diff --git a/examples/statistical_rethinking_lectures/LICENSE b/examples/statistical_rethinking_lectures/LICENSE
new file mode 100644
index 00000000..f288702d
--- /dev/null
+++ b/examples/statistical_rethinking_lectures/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+  For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received.  You must make sure that they, too, receive
+or can get the source code.  And you must show them these terms so they
+know their rights.
+
+  Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+  Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so.  This is fundamentally incompatible with the aim of
+protecting users' freedom to change the software.  The systematic
+pattern of such abuse occurs in the area of products for individuals to
+use, which is precisely where it is most unacceptable.  Therefore, we
+have designed this version of the GPL to prohibit the practice for those
+products.  If such problems arise substantially in other domains, we
+stand ready to extend this provision to those domains in future versions
+of the GPL, as needed to protect the freedom of users.
+
+  Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
+software on general-purpose computers, but in those that do, we wish to
+avoid the special danger that patents applied to a free program could
+make it effectively proprietary.  To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
+standard defined by a recognized standards body, or, in the case of
+interfaces specified for a particular programming language, one that
+is widely used among developers working in that language.
+
+  The "System Libraries" of an executable work include anything, other
+than the work as a whole, that (a) is included in the normal form of
+packaging a Major Component, but which is not part of that Major
+Component, and (b) serves only to enable use of the work with that
+Major Component, or to implement a Standard Interface for which an
+implementation is available to the public in source code form.  A
+"Major Component", in this context, means a major essential component
+(kernel, window system, and so on) of the specific operating system
+(if any) on which the executable work runs, or a compiler used to
+produce the work, or an object code interpreter used to run it.
+
+  The "Corresponding Source" for a work in object code form means all
+the source code needed to generate, install, and (for an executable
+work) run the object code and to modify the work, including scripts to
+control those activities.  However, it does not include the work's
+System Libraries, or general-purpose tools or generally available free
+programs which are used unmodified in performing those activities but
+which are not part of the work.  For example, Corresponding Source
+includes interface definition files associated with source files for
+the work, and the source code for shared libraries and dynamically
+linked subprograms that the work is specifically designed to require,
+such as by intimate data communication or control flow between those
+subprograms and other parts of the work.
+
+  The Corresponding Source need not include anything that users
+can regenerate automatically from other parts of the Corresponding
+Source.
+
+  The Corresponding Source for a work in source code form is that
+same work.
+
+  2. Basic Permissions.
+
+  All rights granted under this License are granted for the term of
+copyright on the Program, and are irrevocable provided the stated
+conditions are met.  This License explicitly affirms your unlimited
+permission to run the unmodified Program.  The output from running a
+covered work is covered by this License only if the output, given its
+content, constitutes a covered work.  This License acknowledges your
+rights of fair use or other equivalent, as provided by copyright law.
+
+  You may make, run and propagate covered works that you do not
+convey, without conditions so long as your license otherwise remains
+in force.  You may convey covered works to others for the sole purpose
+of having them make modifications exclusively for you, or provide you
+with facilities for running those works, provided that you comply with
+the terms of this License in conveying all material for which you do
+not control copyright.  Those thus making or running the covered works
+for you must do so exclusively on your behalf, under your direction
+and control, on terms that prohibit them from making any copies of
+your copyrighted material outside their relationship with you.
+
+  Conveying under any other circumstances is permitted solely under
+the conditions stated below.  Sublicensing is not allowed; section 10
+makes it unnecessary.
+
+  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
+
+  No covered work shall be deemed part of an effective technological
+measure under any applicable law fulfilling obligations under article
+11 of the WIPO copyright treaty adopted on 20 December 1996, or
+similar laws prohibiting or restricting circumvention of such
+measures.
+
+  When you convey a covered work, you waive any legal power to forbid
+circumvention of technological measures to the extent such circumvention
+is effected by exercising rights under this License with respect to
+the covered work, and you disclaim any intention to limit operation or
+modification of the work as a means of enforcing, against the work's
+users, your or third parties' legal rights to forbid circumvention of
+technological measures.
+
+  4. Conveying Verbatim Copies.
+
+  You may convey verbatim copies of the Program's source code as you
+receive it, in any medium, provided that you conspicuously and
+appropriately publish on each copy an appropriate copyright notice;
+keep intact all notices stating that this License and any
+non-permissive terms added in accord with section 7 apply to the code;
+keep intact all notices of the absence of any warranty; and give all
+recipients a copy of this License along with the Program.
+
+  You may charge any price or no price for each copy that you convey,
+and you may offer support or warranty protection for a fee.
+
+  5. Conveying Modified Source Versions.
+
+  You may convey a work based on the Program, or the modifications to
+produce it from the Program, in the form of source code under the
+terms of section 4, provided that you also meet all of these conditions:
+
+    a) The work must carry prominent notices stating that you modified
+    it, and giving a relevant date.
+
+    b) The work must carry prominent notices stating that it is
+    released under this License and any conditions added under section
+    7.  This requirement modifies the requirement in section 4 to
+    "keep intact all notices".
+
+    c) You must license the entire work, as a whole, under this
+    License to anyone who comes into possession of a copy.  This
+    License will therefore apply, along with any applicable section 7
+    additional terms, to the whole of the work, and all its parts,
+    regardless of how they are packaged.  This License gives no
+    permission to license the work in any other way, but it does not
+    invalidate such permission if you have separately received it.
+
+    d) If the work has interactive user interfaces, each must display
+    Appropriate Legal Notices; however, if the Program has interactive
+    interfaces that do not display Appropriate Legal Notices, your
+    work need not make them do so.
+
+  A compilation of a covered work with other separate and independent
+works, which are not by their nature extensions of the covered work,
+and which are not combined with it such as to form a larger program,
+in or on a volume of a storage or distribution medium, is called an
+"aggregate" if the compilation and its resulting copyright are not
+used to limit the access or legal rights of the compilation's users
+beyond what the individual works permit.  Inclusion of a covered work
+in an aggregate does not cause this License to apply to the other
+parts of the aggregate.
+
+  6. Conveying Non-Source Forms.
+
+  You may convey a covered work in object code form under the terms
+of sections 4 and 5, provided that you also convey the
+machine-readable Corresponding Source under the terms of this License,
+in one of these ways:
+
+    a) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by the
+    Corresponding Source fixed on a durable physical medium
+    customarily used for software interchange.
+
+    b) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by a
+    written offer, valid for at least three years and valid for as
+    long as you offer spare parts or customer support for that product
+    model, to give anyone who possesses the object code either (1) a
+    copy of the Corresponding Source for all the software in the
+    product that is covered by this License, on a durable physical
+    medium customarily used for software interchange, for a price no
+    more than your reasonable cost of physically performing this
+    conveying of source, or (2) access to copy the
+    Corresponding Source from a network server at no charge.
+
+    c) Convey individual copies of the object code with a copy of the
+    written offer to provide the Corresponding Source.  This
+    alternative is allowed only occasionally and noncommercially, and
+    only if you received the object code with such an offer, in accord
+    with subsection 6b.
+
+    d) Convey the object code by offering access from a designated
+    place (gratis or for a charge), and offer equivalent access to the
+    Corresponding Source in the same way through the same place at no
+    further charge.  You need not require recipients to copy the
+    Corresponding Source along with the object code.  If the place to
+    copy the object code is a network server, the Corresponding Source
+    may be on a different server (operated by you or a third party)
+    that supports equivalent copying facilities, provided you maintain
+    clear directions next to the object code saying where to find the
+    Corresponding Source.  Regardless of what server hosts the
+    Corresponding Source, you remain obligated to ensure that it is
+    available for as long as needed to satisfy these requirements.
+
+    e) Convey the object code using peer-to-peer transmission, provided
+    you inform other peers where the object code and Corresponding
+    Source of the work are being offered to the general public at no
+    charge under subsection 6d.
+
+  A separable portion of the object code, whose source code is excluded
+from the Corresponding Source as a System Library, need not be
+included in conveying the object code work.
+
+  A "User Product" is either (1) a "consumer product", which means any
+tangible personal property which is normally used for personal, family,
+or household purposes, or (2) anything designed or sold for incorporation
+into a dwelling.  In determining whether a product is a consumer product,
+doubtful cases shall be resolved in favor of coverage.  For a particular
+product received by a particular user, "normally used" refers to a
+typical or common use of that class of product, regardless of the status
+of the particular user or of the way in which the particular user
+actually uses, or expects or is expected to use, the product.  A product
+is a consumer product regardless of whether the product has substantial
+commercial, industrial or non-consumer uses, unless such uses represent
+the only significant mode of use of the product.
+
+  "Installation Information" for a User Product means any methods,
+procedures, authorization keys, or other information required to install
+and execute modified versions of a covered work in that User Product from
+a modified version of its Corresponding Source.  The information must
+suffice to ensure that the continued functioning of the modified object
+code is in no case prevented or interfered with solely because
+modification has been made.
+
+  If you convey an object code work under this section in, or with, or
+specifically for use in, a User Product, and the conveying occurs as
+part of a transaction in which the right of possession and use of the
+User Product is transferred to the recipient in perpetuity or for a
+fixed term (regardless of how the transaction is characterized), the
+Corresponding Source conveyed under this section must be accompanied
+by the Installation Information.  But this requirement does not apply
+if neither you nor any third party retains the ability to install
+modified object code on the User Product (for example, the work has
+been installed in ROM).
+
+  The requirement to provide Installation Information does not include a
+requirement to continue to provide support service, warranty, or updates
+for a work that has been modified or installed by the recipient, or for
+the User Product in which it has been modified or installed.  Access to a
+network may be denied when the modification itself materially and
+adversely affects the operation of the network or violates the rules and
+protocols for communication across the network.
+
+  Corresponding Source conveyed, and Installation Information provided,
+in accord with this section must be in a format that is publicly
+documented (and with an implementation available to the public in
+source code form), and must require no special password or key for
+unpacking, reading or copying.
+
+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
+License by making exceptions from one or more of its conditions.
+Additional permissions that are applicable to the entire Program shall
+be treated as though they were included in this License, to the extent
+that they are valid under applicable law.  If additional permissions
+apply only to part of the Program, that part may be used separately
+under those permissions, but the entire Program remains governed by
+this License without regard to the additional permissions.
+
+  When you convey a copy of a covered work, you may at your option
+remove any additional permissions from that copy, or from any part of
+it.  (Additional permissions may be written to require their own
+removal in certain cases when you modify the work.)  You may place
+additional permissions on material, added by you to a covered work,
+for which you have or can give appropriate copyright permission.
+
+  Notwithstanding any other provision of this License, for material you
+add to a covered work, you may (if authorized by the copyright holders of
+that material) supplement the terms of this License with terms:
+
+    a) Disclaiming warranty or limiting liability differently from the
+    terms of sections 15 and 16 of this License; or
+
+    b) Requiring preservation of specified reasonable legal notices or
+    author attributions in that material or in the Appropriate Legal
+    Notices displayed by works containing it; or
+
+    c) Prohibiting misrepresentation of the origin of that material, or
+    requiring that modified versions of such material be marked in
+    reasonable ways as different from the original version; or
+
+    d) Limiting the use for publicity purposes of names of licensors or
+    authors of the material; or
+
+    e) Declining to grant rights under trademark law for use of some
+    trade names, trademarks, or service marks; or
+
+    f) Requiring indemnification of licensors and authors of that
+    material by anyone who conveys the material (or modified versions of
+    it) with contractual assumptions of liability to the recipient, for
+    any liability that these contractual assumptions directly impose on
+    those licensors and authors.
+
+  All other non-permissive additional terms are considered "further
+restrictions" within the meaning of section 10.  If the Program as you
+received it, or any part of it, contains a notice stating that it is
+governed by this License along with a term that is a further
+restriction, you may remove that term.  If a license document contains
+a further restriction but permits relicensing or conveying under this
+License, you may add to a covered work material governed by the terms
+of that license document, provided that the further restriction does
+not survive such relicensing or conveying.
+
+  If you add terms to a covered work in accord with this section, you
+must place, in the relevant source files, a statement of the
+additional terms that apply to those files, or a notice indicating
+where to find the applicable terms.
+
+  Additional terms, permissive or non-permissive, may be stated in the
+form of a separately written license, or stated as exceptions;
+the above requirements apply either way.
+
+  8. Termination.
+
+  You may not propagate or modify a covered work except as expressly
+provided under this License.  Any attempt otherwise to propagate or
+modify it is void, and will automatically terminate your rights under
+this License (including any patent licenses granted under the third
+paragraph of section 11).
+
+  However, if you cease all violation of this License, then your
+license from a particular copyright holder is reinstated (a)
+provisionally, unless and until the copyright holder explicitly and
+finally terminates your license, and (b) permanently, if the copyright
+holder fails to notify you of the violation by some reasonable means
+prior to 60 days after the cessation.
+
+  Moreover, your license from a particular copyright holder is
+reinstated permanently if the copyright holder notifies you of the
+violation by some reasonable means, this is the first time you have
+received notice of violation of this License (for any work) from that
+copyright holder, and you cure the violation prior to 30 days after
+your receipt of the notice.
+
+  Termination of your rights under this section does not terminate the
+licenses of parties who have received copies or rights from you under
+this License.  If your rights have been terminated and not permanently
+reinstated, you do not qualify to receive new licenses for the same
+material under section 10.
+
+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
+run a copy of the Program.  Ancillary propagation of a covered work
+occurring solely as a consequence of using peer-to-peer transmission
+to receive a copy likewise does not require acceptance.  However,
+nothing other than this License grants you permission to propagate or
+modify any covered work.  These actions infringe copyright if you do
+not accept this License.  Therefore, by modifying or propagating a
+covered work, you indicate your acceptance of this License to do so.
+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
+receives a license from the original licensors, to run, modify and
+propagate that work, subject to this License.  You are not responsible
+for enforcing compliance by third parties with this License.
+
+  An "entity transaction" is a transaction transferring control of an
+organization, or substantially all assets of one, or subdividing an
+organization, or merging organizations.  If propagation of a covered
+work results from an entity transaction, each party to that
+transaction who receives a copy of the work also receives whatever
+licenses to the work the party's predecessor in interest had or could
+give under the previous paragraph, plus a right to possession of the
+Corresponding Source of the work from the predecessor in interest, if
+the predecessor has it or can get it with reasonable efforts.
+
+  You may not impose any further restrictions on the exercise of the
+rights granted or affirmed under this License.  For example, you may
+not impose a license fee, royalty, or other charge for exercise of
+rights granted under this License, and you may not initiate litigation
+(including a cross-claim or counterclaim in a lawsuit) alleging that
+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based.  The
+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
+owned or controlled by the contributor, whether already acquired or
+hereafter acquired, that would be infringed by some manner, permitted
+by this License, of making, using, or selling its contributor version,
+but do not include claims that would be infringed only as a
+consequence of further modification of the contributor version.  For
+purposes of this definition, "control" includes the right to grant
+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
+sue for patent infringement).  To "grant" such a patent license to a
+party means to make such an agreement or commitment not to enforce a
+patent against the party.
+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
+covered work, and grant a patent license to some of the parties
+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.ipynb b/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.ipynb
new file mode 100644
index 00000000..fdcf3b4b
--- /dev/null
+++ b/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.ipynb
@@ -0,0 +1,1262 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "60123f05-5e3a-42b2-b948-db9e8d7fc5ee",
+   "metadata": {},
+   "source": [
+    "(lecture_02)=\n",
+    "# The Garden of Forking Data\n",
+    ":::{post} Jan 7, 2024\n",
+    ":tags: statistical rethinking, bayesian inference, probability\n",
+    ":category: intermediate\n",
+    ":author: Dustin Stansbury\n",
+    ":::\n",
+    "\n",
+    "This notebook is part of the PyMC port of the [Statistical Rethinking 2023](https://github.com/rmcelreath/stat_rethinking_2023) lecture series by Richard McElreath.\n",
+    "\n",
+    "[Video - Lecture 02 - The Garden of Forking Data](https://youtu.be/R1vcdhPBlXA?si=rL3BOz9hHxkPt79m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "14629f37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ignore warnings\n",
+    "import warnings\n",
+    "\n",
+    "import arviz as az\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm\n",
+    "import statsmodels.formula.api as smf\n",
+    "import utils as utils\n",
+    "import xarray as xr\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib import style\n",
+    "from scipy import stats as stats\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "# Set matplotlib style\n",
+    "STYLE = \"statistical-rethinking-2023.mplstyle\"\n",
+    "style.use(STYLE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "125141e5",
+   "metadata": {},
+   "source": [
+    "# Task: What proportion of earth's surface is covered with water?\n",
+    "\n",
+    "## Workflow (Drawing the Owl)\n",
+    "\n",
+    "1. Define **generative model** of tossing the globe\n",
+    "2. Define an **estimand** -- in this case, the proportion of globe covered in water\n",
+    "3. **Design a statistical procedure** to produce an estimate of the estimand\n",
+    "4. **Validate the statistical procedure** (3) using the generative model -- can we recover an accurate estimate of (2) from data generated by (1)\n",
+    "5. **Apply statistical procedure** (3) to real data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9103a6d",
+   "metadata": {},
+   "source": [
+    "## 1, 2. Define generative model of globe tossing\n",
+    "- $p$: proportion of water -- this is the **estimand**, what we'd like to estimate\n",
+    "- $N$: number of tosses  -- we control this via experiment\n",
+    "- $W$: number of `Water` observations\n",
+    "- $L$: number of `Land` observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "75315540-4081-4bd2-b976-a792bd7361d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"214pt\" height=\"111pt\"\n",
+       " viewBox=\"0.00 0.00 214.25 110.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 106.95)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-106.95 210.25,-106.95 210.25,4 -4,4\"/>\n",
+       "<!-- p -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>p</title>\n",
+       "<ellipse fill=\"none\" stroke=\"red\" cx=\"27\" cy=\"-83.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-79.9\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
+       "</g>\n",
