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+cheese,url,milk,country,region,family,type,fat_content,calcium_content,texture,rind,color,flavor,aroma,vegetarian,vegan,synonyms,alt_spellings,producers
+Aarewasser,https://www.cheese.com/aarewasser/,cow,Switzerland,NA,NA,semi-soft,NA,NA,buttery,washed,yellow,sweet,buttery,FALSE,FALSE,NA,NA,Jumi
+Abbaye de Belloc,https://www.cheese.com/abbaye-de-belloc/,sheep,France,Pays Basque,NA,"semi-hard, artisan",NA,NA,"creamy, dense, firm",natural,yellow,burnt caramel,lanoline,TRUE,FALSE,Abbaye Notre-Dame de Belloc,NA,NA
+Abbaye de Belval,https://www.cheese.com/abbaye-de-belval/,cow,France,NA,NA,semi-hard,40-46%,NA,elastic,washed,ivory,NA,aromatic,FALSE,FALSE,NA,NA,NA
+Abbaye de Citeaux,https://www.cheese.com/abbaye-de-citeaux/,cow,France,Burgundy,NA,"semi-soft, artisan, brined",NA,NA,"creamy, dense, smooth",washed,white,"acidic, milky, smooth","barnyardy, earthy",FALSE,FALSE,NA,NA,NA
+Abbaye de Tamié,https://www.cheese.com/tamie/,cow,France,Savoie,NA,"soft, artisan",NA,NA,"creamy, open, smooth",washed,white,"fruity, nutty","perfumed, pungent",FALSE,FALSE,NA,"Tamié, Trappiste de Tamie, Abbey of Tamie",NA
+Abbaye de Timadeuc,https://www.cheese.com/abbaye-de-timadeuc/,cow,France,province of Brittany,NA,semi-hard,NA,NA,soft,washed,pale yellow,"salty, smooth",nutty,FALSE,FALSE,NA,NA,Abbaye Cistercienne NOTRE-DAME DE TIMADEUC
+Abbaye du Mont des Cats,https://www.cheese.com/abbaye-du-mont-des-cats/,cow,France,Nord-Pas-de-Calais,NA,"semi-soft, artisan, brined",50%,NA,"smooth, supple",washed,pale yellow,"milky, salty",floral,FALSE,FALSE,NA,NA,Abbaye du Mont des Cats
+Abbot’s Gold,https://www.cheese.com/abbots-gold/,cow,"England, Great Britain, United Kingdom",North Yorkshire,Cheddar,semi-hard,NA,NA,"creamy, crumbly, dense, semi firm",natural,pale yellow,"mild, sweet, tangy",aromatic,TRUE,FALSE,"Abbot's Gold Cheddar with Caramelised Onion, Caramelised Onion Cheddar, English Cheddar with Caramelized Onions",NA,Wensleydale Creamery
+Abertam,https://www.cheese.com/abertam/,sheep,Czech Republic,Karlovy Vary,NA,"hard, artisan",45%,NA,firm,natural,pale yellow,"acidic, strong, tangy",NA,FALSE,FALSE,NA,NA,NA
+Abondance,https://www.cheese.com/abondance/,cow,France,NA,NA,"semi-hard, artisan",NA,NA,creamy,natural,pale yellow,nutty,"buttery, fruity",FALSE,FALSE,Tomme d'Abondance,NA,NA
+Acapella,https://www.cheese.com/acapella/,goat,United States,California,NA,"soft, soft-ripened",NA,NA,,NA,NA,buttery,"fresh, herbal",FALSE,FALSE,NA,NA,NA
+Accasciato,https://www.cheese.com/accasciato/,"buffalo, cow",Italy,Campania,NA,semi-hard,NA,NA,firm,natural,pale yellow,sweet,"aromatic, fresh",FALSE,FALSE,NA,NA,Casa Madaio
+Ackawi,https://www.cheese.com/ackawi/,"cow, goat, sheep","Cyprus, Egypt, Israel, Jordan, Lebanon, Middle East, Syria",+,Feta,"soft, brined",NA,NA,"elastic, smooth, springy",natural,white,"mild, milky, salty","mild, milky",FALSE,FALSE,NA,"Akkawi , Akawieh, Akawi",NA
+Acorn,https://www.cheese.com/acorn/,sheep,United Kingdom,Bethania,NA,"hard, artisan",52%,NA,"crumbly, firm",NA,NA,"burnt caramel, citrusy, herbaceous",fruity,TRUE,FALSE,NA,NA,NA
+Adelost,https://www.cheese.com/adelost/,cow,Sweden,NA,Blue,"semi-soft, blue-veined",50%,NA,creamy,natural,blue,"salty, sharp, tangy",strong,NA,NA,NA,NA,NA
+ADL Brick Cheese,https://www.cheese.com/adl-brick-cheese/,cow,Canada,Prince Edward Island,Cheddar,semi-soft,12%,NA,"elastic, firm, open, soft",rindless,ivory,"buttery, mild, milky, subtle","buttery, sweet",NA,NA,NA,NA,ADL - Amalgamated Dairies Limited
+ADL Mild Cheddar,https://www.cheese.com/adl-mild-cheddar/,cow,Canada,Prince Edward Island,Cheddar,semi-hard,14%,NA,"firm, springy",rindless,yellow,"acidic, buttery, milky, subtle",NA,NA,NA,NA,NA,ADL - Amalgamated Dairies Limited
+Affidelice au Chablis,https://www.cheese.com/affidelice-au-chablis/,cow,France,Burgundy,NA,soft,55%,26 mg/100g,"creamy, smooth",washed,orange,"fruity, mild, tangy","perfumed, strong",FALSE,FALSE,NA,NA,Fromagerie Berthaut
+Affineur Walo Rotwein Sennechäs,https://www.cheese.com/affineur-walo-rotwein-sennechas/,cow,Switzerland,NA,Swiss Cheese,"hard, artisan",NA,NA,smooth,washed,cream,"creamy, pronounced, spicy","rich, strong",FALSE,FALSE,Affineur Walo Red Wine Farmer,NA,Walo von Mühlenen AG
+Afuega'l Pitu,https://www.cheese.com/afuegal-pitu/,cow,Spain,Asturias,NA,"soft, artisan",NA,NA,smooth,cloth wrapped,NA,"spicy, strong",NA,FALSE,FALSE,NA,NA,NA
+Aged British Cheddar,https://www.cheese.com/aged-british-cheddar/,cow,United States,NY,Cheddar,"hard, artisan",NA,NA,"crumbly, crystalline, flaky",natural,pale yellow,"sharp, tangy","nutty, sweet",NA,NA,NA,NA,Muranda Cheese Company
+Aged Cashew & Blue Green Algae Cheese,https://www.cheese.com/aged-cashew-blue-green-algae-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,blue,"creamy, mellow, oceanic, tangy",rich,TRUE,FALSE,NA,NA,Dr. Cow Tree Nut Cheese
+Aged Cashew & Brazil Nut Cheese,https://www.cheese.com/aged-cashew-brazil-nut-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,brown,"creamy, nutty, sweet","nutty, rich",TRUE,FALSE,NA,NA,Dr. Cow Tree Nut Cheese
+Aged Cashew & Dulse Cheese,https://www.cheese.com/aged-cashew-dulse-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,brown,"creamy, oceanic",rich,TRUE,FALSE,NA,NA,Dr. Cow Tree Nut Cheese
+Aged Cashew & Hemp Seed Cheese,https://www.cheese.com/aged-cashew-hemp-seed-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,brown,"creamy, mild, nutty, spicy","nutty, rich",TRUE,FALSE,NA,NA,Dr. Cow Tree Nut Cheese
+Aged Cashew Nut & Kale Cheese,https://www.cheese.com/aged-cashew-nut-kale-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,green,"creamy, oceanic, tangy",rich,TRUE,FALSE,NA,NA,Dr. Cow Tree Nut Cheese
+Aged Cashew Nut Cheese,https://www.cheese.com/aged-cashew-nut-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,golden yellow,"creamy, nutty, subtle","clean, fresh, nutty",TRUE,FALSE,"Cajun Aged Cashew Cheese, Smoked Aged Cashew Cheese, Truffle Aged Cashew Cheese, Reishi Mushroom Aged Cashew Cheese",Plain Aged Cashew Cheese,Dr. Cow Tree Nut Cheese
+Aged Cheddar,https://www.cheese.com/aged-cheddar/,cow,United States,NA,NA,hard,NA,NA,"buttery, creamy, crumbly",rindless,golden yellow,"creamy, earthy, nutty","earthy, nutty",TRUE,FALSE,NA,NA,"Maple Grove Cheese, INC."
+Aged Chelsea,https://www.cheese.com/aged-chelsea/,goat,United States,"Ann Arbor, Michigan",NA,soft,NA,NA,"creamy, spreadable",mold ripened,ivory,"acidic, buttery, sweet",goaty,FALSE,FALSE,NA,NA,Zingermans.com LLC
+Aged Gouda,https://www.cheese.com/aged-gouda/,"cow, goat, sheep",Netherlands,NA,Gouda,hard,NA,NA,"crumbly, crystalline, dense",waxed,yellow,NA,rich,NA,NA,Oud Gouda,NA,NA
+Aggiano,https://www.cheese.com/aggiano/,cow,United States,Utah,NA,"hard, artisan",NA,NA,"creamy, dry",NA,cream,"butterscotch, tangy",fruity,TRUE,FALSE,NA,NA,Beehive Cheese Company
+Ailsa Craig,https://www.cheese.com/ailsa-craig/,goat,"Scotland, United Kingdom",Stewarton,NA,"semi-soft, artisan",NA,NA,"creamy, fluffy",rindless,white,creamy,goaty,TRUE,FALSE,NA,Paddy's Milestone,Dunlop Dairy
+Airedale,https://www.cheese.com/airedale/,cow,New Zealand,Airedale farming district,NA,"semi-soft, artisan",NA,NA,smooth,waxed,pale yellow,"full-flavored, milky, salty, tangy","grassy, strong",TRUE,FALSE,NA,Aged Airedale,Whitestone Cheese
+Aisy Cendre,https://www.cheese.com/aisy-cendre/,cow,France,Burgundy,NA,"semi-soft, smear-ripened",50%,NA,"creamy, smooth",washed,white,"full-flavored, herbaceous, salty, smokey","earthy, nutty, smokey",FALSE,FALSE,"Cendre d'Aisy, Ashen Aisy",NA,NA
+Alex James Co. No 1 Cheddar,https://www.cheese.com/alex-james-co-no-1-cheddar/,cow,United Kingdom,NA,NA,hard,NA,NA,"creamy, crumbly",NA,yellow,sweet,NA,NA,NA,NA,NA,NA
+Alex James Co. No 2 Blue Monday,https://www.cheese.com/alex-james-co-no-2-blue-monday/,cow,United Kingdom,NA,NA,semi-soft,NA,NA,"creamy, semi firm",NA,blue,NA,"spicy, sweet",NA,NA,NA,NA,NA
+Alex James Co. No 3 Valley Brie,https://www.cheese.com/alex-james-co-no-3-valley-brie/,cow,United Kingdom,NA,NA,"soft, semi-soft",NA,NA,"buttery, creamy, semi firm, smooth, soft",NA,pale white,NA,NA,NA,NA,NA,NA,NA
+Alex James Co. No 4 Goats',https://www.cheese.com/alex-james-co-no-4-goats/,goat,United Kingdom,NA,NA,fresh soft,NA,NA,"smooth, springy",NA,pale white,NA,NA,NA,NA,NA,NA,NA
+Alex James Co. No 5 Grunge,https://www.cheese.com/alex-james-co-no-5-grunge/,cow,United Kingdom,NA,NA,"soft, semi-soft, organic",NA,NA,"creamy, soft",washed,orange,NA,NA,NA,NA,NA,NA,NA
+Alisia-Victoria,https://www.cheese.com/alisia-victoria/,cow,Switzerland,NA,NA,firm,NA,NA,smooth,natural,golden yellow,buttery,nutty,NA,NA,NA,NA,Eyeweid
+Allgauer Emmentaler,https://www.cheese.com/allgauer-emmentaler/,cow,Germany,Swabia,NA,hard,45%,NA,firm,natural,yellow,nutty,pungent,FALSE,FALSE,NA,NA,NA
+Allium Piper,https://www.cheese.com/allium-piper/,goat,Australia,South Australia,NA,"fresh soft, artisan",45%,NA,"creamy, soft",NA,white,"garlicky, spicy","fresh, garlicky, spicy",TRUE,FALSE,"Woodside Chevre - Allium Piper, Woodside Allium Piper",NA,Woodside Cheese Wrights
+Alma Vorarlberger Alpkäse (3-5 months),https://www.cheese.com/alma-vorarlberger-alpkase-3-5-months/,cow,Austria,Vorarlberg,NA,"hard, artisan",34%,NA,firm,natural,yellow,"mild, spicy","mild, spicy",FALSE,FALSE,Vorarlberg Alpine cheese 3-5 months,NA,Rupp AG
+Alma Vorarlberger Alpkäse (6-9 months),https://www.cheese.com/alma-vorarlberger-alpkase-6-9-months/,cow,Austria,Vorarlberg,NA,"hard, artisan",34%,NA,firm,natural,yellow,"spicy, strong","spicy, strong",FALSE,FALSE,Vorarlberg Alpine cheese 6-9 months,NA,Rupp AG
+Alma Vorarlberger Bergkäse (10 months),https://www.cheese.com/alma-vorarlberger-bergkase-10-months/,cow,Austria,"Bregenzerwald, Kleinwalsertal, Großwalsertal, Laiblachtal (Pfänderstock) and Rheintal",NA,"hard, artisan",34%,NA,"brittle, firm, flaky, open",natural,yellow,"piquant, spicy, strong, tangy","aromatic, strong",FALSE,FALSE,Rupp Vorarlberger Bergkäse,Vorarlberger Bergkase 10 months,Rupp AG
+Alma Vorarlberger Bergkäse (12 months),https://www.cheese.com/alma-vorarlberger-bergkase-12-months/,cow,Austria,"Bregenzerwald, Kleinwalsertal, Großwalsertal, Laiblachtal (Pfänderstock) and Rheintal",NA,"hard, artisan",34%,NA,"brittle, firm, flaky, open",natural,yellow,"piquant, spicy, strong, tangy","aromatic, strong",FALSE,FALSE,Rupp Vorarlberger Bergkäse,Vorarlberger Bergkase 12 months,Rupp AG
+Alma Vorarlberger Bergkäse (6 months),https://www.cheese.com/alma-vorarlberger-bergkase-6-months/,cow,Austria,"Bregenzerwald, Kleinwalsertal, Großwalsertal, Laiblachtal (Pfänderstock) and Rheintal",NA,"hard, artisan",34%,NA,"firm, open, supple",natural,cream,"piquant, spicy, tangy",aromatic,FALSE,FALSE,Rupp Vorarlberger Bergkäse,Vorarlberger Bergkase 6 months,Rupp AG
+Almnäs Tegel,https://www.cheese.com/almnas-tegel/,cow,Sweden,Västra Götaland,NA,"hard, smear-ripened",NA,NA,"open, smooth",washed,straw,"caramel, fruity, nutty","fruity, sweet",FALSE,FALSE,NA,Almnas Tegel,Almnäs Bruk
+Alpe di Frabosa,https://www.cheese.com/alpe-di-frabosa/,cow,Italy,NA,NA,semi-soft,NA,NA,,NA,NA,bitter,"milky, mushroom",FALSE,FALSE,NA,NA,NA
+Alpha Tolman,https://www.cheese.com/alpha-tolman/,cow,United States,"Greensboro, VT",Swiss Cheese,"semi-hard, artisan",NA,NA,"dense, elastic, smooth",washed,yellow,"buttery, caramel, fruity, full-flavored, nutty","fruity, nutty",NA,NA,NA,NA,Jasper Hill Farm
+Alpicrème,https://www.cheese.com/alpicreme/,goat,France,NA,NA,soft,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Alpine Gold,https://www.cheese.com/alpine-gold/,cow,Canada,British Columbia,NA,"semi-soft, brined",NA,NA,supple,washed,cream,"floral, grassy, savory","earthy, floral, rich",FALSE,FALSE,NA,NA,The Farm House Natural Cheeses
+Alpkäse,https://www.cheese.com/alpkase/,cow,Switzerland,NA,NA,hard,NA,NA,dense,natural,golden yellow,herbaceous,rich,FALSE,FALSE,Alpkase,NA,Jumi
+Alps Rebel,https://www.cheese.com/alps-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",48%,NA,"creamy, open",natural,yellow,"buttery, citrusy, milky, piquant","fruity, lactic, milky, nutty",NA,NA,Alpenrebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Alta Badia,https://www.cheese.com/alta-badia/,cow,Italy,NA,NA,semi-soft,33.5 g/100g,NA,firm,NA,NA,"milky, sharp, tangy",NA,FALSE,FALSE,NA,NA,Mila LATTE MONTAGNA ALTO ADIGE
+Alverca,https://www.cheese.com/alverca/,"goat, sheep",Portugal,NA,NA,semi-hard,40-50%,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Amablu Blue cheese,https://www.cheese.com/amablu-blue-cheese/,cow,United States,NA,Blue,"semi-soft, artisan, blue-veined",NA,NA,crumbly,NA,NA,tangy,NA,FALSE,FALSE,NA,NA,Caves of Faribault
+Amalthée,https://www.cheese.com/amalthee/,goat,France,Charentes-Poitou,NA,"soft, soft-ripened",NA,NA,,bloomy,NA,"grassy, mild, sweet","goaty, grassy, mild, sweet",FALSE,FALSE,NA,NA,NA
+Amarelo de Beira Baixa,https://www.cheese.com/amarelo-de-beira-baixa/,"goat, sheep",Portugal,Beira Baixa Province,NA,"semi-firm, artisan",45-60%,NA,,NA,yellow,acidic,NA,FALSE,FALSE,"Amarelo da Beira Baixa Cheese (DOP), Queijo amarelo da Beira Baixa",NA,NA
+Ameribella,https://www.cheese.com/ameribella/,cow,United States,Indiana,NA,"semi-soft, artisan",NA,NA,"creamy, runny, smooth, spreadable",washed,straw,"mushroomy, salty, savory, sweet","earthy, grassy, lactic",NA,NA,Arabella,NA,Jacobs & Brichford Farmstead Cheese
+American Cheese,https://www.cheese.com/american-cheese/,cow,United States,NA,NA,"semi-soft, processed",NA,NA,smooth,rindless,yellow,NA,mild,FALSE,FALSE,NA,NA,NA
+Ami du Chambertin,https://www.cheese.com/ami-du-chambertin/,cow,France,"Gevrey-Chambertin, Burgundy",NA,"semi-soft, artisan, brined",45%,NA,smooth,washed,white,"buttery, sharp",NA,FALSE,FALSE,L'Ami du Chambertin,NA,NA
+Amish Frolic,https://www.cheese.com/amish-frolic/,cow,United States,"Milford, NJ",NA,"hard, artisan",NA,NA,"flaky, open",natural,pale yellow,"grassy, nutty, sweet",grassy,FALSE,FALSE,NA,NA,Bobolink Dairy
+Amou,https://www.cheese.com/amou/,sheep,France,"Amou, Gascony",NA,firm,45%,NA,,NA,golden yellow,NA,NA,FALSE,FALSE,"l'Amou, Amu",NA,NA
+Amsterdammer (British Columbia),https://www.cheese.com/amsterdammer-british-columbia/,,Canada,"Comox Valley, Vancouver Island",NA,"semi-soft, firm",30%,NA,"buttery, creamy",natural,pale yellow,"buttery, creamy","aromatic, buttery",FALSE,FALSE,NA,NA,Natural Pastures Cheese Company
+Amul Cheese Spread,https://www.cheese.com/amul-cheese-spread/,cow,India,Gujarat,NA,"soft, processed",NA,NA,"creamy, spreadable",NA,NA,"creamy, salty, savory, spicy",NA,TRUE,FALSE,NA,NA,Gujarat Cooperative Milk Marketing Federation (Amul)
+Amul Emmental,https://www.cheese.com/amul-emmental/,cow,India,Gujarat,Swiss Cheese,semi-hard,46%,488 mg/100g,"firm, open",artificial,yellow,sweet,nutty,TRUE,FALSE,NA,NA,Gujarat Cooperative Milk Marketing Federation (Amul)
+Amul Gouda,https://www.cheese.com/amul-gouda/,cow,India,Gujarat,Gouda,semi-hard,46%,492 mg/100g,"compact, dense, firm, springy",plastic,yellow,"fruity, sweet",mild,TRUE,FALSE,NA,NA,Gujarat Cooperative Milk Marketing Federation (Amul)
+Amul Pizza Mozzarella Cheese,https://www.cheese.com/amul-pizza-mozzarella-cheese/,cow,India,Gujarat,Mozzarella,"semi-soft, processed",30-40%,492 mg/100g,"elastic, stringy",artificial,yellow,salty,pleasant,TRUE,FALSE,NA,NA,Gujarat Cooperative Milk Marketing Federation (Amul)
+Amul Processed Cheese,https://www.cheese.com/amul-processed-cheese/,"cow, water buffalo",India,Gujarat,Cheddar,"hard, processed",26%,343 mg/100g,"crumbly, dense",artificial,yellow,"buttery, creamy, salty",NA,TRUE,FALSE,NA,NA,Gujarat Cooperative Milk Marketing Federation (Amul)
+Anari,https://www.cheese.com/anari/,"goat, sheep",Cyprus,island wide,Cottage,"fresh soft, hard, artisan",8%,NA,"brittle, creamy, flaky",natural,white,"creamy, mild, salty",NA,NA,NA,"Dry Anari, Fresh Anari",NA,NA
+Anejo Enchilado,https://www.cheese.com/anejo-enchilado/,"cow, goat",Mexico,NA,NA,semi-hard,NA,NA,"crumbly, firm",NA,white,"salty, sharp, spicy, strong",strong,FALSE,FALSE,"Queso Añejo, Añejo",NA,NA
+Anneau du Vic-Bilh,https://www.cheese.com/anneau-du-vic-bilh/,goat,France,Pyrenees-Atlantiques,Cottage,"soft, artisan",45%,NA,"creamy, smooth",NA,white,"acidic, nutty, salty, smokey","earthy, nutty, smokey",FALSE,FALSE,NA,NA,NA
+Anniversary Ale Cheddar,https://www.cheese.com/anniversary-ale-cheddar/,cow,United States,Northwest,Cheddar,"semi-hard, artisan",NA,NA,"dense, firm",NA,NA,"creamy, mild, sweet","floral, fruity",TRUE,FALSE,NA,NA,Rogue Creamery
+Anster,https://www.cheese.com/anster/,cow,Scotland,Fife,NA,"semi-hard, artisan",NA,NA,"crumbly, dry",NA,ivory,milky,"milky, rich",NA,NA,NA,NA,St Andrews Farmhouse Cheese Company
+Anthotyro,https://www.cheese.com/anthotyro/,"goat, sheep",Greece,"Macedonia, Thrace, Thessalia, Peloponissos, Ionian Islands, Aegean islands, Crete Island and Epirus",NA,"hard, whey",30%,318 mg/100g,crumbly,natural,white,"salty, tangy",strong,NA,NA,"Anthotyro Fresco, Anthotyro","Anthotiro, Antotiro",NA
+Anthotyro Fresco,https://www.cheese.com/anthotyro-fresco/,"goat, sheep",Greece,"Macedonia, Thrace, Thessalia, Peloponissos, Ionian Islands, Aegean islands, Crete Island and Epirus",NA,"semi-soft, whey",20%,NA,"creamy, smooth",rindless,white,sweet,NA,NA,NA,"Fresh Anthotyro, Anthotiro",NA,NA
+Aphrodite Goat Milk Halloumi,https://www.cheese.com/aphrodite-haloumi/,goat,Cyprus,NA,NA,"semi-soft, artisan",NA,NA,soft,rindless,white,"lemony, tangy",strong,NA,NA,Aphrodite Artisan Goat Milk Halloumi,NA,NA
+Appalachian,https://www.cheese.com/appalachian/,cow,United States,Virginia,Tomme,"semi-soft, artisan",NA,NA,firm,mold ripened,white,"buttery, lemony, mushroomy","earthy, grassy, lactic",NA,NA,NA,NA,Meadow Creek Dairy
+Appenzeller,https://www.cheese.com/appenzeller/,cow,Switzerland,NA,NA,"hard, artisan",NA,NA,firm,washed,NA,"fruity, nutty",strong,NA,NA,NA,"Appenzeller Classic, Appenzeller Surchoix, Appenzeller Extra",NA
+Apple Walnut Smoked,https://www.cheese.com/apple-walnut-smoked/,cow,United States,Utah,Cheddar,"hard, artisan",NA,NA,"creamy, smooth",NA,yellow,"nutty, smokey , sweet","nutty, smokey, sweet",TRUE,FALSE,"Smoked Apple Walnut, Promontory Apple Walnut Smoked",NA,Beehive Cheese Company
+Appleby's Double Gloucester,https://www.cheese.com/applebys-double-gloucester/,cow,"England, Great Britain, United Kingdom",Gloucestershire,NA,"hard, artisan",54.23 g/100g,NA,"crumbly, dense, firm, flaky, smooth",natural,golden yellow,"mellow, milky, nutty, subtle, tangy","subtle, sweet",TRUE,FALSE,NA,NA,Appleby's
+Applewood,https://www.cheese.com/applewood/,cow,"England, Great Britain, United Kingdom",Somerset,Cheddar,semi-hard,NA,NA,"crumbly, dense",natural,yellow,"smokey , spicy",smokey,TRUE,FALSE,Applewood smoked cheddar,NA,Ilchester Cheese Company
+Applewood Smoked Chevre,https://www.cheese.com/applewood-smoked-chevre/,goat,United States,Colorado,NA,"semi-soft, artisan",NA,NA,"creamy, crumbly, firm",rindless,white,subtle,woody,TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Aragon,https://www.cheese.com/aragon/,,Spain,NA,NA,NA,NA,NA,,NA,NA,NA,NA,NA,NA,NA,NA,NA
+Ardi Gasna,https://www.cheese.com/ardi-gasna/,sheep,France,Midi-Pyrénées,NA,"hard, artisan",45%,NA,"firm, supple",washed,pale yellow,"mild, nutty, sharp",fresh,FALSE,FALSE,NA,NA,NA
+Ardrahan,https://www.cheese.com/ardrahan/,cow,Ireland,Duhallow,NA,"semi-soft, brined",25%,NA,"chalky, smooth",washed,yellow,"acidic, buttery, nutty","earthy, pungent",TRUE,FALSE,NA,NA,Ardrahan Dairy Products Ltd.
+Ardsallagh Hard Goat's Cheese,https://www.cheese.com/ardsallagh-hard-goats-cheese/,goat,Ireland,Carrigtwohill,NA,"semi-hard, artisan",NA,NA,"firm, smooth",natural,white,"nutty, salty, smooth",NA,TRUE,FALSE,NA,NA,Ardsallagh Goats Farm
+Ardsallagh Smoked Cheese,https://www.cheese.com/ardsallagh-smoked-cheese/,goat,Ireland,Carrigtwohill,NA,"semi-hard, artisan",NA,NA,"firm, smooth",natural,white,"mild, nutty, smokey , sweet",NA,TRUE,FALSE,NA,NA,Ardsallagh Goats Farm
+Ardsallagh Soft Goat's Cheese,https://www.cheese.com/ardsallagh-soft-goats-cheese/,,,Carrigtwohill,NA,NA,NA,NA,,natural,white,NA,NA,TRUE,FALSE,NA,NA,Ardsallagh Goats Farm
+Armenian String Cheese,https://www.cheese.com/armenian-string-cheese/,"cow, goat, sheep",Armenia,NA,NA,semi-soft,NA,NA,"creamy, smooth, springy",NA,white,mild,NA,NA,NA,"Syrian String Cheese, Chechil",NA,NA
+Aromes au Gene de Marc,https://www.cheese.com/aromes-au-gene-de-marc/,"cow, goat",France,Rhône-Alpes,NA,"semi-soft, artisan",25%,NA,"creamy, flaky",natural,white,strong,"fermented, pungent",FALSE,FALSE,NA,NA,NA
+Arådalen,https://www.cheese.com/aradalen/,cow,Sweden,Oviken,Blue,"soft, artisan, blue-veined",34%,NA,creamy,mold ripened,ivory,sweet,NA,NA,NA,NA,NA,Oviken cheese
+Ascutney Mountain,https://www.cheese.com/ascutney-mountain/,cow,United States,Vermont,Swiss Cheese,"hard, artisan",NA,NA,"chewy, dense",natural,NA,"mild, nutty, sweet","herbal, sweet",TRUE,FALSE,NA,NA,Cobb Hill Farm
+Asher Blue,https://www.cheese.com/asher-blue/,cow,United States,Georgia,Blue,"semi-soft, blue-veined",NA,NA,"creamy, crumbly",natural,straw,"grassy, milky, mushroomy, salty, sweet, tangy","earthy, rich, strong",TRUE,FALSE,NA,NA,Sweet Grass Dairy
+Ashley,https://www.cheese.com/ashley/,cow,United States,Colorado,NA,"soft, artisan, soft-ripened",NA,NA,"creamy, soft",ash coated,cream,sweet,NA,NA,NA,MouCo Ashley,NA,MouCo Cheese Company
+Asiago DOP,https://www.cheese.com/asiago/,cow,Italy,"Veneto, Trentino",NA,hard,34-48%,NA,"compact, crumbly, open, smooth",natural,yellow,"full-flavored, milky, sharp",pungent,FALSE,FALSE,NA,NA,NA
+Asiago d’Allevo DOP,https://www.cheese.com/asiago-dallevo/,cow,Italy,Veneto,NA,hard,34%,NA,compact,natural,pale yellow,savory,"pleasant, yeasty",FALSE,FALSE,"Asiago Aged, Asiago D’Allevo DOP Mitica®, Asiago Mezzano",NA,NA
+Asiago Pressato DOP,https://www.cheese.com/asiago-pressato/,cow,Italy,Veneto,NA,semi-soft,48%,NA,"creamy, smooth",natural,white,"nutty, sweet",fresh,FALSE,FALSE,"Asiago Fresco, Asiago Fresco DOP Mitica®",Pressato,NA
+Aspen Ash,https://www.cheese.com/aspen-ash/,goat,United States,Colorado,NA,"soft, soft-ripened",NA,NA,creamy,ash coated,white,"acidic, creamy",earthy,TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Assa,https://www.cheese.com/assa/,goat,United States,"Tomales, California",NA,NA,NA,NA,,natural,NA,NA,NA,NA,NA,NA,NA,Tomales Farmstead Creamery
+Aubisque Pyrenees,https://www.cheese.com/aubisque-pyrenees/,"cow, sheep",France,Béarnaise in Pyrénées-Atlantique,NA,semi-hard,NA,NA,smooth,natural,NA,"mild, smooth",NA,FALSE,FALSE,NA,NA,NA
+Auld Lochnagar,https://www.cheese.com/auld-lochnagar/,cow,Scotland,NA,NA,hard,NA,NA,"compact, crumbly",natural,orange,"grassy, mellow, subtle, tangy",mild,NA,NA,NA,NA,The Cambus O’May Cheese Company
+Auld Reekie,https://www.cheese.com/auld-reekie/,cow,Scotland,NA,NA,hard,NA,NA,"buttery, compact, crumbly",natural,orange,"smokey , tangy, woody","fruity, smokey, woody",FALSE,FALSE,NA,NA,The Cambus O’May Cheese Company
+Aura,https://www.cheese.com/aura/,cow,Finland,Äänekoski,Blue,"semi-soft, blue-veined",NA,NA,creamy,mold ripened,pale yellow,"salty, sharp, strong, tangy",NA,FALSE,FALSE,Auro Gold,NA,Valio
+Austrian Alps cheese,https://www.cheese.com/austrian-alps/,cow,Switzerland,NA,NA,NA,NA,NA,smooth,NA,NA,"nutty, spicy","aromatic, nutty, spicy",FALSE,FALSE,NA,NA,NA
+Autun,https://www.cheese.com/autun/,"cow, goat",France,Burgundy,NA,"fresh soft, artisan",40-45%,NA,creamy,rindless,white,acidic,NA,FALSE,FALSE,NA,NA,NA
+Avaxtskyr,https://www.cheese.com/avaxtskyr/,cow,Iceland,NA,NA,fresh soft,NA,NA,,NA,NA,NA,NA,TRUE,FALSE,"Skyr, Rjomaskyr",NA,NA
+Avonlea Clothbound Cheddar,https://www.cheese.com/avonlea-clothbound-cheddar/,cow,Canada,Prince Edward Island,Cheddar,"hard, artisan",32%,NA,"creamy, crumbly, firm",cloth wrapped,straw,"fruity, mushroomy, savory",NA,TRUE,FALSE,NA,NA,COWS Inc.
+Azeitao,https://www.cheese.com/azeitao/,sheep,Portugal,"Setubal, Palmela and Sesimbra",NA,"semi-soft, artisan",NA,NA,"creamy, supple",washed,pale yellow,"herbaceous, salty, sour",NA,TRUE,FALSE,Queijo de Azeitao,NA,NA
+Baby Brie,https://www.cheese.com/baby-brie/,cow,France,NA,Brie,soft,NA,NA,creamy,bloomy,cream,"creamy, mild",aromatic,FALSE,FALSE,"Petit Brie, Mini Brie",NA,NA
+Baby Swiss,https://www.cheese.com/baby-swiss/,cow,United States,"Charm, Ohio",Swiss Cheese,"semi-soft, processed",43%,NA,"creamy, open, smooth",rindless,pale yellow,"nutty, sharp, sweet",NA,FALSE,FALSE,Lacy cheese,NA,Guggisberg Cheese
+Babybel,https://www.cheese.com/babybel/,cow,France,NA,NA,semi-hard,NA,NA,smooth,waxed,NA,NA,NA,NA,NA,NA,NA,NA
+Bad Axe,https://www.cheese.com/bad-axe/,sheep,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,"creamy, firm, smooth",waxed,white,"creamy, smooth, tart",fresh,TRUE,FALSE,NA,NA,Hidden Springs Company
+Baguette Laonnaise,https://www.cheese.com/baguette-laonnaise/,cow,France,Ile-de-France/Champagne,NA,"soft, artisan",28.5%,NA,"open, supple",washed,pale yellow,spicy,pungent,FALSE,FALSE,Baguette Lyonnaise,NA,NA
+Baita Friuli,https://www.cheese.com/baita-friuli/,cow,Italy,Friuli-Venezia Giulia,NA,firm,NA,NA,compact,natural,pale yellow,"fruity, spicy",rich,FALSE,FALSE,NA,NA,NA
+Baladi,https://www.cheese.com/baladi/,"cow, goat, sheep","Lebanon, Middle East",NA,NA,"fresh soft, artisan",22%,NA,"creamy, dense, smooth",rindless,white,"buttery, mild, salty, sweet",fresh,FALSE,FALSE,"Jibneh Khadra, Jibnah Baladi, Baladieh, Jibneh Baladi",NA,NA
+Balaton,https://www.cheese.com/balaton/,cow,Hungary,NA,NA,semi-hard,NA,NA,firm,natural,pale yellow,"acidic, mild",NA,FALSE,FALSE,NA,NA,NA
+Balfour,https://www.cheese.com/balfour/,sheep,New Zealand,Queenstown,NA,hard,NA,NA,"crumbly, flaky, grainy",natural,NA,"nutty, sweet",NA,FALSE,FALSE,Balfour Pecorino,NA,The Gibbston Valley Cheese Company
+Baluchon,https://www.cheese.com/baluchon/,cow,Canada,Quebec,NA,semi-soft,28%,NA,creamy,washed,ivory,"acidic, creamy, earthy, salty","earthy, mild",FALSE,FALSE,Le Baluchon,NA,Fromageries Jonathan
+Bandal,https://www.cheese.com/bandal/,cow,India,West Bengal,Cottage,"semi-soft, artisan",NA,NA,crumbly,rindless,NA,"salty, smokey , strong",strong,TRUE,FALSE,NA,Bandel,NA
+Banon,https://www.cheese.com/banon/,goat,France,Banon,NA,"soft, artisan",NA,NA,"creamy, smooth",leaf wrapped,white,"fruity, mild",earthy,NA,NA,"Banon à la feuille, Banon AOC",NA,NA
+Barambah Organics Marinated Feta,https://www.cheese.com/barambah-organics-marinated-feta/,cow,Australia,Brisbane,Feta,fresh soft,25.22 g/100g,NA,"creamy, crumbly, springy",rindless,white,"herbaceous, mild, milky","herbal, mild, milky",TRUE,FALSE,NA,NA,Barambah Organics Pty Ltd.
+Barber's 1833,https://www.cheese.com/barbers-1833/,cow,England,NA,Cheddar,hard,NA,NA,creamy,NA,NA,"sweet, tangy",NA,TRUE,FALSE,NA,NA,AJ & RE Barber Ltd
+Barberey,https://www.cheese.com/barberey/,cow,France,"Troyes , Aube",NA,soft,20-30%,NA,"dry, smooth, soft",natural,NA,"pungent, woody",mild,FALSE,FALSE,"ash trojan, cheese Troyes",NA,NA
+Barden Blue,https://www.cheese.com/barden-blue/,cow,United States,"West Pawlet, VT",Blue,"semi-hard, artisan, blue-veined",NA,NA,"buttery, dense, open",natural,yellow,"mild, nutty, spicy, tangy","barnyardy, herbal",FALSE,FALSE,NA,NA,Consider Bardwell Farm
+Barely Buzzed,https://www.cheese.com/barely-buzzed/,cow,United States,Utah,Cheddar,"hard, artisan",NA,NA,"creamy, smooth",natural,pale yellow,"butterscotch, caramel","aromatic, rich",TRUE,FALSE,NA,NA,Beehive Cheese Company
+Barilotto,https://www.cheese.com/barilotto/,buffalo,Italy,Campania,Brie,"hard, soft-ripened",NA,NA,firm,washed,pale yellow,"creamy, sharp","buttery, fresh",FALSE,FALSE,NA,NA,Casa Madaio
+Barlocco,https://www.cheese.com/barlocco/,cow,Scotland,NA,NA,semi-soft,NA,NA,creamy,natural,blue,full-flavored,strong,TRUE,FALSE,NA,NA,The Ethical Dairy
+Baron Bigod,https://www.cheese.com/baron-bigod/,cow,England,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",NA,NA,"buttery, mushroomy","grassy, milky",NA,NA,NA,NA,Fen Farm Dairy
+Baron Bigod Baby Truffle,https://www.cheese.com/baron-bigod-baby-truffle/,cow,England,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",NA,NA,milky,"earthy, mushroom",NA,NA,Baby Truffled Baron,NA,Fen Farm Dairy
+Baronerosso di Capra,https://www.cheese.com/baronerosso-di-capra/,goat,Italy,Veneto,NA,semi-hard,NA,NA,"crumbly, firm",washed,white,"full-flavored, sharp","fruity, musty",FALSE,FALSE,NA,Baronerosso al Barbera,La Casearia Carpenedo S.r.l.
+Barrel Aged Feta,https://www.cheese.com/barrel-aged-feta/,"cow, goat, sheep",Greece,NA,Feta,"soft, artisan",NA,NA,"creamy, crumbly",NA,white,creamy,rich,NA,NA,NA,NA,NA
+Barricato al Pepe,https://www.cheese.com/barricato-al-pepe/,cow,Italy,NA,NA,"firm, artisan",NA,NA,crumbly,natural,ivory,NA,"fruity, spicy",NA,NA,NA,NA,NA
+Barriquet,https://www.cheese.com/barriquet/,goat,France,NA,NA,soft,NA,NA,"smooth, supple",washed,white,"earthy, full-flavored, meaty, nutty",aromatic,FALSE,FALSE,NA,NA,Pierrick Brendani and Nicolas Trotot
+Barry's Bay Cheddar,https://www.cheese.com/barrys-bay-cheddar/,cow,New Zealand,Banks Peninsular in Canterbury,Cheddar,"hard, artisan",NA,NA,creamy,cloth wrapped,NA,NA,NA,FALSE,FALSE,NA,NA,Barry's Bay
+Bartlett,https://www.cheese.com/bartlett/,sheep,"England, Great Britain, United Kingdom",Somerset,NA,"soft, artisan",NA,NA,"dense, smooth",mold ripened,white,"buttery, milky","aromatic, strong",TRUE,FALSE,NA,NA,NA
+Basajo,https://www.cheese.com/basajo/,sheep,Italy,NA,Blue,"semi-soft, blue-veined",NA,NA,creamy,NA,NA,sweet,fruity,FALSE,FALSE,NA,NA,NA
+Baserri,https://www.cheese.com/baserri/,sheep,United States,NA,Tomme,"semi-hard, artisan",NA,NA,"creamy, crumbly",NA,NA,"milky, tangy","milky, nutty",NA,NA,Txiki,NA,"Barinaga Ranch, Inc."
+Basils Original Rauchkäse,https://www.cheese.com/basils-original-rauchkase/,cow,Germany,NA,NA,semi-soft,25.5%,700 mg/100g,"compact, dense, soft",natural,pale yellow,"salty, smokey , spicy",smokey,FALSE,FALSE,Basil's Smoked Cheese,NA,Bergader Privatkäserei GmbH
+Basing,https://www.cheese.com/basing/,goat,United Kingdom,Kent,NA,hard,NA,NA,crumbly,NA,NA,"herbaceous, smooth",NA,TRUE,FALSE,NA,NA,NA
+Baskeriu,https://www.cheese.com/baskeriu/,sheep,France,"French Basque Country, Midi-Pyrénées",NA,semi-soft,50%,NA,"dry, smooth",NA,NA,"buttery, nutty","buttery, nutty, rich",TRUE,FALSE,NA,NA,NA
+Basket Cheese,https://www.cheese.com/basket-cheese/,cow,Middle East,NA,Cottage,"soft, semi-soft",NA,NA,"chewy, supple",rindless,white,"mild, salty",milky,TRUE,FALSE,NA,NA,"Specialty Cheese Company, Inc"
+Basket Molded Ricotta,https://www.cheese.com/basket-molded-ricotta/,cow,,NA,Cottage,fresh firm,NA,NA,firm,NA,white,"creamy, sweet",fresh,NA,NA,NA,NA,NA
+Bassigny au porto,https://www.cheese.com/bassigny-au-porto/,cow,Belgium,NA,NA,semi-soft,45%,NA,,washed,yellow,"acidic, buttery",aromatic,FALSE,FALSE,Langres,NA,NA
+Bath Blue,https://www.cheese.com/bath-blue/,cow,"England, United Kingdom",South West England,Blue,"soft, artisan, blue-veined",NA,NA,"creamy, smooth",natural,cream,creamy,clean,NA,NA,NA,NA,The Bath Soft Cheese Co.
+Bath Soft Cheese,https://www.cheese.com/bath-soft/,cow,England,South West England,Brie,soft,NA,NA,creamy,bloomy,ivory,"citrusy, lemony, mushroomy","aromatic, grassy",NA,NA,NA,Bath Soft,The Bath Soft Cheese Co.
+Bath Soft Cheese Truffled,https://www.cheese.com/bath-soft-cheese-truffled/,cow,United Kingdom,NA,Brie,soft,NA,NA,"buttery, soft-ripened",NA,white,NA,NA,NA,NA,NA,NA,The Bath Soft Cheese Co.
+Batzos,https://www.cheese.com/batzos/,"goat, sheep",Greece,"Central and Western Macedonia, Thessaly",NA,semi-hard,20%,NA,dry,NA,white,"piquant, salty, sour, spicy",pleasant,FALSE,FALSE,NA,NA,NA
+Bavaria blu,https://www.cheese.com/bavaria-blu/,cow,Germany,NA,Blue,"soft, blue-veined",43.3 g/100g,450 mg/100g,"creamy, soft",mold ripened,cream,"creamy, sharp, strong","aromatic, rich",FALSE,FALSE,"Bavaria blu - Tasty Blue, Bavaria blu - Rich & Creamy, Bavaria blu – Classic Blue, Bavaria blu – Mild & Blue",NA,Bergader Privatkäserei GmbH
+Bavarian Bergkase,https://www.cheese.com/bavarian-bergkase/,cow,Germany,Allgaeu Alps,NA,"hard, artisan",62%,NA,"crumbly, firm, open",natural,pale yellow,"full-flavored, nutty, spicy","aromatic, rich",FALSE,FALSE,"Allgäuer Bergkäse DOP, Allgauer Bergkase",Bawarii Bergkäse,NA
+Bayley Hazen Blue,https://www.cheese.com/bayley-hazen-blue/,cow,United States,"Greensboro, VT",Blue,"semi-hard, artisan, blue-veined",NA,NA,"creamy, dense, firm",natural,cream,"buttery, grassy, licorice, nutty, tangy","grassy, spicy, strong",FALSE,FALSE,NA,NA,Jasper Hill Farm
+Baylough,https://www.cheese.com/baylough/,cow,Ireland,"County Tipperary, Clogheen",Cheddar,"hard, artisan",NA,NA,"close, firm",waxed,yellow,"garlicky, herbaceous, mild, smokey","herbal, mild",TRUE,FALSE,NA,Bay Lough,BAY LOUGH CHEESE
+Beach Box Brie,https://www.cheese.com/beach-box-brie/,cow,Australia,"Mornington Peninsula, Melbourne",Brie,artisan,NA,NA,creamy,ash coated,golden yellow,creamy,rich,TRUE,FALSE,NA,NA,BoatShed Cheese
+Bear Hill,https://www.cheese.com/bear-hill/,sheep,United States,Vermont,NA,"semi-hard, smear-ripened",NA,NA,smooth,washed,pale yellow,"fruity, mild, milky, nutty, sweet",NA,NA,NA,NA,NA,Grafton Village Cheese Company
+Beaufort,https://www.cheese.com/beaufort/,cow,France,NA,NA,semi-firm,NA,NA,"creamy, smooth",NA,pale yellow,NA,"fruity, nutty",FALSE,FALSE,"Beaufort AOP Chalet Alpage Meule, Beaufort AOC",NA,NA
+Beaumont,https://www.cheese.com/beaumont/,cow,France,Rhône-Alpes,NA,semi-soft,50%,NA,"creamy, smooth",washed,pale yellow,NA,"barnyardy, earthy, mild, nutty, rich",NA,NA,NA,NA,NA
+Beauvale,https://www.cheese.com/beauvale/,cow,England,East Midlands,NA,"soft, blue-veined",NA,NA,"creamy, smooth",NA,pale yellow,creamy,NA,FALSE,FALSE,NA,NA,Cropwell Bishop Creamery Limited
+Beauvoorde,https://www.cheese.com/beauvoorde/,cow,Belgium,Flanders,NA,semi-hard,NA,NA,"creamy, firm",natural,yellow,mild,spicy,NA,NA,NA,NA,NA
+Beehive Fresh,https://www.cheese.com/beehive-fresh/,cow,United States,Utah,Mozzarella,"fresh soft, artisan",NA,NA,"buttery, soft",NA,pale yellow,"buttery, mild","buttery, fresh, mild",TRUE,FALSE,NA,NA,Beehive Cheese Company
+Beemster 2% Milk,https://www.cheese.com/beemster-2-milk/,cow,"Canada, Denmark, France, Germany, Netherlands, United States",NA,NA,semi-soft,8%,NA,smooth,NA,NA,nutty,"aromatic, floral, fruity",FALSE,FALSE,NA,NA,"Beemster Cheese, CONO Kaasmakers / Beemster"
+Beemster Aged,https://www.cheese.com/beemster-aged/,cow,Netherlands,NA,Gouda,hard,NA,NA,"crystalline, firm, smooth",natural,yellow,"full-flavored, smooth, spicy, strong",rich,FALSE,FALSE,Beemster Old,NA,CONO Kaasmakers / Beemster
+Beemster Classic,https://www.cheese.com/beemster-classic/,cow,Netherlands,NA,Gouda,semi-hard,NA,NA,"creamy, firm, smooth",natural,yellow,"buttery, nutty","buttery, rich",FALSE,FALSE,NA,NA,Beemster Cheese
+Beemster Extra Aged (XO),https://www.cheese.com/beemster-extra-aged/,cow,Netherlands,NA,Gouda,hard,NA,NA,"brittle, crumbly, grainy",natural,golden yellow,"butterscotch, nutty",aromatic,FALSE,FALSE,"Beemster Extra Old, Beemster X-O, Beemster XO",NA,"Beemster Cheese, CONO Kaasmakers / Beemster"
+Beemster Graskaas,https://www.cheese.com/beemster-graskaas/,cow,Netherlands,NA,Gouda,semi-hard,NA,NA,"creamy, open, smooth",natural,yellow,full-flavored,rich,FALSE,FALSE,NA,NA,"Beemster Cheese, CONO Kaasmakers / Beemster"
+Beenleigh Blue,https://www.cheese.com/beenleigh-blue/,sheep,England,NA,Blue,"soft, blue-veined",NA,NA,smooth,natural,NA,lemony,fresh,TRUE,FALSE,NA,NA,Ticklemore Cheese Dairy
+Bega Processed Cheddar,https://www.cheese.com/bega-processed-cheddar/,cow,Australia,NA,Cheddar,soft,NA,NA,"creamy, smooth",NA,yellow,creamy,NA,NA,NA,NA,NA,Bega Cheese Limited
+Bel Ceillo,https://www.cheese.com/bel-ceillo/,cow,United States,NY,Parmesan,"semi-hard, artisan",NA,NA,"creamy, crumbly",natural,pale yellow,sharp,pungent,NA,NA,NA,NA,Muranda Cheese Company
+Bel Paese,https://www.cheese.com/bel-paese/,cow,Italy,Lombardy,NA,semi-soft,NA,NA,"creamy, smooth",plastic,pale yellow,"buttery, mild, milky, sweet",pleasant,FALSE,FALSE,NA,NA,Galbani
+Bella Lodi,https://www.cheese.com/bella-lodi/,cow,Italy,Lodi,Parmesan,"hard, artisan",NA,NA,"crumbly, flaky, grainy",natural,white,full-flavored,"aromatic, rich",TRUE,FALSE,Black Parmesan,"Bella Lodi Raspadura, Lodigrana Bella Lodi",Lodigrana
+Belle Creme,https://www.cheese.com/belle-creme/,cow,Canada,Québec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, smooth, soft-ripened",bloomy,white,"acidic, buttery, creamy, salty","mushroom, nutty",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Belletoile,https://www.cheese.com/belletoile/,cow,France,NA,Brie,"soft, soft-ripened",70%,NA,creamy,NA,NA,"garlicky, mild, mushroomy, nutty, tangy",mild,FALSE,FALSE,NA,NA,Fromagerie Henri Hutin
+Bellwether Farms Crescenza,https://www.cheese.com/bellwether-farms-crescenza/,cow,United States,"Sonoma, California",NA,"soft, artisan",NA,NA,"buttery, soft-ripened, spreadable",rindless,ivory,"buttery, tart","buttery, rich",TRUE,FALSE,Crescenza,NA,Bellwether Farms
+Benedictine,https://www.cheese.com/benedictine/,"cow, goat, sheep",United States,"La Velle, Wisconsin",NA,"semi-hard, artisan",NA,NA,creamy,washed,yellow,NA,NA,NA,NA,NA,NA,Carr Valley Cheese Company
+Bent River,https://www.cheese.com/bent-river/,cow,United States,"Mankato, MN",Camembert,"soft, artisan, soft-ripened",NA,NA,"smooth, soft",bloomy,NA,"buttery, mellow, milky, subtle",mushroom,NA,NA,NA,NA,Alemar Cheese
+Bergader,https://www.cheese.com/bergader/,cow,Germany,NA,NA,NA,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,Bergader Privatkäserei GmbH
+Bergere Bleue,https://www.cheese.com/bergere-bleue/,sheep,United States,"Marathon, NY",Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly, smooth",mold ripened,pale yellow,burnt caramel,"lanoline, rich, yeasty",TRUE,FALSE,NA,NA,Northland Sheep Dairy
+Bergues,https://www.cheese.com/bergues/,cow,France,Bergues,NA,semi-hard,15-25%,NA,smooth,washed,NA,"subtle, sweet",aromatic,FALSE,FALSE,NA,NA,NA
+Berkswell,https://www.cheese.com/berkswell/,sheep,United Kingdom,NA,NA,hard,NA,NA,"chewy, firm",natural,pale yellow,NA,sweet,NA,NA,NA,NA,NA
+Bermondsey Hard Pressed,https://www.cheese.com/bermondsey-hard-pressed/,cow,"England, Great Britain, United Kingdom","Bermondsey, London",Cheddar,"hard, artisan",NA,NA,"creamy, crumbly, open",natural,yellow,"creamy, nutty",barnyardy,NA,NA,NA,NA,KAPPACASEIN DAIRY
+Bermuda Triangle,https://www.cheese.com/bermuda-triangle/,goat,United States,California,Brie,"semi-soft, soft-ripened",NA,NA,"creamy, crumbly, dense, firm, smooth",bloomy,white,"earthy, piquant, spicy, tangy","earthy, fresh, spicy",TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Bethmale des Pyrenees,https://www.cheese.com/bethmale-des-pyrenees/,cow,France,Pyrenees,NA,semi-hard,NA,NA,,natural,brownish yellow,mild,"aromatic, earthy, mushroom",FALSE,FALSE,NA,NA,NA
+Bettine Bleu,https://www.cheese.com/bettine-bleu/,goat,Netherlands,NA,NA,semi-hard,39.6 g/100g,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,Bettinehoeve BV
+Bettine Grand Cru,https://www.cheese.com/bettine-grand-cru/,goat,Netherlands,NA,NA,semi-hard,NA,NA,,NA,NA,"spicy, strong",strong,FALSE,FALSE,NA,NA,Bettinehoeve BV
+Beyaz Peynir,https://www.cheese.com/beyaz-peynir/,"cow, goat, sheep",Turkey,NA,NA,"semi-soft, brined",NA,NA,,NA,pale white,NA,NA,TRUE,FALSE,NA,NA,NA
+Bianca,https://www.cheese.com/bianca/,"goat, sheep",United States,"Tieton, Washington",NA,"fresh soft, artisan",NA,NA,"soft, spreadable",rindless,ivory,"creamy, tangy","fresh, rich",NA,NA,NA,NA,Tieton Farm & Creamery
+Bianco,https://www.cheese.com/bianco/,cow,Germany,NA,NA,semi-hard,32.5 g/100g,725 mg/100g,"creamy, open, soft",natural,pale yellow,"buttery, garlicky, mild",NA,FALSE,FALSE,NA,"Bianco Garlic, Bianco Original",Bergader Privatkäserei GmbH
+Bica de Queijo,https://www.cheese.com/bica-de-queijo/,"cow, goat, sheep",Portugal,Póvoa de Lanhoso,NA,"semi-soft, artisan",45%,NA,"creamy, firm, smooth, springy",natural,ivory,"buttery, mild, salty",goaty,FALSE,FALSE,NA,"bag of cheese, Queijo de Bica, Bolsa de queso",NA
+Bierkase,https://www.cheese.com/bierkase/,cow,Germany,NA,NA,"semi-soft, smear-ripened",NA,NA,smooth,washed,pale yellow,"salty, tangy",strong,NA,NA,"Weisslacker, beer kaese, beer cheese, Whitewash, bierkäse",NA,NA
+Big John's Cajun,https://www.cheese.com/big-johns-cajun/,cow,United States,Utah,NA,"hard, artisan",NA,NA,"creamy, smooth",natural,pale yellow,"smooth, spicy","rich, spicy",TRUE,FALSE,NA,NA,Beehive Cheese Company
+Big Rock Blue,https://www.cheese.com/big-rock-blue/,cow,United States,California,Blue,"semi-firm, artisan, blue-veined",NA,NA,"buttery, crumbly",rindless,ivory,"buttery, salty",mild,TRUE,FALSE,NA,NA,Central Coast Creamery
+Big Woods Blue,https://www.cheese.com/big-woods-blue/,sheep,United States,Minnesota,Blue,"semi-hard, blue-veined",NA,NA,firm,natural,ivory,"full-flavored, sharp, spicy, tangy","pronounced, sweet",TRUE,FALSE,NA,NA,Shepherd's Way Farms
+Bijou,https://www.cheese.com/bijou/,goat,United States,"Websterville, VT",NA,"semi-soft, artisan",11%,NA,"creamy, smooth",mold ripened,NA,"sharp, sweet, tangy, yeasty","fresh, yeasty",NA,NA,NA,NA,Vermont Creamery
+Binnorie Marinated Fetta,https://www.cheese.com/binnorie-marinated-fetta/,cow,Australia,"Pokolbin, Hunter Valley",Feta,"soft, brined",NA,NA,creamy,natural,white,"herbaceous, mild, milky","clean, fresh, herbal",TRUE,FALSE,NA,Marinated Feta,Binnorie Dairy
+Bishop Kennedy,https://www.cheese.com/bishop-kennedy/,cow,Scotland,"Kinfauns, Perthshire",NA,soft,45%,NA,smooth,washed,yellow,"creamy, strong",pungent,TRUE,FALSE,NA,NA,"Kinfauns Home Farm, Scotland"
+Bismark,https://www.cheese.com/bismark/,sheep,United States,Vermont,NA,"semi-hard, artisan",NA,NA,"crumbly, firm",rindless,yellow,"buttery, creamy, nutty, sweet, tangy",NA,TRUE,FALSE,NA,NA,Grafton Village Cheese Company
+Bix,https://www.cheese.com/bix/,cow,United Kingdom,NA,NA,"soft, semi-soft",NA,NA,"buttery, creamy, soft, soft-ripened",NA,pale yellow,NA,NA,NA,NA,NA,NA,NA
+Black Betty,https://www.cheese.com/black-betty/,goat,Netherlands,NA,NA,"hard, artisan",NA,NA,firm,waxed,NA,NA,NA,NA,NA,NA,NA,"Essex St. Cheese Co., Fromagerie L'Amuse"
+Black Bomber Cheddar,https://www.cheese.com/black-bomber-cheddar/,cow,United Kingdom,NA,NA,hard,NA,NA,"buttery, compact, creamy, crumbly",NA,yellow,NA,rich,NA,NA,"Little Black Bomber, Black Bomber",NA,Snowdonia Cheese Company Limited
+Black Pearl,https://www.cheese.com/black-pearl/,goat,Australia,"Mornington Peninsula, Melbourne",NA,"semi-hard, artisan",NA,NA,firm,ash coated,NA,mild,fresh,TRUE,FALSE,Boatshed Black Pearl,NA,BoatShed Cheese
+Blackmount,https://www.cheese.com/blackmount/,goat,Scotland,NA,NA,soft,NA,NA,creamy,ash coated,green,earthy,clean,TRUE,FALSE,NA,NA,Errington Cheese Ltd.
+Blacksticks Blue,https://www.cheese.com/blacksticks-blue/,cow,United Kingdom,NA,Blue,"soft, blue-veined",NA,NA,"creamy, spreadable",NA,orange,"creamy, tangy",NA,TRUE,FALSE,Butlers Blacksticks Blue,NA,Butlers Farmhouse Cheeses
+Blaenafon Pwll Ddu,https://www.cheese.com/blaenafon-pwll-ddu/,cow,Wales,NA,NA,hard,NA,NA,compact,NA,cream,savory,NA,NA,NA,NA,NA,NA
+Blarney Castle,https://www.cheese.com/blarney-castle/,cow,Ireland,Blarney,Gouda,semi-soft,NA,NA,creamy,natural,golden yellow,"smooth, tangy",NA,FALSE,FALSE,NA,NA,Kerrygold
+Blenda,https://www.cheese.com/blenda/,"cow, sheep",Sweden,Oviken,NA,"hard, artisan",30%,NA,,natural,yellow,"acidic, fruity, mild, nutty",NA,NA,NA,NA,NA,Oviken cheese
+Bleu Bénédictin,https://www.cheese.com/bleu-benedictin/,cow,Canada,Quebec,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly, firm",natural,pale yellow,"creamy, salty, woody","mushroom, pleasant",FALSE,FALSE,NA,NA,Fromagerie de l'Abbaye Saint-Benoît
+Bleu d'Auvergne,https://www.cheese.com/bleu-dauvergne/,cow,France,Auvergne,Blue,"semi-soft, artisan",NA,NA,"creamy, smooth",NA,ivory,"buttery, creamy, pungent",strong,NA,NA,Bleu d'Auvergne AOC,NA,NA
+Bleu de Laqueuille,https://www.cheese.com/bleu-de-laqueuille/,cow,France,Laqueuille,Blue,"soft, blue-veined",30%,NA,"creamy, smooth",natural,blue,"salty, spicy, tangy",earthy,FALSE,FALSE,NA,NA,NA
+Bleu Des Causses,https://www.cheese.com/bleu-des-causses/,cow,France,NA,Blue,"semi-soft, blue-veined",NA,NA,creamy,NA,NA,"milky, spicy",strong,NA,NA,Bleu Des Causses AOC,NA,NA
+Bleu L'Ermite,https://www.cheese.com/bleu-lermite/,cow,Canada,Quebec,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly",natural,cream,"mushroomy, nutty, salty, sharp",fermented,FALSE,FALSE,"Ermite (L'), Bleu Ermite, Blue Hermit",NA,Abbey de Saint-Benoit-du-lac
+Bleu Mont Dairy Bandaged Cheddar,https://www.cheese.com/bleu-mont-dairy-bandaged-cheddar/,cow,United States,Wisconsin,Cheddar,"hard, artisan",NA,NA,"crumbly, crystalline, flaky",cloth wrapped,yellow,"caramel, nutty, sweet",earthy,FALSE,FALSE,NA,NA,NA
+Bleubry,https://www.cheese.com/bleubry/,cow,Canada,Quebec,Blue,"soft, blue-veined",37%,NA,"creamy, smooth, supple",mold ripened,cream,"creamy, mild, savory",pungent,FALSE,FALSE,NA,NA,La Maison Alexis de Portneuf Inc.
+Blissful Blocks,https://www.cheese.com/blissful-blocks/,,"Canada, United States",NA,Cheddar,hard,NA,NA,"creamy, crumbly",plastic,yellow,"creamy, savory, sharp, spicy",NA,TRUE,FALSE,"Lactose Free Cheddar Blocks, Lactose & Soy Free Cheddar Blocks, Lactose Free Mozzarella Blocks, Lactose & Soy Free Mozzarella Blocks",NA,GO Veggie!
+Blissful Toppings,https://www.cheese.com/blissful-toppings/,,"Canada, United States",NA,Parmesan,soft,NA,NA,crumbly,artificial,yellow,"savory, sharp",NA,TRUE,FALSE,"Lactose Free Parmesan Grated Topping, Dairy Free Parmesan Grated Topping",NA,GO Veggie!
+Bloomsdale,https://www.cheese.com/bloomsdale/,goat,United States,Bloomdale,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, runny, soft-ripened",mold ripened,white,"earthy, milky, tangy",NA,FALSE,FALSE,NA,NA,Baetje Farms LLC
+Blu '61,https://www.cheese.com/blu-61/,cow,Italy,Veneto,Blue,"soft, blue-veined",NA,NA,"creamy, soft",NA,white,"creamy, pronounced, strong","fruity, rich, sweet",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Blu Della Casera,https://www.cheese.com/blu-della-casera/,"cow, sheep",Italy,Piedmont,NA,"soft, blue-veined",54%,NA,soft,leaf wrapped,ivory,"creamy, strong",NA,NA,NA,NA,NA,La Casera srl
+Blu di Bufala,https://www.cheese.com/blu-di-bufala/,water buffalo,Italy,Lombardy,Blue,"semi-firm, artisan, blue-veined",NA,NA,"creamy, crumbly",natural,cream,"acidic, milky, sweet, tangy","fresh, milky",FALSE,FALSE,NA,NA,NA
+Blue,https://www.cheese.com/vegan-blue/,plant-based,United Kingdom,NA,NA,soft,NA,NA,"creamy, soft-ripened",mold ripened,blue,"creamy, mild, smooth","milky, ripe",TRUE,TRUE,NA,NA,Honestly Tasty
+Blue Benedictine,https://www.cheese.com/blue-benedictine/,,Canada,NA,NA,"semi-soft, blue-veined, soft-ripened",NA,NA,"buttery, creamy, crumbly, smooth",natural,brownish yellow,"buttery, creamy, earthy, grassy, herbaceous, mushroomy, salty, smooth, sweet","buttery, earthy, grassy, herbal, mushroom, sweet",NA,NA,NA,NA,"Benedictine Abbey of Saint-Benoît-du-Lac, Quebec."
+Blue Castello,https://www.cheese.com/blue-castello/,cow,Denmark,NA,Blue,soft,NA,NA,"creamy, smooth",washed,blue,buttery,spicy,NA,NA,NA,NA,NA
+Blue Cheese,https://www.cheese.com/blue-vein-cheese/,"cow, goat, sheep",,NA,Blue,"semi-soft, blue-veined",NA,NA,creamy,NA,blue,"salty, sharp, tangy","stinky, strong",FALSE,FALSE,"Blue cheese, Bleu cheese, Erborinato, Blue-Vein Cheese",NA,NA
+Blue Clouds,https://www.cheese.com/blue-clouds/,cow,United Kingdom,NA,NA,semi-soft,NA,NA,creamy,NA,blue,NA,NA,TRUE,FALSE,NA,NA,NA
+Blue Earth,https://www.cheese.com/blue-earth/,cow,United States,"Mankato, MN",Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, runny, smooth, soft",bloomy,pale yellow,"buttery, grassy, smooth",NA,TRUE,FALSE,NA,NA,NA
+Blue Ledge La Luna,https://www.cheese.com/blue-ledge-la-luna/,goat,United States,Vermont,Gouda,"semi-hard, artisan",NA,NA,"firm, smooth",waxed,white,"creamy, milky, tangy",grassy,FALSE,FALSE,La Luna,NA,Blue Ledge
+Blue Lupine,https://www.cheese.com/blue-lupine/,goat,United States,California,Blue,"semi-soft, blue-veined",NA,NA,"creamy, dense",natural,NA,tangy,goaty,NA,NA,NA,NA,Weirauch Farm and Creamery
+Blue Rathgore,https://www.cheese.com/blue-rathgore/,goat,Ireland,County Antrim,Blue,"semi-soft, artisan",NA,NA,"creamy, crumbly",natural,ivory,"buttery, spicy",NA,TRUE,FALSE,NA,NA,NA
+Blue Vein (Australian),https://www.cheese.com/blue-vein-australian/,cow,Australia,NA,Blue,"semi-soft, blue-veined",NA,NA,"creamy, dense",natural,blue,"salty, sharp","stinky, strong",NA,NA,NA,NA,NA
+Blue Wensleydale,https://www.cheese.com/blue-wensleydale/,cow,England,North Yorkshire,Blue,"hard, blue-veined",NA,NA,close,cloth wrapped,blue,"acidic, salty, savory, sharp, strong",rich,TRUE,FALSE,NA,NA,Wensleydale Creamery
+Blue Yonder,https://www.cheese.com/blue-yonder/,cow,United States,NY,Blue,"semi-soft, blue-veined",NA,NA,creamy,natural,white,subtle,rich,TRUE,FALSE,NA,NA,Lively Run Goat Dairy
+Bluebell,https://www.cheese.com/bluebell/,cow,Scotland,NA,NA,semi-soft,NA,NA,soft,natural,blue-grey,sweet,mushroom,TRUE,FALSE,NA,NA,The Ethical Dairy
+Bluebell Falls Cygnus,https://www.cheese.com/bluebell-falls-cygnus/,goat,Ireland,Co. Cork,NA,"fresh soft, artisan",NA,NA,"creamy, smooth",natural,white,"garlicky, herbaceous, sweet",NA,TRUE,FALSE,"Cygnus Goats Cheese, Cygnus Honey Garlic and Thyme Goats Cheese, Cygnus Pepper and Garlic Goats Cheese",NA,Bluebell Falls Goats Cheese
+Blythedale Camembert Vermont™,https://www.cheese.com/blythedale-camembert/,cow,United States,Vermont,Camembert,"soft, artisan",NA,NA,"creamy, dense",natural,ivory,"earthy, mushroomy",mild,TRUE,FALSE,Camembert Vermont,NA,Blythedale Farm Cheeses
+Bocconcini,https://www.cheese.com/bocconcini/,"cow, goat, water buffalo",Italy,NA,Mozzarella,"semi-soft, brined",NA,NA,creamy,rindless,white,"buttery, mild, sweet",NA,NA,NA,"bocconcino di bufala campana, bocconcini alla panna di bufala, Bocconcino Di Langa",NA,La Casa Del Formaggio
+Boeren-Leidse met sleutels,https://www.cheese.com/boeren-leidse-met-sleutels/,cow,Netherlands,Leiden,Gouda,"hard, artisan",30-40%,NA,"crumbly, firm",waxed,yellow,"fruity, spicy","aromatic, rich",FALSE,FALSE,"Farmers Leiden, Leidse kaas, Leyden cheese, Boeren-Leidse kaas, Boeren-Leidse, cumin cheese, Leyden, Boeren-Leidse met sleutels, Boeren Leidenkaas",NA,NA
+Bohemian Blue,https://www.cheese.com/bohemian-blue/,sheep,United States,Southwestern Wisconsin,Blue,"firm, artisan",NA,NA,"creamy, crumbly, dry",rindless,white,"piquant, sour, sweet",sweet,FALSE,FALSE,NA,NA,Hidden Springs Company
+Boivin Extra Aged Cheddar,https://www.cheese.com/boivin-extra-aged-cheddar/,cow,Canada,Quebec,Cheddar,hard,NA,NA,"firm, smooth",rindless,straw,"acidic, buttery",NA,NA,NA,NA,NA,La Fromagerie Boivin
+Boivin Marbled Cheddar,https://www.cheese.com/boivin-marbled-cheddar/,cow,Canada,Quebec,Cheddar,"semi-soft, processed",31%,NA,"firm, smooth",rindless,golden orange,"mild, salty, sweet",buttery,NA,NA,NA,NA,La Fromagerie Boivin
+Boivin Medium Cheddar,https://www.cheese.com/boivin-medium-cheddar/,cow,Canada,Quebec,Cheddar,semi-hard,NA,NA,"firm, smooth",rindless,pale yellow,"acidic, buttery",NA,NA,NA,NA,NA,La Fromagerie Boivin
+Bonchester,https://www.cheese.com/bonchester/,cow,"Scotland, United Kingdom",Roxburghshire,NA,"soft, artisan",20%,NA,"close, smooth",natural,yellow,mild,grassy,FALSE,FALSE,NA,NA,NA
+Bonifaz,https://www.cheese.com/bonifaz/,cow,Germany,NA,NA,soft,43.3 g/100g,430 mg/100g,"creamy, soft",natural,cream,"creamy, garlicky, herbaceous, mild, milky, mushroomy, spicy",NA,FALSE,FALSE,NA,NA,Bergader Privatkäserei GmbH
+Bonne Bouche,https://www.cheese.com/bonne-bouche/,goat,United States,Vermont,NA,soft,21%,NA,"creamy, fluffy, smooth",mold ripened,ivory,"citrusy, grassy",yeasty,TRUE,FALSE,NA,NA,Vermont Creamery
+Boo Boo Baby Swiss,https://www.cheese.com/boo-boo-baby-swiss/,cow,United States,Utah,Swiss Cheese,"hard, artisan",NA,NA,"crumbly, firm, open",natural,yellow,"nutty, sweet","milky, pleasant",FALSE,FALSE,NA,NA,Rockhill Creamery
+Bootlegger,https://www.cheese.com/bootlegger/,"cow, sheep","Canada, Italy",Lombardy,NA,"hard, artisan",NA,NA,"crumbly, firm",natural,pale yellow,"fruity, full-flavored, strong",floral,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Bossa,https://www.cheese.com/bossa/,sheep,United States,Missouri,NA,"semi-soft, artisan",NA,NA,"creamy, firm, spreadable",washed,cream,"floral, meaty",stinky,TRUE,FALSE,NA,NA,Green Dirt Farm
+Bosworth,https://www.cheese.com/bosworth/,goat,United Kingdom,NA,NA,"soft, artisan",NA,NA,crumbly,mold ripened,white,"mild, salty, sweet",goaty,NA,NA,"Bosworth Leaf, Bosworth Ash Log",NA,Highfields Farm Dairy
+Bothwell Black Truffle Cheddar,https://www.cheese.com/bothwell-black-truffle-cheddar/,cow,Canada,Manitoba,Cheddar,semi-hard,NA,NA,firm,rindless,pale yellow,"earthy, fruity","buttery, rich",NA,NA,NA,NA,Bothwell Cheese Inc.
+Bougon,https://www.cheese.com/bougon/,goat,France,NA,NA,"soft, artisan",50%,NA,firm,bloomy,white,acidic,fresh,FALSE,FALSE,NA,NA,NA
+Boulder Chevre,https://www.cheese.com/boulder-chevre/,goat,United States,NA,NA,"semi-soft, artisan",NA,NA,"creamy, crumbly, firm",NA,white,"citrusy, tangy","clean, fresh",TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Boule Du Roves,https://www.cheese.com/boule-du-roves/,goat,France,NA,NA,"soft, artisan",NA,NA,creamy,natural,white,NA,fresh,FALSE,FALSE,NA,NA,NA
+Boulette d'Avesnes,https://www.cheese.com/boulette-davesnes/,cow,France,Avesnes,NA,fresh soft,45%,NA,"creamy, smooth",washed,NA,spicy,stinky,TRUE,FALSE,NA,NA,NA
+Bouncing Berry,https://www.cheese.com/bouncing-berry/,cow,United Kingdom,NA,NA,hard,NA,NA,creamy,waxed,pale yellow,fruity,NA,NA,NA,NA,NA,NA
+Bourdin Goat Log,https://www.cheese.com/bourdin-goat-log/,goat,France,NA,Cottage,soft,NA,NA,"creamy, soft",rindless,NA,"creamy, tangy",fresh,NA,NA,"Bourdin Chevre, Chevre by Bourdin",NA,NA
+Boursault,https://www.cheese.com/boursault/,cow,France,NA,Brie,"soft, soft-ripened",NA,NA,"creamy, spreadable",bloomy,NA,buttery,NA,NA,NA,NA,NA,NA
+Boursin,https://www.cheese.com/boursin/,cow,France,Croisy-sur-Eure,NA,"soft, processed",60%,NA,"creamy, crumbly, spreadable",rindless,white,"buttery, full-flavored, herbaceous, smooth","fresh, strong",TRUE,FALSE,Gournay cheese,NA,Boursin Bel UK Ltd
+Bouyguette,https://www.cheese.com/bouyguette/,goat,France,NA,NA,fresh soft,NA,NA,"creamy, crumbly, soft",edible,white,"lemony, milky, smooth","floral, fresh, goaty, grassy",FALSE,FALSE,NA,NA,NA
+Bouyssou,https://www.cheese.com/bouyssou/,cow,France,Aveyron,NA,soft,NA,NA,,NA,NA,fruity,NA,FALSE,FALSE,NA,NA,NA
+Boyne Valley Bán,https://www.cheese.com/boyne-valley-ban/,goat,Ireland,NA,NA,hard,NA,NA,"creamy, crumbly",natural,ivory,"earthy, full-flavored, garlicky, grassy, lemony, mushroomy, umami","barnyardy, buttery, earthy, goaty, musty",TRUE,FALSE,Boyne Valley Ban,NA,Boyne Valley Cheese
+Bra Duro DOP,https://www.cheese.com/bra/,cow,Italy,Piedmont,NA,hard,35%,NA,firm,natural,pale yellow,"salty, savory",NA,FALSE,FALSE,Bra Duro DOP,NA,NA
+Braudostur,https://www.cheese.com/braudostur/,cow,Iceland,NA,NA,semi-hard,25%,NA,,NA,NA,sweet,strong,FALSE,FALSE,NA,NA,NA
+Breakfast Cheese,https://www.cheese.com/breakfast-cheese/,cow,United States,California,NA,"fresh firm, soft-ripened",7 g/100g,90 mg/100g,"dense, firm",rindless,white,"citrusy, tangy",fresh,TRUE,FALSE,Petite Breakfast,NA,Marin French Cheeese Co.
+Brebirousse d'Argental,https://www.cheese.com/brebirousse-dargental/,sheep,France,NA,Brie,soft,NA,NA,"creamy, smooth",washed,orange,"creamy, sweet","grassy, milky",TRUE,FALSE,NA,NA,NA
+Brebis d'Azure,https://www.cheese.com/brebis-dazure/,sheep,"Canada, Italy",Lombardy,Blue,"semi-hard, artisan, blue-veined",NA,NA,soft,natural,pale yellow,sharp,aromatic,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Brebis du Lavort,https://www.cheese.com/brebis-du-lavort/,sheep,France,Auvergne,NA,"semi-hard, artisan",NA,1050 mg/100g,"creamy, open",natural,ivory,"mild, nutty",earthy,FALSE,FALSE,NA,"Lavort, Guillaume de Lavort",Fromagerie De Terre Dieu
+Brebis du Puyfaucon,https://www.cheese.com/brebis-du-puyfaucon/,sheep,France,Haute Vienne,NA,"soft, artisan",NA,NA,smooth,natural,NA,sweet,grassy,FALSE,FALSE,NA,NA,NA
+Bree,https://www.cheese.com/vegan-bree/,plant-based,United Kingdom,NA,NA,soft,NA,NA,"creamy, soft-ripened",bloomy,white,"buttery, earthy, mushroomy","earthy, mild",TRUE,TRUE,NA,NA,Honestly Tasty
+Brefu Bach,https://www.cheese.com/brefu-bach/,sheep,Wales,NA,NA,soft,NA,NA,soft,NA,golden yellow,NA,NA,NA,NA,NA,NA,NA
+Bresse Bleu,https://www.cheese.com/bresse-bleu/,cow,France,NA,Blue,"soft, blue-veined",NA,NA,creamy,NA,white,creamy,"milky, mushroom",FALSE,FALSE,Bleu de Bresse,NA,NA
+Brewer's Gold,https://www.cheese.com/brewers-gold/,cow,Ireland,Stoneyford,NA,"semi-soft, artisan",NA,NA,creamy,washed,pale yellow,NA,NA,TRUE,FALSE,NA,NA,Knockdrinna Farmhouse Cheese
+Brick,https://www.cheese.com/brick/,cow,United States,Wisconsin,NA,"semi-hard, smear-ripened",NA,NA,"open, smooth",washed,ivory,"mild, nutty, sweet, tangy","pungent, rich",FALSE,FALSE,NA,NA,NA
+Brick Lane Bree,https://www.cheese.com/vegan-brick-lane-bree-cheese/,plant-based,United Kingdom,NA,NA,soft,NA,NA,"buttery, creamy, gooey, soft",mold ripened,cream,"buttery, mild, mushroomy","buttery, mild, mushroom",TRUE,TRUE,NA,NA,La Fauxmagerie
+Bridgewater,https://www.cheese.com/bridgewater/,cow,United States,"Ann Arbor, Michigan",NA,"soft, soft-ripened",NA,NA,"creamy, soft-ripened",bloomy,ivory,"citrusy, mushroomy, piquant, spicy","mushroom, spicy",FALSE,FALSE,NA,NA,Zingerman's Creamery
+Brie,https://www.cheese.com/brie/,cow,France,NA,Brie,"soft, artisan",NA,NA,"buttery, soft-ripened",bloomy,cream,mild,"buttery, mild",FALSE,FALSE,NA,NA,NA
+Brie au poivre (Brie with pepper),https://www.cheese.com/brie-au-poivre-brie-with-pepper/,cow,France,NA,Brie,"soft, soft-ripened",NA,NA,creamy,bloomy,NA,"creamy, spicy",spicy,NA,NA,NA,NA,NA
+Brie Coco,https://www.cheese.com/brie-coco/,cow,Canada,Québec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, smooth, soft-ripened",bloomy,pale yellow,"acidic, buttery, creamy, salty","mushroom, nutty",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Brie d'Alexis,https://www.cheese.com/brie-dalexis/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, supple",bloomy,cream,"creamy, nutty",nutty,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Brie de Meaux,https://www.cheese.com/brie-de-meaux/,cow,France,Ile de France,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, smooth",bloomy,straw,milky,"buttery, sweet",NA,NA,NA,NA,NA
+Brie de Melun,https://www.cheese.com/brie-de-melun/,cow,France,Ile de France,Brie,"semi-soft, soft-ripened",45%,NA,firm,bloomy,yellow,"salty, sharp, sour, strong","grassy, musty",NA,NA,Brie Noir,NA,NA
+Brie de Portneuf,https://www.cheese.com/brie-de-portneuf/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, supple",bloomy,cream,creamy,fruity,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Brie de Portneuf Double Cream,https://www.cheese.com/brie-de-portneuf-double-cream/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, supple",bloomy,cream,creamy,nutty,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Brightwell Ash,https://www.cheese.com/brightwell-ash/,goat,United Kingdom,South East England,NA,"semi-firm, artisan",NA,NA,smooth,ash coated,pale white,"citrusy, nutty, tangy",mild,TRUE,FALSE,NA,NA,NA
+Brillat-Savarin,https://www.cheese.com/brillat-savarin/,cow,France,Ile de France,NA,"soft, artisan",75%,NA,"creamy, dense",mold ripened,white,"buttery, nutty, sour",milky,FALSE,FALSE,NA,NA,NA
+Brillo di Treviso,https://www.cheese.com/brillo-di-treviso/,cow,Italy,Veneto,NA,soft,NA,NA,compact,natural,ivory,"fruity, tangy","aromatic, subtle",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Brimstone,https://www.cheese.com/brimstone/,cow,United States,Missouri,Gouda,"semi-hard, artisan",NA,NA,crumbly,rindless,pale yellow,"smooth, spicy",spicy,TRUE,FALSE,NA,NA,Heartland Creamery
+Brin,https://www.cheese.com/brin/,cow,France,Rhone-Alps,NA,"semi-soft, artisan",40%,NA,"creamy, spreadable",washed,golden orange,"buttery, sweet","aromatic, nutty, yeasty",TRUE,FALSE,NA,NA,Fromagerie GUILLOTEAU
+Brin d'Amour,https://www.cheese.com/brin-damour/,sheep,France,NA,NA,"semi-soft, artisan",NA,NA,"creamy, firm",NA,white,"citrusy, mild",herbal,NA,NA,NA,NA,NA
+Brinza - Feta style,https://www.cheese.com/brinza---feta-style/,sheep,New Zealand,Queenstown,Feta,"soft, brined",NA,NA,"creamy, crumbly, open",natural,white,"citrusy, salty, sweet, tangy",NA,FALSE,FALSE,Briza Feta,NA,The Gibbston Valley Cheese Company
+Briquette de Brebis,https://www.cheese.com/briquette-de-brebis/,sheep,France,Averyon,NA,soft,NA,NA,creamy,natural,white,nutty,nutty,NA,NA,NA,NA,NA
+Briquette du Forez,https://www.cheese.com/briquette-du-forez/,"cow, goat",France,Auvergne,Brie,"soft, soft-ripened",NA,NA,creamy,natural,white,smooth,"goaty, pleasant",FALSE,FALSE,NA,NA,NA
+Briscola,https://www.cheese.com/briscola/,cow,Italy,Veneto,NA,semi-hard,NA,NA,"firm, open",natural,ivory,"fruity, savory, spicy, strong","milky, pleasant, spicy",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Brise du Matin,https://www.cheese.com/brise-du-matin/,cow,Canada,Quebec,Brie,"soft, soft-ripened",30%,NA,"buttery, creamy, runny, soft, soft-ripened",bloomy,cream,"buttery, mushroomy, nutty, pronounced",fresh,NA,NA,Légère Brise du Matin,NA,La Maison Alexis de Portneuf Inc.
+Brixton Blue,https://www.cheese.com/vegan-brixton-blue-cheese/,plant-based,United Kingdom,NA,NA,semi-firm,NA,NA,"creamy, semi firm, soft-ripened",mold ripened,blue,"creamy, full-flavored, piquant, tangy, umami","earthy, fermented, pungent, strong",TRUE,TRUE,NA,NA,La Fauxmagerie
+Brocciu,https://www.cheese.com/brocciu/,"goat, sheep",France,Corsica,Cottage,"fresh soft, whey",40-50%,NA,"creamy, crumbly, smooth",rindless,white,"milky, sweet",sweet,NA,NA,"Brocciu AOC, Brocciu AOP",NA,NA
+Broncha,https://www.cheese.com/broncha/,"cow, goat",United States,California,NA,"semi-hard, artisan",NA,NA,creamy,mold ripened,straw,"creamy, mild",NA,FALSE,FALSE,NA,NA,Achadinha Cheese Company
+Brousse du Rove,https://www.cheese.com/brousse-du-rove/,"cow, goat, sheep",France,"Bas-Languedoc, Comtat Venaissin",NA,"soft, artisan",NA,NA,creamy,NA,white,"mild, sweet",milky,FALSE,FALSE,NA,NA,NA
+Brown’s Gulch,https://www.cheese.com/browns-gulch/,goat,United States,Oregon,Parmesan,"hard, artisan",NA,NA,crumbly,natural,pale yellow,"herbaceous, salty, sharp",strong,FALSE,FALSE,NA,NA,Pholia Farm
+Bruder Basil,https://www.cheese.com/bruder-basil/,cow,Germany,Bavaria,NA,"semi-soft, artisan",45%,NA,"creamy, open, smooth",washed,pale yellow,"mild, savory, smokey","rich, smokey",FALSE,FALSE,NA,NA,Bergader Private Cheese Dairy
+Brunost,https://www.cheese.com/brunost/,"cow, goat","Denmark, Finland, Germany, Iceland, Norway, Sweden",NA,NA,"semi-soft, whey",27 g/100g,360 mg/100g,dense,natural,brown,"caramel, sweet",NA,NA,NA,"mysost, mesost, meesjuusto, mysuostur, myseost, Braunkäse, geitost, Ekte Geitost, Gudbrandsdalsost",NA,NA
+Brusselae Kaas (Fromage de Bruxelles),https://www.cheese.com/brusselae-kaas-fromage-de-bruxelles/,cow,Belgium,NA,NA,"soft, artisan",NA,NA,smooth,washed,pale yellow,"salty, sharp",strong,FALSE,FALSE,NA,NA,NA
+Brutal Blue,https://www.cheese.com/brutal-blue/,cow,United States,Oregon,Blue,"semi-soft, artisan",NA,NA,creamy,NA,cream,"creamy, spicy, strong, woody",strong,TRUE,FALSE,NA,NA,Rogue Creamery
+Bryndza,https://www.cheese.com/bryndza/,sheep,"Hungary, Poland, Slovakia",NA,NA,"soft, artisan",NA,NA,spreadable,rindless,white,"mild, salty",NA,NA,NA,"Slovenská bryndza, Bryndza Podhalańska, Liptauer, Brinza, brynza","ovčia bryndza, Slovenska bryndza, Bryndza Podhalanska",NA
+Brânză de Burduf,https://www.cheese.com/branza-de-burduf/,sheep,Romania,Romanian Carpathians,NA,"soft, artisan",NA,NA,,natural,white,"salty, spicy",woody,FALSE,FALSE,"Brinza (Burduf Brinza), Brânză frământată",NA,NA
+Buche de Chevre,https://www.cheese.com/buche-de-chevre/,goat,France,NA,NA,"soft, soft-ripened",NA,NA,creamy,NA,white,sweet,NA,NA,NA,"Bûche de chèvre, Buche de chèvre",NA,NA
+Bucheret,https://www.cheese.com/bucheret/,goat,United States,California,Brie,"soft, artisan, soft-ripened",10%,NA,"buttery, chalky, dense, smooth, soft-ripened",bloomy,white,"buttery, mushroomy, nutty, tangy",rich,TRUE,FALSE,NA,NA,Redwood Hill Farm & Creamery
+Buchette d'Anjou,https://www.cheese.com/buchette-danjou/,goat,France,Loire,NA,soft,45%,NA,"firm, grainy",artificial,ivory,"acidic, citrusy",aromatic,FALSE,FALSE,NA,NA,Various
+Buchette de Manon,https://www.cheese.com/buchette-de-manon/,goat,France,NA,NA,soft,NA,NA,creamy,natural,cream,mild,clean,FALSE,FALSE,NA,NA,NA
+Bufala Soldier,https://www.cheese.com/bufala-soldier/,"cow, goat, water buffalo",United States,Colorado,Camembert,"soft, artisan, soft-ripened",NA,NA,"buttery, creamy",bloomy,pale yellow,"acidic, buttery, creamy, earthy, sweet",rich,TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Bufalino,https://www.cheese.com/bufalino/,buffalo,Italy,NA,NA,soft,NA,NA,"creamy, soft",NA,white,"creamy, strong",buttery,NA,NA,NA,NA,Casa Madaio
+Bufarolo,https://www.cheese.com/bufarolo/,water buffalo,Italy,Lombardy,Cottage,"fresh soft, artisan",NA,NA,"chalky, crumbly",rindless,white,"mild, milky, subtle","fresh, mild, milky, pleasant",FALSE,FALSE,NA,NA,Azienda Agricola Gritti Bruno E Alfio S.s. Societa Agricola
+Buff Blue,https://www.cheese.com/buff-blue/,buffalo,United States,Southern California,Blue,"firm, blue-veined",NA,NA,,natural,blue,smokey,"earthy, smokey",NA,NA,NA,NA,Bleating Heart Cheese
+Burgos,https://www.cheese.com/burgos/,"cow, sheep",Spain,Castille-Leon,NA,fresh soft,46-60%,NA,creamy,rindless,white,"mild, milky",fresh,FALSE,FALSE,"Queso de Burgos, Fromage Burgos, Burgos käse",NA,NA
+Burrata,https://www.cheese.com/burrata/,water buffalo,"Italy, United States",Apulia,Mozzarella,"fresh soft, artisan",NA,NA,"creamy, stringy",leaf wrapped,white,"buttery, milky","fresh, milky",FALSE,FALSE,NA,NA,NA
+Burwash Rose,https://www.cheese.com/burwash-rose/,cow,"England, Great Britain, United Kingdom","Stonegate, East Sussex",NA,"semi-soft, artisan",NA,NA,"creamy, springy",washed,cream,creamy,"aromatic, floral",TRUE,FALSE,NA,NA,Traditional Cheese Dairy
+Burwood Bole,https://www.cheese.com/burwood-bole/,cow,"England, Great Britain, United Kingdom",Dorset,NA,"semi-hard, artisan",NA,NA,firm,washed,pale yellow,"lemony, nutty, sweet",NA,TRUE,FALSE,NA,NA,James’s Cheese
+Butte,https://www.cheese.com/butte/,cow,France,NA,NA,"soft, soft-ripened",NA,NA,smooth,washed,yellow,"bitter, salty","rich, ripe",FALSE,FALSE,NA,NA,NA
+Buttercup,https://www.cheese.com/buttercup/,"cow, goat",United States,Colorado,Monterey Jack,"semi-soft, artisan",NA,NA,"creamy, firm, open",waxed,pale yellow,"buttery, creamy",NA,TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Butterkase,https://www.cheese.com/butterkase/,cow,"Austria, Germany",NA,NA,semi-soft,50%,NA,"creamy, smooth, spreadable",natural,pale yellow,"buttery, mild",NA,FALSE,FALSE,"butter cheese, Butterkäse",NA,NA
+Buttermilk Blue,https://www.cheese.com/buttermilk-blue/,cow,United States,Wisconsin,NA,"semi-hard, blue-veined",8 g/100g,NA,"creamy, crumbly",natural,pale yellow,"piquant, tangy",fresh,NA,NA,NA,NA,Emmi Roth USA
+Buttermilk Blue Affinee,https://www.cheese.com/buttermilk-blue-affinee/,cow,United States,Wisconsin,Blue,"semi-soft, blue-veined",NA,NA,creamy,natural,ivory,"earthy, piquant, strong",rich,TRUE,FALSE,Blue Affinee Cheese,Buttermilk Bleu Affinee,Emmi Roth USA
+Buttermilk Gorgonzola,https://www.cheese.com/buttermilk-gorgonzola/,cow,United States,Wisconsin,Blue,"semi-soft, blue-veined",NA,NA,crumbly,natural,pale yellow,"piquant, spicy",rich,TRUE,FALSE,NA,NA,Emmi Roth USA
+Butternut,https://www.cheese.com/butternut/,cow,United States,Vermont,NA,"semi-hard, artisan",NA,NA,"creamy, firm",natural,pale yellow,"buttery, nutty","earthy, mushroom",FALSE,FALSE,NA,NA,Willow Hill Farm
+Buxton Blue,https://www.cheese.com/buxton-blue/,cow,"England, United Kingdom","Buxton, Derbyshire",Blue,"soft, artisan, blue-veined",45%,NA,crumbly,natural,yellow,tangy,NA,TRUE,FALSE,NA,NA,NA
+Byaslag,https://www.cheese.com/byaslag/,yak,Mongolia,NA,NA,"fresh soft, artisan",NA,NA,firm,natural,pale yellow,"creamy, mild, salty",aromatic,NA,NA,NA,NA,NA
+Bûchette à la Sarriette,https://www.cheese.com/buchette-la-sarriette/,goat,France,NA,NA,"soft, processed",45%,NA,,NA,NA,NA,NA,NA,NA,NA,NA,NA
+Cabecou,https://www.cheese.com/cabecou/,goat,France,Midi-Pyrenees,NA,"soft, artisan",NA,NA,"creamy, smooth",leaf wrapped,white,"smooth, tangy",aromatic,FALSE,FALSE,NA,NA,NA
+Cabecou Feuille D'Armagnac,https://www.cheese.com/cabecou-feuille-darmagnac/,goat,France,NA,NA,"fresh soft, artisan",NA,NA,smooth,natural,white,"fruity, spicy, tangy",aromatic,FALSE,FALSE,NA,NA,NA
+Caboc,https://www.cheese.com/caboc/,cow,Scotland,NA,NA,soft,NA,NA,"creamy, smooth",natural,white,"buttery, creamy","buttery, fresh",NA,NA,NA,NA,Highland Fine Cheeses Limited
+Cabot Clothbound,https://www.cheese.com/cabot-clothbound/,cow,United States,Vermont,Cheddar,"hard, artisan",NA,NA,"crumbly, flaky",natural,yellow,"nutty, savory, sweet, tangy",NA,FALSE,FALSE,Cheddar Clothbound,NA,"Cabot Creamery , Jasper Hill Farm"
+Cabrales,https://www.cheese.com/cabrales/,cow,Spain,NA,Blue,"semi-hard, artisan, blue-veined",NA,NA,"creamy, firm",NA,NA,"acidic, salty, sharp",strong,NA,NA,"Cabrales DOP, Cabrales PDO",Queso de Cabrales,NA
+Cachaille,https://www.cheese.com/cachaille/,goat,France,Puimichel in Provence Alpes,NA,soft,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,"wrestling, fuorte, toupina",NA,NA
+Cacio De Roma®,https://www.cheese.com/cacio-de-roma/,sheep,Italy,NA,NA,"semi-soft, artisan",20%,NA,semi firm,NA,ivory,mild,sweet,FALSE,FALSE,Rustico Red Pepper Pecorino,NA,NA
+Cacio di Bosco al Tartufo,https://www.cheese.com/cacio-di-bosco-al-tartufo/,"cow, sheep",Italy,Tuscany,NA,"semi-firm, artisan",55%,NA,"crumbly, firm",NA,ivory,"nutty, sour, sweet",strong,NA,NA,NA,NA,Cooperativa Agricola IL FORTETO
+Caciobarricato,https://www.cheese.com/caciobarricato/,cow,Italy,Veneto,Pasta filata,semi-soft,NA,NA,"elastic, soft, stringy, supple",natural,ivory,"pronounced, sharp, tangy","clean, mild",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Caciobirraio,https://www.cheese.com/caciobirraio/,cow,Italy,Veneto,NA,soft,NA,NA,compact,natural,straw,"bitter, subtle","aromatic, toasty",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Caciobufala,https://www.cheese.com/caciobufala/,water buffalo,Italy,Campania,NA,semi-hard,NA,NA,compact,NA,ivory,sweet,"aromatic, buttery, pleasant",FALSE,FALSE,NA,NA,Casa Madaio
+Caciocavallo,https://www.cheese.com/caciocavallo/,cow,Italy,NA,Pasta filata,semi-hard,NA,NA,"elastic, firm",natural,NA,NA,earthy,NA,NA,NA,NA,NA
+Caciocavallo di Bufala,https://www.cheese.com/caciocavallo-di-bufala/,water buffalo,Italy,NA,Pasta filata,"semi-hard, artisan",NA,NA,smooth,NA,NA,savory,earthy,NA,NA,NA,NA,NA
+Caciocavallo Podolico Vetus,https://www.cheese.com/caciocavallo-podolico-vetus/,cow,Italy,NA,Pasta filata,soft,NA,NA,firm,natural,NA,"buttery, sweet",earthy,NA,NA,Caciocavallo Vetus,NA,Casa Madaio
+Cacioradicchio,https://www.cheese.com/cacioradicchio/,,Italy,Veneto,NA,soft,NA,NA,creamy,leaf wrapped,white,"bitter, herbaceous, subtle","aromatic, fresh",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Caciotta,https://www.cheese.com/caciotta/,"cow, goat, sheep, water buffalo",Italy,NA,NA,"semi-soft, artisan",NA,NA,"compact, firm",NA,NA,mild,NA,NA,NA,NA,NA,NA
+Caciotta Al Tartufo,https://www.cheese.com/caciotta-al-tartufo/,"cow, sheep",Italy,"Umbria, Lazio",Caciotta,"semi-soft, artisan",NA,NA,firm,natural,ivory,"spicy, tangy",earthy,NA,NA,NA,Caciotta Al Tartufo with Black Truffles,NA
+Cacow Belle,https://www.cheese.com/cacow-belle/,cow,United States,Oregon,Cheddar,"semi-soft, artisan",NA,NA,"creamy, smooth",natural,pale yellow,"savory, spicy, sweet","aromatic, spicy",TRUE,FALSE,NA,NA,Rogue Creamery
+Caerphilly,https://www.cheese.com/caerphilly/,cow,"United Kingdom, Wales","Wales, London",Cheddar,hard,48%,NA,"crumbly, dense",natural,white,"citrusy, mild, tangy",fresh,NA,NA,"Duckett's Caerphilly, Duckett's Aged Caerphilly",NA,NA
+Cahill's Irish Porter Cheddar,https://www.cheese.com/cahills-irish-porter-cheddar/,cow,Ireland,NA,Cheddar,"semi-hard, artisan",NA,NA,firm,NA,brownish yellow,"full-flavored, tangy",rich,NA,NA,Original Irish Porter,NA,Cahills Farm Cheese
+Cahill's Whiskey Cheese,https://www.cheese.com/cahills-whiskey-cheese/,cow,Ireland,Co Limerick,Cheddar,"semi-hard, artisan",NA,NA,"creamy, soft",waxed,straw,"butterscotch, creamy","pecan, whiskey",NA,NA,Cahills Original Whiskey cheese,Cahill's Irish Whiskey Cheese,Cahills Farm Cheese
+Cairnsmore,https://www.cheese.com/cairnsmore/,sheep,Scotland,Wigtownshire,Cheddar,"hard, artisan",NA,NA,"crumbly, open, smooth",natural,ivory,"burnt caramel, nutty, sweet",aromatic,TRUE,FALSE,NA,NA,Galloway Farmhouse Cheese
+Calcagno,https://www.cheese.com/calcagno/,sheep,Italy,Sardinia & Campania,Pecorino,"hard, artisan",NA,NA,"firm, flaky, smooth",natural,pale yellow,"herbaceous, savory, sweet",herbal,FALSE,FALSE,NA,Calcagno Pecorino,Casa Madaio
+Calenzana (Calinzanincu),https://www.cheese.com/calenzana/,"goat, sheep",France,Upper Corsica,NA,"semi-soft, artisan",NA,NA,creamy,natural,pale yellow,strong,rich,FALSE,FALSE,Calinzanincu,NA,NA
+California Crottin,https://www.cheese.com/california-crottin/,goat,United States,NA,Brie,"soft, artisan, soft-ripened",NA,NA,"dense, firm",mold ripened,cream,"earthy, full-flavored, tangy",mushroom,NA,NA,NA,NA,Redwood Hill Farm & Creamery
+cambazola,https://www.cheese.com/cambazola/,cow,Germany,NA,Blue,"soft, soft-ripened",NA,NA,creamy,NA,NA,NA,NA,TRUE,FALSE,NA,NA,NA
+Cambozola Grand Noir,https://www.cheese.com/cambozola-grand-noir/,cow,Germany,Allgäu,Blue,"semi-soft, blue-veined",NA,NA,creamy,waxed,pale yellow,"sharp, sweet",aromatic,TRUE,FALSE,"Cambozola Black Label, Cambozola Classic, Cambozola Finesse, Cambozola Balance, Cambozola",NA,Käserei Champignon
+Cambus o’May,https://www.cheese.com/cambus-omay/,cow,Scotland,Aberdeenshire,NA,"semi-hard, artisan",NA,NA,creamy,cloth wrapped,cream,sharp,strong,NA,NA,NA,NA,The Cambus O’May Cheese Company
+Camembert,https://www.cheese.com/camembert/,cow,France,NA,Camembert,"soft, artisan",NA,NA,"smooth, soft-ripened",NA,pale yellow,sweet,"buttery, rich",NA,NA,"Camembert Le Châtelain, Camembert St Loup, Camembert Le Chatelain",NA,NA
+Camembert Calvados,https://www.cheese.com/camembert-calvados/,cow,France,NA,NA,"soft, semi-soft",NA,NA,"creamy, smooth, springy",NA,yellow,savory,"aromatic, fruity",NA,NA,NA,NA,NA
+Camembert de Normandie,https://www.cheese.com/camembert-de-normandie/,cow,France,Normandy,Camembert,"soft, soft-ripened",NA,NA,creamy,bloomy,pale yellow,creamy,NA,NA,NA,"Camembert de Normandie AOC, Camembert de Normandie PDO",NA,NA
+Camembert de Portneuf,https://www.cheese.com/camembert-de-portneuf/,cow,Canada,Quebec,Camembert,"soft, soft-ripened",NA,NA,"creamy, soft-ripened, supple",bloomy,pale yellow,"buttery, creamy",aromatic,NA,NA,NA,NA,Alexis de Portneuf
+Camembert des Camarades,https://www.cheese.com/camembert-des-camarades/,cow,Canada,Quebec,Camembert,"soft, soft-ripened",30%,NA,"creamy, soft, soft-ripened",bloomy,ivory,"buttery, nutty, subtle, sweet","aromatic, fresh",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Cameo,https://www.cheese.com/cameo/,goat,United States,California,Camembert,"semi-soft, artisan",NA,NA,"buttery, creamy, soft",bloomy,white,"creamy, smooth","floral, goaty, herbal",NA,NA,NA,NA,Redwood Hill Farm & Creamery
+Campfire,https://www.cheese.com/campfire/,cow,United States,Port Townsend,Monterey Jack,semi-hard,NA,NA,"compact, creamy, firm, open, supple",natural,ivory,"buttery, smokey , sweet",smokey,FALSE,FALSE,Smoked Washington Jack,NA,Mt. Townsend Creamery
+Campi,https://www.cheese.com/campi/,water buffalo,Italy,Lombardy,NA,"semi-hard, artisan",NA,NA,"elastic, firm",natural,ivory,"spicy, sweet",strong,FALSE,FALSE,NA,NA,Azienda Agricola Gritti Bruno E Alfio S.s. Societa Agricola
+Cana de Cabra,https://www.cheese.com/cana-de-cabra/,goat,Spain,Murcia,NA,"semi-soft, soft-ripened",NA,NA,creamy,bloomy,ivory,"creamy, mild","mild, mushroom",NA,NA,"MitiCana de Cabra, MitiCaña® de Cabra",NA,NA
+Canadian Cheddar,https://www.cheese.com/canadian-cheddar/,"cow, goat, sheep",Canada,Ontario,Cheddar,"hard, artisan, processed",NA,NA,"crumbly, open",natural,yellow,"full-flavored, milky, salty, sharp","fresh, rich, strong",FALSE,FALSE,NA,NA,Balderson Cheese Company
+Canarejal,https://www.cheese.com/canarejal/,sheep,Spain,NA,NA,soft,NA,NA,"gooey, runny, smooth",NA,pale yellow,NA,NA,NA,NA,NA,NA,NA
+Canastra cheese,https://www.cheese.com/canastra-cheese/,cow,Brazil,"Serra da Canastra, Minas Gerais state",NA,artisan,NA,NA,,NA,yellow,spicy,aromatic,NA,NA,Queijo Canastra,NA,NA
+Cancoillotte (Cancoyotte),https://www.cheese.com/cancoillotte-cancoyotte/,cow,France,NA,NA,soft,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,Cancoyotte,NA,NA
+Canestrato,https://www.cheese.com/canestrato/,"goat, sheep",Italy,Moliterno,NA,"hard, artisan",NA,NA,"dense, flaky",NA,straw,full-flavored,"spicy, strong",NA,NA,"Canestrato di Moliterno, Canestrato di Moliterno IGP",NA,Casa Madaio
+Cantal,https://www.cheese.com/cantal/,cow,France,NA,NA,"semi-hard, artisan",NA,NA,firm,natural,pale yellow,NA,NA,NA,NA,"Cantal jeune, Cantal entre-deux, Cantal vieux",NA,NA
+Cap Cressy,https://www.cheese.com/cap-cressy/,goat,"Canada, Italy",Lombardy,NA,"semi-hard, artisan, smear-ripened",NA,NA,"compact, dense",washed,pale yellow,"mellow, savory, sweet",lactic,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Cape Vessey,https://www.cheese.com/cape-vessey/,goat,Canada,"Prince Edward County, Ontario",NA,"semi-soft, artisan",NA,NA,"chewy, firm",washed,pale yellow,"acidic, creamy, subtle, sweet","goaty, rich, strong",NA,NA,NA,NA,NA
+Capra al Fieno,https://www.cheese.com/capra-al-fieno/,goat,Italy,"Treviso, Veneto",NA,"semi-soft, artisan",NA,NA,firm,natural,ivory,"strong, woody","floral, grassy",NA,NA,NA,NA,Moro Latteria di Moro Sergio
+Capra al Pepe,https://www.cheese.com/capra-al-pepe/,goat,Italy,"Treviso, Veneto",NA,"soft, artisan",NA,NA,"creamy, soft",natural,pale yellow,"mild, spicy",spicy,NA,NA,NA,NA,Moro Latteria di Moro Sergio
+Capra Nouveau,https://www.cheese.com/capra-nouveau/,goat,England,"Chelmarsh, Bridgnorth, Shropshire",NA,semi-soft,NA,NA,"creamy, smooth",washed,ivory,"creamy, herbaceous, nutty, smooth, sweet","herbal, nutty, rich, sweet",NA,NA,Capra Baby,NA,Brock Hall Farm Dairy
+Caprano,https://www.cheese.com/caprano/,goat,Canada,Quebec,NA,"hard, semi-hard",26%,NA,"crumbly, open, smooth",natural,pale yellow,pronounced,"goaty, strong",TRUE,FALSE,NA,Aged Caprano,La Maison Alexis de Portneuf Inc.
+Capraricca,https://www.cheese.com/capraricca/,goat,Italy,Veneto,NA,"soft, artisan, soft-ripened",NA,NA,"creamy, runny, supple",bloomy,white,sweet,"fresh, goaty",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Caprese di Bufala,https://www.cheese.com/caprese-di-bufala/,water buffalo,Italy,Veneto,NA,soft,NA,NA,"oily, smooth",natural,straw,"herbaceous, mild, subtle, vegetal","aromatic, fresh",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Capri Blu,https://www.cheese.com/capri-blu/,goat,"Canada, Italy",Lombardy,Blue,"soft, blue-veined",NA,NA,"creamy, soft",natural,pale yellow,"creamy, subtle, sweet",goaty,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Capriago,https://www.cheese.com/capriago/,goat,United States,"Sebastopol, California",NA,"hard, artisan, brined",NA,NA,firm,washed,NA,"nutty, sweet","nutty, spicy",NA,NA,NA,NA,Bohemian Creamery
+Caprice,https://www.cheese.com/caprice/,goat,"Canada, Italy",Lombardy,NA,soft,NA,NA,"creamy, smooth",natural,white,subtle,goaty,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Caprice des Dieux,https://www.cheese.com/caprice-des-dieux/,cow,France,Illoud (Haute-Marne),NA,"soft, soft-ripened",60%,NA,"creamy, smooth",bloomy,white,"buttery, nutty, smooth",fresh,TRUE,FALSE,NA,NA,Savencia Fromage & Dairy
+Capricious,https://www.cheese.com/capricious/,goat,United States,"Petaluma, California",NA,"hard, artisan",NA,NA,creamy,mold ripened,ivory,"caramel, nutty",nutty,NA,NA,NA,NA,Achadinha Cheese Company
+Capricorn Somerset Goats Cheese,https://www.cheese.com/capricorn-somerset-goats-cheese/,goat,England,Somerset,NA,"soft, artisan",NA,NA,"creamy, crumbly, firm, smooth",bloomy,white,nutty,NA,TRUE,FALSE,NA,NA,Lactalis McLelland Ltd
+Capriny,https://www.cheese.com/capriny/,goat,Canada,Quebec,NA,soft,NA,NA,creamy,rindless,white,"creamy, mild, sharp, sour",goaty,NA,NA,NA,"Capriny with Fine Herbs, Capriny Pepper",La Maison Alexis de Portneuf Inc.
+Capriole Banon,https://www.cheese.com/capriole-banon/,goat,United States,"Greenville, Indiana",NA,"soft, artisan",NA,NA,"creamy, dense",leaf wrapped,pale yellow,"citrusy, strong, sweet","aromatic, fresh, goaty",FALSE,FALSE,O'Banon,NA,Capriole Goat Cheese
+Caprotto,https://www.cheese.com/caprotto/,goat,Italy,Campania,NA,"hard, artisan",NA,NA,compact,natural,straw,"sharp, spicy","floral, strong",FALSE,FALSE,NA,NA,Casa Madaio
+Carabiner,https://www.cheese.com/carabiner/,cow,United States,California,NA,"semi-hard, artisan",NA,NA,"dense, firm",natural,yellow,"nutty, salty, sweet",earthy,TRUE,FALSE,NA,NA,Weirauch Farm and Creamery
+Caravane,https://www.cheese.com/caravane/,camel,Mauritania,NA,NA,"soft, artisan",22%,NA,creamy,bloomy,white,"salty, sweet",NA,TRUE,FALSE,Camelbert,NA,Tiviski
+Carboncino,https://www.cheese.com/carboncino/,"cow, goat, sheep",Italy,NA,NA,fresh soft,NA,NA,"creamy, gooey, runny, soft",NA,white,NA,NA,NA,NA,NA,NA,NA
+Cardo,https://www.cheese.com/cardo/,goat,"England, Scotland, Wales","Timsbury, Somerset",NA,"semi-soft, artisan",NA,NA,"firm, open, runny",washed,ivory,"floral, pungent, savory",pungent,TRUE,FALSE,NA,NA,NA
+Carlina,https://www.cheese.com/carlina/,cow,Italy,Veneto,NA,"fresh soft, artisan",NA,NA,creamy,bloomy,white,subtle,fresh,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Carlow,https://www.cheese.com/carlow/,cow,Ireland,County Carlow,NA,"semi-hard, artisan",NA,NA,"firm, smooth",waxed,golden yellow,"mild, savory, spicy",NA,NA,NA,NA,NA,Elizabeth Bradley @ Carlow Cheese
+Carmody,https://www.cheese.com/carmody/,cow,United States,California,Gorgonzola,"semi-hard, artisan",NA,NA,firm,natural,cream,"buttery, caramel, sweet",fresh,TRUE,FALSE,NA,NA,Bellwether Farms
+Carnia Altobut,https://www.cheese.com/carnia-altobut/,cow,Italy,Carnia,NA,"hard, artisan",NA,NA,"compact, elastic, firm, open",natural,pale yellow,"pronounced, strong, subtle","aromatic, floral",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Caronzola,https://www.cheese.com/caronzola/,cow,Canada,Quebec,Blue,"soft, blue-veined",NA,NA,creamy,bloomy,ivory,mild,NA,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Carr Valley Glacier Wildfire Blue,https://www.cheese.com/carr-valley-glacier-wildfire-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly, soft",rindless,ivory,creamy,clean,NA,NA,NA,NA,Carr Valley Cheese Company
+Carre de l'Est,https://www.cheese.com/carre-de-lest/,cow,France,NA,NA,soft,NA,NA,smooth,washed,pale yellow,smokey,NA,NA,NA,NA,NA,NA
+Carrick,https://www.cheese.com/carrick/,cow,Scotland,NA,NA,"hard, organic",NA,NA,creamy,edible,cream,citrusy,earthy,TRUE,FALSE,NA,NA,The Ethical Dairy
+Carrot Rebel,https://www.cheese.com/carrot-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,creamy,natural,orange,"creamy, fruity, nutty, sweet","aromatic, fruity, sweet",NA,NA,Rüblirebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Carrowholly,https://www.cheese.com/carrowholly/,cow,Ireland,Co. Mayo,Gouda,"hard, artisan",NA,NA,"crumbly, crystalline, firm, smooth",waxed,yellow,"acidic, crunchy, fruity, herbaceous, mild, spicy",NA,TRUE,FALSE,"Carrowholly Original, Carrowholly ""Old Russet"", Carrowholly Cheese Flavoured, Carrowholly Nettle, Carrowholly Pepper, Carrowholly Garlic & Chive",NA,Carrowholly Cheese
+Casatica,https://www.cheese.com/casatica/,water buffalo,Italy,Lombardy,NA,"semi-soft, artisan, soft-ripened",NA,NA,"creamy, soft-ripened",bloomy,white,"creamy, subtle","aromatic, milky, pleasant, rich",FALSE,FALSE,Casatica di Bufala,NA,NA
+Casciotta di Urbino,https://www.cheese.com/casciotta-di-urbino/,"cow, sheep",Italy,Pesaro-Urbino,Caciotta,"semi-soft, artisan",45%,NA,"creamy, crumbly, open",waxed,straw,"acidic, milky, nutty","grassy, pleasant",FALSE,FALSE,NA,"Casciotta di Urbino D.O.P, Casciotta di Urbino PDO, Casciotta d’Urbino",Caseificio Val D’Apsa
+Cashel Blue,https://www.cheese.com/cashel-blue/,cow,Ireland,NA,Blue,"semi-soft, blue-veined",NA,NA,creamy,natural,blue,tangy,sweet,TRUE,FALSE,NA,NA,J&L Grubb Ltd.
+Cashew Nut Cream Cheese,https://www.cheese.com/cashew-nut-cream-cheese/,,United States,Brooklyn NY,NA,"soft, artisan",NA,NA,"creamy, smooth, soft, spreadable",NA,white,"creamy, nutty, sweet","clean, fresh, nutty",TRUE,FALSE,"Cream Cashew Nut Cheese with Chives, Cream Cashew Nut Cheese with Tomato-Turmeric-Garlic",Plain Cream Cashew Nut Cheese,Dr. Cow Tree Nut Cheese
+Castelmagno,https://www.cheese.com/castelmagno/,"cow, goat, sheep",Italy,Piedmont,Blue,semi-hard,34.2 g/100g,4768 mg/100g,"crumbly, dense, grainy",washed,ivory,"sharp, spicy, strong",strong,FALSE,FALSE,NA,"Castelmagno PDO, Castelmagno di alpeggio, Castelmagno prodotto della montagna",NA
+Castelo Branco,https://www.cheese.com/castelo-branco/,"goat, sheep",Portugal,"Castelo Branco, Fundão and Idanha-a-Nova",NA,"semi-soft, artisan",45%,NA,"brittle, creamy, crumbly, firm, smooth",natural,pale yellow,"sour, spicy, tangy",aromatic,TRUE,FALSE,NA,Queijo de Castelo Branco,NA
+Castigliano,https://www.cheese.com/castigliano/,"cow, goat, sheep",Spain,Castile-Leon,NA,hard,NA,NA,firm,natural,yellow,"acidic, salty, spicy",rich,FALSE,FALSE,NA,Queso Castellano,NA
+Castillon Frais,https://www.cheese.com/castillon-frais/,sheep,France,NA,NA,fresh soft,NA,NA,"creamy, open",rindless,white,"citrusy, creamy, floral, herbaceous","clean, floral, fresh",FALSE,FALSE,Fresh Castillon,NA,David and Fanette Ladu
+Castle Blue,https://www.cheese.com/castle-blue/,cow,Canada,British Columbia,Brie,"semi-soft, blue-veined, soft-ripened",NA,NA,"buttery, creamy",natural,cream,"creamy, piquant, sweet","earthy, rich",FALSE,FALSE,NA,NA,The Farm House Natural Cheeses
+Casu marzu,https://www.cheese.com/casu-marzu/,sheep,"France, Italy","Sardinia (Italy), Southern Corsica (France)",NA,"soft, soft-ripened",NA,NA,soft-ripened,natural,NA,NA,NA,NA,NA,"casu modde, casu cundídu, casu fràzigu, formaggio marcio, Casu martzu, Casgiu merzu",NA,NA
+Cathelain,https://www.cheese.com/cathelain/,cow,France,NA,NA,soft,NA,NA,smooth,NA,NA,sour,NA,FALSE,FALSE,NA,NA,NA
+Catupiry,https://www.cheese.com/catupiry/,cow,Brazil,NA,NA,"soft, processed",NA,NA,"creamy, spreadable",NA,white,"creamy, mild, milky","fresh, milky",FALSE,FALSE,"Catupiry Original, Catupiry Light",NA,NA
+Cave Aged Marisa,https://www.cheese.com/cave-aged-marisa/,sheep,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,firm,natural,pale yellow,"sweet, tangy",sweet,NA,NA,NA,NA,Carr Valley Cheese Company
+Cave Rebel,https://www.cheese.com/cave-rebel/,cow,Austria,Sulzberg,NA,"hard, artisan",50%,NA,"compact, crumbly, firm, open",natural,pale yellow,"creamy, grassy","aromatic, nutty",NA,NA,Höhlenrebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Caveman Blue,https://www.cheese.com/caveman-blue/,cow,United States,Oregon,Blue,"semi-hard, blue-veined",NA,NA,"creamy, dense",natural,pale yellow,"buttery, mushroomy",fresh,TRUE,FALSE,NA,NA,Rogue Creamery
+Caws Cenarth Black Sheep,https://www.cheese.com/caws-cenarth-black-sheep/,sheep,Wales,NA,NA,semi-hard,NA,NA,buttery,NA,cream,NA,NA,NA,NA,NA,NA,NA
+Caws Penhelyg Abaty,https://www.cheese.com/caws-penhelyg-abaty/,cow,Wales,NA,NA,soft,NA,NA,gooey,NA,white,NA,NA,NA,NA,NA,NA,NA
+Cayuga Blue,https://www.cheese.com/cayuga-blue/,goat,United States,NY,Blue,"semi-hard, blue-veined",NA,NA,compact,bloomy,ivory,creamy,"mild, rich",TRUE,FALSE,NA,NA,Lively Run Goat Dairy
+Cello Thick & Smooth Mascarpone,https://www.cheese.com/cello-thick-smooth-mascarpone/,cow,United States,NA,NA,"fresh soft, processed",NA,NA,"buttery, creamy, firm, smooth",rindless,white,"creamy, milky, sweet","fresh, milky",NA,NA,NA,NA,Schuman Cheese
+Celtic Promise,https://www.cheese.com/celtic-promise/,cow,United Kingdom,Ceredigion,NA,"semi-soft, artisan",NA,NA,creamy,washed,yellow,full-flavored,pungent,TRUE,FALSE,NA,NA,Teifi Farmhouse Cheese
+Cendre d'Olivet,https://www.cheese.com/cendre-dolivet/,cow,France,Centre-Val de Loire,NA,"soft, artisan",45%,NA,"smooth, supple",natural,ivory,"mild, smooth","earthy, pungent",FALSE,FALSE,NA,Olivet Cendre,NA
+Cendré des Prés,https://www.cheese.com/cendre-des-pres/,cow,Canada,Quebec,Brie,"soft, artisan, soft-ripened",27%,NA,"buttery, creamy, soft",bloomy,ivory,"acidic, buttery, creamy, fruity, mushroomy","aromatic, buttery, floral, lactic, mushroom, woody",NA,NA,NA,NA,Fromagerie Domaine Féodal inc.
+Cerney Pyramid,https://www.cheese.com/cerney-pyramid/,goat,"England, Great Britain, United Kingdom",Cotswolds,NA,"fresh soft, artisan",NA,NA,creamy,natural,white,"citrusy, lemony, mild, sweet",NA,TRUE,FALSE,NA,NA,NA
+Chabichou du Poitou,https://www.cheese.com/chabichou-du-poitou/,goat,France,NA,NA,"semi-soft, artisan",NA,NA,creamy,NA,white,"salty, sweet, tangy",goaty,NA,NA,"Chabichou du Poitou AOP, Chabichou du Poitou AOC",NA,NA
+Chabis de Gatine,https://www.cheese.com/chabis-de-gatine/,goat,France,Poitou-Charentes,NA,"soft, artisan",NA,NA,"firm, smooth",natural,white,"salty, sharp",goaty,FALSE,FALSE,NA,NA,NA
+Challerhocker,https://www.cheese.com/challerhocker/,cow,Switzerland,"St. Gallen (canton), Tufertschwil",Swiss Cheese,hard,NA,NA,"creamy, dense, smooth",washed,pale yellow,"caramel, nutty, salty, sweet","nutty, sweet",NA,NA,NA,NA,Käserei Tufertschwil
+Champignon de Luxe Garlic,https://www.cheese.com/champignon-de-luxe-garlic/,cow,Germany,Allgäu,NA,"soft, soft-ripened",NA,NA,creamy,natural,cream,"garlicky, herbaceous","herbal, spicy",TRUE,FALSE,"Champignon de Luxe Knoblauch, Champignon Garlic",NA,Käserei Champignon
+Champignon de Luxe Pepper,https://www.cheese.com/champignon-de-luxe-pepper/,cow,Germany,Allgäu,NA,"soft, soft-ripened",NA,NA,creamy,natural,cream,"creamy, sharp",spicy,TRUE,FALSE,"Champignon Pepper, Champignon de Luxe Pfeffer",NA,Käserei Champignon
+Champignon Mushrooom,https://www.cheese.com/champignon-mushrooom/,cow,Germany,Allgäu,NA,"soft, soft-ripened",NA,NA,creamy,natural,cream,"creamy, mushroomy","fresh, mild",TRUE,FALSE,Champignon Mushroom Mini,NA,Käserei Champignon
+Chaource,https://www.cheese.com/chaource/,cow,France,NA,NA,"soft, soft-ripened",NA,NA,"creamy, runny",NA,cream,creamy,NA,NA,NA,NA,NA,NA
+Chapman's Pasture,https://www.cheese.com/chapmans-pasture/,cow,United States,Vermont,Parmesan,semi-hard,NA,NA,grainy,washed,ivory,"sharp, sweet","strong, sweet",FALSE,FALSE,NA,NA,Parish Hill Creamery
+Charolais,https://www.cheese.com/charolais/,goat,France,Bourgogne,NA,semi-soft,NA,NA,firm,NA,NA,"acidic, salty, sweet",subtle,FALSE,FALSE,Charoles,NA,NA
+Chaumes,https://www.cheese.com/chaumes/,cow,France,St Antoine,NA,"soft, semi-soft, soft-ripened",50%,NA,"creamy, smooth, springy, supple",washed,pale yellow,"full-flavored, nutty","aromatic, strong",FALSE,FALSE,NA,Chaumes la Crème,NA
+Chavroux,https://www.cheese.com/chavroux/,goat,France,NA,NA,soft,12%,NA,"creamy, spreadable",NA,white,"creamy, mild","fresh, goaty",NA,NA,NA,NA,NA
+Checkerboard Cheddar,https://www.cheese.com/checkerboard-cheddar/,cow,United States,NY,Cheddar,"semi-hard, artisan",NA,NA,crumbly,natural,pale yellow,"sharp, sweet, tangy",pleasant,NA,NA,NA,NA,Muranda Cheese Company
+Cheddar,https://www.cheese.com/cheddar/,cow,England,NA,Cheddar,hard,NA,NA,compact,NA,pale yellow,"creamy, sharp",NA,NA,NA,NA,NA,NA
+Cheddar LaDiDa Lavender,https://www.cheese.com/cheddar-ladida-lavender/,cow,United States,Oregon,Cheddar,"semi-hard, artisan",NA,NA,"creamy, smooth",natural,white,"herbaceous, savory, subtle",earthy,TRUE,FALSE,NA,La Di Da Lavender,Rogue Creamery
+Cheddar with Irish Porter,https://www.cheese.com/cheddar-with-irish-porter/,cow,Ireland,Kilmallock County Limerick,Cheddar,"semi-hard, artisan",NA,NA,smooth,NA,pale yellow,"fruity, tangy","pungent, rich",NA,NA,Irish Porter,NA,NA
+Cheddar with Red Wine,https://www.cheese.com/cheddar-with-red-wine/,cow,Ireland,Kilmallock County Limerick,Cheddar,"semi-hard, artisan",NA,NA,firm,NA,pale yellow,"fruity, tangy","fruity, rich",TRUE,FALSE,Red Wine Cheddar,NA,J.O.D. Foods
+Cheese Curds,https://www.cheese.com/cheese-curds/,,"Canada, India, United States",NA,Cheddar,fresh firm,NA,NA,"firm, springy",natural,white,"mild, milky",fresh,NA,NA,"Squeaky Cheese, cheeseballs, paneer, Boivin Cheddar Curds",NA,NA
+Chelsea Blue,https://www.cheese.com/chelsea-blue/,,Australia,"Mornington Peninsula, Melbourne",Blue,artisan,NA,NA,crumbly,NA,NA,"nutty, salty, sweet","nutty, sweet",TRUE,FALSE,NA,NA,BoatShed Cheese
+Cherni Vit,https://www.cheese.com/cherni-vit/,sheep,Bulgaria,Central Balkan Mountains,NA,"soft, brined",NA,NA,soft,mold ripened,green,"nutty, sharp",NA,NA,NA,NA,NA,NA
+Cherokee Rose,https://www.cheese.com/cherokee-rose/,cow,United States,Georgia,Raclette,"hard, artisan",NA,NA,"creamy, smooth, soft",natural,pale yellow,"buttery, nutty, sweet, tangy","fresh, milky",NA,NA,NA,NA,Nature's Harmony Farm
+Cheshire,https://www.cheese.com/cheshire/,cow,United Kingdom,NA,NA,hard,NA,NA,"crumbly, dense",cloth wrapped,orange,earthy,"grassy, spicy",NA,NA,Appleby's Cheshire,NA,Appleby's
+Chevre en Marinade,https://www.cheese.com/chevre-en-marinade/,goat,United States,Colorado,NA,"semi-soft, artisan",NA,NA,"creamy, crumbly, firm, oily",rindless,white,"garlicky, herbaceous, spicy","aromatic, pungent",TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Chevre Log,https://www.cheese.com/chevre-log/,goat,United States,California,NA,fresh soft,NA,NA,"creamy, crumbly, smooth, spreadable",rindless,white,"citrusy, creamy","fresh, mild",TRUE,FALSE,NA,NA,"Cypress Grove Chevre, Goat Lady Dairy"
+Chevrotin des Aravis,https://www.cheese.com/chevrotin-des-aravis/,goat,France,Haute-Savoie / Upper Savoy,NA,"soft, artisan, brined",45%,NA,"creamy, smooth",washed,pale yellow,"full-flavored, herbaceous, sweet","aromatic, floral, goaty",FALSE,FALSE,NA,NA,NA
+Chhurpi,https://www.cheese.com/chhurpi/,"cow, yak","China, Nepal, Tibet",NA,Cottage,"soft, hard, artisan",NA,NA,dense,natural,pale yellow,tangy,NA,NA,NA,"Durkha, Chhur singba, Sherkam, Chhur mingba",Churpi,NA
+Childwickbury,https://www.cheese.com/childwickbury/,goat,"England, Great Britain, United Kingdom","Odell, Bedfordshire",NA,"fresh soft, artisan",NA,NA,"creamy, smooth",natural,white,"citrusy, lemony, mild, milky","floral, fresh, mild, milky",TRUE,FALSE,NA,NA,Childwickbury Estate
+Chile Jack,https://www.cheese.com/chile-jack/,goat,United States,Colorado,Monterey Jack,"semi-soft, artisan",NA,NA,creamy,waxed,cream,"creamy, spicy, subtle, tangy",mild,TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Chilli Caciotta,https://www.cheese.com/chile-caciotta/,"cow, sheep",Italy,NA,NA,"semi-soft, artisan",NA,NA,"creamy, smooth",NA,NA,spicy,NA,NA,NA,"Caciotta Ancho Chile, Caciotta Mexican Marigold Mint, Caciotta Basil, Chile Caciotta",NA,NA
+Chimney Rock,https://www.cheese.com/chimney-rock/,cow,United States,California,NA,soft,NA,NA,soft,bloomy,pale yellow,"earthy, piquant, savory","fruity, rich",TRUE,FALSE,NA,NA,Cowgirl Creamery
+Chiriboga Blue,https://www.cheese.com/chiriboga-blue/,cow,Germany,Allgau,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, dense, smooth, spreadable",mold ripened,cream,"buttery, grassy, mild, sweet",mild,FALSE,FALSE,NA,NA,NA
+Chocolate Lab,https://www.cheese.com/chocolate-lab/,cow,United States,Fairview,NA,"hard, artisan",NA,NA,firm,washed,pale yellow,"pungent, sharp, sweet",rich,NA,NA,NA,NA,Looking Glass Creamery
+Chocolate Stout Cheddar,https://www.cheese.com/chocolate-stout-cheddar/,cow,United States,Oregon,Cheddar,"semi-hard, artisan",NA,NA,firm,natural,pale yellow,"savory, sweet, tangy",buttery,TRUE,FALSE,NA,NA,Rogue Creamery
+Chontaleno,https://www.cheese.com/chontaleno/,cow,Mexico,NA,Parmesan,semi-hard,NA,NA,firm,natural,white,salty,NA,FALSE,FALSE,NA,Chontaleno Ahumado,Peluso Cheese
+Chorlton Blue Cheshire,https://www.cheese.com/chorlton-blue-cheshire/,cow,"England, Great Britain, United Kingdom",Cheshire,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly",natural,pale yellow,"citrusy, lemony, salty",strong,NA,NA,NA,NA,Chorlton Cheshire Cheese
+Chura Kampo,https://www.cheese.com/chura-kampo/,yak,"China, Tibet",Tibet,NA,"hard, artisan",NA,NA,"dense, dry, firm",natural,NA,NA,aromatic,NA,NA,"chura loenpa , ser",NA,NA
+Château de Versailles,https://www.cheese.com/chateau-de-versailles/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, runny, soft, soft-ripened",bloomy,ivory,"buttery, creamy",pungent,NA,NA,NA,NA,Tre Stelle
+Chèvre,https://www.cheese.com/chevre/,goat,France,NA,NA,NA,NA,NA,,NA,white,tangy,goaty,NA,NA,"Goat cheese, Chevre, Chèvre",NA,NA
+Chèvre des neiges,https://www.cheese.com/chevre-des-neiges/,"cow, goat",Canada,Quebec,NA,fresh soft,24%,NA,"soft, spreadable, supple",rindless,white,"fruity, mild, nutty","aromatic, fresh, fruity",NA,NA,NA,"Chèvre des neiges coconut, Chèvre des neiges fig & orange",La Maison Alexis de Portneuf Inc.
