PAD]']\n", "105\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return BoolOp And Compare Attribute stop signal Name IsNot NameConstant UnaryOp Not Call Attribute is set Attribute stop signal Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "106\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg sequence arg use multiprocessing arg shuffle NameConstant NameConstant Expr Call Attribute init Call Name Name Name Name Name Assign Attribute shuffle Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ki', '[PAD]', '[PAD]', '[PAD]']\n", "107\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['response', '[PAD]', '[PAD]', '[PAD]']\n", "108\n", - "[CLS] While NameConstant Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Num If BoolOp Or Compare Attribute unfinished tasks Attribute queue Name Eq Num Call Attribute is set Attribute stop signal Name Return\n", - "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['chunk', '[PAD]', '[PAD]', '[PAD]']\n", "109\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg workers Expr Str Return Lambda arguments arg seqs Call Attribute Pool Name Name keyword Name keyword Tuple Name Attribute random seed Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['extractall', '[PAD]', '[PAD]', '[PAD]']\n", "110\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Expr Call Attribute add Name Str\n", - "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cache', 'dir', '[PAD]', '[PAD]']\n", "111\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num If Compare Name Str Assign Name pad BinOp Name Sub Num\n", - "Label = ['pad', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['exists', '[PAD]', '[PAD]', '[PAD]']\n", "112\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num Name keyword Tuple Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", "113\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute zeros Name BinOp Tuple Name Add Name keyword Attribute float32 Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "114\n", - "[CLS] If Call Name Name Name If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute build Name\n", - "Label = ['built', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['item', '[PAD]', '[PAD]', '[PAD]']\n", "115\n", - "[CLS] If Call Name Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute join Str ListComp Call Name Name comprehension Name ishape Attribute input shapes Name Assign Name inputlabels Str\n", - "Label = ['inputlabels', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['stop', 'signal', '[PAD]', '[PAD]']\n", "116\n", - "[CLS] keyword Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sleep', '[PAD]', '[PAD]', '[PAD]']\n", "117\n", - "[CLS] If Compare Name In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name fn Call Attribute get Name Name If Compare Name Is NameConstant Raise Call Name BinOp BinOp BinOp Str Add Name Str Name\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", "118\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp Str Mult BinOp Attribute width Name Sub Name\n", - "Label = ['bar', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['seqs', '[PAD]', '[PAD]', '[PAD]']\n", "119\n", - "[CLS] If Compare Name Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Str Mod Tuple BinOp Name FloorDiv Num BinOp BinOp Name Num Num BinOp Name Num If Compare Name Num Assign Name eta format BinOp Str Tuple BinOp Name Num BinOp Name Num Assign Name eta format BinOp Str Name\n", - "Label = ['eta', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['executor', 'fn', '[PAD]', '[PAD]']\n", "120\n", - "[CLS] If Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name AugAssign Name info Add BinOp Str Mult BinOp Name Sub Attribute total width Name\n", - "Label = ['total', 'width', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'set', '[PAD]', '[PAD]']\n", "121\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Num Assign Name fpath Call Attribute join Attribute path Name Name BinOp Str Add Call Name Name Assign Tuple Subscript Name ExtSlice Slice BinOp BinOp Name Sub Num Mult Num BinOp Name Num Slice Slice Slice Subscript Name Slice BinOp BinOp Name Num Num BinOp Name Num Call Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['put', '[PAD]', '[PAD]', '[PAD]']\n", "122\n", - "[CLS] With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Str Name imgpath Assign Name x train Call Attribute reshape Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num Call Name Name Num Num\n", - "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "123\n", - "[CLS] Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute frombuffer Name Call Attribute read Name Attribute uint8 Name keyword Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", "124\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Lt Tuple Num Assign Name d Call Attribute load Name Name Assign Name d Call Attribute load Name Name keyword Str Assign Name d decoded Dict For Tuple Name k Name v Call Attribute items Name Assign Subscript Name Index Call Attribute decode Name Str Name Assign Name d Name\n", - "Label = ['version', 'info', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['stop', '[PAD]', '[PAD]', '[PAD]']\n", "125\n", - "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Call Name Name comprehension Name x Name\n", - "Label = ['num', 'words', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['single', 'value', '[PAD]', '[PAD]']\n", "126\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Str Expr Str Assign Name path Call Name Name keyword Str keyword Str Assign Name f Call Name Name Assign Name data Call Attribute load Name Name Expr Call Attribute close Name Return Name\n", - "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "127\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp BinOp List Name Add ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name If Name Assign Name xs ListComp ListComp BinOp Name Name comprehension Name w Name comprehension Name x Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "128\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp ListComp IfExp Compare Name LtE Lt Name Name Name Name comprehension Name w Name comprehension Name x Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", "129\n", - "[CLS] ListComp ListComp Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Compare Name LtE Lt Name Name comprehension Name x Name\n", - "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", "130\n", - "[CLS] If BoolOp And UnaryOp Not Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute FunctionType Name UnaryOp Call Name Attribute build fn Name Attribute MethodType Name Expr Call Attribute append Name Attribute call Attribute build fn Name Expr Call Attribute append Name Attribute build fn Name\n", - "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "131\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name output Call Name Attribute metrics names Attribute model Name Name If Compare Name Eq Str Return Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['fn', '[PAD]', '[PAD]', '[PAD]']\n", "132\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute preprocess input Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['raw', 'code', '[PAD]', '[PAD]']\n", "133\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute VGG19 Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kind', '[PAD]', '[PAD]', '[PAD]']\n", "134\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", "135\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute DenseNet121 Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['last', 'update', '[PAD]', '[PAD]']\n", "136\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Return Call Attribute decode predictions Name Starred Name keyword Name Name\n", - "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "137\n", - "[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name Num Expr Call Attribute append Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name Expr Call Attribute append Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['info', '[PAD]', '[PAD]', '[PAD]']\n", "138\n", - "[CLS] If Compare Name Eq Num Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name If Compare Name NotEq Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name Expr Call Attribute append Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "139\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", "140\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List BinOp Name Sub Num Add Call Name Call Name BinOp Name Num\n", - "Label = ['dims', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['fpath', '[PAD]', '[PAD]', '[PAD]']\n", "141\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] NameConstant Assign Name output shape Subscript Subscript Name Index Num Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['open', '[PAD]', '[PAD]', '[PAD]']\n", "142\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Subscript Name Index Num comprehension Name s Name Compare Name IsNot NameConstant\n", - "Label = ['batch', 'sizes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "143\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Call Name Name NotEq Num Raise Call Name Str Return BinOp Subscript Name Index Num Sub Subscript Name Index Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "144\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Subscript Attribute axes Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['idx', '[PAD]', '[PAD]', '[PAD]']\n", "145\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Mod Call Attribute ndim Name Subscript Name Index Name\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "146\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg kwargs Expr Str Return Call Call Name keyword Name Name\n", - "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "147\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Call Name Attribute alpha Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", "148\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['load', '[PAD]', '[PAD]', '[PAD]']\n", "149\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute shared axes Name Assign Subscript Name Index BinOp Name Sub Num Num Assign Subscript Attribute param broadcast Name Index BinOp Name Num NameConstant\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", "150\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Num Call Name Name If Compare Name NotIn Attribute shared axes Name Assign Subscript Name Index Name Subscript Name Index Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['build', 'fn', '[PAD]', '[PAD]']\n", "151\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute axis Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "152\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute cast to floatx Name Name\n", - "Label = ['max', 'value', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "153\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Call Name Attribute layer Name Str Return Attribute updates Attribute layer Name Return List Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['probs', '[PAD]', '[PAD]', '[PAD]']\n", "154\n", - "[CLS] Dict Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Attribute layer Name Call Attribute get config Attribute layer Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "155\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Num\n", - "Label = ['inner', 'input', 'shape', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "156\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get shape tuple Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "157\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple UnaryOp USub Num Name Name Num Subscript Name Slice Num\n", - "Label = ['get', 'shape', 'tuple', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['compute', 'elemwise', 'op', 'output']\n", "158\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg value Assign Attribute trainable Name Name Assign Attribute trainable Attribute forward layer Name Name Assign Attribute trainable Attribute backward layer Name Name Attribute setter Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "159\n", - "[CLS] Return BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute forward layer Name Add Call Attribute get weights Attribute backward layer Name\n", - "Label = ['get', 'weights', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "160\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Return BinOp BinOp Name Add Name Call Attribute copy Name Name\n", - "Label = ['merge', 'mode', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "161\n", - "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "162\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Name FloorDiv Num Add Num\n", - "Label = ['pivot', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['m', '[PAD]', '[PAD]', '[PAD]']\n", "163\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp UnaryOp Not Attribute merge mode Name List NameConstant NameConstant NameConstant\n", - "Label = ['output', 'mask', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "164\n", - "[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Add Name Name\n", - "Label = ['get', 'updates', 'for', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "165\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Return BinOp Attribute losses Attribute forward layer Name Add Attribute losses Attribute backward layer Name\n", - "Label = ['forward', 'layer', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "166\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "167\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Call Name keyword Tuple NameConstant Name comprehension Name dim Name\n", - "Label = ['state', 'spec', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", "168\n", - "[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Attribute go backwards Name Attribute stateful Name\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", "169\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "170\n", - "[CLS] If BoolOp And Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute built Name Return List Attribute kernel Name Attribute recurrent kernel Name Attribute bias Name\n", - "Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", "171\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Attribute