+       "<!-- W -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>W</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"179.25\" cy=\"-83.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"179.25\" y=\"-79.9\" font-family=\"Times,serif\" font-size=\"14.00\">W</text>\n",
+       "</g>\n",
+       "<!-- p&#45;&gt;W -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>p&#45;&gt;W</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M54.17,-83.45C78.05,-83.45 113.61,-83.45 140.55,-83.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"140.36,-86.95 150.36,-83.45 140.36,-79.95 140.36,-86.95\"/>\n",
+       "<text text-anchor=\"middle\" x=\"103.12\" y=\"-89.65\" font-family=\"Times,serif\" font-size=\"14.00\">influence</text>\n",
+       "</g>\n",
+       "<!-- L -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>L</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"179.25\" cy=\"-20.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"179.25\" y=\"-16.9\" font-family=\"Times,serif\" font-size=\"14.00\">L</text>\n",
+       "</g>\n",
+       "<!-- p&#45;&gt;L -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>p&#45;&gt;L</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M50.82,-73.88C75.86,-63.38 116.29,-46.42 144.89,-34.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"145.95,-37.78 153.82,-30.69 143.24,-31.33 145.95,-37.78\"/>\n",
+       "</g>\n",
+       "<!-- N -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>N</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-19.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"27\" y=\"-15.9\" font-family=\"Times,serif\" font-size=\"14.00\">N</text>\n",
+       "</g>\n",
+       "<!-- N&#45;&gt;W -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>N&#45;&gt;W</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M50.82,-29.17C76.01,-39.89 116.76,-57.26 145.39,-69.45\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"143.74,-72.55 154.32,-73.25 146.49,-66.11 143.74,-72.55\"/>\n",
+       "</g>\n",
+       "<!-- N&#45;&gt;L -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>N&#45;&gt;L</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M54.17,-19.62C78.05,-19.78 113.61,-20.02 140.55,-20.19\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"140.34,-23.69 150.36,-20.26 140.38,-16.69 140.34,-23.69\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x7f654df278c0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.draw_causal_graph(\n",
+    "    edge_list=[(\"p\", \"W\"), (\"p\", \"L\"), (\"N\", \"L\"), (\"N\", \"W\")],\n",
+    "    graph_direction=\"LR\",\n",
+    "    node_props={\"p\": {\"color\": \"red\"}},\n",
+    "    edge_props={(\"p\", \"W\"): {\"label\": \"influence\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "953f3287",
+   "metadata": {},
+   "source": [
+    "- This graph defines a causal model, of how $p, N$ effect the values of $W, L$. This is the same as saying it defines some function $f$ that maps $p, N$ onto the values of $W, L$, i.e.  $W, L = f(p, N)$\n",
+    "- Scientific knowledge defines what $f$ is or can be\n",
+    "\n",
+    "The unglamourous basis of applied probability:\n",
+    "> **Things that can happen more ways are more plausible.**\n",
+    "\n",
+    "\n",
+    "#### Bayesian data analysis\n",
+    "\"Very simple, very humble\"\n",
+    "- For each possible explanation of the sample\n",
+    "- Count all the ways the sample could occur\n",
+    "- **The explanations with the largest number of ways to produce the observed sample are more plausible**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e7c5bc9",
+   "metadata": {},
+   "source": [
+    "## 3. Design a statistical procedure to produce an estimate\n",
+    "### Garden of Forking Data\n",
+    "Following the mantra above...\n",
+    "\n",
+    "- for each possible **proportion of water**, $p$\n",
+    "- count all the ways the sample of tosses could have occurred\n",
+    "- the $p$ that are associated with more ways to produce the sample are more plausible\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "38525410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observations: 'WLW'\n",
+      "(1) LLLL p(W) = 0.0\t\t0 Ways to Produce\n",
+      "(2) WLLL p(W) = 0.25\t\t3 Ways to Produce\n",
+      "(3) WWLL p(W) = 0.5\t\t8 Ways to Produce\n",
+      "(4) WWWL p(W) = 0.75\t\t9 Ways to Produce\n",
+      "(5) WWWW p(W) = 1.0\t\t0 Ways to Produce\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calculate_n_ways_possible(observations: str, n_water: int, resolution: int = 4):\n",
+    "    \"\"\"\n",
+    "    Calculate the number of ways to observing water ('W') given the toss of a globe\n",
+    "    with `resolution` number of sides and `n_water` faces.\n",
+    "\n",
+    "    Note: this method results in numerical precision issues (due to the product) when the\n",
+    "    resolution of 16 or so, depending on your system.\n",
+    "    \"\"\"\n",
+    "    assert n_water <= resolution\n",
+    "\n",
+    "    # Convert observation string to an array\n",
+    "    observations = np.array(list(observations.upper()))\n",
+    "\n",
+    "    # Create n-sided globe with possible outcomes\n",
+    "    possible = np.array(list(\"L\" * (resolution - n_water)) + list(\"W\" * n_water))\n",
+    "\n",
+    "    # Tally up ways to obtain each observation given the possible outcomes\n",
+    "    # Here we use brute-force, but we could also use the analytical solution below\n",
+    "    ways = []\n",
+    "    for obs in observations:\n",
+    "        ways.append((possible == obs).sum())\n",
+    "\n",
+    "    p_water = n_water / resolution\n",
+    "    # perform product in log space for numerical precision\n",
+    "    n_ways = np.round(np.exp(np.sum(np.log(ways)))).astype(int)\n",
+    "    return n_ways, p_water\n",
+    "\n",
+    "\n",
+    "def run_globe_tossing_simulation(observations, resolution, current_n_possible_ways=None):\n",
+    "    \"\"\"Simulate the number of ways you can observe water ('W') for a globe of `resolution`\n",
+    "    sides, varying the proportion of the globe that is covered by water.\n",
+    "    \"\"\"\n",
+    "    # For Bayesian updates\n",
+    "    current_n_possible_ways = (\n",
+    "        current_n_possible_ways if current_n_possible_ways is not None else np.array([])\n",
+    "    )\n",
+    "\n",
+    "    print(f\"Observations: '{observations}'\")\n",
+    "    p_water = np.array([])\n",
+    "    for n_W in range(0, resolution + 1):\n",
+    "        n_L = resolution - n_W\n",
+    "        globe_sides = \"W\" * n_W + \"L\" * n_L\n",
+    "        n_possible_ways, p_water_ = calculate_n_ways_possible(\n",
+    "            observations, n_water=n_W, resolution=resolution\n",
+    "        )\n",
+    "        print(f\"({n_W+1}) {globe_sides} p(W) = {p_water_:1.2}\\t\\t{n_possible_ways} Ways to Produce\")\n",
+    "\n",
+    "        p_water = np.append(p_water, p_water_)\n",
+    "        current_n_possible_ways = np.append(current_n_possible_ways, n_possible_ways)\n",
+    "\n",
+    "    return current_n_possible_ways, p_water\n",
+    "\n",
+    "\n",
+    "RESOLUTION = 4\n",
+    "observations = \"WLW\"\n",
+    "n_possible_ways, p_water = run_globe_tossing_simulation(observations, resolution=RESOLUTION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51fbb48f",
+   "metadata": {},
+   "source": [
+    "## Bayesian (online) Updating"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "29063cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observations: 'W'\n",
+      "(1) LLLL p(W) = 0.0\t\t0 Ways to Produce\n",
+      "(2) WLLL p(W) = 0.25\t\t1 Ways to Produce\n",
+      "(3) WWLL p(W) = 0.5\t\t2 Ways to Produce\n",
+      "(4) WWWL p(W) = 0.75\t\t3 Ways to Produce\n",
+      "(5) WWWW p(W) = 1.0\t\t4 Ways to Produce\n",
+      "\n",
+      "Updated Possibilities given new observation:\n",
+      "(1) p(W) = 0.0\t\t0 Ways to Produce\n",
+      "(2) p(W) = 0.25\t\t3 Ways to Produce\n",
+      "(3) p(W) = 0.5\t\t16 Ways to Produce\n",
+      "(4) p(W) = 0.75\t\t27 Ways to Produce\n",
+      "(5) p(W) = 1.0\t\t0 Ways to Produce\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_observation_possible_ways, _ = run_globe_tossing_simulation(\"W\", resolution=RESOLUTION)\n",
+    "\n",
+    "# Online update\n",
+    "n_possible_ways *= new_observation_possible_ways\n",
+    "\n",
+    "print(\"\\nUpdated Possibilities given new observation:\")\n",
+    "for ii in range(0, RESOLUTION + 1):\n",
+    "    print(f\"({ii+1}) p(W) = {p_water[ii]:1.2}\\t\\t{int(n_possible_ways[ii])} Ways to Produce\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a78d8f8",
+   "metadata": {},
+   "source": [
+    "## The whole sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "58c70ffd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Observations: 'WLWWWLWLW'\n",
+      "(1) LLLL p(W) = 0.0\t\t0 Ways to Produce\n",
+      "(2) WLLL p(W) = 0.25\t\t27 Ways to Produce\n",
+      "(3) WWLL p(W) = 0.5\t\t512 Ways to Produce\n",
+      "(4) WWWL p(W) = 0.75\t\t729 Ways to Produce\n",
+      "(5) WWWW p(W) = 1.0\t\t0 Ways to Produce\n"
+     ]
+    }
+   ],
+   "source": [
+    "RESOLUTION = 4\n",
+    "observations = \"WLWWWLWLW\"\n",
+    "n_W = len(observations.replace(\"L\", \"\"))\n",
+    "n_L = len(observations) - n_W\n",
+    "\n",
+    "n_possible_ways, p_water = run_globe_tossing_simulation(observations, resolution=RESOLUTION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7f03414",
+   "metadata": {},
+   "source": [
+    "show that we get identical answers with the analytical solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c4af49-1acf-459c-9585-52fbb09387b0",
+   "metadata": {},
+   "source": [
+    "#### Results suggest the Analytical Solution $W,L = (Rp)^W \\times (R - Rp)^L$\n",
+    "where $R$ is the number of possible globes, in this case 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d17c330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_analytic_n_ways_possible(p, n_W, n_L, resolution=RESOLUTION):\n",
+    "    \"\"\"This scales much better than the brute-force method\"\"\"\n",
+    "    return (resolution * p) ** n_W * (resolution - resolution * p) ** n_L\n",
+    "\n",
+    "\n",
+    "analytic_n_possible_ways = np.array(\n",
+    "    [calculate_analytic_n_ways_possible(p, n_W, n_L) for p in p_water]\n",
+    ")\n",
+    "assert (analytic_n_possible_ways == n_possible_ways).all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c25c2e82-9b21-42c6-b25f-67333dcea026",
+   "metadata": {},
+   "source": [
+    "## Probability\n",
+    "- non-negative values that sum to 1\n",
+    "- normalizes large sums by the total counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "27508218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Proportion\tWays\tProbability\n",
+      "0.0\t\t0\t0.00\n",
+      "0.25\t\t27\t0.02\n",
+      "0.5\t\t512\t0.40\n",
+      "0.75\t\t729\t0.57\n",
+      "1.0\t\t0\t0.00\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_possible_probabilities = n_possible_ways / n_possible_ways.sum()\n",
+    "\n",
+    "print(\"Proportion\\tWays\\tProbability\")\n",
+    "for p, n_w, n_p in zip(p_water, n_possible_ways, n_possible_probabilities):\n",
+    "    print(f\"{p:1.12}\\t\\t{n_w:0.0f}\\t{n_p:1.2f}\")\n",
+    "\n",
+    "probs = np.linspace(0, 1, RESOLUTION + 1)\n",
+    "plt.subplots(figsize=(5, 5))\n",
+    "plt.bar(x=probs, height=n_possible_probabilities, width=0.9 / RESOLUTION, color=\"k\")\n",
+    "plt.xticks(probs)\n",
+    "plt.ylabel(\"probability\")\n",
+    "plt.xlabel(\"proportion water\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49953df1",
+   "metadata": {},
+   "source": [
+    "## 4. Validate Statistical Procedure (3) using Generative Model (1)\n",
+    "\n",
+    "### Test Before You Est(imate) 🐤\n",
+    "1. Code generative simulation (1)\n",
+    "2. Code an estimator (3)\n",
+    "3. Test (3) with (1); you should get expected output\n",
+    "\n",
+    "**IF YOU TEST NOTHING YOU MISS EVERYTHING**\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ae34bb3",
+   "metadata": {},
+   "source": [
+    "### 4.1 Generative Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "87d9a518",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "\n",
+    "def simulate_globe_toss(p: float = 0.7, N: int = 9) -> list[str]:\n",
+    "    \"\"\"Simulate N globe tosses with a specific/known proportion\n",
+    "    p: float\n",
+    "        The propotion of water\n",
+    "    N: int\n",
+    "        Number of globe tosses\n",
+    "    \"\"\"\n",
+    "    return np.random.choice(list(\"WL\"), size=N, p=np.array([p, 1 - p]), replace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "299b93f2-36ef-44e8-a8b9-2e3c42438b36",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['W' 'L' 'W' 'W' 'W' 'W' 'W' 'W' 'W']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(simulate_globe_toss())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b16ed1b9-ce5d-4f07-9953-5495afb50a70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W'],\n",
+      " ['W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W', 'W']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint([simulate_globe_toss(p=1, N=11).tolist() for _ in range(10)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30937aff-7dae-454f-915e-d352478b2111",
+   "metadata": {},
+   "source": [
+    "#### Test on Extreme settings\n",
+    "With a large number of samples N, estimator should converge to known $p$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0319f9e4-8e58-4510-928e-90bf73a4ef77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "known_p = 0.5\n",
+    "\n",
+    "simulated_ps = []\n",
+    "sample_sizes = np.linspace(10, 100_000, 10)\n",
+    "for N in sample_sizes:\n",
+    "    simulated_p = np.sum(simulate_globe_toss(p=known_p, N=int(N)) == \"W\") / N\n",
+    "    simulated_ps.append(simulated_p)\n",
+    "\n",
+    "plt.axhline(known_p, label=f\"Known p={known_p}\", color=\"k\", linestyle=\"--\")\n",
+    "plt.legend()\n",
+    "plt.plot(sample_sizes, simulated_ps);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfa7f8cd",
+   "metadata": {},
+   "source": [
+    "### 4.2 Code the estimator\n",
+    "\n",
+    "The estimator takes in observations and returns a probability distribution (posterior) over potential estimates. Higher probability estimates should be more plausible given the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6338583a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAHrCAYAAAAe4lGYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABp2klEQVR4nO3dd3gU5d7G8XtDEhASUmgiByGQQq8hgGChqCBdUJFDL4IIUgQFe0E8WFAJB0UB6QhSRJogICIoCS1SDNJSIHQCISuEFOb9I+/uYU0CCcmym+T7uS6vy0x55re7M8y9s888YzIMwxAAAACAPOfi6AIAAACAgoqwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQBOIDQ0VEFBQQoNDXV0KQ5z8uRJBQUFqWXLlg6rYdy4cQoKCtLy5csdVkNeWL58uYKCgjRu3DhHlwIUeq6OLgBA4dOyZUvFxcXZTCtatKjKli2rRo0aqX///goICLirNV25ckVz5syRp6en+vbte1e37eySk5O1YsUKbd68WZGRkbp06ZJcXV1Vvnx5NWjQQB07dlRISIijy3RKy5cvV1xcnLp06aJ//etfji4HgAMQtgE4TOXKleXr6ytJSkxMVHR0tJYvX67Vq1fr888/v6tXOK9cuaKpU6eqQoUKDgnbPj4+8vPzk4+Pz13f9q3s379fI0aMsH45KlWqlAICApSSkqKTJ0/qu+++03fffadHHnlE06dPd3C1zmfFihUKDw9XSEgIYRsopAjbABxm8ODBevLJJ61/X7hwQWPHjtVvv/2m8ePHa/PmzSpRooQDK7x7evbsqZ49ezq6DBv79+9Xr169dO3aNTVr1kyjR49WrVq1rPOTk5O1fft2/fe//9Xu3bsdWCkAOC/6bANwGqVLl9aHH34od3d3Xb58Wb/99pujSyq0kpOTNWrUKF27dk3t27fXjBkzbIK2JLm7u6tFixZasmSJnn/+eQdVCgDOjSvbAJxKmTJlVLlyZR0+fFjR0dE287Zs2aJ58+bpwIED+vvvv1W2bFk99NBDGjx4sMqXL5+hrUuXLumrr77Szz//rLi4OBUpUkS+vr7y8/NTy5Yt9e9//1tS+k1xK1askCTFxcUpKCjIpp2//vrL5u9jx45pxowZCgsL07lz51SiRAnVrVtX/fr1U9OmTTPUYemjvmnTJp08eVIzZszQ/v37dfnyZc2dO1eNGzdWaGiopk6dqmHDhmn48OEZ2tizZ49mzZqlPXv26MqVK/L19VWTJk00ePBgVa1aNcPyvXr1Unh4uObOnauSJUvqiy++0K5duxQfH6+JEyfa/KKQmR9++EEnTpyQj4+P3nnnHbm4ZH1txsXFRQMGDLCZduLECa1bt06//vqrYmNjdfHiRXl4eKhmzZrq1auXHnnkkVtuPzOpqalavny5Vq1apcOHD+vq1asqW7asatSooU6dOql169bWZW9+zzPrvnHz+9O4cePbbjspKUkbN27Upk2b9Oeff+rcuXOSpPvvv1+PP/64+vbtq+LFi1uXDwsLU+/eva1/3/z/kvTBBx/YfAbXrl3TvHnz9OOPPyo6OlppaWmqXLmyOnTooN69e8vd3T1DTYZhaOnSpZo/f76ioqJUokQJNWnSRCNHjrzt6wFw9xC2ATgdwzAyTPvkk0/01VdfSZLuvfdeVahQQcePH9eiRYu0du1azZw5U7Vr17Yun5iYqKefflqxsbFyc3NTpUqVVLRoUZ05c0bbt2/XH3/8YQ3blStXVq1atXTgwAG5u7tnuIJ7s7Vr1+rll19WSkqKSpQoIX9/f124cEG//PKLtm7dqtdee029evXKdN01a9bos88+k6enp+6//34VK1YsW+/HwoUL9e6778owDJUqVUpBQUGKjY3VypUr9eOPP2rKlClZhtedO3dq+vTpcnV1lZ+fn00gvJW1a9dKkjp16iQPD49srXOzL7/8UkuXLlXx4sVVtmxZBQUF6ezZs9q2bZu2bduml156Sc8991y220tISNCQIUO0Z88eSVKFChX0r3/9S6dPn9aGDRt08OBBm7Cd1w4cOKCXXnpJrq6uKl26tKpUqSKz2ayjR4/q0KFD2rhxoxYuXGj9TD09PdWgQQMdPnxYZrNZgYGBNu9jqVKlrP9/9uxZ9e/fX0ePHpWrq6sqVKggV1dXHT16VB999JE2b96sWbNmZdhf3nnnHS1atMj6fnh7e2vjxo3atm2bevToYbf3AkAOGQBwl7Vo0cIIDAw0li1blmHeuXPnjFq1ahmBgYHG+vXrDcMwjM2bNxuBgYFGjRo1jJUrV1qXTUxMNF544QUjMDDQaNGihXHt2jXrvJkzZxqBgYFG//79jUuXLtlsIy4uzvjmm29spp04ccLaTlYiIyONWrVqGbVr1zYWL15spKWlWedt2rTJaNCggVG9enUjMjIy09dbvXp1IzQ01EhJSTEMwzBu3LhhXL9+3TAMw5gyZYoRGBhoTJkyxWbdP//806hRo4YRGBhofP3119ZtXr9+3Xj77beNwMBAo2HDhsbZs2dt1uvZs6d1m2+88YZx9epV67yb36es1KtXzwgMDDR++umn2y6bmS1bthgRERHGjRs3bKbv3LnTaNasmVG9enUjJibGZt6tPoOhQ4cagYGBRuvWrY2IiAibedHR0cbXX39tM83ynp84cSLT+izvz44dO2ymv/LKK5numydPnjTWrl1rmM1mm+nnzp0zhg8fbgQGBhqhoaHZ3o5FWlqa8cwzzxiBgYHGqFGjjPPnz1vnnT592ujRo4cRGBho/Oc//7FZb+PGjUZgYKBRq1Yt63FiGIZx8eJFo2fPnkbNmjWNwMBA45VXXsl0uwDuHvpsA3AaFy9e1Msvv6zk5GR5eXnpgQcekCTrFe0ePXqoY8eO1uU9PDz00UcfycfHR3FxcVq9erV1nqULSo8ePeTt7W2znfvuu++ORhz573//q+TkZI0ZM0ZPP/20TdeKli1batSoUUpLS9PcuXMzXf+hhx7SsGHD5Oqa/qOiyWTKtHvAzWbOnKnU1FS1atVKAwcOtG7T3d1db775pgICApSYmGi9wvlPAQEBevvtt3XPPfdYp93uinpiYqKuXr0qSXc8gsbDDz+sunXrymQy2UwPDg7WiBEjlJaWZr16fjv79u3Txo0b5e7urq+//lp169a1mV+pUiUNHDjwjurMrgoVKqht27YZbtgtU6aMPvzwQ7m5uWnVqlU5bnfLli3au3evateurQ8//FClS5e2zrv33nv16aefqnjx4vr222+VlJRknTdz5kxJ6d1hHnvsMet0X19fTZ48Ocd1ALAfupEAcJjp06fru+++k/S/of9SUlLk5uam9957Tx4eHvr7778VEREhSZl2z7jnnnv09NNPa/r06dq+fbu6desmSdY+3Bs3btTDDz9sDbh3Kjk5Wb/88ouKFCmSZX/nli1b6r333tPOnTsznd+5c+ccb3f79u2SMn/tJpNJvXr10ptvvqnt27drxIgRGZbp2LHjLftbZ+bvv/+2/n92u51kJj4+XqtWrdK+fft08eJFXb9+XZJkNpslSYcOHcpWO5s2bZIkPfroo6pcufId15NbN27c0ObNm7V9+3adOHFCV69etXZ5MplMio6O1rVr12y+2NzOTz/9JEnq0qVLpvto2bJlVbt2bYWFhenAgQMKDg7W33//rb1790qSnn322QzrlClTRo899pjWrFlzJy8TQB4jbANwmOjoaOsVaDc3N5UpU0bBwcHq37+/qlevLkmKjY3VjRs35O7urooVK2bajr+/v7U9iyeffFIzZ87U8uXLtXXrVj344INq2LChmjRpkmU7t6v1+vXrcnNz06BBgzJdxhK8zp49m+n8zG5kvJUrV64oPj7+lutm9tpzs01JNldvLVe4c2rbtm0aOXKkEhMTs1wmISEhW20dP35ckjJc0b6brly5oueee84acm+1XE7CtuXm22+//dbml5mbWT5by02ZlmOiaNGiWe7LVapUyXYNAOyLsA3AYf45IkNmLFdZfX19M3RJsLD89H7zFdly5cpp8eLF+vzzz7VlyxatWLHCOuJIvXr1NG7cONWvXz/btVpCY0pKivUmvaxYruD+U05CmGQbdG++oe5mmb323GxTSr+5r3jx4rp69apOnjypatWq5Wj9K1euaPTo0UpMTFTnzp3Vo0cP+fn5ycPDQy4uLvrtt9/Ur18/paamZqs9y5XwkiVL5vi15JUPPvhAe/fulZ+fn0aPHq26devKx8fH2g3ooYce0tmzZ5WSkpKjdi2v7fDhw7dd1tKNxPJZ3+oBSDd3RwHgWIRtAE7NcpU1Pj5ehmFkGrgvXLhgs6xF1apVNWXKFCUnJ2vv3r3auXOn1qxZo4iICPXv31+rVq3Kdp9kS9vlypXT1q1bc/OSsu3mLhwXL15U2bJlMyyT1WvPrfr162v79u3auXNnjkf52Lp1qxISElS/fn395z//yfCZnT59OkftWV7blStXcrSelPnINlLOrtinpqbqxx9/lCRNmzYtw1Xj1NRU6+eQU5bP+JtvvrHeo3A7lvfj0qVLWS5z8eLFO6oHQN7jBkkATu3++++Xi4uLkpOTdeLEiUyXOXr0qCRl2Z/X3d1djRs31rBhw7R69Wo1aNBAV69etenTmtVVc4tKlSrJzc1N58+f1+XLl+/oteRUyZIlrY+zP3bsWKbL3O6136m2bdtKSh9v23L1Nbssj3avV69epu9rdvtqW1i6yvzxxx/ZXscSYrMKpFntS5mJj4/X1atX5e3tnWn3jCNHjigtLS3b7d3M0s0nO1e2LSzHxPXr13Xy5MlMl7F0vQHgeIRtAE6tRIkS1u4e8+bNyzA/KSnJepNl8+bNb9tekSJFrONxW/rASv8boePmER9uds8996h58+a6ceNGpnXYi+U1ZbZNwzA0f/58m+XySqdOnVShQgXFx8frrbfe0o0bN7Jc1jAM6+gYklS0aFFJyvRq76VLl7R06dIc1WK5sr5x40bFxsZmax3LLxb79+/PMG/9+vXZ7i8u/W/fMJvNme4fM2bMuO26We1XlpFEFi9enGX3o38qUaKE6tWrJym9r/c/XbhwQRs2bMhWWwDsj7ANwOlZbkhctGiRzfBqZrNZL7/8suLj41WhQgW1a9fOOu/TTz/Vd999l6HrweHDh7Vu3TpJUo0aNazTfX19VaJECV28eDHLq8gjRoyQu7u7vvjiC3311VcZAtS5c+c0Z86cLIfhuxP9+/eXq6urNm3apFmzZllDb3Jyst5//30dPnxYnp6emY5KkRvu7u767LPPVKxYMa1evVqDBg3SwYMHbZZJSUnR1q1b9cwzz+iLL76wTg8ODpYk/fjjj/rtt9+s08+dO6cXX3wxx1eBa9WqpUcffVTXr1/XoEGDtG/fPpv5MTExNmFfSu9DLaUH4ZtvHt23b58mTJggNze3bG+/ZMmSCggIUGpqqiZOnKjk5GRJUlpamr766iutXbs2y/YsNzBmNULNo48+qnr16un48eMaMmSIYmJibOYnJydry5YtGj9+vM10yxM7586dq40bN1qnx8fHa8yYMVl2nwFw99FnG4DTa9GihZ577jl99dVXGjNmjD755BOVKlVKx48f19WrV+Xl5WUNhhZHjhzRl19+qTfffFMVK1aUl5eXEhISrGGmcePG6tSpk3V5k8mkNm3aaNmyZerSpYsCAgKsXREsV5WrV6+uyZMna+zYsfrkk080depUValSxdq9xNIXOavRSu5E9erV9dprr+ndd9/VpEmTNHPmTJUvX14xMTG6cuWK3N3d9fHHH6tMmTJ5tk2LOnXqaO7cuRo5cqT1yY+lS5dWuXLllJKSopMnT1r7Prdq1cq6Xq1atfT4449r/fr16tevnypVqqTixYvryJEjKlq0qF566SVNnDgxR7W8//77unDhgvbu3aunnnpKFSpUkI+Pj86cOaMLFy6oQoUKNo+M79q1qxYuXKgjR46oXbt2qlKlipKTkxUdHa127drp/PnzCg8Pz/b2R48eraFDh2rx4sVav369/vWvfykuLk6XLl3S0KFDtXLlSmv3mZs98cQTWrBggb7++mv99NNPKlOmjEwmkwYNGqSHHnpILi4uCg0N1eDBg/Xbb7/pscceU6VKleTt7a2///5bMTExSklJyXDDY+vWrfXMM89o8eLFeuGFF/Svf/1LXl5eOnr0qIoWLaoBAwboyy+/zNF7DMA+CNsA8oWXXnpJDRo00Lx583TgwAFduHBBZcuWVceOHTVkyBDruNoWzz//vKpWraqwsDCdOnVKp06dkq+vr0JCQtS1a1e1b98+w7jGr732mkqUKKFNmzbpr7/+ynRkiUcffVRr1qzR7NmztW3bNkVFRcnFxUXlypXTo48+qtatW6tly5Z5+tp79OihoKAgzZo1S3v27NGhQ4fk4+OjRx55RIMHD7b2abaHunXrav369Vq+fLk2bdqkQ4cO6fDhw3Jzc1P58uXVsGFDde7cWQ0bNrRZ7+OPP1bVqlW1cuVKnTp1St7e3nr88cc1bNgwnT9/Psd1eHl5ad68eVqyZIlWr16tI0eO6MKFCypTpowef/xxdenSxWb5okWLas6cOZo8ebJ+/vlnRUdH61//+pdeeeUV9e3bV3369MnR9lu2bKmvv/5a//3vfxUZGamoqCgFBATo1VdfVceOHbVy5cpM1wsODtYnn3yiOXPm6OjRo9ar7DfXW7ZsWS1evFhLly7V2rVrdfjwYZ06dUqlS5dWnTp19MADD1j70N/snXfeUc2aNbVgwQJFRUXp6tWr1ocr7d69O0evD4D9mAx+awIAAADsgj7bAAAAgJ0QtgEAAAA7IWwDAAAAdkLYBgAAAOyEsA0AAADYCWEbAAAAsBPCNgAAAGAnhG0AAADATgjbAJzSoUOHNHjwYIWEhKhatWoKCgpSWFiYo8tCPhQaGqqgoCCFhoY6uhQAhRCPawfgdC5evKjevXsrISFB5cqVU5UqVWQymeTp6eno0u6K+Ph4NW3aVI8++qimTp0qSTpx4oRat26tJ598Uh988MEdtXv06FHNnTtXYWFhOn36tFxcXOTr66sKFSqoYcOGeuihh9SgQYO8fCnIAxs2bNDChQv1559/KikpSeXLl9ejjz6qwYMHF5pjAsjPCNsAnM6aNWuUkJCgVq1aaerUqXJxKVw/wkVEREiS6tWrZ522Z88eSVL9+vXvqM0ffvhBr776qlJSUuTm5qby5cvLy8tL8fHxCg8PV3h4uLZu3arly5fntnzkobfffluLFi2SJN13332qWLGijh8/rq+//lrr1q3TokWLVLZsWQdXCeBWCNsAnM7x48clSc2bNy90QVv6X9i+OVjv3bs3w7TsOnnypF577TWlpKSoa9eueumll1SqVCnr/PPnz2vDhg36448/clc48tT333+vRYsWydXVVR9++KHatWsnSfr777/1+uuva+3atRo7dqzmzJnj4EoB3ErhO4sBcHrXr1+XJBUrVszBlTjG3r175ebmplq1almn7dmzR56envL3989xe2vXrlVycrL8/Pw0YcIEm6AtSWXKlNG///1vffjhh7muHXln9uzZkqRu3bpZg7YklShRQu+99558fX21Y8cO7dq1y0EVAsgOwjYAp2G5kc3SlWH8+PEKCgpSUFCQevXqJSn9Km1QUJBatmwpSVqyZIm6du2q+vXrKygoyKa9PXv2aNiwYXrggQdUq1YtPfTQQ3r55Zd17NixTLffq1cv642Yhw4d0vPPP6/GjRurQYMG6tu3r/bv329ddteuXRowYIBCQkJUv359Pffcc1m2mxNpaWk6cOCAatSooaJFi0qSzGazjhw5orp168pkMuW4zRMnTkiSAgMDc/xLweHDhzVlyhQ988wzat68uWrVqqXmzZtr2LBh1q4t/7R8+XIFBQVp3LhxSkpK0ieffKJWrVqpTp06evzxxzVv3jzrspcuXdKECRPUokUL1a5dW+3atcuyK8vNn8++ffv03HPPKSQkRPXq1VP37t21cePGHL02izNnzmjChAl6/PHHVadOHQUHB6tXr1768ccfM13+6tWrmjp1qjp06KB69eqpdu3aevjhh9WrVy999dVXSklJuaM6/rmNQ4cOSZLatGmTYb6Hh4eaNWsmSVq3bl2utwfAfuhGAsBplC9fXg0aNFBMTIwuXryoypUry9fXV1J6UPynt956S99++63Kly+vKlWqKDY21jpv4cKFevfdd2UYhkqVKqWgoCDFxsZq5cqV+vHHHzVlyhQ98sgjmdbxxx9/6L///a/c3d1VsWJFxcbG6vfff1efPn20ePFiHT16VGPGjJGXl5f+9a9/6fjx4/rll1+0f/9+rVq1SqVLl87R6/7nlwRLDf+cvm3bNptpf/31V7ba9/DwkJQ+woulz3Z2TZw4Ub///rtKliypMmXKqGzZsjp16pR++uknbd68WZMmTVKHDh0yXTclJUV9+/bVvn375O/vL8MwFB0drQkTJighIUHPPvusevToobi4OAUEBCg1NVVHjx7V+PHjZRiGunbtmmm7u3bt0hdffCE3NzdVqVJFZ8+e1d69e/XCCy9o3Lhx6tevX7ZfX3h4uIYOHarExEQVK1ZMlSpVUmJiorUfe//+/fXKK69Yl09NTVW/fv0UEREhFxcXVapUSSVKlNC5c+e0a9cuhYeHq3v37jbvcWhoqKZOnaqQkBCbLxq3kpiYKMMwJCnLPtnlypWT9L9uRwCcE2EbgNPo1q2bunXrpnHjxmnFihUaPHiwnnzyyUyXPXPmjH744QdNmzZNrVq1kiQlJSVJkiIjI/X+++/LMAyNHTtW/fv3l4uLi5KTk/XBBx9o4cKFGjNmjNauXZtpkJkyZYp69uyp0aNHy93dXcnJyRo5cqQ2bdqkd999V4cOHdKYMWPUp08fubi46MqVKxowYID27dunb775RmPHjs3R6w4JCbH+/4kTJ3T69GnVqFHDGpKjoqJ0/vx51alT54661jz44IOaNWuWYmJiNGjQIPXr108hISG65557brtu9+7drb8wWBiGoU2bNmns2LF6++231aJFC2utN1u/fr38/Py0fv16VaxYUVL6za+jR4/W119/rb1796pcuXJauHChtWvLl19+qU8//VSffvqpOnfurCJFimRod9q0aXr88cc1YcIEFS9eXIZhaP78+ZowYYI+/vhjNW3aVNWqVbvtazt79qyGDx8us9ms0aNHq1+/fnJ3d5eU/qvIyJEjNWvWLIWEhKhFixaSpE2bNikiIkLVqlXT9OnTde+991rbi4+P16pVq3L0ZSYrJUqUsP7/uXPnVLVq1Uzrl9L3DwDOi24kAPKltLQ0vfjii9agLf2vj/fMmTOVmpqqVq1aaeDAgdauE+7u7nrzzTcVEBCgxMRE6ygP/xQQEKBXXnnFGrzc3d2tATo8PFyNGjVSv379rO2WLFlSL774oiTp119/zfFrmTdvnvW/atWqyWQyadasWdZplStXVtGiRbVgwQKbZbPrgQce0DPPPCNJ+v333/Xcc88pODhYHTt21Jtvvqmff/5ZaWlpma7bpk2bDFfYTSaTWrdurT59+shsNuvnn3/OdN3U1FRNmjTJGrQlqV27dqpfv76SkpK0a9cuffTRRzZ9yAcNGqRy5crp/PnzWV659/Ly0gcffKDixYtb6+nVq5cee+wxpaam6ptvvsnW+/LNN9/o8uXL6tOnjwYPHmz9vCWpQYMGeueddyT9r++0JMXExEiSunbtahO0JcnX11d9+vTJ8CXGw8ND5cqVk4+PT7bqsqxTpUoVSelD//3T33//rd9++836/1l9fgAcj7ANIN/q1KlTptO3b98uSdZ+3jezBLObl/unJ598MkPfaD8/P2uI6tatW4Z1atSoIel//aPvRFpamnbu3Klq1apZg1lSUpIiIiJUv359mzCYU++++65CQ0MVEhKiIkWKKDU1VX/99ZcWL16sIUOGqFOnTlmG21OnTumrr77SiBEj1Lt3bz377LN69tlnrX2FLX2L/6lGjRrW9+VmlqvODz30kLUrhEWRIkWs4T6r97Jbt27W/uw369Gjh6T07jbZYQmxTz31VKbzH3zwQbm5uWnv3r1KTU2VJGvA/uWXX3Tt2rVsbadfv37aunWrpkyZkq3lLSyvZ8mSJVq6dKl1utls1iuvvKKLFy9ap1l+1QHgfOhGAiBf8vHxsfbnvtmVK1cUHx8vSZn+9C7JOqJHdHR0pvPvv//+LLd57dq1TOdbarl69epta8/KgQMHZDab1bRpU+u0PXv2KCUlRU2aNLnjdi0ee+wxPfbYYzKbzdq3b58iIiL0888/a9++fTpy5Ij69eun1atX27yvK1as0FtvvWUdISYzCQkJmU6/+Yr2zSzt325+Vu+l5YpvVtMvXLggs9mcadcWi7///ltxcXGSpDfeeCPL5aT00XEuX76s0qVLq3Xr1qpQoYK2bdumBx98UA8++KCCg4MVEhKigICAW7aTU//+97+1c+dOrV+/Xq+99po+/vhjlS1bVlFRUUpOTlanTp20cuVKmUymbHUJAuAYhG0A+ZKlC8E/3RzQ/jnEnYXlBsa///470/lZ9Yu2XO3ObP6djBLy559/6r333rP+bfmSYOkXLKWPgS2lP5Rm69atktKvGN8uIN6Kh4eHHnjgAT3wwAMaOnSofvzxR40aNUoXL17U4sWL9fzzz0uSYmNj9cYbbyglJUX9+/dXx44dVbFiRZUoUUImk0nfffedXn/9detV33/KKgBa3qvbzbfcIPhPWX2uN0//+++/bxm2zWaz9f+zGlXlZpYrx8WLF9fChQv1+eefa/369Vq7dq3Wrl0rKf1L3JgxY6z9u3PLxcVFn332mZYtW6alS5fq8OHDio2NVc2aNdWvXz95enpq5cqVKlWqVKEcjx7ILwjbAAqUm0P4xYsXM70B8sKFC5Jsb0JzhMTExEyDXkxMjLVvsIXlQT+S5Oqat/90t2nTRuvWrdOPP/6offv2WaevW7dOKSkpateunc2IHBanT5/O0zqyy/Kl5FbTb/fZ3ryfHDhwIEc3Nd5777364IMP9N577+ngwYMKCwvT+vXrdeDAAb3wwgtatGiR6tatm+32bsXFxUVPPfVUpl1dvvrqK0lSzZo182RbAOyDr8IACpSSJUtauyFkNe710aNHJUmVK1e+W2VlqnHjxvrrr7+s/3l7e6t69erWv/fv3y83NzcFBwfbLJeTmyOzy9Kl4+Yxoi3dLLJ6amVWfbXt7eYvHjezfN6lS5e+5VVtSfL09LR+EbPsDznl6uqqunXr6rnnntOyZcvUrl07paWladmyZXfUXk6tX79ekrIcwhKAcyBsAyhwmjdvLkmZhlLLMHE3L+cMjh8/rsuXL6tevXrWaQcPHlRKSsodPaL9ZjffSJcVy+Pgb/4CYrkJ0fJLwM2OHTuW5Sgk9rZ06VIlJydnmG4ZXcbysJfbeeyxxyQpzx53brmafe7cuTxp71YsV9K9vb2zHOccgHMgbAMocPr37y9XV1dt2rRJs2bN0o0bNyRJycnJev/993X48GF5enrq2WefdXCl/2Ppo31z2LZMa9CgQa7a/vLLL9WjRw+tXr3apq+ylB4M33zzTe3atUsmk0mdO3e2zmvYsKGk9BAbGRlpnR4VFaWRI0fmyXjSd+Ly5ct69dVXrf3zDcPQggULtGHDBhUpUiTbD7UZNGiQvL29tWLFCn3wwQe6cuVKhu0sXbpU06ZNs06bPXu2Zs+eneELyKlTp6wjhvxzBJbZs2erZcuWGjVqVI5eZ1JSkhYtWqTExETrtLS0NK1cuVLjxo2TJL366qvy9PTMUbsA7i76bAMocKpXr67XXntN7777riZNmqSZM2eqfPnyiomJ0ZUrV+Tu7q6PP/5YZcqUcXSpVpYryzdfxbZMuzmA3wmTyaTdu3dr9+7dcnFx0f3336+SJUsqPj5eZ86cUWpqqooUKaJx48apVq1a1vVat26tevXqKSIiQl27dlXlypVVpEgRHTlyRKVLl9bzzz+vzz77LFe13YmhQ4fqiy++0ObNm+Xn56dz585ZryaPHj1a1atXz1Y79957r6ZNm6YXXnhBs2fP1oIFC6xDPMbHx+vkyZMyDENPPPGEdZ24uDjNnTtXH3zwgSpUqKBSpUrJbDYrJiZGaWlpCgwMzBD2ExMTFRcXpwoVKuTodSYnJ+vtt9/WhAkTVL58eXl5eenkyZO6fPmyihQpopdffjnL4S8BOA/CNoACqUePHgoKCtKsWbO0Z88eHTp0SD4+PnrkkUc0ePBg6/B/ziIiIkI+Pj6qVKmSzbSbH1l/p0aPHq1mzZpp69at+uOPP3T27FnFxcXJzc1NlStXVnBwsJ599tkMT110dXXVzJkz9dlnn2n9+vWKjY1VqVKl1K1bN7344ovZHs86rwUHB2vhwoUKDQ1VRESEkpOTVa9ePQ0cOFCPPvpojtpq2LCh1q5dqzlz5mjLli2KjY3VjRs3VK5cOT344INq0aKFtbuJlP5ETS8vL+3YsUOxsbGKjIyUl5eXateurQ4dOqhbt2539JTPzBQrVkzPPfecdVtnzpxRqVKl1KlTJ/Xp04cbI4F8wmRkNbYSAABOpFevXgoPD9fcuXPVuHFjR5cDANlCn20AAADATgjbAAAAgJ0QtgEAAAA7cdobJPft22e9+SUlJUX+/v7q06dPjscTNZvNmjVrljZs2KATJ07Izc1NFStWVKtWrTRs2DA7VQ8AAAA46Q2SYWFhGjBggNzc3NSuXTt5enpqw4YNOnnypEaNGqUhQ4Zkq51Tp06pT58+OnHihB544AFVr15dycnJio2N1alTp7Rq1So7vxIAAAAUZk4XtlNTU9W2bVudOXNGixcvtj4cwGw2q3v37oqKitKaNWtu+5jltLQ0PfPMMzpy5IimT5+uJk2aZNiOq6vTXtgHAABAAeB0fbYt44m2b9/e5ilcHh4eGjp0qFJTU7V8+fLbtrN+/Xrt379f/fv3zxC0JRG07yLDMHT58mU52fc6ADnAcQzkfxzHjuF0iTM8PFyS1Lx58wzzmjVrZrPMraxdu1aS1KZNG50+fVpbtmxRYmKiKlasqIceekglSpS45frsiHnHMAzrfwDyJ45jIP/jOM57JpPptss4XdiOjo6WJJunqFl4eXnJx8dHMTExt23nwIEDkqTdu3frgw8+UHJysnWer6+vPvvss1s+FCEhIYGdMY8lJCQ4ugQAucRxDOR/HMd5x8fH57bLOF3YNpvNkiRPT89M53t4eOjMmTO3befixYuSpAkTJqh///7q2bOn3N3dtWbNGk2aNEkvvPCC1q5dq7Jly2a6vpeX1x2+AvyTYRhKSEiQl5dXtr4BAnA+HMdA/sdx7BhOF7bziuWq9COPPKIxY8ZYp/fq1Utnz57V119/raVLl2ro0KGZrs9OmPdMJhPvK5DPcRwD+R/H8d3ldDdIenh4SJISExMznW82m7O86p1ZOy1btswwr0WLFpL+19UEAAAAsAenC9uWIf0y65edkJCgS5cuZdqf+5/8/PwkSSVLlswwzzLt+vXruagUAAAAuDWnC9uNGjWSJG3bti3DvO3bt0uSQkJCbtuOZbi/o0ePZphnmVahQoU7rhMAAAC4HacL202bNlXFihW1evVqRUZGWqebzWZNmzZNrq6u6tKli3V6fHy8jh07pvj4eJt2nnzySbm7u2v+/Pk6e/asTTvTp0+XJLVt29bOrwYAAACFmdM9QVJKf7DNwIED5ebmpvbt28vDw8P6uPaRI0fq+eefty4bGhqqqVOnatiwYRo+fLhNO/PmzdOECRPk7e2tRx99VO7u7tqyZYvi4uL0zDPP6N13373bL61Qsgyi7+3tzQ0ZQD7FcQzkfxzHjuGUo5E0adJECxcu1JQpU7Ru3TqlpKTI399fI0aMUMeOHbPdTq9evVShQgXNnDlTa9asUVpamvz9/TVkyBA9/fTTdnwFAAAAgJNe2UbBwjdpIP/jOAbyP45jx3C6PtsAAABAQUHYBgAAAOyEsA0AAADYCWEbAAAAsBPCNgAAAGAnTjn0HwAAzopRHPIfBl6DI3FlGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAO3F1dAFZ2bdvn0JDQxUREaGUlBT5+/urT58+6tChQ7bWDwsLU+/evbOcv3jxYtWrVy+PqgUAAAAycsqwHRYWpgEDBsjNzU3t2rWTp6enNmzYoDFjxiguLk5DhgzJdlshISEKCQnJMP3ee+/Ny5IBAACADJwubKempur111+XyWTSggULVKNGDUnSCy+8oO7duys0NFRt2rRR5cqVs9VeSEiIhg8fbseKAQAAgMw5XZ/tHTt2KDY2Vu3bt7cGbUny8PDQ0KFDlZqaquXLlzuwQgAAACB7nO7Kdnh4uCSpefPmGeY1a9bMZpnsiI6O1ty5c5WUlKT77rtPDzzwgHx9ffOmWAAAAOAWnC5sR0dHS5IqVaqUYZ6Xl5d8fHwUExOT7fZWr16t1atXW/8uVqyYhg8froEDB95yPcMwsr0N3JrlveQ9BfIvjmPkZ+y36TiO857JZLrtMk4Xts1msyTJ09Mz0/keHh46c+bMbdvx9fXVyy+/rEceeUT33Xefrly5orCwMH388cf66KOP5OHhoe7du2e5fkJCAjtjHktISHB0CQByieMY+dHly5cdXYJT4TjOOz4+PrddxunCdl4JCAhQQECA9e977rlHHTt2VLVq1fTkk08qNDRUTz/9tFxcMu+27uXldbdKLfAMw1BCQoK8vLyy9Q0QgPPhOEZ+5u3t7egSnALHsWM4Xdj28PCQJCUmJmY632w2Z3nVOzsCAwNVt25d7dq1SzExMfLz88t0OXbCvGcymXhfgXyO4xj5EfusLY7ju8vpRiOxDOmXWb/shIQEXbp0KdP+3DlhueSflJSUq3YAAACAW3G6sN2oUSNJ0rZt2zLM2759uyRl+pCa7EpNTdWff/4pk8mk8uXL33E7AAAAwO04Xdhu2rSpKlasqNWrVysyMtI63Ww2a9q0aXJ1dVWXLl2s0+Pj43Xs2DHFx8fbtLN3794MNzimpqbqww8/VFxcnJo3b04fLgAAANiVyXDCITd27NihgQMHys3NTe3bt5eHh4c2bNigkydPauTIkXr++eety4aGhmrq1KkaNmyYzZMiW7ZsKUmqX7++ypUrp8TERO3cuVNRUVG67777NH/+fFWoUOGuv7bCyDAMXb58Wd7e3vQRA/IpjuP/KeyvPz9ywqjjEBzHjuF0N0hKUpMmTbRw4UJNmTJF69atU0pKivz9/TVixAh17NgxW210795dv/76q8LDw3Xp0iW5urrq/vvv15AhQ9S/f39GGwEAAIDdOeWVbRQsfJMG8j+O4/8p7K8/PyLqpOM4dgyn67MNAAAAFBSEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADshbAMAAAB2QtgGAAAA7ISwDQAAANgJYRsAAACwE8I2AAAAYCeEbQAAAMBOCNsAAACAnRC2AQAAADtx2rC9b98+DRo0SI0aNVK9evXUrVs3rVq16o7bS0lJUadOnRQUFKQ2bdrkYaUAAABA5lwdXUBmwsLCNGDAALm5ualdu3by9PTUhg0bNGbMGMXFxWnIkCE5bnPatGmKjY21Q7UAAABA5pzuynZqaqpef/11mUwmLViwQBMmTNArr7yilStXKiAgQKGhoYqOjs5RmwcPHtRXX32l0aNH26doAAAAIBNOF7Z37Nih2NhYtW/fXjVq1LBO9/Dw0NChQ5Wamqrly5dnu73k5GSNGzdOdevWVc+ePe1RMgDYMJlMBe4/FxcX+fr6ysXFxeG15PV/AGBPTteNJDw8XJLUvHnzDPOaNWtms0x2TJ06VTExMVq5ciX/qAIAAOCucrqwbekiUqlSpQzzvLy85OPjo5iYmGy1tW/fPs2YMUOjRo2Sn59fjuowDCNHyyNrlveS9xSAM+LfpoKPzzgd5+O8l50LuU4Xts1msyTJ09Mz0/keHh46c+bMbdtJTk7W+PHjVb16dfXv3z/HdSQkJLAz5rGEhARHlwAAGVy+fNnRJcDO+IxtcT7OOz4+PrddxunCdl757LPPFBMTo2XLlqlIkSI5Xt/Ly8sOVRVOhmEoISFBXl5edOUB4HS8vb0dXQLsjM84Hedjx3C6sO3h4SFJSkxMzHS+2WzO8qq3xcGDBzV79mwNHTpUQUFBd1QHO2He42YkAM6If5cKPj5jW5yP7y6nG42kcuXKkpRpv+yEhARdunQp0/7cN/vrr7+Ulpam0NBQBQUF2fwnSVFRUQoKClJwcHCe1w8AAABYON2V7UaNGmn69Onatm2b2rVrZzNv+/btkqSQkJBbtlG5cmV169Yt03lLly6Vp6enHn/8cd1zzz15UzQAAACQCZPhZHcBpqamqk2bNjp79qyWLFmi6tWrS0rvPtK9e3dFRUVp9erV1tFF4uPjdenSJfn4+MjX1/e27QcFBcnPz08//vijXV8H/scwDF2+fFne3t78bIVCgf08f8npaZDPN/9xsqjjMJyPHcPpupG4urpqwoQJMgxDPXr00BtvvKFJkyapU6dOOnLkiIYNG2YzjN+CBQv0xBNPaMGCBQ6sGgAAAMjI6bqRSFKTJk20cOFCTZkyRevWrVNKSor8/f01YsQIdezY0dHlAQAAANnidN1IUPDwsxUKG/bz/IVuJAUfUScd52PHyFU3kokTJ+ro0aN5VQsAAABQoOTqyna1atVkMplUt25dPf3002rbti0jfCADvkmjsGE/z1+4sl3wcWU7Hedjx8hV2P7ll1/03XffacuWLUpLS1Px4sXVvn17PfXUU6pVq1Ze1ol8jIMbhQ37ef5C2C74CNvpOB87Rp702Y6Pj9fy5cu1bNkyRUVFyWQyKSgoSE8//bQ6duxofSokCicObhQ27Of5C2G74CNsp+N87Bh5foPkrl27tHTpUq1fv15JSUkqWrSo2rRpo6eeekoNGzbMy00hn+DgRmHDfp6/ELYLPsJ2Os7HjmG30Uh27typl156SefOnUvfkMkkf39/vfDCC2rTpo09NgknxcGNwob9PH8hbBd8hO10nI8dI0/H2TabzVq1apW+++47RUZGyjAM1a9fX126dNGff/6plStXatSoUTpx4oQGDRqUl5sGAAAAnE6eXNm+uevItWvXVLJkSXXs2FHPPPOMAgICrMvFx8erb9++SkhI0C+//JLbzSKf4Js0Chv28/yFK9sFH1e203E+doxcXdmeNWuWvvvuO0VHR8swDNWrV0/PPPOMnnjiCRUtWjTD8r6+vnr88cc1derU3GwWAAAAyBdyFbY//PBDeXp6qkePHnrmmWcUGBh423Vq1aqlzp0752azAAAAQL6Qq24ky5cv1xNPPKFixYrlZU0oYPjZCoUN+3n+QjeSgo9uJOk4HztGrh7X7uLioujo6Fsuc/jwYX3//fe52QwAAACQL+UqbI8fP14bN2685TJbtmzR+PHjc7MZAAAAIF/KVdjOzs8yN27ckItLrjYDAAAA5Et2T8F//vmnvLy87L0ZAAAAwOnkeDSS3r172/y9YsUKhYeHZ1juxo0bOnPmjOLi4tS2bds7rxAAAADIp3Ictm8O1iaTSXFxcYqLi8uwnIuLi7y8vNSmTRu9+uqruasSAAAAyIdyHLYPHTpk/f9q1app2LBhGjZsWJ4WBQAAABQEuXqozdy5c1WhQoW8qgUAAAAoUHIVtkNCQvKqDgAAAKDAyVHYtjycpnXr1vLw8MjRw2p4RDsAAAAKmxw9rr1atWoymUxau3at/Pz8rH/fimEYMplMioyMzHWxyJ94PCwKG/bz/IXHtRd8PK49Hedjx8jRle2JEyfKZDKpTJkykqQPPvjALkUBAAAABUGOrmwDd4Jv0ihs2M/zF65sF3xEnXScjx2D56gDAAAAdkLYBgAAAOwkR322W7VqdUcbMZlM2rhx4x2tCwAAAORXOQrbd9rnib5SAAAAKIxyFLY3b95srzoAAACAAoc+2wAAAICdELYBAAAAO+Fx7QAAAICd8Lh22B2D6KOwYT/PX3ioTcHHQA3pOB87Bo9rBwAAAOyEx7XD7vgmjcKG/Tx/4cp2wUfUScf52DG4QRIAAACwkxx1I8nK1atXtWnTJkVGRspsNsvDw0PVq1dXq1atVLx48bzYBAAAAJDv5Dpsr169Wu+9956uXLli8zONyWRSyZIl9eabb6pdu3a53QwAAACQ7+QqbG/evFljx45V0aJF1aNHDzVs2FClS5fWhQsXtGvXLi1fvlxjx45ViRIl9Mgjj+RRyQAAAED+kKsbJJ966ilFRUXpu+++k5+fX4b5x44d09NPP62qVatqyZIluSoU+Rc3ZKCwYT/PX7hBsuDjBsl0nI8dI1c3SB4+fFhPPPFEpkFbkqpWrap27drpr7/+ys1mAAAAgHwpV2Hbw8NDJUuWvOUynp6e8vT0zM1mAAAAgHwpV2G7RYsW2rJli9LS0jKdn5qaql9++UUtW7bMzWYAAACAfClXfbavXLmivn37ytvbWyNGjFDdunWt8yIiIvT555/rypUr+uabb257Bfyf9u3bp9DQUEVERCglJUX+/v7q06ePOnTokK31w8LCtGTJEv355586f/68UlJSdO+996pBgwYaNGiQqlSpkqN6cOfoI4bChv08f6HPdsFHn+10nI8dI0dhu1WrVhmmpaSk6Pz585IkV1dXeXt76/Lly0pNTZUklSlTRu7u7tq4cWO2iwoLC9OAAQPk5uamdu3aydPTUxs2bNDJkyc1atQoDRky5LZtfPrpp1q5cqXq1KmjcuXKyc3NTcePH9fWrVtVpEgRff3112rSpEm2a8Kd4+BGYcN+nr8Qtgs+wnY6zseOkaOwnZvuIJs3b87WcqmpqWrbtq3OnDmjxYsXq0aNGpIks9ms7t27KyoqSmvWrFHlypVv2c7169dVtGjRDNN///139e3bV7Vq1dKyZcty/DqQcxzcKGzYz/MXwnbBR9hOx/nYMXI0znZ2A3Nu7NixQ7GxsXryySetQVtKvxlz6NChGjVqlJYvX67Ro0ffsp3MgrYkNW3aVF5eXoqNjc3TugEAAIB/ytUNkvYQHh4uSWrevHmGec2aNbNZ5k7s3btXCQkJCggIuOM2AAAAgOzI9ePa81p0dLQkqVKlShnmeXl5ycfHRzExMdluLywsTOHh4UpOTlZMTIx+/vln+fj4aPz48bdcj5+c8o7lveQ9BeCM+Lep4OMzTsf5OO9lpztOnoTtvXv36rffftO5c+eUnJycaSETJ07MVltms1mSshyb28PDQ2fOnMl2beHh4Zo6dar170qVKmny5MmqVavWLddLSEhgZ8xjCQkJji4BADK4fPmyo0uAnfEZ2+J8nHd8fHxuu0yuwnZqaqpGjx6tn376SYZhyGQy2QRUy985Cdt5bfjw4Ro+fLiuXr2qo0ePatq0aXr22Wc1ceLEWw4j6OXldRerLNgMw1BCQoK8vLy4IQOA0/H29nZ0CbAzPuN0nI8dI1dhe9asWdqwYYO6du2qHj16qGvXrurTp4+eeOIJ7dy5U19//bWaNm2qMWPGZLtNDw8PSVJiYmKm881m8x09kbJ48eKqU6eOpk6dqq5du+rNN99Us2bN5Ovrm+ny7IR5z2Qy8b4CcDr8u1Tw8Rnb4nx8d+XqBslVq1YpICBA77//vmrWrCkpvftH3bp1NXDgQM2fP19btmzRtm3bst2mZUi/zPplJyQk6NKlS5n2584uV1dXNW7cWFevXtX+/fvvuB0AAADgdnIVtmNjY9W4cWPr3yaTyfowG0kKCAhQixYttGjRomy32ahRI0nKNKBv375dkhQSEnKnJUuSzp07Jyk9eAMAAAD2kquw7ebmpmLFiln/Ll68uOLj422Wue+++3I0ekjTpk1VsWJFrV69WpGRkdbpZrNZ06ZNk6urq7p06WKdHh8fr2PHjmXY7s6dOzO9wXHbtm3auHGjPD09Vb9+/WzXBQAAAORUri7tli9fXqdPn7b+XaVKFWvItfQF+uOPP3J0s6Grq6smTJiggQMHqkePHmrfvr08PDysj2sfOXKk/Pz8rMsvWLBAU6dO1bBhwzR8+HDr9Oeff14+Pj6qXbu27r33Xl2/fl1//fWXdu7cKTc3N02YMEHFixfPzcsHAAAAbilXYbtRo0batGmTNVw/8cQTmjRpkgYPHqyHH35Yu3fv1u7du9W1a9cctdukSRMtXLhQU6ZM0bp165SSkiJ/f3+NGDFCHTt2zFYbw4cP16+//qrdu3crPj5eJpNJ5cuX11NPPaU+ffrwUBsAAADYncnIxWDSBw8e1JIlSzRkyBCVL19eKSkpGj58uLZs2WJdpk6dOpo+fXq2xiFEwWQYhi5fvixvb2/ufkahwH6ev+T0NMjnm//w3Ix0nI8dI1dhOyv79+/XiRMndN9996lOnTpycXG6p8LjLuLgRmHDfp6/ELYLPsJ2Os7HjmGXsA3cjIMbhQ37ef5C2C74iDrpOB87Rp6NfXf27FkdOnRIiYmJ8vT0VLVq1VSuXLm8ah4AAADId3Idto8fP6733ntPO3bsyDCvadOmev3111WlSpXcbgYAAADId3LVjeTYsWPq0aOHEhISFBAQoIYNG6p06dK6cOGC9uzZo8OHD8vLy0sLFy5U1apV87Ju5CP8bIXChv08f6EbScFHN5J0nI8dI1dXtj/66CMlJibqP//5jzp37pxh/vfff69XX31VH3/8sb744ovcbAoAAADId3I1TMjOnTv16KOPZhq0Jalz585q3bq1wsPDc7MZAAAAIF/KVdg2mUyqXLnyLZfx8/PjpwoAAAAUSrkK2/Xq1dOBAwduucz+/fvVoEGD3GwGAAAAyJdyFbZfeeUV/fHHHwoNDVVSUpLNvKSkJH3++efav3+/xo4dm6siAQAAgPwoR6ORjB8/PsO0EydOaPfu3SpZsqRq1KghX19fxcfH688//9SVK1cUHBysihUrauLEiXlaOPIP7n5GYcN+nr8wGknBx2gk6TgfO0aOwna1atXubCMmkyIjI+9oXeR/HNwobNjP8xfCdsFH2E7H+dgxcjT036ZNm+xVBwAAAFDg5ChsV6hQwV51AAAAAAVOrm6QBAAAAJC1XD1B0mLVqlVasWKFIiMjlZiYKA8PD9WoUUNdunRRhw4d8mITAAAAQL6Toxsk/+nGjRsaOXKkfvrpJxmGoWLFillHI0lKSpLJZFLr1q31+eefy8WFi+iFFTdkoLBhP89fuEGy4OMGyXScjx0jVwl43rx52rBhg4KDg7V48WJFRERo8+bNioiI0JIlS9SoUSNt3LhR8+bNy6t6AQAAgHwjV1e2O3furOvXr2vVqlVydc3YIyU1NVUdO3aUm5ubVq5cmatCkX/xTRqFDft5/sKV7YKPK9vpOB87Rq6ubEdFRalFixaZBm1JcnV11SOPPKLo6OjcbAYAAADIl3IVtt3c3HTt2rVbLnPt2jW5ubnlZjMAAABAvpSrsF29enWtW7dOZ8+ezXT+uXPntG7dOtWoUSM3mwEAAADypVyF7f79++vy5cvq2rWrZs2apf379+v06dPav3+/Zs6cqSeffFIJCQnq169fXtULAAAA5Bu5ukFSkubMmaOPPvpIaWlpNtMNw5Crq6vGjBmjvn375mYTyOe4IQOFDft5/sINkgUfN0im43zsGLkO25J04sQJ/fDDDzp06JDMZrM8PDxUvXp1dejQQRUrVsyLOpGPcXCjsGE/z18I2wUfYTsd52PHyFXY/v7771WqVCk9+OCDeVkTChgObhQ27Of5C2G74CNsp+N87Bi56rP92muvadu2bXlVCwAAAFCg5CpslylTRikpKXlVCwAAAFCg5Cpst27dWtu3b1dycnJe1QMAAAAUGLkK2yNGjFCJEiX0wgsv6MiRI3lVEwAAAFAg5OoGyVatWik5OVkXLlyQJBUtWlS+vr4ZOt2bTCZt3Lgxd5Ui3+KGDBQ27Of5CzdIFnzcIJmO87FjuOZmZcMw5ObmpvLly2eYfqu/AQAAgMIgV2F78+bNeVUHAAAAUODkKmzfLDU1VdHR0UpMTJSnp6cqV64sV9c8ax4AAADId3KdhuPj4zV58mStWbNGSUlJ1unFihVT+/btNWrUKPn6+uZ2MwAAAEC+k6sbJM+ePatnn31Wp06dkq+vr2rWrKnSpUvrwoULOnjwoOLj43Xfffdp0aJFKleuXF7WjXyEGzJQ2LCf5y/cIFnwce9YOs7HjpGrK9sffvihTp06peHDh2vgwIEqWrSodd7169f19ddfa+rUqfroo4/08ccf57pYAAAAID/JVdjetm2bHnzwQb3wwgsZ5hUtWlTDhg3T3r179euvv+ZmMwAAAEC+lKuH2iQnJ6tGjRq3XKZWrVo8YRIAAACFUq7Cdq1atRQVFXXLZY4fP65atWrlZjMAAABAvpTrx7X//PPPWr58eabzly5dql9++UUjRozIzWYAAACAfClXo5FMnTpVERER2r59u/z8/NSgQQOVKlVKFy9e1J49exQVFaVmzZqpXr16ths1mTLt542CibufUdiwn+cvjEZS8DEaSTrOx46Rq7BdrVq1O9uoyaTIyMg73SzyGQ5uFDbs5/kLYbvgI2yn43zsGLkajWTu3Ll5VQcAAABQ4OQqbIeEhORVHRns27dPoaGhioiIUEpKivz9/dWnTx916NAhW+vv2rVLGzduVHh4uOLi4nT16lVVqFBBrVq10uDBg1WyZEm71Q4AAABIuexGYi9hYWEaMGCA3Nzc1K5dO3l6emrDhg06efKkRo0apSFDhty2jWbNmunSpUtq2LChqlevLpPJpPDwcP3555+6//779e2336pUqVJ34dWAn61Q2LCf5y90Iyn4nDDqOATnY8dwurCdmpqqtm3b6syZM1q8eLF1HG+z2azu3bsrKipKa9asUeXKlW/ZzldffaXOnTurbNmy1mmGYeidd97RokWL1KNHD7311lv2fCn4fxzcKGzYz/MXwnbB52RRx2E4HztGrob+s4cdO3YoNjZW7du3t3lgjoeHh4YOHarU1NQshxq82XPPPWcTtKX0fyCHDh0qSdq5c2feFg4AAAD8g9OF7fDwcElS8+bNM8xr1qyZzTJ3wtU1vZt6kSJF7rgNAAAAIDtydYOkPURHR0uSKlWqlGGel5eXfHx8FBMTc8ftL1u2TNL/gntW+Mkp71jeS95TAM6If5sKPj7jdJyP8152uuM4Xdg2m82SJE9Pz0zne3h46MyZM3fUdmRkpP773/+qVKlSGjhw4C2XTUhIYGfMYwkJCY4uAQAyuHz5sqNLgJ3xGdvifJx3fHx8bruM04Vtezlx4oQGDx6stLQ0TZ48Wb6+vrdc3svL6y5VVvAZhqGEhAR5eXlxQwYAp+Pt7e3oEmBnfMbpOB87htOFbQ8PD0lSYmJipvPNZnOWV72zEhcXpz59+ig+Pl6hoaFq0qTJbddhJ8x7JpOJ9xWA0+HfpYKPz9gW5+O7y+lukLQM6ZdZv+yEhARdunQp0/7cWTl58qR69eqlc+fO6bPPPlOLFi3yqlQAAADglpwubDdq1EiStG3btgzztm/fLin7T648efKkevfurXPnzunTTz9V69at865QAAAA4DacLmw3bdpUFStW1OrVqxUZGWmdbjabNW3aNLm6uqpLly7W6fHx8Tp27Jji4+Nt2rEE7bNnz2ry5Ml69NFH79prAAAAACQnfIKklP5gm4EDB8rNzU3t27eXh4eH9XHtI0eO1PPPP29dNjQ0VFOnTtWwYcM0fPhw6/SWLVsqLi5O9erVy3TMbkk2y8N+eGIVChv28/yFJ0gWfE4YdRyC87FjON0NkpLUpEkTLVy4UFOmTNG6deuUkpIif39/jRgxQh07dsxWG3FxcZKkiIgIRUREZLoMYRsAAAD25JRXtlGw8E0ahQ37ef7Cle2Cj6iTjvOxYzhdn20AAACgoCBsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2ImrowvIyr59+xQaGqqIiAilpKTI399fffr0UYcOHbK1/sWLF7V06VIdPHhQBw4cUFxcnCTpr7/+smfZAAAAgJVThu2wsDANGDBAbm5uateunTw9PbVhwwaNGTNGcXFxGjJkyG3bOHr0qCZPniyTyaRKlSrpnnvu0bVr1+5C9QAAAEA6k2EYhqOLuFlqaqratm2rM2fOaPHixapRo4YkyWw2q3v37oqKitKaNWtUuXLlW7Zz4cIFRUVFqXr16vLw8FCbNm0UFRXFlW0HMAxDly9flre3t0wmk6PLAeyO/Tx/yelpkM83/3GyqOMwnI8dw+n6bO/YsUOxsbFq3769NWhLkoeHh4YOHarU1FQtX778tu2ULl1ajRo1koeHhz3LBQAAALLkdGE7PDxcktS8efMM85o1a2azDAAAAODMnK7PdnR0tCSpUqVKGeZ5eXnJx8dHMTExdq+Dn5zyjuW95D0F4Iz4t6ng4zNOx/k472WnO47ThW2z2SxJ8vT0zHS+h4eHzpw5Y/c6EhIS2BnzWEJCgqNLAIAMLl++7OgSYGd8xrY4H+cdHx+f2y7jdGHbWXh5eTm6hALDMAwlJCTIy8uLGzIAOB1vb29HlwA74zNOx/nYMZwubFtuaExMTMx0vtlszvKqd15iJ8x7JpOJ9xWA0+HfpYKPz9gW5+O7y+lukLQM6ZdZv+yEhARdunQp0/7cAAAAgLNxurDdqFEjSdK2bdsyzNu+fbskKSQk5K7WBAAAANwJpwvbTZs2VcWKFbV69WpFRkZap5vNZk2bNk2urq7q0qWLdXp8fLyOHTum+Ph4R5QLAAAAZMnp+my7urpqwoQJGjhwoHr06KH27dvLw8NDGzZs0MmTJzVy5Ej5+flZl1+wYIGmTp2qYcOGafjw4TZtjRs3zvr/58+fzzDt5Zdflq+vr51fEQAAAAorpwvbktSkSRMtXLhQU6ZM0bp165SSkiJ/f3+NGDFCHTt2zHY7K1asuOW0YcOGEbYBAABgNyaDwaRhZ4Zh6PLly/L29ubuZxQK7Of5S05Pg3y++Q9RJx3nY8dwuj7bAAAAQEFB2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ4RtAAAAwE4I2wAAAICdELYBAAAAOyFsAwAAAHZC2AYAAADshLANAAAA2AlhGwAAALATwjYAAABgJ66OLgAobEwmk6NLQA4ZhuHoEgAA+RRXtgEAAAA7IWwDAAAAdkLYBgAAAOyEsA0AAADYCWEbAAAAsBOnDdv79u3ToEGD1KhRI9WrV0/dunXTqlWrctTGjRs3NH/+fHXo0EF16tRRkyZNNGLECEVHR9unaAAAAOAmJsMJx7QKCwvTgAED5Obmpnbt2snT01MbNmzQyZMnNWrUKA0ZMiRb7bzxxhtasmSJ/P399fDDD+vixYtau3atihYtqm+//Vb+/v52fiWQ0odNu3z5sry9vRn2Tgz9lx/l9J9JPuP8hc+34HPCqOMQnI8dw+nCdmpqqtq2baszZ85o8eLFqlGjhiTJbDare/fuioqK0po1a1S5cuVbtrNjxw716dNHwcHB+uabb+Tu7i5J+v3339WvXz8FBwdr/vz59n45EAf3P/Ee5D+EsYKNz7fgc7Ko4zCcjx3D6bqR7NixQ7GxsWrfvr01aEuSh4eHhg4dqtTUVC1fvvy27Xz33XeSpJEjR1qDtiQ1bdpUzZs3186dOxUVFZX3LwAAAAD4f04XtsPDwyVJzZs3zzCvWbNmNsvcSlhYmIoXL64GDRpkmGdpe+fOnbkpFQAAALglp3tcu+XmxUqVKmWY5+XlJR8fH8XExNyyjatXr+r8+fMKDAxUkSJFMsy3dEG51Y2S/OSUdyzvJe8p8iv23YKNz7fg4zNOx/k472WnO47ThW2z2SxJ8vT0zHS+h4eHzpw5c8s2EhMTrctm1cbN28oMfZnyjslkko+Pj6PLcBr8I1fw8RkXbHy+yK84HzuG03UjAQAAAAoKpwvblqvOlqvT/2Q2m7O86m1hmZ/VlWvL9KyufAMAAAB5wenCtqU/dWb9shMSEnTp0qVM+3PfrHjx4ipTpoxOnjyptLS0DPMtfbVvN3wgAAAAkBtOF7YbNWokSdq2bVuGedu3b5ckhYSE3LadkJAQXb16VXv27Mkwz9K2ZVsAAACAPThd2G7atKkqVqyo1atXKzIy0jrdbDZr2rRpcnV1VZcuXazT4+PjdezYMcXHx9u08/TTT0uSPvvsMyUnJ1un//7779q2bZsaNWokPz8/O78aAAAAFGZO9wRJKf3BNgMHDpSbm5vat28vDw8P6+PaR44cqeeff966bGhoqKZOnaphw4Zp+PDhNu28/vrr+u6773hcOwAAABzC6Yb+k6QmTZpo4cKFmjJlitatW6eUlBT5+/trxIgR6tixY7bbeffddxUUFKTFixdr3rx5Kl68uFq0aKFRo0ZxVRsAAAB255RXtgEAAICCwOn6bKNwuXHjhqNLAJBDSUlJji4BAPINwjbuqpSUFJ09e1bXrl2TJLm4sAsC+Un//v01b948paSkOLoUAMgXnLLPNgqmDz/8UGFhYYqMjFTFihUVEhKiQYMG6d5775W7u7sMw5DJZHJ0mQCyMHjwYP32229q3rw5jywH8rGjR4/q1KlT8vLykre3t83zSzgX5z36bOOueP755/X777+rWrVqCggI0K5duxQVFaVKlSqpR48e6ty5s7y8vDjIASc1cOBAhYeHa/To0eratWumT/Ll+AWc3/jx47V582YlJCRISn8Q4ODBg9W6dWtVrVpVEsdyXiNsw+5mzJihyZMna+TIkXrqqafk4+Ojs2fPasOGDZo/f77OnTunf//73xowYIB8fHw4yAEnM3jwYO3YsUMjR47Uk08+KS8vL+u8tLQ0FSlSxPo3xy/gvEaMGKGtW7eqTZs2evjhh3X06FGtW7dOx44dU5MmTdSnTx+1aNFCEsdyXqIbCexu3759Klu2rJ599ll5enoqNTVV5cqV09NPP63q1avr/fff1+zZs1WkSBENHDgw0ytmAByjf//++u233/Tiiy/q3//+t9zd3ZWUlKTz589ryZIlio6O1j333CN/f38NGDDAJngDcB7r16/X5s2b1atXLw0ZMkQlS5aUJLVu3Vpr1qzRrFmzdOHCBSUnJ+vxxx+XyWQicOcRwjbsxjAM/f3334qJiZG7u7uKFi0qwzDk6pq+2xUtWlTBwcF699139frrr2vRokXy8/NTp06dJIkDHHCwuLg4Xbp0SZJ0/fp1mUwm3bhxQz/88INmzJih2NhYubq6KjU1VZIUFhamN998U5UqVeIkDTiZ2NhYpaam6sknn1TJkiWVkpIiNzc3VatWTRUqVFCpUqX08ccf64svvpCHh4eaNWvGMZxHGAoCdmMymeTh4SF/f3+dPXtWJ06ckMlksp6YLWrVqqWxY8eqePHimjdvnq5du8YBDjiB++67T//5z38UHBysuXPnavr06VqxYoUmT54sT09PzZo1S2vXrtWyZcvUokULbd++Xe+9954kviwDzsIyxO65c+dkGIbOnj0rwzBsfoXy9PRU9+7dNXr0aB05ckTffvut9Ys2co+wDbux3A7w4IMP6vr16/r4448lSa6urkpLS7MuZzKZVL9+fT3xxBM6ePCgFi5c6JB6AdgymUwKCgrSG2+8oRo1amj69OmaMGGC/Pz8tGjRIj3wwAO6//77VbNmTb3xxhtq2rSptm3bpjlz5ji6dAD/zzLEbrNmzSRJu3btkslkkouLi82oQsWKFVPnzp3VrVs3/fTTT9qyZYsjyi2QCNuwG8uVrYceeki1a9fWzz//rI8++kiSVKRIEZvAXaJECT355JMqWrSoYmJiHFIvgMwFBQXpzTffVO3atXXPPfdo0qRJcnd3tx7DN27c0H333ae+ffuqSJEiOnfunIMrBvBPVapUUdWqVfXFF19o06ZNkmTtl23h6+ur9u3bq3jx4po/f76uXbvGMJ95gLANu7px44Z8fX01adIkeXt7a+bMmfr8888lpQfu1NRU6wn7vvvuU/Hixa3DEQFwHkFBQXrrrbc0duxY+fr6SpL1Z2jLybhEiRJKS0uT2Wx2WJ0AMnf//ferf//+kqT3339fv//+u6SMgbtRo0Zq1qyZTp48qdTUVLqE5QHCNuzKxcVFaWlpqlKliqZOnSpvb2998cUX1n6drq6u1hP2zz//rGvXrqlWrVqOLBlAFgIDA9WuXTt5eHhYp93c9/OXX36Ru7u7mjRpYp0HwPEsx2LXrl31/PPP69SpU3rvvff022+/SUoP3Df/2pyUlCRPT0+5u7s7pN6ChrCNXLvdCdVyIg4ODtb06dNVunRpLViwQL1799aqVat09OhRLViwQF999ZW8vLzUtm3bu1E2gP+X3VBsMplsTr43X/XauHGjVq9erWrVqik4ONi6PADHs4wkJKWPtT1gwAAdP35co0eP1po1ayT971y9ZcsW/fXXX6pZsybHcB7hoTbIc1kN+WWZfvLkSb3//vvatWuXEhMTJaUf5BUqVNDUqVMVGBh4t0sGcJOcDtv37bffav78+Tp//rwWLlxofQodAOf15Zdf6rPPPpMktWrVStWrV9fly5e1ZcsWmc1mLVy4UFWqVHFskQUEYRt3bOPGjYqOjtaVK1dUt25d1apVS+XKlZOU8alyFpbpZrNZsbGx2rZtm65fv66KFSuqadOm1vUB2N+dHMM3O3jwoD766CPt27dP9957rz7//HMFBATcjdIB/MOdjG2/fv16rVq1Sr/99puuXr0qLy8vVa1aVe+++678/f3tVGnhQ9jGHRkxYoQ2bdpkM2Z2jRo11K1bN/Xo0UNS1idrHnYBOF5ujmGLs2fPauzYsfLz89Nzzz2nChUq2L1uALYiIyPl7+8vNze3bK9z48YN65CAV69e1blz5xQTE6Py5curXLly8vLysle5hRJhGzk2fvx4/fjjj+rVq5e6du2q06dPa+/evQoNDdWNGzfUu3dvvfrqq5JufbJOTU21Pk2SAA7cPXl1DEvpJ2pJKl68+F2pHcD/DBo0SKdPn9b48ePVpEmT2/4adTPLeffm4A37IGwjR44ePap///vfeuSRR/Tqq6/afPvdtWuXxo4dq9OnT+uZZ57RO++8I8n2ZH3u3DmdP39eNWvWdEj9QGHHMQwUDBMnTtTcuXMlSXXq1NHIkSPVuHHjbAfu7HQVQ97gqwxy5PTp00pISFCLFi3k5eWllJQUSek/SQUHB+u///2vKleurMWLF2vy5MmS/neH88WLF/XKK6/oxRdfVHh4uMNeA1CYcQwD+d+PP/6oZcuWKSAgQH379tXRo0f1ySefaMeOHTZD+GXl5MmTWrp0qWJjY+9CtSBsI0fuueceSdKBAwckydpHzPLY1xo1auiTTz6Rt7e3ZsyYYR1SSJISExOVlJSkq1evqmLFine/eAAcw0A+l5SUpBUrVig5OVmTJ0/Wyy+/rP79++v48eOaPHnybQP333//ralTp+qtt97SDz/8kK1wjtwhbCNHKlasKC8vL23btk2HDx+2mWd5ClXNmjX14YcfSpL1kbCSVLlyZb311lv6/vvvVb58+btaN4B0HMNA/lasWDG98MILGj16tAICAuTi4qJnnnnGOnb27QJ30aJFVa1aNdWpU0ft2rWjK8ldQNhGjpQrV059+vTRoUOHrN+sb2Y5WTdr1kytW7fWunXrdOTIEev8atWqMbwf4EAcw0D+V6dOHfXs2dP6d5kyZdS9e/fbBu6rV6/K1dVVPXr00KxZs+Tn53e3Sy+UCNvIsaeeekr16tXTN998ozlz5mS6TJEiRVS3bl0ZhqGLFy/e5QoB3ArHMJD/WbqAWca5KF26dIbAbXkcuyStWbNGixYt0tmzZ+Xu7i4PDw+H1F0YuTq6AOQ/ZcqU0QcffKCBAwfqk08+UVpamnr27Gk9cC1D+J07d04eHh7y8fFxZLkA/oFjGCg4bh421xK4JWnmzJn6/PPP5erqqri4OE2aNEm+vr7q3LmzgyotvBj6D3fs2LFjGjp0qGJiYtS1a1d16NBBTZo0kST98ssvmjhxonx9fTV9+nSVLFnSwdUC+CeOYaBgOnv2rFasWKHp06erbNmyOn/+vFxdXTV37lxVq1bN0eUVOoRt5EpsbKwmTpyoX3/9VcWKFVP9+vVlGIb++usvpaWlad68eTzyFXBiHMNAwWIZPzs+Pl5vv/22NmzYIC8vL82fP18BAQGOLq9QImwj165cuaItW7Zo1qxZOnfunIoXL65atWrpxRdfVJUqVRxdHoDb4BgGCp6VK1dq0qRJSk1N1aJFi1S1alVHl1RoEbaRZ5KSkpScnKwiRYrIzc1N7u7uji4JQA5wDAMFw9atW/X666/rypUr+u6777ii7WDcIIk8U6xYMRUrVszRZQC4QxzDQMFQpUoV1ahRQy+99BJB2wlwZRsAAKCASU5O5tcpJ0HYBgAAAOyEh9oAAAAAdkLYBgAAAOyEsA0AAADYCWEbAAAAsBPCNgAAAGAnhG0AAADATgjbAAAAgJ0QtgEAkqSWLVuqZcuWji4DAAoUwjYAFBLjxo1TUFCQTp486ehSnM7y5csVFBSk5cuXO7oUAAWMq6MLAAA4h9mzZzu6BAAocAjbAABJ0v333+/oEgCgwKEbCYBCISwsTEFBQQoNDdXOnTvVs2dP1a9fXyEhIXrppZd05syZDOtY+jBfuXJFEyZM0MMPP6waNWrYdDX4+eef1atXLzVs2FB16tRRp06dNHv2bKWlpdm0dfLkSQUFBWncuHE6fPiwBg4cqIYNG6pBgwYaMmSIjh49mmndp06d0quvvqoHH3xQtWrV0kMPPaRXX31Vp0+fzrBsr169FBQUpOTkZH3++ed69NFHVbNmTYWGhqply5ZasWKFJKlVq1YKCgpSUFCQevXqleH1/tO1a9cUGhqqNm3aqHbt2goJCdFzzz2nPXv2ZFg2NDRUQUFBCgsL09q1a9WlSxfVqVNHzZs314QJE5SUlJTFJ/Q/N27cUEhIiDp16mQzPT4+XtWqVVNQUJB27dplM2/kyJEKCgrShQsXJEnJycmaN2+eBgwYoIcffli1atVS06ZNNWzYMP355582644bN07jx4+XJI0fP9763gQFBdksZzabNWXKFLVr10516tRRcHCwBgwYkKEW6dafBYDChSvbAAqViIgITZ8+XY888oh69eqlgwcPavXq1dq9e7eWLl2q0qVL2yyfnJysPn366O+//1aLFi3k5uamUqVKSZLmzJmjiRMnytvbW+3bt1fx4sW1efNmffDBB9q9e7emTJkik8lk096JEyf07LPPqk6dOurRo4diYmL0008/affu3fr2229VtWpV67LR0dHq0aOHLl68qBYtWiggIEBHjhzRsmXLtGXLFi1atEiVKlXK8BqHDRumQ4cOqXnz5vLy8lLFihXVu3dvrVixQocOHVLv3r1VsmRJSVKFChVu+X4lJyerb9++ioiIUM2aNdWnTx9dvHhR69at0/bt2/Xpp5/qsccey7DeggUL9Ouvv6ply5YKCQnRr7/+qnnz5unSpUv65JNPbrlNFxcXNWrUSJs2bdKlS5fk4+MjKf0Lk2EY1v8PDg62rrNz505VrVrV+vklJCRo4sSJCg4O1sMPP6ySJUvqxIkT2rx5s7Zu3ar58+erTp06kqTWrVvrypUr2rRpk1q1aqXq1atnqOny5cvq2bOnjhw5ouDgYDVv3lyJiYnatGmT+vTpo88//1ytW7fO1mcBoJAxAKAQ2LFjhxEYGGgEBgYaS5YssZkXGhpqBAYGGuPHj7eZ3qJFCyMwMNDo37+/ce3aNZt5sbGxRo0aNYymTZsap06dsk6/fv260bNnTyMwMND4/vvvrdNPnDhh3f6nn35q09aKFSuMwMBAo3fv3jbTe/fubQQGBhrffvutzfRvv/3WCAwMNPr06WMz3bLdTp06GZcuXcrwHrzyyitGYGCgceLEiUzfoxYtWhgtWrSwmTZ16lQjMDDQeOmll4wbN25Yp0dGRhq1atUyGjVqZCQmJlqnT5kyxQgMDDQaNmxoHDt2zDr92rVrxmOPPWYEBQUZZ86cyXT7N5szZ44RGBhorF+/3jrtrbfeMoKDg40uXboYvXr1sk4/cuSIERgYaLz99tvWadevX890O4cPHzbq1atn9O3b12b6smXLjMDAQGPZsmWZ1jN69GgjMDDQWLp0qc308+fPGw8//LDRpEkTIykpyTr9dp8FgMKDbiQAChU/Pz9169bNZtrAgQPl6+urNWvWKDk5OcM6Y8eOVbFixWym/fDDD0pNTVW/fv1Uvnx563R3d3e99NJLkmTttnEzLy8vPffcczbTOnXqpMDAQO3YscPaPeT06dPasWOH/P399fTTT9ss//TTT6tq1ar6/fffM+1OMnz4cHl7e9/iXci+FStWyM3NTWPGjLG5Sl+tWjV16dJFCQkJ2rRpU4b1evfurSpVqlj/LlasmNq3by/DMHTw4MHbbjckJESStGPHDuu0sLAwNWrUSE2bNlVERISuX79unX7zOlL651CuXLkM7QYEBKhx48bauXOnUlJSbluHlN59Zd26dWratKm6du1qM6906dIaMGCA4uPj9dtvv2VYNy8/CwD5E91IABQqDRo0yNC1o1ixYqpZs6Z+/fVXRUdHKzAw0DqvaNGiGfruSlJkZKQkqXHjxhnm1atXT8WKFbMuc7Pq1aurePHiNtNMJpMaNGigw4cP69ChQypfvry1X3GjRo0y1GsymRQcHKxjx45Zl7+ZpXtEbpnNZp04cUJVq1bVvffem2F+48aNtXjxYh06dChD/+oaNWpkWN7SxpUrV2677aCgIPn4+FiD9Pnz53X8+HF1795dfn5+mjFjhvbu3asmTZooLCxMJpMpw2cRGRmpGTNmaPfu3bpw4UKGcH3p0iWVLVv2trXs379faWlpun79eqZ9rqOjoyVJx48fV4sWLWzm5dVnASD/ImwDKFQs/a3/ydLXNzExMcPy/wy7UnoQvVV7pUqV0tmzZ+94+5b2/9mH3KJMmTKZ1nurdXLqdq8xq/dMkjw9PTNMK1KkiKT0GyBvx2QyKSQkROvXr9eFCxesobtx48aqWLGiXF1dFRYWpsaNGys8PFwBAQHy9fW1rr9nzx716dNHktSsWTNVrlxZxYsXl8lk0saNG3Xo0KFMf8XITEJCgrXNzG4Ktbh27VqGaXn1WQDIvwjbAAqVixcvZjrdMorFP0NiZkFbkjw8PKztZXaT4cWLF63L3Mn2Letapme1fGbbyKrmnLr5NWbGMj2zGvJC48aNtX79eoWHhys8PFw+Pj4KCgqSyWRS7dq1FRYWpsOHD+vSpUtq3769zbpffvmlkpOTtXDhQjVs2NBmXkRERI7qsLy+/v3765VXXsnRunn1WQDIv+izDaBQ2bNnj3VEC4ukpCQdPHhQxYoVU+XKlbPVjmXECssV15vt27dPSUlJqlatWoZ5kZGRunr1aqZ1SbKuY2l/165dGeo1DEO7d++2WS47XFzS/8nPzpVlKT1kVqxYUbGxsZlepQ8PD7epOa/d3G87LCxMISEh1vDauHFj7du3T1u2bLFZ1iI2Nlbe3t4Zgva1a9cyDP0n/e+9+eeQjZJUu3ZtmUwm7d27N9evCUDhQ9gGUKhERUVp6dKlNtNmzJih+Ph4tWvXTu7u7tlqp0OHDnJ1ddXs2bNtgmhKSoo+/vhjSVKXLl0yrJeQkKCvvvrKZtr333+vw4cPq0mTJtb+1/fdd58aN26sI0eOZKh36dKlOnLkiM3y2eHl5SVJmY4pnpXOnTsrJSVFn3zyiU3oP3z4sJYvXy5PT89Mh7zLCwEBASpVqpR++uknRUdH2/TJbtKkiVJSUjR79myZTCY1atTIZt0KFSooISFBR44csU5LS0vTpEmTFB8fn2FblpsYM/tSUaZMGbVt21Z79+7VjBkzMnz5kaQ//vgj024kAEA3EgCFSvPmzfXOO+/ol19+UZUqVXTw4EFt27ZN5cuX1+jRo7Pdzv33368xY8boP//5jzp27Ki2bdvqnnvu0ZYtW3T8+HG1atUqw02DkhQcHKx58+bpjz/+UO3atRUdHa2ffvpJnp6eevPNN22Wffvtt9WjRw+98cYb+vnnn+Xv76+jR49q8+bN8vX11dtvv52j196kSRPNmjVLb775ptq0aaPixYurfPny6tChQ5brDBo0SL/88otWrlypY8eOqWnTptbROVJTUzVp0iS7dSOR0q9Yr1u3zlq/Rf369eXu7m590I1lLG6Lnj17atu2berRo4fatm0rd3d3hYeH6+zZswoJCbFelbew3NQ6Z84cmc1ma/9vy8gxb731lqKiovTRRx9p5cqVql+/vjw8PHTmzBkdPHhQ0dHR2rZtm+655x67vRcA8ieubAMoVOrVq6dvvvlGly5d0ty5c7Vv3z61a9dOixYtyvHNbP369dO0adMUGBioH374QfPnz5erq6vGjRuX6QNtJKlixYpauHChihQpovnz5+vXX3/VQw89pEWLFtk80EaSqlSpomXLlqlLly7at2+fZs6cqX379qlLly5aunSp/Pz8clTvww8/rLFjx8owDM2YMUOffPKJlixZcst1ihYtqjlz5mjo0KEym82aPXu2fvrpJwUHB2vu3Llq27ZtjmrIKcvV7NKlS9u8P8WKFVPdunUlZexCIkktWrTQlClTVLFiRf3www9avXq1qlSpoqVLl2bax97b21tTpkxRpUqVtGjRIn3yySc2D9/x9vbWt99+q7Fjx8rNzU2rVq3SggUL9Mcff8jf31+TJk3KEPgBQJJMRma/hwFAARMWFqbevXtr2LBhGj58+F3f/smTJ9WqVSt16dJF//nPf+769gEAjsGVbQAAAMBOCNsAAACAnRC2AQAAADuhzzYAAABgJ1zZBgAAAOyEsA0AAADYCWEbAAAAsBPCNgAAAGAnhG0AAADATgjbAAAAgJ0QtgEAAAA7IWwDAAAAdvJ/OKw4r3f/ta4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def compute_posterior(observations, resolution=RESOLUTION, ax=None):\n",