+Cilentano ai fichi,https://www.cheese.com/cilentano-ai-fichi/,"buffalo, cow, sheep",Italy,Calabria,NA,"semi-soft, artisan",NA,NA,creamy,natural,cream,"creamy, fruity","nutty, sweet",NA,NA,NA,NA,Casa Madaio
+Cinerino,https://www.cheese.com/cinerino/,sheep,Italy,Campania,NA,"hard, artisan",NA,NA,flaky,ash coated,ivory,"grassy, herbaceous","herbal, nutty, woody",TRUE,FALSE,NA,NA,Casa Madaio
+Cirrus,https://www.cheese.com/cirrus/,cow,United States,Port Townsend,Camembert,"soft, soft-ripened",NA,NA,"creamy, soft",bloomy,ivory,"acidic, buttery, milky, nutty, salty","earthy, mushroom, rich",FALSE,FALSE,NA,NA,Mt. Townsend Creamery
+Civray,https://www.cheese.com/civray/,goat,France,NA,NA,"soft, artisan",45%,NA,"creamy, firm",natural,pale yellow,"acidic, sweet",pleasant,FALSE,FALSE,NA,NA,NA
+Classic Blue Log,https://www.cheese.com/classic-blue-log/,goat,United States,Massachusetts,NA,"semi-soft, artisan",NA,NA,"creamy, dense, soft",mold ripened,cream,"creamy, tangy","clean, fresh",TRUE,FALSE,NA,NA,Westfield Farm
+Classico Pecorino Senese,https://www.cheese.com/classico-pecorino-senese/,sheep,Italy,Tuscany,Pecorino,"semi-hard, artisan",NA,NA,"buttery, compact, firm",natural,straw,sharp,"aromatic, strong",FALSE,FALSE,NA,NA,Caseificio Pinzani Srl
+Classico Riserva,https://www.cheese.com/classico-riserva/,sheep,Italy,Tuscany,Pecorino,"hard, artisan",NA,NA,"chalky, compact, crumbly",natural,straw,full-flavored,"aromatic, strong",NA,NA,NA,NA,Caseificio Pinzani Srl
+Classics Fresh Mozzarella,https://www.cheese.com/classics-fresh-mozzarella/,cow,United States,Wisconsin,Mozzarella,"soft, brined",NA,NA,"creamy, elastic, smooth, soft, stringy, supple",rindless,white,"acidic, mild, milky, spicy",fresh,TRUE,FALSE,"perline, ciliegine, ovoline, marinated ciliegine",NA,Crave Brothers Farmstead Cheese
+Clava Brie,https://www.cheese.com/clava-brie/,cow,Scotland,NA,NA,soft,NA,NA,"buttery, creamy, soft",bloomy,straw,"mushroomy, pungent, rustic, vegetal","garlicky, herbal, mushroom, pungent",TRUE,FALSE,NA,NA,Connage Highland Dairy
+Clonmore,https://www.cheese.com/clonmore/,goat,Ireland,Co. Cork,Gouda,"hard, artisan",NA,NA,"firm, open",plastic,pale yellow,"earthy, mild, milky, nutty, smooth, sweet, tangy","goaty, strong",TRUE,FALSE,NA,NA,Tom and Lena Beggane
+Coalho,https://www.cheese.com/coalho/,cow,Brazil,Northeastern Brazil,NA,semi-hard,NA,NA,"elastic, firm, springy",natural,yellow,"acidic, salty",fresh,FALSE,FALSE,"Queijo coalho, Queijo de coalho , Rennet Cheese",NA,NA
+Coastal Cheddar,https://www.cheese.com/coastal-cheddar/,cow,England,Dorset,Cheddar,firm,NA,NA,,rindless,pale yellow,"crunchy, sweet",nutty,NA,NA,NA,NA,Ford Farm
+Coeur de Camembert au Calvados,https://www.cheese.com/coeur-de-camembert-au-calvados/,cow,France,Lower Normandy,Camembert,"soft, artisan",NA,NA,creamy,bloomy,ivory,"fruity, nutty","aromatic, rich",FALSE,FALSE,Calva d'Auge,NA,Isigny Sainte Mère
+Coeur de Chevre,https://www.cheese.com/coeur-de-chevre/,goat,France,Gâtinais,NA,"soft, soft-ripened",NA,NA,creamy,leaf wrapped,pale yellow,"salty, spicy","fresh, milky",FALSE,FALSE,NA,NA,NA
+Colby,https://www.cheese.com/colby/,cow,United States,"Colby, Wisconsin",Cheddar,semi-hard,NA,NA,"firm, open, springy",rindless,yellow,sweet,"mild, sweet",FALSE,FALSE,NA,Colby Swiss Cheddar,NA
+Colby-Jack,https://www.cheese.com/colby-jack/,,United States,NA,Monterey Jack,"semi-soft, processed",NA,NA,smooth,rindless,NA,"creamy, sweet, tangy",NA,NA,NA,"Marble jack, Cojack, Co-jack, Colby Jack",NA,NA
+Cold Pack,https://www.cheese.com/cold-pack/,cow,United States,Wisconsin,NA,"soft, blue-veined, processed",NA,NA,"creamy, smooth, spreadable",rindless,NA,"full-flavored, sharp, smokey , spicy",strong,NA,NA,"club cheese, comminuted cheese, crock cheese",NA,Brunkow Cheese Factory
+Colony cheese,https://www.cheese.com/colony-cheese/,,Brazil,Rio Grande do Sul,NA,"semi-hard, artisan",NA,NA,"creamy, open, soft, supple",natural,pale yellow,"creamy, pungent, spicy","lactic, pungent, spicy",NA,NA,"queijo de colônia, Queijo colônia, queijo colonial",NA,NA
+ColoRouge,https://www.cheese.com/colorouge/,cow,United States,Colorado,NA,"soft, artisan, smear-ripened",12%,NA,"creamy, soft",washed,white,"acidic, buttery, creamy, earthy","buttery, earthy, mild, spicy",NA,NA,MouCo ColoRouge,NA,MouCo Cheese Company
+Colston Bassett Stilton,https://www.cheese.com/colston-bassett-stilton/,cow,"England, Great Britain, United Kingdom",East Midlands,Blue,"semi-hard, artisan",33%,NA,"buttery, creamy",natural,cream,"buttery, fruity",NA,FALSE,FALSE,NA,NA,Colston Bassett Dairy Limited
+Comox Brie,https://www.cheese.com/comox-brie/,cow,Canada,British Columbia,Brie,"soft, soft-ripened",26%,NA,"buttery, creamy, runny, soft, soft-ripened",bloomy,ivory,"buttery, creamy","mushroom, pungent",NA,NA,NA,NA,Natural Pastures Cheese Company
+Comox Camembert,https://www.cheese.com/comox-camembert/,cow,Canada,Quebec,Camembert,"soft, artisan, soft-ripened",28%,NA,"buttery, chalky, creamy, soft, soft-ripened, supple",bloomy,pale yellow,"buttery, creamy, mushroomy","milky, mushroom, pungent",NA,NA,NA,NA,Natural Pastures Cheese Company
+Comte,https://www.cheese.com/comte/,cow,France,NA,NA,"semi-hard, artisan",NA,NA,"dense, firm",natural,pale yellow,NA,NA,NA,NA,NA,NA,NA
+Comte 12 months,https://www.cheese.com/comte-12-months/,cow,France,NA,NA,semi-hard,NA,NA,buttery,washed,yellow,milky,nutty,FALSE,FALSE,Comté Grande Réserve 12-18 months,NA,JuraFlore
+Comte 18 months,https://www.cheese.com/comte-18-months/,cow,France,NA,NA,hard,NA,NA,firm,natural,yellow,fruity,nutty,FALSE,FALSE,Comté Grande Réserve 18-24 months,NA,JuraFlore
+Comtomme,https://www.cheese.com/comtomme/,cow,Canada,Québec,Tomme,"semi-soft, artisan",28%,NA,"firm, supple",washed,golden yellow,"buttery, fruity","buttery, fruity",NA,NA,NA,NA,Fromagerie La Station
+Conciato Al Pepe,https://www.cheese.com/conciato-al-pepe/,cow,Italy,Veneto,NA,hard,NA,NA,"compact, crumbly",natural,pale yellow,"sharp, spicy",aromatic,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Condio,https://www.cheese.com/condio/,cow,Italy,"Piave Valley, Italy",NA,"semi-soft, artisan",NA,NA,"creamy, smooth, soft",natural,ivory,"herbaceous, piquant, spicy","pungent, spicy",FALSE,FALSE,NA,NA,NA
+Connemara,https://www.cheese.com/connemara/,cow,United States,Fairview,NA,"semi-hard, artisan",NA,NA,"creamy, firm",natural,white,"fruity, mild","fruity, goaty, rich",NA,NA,NA,NA,Looking Glass Creamery
+Consider Bardwell Farm Manchester,https://www.cheese.com/consider-bardwell-farm-manchester/,goat,United States,Vermont,NA,"semi-hard, artisan",NA,NA,"firm, open",natural,ivory,"nutty, tangy, woody","lactic, mushroom, woody",TRUE,FALSE,NA,NA,Consider Bardwell Farm
+Coolattin Cheddar,https://www.cheese.com/coolattin-cheddar/,cow,Ireland,Co. Carlow,Cheddar,"hard, artisan",NA,NA,"crumbly, open",waxed,pale yellow,"fruity, nutty, sweet",NA,FALSE,FALSE,NA,NA,Thomas and Fiona Burgess - Coolattin Cheddar
+Coolea,https://www.cheese.com/coolea/,cow,Ireland,NA,Gouda,hard,NA,NA,firm,NA,NA,sweet,NA,TRUE,FALSE,NA,NA,Coolea Farmhouse Cheese Limited
+Cooleney,https://www.cheese.com/cooleney/,cow,Ireland,Tipperary,Camembert,"soft, artisan",45%,NA,"creamy, smooth",mold ripened,white,"bitter, buttery, mushroomy",NA,TRUE,FALSE,NA,Cooleeney Farmhouse Cheese,Cooleeney Farm
+Coquetdale,https://www.cheese.com/coquetdale/,cow,England,Coquet,NA,"semi-hard, soft-ripened",55%,NA,creamy,natural,pale yellow,fruity,rich,TRUE,FALSE,NA,NA,Northumberland Cheese Company
+Corleggy,https://www.cheese.com/corleggy/,goat,Ireland,County Cavan,NA,"hard, artisan",40%,NA,,natural,NA,"mild, nutty",NA,TRUE,FALSE,NA,NA,Corleggy Cheeses
+Cornish Blue,https://www.cheese.com/cornish-blue/,cow,England,Cornwall,Blue,"semi-soft, artisan",NA,NA,"buttery, dense",NA,NA,"creamy, mild, sweet","buttery, mild, sweet",TRUE,FALSE,NA,NA,Cornish Cheese Company Ltd
+Cornish Brie,https://www.cheese.com/cornish-brie/,cow,"England, Great Britain, United Kingdom",Cornwall,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, soft-ripened",bloomy,yellow,"creamy, mild",NA,NA,NA,NA,NA,"Cornish Cheese Company Ltd, Cornish Country Larder ltd."
+Cornish Crumbly,https://www.cheese.com/cornish-crumbly/,cow,England,North Cornwall,Cheddar,"semi-soft, artisan",NA,NA,chalky,mold ripened,ivory,creamy,rich,TRUE,FALSE,NA,NA,Whalesborough Farm Foods
+Cornish Kern,https://www.cheese.com/cornish-kern/,,,Cornwall,Cornish,NA,NA,NA,,washed,NA,NA,NA,TRUE,FALSE,NA,NA,Lynher Dairies
+Cornish Pepper,https://www.cheese.com/cornish-pepper/,cow,England,NA,Cornish,"soft, artisan",45%,NA,creamy,natural,white,smooth,rich,FALSE,FALSE,NA,NA,Lynher Valley Dairy
+Cornish Smuggler,https://www.cheese.com/cornish-smuggler/,cow,England,North Cornwall,Cheddar,"semi-soft, artisan",NA,NA,creamy,mold ripened,ivory,creamy,rich,TRUE,FALSE,NA,NA,Whalesborough Farm Foods
+Cornish Wild Garlic Yarg,https://www.cheese.com/cornish-wild-garlic-yarg/,cow,England,NA,NA,"semi-firm, artisan",NA,NA,crumbly,leaf wrapped,pale yellow,garlicky,"garlicky, herbal",TRUE,FALSE,Cornish Garlic Yarg,NA,Lynher Dairies
+Cornish Yarg,https://www.cheese.com/cornish-yarg/,cow,England,NA,Cornish,"hard, artisan",NA,NA,"creamy, crumbly",leaf wrapped,NA,mushroomy,fresh,TRUE,FALSE,NA,NA,Lynher Dairies
+corra linn,https://www.cheese.com/corra-linn/,sheep,Scotland,NA,NA,firm,NA,NA,crumbly,cloth wrapped,ivory,caramel,buttery,NA,NA,NA,NA,Errington Cheese Ltd.
+Cote Hill Blue,https://www.cheese.com/cote-hill-blue/,cow,"England, United Kingdom",Lincolnshire,Blue,"soft, artisan, blue-veined",NA,NA,creamy,natural,cream,"buttery, salty, sharp, smokey , smooth","smokey, strong",TRUE,FALSE,NA,NA,Cote Hill Farm
+Cote Hill Snowdrop,https://www.cheese.com/cote-hill-snowdrop/,cow,,NA,NA,"soft, artisan, soft-ripened",NA,NA,"creamy, smooth",NA,white,savory,NA,NA,NA,NA,NA,Cote Hill Farm
+Cotherstone,https://www.cheese.com/cotherstone/,cow,"England, United Kingdom",Cotherstone,NA,"semi-hard, artisan",45%,NA,"crumbly, open",waxed,pale yellow,"acidic, citrusy, tangy",fresh,NA,NA,NA,NA,NA
+Cotija Cheese,https://www.cheese.com/cotija/,cow,Mexico,NA,NA,"hard, artisan",NA,NA,"crumbly, dense",rindless,white,"salty, strong",NA,NA,NA,"Queso Cincho, Queso Seco",NA,NA
+Cotswold,https://www.cheese.com/cotswold/,cow,"England, United Kingdom",Gloucestershire County,NA,semi-firm,NA,NA,"creamy, smooth",natural,NA,"sweet, tangy",NA,FALSE,FALSE,"Double Gloucester with Chives, Double Gloucester with Onion and Chives, English Cotswold",NA,NA
+Cottage Cheese,https://www.cheese.com/cottage-cheese/,cow,"United Kingdom, United States",NA,Cottage,"soft, artisan, processed",NA,NA,"creamy, crumbly",rindless,white,sweet,NA,TRUE,FALSE,NA,NA,NA
+Cottage Cheese (Australian),https://www.cheese.com/cottage-cheese-australian/,cow,Australia,NA,Cottage,fresh soft,NA,NA,"crumbly, firm, grainy",rindless,white,"mild, sweet","lactic, milky",TRUE,FALSE,NA,NA,NA
+Cougar Gold,https://www.cheese.com/cougar-gold/,cow,United States,"Pullman, Washington",Cheddar,semi-soft,14%,NA,"creamy, crumbly, crystalline, firm, smooth",rindless,white,"nutty, sharp",NA,FALSE,FALSE,NA,NA,WSU Creamery
+Coulommiers,https://www.cheese.com/coulommiers/,cow,France,NA,Brie,"soft, artisan",NA,NA,creamy,bloomy,NA,"buttery, nutty",NA,NA,NA,NA,NA,NA
+Counting Sheep…and Goats...,https://www.cheese.com/counting-sheepand-goats/,"cow, goat, sheep","Canada, Italy",Lombardy,NA,"soft, soft-ripened",NA,NA,"creamy, soft",NA,white,"creamy, subtle, sweet",nutty,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Country Morning,https://www.cheese.com/country-morning/,cow,Canada,British Columbia,Cheddar,"hard, artisan",NA,NA,"creamy, crumbly",natural,pale yellow,"nutty, sharp",strong,FALSE,FALSE,NA,NA,The Farm House Natural Cheeses
+Coupole,https://www.cheese.com/coupole/,goat,United States,Vermont,NA,"soft, artisan",NA,NA,"buttery, creamy, dense, fluffy",mold ripened,pale yellow,"mild, milky","fresh, mild, milky",TRUE,FALSE,NA,NA,Vermont Creamery
+Couronne Lochoise,https://www.cheese.com/couronne-lochoise/,goat,France,Loire Valley,NA,"soft, artisan, soft-ripened",NA,NA,"buttery, creamy, smooth, soft-ripened",bloomy,ivory,"acidic, herbaceous, mild, salty","earthy, goaty, grassy, musty",FALSE,FALSE,La Couronne,NA,"Fromages de chèvre FREVAL, Various"
+Coverdale,https://www.cheese.com/coverdale/,cow,"England, United Kingdom",North Yorkshire,NA,"hard, artisan",NA,NA,"creamy, firm, open",natural,white,"buttery, lemony, mild, sharp",NA,TRUE,FALSE,NA,NA,Wensleydale Creamery
+Cow's Milk Gouda,https://www.cheese.com/cows-milk-gouda/,cow,United States,Maine,Gouda,"semi-hard, artisan",NA,NA,"compact, creamy, crumbly, dense",natural,NA,"caramel, nutty",NA,NA,NA,NA,NA,Fuzzy Udder Creamery
+Cracked Pepper Chevre,https://www.cheese.com/cracked-pepper-chevre/,goat,United States,Colorado,NA,"semi-soft, artisan",5 g/100g,NA,"creamy, crumbly, firm",rindless,white,"savory, spicy","clean, fresh",TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Cranborne,https://www.cheese.com/cranborne/,cow,"England, Great Britain, United Kingdom",Dorset,Camembert,"soft, artisan, soft-ripened",NA,NA,"buttery, creamy",bloomy,pale yellow,creamy,mushroom,FALSE,FALSE,NA,NA,Chalke Valley Cheese Ltd
+Cratloe Hills,https://www.cheese.com/cratloe-hills/,sheep,Ireland,"Brickhill, Co. Clare",NA,"hard, artisan",NA,NA,"firm, grainy, open",plastic,pale yellow,"mild, nutty, strong, sweet",NA,TRUE,FALSE,NA,NA,Cratloe Hills
+Cravero Parmigiano Reggiano,https://www.cheese.com/cravero-parmigiano-reggiano/,cow,Italy,Modena,Parmesan,"hard, artisan",NA,NA,dense,natural,golden yellow,"bitter, creamy, nutty, savory, smooth, sweet","aromatic, fruity",FALSE,FALSE,"San Pietro, Baruffi",NA,G. Cravero Sas
+Crayeux de Roncq,https://www.cheese.com/crayeux-de-roncq/,cow,France,Roncq,NA,"soft, artisan, soft-ripened",45%,NA,"creamy, grainy",washed,orange,"full-flavored, strong, sweet",NA,FALSE,FALSE,Carré du Vinage,NA,NA
+Cream Cheese,https://www.cheese.com/cream-cheese/,cow,United States,NA,NA,"fresh soft, processed",NA,NA,"creamy, spreadable",rindless,white,"creamy, mild, sweet","fresh, pleasant",TRUE,FALSE,fruit cream,Fruit cream cheese,NA
+Cream Cheesy Bliss,https://www.cheese.com/cream-cheesy-bliss/,,"Canada, United States",NA,NA,soft,NA,NA,"creamy, spreadable",artificial,white,"creamy, garlicky, herbaceous, sweet",rich,TRUE,FALSE,"Dairy Free Cream Cheese, Dairy Free Classic Plain Cream Cheese, Dairy Free Chive & Garlic Cream Cheese, Dairy Free Strawberry Cream Cheese",NA,GO Veggie!
+Cream Havarti,https://www.cheese.com/cream-havarti/,cow,Denmark,NA,Havarti,"semi-soft, processed",45%,NA,smooth,rindless,pale yellow,"buttery, creamy, sweet",sweet,NA,NA,"Havarathi, Flødeis Havarti, Smoked Havarti",Flodeis Havarti,NA
+Creamy Gouda,https://www.cheese.com/creamy-gouda/,cow,Netherlands,NA,NA,semi-soft,NA,NA,creamy,natural,cream,creamy,aromatic,NA,NA,Extra Belegen Gouda,NA,dutchcheeseman uk
+Creamy Lancashire,https://www.cheese.com/creamy-lancashire/,cow,England,NA,NA,NA,NA,NA,fluffy,NA,pale yellow,creamy,NA,NA,NA,NA,NA,NA
+Crema de Blue,https://www.cheese.com/crema-de-blue/,"cow, sheep",United States,New Jersey,Blue,"semi-soft, artisan, blue-veined",NA,NA,creamy,natural,cream,"full-flavored, spicy",NA,FALSE,FALSE,NA,NA,Valley Shepherd Creamery
+Crema Mexicana,https://www.cheese.com/crema-mexicana/,cow,Mexico and Caribbean,NA,NA,soft,NA,NA,"creamy, smooth, spreadable",rindless,white,"buttery, sour, tangy",rich,TRUE,FALSE,NA,NA,Cacique Inc.
+Crema Mexicana Agria,https://www.cheese.com/crema-agria/,cow,Mexico and Caribbean,NA,NA,fresh soft,NA,NA,"creamy, spreadable",rindless,white,"acidic, sour, tangy","aromatic, fresh",FALSE,FALSE,"Crema Centroamericana, Crema Agria",NA,Cacique Inc.
+Cremet,https://www.cheese.com/cremet/,"cow, goat","England, Great Britain, United Kingdom",Devon,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, smooth, soft, soft-ripened",bloomy,white,"creamy, lemony","fresh, rich",TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+"Cremig Extra Würzig, Bergkäse Aus Dem Schweizer Jura",https://www.cheese.com/cremig-extra-wurzig-bergkase-aus-dem-schweizer-jura/,cow,Switzerland,Jura,Swiss Cheese,"hard, artisan",51%,NA,creamy,natural,cream,"creamy, spicy",NA,NA,NA,extra-creamy spicy mountain cheese from the Swiss Jura,NA,Walo von Mühlenen AG
+Cremont,https://www.cheese.com/cremont/,"cow, goat",United States,Vermont,NA,"soft, artisan",14%,NA,"creamy, smooth",mold ripened,cream,"creamy, nutty, smooth, yeasty","nutty, yeasty",NA,NA,NA,NA,Vermont Creamery
+CreNoble,https://www.cheese.com/crenoble/,,Germany,Landshut,NA,semi-soft,NA,NA,"creamy, open, smooth",NA,pale yellow,"creamy, savory",aromatic,NA,NA,NA,NA,Bayerische Milchindustrie eG
+Crescenza di Bufala,https://www.cheese.com/crescenza-di-bufala/,water buffalo,Italy,Lombardy,NA,"fresh soft, artisan",NA,NA,"buttery, creamy, spreadable",rindless,white,"buttery, creamy, mild, sweet","fresh, pleasant, rich, sweet",TRUE,FALSE,NA,NA,Azienda Agricola Gritti Bruno E Alfio S.s. Societa Agricola
+Crescenza-Stracchino,https://www.cheese.com/crescenza/,cow,Italy,NA,Italian Cheese,fresh soft,NA,NA,"buttery, creamy, spreadable",rindless,white,"creamy, mild, sweet","pleasant, rich",NA,NA,Stracchino,NA,NA
+Cressy Blu,https://www.cheese.com/cressy-blu/,cow,"Canada, Italy",Lombardy,Blue,"semi-hard, blue-veined",NA,NA,"creamy, crumbly, firm",natural,pale yellow,"creamy, savory, sweet",NA,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Criffel,https://www.cheese.com/criffel/,cow,"Scotland, United Kingdom",Dumfriesshire,NA,"semi-hard, artisan",NA,NA,"creamy, smooth",washed,straw,smooth,"pungent, strong",TRUE,FALSE,NA,NA,Loch Arthur Creamery
+Criollo Cheese,https://www.cheese.com/criollo-cheese/,cow,Mexico,Taxco,NA,semi-firm,NA,NA,smooth,NA,pale yellow,"savory, sharp",strong,FALSE,FALSE,Criolla,NA,NA
+Crocodile Tear,https://www.cheese.com/crocodile-tear/,goat,United States,Indiana,Brie,"soft, semi-soft, artisan, soft-ripened",NA,NA,"creamy, firm",bloomy,white,"creamy, spicy",pungent,NA,NA,NA,NA,Capriole Goat Cheese
+Croghan,https://www.cheese.com/croghan/,goat,Ireland,County Wexford,NA,"semi-soft, artisan",NA,NA,supple,washed,white,full-flavored,"earthy, grassy",TRUE,FALSE,NA,NA,Croghan Goat Farm
+Crotonese,https://www.cheese.com/crotonese/,sheep,Italy,Crotone,NA,"semi-hard, artisan",NA,NA,compact,natural,pale yellow,"salty, savory, strong",spicy,FALSE,FALSE,Pecorino Crotonese,NA,NA
+Crottin de Champcol,https://www.cheese.com/crottin-de-champcol/,goat,France,NA,NA,soft,NA,NA,"crumbly, dense",NA,white,full-flavored,strong,NA,NA,NA,NA,NA
+Crottin de Chavignol,https://www.cheese.com/crottin-de-chavignol/,goat,France,NA,NA,"firm, soft-ripened",NA,NA,crumbly,natural,white,full-flavored,strong,NA,NA,"Crottin Du Chavignol, Crottin",NA,NA
+Crowdie,https://www.cheese.com/crowdie/,cow,Scotland,NA,Cottage,fresh soft,NA,NA,"creamy, crumbly",natural,white,sour,NA,TRUE,FALSE,NA,NA,Highland Fine Cheeses Limited
+Crowley,https://www.cheese.com/crowley/,cow,United States,Vermont,Cheddar,"semi-soft, hard, artisan",NA,NA,"creamy, crumbly, firm, smooth",cloth wrapped,pale yellow,"buttery, full-flavored, mild, savory, smokey , spicy, sweet, tangy","aromatic, fresh, mild, pleasant, smokey",FALSE,FALSE,NA,NA,Crowley Cheese
+Crozier,https://www.cheese.com/crozier/,sheep,Ireland,"Fethard, Co Tipperary",Blue,"semi-soft, artisan, blue-veined",NA,NA,"buttery, chalky, creamy, crumbly",natural,pale yellow,"acidic, creamy, mild","rich, strong",TRUE,FALSE,NA,Croizer Blue,J&L Grubb Ltd.
+Crucolo,https://www.cheese.com/crucolo/,cow,Italy,NA,NA,"semi-hard, artisan",NA,NA,buttery,NA,ivory,"savory, sweet","mild, rich",NA,NA,NA,NA,Rifugio Crucolo
+Crumbly Lancashire,https://www.cheese.com/crumbly-lancashire/,cow,England,NA,NA,hard,NA,NA,crumbly,NA,pale yellow,acidic,NA,NA,NA,NA,NA,NA
+CréMonté,https://www.cheese.com/cremonte/,cow,Germany,Landshut,Blue,"soft, blue-veined",60%,NA,"creamy, smooth",mold ripened,ivory,"creamy, mild",pleasant,NA,NA,NA,NA,Bayerische Milchindustrie eG
+Cuajada,https://www.cheese.com/cuajada/,"cow, sheep",Spain,NA,NA,soft,NA,NA,"creamy, smooth",NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Cubetto,https://www.cheese.com/cubetto/,"buffalo, cow",Italy,Campania,NA,"semi-soft, artisan",NA,NA,"compact, smooth",NA,ivory,"smooth, sweet","fresh, fruity",NA,NA,Cubetti,NA,Casa Madaio
+Cuor di burrata,https://www.cheese.com/cuor-di-burrata/,,Italy,Campania,NA,soft,NA,NA,"creamy, smooth",NA,white,"buttery, milky, sweet","fresh, herbal, nutty",FALSE,FALSE,NA,NA,Casa Madaio
+Cup Cheese,https://www.cheese.com/cup-cheese/,cow,United States,Pennsylvania,NA,"soft, artisan",NA,NA,"creamy, spreadable",NA,NA,"sharp, sour, strong",strong,NA,NA,NA,NA,NA
+Cure Nantais,https://www.cheese.com/cure-nantais/,cow,France,Anjou,NA,"soft, artisan",40%,NA,"open, sticky, supple",NA,straw,"smokey , spicy",NA,FALSE,FALSE,Nantais,NA,Various
+Curworthy,https://www.cheese.com/curworthy/,cow,England,Devon,NA,semi-hard,48%,NA,creamy,natural,cream,buttery,fresh,TRUE,FALSE,NA,NA,Rachel Stephens
+Cwmtawe Pecorino,https://www.cheese.com/cwmtawe-pecorino/,sheep,Italy,Oristano,Pecorino,hard,NA,NA,creamy,washed,orange,NA,aromatic,TRUE,FALSE,NA,NA,Irranca Giovanni Antonio
+Cypress Grove Chevre,https://www.cheese.com/cypress-grove-chevre/,goat,United States,California,NA,NA,NA,NA,,NA,NA,NA,NA,NA,NA,NA,NA,Cypress Grove Chevre
+L'Affine Au Chablis,https://www.cheese.com/laffine-au-chablis/,cow,France,NA,NA,"soft, semi-soft",NA,NA,"creamy, soft",washed,pale yellow,NA,"floral, fruity",NA,NA,NA,NA,NA
+L'Amuse Brabander Goat Gouda,https://www.cheese.com/lamuse-brabander-goat-gouda/,cow,Netherlands,Northern Holland,Gouda,hard,NA,NA,smooth,waxed,brownish yellow,"caramel, salty","buttery, nutty",NA,NA,NA,NA,Essex St. Cheese Co.
+L'Amuse Signature Gouda,https://www.cheese.com/lamuse-signature-gouda/,cow,Netherlands,NA,NA,"hard, artisan",NA,NA,"crumbly, crystalline, smooth",waxed,orange,"burnt caramel, caramel, full-flavored, salty",nutty,NA,NA,NA,NA,Essex St. Cheese Co.
+L'Aveyronnais,https://www.cheese.com/laveyronnais/,cow,France,massif des Causses,NA,soft,NA,NA,,leaf wrapped,white,NA,grassy,FALSE,FALSE,NA,NA,NA
+L'Ecir de l'Aubrac,https://www.cheese.com/lecir-de-laubrac/,cow,France,Auvergne,NA,"soft, artisan",NA,NA,smooth,natural,white,sweet,pleasant,FALSE,FALSE,NA,NA,NA
+L'Empereur,https://www.cheese.com/lempereur/,,Canada,Quebec,NA,"soft, artisan",15%,NA,"creamy, supple",washed,cream,"buttery, salty","fruity, milky, nutty",NA,NA,"Empereur allégé, L’Empereur Léger",NA,Fromagerie Fritz Kaiser
+L'Étivaz,https://www.cheese.com/letivaz/,cow,Switzerland,NA,NA,hard,NA,NA,buttery,NA,pale yellow,NA,smokey,NA,NA,"L’Etivaz AOP, LEtivaz",NA,NA
+L'Étoile de St-Raymond,https://www.cheese.com/letoile-de-st-raymond/,,Canada,Quebec,NA,soft,35%,NA,"buttery, creamy",ash coated,ivory,"buttery, grassy, milky",mushroom,NA,NA,NA,L’Étoile De Saint-Raymond,La Maison Alexis de Portneuf Inc.
+La Bonaparte,https://www.cheese.com/la-bonaparte/,cow,Canada,Quebec,Brie,"soft, soft-ripened",28%,NA,"creamy, soft, soft-ripened, supple",bloomy,cream,"creamy, mild, milky","fresh, mild",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+La Casatella,https://www.cheese.com/la-casatella/,cow,Italy,Veneto,NA,"fresh soft, artisan",NA,NA,"compact, soft",natural,white,"subtle, sweet",lactic,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+La Couronne - Fort Aged Comté,https://www.cheese.com/la-couronne-fort-aged-comte/,cow,"France, Switzerland",Franche Comté,NA,semi-hard,NA,NA,dense,NA,pale yellow,"caramel, nutty, sweet",rich,NA,NA,NA,NA,Cheese Slices
+La Fleurie,https://www.cheese.com/la-fleurie/,cow,United States,Vermont,NA,"soft, artisan, soft-ripened",NA,NA,"buttery, runny, smooth",mold ripened,ivory,"buttery, earthy, mushroomy, nutty","earthy, mild",TRUE,FALSE,NA,NA,Willow Hill Farm
+La Peral,https://www.cheese.com/la-peral/,cow,Spain,NA,Blue,"semi-soft, blue-veined",NA,NA,"creamy, firm, grainy",NA,pale yellow,"spicy, sweet","buttery, rich",FALSE,FALSE,La Peral Blue,NA,NA
+La Pyramide,https://www.cheese.com/la-pyramide/,goat,Canada,British Columbia,NA,"semi-soft, artisan",NA,NA,dense,ash coated,ivory,"salty, tangy",mild,FALSE,FALSE,NA,NA,The Farm House Natural Cheeses
+La Retorta,https://www.cheese.com/la-retorta/,sheep,Spain,NA,NA,"soft, semi-soft",NA,NA,"creamy, runny",NA,pale yellow,creamy,NA,NA,NA,NA,NA,NA
+La Rumeur,https://www.cheese.com/la-rumeur/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"buttery, creamy, runny, supple",bloomy,cream,"creamy, mild","mild, milky",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+La Sauvagine,https://www.cheese.com/la-sauvagine/,cow,Canada,Quebec,NA,soft,32%,NA,"buttery, creamy, runny, supple",washed,ivory,"buttery, creamy",mushroom,TRUE,FALSE,NA,NA,La Maison Alexis de Portneuf Inc.
+La Sauvagine Réserve,https://www.cheese.com/la-sauvagine-reserve/,cow,Canada,Quebec,NA,soft,38%,NA,"buttery, creamy, runny, supple",washed,ivory,"buttery, creamy, mild, milky",mushroom,TRUE,FALSE,NA,NA,La Maison Alexis de Portneuf Inc.
+La Serena,https://www.cheese.com/la-serena/,sheep,Spain,Extremadura,NA,soft,50%,NA,"creamy, spreadable",washed,straw,"bitter, full-flavored, sharp, strong","pleasant, strong",TRUE,FALSE,NA,"Torta La Serena, Queso de la Serena, Torta de la Serena",NA
+La Taupiniere,https://www.cheese.com/la-taupiniere/,goat,France,Poitou-Charentes,NA,"soft, artisan",45%,NA,"creamy, smooth",natural,white,"sour, tangy","aromatic, nutty",FALSE,FALSE,Taupinette,Taupiniere,Fromagerie Jousseaume
+La Tur,https://www.cheese.com/la-tur/,"cow, goat, sheep",Italy,Alba,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, fluffy, soft-ripened",mold ripened,straw,"acidic, buttery, creamy, grassy, mushroomy","earthy, lactic, yeasty",FALSE,FALSE,NA,NA,Caseificio dell'Alta Langa
+La Vache Qui Rit cheese,https://www.cheese.com/la-vache-qui-rit/,cow,France,NA,Swiss Cheese,"semi-soft, processed",NA,NA,"creamy, smooth, spreadable",rindless,pale yellow,"buttery, mild",fresh,NA,NA,Laughing Cow Cheese,NA,BEL Group
+Labneh,https://www.cheese.com/labneh/,cow,Middle East,NA,NA,soft,NA,NA,creamy,rindless,white,"creamy, milky, sour","fresh, mild",NA,NA,"labaneh, chakka, lebnah, suzma",labne,NA
+Lacey Grey,https://www.cheese.com/lacey-grey/,goat,Canada,"Prince Edward County, Ontario",NA,"soft, artisan, soft-ripened",NA,NA,"creamy, soft",bloomy,white,"acidic, bitter, nutty","clean, herbal",NA,NA,NA,NA,Fifth Town Artisan Cheese
+LaClare Farms Chandoka,https://www.cheese.com/laclare-farms-chandoka/,"cow, goat",United States,NA,Cheddar,"semi-firm, artisan",NA,NA,creamy,NA,ivory,"fruity, tangy",fruity,TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Farms Cheddar,https://www.cheese.com/laclare-farms-cheddar/,goat,United States,Wisconsin,Cheddar,"semi-soft, artisan",NA,NA,creamy,NA,ivory,"mild, sweet, tangy","fruity, sweet",TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Farms Chevre,https://www.cheese.com/laclare-farms-chevre/,goat,United States,Wisconsin,Cottage,fresh soft,NA,NA,creamy,rindless,white,"mild, sweet",fresh,TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Farms Evalon,https://www.cheese.com/laclare-farms-evalon/,goat,United States,Wisconsin,Gouda,"semi-hard, artisan",NA,NA,creamy,natural,ivory,mild,goaty,TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Farms Evalon with Cummin,https://www.cheese.com/laclare-farms-evalon-cummin/,goat,United States,Wisconsin,Gouda,"semi-hard, artisan",NA,NA,creamy,natural,ivory,savory,goaty,TRUE,FALSE,Cummin Evalon,NA,LaClare Farms
+LaClare Farms Evalon with Fenugreek,https://www.cheese.com/laclare-farms-evalon-fenugreek/,goat,United States,Wisconsin,Gouda,"semi-hard, artisan",NA,NA,creamy,natural,pale yellow,mild,goaty,TRUE,FALSE,Fenugreek Evalon,NA,LaClare Farms
+LaClare Farms Fondry Jack,https://www.cheese.com/laclare-farms-fondry-jack/,goat,United States,Wisconsin,Monterey Jack,"fresh soft, artisan",NA,NA,creamy,natural,ivory,mild,fresh,NA,NA,NA,"Pepper Jack Fondy Jack, Tomato Basil Fondy Jack",LaClare Farms
+LaClare Farms Martone,https://www.cheese.com/laclare-farms-martone/,goat,United States,Wisconsin,Cottage,semi-soft,NA,NA,creamy,ash coated,white,"sweet, tangy","buttery, fresh",TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Farms Raw Goats Milk Cheddar,https://www.cheese.com/laclare-farms-raw-goats-milk-cheddar/,goat,United States,Wisconsin,Cheddar,"semi-soft, artisan",NA,NA,creamy,NA,ivory,"mild, sweet, tangy","fruity, sweet",TRUE,FALSE,NA,NA,LaClare Farms
+LaClare Ziege Zacke Blue,https://www.cheese.com/laclare-ziege-zacke-blue/,"cow, goat",United States,Wisconsin,Blue,"semi-hard, blue-veined",NA,NA,"creamy, firm",natural,cream,"creamy, spicy, sweet, tangy","earthy, rich",FALSE,FALSE,Ziege Zacke Blue,NA,LaClare Farms
+Lacy Swiss,https://www.cheese.com/lacy-swiss/,cow,United States,NA,Swiss Cheese,"semi-firm, processed",NA,NA,"creamy, open, supple",NA,ivory,"mild, nutty",mild,NA,NA,NA,Lacey Swiss,NA
+Lady Jane,https://www.cheese.com/lady-jane/,cow,Canada,British Columbia,Brie,"semi-soft, artisan, soft-ripened",NA,NA,"creamy, dense, soft-ripened",mold ripened,pale yellow,tangy,"earthy, mushroom",FALSE,FALSE,NA,NA,The Farm House Natural Cheeses
+Laganory,https://www.cheese.com/laganory/,cow,Scotland,NA,NA,firm,NA,NA,"brittle, compact, dry, grainy",natural,pale yellow,"nutty, salty, subtle","earthy, grassy, raw nut, subtle",TRUE,FALSE,NA,NA,The Ethical Dairy
+Laguiole,https://www.cheese.com/laguiole/,cow,France,"Aveyron, Laguiole",NA,"semi-hard, artisan",NA,NA,"creamy, firm, supple",natural,straw,"sharp, sour, tangy",aromatic,FALSE,FALSE,NA,Tome de Laguiole,NA
+Lairobell,https://www.cheese.com/lairobell/,goat,"Scotland, United Kingdom",Orkney Isles,NA,"hard, artisan",NA,NA,"crumbly, open",natural,pale yellow,"herbaceous, sweet",goaty,TRUE,FALSE,NA,NA,NA
+Lajta,https://www.cheese.com/lajta/,cow,Hungary,NA,NA,soft,50%,NA,"creamy, open",washed,yellow,piquant,"aromatic, stinky, strong",FALSE,FALSE,NA,NA,NA
+Lake District Extra Mature Cheddar,https://www.cheese.com/lake-district-extra-mature-cheddar/,cow,United Kingdom,Cumbrian,Cheddar,"semi-soft, artisan",NA,NA,creamy,NA,pale yellow,sweet,rich,TRUE,FALSE,NA,NA,The Lake District Cheese Company
+Lake District Mature Cheddar,https://www.cheese.com/lake-district-mature-cheddar/,cow,United Kingdom,Cumbrian,Cheddar,"soft, artisan",NA,NA,"crumbly, dense",NA,pale yellow,"savory, sweet",fresh,TRUE,FALSE,NA,NA,The Lake District Cheese Company
+Lamb Chopper,https://www.cheese.com/lamb-chopper/,sheep,"Netherlands, United States",California,Gouda,hard,NA,NA,"dense, firm, smooth",waxed,ivory,"buttery, caramel, creamy, nutty, sweet","mild, sweet",TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Lanark Blue,https://www.cheese.com/lanark-blue/,sheep,Scotland,NA,NA,"semi-soft, artisan, blue-veined",NA,NA,"brittle, grainy",NA,NA,NA,NA,TRUE,FALSE,NA,NA,Errington Cheese Ltd.
+Lanark White,https://www.cheese.com/lanark-white/,sheep,Scotland,NA,NA,hard,NA,NA,"buttery, crumbly, flaky",natural,pale white,"grassy, milky, nutty, salty","grassy, milky, musty",TRUE,FALSE,NA,NA,Errington Cheese Ltd.
+Lancashire Smoked,https://www.cheese.com/lancashire-smoked/,cow,England,NA,NA,hard,NA,NA,"buttery, crumbly",natural,red,"acidic, nutty, smokey , tangy, woody","buttery, fresh, grassy, smokey",FALSE,FALSE,NA,NA,Mrs. Kirkham's
+Landaff,https://www.cheese.com/landaff/,cow,United States,New Hampshire,NA,"semi-hard, artisan",NA,NA,"creamy, firm, open",natural,straw,"mild, tangy",aromatic,NA,NA,NA,NA,"Jasper Hill Farm, Landaff Creamery, LLC"
+Langres,https://www.cheese.com/langres/,cow,France,NA,NA,semi-soft,NA,NA,"crumbly, firm",washed,orange,NA,strong,NA,NA,NA,NA,NA
+Lappi,https://www.cheese.com/lappi/,cow,Finland,Lapland,Swiss Cheese,semi-soft,NA,NA,"creamy, firm, open, smooth",rindless,yellow,"mild, sweet",NA,FALSE,FALSE,NA,NA,NA
+Largo,https://www.cheese.com/largo/,cow,United States,California,NA,"soft, artisan",NA,NA,"creamy, smooth",bloomy,pale yellow,"mushroomy, nutty, sweet","pleasant, sweet",NA,NA,NA,NA,Andante Dairy
+Laruns,https://www.cheese.com/laruns/,sheep,France,Laruns,NA,"soft, artisan",NA,NA,"brittle, firm, supple",natural,straw,"acidic, mild, nutty, salty, sweet",mild,FALSE,FALSE,NA,NA,NA
+Latteria Navel,https://www.cheese.com/latteria-navel/,cow,Italy,Friuli-Venezia Giulia and the Veneto,NA,"hard, soft-ripened",NA,NA,brittle,natural,straw,full-flavored,aromatic,NA,NA,NA,NA,NA
+Latteria San Biagio,https://www.cheese.com/latteria-san-biagio/,cow,Italy,Veneto,NA,"soft, artisan",NA,NA,creamy,natural,white,"acidic, sweet",subtle,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Laura Chenel Taupiniere,https://www.cheese.com/laura-chenel-taupiniere/,goat,United States,"Carneros, Sonoma, California",NA,"soft, artisan",NA,NA,"creamy, soft",ash coated,white,"creamy, tart","aromatic, fresh",NA,NA,NA,NA,Laura Chenel's Chevre
+Laura Chenel Tome,https://www.cheese.com/laura-chenel-tome/,goat,United States,"Sonoma, California",Cheddar,"firm, artisan",NA,NA,"crumbly, dense, firm",cloth wrapped,ivory,"caramel, savory","rich, sweet",TRUE,FALSE,NA,NA,Laura Chenel's Chevre
+Laura Chenel's Cabecou,https://www.cheese.com/laura-chenels-cabecou/,goat,United States,California,NA,fresh firm,NA,NA,"creamy, dense, smooth",rindless,cream,"creamy, mild, nutty","mild, nutty, rich",NA,NA,NA,NA,Laura Chenel's Chevre
+Lavistown,https://www.cheese.com/lavistown/,cow,Ireland,Stoneyford,NA,"semi-hard, artisan",NA,NA,"crumbly, dry, firm",natural,yellow,"creamy, mild, milky, tangy, vegetal",NA,TRUE,FALSE,NA,NA,Knockdrinna Farmhouse Cheese
+Le Brebiou,https://www.cheese.com/le-brebiou/,sheep,France,Pyrénées-Atlantiques,Brie,"semi-soft, artisan, soft-ripened",26%,NA,"creamy, firm, smooth",bloomy,white,"mild, milky, sweet",musty,FALSE,FALSE,Brebiou sheep cheese,Brebiou ligne et plaisir,NA
+Le Brin,https://www.cheese.com/le-brin/,cow,France,Rhone-Alps,NA,"semi-soft, artisan",40%,NA,"creamy, spreadable",washed,white,"buttery, sweet","aromatic, nutty, yeasty",TRUE,FALSE,NA,NA,Fromagerie GUILLOTEAU
+Le Cendrillon,https://www.cheese.com/le-cendrillon/,goat,Canada,Quebec,NA,soft,25%,NA,"creamy, smooth",ash coated,ivory,"acidic, pronounced, sour",goaty,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Le Chevrot,https://www.cheese.com/le-chevrot/,goat,France,Loire Valley,NA,soft,50%,NA,"creamy, dense, firm",mold ripened,cream,"herbaceous, nutty, strong","goaty, strong",FALSE,FALSE,NA,NA,SEVRE & BELLE
+Le Conquerant Camembert,https://www.cheese.com/le-conquerant-camembert/,cow,France,"Pays d’Auge, Normandy",Camembert,"soft, artisan",NA,NA,soft-ripened,waxed,white,"fruity, yeasty",rich,FALSE,FALSE,NA,NA,NA
+Le Conquerant Demi Pont L'eveque,https://www.cheese.com/le-conquerant-demi-pont-leveque/,cow,"Australia, France",NA,NA,"soft, artisan",NA,NA,"chalky, creamy, soft",washed,NA,"creamy, mild",pungent,NA,NA,NA,NA,"Fromagerie Graindorge, Will Studd Enterprizes Pty Ltd"
+Le Double Joie,https://www.cheese.com/le-double-joie/,"cow, goat",Canada,Quebec,Brie,"soft, soft-ripened",27%,NA,"buttery, creamy, runny",bloomy,cream,"buttery, creamy",mushroom,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Le Duc Vacherin,https://www.cheese.com/le-duc-vacherin/,cow,France,Franche Comté,NA,"soft, soft-ripened",NA,NA,"smooth, soft",washed,ivory,"buttery, creamy, lemony, mild","rich, woody",NA,NA,NA,NA,Fromagerie Jean Perrin
+Le Fium Orbo,https://www.cheese.com/le-fium-orbo/,"goat, sheep",France,NA,NA,soft,50%,NA,soft-ripened,natural,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Le Gruyère AOP,https://www.cheese.com/le-gruyere-aop/,cow,Switzerland,NA,NA,hard,49-53%,NA,compact,NA,NA,sweet,earthy,FALSE,FALSE,NA,NA,NA
+Le Lacandou,https://www.cheese.com/le-lacandou/,sheep,France,Aveyron,NA,"soft, artisan",45%,NA,creamy,natural,pale yellow,fruity,grassy,FALSE,FALSE,NA,NA,NA
+Le Marquis Chevre,https://www.cheese.com/le-marquis-chevre/,goat,France,Rhone Valley,Cheddar,"soft, artisan",NA,NA,soft,natural,ivory,"lemony, tangy",mild,NA,NA,NA,NA,Cheese Slices
+Le Maréchal,https://www.cheese.com/le-marechal/,cow,Switzerland,NA,NA,hard,NA,NA,"firm, smooth",natural,yellow,"buttery, creamy, floral, herbaceous","buttery, floral, grassy, herbal",FALSE,FALSE,Marechal,NA,Jean-Michel Rapin
+Le Moutier,https://www.cheese.com/le-moutier/,goat,Canada,Québec,NA,fresh firm,NA,NA,firm,natural,white,sweet,NA,NA,NA,NA,NA,Fromagerie de l'Abbaye Saint-Benoît
+Le Reflet de Portneuf,https://www.cheese.com/le-reflet-de-portneuf/,,Canada,Quebec,NA,soft,30%,NA,"buttery, creamy",washed,cream,"buttery, creamy, mushroomy",NA,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Le St-Raymond,https://www.cheese.com/le-st-raymond/,,Canada,Quebec,NA,soft,20%,NA,"smooth, supple",washed,ivory,"fruity, nutty, woody",NA,NA,NA,NA,Le Saint-Raymond,La Maison Alexis de Portneuf Inc.
+Le Wavreumont,https://www.cheese.com/le-wavreumont/,cow,Belgium,Wallonia,NA,"semi-soft, artisan",NA,NA,"buttery, creamy, semi firm, smooth, soft",washed,pale yellow,"buttery, creamy, nutty, smooth, strong","buttery, fresh, grassy",NA,NA,NA,NA,Fromagerie des Ardennes sprl
+Leafield,https://www.cheese.com/leafield/,sheep,Great Britain,Oxfordshire,Brie,"hard, artisan",48%,NA,chewy,natural,pale yellow,fruity,fruity,TRUE,FALSE,NA,NA,NA
+Lebbene,https://www.cheese.com/lebbene/,"goat, sheep",Israel,NA,NA,soft,45%,NA,,natural,white,mild,mild,NA,NA,Gibne,"Lebney, Labaneh",NA
+Leerdammer,https://www.cheese.com/leerdammer/,cow,Netherlands,"Schoonrewoerd, Leerdam",Gouda,semi-hard,NA,NA,"creamy, open, smooth",natural,white,"mild, nutty, sweet",NA,TRUE,FALSE,NA,NA,NA
+Legacy,https://www.cheese.com/legacy/,cow,United States,Missouri,Gouda,"semi-hard, artisan",NA,NA,creamy,rindless,pale yellow,creamy,aromatic,TRUE,FALSE,NA,NA,Heartland Creamery
+Legato,https://www.cheese.com/legato/,cow,United States,California,Camembert,"soft, soft-ripened",NA,NA,"creamy, soft-ripened",mold ripened,pale yellow,full-flavored,strong,TRUE,FALSE,NA,NA,Andante Dairy
+Lemon Fetish,https://www.cheese.com/lemon-fetish/,sheep,Canada,"Prince Edward County, Ontario",Feta,"fresh firm, artisan",NA,NA,"crumbly, dry",natural,white,"citrusy, lemony, salty, tangy","clean, lactic",NA,NA,NA,NA,Fifth Town Artisan Cheese
+Lemon Myrtle Chevre,https://www.cheese.com/lemon-myrtle-chevre/,goat,Australia,South Australia,NA,"fresh firm, artisan",45%,NA,"creamy, firm, smooth",rindless,white,"acidic, herbaceous","fresh, herbal",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Leonora®,https://www.cheese.com/leonora/,goat,Spain,Castilla Leon,NA,"semi-soft, artisan",NA,NA,soft-ripened,bloomy,white,"lemony, mushroomy",lactic,FALSE,FALSE,NA,NA,NA
+Les Calendos,https://www.cheese.com/les-calendos/,cow,Canada,Quebec,Camembert,"soft, soft-ripened",NA,NA,"creamy, smooth, supple",bloomy,cream,"creamy, full-flavored",NA,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Leyden,https://www.cheese.com/leyden/,cow,Netherlands,Leiden,Gouda,"semi-hard, artisan",30-40%,NA,firm,NA,yellow,"spicy, tangy",aromatic,FALSE,FALSE,"Leidse Kaas, Cumin cheese, Boeren-Leidse, Boeren-Leidse kaas, Farmers Leiden",NA,NA
+Liburnski Sir,https://www.cheese.com/liburnski-sir/,"cow, goat, sheep",Croatia,old Liburnia (Dalmatia),NA,"hard, artisan",NA,NA,"creamy, open",natural,golden yellow,"creamy, earthy, nutty, tart","buttery, clean, goaty, mild",TRUE,FALSE,NA,NA,SIRANA GLIGORA D.O.O
+Lil Moo,https://www.cheese.com/lil-moo/,cow,United States,Georgia,NA,soft,NA,NA,"creamy, dense, spreadable",rindless,pale yellow,"creamy, smooth, sweet","fresh, grassy",NA,NA,NA,NA,Sweet Grass Dairy
+Liliputas,https://www.cheese.com/liliputas/,cow,Lithuania,Belvederis,NA,semi-hard,50%,NA,"elastic, firm",waxed,yellow,milky,"fresh, lactic",NA,NA,NA,NA,NA
+Limburger,https://www.cheese.com/limburger/,cow,"Belgium, Germany, Netherlands",Duchy of Limburg,NA,"semi-soft, smear-ripened",42%,497 mg/100g,"crumbly, firm, smooth",washed,straw,"grassy, mild, mushroomy",stinky,FALSE,FALSE,NA,Limburger Kase,NA
+Lincoln Log,https://www.cheese.com/lincoln-log/,goat,United States,"Ann Arbor, MI",NA,"semi-soft, soft-ripened",NA,NA,"creamy, dense",mold ripened,white,"citrusy, lemony, mild, mushroomy, tangy","goaty, milky",FALSE,FALSE,NA,NA,Zingerman's Creamery
+Lincolnshire Poacher,https://www.cheese.com/lincolnshire-poacher/,cow,England,NA,Cheddar,hard,NA,NA,creamy,NA,yellow,"nutty, sweet",earthy,NA,NA,NA,NA,F.W.Read & Sons Ltd
+Lindale,https://www.cheese.com/lindale/,cow,United States,North Carolina,Gouda,"semi-soft, artisan",NA,NA,"creamy, smooth",natural,pale yellow,"buttery, sweet",rich,FALSE,FALSE,NA,NA,Goat Lady Dairy
+Lindy Hop,https://www.cheese.com/lindy-hop/,cow,United States,Vermont,Blue,"semi-soft, blue-veined",NA,NA,creamy,natural,straw,"creamy, grassy, mild","rich, strong",FALSE,FALSE,NA,NA,Dancing Cow Farm
+Lingot des Causses,https://www.cheese.com/lingot-des-causses/,goat,France,NA,NA,soft,NA,NA,"creamy, firm",NA,NA,NA,"buttery, clean, fresh",NA,NA,NA,NA,NA
+Lingot Saint Bousquet d'Orb,https://www.cheese.com/lingot-saint-bousquet-dorb/,goat,France,Herault,Brie,"soft, artisan",NA,NA,smooth,natural,white,sweet,herbal,FALSE,FALSE,NA,NA,NA
+Liptauer,https://www.cheese.com/liptauer/,cow,United States,"Ann Arbor, Michigan",NA,"fresh soft, artisan",NA,NA,,rindless,brown,"garlicky, savory, spicy, sweet","garlicky, spicy",TRUE,FALSE,NA,NA,Zingerman's Creamery
+Little Bloom on the Prairie,https://www.cheese.com/little-bloom-prairie/,goat,United States,Illinois,Camembert,"soft, soft-ripened",NA,NA,"creamy, dense, smooth, soft-ripened",mold ripened,white,"citrusy, creamy, mushroomy, tangy","fresh, lactic",FALSE,FALSE,NA,NA,Prairie Fruits Farm
+Little Colonel,https://www.cheese.com/little-colonel/,cow,"England, Great Britain, United Kingdom",Dorset,NA,"semi-soft, artisan",NA,NA,"smooth, supple",washed,pale yellow,spicy,pungent,TRUE,FALSE,NA,NA,James’s Cheese
+Little Dragon,https://www.cheese.com/little-dragon/,goat,United States,"Ann Arbor, Michigan",NA,soft,NA,NA,creamy,NA,white,"citrusy, creamy, mellow, sweet","fresh, herbal",FALSE,FALSE,NA,NA,Zingerman's Creamery
+Little Napoleon,https://www.cheese.com/little-napoleon/,goat,United States,"Ann Arbor, Michigan",NA,"hard, artisan",NA,NA,firm,mold ripened,pale yellow,"acidic, savory","goaty, pungent",FALSE,FALSE,NA,NA,Zingerman's Creamery
+Little Qualicum Raclette,https://www.cheese.com/little-qualicum-raclette/,cow,Canada,British Columbia,Raclette,"semi-soft, artisan",NA,NA,"firm, supple",washed,ivory,"meaty, strong","pungent, stinky",NA,NA,NA,NA,Little Qualicum Cheeseworks
+Little Rydings,https://www.cheese.com/little-rydings/,sheep,England,North Wootton,NA,"soft, artisan",48%,NA,"creamy, smooth, springy",mold ripened,white,"mild, sweet","rich, sweet",TRUE,FALSE,NA,NA,Wootton Organic Dairy
+Little Ypsi,https://www.cheese.com/little-ypsi/,goat,United States,"Ann Arbor, Michigan",NA,"semi-soft, artisan",NA,NA,"buttery, firm",natural,ivory,buttery,buttery,FALSE,FALSE,NA,NA,Zingerman's Creamery
+Livarot,https://www.cheese.com/livarot/,cow,France,NA,NA,"soft, artisan",NA,NA,creamy,washed,NA,full-flavored,"pungent, strong",FALSE,FALSE,NA,NA,NA
+Llanboidy,https://www.cheese.com/llanboidy/,cow,Wales,Carmarthenshire,Cheddar,"hard, artisan",NA,NA,"firm, smooth",natural,white,"buttery, spicy","grassy, sweet",FALSE,FALSE,NA,NA,Llanboidy
+Llanglofan Farmhouse,https://www.cheese.com/llanglofan-farmhouse/,cow,"Great Britain, United Kingdom, Wales",Pembrokeshire,NA,"hard, artisan",45%,NA,"crumbly, firm, smooth",natural,yellow,"citrusy, savory, smokey , spicy","lactic, smokey",TRUE,FALSE,NA,"Llangloffan White Farmhouse Cheese, Llangloffan Red Farmhouse Cheese, Llangloffan Smoked Farmhouse Cheese, Llangloffan Garlic & Chive Farmhouse Cheese",CARMARTHENSHIRE CHEESE COMPANY
+Lo Sburrato,https://www.cheese.com/lo-sburrato/,sheep,Italy,Tuscany,Pecorino,"semi-soft, artisan",NA,NA,"compact, creamy",natural,straw,"creamy, smooth, sweet",aromatic,NA,NA,NA,NA,Caseificio Pinzani Srl
+Lo Speziato,https://www.cheese.com/lo-speziato/,cow,Italy,"Treviso, Veneto",NA,"soft, artisan",NA,NA,compact,natural,ivory,"full-flavored, spicy",fresh,NA,NA,NA,NA,Moro Latteria di Moro Sergio
+Loch Arthur Farmhouse,https://www.cheese.com/loch-arthur-farmhouse/,cow,Scotland,Dumfries,Cheddar,hard,48%,NA,firm,NA,NA,nutty,NA,TRUE,FALSE,NA,NA,Loch Arthur Creamery
+Loddiswell Avondale,https://www.cheese.com/loddiswell-avondale/,goat,England,Devon,NA,semi-soft,NA,NA,,washed,orange,sweet,goaty,FALSE,FALSE,NA,NA,Jocelyn and Bill Martin
+Loma Alta,https://www.cheese.com/loma-alta/,cow,United States,California,Camembert,"semi-soft, soft-ripened",NA,NA,firm,mold ripened,white,"buttery, nutty",rich,FALSE,FALSE,Blue Mountain,NA,Nicasio Valley Cheese Company
+Longhorn,https://www.cheese.com/longhorn/,cow,United States,"Colby, Wisconsin",Cheddar,semi-hard,NA,NA,"firm, open, springy",rindless,orange,"mild, sweet",NA,FALSE,FALSE,NA,"Colby Longhorn, Longhorn Cheddar, Colby Jack Longhorn, Pepper Jack Longhorn, Low Sodium Colby Longhorn",Williams Cheese Company
+Lord of the Hundreds,https://www.cheese.com/lord-hundreds/,sheep,United Kingdom,East Sussex,NA,"semi-hard, artisan",NA,NA,"firm, grainy, open",natural,golden yellow,"burnt caramel, nutty, savory, sweet","grassy, sweet",TRUE,FALSE,NA,NA,Traditional Cheese Dairy
+Lost Lake,https://www.cheese.com/lost-lake/,sheep,Canada,"Prince Edward County, Ontario",NA,"fresh firm, artisan, soft-ripened",NA,NA,"creamy, semi firm, soft",mold ripened,white,"creamy, mushroomy","rich, yeasty",NA,NA,NA,NA,Fifth Town Artisan Cheese
+Lou Bergier Pichin,https://www.cheese.com/lou-bergier/,cow,Italy,Piemonte,NA,"semi-soft, artisan",NA,NA,"buttery, open, smooth",natural,brown,"creamy, floral, mushroomy","grassy, milky",TRUE,FALSE,Lou Bergier Pichin,NA,NA
+Lou Palou,https://www.cheese.com/lou-palou/,cow,France,Pyrénées,NA,semi-soft,NA,NA,"elastic, smooth",NA,NA,NA,pleasant,FALSE,FALSE,NA,NA,NA
+Lou Pevre,https://www.cheese.com/lou-pevre/,goat,France,Provence,NA,"soft, processed",NA,NA,smooth,natural,white,acidic,goaty,FALSE,FALSE,NA,NA,NA
+Lunetta,https://www.cheese.com/lunetta/,cow,"Canada, Italy",Lombardy,NA,"fresh soft, artisan",NA,NA,creamy,natural,white,"creamy, salty","aromatic, fresh",NA,NA,NA,NA,Fifth Town Artisan Cheese
+Lyburn Garlic and Nettle,https://www.cheese.com/lyburn-garlic-and-nettle/,cow,"England, Great Britain, United Kingdom",Landford,NA,"semi-soft, artisan",NA,NA,"creamy, open, smooth",washed,pale yellow,"garlicky, herbaceous, pronounced, spicy, strong","aromatic, rich, strong",TRUE,FALSE,NA,NA,Lyburn Farm
+Lyburn Gold,https://www.cheese.com/lyburn-gold/,cow,"England, Great Britain, United Kingdom",Landford,Gouda,"semi-hard, artisan",NA,NA,"creamy, open, smooth",washed,brownish yellow,nutty,NA,TRUE,FALSE,NA,NA,Lyburn Farm
+Lyburn Lightly Oak Smoked,https://www.cheese.com/lyburn-lightly-oak-smoked/,cow,"England, Great Britain, United Kingdom",Landford,Gouda,"semi-hard, artisan",NA,NA,smooth,washed,pale yellow,"smokey , smooth, subtle",smokey,TRUE,FALSE,NA,NA,Lyburn Farm
+Lyburn's Winchester,https://www.cheese.com/lyburns-winchester/,cow,"England, Great Britain, United Kingdom",Landford,Gouda,"hard, artisan",NA,NA,creamy,natural,brownish yellow,"creamy, nutty",NA,TRUE,FALSE,NA,NA,Lyburn Farm
+Maasdam,https://www.cheese.com/maasdam/,cow,Netherlands,All Holland,Gouda,semi-hard,45%,NA,"creamy, open, supple",NA,pale yellow,"buttery, nutty, sweet",fruity,FALSE,FALSE,NA,Maasdammer,NA
+Macadamia Nut Cheese,https://www.cheese.com/macadamia-nut-cheese/,,United States,Brooklyn NY,NA,"firm, artisan",NA,NA,"firm, spreadable",NA,brown,"creamy, nutty, sweet",nutty,TRUE,FALSE,NA,Aged Macadamia Cheese,Dr. Cow Tree Nut Cheese
+Macconais,https://www.cheese.com/macconais/,"cow, goat",France,Bourgogne,Blue,"soft, artisan, soft-ripened",NA,NA,"creamy, firm, smooth",bloomy,cream,"salty, tangy",herbal,FALSE,FALSE,Chevreton de Macon,NA,NA
+Madrona,https://www.cheese.com/madrona/,goat,United States,Oregon,NA,"semi-soft, artisan",NA,NA,creamy,natural,ivory,mild,"fruity, herbal",NA,NA,NA,NA,Briar Rose Creamery
+Maffra Aged Rinded Cheddar,https://www.cheese.com/maffra-aged-rinded-cheddar/,cow,Australia,"Gippsland, Victoria",Cheddar,"hard, artisan",NA,NA,"buttery, crumbly, firm",cloth wrapped,straw,"earthy, meaty, sweet",grassy,FALSE,FALSE,Maffra Cloth Bound Cheddar,NA,Maffra Cheese Company Pty Ltd
+Maffra Cheshire,https://www.cheese.com/maffra-cheshire/,cow,Australia,"Gippsland, Victoria",NA,"hard, artisan",NA,NA,crumbly,natural,pale yellow,"mellow, salty, sharp, tart",NA,TRUE,FALSE,NA,NA,Maffra Cheese Company Pty Ltd
+Maffra Dargo Walnut,https://www.cheese.com/maffra-dargo-walnut/,cow,Australia,"Gippsland, Victoria",Cheddar,"semi-hard, artisan",NA,NA,"crumbly, dense, firm, flaky",natural,golden yellow,"mild, nutty, sweet","nutty, sweet",NA,NA,Maffra Dargo Walnut Red Leicester,NA,Maffra Cheese Company Pty Ltd
+Maffra Mature Cheddar,https://www.cheese.com/maffra-mature-cheddar/,cow,Australia,"Gippsland, Victoria",Cheddar,"hard, artisan",NA,NA,"creamy, crumbly, firm, smooth",waxed,yellow,"acidic, salty, smooth",NA,TRUE,FALSE,"Mature Cheddar Red Wax, Maffra Red Wax Cheddar",NA,Maffra Cheese Company Pty Ltd
+Maffra Peppercorn Cheddar,https://www.cheese.com/maffra-peppercorn-cheddar/,cow,Australia,"Gippsland, Victoria",Cheddar,"semi-hard, artisan",NA,NA,"creamy, firm",natural,yellow,"creamy, spicy",spicy,NA,NA,NA,NA,Maffra Cheese Company Pty Ltd
+Maffra Red Leicester,https://www.cheese.com/maffra-red-leicester/,cow,Australia,"Gippsland, Victoria",NA,"hard, artisan",NA,NA,"buttery, creamy, dense, smooth",NA,brownish yellow,"creamy, mellow, smooth",NA,TRUE,FALSE,NA,NA,Maffra Cheese Company Pty Ltd
+Maffra Sage Derby,https://www.cheese.com/maffra-sage-derby/,cow,Australia,"Gippsland, Victoria",Cheddar,"semi-hard, artisan",NA,NA,"firm, smooth",NA,pale yellow,"creamy, herbaceous, smooth","herbal, sweet",TRUE,FALSE,NA,NA,Maffra Cheese Company Pty Ltd
+Maffra Wensleydale,https://www.cheese.com/maffra-wensleydale/,cow,Australia,"Gippsland, Victoria",NA,"hard, artisan",NA,NA,crumbly,natural,pale yellow,"creamy, lemony",NA,NA,NA,NA,NA,Maffra Cheese Company Pty Ltd
+Maggie's Round,https://www.cheese.com/maggies-round/,cow,United States,Massachusetts,Tomme,"semi-hard, artisan",NA,NA,"firm, smooth",natural,blue-grey,"buttery, citrusy, nutty, sharp","earthy, fruity, herbal",TRUE,FALSE,NA,NA,Cricket Creek Farm
+Magna,https://www.cheese.com/magna/,cow,Sweden,Oviken,Blue,"firm, artisan, blue-veined",34%,NA,,mold ripened,ivory,"full-flavored, salty, sharp",NA,NA,NA,NA,NA,Oviken cheese
+Mahoe Aged Gouda,https://www.cheese.com/mahoe-aged-gouda/,cow,Netherlands,NA,Gouda,NA,NA,NA,firm,NA,yellow,nutty,NA,NA,NA,NA,NA,Mahoe Farmhouse Cheese
+Mahón,https://www.cheese.com/mahon/,cow,Spain,"Menorca, Balearic Islands",NA,"semi-hard, artisan",NA,NA,"crumbly, dense",washed,pale yellow,"buttery, salty","nutty, sweet",FALSE,FALSE,"Mahon Reserva, Aged Mahon, Artesano Mahón, Artisanal Mahón, formatge de Maó, queso de Mahón",NA,NA
+Maida Vale,https://www.cheese.com/maida-vale/,cow,England,NA,NA,semi-soft,NA,NA,"buttery, soft, supple",washed,pink and white,"grassy, meaty, nutty, savory, umami, vegetal","barnyardy, earthy, grassy, stinky, yeasty",TRUE,FALSE,NA,NA,Village Maid Cheese
+Maisie's Kebbuck,https://www.cheese.com/maisies-kebbuck/,cow,"Great Britain, Scotland, United Kingdom",Lanarkshire,NA,"semi-hard, artisan",29.8%,NA,"creamy, crumbly",natural,white,NA,fresh,TRUE,FALSE,NA,NA,Errington Cheese Ltd.
+Majorero,https://www.cheese.com/majorero/,goat,Spain,Canary Islands,NA,semi-hard,NA,NA,"buttery, creamy",natural,pale white,acidic,"milky, nutty",FALSE,FALSE,"Queso Majorero, Queso Fuerteventura, Majorero PDO, Majorero DOP",NA,NA
+Malvarosa®,https://www.cheese.com/malvarosa/,sheep,Spain,Valencia,NA,"semi-firm, artisan",NA,NA,"firm, smooth",natural,straw,"buttery, sweet","fresh, rich",FALSE,FALSE,Malvarosa,NA,NA
+Malvern,https://www.cheese.com/malvern/,sheep,"England, Great Britain, United Kingdom",Severn Valley,NA,"semi-hard, artisan",50%,NA,"creamy, dense",NA,ivory,"butterscotch, herbaceous, sweet",NA,TRUE,FALSE,NA,NA,NA
+Mamirolle,https://www.cheese.com/mamirolle/,cow,"Canada, France","Plessisville, Quebec",NA,"semi-soft, artisan",23%,NA,"chewy, supple",washed,ivory,"buttery, fruity, salty, sweet","earthy, pungent",FALSE,FALSE,NA,NA,Eco-Delices Inc.
+Manchego,https://www.cheese.com/manchego/,sheep,Spain,NA,NA,hard,NA,NA,"compact, firm",NA,pale yellow,"buttery, nutty",NA,NA,NA,Queso Manchego,NA,NA
+Mandolin,https://www.cheese.com/mandolin/,cow,Australia,South Australia,NA,"semi-hard, artisan, smear-ripened",45%,NA,"crumbly, soft, supple",leaf wrapped,pale yellow,"creamy, grassy, smooth","grassy, herbal",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Mango Rebel,https://www.cheese.com/mango-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,"creamy, firm",natural,pale yellow,"fruity, spicy","fresh, fruity",NA,NA,Mangorebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Manon,https://www.cheese.com/manon/,goat,Australia,South Australia,NA,"fresh soft, artisan",45%,NA,"creamy, smooth",leaf wrapped,white,"creamy, garlicky, spicy, strong","aromatic, garlicky, spicy, strong",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Manouri,https://www.cheese.com/manouri/,"goat, sheep",Greece,"Central and Western Macedonia, Thessalia",Feta,"semi-soft, whey",36-38%,NA,creamy,rindless,white,"mild, milky","clean, nutty, subtle",NA,NA,Manoypi,NA,NA
+Manteca,https://www.cheese.com/manteca/,cow,Italy,Basilicata,NA,"semi-soft, artisan",NA,NA,"dense, elastic, stringy",natural,pale yellow,buttery,"floral, fruity",FALSE,FALSE,"Butirro , Burrino, Burriello",NA,Casa Madaio
+Manur,https://www.cheese.com/manur/,"cow, sheep",Serbia,NA,NA,"hard, artisan",40%,NA,creamy,natural,NA,salty,NA,FALSE,FALSE,NA,NA,NA
+Marble Cheddar,https://www.cheese.com/marble-cheddar/,cow,United Kingdom,NA,Cheddar,"hard, processed",NA,NA,"firm, smooth",rindless,NA,creamy,rich,NA,NA,NA,NA,NA
+Marble Cheese,https://www.cheese.com/marbled-cheeses/,cow,United Kingdom,NA,NA,"hard, processed",NA,NA,firm,rindless,golden orange,"mild, smooth, sweet, tangy",NA,FALSE,FALSE,NA,"Marble Cheddar, Marbled Cheese",NA
+Marco Polo,https://www.cheese.com/marco-polo/,cow,United States,"Seattle, Washington",Cheddar,"hard, artisan",NA,NA,"creamy, crumbly",cloth wrapped,ivory,creamy,"clean, rich",NA,NA,NA,NA,Beecher's
+Maredsous,https://www.cheese.com/maredsous/,cow,Belgium,NA,NA,soft,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Margot,https://www.cheese.com/margotin/,cow,Italy,Piemonte,NA,semi-soft,NA,NA,firm,natural,cream,"bitter, full-flavored",NA,FALSE,FALSE,NA,NA,NA
+Maribo,https://www.cheese.com/maribo/,cow,Denmark,Maribo,Gouda,"semi-hard, artisan",35-40%,NA,"creamy, firm, open",waxed,pale yellow,tangy,strong,FALSE,FALSE,NA,NA,NA
+Marin French Triple Crème,https://www.cheese.com/marin-french-triple-creme/,cow,United States,"Petaluma, California",Brie,soft,NA,NA,"fluffy, smooth",bloomy,NA,sweet,"rich, sweet",NA,NA,"3 Triple Crème Brie, Truffle Brie, Triple Crème Brie with Truffles",NA,Marin French Cheese
+Marisa,https://www.cheese.com/marisa/,sheep,United States,Wisconsin,NA,"semi-soft, artisan",NA,NA,"dense, firm",NA,white,"mellow, sweet",pleasant,TRUE,FALSE,NA,NA,Carr Valley Cheese Company
+Maroilles,https://www.cheese.com/maroilles/,cow,France,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",washed,NA,sweet,NA,FALSE,FALSE,NA,Marolles,NA
+Martha's Heat,https://www.cheese.com/marthas-heat/,cow,Australia,"Mornington Peninsula, Melbourne",Cheddar,"hard, artisan",NA,NA,,NA,brownish yellow,"smokey , spicy","smokey, spicy",TRUE,FALSE,NA,NA,BoatShed Cheese
+Mascares,https://www.cheese.com/mascares/,"goat, sheep",France,Provencale,Tomme,"fresh soft, artisan",NA,NA,smooth,leaf wrapped,white,NA,"earthy, floral",FALSE,FALSE,NA,NA,NA
+Mascarpone,https://www.cheese.com/mascarpone/,cow,Italy,Lombardy,NA,fresh soft,NA,NA,"buttery, creamy, smooth, spreadable",rindless,white,"buttery, creamy, mild",fresh,NA,NA,Mascarpone Australian,NA,NA
+Mastorazio,https://www.cheese.com/mastorazio/,sheep,Italy,Campania,NA,"hard, artisan",NA,NA,"compact, dense",natural,pale yellow,"herbaceous, nutty, sharp, spicy","herbal, milky",FALSE,FALSE,NA,NA,Casa Madaio
+Matocq,https://www.cheese.com/matocq/,"cow, sheep",France,NA,NA,"semi-hard, processed",50%,NA,chewy,NA,cream,"acidic, nutty",nutty,FALSE,FALSE,NA,NA,NA
+Mature Wensleydale,https://www.cheese.com/mature-wensleydale/,cow,"England, United Kingdom",North Yorkshire,NA,"hard, artisan",48%,NA,crumbly,cloth wrapped,pale yellow,strong,"rich, ripe",FALSE,FALSE,NA,Ryp Wensleydale,Wensleydale Creamery
+May Hill Green,https://www.cheese.com/may-hill-green/,cow,"England, Great Britain, United Kingdom",Gloucestershire,NA,"semi-soft, artisan",NA,NA,runny,leaf wrapped,cream,"creamy, milky",strong,TRUE,FALSE,NA,Mayhill Green,Charles Martell & Son Limited
+Maytag Blue,https://www.cheese.com/maytag-blue/,cow,United States,Iowa,Blue,"semi-hard, blue-veined",NA,NA,"crumbly, dense",rindless,cream,"lemony, savory, tangy","pungent, strong",NA,NA,NA,NA,Maytag Dairy Farms
+McLaren,https://www.cheese.com/mclaren/,cow,Australia,South Australia,Camembert,"soft, artisan, soft-ripened",35%,NA,"chalky, creamy, soft, soft-ripened",bloomy,pale yellow,"creamy, mushroomy, smooth","mushroom, rich, strong",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Medallion,https://www.cheese.com/medallion/,cow,United States,Maine,NA,"soft, artisan",NA,NA,"creamy, smooth",bloomy,pale yellow,"mushroomy, sweet",rich,TRUE,FALSE,NA,NA,Lakin's Gorges Cheese LLC
+Meira,https://www.cheese.com/meira/,sheep,Iraq,NA,NA,semi-hard,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,Mira,NA,NA
+Melange,https://www.cheese.com/melange/,"cow, goat",United States,California,Brie,"soft, soft-ripened",NA,NA,"firm, soft-ripened",bloomy,cream,"piquant, spicy, sweet, tart",goaty,NA,NA,NA,NA,Andante Dairy
+Melange Brie,https://www.cheese.com/melange-brie/,"cow, goat",United States,California,Brie,"soft, soft-ripened",NA,NA,"creamy, smooth, soft-ripened",bloomy,ivory,"buttery, creamy, mild, piquant, sweet, tangy","earthy, goaty, mushroom",TRUE,FALSE,NA,NA,Marin French Cheeese Co.
+Melinda Mae,https://www.cheese.com/melinda-mae/,cow,United States,"Lebanon, CT",Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, fluffy, runny, smooth, soft",bloomy,ivory,"buttery, mild, piquant, pungent, savory, subtle, sweet","fruity, musty, yeasty",NA,NA,NA,NA,The Mystic Cheese Company
+Melville,https://www.cheese.com/melville/,goat,United States,"Lebanon, CT",NA,"fresh soft, artisan, soft-ripened",NA,NA,"elastic, oily, soft, supple",NA,white,"acidic, buttery, mild, sweet, tart",fresh,NA,NA,NA,NA,The Mystic Cheese Company
+Menage,https://www.cheese.com/menage/,"cow, goat, sheep",United States,Wisconsin,NA,semi-hard,NA,NA,"creamy, dry, firm",waxed,ivory,"full-flavored, strong",pleasant,TRUE,FALSE,NA,NA,Carr Valley Cheese Company
+Menallack Farmhouse,https://www.cheese.com/menallack-farmhouse/,cow,England,NA,NA,hard,NA,NA,,NA,NA,NA,NA,TRUE,FALSE,NA,NA,NA
+Menonita,https://www.cheese.com/menonita/,cow,Mexico,NA,Cheddar,semi-soft,NA,NA,firm,natural,pale yellow,"buttery, mild",NA,NA,NA,"Queso Chihuahua, Chihuahua cheese, Queso menonita, Campresino Menonita",NA,NA
+Meredith Ashed Pyramids,https://www.cheese.com/meredith-ashed-pyramids/,goat,Australia,Victoria,NA,"fresh soft, artisan",NA,NA,"creamy, soft",ash coated,white,"creamy, smooth",fresh,NA,NA,NA,NA,Meredith Dairy
+Meredith Blue,https://www.cheese.com/meredith-blue/,sheep,Australia,"Ballarat, Victoria",Blue,"soft, semi-soft, artisan, blue-veined",NA,NA,creamy,natural,blue,"mild, mushroomy, sweet","fresh, goaty",FALSE,FALSE,NA,NA,Meredith Dairy
+Meredith Chevre Ash,https://www.cheese.com/meredith-chevre-ash/,goat,Australia,Victoria,NA,"fresh soft, artisan",NA,NA,"creamy, soft",ash coated,white,"creamy, smooth","clean, fresh",TRUE,FALSE,NA,NA,Meredith Dairy
+Meredith Chevre Dill,https://www.cheese.com/meredith-chevre-dill/,goat,Australia,Victoria,NA,"fresh soft, artisan",NA,NA,"creamy, soft",rindless,white,"herbaceous, smooth","aromatic, clean, fresh, herbal",TRUE,FALSE,NA,NA,Meredith Dairy
+Meredith Chevre Plain,https://www.cheese.com/meredith-chevre-plain/,goat,Australia,Victoria,NA,"fresh soft, artisan",NA,NA,"creamy, soft",rindless,white,"acidic, smooth","clean, fresh",TRUE,FALSE,NA,NA,Meredith Dairy
+Meredith Goat Cheese in Extra Virgin Olive Oil,https://www.cheese.com/meredith-goat-cheese-in-extra-virgin-olive-oil/,goat,Australia,Victoria,NA,"fresh soft, artisan",NA,NA,"creamy, soft",NA,white,"garlicky, herbaceous, spicy","fresh, herbal, spicy, strong",NA,NA,NA,NA,Meredith Dairy
+Merry Wyfe,https://www.cheese.com/merry-wyfe/,cow,United Kingdom,South West England,NA,semi-hard,NA,NA,"creamy, springy",washed,orange,NA,aromatic,TRUE,FALSE,NA,NA,The Bath Soft Cheese Co.
+Mersey Valley Original Vintage,https://www.cheese.com/mersey-valley-original-vintage/,cow,Australia,Tasmania,Cheddar,hard,NA,NA,"creamy, crumbly, spreadable",natural,yellow,"buttery, full-flavored, sharp",strong,NA,NA,NA,Mersey Valley Vintage,Mersey Valley - National Foods
+Metronome,https://www.cheese.com/metronome/,"cow, goat",United States,California,NA,"semi-soft, soft-ripened",NA,NA,"creamy, firm",natural,pale yellow,"buttery, tart","goaty, grassy, rich",NA,NA,NA,NA,Andante Dairy
+Mettowee,https://www.cheese.com/mettowee/,goat,United States,Vermont,NA,"fresh soft, artisan",NA,NA,"creamy, smooth",natural,white,"acidic, creamy, tangy, tart","clean, fresh, subtle",TRUE,FALSE,NA,NA,Consider Bardwell Farm
+Meyer Vintage Gouda,https://www.cheese.com/meyer-vintage-gouda/,cow,New Zealand,Hamilton,Gouda,"semi-hard, artisan",NA,NA,crumbly,NA,NA,"nutty, sharp, spicy",aromatic,FALSE,FALSE,NA,NA,NA
+MezzaLuna Fontina,https://www.cheese.com/mezzaluna-fontina/,cow,United States,Wisconsin,NA,semi-hard,NA,NA,creamy,washed,NA,"earthy, mild, mushroomy, yeasty",rich,NA,NA,NA,NA,Emmi Roth USA
+Mezzo Secco,https://www.cheese.com/mezzo-secco/,cow,United States,"Sonoma, California",Monterey Jack,"semi-hard, artisan",NA,NA,"creamy, smooth",natural,ivory,milky,spicy,TRUE,FALSE,Oro Secco,NA,Vella Cheese Company
+Mi-Ewe,https://www.cheese.com/mi-ewe/,"cow, sheep",United States,California,NA,"semi-hard, artisan",NA,NA,crumbly,natural,pale yellow,"buttery, citrusy, nutty, sharp","caramel, rich",TRUE,FALSE,NA,NA,Weirauch Farm and Creamery
+Midnight Blue,https://www.cheese.com/midnight-blue/,goat,United States,Colorado,Blue,"semi-soft, blue-veined",NA,NA,"dense, dry",natural,pale yellow,"pungent, sharp, spicy",goaty,TRUE,FALSE,NA,NA,Avalanche Cheese Company
+Midnight Moon®,https://www.cheese.com/midnight-moon/,goat,Holland,NA,Gouda,hard,NA,NA,"creamy, crystalline",NA,ivory,"caramel, sweet",NA,NA,NA,NA,NA,NA
+Miette,https://www.cheese.com/miette/,"goat, sheep",United States,Missouri,Brie,"semi-soft, artisan",NA,NA,"creamy, runny",bloomy,cream,"mushroomy, sweet, yeasty",barnyardy,FALSE,FALSE,NA,NA,Baetje Farms LLC
+Mihalic Peynir,https://www.cheese.com/mihalic-peynir/,sheep,Turkey,Bursa,NA,"hard, artisan",45%,NA,"crumbly, grainy, open",NA,pale yellow,salty,strong,TRUE,FALSE,NA,NA,NA
+Milawa Affine,https://www.cheese.com/milawa-affine/,goat,Australia,North East Victoria,NA,"semi-soft, artisan",NA,NA,"creamy, crumbly, smooth",ash coated,white,"acidic, creamy",NA,FALSE,FALSE,Milawa Ashed Chèvre,NA,Milawa Cheese Company
+Milawa Aged Blue,https://www.cheese.com/milawa-aged-blue/,,,North East Victoria,Blue,NA,NA,NA,,mold ripened,blue,NA,NA,NA,NA,NA,NA,NA
+Milawa Blue,https://www.cheese.com/milawa-blue/,cow,Australia,North East Victoria,Blue,"soft, blue-veined",NA,NA,creamy,mold ripened,blue,"buttery, creamy, sweet",rich,NA,NA,NA,NA,Milawa Cheese Company
+Milawa Brie,https://www.cheese.com/milawa-brie/,cow,Australia,North East Victoria,Brie,"soft, soft-ripened",27%,NA,creamy,mold ripened,pale yellow,"buttery, milky",NA,NA,NA,NA,NA,Milawa Cheese Company
+Milawa Goats Tomme,https://www.cheese.com/milawa-goats-tomme/,goat,Australia,North East Victoria,Tomme,semi-hard,NA,NA,"creamy, crumbly, dense",natural,yellow,"savory, spicy",earthy,TRUE,FALSE,NA,NA,Milawa Cheese Company
+Milawa White,https://www.cheese.com/milawa-white/,cow,Australia,NA,NA,soft,NA,NA,"creamy, open",NA,NA,sweet,NA,TRUE,FALSE,NA,NA,Milawa Cheese Company
+Milbenkäse,https://www.cheese.com/milbenkase/,cow,Germany,NA,NA,"semi-soft, smear-ripened",NA,NA,firm,NA,NA,tangy,strong,FALSE,FALSE,"Milbenkäse, Mite Cheese",NA,NA
+Milleens,https://www.cheese.com/milleens/,cow,Ireland,"Beara Peninsula, Co. Cork",NA,"soft, artisan",45-50%,NA,"creamy, smooth",washed,pale yellow,"floral, herbaceous, sweet",aromatic,FALSE,FALSE,NA,NA,Milleens Cheese Ltd.
+Millstone,https://www.cheese.com/millstone/,sheep,England,Somerset,NA,"hard, artisan",NA,NA,"crumbly, dry",natural,pale yellow,NA,mild,TRUE,FALSE,NA,NA,Wootton Organic Dairy
+Mimolette (Boule de Lille),https://www.cheese.com/mimolette-boule-de-lille/,cow,France,NA,NA,semi-hard,NA,NA,firm,natural,orange,"bitter, buttery, sweet",NA,FALSE,FALSE,"vieux Hollande, Boule de Lille",NA,NA
+Minas cheese,https://www.cheese.com/minas-cheese/,cow,Brazil,Minas Gerais,NA,"semi-soft, artisan",40-50%,NA,"grainy, open, soft, springy",rindless,white,"bitter, mild, salty, strong",NA,FALSE,FALSE,"Queijos de Minas Frescal , Queijo Minas Meia Cura, Queijo Minas Curado, Queijo Padrão",Queijo Minas,NA
+Mine-Gabhar,https://www.cheese.com/mine-gabhar/,goat,Ireland,County Wexford,Blue,"soft, artisan",NA,NA,creamy,natural,white,"acidic, sweet","earthy, pungent",TRUE,FALSE,NA,NA,Luc and Anne Van Kampen
+Minger,https://www.cheese.com/minger/,cow,Scotland,NA,NA,soft,NA,NA,"buttery, creamy, runny",washed,orange,"full-flavored, garlicky, meaty, pungent, strong","barnyardy, ripe, stinky, yeasty",TRUE,FALSE,NA,NA,Highland Fine Cheeses Limited
+Minuet,https://www.cheese.com/minuet/,"cow, goat",United States,California,NA,"soft, soft-ripened",NA,NA,"creamy, smooth",mold ripened,white,"sweet, tangy",pleasant,TRUE,FALSE,NA,NA,Andante Dairy
+Mirabo Brie with Walnut,https://www.cheese.com/mirabo-brie-with-walnut/,cow,Germany,Bavaria,Brie,"semi-soft, soft-ripened",NA,NA,"creamy, smooth, soft",natural,ivory,"nutty, savory",nutty,NA,NA,"Mirabo Walnut, Mirabo Brie Walnut",NA,Käserei Champignon
+Miss Muffet,https://www.cheese.com/miss-muffet/,cow,England,North Cornwall,Cornish,"semi-soft, artisan",NA,NA,"smooth, supple",washed,ivory,creamy,nutty,TRUE,FALSE,NA,NA,Whalesborough Farm Foods
+Mistralou,https://www.cheese.com/mistralou/,goat,France,NA,NA,soft,NA,NA,creamy,natural,white,creamy,floral,FALSE,FALSE,NA,NA,NA
+MitiCaña® de Oveja,https://www.cheese.com/miticana-de-oveja/,sheep,Spain,Murcia,NA,"semi-soft, soft-ripened",13%,NA,"buttery, crumbly, flaky",bloomy,white,"buttery, tangy",buttery,NA,NA,"Miticana de Oveja, MitiCaña de Oveja",NA,NA
+Mladi Trapist,https://www.cheese.com/mladi-trapist/,cow,Croatia,Dalmatia,NA,"semi-hard, artisan",50%,NA,"crumbly, semi firm",natural,white,"grassy, savory",aromatic,TRUE,FALSE,NA,NA,SIRANA GLIGORA D.O.O
+Mobay,https://www.cheese.com/mobay/,"goat, sheep",United States,Wisconsin,NA,"semi-soft, artisan",NA,NA,"soft, supple",plastic,ivory,"earthy, sweet, tangy",goaty,TRUE,FALSE,NA,NA,Carr Valley Cheese Company
+Molbo,https://www.cheese.com/molbo/,cow,Denmark,Mols,Cheddar,"semi-hard, artisan",NA,NA,firm,waxed,pale yellow,"salty, tangy",fresh,FALSE,FALSE,NA,NA,NA
+Mona,https://www.cheese.com/mona/,"cow, sheep",United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,"crumbly, firm, supple",plastic,pale yellow,"buttery, nutty, savory, sweet",grassy,FALSE,FALSE,NA,NA,Wisconsin Sheep Dairy Coop
+Monastery Cheeses,https://www.cheese.com/monastery-cheeses/,cow,"Belgium, Canada, France, Switzerland, United States",NA,NA,"soft, semi-soft, brined",NA,NA,"chalky, creamy, firm, grainy",washed,golden yellow,"mild, pungent","pungent, strong",FALSE,FALSE,"monastery, trappist cheese, monk cheese",NA,NA
+Mondseer,https://www.cheese.com/mondseer/,cow,Austria,NA,NA,semi-hard,NA,NA,"creamy, smooth",washed,pale yellow,spicy,strong,NA,NA,NA,NA,NA
+Monet,https://www.cheese.com/monet/,goat,Australia,South Australia,NA,"fresh soft, artisan",22%,NA,"creamy, soft",NA,white,"floral, herbaceous","floral, fresh",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Mont D'or,https://www.cheese.com/mont-dor-lyonnais/,cow,France,NA,NA,"semi-soft, artisan",NA,NA,creamy,natural,NA,NA,"earthy, strong",NA,NA,Vacherin Mont D'or,Mont Dor,NA
+Mont Saint-Francis,https://www.cheese.com/mont-saint-francis/,goat,United States,Indiana,NA,"semi-hard, artisan",NA,NA,"brittle, firm, supple",washed,golden yellow,"earthy, full-flavored, strong","pungent, stinky",FALSE,FALSE,Mont St. Francis,NA,Capriole Goat Cheese
+Mont St-Benoît,https://www.cheese.com/mont-st-benoit/,cow,Canada,Quebec,Swiss Cheese,"firm, artisan",31%,NA,"creamy, elastic, smooth, supple",rindless,NA,"buttery, creamy, mild, nutty","fermented, mild, nutty",NA,NA,NA,NA,Abbey de Saint-Benoit-du-lac
+Montagnolo,https://www.cheese.com/montagnolo/,cow,Germany,NA,Blue,"soft, blue-veined",NA,NA,"creamy, smooth",natural,NA,creamy,"buttery, rich",NA,NA,Montagnolo Affine,NA,Käserei Champignon
+Montasio,https://www.cheese.com/montasio/,cow,Italy,Friuli Venezia Giulia and Veneto,NA,semi-firm,NA,NA,creamy,NA,NA,"mild, smooth","aromatic, pleasant",FALSE,FALSE,"Montasio Mitica® DOP, Montasio Mitica DOP",NA,NA
+Montasio Mezzano,https://www.cheese.com/montasio-mezzano/,cow,Italy,Friuli Venezia Giulia and Veneto,NA,"semi-firm, artisan",NA,NA,creamy,natural,NA,"full-flavored, nutty, sweet",strong,FALSE,FALSE,NA,NA,NA
+Montasio Vecchio,https://www.cheese.com/montasio-vecchio/,cow,Italy,Friuli Venezia Giulia and Veneto,NA,"semi-firm, artisan",NA,NA,creamy,natural,NA,sharp,strong,FALSE,FALSE,NA,Montasio Stagionato,NA
+Montchevre Goat Cheese Log,https://www.cheese.com/montchevre-goat-cheese-log/,goat,United States,NA,NA,"semi-soft, artisan",NA,NA,smooth,rindless,white,"creamy, tangy","fresh, rich",NA,NA,"Montchevre fresh goat cheese, Fresh Chevré (Montchevré), Montchevre fresh goat cheese log",NA,Montchevre Cheese Company
+Monte Enebro,https://www.cheese.com/monte-enebro/,goat,Spain,Avila,NA,semi-soft,NA,NA,smooth,NA,NA,"creamy, lemony","goaty, strong",FALSE,FALSE,"pata de mulo, mule’s-hoof cheese",NA,NA
+Monteo,https://www.cheese.com/monteo/,cow,Italy,Veneto,NA,"semi-firm, artisan",NA,NA,"creamy, open",natural,pale yellow,"buttery, milky, smooth","fresh, milky",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Monterey Jack,https://www.cheese.com/monterey-jack/,cow,"Mexico, United States","Monterey, California",Monterey Jack,semi-hard,NA,NA,"compact, creamy, firm, open, supple",NA,pale yellow,"buttery, mild",aromatic,NA,NA,Fresh Jack,NA,NA
+Montgomery's Cheddar,https://www.cheese.com/montgomerys-cheddar/,cow,United Kingdom,NA,Cheddar,"hard, artisan",NA,NA,,natural,yellow,NA,rich,FALSE,FALSE,NA,NA,NA
+Montsalvat,https://www.cheese.com/montsalvat/,cow,Germany,Landshut,Blue,"soft, blue-veined",60%,NA,"creamy, soft",NA,ivory,"creamy, mild",NA,NA,NA,NA,NA,Bayerische Milchindustrie eG
+Moody Blue,https://www.cheese.com/moody-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan, blue-veined",NA,NA,creamy,natural,pale yellow,"creamy, nutty, subtle, tangy","rich, smokey",TRUE,FALSE,NA,NA,Emmi Roth USA
+Moonglo,https://www.cheese.com/moonglo/,goat,United States,Illinois,NA,"semi-hard, artisan",NA,NA,"dense, firm, open",washed,ivory,"caramel, citrusy, fruity, nutty, sharp, tangy","aromatic, goaty",FALSE,FALSE,NA,NA,Prairie Fruits Farm
+Moonlight Chaource,https://www.cheese.com/moonlight-chaource/,cow,United States,New York,NA,"soft, soft-ripened",NA,NA,"dense, firm",ash coated,white,"savory, tart","rich, sweet",TRUE,FALSE,NA,NA,Chaseholm Farm
+Moose,https://www.cheese.com/moose/,moose,Sweden,Bjurholm,NA,fresh soft,12%,NA,"creamy, smooth",NA,NA,smooth,NA,NA,NA,NA,NA,Algens Hus
+Morangie Brie,https://www.cheese.com/morangie-brie/,"cow, sheep",Scotland,Tain,Brie,"soft, artisan",NA,NA,"creamy, smooth",NA,cream,"creamy, smooth, sweet","mild, milky, sweet",NA,NA,Jezebel,NA,Highland Fine Cheeses Limited
+Morbier,https://www.cheese.com/morbier/,cow,France,NA,NA,"semi-soft, artisan",NA,NA,creamy,NA,ivory,citrusy,"fruity, grassy",FALSE,FALSE,"Morbier Cru de Montagne, Morbier AOP",NA,NA
+Morcella,https://www.cheese.com/morcella/,sheep,United States,Minnesota,NA,"semi-soft, soft-ripened",NA,NA,creamy,bloomy,ivory,"creamy, earthy",strong,TRUE,FALSE,NA,NA,Shepherd's Way Farms
+Morgan,https://www.cheese.com/morgan/,cow,United States,Maine,NA,"hard, artisan",NA,NA,firm,natural,ivory,"citrusy, salty",grassy,FALSE,FALSE,NA,NA,Lakin's Gorges Cheese LLC
+Morimoto Soba Ale Cheddar,https://www.cheese.com/morimoto-soba-ale-cheddar/,cow,United States,Oregon,Cheddar,"semi-hard, artisan",NA,NA,"creamy, crumbly",natural,pale yellow,"buttery, smooth, strong","strong, toasty",TRUE,FALSE,NA,NA,Rogue Creamery
+Moringhello,https://www.cheese.com/moringhello/,water buffalo,Italy,Lombardy,NA,"semi-soft, artisan",NA,NA,"chalky, crumbly",natural,white,smooth,"pleasant, subtle",FALSE,FALSE,Moringhello di Bufala,NA,Quattro Portoni
+Morlacco,https://www.cheese.com/morlacco/,cow,Italy,Veneto,NA,"soft, artisan",NA,NA,soft,natural,white,NA,fresh,NA,NA,"Morlacco del Grappa, Morlacco del Montegrappa, Morlacco del Grappa di malga",NA,NA
+Moses Sleeper,https://www.cheese.com/moses-sleeper/,cow,United States,Vermont,Brie,"semi-soft, artisan",NA,NA,"soft, soft-ripened",bloomy,white,"buttery, creamy, nutty","milky, mushroom",NA,NA,NA,NA,Jasper Hill Farm
+Mossfield Organic,https://www.cheese.com/mossfield-organic/,cow,Ireland,Co. Offaly,Cheddar,"hard, semi-hard, artisan",NA,NA,"crumbly, dry, flaky",natural,brownish yellow,"nutty, tangy","fresh, lactic, mild",TRUE,FALSE,NA,NA,Mossfield Organic Farm
+Mothais a la Feuille,https://www.cheese.com/mothais-a-la-feuille/,goat,France,NA,NA,"soft, artisan",NA,NA,"creamy, soft",NA,NA,"lemony, woody",mushroom,FALSE,FALSE,"Le Mothais sur Feuille, Mothais",NA,NA
+MouCo Camembert,https://www.cheese.com/mouco-camembert/,cow,United States,Colorado,Camembert,"soft, artisan",NA,NA,"creamy, soft",bloomy,ivory,"creamy, milky","caramel, clean, grassy",NA,NA,NA,NA,MouCo Cheese Company
+MouCo Truffello,https://www.cheese.com/truffello/,cow,United States,Colorado,NA,"soft, artisan, soft-ripened",12%,NA,"creamy, soft",washed,white,"buttery, creamy, earthy","earthy, fruity",NA,NA,Truffello,NA,MouCo Cheese Company
+Mountain Goat,https://www.cheese.com/mountain-goat/,goat,Australia,"Mornington Peninsula, Melbourne",Tomme,"hard, artisan",NA,NA,firm,natural,ivory,NA,subtle,TRUE,FALSE,NA,NA,BoatShed Cheese
+Mountain Herbs Rebel,https://www.cheese.com/mountain-herbs-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,"creamy, smooth",natural,pale yellow,"creamy, nutty, spicy","aromatic, grassy, sweet",NA,NA,Bergkräuterrebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Mountain Rebel,https://www.cheese.com/mountain-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,"compact, creamy",natural,yellow,"creamy, fruity","aromatic, spicy",NA,NA,Bergrebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Mountaineer,https://www.cheese.com/mountaineer/,cow,United States,"Galax, Virginia",NA,"semi-soft, artisan",NA,NA,"dense, supple",washed,yellow,"full-flavored, mellow","caramel, fruity, rich",NA,NA,NA,NA,Meadow Creek Dairy
+Mouse House Chilli Cheddar,https://www.cheese.com/mouse-house-chilli-cheddar/,cow,England,NA,NA,semi-hard,NA,NA,creamy,waxed,golden orange,spicy,spicy,TRUE,FALSE,NA,NA,Lymn Bank Farm
+Mouse House Garlic & Chive Cheddar,https://www.cheese.com/mouse-house-garlic-chive-cheddar/,cow,England,NA,NA,semi-hard,NA,NA,creamy,waxed,green,garlicky,garlicky,TRUE,FALSE,NA,NA,Lymn Bank Farm
+Mouse House Smoked Cheddar,https://www.cheese.com/mouse-house-smoked-cheddar/,cow,England,NA,NA,semi-hard,NA,NA,creamy,waxed,golden yellow,smokey,smokey,TRUE,FALSE,NA,NA,Lymn Bank Farm
+Mozzarella,https://www.cheese.com/mozzarella/,"cow, goat, sheep, water buffalo",Italy,NA,NA,"semi-soft, brined",NA,NA,"elastic, stringy",rindless,white,"mild, sweet","fresh, milky",NA,NA,mozerrela,NA,NA
+Mozzarella (Australian),https://www.cheese.com/mozzarella-australian/,"cow, water buffalo",Australia,NA,Pasta filata,"semi-soft, brined",45%,NA,"springy, stringy, supple",rindless,white,milky,"fresh, milky",TRUE,FALSE,NA,Australian Mozzarella,NA
+Mozzarella di Bufala DOP,https://www.cheese.com/mozzarella-di-bufala/,water buffalo,Italy,"Campania, Paestum, Foggia",Pasta filata,"soft, brined",21%,NA,"creamy, smooth, springy",NA,white,"floral, mild, milky, sour",fresh,FALSE,FALSE,"Buffalo mozzarella, Mozzarella di Bufala Campana",NA,Casa Madaio
+Mozzarellissima,https://www.cheese.com/mozzarellissima/,cow,United States,NA,Pasta filata,"semi-soft, artisan",NA,NA,"elastic, springy, stringy, supple",rindless,pale yellow,"buttery, mild, milky",mild,TRUE,FALSE,Italiano 4 Formaggi Shredded Cheese,"Lite Mozzarellissima, Mozzarellissima Shredded Cheese",Saputo Dairy Products Canada G.P.
+Ms. Natural,https://www.cheese.com/ms-natural/,goat,United States,California,NA,fresh soft,NA,NA,"creamy, crumbly, smooth, spreadable",rindless,white,"citrusy, creamy","fresh, mild",TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Mt Scott,https://www.cheese.com/mt-scott/,cow,New Zealand,Queenstown,NA,semi-soft,NA,NA,"smooth, supple",natural,yellow,"buttery, sharp, sweet",NA,TRUE,FALSE,Mt Scott Havarti,NA,The Gibbston Valley Cheese Company
+Mt Tam,https://www.cheese.com/mt-tam/,cow,United States,California,NA,soft,NA,NA,"creamy, firm",bloomy,pale yellow,"buttery, earthy","grassy, mushroom, rich",TRUE,FALSE,NA,NA,Cowgirl Creamery
+Mt. Mazama Cheddar,https://www.cheese.com/mt-mazama-cheddar/,"cow, goat",United States,Oregon,Cheddar,semi-hard,NA,NA,"creamy, crumbly, crystalline, dense",natural,pale yellow,"acidic, caramel, nutty, sweet, tangy","pleasant, rich",TRUE,FALSE,Mount Mazama,NA,Rogue Creamery
+Muddlewell,https://www.cheese.com/muddlewell/,"cow, sheep",England,North Wootton,NA,"hard, artisan",NA,NA,"creamy, crumbly",natural,ivory,mild,strong,TRUE,FALSE,NA,NA,Wootton Organic Dairy
+Muenster,https://www.cheese.com/muenster/,cow,United States,NA,NA,"soft, processed",NA,NA,smooth,NA,pale yellow,mild,"pungent, strong",NA,NA,NA,American Muenster,NA
+Muffato,https://www.cheese.com/muffato/,cow,Italy,"Treviso, Veneto",Blue,"semi-firm, artisan",NA,NA,flaky,NA,ivory,"full-flavored, grassy, herbaceous",herbal,NA,NA,NA,NA,Moro Latteria di Moro Sergio
+Mun-chee,https://www.cheese.com/mun-chee/,cow,United States,"Richfield, Wisconsin",NA,"semi-soft, processed",NA,NA,"creamy, smooth",natural,pale yellow,"mild, sweet",mild,NA,NA,"Munchee, Sweet Munchee",NA,NA
+Munster,https://www.cheese.com/munster/,cow,France,NA,NA,soft,NA,NA,smooth,washed,NA,"savory, spicy, sweet",NA,NA,NA,"Munster-géromé, Minschterkaas",Munster gerome,NA
+Muranda Blue,https://www.cheese.com/muranda-blue/,cow,United States,NY,Blue,"semi-hard, blue-veined",NA,NA,crumbly,natural,pale yellow,"salty, sharp, strong",strong,NA,NA,NA,NA,Muranda Cheese Company
+Murazzano DOP,https://www.cheese.com/murazzano-dop/,"cow, goat",Italy,Murazzano,NA,"fresh soft, processed",NA,NA,soft,rindless,white,spicy,mild,NA,NA,NA,NA,La Casera srl
+Murol,https://www.cheese.com/murol/,cow,France,NA,NA,"semi-soft, artisan",45%,NA,"creamy, firm, springy",washed,ivory,"mild, milky, savory, sweet",aromatic,FALSE,FALSE,NA,"murolait, trou de murol",La Fromagerie du Grand Murols
+Mycella,https://www.cheese.com/mycella/,cow,Denmark,Bornholm,NA,"soft, artisan, blue-veined",50-60%,NA,crystalline,rindless,pale yellow,mild,NA,FALSE,FALSE,Danish Gorgonzola,NA,Various
+Myzithra,https://www.cheese.com/myzithra/,"goat, sheep",Greece,NA,Cottage,"soft, whey",NA,NA,"creamy, crumbly, spreadable",NA,white,mild,NA,NA,NA,"Xinomizythra, Sour Mizythra, Mizythra, Fresh Mizythra, Mizithra, Dry Mizythra",NA,NA
+Météorite,https://www.cheese.com/meteorite/,cow,Canada,Quebec,Blue,"soft, blue-veined",37%,NA,"creamy, supple",ash coated,straw,mild,NA,NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+P'tit Basque,https://www.cheese.com/ptit-basque/,sheep,France,"Basque, Pyrenees Mountains",NA,"semi-hard, artisan",45%,NA,"creamy, dry, smooth",natural,ivory,"earthy, mild, nutty, pungent, subtle, sweet","fresh, lactic, pleasant",NA,NA,NA,Petit Basque,NA
+P'tit Berrichon,https://www.cheese.com/ptit-berrichon/,goat,France,Berry,NA,"soft, artisan",NA,NA,soft-ripened,leaf wrapped,white,sweet,goaty,FALSE,FALSE,NA,NA,NA
+Pacific Rock,https://www.cheese.com/pacific-rock/,cow,Canada,Quebec,NA,hard,30%,NA,"crumbly, firm",washed,orange,"buttery, earthy, nutty, subtle","fruity, rich",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Pack Square,https://www.cheese.com/pack-square/,cow,United States,Fairview,Brie,"semi-soft, artisan, soft-ripened",NA,NA,"buttery, creamy, soft, soft-ripened",bloomy,white,"buttery, creamy, earthy, spicy","grassy, rich",NA,NA,NA,NA,Looking Glass Creamery
+Paesanella Bocconcini,https://www.cheese.com/paesanella-bocconcin/,cow,Australia,NA,Mozzarella,semi-soft,NA,NA,elastic,rindless,white,"creamy, salty, sweet","milky, sweet",TRUE,FALSE,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Buffalo Mozzarella,https://www.cheese.com/paesanella-buffalo-mozzarella/,"buffalo, cow",Australia,New South Wales,Mozzarella,NA,25.8 g/100g,NA,,rindless,white,NA,NA,TRUE,FALSE,Bufala Mozzarella,NA,Paesanella Cheese Manufacturers
+Paesanella Buffalo Ricotta,https://www.cheese.com/paesanella-buffalo-ricotta/,buffalo,Australia,NA,NA,"fresh soft, whey",NA,NA,"creamy, soft",NA,white,creamy,"milky, sweet",NA,NA,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Burrata,https://www.cheese.com/paesanella-burrata/,cow,Australia,NA,Mozzarella,"fresh soft, artisan",25.4 g/100g,NA,"creamy, soft",NA,white,"buttery, milky, smooth","fresh, milky, rich, sweet",NA,NA,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Caciotta,https://www.cheese.com/paesanella-caciotta/,cow,Australia,NA,Caciotta,semi-soft,NA,NA,"buttery, soft",rindless,cream,"mild, milky","buttery, milky",NA,NA,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Caciotta with Rocket & Chilli,https://www.cheese.com/paesanella-caciotta-with-rocket-chilli/,cow,Australia,NA,Caciotta,semi-soft,NA,NA,"buttery, soft",NA,ivory,"herbaceous, spicy",herbal,NA,NA,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Cherry Bocconcini,https://www.cheese.com/paesanella-cherry-bocconcin/,cow,Australia,NA,Mozzarella,semi-soft,NA,NA,elastic,rindless,white,"creamy, salty, sweet","milky, sweet",TRUE,FALSE,Cherry Bocconcini,NA,Paesanella Cheese Manufacturers
+Paesanella Dry Ricotta,https://www.cheese.com/paesanella-dry-ricotta/,cow,Australia,NA,NA,"soft, whey",NA,NA,firm,NA,white,"creamy, salty",sweet,NA,NA,NA,NA,Paesanella Cheese Manufacturers
+Paesanella Fresco,https://www.cheese.com/paesanella-fresco/,cow,Australia,NA,Pecorino,"semi-hard, artisan",18.4 g/100g,NA,"chewy, firm",rindless,pale yellow,"mild, tangy",mild,TRUE,FALSE,Pecorino Fresco,Fresco Fresh Pecorino style,Paesanella Cheese Manufacturers
+Paesanella Fresh Ricotta,https://www.cheese.com/paesanella-fresh-ricotta/,cow,Australia,NA,NA,fresh soft,NA,NA,firm,NA,white,creamy,"fresh, sweet",NA,NA,Paesanella Fresh Pure Milk Ricotta,NA,Paesanella Cheese Manufacturers
+Paglierino,https://www.cheese.com/paglierino/,sheep,Italy,Campania,NA,semi-hard,NA,NA,"compact, elastic",natural,pale yellow,"citrusy, spicy, sweet",grassy,FALSE,FALSE,NA,NA,Casa Madaio
+Paillot de Chèvre,https://www.cheese.com/paillot-de-chevre/,goat,Canada,Quebec,NA,"soft, soft-ripened",26%,NA,"firm, runny, smooth, soft-ripened",bloomy,white,"acidic, nutty, tangy","goaty, lactic",NA,NA,NA,NA,La Maison Alexis de Portneuf Inc.
+Palet de Babligny,https://www.cheese.com/palet-de-babligny/,cow,France,Burgundy,NA,soft,NA,NA,creamy,washed,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Pallone di Gravina,https://www.cheese.com/pallone-di-gravina/,cow,Italy,"Gravina in Puglia, Murgia",Italian Cheese,"semi-hard, artisan",NA,NA,smooth,NA,golden yellow,"spicy, strong",NA,NA,NA,Ball of Gravina,NA,Caseificio Artigianale dei Fratelli Derosa
+Paneer,https://www.cheese.com/paneer/,"cow, water buffalo","Bangladesh, India",NA,Cottage,fresh firm,20.8 g/100g,208 mg/100g,"crumbly, firm",rindless,white,milky,"fresh, milky",TRUE,FALSE,"Chhena, Chhana",NA,NA
+Panela,https://www.cheese.com/panela/,cow,Mexico,NA,Cottage,"fresh firm, artisan",NA,NA,"creamy, crumbly",NA,white,NA,"fresh, mild",NA,NA,Queso Panela,NA,NA
+Paniolo,https://www.cheese.com/paniolo/,cow,United States,Vermont,NA,"soft, artisan",NA,NA,"buttery, creamy, runny",washed,pale yellow,"buttery, creamy, meaty",mushroom,NA,NA,NA,NA,Willow Hill Farm
+Pannerone,https://www.cheese.com/pannerone/,cow,Italy,Lodi,NA,"soft, semi-soft, artisan",50%,NA,"creamy, grainy, open",natural,ivory,"bitter, buttery, smooth, sweet",rich,FALSE,FALSE,NA,NA,NA
+Panquehue,https://www.cheese.com/panquehue/,cow,Chile,Aconcagua,NA,"semi-soft, artisan",NA,NA,"open, smooth",natural,cream,"creamy, nutty, savory, spicy",NA,TRUE,FALSE,NA,NA,Andes Cheese
+Pant ys Gawn,https://www.cheese.com/pant-ys-gawn/,goat,Wales,NA,NA,fresh soft,NA,NA,creamy,rindless,white,"lemony, salty",NA,NA,NA,"Pant-Ys-Gawn, Pant-Ysgawn",NA,Abergavenny Fine Foods
+Paprika Rebel,https://www.cheese.com/paprika-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,creamy,natural,pale yellow,"smokey , spicy","smokey, spicy",NA,NA,Paprikarebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Parmesan,https://www.cheese.com/parmesan/,cow,Italy,NA,Parmesan,"hard, artisan",NA,NA,"dense, grainy",natural,straw,"salty, savory","nutty, strong",FALSE,FALSE,"Parmigiano Reggiano, Parmesan Regiano, Parmesan Reggiano, Parmesan Parmigiano",NA,NA
+Parrano,https://www.cheese.com/parrano/,cow,Netherlands,Het Groene Hart,Gouda,"hard, artisan",NA,NA,"creamy, firm, open, smooth",plastic,pale yellow,"buttery, creamy, nutty, savory, sweet",NA,NA,NA,"Parrano Originale, Parrano Robusto, Parrano Olifesta",NA,Uniekaas Nederland B.V.
+Pas de l'Escalette,https://www.cheese.com/pas-de-lescalette/,cow,France,Larzac,NA,"semi-hard, artisan",NA,NA,creamy,natural,straw,sweet,fresh,FALSE,FALSE,NA,NA,NA
+Passendale,https://www.cheese.com/passendale/,cow,Belgium,Passendale,NA,"semi-soft, soft-ripened",28%,NA,creamy,natural,golden orange,mild,sweet,FALSE,FALSE,NA,NA,NA
+Pastoral,https://www.cheese.com/pastoral/,goat,United States,California,NA,soft,NA,NA,smooth,NA,white,"creamy, herbaceous","fresh, goaty",NA,NA,NA,NA,Andante Dairy
+Pate de Fromage,https://www.cheese.com/pate-de-fromage/,"goat, sheep",France,NA,NA,soft,50%,NA,,NA,NA,NA,NA,FALSE,FALSE,"Pâte de fromage, Pâte fromage",NA,NA
+Patefine Fort,https://www.cheese.com/patefine-fort/,cow,France,Isere,NA,"soft, artisan",NA,NA,smooth,natural,white,sour,fresh,FALSE,FALSE,NA,NA,NA
+Pave d'Affinois,https://www.cheese.com/pave-daffinois/,cow,France,NA,NA,"fresh soft, soft-ripened",60%,NA,"creamy, smooth",bloomy,ivory,"grassy, mild, milky, sweet","fresh, milky, pleasant",TRUE,FALSE,Fromager D'Affinois,Pave Affinois,Fromagerie GUILLOTEAU
+Pave d'Auge,https://www.cheese.com/pave-dauge/,cow,France,NA,NA,semi-soft,50%,NA,smooth,washed,pale yellow,"buttery, smooth",NA,FALSE,FALSE,"pavé de Moyaux, pavé du Plessis, Trouville",NA,Various
+Pave de Chirac,https://www.cheese.com/pave-de-chirac/,goat,France,Chirac,NA,"soft, artisan",NA,NA,"creamy, smooth",natural,ivory,mild,fresh,FALSE,FALSE,NA,NA,NA
+Pawlet,https://www.cheese.com/pawlet/,cow,United States,Vermont,Swiss Cheese,"semi-hard, artisan",NA,NA,"buttery, creamy",natural,pale yellow,"creamy, meaty, mushroomy, nutty","herbal, mushroom, stinky",TRUE,FALSE,NA,NA,Consider Bardwell Farm
+Paški Sir (PDO),https://www.cheese.com/paski-sir/,sheep,Croatia,Island of Pag,NA,"hard, artisan",NA,NA,"crumbly, flaky, grainy",natural,yellow,"salty, savory, tangy",pleasant,NA,NA,"Pag Cheese, Pag Island Cheese",Paski Sir,NA
+Peau Rouge,https://www.cheese.com/peau-rouge/,cow,Canada,Quebec,NA,hard,25%,NA,"crumbly, firm",washed,pale yellow,"caramel, nutty, woody",strong,NA,NA,NA,NA,Les Dépendances
+Pecorino,https://www.cheese.com/pecorino/,sheep,Italy,NA,Pecorino,hard,NA,NA,creamy,NA,pale yellow,NA,NA,FALSE,FALSE,NA,NA,NA
+Pecorino a Latte Crudo,https://www.cheese.com/pecorino-a-latte-crudo/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"compact, crumbly",natural,straw,"piquant, savory, sharp, smooth",strong,NA,NA,NA,NA,Romaniae Terrae
+Pecorino al Pepe,https://www.cheese.com/pecorino-al-pepe/,sheep,Italy,Tuscany,Pecorino,"semi-soft, artisan",NA,NA,"compact, crumbly",natural,cream,"sharp, spicy, strong","pleasant, strong",NA,NA,NA,NA,Caseificio Pinzani Srl
+Pecorino al Tartufo,https://www.cheese.com/pecorino-al-tartufo/,sheep,Italy,NA,Pecorino,"hard, artisan",NA,NA,firm,NA,cream,"sharp, spicy","grassy, nutty",NA,NA,Truffle Pecorino,NA,NA
+Pecorino alla Canapa,https://www.cheese.com/pecorino-all-canapa/,sheep,Italy,Emilia Romagna,Pecorino,"semi-soft, artisan",NA,NA,smooth,leaf wrapped,white,"herbaceous, smooth","aromatic, herbal",NA,NA,Pecorino Canapa,NA,NA
+Pecorino allo Zafferano,https://www.cheese.com/pecorino-allo-zafferano/,sheep,Italy,Tuscany,Pecorino,"semi-hard, artisan",NA,NA,"buttery, compact, smooth",natural,yellow,"creamy, floral, mild, sweet","aromatic, rich",NA,NA,NA,NA,"Caseificio Pinzani Srl , Romaniae Terrae"
+Pecorino Barba Del Passatore,https://www.cheese.com/pecorino-barba-del-passatore/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"buttery, smooth, soft",natural,white,"buttery, smooth","pronounced, strong",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Boccondilatte,https://www.cheese.com/pecorino-boccondilatte/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,crumbly,natural,white,"subtle, sweet",pronounced,NA,NA,NA,NA,Romaniae Terrae
+Pecorino Camomilla,https://www.cheese.com/pecorino-camomilla/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"creamy, smooth, soft",natural,white,"creamy, smooth","aromatic, floral",NA,NA,NA,NA,NA
+Pecorino Con Caglio Vegetale,https://www.cheese.com/pecorino-con-caglio-vegetale/,sheep,Italy,Tuscany,Pecorino,"semi-hard, artisan",NA,NA,"compact, creamy",natural,white,"bitter, subtle, vegetal","earthy, pleasant",TRUE,FALSE,NA,NA,Caseificio Pinzani Srl
+Pecorino dei Malatesta al Sangiovese,https://www.cheese.com/pecorino-dei-malatesta-al-sangiovese/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,smooth,natural,white,"acidic, smooth, subtle, sweet",pleasant,NA,NA,NA,NA,Romaniae Terrae
+Pecorino Dei Malatesta Sotto Cenere,https://www.cheese.com/pecorino-dei-malatesta-sotto-cenere/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,smooth,ash coated,white,"smooth, subtle, sweet","fresh, milky",NA,NA,NA,NA,Romaniae Terrae
+Pecorino dei Monaci,https://www.cheese.com/pecorino-dei-monaci/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"creamy, smooth",natural,white,"milky, smooth, sweet","milky, sweet",NA,NA,NA,NA,Romaniae Terrae
+Pecorino di Sogliano,https://www.cheese.com/pecorino-di-sogliano/,sheep,Italy,Emilia-Romagna,Pecorino,"hard, artisan",NA,NA,"crumbly, grainy",washed,white,"earthy, meaty, strong","pungent, strong",FALSE,FALSE,NA,NA,Romaniae Terrae
+Pecorino di Talamello,https://www.cheese.com/pecorino-di-talamello/,sheep,Italy,Emilia-Romagna,Pecorino,hard,NA,NA,"compact, crumbly, grainy",washed,white,"earthy, meaty, strong","pungent, rich, strong",FALSE,FALSE,NA,NA,Romaniae Terrae
+Pecorino di Vigna,https://www.cheese.com/pecorino-di-vigna/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"smooth, soft",leaf wrapped,straw,"herbaceous, smooth","aromatic, herbal",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Erica,https://www.cheese.com/pecorino-erica/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"compact, smooth",natural,white,"milky, subtle, sweet","aromatic, floral, strong",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Fiordaliso,https://www.cheese.com/pecorino-fiordaliso/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,compact,natural,ivory,"smooth, subtle","aromatic, floral, pronounced",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Foglie Noci,https://www.cheese.com/pecorino-foglie-noci/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"crumbly, grainy",leaf wrapped,white,"earthy, milky, nutty, sweet","fresh, nutty",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Gelsomino,https://www.cheese.com/pecorino-gelsomino/,sheep,Italy,Emilia-Romagna,Pecorino,"hard, artisan",NA,NA,"crumbly, flaky",natural,ivory,"creamy, milky","aromatic, floral",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Ginepro,https://www.cheese.com/pecorino-ginepro/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"chalky, grainy",natural,pale white,"fruity, savory","fruity, woody",NA,NA,NA,NA,NA
+Pecorino Gran Riserva Del Passatore,https://www.cheese.com/pecorino-gran-riserva-del-passatore/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,smooth,natural,white,"smooth, sweet","fresh, milky, pleasant",NA,NA,NA,NA,Romaniae Terrae
+Pecorino in Walnut Leaves,https://www.cheese.com/pecorino-in-walnut-leaves/,sheep,Italy,Emilia-Romagna,Pecorino,"hard, artisan",40%,NA,"dense, firm",leaf wrapped,white,"buttery, herbaceous, nutty, sweet","earthy, herbal",FALSE,FALSE,"Pecorino Foglie De Noce, Pecorino Aged in Walnut Leaves",Walnut tree leaf Pecorino cheese,NA
+Pecorino Mallo di Noce,https://www.cheese.com/pecorino-mallo-di-noce/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"crumbly, grainy",natural,white,"milky, nutty, subtle, sweet",nutty,NA,NA,NA,NA,Romaniae Terrae
+Pecorino nel fieno,https://www.cheese.com/pecorino-nel-fieno/,sheep,Italy,Pienza,NA,"soft, artisan",NA,NA,soft,natural,pale yellow,NA,NA,NA,NA,NA,NA,La Casera srl
+Pecorino Nel Granaio,https://www.cheese.com/pecorino-nel-granaio/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"creamy, crumbly, smooth, soft",natural,pale yellow,creamy,"rich, strong",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Nero,https://www.cheese.com/pecorino-nero/,sheep,Italy,Tuscany,NA,"soft, artisan",NA,NA,"creamy, smooth",NA,ivory,"pungent, subtle","mild, milky",NA,NA,NA,NA,Caseificio Pinzani Srl
+Pecorino Ortica,https://www.cheese.com/pecorino-ortica/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-soft, artisan",NA,NA,"compact, smooth, soft",leaf wrapped,straw,"herbaceous, piquant, savory, sharp","aromatic, strong",NA,NA,Pecorino Ortica a latte Crudo,NA,Romaniae Terrae
+Pecorino Papavero,https://www.cheese.com/pecorino-papavero/,sheep,Italy,Emilia-Romagna,Pecorino,"hard, artisan",NA,NA,"crumbly, flaky",natural,straw,"floral, herbaceous, smooth","aromatic, floral, herbal",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Pepato Mitica® Aged,https://www.cheese.com/pecorino-pepato/,sheep,Italy,Sardegna,NA,"semi-firm, artisan",NA,NA,"creamy, flaky",natural,ivory,"spicy, tangy",spicy,NA,NA,"Pepato, Pecorino Pepato Mitica® Aged",NA,NA
+Pecorino Pera,https://www.cheese.com/pecorino-pera/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"crumbly, smooth",natural,ivory,"fruity, smooth, subtle, sweet","fruity, milky, sweet",NA,NA,NA,NA,Romaniae Terrae
+Pecorino Querciaiolo,https://www.cheese.com/pecorino-querciaiolo/,sheep,Italy,Emilia-Romagna,Pecorino,semi-soft,NA,NA,smooth,natural,white,"smooth, subtle",strong,NA,NA,NA,NA,Romaniae Terrae
+Pecorino Romagnolo,https://www.cheese.com/pecorino-romagnolo/,sheep,Italy,Emilia-Romagna,Pecorino,"semi-hard, artisan",NA,NA,"compact, crumbly",natural,ivory,"smooth, subtle",pronounced,NA,NA,NA,NA,Romaniae Terrae
+Pecorino Romano,https://www.cheese.com/pecorino-romano/,sheep,Italy,"Lazio, Sardinia",Pecorino,hard,NA,NA,"crumbly, grainy",natural,pale yellow,"salty, sharp",strong,FALSE,FALSE,Pecorino Romano PDO,NA,NA
+Pecorino Toscanello,https://www.cheese.com/toscanello/,sheep,Italy,Tuscany,Pecorino,semi-hard,NA,NA,"creamy, smooth",NA,pale yellow,mild,nutty,FALSE,FALSE,"Pecorino Toscano, Pecorino Toscano DOP, Tuscan Pecorino, Pecorino Toscanello",NA,NA
+Peekskill Pyramid,https://www.cheese.com/peekskill-pyramid/,cow,United States,Peekskill,Brie,"soft, artisan",50%,NA,creamy,rindless,pale yellow,"buttery, sour, sweet",rich,FALSE,FALSE,NA,NA,Egg Farm Dairy
+Pelardon des Cevennes,https://www.cheese.com/pelardon-des-cevennes/,goat,France,Languedoc,Tomme,"soft, soft-ripened",NA,NA,creamy,natural,white,"acidic, fruity",goaty,FALSE,FALSE,NA,NA,NA
+Pelardon des Corbieres,https://www.cheese.com/pelardon-des-corbieres/,goat,France,Languedoc-Roussillon,NA,soft,45%,NA,,NA,NA,"acidic, sweet",NA,FALSE,FALSE,NA,NA,NA
+Pembrokeshire Extra Mature Cheddar,https://www.cheese.com/pembrokeshire-extra-mature-cheddar/,cow,United Kingdom,Pembrokeshire,Cheddar,"semi-soft, artisan",NA,NA,"crumbly, dense",NA,yellow,"strong, tangy",rich,FALSE,FALSE,Extra Mature Welsh Cheddar,NA,Pembrokeshire Cheese Company
+Pembrokeshire Mature Cheddar,https://www.cheese.com/pembrokeshire-mature-cheddar/,cow,United Kingdom,Pembrokeshire,Cheddar,"semi-soft, artisan",NA,NA,dense,NA,pale yellow,"smooth, tangy",strong,TRUE,FALSE,Mature Welsh Cheddar,NA,Pembrokeshire Cheese Company
+Penamellera,https://www.cheese.com/penamellera/,"cow, goat, sheep",Spain,Asturias,NA,"semi-hard, artisan",NA,NA,"creamy, dense, supple",natural,pale yellow,"acidic, bitter, herbaceous","aromatic, strong",FALSE,FALSE,NA,CUAYAU de Penamellera,NA
+Penbryn,https://www.cheese.com/penbryn/,cow,"Great Britain, United Kingdom, Wales",NA,Gouda,hard,45%,NA,,NA,NA,"buttery, fruity, grassy, nutty, sweet",NA,TRUE,FALSE,NA,NA,NA
+Pencarreg,https://www.cheese.com/pencarreg/,cow,Great Britain,Wales,Brie,"soft, blue-veined",40%,NA,creamy,natural,pale yellow,smooth,rich,FALSE,FALSE,NA,NA,NA
+Pepato,https://www.cheese.com/pepato/,sheep,Italy,NA,NA,"semi-hard, artisan",NA,NA,"creamy, flaky",natural,straw,"salty, spicy",spicy,NA,NA,Pecorino Pepato,NA,NA
+PepBert,https://www.cheese.com/pepbert/,cow,United States,Colorado,Camembert,"soft, artisan",NA,NA,soft-ripened,NA,ivory,"creamy, spicy","buttery, strong",NA,NA,MouCo PepBert,NA,MouCo Cheese Company
+Pepper Jack,https://www.cheese.com/pepper-jack/,cow,United States,"Monterey, California",Monterey Jack,semi-soft,NA,NA,"creamy, smooth",natural,cream,"herbaceous, spicy","aromatic, herbal",NA,NA,NA,NA,NA
+Pepper Rebel,https://www.cheese.com/pepper-rebel/,cow,Austria,Sulzberg,NA,"semi-hard, artisan",50%,NA,creamy,natural,yellow,"creamy, spicy","grassy, spicy, sweet",NA,NA,Pfefferrebell,NA,Sulzberger Käserebellen Sennerei GmbH
+Peppercorn Gouda,https://www.cheese.com/peppercorn-gouda/,cow,United States,Utah,Gouda,"semi-hard, artisan",NA,NA,"compact, crumbly, dense",natural,yellow,"mild, nutty, sharp, spicy",NA,NA,NA,NA,NA,Rockhill Creamery
+Perl Las Blue,https://www.cheese.com/perl-las-blue/,cow,United Kingdom,NA,NA,semi-soft,NA,NA,creamy,NA,golden yellow,"creamy, salty",NA,NA,NA,NA,NA,NA
+Perl Wen,https://www.cheese.com/perl-wen/,cow,"United Kingdom, Wales",NA,NA,semi-soft,NA,NA,"creamy, smooth, soft",NA,white,citrusy,NA,NA,NA,NA,NA,NA
+Perlagrigia Sotto Cenere,https://www.cheese.com/perlagrigia-sotto-cenere/,cow,Italy,Veneto,NA,soft,NA,NA,compact,ash coated,pale yellow,"sharp, spicy","aromatic, smokey",FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Perroche,https://www.cheese.com/perroche/,goat,United Kingdom,"Herefordshire, West Midlands",NA,"fresh soft, artisan",NA,NA,,NA,white,"lemony, mild","aromatic, fresh, herbal",TRUE,FALSE,NA,NA,Neal's Yard Creamery
+Petida,https://www.cheese.com/petida/,cow,Germany,NA,NA,"soft, brined",55%,190 mg/100g,creamy,artificial,white,"mild, milky","clean, fresh",TRUE,FALSE,NA,NA,Bergader Privatkäserei GmbH
+Petit Blaja,https://www.cheese.com/petit-blaja/,goat,France,NA,NA,soft,NA,NA,soft,NA,golden yellow,NA,NA,NA,NA,NA,NA,NA
+Petit Pardou,https://www.cheese.com/petit-pardou/,cow,France,Laruns,NA,semi-hard,50%,NA,,natural,NA,NA,aromatic,FALSE,FALSE,NA,NA,NA
+Petit-Suisse,https://www.cheese.com/petit-suisse/,cow,France,"Normandy, Auvilliers",NA,fresh soft,40%,NA,"creamy, smooth",rindless,white,"mild, sweet",fresh,NA,NA,NA,"double Suisse, double Petit-Suisse, Suisse double",Danone
+Petite Swiss,https://www.cheese.com/petite-swiss/,cow,United States,Wisconsin,NA,semi-hard,NA,NA,compact,natural,pale yellow,"mild, nutty, sweet","fresh, fruity",NA,NA,NA,NA,Emmi Roth USA
+Phoebe,https://www.cheese.com/phoebe/,"goat, sheep",United States,"Tieton, Washington",Feta,"fresh soft, brined",NA,NA,soft,rindless,white,"milky, salty",fresh,NA,NA,NA,NA,Tieton Farm & Creamery
+Pianoforte,https://www.cheese.com/pianoforte/,cow,United States,California,NA,"soft, soft-ripened",NA,NA,"creamy, soft",natural,pale yellow,"acidic, mushroomy, nutty",pleasant,NA,NA,NA,NA,Andante Dairy
+Piave,https://www.cheese.com/piave/,cow,Italy,Veneto,NA,"hard, artisan",NA,NA,"crystalline, dense, flaky",natural,NA,NA,NA,NA,NA,NA,NA,NA
+Piave Fresco,https://www.cheese.com/piave-fresco/,cow,Italy,Veneto,Parmesan,"semi-hard, artisan",NA,NA,"creamy, smooth",natural,ivory,"mild, milky, smooth","mild, milky, pleasant",NA,NA,NA,NA,NA
+Piave Mezzano,https://www.cheese.com/piave-mezzano/,cow,,Veneto,Parmesan,"hard, artisan",NA,NA,"dense, firm",natural,pale yellow,"full-flavored, salty, sharp, strong","milky, pleasant",NA,NA,NA,NA,NA
+Piave Vecchio DOP,https://www.cheese.com/piave-vecchio/,cow,Italy,Veneto,Parmesan,"hard, artisan",NA,NA,"dense, firm, flaky",natural,golden yellow,"nutty, strong, sweet",NA,NA,NA,Piave Stravecchio,NA,NA
+Piave Vecchio Selezione Oro,https://www.cheese.com/piave-vecchio-selezione-oro/,cow,Italy,Veneto,Parmesan,"hard, artisan",NA,NA,"crumbly, dense, flaky, grainy",natural,yellow,"fruity, full-flavored, mild, sweet",NA,NA,NA,Piave Vecchio Gold Selection,NA,NA
+Picobello,https://www.cheese.com/picobello/,cow,Netherlands,Huizen,Gouda,hard,45%,NA,"crumbly, open",waxed,golden yellow,"caramel, nutty, sweet",NA,NA,NA,NA,"Picobello Fino, Picobello Maturo",Westland Kaasexport B.V.
+Picodon de Chevre,https://www.cheese.com/picodon-de-chevre/,goat,France,NA,NA,"soft, artisan",NA,NA,"firm, smooth",natural,white,"sour, sweet","goaty, pungent",NA,NA,"Picodon AOC, Picodon PDO, Picodon AOP",NA,NA
+Picolo,https://www.cheese.com/picolo/,cow,United States,California,NA,"soft, artisan",NA,NA,soft-ripened,bloomy,pale yellow,"mushroomy, nutty, sweet",sweet,NA,NA,NA,NA,Andante Dairy
+Picos de Europa,https://www.cheese.com/picos-de-europa/,cow,Spain,NA,Blue,"semi-soft, blue-veined",NA,NA,creamy,NA,blue,"spicy, strong",NA,NA,NA,Picos Blue,NA,NA
+Pied-de-vent,https://www.cheese.com/pied-de-vent/,cow,Canada,Quebec,Blue,"semi-soft, blue-veined",27%,NA,"creamy, smooth",washed,cream,"mushroomy, nutty",rich,FALSE,FALSE,NA,NA,Fromagerie du Pied-De-Vent
+Pierce Pt,https://www.cheese.com/pierce-pt/,cow,United States,California,NA,"soft, artisan",NA,NA,soft,bloomy,pale yellow,"savory, tangy","floral, herbal",TRUE,FALSE,Pierce Point,NA,Cowgirl Creamery
+Pigouille,https://www.cheese.com/pigouille/,sheep,France,Charentes,NA,"soft, artisan",NA,NA,"creamy, crumbly, grainy",mold ripened,ivory,"acidic, salty, sweet, tangy",barnyardy,NA,NA,NA,Pigouille des Charentes,NA
+Pimento,https://www.cheese.com/pimento/,cow,United States,"Ann Arbor, Michigan",NA,"soft, artisan",NA,NA,"creamy, crumbly",rindless,brown,spicy,NA,FALSE,FALSE,NA,NA,Zingerman's Creamery
+Pinconning,https://www.cheese.com/pinconning/,cow,United States,"Pinconning, Michigan",Cheddar,semi-hard,NA,NA,"creamy, open",natural,yellow,"mild, sharp",NA,FALSE,FALSE,NA,NA,Pinconning Cheese Co.