units Name\n", - "Label = ['recurrent', 'kernel', 'z', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "172\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Attribute units Name BinOp Attribute units Name Mult Num\n", - "Label = ['kernel', 'r', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shared', 'axes', '[PAD]', '[PAD]']\n", "173\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['bias', 'r', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "174\n", - "[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num\n", - "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", "175\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "176\n", - "[CLS] ExtSlice Slice Slice BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['units', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "177\n", - "[CLS] If BoolOp Or Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute return state Name Assign Name h Subscript Name Index Num Assign Name c Subscript Name Index Num\n", - "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "178\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name output Call Attribute transpose Name Name Tuple Num Num Num Assign Name output Subscript Name Index UnaryOp USub Num\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", "179\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pooling function Name keyword Name keyword BinOp Attribute pool size Name Add Tuple Num keyword BinOp Attribute strides Name Tuple Num keyword Attribute padding Name keyword Attribute data format Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['max', 'value', '[PAD]', '[PAD]']\n", "180\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'map', '[PAD]', '[PAD]']\n", "181\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Return Tuple Subscript Name Index Num Name Name Subscript Name Index Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "182\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['get', 'shape', 'tuple', '[PAD]']\n", "183\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['nw', '[PAD]', '[PAD]', '[PAD]']\n", "184\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute pool size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['len', 'dim3', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['get', 'weights', '[PAD]', '[PAD]']\n", "185\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute pooling function Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['set', 'weights', '[PAD]', '[PAD]']\n", "186\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Attribute pool size Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name\n", - "Label = ['pooling', 'function', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", "187\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pool size arg strides arg padding arg data format arg kwargs Tuple Num Num Num NameConstant Str NameConstant Expr Call Attribute init Call Name Name Name Name Name Name Name keyword Name Attribute legacy pooling3d support Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['int', 'shape', '[PAD]', '[PAD]']\n", "188\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs Str Expr Call Attribute init Call Name Name Name Name keyword Name Assign Attribute supports masking Name NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'keras', 'tensor', '[PAD]']\n", "189\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format arg kwargs NameConstant Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute data format Name Call Attribute normalize data format Name Name Assign Attribute input spec Name Call Name keyword Num Attribute legacy global pooling support Name\n", "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "190\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name config Dict Str Attribute data format Name Assign Name base config Call Attribute get config Call Name Name Name Return Call Name BinOp Call Name Call Attribute items Name Add Call Name Call Attribute items Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['get', 'updates', 'for', '[PAD]']\n", "191\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute mean Name Name keyword List Num Num Return Call Attribute mean Name Name keyword List Num Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "192\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs If Compare Attribute data format Name Eq Str Return Call Attribute max Name Name keyword List Num Num Return Call Attribute max Name Name keyword List Num Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['stateful', '[PAD]', '[PAD]', '[PAD]']\n", "193\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['return', 'state', '[PAD]', '[PAD]']\n", "194\n", - "[CLS] Raise Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Attribute shape Name comprehension Name spec Attribute state spec Name Attribute state size Attribute cell Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "195\n", - "[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Subscript Name Index Name keyword Attribute padding Attribute cell Name keyword Subscript Attribute strides Attribute cell Name Index Name keyword Subscript Attribute dilation rate Attribute cell Name Index Name\n", - "Label = ['conv', 'output', 'length', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "196\n", - "[CLS] If BoolOp And Compare Name Is NameConstant Compare Name NameConstant Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kernel', 'r', '[PAD]', '[PAD]']\n", "197\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg mask arg training arg initial state arg constants NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "198\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "199\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg states Assign Name constants Subscript Name Slice UnaryOp USub Attribute num constants Name Assign Name states Subscript Name Slice UnaryOp Attribute num constants Name Return Call Attribute call Attribute cell Name Name Name keyword Name keyword Name\n", - "Label = ['inputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "200\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name state shape BinOp Subscript Name Slice Num Add Subscript Name Slice Num\n", - "Label = ['return', 'sequences', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", "201\n", - "[CLS] BinOp Attribute [MASK] [MASK] [MASK] [MASK] Name Add Tuple Name BinOp Attribute filters Name Mult Num\n", - "Label = ['kernel', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "202\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice Slice Slice Attribute filters Name\n", - "Label = ['kernel', 'i', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "203\n", - "[CLS] Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pool', 'size', '[PAD]', '[PAD]']\n", "204\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice Slice Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['recurrent', 'kernel', 'o', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "205\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num BinOp Attribute filters Name Num\n", - "Label = ['bias', 'c', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "206\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute bias Name Slice BinOp Attribute filters Name Mult Num\n", - "Label = ['bias', 'o', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "207\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute kernel i Name Attribute bias i Name keyword Attribute padding Name\n", - "Label = ['input', 'conv', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "208\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "209\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "208\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", + "209\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "210\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Assign Name outputs Call Attribute conv1d Name Name Attribute kernel Name keyword Subscript Attribute strides Name Index Num keyword Attribute padding Name keyword Attribute data format Name keyword Subscript Attribute dilation rate Name Index Num\n", - "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "211\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Name Assign Name new dim Call Attribute conv output length Name Subscript Name Index Name Subscript Attribute kernel size Name Index Name keyword Attribute padding Name keyword Subscript Attribute strides Name Index Name keyword Subscript Attribute dilation rate Name Index Name Expr Call Attribute append Name Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "212\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "213\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "214\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "215\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "216\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Tuple Name h axis Name w axis Tuple Num Num Assign Tuple Name h axis Name w axis Tuple Num Num\n", "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "216\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "217\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Assign Name output shape Tuple Name Name Name Attribute filters Name\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", "218\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv2d transpose Name Name Attribute kernel Name Name Attribute strides Name keyword Attribute padding Name keyword Attribute data format Name keyword Attribute dilation rate Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", "219\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "220\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute output padding Name\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "221\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", "222\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "223\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute deconv length Name Name Name Name Attribute padding Name Name\n", - "Label = ['out', 'height', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "224\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Name output shape Tuple Name Attribute filters Name Name Name Name Assign Name output shape Tuple Name Name Name Name Attribute filters Name\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['recurrent', 'kernel', 'c', '[PAD]']\n", "225\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', 'f', '[PAD]', '[PAD]']\n", "226\n", - "[CLS] Assign Subscript Name Index Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', 'f', '[PAD]', '[PAD]']\n", "227\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Name Name Attribute padding Name Name\n", - "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", "228\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "229\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Attribute data format Name Eq Str Num UnaryOp USub Num\n", - "Label = ['channel', 'axis', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "230\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Tuple Attribute filters Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "231\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name keyword Num keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "231\n", + "Label = ['out', 'height', '[PAD]', '[PAD]']\n", "232\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute depthwise kernel Name keyword Attribute strides Name keyword Attribute padding Name keyword Attribute dilation rate Name keyword Attribute data format Name\n", - "Label = ['depthwise', 'conv2d', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "233\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outputs Call Attribute bias add Name Name Attribute bias Name keyword Attribute data format Name\n", - "Label = ['use', 'bias', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "234\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Call Name Num BinOp Num Add Attribute rank Name\n", - "Label = ['spatial', 'axes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", "235\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name output Call Attribute repeat elements Name Name Subscript Attribute size Name Index Num keyword Num Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['deconv', 'length', '[PAD]', '[PAD]']\n", "236\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute resize images Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "237\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Subscript Attribute size Name Index Num Subscript Attribute size Name Index Num Attribute data format Name Attribute interpolation Name\n", - "Label = ['resize', 'images', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "238\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name Str keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "239\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute normalize tuple Name Subscript Name Index Num Num Str\n", - "Label = ['dim3', 'padding', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", "240\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg cropping arg data format arg kwargs Tuple Tuple Num Num Tuple Num Num Tuple Num Num NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "241\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Tuple Name Name Tuple Name Name Tuple Name Name\n", - "Label = ['normalized', 'cropping', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", "242\n", - "[CLS] BinOp Str Mod Tuple Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name\n", - "Label = ['input', 'length', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['separable', 'conv2d', '[PAD]', '[PAD]']\n", "243\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv1d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "244\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name NotEq Str Raise Call Name BinOp Str Add Name\n", - "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "245\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv output length Name Name Subscript Attribute kernel size Name Index Num Attribute padding Name Subscript Attribute strides Name Index Num\n", - "Label = ['output', 'row', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['size', 