+    "    n_W = len(observations.replace(\"L\", \"\"))\n",
+    "    n_L = len(observations) - n_W\n",
+    "\n",
+    "    p_water = np.linspace(0, 1, resolution + 1)\n",
+    "    n_possible_ways = np.array(\n",
+    "        [calculate_analytic_n_ways_possible(p, n_W, n_L, resolution) for p in p_water]\n",
+    "    )\n",
+    "\n",
+    "    posterior = n_possible_ways / n_possible_ways.sum()\n",
+    "    potential_p = np.linspace(0, 1, resolution + 1)\n",
+    "\n",
+    "    return posterior, potential_p\n",
+    "\n",
+    "\n",
+    "def plot_posterior(observations, resolution=RESOLUTION, ax=None):\n",
+    "    posterior, probs = compute_posterior(observations, resolution=resolution)\n",
+    "    if ax is not None:\n",
+    "        plt.sca(ax)\n",
+    "    plt.bar(x=probs, height=posterior, width=0.9 / resolution, color=\"k\")\n",
+    "    plt.xticks(probs[::2], rotation=45)\n",
+    "    plt.ylabel(\"probability\")\n",
+    "    plt.xlabel(\"proportion water\")\n",
+    "    plt.title(f\"Posterior Calculated\\nfrom # Samples: {len(observations)}\")\n",
+    "\n",
+    "\n",
+    "plot_posterior(observations, resolution=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6dc89d17",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(2)\n",
+    "known_p = 0.4\n",
+    "simulated_observations = \"\".join(simulate_globe_toss(p=known_p, N=100))\n",
+    "plot_posterior(simulated_observations, resolution=20)\n",
+    "plt.axvline(known_p, color=\"C0\", label=\"True Proportion\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf85153d",
+   "metadata": {},
+   "source": [
+    "## Infinite Possibilities\n",
+    "\n",
+    "### Moving from an N-sided globe to an infinitely-sided globe.\n",
+    "As we increase resolution of globe\n",
+    "- there are more bars/finer-grained resolution along the proportion axis\n",
+    "- bars get shorter with more possibilities -- they must sum to 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4183724f-ba16-43d0-90c6-ec9e90f0ce34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(12)\n",
+    "known_p = 0.7\n",
+    "simulated_observations = \"\".join(simulate_globe_toss(p=known_p, N=30))\n",
+    "_, axs = plt.subplots(1, 3, figsize=(10, 4))\n",
+    "for ii, possibilities in enumerate([5, 10, 20]):\n",
+    "    plot_posterior(simulated_observations, resolution=possibilities, ax=axs[ii])\n",
+    "    plt.ylim([-0.05, 1])\n",
+    "    axs[ii].set_title(f\"{possibilities} possibilities\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d86faad4-b59b-4255-b8ac-4d862ae4be7e",
+   "metadata": {},
+   "source": [
+    "### Beta Distribution\n",
+    "\n",
+    "Analytical function that gives us the pdf as the limit as number of possibilities $\\rightarrow \\infty$\n",
+    "\n",
+    "$$\n",
+    "p = \\frac{(W + L + 1)!}{W!L!} p^W(1-p)^L\n",
+    "$$\n",
+    "\n",
+    "where $\\frac{(W + L + 1)!}{W!L!}$ is a normalizing constant to make the distribution sum to 1\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fb58c96-f238-4066-b846-5645cf7632f2",
+   "metadata": {},
+   "source": [
+    "### Tossing the Globe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "87485419",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x800 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.special import factorial\n",
+    "\n",
+    "\n",
+    "def beta_posterior(n_W: int, n_L: int, p: float) -> float:\n",
+    "    \"\"\"Calculates the beta posterior over proportions `p` given a set of\n",
+    "    `N_W` water and `N_L` land observations\n",
+    "    \"\"\"\n",
+    "    return factorial(n_W + n_L + 1) / (factorial(n_W) * factorial(n_L)) * p**n_W * (1 - p) ** n_L\n",
+    "\n",
+    "\n",
+    "def plot_beta_posterior_from_observations(\n",
+    "    observations: str, resolution: int = 50, **plot_kwargs\n",
+    ") -> None:\n",
+    "    \"\"\"Calculates and plots the beta posterior for a string of observations\"\"\"\n",
+    "    n_W = len(observations.replace(\"L\", \"\"))\n",
+    "    n_L = len(observations) - n_W\n",
+    "    proportions = np.linspace(0, 1, resolution)\n",
+    "\n",
+    "    probs = beta_posterior(n_W, n_L, proportions)\n",
+    "    plt.plot(proportions, probs, **plot_kwargs)\n",
+    "    plt.yticks([])\n",
+    "    plt.title(observations)\n",
+    "\n",
+    "\n",
+    "# Tossing the globe\n",
+    "observations = \"WLWWWLWLW\"\n",
+    "fig, axs = plt.subplots(3, 3, figsize=(8, 8))\n",
+    "for ii in range(9):\n",
+    "    ax = axs[ii // 3][ii % 3]\n",
+    "    plt.sca(ax)\n",
+    "    # Plot previous\n",
+    "    if ii > 0:\n",
+    "        plot_beta_posterior_from_observations(observations[:ii], color=\"k\", linestyle=\"--\")\n",
+    "    else:\n",
+    "        # First observation, no previous data\n",
+    "        plot_beta_posterior_from_observations(\"\", color=\"k\", linestyle=\"--\")\n",
+    "\n",
+    "    color = \"C1\" if observations[ii] == \"W\" else \"C0\"\n",
+    "    plot_beta_posterior_from_observations(\n",
+    "        observations[: ii + 1], color=color, linewidth=4, alpha=0.5\n",
+    "    )\n",
+    "\n",
+    "    if not ii % 3:\n",
+    "        plt.ylabel(\"posterior probability\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aee318f2-526e-4bf2-baa2-51e024210533",
+   "metadata": {},
+   "source": [
+    "## On Bayesian Inference...\n",
+    "- **There is no minimun sample size** -- fewer samples fall back to prior\n",
+    "- **Posterior shape embodies the sample size** -- more data makes the posterior more precise\n",
+    "- There is no point estimates -- **the estimate is the entire posterior distribution**\n",
+    "- There is no true interval -- there are an infinite number of intervals one could draw, each is arbitrary and depends on what you're trying to communicate/summarize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3bfa4ed-5fef-4065-9767-32a5cdd0f41e",
+   "metadata": {},
+   "source": [
+    "## From Posterior to Prediction\n",
+    "- To make predictions, we must average (i.e. integrate) over the entire posterior -- this averages over the uncertainty in the posterior\n",
+    "- We could do this with integral calculus\n",
+    "- OR, we could just **take samples from the posterior and average over those**\n",
+    "\n",
+    "**TURN A CALCULUS PROBLEM INTO A DATA SUMMARY PROBLEM**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3ac9be4",
+   "metadata": {},
+   "source": [
+    "### Sampling from Posterior Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d0d139e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a, b = 6, 3\n",
+    "# draw random samples from Beta PDF\n",
+    "beta_posterior_pdf = stats.beta(a, b)\n",
+    "beta_posterior_samples = beta_posterior_pdf.rvs(size=1000)\n",
+    "\n",
+    "# Show that our beta postorior captures shape of beta-distributed samples\n",
+    "plt.hist(beta_posterior_samples, bins=50, density=True, label=\"samples\")\n",
+    "probs = np.linspace(0, 1, 100)\n",
+    "plt.plot(\n",
+    "    probs,\n",
+    "    beta_posterior(a - 1, b - 1, probs),\n",
+    "    linewidth=3,\n",
+    "    color=\"k\",\n",
+    "    linestyle=\"--\",\n",
+    "    label=\"beta distribution\",\n",
+    ")\n",
+    "plt.xlabel(\"proportion water\")\n",
+    "plt.ylabel(\"density\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a984483a",
+   "metadata": {},
+   "source": [
+    "### Sampling from Posterior Predictive Distribution\n",
+    "**Posterior Prediction**: a prediction for out-of-sample data based on the current posterior estimate\n",
+    "- 1. Draw a sample of model parameters from the posterior (i.e. proportions)\n",
+    "- 2. Generate/simulate data predictions using our generative model and the sampled parameters\n",
+    "- 3. The resulting probability distribution is our prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e04824e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 1. Sample parameters values from posterior\n",
+    "N_posterior_samples = 10_000\n",
+    "posterior_samples = beta_posterior_pdf.rvs(size=N_posterior_samples)\n",
+    "\n",
+    "# 2. Use samples for the posterior to simulate sampling 10 observations from our generative model\n",
+    "N_draws_for_prediction = 10\n",
+    "posterior_predictive = [\n",
+    "    (simulate_globe_toss(p, N_draws_for_prediction) == \"W\").sum() for p in posterior_samples\n",
+    "]\n",
+    "ppd_unique, ppd_counts = np.unique(posterior_predictive, return_counts=True)\n",
+    "\n",
+    "# ...for comparison we can compare to the distribution that results from pinning the parameter to a specific value\n",
+    "specific_prob = 0.64\n",
+    "specific_predictive = [\n",
+    "    (simulate_globe_toss(specific_prob, N_draws_for_prediction) == \"W\").sum()\n",
+    "    for _ in posterior_samples\n",
+    "]\n",
+    "specific_unique, specific_counts = np.unique(specific_predictive, return_counts=True)\n",
+    "\n",
+    "plt.bar(\n",
+    "    specific_unique,\n",
+    "    specific_counts,\n",
+    "    width=0.5,\n",
+    "    color=\"k\",\n",
+    "    label=f\"simulation at p={specific_prob:1.2}\",\n",
+    ")\n",
+    "plt.bar(ppd_unique, ppd_counts, width=0.2, color=\"C1\", label=\"posterior predictive\")\n",
+    "plt.xlabel(r\"$\\hat n_W$\")\n",
+    "plt.ylabel(\"count\")\n",
+    "plt.title(f\"number of W samples predicted $\\hat n_W$ from {N_draws_for_prediction} globe flips\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d0b4748-3e97-43a7-b4a8-bc939adfc3b4",
+   "metadata": {},
+   "source": [
+    "### Sampling is Handsom & Handy\n",
+    "Things we'll compute via sampling\n",
+    "- Forecasts\n",
+    "- Causal effects\n",
+    "- Counterfactuals\n",
+    "- Prior Predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e318c01",
+   "metadata": {},
+   "source": [
+    "# Summary: Bayesian Data Analysis\n",
+    "- For each possible explanation of data\n",
+    "- Count all the ways that data could occur under that explanation\n",
+    "- The explanations with more ways to produce data are more plausable\n",
+    "\n",
+    "## Bayesian Modesty\n",
+    "- If your generative model is correct, you can't do better: this will be an optimal solution\n",
+    "- Gives no gaurantees, only provides what you put into it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756a92cb",
+   "metadata": {},
+   "source": [
+    "# Bonus: Misclassification\n",
+    "In previous examples, we  do not consider sampling error or noise in measurement. In other words the number of `Water` observations that we measure may not be the _true_ value.\n",
+    "\n",
+    "This means that the _true_ value for $W$ is unknown / unmeasured, but we instead measure $W^*$ that is caused by both the true, unmeasured $W$ and the measurement process $M$. If we know our measurement error rate, we can attempt to model it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d0eb1ed0-b31c-41c5-8b89-43e516be641c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"591pt\" height=\"189pt\"\n",
+       " viewBox=\"0.00 0.00 591.24 188.89\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184.89)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-184.89 587.24,-184.89 587.24,4 -4,4\"/>\n",
+       "<!-- p -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>p</title>\n",
+       "<ellipse fill=\"none\" stroke=\"red\" stroke-dasharray=\"5,2\" cx=\"67\" cy=\"-104.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"67\" y=\"-100.9\" font-family=\"Times,serif\" font-size=\"14.00\">p</text>\n",
+       "</g>\n",
+       "<!-- W -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>W</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"288.97\" cy=\"-76.45\" rx=\"54.27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"288.97\" y=\"-72.9\" font-family=\"Times,serif\" font-size=\"14.00\">actual W</text>\n",
+       "</g>\n",
+       "<!-- p&#45;&gt;W -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>p&#45;&gt;W</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M94.1,-101.12C126.38,-97.01 182.39,-89.88 225.94,-84.34\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"226.31,-87.82 235.79,-83.09 225.43,-80.88 226.31,-87.82\"/>\n",
+       "</g>\n",
+       "<!-- W* -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>W*</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"513.59\" cy=\"-47.45\" rx=\"69.65\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"513.59\" y=\"-43.9\" font-family=\"Times,serif\" font-size=\"14.00\">noisy W, W*</text>\n",
+       "</g>\n",
+       "<!-- W&#45;&gt;W* -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>W&#45;&gt;W*</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M340.48,-69.86C369.52,-66.08 406.51,-61.26 438.75,-57.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"438.97,-60.56 448.44,-55.8 438.07,-53.62 438.97,-60.56\"/>\n",
+       "</g>\n",
+       "<!-- M -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>M</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"288.97\" cy=\"-19.45\" rx=\"118.97\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"288.97\" y=\"-15.9\" font-family=\"Times,serif\" font-size=\"14.00\">measurement error, M</text>\n",
+       "</g>\n",
+       "<!-- M&#45;&gt;W* -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>M&#45;&gt;W*</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M384.7,-31.36C402.66,-33.62 421.21,-35.95 438.42,-38.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"437.66,-41.55 448.01,-39.32 438.53,-34.6 437.66,-41.55\"/>\n",
+       "</g>\n",
+       "<!-- N -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>N</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"67\" cy=\"-47.45\" rx=\"27\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"67\" y=\"-43.9\" font-family=\"Times,serif\" font-size=\"14.00\">N</text>\n",
+       "</g>\n",
+       "<!-- N&#45;&gt;W -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>N&#45;&gt;W</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M93.66,-50.83C125.91,-55.08 182.34,-62.52 226.1,-68.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"225.63,-71.76 236,-69.59 226.55,-64.82 225.63,-71.76\"/>\n",
+       "</g>\n",
+       "<!-- unobserved -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>unobserved</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"67\" cy=\"-161.45\" rx=\"67\" ry=\"19.45\"/>\n",
+       "<text text-anchor=\"middle\" x=\"67\" y=\"-157.9\" font-family=\"Times,serif\" font-size=\"14.00\">unobserved</text>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x7f654db68a70>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.draw_causal_graph(\n",
+    "    edge_list=[(\"p\", \"W\"), (\"W\", \"W*\"), (\"M\", \"W*\"), (\"N\", \"W\")],\n",
+    "    node_props={\n",
+    "        \"p\": {\"color\": \"red\", \"style\": \"dashed\"},\n",
+    "        \"W\": {\"style\": \"dashed\", \"label\": \"actual W\"},\n",
+    "        \"W*\": {\"label\": \"noisy W, W*\"},\n",
+    "        \"unobserved\": {\"style\": \"dashed\"},\n",
+    "        \"M\": {\"label\": \"measurement error, M\"},\n",
+    "    },\n",
+    "    graph_direction=\"LR\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2c47ad8",
+   "metadata": {},
+   "source": [
+    "## Missclassification Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "e0149a06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['L', 'W', 'W', 'L', 'W', 'W', 'W', 'W', 'W'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def simulate_noisy_globe_toss(p: float = 0.7, N: int = 9, error_rate: float = 0.1) -> np.ndarray:\n",
+    "    # True sample\n",
+    "    sample = np.random.choice(list(\"WL\"), size=N, p=np.array([p, 1 - p]), replace=True)\n",
+    "\n",
+    "    # Error-induced sample\n",
+    "    error_trials = np.random.rand(N) < error_rate\n",
+    "    errors_effect_sample_trials = (sample == \"W\") & error_trials\n",
+    "    sample[errors_effect_sample_trials] = \"L\"\n",
+    "    return sample\n",
+    "\n",
+    "\n",
+    "simulate_noisy_globe_toss()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a6aec7b-7418-4cd9-8595-3723b1730462",
+   "metadata": {},
+   "source": [
+    "## Missclassification Estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9a6d40a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAJjCAYAAABAyBZ9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADcgklEQVR4nOzdd1gU19cH8O+y9N4VQbFSlC5gVwTs2GvsNZrYookxtiSmaKLRxGA0xpafNcTeGxasiCL2rlhApfelbJn3D142rjNLWbbC+TxPniecmZ05O+Muh3vv3MtjGIYBIYQQQgjRCD1NJ0AIIYQQUptRMUYIIYQQokFUjBFCCCGEaBAVY4QQQgghGkTFGCGEEEKIBlExRgghhBCiQVSMEUIIIYRoEBVjhBBCCCEaRMWYhjAMg+zsbNCcu5pB11/z6B5oFl1/zaN7oHnacg+oGNMgTd/82o6uv+bRPdAsuv6aR/dA87ThHlAxRgghhBCiQVSMEUIIIYRoEBVjhBBCCCEaRMUYIYQQQogGUTFGCCGEEKJBVIwRQgghhGiQvqYTqAnEYjGEQmGVXsMwDEpKSlBUVAQej6eizIg8dP01j+6BLAMDA/D5fE2nQQjRACrGqoFhGLx79w7Z2dkKvV4ikSAzM1O5SZFKo+uveXQPZFlbW6Nu3bpUnBJSy1AxVg1lhZijoyNMTU2r9AXKMAzEYjH4fD598WoAXX/No3vwH4ZhIBAIkJqaCgBwcnLScEaEEHWiYkxBYrFYWojZ2dlV+fX0i0iz6PprHt0DWSYmJgCA1NRUODo6UpclIbUIDeBXUNkYMVNTUw1nQgipKcq+T6o6BpUQotuoGKsm+oueEKIs9H1CSO1ExRghhBBCiAZRMUYIIYQQokFUjBGdkJSUBHd3d+zdu1fTqWiNp0+fIjIyEklJSSo5fmRkJNzd3VVybEIIIf+hpymJTnB0dERUVBQaNGig6VS0xtOnT7F69WoEBwfDxcVF6ccfPHgwOnTooPTjEkIIkUXFGFG6oqIiGBsbK/WYhoaG8PPzU+oxCbfCwkKYmJigbt26qFu3rtKPSwghRBYVY0oUHR2NzZs34/nz55Xan2EYlT491bhxY4wbNw7h4eFVel1kZCRWr16Nffv2Yc2aNbh8+TJ4PB46d+6M+fPnw9bWVrpvaGgomjVrhoEDB2LNmjV49uwZxowZgy+++AJpaWmIjIzEuXPnkJmZCUdHRwwYMABTpkyBvr4+hEIhOnbsiPbt22P58uUyOeTm5qJ9+/b46KOPMG/ePCQlJSEsLAxLly7FgAEDpPtdv34dkZGRuH37NiQSCTw9PTFlyhSEhISw3s+jR49kzrFv3z4sXLgQp0+flrYsXblyBWvWrMHjx49RWFgIW1tbeHt7Y9myZeUWEmXXYdCgQfj999/x4sULODo6YsyYMRg9erTMvm/evMHKlStx6dIl5OXloX79+hg8eDDGjh0LPb3/Rg7s2LED//zzD16/fg0AqFOnDrp27YrZs2dj7969mDdvHgDIHP/963P58mWsW7cOd+7cgVgshqenJ2bOnIk2bdqwrs3evXuxbt06XLlyBUZGRrh48SLndZNIJNi4cSP27NmDpKQkWFhYoEOHDpg9e7ZM4TZq1ChkZWXhm2++wYoVK/DgwQOEhobi119/lXsNCSGktqJiTEmio6PRo0cPiEQiTaciFRsbi3///RfHjx9HWFhYlV8/bdo0dO/eHcOGDcPTp0+xatUqPHv2DP/++y8MDAyk+927dw/Pnj3DJ598AhcXF5iYmCAtLQ2DBw+Gnp4epk6digYNGiAhIQFr165FcnIyli5dCgMDA/Tp0wf//PMPvvnmG5ibm0uPefjwYRQXF8sUXh+Ki4vD+PHj4ebmhh9//BGGhobYuXMnpkyZgpUrV6Jnz55Ver9JSUmYPHkyAgMD8eOPP8LS0hIpKSm4cOEChEJhha06Dx48wJIlSzBt2jTY29vj0KFD+PHHHyEUCjFhwgQAQGZmJoYNGwahUIiZM2fC2dkZ586dw88//4xXr17h22+/BQAcOXIEixcvxqhRozB37lzo6enh5cuXePr0KQAgJCQEs2fPxsqVK/H111+jRYsWACDtxj1w4ADmzp2LsLAw/Pzzz9DX10dUVBQmTJiAjRs3yhRkADB9+nT07NkTw4YNg0AgkPsev/32W0RFRWHkyJEICQlBcnIyVq1ahbi4OOzdu1emUE9LS8OcOXMwceJEzJo1S6bQJIQQ8h6GKKSwsJC5f/8+U1hYyDAMwwwfPpwBoJX/DR8+vErv7ffff2fc3NyYJUuWyMQPHjzIuLm5MQcOHJDGOnfuzHh6ejLPnz+X2XfRokWMn58fk5ycLBPfuHEj4+bmxjx58oRhGIZ5+PAh4+bmxkRFRcnsN2jQIKZ///7Sn1+/fs24ubkxe/bskcaGDBnCtGnThsnPz5fGRCIRExERwXTs2JGRSCQy7+d9EomE+ffffxk3Nzfm9evXDMMwzPHjxxk3NzfmwYMHlbtQ7+ncuTPj7u7Oeu24ceOYgIAARiAQMAzDML/88gvj5ubG3Lp1S2a/b775hnF3d5dex++++44JDAws95zHjh1j3NzcmNjYWJm4QCBggoODmcmTJ8vExWIx06dPH2bQoEHSWNm1WbVqFev4H163p0+fMm5ubsy3334rs9+tW7cYNzc3ZuXKldLYyJEjGTc3N+by5cty85dIJIxQKJTeJ8L+XlEliUTCZGZm0vXXILoHmqct94D+VCVy9e7dW+bnHj16QF9fH1evXpWJu7u7o1GjRjKxc+fOoVWrVnB0dIRIJJL+17FjRwClrVplr23RooXMU5LPnj3D7du3MXDgQLm5CQQC3Lp1C926dYOZmZk0zufz0adPH7x7967S3cVlPD09YWBggEWLFmHfvn3S7sHKatasGTw8PGRiERERyM/Px7179wCUtlY2bdoUPj4+MvsNGDAADMMgNjYWAODt7Y3c3FzMnj0b0dHRVVpMOyEhAdnZ2ejfv7/MtZdIJOjQoQPu3LnDav3q2rVrhcctu+/9+/eXifv4+KBJkya4cuWKTNzKyorVAkdqN5FIhFu3bmHDhg2YPHky+vbti4kTJ+Kvv/7CzZs3tapngRB1om5KJRk3bhz+/fdfrfsy0dfXx/jx4xV6rYODA+tY1tbWyM7OLnc/AMjIyMDZs2el3WcfysrKkv7/wIED8d133+HZs2do0qQJ9uzZA0NDQ0RERMjNLTc3FwzDcJ7b0dERAFh5VqRBgwb4+++/sWHDBnz33XcQCASoX78+Ro0ahTFjxlT4ent7e7mxslyys7Ph7OxcYc79+vWDWCzGrl27MGPGDEgkEnh7e+Ozzz5Du3btys0jPT0dADBjxgy5++Tk5Mgs5VV2/vKU5ca1r6OjI968eSMT47o3pPYRCARYu3Yt9u7di4SEBBQWFrL22bhxI4DS9TkDAgLQr18/fPLJJzJ/aBFSk1ExpiTh4eE4fvw4Nm3apFUD+MePH6/QeDGgdMxPnTp1pD+LRCJkZ2fD2tpaZj+u92BjYwN3d3d89tlnnMd+/xd6REQEfvrpJ+zbtw+zZs3CwYMHER4eDisrK7m5WVpaQk9PD2lpaaxtqamp0hwAwMjICABQUlICQ0ND6X5cxVpgYCACAwMhFotx9+5dbN26FUuWLIG9vT169eolNx/gvyKIK1Z2zaytrSuVM1BapA4cOBACgQDXrl1DZGQkJk+ejBMnTnAWdGXKjrFo0SL4+vpy7qPI4vZl7yE1NZX1lGVqaqpM7gAt7UNKx35Onz4dL168qNT+hYWFuHTpEi5duoQ1a9Zg3bp16NKli2qTJEQLUDGmRGFhYZUufBiGgVgsBp/P19pfWocOHYKXl5f052PHjkEkEiE4OLjC14aEhCAmJgYNGjQot6gCSruzwsPDsX//fvj5+SEtLa3cLkqgdEFlX19fnDp1CnPnzpVOpSGRSHDw4EHUrVtX2nVaVrg8fPhQpnvw3Llzco/P5/Ph6+uLxo0b49ChQ7h3716FxdiTJ0/w8OFDma7Kw4cPw8zMTNpC2KZNG6xbtw737t2TaTXcv38/eDweWrVqxfleO3XqBKFQiKlTp+Lp06dwdnaWFpZFRUUy+wcEBMDS0hJPnz7FyJEjy825Klq3bg0AOHjwoMx1vH37Np49e4YpU6Yo7VxEt7169QozZ87E/v37FT5GYmIiunbtijFjxmDFihUK/QFBiK6gYozIderUKfD5fLRr1w5PnjzBqlWr4OHhgR49elT42hkzZuDy5csYNmwYRo0ahUaNGqGkpARJSUk4f/48Fi9eLNO6MnDgQBw9ehTff/896tati7Zt21Z4jtmzZ2P8+PEYPXo0xo8fDwMDA+zYsQNPnjzBypUrpUVup06dYG1tjQULFmDmzJng8/nYu3cv3r17J3O8nTt3IjY2FiEhIXByckJxcTH27NkDAJXKx9HREZ988gmmTZsGBwcHHDx4EJcuXcIXX3whfRJz7Nix2L9/PyZPnowZM2agXr16OHfuHHbs2IGPPvpIWkAuXLgQxsbGCAgIgIODA9LS0vDXX3/BwsIC3t7eAErHqAHAv//+CzMzMxgZGcHFxQU2NjZYuHAhvvrqK+Tk5KBbt26ws7NDZmYmHj58iMzMTCxevLjC9/Ohxo0bY+jQodi2bRv09PTQsWNH6dOUTk5OGDt2bJWPSWoWiUSClStX4ptvvin3qdyq+N///oejR4/i999/x7Bhw5RyTEK0DRVjRK7IyEhERkZi586d4PF4CA0Nxfz582W6+uRxdHTE7t27sWbNGmzcuBEpKSkwMzODs7MzOnToAEtLS5n927ZtCycnJ7x9+xZTpkyp1DQIwcHB+PvvvxEZGYl58+ZBIpHAw8MDa9euRefOnaX7mZubY/369ViyZAnmzJkDCwsLDBo0CO3bt8fXX38t3c/T0xOXLl1CZGQk0tLSYGpqCjc3N6xduxbt27evMB9PT08MGDAAkZGR0nnG5s2bJ1Ok2Nra4p9//sGKFSuwYsUKFBQUwMXFBXPmzMG4ceOk+wUGBmLv3r04duwYcnJyYGNjg5YtW+Lnn3+WTh9Rv359zJ8/H1u2bMHo0aMhFoul84z17dsX9erVw4YNG/DNN9+goKAAtra28PT0ZA3Ar4pvv/0W9evXx+7du7Fjxw6Ym5ujQ4cO+Pzzz1ndlKR2EYvFGD9+PLZs2VLp19jb23N2738oLS0NH330Ee7du4fvv/++OmkSopV4DMMwmk5CFxUVFSExMRGNGjVSaLZ5be6mLJvs88qVKzLzRtUkyr7+ZZO+rlu3TgnZ1Q7a/BnQlOp+r1QFwzDSMaDVvf5isRjjxo3D1q1by90vMDAQvXv3RlBQEIKCgmBvb4+MjAxcu3YNcXFxOHToEK5fv17uMZYvX44vvviiWvlqC2XeA6IYbbkH1DJGCCFEYWKxGGPHjsW2bdvk7mNnZ4dly5axVpko29a9e3d0794dCxcuxLp16zB37lzk5eVxHmvOnDmwtbVV+ClxQrQRzTNGCCFEIWKxGGPGjCm3EJs4cSIePXqE8ePHVzj8QE9PD5988gnu37/PmufwfZMmTZKZm5AQXUfdlAqqyd2UtQFdf82je8CmS92UYrEYo0ePxo4dOzi3m5iYYP/+/ZWaUFhefps2bcLEiRM5txsaGuLIkSNVXntXm2hLF1ltpi33gFrGCCGEVNn8+fPLLcQOHz6scCEGlM5TN2HCBERGRnJuLykpQb9+/SocY0aILqBijBBCSJWcOHECy5Yt49xmYmKCI0eOIDQ0VCnnmjZtGr777jvObQUFBRg+fLjSptEgRFOoGCOEEFJpb9++xahRozi3lRVi708towwLFy6Uu5rHkydP8M033yj1fISoGxVjhBBCKkUikWDUqFGcS3oZGhri6NGjSi/EgNIuyxUrVshdI3blypXShewJ0UVUjBFCCKmUn3/+GadPn+bctnLlSoSEhKjs3Hp6etiwYQPncmwSiQTjxo1jLQ1GiK6gYowQQkiFrly5gkWLFnFu69evHz799FOV56Cvr4/NmzdzrgLy4MEDuWPLCNF2VIwRQggpV3Z2Nj766COIxWLWtvr162Pjxo1qmxagefPmcseILVu2DPHx8WrJgxBlomKMKF1SUhLc3d1VMiljZGQk3N3dlX7cqnB3d2c9bn/lyhUMGDAAfn5+cHd3R3R0NPbu3Qt3d3ckJSWpJI+nT58iMjKS8/hfffWV0p5m0wUxMTFyp0BQhlGjRskdtF4bzJ07Fy9fvmTF9fT0sGPHDrUvmzZnzhwEBASw4mXLMpWUlKg1H0Kqi5ZDIkrn6OiIqKgoNGjQQNOpqERUVBTq1q0r/ZlhGHz22Wdo2LAh1q5dCxMTEzRq1AhisRhRUVFwdHRUSR5Pnz7F6tWrERwcDBcXF5ltn376KUaPHq2S82qjmJgYbN++HdOnT1fJ8Wvz03o3btzA+vXrObctXrwY7du3V3NGgIGBATZv3ozAwEAIhUKZbXfu3MHy5cuxYMECtedFiKKoZYwonaGhIfz8/GrsIuN+fn4yxVhqaiqys7MRHh6ONm3awM/PD1ZWVrC1tYWfnx/n+BZVa9CgAZo3b67289Y0hYWFAICmTZuiadOmSjkmwzA6M9CcYRhMnz4dXAu1dO7cGfPmzdNAVqV8fHzkFlzLly9HVlaWmjMiRHFUjBGWsq7Ahw8fYsaMGWjZsiWCg4OxdOlSiEQiPH/+HBMmTIC/vz9CQ0NZfzVzdVNmZmZi0aJF6NSpE7y8vNC6dWsMGzYMly9flnnt+fPnMWbMGLRs2RK+vr7o0aMH1q1bV26+R48exfjx49G+fXv4+PigR48e+OWXX1gTQb5+/RqzZs1C+/bt4e3tjY4dO2Ls2LF48OCBdJ8rV65g1KhRaNWqFXx8fBASEoLp06dLfykDst2UkZGR6NixIwDgl19+gbu7u7R7UF43ZUXv8c6dO5g1axZCQ0Ph4+OD0NBQzJ49G8nJydJ99u7di5kzZwIARo8eDXd3d5lrztVNWVxcjBUrViA0NBReXl7o0KEDFi9ejNzcXJn9QkNDMXnyZJw/fx79+/eHj48Punfvjt27d5d7H4D/7v369euxdu1ahISEwNvbGwMGDMCVK1dY+8fHx2Ps2LHw9/eHr68vhg0bhnPnzsnsU1hYiJ9//hmhoaHw9vZGcHAwBgwYgMOHD0vf6/bt26X3puy/suvOMAy2b9+Ovn37wsfHB0FBQZgxYwZev34tc55Ro0YhIiIC165dw7Bhw+Dr64v58+dLt33YTZmdnY1vv/0WHTp0gJeXF8LCwvDrr7+yusjc3d3x3XffYefOnejRowe8vb2xb9++Cq+lNtixYwfrMwoAxsbG2Lx5M/h8vgay+s+8efPg4+PDiufk5GDFihUayIgQxVA3JZHrs88+Q58+fTBs2DBcunQJGzZsgEgkwuXLlzF8+HBMmDABhw4dwi+//AJXV9dylz6ZM2cO7t+/j1mzZqFhw4bIzc3F/fv3kZ2dLd1n165dWLRoEYKCgrB48WLY2dkhMTERT548KTfPFy9eoGPHjhgzZgxMTEzw/PlzrF+/Hrdv38aWLVuk+02aNAkSiQRz5syBk5MTMjIycOvWLWkxkpSUhMmTJyMwMBA//vgjLC0tkZKSggsXLkAoFMLExIR17sGDB8PDwwPTpk2T/jIvryWsMu8xOTkZjRo1Qq9evWBlZYW0tDTs3LkTgwYNwpEjR2Bra4uQkBDMnj0bK1euxNdff40WLVoAgNyuYYZh8OmnnyI2NhYff/wxAgMD8ejRI0RGRuLmzZuIioqSyfvhw4f4+eefMWnSJNjb22PXrl1YsGABXF1dERQUVO79AIDt27ejXr16mD9/PiQSCTZs2IBJkyZh69at8Pf3BwDExcVh/PjxcHd3x48//ghDQ0Ps3LkTU6ZMwcqVK9GzZ08AwNKlS3Hw4EF89tln8PT0RGFhIR4/fiz9t/Ppp59CIBDgxIkTiIqKkuZQ1j389ddfY9++fRg1ahS++OIL5OTk4I8//sCwYcNw4MAB2NvbS1+TlpaGOXPmYOLEiZg1a5bcha2Li4sxevRovH79GtOnT4e7uzuuX7+Ov/76Cw8ePMBff/0ls390dDSuX7+OqVOnwt7eHnZ2dhVeQ03Ly8vDnDlzOLd99dVXcHV1VXNGbIaGhvjzzz/Rtm1b1rbffvsNM2fOhIODgwYyI6SKGKKQwsJC5v79+0xhYaFCr5dIJIxQKGQkEomSM6u+33//nXFzc2M2bdokE+/bty/j5ubGnDx5UhoTCoVM69atmWnTpkljr1+/Ztzc3Jg9e/ZIY35+fsyPP/4o95z5+flMQEAA89FHH5V7Tcpyk6fsusbFxTFubm7MgwcPGIZhmMzMTMbNzY35+++/ZfZ7/1zHjx+XeY08bm5uzO+//856vxs2bJDZb8+ePYybmxvz+vXrKr3HD4lEIqagoIDx8/Nj/ve//0njx44dY9zc3JjY2FjWa+bOnct07txZ+vP58+cZNzc3Zv369TL7HTlyhHFzc2OioqKksc6dOzPe3t5McnKyNFZUVMQEBwczixYtKjfXsmvRvn17pqioSBrPy8tjgoODmbFjx0pjgwcPZtq0acPk5eXJvNeIiAimY8eO0msUERHBfPrpp+Wed/HixZz/LhISEjj/Lb99+5bx8fFhli1bJo2NHDmScXNzYy5fvsw6zsiRI5mRI0dKf965cyfj5ubGHD16VGa/v/76i3Fzc2MuXrwojbm5uTEtW7ZksrOzy30PDFP975WqkEgkTGZmptx/i1999RUDgPWfq6srIxAIVJ5fVfTq1Ysz1y+++ELTqZWrontAVE9b7gF1UxK5PpzAsUmTJuDxeNJuOaB03h9XV1eZLjQuPj4+2LdvH9asWYObN2+yBt0mJCQgPz8fw4cPr/Ij8q9fv8bnn3+Odu3awdPTEy1atMDIkSMBAM+fPwcAWFtbo0GDBti4cSM2b96M+/fvQyKRyBzH09MTBgYGWLRoEfbt28fqxqquyr7HgoICLF++HF26dEHz5s3RvHlz+Pv7QyAQ4NmzZwqdOzY2FgAwYMAAmXiPHj1gamrK6kL09PREvXr1pD8bGRmhYcOGePPmTaXO17VrVxgZGUl/Njc3R+fOnXHt2jWIxWIIBALcvn0bXbp0gZmZmXQ/Pp+PPn364N27d9J75+3tjfPnz+OXX37B1atXqzTe6uzZs+DxeOjTpw9EIpH0P3t7e3h4eCAuLk5mfysrK7Rp06bC48bGxsLU1BTdu3eXiZdd3w+vZ+vWrWFlZVXpvDXtyZMnWLlyJee2FStWcLYSa5K8+cX++OMPvHv3Ts3ZEFJ11E1J5Prwl4eBgQFMTExkfsmWxfPz88s91q+//oq1a9di9+7dWLVqFUxNTdGlSxfMmTMHDg4OyMzMBACZgfGVUbZQsJGRkfSJRmNjY7x79w7Tpk2T/uLm8Xj4+++/8ccff2DDhg346aefYGVlhd69e2PWrFkwNzdHgwYN8Pfff2PDhg347rvvIBAIUL9+fYwaNUruMixVUdn3+PnnnyM2NhaffvopvL29YWZmBh6Ph48//hjFxcUKnTs7Oxv6+vqshyp4PB7s7e1luouB0uL1Q4aGhpU+//tdf+/HhEIhBAIBCgoKwDAMZxdSWfdiWU4LFy5E3bp1cfToUaxfvx5GRkZo3749vvzySzRs2LDcPDIyMsAwDGc3FlA6R9b7KtullZ2dDXt7e1ZRbWdnB319fdb11LWustmzZ3NODxEaGsoq6LVBQEAA+vfvzxqLV1hYiKVLl2LVqlUayoyQyqFijKiFra0tFixYgAULFuDNmzc4c+YMVqxYgYyMDGzcuFFaJFT1r9jY2FikpqZi69atMsuk5OXlsfZ1dnbGkiVLAJS2mB09ehRr1qyBUCiU/mUdGBiIwMBAiMVi3L17F1u3bsWSJUtgb2+PXr16Kfr2AaBS7zEvLw/nzp3DtGnT8PHHH0vjJSUlyMnJUfjc1tbWEIlEyMzMlCnIGIZBeno6vL29FT42l/T0dM6YgYEBTE1Nwefzoaenx7nGYWpqKgDAxsYGAGBqaooZM2ZgxowZSE9Px/nz57FixQpMmTIFx48fLzcPGxsb8Hg8bN++nXMs34exyrbKWltb49atW2AYRuY1GRkZEIlE0tyrelxtcOLECenDEe/j8/lYtWqV1r6XxYsXY//+/awnP//880/MmTOHNf0LIdqEuimJ2tWrVw8jR45E27Ztcf/+fQCAv78/LCws8M8//3A+Ri9P2S+GD3+p/vPPP+W+rlGjRpgyZQrc3NykObyPz+fD19dXOr/UvXv3Kp2TPJV5jzweDwzDsN7Prl27WLOfl+1TmW67sq63gwcPysRPnDgBgUBQqa65qjh58qRMK1p+fj7Onj2LwMBA8Pl8mJqawsfHB9HR0TL5SyQSHDx4EHXr1kWjRo1Yx7W3t8eAAQPQq1cvJCYmSp9ylXctQkJCwDAMUlJS4O3tzfpP0QmE27RpA4FAgOjoaJn4/v37pdt1EcMw+Prrrzm3TZ06FV5eXmrOqPK8vb0xZMgQVrykpAQ//vijBjIipPKoZYyoXF5eHkaPHo2IiAg0btwYZmZmuHPnDi5cuIAuXboAAMzMzDB37lwsXLgQY8eOxZAhQ2BnZ4dXr17h4cOHcn9B+Pv7w8rKCt988w2mTZsGfX19HDp0CI8ePZLZ7+HDh/j+++/RvXt3uLq6wsDAAJcvX8ajR4+kLVA7d+5EbGwsQkJC4OTkhOLiYuzZswcA5HZzVUVl3qO5uTmCgoKwceNG2NjYwNnZGXFxcdi9ezcsLS1ljtesWTMAwL///gszMzMYGRnBxcWF1SoDAO3atUP79u3xyy+/ID8/HwEBAXj06BF+//13NG/eHH379q32+3sfn8/HuHHjMG7cOEgkEqxfvx75+fkyk7LOnj0b48ePx5gxYzB+/HgYGBhgx44d0vFKZYX24MGDERISAnd3d1hZWeHZs2c4cOAA/P39pWOX3NzcAADr169Hx44doaenB3d3d7Rs2RJDhw7F/PnzcffuXQQFBcHExARpaWmIj4+Hm5sbhg8fXuX3169fP2zfvh1z585FcnIy3NzcEB8fj3Xr1qFTp05K+feiCSdPnmSNowNKi+Bvv/1W/QlV0bfffotdu3axxoNu3LgRc+fOrbBbmxBNoWKMqJyRkRF8fHxw4MABJCcnQyQSwcnJCZMmTcLEiROl+w0ePBiOjo7YsGEDFi5cCIZh4OzsjH79+sk9to2NDdatW4eff/4Zc+bMgYmJiXS+p/79+0v3c3BwQIMGDbBjxw5pN6GLiwvmzp0rnT/K09MTly5dQmRkJNLS0mBqago3NzesXbtWabOMV+Y9rlixAj/++COWL18OkUiEgIAAbN68GZMnT5Y5Vv369TF//nxs2bIFo0ePhlgsxtKlSznH9PB4PKxZswaRkZHYu3cv/vzzT1hbW6Nv376YPXu20iemHTFiBIqLi/HDDz8gIyMDzZo1w7p169CyZUvpPsHBwdi0aRPWrFmDefPmQSKRwMPDA2vXrkXnzp2l+7Vu3RpnzpzB//73PxQWFqJOnTro168fpkyZIt0nIiICN27cwI4dO/DHH3+AYRicPn0aLi4u+O677+Dr64uoqCjs3LkTEokEjo6OCAgI4JyjqjKMjIywZcsW/Prrr9iwYQOysrJQp04djB8/HtOmTVP8wmkQwzBYvHgx57ZFixZxFvnaxsPDAyNHjpSZ0gYAhEIhli9fjj/++ENDmRFSPh5TlT4hIlVUVITExEQ0atQIxsbGVX49wzAQi8Xg8/laOwajJqPrrxpJSUkICwvDl19+iQkTJpS7L90Dtup+r1QFwzDIzs6GtbU1eDweoqOjpS3V76tbty6eP3+udU9QyvPs2TO4u7uzuvXNzc2RnJzMamHWpA/vAVE/bbkH1DKmRHnnEpC14xRKXlQ8CJ0BAIYBeDyo6vYbNqwLm+FdYBHir6IzEEJqgvJaxebOnaszhRhQOgXP6NGjsXnzZpl4fn4+tmzZorMtl6Rmo2JMSfLOJSBxyNeASFzxzmoiuP4Q2fsvoNGu72HRyU/T6RBCtFRMTAwuXrzIijs6Oso81asrZsyYwSrGgNJ5x6ZOnUqtUETr0NOUSpK145RWFWJSIjGytp/UdBaklnBxccGjR48q7KIk2kVeq9icOXNgamqq5myqz8/PD+3atWPFHz58iDNnzmggI0LKR8UYIYTUYufPn2ctzg6UPkH5ySefqD8hJZk6dSpnnAbxE21ExZiS2AzvAujzNZ0Gmz4fNiPkL+BNCKndfvjhB874F198IbNUla4ZOHAg6tSpw4ofOHAAr1690kBGhMhHY8aUxCLEH412fY+s7Se1awD/iK4KjxdbvXo1jh49isOHD0NPr3bX7U+fPsWxY8fQv39/jczknZGRgeXLl+Ps2bMoKiqCh4cHPvvss0pNLvrkyRNs374dDx48wKNHj1BYWIgtW7agVatWMvvl5OQgLCwMP/30E8LDw1X1VogWuXbtGmviWqB0WSd5LUu6wtDQEB9//DG+//57mbhEIsG6detoIliiVagYUyKLTn6VLny0/bH+lJQUbNy4EUuXLq31hRhQWoytXr0awcHBai/GSkpKMHbsWOTm5mLBggWws7PD9u3bMXHiRGzevFlmGSgud+/eRXR0NJo3b47WrVvj7NmznPtZWVlh7NixWLZsGTp27Kj0uceI9lm7di1nfPbs2TA3N1dzNso3efJkLFmyhDXNxfr16/H111+z1tklRFPotyzhtGXLFlhYWKBrV+V1ccpbtkcoFEIkElXr2GXL4qhqf03atWsXHj9+jN9++w19+vRBu3bt8Pvvv6Nhw4ZYvnx5ha/v27cvLl68iL/++qvcCXQBYNiwYUhOTsaJEyeUlD3RVq9fv8ahQ4dYcWtr6xoz/YOzs7PM5M9l0tLSsGvXLg1kRAg3KsYIS0lJCXbv3o2IiAhWq1hJSQnWrFmD7t27w8vLC61bt8a8efOQmZkps19oaCgmT56MkydPol+/fvD29sbq1atx9epVuLu7Y//+/fjpp5/QoUMHeHt74+XLlwCA3bt3o0+fPvD29kZwcDCmTp2KZ8+eyRz7q6++gr+/Px49eoTx48fD398fY8eOlft+IiMj4e7ujnv37mHGjBkICgqSFpl37tzBrFmzEBoaCh8fH4SGhmL27NlITk6Wvn7v3r2YOXMmAGD06NFwd3eHu7s79u7dK93n8uXLGDNmDAICAuDr64thw4bhypUrVb/4HKKjo9GoUSP4+/83X5y+vj769OmD27dvIyUlpdzXV6Vl097eHm3btq1wbU+i+9auXctqMQKAKVOmaNXEqNVFA/mJLqBuSsJy+/ZtZGdns8YUSSQSfPrpp4iPj8eECRMQEBCA5ORkREZG4vbt29izZ4/MrOH37t3Ds2fP8Mknn8DFxQUmJibSFqmVK1fCz88Pixcvhp6eHuzs7LBu3TqsXLkSERER+Pzzz5GVlYXVq1dj6NCh2L17t8y6ckKhEJ988gmGDRuGSZMmcf5S+dD06dPRs2dPDBs2DAKBAACQnJyMRo0aoVevXrCyskJaWhp27tyJQYMG4ciRI7C1tUVISAhmz56NlStX4uuvv0aLFi0AAA0aNABQOiB47ty5CAsLw88//wx9fX1ERUVhwoQJ2Lhxo3RcV1nXdGXo6//30Xzy5InMMkJlyha5fvLkCedAZUUFBwdj5cqVyM3NrVG/lMl/CgsLsX79elacz+fj008/1UBGqtOpUye0aNEC9+7dk4nHxsYiPj6e87NFiLpRMUZYEhISAEBadJQ5duwYLly4gMjISJnuSw8PDwwaNAh79+6VWXQ5MzMTR44cQaNGjaSxq1evAigtZH7//XdpPDc3F2vWrEGnTp2wYsUKabxVq1bo2rUrIiMjZeJCoRBTp07FwIEDK/2++vXrhxkzZgD4rzDq3r07evToId1HLBYjJCQE7dq1w+HDhzF69GjY2trC1dUVANC0aVP4+flJ9y8sLMSSJUsQEhIi85d2p06d0L9/f6xcuVLaHRIXF4fRo0dXKteydRUBIDs7G1ZWVqx9ymLZ2dmVvgaV0aJFC0gkEty8eRMdO3ZU6rGJdtixYwcyMjJY8QEDBqB+/foayEh1eDwepk6dyllkbtq0iYoxohWoGCMsqamp4PF4rIWBz549C0tLS3Tu3FlmjJenpyccHBwQFxcnU4y5u7vLFGLv+3AsWkJCAoqKiljjO5ycnNC6dWvExsayjtGtW7cqvS+u8W8FBQVYu3YtTp48ieTkZJmWqw+7R7kkJCQgOzsb/fv3Z41769ChAzZs2ACBQABTU1O0aNECu3fvrlSujo6OMj+X95CHsh8AsbW1BYAKuz+JbmIYBqtWreLcVvbHSk0zcuRIzJ07F3l5eTLxqKgo/PbbbzAwMNBQZoSUomKMsBQXF0NfXx98vuy8aRkZGcjNzYWXlxfn67KysmR+dnBwkHuOD7eVte5wvcbR0RGXL1+WiZmYmFT5aa8PCxygdC6l2NhYfPrpp/D29oaZmRl4PB4+/vhjFBcXV3jM9PR0AOX/EsvJyYGpqSnMzMzg6elZqVzf76a0trbmbP3KyckBAM5Ws+ooe8KsMu+f6J6YmBjcuXOHFQ8ICOCctb4msLCwwNChQ7FhwwaZeEZGBk6cOIGIiAgNZUZIKSrGCIuNjQ2EQqG0Ref9uLW1NesLrcyHE0RWpTXH2toaQOlTTh9KTU1ltdIpozUoLy8P586dw7Rp02TW3yspKZEWOhUpy2vRokXw9fXl3MfOzg6A4t2Ubm5uePz4MWufslizZs0qdczKKnvvH15zUjOU1yqmjdPsKMuIESM4v7u2b99OxRjROK0rxlJSUnDs2DGcP38ez58/R3p6OqysrBAQEICJEyfK/YX3oatXr5b7iy8qKkpm7A/5T1nX4qtXr+Dh4SGNh4SE4MiRI5BIJJW+D5Xl7+8PY2NjHDx4UGYM17t37xAbG1vlLsnK4PF4YBiGNZ/Wrl27WAPty/b5cHqOgIAAWFpa4unTpxg5cmS551O0mzI8PByLFy/GrVu3pNddJBLh4MGD8PX1VergfaB0ygMAaNKkiVKPSzQvMTERBw8eZMUdHBwwdOhQDWSkPh07doSLiwuSkpJk4gcOHEBeXh4sLCw0lBkhWliMbd26FevXr0eDBg3Qtm1b2NnZ4eXLl4iOjkZ0dDRWrFiBnj17Vvp4wcHBnJNi1q1bV5lp1yhlT1HeunVLphjr1asXDh06hI8//hijRo2Cj48PDAwM8O7dO1y9ehVhYWHo0qWLQue0tLTEp59+ipUrV+LLL79Er169kJ2djT/++ANGRkYqmffI3NwcgYGB2LhxI2xsbODs7Iy4uDjs3r2b9RRhWevTv//+CzMzMxgZGcHFxQU2NjZYuHAhvvrqK+Tk5KBbt26ws7NDZmYmHj58iMzMTOkizObm5vD29q5ynoMGDcKOHTswc+ZMfP7557Czs8OOHTuQmJiIzZs3y+w7ZswYXLt2Dffv35fGCgsLERMTA6D0ngKlM69nZWXBxMQEnTp1kjnGrVu3YG1tLX1ak9Qca9asgUQiYcWnTJki8yR0TaSnp4fhw4dj2bJlMvHCwkLs27ev0q3WhKiC1hVjPj4+2L59OwIDA2Xi169fx9ixY7F48WKEh4dXenbw4OBgTJ8+XRWp1lhOTk4IDAzE6dOnZf5a5vP5WLt2LbZs2YIDBw7gr7/+Ap/PR926dREUFAQ3N7dqnXfy5MmwtbXF1q1bcfToURgbGyM4OBizZ8+WmdZCmVasWIElS5Zg+fLlEIlECAgIwObNmzF58mSZ/erXr4/58+djy5YtGD16NMRiMZYuXYoBAwagb9++qFevHjZs2IBvvvkGBQUFsLW1haenJ+eEk1VlaGiIv//+G8uXL8cPP/yAwsJCeHp6Yv369aw/NCQSCatVLyMjQzpPWpnIyEgApZNinjlzRhpnGAZnzpxB7969a3SXVW1UUFDA2U2nr6+PKVOmaCAj9RsxYgSrGANKuyqpGCMaxeiQ8ePHM25ubszt27cr3Dc2NpZxc3Njfv/9d5XkUlhYyNy/f58pLCxU6PUSiYQRCoWMRCJRcmbKcfz4ccbT05N59+6dplNRCW2//ppy+fJlxsPDg3n69KnKz0X3gK263yvl2bBhA4PSZXFl/hs+fLjSz6XNvL29WddAT0+Pefv2rdpzkUgkTGZmJn0GNEhb7oHWtYyVp+wJs/efNKvIixcvsGXLFhQVFaFevXpo27at9NH98jAMU6ntDMNUuG91z6UJXbp0gZeXF/788098/fXXmk5HpbTx+mvKmjVrMHDgQDRu3Fit14XuQSllfq98aOPGjZzxadOm1arrP3z4cMybN08mJpFIsHPnTnz22WdqzeX9+000Qx33oDK9DDxGR/4VvHnzBt26dYOVlRViYmJY0y58SN4AfmNjY0yfPh0TJ04s9/XZ2dnl3pySkhKkp6fD1dW1xo61ePLkCc6ePYuJEyfSYuG1QE5ODrZt24Zhw4ZJnwAl6lVUVISXL1/C3t5eqQu1P3r0SLoSxPt8fHxw7tw5pZ1HFyQlJcHHx4cV9/f3x+nTpzWQEanpKvNkuk60jAmFQnz55ZcoKSnBF198UWEhBpROXPnll18iJCQE9erVQ25uLq5evYpffvkFy5cvh7m5OYYNGyb39RXN3VRUVITMzEzw+fxK5cNFLBYr/Fp18PDwkBnAX9No+/VXN1tbW7VP+kn3QBafz4eenh4sLS2V+keevEWxR40aBSsrq1o1PtDa2hqdOnWSPtRSJiEhASkpKWp9cIVhGOTk5NS6e6BNtOUeaH0xJpFIMH/+fFy7dg1DhgxBv379KvW6Zs2aycy/ZGJigj59+sDDwwMDBgxAZGQkhgwZIrfFp6KbUradx+MpdAPfb3WjD6H60fXXPLoHbNX9XuFSUlKCrVu3suLGxsYYNGiQUs+lK0aMGMEqxoDSZaK+++47tedTG++BttH0PdDqvieGYbBw4UIcPHgQffr0kU4RUB1ubm7w9fVFeno6Xr58qZQcCSFEGVTxfXL48GHOyZQHDhyo9NUbdMWgQYM4u4G3b99O3+lEI7S2GCtrEduzZw8iIiLw008/KW3cUln/7YcTeFZF2UMEH65HSAghiir7PqnKQ0oVkTdwf/z48Uo7h66xsbFBr169WPHnz5/j6tWrGsiI1HZaWYxJJBIsWLAAe/fuRc+ePbFs2TKljSsRiUS4f/8+eDwenJycFD5O2Vix3NxcpeRFCCG5ubnVGof6oeTkZBw/fpwVb9y4MWuy39pmxIgRnPE9e/aoORNCtHDM2PuFWPfu3bF8+fJyv5gyMzORlZUFGxsbmSkrEhIS4OfnJ9MHLBKJsGzZMiQnJ6NDhw7S9RAVwePx4OjoiLdv38LIyEi6wHRlMQwjHbxMYwXUj66/5tE9+A/DMCgoKEBubi6cnJyUdj3+/vtvzhn3x48fX+ufkO7VqxcsLS1Zf1Dv27cPy5Ytq/X/Jol6ad3UFpGRkVi9ejVMTU0xevRozub68PBweHp6yuw/bdo0mZn2Q0NDAZQ+rlynTh3k5eXh2rVrSExMRL169bBt2zY4OztXK1eGYfDu3Tvk5OQoNM5AIpHU+i9ETaLrr3l0D/7D4/FgZWWFunXrKqUQkEgkaNasGZ4/fy4T19PTw6tXr1CvXj1kZ2fD2tq61hYeH330Ef755x9W/O7du2jRooXKz88wTK2/B5qmLfdA61rGkpOTAQACgQB//vkn5z7Ozs7SYkyeYcOG4cKFC4iLi0NWVhb09fXRoEEDTJkyBePHj1fKwNWyrk5HR0cIhcIqvZZhGOTm5sLS0pI+hBpA11/z6B7IMjAwUOo0HzExMaxCDAC6d+8OZ2dnGqgOoG/fvpzF2IEDB9RSjBFSRutaxmoLbanGayu6/ppH90C1Ro4cie3bt7Pie/bswYABA+j6o3SiYwcHB9Yf00FBQYiLi1P5+ekeaJ623APqHyCEkBomOzubcyC6g4MDIiIiNJCRdrKyspIOaXnftWvXkJSUpIGMSG1FxRghhNQwe/bs4Zy6Z/To0UpdZqkmkDeR+MGDB9WbCKnVqBgjhJAahqt7Eqjdc4vJ06dPH874/v371ZsIqdWoGCOEkBokOTmZc/HvgIAANG/eXP0Jabl69eohODiYFT979iyys7PVnxCplagYI4SQGmTnzp2cT0rKm+SUcHdVikQiHD16VP3JkFqJijFCCKlBduzYwYrxeDwMHTpUA9noBnnjxg4cOKDeREitRcUYIYTUEA8ePEBCQgIr3rlz52pPcl2TeXh4wM3NjRU/evQoiouLNZARqW2oGCOEkBpC3sB96qIsH4/H42wdy8/Px5kzZ9SfEKl1qBgjhJAagGEYzi5KIyMjDBw4UAMZ6Za+fftyxumpSqIOVIwRQkgNEBsbi8TERFa8V69eSln+raZr1aoV6tSpw4ofOHCAc7F1QpSJijFCCKkBqIuyevh8PuecYykpKWpZGonUblSMEUKIjhMKhYiKimLFrays0LNnTw1kpJvkPVV5/Phx9SZCah0qxgghRMedOnUK6enprPigQYNgbGysgYx0U2hoKExMTFhxKsaIqlExRgghOo5r4D4ADB8+XM2Z6DZjY2N06tSJFb927RoyMzM1kBGpLagYI4QQHSYQCDif+KtXrx5nYUHK161bN1ZMIpEgOjpaA9mQ2oKKMUII0WFHjx5FQUEBK/7RRx+Bz+drICPd1r17d874iRMn1JwJqU2oGCOEEB22e/duzvhHH32k5kxqBnd3dzRo0IAVP378OOean4QoAxVjhBCiowoLC3H48GFWvFGjRggICNBARrqPx+NxdlW+efMG9+7d00BGpDagYowQQnTU8ePHObsoBw8eDB6Pp4GMagbqqiTqRsUYIYToqF27dnHGBw0apOZMapawsDDO8XY0xQVRFSrGCCFEBxUVFeHQoUOsuKurKwIDAzWQUc1hZWWF1q1bs+IXLlyAQCDQQEakpqNijBBCdNCJEyeQn5/Pig8aNIi6KJWAa9xYcXExYmJiNJANqemoGCOEEB0k7ynKwYMHqzmTmkneuDHqqiSqQMUYIYTomOLiYhw8eJAVr1+/PoKDgzWQUc0TEBAAOzs7VpwG8RNVoGKMEEJ0zKlTp5Cbm8uKUxel8vD5fHTp0oUVf/ToEV6+fKmBjEhNRsUYIYToGHlPUVIXpXLRFBdEXagYI4QQHVJSUoIDBw6w4s7OzmjVqpUGMqq5unbtyhmncWNE2agYI4QQHRIdHY2cnBxWfNCgQdDTo690ZXJycoKPjw8rfvr0aQiFQg1kRGoq+uQSQogOkfcUJU30qhpcXZW5ubm4du2aBrIhNRUVY4QQoiOEQiH279/Pijs5OaFt27bqT6gWkNdVefbsWTVnQmoyKsYIIURHXLhwAVlZWaz4wIEDqYtSRdq2bQtDQ0NW/Ny5c+pPhtRY9OklhBAdwTVwHwAGDBig5kxqDxMTE7Rp04YVv3TpEoqLizWQEamJqBgjhBAdwDAMZxelra0tOnTooP6EapHOnTuzYoWFhYiLi9NANqQmomKMEEJ0wM2bN/Hq1StWPCIiAvr6+hrIqPYICQnhjNO4MaIsVIwRQogO4GoVA4B+/fqpNY/aqHXr1jA2NmbFqRgjykJ/ThFCiA7gKsaMjY3lPu1XGzAMA3FWHkpevEPJy3dghCLo21qCb28FfXsr6NtZQc/EqNrnMTIyQtu2bXHmzBmZ+JUrV1BUVMRZqBFSFVSMEUKIlktMTMTt27dZ8a5du8LMzEwDGWmGpLAYedHXkXP0CooevETJi3eQ5BaU+xp9RxuYh/jBslsrWIS1BN9SsevVuXNnVjFWXFyM2NhYud2YhFQWFWOEEKLl5D1FWRu6KBmhCHnnEpC9Nwa5R65Akl9YpdeLUrOQ/e9ZZP97FtDnw7ydNyx7toHN0NAqFWZcg/iB0q5KKsZIddGYMUII0XJcXZR6enqIiIhQfzJqIiksRupv/+J+85F4MfQbZEedqXIhxiISIz/mJt7MXYuH/uOR+vtuSAorNz1FUFAQTE1NWXEaN0aUgYoxQgjRYunp6bhw4QIr3q5dOzg4OGggI9VixGJk7jiFR0GT8O67vyHOyFXJecRZeXj37SY8DJyIjL+PghGKyt3f0NAQ7du3Z8VjY2MhEAhUkiOpPagYI4QQLXb48GFIJBJWvCZ2UeZFX8eTTtORNO1XCN+kq+WcorcZSJ69Go9aT0bemRvl7svVHSkUCnH58mUVZUdqCxozRgghWkzeeLG+ffuqORPVEecKkPx5JLL3xFT6NQYN6sDUrxkMG9aFYUMnGDasCz0zE4gzcyBKz4EoIwfC16nIi45Hyct3FR6vJPEtEgcthOPsoajz1Ujw9PmsfcobNxYeHl7p3An5EBVjhBCipQQCAU6cOMGKe3t7o0mTJhrISPkEN5/g1YSfUJL4tsJ99Z3sYN2vA6wHdIJJgBt4PF6Fr2EYBsUPXyH3eCxyj8ZCEP+o3P1TV0ahIPYeGqyfCwMnO5ltLVu2hLm5OfLz82XiNG6MVBd1UxJCiJY6deoUCgvZg9ZrQhclwzBIX3cAz7p9XmEhZhrkiUb7lsDzzv9Q78ePYdrSvVKFGADweDwYe7rCcdZQND31Kxof+hmmQZ7lvqbg8l087jQNeWdluy0NDAw4l566du0aq0AjpCqoGCOEEC0lb9Z9Xe+iFOfk4+XoH/Bm3rpyB84bNqkH1/8tQJPjv8Cikx94etX/lWXezhtNjv+Chju/gXGLRvJzTM9B4qBFSF21SybO1VUpEolw6dKlaudGai8qxgghRAuJxWIcPnyYFXdxcUFAQIAGMlIO4dsMPO0xB7lHrsjdh29tjnrLP4X75T9h1btdpVvBKovH48GyWys0i4lEvZ+mgGcoZ8QOw+Dd4s1499M2MAwDoPxxY4QoiooxQgjRQnFxcUhPZz9R2LdvX6UXJ+pS/PwNnvb4AsUPX8rdxzTYE83Or4b9hAjwDFQ7rJmnpwf7j/ugybFfYOhaV+5+qct2IOXHLWAYBv7+/rCysmLtQ8UYqQ4qxgghRAsdOnSIM96nTx81Z6IchfcS8aznHAhfpcjdx2HWEDQ59DMMXRzVmBlg6u+GZud+h2VEW7n7pK6MwrtvN0FPTw8dO3Zkbb9+/Try8vJUmSapwagYI4QQLcTVRWlmZoZOnTppIJvqKYi9h2e9voQoNYtzO9/eCo12fQ+nRWNV3homD9/KHK7/WwCnHyYBcloe0yL34O2C9QjhuAcSiQSxsbGqTpPUUFSMEUKIlnn58iXu3LnDinft2hVGRkYayEhxeecS8HzgQrkLehu5N0Czs5GwCGup5szYeDweHD7tD5fVs+QWZOl/7kfnR9zLMl28eFGV6ZEajIoxQgjRMkeOHOGM69palIIbj/Fy1Pdg5Kz/aBLghiZHlsHQ2V7NmZXP9qNw1F/3BSDn6U3+7ssYZsGe542KMaIoKsYIIUTLcHVRAkCvXr3UnIniip8mIXHo15AUFHFuN+/kh8b7l0Lf1lLNmVWOzaDOaLBhLsDn/jU5x6AZAvRtZGKxsbEQCoXqSI/UMFSMEUKIFikoKMCZM2dY8eDgYNSpU0cDGVWd8F0mng9aJHeRb8uItmj4z2LwzU3UnFnVWPfrANdN8wCOpZH0GR5WmAXAWe+/9yAQCHDz5k01ZkhqCirGCCFEi0RHR6O4mN2tpytdlOLcAiQOXiT3qUnrIZ3humke9IwM1JyZYqx6t0ODtV9wbrPVM8Qq85YwxX/FGk3+ShRBxRghhGgReV2UulCMSYpK8GLEdyi6l8i53aJrEOpHzuJchFubWQ/sBMfPh3Fua8a3wFIzX+kvUxo3RhRBxRghhGgJiUTCOXjf2dkZfn5+6k+oChiGQdLMVSi4xH4KFABMAz3gummexqauqK4680bKnYcsxLAOPjFuBqC0GCubrZ+QyqJijBBCtMSNGzfw9i170eyIiAitn3U/Y/0hZO/inoXeyK0+Gv7zLfRMjdWclfLw9PRQf83nMPZqzLl9onET+OvbICUlBc+ePVNzdkTXUTFGCCFaQle7KAti7+HNwvWc2wyc7NBo9/da+9RkVfDNTdBw+yLoO1iztunxePjR1Adm0KeuSlJlVIwRQoiW4CrGjI2NERoaqoFsKkf4LhMvxy0BRGLWNj1LMzTa/YPalzdSJcP6deC6ZSHn4uLOfFPMNfWkYoxUGRVjhBCiBd68eYP4+HhWPCwsDKamphrIqGKSEiFejlsCUQr3MkcN/poDY09XNWelematmqPugjGc2/oauUAYnaDmjIiuo2KMEEK0gLxZ93v37q3mTCrv7dcbIbh6n3Ob45fDYdk1WM0ZqY/91P4wa+/DuW1Ctg3e3X2s5oyILqNijBBCtICuzbqfvScGGX8d5Nxm0SUIdb4cruaM1Kt0QP9siIzY3ZXWeoZ48ekv9FQlqTQqxgghRMOKi4tx+vRpVtzPzw8uLi4ayKh8JUmpSPp8Nec2w4Z1UX/dF+DJWdexJjF0cYTR3CGc20zvJiFjE3drJyEfqvmfFkII0XIXLlxAQUEBK96zZ08NZFM+RizG609WQJLLzpdnYgTXrYugb22hgcw0w3PGcJxFBue2d4s3Q/guU80ZEV1ExRghhGjY0aNHOePaWIyl/bFP7sSuLr/NgEmLRmrOSLP09PRwpXUdpEjYC6JL8gvx9usNGsiK6BoqxgghRMOOHTvGitnY2KBVq1YayEa+wrvPkfLjFs5tNsPCYDO4s5oz0g4tO3fA1wW3Obdl7z6H/Au31JwR0TVUjBFCiAY9f/4cDx8+ZMW7desGfX3tWTpIUlSCVx8vByMUsbYZNKiDej99ooGstEP79u0RK8rAkeJkzu3Jc9ZAUiJUc1ZEl1AxRgghGsTVKgYAPXr0UHMm5Xv33d8ofviSvYHHQ4O1n4NvqZ1zoalDQEAAjI2NsaLwIfIYdtFV/Pg10v/cr/7EiM6gYowQQjRIXjHWvXt3NWciX/75m3KLCYeZg2DWxku9CWkZIyMjBAcHI4MpwZrCJ5z7pCzbgZKkNDVnRnQFFWOEEKIhRUVFOHPmDCseGBgIR0ftWEJIIihC0me/c24z9mmCOl+NVHNG2qldu3YAgKjiV3goymVtZwTFeLvgL3WnRXQEFWOEEKIhMTExKCwsZMW16SnKlGU7UPLiHSvOMzZEg3VzoGdooIGstE+bNm0AAGIw+FFwj3OfnEOXkHeaveQVIVSMEUKIhmj7lBaFt58h7Y+9nNucvhkHY/cGas5Ie73/5OttcTb2Fb/m3O/Nog1gxOxF1UntRsUYIYRoCFcxZmdnh8DAQA1kI4sRiZH02SpALGFtMw3yhN0k7V0zUxMcHR3RuHFj6c+rCh8jR1LC2q/44Utk/XtWnakRHUDFGCGEaMCTJ0/w9OlTVrx79+7g8/kayEhW+roDKLzJzo9noA+X32bUiuWOqqp169bS/89iShApbzD/0q2QFLELNVJ7ad2nKSUlBX///TfGjx+PkJAQeHl5oV27dpg+fTpu3araxHkSiQTbtm1D79694ePjg9atW2PmzJl48eKFapInhJBK0uYpLUpevsO7pVs5tznMHAxjT1c1Z6Qb3i/GAGBvyWu8EOez9hMmpdG6lUSG1hVjW7duxdKlS/H69Wu0bdsW48aNQ8uWLXH69GkMGzZM7hgLLt988w2+//57SCQSjBw5Ep06dcKZM2cwaNAgzr9ICSFEXbi+y3g8Hrp166aBbP7DMAySPv8DjKCYtc2omQscZw/VQFa6oWwQfxkxGLmtY6kroyDmWN+T1E48hmEYTSfxvpMnT8LW1pY1ZuL69esYO3YszMzMcOHCBRgaGpZ7nNjYWIwZMwaBgYHYvHmzdP8rV65g3LhxCAwMxLZt21T2PirCMAyys7NhbW0NHo+nsTxqK7r+mleb74FAIICtrS2Ki2ULnlatWiE2NlYtOci7/tl7YvBq0s+cr2lyZFmtn1OsPCUlJbCyskJRkew6lYfr90B9dgMZHD4fBuNPe9fKz4C20JbvIa1rGevatSvn4NXAwEC0atUK2dnZePToUYXH2bVrFwDgs88+kync2rRpg/bt2+PatWtITExUXuKEEFJJZ8+eZRVigOafopQUFMld2Np2TA8qxCpgaGiIli1bsuK/FTzg3D997T6I07JVnBXRBVpXjJWnbJ22yqzXdvXqVZiamiIgIIC1rX379gCAa9euKTdBQgipBHnjxTRdjKX+FgXh2wxWXL+uLZy+HaeBjHTPh+PGACA68wX0Wnmw4oygGDmR+9SRFtFy2rMKbQXevHmDy5cvw8HBAW5ubuXuKxAIkJaWBjc3N86nkho2bAgA5Q7kV3XvbdnxtayXuNag6695tfkeHD9+nBVzcHCAv7+/2q7Hh9e/OPEt0iL3cO7r9MMk6Fma1cp7VVXvzzf2vgcdXOF+lb0gfEHUWRTNHALjJs6qTo1wUMf3UGW6P3WiGBMKhfjyyy9RUlKCL774osLHvvPy8gAA5ubmnNvL4vn5HJ34/y8nJ0ctXzw5OTkqPweRj66/5tW2e/D8+XM8e/aMFQ8NDUVuLnsZHVUru/5pX60BUyJibTdq5QkmxBvZ2dlqzkw3eXp6csZPPL8D/95tIDh0RXaDSIzkJVtgt+ITNWRH5FHl95CNjU2F+2h9MSaRSDB//nxcu3YNQ4YMQb9+/dRyXisrK5Uen2EY5OTkwMrKigZuagBdf82rrffgypUrnPHevUsHcqvL+9c//0w8iqJvsHfS00P95VNhUolfJqSUtbU1XFxckJSUJBNPSEiAy67v8PhYHCCSnYFfcOgy6i8aC8OGTupMlUB7voe0uhhjGAYLFy7EwYMH0adPHyxevLhSr7OwsAAgv+WrLC6v5QyoXLOiMvB4vFr1i0jb0PXXvNp2D06cOMGK8Xg8dO3aVSPXgRGK8HbBes5tduN7wtSrMec2Il/r1q2xe/dumditW7cgqWsNuzHdkbHxgznGxBKkRe6By8rpasySvE/T30NaO4C/rEVsz549iIiIwE8//QS9Ss74bGpqCgcHByQlJUHMsQZY2VixsrFjhBCiDsXFxThz5gwr3rJlSzg4OGggIyBj/SEUP0lixfk2Fqgzb5QGMtJ9XIP4xWIx4uPj4TBzMKDPHmqTteMUhG/S1ZEe0UJaWYxJJBIsWLAAe/fuRc+ePbFs2bIqLw8SHBwMgUCAGzfYTe8XL14EAAQFBSklX0IIqYxLly5BIBCw4t27d9dANoA4PQepy3Zwbqu7cDT0bSzUnFHNwFWMAaVd1IYujrAZFsbaxpSIkLaae1F2UvNpXTH2fiHWvXt3LF++vNxCLDMzE8+ePUNmZqZMfMiQIQCA3377DSUl/60BduXKFVy8eBFBQUFo1KiRat4EIYRw4HqKEoDGZt3PWbUHkvxCVtzYqzFsR2umQKwJAgICOKdgKpvQ1/GzIQBHT0/G/45BlF67HmghpbRuzNgff/yBvXv3wtTUFA0bNsTatWtZ+4SHh0ufWNm+fTtWr16NadOmYfr0//rbW7dujcGDB2PXrl3o378/OnXqhIyMDBw9ehTm5ub49ttv1fWWCCEEAHcxZmVlJbclRZWKHr9GQdRZzm3OP08BTwsWK9dVJiYm8PPzw/Xr12XiV65cAcMwMGpcD9b9OyB7T4zMdqawGGlr98Fp0Vg1Zku0gdYVY8nJyQBK5wr7888/OfdxdnaW+/jw+7777ju4u7sjKioKW7duhampKTp37oxZs2ZRqxghRK3evHmDO3fusOLh4eGVmsha2VK++xsQS1hxq/4daaZ9JWjTpg2rGHv37h1ev36NBg0awHHWUFYxBgAZGw7DccYg8K3kP2BGah6tW5uyttCW9bBqK7r+mlfb7sHmzZsxfvx4Vnz9+vWYOHGiWnMpuHIXz3p9yYrzDPXhfvUvGLrWVWs+NdGOHTswYsQIVjwqKko6jObFiO+Qe4y9Fmmd+aNQ54uPVJ4j0Z7vIa0bM0YIITWRtowXYxgGb7/eyLnNbmJvKsSUpLxB/GUcZg/l3Cd97X6IOcbykZqLijFCCFExsViMU6dOseLNmzdH/fr11ZpLzsFLEMQ/YsX5VuZw/HyYWnOpyRo1asQ5XUnZIH4AMA1wg3EHb9Y+4qw8ZG7hLt5JzUTFGCGEqNi1a9eQlZXFiqt7SgtJiRDvvtvMuc1h1hCaykKJeDweZ+tYQkIChEKh9GeLT/tyvj79r4NgOObJJDUTFWOEEKJi2tJFmfn3MZQkvmXFDVwcYP9xH7XmUhsEBwezYsXFxTIPchgHe8K0dQvWfsJXKcg9dlWl+RHtQcUYIYSoGNcSSCYmJujYsaPachDnCpCyXN4Er2OgZ2yotlxqC3kTi1+7dk3mZ/tP+nHul752n7JTIlpKoWJsyZIlePr0qbJzIYSQGicjIwNxcXGseEhICIyNjdWWR/rafRBn5LLixt6NYT0oRG151CaVLcYse7TifHCi4Mo9CG4+UUluRLsoVIxt2bIFvXv3xrBhw7B3714UFtJTH4QQwiU6OhoSCXs+L3WOFxNl5iJtDXcri9O348Gr5Lq/pGpsbW3RpEkTVvzD4pzH58NOTjdx+p/7VZEa0TIKfQLXrVuHsLAw3L17FwsWLED79u3xzTff4O7du8rOjxBCdBpXFyWg3vFiaav3QJLHXhPTqL0XzEP81ZZHbcTVOnbv3j0UFBTIxGxHdIWeuQlr35x9FyB8m6Gy/Ih2UKgY69SpE1avXo3z58/j888/h6OjI6KiojB48GD069cPO3bsQH5+vrJzJYQQncIwDE6ePMmKu7q6ws3NTS05CFOzkP7XQc5tVrOHqCWH2oxrEL9EIkFCQoJMjG9pCtuR7AKdEYqQsemIyvIj2qFabdO2traYOHEijh07hm3btqFv3754+fIlvv/+e7Rv3x5fffUV4uPjlZUrIYTolPv370uXeHtft27d1Dbbd9qv/4IRFLPiFt1bwciX3YVGlEveuDGucYT2k/twLyD+91FICtn3kNQcShsoEBgYiJ9++gl//fUXHBwcUFRUhP3792PkyJHo3bu33Ee7CSGkpuJqFQPU10VZkpSGjM3crSp15o1USw61nb+/P/Q4CqwPB/EDgKFrXVj2ZM9NJs7IRdYu7kXdSc2glGIsPz8fO3fuxIABAzB69GikpqbC398f3333HYYOHYqkpCTMmjUL69evV8bpCCFEJ3AVY3p6eggNDVXL+VNX/gOmRMSKW/XrABOvxmrJobYzMzODlxd74XWuYgwAHORNc/HnAdBS0jWXfnVefP36dezevRsnTpxAYWEhLC0tMWLECAwdOhTNmjWT7jdjxgyMHTsW27Ztw6RJk6qdNCGEaLuioiLExMSw4q1atYK1tbXKz1/84i0yt3G0zOnpoc5X1CqmTkFBQbh9+7ZM7NmzZ8jIyACfz5eJm7ZuARPfpii8JTt9VPHDl8g/lwCLzgEqz5eon0ItY5s2bUKPHj0watQo7N+/H+7u7li6dCkuXLiAhQsXyhRiQOnYsm7duiE1NVUpSRNCiLa7ePEi57Q/6uqiTF22AxCxl9OxGRoKYzf1rodZ23EN4gdKGzQ+xOPx5E4Cm7H5qDLTIlpEoWJs2bJlSE9Px/Dhw3Hw4EH8888/6N+/P4yMjOS+xsvLC/369VM0T0II0SnyprTo2rWrys9d/DQJWf9yjDHS58NxzkcqPz+RVdnJX8tY9esA/bq2rHjusVia5qKGUqibcsmSJejZs2eVZo/u1KkTOnXqpMjpCCFE53CNF7O2tpb7i1mZUldGARwTzdqO7Aqjhk4qPz+R5eXlBWNjYxQVFcnEuVrGAEDP0AC2I7sh9ZedshvEEmRuP4k6X1BBXdMo1DKmp6eHFy9elLvP48ePsX//fkUOTwghOu3t27esMUIAEBYWBn39ag3VrVDx8zecT97xDPXhOHuYSs9NuBkYGMDfnz25blxcnNxB+baju3FOc5H5v+NgxOzuZ6LbFCrG5s2bh+jo6HL3OXfuHObNm6dQUoQQostOnTrFGVfHeLHUX/8FxBytYqO6wdDFQeXnJ9y4WkTfvXuHN2/ecO5v6OIIi67s1wiT05AXzd2iRnSXQsVYZR6vlUgknHOrEEJITaep8WIlL98hK+o0K84z0IfDzMEqPTcpn7zu6Q9n4n+f3bienHEayF/zqKxaun//PqysrFR1eEII0UoSiYSzZczd3R2urq4qPXfqb7u4n6Ac3gWGLo4qPTcpn7wnKm/cuCH3NRahATDgaM3MO3UdJa9TlJYb0bxKD14YPXq0zM/79u3jXM5BIpHg3bt3SE5ORo8ePaqfISGE6JCbN28iLS2NFVd5q1hSKrJ2cHSP6vPhOIvWoNS0pk2bwsrKCjk5OTLx8lrGeHw+bMf0QMqPW2Q3MAwytxxH3QVjVJEq0YBKF2PvF148Hg/Jycmca67p6enBysoK3bt3x/z585WTJSGE6Ah5SyCpuhhLW7ULjJA9277N0DAYNqij0nOTiunp6SEwMBCnT8t2IyckJEAikbAmfy1jO6IrUn7ezmrxzNx6EnW+HAGegWofCCHqUem7+PDhQ+n/e3h4YNq0aZg2bZpKkiKEEF3FNV7MwMAAISEhKjun8E06MrdyjFPj68Fx9lCVnZdUTXBwMKsYy83NxZMnT+Dh4cH5GoO6trDq2QY5By/KxEWpWcg5Ggvrvu1Vli9RH4XGjG3ZsgX9+/dXdi6EEKLT8vPzcenSJVa8Xbt2MDc3V9l5U3/fzbkGpc3gzjBqRPOKaYuqTv5axlbOQP7Mv2kgf02hUDEWHBwMZ2dnZedCCCE6LSYmBkKhkBVX5ZQWwtQsZG45zt6gpweHWdQqpk0ULcbMO/jAsEk9Vjw/5iaKn7GHCxHdU6luyrLJW8PDw2Fubl6lyVxpCSRCSG2hifFi6esOgCkqYcWtB3SEcTMXlZ2XVJ2zszOcnJzw9u1bmbi8mfjL8PT0YDemJ95+vYG1LWtnNOoupIH8uo7HVGLSMA8PD/B4PBw9ehSNGjWS/lwehmHA4/Hw4MEDpSVbkzAMg+zsbFhbW1d4LYny0fXXvJp4Dzw9PWXG1wKAvb09UlJSVDLvoji3AA+8x0CSJ2Btc7v8J4w9Gsh9bU28/rqgT58+OHTokEzMxMQEubm55a7OIMrIwYMWo1jd0QZOdvC4/Td4ch4AIOXTls9BpVrGlixZAh6PBweH0vlOli5dqtKkCCFE17x+/ZpViAFAly5dVDYBdsamI5yFmGVE23ILMaI5LVu2ZBVjhYWFePjwIby8vOS+Tt/OCpY9WiPngOxAfuHbDOSfuwmLsJYqyZeoR6WKsQEDBsj8TIP3CSFElrwlkFTVRSkpLEb62v2c2xw/o3nFtFVgYCBnPD4+vtxiDCidvPfDYgwAMnecomJMx9F6RYQQogTyxot16dJFJefL3HEKorRsVty8kx9MA9xUck5SfS1bchdN8fHxFb7WIjQA+k52rHju0SsQZedVOzeiOVSMEUJINUkkEkRHR7PizZs3V8mT54xIjLTIPZzbHKhVTKvVrVsX9eqxn4ysaBA/UDojv83QUFacKRYie0+MUvIjmlGpbsqwsDCFDs7j8Ti/oAghpCZJSEhARkYGK66qLsrsvTEQvmKvTWgS4Abzjr4qOSdRnpYtW+LNmzcysZs3b0IkEpU7iB8AbId3Qdpvu1jxrB2nYD8hQql5EvWpVMsYwzAK/SeRSFSdPyGEaJw6p7RgJJLSBcE5OH42hJ6M1AFcXZVlg/grYtTUBabBnuzXJzxB0f0XykiPaEClWsbOnDmj6jwIIURncRVjhoaG6Nixo9LPlXsiDsUPX7LiRs3qw7Jna6WfjyhfdQbxA6UD+QVx7GmjMnecQr0fJlU7P6J+NGaMEEKqoaCgQO4SSGZmZko/H1cXFQA4zBwEnoqm0CDKJW8Qf2XGjQGAdb+O4JkYseLZu85yLhZPtB99cgkhpBrkLYGkii7Kgth7EFxjt4gYODvAelCI0s9HVEPeIP7KPFEJAHxLU1j1aceKi9KykXuq/KWViHai5ZAIIaQa1DleLG21nCcopw2AnqGB0s9HVKc6g/iB0oH82VHsIURZO07BqmcbpeVJ1KNSxdhXX30FHo8HX19fmJubS38uT9lySFSMEUJqMq5izN7eHn5+fko9T9GTJOQeu8qK820sYDtSdQuRE9VQdCb+MmbtvGHQoA7rqdrck9cgSsuGvoO1MtMlKkbLIRFCiIKSkpI4198NDw9X+hJI6X/sBTiWErabEAE9M2OlnouoXnUH8fP09GAzLAypy3bIbhCJkb3vPOw/7qOMNIma0HJIhBCiIHUtgSRMzUJW1GlWnGdkALuJNLeULipvEP+YMWMqdQzbj8LZxRhKB/JTMaZbaAA/IYQoSF1LIGWsPwSmmP2QgM2wcBg42ij1XEQ9qjuIHwAMXevCtFVzVlwQ/wjFz99wvIJoq0q1jMkjEAhw+vRpPHjwAPn5+TA3N4enpyfCwsJgamqqrBwJIUTryFsCydPTEy4uLso7T0ERMjYdYW/g8eAwlXopdFl1B/EDgM2QzhBcvc+KZ+86izpzRyglT6J6Chdjhw8fxvfff4/c3Fww741j4PF4sLS0xNdff41evXopJUlCCNE2N2/eRHp6Oiuu7C7KzO0nIc5iLwJt2bM1jJoqr+gj6lfdQfwAYNW3A5Ln/gmIxDLxrN1n4fjlcFqRQUcoVIydOXMGc+bMgZGREYYPH46WLVvC3t4e6enpuH79Ovbu3Ys5c+bAzMwMISEhSk6ZEEI0Tx1dlIxIjLQ1+zi3OUwbqLTzEM0ob9xYZYsxfVtLWIQHIu+47JO2Jc/eoDDhCUwD3KqdJ1E9hYqxtWvXwszMDLt27UKjRo1ktvXs2RMjRozAkCFDsGbNGirGCCE1EtfgfQMDA6V+5+UcvMi5ILhpkCfMOMYKEd0irxiLj4/H2LFjK30cm8GdWcUYAGT9e4aKMR2h0AD+x48fo2fPnqxCrEyTJk3Qq1cvPHr0qFrJEUKINhIIBLh48SIrrswlkBiGkd8qNp1axWqCunXrwsnJiRWvyiB+ALDs3gp65iaseM6+82A+6L4k2kmhYszc3ByWlpbl7mNhYQELCwuFkiKEEG12/vx5lJSUsOLK7KIUXL2PwhuPWXHDJvVg2aOV0s5DNMvX15cVKxvEX1l6Jkaw6s29PFJ+zM3qpEfURKFirHPnzjh37hzEYu6KWyQSISYmBqGhodVKjhBCtJE6lkCS2yr2aX/w+HylnYdoFtdKDWWD+KtC3tqkWbvYSyYR7aNQMfbll1/C0NAQkyZNwq1bt2S23bx5E5MmTYKRkRG++OILpSRJCCHahGu8mK2tLfz9/ZVy/OLEt8g9coUV59tawmZomFLOQbSDvGWzrl+/XqXjmHf0hX4d9pxzuUeuQFJQpEhqRI0qNYA/LIz94RcKhXjw4AGuXLkCfX19WFtbIzs7W9q06uDggAEDBnDOw0MIIbrqzZs3uHv3LiseHh4OvpJarNLXHeBe+mhcT+iZ0tJHNQlXNyVQ9UH8PD4f1gM6IX3tfpm4pKAIOcdjYTMwRPEkicpVqmWMYRjWf/r6+nBycoKTkxMcHBxgYGAABwcHaUxfXx8SiUTV+RNCiFrJ+wNTWePFxDn5yNrO7gblGerT0kc1UJ06dThn4k9ISKjysawHd+aMZ+86W+VjEfWqVMvYmTPU50wIIYDq5xfL+N9xzm4l64EhMKhjq5RzEO0SEBDAORO/WCyuUmuriW9TGDWrj+Inr2XieafjIcrIgb6dlVLyJcpHa1MSQkglyVsCyc3NDa6urtU+PiMUIeOvg5zb7D/pV+3jE+3ENdawoKAAT548qdJxeDwerAeHsDeIJcg5zB6DSLQHFWOEEFJJd+7cQUoKexJWZT1FmX3wIoRv2EssmXfyg4lXY6Wcg2ifgIAAzviNGzeqfCxrOWPDcvafr/KxiPpUa6HwhIQEXL58GampqZxz7vB4PCxZsqQ6pyCEEK3B9RQloJwuSoZhkC5nOgv7T2lB8JpMXjGWkJCA4cOHV+lYRo2cYOLXFIU3n8rE8y/chjA1CwaO7CcuieYpVIyJRCLMnj0bp06dAsMw4PF4rMXCy+JUjBFCagqu8WL6+vpKWQJJcPU+ChPY3VJGzerDIox72RxSM7i4uMDOzg4ZGRkycUVaxgDAql9HVjEGiQQ5hy7BfgI9BKKNFOqm3LRpE06ePIkBAwZgz549YBgGY8aMQVRUFL744gtYWlqie/fucv+KJIQQXVNYWIgLFy6w4q1bt65wRZLKkDfJq/0n/cDToxElNRmPx+NsHbtx44ZMQ0dlWffvwBnP2UddldpKoU/4oUOH0KxZM/z4449o0aIFgNLlj3x9fTFx4kRs27YN586d41y7jRBCdNHFixdRVMR+ylEZ48VKXqUg92gsK146ySutZFIbcBVj2dnZePHiRZWPZVi/DkxburPiBVfuQfg2g+MVRNMUKsZevXqFVq3+WxuNx+PJrKPVrFkzdO7cGTt37qx+hoQQogVUOV4sff0hgGNeRrtxPaBnYlTt4xPtp8xB/ABg1b8jO8gwyDlIjSTaSKFizMDAAMbG/80CbWpqiszMTJl96tWrh5cvX1YvO0II0RJc48Wsra0RGBhYreOK8wuRufUEe4M+H3bjaXxPbSFvKS1FJn8FAKu+3F2V2dRVqZUUKsacnJzw9u1b6c+NGzfGtWvXZPq2b926BSsrmmCOEKL7UlJSWOvwAkBoaCj09av1UDqy/omGJLeAFbfu1wEGTnbVOjbRHU2aNIGFhQUrrmjLmKGzPUxbt2DFBXEPUJKUptAxieooVIwFBQXh+vXr0uKrZ8+eSExMxOTJk7F9+3bMnj0b8fHx6NCBuzInhBBdoqolkBiJBOnyJnmd3Ldaxya6RU9Pj7N1LD4+XqFB/ABgzdVVCSDnAPtBFKJZChVjAwcOREhICN69ewcAGDlyJEJCQnD+/Hl8//33OHr0KLy9vfH5558rNVlCCNEEeePFqjt4P+90PEqeJrPipkGenAOwSc3GNW4sNTVVpieqKqx6twN4PFacuiq1j0Lt6y1atMDixYulPxsYGODPP//EnTt38Pr1a9SrVw8+Pj7Qo8exCSE6jmEYzvFiTZo0QePG1ZsVP32dvFaxPtU6LtFN8saN3bhxg3Mx8YoY1LWFWTtvFFy8LRMvvPEYJS/fwdC1rkJ5EuVTarXk7e2Nnj17ws/PjwoxQkiNcO/ePc6Wieq2ihU9eoX8M/GsuIGTXWmLBql1ypuJX1Hy5hzL3k9dldqk2hVTSkoKYmJicPjwYcTExHCu20YIIbqKq1UMqH4xJm+smN3ECPAMqvdQANFNHh4eMjMVlFF0ED8AWPVuD/DZv+qpq1K7KPyJf/78Ob7//nvExrInKmzTpg0WLlxY7SZ8QgjRNK7xYnw+H507d1b4mKLsPGRFnWbFecaGsB3TQ+HjEt2mr68PX19fXL16VSZenWJM394K5h18kX9OtnWt6PYzFL94C6OGTgofmyiPQi1jz549w0cffYQrV66gadOmGDp0KKZOnYqhQ4eiWbNmuHz5Mj766CM8e/ZM2fkSQojaFBUVISYmhhVv1apVtabuydxyAoygmBW3GRIKfdvqL61EdBdXV+WrV6+Qnp6u8DGt+slZHungJYWPSZRLoZax5cuXIy8vDz/99BP69evH2r5//37Mnz8fv/zyC9auXVvdHAkhRCMuXbqEwsJCVrw6XZSMWIyMjYc5t9l/TAP3a7vyJn9VdCoVq55tkDx7NWuVh9xDl+A4Y5BCxyTKpVDL2LVr19ClSxfOQgwA+vXrh/DwcMTFxVUnN0II0ShVLIGUe/wqhK9TWXHzTn4wbt5Q4eOSmkEVg/j17a1g3t6bFRfEP0JJsuItbkR5FCrGeDweGjZsWO4+jRo1Ao9jfhNCCNEVXIP3LS0tERwcrPAx5Q7cp1YxAsDLy4tzVYfqjBsDAEs5T+jmHqauSm2gUDHm5+eHu3fvlrvPnTt35Fb4hBCi7VJTUzlbI8LCwhReAqno/gsUXLjNihu61oVl1yCFjklqFiMjI3h5ebHi1S3GrHq15ZwANucQFWPaQKFibO7cubh16xYiIyNRVFQks62oqAirVq3CnTt3MGfOHKUkSQgh6qaKJZDS1x/ijNtNiACPz1f4uKRm4Ro39uTJE+Tm5ip8TIO6tjAN9mTFC67cgzA1S+HjEuWo1J938+bNY8U8PDywZs0abNu2Dc2bN4etrS0yMzNx//595ObmIjAwEJs3b8aSJUuUnjQhhKiaspdAEmXnIevfM6w4z9QINiOrt8YlqVkCAgKwefNmVvzmzZvo2JF7vcnKsOrdDoKr92WDDIPco1dgN7anwscl1VepYmzfvn1yt+Xk5ODKlSus+LVr13D9+nUqxgghOkfeEkiNGzdGkyZNFDpm1taTYArlTGdhbaHQMUnNJG+Iz40bN6pZjLXF24XrWfGcAxepGNOwShVjp0+zJydUpQMHDiA+Ph53797F48ePIRQKsXTpUgwYMKDSx7h69SpGjx4td3tUVBT8/PyUkC0hpKa5f/8+3rx5w4or2kXJiMVIlzedxSQauE9k+fr6gsfjgWEYmXh1nqgEAMP6dWDi3wyFCU9k4vkXb0OUmUtz3GlQpYoxZ2dnVechY9WqVUhOToaNjQ0cHR2RnJys8LGCg4M5n3yqW5cWSCWEcFN2F2XuiTgIX7GXijPv6AtjT1eFjklqLjMzM7i5ueHRo0cy8eoWYwBg1ac9qxiDWILcY7GwHVG9Jb6I4rRyAbQffvgBrq6ucHZ2xl9//YUVK1YofKzg4GBMnz5didkRQmo6ri5KPT09hIaGKnS8DHkD92k6CyKHv78/qxi7f/8+ioqKONevrCyr3u3wbjF7PFrOoUtUjGlQtRYKP3ToEMaPH482bdrAy8sLrVu3xvjx43HoEPcXT2W1bdtW7a1xhBACAMXFxXKXQLK2tq7y8YoevER+zE1W3KBBHVh2U3y+MlKzcT1RKRaLce/evWod16hxPRi3aMSK559LgDhXUK1jE8Up1DImkUjw2Wef4dSpU2AYBsbGxnB0dERmZiYuX76MK1eu4OTJk1i1ahX09KpV71XbixcvsGXLFhQVFaFevXpo27YtbG1tK3zdh331ylZ2fFWfh3Cj66952noPLl68CIGA/UspPDxcoVzlT2fRC9DT09j719brX5uUdw/kjWm+ceNGtefwtOzdFkX3EmVzKREh98RVWA8KqdaxdY06PgeVmQBfoWJs69atOHnyJIKCgvDFF1/A19dXuu327dv45ZdfEB0dja1bt2LMmDGKnEJpDh8+jMOH/xs4a2xsjOnTp2PixInlvi4nJ0ctX1I5OTkqPweRj66/5mnbPTh4kHuG/DZt2iA7O7tKx5LkFiAriv0AFM/YEHoRrap8PFXQtutfG3Hdg8aNG3Pue/XqVQwaVL31JPU6+QA/bWfF0/eeA8L9qnVsXaXKz4GNjU2F+yhUjO3btw+NGjXC5s2bWTNR+/j4YNOmTejTpw/27t2rsWLM1tYWX375JUJCQlCvXj3k5ubi6tWr+OWXX7B8+XKYm5tj2LBhcl9vZWWl0vwYhkFOTg6srKxo2SgNoOuvedp6D86fP8+KWVpaIjw8vMoz76dHxXBOZ2E9pDPsGroonKMyaOv1r03KuwfW1tZwcXFBUlKSTPz+/fsKdZfLnDfICllNXVD8VPbYRedvw9LQGHqmio9J0zXa8jlQqBhLTEzEiBEj5H4x6evrIyQkBNu3sytvdWnWrBmaNWsm/dnExAR9+vSBh4cHBgwYgMjISAwZMkRuN6q6bgqPx6MvQg2i66952nQPUlJScPPmTVY8LCwMBgYGVToWI5Ego5zpLLTlPWvT9a+t5N0Df39/VjF269YtSCQS8KuxYgOPx4NVn3ZIXRklE2cKi5F//haserRW+Ni6StOfA4UGdBkYGKCwsLDcfQoLC6v85aUObm5u8PX1RXp6Ol6+fKnpdAghWkSZU1rkn7mBkmfsucrM2nrBhGMANSEf4hrELxAI8OTJE469q8ayVxvOeO7hy9U+Nqk6hYoxT09PHDt2DCkp7HlzgNIFdo8dO4bmzZtXKzlVKeu//XBdTUJI7cY1pQUAdOvWrcrHSt8gZ+D+xN5VPhapnbiKMUA5842Z+DWDgZMdK557Ig6MSFzt45OqUagYGz9+PLKzszFw4EBs2rQJd+7cwdu3b3Hnzh1s3LgRAwYMQE5ODsaNG6fsfKtNJBLh/v374PF4cHJy0nQ6hBAtIW8JpKZNm6JRo6q1ZBUnvkXeqeusuIGTHazktEgQ8iFVFmM8Ho+zdUycmYuCD9evJCqn0Jixzp07Y968eVi+fDmWL18us41hGOjr62Pu3Lno3LmzUpIsT2ZmJrKysmBjYyMzZUVCQgL8/Pxk+oBFIhGWLVuG5ORkdOjQodqDIAkhNcft27c5W/sV6aLM2HQE4Hga23ZcT/AMtHKubaKFGjRoABsbG2RlZcnElVGMAYBlr7bI2MAe15h7+DLM23kr5RykchT+VhgzZgxCQ0Nx8OBBPHz4EPn5+TA3N4enpyd69+6N+vXrK5zUrl27EB8fDwB4/PixNBYXFwegdL6f8PBwAMD27duxevVqTJs2TWam/c8//xxA6V8WderUQV5eHq5du4bExETUq1cPixcvVjg/QkjNo6wuSklBEbK2sY/FM9SH7ejuCuVGaicejwd/f3+cOXNGJp6QkACGYao94Ny8rRf4VuYQ5+TLxHOOXoHTko/pwQ41UqgY279/P+zs7NChQwdMnTpV2TkhPj4e+/btk4nduHEDN27cAFC6VmZZMSbPsGHDcOHCBcTFxSErKwv6+vpo0KABpkyZgvHjx6t86gpCiG45ceIEK1b2ZHhVZO0+x/rlBgBW/TrAwLHi+YYIeR9XMZaRkYGkpKRqNXoAAM9AHxbdgpD971mZuPB1KoruPoeJd5NqHZ9UHo9RYGbTFi1aYOTIkZg3b54qcqoVGIZBdnY2rK2t6a8PDaDrr3nadA8EAgFsbGxQUlIiE+/QoQPnvGPyMAyDJx2nsWY3B4CmJ1fCNNCj2rkqizZd/9qqMvdg27ZtGDVqFCt+8OBB9O5d/YdBcg5dwssxP7LijnOGo+68kdU+vrbTls+BQgP4HRwcIBQKlZ0LIYRoxPnz51mFGFD1LkpB7D3OQszEvxlMWrornB+pvVQ5iB8ALEJbgmdsyIrnHr2ilOOTylGoGAsPD8elS5c4v7wIIUTXcHVRAlUfvC93HcqJvan1iSjE3d0dxsbsGfGVVYzpmRnDPIRd8BXdS0Txi7dKOQepmELF2MyZM2FmZoapU6cqZfI5QgjRJK7B+7a2tlVakFn4NgM5HBNm8u0sYd2/Y7XyI7WXvr4+fHx8WHFlFWMAYNVTzgSwR6h1TF0UGsDfr18/lJSU4MGDB7h48SKMjIxga2vL+suPx+MhOjpaKYkSQogqJCUl4f599rxKXbp0qdKSMxn/OwZwTJZpO6ob9Di6gQipLH9/f+lsAmVevnyJzMxMmSmdFGXZvRWgpwdIJDLx3KNX4DB1QLWPTyqmUMsYwzAwMDCAk5MTnJycpP8YGIaR+U/ywY0lhBBtI29Ki6p0UUpKhMj83zH2Bj092I3rqWhqhACQP26Max1VRejbW8GsNXvFnIKrDyBKy1bKOUj5FGoZ+/AxW0II0VXKKMZyD12GKCWLFbfsHgzD+nUUzo0QoPxB/KGhoUo5h2XPNii4fFc2KJEg9/hV2I6q+nJgpGoUahkjhJCaQCwWcy4O3rx5c7i4uFT6OOkb2bOYA7QOJVEOb29vzi5zpY4bk7NMVw49VakW1S7GRCIRnj59ioSEBDx9+hQikUgZeRFCiMrFx8cjMzOTFa9Kq1jh3ecQxN5jxY2aucC8k1910iMEAGBiYgIPD/Ycdcosxgxd68LYqzErnh9zExJBkdLOQ7gpvBxSZmYmVq5ciSNHjqCo6L8bZWxsjIiICMyaNUspAwsJIURVjh8/zhmvyvxiXGv7AYDdhAiazoIojb+/P+7dky36Hz58CIFAAFNTU6Wcw7JHaxTdfS4TY4pKkHcuQe4Tl0Q5FGoZS0lJwaBBg7B7926YmJigffv26NevH9q3bw8TExPs2rULgwYN4lx0lxBCtAXX/GLGxsbo1KlTpV4vys5D1q6zrLiemTFshoVVOz9CynCNG5NIJLhz547SzmHZoxVnPPfYVaWdg3BTqGVs2bJlePPmDaZPn46JEyfCyMhIuq24uBjr16/H6tWrsXz5cvzyyy9KS5YQQpQlKysLsbGxrHjHjh1hYmJSuWNsPwWmsJgVtxkaBr6lWbVzJKRMeYP4W7XiLqKqysS3KfSd7CB6myETzz1xFYxYDF4VpnohVaNQy9jFixeli4S/X4gBgJGREaZNm4Z27drhwoULSkmSEEKU7fTp05zT73Tv3r1Sr2ckEmRsOsK5zW5CRLVyI+RDfn5+nHFlTW8BlM4NatmdXdiJ03MgiH+stPMQNoWKsZKSEjRvzp6T5H1eXl60XBIhRGtVd7xY3ul4lCSyl4sxa+8DY0/XauVGyIdsbGzg6sr+d6XMQfwAYNWjNWc89zi7FZkoj0LFmJeXFxIT2Yvhvu/58+fw8vJSKClCCFElhmE4x4vVr18fnp6elTpGhpzpLOwnUqsYUQ2ursrbt28rdRYDsw6+0DNnd9PTuDHVUnhtyrNnz2Lv3r2c23fv3o2YmBjMnDmzWskRQogqPHjwAElJSax4t27dKvUEZPGLt8g7dZ0VN3Cyg6WclgVCqourGCsqKsLjx8rrQtQzMoBFZ/aarMWPXqH4+RulnYfIUmgAf2xsLFq1aoUFCxZgw4YNCAgIgJ2dHTIyMnDjxg0kJiaiXbt2iI2NlRkgy+PxMHXqVKUlTwghiqhuF2XGpqMAw7DitmN7gGeg8IxBhJSrvHFjFQ0dqgrLHq2Rc+gSK557/CocPu2vtPOQ/yj0rbF69Wrp/z9//hzPnz9n7XPx4kVcvHhRJkbFGCFEG3B1UfL5fISHh1f4WomgCFnb2K/nGejDdnTlBv8ToojynqgcPny40s5j0SWQe+FwKsZURqFibMuWLcrOgxBC1EIgECAmJoYVb9WqFaytrSt8ffbe8xBn57PiVn3awaAOTXRNVMfFxUXaC/U+ZQ/i17ezglkrTxRckZ1ktuDKXYiy8qBvY6HU8xEFi7Hg4GBl50EIIWpx/vx5FBez5warzJQWDMMgfcMhzm00nQVRNR6PBz8/P5w+fVomfvPmTTAMo9QVHyx7tGEVYxBLkHfqGmyGKGdxcvIfWiicEFKrVGe8mODaQxTdfsaKG3s3hmkr5Y3ZIUQerq7KjIwMzgdSqkP+bPw0xYUqUDFGCKlVuMaL2dnZoWXLlhW+Vt50FrQOJVEXeYP4ld1VadTEGUbN6rPieafjISkWKvVchIoxQkgt8vLlSzx8+JAV79KlC/gVLPUiTM1CzgH2qiJ8K3PYDApRVoqElEveIH5lzsRfhqt1TJJfiIJLylsPk5SiYowQUmtwtYoBlRsvlrn1BJgS9uSaNiO6QM/UuNq5EVIZ7u7unGunKrtlDADn0kgAzcavClSMEUJqDXnFWNeuXct9HSMSI3PzUc5tduN7VTsvQiqLz+fD29ubFVdFMWYa5AG+vRUrnnv8KhiOefaI4qgYI4TUCkKhENHR0ay4r68vnJycyn1t7vGrEL5JZ8UtwgNh1Lie0nIkpDK4uipfvnyJrKwspZ6Hx+fDsksQKy5MSkPRgxdKPVdtp1AxNnr0aKxatUrZuRBCiMpcvnwZubm5rHhlnqLMkDedBa1DSTRArePGunFPZZV3PE7p56rNFCrGbt++DbFYrOxcCCFEZY4dO8YZ79GjR7mvK3r4Cvnnb7Hihg3rwiKs4icwCVE2dT1RCQDmIQGcS3zlnqBiTJkUKsYaN26M5ORkZedCCCEqw1WMWVhYoF27duW+LmOTnOksxvUCr4InMAlRBW9vb+jpsX99q6JljG9pCrN27DFqgusPIUrLVvr5aiuFirFRo0bhzJkzePr0qbLzIYQQpUtOTsbt27dZ8fDwcBgYGMh9nThXgKx/TrPiPGND2Iwsf9A/IapiamoKDw8PVlwVLWOAnKcqGQa50ddVcr7aSKHlkFxcXBAcHIwhQ4Zg6NCh8Pb2hr29Peekh0FB7MF/hBCiTvJm3a+oizLr39OQ5Bey4tYDQ2h9PqJRfn5+uH//vkzswYMHKCws5Jz6ojosuwXjzVd/suJ5x6/C9qNwpZ6rtlKoGBs1ahR4PB4YhsHmzZvLnXn6wYMHCidHCCHKoMh4MYZhkLFBThclDdwnGubv748dO3bIxMRiMe7du4fAwEClnsvQtS6MPFxR/PClTDzv7A1IioXQM5LfukwqR6FibOrUqbT0ByFEJwiFQpw6dYoV9/LygouLi9zXFVy4heLHr1lx0yBPmPo2VWqOhFRVeYP4lV2MAaWtY2kfFGOS/EIUXL4Di84BSj9fbaNQMTZ9+nRl50EIISpx5coVziktKuqiTJfXKjaJWsWI5qlzegvg/4uxVbtY8dwTcVSMKQFN+koIqdEU6aIsSUpF7lH2ki/6Dtaw6t1eabkRoig7OzvUr89eyFtVg/hNgzzAt7VkxfNOxNFs/EqgUMtYGYFAgNOnT+PBgwfIz8+HmZkZPD09ER4eDlNTU2XlSAghCuMqxszNzcud0iLz72OARMKK247uTuNjiNbw8/PD69eyXem3bt2CWCyucOH7quLx+bDoEojsqDMy8ZKX71D88BWMPV2Ver7aRuFiLDo6GgsWLEBubq5MVczj8WBpaYnvv/++wvXeCCFEld68eYNbt9gTtoaHh8PQ0JDzNZJiITK2cDx9ydeD7dieyk6REIX5+/vj0CHZ1SEEAgGePHnCOfVFdVl2a8UqxgAg92QcFWPVpFA3ZUJCAj777DMUFhZiyJAhWLlyJbZs2YKVK1di6NChKCoqwuzZs1XWXEoIIZUhb2Hw8rooc/afhzg9hxW36tkGhs72SsuNkOpS97gxi9AAQJ/d4pZ7/KpKzlebKNQy9ueff8LAwABRUVFwc3OT2dazZ08MHz4cQ4cOxbp16/Dnn+y5SQghRB0UGS+Wvp7WoSS6obwnKocNG6b08/EtzWDezhv5MTdl4oJrDyHKyIG+nZXSz1lbKNQydvPmTfTs2ZNViJVxc3NDjx49qGWMEKIxIpGIc0qLFi1acA58BgBB/CMU3njMiht5uMKsvY/ScySkOlxdXWFjY8OKq/J3r0VXjoXDJRLknaLZ+KtDoWKssLAQdnZ25e5jZ2eHwkL2zNWEEKIOsbGxyM7OZsXLaxXL2Mg9nYX9pAiaW5FoHR6Px9k6lpCQoLInHDmXRkLpuDGiOIWKMWdnZ1y6dKncfa5cuQJnZ2eFkiKEkOqqahelKC0b2XtjWHE9SzNYDw5Vam6EKAvXuLH09HQkJyer5HxGjZxg1Izdspx35gYYoUgl56wNFCrGevbsiXv37mHu3LlISUmR2ZaamoqvvvoK9+7dQ8+e9OQRIUQzjhw5woqZmZnJndIic+sJMCXsXya2w7uAb67ctf4IURZ5g/hV2lXZjd1VKcktQMHV+xx7k8pQaAD/pEmTcPHiRRw4cABHjx6Fq6sr7OzskJGRgZcvX0IoFMLHxweTJk1Sdr6EEFKh5ORkuVNaGBkZseKMSIyMTeziDQDsJvRSen6EKEt5xVjv3r1Vck7LrkFIX72HFc89eQ3mNLZSIQq1jBkbG2Pr1q2YPn066tSpg6dPn+Lq1at4+vQp6tati+nTp2Pbtm0wNjZWdr6EEFKho0ePcsZ79eIurHKPxUL4Jp0VtwgPhFETGm5BtJe7uzvn71pVtoyZtWoOPUszVjzvBI0bU5TCk74aGhpi6tSpmDp1KvLz81FQUAAzMzOYm5srMz9CCKkyri5KAHKHTqRvkDOdxSTVtCwQoiz6+vrw8fFBXJxsIaTKYoxnoA+L0ADk7L8gEy9+8hrFiW9h1MhJZeeuqZSyNqW5uTnq1KlDhRghROOKi4sRHR3Nivv5+XE+VFR0/wUKLtxmxQ0b1oVFWEuV5EiIMnF1Vb58+RJZWVkqO6clx7gxAMijpyoVQguFE0JqlJiYGBQUFLDiclvF5ExnYTchAjw9+ook2k/dM/EDpV344JjuJZe6KhVSqW7KsLAw8Hg8bN68GfXr10dYWFilDs7j8Tj/QiWEEFWR10XJNV5MnJOPrKjTrDjP1Ag2I7ooPTdCVKG8QfydO3dWyTn17axgGugBwbUHMvGCy3cgzhOAb2GqkvPWVJX6s49hGEgkEpmfK/Pf+68hhBBVYxiGsxizs7NDq1bsySozt58CIyhmxW0GdYa+tYVKciRE2by8vKDH0Yqr6lVwuLoqmRIRa7kkUrFKtYydOXOm3J8JIUQbPH78GM+ePWPFu3fvDj5fdoFjRixGhryB+x/TwH2iO0xNTeHh4YH792Xn+VJ1MWbRNQjvfvgfK557Mg5WEW1Veu6aRqEBEW/evEFaWpqycyGEkGqpShdlXvR1lLx4x4qbtfeBSfNGSs+NEFXi6qp8+PChSpclNG7RCAb17FnxvFPXwFDPWJUoVIyFhYXh119/VXYuhBBSLVzFmJ6eHrp168aKp//F3SpmT9NZEB3EVYyJxWLcuXNHZefk8Xiw6BrEiotSslB4m91CTeRTqBizsrKClZWVsnMhhBCF5ebm4vz586x427ZtYWtrKxMrevwa+WdvsPY1cHGAZY/WKsuREFXRxLJIQDlTXNBTlVWiUDEWGBjIudQIIYRoyqlTpyASsdeW5JrSQu5YsQkR4OnzObcRos38/Pw446ouxsw7+IJnbMiK5566ptLz1jQKFWOzZ8/GkydPsHr1as4vP0IIUbfKjhcT5xYg6x+O6SyMDWE7it2dSYgusLW1haurKyuu6mJMz9QY5h18WfHCG48hTMlU6blrEoWWQ1q/fj2aNWuGP/74A1FRUfDw8IC9PXsQH4/Hw5IlS6qdJCGElEcikXCuR+ni4gJvb2+ZWNaOU5Dkswc1Ww8Mgb6tpcpyJETV/P398fLlS5nY7du3IRKJoK+v8OqHFbLoGoQ8jpawvFPXYTuyq8rOW5ModHf27dsn/f+0tDS5T1ZSMUYIUYcbN24gJSWFFe/Vqxd4780SzkgkSN/APeO+PU1nQXScv78/9u/fLxMrKirC48eP0bx5c5Wd17JrEN7MYcdzT8ZRMVZJChVjp0+zm/gJIURTKttFmXc6HiXP37D2M2vTAibeTVSSGyHqUt4gflUWY4b168DY0xVFD2Rb5fLPJUBSIoSeoYHKzl1TKFSMcS22SwghmnLoEHtAvpGREUJDQ2ViGX8d5Hy93aQ+KsmLEHUqrxgbMWKESs9t0TWYVYxJ8gtRcOUeLDr5qfTcNYFSVsHNzs7G27dvlXEoQgipkuTkZMTHx7PioaGhMDMzk/5c9Pg18k6z9zOoZw+rXm1UmiMh6uDs7Mw5flvVg/iB0q5KLnknaYqLylC4GMvLy8MPP/yAtm3bok2bNjKLh9+6dQuTJk3C3bt3lZIkIYTIc/gw9xiw3r1lx4BlrJfTKja+F3gGqhvcTIi68Hg8ztaxhIQEMAyj0nObBnmCb23OitMUF5WjUDGWnZ2NwYMHY9u2bahbty6aNGkic6Pd3d1x48YNzq4DQghRJnnfM+8XY+KcfPnTWYzprrLcCFE3rvnGsrKyWE9ZKhtPnw+LsJaseMnTZBQ/S1bpuWsChYqx1atX48WLF1i5ciX27t2L7t1lv8yMjY0RFBSE2NhYpSRJCCFcCgoKOB8o8vf3h4uLi/TnzG0nISkoYu1nPSgE+na0mgipOQICAjjj6uiqtOjKPRt/7klqHauIQsXYmTNnEBISwjmzdRlnZ2e8e8dehJcQQpQlOjoaRUXsIqtPn/8G5DNiMdLXy1mHcnJfleVGiCZotBgLawnoscsKGjdWMYWKsdTUVDRt2rTcfQwNDVW6WjwhhFSmizL3+FUIX7HnIDNr7wOTFo1UlhshmtC0aVOYm7PHbt24wV6LVdn0bS1hGujOihdcvgtxnkDl59dlChVj1tbWFT49mZiYCAcHB4WSIoSQikgkEs7B+/Xq1ZNpHUhfxz1w334yTWdBah49PT3OcWPqKMYA7oXDGaEI+TE31XJ+XaVQMRYUFIQzZ85wzngNAE+fPsWFCxfQtm3baiVHCCHyXLt2jfM7qHfv3tJZ9wvvJaLg4m3WPoaudWHZvZXKcyREE7i6Kt++fauWKajkjxujrsryKFSMTZkyBWKxGB999BEOHTqErKwsAMCzZ8+wa9cujBkzBoaGhpgwYYJSkyWEkDKV6aJMX3eAcx+7iRHg8fkqyYsQTdPkuDHj5g1hUI8911neqWtgJBKVn19XKVSMubu749dff0Vubi6+/PJL7NixAwzDICIiAosWLUJRURF+++03NGzYUMnpEkJIKa5izMTERDrrvigjB9m7z7H20TMzpvXySI0mrxhTR1clj8eDBccEsKKULBTefqby8+sqhWc6DAsLQ3R0NPbv349bt24hJycH5ubm8PHxwYABA2Bra6vMPAkhROrly5e4fZvd/di1a1eYmJgAADL/dxxMUQlrH5uPwsG3Yg9wJqSm8PT0hLGxMetJY3W0jAGAZddgZP59jBXPO3kNpn7N1JKDrqnWtNPW1tYYO3asklIhhJDKqaiLUlIiRPoG7n1oHUpS0+nr68Pb2xvXrsnO76WuQfzmHXzBMzIAUyyUieeejEOdL4erJQddo1A35bx58zgnWnxfTEwM5s2bp1BShBBSHnnFWK9evQAAOQcuQvQuk7XdIqwljJu5sOKE1DRcXZUvXrxAZib7c6FsembGMO/gy4oXJjyBMDVL5efXRQoVY/v27cODBw/K3efRo0fYv3+/IocnhBC5cnNzcfbsWVa8VatWqFu3LhiGQfrafZyvtf+kv6rTI0QraHIQPwDOcWNgGOSdjlfL+XWNwguFV6SkpAR8elqJEKJkJ0+ehFAoZMXLuigFsfdQePMpa7uRewOYd2YvokxITaTJQfxA6bgxLnknaIoLLgoXY2Xz+HyIYRi8ffsWMTExcHR0VOjYBw4cwNdff40BAwbAy8sL7u7u2Lt3b5WPI5FIsG3bNvTu3Rs+Pj5o3bo1Zs6ciRcvXiiUFyFE8+S1uJcVY2lrubfbT+kn93uLkJrGy8sL+vrsYeHqahkzbFAHRu4NWPG8szfACEVqyUGXVHoAv4eHh8wX2erVq7F69Wq5+zMMg0mTJimU1KpVq5CcnAwbGxs4OjoiOVmxFd+/+eYb/Pvvv2jatClGjhyJjIwMHD16FJcuXcI///xT4ZJOhBDtIhQKOWfdb9iwIby9vVHy8h1yj8aytvNtLWEzpLM6UiREKxgbG6N58+asp47V1TIGlM7Gn/bolUxMkidAQew9zjFltVmli7GgoP/6f69fvw4nJyc4Ozuz9uPz+bCyskLr1q0xZMgQhZL64Ycf4OrqCmdnZ/z1119YsWJFlY8RGxuLf//9F4GBgdi8eTMMDQ0BAP369cO4cePw7bffYtu2bQrlRwjRjHPnziEnJ4cV79evtNUrfd1BgGNiSbtxPaBnYqSOFAnRGgEBAaxi7PHjx8jLy4OFhYXKz2/RNQhpv+9mxXNPxFEx9oFKF2Nbt26V/r+HhwcGDBiAadOmqSQpZSyjtGvXLgDAZ599Ji3EAKBNmzZo3749Lly4gMTERDRqRAsFE6Ir5HVR9uvXD+JcATK3nWBt4xnow25Cb45XEVKzBQQE4O+//5aJMQyDW7duoX379io/v1lwc/CtzCHOyZeJ5526BvygWM9ZTaXQmLGHDx+qrBBTlqtXr8LU1JRzEGPZP8IP52AhhGgviUSCAwfYyxvZ29ujXbt2yNx+EpL8QtZ2qwEdYVCXJqEmtY+mB/Hz9PkwD2vJihc/SULx8zdqyUFXVGvS1w8lJCTg3LlzMDIywsCBA1GnTh1lHr7SBAIB0tLS4ObmxvlEZ9kyTeUN5GcYRkXZyR5f1ech3Oj6a15V78H169c5x49GRERAD0D6X9zrUNpP7kv3mQN9BjRP1ffAx8cHPB6PdfwbN26o7b5bdAlEzt4YVjz3ZBzsJ/dVSw7lUcfnoDIPDilUjP3888/Ytm0bLly4AGtrawDA8ePHMXv2bEj+f7zG9u3bsXfvXo0UZHl5eQAAc3PuJU/K4vn5+ZzbASAnJ0ct/1i5xr8Q9aHrr3mVvQdRUVGc8S5duuDt7jMQvkxhbTMK9kCxqz2Ks7Ork2KNRp8BzVPlPWjatCmePHkiE7t+/Tqy1fSZkAQ2A3g84IPfp5lHL0N/aCe15FAZqrwHNjY2Fe6jUDF29epVtGrVSlqIAaVPQFpYWGD+/PlIT0/HypUrsWnTJp2dhd/Kykqlx2cYBjk5ObCysqLH7TWArr/mVfUeHDvGXuvO1NQU/fr1w5sBizhfU2fqQFi99z1F/kOfAc1Txz0IDAxkFWMPHz6EsbExjI2NVXJOGdbWyA50h+DaQ5lwSdxDWPANwbcwVX0O5dCWz4FCxdibN29k+qJfvHiBxMRETJs2DX37ljY7Xr9+XWNLIpU9JSKv5assLq/lDKhcs6Iy8Hg8+iLUILr+mleZe/Do0SPOVT+6d+8O5t4rCOLY2wxd68KqZ2u6vxWgz4DmqfIeBAQEYOfOnTIxsViMu3fvysySoEqW3VqxijGmRISC87dgFVH9B/aUQdOfA4UG8AsEApia/lfNxsfHg8fjoWPHjtJY06ZNkZLC7jZQB1NTUzg4OCApKQlisZi1vWysWNnYMUKIduMauA+UPkWZ/gf3hND2U/qCR6uAkFpO04P4ATlLI6F03BgppVAx5uDggMTEROnPFy5cgKmpKVq0aCGN5efny0wpoW7BwcEQCASc/+AuXrwIAGr7q4AQUj1cU1rw+Xx09w1GzuHL7G1W5rAZ0VUNmRGi3fz9uZcAU2cxZtyiEQzq2bPiedHXwXDMC1gbKVSMBQcH49y5c9i+fTt27dqF6OhotGvXTubJxVevXqll8H5mZiaePXvGWom+bMLZ3377DSUlJdL4lStXcPHiRQQFBdEcY4TogLdv3yI2lj2rfkhICEp2nOWc5NV2XA/wzU3UkR4hWs3Gxobzd526lkUCSrsAuVrHRO8yUXj7mdry0GYKjRmbMmUKoqOj8cMPP4BhGJiYmMjMO5aZmYmrV69i0KBBCiW1a9cuxMeXruz++PFjaSwurrRJMzw8HOHh4QBKn9pcvXo1pk2bhunTp0uP0bp1awwePBi7du1C//790alTJ+lySObm5vj2228Vyo0Qol4HDx7kfLJ5ULdeyPr9JCvOM9CH/aQ+6kiNEJ3g7+8v05sFALdv34ZQKISBgYFacrDsGozMv9kP4eSdvAZTv2ZqyUGbKVSMubq64siRIzh5svSLMCQkBC4uLtLtycnJGD58OCIiIhRKKj4+Hvv27ZOJ3bhxQ9qs6uzsLC3GyvPdd9/B3d0dUVFR2Lp1K0xNTdG5c2fMmjWLWsUI0RHyZt0PE5ijsKCIFbce2AkGTnYqzooQ3REQEIC9e2XHVhYXF+PevXvw8/NTSw7mHXzBMzIAUyyUieeejEOdL4erJQdtxmNoxj+NYBgG2dnZsLa2pieZNICuv+ZV5h7k5ubC3t4eQqHsF3irloHYVOgB0dsM1muanV8NE6/GKsm5JqHPgOap6x6cOHEC3bt3Z8XXr1+PiRMnquy8H0ocvAh5p+NZcc+H22HgWPFcXKqgLZ8DhcaMvU8kEuHp06dISEjAkydPIBKJlJEXIYTgyJEjrEIMAD5xb8tZiJl38qNCjJAPtGzJXpIIgHQ4kLpYdAvmjOdFX1drHtpI4eWQcnJy8Msvv+Dw4cMoKvqvq8DY2BgRERGYPXt2pWadJYQQeXbv3s0Z93/CPYegw9QBqkyHEJ1kb28PV1dXvHz5UiZ+/bp6iyDLrsF48+VaVjzvRBxsh3dRay7aRqGWsZycHAwdOhS7du2CkZER2rVrh379+qF9+/YwNjbGrl27MGzYMLUtt0AIqXkKCgo4Z90f2MgXePqWFTfycOVclJgQwt06dvv2bZnZBlTNsEEdGHu6suJ5Z29AUsJuAa9NFCrG1qxZgxcvXmDy5Mk4e/YsNmzYgKVLl2L9+vU4e/YspkyZgpcvX2LtWnYFTAghlXHs2DEUFhay4h9buHPu7/Bpfxr7RIgcXMVYSUkJ7t27p9Y8uLoqJfmFKLh0R615aBuFirHo6Gi0atUKs2bNgomJ7Fw+xsbG+Oyzz9C6dWtER0crJUlCSO3D1UXpzrdA3dd5rLi+ow2sB3dWR1qE6KTAwEDOuCa6Krnknqjds/ErVIylpqZW+Disr68vUlNTFTk8IaSWKywsxJEjR1jxqfY+nPvbT+4DPSP1zJdEiC7SlkH8pkEe4NtYsOJ5J+I45xOsLRQqxiwsLJCcnFzuPsnJydIFuwkhpCpOnjyJ/HzZQfr19EzQQWjJ2lfP3AS243qqKzVCdJKdnR3neszqbhnj8fmw6MJupSt5+Q7Fj1+rNRdtovBySMePH8fly+w14YDSJYeOHz+O4GDu5khCCCkPVxflSKOG0OP4w9l2dHfoW9MffoRURN4g/uLiYrXmQV2VbApNbTFt2jTExMRgwoQJ6NSpE4KCgmBnZ4eMjAzExcXh/PnzMDY2xtSpU5WdLyGkhisuLsbBgwdlYlY8Aww0bsDeWZ8P+yn91JMYITouMDAQe/bskYkJhULcvXtXbjemKliEtQT4eoBYdl3ZvJNxcJyh2DKKuk6hYqxp06bYuHEj5s2bh3PnzuHcuXPg8XjS/t4GDRpg6dKlaNaM1psihFTN6dOnkZubKxMbatQAxhwN+TaDQmDo4qCu1AjRaeWNG1NnMca3ModZGy8UXLwtEy+4eh+irDzoc4wpq+kUnvQ1ICAAx48fR3x8PB48eID8/HyYm5vD09MTLVu2pEfMCSEK+bCL0gh6+MiIPTcRANhPo0leCamsgIAAzvj169fx8ccfqzUXy67BrGIMYgnyzsTDZmCIWnPRBgoXYwDA4/EQGBgo95FZQgipCqFQyFoYvI+hM2z1jFj7WoQHwqR5IzVlRojus7OzQ6NGjZCYmCgTV/cTlUDpfGNvv97AiuediKuVxZhS16Z8+vQprU1JCFHY2bNnkZWVJf1ZD8BoY+6Cy6GWji0hpDq4uiPv3Lmj9kH8Rk2dYdi4HiueF30djEis1ly0gcLFWGZmJhYuXIigoCD07t0bw4cPR+/evREUFIRFixYhMzNTmXkSQmqBDwcXhxvURQO+GWs/kwA3mLXzVldahNQYXD1ZQqEQd+6odwZ8Ho/H+VSlODsfgmsP1JqLNlCoGEtJScGgQYOwe/dumJiYoH379tK1KU1MTLBr1y4MGjQIKSkpys6XEFJDiUQi7Nu3TyY2wbgJ574O0wfRuFRCFKAtk78C3EsjAbVziguFirFly5bhzZs3mD59Os6ePYv169fLrE05bdo0vHnzBsuXL1d2voSQGiomJgZpaWnSn9vp28NDnz3Jq2HjerCKaKPO1AipMeQVY+qe/BUAzNq0gJ65CSuee/yq2nPRNIWKsYsXL6JDhw6YOnUqjIxkB9YaGRlh2rRpaNeuHS5cuKCUJAkhNd8///wj83O5rWJ8vjpSIqTGsbGxQePGjVlxTbSM6RkawKIz+wnP4sevUZz4Vu35aJJCxVhJSQmaN29e7j5eXl4oKSlRKClCSO1SUlIiM17Mn2+Dlga2rP30nexgMyxMnakRUuPIG8RfVFSk9lzkdlXWstYxhYoxLy8v1qOxH3r+/Dm8vLwUSooQUrtER0fLPEU53oT9lzsAOHzanxYEJ6SauAbxi0QitQ/iB/5/aSSO8Z95J6gYq9DMmTNx9uxZ7N27l3P77t27ERMTg5kzZ1YrOUJI7fB+F6Ub3wIdDRxZ+/BtLGA7poc60yKkRtKmQfz69lYwDfJgxfMv34U4J1/t+WiKQpO+xsbGolWrVliwYAE2bNiAgIAA6dqUN27cQGJiItq1a4fY2FjExsZKX8fj8Wi9SkKIjMLCQpmJXscbc7eK2X/cB3yOwb6EkKopbyZ+TbDs0RqCuA+msxCJkRcdD+uBnTSSk7rxmLIFJavAw4NdxVbqZDweHjyoffOHcGEYBtnZ2bC2tqZH9DWArr/mld2DM2fOYNCg0glc6+uZ4oBlR/A/uCd6ZsbwuP2/WrlmnarQZ0DzNHkPmjZtimfPnsnEfH19cfPmTbXmAQBFD1/hcdsprLj1oBA0+OtLlZ5bWz4HCrWMbdmyRdl5EEJqqX///Vf6/2ONG7EKMQCwHdODCjFClCgwMJBVjN29excCgQCmpqZqzcXIvT4MG9ZFyYt3MvG8U9fBCEXgGVRr5UadoNA7DA7mfvqBEEKqIj8/H4cOHQIAOPKM0MfQhbUPz0AfDp/2V3dqhNRoQUFBiIqKkomJxWIkJCSgXbt2as2Fx+PBsntrpP+5XzafnHwUXL0P8/Y+as1HE6q9NiUhhPxfe/cdHkW1/gH8u+mkFzrSw4ZeQhISQGIAFaQGFJQuoHI1KAgI3CsCXgQBUSTKT9CL9F6lSRMhlCS0gDRDSacEUjY9m92d3x+YyDqzIcBmZ7P5fp4nD+Y9s7PvznF338w5c+ZZHThwAPn5+QCAUQ6NYKcQfyR5vNUdtrWrmjo1Iotm6KRKdLQ8q9+79qjcS1ywGCMi2RRfke2lsMNA+7riDayseENwonLg6+sLKytxCXDmzBkZsgGcglrCylV8H9qsX6PwDFPbKxwWY0Qki8zMTBw5cgQAMMKhIRwU4lX13Qd0gX2j2qZOjcjiOTk5Sa4FKteZMYWtDVy6i9c/U9++g8IbyTJkZFosxohIFjt37oRarYa7whaD7OuJN1AoUP3jN02fGFElITVUeevWLaSlpcmQTeUeqmQxRkSyKJ48PNS+ARwV4muJ3Pp0gkNTiSKNiIzC0LwxuYYqXbr7AdbisqQyrMbPYoyITC41NRWHDx+Gi8IGbznUl9ym+mSeFSMqT/7+/pJxuYYqbdxd4BQkHjrNjboGTXqWDBmZDosxIjK5jRs3QqvVYoh9A7goxPeadO0ZiCotpVfiJyLjaNGiBapUEd/VQq4zYwDgKnXjcJ0O2Yfky8kUWIwRkcmtWbMGTrDBUHueFSOSi62treStkaKjo2W7gtG1RwfJeNZ+yx6qZDFGRCZ1/fp1nD17FoPt68HNyk7U7tKtPRzbKWXIjKjykZo3lpqaisTERBmyAewb14F9E/EyN9m/nYOusEiGjEyDxRgRmdTatWtRBdYY7tBQsr365LdMnBFR5WVui78CgGtP8dkxXU4+co7HmD4ZE2ExRkQmo9PpsHbtWrxlXx+eEmfFnLu0gVOH5jJkRlQ5mWcxFigZz9ofaeJMTIfFGBGZzMmTJ/EwIQUjeVaMyCw0bNgQXl5eorick/gd/XxgU91DFM/6NQqCTidDRuWPxRgRmcyaNWvwlkN9uEucFXPq3LpS3BCYyJwoFArJJS7Onj0LrVYrQ0aAwtpa8qpKzb105J+PlSGj8sdijIhMoqCgAHs3b8MI+waS7TWmDTVtQkQEQHqoMjc3F9euXZMhm0dcX5MeqlRZ6FAlizEiMom9e/eiT6Gn5BWUzsFt4dyxlQxZEZE5zhtz7tIWVk4OonjW3tMyZFP+WIwRkUlsXbkGwwydFZvKs2JEcjG3lfgBwKqKPZxDxGugFcYmofCm5d04nMUYEZW7tLQ0eB29Dlcr8Wr7Ti+1g1NgCxmyIiIAqF69Oho0aCCKy1mMAYBbryDJuGqf5Q1VshgjonK3bdVavGUrXsgR4FwxInMgNVT5xx9/ID8/X4ZsHnF5JUDyxuGWuMQFizEiKndp/7dL8h6UOj9vOPk3kyEjInqcVDGm0WgQExNj+mT+YuPhAqeO4huH50Vfg+ZBpukTKkcsxoioXF07dQYhD20k22r+e4SJsyEiKeY4bwwA3KQWgBUEZP1qWfeqZDFGROXq0rQlcFSIi7E0ZXXYt2ksQ0ZE9E++vr6wshKXBFFR8hY9rq8ZmDdmYUOVLMaIqNzkxd9B48sPJNuaLRhv4myIyBBnZ2e0aCG+kCYyUt6ix65eDTi0bCSK5/x+AbrcAhkyKh8sxoio3FyYuAh2Eh8z12vYosaL4svWiUg+gYHiIcG4uDjcv39fhmz+JrUArFCgRvbR8zJkUz5YjBFRuSi8mQzHY+IVvLWCgGpTh8iQERGVJihIekjw9Gl5F1p1MzBUmbXPchaAZTFGROUiftb/YC0RP2qbieARb5g8HyIqnbkWYw6tGsH2hWqieNavURCKNDJkZHwsxojI6PIv30bhPvHE3yJBB82wlyQnChORvJRKJTw8PETxU6dOyZDN3xQKheREfm1mDnJOXJIhI+PjJyIRGd29Oask41sLEzHow/dMnA0RlYWVlZXk2bGzZ89CrVbLkNHf3Pp0koyr9shbKBoLizEiMqrcyCvIPnhGFM8XNLjuX1vytitEZB6kirGCggJcvHhRhmz+5hTYHNZV3UTxrL2nIWi1MmRkXCzGiMhoBEHAvf+ulGxbV5CAN94bbdqEiOipGJo3JvtQpbW15ER+TWoG8s5clyEj42IxRkRGk/VrFHJPXxHHdUXYbv8QoaGhMmRFRGUVEBAgOadT7kn8AODWu6NkXLX7pIkzMT4WY0RkFIJGi3uzfpZsW1l4G32HDEaVKlVMnBURPQ0XFxe0atVKFDeHYsypSxtYuTqJ4qo9pyAIggwZGQ+LMSIyivS1B1F4I0kUT9UVYF1BPEaP5hAlUUUgNVSZmJiIlJQUGbL5m5WdLVx7iG9oXpSUivyLN2XIyHhYjBHRc9Pm5OP+/LWSbUvzb6BJqxZo3769ibMiomdhruuNAYBbb+mrKrMq+FWVLMaI6Lk9XLoDmvsZovhNbTZ+UadgzJgxUCgUMmRGRE/LnIsxl66+UDjai+Kq3Scr9FAlizEiei5FqRl4EL5Vsm1x3p+wq+KAESNGmDgrInpW3t7eqFq1qihuDsWYlaMDXLv5ieKFN5JR+Kd4mkRFwWKMiJ5L6oL10OUWiOJnitIQoXmAN998U3JVbyIyTwqFQvLs2Llz51BYWChDRvpc+xi6qvKEiTMxHhZjRPTMCm4kI23Vfsm2b/L/BACMGzfOlCkRkRFIFWNqtRrnz5+XIRt9rq90gMLORhSvyKvxsxgjomd2d+b/AK1OFN+vvoMrWhV8fX3h7+8vQ2ZE9DzMed6YtasjnIPbieIFf9xGYfxdGTJ6fizGiOiZZB89j+xfpW8GHp4fC+DRWTFO3CeqePz9/WFtbS2Km0MxBhheADZrd8U8O8ZijIiemqDR4u6nP0q2bSpMRIouH66urnjrrbdMnBkRGYOTkxPatGkjip86ZR4LrLq+FghI3Ckgc1eEDNk8PxZjRPTU0tf8ioJrCaJ4pk6NZQWPFl8cPnw4nJ2dTZ0aERmJ1FDlnTt3kJQk/1WLNl5ucO4svlNA/vlYqBPvy5DR82ExRkRPRavKwb25ayTb/q/gBrKEIgCcuE9U0ZnzvDEAcOv/omQ8c2fFOzvGYoyInsr9hRugTcsSxW9pc7C18NFfzJ07d0bLli1NnRoRGVHHjtLzsk6eNI8bc7v17iQ5VKmqgEOVLMaIqMwKbybj4fJfJNu+yrsGDR7NJeFZMaKKr0GDBqhRo4YofuKEeaznZVPVDc4vthbF8y/cqHBXVbIYI6IyuztzBaDRiuLHi1JxSvMQAFC1alW8/vrrpk6NiIxMoVDgxRfFQ4ExMTFQqVQyZCTm1r+LZFy1yzwKxrJiMUZEZZJ99Dyy9keK4hpBh0V510t+Hz16NOztxfeOI6KKp0sXcbEjCIIZDVUGAdYSV1XuOC5DNs+OxRgRPZFOXYQ7036QbNtUmIh4XS4AwNraGmFhYaZMjYjKkVQxBgDHj5tHsWPj5QbnLm1F8YJLt1B4K8X0CT0jFmNE9EQP/28nCm8ki+KZOjV++GspCwAYOHAg6tata8rUiKgctWzZEu7u7qK4uRRjAOBu4KrKijRUyWKMiEqlTn6A1K82SLZ9/9hSFgAwYcIEE2VFRKZgbW2Nzp07i+JnzpxBXl6eDBmJufYKAmzEdwuoSEtcsBgjolLdnfEjdLkFovg1jQpbCxNLfg8ICEBgYKApUyMiE5AaqtRoNIiMFM8hlYONpytcgtuK4gWXb6NA4oy+OWIxRkQGZf9+weCp/rl5V/H4LcI/+ugj3oeSyAKZ+7wxwPACsBVlzTGzLcYuXbqEd955B/7+/mjbti1ef/117N69u8yPj4qKgo+Pj8GfmJiY8kueyALo1EW4M1V60v7OwmRc0maW/F67dm0uZ0FkoXx9feHo6CiKm1Mx5torCApbG1FcVUGGKsWZm4GoqCiMGTMGtra26NWrF1xcXHDw4EFMnjwZKSkpT7WgZEBAAAICAkTxmjVrGjNlIovz8IedKLwhvgddlq4I3+b/qRf74IMPYGdnZ6rUiMiEbG1t0bFjRxw+fFgvfvr0aajVarN479u4u8A5pB2yD