+Piora,https://www.cheese.com/piora/,cow,Switzerland,Piora Valley,NA,hard,NA,NA,,NA,NA,NA,"aromatic, rich",FALSE,FALSE,NA,NA,NA
+Piper's Pyramide,https://www.cheese.com/pipers-pyramide/,goat,United States,Indiana,Brie,"soft, artisan, soft-ripened",NA,NA,"creamy, dense, fluffy, soft-ripened",bloomy,white,"buttery, milky, sweet","lactic, musty",FALSE,FALSE,NA,NA,Capriole Goat Cheese
+Pistol Point Cheddar,https://www.cheese.com/pistol-point-cheddar/,cow,United States,Oregon,Cheddar,"semi-hard, artisan",NA,NA,"creamy, crumbly",natural,pale yellow,"smokey , spicy",NA,TRUE,FALSE,NA,NA,Rogue Creamery
+Pitchfork Cheddar,https://www.cheese.com/pitchfork-cheddar/,cow,England,NA,NA,hard,NA,NA,"compact, creamy, firm",cloth wrapped,pale yellow,"acidic, fruity, grassy, nutty, tangy","buttery, fruity, grassy, nutty, subtle",FALSE,FALSE,NA,NA,Trethowan Brothers
+Pithtiviers au Foin,https://www.cheese.com/pithtiviers-au-foin/,cow,France,"Centre , the department of Loiret",Camembert,"soft, artisan",NA,NA,smooth,natural,pale yellow,"burnt caramel, fruity",grassy,FALSE,FALSE,Bondaroy au foin,NA,Kraft Foods Company
+Pizy,https://www.cheese.com/pizy/,cow,Canada,Quebec,Tomme,"soft, artisan, soft-ripened",27%,NA,"creamy, smooth, soft",bloomy,ivory,"bitter, buttery, mild, milky, salty",mushroom,NA,NA,NA,NA,Fromagerie La Suisse Normande
+Plancherin d'Arêches,https://www.cheese.com/plancherin-d-areches/,goat,France,NA,NA,soft,NA,NA,"creamy, smooth, soft",natural,white,"creamy, floral, herbaceous, mild, nutty, woody","floral, fresh, herbal, perfumed, woody",FALSE,FALSE,NA,Plancherin d'Areches,Caroline Jouguet
+Pleasant Creek,https://www.cheese.com/pleasant-creek/,goat,United States,Oregon,Swiss Cheese,hard,NA,NA,"open, smooth",natural,yellow,"buttery, mild, nutty","fruity, grassy",TRUE,FALSE,NA,NA,Pholia Farm
+Pleasant Ridge Reserve,https://www.cheese.com/pleasant-ridge-reserve/,cow,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,"crystalline, firm, smooth",washed,golden yellow,"creamy, full-flavored, grassy, nutty",NA,FALSE,FALSE,Uplands Pleasant Ridge,NA,Uplands Cheese Company
+Plymouth Cheese,https://www.cheese.com/plymouth-cheese/,cow,United States,Vermont,Cheddar,"hard, artisan",NA,NA,"firm, flaky, grainy",waxed,yellow,"buttery, fruity, full-flavored, nutty, smokey , spicy, tangy","rich, smokey, strong",NA,NA,NA,NA,Plymouth Artisan Cheese
+Podhalanski,https://www.cheese.com/podhalanski/,"cow, sheep",Poland,NA,NA,"semi-hard, artisan",40%,NA,"creamy, open",natural,pale yellow,smokey,smokey,FALSE,FALSE,NA,NA,NA
+Point Reyes Bay Blue,https://www.cheese.com/point-reyes-bay-blue/,cow,United States,California,Blue,"soft, artisan",NA,NA,"creamy, crumbly",natural,pale yellow,"buttery, caramel, creamy, salty, sweet","earthy, strong",NA,NA,NA,NA,Point Reyes Farmstead Cheese Co
+Point Reyes Original Blue,https://www.cheese.com/point-reyes-original-blue/,cow,United States,California,Blue,"semi-soft, artisan, blue-veined",NA,NA,"buttery, creamy",natural,white,"creamy, milky, strong, sweet","fresh, milky, strong",TRUE,FALSE,Original Blue,NA,Point Reyes Farmstead Cheese Co
+Point Reyes Toma,https://www.cheese.com/point-reyes-toma/,cow,United States,California,NA,"semi-hard, artisan",NA,NA,"buttery, creamy",waxed,ivory,"buttery, creamy",buttery,TRUE,FALSE,NA,NA,Point Reyes Farmstead Cheese Co
+Poivre d'Ane,https://www.cheese.com/poivre-dane/,"cow, goat",France,NA,NA,"soft, artisan",NA,NA,smooth,natural,white,herbaceous,herbal,NA,NA,NA,NA,NA
+Pokolbin,https://www.cheese.com/pokolbin/,cow,Australia,Hunter Valley,NA,"semi-soft, smear-ripened",NA,NA,,washed,NA,"sharp, spicy",NA,FALSE,FALSE,NA,NA,Hunter Valley Cheese Company
+Pompeii,https://www.cheese.com/pompeii/,cow,Australia,South Australia,NA,"semi-hard, artisan, smear-ripened",45%,NA,"crumbly, soft, supple",washed,cream,"herbaceous, sweet",herbal,TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Pong's Extraordinary... Cheddar,https://www.cheese.com/pongs-extraordinary-cheddar/,cow,United Kingdom,NA,NA,"hard, semi-hard",NA,NA,"creamy, crumbly",NA,yellow,NA,NA,NA,NA,NA,NA,NA
+Pont l'Eveque,https://www.cheese.com/pont-leveque/,cow,France,NA,NA,soft,NA,NA,creamy,washed,pale yellow,"creamy, full-flavored",NA,NA,NA,Pont-l'Évêque,NA,NA
+Port Nicholson,https://www.cheese.com/port-nicholson/,cow,New Zealand,NA,NA,semi-soft,40%,NA,"creamy, smooth",washed,orange,"sour, sweet",smokey,NA,NA,Kapiti Port Nicholson,NA,Kapiti Brands NZ Ltd
+Port-Salut,https://www.cheese.com/port-salut/,cow,France,Brittany,NA,semi-soft,72.7%,NA,"creamy, smooth",washed,pale yellow,"acidic, mellow",NA,FALSE,FALSE,NA,"Port du Salut, Port Salut",NA
+Postel,https://www.cheese.com/postel/,cow,Belgium,Postel,NA,NA,NA,NA,,washed,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Pouligny-Saint-Pierre,https://www.cheese.com/pouligny-saint-pierre/,goat,France,Berry,NA,soft,NA,NA,"creamy, crumbly",natural,ivory,"acidic, sweet",goaty,FALSE,FALSE,NA,NA,NA
+Pourly,https://www.cheese.com/pourly/,goat,France,Burgund,NA,"soft, artisan",45%,NA,smooth,natural,pale yellow,sweet,fresh,FALSE,FALSE,NA,NA,NA
+Prairie Breeze Cheddar,https://www.cheese.com/prairie-breeze-cheddar/,cow,United States,Iowa,Cheddar,"hard, artisan",NA,NA,"creamy, crumbly, grainy",natural,yellow,"nutty, sharp, sweet","grassy, mild",TRUE,FALSE,NA,NA,Milton Creamery LLC
+Prairie Rose,https://www.cheese.com/prairie-rose/,,United States,Iowa,Swiss Cheese,"semi-hard, artisan",NA,NA,"creamy, open",natural,yellow,"creamy, full-flavored, smooth","grassy, mild, nutty",TRUE,FALSE,NA,NA,Milton Creamery LLC
+Prairie Tomme,https://www.cheese.com/prairie-tomme/,sheep,United States,Missouri,Tomme,"semi-hard, artisan",NA,NA,"firm, smooth",natural,cream,"buttery, nutty",NA,FALSE,FALSE,NA,NA,Green Dirt Farm
+Prastost,https://www.cheese.com/prastost/,cow,Sweden,NA,Cheddar,semi-soft,45-50%,NA,creamy,NA,yellow,"salty, spicy, strong","aromatic, rich",FALSE,FALSE,"Priest Cheese, Saaland Pfarr, VODCheese",NA,NA
+President Brie,https://www.cheese.com/president-brie/,cow,France,NA,Brie,"soft, artisan, soft-ripened",NA,NA,"buttery, creamy, runny, spreadable",bloomy,pale yellow,"buttery, creamy, subtle",rich,NA,NA,"President Lingot Brie, President Wee Brie",NA,NA
+President Camembert,https://www.cheese.com/president-camembert/,cow,United States,New York,Camembert,"semi-soft, soft-ripened",NA,NA,"creamy, firm, soft",bloomy,cream,"creamy, earthy",rich,NA,NA,NA,NA,President Cheese
+President Fat Free Feta,https://www.cheese.com/president-fat-free-feta/,cow,"France, United States",New York,Feta,"firm, artisan, brined",0 g/100g,30 mg/100g,crumbly,natural,white,"herbaceous, salty, tangy",fresh,NA,NA,"Fat Free Feta Crumbles, Fat Free Feta Chunk",NA,President Cheese
+President Light Brie,https://www.cheese.com/president-light-brie/,cow,France,New York,Brie,"soft, soft-ripened",NA,NA,"creamy, runny, smooth",bloomy,white,"creamy, mild, nutty, tangy",strong,NA,NA,NA,NA,President Cheese
+President Madrigal,https://www.cheese.com/president-madrigal/,cow,France,NA,Swiss Cheese,"semi-hard, artisan",NA,NA,"smooth, soft",natural,straw,"nutty, sweet",NA,NA,NA,NA,NA,President Cheese
+Prima Donna,https://www.cheese.com/prima-donna/,cow,Netherlands,NA,Parmesan,hard,NA,NA,"crumbly, crystalline, firm, grainy",natural,yellow,"full-flavored, nutty, sweet",NA,NA,NA,NA,NA,Vandersterre Groep International B.V.
+Prima Donna fino,https://www.cheese.com/prima-donna-fino/,cow,Netherlands,NA,Parmesan,hard,30.5 g/100g,921 mg/100g,"crumbly, crystalline, firm",natural,pale yellow,"full-flavored, nutty, sweet",rich,NA,NA,NA,NA,Vandersterre Groep International B.V.
+Prima Donna forte,https://www.cheese.com/prima-donna-forte/,,Netherlands,NA,Parmesan,hard,33 g/100g,990 mg/100g,"crumbly, crystalline, flaky, grainy",natural,yellow,"nutty, strong, sweet",NA,NA,NA,NA,NA,Vandersterre Groep International B.V.
+Prima Donna leggero,https://www.cheese.com/prima-donna-leggero/,cow,Netherlands,NA,Parmesan,hard,18.3 g/100g,1071 mg/100g,"crumbly, crystalline, firm",natural,yellow,"savory, sharp",NA,NA,NA,NA,NA,Vandersterre Groep International B.V.
+Prima Donna maturo,https://www.cheese.com/prima-donna-maturo/,cow,Netherlands,NA,Parmesan,hard,32.3 g/100g,749 mg/100g,"crumbly, crystalline, firm, grainy",natural,yellow,sharp,NA,NA,NA,NA,NA,Vandersterre Groep International B.V.
+Primo Fresco,https://www.cheese.com/primo-fresco/,sheep,United States,California,NA,"fresh soft, artisan",NA,NA,"creamy, fluffy, spreadable",natural,white,"salty, savory, subtle, tangy","fresh, mild",TRUE,FALSE,NA,NA,Weirauch Farm and Creamery
+Prince-Jean,https://www.cheese.com/prince-jean/,cow,Belgium,NA,NA,"fresh soft, artisan",NA,NA,creamy,natural,white,buttery,"fresh, rich",FALSE,FALSE,NA,NA,NA
+Prix de Diane,https://www.cheese.com/prix-de-diane/,cow,United States,Maine,Brie,"soft, artisan",NA,NA,"creamy, soft, soft-ripened",bloomy,pale yellow,"buttery, citrusy, creamy, subtle",rich,TRUE,FALSE,NA,NA,Lakin's Gorges Cheese LLC
+Processed Cheddar,https://www.cheese.com/processed-cheddar/,cow,,NA,Cheddar,"semi-hard, processed",NA,NA,"creamy, smooth",NA,NA,sharp,NA,NA,NA,NA,NA,NA
+Processed Cheese,https://www.cheese.com/pasteurized-processed/,cow,,NA,NA,"soft, processed",NA,NA,"creamy, smooth, spreadable, springy",plastic,NA,NA,NA,FALSE,FALSE,"cheese spread, cheese food, singles",NA,Various
+Processed Smoked Gouda,https://www.cheese.com/processed-smoked-gouda/,cow,United States,Wisconsin,Gouda,"semi-hard, processed",NA,NA,creamy,natural,straw,"creamy, mild, smokey",smokey,NA,NA,NA,NA,Emmi Roth USA
+Promontory,https://www.cheese.com/promontory/,cow,United States,Utah,Cheddar,"hard, artisan",NA,NA,"creamy, firm",NA,pale yellow,"buttery, citrusy","fruity, rich",TRUE,FALSE,NA,NA,Beehive Cheese Company
+Provel,https://www.cheese.com/provel/,cow,United States,"St. Louis, Missouri",Cheddar,"soft, processed",NA,NA,"buttery, gooey",plastic,white,buttery,smokey,FALSE,FALSE,NA,NA,NA
+Providence,https://www.cheese.com/providence/,goat,United States,North Carolina,NA,"semi-soft, artisan",NA,NA,"compact, crumbly",washed,pale yellow,creamy,yeasty,FALSE,FALSE,NA,NA,Goat Lady Dairy
+Provoleta,https://www.cheese.com/provoleta/,water buffalo,Argentina,NA,Pasta filata,"semi-hard, artisan",45%,316 mg/100g,"creamy, smooth, springy",NA,pale yellow,"mild, smokey",fresh,FALSE,FALSE,Spinning Argentine provolone cheese,NA,NA
+Provolone,https://www.cheese.com/provolone/,cow,Italy,Po valley region,Pasta filata,"semi-hard, artisan",NA,NA,firm,NA,pale yellow,tangy,pleasant,NA,NA,NA,NA,NA
+Provolone del Monaco,https://www.cheese.com/provolone-del-monaco/,cow,Italy,Naples,Pasta filata,"semi-hard, artisan",40.5%,157 mg/100g,"firm, grainy",NA,pale yellow,"buttery, sweet",pleasant,FALSE,FALSE,Provolone delmonaco,NA,NA
+Provolone Mandarino Gran Riserva,https://www.cheese.com/provolone-mandarino-gran-riserva/,cow,Italy,Veneto,Pasta filata,"semi-hard, artisan",NA,NA,"elastic, stringy, supple",natural,white,"pronounced, spicy, subtle",spicy,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Provolone Valpadana,https://www.cheese.com/provolone-valpadana/,cow,Italy,Valpadana,Pasta filata,"semi-hard, artisan",NA,NA,firm,NA,pale yellow,full-flavored,rich,FALSE,FALSE,NA,NA,NA
+Président Fresh Goat Cheese,https://www.cheese.com/president-fresh-goat-cheese/,goat,France,Poitou-Charentes,NA,"fresh firm, artisan",NA,NA,"dense, smooth",natural,white,"herbaceous, mild, sharp, smooth, tangy","fresh, goaty",NA,NA,"Président plain goat log, Président herb goat log",NA,President Cheese
+PsycheDillic,https://www.cheese.com/psychedillic/,goat,United States,California,NA,fresh soft,NA,NA,"creamy, smooth, spreadable",rindless,white,"creamy, full-flavored, herbaceous",aromatic,TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Pule,https://www.cheese.com/pule/,donkey,Serbia,Zasavica,NA,artisan,NA,NA,crumbly,NA,white,NA,NA,NA,NA,magareći sir,NA,NA
+Purple Haze,https://www.cheese.com/purple-haze/,goat,United States,California,NA,fresh soft,NA,NA,"creamy, crumbly, smooth, spreadable",rindless,white,"earthy, herbaceous",aromatic,TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Purple's a Must,https://www.cheese.com/purples-must/,"cow, goat","Canada, Italy",Lombardy,Blue,"semi-hard, artisan, blue-veined",NA,NA,"creamy, crumbly",mold ripened,pale yellow,"full-flavored, strong",rich,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Pyengana Cheddar,https://www.cheese.com/pyengana-cheddar/,cow,Australia,Tasmania,Cheddar,"hard, semi-hard, artisan",NA,NA,"creamy, crumbly, smooth",natural,pale yellow,"acidic, full-flavored, herbaceous, savory, spicy","grassy, herbal",FALSE,FALSE,NA,NA,Pyengana Dairy Company
+Pyramide,https://www.cheese.com/pyramide/,goat,France,Loire Valley,NA,"soft, artisan",NA,NA,crumbly,natural,ivory,NA,pungent,FALSE,FALSE,NA,NA,NA
+Pérail de Brebis,https://www.cheese.com/perail-de-brebis/,sheep,France,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",NA,pale yellow,"full-flavored, strong",goaty,NA,NA,"Perail, Pérail",NA,NA
+Saaland Pfarr,https://www.cheese.com/saaland-pfarr/,cow,Sweden,NA,NA,semi-soft,45-50%,NA,creamy,NA,yellow,"salty, spicy, strong","aromatic, rich",FALSE,FALSE,VODCheese,NA,Norrmejerier
+Saanen Silk,https://www.cheese.com/saanen-silk/,goat,Canada,Ontario,NA,"semi-soft, artisan, soft-ripened",NA,NA,"creamy, fluffy, runny, soft-ripened",bloomy,white,"mild, mushroomy","floral, pleasant",FALSE,FALSE,NA,NA,Cross Wind Farm
+Saanenkaese,https://www.cheese.com/saanenkaese/,cow,Switzerland,NA,Parmesan,"hard, processed",NA,NA,"brittle, firm",natural,pale yellow,fruity,strong,FALSE,FALSE,NA,NA,NA
+Saga,https://www.cheese.com/saga/,cow,Denmark,NA,Brie,"soft, blue-veined, soft-ripened",NA,NA,creamy,bloomy,white,"creamy, mild",mild,FALSE,FALSE,"Saga Classic Blue Brie, Saga Blue Brie",NA,NA
+Sage Derby,https://www.cheese.com/sage-derby/,cow,"England, United Kingdom",East Midlands,NA,"semi-hard, artisan",45%,NA,firm,NA,green,"herbaceous, mild",herbal,TRUE,FALSE,NA,NA,NA
+Saint Agur,https://www.cheese.com/saint-agur/,cow,France,Auvergne,Blue,"soft, blue-veined",60%,NA,"creamy, smooth, spreadable",NA,blue,"fruity, mellow, sharp",strong,FALSE,FALSE,NA,"Saint Agur Coupe, Saint Agur Crème",Savencia Fromage & Dairy
+Saint Albray,https://www.cheese.com/saint-albray/,cow,France,Aquitaine,NA,semi-soft,NA,NA,"creamy, smooth",washed,NA,"mild, sweet","buttery, mild",NA,NA,NA,"St. Albray, Saint-Albray, St Albray",NA
+Saint André,https://www.cheese.com/saint-andre/,cow,France,NA,Brie,"soft, soft-ripened",NA,NA,"creamy, dense",bloomy,ivory,"buttery, tangy",rich,NA,NA,NA,"St. Andre, Saint Andre, St André",NA
+Saint Felicien,https://www.cheese.com/saint-felicien/,cow,France,NA,NA,"soft, artisan",NA,NA,creamy,NA,white,creamy,nutty,NA,NA,Saint Félicien,St Felicien,NA
+Saint Honoré,https://www.cheese.com/saint-honore/,cow,Canada,Quebec,Brie,"soft, soft-ripened",NA,NA,"creamy, smooth, soft, soft-ripened, supple",bloomy,cream,"creamy, mild",mild,NA,NA,NA,"Saint-Honoré, St Honoré, St. Honoré",La Maison Alexis de Portneuf Inc.
+Saint Marcellin,https://www.cheese.com/saint-marcellin/,cow,France,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",NA,white,"mild, tangy",NA,FALSE,FALSE,NA,"St Marcellin, Saint-Marcellin",NA
+Saint Nectaire,https://www.cheese.com/saint-nectaire/,cow,France,Auvergne,Tomme,"semi-soft, artisan",NA,NA,"creamy, smooth",washed,ivory,pungent,grassy,FALSE,FALSE,NA,"St. Nectaire, Saint-Nectaire, St Nectaire",NA
+Saint Paulin,https://www.cheese.com/saint-paulin/,cow,France,NA,Saint-Paulin,"semi-soft, artisan",NA,NA,"creamy, firm",washed,NA,"buttery, sweet",milky,NA,NA,NA,Saint-Paulin,Fromagerie Fritz Kaiser
+Saint Rose,https://www.cheese.com/saint-rose/,sheep,United States,California,NA,"semi-soft, artisan",NA,NA,firm,natural,pale yellow,"citrusy, floral, nutty, sharp","grassy, mild",TRUE,FALSE,NA,St. Rose,Weirauch Farm and Creamery
+Sainte-Maure de Touraine AOC,https://www.cheese.com/sainte-maure-de-touraine-aoc/,goat,France,Loire,NA,"semi-soft, artisan",NA,NA,"creamy, fluffy, soft",NA,white,NA,NA,FALSE,FALSE,NA,"Saint ​​Maure de Touraine, St ​​Maure de Touraine, Sainte Maure de Touraine",NA
+Salemville Amish Blue,https://www.cheese.com/salemville-amish-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly",NA,ivory,"creamy, earthy","earthy, rich",NA,NA,Salemville Reserve,NA,"DCI Cheese Company, Inc"
+Salemville Amish Gorgonzola,https://www.cheese.com/salemville-amish-gorgonzola/,cow,United States,Wisconsin,Blue,"semi-soft, artisan, blue-veined",NA,NA,"creamy, crumbly",NA,ivory,"creamy, earthy, piquant","earthy, rich",NA,NA,NA,NA,"DCI Cheese Company, Inc"
+Salemville Smokehaus Blue,https://www.cheese.com/salemville-smokehaus-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan, blue-veined",NA,NA,creamy,NA,ivory,"creamy, smokey","earthy, rich, smokey",NA,NA,NA,NA,"DCI Cheese Company, Inc"
+Salers,https://www.cheese.com/salers/,cow,France,"Auvergne, Salers",NA,semi-hard,NA,NA,,NA,red,"fruity, spicy",NA,FALSE,FALSE,NA,NA,NA
+Salsa Asiago,https://www.cheese.com/salsa-asiago/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"compact, firm, open, smooth",natural,pale yellow,"creamy, garlicky, nutty, sharp, spicy","aromatic, nutty, pungent",TRUE,FALSE,NA,NA,Sartori
+Saltbush Chevre,https://www.cheese.com/saltbush_chevre/,goat,Australia,South Australia,NA,"fresh firm, artisan",45%,NA,creamy,rindless,white,"acidic, grassy, herbaceous, salty","goaty, grassy, herbal",TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Samso,https://www.cheese.com/samso/,cow,Denmark,NA,NA,semi-hard,30-45%,NA,supple,waxed,yellow,"nutty, sour, sweet",NA,FALSE,FALSE,NA,NA,NA
+San Andreas,https://www.cheese.com/san-andreas/,sheep,United States,California,NA,"hard, artisan",NA,NA,"creamy, firm, open, smooth",natural,straw,"butterscotch, nutty, sweet","clean, mild, rich",TRUE,FALSE,NA,NA,Bellwether Farms
+San Geronimo,https://www.cheese.com/san-geronimo/,cow,United States,Nicasio,NA,"semi-soft, brined",NA,NA,smooth,washed,white,"acidic, meaty, mellow, mild, tangy, tart",earthy,FALSE,FALSE,NA,NA,Nicasio Valley Cheese Company
+San Simón DOP,https://www.cheese.com/san-simon/,cow,Spain,Galicia,NA,semi-soft,NA,NA,"buttery, creamy",natural,cream,buttery,"smokey, woody",FALSE,FALSE,NA,NA,NA
+Sancerre,https://www.cheese.com/sancerre/,goat,France,NA,NA,hard,40%,NA,,natural,NA,"nutty, strong",NA,FALSE,FALSE,NA,NA,NA
+Sandy Creek,https://www.cheese.com/sandy-creek/,goat,United States,North Carolina,NA,"soft, soft-ripened",NA,NA,"runny, smooth",mold ripened,ivory,"citrusy, lemony, mushroomy, tangy","earthy, grassy",TRUE,FALSE,NA,NA,Goat Lady Dairy
+Santa Gadea,https://www.cheese.com/santa-gadea/,goat,Spain,NA,NA,"soft, semi-soft",NA,NA,"creamy, smooth, soft",NA,white,NA,NA,NA,NA,NA,NA,NA
+Sao Jorge,https://www.cheese.com/sao-jorge/,cow,Portugal,Azores,NA,semi-hard,NA,NA,"chalky, crumbly, firm",natural,golden yellow,"spicy, tangy",NA,FALSE,FALSE,"S. Jorge cheese, Queijo São Jorge, St. George",NA,NA
+Sap Sago,https://www.cheese.com/sap-sago/,cow,Switzerland,Canton of Glarus,NA,hard,NA,NA,dry,natural,green,NA,herbal,NA,NA,"Schabziger, Swiss Green Cheese",sapsago,NA
+Saporito,https://www.cheese.com/saporito/,cow,Italy,"Treviso, Veneto",NA,"semi-hard, artisan",NA,NA,creamy,natural,ivory,"creamy, mild",herbal,NA,NA,NA,NA,Moro Latteria di Moro Sergio
+Sardo,https://www.cheese.com/sardo/,cow,Argentina,NA,NA,hard,NA,NA,"crumbly, firm, flaky",natural,pale yellow,"full-flavored, salty, sharp",NA,FALSE,FALSE,NA,"Argentine Sardo, Sardo Argentino",NA
+Sarró de Cabra,https://www.cheese.com/sarro-de-cabra/,goat,Spain,Barcelona,NA,"semi-soft, artisan",NA,NA,buttery,cloth wrapped,white,"citrusy, strong, tangy","mild, sweet",NA,NA,Sarro,NA,Formatgeries Montbrú
+Sartori Classic Cheese Asiago,https://www.cheese.com/sartori-classic-cheese-asiago/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"compact, crumbly, open, smooth",natural,NA,"creamy, mild, nutty",pungent,TRUE,FALSE,NA,NA,Sartori
+Sartori Classic Cheese Fontina,https://www.cheese.com/sartori-classic-cheese-fontina/,cow,United States,Wisconsin,NA,"semi-soft, artisan",NA,NA,creamy,natural,pale yellow,"creamy, nutty, smooth, sweet","aromatic, lactic",TRUE,FALSE,NA,NA,Sartori
+Sartori Classic Cheese Parmesan,https://www.cheese.com/sartori-classic-cheese-parmesan/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"crumbly, dry, grainy",natural,yellow,"mellow, nutty, sweet",NA,TRUE,FALSE,NA,NA,Sartori
+Sartori Classic Cheese Romano,https://www.cheese.com/sartori-classic-cheese-romano/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"brittle, crumbly, dense, flaky",natural,straw,"nutty, savory, tangy",spicy,TRUE,FALSE,NA,NA,Sartori
+Sartori Classic MontAmore,https://www.cheese.com/sartori-classic-montamore/,cow,United States,Wisconsin,Parmesan,"semi-hard, artisan",NA,NA,"dense, firm",rindless,pale yellow,"creamy, fruity, sweet, tangy","fresh, fruity, pleasant",TRUE,FALSE,NA,NA,Sartori
+Sartori Limited Edition Cannella BellaVitano,https://www.cheese.com/sartori-limited-edition-cannella-bellavitano/,cow,United States,Wisconsin,NA,hard,NA,NA,creamy,NA,pale yellow,"buttery, fruity, mild, sweet","fresh, rich",TRUE,FALSE,NA,NA,Sartori
+Sartori Limited Edition Cognac BellaVitano,https://www.cheese.com/sartori-limited-edition-cognac-bellavitano/,cow,United States,Northern Wisconsin,NA,hard,NA,NA,creamy,NA,NA,"buttery, mild, smokey , sweet","nutty, smokey",TRUE,FALSE,NA,NA,Sartori
+Sartori Limited Edition Extra-Aged Goat,https://www.cheese.com/sartori-limited-edition-extra-aged-goat/,goat,United States,Wisconsin,NA,"semi-firm, artisan",NA,NA,creamy,NA,NA,"mild, savory","mild, pleasant",NA,NA,NA,NA,Sartori
+Sartori Limited Edition Family Heirloom BellaVitano,https://www.cheese.com/sartori-limited-edition-family-heirloom-bellavitano/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"crumbly, dry",natural,pale yellow,"full-flavored, strong, sweet","fermented, grassy, rich",TRUE,FALSE,NA,NA,Sartori
+Sartori Limited Edition Family Heirloom Parmesan,https://www.cheese.com/sartori-limited-edition-family-heirloom-parmesan/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"crumbly, flaky, grainy",natural,golden yellow,"caramel, fruity, nutty, piquant, sweet","buttery, nutty, woody",TRUE,FALSE,NA,NA,Sartori
+Sartori Limited Edition Pastorale Blend,https://www.cheese.com/sartori-limited-edition-pastorale-blend/,"cow, sheep",United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,creamy,natural,pale yellow,"nutty, sweet","earthy, nutty, sweet",NA,NA,NA,NA,Sartori
+Sartori Reserve Balsamic BellaVitano,https://www.cheese.com/sartori-reserve-balsamic-bellavitano/,cow,United States,Wisconsin,NA,hard,NA,NA,firm,NA,NA,"fruity, nutty, sweet, tangy","earthy, fruity, sweet",NA,NA,NA,NA,Sartori
+Sartori Reserve Basil & Olive Oil Asiago,https://www.cheese.com/sartori-reserve-basil-olive-oil-asiago/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,,NA,pale yellow,"herbaceous, savory, sweet","herbal, rich",NA,NA,NA,NA,Sartori
+Sartori Reserve BellaVitano Gold,https://www.cheese.com/sartori-reserve-bellavitano-gold/,cow,United States,Wisconsin,NA,hard,NA,NA,creamy,natural,pale yellow,"fruity, nutty, sweet","fruity, nutty, rich",NA,NA,NA,NA,Sartori
+Sartori Reserve Black Pepper BellaVitano,https://www.cheese.com/sartori-reserve-black-pepper-bellavitano/,cow,United States,Wisconsin,NA,semi-hard,NA,NA,creamy,natural,pale yellow,"creamy, nutty, salty, spicy","nutty, rich, spicy",NA,NA,NA,NA,Sartori
+Sartori Reserve Chai BellaVitano,https://www.cheese.com/sartori-reserve-chai-bellavitano/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,creamy,natural,pale yellow,"creamy, sweet","rich, sweet",NA,NA,NA,NA,Sartori
+Sartori Reserve Cheese Mediterranean Fontina,https://www.cheese.com/sartori-reserve-cheese-mediterranean-fontina/,cow,United States,Wisconsin,NA,"semi-soft, artisan",NA,NA,creamy,natural,cream,"garlicky, piquant, spicy, sweet","earthy, herbal",TRUE,FALSE,NA,NA,Sartori
+Sartori Reserve Dolcina Gorgonzola,https://www.cheese.com/sartori-reserve-dolcina-gorgonzola/,cow,United States,Wisconsin,Blue,"semi-firm, blue-veined",NA,NA,"creamy, soft",natural,ivory,"mild, smooth, sweet","rich, spicy",NA,NA,NA,NA,Sartori
+Sartori Reserve Espresso BellaVitano,https://www.cheese.com/sartori-reserve-espresso-bellavitano/,cow,United States,Wisconsin,NA,"semi-firm, artisan",NA,NA,firm,natural,pale yellow,"smokey , sweet","smokey, sweet",NA,NA,NA,NA,Sartori
+Sartori Reserve Extra Aged Fontina,https://www.cheese.com/sartori-reserve-extra-aged-fontina/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,creamy,natural,yellow,"buttery, fruity, mild, tangy","lactic, rich",TRUE,FALSE,NA,NA,Sartori
+Sartori Reserve Extra-Aged Asiago,https://www.cheese.com/sartori-reserve-extra-aged-asiago/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"creamy, crumbly",NA,pale yellow,"creamy, nutty",rich,FALSE,FALSE,NA,NA,Sartori
+Sartori Reserve Merlot BellaVitano,https://www.cheese.com/sartori-reserve-merlot-bellavitano/,cow,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,"creamy, crystalline",natural,pale yellow,"creamy, fruity","fruity, pleasant, rich",NA,NA,NA,NA,Sartori
+Sartori Reserve Raspberry BellaVitano,https://www.cheese.com/sartori-reserve-raspberry-bellavitano/,cow,United States,Wisconsin,Cheddar,"hard, artisan",NA,NA,creamy,natural,pale yellow,"buttery, creamy, fruity, nutty, sweet","fruity, nutty, rich",TRUE,FALSE,NA,NA,Sartori
+Sartori Reserve Rosemary & Olive Oil Asiago,https://www.cheese.com/sartori-reserve-rosemary-olive-oil-asiago/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,,NA,pale yellow,"fruity, nutty","fruity, nutty, rich",NA,NA,NA,NA,Sartori
+Sartori Reserve SarVecchio Parmesan,https://www.cheese.com/sartori-reserve-sarvecchio-parmesan/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"crumbly, crystalline, dry, flaky, grainy",natural,yellow,"caramel, fruity, nutty","fruity, sweet",TRUE,FALSE,NA,NA,Sartori
+Sbrinz,https://www.cheese.com/sbrinz/,cow,Switzerland,"Lucerne, Schwyz, Unterwald, and Zoug, and the following additional places: Muri district in d'Argovi",Swiss Cheese,"hard, artisan",45%,NA,"dense, flaky",natural,yellow,"butterscotch, full-flavored, nutty, spicy, strong","aromatic, spicy",FALSE,FALSE,NA,NA,NA
+Sbronzo,https://www.cheese.com/sbronzo/,water buffalo,Italy,Campania,NA,"semi-soft, artisan",NA,NA,"creamy, dense",natural,ivory,"buttery, creamy, fruity, sweet",fruity,NA,NA,NA,NA,Casa Madaio
+Scallion Onion Cheddar,https://www.cheese.com/scallion-onion-cheddar/,cow,United States,NY,Cheddar,"semi-hard, artisan",NA,NA,crumbly,natural,pale yellow,"full-flavored, spicy",strong,NA,NA,NA,NA,Muranda Cheese Company
+Scamorza,https://www.cheese.com/scamorza/,cow,Italy,NA,Pasta filata,semi-soft,NA,NA,"elastic, smooth, springy",natural,white,NA,NA,NA,NA,Smoked Scamorza,Scamorza Affumicata,NA
+Schloss,https://www.cheese.com/schloss/,cow,United States,NA,NA,"semi-soft, brined",NA,NA,"chalky, creamy, smooth",washed,straw,"citrusy, earthy, fruity, full-flavored, meaty, pungent","pungent, strong",FALSE,FALSE,NA,NA,Marin French Cheeese Co.
+Scotch Bonnet Cheddar,https://www.cheese.com/scotch-bonnet-cheddar/,cow,United Kingdom,NA,Cheddar,"hard, processed",NA,NA,creamy,plastic,pale yellow,"creamy, spicy, strong",NA,NA,NA,NA,NA,Tesco
+Sea Change,https://www.cheese.com/sea-change/,cow,United States,"Lebanon, CT",NA,"semi-soft, artisan",NA,NA,"elastic, smooth",natural,cream,"buttery, mild, milky","fruity, lactic, yeasty",NA,NA,NA,NA,The Mystic Cheese Company
+Seahive,https://www.cheese.com/seahive/,cow,United States,Utah,Cheddar,"hard, artisan",NA,NA,"dry, firm",natural,pale yellow,"salty, sweet",floral,TRUE,FALSE,NA,NA,Beehive Cheese Company
+Seascape,https://www.cheese.com/seascape/,"cow, goat",United States,California,NA,"semi-hard, artisan",8 g/100g,NA,"crumbly, smooth",natural,pale yellow,"buttery, caramel, tangy",pleasant,TRUE,FALSE,NA,NA,Central Coast Creamery
+Seastack,https://www.cheese.com/seastack/,cow,United States,Port Townsend,NA,"soft, soft-ripened",NA,NA,"creamy, firm, runny, soft",ash coated,white,"citrusy, earthy, garlicky, tangy","fresh, milky",FALSE,FALSE,NA,NA,Mt. Townsend Creamery
+Seator's Orkney,https://www.cheese.com/seators-orkney/,cow,"Great Britain, Scotland, United Kingdom",Orkney Islands,NA,"hard, artisan",NA,NA,crumbly,NA,cream,acidic,lactic,NA,NA,NA,NA,Grimbister Farm
+Selles sur Cher,https://www.cheese.com/selles-sur-cher/,goat,France,NA,NA,soft,NA,NA,firm,ash coated,NA,tangy,"lactic, nutty",NA,NA,NA,NA,NA
+Selva,https://www.cheese.com/selva/,cow,Spain,"Fornells de la Selva, Gironès",NA,"soft, artisan",45%,NA,"smooth, supple",natural,pale yellow,"bitter, buttery, salty",pleasant,FALSE,FALSE,NA,Queso De La Selva,NA
+Serat,https://www.cheese.com/serat/,sheep,Afghanistan,NA,NA,hard,NA,NA,,NA,NA,NA,NA,FALSE,FALSE,NA,NA,NA
+Seriously Strong Cheddar,https://www.cheese.com/seriously-strong-cheddar/,cow,"England, Scotland, United Kingdom",Stranraer,Cheddar,hard,34.4%,740 mg/100g,"crumbly, dense, firm, flaky",natural,yellow,"full-flavored, savory, smokey , spicy, tangy","rich, smokey, strong",FALSE,FALSE,NA,"Seriously Strong Spreadable, Seriously Strong Vintage Cheddar, Seriously Strong Grated Cheddar",Lactalis McLelland Ltd
+Serra da Estrela DOP,https://www.cheese.com/serra-da-estrela-dop/,sheep,Portugal,Serra da Estrela,NA,semi-soft,NA,NA,,NA,ivory,NA,pungent,FALSE,FALSE,"Serra da Estrela, Queijo Serra da Estrela",NA,NA
+Sgt. Pepper,https://www.cheese.com/sgt-pepper/,goat,United States,California,NA,fresh soft,NA,NA,"creamy, smooth, spreadable",rindless,white,"full-flavored, spicy","fresh, spicy",TRUE,FALSE,NA,NA,Cypress Grove Chevre
+Shaker Blue,https://www.cheese.com/shaker-blue/,sheep,United States,New York,Blue,"semi-firm, blue-veined",NA,NA,creamy,rindless,ivory,"acidic, creamy, full-flavored, sweet","rich, sweet",TRUE,FALSE,Shaker Blue,NA,Old Chatham Sheepherding Company
+Shamembert,https://www.cheese.com/vegan-shamembert/,plant-based,United Kingdom,NA,NA,soft,NA,NA,"soft, soft-ripened",mold ripened,white,"earthy, full-flavored, mushroomy, savory, strong",earthy,TRUE,TRUE,NA,NA,Honestly Tasty
+Shanklish,https://www.cheese.com/shanklish/,"cow, sheep","Egypt, Lebanon, Syria",NA,Feta,"fresh firm, hard, artisan",NA,NA,"creamy, crumbly, firm",mold ripened,white,"sharp, spicy, strong","pungent, strong",FALSE,FALSE,NA,NA,Grandpa's dairy
+Sharon Hollow Garlic and Chive,https://www.cheese.com/sharon-hollow-garlic-and-chive/,cow,United States,"Ann Arbor, Michigan",NA,"fresh soft, artisan",NA,NA,flaky,rindless,ivory,"garlicky, milky","fresh, garlicky",TRUE,FALSE,Sharon Hollow Garlic and Pepper,NA,Zingerman's Creamery
+Sharp Cheddar,https://www.cheese.com/sharp-cheddar/,cow,United States,NA,Cheddar,"semi-hard, artisan",NA,NA,creamy,natural,NA,"sharp, strong, tangy",NA,NA,NA,NA,NA,NA
+Sharpham,https://www.cheese.com/sharpham/,cow,"England, United Kingdom",Devon,Brie,"soft, artisan",45%,NA,"creamy, smooth",mold ripened,white,buttery,fresh,TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+Sharpham Elmhirst,https://www.cheese.com/sharpham-elmhirst/,cow,"England, Great Britain, United Kingdom",Devon,NA,"soft, artisan, soft-ripened",NA,NA,"creamy, smooth, soft, soft-ripened, spreadable",bloomy,white,"creamy, full-flavored, milky, smooth","fresh, mild, milky, rich",TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+Sharpham Rustic,https://www.cheese.com/sharpham-rustic/,cow,"England, Great Britain, United Kingdom",Devon,NA,"semi-hard, artisan",NA,NA,creamy,natural,white,"lemony, nutty",fresh,TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+Sharpham Rustic Chive & Garlic,https://www.cheese.com/sharpham-rustic-chive-garlic/,cow,"England, Great Britain, United Kingdom",Devon,NA,"semi-hard, artisan",NA,NA,creamy,natural,white,"creamy, garlicky, savory","garlicky, nutty, strong",TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+Sharpham Savour,https://www.cheese.com/sharpham-savour/,"cow, goat","England, Great Britain, United Kingdom",Devon,NA,"semi-hard, artisan",NA,NA,"close, creamy, supple",natural,cream,"smooth, sweet",fresh,TRUE,FALSE,NA,NA,Sharpham Wine & Cheese
+Sheep Gouda,https://www.cheese.com/sheep-gouda/,sheep,United States,Maine,Gouda,"semi-hard, artisan",NA,NA,"compact, crumbly, dense",natural,pale yellow,"caramel, nutty",NA,NA,NA,NA,NA,Fuzzy Udder Creamery
+Shelburne Cheddar,https://www.cheese.com/shelburne-cheddar/,cow,United States,Shelburne Farms,Cheddar,"hard, artisan",51%,NA,firm,rindless,pale yellow,strong,rich,TRUE,FALSE,NA,NA,Shelburne Farms
+Shepherd's Crook,https://www.cheese.com/shepherds-crook/,sheep,England,Somerset,NA,"soft, artisan",48%,NA,"creamy, smooth, springy",mold ripened,white,"mild, sweet","rich, sweet",TRUE,FALSE,NA,NA,Wootton Organic Dairy
+Shepherd's Hope,https://www.cheese.com/shepherds-hope/,sheep,United States,Minnesota,NA,"fresh soft, artisan",NA,NA,"creamy, firm, soft",rindless,white,"citrusy, garlicky, herbaceous, mild, milky","aromatic, fresh, herbal",TRUE,FALSE,NA,NA,Shepherd's Way Farms
+Shepherdista Crush,https://www.cheese.com/shepherdista-crush/,sheep,United States,California,NA,"firm, artisan",NA,NA,compact,natural,ivory,tangy,"grassy, woody",NA,NA,Shepherdista,NA,Bleating Heart Cheese
+Shepsog,https://www.cheese.com/shepsog/,"cow, sheep",United States,Vermont,NA,semi-firm,NA,NA,buttery,natural,yellow,"nutty, sweet","earthy, nutty, rich, sweet",NA,NA,NA,NA,Grafton Village Cheese Company
+Ships Wheel Brie,https://www.cheese.com/ships-wheel-brie/,cow,Australia,"Mornington Peninsula, Melbourne",Brie,artisan,NA,NA,creamy,NA,NA,"mild, nutty","mild, nutty",TRUE,FALSE,NA,NA,BoatShed Cheese
+Shoreditch Smoked,https://www.cheese.com/vegan-shoreditch-smoked-cheese/,plant-based,United Kingdom,NA,NA,semi-firm,NA,NA,"creamy, semi firm, soft, spreadable",rindless,pale yellow,"nutty, smokey , umami, yeasty","mild, nutty, smokey, yeasty",TRUE,TRUE,NA,NA,La Fauxmagerie
+Shorrock's Lancashire Bomb,https://www.cheese.com/shorrocks-lancashire-bomb/,cow,United Kingdom,NA,NA,semi-hard,NA,NA,"creamy, crumbly",NA,yellow,NA,NA,TRUE,FALSE,NA,NA,NA
+Shredded Bliss,https://www.cheese.com/shredded-bliss/,,"Canada, United States",NA,Mozzarella,semi-soft,NA,NA,"elastic, smooth, springy, stringy",plastic,pale yellow,"mild, milky","fresh, mild",TRUE,FALSE,"Lactose Free Mozzarella Shreds, Dairy Free Mozzarella Shreds, Lactose & Soy Free Mozzarella Shreds",NA,NA
+Shropshire Blue,https://www.cheese.com/shropshire-blue/,cow,United Kingdom,NA,Blue,semi-hard,NA,NA,"creamy, smooth",natural,orange,creamy,rich,TRUE,FALSE,"Blue Shropshire, Blue Stuart, Inverness-shire Blue",NA,NA
+Shtayburne Farm Cheddar,https://www.cheese.com/shtayburne-farm-cheddar/,cow,United States,NY,Cheddar,"hard, artisan",NA,NA,"creamy, smooth",natural,yellow,"creamy, garlicky, sharp, smokey , smooth, spicy",NA,NA,NA,NA,NA,Shtayburne Farm
+Shtayburne Farm Monterey Jack,https://www.cheese.com/shtayburne-farm-monterey-jack/,cow,United States,NY,Monterey Jack,"semi-hard, artisan",NA,NA,"compact, creamy, soft, supple",natural,pale yellow,"creamy, garlicky, herbaceous, smooth, spicy, sweet, tangy",NA,NA,NA,NA,NA,Shtayburne Farm
+Sicilian Blend,https://www.cheese.com/sicilian-blend/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"crumbly, dry, firm",natural,pale yellow,"piquant, savory, sharp, spicy",NA,TRUE,FALSE,NA,NA,Sartori
+Siltcoos,https://www.cheese.com/siltcoos/,goat,United States,Coast of Oregon,NA,"soft, artisan",NA,NA,soft,ash coated,ivory,"spicy, strong","clean, fresh",NA,NA,NA,NA,Rivers Edge Chèvre
+Sinodun Hill,https://www.cheese.com/sinodun-hill/,goat,England,NA,NA,soft,NA,NA,fluffy,natural,pale yellow,creamy,clean,TRUE,FALSE,NA,NA,Norton & Yarrow Cheese
+Sirene,https://www.cheese.com/sirene/,"cow, goat, sheep","Albania, Bulgaria, Croatia, Greece, Israel, Macedonia, Romania, Serbia",Trakia,Feta,"fresh soft, brined",NA,NA,"crumbly, grainy, smooth",natural,white,"lemony, salty, sharp, tangy",strong,FALSE,FALSE,Sirenje,NA,NA
+Sleightlett,https://www.cheese.com/sleightlett/,goat,"England, Great Britain, United Kingdom","Timsbury, Somerset",NA,"fresh soft, artisan",NA,NA,"creamy, fluffy, smooth",mold ripened,white,"citrusy, lemony, nutty","goaty, lactic",FALSE,FALSE,NA,NA,NA
+Slices Of Bliss,https://www.cheese.com/slices-of-bliss/,,"Canada, United States",NA,Cheddar,soft,NA,NA,creamy,plastic,yellow,"creamy, savory, sharp, spicy",NA,TRUE,FALSE,"Lactose Free Slices, Dairy Free Slices, Lactose & Soy Free Slices",NA,GO Veggie!
+Smoked Fior Di Latte,https://www.cheese.com/smoked-fior-di-latte/,cow,Italy,NA,Mozzarella,semi-soft,NA,NA,"elastic, smooth",NA,brownish yellow,"smokey , tangy",smokey,NA,NA,NA,NA,NA
+Smoked Gouda,https://www.cheese.com/smoked-gouda/,"cow, goat, sheep",Netherlands,NA,Gouda,hard,NA,NA,"buttery, crumbly",waxed,brownish yellow,NA,smokey,NA,NA,NA,NA,NA
+Smoked Lincolnshire Poacher,https://www.cheese.com/smoked-lincolnshire-poacher/,cow,United Kingdom,NA,NA,hard,NA,NA,"creamy, crumbly",NA,pale yellow,NA,NA,NA,NA,NA,NA,NA
+Smoked Sulguni,https://www.cheese.com/smoked-sulguni/,"buffalo, cow",Georgia,"Svaneti, Samegrelo",NA,semi-firm,NA,NA,"dense, elastic",NA,yellow,"salty, smokey , sour",smokey,NA,NA,"smoked suluguni, Georgian smoked suluguni",Shebolili Megruli Sulguni,NA
+Smokey Jalapeño,https://www.cheese.com/smokey-jalapeno/,,Canada,Ontario,NA,"semi-firm, artisan",NA,NA,"creamy, firm",NA,golden orange,"creamy, mild, spicy",NA,TRUE,FALSE,NA,NA,Zengarry Vegetarian Cuisine
+Smokey Mountain Round,https://www.cheese.com/smokey-mountain-round/,goat,United States,North Carolina,NA,"semi-soft, artisan",NA,NA,compact,natural,white,"savory, woody","aromatic, lactic",TRUE,FALSE,NA,NA,Goat Lady Dairy
+Smokey Oregon Blue,https://www.cheese.com/smokey-oregon-blue/,cow,United States,Oregon,Blue,"semi-hard, blue-veined",NA,NA,"dense, firm",natural,pale yellow,"caramel, earthy, savory, sharp, sweet","milky, nutty, smokey",TRUE,FALSE,NA,NA,Rogue Creamery
+Smokey Touvelle,https://www.cheese.com/smokey-touvelle/,cow,United States,Oregon,Cheddar,"semi-hard, artisan",NA,NA,"crumbly, firm",natural,pale yellow,"nutty, sweet, tangy","mild, smokey",TRUE,FALSE,NA,NA,Rogue Creamery
+Snow Camp,https://www.cheese.com/snow-camp/,"cow, goat",United States,North Carolina,NA,semi-firm,NA,NA,soft,bloomy,ivory,"buttery, creamy",buttery,TRUE,FALSE,NA,NA,Goat Lady Dairy
+Sofia,https://www.cheese.com/sofia/,goat,United States,Indiana,NA,"soft, artisan, soft-ripened",NA,NA,"close, creamy, dense, soft, soft-ripened",mold ripened,cream,"citrusy, creamy, sweet, tangy",NA,FALSE,FALSE,NA,NA,Capriole Goat Cheese
+Somerset Brie,https://www.cheese.com/somerset-brie/,cow,"England, United Kingdom",Somerset,Brie,"soft, artisan",NA,NA,"creamy, smooth",natural,white,mild,"fresh, grassy, mushroom",TRUE,FALSE,NA,NA,NA
+Somerset Organic Cheddar,https://www.cheese.com/somerset-organic-cheddar/,cow,United Kingdom,NA,NA,"hard, organic",NA,NA,"creamy, crumbly",NA,pale yellow,NA,NA,NA,NA,Somerset Cheddar,NA,NA
+Sonnet,https://www.cheese.com/sonnet/,"goat, sheep",United States,"Tieton, Washington",NA,"soft, artisan",NA,NA,"smooth, spreadable",bloomy,NA,"lemony, smooth",rich,NA,NA,NA,NA,Tieton Farm & Creamery
+Sonoma Jack,https://www.cheese.com/sonoma-jack/,cow,United States,"Sonoma, California",Monterey Jack,semi-hard,NA,NA,"brittle, creamy, crumbly, firm, open, supple",natural,pale white,"buttery, herbaceous, mild, mushroomy, nutty, sharp, spicy","aromatic, earthy, herbal, mild",TRUE,FALSE,NA,NA,Vella Cheese Company
+Sosha,https://www.cheese.com/sosha/,"goat, yak","China, Nepal, Tibet",Tibet,NA,"soft, artisan",NA,NA,creamy,natural,white,"pungent, strong","pungent, strong",NA,NA,Churul,NA,NA
+Sottocenere® al Tartufo,https://www.cheese.com/sottocenere-al-tartufo/,cow,Italy,Veneto,NA,"semi-soft, artisan",NA,NA,"firm, smooth",natural,pale yellow,"creamy, salty, savory","aromatic, spicy",FALSE,FALSE,Italian Truffle Cheese,NA,NA
+Soumaintrain,https://www.cheese.com/soumaintrain/,cow,France,NA,NA,"soft, artisan",NA,NA,"creamy, smooth",washed,NA,creamy,rich,NA,NA,Soumaintrain AOC,NA,NA
+Sourire Lozerien,https://www.cheese.com/sourire-lozerien/,cow,France,Cevenes,NA,"semi-soft, artisan",25%,NA,"creamy, smooth",natural,white,"mild, sweet",musty,FALSE,FALSE,NA,NA,SARL LAITERIE RISSOAN
+Sparkenhoe Red Leicester,https://www.cheese.com/sparkenhoe-red-leicester/,cow,United Kingdom,NA,NA,hard,NA,NA,"brittle, close, creamy, crumbly, flaky",NA,orange,nutty,NA,NA,NA,NA,NA,NA
+Spenwood,https://www.cheese.com/spenwood/,sheep,England,NA,Parmesan,hard,NA,NA,firm,natural,pale yellow,nutty,NA,TRUE,FALSE,NA,NA,NA
+Speziato,https://www.cheese.com/speziato/,cow,Italy,Veneto,NA,soft,NA,NA,compact,natural,ivory,"earthy, spicy",aromatic,FALSE,FALSE,NA,Tartufino Speziato,La Casearia Carpenedo S.r.l.
+Squaquerone di Bufala,https://www.cheese.com/squaquerone-di-bufala/,water buffalo,Italy,Lombardy,NA,"fresh soft, artisan",NA,NA,"creamy, soft, spreadable",rindless,white,"acidic, piquant, subtle, sweet","pleasant, subtle",FALSE,FALSE,NA,NA,Azienda Agricola Gritti Bruno E Alfio S.s. Societa Agricola
+St Andrews Farmhouse Cheddar,https://www.cheese.com/st-andrews-farmhouse-cheddar/,cow,Scotland,NA,NA,hard,NA,NA,"compact, crumbly, dry",waxed,pale yellow,"fruity, grassy, nutty, tangy","buttery, grassy, subtle",FALSE,FALSE,NA,St. Andrews Farmhouse Cheddar,St. Andrews Farmhouse Cheese
+St Cera,https://www.cheese.com/st-cera/,cow,England,NA,NA,soft,NA,NA,"buttery, creamy",washed,yellow,"full-flavored, pronounced",pungent,FALSE,FALSE,St. Cera,NA,Julie Cheyney
+St Fidèle Swiss,https://www.cheese.com/st-fidele-swiss/,cow,Canada,Quebec,Swiss Cheese,semi-hard,17%,NA,"elastic, firm, open",rindless,ivory,"nutty, sweet","aromatic, sweet",NA,NA,La Belle Brune,"St-Fidele Swiss, Suisse St-Fidèle, Saint Fidele Swiss",Fromagerie St-Fidèle
+St Gall,https://www.cheese.com/st-gall/,cow,Ireland,Co. Cork,Swiss Cheese,"hard, brined",NA,NA,"creamy, smooth, soft, springy",natural,yellow,"fruity, mild, milky, nutty, smooth, yeasty","fruity, mild, milky, nutty, rich, yeasty",FALSE,FALSE,NA,NA,CAIS Cheesemakers Association Ltd
+St James,https://www.cheese.com/st-james/,sheep,England,NA,NA,semi-soft,NA,NA,close,washed,pink and white,meaty,barnyardy,FALSE,FALSE,NA,St. James,Martin Gott
+St Jude,https://www.cheese.com/st-jude/,cow,"England, Great Britain, United Kingdom",NA,NA,"soft, artisan",NA,NA,"creamy, fluffy",mold ripened,cream,NA,"buttery, rich",FALSE,FALSE,NA,"St. Jude, Saint Jude",NA
+St Killian,https://www.cheese.com/st-killian/,cow,Ireland,"Adamstown, Co Wexford",Brie,"semi-soft, artisan",NA,NA,creamy,bloomy,pale yellow,"mushroomy, salty","aromatic, mushroom",TRUE,FALSE,NA,"St. Killian, Saint Killian","Carrigbyrne House, Adamstown, Co. Wexford"
+St Mang Original Allgäuer Limburger,https://www.cheese.com/st-mang-original-allgauer-limburger/,cow,Germany,Allgäu,NA,"soft, artisan",NA,NA,creamy,natural,pale yellow,spicy,"mild, spicy",TRUE,FALSE,"St. Mang original Allgaeuer Romadur, St. Mang Masterpiec",NA,Käserei Champignon
+St Pat,https://www.cheese.com/st-pat/,cow,United States,California,NA,"semi-soft, artisan",NA,NA,"creamy, soft",bloomy,pale yellow,"full-flavored, mellow","rich, smokey",TRUE,FALSE,NA,NA,Cowgirl Creamery
+St Tola Ash Log,https://www.cheese.com/st-tola-ash-log/,goat,Ireland,NA,NA,"soft, artisan",NA,NA,smooth,ash coated,NA,full-flavored,NA,FALSE,FALSE,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Cranberry,https://www.cheese.com/st-tola-cranberry/,goat,Ireland,"Inagh, Co Clare",NA,"fresh soft, artisan",18.2 g/100g,NA,"compact, creamy, smooth, soft",mold ripened,white,"creamy, fruity, full-flavored, sweet","fresh, fruity",FALSE,FALSE,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Crottin,https://www.cheese.com/st-tola-crottin/,goat,Ireland,NA,NA,soft,NA,NA,smooth,NA,white,"lemony, sweet","clean, floral",NA,NA,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Divine,https://www.cheese.com/st-tola-divine/,goat,,"Inagh, Co Clare",NA,"fresh soft, artisan",14 g/100g,NA,"creamy, soft, spreadable",rindless,white,"lemony, mild, milky","floral, mild, milky",FALSE,FALSE,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Greek Style,https://www.cheese.com/st-tola-greek-style/,goat,Ireland,Co Clare,Feta,"firm, brined",NA,NA,smooth,rindless,white,"creamy, salty",goaty,NA,NA,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Hard Cheese,https://www.cheese.com/st-tola-hard-cheese/,goat,Ireland,"Inagh, Co Clare",Gouda,"hard, artisan",36 g/100g,NA,"compact, crumbly, dense, smooth",waxed,cream,"nutty, sweet, tangy",NA,FALSE,FALSE,NA,NA,Inagh Farmhouse Cheese Ltd
+St Tola Log,https://www.cheese.com/st-tola-log/,goat,Ireland,NA,NA,soft,NA,NA,creamy,natural,NA,citrusy,NA,FALSE,FALSE,St Tola,NA,Inagh Farmhouse Cheese Ltd
+Staffordshire Organic,https://www.cheese.com/staffordshire-organic/,cow,"England, Great Britain, United Kingdom",Staffordshire,NA,"hard, artisan",48%,NA,"creamy, smooth",NA,yellow,NA,NA,TRUE,FALSE,NA,Staffordshire Organic Cheese,Staffordshire Dairy
+Stawley,https://www.cheese.com/stawley/,goat,"England, Great Britain, United Kingdom","Stawley, near Wellington, Somerset",NA,soft,NA,NA,"dense, firm, smooth",mold ripened,pale yellow,"caramel, floral, milky, sweet","mushroom, nutty",FALSE,FALSE,NA,NA,NA
+Stella Asiago,https://www.cheese.com/stella-asiago/,cow,Italy,Asiago,NA,"semi-hard, artisan",NA,NA,"buttery, creamy, crumbly",natural,cream,"nutty, sharp, smooth, sweet","fresh, mild, rich",TRUE,FALSE,NA,NA,Stella
+Stella Black Pepper Romano,https://www.cheese.com/stella-black-pepper-romano/,cow,United States,Wisconsin,NA,"hard, artisan",NA,NA,"compact, crumbly, flaky",natural,pale yellow,"piquant, salty, sharp","pleasant, spicy",TRUE,FALSE,NA,NA,Stella
+Stella Blue,https://www.cheese.com/stella-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan",NA,NA,crumbly,natural,white,tangy,rich,TRUE,FALSE,Stella Blue Cheese crumbles,NA,Stella
+Stella Feta,https://www.cheese.com/stella-feta/,cow,United States,NA,Feta,"firm, artisan",NA,NA,"crumbly, firm",NA,white,tangy,fresh,TRUE,FALSE,NA,NA,NA
+Stella Fontina,https://www.cheese.com/stella-fontina/,cow,United States,Wisconsin,NA,"semi-soft, artisan",NA,NA,"creamy, open",natural,pale yellow,"buttery, sweet","earthy, mild",TRUE,FALSE,NA,NA,Stella
+Stella Fontinella,https://www.cheese.com/stella-fontinella/,cow,United States,NA,NA,"semi-hard, artisan",NA,NA,crumbly,natural,pale yellow,"creamy, sharp, smooth, sweet",NA,TRUE,FALSE,NA,NA,Stella
+Stella Goat,https://www.cheese.com/stella-goat/,goat,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,crumbly,natural,cream,tangy,"mild, pleasant",TRUE,FALSE,NA,NA,Stella
+Stella Gorgonzola,https://www.cheese.com/stella-gorgonzola/,cow,United States,NA,Gorgonzola,"semi-soft, blue-veined",NA,NA,creamy,natural,cream,"earthy, mellow, tangy",pleasant,TRUE,FALSE,NA,NA,Stella
+Stella Italian Sharp,https://www.cheese.com/stella-italian-sharp/,cow,United States,NA,NA,"semi-soft, artisan",NA,NA,creamy,natural,pale yellow,"creamy, full-flavored, sharp",strong,FALSE,FALSE,NA,NA,Stella
+Stella Kasseri,https://www.cheese.com/stella-kasseri/,cow,United States,Wisconsin,NA,"semi-hard, artisan",NA,NA,firm,natural,pale yellow,"salty, sharp, tangy",strong,TRUE,FALSE,NA,NA,Saputo Inc.
+Stella Mediterranean Parmesan,https://www.cheese.com/stella-mediterranean-parmesan/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"buttery, crumbly, flaky",natural,NA,"buttery, earthy, garlicky, savory, spicy",nutty,TRUE,FALSE,NA,NA,Stella
+Stella Parmesan,https://www.cheese.com/stella-parmesan/,cow,United States,NA,Parmesan,"hard, artisan",NA,NA,"crumbly, dense, grainy",natural,pale yellow,"nutty, subtle",NA,TRUE,FALSE,NA,NA,Stella
+Stella Parmesan & Romano Blend,https://www.cheese.com/stella-parmesan-romano-blend/,cow,United States,Wisconsin,Parmesan,"hard, artisan",NA,NA,"crumbly, flaky",natural,cream,"salty, sharp",nutty,TRUE,FALSE,NA,NA,Stella
+Stella Reduced Fat Blue,https://www.cheese.com/stella-reduced-fat-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan",NA,NA,crumbly,natural,white,full-flavored,pleasant,TRUE,FALSE,NA,NA,Stella
+Stella Smoked Blue,https://www.cheese.com/stella-smoked-blue/,cow,United States,Wisconsin,Blue,"semi-soft, artisan",NA,NA,"creamy, crumbly",natural,white,"creamy, subtle","pleasant, smokey",TRUE,FALSE,NA,NA,Stella
+Stella Swiss,https://www.cheese.com/stella-swiss/,cow,United States,Wisconsin,Swiss Cheese,"semi-soft, artisan",NA,NA,"creamy, open, smooth",natural,cream,"nutty, tangy","mild, pleasant",TRUE,FALSE,Natural Baby Swiss Cheese,NA,Stella
+Sternschnuppe,https://www.cheese.com/sternschnuppe/,cow,Germany,"Allagau, Bavarian Alps",NA,"firm, artisan",NA,NA,"buttery, firm",washed,golden yellow,"buttery, fruity","caramel, herbal, nutty, rich",FALSE,FALSE,Shooting star,NA,Käsküche Isny
+Stichelton,https://www.cheese.com/stichelton/,cow,"England, Great Britain, United Kingdom",Nottinghamshire,Blue,"semi-soft, artisan, blue-veined",NA,NA,creamy,mold ripened,ivory,"buttery, caramel, creamy, savory, spicy, sweet",rich,FALSE,FALSE,NA,NA,NA
+Stickney Hill Chevre,https://www.cheese.com/stickney-hill-chevre/,goat,United States,Kimball,NA,"semi-soft, artisan",NA,NA,"crumbly, spreadable",natural,white,"garlicky, herbaceous, spicy, tangy","goaty, mild, milky",TRUE,FALSE,NA,NA,Stickney Hill Dairy
+Stilton,https://www.cheese.com/stilton/,cow,England,"Derbyshire, Leicestershire, Nottinghamshire",Blue,"hard, blue-veined",NA,NA,"creamy, crumbly, smooth",natural,NA,"spicy, strong",NA,NA,NA,"Cropwell Bishop Blue Stilton, Stilton Colston Bassett",NA,NA
+Stinking Bishop,https://www.cheese.com/stinking-bishop/,cow,United Kingdom,NA,NA,"semi-soft, artisan",NA,NA,"creamy, smooth",washed,NA,NA,pungent,TRUE,FALSE,NA,NA,Charles Martell & Son Limited
+Stoney Cross,https://www.cheese.com/stoney-cross/,cow,"England, Great Britain, United Kingdom",Landford,Tomme,"semi-hard, artisan",NA,NA,"creamy, smooth",mold ripened,yellow,"earthy, subtle, sweet",NA,TRUE,FALSE,NA,NA,Lyburn Farm
+Stracchinata,https://www.cheese.com/stracchinata/,cow,Italy,Veneto,NA,"fresh soft, artisan",NA,NA,"creamy, open",natural,pale yellow,"creamy, sweet",subtle,FALSE,FALSE,NA,NA,La Casearia Carpenedo S.r.l.
+Strathdon Blue,https://www.cheese.com/strathdon-blue/,cow,Scotland,Tain,Blue,semi-soft,NA,NA,creamy,NA,NA,"creamy, spicy","aromatic, rich",TRUE,FALSE,NA,NA,Highland Fine Cheeses Limited
+Strawberry Moon,https://www.cheese.com/strawberry-moon/,cow,"Canada, Italy",Lombardy,NA,"semi-hard, artisan, smear-ripened",NA,NA,"compact, creamy, dense",washed,straw,"subtle, sweet",strong,NA,NA,NA,NA,Fifth Town Artisan Cheese
+Striegistaler Zwerge Camembert,https://www.cheese.com/striegistaler-zwerge-camembert/,cow,Germany,Allgäu,Camembert,"semi-soft, soft-ripened",NA,NA,"creamy, soft",natural,white,"full-flavored, mild","aromatic, fresh, strong",TRUE,FALSE,NA,NA,Käserei Champignon
+String Cheese,https://www.cheese.com/string/,,,NA,NA,semi-hard,NA,NA,"chewy, firm, stringy",NA,NA,NA,NA,NA,NA,NA,NA,NA
+Suffolk Punch,https://www.cheese.com/suffolk-punch/,cow,United States,Vermont,Pasta filata,"hard, artisan",NA,NA,"dry, firm",natural,ivory,"buttery, tangy","buttery, spicy",FALSE,FALSE,NA,NA,Parish Hill Creamery
+Sulguni,https://www.cheese.com/sulguni/,"buffalo, cow",Georgia,"Svaneti, Samegrelo",NA,semi-firm,NA,NA,"dense, elastic",NA,NA,"salty, smokey , sour",NA,NA,NA,Georgian Pickle Cheese,"Megruli Sulguni, Shebolili Megruli Sulguni",NA
+Sun Dried Tomato and Basil Cashew Cheese,https://www.cheese.com/sun-dried-tomato-and-basil-cashew-cheese/,,Canada,Ontario,NA,"soft, artisan",NA,NA,creamy,NA,white,"creamy, full-flavored, herbaceous, nutty, tangy","herbal, nutty",TRUE,FALSE,NA,NA,Zengarry Vegetarian Cuisine
+Sunlight,https://www.cheese.com/sunlight/,goat,United States,Colorado,NA,"semi-hard, artisan",NA,NA,"compact, creamy, firm, open, smooth",washed,cream,"buttery, caramel, grassy, piquant, sweet, tangy","pungent, strong",TRUE,FALSE,NA,NA,Haystack Mountain Creamery
+Sunset Bay,https://www.cheese.com/sunset-bay/,goat,United States,Oregon Coast Range,NA,"soft, artisan",NA,NA,"creamy, dense, smooth",bloomy,ivory,"creamy, savory, smokey , smooth","buttery, smokey, yeasty",TRUE,FALSE,NA,NA,Rivers Edge Chèvre
+Sussex Slipcote,https://www.cheese.com/sussex-slipcote/,sheep,England,NA,NA,soft,NA,NA,,NA,NA,sharp,NA,TRUE,FALSE,NA,NA,High Weald Dairy
+Sveciaost,https://www.cheese.com/sveciaost/,cow,Sweden,Low-laying regions,NA,"semi-hard, brined",45%,NA,"creamy, supple",rindless,pale yellow,acidic,NA,FALSE,FALSE,NA,NA,NA
+Swag,https://www.cheese.com/swag/,goat,Australia,South Australia,NA,"fresh firm, artisan",NA,NA,"creamy, crumbly",ash coated,white,"acidic, creamy",fresh,TRUE,FALSE,NA,NA,Woodside Cheese Wrights
+Swaledale,https://www.cheese.com/swaledale/,sheep,England,"Swaledale, North Yorkshire",NA,hard,NA,NA,semi firm,NA,yellow,"smooth, sweet",floral,TRUE,FALSE,Swaledale Sheep Cheese,NA,NA
+Sweet Style Swiss,https://www.cheese.com/sweet-style-swiss/,,Switzerland,NA,NA,"semi-hard, artisan",NA,NA,"firm, supple",waxed,NA,nutty,"nutty, sweet",FALSE,FALSE,NA,NA,NA
+Swiss cheese,https://www.cheese.com/swiss/,cow,United States,NA,Swiss Cheese,"hard, artisan, processed",7.8 g/100g,NA,firm,rindless,pale yellow,"nutty, sweet",NA,TRUE,FALSE,American Swiss Cheese,NA,Various
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+# Cheese Type Classification - Models and Results
+
+## Table of Contents
+
+- [Models](#models)
+- [Results](#results)
+- [Conclusion](#conclusion)
+- [Signature](#signature)
+
+## Models
+
+The project explores the following machine learning models to classify different types of cheese:
+
+### 1. K-Nearest Neighbors (KNN)
+
+**Description**: 
+KNN is a simple, instance-based learning algorithm where classification is based on the majority vote of the nearest neighbors.
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_1.png)
+
+### 2. Logistic Regression
+
+**Description**: 
+Logistic Regression is a linear model for binary classification that uses a logistic function to model the probability of a certain class.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_2.png)
+
+### 3. Decision Tree
+
+**Description**: 
+Decision Trees are non-parametric supervised learning methods used for classification. The model predicts the target variable by learning simple decision rules inferred from the data features.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_3.png)
+
+### 4. Support Vector Machine (SVM)
+
+**Description**: 
+SVM is a supervised learning model that analyzes data for classification and regression analysis. It finds the hyperplane that best divides a dataset into classes
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_4.png)
+
+### 5. Random Forest
+
+**Description**: 
+Random Forest is an ensemble learning method that operates by constructing multiple decision trees and outputting the class that is the mode of the classes of individual trees.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_5.png)
+
+### 6. Gradient Boosting
+
+**Description**: 
+Gradient Boosting is an ensemble learning technique that builds models sequentially. Each new model attempts to correct errors made by the previous model.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_6.png)
+
+### 7. AdaBoost
+
+**Description**: 
+AdaBoost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It adjusts the weights of misclassified instances to focus on hard-to-classify cases.
+
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_7.png)
+
+### 8. Extra Trees
+
+**Description**: 
+Extra Trees is similar to Random Forest but with more randomness in node splitting, reducing variance and improving performance.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_8.png)
+
+### 9. Naive Bayes
+
+**Description**: 
+Naive Bayes classifiers are probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_9.png)
+
+### 10. XGBoost
+
+**Description**: 
+XGBoost is an optimized gradient boosting framework that is efficient and performs well on structured data.
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_10.png)
+
+### 11. CatBoost
+
+**Description**: 
+CatBoost is a gradient boosting algorithm that handles categorical features automatically and efficiently, often providing high accuracy with minimal parameter tuning.
+
+
+![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___69_11.png)
+
+## Results
+
+The accuracy results for each model are as follows:
+
+- **K-Nearest Neighbors**: 0.62
+- **Logistic Regression**: 0.633
+- **Decision Tree**: 0.713
+- **Support Vector Machine**: 0.647
+- **Random Forest**: 0.72
+- **Gradient Boosting**: 0.767
+- **AdaBoost**: 0.727
+- **Extra Trees**: 0.70
+- **Naive Bayes**: 0.64
+- **XGBoost**: 0.727
+- **CatBoost**: 0.74
+
+## Conclusion
+
+After evaluating various machine learning models, it is evident that ensemble methods such as Gradient Boosting, CatBoost, and Random Forest perform significantly better than single classifiers like K-Nearest Neighbors or Logistic Regression. These models effectively capture complex relationships within the data, leading to higher classification accuracy.
+
+- **Best Performing Models:** Gradient Boosting and CatBoost achieved the highest accuracy scores, indicating robust predictive performance.
+- **Important Features:** Features such as fat content, moisture content, and aging time were consistently found to be the most influential in classifying cheese types.
+
+## Signature
+
+Aditya D
+* GitHub: [adi271001](https://www.github.com/adi271001)
+* LinkedIn: [Aditya D](https://www.linkedin.com/in/aditya-d-23453a179/)
+* Topmate: [Aditya D](https://topmate.io/aditya_d/)
+* Twitter: [@ADITYAD29257528](https://x.com/ADITYAD29257528)
diff --git a/Cheese Classification/Model/cheese-classification-eda-and-models.ipynb b/Cheese Classification/Model/cheese-classification-eda-and-models.ipynb
new file mode 100644
index 000000000..b38cfaaa2
--- /dev/null
+++ b/Cheese Classification/Model/cheese-classification-eda-and-models.ipynb	
@@ -0,0 +1,6334 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "af7e1474",
+   "metadata": {
+    "papermill": {
+     "duration": 0.018888,
+     "end_time": "2024-06-26T13:55:51.133183",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:51.114295",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Cheese Classification EDA and Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35193a21",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016863,
+     "end_time": "2024-06-26T13:55:51.167415",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:51.150552",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Importing Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fd59602a",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:51.205441Z",
+     "iopub.status.busy": "2024-06-26T13:55:51.205061Z",
+     "iopub.status.idle": "2024-06-26T13:55:53.752830Z",
+     "shell.execute_reply": "2024-06-26T13:55:53.751862Z"
+    },
+    "papermill": {
+     "duration": 2.569076,
+     "end_time": "2024-06-26T13:55:53.755224",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:51.186148",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import plotly.express as px\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5511d55d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017126,
+     "end_time": "2024-06-26T13:55:53.789966",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:53.772840",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Loading Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "33d1179d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:53.825639Z",
+     "iopub.status.busy": "2024-06-26T13:55:53.825138Z",
+     "iopub.status.idle": "2024-06-26T13:55:53.893708Z",
+     "shell.execute_reply": "2024-06-26T13:55:53.892854Z"
+    },
+    "papermill": {
+     "duration": 0.08909,
+     "end_time": "2024-06-26T13:55:53.896099",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:53.807009",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cheese</th>\n",
+       "      <th>url</th>\n",
+       "      <th>milk</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>family</th>\n",
+       "      <th>type</th>\n",
+       "      <th>fat_content</th>\n",
+       "      <th>calcium_content</th>\n",
+       "      <th>texture</th>\n",
+       "      <th>rind</th>\n",
+       "      <th>color</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>vegan</th>\n",
+       "      <th>synonyms</th>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aarewasser</td>\n",
+       "      <td>https://www.cheese.com/aarewasser/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Abbaye de Belloc</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belloc/</td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Pays Basque</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Abbaye Notre-Dame de Belloc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Abbaye de Belval</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belval/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>40-46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>ivory</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abbaye de Citeaux</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-citeaux/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Burgundy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft, artisan, brined</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbaye de Tamié</td>\n",
+       "      <td>https://www.cheese.com/tamie/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Savoie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tamié, Trappiste de Tamie, Abbey of Tamie</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>Sveciaost</td>\n",
+       "      <td>https://www.cheese.com/sveciaost/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>Low-laying regions</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, brined</td>\n",
+       "      <td>45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, supple</td>\n",
+       "      <td>rindless</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>acidic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>Swag</td>\n",
+       "      <td>https://www.cheese.com/swag/</td>\n",
+       "      <td>goat</td>\n",
+       "      <td>Australia</td>\n",
+       "      <td>South Australia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>fresh firm, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, crumbly</td>\n",
+       "      <td>ash coated</td>\n",
+       "      <td>white</td>\n",
+       "      <td>acidic, creamy</td>\n",
+       "      <td>fresh</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Woodside Cheese Wrights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>Swaledale</td>\n",
+       "      <td>https://www.cheese.com/swaledale/</td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>England</td>\n",
+       "      <td>Swaledale, North Yorkshire</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>hard</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi firm</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>smooth, sweet</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Swaledale Sheep Cheese</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>Sweet Style Swiss</td>\n",
+       "      <td>https://www.cheese.com/sweet-style-swiss/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>firm, supple</td>\n",
+       "      <td>waxed</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>Swiss cheese</td>\n",
+       "      <td>https://www.cheese.com/swiss/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Swiss Cheese</td>\n",
+       "      <td>hard, artisan, processed</td>\n",
+       "      <td>7.8 g/100g</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>rindless</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>American Swiss Cheese</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Various</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1187 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 cheese                                        url   milk  \\\n",
+       "0            Aarewasser         https://www.cheese.com/aarewasser/    cow   \n",
+       "1      Abbaye de Belloc   https://www.cheese.com/abbaye-de-belloc/  sheep   \n",
+       "2      Abbaye de Belval   https://www.cheese.com/abbaye-de-belval/    cow   \n",
+       "3     Abbaye de Citeaux  https://www.cheese.com/abbaye-de-citeaux/    cow   \n",
+       "4       Abbaye de Tamié              https://www.cheese.com/tamie/    cow   \n",
+       "...                 ...                                        ...    ...   \n",
+       "1182          Sveciaost          https://www.cheese.com/sveciaost/    cow   \n",
+       "1183               Swag               https://www.cheese.com/swag/   goat   \n",
+       "1184          Swaledale          https://www.cheese.com/swaledale/  sheep   \n",
+       "1185  Sweet Style Swiss  https://www.cheese.com/sweet-style-swiss/    NaN   \n",
+       "1186       Swiss cheese              https://www.cheese.com/swiss/    cow   \n",
+       "\n",
+       "            country                      region        family  \\\n",
+       "0       Switzerland                         NaN           NaN   \n",
+       "1            France                 Pays Basque           NaN   \n",
+       "2            France                         NaN           NaN   \n",
+       "3            France                    Burgundy           NaN   \n",
+       "4            France                      Savoie           NaN   \n",
+       "...             ...                         ...           ...   \n",
+       "1182         Sweden          Low-laying regions           NaN   \n",
+       "1183      Australia             South Australia           NaN   \n",
+       "1184        England  Swaledale, North Yorkshire           NaN   \n",
+       "1185    Switzerland                         NaN           NaN   \n",
+       "1186  United States                         NaN  Swiss Cheese   \n",
+       "\n",
+       "                            type fat_content calcium_content  \\\n",
+       "0                      semi-soft         NaN             NaN   \n",
+       "1             semi-hard, artisan         NaN             NaN   \n",
+       "2                      semi-hard      40-46%             NaN   \n",
+       "3     semi-soft, artisan, brined         NaN             NaN   \n",
+       "4                  soft, artisan         NaN             NaN   \n",
+       "...                          ...         ...             ...   \n",
+       "1182           semi-hard, brined         45%             NaN   \n",
+       "1183         fresh firm, artisan         NaN             NaN   \n",
+       "1184                        hard         NaN             NaN   \n",
+       "1185          semi-hard, artisan         NaN             NaN   \n",
+       "1186    hard, artisan, processed  7.8 g/100g             NaN   \n",
+       "\n",
+       "                    texture        rind        color                 flavor  \\\n",
+       "0                   buttery      washed       yellow                  sweet   \n",
+       "1       creamy, dense, firm     natural       yellow          burnt caramel   \n",
+       "2                   elastic      washed        ivory                    NaN   \n",
+       "3     creamy, dense, smooth      washed        white  acidic, milky, smooth   \n",
+       "4      creamy, open, smooth      washed        white          fruity, nutty   \n",
+       "...                     ...         ...          ...                    ...   \n",
+       "1182         creamy, supple    rindless  pale yellow                 acidic   \n",
+       "1183        creamy, crumbly  ash coated        white         acidic, creamy   \n",
+       "1184              semi firm         NaN       yellow          smooth, sweet   \n",
+       "1185           firm, supple       waxed          NaN                  nutty   \n",
+       "1186                   firm    rindless  pale yellow           nutty, sweet   \n",
+       "\n",
+       "                  aroma vegetarian  vegan                     synonyms  \\\n",
+       "0               buttery      False  False                          NaN   \n",
+       "1              lanoline       True  False  Abbaye Notre-Dame de Belloc   \n",
+       "2              aromatic      False  False                          NaN   \n",
+       "3     barnyardy, earthy      False  False                          NaN   \n",
+       "4     perfumed, pungent      False  False                          NaN   \n",
+       "...                 ...        ...    ...                          ...   \n",
+       "1182                NaN      False  False                          NaN   \n",
+       "1183              fresh       True  False                          NaN   \n",
+       "1184             floral       True  False       Swaledale Sheep Cheese   \n",
+       "1185       nutty, sweet      False  False                          NaN   \n",
+       "1186                NaN       True  False        American Swiss Cheese   \n",
+       "\n",
+       "                                  alt_spellings                producers  \n",