'all', 'dims', '[PAD]']\n", "246\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute local conv2d Name Name Attribute kernel Name Attribute kernel size Name Attribute strides Name Tuple Attribute output row Name Attribute output col Name Attribute data format Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "247\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "248\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Attribute gamma Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute gamma initializer Name keyword Attribute gamma regularizer Name keyword Attribute gamma constraint Name Assign Attribute gamma Name NameConstant\n", - "Label = ['scale', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['end', '[PAD]', '[PAD]', '[PAD]']\n", "249\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute add weight Name keyword Name keyword Str keyword Attribute beta initializer Name keyword Attribute beta regularizer Name keyword Attribute beta constraint Name\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['slices', '[PAD]', '[PAD]', '[PAD]']\n", "250\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Str keyword Attribute moving mean initializer Name keyword NameConstant\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cropping', 'all', 'dims', '[PAD]']\n", "251\n", - "[CLS] BinOp Name Div BinOp Name Sub BinOp Num Add Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['epsilon', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "252\n", - "[CLS] Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "253\n", - "[CLS] Return Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute cells Name Index UnaryOp USub Num Index Num\n", - "Label = ['state', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "254\n", - "[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute cells Name Slice UnaryOp USub Num Attribute cells Name\n", - "Label = ['reverse', 'state', 'order', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "255\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Slice Num Assign Name input shape Subscript Name Index Num\n", - "Label = ['constants', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "256\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute cells Name If Call Name Name Name AugAssign Name weights Add Attribute non trainable weights Name\n", - "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "257\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs NameConstant Assign Name losses List For Name cell Attribute cells Name If Call Name Name Name Assign Name cell losses Call Attribute get losses for Name Name AugAssign Name losses Add Name Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "258\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] If Compare Attribute states Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name Return Attribute states Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['conv', 'output', 'length', '[PAD]']\n", "259\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant If Call Name Attribute state size Attribute cell Name Name Assign Name num states Num Assign Name num states Call Name Attribute state size Attribute cell Name Return ListComp NameConstant comprehension Name Call Name Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "260\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Tuple Subscript Name Index Num Name comprehension Name dim Name\n", - "Label = ['state', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "261\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num\n", - "Label = ['input', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "262\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cell Name Str Return ListComp Call Attribute tile Name Name List Num Name comprehension Name dim Attribute state size Attribute cell Name Return List Call Attribute tile Name Name List Num Attribute state size Attribute cell Name\n", - "Label = ['state', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['center', '[PAD]', '[PAD]', '[PAD]']\n", "263\n", - "[CLS] If Call Name Name Name If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Is NameConstant Assign Name initial state Subscript Name Slice Num Assign Name initial state Subscript Name Slice Num UnaryOp USub Attribute num constants Name If Compare Call Name Name Eq Num Assign Name initial state NameConstant Assign Name inputs Subscript Name Index Num\n", - "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "264\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Tuple Name Name Str Call Name Attribute shape Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['state', 'size', '[PAD]', '[PAD]']\n", "265\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reverse', 'state', 'order', '[PAD]']\n", "266\n", - "[CLS] BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reverse', 'state', 'order', '[PAD]']\n", "267\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Subscript Name Index Str Attribute num constants Name\n", - "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "268\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "269\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Name If UnaryOp Not Attribute trainable Name Return Attribute weights Attribute cell Name Return Attribute non trainable weights Attribute cell Name\n", "Label = ['cell', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "269\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "270\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant Assign Name h Call Attribute bias add Name Name Attribute bias Name\n", - "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['state', 'shape', '[PAD]', '[PAD]']\n", "271\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num\n", - "Label = ['kernel', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "272\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['ones', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['build', '[PAD]', '[PAD]', '[PAD]']\n", "273\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Mult Attribute units Name\n", - "Label = ['matrix', 'inner', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "274\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Subscript Name ExtSlice Slice Slice BinOp Num Attribute units Name\n", - "Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['additional', 'inputs', '[PAD]', '[PAD]']\n", "275\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dot Name BinOp Name Mult Name Subscript Attribute recurrent kernel Name ExtSlice Slice Slice BinOp Num Attribute units Name\n", - "Label = ['recurrent', 'h', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['state', 'spec', '[PAD]', '[PAD]']\n", "276\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "277\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "278\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['set', 'value', '[PAD]', '[PAD]']\n", "279\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg config If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Num Assign Subscript Name Index Str Num Return Call Name keyword Name Name\n", - "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "280\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Call Attribute bias initializer Name Tuple Attribute units Name Starred Name keyword Name Call Call Attribute Ones Name Tuple Attribute units Name Starred Name keyword Name Call Attribute bias initializer Name Tuple BinOp Attribute units Name Mult Num Starred Name keyword Name\n", - "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['num', 'constants', '[PAD]', '[PAD]']\n", "281\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute kernel Name ExtSlice Slice Slice BinOp Attribute units Name Mult Num BinOp Attribute units Name Num\n", - "Label = ['kernel', 'c', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['get', 'losses', 'for', '[PAD]']\n", "282\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Call Attribute ones like Name Name Attribute dropout Name keyword Name keyword Num\n", - "Label = ['dropout', 'mask', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['backend', '[PAD]', '[PAD]', '[PAD]']\n", "283\n", - "[CLS] If BoolOp And Compare Num Lt Attribute [MASK] [MASK] [MASK] [MASK] Name Num Compare Attribute recurrent dropout mask Name Is NameConstant Assign Attribute recurrent dropout mask Name Call Name Call Attribute ones like Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['recurrent', 'dropout', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "284\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Attribute recurrent dropout Name keyword Name keyword Num\n", - "Label = ['ones', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "285\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute recurrent activation Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel f Name\n", - "Label = ['f', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "286\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Add Call Attribute dot Name Name Attribute recurrent kernel o Name\n", - "Label = ['recurrent', 'activation', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "287\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Attribute units Name BinOp Num Mult Attribute units Name\n", - "Label = ['z1', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['recurrent', 'kernel', '[PAD]', '[PAD]']\n", "288\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "289\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "290\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute sqrt Name BinOp Attribute rate Name Div BinOp Num Sub Attribute rate Name\n", - "Label = ['stddev', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['recurrent', 'dropout', 'mask', '[PAD]']\n", "291\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "292\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assign Name input shape Call Attribute shape Name Name If Compare Attribute data format Name Eq Str Assign Name noise shape Tuple Subscript Name Index Num Subscript Name Index Num Num Num Assign Name noise shape Tuple Subscript Name Index Num Num Num Subscript Name Index Num Return Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", "293\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Num Num Subscript Name Index Num\n", - "Label = ['noise', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", "294\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name Str\n", - "Label = ['unknown', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "295\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "295\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "296\n", - "[CLS] Return Call Name BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "297\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg n arg kwargs Expr Call Attribute init Call Name Name Name keyword Name Assign Attribute n Name Name Assign Attribute input spec Name Call Name keyword Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cls', '[PAD]', '[PAD]', '[PAD]']\n", "298\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg input shape Return Tuple Subscript Name Index Num Attribute n Name Subscript Name Index Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "299\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Return Call Attribute repeat Name Name Attribute n Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "300\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute placeholder Name keyword Name comprehension Name shape Name\n", - "Label = ['xs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kept', 'idx', '[PAD]', '[PAD]']\n", "301\n", - "[CLS] BinOp Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Add Call Name Call Attribute items Name\n", - "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "302\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str If Call Name Subscript Subscript Name Index Str Index Name Name Assign Name arg dict Subscript Subscript Name Index Str Index Name If BoolOp And Compare Str In Name Compare Subscript Name Index Str Eq Str Assign Subscript Subscript Name Index Str Index Name Call Attribute array Name Subscript Name Index Str\n", - "Label = ['key', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "303\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Name Attribute units Name keyword Attribute kernel initializer Name keyword Str keyword Attribute kernel regularizer Name keyword Attribute kernel constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "304\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Tuple Attribute units Name keyword Attribute bias initializer Name keyword Str keyword Attribute bias regularizer Name keyword Attribute bias constraint Name\n", - "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['boolean', 'mask', '[PAD]', '[PAD]']\n", "305\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute asarray Name Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "306\n", - "[CLS] BoolOp And Call Name Name Compare Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute cntk py Name Is NameConstant\n", - "Label = ['Function', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "307\n", - "[CLS] If Compare Name Eq Str Return Attribute [MASK] [MASK] [MASK] [MASK] Name Return Attribute float32 Name\n", - "Label = ['float16', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "308\n", - "[CLS] BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Parameter Attribute variables Name\n", - "Label = ['Constant', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "309\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Attribute shape Name Index Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['s', '[PAD]', '[PAD]', '[PAD]']\n", "310\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute input Name keyword Name keyword Call Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['fix', 'unknown', 'dimension', '[PAD]']\n", "311\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index BinOp Name Add Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "312\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['data', 'format', '[PAD]', '[PAD]']\n", "313\n", - "[CLS] For Name Name If Compare Name Is NameConstant Raise Call Name