57RixdcjUfBn4lw8KknU2ZlY3bFmEajwaeffgqFQoF169ahefPmAB592L/55psIDw9Hjx490KBBgzLtLyAgAOPHjy/HjIksjzrlIVIXGpq0H4t0QV3yu4ODA959911TpUZEMujSpYuoGCsoKMDZs2cN3jbJ1Nz7dxEVYwCQuf04ak4fJkNGZWd2w5SRkZFITExE7969SwoxAHB2dsb7778PjUaD7du3y5ghkWUTBAF3PlkqOWn/uiYLWwr1z5YNGzYMVatWNVV6RCSDijBvzLVnByjsxOeYMrcehSAIMmRUdmZ3Ziw6OhoAJC+l7dSpk942ZREfH4/Vq1ejoKAAtWvXRseOHeHp6fnEx5V3xxXv39z/B7FUPP6GqXaflFxpHwDm5l2BFvrH7MMPP3ym48g+kBePv/wqUh/4+/vDzs4OarVaL378+HFMnTpVpqz0Wbk6waWbn+jzSx13F3lnr8PRr6noMabog7Jc2GR2xVh8fDwAoH79+qI2Nzc3eHh4ICEhocz727NnD/bs2VPyu4ODA8aPH4+xY8eW+jiVSmWSN4i53N+rsuLx16fLzsO9qf8n2barMBkXH5u0DwDBwcF44YUXkJmZKfmYsmAfyIvHX34VpQ98fX1Fy1lEREQgLS0N1tbidb7kYPtaACDxx+T9dQfg4W14rnh59oGHh8cTtzG7YiwnJwcA4OLiItnu7OyMe/fuPXE/np6e+OSTT/DSSy+hdu3ayMrKQlRUFL766issXLgQzs7OePPNNw0+3s3N7dleQBkJggCVSgU3NzcuByADHn9pKXM3QHs/QxRXKTRYlH9dFP/0008lV+cuC/aBvHj85VfR+iAkJERUjOXk5CAhIQG+vr4yZaXPNfQlZEz/EbqcfL14wf5ouC0Mg+Ifi8OaSx+YXTFmLE2aNEGTJk1Kfq9SpQr69u2Lpk2bYsCAAQgPD8egQYNgZSU9bc5UnaJQKCrEm9BS8fj/LTf6GtJX7JVsW5BzBarHVtoHAD8/P3Tr1u25jx/7QF48/vKrKH0QHByMefPmieIRERFo3769DBmJWTs6wK1PJ2Rs0L/YQPMgE7nHYuDS3U/ycXL3gdlN4Hd2dgYAZGdnS7bn5OQYPGtWFkqlEm3atMHDhw+fariTyJIJRRqkTFwCSAzN/2FfgD3qO6L49OnTK8QXCBEZR8eOHSVPYJjTJH4AcB/UVTKesfWoiTMpO7MrxoqXrJAqlFQqFTIyMiTnkz2N4vHbggLx1WJEldGD77ah4Jr4Paeztca01ChR3MfHB/379zdBZkRkLlxcXCSHI48fP25WFyE4d24Fm5riC/Wy9p6WvErcHJhdMebv7w8AOHFCfAuWkydPAoDkIq5lpdFocPXqVSgUCtSqVeuZ90NkKQpvJuO+gTXFfvHIQ7JOfDPgqVOnGhziJyLLJbXERVpaGq5duyZDNtIU1tZwHxAsiutyC6D61Tzup/lPZvdpGhQUhLp162LPnj16nZuTk4OlS5fCxsYGoaGhJfH09HTcunUL6enpevu5cOGCqFLXaDRYsGABUlJS0Llz52eeeExkKQStFklh30AoUIvatPWr4b9/ik/r16lTB0OHDjVFekRkZgytN3bs2DETZ1I69zdCJOOZW8xzqNLsJvDb2Nhgzpw5GDt2LIYMGYLevXvD2dkZBw8eRHJyMiZMmICGDRuWbL9u3Tp89913CAsL01tpf9KkSQCAdu3aoUaNGsjOzsaZM2cQFxeH2rVrY/bs2SZ/bUTm5uGyX5AXLfEXrUKB5a4PoYF46GHSpElmcfsTIjI9qTVAAeDIkSP417/+ZeJsDKvSujHsm9QV3dIt+7fz0DxUwaZq+a6Y8LTM7swYAAQGBmL9+vVo37499u/fj/Xr18Pd3R0LFy4sc2e/+eabqFOnDqKjo7F69Wrs3r0bdnZ2GDduHHbu3Ik6deqU86sgMm+Ft1Jwb84q6cbQIPxwfJ8o7OnpiXfeeaecMyMic+Xl5YU2bdqI4r/99hu0Wq0MGUlTKBRwf+MlcYNGi8yd5nXBAQAoBHOadVeJCIKAzMxMuLu784o0GVT24y9otbjVeyryoq6K2uwa1MTMuhnY/MtOUdvMmTMxa9Ys4+RQyftAbjz+8quofTBlyhR89dVXonh0dHTJvG9zUBh/F3/6jhHFHf2bwfvAIgDm0wdmeWaMiMrXw+W7JQsxAMgb30uyEHN0dNSbCkBElVP37t0l44cOHTJxJqWzb1ALjgHNRPG8M9dQGH9XhowMYzFGVMmUNjzp9U4fzNoh3fbBBx/Ay8urPFMjogrgxRdflJw3am7FGGB4In/GxiMmzqR0LMaIKhFBq0XS+MUQ8gtFbXYNaiKlVyvs3r1b1Obk5IQpU6aYIkUiMnOOjo7o1KmTKH7q1Cnk5ubKkJFh7v1fBGzE983M2HAYgk4nQ0bSWIwRVSIPlmxFXuQVybYXlkzAzHlfSLZ99NFHqFatWnmmRkQVyMsvvyyKqdVqREREyJCNYTZebnB9Vbw2aVFSKnIiLsmQkTQWY0SVRF7MDdybt1ayzWtsb1yyysGvv/4qanNxcSlZKoaICJAuxgDg8OHDknE5eQ59RTKese6giTMxzOzWGSMi49PlFSDpvYWARnzpuV39mqj52dsYFdpX8rETJ06Ep6f41iJEVHm1a9cOHh4eyMjI0Iub47wxl+5+sKnuAU2qfq6qPadQW5UjU1b6eGaMqBK4+9n/UHgjWdxgZYW6P0zGyfNnJP+idXd3x8SJE02QIRFVJNbW1ujWrZsofunSJdy/f1+GjAxT2FjDY7D45uFCgRqZ281jzTEWY0QWLutAFNJW7JVsq/7xYDgGNMOMGTMk2ydNmsTbhhGRJENLXBw5Yl5XKgKAxxDpYdWM9eZxJo/FGJEFK0rNQNL4xZJtVXyVqDHlLRw8eBDHj4v/OvT09MSHH35YzhkSUUVlaN6YOQ5VOvjUg6NfU1E8/3wsimIlRg1MjMUYkYUSdDokj18M7UOVqM3KyQH1lk2BzkqByZMnSz7+k08+gaura3mnSUQVVKNGjfTuFV3s8OHDMMeb+xg6O5a7Vf6bnLMYI7JQD77bjuxDZyTban3xLuwb18HKlStx+fJlUXu1atXwwQcflHeKRFTBSZ0dS05Oxp9//ilDNqVzH9AFiir2onjuzhMQijQyZPQ3FmNEFig36iru/XelZJvra4HwHP4qcnJyDM4Vmz17NpydncsxQyKyBBXl1kgAYO3qBLc+HUVxXVqWwT9cTYXFGJGF0aRnIXHMl4BWvLq0TQ0PvLD4IygUCixatAh374rvz9a0aVOMHTvWFKkSUQXXtWtXyRtsm+N6Y4DhNcfS18lbPLIYI7Iggk6HpPcXoejOQ3GjlRXqLf8ENlXdcPfuXSxYsEByHwsWLICtrW05Z0pElsDLywvt27cXxY8ePYqioiIZMiqdU6dWsK1XQxTPPnQGmoxsGTJ6hMUYkQV58N12ZB+UPt1eY+oQOL/YBgDw2WefIS8vT7RNcHAwevfuXa45EpFlkRqqzM7ORnR0tAzZlE5hZQVPqYn8Wh3UceKRAlNhMUZkIUqbJ+Yc3BbVPx4MALh8+TJWrFghud1XX30lOeRARGSIoSUu9u6VXt9Qbh5DukNhr3/2X2FvC7u61WXKiMUYkUUoupeOhLfnGpwnVnfZFCisrQEAU6ZMgU4n3m7o0KHw8/Mr91yJyLJ07NgRTk5Oovgvv/wiQzZPZvdCddT+8l9Q2P51R0gba9ScPQY21dxly4n3piSq4HTqIiSM+gKae+nixr/midlW9wDw6MNR6mbg9vb2+OKLL8o7VSKyQA4ODnjllVewY8cOvfiVK1dw+/ZtNGrUSKbMDPMa2QOuL/sh749bUNd0R9XWSlnz4ZkxogruzvRlyIu+Jtn2+DyxvLw8gyvqf/TRR6hfv3655UhElq1v376S8d27d5s4k7KzrV0Vrq8EwKaefMOTxViMEVVgaat/RfrP+yTbnLu2L5knBgBffPEFEhISRNtVrVoV06dPL7ccicjy9erVS3K+qbkOVZobFmNEFVTumeu488lSyTa7hrVQ76dPSuaJ/fnnn1i4cKHktgsXLuTNwInouVSrVg1BQUGi+PHjx5GZmWn6hCoYFmNEFVDRvXQkjPoCglp8Cw8rJwc0WDMDNu4uAABBEPDBBx9IrvnTqVMnjBgxotzzJSLLJzVUqdFoJOepkj4WY0QVjC6vAPFDZ0NzN02y/YXvPoZD8wYlv2/evBlHjhwRbWdtbY2lS5fCyoofA0T0/AzNG+NQ5ZPxU5ioAhF0OiSO+wr5F25Itleb8Abc+3Uu+T07Oxsff/yx5Lbjx49H69atyyVPIqp8mjZtCm9vb1F83759ZrkavzlhMUZUgdyd9TOy9pySbHPp1h41/6M/5Dhz5kzcuXNHtG2tWrUwe/bscsmRiConhUKBPn36iOIqlQonTpyQIaOKg8UYUQWRtnIfHn63TbLNzrsO6v7494R9ADh9+jS+/fZbye2//vpruLq6lkueRFR5cajy2bAYI6oAso+cQ8oU6SsnrT1d0XDj7JIJ+wCQn5+PUaNGSa6037VrVwwePFgUJyJ6Xp06dYKHh4covmvXLgiCIENGFQOLMSIzl3/5NhJGz5O81ZHCzgYN1s6AfaPaevEZM2YgNjZWtL2dnR2+//573n+SiMqFra0tevbsKYrHxcXh6tWrMmRUMbAYIzJjhXF3Eff6DOiy8yTbXwifCKfAFnqxU6dO4euvv5bcftasWWjatKnR8yQiKlYRV+OXG4sxIjNVdC8dcQP/A01qhmR7jWnD4PFGiF4sLy8Po0aNkhwO8Pf3x5QpU8olVyKiYj169ICNjfjW15w3ZhiLMSIzpFXlIO6NGVDH35Nsdx/cFdWnvCWKf/rpp7hxQ7zshZ2dHVauXCn5AUlEZExubm4IDg4WxSMjI3H//n0ZMjJ/LMaIzIwurwBxb81GwZU4yXbn4LZ4YfFHonlfJ06cwOLFiyUf8/nnn6N58+bGTpWISJLUUKUgCNiyZYsM2Zg/FmNEZkSnLkLC6HnIi7wi2V7FV4n6a2bAyt5WL56RkYFhw4ZJDk8GBARg0qRJ5ZIvEZGUfv36ScbXrVtn4kwqBhZjRGZCKNIgccyXyD54RrLdvkldNNw0G9bOVfQfJwgYO3YsEhISxI+xt+fwJBGZXP369dGxY0dRPDIyErdu3ZIhI/PGYozIDAhFGiSM+RJZe09LttvWqYaG2+bAxstN1LZ06VJs375d8nGff/45mjVrZtRciYjKYujQoZLxDRs2mDgT88dijEhmQpEGiWPnG7zNkbWnKxpumwO7F6qJ2mJiYgzeezI4OJjDk0Qkm0GDBkmelV+3bh0XgP0HFmNEMhI0WiS+uwCq3Scl261cHNFw8+dwUNYVtWVnZ2PQoEFQq9WitqpVq2L9+vWwfuz2SEREplS1alW8+uqrovj169dx4cIFGTIyXyzGiGSiUxc9KsR2Sd9A18q5ChptmwNHX6WoTRAE/Otf/5JcxgIAVq9ejdq1a0u2ERGZypAhQyTj69evN3Em5o3FGJEMdHkFSBj2X6h2Rki2WzlXQcOtc+DoJ71a/o8//mjwqqRPPvlE8nYkRESm1q9fPzg5OYniGzZsgFarlSEj88RijMjEtKoc3B74KbIPn5Vst3KugoZb/gunAOmJ98eOHcMHH3wg2RYYGIg5c+YYLVcioufh5OSE/v37i+J37tzBsWPHTJ+QmWIxRmRCRffTcavPNORFSd8w18rJAQ03fw6nDtILtMbFxWHgwIHQaDSiNnd3d2zcuBG2trYSjyQikoehqyo5VPk3FmNEJqJOvI9bvT5BweXbku1WLo6Pzoj948bfxbKystCnTx+kpaVJtq9YsQL169c3Wr5ERMbQvXt3VK1aVRTfunUrCgoKZMjI/LAYIzKBvPOxuPnKx1DfviPZbl3VDY1++dJgIabVajF06FBcuSK9Mv+MGTMQGhpqtHyJiIzF1tYWgwcPFsVVKhX27dsnQ0bmh8UYUTlT7T6JW32mQpOaIdluW6caGu9dCMc23gb38Z///Ad79uyRbBswYABmzZpljFSJiMqFoaFK3h7pERZjROVEEASkLtmKhJFfQMgvlNzGvskLaLz/Kzg0ecHgfpYvX4758+dLtrVp0warV6+GlRXfykRkvgIDA9GwYUNRfM+ePXj48KEMGZkXfoITlQOhSIOUieG4N2uFwW2qtPFG470LJVfWL7Zp0yaMGzdOsq169erYtWuX5GXjRETmRKFQSK45plar8eOPP8qQkXlhMUZkZEWpGbg94D9IX/2rwW1curVHo11fwqaq+F6TxX799VcMHz5c8rYhdnZ22LFjByfsE1GFMWLECMn40qVLUVRUZOJszAuLMSIjyo26ihshHyL35B8Gt/Ea0wsNNsyCtaujwW1OnjyJAQMGGPyAWrZsGTp27Pjc+RIRmYpSqUSPHj1E8eTkZOzcudP0CZkRFmNERiAIAh7+uPvRRP270ktPQKFA7bnvovaC96GwMXzPyIsXL6JXr17Iz8+XbJ87dy5GjRplhKyJiEzrww8/lIwvWbLExJmYFxZjRM9Jl1uApH99hTtT/w/QSN/ew8rJAQ3WfYaq4/pDoVAY3Nfly5fx6quvQqVSSbZPnjwZ06ZNM0reRESm9uqrr6JJkyai+IkTJ3D+/HkZMjIPLMaInkPehVjEhoxH5uajBrexrVcDjfcuhGuPDqXu6+zZswgODsb9+/cl28eMGYMFCxaUWswREZkzKysrjB8/XrItPDzcxNmYDxZjRM9A0GqR+u0W3Hx1EtQ3Uwxu59KtPZr89i2qtG5c6v4iIiLQtWtXpKenS7YPHDgQy5YtYyFGRBXeyJEj4eLiIoqvX78eqampMmQkPxZjRE9JnfIQt0P/g3uzfzY4LAkA1acMQYONs2Dj6Vrq/g4cOIBXX30V2dnZku0vv/wy1q1bB2trw/PMiIgqCldXV7z99tuiuFqtxvLly2XISH4sxojKSBAEpK8/hBsvvo/cE5cMbmft5owGG2ai5vRhUDyhgNqxYwf69u1rcLJ+165dsX37dtjb2z9X7kRE5iQsLEwyXlmXuWAxRlQG6sT7iBv4KZLDvoE2M8fgdo7tfeB99Fu4vlr6/DBBELBw4UIMHDgQarVacpvevXtj7969cHZ2fq7ciYjMTZMmTfDaa6+J4nfv3sW2bdtkyEheLMaISiFotXj4w0782XEccn6/YHhDKytUn/wWGu9bCPsGtUrdZ2FhId5++2188sknkgu6AsDgwYOxfft2ODg4PE/6RERmy9AyF4sWLTL42WipWIwRGZB3PhY3X52EO/9eDiFP+t6SAGD7QjU03v0lav57OBS2NqXuMzU1Fd26dcOqVasMbjN69GisW7cOtra2z5w7EZG5e/nll+Hj4yOKnz17ttKdHWMxRvQPRakZSAr7Bje7T0D++dhSt3UfGAxlxPdwCmr5xP1evHgRAQEBOHnypMFtPvzwQ/z444+crE9EFs/Kysrg2bFp06YZnMJhiViMEf1Fpy7Cg++240//d5Cx/lCp29rU8kL9tTNQ78epsHYrfU6XIAgIDw9Hhw4dkJCQILmNlZUVFi1ahMWLF8PKim9LIqocRo8ejXr16onit27dwrJly2TISB781KdKT9DpkLH1KGKDxuHuZz9Bl51X6vaeI3vC5/QPcHst6In7fvjwIfr164cPP/wQhYXSQ50uLi7YvXs3Pv74Y64jRkSVioODA7744gvJttmzZxu8G4mlYTFGlZYgCMj6NQo3uoQh6d2FUMfdLXV7u0a10eiXL/HCN+Nh7er0xP3/9ttvaN26NXbv3m1wm4YNG+L06dOSVxUREVUGQ4YMQdu2bUXxtLQ0zJ8/3/QJyYDFGFU6giAg++h5pL4xCwlDP0fB1fhSt1c42qPGv4dDGfE9nDu3fuL+s7Ky8OGHH6J79+64e9dwgdelSxdER0ejRYsWT/sSiIgshpWVFRYuXCjZ9s033yApKcnEGZkeizGqNAStFpk7juNGyIeIf30G1BduPvExbgOC4RO5HDUmvwWrKqUvvCoIArZu3YpmzZohPDy81EuzJ0+ejEOHDqFq1apP/TqIiCxN9+7d0aNHD1G8oKAAn332mQwZmZZCqGyLeZgJQRCQmZkJd3d3zhMqZ7q8AmRsPooH4VufOBRZzKFlI9T+8j04d2xVpu3j4+MRFhaGvXv3lrpdjRo1sHr1arzyyitl2q8l43tAXjz+8mMf6Lt06RLatm0r+kNWoVAgJiYGrVs/eWTiaZlLH5S+KBJRBVZ4+w7SVuxFxvpDpa6a/zi7hrVQY/owuId2eeKtjAAgMzMTCxYswOLFiw3e0qjYa6+9hp9//hnVq1cvUy5ERJVJ69atMXLkSKxcuVIvLggCxo4dixMnTsDOzk6e5MoZz4zJxFyqcUsjFGmQdfgs0lfsRfaRc2V+nG0tL1SfMgSeQ19+4sKtAJCfn4/w8HB8+eWXyMjIKHVbBwcHzJs3Dx999BH7+jF8D8iLx19+7AOx5ORkNGnSBAUFBaK2CRMm4JtvvjHq85lLH/DMGFV4giCg4I/byNh4BJnbfofmQWaZH2tTwxPVwgbAa3SvJ84JAx7NX1i1ahX++9//IiUl5Ynbv/zyy1i6dCm8vb3LnBMRUWX1wgsvYOLEiZg3b56obfHixXjppZfQr18/GTIrXzwzJhNzqcYrssK4u1DtPoHMTb+h4Jr0YqqG2DWuDacxr6H2qN6wdnjyae8HDx5g6dKl+P777/HgwYMnbl+9enUsXrwYb775JvvXAL4H5MXjLz/2gbScnBz4+fnhzz//FLW5u7vjwoULaNCggVGey1z6gGfGqMIQBAGF1xKg2nMKqj2nUHD59lPvo0pbb1T7aBBcewVClZ0NK/vS7/945coVhIeHY9WqVZKnzf9JoVBg7NixmD9/Pjw8PJ46PyKiys7Z2RmbN29Ghw4dRJ+7mZmZGDx4MCIiIixq/hiLMTJrutwC5Jy4iOwj55B95FyZr4Z8nMLOBm79X4TX6N5w9G8KhUJR6rITGRkZ2LhxI37++WecOXOmzM/z2muvYe7cuWjTps1T50hERH9r3bo1wsPD8c4774jaoqOjMX36dCxatEiGzMoHizEyKzp1EfIv3EDu6cvIORaD3NOXIag1z7Qv27rV4fX2a/Ac+gpsqrmXum1ubi4OHDiAzZs3Y+fOnQZvXSQlMDAQ8+fPR5cuXZ4pTyIiEhszZgyOHj2K9evXi9q+/vpr1K1bFxMmTDB9YuWAxRjJSpORjfzzscg7ex25py8j98x1CPllL4T+ycrJAW59O8NjcFc4dW4NRSk33X7w4AG2b9+OXbt24dChQ2Uahnxcu3btMHPmTPTt25fzPYiIjEyhUOCHH37A2bNnERsbK2qfOHEikpKSsHDhQliV8llfEbAYI5PRpKlQcCUO+ZfjkH8hFnnnY59p2FHE2grOL7aBx5vd4NarI6ycHCQ3y8zMxPHjx3H06FEcPXoUly5dKnW40pBevXph0qRJeOmll1iEERGVIxcXF2zZskVy/hjw6AxZcnIyVq1aBQcH6c/+ioDFGBmVIAjQpGag8GbKXz/JKLiWgIKrcdDcSzfa8yjsbOD8Uju49ekE156BsPF01WsvKirC1atXcebMGZw9exbR0dG4ePEidDrdMz2fg4MDhg8fjokTJ6JZs2bGeAlERFQGpc0fA4DNmzfj7t272LlzJzw9PU2cnXGYbTF26dIlhIeHIyYmBkVFRfD29sbIkSPRp0+fMu9Dp9Nh/fr12LRpExISEuDo6IgOHTpg4sSJRrsstrIRBAHajGwU3U1DUcoDqBPvQ51wH0WJ96FOvI/C23egy84rl+e2qeYO566+cOnuB9eXA2Dt6giNRoOEhARcPXkMV69exbVr13D16lX88ccfTz3sKCUoKAijRo3C4MGD4ebmZoRXQURET2vs2LHIzs7Gxx9/LNkeERGBVq1aYerUqXjnnXdQpUoVE2f4fMxynbGoqCiMGTMGtra26NWrF1xcXHDw4EEkJydj4sSJGDduXJn2M2PGDGzevBne3t4IDg5GWloa9u3bB3t7e2zcuFHWhTjNZW0TQRAg5BdCm5kDTWY2tBk50KZnQfMwE5oHqr/+zYQmNeNRAXYvHUKB2iS5KexsYN2qIQrb1MeDhp5ItC3CnXt3ER8fj7i4OMTHxyMpKQlardaoz1uvXj0MGTIEI0eORNOmTY26b/qbubwHKisef/mxD57e5s2bMXz4cKjVhr+HatSogU8++QTvvfcenJycSt2fufSB2RVjGo0GPXv2xL1797Bp0yY0b94cwKNF4N58803ExcVh7969TzyzFRkZiZEjR8LPzw8///xzyXokp0+fxttvvw0/Pz+sXbu2vF+OQcX/A7i5uUEhCIBOgKDVQdBoAd1f//71u6DRQCjSQlBr/vpvDYTCopJ/deqiR/8WqCEUFEKXr4ZQoIYuvwC63ELo8gqgy82HLq8Q2pw86LLyoM3OgzYrF7qs3Ge+WtHY1FYCbtlrEAMVovJTcTozGQWCcQstQ9q0aYP+/fujf//+aNOmDT8YTcBcPgQrKx5/+bEPns3x48fRr18/ZGZmlrqdh4cHgoKC4OvrC19fX7Rv3x516tSB9WP3HTaXPjC7YcrIyEgkJiZiwIABJYUY8GgRuPfffx8TJ07E9u3bDZ6qLLZlyxYAj+5l9fjCcEFBQejcuTMiIiIQFxeHhg0bls8LMSDtRjwiBkxCzTs5sBesYI3K+QbUCgJuabNxRavCZY0Kl7Uq3NRmQwPT/G3g5eWFTp064ZVXXkGvXr04bE1EVEF06dIFJ0+eRM+ePZGYmGhwu4yMDOzbtw/79u3Ti1tbW8PBwQEODg5wcXFBUFAQvvrqK9SuXbu8UzfI7Iqx6OhoAEDnzp1FbZ06ddLbpjRRUVFwdHSEr6+vqK24GDtz5oxJizFBEBAZPA6NCqwAWD9xe0uRoVPjpjYbsdps3Pjr55Y2B/kwzVkvAKhfvz78/Pzw4osvIiQkBC1atEBWVpbsfw0REdHTa968OU6fPo233noLx48ff6rHarVa5ObmIjc3F2lpaYiPj8cff/yBmJgYvbNmpmR2xVh8fDyAR1+e/+Tm5gYPDw8kJJR+H8K8vDw8ePAASqVS8sAWnwUpfi4p5TF6mxAVg7oFFXstFENUOjXu6PKRostHgjYX8brckn9VQpHJ8rCxsYG3tzeaNWuGtm3bwt/fH+3bt0e1atX0tivuXzMbpa9U2Afy4vGXH/vg+dSqVQtHjx7Fr7/+is8//xxRUVHPvK/Lly/jjz/+KJc7qJTlD36zK8ZycnIAPFpbRIqzszPu3btX6j6ys7NLtjW0j8efS4pKpTL6G0TnZI8iQQdbRcUpyHSCgExBjXRBjQe6QqTqCnBfV4BUXSEeCAW4o8vHHW0+cmC6eWe2traoW7cu6tWrV/LTuHFj+Pj4oFGjRpL3KzM0t0ClUpVztvQk7AN58fjLj33wfIKCgrBv3z78/vvvWLBgwTMXZTqd7onz0J5FWe5TbHbFmLkoj2UM3N3dcayZG9pfzzb6vkuTL2iRL2iQL2iRK2iQCw1yhEc/uYIG2YIGKl0RVIIaKqEI2UIR0nWPCrBMQY1nW5mr7GxtbeHq6go3Nzd4eXmV/Hh4eKB69eqoVatWyU/NmjVRo0aN515tWRAEqFSqRxdQcJhSFuwDefH4y499YFyhoaHo378/IiIisGPHDpw/fx4XLlwo9cTL449t166dCbKUZnbFWPFZq+KzW/+Uk5Nj8KxZseJ2Qx1QHDd05gwo22nFZzEiYj12/XsB0o+ehTavEAprKwgKQFAoIADQKf7+Ef76V6tQPPrXqrhNAY3Vo98f/fvX79YKFFkpoLUCiqwBjbUViqwBPPZaFBL/rVAooFAo4ACgikKBWgoFrKys9H4UCgWsra1hbW0NKyurkn9tbW1ha2sLGxubkn/t7e1hb28POzu7kv92dHRElSpVSv51cnKCq6srXF1dYW9vXy7HuiyKXzvJh30gLx5/+bEPjEehUCA4OBjBwcEAHp3tunHjBs6fP49r164hLy8PBQUFKCgoQGFhIaysrNCmTRuEhYXxasrHFc/nSkhIQMuWLfXaVCoVMjIynli9Ojo6olq1akhOToZWqxXNGyueKybHFXTW1tYYMH+62VxOS0REZKmsrKzg4+MDHx8fyfbi72JbW1sTZ6bP7CYv+fv7AwBOnDghajt58iQAICAg4In7CQgIQF5eHs6fPy9qK9538XMRERERycXsirGgoCDUrVsXe/bswbVr10riOTk5WLp0KWxsbBAaGloST09Px61bt5Cern/fw0GDBgEAFi9erLdS7+nTp3HixAn4+/ubfI0xIiIion8yu2FKGxsbzJkzB2PHjsWQIUPQu3dvODs7l9wOacKECXpF1Lp16/Ddd98hLCwM48ePL4kHBgbijTfewJYtWxAaGqp3OyRnZ2fMmjVLhldHREREpM/sijHgUSG1fv16LFmyBPv37y+5UfhHH32Evn37lnk/n3/+OXx8fLBp0yasWbMGjo6OCAkJwcSJE3lWjIiIiMyC2d2bsrLgBH558fjLj30gLx5/+bEP5GcufWB2c8aIiIiIKhMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyYjFGREREJCMWY0REREQyUgiCIMidBBEREVFlxTNjRERERDJiMUZEREQkIxZjRERERDJiMUZEREQkIxZjRERERDKykTsBS3Hp0iWEh4cjJiYGRUVF8Pb2xsiRI9GnT58y70On02H9+vXYtGkTEhIS4OjoiA4dOmDixIlo0KBB+SVvIZ63D86ePYvDhw8jOjoaKSkpyMvLQ506ddCtWze89957cHV1LedXULEZ4z3wuKKiIrz++uu4fv06GjZsiF9//dXIGVseY/VBTk4OVqxYgYMHDyIpKQm2traoW7cuunXrhrCwsHLK3jIYow+ysrLw888/4/Dhw0hOToadnR1eeOEFhIaG4o033oC9vX05voKKa9euXTh37hwuX76M2NhYFBUVYd68eRgwYMBT7UeO72IubWEEUVFRGDNmDGxtbdGrVy+4uLjg4MGDSE5OxsSJEzFu3Lgy7WfGjBnYvHkzvL29ERwcjLS0NOzbtw/29vbYuHEjvL29y/mVVFzG6INOnTohIyMD7du3R7NmzaBQKBAdHY2rV6+iXr162LhxI7y8vEzwaioeY70HHvftt99i5cqVyMvLYzFWBsbqgzt37mDkyJFISkpCx44d0axZM6jVaiQmJuLOnTvYvXt3Ob+SissYfZCVlYUBAwYgKSkJ7du3R5s2baBWq3H8+HEkJiYiMDAQP//8M6ysOLD1T127dkVKSgo8PDzg6OiIlJSUZyrGZPkuFui5FBUVCd27dxdatmwpXLlypSSenZ0t9OrVS2jevLkQFxf3xP2cPn1aUCqVwpAhQ4TCwsKS+KlTpwQfHx9h6NCh5ZG+RTBWHyxbtky4f/++Xkyn0wkzZ84UlEqlMGvWLGOnbhGMdfwfd/nyZaF58+bC6tWrBaVSKbz66qtGztqyGKsPNBqNMHDgQKF169bC6dOnJZ+HpBmrD5YvXy4olUph7ty5evHCwkJh4MCBglKpFKKjo42dvkU4efKkkJycLAjCo89zpVIpbNu27an2Idd3MUvr5xQZGYnExET07t0bzZs3L4k7Ozvj/fffh0ajwfbt25+4ny1btgAAJkyYADs7u5J4UFAQOnfujDNnziAuLs74L8ACGKsP3n33XVSvXl0vplAo8P777wMAzpw5Y9zELYSxjn8xtVqNadOmoU2bNhg2bFh5pGxxjNUHBw4cwB9//IHRo0cjMDBQ1G5jw5kthhirD5KSkgAAwcHBenE7Ozt06tQJAJCWlmbEzC1Hx44dUadOnefah1zfxSzGnlN0dDQAoHPnzqK24jdO8TaliYqKgqOjI3x9fUVtxftmMSDNWH1gSPEXkLW19TPvw5IZ+/h/9913SEhIwBdffAGFQmGcJC2csfpg3759AIAePXrg7t272LBhA5YvX479+/cjNzfXiBlbHmP1QZMmTQAAERERevGioiKcOnUKDg4OaNeu3fOmSwbI9V3MP3OeU3x8PACgfv36ojY3Nzd4eHggISGh1H3k5eXhwYMHUCqVkl/4xRMGi5+L9BmjD0qzbds2AH9/oJI+Yx7/S5cu4aeffsLEiRPRsGFDY6Zp0YzVB5cvXwYAnDt3DvPmzYNarS5p8/T0xOLFi9GhQwfjJG1hjNUHb7zxBnbt2oUVK1bg8uXLaNmyJYqKihAREQGVSoVFixahRo0axk6fIO93Mc+MPaecnBwAgIuLi2S7s7MzsrOzS91Hcbuzs7PBfTz+XKTPGH1gyLVr1/D999/Dy8sLY8eOfeYcLZmxjr9arcb06dPRrFkzjB492qg5Wjpj9UHx8NecOXMwcuRIHDt2DKdPn8ann36K7OxsfPDBB0hNTTVe4hbEWH3g4OCANWvWoG/fvoiOjsaKFSuwZs2akiFQqTM2ZBxyfhezGCMyICkpCe+99x60Wi2+/vpreHp6yp2SRVu8eDESEhIwd+5cDgnLRPjr4vqXXnoJkydPRs2aNeHp6Ynhw4dj1KhRyM7OxtatW2XO0rKlp6fj7bffxsWLF7F8+XKcPXsWJ0+exOzZs7F9+3YMGjQIKpVK7jTJyFiMPafiStnQXzw5OTkG/1IqVtxuqNoujhuq1is7Y/TBP6WkpGDkyJFIT0/HkiVLJCcz0yPGOP5XrlzBypUrMW7cOPj4+Bg9R0tnrPdA8X66du0qagsJCQHw91Am6TNWH3z55Ze4cOEClixZguDgYLi4uKBq1aoYNGgQpkyZgqSkJKxatcqoudMjcn4Xsxh7TsVjyFJzAVQqFTIyMiTnEDzO0dER1apVQ3JyMrRarai9eHyaC79KM0YfPC45ORnDhw9HamoqFi9eXPIlRNKMcfz//PNPaLVahIeHw8fHR+8HAOLi4uDj4wM/Pz+j528JjPUeKJ6nJ7XAcXGssLDwOTK1XMbqg2PHjsHd3R1NmzYVtRX/UXjlypXnS5YkyfldzGLsOfn7+wMATpw4IWo7efIkACAgIOCJ+wkICEBeXh7Onz8vaived/FzkT5j9QHwqBAbMWIEUlNT8c0336B79+7GS9RCGeP4N2jQAK+//rrkD/DoL9bXX38d/fv3N27yFsJY74HiL/ubN2+K2opjz7t0gKUyVh+o1Wrk5OToXTxRLD09HQD0llwg45Ltu7hcVi+rRIqKioRu3boJLVu2FK5evVoSf3yhv9u3b5fE09LShJs3bwppaWl6++Gir8/OWH2QlJQkhISECM2bNxcOHDhgsvwrOmMdf0O46OuTGasPEhMThZYtWwpBQUHCvXv39PbTr18/QalUCqdOnSr/F1QBGasPRo8eLSiVSuGbb77RixcWFpa0rVmzplxfiyV40qKv5vZdzNshGUFkZCTGjh0LW1tb9O7dG87OziW3wJgwYQL+9a9/lWwbHh6O7777DmFhYRg/frzefj799FNs2bKFt0N6Bsbog+JbabRt21ZyrSAAoj6jR4z1HpDi4+PD2yGVgbH6YM2aNZgzZw7c3d3x8ssvw87ODr///jtSUlIwePBgfP7556Z+aRWGMfrg2rVrGDp0KHJzc9G6dWv4+vqisLAQJ06cQFJSElq0aIENGzbw/pQStmzZgnPnzgEAYmNjceXKFfj6+pYMD3fv3r1ktMPcvou5zpgRBAYGYv369ViyZAn2799fcnPYjz76CH379i3zfj7//HP4+Phg06ZNWLNmDRwdHRESEsI1l8rAGH2QkpICAIiJiUFMTIzkNizGpBnrPUDPzlh9MHz4cNSpUwf/+9//sHfvXmi1Wnh7e2PcuHEYNGhQOb6Cis8YfdCsWTNs374dy5YtQ2RkJNatWwdra2vUq1cP48ePx5gxY1iIGXDu3Dns2LFDL3b+/PmSIcc6deqUaeqJHN/FPDNGREREJCNO4CciIiKSEYsxIiIiIhmxGCMiIiKSEYsxIiIiIhmxGCMiIiKSEYsxIiIiIhmxGCMiIiKSEYsxIiIiIhmxGCMiIiKSEYsxIqo0unbtiq5du8qdxnNZuXIlevbsidatW8PHxwfbt2+XOyUiek4sxojIYkybNg0+Pj5ITk6WO5VysXv3bsybNw9VqlTBqFGjEBYWhmbNmsmdVont27ezQCR6BrxROBFVGitXrpQ7hedy7NgxAMCyZctQrVo1mbMhImNhMUZElUa9evXkTuG5pKamAgALMSILw2FKIgsRFRUFHx8fhIeH48yZMxg2bBjatWuHgIAATJo0Cffu3RM9pngOVVZWFubMmYPg4GA0b95cb5jp6NGjGD58ONq3b4/WrVujX79+WLlyJbRard6+kpOT4ePjg2nTpiE2NhZjx45F+/bt4evri3HjxuHmzZuSed+5cwf//ve/8eKLL6Jly5bo0qUL/v3vf+Pu3buibYcPHw4fHx+o1Wp8++23ePnll9GiRQuEh4eja9eu2LFjBwCgW7du8PHxgY+PD4YPHy56vf+Un5+P8PBw9OjRA61atUJAQADeffddnD9/XrRteHg4fHx8EBUVhX379iE0NBStW7dG586dMWfOHBQUFBjoIWllOb7Fw39RUVEAUPLaSpv/ptPpEBAQgH79+unF09PT0bRpU/j4+ODs2bN6bRMmTICPjw8ePnwIAFCr1VizZg3GjBmD4OBgtGzZEkFBQQgLC8PVq1f1Hjtt2jRMnz4dADB9+vSSHH18fPS2y8nJwZIlS9CrVy+0bt0afn5+GDNmjCgXoPT+JrIkPDNGZGFiYmKwbNkyvPTSSxg+fDiuXLmCPXv24Ny5c9i6dSuqVq2qt71arcbIkSORm5uLkJAQ2NrawsvLCwCwatUqzJ07F+7u7ujduzccHR3x22+/Yd68eTh37hyWLFkChUKht7+kpCS89dZbaN26NYYMGYKEhAQcOnQI586dw8aNG9G4ceOSbePj4zFkyBCkpaUhJCQETZo0wY0bN7Bt2zb8/vvv2LBhA+rXry96jWFhYbh+/To6d+4MNzc31K1bFyNGjMCOHTtw/fp1jBgxAq6urgCAOnXqlHq81Go1Ro0ahZiYGLRo0QIjR45EWloa9u/fj5MnT+Kbb77BK6+8InrcunXrEBERga5duyIgIAARERFYs2YNMjIysGjRojL1VVmPb7NmzRAWFoYdO3YgJSUFYWFhAAAXFxeD+7aysoK/vz+OHDmCjIwMeHh4AHhUtAuCUPLffn5+JY85c+YMGjduXPL/iEqlwty5c+Hn54fg4GC4uroiKSkJv/32G44fP461a9eidevWAIDu3bsjKysLR44cQbdu3STnsmVmZmLYsGG4ceMG/Pz80LlzZ2RnZ+PIkSMYOXIkvv32W3Tv3l30OKn+JrIoAhFZhMjISEGpVApKpVLYvHmzXlt4eLigVCqF6dOn68VDQkIEpVIpjB49WsjPz9drS0xMFJo3by4EBQUJd+7cKYkXFhYKw4YNE5RKpbBz586SeFJSUsnzf/PNN3r72rFjh6BUKoURI0boxUeMGCEolUph48aNevGNGzcKSqVSGDlypF68+Hn79esnZGRkiI7B1KlTBaVSKSQlJUkeo5CQECEkJEQv9t133wlKpVKYNGmSoNPpSuLXrl0TWrZsKfj7+wvZ2dkl8SVLlghKpVJo3769cOvWrZJ4fn6+8Morrwg+Pj7CvXv3JJ//cU97fB9//WW1atUqQalUCgcOHCiJzZw5U/Dz8xNCQ0OF4cOHl8Rv3LghKJVKYdasWXq5SL2W2NhYoW3btsKoUaP04tu2bROUSqWwbds2yXw+/vhjQalUClu3btWLP3jwQAgODhYCAwOFgoIC0es11N9EloLDlEQWpmHDhnj99df1YmPHjoWnpyf27t0LtVotesyUKVPg4OCgF/vll1+g0Wjw9ttvo1atWiVxOzs7TJo0CQBKhgUf5+bmhnfffVcv1q9fPyiVSkRGRpYMP969exeRkZHw9vbGoEGD9LYfNGgQGjdujNOnT0sOV44fPx7u7u6lHIWy27FjB2xtbTF58mS9s3xNmzZFaGgoVCoVjhw5InrciBEj0KhRo5LfHRwc0Lt3bwiCgCtXrjzxeZ/1+D6NgIAAAEBkZGRJLCoqCv7+/ggKCkJMTAwKCwtL4o8/pjiXGjVqiPbbpEkTdOjQAWfOnEFRUVGZcklPT8f+/fsRFBSEgQMH6rVVrVoVY8aMQXp6Ok6dOiV6rDH7m8gccZiSyML4+vqKhg4dHBzQokULREREID4+HkqlsqTN3t5eNK8HAK5duwYA6NChg6itbdu2cHBwKNnmcc2aNYOjo6NeTKFQwNfXF7Gxsbh+/Tpq1apVMufI399flK9CoYCfnx9u3bpVsv3jiofGnldOTg6SkpLQuHFj1KxZU9TeoUMHbNq0CdevXxfNvWrevLlo++J9ZGVlPfG5n/X4Pg0fHx94eHiUFFoPHjzA7du38eabb6Jhw4b46aefcOHCBQQGBiIqKgoKhUKUz7Vr1/DTTz/h3LlzePjwoaj4ysjIQPXq1Z+Yyx9//AGtVovCwkLJOV/x8fEAgNu3byMkJESvzVj9TWSuWIwRWZji+V7/VDwPKDs7W7T9P4sh4FGhUtr+vLy8cP/+/Wd+/uL9/3MOW7HiKwb/mW9pj3laT3qNho4ZID1fy9raGsCjyfPP+9yGju/TUCgUCAgIwIEDB/Dw4cOSoqxDhw6oW7cubGxsEBUVhQ4dOiA6OhpNmjSBp6dnyePPnz+PkSNHAgA6deqEBg0awNHREQqFAocPH8b169clz7RKUalUJfuUujCiWH5+vihmrP4mMlcsxogsTFpammS8+Aq5fxYRUoUYADg7O5fsT2oSfFpaWsk2z/L8xY8tjhvaXuo5DOX8tB5/jVKK41I5GPO5n+b4Pq0OHTrgwIEDiI6ORnR0NDw8PODj4wOFQoFWrVohKioKsbGxyMjIQO/evfUe+8MPP0CtVmP9+vVo3769XltMTMxT5VH8WkaPHo2pU6c+1WON1d9E5opzxogszPnz50uulitWUFCAK1euwMHBAQ0aNCjTfoqvhis+m/K4S5cuoaCgAE2bNhW1Xbt2DXl5eZJ5ASh5TPH+z549K8pXEAScO3dOb7uysLJ69JFWljNTwKMCoW7dukhMTJQ8CxUdHa2XszE96/F9Wo/PG4uKikJAQEBJcdOhQwdcunQJv//+u962xRITE+Hu7i4qxPLz80VLWwB/H/9/LnsCAK1atYJCocCFCxee+zURWRoWY0QWJi4uDlu3btWL/fTTT0hPT0evXr1gZ2dXpv306dMHNjY2WLlypV6hUlRUhK+++goAEBoaKnqcSqXC8uXL9WI7d+5EbGwsAgMDS+Z/1a5dGx06dMCNGzdE+W7duhU3btzQ274s3NzcAEByTTVD+vfvj6KiIixatEivKIyNjcX27dvh4uIiudzC83rW4/u0mjRpAi8vLxw6dAjx8fF6c8ICAwNRVFSElStXQqFQwN/fX++xderUgUqlwo0bN0piWq0W8+fPR3p6uui5iifZSxW21apVQ8+ePXHhwgX89NNPogIcAC5evCg5TElk6ThMSWRhOnfujNmzZ+PYsWNo1KgRrly5ghMnTqBWrVr4+OOPy7yfevXqYfLkyfjyyy/Rt29f9OzZE1WqVMHvv/+O27dvo1u3bqJJ7QDg5+eHNWvW4OLFi2jVqhXi4+Nx6NAhuLi44LPPPtPbdtasWRgyZAhmzJiBo0ePwtvbGzdv3sRvv/0GT09PzJo166lee2BgIFasWIHPPvsMPXr0gKOjI2rVqoU+ffoYfMw777yDY8eOYdeuXbh16xaCgoJKrvzTaDSYP39+uQxTPuvxfRYBAQHYv38/gEfHqFi7du1gZ2dXshBs8VpkxYYNG4YTJ05gyJAh6NmzJ+zs7BAdHY379+8jICCg5MxhseILD1atWoWcnJyS+WfFV9fOnDkTcXFxWLhwIXbt2oV27drB2dkZ9+7dw5UrVxAfH48TJ06gSpUqRnndRBUFz4wRWZi2bdvi559/RkZGBlavXo1Lly6hV69e2LBhw1NPhH777bexdOlSKJVK/PLLL1i7di1sbGwwbdo0yQVfAaBu3bpYv349rK2tsXbtWkRERKBLly7YsGGD3oKvANCoUSNs27YNoaGhuHTpEv73v//h0qVLCA0NxdatW9GwYcOnyjc4OBhTpkyBIAj46aefsGjRImzevLnUx9jb22PVqlV4//33kZOTg5UrV+LQoUPw8/PD6tWr0bNnz6fK4Wk8y/F9FsVnw6pWrarXBw4ODmjTpg0A8RAlAISEhGDJkiWoW7cufvnlF+zZsweNGjXC1q1bJee5ubu7Y8mSJahfvz42bNiARYsW6S2A6+7ujo0bN2LKlCmwtbXF7t27sW7dOly8eBHe3t6YP3++qCAkqgwUgtS5YiKqcKKiojBixAiEhYVh/PjxJn/+5ORkdOvWDaGhofjyyy9N/vxERBUVz4wRERERyYjFGBEREZGMWIwRERERyYhzxoiIiIhkxDNjRERERDJiMUZEREQkIxZjRERERDJiMUZEREQkIxZjRERERDJiMUZEREQkIxZjRERERDJiMUZEREQko/8HxCNLKeyTbMkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def calculate_unnormalized_n_ways_possible_with_error(\n",
+    "    p: float, n_W: int, n_L: int, error_rate: float = 0.1\n",
+    ") -> float:\n",
+    "    n_W_error = (p * (1 - error_rate) + ((1 - p) * error_rate)) ** n_W\n",
+    "    n_L_error = ((1 - p) * (1 - error_rate) + (p * error_rate)) ** n_L\n",
+    "    return n_W_error * n_L_error\n",
+    "\n",
+    "\n",
+    "a, b = 6, 3\n",
+    "resolution = 100\n",
+    "proportions = np.linspace(0, 1, resolution)\n",
+    "error_rate = 0.1\n",
+    "error_posterior = np.array(\n",
+    "    [calculate_unnormalized_n_ways_possible_with_error(p, a, b, error_rate) for p in proportions]\n",
+    ")\n",
+    "beta_posterior_values = beta_posterior(a, b, proportions)\n",
+    "\n",
+    "# Infer normalization constant Z directly from samples\n",
+    "error_posterior *= resolution / error_posterior.sum()\n",
+    "beta_posterior_values *= resolution / beta_posterior_values.sum()\n",
+    "\n",
+    "plt.subplots(figsize=(6, 6))\n",
+    "plt.plot(proportions, beta_posterior_values, label=\"previous posterior\", color=\"k\", linewidth=4)\n",
+    "plt.plot(\n",
+    "    proportions,\n",
+    "    error_posterior,\n",
+    "    label=f\"misclassification posterior\\n(error rate={error_rate:1.2})\",\n",
+    "    linewidth=4,\n",
+    ")\n",
+    "plt.xlabel(\"proportion of water\")\n",
+    "plt.ylabel(\"posterior probability\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e2837d6",
+   "metadata": {},
+   "source": [
+    "## Measurement Matters\n",
+    "- better to model measurement error than to ignore it\n",
+    "- same goes for mssing data\n",
+    "- what matters is _why_ samples differ, and that we are explicit about how model it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "849db332",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "* Ported to PyMC by Dustin Stansbury (2024)\n",
+    "* Based on Statistical Rethinking (2023) lectures by Richard McElreath"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d06d871f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Tue Dec 17 2024\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.5\n",
+      "IPython version      : 8.27.0\n",
+      "\n",
+      "pytensor: 2.26.4\n",
+      "aeppl   : not installed\n",
+      "xarray  : 2024.7.0\n",
+      "\n",
+      "numpy      : 1.26.4\n",
+      "scipy      : 1.14.1\n",
+      "matplotlib : 3.9.2\n",
+      "statsmodels: 0.14.2\n",
+      "xarray     : 2024.7.0\n",
+      "pymc       : 5.19.1\n",
+      "pandas     : 2.2.2\n",
+      "arviz      : 0.19.0\n",
+      "\n",
+      "Watermark: 2.5.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "741e5b25",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.myst.md b/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.myst.md
new file mode 100644
index 00000000..aff2449e
--- /dev/null
+++ b/examples/statistical_rethinking_lectures/Lecture_02-The_Garden_of_Forking_Data.myst.md
@@ -0,0 +1,588 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+(lecture_02)=
+# The Garden of Forking Data
+:::{post} Jan 7, 2024
+:tags: statistical rethinking, bayesian inference, probability
+:category: intermediate
+:author: Dustin Stansbury
+:::
+
+This notebook is part of the PyMC port of the [Statistical Rethinking 2023](https://github.com/rmcelreath/stat_rethinking_2023) lecture series by Richard McElreath.