+       "0                                           NaN                     Jumi  \n",
+       "1                                           NaN                      NaN  \n",
+       "2                                           NaN                      NaN  \n",
+       "3                                           NaN                      NaN  \n",
+       "4     Tamié, Trappiste de Tamie, Abbey of Tamie                      NaN  \n",
+       "...                                         ...                      ...  \n",
+       "1182                                        NaN                      NaN  \n",
+       "1183                                        NaN  Woodside Cheese Wrights  \n",
+       "1184                                        NaN                      NaN  \n",
+       "1185                                        NaN                      NaN  \n",
+       "1186                                        NaN                  Various  \n",
+       "\n",
+       "[1187 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/kaggle/input/cheese-across-the-world/cheeses.csv')\n",
+    "df   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "450140f2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01767,
+     "end_time": "2024-06-26T13:55:53.931953",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:53.914283",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "101756c6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:53.969197Z",
+     "iopub.status.busy": "2024-06-26T13:55:53.968868Z",
+     "iopub.status.idle": "2024-06-26T13:55:53.989547Z",
+     "shell.execute_reply": "2024-06-26T13:55:53.988628Z"
+    },
+    "papermill": {
+     "duration": 0.041889,
+     "end_time": "2024-06-26T13:55:53.991654",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:53.949765",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cheese</th>\n",
+       "      <th>url</th>\n",
+       "      <th>milk</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>family</th>\n",
+       "      <th>type</th>\n",
+       "      <th>fat_content</th>\n",
+       "      <th>calcium_content</th>\n",
+       "      <th>texture</th>\n",
+       "      <th>rind</th>\n",
+       "      <th>color</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>vegan</th>\n",
+       "      <th>synonyms</th>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aarewasser</td>\n",
+       "      <td>https://www.cheese.com/aarewasser/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Abbaye de Belloc</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belloc/</td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Pays Basque</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Abbaye Notre-Dame de Belloc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Abbaye de Belval</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belval/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>40-46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>ivory</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abbaye de Citeaux</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-citeaux/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Burgundy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft, artisan, brined</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbaye de Tamié</td>\n",
+       "      <td>https://www.cheese.com/tamie/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Savoie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tamié, Trappiste de Tamie, Abbey of Tamie</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              cheese                                        url   milk  \\\n",
+       "0         Aarewasser         https://www.cheese.com/aarewasser/    cow   \n",
+       "1   Abbaye de Belloc   https://www.cheese.com/abbaye-de-belloc/  sheep   \n",
+       "2   Abbaye de Belval   https://www.cheese.com/abbaye-de-belval/    cow   \n",
+       "3  Abbaye de Citeaux  https://www.cheese.com/abbaye-de-citeaux/    cow   \n",
+       "4    Abbaye de Tamié              https://www.cheese.com/tamie/    cow   \n",
+       "\n",
+       "       country       region family                        type fat_content  \\\n",
+       "0  Switzerland          NaN    NaN                   semi-soft         NaN   \n",
+       "1       France  Pays Basque    NaN          semi-hard, artisan         NaN   \n",
+       "2       France          NaN    NaN                   semi-hard      40-46%   \n",
+       "3       France     Burgundy    NaN  semi-soft, artisan, brined         NaN   \n",
+       "4       France       Savoie    NaN               soft, artisan         NaN   \n",
+       "\n",
+       "  calcium_content                texture     rind   color  \\\n",
+       "0             NaN                buttery   washed  yellow   \n",
+       "1             NaN    creamy, dense, firm  natural  yellow   \n",
+       "2             NaN                elastic   washed   ivory   \n",
+       "3             NaN  creamy, dense, smooth   washed   white   \n",
+       "4             NaN   creamy, open, smooth   washed   white   \n",
+       "\n",
+       "                  flavor              aroma vegetarian  vegan  \\\n",
+       "0                  sweet            buttery      False  False   \n",
+       "1          burnt caramel           lanoline       True  False   \n",
+       "2                    NaN           aromatic      False  False   \n",
+       "3  acidic, milky, smooth  barnyardy, earthy      False  False   \n",
+       "4          fruity, nutty  perfumed, pungent      False  False   \n",
+       "\n",
+       "                      synonyms                              alt_spellings  \\\n",
+       "0                          NaN                                        NaN   \n",
+       "1  Abbaye Notre-Dame de Belloc                                        NaN   \n",
+       "2                          NaN                                        NaN   \n",
+       "3                          NaN                                        NaN   \n",
+       "4                          NaN  Tamié, Trappiste de Tamie, Abbey of Tamie   \n",
+       "\n",
+       "  producers  \n",
+       "0      Jumi  \n",
+       "1       NaN  \n",
+       "2       NaN  \n",
+       "3       NaN  \n",
+       "4       NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "72a5780c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.032270Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.031657Z",
+     "iopub.status.idle": "2024-06-26T13:55:54.055136Z",
+     "shell.execute_reply": "2024-06-26T13:55:54.054096Z"
+    },
+    "papermill": {
+     "duration": 0.04586,
+     "end_time": "2024-06-26T13:55:54.057635",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.011775",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1187 entries, 0 to 1186\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   cheese           1187 non-null   object\n",
+      " 1   url              1187 non-null   object\n",
+      " 2   milk             1151 non-null   object\n",
+      " 3   country          1176 non-null   object\n",
+      " 4   region           855 non-null    object\n",
+      " 5   family           489 non-null    object\n",
+      " 6   type             1174 non-null   object\n",
+      " 7   fat_content      248 non-null    object\n",
+      " 8   calcium_content  25 non-null     object\n",
+      " 9   texture          1129 non-null   object\n",
+      " 10  rind             945 non-null    object\n",
+      " 11  color            1045 non-null   object\n",
+      " 12  flavor           1089 non-null   object\n",
+      " 13  aroma            929 non-null    object\n",
+      " 14  vegetarian       748 non-null    object\n",
+      " 15  vegan            748 non-null    object\n",
+      " 16  synonyms         294 non-null    object\n",
+      " 17  alt_spellings    109 non-null    object\n",
+      " 18  producers        787 non-null    object\n",
+      "dtypes: object(19)\n",
+      "memory usage: 176.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "80daadbb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.099711Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.099035Z",
+     "iopub.status.idle": "2024-06-26T13:55:54.142522Z",
+     "shell.execute_reply": "2024-06-26T13:55:54.141551Z"
+    },
+    "papermill": {
+     "duration": 0.06522,
+     "end_time": "2024-06-26T13:55:54.144469",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.079249",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>unique</th>\n",
+       "      <th>top</th>\n",
+       "      <th>freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cheese</th>\n",
+       "      <td>1187</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>Swiss cheese</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>url</th>\n",
+       "      <td>1187</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>https://www.cheese.com/swiss/</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>milk</th>\n",
+       "      <td>1151</td>\n",
+       "      <td>21</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>country</th>\n",
+       "      <td>1176</td>\n",
+       "      <td>82</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <td>855</td>\n",
+       "      <td>349</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>family</th>\n",
+       "      <td>489</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <td>1174</td>\n",
+       "      <td>84</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fat_content</th>\n",
+       "      <td>248</td>\n",
+       "      <td>85</td>\n",
+       "      <td>45%</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>calcium_content</th>\n",
+       "      <td>25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>492 mg/100g</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>texture</th>\n",
+       "      <td>1129</td>\n",
+       "      <td>309</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rind</th>\n",
+       "      <td>945</td>\n",
+       "      <td>12</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>color</th>\n",
+       "      <td>1045</td>\n",
+       "      <td>17</td>\n",
+       "      <td>white</td>\n",
+       "      <td>281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>flavor</th>\n",
+       "      <td>1089</td>\n",
+       "      <td>626</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aroma</th>\n",
+       "      <td>929</td>\n",
+       "      <td>330</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vegetarian</th>\n",
+       "      <td>748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vegan</th>\n",
+       "      <td>748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>synonyms</th>\n",
+       "      <td>294</td>\n",
+       "      <td>292</td>\n",
+       "      <td>Rupp Vorarlberger Bergkäse</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <td>109</td>\n",
+       "      <td>109</td>\n",
+       "      <td>Megruli Sulguni, Shebolili Megruli Sulguni</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>producers</th>\n",
+       "      <td>787</td>\n",
+       "      <td>318</td>\n",
+       "      <td>Sartori</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count unique                                         top freq\n",
+       "cheese           1187   1187                                Swiss cheese    1\n",
+       "url              1187   1187               https://www.cheese.com/swiss/    1\n",
+       "milk             1151     21                                         cow  696\n",
+       "country          1176     82                               United States  305\n",
+       "region            855    349                                   Wisconsin   67\n",
+       "family            489     21                                        Blue   94\n",
+       "type             1174     84                          semi-hard, artisan  133\n",
+       "fat_content       248     85                                         45%   50\n",
+       "calcium_content    25     24                                 492 mg/100g    2\n",
+       "texture          1129    309                                      creamy  162\n",
+       "rind              945     12                                     natural  439\n",
+       "color            1045     17                                       white  281\n",
+       "flavor           1089    626                                      creamy   34\n",
+       "aroma             929    330                                        rich   56\n",
+       "vegetarian        748      2                                       False  386\n",
+       "vegan             748      2                                       False  742\n",
+       "synonyms          294    292                  Rupp Vorarlberger Bergkäse    3\n",
+       "alt_spellings     109    109  Megruli Sulguni, Shebolili Megruli Sulguni    1\n",
+       "producers         787    318                                     Sartori   27"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include='all').T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ce1ca97c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.184338Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.183600Z",
+     "iopub.status.idle": "2024-06-26T13:55:54.221415Z",
+     "shell.execute_reply": "2024-06-26T13:55:54.220562Z"
+    },
+    "papermill": {
+     "duration": 0.060335,
+     "end_time": "2024-06-26T13:55:54.223514",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.163179",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>unique</th>\n",
+       "      <th>top</th>\n",
+       "      <th>freq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>cheese</th>\n",
+       "      <td>1187</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>Swiss cheese</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>url</th>\n",
+       "      <td>1187</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>https://www.cheese.com/swiss/</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>milk</th>\n",
+       "      <td>1151</td>\n",
+       "      <td>21</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>country</th>\n",
+       "      <td>1176</td>\n",
+       "      <td>82</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>region</th>\n",
+       "      <td>855</td>\n",
+       "      <td>349</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>family</th>\n",
+       "      <td>489</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>type</th>\n",
+       "      <td>1174</td>\n",
+       "      <td>84</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fat_content</th>\n",
+       "      <td>248</td>\n",
+       "      <td>85</td>\n",
+       "      <td>45%</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>calcium_content</th>\n",
+       "      <td>25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>492 mg/100g</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>texture</th>\n",
+       "      <td>1129</td>\n",
+       "      <td>309</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rind</th>\n",
+       "      <td>945</td>\n",
+       "      <td>12</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>color</th>\n",
+       "      <td>1045</td>\n",
+       "      <td>17</td>\n",
+       "      <td>white</td>\n",
+       "      <td>281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>flavor</th>\n",
+       "      <td>1089</td>\n",
+       "      <td>626</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aroma</th>\n",
+       "      <td>929</td>\n",
+       "      <td>330</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vegetarian</th>\n",
+       "      <td>748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vegan</th>\n",
+       "      <td>748</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False</td>\n",
+       "      <td>742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>synonyms</th>\n",
+       "      <td>294</td>\n",
+       "      <td>292</td>\n",
+       "      <td>Rupp Vorarlberger Bergkäse</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <td>109</td>\n",
+       "      <td>109</td>\n",
+       "      <td>Megruli Sulguni, Shebolili Megruli Sulguni</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>producers</th>\n",
+       "      <td>787</td>\n",
+       "      <td>318</td>\n",
+       "      <td>Sartori</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count unique                                         top freq\n",
+       "cheese           1187   1187                                Swiss cheese    1\n",
+       "url              1187   1187               https://www.cheese.com/swiss/    1\n",
+       "milk             1151     21                                         cow  696\n",
+       "country          1176     82                               United States  305\n",
+       "region            855    349                                   Wisconsin   67\n",
+       "family            489     21                                        Blue   94\n",
+       "type             1174     84                          semi-hard, artisan  133\n",
+       "fat_content       248     85                                         45%   50\n",
+       "calcium_content    25     24                                 492 mg/100g    2\n",
+       "texture          1129    309                                      creamy  162\n",
+       "rind              945     12                                     natural  439\n",
+       "color            1045     17                                       white  281\n",
+       "flavor           1089    626                                      creamy   34\n",
+       "aroma             929    330                                        rich   56\n",
+       "vegetarian        748      2                                       False  386\n",
+       "vegan             748      2                                       False  742\n",
+       "synonyms          294    292                  Rupp Vorarlberger Bergkäse    3\n",
+       "alt_spellings     109    109  Megruli Sulguni, Shebolili Megruli Sulguni    1\n",
+       "producers         787    318                                     Sartori   27"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "11e9712e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.263421Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.263097Z",
+     "iopub.status.idle": "2024-06-26T13:55:54.821750Z",
+     "shell.execute_reply": "2024-06-26T13:55:54.820867Z"
+    },
+    "papermill": {
+     "duration": 0.580999,
+     "end_time": "2024-06-26T13:55:54.824124",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.243125",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.describe().T.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7c44edf1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.865284Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.864939Z",
+     "iopub.status.idle": "2024-06-26T13:55:54.873126Z",
+     "shell.execute_reply": "2024-06-26T13:55:54.872149Z"
+    },
+    "papermill": {
+     "duration": 0.030919,
+     "end_time": "2024-06-26T13:55:54.875027",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.844108",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vegan\n",
+       "False    742\n",
+       "True       6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['vegan'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0ddcc22c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:54.916283Z",
+     "iopub.status.busy": "2024-06-26T13:55:54.915934Z",
+     "iopub.status.idle": "2024-06-26T13:55:55.167556Z",
+     "shell.execute_reply": "2024-06-26T13:55:55.166690Z"
+    },
+    "papermill": {
+     "duration": 0.274828,
+     "end_time": "2024-06-26T13:55:55.169747",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:54.894919",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='vegan'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['vegan'].value_counts().plot(kind='bar' ,color='g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "80c98cbf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:55.212851Z",
+     "iopub.status.busy": "2024-06-26T13:55:55.211996Z",
+     "iopub.status.idle": "2024-06-26T13:55:56.264413Z",
+     "shell.execute_reply": "2024-06-26T13:55:56.263445Z"
+    },
+    "papermill": {
+     "duration": 1.079511,
+     "end_time": "2024-06-26T13:55:56.269863",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:55.190352",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribution of cheese per country\n",
+    "Freq = df[\"country\"].value_counts().sort_index() \\\n",
+    "          .plot(kind = \"bar\", title = \"Distribution of cheese per country\", figsize = (16, 8))\n",
+    "Freq.set_xlabel(\"country\")\n",
+    "Freq.set_ylabel(\"Frequency\")\n",
+    "plt.grid(True, axis='y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e438243b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:56.319991Z",
+     "iopub.status.busy": "2024-06-26T13:55:56.319375Z",
+     "iopub.status.idle": "2024-06-26T13:55:56.326192Z",
+     "shell.execute_reply": "2024-06-26T13:55:56.325177Z"
+    },
+    "papermill": {
+     "duration": 0.033589,
+     "end_time": "2024-06-26T13:55:56.328280",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:56.294691",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['cheese',\n",
+       " 'url',\n",
+       " 'milk',\n",
+       " 'country',\n",
+       " 'region',\n",
+       " 'family',\n",
+       " 'type',\n",
+       " 'fat_content',\n",
+       " 'calcium_content',\n",
+       " 'texture',\n",
+       " 'rind',\n",
+       " 'color',\n",
+       " 'flavor',\n",
+       " 'aroma',\n",
+       " 'vegetarian',\n",
+       " 'vegan',\n",
+       " 'synonyms',\n",
+       " 'alt_spellings',\n",
+       " 'producers']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "549ab260",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:56.377754Z",
+     "iopub.status.busy": "2024-06-26T13:55:56.376832Z",
+     "iopub.status.idle": "2024-06-26T13:55:56.403251Z",
+     "shell.execute_reply": "2024-06-26T13:55:56.402235Z"
+    },
+    "papermill": {
+     "duration": 0.053726,
+     "end_time": "2024-06-26T13:55:56.405559",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:56.351833",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cheese</th>\n",
+       "      <th>url</th>\n",
+       "      <th>milk</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>family</th>\n",
+       "      <th>type</th>\n",
+       "      <th>fat_content</th>\n",
+       "      <th>calcium_content</th>\n",
+       "      <th>texture</th>\n",
+       "      <th>rind</th>\n",
+       "      <th>color</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>vegan</th>\n",
+       "      <th>synonyms</th>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <th>producers</th>\n",
+       "      <th>calcium (mg/100g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aarewasser</td>\n",
+       "      <td>https://www.cheese.com/aarewasser/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jumi</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Abbaye de Belloc</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belloc/</td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Pays Basque</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Abbaye Notre-Dame de Belloc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Abbaye de Belval</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-belval/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>40-46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>ivory</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abbaye de Citeaux</td>\n",
+       "      <td>https://www.cheese.com/abbaye-de-citeaux/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Burgundy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft, artisan, brined</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbaye de Tamié</td>\n",
+       "      <td>https://www.cheese.com/tamie/</td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Savoie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tamié, Trappiste de Tamie, Abbey of Tamie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              cheese                                        url   milk  \\\n",
+       "0         Aarewasser         https://www.cheese.com/aarewasser/    cow   \n",
+       "1   Abbaye de Belloc   https://www.cheese.com/abbaye-de-belloc/  sheep   \n",
+       "2   Abbaye de Belval   https://www.cheese.com/abbaye-de-belval/    cow   \n",
+       "3  Abbaye de Citeaux  https://www.cheese.com/abbaye-de-citeaux/    cow   \n",
+       "4    Abbaye de Tamié              https://www.cheese.com/tamie/    cow   \n",
+       "\n",
+       "       country       region family                        type fat_content  \\\n",
+       "0  Switzerland          NaN    NaN                   semi-soft         NaN   \n",
+       "1       France  Pays Basque    NaN          semi-hard, artisan         NaN   \n",
+       "2       France          NaN    NaN                   semi-hard      40-46%   \n",
+       "3       France     Burgundy    NaN  semi-soft, artisan, brined         NaN   \n",
+       "4       France       Savoie    NaN               soft, artisan         NaN   \n",
+       "\n",
+       "  calcium_content                texture     rind   color  \\\n",
+       "0             NaN                buttery   washed  yellow   \n",
+       "1             NaN    creamy, dense, firm  natural  yellow   \n",
+       "2             NaN                elastic   washed   ivory   \n",
+       "3             NaN  creamy, dense, smooth   washed   white   \n",
+       "4             NaN   creamy, open, smooth   washed   white   \n",
+       "\n",
+       "                  flavor              aroma vegetarian  vegan  \\\n",
+       "0                  sweet            buttery      False  False   \n",
+       "1          burnt caramel           lanoline       True  False   \n",
+       "2                    NaN           aromatic      False  False   \n",
+       "3  acidic, milky, smooth  barnyardy, earthy      False  False   \n",
+       "4          fruity, nutty  perfumed, pungent      False  False   \n",
+       "\n",
+       "                      synonyms                              alt_spellings  \\\n",
+       "0                          NaN                                        NaN   \n",
+       "1  Abbaye Notre-Dame de Belloc                                        NaN   \n",
+       "2                          NaN                                        NaN   \n",
+       "3                          NaN                                        NaN   \n",
+       "4                          NaN  Tamié, Trappiste de Tamie, Abbey of Tamie   \n",
+       "\n",
+       "  producers  calcium (mg/100g)  \n",
+       "0      Jumi                NaN  \n",
+       "1       NaN                NaN  \n",
+       "2       NaN                NaN  \n",
+       "3       NaN                NaN  \n",
+       "4       NaN                NaN  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['calcium (mg/100g)'] = df['calcium_content'].apply(func=lambda x: x if isinstance(x, float) else int(x.split()[0])) \n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4ab5bf54",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:56.457286Z",
+     "iopub.status.busy": "2024-06-26T13:55:56.456915Z",
+     "iopub.status.idle": "2024-06-26T13:55:56.920544Z",
+     "shell.execute_reply": "2024-06-26T13:55:56.919623Z"
+    },
+    "papermill": {
+     "duration": 0.491308,
+     "end_time": "2024-06-26T13:55:56.922758",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:56.431450",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "country\n",
+       "United States                             305\n",
+       "France                                    169\n",
+       "Italy                                     141\n",
+       "Canada                                     65\n",
+       "Australia                                  53\n",
+       "United Kingdom                             43\n",
+       "England                                    42\n",
+       "Ireland                                    36\n",
+       "England, Great Britain, United Kingdom     31\n",
+       "Germany                                    25\n",
+       "Spain                                      24\n",
+       "Netherlands                                24\n",
+       "Scotland                                   22\n",
+       "Switzerland                                16\n",
+       "Austria                                    14\n",
+       "England, United Kingdom                    10\n",
+       "Canada, Italy                              10\n",
+       "Sweden                                      9\n",
+       "Belgium                                     8\n",
+       "Portugal                                    7\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top_countries = df['country'].value_counts().head(20)\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x=top_countries.index, y=top_countries.values)\n",
+    "plt.title('Top 20 countries in the cheese industry')\n",
+    "plt.xlabel('Country')\n",
+    "plt.ylabel('Number of content')\n",
+    "plt.grid(True, axis='y')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.show()\n",
+    "\n",
+    "top_countries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "af1fbcb0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:56.975744Z",
+     "iopub.status.busy": "2024-06-26T13:55:56.975080Z",
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+     "start_time": "2024-06-26T13:55:56.948752",
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('03cd21e9-dc92-420a-954f-4c175368ee4d');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plotly import express\n",
+    "for column in ['milk', 'family', 'fat_content', 'calcium (mg/100g)', 'rind', 'color', 'vegetarian', 'vegan']:\n",
+    "    express.histogram(data_frame=df, x=column).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b0f44b4e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:59.062073Z",
+     "iopub.status.busy": "2024-06-26T13:55:59.061721Z",
+     "iopub.status.idle": "2024-06-26T13:55:59.437409Z",
+     "shell.execute_reply": "2024-06-26T13:55:59.436459Z"
+    },
+    "papermill": {
+     "duration": 0.406252,
+     "end_time": "2024-06-26T13:55:59.440104",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:59.033852",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Distribution_milk = df['family'].value_counts().sort_index()\n",
+    "plt.figure(figsize=(16, 8))\n",
+    "sns.barplot(x=Distribution_milk.index, y=Distribution_milk.values, palette='Set2')\n",
+    "plt.title(\"Distribution of cheese per family\")\n",
+    "plt.xlabel('family')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.grid(True, axis='y')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "8c8c84c2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:59.496228Z",
+     "iopub.status.busy": "2024-06-26T13:55:59.495932Z",
+     "iopub.status.idle": "2024-06-26T13:55:59.688562Z",
+     "shell.execute_reply": "2024-06-26T13:55:59.687608Z"
+    },
+    "papermill": {
+     "duration": 0.223002,
+     "end_time": "2024-06-26T13:55:59.690755",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:59.467753",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numeric_df = df.select_dtypes(include='number')\n",
+    "# Compute the correlation matrix\n",
+    "corr_matrix = numeric_df.corr()\n",
+    "# Create a heatmap\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
+    "# Display the heatmap\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3a7c4d34",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:55:59.790502Z",
+     "iopub.status.busy": "2024-06-26T13:55:59.790143Z",
+     "iopub.status.idle": "2024-06-26T13:56:00.132607Z",
+     "shell.execute_reply": "2024-06-26T13:56:00.131720Z"
+    },
+    "papermill": {
+     "duration": 0.416464,
+     "end_time": "2024-06-26T13:56:00.134994",
+     "exception": false,
+     "start_time": "2024-06-26T13:55:59.718530",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Distribution_color = df['color'].value_counts().sort_index()\n",
+    "plt.figure(figsize=(16, 8))\n",
+    "sns.barplot(y=Distribution_color.index, x=Distribution_color.values, palette='viridis')\n",
+    "plt.title(\"Distribution of cheese per color\")\n",
+    "plt.ylabel('color')\n",
+    "plt.xlabel('Frequency')\n",
+    "plt.grid(True, axis='x')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3b72cdb7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:00.193868Z",
+     "iopub.status.busy": "2024-06-26T13:56:00.193533Z",
+     "iopub.status.idle": "2024-06-26T13:56:00.212171Z",
+     "shell.execute_reply": "2024-06-26T13:56:00.211347Z"
+    },
+    "papermill": {
+     "duration": 0.049777,
+     "end_time": "2024-06-26T13:56:00.214130",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:00.164353",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cheese</th>\n",
+       "      <th>url</th>\n",
+       "      <th>milk</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>family</th>\n",
+       "      <th>type</th>\n",
+       "      <th>fat_content</th>\n",
+       "      <th>calcium_content</th>\n",
+       "      <th>texture</th>\n",
+       "      <th>rind</th>\n",
+       "      <th>color</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>vegan</th>\n",
+       "      <th>synonyms</th>\n",
+       "      <th>alt_spellings</th>\n",
+       "      <th>producers</th>\n",
+       "      <th>calcium (mg/100g)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aarewasser</td>\n",
+       "      <td><img src=\"https://www.cheese.com/aarewasser/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jumi</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Abbaye de Belloc</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbaye-de-belloc/\" width=\"60\" ></td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Pays Basque</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Abbaye Notre-Dame de Belloc</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Abbaye de Belval</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbaye-de-belval/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>40-46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>ivory</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Abbaye de Citeaux</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbaye-de-citeaux/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Burgundy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft, artisan, brined</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Abbaye de Tamié</td>\n",
+       "      <td><img src=\"https://www.cheese.com/tamie/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Savoie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>white</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Tamié, Trappiste de Tamie, Abbey of Tamie</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Abbaye de Timadeuc</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbaye-de-timadeuc/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>province of Brittany</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>salty, smooth</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Abbaye Cistercienne NOTRE-DAME DE TIMADEUC</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Abbaye du Mont des Cats</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbaye-du-mont-des-cats/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Nord-Pas-de-Calais</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-soft, artisan, brined</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>smooth, supple</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>milky, salty</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Abbaye du Mont des Cats</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Abbot’s Gold</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abbots-gold/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>England, Great Britain, United Kingdom</td>\n",
+       "      <td>North Yorkshire</td>\n",
+       "      <td>Cheddar</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy, crumbly, dense, semi firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>mild, sweet, tangy</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Abbot's Gold Cheddar with Caramelised Onion, Caramelised Onion Cheddar, English Cheddar with Caramelized Onions</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Wensleydale Creamery</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Abertam</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abertam/\" width=\"60\" ></td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>Czech Republic</td>\n",
+       "      <td>Karlovy Vary</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>hard, artisan</td>\n",
+       "      <td>45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>acidic, strong, tangy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Abondance</td>\n",
+       "      <td><img src=\"https://www.cheese.com/abondance/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>buttery, fruity</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Tomme d'Abondance</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Acapella</td>\n",
+       "      <td><img src=\"https://www.cheese.com/acapella/\" width=\"60\" ></td>\n",
+       "      <td>goat</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>California</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, soft-ripened</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>fresh, herbal</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Accasciato</td>\n",
+       "      <td><img src=\"https://www.cheese.com/accasciato/\" width=\"60\" ></td>\n",
+       "      <td>buffalo, cow</td>\n",
+       "      <td>Italy</td>\n",
+       "      <td>Campania</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>pale yellow</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>aromatic, fresh</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Casa Madaio</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Ackawi</td>\n",
+       "      <td><img src=\"https://www.cheese.com/ackawi/\" width=\"60\" ></td>\n",
+       "      <td>cow, goat, sheep</td>\n",
+       "      <td>Cyprus, Egypt, Israel, Jordan, Lebanon, Middle East, Syria</td>\n",
+       "      <td>+</td>\n",
+       "      <td>Feta</td>\n",
+       "      <td>soft, brined</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic, smooth, springy</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>white</td>\n",
+       "      <td>mild, milky, salty</td>\n",
+       "      <td>mild, milky</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Akkawi , Akawieh, Akawi</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Acorn</td>\n",
+       "      <td><img src=\"https://www.cheese.com/acorn/\" width=\"60\" ></td>\n",
+       "      <td>sheep</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Bethania</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>hard, artisan</td>\n",
+       "      <td>52%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>crumbly, firm</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>burnt caramel, citrusy, herbaceous</td>\n",
+       "      <td>fruity</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Adelost</td>\n",
+       "      <td><img src=\"https://www.cheese.com/adelost/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Sweden</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>semi-soft, blue-veined</td>\n",
+       "      <td>50%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>natural</td>\n",
+       "      <td>blue</td>\n",
+       "      <td>salty, sharp, tangy</td>\n",
+       "      <td>strong</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ADL Brick Cheese</td>\n",
+       "      <td><img src=\"https://www.cheese.com/adl-brick-cheese/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>Prince Edward Island</td>\n",
+       "      <td>Cheddar</td>\n",
+       "      <td>semi-soft</td>\n",
+       "      <td>12%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>elastic, firm, open, soft</td>\n",
+       "      <td>rindless</td>\n",
+       "      <td>ivory</td>\n",
+       "      <td>buttery, mild, milky, subtle</td>\n",
+       "      <td>buttery, sweet</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ADL - Amalgamated Dairies Limited</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ADL Mild Cheddar</td>\n",
+       "      <td><img src=\"https://www.cheese.com/adl-mild-cheddar/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Canada</td>\n",
+       "      <td>Prince Edward Island</td>\n",
+       "      <td>Cheddar</td>\n",
+       "      <td>semi-hard</td>\n",
+       "      <td>14%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>firm, springy</td>\n",
+       "      <td>rindless</td>\n",
+       "      <td>yellow</td>\n",
+       "      <td>acidic, buttery, milky, subtle</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ADL - Amalgamated Dairies Limited</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Affidelice au Chablis</td>\n",
+       "      <td><img src=\"https://www.cheese.com/affidelice-au-chablis/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Burgundy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>55%</td>\n",
+       "      <td>26 mg/100g</td>\n",
+       "      <td>creamy, smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>orange</td>\n",
+       "      <td>fruity, mild, tangy</td>\n",
+       "      <td>perfumed, strong</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fromagerie Berthaut</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Affineur Walo Rotwein Sennechäs</td>\n",
+       "      <td><img src=\"https://www.cheese.com/affineur-walo-rotwein-sennechas/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Swiss Cheese</td>\n",
+       "      <td>hard, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>smooth</td>\n",
+       "      <td>washed</td>\n",
+       "      <td>cream</td>\n",
+       "      <td>creamy, pronounced, spicy</td>\n",
+       "      <td>rich, strong</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Affineur Walo Red Wine Farmer</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Walo von Mühlenen AG</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Afuega'l Pitu</td>\n",
+       "      <td><img src=\"https://www.cheese.com/afuegal-pitu/\" width=\"60\" ></td>\n",
+       "      <td>cow</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Asturias</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>soft, artisan</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>smooth</td>\n",
+       "      <td>cloth wrapped</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>spicy, strong</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import HTML\n",
+    "def path_to_image_html(path):\n",
+    "    return '<img src=\"'+ path + '\" width=\"60\" >'\n",
+    "HTML(df[0:20].to_html(escape=False,formatters=dict(url=path_to_image_html)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a48a91df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:00.277169Z",
+     "iopub.status.busy": "2024-06-26T13:56:00.276877Z",
+     "iopub.status.idle": "2024-06-26T13:56:01.372560Z",
+     "shell.execute_reply": "2024-06-26T13:56:01.371649Z"
+    },
+    "papermill": {
+     "duration": 1.130421,
+     "end_time": "2024-06-26T13:56:01.377634",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:00.247213",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pn/6JD3/4w2zevJkPfOADVFRU0Nvby49//GN27tzJP/7jP5bOr6ys5NZbb+Xv/u7vSCQS3HvvvRes8+abb+YTn/gEf/mXf8mBAwd461vfiqZpdHR08Mgjj/CFL3xhjqjXjTfeyLe+9S3WrVtXWgnfvHkzXq+XU6dO8aEPfWhO+W9961uprq5m586dVFVVcfz4cf7xH/+Ru++++7IEvxwcHBwc3gC8zirIDg4ODg6vEZ/5zGcEIHbs2DFv33e/+10BCL/fL0zTnLOvUCiIz372s6KlpUVomiYaGhrEZz7zGZHNZucc19TUJO6+++4F6+7p6RHvete7hMfjEeXl5eL3fu/3SilVzpdGZzanTp0SH//4x0Vzc7PQdV34/X6xc+dO8Q//8A9z2nJuGh0hhOjs7BTve9/7RCgUEi6XS2zfvl386Ec/mldHZ2enuOOOO4RhGKKqqkr86Z/+qXjiiScWbOeXvvQl0dLSIgzDEFu3bhXPPvusuPnmmy+YRkeIpffppaTRWYjOzk6hKMqcfnnqqafEnXfeKYLBoHC5XKKtrU3cd999Yt++ffPO//KXv1y6PzKZzIJ1z06jM8P9998vtmzZItxut/D7/WLdunXi05/+tBgcHJxz3Be/+EUBiN/8zd+cs/2OO+4QgPj5z38+Z/u//Mu/iJtuukmUlZUJwzBEW1ub+NSnPiWmpqYW6x4HBwcHhzcJkhDnBCU5ODg4ODg4ODg4ODg4OFyDODGwDg4ODg4ODg4ODg4ODm8IHAPWwcHBwcHBwcHBwcHB4Q2BY8A6ODg4ODg4ODg4ODg4vCFwDFgHBwcHBwcHBwcHBweHNwSOAevg4ODg4ODg4ODg4ODwhsAxYB0cHBwcHBwcHBwcHBzeEDgGrIODg4ODg4ODg4ODg8MbAseAdXBwcHBwcHBwcHBwcHhDoL7eDXC4tnjlRB8/ffEkx7uHAZAkic0rGnj/WzZSUx5Y8JyfPH+MJ/d2MDKRACDk93DDumY+eOfm16zdDpfHXz+/m929Z5Z8vKYo3LdhMzc3NhN0ua5ewxwcfgEpWBY/6+7kkeNHGE2llnSOIsssj5Tz13fceZVb9+YgXSjw8+7TPHTsCFPZ7JLOUWWZ9VU1fPbm265y6xwc3oAIEyv3DFZuN6rnQ0jqMiRJuswyC5jpBxEigeJ6J7LaeGXaerHNsBNY2R9j5V9E8/4msrbiNa3fyjyKlX8OxXUninHzrIYJoEA+8VfI6ioU42YkpeLyKhM2wh4jP/VnqJ4PIuubkOSFx/+vJ9esATs8HudLj+yhYFrs2NDMdWubqAz7AbAsm+hkgi8+8hyWZbNrYxvb1zRSEfa9zq1+41MV8bN1ZT1lQQ+Do3F+8vxxqsJ+8gVz0XNaasu4YX2B4fEER04PcbpvlLa6stew1Q6XS198iiOj0SUfrysK4+k0pm1fxVY5OPxiIoCJTJqTY2MMJhNLOkeVZBTJcapaKrYQjKXTnBgbZTyTWdI5qiwTNJwJO4c3P8JOYBcOYaUfRPX/CZJSiyQpFzoLYU8irF4gd4VaYiOsEYSYAvKL1yxMEPnpNupwuYbzPEyEFUWYpxFiae+Ls23LYOf3YmV+hBBzJyQlyUBS21G9v4YkLf5uEfY4wuxG2LEF9toIswchVwGLj9WFyIOwkCQVJO18LQZRQJinECIBXJvjvGvWgE1l8jz9Sgd500JRZJY1VJQM2FzB5MzQJE/u7cC0bKrKAqxpq+Yy5xwcKBqwAa+LNW01nOiJ8pPnj1/wnJbaMqoifuKpLMIWnBmaeA1a6uDgcClMZbI8cuAIU5ksdaEgO1oaaAyHXu9mOTg4OLwpeOp0F8dHRknm5xpcmqLwkS0bCbldKPKFJ5u6xid4qbefvtjUvH3bG+rZ0dyArl6tYXweYfViZR9F9f0mUA1cyICVkbV1SJIHSa65Qu1QkY2bgDySXL7oUcIawC4cRVbbkdR2JK60AXsZCBPb7MXKPY2iX4ek1M7aqU9f1+W0V0V1/1KxXMm/6FF24RjYE0hq+wVWsmWQA6i+jyNrq0ByX0bbrh6viwFbyJtMTaaITaRoW1mzqIuBJEmEfG5GxhMk02dnczK5Aj1DE+iaihDmlZ9o+QVGUxU0VcHj0hiPp5d0jqGrGLqK26UT9F2bN7rDtYtl20xlswxOJRhLpkjm8uRMC4FAkWVcmkrQ5aLC56U26MetacjOQ3/JJHI5Ht5/mL7YFFsb6mmOhBwD1sHhCmLbgufP9DCSTFGwrNJ2CQlJAlVR8GoaEY+HumCAcq8HXb2QceDwRqFzfIJnu84wnEiSLZikC3kyBRO3pvLO1SsIuowllTORzrC/f5BX+gfJWxaZQoFkLo8lBEIItjXWoV/la7koJKXoWnsl3WslBcW47gIHCYR1Bjv7UyS3F0ltu3L1X1FUFNc7kPWts7ZJIKnA+VZEz4MkFct133WBAwWisB/b7EeRw8B5DFhJQpL8qJ4PXlqbXiNeFwM2mchydH8Ph/Z281t/+k4kZeHBqCxJNFaHmZhKk0jnsG2BLEtkcwX6RmJUhn2MTCSurZkWhzclAkAIsqZZ/LNM8qZFzjLJWRambWELgS0ElhDYtkAgSgMWCQlZllAlGVUu/mmKgqEoGIqKoaq4VRVNUX6hjLNYJkvfZIyjw1Fe7Rvk1OgY0UTRiLVsG11VCbpd1IcCrKwsZ1tjPTe2NePW1MuPrXH4hSOaSDIcT+I1dFoiYWTZuYeuNkKI0v//RXlmLWHzjVcP8WJP37xVOFmScGkq5R4PrWURttTXsb2xjmXlZfiXaNg4XNtsqqvFrWmMpdKMJVMcj45ycHD4osupDfi5dVkbLWURkrkcvbEpXjzTR2yJMduvJXahA2GPAjaS5EVSW0AKlp55YScR9iSIFGAj7ASSUgcIsCcAgaRUISlnV27t/MFpl1sbSS5DUuqR5LkrjLbZi7Cj2PlXsM1jSOZykGRAQpLLi67P0/GbQphgTyCsoaIbsCQjSQEkpRJJDnN2FVQgrDGEPYKwkyCp03bG5brSSiAHkJTzhdgJhD2FsIYRdnHlXZKDIHJMj0TPHmmnsAuHAYti/9UhKdVI56yY2oWTCBHDzh9A2KOIQiOWSBX7SKmZdg8vui8LaxTbPD19rQJJbSv2vTR3qkQIAXa06FY9U5bsR5Kr5lyfbXZNt1sHEUfYcUBGkgNISj1IXqRLDH15XQzYVCJLx7FB9u4+Nefjdi6SBNXlfvqGJ4klMuRNC5euksmbDERjtDWUMxpLYs8qQwiBZQvSmTzZfAHTKt5wqqrgNlQ8ho6izO+sZDrHVHJxv3ZVVfC5dbzusx+Y8akU2Vxh0XMMXcPn0XHpc2dWcnmTbL5AvmBhWjZCCCRJQlNlvG4DQ1PnDKwsyyaRzpHLm4QDHmxhk84WyOdNbFE06t2Ghs9jzHFLMS2bTDZPJjerHxQFt0vDbaioyus34yuEIJc3yeQLFAoWlm0jBMiyhK4qeN0Guqa8bgMeQXFlMD9toOYti5xp0RePMZBIMJJMEk0niaaSjKbSTOWy5Kxpg9Ys/plCIEsSmiyjygq6ouDTdQKGgV83CLvdVHq8VHm9VPl8NAZCRNxu3KpWMm51RSn9Tm+2oV8im+P5rh6+c/AoL/f2U7AsDLVozHsNvfi5EIJENsuB/hT7+wfZ093L+toqXJr/TdcfDlefp0938419B9lQV82fvuUWXPLV+wSWvm2C4iT76/QuM22bZCFHyszjU3UCugtJkijYFlP5LAXLwqsVt18NCrbFeDZNlccH4hfHiJ3BUBT8LgPXtKvnzLclkcux50wve870cn1jPfdt28zOlsYluZY6XNtsqa9lS33RTXQonuC7h49dmgEbDFAbLBpfti04MDRE1/jENWnAWpnvYOV+hrAmkJRqtMB/Q9ZvKO0X1hms7BPYhZMg6diFA6iuu0EysPP7ECKN4r4b1fMRpOn4TDP1pZJhLBs3oPp+G0VeP6deO/ckZvYnCPMM2BOYqQdKxphs3IbquQdJXosQFtjjWJkfY+WeQthDIOnIajuycRuKcdssQzeDlf0pVvanCKsPZD+y2g6SB8TlGrFW0ZCew7TBLUkIkcPKvYCV+Q+EeQokFVldDbIXIebaG8IewYz/T4RIIaxhFO9Hp8WzmuccZ6a/hV14FWH2gMhhm53TBqmC4v5PqJ73g1Jd7M/CIQqJvwE7gbCHUAOfRXW9DWYJQwlhgUhiZn6AnXsOYQ0WJwPUNhTjNhTXXdOGqYSV+jpCJJCUKuzCaYR5unis0oTqvQ9Z2wSS95J68nUxYC3LIn8ew28GSZKoKQugaQrRySSxeJrq8kBpBfat16/klWN9c86xhWA8luJ7Tx9i94EuBkenkCWJppowt2xdxttuWFmKpZ3NT54/zt98/clF29JaV8Y9b9nIL926obTtr//95zy17/Si5+zc0MK9b93E9Wub52w/1jXM7gNdHOwYZCAaI5XN43MbtNWX8f47NrFxRR0B79nBxEQ8zb8/upcXDp3hLz55N7Fkhkf3HGPfsT4S6RyRoIfbty3nl+/aQtjvKZ03Opng8RdP8rOXTtIXnUIGmmoi3L59GbdtXUZdZWjRtl9tTMvm1ZMDPPPKaY51DzM8HidfsAj63axtreb9d2xiVUsVhv76hWkPJhMciQ5zcGSEw9Fhjo2Oksjn5kyYXAhbiGmho+ILazxzfrfsiNtNczDM6ooKNlbXsKGyhrZI5HIu45rle4eO8eCrh+gcK8ZMB10G25vqub65geqAH02WiWWydIyOs69vgIFYnPesW4XfZTjGq8Ml0Tk2QTSxNFXfK4GwBWbBQtEUlEU8ja42I5kED3Yc4Dvdh7m3bQO/vXYnqiQxkJzi7w49y4nYKB9o38ivrdx2VervjE/w315+jAdufT9+7RdvhXFDbQ33bdvEre0t2ALylsVoMsXzPb08cvAIJ6NjPH+mF4AVVeVU+RwxSoc3Hqr/T1D9f4CVfhAz/fCCxwhrEGFHUT33IqttmKl/RzGuQ3G/Dzu/Bzu3F2HcXjLAtNA/AzkK8f+JsMcWLFPxfBDF/V6szA8wU19D838a2dhB0ShUzooV2TGs3NMUkv8Xzf+HyNpmBGmszPex0g+CsFA9v1Q8NLcHM/MIstKE6v0okuTHyu/GTH0Z5KrL6CWBsCfAnj2ZoYDsA4rPvSgcwMp+D0QSzf9HSGojdn4fheT984SpJKUFvfxHIDLkJj60aK1a4NMgchTi/xthj6B6P46szdgy2rQLcxHZuA3DuBmsfrKjb1vkMpKYme9ipb6G4rkHxfgdhMhjZX9a7CNAcb+HGbdoK7cbSWlAdb8b2f972NYIZvzPsdLfRvL6kPSNS+/CWbw+BmzBJpe5sAELxZXPirCPRCrLRCJNJOhhMp4hmc7RWhdB186uItq24MSZKF/+/gsc7RxiVXMVm1bUI4Sgq3+M7z55iGNdw/zu+2+iuiIwx1Vz44o6/vjDt86rv3tgnFeO9+PSVcoDc2cJ3nXTOrasaph7goCDHYPsPdZLwOvC757/wX7m1dMc7hom6HOxpmVFyUB/+UgPn3/waX7/Q7ewbXUjbuPsyq0Qglgiw6PPH2dodApbCO7YvryoVjmVxuvW51zP4dODfOfJQ7xyvI+2+nK2rW7Csm1O9ozw3ScP0TUwzkfu3kZL7eujFpwvmDy65yj90SnqK0NsWdkAEvRHY7x6sp+O3lE+95tvp7WufM5vfDUp2BZnYjGe7+/l+b5eOicnmMpmSyuweasYl3k1iWWzHMmPcGpijJ+cPoVL1SjzuNlaU8d1dfWsr6yh0ut9Q7sZW7bNMx3dfO/QMXomYrhUleua6vnwtk2sqq7ApaoosowkFZ/pW9pb+OCW9cQzOcp8Htya9gu3iuNw+SRzeXonp4gtUXF2IfLZAj0dI8TGk2y7ZeV5jxW2IDo4yb5nTrLh+jYa2i9n4HN5aNNeHB2xsdI7rDcZI1UoYCgq57qmXSmypslIJkF/auqqvzuvVSSp6FmkyDIKoCoy9aEA7/KtpL0swldeeoWnOrvpn4rz81OdfGjzhguW6eBwrSFJEgiFouG4GDKyUofiuhNh9mKmHkDWdyIb1yOsAYQ4XjTwaJ5VZnF1ctF60RGSXDRUJRkkAyQ3EnPPE1Y/Vu5JFPd7UVxvBTmChA12AtNOYOeehWkD1so+iSRXILvuLK4iSxKKHC6uFNujl9FLRWPclGbZBXIFqueD0wafgZXbC8JEdr0V2bgFJBVFacTOH8Q2T8y9dkma9vBRL9BHxnQfqRSNetcsN+NzjOLpPhfnEewSdgwr9U0U1x0orrcjqY3FUiQDRAor/RCK6+7S5IEkV6AYNxWvUTJQlGaE6zZs83TRrfwSuWgD9qkfH6Sve5QV6+u57qazH/AH/+Up4rGlif7EJlJ0nhhcWoUC6itDpLP5kuE6PD6Fosi01JajzRI+GBid4rkDXXT2j3Hr1mW8Y9caIgEPAhidTPLUKx0880onD//8AL91zy7kWec2VIaIBDxzqh6LpTjePYJLV7lhfQvrl9fO2b9+WS0rmytL/7Ysm6GxOD/ec4ymmjA7NrTQXDt/9eyunau5ddsy3IaGx9CRZYl4Kkt9ZYjvPHmQk2eitNeX464IzjkvXzA51DHIXTtWsbatBr+3uBKVKxRdq72uoo/66GSSl472cqo3yq6NLbz3lvV4PQZCCEYnkvzg2SMcOT3EEy+d5Nffu2Npv8MVxtBU3n/HJgTgdem4jeIDODKR4Mm9p/jOkwc51jVCVcSPrnkuVNwlYwtBNJXk5YF+nu09Q1dskvFMmolMhnShcFGrrVeqPTPGchKADNFUkqFEgud6e6j0+lhVXsF1dfVsrKqhzHP1+uZqYAtBPJvj668coHu8+OK6pb2FD23dwLqaKrzGwpIUAbeLKr+NLElvaOP9WqMYn/2LwcnoGOOpNNZlPNOWZTMxGme4b67SuhCCiWic8ZE4iiJT1RDB7TUwTZtUPEOhYJGYSpPPFdANjVymwHDfOHXNFfhDHtSrPEnnVjS8qk5PchLTtlElmd5kDEmCMsNTuoaCbfHj3hO8HO1jLJsirLvZXtXIbXVtRIyz75p/OfYihqzi1XReGe0vHfv2ppVsqajHo+o8O9TFD88coys+wWQuzSef/R7qtHvs+9s2cFfjyl+oZ3lm0k0CZEVBUxTW1VSxoa6GoyNRYpksR4eXnsrMweENh6QDMkg+kIvjW0kuR5L8ReMHaTrWc96J5ylTQhLSrGOm//85cZVCJLDzB5Ck4+TNE8yYP0IU401ltYViLKmMsHqLcaFKDZI8bejJISS1DZG/dIMLtGm32VWzthnFWNCZ9ljDgIasNCDJ0+9cSUdSm8DqvbRqJRlJzO6P+f2zVIQwp2N0+5HUldO/n2u6mmokpQkrt7tomE7HzUpKJZLaVIpfFqggV4A4xvlSI12IizZgTxzq49UXTiMr0hwD9qVnTzIysLQf1jRtcpk8obILu8oIBPWVITr7x5iMp4mnc4xOpqiM+An6XXNiRQeiMQ51DKCrCu+5eR3LGitKBm5VxE8ileXgqQFePHyGD925mbKgtxQP6zI0XNMrnkIIMrkCLx3uYXB0ig3L67h92/I57rkAfo8BFGdSLMtmeDzBj547hmlavGPXGjatqJsTMztDW315cSA+q+3lIS8F0+KHzx4hOpkgkZ7/EAshaK6NsH5ZLcsaKhYVIOkZnuTEmREMTeX27ctZ3nTWyK6K+DnVO8qhjkGOdg6TzhVw66+9II6iyKxuqUZRpDl1B3wuJhNpHv7ZAQbHpsjmF89pdTlYtk13bJJXhwbZPzzE8bFRumITJPKX/jBdLQq2zWg6zWg6TVdsko6JMQ6MDNEWjrCusorr6hpoCATQlWs2K1aJdL7ASz19nBgeJV0osL62mtuWt7G+thqPvrgKnyxJyIvEbNtCkMrn6RyboHtsgqF4kng2S96ykCUJj65T4SsKpqyvq8ar6/MGzjnT5DsHj9I9PsnbVi2jLhjg2PAoBwaGyFsWDaEgty9vpdznJZpIcWBgkGPDoxQsi9pggNuXt1Ll95VWus6la3yC48Oj9E7GmExnMW0bt65S4fWyorKcNTWV+A1jwefw+4eOcSI6xo7mBlZWVdIxOs7LvX2k8wXqQ0Fubm+mLhhgMpPl0MAwBweHyBZMqvw+blnWQkMoiLFIqoXiSrfE4FSCo0MjdIyOMZnJIoQg4HLRWhZmbW0VDaHgkmLzLNuma3ySY8NR+ianmMpmsYXAo2tU+XysqCpnTXXVBUW4uscn2dPVw0giyYa6Gm5fUVSVPD4c5ehQlIGpOKl8HgkJv0unJuBna2MdtQE/uqpi2TZD8QSHB0cYS6UYTaY5ER2lZyIGwN7eAT73+FMoC7xDb2xr5rZlrQterxACM2+Rz871ILItm/GROKNDMVLxDCcO9PK2D0wrZ0oS4yNTjI8UBTnC5X5O7O+lqiHMMz86wNabV1LTWIaiXr3YR0WScCkqLkVlOB2n3heiNxlDkWRC7uIALWnmeXawi8f7TtHkD9HkCzGZy/DKaB9T+Qz3Ld+KLBXf16dio3TGJ1gVrqTRF6LJH6ZzapxvdOwnbHhYHa6kwRfiuqpiTOfp+Bi31bXjVovPeEsg8gszcXI+PLpObcBPudfLmYlJosmF3dvzpsloqngP905OMZ5Kky4UEIB7WhCqpSzM5vpa/LpxXnGybMHkRHSU49FRhqYSpAp5ZEki5HbRXl7Gxtoayr2eCz7vqXyenskYR4ZGGIgnSOXy2EJgqCp+Q6fc66E5EqatLELY7Z7XpoJlMZnJcGJkjJ7JGKOpFKn82TIibjeN4RBb6msJe9ylyY9z+efnXyaaTHHH8jY219cyOJXg1f4BeianSOXzyLJE2OWitTzCmqrKCyqu20KQK5i80j9Ix9g4o8kUOctEVxTKvV6WV5Sxub72DaOGb9k2sWx2up+L91hRydhGVxRCbjeNoSCb6mup8HoW/YZdGZRZtuj0CqmkgaTMco+9GosGAkQeRA5Z24CkrSkZVzNIchWlxoksoJdicacPKBre511hvhAKsrYexbj+nO2z76N8cfX43JywkrfYT687FogMUCga2LPbJKkgu0AUmC14JUkeJOncxRaleMxlxBRf9Ii3aVkVhYJFbeNc19OpiRSGW6extYJA6PyrQompNL2do1jW0hpeXxXkWPcwk/E0sUSG8XiKlpoIqjJX5GcinmZoLE444GF5Y8UcsSZdU6iI+KivDLH3WC/D4wmCPvc8QSchBJZl88wrnew+0EljdZjbti2jtW7xOEQhBNHJJD976SQvHTnD3btWs3NDC+WhhQ30XL4Ywzs8niCezJItFAWdYokMmVyBbN4siS6dy+qWKipCvvN+oIbH4oyMJ0hl8rx8pIdjXSNz9p/oGSGdzTOVzJBMZXHrr33MjQAy+TxnBieITiRJpHPkC0Vxq4HROFBMl2TZVzaBcnHFNcW+wX5eHhzg1aFBumKTZMylubS/3pi2zUgqxUgqxaGRYfYPD3IkGuW2llY2V9cScV/baYxS+TxPdXSTyhdQJInrmurZ3FBzXuP1fAgh6J2I8cTJTo4Oj9AzESOaTJHI5ihMG7BuXaPM66EpHOL65gbuWNFGbTAwZ0BkWja7u3p4sbuXMo+bEy4XPzvVyeHBYQqWRU0gQKZQYEdLI6/2DfLjoyc5OTqGadlUB3yMp9J8cMt6qv2+s6qLQpDKF3jxTB/Pd/dwdHiUgVjRqDNtgUtVKfO6aS8vY3tTPTe1NdMUCc0bQLzU08+Tp7qKHhTJND871cm+vgEy+QI1QT9TmSw3tTfTPT7J9w8d48hwlGzBpMLnJZpIcu/m9bSWhRc0GFVZ5vjIKC/39PNSTx9d4xNMZYpx3gGXQWM4xOaGWm5pb2FTfc2ihrAtBFOZLC+e6WNPdy8nRkYZnIoTzxbLcmsaFT4PbeURbmhu5Kb2ZmoDAdQFBPUARhJJnu08w9HhKDnTZEdLI3u6e3iqo4sjQ1GG4wnS+QKSBF5Dpy4QoCbop8LnRaf4nJyKjvHgK4cYT6cZT6WJZ3PT8ejQNTZRMmbPJex2c0t7ywUzHs5FIpctkJzKMNA9yuhQjLfdux2A5FSao/u6qW0qJ1zhp/vEEMde7UaSJQ7s6aCqPkK43Ic3cPWeXYHAUFQafCE6psYI6m7Gsyl0RSFsuBECJnNpHuo8SJ0nwNsaVtDsjzCcTvDj3uM8M9jFTTWttAXKSkOtrFmg0uXjXc2r8WoGRyeG+dQLP6IrPk5rIMKyYDllLg8S8NTAaX6pbR1BzeW4/5+Doaq4VBWBKN2fs8mbFsdGRvnhsRN0jI0zEIszmc6Qt21sIdAVmYjbQ2MkxLGRUd69ZuWCk2m2EEykMzx1uosXzvRxMjpKNJkia5pIkoRP12iJhDneOMqt7a2sqCjHpS38vI8mU+zrG+DJ012ciI4yHE+Ss0wsW6ApMl5dJ+Jxs6y8jLeuWMYtbS1zBNMKlkXPZIxvHzzKqbFxBmJTjKfTZE0LIYrp00JuF/XBAMdGorxj9Qqaw+EFUw399ORpjkdH8eoaedPima5uXu0fZGAqQdY0AUHAMGiKhLiusYG3rmhndVXlvHJm+nowHueJU6fZ2ztA5/gEk5mi0Jkqy4TdRUO4Y2yc25e1URvwX2WD7/IwbZvheIIH9x+iY2ycvtgUY8k0WXNaAFSSCLpc1AUDHBmJ8vaVy1leXoaxyO9+ZTjn+b8i74PZK7CLjBklDUmOIKlNKJ73I0nnjntlSsap5AOyCDFLLEvYIOKLl39RbV3cCJYkDzZTIJJzd4gkzBN/uth6r8QkgVxccZVcCHu8ODFQamMW7DiS7EOSvJy9zul45CvMRd+lO25bxeYb2nF75rv6LVtdyx3v2kTTBWJ9ejujPPGDVzl5uH9JddZXhlBkmclEhuGxOJPxNJtX1M+773MFk1zewuvSURd40emqit9jYNuCqWRmQeMoX7A4cWaE7z19iIDXxe3blrOmtRr5PLORE/E0Lx/t4Wd7T7JhWS3v2LWGqoh/3uycEIJEOseLh89w4NQA47EUli2wbBvLtsnkCuQKJmI6x9dCBH1udP38N0J6Wnl4MpHm+UNnFhwwVIR9NFSFse3XPi7JtGxiiTTPHeji0Okhkukctm1j2QLTtImnii8NIcQVm4yzhSBVyNM5McEL/X38qOMkXbGJ6Q/cG5OcZXFyfJyT4+NE3G6WR8quaQNWCEE6n2d//xAFyyLodrGyqoKawOKJt5fCwFScHx87yZmJSSq8XuqCAbwVGqoiY9lFw2pgKs7T490cHhrBUFXeurKdCt/Cynev9g+iyDKZfIHV1ZUMTsXpGp/g4f2HGU2m6J2Ikbcs1tVUMZZKc3p0nIdePcz2pnrCbndp0JfI5Xm5t5/7n9/LiZFRgm4XVX4fyyrKiuUXCoyl0iVDLZ7N8c61Kxc1No8ORRmKJxhPpVlbXclgPEHvZIzvHz7GWCpNIptjKpNlfU0VE+kMJ6NjfO/QcdbWVlMT8C84SRDLZHj8eAeJXA5ZklheWYGuyORNi+FEkpPRMTrHJhhLpvBoGuvrqhfss1gmw56uXv7txVc4GR2jwlf8HVZWGUhSceV9NJni56eKBmiqUOCuVctpCAXOa9DkTJOheIL9A4P86wv7GEul8RsGyyrKkSXImRbJXI6JdAaPpqHMcovSFZUKv5cKf/F37puc4vTYOMlcnsZwiLU1VQtOBK6oKr/olZWpiSTZVA5VlTFcxX6eebemkznikyka26vwBtycOTmMP+RBUWVaV9ficmtX/T0sALeq0RKIcCI2SrUnABL4NQOXomEKm/FsmldH+/nQDe+kPVCOXzcI6S56k5Psi/axf2yAFn+k1DfN/jAby2up94UQQrC9shFDURnPpkmbhaumavxmI1sokDULqLKCf4EQCkvY9MWmeLKjC01RqPL7aC8vw6NpWEIQy2QYjMd54Uwv+wcGCbvd3LminTLv2YWEGS+Vx0928O/79jORzlDt97O5vhavrmHagol0mo6xcTrGJ5jMZLhn/VrWVFfOez4t2+bg4DDf3H+IAwND1AUDbKyrwTv9fsmZFsl8nslMhtNjE2yuT88LxbGFIJpI8fjJDhRZpszjoTEcwqtrCCCezTEUT7Cvb4AjwyO4NY13rV5JXSiwYB/OtOn02ATHo6PUBQLc0BxClWWSuZmV4ij9sTg506QlEsalzvUCsWyboUSC7x85ztf27kdXFRpDIVZWVmCoClnTYiSR5JW+QY4MR8mbFu9YvYK64PnfYa8nQggmM1kePX4KRZYJu93U1QXwajqSJJHI5RhOJDg4OMSxaBRVkvDpOi1l4XPKySCJNELMmAwSSAqSdK6HoZj13yv5TpseCy4W/iFJ0yuqEsIeBJFCCGXaTVZDklQkOYKsrcYuHEGxhkBtopjaJUfRjVUDit8KWVuBbXYgzA6E0gSSim31IgqnmBHkvPTryCLs2Z4WM+68RQ8sSWkCqx+7cKIotCR5ii67ZgeIxcI0xTl/C/VR0fAUIgfWyNk2SArF1eZzbZyZcubaSZKkIuQyZG0tdv4gkroGWVJB2AizA9vsQtbWg+y+5PQ4S+WiDdhQZPHVulCZj6q6MNV14UWPAchm8gQjS5dNLg/7CPpcTCUzdPaPEU/maK0vn5f/VZFlZEWiYNnYtije07NeLLZtUzDt6ZQ181O0FEyL3pFJvv7oPlKZHB+8czPrl9Win2c2Kp3Nc+DkAD976SQBr4uPv2cHVRH/gql6TMvmSOcQX/nBi0iSxHVrm9i4vI6KsA9NVRibTPG//u2n5+0LWZYu+LJUZBlZkqirDPHBOzcvOovq97oI+F77QUYmm2ffsT7+8eHd1FYE2bWxlVXNVUSCHiQkOvrGLtgPF0PBspjIZjg4MsxDRw/zXO8Z8ld4Zff1QgJ8us6Gqmoi7ms7HtYSgkQuT19sCksImiIhyi/TZUmSJKoDfm5pb+FU1M+WhlpWV1dSFwzg0TUyBZOO0XEeP9HBT46dYiKV5omTp2mviCxqwL7cO8Dty9v4la0bqfB5+emJ03xx94ucmYjxg8PH2dHSyG/eeB0tkTAvnOnlr3/2LJOZDCdHxlhWXoZL81GwLE6PjvOVF/ZxcGCI6oCft6xoZ2drE23lYQxVZSSR5ED/II8cKLouf+/QUQIug6qAD58+fyB7eGiYG5ob+Y2d22grL+Opji6+uPsl+mNxfnLsFFsba/nYjq2sr63m4MAwn/3Jk8SzWU6PjrOxrppGPTSvzOPDo3gNne2Ndbxt9XLW1VQTcBlMZbLs6e7hB4ePc3x4lOe7ewm6XSyrLMOtzTWE86bJkaEoX3v5VY6NRKkNBHj76hXc0NJAc6Q4+Tg0lWBvXz/f3n+Ugak4D716iIjHTbl3xXlX33MFk5PRMb657yCxdIbbl7WxubGW+ukV9Ml0hp7JGN3jk7SVRzCmJy4NVWVXWxO72ppKZX334FH+9YV9nM5NcENLQzGNziWsNJhmMZ51uH8CCQhX+OnpGGbgzBjVDRGaVlQzHo2Xjvf6XYTLfGTTeYb7JohUBgiGvWy6cTnCFgTLvOjGJSaxvwjcqkZroIxHe09Q5fETMTwY06tiBcsilstQsG2qPX70mdRdkoRX1fFrLkbSyTlCTEHDRchwlY6TJNBkBXN6QtbhwmQLBYYTCUanJ4gaQ6F5x7g1jeUVZbxn3WqCLheb6mpojoTx6Rq2EPROTvHYiVP8+74DTGQy/PDoCTbUVs8xYHNm8Tm6/4W9TKQzbG2o473rVrOzuZGI10PBsugam+Df9r7KM53dPHaig7DbTVMkhN+Ya6RkTZNX+wfZ1zdATcDPr2zZwF2rlhNyFVfX0/kCg/E4x0dGGU4kuaW9pfRczmCoKk2REO9eu6r4/aqrobUsQtBlgIDhRJKnTnfypedfZiKd4YlTp1lbXbWoAQvwUm8/1X4/N7U2ce/GdayqqkCSJMZSaX58/CTfP3yM4yOjPNt5hveuXU1beQRl1lgqns3xcm8//75vP3nL4tZlrbxv/RrW1VThMwwS2Rz7Bwb52t79vNjTxzdfPUhdMEDE68GjXf3n91LQFIW6oJ93rVmJ1zBYW13JsvIywm43kgTjqTTPdp3hi3teYiiR5OnOM6yuqpxnwNqFQ2CNU1pFk/RiXKNWDCMU9kQx5tFOFlOqiCzC7EbIIYRkIMm1XAwzKW+KeWPTxTyxIo4wO7ElHSQDSWlEKqnnytN5Yiuxc88hSeVn26jUgxREUmqRXW/FTHwBK/MDJG0dyF6wkyDySGoDynSqGNm4Gds8jZ3bA8ICOYAwu6bzsl7Ou83GLhwtljmDpIAcQtbWASqSvgXJPIFd2IeV9SPJNdjWYDE2dlYaHSGy04rGsaJLr8iANVY0IkWumPpHqZtVt1zsC/MEVn7PtMCShqTWIikNgGs6V+90X1tDgABrENs8iWRPIslhJKUCSQ6iuO/FTH8VKfczhDUAmNiFA2BPoXg/xowC8dXkivkJuDw6gZBnSR9iVVVwufQlew6oskxlxE/XwDjRySSWbdNaVzbvfL/HIORzMRlPk8rm8c6qQwhBKpsnOplEVWWqy+a6r9m2KAowPXeMvcd6+NSHb2fTinp8nsVl/y3b5kjnMD996QSpbJ5Pvm8XjTWLG++mafGzl04yFkvxq++8jrt3rSY8LRyVzZvEEpkroqgSCrgJ+l0okszq5iqap5WGZ/fXzArv6zFzGEtm+NnLJ0llC3zk7du4fl1zqZ+nkhm6BycuUMLSsWyboWSCH3Wc5F/3v8Jk9tIVSK9FVFlmR30jK8sqFjR6riUKpkUsnS0NbmsCfnyLiDZdDM2REL+xazu6ohSdZM65p+tDAVZUldM1NsGxkVFOjIwSTaRK+ZfPJegyuL6pgZ2tTQghcGsq/753P/FsDsu22dHSyK3tLdMTRdAcCdMxOs7AVJxkPk8VEE0keb67l1f6BpEliV+9bjNvX72cSv/ZCcAqv4/V1ZVU+f38n6ee48xEjL29A6ytrWJ7Y/28dvkMnU31Ndy2vA0hBPduXscjB46QzOewhc2m+lruWrUcRZYJuAzaKyIcHY4ykkgylcnBAq+mvGVxR3MDH962kS0NZz92IbeLpkgIt6bx//Yd4PDgCPv7BzkVHWNDXc2cMgam4jzf3cOhwRG8usYnd13HW1a2E3KfnRyrCfhZWVVOTcDPX/3sWfpjcV4608fyinI2LLKqC0Uvg56JGNmCyefuvoNtjfXzDN4bWhoXPf9KI0kS6WSWo3u7iY0lkSSJ9//mrQRCXhKTKXo6hlEUGUkuqmPJkkSo3Mfmncvp74oyHo1T21zOcz85RE/HCIV8gbs+eANNy6qQrnKaHY+q0ewPcyo2RrnLQ7XHj1vRmMhlSm2VKK74zXwfhBDY03/nrkqrsrxoXOIM1+a61OvHTL/a0/nqj4+McmhohLFUmlVVFWyuX3igv6KyghWVFfO2K0BbeYS3r1pB31Sc7xw6yonoKMlcrvR+E9Ouww8dOMxYKk1NwM9Htm7kptbmUpyrriisrKrgD27awfBUnJf6Bjg4OMyhwRF2nvN8xbNZ4rmiO36F18O7Vq/E7zKKYjoU31PLK8pZXlF+3r6oCwb4/ZsWFpGsDwW4a9UKzkxO8e2DR+gcm2AynV70nQ3FOO8bmhv47V3XUzXrPVvp8/JL69YwlS6mZEvk8+wfGKI5EmZmCCiEoGNsnMdOdJAtmLSWRfj9m26gPhgs3fcBl8ENTY1U+Xzc963vMJxI8lJvP+3lEVYt4pJ8LRDxePiDm3cuuK/S7+Ptq5ZzZnKKRw4eLsUiF+/T6VVNyYeZ+Pyc8yQ5guy6A137bwBY2Scw098u5vikaJwVEn8NKMhqI1rgfxYNSsmFYMZ4k5FkH0UzREJCn1bGVUCkMTOPYKW/W3RTnTYa7cJnQXIhq8vQw/8A0tmPmqyuQXG/Hyv1VfLx/4Yk+VHc70ZxvxNJDiLJYRTjdiTZj5l6EDv3FIgckhxG1regaMtKZSnGDQh7EivzPczkl0AOIuvXofk/iZl+aFrh+GKQkCQNSdIxU/efs8uDrG9BD/4VSD4UfR2Id2Nlvo2Z+jcEMop+A4rnXqzc08wYhsIawEx9Ayv7w5LwlWUNY+WeKhqYxs3Iwf85pyrZuA1EFjP7H1jZJ5DkMKr3oyiuCEgu7MLRYs7Y/LNFY1kyMNMPQvohJKUC1fMBVO9/RpL9KO53gaRgZb6PnXkUpGJ8r+r9CIrrllnXV1SFnmduSjpIHman8LlYrpgB+z++8Ct4vAYe34XzvGm6SjDsIRD2Ltn/vbrMz/6T/fQMTbKqpYrykI/MOblk6yqDrG6p4dn9p3n0uaO886a1eKaVeVOZPJ39Y5zuG6W9oZyqMv8cA3YsluTZ/Z38fO8pPnDnFnZuaLng6uSZwQke3XOMsViKd9+8ng3L6857fDGBeXEO2+815qTJyeYKPLnv1LxruhTa6stpqytnz6FufvrSCX79vfNfXpYtMC0b1+uQZ1WIYv0AQb97zgr3RDzN84e6r1hdR6IjfPPoIR49fYrUNSjQdLkYisqH1m6gzHPtug7PYNr2nN/Ao2tXJH5IliSMC5Tj0TSua2qgc3yCWCZLprD4c1YbDFDmPdufuqrSHAlzbDhKcyRMhe9sGiNVLrr0dY5NFIWjpl3Seyen2NPdgyrLtERC3NzeQrl3/oqvIknc3N7CQ/sPMzAVp3ta7GkhA7bS55tjAKuyTEMoyOBUgrpgkGq/r9QuWZKo8vs5FR0nmcuRXSTGW5VlttTXLRoTdmNbM/v7BzkyOMJEOsOLZ/rmGbAdoxPs6x3AUBWWVZRz2/JWAq753wGXpvGWFe189aVXmUxnORkd4/TY+HkNWICI18NbVy1je1M9rkVicF8rXB6d29+zhRvv2oA8bXAabh2EoLoxgrAFslw0YFVVoaohwt0fugFVU6hrqcAWNrIss3pLC2beQtVkXB69aPBeZXRZodzlJWcVODA2yPvbNuDXDCZyGVyKSqXbh6Fo9CQmaQuU4VKL7pwJM8dkPk2DLzjP6+lCSEgokoygaLRND4t/YRECMqbJSCLJy739fP/IMY6NRAm6XayvqWZHc8OFC1kAn0tnVVXRwE3l89Mp3872dSyb5enObgqWxc1tzTSGQwu6yVf6vCyrqODU2AQDU3FORKPzDNiAy4XPKLqgjqbSPHqig/dtWDNnNfNK4FIVNtRW84Mjx0vp7M53/zRFQqyrqaJigfesz9CpCviIeNyYlkU0lcKe5U0ggL7YFPsHBvHoOu9eu4qgyzWvLk2RqQsFWFVZyasDg5yMjtIfi1/TBuyFUGSZdTWV/OiYxkQ6Q7YwEx8bQnG9A8W4dYGziq65pTLc70ZxvQ2wFjhWKQoQAarvdyn2toKkVGOU/8f0Pq2YT1S8d1qNWEH1/hqq55eZv+IpTZd5jjeoHEZx3Yli3Dx9jgTo0+XNnOpB1neia1s56wosFa9ljqiThOJ6y3RZ5qxjVHTjtqLhdTFI3uk+eivzXXwlQC31EUjI+vbpPK0z3+1i3Yrr7bOUfZvR/H+E5v+dRcqcvzggKbUo3o+geD5AqY8kV+lYWd+Mrq2ZrvfcMuW5fYk83d+3cPZ3V885ZuY3t+f1r+q5B6bT6lwqV2w0UFEdLIp0LeElFin3cfs7N7H9phXnFSOaTVVZAJehocgSDVULv3wbqsLcuLGVEz0jfOOxV+iPTtFaV4YQcKo3ysGOASrDPn75bVvQtbMuxMlMjucPneHR544R8rlpqglzsjc6R4VPVxXKgl7qq0IATCYyfO+pQxztHKKtoRyvW+PAqYE57fG4dKojfspCxRtTUWTWtFbzyvE+fvriSdLZArUVASbjaY6fGaFnaLK4OnWZH4La8gA3rGtmYDTGo3uOE51IsbK5Ep9bJ50rMDgaJ5MrsLqlinfcuKZ0nmlaTCWzxFNZUpk8p/tGAUEsmeFUT5Rs3sRtaAS8LkJ+dzHtgmkxPpUinSkQT2cZHJuiYFpEJ5McPj2I29BwGxrhgKc0meBx6axurWbvsV6+/fODDESnCPndjIzHOdk7yuDo1GWvDNtC8NjpU3znxDFeHRok+SY0Xr2axva6etZUVOJSr00XptkUozPOvhRlSb4i+g0XulckqZiDscLnRZFkbFEUS7GEQF3g3IBhlNxkpem0PQGXgSxJhD3uOXloZUnCpWkgSRSmy7SFYDSZ4vToBJqisLamGt90uqyFMFSFSp8Xj6YzlkozHE8seJzP0Eur7DP1+wwDRZYIug18s1SMJSRc00q/BcsuTRidS6XfR4Xfs6hoR9jtojYYIOx2k8oV6Byb6x1hC8FwPEH3eAy3prGmunJRdU4JcKkq1X4/3eMxookUo4uors4m5Haxs7kRQ33t1dLPRZIkdJeG7pr/vHlmTXjOtFNRJBR38TebLRKuasqc1aTX4rokikZsW6CMIxPDhA03fs0o1R8xPLyjaRX/0XOMsMvDqlAlXfEJnhs6Q1h3s6O6+aKNFJeiUubyYAvBC8M9XF/ViCLJGIpaUiR+s3NkOMr//vmzfGnPSyVDvmBZJHN5JtIZfLrOHcvb+OXNGxYVSbsQmqyUPB5KkwVCgCSRLhRKgmoCaCsvKxpnC/yWiixT4ffgM3SmsllGEvOfT7eqsaGmmo211RwZjnL/i3vZ3X2GG1ua2N5YT10wcMnXcW5bIh536V1iT4tWLRafXu33UxcMLPielSUJj6bhMwximSy5gjknnjKWyTKcSJLM5yn3ellbU4WhzH/fSJKEKsvUhgIcHh4hmkwxmcmeW90bClmSiHjcJUX2mXtHkhXAWJKBMSPqc+EDZ98XypwVVHCdMzvhnl65WxrSdBzp+dpb/D11zlUgXuDIYnzvQmVdgsFVrHeJfTS9Wou0wPtRmv2NmZkYWHo4piQpnK9fF613wWMllnJ/lFIBzTt3qf2xOFfMgL2YFACarhKp8BOpWLp4S3WZn7Dfjc9j0DBtRJ6LS1dZ017NR+/ezhMvn+RY1zAHOwaKMa+KwoqmKnZuaGFde+2cl2AsnqF7cJze4UncLo3vPnloXtlhv5vr1jbxvqqNAAyPTdHZP87weIJcwWQsNv9F31gV4i3XreCGUEvxuhWZXRtbGR5PcKRziMdeOI5L1/C4NKrLA3zobVv4yg9exLjMnIC6prJ+WS2yLPHcwW66+sfp7C8mX1YUGUNXaa6OUBace+NHY0kee/44+08MFD+w6RxCQPfAON947BU8bh2PS+f6tU287/aNWLagbyTGNx57hehEgnyhaLimMvmSSJWiyPi9BvfcvpEtq4qzywGvwa1bljE8lqBrYIwfPHMYQ1fxuQ2aasJsunU9E/EXF4wjvhBiepb/P06d4NvHj3BoZPiqpcZxT0v9l7u9RNxuIm43LlXDUBV0RUGRJCwhKFg2ecsikc+RzOeZyGQYS6eYyKRJnmcl8EKUuT38p5Wr8RvGG0LOX5GkOQObvGliXiHxGtOymUinOT5SnBEfTxVTBWRNi4JlkS0U6JmIkS4U74WiQNjCdRuaWooBhOm5V7lobLs0de6qsQTT3qKlFaZswSSWzZLI5ZCkoijU/3j05wsqaM5wZGiETKGAQJDMLXy/GqoyL5ZMlSUkiv2qn7MKXRr4CTFn4mA2EY8bzwJphWZQZJmQy0XAbTCSSM4zODOFArFMllQ+jyrLvHCmjz/5j8cXTE8zw4yycMGySOcvfP+7NY2mSOiaX7m7GCNUki6sZXA10GSFVeFKopkkQd2FJk/HugIhw80Hl23iodP7+W7X4emBLFS4vdzbvmFOHtiloisKzf4IdzWu5Dtdh3ms7yR+zeDuxlXsrGm+shd3jZLK5+d4nswYU9UBHxvratjaUMe2+jpayyKL3hNiWoTp5Og4XWPjjCRTxLM5MoUCBcsmYxYYTiRnHX/23JxpMpHOlISUvnvoKM919aAv8n3tGp9kLFV0111o4leWJbY01GEKm0ePneSl3gH2dPfQPT7JE6c6aS8vY1NdDetrqqgNLh6zKqZznp+MjtE5PsFwIslUJku6UCjmQjdNxlLpUt+Jcy/sHHyGvqAIVqndkjT9XhLY56TuSOXzJLLF8U48m+Nfnn+5NGl5LpYoun6nC8VVqsW8W64VZsIATkTH6BwfZyieJJbJkM4XSjnnZ5TaS+e8ju39RUaUVIbleQJItp0tLpiiTRuiDtds4shIwMOvves6VrdW4502nN5y3Qo2La9nTVs1klRMjfPBOzfTUBUi5HcjSRIhn5vr1zURCbjpGZ4knsoiSRJBr4ummgit9WX4zsnN6nXrbF/TSNC3+GyP16XTVHM2lU7I7+buXavZtmbx+KuyoIeK8Fk3B1mWqa0I8vadq1jRVMn4VArbFvg9Bg01Yda21pDLm3hcOlWRs8a9162za0MrlWE/bXXl5x0IzxAOeNiyqoHKiJ/O/jFiiQymZaGpKgGvQU15kOZZ1wPg0rXpbWdf2rdtWz7nGE2VqaucTkAtFdu2qqWK+srQom0xdIWQf5ZLpqbSXBvhP922nq6BcaaSmeIAyu+huTZCfWUQSwhqygMXJTIlKLpnPdtzhm8dPcSR6AipyzAQz0WTZRqDIeoDQer9fqp9fso9HoKGC79uEDAMdEVBlWU0RUGm6KRh2TYF2yZTKJAuFEjkc8RzOSazGcbTaUZSSYZTSfriccbSKfLWQm44c/HpOqvKK7i+ruGCcWjXCqoiF3OdUvytJtMZspf5+wghiCaSvNw7wCt9g/RMTDKaSpOcXm2YiVMVAlK5/KIrkbNRZHneLH5pRW1aIG2RxoAoDhgz+UJxwCigZzJGz2RsSdcjw4KpNKC4Yn2uGvqMeM6CbWbWk7zIZbtU9YL3j6EquFQV0y6mBZpNtmCSKRSvNW9ZdI1P0DW+tBh2WZKWJPajyNIViZX+RSWgudhaUU9rIIKhqLylfjkrQhW0BoraCLuqmzEUFV1WWBmq4D0tazk1NUoin8Oj6rQFy1gbrppz39/VuBJVlqlyn/1OKZLEr67cxopQRUmBWJZkqj1+PtC+kUPjg2TMAm5VI+Ja3Bheqj7DbKX+13tl/nzUBwNsrq+lNRKGaW8Ql6IQ9ripDwVpDocIz1ppPJdMoUDH6DjPdHZzbGSUoXiCjFlAkYoxyLIkYQtBMj8/dzwUw3Vmf1OOj4xyMjp2wXa7VHXRNlX4vOxqaaLc42FDXQ3HR0Y5NTrOvr4Bjg5HOTQ0zLrqKm5oamBXaxPqdL7pGXKmSc9krJgWazjKYDxOKl8oLjZMX5NAkC4UsJaYJ1KT5dKEzIU493Vo2jYFu9hHedNkX//gBSfMFElCleeLgV5LzKQF+tmpTo6OROmPTZUmSDVFKf2+MxMhDleOZGGMkWwHE7keBIKAVk2zbxuG7EWSJCw7Qd7sIWd2AxIudTmC4jOsKtVoytyYd1skyeaPo6uN6GrTAjVeG9i2TT5vYRhF81KSJNLpPEIIvN5LdxdeiGvWgA0HPPzyXVvnbNuxvmXOvw1N5Zdu2zBnmyRJeFw6G1fUs3HF/DiyxerauaGVnRtal9y+mvIg77gxuOTjZ9PeUEF7w3xBBoC3XLdi3jaPS+f6dc1cv675ourxuHRWNFWyomlp8RmRgGeewXo+FLkohnXP7Rsvql1QNGbWtFazpnXh+Lf33LzuostM5HK8PNjPvx14hUPRYXJLMAQvhCYrlLndNAVDNIVCrCqvoDUcoTkYotLrvSzXXdO2SeRyDCTjDMTjdE1O0jMVYzARZzCZYCSVXNT1udbn55bmFso817by8Gw0WSHgMvC7ioqOAzNubecR5rgQ0WSKZzrP8Mj+IxwZjlLp89JSFmZVVQUhtwuvrmOoCpYtODocZU9XzwVTJ0mz/nfhfedntpC9JsssryynMRxCW4JHgSxJbDwnxrRUt/Q6xQ8W5dwpOoDPHfrNeDxAcXV6eUXZovF15yJLEqurF34PzqkeaU44h8PF4dcNNpXXkc8VGO2fRB3J0SZ5SaXiVNaG2V7ZUHr+VEliS0U9WyrO/+28o37ZvG2yJPOhZZvmbXerGhvKathQtvB9fS4j/ZMoqkxFTei8x6WTOYb7J2hdubRyXy/qggHuWrmc29pbYDokYakIIegYHeeRQ0f46cnTAKyrqaIuEKDcW3T1NVSVvGXRMTpOX+zIvDLkaaNwhh0tjdT6/YvmYJ5Bk5VFn0+JYr7k65sa2FhXw6nRcY4MjXAiOkrn+ATdE5OcjI7SNT6B19DZWFdT8hARQtAXm+J7h4/xvcPHKVgWa6orWVVZSbnPQ8AwMKbz4vbFpvjWgcNL8tS4HK8GZdo1GMCta9y5YhlefeFQiNkYisry8rJLqvNqI4QgmkzygyPH+db+w+RtixUV5Wyqq6HC5yXgMkqphAam4nz70FEm0m8ukcvXC1tYDGaOcXjyxwxnTwKCkF6PWwlS416JKivkCidJ5w8VU/ygYos0easXIfLoIgPYyJIPRS56SgqRJ2d2I0kGily852TJDcjF1DhYSJKBwMQWWRAWQuRRlcriMeSw7AQSErIcQL6gG/WlYVk2k7EUk5MpWlsqyeYKnDkzhmGorFp5cWrUF+KKGbDZbB5FllHU+asEDg5Xm3Qhz+HoMA8ceJW9QwMXPuECqLJMxOWmJRxmQ1UNOxsa2VJdu2h836XWEXa7CbvdrK0o5k5O5HKcnhzn1eEhDo0M0zk5Me1uXExxAcWZ8RVl5dzc2HxF2vFaIcsSXl2nrSzCkaERBqYSDMcTpAsFvJegoCyE4NDgMN87dIwjQyNEvB7euXYFty1rY1llGd5ZrrHxbJaH9x9hb2//Vc/9qyvFFUuJ4iTbze3NvH/TOoLupXkTvJYr6plCAfMCM+850yRXMFFked7vZKgqLrWo/uw3DO5Y0c77N62b5+q8GFdCxMthaaQSWQ6/3EVf9yj+gAfbtlm7tYUV6xvQNIV8ziSXLeDxGajTGhG2bZe2q6qC22sghCCTzmFbAt1Qi3lvBeTzJtlMHpdbR9NVhBBYpo1t2+SyBQyXhm5oyPJ0uVmTfK6Aoiol8cdMKkfXySF8ATdllQFMs6iInM3k0XUV3aWhKDL5nEl0KMaJA73XvAELxfmfSxkX5UyTl3v7eex4BzaCW9pa+M/bt0ynjJolfphO87NTnTx8cL4B61JVIh5PaaX27lUruLWtZcnvo/MhSRJuTWNDbTUbaquZymY5MDDEYydO8UznGV7pH+TB/YdZUVleMmDzlsWR4RG+e/gYyVyeXS1N/Nr2zaytqZrzfknl87xwppfvHj5GmqvrpuszdAJGMWe1Xzf48JaNrKgoQ3+dReMuh4Jtc2psnG8dOMx4Os2uliY+snUTm+tr54js5S2Ll3r6+MnxU0xwYQM2XogylZ9OsbIkJCRJRkZGlhQUSUeVdTTJjUvxoUjaVc8X+lpj2jkG00fpSx9ETItQRbMd9KX3EzbqcAuLTOEYQuSJ+D6EhIGggJmNkrNGEAUTy46hyGE8+iaEMClYQyhyCMueIps/iix70ZQqFDlCzuxAljyoSjV5s5eC1YcsebBFHp9SBpjkCp2YVhQATanG0JZfFVdkMf0tOHCgF8u0SSSyDI9MUX+B9KqXwhV7OntOR5ElicqaEP6QZ8niTA6Xj1ggLuRadmu50pi2zanxcb574hjP9/dednluVaXOH+D2ljbuXraCNRWVr1mMqd8w2FRdy6bqWtKFAqcnxnm8q4Nnes4wnEyQyOdpDobYVltPjX/x+KJrFY+usbWxjpOjY6TzBQ4NjrCutpo11ZUXfc8WbJuO0XEODQ7j0TW2NdbxWzdePy9PKRTjQGdSS1xt3JpanN3WNCxbMBRPoikKnlniT9cKk9MxZ4utgttCEM/mmMpmMRR1npKyR9cIuFwYmkrBshhJJHGpyhyhK4drA7NgkUpmWb+9lZUbGnnm0UNEB2NU1oXxeHQGe8aZGEtQ11xOVV0Yw9BIxrMM900wPhrH63OzcmMDsbEEAz3jFPIm1fURapvKKORM+rtHGYvGqagJUV0fxrYEUxNJUskssbEkgZCHlhU1GG6dxFSGob4JYmMJfEEPqzY2IssSAz1jaJpCqMxHLltgPBonk8kzPhLH53fR0FpJIOwhPpliIhqnrvn8qVre6Iyl0vRNTRHLZmkri/CRrRtZUVk+xyNBADnTIrqIIJpb16gLBgi6XcTSGU6PjrOptpjv+Uo/o0GXi5vbWoh4PNgCvn/kOC/19pE3zyoIxzJZeienmEhnCLld3LdtE+trq+e8twXF7/pQPIl9hXQSzkfI7aY64MdvGOQtk8NDwzSFQ2jKte0ifD7i2Ry9kzHGUmkUSeIDm9azpaF2Tl5fQTGF5HAiWZogvxCn4s/wXPTfZqXDOT8yCoqso0o6LsWHVy0joFUR1huoda8hqFdhKH5USUd+k8R2FkQWU+RKxusMWSuBJUzyVi8SKm59I/K0kJGEDkjkzTPoSlNxRdZ8AUNtxbQnmEx9B7e+hkyhaBS79TXkzT787luZSv8In+tWVKWKVO4F0rm9+N23oyplgETBHCSefhRZDiBElpR4iXL/b6AqoSt2zUIITNMilzMJBty0tVXyo0cPoGkqu3YuY/36S1NYPx9XbNrje/++h//2m1/jif94lUx64VgMh6tHQZgUhIkprEUFW96sDCUSPN51mh+cOnFFyntrWzv/5y138YfX72RtReXrJh7jVlXWVVbxh9ft5BvvvYc/2XETm6trua6ugVuaml+nVl0ePkPnrtXLS+lQnuro4vnuXnLmxbt7J3M54pksBcsuuqnV16AsMpObN21ORsdfkzgfWZKo8HlpKw+TN01ePNNHahFhptebkXiC8VRq0f5P5nIMTSWYSGfw6Bqt5XNnUWVJotrvoykcIpXL83JP/yX9lq8FUskVujhoWyzW+M2MWbDoPjHMwRc7SUymCIQ9SMDhfd08+cMDaLrCt/7lKfq6RslmC7zw86P89Hv7yOdMJkbjFPIm3/zSz+k6PkgyniUVz5LLFDh1pJ8f/L8XELbgsUf2cuzVHob7J9j92GEee3gvSBIP/vNTDPVNkMvmefYnh3j6RwfIZc3pfLrF9rncBof3neHoq2eIx9LsffYkD/3zU8iyxKMPvUzHkX7MgoWqKSRiGR7/7r7XtT+vNnnLKr2zirHgC8eQjaXS7O46s+A+iaKS9y1tLWiKwuMnOzg9PnnFBPQWwmfolHmL6tPZgjlnRFKw7VJMroSEfxGxpGQuz09Pdlx1j5liO6AhFGRTXS3JXJ4HXz3EeDpdEr56I2La9px3sc/QF/w+5iyLn5/qJJ69OuN2G4uCnSFjTTGZH6A