Str\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", "314\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", "315\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['array', '[PAD]', '[PAD]', '[PAD]']\n", "316\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name Name keyword Call Attribute uniform Attribute initializer Name Name keyword Name keyword Name keyword Name\n", - "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['add', 'weight', '[PAD]', '[PAD]']\n", "317\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute parameter Name keyword Name keyword Call Attribute normal Attribute initializer Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['p', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['input', 'spec', '[PAD]', '[PAD]']\n", "318\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "319\n", - "[CLS] For Name Attribute [MASK] [MASK] [MASK] [MASK] Name If BoolOp Or Compare Name Eq Attribute InferredDimension Name Compare Name Attribute FreeDimension Name Raise Call Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "320\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Call Name BinOp Call Name Name Sub Num\n", - "Label = ['permutation', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['items', '[PAD]', '[PAD]', '[PAD]']\n", "321\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] List BinOp Call Name Name Sub Num BinOp Call Name Name Num\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['parameter', '[PAD]', '[PAD]', '[PAD]']\n", "322\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp IfExp Compare Name Is NameConstant Attribute InferredDimension Name Name comprehension Name Name\n", - "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "323\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name IfExp Compare Name GtE Num Name BinOp Name Add Call Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "324\n", - "[CLS] Call Name ListComp Num comprehension Name Call Name BinOp Call Name Name Sub Call Name Name\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "325\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name rep Call Name Name If BoolOp And Compare Name GtE Name Compare Subscript Name Index Name IsNot NameConstant Assign Name tmp BinOp List Name Mult Name Assign Name x Call Attribute splice Name Starred Name keyword BinOp Name Sub Name AugAssign Name i Add Num\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "326\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute all axes Attribute Axis Name\n", - "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "327\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute element select Name Name Call Name Name Call Name Name\n", - "Label = ['any', 'matrix', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bernoulli', '[PAD]', '[PAD]', '[PAD]']\n", "328\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg increment Assign Name result BinOp Name Add Name Return Call Attribute assign Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['zeros', '[PAD]', '[PAD]', '[PAD]']\n", "329\n", - "[CLS] If BoolOp And Compare Call Name Name Eq Call Name Name Compare Subscript Call Name Name Index Num Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name List Num\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['size', '[PAD]', '[PAD]', '[PAD]']\n", "330\n", - "[CLS] BoolOp Or Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name Call Name GeneratorExp Compare Name Attribute FreeDimension Name comprehension Name Attribute shape Name\n", - "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "331\n", - "[CLS] Call Name GeneratorExp Compare Name Eq Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name Attribute shape Name\n", "Label = ['InferredDimension', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "332\n", - "[CLS] BinOp Call Name ListComp UnaryOp USub Num comprehension Name Call Name BinOp Name Sub Name Add Name\n", - "Label = ['[PAD]', '[PAD]', '[PAD]', '[PAD]']\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "333\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name ListComp Name comprehension Name i Call Name Name\n", - "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "334\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs arg initial states arg go backwards arg mask arg constants arg unroll arg input length NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['step', 'function', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['has', 'seq', '[PAD]', '[PAD]']\n", "335\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute ops Name Name Name Name BinOp Name Add Num\n", - "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "336\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute element select Attribute ops Name Name Name Name\n", "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "337\n", - "[CLS] If BoolOp And Compare Name Is NameConstant UnaryOp Not Call Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Name Index Num\n", - "Label = ['num', 'time', 'step', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "338\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name If Compare Call Name Name Eq Num Expr Call Attribute append Name Call Attribute broadcast as Attribute sequence Name Name Name Expr Call Attribute append Name Name\n", - "Label = ['c', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "339\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name new states Call Name Name BinOp Call Name Name Add Call Name Name\n", - "Label = ['new', 'output', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", "340\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute element select Name Name Name Name comprehension Tuple Name n Name s Call Name Name Name\n", - "Label = ['new', 'states', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "341\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute swapaxes Name Name Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['assign', '[PAD]', '[PAD]', '[PAD]']\n", "342\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['gamma', '[PAD]', '[PAD]', '[PAD]']\n", "343\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Name keyword Name keyword List NameConstant Name Name keyword Subscript Attribute shape Name Index Num\n", - "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", "344\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute reshape Name Call Attribute transpose Name Name Tuple Num Num Num Num BinOp Tuple UnaryOp USub Num Num Add Subscript Attribute shape Name Slice Num\n", - "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axis', '[PAD]', '[PAD]', '[PAD]']\n", "345\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format Tuple Num Num Num Str NameConstant\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "346\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute clip Name Name Call Name BinOp Num Sub Call Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", "347\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute one hot Name Name Subscript Attribute shape Name Index Name keyword Name\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "348\n", - "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Expr Call Attribute append Name Name Expr Call Attribute append Name Name\n", - "Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", "349\n", - "[CLS] If Compare Name In Name Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name Expr Call Attribute append Name Name Raise Call Name BinOp Str Mod Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['current', 'layout', '[PAD]', '[PAD]']\n", "350\n", - "[CLS] If Compare Call Name Name Gt Num Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute combine Name ListComp Attribute output Name comprehension Name Name\n", - "Label = ['unrelated', 'updates', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", "351\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Attribute arguments Attribute loss Name If Compare Name In Name Assign Subscript Name Index Name Subscript Name Index Name Raise Call Name BinOp Str Mod Attribute name Name\n", - "Label = ['argument', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['slice', '[PAD]', '[PAD]', '[PAD]']\n", "352\n", - "[CLS] If Compare Subscript Name Index Num Gt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Assign Subscript Name Index Name Subscript Name Index Num Assign Name prefix shape Call Name Name Assign Name x Call Attribute splice Name Call Attribute constant Name keyword Num keyword Name Name keyword Name Assign Name base shape Attribute shape Name\n", - "Label = ['prefix', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['current', '[PAD]', '[PAD]', '[PAD]']\n", "353\n", - "[CLS] Assert Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Num Sub IfExp Compare Name Gt Num Num Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "354\n", - "[CLS] If BoolOp Or Call Name Name Attribute [MASK] [MASK] [MASK] [MASK] Attribute variables Name Call Name Name Attribute Constant Attribute variables Name Expr Call Attribute append Name Attribute value Name Expr Call Attribute append Name Call Name Name\n", - "Label = ['Parameter', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "355\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg message Str Return Call Attribute user function Name Call Name Name keyword Lambda arguments arg x NameConstant keyword Lambda arguments arg x Call Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "356\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Subscript Name Index BinOp Name Add Name\n", - "Label = ['condition', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['depthwise', 'kernel', '[PAD]', '[PAD]']\n", "357\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg data format If Compare Name Eq Str Assign Name x Call Attribute transpose Name Name Tuple Num Num Num Return Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", "358\n", - "[CLS] If Call Name Name Str Return Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Return Num\n", - "Label = ['dynamic', 'axes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "359\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Slice Index Name Index Name Tuple UnaryOp USub Num Num Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['convolution', '[PAD]', '[PAD]', '[PAD]']\n", "360\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Subscript Name ExtSlice Slice Index Name Index Name Slice Tuple UnaryOp USub Num Num Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "361\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute as shape Call Attribute data Name BinOp Tuple Name Add Attribute target shape Name\n", - "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "362\n", - "[CLS] BinOp Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Mult Call Attribute prod Name Call Attribute asarray Name Attribute target shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "363\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Name List Name keyword NameConstant keyword Name\n", - "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "364\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Subscript Attribute inputs Name Index Num Slice Num Attribute dtype Subscript Attribute inputs Name Index Num List Name\n", - "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['axis', 'without', 'batch', '[PAD]']\n", "365\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg state arg root gradients Return Call Attribute Value Attribute cntk py Name Call Attribute data Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", "366\n", - "[CLS] FunctionDef arguments Expr Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute get default graph Name If Compare Name NotIn Name Assign Name phase Call Attribute placeholder with default Name NameConstant keyword Tuple keyword Str Assign Subscript Name Index Name Name Return Subscript Name Index Name\n", - "Label = ['graph', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "367\n", - "[CLS] If UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword NameConstant Assign Name num thread Call Name Call Attribute get Attribute environ Name Str Assign Name config Call Attribute ConfigProto Name keyword Name keyword NameConstant\n", - "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "368\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return Call Attribute eval Call Name Name keyword Call Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['postfix', 'shape', '[PAD]', '[PAD]']\n", "369\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute transpose Name Name keyword Name List Subscript Name Index UnaryOp USub Num UnaryOp Num\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['splice', '[PAD]', '[PAD]', '[PAD]']\n", "370\n", - "[CLS] If Call Name ListComp Call Name Name Tuple Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp BinOp Str Add Str Str Call Name Name\n", - "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pattern', '[PAD]', '[PAD]', '[PAD]']\n", "371\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp Compare Subscript Name Index Num Eq BinOp Call Name Name Sub Num NameConstant NameConstant\n", - "Label = ['adj', 'x', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['a', '[PAD]', '[PAD]', '[PAD]']\n", "372\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Expr Str Return Call Attribute reduce max Name Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "373\n", - "[CLS] Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name keyword Name keyword Name\n", - "Label = ['sqrt', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['user', 'function', '[PAD]', '[PAD]']\n", "374\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute not equal Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "375\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Expr Str Return Call Attribute greater equal Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "376\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute nn Name Name Name Name keyword Name keyword Name\n", - "Label = ['fused', 'batch', 'norm', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['version', '[PAD]', '[PAD]', '[PAD]']\n", "377\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name If Compare Call Name Name Gt Num Assign Name beta Call Attribute reshape Name Name UnaryOp USub Num\n", - "Label = ['beta', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "378\n", - "[CLS] If Compare Name Lt Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num If Name AugAssign Name axis Mod Name Assign Name axis Num\n", - "Label = ['rank', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['num', 'element', '[PAD]', '[PAD]']\n", "379\n", - "[CLS] If Call Name ListComp Call Name Name comprehension Name [MASK] [MASK] [MASK] [MASK] Name Return Call Attribute sparse concat Name Name Name Return Call Attribute concat Name ListComp Call Name Name comprehension Name x Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['Value', '[PAD]', '[PAD]', '[PAD]']\n", "380\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg shape Expr Str Return Call Attribute reshape Name Name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "381\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg pattern Expr Str Return Call Attribute transpose Name Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "382\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Name keyword Subscript Name Index Name keyword Name\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'variable', '[PAD]', '[PAD]']\n", "383\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Assign Name x Call Attribute reshape Name Name Call Attribute stack Name List UnaryOp USub Num Call Name Subscript Call Name Name Slice Num Return Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "384\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg padding arg data format Tuple Tuple Num Num Tuple Num Num Tuple Num Num NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "385\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Attribute outputs Name Add List Attribute updates op Name Attribute fetches Name\n", - "Label = ['fetches', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['run', '[PAD]', '[PAD]', '[PAD]']\n", "386\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp List Num Num Add Call Name Call Name Num Name\n", - "Label = ['axes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "387\n", - "[CLS] If Compare Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Eq BinOp Name Sub Num Assign Name mask Call Name Name\n", - "Label = ['get', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "388\n", - "[CLS] UnaryOp USub Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Mult Call Attribute log Name Name Name\n", - "Label = ['reduce', 'sum', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "389\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg level arg noise shape arg seed NameConstant NameConstant Expr Str Assign Name retain prob BinOp Num Sub Name If Compare Name Is NameConstant Assign Name seed Call Attribute randint Attribute random Name Num Return Call Attribute dropout Attribute nn Name BinOp Name Mult Num Name Name keyword Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "390\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['type', '[PAD]', '[PAD]', '[PAD]']\n", "391\n", - "[CLS] If BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str Assign Name x Call Attribute cast Name Name Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "392\n", - "[CLS] BoolOp And Compare Call Name Name Eq Str Compare Call Name Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute version Name Str Index Num Lt Call Name Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "393\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Name Eq Str Assign Name padding Str If Compare Name Str Assign Name padding Str Raise Call Name BinOp Str Add Call Name Name Return Name\n", - "Label = ['padding', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['xt', '[PAD]', '[PAD]', '[PAD]']\n", "394\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult BinOp Subscript Name Index Num Sub Num\n", - "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "395\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute convolution Attribute nn Name keyword Name keyword Name keyword Tuple Name keyword Tuple Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", "396\n", - "[CLS] If Call Name Name Tuple Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute stack Name Name\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['adj', 'x', '[PAD]', '[PAD]']\n", "397\n", - "[CLS] If Compare Subscript Name Index Num Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Subscript Call Attribute shape Name Name Index Num Add Call Name Subscript Name Slice Num Assign Name output shape Call Attribute stack Name Call Name Name\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "398\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute atrous conv2d transpose Attribute nn Name Name Name Name Subscript Name Index Num Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "398\n", + "Label = ['normed', '[PAD]', '[PAD]', '[PAD]']\n", "399\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Num Assign Name strides BinOp BinOp Tuple Num Add BinOp Name Mult Num Tuple Num Assign Name spatial start dim Num Assign Name strides BinOp Tuple Num Num BinOp Name Num\n", - "Label = ['spatial', 'start', 'dim', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['mean', '[PAD]', '[PAD]', '[PAD]']\n", "400\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Tuple Num Num Add BinOp Name Mult Num\n", - "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['concat', '[PAD]', '[PAD]', '[PAD]']\n", "401\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "402\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "403\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['splits', '[PAD]', '[PAD]', '[PAD]']\n", "404\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute conv3d transpose Attribute nn Name Name Name Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['result', '[PAD]', '[PAD]', '[PAD]']\n", "405\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute max pool Attribute nn Name Name Name Name keyword Name keyword Name If Compare Name Str Assign Name x Call Attribute avg pool Attribute nn Name Name Name Name keyword Name keyword Name Raise Call Name BinOp Str Add Call Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "406\n", - "[CLS] If BoolOp And Compare Name Eq Str Compare Name Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute transpose Name Name Tuple Num Num Num Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", "407\n", - "[CLS] If Compare Call Name Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Num Num Num Num Subscript Name Index Num Assign Name new shape BinOp Tuple Num Add Name\n", - "Label = ['new', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['assign', 'placeholder', '[PAD]', '[PAD]']\n", "408\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute randint Attribute random Name Num\n", - "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['update', '[PAD]', '[PAD]', '[PAD]']\n", "409\n", - "[CLS] Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute range Name Subscript Name Index Num Num Lt Call Attribute fill Name Name Name\n", - "Label = ['expand', 'dims', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "410\n", - "[CLS] If Name Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name log prob Call Attribute ctc greedy decoder Name keyword Name keyword Name Assign Tuple Name decoded Name log prob Call Attribute ctc beam search decoder Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['decoded', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sparse', 'coo', '[PAD]', '[PAD]']\n", "411\n", - "[CLS] If Call Name Name Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Return Name\n", - "Label = ['dense', 'from', 'sparse', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", "412\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg ndim arg dtype arg sparse arg name NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "413\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str Return BoolOp And Call Name Name Str Attribute theano placeholder Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "413\n", + "Label = ['step', 'function', '[PAD]', '[PAD]']\n", "414\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg name NameConstant NameConstant Expr Str If Compare Name Is NameConstant Assign Name dtype Call Name Return Call Name Call Attribute zeros Name Name Name Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['stack', '[PAD]', '[PAD]', '[PAD]']\n", "415\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute random Name keyword Num keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['stack', '[PAD]', '[PAD]', '[PAD]']\n", "416\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg indices Expr Str Assign Name y Subscript Name Index Name If BoolOp And Call Name Name Str Call Name Name Str Assign Attribute keras shape Name BinOp Attribute keras shape Name Add Subscript Attribute keras shape Name Slice Num Return Name\n", - "Label = ['reference', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "417\n", - "[CLS] BoolOp Or Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Attribute dtype Name Eq Str\n", - "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['logits', '[PAD]', '[PAD]', '[PAD]']\n", "418\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis arg keepdims NameConstant NameConstant Return Call Attribute var Name Name keyword Name keyword Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "419\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis UnaryOp USub Num Return Call Attribute argmin Name Name keyword Name keyword NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['dropout', '[PAD]', '[PAD]', '[PAD]']\n", "420\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg y Assign Name z Call Attribute neq Name Name Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name If Call Name Name Str Assign Attribute keras shape Name Attribute keras shape Name Return Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "421\n", - "[CLS] Return Tuple Name Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Name Pow Num\n", - "Label = ['inv', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "422\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute bn Attribute nnet Name Name Name Name Name Name Name Name\n", - "Label = ['batch', 'normalization', 'test', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", "423\n", - "[CLS] BoolOp And Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Num Compare Attribute ndim Name Gt Num\n", - "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "424\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute dnn Attribute cuda Attribute sandbox Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Call Attribute dimshuffle Name Name Str Name\n", - "Label = ['dnn', 'batch', 'normalization', 'test']\n", - "Pred =\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "425\n", - "[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute hstack Attribute basic Name Name keyword Str Raise Call Name Str Name\n", - "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "426\n", - "[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Attribute uses learning phase Name Assign Attribute uses learning phase Name NameConstant\n", - "Label = ['uses', 'learning', 'phase', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "427\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Is NameConstant AugAssign Name output shape Add Tuple NameConstant AugAssign Name output shape Tuple BinOp Subscript Attribute keras shape Name Index UnaryOp Num Mult Name\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['strides', '[PAD]', '[PAD]', '[PAD]']\n", "428\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Subscript Name Index Num BinOp BinOp Subscript Name Index Num Add Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num\n", - "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "429\n", - "[CLS] ExtSlice Slice Slice Subscript Name Index Num BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Slice\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "430\n", - "[CLS] If Call Name Name Str Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Attribute keras shape Name Index Num BinOp Subscript Attribute keras shape Name Index Num Add Call Name Name Subscript Attribute keras shape Name Index Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "431\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Tuple Call Name NameConstant Call Name Name BinOp Subscript Name Index Num Add Name Call Name Name BinOp Subscript Name Index Num Name Call Name NameConstant\n", - "Label = ['indices', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['random', 'normal', '[PAD]', '[PAD]']\n", "432\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n", - "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['seed', '[PAD]', '[PAD]', '[PAD]']\n", "433\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num IsNot NameConstant Assign Name w BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num Assign Name w NameConstant\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "434\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Subscript Attribute keras shape Name Index Num Add Subscript Subscript Name Index Num Index Num Subscript Subscript Name Index Num Index Num\n", - "Label = ['w', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "435\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Subscript Name Index Num Index Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['batch', 'array', '[PAD]', '[PAD]']\n", "436\n", - "[CLS] GeneratorExp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name comprehension Name i Call Name Attribute ndim Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['SparseTensor', '[PAD]', '[PAD]', '[PAD]']\n", "437\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Attribute shape Call Attribute get value Name keyword NameConstant keyword NameConstant\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['slice', 'row', '[PAD]', '[PAD]']\n", "438\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute function Name Name Name keyword Name keyword NameConstant keyword Str keyword Name keyword Name\n", - "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "439\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg inputs Assert Call Name Name Tuple Name Name Return Call Attribute function Name