+
+[Video - Lecture 02 - The Garden of Forking Data](https://youtu.be/R1vcdhPBlXA?si=rL3BOz9hHxkPt79m)
+
+```{code-cell} ipython3
+# Ignore warnings
+import warnings
+
+import arviz as az
+import numpy as np
+import pandas as pd
+import pymc as pm
+import statsmodels.formula.api as smf
+import utils as utils
+import xarray as xr
+
+from matplotlib import pyplot as plt
+from matplotlib import style
+from scipy import stats as stats
+
+warnings.filterwarnings("ignore")
+
+# Set matplotlib style
+STYLE = "statistical-rethinking-2023.mplstyle"
+style.use(STYLE)
+```
+
+# Task: What proportion of earth's surface is covered with water?
+
+## Workflow (Drawing the Owl)
+
+1. Define **generative model** of tossing the globe
+2. Define an **estimand** -- in this case, the proportion of globe covered in water
+3. **Design a statistical procedure** to produce an estimate of the estimand
+4. **Validate the statistical procedure** (3) using the generative model -- can we recover an accurate estimate of (2) from data generated by (1)
+5. **Apply statistical procedure** (3) to real data
+
++++
+
+## 1, 2. Define generative model of globe tossing
+- $p$: proportion of water -- this is the **estimand**, what we'd like to estimate
+- $N$: number of tosses  -- we control this via experiment
+- $W$: number of `Water` observations
+- $L$: number of `Land` observations
+
+```{code-cell} ipython3
+utils.draw_causal_graph(
+    edge_list=[("p", "W"), ("p", "L"), ("N", "L"), ("N", "W")],
+    graph_direction="LR",
+    node_props={"p": {"color": "red"}},
+    edge_props={("p", "W"): {"label": "influence"}},
+)
+```
+
+- This graph defines a causal model, of how $p, N$ effect the values of $W, L$. This is the same as saying it defines some function $f$ that maps $p, N$ onto the values of $W, L$, i.e.  $W, L = f(p, N)$
+- Scientific knowledge defines what $f$ is or can be
+
+The unglamourous basis of applied probability:
+> **Things that can happen more ways are more plausible.**
+
+
+#### Bayesian data analysis
+"Very simple, very humble"
+- For each possible explanation of the sample
+- Count all the ways the sample could occur
+- **The explanations with the largest number of ways to produce the observed sample are more plausible**
+
++++
+
+## 3. Design a statistical procedure to produce an estimate
+### Garden of Forking Data
+Following the mantra above...
+
+- for each possible **proportion of water**, $p$
+- count all the ways the sample of tosses could have occurred
+- the $p$ that are associated with more ways to produce the sample are more plausible
+
+```{code-cell} ipython3
+def calculate_n_ways_possible(observations: str, n_water: int, resolution: int = 4):
+    """
+    Calculate the number of ways to observing water ('W') given the toss of a globe
+    with `resolution` number of sides and `n_water` faces.
+
+    Note: this method results in numerical precision issues (due to the product) when the
+    resolution of 16 or so, depending on your system.
+    """
+    assert n_water <= resolution
+
+    # Convert observation string to an array
+    observations = np.array(list(observations.upper()))
+
+    # Create n-sided globe with possible outcomes
+    possible = np.array(list("L" * (resolution - n_water)) + list("W" * n_water))
+
+    # Tally up ways to obtain each observation given the possible outcomes
+    # Here we use brute-force, but we could also use the analytical solution below
+    ways = []
+    for obs in observations:
+        ways.append((possible == obs).sum())
+
+    p_water = n_water / resolution
+    # perform product in log space for numerical precision
+    n_ways = np.round(np.exp(np.sum(np.log(ways)))).astype(int)
+    return n_ways, p_water
+
+
+def run_globe_tossing_simulation(observations, resolution, current_n_possible_ways=None):
+    """Simulate the number of ways you can observe water ('W') for a globe of `resolution`
+    sides, varying the proportion of the globe that is covered by water.
+    """
+    # For Bayesian updates
+    current_n_possible_ways = (
+        current_n_possible_ways if current_n_possible_ways is not None else np.array([])
+    )
+
+    print(f"Observations: '{observations}'")
+    p_water = np.array([])
+    for n_W in range(0, resolution + 1):
+        n_L = resolution - n_W
+        globe_sides = "W" * n_W + "L" * n_L
+        n_possible_ways, p_water_ = calculate_n_ways_possible(
+            observations, n_water=n_W, resolution=resolution
+        )
+        print(f"({n_W+1}) {globe_sides} p(W) = {p_water_:1.2}\t\t{n_possible_ways} Ways to Produce")
+
+        p_water = np.append(p_water, p_water_)
+        current_n_possible_ways = np.append(current_n_possible_ways, n_possible_ways)
+
+    return current_n_possible_ways, p_water
+
+
+RESOLUTION = 4
+observations = "WLW"
+n_possible_ways, p_water = run_globe_tossing_simulation(observations, resolution=RESOLUTION)
+```
+
+## Bayesian (online) Updating
+
+```{code-cell} ipython3
+new_observation_possible_ways, _ = run_globe_tossing_simulation("W", resolution=RESOLUTION)
+
+# Online update
+n_possible_ways *= new_observation_possible_ways
+
+print("\nUpdated Possibilities given new observation:")
+for ii in range(0, RESOLUTION + 1):
+    print(f"({ii+1}) p(W) = {p_water[ii]:1.2}\t\t{int(n_possible_ways[ii])} Ways to Produce")
+```
+
+## The whole sample
+
+```{code-cell} ipython3
+RESOLUTION = 4
+observations = "WLWWWLWLW"
+n_W = len(observations.replace("L", ""))
+n_L = len(observations) - n_W
+
+n_possible_ways, p_water = run_globe_tossing_simulation(observations, resolution=RESOLUTION)
+```
+
+show that we get identical answers with the analytical solution
+
++++
+
+#### Results suggest the Analytical Solution $W,L = (Rp)^W \times (R - Rp)^L$
+where $R$ is the number of possible globes, in this case 4
+
+```{code-cell} ipython3
+def calculate_analytic_n_ways_possible(p, n_W, n_L, resolution=RESOLUTION):
+    """This scales much better than the brute-force method"""
+    return (resolution * p) ** n_W * (resolution - resolution * p) ** n_L
+
+
+analytic_n_possible_ways = np.array(
+    [calculate_analytic_n_ways_possible(p, n_W, n_L) for p in p_water]
+)
+assert (analytic_n_possible_ways == n_possible_ways).all()
+```
+
+## Probability
+- non-negative values that sum to 1
+- normalizes large sums by the total counts
+
+```{code-cell} ipython3
+n_possible_probabilities = n_possible_ways / n_possible_ways.sum()
+
+print("Proportion\tWays\tProbability")
+for p, n_w, n_p in zip(p_water, n_possible_ways, n_possible_probabilities):
+    print(f"{p:1.12}\t\t{n_w:0.0f}\t{n_p:1.2f}")
+
+probs = np.linspace(0, 1, RESOLUTION + 1)
+plt.subplots(figsize=(5, 5))
+plt.bar(x=probs, height=n_possible_probabilities, width=0.9 / RESOLUTION, color="k")
+plt.xticks(probs)
+plt.ylabel("probability")
+plt.xlabel("proportion water");
+```
+
+## 4. Validate Statistical Procedure (3) using Generative Model (1)
+
+### Test Before You Est(imate) 🐤
+1. Code generative simulation (1)
+2. Code an estimator (3)
+3. Test (3) with (1); you should get expected output
+
+**IF YOU TEST NOTHING YOU MISS EVERYTHING**
+
+
++++
+
+### 4.1 Generative Simulation
+
+```{code-cell} ipython3
+from pprint import pprint
+
+np.random.seed(1)
+
+
+def simulate_globe_toss(p: float = 0.7, N: int = 9) -> list[str]:
+    """Simulate N globe tosses with a specific/known proportion
+    p: float
+        The propotion of water
+    N: int
+        Number of globe tosses
+    """
+    return np.random.choice(list("WL"), size=N, p=np.array([p, 1 - p]), replace=True)
+```
+
+```{code-cell} ipython3
+print(simulate_globe_toss())
+```
+
+```{code-cell} ipython3
+pprint([simulate_globe_toss(p=1, N=11).tolist() for _ in range(10)])
+```
+
+#### Test on Extreme settings
+With a large number of samples N, estimator should converge to known $p$
+
+```{code-cell} ipython3
+known_p = 0.5
+
+simulated_ps = []
+sample_sizes = np.linspace(10, 100_000, 10)
+for N in sample_sizes:
+    simulated_p = np.sum(simulate_globe_toss(p=known_p, N=int(N)) == "W") / N
+    simulated_ps.append(simulated_p)
+
+plt.axhline(known_p, label=f"Known p={known_p}", color="k", linestyle="--")
+plt.legend()
+plt.plot(sample_sizes, simulated_ps);
+```
+
+### 4.2 Code the estimator
+
+The estimator takes in observations and returns a probability distribution (posterior) over potential estimates. Higher probability estimates should be more plausible given the data.
+
+```{code-cell} ipython3
+def compute_posterior(observations, resolution=RESOLUTION, ax=None):
+    n_W = len(observations.replace("L", ""))
+    n_L = len(observations) - n_W
+
+    p_water = np.linspace(0, 1, resolution + 1)
+    n_possible_ways = np.array(
+        [calculate_analytic_n_ways_possible(p, n_W, n_L, resolution) for p in p_water]
+    )
+
+    posterior = n_possible_ways / n_possible_ways.sum()
+    potential_p = np.linspace(0, 1, resolution + 1)
+
+    return posterior, potential_p
+
+
+def plot_posterior(observations, resolution=RESOLUTION, ax=None):
+    posterior, probs = compute_posterior(observations, resolution=resolution)
+    if ax is not None:
+        plt.sca(ax)
+    plt.bar(x=probs, height=posterior, width=0.9 / resolution, color="k")
+    plt.xticks(probs[::2], rotation=45)
+    plt.ylabel("probability")
+    plt.xlabel("proportion water")
+    plt.title(f"Posterior Calculated\nfrom # Samples: {len(observations)}")
+
+
+plot_posterior(observations, resolution=4)
+```
+
+```{code-cell} ipython3
+np.random.seed(2)
+known_p = 0.4
+simulated_observations = "".join(simulate_globe_toss(p=known_p, N=100))
+plot_posterior(simulated_observations, resolution=20)
+plt.axvline(known_p, color="C0", label="True Proportion")
+plt.legend();
+```
+
+## Infinite Possibilities
+
+### Moving from an N-sided globe to an infinitely-sided globe.
+As we increase resolution of globe
+- there are more bars/finer-grained resolution along the proportion axis
+- bars get shorter with more possibilities -- they must sum to 1
+
+```{code-cell} ipython3
+np.random.seed(12)
+known_p = 0.7
+simulated_observations = "".join(simulate_globe_toss(p=known_p, N=30))
+_, axs = plt.subplots(1, 3, figsize=(10, 4))
+for ii, possibilities in enumerate([5, 10, 20]):
+    plot_posterior(simulated_observations, resolution=possibilities, ax=axs[ii])
+    plt.ylim([-0.05, 1])
+    axs[ii].set_title(f"{possibilities} possibilities")
+```
+
+### Beta Distribution
+
+Analytical function that gives us the pdf as the limit as number of possibilities $\rightarrow \infty$
+
+$$
+p = \frac{(W + L + 1)!}{W!L!} p^W(1-p)^L
+$$
+
+where $\frac{(W + L + 1)!}{W!L!}$ is a normalizing constant to make the distribution sum to 1
+
+
++++
+
+### Tossing the Globe
+
+```{code-cell} ipython3
+from scipy.special import factorial
+
+
+def beta_posterior(n_W: int, n_L: int, p: float) -> float:
+    """Calculates the beta posterior over proportions `p` given a set of
+    `N_W` water and `N_L` land observations
+    """
+    return factorial(n_W + n_L + 1) / (factorial(n_W) * factorial(n_L)) * p**n_W * (1 - p) ** n_L
+
+
+def plot_beta_posterior_from_observations(
+    observations: str, resolution: int = 50, **plot_kwargs
+) -> None:
+    """Calculates and plots the beta posterior for a string of observations"""
+    n_W = len(observations.replace("L", ""))
+    n_L = len(observations) - n_W
+    proportions = np.linspace(0, 1, resolution)
+
+    probs = beta_posterior(n_W, n_L, proportions)
+    plt.plot(proportions, probs, **plot_kwargs)
+    plt.yticks([])
+    plt.title(observations)
+
+
+# Tossing the globe
+observations = "WLWWWLWLW"
+fig, axs = plt.subplots(3, 3, figsize=(8, 8))
+for ii in range(9):
+    ax = axs[ii // 3][ii % 3]
+    plt.sca(ax)
+    # Plot previous
+    if ii > 0:
+        plot_beta_posterior_from_observations(observations[:ii], color="k", linestyle="--")
+    else:
+        # First observation, no previous data
+        plot_beta_posterior_from_observations("", color="k", linestyle="--")
+
+    color = "C1" if observations[ii] == "W" else "C0"
+    plot_beta_posterior_from_observations(
+        observations[: ii + 1], color=color, linewidth=4, alpha=0.5
+    )
+
+    if not ii % 3:
+        plt.ylabel("posterior probability")
+```
+
+## On Bayesian Inference...
+- **There is no minimun sample size** -- fewer samples fall back to prior
+- **Posterior shape embodies the sample size** -- more data makes the posterior more precise
+- There is no point estimates -- **the estimate is the entire posterior distribution**
+- There is no true interval -- there are an infinite number of intervals one could draw, each is arbitrary and depends on what you're trying to communicate/summarize
+
++++
+
+## From Posterior to Prediction
+- To make predictions, we must average (i.e. integrate) over the entire posterior -- this averages over the uncertainty in the posterior
+- We could do this with integral calculus
+- OR, we could just **take samples from the posterior and average over those**
+
+**TURN A CALCULUS PROBLEM INTO A DATA SUMMARY PROBLEM**
+
++++
+
+### Sampling from Posterior Distribution
+
+```{code-cell} ipython3
+a, b = 6, 3
+# draw random samples from Beta PDF
+beta_posterior_pdf = stats.beta(a, b)
+beta_posterior_samples = beta_posterior_pdf.rvs(size=1000)
+
+# Show that our beta postorior captures shape of beta-distributed samples
+plt.hist(beta_posterior_samples, bins=50, density=True, label="samples")
+probs = np.linspace(0, 1, 100)
+plt.plot(
+    probs,
+    beta_posterior(a - 1, b - 1, probs),
+    linewidth=3,
+    color="k",
+    linestyle="--",
+    label="beta distribution",
+)
+plt.xlabel("proportion water")
+plt.ylabel("density")
+plt.legend();
+```
+
+### Sampling from Posterior Predictive Distribution
+**Posterior Prediction**: a prediction for out-of-sample data based on the current posterior estimate
+- 1. Draw a sample of model parameters from the posterior (i.e. proportions)
+- 2. Generate/simulate data predictions using our generative model and the sampled parameters
+- 3. The resulting probability distribution is our prediction
+
+```{code-cell} ipython3
+# 1. Sample parameters values from posterior
+N_posterior_samples = 10_000
+posterior_samples = beta_posterior_pdf.rvs(size=N_posterior_samples)
+
+# 2. Use samples for the posterior to simulate sampling 10 observations from our generative model
+N_draws_for_prediction = 10
+posterior_predictive = [
+    (simulate_globe_toss(p, N_draws_for_prediction) == "W").sum() for p in posterior_samples
+]
+ppd_unique, ppd_counts = np.unique(posterior_predictive, return_counts=True)
+
+# ...for comparison we can compare to the distribution that results from pinning the parameter to a specific value
+specific_prob = 0.64
+specific_predictive = [
+    (simulate_globe_toss(specific_prob, N_draws_for_prediction) == "W").sum()
+    for _ in posterior_samples
+]
+specific_unique, specific_counts = np.unique(specific_predictive, return_counts=True)
+
+plt.bar(
+    specific_unique,
+    specific_counts,
+    width=0.5,
+    color="k",
+    label=f"simulation at p={specific_prob:1.2}",
+)
+plt.bar(ppd_unique, ppd_counts, width=0.2, color="C1", label="posterior predictive")
+plt.xlabel(r"$\hat n_W$")
+plt.ylabel("count")
+plt.title(f"number of W samples predicted $\hat n_W$ from {N_draws_for_prediction} globe flips")
+plt.legend();
+```
+
+### Sampling is Handsom & Handy
+Things we'll compute via sampling
+- Forecasts
+- Causal effects
+- Counterfactuals
+- Prior Predictions
+
++++
+
+# Summary: Bayesian Data Analysis
+- For each possible explanation of data
+- Count all the ways that data could occur under that explanation
+- The explanations with more ways to produce data are more plausable
+
+## Bayesian Modesty
+- If your generative model is correct, you can't do better: this will be an optimal solution
+- Gives no gaurantees, only provides what you put into it
+
++++
+
+# Bonus: Misclassification
+In previous examples, we  do not consider sampling error or noise in measurement. In other words the number of `Water` observations that we measure may not be the _true_ value.
+
+This means that the _true_ value for $W$ is unknown / unmeasured, but we instead measure $W^*$ that is caused by both the true, unmeasured $W$ and the measurement process $M$. If we know our measurement error rate, we can attempt to model it
+
+```{code-cell} ipython3
+utils.draw_causal_graph(
+    edge_list=[("p", "W"), ("W", "W*"), ("M", "W*"), ("N", "W")],
+    node_props={
+        "p": {"color": "red", "style": "dashed"},
+        "W": {"style": "dashed", "label": "actual W"},
+        "W*": {"label": "noisy W, W*"},
+        "unobserved": {"style": "dashed"},
+        "M": {"label": "measurement error, M"},
+    },
+    graph_direction="LR",
+)
+```
+
+## Missclassification Simulation
+
+```{code-cell} ipython3
+def simulate_noisy_globe_toss(p: float = 0.7, N: int = 9, error_rate: float = 0.1) -> np.ndarray:
+    # True sample
+    sample = np.random.choice(list("WL"), size=N, p=np.array([p, 1 - p]), replace=True)
+
+    # Error-induced sample
+    error_trials = np.random.rand(N) < error_rate
+    errors_effect_sample_trials = (sample == "W") & error_trials
+    sample[errors_effect_sample_trials] = "L"
+    return sample
+
+
+simulate_noisy_globe_toss()
+```
+
+## Missclassification Estimator
+
+```{code-cell} ipython3
+def calculate_unnormalized_n_ways_possible_with_error(
+    p: float, n_W: int, n_L: int, error_rate: float = 0.1
+) -> float:
+    n_W_error = (p * (1 - error_rate) + ((1 - p) * error_rate)) ** n_W
+    n_L_error = ((1 - p) * (1 - error_rate) + (p * error_rate)) ** n_L
+    return n_W_error * n_L_error
+
+
+a, b = 6, 3
+resolution = 100
+proportions = np.linspace(0, 1, resolution)
+error_rate = 0.1
+error_posterior = np.array(
+    [calculate_unnormalized_n_ways_possible_with_error(p, a, b, error_rate) for p in proportions]
+)
+beta_posterior_values = beta_posterior(a, b, proportions)
+
+# Infer normalization constant Z directly from samples
+error_posterior *= resolution / error_posterior.sum()
+beta_posterior_values *= resolution / beta_posterior_values.sum()
+
+plt.subplots(figsize=(6, 6))
+plt.plot(proportions, beta_posterior_values, label="previous posterior", color="k", linewidth=4)
+plt.plot(
+    proportions,
+    error_posterior,
+    label=f"misclassification posterior\n(error rate={error_rate:1.2})",
+    linewidth=4,
+)
+plt.xlabel("proportion of water")
+plt.ylabel("posterior probability")
+plt.legend();
+```
+
+## Measurement Matters
+- better to model measurement error than to ignore it
+- same goes for mssing data
+- what matters is _why_ samples differ, and that we are explicit about how model it
+
++++
+
+## Authors
+* Ported to PyMC by Dustin Stansbury (2024)
+* Based on Statistical Rethinking (2023) lectures by Richard McElreath
+
+```{code-cell} ipython3
+%load_ext watermark
+%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray
+```
+
+:::{include} ../page_footer.md
+:::
diff --git a/examples/statistical_rethinking_lectures/statistical-rethinking-2023.mplstyle b/examples/statistical_rethinking_lectures/statistical-rethinking-2023.mplstyle
new file mode 100644
index 00000000..38b80b8b
--- /dev/null
+++ b/examples/statistical_rethinking_lectures/statistical-rethinking-2023.mplstyle
@@ -0,0 +1,36 @@
+figure.figsize: 7.2, 4.8
+figure.dpi: 100.0
+figure.facecolor: white
+figure.constrained_layout.use: True
+text.color: .15
+axes.labelcolor: .15
+legend.frameon: True
+legend.numpoints: 1
+legend.scatterpoints: 1
+xtick.direction: out
+ytick.direction: out
+xtick.color: .15
+ytick.color: .15
+axes.axisbelow: True
+grid.linestyle: -
+lines.solid_capstyle: round
+
+axes.labelsize: 14
+axes.titlesize: 16
+xtick.labelsize: 14
+ytick.labelsize: 14
+legend.fontsize: 12
+
+axes.grid: True
+axes.facecolor: white
+axes.edgecolor: black
+axes.linewidth: 0
+grid.color: eeeeee
+image.cmap: viridis
+xtick.major.size: 0
+ytick.major.size: 0
+xtick.minor.size: 0
+ytick.minor.size: 0
+
+
+axes.prop_cycle: cycler('color', ['d11149', '1a8fe3', '1ccd6a', 'e6c229', '6610f2', 'f17105',  '65e5f3', 'bd8ad5', 'b16b57'])
\ No newline at end of file
diff --git a/examples/statistical_rethinking_lectures/utils.py b/examples/statistical_rethinking_lectures/utils.py
new file mode 100644
index 00000000..d0ab4c00
--- /dev/null
+++ b/examples/statistical_rethinking_lectures/utils.py
@@ -0,0 +1,381 @@
+import os
+import pandas as pd
+import numpy as np
+import pymc as pm
+import arviz as az
+import graphviz as gr
+import networkx as nx
+from matplotlib import pyplot as plt
+from pathlib import Path
+from typing import List, Union, Callable
+
+HERE = Path(".")
+
+
+def load_data(dataset, delimiter=";"):
+    fname = f"{dataset}.csv"
+    data_path = HERE / "data"
+    data_file = data_path / fname
+    return pd.read_csv(data_file, sep=delimiter)
+
+
+def crosstab(x: np.array, y: np.array, labels: list[str] = None):
+    """Simple cross tabulation of two discrete vectors x and y"""
+    ct = pd.crosstab(x, y)
+    if labels:
+        ct.index = labels
+        ct.columns = labels
+    return ct
+
+
+def center(vals: np.ndarray) -> np.ndarray:
+    return vals - np.nanmean(vals)
+
+
+def standardize(vals: np.ndarray) -> np.ndarray:
+    centered_vals = center(vals)
+    return centered_vals / np.nanstd(centered_vals)
+
+
+def convert_to_categorical(vals):
+    return vals.astype("category").cat.codes.values
+
+
+def logit(p: float) -> float:
+    return np.log(p / (1 - p))
+
+
+def invlogit(x: float) -> float:
+    return 1 / (1 + np.exp(-x))
+
+
+def draw_causal_graph(edge_list, node_props=None, edge_props=None, graph_direction="UD"):
+    """Utility to draw a causal (directed) graph"""
+    g = gr.Digraph(graph_attr={"rankdir": graph_direction})
+
+    edge_props = {} if edge_props is None else edge_props
+    for e in edge_list:
+        props = edge_props[e] if e in edge_props else {}
+        g.edge(e[0], e[1], **props)
+
+    if node_props is not None:
+        for name, props in node_props.items():
+            g.node(name=name, **props)
+    return g
+
+
+def plot_scatter(xs, ys, **scatter_kwargs):
+    """Draw scatter plot with consistent style (e.g. unfilled points)"""
+    defaults = {"alpha": 0.6, "lw": 3, "s": 80, "color": "C0", "facecolors": "none"}
+
+    for k, v in defaults.items():
+        val = scatter_kwargs.get(k, v)
+        scatter_kwargs[k] = val
+
+    plt.scatter(xs, ys, **scatter_kwargs)
+
+
+def plot_line(xs, ys, **plot_kwargs):
+    """Plot line with consistent style (e.g. bordered lines)"""
+    linewidth = plot_kwargs.get("linewidth", 3)
+    plot_kwargs["linewidth"] = linewidth
+
+    # Copy settings for background
+    background_plot_kwargs = {k: v for k, v in plot_kwargs.items()}
+    background_plot_kwargs["linewidth"] = linewidth + 2
+    background_plot_kwargs["color"] = "white"
+    del background_plot_kwargs["label"]  # no legend label for background
+
+    plt.plot(xs, ys, **background_plot_kwargs, zorder=30)
+    plt.plot(xs, ys, **plot_kwargs, zorder=31)
+
+
+def plot_errorbar(xs, ys, error_lower, error_upper, colors="C0", error_width=12, alpha=0.3):
+    if isinstance(colors, str):
+        colors = [colors] * len(xs)
+
+    """Draw thick error bars with consistent style"""
+    for ii, (x, y, err_l, err_u) in enumerate(zip(xs, ys, error_lower, error_upper)):
+        marker, _, bar = plt.errorbar(
+            x=x,
+            y=y,
+            yerr=np.array((err_l, err_u))[:, None],
+            ls="none",
+            color=colors[ii],
+            zorder=1,
+        )
+        plt.setp(bar[0], capstyle="round")
+        marker.set_fillstyle("none")
+        bar[0].set_alpha(alpha)
+        bar[0].set_linewidth(error_width)
+
+
+def plot_x_errorbar(xs, ys, error_lower, error_upper, colors="C0", error_width=12, alpha=0.3):
+    if isinstance(colors, str):
+        colors = [colors] * len(xs)
+
+    """Draw thick error bars with consistent style"""
+    for ii, (x, y, err_l, err_u) in enumerate(zip(xs, ys, error_lower, error_upper)):
+        marker, _, bar = plt.errorbar(
+            x=x,
+            y=y,
+            xerr=np.array((err_l, err_u))[:, None],
+            ls="none",
+            color=colors[ii],
+            zorder=1,
+        )
+        plt.setp(bar[0], capstyle="round")
+        marker.set_fillstyle("none")
+        bar[0].set_alpha(alpha)
+        bar[0].set_linewidth(error_width)
+
+
+def plot_graph(graph, **graph_kwargs):
+    """Draw a network graph.
+
+    graph: Union[networkx.DiGraph, np.ndarray]
+        if ndarray, assume `graph` is an adjacency matrix defining
+        a directed graph.
+
+    """
+    # convert to networkx.DiGraph, if needed
+    G = (
+        nx.from_numpy_array(graph, create_using=nx.DiGraph)
+        if isinstance(graph, np.ndarray)
+        else graph
+    )
+
+    # Set default styling
+    np.random.seed(123)  # for consistent spring-layout
+    if "layout" in graph_kwargs:
+        graph_kwargs["pos"] = graph_kwargs["layout"](G)
+
+    default_graph_kwargs = {
+        "node_color": "C0",
+        "node_size": 500,
+        "arrowsize": 30,
+        "width": 3,
+        "alpha": 0.7,
+        "connectionstyle": "arc3,rad=0.1",
+        "pos": nx.kamada_kawai_layout(G),
+    }
+    for k, v in default_graph_kwargs.items():
+        if k not in graph_kwargs:
+            graph_kwargs[k] = v
+
+    nx.draw(G, **graph_kwargs)
+    # return the node layout for consistent graphing
+    return graph_kwargs["pos"]
+
+
+def plot_2d_function(xrange, yrange, func, ax=None, **countour_kwargs):
+    """Evaluate the function `func` over the values of xrange and yrange and
+    plot the resulting value contour over that range.
+
+    Parameters
+    ----------
+    xrange : np.ndarray
+        The horizontal values to evaluate/plot
+    yrange : p.ndarray
+        The horizontal values to evaluate/plot
+    func : Callable
+        function of two arguments, xs and ys. Should return a single value at
+        each point.
+    ax : matplotlib.Axis, optional
+        An optional axis to plot the function, by default None
+
+    Returns
+    -------
+    contour : matplotlib.contour.QuadContourSet
+    """
+    resolution = len(xrange)
+    xs, ys = np.meshgrid(xrange, yrange)
+    xs = xs.ravel()
+    ys = ys.ravel()
+
+    value = func(xs, ys)
+
+    if ax is not None:
+        plt.sca(ax)
+
+    return plt.contour(
+        xs.reshape(resolution, resolution),
+        ys.reshape(resolution, resolution),
+        value.reshape(resolution, resolution),
+        **countour_kwargs,
+    )
+
+
+def create_variables_dataframe(*variables: List[np.ndarray]) -> pd.DataFrame:
+    """Converts a list of numpy arrays to a dataframe; infers column names from
+    variable names
+    """
+    column_names = [get_variable_name(v) for v in variables]
+    return pd.DataFrame(np.vstack(variables).T, columns=column_names)
+
+
+def plot_pymc_distribution(distribution: pm.Distribution, **distribution_params):
+    """Plot a PyMC Distribution with specific distrubution parameters
+
+    Parameters
+    ----------
+    distribution : pymc.Distribution
+        The class of distribution to
+    **distribution_params : dict
+        Distribution-specific parameters.
+
+    Returns
+    -------
+    ax : matplotlib.Axes
+        The axes object associated with the plot.
+    """
+    with pm.Model() as _:
+        d = distribution(name=distribution.__name__, **distribution_params)
+        draws = pm.draw(d, draws=10_000)
+    return az.plot_dist(draws)
+
+
+def savefig(filename):
+    """Save a figure to the `./images` directory"""
+    image_path = HERE / "images"
+    if not image_path.exists():
+        print(f"creating image directory: {image_path}")
+        os.makedirs(image_path)
+
+    figure_path = image_path / filename
+    print(f"saving figure to {figure_path}")
+    plt.savefig(figure_path, dpi=300, bbox_inches="tight")
+
+
+def display_image(filename, width=600):
+    """Display an image saved to the `./images` directory"""
+    from IPython.display import Image, display
+
+    return display(Image(filename=f"images/{filename}", width=width))
+
+
+def simulate_2_parameter_bayesian_learning_grid_approximation(
+    x_obs,
+    y_obs,
+    param_a_grid,
+    param_b_grid,
+    true_param_a,
+    true_param_b,
+    model_func,
+    posterior_func,
+    n_posterior_samples=3,
+    param_labels=None,
+    data_range_x=None,
+    data_range_y=None,
+):
+    """General function for simulating Bayesian learning in a 2-parameter model
+    using grid approximation.
+
+    Parameters
+    ----------
+    x_obs : np.ndarray
+        The observed x values
+    y_obs : np.ndarray
+        The observed y values
+    param_a_grid: np.ndarray
+        The range of values the first model parameter in the model can take.
+        Note: should have same length as param_b_grid.
+    param_b_grid: np.ndarray
+        The range of values the second model parameter in the model can take.
+        Note: should have same length as param_a_grid.
+    true_param_a: float
+        The true value of the first model parameter, used for visualizing ground
+        truth
+    true_param_b: float
+        The true value of the second model parameter, used for visualizing ground
+        truth
+    model_func: Callable
+        A function `f` of the form `f(x, param_a, param_b)`. Evaluates the model
+        given at data points x, given the current state of parameters, `param_a`
+        and `param_b`. Returns a scalar output for the `y` associated with input
+        `x`.
+    posterior_func: Callable
+        A function `f` of the form `f(x_obs, y_obs, param_grid_a, param_grid_b)
+        that returns the posterior probability given the observed data and the
+        range of parameters defined by `param_grid_a` and `param_grid_b`.
+    n_posterior_samples: int
+        The number of model functions sampled from the 2D posterior
+    param_labels: Optional[list[str, str]]
+        For visualization, the names of `param_a` and `param_b`, respectively
+    data_range_x: Optional len-2 float sequence
+        For visualization, the upper and lower bounds of the domain used for model
+        evaluation
+    data_range_y: Optional len-2 float sequence
+        For visualization, the upper and lower bounds of the range used for model
+        evaluation.
+    """
+    param_labels = param_labels if param_labels is not None else ["param_a", "param_b"]
+    data_range_x = (x_obs.min(), x_obs.max()) if data_range_x is None else data_range_x
+    data_range_y = (y_obs.min(), y_obs.max()) if data_range_y is None else data_range_y
+
+    # NOTE: assume square parameter grid
+    resolution = len(param_a_grid)
+
+    param_a_grid, param_b_grid = np.meshgrid(param_a_grid, param_b_grid)
+    param_a_grid = param_a_grid.ravel()
+    param_b_grid = param_b_grid.ravel()
+
+    posterior = posterior_func(x_obs, y_obs, param_a_grid, param_b_grid)
+
+    # Visualization
+    fig, axs = plt.subplots(1, 2, figsize=(10, 5))
+
+    # Plot Posterior over intercept and slope params
+    plt.sca(axs[0])
+    plt.contour(
+        param_a_grid.reshape(resolution, resolution),
+        param_b_grid.reshape(resolution, resolution),
+        posterior.reshape(resolution, resolution),
+        cmap="gray_r",
+    )
+
+    # Sample locations in parameter space according to posterior
+    sample_idx = np.random.choice(
+        np.arange(len(posterior)),
+        p=posterior / posterior.sum(),
+        size=n_posterior_samples,
+    )
+
+    param_a_list = []
+    param_b_list = []
+    for ii, idx in enumerate(sample_idx):
+        param_a = param_a_grid[idx]
+        param_b = param_b_grid[idx]
+        param_a_list.append(param_a)
+        param_b_list.append(param_b)
+
+        # Add sampled parameters to posterior
+        plt.scatter(param_a, param_b, s=60, c=f"C{ii}", alpha=0.75, zorder=20)
+
+    # Add the true params to the plot for reference
+    plt.scatter(true_param_a, true_param_b, color="k", marker="x", s=60, label="true parameters")
+
+    plt.xlabel(param_labels[0])
+    plt.ylabel(param_labels[1])
+
+    # Plot the current training data and model trends sampled from posterior
+    plt.sca(axs[1])
+    plt.scatter(x_obs, y_obs, s=60, c="k", alpha=0.5)
+
+    # Plot the resulting model functions sampled from posterior
+    xs = np.linspace(data_range_x[0], data_range_x[1], 100)
+    for ii, (param_a, param_b) in enumerate(zip(param_a_list, param_b_list)):
+        ys = model_func(xs, param_a, param_b)
+        plt.plot(xs, ys, color=f"C{ii}", linewidth=4, alpha=0.5)
+
+    groundtruth_ys = model_func(xs, true_param_a, true_param_b)
+    plt.plot(xs, groundtruth_ys, color="k", linestyle="--", alpha=0.5, label="true trend")
+
+    plt.xlim([data_range_x[0], data_range_x[1]])
+    plt.xlabel("x value")
+
+    plt.ylim([data_range_y[0], data_range_y[1]])
+    plt.ylabel("y value")
+
+    plt.title(f"N={len(y_obs)}")
+    plt.legend(loc="upper left")
diff --git a/pixi.lock b/pixi.lock
index 252f0e44..6a82767a 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -251,6 +251,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-7.2.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py312h83e6fd3_0.conda
@@ -3732,6 +3733,22 @@ packages:
   - pkg:pypi/nest-asyncio?source=hash-mapping
   size: 11638
   timestamp: 1705850780510
+- conda: https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda
+  sha256: 39625cd0c9747fa5c46a9a90683b8997d8b9649881b3dc88336b13b7bdd60117
+  md5: fd40bf7f7f4bc4b647dc8512053d9873
+  depends:
+  - python >=3.10
+  - python
+  constrains:
+  - numpy >=1.24
+  - scipy >=1.10,!=1.11.0,!=1.11.1
+  - matplotlib >=3.7
+  - pandas >=2.0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls: []
+  size: 1265008
+  timestamp: 1731521053408
 - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-7.2.2-pyhd8ed1ab_0.conda
   sha256: 613242d5151a4d70438bb2d65041c509e4376b7e18c06c3795c52a18176e41dc
   md5: c4d5a58f43ce9ffa430e6ecad6c30a42
diff --git a/pixi.toml b/pixi.toml
index 59565d0f..ae45c3f4 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -27,6 +27,7 @@ nutpie = ">=0.13.2,<0.14"
 numba = ">=0.60.0,<0.61"
 scikit-learn = ">=1.5.2,<2"
 blackjax = ">=1.2.3,<2"
+networkx = ">=3.4.2,<4"
 
 [pypi-dependencies]
 pymc-experimental = ">=0.1.2, <0.2"