/fYhjUz/j+dEH+Hbvp/jRwJ9zKv4MGWvqqtT/emDIXnTZg8xcgzyo1aBJLuxpjwJpATdet7Yen2sXHn0julpL3hoklduDaY+Rzu8nZ3YiSTJCmKRze0urs5pSjix5kSU3Ln0NIc9/wmfcjIRKJr+fvDVAtnCMbOEYpjVC3uq5otdsWTanT4/wzQdf4KGHX2b/gV5SqTypVJaXXupk374rlx5zhiu2AjvUP4E/6KaiOojb4whuvJZM5ON85tAXsYTFcn8jd9fuYm2w7ZLLy1kFDsU6+EbvT5AlmZsrNnNTxSbC+rW34hfPZflhxwl+cPL4kgRhFkOVZep8AX5jyzZ2NDRS5fVNK8++frOvpVQtFN2a3ta+jB31jeiKQsh1+e5frwe6otBSFuaOFW389MRpYpks3z90DFsI3r9pLZHzxPTOHkpI02Wp025ppmUzkcosOHmTyOY4NDjMi2d6yb0WgyFJoikS4qa2Fo4MRYkmkzz46iF+eesGGsOhRc8zZ9JEQOm6rjaWEOztHWBlVQVbGubnsX628wzHRopuRxGPmxua54rWSZLE8spyrmtq4GR0jIGpOF97eT8f2rqBSt/i8v6mXXQtlWX5NXOZDriMUixgLJNlcCrO8so39wreQti2XZopTydz9HePMjWRYteda2lbXctAzzjjI1Nk0zkURWH9tla23rQCs2Bx5tQw9a2VbLyhnZqGCIqqMDmaoOf0CDe/fT0rNzSQTuXIpvOMF6YIV/ipay5n5YYGjr5yhkw6R29nFMOlsX57KxtvaC+KjU2/66obwgTDXmRZRgiBx2uweecyVm9qoqdjBFsIMuk8/pCHmsbIJSnVv5Eo83oIu4v5SCfSGX5w5Dgfu25rMS2XJBHLZNnb18/3Dh/jxOjCwkySJFHm9fChzevZ1zfAcCLJF/e8SMfoGDe2NlMfCqArColcnsl0hq7xCQ4MDtMQCvC2lcsp9859Jx8aHGb/wBACwfraalrDYYLuYloe07YZiE3x+IkOfnaqE7emsqG2ek78f8jlosLrRQKyZoHvHjrKf75uK9V+H4osk8zlODQ0wncOHeXIcJTCFdCyuBCSJLG8opy7Vy3n4OAQnRMT/I/Hf847V69kc30tlT4vEhLxbJaJdIZTo+O80j/ALe0tXNfYsGDe69ebgGFQ5fdNi0kKfnTsZFEjIhJGkWXS+TwnomM8cvAIrw4MvSYTBWeZ+VILRrOd7Ik+wHDmBGtDd1Htnq8D80ZDkXRafdeRMic5k9qLhEyjdyPtgV141CB5qiiYPeTNLrzG5jnnSpKKJKmI0tKJQMKFz9iJx9iOKoeRJI28eYZs4TjJ7NO49XXIsn86DlxBQp3jli1JOh59My59FbraCEgo8pUdz8uyTHVNiJtuOvv7FUwLaXpfJLL0dD9L5crFwGYKBMJevH6XEwP7GmMJm8HsKJawiRhBstblrfaosoKJRXdqEAFUGhG2hlddkwbsz7o6+Vl3J9FU8sIHL4JbVVlfVc2vbdjMlpo6Qi7XNSUaI0lSMcZQN/Bpc/OAzsa2xTnKnK9fuo7FmImZ+tCWDUQTKfb2DtA7OcUjB45wfDhaVOssj5QGBOl8gfFUmv6pOFOZLDe1NbO5oRZDVXFrGhU+LxGPm3g2xzOnu9nWWMf62mr8LoNsoah0+Xx3D48d73hNUw1V+X3sbG1kb28/+3oHePTYKSbSGa5vaqC9IlKKP5u5vp7JGKeiY6yuruQtK9op87424lyqLLO3tx9VlplIZVhdXYnfpZPI5tnXN8D3Dh3jVHScMq+HDXXVLKuYL1hSFwqws7WRQ4PDHBwY4nuHjjGaSnFdYz0tZWH8LgMhIJ3PM5ZKc2YixsmRUa5vbuCm9mZC7qXn+rscWsvO9vuxkSjfPXiM96xfRXWgqKSbLRRI5vIEXS4q/Ff+Y3stoKgKNY1lLFtTjywXY0nHo/Gi+qumYLi0uR4nQqCoMh6vQaFglhTKdEObk/cWAaoqY7i0OeFDmqbgC7gxXDq6oWLbAtsqpudRVAXPOaqUmqbOScknqzIev6sUP4sAYQsURS7G6V7zyZUuD6+us7a6ivW11RwdjvKDI8c5PjJKxOOhYFtMZbKMpdJ4dY2Pbt3EV156ZUEVe11RWFZexu/ftIMH9r5K72SMbx04zM87Oou6DrKEOZ3uLZXPE8tkecvydix7flmJXI5XBwY5MjRC0O3Cp+t4dA1FlsmZJqlcnpFkiol0huZwmI9u3VgMn5g+3z0t9ra9sZ69fQM8fvI0XeOTVPi8CGAqm2U8lcYWNp/cuZ1/felVJtPpee240vgNne2N9fzGju185aVXODI0wkg8SdjjLsUbF6bTziRzeSYzGVZWls9RSc6ZJgcGhugYmyCRy5ErmIyn06XJhYJl8y8v7KXc68GtabhUlUq/j9VVlSw/5916ZmKSYyOjDEzFyRZMsmaB/ql4yVX8hZ4+cpaFT9dxqSouTWVlZQWrqyoJuV0YqkJrJMzO1iZ2d/Xw1OkueidjVPv9SFLRxXg8nSaZz/Nr123mW/sPM7RIDvILUW604lHDKNJ8k0JgYwuTvJUmbcVImzFMcXa11xIF0tYknYnnkZDRZQ8R48q7m76WSJJEhaud7eUfYE3orUhIuNUQAa0SSVLQlQbySi+p3EtEp/4vsuzFa2wrii+d806TJRde1w1Mph4imX0aSdJxaUVD1FBbmUg9RFXgj1Cks6Ky574X3fompjI/JJV9nox8GE2pJeC+4wpfM/h9LowmDWGL0vgTigsMFxKNuxSumAHrD7iRZOl8aboum7yVYix3nJHMYVxKCFuY1Hm249dqUGSdeL6flDlKxGjDUC7f2MpbSRRJQ5FnxQwIm5ydQJd9bxp//XORJQldUlEllYyVZSAzSs6+9nKdnRof46mebk6Nj2Nd4o0XNFxcV1fP+1atYWdDI2712ondS6ay9A9Moigy9XVhhF0cEBZFUCxAwrJsMpk8fr+LQsFibCKJsAVut87UVJrmpgpy+TyGoV2VF8ilIAHLKsr4lW0bCLgMXjjTR//kFGPJNKdGxyn3enBpGiDIm/b0gCqDKsusqCwvpcJRZJm1NZXc0NzAY8c76Bqf5Msv7KM26MetaRQsm/FUmuF4AoHg4zds419f3HfVXKVmY6gqyyvK+ej2TaiyzP7+IZ453U3H6DiVPi8u7eyAKJnLM5HOEE2k0BWVW9pfO9fWXa1NZAsmJ6bz59YE/RiqSq5g0jsZo3tiElmC65rquXvNigXT2bg1jXW11fzy1g0ossTBgWF+frKTE8OjlPs8pcFf3rSKeZBTaUYSKaoDfm54DVM31AR8bG2ooz82xUgiyeMnOuiZjBGcniwpWDambfPWle3cveaNvwqwEGbB4vTRQVKJLKNDU9Q1lVHfUsnkWJJDL3cxOjzF2MgUKzc0Ul4dZGI0SffJYfJZE0WVWba2nmw6z/7nO+g42k9NYxk1DWU0tFVy9JUzTE2mGTgzxqpNTbjdOulUbt77NFLhZ7B3nM5jA6QTWQyPxtYbV5DPFeg+MUz3qWEMl4bXV0y5Jc+S3xZALlegtzPKoZe7iA7FeOmp46xY30Ag5EFZonjYGwVVltlUV8OHt2zk5x1dHBkaYf/A4HRO06II4MrKcm5sa2ZZeRk/PXl6wZRdM7lnb25rxqWqvNTbx9HhKP2xOLFsFtOycGkqXl2nyufjusZ6tjbULeiyXBsMsLKygmgiyWA8Qdf4ZGmVVJLAZxjU+P1c31TPjuZGtjTUzRFrU6aV2T+ydRM1AT8HBoY4OhKFkeL1+g2D9vIIN7Y2c3NbM0+e7ubIVcrlPhtFlqn0eXn7yuUEXS5e7u3n1OgYZyYmSeSKaUDcmobP0Kn2+9lcX8vKygrcszIR5EyLl3sHeKqzi6lMtuTGm55OF2faNk93dqMrCpqioMkyreWRolr9OQZsz2SMJ06d5shwFNOazmdumqU0OJ1jE4wkivoKxdR9MneuWEZdMEDIXZyEb4qE+OjWTVT5fNOG9TgnR8dQZBmfrtMSCfOO1St5y/I2Xu0fIpa5NBXiZu9Waj1rMOSFJv4ENjamnadgp0lZk0zkeulPHyaWHyjFiaatGL2p/QS0KiLG+6fPvTbGYpeCoXgxFC9lxvyUN5Lswa2tnRZayiJJGopchlvzAQJFKQNRQDJ0VKUKRfLid92MLXJISGhKFapchs91I4ocwNDaSu7IHn0ztpg7xtGUKrzGDVh2bFrVf266zCuBJEmoqoJl2QxH46RSOVpbK3G5NE6cHMKybFavquNKDq+vmAG7Yn0DR17pJjaeJJ8rFGdLrzCmyDKV72M8d4p6z/VkrAlGMgfRZDdeuZLivIPM5f4wQggskWMsdxK/VotfrpnebpO3U4ykD1Dt2Yyh+C9Q0hsTiWJ+M4/qIm1lmczHKdivpXvJ+RFCkLMsfnL6FAdHhkkVLu3j5tN1dtQ3cM/qtexoaMRQrpziYDKeASHwBS99JS2TKXCmZwy3WyeZypFK5WhuLMflUkkksrjcOi5DpW9ggva2KmRZIpHIYlk2qqrQNzBJJlsgFkuzckUNZRHvNeEdIUkSmqJwQ3Mjbk2jMRziyNAI/VNxxpIp+mNTFCwbRZbQlaIgUMjjpr08Qm0wMJ2Mvsiy8jLeuXYVthAcGxnl0OAwL/cUVxTdmkqZ18OKynJ2tjZx27JWfnDk+GsWjxpwGexqbUKRZVrLwpyIjjE4leDMxCQ500QI0BQZn25Q5nWzsb6GNdWVePUr/+5cjF2tTVT4vOzvH+KVvgGe7+4lkc0BAq+uUx8KsqG2mjtWtLG+duGUVwBlHje3tLegKwrPlZ2hY3ScoXiS02MT5M1iDJyuKKXYuG2NdayoLF9QcOtq4dI07ljRhiVsXujupS8W59nOM5iWhSrLuDSNMq+b7U1LS7/2RsPtNVi2to7oQAzDpVPTEKF5WTX1LeUEwl7SqSyyIrNsTT1V9UVX3qb2ypJXh6orhMv9LF9fTzqRA6moLO7xGbQsr2YimkBRZZqWVVHXVI5uqOiGittroKoyqzY1UV4VJFTuo3lZVcn9d+a/EhKyIrF8bR2KouALuqmsC09P2sm0ra4lEPJguDRy2QKRCj/bblqOos4I7Vw7g11ZkrhjeRvLKsqo8vtoDIUuuazqgJ9b21uoDwY4OhIllsmSMy0MVaHC52VFRTkrKsuxheDDWzYyGI/TEArMmziQJImgy8Vty1ppLQtzemyCgak4U9mioeVSVTy6RqXXS2M4RGM4hGeB57Mm4OeO5W20RMIMJxLEszmypokQAlWW8RkG1X4f7eURlpUvrORb5vGwq6WJmoCPjbU1TGYyZE0TTZYp83poLytjTU0lHk3j/RvWsq2hjhWVFQt60bxrzUq2N9azurpynrvzbFrLIrx37WpShQLraqoW9LLSFIVqv493r1nJiooyOscnGUkkiwKAFBWdfbpOld9HS1mY+mBwjuK6psi0l0co2NaSw1XKvd6SF8i52zfW1pw3FONclleUzfnNgi4X1zc1UOnzsbGuhrFUmmyhUJr8aC0Ls6G2Br+h8561K1ldVcH62uqL9lYK6XXUuFfjUS+UWlJQsHNM5Ycod7VwNPY447leLFH8JifMKP3pQ6wx78StBK+osXOtoak1qGo1CBuBPe32O/eC1Vm5YL3GdQhhMqPqDBKG3IqhzU3/aWjt8+qSJAWPvh4hLEBMqw9fnc4tFCyGh6dIprI0NJThcmkMD8fI5y1Wrqi5omEfV2zEfsOtKxnoGaO7Y5i6pjKa26tL0vhXEl32UG6soD1wJ/3plxnNHCVvJ1FMnbydRJUN5HPcGLJWnIw5TsFOo0gaLiWERy0nXuinYGcQWOiyH69aiSJp5O0UE7nT9Cb3UGYsw3YX0GU/Nibj2ZOcST6LIhsE9UZcSgjLzpE0R0quEkG96Q2/QisjY8jFF2HaymKx9JWSxRLRCyHmrNDP7L7YFU9LCI5GR3iiu5PB5KW5vBiKwpbqWv7TqjXsami64uk8Oo8NIIRg4w3z8yUuFSEESFAW8XLqdJR4PEMo6EHPqPT0juHzumhqLKNvYJKG+jI8bg3TtDDNYixlIpkhOpoglcpRXx8hHPZeOdW2K4CmKGxrrGdZRTmdY+McHYrSMxkrDogKJpoi49Y0Ai6DmoCfZZVlrKyqmPNbBdwurm9uoDrg47muHs5MTJLM5VEkmaDboCkSYmNdDetrq5GmB5U9kzHqQ8E5950iy2yorcalqqytqSLoPvvu0lWVDXXV6KpS3Dcr3smlqqyrrUZQzM84OxZKkiQ8us7ty9tYU13JgYEhTkbHGEkkSeXy2ELg0jQiHjcNoSArKstpqyjDf0481erqSkzbpqUsTGTWAK2YR7US07JpLQ8T8Zx1xVUVmVXVFeQsi9ayufvcmsaOlkbGUmnW1VaxurqSNTWVrK6u4OhwlLFkGoEg7HGzorKcrY11pbipxZAkiaDbxZ2rlrGhrppX+gbpGB1nNJkknS8gRFENNeJx0xgOsbKqgtay8IIrulCMt91YV4Mqy7SUhUspOC6Xoou0QXt5GUeHoowmU+RME11V8BsGVQEfa6qXljf7jYYv4GbLzoXzfNc0RKiuD2OZNop6Nva/eXk1je1V2LZdGnhsv3kllmmXDFhJktDLfNz2ro1Ypo2syCU34rLKs55QW3edrbttVS0tK6qxp92BJUlC11XWbG5mzebmBdu4dsvZ7cGwl4aWC+cQfr1QZJlfWr/mipUXcLnY0lC3YJz6bH55y4YLlqXKMu3lZbRfYv5Sl6qyrLyMZZeZ/9Sja6yrqWZdzeITYwDvXbf6vPvv2775vPtnWFNduaRne2aCdSltOxe3pnHXquXctWrh5+xiWGp7L4ShqqyqqmBV1fmfl7evWsHbV112dRdAQpNdlLuaCet1CCF4ZeLbJApFnQVLFEiaY4zne6h3r+NampS6GkhIIClILO37Ji3gon1R9b0GNoksS+iGykRfisGhSdyTOvF4BtdVWNS8YgZsY2slG7e38sLTx4mNJdn1lrXUNZehKMqisyiKqlBVG7ooA8YWFjkrTizfQyzXhVetRJM9JMxBOuNPULCTrI98hKDeUDp+LFt0O87bCVxygHLXSlTZRXfiKXJ2AiFMfFoNdZ5tBPQGMuY4fakXmMidpmAnsSkQ0luxhUlvag9ThT76Uy+St5NUutaQMSfoSe3GtHOkrVGWB95JpWsNuuK7El37uiCgFNuRtwsLph8RQpDJ5LFtga6r0wMRsKzido9Hn84lKJCn3cvzeRPLtNB0lXyugNfnQlEu5vcXpPJ5/t/hg/RNTV2ScJMELI+U86F1G9jZ0HhVclEeerET27Yvy4DVNZWqigBrVtWRzRZnc6uqAmQyBWRZJpHMYts2llUUxEml82QyeUzLJpnMIgTU1YVwGxqRsHfOyuW1RMi9tMHZYnh0jdXVlaxewsf+j27bteB2l6byGzu3L7gv4DL4xCL7yrwePnbD1gvWWx3w87aAn7ddwsDmw9s2LrhdkeXivm3z97k1jQ9v28SHF9hX5vXw/911+5xtMysu71l//sHihZAlidpggNrg5YVwLK8sv2oCSw2hIA2hIO9cu/KqlH+tIITAFPacGL3ZaLIyR5VUkiQUVSY5lcG2bVweA1VVQCqukgoBtmUjKxLFWfy58fWSJKFqS3+XyrLMNeAQ4uDg8JojIUsaq4N30JF4llRhHHs6LU/BzjCR66HOve5Nbr6+OXG5NBobynj6mRNYlo0kSWQyedrbq6646N4VM2CffPQAQwMTZDMFnn/qOM88dhiP3yBSHigZNucSKffz//3jhy/KgMnZcfpSLzCe68BQAlxX8Tt41Sp8WjV5K8lo9uic4wt2mpHMIXxaNcuDv4aEjGXn6U+/jITMisA7cathelPPcyb5NJvKfo2g3kSr/3Y02U2dZxuV7rWlskCgSs+yLvxBvFrRvcqjVhIxlmGKLIPpfUzkOgjqjW9YA9YWgpydJ1YoCiOp0sJiGWbB4uiRfnI5k8bGMrxeA91QyWYLHD08wOo1dbjcGslEhkDAg6zIDA5OMhqN09pWyfGjA2zZ2oI/sHQRl7xl0TU5wVM9XcTzlxbL6NV1fmPrdm6oa5gTv3I+bFuQzy49DjiTzl92LG0o5GFTqBg/sXVzM7YQJcXOhrowgqLBUF8XKZ1z3baz6tMrlxdd32ef5+Dg8IuBjWAoM0U8nylqLwmBLEnTOkyCZm8ZAX3uu9e2bJ5//BCZVI4121qpqAkhKTI+v4tsJk8ylsEbcJHLFHB5dDx+l/NecXBwuGgkScJQfIS0OsaVHrJW0ZvOEiZZM8HcvAMLUwz3K5SMXwkJRdKQmJtBQggbGxsx7a57VgV5RvBIKoYwSHJpNXQp7zVLmNiisOTElaqkz2vblUQIgZh1naL0tj8r7FSsX0ae/u+VZkZx+Dc/cRtdXaNYto3f77q2VYgf+/Y+hvomME2r6F4EJKcypBK5RWdR0skcF6v65FKCNPlvotV3O89H/2bad34mRfZ8MtYEmuzGUIKlY2wskoUh3GoEVXahyz4UFLKXkIfKpsBw+gB9qRdQJI20OYZHrUQsMuv9RmAiP0VXcoCsVTQQA5p3wVQXli0YGZ7ihh3LyOdN9r7chc/vorWtkpGRGLlcgW3XtTE8PIUky1RXB/F5DWKqQijkKcZjXuRzPJ5J8+DRw2QuUfJdVxQ+tnELm6pq8OpLT/c03DfOn//W15d8/ER0ipvfsfESWrg453bVUrvOGV46OPziYdk2xyaH6EhEGc0miGYTLAtUkrdNRjIJPrZsJ2v1+V4P+ZxJLlvg5IFeJscSNLZXYeZNnvr+K4yPxAlX+EnFM6y/vp1117ddFb0LBweHXwwMxYsinX2HCGwKIrukc007y9PRf6Yr8QIAYb2BHZX3Uedew+yRT9IcZyB9mL70QSZzfaTMSQoih4KCofjxa+VEjEaqXCuo96zDpfiX5NZ7LPYEr0w8Qs5aShYMiTtqfo96zwYM5eqo3OfsFGO5bgbSR4hmO0gWxshaCSxRQFNcuJUQYb2OGvdKat1riBiNFy70EjEMleXLqxACDhzsIToyxfbtl57ecyGumAH7q7/3VjLpixNHcbm1ixaVkZBRJRderYoK12pGs8fRZC8edeF4DEP2Y4ocBTtVup0lZDxqBcnCIJbIUbAz2Njo8tkVU1lSEcLCFrNl5CVkSZmecSka3on8EElzhIBeT7V7I/3J5ymIq69yejUwbZPB7BgvjB3myWgxQbyERKOnGpc8P55ZkkDXNbq7RkmlskxOpPB6DTRVIRTyEgy5SSWzDA9N4fe7SfpcRKNxhodjVNcEGR6OkcvV4fOJJc1IZQoFuiYnePJM5yXlhvOoGltr63j3ilWUezwXJVRQyFuMDEzwjl++AbfnwrHd+545ecVn2c5117uU8xwcHH4xUGWF6yqaKXf56E9P4lI01oZqEQi+27MfRZKxhY08axZeUPQ20fRiOptg2EukMkAqnsUbcBOI+LAsi2CZj0hVoOhi7ODg4HCJ2MKam/4PBV1ys5Spd4Egb6VIWzEAdMtLypxATGdRzZhTdCae53RiDxP5PnJ2EtPOYQlzWjhJImmOEysMMJw5SZfyEm3+G9gUeQ9eNXLeugFMkSNtTpGzl6bFYtoFFspVfzkUQ0VydCdf5nTiOcZyZ8haCQp2prg6LSwEAtmUiUsjjOe66U29ileNUOtZy5rgnQS1alR56Qs6i7Wjr3+C48cGqG8o4+mnjxd/B0liaGiKNatrr10Ddv3WFuyLXE2VZsnjX9R5SGiyhxb/7XRMPYpfrSZRGGAgvZd4oQ9FclHuWkmlew264iekNzGV7+PI5ENospeAVk+FaxVT+R56ks8CEoqkU+s5GzDmUcuxRJ7+1Avk7CkixjK8agVupYyclaAz8TjlrlUYcgAhbCZzXUhIFESaq7HmZQmbE/EzvDJ5nIQ5NydaxsqV+n4wM8qPBnfz0sSRiypfCEHWyjNVSDKUHWckOw4UXVQ3h1fi1+ar+6mqwroNDUiAado0NJbj8xsEAm42bGxE14sqlOvWNxAIujEMlbq6MKGQh2DIw9Ztrfhm5xK8AMOpJM/0nGEik7noV4AsSVT5fHxk/SZq/YGLjnuVJHC5dXa8dR3B8IVnz8ZGptB0Z3C3EEKIYsL7RJzhZJLRdIrxTIbJbIZUPk/eMslbNuZ0qiBVLopq6IqCXzfw6wYhl4sKr5dqr49Kr4+Qa+n30ZuVnGkylk4zkIgzlk4xmc0ymc2QyOfImcX8hQXbwp5WC9XkYp96NZ2AyyBkuIi43dT4/TT4g3g07ZrKh/xaU7AtJjMZzkzFiKZSjKfTTGQzJPN5cqZJ3raKit+KjK6oGEpRCKrM7abc7aHS66M5FCZoGJfUj/Ilfh9nnx82vNTbFqO5BAPpGHWeEAXbJFbIYAkxLzRElmU237iimP9VUwiEPMUV1gBs3LkcRZGxLRtJlvCHPEiXGVeftywmMmnOxGJE08U+nnkP5KZzblrCRpVlDEXFUBUCukHE7aHc46HK66MlVBQDUy7BHU6+Ci501xJCCLKmyVgmzdh0/45nMsTzOZL5PKlCnmzBxLQtTCGKehWShCJLKFIxNYtH06b/dIKGi7Cr+J6o8Hgp83iWHIbjcH5sIUgX8gwkEkRTSUZTKcYyaaayWbKWSX76eTDt4vOgT38TXYqK3zCIuNyE3W4ibg/VPh9lbg+uBRSgrx2Ko7jiaujZFVdNNgjoNZf06rOFScYselImC2McjT3O6eTzTOb6FlzVFQgEJrZtUiBDQWTRZc+SRZUM2YtfK0c2JQp2FlO8NhkOZrCFRdqMcTD2H/SlDjKR6yVnL7wabFNckDOtHFkrQaIwRqIwSiw3wLrw26lxr8J1mZlVQkEPjU3lJJNZmpvLKS/3o2kKx08MEfr/2fvv4LqyPL8T/Jzr7/MWwIMnCHqbPrMyy2dVl+3ual/qlumQWgoppNnd2JnVrEIbmtjYidjYnYjZUWhX0si0ulvtfauqurxJU+mT3hOEdw/meXP9/vEAkEyCJB74QILV+c1gJJ655p177zk/+/0mdnEJsRnuPOPwB6FKJkl9lLDag4RCWt9PNbSAoSTxAoeMcYC4NoAhJ9GkMAIJSSh0GUfRpRiWX0YRJrocJaxk6AmdxPJK+IFLSMmS1G7SUWtShN7Q01h+BV2OIQsNgYyppBiKfKzFmihFCClpssahtVLkMBE1hyy0jkvs+IHPTCPPa8tnWGoWbvssWPsPoGhXOFW8gtQm32xAgOO7+Le4hpqkMBLu50RiH2Hlzj5VWZbI5RK3RM/ERq9zTy6x8b1bndR0Jko60xqbaHTrva+e7zNdKvHq1MS24lcpw+SF/gGe6+tH3YYxGU2E+NwvP8fASJbQFpzugZEsvr+Dosi7AO0kdj3fp9BsMlZYZapUZK5SZq5aYaVep9hsUrIsKrZFw3VwfR/H8za0fWUhNhwuU1UJqyoRTSNhmKRMk65QuOV0xeLsSSTpi8XQZOUnvnQ6CAIWa1XGiwWmyyXmq1UW1wICpWaTim1Rti0ajrMREHD9FkO1LElrYyphKCphTSOiacQ0nUwoRDYUpjscYSAWZyiRoC8aI7aJJuRPGmq2zUy5xPXCKlPlEvOVMvPVKoVmg1Jz7R51HOy1sfR8H3kjGNAy9mO6sWHo56JR+qJxhuJxRpNpctHolo1KVdqce6BdxFWD/lCSkt1grLJEEAQcTfSS1sN3VGdIkmBg751kaJqu0jNwZ0ZiO9UdVdtmqlRkrLDKdLnEXKXCfLWyNg8018bYxblljNeff1WWWgEXXSduGKQMc22MYwzFE4wm0/REIhvaw/eCoNVS0okx3g1osfwH5Os15ioVFmrVliNUr7PaqG+Mb9myqDkODceh6TpYnofn+3hr2wshWk7s2ryrKwqGomDILZ3YqKYT01tBxLQZoivcmisG43H6onEimta2DMtuQbHZ5NTCHKcW5u/73ZRp8omhPQwnkts+nuW6LNVrTBSLTJaLTK0FywqNBoW169UK6raCj87a/C0JgSpJKBtBSJWorhNbuzYpM0Q2HKZ77drkIlEG4nFCyu4JSgZBQNlZoOLmcf1W1aJAJiQn6DJG2U70zg9cGl4Ry6txsfRdLpd/QMGeJcBbUyCJocsRFKESBAG236DplbH9OgE+ilDpCx1FlbYWEO829/NU6uex/Bqub+MGNm5g4fk2C82rLDWv75hT6wcuZSfPueJfc7n0/bXMc6syURE6ESVNWEmhSiYIgefbNLwiFWelRV6LR9Vdpl4t4gUOQdKnL3QcXd6e9KMQgljMxDBUlper7BnOkkiEUNYqMv1tEK7eD7s5PHMHVClESt+7IcciCZ2R6Gc2FtGUvnl6OqL2EFFbdOitJucWuo0TmxrhQggEMn3hO5lHVWEyErudwTOkZEgbD06bfi9IQtClJxmNDKBLKkW7StmtYfu3Ewv5BNgPqNkqEEQUk72Rfj7d/Qx9ZheadPco68MoUS1ZFtcLK4wXC/f/8gegSBJ7Uym+uO8AkTb6Xm9FNBHii199HsPc2vYHTgy229792EGV5Pte+6brMlcpM14scHVlmbP5Ra6trjBTLmFtsQzcDwIc36eBuylxlyJJpEyTvckUhzJZDme6GEkmGYwnSBrmY2tMbQY/CChbFlOlIjOVEldXVri4lGessMpCrUrd2RrRmOv7uL5PE6jYNkv12h3fSRomexJJDmQyHEhlGEkmGY4n6Y1GkT7AQPs4IwgCCs0mNwqrXFtd4cJSnvNLi0wUC5Ss+7eDrI9lw23NU/PV2yPgmVCIkUSKY13dHMl2sTeVamUNVe2uYyhoMUnLHRhjU9HYF+0irprM1ouoksxAOElKb89QeZDr7QcBhWaDsdXWGJ9fWuRCPs9kqbglMj7H93F8H9bG+IPyadlQmNFkimPdPRzOZBlNpRmKJ+453wshMFX1sb6P/cCnatvka7WNapbJUoHxYpHpconZSpnVRgO3DeMxCAL8IMAFLM+jdo85RZUk0maI/licfanU2rgnGYrHyUWibfFM7AaUrSavTE7wX86euu9314Mm23Fg647DTLnEtdUVLi4vcWk5z/XVVWYr5S1VMvprVUzQsvVWGnd+x5AVMuFQKwgZS7AvnaY/1rouuUiUlPno1kY/8Gl6Za6Uf0TFWVojVoKwkiQXOkRczW1rv17gUnHyjFff4mLpO5SceUJynITWR0LrJap2YSpxFKETBD6WX6PmrlJ1lqh7BSQhk9IGUcTWqgrS+hBpvUW02fItAly/iRNYnF79Swr2DK63Ew5sQM0tMFb5MecKX8PaIJjVSWi9dBn7yOjDRNUudCkMQuD6FjV3hRVrisXmNZabYzhBEx+Xydq76HIYTQ7Tax5+IAlQVZXp6bldw7c3l+h46TTsgAPruR61qkW5WKdRt3CdtYiAKmOGNKLxEOGocc/eGd8PsFyXqm2TMFuREFkIWJNk8YKWdEjNdkiGTaS1bQKC2x7I9UzD+j6hNTFXmjYRQ0eTW2LAjwMkJI7ERoipESZqc0zU5pmpL7JsF6k4dRatVQBUoRBWzA0N17aOIQSKpBCSDQZDPbyQPsbzmaOd/inbwmSpyNnFxZYR0yZShsmJ7h6e6d2eTAu0yqVTt2gaNmoWjn33QEHvUAZNf6ziQ21B0NID3GwBXA8SLdVqXFtd4bXpSV6ZmuD66sq2rt/94Po++VqNfK3GGzPTJA2TZ3v7+OTwCE/05MhFokQf8+xhy3FtslCtcnF5iR9OjvPW7DTL9XrbrRtbRaHZoLDQ4P2FOUKqyr5Umk8OjfCRgQH6ojEyodCWghi7FX4Q0HAcZitlTi/O883r1zi1OE+xuTUCka1iuV5nuV7nnbkZusIRnuvr57N793E000V3JIIu3zmGQrTY0qUOSV8ZispgJMVgpJVFvV7OE1I0VGln5ygv8NeM9TKnF+b5xvWrnF1c2DaD/N2wVK+xVK/x5uw0uWiMF/oHeHnPXg5nuuiJRFClO5k/JQHRxyxbuP6k12ybQrPBSqPORLHAmcUFziwscHllmaa7dVbUB4Xj+yzUqizUqrw7P9vSmE2m1qqdBtifytATiWAoymM7T9wNJauJ5bobWev7Yb11ZqFa5erqMq9MTfDq1CQTxcKOXK+m5zJTLjNTLvMG00hCMBxPcry7my/vP8hHB4aQdkBCcDOsswB7gYPjN6i7RfLNa5wrfIO6WwRAk8L0hY6yL/pR5G3OS65vs2xNsNi8RsXJE1WyDIWfYjT6IjnzENom2UU/8Kh7JVatSRpeEV0ObYudt5X4EmhyCI0QuhxquxJyq3D8JguNK5wvfhPLbwWfW9WmoxyKv8ze6At37eFtelVm6+c5U/hLpmun8Wn1yI5X3yaqdpPU+rbU/3s3eJ5PpdKScYzFWr6eYexMm0FHVy/HcVleLHPu3XHeefUq41cXKBVagxtPhhke7eapF/dx/Jk9dOUSqNrmh2+6DhMrRc7NLvDi3lZ0IxFqlZtWmtaG9ueZmXk+tm8PAkGp0SQgwFSVlrwIgobjko6YuH5AzbJbJQOex6X5PCcHcqTDYZQ2JHweJYQQaLLK3kgfeyMtR8zyHPLWKu8XrvAfbvw5XuCT0RM8lTzIYLg9AW4AXdJIaBF6zSzdemrHjZutwg8CbhRWOZO/f1nPZjiQzvDSwBCq1LnJ+vqFWeanlm9/M7hpYMiyYHC0m/3Hd47l7dFCtHokP7Bwrz9jy/U6f3r5In9x5SKTpeKOOVmbodBs8K0b1/nxzBTP9w/yi4eP8nxvP2FNeyzlhGyv1Yv545kp/urqZd6anaHhbl3SqROoOw5nFhc4l1/kt8+d4qtHjvH50QMMxxMYirJrytK2Ctf3KVlNzucX+c0zp3hzZmrLFQHbRQAs1qr81dXL/GBynM/uGeVXjx1nbzJN+I5+Y0FU07fV17lxvCCg6bmsyyhYnsN6wPa785d5OXeQqLpzveOu71NoNji9MM9vnT3NmzNTG20BO4UAmKuU+dNLF/j+xA0+N7KPXz12nKF4gpB6u7MqEER1/bFwYFtyIa1+1obrcG5xkR9NjfPj6SkmSsW2Mqw7Cdf3ubyyzOWVZf7iyiVe7B/ibx8/yb50mpim/0RVblRtm4br4gUByn1+kx8ENFyH2XKZ3z57mm+NXWO5Ub/nNp2GHwTcKK6yUK2wJ5HkY4PDHdu35Vepuav4webrkhc42H6DmrvCsjXOTO0sk7X38XERSGhSiIHwSQ7FP0OPuX19bidosNi8CoApJ3gi9bPsi36MiLo5ySuAJGQiSorIAzhtDxcBq9Y049U3WbWnNt6NqzmeTP08eyLP3pOQyZAjDIefwpRjFKwZKm4rA277deYbF5mrH2Bf7EW2m9xrNB0uX5mjUrV44bm9bbUKtouOeihjl+b5k//yKu++dg3Lcghu6QGslBrMTq3w9qtXOfncCL/46x/l2NN7Nt1Pod7kzMw8q/UG37p4jbCm8vRQH6u1BlcWl+mNR3lysG+DQOK1sQnOzi6gSBLZ6E3JF0NVONnfy9RqkXy1iiIkbK+lAfWTAE1S6De70CWVP5j6NmWnSkqL8Wz6CE8mtzcJrCti7SaU1nonJ4vFtrfVZZmDmSxP5baffd0MP/raaV79xpmN1wEBtuXiOT5Cgq7eJF/61Y/8xDqwQkBIVe8w/pquy/mlPP/qR99jvFhoRagf0TlWbJvvjY9xcSnPl/cf5J8983wrE/CIzme7OJ9f4H9//13enJ2hbFk7UoqzVfhBwGqjwb977x2+fWOMXz16gs+N7qM7/HhpXs9VyvzllUv85plTFJvNhz6mFcviz69c5I2ZKf7Zs8/z8p69ZEI3SS4EtMifHsDYD4DvL1zG8lyansMPFq6grpWGTdZWOZkaYPQBf8e9MFUq8meXL/K7585QspoP/a4tNBr88cXzvDI1wX//wot8fHAPSfOmMSUJQUI3OlKmvdNwfZ+5apmvX7vK165dYbJUpLmW/dut1kyh2eQbY1f54dQ4v3r0BH/72El6o53lBnnUWO+Jv1+FT9lq8vr0FP/P119hoVZ9pAGHoUSCgVi8o/f9a/n/zOviN+9hO67dp8H6X/7GnBtVsxyJf5b9sY+T1Po7dk4nUz/D3uhHHiibuFuxbI0zVb+9zP1Y4gvkzIO3SRLdDZJQSGi9HE38FO+s/jGO36pBX2qOMVs/u+bAbg+qIpNKRVhcLO84D0zHHNiFuQJ/8luvce7dCYZGu3jqI/vYs7+HWKKVsq+U6ty4ssD7b1zn8tlp/vS3XiOVjdI7mL4jIhcEAZoic6A7w/hygYpl4/otWd6641BsNlmq1phaKWEPuUR0jX1daeq2Q7VpE9Y1FElQaVqwVlZsux42HrKQqDsPr8RmJ7E+bqqkMBDq4kqluVHG8CCR+92G8WKBqVJxW9fsSFc3x7t7Os7G97f+6cv8zN976eYbwVov3XKFH32t5dhuRobyk4SQqiHfUuK4WKvyvfEx/vPp95kul7B3OKO1FfhBwEK1wp9dusBUqcj/8MJL9Mfim+oa7yb4QUDJavLbZ07zzbGrTJdLu2re8oKAiWKBf/ve21xczvOLh47y9AOU6D9MvDEzxR9fPM+PJicoPQLndR3+GgnX//bWG1xfXeErBw9zJNsNrBFi6MYD3acC+Fj3PmbrRZaaFX5j30v0h1v9en86eYp0mz2w7eCVyQn+6OI5fjwzRcmyHtl96wY+C9UK/68fv8bVlWV+9sBh9qczQMuBjevGrs7AViyLM4sLfGf8Om/OzrBcr1GxbBz/0c+tW4EfBNRsmz+8eI4bhVV+/eSTPJ3ra1sFYLeiZFnU3Xs7sOPFAv/t6mX+8OI5Fqs1vODRZstHkykG4/GOZsIDvLY4PzQpzHDkKUbCz5Ex9hJR0mjbLN39IAQScTXHSOQ5wsqd/sXjjrKTZ9kap+asrr0jiCoZcuZBQkpyS7+3RUIbYjjyLKcKf4lDy4G1/QYVd4mKs0RU3b796jger752hXPnZzAMFUkSnDg+yMufPrLtfW6Gjln1P/7uBW5cmufkcyN88gsn2HOgh0jU3JAScWyPfUf6OHCsnx9+4ywXz0zx+vcu8gt/76U7BjwdCfHiyBCyLFG3HEayKfoSMVzfZ39Xhv3dGWKGzk8d3kfE0DmS696gF3c9D1mSkITA9X3SkRBx02BPJtkqLRYC1/NImGbH+oseNWQhMxDqZqw6+6hPZUewzrK6HRzJdHEok+24kZLqit3WE7uOnoEUuqHxytfPMH0jz1MfO9DR4+4WCNYzsK0FZ7Zc5q/HrvIHF84xXiw81JLh+8ELApbqdV6bnkQSgn/45DMcTGd2rRFlex7XV1f4r+fP8OrkxCOP2N8Nju+Tr1X57vgYy/U6Xyof4Gf2H2wF0Xah0eAHAd+fuMGfXrrAW7MzFJqbMJ88ZHhBwEKtyteuXaHmOPzCoaM8letFADFdR3mAtgchBFHVYDCcotuMoUsyIaVlaH8qd4Auo7PZsPVs4LfGrvGnly7w7vwcJauz/cTbgRcEzFcr/MWVS9Qdl68cPMyJ7p6WA2sYu7r8/Xphha9fv8J3x8dYbTR21by6VQS02H3fnJ0mAGqOw8cGh9Dk3dGi9CCoWNY9ifPO5Rf5yyuX+NbYNeYqW9MK3WmMJFMMxOKP9Bxcv8l84zJNr0KvM09/6DgZfaQjkkyK0BiOPENYSSOLx/8e+yBK9gIlewF/jXFYQqLb3I+pJNoiX5KFQlzLoUlhml5ljUgroOGWKNgz23ZgVVVmcDDN3/07H0WSBJLUSqolk50PmHbs6p59ZxxZkTjx7AjHnx0h9AFZHU1XCUcNYvEQxZUa1y/Nce7dcX7+7750x75MVcWMq/hBwMmBHF3RMLqi0JeI0R2NkImEWouz0TqGrigbUi7rhtOtwshhTSMZunPi341G1nagCJl+s3vX9Kx2En4QMFkqMlMpt71tXNcZTaXIRR5e2ZKmq/T0p3Adj9V8++f8+EAQUlskTgvVKt+6cY0/u3yRscLq/Td9BAhosff+aHKchGHwK0eO70hg40FRs21OLc7zp5cu8MrkBKu7wMm6FwJgtdHgrdlpqraN6/t8Yd9+QsruY3f9/sQN/vDCOd6dm6W4CxyrW7FYq/H9iRv4QYAAnujJockypqogC/FAvaOGrNL0HK6Vl2isMWI2PYdu484A3IMgAL594zp/eOEcpxbmO07U9KCYr1b59o3rGxmw413da2OsPvAY7xSShokiSRSbzcfSeb0VFdvmrdlpNFlCl2VeGhx+7No5PoiKfXcH9lx+kT+7dIHvjI8xuw37ZSeQNkwGY3ESRmf7EhNqH4Ycu6sDFeDhBQ6WV6PhlbD9OhUnT81dpWjPM9e4SI9xkKHIU+TMQw/keErrUjji8SZuvBvKziIVN7/xWghBShts+/cKIaEIDV2OILnyxrxo+01q7vbsuCBokTg16jb7RruxbZfllSrhkE4qtYt1YPMLJSJxg2xP/A7n9VaYYZ1sT4xYIkR+vnjPdlRJCAZTiY3XydDdH7o7WRzv/fonCYokMxrp52h8LxktTlTdudKwh41Co8FspUxpG8ygo8n0GnlH5xnQVpfK1Kt3Gmie6zF5dZHSapVEpn0irccFkhAYikrTdfjh5Dh/dfUyl5eXHvVp3RcV2+bbN67THY4Q03QG4o82En0r6o7De/Nz/PGl83xv/MZDJ2p6ENQchzOL89RdB0kSfGp4hIRu7Ip51/E8Li0v8UcXz/H2XKuPeDciX6vxg4kbyEKQMk0GYnEimo4qy3ju9qXRyk6DG5UlxqsrRNSba7PfwcLeputycSnP750/w/vz89ScndE+fFDMVyt8d3wMRUgkDYOBWJyYpqNIEt4uaHn4IPqiMY5muxmITW1LQm63oWLbvDk7Q0w3yEVjjCYf7/7E8l0ysOPFAn9x5dKucl4B9qbS9ESiaB2uPhoMP0G3uR9N2tz29AMPP3Cw/Do1d5Wys8iqNcWKPUnZWaDsLLBiTVJ2FnH9JoPhJxHcyRy+FUhCIqn1bakX9HFE3S1sMDdDK8mzbE1wofRtNKm9wIQXuDS9MsEtZe1eYGN5d8rqbQXlSoOJ8SUWFkvEYiaNho1tuUiyYGgww4ED25NHuhs65sAGfoAkSRvESveEEAhJ8IhbATbFrf1Q9yMzanhNlq0VZKGQM7seGfmRImSGw718MfcShqzSo9+dce1xw0SxwFKttq3o+LHubnp3KPt68d0Jblyau+N9z/OZGV8iFNbZc7CzD+tugQBUWUaRJM4uLvJXVy9xPr/YtjnccoIVTEVFV2Q0SUYSrZKTIGiV/jmeh+W61F2HRod6QPO12oYTmwmFMHcgwNEuLNflzOLCmvM6RuMBHBZoXSNdUQgpKrqioMkysiSQ1uYoPwhwAx97fXwdF8t7sGNansfFpTz/4f13iagaz/cNEDd2juV2K3A8j5lymd85d5o3Z2aodCArqEgSpqJiqgqGoqCIVstKwLpmsYflejRdl7pjtzV3LdXrfOfGGNlQmF87dpKIpqHLMs0HuB/qrs1svcRcvcShRM/GOtWp1cryXMaLBX7r7CnenZt94HsXWmMcUlRMtXX/KkJsjLEX+Liej+V5NF2HmuO0lZ1cWMvEZkJhfu3YCaJ6K0iw0yzU24EqyxzJdvFMb19H5VZ0WUZXFHS5NTeokoQkJCQBPuCtaRu7vkfT9ajaVscy1KuNBm/MTNMXjfEPnnh6UxmpxwUtB/ZmsMbzfcqWxV9eucQ3x652rGx4vVLoQbPwR7JddIU7nwnrMvYxEnmBkHL/gLDnO1TcJaZqp7lW/hELzSvYfp2au8KN6hvYfp2o2kVK2x4BpkDClBMd6afdjbD8GrZ/k8E6wONG9Q1uVN/oyP59PJxge+vk8lKZM2eniMVDLCyWsC2XPXuyzM4VsG1v9zqwqUyU5XyJ4moN23LvqoFpWy7F1SqVUoN0V3TTVdTzPepeg6JzZ+RKkzSiSpiQsjPUzJ7vsWSvktXSyOLeEaBla5XvLr5GVAnz8wNf3JHz2QokIRFVQzyR3P/IzmGnMFEqbqtXTSDYn8qQ3SF21JnxJc69M37H+7qh0N2f4oWXj3D46eGOH9e2XVzXQ5IkVLUVRV13+KDFDuwHLT2f9R5vz/M3+Q4o8vYm+Jakk8xircrvnT/DhaX8loybdcc3ommEVLUVhY9E6AlHyYRCxHUDXZFbGSffx3I9ypZFvl5lrlJhtlKmYllUHZua3Z5j8EGcX8rzo6kJDmezG8Q5jwLrfYNjxVV+59xpfjgxvm0HQJdlQmprbMOqSk8kSm/05tiGVBVFkhG0+mzrrkOh0WCpXmOuUiFfq1JzHOqOTXWb4+sHAVdWlvmts6cwFIXn+voxOtDXtB34QcByo85fj13lL69ceiANYkWSiGgaEVUjZYboj8XojcboCoUx1ZYT4AcBlutRdWxW63UW61WmSiXKlkVt7Z61PO++RuhKo85vnT3FQDxOw3Ee2LiPqgaKkLheyeMG3gbB395oluwD9sH6QcBitcpfX7/KX129/ED7UiWJiKYT1lQyZrg1xpEo2XAYU1FRZRk/aM0LFdti5ZYxrtoWNduh5mxtjOerFX7n3GkG43Ecz911zPu3YiSZ4tnefr45dq3t6gFJCDRJxlAUdEVGlxV0RSEdCtEVCpM2QyQMg5imo8kKqizh+j4N16Fm21Rsm6V6nfHCKiWrScN1qNg2Tdd9IGdqulziOzeu86nhEQ5msrt49O+NitViIYY1wiqnVZH0BxfOsVirbnk/iiS1rpEso8kKiiShSBKyEMiShCpJBLTmbc/31wJlPo7nYa/9a3r3viaSEBzKZG9jO38UkCWVhNZLTO2m29jHK/l/z0LjMm5gY/t1FhqXuVD8Fi92/TpS0L5+sBACVTIQO6TB+qjh+Taev3PVWUHg4wfbs0GaVosZ/VOfOMSp05M4jsfBgzlcrz2Sr62iYw7s/mN9TP7FIufeGWdopIs9B3rWyDxanwdrLK2T1xc5+/Y4tUqDlz5zhM3uzZJT4fWVd/ivk3+KImRu9XJHI8N8sfdlnk2d7NSpbyAIAlbsAv/L5X/Lvzj035HSEvf8vqDFAKxIyq5eAB9nzFXKlLZR8hdWVQZiceL3obffLn7ln3yaX/knn96Rfd8L03MFFhZLRMI6g/0pfD8gmQhj2Q5B0FqkbNvF9XySiRC27VIqNzBNDdfxEFJrQbRsl0x6+85903X4/7z1Y96bm9uSUSUJQVhVGUkm+dTwKC8ODLI/nSG8iRTPZvCDgIplcWpxnu+Pj/GDyXEWqy1yo+3Mi0EQcGp+ju/cGONQpuuR9sI2HIf//f13eX16cltlw5IQ6LLCgXSalwaHeaF/gONd3YRVbcuLv+f7LNfrnF6c5/XpqTXSmDq2521rfN+YmaYrHCaq6zzRnXskGRZrraz137//zradVwHIksRgPM5n9oxuGNyxLcwr69qd11dXeHN2mh9OjnMuv0ip2bxncCCgxW76P3z3W9s65w+i5ljENZNfGHqS57N7NojXOsFU31jTB/4vZ0/d/8t3wfoYDycS/NTe/XxiaA/7U+n7SpPAzTG+srLMGzNT/HBynItLeUqWdU9jfl2T97/71te3fd4PCyFVZTTVcmK/Oz523+8LWnOCtMZkPZJIcry7h0OZLIcyWQZjcUKatuU5b32MJ0tFTi3M862xq5xaWKDYbGw7iOgHAdPlMr977iz/6uOfQJUezyxsea0Hdl3//NrqMv/z6z+i0Lh/0H39OumyQlc4wonubg5ksuxNpMhFo3SHw8R1A21N+i0IAlzfp2bblCyL+WqF8WKBG8UCNwqrnMsvUrastTUxuOP+T+gGe5KpR14Vsw5JyHSb+zgQ+yQNr8SKNQlA3Styvfpjnk7/EoYc25Zt/ZPqvAK4OHjcbie0aqs68/xIQt72+AVBQLVqcfHSHLNzBVy3lTyZnS2S3s09sJ/+0kkunZrite9eYOpGnhPP7WXvwRyJZJggCCgX61y/NM+Zt8eYmVhm3+FeXv7yybtOWhKCqBLhn+77dcLyzbp6Q9ZJajvTt+YGLgW7xGJzeUtU5zmzm5/r+8JjOfE+LpitVLbVs3YwkyVu/OQ18ZfLDaJRg8G+FOOTy5w5P8NXf+FZLlyeJwgCfD/AcT1SiRCmqfHu6QlkSWJ5tcrScplI2KAvlyQWNbbtwK47k69NTW5JKidpmHykf4CfPnCIJ3O9GGtla4okbXnKFUBU13mhb4Ane3L86rGTfO3qZb5+/eq2e8MWalVOLcxzdWWZg5nstvbxoHB8n3/3/ju8PTtDZRv3uaEofGZklC/tO8DRbBextXJItU32WkkIMqEQHx/aw/P9A/zGk0/znRvX+bNLF7m6urwtB/C7N27QH43TH43vSNna/XBhKc9vnz29rXGFVlYkF4nwd088yaeGR8iYoVY5axustbIQ7E2mGIjH+dK+A1xdabHKfuPa1YdGJKXJCnXX5o2lG7ySv77xzP3y8NPsiz2Y1NephXn+4MI5qtscY3UtOPB3TzzJJ4b2kDAMdLn9Md6fSjMcT/CzBw5xYSnP169d4ds3xnYFC3InMBhP8NmR0fs6sAI4nM3ybO8AT+d62ZdKkw6FUCX5ZlavjXl3HbIQDMUT9EaifGp4hKsry/z19at8Y+wqy/X6/XewCUpWk1enJ1is1shFoyiPoR1VXpPRCYAbxQL/61tvUNgCW7QkBIezXXx8cJiP9A+wN5nemFvWM6/rQYhboUgSMcMgouvkohGOd/dslHtbrsuV1WXOLC7w3vwsF5eWWG60ro0Ansz17lhA/0EwGD7JWOXHGw4sBNhenYXGFQbCJ5DE7nC4dwtkoSAh38JCLHMw9ikSei+y0B54/4YUIW0Mb2vbdCrCnj1dTE2tAK0qv6WlCqah0tOTeOBz+yA65sB29yb5ub/7El/7w7e4cm6GxbkiZlhHUWQgwHV9GjWLIAg4/vQevvjLz5G95w8SLXkYs5eoetPQlhBIQmoZ6/i8sfIe50tXWLULhOUQB2OjPJ96krh2k2HxG/Pfw/Jsklqci+VrrNoFIkqY59NPcTi2n7BicqF0hR/kf8xCM48TOPxv1/7jBqvvRzPP8amuFxFC4Ac+FafGn81+nfnmIkEATyWP87ncJ287+z+c+itiahRZSFypjFF2KyTUOC9mnuFAdC+mbFByylypjHGmeIkVaxU/8MnoKV5IP8XeyBAh5SeHjKldBGslMvlalZrdPiHIgXSaiKbvWHBhZbHE5dNTXL8wS7XcwHPvdOROPD/Kx790sqPHlWWJYrFOvW6zmC/jeh4EAZbtsLBYJp0MI8uC/FKF0T1dJOMhrt1YRCDo7oqTiJlIEizkSxw/sn3R8ADu2zMmgBf6B/jy/oM8k+unKxwmouvbihO29I1bfZ36Wt/s3zp6gv3pDH919TLfm7hxG/P4VuAHAVOlIq9MTjwSB7Zm27wxM8XXr11hqV5rK9OpyzKHMl38neMnOZrtpicSIdxGVuWDEEJslKvpa2XeP73/IEez3Xz9+hW+feM6C9Wtl8QB1Byb742PkYtE+ZUjxx6qXMl8tcJbs9O8vzC3rQxyXDd4rq+fv3fiCUaTaZKm2bYm64ZOt9wqiw8pKid7VHLRKC/0D/Jfz57m9OL8jvdeRlWDZzJD7It14XEzEJEzHywQPFMu8ebsNGcW57c1xinT5MWBIX716AlGUykSevuSNneMsarydK6P/lic5/sH+b3zZzizuLArZajaQVTTOZTJcryrm/NL+dscpIimsS+V5vm+AZ7s6aUnEmmVBev6Run1g2B9jBUhNkpdj3f30BOJcKy7m985d4Zziwtt3wN+EFBsNvnuxBhfOXCo48y4DwPrLMSTpSLfuHaV0wtz93ReZSF4prefL+7bz5FsNz3hCHHDwFTuXyq7/vl65hYk1lQqN9a+qK5zIJ3l86P7ma+UOb+U563ZGU4vzvN0bx+xba6/O4mo0oUuRxBIa1Iu4AcuRXuO3tARHj1Dxe6CIjQUSd/ogxVCosscZSj8FLr84C1zEjKKtD1HOJ2O8MLze3E2sYdN48Gd6w+iYw6sqikceWIIM6Rx8cwUY5fnyc8VqddakdlY3GT/4V72Hurl8MlB9uzrQVHvPbEKQJEUNOnOW9jybE4Vz/HOymm6jAwDZi9Vt8Z4bYqKU+Ur/Z9HWmMxW2wuc7l8naFwP/1mjj3hQabrs/wg/zph2eRgbJRuI8uTyeNcq9xgojbNC+mniKoRBDAQ6r3lnAS6rPFE8hiJapzTxQvkreU7zm+2scCZ0kWGQwMMhwcQwHR9nq/PfZfEUIwBs7dV8olEWkswGOpDQnC5cp03Vt5DlVQOxkYf5JIAa6RUAWsZ5e0Xocvi4Zf45Gs1yraFuw22r51iH17HG9+9wNWz04SjBvG7lEaEop2PHA70pcikIwRBQHc2hut5KIrMvj1d9HTFCZkqkiThuh6KIjHYnyIU0lBkGVWV0VQZ3w9oWDvLcKvJMj+z/xA/tXeUE9050qbZ0ftHlWVy0SgfVYcIaxq6ovCNa1favsPztRpvz83wa8dPYD5E6RfX95mrlvmts6eZKZfbMrBTpslHB4f5hUNHOJbtJqJpHXUOhRDICDKhMDFdJ27oZMwQf3n1ctsySRPFIq9PT3K8q5tj3Q+HlTsIAs4uLvKjqQmq2wh+dYXDfGJoD790+BjHurpRpe2xYX4QQghCqsZQXCVjhsiYIX7/wllenZqgsA2W9a3CC3wmqiu8lh/D8T0iqo7re3yh/xjhyPYyMkEQ8N7CHK/PTFG7hw7m3ZCLRHh5zyg/f+gIhzLZjrGiCiEIaxrDaoK0aW6M8evTUx0h8HpUUCSJnkiEL+47wJWVZbwgYG8yxcnuHo5kuxlJJumLxuiJRHecFKl1H6sMxhPEdJ2YbvBfzrzPqYX5tsnGLM/lhxPjfGbP6GPpwDqex0ylzA8nx/nr61fv+iyokkR/LM5P7z/Is739HEhniBtG20Gxu2H9eoc1jbCm0UOEwVic0VSaZ3r7mKuUOZLtJq7vvmymLKkoQkcSN6VcAgIsv3IbO+6HaEGTQqiScRuRkxe46HKUsJJ8hGcGmqagaQ9PzrOjRwpHDY48MUTfUIajT65SWK7QqLcMCDOkke6K0dOfIp4MbxDM3A0BAQ2vyX+b+w7GLdGAXrOHE4nDOIHL9xZfI6uneTJ5jAGzl5Jb4Z3V07xfOMfTqRP0m71rPbTgBR4JNcYL6acIKyGmG3P8u+u/zVxjkeHwAN1GlogSRhYS382/wrOpJ8jqqU3leAxZ52TiCDElwnT9TibadXi+R7eR4SPpp1EllbHaBP/m2m+y0MjTpWcwZYOhcD85s5ukFkdCQpVUvp9/jby1zAH2tl3X7gc+eavATD3PklWg4tRxAmdLJdH3wk/1vEC38XAp7xeqlW2zb/bH4oR2kDxmcaZVtnr02RH6hjKbsm9HE53PoCcTIRLB+kJ/s8e8Kxsjm7ndfRNCEIuaRCMPd9GKqBpf3L+fXz58nAPpzI4GEhKGyTO5PgSt++X0wnxbfVl112GyXOLKyjLHunoeWhnbcr3GDybGeXd+FsffegauOxzmY0N7+KXDR3kq1wd0jk12M2iywv5UBkNWkCWJ3zt/ti1ZiKbncn5pke9OjHEwk22Vje/wGM9XK7w3P8ulbcg6pQxzw3l9oie3I73RkhBEdZ3n+weQ18hZXp+eZHULfXPbQcGqM98oUbIblJ0Gz0dGeG9liprTqojazvWYrZR5Z26WaysrbW+bNkN8es9efv7QEY51de/IGMtCImGYfGRgEFWWCQJ4a3Z61+n/toOYbvDJ4T2cyy/SE4lwIJ3lUCbDYCyxpX7hTkMSgqQZ4qODQwCUrTe4trqypbaSdTiex/mlRVYaNbrD4QfOFj9sBMDbszOcXVxgvLR5K0tU0zja1c0XRw/wqeERusLhh1KNoisKvdEYuUgUv6eVhNltuufQsvUD/DsqqISQd3Zxe0wRUpKE5OSGVmsQBBTsGVz/8Q3QbRcdd5UlWSKVjZLKPrh8iRd4TNdnUW8RNVYkBdu3qbl1rlbG+Fj2efpDvUSUMGElxN7wMO+unuVq5QY9RhcKrQkxo6cZjewha6QJCNgfHcGQdSpujaZn3Vam3CnkzC5GwoOk9SQBAfsie1CETMmtYPs2UTWCImRW3SLzjTx+4FF1q1ScKk3Pas2ObTzAludwrTrFmeJVLpUnmKnnKTgV7A4wlj2ZPPjQHdiVNRKZdiELQW80iqHsXCRocLSb6THBar6CositUvkPXCshCVLZ2OY7eADczeBs9/2dQEzT+Uj/IH//5FMMxZMd15vbDFFd54meXn7lyDGmSiVWGvW2GDIrVpN35mY5lOnqWET8XrBcl6sry3zt6pW2AjQJ3eCF/kF+6dBRnl5zXh8GhBAMxhN8ad8Bis0mv3P2NJbnbjnbPV+t8ubMNJ/fu58D6cyOnivAmcUFzizOt519VSWJZ3r7+On9B3fMef0gnunto+E61B2HN2amNtWUfFA0PQddVjme6qPiNDmR7Ge8uoxYk6XZzq98d36W8/nFtvVeVUlq9cPvP7hjzuutUCSJF/oHWqy6js17852R+XmYKNWazK6UWKnUUWSJg3qal4aGGe1KE9Y3L8tbrdQp15uEDZ1sfOf6zwVgyAqfGh7hRmGV379wlqlSacvbB7RkdSZLJYbjSeKPmQMLcGXlzgq8dcR0nadzfXzlwGFeHtm7qU1iWy6VShOraWOYGvFECHmbCgHrqNcsqtUmvh8QDutEY5tnt33fp9l0WM5X6Mkl7qoespOwvRqO39jo6YRWpaMhRZF+gsmYtouY2kNU7WLJavXDB/gsNq7R9CtEg64Nkr6/CeicDuwtBmNHyq0QhJQQvzr0c0SVm86lKikoQmahuYSzllXV1gSLhWiV90aUEKt2Af+WrGNIMYmq4Y19Q6sZ2g88PHamBymiRAiv9bGKtf8UoeD6Hn7gs2IVOF++wpXKGA2vie97NHyLklPBX4tLbSUDu65de6M2w59Mf49zpevUvQePxshCxpA0DFm7LYjwsLDaaOC06cCuZzcShomyg4thd3+SM29c550fXiLVFcUI6XdkYZ/5+EEG9z46iZaHDV2WOZTN8g+efIq9yfRDjfbGdZ2X9+zle+M3eHVqsi3DuuY4nFqY51ePntjBM7yJfK3Ku/NznFta3PI2iiRxsifHT+87yNO9D895XYcQgr5ojK8eOc77a+e+1eCS6/tMlUt8c+wa+9IZpG1m/baChuPw3vwc11bbzwwOJVpO+lO5vod67740MMRirUa+VuVcfuv3xFYRVQ2SWggBRBSdt5fHqbn22rrRvgtbdxzenp1lYhvkaSPJFD9z4BDHunoe6hh/YmgPc5UKy406l7eRmX+UmFst8b0z17k2t8xQV5Jqw+Ic82RCobs6sFNLRS5NL7KnO7WjDiy05gZFCH7p8FHOL+VZqtXbZlO/vrrCkz25XcOQ2wkYisKTPb189cgxPjk8ctesa6XS4Mb1PI7jkslG0XSlJX0nBLIs4fk+nuujaQq27W7I4qmqAgJ8L8AwVVT1Zul4frHM3OwqmqaQ7W4F0cMRg0bDbsnoeQGe56MoErWqxbtvjfHJzx5tle5aLgQgKxKO4xH4rWMbpvbAjvVmWLYmqHtFbm1xk4RMTO1GegR2525HXMuR0HqRUPBxCfBZsSZYtSaJqz0Y8oMnDx8XdOzuKKxUURUZI6y1Hqx7wLFdmg0bSZIIRe5OtCMhiKsx4urtF8T2nTWqZ4GHt+HAtTQVW+ROEvJty7KMtFFOvDU8uGiRImTkuxwzAN4vnOPt1dPsj47wtwa/QkyJcKM2yX+68fvtHSgAJ3D50+nvc640Rt2zWtIEQkaTlI2ITN218PFRhIy6/n7QcoC9wMPxXdZFSXRJpctIMRLuYzicI63vDPPzvbDaaLSdgZWFoCccaYvhdjt4/VvnWV4o8uLnjjE02o28ST93biC9g2ew+zAUT/D50f0bpa0PE+v9hV89epxLy0vUSlt3YBuOw4V8Htv3CO2gcwWtfsQLS3l+OHGnhvC90B0O8/nRfbw4sD1x905AliS6IxH+8dPP8i9/8F0Wa9Utz5Ir9QY/mBjnHzzxNCFV3bFn83phlSsry233lCqSxM8eOMwTPb07WrmxGSQh+NTQHqZLRa6vrnQ8QxhXDfbHuhG0npNXFq/yhfhRBkLJbUXrr64sc3VluW15M0WS+MXDRzma7X4kY/xTI6OMFwtMFAvbbk15VOhKRDjQn+XlE6MslWr8f7/+Yw4PdJGJhbFdbyOBENI15HX9bz+g6biU602EEJiaiuN5Lfk/RW5JrrkejucR6QBjf8oM8XxfP9dXV+6ZldwMk6XitvrVdysEcCCd4atHj9/TeQWo1y0aDYsTTw4TCuks5ctcv7qALEtEIgZW06bRcOjOxZmaXKZSbmKaGtmuGLIsUas1Gd2fo7snvtFSVCzUEEKw/2AOP4BzZ6Y58eQQY1cXUFSZes2iUm4yMJRmYDCNEC17aWGuyNxsKzCl6yqrK1UgIJWOsO9A7q6Z3O2gJc/kMFF9l7KTv230VMmky9iHLD6kcPogYmoXWX2EiJqh7CwA4ONypfwKUbWbnHkIeRuOf6eTkA8DHVtF/q+/8ZuMHurlZ/7W8+w/em9207PvjvNXv/sGqWyMf/Z/+2mE3N5gqUIhpSXQZY18c4Xh0CCGrBMADbdJyS7Tk8kiteWwtrKk6yULfhBsu7xqK/ADjxW7gCapPJ06Tmwty7xsF9ou+XV8lwvlG1ytTlP3WoZbWDHZFxnkmfRhsnoCBYX/PP5XTDcW2RcZ4PnMMXqMNJ7vUXZrTNTmOVO8St4qEAQBeyP9/PqeL7M/OriWOX74pT2rjUbb8h2SEKTMEPIOP4CJdJjkR/bx8S+dpKs3sel3NuuL/UmFqSg8levl5w4cfmTn0CpPHGQwnmChWsXytmakekFA0WqyVKsSUbUd7cNabTQ4m1/kwnL+/l++BV8cPcDTub5H3iNmKgofH9rDgXRmQwNxK3B8j7lKmR9PT/LS4BAhtfOMhACvTU8yXd56CSO05vh9qTQfHRgiF3000euUafJ0bx+nF+d5dWry/hu0gdlGkbHKMiFZ4/nsHl7OHUKIlnLgdvCDyXHma5W2tpGF4GA6w8cHh8k+AkklgEwoxHN9/VxcyvPm7PQjOYcHQgCuHzC9XCQZMTE0lVfO3+C1i+M0bRdZkviHn3uOvnQr2Dy/Wub0jTm+/f5VDE3lqx8/wekb85iaylOjfXQlIrxxeZL3rs/yf/7Kxzpyih8bHObt2Zm2HdjpUmlHyucfFWK6wT84+RQv9g9uqdKgXGrwxqtXGdqTRZIEpqmxtFRmdaVKV1eMA4dypLMxJieW0TSl5fx5LdvItlx0XeHWwwTA6kqVa1cWSKYiOI7L5PgStZqFaWrIiozn+0xOLDMweDPQPj9XJJmKIMuC99+dwDRVvvDlJ/jet87TP5jprAOLx2z9LGOVH1O5xYHVpBBdxiimEvuJ1nN9EGSNvQxHnuZs4Wsb701U3yaqpNGlEFljb9v79AIXHweBjCp2n9zSZuiYA2s3HRzbxffvH5MXQtC0HCbHFred5wzJBh/PvsC7q2eIKhH2R0dYtJZ4t3CGkBLiRPzIhgzOVqFKCgmt9dBcKl9FkxRUWUMRMqbc2dIWScjoko7t28w38gyEellqrvBq/i1W7PZKs2zf4b3CZRprzmuXnuTT3c/wcvezRJQQylq2OqFFmG0sEVPDjEb6ORgdXhO89rF9ly/mXuSV5VO8unSG8docvzv5TX5j5GcYDOUeSUSm0Gw/AysJibRpIu9wH0AiHeH1b5/nvVevku6OoRvqHQ7ryRdG+cSXn9jR89gtON7dw8eGhglrO+OYbAlrUjDP5PoYL6wy0wbZkBf4TJSK9EZjO+oknl1c4OziQls9unsSSV7oH6Q/Fn/kkdEWQzF8bnQfk6UiE6Xilretuw7fHR/jyVxvxx3YIAjwg4DTC/Pka+3J/UhC8JUDh8hFoo+M5EQIwaFMlhcHhnhteqptSah7ISRraJJMzbXWyj23d3+3MiYB78/Ptk04ZSgKv3j4KOlQ6JGO8fGubp7t6+etuZmOjvFOY6FQ4Ydnx3j76jQrlSqfODZKNh5mIJvg2HCOYrXBhakF3rw8yZeebQURdVXhuf0DnNzbx/W5Zb7xzmW+/Oxhvv7OJYa7Ei0pNMdlb65zlUK5SJQ9iSRJw2irCmK5Uae5xYDj44C/fewEx7t7MNWtMdtHYwZHjg0QiRq88do1bNvFdbxWoEkW6IaGJK1pw0ot8sYgCJBkCYTgxtgiTyT2IN+SDLJtt6UbHzM5emKQP/jt1/nkZ45Qq1qsLFfw/QBJEszOrjIzs8L0ZKvtIr/QCgBGokZLWk2R1p6Vzjwvrm9Tdha5XnmdS6XvUnLmb9t3VM1wNPG5DRWRD3E7BIKk1s9I5HkWGlfIN68BrV7YK+UfUXGX2Bf9KEPhp4mod3+2/cDD8mssN2+w2LjKbOMCWWMPh+Ivk9S2L7H4MPFICsxlWUISgkqpsa1nYp0J+OWuj/L9/Gu8sfIury6/tXZh43y+51NE1XDbDL6yUMjqaT7d/RJvrZ7i3cJZQrLBs+kneCZ1Emg5i2+svMv1yjgLzWVmG/NokkrRKRNXo7yUeY7B0P1LKAVwKDZKySnzo/wbvLN6GlM2ORDby0Jzsa3ouBt43KjO4PitBeB4Yh8vpI/RY2RuMxY0SUVaI+6QhURIud0pj6sRTMVAk1S+Nf8mVytT/N7Ut/nHe3+epBZ96M3hdcdumz1ZXsvA3o/l+kGRzSU4eHIIq2Gj6SqycufYROIPzkLs+z7NqoUkCzRDay1YuwyGonCyO8dTPQ+3d/CDWD/ysa5uvjs+1pYD29KELfN0buc0OT3f5+LSElfb7M/81PAehhOJh0KItVW82D/In1++yFS5tGVn3HJd3pydpmrbrWe0g/dKAFxbXWGuUm6rPFQWgq5whBcHhh55/13SMDmQzjCaTG2rh/dukITEUrPKq4vXuVCc23BgvzhwjOHI1p0X1/e5urrCfLWC1cYYK5JELhLlY4PDRHYo875VpM0QB9MZ9iSS3GhTEupRIh4yeGq0j2f2D9CwHL5/5joDmTiu7zM2v4rn+ywUKoR0bYOFPWbqdCUiDGYT1C2bb5+6SjrWWhsbtsvEwiqW43FyT+99jr51qLJMX6wl59OOA7udYPVuhCJJ7E2meHlklO5wZEtzXCYTIxTSSaypcxw51o/vBxsM4bquEonqSJLgxJNDG4EXVVUQksC2XBRFus3mGdnbRXdPHFWVMU0NM6Txmc8fp7cvieN49A+mWqwskiAU1vn8l06SyUbx/QDP9TfkF9fxxDN7iN/Dnpmtn8P2a6jS5hnaIPDxAoemX6HqLFNyFig7i1Sc/Ib2K0BM7WYk8gI58zCPEwVxQLAW4LNxfRs3sGh6VXxut1/rXoGau0IQeCiSjizUtitFARRJpdvYzxPJn+XHS/+Fqru8Jj1UZbZ+npK9wLXyq0TVLqJqFlUyEMj4gYsTNLG8KnW3SN0rYnkVGl6ZulfEkCP4weMTSHokDqxjuzRq1l0bwkOKwaHYPnRZw5A2T2VLSPSa3byYeYa5xgJVr4EmqXTrWfaEB25ztp5JncT1XTL67Yv153OfJKOnNkiiJCGIqRE+3fVRJmpTND0LVWo5tTePK+jSMwRBQJ+Z42TiCEK0+k1DsklECSMJwcezz2PIOkntZu+oJqn8XP8X2BMeJKSYDIUHUCSFmfo8ju8QVkLsi+4hrSXoM3NbdsC9wGfRKuAGHqpQGA7n6A/dyfC43vfqBt6m0h2KJJMzMjybOsJic5XvLb7D6cJVLpZv8ETyIBHl4eq02Z7XVqYKWsGNqKZvuzxuqxg+kCN7S+lwEIDreMxPrTBzI8/s+DLx9J3M1rPXF5i+Mkej3CCVSzB0ZIDEPZiK7abDpbeuMn1lno/89NN0De48i2u7GEmkOJTJkg51XjZoO9ifThNvs6crCALytSqOt3O6c9PlEmOFFVYb9ft/eQ2movDiwBDZ0KMpu7wbeiJRhuJJrrTRC+kFAfPVKhOlItlwmHAHnRk/8Hlvfo5is9lWTNRUVJ7t7ScXiaI+BAbqe0GRJHojUZ7M9XbUgQ0rGofiPchCQpeVtfYKQVhpb/wd3+ft2RnKlt3WGEdUjad6++he4yZ4lFBlmYFYnBPdPY+VA2vqKslIjOPDOZq2y+/98BRXZpZQFBnf9+lORFitrLGvr10cx/dxfR/P93E8H11VUGSZg/1dVBsW84UK0ZBOLtXZsvneSJRcJNqWjFXdcbBdFz8IdqXUy1YRUlV+5sAhhuIJ9C32eZuhloO5jq7u+IaT+sEMZFf37VwkQRAQBNwRsI8nQsQ/IOO3/2Dulu1Ct+0/Gu255bM7j93dc28OlOn6GfLN64i7JjkC/MDD8RtYfu02DdN1xNRuRqMvciD2SQy586ognUS+OcZS8zo1dxUvcHADBy9w8AMXP3DwApdlaxzHvz2Ic638CguNSyiSgSwUJKEiCwVZqBv/BsNPkNIGUaR7zc8CU44xFHkK229wsfRtVqxJ3MDC9uus2lMU7Bk0KYQhR1GEBkJsBBIcv4nt13GD29fuoENZ9oeFbTmwvuevNYHfLCNyPY9mw2YlX2Z+ZvOFIfADKqUGF89MUVyt0TuU2TTIYsgGI5FBRiJ3JyxZf7hGIkOMRIbueb5H4wc2ff/FzDN3vKdKKkPhPobCm2dRFUnhUGwfh2L77nnMp1LHN933p7tfuu29A9G9HIjeXq/eY3Tdc98fhI9PxanhBwFR1WhlUuU7DXhVUpGFhOO7WHfps5WEoN/s4kRiH68unabhNTlVuMpoZOChO7CO57Vd5iUEGKqy46UnmZ44qWyUWrXJ8nyRxdkiizOrzE0uUy7UsC2XzZLA05dnmb4yh6zKNGpNivkSz37xSYyQjlW3qRSqqJpCOBFG1Vp9LYqqcPoH5zn64oFd6cCe6O5hX+rhsg7fC9lwmKRhokrSlnuo/QBWG3XcNnuu28G5/CJTpdKWdWoVSWI0lWYkkdpRLd12IYRAlWX2pdK8Nz/bFpmP6/tcWs5zMJ3pqAPrBQHvL8xRaZMIJqypfGrPCIay83PGVpANhXk618sfXTjXMVNCFhL94SQD4RQRdfu9TY7n8c7cTNu9ijFD5+ODww9FA3gr6IlEONndw19cvritMV4u1fjzH5594PP41NP7iEe2tqZWGxYLq2V0RaZmOfQkY5iGRrnWxPU8JCHQVeW2AFzTdhlfKEAAi6UqT432ocgSR4dzvH5xnHKjyWBXAv0+pJvtIhuOtB1w84MA22s529IuqjRpB+sBqM/tHSX8gPP1Vp8TIQTbeaTutf/tPKNVd4kq22P31qQQKX2Q4fDTjESeJ2vs2dZ+HiaWmte5WPoOK9bkWtbVwef+mcvZxnn4QPeFhIwsNGRJRREqhhwlruZQuPf6KIRESE5wOPEymmwyXnmLxeY1qu4yXuAQ4GP5VSz//i01AomwkiKqZFHF48MEvq2Zy3E8xq8tcPrNsY0MWaNmsTBb4K1XLnP98tym2/muT36hxNileYIg4OgTg9t6+D7EBxC0iJwCAjRJvSvzsSG1+nmbnrXRL7sZQopBl54ipcdYaCxzvTpNowOyPO3C9v32M7AINFnekfsqCAJ8P6BSrFMq1CguVViYXmFqLM/sxDKnXr/G6JE+Dp4c5ODJQfYdHbhjH7VSnUx/ipHjQxQWirz77bMMHOwjN9LNjTOTVIo1JEnQNZhh4EAvuqlz7KOH+OZv/mBXGH8fhCbLHMpkGYg9fJbqu0GVZFKmSVjTKG6xjC0goNBs4rZZsr5VBEHAhaVF5qpbL2vWZJkX+gcJa9quvPZ7kklSpsmNNuVUrq6sdJRxNAgCbNfj6soy9TbkkyQhiOsGz+T6dk15dkzX2ZdME9MNylZ72eS7wfJd5uslVu0aMdUkoZmk9PAaQ/3W7is/CGi6LpeWl7ZMjgZrpHpGiCd6eu/JxPowkTRMRpNporpOuU0mZYDFlQr/8fRbt73XYnheGyfbxXY8VEVGU1tsv57vYzutiiJTV0lGTZ46OLAlBzZs6CQjJkulGhemFmnYDi8dGWakJ8XFqTxj8yssFqskIyamrqLIEjFTZ6gryUq5xvhiAUNT+PiJvaiyTFc83NKI1TWGupJt//77Ia7rxPT2AyWO7+H6/iMnqtsuoprOyZ4cg7HErrnXdxdaJKmSUFAkHV0KY8hRUvogeyLP0mceu2fP5m6C5depu0UaXnuEgZvBx8MPGjhey7N1/MZtZdX3ghASmjA5HH+ZlDbIZO1d5huXqTh5LK+K5dc3MsPrspySkJGFgiIMVMlAlyOE5ARZY4TB8JOYyu6x5e6HbTmwnuczO7nCq98+T61qYTcdalWLRn2ZxbnC3R9e0ep/NUM6R54Y4hNfOLErDbNHjbbprNdKmAWtRcAL/E01ZMOKgSIp1LwmZad2z13qkkpKizPfWGbJap8ZuRNolRC3t40ANEluu/95KwiCgOmxPNfOz3D9/Azjl+epFOuku2PkhjK4jscv/+NPcezZvbeVBG0GVVPoGe5i6HAfV9+7AQH88I9+TP+BHEtTK0TTUTK9KSLJ3VU6+kHkIlH6YzGi2zBYdhIJ3SCstuHABlCz7S2R0G0HTc/lRrHQFvmNJsk8nevFfMiSI1tFXyRKTG8/WnujUKDWQcZR1/dZbtRZqFbaYi03FIW+aOyRMQ9vBlmSiBkG+9NpTi3Md6QiQBESNdfi3eVJSnaDkWiGg/EcXWaUuGpiKup9Se9c32epUWOhWmnrnEKqSl80Rk9k95QEypJEwjDYm0xxemG+7SBBJKTz1MHNK7SK1QY3ZlcwdZWuZIR0PISqyNSbDsulGqVqg2jI4NPP7CMS2tqcOZhNMJhNbPpZf2bz90dyaUY+QM4UBAF1y6HasLBdn5GeBH2pu7evbBe6rKAr7TuhzjYC1rsJKdPkY0PDu8amdT0f3/Nv04d9UARBgHANNCcLwgcBprE1oipJyCjCQJPCGCSJBH3szRyn29yHJm+vuk8IibCSvo10yJQTO34NDClCTO3esqPZDnQp0ib7cuu39pgH6Db3UXHy5JvXyTfHWLWmaHhlLL+GH7hIQkYVBqYcI6pmias5ssZeMsYedCmyrX7cR4ltWUahsM5nf/ZJnv/EQa5dmOXC+5N87Y/eIhw1GBzpuqP2fuNgqkw8FWH/0T6OPbWH2F2+95OOlpF8d71Jz/NbDHOSxAdjAZttIxCEVYOGb1Hz6lietak+fVQJoQmFkl1h2SpukARsBiHYUNKtuc22yZQ6Adf3267JFwI0Rd6RDljHcvmf/uF/brFZPr+XL3z1efYfH6BnIEVhucq3//ht5Dayv0ISCEnCqjc4++olivkSzZpFpVCjx/XIz6zsegf2SLZr1/S+3oqIprepNRlgb+N+2yrGCqss1+ttOViaLHO8u+eha2ZuFd2RCNFtsE5PFAvUbPue8087sDyXi0v5tiW3YrrO4Wz2gY/faZiKwtFsN2cXF7ZQlHZ/GLLKydQAx1P9VJwmfzV1lv/p9NfoDyf4Uv8xns+OkNTv/QzXHZvLy8ttBxRbxFS7L6sS0TQOZ7o4s7DQ9jM/1JPk//4zn7/j/YWVMt984zKLKxX++1/9JCf29aLdUp67Uqrxo1Nj/M5fv8PBwS4M7eE+167n88blSb535joH+7sY7c0g7UCmUFdkdLn93+avMYk/rkgYBs/m+ncN9VChUKNcajA8nNmwI9eH9zbJnYA1VuPbP1t//9Y52rJd9OUnGJ4bIhzWCAL4yPOjqKp8x/432x+A7bjMzRd5/9QkL3zhEJq8/XJrVTL4RM8/Bv7xtvexHRxOfIbDic881GNuBQKJmNpDTO1hNPrS/Tf4ADqlBXtb659gRxJK8AAkTkIIYvEQx58Z4dDJQd574zq5/hSf+7mnGD28eXRSiJbRrqoKqvZ4efqdxFuvXKZcqDFyIEffYBrfDwhFDGzLaTEE1m1mJ1cIRXQGR7JrpFc2sWR4U+dIFjK9RpaiXcX2HQpOhYpbJ6be7vx0GSnCikneKrDYXCXfXKXb3Ny4sH2Xgt2e1t9uwU49LJIscfKFUU6/cZ2zb45RLtRYWSxz+MmhTQmb7ofVhSJTF2d47gtPMnNtnhOfPMqRj+wn1ZNAlmWM8O7Kam6GvckUiW1k4XYamiy3TRhje+6OGVAX80uUmlsvV9RkmZ5IhLiu75re4g8iphstmQjaI5Ov2BYlq4nte9sydD8Iy/W4trrSdrYypunsS+2+nnJdUdibTLUqmTrAzOoFPjP1Im8tj3OxME9KD/E/HvspknqI91YmeWPpBl/oP3rPfTRclxuFVfw2nb2EbjCa2n0OrKmoDCeStH3z3gOXJ/OcHZvjoydHOL6vF/UDWchE1OTJA/1cuLHAH373NMO9aULGw2NlVmSJlw4P8/S+flRZ7njv6zp0WenIc/04IarpDMUTJB4xk/mtqFSaLCyWGBxMIUky+XyZ62N5TFND1WTCIZ2urhhXrs5j6CqrhRqxqEE8HqJabVIo1OnvT7Fn+OYcefHSHL4f8NJH9lGrW3z/B5f44SuXyfXECZk6K4UqjuMRixoUSw0sy6GnJ45paCwtVwiCgIH+FPGYuWsc/Q9xE35g4wc2Qigo4sE4b3xsBMqOavk+kAMrZIEuS+iGSm4gRSRqEIoYf2Mzq1tFtdygtFrj+qU58vNFYokQ/cMZxi7NE46ZJNNhPNfDcz2WFkq8/+PrVMoNPveVp4nEjTuipoqQGQrluFaZxsZhobHCQnPlDge23+wiqoQICJiqL/D6ylm+0v8J4Hanr+LUma4vkrdavW1hZed1VTeDIrfPJRzQIhs5dWoCxRUMDWdIpSP4foBpati2i6LIeJ6PY7uoqoysSDQaNlbTIRYL0WjarUlevV0cXFZkfvEffZLP/MIzzE2sMD2W5+L7E7z+rXMAOLbH9Qsz9PQn6e5PoaibB2kuv3WdS29eI5qM0H+glz3HBsgMpPnmf/4BP/7Ld9EMleEjAxz76EEWp5Y5/f3zzFyd57U/f5sgCBg82IdmPlo5inUMJxLEd6EDq8hSW31IAWyZXGk7uFFcpdZGf6YhKwzGE0hidxDfbAZZCEKKiq4obUnXBECx2aThuB0xdB3fY6pUbPv6hVSNoXjigY/faeiywnAi2bHARdW1KNp10lqEr448Q1jRSektffCS3djS3G65LtPlUtukehFdo38X9cevw1QVhuKJjhrRhXKd5WKNF4/v2dQ5lCWJkKGSjod5/cwNLPvhylUIITA0FUPbWUI4SYhdG3TbKaRMg5Fkclf1vvp+sFbN13pdq9sIIcgvlXEcj95cgmQixOxckWhEJ5uN0tMVx3Y8HMfDsh3GJ/K3ObC25aJpCpGIgeN4SLLg+NF+Ll9d4NpqHlmW0DSZ1dVqKzv7wihnzk5TLNZRVRlVldE1hT17dl/ly07C9vKMrfxLuiO/RMx4BkW6/5zoeAUq1vuUmm8ynPwf1xieH/y5qruLrDbPU7HHEULBlDP0hD+CLiepujPkG+8QBC77E7+27WP4gcVU9Zsk9INE1UEUaWd8wo6FyZ58YRQCiMR2hqnW8asIZJQ1nSk/cKjY4zS8RRLaIQzlwSPpFXucudp3yIU/TUQdQhIKJesKq9YZZKHTE/oEmvzgi7HvtQSobctldaklKJ3rT1FcrTI/W2D/4V7KxTpBAEZIx3U8wlEDWZE2zS6qksKR+AivLp+i5jWYqi8wU8+zP3o7i3Of2UXWSKJVVJasAq8snUKXVE4k9pPW4/iBz6pd5lzpOj/Kv4/l2wgEfWYWQ374DpMqSW0vhEHQMmjz+TKqJ2g0HQI/IBLVOXy4j/HxZQxDpVCoEfgBff1JurrjNBsOkxPLCEkQj5v09iZRP2CESJKgbzhLbjDDwEgXo0f6WFoosjhTYPr6IpIs8d6rVxi7MEvfcIYnXtrP8eduZ5g+9Pw+sgMZHMvBjBike5OYURM9pPPcF57AqlsISZDqSaKoCtFkmH1P7iGejRHPxkj2JJC20V+0E1DX9B3DO2wQbQebPyn3hh+0m1/aOqZKxbYIhnRFpjcS3dVRaiEEmqKgyXJbDixA0WrScB0SPHjww/F8pitlvDYzsGFN3VXkY+vQ1qReOiU54/o+qpAZDCcZjWVb+o9r8+r+WPeW7jHL85itlNt+PqKaTt8u6jFehy4r9MViLYKlDgWuAlrMvyulu8tk2Y5HfrWCt4PtCo8auzXgtpOI6waDscQd73u+ixO4QIAsFNzAwZBM3MDF8ptISGiSvjZmLUkc27dQJRU/8FskPfeUU7k3KpUmFy7OkslEmZpewXU9LMvB9wLK5QZXry9SrTTQNYVoxCQaNZiaXmVltZVJ/WBFUixmsrxS4dyFmRbztaaSSkVw3dbcKwSoqoxhqFSrFulUBNv2Nu4JTVNQVYXl5SpLyxXy+Qo9a3q1DwMtGZkq08V/jeevESoKCUkYaHI3Ef0Ecf05RId7Qf3AomK9R9L8OH6wNf6HIHBwvDx152pHz8X2SjS9FQwlS1jtI994m7Ddj6TrGHIKVQpTtK7cto3j12h6K4BAlxJAa/5quHlUOYouJVEkA9dvYnkFmt4yVWeWsNKHr+ycvnPHHNiTz43geQGJVOd79jy/Sdm6iirHiGmjQOtGbHiLlO1rRJRB4MEcWNdvUHOmWGq8jSbFCSk5JKEgCQ3LW8X1a2TN54EHN3hG9vdg2y6yLBEEAbqhEooYDO/roVpuEEuEkRUZw9SIxU0OPzGIpqsYprppAEaVZA5Eh4irEUpOlfnmMlP1Bepek5B800CMqCb7I4Ncq0wzWZ9nvDbH1+Ze43p1hrgaISCg5FQZr80xUZsHWpnZE4l9RJSHn1VXtyW7EOB4HrIs09+bolJusLxSRdPk1uJgOSwuFGk2HQxDpdGM0Gw4lMsNZmZW0HWVrq4YsnL3XlZJEsSSYWLJMMMHemg2bBZnCsxOLDE7vszC9Arz0yvkplfvcGB79/bQu7fnjn3KiszBZ0fx3NZkL6SWkRlLR4mlo+x/eu8d2zxKCCBpmkQ0HUXaHQ71rRBsI1a5A/ZkEAS4vs9cpUKjDSdPk2W6wpFdbwyqkoSyjeqMimVhten03g2O7zFbLrfliKiSREzTie+ikr91yEIQ13XCqkbZsh6orH2mVuBqeZGlZpWYaiCEYG80u/Fs3K/3dR225zFXqbSVgdVkmZiub4voa6ehSBJxrTXGpQ6xPSciBmFT4/0r07x2ppu9fRmiYR1FlrAdj6VilfevzHBpYpHhXGrHSng/xMNHVNfJRe4M1KzYy5ScVYSQkGmtkz1mHwJByS5g+U00SceQTSJKhIXmPJqkYflNqk6ZmJqg3xxCltq/V5LJEENDGRRFQlVkurJRJEnC932EECiyhOv7HDrYSyRikIiHUBSZaMSgN5docbB8YP3J9cTRNBnX8zF0lUMHc8iyxJ6hTCsoEwSoqoKuKVRrrZaZvSOtbKvvB+i6QjRqYNseRw73oelKJ5KKbSDACxos1/8bIXUfhjKEJDT8wKFmX6JmX0CTshhq6/1HCVkKEdaOIITGNi2aTeGvsRLrchJdTrZe40IQoMtJTLmLEtc2vu/4NVaaZ3H9GgEBQeCjywmq7gymnKFhnSOtHyOs9lF1pihYV9CkKDVnBt94oiPnfDd0bAbt6Utt/N10l2l6S4DACxpIaAR4hNUB/MCm6ebxcRHIRLURFBHC8cs03DxuUIfAByGIKIMIoVK2r7FYfxVDyRDgY8gZFNFylB2vTMm+StNbQpUihJReJKFRdm4QBDZB4GMoWQw5gyzdPTtse6vYXpGouodV6yx9kZ8CQkS1PcScfVTt8du+X7HHNm6EAA+BjBAyce0gJfsyfmDhBy6anCSk9KFINxfxfUdu9gjfKho9eqiXwA9aTc+3TByRmLlByrSZQSsJiayRYDQygOU7GJJGRDFbGYlbfAuB4HhilKn6AstWkZrXYKI+z0R9ftMxUYRMn5nlqeQhoo/EgZXbz8DSMrb6+rPsH8lRLtXJdsVIJELoukIkalCtNslkY0SjOvF4CCG1ooapdJRkIkxXVwxd39qjIYTADOkM7+9heH8PnueTny0wcXWBWLL9MZN3SXb1fhBC0BOO7hr5kU6jWG9Qalhoikw6HMJ2XUKahk+A5/trWawW8Ygmy/hBgOP5KFKrdHn9tvWDgEKzQbHZaKtHs+l6jBVW+Nq1K7u6HO/a6gqO336EteE62B3o71yXd1mu19ty9ExVJWGYHctydhJCCGRJIm2aLNVrDzROl0sLTFZX0WWFZavGytI4I5EMd43ObYLWGDusNuptOXohVSWuG7t2jBVZIh0KUXXsjrA9D/WkOL43x3ffucp//ea7nNjXRzoWRlUkmrbLTL7IhfEFmrbDz33yGNEtshB/iN2PsKqRDd+ZvFmx88w1plGFghASilBJamkUoVCwV6h7NVRJxZBNQvJe5pszDJp7WLbyrFotGzowtxdeSacipFM3uTl6euK32Zu3/n0renridHfHNv0smQyTWGsRvPWzvXu7ADbd5/59PZt+1ptLbOt3dQop82WS5qeQpSiuX6DYeJ2Z8v+Pqn0WTekmCBwaTsvuV+UUdec6nl9BEhph7RianN3I1Pp+k4Y7ieXOEAQ2kjDR1X50uRf5lvJZz29QtU4T4AECTe7GVEdQpJvBDz+waThjNJ1JAjxUOc2dzmuA61doOOPY3iJB4CCEiiZn0JVBNPneCT0vaFJ35tf+tpGFgRCb27xVZ5p8421kYbTOzV0iZRyjZF8nEd3PUuM9DCmFEDJlexzLWyWm7sEN6jvC0nwrOubA2paDkCRkWaJsX2Op8SaKFKJkXyOmjlJ35+iPfB7Hr1CwziEJhYAAz6+TNE5QdaZZqP8I2yugSGH8oEnaeJqoNspq8xRl+zpNbwlZ6CT1Y0TUlthxw53HCyzAR5PiZM3nCKn93Cj9PrqcRAAp4wkUI4TM3R3YhpfH8lfpCr3AeOmPcP0GqhS9aynBfO0HBHhYXgE/cFClGH7QJJ4+wErjfWy/gOWtElaH6Al/nKg0vOl+PjhBCGkTlmEh7hl8WS+W/EjmOFk9QZ+Z5VhilKh6pwM1EOrm2dRhlqwiF8pjVN3NZT10SaPPzPLZnufYE+lFfxQlxHL7cjhBEFBzHEaP9RA1DaJRg96+JAQtBj0hBPsO5OjqiqHdwgIZj4cYHHrwMnRZlsgNpskN7j7ikk5CAF3hMNouzL52ApMrRS7O58lEQjw91M9cscxod5pivUnVspHWDADH89mbTbFcreF6PsmwSegWZl4vCJitlHHaZPFeadT5/Qvn+P0L5zr903YFHM/vSOmm6/tUbbttJzqkqLuKcOWDkARkQmHUwuoDObBLzSoZI8JHukaYrhX4i6nTLFtVZCERUw20LfQgO563NsZtlmir2q7McK9DFoKuUJjZSrlDDmySzz53kEKlwfkb8/zlj87heB6+D7Ik0DWFdDzMJ57cx+deOETUfLRj469Vh9z6zw/8DTbgm/9aOtl+EBAErTJXn9bfftAqhF7/zF8rg21H7/onAYaibEpmKCGhSiohOYyyVhasSCqu7xAQEFYiGLKJF7iokkZCTZLRu3ADl6gSJ61nt5V9vRtutTfvVd3zsD97FJAkA1mKokgJZClESNuPIqL4QYMg8HH9Evnqn+AFVWLG8xQaP8DxFhCoDCT+j6hyEoGMH1g03Any1T+h4VzFDywkYRDWTpAKfZKIdnzjmA3nOjX7PLafxw8sTGWETPjLJMyPAi1b3veblJvvslL/Ora3iBAGJ3Mf5dZslOc3qdkXyVf/BNtdwKeJECph9TCZ8Bfv68AKISMLE1WKIqFie0W8oImySUtPxZ7A8y2QBAIJXW6VC2eME8S1UYrqVYQQ1J15/MAhbRwjro8SVfcg73AWu2NPxvT4Eooik+6KEcgBmpwkrh2g7NygN/wy45U/omRfRRY6aeNJukIfoWqPc6nwbzmhjQCti5cyTpILf5KidZHpyl/RFXqBnvAnkIRKVNtLd6hFDe35rfIEWQoxGP0yhtzFYuM1Vq2zGEoXdXeGvfGvrmU/Q4h7/NQgCLDcZRruIt2hj2IqPdTcGVQ5hio2L4kOCIhp+6k5UwQERNW9zNe/D8BQ7CsAlO2rlOyrVOwxoupwp4b6rng+fZTn0/dmk5SExBOpg4TVEF+fM3hn9SJucLPXQRICTVIZDuX4bM/zfLL7qdu2dz0fz/NbvQ6KvEG1DjeVezo1URmKgryJQ38v+EFAqdm8LRuzHgAwDJWDh3o7cm5/0yHWyhzbvT6PC9w1TUJVlnE8j+tLKwxlkrw/OcdytYYfBBTrTVRFpjsW4cc3phhIxoma+m3JLS/wWa7X8XZIX/ZxheN7+B2Q5nI8j4q9dXbndZiququdK4Eg2YEMsRCC8eoyNdemYNdYbFT4ztwlJCHx8Z599IUS992H7XlU7K33b68jpKrEdpk+9K2QhCBpmh2rcFAUmcN7uvm//O1PcfrqLOeuz5MvVLFdl7ChMdCd4PhoL8dGezu6Tt4L606mF/h4foC34ZT6NByXotWk0GxQaDQoNpvUHJuG69BwHBquS9N1sTwPx/M2/m97LpbrYfsetrf+7+Z7lud1JCDwuEDQslUim0iKhZUIe9WD9JoDH9hGkNBSd3z/UKzl7AyFRzZ6pHdKVWGn4Xs+tUqjJRsZgKxIaIaKpu8OzgzPr+P6RQJcbG+RivU+CJmI/iSyZOL5FbygRtU6jSZnGYj/U3QlR8O5gamOIonW3Ga5c6zUv0nNvsBQ8p9jKH2UrfdZrPwxrl/ASAxvHLNsvUF//J8S05+l7lxjqfYXzFd+k5jxLBI6CFDkGLnY3yYV+hTLta+Rr/3FHeduudMUG69ge4uMpP8fKFIS21vA8ytocvd9f7siTKLaEEntIA03j+Wt4ngVFCmEF9j4uLh+E1loqFKYpH6AuL6fqDpEw11mxTqDJBTWM2sBIEsqwpdwvCqe38QLGjve598xB/bPf/vHnHlnnJ/7Oy/yxOcsJE1BkcKtAZCjCOS1jGQ/upxGQkOTUzTdJYKg1Qulyyl0OYmEgiwM3KB5356bkNKLLEwkoSGQ8AIbXU5zIPEb3Cj9AUJI9Ec+T8o4iczmi6kb1Gh6y6w03qPuzOMGFZYb7xBW+lGlu/f0ysJcK0sO1li2Ahy/xvXif1q7AWoIFNJm58XCHwQyEgeiQwyN9rBkFblcnqDgVCAIiKsRBsM9DIS6Cct3GnjXZpe4MLlIImLy0pFhak2beNgkCAIatoMkBBGzM0ZLXNfbNuD8IKBoNTtiHH+Iu0PQklHZjeWBnYCpKhzKZdnflWFqtcT0agnLcXE8j7rtENZU9mSSZCIhTE3BVBVGMilixu33vu8HrDTqbRMM/aTD8fyOOPWO71Gymm1vp8kyYXV3GFJ3Q0hVkR6Q/f2ZzBBz9SJNzyVjhBmJZlDWqoq2Wj1hex6VbYyxoSiElN07xgJBRNM6XqIfNjSePzrMM4cHW/bLWmRXWisNf5juSNN1ma9WuLqyzLXCKtPlErPlMrOVMqVmS8oqWMuwtkytmybnuu218ZQGNz+99cm91Ub7mximUyQJXd683anXHNxki78ZWM2X+Zdf/TesLJbwXZ+hgzm+/Osf55M/98yjPjUAJov/b6aK/+taMEknrB1kT/JfEFL3bSS8AjwMdZh06POE1P2AREQ7AbdIwzTdSSrWe/RE/xZh9SBCaCTNT1GzLlBzLlGzL2GorQBGwvgkUf1pdGUAVc7i+gVmy/+RqnWGqP4kgq3NlwEefuAgEMiSiSJFUKRRWk/g/deMmjvPYuMtdDmF7RbZn/hVJKGyUP8xM9XvUXWmGCv/MUORz5Mxn2Cs9Ceslv8cSWjIwiCqDd2xz7DSj+c3maz+NSvWWarO7JYJq7aLjjmwM5PLaLpMMh3GMANq3vrDfPOh1uUkBD62V8DHwfaL6HL6ljJdidsHfy0riEyAt+Ho3goh5Dt0hmRhkDJOElGHWWq+Q9WeRJViJPRDm557zZlGIDMS/ypp82k8v8G14m/i+BUgd59fflNIzvObrDbfR5YMcuZHaHh5KvaN+2z/8CGEQEYQkg36zCxpLY4XtMrUZCGjSQqqpG46IduuR6Vh0XQcfnB2DFWWODqcYzJfoFRtkEvF2NubJqQ/eOlAXDdR2yxR9dZ6DndSEmUztBb2lqWy2yKmzloJorwNVud7IaJpj0Re6WFgJJsiCEBXFfZkk/xy9Dhx0yAdDjGcSdKXjK31uwpUWebFvUOEdO2OrIofBCzV63gfBlRuQ9Ch2Kzj+ZSt9jOwmixj7mbnSoi15+vBnteBUJIeI7apfqspb+33bzcDq8kyhrJ7iYokIYionXVg159/RW712D5M+EFAw3W4uLTE+aVFLiwtMlksUmg2W5lU18X217Oo/odzUoegSBKKJN+Vn2S7eBR2RKVYZ/r6AtfPTDF5dYHicpl6uUG92kSSJDRDI5oMkckl6B3OsvdoP3uO9GOEdKQPVGMFfkCjZtGoNPE8n2bdxnV2jpG2XfREfo2Y/hSSFMLxVqnZ55ku/WsGhXZb2a8iJdCV/lv8lJs2aRD4eH6FinWapjPJTOnfsu7zON4SujKA469g0HJgdSWHLEyEkJAwUKQkkjCx3Dmi+sktn7uuDBAznqVuX+By/h8SNZ4lZb5MWDuEIt07YRbT9rJfyeIFNgJ5jVcohSQUstJTJLT9a22RETQ5jkBiOPpl3KDVbigJFUUYgEAWGv2RT7eSjpKOLseJaXsRSAR4rXLjB9STvRc6tro06zbxZJhIzESSarDJfRpRh/ADi4J1jop9Ax+HwehPo0oRGuTvum9dThMEPvnGmzh+jYR+hJCyuWMZBC4Nd4GZ6jeQhEbdnSOuHUC7x0WtOZN4gUXGfJawMoCPQ4CPG1RZarzJUuMtmm4eWTJJaIdJGsc33Y8QMrqSpVb7HnneBASe337k+mFBCIEqFNQ2eiz8IECskUlV6hae7+N6Pk3LoVBtEDX1joVhE4aBuo0M7GpjrWxlEwSBR8G6wnjlazTd5ds+kyWTwchn6DKfbFu3yvVrXFj9D+yP/yqmku1YeVjTcbk4l+fyYh4vCIgbBodyWUazmbvysDieR9NxcX2fhGlwfnYRx/MYzqToinaKJVygycqu62vpFIxbsnOmqmKuvT7Um0VXFEK3SAcJIYjdpZ/NDwLK1u0l7R9iHQ8+Jl7gY22jR1SVdrkDy3oG9sGeL01WttTnei+42xxjTZIxdvMYCwhrGjuREw2CgLrl0GjaqIpMyNBQd4Cgzw8CVup1Li0vcWZxngvLeZbrdVYbDQrNBlW7MwRVH+LukKX2NMd3G3zfp7xS463vnufcG9eYn1yivFKjWqpjNW1c28N1PIQASZFRNQUjpBGOmsRSYTK5BEee28tznzlG98Djw/1hqkNE9ZPIUhw/sNDkLHXnMqv1b2MqwxvfE0JFuisBrI8fuMhShGzkK5jKCNwStFCkBKa6h/W1rkWUJK39La0l4ARBm5lKWYSI6U8ix01q9kUa7hiz5X9PTH+WVOgzhNS7q1YoknEbqeyt0GQVTb6TTdtQWj7Y2onfFlzR5cTG3y3nNsxGMmeH7cOOObDhqIG0FnGMqENocgJDztIX/iyqFCEX/gQRdQgvsFGkKH7gIAmFpH4MWRiElB4koa4RJymElBwDkS8hSzoSChnzWZreEpqUaPW0CoWougdNSqDJCWRhkNAPE1b6kSWDiDaMhEJI7SOqDqPLd3+wIuoIIaUfU+lGCIEUKAxEvoSp5HD8ClnzWTy/iS6n0eQEAkHWfBZDzrayyrR6fvsjnyeiDpILfwKBjCQ0JKGg36eh+nFCbzqGKssocotp1XF94mGDkVyabCJCLKSjdUjTK2EYqG2y3Hp+QL5Ww/E9giC44wFqaVctk2+8S1QdInZLb7Is6WhSq9y9HfiBR9MtMFX5NnuiP41J50S6Hc/jyuIStueTi0dZKFW4nl8hHQ4T0TXylSpVyyYbDW+Ur84Wy1xfWoEA9mSSzBXL2J5HAFiOSyJkEDUerMxbCFAl0Q6Z6U8EUuH2AhsBLZbcD1tgdwZ+cLPCoB2sZ853MzR586zOw4YfBLjbGGNFklAfchayHQjE2hh3bp9Ny2F6scjbl6ZYWCnTsByOjuR45vAguXSM1Uqd82Pz7O3L0JOObtupdTyP8WKBs/lFLuQXuV5YYaJYZK5a+TBY9pAhELuaKf5eqFebXD01yRvfPMPFd28wM5anWdu8oiUIwLddXNulUW1SyJdhDBRVxvN8Dj018lg5sEIoCKEjSyYyJrqSQ5HiWN4sPrc6lPeSsGlpyKpSGlPZQyr0WSSh3r6tEFjuLACOVyRY23cQuHh+Ax8bRU7c4xibnbuEKqeJGc9jqvtoONcoNH5AxXoXXe65pwO7XYgtVhO01qyH8zx0zIHdf7Sfi6enKK7WUIIRQnpLKsZQWsZ81nxu47sR9c6+AEPJbnx3/XVO+eTG69QmWc+w2k9Y7d94va4RC9AbfnnL5x7X99/2WgiJnvDH1l7liGv779gmZZy44z1TaTVP58Kf2vKxHzd0J6J0J1oRmmCNgVAAUVO/Ky37drGdDGxAQNm2KFkWru/f1UiVUOkNf5S+8MdveVegCH1D/6vu5Ck7EzTcPK0gRZyUfhB9rdwiCHxq7jzLjbNUnEmaXoGp6rcxlRalfFzbS8Y8QRD4WF6Bkn2dpreKHzjIQiek5IhrI5tGvNbhBwEVy6YrGmYwGadQa1C3W+VgkoBr+RUcz+PifJ5nh/tJhkzmihXOzy4S0TV0VcH1feZLFVzfZ7laJ24aPL9n4IFL3BRJ2nXl0rsN67JOO01m8DcV6zq77aLVj7i7791b5ZgeJYIgwN1GuakkxLY0gh8mWj38HaqWsR2uTi/xtdcu8NaFSVzPp1Bp4Lo++way9KSjFCsNvvXmZU7u7+Onnj9IItJeeZ3n+0yXS5xZXOC9+Tnem5/lRrFAs0Oayh+ifeyCR3RbKK9WufD2GN/5wzd59wcXcayb95AR0kh2xUh1xzFDOqqu4PsBdtOhVm5QXK5QXKpgWw6u49HVn8KM7F7Cts3QksFs4vk1vKBBwxnHcudbZcVo+Ny/NUUIaUMKp9R8g7B+DF1uyQa5fgmQ0OSbfk3NuUjTnUaWojjeMg3nGpLQMdW9bSVOXL+C55cRQkeR40Slp9fKoC/j+Kttj8Xjio45sM9/8iAzk8tMXF9k8GqWgZEs5oc6Z48cLePDo+41aXo2ju/iB62OKFlIKELGkDVCioEilLYiiULc7r50OluQCYW2pTMaBAEL1QojieQ9siwCVQqhy/FNP7W8IouNd8g33sXyikCALBnUnXn6Ip/AlLMb2dzFxtuU7XECfPL1dzfKj4OwT8Y8ThC41N08s9VXaHh5vMAhCHzCai/94U+QC79w398zX6ogBKzWGwwk45iqiu25rNTq6IrCX5+/SncsQjJkIomWBFHcNMhGwpTqTYqNJr2JGIV6g4mVAsf6uok/oIyDJB4uIcljiWDNgf3Qf90R+ATb6uWThHjg/tKdhBC7J0DkB8G2SMgksbszUxtj3KFTXFyt8MrpG/zo1BgHh7vZk0vxnbevbHwuSxKSJCjXmrx25gYvnRjZsgPrrclFXVtd4YeT43z92hVmOiT/8yH+5qFWaXDx3XH+22++wqlXLm+8b4Q0sr1Jhg7k2HtsgOGDvcTTEcyIgef61MoNVhaKzFxfZOLyHIszq1gNm2dfPkq2N/kIf1H7qNuXkFCRhInnl6k71wBImC8hS2F8b2vcCoY6QNL8GIvVP2a1/u1WvywSjreKruRQ1yRyAHy/RsV6D9tboOnMUHeuEdOewlSHWS8ttt1FbG+RpjtFw53AD2qUmm8ghIqu9KFJGRxvmap9Ft+vo8gpCKBmn0dXutGV/s1P9CcQHXNgRw/28vRHRnnjB5corlT5xOeP0z+cQZKluzo2kixIZaIdd3yCIMALXNzAQRYKkpDWSIoCNMlY+9xBFuqWj+0HPl7g4gUOitCQhYKPt0bZI9aO52LI7ZUY7hRavWE2NbfBqlNmojbPXGOJslOj4VkEBBiyTkQ2yZkZhsI9ZLQEUTWEIekou0DfMxeJbruHaq5Soem6RO8q4+DTcFeo2NMb7wghYyoZJFTyjfdZbLxNROnjSOrvI5CZrb3C5eLvElJ70MwYimSSNU+Q0EdZarzP6/P/nKey/5y4vu8DemsyupygJ/QCUW0QTY6y3DjDVOU7TFS+QS78/Po3Nz1TIQSKJNDXZIXqtkOx0VgzcKHYaNJ0HTzfx9RUMpEwuXiU3niUA90ZplYKHOrJcqK/h6Vqnev5FUqN5gM7sB/i/ggI1kpcP/RgdwJBEGyLzbgTZX+3sq/ebx1pvzql5WDvBvcvINgWKZ4Q4g5il92FzgYxxmZXOHVlhkPD3fxPv/E5YiGDCzcWbvuOqans6U3zyukxbGdrWVPX91ipN3h7boZ///47XFpe2nVlwuuyQOv/X9eQ/RC7D57rMXZumu/98Vu3Oa+6qbH/5BAv/9JzPPPpoyQyd68MC4IWQdPYuWlmx5fYc6gP3dxZzc9OQSCjy/2Umm9Qar7RekeEMNQRemN/n7jxIkIouH4ZTcogJP2egURNzpI0Pw0BrDS+wXLtrxBCQVcGSEufpbXatF7non+PsvU2q/Xvt7gz9OfpjvzibdnXinWKlfo3abjX8fwmAoWJwv+MJMJ0R36BhPkJIMB2F1ratH4RIRRMZYhM+GeI6c/u9BDuGnTMgf3BN86Qny9hWS6vf/cCP/j6GcJRg1Q2inKXPo9UNsq/+te/hix3dpHzAocla47p+lW6jUGiSpJle5YgCNgffRIfj/nmJN3GICr3fujWDQ7Lb5BvTrPYnGQgdICM3kvJWUKTTHQpxJI1w3xjnGfSn+nob2kX64ZS0a5wuniV7yy+xZnitS1tOxoZ4GPZJ3ghfYw+s1X28Ch7sLrCkQ2WyHYXw+lyibp798Z4N2hyavl/4dQtE5Mhp/h4778hqg2xUH8DTYrSG/4oYaWlHTsS+2mul/+EgnWZuLaXiLS1SJdAJqzkCCs51h2ZpH6Ion2NxfrbG9m5ew31aFeaJ/pzZCIhxpcLvDc52zIS/IBfevIo1xeXN7LVa7K3GyLzAa1Mw4NKcnyI7WE3m/CPFg8+Mtt1km5lQf6gXMjG3rbgQPpBsCVH+I59bwG75b7ZrrPfkmbZ/U5Mp8a5UG7QtBw++9yBu2ZWFUUiHjEoVRpbCrwEQcBcpcqfX7nIfzr9HpVtMG5vF1sdF0kIdFnBUBUMRUGVZCq2xWqjsaPn9yG2h8JShTe+dY43vnX2tvef+OhBvvp/+hz7Tgze1+4TQhCKGBx7YR/HXti3k6fbUQgho8kZjuf+9L7f1ZUcw6l/saX9qnKSrugv0BX9hbvu60TuLwBIhz93z32lw5+773cA+uL/iL74P9rS+f2komMO7F//6TvMT6/i2C6u0yqZq5Qb1KvWXWfCes1iJ2rrLL/Jjeo5jide4kb1HIZsokkmllfHx6PiFMg3p0lrOVacOUw5AkDBzqNJBnmr9Vm3MYgutxaiilNgxVrgcOw5zpd+jCIUVuxFEmqGqJHY2MejRkDA6eJVvr3wFqeLV2l4W2dBnqjNsdBc4f3CZT7d/Qwfzz6JKh6dDIIAuiJhIprWtlTG1dVlqvbdt5GFztHUP6LLfHrjPQmFiNrq3W66qyw13mem+v2NpvxWyfASTXcVtw126QAf2y8xWflrVhrnaHqreIFF0ytgymnA517aXUEQ8PWzV3j12gRNx+V4Xw8nh3q5srjEty5cY7XeYK5U3nDy46aBpij86akLXJzPkzAN9F0sZ/GTDIFAU5S2S0G7wmE+v3c/UU3bPZ5MB3E400V3+MEZsSUhtqVFHARslMUGwBtzU7y9MEPNtUnoBieyOV7qG77nPkp2k/966Qx/5/ATxLR7t8t8a+IaXaEIT3X3tn2ujxrbHWM/oCNav48L1ltzDO3uc63vBzQtF0XZWvvFtdUVfv/CWf7yymWqO+S8arJM2gzRHY6QCZkkDJO4bhDTdUKqSkjVCCkquqJgKDK6rLT+lhV0RUZTFCRuZmArls0fXjzHb555f0fO90M8GN7+zjlOv3oZb03SRlZkDjw5zK/8H36K4UOP3/z0If7momNW7a/8xido3IW97G4wQhpiR+jHA9zAQZMMfHyC4KamloSELpk4vrXmWFhYfgMQFO0lGl4VXW5lVJNa14YDG7S6rdBkE2dt3wGtsmIh1rNbj9bSDAh4Zek03198h0uVCapu/bbPJQSapKJJKkKA7blYvr2hEegGHlW3zuXyBA3PouzU+Nm+j7eKpB9yJnb9eD3hKFFNb9uBvbayQsWyN2UiBhBIRNR+UrdpAwskoeDj4uOSDT1Jb/ijhOSu27YNKV2E1PvpA9+E5RW4sPofsL0yPaEXCKt9OH6VudqrlOyxe24b1lS+dOwgFctCEoIASJgGcdMgYRoMpRJEdI2P7x+mNx5D0HJgn93Tz1AqTkjTCOsqkpCIGhrJkMFgMk4i9GH58MOAEKArStt9dknD5HN79zEQi+8KIp9Ow1RUwtqDl5xJiLa1omGtr3M98xoETFdL9EZiDMcSzNcqjJcKDMWS9EeiLDfqzFbLdIciZMwQmqxQti3GiqvsTaRRJYnFehVDVoioGl4QUHNs/CAgaZhcL65gKApJo/XMOZ5H2W61ccxUysR1na5QhLB663gE+MHuKDyXxPbGOAh8/F2tNRrg07kxDhkamiIzvVDc/GgB1Js2V6by9GUTqPdh679RWOV3z5/hm2PXKDQfPJspgLhhMJJIsTeZZDCeoDcaIxsKEVJVdFlBk+U19ujW/2XR0rqWhbTRNy7d+u8WbfH1aaqgNDrybH+IzmN1scTl9yeYG18CQEiCWCrML/6TlxnY34Oq/eRK432Inzx0zIF96oXRu2pv3g1ih2Q4BBK6ZDBTv0YQBDi+RcVZpe5VKTuruIFLyVmm5CwTUeJM168SEJDSu5lvNAgCj5ASR74l+ygLhYCA6doVdMnEDlr7DAKfiB2n7KxSdpcp2ctE1SSSePg9pOdLY/ww/x4Xyjeoe01UoZDRE+yPDtJrZkmqUQxZQxYt6QDP97F8m5JTZbaxxPXqDIvNFZq+zXhtjiAI6DHSPJU8hL5F0ftOIxeNENN1ZivtbbfabLBYq1J3nLsspi1nVZbuzJyIQEaToshCI67uIWUc/cCWH6QJF4AEAm4W7bY+DwIf268wW3uV0dhXyJpPElZzVJ0Zlhr3j1ArskxfcnMNY12RSYXNmwbE2v9VWSYTDpEOmRvvr39mqOpdNUs/xE5AoMty2xlYWQjihk4uGt3VRDiPGkKItpnKoTX33Sq/Y3keArB9b4M1WhAwVSnxg+kb9ISiXFzNcyjVxfFMd4sd3LZ5f3GWj+QGmK2WsVyXwVgCPwg4t7zA4XQXCcPA8TyuF1fRZYWReIqqY3MqP8/l1Tz7kxneXpzhqa5eTmRzG5nOINg97NXbzcC6foDt7V4HdmOMOzTEuXSM3myMdy5P8f13r3JkpAfX8/CDgFrT5tL4wv+fvf+MkivN0vPQ59g44b1J7xOZ8K6Acl2uq7vaTtsxPRzNcEZXpEY0I1LUupIoXemSS3dJSxJlrngpw+FwkUOOd93NttXV5T28TSC9N+F9HHt/RCIBFDKBTCABJLrxYOFHRsYx8eWJc7797b3flzdPjzG1mOWLT+/Gq60f5DmOQ6HR4M8uX+SnkxMsVyp3fU6KKJL0+tibSDIQjdIdDBNze4i6m5nWa1nWR9nH9DGb59KJCWZGF9HrzfYqj0/jwDODDB/tbSaU7uOzRhCut0kZukl6Ps/4hVnmJ1co5So0anqzgsGjEoz4SHbGGDzYRTjuR7lNVcPtcByHzEKemdEl5idWSC/mqVcaGLqF6pLw+N1EUkE6+pJ0DqYIRjfu+13b32KBV//4fZZmm2q/8ZYQh54bYvho75bPL7OY59SbI1z46Hoi41NfOsTw0R7c3vXnaR/86Bxn371CtdIgmgzy9b/5adxeF4Io4NgOuXSR6ZEFpq4sUkiXqJbrOLaDy6PiDbiJpUK09SZo7U0QCG++CspxHIqZMjOji8yOL7Myl6NWaWA0TGRFwu1zEY4HaO1J0LO7lXAsgHCfNRC2LYD1+HbOpFgRVdo9A9iOQ1LrbJb3CuCxA4iChCwItHv6UUUNvxImoqZwcEho7SiiC9M28Cth1BvMfj2Sn5TWheM4tLn7cIkaMVcbquhCFCQ8ko9Wd++mvZK2E9txaFg6ry19zOXSJFWrTlgNMOzv5mB4kD5fOylXBL/iRRauews6joPt2JTNGouNDJOVec7kR7lQGGe5kWWqusj3Ft6h19tGXAshPYSgvDMYIqRt/doybZuxXJZDqZYtrwaLgkRE202mfo5M4zwepQVVDGA5DWrmCh45iSJ6YS2rL636x4qUzVk8SgpJcCEgrpYfO5h2DVn0IIsapl0j37hCXt9cb/JGCLdRUr0xaH3Mw0MUwKtsfWLQzOJtzdx8MxTLdcamVihXGzyxv4tSuc7F0UWiYS97B1upVBtcmVympz2K36shrVotZfMVRqdWyBWqPHW4F5/HhWXbjIwv4VJlBroTdzjy/UESBdx3UR6v2xa1T1iPFBp15stFco06miTTsCwuZZcxbZs2X4D5cpGZYoF2X5CA6iKiubmUXUa3LfyqykgujenY+BSVi5lljqc6EBBIeH1k61XStWYgolsWS9Uy2UaNFp+fs5kllqplqqZxUyly0z/44Qewsiii3c0YWxZ1a+fauzhA3TC2bZGgKxXm+J4u/uynZ/mjV08x0BFnOVdGNy2+9+5FDNNiciFLZzLM84f78W3g0uAAP54Y5dXxUeZLxbs6F1WS6A1H2BdPsDueZDgWoycUJubxPl4Q+zlm5OQk6fn82s+BiJdnvngQb8CNeJ8XMSRJQhAFsksFLn40zpm3rzB1ZZ6lmSzlQnUtqHa5VfwhD7HWMH17O9j/9ABDR7qJJNZ3jNiI7HKBkVNTXP54oqmYPJMht1ykXtMxDQtFlXB7NUJxPy3dcXp3tzF0pIe9x/txacqGwZcoCWvnrzcMEu0RLNPecgDrOA6L0xl+9Ifvce695lwwGPWx93jfbecLo+dm+MmffkghU6a9P8lL3zyGy6OilxtcOjHBhQ/GGD0/w/z4CsVcmVqlgeM4uDQFj89NOBFgYH8nz375EAef3bWpcy1ky4ydn+HSRxOMX5hlcTpNZrFAvdocS0kS0bwqwaifZGeU7l2tDB3pZu/xfvwhD+J98gP/mWyMU0QXbZ7rnrACAhGSN70nGLxuXdLpvf5H1DTv6ur7zQGAR/bjlq/3uQoIBJTrps1+JUxcezjy1ZZjMVGZ52z+KkWjgk92cyA4wOdbnmZvsHdD8Z5mACQRVH0EVR8Dvk56vG14ZTdvrZyiYJQ5XxhjrDyDX/HglbfmWbcddAdDxNweBLZe6nVxZZnFji46g6EtHzfpPk7dzJBrjDS//FIQ2zGoWzk6fZ9BFt0Iq32roiDjlhNEXLubtjtmDkXy4Ve6iGjDKIKXhPsQucZlJFFFFFyUjVlsx1jbx2N+NhEFgbDm3rLnqGnbFOvNB892lqkUSjXePz3B3GKefbtamZjN8P03LrB3sJW9g60Ypk06W6Y1EcTnuf6NqzdMxqfTfHx+mj2DrXg0BdOwOHVhlqBfe2gBrCKK+O7Qf7oehmVR+8QCgU9Vibm9CIJAWdeZLRWYL5fYF03SF4qwWClRNQ2KjToJj5eEx4u0aiXVHQjzwcIsU8U8Ld6menrU3VSkj7u9eGTlpsBBlST2RJP0BCK0+wKIgkjDNGH1szhA3TR2RACriNInyps3R8Myqd9GSG+72arSs+M41Exz24SmokEvx/d0Ua7pvHV6jPfPT2E7Dul8hXypRsCr0dMa4dNPDDLQEV/Xh9u0LebLJf700gVmioW7Un+OezzsT6Z4oauHp9o76Q6GHmdYf85xHAfTsJgbX6aUay6kSZJIOB5g7/H+tYXK+4msyhSzFT549Tyv/tH7XPxo/Povb5jgmUaNSrHG4nSGCx+MMX5hhlfKT3Pkxd23VUa+hmXZZBbzvPeDs7z17ZOMX5i9pb1REAQaNYNGzSCfLjF5aZ7Tb40wdLibcqHKwWd3EQh7bwm8BEEgkgjSv7+TqZEFlmez5NMlLp+cpJSv4At4Np11bNR0FqbSjF+YXXutf38HrT0JtE1akFqmRX6lRCDs5ew7V/jhH7zHhQ/HqBRvbTkwdYtKsc7KfA6jYbL/mcFNHSOzVODk65d48zsnufzxOOXCzfsWBAHLtNAbBsVshZmri5x+8zL9H3SQXylx9KXdxFpCSBuI+d4L9y2A3cpD4X5kiu7WgKApRLBBVmuHqqk0bJ0PcxcomVUcHHq97TwXP8T+UP+dN74BURAY9Hdi2CbZRoF3M2cxbJOT+Sv0+ToeSgCb8vpIeH24FYXqFjNSl9IrzJWKWLZ9wwNcQJF8BNRuFLFZmmtZNtWaTr7U/GI6jkMs3EGn77MsVN9hufYRul1CEbwEXD2re7nZJkeTwuwK/xozpR8zXv0hquShK/AyEW0YVQoyFP51xot/zlj+B2hygFbvU3T7v0imfgHbdsjmy/i8LlyqfN9XQh/z4BAFkYTXi7zFygzDssjUqnd+493ggGna2LbD4koR07JxqTIN3UQ3TAa6EwR8N6/ItyaDHN3ftWF/X0M3KVcaCAKEAp4HZp+iiBKBDa2yNka3rFvuJx5ZJaJ5kAQB3bIoGwZxt5elaoWFSolso4ZXUdFkmYqhs1Kr0LAsVqoVPLJKWHMzkl1BtywOxFPNSaNtU9DrlA2dfKNOvlFb862VhZs9SG9+YjYz8Ftty7kfqNLdjXHDtKhu0ipmM+hWAQcLSdDWhPUEZFaNfjDtKqKgIOGiKYrn4GAjIKwuSos3VUg5OJR1fVsXCVLRAN986QBHhto5NTLHYqZIw7DwulV6WiPs72ulM7W+X6YDlHWD749e4XJ65ZYKgTshCgIRzc1ne/v5a/sO0h+O3MYH/TE/bxSzZbLLBRqrmU7N6yLZGd1UULgdWKbFhQ/HWJxKM35xDo9fIxD2onlcKK7m99k0TGrlOsVchWqpjuM4nHtvFEEUcXtdHH9l/22DbcuyKWbKvP4XH/NX//x1sssFRFHEG3DjD3max1JlREnENC30uk6lWKdcqFIr1zn99ggzo4v8+//l1zj8/BD+sHfd+GTfU/1c+GCU5dkset1gaSbD1TPT7H96cNMWlCtzOcbOza4Fm4IgcPDZXcRbQlsYU5vMQp5qqc4f/+8/YvziHJIkEkkG0TwqstLMetuWjdEwqVd16tUGobif/v0dt923YzvUazrv//As3/kXbzB9ZREEcHtd+MNe3D4XiiIjydJaAFst1ynnq9SrOpdPTLI0k8VoGDz75cNEU8Ftj/W2NYA1TQtTt7AsG8u27/jwFUUBf+D+B0WmZWOYFrbtrE0YFFlCvo1H7Z3QDbMpanDDPhzHQTcsTMtCVeR72v9WMGyTC4UxGrYOwN5gL8OBnrveX5e3haORYd7NnMXB4UppmuoW1Iy3E1EUafcHSHl9jOdzW9p2vlxiulig2GgQdjevM1GQSLgPk3AfBpp/s3ypxolz07z23giiKGJaNr/2lSfYPTBAOLK5EgtJdNHmfY4Wz6dYzpRwuxSC/uYxZVEjph0gqh1gcaWI3+vCv9rf0Bf8GrW6zndfO8+Th3robo/g3qA36jGPHpIgEPd6t5wBMWyblWrlvnRACoKAqsjU6gZL6RKKLOH3aSxnSvzgzYt8eHqSv/dbL9HXFcd1h94jgabS7OxijvdOTuByyfzCp/ffcbvtQpEkAq6ttxjUTJNC4/o9TRUlPlqc5aPF5mr4YCTGl3qGqFsG//vp97m4Wkr86c4+wpqbK7kMP5i4Qr5R49vjl/lizy76g1EuZ1aYLuX55V37cIBso8YPJ69yObvCYqVERHMzEIqhihKqJCGsHlsRRW6M+R0H8vU65g4QQVIlCf9dZLmrprGtti/L1fdoWBlCrmE8SjuOY+CWW7CcGrpVoKCP4JLC+JQeZNGDaVcxnRqSoGLbDRQpiIR77ZlsOw65eu2uspy3w6XIDHUlGepK3vnNN+Csns8fnj9HRde3tK1AU/jtW3v381sHj9xV28228vDXXR5zIw7MTazQqF5ftPMF3bR2xx/YKUxfWWBqZAG9YRAIe9n/9AAvfuMYgwc6CUb9iKJAIVvm8olJXvuzD/nwx+fQG81FnAvvj9LRn2TPk/0EI+s7fjiOQ7VY49Rbl/n9//F7GA0DQRQIx/wceGaQZ750iF0Hu4gkgyBAo2awMLnCidcv8u73z3D1zDSmYZGez/MH/+sP8IU8HHx2F/I6YmtDh3to7Ylz6eMJDN2klK/wwY/OsftYH5J853m/4zjMji0zcmpi7TWP38XuJ/oIJ9bXPFkPo2EweXmed39whoWpNC5NZfBQF4efG6JvXwfRZBCXW6VcqLI4leHKmSlGz87Q1hunpSt22/MzDJOLH43zp//0xyxOZ5q+tSEvQ0e6eeFrR9l9tJdIKoQkixgNk+W5LGffvcrb3z3F+fdHMXST3HKRb//em7j9Gi99/RiKa3vnBdu6twsnp3j9+2c5f2KSQraCbd/+4RtLBfmnf/y3keT7G+SdG53nJ+9fYXIhi9/rAge+9NweDg2147nLYOGvXj9PT2uEXd2JtWCkXG3w7TfO88aJUX7tC0c5srsT7wMwd7Ycm+V6DtOx8coacVeYgHL3FhVeSSPhChOQPRTNKplGHsN+eL1MHcEgrf7AlgNYgEsry4xk0zzZtv5qk2XZnL08x3unxvntX3uOWNgHDrhUectlnwClcp33T07Q0xHlwPDNJeXFUo23Pxpj365WhvtTa69rLoVf/cpRZEl6YJmrxzwYpFURla1eSw3T/owQTAABAABJREFUZL5UvC8+mrIs0t4SYjFdpK4biGLTS7U1GeQzzwyRyVU2fR2KosDkTBrDNPF7XXz++T13VFfdTpolxCqSIGwpEKkY+ppPpSgIfH1gN1/pG1r7vSiIzcwjLv6LYy9g2BaSKOKSJGRBZF8syVAkxt869CSKKKKIzWD0Pzp4HMdhrS837vbyy7v28fX+PQhCM2MsCQJdgdDa+X+pdxcCwk2LHA6rwdUdnqEPAkWS8N7FGJf1BvltUM+9ho2ORR0HC8MuoVs5VCnCcvU9BEGmZi5SMxdxHAdVCpJtnEZERbeLSIKLqHYIr9KBQDPbY10LYHdAlhuaPdgnF+eZLha2nBWOuD18oW+Q3z56DJf0cDvDbMfB5uFft4+5jkMzuDPN68J1msfVDOYeEPVqc1GmtSfO53/tWb74G88iK/JNAV8w6ueJl3bTv78Dr1/jre+eolqqY1k2M6NLXD4xyfHP7F13/7btMDO6xO//D/8OU28G6qnOKN/4D1/mU18+hNunIcnimv6my63QuauFtt4EQ0d6+Kt//jpvf/cUALNXFznx04vE28J0Dd7qOKF5VPr2dnDp4wmmRhaoFGuceOMS3/r7n0d1KXfs+jF0i9mxJcYvzAEgKxKHnx8mnAhsqV+0kCnzV7/7OrVKg9aeON/87Zd54tN78AWbfafXhHKjLSE6BlIceXEY07DuaJjiOA6FTJnf/Ud/Tnq16iraEuSzv/IUX/7rz+MNupvJu9V5guKSae2Ok2iPsPd4H3/2f7zGT/7kAyzTYnE6zem3Ruja1cLQ4btPrK3Htt3pLp2Z5s/+5dtcOjtNraojiiKqS6JcrKNpCgig6ya25eAPuunqS7L7UCcPQvPIshxSsQBDPUn29KVYypT43tsX6euI3XUA+9KxAVyKjNt1XZ3Xo6l89qkhxmbTyLL0wEzcHcehbFZxHBu3pOGSlHsSamgqe8p4ZTdFs0rFrGE51p03vE/0hSN0BUO8PTO15W3PLi9xfnmJYy1t65bmLmfKlMp1utqitCaCq31JzbF744MrTMxm8Hlc1OoGvR1RnjjQjarIvHNijJHxJRq6STjo4cUnBzEMi5+8N8LZy3Ocv7rA5FyWPQMtdLVGmF8q8KO3L3H+yjxjMyuMTafZM5AiHPTw1kdjfHhmkm9+/hCDPQlyhRpnR+aYmMkgAPlSjc88O8RQbxLvJnsjHrMzkASBFp8fr6IiCsKmJ6Y102SykL8viQxFlmhJBJmczTR9apXmY0ASRRRF2lI/VK5Y5dTFWSzb4eufO4j6gDKvN+KSJJI+P4vl0qbHt2oY5Oo1bMdBFITmpP+GuNtZm4Q3RaI05Kb2+Op9VV5V5r2xfsh2bFyStKY54DjNElZZFFFF6aZV+RtDfHWdgMN2YKVawdgBAawAaLJMwutjsVza9DVZ0Ztl09fG+F5xsBEFpalFYC7TsLKEXHvQ5AT5xgUEQUYVQ0iiC9vRsW0TkxqWXcMWGjiOxY1+27btsFwpr5V03yujMytMLebY29dCMrJ+WWa1rjM+n6FcbbCvv/UmJeJMtcr7szNbDl5dksTeeILfPHQYt/xw3AJuxLAtzB2sPv3zSrVUx77h7yIpEm7vg51PhGJ+jrywm5d/6fi6KruCAKIqE477+cr/4wXOvneVarkODuSWi8yNL2+47/R8jtNvj7A8l8VxQJREPvsrT3H003vwhbzrLspKUrOKsm9vO5/+5jHOvHOFUq6CbTucevMyw0d71g1gBUFg4EAnXbtamRpZwDJtCuky598b5fDzQ3j8t68snVnNRjfqzaBekiWefGU//pBnS1Wbtu1QK9cJRH38yu+8wuHnhwlGfOsGwdee66p2Z62AUr7Kx69dYGEqjalbiKLIU68c4IWvHSUQ9a47lxYkAVUSSXXF+PJf/xSn37pMeiGPbdlcPjnJ4McTOzeA/eCNy4yNLNDaGeXos4N09ydJLxf5l//rj3jpywcZPtiJZVic/XiSqdFlUu1hXvrigQemlKq5FOJhH4mIn2xhNcPgwDunx5ldytMwLEJ+N88e7CESbGYv3z0zwfxKgYZuEgt5ObirjUjAy0cXpzlzZZ6juzvY3ZvE627eBCRJJBry4tHUB5tJE0AWJEDAduxt6elxYO3B/rA9blM+Hz2hECGXRr6xtVLmlWqFS+kVJgp5+sKRW35frtYxDIt42IvyiSbzuaUCxXKdg8PtuDWVgF9DlkSm5jIsrhRJxvy0JIKsZMr8+J3LfPmlffS0R8nmK/S0R9m/q5VI0IskCYRDHno7YuSKVXb3p9g72EIk5MGlKgz0JHjv1AQN3cS2HRqGSTZfwbJsnn2ij/nFPKcuzJCKBR4HsI8gLkmizR9gpljYdB93wzKZK5WoGQbyDV6L24EsS7QmQ/zFD0/R3hJGv4c+RU1V2DvYSldbhA/PTNKWDKEq0gO7rwuCgCJJdAQCrFTKm773mbZNSW+QqVWJe65Xq0xXZ8nqOXyyj7ASJKvn6fV1Y9kmdbuBLMiIgojtWLglN5ZjU7Nqa9u6JBddnvbVfkuomnWWGyvEXVECyuZ6zZqq8iaZavUmq5+HhSA0raBa/QGWKuVNL8watk2x0SBXqxH1eO75PGLaEziYyIIXcLAcHVFQ8SndKKIfUZARBReyoOHQFDOyHZ18/QI+tRe3nFwTzbMdh7plslKtbluW+8rMCm+cGiMW8m4YwNYaBufHFvjo0jSdyfBaAOsAuXqdU4sLWz5udyjMSz29dAYeXDbtdjQsi8YOVp/+ucRpZkBvCmAlEVV7sAsenYMpDj8/dMe+W0mWaO9PkeqMkl0q0KgZVEs1CumN/RSXZjKcfmsEy7RXj9XCrkPdRJPBO87HNY+L1p44/fs6OPP2CLbtsDCVZnE6Q63SWDfQb+9P0j3Uwsc/1aiW6jTqOh/8+DyDh7ruGMCOnZ9l8vI8OM3PGm8NMXyk564WFERJ5FNfOszuo70Eo747aqhs5tlczJR5/4fn1pShW7qblkapztgd96+oMsnOKIMHuyjlm73FmYU8c+PLVEo1vHcYm62wbfnPK+fnECWB48/t4pWvHeHZz+xh/9EeVJdCR3ecJ54d5IXPH+CLv3SMfUe7yWfLZNPlB9YrsZwt8cG5Sb7/9kXePzfFvv5W3C6FcMBDSzyI3+MiX6oxMrWC4zR7kE5emqFQqpGI+IiHfaiK3KypD3hYyhTJFCoY5sOfYIiI+JWmPH7FrFMxa/dU8mvaFlWzRslsisj4Ze9d+QBuF5qs0B0K0x+5NQC9E6Ztczm9wodzs+teaoIggNDMeHwS27YJ+DR29SUZ6kvSmggiSSKzi3kEAXo6YuwfaqMtFeLClQU8boW2ZIh4xEdXW4SejhjBVXl6n9dFe0uIeNhHd3uU7vYoAZ8blyrTkQoRCrhvKiF0qTKpeIC9Ay3s29XKcqZEvfHgFD0fsz1cszPqCYXxbUHJ1XYcSo06E/nctgcxogAet0Kx0iAVDxDwu7FMm/mlPB+fnWZ+Kc/J8zNcuDJPsVxHNyym5rOcujDD/FKeD89McmlskUpNR1Vlejtj7NvVimnYvP7+FQxj+7w1N4MiSnQEglvuM67oBnPFm21KcnqB2eoCWT2H6VjkjALN3J9NppFltjbPXG2BnF7AdEymq7Ms1pcpmiVKZoWVeprZ2jxpPUvD0pFFiaJRQrc3/901bZt0tUrNNHZMK6FLlmn3B7a8jFnSG8yXt2jivQFepQ2f0oUmx9DkOF6lDVGQUKUAPrUbj9KOW06gSAFUKYBX6cCn9BDWDhBQ+5El/9rkzbAsMtXqqo3O9lCsNJhfKVC9zX3adhzK1Qbnxxap69ff1zBN0tXKLdfjnZAEgb5whGc6unaM0nBF1ynfBwuwx9wb66njPqgqQWg+C1t7Egwc6NzUexVVJpIMrQXZet2gWlk/gWGZFpnFApOX5tZe693dRjgRWLeHdb3jaR4X7X2JtXHS6wa55SLFbHndbTw+jY7BFB0DzXYw07A4++4VcsvFZpnuBlRLdSYuzbE4lV7dj4t9Tw8QigfuSqlXUWWe/sKBZvnxNtwDbMummK0wcmpyTceovS9Joj2yKT9eQRCQFZmOgRTqas+roZvk0yVyy3dnC7YR25aBzaZLBENeeodaSKyqaAmCgKrK1Os6tu2geVR27WsnvVzg8rkZ3v3JRQ4e630g6r6WbVPXTXTTQnMplKp1dNNCkSUCXhemZVOs1JldysO+5jaRQDN75tFUgj43Po8LWRIZ7knSEg88MKGSOyEJIu3uBJlGgbqts1BPk27kaXFv3KR9O/JGienqEjWrgYBAqzuGS3y4wkKdwRD7Ekk+Xpjf8rbThQIfzM3wXFc3bf6bG+QDPjeqIrOwmmm/MXskiiKaLN1SZu44zmpgwmpZ4Y0WDs2yjk9mggSavXaWbW/qoeFSmyJgCAKKImNZzgMNCh6zvfSFI00/4mpl09volsXJxXm6Q2Fcd+HDuR5ej0pfV5yAV+OpQz0M9iQJBz24NQXTspEkgd0DLWirPzd1DBxMs6lUvHugBVkSMUwbURQY6I7j1hQ6WiM4DoxNrzzwoEuVRLqCIaQt9qNUDJ2JfI6DqeslYpZjYTvN76jpmFTNZpmxbpuk9SyGbSIJEprkIuaKUDRLFPQind52/LKPvJFnpjqPR3LT7+9FQaZm1TEdc+2+cSd0y2SqkN8xvZkALkmmM7iqIrmFG1Gp0WCqkGdfYmuCRltlvTlE0wpPwq923/K7umkyUyw88GvVcRyMVVHJG4exoussVcpb9s0Nahq94QhdwZ2RfYVmL2+x/nBEHx+zAULTX/XG0lLLtNHrDy5TrnlUoqkg4fjmRYrcXhfSqpK2ZdmY+vqBYa3cILtUoJS/rtyfTxd5/4dnufTxxLrbfJJKscrCVPqm72W5UKVcqJLsiN7yfkEQ6BhIMXS4m5GTkzi2w/JclomLc6Q6owSj62eZZ8eWmB1bXlMf9oe9PPnK/rvSjhAlEX/ES+dACtc2iX82ajrZpQKFGwL3Yq7CqTcv37aE+0ZMw2Tm6iKGfv36qlcaTe/avu17FmxbBGaZNl6fhqJc36UoCWgelWK+tpbWl2WJcNRPMOTl6sW5B/YAaYkF6UiFOLq7g+VsmX/wT/6SPX0pRiaXqTUMQCBfqtKRCq81YD9/tJ8Tl2Y4cWmGRMSP5pJpS4Qe0BlvHkVU2Bvs41Jxkrqtc7k4Sa+3jaDiwyNvTY2wZjW4UprmVH4EaE4Cdgd7H4qFzo20+vwMxxJ4FYXKFld3y4bO+ZVlfjoxzq/uO3BTL1sk6CEa9jIyvsS5K/P4PS4kSaTlNqbZbckQS+kSMwt5cGBxpchgTwJREPC4VUzLZnGlyNxSnpDfvVb263GrGIbF/FKRaNhHwKchCgKzi3lKlQZL6RKRkGftetxO/8/HPFyGYnGCW1TL1S2LD+dm+VzfIEGXa1vKciNBL8cONEtmv/7KQQD6Oq8vdHW2rl/l0NcZu+l913jy0PWeluH+1E3iZA8KlyQzGIluuUqk2GhwJZO+6TW/7COhxQgofmzHRhHlpkmLYyEJEl7FgyRI6I6BJEikXAlqZg2PpKGJLkRBoGxWsHGQBIm6VV/1ihVw2FwjRt00uZpNb1tv5nbglmX6wpEtl7Ln63XGspn7dFZ3T9UwGMtl73n+USjXqOtms2KiUsewLHLFKguZWzMNtu0wt5JnYj5D0Kfd1Gte0hssVza/uHWNFp+fzmBw09YdD4JMrUamtn3iXY/ZHtw+100BrGlY1DbIaN4P/GEP/pBnSyJF4qoIEbBaGbn+N7aUr5D/RHnxyTcuc/KNy3d9vgB6w1gro12PZHuEvn0deAPuZkDqwOm3Rhg40HVLAHvt3M9/MMbidPO5o7pkEu0Rdj/Rd1fZV1mRiLeGUVzKpv1n70St0mgKN90w1Jc+HufSx+MbbrMZDMOkXt0+VXrYxgDW43VRrTSo1/S1lWZZEgmGPSzMZG95XZREKqX6AyshNkyLUqVOtlAlX6zh0VSmF5ur3Hv7W/FoCmevNrN7juPgOA4+j8qzB3tpiQUYnVnh8sQyqWiAck2noZtUazrlqo5bU1FlCdOyqdR0GoZJpao3S+wUGfU+q3K6RJnD4SF+tPgBZbPKlfIMvvQpQqqfIX83mqQiC7fvSzNtk7qtM1qa5a2V05zLjyIg4JXdHAoP4pPvvYfpXvCqKj2hMHviCT6cn7vzBp9gpljgO1dH+ExfP3GPd20iqSgSQ71JqjWdv/rRGRRZwqUqfPMLh/B61HXLsno6YkzPZ7lwdYGzl+eIhrx86aW9SJJIPOLDo6mcv7JAudLgyL4uBlal6pMxP5qmcPriDKVKnf1DbaiKxOsfXKVUqnPm0iy1uk4qHsCjKciyiEBTaCAUcCPLO6NE7DFbZygaI+71oojipoV5dMvig/lZcvXaqpfs4wWN9XDJMkOxOC5Zgi08H4uNBpcyK1i2jbha6j3g72WA3rX3dHjaAFDVIGF1P9BU9ITm4l7SHSfpjq/93OJO3vR7ZC9R19ZaH+qmyeV0ekdlYD2KwmA0hiyK6Fsoac/V61zJZm4a451AxdC5ks3ccwnlhfFFxubSlGsNLowvki/VeOfsBBPz2Vve29ANZpYLnB2d56l93Wjq9f7Da6JiWyXu8ZL0rm8r8jBwHIeFUpGlyvaUjT9mexAQ8AXdyDcESY2aTj69fnns/cDldqHeJ4tAvW6sqRzfhHBv6i225WDdRpBM87ho644zsL+T0283kz7n3x/lha8fpXuo5Zag1GiYjJycID3fdNQIxQPsPtqL7y7tREVRxOt3b2uuw9CbXry3cI9j6disJTK3i20LYJNtYS6enqJUqGHbDpIkoLhkWtojnHhvlMxykWRbGFWVKZfqZJYKuD0Prix1bGaFH79/me+8eQHbsvnGywfY1ZXgz39yhovji4T8zX7E1ngz81aq6vzLb3/IQrppZdHdGuHZg72s5Cv8yY9PceLiLFen00wv5nl6fze7e1PMrRT4s5+c4fzoAulchanFLE/t72G45/6WT0mCRI+3lQPhAUpmlbxR4uPsJWYqyzwfP8QLiSN0eJKrQk/rM1df4Z30Gd5cPs10dREHB6+k8UxsP92eVlziw1c37AgGeaVv4K4CWN2yuJrN8HunT/J3nniyWc65Sjjo4dNP7+Klp3atfUMFoHedspFrPH98gOePD9ySVZEkkV/58pF1sy2SJPLXv/HkLb/7G7/yTLNsZYMbRCzs42//+vNb+LSP2WlossxAJMr55SUWNtkT6NDMYJ1eXCDp9RHbBiGcn0UkQSDh9ZLweMnVapteIKiZBtP5POlalZjbg7RDgquqYXBmaXFHeMBeQxZFEp7mGM+WipibHOOKoTNdyJNdFXLaGSMMZV3n/PLSPa+f50o1PrgwzcXJRUqV5urJjz4Y2fD9LkWmrz3Gr3/+CSI3TFoN26Zubr2cM+hyEb4LH+T7hW5bzJVLLN1FNvkx9xEBkh1RVPf1eVylVGNxauWBnYKsSJvqR70bDN28JVPatOi5tzuOosp37CuNt0U4+KldawFsZqnA5KV5+vd1Elttp7zG+MU55idX1oLtWEuIQ88NfXKXm0YQ2JTv7FYwDYt67ebFAFmWbrIguhtUl7yl7Ptm2LYAdvhAB1cvzjF5dZGFmQzt3XG8Po1DT/Xx3uuX+Of/0w/Yc7gLRZG5cmGWlaUCR54aeCDitnv7U/S2RTFMa02NzOtWUWSJ3/rqk1i20/RCFK5LTfs9Kr/x5WNrcvCKLDYVAwWBf++LT/BLnzmEKAqoioSmNrNlbYkgv/WV4/y1LxxpegYqMto2G/eux7WL96ttz5PTi5zMjVCzGqT1HN9bfIefrpwgpPhJamH8sheXpCAg0LB0imaV5UaWglGmatapWQ0cHNySi+FAD7/S+Rk8srYjVs6jbg9HWtroCASYK23eMuMahUadv7h8kSda2zje1o5PbZb2Xvtsm/2IzfetbrPRe27ZRtjwd1s59qOA4zhYpt2UxBfFbStteZQRBIG9iQQfzM1sOoC9xg/HrrI/kXwcwG6AIAiIgsDueILZUpH8FvrvyobBG1MTfGlgCM8mS5Bv7LfcqPfybqkaBnOlIgt3cX+7n1yrnNqfTJGuVSnr62Q7NqDQqPP2zBRfGtiFKD38UteKrjNbLLC4DeJSLxzu59juTrLFKt99+wJvn53g5WODDLR/stxeQJUlgj6NVDRANOhFuaGixrTtu1LuVWV52/rjt4ORdJq54uYXOB7z4IgmgwQiPmRFapYPl+ssTWeoVxu43OqOmOPdLbIi3SIw9I3ffonjn9l3R8Xj26F5XXdUzQ3F/Qwf7bleRgxcPjHJ8NHeWwLYj1+7uFbq7HIrTeXj/R13fX73A0kWUV03J6xe/uUnee4rh0ndJqlzJ1RNwRvY3jnMtt35Dj89QCFXpXsggT/YPEm3R2X/E73sOdTFyLlZMj8oIogChm7S3hXjpS8f3FZ7iI3QVOWmcp0bCW8woIIgENrgwt3odUWWNtzfgyDpivCVtudxiSofZS9SMquUzRpls0ZWLzJXW0YRZcTVCZaNg2mbNGwD8waf14Ds5VB4F19ufZaYK7z2/oeNJIokvT4+1z/IvzpzisYW1VltxyFTq/J/nfyYkKaxN57cUQ//Rx29rpOZzzE3tkTfgS7mRhfpGGxFlASKmTKSLBFOBjDqJp6ge1sU8x4lDiZb6A6Fm9m1LUzwTi8tciG9QkcwREjbOdmWnYQoiDzR2sb7szNbCmArhs6r4+O83NOPR3n4VSaZapWTiws7Kvt6DVWUeLKtgw/mZrYWwNYbvDE1wef6BlB2QAC7VClzdnkJaxsWCLxuFa9bJex309ceY3Ixy3B3kqNDN09KBZoqsIokrbUU3Rww3J1I3416DjuBEwvzTBZyD/s0HvMJBEFAkiVauqJcDXnIr5SwTJtcusTFD8c58OzgXfVg7hRUTcH1iYpOSZKIJIPrCjBtJ7IiEU2F2P/0IB++eh7LtLh6dorZsSX2Hm/2tjqOg9EwOfvuCMVsszqhpatpTeNyP1yB1E+iqPItdj6SJBKK+mlZbYfbKWzbDLKlPcJLXzzA3iM9eP3NSZYkS0Tjfr7xG8/yyteOsPtQJ0P7O/j0Lxzia7/+DLv2tj9Me9GfOWRRZsDXyRdbn+VLrZ9il78LdbX013IsqladglEmZ5TIGSUKRpmKVV8LXt2iiyF/N19sfZZfaHuOAX9nU4BkhzwgBSCkaXy+b5AWn/+urH0sx+HM0iL/5twZLqaXd4TP4s8KxUyZ0TNTzI8toWoK5VwFQzco56tkF/PklwssjC/z3ndP3FYY4WeVuMfDcCx+ixL2nSjpDV6bGGMks/JAbQ8eJURB4EhLGyHNvaVHSt00ObO0wEQuS918uNekAyxWyrw3O/1Qz2MjFEniWGs7ftW1pTGurJZET+Rz6A/ZH9QBZktFPpyf3db9yrJEazzAnp4U0YCXgFe76b/fq+Fzu3Cp8pq11o2IgnhXzzNjB3mupqtVTi7Os1B63P+6Uxk80EU0FVr7uZgp8873z2Do5iP9bPEGPAQjN/eCL86kqVW2VzRoPQRBIBD28uRn9yErze9wPl1m5uoiK6u9roZuMn5hlqXpLPqqzVZ7f4rhIz07Zn59Dc3jIpK8WcQ0vZC7SeF5p7Bt6SfVJdPZl7jldVESOfBEL/FUiMxKERyIxP3EW4K4XA9/xfsa80sFLo0uYJgWzx8fRHPJ615YpmUzOZPm/Mg81Wt14oJASyLIi08Nbrj/esNgfDrNucvzWNeCJkGgvyvOsYPd2/Y5NEllyN9FRAnQ6UkyWp5hrpYmpxcpGZW1bKuDgyzIuESFgOIlogZoc8fp93Uw6O8k5Y4+EHujreKSJAajUT7T28+fXb5A9i7UDhuWyWuT4/hdLiRBZHc88VB9bn9WqFcalLJlvAE3es2glKtgNExEUcQyLIqZEkbDZOL8DE//wtGHfboPHFmU2J9IsTseZ6qQ39K2Hy/MszueoD0Q3HIA/POAALT7A3SHQkzks5Q2mSG0HYd0rcprkxO0+gO0+B/eM6lQrzGSSTPyCWXknYIkCHQEg3SHwiyUS5tWg7ccm6VKhVcnxkh4fcQ8D6/qJVerMpJJM5q9VWTpXulORfC4VBKRrZcsKqKIdhfVQDXTpLZDPFffnJ5kJJPeskvAYx4cQ0e6aflRjMlLc1imTaVU5+w7Vxg5NcWuQ11oHtedd7ID8fg1IqkgvqCHcqEZaI1fmKOQKWNb9rb3Xn4St09j+IkeoqkQSzMZLNNmamSBqcsLpDpjNGo6J16/RLXcFK71hTx0DqRo7b01ZnrYNO2OQgQivjUP3JmxJdILOSzT2lGZ+vv+JBFWzTLbuqK0dW1/Kv/qxDLpbJlUIkhbKnSL4m9DNxmdXKZS02lNBGlvCa+7n+n5LN/+8VlqDYMn9nejqtK6oh627bCULvHhmSkyuTLpbJlsvsrhfR23DWCb9ikF3js5TrWms5QuUijV+OJLe7c1gIXmam7KHSWhhXkispvp6hILtRXSeoGKWcewDRwcVFHBI2nEXSFa3XHaPQm8knvHrQjdiCAIaJLM14d3c2ZpgTP64pZLiQEKjQY/GL26VsK+Kxp7XE58j7h9Gm0DKQIRP3rdwBNwIysSnoCbUDKIaVoIokjfga77Juaw0xmIRDmQSPH+7Ay5LZS65uo13piapNXv5wv9g2v9249pIgjN8sxDqVYurixT0jcfoNiOw48nRnmqvYOQW8MtP/gg1nEcRrPZLZfnPkgEQUCVJJ5obeNKNk2lUNj0trpl8r3RKzzd0YlfVR/KvdZxHC6n03w0P0fF2P4xTkT8dxW8ArhkCZ+69VLCYqOxpZL5+4Fl2yxWynznymXmH2dfdzSprhh9e9u5cnqK5dkslmmxNJvhB//mHdxeF127WnZcSetmUFSZeGuIrl0tXPhwDIC58WXGL8zSMZAikri/i77Xyoj3HOujkC1TKdSYHVti4tIcR17cTa3c4MTrl2jUm/edjv4kXUOteHw7ryVIkiVCMR8DBzo59cYlbNtheTbL+IU59hzru+8l2VvhkZ+xf+cnZ3n9vSt8+eUD/OIXD6MqN/enFko1/s1ffsTkTJqvvnKQX/rSkXX343WrtLeEME0bWZY2zD7Kssi+oTYSMT+lcp3X3hnhjQ+u3vE8PW6Vo/s7aW8JUa40+PaPz/Lx2amtf+AtIAoiXtnNcKCb4UD3fT3Wg0QSRYaicV7o6mG5UmFyi9msayxXK/zF5UsU6nV+Y/8hBqIx3PL6mfdHjQ3LgYT7l1ePtoaJtl5fIEp2XRcy8Qy76RpqW7W3ffTH924JaRoHki0caWnj1YmxLW17ZmmBgMtFq8/PE63tjxdc1uHJtnbemp5kIp/bUo/j1WyGN6YnSPp8d+V3eq+U9AYnF+d5f3bmgR73bnimo4vXJyeYLRY3LTRlOQ4jmTSvT04Q93jpCAQf+BgXGw0+nJ/lxMLWVezvNx5FJaxt3UpjsVJivlzatMfwdmM7Dvl6nb8cucSZpcVtXxhwHAecVU9dwQ2OATRAcCOgAfbqfwmEx1VUd0KSJA48u4upkQVyy0UM3cRomLz57RMEo36e/+oRugZTaF5tTfB0I67NMYyGiaxKD13TItkR5dBzu7h8chLLtDAaJu//4CyxlhBHXtyN23tnL/Vrn6lWaSBJYlOFeJPZW0WVeepz+zn3/lUqhRqZxQKzY8vkVopklwpcPTuNqZsIgkD//k66h1vv+TPfL4JRH099bh8XPhilXtUxdYuz71yhpTvG8185gjewuUSX4zg0ajogoKjStmdv72oGdOPk+HYfoumnCpZpYTvOqpCBiCQJD/1i/yT7htrYN9R2x/eJgkDApxFYXTkZnVpBPXXnP4okiYQCHkKrIk8fnZ1C+TnNQm0XXx/aw6V0msVK+a4sCKCpkPm90SvMlUr8zrGnOJRqwSXLO04cYyMcp+k6aTk2kiCsLbw4NJUtbWxUUcZ0bAQEJB7O57pWifEY2BNP8Jneft6emdrSdesA78/NgAARt4dd0diO8tZcD9tx1oKcB1GmvysWZzAa4/TSwpbbC/788iV6