Starred Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['const', '[PAD]', '[PAD]', '[PAD]']\n", "440\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute switch Name Subscript Name Index Name Name Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['out', '[PAD]', '[PAD]', '[PAD]']\n", "441\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name Call Attribute scan Name Name keyword List Name Name keyword BinOp List Name Add Name keyword Name keyword Name\n", - "Label = ['results', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "442\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name states Subscript Name Slice Num Assign Name outputs Name Assign Name states List\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "443\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute stack Name Starred ListComp Subscript Name Index Name comprehension Name states at step Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bn', '[PAD]', '[PAD]', '[PAD]']\n", "444\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] ListComp Call Attribute squeeze Name Subscript Name Index UnaryOp USub Num comprehension Name state Name\n", - "Label = ['states', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "445\n", - "[CLS] If Compare Name Lt Name Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Sub Name For Name Call Name Name Assign Name condition Call Name Name\n", - "Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shuffle', 'pattern', '[PAD]', '[PAD]']\n", "446\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Mult Call Attribute cast Name Call Attribute gt Name Name Name Call Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', '[PAD]', '[PAD]', '[PAD]']\n", "447\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute to one hot Attribute extra ops Name Name keyword Subscript Attribute shape Name Index UnaryOp USub Num\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "448\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg axis NameConstant Assign Name square sum Call Attribute sum Name Call Attribute square Name Name keyword Name keyword NameConstant Assign Name norm Call Attribute sqrt Name Call Attribute maximum Name Name Call Name Return BinOp Name Div Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "449\n", - "[CLS] If Compare Name Lt Num Try Return Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Str ExceptHandler Name Return Call Attribute zeros like Name Name keyword Str\n", - "Label = ['zeros', 'like', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "450\n", - "[CLS] Index Tuple Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Name\n", - "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "451\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Str Assign Name th padding Str Raise Call Name Str Call Name Name\n", - "Label = ['th', 'padding', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "452\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Try Return Call Name Name ExceptHandler Name Return NameConstant\n", - "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", "453\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", - "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "454\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name GeneratorExp Call Name Name comprehension Name v Name\n", - "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "455\n", - "[CLS] BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num\n", "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "456\n", - "[CLS] BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'keras', 'shape', '[PAD]']\n", "457\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name ExtSlice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute shape Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num Slice Slice\n", - "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "458\n", - "[CLS] BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "459\n", - "[CLS] Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "460\n", - "[CLS] ExtSlice Slice Slice Slice Slice Slice BinOp BinOp BinOp Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Add Subscript Name Index Num Sub Num FloorDiv Subscript Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['h', '[PAD]', '[PAD]', '[PAD]']\n", "461\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n", - "Label = ['conv', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "462\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Num Str NameConstant Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "463\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Num\n", - "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['variables', '[PAD]', '[PAD]', '[PAD]']\n", "464\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['scan', '[PAD]', '[PAD]', '[PAD]']\n", "465\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg kernel arg output shape arg strides arg padding arg data format arg dilation rate Tuple Num Num Str NameConstant Tuple Num Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndim', 'diff', '[PAD]', '[PAD]']\n", "466\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute AbstractConv2d gradInputs Attribute abstract conv Attribute nnet Name keyword NameConstant keyword Name keyword Name keyword Name keyword UnaryOp Not Name keyword Name\n", - "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['negative', 'part', '[PAD]', '[PAD]']\n", "467\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute shape Call Attribute eval Name\n", - "Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", "468\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] IfExp BoolOp And Compare Subscript Name Index Num Gt Num Compare BinOp Subscript Name Index Num Mod Num Eq Num BinOp Subscript Name Index Num Sub Num BinOp Subscript Name Index Num Num\n", - "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "469\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str\n", - "Label = ['pool', '2d', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['clip', '[PAD]', '[PAD]', '[PAD]']\n", "470\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute pool 3d Name Name keyword Name keyword Name keyword NameConstant keyword Name keyword Str Raise Call Name Str Name\n", - "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['to', 'one', 'hot', '[PAD]']\n", "471\n", - "[CLS] If Compare Name Eq Str Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute dimshuffle Name Tuple Num Num Num Num Num\n", - "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['random', 'tensor', '[PAD]', '[PAD]']\n", "472\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name BinOp Tuple Num Subscript Name Index Num Subscript Name Slice Num\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['norm', '[PAD]', '[PAD]', '[PAD]']\n", "473\n", - "[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['targets', 'values', '[PAD]', '[PAD]']\n", "474\n", - "[CLS] If Compare Name Eq Str If Compare Call Name Name Num AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name Tuple Num Num Subscript Name Index Num AugAssign Name x Call Name Name BinOp Tuple Num Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "475\n", - "[CLS] BinOp BinOp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Attribute shape Name Index Num Mult Num Add Num\n", "Label = ['arange', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "475\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "476\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Assign Name skip idxs BinOp BinOp Call Attribute arange Name BinOp BinOp Subscript Attribute shape Name Index Num Sub Num FloorDiv Num Mult Num Add Num Assign Name non repeats Call Attribute neq Name Subscript Name Index Name Subscript Name Index BinOp Name Num Return Subscript Name Index Call Attribute nonzero Name\n", - "Label = ['Y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['volume', 'shape', '[PAD]', '[PAD]']\n", "477\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute exp Name BinOp Subscript Name Slice Name Sub Name\n", - "Label = ['p', 'prev', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['value', '[PAD]', '[PAD]', '[PAD]']\n", "478\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute inc subtensor Name Subscript Name Index BinOp Name Add Num Subscript Name Index Name\n", - "Label = ['p', 'prev', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", "479\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Compare Name Lt Call Attribute dimshuffle Name Num Str BitAnd Subscript Compare Name Call Attribute dimshuffle Name Num Str ExtSlice Slice UnaryOp USub Num Slice UnaryOp Num\n", - "Label = ['mask', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['filter', 'shape', '[PAD]', '[PAD]']\n", "480\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Assign Name elems Subscript Name Slice Num\n", - "Label = ['initializer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "481\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Lambda arguments arg x arg acc Call Name Name Name Name Name keyword Name\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Label = ['foldl', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "482\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Name Name Subscript Name ExtSlice Index BinOp BinOp Name Mult Name Add Name Slice Slice\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "483\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg data format arg file format arg scale arg kwargs NameConstant NameConstant NameConstant If Compare Name Is NameConstant Assign Name data format Call Attribute image data format Name Return Call Attribute save img Name Name Name keyword Name keyword Name keyword Name keyword Name\n", - "Label = ['path', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['left', 'pad', '[PAD]', '[PAD]']\n", "484\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['keras', 'shape', '[PAD]', '[PAD]']\n", "485\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp BinOp BinOp BinOp Name Add Str Attribute name Name Str Call Name Attribute name Name\n", - "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pointwise', 'kernel', 'shape', '[PAD]']\n", "486\n", - "[CLS] Assign Attribute [MASK] [MASK] [MASK] [MASK] Name BoolOp Or Call Name Attribute call Name Str Call Name Name Str\n", - "Label = ['compute', 'previous', 'mask', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'shape', '[PAD]', '[PAD]']\n", "487\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name nodes by depth Name layers Name layers by depth Call Name Attribute inputs Name Attribute outputs Name\n", - "Label = ['nodes', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['op', '[PAD]', '[PAD]', '[PAD]']\n", "488\n", - "[CLS] If BoolOp And UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name UnaryOp Attribute stateful Name Return List\n", - "Label = ['trainable', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "489\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Name ListComp BoolOp And Call Name Name Str Attribute stateful Name comprehension Name layer Attribute layers Name Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['w', 'pad', '[PAD]', '[PAD]']\n", "490\n", - "[CLS] If Compare Name Is NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] ListComp NameConstant comprehension Name Call Name Call Name Name Assign Name masks Call Name Name\n", - "Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "491\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Name Str Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['input', 'layers', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "492\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Attribute name Name Add BinOp Str Mod Tuple Name Name\n", - "Label = ['shape', 'key', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['pool', 'out', '[PAD]', '[PAD]']\n", "493\n", - "[CLS] If Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str If Compare Str NotIn Name Assign Subscript Name Index Str Name\n", - "Label = ['call', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "494\n", - "[CLS] BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant\n", - "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "495\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name y Name mask Call Name Name Name Name Assign Subscript Name Index Call Name Call Name Name Tuple Name Name\n", "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "496\n", - "[CLS] If BoolOp And Call Name Name Str Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Attribute keras shape Name Expr Call Attribute append Name Name Assign Name output shapes NameConstant\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "497\n", - "[CLS] BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute arguments Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['normal', '[PAD]', '[PAD]', '[PAD]']\n", "498\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node data If Compare Name NotIn Name Assign Subscript Name Index Name List Name Expr Call Attribute append Subscript Name Index Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['non', 'repeats', '[PAD]', '[PAD]']\n", "499\n", - "[CLS] Dict Str Str Str Str Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Name Name Call Attribute backend Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['active', 'skip', 'idxs', '[PAD]']\n", "500\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Call Name Name Eq Attribute name Name Return Attribute name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['cast', '[PAD]', '[PAD]', '[PAD]']\n", "501\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name depth Call Attribute items Name If Compare Name NotIn Name Assign Subscript Name Index Name List Expr Call Attribute append Subscript Name Index Name Name\n", - "Label = ['node', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['minimum', '[PAD]', '[PAD]', '[PAD]']\n", "502\n", - "[CLS] If Name For Name [MASK] [MASK] [MASK] [MASK] Attribute input tensors Name If Compare Name NotIn Name Raise Call Name BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute name Name Str Call Name Name For Name x Attribute output tensors Name Expr Call Attribute append Name Name Expr Call Attribute append Name Attribute name