QmFafD68yoNT5bRsm/dnZ3ljapJ0bef1GX2S3fEEA9EYlzIrW87+/eGFc/RHosQ8Hjyy8kDH+K3pSd6ZmbqrtpPtwnEcbNuhrpu4XdctOryKSszj3fL+5kslpgt5LNt+4G0wjuNQ1hucWlzg//j4w/uS1QYb6j8FwQHlCFjzOOYVBPUQjrwb7Bw4FRAjgHf1etq598OdwNDhbhan9jI1ssDkpTkcp+l3+u1/8Tpz48u88q2n2PfUAG6vC1Fafb4IAjhND2zHbv63bRujYTI3vkzXUOstwj8PmkR7hCMv7ua1P/uYhcmmXsTZ964iu2QkReLwc0NIsoQoidddH5zmc/Xa57Gt5me68NEY0WSQ1p4E3k16tMqKxMFP7SKSCLIyl8MybVbmc5x95wrFXAVTbz7r3T4XfXvaad1hgkg3Egj7ePKz+/n+v36H6SuLGLrJ1bPTCGKzXfTZLx1CVmREUbhpbuc4zbF0HGdtLK+enUbVFNr7k7f0Kd8rdxXAGrpJbdXHKBje+KZrWTb5bIX3fnKBhdkcgijQM5hi98FOWndQGvoxjyZxr5dvDu+h0Kjx5vTdZ7MblsXJxXn+kx9/n68MDfPr+w+S9Ph2jC/k7XCAdL3MqewsA4E4Le4gkiBi47BSL3Mxt8CxeBejxRUCika7L4xXfvRKhH6W8Koqe+MJXu7t47tXNvaMXA/dsvhobpb/5s3X+C+ffYHhWHxHKLtuRLZWZSybRbctPtXZ/UCOeay1jfPLi7w9szUxpFytyp9evEDApfGF/sEHNg0ezWX4ztXLOzIzuBFPt3dwKb3MR1v05M7Wqvzbc2fwKSov9fQ+sDEeyaT5yyuXOLu89ICOuD66YXF1ZoX/6v/8Hv/kP/4qPa3NeZBfVWn1+XHLMrUtLGrVTZOxbJbTSwscbbnzAvx2UjF03pud4b99+w0qhn7PnrobYs+DUwa5H+wlMK+C1AliGurfA0cHuQOU/SA92DF4VDn8/BCVUo0/+J9/QHap2Qrg2A6n37rM6Nlp2voSDB/poW9vO8GoH49fw7ZsyoUamcU8C1NppkcWmLqygN4w+Me//7fo2f1wx14QBNr7kvzmP/wF/ue/9/vUynUcB86+c4XZ0SV2HermyIvDdA+14Q95kGSpKTJZqJJZLDA/scL4hVmunpmmmC3zy7/zCpFkcNMBLDQFkPY+2c/yXJb0fJ7sYp4z71yhVr4uJjV8pIeW7ljTV3WnIjSD2L/5j3+R/+0//bfMjS/j2A5j52f5vf/Pt3nz26c4/pm99OxuIxj1oagypmlRLdbILhdZmEozcWGWq2dnyC4V+PQ3j/Gl33x+ZwSws5Np3vjBOWYn03z+m09w5On+W1ZSK+U6F09N8Qf/9+ukFws06iaCAC63ysDuVj795UM89eLwtnyIu+XcyDzf/tEZpuYya68lYgH+i7/1OTRNeeAlTo/ZGqIgcLillZniAMuVCpfvQfzEtG2WqxX+9OIFzi0t8XJvHy90dtMZDCHtkGqBUqPBWD7LhZVlRASe7egi6fdxJjvHXCVPwzJZrpeJuXwkNB8BxYVhW3yYnqKo1+kPxB9ppcGfFURBoDsU4peG93JqYYGF8tY8P+umyfnlJf7z137Mr+7dz2f7+om5PTsiE+s4DmVD5+zSIh/MzXJ6aYGaYfB0R+cDCWAF4FCqhcOpVs4sLW5azAmai0GXMiv88cVzSILAK30D9/0ZMJrL8H+d/Jh3Z6fvqpf/YSAAx1rb+Xh+jkvplS317DrA+ZUl/vDCWQQBXuzuva9jbNk2Y7ks//TjDzgxP4/+kMfYsm1KlQbpQmXNYx5WK7s0F33hCOdXlre0z8uZND8aH+VQsuWBPauWKxV+MjHG7587w3ypeP+CVwDHbAapdhkwQZABC5xaMwMrqKwaCt3Ps/iZQRAEfCEvT352P4qq8If/yw9YmsngOA6mYVHIlKmW68yNLfP2d08hKRKSJDarKS0LU7fQGwaNmk6jZqCoErb18G2/BEHA49PY9+QAv/VffpU//2c/YWkmg2lYpBfylAsXGTk1ieZRkWQJQWhq2timjWGY6HWDelWnXmlgWTambm3J2ura8/fQc0OcffcK6fk8uZUSFz8av8l1Yd9TA7R0xXfE83ojBEFAViUG9nfwq3/v8/zVP3+dsfMzmIZFPl3i/AejTI3M3zCWzQy9ZVmYRvP6uDaWpmmhN+6PyvVdBbDz0xnOn5ykVtHXXUWwbYfp8WW+84cfcPXCHB09cQ4eb9Z7j40scOHkFIIg0NmboLUz8tD+kJGghz2DLYSCbqZms1y4skCx3MC2V+sKdu71tSVsx2aquojlbO3hLSDikVwktDCSsDOzPD5V5bmubtLVCsvVyj2Vh13ziT2xMMdipcyHc7McTLVwJNXKrmgMv+vBlcjolkW2VmOmWGAyn2Myn2O2VGS5UiFTqzIYiXIgmSLheCmbDeKaD9txmCxlqJkGAcVFxdRZqpXwKS7KRgPdfjQmyD8PuGWF3fEEv3ngMP/bR+9RbGxe7t+hqT56ObPCvzp7igsry7zY3cOhVCsxz4P3oa6bBovlMlezGa5kMlzOrLBYLrFQLpOpVvG7XOxPPjhhooBL42hrG+eWl/jp1MSWtq2bJqcXF3EcqOg6XxzYhXYf+uLrpsmpxXn+4MI53puZJvcQy1rvhqCmcbytnUuZFd7ZYqa7Zpp8vDCH7TiUdZ3P9vXfF+Gsiq7z8cIcf3jhHB/MzVJoPFyxI2jOjaq6fsuClSAIhDU3h1ItWw5gM7Uq785M81eRS3xteE9zf9t2xjdjOw4XV5b54fhVfjIxztVsZlv8dO+M1sy+OjqCmAAkEIPN0mHRD1LfahnxYzaDJImEEwGOvbwHb0Dj459c5MTrF8kuF7EtG71ubM7qTgBZcSHcoV/2QSFKIv6Qh6c/fwCPT+Odf3eaCx+NkV8pUS3VqZY2dw8IJwIEoz5U19ZDpL69HbT2JJi8NE+t0sBomNirvu/RVJDeve0EY9ubibwfCIKA5nFx+IVhNI+L935whjNvj7A8l6NeaVDfpEVRIOIjHPej3QdxsLsKYLPpMtmVErFkgI516rjz2TKXTk9z+ewMqbYIX/u1Z+gaSCIAVy7M8uYPzzN6cZ7TH4zR0hHhYS1ExKM+PnWsn2rN4KMzk8wt5rHsnZ2hulqapmbpxF0hYq4QinjnP6HpWHxn/i0q5tYmSaIgElL8/LWuV/DK7h1pqwPQ6vPzcm8fmVqNvxi5eM8qng3LYjyXZa5Y4HImzcmFeXpDYfoiEToDQVI+PzGPd1sEn2zbpmaaZOs1MtUq6WqVldVgfLlSZrlSYalSZrFcIlevY167Ebo9WI6DLIgMh1K4JQXDtigZDbyyime1THhvpBWfrFK3TJJuP6q4Mxcift4QBIGgS+Nz/QNcSC/x2sTElifYtuNwJZthqVJhspDj5OI8e2IJdkVjdASCzV7ubb65Wo5NqaGzXCmvXpdl5ktFZktF5kpFZotF5kslLOf6iryfB9sbJQoCw7EEn+rs5tzKEunq1vpKS3qDU4vzlHWddK3Ksx1d9Eeid2Vz8kmqhs5UPs+HC3O8OTXJB3Oz96l38P4iCgL7kymebu/kcnqFzBYD8EKjwUcLc5QNneVqhU91dNEbjqBuQzl8WdeZyOf4cH6WN6cm+XBulvo2eqXmy3V+euIqjuNwfE8XXnfz+p5cyHJxYvG229YaJldnltedZ0Q0N0+0tvMHF86t3ec3g2nbTORz/NHFc3hUlafbO/Cprm3NbNuOw3ypyMcL87w9PcWH87PMFgv3N/N6I1ISx5wGQUWQWgCxKeok+Jr/xTCCsHURrJ1CplThjQsTLGSL9KWiHO1vJxbYek/0VpAkkVDMz/GX95HsiNK7t53pKwssTKVJz+co5iroNYNGXce2HURJRHXJaB4X/pCHUMxPrCVEx0CKcHznWLuJkkg4HuDJz+4jmgqy61AXk5cXWJhaIbNYoFKo0Wjo2GbTXkfVFDw+jUDERzQVJNEeoaM/xfDRHrS76Ov1hzz07+vg6plpZq4uot+Qnd79RC/J9gjqDrIQvRPBiI9Dz+0ikgzQt7ed8YtzzE+skF7IUc7XaNR1LNNqOgG4FDw+F/6Ql0gqSLItSlt/gr3H+7ZUir1Z7uqJXC3XadR1AiEv0fitsvGzk2nOnZhEEAWe/vRuPvXK3rXoO9Eaolpu8Be//y5nPx7nc984ynasF45OLvPjty7h1m6+MEqVBosr68v9q4pMNOwjGoapuQyaS6ZS25keZo7jYDoWb66cZqGepkWLsSfYy6C/k4h6+5uH7di8nz5HztiaxL2AgF/28JnkMTo9KvImguWHgSSKDERifHN4DyW9wQ/HrlI3zXt+uDYsay376ZIkuoIh+sIROgJB2gIBom4PftWFR1HwKAqaLCOJIrIgIolNQSWHpoiNYdurpvMWddOgZphUDJ2yrlOo11muNgPVpXKZ+XKJlWqF6ib89CRRZCiYXPv5WpmGIAj4FBcJt/+W1x+zM5BFkZTXx7f2HKDU0PlwbpaivnXj9UKjznuzM5xfXqI3FGF/MsVAJEqr309IcxNyufCpLtyKjEuWUUXpluvAcW6+TnXLanpMmgY1w6Bi6JQaOkW9TrpaZa5UZK5YZKZYYLFcprzDgrCox8MTbW08t9LNX41c2nKWqGIYnFteZK5UYDyX43hbO33hyNqYbiXQqhkGmVqVxXKZ8XyWU4sLvDc7c1svYGH1M5QaDXTLenCBwhaIe3082dbBpfQK3x+9suUxLus6pxYXmC0WmMjnONbaRm8oQovfT9ClbWmMq4ZBulphsVxmLJfl5OI8H8zNMlPc2OpHFARibg/5Rh1jC2OcKVT4qzfP4zgOu3tSawHs5allfv8HJ267rW3blGv6uiWXPlVlKBanNxxmPJfbUhBbNQzOLC3yu6dOkK5WOJxqpSMYxHcPYmSWbVMx9KZQVLHA6cUF3pyeZDyXvW2frigI+FUVWZTIbJcomdQJ1gyggRC6diQQw2BNg6DhsAdBit1mJzuXbKnGdz+6yMnxOT5zYJDeVPS+B7Cwaj/mkhnY30n3UCsr8znmx5dZmEqTT5eoV3Rq1QaObSNKEi5NwePXCEZ9RFMhkh1R2vsS6wZkmkfluV84TClfxbEdYq0hWnu2Jlw0dKQHURKpluqomszgFiwn3T6N/U8P0revg6XpDNNXF1meyVDMVqjXmkGXKIloHhe+oJtwPECiI0pbT4JoKnhPVn/7nhrA0E3mxm6upnjylX33HOz37m3nha8dpVZuoLhkOgdT992WUPO42HWom65dLazM5Zi6ssDiVLo5ltUGptEMYFW3gjfgJhzzE2+L0NabINYSum/WTHcVkTTrmcHn124pHTAMk5nxZcYuLRBLBHjxC/tR1OuHCYa9pNojuDSF+entMxM/eX6ai1cXbll1tByHarVBagetEN0NDg7LjRyn81cYK88iCgIz1UUiauCOAey9HLNi1ZiozBN3hfGvBrCFYo18oUrXJoW4ypUGtZqOpin475PvlSpJDMVi/I3DT5Cv1zix0MygbNfEr2FZXMlmuJJt9ksrokjE7SHp9RJ1e4h5PIQ0Ny5JQpUkVElGEoW1oKBumlR0nVKjQa5eI1uvka5WydSqmwpUN8tGk5XHgevORBJFnmhtI1erUTdNTizMUzPv7noo6Tpnlhc5s7yIIop0B8N0h0J0BIOkvD4ibg9BTcOjKEiCiCisqgbSzLCYtkXVMKgaOmXDIFerkavVyNSqLFcrLJTKLFVKj0SvpgD0hSJ8bdduzi0vMZrNbPleYDkOK9Uqf3LpPG9MT3C0pY3DqVb6IhGibjearKBKTb/wa88d23GwHOemRYDlSpkr2TRnFhe5mF5hqVK+7XFFBPwulVf6+vlgbpbpQuGh926uhwAMxeL8wuAQl9LLjOVyW96HadsslMv8wfmzvDYxxrHWDg63tNAbihB2u9Fk+YYxFmF1QfDaGF9bEFyqlBlJpzm9tMil9DIrd8i6i4JAxO3mCwODvD45wVypiLHJgFGSRPyeZtB6o9VIvlRjfqVAezK09vtbPq9lN8uH17kdS6JI3OPhSwO7+N1TJyhsoa0Ams+ojxfmmMzneLm3j6fbO+kJhQm6mt95t9K8XsVVKzXhxmvWtjFsi7ppUTMNqoZBvl5julBo2jvNzTCZz2+qV7/NH2BvPIED/GDszhaDd0RqA7kLwV5p/ixGAKepqS8P4JgjYIyCmIRHNIDdCSiqTGt3fNvUcf1hL//+f/W1e9rHi19/ghe//sQ97cPrd9O7p53ePe33tJ+t0L+vg/59Hfdl30+9sp+nXtl/X/Z9JzSPi46BFB0DqYdy/E9yVwGsvVr+0mzsvjmzk10pMTW2TLVcZ8/hLrrX+aAuTcbtUSkXa2ypS/o29HbG2dWbxPWJmvVqTefEua316OxELMfmfGGMolHBwUEUJIKqnxb31m/YmqjillxsVLtt2RZ1W0e3DRwHJirz7Av241ea/XUXL8/z2puX+Yf/4IubOt745ApXxpbo6Yxx5GDXls93s6iSTH84wn/1qRf5b958rSni0mjcl+yFYdvNjOkdJqOPecxm+ExfP2WjQd00Obu8eM8Bi2HbXM1luJrLrPt7WRRRJAnHdjAdG8u2d2SW715wKwrD8Ti/sf8Q/8N7b1G8h3vBcqXC90av8L3RK/gUlVZ/gPZAgJTPj0eRcUkytuOgW81FgFy9xnKlwlQhT75e21J20qMqHG1p4+8ff4b/8b23VxcRdmaPrEdR2J9I8a09B/gnH7xDzTDueoyXKhW+c/Uy37l6Gb/qom11jJNeHx5FRr1hjCuGTrZ2bYxzFBqNLQmh+VWVJ9s6+E+fepZ8vUauXt90CX9XMsx//eXP4TgO8ic8IrtbIvzHv/I8BwfXV2QtVuq8e3aCf/wvfrTu732qi68P7eH7o1epGplNB9U3kq5V+cML5/j2lcsMRKIcSrbQH4nSEw4T0Tx4FAVZXLUScZqLCEW9QbZWZaFcZjKf40o2zeV0uukxu4VxDbhcfHlwiC/17+Lc8hI/Gh/d0t/lkwiCBJ5vNH+Qu299g9yJ4P0bzQ/CzhBbfMxjHvNguKsA1u1plqUU8pVbxI4unZnh6sV5Yskgh5/s32APTV8pZxv7TY/s6+SbXzhE0H9znfVKtkw2X2Fqdv2J3KOC7dhcLk5Ss5oP2VYtzoCvA5+09bryl5JH+WLLs8jC+n/+6eoiP13+mHczZ3FwmK0u0bDvrUTwQWkEyqJITyjMf/fSZ/mf3n+H1ybHt+xV+JjHPAx+YXAYn+ri906f4P252ft6LNO2t1Si+KgS0dx8aXAXo7ksf3H54rYI+ZQNnSvZNFezaUC4ZR2wOV9vPtu2+oSTBIHeUJh/+OzzBF0a7YEgXkXdsQEsQMLr5cuDuxjP5/jLkYvbUlFS0htczqwwkllpai9s4xjLoshQLM4/ePIZ3LJCRyCER1E2fW0IAkji+k80WRbxuDfub5NEAbembvgwlASBlNfHN4f38nunTzBbKm7qnNajahicXVrk3Kp1UPOMm2N5Lat97T5gf2I8r/mLb5VfHN7L13YN0xeOsFKrENbc21dGvCGPdR0e85ifR+4qgI0lA0RiPhZnc5z6YJRDq4Hq7MQKH701wvToMvuf6OHQU33rbq83DKrl+mrd/PaENYIgIIoi0idWRNeMdh9xbBzmaivodnNy0OqO0eVJ3dVn88oeUloUdYOeVlEQ6PAkIQPgkG7k1467ESdOT1Eo1ujqiNDbHWclXeb//L03+Lt/8yUAFpcKXBld4q33rqJpCl/4zD4yuQqzc1kO7e8kHPJy+tw0cwt5fvGrR3Ech9/9V2/hdqsYpoXHrdLaEsK2HU6fnUGWRfKFGkcOdnL4YBfxaLPX89p4JLw+/t7xp+kMBPnLkUtM3qbf7DGPedgINCevz3R04pZlgi6NH46PPuzTeuRp9oKr/PaRY+TrNd6YmiC3TQta16b72yXCKgoCB5It/PbRY7QHgoiCQFsggE/d2b7NgiAQcXv4W08cJ1ur8s7MNKW76OVeD4dmK8t2lQdIgsATLe38zSNHafU3W286g0E8ytZEVdZ77u7qiiOJAvHQrbog1xBFEY9L2XDWIwgCIvC1oWFGMiu8Oj5Gtn73ixfXgtGbXnGa/a3Xfr9d/Nq+A3x9aDedwdCaSN2uaIx3Z+9zBdzPwPzuMY95zNa5qwC2d1cLA3vaef37Z/nX//Q1Tr43hgBMji4xenGeVHuYo88OEoreKhVtmRaVcp1qpUE8FVqLX69k03xn7DLnVpqrhWHNzdcGdvNkazuqtDPFgx4kjuOQN0qYq1Y4ETVAXAvf1b5EBGRR2lCUya941vpqHSBvlDHvYMHS1hpidj7H1EyGZCLI3HyOeNSHuLqgIEki/b0JBvqSzM5nee2ty/R2xymW6ximhe3YVGs6heL1h/XYZJpnjvfR15PA63VhmhYjo0sUilW+/uXDZPNVpqbTzMxm1wLYa8iiSIvPzzeG99LqD/DvRkd4d2b6rkqyHvOYB4EgCHgVlYOpFtyyQnsgyJ9cOn/fyuB/XhAFgbjHw39w6CiqJPHTyQlWqpWHfVo3IQoCR1vb+NU9+znW2o6yKmDU7g/gUx+sivPdcC1z+B8dPYZLlnlrevKeLM3uB6Ig8HRHJ7+yZz+HU61rY9wR2HoAux4D7XHa4xv3vwIoskR3a4T/5K+9SDy8sZVGyKXxrT37qRgGr09ObLtS9XbeT/yqi1/avZevD+2mNxxeE98KuFwPJoB9zEPDcRxs26HeMFEVCXnVX/XnhR/OjHAhu8jnO4cYDic3fF+6VuEfnfwRX+vex5F4OwH1/mjBPAz+13NvkXT7+FRLL23e4AM99l1Fhqn2MMef38XKQp5zJ6dYmm+KN9QqDdq6Y7zwuf0ceWYAWb61tCOXKbM0n0dAaHrArr6eb9Q5s7zIm7OTACQ9Pp5s6XhAHmOPBjWzvtZP4pE1fPL9kY13iQoe+foXrGrWsbh94BeL+HC5ZKpVnUy2zORMmqeO9+FaFfDyeV20tYQYGkiBAO9/NE535w39u86t7dCWZdOSCtHfm0BRJNLZMrIkEov46OtJkCjXGR1fplReP6MiiSJtgQAv9fSS9PnYFY3x6sTYpsUoHvOYh4FfdbEvkSSoabT4fHznyghXspm7Fnf6eedatmxXNMa39u7HIyu8OjF2T+WZ24kqSRxva+dru3bzbGcXgRv8ptv9wR2fgYXVMXYcdscS/Hv7DuJVFH46OcFCeWvK9/cLlyTxTEcXXx/azVNtHTd5encGQni2wYfW53Hhu03wCs0S4rDfw0tHB/Bp67/32vU6HIvzS7v3guPw+tTkjrNbEgWBVp+fLwzs4pvDe+gKhm5Sjg66XAxGH4sqAVTqOpPLWc5OLbBcqDSrylwK7dEQuzsSONzaT30j6WKFf/HqR1i2zbO7e9jXlSLkXX/+9+aFcc5NLeJSZA73tnK4b3PiRZZtc3lkgcWlAqZpE434OHyw6yahshtxHIda3eCHr57H79Po7YkTj/rX3u+9CwuanchYIcPbixOUjDr/wfCTN6n4z1bynM0u8GTy9touNcvgncVJjse72BfdeYJ898K57AIlX4TDsQcnknWNu+yBdTG8vxNRFGjriZFbKSNKAoGgh4E9bew53EWqbf3sYK2qoygyew53sf9oz0Mp/3CcZtlXoVijYRiYps1KpkxDNzFNi+n5LH6vhqJIuDUFj6aiKFLTysayKRRrGKaFadrk8lVM06ZeN5iayyBLErIs4vW4cLuUNaEr3bAolGqYpo1pWRRLNSzLplRuMDWXRZFFZFnC53HhcjXtWD5JU4Xx2oeAzZZfC4j0+tooGs1elJgrdFtPVwHhpmMZjnlHIQdZlkglAqyky0xOpcnlqwz0JdYWMZwb/uM0H9KyJGKZdnMFTzepN26eoEuSiNutoNwgES5JIn6/G0Fo9huxugK48WdpeqY+1dZBTyhMuz/Au7MzXFhZZqlS3pHqno95jEuW6QtHSHm9RNwe3pmZ5tTiPLOl4iN1zWqyTKvfT0fgwa7MrockiuxPpJAEEb/LxWuT41xOrzzURdKgS+Ppjk5+YXAXx1rbibg9N/0+7HYT1DQUUdzx1SOCICAJAodSLYiCQEB18fr0BFczmYc6xiFN47nObn5hcIgjLa2EtJsn/lGPh4CmIYviPfWFO07zObaSK1Oo1DBM+7bPzeHuJB5p48UJlyzzREsbOA6KJPH29BTp+95Pujk8isJgJMane3r54sAuulfLhm/Eq6j0hsJokrytPryPGplihVMT87x2bpTzU4usFCvYtoNbVWiJBDjU00pHPIRLltloTleo1vnjd85gWDYhr5veZGTDAPbc1CLf/vAifreK3+3adAC7vFJicjqD3jDxelWudUE7jkO1plMq1XFrCl6vC0EQqNZ0pmeyfHhigj1DrbSmgiws5SkUasiyRFtLiHjcz3o6AY8KtuNwpbDMH4+dRhREXm4bpC8YRRHusu/6ER2Hncpd1+YGI16OPjvIvqM9rCwUkBSRUMSH262ulY2uh9ensfdwFwO7W+keTD20C9swLN76aJR0tkzDMJmezZIv1jAtm+//9DwejwtNlenuiDHcnyIVD2DbDvlilVffvkylqqMbJpeuLlKr6SylS3z7x2dxqQqqKrFnoJXB3gRBvxvTsllKF3n9vSs0dBPdsLg6sUK9YTA1m+E7r57FpcioqsyhvR30dcXWvOWuIdDsXc3qRUynqRJcM+ubysLKoshnU0+u9bF2e1uRhI3/RqZj0bCur/aKgnjT9862HYqlGhcuzQHg82kkYn4626NUqzpXRpcIBtx4bvgMtZrB/EIet6Ywv5BneLCFWNTHhctzTM1k8LhVMpnyTcdZE534BHdzzSiSREcgyK/uPcCBZAtvTE9yanGB8VyW5Ur5tr52OwlJEPC7XCR9Ptzy49L6n2VEQcDv0vja0G72xBP8dHKC92anGctlWa5WdmwgKwoCEc1NwuulKxTmcKqF420PfnV2PURBYF8iSdTtpsXn57tXL3M5kyZfrz/Qqgx19X50pKWVX96zj+FYHPc6WUBVkoit+k3fSy/kg0RcDWKjbjctfj/fG73ClUyGfL32QEvhNVmmMxDkaGsb39qzn4FIdDVIuBlVkoh7vPgUlfw9iHzlyzUuTixx8vIMi9nSHQPYv/+tF/Bot8+uuxWFJ9s7CWpu/KqLd2anmS7kH5oAmygIJDxe9iWSfLZvgJd7+ghp65dDumSZuNdLq9/PVCH/c1lNV9MNTk8u8Edvn+HE2BxRv4e9nSkCHg3btilWG3w8Nsup8TlkSdo2V467IZerUK3qdHdG2T3culY9V6k2GLmySL1uIssirauBaaFQY2JyBVmWiMf8+HwaUzMZro4u4fdrCALEYv5HNngFKBsN5ipFKobOUDjJ24sTtPuCKOL1ANZybDKNCu8vTVExddySQqcvTNztxfWJ9se6ZXAhuwg057Yxt5cuXxivoq4db75SJF2v0LBMJFEkoGj0BiL4FBVREKmaOtPlPA3LZE84hXxDsmumnKeg1wipHtp9QUzbZq5SYL5apG4ZuESZhNtHqzeAR95aZY/jOGQbVabLefKNGpIg0uYLolvWTX/jhmUyWymwUCli2BaaLNPiCZBy+9G2odLlRu5pBiyKIm6Pi86+xKa3icT9ROIbixxslVjYR1dblEjIs6oMeDOyJJKM+bEse02h2HGafrXvnhhjfum60Xkk1DSOPrMamAEcLtVIxvyk4gEs2yZfqPHauyM0GtcDnlSimV346MzU2muSKNKaDDYDWNNmcaXIq29fvuncWpMhHODD05Nrr/l9LtqSwVsDWEEgpPhYECRMxyKvl8johU31wUqCxHPxQ3d83zWqVp28cb30yye7kW5YcVJUCdOy+cFPzgPQ2xXn6eP9JOLN8pFiuc6xIz1r79dcCuGQh0y2zPJKEbem8oXP7iMS9vLhiQkuXJ7H79NwgEjkunl3MhG4yRZJlkS8XhehoKe52i+KhENefFsoVZFEkf3JFPuTKUazGX46NcFb05NM5nOUdZ2yru+4TIcsingUBY+iENE8DMfivNDdQ9K3cQ/VY362GIzG6AmH+VRXF6+Oj/HWzBSzxQKlRoOaaT7UkniB5gKRR1HwKiphTeNQqoWnOzo5lGol6d1512mrP8DXhobZm0jyb8+f4aP5OZYrFaqGfl8n2qokEXRpdIdCfHXXMF/oGySgabf4l99I0usjpGmPTAB7jc5giG8O72VvPMkfXjjLR/NzpFd9r+/n9eqSJEKam75wpDnG/YN4FOW2goctPh8Bl+ueAthLk0v8/g8+ZnRmhUjAi6auVlJtcFjT2txzRpUk9ieStPkD7IrG+JNL55krFSnU6w/sWSUKAn5VJenz80JXN1/q38XueGLdSrEb0WSZfYkk86US1s9hFnY2XeCtC+OcGJ0l4vfwhSND/NIz+0mEfE3dmOUcPzx1hb/64ALLhfJD1TpIJgKcOTvD1EwGn08jmQjgcauMja9w4tQUw0MtXBldIZ0p8/STfYiigKYpuDWZgF9DliVs20GUmhWIfp/2SAevANPlPEu1Ej2BCPujLby1MM5XuvfgldW1+0lRr/Px8iwzlTxL1RIuSeazHbt4pX0X3f7wTfed8WKW0+l5FqpFdNtiVzDB13v28WSyE4CFaol/N32RU+l5inq9qd/g9vGLvQd4OtWFR1bJNWr8xcR5pkpZ/tETnyPhbj5fHcfhBzMjXM4t81xLDymPj6VamT8dP8uZTHN/bllhf7SFz7QPcijadsfv742UjQbvLU3xFxPnWayW8CoqxxMdZBvVtUSTbllMlXL86fhZzmYXqJk6PsXFE4kOXm4bZDicuG3ybKs88imcX//Gk/z6N57c8PeRkJff+a2XbnpNFAV8Po3/7j/bmsmyqsgM9CT4v/+7X9vSdm5N4diBbv7V//zXt7TdjQiIdHgSjFVmqds6s7VlxitzDPq7bjv52SqO47DSyDNRWVg9LgQVH/INK07HDvdw7HDPTdvZto1hWDQaJm63wq4b/H8H+5MM9q/f4P7v/cpT674uCAK/89sv3/RaKOjh+A2BsarKfPVLmw/MP0l/JEp/JMov797LxZUVfjo1weuT48ytlmnajoN9l3YCd8s1g/mm2byAIokkvD6Ot7bxZHsnT7S0kfL5tnTjeczPBooosTuWYCga59f3H+SN6Un+3dUrnF5coNCoY9n2A7texdVrVETArch0BkI80dbGk20dPNnWgVdVt/W+dD/QZIV9iST/7Yuf4eTiPH94/izvzs6QrlawVr/724FAc7xckkxfJMIX+gf56q7dJL3eTanIJ30+wm435HPbcj4PEo+icLillX2JJCcX5/k3587w4fws2Vrtvo3xrliMLw3s4ksDQyS83jtuC9Dq8xPUNCgW7vzmDVjMFFnKlvjiM3v4G199Cs2lbNt3QBAEYh4P39q7n8/09vHHF8/x3atXmCrkMVafVdv9vb82prIoEXS5+GxfP9/au5++UGTdTPZ6XAtgfzI5DjuzYOS+4TgOpybmODO5gKYq7O9u4e9+6Znms331uuhviaHKMpZt87uvfvRQzzca8fGlLxzgzLkZfvzaBXxeF7/wxYOcPjfDkUNd7BpI4XarZLJl0pkynR1RuruijI0v09ebIBEPkC9UaTRM2lrD9PVuPrG1E3Ech9FCmoJe51iig/5gjP/fhXfINWqEXR7k1b/hQrVEQMnwnx58gZQnwB+PneFHMyN4ZYUO3+G19wG8MT/G39n7LMeTnYzkV/jLifP835ff52iiDVmQ8Ckqh2JtPNfSR4cvyFylwL++eoL/5dyb7A5/E7es0uYN0h+IMlZI89HyDF/sGgZAty2u5FfwKgp7oyky9Srfm77MT+dG+QcHX2AolOBSbok/nzjL713+kL5jnyfs8qz72dfjZHqOH82MoMky/9NTXyaguvhnF99luVamsbo4NVcp8P2Zy5xOz/GfH/407d4gHy7P8GfjZ8g1avz9/c8RVLdPu+eRD2B/XpAEkb3BPj7KXqRoVJipLnE6f5WDoUFa3fFtO45hm0yU5zmXb1p4CAgM+DrwSLdXTbs0ssB7H41jmBbHD/ds2Pi/E/GpLg63tLI3keBvHD7KlUyGc8uLXFxZ5nI6zXSx8EAEdCRRJOX10R0MMRCJMhyLMxSL0+YPoEgSqiShiOKODwwec38RgIBL45XeAV7o7CFTq3F2eZEP52Y5s7TAZD5P9T5dr6IgEHW76V+9PnfHEuyKxmjx+VFlCVWUUCTpkWr1EYD9iRSDz0WZLRb5YG6G16cmObe8RG4bsp5doRBHWtp4sauHA8kWwm4NbQvK+kmvj7B2fwT7HhSyKHIo1cpQLM50ocD7s9O8MT3JheXle8p6XqM3HOZoSzsvdfeyN5EgpLlxSZvvU0v5/QRd96YM6vdotMYC+Fb1L+4H1zQdfvPAEb4yuJtTSwu8OTXJxwtzTBfy2xbEKpJEq8/PoVQLz3Z0caytnaBLw63IyFvIoLhlmX3JFMrP4YKr4zhMr+SZzRRoiwZ5emj9ZEMq7Odwbxv/Sj6JYT7cKN/rcXHsSA9ej4urY0ssLRfxuBUauollOxiGheM0kznrce3j3Ukz5VHAchzGSxnKhs6hWDtxtw+3rHIqM0dM8xDRmotjrZ4AL7cPMBxOIgoCv9J/kHOZBUYLGWbKeXoCkbV9vtjWz5F4O+3eEDHNS75R4/dGPuJ0eoFDsVYSbh8RlwcRAVEUUESJF1r6+OHMFfQbnEB6A1HafEHeXZpcC2Av5paomjrD4QRdvgiX8kv8eHaEXx04xP5oC0FVI+zqZrKU40ezI5xJL/BC2/pWp+txMbeEblt8sXOY/mAUEPjNXcc4k1lY65eeKmc5lZ7jWwOHGAolUCWJT7cPcCG3yFghw6Xcylq2eTt4HMA+IoiCyO5ALzFXmJVGHtOxuFgY5y/n3uCXOl4mqgbv2e/Wsi0+yl7k7fRpikbTZkIQBPaHBu7Ya9vdGSMSbn6hAwH3I+W9KwoC6mqA6AMOJhX6IxFe6R2gYhrk6zXmi0UWK2XS1SrpapVMrUKuVqds6OiWiW7ZNCxzbTX8mqCJJIqoooQmy2iyjFdV8KsuAi6NoKYR0dzEPV7iHi8Jnxe/6sIty7hlZa1seLOr3ffC3zv+NH/9wOaz2YIg0O4P3KSYupN4qaeXfckU1U0qdwoIaLJM+6o35E5GEJoFO9evKZWo282RllbKuk6x0WC+VGSqkGdp9ZrN1Krk63XKuk7DMtEta+1aFVevU0UU1/bnVVSCLo2w203U7Sbm8ZLy+mjx+wm5NNyr16ZXUfEoMsoNyoyPGsIN3//+iELS6+O5zm5y9TozxQJXsxnmSkWWKmVWqlWKjTp1ozmGpm2hyDKa1PxbhDSNhNdHi89HeyDAUDS+VgIc0tx4FWXL1RPDsTj/2TPP8R8eeeL2nwNhW+xg7gc3jvFgRCbl8/Fidy/ZWo2ZYp4r2SwL5SJL5Qor1QqlRoO6uTrGjo0qSbgkGbfSHOOk10eLrykONhSNk/B6CWoaIZeG5y7GeF8iyf/ruRcp3iGYFhBuUjC+kQMDbVTrOq+fHON/+aM3aYsH8WrKhmXEz+zrwe/dWtB87bniVVU0WSaguTiYTJGr11kql7mayzBTLLBQKpGuVSnUa5R1nfrq991ZFYRSRBFVktBkhaDLRcTtJur2EPd66QqG6AyEiHk8+F0ugi4N/2pFxVa/46oksyeW4Pd+4Rt3tOLrCoYe+vWb9Pr4rYNH+NLgrk1vIwkiMc+tmaxsuUa+UkM3LfxuFz3JyLrjp0giQa9GIuBlLvvwlNGvjC5yeWSBcrlBparj8aq0t4UJ+N388NXzTEyuUKnq9PcmSSbXf06Ggh5Gx5d59acXWVgs8PKLw4/sc2GylGWmnCeguhgMxtFti0PRVk6uzHI03rEWwHoVlRbP9b7YsMtN2OWmZhnkGlV6uB7AtnmD+BRXsy1MUIlqHlySxHylwIFoC7pl8u7iJB+vzLJQLVIzDfJ6jbppNFtbHAcEgR5/hP5AlL+YOM9SrUxc83BqZY6Y5qUvEEUUBCqGzqXcMun6B/zJ+Fmk1b/DSq2CW1ZYqpc3PRa2Y5PXmxoGbd4gstgUtW33hvDJLkRBxHYcCnqd0+k5Zst5fv/qybXb3nylSJs3SKaxvfZ1jwPYG6iZBnOlIpezK0zkc+QbdSqGAThoskLIpdEVCLIrEqc3FEF7gCI6AhBSfDwZ3UtWLzBXWyGvl/ggcx7TNnkufoihQDcuSb2twvBGZPQCH2cv8tbKaa6WZrCxUQSZXl8bg/5O3HfIwHq9rkdSNr1abXD69DRvvH7pptfbWsM8/ewgw/1JTNuiEotT0XWqpknNMKiZxtoEy3YcLNvBcmwsx752j1lVcxZWy7BEZFFEEZuTOJfcnJBpsrwaqKrNidddTBK2g95w5M5veoSIebzEPJsrH3zUkUWRoNZcEHFo2iEMx2IU6g0qhk7NNKgZJvXVBRbTttfKN53VxRZRaC6Syaurvook4Vqd4LplGbei4FNUvKp6k1XGzxqqJBH1eIh6PFi2zUAkyqFUC2Vdp2LoVA2DxrUxtJsiPaIoIq+OnUuW8SgqPlXFr6pE3R5csnxPVRM+VcWnPvzv5zX1ftu2EUXxrqtsXLJMXJaJe7yYts1gNMbhVI2y0aCiG1RNY3Vx4BNjvPpfk5r3TJ+qEnC5iLg9qJJ0T2PsV134I/f2/KrVdeZWClwYX2BkagmfR0ORN66Y2d2T2nIAeyOSKBJ0aQRdGu0Bh8FIlD2JBKVGg7Kurz2jDNvCtB3s1WeTJDSzO5LQHE9VknDLCm6lee0GXC4CqgtVuvdFKXE12D6QTN35zTsAlyzTEQzSEbx31fRyvUFdN1f3KxH2rV+uKQgCiiQR8GjMP8QANhb1s2sA6rqBgIDP58Ln0/C4XTzzVD+WZTeFh2I+XKqCbdsk4wFefH6IwKq+TDjs5cDeDlpbQgSDmy9PvZ8s58ucmZjn7MQClbqOT3NxuL+Nw/1tBDwbf/8u5ZaYKefJNmr8v0/8CNO2GcmvIAjwjd79dPub+jMiwk0VBte+Vw3LxHBu7lFXhOv3KVFofgclQaRhWzjAX0yc50J2kaTHz8vtgziOw0hhmVPpOW50bw6oGl3+CEFV44OlKV5uH+BMdp7d4SS9gSgODqZtIwAvtQ7QE4jcJDwVUFwMhDZvceXAmnCculo9dG1RUpFERFg7pltW+EzHLrp9N/f/RlxuBkLbVy0KjwNYABqmyWQxx4cLs5xeXmSqmGO+VKJs6KvqtA4uScanqqS8Pjr9IQ4kWni6rZPuYGhd9cjtprnyKnE8uoeZ6hJls0rBqLDSyPPWymnSeoHdpR46PSlSWpSoGsQra8jirX9i23HQbYOSWSHTKDBfW2G0PMu5whhztWVqVgMRgZgryOdTTxN3hW7qgV2PifFlLl6ax7Ydhodb6d+g5/V+4TgO1arO9FSa2dks+XyNRsNAkkRcmkIw4KalNURnZxSvx4WwOvlyHLBMm2pVp143yOcqzMxk6e9PMrirhf7+JIrYFAX5pP3CzyPlcp2x0WVOnZq643vb2sMcPtxNNLrzRHweJrPZAuNLWbyaypGetrXXp9I5RhczNAyT3mSEodat9xAJNAPagEsjsEFJZKWu89bIBIOpBK2RAJoiU6jWmcnkKdUbPNV3e0+77SZfrTEyv8J0Ok9nLMSe9iQ+zUWpVmc6U6DS0DnW1/FAzwlWA4TVhYGfBQzDIpstc3lkAUEUiMf8tLWGCQQ2d19zHCiWaoxPrNDddb3i5l6QRXE1O/3oj/HEQpYTl2YI+z0cHmrH61aRRHHD5WTvHRSIt4IoCLgVhTZFge3TyLxrLMfEdixEQUQStnd+dK1c8caFetM2ABtBkJAEGcexMR0dARFJkBG2UThms5hWcwEGmoKnqrzxHEoUBVyKvC02K87qv60SCXsJBd3Yqwvw16oYJBGGd7U2A9jVxQ+4bmno91+/f2guhY72CG0b2Gg+aBqGyfuXp/j2Bxe5MrdCrWHgdqlcml1u9iX3pPC4bv0e2o7DhdwSqiixL5Iitpptjbu9/LupS0wUswwEmwGgblsUjcbatrplUTUNVEnCI9187Rf0OsZqJYJp29Qtg4ZtElQ1bMfhp/OjRDUPxxOdHI61U7V0Gvat4meyKNLmCbAnkuK1uVF6AhFqpkG7N0jC7UOgGVyGXR76gzFe6diFX7m+QHdNa2WzCAhrisrl1c/qOA711Wou+4b3xDQvw6EEn20fvClu2OoxN8PPfQBbMXTOrizxo4mrvD4zzkQhv+77DFunbOgsVsqcXl7ko8U5poo5Pt8zyL54Ep/6YLKPre44z8YOUDFrnMpfoWxWKVs1Pspe5FJxki5Pik5viqQrQlDx4ZZdyIKEiIhDM0to2CYVq0ZOL7FYSzNZXWC2uozhNL8okiCScIV5Nn6QZ2L7UcU7P2gnJtN8/3tnsEwbVZUeaABrGBaLi3nOnp3hzOlpxseWKZXqqzfcpl+sP+BmaFcrX/jiAfoHkmtfLE1TGB5uJRh0Uy43uHJlgb/6y5MP7NwfNSqVBmfPTvOv/9XbTZ9kt4K4QbneocPd9PUmHgewN2DbDlMrOX5yYZREwMfBrpa10ryGYTKxkmUuW8Sw7LsKYDdDwzQ5N7NIqa7jd7vQFJnFfIlTk/OIgsBTAw82gLUsm0ypyofjsyyXKnTHw80Atq5zaW6Z5WLloQSwP2vohsncQp7TZ2foaA8zP5/Hth12DbYgCFCr6VQqDYIhD6oiI4oCltX0OK9UG4iiSLXa4OLFecIhD16PimXZKKqMss7kfLFcZjSbYam8+VK1nUzE7aEvEqFzg+xcsVynUtc5OtzBr3/hCQJeDVnaOICVbmM3CKCbJucmF5nLbC0rJ64GH7IkIksSmiLjc7sIuF0EfRp+zXXHY9+I4zjYWKQbU+h2HRyHgJIgoMQ3nJDWrRJZfQ7LMej23r3Q4rr7NktUrDwx1/VeupK5Ql5fwiMHSGp9WI5JujFN2czS5h7GIz94H2pZEteejbbtYN6mv7VZ2eCwHU3MhmmtBc5bRRSb2bT12Ow1c63MfSeQK9f4+Oosp8bmsOzm4BrVOh9dmeFofzsd8eC6AWy6XmG8mKE3EOUX+w7Q4QuBA4ZtcTm3zJXCCvujrQDk9RqXcks8m+rGI6tczi+TbVTZHU4Sd98897mcX2KuUiCgaGQbVcaKGVRRone17Leo1+n2hYlqHsBhrlJYzb7eStztY3+0hR/OjPDWwjht3iBt3tBaoBl2udkbSfHB8jR7Iym6/WEkQaRsNDAdm7jm23TViigItHoCTJSynM8urvW3nkrPkWvU1lqREm4fXf4IHy5PcyDauvr5HYp6o6mf4fLcnQ/mBvxcB7CmbXN+ZYnfv3Ca16fHqZjGmgF7RHPjUdS10gDDtqkYOtl6jUKjzkKlxB9dPkeuXuevCQc4kmxFeQCldQICh8K7sHEQBDhfGCenl3BwKJtVLhTHuVAcB5qBqFtyoYoKiqhgORaGbVK3dBr2+r2BmqiS1CIci+7hq23P41N2RhnIRti2zfx8jldfvcCPfniOWlUnmQzS358gEGhaGBUKVfL5KlNTaUql+k1Wa5IkEov7icX96LqJqkr84PtnH94HekQQBIjH/QwPt+LZoHS8pyeOz//oZ1a2k0pDp9LQcQDDsshWakS9zZ7xwZY4umlxanL+pm2WCiV006JhWM1ydEmkYZqkgn68mspKsUK53sC07WY5sUcj5t84O+ZWFZ4Z7Ob1i+Mc7WkjHvCykC9Rqjd4bqgH27ZZKVUo13Us20ZTFEJejYBbwzAtpjJ5fC6VxmofaNDdPJ5pWyzmyzRME9t20FSZqM+DT3NR1Q0W8yW8LoWqbmCYNjG/h5DXTdTv5enBLkr1BoXqzT2ItuNQbjQYX85imBZeTSXm96IpMjXdIFepUa4372Vel0I84EWVZbLlKqV6A1WSqOoGpm3TGQ01s821OsVqo9kHbDZbAFpCfgJujZpukK/WqOkmkijg1VQSgeaDfiFfwrQsBEGgbpiIgkBXLHRXvYEPA9tyMHSTVDLIi88P8+d/eYKFxQJdnVFqNYPpmQy1ukEo6Ka7K4bH0/TmnpvPUak2cLkUEnE/CFCvG8zN53Ech9aW0LoB7Egmze+ePMHb03eu1ngUOJhq4TcOHNwwgI2FfezqSuD3ujBMi3K10bT22+DaaAa4G183tYbBn7x1lh+cGNnSeYqCgEuR0FQFt6v53W2NBGmLBuhORuhJRYgFvIR9bjyu29sLAdhYlM0co6UPsWmWJXZ69hFQYtiOjWHXsRwTRdSQBQVBEGlYVearI6w0ptcCWMsxMewGIiKyqAIClqNjOzaSIGM5xmq2VMJZfQ0EbJq9u6IgYTsmOWOBTGPmpgC2ahYYK39EQEmQ1PqwsSgYy5zL/wS/HFsLYG3HxnIMTLuBKroRBfm+fXd9WnNxEJqLEblKje513uc4DqZlUajVN4xfrylCA2tq8+th2w6lmt4sXf756KC5LdlihWK1vha83shirkS5tv48+FxmgYqp0xeIMRRKrAWFlmNzLNnFxewiC9UijgN1y2S6nOPNhXH8isabC+OoosSeSGrN4uYamXqFEyuzZOpVpss5LuWWORBtawawwEAwRrpR4aPlGRaqJSaKGWZK+XXbAv2Ki75ADLes8Pr8GN/o2U+L53r5RVzz8YXOYf6vS+/z07lR+oMxFFEi26jikVVebhu4yUP2TuyNpBgrZfhoZYao5iGgary/NIVhm2txUpc/wrOpHv50/Aw/nh2hyx9BAFbqFVJuP0+nupE2XCLZOj/XAexipcTvnT/J69MT1C0TWRCJe7w8197Fp7v7GIrECWtuBCBTq3Exs8yPp0b56dQ4Bb1Bw7L48eRoU/zFF6A98GBW+URB5GhkmJQW5afLH/Pq4keUrSoNy8Dm+sqb5diUzRpwZyVNRZBRJYUBXwcvJY7yVGz/HYWbdgKlUoNXf3yB737nFIIAR5/o4Zd/5Ul6euKoq0bctZrO7EyW+YU8h490P1IKyTsVURQZGEzxG7/53I4pF3oUmEznKNUb7GqJISBwYmKOF4d7cd3mQfK9UyMslyrMZYuokkjY62axUOKXnzrAE73tvH5xjHMzSxSqdTRV5mBXC7/6dHPSuN7cTFNkDnW18m/fPU250QxSs+UqumkxkIpRbhj81ccXGVvK0DBNEgE/z+zq4pnBbvLVOv/ke29xsLOFTLlKulThqYEuvnx4mHy1xh+/f5bFfImaYRD3e/n8wV0c6+tkJp3nn/3kfQ51tTKdzpOpVPnSoWGeG+q5bWld3TC4spDmX755gkypQkc0yDeP7aM7HmF8OctPL44xtpQFHNrCAX7pyQO0R4K8d3WaD8amaQsHmcnkyVfr/P0vPEtnNMyHY7N8cHWaSkNnqVhmOV/m737+GY73dXBhbol3r0wznyugShI9iQi//qnDuFWFb5+4SK5Sw+tSmM+VUGWJ/+eXn0dTlUdGddlxQNdNMtkyDg6qIlGpNLg0ssClkQWOP9HLd793hq98+RDtbWE+OjHBxOQKhw92Ua3qzUyRZTMxmaahm6SSAXp7trev6VGlpyXCQEec7717keVsmdZ4EI+2saDUK8eHCPm3/xlrOw413aSmm+TKNeYzRS5OL6/9vi0a5PiuDl460M/erhQ+twv5Ntk1026wVLuKImrsCjyDVwqtluQK6HaVxfo4NatASEkRUdtwSR4irjbarT1kjflm7zQ2RWOFgr6EJMiE1BZU0U1Gn8FyTFTRTdXM45OjuEQ3ulPHL8dQRTdlI4ODg0cOUjHz1K0yYbX1pnNs8wyz0pjCcprq66roZtD/NFdL76+VDzuOTcOukNcXKRsZwmoLQTWFjHpfgtiwz03I60aVJUq1BhNLOQ72NM/7xuNZtk2p1mApV9pwX4Ig4FIUdMuiUK3TMNb31C3VG6wUylQa99814VFAkjbu1VdkqbnAtA5jxTQdvhC9gcha8ArNftenk12cXfVVVSWJo/F2nmvp5c8mzjFVzJHy+PlW/yGeTFxXnZYEkbjm5beGjvPe0iTfnb6IgMDTyW5+deDQWsb6VwcO80ejp/n9qycRBYEj8Tb+/eFjzFYKyMLNz0lBEAgoLp5J9vDd6Yv0B2Nrpc4AIZebl9r6MR2b70xe4C8nzyMI0OYJ8mJb/5Y1A/ZGWqhbJn84eor/7/m38cgq3+zdz5F4By2eALIoknT7+Gz7IA4O35m8yHz1JIoo0eUL84XO7Rf0+rkOYP/FuZOcXJqnvuph1B+O8HcOP8Vnu/vXVtWvDbdHUWn3B3i2vYv98RT/7NQHLFUr1C2TDxZm+PPRIH/38PqepveLdneCX+x4madjB3hj+STvpM+w0shhOluTYlcFhQPhAZ6NHWB/aIC4K8zGhU87i3ffucJHH43jOA7Hjvfxt//OZ/B6bzbQ1jSFvv4kvX2Jx8HrYx4qo4sZSjWdJ/rayVZq/OjsFZ4Z6Gr2P92GvkSE3niYfLWOpsgc6+/g1OQ8B7ta+OrRPXz16B4AfnphjO+dGVkLYDdCVSRagn4qdZ35XBEEiAeaojoXZ5e4MLvM73zuGdojQb598iLnZpbojIbxrfbtKbLEr3/qcDM7KTaFyuJ+H3/rs08hiSKO4/Dff+d1xpezHOtrZkoEBFRZ4m+/8jQ+l7q23e0wLZu438s//MqLOMB//oc/YHQpgypLXJhdwrRs/vtf+TyGZfFf/+mPGVlYIeBuVgSIgkhvIsJvvXAUx2mW9Nm2zeRKlnjAxz947jBjSxn+8V/8hMPdreimycmJeXriYX7nc88wuZLln/7oPUYW0gy3NoM0SRQ43t/Joa5WENjSCvZOQNdNPvhojLfeucJLLwzT358km6tQKFR56flhenvizM3lSGfKVKo6siJx+FAXx4/1YVs2i0sFFhYLXBpZ4Auf28/xJ3of9kfaMbx1Zpz//U/fxrZtxuYyzayJsHFb45Gh9vsSwN6JuUyB/z97/x0cSZZueWI/l+GhNQJaI7XOrMzSsqu6RIvqfqKfaL73dhRnOCSXu+Qa15Y0W5pRGG2Xxh2bWRtbzuzMe7NPTk/3a91VXVpXpdYJJLQGAoHQOlzxjwCQiQKQAokU1Z3HLFJEhHtcd79+/X73O985P/kixyeXx3nhQC/fe3o/nbGNRcIkQSWgNnEx+y4+JUK7ax9OyYeNRdUqkdPjOCUfny7+LY+Gf49W1841+ygYKS5k3savNFAyssyUB+jyHORi5l2andu5kvuQkNpM2NGOKtRZOxkpTrfnEJey79Hi2okmeZAFlZy+wEJ1nFbXrts67ppVZqbUz3jxPF3uA3yc+BueiH6PsKMd6S5MhUVBoD0aoCXsZyFb4NOBMV4/tnvNouJCtsiFiXkqGwSlUB+7GgJuivEaI/EU6eL6SYnPr04wl8n/RtjYbAWaQj4iPg+yJGKY15I7ggA72qKEfeunqf/BjmPY2Os+n442tHEo0oIkiNjAH/YeRETglbYd2NTvd0lYLd7W5PLy05f/AbIg8lLbtpUM+rKQ0zJ2Bxv5b4+8tOpzURB46xtdq0SYlmEv/flUYxdRzb2mvZok8432nUttW64dZ6k2/fbmwpIgcLShjcORVqylfdXPgb0iWAp16vLvde/nd7r2rjAKBOq08q22gPytDGANy2I8m+aL2UmS5RIA3f4g3+rdyTNtnetSgQUAQcCtqLzet4tz8Tk+mBojU60wV8hzam6a6XyWVu+9ycIuPxw1UaXdFeO7rc/xYuMx5soJpksLzJQTLFYzFM0yFbNGzdKRRQmHqOKUHAQUD41amGZnlHZ3IwHFi1t2oonqqhvqXqBcrjE0FOfC+UlGhuMkEnmqVQOHQyEW83HgYDsHD3XS2hpas92lS9NMTSbZuauZV17dj9vtqCsAX3ejCIKw9NDY2punWtWZmkpx7uwEV6/OEZ/PUqnqKLJMOOxh565mHjnavW49cD5f4dNPBnnnnUu8/PI+nn1uJ+fOTfLF58OMjS5QLuu43SqdXVG+/fphGhv9yNdlqkzTIpHI8dFHV7l0cZrFxfrqbSTiZe/eNo492kN7e3hLj/dOoesmyWSB06fHGOifZXY2Q7FQQRAE/H4X3T1RHn2slz17WtfU1VYqOoODc/zLf/EWL7+yj+ee30Uikef4F8NcuTJDLltGdcjEYn6++a2D9PbGcDgUPnj/Cu++e4UXvrYbzSHzi5+fpVI1OHash8ef6KNaMfjVr85z9eocDVEfr3/3MNu21Q3bl2GaFslknk8+GqS/f5ZEIketZuJwyASDbjo6Ixw82EFfXyNO18b14jXDZDKZ4bPBCU6OTVPTDRZyBYrVGm5NveHg7nbUswSWbaOpCqos1Sm8hsmvLwwylcwCMLmYoVSt0/Lq+1u7z+WFuaO9bcxn88xlckiCwI7mOoV5Np2jIxLArakoskTE5yZVLDOfydHbWBeu6I1F8Dm1FeqoZVnMZ/P8+ORlqoaJJIpcmorTHg5gLykxKrLIzuYGXKqyLuV0PThXaMH17zcHfZSrOtOpHFPJLCdGpkjlS9hArlxX/VxWSwy4NLqioVXjuSCKNAd8nBqd4V+88QmSKPDawR24HCqjCymmU1nOjM9wYXKOmmFiWjalqr4yoYj5vTQHfbfc/gcNqipx+GAnfb2NDI/EicezuN31msiabiAI9SBXkkRURcK2bHTdqmcRl2YjLpeKLItkc2Vm57K0PmRgAPDk/m5aorf+/G8K3/u6zGVYlk0yX+RXJweYSeb4R18/yt7OxnUzJJIgE1KbeSL6R8yWB/gi+UPaXXvp8hxCpE73TVYnKRkZTFtfmdAuw8ZiunSFvJ6gbOaomkVckp9MLQ7YdHsOMV8ZplHrRRQkHKILRdQYK56h072fnJ6gVzqGuvS+T2kgUZ287WPO6gvMV0bIGwkmSucpmmlStVm8cgSnvPX2aYIgcKCrmcuT8/z8ZD/nx+b4l7/4hN99Yh+xQL0sYXoxy1vnBvnp8cuIolCvg10HTlXhSE8rM8kclyfmeeP0VVRZYmdrA5Ioki9X+OLqFH/5wWlmktmtLDP8SsOtqbxyZDuWbfPx5VFyxSoRv5tvHtvJ0W3t+Fzrlz9ttDBZf3YKqOswFm5EjRWEayJIN3py1APWtd+4vv7YtC0MyyJbq3AyMcm7M8P8Xw69QEhzrbl/V+wcb/Cbt4rlY7+JniuCICALAmwhVXgj/FYGsFXT4M2xIeKlIqZtIwDbQ1Fe7Oy5qRiTKAgEHBpPt3XSn0qQqVbQLYu5JXGnexXALkMQBFRBQVUV/IqHqOqn19NKyaxSNWsYtolpW1i2tdSZRSRBQhVlNMmBS9JwSRqSIN63Oq6B/jneeusily9NY1k2bk/dkieXK3PhwhQjI3FmZtJ8/eW9dHVdE7aZmkoRj2eRJJHOzijb+ho3FBS6G5iaTPHOO5f4/LNhdN3E7XHgdKqUSzUGB+cYGY0zPp7g268fZteullXbWla9NndyIsn4+CJvvnGBjz66SrlcQ5ZFBFFgfj5LPl/h97/36KprU60a9F+Z4ec/O8vQ0DxQ9941TYux0QUmxhMMDc3x2jcOsm9f2wNTn5dOF/nwg37efOMClaqOy6nidKkYusXUVJKxsQWuDszxB3/0KHv2tK0KIm3bplzSGR9fZHoqxUcf9tN/pR4EK4qEqspkM2WmppK8/Mq+pUy7TS5XYWhwHrfLQSZbpFo1mJlOkUoWmJ3N4PFo9F+ZwQbOnh3HME3++I8fZ8fOOtWrVjOYnEjy53/+IdNTaVRVwuVyoKh16mU8nmNoaB4BgZaW0A0D2PFECkUSeX53D4e7WsiUKrx57ipD84t4NBWPtvHYs5ytFEURSawvyFi2zXA8ydhCmm1NETqjQT65Os7JkembXgsB2N/RxC/PDjCbzrG/o4meWBhJFAl6nJwcmUJfEh3Jl6vUDGOV5YCmyqvoV7lKlcG5RebSeX73WH3ltX9mYVWtligIONWb19xdj5pukitX63XrAqSKZRRZIuh2EnBrdDeE+M7RPfXzYdk0Brx4l86jJIprMtvLiwBRn5v9HU2EPS6CbieaIuNfErppCnh5dmf3yvdbQj7UJcs0ZcnL9KsKQRBwuRxs64sRX8iSWMzj9TqJRHwcPz7K+Pgic/NZ9u1to6HBRypdZODqHMlkAVkW2bunFY9HY8f2RjLZMpf7Z3C7VIJboEj8VUdD0E3wNjKqN2Nd3AhP7+ki7FubcQGW6uUNssUyC9kiiUxhpU78epiWTbZU4ezIDP/+rZP87771BO3RtfXMgiAgoRJR29FEN8P2cSpmgURlnLyxCECf91Fmy1cREVe8Kq+HhUVE6yTq6MAl+ZAEBcOuAQIOyYMiOlBEDds2kQQFh+hCtypMli4SUBvRJA+iIMKyqvAmzpmNhSZ56HDvp9W5iz7vYwSUGKp497Lg7dEAT+/uYjqV5dzoHL88PcClyXl8rrrqbL5cpVzViQU8dDeG+PjK+Lr78TodvHZkB6dHZxiPp/jw8ihDswmCHheKJFKs1VjMlnAoEge7W5hMZO7aMX2VIIki21qiBDxOXjrYR80w0VSZlrCfiM99237RDwLmS3nemR7ik/kxVFHi1fYd7Ak13hM3lAcNv5UBbM00+XRmgqJeH9T9Do3uQJB2X+CW97E7EiN4na1KrlrlUiLON3pu3QB7qyEKAk5ZwylrPFi5txvD53fS2RUlGHTT0hLE69NwqAqlUpWzZyc4dXKUkydHaWwMrApgZ2ZS5PMVAkE3TU2BDcWE7hZcbpW2tjDGUYv29jA+vxPNoVCtGgwMzHLyxCjnzk4SjfrWBLDLKJdrXL40zdDgPL29MTo6wvh8TizLJpcrU60a+HzOVdTn0dEF3nnnMpcuTdPT28DjT/QRDLixbZv5+SynTo1x+tQ4AJ2dEbxe7Z4G9htBVWWamgLs3tNKV1eEQNCN01lXMp2aTHLi+AhXrszy9luX6eyIrgpgl2HbNkND88zMpmlrDfHS1/cSjngQRZFioUI8nqOlJbgqW53PVxgbW+DZ53fR1hris8+GOHtmgpMnRtmzt5VvfOsgTk3h7/72C/qvzDA7m6GnN4aiSORyZU6dGuPUyTEOHe7i6NFuYjE/oihQqehkc2USCzm6extwOG48nF6ejuNSVQ51NbOvvYlsqUK6UOLsxCwdkSADswmOD08xlkjhUhUcisyx3jbMJT+39aApMsVqrS5yZFoUqzUC7lubkIU9LiRRXBFq8jk1TMuiNxbmpMfFWxcH64JIxRKtQT+tIT+1DZQ0JVFEkiSShTJXZhbQVLlu1q5+6Rp+aWJrWhaz6RyfXp3g3MQs5ZqOU1V4tK+97vspiRQqVX586jKFShVVEmkO+mgJ+djRHKVU1bkyE0cUhJUA9kZierppUjNMEvkiA7MLuFQVy7J5fk8PAbeTfe2NzKRyXJ6J1/39ZIm+xvCGtVJfJTgcMh3tEYJBN/6Ai4MH6mrT4ZAbv99ZV6+VRZqaAjTG/Hg8Gn29MdwuBzY2iiITCrp57FgPTU0BMpkS1aq+ojXw2w5ZkpBvsrihGybxVIGffHiB7714kGhgcwrtz+7tYXtrdN3fMy0L3TQpV3VypXpN5Oh8iksT8wzNLq7J8uXLVc4MT/PDTy7wj18+RtDjXLXIZFg1krVpktVpBKBk5HE5goiCRNnMs1AZwyn5MG2Tel1shay+wHTpMtlanNHCaYJKEwU9SU5foGoW8SlRNKl+7HWeyGrGlEv2EVAauZB5mwOBl9FED7pVIV2bY7rUT7o2z2jhNE3ObQAkq1MsVEYxbZOp0mUCSoz5yjA5PcFU8RKK6MAp+fArUeKVUVK1aUzbJKy2Igp3r/86VYUDXS1YNjT6vVyanOfSZBzbtnGqCs0hH4d6WtjdHmNyMcun/ePr7keVJbY1R/mz547w0ZVRrs4kGJlPoZsJHIqMz+VgW3OU5/b0UNUN3jhzlXy5su6+ftvg1lS6tBBdN6DJf5XgklX6/JG6XZ7qYE+wiYDqvOfMyQcBv3VPHtOyyNeqjKRT1Mz6ZCzictPk8a4q1r4ZGt0eXNeteJQMncl8Zqubuy4qZo3Fap65cppUtUB5KdPqEBWCDjed7gYaND/qlzxg58tpLmenEQWBPm8Tra61Ye7FzCTD+TkaND9PRHfck+NpbQ3h8TgQhLpJ9vXBVmtriORingsXphgbXVi1XTpVolox8Hk1/IF7r5Ycjfp49LFe9h9op6kpgCheUyPt7olSq9bpqVcury+DDnVq7ORkkscf7+Olr++hpSWEotQnJYZhUasZqwKjcrnGlcsznD0zTiDo4vXvHOHgwY561lYQKBQquFwqc7Npzp2d5MqVGQ4e7ETTtnZwsyybmek0b/364oaG5UeOdNLYGEBdar/Xq3HgYAetbSFaW+vHuXy+kskCmqZw5coM585OUN5AHRBgYmKRw0e6efzJbezZ04qm1e/DZS9gTVOQluodAUyzrmL5yiv78PtdmJbN1GSShYUckYiHF1/cg2lanDkzwccfXyWTLVGp6CiKRKWsMzm5iGFY7NrdwpNPbiPacI1uZhgm6XQJp1NZN+C+Hg0+D62hAG2hALBEC+tu5dJ0HMfSNQ95nDjk6JKoig3Y7GqNEfI4AYGIV0eRRDRV4XBXXVn3kZ5WsqUKkijQEwuzuyV20wzFMrVoT1uM5qCXzkiwnuGVJBoDXp7Z1c1kIk3VMOiNhdneFMXv0ihUqjy7s5uYz7Nq8uxUFLqjIR7b1o4kiiiSxJM7OtnV0gCCQMDt5PG+DoIubd06dFWW6GuMYFoWTlXBtuu06V0tMZyqskJffXpHFx3RID6nxvamKNh1Yax60ufa/dcZDeLVHCs1u8uYTmbRzboCslNVqOgGV6bjtEcDHOxo5mBHC05VJZ7N1+uZrtvnnrYYmqLU2/MVhKrKNDb6aWyss4S+LL4UCXswTQtJusbGaWkO0tQYqKvALo3Ly/e7/xb9Yx/iGgzTYm4xy3969xwvP7pj0wFsWyRAX0v0hgJoy6jUdGaSOS6MzfHJlTFODU6TK61WvC1Uarx9dohn9nazr6tpHWsRGxsTGwiqzYQdrfiUBgy7hmHVfSG7PAfxKMGVvuOWg3S49yMgEHI0UzIzVM0SNhZgo4ke2t17ERBpce7Eq0SwbQtF1HBKPtpce6haJaJaJ4qoYdr1Z4JXidC2KuisH0nE0bFS57d8bF3uQyv0YKfkI+xop2IWMG1zU16pm0HE5+aJHZ00h3xcmpgnni1gmBYuh0Jr2M+uthixgIfBmUWyTx1ge3OUgHu1en9dxEnmxQN9NIW8DM8lSWQLVA0TRRIJeVxsa4mypy3GQraADRQqVfqaIvfkGB/i3iHocPJ4YyePN3be76bcd/zWBbC6ZbJQKpCtVVbobZZtM5nL8u7EyC3vx7AsUpVrhfS6aZKu3FztdyswU0rx6eJVzqfGSFRzVC0Dw6oPyBGHj6PhXp5u2EWvt2kVxWi8kOA/TnyCLEh8r+OJdQPYTxMD/HDycw6Fuu9ZAOtYql1cDz29DcRifnR9nHyhgm3bKw/IWs3AtCwUVcKh3ntan6JIG3qctrSEaO8IAzaZbGlFVOHL9Mnl1fBXX9tPU1NwJXiFun+tLK+eSCQXC4yPJyiVauw/0MGRI12ran49Ho2urgb6+ho5fnyEUyfH2LWrZSXI2yrYts34eIK5ucyGwljBgItg0L0SwEqSiM/nxLfOxDcUcrN9exOyLJFOFzFMa9W1vh7VqsFjj/WyfXvjquMSBAH3Oll4RZEJhT2EQvVrFWvw4fFo6LpJc3MQVZWpVnWiUS+KLFEp16jVjKVtJYKBOj1ybibN+Pgigijg8zlRVRlZlohGvat+L1MrUzSqOCUFTVKQBBFVlHhsWzuGbYENNdPAxKYzGqQ9EsC0LUJeF/s7m1ZqXCzbxqs4eLSvHcOqexpXTYOqaeBVNXpj9fv31QObv08Pda5lBiiSxLGeNo6t473q0Rz8/qP71rwvSyItIR9/8tShdX8n5vfwrcNrRVckUaQtHKAtHFh3uwa/h4Odzet+FvG6iWx388T2zjWf7W5d34d6MV+iZhg0B311xeVKlcV8vb9Ztk1r2E/rBrWJT2xb+zu/SRAEYRVzYRn1+/urn4F+EGBZFqVK3dbpXsnsaKpCT1OYzliQHW1RREHkxOAk2eK1DJ1l2yzminx6ZZzWSGBVACuLKg2ObqJLAaKItKLs26Rto1HrQUBaEqyq95MGrZMGrXNVO/q8j2LaBiAgCiICAjuVpwDo9R5d0+5GZy+Nzt6V/4uCRoPWRYPWtea7ra5da0SdfEqEPu+jq94Lqc0ElUZM26zb9Airs793C25NZU97I3vaGzf8zqGeFg71rM/UgmtB7OGeVg73tG74vU4tdENRrod4iN8U/BYGsBbJcnnVw2M0k+LfZFL8m/MnN71fw7Yo6htLl9el5K9NA+6kLnGimOBiZoJUrbCSbXWIMsOFOFeyU/xw6gtqlkmLK4Rb/mr4cOq6SaVSo1IxMAwTcymAWbZ9qGfUbCzLRlryzhPEpUePzSpv13sJwzCpVgzKlRqGXhd9sSwb27YpFCooirzS7vUCPUkS8Xg1OruiN6WgAszPZ1lcLOBwyLjdDqamkmu+k8mWkGUJ07SYnkphGJszNb8RBAFCIQ89vQ24Nsg8RqLeNRNiy7KpVnTKlRp6rX6dl8/XYrKAY4mCbZkbX1CXy0FrawjvLdabSbKIx3PtPlBVGUkW0TRlVcCrKDKCwFKb6ufM63Ny4FAH771/hY8+uspCIsfRoz3s2t1CLObD7XasZF6X7+nB7AKDuQXa3EGaXX5soNMdpGTqlIxaXSDChoJRpdMTJlEtUDF13LKKblnk9SqGVa9d3xNsJl0rka6WUUWJTK1EVi+zO9BEyPGw7vB20d1Qt985Nz7Hpak4ggBezcHettiabO1DPMR60A2TTKEMNgR9rhULmlKlRr5UveG2hSW7lPvxvJJEkZ1tMb7/3EHypQonh6YxrdXPhpOD07x4cBstYd9aIcR1posbvb9hG+4iXfdWIQgi8m8h3fIhHuI3DVs+mtSDjmuT+Ot0lBEEYYVmeb+EZUzLpqDX2OrlT9u2V9Qv1/vMtG2KtRouRUFespnY7Dl4JNzLTn8rqigTdlzL/hi2yb8e/DVvzZ1nOD/HcH6e/cHOTf3GvcJyX5meTnH2zDgXL0wzO5smlytTqejUaga6Xg90vgynpiDLItWqTqVyb33Plvv54mKB82cnOHNmnKmpFNlsmUqlnsGr1QwMwyK0QZYW6lnWcMhzy6qBxVKVcrlGIpHnx39/kh///akNv6uqEuVKDXsDZcM7gSiK7NzVzD/8R8/ekg/s8mJEsVjlwvlJTp4YZWJikVSqSLFYXTlfun5zC6hQyL0qU33TtgrCKmESYcnaQhRFJFlc9X69saxSXN23r43/8r98hb/5m88ZvDrP+XOT+PxOdu9u5bnndvHY4z2oqgLU7+mKqZOplfEpGi5J4Y2Zfv43O57kUnoOgG5vBNO2mCikaXMH+fV0P5Ig0uUNMVvKkq6V2R1swiFIgM1bMwOM5BYJOVwEVCcBx72ny/+mIOx18b3H1maQH+IhbhWzizn+4hcnsGyLf/67T9EQrI/vJ/un+Pv3L9xwW8M0SeVKGObtWd1tJfZ3N3Oot4WJRJq51Grv0ZH5JJliGdOykaU7m6PZ1zHclsf/Lz+Jls0BrrctvNdzw5U5q10nSrOqndfSDtcnH4SlzO1yU7e6zavP3bUEyPVtWj53yxYmd6stt4PbbbewdO1h/XYv7++6MOKBEaW8E2x8nr7U3+7h9f0q9rktD2BLhQoDF6c59ckgo1fnyaaLAPiDbjr7Yhx+vI+d+9vwblA3d7dhY2PeQ/oOQNU0Gcmk+PlwP480tfJIYwvem6gd3whuWcMlr91eQmR/oJPLmSmKRoVUdWNj7AcJv/rlOd566xKzM2n8fie9vY1Eo15cLhXVIfPF58NcXqeOtKHBh9OpkkoVSSYL97zdx78Y4ac/PcPg1Tk0TWH79ib27G3D63Hg0BQGB+f49JOhG+5DEIQlifRbGwCWH7Yut0pra4jGxsCG35VlcVUN6v3G2NgCf/s3n3P61DiyLNLTE+Pw4U58fhdOp0IqVazb3NxkMaJeo3d7v30nA6yqyuw/0E5vX4z+/ro41/lzE5w+NUb/lRnefPM8//SfPb9U1ysTcrjwKg5mShlyegVVrNf6qpJMVPPgVRwMZheYK2WxLJuI5sEpKSxWSySrJYKqC7/iZLKQwgbCDhfuUDOWbZPTK1TMhyb1D/EQ9wulSo3+8XlsoHadd+dCKs+pgckb2mHZgGXa940xtIx9XU2cG5tdE8DqhsnMYpZcqULIe2dztJphMpfKcWpomv6pBWaSWVL5EqWqDgh4nCp+l0bE72J3eyP7u5roaAjicd5bMcZ4Js/AVIKB6QUmFzIsZAqkC2UquoFlWciyhEOR8WgqYZ+LBr+b1kiAnqYIPU1hgh7nHQf7X4Zt20wsZLg4Pkf/1AKTiQyJTIFSTcc0LVRZJuDRaA776G2KsLeriQNdzSjy/c8sL2QKnB+b49zYLBPxNIlsgVJVx7ZtVFnG59KIBT20RQJsb42yt7ORiM+9rjVZ2dRJV0tUrfp95pJVGp1bb3l0r2HbMJfKcX5slovj84wvpFnMFilVayDUNSW8LgcNfg/bWiLsbI9xoKsZp+Pu6S/Yts1MMsfF8TmuTC4wvpAikam3yTAtFFnC79ZoCvnoaQqzr7OJ/d1N91UTYktnthPDcd7+2VnOfDZMajFPuVjFWFKtnJtKMTEc5/yJUQ4c7eZr3z5E7871a5vuJiRBxKOoq8KFfdFGXu7q40BD06b3KwgCbmX9CymLIm5FQZVkMtUK5h0+vW7k7xhQ3bhklZxepmbdv1XeW8Xg1Xm++HyYudkMhw518to3DhAOe1AdMpIkIooCk5OpdQPYtvYwXp/GyMgCMzP17Kfff2+ERSYnkpw4PsrQ0DwdHVH+6PuP0dDgw6HKSLK0olr62ac3DmCB2yovczlVNE3B49E4cKCDV1/dv/FuBQFlyfblfmMxkef0qXHOnpnA6VT5h//oGTo6I3VLGllElERGhuP88hfn7ndT10AQBFRVRlEk9u9vp6srynPP7+LKlRk++nCA/iuz/KcfnOBP/uRJGpsCdHkjRDUvhm0iCSK6ZeJTNPYEmlBECUUU2RlopNMbRpEkHmvoQhQEDMukapmoooRLUml0ehEReCTSUV8Vpc7yEAURr3J7pQEX4vO8OTzE6dnZu3SW7i1EQUASl2zBxLopu7xkMaSIEpos41QUXIqCS1ZwqQpOWcGtqvgdGiGnRtDpIqhpODcYtx/iIdZDeyzIf/NnLwIQCVyj8ZuWTXPEz+8+v59tbdF1ty1ValwcmeM//Grz5Upbga5YiFjAu+5ny37Km0WuVOHC2BwfXhzlymScTLFMsapTrRnoprlEW67fv4okosgSZ4dn+YnTQW9TmCd3d/HojvY7DqA3gm3bVHWTE4OTfHplnOHZJKl8iWKlRkXXqekmumnWWYQse3MKSJJQt9GSJRyqjMuh4nU6aIsGONzbyrHtbTSFNh9cLbPRPh+Y4KNLYwzOJEjmihSrNSo1Y8mX2sK2622aS4uMzqc4MzzDm6ev0hTy8szeHp7c1Ul0yWf2TpEtVvibD85yYWyO6tJiTU9TmK8d6OPYjvZV352Ip/nw4iifD0wwm8qRL1ep1PSVdsPyuC0yOp/krDKDS1Pxuhx88+gunt3bTSxY75OmZbFQyfODsTMooowm1xlO7e7QqgD21NAUb5y8ylg8ddvH9s9ee4y9nU1oW6SiXtUNfnmyn48ujpJbKiVoCHh4ancXrx3dufK9wZkE750f5vTwDPF0nkKlRrmqoxv1siGonydZEhmWk5wbncWtqXQ2hHh2XzfHtrevnKc7xXKfOzU0zUeXRumfWmAxV6RYqVFeuXb1pIkoCMyn84zH05wbneWtM4M0Bb08uaeLJ3d10hz2b0mfux1sWQCbWszz5t+f5osP+lFVmcOP99HUFsS9VHtWLFSYm0ozfGWGzz8YwDQtAv/oGcINvnuafpZFEb+mrcrgaLJMpz/Ao81rRUtuBxsdRcXQKdRq9ARDVA1zlTfiZmDbNvFKlpHCPJPFRTK1ImWzhm4ZLFbzjBTiRBzee6aydyeYnEwSX8gRDLrYt7+N3XtaV9FD8/nKSk3slxGJeGhvCzN4dZ6R4QVOHB/may/uAe4+pWE+nmV2No1Dldm7r5X9+9tXqeqWSjVM07olSuztoCHmIxz2MjQYp1Cs0twSXKV+/KAinSkxMbFIrWZw+EgXBw52EAy6V+qCq1UDy7ZXxJMeRCyLRLndDqJRL42NfrwejX/1L9/i9KlxvvvdIwC4ZRW3rK66+wRAuU5ozKdK+KiPjaElSvD1NCnbtldUzoMO1wq95/q23A5y1SqDySQnZzdWxP6qQVyiHC7TD8XrXrIooogiili3NlFEEUWqi2k5ZBmnUp8UORUFt6IsBbVOom43DUuviMuNz3F3Fn+WmRRfLqdZfn9Z8bc+ibZXjvEh7j9cmsKe7iaWSwauh9flYG9PE7u61hfrKZSqVHRjQ+G7e4WAx4l3g0xnqVKjZmxuHL46neDDiyN8cXWS8fkUqcJGwpY2hmljmBblmrEy4Z9L5RhfqFv+fO1AL4d6NxYr2gxK1RrDs0neOjvIpfF5JhNpssXqmlrg62HZNpZtY1hQ/dLzXBBgZC6JKAgc7Nl8QkY3TObTeX5+vJ8zI9OMziXJliqYG5T/WLZNzahbguXLVeKZAhMLdUp4/9QCXzvQu4Ga9G22yzQZm09xdnRmZVGjWKmxo62BY9d974uBCd4+O8SpoWnmUrkN7dZM28a0rrWbXBFBgK/t712VgTVsi8limojmodMTxq/Wn5VfXrhNF8oMTC9weTJ+28eWLpRXtC62ApZlM5vMcX5sjvRSvw95XQQ9Tl59ZCdg8/6FEd4+O8T5sVkSmQL6OvNauHaeqvrSeQLmUnlmUlnGF9J87UAfO9sa7uiZYJgWyVyRn5/o59TQFMOzSdKF8ob3wpf73EKmwFQiw2w6z8DUAs/v7+VwbwvuG/jZbzW2LIA9d3yEM58PE23089hzO9l1oINIo29F4KRcrrE4n6P/wiSfv9fP2S9G6dvdwte+dei26YB3AlWSiLnqFjg1sy4Ln6qUSVXKd231wLRtDNsirLlAqLdhs7Bsi3Ppcb5YHGSkEKdmGbgkB4pYV/WrWPrKKs6mYN/bsNdaKoyRFQmHQ1kJXm3bxjAszp4ZZ3oqtca7DsDhUDh4uIuxsUUGB+d5553LOF0q+/a24bnO+9SybCqVGqVSbSVoutPJoL30UJMkEYdDWeWFaBgmVwdmuTowt2677wTRqI/Orgjnzk0wPDjPmdPj7NvfhqrKK8dk2zblsk4mUyQS8a4KrO8XVibsooDmUHCo8sokzjQtZmZSnD41vu5Cxf1EpVJjcbFAKORB05SVNsuyhM/npKHBV1/Rr+l8+VLf7hm//vtfvl73+/o9iFheCLxTRosiSXgUFb/mIKg5CTmXXy5CTidhl4smj4dGj5eo241bUTZ9PQrlKsmlGkjdsGgKe/FoDhDqE4p8qUquVCEW9OJyKJRrOqlcCYcq49bqnrWaQ6ZSNXBrygPh77wRGt0enmhvx+9wUDVNKoZBxTCoGnUl7fq/6+9XTYOaYfBg3f3rY7kG8st3eCzkYXd3I8EbZA5FUcD1AIiFqbKEQ5FWfKCvR1U3MTYxDp8dqWcCP70yzkwyt2bR7VaQL1e5MrnAXCpPMl+kXDM43NuKY0lg706QzBU5u5Q5Oj4wucZKaDOw7Xq2cJleuRmUazojs0l+cbKfd88NkcqXNgxcb4SaYTI0u0g8UyCZK5ItVnhsZwc+19YKeZZqOrlSXcHaME3ODM/wo08vcnJwmkzx9p04/C6NWNCLx+nAtm2KRo3JQpqFcoGCUat7UIsyIqCI995t4k5QKFeZT+fJlytcHJ/nh59c5PzYLMXKxjaBG6Fc07k6nSBfqi7RyCV6mzdnk1TVDSYW0vz8eD/vnh9iIVPY1D1fM0xG55IkMgUS2Xqfe3pPF/5b9KK/U2xZAHvyo6vUKjrHntnBC988gC+wWiHT5dEIR300tYXAhp/89eec/HiQF755kHsp0S+JIiGni3Z/gFIyQdU0WSgWmMplKeo13MrWP1xUSSLqdOOSFYKaE6e8+QnQfDnLr+fOcSI5TNTh41ikj053FL/iwiEpjOTjlI0a1npTgeseuusNj/ZSoG3eAfW4WKoyPrbIqZOj637u8Wg0twRXrFSam4P4/E7mZjNcuTJDQ8yHU1Op6QaJhRyffz5MLlfe0AZm9+4WZmfTpNIF+vtnqVR0ZmfSxBoDaA4F27apVHWymRKmafPKq/vRtGvdvlCokM9XMA2LcqXG3FwWw7CoVHTi8SwTE4tIkoiiSHi9Gk6niiAIRKM+IhEvoyMLDF6d48zpcdweB7pukkoVOHdmgumZ1Lq2LncCt9vB7t0tjI8mOHNmnB//+BSpdJFwyI2qypimRbmsk0wWSKeLfPNbB/H5nCvKzfcLfr+TxqYAtmUzPBznzJlxIlEv2JDNlbg6MMeVy9P4fE5yuXtjR3UrSKdLvP/+FbxeJ9GIF6dLRZYlLNMilSpy4cIktg07d7ZsqMb8EA82dNMkbZZJV8qMk1l5XwBcikKT10tnIEhnIEC7P0Cjx0PE5SLqqmdqZVG85fE8XSgzPp/C63IwMpukVK3R0xxGFETi6TxTiQyFcp0y2NMcxrZt5tN5PJpa9xwulPE4HZSrOt1NIdQHOIBt8/v5Rt92nmhrp6Trq17lL/2/ZOhUdJ2qaVIz6xRO3TKpmdZ1/zbRTeu6fy/9bVkbCijeS2xrbyDkcxG4gTq6LIlEAx4e39uF+z4HsteLEF0PSRRuazHfME3GF9L86LOLfHp5fCX7tAxZEon43ATcTlwOZSXTphsmpapOulAimS+tmkCnC2U+vDhKvlTF5VDZ0Rq9o/q/TKHMicEpfvLFZU5enVqXBScAqiLhc2m4HCqKLCGLQt0JwTSp1HSKlRqFcm3V9s0hH9tbIgQ2MWmvGSYjc0l++sVlfn78ChV9deZbEgU8moOwz4XPpaHK9cVow6yfu0yhTKpQXqH3Qp3C/emVcYqVGqIg8OSeri2tUyxVa+TLVaq6wXg8xd99eI7jg1MrQZkoCDhVGb/biabKK9fbNC3KNZ18uUaxUl0J0rsaQzQEPKiyhGlbFIwqQ7kEumWQKOcZFiUWK0VEQaDR6aPZdc3mzOfS6GgIUtENdGNp3DCWxgyjPl4Ypnnfas5rhkk8k+fU4BQ//PQSp4enV66VLIl4nQ5CXhdepwNVlpZKiSwKlRqpfIl0vrQmSzubyvH+hRE8TgexoHdDJsVG0A2TiYU0P/viCj/89MKacgFJFHA5VCI+Nz6XA1WR6+0yLcq1GtlihWSutKqv5stVTlydpFCuIgoCz+/vRbsuqXK3sGUB7PT4Ir6gi9bOyJrg9Xr4Ai5auyIEw26mxxe3XA34ZhAAVZR4oqWdyVyWqlkmV6symFpkMLXIgYamLT/piijidzjwqg5qS36tm1UhvpSdoD87g1ty8HLzQV5vfQTxOkn4nF5GESWq6zzQ695rS/SEdbK0FVOnZFbR7c0HsIlEnvffu7JhANvbG+Pbrx9eCWC7u6Ps2tVCKlngxPFRJsYXCYY8VCo6iYUsLa0hunsaEDagXPl8Tp56ajuCAO8sCUH9xZ9/jNuj4XKp2JZNpaKj6ybdPQ28+NIebPvaau7I8ALnzk1QruiUS1Xm57KUyzUsy+Lzz4aZmEiiaQp+v5P9+9vp6Y2hKBItLUF27WpmZDhOf/8sqVSBaIOfWs0gmcwTCLjo7IyiqjKl0u2vtt0I27Y1Yry8l1KpxuDQPGP//iOCQTeaU8EwLAr5CuVyjYaYj1duUCN7LxEKedi1q4XOzgjT02n++q8+oyFWLx/IZIp1P9D2MC63g3NnJ+53c1dQLFY5f3aSoaE4/oATr1dD01QMwyKbKVEoVOjpifLqa/sJBB+qA/8mwQaKus5wKsVwql5jpYgiMY+HHZEo+2ONHGxsIup2E9A0fA4HqnRjtkNNr9cBNoa8XBqbZzKeIexzY5gmF0bnmFxIE3A70Q2D1ogfVZGwLBvdNMmWKozOpXBrKpWaQVs0wH3Uz7gpXEs1yC2+m9cFLitelg2DYq1GoValUKtRqNUoLr3yS38X9NrKZ8uvsl73Vq17KNcV/xOlIrV7qPbbEvXTEl3fQ3gZiizRHgvwf/iDZ4gGN1anv9swLYuasX6m1aWpqLeo8m5aFsl8mR98dJ4PL4yu0B0BFEkk5HXRFg2wr6uJ3uYIDX4Pbq2uQVIoV0lkiwzOLnJ+dJbxhTS5YmVlMaJSM/hiYJKQ18WfvHCYrsYQyibYazXD5OzoDD/94jLHBybXfC6LIj63RtjnIhbw0hUL0eB349YcaKqMaVkUKzWS+RLzqRzTyRy5UoV8uUqhXGV7a5Rd7ev7Tt8Itm0zn8rx7rnhNcGrAHhdGi1hH9taIuxub6QtGsDnciCKIuVqjYVskZG5JFcm4ozGkyxkiivZ9JphcnZkBtu2iQbc7O+q05u3Ym5bruqkC2XimQI/+fwyn1wZp2aYqLJEwO0kGnDTEvLT3Rgi6HXVr7cAlZpOMldiOpllOpElXSiRLVbY29lEeIm1ICAgC9IKZfhJbfU94pRXD3idsSCvPLKDg70tlCo1isuv6rV/j8VTzN8n6yqoU3//40fnOT44BdRp5wG3k9aIn20tUXa2N9AaDuB1qsiSSLmmM5eq03LPj80xFk+t8mwGmExk+OTKGAd7mjnSd+ulj7Zts5At8OHFUX706cU1wavX6aAp5KWvOcKeziY6okF8LgeSJFCpGSSyRcbmU1yeiDMyv8hCpoi+NMbqpsXlyTh/88FZYkEPB7pbkMS7yx7bsgC2WtEJhD0ot1AQrcgyDodCLl3inkewgCZLfLNnJ2+Pj5CtVLCwubS4wE+H++kOhPGq6i3JddvUjcmNpWDQIa1/7LlajZF0koppkigXeba1k6C2uRT7YjVP2azR5Wmg0x1dFbyalsVMKUVWL6FJa2c2TlFFFiWyeomcXsJaKsyGeseeKi2SrOY3Rf1RFQmf14nLpaIbJqkl9ekvI5evrKoLdbsdfOc7R4jF/Hz+2RCzs2kWFnL4Ay6OHuvlpa/vYX4uw5tvXsTtWn/FuqHBx2uvHWT/vnY+/HCAK5dnWFwsUK3WUBSZWMxPc0uAffva0TRl1apzf/8Mv/j52VUrqpqzfu6GhucZGp4HIOB34XSqdHZFUBQJTVN45pmdBAJu3n33MpMTSRJXpvH7XOza3cLzz+9ClkV+/euLjI0m1rRZEAQ0TSEQcOH1aLfFQVAUmb372mlpDfPpp1c5dXKMmek0mUwRh0MhHPbQ2dnJwUMdBIOuLa23EkUBp1MhEHThdmu3vG9Vldm9u4V/+s9e4Oc/P8vYaILRkQU0p0pPTwNPPrmN3r4Yb791icmJ5Cp7G1gSpFIkgkF3PaN8S2qLAg5NJhBwrcqES5KI21OnmKuKvLL/5euhOdUVamYk4uHlV/YRCA7Xz3G6hGHkUVSZSNjDscd6ePbZnfT1xdZ43j7Ebx50y2I6l2M6l+Od0REcksTh5haebG/n0dY2OvwBXIqyYSCrKjLYMDy9SHPYh9/tRJZE5lM5yjV9ZcXbsusr4Zl8mWSuiMdZf98wTGJBL2fW8fC8E9i2hWGbGLYONkiijCzIq54vdxPLFirupZrkBvfteRwblkWhViNXrZKvVjk1N8N/OHeW8UzmrrR3sxAEAVWRbxro3m3ky7UlNeC18Gjqyrh4I9i2TbFS4+zwDD/+/BL6dX7jsijSFg3w2iM7+b0n9+FxOjZ8VrwCzCVz/M2HZ3n3/DDzqfzK89iybX51coC+lih+t7ah8NRG7QOYXszyq5MDnBqaXvMdRRJpCvl4dl8PLx3axvaW6LpquMuwbJuqbjA0s8ipoWnOjs5woLuZ3qbbp3LWDJNP+sf59ZmrazKvHqeDp/d08fpjezjU07xhqYBtQ7pQ4p1zQ/zVe2eYS+VWMna6adE/neCv3jtL3/frGeytmAlUagZzyRynh6b54ScX0JforD1NYb52sI/n9/fR2RDckPJtLfWb/sk4H18e4/GdHSuCXaIgENHcPNPYi2lbS3NUkZplYFjWGgpxLOC9aZ/4Fz/9mL/54Cy1LdYjuVWkC+WV4BUg6Hby4qFtfPPYLna1x9ZlOxzohpcObmM8nuavPzjDL0/2r6m/nlnM8d75EQ71tt6yvZBumJwamuZnx69Qrq2+/z2ayqM72vnO43s4tr0d6QZ9rlCu8t6FYf7q/TNMLKRXzq1hWozOp/h3b53k//EnYYJu7asRwHr9Lor5CpVbyDaVS1Vy2TLegIs7LmzYBCRBZFsozBMtHWSqFRKlIovlIm+MDQEC/8WRx/Gqjlu62WeLeQZTi4iCwHPt3et+RxFFXIpKzaxwINqIW908dahOFZYp6BVy+mqqznRpkU8S/UwWF9nmW6uo3OQK4pGdXMnOcDU3S8mo4lkqiq+YOu/ELzKQ25zIy6OP9fHI0e6b1nyKorBmoh8Ku3nl1X289PU9K9svewbLskRra4gDBzvq3lMbPARVVaKrO0pbe/iaB/ESloVSJGnZsuYafud3j/Lt1w/f0jHKsoR8XeDk9Wk89ngfR491X9fuJX/Rpd/p6Y1hWfaadnu9Gq++doCXvr4XURRW1dDeCgQBwmE3r756gJdf3rfqvC+fu3o7tvb+Coe9fPv1I7z2jYNIknhbQZumKezY2UxvXyO2ba2siC63UxRF/uiPH+P3v3cMx5esfxyOupXNX/71P11SBb757woCvPjiHp59dueq697eEeb/+H96FbBX2q8oEt/45kFeeWUf0nXX2e938exzO3nq6e0r3mjX9l8/z8tq2Q/x24eqaXJ8eopTszP4HWd4pLmVb+3YwZPtHbjWUTduCftpCnqxbRDEJd9LUSAW9HKwry5YI1Cf5KmyhFNTeenINpY9+Ky+FkRRoLcljLqFCyYls8xYcYSz6ROYtsUO3252eHcRUINb9ht3E5Ig4Hc48DnqdXS5ahXPXSgH+k3BTDJLMr/+InPY58Z9C+I/lm0zHk/zb399HMNYvZiyo62B7z29n68f3o4i3XwRJBby8k9eeRSP08GPPr3IQuaaLZ5l2/z8+GW6GzdWTt4Itg1/9+E5Tg1Nr5ttfmRbG3/2tSPs7WpaoXDeCAKgKTK7O2LsaGvgj549gCSKyLdwjF/GyaFpPrk0xlwyt+p9URD43tP7+cbRnbRFAzcNAIIeJ988tottLVH+73/7DmPxa5ohhXKVs6Mz/PizS3zn8T1bQlu3bJsL43OML6RXguVn9vbwx88dZFdbA/JNsuQC9WDpcF8rB7qbkcS1z0/DthjKJUiUC7S6A7w7d5WL6Tkeb+jiD7tvbb72IEKVJf70a0d46dA2GgKeG8YYoijQ1Rjin7zyKJlChc8HJlYFncl8iTPDMxTKVTzOW4tXzo/N8cHFEaYXM6t/SxD49qO7+c7je+iKhW56H3icKq8e2UF3Y4j/7ocfcGUyvkIJL9d0LozN8ePPLvHdx/fcNTVx2MIAtndXM5+/d4VLZ8fp6G2gpWP9FanpiUUunhknnyvxxAu7NzzpumUynstwNj5726JHDklmWzCy4Sq4IAhICPzZnoMslot8MDlGQa+xWCry0+F+zsbneKK1nZ3hKI1uDx6l3jkqpkG+WmW+VGAyl+FqapGpfBZVlHiho3vDAFYWRAzL4uOZcY42thBxum5pAr4e9gQ6aIpf5nx6nP848Smj+XncisZCJcfp1AhexUmDtv7qbkBxsTvQxmghzseJfharOfq8TZi2xXBhnmS1gCoq+NTb73CyLK4K7m4HgiAsBYcbnZO1ged6+6gHNrfXBkWRVqke3w7q7RZueNwbtXs5o3gnvw0sbX/vMn/1wHhz7d5oEWH1/mXWczVZ3tZ5G3WmG/UrURRxONbP8H75uG7eNx/itx2mbWOaJslymY8mxrmUiNMdDPFCdzev9Pbhd2grq9l1AblrfW/5PhZEAYn6osv1iySiICBe9/yTYMlPcev6Y9WsMlka41L2HC/GXkURVRRRwSk5KRoFCkYB0zYomyU6XN1IgkTJLJGqJQCBiKMBVVBI62kS1Thu2UNEjeKS3Zi2ScHIM1+ZRRUdNGnNOESNilUhXUtSNavEtEackouCUcDCJKtnwLYJqmHcsoeaVWWmPIW1VNoSdkRRRQeJ6gI1q0qj1oJX9iAK0pLMw+3VcP42YmhmkZlkds37jUEvsYDnlmom59N5vhiYYHIhs4pH1xr288L+Xp7d23PL/VQUBNyaytcO9JHMFvlPn1xYtc+JhQxXJubZ1hK5rSD27OgMVybn11AwZUnk0R0d/KOXjrK9NYqm3Fq93vJ3JEGg/hjb3H2oGyZf9E9weTK+hoP46iM7eG5fLy1h/4ZZsGvtARDQFJkdrQ386dcO8x/eOc3IXHLlO9lChR98fJ6n93ShqfJN93krKNd0qrqBLIk8vaeLP3nhMH3NERT55oKRq8/h+m0xLIuZYgZNVji5OEGj04dP0RAFgappbMh2fNDxu0/u5andXUT9nlu4tvUa9ZDXxT/4+iP0T8cpp64FsLZtU6hUGZpZZF9XE+JN7jXdNDk9PM25kdk1dOqvHezjhQN9tDcEb2GuDVC3+dnWEuWPnzvIX753hssT15Sgy1WdH31ykceXRMQ2s8BzK9iyXvDUS3sYvDTNF+8PkEkW2Xu4k5aOMG6vExubUr7C9ESSS6fHuXpxmoZGP09/fe+GtY35WpVfjl7l89lJbje50eT28X974jlCmgvpBjdTq9fP/2rXAVRR4v2pMdKVupBHrlploVTgfYeGU5GRBQmEOkVXtyzKhk5Rr5GtVikbOg0uNyVjfToO1FUuvaoDWRTv2Ae2UfPzzZYjuGUHQ7k53p4/jyzKuGUHu/ytPBbZzrvxi8yV02u2lUWJ5xp2IyHwcaKfofwco4U4DlElqnl5pekAyWqez5O34F36EA/xFcXf/uv3GOmfpaE5wJGnt3Poib773aRNwbIsLp4c56NfnSedyHP4qW08+fJe/MHbo18+xNbAsm0Keo1itsZiqcRULsvnU5O82redYy2thF1LdV7rLqpe/+9bmwBuFXJ6hkwtTZPWTFSLISJiYyMgcDl/gbHiCC2udlRRwcamaBa5nD2PhbVUejLOdu9uPky8S4+7F9M28co+HLbGVGmCK7kLNGkt5O0cDY4YZavMcH6ArJ5Bk5wMF6/ydOR5ZsqTjBdH8Sl+dLvGVHmSR4KPYgOmbVA1q4yXRukwu2nQGklVF7GwOJs+xWtNr+NVNu/Bea+QL1U4OzjDhaE5/uDFg6t8ZO8V6mq8M0wl1gawO9saCLidt8QomVnM8vnAxEoN3DIO9rbw6M52vLfpPy4KAq0RP3s6G/l8YIKpxWvt0w2TC+Pz7O1quuUA1rbhvfPDzF1HSYa6kGdj0Mv3nzvIttYomrq6rOheYGB6geG5RfKlazXDglBX5H358HY6Y8EbUpm/DEEQ0FSZJ3d1cWZ4hkS2sGJNZFgWc6k8xweneGFJNOhOscwgaQn7+MNnDtLbFN4yP1WoB7eNLi9vz1wlUyvzQvN2fKrGbDGLeA9FX7cKkigQC3h56dB2msP+2wroFEmktznCtpYouWKF4nXU/6puMDS3yO6OGMpNFlOGZxcZnFlcs5jjc2m8sL+XvpbIbS2MCoKAQ5E5tr2Di2PzzCzmVlSoLduui1cNTRPxuWkI3J16/y3rcT3bm3jhmwd552dnOXd8hInhOMGIF82pYNv1GtnUYp5cukRjS5Cvfesg3dvX90qDeq3RZC7DZC5z223p9Jep3oJ4gyyK7G9oRBQFYm4PH02NM5heRLcs5op55or5m+5DEkSCmpNG98aD6nJtT6c/SKpSviPFRIekcDDURdDhYaq4SNGogAAe2Um3p4E2VwSPrJGs5enxrD2/La4Qz8X20OGOslDNUTN1FFGmQfOzzddEXq/Q52tCk36z6VfvXhjm6myCUvXOBJa2t0R5af82HLdQM/QgwzItFuNZpkYSzE2lyKYLVMs6tmUjyRKaS8XrdxKK+oi1BmnuCOPQNq+mfT/Rf26SM58M0rEtRltPw/1uzqZh25CYy3D2s2HmJpMEo16OPrfz5hs+xF2FDZR0nZFUiulsloVikYHFBE+1d7IrGr2jEpK7gYpVQbdrhB0RJKE+gRGWJokFM0/VqtKkNaOKKpIgMVeeZqI0hiRIGJaBhUXE0cBibYGAEqDV1Y4iKpSMIhk9hY1Ns7MV0zZRRJW58gwTpTEKRgGn5CRemWe3bx95I4eJiV8JUDDyZPUMZbOMW/YQdTRyKXsOj+zFrwSwbYuyVaZoFBkqDFCzapsWRryXKFcN+sfivPlFP689sfOeBrD2Uv3mG6eucmFsbo2VhySKPL6zk7Dv5gFOrlRhdD7F8Gxy1ftBj5Pd7TE6GjZHPXcoMu0NQba1NqwKYAFG51NML64NutfDssfl6eGZFcuXZfhdDl7Y38uezkac6p3b82wGJwanmF7MrgqsVVnmSF8bPU1hXJtUXA55XRzubWVoZnGVN6phmrx/fpgD3c0EPa4tOWav08GTu7vY2d6wpcEr1Ofm7e4Qe4PNCIJArzdK1TLwyI4tySDfa2iKwqM72mmPBnDcJntNEOoZ9r7mCP1TC6sCWN0wmU3m1lXV/jLOjswwHk+t+u6yf3FfSwTPJunlQY+TAz3NXJla4OzItRJEy7b5+PIYB7qbifo9d+U+27Je53Q7ePyFXWhOhXPHR5mZSDI3laRaqZ9sh6YQCHno29XMgWM9HH6sD+0BsJ3QZIVDsWaiTjed/iBn4rOMZzMkSgUy1QplQ6/7xdo2kiiiSnLdM9DhIOx00ezxsTMc5XBsYxNrm3on9CoqmXKJO3Va9SpO9gba2RtoX/fzg6GuDbcVBZFmV4hmV2jdz2Ma9Ho3Xli4U9TpcV8+/jv3Zb1dHB+a4u0LgyTzpTvaz9cPbOPZ3T1f6QC2VKhy9fwkF0+OMXR5htmJJNlUnsp1AazTpeINuAg3+GjpirDnSBdPvLQHxwb2Rg/xEA9Rr5E9NTvLRCbLeCbDSz29HGtpJXqbIkV3E7IgIwkSRaOwzmcKATVIm6tj5b2snsUhOvDIXhyShiTIuCQ3+/wHsW2LxWqCgBJCFiRMyySohIhp1zQZCkYeAZGgEsSvBAkooZXAOaxGiDoaUESFilWhZtXwCAIFo55F63b34lG8JKoLSIJEQA0iChL3QwxyM7CWFW2zxU35Lm4WNcNkPpXj1NA0Pz9+hdkv1V0qkkhfS5SDPc34biFzGk8XGJlLrlIdhrolSls0gOsWamg3QsTnprsxxLtfej+ZK7KQKVCu6je11NENk4HpBeLp/CobElEQiPg9fP3wdpzqvV+AtW0bw7S4ND5PMre6BtmhSDy7rwev03FH7drb2cRn/RNcmYqvUEVt4OL4PAvpAh3R4C2rTN8IQY+TFw/0od2BL/ZGEAURv+rk6cZeZFFEFESmi2kckvyVLA9wOmSe29dTF9LaZPtbwv411H7TsskWKzcNYA3TpH8qQTy9OikniQJP7OpcWtTY/Hnd2dZAT1OYc6Ozq0pg+qfizKVybG+N3pU58pbuMRTx8tyr+9l1sIOR/jnmplIU8vWUstfnpKUjQveOJhqa/GtU1fwOjX3Rxi0hB8TcHjRJvmV7WUkQ6fQHafX6eaGjmwuJOMPpJNP5bD2I1fW6qIYk4VJUIk4XTR4v3f4gvcEwDS4P8g1WhQRARECVJLoCIWRBrAe1W3CsDyos26Bs1NV3HdLyJKN+xIZVwbBKKJKn/rfoQcKBjQnYiMLDoOhewLZtLNPm0qkxfv7Xn3P51BiGYeLxOQlGfCiqVKfv6SbVik42XWR+OsXw5RmKuQpHn93xMIB9iIe4BSRKRd4cHmI2nyNfrfJMZydNHu8DkTH0yj68so/x4ihTpXFEJDyKF7fkrnuFfulJFVADlM0iATVEWI2giCoeyYMoiOT0LCdSnxFQgrS7OlFEhUR1gdnyNDY2ETWKV/ESVsP1mlhnC5IgEXXEmC5P1bOoK79nY2FSNAqcTH1Og9aEhc1idYF4ZQ7Ltmh3daCJ2po2/jbCtuvihaZlY1gWVd2gXNXJl6vMp/OcH53lFyf6WcwVVwRXoD6Jjfg9vP7YbmJB701FeADm0zkmE5k173fHQkR9d7Y443U51qUc1gyTZK7uGdvquLGKc9UwuDQeRzdWM/GcqkJ7NEBfS+S+BEI2dVXaeDpPubbaNsflUNnf1XTH2cyWsI/mkA+XqlK8jmGWL1eZTmbYWWkgpNwZjViVJaJ+D7s7GpG2UMDQtm0qpk68nCfgcDFbytbV1gUYyMQpmzp7gxsnix5ECAK4NXVFKGyz8Lu1NYJolm1TqtZuahGUKVRYSOdXZW+hznjY09l4x+JesYCX1rAft0OhcB2zo1CuMZPMki1W7gqNeMtDYkmWaG4L09wWvq3ttocibD/65FY357YgiyIRp5vn27t5fgNBpk1BENBkmU5fEMu2KBk6TlNZ42n1mwTDKjFT/ADbtmlwHUYWNGTRjSJ6qJop0tVBIs59pCsDBBzbUCUvVTMDgFtuWiV08hB3B7YN6WSBv//zj7hyZhJJEmnvjbH7cCe9u5rxBdx1q4R8hcR8lqmRBSZHFhBEgUdf2HlLllkP8RAPUYdhWZyZmyNeKJIsl/iDPXsJas77TonzKF4atWYmSxN8uvghiqiy3buTDlcPmuTEba2e9HS6upkrzzCcv8qYMExACdLj2cbp9HFU0UFACeKRvXgUL0E1zEhhiBOpz7CBpyLP0qS1kKwuMlYcYaY8jSZpPB/7OpqoYQomkiCjiCpOqT7JzuhpEtUFFFGhbBYIqw2oosJ4cYySUcQr++6Z3c+Dgngmz+RCelWdpG3b6IZJRTcpVqosZItMJzJcnUnUM37rsI2WPSmPbW/nG0d3ot2CeJNtQyJXZD69tsSqMeTF59bu6NiciozPuX4WOF+pkimWaY3cOICt6SYD0/FV2VeAoNfJzvbYpvxktwK2ZTM8t7jGwkiRJSI+9y0vINwIiizRGPQQ9bspLqymiY8vpMkUy3dcB+vWVNqigduq071VpKol3p8fYmegkV9MXqJmmciCSKJaoN391VBFvx6KLBH2uQm4N2eduQxNldck/izLri+E3CSAHY0nyVdWsyVkSSTic9MY8N6xKKAiS0QDbhqDXobnVpcVTC1mSeVLX40A1rZtsJfP581oPXWVrQdhFfpuQjdNZgo5PpmeIF+rsCvSwKFY830PYK+nMgvX/blV+zbseu3JXPETqmaWsLaHZvfTqFJ9wqFJQUSxrv6XqlxmvnQchxSgx/87KMLdk95+iDpMw+SLd68wO5FErxkcfn4nr//Zk+w/1rPhNvlMifhshqa20G1b/zzEQzwEzORz/JvTp4gXi/znxx4j7HTe92dgg9bIa02vY9kmFhbSkgfs/sChNd+VRYWnos9jWPUMkiRKYENMa8LERBGUlYCyzdVBi7MN0zaRBGnJ71XgYPAR9gUOYdkWsigjILDbv2/lN0KOMF3ua+PQ/7rnf7+qDZZt8Ujo8TU1u78t+L/+5a+3ZD9uR93O5D//9pO3Qam1SeZKxDNrA1i/24mmyHdEjxYEYcPAqKoba/wr17RuKZC/Op1Yk4H1Oh2030cPXsu2GZ5NrjkGVZFpCvm2rE4w4HES8DhhYbWY50Q8Q6ZQ2WCrW4fH6aDtJosIm4EgCLS4A/xh92FOL07xe10H6faG8SoapxcnmS5mtvw37zacikJL6M7PlSiI6/aPW6l/HZtPUyivXsxQJImWiH/LLAB9Lq2+MPKlAHZ6MXvHpXobYctnoIn5LFNjCRLz2Xr96w1Orsvt4IVvHbovRfT3ErIo4lEUnLKMS/GyL9pI0HFnqzFbh7tLZpYEDVnUcClNeNUOLAwK+jR5fYr80t8uKYYsuvCrXTjlGJJwe+qFD7E5WKbF6MAclbKO062y82AHO/avX1e9DLdPo8PVgCRLv9kc+Id4iLuIYq3GT/qv4Hc4+IM9+2j1PRgKuqIgId6iNchy8Hj9/6V1thUQkIW1Uw0RcdOZUwFh3d96iFtHa8TPy4e38/pje26p7nUZpapOrlSheh0Fdhn/488+5X9+8/gd03Mr+tp9Qz2zWlnnd78M3TRZzJXWTO49mkpL+P4FsLZtk8wV12SGHbJEU9C7ZQsxfrdG0LN2jpkqlG66AHArUCQJj3b35mkOUeZwpA0RAVms3+c9vgjNrvt37TYLRZbWvRb3Eul8idqXFnNkSaQl5NsyBpBvA4XrTKG8RjBuq7BlAWytavDWj09x8pNB4jMZqtW6AMyNEG7w8fw3D/KbPhOumSZ5vYYqSSyUCog38L/aKlTNBCV9BhDxKF3YGMiiF7Aw7TqVwLJ1ivoYHqUbSXRiWVUEQQYEbCwUcfMpf0V00+F5GUEQEaivvEuCE0lQ8SqdaFIYVfLT6n4WVfRiY+OWmxAF9SF9+B7BBsrFCpZpoagyTpeK6rjxkCCKIuI6XrtnPxviw19eQJQEfv+fPIskifz6R6cYH5xHUWX2Hunkkad3EIr5yKVLvPWjUwxfnsGyLDq3NXLk6e3sPHDj4Lla0Rntn+PiyVGmRxNk00Vsy8bl1WhqC7HjQDsHHu1B3SJ15ImhOB/96gKXTo8BcOiJPp775kEamgMbbpNNFRm8OM2VMxPMTi5SzFcQAG/QTXt3lL1Hu+na3ojLc2OanW3bVMs6Zz4d4vKZceLTaWoVHc2t0tweYffhDg481oskrb8q+xAPNmygqOv86MoVXIrCN7Ztp90fuK9tut17ZtX3b7DpRvu9k3v0fmesc8UK/91fvYdxC24H16NSNZiMp+t1ffcJIa+LI32tPLuvh4NLCqG3Mx9Z9gBdb3aXLVXI3p1kC1AXo/lyVvXLMC2LclVfNzOlKjL+O6Q43wlsoFCprbn+siTi3YA2vRloioy2jmhOqVq76fm7FciSeFMhrTuFIkicS82QrBYxLBPTtohqXpq+YkGsJAo4bzKvutsoVGprWBGSKOB1aVtWC+5QZJzrsPLKNZ2acfNFp81gy87qqU8H+fDXFxkdmMfrd9LUFsLpdtxwcuUPum85dNUtHcu2kAQJWVzd7GUq7INKIxIFAZ+qsScaY77gQpPuPnW4ZmapWRlcchuL5c+oWRkaXE9RMRapWUlEQcW2zaXvtFKojiEJDgyrSMVcQBbdRLTHkcTNDaqiIONWrilPXn+NVMmDKtWDY0W8tmIjL/37Qb2Ov2kQALdXQ5JEKqUahVyZallHc91+QX96scDgpWks02L4yiznPx/m9CdDJBdySJLI1MgC+UyZp1/bxy//9jjH3+snMZfBtm2Gr8yQSxdxuVU6+tYqYFumRXwmzcdvXuTc5yPEZ1JkU8W6zQ82qirj8bu4eHKMC8dHePG7R2juiNw0GL8Rxq7O8f7Pz/HhL8+TSRbYdaiTru1NuDwb3w8D5yb57J3L9J+dJD6TJp8pUavp9T7vVBg46+biyTEOPtHHo8/vpLUruu5+zKXjfefHp1eOt5CrYBomsiLhC7gYODfJ1YvTRGK++z6Zf4jNI14s8JP+fkKaE2+vg6DzQWHmPMSNYJgmVycWbjsYWFahDXldSLfhBXknUBWJsNdFY9DH9pYIO9tidDeFaY/68W+iLq+qG2uyOfcKNjenTJqWvUasZhmyJOK+A4XkrUCxUsP6UnJHFAQ0h7xlMx9FltelYZer+hrf3s1AFAUU+e71X8O2GM4lGM4l8KsaLrl+zTzK/XcuuV2IooBDub/lgqXq2kUTQdjawFqWRNR1Fk3KVf2ujRdb1vrjHw4wPb7Ijn2tHH5iG60dYRxOFeEG/GpFlVd9ntUzLFYXKBgFJEHCLXtoc3YgiRLJaoK5yixu2cM2745V+5kuTeKWPYTU2xOOuldQRJGQ5iTsdBJzuXHdg85sY2HZNUy7TMVMYNlVbNtEt7LUzAyiIGNaJSzqKyMVcwG33IFpl6maSQS2liJ6K0Hpw8D13kKURDq3NXLigwFymRKDF2foPzfJ3ke6kDcps59LF/n8ncvMjC+yfX8b5UKV0YFZxq7OIwgChmly/L1+2nqi9O1pYezqPLMTi1w4PkpbT8O6AezsZJIPf3med396hrnJFE3tYXYd6sAf8iCKAsV8hbmpFIMXp5gcjlOt6Lz8+0fp7Gvc1HFMDMf58FcX+PStS+SzZXYf7uTbf/IE+452rxvcW6bF+NA8v/7hSU5+eJVqpUasNcT2/W04XSqWaZPLFBm7Os/Fk2Mk5jJUyzW+/ruPEGlcu5qcmMvw8RsXeOtHp0jGc0Qa/ex9pItgxIsgQC5TIj6d4tf/6SQ7D7RTyJVv+xi3CpIoIgv3585dnswuv74aRiprMZJO8ebwMFG3m2c6u26oaP8QDwY0VeF3ntu3Ss33dhH0bn6xYld7DK/TwerpVV1TRJElFElClSVcmkLA7STicxP1e+hqDNIS9qPKm/c/reoGunH/Msg3g2HWM7DrQRbF+2p5Z9s2lZq+JoAVRKEupLNFA6ksiuuKQVVqxpbYNwncXRaEDZi2zVBugajmxa/Wlca/imOjgIB8jxarNkJFN9YEsKIg4FCkLWNwyZK4rjhaVTcw7tJ4sWV38vhgHEWWeOJru3nutf04b6OmYhnJaoKx4gg1q4YmOSkXx/ApfgJKEMM2mCyNo0naqgA2r+eJV+aIaY2E1DCmbVI1K1TMMqrkwCm5lgzX9bphu6XjEOvv36vMRf1mtNANE7/DeU/k2xXRh0MKY9sGPscOJMGBIoVw2QaqVFdyM8wcJlVEQcMlt+KUm5BFN7LoQ5WCG9rZLCbyDA3MUdMNIlEvPX2NaA/tVL5yECWRPUc6+eiNENlUkYFzkzhdKrWqTltPA9FGP7Ii3dZ9ksuUuHxqnJd+5zDHnt9FqVjljb87zhfvXmFqdIFqRae5I8zrf/IEgbCXj9+8wPu/OEdyIcfAuUm++cePAdcejvlMiXOfD/PuT86QmM/SsS3GM6/sZ9ehDqJNAURJJJsqMHx5hi/e6+f8F8O897Oz+EMe3B6NpvbbW9SanUjy4S/O88kbF8ikiuw80M6r3zvK0Wd3rHseLMumWKjw9t+f5vN3ryAIAgce6+XwU9vp2dWEx+fENCwW4znOfz7MF+9dYXIkwUdvXCAc8/P13z2y6nir5RqDF6Z576dnSSXyhKJennltP4ee6KOxNYQgQHIhR/+5ST745XnOfDpEuVhd0657hb5QiN5QGPd9WGG2bRtz6bUcxBqWtfLSTRPdNKmYBhWj/irpOsWaTtU0bkn84l7ABk7PzdDk9dDu99MXjtzvJj3ETeDSVP7gxbUCV/cKLx3so7spvGZCLwgCDlVeoZDKDh1JreFSNfxKGMs2kQQRG3NlQisIArZtIQkKFtbSd6Ql67u1MExrQwr0jtYoAY/zri1o9TZHCN9EQdfG3piiLbBlojWbxXqjzlYHhHVx1HV+27ZvarnyIEAEfIqGhY1hm5i2jUB9rP/K4UHIy2x42raycevvy2a1YOxWYssC2HKpRjjmI9oU2FTwClCxKqiig05XN27Zw8eL75OppfFIXlpd7UyXJ8nrqw24E9U4oiChio665YdRYKo0QdWq4FX8tDrbcYgOUrVU3TsOE/+SR93t5g1suz4AFItVioUKlXINXTexLbte4ylLODQZt1vD7XEgy2I962RZpMol5op5uv0hAprzrq8kOeVGNDnGNZGm+t+K2nXteJa6FggEpD0AqFIAl9IG2Ais38ahgTn+x//vm2TSRR5/ajv/+H/7Apr21apLeIj6g7yjr5Fjz+2klK8yObrAJ29dZKR/lkdf2MWhx3sJNfjwBV24PNotBbO2XWdWfOOPHsPjdyKKItMjCUb755gYjlMpVXntjx5lx4F2VIfCwSf6GLs6z2cTl0nMZTBNaxW1bmRgjjOfDDE/nSIc8/Ot7z/Oc984gNN9bYxpbA3Stb2Rvt0txGfTzE0k+eTXl2juiBBtCmyYhRUEYSWDYVk2mWSBd35ymvd+fpZcqsjOQx288ntHeeLrezc8Xr1mMDm0wPu/OE8+W+Kpl/fyre8/zq5Dnau+19oVZce+VkRRIJM8wfx0mg9/eZ5nXt2H5lRXxv7EfJb+c5NMjizgcCo8+vwuvvNnTxKKXvMMbWoP07W9iWDYw7/7739FsXD/HupPd3Ty/X37afXd3/t/+QxUdJ2yrlM0dEo1nXytSrpcJlUuk6qUiRcKzOXzLJZKFGo1yoZOSa+/qsb6dX33AkVd5/PpKVp8Ptr8frSvuMVaplRmsVhasQsRBPBpGk3+27dsMC2LyVSWsq7TEvDhd96/GsYHBbvbG9nf03zTczldGmasOICz5mGH7xA5PUnY0UzVLFE2S3UxLEHGxiSgRsnpKXSrhlv24ZI86+pRiIKw4SL8dx7fy5G+1rtWl6+pCv6bzC8lQcSxwZhvWTY1w7xvWVhBEHCqypog2rJtqvrN7VBuFYZprVufranyfc8G3gosIKuX2RlopNcbJeRwISDcd+eOryo0VVlT527bNlXd3LIFDdMy1+1zDkW6Y2uojbBld7Hb60B1KEurefamV5OKRoHJ0jguuW6iHlACa2per4dTcnI89SkCEFIjzFZmOJ76lGOhJygZJSzbxLANhgoDzJan2enbQ9Es3na7bNvGNCzy+QqnT45y9uQYw4PzJBJ5qhW9rjQWctPeFeXA4U6OPdZDQ6MfWZYQBQHTthlMJZnK53i+vZugdvdrneoBurDyvxt/fvP3H+I3C8v36Df/+HEUReadn5xmYniB6bEEP/g3H/Djv/iY3Ye7ePT5new71k2sJYSqycg3mDQ5NIWm9jAuj7biWRZq8BJq8DIxHEdzquw80IG8NIEIhNx4fE5sy6ZWNSgXq3h89XvDtmwGL05z9cIUqkOhrTvKS79zZN3aMdWh0N4b47nXDvCzv/qMyeE4o/2zHHi0m3Bs/eBKkkQkRapnUfMV3vzBCd78wQnymTJ7j3XxjT98lMe+tvuG57CQK/Pxry9SLlRwOBQefW4X2/a2rftdzeVg58EOrl6c5vTHg8xPpxgfjNO3p2VFaXFqNMHIlRkAXG6Nb37/MXyBtWwRl8fBU6/s5Rd/8wXFfAW9dn9q0h4ULJ8dp6LgVBRCN/iuTV0FeDqX5eriIhcX4lyIx5nIZijWauimiWFZ9zyYncxm+WRykmc7u9jTELsrv2EvUa1rRj07bS1lZEShTgdXJAlFEu84G3RyfIa/O3WBc9NzmKaFZds8v6Ob/+qlp2kJ3J7ickU3+H//+kMuzy7w337jeV7c2XtHbfttgmVb6HYN0SqTqM5yKfsFT0ZeY6Y8RkZPIAkKNjYuyY1DdDGQOwNAu3sbqqihCGtLJhRZ2jAICrg12iL+dWvh7hUkUcS1QZ2raVlUasaWCibdLjyaA+nLAaxlU6lt3QKabprU1qFtOlUF5SsQwNYzsA5mihkupecw7XrWf7s/xp/1HbvfzfvKwe1Q1yTNLBsqW6BIvQzdtNatddUU5Y59ZjfClo0yvTubOX9ilFQih2laN5zk3gh2vXqzTgW2qpTMIj7bv0ayfxkxrQmX5EYQRGRBxik5kQSJ0eIQR0KPoYqOpfdd1KwqiWqcA4Ejt519rVYNBvtn+Q//9kOGBufRawaWZa/UMtSqBqVSjbnZDKeOjzA7neK1bx+iszvKbCHPB1PjlPTaCt3tIR7iQYGiSrzyvaPsPNjOJ29d4uM3LjI3mUSvmVw8McqVM+OEGnwcfmIbz3/7ILsOdWy4L1mRCEZWq1c7nAoOTUGSRFweDY/vWgZFVmVkpT6wWpZNrWLA0vy2WKgSn06RXswTaQyw61DnDYVPZFXi0FN9vPOT02SSBeYmk8yML24YwMqKhMOhkEsXefvvT/Pjv/iEYr7MoSe28e0/eZyDj/fd9NxVSlUunxnHNCw6+mIEIh6kG4hb+ENu/EE3UB8zxofm6dpxrVY3tZBjfjqNosrEWgM0tYc3zCBLkkTf3lbmp+uiVg9x63ArCn2hMD3BEF/v7cW0bGbzeU7MTvPR+Dhn5+dYLN1FOdUNMJ5J85OB/rsXwAK5coUfn7vCJ8MTTCTT6JZF0OWkryHMizt7ebqva10F09vBI10ttIZ8zGXyXJyJ8+8+PbU1B/AQtwUbGxERsJktjy3RggWckgcbG9u2KBhZqmYZSZDwygEkUUYWFCx7/UUxt6aibeABXloSbLmvAawk4NpAIVc3LIqVKlG/+x63qg4BcGvKyuLuMkzL3lKrkUrNqGd0vwTNoWx6bn4vIQkibe4g/3THk9g2LFYKXEjPYtoPbu31gwzX0vzreli2Rb5S27J4ZCOLK01dX1BsK7Blo8yLrx9iajzBx29dQtNUHnl6O85NqJl6ZR9NWgsBNUDVrDJXniWohlHE9fclCuK1YFSAJq2ZVxq/RbqW4t34G7wYe5UGrZEd3t00as3MVaZ5c/5n/F7rHyNvUOP5ZZRLNU6fHOU//uXnjI3EqVYNmpqD7N7XSltHBKdToVSqMT2Z5NL5KdKpAtt2NBFp8ALLNRn1h0FJX1/e/SF+c1Gu6lwcnqU9FiQccN+1m3mzEAQBRZXp6IsRjvl58qU9jA7McfazYS6eGCWTKpKYzfDRG+cZGajTi1//0ydQVXlNpkaSBJxudVUCXxJFRElElEU0l7pqG+FLxTqWZa2w3pMLWfLZErYNTrdKU3v4hpkhSRRp7YyiLk1e0ot5kgv5Db8vKxKFfIXP3r7MT/6XTynmKxx5enudAny486YCUHWrG4Pp0QSmaTE5vMC/+m9/fMMSimqlRmYp2LRMi0K2hH3deJDPlsmmCqiaTKwliCgJ6x6zINTfb2gK3JHa8m8jBABBQBIEJEBZ8hPtDASIul083d7JdC7H8Zlp3hkdYWAxcc/G7GSpzImZafoTCfrCa2sc7wRVw2A0keK/f/tjRhMpQm4XOxqjKLJEIl/k8uwCYY+bx7rb7ziA9TocOCMKzX7fV1Zc6zcBTc4OwmoMBKGeHLAtPLIPp+TBoj4nMZcCVYfkpMe7d4VWLK3j3QvgdTpwa+oK2+56JHNFCpUanvuY4ayL08i4HArlqr6q/xWrNeZSeTpjN+Jo3D0IgkAs6FmTkarqBvPp/JbVCuZKFTLFteJ+IY8Lp/rg03BN22K6lOH04hQX07PYQLc3wuFw6/1u2lcSUb97zZiuGxZzyWx9zrUFyJcrpAprF30Dbifuu2S5tGUzn1KxSqTBz9WL0/zg33/EFx8O0NAUwOVxbFg07/I4ePm7j6z6PFVbZLGWwC25SdYW6XQ/gW7pDOQuM1IYomKWuZy9QJenF8s2GSkMMlOeompV8EgeNMnJaHEI0zKpmdW66ptZYbo8yVx5hpJZ95S6Vdg29F+e4e1fXWRkaB6AJ5/ZwTNf20V7RxiPV0OSJAzDpFio8PxLe5idTrN7XxsuZz3obnC5eb6jB8u2sG3wqfdvcH+Ie490rsT/9KPP8LhUAh4nsZCXxoiPprCPxoiPWMiDS1O3VMRhM1AdCqGogj/ooqE5SO/uFp55dT8D5yc5+9kwk8NxRq7MUi3XcLkdvPjdw/X6zesgCMLawG8pRhWX6sS/jGtHvfrhXcpXqJbrFJe6fcxNaPdCPdB1aPUao1KxRqlQ2fDr5WKNy6fHGRuYI7VQr60PhD2EYz5c7pvfo5ZpUSlXqZTqK+fVis7sRPKm2y3Dtm103Vw5bNMwqVV09JqB0+XA43PdmCki1G2QxK8AJeyrAEWSCEhOApqTqNtNm9/HI83NnJqd5Y3hQSYyGapbYEFxI5i2xVw+zy8Gr/LPjx7bsgDWtm1mMzn+/LPTnJua4+Vd23iqr5PWoA9RECnWamRLFbxOB44tWGCTRLG+cLVU8/cQ9weqqKGK2qrASEBAkq5N/a7/zCndPDOpyBIBtxO/S1sTJM2l8mSLFRqD3i1o/eYgCAKqItMZCzI0s4h+nepuoVxlOpm9b20TBYHuxvCaDHZVN5hN5basBjZdKJMprA1gW8I+fJvUqLlXmCvl+MXUJebLOfaHWmhwenFKCrsDjXR6HkynkQcdnbEgLm31XE03TaYXs3ekpH49cqUqqdzaALYp5CXguTslk1sWwL73i/OMXp0nlchTLFSYHF3A63fh0NZmaZYRbvDx9e8cYXkK2+BoBIQlOotMg9ZIo9aEIIh4ZB87fXswbROv4qur5CHgUwIcCj6CQ9Rwyx4UUSWkRhARiWoxvIofCRG37CbiiCLQQJe7Z0OFvS8jmchz/swEly5MIkoi23Y08a3fOcyO3S1omrpKrMC2/XT1NJDfWcHpUlcmlm5Fpcv/1fOveoitgeZQOLSjlVSuRKFUZWRmkeHpxfrkzqEQ9DkJ+d1E/G7Cfjc9rRFi4fs3AZBkqU51Dbnp7Gukoy9G944mPn37Eqc+GmR6bJG3f3yaR57eTrQpsJoyKwiIorh+2LWBMuJGME0Le2lwFTYIflftfikrKUr1H7JMC/MGlgELM2myqQKpRB6Pz0khX2bo8gxDl2YIRrwEwp4Nt4U65dm4ruajpTPMjv3tBKO3VuOnuVR2HmhfCfhty8ay6otcgrjOQsA6UFTpnqia/7ZBk2VafX6aPF46A0G6ggHeHR3lxMw08eLdpWvnqzU+GB/j+/v245CkNeIbm0GuUuXiTJzPRyfpiYb41v6d7GuJrdQKWnZdudWwrHWtEO4IWzA/eqjKcGe40ULY7ZZTiYJA1O+hMehdE8COL6RI5orA+h7X9wqKLLG9tYHxeHpVAJsplBmcrjMq7rYVzHoQxHoA63aoK9KaUK8fXMwVSWSLNAQ8dyS0pJsmiWyRxezacaq9IYjf/WCLoKmiRERzk6gUmCllmC/laHL5UEQJh/SQbbQZdDQE8Tkdq/qceV2f8zodd8QMNEyTxWyReLaw5rOWsJ/QTZTDN4st6w0CEG7wEm649Yl3ILR6ghhxRIk41h/4Wl1ttLrWiqN0uXvocves/N+2bYJqCMu2kIVrwXOz1kqj1oyNjbwBNWY9DA/NM3B5hly2TKzJz4uv7GPP/naUdSaX9Z8S8PlvvtpgWTbpVIH5uQypxQKlUg3DMFEUCa/PSazRT0PMj8e78WAzP5vhyqVpKhWdhpiPPfva1mTErsep4yMkFnK4PRqd3VHaO9baNRiGSSpZYGYqRSpZoFrRkWQJj1ejsSlArMl/S1L0tl2vD15cyLGYyJPJlCgVq/WME6AoEi6Xg0jUS1NLEI9Xu+/y9ncLfo/G9185zGKm/lBJZoosZooksyUy+RKT82n6xxewbRuvy8F3ntt3XwPY6yHJIs0dYaJNfmRVIhnPceXMBJPDcWYnkvjDHpzy3VmcURQZcSk4ti0bfZ36iuux3OdMw8K2bSRZvGG9TyFfxulWeeyFXYRjPj785Xkmh+J88MvzeHwaBx/vW6V2/GWIorBq/9GmAM+8doC+PS23dHyCKODQlJVAVViiWgtC/XgN/ebZPsv66vqffhUgiSItPh9NXi/NXh9Rt5sPxscYTafv2m/WLJPJbIaBxUV8Dgdu9c7vr4V8gXNTs5R1g5d29rGjMbJK6EYUBERJWhO8WpZNulxmfDHNXDZPSdeRRZGgy0lXJEijz3tzuvEtDOv2kgXSSCLFVDpLrlxFEgUiHhctAX99keY38/HwlURzyEtHQ4CB6YVV74/H08wks1RqOtp9zLw7ZInd7THeOzcMXBOqyZWrDM8tksgWiPo8d00teSMIQNjnojHoZTSeWvGrtW2bYrnG5cl5/O5OZGnz9/xitshsKkf+SzW1DkWiNeLH94CreAccTp5v2kaHO8RYIbm02CAwlEtg2zYHHtKIbxshr4tY0ItLU1dqrW2gXDUYmFqgMejBL28+S5rMl5hN5ciVVlv6qbJES8RPwP2AZ2Bf//7jN51grvlxRdryoEUQBCSkNaJPy+/fDmzLZvjqPJMTiyiKRFNzkCee3o58A5GWW0GtajA9leTCuUnOn5lgbGShHixWDZxOhcamADt3t3DgcCc7drfQ0OBDWOc8jQzF+Yt/+yGpxTxHjvXQ2d1wwwD2lz85w6njo7S2h/n27xxZE8BWqwZTE4ucPjHKqROjTIwmyGXLqA6ZhpifPfvaeOSxHgr5yroB/DJs26ZaNRi4PMPlC1MMXZ1neipJKlmgvES31Jwq4YiXnr4Yh492s3d/Gw2N/huK9HxVIYkifo8Lv8dFayxAuaJTrNTIFSrEU3mGphL0j8WZmEuxkMqTyt578ZibQVFlOvti7DrUwZUzE1imTXw2Te/u5k3Vut8KPH5tpT/rNYNc+saZL9uGYq5CrWpgWzZOtwOXZ+MA1Ot3cfCJPr7zp0/iC7kpl2p88e4VLp4YxRdw4g+52bG/fcPMryiJaE4Vza1SKdYo5isojnr2ejMr+5Is4tAUVIeCYZj1+t+bhKeFXAXzLpmEP8Q1iILAkeYWIi4XPofGj/ovM5W9e1RE3bL4cGKM7ZHIlgSwyUKJq/FFHLLMEz0duG4huLBtm/lcni9Gp/hgcJSpdI6aadTHM6eDw+0tPLOti12NDXdkS7JcR3llboGfne/nwsw8xaqOU1FoCXjZ0xKjrOtfCf/K3xa0RPz0NIdRzourMpzJfImr0wkO9eboabp/dE9Vkdnd0YjH6SBfrq7UsBumxXw6z+f9E7x6ZAeKcHs+53cKYan2fk9nI4Ozi0wlMiufVQ2DTy6Psa+rGdeSo8dmcGUyzvRiZk19cntDkKaQb0MBrgcFkiDiV50cirRxKNJGxdAZKyS5kplnupR5GMBuApIosrOtgUsT84zMXStzsmyLL65OcKCnGZ9L23SfG5pZZDyeXtPnWqMBmkK+DUXV7hRb1pO7tjVu1a4eGNRqBnOzaZLJAqGQm56+2C1lVzdC3UTaZnwswQ/++jNOnxilVjVxuVU8Xg2fT8C0LBbiOSbGFzlzaowXXtrDN797hGDo7qrm2bbN9GSSH/7dcT79cABDN3G5HYQjHgRBoFio8MG7lxkbidPQFLgpnTOTKvKv/4dfMzOTRpJEVFXG4VDqwY4NhmERn8swOZ7gzKkxvvN7j/DKNw/e9eO8HzBMi3SuRE03mU/mmJxPMxlPMxXPMJ/IkSmWcSgybbEgXc1hetvWZsYfBDg0FZfn2uqtfZezf+GYn0C4rupbKlaZHltcGSDXG2gt02JyOE61Ul/VDkW9GyoQQ93ep29PK209DViWxR//8xfIJgtcODHKyQ+vojkdhBv8xFqD6/6mIAg4nCptXVFG++eYm0qRSRaWmBSbG1q9fheBsIdUIk98Jo2hm8jy+pMs27ZZmE1Tq26dFP5DrMay7czy2e8MBPnuzp3IosC/PX2KbLV6o803DcOy+HRygt/fvYdGj+eOaeKFao14voBDlugIB1YyrflKlXylirEk5CEgEPW6UWWJck3nnYER/u7keSzb5qm+LnqiIQqVKsfHpvnJuX6m0zn+8VNH2B7bPGXUpi6u8z99eJyLM3H6YmGe6evC79QYT6X54ZnLK7Y/D/FgIOJz09scoTHkZSqxeiHn7Ogs21qjtEb8981vVZEktjVH6G0KkymUKVavZSNT+TI//uwSR7e1L9F1731q/9EdHZwcnGJmMbsSXFd1k48ujfP6Y3sJuLVNnbtKTefk0DTj8dUMEVEUeG5vL2HvWku2Bx2arLAz0MjOwG9ejHEvcbivlVPD04zNp1b6nGnZfN4/yauP7CQW8GyKNVGp6ZwdnWVodnHNZ0/u6qQx6L1rfe7BXoq5z4jHs+RyZWzLxuN10tZ+5yuKpWKNP///fcDF85PYls2uPa187ZV97N7XgtvtILVY4NTxUd57+xKjIwv8+pfnMS2b/+yfPHvnB3QDGIbFT390ii8+GaRWNWjvjPCN7xzmyLFuNE0hEc/x8QcDfPrRVa72z97QKgRAcyocfKQL07TYvquF/Yfa6eppIBB0YxgmM1NpfvWzs5w5OUYmVeT4Z8M0twR59ia+m19FLGYK/D///dsMTiTIFSu4NIXWWIBt7VEe3d3Bto4GelsjaI6N68UfBBRyZdKJuqqvIEAw4rlhJv5O4dAUmtpCRBv9JBfyXFmyq9mo7+m6welPBleEm2KtIZo7bu2eFQSBSKOfP/hnz1Mu1rhwYoQTH/TjcCr84//6tQ2ZAZpLZe8j3UwML1DIlhm+PEvf7lZaOje3CBFq8NLQEiA+kyY+nWJhJkNzRxhlnVVz27IZvjxDuXh3gqiHqKNmGciCiCTU+0CL18c3tm0nW63wb0+fviu/adk2I+k08UKR7mAITb6zR7VuWlR0E79TW0UT/vuzl/nL4+eYTteDEIcs8T//yXfZ2xzj3NQcnwyN45Bl/vSxg3x7/66V7V4/sJt/9f5nfDQ0TvScm//z1zcfwFZ0g+NjU5yfmWdXUwP/5KmjPNJRp+EXqjWOdbbxX//415ve/0PcHXQ1hHh6dzd//cHZVe+PzCX55PIY21qiHOhuvk+tq5dovHiwj/GFFMWFawFsVTcYmE7wg4/P88fPHSTqv7HWwd3AjtYoPU0RLk/EyZbqzyvbtkkXSrx5eoCQ10lHQ/C293t6eJoLY7OkvyTgpEgiz+7rvmu1iA/x4KO3KUxfc4TTQ9Or+kemWObdc0PEAh62tdz+OH5hfI7zo7Ms5lYz5CRR4ImdncQCd+/+umsB7HI92s0giuvbRDwIyGcrdV9KwOlUCEXu7ELUaga/+PFpxscWMHSDr728l2999witHeEVSxKPVyPWFKClPcQvf3KGM6fG+PzjQY4c62Hn7ua75uF1+vgII4PzlIo1du1t4fXffYRHHuvFsRRUeX1OmluCNMR8/PRHp5iaSKKq67dFEAS8Xo3f+8NH+e7vH12iRda9oERJxLZtQmEv3b0x/uX/51ecOz3BzFSKyfFbV3D9KqFSMzg3OIMiiTy+v5ND21vZ0RmjJerH43agyhKyJFF3lLl394JpWFy9MEl7bwy398b0kUKuzIUTo5z4YABRFPCHPHTvbL4hZf1OIQgCOw62s+tSJ+/99Cwz40l++r98yivfO7oqEwx1BeCxgXne/8U5CtkybT1RenY23VSI6frfAuje0cQr3zuKrhsMnJvk1EeDRBr9/O4/fHrd7bx+F8998wDv/+IcelXn4zcuEAx7ePG7h/EF12cT2LZNtaJTKlQJRVfXOrf1NNC3u5WLJ8YoF2v88N99xJ/+Fy8RbvCtuj6lQoVTHw8yO5GkVr290o3fNhiWyVQpxZuzFyibVTrcEQ4EO+jx3txrtWLp/GjyJPsCbfR4Y7hlB4Ig0OT18Ts793B6dpZLCwvU7lJ2cDidZGc0SqNnayYBy11ouS9tb4zyrX07mEhmuDK3wEzmmhLqQDzBZDrDzqYGnt3Wvar/BV0a+1oauTQbZ2hhkcVCkYhnA/bMTaYBVd3g1MQMNcPk0e52OkKBld9yO1QOtjUT9bhJl9aqqj7E/YEgCLSEfTy1p5v3L4zUFXSvw6mhaQD+wUtH7yiItW2bYrXG8GySppDvlifDwpJazRO7u3jn3BDxdIHKdb6oVd3gh59eAGxef2wvHQ3BTdfDGqYF2Mi3JX4m8OzebiYX0nx4aXTVJ7860U/M7+GVR3bQFLo1QUDDtJhYSPPnb59idC616jOPU+WlQ9toCnpRfgNLtB7iViHw2I4OxuZTvHn66qpP3j47RNRXz8C2RwO3tDfbtplYyPBX753hymR81WeqLPHSoW20RQNrLKO2ElsawA5dmeHMZ8OMDMxRyJVv6i8UCHv4r/5fv4d0Hygct4JKpYa+pDIqyxLOdSbrH73fz8fv95PNrK5bVBSJ7/z+UY4cqwtMLU9aP/5ggGy6REd3A/sPddLZ3bDKx1GSRBRFZv/BTqYnUwz2z7GYyPPhO5fp7YvdtQD23JkJkot5NE1h2/ZmDj3Sjfs6AZt6uyT27m9nbDTB1E3sQkRJJBT2rNTurvULFXE4ZHbsamFseIGFhRy5XAnbstet9/0qI+Bx8p998yjTC1lyhQofnR3lg9PDeN0OGsM+OppCtDcGaW8MEgm47xntqlbV+Yv/4ddomkpLV4SWzgiRxgDegKveJy2bQr7C/FSKgfOT9J+dIBnP4Q95+MYfPYov4Lrr16q9p4EjT21jYijO+OA8v/ibL8gkC+x/tIdYSwhJFkgvFrh6fpJP375Mcj6HpEg89co+dh3quO2aakWVOfh4L5lkgWK+wuz4Iu/97CwtHREOPtG3pt5XUSWaO8J86/uP84u//pzkQo43fnCCyZEF9jzSRVN7CE1TMfR6TetiPMfMWIJcpkT3jiZe/9MnV+0v2uhn58F2Oj5tZHIozokPBnBoMo88s53mjgiiKJJK5Og/M8EHvzgP1MWqHtbBbgwbm0ytiCYpHAi2M1Fc5FJmmmZnEFWUyehFZkppYpqfoMONKsqUjRqJao6FSp5MrUTVMlZ5wcqiSKvPxz89fJT/5r23WSzdnbr14WSSTKV8xwGsLIqokkhF15fs3EQEQWBXUwPtQT/DiSQVw6gHsEtYLJSoGiZhlwuftnrBSBJFIh43QaeTfKVGPFfYOIC9yRBhWBazmRyGZdHo9eD5kriUU5WJet3k7xJd+yE2B0WW6G0K88fPHeRf/uwTqteJzpWqOmdGZij87BOe2dPN8/t7aQx6b0nptFzTiafzDM8lGZpJcHkijiAIfP+5Q7edzfG7NL7z+B6ypSrnRmZWraUUyjV+eXKAyUSWZ/Z288i2NhqD3pvS9W3bJl+qMpFIc3U6Qf/UAt2NIf74uUO33C5BgB1tDTy5u4uZZJbh6+oS85UaP/78Mulima8f2s7O9oYbKpHnShVOXJ3iR59epH9qYVWg7lAkeprC/NGzB/G4HA9ssugh7j4EoZ6FfXJXJ+PxFAPTiZXPipUavzzZT7ZU4ZUj29nX1XTDPleq1jg9NMOPPr3I+dG5FTEyqGf72xuC/MEzBwj77i5lfctmyhPDcd744UnOHR8llchhmjayLFIu1VBVGQQwdBPLsnG5HcRagjQ0Bbbq5+8KLNNaVXO33mR4IZ7j0vkpFpeolctQVImnn79GudJ1k/m5LHOzGXTdpLungbaO8Krg9Xp4vBrtHWFa2oIMXY1z4dwEtaqO5tx8cf96WJ6TjY0sUMhXiMZ8tLaH8PrW1voKgkCsOUBn181pBoIgINxgYWL5GKINXjSnimlY6LqJZVlI4t1bsbkf8DhVXnl8F5l8mXS+RDpXJpEpkMoWKVV0+sfiXBqZA8DvcfL8kT4O7bj7QgWWZTM+GKdcrDJ8ZRZ/yIXb68ThVJAkCbCpVQxy2RKL81nKxSoNLQGeeGkPT7+yD1Xb2r64HpwuB3se6aZUqPLGD04wcmWWD395nqsXpvD66wF0uVglMZ9lbjKJw6nwwrcP8tgLu4jcoP71RvAF3Rx9dgeFXJk3/uMJZsYW+fs//xhf0EXv7pZVWWdBENBcKs+8th+9qvPRGxeYn0qRz5QYHZjFF3AjKxKmadWzrvkK2VQRWZXwrlNPrzoUtu1t46XvHubHf/EJi/NZPnvnCuODcfxBN4IIpUKV9GIeXTf5+u8d5f2fnWVhNrPZU/wbD5u6KJJTUok5/UwUF8nrFXTbJFkp8FF8gBZXiP7sLPuCbbS4QozmF7iSnSGm+RktLHAw2LFqnwJ1q51HWlp4pKWFTycnyd2FAGs4lSJb2djL+FbhVBVCbhfxXIFEvkST34ssCfg0Bz7NQbFWw/2l+ifdNLFtG1kS1xVbVCQRWRKxbIuqsXkWgI1NxTCwbXDI8jretwKODerAH+L+QRAE/B6Np/d0Mx5P8+bpq+TL1+6BQrnGxbE5krkSF8bnaIsEaAn7CXg0nKqCLNfHxZphUqnpZIoVMoUyqXyJZL7EYq6u2r+QLRALeFbVsd5q+wQBDnS38Ny+DLlSZZWADUAi+/9n77+j5ErP9E7wd314H+l9JhLem/KGVWTRFU2zyWZ3s7ulbqllZkdzJI10pNWZo52dnd0zUq+2V9rRyI5c+242m2ySxWKR5QsFVAGFgk+D9D4zvI+4dv+IzASykAASQAKVqMLDAwIVce+NL27c+93vfd/nfZ4S7w1NMZfOcXJwktZokJZogIBbw6UqyJKIZTvUDJNSVSdbqpIplEnkSqSLZZZyRZayRZ7f33fb58/rUnlkezuL2QJLuRL58tX7fCaZ5WcfXmE6mWNXRwM9TVGawn48mookCtQMk1ypymwqx/BskkuTCwxOL1G7xtZNFATaYyG+/ugeuhsjnxi7NcO0WMgUmEnmqOgGummhGya6aVG79m/DomaanBmZXa6Sr8X3T1zizOgsXk1BVSQUWUaTJVRl+W9ZRlUkXKpMPOBl2x3Qa7caPC6VQ32tLOaKLGQKZEtXr7n5dJ43zo8yn86zq7OR3qYozZH6NSdLIrppkStVmE8XuDKb5OLkAoMzS1RqxmpiSBCgMeznW0/upa85ek+rr7CJAewHx69w5t0RJFni0c/spLktQjFf5eXvneLgo72098QxDIvx4QWyqSJdfY08/5WDW9o6RZKvqiQ7jrOup2R7R5THn9pOIV9BN0wmRpdYmL9enVLXTeZnMxjL2bHGpiChG1AMoX4hBMMeGppCDFyaI7lUoFis4fO7keTNPGcOhm6RSZfQdZNwxEc44r0hncbtVgmGPLhcyi0r7I7jUCnrLC7kSCeL5PMVqhUdw7AwLRvbshkdXiC/rLrrOA62w21qRW99yLJEcyxAc6xOB6rWDOZTBcZmk4zPpplaqAs6TS9mME2b9obQfQlgZVnk6S/uY2pkkXSiQCZZZH46janXE02CWK9I+vxuGlvDtHZG2XGgg0NP9tPUHrnn41tBrDHAo8/vQlFlzrwzzOToEhNX6oG34zhoLpVg2MO+Yz1s39/B01/cS0vH+n2jG0VjW4THPrubXKbMz7/3AZfPTPLT755Ccyl09TetObYoijS3R3jua4cIRX1c/GCCmbEEyfkck1eWMA0LURTQ3Aq+gJtYc5CubU3sPtK97mdHGwM8/rndmIbF2RMjzI4nGb08i2lYKJpMKOqjc1sTh57cxsHHt3H2xMjDAHYDmC2neWOhhoNDt6+BmmUykJvjw8wkDnA+M4VbVrEcm9lynYbX4glxA1djREEgoGl8qa+f4WTqngSwk7ksuVr9Or+bAC7scdEVCzOdyXF+dp6w143vFlYdHlVBFAQqholl29dl5CuGSdUwkUURn3Zjte9bQRQEfJqKKEDFMDBsmzVHcxxKurGhlqSHuL9QJImmsJ9vPrkPw7R45/LEml44Y5naOrmUIeDWiId8BD0uXKqMJIlYll3vz9ZNCpUquVKVYkW/TrDrbhSog14XT+/pplzTMS2LyaXsmvdLVZ3B6QRXZpP4XBqNYR8+l1Yfo1gPYA3TpFwzyJVXxlhbU82173CArdEgn9nXS7pY5mdnrqwmABxgIVMgmS9xaXKB9niIhqAPt6YgiQK6YZEvV+uBXCpPsbJ27hEFga7GMJ8/3M/Te3o2xUt6q6Ci16v7r58fpVipYZhWXeRt+e9r/ximiWHa6/4+J4cmOTMygyJLqLK0+vfKv1f+26MpHOht/UQEsFAPMJ/Z00MyV+LHpwYoVGo4Tv2aW8oVSRXKXJ5apCMeoiG0cs2JGJZFoVxjMVtkOpFdk6yC+jXXGg3wwqF+ntu/DVW595oumxbAnj89jq6bPPPcTj7/jSN09DQwPjzP6y+d4+BjfTz7pf3Issj5U+Mcf/UStZq5bgViK8HlVlYpu4ZpUS5fnwHcubuF1rYwpmlTKFT4yz8/TSpZxPpIcGeZNrlseXUi9vo0XK6bK365XOqqD6xhWhQKFeINgVsKKN0OHMehUtEx9HoG3O1W16VKr0AUBRRVwu1RKRXXrww4Tj0onppMMjK0wJXhhVVf2Uq5Lmnv2PXtqhWdSuXGKqrXqoA+qBl43TCZmEtTquqUKnX7nGSuRDpXoljREQRoiQUI+931Kvd98oBVNJlf/K2nGR+eZ34qRSZRpFiooFcNLMtGFAVcbpVQ1EdzR5Senc20d8fR1rk+GlpCHHq8j1KxRld/05rfKhj2svNgJ6pLoaO3Yc1+mkuhd1crT35hL01tETSXch3lUBAEog0BPvOVA/TvbePiBxPMjicoLN9PHq9GvDlEz85m9hzpRnMpN6U27z7cidur4va7yFVqvPHW4DUfBrt2tNAQD9DR28AL3zi8Sv+FugXWjRbSdQp2kJ0HO7lycYa5yRS5dIlazUCSRDw+F+FY/Vx29TfR3rP+A1GSRBpbw3z11x6ne3szwxemSS7k0GsmLrdCY1uEHfs72H2kC4AjT22noSVE3+7W+vl7iHVRNGvk9DKtnggh1YPpWCxVc4RUD6oo0etvIKi4yekVTMemz99IhzdGqyeMchNWyKNt7bQHLzOdz216L2ymWiVfq2HYNupt9ditRdznZW9LI28Nj/PK5RG2N8bXqBGvh9ZQgIDbxVKhyEwmR2f0qqhMbZlunC5XaA8HafDfuXq8LEp0xcLIIxLjqQyZUnmVRmxaNqlShUShtKqU/BBbC4ossb0tzi89vR9NlTk5MMV8Jo9urr0X8pUa+cqdJXnu9tHf1RjhhUP9iILAz89eYXwhvcb+B+qKrLlydVVU6X5AkkS2tcb41pP7MC2bkwNTpAolLPuq7U8iVyKRu7mF3LXQFImuhgifPbiNLx3dQTTwyRJuMkyLsfk07w1OUdHvXIHfth2qtrmGcr0eVFna8tZDtwNJFOlsCPNLT+1DN01ODE6xlC2uVqkt2ya1zILYKFRZoj0e4jP7evnqo7uIB++Pm8im/SpL81kCIQ97D3fT1VcXxxAEAUWVqVUNHNvGF/By9Kl+yuUa3//9d3njJxfo2ta4ZQOTlUojQLVikEkXr9smEPQQCNYniGymhD/gri+eP/KsdVhu9l9e+wqieMv+QVEUkFa2ceo05I/6LN0ObGcd2xMHTNO6SgEQhQ2N62ZeuLpuMjWR5M/+8CQnj18BB/wBF16/i3hjkEDAjdujoigSC/NZRq8sks9dFehYCVptx8ZybGqWiSxKeOR7Jxh0L5ErVvnDl8+QypdI5UpUayaSKOD1qMRCPjoaw7Q3hpb/hAnfgweO4zhUqgbFYpVKxcA0LWzHQRJF2rY30be/Ha9Xw+1SENfJ1larBuVyjXLNWDeA3XOkmz03qCh29DXS0be+YE4g5OEL3zrK45/fw8JSjoVUgYVUYd1tARCh/2gXz339EJ47FJD69t/8DADjk0l+/tolfvhf3sQ0bTKZEqZl80//8VdpiAeQFYm+3a38D//LL2z42JpLoW93K327W+9obCtYoSYffWY7R5/ZftNtf+3vfPauPuvTgu2BZnYEmhkuLDBZStLoCtDoCiKLEsdivaiiTEBxs1TNkTXKJGoFEtU8RbN2wwqLIAhEPR52xuIMJZPMF29y7d4BHMchV61S0nVU950nfKM+Dwfbm+mMhnhrZIK9rU082t1OxFtPmi3mixQ/QtHc3dzI9sYYQ4sJfnJpmBf37sClyFi2w2w2z4fTc1i2zb7WJkKeq2OzHQfTsrHsOrW4ohurz7+yrlPWdURBQBYlZEnEpUgc6WzjL88Ncmpihp5YBFmSkEWRXKXKyfEpijX9jqtcWwm27VDVDWaWcnQ2hdE+QQvjXR2NRHxumsMBXj8/wtRylWY9+uZGIAigKTJ+t0ZHPITffedVfoCepii/+ISblmiQPz9+nplkjlypinHN+ud2IYkiHk25q7Gpskx/a5z/y5cfJ+hxcWJwioV0nnLNuK1rXhQEgl4X3U0RvnJsF0/v6fnEBa8PsTlQZInOhjB/5ytPEvZ9yPHL48wkc5Rq+m0xXURBIODR6GyM8IVD/Xz2YP99C15hEwNYvWYSjvpwXSNyIogCmkumVKxiWfWToqgy8aYg4ZifSx9MbGlz8ng8gG85IC2VqszPZriZD+XNIIkCHo+6mkk0dBPTuPnEbpoWum6ufp7bpa6lXG9wCCtjNnQL+6MPE0HA5VJW+yMs07qpIMyKurR5g20cx2FhPsdf/OkpXv/ZJURR4OCRbp57YQ8Hj3QRjfnXfIdXf3qRP/n9d9cGsEDF0ikYVUQEJksp/IqLHcHmjX3hLYaqbnL2yizdLREe39dNd0uU7tYIrQ3BuzKP3ijqwavO+YszHD8xwqWBWTLZMtWqjs/rIhL2sK2viaee6Gfv7rZ1A8PxyQTvnx7H7VL4pV88tuljfO/0GP/s//PSmtcsq+6bLIrCmmtGFAV+5//5bfbvbb+rz2xuDPKlz+/n8MEu0pkSv/dH7zK/kL2rYz7E1oQsiKiiRNzlJ29UWKjmKBhVdgZb+fcjrzNTSmM6Np9r3sOOQDPzlSwvzZ5jtLBEVi+v8YJdD/ubmnhvdnrTA1iAbLVCUdcJ30UAK4ki3bEIf/OpY/zPP3qVf/3mCX4+OEJ3LIwqScznClyeX0JT5NV7bVdzA8/0dzOZyvD7751lYD5Bf0OMQq3G+xPTJIplnt/Ry4v7dqz5rJphMpvLkyyUyVerXJ5fqnsWF4p8MDnHfK6AT1NpDvppDgbQZJljXW0c7mjhnZFJ/s/jpzk1MUPU62Y2V+D0xAxNAR+J4sarUFsVumkyNJng7//Lv+A//JNfpq/txnZbgiDgVtcPjiRJ2Ojj/76iKRLg158/xGM7O/npmSFePzfKQqaAZdvYtlNPon8kkS4s/58o1B0pxGW9EY+q0Nsc5Zm9PTy1u3vDirw3QzTg4YtHtvP4zk5+9P5lXjkzzHQyR1Wv0+RXx/eRdekKA0xYHqcoikiiQMzvZX9PC4/u6Fz38zYKSRRpDPv5H7/xDCcGJvmLExc5NzZHtlRdPXfXnbdrxqJIIkGPiy8e3cGLx3bSGQ/ftoDherj2GryWrbHSF/lxQBAENEXG574/Y1BkaWP+qMsJF59bW5O08blU1Lu0QYP6Pe/V1DXzgUuV8WjXs9Y2AlEUCfnc/PdfeZxHd7TzgxOXOL1ssWPZNtYtrjlZFAh63Xzu4Da+fHQnfS2x+35NbFoAq7oUKmUdvXq1pC9LEv6Ah6W5LIZ+tUyvqjKaJjOXLd1dc8M9hqJKNDUHiUS85DJlrgwtYBjWHXlfyopEQ2NwtbqZzZQplapEb2LNUyrWyGbKCEL9nIXCHqRrmqLrk2o94Kv3o974XBq6SalYxTDW0noEoU4bVl0ygihQLNYo3oAavHqsmknpBt6TjuOwtJDjxNvDAPRtb+LXfusp+nc0I8vidVQgvWZc10trOhYzpSyvzl/mS217cUkPNi2yJR7gv/3fv4Mk1m/8mmFSquiUyjqKJOFx3dvKsgP88Xff57U3BigUqsSiPnZtb0YURbK5EjNzGWbmMjTEA+zbvX5QOD2TZmR0ka7Oe9MH0tQY5DNPr10IX7g0SypdpK0lTN811GNRFAmF7j6zrGkyzU0BGhv8lMo6P3zpLItL+Vvv+BAPFBRBYl+4nd2hNmRRpMEVxHZsZKFuXfWPdn0Z3baQBBGXpCALIkejvewPd2I7DqIgoIoysnDjh3NvJELEfW+qHelKPYC9W/g0lSd6O/lX3/4Kf/rBBc5Oz/HaYBpZFAl73OxuaeRQRwudkTq1WBDgue29NPn9/PD8ACfGpzk+NolLlumNRfnmob08099Fc2DtM2wqk+Nfv3GSd0cncRwH03YwbZtL80tcWXoLURDwagrfPLyH/+EzjwMgCQL/4HNP0RkN8frQGD8fHMEly+xpbeSffPFZLs0t8eOLQ+t9rQcKtu1QruoYpn1LNpXPrfEPv/kMf/frT133ntelbNm+RlEQ6GuO0vH5R/jlpw8wML3IubF5RuaTzKbypAtlSlUd07LrwYEi43WpNIR8NEcCdDSE2N4ao7+1gZDXhSLXK/WbJUIkCgIhn5tfevoAX3t0NyNzqWVBmgTTiSyJXJF8uUrNtBBYDmAUhYBHoyHkozHkp7cpwo72BrqbIgS9rptS8W8XR7a1sa+7mYnFDGfH5rgwMc/YQorFTJFSTa/rPigyIa+blmiAbS0x9nU3c7ivDZ9bQ1OkTTtXYZ+bf/RLz9bp1tdcrpIo3De3hI8i5HPzWy8c4deeO3jfwgdlA217LkXm1587xLef3r9mXKIgoN5BzPBRHOxp4V/9ra+t0suhvn5fYQHcDQ70tLCzvZHpZI5zY3OcG59nbD7JQqawWpVVZYmg101LJEBvc7R+zW1rI+RxrUl83k9s2hXY2BxibGieQr6CbdmIkoiiyTS1hRm+OEsqkSfWFERRJMrFGulEAZemcEepg/sEQRDo62+ivSvG2dMTLMxnOf7mEE89uwP5Ni9ITZVp74zi97uoVQ2mJhIkFvN0dK6fgXUcSCTyTE0mUVWZrt54vbfvmtMlSiKqKmPbDulUabXKff2xHMZHl9bt4YV6pbylJcLCbJalxRyLC3kcZ/2+k3yuQiJRwNDNdQP5WtUkly1TKtUQBNi+s4VI1FdXol4Hc3MZyqW14zIsi4HcHLptkqqVSFQLhFQPtmMj3mQRuVUhiSJ+j8bYbIqfHL/MpbEFcqUqPS1RvvbMXo7saqemW7z07mWaowG2dzYQCW7eYnhkdJFz56dRZIlvfO0wTz3Rv0pdsyybUrlGKlWkuSmEpl3/m9q2w+xclvnFHJ0dN64Y3A36ehr4rd9Yu1D71//uNUrlGvv2tvPL37xa9RUEgchNBNA2CmEl4y+ynFzZmlWNh7g7CIKALEirD7u6OPrV69wtqbikq08iQRBQBGlNwHorlkSrP0DM40ERRYxN7tXM12qUjbsPYAVBwKXI7GqO83eefZSiXg8iBEFAFkU0WcKrqYTc7tVqmCZL7GyO0xT08ctH92HYNuLyccIeN35Nva7loCMS5O8//wS//eSRG45FFAQiXs/quBzHIeJ1861De3lh1zZqhokoCHhUlajXzb7WJr6wp5+mwOb44X5csGyHiq6zTjMPAMVcmTe/9z7PfetRJgZmiTaHaWi7f4J5m4G6Y4OASxTRFJkj29rZ2d5I1TDRTbMu4Lhc3VmpuIqisCqao8kyLlXGrSqbUkVcb3wC9YBDkyV2dTbS3RThswcNakZ9fJZ1Vfjn2qqwIomrQbdbU+pBnAOObWNZ1rJ6/1o4ywo5G7WcU5Z94ftaorRE/Tyzt4eaYWIst/04DohifV2hLp8rr6bgdWmb7iUvCgIebW2CfTad4+zUPKNLKVRJpiHg5TO7egl53Hfdq3wj1AyTsm4gCBDyuHGpysaqovcRgiDc03HJkoTPfW8kTmVJQhIlehrDNIV8PLm7i6r+kWtOqPdsq8vXv8el4nWpq8+KjwObFsBu29XK+PACMxNJkot5GlpCuD0quw918uGJEf7i946z+2AXiioxcHaK5GKe7XvatnL8CsC27c3s2NnC8MAcqWSRH37/A+KNAbb1N92WYIogCvh8Lg4/0sM7rw8yMrzI0OU5erc1rqtGPDWRYODiLMmlAj6/i8ee7EdV16p6ud0q4YiXyfEkczNplhZyhMJetGuseRzHQdct3nxtgGz2+qbslePt2tvK6JUFlhbzjI8uMjG2RPdHBHcAJicSDF6avWHma2UCrT+cwLacdavslmUzP5vh8oUZcrm141IlmQORdnYGm4m5fDS5A6iizIMaXhimxWwix//5g5MMTtQ97UoVHVWWKVbqC1NZFhkYX+TKVAK3pmxqALu4lCeXr9DaEmLv7la6O2NrriPbtql2mEiSuLoYrekmFy/NcHlwjoXFHJcuz7GUzPP2u8PMzmXWHP/rXznEvj1tdzWJrSce5nLX+3G9Xo3mptBN9y+XdQaH5zl/cYalRB7TtAj4XfT2NLB3dxttrZu3CMxmy4xNJhgZXWJxMUehWMVxIBhw0dPdwN49bbS1hLdsb/9DrMWNEhe38/tpkkTM48GvaaQrlVvvcBuomuamiUPVg1iF1vDGLKZWKITu21iUuRWFrlj41ht+5HMkQSDu9xJfRxDK59Jo2CLBazJb5M0PR3Echxce2UHAWxdaHJhY5P3LUzfdt6abTMyn13U0ANCrBoOnx3nqa0cYuzSDKIkPXAC7gpW1gHd5oftxY3JonmKuzI7D3QgCZBMFFiaTtPQ0EInduXDi/ESCxFwGf9hL987rtQ+mryyQTRbY93j/ho+50v+rKTLhrXHZryJbrpKv1OiORWiNBHhrcJyJRIa2iL2q4iuJAvlKDZ9LQ5UlMqUKNdOkqpvIokBj0E+qVF/35cs1GoM+gm4XpZrOYq5AxTBpjwTxuzUMy2ZwbompVBa3otDTEKGnIQrAQi5PuljBq6k0BHx3XYn8NEMQQFVkVEUm5NvaArsr2LQA9sAjPSzOZYg1BhCX/T89Xo39x3p48ycXuHRmkumxBKIoks+VCYa9PPrczi2/yAtHvOw/3MXEWIL3T4wwdHmOP/vDExw62kNXd4xgqE7r1Wsmiws55ucy6z6c6oJWEp/9wj4mxhKMXlnkxPErKKrEgcNdNDaFUFWJcllnZjrNiXeG+fD0OJIs0tffyKNPbLuu6huOeunpa+TD0xMUClVeeek81apBV3cct1fFNKy6T+35aU6+e4Va1bhhmf/Q0W7OnBonsZRn8NIsP/3ROZ5+fidt7dF61bxcY3oyxdtvDDA0OLeqzvxRKIqMP+AiGPKQy5YZvDzLlaEFPF6NQNCNbTvksmXGR5d4+41B5mYymB9RK5RFkU5ffYJ6UIPWa1Gq6Lz6/jAXR+d5bG8XO7oaef/yFPOJOl1VEARkScDnVhmcXGIxnQfuTgToWqjL9A7TtDFN+7p7ThTF6/peHduhVK5RKtWo1Qx0w8Q065lz8zqLg4+3DSCZKnL6zDhvvD1EMlVcFqISmJ5Jc2V0ifGJJM89u5Md/ZvTQz0ytsQbbw8yPplEkSUkScC2HUbHlxgYmmcpmefZJ3fQdQN2xUN88iAIAiGXi8A9CGB123qowLuFkC1U+PmpYRzH4Yl9PasB7Ohsku+/eWG1p3M92LZDpWasoQFeC0EQkFWZn//xCc4fH2J6eIGhD8ZX31ddCs9/+7G7sgj7tKKQLTE3niDWHCLSGGTwg3Fs26GhPUKtqlPIlNGrBpGGAJpHRRAEDN0kmyxQK+t4Am6CER+GbpKYTSPKIuFYgGKuwuKy97csS/gjXvwhL5Ikkk0WyCQK12mPlPIVcukiqqYQjPoeqN/TtG0M00L2iLgUGX1ZBGs+m0e3rHogqSqcmZhjb1sT8YCXNwfGMG2bsNdN1OdBlWW++94FtrfUW5J8LhW/SyNbrjKRzGLaNu+PTvOLx/YgIDCXKTCRzBLzeYgHvMuWiw4z6Ty5cpVsuULc7+WZnT33/XzYjkPNNMnXauT1GvlqjXytusycMdCt+vxtLM/jhmVjO/V1mCKKdTE7UUQWRVRJwqeq+FUNv6bh1+r/DrlcuBVl06jhnxRs2l3Tvb2Zz37tEKGIl+ByRVFRZdo6Y3zxF4/w/ttDJBZy4MC21lZ2H+rk0GN994xysFmQJJH+7U0898IeioUqQwNzvPvWMNOTKTq6YkQiPmRFpFY1SaeKjC37Pq6n0itJIrv2tPLs87uwLJupiQQ/+csqk+NJWtoiaJpMqVRjYqxe5SyVauzY1cJzL+yhdR3fzWjUz5597Zw8foXZ6TRvvzFAPlemsyeOz6eh6xYLc1kunp/GH3CjtsoszmfX/Z4dXTGOPdZHJl1kYjzJm69dJpMp0dUTR1VlisUqYyOLpJNFQiEviiKTWLze71aSRRoagxw62s0bP7/MxFiCn//0AjPTKUJh7zLducjolUVGryyyc08rQwPzLC1cPdYnIWi9FuWqzpsfjNLaEOIrT+1mR1cj6Xx5NYBdQUs8yNnhWQrlzfWTbGsNE4v4WFjIcebsFOGQl5aWEF7PjZUTZVmkuzNOJOzFMCwqFYNyWWfn9mZe/OL+Ndu2t0X4uKgUlWq98vrSy+dZSuZ5+ontdHfFUFWZZKrI+YvTnDw1im6YtDSH8Hm1dVWWbweCCOGQF7/PRWtLmIDfhe04jE8mefudYY6fGCES8j4MYD9lCGku/OrdKaWuB8OyHwawWwget8runmbAWaMiXCzrlCo19vQ0Ew2t3+JgmBYLqTzZwvpJDpdH5chzu5kdXcTULarlGsVrGEqarn7sCcMHFYGwl8XpNCMXpjkc9XHp/VEeeWEfsiwxNbxAYjaNIIjMjC5y8OkdCILA4Afj5NIlRFEg0hhEFAUunhxBWNazUFUFx3Eo5StkEgVESSR/ushjn9+HL+StB6qp4ipbz3EcirkKwx9OUqvqIECsKUTvnrY1+iZbHVXTZCFbwHFAt+qV10yxTLZcRZPrtOrRpTRdsTBRv4dLM4vEAz564hHCXjdl3eDkyDR72psIe1x4lhkeumWSr9SoGAYvnx/m8/v6CXvcqLKET1OJ+T3E/d669ovjUKzWKFRrXJpZJOz18PSO7ntaFHMcB8OyyFSrpCsVUpUy6UqZVLlCtlohW62RqVbIVqpka3X1+JplYljWaiCrWxbWsraCIoooy4rriijhkmX8mkpQcxF0uQhqLkIujYjbQ8zjIeL2EHa7iLjdhDQXHvXjZzZ8nNi0ANbtUdl7uOv6D1AkPve1Q/Rsb2ZuOoXjQHNbmPaeOO6bLKC3EgJBD4eOdSMrEi/94AyzMxkyqRILc9llNV6n3h+hSGguhabmEOGo7zpqsCAIyLLE57+8H0mWOPH2ENOTad5+fYBazcS2bSRJxOVWCQTcHDrSxZPP7uDp53auOy6vT2PH7ha+8OX9vPazS6RTRU6/N8aJd64gCPUEgs+n0dAY4CvfOMzEeIJ33hhc91iyLPH0czvRdZO33xhgYS7H228M8PrPLiKKIi63Qijk5fCxbto7Y1w4N7VuAAvQ0BTgCy/uZ3Ehx+J8jg9PT/D+uyMIQv1zVJcxAWwAAQAASURBVE0hHPFy+FgPX/7aQf7gv7xD8hMsnmOYFjNLWb727B6iId8Nq9duTcG0bIybqEDfCZqbQxw62Mmrbwzw7nsjZHNljh7qoq0tQijoIRTyoCrSmolfliXa2yK0E8EwLF57cxBFkepCT3vuTv13M7G0VODsuSkmp1M898xOfuM7T+Bdzp6bpkVXZ4w//u77vHPiCo8/uo39e9rQtLsLYHfvaGXn9hYUpW52voLHHjHIZcu8c+IKk1Op1R6vjeBO1c0fYuvAq6p4lM2nsBmWhXkb1gYPcW/RHA3wt37hccC5TkipJR7kr7x4jP19LevuWyhXeffCBBdG5td93+1z8cRXDgEQaw3TtbOVni00394JLNvm7MI8tU30SPYoCr3hCD5V3fCcGW8JszSbYeD0GHse6SW1mCPWFKRcrHH++DDZVJFw3M+Vc1N072pFFEXOvDlItClIW28jtm2zMJXi+Etn+ewvPQqAKNX7/6plnWqphsfr4md/fILtB7pw+1x4/C5yqQLZRIFHXtiLbTtMDM5x5fwU7X2NpJdyLE2naWiLENogjblsGExms2Sqm8v02N/YhHeDAZEsivhcKiGvC5cir1KEV+DgrGEiulWF7c0xjvS0ATCVzBLyuDja04bfVY8DitUapaoOOAiwqlrt0RQiXjcV3aAlFKA1EsRxHLLlar2Hf+UzHQfbWdE42DxYtk3ZMEhXKmSqFVLlMuOZDKOZNKPpNBO5LKly+batmGynLm5XMW/uQ7uCqNtDZyhIdyhMdzhMVzBMayBA2O0i7HLjUbausNu9wj3nLQiCAAL07Wqhb9f6k/qDgGDQw+NP9rNrdyvH3xzi/LkpFuaylJb739zeej9qc0uY7t4Gdu9ro7ll/T4gr8/FV37hMHv2tXPq5CiXzk+zuJCjVtXxeF20dUTYf6iLg4e71q28XotYPMAvfPsY3X0NnDx+hfHRJXLZSj1jGPWxfWczT39mJz3bGjnxzjDjI0sIooDLc/1CKxL18dVvHKZ/RzPH3xxkeHCeXLaM5lJobY9w7LE+Dh7pxjQt8vkKo1cWCYQ81900Ho/G3gOd/P1//GV+/vIFBi/PkU4VcWwHn99FW2eUQ0e6efLZHWiaTFd3nLmZDIHAvRMB2Aq4Ua/dSkK9UK4LX222FLkoCHz5C/txu1Ree3OAE++N8O57IzQ1Bjl2pJvHjvXS1hLB61VvGFxvVczMphm+skAo5OGF5/fgdimrCxpZlujtinPscDdDw/O88+4w27c1od1ln4ymyZimRbVqUDCqWJa9ar2gafXPL1f0NdYrjmNhOzqCICGsTrsCDibgYNsGoiADK8J2DvVlQD1BJiAB114XzvKflW3Fh8HvOnCWbTFAuE7gZLOTBookbaoa6QoM28Z0HlZgtwpWWj4+ClWRCHhd+G/iTy2KIh7Xxmwveva0o2oK2WuYOoIoEAj7NiwItBWgWxZ/56Ufs1Aqbtoxt0Wi/LPPvcDehkakDd6/bp8Lt1cjny4xP5GkoTWC6lKYGJwjtZCjXKxiGhaSLJGcy1Kr6ESbguw80kPvnjYyiQKDZ8bpP9DJnkd70Zb7etNLeaJNQSINAXr3ttG+rZlyqYqpm4TjAcLxAPl03QLKtmxGzk3Ss7uV/gOdzI0nmBlZYHE6teEAdiaf43fefYc3JsZvvfFt4Ee/+mvsil+ve7IeXIpMSzhIX2OU6VSOdKmMJslYjkO+UsNxHPKVq24WsrT2+bTeOmc6nWNwLoGmyDzR38mrl0ZWf1tJErEdh0K1RrGq41JkzozPspArcKirlapRr9xyS8OzjcF2HHTLomoaZCpVhtNJ3pma5N3paaZzuY+FEZOqlElVypyZrye/XJJMezDIY+3tPNbWQX80uko1VqXNU6LeynhwiPdbAKIkEIn5ePEbh3nxG4fq6nPXvC9c0/xyq2tHEKCnr6EulPSdR9foHK0cZ6PXn6rKHHusj6OP9sE1vk0fPc6Tz+zgiae3L7+3/sE1l8L+Q53sO9h53bEEhNW54dd+8ym+81efXPVG+yhkWaSjK8Zv/s3PAFd91VZO0bWf/1f/xrP8ld9+5obHetAhyxKtDUHGZpLLGcb1cXlsAZeiEPJvfgO9z6vxlS8d4InH+rh4aYa3jg9z6swEf/gnJ/nLH53la18+wAuf3bNMB35wkM2VSSQL+ANuOjoi1/V4+wNumhqDGIbF6Fjiun7rO4Fp2YyMJThxcoRLg7MsLOYol2pUayY13bzq2XdNOli30mRrZ3HJLbjlVgRAFN2UjWksu0DVWsAjd+BRuhAFF7ZTxbSLmE4R0yrikptQxBCCIOJg4zgmpl1AFgPYThVJ8CAJrrv+bp80WJZNuVhDkkTcHhXho4HH5qx3AFClujfeZqOuYvqwArvVsaOzAbeqEA/dOBARBQG3pm6oTeb1777PxOXZqy8I4Au6+Tv/4tdwPSDsta0Gf8hLIOLjlT85yWMv7MPjd6NoMt27WmjrbWT7oS6gvj4ZuTCNoRuYRr1CJkl1Bl2lVL3udhRFAelajZKbiFy6PBqGbmKZFqZhYlk26gMmPjSdzvHO8AQhj5tcpcrffu4Ron4vPzk3xFsDYwQ9LrLlm9sxfhR+l4ZuWRy/MsnA7BIV3VhV624I+Dg/Nc8bA2OMLKb51cf30xj08/bQBGNLGYrVGj2Nm7d2qZkmp2ZneXV8jHenJxnPZq/zRP24UbVMrqRTjKRT/P75czT5fDzV0cmL/ds52NxyT9hAWw0PA9jbwErQdW2gunJR326246PH0k2TgUSS/3L6DH/t6GF2NzVueF213riuhWXbnJtdYKlYosnvoysSIuRZGySNJdPE/V78mrbmWI7jkCyWCbpdqNf09Rb1GpfmF0kWK+xojNEaCuC+5oa51ZjWjv/W2zzI8LlVnj/Wz5+88iHfffUszx/rp1Cu4uBQ0Q1GZhK8/O4AZwan+czRbXS3RDd9DCvJgXDIy7Gjvezd3U4uX+G9U6P85JUL/PiV88iKxFe/fJDwJnis3i8Ypo1uWiiyiHsdpUtFllYFqkrlGvZdUjGrVYO/fOksr7x6kVrNpK+ngQP7Ogj43WiazMn3Rzl7/nol0px+kYo5S8WcpSBexi234Ve3o1spwEG3Mhh2AQcHSXCR1y8jChqKWFeLzdcuIQgSLrl1+f1LuOU2MtXTgIhP3YZHbkcSt04QWy3rnHp7GMuyefZL+257f71mUinVcBwIRe/MNqmQq3Di1ctcOD3Ob/39zxO/Rs16fjrNuffG+OK3jt7RsT8KURCR7oHNlyKJnzpq2IOIvrYYnU0R3DcJRlRFor89zj//O1+hJRa46fFe+NXHqSwzvCqlKvMTCebHEg9U9XWrIdIYoHtnCydfOc93/v6XcHlU+vZ2kJjN8Mb3P+D06wO4PCpf/+3P0LGtiXPvDPPm909z4uVztHQ3sG1fB8dfOssf/e5PAHj8SwdwbqAoXciWOffuFU6+cp7MUp5X/vhdjn12D0ee380rf3SC4bOTWJZNW28Dres4PmxV7GiO0x4NYlp1Sy3bcQi6XUiiyDeO7uFLB7bXrVUQCHpcKJLIX3nq8Brf2KaQn//p68+toSw3BX28eHAnz+3qRZUlDMsm5q+vReJ+L185tJPn9/ThVmREQWBnS5z//oXHVtfeqnx3VUfLtslWq7w8coWXrgwzmctSqNWomOaqpdJWhEM9DlksFvnxlWHemZpiZzzG53r7eK6rh4jb/YllZ21aAGvbNuvRtO4ExVqNs/ML/NfTH/Jkdydf3tFPzOslU6nw5tgEb4yO80hHG7+0b8/H/mB/efgKNdPkqzt33JU4jEM965MolTbNMmH12A5MprPsbIozncmRrVSJLjfSNwX8+F0aY8k0HlUhXaowk80jiwJRrwdJEHjjyjiHO1rpi0dWm8ZrhslMNs+xzjYuzC1SrOmrnP6AS8N2HHY2xrk0v4RLkcku00nCHjeWbZMpV2gPB4n7vPeEdreV4HWrvPDIdibmUnw4NMvF0QVyxQqFco3f+/Ep3JrCYirPto44T+7vobVhYxYXdwJJqisOu10KoZAHv9+Fokj86CfnuDKyyORUknCo4559/mZDkkRkScSybHTdXGMhBfUKXE2vZ9A1Tb7ruenC5RlOnxnHMm2efqKf557Zic9XP4eSKDA5leLitVWTZdh2FU2KYzsmgiAhCiq2Y6FKIYr6GKKgIQkeBEQsp4pplxCo4FW6KBmTKKIfw8pj2FlkuQUQ8chtFPUrmE4J29lc4a/NwPR4giuXZ1E1mVQiTzjio5CvIAjg8bqoVHRsy8bj1RAEgUyywOJcFl/QTTDsZWE6zeToEqqm0NEbp2d7E4IgkE2V0KsG1aqOKAq0dMYQgLnpNPlMmVDUSyTux+VW8fpd7NzfwYnXBtb4ZGdTRdKJAvHm0CqVeHEuSzZVxO3ViDcFUTWZUqGK4zgk5nJ4fBrhuB+Pd/3ql+04WPdgoaMsq1Q+xNZG3YLi5tuIgoDXrXJgWyuuWyjPNrRH6ywOwDItPH43o+enb1jde4hbw+11sefRPv52/FsEo3Uqttvn4uBTO+je1QoIqJqMJIvIisSTLx6gVKivXXwBN4GIj6/99WexTBtREog3hxFEgXhrGEVTcHk0PvftRwnF6srC2/Z3EGsOYRp1OrHX70YQRZ75+hH0qo4gCviDngdKhXjF3mc9BN0aQbd2XVEn7F1bMFEk6TpbLFmSCLpFAm5tjSd3/T2BoNtFwO1afV1TZBoCvuu2vV0YlsVULsfbU5O8PTnBRDbLfLFAzTQfqFvNchyKuk5J18nVqoxnsvx0ZIQnOzr5fG8fca/3E/cc2bS75vf+j9dobA5x4NFemlpv7gE3ObLI2fdG8frdPPfigetof4ZtkyiWODUzi+04PNXVSczrJVEqcWFhkQ/n5ol5PR87q6psGFyYX8SwLb6yc8ddHUsWRTrDIf7q4UO0h4KbWo90qPcOLOQLqLJEWTfIlCHkdjO0mODxno5VU/vFQpGyrhPzeZlIZ9jb0oRhW2iyjCxeDTQt2yFdqvDB1Bxhj5upTJaaaSGLIsllxbhJVWGxUCRXqWI5DookUqjW8Gkq09kczQH/AzVB3ClkSaIpFuCbzx/g1OUpBicWEQUBTZVxnLqS5fPH+nl0bxc7uxtvmsHfLKyYzcdjfnbuaOHtd6+QK1Ru4BW83Gn5cd9w6yDgcxEOe6nVTBaWcnS0RdY8yErlGqlUAVkSaWkK3XV/8dR0mqWlAk2NAY4d6aa352rm3LYddN2kWjWuH6e2GwFpuacVJNFTp/2KLoTlvldJUJFELw4WoqABDi65GVHQMK0CSKCKIRQxRFDdhSwGCLkOYNlVNDmOKGwtytDElUVUVSYY9jI+tIDvSDdjQ/NIokj39iZmxpNUqwadvQ0YhsXrPzpLZ18D1WW7r1SiwOJshmDYS6UUwHHq1+KJ1y5j6BaRuJ9w1EtzRxTTtJmdTGEaFkMXpunZ0cz+Yz2oqrxs7SaumVMtyyabKrIwm+HwE31Yps3ibIZivkImWSQY8XLosT6uXJxl/MoiLe0REgs5enY0s+tAx3WWZlB/bln3oDdKk+VPfJLv04IVv1vPBnxRR85NUsjUeyctyya9kKOUL9/RPFwXuKlfm6Lw6e2Xl2SRUMy/pt9UECDaHCLcWBcHWlmPCoJAU0dsuTizwmIS6NrRgm2v3c53jW972zXVVM2tEm28PiHd1tuwety7VcXfSlhl3t3F/jfad7337rbPczKb5eTMNCempxlIJpjIZjAecMV3ByjqOkU9zVQ+x1Qux2AywbNd3RxqbiHu9X5i+I6bFsC+8eNzbNvdSte2xlsGsMnFPCdeH0BVFT7z5f2sd7nLokjE42Yym6VsGPUseLFMtlIh5r06WdiOw5VkkivJNOlKBVkUaQ34Odjagk9VVy/wC/MLzBUK7GlspDV4lbrz+ugYqiSxoyFO1TB5b3qaA83NDCVTpEolBEGgIxRkezxGg8+36j/15tgEY+k0p2fncByH/3z6DMJyBfqbe/fg11SKus54OsNYOkOhWsMBQm4X+5ubaPL70GQZw7KYyeV5d3IK3bQQRYFdjfHrzodhWfzg8gD9sRjZSpW5QgHDsoh5POxubKgHvbe4maXlIHkhXyBXrWHZNoWajm5apEtlkqUyxVoNr6oQ83qYTGWIeNy4ZIX6XL32wVmvttf/XTFMLNtGlUS8mkrQ7eL14TEOtDaTKVcwLQtNlhBFoX4UB1LlMnGfFz4FizNRENjT20xT1M/+bS0spYtUavWFesjvpqc1RlPUv0bVdrOQL1RQFbleJbwmgFtZCCWTBSpVnWDAjct1fRBUtwuoB9vlio5t21vmodvUFKSnK86pD8Z5/9QYjQ0BXMtCSrZtMzef5fLgHJomc3B/J6p6d+fXtmxsx0FzKdfZEI1PJpidz1BZJ4D1KOtXtR0cZLWeib728axKV/t5ZNFHTVhCJYosBpBFD7JYnwO9Stdqn9BWsp8ydJP5mTS25WDoFlcuzbH7UCfJhTyKItHWHSeTKlIqVGluC9d7lAfm8QfdtHbFcLlUXC4Fr99FtMFPQ0todX6bHF0iHPURa2ojGPYgCgIWUC5WqVUNBs5No6gy+47e2FLB49VQVJmpkaXV16plnWKuwtjQPP6Am72Hu0gu5sgkC+w51MnEyCKpxTzVio5Pub5P3bAsDHtz2TMAPlXFLT84FZpPKxKZInPJHF6XSl/79c/w20U2WSC1bHu30gd94Omdd2S3kjPyvJl4F0mQeDL2KCH15vTlFdQsncXqEpfzwzS4Ymz39+GVH5wWk9tBPSC9fr746LNuJfl795+3NZ6hnzY4jkPZMDi3sMA7U5O8PTXJcCr5wAeu68GwLEbSKWbyOaZyWUbSaR5ra2d7LIbvE2DB87E8FaXlXsrEYu6GdBhJFAi6XKTKZQq1GrplkSiVKOg6rYGrk+9EJsPLQyOMplLoyxdg0KWRq1b5bF8vbqW+mH1veob3pmbwHVHXBLA/uDxIQNOIejykKxX+zcn3+UL/NpLlCoVqjZJhEHG7ebq7ky/t6EcW62poY+k0Z+cWWCwUEASBM7Nzyxkiga/s2oEflUJNZ2ApwamZWapGnUdfXaYJv7Ctj/ZQEMtxyFaqfDg3z0wuz1gqTX8sRktg7QNGtyz+w/sfcKS1BUkUyVWrlHQDWRSZyGT5zSOH0GRp3QWbKAh0RcPsbWkitNyrIOTqptPdkTA10yLkcePgEHK78WkqbkWmOehHFAQa/PUF9rXm65oi098Q40h7K+dmF2gPBUEAtyLT6PdhWDZjyQxf2bsTt6qQLpXxaioht7tOkXbqx9tabfH3HrGQj1jId+sNNxGDwwsUi1W8HhWv14Wq1ntFDNMmn6/wxjtDlEo19u1pp3Ud5WxRFIhEvGiawtR0ivMXZwiHvDg4mKZNYzyA3//x9F62NIfYt6eNgcE5XntzkMbGIE2NQWRZpFis8f4H4wxdWaSnO86RQ12oyxy/1WppzcCybIqlGrpu4jgOuXyFRLJQt8aSJVwuBVmuVy0aGwME/C4SiQKXB+dR1TotuVCo8ubxIZaWCmuSBLfCjYLOj77ukhtv+xgfJ5KLeQy9Hszlc2XKxRqVUp0yt1JAsiwb26onQ/xBld2HOrEsh1y6RGtnDH/IQyjqI9oYoPGa69LlVmnvibNzf91exDQs0kv5uhiKS6k/X5bVh28UwLq9GqGIF3H5t8oki+i6iazKyIq02lckyRI925tp644zN5VCkkVqVQNf4PoAtmoY1DZBJOyjCGkuvMrmLjbm8wXG0xmK+lVROZ+q8njX1mkfWCqWGFhaQpVkjra1IG/xROfITJJXTw3T2x7blAC2ra+J6HLPtigKuP0uGttjq+un20Faz/LH03+BKqjsDu7YcACr2zoT5Wn+ePp77PT30+Rq+MQGsA/xyYdhWcwVCpxdmOcvhwb5cH6ebO32xKYeRFRNk5MzM1xJpRlOJfnytn4Ot7QQ89yZtsRWwceW1rVtB71Wt5BYDysLiPZQkESpTL5aI1UuUzEMesJhTNvGweEHlwYYWErybE83T/d0kqtUeWnoCv/6xPvsbGigKxy6LfpVsabz8tAI//DZJ9nX1MR4OsMfnj3Pz66M8kRXJzGvB0WS+OvHjjCezvD/e/ckXkXhf/7cc9fbySgKOxvi9EYjdIVDWI7D7585y0+GrrAtFqUtFMQlyxxsbWZHQ4wTU9P8v15766bjOz4xxXcO7uc3jxyiapr8eGCIPz1/gRd3bqfZ719X3l8SRZ7u61r979ZQgJagf02fwjf2775uv5jPi+M4vLCzD4G1PQYht4tn+roBeLynvuhZaXSvGibTmSyP93TUq7mN8dX3RKEuDGVH60bOn1Yq0/3E4NAcr71Z7wGMRrz1nk1ZolzRmZnNoOsm/dsaOXq4m9YbsCe29TWyra+RwaF5/tN/e5vuznjd1Nu0ePGL+9m9s/U+f6s6/D4X+/a0s7SU56VXLvCf/tvbdHZEcakKiWSBRLJALObny1/YT0tzaHU/wzCZmEwyMrZErWZQKFZJZ4pYls35i9NUqgYuTSYc9rKtr5HmZRrYjv5m+vsaOX5yhJd+eo6JqSSyJDIzmyGTLRMJe9GNjfm6fZIxeH6axpYQjS0hKmWduakUMxNJPF6N1FKedKJANlXEtuqMFpdb5fHP7iIxn+OlP32fQNhLIOSp+/2lSmRTdVqvIAiIkrimelGt6Fw+N0VqMc+RJ7cxPbqEoinLzxidTKqIXjPJpksEI15kRUKv1j17a1WDdKLA4IVpFmcz9O9po1ysUshd9fWrJy9u/Z3zem1NQLhZCLld62bLFwoFCjX9OksHURCQRRGXLBNwaev6A15eWuIPPjzPWCpNSdcp1HR6I2F+/Nd+Y9PHf6c4P7/AP33lVWIeL3/wq9/Ev8UD2IVUnotj8zRENmaHcis0dcYoZsuYhonb68Lt06gUa3iDm69SfyPIooxHcqPbBnPVBXT7enbJQzzEVoezXDyayGb54fAQf3b5IplKZUuLM90LpCplfnJlmNF0mu/s28/z3T3EPJ4Hdi1+RwHsireec01FzqH+mm3ZWDfIQjuAYzvks2Wy6RLegOvGfjPLFjXbYzEWC0XmCwXKuoEiSoTcLhKlMrpl8erIGJ/v7+Pp7k7agkFaAwE0WeaHA0OcmJwi7HavoRzfCl5V5bPbenm0vY2Ay0WDz8uHc3O8NzXDXD5/W8cKujR2NTZg2ja2XRc4erKrk1eGRynWdFabum4DRzvaeLyrg+5IuF4tqlb53qXLTGSyxLyeDff43azX4E62g6vBsCgIhD1uPt8QW9332l6FlT6gTwucZc60ZdfNve1Vb8r1ocjSplKJD+zrJJ+vMjg8z/xCjnI5AYDHo9LWGuHo4W6eenwbLc2hG05iB/d1oCgyP3vtEhcuzvD6W4NomkxLcwhdvzcBm6bKuN0Kyjr9hteipTnEL3ztMB3tUV7++UWGhhfQdZNIxMejj/Ty7FM72LNrbYBdKuu8/e4w3/vLM2telxWZd98b5d33RgFoawnzy988thrANsQDfO3FgwSDHt55d5g33hxEUSX6ehv5zd94EtuyeflnF1AfIFGOe4HJkSUOPtrLroMdFPNVXG6VscF5HntuJ+++epnpsQTlUo3OvgZM3WRqZIlXf3gWl1fFG3Dj8WqEoz4c2+HNn1xgfjrN13/9cSRJuI4KLysSscYQp94eJpsukVrK09weoVY1mB5Lcvznl8ikCrz5k/M8+6V9xBqDjA8vcOK1AeZn0vz0zz9g58EOzr8/xsJshmpZp6UjWp+nZBFJlhCEOnNIkm7cP5iv1Sjqmy+mFXV7CGjXC0f9Hyfe56dDV8hUqsuqn8viJrJE1ONhezzOF3ds45H2NqJez5o5+HBbKw0+H6lSmXcmJvnxwNCmj/vTBtOykUSR5g36ed4Ki1NJ3vrBaRLTafY9uZ2Dz+7i9M8v8sw3jt430R9JkHBJGg4OeaOA5Ww+w+CTimuf8R+dM2723icVpm0ADqIgI95Erb3u7GHV+5GFuxdedJY9XS8uLfJvT5/i9U32z33QYDkOA8kEv3P8HUZSKf7GkSPEPF4kHrxr8Y5mQcu0WZrPMjOZXH2tVjXIpIoMXpyhWFy/JG+ZNrOTSU6/c4VcukTfzpabfo4oCGyLRRlNpRlYSoAALcvKZbazLDhkGMR9PgKuqxRGlyzTEQoyl68rid0OFEmiKxJezVgL1FUgBUGgepvHKhsGp2dmeeXKKFeSSSq6iWFbzORy2I59RxaELQH/mmy8JIqokkzZMLZMNkmTJWJezxYkNn58sG04fm6Mtz8cY3wuRfEmli6//uWjfPXpPZv22Tt3NNPXE8e07FWPUlhOJIgiiiLdMkiUJJHdO1rY1tOAaVnYdj33IorCuvY1m4H/7refwzAttA0s1rwejccf7ePwoS4sywYHBFFAkaV1+15DQQ/f+eXH+OYvHLnpcUVRvK4vuK01wi994yhf/8qhehJPqJuya5qCg8OeXa31xM+n2O7i27/9NLIiIcsSwYiHg4/1su9YN4oq87f+8Zfr9F4EZFVCUWVs2+E7HRFsy0ZWJFxuFVEUePrze3nkmR3IirQqmvILv/EE8jU0Ss2lsOtAO1198dX+QEWVUVWZvp3NdPbG+dZvPYWsSKiajCgK7Dvazc797ZimjaLKyLJI747meoVXAFGW0FwyT35uN1APmh9/fhcgICvrL77SlQq52uYGsB5FocHnW7U3Ww+7Gxvoj8fQZAnbdijUaoynM5yYnOKD2Vl+9eA+vnNwP1HP1eRrQNPYGY9hx+rjfm1kbFPH/WmEx6XidasUSptzDbz5F6doaIsSCPnIp0tUChUuvzfCU189DPe1fW3Zy9oxVkXoHuLWsJYT1eI6CfuiqeM4Di5JRpU+HcnO0+nvUrHy7A19npjWdcPtanaR2cpl0tUp9oS/gFu6u4RQ2TD4+dgo/+XsGS4nkrfe4VOCfK3Kn16+yEgmzT94/Al2xuIPnFjgHd05es3k/Klx/uL3j2May31O2TLV8hyLsxlUbf3DOg7oNYNqWae1M8bnvnbwOgXiayEKAv2xGO9OTmHYFnGv92pvqMPqVCote1zCcgZBqO9rOfYteyxN217T2ykK4JHl1Wz1yvFWxn87eHnoCu9MTNLk9/M/PvUEbkVhLJ3md98+cXsHugYuWb4aXK9k3dlaCrEran0PUUe5qnPywgT/5Ufvk8wWcWsKbk1BusFC+G6Vcj+KzajoCoKwoUB3M+G9gV3JehBFAXU5aNno9h63uuoRu4LpxSynLk+RyBYJ+d30d8Q52N+2ZhtJEnG7VdzuundcpWpQ1Q00l4Iqy6iKzNhsiomBFM8d6V/dr1ozqNQMFFnC59n4d3sQ4b7m+wmCgKiKrKQBvNf0S6/ME6Lo4A+4V/tWV17X3AqaS1mT6XN71v5mgiAgKxKBkHd1u1X7BVFCViQ+SroUVfG6Kta1ysIr+6va1XtRvYk6eLpSIVWuULnNJOet0BEMEdJcN51PD7e18I09uwi6XDjUr8mqafK9C5f56fAIH87Os6+piWd7u1f3EQUBcXmxooifXlXazURHU5ju1ijnR+b45nP7UW6gSbFROLZDuCFIuVAhvZDDcer93vdTN6Jq1UjUUgCoooLAp0d46PTSLGcTc1RMg22hGAfjLTR6Nq5fcSG1QF6v0uEP0R2IrHnvbGKe9xanORhr5vn2vs0e+pZDwUhg2jolM02iOn7TANbBwbINDKcKzt0lTBaLRf5i8DI/HBpiLJO+JyJ7Dyoc6sH9mfk5fvfEu/zWwUMca21De4AEA+9opKoms3N/O47zOLOTKabHEyQX8yiajC/gWrN4uRaSLOLxuWjtiLLncBf9u9vW3W4FgiDQHPBTM00GE0nCbg/d4SDzhQKiKBB1u3EpMplKhZKu49e0el+eZbFULPJ4Z/tqRkEWRURRwLjGdDpXqVAxDPzqteMVNizNvWovwvWdvKZtM7CUoGqaHGtv5UBLM6IgkCiV7kr6W9ySki11mJZNvlxlKV8kXaiQLpZJFcsUKjVquoluWeimibFcCVRkCVUWUWUZTZbwulTCPjdRn4eI30vM7yHi86DK8u0yrW8JUbxzqffbRalS4+UTg+RLVb72zF76OxrwuJQbLm46loU7HkQYpkWmVP/ts6UKmVKFbKlKvlyjohvoplVXa13+G+osAmVZMEmRJDRFJuDWCHndBD0uQl4XUb+HeMB3Q/+5zUImX0Y3TTqawtiOw8XRBdriIWIhL4VyjaVMEa9LIRLwIkki6XyZD4dmMCyLtoYQ3c1RDNMiWyyvCVIrNYMLo/Mks0UCXhddzRFa46FNv64fBKx33QvXJgw/+voGztFGt7vdcW0Us/kc2Wpl0xOJ2yJRQi7XTb9aQNNo8vuJeNaG6c/0dnFufp5kqcxisXjXY7Fsm9l8gQvzC4ylMyRKJaqGiSpJNPq87Gtu4lhHGy75etrfCo3v9MwslxaWmFtuCZJFEb+m0RYM8GxvN01+34aqAOPpDK+PjnMlmeRoWysv9G/Dp328qppdzWGe3t/Dq6ev8B9/cJJH93TRGPGhqvK6z/ygz33TZKU/7GNyYJbkfIZ8qoiDQ7Q5uCFmh2mb2NesSkzHXPPerXpZHcemZJUZLoxyMnUKAYG4GkMRH5zF7d1iqpDBJcu0+4Nkq1XemB3ji53b8SkqmVqFxXKBiMtDRPPg4FAxDURBxK+opKpl0tUyWb1uIWjaNk0e/+paszcY4d35SXL6Vbai7Tjk9CpzxTxRl4eIy/2Jqc4maxN45TC6XSZnLABgOSbD+XeoWnksxyCittHo7kcUJGwsMvosF3M/w3JMYlonze7teOXILT7pKuYLBf7o4nleHrnCZDb7iVQZ3gyUDYPTc7NIokjFNHmqoxO3cnNLPl03mZhJYZo2bc0hJKneXqMqMqZpUdNN3C6Fas1E02QEQF8udn60aHA3uKO7Q1YkOnobaGwNk1rKk5jPMT2eINrg59hT22ntjK27nygJdV+suJ/GlnA9s34TCNRVbSMeD6PpDA4OzQE/84UCAgJeTeVgczMjqTQXF5bQlqm070/PUjMt9jY34l+m2wZc9YljIJHgUGszDvDG+ASLhRIN3jtThfWqKi5FIVetMpXN0hoIoFsWXlVFoB7EOo6z+kCeyxd4a3yCwj3ok/o4YNk2qUKZ+UyeuXSe+WyBpVyRdLFCoVwlX6mRr9Qo1/TlgMXGtKx6T7ADiiQiL/9RJAm3quB3qQQ8dcPqgMdFPOClOeynJRKgJRKgYZOCGPkmvWybjZpuMjC+wK7uJj5zZBu9bbFNr7J+XMiVqyRyJRZzBRazRZbyRZL5EvlKjWK1RrFSo1DVKdd0aoa1+vubVv0P1BXHJXHtteDVVHwuFa9LxefSCHo0on4vsYCXhoCPhqCPloifoOfmi8DbhW5auDWVruYIxXKNibk0xYqOS5U5eWECRZGo1AxaYgG2dTSQL1YZmUmiqhIel0prPIRumCSzJWrG1WxvVTeZXsySzBZpiPiI32cl6q2KCzMLnJ2ex++qm9eHvW4OtLcQcD9YFerhVIpk+XoP5bvFnoaGNdTf24FfU1EkCUk0N8XAvmZZvD4yxptjE5QNY/WYNdPkw9k5zszOU7MsHuloI3hNS49t17UafnB5kHcnpkhXysutLxKW7VAzTU7PzHKsvW1DtcXxdIaXBod5dWSMkEsj5vUibQG6/mwizwdDM0wupDl5cYKR2SRhvwdVltZNrvzmi4/QEL7xPLD70T5mRhapVmrIqozH72L7oS7kDbBp3kqcYKF61SIqZ+QBsByLny++SVC5uQqxg0PVqrJQXWK0NIEoiOwKbMcjfXoUiCumQdTlYVe4gYlClsHMEleySVq8ft6Zn8SvaAxlkvSFosRcHqaKOTySwp5oI+eS82RrFRYrRfJ6jbJpcCm9yItdO5EEgRZvgICqrSY2TNsmVS3z5uwYYc3NQGaJHeEGdoRjyOKDRetcD4naOD45RlTroGRmqJg5ZFFjtHiCZlc9MHVJASRBruvp4FCzy7ilAIZdJa1Po4ruDQWwlm2Trlb400sX+dHwEFO53JZpr9uqKBkG781MA/VEyjNdXbjlG8dnhmkxPZPG5VLQdROvRyUW8VMq6VwZXyRfqBIJecjmK+zc1oxhWiRTBQJ+N92dsU3TeLnjSEAQBFxuldbOGK2dMZrbI8QaAuw90s2Ofe2bMrgVdIZDXF5K4FVUQtc8GEUEvrxzOy8NDnN8YpLJbJaqYTKVzfJMTxe7GhpWMwndkQgdoSDn5uZxKzKSIDKZySII4LrDgCigafTHorw3NcP3Lw3QFgggiSJf2L4Nr6qyPR4jU6nwxug4s7k8JV3HsGzEVeIvpEplRlIpZnJ5BhNJinqN45OTJEslmgN+eqMRwu77pzp4K1i2TaFSYy6dZzqVY3QhxWQiw2Qiw2w6T75S3TDVWjct9FvYTrgUmeawn854mK6GMF3xCB3xEO2xEBGf+445++o1VOx7DdtxqOgG7U0hgl7XAx28mpZNsVpjPlNgPlNgOpllJpWr/0nnWMwVqN6mqJNtOXVmxDVFgQSl67aTRZGo30NLJEBrJEhXQ5iOWIjmcIDGkI+Y31O3iLrLxEQ6X2Jo0kFVJGJBLy5NZmQmyXuXp+hsCjObyJHMlmiOBVFkEVWWCPncNIZ9uFUZTZEwLZuR6av9NrJU387n0YiHfIT8W+ee/jgxuJDgjaExnuzrqgslSSLWXdLG7ifqgiNwKbHEUunuq5zXwqeq7IjF1zzzNjIew7LJ1aqcnVsgX63RGQ7RFb65N/vGDl5nGnWEQzT6vKte5rlq/bN+PDCES5HpiYTXBLBFvcbxiSn+6+kzeFWVw20t7GxoIOR2UTNNloolZvN5wh73+oH2NbfzVDbHy0NXeGtsgrDbxS8f2MtT3Z1bggI9l8hx7soslZqB3+tiNpFjNpG74fa//LmDNz1eQ1uEbKKAy6PVhcQQVlu2boXR0jjnspcomCV0+6oytoXFW8nba2FSRYVubwdHIgfwyQ+27cadQBYlmjw+FstFRnIpUtUSS5UiXf4wZ5NzkIOaZTJTyBFUNXZHGxnNpXFJEqbt4JEVJEHgw9QCjzV1EnN7kD6S0SibBpfTi7w5O87RxjbOJ+cxbZtWb4Cw68F+Vph2jaw+j2FXkQUN3S6TrE0uV1TDaJIXjxTEK4dRRQ81u4SAiFcK0+07gu3YDOZfJ28sbeCzbJLlMj8aHuS7ly8xXyw8DF43iJJhcGJ6Gsdx8CgKT7R33FCd2HEcaroJgkAuX6G9JYIkieQLJcYnk9R0k0y2RLVm0NUeJV+oMrtQTyTUaubHH8B+FL07WpAVEWUdwZTbhSyKRDxudjXGkUWRPY0NVAyTvlgEr6rS7PfjUVUEAR7taMewbN4en+DE5DSaLLG7sYFf2L2LgKatnvwd8Ri6afLS0DBvjU3ikiU+09uDX9NoDvhxKwoB22ZPUwMRz1rFxma/nx3x2GoVdwWaLPNkVwembXN6ZpbLi0vEvV6e6+3Bq8JTXZ3YjsO7k1OMZzK0B4P88v69mLZNbDmrniqXeW96lksLi5RNk55IhAsLi1xJpjjY0kLE4yHsdiMKAnubGmkO+NGu+fF9msb+5ibCHvc9VfY1LZtMscx8tsDIfJJTIzOcHp1hKVe8px05VcNkfCnD+FIGLoHPpbKno4nHt3eyu72RlnCAiM+DS1Vui47pUuT7FkjKkkg87CNfqqKb1k39KbciHAcquk6mVGExW2RiKcOZ8VnOjM2ymC2soeXfS5i2zWKuyGKuyIfjc3WP4qCPXW0N7OtqZnd7I80hP7GAF5ei3LS//mZI5cokMiV626Ls6m4k4NF4ZzaJJAnopoXfo+Fxq1iWTdDnJhryEg16aG8Mr9KGI37PmqqQ36MRCXhQVYmWWJDGTbLaeNChSBL9jTF+88nDa16fzxWoGSaiWLfd8mkaEa8bQRBIFkoUl+1jNFkm7veiyhJz2Ty6aWE5Dj5NJeypt5jYtkOmXCFbqWDbDm5VIeR2IYoiS/kill1XBm8I+PBp6m0nttKVMsOpFOnq5vkJSoLAzlicFr8f1y16kmZyec7Mzq220FQMg+lcnrfHJwi73Tzd3cXOhvVZUbcDlyLzjT07USQZVZJWr2/Ltjnc2sLJqWnOzM6Rq1ZX5zjHcVgslvijs+dJlSv88oF9fHXXDpoDV69/x3GwbPuGyaeV59piochLA8O8NjJG0OXil/bv4XPbtk7/YEdjiC8+tnPD2wd9Nw9Mhj4YZ+T8FMVsGduyySUKzE8k2H64e1Ws7EY4Ej6AX/YzV1kgpWcomIXVXtaIGkIWbr30EwURVVSIaVEejRyh39+LKt6cNfdJhW5Z6LaJKIiM5tIciLWwMxInb1TJ6zXS1TKCcLWVzHRsbCRavH76QzHcssJEIcNSuUjE5eajbocV02Asn8Yty6uVX1WSqFkPviVbPfB0yOkLuCQ/NhYL1WFaPXvYF/oSY8X3SVTHaXBto9N7AElUEBHxKTFEJAShfq3bt1DAtmybZLnEq2Nj/JtT75Or1e578CoKAm5Zxi0rqLKEKkkoooQiiavBoLicvHCoW8jZyxRz3aqzFGuWScUwqJom1n0ef8Ws04kVSaLZ56M3El23fUWSRGJRP6VSjUjIS3NjkIDfhW6Y9PU0oCoytZqBqsrEosttFJKwnLjfvO+0aQHsY5/ZgWnahKN3vzDzaxqPdXbwWGfdX/Sp7i6e6u5afb81uJb+8kxPF8/0dHEzuBWFRzraeaTj5tXhf/bFz1/32os7t/Pizu3rbt8bjdIbjfLXjh6+7r22UJDvHNzPdw7uX/P6P29uWv13fzxGf/zWiwu3ovD//vIXrnt9RzzG//erX7rl/neClepCVTdYyhV57cIoPz17heH5BNbH1E9QrOqcHJ7i5PAUzWE/z+7p5bN7+9je0oBHUzYclN7OtrcD07KvU/tSFJlHdndydniOmaUsIb8bTamroa43OYjL/dofJ1a+gm6alGsGQ3MJ3rw0xjuD40wmsh/r2FZgOw4L2QIL2QJvXBoj4Nb4zN5evnCgn57GKEGPC1WWb/tcbmuP098RZ2dXI1DvXw363OzqauJAf2s9EJUlNFUmX6qiSCKVqkFpmWq8UnHXTav+mlbvgZMlAcu0KVVqVGoG7puIAn1a4FCnkGZKFQBUWcKlyPzo/CDz2TwRrwfLttnd0shzO3oxbYs3h8cZWkxS1g3iPi8v7OqjtyHKH753jlJNx3Js9rQ08tS2LlpCASqGwWsDo1ycX8SybLpjYY50tSGLEt87cxEbh5ph8tmdfRzsaCHs3XjFwwFOzsywUCxu6pyoSBKf6+ndUPX1h5cH+cnQldW5pG5pV39ufnv/Xg63teC6RU/TRiBQfzZXTZNCrbbcClJXWq2YJo0+HxcXFtGtqwtNw7ZZKhY5MzdPVzjE57b1rgleYVmE6wZMGlEQcMkyFcPgB5cG+MvLAzT5/fzKwb0839d7199pM9HXHqevPb5px/vg9Ut07WrjM998ZFUYUxBFFPXWv+XB8D4Ohvdh2AbJWpoPMuf4o6nvIQoiT8cfI6renIopIKCKKlEtQpu7hYDy6Wx5qFomeb3KVDFLtlbh8aYuBjNLZGt1xfGCXkNAwKdoVEyTglF/LVOroIgiVdMgr9fvlZpp4VFUbMehZOj1QMU0l3tnBWJuL4caWnmmpQefouKSZdRPAH14rjJAk2s7bZ49hNQWpsrnGMi9Djh45BB7gi8wU7lA3lgipU/R4Krf17fz1HYch1ytyjtTU/yLE8fJbmIy8WaQBAFFqgeqmiThUzW6QiG6QmGafD5iXg9Rt4eQy4Umy6vbAcvBqkXNNCnoNRKlEslymfligYlslqlcXVdBtyxqpoVhW/clIC/oOu/PzPDvTp/inzz9LEFNuy6p63apHD3QueqkspJ4jEV8RMPe1ddX/g4FPHS0brx/eaPYtAB2+956YPgAFZYe4hZYCV5/cOoyf37iAhOJDIZp3UcNxJtjPlPgT945x1uXxvjsvm384qN76GrY2E3i0VSUexDAzidzq83qK3Ach88c2cblsQX+/fdO8MieTvZvayES9KxLpYgGvVuGYvrh+BzfO3mB06MzpItb1/jbdhyy5So/eP8yL50Z4mhvG18/tptH+zsIeDZOwVwPmirz1L4e/vc/f5uByUUE4ND2dp47sg1NlelsivDfXj7F5fFFnjnUS65Y5dXTw8wlcvz56+f4wmM7iQY8tMSDnL0yx3uXJ3l0T9dtVWs+qdBNi1PjM/xfv/cykihyrLudL+/bTrZUoSHg4xcP7Sbi9SBQV5ofWUyRKpZ5fkcv/Y0xPpic5ftnL/P3Pvskl+YW+ZvPHGN/ezOuZSV5y7ZJFEv8+MIg/7evPk9bOIgoCMxm83w4OUdHNMR3HtnPQr7IH5w8h0dTeLSnY8PjdxyHHwwObopI0goEwKcofK63bw0V90bY3dTIzoZ43UbHcSjrBpPZLO9NTbNULPIrB/fx+f6+m1rxbBQ1y+LnI6O8OzHFlWSaZKlESTeoLlcMHOrJr5WFS7FWYzqbQwT2NDXcUhzko6gnfkR+NDDEH3x4no5wkF8/vJ9nerpvvfMDjmDUT3NnjMaO6B0fQxZkmlwN7A3u5CUlQNmqcDR8kC7vRq/xrSsaeT/w+swoP5kYojMQ4qnmbvZEG2ny+Pjds8c5tTiDYVs8197H3mgTHyzN8p8HP2Aok2QklySkukhXy1zKLCELIm5ZpSsQYrFc5JWpYU4uTuFXNDyywjOt3RxrbOeff/AGA+klLMfhCx39PN7cifyAL6rnKpfZ5n8CrxxFQEQWVAQE8sYSxxP/FQcbyzHo8Bwgqm187r0WluNwanaWf3v6/fsWvApAo8/HwaYWHmtv53BLCx2BIPIyk2TlV7uWVbLeL7myoloRAHSW/101TWYKeT6Ym+WdqSlOz82SrVbvyxosW6vyytgorcEgv7HvwA11GG50ZQq3eH+zsGkB7MddMXqIzUWxWuP85Dz/6dXTjC+lyZYq940qejuoV+GKfP/9i3w4Pstn923j20/sr1c4bzLxe1X1hln/u8Hv/N7rTC1kPvKqg21DplDGcRxmE1l+fPzSDSlzf/XFY3z92b2bPraNolwzGJpd4vff/pDB2QTpQomKbm7Z4PVa2E69mvbB2Axji2kOdLfw4uEdPNbfibSBhMXunia2d8ZRr6FtCoCmyfzWi4+gmxaCIOB1qUiigCjK9HfG+Xu//AyyJOH3aDgO7OltwjRtPC4Vn0dDFAWaogG++dx+aoaJ9x555z5oUCWJQ52t/K1njoEg4FZkfC4NSRIJe9xEvJ412d/5XJGhxSTDS0kiXg+O49AU9KHIIr/5xGHeHZnireEJHutp55GedmzHIVUs0xIK4FaU1cVFuWZQrOm0hPxIokhTwEehVqWs31yd9VpUDIMz8/MMJJcoG/qtd9ggAprGZ3v7iLg31hZypK2Fb+3bsxzs1ilphmXz/tQMf3zuAn/04Xls2+aX9t/5nGLZNovFEv/rq29weXGJRr+P3Y1x2oJ9+DSNiq7zZ+cvMp7JrtnPtG0qhokgCPivEa3ZKHTLYjyVYSSZJlOpoEoSqXIFy7ZXbYC2IhzHIZ0rs5QpopsmPrdGPOQlcAva8LWwTIvXv/se548PE4h4EUQBVVN4/tuPXWcBdSOs2kGJKs3uJsaLkwiCiCg8uDoM9wsvdu3k+bY+bBxUUVrtZY25vfz9g09iOTaiIOBXNNyywtHGNnpDURRBxHIc3HKdjVNXv3VQRAkRgbjby9d7dvPZ9m2r+3tkBZ+i8Y8OP4tp148bVF33tC3sfuHx+K+jih4Uod7S1+jqIxRvxi0HeLrhry9v5aCILlSxHih1eg/R5tmLJtUr/3uCn0e4yTV7cmaa716+xHTuxj3nm4Wwy8XjHR0819XDznickMuNR5FxyQrKNRaXG8Xqlh/ZRxJFesNhWnx+nu/pJVet8f7sDD8fG+X84gL5TfYc/yhKus7vnztLbzjC051d17GBbvQd72d73Mei0V0p1ZidSjE7keSpz+99GPxuITiOw1Qyy2sXRnnl3DAjCylqxtbuw7Bsm1y5xuBsgly5ysRSml958iBdDeEbKhZ7Xco9MW3ubYvi89xdcBINfTxKj5ZtM7GU4bWLo7xxaZTxxTSlmn7b/sdbARXdpKrnKdV0ZtM5To3O8PWju2mLBW/6u9dpvWurRCvZ1GjQi207IFylzQiApsg0R4PL29Zf964jFa/IEpGAp34+H055QP1ceVSF5tDathCRujL1R6lLIbdGzO+hNxZlT2sjoijg01REQeBQZystoQAXZxeZyxUYWkiyozmOV1NJlyqYts3KiXcpct2CrVzP1uertTplb4Nzgu04JMtl/uOZ06TKlU1jpYiCQKPPx7d27cGt3Nhq61p4FIWw232djc6zvd28OzXFG6MTXF5KYFjWHc952WqVn18Z5b2pGfY0NvCL+3azqzGOV6krHSeKRX40OHTdZS0KdVVxx3Eo1Gq33dNl2w41x+TFXTuwHYdT0zP8bHiEmNfDs1uwClvTTSYX0vz43QHGZpOUKjq27SDLIpGAhz09zbzwyHYaIv5bBvN9+zoJxQPIqoyi1q0oFO3OLOVUUaHZ1chkafrOvtinEEHNRVC7ngEhCwKNHh+246yhT3pkBY+sXEerXGnFWvm9FUEipLkJadcnM5o8fizHrjNOuL/BwL2CT17LIFBEF4pYP69+Zf32Oe0jatdu+caq2ZcTS7x85Qqn52bvmVWOLIo0eL18rqeXx9s7aA8GafL58Knapii8rwdREFAlGVWSCeKiweMl6nZzsLmZgUSCNybGeWdykuImJk+vhQNkqlX+4Pw5Gr0+DjU335M1893gYwlgi4Uql89OcurtYZ58YQ+bsZq7eHqcC++N4Qu4ef4XDuHx3R1t8NMIw7I4Oz7Hzy+McHJoioml9JahC28EumkxlcySKVYoVg2+eHA7B3taiPiuDwi9LhVF3vyJ54uP76J2G1Wc9dAUvbnFwb1Apljmg7FZ3rw0xofjc0wls/d9DJsNh7rNz6WpxWXV5DxfOrSTQ90td0wrXi/ZJgjChheVt7PtQ1yPrliYzkiIYq3GwPwSXk2lMxqiWNN5dWAU27aZzeYJeVxoiowiSsR9XrY1RnltYBSPqhD1e2kM+GgLBzk+OslfnLlEvlqjvzFOezi4oXEslUq8PFJfNOmbKLQS83h4vL2DHbHYXVdfwh43HkWhZprkq/U+vDtdgJR0nQsLixRrNR7tbOdoW+tqL6vjOBRrNTLl61sM3IpMzOvBdhwmMllqpnlbInaiIODVNH5hz040qc6qOTU9w0sDw0TcbvZdoyfxcaOmmwxMLPLd185ybmSOsN+96vVaqZlMLWaYXMiwlCnyG188QiTovakOQ/+hLrqqrWteEyUB8Q5aX7yyh8eiR2hxNxJWNnaNP8TN8dEExKqX9Ue2u5ZOuhFID6vjG0ayVOLlK1d4e2qS3D2qSDZ5fRxtbeXJjk72NzXRFQqhiNJ9Ty5Iokjc6yXm9dIeCNIdCrE73sBr42NcWFpcTtBuPi4sLfKzsRHCbhf90bsXA9xMfCwBbLWiszCTYXJk6TrBmzvFxNA8r/3lGeJNQZ784t6HAextQjctPhid4S9PX+bE0CTpYuXjHtIdwXEgX6nx2oURCpUqxarOY9s7aAiuFaHwudQNV1tuB/0dmyficb8wm87xzsAEPzt3hYvTC5RrdxeAbzWYts1Srsgr566QL9dIFUo8uq2DprD/vlkp3Q1WemM++sB0HAfLcerqhuu8/yCgNx7Bvw6d+mBHC6F1kgwRr4f9bc1MpeuWaYJwtUtPFMARBFpCATojIVpDAURRIODW+NyubYwl0svVjboYUdznJVOuUDUMfC6NQx0tNAVvLUKYKpc5MT3F9wcHKBmbd69oksSOWJwvbeu/7V7R9TCTy5Eql5FEAa96++rK18JxWPVtDrlda+bOQq3G5aUlEqXydQGsS5Zp8vtoDviZzGS5uLBI2O0m5HZdc2xnte/ro7YN4vLYuyNh/JrGizu3k69VubS4xA8vDxLzeGgO+LfEtT+fyvPOuTHOj8zzxL5udnU3EQ16kEWJim7UbXZG5njjzAi9rVGeP9pPwHvjdYpt2QTCXjyBeqVOrxlklvJ3NDZN1Nju76XX1/WpVRJ+iE8OVuaMt6YmeX1ynJn85lOHVUmiPxLlqc4uPtPdzb7GpnuyZrxdCEDI5eJwcws94QhtgQAvXRle7ZHd7KJT1TR5bXyMzmCIuMe7pWw9P5YA1jJt9E/YIvlBheM46KbF2Yk5/vj4OU6PzlCo3Ftu/f2AaducGJ6iYpiYtsUzu3qIBa562Plc2qZ5Ud0KtuNgGBaiWBckuZZaZJgW6XwZw7QI+dx4XOqGejXvFs6yfPtUMsvPz1/hxx8MMr6UfiDpwhuFbTu8OzTJUq5EtljlM3t76YiFNqxGnderLJSL6JaJJslEXG6irnvvizhRyBB3e/Epa0V4HGA0lyK8TEfbCg/X28XetvUraM9svzE9dHdrI7tbG697/WsHdq27vSJJ7GtrYt86n/WFPf0bHGkdmUqFkzPTfH9wgIFk4rb2vRU6giGe6eziQFPzbe2XKlcYS6dJlV2r93VZN3h3cooridSqDZy6TOW1HWfVk9xybLKVKqZlYwoCC/nCMnW7rv6rLftluxSZ7kgYURC4tLBEZyhEi+5HNy1GUmleGhxGk2Vq5tpqtCSKNPl9vNDfx5+eu8gPLw9h2jbbYlE0WV61/UmXK+xuaiDidt9Um+BgazO5apVspcpbYxM0+f18e/9ePKpy2/21m42J+TTnR+boa4/xN7/+OEFf3appBTXDZHtnA7+beJNX3hvi2K6OmwawV85O0trbSMdyAFvOVzj31iCf+dYjiOrtPSMEQUAWZOSPZ8n3EA+xqXCA6VyOHw0PMZbObHrQ5pYV9jc18tX+nTzb1UWTf+vZ3omiSNTj4cX+7bQHgwQuaLwzNclSqbTp52Mim+WNiXE6QyGe6tgavttwBwFsNl2kVjXxeFX8wavUzMR8djVDeysszmXIpUu3+9H3DLWqQblQpVyqYRoWggCKKuMNuPF4NWTl+geq4zhUyzqlQpVqRccy7dVKybUIRryEY1vv4oerwevg7BL/5qcnuTy9SHWL97veLs6Oz2GYdVXgz+3btkod9bk01PsUwBqmxcR8CtO06WqJ4HGpCIJApWYwOZ/mxIUJcsUqe3qb2dfXQizkvadBrOM4GJbFbDrPH759lp+eHSZbejAr7neCkYUkf/D2h2TLFb712D5ao4ENVacm8hl+Oj1M2TQIaS52hBp4prWn3psm1q3pbcfBxkGkXkky7Pq1Z9g2LknCdlh9TZNkJEHAsG1sx0YURGzHXn3Poe7L9tbcOE+1dK8GsLZjo9s2NdPkZ9PD7I+1sDfahCRqq0HJyrG3yoPmQUfdpqHG8ekp/vTSBd6ZmtrU4wc0jac6O/lC37bb7qn6YHaObLVaD1Cpz+nz+QLT2RyqLPGVnTt4sruz/j2oi0+dmJomXa5QMUzOzy1Q0nVqlsgPLg/gUhTcskJvNExvNELI7SaoaTzZ3cGPBwZ5fXSMbLVKS8BPoaYzk8tRrOkcbm3m+OT15yXm8fCrB/YxlsowsJRgOpejPRgk4nXjOA7JUpmxVJp/9qXP42/WbhrAioLA410dVAyDf/feKX7vw7N0RyIca2/Fq26sZ/heIZMvU6roPHOwl3Dg+rYVTZFpjQc5sK2VV94bXFdbwnEcTMOimC0zdGYc27bxBt3gwOJUkvPHh3j6F45sWMTpIR7ik4a6Qq/B9wcvczmxRMXc3GKYS5Y52NzM3zx8lCMtLZvChrmXkESRQ80tRNz1JPbPxkZJlcubHsSenpulNxzhQFMTgXV6wz8O3PYs+NKfnmLwwjTHnt7Oi99+ZPX1f/c7PyG1tLEyfqWsk04UcK0jdHK/YVk2YwNzvPPTC3zw1jCJ+QwCAm09cZ79ygEeeW4XjW3hNZlUqFeRL7w/xts/Oc/lM5NkU0Vq1XogC8u9KqLIN3/7Gf7K37vev/XjhrNsnjybzvG//NmrTCylt6TK8Gbg0vQiVd0EBL5+bFe9r8qlosryqlfVvUShVON7r1/g5IUJ/h9/60vs7WvGcRwujy/y77/3LhdG5hBFke+9fp7/7ptP8PlHdxAJ3pvKnuM42LbDVCLLv/jhW7x3ZXrDiadPEpKFEj84dYmyrvO3X3iMqL++4LzZAjhnVGnzBTkQa2GmmONCeoEOXxBBEGjxBvEqCnmjRtnU8cgKiigznk8jigKL5SK7ww1UTJPJQgZBgL5gjCaPn6lihqKh41NUCrqOJAjsCDfg4PBhco6+YJSQWn9gOI5D0dCZKGRYKBcpW+aq72dBrzFVzJKpVdgejNPg8cFt9Bs+xPVYoaqVDZ2/HBrgTy5eYCCZ3NTPEAWBpzu7+ELvNppvI9OviCKaLDOeTjOeTgN1OrUoCARcGnubG/nKrh081tlO3FufT2zHIVEq87tvvXu9YqcJ/+r4ydX//MquHfzqgX2E3G5cisKexkb+6eee4w/OnOPC/AKnpmdo9Pl4sruTL+7oZziR5OLi0nWVUFmSaAsF+Z0XP88PLg3wxugEg4kkpdkamiTT4PPxVE8nTX7fmuBdEgRUSbou0eiSZZ7s6kS3LP63N97mf3v9Lf7l175EXzTysYqMmFbdF9frvrFdkSSK+D0q1RuoulumRWImzat/coILx4eZGVng/PEhLNOmupxgv1vUr+nl/90m5UYS7n/v30M8xLUwbJvJbI4/vXSRRGlzC2GqJLG/sYn/6aln6ItE7olTxb1CVyjM3zh8FFkU+fOBy5Q3sb0F6v6wZxcXODkzw+d6erfEPHDbAeziXIahCzN0dK/t9Zu4ssDCzEftQ9bHyiJ6KwSwx1++wEt/fJKZsQQNrWGe/tJ+HBuGL0zzJ//2dSaGF/nitx9h+/72Nfu98t1T/ORP30MURR59fifNHTESC1le+/4Z0ok8z754kKPPbGfHgTvztbrXcIDJRIZ/+aN3PtHB6womEmn+8O0ztIQDHOltRZZEPKqCqsj3XGW5ZphcHJ2nuyWyqk67lC5ydmiGuWSOX3x+P0d3dfCff/g+pwem6W6N8ui9CmCBgdkl/uOrpzg1MvOpDF5XkCtXefX8CBXd4B989WlCXs+GxDYEBCRBRBElLMdhqpDhSi7Fk81dHJ+fQBAEjsRbmS3l+Wcfvs4L7dvxySqGbZOslsgbNZLVEi9PDfM/H/0c3xu7SKs3yMX0AiCwK9xA2TQ4HG/Fr2j83vAZvIpKxOWhYNT4YGmW1+ZG+UxLL2cTs+wON5DTK5xLzjNdytHuC/KvLhznHxx8um7F8FDy+I5hOQ5DyST/7oP3OTU7S6qy+UyFY61t/MLOXexruj1Bor/71OP87ceOrRMICYhCXTnTJctrgjpJEGgPBfmDX/kWlnPze98ly2uqD4okcrSthd2NDZi2hePUe1Q1SUKTZfqiEZ7p6SKgua4XsqHed/zNvXv46q4dmLa9qsYtCQKyKOFRlTXCVY93dfDdX/+VesJRXbtW8Ls0vrB9G092dyJSD9jvlRroRuF2KciSyORC+obbVGoGY3NpYiHvuotjSZZo7IjywneeoFqu0dgRo3NHC4IgoHk0GtoiqHdpwVW1a4wXJ5kqz5Iz8hiOseEk7heaniOu3bkv7UM8xN0iWS7xv79/knRlc/s9NVlmVzzO//rcZ2kPbIyVtdXQGgjwzV17qFkWf3rp4qYffyiZ4Gejozzd2YVL/vhZILc9gs9+9SD7jnbT2rlWjcoybbbtbuXQY300toZveoyluQxnToyQXLwzQYLNwuxEkrd+cp7kYp6nvriP579+CF+w7i2YTRf58//4FhdPjxOJ+2loDRGO+ZcpPjYnfn6JcqHK5791jGdfPIDmVigXa7S0R/lPv/MSlmUTbwkTawp9rN/xRhhdSPFn757n9OjsJz54BbBsh6lkjn/z03f5J994nu7GMB6Xius+BLCWZZPMFjnQ34pLq9PcxudSDE4s0hoP8csvHCLkdzM4scQHA9MkM/eGXu84DmfH5/izE+d5/8oUunn32fwHGY4D2VKVk8PT/KuX3uXvfvlJAh7XTXvpLqQWGMwkaPT42BaM0h2IENJc/IfLp9gTbWKxUqTB7SOseUhUS7gkhc+29aGKEj5FZalSJFerMFnIkKiUsByboq6zq62RvF5FAOJuL+laGUkU6QtGV6nJAIlKiVStxNF4GwdizWwPx3HLCmP5NEO5BAvlIqlqiZlSjrFcmt2RRtz3QG37k46irjOYTPLyyDAnZ2aYyeco6vqmeyHviMb49X37OdzcvKEe5nRtlPPp32db4Is0uvfh124v0VXvhRSIem/fqksQBFRZXuORfC0kEU6n/iVN5gFavEfxyvE1+wqAR1XwsDFKnrbcg7seREHArShbit7XHA3QFA1w/PwE/R0NPLmvG6+77gFtWjYzS1l+9v4QF0bnefZg77pe0IJQVxmONod44sVDBGN+wo1BBECURBRNuWMVc9O2uJwf5PWl48xW5qlYFUzHWq3GbgRPRI89DGAf4mNDrlrlzPw8x6enNlX9XZUkdsXi/MPHn6Q9GEQRxS1RYbxdyKJIXyTCF/q2MVfIb3qrS/25WLfw+ULftk099p3gtgPYvl0tdG1rXLcHo6UjyoFHe+nqa7jpMSZHlpifSX/sAeyF98eYGlmko7eBR57fRc/OFkRJxHEcGlrDPPn5vSTmswxfmGZ0YI4jT22vU/jyZZKLOQJhL209DTQsB+zegJsDj/ehuhRy6SKmYa3bP/txYzad483LY7x+cZRS7d54SF0LRRJpCPpoDPmJ+T2EfW58LheqLKLKMqJQDzANy0I3Lcq6Qa5UIVeuksqXWcgVyFdqdQ/Ou0DNMBmYTfBf3/iAX3vmIJIooN2H32dFrCng1ZAlcbknNk0yV+borg5aYkEEAVpiAU4YJqXqvflNzk7M8YNTlzgxNEnxHn3GCgQBIj4PDUEfMb+HoMeF3+3C51JRZAlZFJElEdtxMC0bw7Ko6iaFSo18pUamWGYhWyBVKN/TvmzbcUgXy7x9eZyWcIBvPLKHqN97w0Vik8dPmy9IfyhG3OXDIyuIghdNkjmXnEMAGlxeVKluWu+RVRrcdWrkUGaJ2VKOBo8Pt6yQqdb9Qx3qnoN+1QWOgybJFIxavfKkqEiCuFrRMp06TdGnaARUF26pLl5TtUwimod2X4htwRhl06DdF0QRt978s1VRM02mcjkuLi1yYWmRoWSSsUz6nohiKKJITzjC3zhyhGOtbfhVDdsxKRhzzJVPk9OnMOwKsqjhkWI0ew4Sde3AsEukqsO0ex/DZqsloBzS1RH8SguWc++fK1sN3c0RHt/bxcR8ij/86QccPzdOyL9io2OQyBZZTBVojQf50uO7bijgJAgCsiLX/WMVaVMcFQzbYLaywI/mf8ZIcYyKVfc/lgUZTVQRNmzd8glW+XuILY+5YoGfjo6Q32TLnN5wmF/avYf9jU03DF6zeoE/nn6FiBJAFER8soft/g66fa3rHPHOMFNeYrGWplGL0Oa5eRx1I7gVhX2NTXx1+w4GEklSlfKmjc8BZvI5fjpyhed7epCFjzfQv+0A1u25cX9HIOQmGPasEXdafzsPvsDHL8U8enmWQrZMe28DndsaV/3VBEFAkgS2728n2hBgemyJ6ZEljjy1HRAQVrwg15nLVxP0W3Ser+gGH4zO8ur5ERZzxXv2OW5VpqshQlc8TGskSHPYTyzgJehxEXBruDUVRRJRllV5bbvek2tYFjWjHsgUqzq5cpVkoUQiV2I+U2AqmWUqmbljq5eaYfLW5TFsbObT+ftiGSOKAl6XRrliYFk2s4ksIzNJNEXi8M72Nd6ihmlj3YOK+MRSmlfOXuH44MQ9sUiSRIGgx017NEhrNEhLJEDU7yHqqwevPreKR1VxawqKJC4rndYDWMuuf+eaaVGu6ZSqOvlKjVShTKpQYiFbYCaVYzqVYz6d3/Rby7YdUoUyf3n6MvGAl6d3dRP1r1/Ziru97I40sj10tbqkiCLHGtr5+cwVDsZa6PCHgOVqyjWTe8k0mCnmUEQJRZSQRHG18iEs/3GWt7dsm3S1zGA2wUK5wPnUAmHNjVdWCKguzqfmEQVYrBQxbZtWb4CKaZCpVcjpVSRBJKy579pL9JMKy7apmCapcpmlUpGZfJ6ZfJ7JXJbRdJrJXJZstXpPPtsty+yIxfmVvfv4THcPAVVDEARy+gxTxeOkqoO45RiK6MF2TErmEjUrz+25ST7E/UbI7+GR3Z1UagYnLk5yeWIB3bBwqFs8Bb1u+jsaeGJ/N/2dDSi3EOqbHV1Ec6s0dty992LJrHAmc46hwgiWY9Hn66bL005YDaNJ6oavrbB6c3bdQzzEvUKhVmMwmeD9mZlNPW6Tz8eTHXWrnJsxOqq2zun0IF9reRqv7MItudCkzW2DzBoFpsuLaKJCG3cWwAKEXS6OtLTyQm8vf3Lp4qYyhwq6zsWlRYZTSfojsY9Vd2DTSMzHnt5O/562DQWmsiLh8bpQNZnrbZ/vH9KJAnrNxB90rxt0R+J+3D6NUqFKLl0P9gQBvD4XLR1RpkeXGBuco607htfvopivcvbdKxi6SVtPnGD43lts3A4cB4bmErw9MM7Q7OZaQMCyP5XXTWc8TG9TlH1dTexsaaA9FsKj3blCpG07FCpVplM5hueSDM4uMZHIML6YJl0s3zYFulCt8ZMzQ3c0ljuBKsv0tscYmUnyweAMi6k8U/MZOpvD7Oi8OkllixUQ2HR7n1y5ys/Pj3B8cIKl3ObSk92qQnPIT0c8RG9jlG0tMXqbonTFw6jy3Qt+6KbJUq7E6EKK4fn6bz+VyDCTylOu6ZsWzNpOXdjq++9fIuxzc6yvHY+29uHU4PahiCKBj9jZiILI7kgD3x+/RNTlIe6u3/dB1cXRhrbVam6zx0+7L0SmVkERRQ7GWpAFkQOxZnyKSncgAkBYdeFTVEzbIlersjvSiCJKVC2TVneQ3kCU2VK+LtYUitPg9tHpD2PaNmVTJ1ktoYh1xeNPA1aElmzHWbWJMW0bYzkhptv1pFjFNCgbJmVDJ1+rkapUmC8UmMnnGMukmcrnqWyy8MVHEXK52N/YxJf7t/O17TuQr8n2Z2pjLFUuEFQ76A++iCK60a0SJTOBT2lEFFYe1w6GXWGhfBbLqQICHjlGQGnHLV8NMHSrSN6YoWwmMZ0qAiKaFCSi9aGKPkRBwrSr5I1ZdKtIQG0jXbtCzSogChIRrR+/0oQoyFStLAvlc0Rd2ygY89SsHAIiXqWJoNKOKl191plOjVT1CunqCLZjoEoBQmonXjmOIIjodomiMU/RWMR0KoCAKvqIaH24pCCiIFOzCpTNJLpdwLDLGHaFqNZP1cpQsdK4pBBhtQdVWuvv/XFCFAWaYwG++vQeuluiDE8vkc5XME0Lj0ulJR5gZ2cj/R0Ndf/iW8yN5WKVUr6CUTNQtP8/e/8dJdeZ5meCz3d9eJ/eJ5AG3hEAPVkkq4rlust0d3WpJXWr1dpWy4ykHc2MzuyZnXM0s3Mk7YzOtEa7mpUbqWXaqau6vGGRLBJ0IEh4IOHSexsZ/vr9IxIJgHCZyIAlHh6SQMSNe2/cuOZ7v/d9f7+NlUqX3BLHsqdxPIe2YDPPpZ9kd2I7SS2BtObs62Mec/8YWc7ywfg4c6XajWE0SWZPYxOvdHdTF7r1vUQgCEgaT2d2ktSiq68vWXnGSzMEFYOKaxFRgmSMBAFZZ9kuMFNZxHQtPN+jLdRIRAkyb2VZNHM4vosqyTQHMoSV6j205FYYLU3j+h6KqL4XU8PrGksJIagPhfml3j7eHh1hKp/HrVEQ6/k+i+UyP7t0ibZo/NEIYP/y33oFRZXXVDIbCOq0dWfYtrfjjvs5aoG70gMoKzKSfP2OKGo1S+K53qq6sBACVVN48pWtLM7lOfKLcxSyZepbEuSzJY68dY761iR7n+mlrjl+L7/ObckWy7x2/CIfXZrA8Wqb5QtoCnWxCLs7m3hp+yb2dbfcsMfnTpAkQSwUIBYKsLW1HsfrZ2B8jtdOXODY8CTDc0ssFys170+rFcGAyvO7u/kPPzrCn7x2lGLJoiEVYW9fK/FI1UrC92F8JouhKqtCTxvFX8lufnhhjB8fPcfI3NpE1taCKktkomE2N6Y52NPGwZ422jOJNXuqrhVNUWhJxWhJxXimv4OlQplDA0P84vQQ5ybnmM8Va1pifHRoko5Tl8hEw/Q3112THb8663oZx/PImmUmizl2phppCcdRVsp2m0JR/kLP7tVlG0NRXg2EVzIyV7KzX+/eDkAmcP0D9NX2Xl5t773mtUhcZ1Mshet71fLilfXszjSzI9VYffCt2Prc7fKe6UKBkzMzTBfuXjXHWvA8H8f3cLzqvxXHoWTbFC2Lgm2RLVdYKJdWM64zxSLLd8H0/WbIQpAMBDnQ0sLX+7fwXEfV5/bqX8f2yvh4xPUOImozAomgkiGud1yzLte3WTQvMl0+StldwvUsImojbeFnaA8/txroltwFxovvM18ZwPZK+PhoUoju6Cs0hw6iiRCml2Ok8BZzlbNsinyO0eIhSs4cAkF//OuElDSSUMjbk3ww9/v0xX+ZnDVG0Z7B8S0Sejedkc/QGLhynuesCYrOHKabxXKLaFKYpuA+euNfQcGg4mSZLH3EdOkYtlfEx0cSMt2RV2gJPUVASVB0ZhkrvMOCeQFZaCyY5+mKvETFzbJoXiKiNtIb+zJ1ge13+6dbF0IIwgGdJ7d38OT2jg2tS9VUJodmkWRBKBZc7YNt62lcrRZbK7ZnM1WZwcNjb3IXOxPbSD/uZX3MQ4LpOJyeneW9sbGarrc5GuWZtrY1e287vstEaZa8XcSQNSJKiNHSNN+fPMT22CaKbolmI0NYCSALiZHiFB8tDiAJQcmtkHNK7Ihv4kT2AhfyowQUg7ASJCwHCCnVJFrWyjNXyTJRnsP3fbbHNnEwtW3d3y2gqvSmM7zQ0cl3BwbIWbUruy7aNq8NDvL1/q0EVfW+CV7VLIANhm9eWvxJEukwL35hJy9+YWetNn9HBMMGkiSolCzMsk0gdO13KOYrWKaNpisYwWuDimc/v4Op0UUO/egEv/jBMYygRjQRpKOnnle/eZDO3sbrPnO/uJyheGdgmMMXR5nP1zYLp6sKW1sb+OqBbTy7pYN48O6VhwshUGWZ7e0NbG9v4Oz4DN85fIY3Tl1iPl98IFV1QwGdzz3Zx9hMlqPnxmmqi/HZg308s6trdRnHdZleyNGYiVKfqo1vsOt5TC7m+VevH2ZodrEmg3UhBEFNoTkV41ee3M7L2zeTjATvSR+ELEmkoyF+ef82nt/azU+PnecHHw8wMD5b0yD25ycu0pKsBs2x4K37z0qOzeHZcd6bGeFrnVtpXykfvtV3qAVVMZ4bqJhKEjL37mHy3XMDfPfcwD3b3sOIJsvUhUJ8uaePr/b3syl548AhqCRRhMFU6WPqjR3ocgxF6EhCuaZH0fVNRouH2Jf+XRoCO1i2xji3/D0u5n5KY2APhhKvbleEyBhbaQo+QURtoOjMMZD9NscX/4C00Y8mVQdMrlchaw4yoxxnW+LXCSt1FJwZQkoGRbpy/ju+ycXcj9iX/l1Seg+z5VMM5l/j/PL3qTO2Iq2cj7OVE/REv8SOxLfwfIfB/GucXPrPtIafIqI2okoB0novaaOXqNqK7RU5vfTHDCx/h7jWQWBl/8vuEkVnjifr/h4Xct/jwvIP6U98jaS+ifnKAFOlow9cAFtLCtkip96/gF2xkdVqNUswYvB//z9+CyO09vEWgIdP2a22jrQGmogoD1Z12GMecyvGczlOzMwwllubVedakIXg+Y4O9jU1r0nB3PM9im6Z70z8AlVSaAs28ESyH8uzcX2Xg6ltpPXYakXDVHmeqfI8ETXIy/UHmDOX+MHkIZoDGbJWnqQe46W6JwgrQQxZX53M1CWNXfEOtsc2cSY3xJHFMxxIbb2jFpKAqvL1/q28PTJC3jJrNmFruS7nF+Y5tzBPImAQ1tZ3P6oV918H+T7S3Jnh9EfDLM7mWJzN0fwJa6DpsUXyy2ViyTDpxvg17w1fmOHUh0N0bWniS996kk1bm0EIJEkgy9KVPtkHhLJl88OjAwzN1C4Ld5nP7dzMt57bzebG9D23MuhtquNvfD7G7s4m/uPbRzk+PHVPt78WBBDQVf76N55eFaKSZQn5qnNEliT+wW++jKaphG/hI7gelksVfv+HhxieXaqZ0nQiFOCFrV389c8dJBEO3raP624RDxp8/eA2trbW8+0PTvGdw2ew3dqI2uTKJm+eHqQxEeVL+/pvuWxE1fhs62ZebulGliSkx32Kj/kEB5pb+M3de9jX2HSdHczVZIxtmG6Bk0v/iR+O/y1ag0/SEX2RtN6HLl+Z1JKFQWv4SZqC+wjICYJKhkXzImPF9yg4U6sBbEBJYSiJlYGPQBYaLaEnGSy8fo3Iko9HQEnSFX2ZlL4JEOhylE+298hCoz30HBljKwE5QWv4aYrOLCPFt1k0L5A2+gCoD+ykOXSAuNaBj0+bX+F87gfkrDGCSgpDTqAHYgAIJDQpRGfkM4wV38Pxr/QdK5JBRG0grrWTMbaxaA4SU9sIKmny9hQVt3aD2QeRb/ytz/K133uFqqDGym8huKGA5u2QEBiSTtEtocs68g0mvx7zmAeV4zPTnJqdqek6W2MxDra00plIrml5SUhElTB/rfurJLQIEgJJSBxdOkdA1sno8WuWzzslRkvTnC+MMlScxMcnoUbRZY2n0jv4YPE0/2boe7QE6/hq8/ME5WriJ6lFiChBFElGkxQs787bWlRJYltdHVszGRbLZfI1zMJ6vs/rQ4P0pFIPVwD7/psDjA/Ps3VXG119DWgb6G+8n+x9toePD53j9MfDdPY1UtecRNXkqmqs5fLuz04zPjTH1r0d9O641gd28Owk2YU87ZvrSdZFq8fgAQtaL+N6Hj/6+BzDs0uYTu0yVUFd5S8+t4fP7+mlJRm7pp/rXiFJ1ZKtZ/o6SEWC/OdDx3jtxMV7ug+34/IxuVVvqyQJ0okIQnBLG5e1slyq8P75Ud4ZGKFs1aavr685wy/v38rLOzaTigSRhLhv170QAkWW2dyY5jee20NLKs6/fO2Dmqkrn5+c49DAMNva6umou/kD7rItCfcw6/mYBx9DUehPZ/jVrdvY29REUySCoai3vLY1KURr6EkSeiez5dPMVE5wZO7/Q0LfRE/0CzQEdwEgCZmY2oYsLqvHVoNTgcD2rgSAtl9ioniY6dJRCvYUtl/B9oo4XgXP97i640KVgkTVlqsyvdfvp4RMWGta3a6Cji7HkFAoOXNANYANK/XoUnRlXT6SUJFRsf0yvu/hUGa6dJzJ0ofk7HEcr4Ljm5TdJTzfxV/ZMUkoqFIQISRkoaFJoZVtyyvWUnfX/my9nB+dY3hqgR2bmmhIRW+4TKlicXFinnzRZNfm5lu2i+SXSrz3w2Oc+fASruPS1tvIy7/2JKnG+Lrvu6qk0mDUMVQcZdHKUnFNNOnBqBB7WJgoz/D67PscWTy5+lpnqJWX6p9ke6znPu7Zo03FcTg3P8fg0s39le+EFzu66Eok1jXeEoAqKWiSes2rguvHQmElSEMgjSHrfL7hSQBkSSayUir8cv1++iMdnM+P8t7CKZ7LVNswxEpbUC1GVkIIZCF4uq2dgYX5mgewRyYmyG3bAbGarXZd3FEAO3R+ip9+52Peee006YYonT0NbO5voquvkVgihFJjEZr1MDe1zE//5MPryoEB0g0x+na1kayrPlzaN9Xz1Mvb+MUPjvOTP/2QqdEFmjsz+J7P4Lkpjr5zgbqmOHuf7aWh9dpBbGdvA6m6GEffucDY4Cy6UQ3iZUUiGg/Ru7OVXU9uoq45cV+De9fzWCqW+cHHA8znalc6nAwH+eLePr7yxBYaEpH72sgtiarK79bWBv7S83vRFIU3T1+6JwrDteDy+aHcoA/7TnA9j5G5Jf70/ZM1s0na193Cl/f183RfB3WxB0c4RVcVWtIxXt6xCQT829c/ZLlkrg6C7xTTcTkzNsObpwf5zVsEsI95zNWkAgG21dXzdFsbexqb6IjFiRrGmipThJDQ5DBxqYOAnCRt9LFoXmA4/wYTpQ8Jq9U+LYGEKocQK5MmlzOsUM2mXubC8g9YMM8TUZtpDj2BDyyaF1k0L12/beRryoVvuo9XDauqA60VFfmrLH0UyVjtwxWr/0ir1+RQ/g1myscJyEl6Yl9CQiFnj7NsjV5T4nb5c1f+LMM1A7sHS/Pgwtgcb358kbpE5KYBbNm0OXVpisOnR+loTN4ygH3/x8eRZMELX9+PJAmW5/P87D+/yzf+5ufQjPVNmAXlADvj2xgrT3Aie5qOYCsRNfypVLaeN5f44dQvmCzP4vlXrhdFkgnIBmktwaZIO9timzEkffX5HFFCbI/1EJYD5J0Sb85+wExlnrJzd9TKH1Olame2RKmGAntRXedgSwtNkehduwJSWpTWYD2L5jJvzH4EQEeokc5wM+fzI4yVZrA8G8uz2RxpRbqLk+AHWlr483MDDC7VtgJzLJdjPJ+jK5EgeIvqorvFHQWw6foYyUyE6bFFLg1McuH0JMc/GKShJUFTW4rWzgytnRnqm+JoG1TPWy/zM8u89p2PkG9Q2tizo5W6lsRqABsI6Rx8eSuyInHsvUsce/8iJz8cBB8c16Ozt4H9L/az40AXxsqDxvOqPrBnj45iViz0gIYkSfiej4ePbTnMTmQZuTBNMV/hqVe20tB6/8QSiqbFobPDXJqar1mfYDIc5Jn+Dr66fyvNydg1Qjf3CyGqGeEtrXV869ld5EoVjg5P3jVP1QeZycUchwaGOT1Wm5KbXR1NfHlfP8/2d5KOPni9U6os05iI8rmdPWQLZf78w9M1sQqazuY5fHGMF7Z20Z65vxNRj3lwCakanYk4fekM/ekMvek03Ykk9eE7m+iRhEJASRJQkkTURqbLxyg5i5Td7JVlkG4q4F/VPHCZLB3BkBM0BHZSZ2zD9suYNy27vX0VgY9Hxcni+9Vg1fMdHK+M57to0pUSZ3GLffNwmSmfwPEq1Id30Bjci4+HV7JXe2hvxoN++eVLJtMLuVtWvHi+T6licWZomsptKmPmJ5fo2d3Bjqd6kGTByMAkH79xBu8OBBiDSpB9iV0M5C8wXBrlnYXDOH5VkdiQN+4z+zBRcU3O5i5xIT9MT6SToBIAfMquQ9bKczE/wpncRbJWjieS24mvKM6GlCC9kU7aAo3knCIfLZ26v1/kPuF5Hr7nV23i7kEL0cfTk4wsZ2s2XSUJwd6mJjriCQLK2kOgiBLkay0vEpSvTY61BOt4sW7vdcvrskZ7sAFZSJScauYzpccxJJU6PbHi7y7QJJW2YCOykGgwUhiyTlwNowiF5kAdz9ft2fBEU3MkSmc8wcD8XE09dE3X4dz8PFszdQ9PALt1dztGQGNieJ7JsUXmZrLMT+e4eHaSQFCnqS1JW1cdLZ1pGpqT1DfFyTTECIT0GwaWtaCjt5HPfGUPSwv5my7T3FG1u/nka8arO2nqyDA0MEluqQgIEpkIm7e10NnXSDR+xWLHsRw+eP0sb//4BC2dGfp3t5Oqj6IoMr4Pju0wNbbIj/7oAz566xxN7an7FsB6vk+2UM2+1ioTZ6gK21rr+cq+fjY1btyfrtZoisKO9ka+dnA7RdPizNhsTcum7xTf96lYDgvZIoWyie143CyD0JCKkknc2eDXsh3Ojs/yxslLVKyNf++OugRf2tvH030dD2TwehlFlqiPh/nqgW1MLOZ4//wIufLGbtQV2+HS9AKvn7zEb31mH77/4A+iH3P3CSgKyUCQTChEYzhMWyzOpmSSLZkMrbE44Tt4kPu+R8ldwHSXMeQEijDwcatWM16FgJJCEQaWv/b7uO2VCSkNaFIYH4+8PclM+Th3mrn0fIe5ymmaQ08gCYWCM82yNYYqBYhqLWtej+NXkISCLkcQQMGZZbJ05Jq+3EcV3/exbLcqNnibn0EzVJbn84xdmEaSBbNjixgh/Y4m0iQEAcWgN7KJt+fe48jiUZasLF2hdhJaHEPW15QB6ol0r6qlPuwIIXgus48GI4MPOL7Dsp3nYmGEX8x+iOlZtAWbiKphJCEhC4mAbGBIOrqso4hHW0LG933MssXFk2NYlepkS+vmBhRVYXkhjxHUaKiBR/GtKFhWzVXuhRC82NFJMhBY17UUVAxerN933ev1RpJ648ZVWgktSkK7vhojoobYfIPl03qc9FW9tHVGgjpj497LuqKwOZXio6lQTQNYgNNzszzT1k5r7N7XEd/RFdjUlqKpLYXrehRyZYbOTzN4boqh8zPMTmXJLhR4Z+g0ju3R2JJg89ZmNvU30dCSJJkOE0uGCEcCa7LcWSvb9nWybV/nHX02VR8lVR9l/wt9t13WNG1+8ieHyS+VePp3t7Pv2V5k5cqN31/xIjx1eJDxwTnmp++f0ETFshmeW+LE8FTNRHw66pK8sK2bvd1rH7DcD17Y2sX4QpZssczQbO2Fq9aD63nkiyYfD4xxbmSOuaUCpu3gf2IEIxDomsJnD/becQA7nS1wfHiK81PzG9rny8JTX9jTx7NbOh+osuGbIUsSHXUJvvLEFuZyBU6OTG/YLmohX+L1k5f4pf1biYcM5MeeiZ8aBNVe1kQgQFBVCSgqEV0jEwzREY/TnUzRl0rTHo+jyPKG5sh9fLLmMFPljwkqKVQRxMdbKat1SWpdhJQMlpW7/X4LgUAhqW+i7C4yWTpCzh6nYM9QcbPcqfe6j7e6vqw8TNYaoeTMU29sJ6I24vu3v9YkZOJaB0vmIJOljyg5C5ScBcrOIr7vP3QFrUv5EhXTwfM8lgtlbMdlYbnIxGz2umVdz2dibpnBiQXikcBtJ/NbNzcwPTLP6fcvIMkSZtli0872O8p65Z0i784fJu8UEEKwYC0xuzDPR0vHiakRwkoYdQ0B2V/t+ouPRgArqv/piXTSFb6ib+L5HluimxjIDTJSnGDRXsbxXbRP4X3fNh1mxhb46I0zBMMGQghiyTBCEgyeHiccC6IHNIyAVq1ElCUc28UsWzi2SzBioKgytuVQLpgomowR0BBC4Ngunu9TKVQwgjqaod7wvB5eWmI4u0TBqs3kliQEUU1jd0MjEf3+CA/dL7oTCdLBEBcXa9tLPDA/x1ypiOf7N+0nzi4VMSs24YhBKGzg+z6z08vMTC/juR7pTJS6hui6K3Y3NIUkyxKxRIhdB7rZdaAb1/GYGl/k/Olxzh4f49LZSeZnchz62Wle//5x0vVRerY1s3V3O5v6m0jWRUllIg9VaZ7n+kyOLFDflCAY1m/oH2uWbTzXw8fnflqTzuWKvHtutGalwyFd5em+dj6zvfu++T6tFUWW+NyuHobnlpjK5muSjbxTKqbDR2fH+N//6BdYloumypQqFpbtEg0ZIKBUtnA9n+6WNBXzzvbV9TxOjEzx0dD4hntANVVhZ3sjX97XT328NrY+94qne9s5PjzJ5GKOmeWNzdxajsvYYpYPL4zx7NZOQvpj4ZNPC5os0xGP82JnFx3xBK3RKG2xOOlQCLXm9z+Bj0/BnmGq9DGOV0GRdCJqM12RV2gI7EKTQ8hCJ6I2oUpXemABdDlCSK1HEVcqjHqiX+Bi/icMF95CIEgbfWyN/xolZwFZVM9jCQVDThBSi7ctU5OFRlv4GZasYUasQ8hCpSV0gK7IK9UeV3zCagO6HENGveZzEa0ZbUWQqTP8IuAxXjrMaPFdElonO5J/EdPNo0gBqqJUKrp8WalYoEpBgkoGRRhIQkWX40ji3rYn3YgzQzNcHJ8jXzI5MzjNUr7MO8eHGJxYuG5Z03KYmFvm1KUpnt7ZReA2g7Unv7CL0XNTjJ2fwnFcenZ3sHlX+x3tZ9Ze5tsTP1z9u0CgCAXP91iyllmy1jbRbnq1zd48aEhCIigbNAXqmaksYHkWru8Cd3auld0KBacEQEKNokjXDrmLTomCU0IWMml945m2WlIuVhi/OIsR0nn51w6uBKQKo+ensEyHuclqYiAYMeje3ooR0FicXWZ6ZB6rYpNpStDcXcfE4Czzk1mMoEZje5pAxGBheplyoUJ2Lk8kEaKlu55I4no7vo+mJlkq167HWJNl+tIZGiMRtPuo23I/6IgnSAdrP/k0kc8zXyphuy76TUqyTx8fY3oqy669HXRtrsc0HV774XE+ePcipunwxJPdvPzqDjq66ta17ZrWQMiKREtHmpaONJ/54i7Mis3IxRlOfDjEiSNDXDgzwRs/PM7r3z9GMGKw+0A3/+CffBO5RuI19wIhQTgSYHE2R36phG25KCsZWB/wPZ+j715gcnSBWCpMqv7GYg53G9/3mc7meevsYM3WuauzmX3dLSTDD8cMbGMiytN9HYzMLvHR4MR92498scJ/ef0Ypunwa5/dw4Gtbfzs8DnOjczxG6/uQ5YEb3x0gZGpJf7iF/bxxJa2O9pOrmRyYmSKgfG5De2voNrn/He/9ByZaLgmqsj3ElmW+OyuHkbmsvz0+PkNr69sVi2o9nY3E9QeTsX1x6wfQ1HoS2f4lS3bqAuFVlXWqwq41Wxhrc4FSUi0hPbTEtp/y+XSRi+fbfkn173eHf0s3dHPXvNa0tjMfuP6QrVfCv2b1T8HlCTbk99a0z4KIRFWGuiPfQ1Nvr4iQxIyLzX/v657PaF38YXWf7b697jewW79t9md+u1rlnu19fdX/xzT2ohpV+6DzaH9NF91bK5+736SK1T4+NwEZ4emWS5UqtYSH1246WSArilsaknzG5/fRyJy/XPU933wV/oMfWjZVE9rT0P1vNvAuVbts2u6489fWc+jOYHn46+WdDu+S9bKIUsSUSWEvoHvfCp7ge9PvYGPz1/v/nUaA9cO0N9dOMr3Jt+gXk/x32/56xv5CjVHD+ok62P88A/eprO/ib69XasWTtm5HFMj8zS0pvmT/+Nn/OX/7ivEM2E+fO0US3M56pqTvPFfPuQ3/v4X+dG/f5ueXe2cujhDuinBjqd6OPb2OS4cG+HZX9rDD/7dW7z4tf3serZ3tSrz8gT8qdlZls3aBbBBVeXZ9g40uXahzyeTBQ/q+KAlGiUTDKEICWcN1TJrxfd9ZgsFFstlGiM3TnaMDM0xPZVl/5ObsC2XS+en+dF3j/K5L+1G1WSGB2d5582B+xvAfhJNV+jsbaClM8NLX9nN0Plp3n/jLB8eOs/0+CKD56a5rynKO0DTVF765T187z+8y3/8Zz/jxOFLtHXXI8kSuaUiQwOTnDoyjOO4vPy1fWzZ03Ff9nOhUOLc5BxTi7cvOVsLQU3l2f4OdrQ31GR994p9XS2cm5jj+MhUte/oPmA5LoOTSzy5o5Pn9nTT3pDg8JlRArpKJhGmvTHBlq4Gfv8P3+LQ8SFCAZ1dPc3r3s6754c5Mz6Dt8FrKhkJ8tL2TXTWJ+6bz+tG6apPsq2tnuPDkxvOwpq2w/sXRpnK5omFAmj3UWX9MfeOnGny44sXODQ6SjIQoC0WozORoCeVoi+doSuRxFiHCMhjHj1e2LuJg9vbWcqV+e7bp3jr6CU+d7CPnrbMdctqikIsYtCQjBILGyg3uLc6tsvEpRl+/AdvszCVpa41xYHP7mDrwU3IG7jvNOh1/De9f/uOP3+ZR6J8+CZYns1MZZ73F45xoTDCS/UHqTcyn0qlZgBdV+na2szv/I9f5+hbAxx+7TRPf3EXkXiIZEOclk319D/RyenDF6mUTc4fW2D84gzLCwUWppZZmstx7NA5svMFBj4aZmmuOhadn1wiENbZ9Vwv2w5sYmZ0AatikVsskKy/0kdpeS4D83Pka9izGVAUdjc0oNVwXOPj4/pe1YXkAbbTk4QgGQgQDxjMl0o1XfdUocB8qXTTALZStpAkQTIdplis8JPvH2PLjlYOPttDMh3m+392hNk7aLe8a09fx3FZXixyaWCKi2cnGTw3zdx0lsW5POWSRUNLkl0Huh7Y2YqboekKL311L6qmcOKDS5w8PMhHb1ezPIoiYwQ1tj3Ryb7netn2RCeR2P254U8t5hkYn8X1ajNBsKerhd6mDKGHrG8gGtTpb6ljS0sdJ0am78s+eL5PuWJRlwgTCeioioymysiSwHYcdFVBVxW2dNbzi48vMTGbXXcAa9oOHw9OMDizsf4GSRK0pGL80hNb0BT5obs+L6PKMtvbG9jR0cjPjl/Y0Lp8oGI5fHB+lPpY+KErqX7MneEDZceh7DgsVcpM5HMcm54mqKmENY2EEaAjHqcvnWZbXT2d8cSnrq/q005AVwnoKpGgQWdTksHJBXraMjzRf32GWEgCRZZWJ8BudG/NZ4v82T//Kdue7OGJl7czN7HI8MAkkiKx7eCNZF/WhizJxG4gJvOpxa/2dP/zi/+RgKxXq+d8Hw8PQ9L5tdYvcCC1g7T+6VWfF5JAM1Sau+oIRQL84s8/opgrI6iOdYNhA1VTUDQF36u2yzV2ZNiyv5uOviYKuTIjA5P07e2kb08HwUgAPahimQ4LM8v4PqiaUu19FwJxlZuF7XkMzM+zXKng1ijJJQlBRNfpS2dQpNtPBs1VFhnIDzJZniOsBOmPdl3TL32ZrJVnqDhOQDbojXYg82BOcIuVADZhBGoewE4X87dcp6LKSEJQLJosZ0scef8iv/nXXqSuIYaqyUiSwL6DVseaBrCFXJmpsUVGLs0yNjjL5NgiC7M5FufzWKZDpiHG7oPdtHXX0dSWor4xfs1J+zAgyRLphhjPfmEHm7e3kF0oYFZs8H0UVcYI6STTERrbUoQixj2RGb8Rk0s5zk5srJT0MpIQPN3XTlsm8UBY5qwHWZLoaUyzf1PrfQtgJSEIBjRM28FdERUyVkpR55euePMmokGKZYtCaf2CBeen5hmeXdqw2nQmEmJ3ZxOd9Q9WP86d0F2fYktLHW+dHsR03Nt/4DZ8eGmcZ7d0Pg5gP4U4nkfBsqpiIivPaUWSODs3y+GJcepDYZqjUboTSfrSafrSGVJ3od/ofhFRmngi/Xuk9B5kqVZB+uWBqVgRPywDAiFUhFBWyjo9fFwED3bpviJLNKVjbOtqIBULEQ7e2TFyHY+FqSy9ezupa0myPF/HsbfPMXZ+ekMB7GNugA9hJbiSVfZxfJeCU2LRynIuP0h7sImEFkOT7n+v9f2glC8zcm6KkXNTKKpMdj5H2+Z6tICGEFwzdveBlu46yvkKk4OzWGULIUv07+vi0Pc+ZvDMBLqh0rq5gVg6glm2GLswjRCCqZF52nubCEUCq+uzXJePJycpObXzfg2qKm2xOBFdv21OveiU+WjpDEJAX7QTQ9KJqOFquay5SMEpEVGCxNQIlm+TtfMUnTJqQSGjJwgrQRzfY9nOY7oWaT1BUKnqE9iezYK5TMUzCStBklocQVVobdFaJqqGiSohfMB0LSQhCMgGOaeAJqlokop8G8uxm5EMBEkGArdfcJ1M5wvMl4o3fb+xOcHU+BJ/+O8O4TgeyWSY3q3NhMI6SwsFbNtFN9Zfqn/HAazvVz1PF+cLTI8vMj2xxPT4IlPjS0yNLbAwm8f3fRpbkmzf20Fja4rm9qp6cX1Tgkjs5hLWS3N5LpwYxbYcnvjMlnvuJbsWMo1xMo3xdX3G930qJYsLJ8aYHJ4jny2BgHA0QFNHhh1PbgI2XkNfqJiMLywzWYPyYQG018Xpb6kjHno4/eLqYmH6W+pIRYIs5Gs787QWVEWiOR1jbCZLxazekOPhAAI4dn6C7ZubCOoq47PLmLZzR5MERy6OM50tbLgivzUd56nedrRHoDQyFjToyCRoSce4NL1x5b3zk3PM54vYrov6KROAeMz1OJ7HQrnMQrnM+YUFAopCfSi8Wmbck0rTk0rTEY8TUNV71kvu+x6WM4TljOJjIYkgqtyMrnZyMwVi33fxfWtl+QhCSPi+jeebCCQMJU5n5MWa7qflTOF6CwS07QBU7AFMZ5iAtgND3YTvWzjuLKYzRFh/kjsV0rlXdDQmCQc00neoIA/gux6F5RKDp8aYGV3A8zwmB6tlmamGOIoms+Pp3po6ONyOimsyWBxh2cqxLdZHRH3wFenXghCCZzP7aDLqVspAXQpOmfHyNIfmjvDTmUMYsk5PpANdfjR7f2+FkCR0QyMYMZBlmd7dHbRsqscI6uiGRiCkISsy2w9uoq45STwdxrFd9KCGLEur2dvNO9uxLRsQ6IHqNSzLEpF4iEBYZ+v+bpo6M2jGlevbcl2OTk3V1AIxrGl0JRJrug9PlGcouCU2hdvYFtuMhITrezi+y2R5FtOzuFQYpTXYSEKLYnk209Y8uqwxVJygN9JO0a2wYGaRhcxwcYIn0zsBwbGlc5TdCqqkkNLjxNQIy3aBk8vnMCSDkeIUrcF6omqY2coiuqzRFWrhQn6EjJ6gzkgTuMPxR8zQiRm1H8fPlYosVW7eq9y/tZly0eLc2UkUVeYr39hPXX0MRZEpFk1i8RCZuvXb8NzRKHVuOsvE6ALz0znGh+cYvjjD8IUZCrkKwbBOKhNhy642mtvTdPU20NXbQF1jAk1f2+aW5nK8/7NTFPMVdj7V80AGsOvF8zzKBZMPXjvN8XcvUMiV8T0fBARCOsVche0HN9XEZ3JqKc/o/NItDdXXihCCA5vbqI+HH9pBu64qNCai9LfUcejs8L3fvqayfVMTxy9OULYcPM+nKRMlEQ3ywakREpEg4ZDOoWODBA2N1DrKzn3fp2w5HBueZCF/8xmwtRDSNbrrk/S31G9oPQ8KQggaE1G2tNTXJIBdLJQZm8+ypaX+oREye8y9o+w4DC9nGV7O8s7YKG2xGHsbm9jd2EhPKk17LE7MMO5BIOtStk5iOSNIUgDfd3HkBWQ5gUR18CKEju+bIAQCGddbxnSG8H0LTWlHkRLY7gyOO4sQATSlBUWueh16vonr5RFCRRYhhFDxfRfPL6y+L4kQAgWfCj4+Ehqul0OWkwhUPL+A7Y7huIurAayPRbHyNrIUXQ1gbWeSovkBIe0JPN9CIBBCQzyAHpyZRPiO7c8uo2gKTV11TA3PoajV7+jYLqqmMHx2Aj2gsu3JzXAPyxSLTol35g8zXBylOdjwaASwKzY6XaHWa8pCfXxydoFFM8vb80foiXTSYKTJyDf2+dwovs8DqwMTCOl0bm2mra8RPB9JkVcn16/uVd35TO/qn7u3tdLZ34zneciyDAL2v7INx3YRQiDJgrmJJYyQzuadbex6thd9xVrnMr7vY7kOZ+ZnMd2NV05dppqBXVuQNF2ZJ6FGSWnx1WynjITtOxScErZnc2r5IrbnsDexFc/3sDyblB7nxPJ5FCGzZC2zbBeIqmGGihP0RTvxgQuFETJ6koyRxJB1rJVgeLayyLbYZs7lhwGfOiPFdGWesBKkK9TCWGkaWcgktTjId1bhEVI1Qmrt46mCZZE3zZtO7je3pQgEdTo21SFLEpv7G1FXJuECAY3+bc2EwusPrO/oKXD88CDf+8MPGB+ex3U8wlGDUMSgrauOTVua6N/RSldfI6lM7XouLNMmv1Qily1imw6IapN5qjFOIKQjr/ikFfMVzLKFZTrYpk26MU6lbFHKV9ADGunG2GqqOrtQYHmhgFmxkCWJUDRAIhNBD1yZbfN9n1K+QnahQClfqV6Yikw4FiCRvnbZW2FWbM6fGOM//e8/ob23gWde3UnX1haEgKX5PK7tVssyRLWcKp8tsbxQoFwyEQgCIZ10Y7zql3WbDN3w7BKjc9k7PtZXo8gSBza1Egs8nNnXy6SjIfZ2tdyXADaoqzy7uwvwMVZ6PlobEuzqaeL4+Qn+/Q8/BHyChsbnn+qnsym15nW7ns/w7CJDs4uUzI1NWDQlo/Q0ZYgFH+7f+mrqYmH6muv43pGzNVnfuYl5dnc2Pw5gH3NLLNfl4uIiFxcX+dGFCzzV1sZLnV3sqG+gPhxe7ZW9O6Gsj+MtointGNo2THsA253GdiZXAj8ZTWnDckarQagUw3LHKFTeBFQ8r4Ch9VO2j2Pal1CkFOAgSwl8bEz7PLY7jyRUVLkVTWnF88sUKu+iSElcP4+uVMtdbXd85Vt6uF4WQ92GqjTiuPPVgFaqjhGEEIT0g+TLr19jD1T9Oj6uX8B2xpDlNKpch7i7+pN3RLFiUSiZqIpMMnpn94dIPMSv/d0v3PR9WZZQ7rGInO3bjJbGGSmNYrq18eN8UBEINEmlP9rN+4vHma7MkXeKZLjDAFZc/o9/ne87gOu72H7tgrRaI4RY9/kmydJ1rXNXVwxoukIiE8UIaiiqcl3Foef7FC2LyXx+wz7uVxNSNVqjawtgJVHNuHpXqfV6+BSdMt6KaJPru3h+1S7TkHU2hdvoCDbRZNSRswsUnBIeHrbvkNCi5J0SJadMXI3QHW6lK9wCwJK1zHhpeqXHtoWSW6HolFm28yvPh+p5465sayMEVZXAXQhgPd+naFsUbZv4DQJYIQSpTIRU5vr2q/rGOPXrrGa9zB09BSZGFxgbnCMUMWjvrmPngW52HeiitTODscaAbr0sTC/zzo9O8MFrp1mYyeID6fo4v/xXnmPHk5uIJEJMjy1y5I2zDJ4ZJ5ctM35xhq/8leeYHlng9JFBWrvr+cbvvkjXlmZs0+HQD47x3k9PMj26gG6o9O3p4JVf2U/v7o7VYNKxXU5/OMjP/+wIg6cnqJQtQhGD3c/18uq3nqRt0+1VeX3fJzuX54f/4V2EEPza33iF7q0tyCv2O22br11HpWTx4Rtnefv7Rxk9Pw1C0NnfyFf/6gt0b23BCGo3LTP2fZ+JxVxNyoclIYiHAmxprSd4B/XpDxLJcJAd7Y3IkqiZsNVaMXSVPX0t7OlrWX0tHNB5ckcnkZDBm0cu4noeW7sbeWZnJ/WptU/8OK7LoYFh8uWNK/V11iXoaUpveD0PEvFQgNZ0HFWWsWswm3thao65XJG+9YtEP+ZTSs6qqhm/PTLMvqZmvrFlK8+2dxBUVeQN2qPcCssdxbcsHHcJWYogCYOS9RGgocr1lO3jSCKEoW7B921830GRokhSBPDxfAsQSFIISQoCLrYzwVLxT9HVnmpAKaVIhr+F6y0yk/tfSQZ/DUWpA1EtCS6bR1HkDMvl7xMxXsL1C4TFs0hSANuZwHLHCRtP3/J7+FiUzCOYzkWigZeRxIMplHV+ZJa3jw/SUhfnay/suKN1qLpCR/+DdXPxfI+KV3lQE4U1pdqL7VP2zOqfYUNBgyJkFCFR8RxM79oJZnel57bo3Pu2pvtJPBNl9y2SWxXHYTyXq8nz+mqCqkrLGgPYZqOOj5ZOM2cuktETSELC9hyOLp0l7xTYl9jGsp1fLS33fI+SW6HsmlQ8E0PSaQ02kNGTdIfbEEJgSBrDxQls36HsVig5FWQhISERUoMUnDIlp0LZqSCAgKxjehZl16LsmuSdIs4GJzuCqkrwLgSwAEXLJmeaxG9Souz7Pp7n4zguvuej6ep1FafrfRbeUQC7bU8HDc1Jtu5uI10fQ16ZcbmbAj96QKNzSxMt3XW0dNdhmTbf+Ve/4Lv/19sk6qJs2dcJVLOZI+en+b1/+A1+9seH+eEfvMMLv7SHz/3aAYbOTnHkjQFauut590fHOfTD4zzzxZ1sP9DN/NQyh354nH/9v3yP/+Ff/jbhlR7dS6fH+eDnp9ENlf/H//lbBEI6M+OLWJZDJBZa8/4XcxXOHBnklV/dT6ohhnQL79t3fnyCd354nObODH/h73wO23Z549tH+D//x2/zX/3jb9K1pfmmpcau5zGTzTO/wXJSqCosPrGphaCuPvRC8qoskYoEaU3HGZ3LbthqphZEQwYHtnWwb0WxUpIEsrQ+0S/H8zg6OLnh7KsQgvZMgu76tWd/HwZkSZAIGbTXxbk4tbDh9Y0tLJMtlmuwZ4/5tFG0bQ6NjnBmbpaDLa38zf0H6Ign7mJrhrfS22ri+QLXv7GdlCQFUUUDmtKGKtcT1HYhhIEmjyKhoCub0ZUefN+iaL6P487h+WVcdxFNsbDdCSQRQpGSxIJfQpHTgEzFOosiZwgbz1KxzhLWD1KxB/B9C1VuR1WasN1bC+t5vknFPofpDNEQ++/Qla67cJxqw9hslvdPjfD0jgfXSuNO8PExHWvD2Z+HhbJb4b2FY1Q8k0YjQ3QDJdMhJUhICbBQyjJamqIzdGUSe6Q4yVhpiopbO5uYR4GK4zCyvFzzsy2gKDSE1/ZbtoeauFgY5f2FE7wzf5R6I8X2WA9pPc5HS6eZNZeYN5fYGa+WT1dck0vFcabKc2StHC83PMlsZYFfzH3IB4snCMgGv9r6eVpDjRzNnuUXcx9yaP5j2oNNPJ/Zx974Fv547MeczV2qliUnt9AdbmMgN8iPpt5mtDTJeHmankj7xo6Bqtw167eSbZEzK8CNJwlsy2VmOsvAqQkWFwu8+kt7iEQMHNvD9TxkWVotK14rd/RNtuxsw3U9jIB2z8QEoskQW/Z24vvVyN22HJ754k7+5T/8c0r58qqZsKYrpBvjdPY30b+3nbHBGZq76mhoS7E4m2dpPodZtvjpH3/AtgPd7Di4icb2NHXNSUqFapB5+vAl9jzXhx7QsEwHq2KjqDLRZJhoIkQ0FV6ZQVjb4XNsl3yuRLlk0txZh2bcXFHRdVw+evMs8UyEJz+3g/beRnzPJxwNcvzdf8PpDy8RT4dJN8Rv+PmppQILhRJ2DTxPDVVhV0cjqvzw2qlcRghBUFPZ1trA5GIOqwaqtDdjYHiGs0MzpONh9m9tQ9dufJ4IIVDkK0Hr+6eGCRoqbQ2JG5rcfxLX8yhULM5Pz1G5Awnyq2mIhWlMRAnqD3em/ZMIIQjqGu3pRE0C2FzJZLFQpmTaBB+B3vyNoskymiw/EBNcju/jeB6u63J/HJ9vj+v7LJTLvDk8xNn5Ob61fSevbtpM/RoHVutBkzsJaNuw3RksZxDLGaL6yPcAH9crgqRSLXGUALk6aBQSQlT/DlK1T1bI+D4IoRDU9xPUdqPISYTQkKU4jruIJLSV/tQr14VAutKzKmRYyWlV13/7QE8ICVlEkeU4lj2IIiVR5Adzks2ynapwZfr+2NVUVZvB9EzUDSiVXrde36fi3Vyg5aHErx6vw4snGCqOAWB7DgtWlgv5Ec7nB2kNNNAf7SKhRnF9j7JTIWvnqHgmObtAxTVxfIex8jSJfAxD1gjKAZJabHW81GRkaA02cWL5PN+d+DmLZpaYFmbJynFm+SLTlfkNBciPImXbZiSbrek6dVkmoutoa5wslITEwdROdsb7cH0XRZIJK9WEVVpPoEoKnu8RkA0CssFzdU9wwN2Jj48AomqY9mATW2Ob8AFVUlBWrsfP1B3E9KqTFoaso8saqqTyq22v4vkuAomwEsSQNbZEu2kO1CNLMp7vEVGCGPKdt3jJQkJZZ5JkrRTtagb2RiwtFjj0xll++oPjlEoWpYLJs5/pJxw2GLw4w6njo0RjAV75ws51bfOOAthA6PYlPJfTxbficpnuWoIjx3YZGpjk7JEhZieWMCsW+WyZxZkcjuOtlrfIikwoGkAzVEKxAOFYgEBYJxDSUVQJs2Jjmw7D56ZYXihy7ujIqvpZdj5PMVdmamQBdyUAbO7M0NHXxJE3z/Iv/6fv0L+ng11P99DQllqzsbjv+3iraXPlliIeucUiizM5tuzvorE9haar+L5PpjFOuiHO7ESWYq580wB2bCHLYo2Udg1VYXtb40Mr3vRJDE1hW1sDr5+6eFcD2JMXp3jr40vs6m3mwPa1z5i9e3wIy3b47ME+9vbfPoCtWA7nJ+cpVqzVCZw7pSUdoz4WfuhsktZCQFNoStZmUOn5PnPLBZYKJYL6+lXzHjU+09nJCx1dpO+3ZYxf7VHyfR9v5V/TdSnbNiXbpmhZFGyLxXKZhVKJxXKZuVKR+VKppn1Wa8HzffKWRWFxkT84fozR5Sxf7u1ja6ZuzQOstVCyjmG5I3h+BVmKE1D24Ho5sqU/xfPyVOyzBLTqgEESQWQpRr7ycxxvnqjxMoqcpmIPUCn/GMdbIKw/Q1B7gmzpOxTND5CkALqyiZB+YGUC40YDI1H996pnnucXKJofUjTfxXSGWC79iKC+j5L5IRX7HD4eshRHVVoRKChyPfHQVyhW3kGSQgTETmTpwbOy0lSFoKHi3MVny60oOiV+Mv0Go6VxGo16nkztoz1UFSiaMxf49sQP7nC9Rcpu5ZHKwF72ff3F7GHUFZscf+UfRcjsS27nucw+OkLNyJJMzi5wdOkMr8++X+1bXQl2JQRvzL7PR0un0IRKYyDD73T9KmLliggoAfYktlB2K5xYPsfb80eQEASVAJ2hFlqCjQwWxu7jkXjwKDsOI8vZDY9priagqsQMY12JmIgaIqwEV897SUj4vk+Dka7+voLV3zkmhfEVfyWArcY0hqyvfl4IsbpsXIvg+WG46nVJQEZP4Pne6ucBAko1QPZZuZNuMJGkSBKyuDsBbNm2qzZzN+D0iTEuXZhh175OGpri/LN//EM8tzqZGYkFyOfKzM+uv+2xprnk8eF5LpyeYHJ0gXLJvG0AG44G+ObvPH/rH2XlrePvXuD4OxfwXI/mzgySLDE3leXkBxevOdEl6UrTuSSkqoGuJFaenwLPrc7Ql4sWW/eladtcj36VZ5seUOnZ0Ya6kjWLpyM88UI/wbDO8LlpBo6OcO7YKAde3sr2g5uIp24/eybLEnqgKjmeXy6tBsc3wjRtHMdF0+TVDO9lk+dASMOq2Dj2zT8/Np9lqbjxAFaRJBKhAG2ZOPJ98rKtNbqqsLkxve4y3fUyNrNErlhBU+U1zXZdPv0d1+PC6Bw7Nq+tB6pk2ZwcmcKpQba9LR0ns46S+IcJQ1VpStQuKzKXKzKfL9KcehzAtsXiPNXauubeonuFt5KNtVwX03EwXYeK41TVEi2LgmmSM00WyiVmi0VmCkXGc8uM55ZZNs170mLgA0PZJYoXLLKVMl/u6eNgS2sNRDZkQvoTaMoVdVVFSqMpHXh+kbDxPLKIosj1aEorkggjhEJA24qPhSKlEEJDlRsJaruw3dkVIScZTWklpB/A8ysIxEo2VCBJURKhbyKJKxMZhtqLp7SgSAligVdR5WaEpqPISTyvSEg/SEDdjio3IAkdVW4gFvwlZCmCLCWQRABNaScWfJWAuhXfX9m3B1DACaAhFaU5E+fi+DyO4yLL0j2tXKq4Jofm32fOXCChxukIta4GsEWnyHvzH97Rej18HL92dib3m5ga4QuNz7OU2nVNUC4QKEImpARpMNJ0hprRJA2BQBUqDUaGPYmtq8s/9Yn1SoiVbOqV31wWEi2BBj5Td5CuUCs5p4Dre4SVAO3BZmQhsSXa/UhNDmyUag9sbUuIA4pKTF9/5vLqwPNGf7/VsrdavprEutHr144XrwTKtUGWBPJdSlLYnntT26OxlaTgC69sxTCufb5FYwEkSZBbXn/cUrMnwexUljd/dILDvxhgYmQB07SRJIFlOisBpY/rVjOluqEST4bo2NxwQ2EAx3GplC0CYR0hwHM9jh06z/ToAs99eTcHX9mGrEiceO8ir//ZketXIK7+4ye7hKsqaaGIQe/udp770i5SV2czRfXkumzSLMsSbT0N1LUkmR5bYODjYd7/2Sne+M5HZJriawpgq9sLkGqIcf7YKE+80E8kHrzhwy0Q1NF0BbNsUylZROKhlWy2R2G5TFNHBlW7+Sz95FKO5dLGy30MTaEpGSNs3DzbfjnbgajevO/kYe163i0/f3lyohYDAU2RaUnF0FWFQtm8a4+MbL5MwFDJJNaX0cwkQhw/71CqrE3psWRanBqdue1E0VpoTsZIRR7NAFZTFTLR2n23hXyJpcLjPtgHGUmI1fLmsHbjsviqXYPLfKnEdCHPyPIyw9klxpaXmcjnGFvOMVss3PWh5WyxyGuDgxQtG8fzeKq1jdBN9nktCCET0LYTYPt178kEiAe/uHJfvTbAUuVWYoHLk2fV9wLaLgJcfQ9WCBtP4fsOIFZKgQWyCBMLXqueW/WdrRI2nlvZRt3lHUFXu69ZPqjvIajvuXZ/pTCq0ghASN+/vgNxj+loTLKvv5X3Tw3z+pEL9LTVEQ0bqEr1GH2SoKHWdDJVEhJJNY7l2cTUCJp05RzyfJ+KV3U0SOspVGntQz/Hc5gzFx6aIMv3fUzb4fTYDIWV6qSOugRNiSiaqhBRQzyX2beudQYVg75oF33RtfdgX5peQF959rQEG2gJ3ljwsz30YIl23U98wHQdZosb13C5morjMDA/x384caym633YcH2fY9O31h2443V7PvZNqpks00ZRJJKpCKXitfGJWbFxbPeOEmU1C2CPfXCJN35wnGKhTEtHmkQ6jOO4HH3vEh099STTESzLYW5qGdf12Lyliec+tw18n6W5HIqmIMsSlZLFxOAcS7M59r+0FVmWqyVXS0WMoEZjexpVU8guFDh3bASrsj7xGiEEuqHRs6uNsUszzE5miSbDKIqMZdqYFZtYKrz6uCkVKtiWg6opNHdmyDQlEELw7X/9C5YX13aRCSEIx4PsfqaHI2+c5cxHw/g+hONBJCFWS44i8SCReJCGtjTZhQJjF2cIx4P4ns/4pVmW5vI0tKUI38Qn1Pd95nJFCmsMgG5FSNdoS8evea3s2CyWy+QsE1WSiBsBKo6NJARJI4ChrD9zMF8u4foeUU0nrF0fLE8W8oQ17Y5mzz6JLEnEgjrJcIDlYrkmfcI3wnZcJCFQ1yk/ryoyruvhrEF9r+r/anNpegHX39j3kCWJdDRENPBgqntuFE2WiIcCNVvfcqlCrgaqz4+5vwgh0BWF5miU5miUvU3NOJ7HbLHAqdlZjkxOcnJmmulCgZligcpNZpdrQcGyeHt0hJJto0gST7W2VXuLa57Bq87832i11W3Jn3jtxoOKB9GH9UFAkSVc1+f/9+fv8czOLhrTUQxN5UaH8YXdm4iGbv5cM8sWC9NZrIpNNBkmkgiRXywQr4vdcGI0KAf4bMOLDBZHaDDqaAk0XbeMJmnsT+6pekmukay9zE+m38DyHg4LHdv1mFzK8aOPzxE0NCzbZT5X5IlNrbTXJe7Zfkwu5ogEdOJBA119fL2sBc/3MR2X/E16Ke+UpUqZnw8N8vOhwZqu9zFXcDzvpu048WSIxYUC589OEI5Up0QLhQqzMzkGTk2Qz1doaF7/tVmzq+rIofMUciX2P9fLF3/1AD3bmhk6N83Z4/+aV35pDy+8WpWVf/f1M7z3+lmMgEb/rjYcx+XDN85iBDWMgMbsxCIn37+E5/nsfaEfVZORFZlUY5zR89NcPDGGospMDM1x/N0LmJWq1P960AMqn/vmQf7Lv3idI2+exSpbBKMBlhfy5JZKPPel3eiGCgImh+aYHqva7MRSEcyKzeCZCeqbE4Qjax8Ux5IhXvmV/Zw5MsT3/t3bjF2coXNLE6qmYFVsgmGDvc/3IckS+1/awtvfP8bhn58BwHU83v/ZKWKpMP17O4inb5z1tRyXbKFM2dqYIi1UvUubr7JzcT2PS9lFjkxPMJZbJqLp7GtoZqaYRxYSu+obaQhFUCUJWar2Crh+tYRPleTV12zPw/U9FElCk2ROzk2Ttyx6k2k64wlUqVp26/s+puvwzsQIfakMOzLV2cvLpYFQvWA0WUYWAp/LF5CLJis3taeQhKA1HWdyMYd9lzztgobGXLZIoVQtRbxVzzNcyTIvF6p9RmuZiXI8j3zZZCaX37C9QSxoEHuEH7KKLBMN1i44L1RMiubDMZh7zPpQJImmSJSmSJQXOzqZK5V4fegSPzh/nqHsEtlKBavG9g6XqTgOH05OUHYc0sEQvek0qnRvy1Afc+d8NDDGv/rz98mXTVzX47tvn7rl8ls7G24ZwI5fnOHIayeZn8zS/0QXu1/YwuGfnuSlbz6JdANhQEPWOZDay4HU3huuTyAIKUGeSx+k7So13NsxXprk7bn3sb2NjyvuBblShdOjM0iS4Lc/8wQF0+K7h09zfHiK+kSEfNlElSVcz0OVZYJ6NRNu2k41Y4uPabsYqkI0aGA5DhWrKtAlhCAWNFBkCdNxKZs2ruetvhcJ6GiKTNG06GlKE9BUAitif67nUbZsyiuOAZoiE9BVtLukCvswYjkOecvEfQBcIh6zPly/Ov6+Eb19TUxPZPnJ947R0VUHPpz4aBjLcjl9YoxMfZSdezrWvc2aXTmTowtEEyEOPN9P345q34WQBKpaDdA8zyeRCvPSl3YB8Of/8T1+9udH+epfeIojbw5w5sgglZJJNBlm11Ob+dpf+wxtm+pX1//CV/bwkz96nz/65z/DrNh0b23mN/7eq/yzf/DHq7ORnxSFEqK6D9f8XQg0XeXgy9uwTYef/elhfvqH72NbDumGOLue7eGFr+xZjYnNss1Hbw5w5K0BistldEOls7+Jb/zuS3T2Xz/DeTM0XaWjr4m/979+i+//u7d563tH+c6/ehNZkWnd3MBLX7tS0nLgpa3g+/z0jz7gH/2tP0CSBH172vm//T9/mZauOqSblB3N5YqULLsmfm1BXaP1qh6/pUqZHw9eoCEc5r/Z/yyqLJM3TeZKBcquw8m5GebLJdqjcdKBII7nMV3Kc25hno5YguZwlIrrMLS8xGypSGskSl8yA4DpuZxfWmDZrNAWjdMUrgp0nJidoSUSoz54JWA3XYeh5SwCmCkW2JxI0RAKU3YcxvPV0r+t6TrSwRAy15ceCyFoTcU5qkwCdycIaUhFOTU4zeDEAqbpYOjXm3Vf5vJvZbse50ZmkCSJ8BoyoSXTZmpp48ErQEMiQugRUx++GkWWCOlazbxgCxVrtTTtcXDx6KLKMk2RCL+xYxdf6enj+xfO8ydnTnNqZvquDbAs1+X07Cz/45uv8/uvfpGGcBj5JueYtzrbfe3zrVZcrS3x+Dy/PY3pKC/s7b79givEbhG8Arz1nQ9p620iloqQWyxSLlQ4c/gSL3xj/6pGx3oJKsHr+uxuhyQkDFkn93DErxQqFmMLyxzoaUNTZZpDUWzXZalU5vToNP/50DE661PMLOXoacrwwrZumpJRjo9M8d3DZ6jYDh9fmmB/Tyu/8/IBTo9O8/6FUcqWTUjX+Ksv76ctE+fM2AxvnLrETLaA5TiEdI1vPLmDXZ2NvHbiIt8/cpZn+jr40r5+UpEghYrFz09e5NCZYcCntznD81u66Gupu9+H7IGh4jhkK4+Y4vWnBNfzb1rV2LethWBY5/WfnOLnPzmJJAv+7b94nVQ6wrMvbeGlz+9gU8+NS+xvRc0C2ErZJp4MEQhdGQhXg0WFUsnEW/liqqZQ15QgmYlw8sMhfuW3nuX3/uHXsM3qDJckSWi6SiB87SC+pbuOb/1Xn+Prf+1FfB8UVSYcDfC//KffW+knhaaODF/6y8/gOi6SJNj1bC99ezsIBHVkReYrv/UcnnPlAD/x4ha27e/GthzAR5Kr21avssfZvLOVlk11fOvvfA7f80EIVE0mGAms+yEiSYKWrgx/6e9/kW/+Lbt6TIRAUSSMq5SdhSTY/VwffXs7sc1q2ZqqK4SjgVvaFs1k81RqkH0FCGgqjVcJ35yan6UpHKE/lVlVytRWSmQ/nJ5gX30zgxMjnF2Y4yub+pgs5PjPZ0/wbEsHf3LuFE81t5GzTAYW5jjQ2MJcqURPsjpAOjU3Q0skymKlxHuTY/zurv0EFIWQpvFfzp/C8bqoD1WD2KlCnv/5vTd5paObqKbj+kkKtsWR6QlOzE2zJVXHP/nwEH9775M0h6MoNwhgM9HgSl/S3WFHTxMnLk7ywakR6pNhvvHSbrRb/G5l0+GPfvYxl8YX2Lm5iaZbmHxfplAxmVhcrsn+piJBjDscED0sCCHQVBnHczcc9BcqFkXTWlUGfMyjT1jX+UpvHzvq6/nh+fN8e+AMMzXu07qM6ToMzM/zv733Dn/n4FO0xm4skPU//NMf4Ps+hqESDRns6GvmxSd7arYfw+OLXByZJRI2OLir8/Yf+JSzpaOerqa1W/yEAreeNFQ1lURdlFKuTKW08clWgSAkG+sOYAVixbrj4bjbOSuZzrChr1Y/CSHA9ylWbCzb5ZUdm0hFQuiqjKGqlCoWS4UymViYrx3Yyp+8e5KOugSxoM4ruzbz3NZO5nJFTo3N8MGF0Suq9r7Ps/0d7OhoZGB8jjdPX2JnRwOv7NzMTDZPIlyt0nNcj+mlHG+cvMTf+8qzRAMGmiI/slVPd4rpOiteoo952LhVCTFAU3OSr3/rIF/85T1YpoO3YokaDGoYgTsTLqzZ1aNqCpbp4NhXMhyyLBGOBpifzmFb1deFEAQCKkZAY2J4HoEgnrq9JL6qKdWAMXGtGEu6Mb76Z0WVCatXynqNQLUs+TJXvwdUy5aDt36IaLqKtia/R5+qv94nAhXfq76+4qWnqArRxK0PuxDiun1fC9PZwob9QC+jqzKpq7xIi7ZVLbdRtSsZ7ZUHWn8yzY66ei4uKZRsm9FclhNzM0wW8hybneJidpGmcPWGv2xWmMjnONDUuvpw6Yol6E9lsDyXM/OzLFZKNIejdMTiSELCuarH83Ls8UxzO1FdJ6oZDGYXOT0/y/DyEqbrMpLLMpRdImkEiHyir1YISEdCd9UaaEtnA/u2tPGjd87w7TdPMjKV5akdHXQ2p0hEAyiyjGU7LCwXuTi2wIenRzh8ZhRFlnhyRyedaxgEFSsWk4vrlx2/EYlQAOMRf5AKql5wZcSGxUhcz6Ns2lQs+5HzzX3MjZGEIKxpbE6m+PXtO+hMJvjDkyc5Oj11V7ZXcWzeGB5iZ30Dn7uJT2zZtHj56T42tWdQZIlIeOM6AVdjOy6lso36iN8baoWmKmg1PFaxVJhzHw8zP75IbqlApVQh05K4aQXWrUhqcb7Z9lVSWoKouj4LIkkIDEl7aLLwshBoikLFslerCKr/qyqwGppKWyaBrlzpMTc0lYCmMp8r8pNj55nPF/nsrs1oiszhi2OMzC5hOR4LhSKpcGh1vYlwkEwsTDoSIhrMsVys4FPVEDFUZXWywHQcssUK8ZBBOhIibDw8x/NeUnEcliuP9SUeRnz8m6r3ZxeLnB+Y4tyZCQr5Mrbtgs+qWC7A5r5GPv/l3evaZs3utqlMlMnReYr5Cp7nI0mimm1tjHFpYIrsUoF0QxRFkTErNoVc+ZHwnHTdWTx3Dh8LAchKN0JUPZ58vwK+je/n8f0CstIHuPh+EdC4Yuou4/sOQoQ2dFNbLJRq4m+qyhJhQ8PQrgTuyUCQuYU5lirXq6+mA0FSRpAZrYDlupQdB8/32JquY39jK/sammkIRZCEoCueYKFc4sdDF/jt7dVenbhhkA4EKdgWuqJguS5CCEKqVu0Bu2pbEoKgqtIQiqyqi1qeS0hV2VnXyLZ0PXvrm+iOJ2/oqSghSEWCqHfRGigS1Hludzee5/HGkYu8+dEFLo7PkYwGCegqkiThuh6lisXCconphRyxcIAvPNPP3r4WIsHbD0RLps10Nl+T/Y2HAo/+TLBg3aJat8JxXUzHpYattY95CLgs+vSS1kVE0/nDUyf5YHwMs8Z9sT6QrVT4s4EzNEWjRHX9OnsdWZZoro+zuaNaguj7sJyv8NbhC7ieR8W02bq5ie62NMGAxtxigbcOX6QuFSZfMEnEgziOi2k5+D4UyyZBQ6Ni2Ty9UgqbK1QYnVxkabmIZbs8saOdlvo4py5MMTmTxbZd4rEg23qayOZKzC0W2LKpEV1TODc4Q8W0Obi7E8t2OPThJQolE9vxaGtKsLmjDs/zOHZ2glyhjON47OhrprUxQcDYqJXQw8+u5/uZuDSDqskkyzEaO+po7W1AvkU1z80IKyGeTu1HlRQC8vomOoJykIOpfXQE24iptbMju1sEdZWGeJgz4zPs6WqmkCsiS6IaNEoCRZaum7CVV7Q7AprCjvZGtrU10J5JMLWUZ3IxR1DXaEkFqNg2nn9lClRTZFRZQhICSYibCirKQkJVpKs82x/+se/dwHJd8tbjAPZhxIebpgaOHhni48ODGAGNcMS44ZKBdSbsoIYBbGdPPWNDs0xPLLG8VCSRCmMENXq2NnP66Ahv/ugE89PLqJrCySNDzM/kaGlPP/TXsefO4HnzCGFgOxfx/QqKug3XGcV1x5GkNEIK4bkzyEoPjjOI75fAt/C8ZYQUQpLSeO4Emv7ChvYlV66sScH2dhiaSjRgXCM+1BVLcGlpgZNz0+TMCmFNpykcwYfrjJHDqkZXPMl8uUTZtpElgS7LOJ5XnWEzK5xbnFvtI5OuFlzyq0JNBcvk/NICE4UcA4tztEXjtISjILiuJ6wuGKIhFGGykKPiOMhCkDAMNOn6B70QkIwEUe5iBhagvSHBKwd6ScVCnLgwydhMlpGpJSpWtR9cliWChkoiGuTZ3V1s7WrkyR0dpGKhNU3sVGyHhfzG/X4Bzo7P8l/eO0l0DYHzw0q+bFIy7ZpZQTje2tSiH/PoIQlBIhDkufYODEXB832OTk1RtGvfU39mbo63RoZpiUbpS2euec+yXT4+PcbsQp5ISKe5IUEsEiAY0PA8n/mlIoNj80TDBu3NSbK5EoeOXOSLL27D0BUMTeHi1BKFkommKQyOzrNnaysfnxpjR2/zyjYcbNdD11SW8xU+ODaMuaWFSyNzeJ5POKizmC3y/tEhErEgl0bm6GxJIUkwMrFIoWRyYFcnluXyxgcX2LKpgVjYQNcUTMthZj7H+aFZuttSZHNlTgxMoCoyna1rL8V9lCgVKhx57Yr4U26piFmycF2P7HwePajS1tN0XaHX7VAkhaR+Zwq8Adlgb2IXZtQk/hAEsJGATk9TmrOHZ/n5yYuUTJt4MMCmxvRNxffsFb9oy/HIlU0EMDAxhyQJSqYN2AQ19Tpf9+ue1D6YtsvAxBRDs0vM50qEDY3uhiSpSIh0NMQvzgwR0BSS4SCt6XhNLd4edmzXo+w8JM3Wj1kzczPLlEsWe/Z30d6ZvibzepnQHVQQ1SyA3b6vk9HB6gVvlqs3iWBIZ9eBbt780Ql+8aMTnD85jixLzE4vo8gSOw903VDO/2HC902qJcJBfN/CdSeRlU4c5zyuM4Ki9iJLLdWgFRfPncLz5vG8ZXwvj5BiSNIsrnNpwwFsvmzWxBomoKnXBTP1oTB9qQxnF+YYzS+T0AOkAgGaw1FkIRFQFOqDYYKKSl0wRF0ozLnFeWZLBTzfJ2EEsFyXhXKJgKLyTEsHiiRRHwoTcxzCmoYiSXTGE4RUDdv1mC8XaY3E0CSZnGnihnxCqsrehuZrHiT1wTCdsQRz5SLj+WXcFbXjG85zCkE8FEC5ixlYWOl3rotTl4jwxJZWBkZmmZ7PUyybVfVDRSYaNGhIR+lqSdGQjK6rIsG0nZr4/QJ8eHGMDy+O1WRdnxZuJVjwmEcfAQRVlefbOyg7DpbrcnJmpuaDL8fzODQ6Qm8qTWs0do0/rOd6zC8WqgF1LEgsEiQeDdCQiaDIMhXLZmm5xNJyifbmJK7rYdku23ubSSdCyLLEYraIqsokYyGmZnPs3tLKhydGVvvEDV0lkwzz0lO9DI3N85++ewRNlfF92NHfTFdLiuMDE3z3tZN8/vktn9j7a0WgIiGdZCxIfTpKQyaK43qMTCySy5dpyMQwdJVjZ8ZZypXo5OEMYC9nvpfyZUzbWTmON540a61LoH9Ce8CxHEbPTQIwM7qAqimEE9VJzcmhWUbPTbL7+S231MKoNYqkkNaT92x7GyWgqXTVp9je3sB8rojn++ztamZzY4rxhWV2tF8vFjO3XKBUsUhFgsxk8ziux8SFMb60r4/WdIzJxRxly6YxESUc0JAliUQoQHtdgkQ4iCwJkpEgW9vq8TyP+VyR9MpEedG0qNgOLakYL27r5vzkHEIIPN+nLnZjR4lPK67vPX6uPoI0NidZzpbI58rMzxeu0qC5eswroP5Gn745NQtgt+5ux3FcMvUxEulqj4VuqGza0sTzr+7ggzcHmJ1aBnzC0QDb9rTz3Oe2P/R9AJIUxXXz+N4yitKNJMUQSMhyE5KcQpKSV4JcQFba8KwlZLkOoXRTDWoXkeW1y9rfjFzJxKnBxW+oChHj+trIfQ3N7Klvwl1p1P5kH2ncuLbH+Le27cHyXBRRLbHxgd11jXj46HL11NueufZh0hG7Mkv82Y7NfLZj8zXvB1WV39lxrQm5LElsSdfRl0pjex6qJFfdDm9wbgmq/SnyPSpf11SZxnSMxvSNhVjuBN/3MR3nsRfpfcT1POwalOs/5iFHCD6/aTNL5TIFy2Jgfu6mfUB3ynA2y5HJCXY2NLCt7soT3jBUPvtsPzv6qtnSimkzODbP918/RVNdnNmFHKoqr96vhRDomkIyHrzGqkuRJXRNQVUkAoZ6zX0zFNSIhqvVOLqmYNsuxZJFNFK13VJUGVWRKVcspJVBue+D7XirzyIhwNAVvv75Xbz94SVOX5hie28T9eko2VyZ8eks73x0CYBMMkwiWjvP5nuJ7bjMLxc5OzTDwMgs2XyparHyieUEAl2V+c0vHaBOuzaACcWCfOV3PgPAj/7922SaEvTu6UTRFCYGZzj5zrmHvmrtbnPZzuarB7Zd915PU4aepsx1r88uV/VDdnc20VmfxHZd/v0bH2GoKp/b1XvD7XTWJ+msvxLYb25Ms7kxDcBnd/Xw2V3XC6o92dvOk73td/rVHnk836+JU8BjHizqGqIc+8jmz//kMKlMhGBQv258vnVHK53d61PkrlkAG4oYPPWZT87AVsWXvvXXXmTHvk6GLkzj+9DeXcfmrc13lDJ+0FDUHhR1E9VZ1isBnSbXUQ1aBVc/cRSlE0Vpv+o1/7pl7pR8uVKTi19V5Juq0kpCIK2x/FYIsRqoQvUbrvWzd4IkJPTbZFaFEAQ0FUWu9tY+jG5jjutRsRzMGgl2PWb9uJ5fk8mixzzcXL5rf7Wvn9HlLFP5/A11AjbK0ekp3h0bZUum7qa+0rlCheHxRTLJCF/6zDY+Pj3G2NTShrYrEHyyTKohHWFhJbMbjRgUSiYNmSgBQ6NScShXLPLFCrlCBU1VVuymoDET41e/sIePTo0yNbtctTRrTFAomvylrx4Aqn29yl1UiL+bzC8X+fF7A/xfP/gA3wdZEpRNG1mqfifP83E9D1mW6Gmtw7rB/VuWJaLJalDrWA7RZJh0c3VSt1yosDiTq7ohPKamtKbjjC0s8+Oj5wjoVVXiZ7d0kYk9Lu+9l3gr1XOPebT48L2LXDw3xVPP9dHdU49yA+/jdN36xOWghgHsLRHQv6uN3hV/WEkS18wAP/zcLAC9WVAq1rDM+slXrJoMqlVZJqA92iIal4PYh7FcpWzZ5B9nX+8rnu/j3USw4zGfPgxF4cs9fcwUi/z5wNmar388l+P07CxzxeINFYkBIiGDxkyUH715ioWlArIsY2i1nzDs39zIRydHee3dAUzLoT4V4Ve+sIdMMsz33zjJf/ruEWIRg1LZoq05iev5zC/m+Zd/9A4CQcW02bOtjR19zVRMm4FL0/xv//rnAPR21fPk7k5aGu+sX/N+cmF0jrePXSIVC/GbX9xPV3OKf/QHP2drZyMHtrYxt1Tgw7OjlCo2//1vvkJd8tblo5qh8t6PjnP2yCCqprAwvYT2iQz5Y2pDMhLk87t7eWXHZi7PbCuy9IiNUx98XN+/pRXLYx5OorEg23e388oXd9LYnLhxtHQHVZF3PYC9fLNVaqgA+uCxlkB1La9vjJJlrZaLbQRNka5RIH4UCWoqiiw/lAFsxXZuKkbxmHvESqnkYx4D1edcdzLJ/qZmTkxPMZTN1nT9nu8zlF3ivbExfrm/H4C//ZdfJBm/YnWmawq9XfX8/d95BVWtWoRIkiC0IpXd3pzk7/6Vz1zT///Ezg48z0dVJDpbUkTCBr/zzadJxatWIXWp8GpWtKk+zt/8S8+TSUV44eBmDuxa+awqE48GUWSJ3/7Vp/B9H0WuZhx1Tan2B8bD/JVfeQqoqiWHghqRoI7n+3z91V1YK/Z7AV0l/JBKe2cLZSqWwwt7NvHCnk3oqkI4oJOIBNjUkmZ3TwsdTSn+w4+P8OP3B/jl57eTjAZvur6Xv/kkE4OzLEwu4boem3e307q58Rqf+o1Qtb3w1jURpwjlgQ+gPd/Hdb2qK4QsrWl/JSGqvvZrGKf6KyrEN2tTesyd43ke1uMS4keOaCzAkfcv8fv/6Adk6qPohnqd5svmvkZe/cqeda33vnhnmGWL2ellZiaz7H1y0x1F3o+5Httx8WpQXqTI8rp8Qc/kTnOxcIHWYBt9kX4C8s17mCpumdHSKB8uHiamxnB9l75IP23BdgLKvet90hTlnvXB1hrLcR+XDz9inB+dpWza1CUixCIBPNcjaGi4nle1OVlZTlOqPYe262LbLrqmUCibaKpSLS31fQLGY2/a+4GhKOxubOSp1raaB7AAE7k8RyYn+EpfHwJoaYhf874kCYIBjbbmGwvuGLpK6ycym9Gr2nguB7pNdbFrPnMZXVNoXtmmpgaJ30CQ9urPXo2myjTXx2/4Xia5/tKxB5FqEO7TWhcnGqoeV0NTV0qoBfFIgLb6ON1NKQ4dH+SV/T23DGATdTGMoE5TRwbP8zFCOuHYzZe/Hb7vU/FMTi2fYbA4woK5hOlZq56ma+HX275GY2CdSit3mcn5ZU4NTrOQK5KKhmhKRymbNqois6WjvqbevFBt4Xnj4wvs728nHnk4+7UfVB5nYB9NkqkIm/saKRVNNKNa/fjJPF74fqoQr4dCvsLpo8N89O5Fdh/sRn6sSrAhLs8Iul5tTEIUSbpOoOlWRJQIOXuZrLWE49lwiwDW8R3yTo4Fa4Hu8CaW7SyDxUE0SaMz3FWDvV8bsrS2mdkHEdd7rNT3qDGzkKdYsSiWLWLhAIlIAE1VODM0jed5eJ5PybSJhgyaMjFKFYtzIzM0pmIUVsrJK5ZDLGSwY3NTTT1vH7N2WqMx9jQ28dPBS8wVizVdd86sMJhdZKZQoOEmZcSPuX/IslSt6nGu3JvDQY2yaa9eo5qqkE6EGZ/NrsmzPRA2CNRAK6QavFZ4beYtjmdPM1meIu8Ucfy1T4QG5AAlt/b93XeK7/sUyhZvHx8koKs0JqNEQwa+D9OLeSQhMDSFZDRELGSgqQoVy2YhV6RiOtQlwqtifKoiV5XlHZdMPEzFtKtezIUyhqYQDVVFyyzbYXxuGctxr7Fkq1g22UKZfMlEkSUSkSDyilNCqWJh2g7xcIBIUCdfMlnKl4gEDaJBHZ/qvdv1PBaWS8RCBrGw8fge/phHhpa2FJHYrSd7QqH1V97clwC2XDIZH5rn3IlxHrY6PMt0+PAXAxRyZWLJMF19jdQ1xW/7ObNiMzY4y/D5aUKRAL3bW0hkIjULojzPW9dM6q0Qguuykz4+0+UpsnYW13dRJZXWYBsBKUBrsI2MXocmre0EVIRKg9HAnsRelqwl3pp7kwVrgU66KLslpspTVDwTz3dpMBqQhULeyVF0qgNC13doD3YSVIIsmPNk7WVsz0aXdZqMJgJy4LbHVZbEQxzA+o89SB8xfCARDWLbLtl8ifpkBNfzGJxYwPd9VEWmWDYphAPIkmB2qcDpwWkqpoPluDiOVx1U1cVqdh9Y1/57JVx3Es8dAgSSVIekdCBJD75v5NW47gSeMwioKOoOhLS+jFdI0+hMJNhWV8cbQ0O13TffZ75U5vTcLPXh8ONp3weMUEDD0FXGZ7Orr9UlIkzOLzM4sUhLJk6xbDG7WGBFHuueYXkWQ8VRfj77FvPmAqrQSGkJwkoIy7MZL08SkA3qjQwSEmW3wrKdo+yWCathtkZ7SWkpYg+QD6zr+QxNLTC7VODFPZvob69HCMHMYp4L43NMLeQwdJVLEwv0t9fTVh8nV6wwNLmA5bicHZlGVWRChoZpu5RNm3BQp2zalCoWc9kCkiSwHZfmdJzetgyO6zG/XCBfMnFXqt0s22VsNsuFsTkMTWVoepGX9mzGchxmFguYtoOgqtC9t7eF4xcnEELg+9BWHyceDjA8vchctkjI0DgzPM2enhYaUxEk6WY9uA/XuHmtSEIgP6TjssfcnGQ6TDIdXrUVu9EQ5U5+9vsSwDqOR6XycJoVm2WLP/wXbzByYZreHW38yu88v6YANpct8bM/+5jXvn2ETEOcv/rffoE9qTBC3vjF6lM1gK7VuFVa6Z36JHPmLGPlMUpuqdrnJBQ6Q113/CiuZo59JFHNhjqew5w5x9Hsx+iyTskps2xnCcohJiuTLJoLxLUEOXsZy3PoDHVwoXCeOXMOH1CEjOlW2BLdettty5LEQ1pBjOt5jxVwHzHS8RB1iQgVy8ayXdLxEKbtUJ+MYFo2kZCB5/toirwqLNLZlFrJHHjE4yFi4QD1qcg91xvwfQ/XncCu/AzPmwEhoyh9SHIaeHAGvGvBc2ewKj8BXyDJ7cjrDGAB6sNh9je31DyAhWoW9ujUFC92dN7ZE/8xd410NER9IszUQg7X85CEoLctw5mhad4+fgnP8yiULT4aGKOjMVHz0tZbUXRLfLh4jEUziy7p9EY20RPppk7PMGvO8WfjPyClJ3m57nkMWWfJXma4OMpQYRRZknkqtZ8d8a2o4r4MGW+I63oMjy/S2ZQiHQ+v3hdlWeB6HrPZAge3tvPGhXECukpdMlz1Rs5Xs8jffvsk9YkIB7e2MzaTZTFfYnt3I7liBQFcmpxnb28rw1NLLBdMWuvi1X7vcIDDZ0Z5cmsH6ViIsmUzNLnIwMgsT27r4MOzo7y0ZzNz2SInLk2SjAZJRIIcPjNKKKAxMDrL7s3NnB2eoWRatDckOTc6x9jsEl96eitvHL1IfSJCKhbE0K4PYL1HWH9BFgLtLjpVPOb+YFkO2cUic7M5zIp9XaujEJBIhenatL72hHXfjQr5Mra5sf67pfkCheUHpxTlXmCWLQYHJrEqDlNjC+SyJVzXQ6qFyp1f7X+t1T1NCLFa/nL1NjZHeukMdZO1swwVBxksXqI92IEk1vcdfHxMzyRrZzmfHyCshElqSQpOgenyND4+z6WfZ8la4oPF94mqMWQh0xxs4Zn0s0yUx3lj9ud4vsuStURzoIX2YAejpRGOZj+mP7rltkH1w1xC7HmP+0