Name\n", - "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['float32', '[PAD]', '[PAD]', '[PAD]']\n", "503\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", - "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['int32', '[PAD]', '[PAD]', '[PAD]']\n", "504\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Name Str Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Str\n", - "Label = ['count', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['dimshuffle', '[PAD]', '[PAD]', '[PAD]']\n", "505\n", - "[CLS] AugAssign Name [MASK] [MASK] [MASK] [MASK] Add BinOp BoolOp Or Name List List Attribute history Name\n", - "Label = ['callbacks', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['log', '[PAD]', '[PAD]', '[PAD]']\n", "506\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Subscript Name Index Name Call Attribute toarray Subscript Name Index Name\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ret', '[PAD]', '[PAD]', '[PAD]']\n", "507\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', '[PAD]', '[PAD]', '[PAD]']\n", "508\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Name Name keyword Name keyword Num\n", - "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['args', '[PAD]', '[PAD]', '[PAD]']\n", "509\n", - "[CLS] If Name If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Subscript Attribute inbound nodes Name Index UnaryOp USub Num NotEq Num Raise Call Name Str Assign Attribute outputs Name List Subscript Attribute output tensors Subscript Attribute inbound nodes Name Index UnaryOp Num Index Num Assign Attribute inputs Name Call Attribute get source inputs Name Subscript Attribute outputs Name Index Num\n", - "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "510\n", - "[CLS] If Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index UnaryOp USub Num Gt Num Return Call Attribute argmax Name keyword UnaryOp Num Return Call Attribute astype Compare Name Num Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "511\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Dict Str Str Attribute name Name Call Attribute deepcopy Name Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'graph', 'network', '[PAD]']\n", "512\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Str Assign Name build input shape Call Attribute get Name Str Assign Name layer configs Subscript Name Index Str Assign Name name Name build input shape NameConstant Assign Name layer configs Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", "513\n", - "[CLS] Assign Subscript Name Index Str ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str comprehension Name layer Name\n", - "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layers', '[PAD]', '[PAD]', '[PAD]']\n", "514\n", - "[CLS] Assign Subscript Name Index Str Call Attribute [MASK] [MASK] [MASK] [MASK] Call Attribute backend Name Str\n", - "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['network', 'nodes', '[PAD]', '[PAD]']\n", "515\n", - "[CLS] If Call Name Name Str If Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str Assign Name name BinOp BinOp Call Name Attribute name Name Add Str Call Name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Call Name Name\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['masks', '[PAD]', '[PAD]', '[PAD]']\n", "516\n", - "[CLS] Compare Subscript Call Attribute [MASK] [MASK] [MASK] [MASK] Attribute name Name Str Index UnaryOp USub Num Eq Str\n", - "Label = ['split', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['j', '[PAD]', '[PAD]', '[PAD]']\n", "517\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp Call Name Attribute name Name Add Str Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", "518\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] List For Name value Name Expr Call Attribute append Name Call Name Name Return Name\n", - "Label = ['deserialized', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "519\n", - "[CLS] If Compare Str In Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute decode Subscript Name Index Str Str Assign Name original backend NameConstant\n", - "Label = ['original', 'backend', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['arguments', '[PAD]', '[PAD]', '[PAD]']\n", "520\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Assign Name weights Attribute weights Name If Name Expr Call Attribute append Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", "521\n", - "[CLS] Call Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str Call Name Name Call Name Name\n", - "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['kwargs', '[PAD]', '[PAD]', '[PAD]']\n", "522\n", - "[CLS] If Compare Call Name Name NotEq Call Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str Call Name Call Name Name Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layer', 'data', '[PAD]', '[PAD]']\n", "523\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg custom objects NameConstant Expr Str If Call Name Name Name Raise Call Name Str ImportFrom alias Return Call Name Name keyword Name\n", - "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "524\n", - "[CLS] BinOp Str Mod Tuple Name Call Attribute [MASK] [MASK] [MASK] [MASK] Str ListComp Name comprehension Name x Name\n", - "Label = ['join', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['load', 'weights', 'from', 'hdf5']\n", + "524\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "525\n", - "[CLS] While Compare BinOp Str Mod Tuple Name Name In Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute extend Name ListComp Call Attribute decode Name Str comprehension Name n Subscript Attribute attrs Name Index BinOp Str Tuple Name Name AugAssign Name chunk id Add Num\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "526\n", - "[CLS] comprehension Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute attrs Name Index BinOp Str Mod Tuple Name Name\n", - "Label = ['n', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n", "527\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Tuple Name w Name val Call Name Call Name Name Name If BoolOp And Call Name Name Str Attribute name Name Assign Name name Call Name Attribute name Name Assign Name name BinOp Str Add Call Name Name Expr Call Attribute append Name Call Attribute encode Name Str\n", - "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['num', 'train', 'samples', '[PAD]']\n", "528\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name In List Str Str Assign Name weights Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", "529\n", - "[CLS] Assert BoolOp And Compare Subscript Name Index Num Eq Attribute [MASK] [MASK] [MASK] [MASK] Name Compare Subscript Name Slice Num Tuple Subscript Attribute kernel size Name Index Num Num\n", - "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'sparse', '[PAD]', '[PAD]']\n", "530\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str If Compare Attribute data format Name Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['i', '[PAD]', '[PAD]', '[PAD]']\n", "531\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "532\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['data', 'format', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ins', 'batch', '[PAD]', '[PAD]']\n", "533\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name List Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num Subscript Name Index Num keyword UnaryOp USub Num\n", - "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ins', 'batch', '[PAD]', '[PAD]']\n", "534\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Str Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name Str Call Name Call Attribute prod Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['batch', 'out', '[PAD]', '[PAD]']\n", "535\n", - "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Name\n", - "Label = ['reshape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", "536\n", - "[CLS] If Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Assign Subscript Name Index Num Call Attribute transpose Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", "537\n", - "[CLS] Assign Subscript Name Index Num Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'tensors', '[PAD]', '[PAD]']\n", "538\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Num Tuple Num Num Num Num\n", - "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "539\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg func arg n gates Expr Str Return Call Attribute hstack Name ListComp Call Name Name comprehension Name k Call Attribute hsplit Name Name Name\n", - "Label = ['kernels', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['init', 'graph', 'network', '[PAD]']\n", "540\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Return Call Attribute reshape Attribute T Name Attribute shape Name keyword Name\n", - "Label = ['kernel', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", "541\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Num Mult Subscript Name Index Num Num\n", - "Label = ['tile', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['module', '[PAD]', '[PAD]', '[PAD]']\n", "542\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Subscript Name Index Num Lambda arguments arg k Attribute T Name Name\n", - "Label = ['recurrent', 'kernels', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['encode', '[PAD]', '[PAD]', '[PAD]']\n", "543\n", - "[CLS] If Compare Name Eq Tuple Num BinOp Name Mult Name Assign Name [MASK] [MASK] [MASK] [MASK] Str If Compare Name Tuple BinOp Name Name Assign Name source Str Raise Call Name BinOp Str Add Call Name Name\n", - "Label = ['source', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "544\n", - "[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original backend Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original backend NameConstant\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['format', '[PAD]', '[PAD]', '[PAD]']\n", "545\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Name Str Attribute name Name Str\n", "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "546\n", - "[CLS] If Compare Str In Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name original keras version Call Attribute decode Subscript Attribute attrs Name Index Str Str Assign Name original keras version Str\n", - "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "547\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Call Attribute setdefault Name Attribute name Name List Name\n", "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "547\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", "548\n", - "[CLS] ListComp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name comprehension Name weight name Name\n", - "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "549\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Str Call Name Call Name Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "550\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Subscript Name Index Name Call Attribute format Str Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['weights', '[PAD]', '[PAD]', '[PAD]']\n", "551\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Tuple Subscript Name Index Name Subscript Name Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['extend', '[PAD]', '[PAD]', '[PAD]']\n", "552\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp Name Add ListComp BinOp Str Name comprehension Name n Name\n", - "Label = ['callback', 'metrics', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "553\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "554\n", - "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute evaluate generator Name Name Name keyword Num Assign Name val outs Call Attribute evaluate Name Name Name keyword Name keyword Name keyword Num\n", - "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['filters', '[PAD]', '[PAD]', '[PAD]']\n", "555\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name o Call Name Name Name Assign Subscript Name Index BinOp Str Add Name Name\n", - "Label = ['l', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", "556\n", - "[CLS] If Compare Name Is NameConstant If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name Raise Call Name Str\n", - "Label = ['steps', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['transpose', '[PAD]', '[PAD]', '[PAD]']\n", "557\n", - "[CLS] If Call Name Name Name Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Attribute shape Subscript Name Index Num Index Num If Call Name Name Name Assign Name batch size Subscript Attribute shape Subscript Call Name Call Attribute values Name Index Num Index Num Assign Name batch size Subscript Attribute shape Name Index Num\n", - "Label = ['batch', 'size', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['concatenate', '[PAD]', '[PAD]', '[PAD]']\n", "558\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name ListComp Subscript Name Index Name comprehension Name out Name keyword Name\n", - "Label = ['average', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['bias', '[PAD]', '[PAD]', '[PAD]']\n", "559\n", - "[CLS] Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['convert', 'kernel', '[PAD]', '[PAD]']\n", "560\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute float64 Name Subscript Subscript Name Index UnaryOp USub Num Index Name\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "561\n", - "[CLS] If Compare Name Gt Num If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name Expr Call Attribute start Name keyword Name keyword Name Assign Name output generator Call Attribute get Name If Name Assign Name output generator Call Name Name Assign Name output generator Name\n", - "Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "562\n", - "[CLS] If Name Assign Name [MASK] [MASK] [MASK] [MASK] Call Name Name keyword Name Assign Name enqueuer Call Name Name keyword Name keyword Name\n", - "Label = ['enqueuer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['biases', '[PAD]', '[PAD]', '[PAD]']\n", "563\n", - "[CLS] If Compare Name Eq Num Assign Name [MASK] [MASK] [MASK] [MASK] Call Name keyword Name\n", - "Label = ['progbar', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['k', '[PAD]', '[PAD]', '[PAD]']\n", "564\n", - "[CLS] If UnaryOp Not Name Assign Name [MASK] [MASK] [MASK] [MASK] Str Assign Name name BinOp BinOp Name Add Str Call Name Call Attribute get uid Name Name\n", - "Label = ['prefix', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['asarray', '[PAD]', '[PAD]', '[PAD]']\n", "565\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] arg node index Expr Str Return BinOp BinOp Attribute name Name Add Str Call Name Name Name\n", - "Label = ['layer', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['attrs', '[PAD]', '[PAD]', '[PAD]']\n", "566\n", - "[CLS] If Compare Name IsNot NameConstant With withitem Call Attribute [MASK] [MASK] [MASK] [MASK] Name Str Expr Call Attribute add loss Name Call Name Name\n", - "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "567\n", - "[CLS] BoolOp And Compare Name IsNot NameConstant Compare Name Gt Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['max', 'ndim', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "568\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute max ndim Name Str Call Name Call Attribute ndim Name Name\n", "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "568\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "569\n", - "[CLS] Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name Str Call Name Call Attribute ndim Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['model', '[PAD]', '[PAD]', '[PAD]']\n", "570\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute min ndim Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['val', 'outs', '[PAD]', '[PAD]']\n", "571\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Name Str Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", - "572\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CLS] If BoolOp And Call Name Name Str Compare Attribute [MASK] [MASK] [MASK] [MASK] Name IsNot NameConstant With withitem Call Attribute name scope Name Str Assign Name regularization losses ListComp Call Attribute activity regularizer Name Name comprehension Name x Call Name Name Expr Call Attribute add loss Name Name keyword Call Name Name\n", - "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['warn', '[PAD]', '[PAD]', '[PAD]']\n", + "572\n", + "Label = ['get', 'uid', '[PAD]', '[PAD]']\n", "573\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name If Compare Name IsNot NameConstant If Call Name Name Name If Call Name GeneratorExp Compare Name NameConstant comprehension Name m Name Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Call Name Name Raise Call Name BinOp BinOp BinOp Str Attribute name Name Str Call Name Name Return NameConstant\n", - "Label = ['supports', 'masking', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['batch', 'input', 'shape', '[PAD]']\n", "574\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Raise Call Name BinOp BinOp Str Add Attribute name Name Str\n", - "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'placeholder', '[PAD]', '[PAD]']\n", "575\n", - "[CLS] FunctionDef arguments arg [MASK] [MASK] [MASK] [MASK] Expr Str If Compare Call Name Attribute inbound nodes Name NotEq Num Raise Call Name BinOp BinOp BinOp Str Add Attribute name Name Str Str Return Call Attribute get node attribute at index Name Num Str Str Name\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['config', '[PAD]', '[PAD]', '[PAD]']\n", "576\n", - "[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n", - "Label = ['input', 'shapes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "577\n", - "[CLS] Call Name ListComp Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name node Attribute inbound nodes Name\n", - "Label = ['output', 'shapes', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "578\n", - "[CLS] BinOp BinOp BinOp BinOp BinOp Str Add Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Call Name Name Str Call Name Call Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['variable', '[PAD]', '[PAD]', '[PAD]']\n", "579\n", - "[CLS] If Call Name Name Str Assign Subscript Name Index Str Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['batch', 'input', 'shape', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "580\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg dtype arg shape arg ndim arg max ndim arg min ndim arg axes NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "581\n", - "[CLS] IfExp Attribute [MASK] [MASK] [MASK] [MASK] Name BinOp Str Add Call Name Attribute dtype Name Str\n", - "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['min', 'ndim', '[PAD]', '[PAD]']\n", "582\n", - "[CLS] If Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name outbound layer Attribute name Attribute outbound layer Name Assign Name outbound layer NameConstant\n", - "Label = ['outbound', 'layer', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "583\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg optimizer arg loss arg metrics arg loss weights arg sample weight mode arg weighted metrics arg target tensors arg kwargs NameConstant NameConstant NameConstant NameConstant NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "584\n", - "[CLS] BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['activity', 'regularizer', '[PAD]', '[PAD]']\n", "585\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inbound', 'layer', '[PAD]', '[PAD]']\n", "586\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "587\n", - "[CLS] Raise Call Name BinOp BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Name\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", "588\n", - "[CLS] Call Name BinOp BinOp BinOp Str Add Name Str Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name\n", - "Label = ['output', 'names', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", "589\n", - "[CLS] Call Name BinOp BinOp Str Add Call Name Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str\n", - "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", "590\n", - "[CLS] If Compare Name IsNot NameConstant Assign Name [MASK] [MASK] [MASK] [MASK] Subscript Name Index Name Assign Name target NameConstant\n", - "Label = ['target', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", "591\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name keyword Call Name Name keyword BinOp Name Add Str keyword Call Attribute is sparse Name Subscript Attribute outputs Name Index Name keyword Call Attribute dtype Name Subscript Attribute outputs Name Index Name\n", - "Label = ['placeholder', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['inbound', 'nodes', '[PAD]', '[PAD]']\n", "592\n", - "[CLS] If Compare Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name Eq Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Assign Name weight Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", - "Label = ['get', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", "593\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", - "Label = ['weight', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", "594\n", - "[CLS] If Compare Name Eq Str Expr Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str Expr Call Attribute append Name Str Expr Call Attribute append Name Call Attribute placeholder Name keyword Num keyword BinOp Name Str Expr Call Attribute append Name NameConstant\n", - "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['dtype', '[PAD]', '[PAD]', '[PAD]']\n", "595\n", - "[CLS] Call Attribute [MASK] [MASK] [MASK] [MASK] Name Call Attribute placeholder Name keyword Num keyword BinOp Name Add Str\n", "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "596\n", - "[CLS] BoolOp Or Compare Subscript Name Index UnaryOp USub Num Eq Num Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Name Attribute binary crossentropy Name\n", - "Label = ['loss', 'functions', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", "597\n", - "[CLS] If Compare Name IsNot NameConstant AugAssign Name [MASK] [MASK] [MASK] [MASK] Add Call Name Name keyword NameConstant\n", - "Label = ['all', 'inputs', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['outputs', '[PAD]', '[PAD]', '[PAD]']\n", "598\n", - "[CLS] If Call Name GeneratorExp Call Attribute [MASK] [MASK] [MASK] [MASK] Name Name comprehension Name v Name If UnaryOp Not Call Name GeneratorExp Call Attribute is tensor Name Name comprehension Name v Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Name Str Call Name Name\n", - "Label = ['is', 'tensor', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", "599\n", - "[CLS] If UnaryOp Not Attribute [MASK] [MASK] [MASK] [MASK] Name Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes NameConstant Assign Name feed input names Attribute feed input names Name Assign Name feed input shapes Attribute feed input shapes Name\n", - "Label = ['is', 'graph', 'network', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['output', 'names', '[PAD]', '[PAD]']\n", "600\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg x arg y arg batch size arg epochs arg verbose arg callbacks arg validation split arg validation data arg shuffle arg class weight arg sample weight arg initial epoch arg steps per epoch arg validation steps arg kwargs NameConstant NameConstant NameConstant Num Num NameConstant Num NameConstant NameConstant NameConstant NameConstant Num NameConstant NameConstant\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['append', '[PAD]', '[PAD]', '[PAD]']\n", "601\n", - "[CLS] If Compare Call Name Name Eq Num Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Name val sample weight Name Raise Call Name BinOp Str Mod Call Name Name\n", - "Label = ['val', 'x', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['name', 'scope', '[PAD]', '[PAD]']\n", "602\n", - "[CLS] Assign Name [MASK] [MASK] [MASK] [MASK] BinOp BinOp BinOp Name Add Name Name List Num\n", - "Label = ['val', 'ins', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['train', 'function', '[PAD]', '[PAD]']\n", "603\n", - "[CLS] Assign Tuple Name [MASK] [MASK] [MASK] [MASK] Name val y Tuple Call Name Name Num Name Call Name Name Name\n", - "Label = ['y', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['function', '[PAD]', '[PAD]', '[PAD]']\n", "604\n", - "[CLS] If BoolOp And Compare Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Gt Name Compare BinOp Subscript Attribute shape Subscript Name Index Num Index Num Mod Name NotEq Num Raise Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute shape Subscript Name Index Num Index Num Str Call Name Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['test', 'function', '[PAD]', '[PAD]']\n", "605\n", - "[CLS] Call Name BinOp BinOp BinOp BinOp Str Add Call Name Subscript Attribute [MASK] [MASK] [MASK] [MASK] Subscript Name Index Num Index Num Str Call Name Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['predict', 'function', '[PAD]', '[PAD]']\n", "606\n", - "[CLS] arguments arg [MASK] [MASK] [MASK] [MASK] arg generator arg steps arg max queue size arg workers arg use multiprocessing arg verbose NameConstant Num Num NameConstant Num\n", - "Label = ['self', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['uses', 'dynamic', 'learning', 'phase']\n", "607\n", - "[CLS] Try Assign Name [MASK] [MASK] [MASK] [MASK] ListComp IfExp Compare Attribute name Attribute class Subscript Name Index Name Eq Str Attribute values Subscript Name Index Name Subscript Name Index Name comprehension Name x Name ExceptHandler Name Raise Call Name BinOp BinOp BinOp Str Add Subscript Attribute args Name Index Num Str Call Name Name\n", - "Label = ['data', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndim', '[PAD]', '[PAD]', '[PAD]']\n", "608\n", - "[CLS] ListComp IfExp Compare Attribute [MASK] [MASK] [MASK] [MASK] Attribute class Name Eq Str Attribute values Name Name comprehension Name x Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", "609\n", - "[CLS] BoolOp And Compare Subscript Name Index Name IsNot NameConstant UnaryOp Not Call Attribute [MASK] [MASK] [MASK] [MASK] Name Subscript Name Index Name\n", - "Label = ['is', 'tensor', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ndarray', '[PAD]', '[PAD]', '[PAD]']\n", "610\n", - "[CLS] For Tuple Name [MASK] [MASK] [MASK] [MASK] Name ref dim Call Name Name Name If BoolOp And Compare Name NotEq Name Name Raise Call Name BinOp BinOp BinOp BinOp BinOp BinOp BinOp Str Add Name Str Subscript Name Index Name Str Call Name Name Str Call Name Name\n", - "Label = ['dim', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['is', 'graph', 'network', '[PAD]']\n", "611\n", - "[CLS] For Name [MASK] [MASK] [MASK] [MASK] Name Expr Call Attribute append Name Call Attribute get Name Name\n", - "Label = ['name', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['sample', 'weights', '[PAD]', '[PAD]']\n", "612\n", - "[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name y Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['val', 'x', '[PAD]', '[PAD]']\n", "613\n", - "[CLS] Call Name BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['split', 'at', '[PAD]', '[PAD]']\n", "614\n", - "[CLS] BinOp Str Add Call Name ListComp Attribute [MASK] [MASK] [MASK] [MASK] Name comprehension Name w Name\n", "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", "615\n", - "[CLS] If Compare Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Gt Call Name Attribute shape Name Raise Call Name BinOp BinOp BinOp Str Add Call Name Attribute shape Name Str Call Name Call Name Attribute shape Name\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['do', 'validation', '[PAD]', '[PAD]']\n", "616\n", - "[CLS] BinOp BinOp BinOp BinOp Str Add Call Name Attribute [MASK] [MASK] [MASK] [MASK] Name Str Call Name Attribute shape Name Str\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n", + "Label = ['ins', '[PAD]', '[PAD]', '[PAD]']\n", "617\n", - "[CLS] Tuple Subscript Attribute [MASK] [MASK] [MASK] [MASK] Name Index Num Subscript Attribute shape Name Index Num\n", - "Label = ['shape', '[PAD]', '[PAD]', '[PAD]']\n", - "Pred =\n", - "\n" + "Label = ['x', '[PAD]', '[PAD]', '[PAD]']\n" ] } ], @@ -9548,7 +35378,7 @@ "pred_str = []; score = [0]*4; score_full_name=0; score_no_pad = 0; rank =[0]*4; skipped = 0\n", "for idx in range(nb_snips):\n", " print(idx)\n", - " print(snippet.loc[idx][0])\n", + " #print(snippet.loc[idx][0])\n", " print(\"Label =\", labels_str[idx])\n", " try:\n", " msk_idx = snippet.loc[idx][0].split(\" \").index('[MASK]')\n",