QeNbZ0Nlz3mq4qPL2zE8/3rxEM8X3o72jA933ePzVMvijR2ZSipT6Opso3tVm5W/h+EdcZwHUuEor/z4CBEB4g4XslfL9M1VJMQkhRoCoE43l58C0QUvVLCQ0hgvh+HnwbcPDxESKEJFX7JD1vGXwH8EHoCGGAb+P7JapNNS6IAEIEABffK1aXxQFhIEQIIRR831x5b8WrVIoCKqq2B8+dxnUuffJbXtlfPBAKQkQQ4nqxu3QgyM76BjRZrrkgSXHFa9aH1d7KxzwYNNfF2L+lnYsTc1iOi6Eq7Olt4ei5cV7/6CKHjg8iCUEsHOArz20jcg/Fqsquyfn8RVzfZUukl19u/gKbw10IITiTO4cqKQTlADvj20jp1T7pJWuZDxY+4gdTP+W7kz+mNdhMWk8i8+AEGD7+Dav4DE3lib42+tvrGZ1ZQpIEhbKJ4/l4vs9yoYJlV8t2u5vSBDSV2aUCrXVxzo/NEQno9LbWsaW9Ad/zyZVMFvMl2uoTtNatTD6sXHqCqmJxwFAJBTTqkxHCQR1ZkmirT9DdlELXFC5OzDMwMsu+vla2tDfgej7LhTJzS3kiQZ09Pa30tdXR1ZRc1T64kZim7Xk195p+UJCFhHoXlJ8NRSGi6Xdl3Y+pUh8KE9ZurL8xN73MW6+f4e3Xz7K0WGBpqUgsFsT3oVK2UFWZFz+3jb/5X39hXdtcdwD7oz/5kDPHRtf7sWso5CtMjS3eMMv3qBKKGOx+ajMjF6bp7m+moSWJotbmQeCzEtDU6KYmCXHdINjyLN5beJex0ijgY3s2TYHmO1p/xS1zevkUy3aWlkArTyT302g0MVOZYdqcYiB/lgVrAYCoEiUgB/B9j4AcRBEKCTVBzs4zb84xVh7jXOEcYeUjVKHSaDSuKSP8cJcQe7iPM7CfGm6qZS7gye0d93JXbojnTuF7WRRtH3C5X686ULCtt7EqP8P3CggRwgj/HrLSBqhY5W/j2CeQRBzPL6Jou9EDX6JS/Ld4zhCuM4SPg2Z8mWDkdwGoFP8trjMMfhlF24eqPYXjnMM2fwqoeO48mvESmvESrjuNVf4TfN/Bc4ZR9afRg7+GrHTgWB9jlr+N7xUAFyP8N1DU3qv2/3rM0h/j2Mfw/SKy3IQR+h1kpeO65RRJIhkI0BlPcGFxoaaDzZLtMLSUXfETfXACicdAXSLMl57Zcs1rqViIb31uH60NCY5fmCQY0Hiiv5XP7u+9fpL4LuJ4NrOVeTw8nkjspt6oW31PQkIRCrZnX9PXGVej7IhvYcac5bWZt3hn/jAv1j1DQruxUNe9RpYEnY0p3j81Qk9rhoarxMAEVcG7K894n+GpJSYXcoQNjZf39XD84iSKJCFEVQBNXDaGXzkEZbMa4FpuNWC8WU9qJKgTCwdYLlYYnFzgr//SU8TD1X4/RbpixSMJCOgKlYqN53mYdlWcr7qfK/9fXevN9Uw838d7VEuIJYEq1T7Lv7+5hd/es5dNiYfPnuthQZZkguqN3Us+OjzI6RPjPPH0Jjb1NPAv/ulP+Bv/9atYlsuh18+SrovwhV9enwIx3EEAO3R+mo/fu7juDV2N7/s4jke67uEqL9sI0USIL/+FJ/nMV3ahagqR2O37NNeKYCXgrFE85q/8czUjpWEcz2FnfBcxNcZA/uwd99oZcoCtsW282vBFArKBLhkIBAHZoE6vpyvUzRcbvwxUH65z5iznCgO4vovjOyzZS8TUKGk9Q8Wr0GA0rmZdFbHWQd297UGqJT7w2Mv+08Mn7xNX/vqAnMF+Gd83keQGfD9HKfeP8b1pVP1ZQEbVn0ZRn8Bzh6kU/78EI/8tQs7geUsIkUAL/hqSlACh4Xt5fC+LFvgS+BaucxFZ7cf3LWzrMCAwQn8ZSYpiWx9iVX6ApGwCXyYY/Xv43jKWeQjHuYimP4eidOO5U5Ty/xTVeBVJbsJ1LuG6YyjqLlTjM/juFGb5T5Gk30RWum/6NfXgr6L7v4TnTuDYZ3DMwzcMYIUQBFWVrXV1DC4t1jSA9fEpOxaD2SW6E8malNtVXIsL+Slemz7GmdwYJcckqOh0hxt5uWEne5PdD/Hd8t5xs+d5YzrCl5/ZxucO9CFWhIXudQWQR1XECaqBqSFpq9sXQqBICiW3jM+ViVEhBAktzpZoLz+dfpOTy6c5kNzz4ASwskRvR5pTg1N85+1TyLKgoyFJ603UrsMBDc/zeOv4JUZnl8iXKqjR0E3Xf2poiqmFZYoVi+1dTcRCBpcm5vnFsUsMTy3w3UOneWnvZjoakywXylwan8dxXMbnsnzj+Z14n6iSMnSVLR31/PTwOU4NTVMybfb0ttCYjrFUWHtFolNDvZMHjbuVgVWERFjVyIQei9/dLW5lK7W0WCSZDvPqV/ZgWw5CkujoriORCNPckuC9Qxc49OYA3/rNzLq2ue4A1rZdkukIW3a10dq1vo1dZmE2x5ljo+Q/RWXEsiwRiQVuq8R1RwhQFblmgwzf57oa9Zgap+QWOZ07RUyNUXErK6qEJc4sneZC4Ty6pGN5Jl2hbtpDHbfYXYEu6UTVKJp0peQgrERoCjQxUR7nrbk3AWgwGtEkDcu1mDAnKLkl8k6e3Yk9tAc7MD2T6coUc+YshhygPdjO5nBPTY7Dg4okBMqnqHrhMQ84Ql0tyxUiiBH6C5Tz/29cZxhZ6UKSmpDkRsDDcc7jU9U/EEgIKYGstCGEVh2UCRWQsa0j4DtIcgZZ7QHfwXMGkaQ0klyPJNUBx3DdCWSlD0lpQUgZhAgBLvglhNDxfBvLfBM9+OvISieg4bkL+H4FWW5HkupBhHGd0ZVS5xvjeya2+VY12+yX8L0lJLnlpssbisrmZApZkrBrXO5vex6j2Sxt0diGA9iya/HO3Fl+MvUxkpB4qX4nYcWg5Jo4nod2F7IhnzZkSSKgqwR09RoP3nvN5fFBdZB55fkhCxld0shayziee01pui5pJLUECJiqzGH5D47/uBACXVX47BO9FCsWrusRMjQiQYPOxlTV21WVeXp7J4oso6kyqViILR31BHWNJ7d2oCkyjekojakoluMSNDQy8TAnB6fob69nc0uaSNAgHgmgawoNySgv7+thb28L0aBBKhZiZHqRkmnz66/soT4R4cjAGKOzWdrqExit1d9dCPj8/j5iIYPICwae5yGEIBkNEtBUIkF9JRsseGlvDwFdJXQTOzT3ES4hloRAlWpfWeL6Ho7nodzDqofHXMH3fWRZIhDQEIBuqFimg6rJtLSnUd67xNTE0rrXe0dPp0xjjD1PbWL7vs47+Thjg3Pks2XOHF97KfLsxCKFpSKqrhII67iOR11LEtfxMCtWtadqRcgoHA1gWw625aAZKlbZQkgCI6jXpuf0AUMAqiTVbI7c8/1Vhb3LJLQEO2I7KbpFNElDEjKaUNElnUajkadSzyAJiYgSIXILpUJdMmgJtBBWwsifyJYqkkJGr2N3fC9ltwRAVI1SdEsE5AANRgNd4W5c36Uj2ElICdIT6SGjZ7A9G1VSSaiPfomILFXtGh7zmAcBSUohRBjHOYdAQlH6ESt9qD4+vl+h2mtqIghwZfAsrfS9Vgdq1TGzDvhIUkM1+JXTSFK62vcqwvjeYvXPvoXARwgdhFTtbUXgr/bn+XjuNI59FCHCqPr+lf5XUe2dRVoJWH18v1xdDzd/NrjORTx3HCElkEjj+BVupQSqyTLN0ehdyVy6nsdUIY/rbzwwPp+b4P35c1iewzdan6I32owha5iuTcW1CKsBTNdmuDDL4cULvNq4h/cXzjFanMfDZ1usjYPpXgLylcH22eUxTi2PMlPJIguJtmCGJ1KbSetRJCFYMvOczY1zIT/Fsl1CCEhpUZ7O9NFoJNBklbnKMsPFWebMZbJWkaxd5LnMVibKi4yV5mkKJDmY6iGp3x8PWcd1GZpcZC5boLs5TToWWi0VheqAzbJdDp8Z4czQDIu5Eqoi09aQ4OC2dloy8XvaQiUjEVJCLNs5Ck4R27fRqf5milCIqlFmKnMsWksktQT66u8pqteV71NyizU552pNU7qqvF7VCrhegLIuceUcCRkadYnwaovU5UA9HLjSj6zKEooskYmHaatPkIwGV5cLBTRCAY22+ivjDGVRxvM8lnIlHMejZFpEAjqpWIjAVT7KwZWAtCugrwawl9d7td9yY+rWlYmO616XYHhUUCQJQ6n9pJnteZjuxrR71ovn+Zz/eJDjvzhLy+YGnv7Kvpsu61gOH/7sBKffPU9pRWQslo6y5eBmnvjsjnu1y3eNSDRAfrnM7MwyyVSY9s40h94cYOeeDlzHZXZm+Zr751q5ozMlHAlQ1xinrjF+Jx+nXDSJJtan7ljKl5mdqDbjRxLVso9Mc4Kxi9MIIJIIYZYtrIqNqsmcPzqyErRqlIsmibooTR11t9yG53oU8xUuDUwxO7FEqWACPsGwQV1TnNauDJqhrkib3/rhsziX5/RHw0yPLV73nh5Q2fdsL03tqXUdg1shy7UrSbr8MLgaTdLoCt+4vK491HHLjOvVqJJKSk+T0tM3fD8gB+j6hB/saGkEXTZIaMnrFIYzeh0Z/da/643xH1olelkSNZ3J/8z2bpLh9autfprZ0lJPLLhxf8ZHASElkORWsE9jlr9dLQVGQlZ68HFxnbP4fqFaGmy8jBA3Ptd8H/DLgANU8P0SnjsDCGRlM4q6A6vyA2zrAwQqvl9CUXexUrz0iXWZOPZprMrPUNQtONbHK0FxB7LchOdO4DoXsSrg+0so2n6ECOLYZ3Dtsytlwh+B34ukdFEVhCoACkixG4o3XY0mSzSEw3dF6dz1fWaKRZwNDmJ93+dsbpwZM8vueBdPpvtQLmc/rvp6ObvEaGmO700cJqjo5KwSPj6WVw1yLwfpvu8zUpzjrdnT5JwymqRgeQ4nl4eZrWT5RtvThBSDimezYOXJ2kUkBI7ncnRpkLJr8oWmfTQGEuTsEsezQ5zLTdATbeZkdoScVSKqBpk1l5mrLBNTgzyd6d/QMbhTLNvlvVPDnLw0xTdf3k0yGrxG2sj3fd45OcT33j7N8NQCrucjS4KPz2lMzi/zrc/uIRkN3bOMrCIp1OlpcnaeqcoMRadEWKmOo3RJo15Pcy5/gdO5c9QbGXS5OjapuBVmzXl8fGRqN0lea8Qa7VfWspyiyHQ3p6sZI0O77biqPhnBcV1mlwooskR/ez3NmRj6LVSmb26Pc3vKjvPIijjqikxEv3HmeSM4nkvFWV8A+94PPmbi4jTJhjg9e7to2XRFaNGq2Hz8+ilGz02Saoiz5eBmGjuvHYf6vs/ExWne+fMP2fFc/y0DWADf86mULbLzeYZOjYHvoxnqIxHAdnbXYQSqv2sgoPHMC/384rUzTIwu4DgukiSxa1/Hute77gC2q7cBIQTx1J3XkiuqjB7Q1tXCpaoKvudjWg4B21lVQh6/OEOmKUE4FiQ7n6eYK5Ooi3Lp1BiReAghoFKyUFQF0XXz9duWw9xUlmPvXeSDNwYYPjfN8lLVdzSaCNKxuYFdT22if3c7qiYjbjMyWZrP885PT/HxOxdwbHflXwffh1gyRF1TomYB7OWbsixVhxIbjcs8z3+gRIKCcpCMnlmzz+xaeJjLcKoZ2NoNfL52YBtbW+sf97mtA02Vb6gQ+WlECA1Z6UbVDmLbxxFCQVF3o2i78L1lHLtUzV4KHS3w5ZUyX6r9ptcEgtUSYyEl8X0Lzx3B93IIKYqi9qOom3CdzXjuDD4mstKNrPTheXNUs7kKoCMrm1eCZB8hxUDouO4YoCD5TdWyZGUTvreI544CHprxKkKK4dpjIFQkKY3vLeN5WSRAUtqRlFZ8bwH8CpLcjCTd/P6tSDKZUOiu9Dl6vs98sYi7wUGs7btMV5ZQhExfrOVK8HoDXN9j2S4xVpzjlYbdtARTlF2rmjGRq7+hh88vZk8xXVnimcwWdiW6KLkmh2bP8Gfj77E/3cOmcCMhxaA/2kJ/tIU6PUbJtfjB5BHemj3N/tRmGgJxoFrebHo2n2vYXe1dnDvNr7Q9TW+0hRPZYc7lxu9fAOu4fDwwzvDUIhXLuUb00HU9FvMl/uyNE4zPZultr6O7OY3lOJwdnuFH756lpzXDs7u675kSsSHpdIbaGSqOMFgYZnd8O3VGGoEgqAToDLdzaP59Pl46TlpL0hluRxEyM5V5Pl46jkCQ0pIot5m4+f+z99/RdeXZfSf6OfHmfJFzIJgzK8eu0Dmru2VFy5Yt2bLf2DMej9974+exZ8br2UseOcwbT5Atyx6FVkstqaWOFbq6IqtIFnMAQeR8cXO+J78/LgASBEgiMVQ1v2thATj53HvCb+/93d/vxwGqLDHQsf72uJDPTcjXzJ7uu3dMN6JsGOgf0wDWLcuE3dufGNYti4qxMevON751nHe/c4re/Z186W9/ckUAm08VePX33+GdPz/Jrkf6CMYCqwJYAWhsj3Hw+T107bq94KmkSBx6bg89+zvIJ4v8+f/+CldP3qyE/9FF30AzrR1RvF4Xbo/Co0/2k1ooMDaygCxL7D3QweFHbhOg3QIbDmA/87VH6iIV/s1fZC6PSnNbhO7+pnV72cmqjOKScXlVwvEg85NpcCAcC9DcGUeSRUzdxLYcJFmiva8JX9hLZj6HrpkIwq1Feh3HIZ0o8Nb3z/Mn//EtKiWNUNRHvDmELNfpIWND80yOLDB2dR7LtO441A+GvOw50oWiylTKGoVMianRJIXFoPhuQJakRYPsrQVmpm1v3v7BWZLxE27x3S4d21LW3lr+X1i046kfv7W4jEjc1UDc1XDT+te3sRlopnVX6FATc1l0Y2tUlXjYRyR464qoLIko20ghdikyQa97W7f5ED9dEKU4qudTqJ5PrZonq4fWXEf1fPqmKRamcQFR6kCWBxDEIKZxBtMYYuled3k+t2o7Ep3LfwuCe8UyqvuFNfctKzsXVYdvOibXM6iuZ1ZNF4Qwbu/PrrmttSAJAgHVdVcsjWzHIV+rbTkBp1kGVUtHFWXCyq3FbKAuHiUJIs827qM/0IxbUglzfZ0l1s67qcvsCnbgkhTSWgGAmCuAbptcyU/R7onhVzw0usOUzBoZvYRmm7R5Yot/G8uPeJeo0OAK0eKJ0Bdo4VJhkmZ3hJgrwGhpnqJZ29L5bwWWZTOVyNHeGCLsd6+gv9V0g9NXp7kynuDxvV38/KeOsqenGd0wOTM0w//3P7/Kqyeucmig7d4FsJKbPcEB3k1/QFJL1wWbnPor2it5GfD3EVZDTFVm+cu5H9Hl7cAjeZirJRgujSIJEruCO/DKd0HD4yE2hLKuY2yzPdeDAo+sEHFv/zVWNUwKNW3D6ykuhVpFJzGZWjF9fjxJpVBFlMRbFrNESWT/07vY//SuO+5HEAS8QQ/eoIdIY4hww8dL4DYU9hIKXx/T+gMevvFLTy1WX4VNMxI2HMCGo1tX8Yo3BvncNx7lc994dN3rNHXEaGyPLv/f3tcEAux/csfytANPXR+QPPLSPgDOvHWFaknDtm30moHHt/qFYegWZ48P85e/d5xKWcMfdPO5n3ucR5/bSawpRKVU48qZCd747jne+IszyIqEfgcv3IbWMF/8xSeX/09MZ/kv//YV3vrB+XWf80bhUiREUcC2tjawMUyLqnbnbFU90LQBg/qoQ6r/dkxAqns0rlrHAszr8+w8DiaC6IPlAZFd73XDQRAjgHrD+g6gUfea3OwZ1jPod6OP5H/6Dz9ifHY1bRyhThGxFwUyBGBJt99xrns6SpLIr3/1Cb724qFb7kMWxVtK+m8G2qIf3sMA9iHuLyQU9TG0yh9i6sfBcRClplsGoQ8yhEUrMkWUtoUVcyMcx6FqmtuiRLqRR6gkCHR447e0uTBti5RW5C9nTvCD2Q9XbNslqRTNKpZjUzAqHE9d5XjqCtOVDJptYNk2eaOM7VzXv5cEEddidVcWJLySC1mQEBd7lc372I/pOA6FUpU93U0rqKKO41CsaPzo+FVs2+Hlx3bS2xZDEMClyrQ1hNjf38rZoRk0/d715HkkN7uDA7R7Wun0thGUA9jYiEgookyTO86zDU/wxsK7JGpJ5msLy+tKgkRQ8fNswxOE5PvTc/wQ11ExDAz74xnA1iuw2x/AVgyDnLZx0dhocwjVJbMwmVwhbjY3nsSyLHxBD9I2jsU+btA0A+c24+x68C9S173Y+ID+oysxuM5z3ff4DhzbQZREJHntKH9scI4LJ8fIpkuEoj5+7jde4LnPHsAX9CCKAuG4n8a2CN0Dzfz2v/geV85OPpAy5n533ajZ3CL9V7csqvp66BYmjjGIVfshjpNBlHchSG31PjYhjOReXclwzBFs/TSy7+cAsPQT2NqbiK5nkT2fWVyogl17HUt/F8X/GwjKDd56dhKz8m1k/1/ndp6NdzxH01wlVLUdaGsMrQqsBUFAkSXmUgUWskVUWSYa8uLzqBiWRTpfIVuo0hwPcGhHG71tt6eWu1UZn2v7qFyaYWFZzoq+t4d4iHsPEUnuxRv4b7ke8ok30Yw/OhCoD8i2gxVzIxygZppsNXzzyipeycW8lSWrl9axxvWAfO3jcnAch1/oep5nGvcQVW9IdgsCXknFI6n8l7E3uJifZH+4i1/v/zRuSeVacY5/fulbN+2NFV2XAsKWkpbbCwFEAVle2U5kWjbJXJnTV6fY09NEe0MIt3p9mKXIItGgl0K5dlfeP7eDT/bxD3b+BpIgoYrKciIA6toTX2j5NLIg827qBAtaEsux8Yhu+vzdfK71k3R5O1HEj+a9+HFC2dAxHqAWr+2EKkkEXSqqJG2eBbgGqqZBrrZxxkYgUn+GpeZyaFUd9yJjYn48icfvJtwQxDI/nsmE7cAf/Ke3Sc7n154pCIQjXvYd7GTXvjaisY0nxz5SAexm+okU9c6nODGcYGxoHlkWae6I8uxnDxAIeVcoFouiQHNHlBe+dJihi9MPVI/oEoIe16I67dYyu4ZpUbtDv4DjGDjWLGblvyB5voIgNSMIbhwriW0OgZPC0t6v22dIzXVLC2sex84i3OC1KLkewzEuINw4HBO8SO6XsPUP4Ibpjp3FtiYRld3A1rJeumFtuYdsLfzaV55Av+mBphsWw1NJfvvP3ufrLx7i0b1dNER8iKKI4zjUdJOhiQX+5PWztDeFV6gmrgWPqhD0bF+fSKmmfWwpSQ/x0UH9+S4v9rJ+PCBunz33MhzHQbOsLQfFIiKtnihDxVnO58Z4Ir4TUdh8b70iSsRdQWq2jiJKxFwraXAC9V7asVKCsOLlaKSPZncE3TYpGZUHMil8K4iiQDzkI5kvUbuBjZXMlXj77AiaYfLUwV4iNyjYLsG27Tr75x6er7DI9vHJdRrfzXoHSz7sLzQ+y5HIQQpGEcux8EhuwkqQsBpGFZV76l17NzBTGWGkdJ5nG7+yPG2qPETFKhJVm2lwr92rOF+dYLBwkpKZ45nGrxCQI1u6V0pmHlV0b0rXI1/TNixI9FGBIAiokkzE7SFRXk9SbX2oGAb5TQSwwZgfx3aoFKrMjyfp3lO3TpsfT+LyqIQagpg3tYy9//0zvPFHx5kbq7MYfGEv+5/exc//d1/c+oncgHy6yODJES68M8jc2AL5VAlBgEhTiD2P1dWL227o2/3df/rHVMsaT33pGAee3kWtonH15Ah/+Jt/QUNbjM//2ovsPNqLVtG4/MEw3/xXf8nX//7n2PvkDhYm01x87yrD5yZITqWpFGuoboWWnkae/MIRdh7rI7gGO1dRJJTbFFsSc3muXDzO/sNdfPoLh2i9gWW7Hnx8RgqbhOM4JOdyJGdzeP1uugeaCUVW9wMt9f3uPtK1Lgrx/UDA494WE2jdtKjciULslLGNywhSO6LciyDWG9gdpwxOAducQRLDmJU/QfJ8EUHuARwcewHHmkdy1enjghiBm6jGgiDhiPHFgeyNL0wH7AK2fgJRfWxTlRnHqVN2K7qx5Ur1WmhrDK+alsqV+OPXZuhtj/HM4T729DatEACybYfWeJDh6RSzyTwzyTzttzBjh7rnr9el4FJktC322wLkyrVt2c5DPMRDrIS+aO223bizDv6dIQgC+8NdjJYSnMqMEJp4m2OxHYQUL5ploNkGEdW/Li9YQRAQEXmmcS/ncmOcyY7hkVwEZDcFs0qimmN3qB1FlJEEEc02qVkGum0yXk7wWuI8VevB8Ri9ExRZ4sjOdo5fHOf9i+N1yQcETl+d4s0zI7TEgxzb3UngJqVy07IplDXcLvmOQpDbjduJ9C0FphE1REgJYjrmYt+zhCx8fCiSml0lZ6zsZyyYGQz79v2RASVCRG0io89jORsTA7oRjuOg2VVGSxdo9fQQd91e3GctpKsVqubmj+FBhypJNPn9LJRL29Z6UTNNsrUaFcPAq6x/3KioCi6PilY1mBmep2t3G9VSjcx8ltbeJmzLJjm9smWsubuBg8/tprWviasfjjJ8dpyG9u1zHFlCcjrNW9/+gJmRBLGWMI0dMQzNIDGRYnponoWpND/3330Rf7ieRKuVNSYHZ+g70MmBp3ehVXUGT40wfmma6aF5Hv/cYQaO9FAu1pi4MsPohclFFqrI+bevcPKV8+iaQSgWIBgLUClWOffmFWaG5/nyb3ySIy/uW65QL+GZF/ag3SaW0GomH34wQmIux7kPxz8aAayuGeTSJTKpEjv3t9/XrJ5pWJQKVSqlGg0tYZrbI7c8HkkWCUf9eP1uapUH72Ub9Lq2xR+0ppsUqndoeHd0sBcQ5G4QvLCcjaxXUQQpiiD34mg/wXHSOLQhCMHFSuz05g5M8IIYxjbHYAsEuqpuoBvmPVMhrmoGJy9P8vzRfppjgVXqtaIoEAl66W2N8aP3B5lN3oJysbS8IOBWZUIeFwvbEHhmy1VqDwPYh3iIbcOSlJ1mmdjb7NclCALqIjV5q+jwNvBMwx5sx+bDzAijpXlckoqIQLMnzLHoDlo86/PWFhF4rnEvJbPKUGGGqXJysV/WwSWp9AeaUUWZQ5FezmRH+dH8aU5n60qbXb4GzmXH2P569d2BS5F44dgOzg/P8uaZEYamkgjAbKqAblh85fn9tDeEVmgVOE793Tq1kKUh7H9gvbxFQUD9iNL27wQREQmJmlXhUv447d4dlMwcHslP1SpyIfcuNjaGrdHnP0BQiSIJMj45SNTVxHT1uhBNVl8gWZumZlewHQtJUOjw7iCkxMnqCYpmlrDaiE8KktUXWNCmaPf2M1q6yLXiWXJ6kg7vADFXM0HlzgHOklBatlr92FZgATyKTGcoxIXE/LZt03YcSrrGfKlIb2QDQZIAgaiPWkVjZrh+PAuTKcqFGpGmEKIkrgpgW/uaCDcE0So6iktm+NzEtp3HjQiEfex7aid7n9xJY3sUb9CDqZtc/XCM4989zfm3r/DUl46x+9F+BAGauhoYvThFdqEurqdXdUbPT9K9t4PR85NkEnlqZY1yvkJyOoPX76a5uwFFlWnrb0ZSZLx+N5GmEKpboVKs8u53TvHBD84yeHKEnn0dq5SYu3rurOatKBI/+u5ZRocX7rjszbgvAWy5VOPyuUnOnxxjYG8bgnT/Xlq6ZqLXTGzbQVYk/MFbN5ALgoAoiXj9LnKpe+PfthEEPa5tqcBWdIN8+U4N71I9oLRz3ExZFkQfgtCCIHUgSE3gWOAYi5YWIQQ2d4yC4EYQowhbyAg7QKFau6c+arZdF/bQjNsLr2iGSamirUvF2KXIhP0eFgpbV7XOlqoPK7AP8cDCdhwM26r3kgtb97u2nXpIKSKsuS3TtrEdG1EQbmstczs4joNh3Z02heu9tVvflld2cTDSQ1j1cSY7ynwti2nbKJJMSPHhlVQUUabDG+fl5kN1GuktgkxBEOjyNfJy8yHO58aZrqSpWhp+2U2zO4wsyggIPBYfQJVkhgozVMwazZ4oT8V34TjQ4ArWfd0VLzsCbVSsGqIg0uKJcCzWT8wVxCu52Blsu68VW0WW2N/Xwmee2M3pq9MsZIogCDSE/Tx3uJ/PPrG7XmW94aOybJuaZmBZDvv7WvC4fuoJcPcckqigSm7yRppL+Q+wHYucnsTnCSKLKjW7gmHrpLRZ3JIPVXTjk9dWhM3rKUZKFxAFCZ8coGZVMR2dQ+FnyRpJ5qpjiIKMW/SS1ue4VjxDi6eHopFFt2vodhXdrmE562/fKek6JV3/2PrAQl2JuDcS2XbtgLJuMJHPbSyAxSEY9aPXDGauzeM4MDU0hySLRJvDGPrq7051KagN9QRQKB68aym5hvYYL/3cU6souvG2KLmFAu/+5Smmrs6y+5E+QKCpO44oiuSTBfSaTq2iM355mue+9jipmQzpuSz5VJFyvkImkaOhPYZvsZXywNO7OPissEKwyrZtXG6VS+9fIzGZIruQXxXArgexuB+3R6GQq2x43fvyBK2UNEauzHHqnWv3ve/FvtEPVOCOtB6ButH1g5goDvs826JOqxn1CqxhWbdWphU9iHIfVvW72Ma1emApBsCpsVSFXQHHxLEzdQqxU8Q2pxGkGI6dBSeHY6dwrBSIQXAMHDsFTgnHmseR2kHwgFPFseZw7DKONQ10gOBZtt9ZDxzHIV+u3RX68K0gSyJNUT/XJlOMTKdxuxR8bhVJEnGc+uedyZe5NDqHIAp4PXfui3EpMlH/ra12NoJ0sUztHipiftRgGBZjUylqmklPR4zATRZiS8+wsak0ju0Qj/oJ3SYRtlEspIok00XCIS+NsQCK8uBVbqqmQdHQaPRc74PJaBUUUSSgbK1fW7NMpspZCrrG3kgzHnlr1aG0VkG3LKIu75rbKhg15qsFBAR2h5s2tQ/Ltilq2jbXXusQBAG3LG2bb7NXdrE71MHuUMctl9kf7mZ/uHtd29sRaGVHoPWW85vcYT7dcoRPtxxZMf3vDHx2+e9Gd4hGd2j5/53BNnYG21bMv58QBAFVkfn6i4c4PNDOXLqAIEBLPERfW2zN97Bt27hVmWcP9bK/vxX/Op7zG8XSs6hma8iCtJw0uNM6lmORNwqUzQqGYyIg4JJU/LKPgOzfUr/n3cCSYNhGoYgqqugmqU0TVZvIGklKZg4QCClxFEHFckwkZFK1GVrdPbcMYAEkUabDO0C3bw+T5UEGi6c4GH52zeMVBZmw0kCf/wCGrbMzeJR2b/8GzhkWyiU06+P9rvYqCn2R6LYPscuGzkTu9uy2tRCM+TF1i4vHr2KbFlNDc4TjQYKxAMmZNRwn7hEc6mzWhcWeVEM3sEybcr5CpVjFMiyK2es07OauBjwBN4VMifR8nkK6yMJUmv5D3QyeHKWQKpKZz1Et1cglC3TualuOhxzHoZAqUciW0Mo6pmFimTbZRA7LsKiVa2ibZKXquoXjgKJsPBy9LwGsaVhotQeDw6+oMvKiOrFtOei34Ws7ixL/hrF1AY27gcagH7e89a/UdhyqukGhUiMWWNsfUBC8IO8C6X2s2vcAEVHZX6+4Ct7lvlZBCIDgwqGGY9QViB07g6W9iuR6AVt/D9uaRbArCFIbonIYx8lg1V7HsUtY+gcghhGlbmxrAlt7B9Cwaq8heT6FIHUC6x8IOA5kypV7GsB63SpPHezhO29e5JuvnObpw73s7GrE73FjWRZz6QJvnh7m1OUpDu9qp73xzoMzr6rSHN4eS4O5bIGypi/7At4OmmaQK9ar8z6PC49bWfZAdBwHTTfJF+rz/T4XbpeywiPxo4hCscr/+js/YXwqzT/9B5/n6IHOVctYtsPvfPNdDMPmS588wJOP9K2xpc3hzeND/OkPzvDcEwN87XNHiG+Dldl2Y7qS43Rqmp/tPQzUr4UTCxPE3X6OxNuxHBvLsVFECRGhTqt16qI+DnXLFEkQsBwHc7HaKgsikihSMjVOJCf5wfQVfvORL+KR6/eH7Tirtms5DoJwXb12aT/i4rYtx+ZMapq8XuPRxk6aPUFsx0ERJSShXpFNVIu8Mj1IRqvyPx79zPI5Wo69XFGVRfG2g3rTtklWynflPSFQr1J8xPV0PhZQZIndPU3s7rlzokNVZPo7GujvuDOtbivQbYOh4gh+2UuTuxGv5Lnlteo4DoZtkNCSHE+fYqg4Qs7IIwsSTe4G9gR3cTi8n5grgsgW2A934WK1NhPACi4UQWWifIUe/x4mylcxHQPT0ZksDzJUOoNH8pHTUwSVGM4dUlA+OYhb8iIJEpKoYNr1QfyNSQObOqNjGQJcbzJYPxzHYaZYRPuYq956FIXucBhJFLG2UVyypOuM57LLn/p6r8hg1I/jQHouR7lQYXJwlkhzeE3RonsFy7LJJwtcPTXC+z84y9xYkmKmRK2iodd0KsUavpCXGy+7xo4YwZiffKrI5OAMuYUCgiDQ2ttIz7525seSpOey2LZDIV3isU8fQhAETN1k+to8p9+4yJUTI6SmM5QLFbSqjl4zKGZKtPY1bTpZWypW8XhUorGNf573J4A17QcmgHW5FNweFUkRMTSDXOr2yme2ZVPMV7AfQBXipnBghWT/VlAzTBK50i0DWAAED7LvbwF1b9f65XRj5llA8v4CS48KQW1BVJ9angcCkqcDyfONFdMEosi+PvD9zcXp9ZevKEYRlYMrlt0oHMdhLltapRR8NxH0u/nFzzzCxFyODwen+HBweuX73KlnaHd0NPCFZ/ayp7f5lttaQsCj0hEPb8vxzWQKFNdp8n3q/CT/7F9/F8d2+MWvPs7nX9pHbPFBrhsWp85P8I//xXcA+PVfeoaXntlNwybk0T9qWApUHMe+46Dn44iyoTNdzi3/bwMprYxLkqlaBjPlHDPlAnsjTcRcPvJGDc0ySNbKlAyNTn+EVm+I2Uqea4UUPlmlNxCj0eMn7vLxmfbd/GRueMUAumLW95moltgbaSLi8jJTzuNTVMKqB9OxyOs1HBya3AFmK/nFnwJeWaGoayQqs6S1MnsjzTR7AqiSzK5QI1mtwuuz11acY16vMlbMAAL9wRgh9dZVdsO2SZbL3A2nFEEQ8CgK4sMI9iHWwHhlkj+a+nNSWppPNDzNZ1peJKyunRS1HIuR8jj/efybTFZmVjy7JirTnMtd5mTmDL/W+8vEXdFNV/2lRe/z7bodlij6G92eIqrIospk+SqPxT5DojZF2SyQ0RP45CAdngF2hx7hXPZtSuadq3VCvRFh1XRZUDFtA8sxqVmVxSpvHRISpmOuDGrXAQcYyWQ+1gJOAIoo0uD1EXV7SFbKm0pUrIWCpnElmdzwev6IH7fPjW1aTFyZYfzyNM98+RGCmwi4tgu5hTxvfOs4f/Avv0Mg4ufFn3uS7j0dhOIBTMPk/e+f5fj3Tq9YxxfyEm4IkpnPMXV1lnK+QnN3A/6wj85drYxemCQxmcLtc6HXDDp3tyGKAuOXZ/jt//6bjJyfYOBwDy/8lSdo6WnCu1jN/Z1/8q1bHOX60Lujmc6eBkRx44WODUc7P/iTkwxfmd3wjm5EPlthbGj7GrS3BAGijUFiDUEKuQqjg3PLg9Gbs426ZjIznkKr6vdSAX/daI74VwkEbRYVzWA6k2dPx60zy/XPZ/HHkZcm3rzULf6+3bTbTd8afdLBYT5XuKcBrCgIBHwu/uEvfYKz12a5ODzH9EKOSk1HlkRiYR97ups5uqeDtoYg6jpo4AGPi8749tDoNMMkVShTqmkE7kBrc3Cw7frPxHRqhYVEqVxjdj6/7G94j20O7ytkSeTv/40XcRwHv+/OjIBEssC5y9NEwz4O7GlD3QR95kGCQH1QmagW+Vfnf8wv7XgEt6RgOTYnFia4mJ1jV7iJ37r4E35j99PMVHJ8sDCBR1IIqR5+Mj/Mr+96kv9w9X0eiXcgAmVTA/wIgnDDs6aOgl7jg+Q41/IpegIx/s3Ft/i7e55htpIjb9ToD8YxbZsPU1M83tjDlJXjj0bP0BOIMl3O4ZJkegIx5qp5vLLKv7n4E/727qfZEWqo7++m50+qVuL7U1eQF62vTqem+IX+o3hldc3Pw7As5kqlu5LMkASBRp8f6QGjdT7Eg4GR0hgls4zhmMiijE++dRJ6ojLF6wtvM1Odx8EhqARocjUiCgIpLU1GzzFenuT3Jv6YX+v9ZXzyalug9UCVpG3tabQd0DfRB6qKLnxyCAcbnxwg5mqlYhUJKTEcx+FK4SRJbQbT0VGE+r2tWVWmKkNczL/LXHUCcOjw7kQRbv2cb3S3c7V4itOZH+OTg5iOvhjqCkRcTRi2xsnMK8xVx+jx76PR3b6Oc3YYyWao3sHi8KMOQRCQJYmBeJz8TI3qNglW6ZZFqlJhNJOhKxxGXmfAJIoCvpCXSFOIc29eITWTqVcz72MFdvzyDB++fpFg1M/f/dd/lb6DXbi9rmVRKZdndRwgCALx1gizIwlmRxYwdIP+Q93IikR7fzO2aTN5ZZZQQwC3V6VzZwuCIPDed08zMzzPU184xmd/9RO0dDciq3U/8OFzEyv6YjcDWRaXWbAbXnejKwxemOKDnwxuamdLWKrARu5jBmMJgiDQ3hOno6+R0+9eY2Y8xbn3R9hzpAv1puboQrbCu69cXLNx+0GAR1UIed24ZGnLNJOKpjOdWm+/wF0wO7xLsB1I5Iro97CPpG4xAQ0RP4/v62JPdxMVzcCybASh3s8a8LkJB9xI4vpoWh5VoSkUwKMqVPWtvdAcYDZTIF2s3DGAhTptLh7xMT2Xo3bDvosljUSygN/rQtONj8olsWUsfV8bofYupIu8+f41Hj3Uzb5dt+4X/KhAEesD1FOpKUBgpJBCAHJ6jYVqkbFiBs2yGC9mGC9mqFg6YdVDlz+KT1GZqxbI1CrolslYMcPReDsh9da9s5PlLFdyC0yWshQNjfFimvFSmjZfmOG5YRRBwq+oJKpFoi4P7y2M0+oLsTfSgmU7lE1tWSU4WSszUcpStQwcx1nz/rucTTBSSIFQpwfjOMyU8+wIrU0HrZkmI9nMXRFxkkSRZp9v3QOwh/jpwlRllppVI65GiLuit7TBqZgVRkrjXMhdRgB2Bvr5ZNMnaHTFEQSBBS3Jqcw53s+c4mpxmKvFYfYEB/DIG+vvFwRQJAlRELZN+d92nMVAbmPbU0Q3Xb5dfK71V1FFD72+fbS4u3FLXhzHodnThSyqCAiIgkRgUYW42d2FVw6g2zVcoheP5K9Pd7pwiV5kQaHV3YM/Hqr3D4tuDkeeR7drSIu+1ktJMUVw8Wjskxi2jlvy3bbH9kY4SwHsx1iBeAmqJHGwqYnzifltPd+yoXNmfo6WQGDdz09BAG/AQ6w1ynvfPY0gCMRaI7j9W9N22Ar0qk6lUEVxKfTs6yAUCyAutmotTKWYvEWRMdYSwe1zMTuaQJREHv/sIWRFpqW3CW/Iw+xogkqpSrgxSCgeBAFK2TKGZhBtCdPS04g/VNdesSybS8eHKOXKNHZs3iZoK6KMGw5gKyUNWZHoGWimqXV98vo3I58tMzY0/8B4qfbsbGHXwQ4un54gs1DgT//T25QKVfr3thEIejAMi/npLGePD/P+j68gyRLWA9iHIIki8YAPn1tFK91JRfj2qGgGU+nc9hzYAwTHcZhK5dGNe/v9Ld2kQZ8blypT0wwcB7wuBXUTtG9JFPF7XLRFg4wmMlseGEwkcyTyRbob73xPK4pEa1OYweF5KlUd27YRRZFiuUYmV6a1Oczo5MapOg8shO1t4dJ0k4VUkdGJJAf3tD+QbI6NwiXJuCSZ06lpnm/p51Jujg5fBK+s4pZk9kWa2RNpZm+kma5AhOFCipDqocHjRxUlvJKK7Th8qWsfJVPnWiGFKslEG9auHmmWiVdW2R9pYSDUyKFYG+3eMA0ePx5ZYbqSI6R6aPEGcUsKZUMjILuIuLwEVRc5vUq6VkYSRI7G2nl3fhQBAYe1c3EVU6fZE6DZGyTm8iEJAhHXrQfyVdNgKJ2+K1ZdkiDQEggibWMAW67qXJ1a4JWTQyum7+ps5NE9nbTG1jfIfoj7j5SWQbN14q4YUfXWtoAz1XmGiqMUzRJRNcJnW15iX3A3HsmNIAi0uJsQEJiqTDNXS3CxcIUuX8eGA1gQUEVxWxOapm2Tr9U2/OyUBAm/HMK/2EcfUMIElPDy/JC69kDcr4Tx37DcWvDKAbzyUruMQNy1dmJSQKDRfWvBtLVgLZ7vfLGIvo19oQ8qXJLEY23t/PHlS9u63Yph8P70FC/19m3ID9YbqFvKnPjhWTp2thKI+FZpeziOU28xzJaoVXSMmkF6LotpWpSyJSYGZ1BVBcWtEAh7cS16pjq2g2GYFDNl9JpOKVehkC5h6CbZhTyTV2dRXTKqx0Ug4kNRZQIxPw3tUS4dH+KNb73Prkd6cZy6xc/gqREWptJr3vexlgiqR2VmeJ5A1E//oW4kRcLnVog1Rxi/NI2hm+w40o2yOC5t6WnA43dz7fQ4H/zgLC3dDVRLNaaG5rhyYuS+WopuirfW1BrhyRf2sO9I96Z2OjWWxLEdhi7NbGr97Ua0IcDBx/oYH0pw8s1Bzrx3DV0z6drRhD/owdBNFmZzzE6kiTUGCIS8TFxbmwLtOA75TJnRwTmKuQqGYWEaFrlUkenRJLbtoNUMTr45SCZZQJYlZEVCdSvEGgL07WldVfndCJojAYJeN5ktBrBlTWcylUM3LRRp69YVDwJs26Fc05nLFe8phRjq10U6X+byWIKpRI5CqUYs5OXo7g5622KYls2VsQQBn4umaACve21q4o3wKDI7WxuYSGaxra0NlMcWMsxni7esQN0IRZZobgxy5uIU6WyZmmbi9agUSzWy+Qpd7VGGx1d7epUrGhMzGSZnMpRKGgj1/uCO1ggdrRH8vtUZzZm5LKOTaVRF4vC+Diam04xNpSmWa/Vu6YiP3s44nW0rpfHnF/JMzGRYSBWpaSaKLBIJe9nZ20Qs4l+l5Os4DqZpc21sgam5LKVSDVmWaIgFiC29rG76WAqlKtfGklwbTSxPi4S87N7Rsup4AKZmMkzOZphN5Lk4OEsmV+HMxUk0vX58AA2xAAO9TXSskRy0bYf5ZJ7Ba/MkMyUsy8brUeloi9DVVldHvl+3qUdWCKseRgopjsTbeWP+Gl3+KN3+CIookqgWsR0Hn6ISc/mYFLOIgsCNw9olWRPLdpgoZWhw+zBsi4xW4VJ2npxW4XxmFlkQibp8NHkCZLQyNg5+RSXm9qGKEn3BOG/Nj5DVqrzcthNJEOkNxDmZmuRMapqxYoaaZVI0asyU87R6Q0iiiCiAZhkkqiWu5hdIVIucTU+zI9hIhz9CSisvCk4JBFU3EdfaKuD1wbXGeC57dwJYUVxVQZhNFbg8Ps9cprhiWVEQkCURr0shGvTS3hCmozG8SMu+vpztONR0g0S2iG6YzKYKLORKZIoV+tvjDwPYjxCKZgnTNgkpQQLy2qwQ27GZqEwzWh7HI7nZ4e/hYGgvqqguP/+9sod2Tys7An3MVOcYLU1Qs2qbOiZVlra1Z9u0bbK12k+N2oBmWQxl0pR0/Z75199PyKLI7oZG4l4vqUpl22yDaqbJmblZstUqflVddxXWG3DTvqMZQzPp2t2GZ43qq+M4FLNlXvm9d0jNZDA0k4kr02hVnfHLM/zZ/+9HKKqMy6vyzJcfZeexXgBMw2R+PMmP//BdysUqtbLG8NlxSvkKl9+/hqGZKC4Zf9jHSz//NK29jbT0NHL0pf1k5vO89e0PGDo9iqzIWKZFtDlM/6EuRs5PrjrGaEsYb8BDPl0kFA/QvqMFSRYRRZHGzhiiKKJVdVpvEKXb/8wuJgZnGTk/wau/9zaRpnryxzJtdh7tJTmdRnXfH9/oTQWwwbCX1s4YHb2bU9OzbJtw/P7Th5cgSiJ9e9r4zM8+imXZjA/NM3RhmosnxwBwuRVCUR+9u1t55jMHuHZhisT0reWz56cz/PBbJxhfrDJrNQNdM6hVdGzboVbRee3PTvP2Dy/UMysuBX/Qw96jXbR2xbcUwLbHQkR8HsbJbnobALppsZAvsZAv0RIJIH0MAljDspjNFChVtXv6ErBsm2JZ4/WT13j9xFVmkgWKlRq9rXEaIgF622LYtsOPT10jGvDy9KEeetvjd9yu16VyoLuF1y8MY2xRVGw+V2Q2W6CiGfjuEDxLkkgk7MPjUZhfKFCqaLhUmUKpRqFU49DejlWCHblChYuDs7z9wTATM2n0RRq+okj0dsZ54mgvR/Z3ruofHZtK893XziMKAn6fix+/e5WxqRTFUg3DtOhoieI8vXNFwDg1m+HtD0a4MDhDajHQEwQBt0vmkUNdPPvYDtpbIsuVb8dxMC2b84Mz/PCNS4xNpjAMC49HpSkeoL01gq6v9vCt1gzGp1K8c2IE07IYnUjR3hLhF7766JoB7OxCnjMXp7g2lmRmLoumm1wbWyCZLi0PGgd6GwkHvWsGsPPJPMdPjTIyniSTr2CaNqZp0dfdwEvP7ObAnnYC6+i/vRvwyy4GQg1Yjk3E5WFPuJluf5S+YBxZlJivFBgtprEcm32RFqIuH6ooE1BcSIJIdyCKV1Y4t8j66A/G6fBHsBybkqGRqpXZFa6LK5UXq6F5vUqyWmKsmEYADkRbAZkufxSXOEFhsRdWFkX2RJoYzCVIaWVUSaLVG6LB7WehWmKmkmd3uAm/4sJ0bLJ6BcuxafMGma0U6PbH6A/GmS7nmKsUlve/O9y8Zrm2pOtM5LPkapsb7N8OAuCSZZp8vhXP5IlElm+/dYEPr04R9LnrffSLwjmCIKDKEvGQl/29rbx0bIDe1iiuG/quPS6F3V1N/LXPPkq1pvP66WHeOT+27cf/EHcfuq3j4OAWXbiktZ/lRaPETGWWpJaiwRXnWPTgiuB1CT7ZR7unFRuHpJbCcDbHmvPI2ys6ZtoWmWrlgXSDuBuoGgYnZ6Z/KqqvUE/SRTweBqIxpguFbXuWmrbNVKHA5eQCMa+HoOvWNOCBIz1Ylk0oHsTtc9N3oIsnPn+Eoy/uwx/2IUoinTtbMXWLcGMQHDAWbW1Si/Y6wViAYy/tByCXLNTPTRYpF657ntq2Q61cY248iVatVzNb+5po7asHkdmFeitfKVdBq9bFNmPNYR791EEUVebS8WsUc2UEQaBnXweHP7EXQzP58LULtPU3rXhFhWIBdhzq5tFPHaJjoGVFH2//wS4ynzqAZdr0H+5ent67r5OXf+FpGjtiTA3NYWgGwWiA/kNdPPmFo7i89WdMOH7vxTo3HMBGGwKIokAgtHmfQ1mWcN2niP1W8PpdHHysj9auGG//4CJXz0+STdaz2dHGIDsPdvDIsztp7YqB4zByZY6G5hCeNQaMzqLIjaRIeGTp+jI3Kc8uY5GiaJk2W9Xp64yHt80ftKqbXJqcJx7wIqkf/X4rzTC5Np+65ybgmm5y5uoM//f3T+Jzu9jf18JcurA8XxAEXKpMsVxjdCZFR3N4XQGsz6VwsKsFtyJTM7ZGx9dNi8lkjslUjt3tdzajVmSRlqYwqUyJUlnD41LI5irYllMP3m4YrBiGxYUrs3z7+6eZXyjwxNFeejrj2LbNlWvznL00zex8Dq9b5dihrtXHZljMJfL86CeXmVvIc2R/J5Ggl1yxiigIhIP1633JxucvX73A8VOjxKN+njzWS0MsQL5Y5cSZcb75nVNYpsOnX9hLW3MYqL9AsrkKv/tH7zE5k2HPQCv7drbi8ShMzmR46/g1ShUN66YkQSzs44WndnFwTweVisa/+j9eve1n1t4SweNS2NXfwqlzE7z1wTWeONrH40d6UBcrwgGfm8aGtV8E5y/PkM6W6W6P8fjRXkRB4OTZcU6cHcdx6tXs/bvb1lz3biOounm0oYtHG+rf318feGx53t5IM7vDjRh23e5GAKI3VS9bfSEcx6G15yCWYy9b6AD0BeP0BeN8o/fQinUOxdrYH23BvGG7FdMgq1VodPvp9EdwSfVXnFdW+cUdx7Dset+5KIjYjsOjjZ1Igrgc6AEcjrVzOLZaVOVT7bsw7LryqSyItxyQJ8tlzs7dHZFCRZJo8HoJuFxrUog9LoVHd3fSEgsiiQKGaVOs1JhNFRidS3NpPMFUMsd//fVnaY4Gls9ZlkQiAS+RQP17GZ/PcmZo+q6cw0PcXQgr/lj7Gh2vTDFTm8d2HKJqhL3BXWsup4oyQaU+yC2ZFSxn4wGUAIRc7m3t2dYti9li8aeiGuk4DlXT4NTsLMY9HrvcbxxuaeX0/Ny2JgNtx+GN8TH2NjYSUF23ZJx97e99dsX/A0d6+B+++fdWTHvhZ5/khZ+9/n9De4y/+1u/vKHjcXlUBo708v/63d/Y0Hqxlggv/fzTvPTzT685f9+TA6umqW6FZ7/6KM9+9dFV8w48s5sDz+xec1u7H+1n96NrexZ/5e98agNHvb3YcAD7jb/+LIIo4A9uPoBVVIlA0IvH6+JBUv+RFYmWjhjf+LXnbrvcc587yHOfO7jmPEEQ2H24i3/8v64eiN8LtMdCRP2ebRFMqOkG5ybmeXJXN/evXX37UDVMLk8l7oqwyu1QKNf40x+fw63K/De/8ByHBtr5vR+c4u0zoyuW622LMz6XWfZZvRNURaYjHqY1GqSiG1umRQ/Pp7k0lWBXW8M6KOMC3R1RMrkypbKGLImksiV8XpX2lsgKeuJ8ssCpcxPMLxT46mcO8Y0vHlue9/JzOt/885N897UL/PAnlzh6sO6xeuP+bdshX6ySL1X5n//Rl1AVac3jMy2bqyMJ3nr/Gn1dcb7+haMc2lvvNbIsm898Yi9/7598i9ffHaSvu4HWphCCIFCp6py+MMngcIJHD3fxSz/zOLv661ZGhVKNPQOt/M//5nu4XSurGbIsEQl5iSyKGni96nJleS20NYdpaw6TTBeZX8ijyBIdrRGO7O/Es46EnqpIPPNIP1/7/BFkWcJxHJ5/aoB//C//guHxBcan0/ctgL0TREHEdQc/4Lr3q4DM+ge6kiCu6EW6kJnl7cQoPYEoTzR2r17+hkG0KNSFWjYCRbz98o7jkKyUOTM/t6Htrhd+RWVnLH7Lt2bA6+ZzT+zmkZ0dK5Sts8UKb5we5je/+RPeOD3Mz794mFjQt5w42Qocx8FxwLQsLNvBwanrrQoCsiSsKUxn2zamZWPZDrIkoqyhZGlZ9vIzza3KH4s2lnsBt+RGFESqVg3NWm2PZjs214ojzFbnCCoBurzthNXwLbZ2/R4xndUslPVAEASiHjfyHe6djUCzLKbyeWzHWVfby2bhOIs64jedtiBsTXxmI7Adh4KmcS4xj/FTUoFdwqHmFiJuD+Pktm2btuPw1sQEP7t3Hx3B0MeCXfjTig0HsJFF6u9Wbt54U4gv/cITfOZrxxClhxfPdsKtyDSF/UT8HtLFyp1XuA2qusH71yb41RePAfeHmridqGo6p8dmMO+xh69uWAxNJnnhkR20NYRvOWgM+d0YhkVNX381VZZEjva1M5vdujXQeDLL1ZkkummtoBeuBVEU6GmP8d6Ho5TKNRzboVo1aGlafX5jkynGplI0xPw8frR3xTyvW6GlKYTf62J6PkuxXCOwRi9sUzzIS0/vuu2A2zQtPjg9TrmicWBPO13t1wU5RFEgFPDQ1xnn1PkJkpkimm7idilUqjoXr85i2TaPHe6hqeF6v5/f62LvjhYiIR/6Br6Xu4HdO1rYu7N1RcDm97ppbgiykCpSrqzPy/fjjCPxDvZHW5AEEVW699ZEBU1jOJ3majp1V7bvU1V2xdeTYFqJkM/DQGcjnU1hRmbSZIoVdNPclgBWNy2mFnJ8552LnB+ZI1Os4FJk+lpjPHuwj+cP9eHzrEz+jMym+fN3LnJxdJ4vP72Przy7f9V2P7gyyf/yRz9BEkX+4z/6WQLej/476F4g7ooxV1sgrWXI6llgZTJ9rppguDRORs+xI9DL7uDqSs0SbMfGsOu0RklYO3F4JwhAxONFuUMCayPQLYtEuUTNMvEoyl0rg+iGRa5UoapdV9sXBYG2xvA9C3yytRpn5uaofMztc9bCjliM9mCIwVRyW9WI05UK701N0ewP0BHaHjvCh7j32PAbfjNmszdDkkQ8XhWP985CNR81LGXsbqSk3UsIgkBrJERzOLDlANawbGYzBYbnM3hsSpVyAAEAAElEQVRdKp5t8pi9H9AMk0S+xFTquk/pvcKSQIrf60KWbz0IMMw6PXEjl40sijza38GPL4yQK2+NZqMbJqMLac6MzfL4QOdtlxUEge6OGK+8eYViqUalqlPTjOXezxtPIVeokMtXWEgX+We/9d1V1ZZcoUoqU6KzLUqppOH3rhYj8ngU2prDt72nLMthZj6LYVh8+3unee3tQSRx5fLT8zlKZW35eN0uBdO0WEgVcWyHhlhgRTVUFAVUVaIh6mcuWbh5l/cU4ZCXcPi6D+OSEI+qyjiOg7VFIa+PA1RJQpW2r9KzUQyl05yYmb5rbQo+VWVn/NYV2FtBFOvXSlU3EUSBkM+zZtVzoyhXdT4cmuI///AUUws5WuNB+lvjlGs616ZTjM1luDaV5G9+4XG8bmX52jUtm3JVJ1eq3jJhZ5gW2WIVWRJ/anodtwO9vi5GS+NMVKa5Whqmx99FVI1gOzZ5o8CPEm8wUZlCFRU6PG0MBNamBgIYjkHBKAHgFl2IG2BH3IiIZ3spxACaaXItneZgUzOeDSjKbgTFSo2Lo/PMpgqYlkWxolPTDf7uzzy9LqHF7cBCucTbkxP3ZF8PGhRR5FhbK0Pp1LYmBR0cXhkZ5kBTE23B4Lb2Zz/EvcO9T1GvgYph8J8ufchQNg2AuOgbFnV5ONTYwhMtnYRu02z9oOHfn/uAz3YP0B2K3Jcbo6shTGc8zKWpxJ0XvgM0w+TUyBTdDeF7FsDWByv1NMB2JQFy5SqXpxL3XH0Y6rTFWMhHIl1AW6NXdWlsNjabQZWlDVUaJElkX2czrdEAyUJpS+fnAOMLWd6+PMaxvrbbetKKAnS2RZEkkXS2TFUz0HSTjpbV4kOmWacLej0qvV2rq0dLoXJTQwC3S1kzgJdEEfcdBwz1HljbcWhqCNHcGFy9r8WqbF9Xw/IA3nbq1VuHuqiUeFPQKwgCiiLdN4XfJbhUGdcalktL5/hwkH9/UdZ1Li4kOH2X6MOiIBB2u+mPrhYIuxUs26aqGUwt5Hjz7AilqsYzB3ppjgZQtiHQvzq1wHfeucR0MscXntzL4R1tBH0udMPi/Ogcb50b5d2LY/S0Rvns47u3JWh+iNvjQGgPZ3IXGCmNcypzjrxRpNndiO3YzFTnuVocpmgWGfD3sz+0B798a82MmqWR0tIICITUANIGKfdQfz41eOsK4dsJw7Y5n0gwEIvftQDW53Ex0NlIW0MY23FI5UscvzCOZdt3lbq8BM00mc4XODO3tq/nxx2CIPBYWzvHp6YYSqe2VXV6LJfl9Nwc/dHYwyrsRxTbHsDWqjoL83nmpjIUcxVM06K5LcLA3na8fhemaWHoFo7j4HYrCKKAblu8MzNB2dTZF2vCr6oYts1CpcwfDJ7DIyscbWzFrz7YFCLbcSjpOt8dHeRIYyvdoc355G4V7bEwXQ0RFEncsjqtbTu8NzjB83v7iAd92+I9aNgVctoo4BBy9SAiIwoSAhI2JoZdoWokCajtiNSDFtsxsR0dy9GxHQuvHEcQ1n8syUKZE8NTWz72zcDjUji6u52LI3OcuDgBixVZBwfDtEhmi1wanefc0AwdzRFaG9b/MBUQiPq9HOppZTqdZy5bvPNKt0GmVOX85BxXphfY19l86/0KAsGAm2jERzJdIpmuW/C0r6Ge63bJuFwyfp+Ll57ZRegW/fNuVVmlQnx9f9wxGSQIAgG/C0kS2b+rlUcPd+O+RW9pPOJfDgYlUcDnVREEqFR1TNPmxlyN4ziUKhr2Pa7c3wxRFB5mih9gXFxIcGJmmoVy+a5sfyl4DbtvrT9RrNT4/vErnL02iyjWdRB03aRY0ShrOi8e2cHLxwaIBr2rEjUbRb5c49LYPFcmEvS3xfnKs/tpjgaQF6miLbEgVc3gWz8+yysnrvLi0R3IHxNLtgcZbZ4WjkYOUjLLzNUSZPUcftmHg0PeKGA5Fu2eVh6JHmYg0Id4m/do2awwXZ1b9C5tQBU3HigKQFsgiGubkxembXN6bpbP7hgg6tm8JsvtIAj194MsiTg4KJKEZlj3zL5nrlTi3MI8qcrW2HQfZXSGwuyKxzk9N7utz9aaafLO5AT90ShNfv9dZe5Yjk1WL3IhN4ZumzjYdHmbGAi0I21zYudBRCFfVwwPhdf2dc+mS2iaSXNreEPb3dYANjmfZ/D8JJfOTDJ+LUE2XcK2bI4+tYO2zhhev4tSocbo1TlSiTz7DnfT0nk9m3yksZWf33WQJq+fsqFzNZvi//3uK5xPztMXiuJXXeS1GmP5LNOlPIZtE1Rd9IdjNHn9uOXrp2M7DmP5DOOFHCVdr1sn+AP0hKL4ZIXpUoFsrUrJ0CkbOlG3h5jHy2iu7hF4tKmNuMeLItbFUnJajZF8hrlyEcOyUCSJJq+f3lCEqNtLulrhw4UZZktF5spF3pgaZaZUQADaA0Eea+5YfnFPFLKMF3LktRqSINLk9bMjEiN4gyJaxTAYL2QpGTpdwTCXUgnyeg1ZlNgfb6LdH7olJSfkc9MRC9EY8jOT2Rrt0QGuzia5NpekLRok7Nv6i8K0qyRrFwAbRfJj2GU8UhRVDFK1Uhh2iYI+hS3YBOR2dLtA2ZjHxkQRvdiOhUeOIqyTzlTVDaZSOS5NrfYmvRfweVRefmwnI9NpXj85xFQiy9hsXazp5OVJxmbTnLw8iWFZHNnVTmfz+hMfS2PBJ3d2cXpkhvlcccPm7jfCsm2m03m+f+YqvU0xPK5b2x9IokhrU4jZRJ7ZRJ6BnkaaG4Jk8ytftk0NQZriQZLpIpZls7O3ecWxQz1R4jjOKnPwjUCSRAZ6mzh5doKaZhAOeulqj62qnC7Z6iwN4FVVpqM1iiiMMjaRYqCnCe9iz55hWKSzZdLZEtvFCpUkEVmu0yJrmvGwcvoxQLpS4Sfj45ydn7tryqgt/gCHmltum8Soaianrk7jGpuvW1k5oJsmAgJtDUEO9LbS2xqr2+xsEfPpAqNzGQRB4GB/K+03Jd6aYwEG2hsQRIFrMyny5RpuVUF+qHtx1yAIAqqk8mj0CLZjcS5/mbSWoWbVfbfjrhhxNcaRyH4OR/YTUW+dLDVsk6yRY7o6iyiI9Hg7cUsbLyKIgkB7KLTcq7pdd4dp21xcSJCuVGjy+baFUXAzarrBTCpPKlcGp85Ia42H7sq+boZhWVxNJTk+NflT43e7FtyyzP7GJnbG4tueHLySSvL25AQ7YjH2NNzZfWEzcByHklnljcRZKpaGIsoIQIMrfFf29yDiyqUZUgsF9h/qor0zutyKahgmM5MZLpydRFElPt16eEPb3bYAtlLSeP8nV/jRn37I7FQar6+uMJxLF8mmW5aFc2pVnbMfjPDOq5f46i+bfLbjupyzS5LxKypB1YV38WEXcXmwHQfLcSgbOhdSCX4wdpXJYh7DtvDKCsea23mxo4/+cBRJFDFtm2S1zHdGrnA1k6JoaMiiSHcwwvPtvTza3MaHiRnenZnAxmGuXCLm9rI71sCFVIKJQpa/se8RXujsJer2UDENLqQS/OnwJdLVCpplokgSe6KNfL5nJxG3h4VqiR+OX2M0n6FiGLw1Pca5ZJ1K9nhLJ482d+A4DplahR+MX+NCcp6sVkUURNr8AZ5r7+Xlzj7kRdpmXqvxg/EhrqSTfKFvFz+eHGG+UkIUBP763qM0ef23DGBFQaAlGmSgtWHLASzURTqOX52gvzlOyOvecgbdwcF2dAQEysYcWW2YmHsXAaWdvDaG6dSwMchqQ7ilCHl9glTtIqroJ+oawHJ0NvIanM8VuTSVIFden7rvdqNege3gi8/u4/WTQ7x1ZoRKzcC0bN44NYRLkQn6Pbz06ACP7esiFlo7S3U77G1vorsxwuBsknJN39LxZktV3rgwwvN7ejnY3YL7NtTx9pYI56/MUChWiUV8+H2uVQFsV3uMXf3NTM1mePWtQRqiAfy+eqXUtm1qmolhWnjcCu1rUJDXC0WWOHqgizePX+Pi1VmaFgfUnsXeO9O0qFR1EKAxFiAYqCdjvB6V/bva+MGPL3Lq/ARtLWEs20aWRDK5MifOjGMY1or+/3rPqY1h1pVXbbv+v23bVGsGpXINUawr5N6smuxSZQKLRuhjU2lmE7n6sTh1eyKPR8W9BS/oh7h3cBwH3bJ4b2qK96enSNyl6qsoCLQFgxxoujUrAuqiaI/v7aItHkKSBGzLoVTVmUvnmUrm+MEHV1BkkReP7CAS8GxJ0yKZK7GQLSEI9X7VM9dmVi0zm86jyhK6aZHMlYkFfcsV2oe4e2hyN/Bi43P0+Lq5VholZ+QREYiqEXYHB2j3tOKVb5+MNhwD27FpcMVodTezOziAR9pcK1fU4yHscqNI0rZ5mdqOw2yxyFA6TWcoROQuVGElUcSjKqiKhGnaNEb8PHOwF5dy91Wxk5UyZ+fnuLRwfxLvt8KNCddbfQZrJWXX+3mtWHfRy3p3QwP7Gps4MTODZm2fmJNuWZycmaYjGKI9GCLo2n6Wp+FYJKpZfrJwjv9658/Q5WtaVuSumhqpahoHB8uxaHCF8cpuDNukaFSoWhogYDoWje4wHslFTi9RtTSkRSs4j+wirPgQBIGMVqRs1rCxkQWJBlcYVbz/Cu5z01lOHh8mmSjw1HM7ae+MYRgWU+Mp3n/nGmMjCfbs79jwdrctgB28MM0Pv32Khdkcuw92cuBYD5Zp8wf/549XLNfYHKKzpwHLtLh0ZpLPfO2R5XllQydZrQ8ACrrGmYU5TNtmd6yRmNvD1UyK1yaHKZsG/+SJF2jweHl9coTfu3IOEYFmn5+Qy01R1/juyCDvz03z1/cd5VBDM+OFHH82fInfvnCSvnAUBziXmuev7TuKbTt8b+wqH8xP8bcPPMbrUyO8MzvOwYZmom4Pk8Uc78yMk63V+J+eepmo28N8uUBR12n0+pEEkb2xJv7l05/iYjrB33rtO/z3jz3P023dK7LlmmXyw/FrvDc7yed7d/FUayfJapm/GLnCvz39Hocammn0+pEX16mZJoPZJO0LQX7twCO0+oJMFfO0+YN45NsPcFujQXa3N/KTS6PbUuF5f2iSxwe66GuObbkXVkBAFj3IgoeqmUYQJCTBgyL6cclhKto1PFJssUTnoEr1wNUtRVBEP3l9bN3hq2XbDM4keX9ockvHvFVIosiXn9/PwYFWzlydYXgqSb5UQ5FFWhtCPLavi52djZsOXNyqwqGeVgZnklvufbYdh1SxzH947QT/+Osv0h4L3ZI63tYcRpElAn73CvXeGxGP+nn0UDdziTzvnx5lLpFjV38zPp+LWs1gLpHHtCyOHuziZ79wbM1trAeSJNLbGeeTz+3hu6+d5zs/PMvZS1N0tkWQZYlCocroZIrW5jBf+uQB9u2qW8543AoH9rZxeH8HH56f4pvfOcVAbxMBv4tEssi10QSdbVHmk9fp2ZZlk8mVmZjOUKkZ1Go6xZKGaVpcG03g9ah43ArBgJs9O1pWHKfXo9LaFCYe9fPB6TEUWaQpXv/sGuMB9u1spafzzj7AD3F/URfscxjNZvmDC+e4nLx7A02/qtIVDtMTuX2Cx+9x8clHBlbZ6BQqNd67OMFv/dGb/Na33qI5GuToQPsqdeCNoKIZVDSd+UyJ33/1NH/4+tlbLhvwuNDNzdmwPMTm4Fd8HAjv4UB4z6bWd4suDkcOsC9U94b0Sp7NqRAvBiGtwSB+VSVT3d5E8rtTE+xrbCTs3npy/Wb4PC46GsOIoki2UMbvcaHI8l13f7RsmxMzM3wwM/3AeL8aloW1fP/WrbOWWI/2onjpUl+wQH386pZlaqaJKkkrxhBLlfjrFflFzRPAWkwKqpKEBCAINPsD7G9qojcS4Uoqua3nNVUo8ObEGHsaGvhET0/dF3wbr6OyWWWqusCuYCcN7jCKWP/MHBxmqil+MHcCw7bI6AU+1/o4+0M9pLUCH2QGGSnN4pM9pGo5vtD2BHtCXby1cJ6R0iwN7jC6bdDvb+Ophr0ogsypzFWuFKaoWDV8spsvtD5Bj+/2Sc97gedf3ovbo/DjH11k8NI0X/u5J0ini7z+wwsoqswnXt7HE8/cWg39Vti2APbNH54jkyzy2a8/wme+/ggNTWFGBmdXBbCCKOAPeQiEvCRmMyvm/f7gOb559fxigCPS7AvwD44+xWPN7fgUlfOpOZLVMt8Y2E9fKIoAfLlvD29MjTJWyDBeyHKwoYW8VuNPrl3kF3cf4mhTK3G3l0avn4VKiYupE5xKTGM70OTz0xuM4JZlhnIpdMtiX6yRuXKR744Ootv1TKFp2+i2hSgIeOV6lbg/XB9gbuQy1y2LPxm6yKd7BniipYNWX4AWX4CqYfD65AjH5yb5VNeO5V5fy7Fp9vr5Yu9udkcblw3B17PPppCfPe2NhH1usqWtvzCy5RrvDo7T1RDmSG/7lrbllqIMhH5m8b/646tOBxZodB8g7t53g9qhiFdqwHEtLScQVDsQWB+FJ1kocXFyjmtzd8fWYqPobonS2RxZ9k6se83Xexu3+sx8cmcX58fnuTK9sGUao25anBiZ4rsfXuGrj+2jJbJ2cNreEibgd+FyyTQ1rr0MwK7+ZmJRH7v7m3jtnau8/u4gmmYuB3OH93VweO/GM3Br4XMv7qOrPcqbx4f48PwkZy5O4jgQDnrp7Yxx7GAXzY0rqXMet8rf/xsv8u3vneadEyO88d5VPB6VQ3va+Y1feZ7LQ3P88CeXlpcvVXTefH+Yf/+7PwHqV7G1yDL58x+d4zuvnEcUBVqbQvzuv/6ryDdRNgd6G/nbv/wsf/idU5w8O0G5ouHxqDx5rI/ezoZt+Rwe4u7CAQqazm++9w4XFxbu6kCzPxpjb0PjJjVg6wHkvp4mPnGknz996zzHL03Q1RzZUgDLotZe2O/mYH8rLbFb3/8uRaYx7N9Q9dVxnLtGx36IO0NAQBUUVGl72CAdixWu7Q5g3xwf49P9O+iPRred2lutGXz/+BVmUnmaowGKFZ1XTl7lN77y1F1VIZ4vlXh3cpKLD1D19UoqSaJcIubx4pZlUtUKz3R0oVkWZV2nqOuUdI2Ay4VHVjg1O8PR1lZOz83SE47S4vcvbyvgclEzTURBwLLt5WDXJcssVEpcTiY50NhE1ONdLugcaGri0/07tj2ABbi4sMD/9eEpeiIReiPRbc1PmLZF0agSUwNINz3B+/xt/Eb/lzAdi1OZIWYqKdo89dhCRKDT28jXOp5lrDzPa/MfEneFKJoVQqqPz7Y+SkQJ1MeOi0f8UvNRXmw6QkLLcik3zrncCF2+JqS7nXG5A0JhLy9/5iC79rbzl98+yT/9R3+EIAh85a88xkufOUhbR3RTmgzbFsBODi8Qivro3dVCvCmEJItr0pMEQcDlVvB4VXKZ8gom6Od7d/KZ7gEibg8lXWcwk+TfnzuBblk809ZFVqvx5vQYZxbmVlQg58tFHm/poKhr2I5D1TIYK2T5d2eO8x8vfYi0KFJQ0utU4oVKmZjbi0dSkEURWZTwygqyICIKAqooYtjWcra4JxTlsZYOrmSS/MqPvs1jLe18umuAPbFGAusUlnIcB922mC7l+e0LJ/nDwXM30AgMKobBXLl0Q4YLcOqZ985gaLmSu96vWBQEWiJBntrZzXc/vLLOtW6PD65N0d8SZ0dLAwHP5qkW9Qzd0otm6XwXlVQRERGXb8jr826grawzeHUceHdwgg+uTT0wPSSCINw1/7iIz8vRvjYGZxe4MDG/5e05Dvzxe+eJBby8tH8H8aCPI/s6+a3/4WsE/W5UVaaxIcg/+PWXcYCg310P2ppD/M6//qtEgl58i1ZZoigQi/h58ZndPHa0F8OwlrO1iizhditrDqaP7O+kv7sBB4hH10etliSRnX1NdLRG+JnPHcFcVGYWRRFVlfB5XCvEnQRBAMfB73Pxlc8e5lPP78UwLURRwO1S8Hld7Blo4dOf2Es8Wn8JB3wuPvncbh45eN1u6MZrbOlulWVxVV+vIAh43Cr7drXxj9qi6IaJbdc/C69HXSVk9ann9/DEsV68HnWZ9nwjfuUbj/OzXzy6pn/uQ9wdmLbNVCHPv3znbU7PzVIxtkbbvx1EQeBgUxOPtLZuujIgCMJiEOlDANKF8pqK6BuBx63idasEvG4e293Ji0d33Hr/CHUbscV74cbzWOvZbDsOhmVR04xt6dd9iI1ju6uZA7EYUY+H8VxuW7db1HWOT0/RHQ6zK769yb/x+QzNsQD7+lroaAyTzJX4/ntXMC0b23HuiqieaVl869JF3p+eumt2XJtBslKmxR+gLxIlVamQqpQxbJvXx0ZwgIDqQpFEPLKCLljMFAvo0yapSoWirjOYFhEFkSafn45giMupBUIuFxP5PA4Ou2MNeGSZC8kEumWvei40+nwcbW3lQFMT5xNbd9i4EaZtczWd4l+8/Tb//MWXiHu92yJYCiCJEl7ZRaKWweb692naFsOlWd5NXsQlKiRqWYKKF8uxERFwSypuSUUVZVrdUfJGGd02kASJoKISUQLLMQRAzdJ5K3memUoKw7Yom1VavLFtOYetQhAESqUqw0NzDF6eoa0zRqlQY/DSLP0DLcTifnz+jY9ftq8HtqzhC7jx+d2rqg03Y6lf7GY0ef3sjTXS7A1g2Bb94RjnU/N8MD9FdyiMaVt0+sN8qX8PXcGVFZS4x0dXMAzUs7a24/Clvj3siTXgvUFi3S3JNHn9XEovLNuECNQHCUuekYKw8qXqlRUebW4npLq5lF5gKJvk3509zlOtXXyqawd94TvbGjiAbdvolsXnendxuLGF4A3BryyKdAciq6jBkiDekS68FgRBoCnk55ndPfzgzFWsbXgQFqr1Kmx7LMRnj+zapof3TZYltwjRbzX9drgwOcd7VyeYTOY2c2DbhnJV5+TlSY7t7sDrVu+YadINk1yxSqmq4fe4aIwG1rUfWRI53NPKyHyaqzPJbbEMypSq/Mnxi0iiyIv7+okGvMsCRwCiKK2iDquKTHf76genLIkE/O7l/s/1wOtRV+xvvXC7lA3RseueqgLhoJdwcLWthNejEgldny5JIqGAh9AaAeV6IIoCHreywnP2VggGPGsGrkuIRfy3nPcQ24+qYXApucDvnz/H8alJSrp+VxNkO2Nx9jc1E/duvDf+RhimTa5Uw6FeEZU2oOK+Fpoifpoifkbn0sv9reuFLIkosoRh2RSrq/2rC+UaC9ky5n1W/r6fmMsWeO38MLPZAl6XQsDt5pOHdtAcCmyqWqGbFq+cHWJPexOt0SDuNSy5bsZMpsDFiXmO9LYRC2xNuXpnPE7Mc2vLns3CdhzeHB9jVyxOZyi8Yry3VQS9Lq5OLlCtGXhUhWS2RKWm132Vt20vK/GjkRHemhhnoVy6S3vYHERBYKFSRpEkirrGQqWCZduUDYOqYRD1eLBth6Ku4VNVQm43PkUl6ZTJ1qq4JBlREHDLMm1OgFytxmQ+h0uSCblddRcA06Sk62u6Z8iiRF80yhd37uZiIsF2h/ZlXefU3Ay/+e7b/NqxR+gOR7ZFmdgruWjzxPnezAfk9BJeyY0sSqS1AtOVJD7ZzbHoTk5mrtbF1hah2yaGU7cFzBsVPJILWajXUiVBXBG8AoyX5snrZTq9jfhkN4PF6QemZWPw0gzvvHGFibEkh4728PhTO6hUdD78YITXf3iBsZEETz23i/6dLXfe2A3YtgDW63dTqxpoi6qat8re2ZZNuVijVKgSjvlXxC+SIKKIEi5ZxoWMLEpEXB5yWg3NsvDJKnGvl75QlOc7elZsdykIBVBEiajbQ7PPz1OtXSsemgKgWRZXMslVD6BbPZJEQSDm9vJIcxt9oSgj+UZenxzhxPw07f7g9QD2BqsPx1lsRr+hciqLEhG3hyavn0eb2mkPXA/CBa73iqw6r00+Kn1ulR0tMXa2NTA0k9xyNs9xYHguzY8vDNMeC3Gou3VL27ubSBXK/OjsEOcn5qhtsdKwVRTKNb75ymlaG4I0x4KYpo0oCnjdKqqy8iGUyZc5fmGC04NTlKs6fq+LA/2tvPDIDnwe9Y5Z8Yagn0PdrXzYOcPp0dWiKpvByHya75y4hGXZvLC/n8bQT0fAVNF0qrpJxO9Zvj8f4qcbC+Uy709P8f1rV/lgepqifvcqr1B//j/e3s6+xsYt0SOrmsFEIsPpoWkc26GvNYrfuzUKZEssSH97nBNXJjk9NM3l8QQ72uMrvF5t26amm1RqBtGQd/n9GPS6CPnclKsaQ1NJKpqOW60rnpumxdBUktND02xJUv0jjopmkClV8KgKPQ1RFgolfnxhmG88eQBV3PjQTRQEOuJhAh7XcrL+TvC5FNpii8HuFh9/DV4frYEgAdVFUdfuvMIGMFMs8vbkBD2RCI+3b08rCkA87KOzKcJsMs/l8QSSJPDUgZ67IuKkWxbn5uf448sXGclmHpje1yUMROPULBOvouBXVfyKiiKKHG5uQTMt4t569VASREIuF8daWnHLMq2BAKbtoCxWNL2KQtjt4WBjM0Vdw6+qeBQFv6JiOTaK2IZh2wRUdVWRJOr28FhbOwebW7iwkNjWCrUDFDWN18ZGkSWJv7JvP7vjDbjkrYVJqijT4o5yINzLmwsX8MouVFEmogZwHIe5aoax0jy6ZaxIhGqWwXR1gVfnP2S+lmF3sJOweuuxlyoplMwaZbNGRA1gORbbaTRjWBYFTcOwLZr96yuqLGFsZIFMusSRR3o5dKyb7r5GLNMmHPFx9sMxxkYWOH1i9P4FsF39jZw7McrIlTn6drXStIafj207zE5luHZ5Bl0z2bGndcUz0bAtapZJ2dDRrTrddryQoy8cJai66A5FuJJNciIxxa5YA3G3F8O2SNcq+BUXEbcbSRAJqC6eaO3kw4UZ9sQacckyqihS1HWqlkHcvbEsYF6rUdS1eqbI5eZwYwtz5SIXUvPktevZYxGBoOpGEkXmykVyWg2fogD1rJNLknmytZOhbIqr2RRBlxu3JFM2dAp6jc5gGHmLWfEbIYkisaCPTx0aYCKZxdyiOi1AWdM5Nz7Hd09dIeLz0BEPP1DelPaix+qPzg7x9pUxkvm7owq6EdR0g7NDM7x2YgiPS6GqGYiCQGM0wI6OBvb0NNUzuoLAheE5Xj8xxGwqTzToZSqR48p4gpZ4kL19zXfsu5ElkR2tcV7Y18fIfJp8ZXV1Y6OwbJtLUwks20E3LZ7d00N3451ZBx9FmJZNslBiIpljNJFGEkW+8tg+lIfKqT/VqBoGQ+kU709P88b4KGfm5u7JALMrHOZoaxttgVv3l94IzTC5OrmAY7PsXakbFgu5EmevzTA+n6G3LcaBvlaC3joTwnEcqrpBplDBtGxM02IhW0IzLIoVjclEFo9LQZZEVFkiHvLVhdu8Lg70tjA40M4HVyb55utneGRXB7GQD0kU0E2LQrlGoawRCXp48cgOxMXgNujz0NFYD6YGJxb487cv0tkUQRIFMoUK54bnmJjPbq1H92OAsNdDZzzMsf52zo3P8mcnLvEzj+3n1PgUCAKO4yCLIo1hP62REOBwemyGqmZgWBYNQT+9TVEEQWBkPs1UOkfU78HhOgsmW6pyYXKOsM9DRdMJeNy0R4OYts1oIkOhqtERCy8vP7GQJZEvUTNMvKrCQGucgMd1x4BOkSR6IhGa/X6Kme0NYK1FT9jWQICWQICuUPiO66wHHpfKzs4Ggl4XmWIVv0etX6fb/D6oP1/S/JdzZ/lwdoayYWzr9rcDbcEgtuNcT+YuxjA7orFVlT5BEOiN1McIrYHgmvP7o9FlIacbr50VBaebrilFkmgPBvn63n3MFAukKpVt7ZOv6xpo/ODaNQA+NzDA/sZmwu7Nt+eIgkhA8fFy81HO5UaxnLqtWVDx0uSOULZqSIJAt68ZVZIJKl5KRhVFlPBILkRBJKIGOBjpI6T42BnsWJM90+SOMBBoI2uU8EouIqofv+zZFHvxRmSqVSbyOUYzGTLVKh2hEJ/u31gA29QSwu8f4MCRbkLh+vcrKxI797QSjfs5f3oCZxNsm20LYI89tYORK7OcPj6MokrsP9ZDeqGA40C1rDE9niKVyHP2g1HOvj9COObn2FM7Vlygo/ksb0yNElLd1EyDqVKemmVwtLGVJq+fgOpiplTg/bkpvjc6SIsvgGHbpGuVRUpuC5IEIdXFz+zYy3+4cIqfTI8xWazTFJb8YD/Z1b8hyleyUuZcap6yoRN21S/kC6l5OgKhFVVUSRRp9PrYFW3gXGoey7GJur00+/wcbmxFlSS+0r+H/3TpNO/NTZKqVvApCmXDQLctftYXRJI3W29dG363ygv7+vjhmasMz6cxtoFWmiyUeePiCGGfm68+tp/GkA/5Hvii3QmWbZOv1Dg5PM0fvXue6XT+gREBsW2H771zGVEUlnu6VEVmb28zfo9KV0sEQRA4c3WafKnKUwd7OLq7g6GJBf7wldO8c3aU1obQuoQjGkN+ntjZxfmJeV47f21bPgPLdrg8naBQrZEtV3n54A56GqO4FPmBSmBsFI7jYNo2hUqNdLFCIl/i8vQCp0dnuDqzQG9TjC8e2/MwgP0phO041EyTuWKRoXSK10ZHOD49xXzp3lD7VEnik7397I434FknLbJU1Xj11DXevzyJKAj1c9AM8uUamm6ys7ORTz2yk/62OJ5Fir1lO8wk87x6aghNN9EMk6GpJIVyDcdx+PHpa1wcm8etyvg9Lr7yzD4igfogpL8tzhef3kvNMDk/OsfwTIqgz4Mii+imRbmqIUsinzi8Y8Ug1q3K7Oxs5NmDvbxzYZz/8qMP6WwMI8silZqB36PS2xarW1kVK2ue608LHBws28a0nXo1SIDvnR4kHvQR9XvxqgqqItMaCeEAk8kcuUqNVKFEY8iPS5FpCvuZSuf49vsXCLhdhH2e5ffQfK7It947z/N7+6gZBs3hAFG/B920OD8xz6WpebobIvjdKiVD5+z4LLOZAqJYH6tUdIOnd3WvS4RwZyxOVzjMtUx62z+nhXKZtybGafT5+PqefdtmqxPwugl01sd95ZrO1ckFDvS1IG7TmKek61xJLvCdq4P8cPjaSh2UBwy3etffKXmx1vy1GIfr2VbA5eJTff18MD3NT8ZHyWvbmwwBKOoaf3l1kEy1yud2VHmkrY1mf2DTYx1FlOj0NdLpW+01uyPQtmpayajild3sCLTzqZaVzgxHo2ur9fpkN0817NvU8d2IJVr4Up/zYCrFh7MznJmfQxREvrxrN5/uv7XewVo4dLQb23LQDZOF+Ty6btLUUn/ex+IBnnp+16boztsXwD49wOD5ad59/RJ//nvv8c6rlwiEvdi2w/REmu9+8wPSC3mSiQLBkIdnPrmfA4/0ggCSINAeCHJmYY4/HDxfF5yQJJq8fn5+18FFP9b6C/PT3QMoosSPp0ZYqJTxKSo9oQh7Y43LF5dXUXmqpa6O9qPxa5ycn8JyoMnr47GWDmRRJKi6aPEF8EgyqiQT9/gwF5WG/YqLdn8Q1+IDShAgVS3z1vQ4Ob2GLIj0BCN8sW83Rxqv02jr66r8yp4j/MXIFf7k2iVckswLHT0cbmxFFkUebe5AsyxeGb/Gnw5fQrcsYh4vhxpbFtXEFr8YUaTB68N07C2p0yqSRGskyMsHd5ApVUjktmfwlSyU+f23z+JWZF46MEBrNIgiSVtW0t0sTMsiXaxwcniaf/f9d0kWSlgPWP+UZdk8caCX/vY4giAwNLHA0MQCPzx+hV/90uOookgqXyES9HJsdydPHOhmoLOBy2MJLo/PUyjXaOPWxvNLEAWBjliIn3/mEBcn50nki9vyWTgOTKXy/MHbZxmcWeCXnjtCb1OMyOJg6KNCsXWceiW5qhtUNIN0scylqQVODk9xdnyWTKmy/Hn1Nt3ng32IewrHcTBsm6phkKvVmMjneGVkhNdGh7c92387iIJAXyTKZwcG6Ajd+Z73uhRaYkFS+RKmZZG9IeiTRYnWWJD+tjhP7e/h0d2dK3oZbdtmPlPkjTPDK7bZEKn3tE4kskwksggIeFwKLx8bWA5g3S6FQ/1ttMdDvH56mHPDsyRyJco1G69Lobclxu7uJh7f07VKgXhHe5yfe/EwsaCPD4emyZWquBSZ3tYozx7sIxLw8IP3BxmZSa2b8vpxg26apEsVrs2lGF/IcKS3DUkUyZVrPNLfwbN7evC761oadWV7h2d29+A4DiOJNCOJDOMLGXa0xHl+bx+nR2dwKSuHfYZlUdUMntjZSVMosOJ7enygc4V/+mQyx3yuSGdDmD3tTQzNpnjz8ihP7uxaV6vTznic/miUt8bHl10ethMjmQx/euUKjT4fL/X241WUbU2w5opV/vLdS+zqbNxy0t52HMq6ztn5Ob51+SLfHxp6YMQmH2SIgkDQ5eJXjxxhppjn/HzirlxLVdPktdERxnNZvlTYzecGBmjw+vBs8zW1FhRRJiB7ELeRkXk7WLZN1TQp6zrZWpXRbJYPpqd5b2qSyXweY/HzbQuujwl0M2zbIZspMTq8wMjQPNlMmZ//a88QjniplDXKpRqKKuPZIONm2wJYt0flZ//Gs0Rifl79izNMDC/Ue2FFmJ9Kk5jOIAgCnX0NvPylI3zqq8eQF/v/AqqL33z2M+vaT0cgxK/sPcKv7D1y2+UkUeTlzn5e7uxfc/7LXf283HV93o7IddGZ5zt6eb6jd/n/vnCMvnCMXz/w6B2PTxAEnu/oWdWjeyOeaevmmbbu226nwevjbx987I77Ww9kSeQrj+7lnSvjpAqVbRF0gno28t99/z1mMkV+7ulD9DZFkRapsPcKS1YL87kSr54d4rdfP0FpG6jS2w1RFPjCs/v42gsHaYrV6RfTiRw/en+Qd86O8iuffwwU0HQTURRwLQpsqIrMgf5W/vTH59D09dOK3IvUrl954Sj/x4/eJ7MNVkpLqOoG7w5OcGZsli8c3cOXHt1Df0scRRIX7YAenMHmUlZvyaPOdupZwNFEljNjs7x/bZJz4zMUqw/eNfMQdx9L18eSj6Fp20wX8rw/PcXro6O8OzV5X5RAPbLM3zxylK5QGHkdapgH+1s52L85TQJVkXnmQC/PHOi988JrQJZEmmNBfuHlI/zCy7d/L98ISRTpaIrwq59/jF9l7Xfdwb4HV2fhXmAynePtwTF2tjRwsLuFTx3eiSzWqdyxgHc5eIX6tVyqafzOj09i2TYVzUCRJKK+27dMiUI9MdESDqzpHHEjkoUyI/Npzo7N8u6VCVRForsxsu7Eddjtpj8au2tVWAe4lk7xm++9iyrJPNPZhV+tD4o3+l6ybHvZ7m7VtC0ep+04VA2D10ZH+L3z5zgzP7fFLf50QRJF9jU28fmBneRr2l25lpYwnMnw709+wBvjo/za0Ud4or1jOTFyt8Y6bd44bd674wV/fUzkYDv1azFTrXB6bo63Jsb5YHqKiXx+W/eZTpX40V+e4Xt/9mFd6KtQ5Utff4RQ2MPYcIIT710jFPbxtV94YkPb3b4OX8AX8PDZbzzCo8/tZPxagrFr8+SzZRzbIRj20TPQTM9AEw3NIdx30UPrIVYj4vPw0oF+sqUKYwvZbd329z68wlQqx1cf38dTu7oJee+dnUfNMHl3cJzvnLjEqZEZKtqDF4gICMiSSF9bdJm2B+D31hWG0/kyN74SBa57wkqiQCjgoaqbG66iel0qXzi6l7Njc7xzZYxCdXupNlXN4C9OXeL40ARHett4cX8/R3vb8G/BYuluoKIZTKZyXJle4PzEHJemE6QWbUR009oWWv1DfDRhLb68LyeTXFxY4MPZGUayGfI1Dc0y70vw6ldVnu7s4pmubgKuB+teeoh7i+6GKI/t6OS5Pb0okriczFiLfFkzTE5cmybk9fCJfb0k8iWuzqzXM3N9A/HGsJ+epijdjVGe3NlVV0RdTFyuFzuiUQ40N921oMMBEqUS/+SN1/nFA4f4+p69tAY21rMH8MqJqwxPp6hq1xPH5arO8EwKe4vPhYuJBH948TxvT06QLN9/nY6PKr60azej2SzJSplcbet6H7dC1TS5kEjw/3ztFY61tvKlnbt5rL19y8rw9wMOdV2fwWSS03NznJ6bZTiTpqDp6JaJbt1+PLSZkP3MyVESczl++W8+T09fI//N3/rd5XmtHVFkRWJuduNxybYGsKIo4PG6aOmIEo0H2H2wA9OwcABZlvB4VdwedbnyuhmktDwfpK/w6vzJFdNlUcYjuYiqAbp9zewJdtHrb0VZh1rffDXDpcIYl/PjJGpZqraOR1Rp8cTZH+phT6ibuGttGtfridP8JHGGormyT0cURFyiQkjx0+FtZE+oi32hXmRBvOcVqiWLkJcO7GB4Lk2qWKG4jcFMzTC5MDlPslDmg2tTvLCvj2N97fjuUpJiKdP83tVJ3rkyxqWpBHPZIuUHMHgFkCSBeNjHfKa4wnuxqukks0V002I+XSTs92CYFoosYt8YrDoOsiRumJ4tIOB1Kfz1F45R0w0+uDa1rZ+RA1R1k5lMnnylxoWJOdpiIfZ2NLG3o4k97Y2EfZ571h9t2w7ZcoX5bJHZbJHJVJbxZJbZTIFCRaOs6ZSq9d9ryfQ/xMcLtuPU/fB0naKuU9Q0crUaqXKZ+XKJ+VKJuWKRhXKZkqFT1nUKmkbNNO9b77xbltnX0MjfeeQxQm73R7q//CG2DlkU8anqunzXZUmisyHMGxdH+P7pQWRJxLQcTMtmOpXn1QtDXJlJYlo2s9kCx3rb6GyIAKsHpTXD5Mr0Aq+dv8bl6QSCIHCgq5n9nc10xEJcm0sxMp/G71bZ3d7II/0d6x7Y9kQiHG5u4ZXhkW1XI15CvapU5Y8uXmA0m+ELAzt5pK2NsHv9fbH7elvobIqssHy0bRvdtHCrG7fq0UyT0WyGV0aGeXdqkpFMhoKmPdA9rw86AqqLr+7esyi8NIR2h+BrKzBsm1ytxvvT04xkMnSHIxxtbeWJjk52xeLr1im4l1hqiZnK5xnLZhjJZRnJZJgu5ElXqhR1jaKmUd3AO28zV2syUUBWJI493od5U8HA4627a1TKG38WbGsAu7xRWUIOSPgC7nq/mW5SylcxTWsxc7X5Aa1uGyRqGS7kx4A6V1xAWLbu8Ugq53OjvJ++zJ5gNy80HqbFE1vlmQT1EvqHmSHeTV3gSn6SlJajZNYwHQtFlPAXp7icH+dgpI+n4/vZE+petY2UluNKYYKcUUISpGXOuu3YCAi4JIVL+THO5q6xJ9jDl9qfJKT4t+zBtxk0Bv28fHAHC4US7w6Ob6tDQVU3GF/IkCtXGUtkePPSKPs6m9nd3khHPITXtVoSfSPQTZN0scJYIsPV2RTX5lOMJzJMpfMUq9qGBpwCcLC7ldlsgYX83RdkcasKh3a08c6ZMWwbelpjCAIMTyU5fn4cj6rwf/7puwS9bibmMjRE/Ms9bIZpM5XI4fOoGw4Elz7u3qYY33jqAKZtc+La1LbbClm2Q75SI1+pMZ3JMzKf5r2rEzQG/TSF/TSHAzSFAzQGfUT8XgIeFwG3iixL67ombKc+CKvpdb+5imZQqGgUqjUKi/vNlqukCxXy1RqlqkaxqpOv1siXq5Q1/YHrh/644J3JSZLl8jJN737Ddhwspy56s/Rbty1000K3LGqmScUwKBs6Jb3+UzPvr83WEiRRZFe8gV88cIiBWAxpnffG6eQslzMJKqaBKkrsjzVxKN66Jdudh7j/aAz5eWp3Nz7X6nvr60/uXw4+lyBLIh2xEF9+dC+iKKBIIpIoEvK6CXpdHO1tp6shgldVifg9hHz1YK4tFuIXnz28IrEuiyItkQCfWExGBzwuYgEvYZ+HI73tdDZE0Ayzrkod9G2oKuNVVHbFG3i8o4NXR4bvvMIWkCiXeGtinNlCgZOzMzze3sGBpmbi3js7UTRHAzSG/dxMGF5iVK0Hpm2TqpS5sljturCQYDSbIVEqPXA2OR9FiILAjmiML+/cTdUweHV05K4nH5feG3OlEqPZDMenp2gPhOiNRtgRjdEbidDg9eGWt99q6XaomSbpSoVkpcxCeemnxFypRKZSIVerka1VyVZrlPR7nThxEEQBURLhpgA2n6ugaQbuTSjObzqAtSybQrbM+HCCbKoeBATDXjp6G4k3BhAlkVKhyvmTY4wMzlEsVJEkkWg8QP/uFgb2teMPbk0lThEkvtT2FEHFh2VbVCyNtJZnsrLA2ewwE+UERaPCJ5uP0etvXRXEXivO8FriQ06mB7Edm25fC73+Fnyym4qlMVGeZ6Q0SzpRQLdM/LJ3TRUxgKDi41C4n/5FRTHLsalaGolaltHSHOdyo0xVkgQUDy81Hb2tn9PdgigKHOhqYSqVYzZTYDSR2dbtO0C2XCVXrjI4s8DFqXl6GqO0RYM0R4L1F6DXTcDjxudWUCSp3jcpCtg2i0qLNrphUdZ0yppOsaqRKVVIFyss5EvMZPJMJHPMZ4uboveJgsDB7ha++tg+Xr8wTKFSu+s+sX6vi089sYv/+/un+Mmpa5zwTCBQ7yH2ulW+/Px+rownmExk2dndSE0zOHFxAseBSk3ng0sTdLVENm0pIUsiR3raKFY0NMPi1Mj0tvVB3wzNsJjJFJjJFAAIeFxEfB5iAS8Rn4eg143PpeJ1KbhVZZkWdyMNzXYcbNvBXlTeNCwLzTDRzPrvmmFQrhmUaxqlmk6pVr9O8pUaxl3MwD7EalxNpxjJpB+YvuelPmdr8fdHCf2RKJ8fGODprq51B5/pWoWhbJJsrUq7P4QiinhldcuenR8VOIvftW5ZGFY9SWHYFgvl0rZXYzTTJFWpMFPI199dooQq1X8kcWM02vUg4HHdsvJ6tK991TRREPC6VB7dsbYP6oGutT0WIz4Pjw10rpgmSyLN4QDN4dXU29ZokNbo5sRclo6zOxzhxZ5ejk9NUrrLXsoFTeP0/ByThTxXkkn2NDSyK15XQ+4Ihgi73Wvebzf6Ga8XtmNT0DSS5QrThQIT+RwTi1WvoUyahW2gC++MxfG7VOaKRWaLxS1v76MOj6JwqKWFimlQ0DSOT0/dk/3WTJOJfJ6JfB6XJNHsD9ARCtEWCNLg8xH3eol5PEQ9HgIuFz5FxacqeBUFVZKRb/PMWHqPmbZdT7xaJppZHwMtJWFLuk7J0Chp9YC6oGmLAWqVbK1KplojW62QqVbve5W/uTVCaqHI8beu0tQSxnEguVCgVKxx7vQ4+WyFgd0b1zvYVABrWTazk2lOvj3EuRMjJGZzAEQbAuw/2s0jz+ykoTnE5TMTfPs/v8PVC9PLCnn+oIc9hzp54XOHePyF3ajq5rIUAvXq6+dbn6DD24jjOFSsGnPVDIOFSY6nL3EuN8KriVO4JRWv7KLdWw8+HcfBdCx+kjjLmew1TMfiULifl5qOsjfUU/dhMqtcKUzwauIUZ7LDnMhcIe4K0eZ9HpHVzdshxccTsb28fIPkdcXUmK4u8EH6Cq8nTjNTSfL92fc5EO4loHiQhHufIQ94XDy6o5P5XIlMqbpCYXC74FCnIA3OJBmcSdYVlUM+msMBYgEfUX89kFFlCVWWkBYDWNOyMSyLmmFSqNTqli2lKol8iVSxTE3fWqApiSLNYT8/88R+ntvTy3Qmz+XpBWp3uQrrVmWO7u4gX6px5uo0qVwZB+hoinBoZxvPHO7j2mSSqUSWjqYIl0fn+ODSBH/06mlMyyZbqPDl5/cTCW7Mv3jlMSg8sbOLmmFR0w0uTScw7wGNtljVKFY1JlO5VfNUWUKRJGRJrFcLJBHHqScybHvROsKqU7buRy/iQ9wZlm3zMGWwdXSHwnxmxw4+2ddPcAN9r6VFD/FWX4Av9OxCka6/0gczCxQNDdO2CapueoIRXJJMslpmtlLAsusU66DqosMfJuS6N9oFS4nK5R+nfp+bN09f/rEwbWfx98p5hm1j2FY9uWXWB3k1y2QynydT3d53W6pS4fj0FLlaFdeir7tLlnBJMqpUf5ZJQj0hp0ji8t/r/ZEWfyuLf9/sj/lxQsTt5lBzM0dbWnl7cuKeJJvqtiCTnJqdoTMUZle8gYFYjLZgsB5kqC58qoJHVnDJ9e9UEoR6kCHUVfjtJWaH46BbJjXTpGouJlUXlcsXyiWmCwWGMxmGM2nSlcq2BRBht5vPD+yk0efjRyPDDwPYRQRdLh5pbaNmmmSqVYbSqXuq6KxZVj1Zkc8BdQZDxOOhxe+n0ecn6vYQdLsIuFwEVRduWV58XtSvL1EQVghNLiVhjUXWUNVcZJ8ZJhVDp6jp5LQaea1GrlYjX6tRMYwHNmm7c3cruWyZKxenGR1OAPDWa5dAEMhlyvT0N3LgcNeGt7upALaQr/D+m4P8yX96m1pFJxLzI8kiU2NJpkaTlEsa+4928/p3zzI9nqJnoBmv34WumWRTJS58OE65WKO9p4Henc2bOYRVEAQBn+yhP9BGl6+JPaFu/rdrf8ZgYZJ3Uudp9kRpdEdRRRkbh2Qtx8nMFTJagQPhPl5uPsbTDfuXtxdW/TwR34soiJTNGmezw7yfvsRLzUeJqsF1Jbi9souBQAcB2YthW/z+xKtMVBLMVzO0exrwbiLDtx3oaYzw4v5+5rIF3rg4ctcrkKZtM5ctMpe9fw9bQYCIz82L+/t5YV8fPpdKUyhAxOe56zRiQRBQFZlPP7mb54/2kyvWM2JBn5uAtz5YPbKrnSO72nEch5Z4AJeq8PbZEUzL5pE9nTxzqI+wf2uMhaDXzXN7e5Alkd976zRDs8n72guqm3Vq50M8xE8zWv0BvrRrF18Y2EV78M6WOTci7HKjSjJzlRIX0wmavH6avH5EQeR8ep6ZUp5MrUrc4+NTXQN0ByJczizww8khQqoL3bIIudw81dLFY82dd97hIpZsFzTTXBzQLyadHPt6BfzG/+2lQZm9TOWuGsZyAFA1jHogYBrL/9eWp10PEqrL8+uDunvZr5yslPnx2Cg/HhtdNU8UBFRJwi3LuGUFryIv/i3jkZX6b0VZnuaWFbxL85X6/0vLeGQZl1yv0EiCiCTWB7iSUK/Y3O5/RZLwq1tr17kXEASBZr+fr+/dx6XkAplq9Z59j5plcS2TXhaR8qsqrYEAHcEQzX4/UY+XsNuNX1VRJXlRXb/eKlOv9NvotlmveFWrZKpV0tUK86Uy04U8FWP9bgEbgSJJPNnRyWd27MCvuricWq9A108HYl4vz3R2UTZ0fvvUKWZLm2PpbQdM2yZZLi8KdCVuuZwoCCiiiCxK2I6NZTuYi8/MBxWbebJ09jSguhW8Xhcfnhhl9742RocThCM+Dh3r4ZEn+mnvjN15QzdhUwHsyJVZzn0wgl4zGNjXxktfOEIg7GFhLsfxN64wMjhLKpFn+PIsT764h5/7tU8QawxQKWu888olfvjtk8xNZXjzB+fpGWja9iyjIsp0+Zr4asez/IeR7zJZWWCwMMnh8A7avHEM2+RM7hp5owwIHAz3cTDct+a2DoR7uVKY4FxumJSW51xuhOcaDiJuoHoaU4PsD1231UlqOSqWhle+d2q9N0IQBHa3N/KLzx5hLlvgwuT8x75H0OdSOdDVwt986TG8rnrTeGPIR9i3PWbn64XbpdB8gxLxkqS5YdoYpoVumIT9Xr7+0iG+9uJBbNtBWme/zXoQ9tUVKqN+D//LX7zFeDL7MIh8iIe4Twi6XPzCgYN8ceeuTXnsRVwePtHex6uT1/jtSyfpDUb5KwMHaPEF+Fz3LmzHYayQ4WI6waV0gu5AvW9SFOCLvXto8vh5Y3qU08m5DQWwNdPk/Pw8Q+kUZcNY7iuuLP5dMeoVg7JhUNF1KqZBRa9PN52PH5vCdhxqi8E2bI8aqipJeGUF7yLt0CMr+BQFj6rilWW8iopXqQfLXkXBr7po8Qd4uqsL7wMoKHMzgi43T3d28kJ3Dz8aGSav3R1BpzuhpOsMpdMMpe+eFctWIYsirYEAv370GG2BILIoEnV7kEXxITvpBsS8Xr6yaw+27fA7Z04zXShgPcDPG9tx0CzrropPbTc2Gyk0t4T53FeO8tkvH8Ey60G6LEsrPMk3ik0FsPPTWWYm0nT1N/Ff/X++TEtndFlIqbO3kR/92Yd8+O4wzW0Rfvk3XiIU8yGKAoGgh+c+vR+tpvN7//uPGbwwta1CQjdCROCR6C6+N3uc+VqGyXKCoeIUbd44pmMxWJhCsw0aXCFaPDH88tqBjFtUaXHHaHZHKRgVLufHeTq+H3kDQlSyKK0IVg3HWg5c7hckUWBHa5x/+KXn+G//8/dZKBQ/tkGsKksc62vn//HZpwh6r9PzGkN+Ir77k0RYggPUNIOLI3OcuDzJqctTfOnZfXzlEwcAtnRz3woeVeFIbxv//Oc/zT/71mtcm0uiPQxiH+Ih7ikUUeTvPfYEn9mxg0bf5jURugJh/truo3yqcwfvzI3zv51/n7936Cn+ePg8mVp1MahyONRwvceo3RfCKyu4JBlJFNCtjbFwMtUqfzZ4hb+4Ogg3ydzc6L281v8PsT7oiz29ea0eEC8l+pfeCDcm/gVAlWQGYjEONjd/JAJYAJ+i8l89/gSD6RSXk8mHwdgtEPd6+YdPPk1/NIa62K8b8XiIe73Ml+6+EOVHCR5Z5q/s20/A5eL/OnWSq/eYTvwQd4Yki1uQ8r2OTQWwxXwF07Bo64zR3BFFkq5bw3TvqPu82rZNY2t4OXhdmu/xqcSbQoTC3rr40128shRBosvbXBdi0gvM1+qiRbZjM1NJYtgmjb4IQcV3yyqwIAiEVB8xNUSylme6msRmYw9Z3TYpGNdtdgKyZ132PncTgiDgkiX6mmP8k2+8yL/93juMzKc/dvYiqiz9/9n77yhL8vy6D/z8wsfzPn1mpS3bXaa72o3rsZgBMBiAA4IOJCRylxK1XK2olVZc8eyupKM9knhWXO5KIpeH4CF5aEGCHGAGGADjemba++rq8r7Sm5fPm/Cxf8TL7KquzHJd3VXVlbfP66zMeBHxC/Pi/e7X3MsX90/yFz5/mJFC5rrr3JdOkk3cfV/p3cL1fJbKDU5cXOL9C4ucvrxCrdmlbTmYurpJWj+u/ichBJoSGdD/N3/mK/yLn7/Lz09fZr3ZufXKO9jBDj4SVEliLJPlrx99mmdGRsiZ5l2XfAZhGOlByDJ5M8Z4KsdLi1d5e3UeWUj8wugMtu9xYn35uvUkIbb0Er1dhNDrQd0JfH3c+HAg4IMFH1LHFT5uENygmvsgQxKCUjzBf3DoCP+gRzZ2cD2Gkil+fd9+Pj82hn6Nsm3ONCnFEzsE9kMQQqDKMl8en0CXZf7Z8WO8sbDwQJflPkrYal67Xm5iWy6Dw7k72tZdsSjbiqK1qWwM9UOersm0STyhoygy6UzshvJHSZIwYhqxpEGz1uHjYrAbJ6nfyBKXDdadBjU3+qAHYUjVbeKHAWktTky+uWhGTNZJqjG80KdiN+44a1y26xyvRXLxhqQyaBaIKfffoF4IgaGpHB4f5K/9wjP8ixePcfzq0kcWS3pQoMoSX35sil97+gC7h4poH+o5TpoambiBoSofax9wEIZYtsuFuTJnr65yYW6N+bU61XoHy/XIJEye2DfC1HCRoVKamdHixzaWDWyQ2Im+PH/p+ScY78vxJ8fOcXph9XoP2h3sYAf3DGnd4MnBQf7sgcd4cnCIpKYhSXffIjDbrPLGyjwLrUaUhgvhl3ftYTiR5o2VeV5auoIpK3S86/vyPq3iQDt4uCCEQBGCz42Nsdxq8e9Pn+RC5d66IzzMGEgk+drkJH9q7z4S2vVzxpxh0heP36eRPfhI6jrPjoxGVlK6wc+uXnlgLNN2cD1OvjfH8lKN3/jN5+5ovbsisEEvS6dpN66uKDKyIiPJAnWL5RCVRSqK/LGVD1+LhBpDk1Us36HjRz0WISEdzyYIAwxJu2U2VJUUdEnFDwPavsXNSPdG9NMLfFpel8XuOm9XzvHq+ikUIfNkbg8lI4sq7m8GdgMb0vtPT48ShFGv6FsX52l2708/yr1CyjT44oEJfumJPTw22o+5hfG4IstkEybZhHnPBaYc12O93mF2ucrVpQqzy1UWyw3KtRZBEFJpdAiBp/aP8sUnpunLJ+nPJUnG9Tv2e71bCCFQZMFkf56YrlJIxnnhxEXevDBHrXNv+rh2sIMdRNxyVybD58Z28bWJKY4MDqLL8kcmknFVZzSZIanqSEJgKAqP5ftRJZkvj0ziBQF6z7Iho0c9c1OZPHkjRt6IYSgKj+cHmEjdWeR7Bzu4l8ibMb4+NUXTtrG8M8w3Gvd7SPcd/YkEX5mY4Ff37mMkfaO4W9Y06fsIrQePAjKGwdNDw8RVlWI8zo8vXWKptaPc/FHwcYQ+11YbzM/eeQ/6XbGoDZImtujPE+KawqQHIMgrCwkJQUBIcE0ztxf6hNcsvxkkBJKQCAk31/swmm6Xt6vnaPtW9L4egV3ornG+ucCaXWNPapRfHHiarJp44CLgMV3jM3vG0BSZdMzgzQtzm16eDxNkSVBKJ/jigUl+6Ym9TPXntySvG8glYhRTiXtKYN+/sMj5uTJzy1XmV2tU6h1c36eQibN/YoDhUppj5xaYW6nx+PQgzz8xdc/2fbcYyKb44gGD/kySwVyKdy8vcHZxDcfd+n5/FBDTVPLJ2MfSh7yDRwcZw+BAqcRnR8f43OgudhcK90wltmjGKZpbZ2G+MDSx5d9HkxlGk5nN32eyhXsylh3s4KNgVybLN6ancXyPP75wnoVH2CImIq+T/OqefewvlrZ8T840KSV2COytkDYMjg4NU+h5s/740iXOltdwdvqt7wq3Mx+0bZeXXjiN593eOT5zYh75LlxZ7joN6PsBrabF0vyN5R7NepfAD+m0tl5eLTdxP6EyVS/wCcIw8mS7RjlYFhFtDcKQ4BaXZIP8CgQy8pZ0t+o2+fHK2/xk9R0ABAJFkjEljaQa58ncHj5ffJwjuZn73v+6HQxV5bndY5TSCYqpOD89eYkrq5WHpi82pqmMlbJ8ft84v/7MYxRSceRblOfl4jFKqXtbhvMnr57hh6+fxfUCBgopdu8q8fjUANOjRcYGcsQMDcvxmFup3dP9flTEDY0nJoeY7M/x+vk+fnj8HOcX11mqNbE/ZqulBwWyJMjETIrpOLtKWZ6b2YVyD1Wgd/BoIBLVkRlOpXlycJBfntnN4339JO/A43UHO3jUsL9Y6s3NBN+/cO6R9DkdTCb56sQk3963n33F0rbBrpSuU4zFdpSIbwOaLDOTy9N/MMFIKs13Tp/idHmNmmXdM4/eRwW3E3q1ui7/9B/8lFwhsWWl7oexslxnZu/gLd/3Ydw1k7I6DhdPL/KH/+aNG5adfX8O23K4cn5ly+UrC1Wq6y2UT8AHteV1cQIXXVIxe72uAoEp69RECztw8W4hROEFPm7gReW2srblezRJIaelSKkxQKBKMgnFpM/IsSc5ysHMJP3mg1+mJUsSuweL9KUT7B4s8k9eeJuFap1mx35gH5KqLJGKGeweKvKtJ/fxtUMzkUjJbWQ5somIrNxLtLoOITBUSnN03wgHZ4YYH8xj6CpBEOJ591+FejsIIcgmYnzt0Ayf3buL7711mh++d56ra1Walo3teJ+6jKwkBIamENM0cokYRyYG+fz+CR4b7SMd+2RtlnbwcEMAuqKQMQxG02l+fe8BvjI5SdowHoSCpB3s4IGGEIK9xSJJ/RAxTeVfvn+carf7SJCMDWucb+/dz6/v279l2fC12HjOZA2Dtc69E2B0HA/X9REiagm8HQICUdZNkiRUVSYMQ8IwSnTJcjQX+ziqDn0/QAixOafS9e2r7YQQpA2Db+3Zy55CkX/23jFeX5hnpd2i67qfunnNxwFVkjZVsG+GMAzpWg7Pf/UAff239jZ/+Wdn8O7CCeOuCKwQgm7H4czxec4cn9/2fRdOL3Hh9NK2y0uDmbvZ/W1hgyCsWBXankVKjZHVonKLDWXhVbtG0+tgBc5Nt9X1HVpeF0XIZLXklh/EfiPHnx/9Cl8dePLeH8x9QCZu8uXHp3hqeoR/88p7fPfN0yxWG3gPWDZWEoJdpRzffHIv33xyH/nknakK5xImxdS9LcP5pc/uI5M0eefMHP/2x+/xr3/wLsmYzuMzgxzZPczh3cN07V5GM/zgXn2QysolIUgYOn/us4f4+qHdvHL2Kt9/5wzvXl6gY7ufCkU/IcRmxvXo9DDP75vkqelhMnHzlpn7HezgwxBAXNN4rNTHt/ft5+tT05jXqIZGn/ON1wai++xB+Ox/OKj2IIxpB48mhlNp/srhJxhOpfn/vPYqK+3WAxtAvxfY8Hn9L579DM+PT5DQtk6UfBhJXWc4lb6nBHZ2tsLly6soiszoaJ6JieKmXo0QXPdv+OD3c+eWSSZNxsbyhGGI43isrDTIZuPEYpFY3bWPlDD8YBvXPiOj7YWAuGF/H953pdImHtdYXq7T7brs3Tt43bg+vH2I5jZ7i0X+uy9+iRdnZ/nXJ47z8tzsDom9CQSRa8lwKsVELnvr9wtBKmVy+Og4Y+O3FiVdXqxx5fLqHY/rrgjszIFhvv7tj07Ukmlzyz7aewU7cLncXqbuttljjtBv5AGQkRg0ClxpLbNqVWm47Ztup+G2KTsNdEllKFa4Zc/spwWSEKRMgz//ucN8Yf8kP3rvPD86foFLq5UHIoM41Z/nK49P87l940yUchi3GSm8FqmYQT4VQ1Uk3Nus178VHp8eZM9YiT/3C0dYXm9y/Pwix87Nc+rSCsfOLqDIb2E5LomYzvxqnXKtTT4Tf2DvqnTM4EuPReTu8mqVn528xE9PXGKhUn8oiayuyIz35Xh8bIDD44PsH+knEzfQVQVNke9Zf+IOHh0UYjGeHRnhFyanOTwwQFo3MJQPP48C2s4JKt0f4wdNQDCU+muo8oPSgxrghy38oIMq5xDslDvv4P4hrml8Y2qa6VyO337nLV6em6NmffrEBbOGwWdHx/grR55gKpff4rmxPZKaxlAqxbvL2yeK7hTttoVhqhTySU6dWkRIgsuX18jnEui6Qqtl4fsBuVyCMAxZWq6hyBKNpsXQYJaFBYl220bTFM6cWWJkJI9hqNTrHVRVJpeL02rZzM6tMzyUZXy8iGlGhH1puc7Jk/PYlkcmEyOfT1CptonFdGRJUKt1CMOQ/v40iiLz2msXGB8v4vsBq6sNKpUWpVIKRZFZWalTLKYYHMwQj9/4LJMliWdHRpjJ53lzcYHfPXmCV+bn7tl5/LRgMJnk82O7+PL4BPuKJTKGcct14nGd//xvfZPSbWRfAfoG0nelmXRXBPbxJ8eZ2N1/N6teh2v9Y+81AkJeLZ9k2VrHC31GYn1MJ4cAUCSZ3ckR3q6eZc2us2xVaXsWceXGC2P5Dit2hVWrSkqNsSc1hiQejexMVPYBCUNnopTj288+xrO7xzi7uMabF+Z4+9IC1Vb3kxsPEeE8ODbAZ/eOs3uoyGA2SSZuoqt3Vw2/QdKLqQSL90i0SlcVdFUhHtPJJEyGimk+c3CcarPDxfkyZ66scubyCiuVJj947QwnLiwy3Jdl964Se8dK7BrMkU48OKWrkiQwNRVdVUgYOkO5FL9waIa5co2TcyucmF3h0kqFRvfBm1wIAQldZzifZlcpy/Rggam+PMV0gpSpk4oZJAxtJ+O6gzuCJAQpXedQ/wBPDw2zv1RiMJmkEIuR0PQtgyC2t4DlLaAroyS1g4S4KFIKAC9o4HjLhPgYyiiSMHH8FfywgyziBGEHkNDlftyghuXNYiq7UOQsklDxggZeUCMMPbygTlzdixA6XlDH8RYJsNHkPnQl6jNy/Sq2t4AsxdHkfiSh4vir1KyfE4Y+hjJGXNuLKud4INQYd/DIQRKCmKqyp1DkP33mOQ4NXOFPzl/g/dVlbP/h9x/WZZnDAwN8Y2qG50ZHGU6m0O5QmTyp6wwnU/d0XI7jUS63CAMIgoC337pMPGGwtFRD1WQy6RiDg1kMQ+XUqQVc10eJyRBGWdih4Sy7ZwZYXW1gmhpXr5YZGsrQaluYpkaj0eX06UXiCYMrV9cZHMxuEljP9el0HAih0ehSb3TZs3uAarXNlfkKmbTJ1FQfp04tcPToBNlsnHw+Qatlkc3GGRnJce7cMmNjBer1Lvn89oKpoqfa3p9I8PyucSayWd5bXuaFK5d5Z2mRpm0/khlZWQjysRgH+wc4OjjIgVIfA4kk+ZhJTNVuK8AvKxJTMwPbOtF8GHsfG2Zy5s455V3N+lOZGKnMnZVqfpKwfZcLrQW+v/QaZbvOcKzI7tQIBT2KBihC5vHsJN9bfIWq0+JU/Qp7kiMcyc3csK3zzXnONedxA4+8luLx9ATyI0Jgr4WqyPRnkhSSMcaKGfaP9PHlx6a5uLzO1bUqV8tVlqoNmt2bl2PfKUxNYTCbYlcpx3gpx1gpw2ghy1ghQypmfGSBHSEE+4f7+C9+5fN3ZR00lE9jbEOeJSHQNQVdU8inY4yFWcYHchycHmKt2mJxrc6VpQqXFiocP7/AmSsrvJiK8ae/cpDnn5j+SMf1cUASEZEdyqUZzKaYKOXYO9zH5/e1WKw2WKjUWVxvsFRrslxtstZo436CEw1VlsklTAqpOIVUnL50gv5Mkr5Mgmw8RiZhUEjEySZMNOWjW5js4NFCQtMYSibZlckykc0xmcsxkk4zkkqRM2O37A0SQiUIOzjeIpLxDLo8Dsh4QYWG/RZe0EQSOm3nJLnY16l2f4wiZbC9xR6xHcGSrmC5VzHUMcqd75I1nsdQd9Gy36dhv0FMm0EgEVN3E4Y2detlvKCJrvQDEprcjxfUqXR+gCwl8YMGmjJAUj+CFzSx3KtIIoYq5QnDR0O4bQcPLoQQ6IrCVDZHQtWYyuZ5Z2mRl+dmOb6y/FCWFauSxGN9fXxmZIwnBwfZXShQjCfuKkyU0HSGUveWwAZBSKfjoMgdfD9AVWWCICCdMvH9EFWVSadjCCkSQW21bQqFJN2OQzJpoMgys7PrVCotQgRdy8HzAmRZwuq6EeFVZAI/IJONoSjXz+EEAkkRBGGIJATVaptOxwZCVFUhlTLpdt1N0mvbHkEQEo/rJBIG3a5DEIQoqky12iGXSxCLbV+SLUsSGcMgoZUYSqbYWyxyvrLOsaUl3llaYq5Rx/kUBExuho0S9pl8gb2FIlO5HEOpFEPJFPmeUNidQAiBbmzfjxyGkRbM4nyVKxdXuXJxlUIpxS/92hN3Nu47evcDDD8M6HgWq3aVC80F3qic4VT9Kpqk8Gx+HwfS42hSdEJlITEcK3IgPU7dbXO2OcdPV4+hSgq74v3EZJ1u4DDbXuEnq+9ytjFHTktyKDtFn5FDPMIRaUWWySfj5BJx9g/3sdposVhpsFhtsFRtslpvUWt3aXRtmr1Xx3FxPR/X9/H8AM8PCAlRJBlVllBkCVWRMVSFZC8rljIN0jGdYirBYC7FUC7FUC5NMR1Hvcc+qf3ZJP3Z5D3d5ochhEAWglw6Ti4dZ2a0SMdyWVlvMLtSY361xvxKjYW1Orbz4D8shRAkTJ2EqTPRl8N2PartLqv1FmuNNmuNNuVGm2qrS7Nr0bYd2pYb/bQdLMfD9X38IMAPAjw/xA8CgjBECJCEhNTrUVVkCU2R0ZSoxFdXFUxNIaZrxHWNuKGRMDTSpkE6bpKNR96++WSMQjJOKqbvZFh3cNuQhMBUFDKGSdY0yZkm/YkEg8kkQ8kUw6kUQ6kUfYkk6h3cV4qUwVQn8cMuTestWkIlY36Ojnse16+gyUWEUGnZ72F5V2k7p8nFvoobrCNQCEIXyzlFxz0LQqJuvYaujKDKRVx/DcdfJis/jyRMhFCAENtfwvXXUaU0sjxEiEvbOUXTeQddHsL2l9D9NUxlElnEkKU4ipRHV0aQpXsrbreDHdwthBAMJJMU43Gmcjlm8nneWVri7HqZ8+vrVK3uA01m5V7FxkyhwP5CicMDAxzqH6AvkbhjcnAtEprGQDKJLMQ9E7oaHMyg6wqapuAHATFTx3E80mkTzwvQNAXDUOl2Hbodh3TapNWKSGyhkEBVZRoNC02TUVWF/v4UmXQMPwhwHR8hCfpKKVzXJ59PXCe8lEqZzMz0bQaXZUWCEDKZGH19aUxTRddVZmb6URSZ8fEC8XhUOSlJAtPUmJnpJ59PoOkKYQiadnvzRUWSyMdi5GMx9hdLHCj1cXhgkNPlNS5WKszV6yw2G58KQTFFkkjrRo+kJhlOpRlNpxnPZhnPZCnFb+3gcacIgoBO22Z5sc7SQoWF+QrLizU6LZswDEnfRVL0oSWwIeCFAW9WznCxtYgbeNTdNrOdFU7XZ7ncXiKvp3gyu5vPFw8yEvvAS0sIgS5Uvth3mIrT5HjtIq+tn6LpdTiQniCpmrQ8i9P1KxyvXcINPZ7K7eXzxYMo0t2RJ8/zaTYt0ukYzWYX09DQ9Ds//Y7jUa22MQwVTVXoWg653MfjBeZ5PuvrLXwvwIxpJOL6ZkmAEFEJ+EA2xUA2xROA6/nUOxZzazWuLq4j6Qrr7Q7NjkWjbUWeogL8MCQIwx4pkTdJSVzXyCZMCskY+WSURcvFY6iKzKctWSaEIG5qTAwXmBiOeuBW1ptcWlxnIH9vI6qfBHRVoT+TpD/zQSDA830qrS6VVpd6p0ujY1PvWNQ7XdqWi+P5uIGP5/m4vcCGHwYRcRUSsiSQ5Uj1LiKtKoamEtMU4oZOOhYFO9K9V8o0EOJ6wYYgDAjCAD8MkcXWn92Nfu6AEImPRy1xB/cHgoiQXv+SUDfuK1lBVzZ+KsRUlZSmk4uZlOJx+uIJ+hNJxrMZirE4+h30p30YsmQS1/ajSDma9lvUuj8nps5gewsoUpKYOkOIh+VdxfFXCUMPQ9mFpcwiUFClDLY3jyrlkYRBQnscWSQICRFCQ5P7SeqHr9ljSFzdgyWu4IUt3GANPRzC8maRRRxZimNK46hSESEkZBFDkXJoch5dGUKWdjwmd/BgQZEkhlIp+hMJnh0Z7WXJFjlXWWe+UWet3aZuPxiOCRKQ1A1K8ThDqSRT2TxPDg1xdHCIlH5vgqq6LJMzY6R1g4p1b9q5RkbyjIzkN3/fEFb68Her43ikUiZ9/WmqlTYjIzkKhej7v7//elE4IQRBEP0uSWLbbWYyMTIfIjJbiVzu2xe1A+7ZM3jDsr17o2WFQuKu1Y9NVeWxUh/7CkVW221Ol9c4U17j3Po6y60Wq+0W5U6HjvtwCFqaikLaMMj0XnkzxkgqzVQ+z3Quz65Mhrh2e+XBdwLfD2jUO1TKLcprTVaWaizMVVhfa/L2GxeZnO5j32MjTO8dZGq67463/9ASWAAncPnti39ISESIBAJNUjAVnYnEIE9kZ/ilwWcYMPNblv0eyk5Rd1sIEZUKv1k5y8vlE/hhgCwkdEklqcY4nN7LV/ufZDo5fNdjtbou584sceTJca5cWqNYSpFMRpEjRZEJghDX9dA0BVmWcFyPMARFkQiDENcL0DQZ1/U5e3qRQjFFKmVy9WqZ/QeG0DQFSQhsx0PqTfyDXlZLVRUE4LgebGwTetG0qHchKuuQ0HUVRY0m+pblcvzYLJomE4/pDI/miSd0giDsSaVH66lqJHzj+T5KIMgrOu1Q4+nD07i9fVYqLcIQ0umoZMT1fAxDJQzBsd3NkoMNyfYwDPGDdYJgmSAoIMtZ/KCO5y8hSzlkqYC4RSl3EHQQQkGI21P0+zgQhgFh2MXzlwlxgRBJxJF6E88wtFCV6IHbl0/Sl7+zTHAY+nj+PJKURhKJXuZlY5mL688jSzkkEb9u2ScBRZYppROU0h//RDgMQ7zQp2JXyWkZ1F61Rde3qDo1fALGYlt/fkNCbN+h5jXIa1k08UFE2As8On4XUzawAwdD0lEeUB/nmyGh6Yxnshzs++jaBQ8Loky+QJEkVEmOXrKEKssRUdV1MoZBWjfJmtEXe188QSkeJ6lv3cf6UeAHXfywiUAQ1/bRdk/jhy0UKYUXtLD9BQCC0EKVsiAE0X/SZtWPLJlocpG0/gzoIapc6GVcJQTXB2iC0COm7UaRszSsN6h0fkxSO4Im9wE+ce0AqpxHFnEUKYsfNpGlGE5QwfGXkISGLG0dFddlmbFM+pG6nx50qLLEaDqDepdB9ocJsiSRM02+NDHBFycmuFytcGxpiWPLy1yorlNud2i5Dh3XxfKiap+Pm2ZIvZ7KmKoSVzUyhsFkLseRgUGeGBhkOpdDusdZLSEEGUPnc2NjzNY/mqiiqWxd8rmhhfJhJBI6Tz01SbXaZmw0f4PdzoeJo3SNYOt229xu/3ez7F6ca1mSGEgmGUgmeX7XOLbncWJ1lWPLS7y/usJ8o07Tduh6Ll3Xo+u52N79sRoURG1UuqxgKDK6omDIkR7LYCLJRDbLZC7PVC7HWDpD1vz4dVYunlvm4rkVrl5eY362TKPeJZdPMDndx9tvXOTrv3KEZz+/e0uRrdvBQzcTk4SEIWuk1A/KmyRE9DclzoCZY09qjKfyexg2izfNmAoEz5cOMx4f5M3KGd6pnmehs0bHt4krBmOxPp4p7ONIdpo+Y2sPV01SSapxEoqJepOJret6LC/V6HYd1lYbtNt2dMeFUdmEbXtU1lsMDmWJxTWWl2q4rk8mE8dxPOq1DgNDWcYniptZUMtymLtaptuxKRZTqJrM4kIVw1BJJk0sy6XbdRgYzOB5PivLdYIgJJ2OEQQBtVqH/v4MYRhSqbTJ5RKM7SqQzUXn1vcC1tdbfOZzM5x8f77nARpiWx6FQhLfD6ist+gbyCDLgsp6K2rAJ8rerq7UqdU6mKbG8mI1Uq7LJwmCgNWVBtO7++l2HebnK2iqwvhEieGRD85zu/vHNDvfIZ34iyTMX6Frv0Gl8XdIxX6dVOIvILi5GprjnUWWiqjK3QcePhpCwrCD7b5PrfVP8IMqYehiaAcxtCcJwhaud4lC+r+++z2EFpXm3yWufxVTfwZZ/uD8+UGNSuN/ImH+OqZ+FFncniLcwwg/9Cnb6/z7hT/g28PfpE8vIoSg5tR5vfIOS9YSf33qfw/Q+5IPewGEaN1Ve41X19/iq33Pk9Wi8yQQNLwmJ+qnmUlOsdBZYjIxTkZLEYYhAcFmn45EJEgXhCEhAWEYtSo8KNncQ/39HOrfIRv3E46/SN16ja57ASFk/KCDLg8SU2cod/6QSueHCKGhSjni6m4UkQQhIwkdkBFCJQw9Ws5xut4VwtCmlPgzxNRpBCqSdP2ExAtqVDo/wvYiZU1dGUYInYz5HPO1/42uexmApH6EXOwXkISOqUyx1PzHWO4VivFvEdf2bnksfYkE/9kzz/GfPfPcx3rOdnD/8CDau20FAUxkc0xkc/zavv20HIdz5TJvLy1ytrzGxUo18vr03M02lSBkM/ERhmFkbtU73q3Ih6BHujZ/flDRIUsCRUgkdY2JbI79pRIH+/o52N9P3ox97O0ruzJZ/t9f/8WPdR9bQQiBriv036bS7MMOSQhMVeXo0BBHh4YIwpCa1eVytcrZcpmz62XOrq8zW6/R7WVmP/zauM827rFr/30trv3Effi+2wjMXnsPSiLyBe5PJBhNZ9iVyTCWzkR6DbkcKe3+CFX+vb/zx6yXm+zZP8Rzn9/D3gNDDI8WkCTBn/zhe0hSNPa7xUNHYEt6hl8feZ5vDX32ur8L6F3cqERMER9ErW+FkViRQTPPLw8+SxAGPQeqiCzLvdd2+OXBZ/mF/qMIIVBvkuHSDZXRsQI/e+E0A4MZKustRkbzKIrMyffnyOUT7N43SF9fmtWVGpqm0GhYVKtr5PMJdu8dpK8/fV3DuyRJFEspjj41yQ/++DjttkUun8Sy3EiBrZBk995B8vkEr75ynjAE09RYWanTbtuk0zGuXFkjHjfYNV5kYDBDzPwgWxmEIZ22zZlTiwDMza6jqjKxuM7sbJlk0mRq9wD9/WnOnFrAslwMQwNCVpY7/OF33+XP/IVnqdc6aIbK+loT26lRLCXpdCOZ9bfeuIiiKliyS7ncuI7ARvAJwk6P/HUQ4vYjNa3u9zG0J+8rgbXdk1Qa/yvJ2LeJG19ACIMQHz9Yp2u/fp/G9elDx+9ysnGGmtNg2VolpSSJKSYDZh+Pp/exapc339v22jiBS91t4AYuBT1/3baqTp2QgLgSR5M0Clqeq+1ZRmLDxHpK5R2/y4q1StWpUzDyDJsDyMi0vTYr1hoBAYPmAAnlwRW728EnC0PZhRYfIiQK8gk0JKEBgmL8VwljLiEhklARaIxm/s8IoVGI/2rv/YKUcTSq6sCOSKswAImM+QUyfO66/alSnlL81whwANEjwiDQGc78dYLQ7n1n6r3srUxc28t49r/pfY/u2Og8ytigcw+b5kdCVTnU38+BvhJBEBGHpmOz0GxwpVZnqdlkvdNhvdthvdOh0u3Sdh1sz8PxfZwgiLJoYYgkSahS1G6wUbWR0g0KMZNSPMlgMiIMY5k0/YkkuqwgbxLbR8Vw8dGEADKGyaF+g8f6+nskNaDjeqy1W6y02qy0W6y0W6y226y0WlStbq8qwMf2PGzf6/2M9EAkSepVDEU/NUnG1FSSmkZS00nq+mblUD4WoxRP0BeP05dIkDNNNEneomXm/t2FBw6O8MrPz3Ly+Bz1Wpf1cpMDB0eY2Tt4T6w4PzKBrbt1/vnVf0LFqRKEH/QdCAGyUDBkg4JWZDw+zt7kPvJ64SPZ0EhCQhMS2j0s44sEYyS218zaHqqk3DTzugHDUNk1XuRHPzjBY4+PIEsSqysNhAAzpgOCWExFUSQWF6pUqx081yfsidqYMRVZFpTXmizOV2g1uqQzcSqVNu8du4oZ06Ly3p6ymyTLSJLAMFRkWULXFKqVNsmkSbGUQio3kSXByGieynobw1RQVfl6X94warzudmxcNyAe16Nt6QrFYpJWy94cs6Yp2LaHLMukMya1WodnnpvmzOlF6tV2VCrs+hhSFDkyDJVLl1ZJJE1c1yeVMjf7J66FLPcTBG0c9zR+UENVRjeX1Vv/Cts7TRA0kKUspv4MceNLeN48ze4f0LZewHZO0rFeRFf3kYz/Go57nlb3+6Tjv4mqDNO2XsD15ogbn0eSslQafwdNmcbzV/CCFUztSRKxbxGGDh3rBbr264ShhaKMEDeex9AObXvNfb+G5y8gSQni5peRRBIhJMIwIKAFoYPjnmGt9t8SBDUkKUsq/hvo6m4cb5aO9XNs5z0QIZqyh2Tsl1HkAWznfdrWj3G9WSQpiectEeqR8JNlv03b/imet4gQJp6/DkSfy3b3x3TsV/CDKpJQ0bWDJGN/CkkYNNq/g+eXEULD9a4ihEQ+9X9BiNhmqXa38/u02v+QMLxzteZrIdDI5v4+sjyE2KYv9U7R9S2utOf4ct/nWO6u0KcXiSlmLwglXzeRmO8ucq55EV3W0ITGO7XjPJd/CtFbVrbXGTT7yahpOl6HqlvDkA2+t/gDvt7/JQbNPk43znG5M8tMYpKqU2PI6KfhNXmzeowwDJCExNnmBb7a9zy6pD3wWYz7iSutCt+58h4vLJ3f/NveTD+/NvY4z5R23XL9MAzp+h7/7bt/RE6P8a3Rx9iTufN+mo8bQshISMBGkPCD/iyBRtj7Btr8mzB6yz74vhQoRDF7vbd+bztbfJVHn1sdube/a1suBDoC9bptAIShtNP7+jEhDF0Iu71WDpP7Leyw3K2wYlVQJZWRWJGQkIRiEoYhHd9m2apg+w5j8X4SihkJZfoWipA353CykG5r/vNJIbqVI8FEGYmNqnqjJ8o2nctHegvBBy8/DCKi26vIiTKzsEneRVTlJwkRtWcJCUUSmy0JmiyjyQqq9OBU3Ozg48dGZhRxffOGoUSBjtF0Bi8IcIMAL4h0Pvww+CAbe10VQPTvjWCREBsJOTY1QTay/Rv6IIokRUKoPbKrPID336/92Wf4yjcOsjhf4eL5FS5dWOHln55GkgTV9RYXzy2za6LE8NiNJei3g4/85PFDjyVriTV7jbAnlCKE2Cyxk5CYk+e42DrP6cYpnso9w77UfnT50YruCiGRTJn88rcO09eXJpuLY3Xd3rIom5pKmUiSYHKqH8f5wMJA0xWSSTNSfk0aPPXM1CZpHBjMoCpy9JCVBL4fqcRJkiAk6lOISoRbFAoJOp0oS3vkyXEAYjENa9glmTJvkDOPJ3S+/NUD6LoSyZIr0cdUkgS6rkZN/GkTSZJoNLqYZkRmra7L0acmSaVNOm0b1/U3qqUjAmyolPpSSD0LHN/z0TSFeOLGe0KVR3tluCcJQwdVmdhcZuhH0NQpQhxs5xgd+0XixleQ5QIx4wt07dfQtYPEjS8iS1kIQ8KwjecvEIZRFiQI6vj+KkFoI4U+rjeLLBUw9CNIIoYsFSD08byrtK2fkIx9CyEMutaL2M77KPIQilzc8poHYQs/aKLIfcjSB2U2QmyUm1qEYUjC/Dph6NPo/Ctc7wqyyOC4J/H8OZLxbwMhzfbvYrvThKGP5b5PENRJxn8dQg/LeZsw9PCDGpb7HoQOyfi3CYImVuM1CCNyq6kzSFKaEAfPX6TZ/nckzF8GoeP5ZfxgFUM73CPlopftvkYQKVjHc08Thh9VLELvnf970ynihz4tr81Sd5mpxDgXWpcZT4wxwNYkxglcDNmgzyiiCoV1p4IVWJSdCm9X3uPxzD4Gzf7eJE3gBC5Nt8WitYTlW0hIhIQ0nAbrdoXp5CRCCGY7C8x3FjarOJzAoWyvM2QO3JPj/KhwA5+Fdo1X165wrr5K3emiSjI5PcZ0qsTB3BCTqcInPq60avBEYQRDUak7Xb43e4KrrQot9/YDJUEYcKVVoe3ZdLx7a+V1LxFNMLb3Jfyo29juvcv1JueWyqiyzJFdg9ieh6mpdB2HRteOtBIUGcf1Gc6l8YOAtu30VMBlgjBEVxQ6jkvyUfRNDn0C9zSh9z5haCFEHMn8VYRQIbQJQwtQEDcj/0GZwH6dMJhHjv9VBPdPmwGg7rZxAo9+M8+KXeVE7TJf6X+CNatGw2tTcZq0XQs38NmdGuWd6jnW7BoJxSQmG5iyRp+RZSi29fffgwRZkpAlCeMjiLDdTwRhQMe3+Z2rL7HUrbA7NcTT+d2Mxh/8c/+oQRKiF9T49Pei3wr5QpJcLkGxL8XYRInDT46zttrgyqVVMrkEp96f5/LFVQaGshx5aoLPfGHPHW3/nn6aJxNTTCWmSavRZN0LPJpei/nuLJfaF1l3KgRhgC7p7Evvv5e7fuAhRCSetLennmaikUpt3WNSKCa37T8xTY2R0Q9KHnMfUnrbar0gCBifKKEokbBTLp+gUExuvm87GzFNi/pSr8V24xoYzGBZkRiTYagMDGYANpuzr10vDENSKXNTge5mvTaqMhxlQ/1FJCmLqowTBi0IfXx/GT+oAuD6S3jeAggQIoGu7UOSsmjKBKZ+tHceOlscZciHiZQiD6Grj6HI0WTeD2o43hVs9wS6uxdJxPD8ZRAqQdCAbQhspEMoEYZb2+IIoaLIfRjak4BEq/s9gqCFFyziuBew3VOo8ggQCVr5/hqeiBEE9Yik688QBC0kKdPrqSsTBE1kuZ+Y/gyev4IkUiCk3vlax/MXgADfX8PxLhHibh69LGXRlN3o2/S9Pahoex3K9jolo4gu6Qgh6HhdLN/G2CZQpksaKSWBLBR0KZpIBmGIFVhRGScSTa/NkrWMQDCeGOO9+slNVcPR2DACQde3eL9+ipJeoOt3SSlJ0mqKtJoCIYg/ICXEHc/h3fV5/mj+FKdq0TGZikoQBpyurTDbqhFTtPtCYFOqwZH8CLvTfVSdDi8tX7rjbWiSzG9OPokpqwzFM/d+kA85WpbNxdUKsiRwA59au8vhXYOEIZSbbYIwRJYkgiBgMJvCD0JWGy3WWx3CMApaekHAeqvD52Z2kY2bjxSJDf1lQv8qYegj5NFexlsiDC1C9xRhsIYQKVB2gVSEoApSGoEalYz3AqaIGKF7mo2qGIh0DAiqENoglxDik3lmhIQ4gUvFabDYXWexW8YLfHwC1u0GLa+LImTc0MMKHOpuCz8MKNt1ZNGk38gxJO0QqE8CXhiw0Knw45X3WOpWWexWKBmZHQK7gwceQhLEEwbxhMHgcBbX9dm9b5CDR3axtFhlcb5KrdJidbl+x9u+pwR22BzhmfyzDJlRz6Ef+rS9NlfbV3ix/DPOts5yrnWWktHHVHIaTbq/Ecj7jbtVV7vZ+7ZaTwjB9Ew/rZaFoavohnrXpQbbrTc8kqfTcRBAbAtFsWvX+7AC3c2V5FKE/iJ+0IiUdqUYXtDA8xfp2C8jiRSynCMMOh/K6gmiSULQ+9tG1kLplcAGUSlv0CEI29ftU5FLvd6yDYSAD6EXTViQ0NQ9qMoupJtE3CUpiSyl8fxlXG8RRe7rlcxGpFkIA1nObpbRbpQMRoTXJwzdXrZTYOrPoSpj1ygqi81XNJkSfEDGP1iGiJZ5QRnLeQsvWEdVRgmCdnQerin7l0QSScpsezwPKmpunRVrjadyR+g3SrT9Ni2vzZpdxpAN5roL1N0mF1qXGTT6CcKAutvkamceVVKxAgdNqCSVOLvio9TdBvPdRRJKnDV7nbVeSbEiFAQCN4jEzDRJo+42ONe8wPPFz1DUC9TdBqqkElNMDNmIiOx9RhiGnKwu852rx3mrPMu+TD9PF8coGAmcXlZWCEFKvT9VMbIkkZB04opGTFHvOHIthECTFb45euBjGuHDjyCIFOlBcGWtStdx2et66IoS2Vn5PnFdo96NPPkc32O53mKhUkfteTDXO126rofr33+Lkk8aEcmsI4SGpB7pZVplwqBJ6J0n9JdA6gcphRAZQu8ySCmQxwj9WQgthDyMUCaI6lrF5nZD9yxhsARI4F1E1j8HqNeVGIeEuIHPmlVn2apSdzp0fQevFxxVhIwmKcQVnbQWp6CnyKhxdHn7xqicltzcdlZN8FhmAl1WKWhpHN/FDtwo26oYqEJhKjGEJCSaXhc/9CloabLqJ1tu3nItur6DqagklI9fRfWBwTVCQA8CLN9hobvO8eqVG5aJnqihKsnR/ajG6Tezt7wfd/DphxACTVMYHM4xOJwjCEIq5SZzV8ubFZ53go+1nkIWMik1xWOZxwkJqLpVLrcvsWIt03DrFPSd6NEnASEEiiKTyXx8xvSyLG3aAt1biGjSIBJIm5HpEC9Yw/eraPoMmjqF5y/jB/Vr14r6Q/0VLOc9ZCnfK+XNEuLjuGcJwjauP98r/7rJCISJqoyjqfswtCeQpARhGKLIxajEeBtIIokqjyBJcTrWT9DUSUDrlRP7fEA0r4csZVGVSfSghaEfZSOLq6kTEAbY4jiOdxnLeZcw9PHDBiE+spRHiBi+v4zlHCMImgRhC8KAIKjhBRUkEUdX90XCLSJ2Qx/Wg9VBcbsQxJQY+1K70aQoOLPQXcTybezAoem2yGkZFrpL5LVspKQd2KzZNgklQU7LEFNi7IqPciT7OGebF7B9m4yaJq/lWLXKzHcX6TOKmLKBF3qs21UWu8vIksTe5AyKJDNsDrJirbFqr9H0WmTUNCPmEPJ97ktpuBY/X77AW+VZZtIl/uM9n+Xx3ODmcjfwcQL/Ib32O7gdxHSN4VwKXYn8bg1NoZiMzOq7jkvbdiml4nQcFwT4QYgqS/SlE2R7Xtxty8ZQVdLmR7MYmu3M0XAbN20gMCWdnJ4jp22t/n+78EOf880LeOHNrS2SSpyCXiChbE3IhFwglNKE/iyB+z5C7kMok73npwTCBDkLIk3PpZ7AeRXZSBO6J0DIiGv0GzYQ+ssEzqsQ1EHKErqnkdR9IJU2bZH8MKDmtLnQWuK96mVO1WdZ6FaoO22sIGpD0iWVhGKQ05IMx/JMJvs5kp1iMtGHqWwdmCoZWYp6pucjvNGLLTDliHTA9cHl3erofVUmdgOPc80FLrdXmEoOcDAz/omP4X5BkWSGYzk+X9zPolXhsfQYE/H71+ff9izeqVzi75797g3LBAJFkonJGjktyVAsz/70CPvSo0zE+8hqiQeuV3MH9weSJCiUUhRKdxfo/8QaAiYSU6TVDABWYFF3a1sS2CAMcAIHO7BwA48gDHqCUDKq0NBlHVVSb1DGC8OAjt+l47fRJJ2UksINXSy/ixu4BATRB0so6LKBIfUEMq6NcoYhXuhSdarIQiGlphAI7MDCDmz8XrRTFjK6pKNLxmbP73bYPB7fwg1v/3g24AQOTbdJgE9WzSELuXd+bNzAIeiVIknIaJKGKZsPpU/lVpCkJJKIoyrDyFIWWcoiiTiylEFTppGkJB37RWz3PcLQQ1evLX2VMPWn6NqvUmudw9SeIZ348yjKMLoyQ6v7RwhhIISCqoxHvUxCQpH7kITJtVROEgaqMk4i9g2ane9s9s/Gza+SML8BbB05EkJCU2dIx/8itdY/pW39EBAY2lEM7SCSSES9uT3IUg5JiiNJGQztIH5Qpdb8R5tjySb/IzR1D7p6ANdfoNr8bRQ5j6ZMIYs0slzA0B6n3f0RtdZvI0s5dPUAkpREVUZR5AKW/Q6ev4gQGqb+ZE/IBWQpHfW83iNRpU8So7EhRmNDm7/3GyX6jQ9K36cS10905joLjMQGGTQGIvXgntVWnxE9j47mDm++t88osS81gyyiPvONz+ne1DS7k5OEhJueswBP54/ghZH4miLJD4SC5+naCqdqy6RUgy8PzFxHXoFNj9QPw/LdyNfO93BDPyolFZF4SVLVMWTlBkG+IAxpOF26vktaM5GFRNuzIwuLMEASgpiikVSNj+xXafseTdeifU3Pqy4pZDQTYxtPQwA/CLB8l7bn4AR+9EwmUg015CiLpcufjmfoBoZzaYZzH/ThR77p0fffTH9hs25jMJtCAPlEjM/M7LpuGxuWDxvr3S2+t/CHvFl9i60NJCKMmMN8qe+LfKn0/F3vB6Lvz79/8R9Qc29envZYej+/OPAN9qW2bp8QUhrJ+CKhe47A/jmBvYiS+K96WdYSQpgIeQJJGYEwBPUIofV9Qn+VMGwgRB9CyhH6leu2G3qXwS8Thl1E6IHQCf0VhJQHZMIwpOq0eHH1FL8z+xJznbWeR72CImQSPVV0PwxpeF3WnSZnmvP8eOU4vzHaJq09y8g2BBY2hGi2rtra7v33C1WnzU9X3+e18ll+aejoI0VgJSGRVGP8JzOfvF3O7UCXVAxZ3XTsCMIQPwxY6la40l7hpbVT7E0N8ytDT/GlvsdJqI9Q9nwHHxs+sW9pUzZRepPjIAw2yeC1CMOQltfifOscr6+/xuX2JVpeE1nI5PUCu5N7eCL7JNOJmc11Nh6oTuDw07Uf88dLf8S+1AH+6uR/zKX2RV4uv8iF1nmabhNd0hmJjfBk9imeyj2DKqnXKX/5oc9sZ5b/4cx/z5AxzH84/ldQJJW3q2/yXu1d1qw1QkKKepEj2Sd4KvcMRT2aKG/1JbBxPGebZ3ij8jqX25doey1kIVPQi+xJ7uVI9kmmElM3HM8GLrUu8q9m/wVlp8z/bd//g7xW4FzrLG9W3uBC6xx1t46ETEbLcCD9GM8Xv0Sf8eApcN4phBAkY9+66XuKmf/7TZenYt8mFfv2dX+ThEE+/V9tu04p+z9u+XdZSpI0v0nS/OZN9/lhSFIcUz+62Yd7M+RS/8drfiuQSfwWJH7rhvcZ+hEM/ciW2zD1ZzD1Z7Zclkn8ZUj85S2XpRO/ecvxfVogSwpKoKBI8iZ53Q6RDP2Nj8kN5fKtoPTI7oOCM/UVFjt1JlMFnizcmAXaDscrC/x48Rzvrs8z167R9mySqs50qsRvTh3laGGUlGpc98xyAo9/cuF1Xlg8z/9p//Nk9Ri/f/V9frZ8nnW7TULV+YWhvfzW1FPsSuZvsvdb42x9lX9+4U1+f/Y4EOW9DueH+Rv7v7itenEYhlScDq+sXOIP505xqrZEzeliyApFI8mzpXG+OXqAw/mbW2992AJgOzJ2J+Tgk8S1GdRNNU1uftte+76PhF4g6GYE9l6i5574kfYXXW8ToT6OLOXxu/+e0J9FSLuJtA5CoraUjWymipCHCZzXIiV3eaS3jU0HyOh3oSPUvUjKBEI9yIfvGD8MeHH1FP/s8gus2DUkBAUtxeHcBCOxAnHFwA99qk6b+U6ZM415ynYTXVaZSPQzaGT5tGC+U2a5W7vfw9jBFjicm+Dp/AxFPY0fBnR9mzW7wdnGPO9WL9H2bE435vHCqI3hm0NHH4jn4A4ebnxiBLblNXF75S6qpBGTbyxnvdq5wkvln/NO7R0s3yIIA+JKDNu3WbVWWLfLnG+e44nsk3x94BeRt8h8OYHDojXP6+uv8v2lP6DpNVElFU3S6AZdzjbPMt+d52L7Ar8y+KtktK0f8OvOOsfr7zHbvsrF9gUADNmg63dZspb44coPuNy+zJf7vsq+1NaCVFfal3mx/HOO1d+95nji2L7FirVM2S5zrnWWJ7NH+Vr/17c8ng2EhNTdOq+uv8Lx2jFW7dXNIIATOqzaNuvOIG7w4Cpw7mAHDwKmE+ObiumPApY7DRquRVaLMRi/fdP5Hy6e5XhlkZwe42BuCE1WWO42eGnlEv/jez/kvz74NZ4tjWNuke2su11+sHAGPwyo2h2+OrgHVZKZ61TJarF7IgA0nszzl2ee4UuDM1SdDv/z+z+55TqXWxX++YU3+NHiWUxZ5bnSBP2xJLbvcam5HnXO30afmRVYLHQXON04w5K1zLpdoe23cQMXgUCVVEzZJKOmyWl5hsxBRmMj9BklDPnjaLV4ePDZwnOMxUfpeG06XpeO36Xtd7jUukTX795TYqsKlV8b/hY1p07H79DxOnT8LlW3ytX2LF7o3XojQOidJXTeIuyJ4IWhhVB2ARqSPITvHidw3kTSn0M2vgFISMZX8Rt/G2F8BSEXCd238K0fEnrn8dv/GMn4BpKyh8BfwO/+PsL6CUKKI8f/MvQqYy61VzhRv0rZaZBUTJ4t7OYvTXyJjBpH2aj+CiGgZ9UR+sy21yjbDfamRj6SZeGDhrlOmRWrdr+HsYMt0GdkOJgZj0SlepUafhjgBh5nmvP844s/5mxzgSvtFV5fP8tninvJ6zfaJu5gB3eCT4zAnm6cpuJUkIVCWkmT16/vHVy1V3l1/RXerb5DTDb5QuH5SOhJaAQErFqrHKu9w4XWed6pvkVGzfBc4bM3RLh9fMp2mT9Z+SOmkjPsS+0n0ytdXnfWOVF/nzerb/Bu7R1GY2McyTxBWsvcMF47sHi5/BKjsTF+aeCbDJsjyELGDmzONs/w8vpLnG+dI6flSCkpRmLXZzZWrRVeWX+J92rvElfiPF/4IpPJaTSh9o5nhXdqb3OpdZG3q2+RVjM8l//MtufPDz1+svJDVu1VBs0hPl/8IgW9gCLkTVJd0IskHwDRmIcNYWgTBFV8f4XAXyMIqwRBjSCoE4YtwrDTE4myCEMP8AD/muy90hNiUkAoCGEgiThCxBFSHEkkECKJJOeRpDySVECSskhSjAcqVfeI4FETj2t5Nn4YEFNU9F57QRAG/K+nXuRUbZlurwT38fwQvzX1FAUj6gP8U2MH+cXh/ZiySkzRkIWg5nQZi+f4Fxff5FRtmZl0iWElc8M+nSDgVG2JXxzZz3OlCTKaiSwEXd9Fl1WKxkfvx48rKuPJPIPxNG3X4X879fObvj8IA/5w7gQvrlxiOlXiz0wcYSpZwFBU/DCg7TroskJe335sLa/NueY53q6+w1xnnpbXwgpsnMDBD/xN8iWEQEZGlVRUScWQDWKySUHLsz+9ny8UP9fz+bv9z/+ytcLZ5jkADmUeJ6EkN0v2HiZMJ6YYjY3gh37vFeCFHv/w0j9isbt026TydiALmcOZQ3iht7kvP/S52r7Kv5r7HRpu87a2I+RhhB4jDNtEKWQVRCLK7MpDyMYvE4YdxGZLiECIBEIZQcgDIOURwkSOZcH4Okg5hFQCoSPpn0eoj0VrCZ1rp2WLnXWWu1X8MCCnJ/n26HOMmAVksbXnYxiGpNQYju9iyJ8e/2kv8DcJbGqn/PSBgyJkdFnFlG/8bj0kT/DZ4iJNr8tcp8yqVedCc5G8vvs+jHQHnyZ8LAQ2MueNej/rbp2r7Su8sv4SZWeNQWOQicTkZg/qBk7U3+dM8zSmYnI09zTP5T9DXitsfskPm8Poso4bulxsXeD1yus8kTuKIRlbPqTjcoLP5D/LSGwUXYp6QIb9LgklSdWpcr51lrerbzIW27UlgQ0J0SWN/akDHM4eJqlE/bBBGFDUizTcBsfr73GueZZhc+QGAvt+/Thnm2eIKTGeyj3Ns/nnyGn5zeMZMofRJA03cLncvsSbldc5mnsKla0Vgv3Q51zrHE/lnuFQ5jDD5ggxJYaEhBe6jMXHUYWyZWZ7BxHC0CcMW3jeLIG/hB8s4/vLBP46QVgnDFqEYZsw7PZeFmFoE+JC6EZG9JvKxtcqcUqbLyEkQO311GoItJ5gUiSaJEQcIWJIUhwhpZGlIpLchyz3I0l9yHJf7z2PRnbwYYDj+ZxfLfOHJ87SsiOyN1nI8ZnJMaZKH60M9pOA38sobpihb0CVJDRZZqnbZaFdR5MVbP8D8jCdKvVKqD9Yp2AkcHyff3P5HZa7DZru1gJoYRgykshyOD/MgezARxL92Q6SkNBlCV1WUIV8y30sd5u8X11ClSSeLY3zmdI48TtQXq46Vd6uvsOblbeZ68zR9rey5Yqw4YPu+i74UO/1YNbcOkXjzsUL3cDlSvsqP175CYZsMB7fRUyOP5QENqbEiHG9VcyGvd69vkside0bg7pdv3tHFRhCSoCIITayw9esKzBAHt0ce6QsfIbAPY5QH0coE5F6vNAQW6m8y30IqQiEkeXZNWeh7dl0/eiZo0squ+KlbcnrxvGasrYlkfgw2p7V61FcY7lbpeq2cHqff01WSCsxBswsk8kBxhN9vWPd/go13S5/sPgms+01DmUneL50AFWSaXs2J+uzXGotU3VaOIGHImRSaowBM8eB9ChFI31dT3zNaXO6MUfZbrBuN6k4Td6uXKLtR8G4n6+eZLlb3XIcu+J9fHvkWZSbtIcEYUDZbnC6Mc9cu0yt54krC4mUGmPYzHEgs4uCnkS9haaI5Tscq17mZ6snSKomvzX+JUxZxwlcrrRXOddcZKlb6flTh8QUnZKeYXdqiNFYkYR6Y0VGEIb8eOU93qlc3HKfo/EiR3PTTCUfDI/xmyGm6OxODfFm5QJznTId32b5Fpn0IAzp+jYn63Ncaa+wbjfp+pHLRVwx6DMyTCcHGU+UokDNbTw5up7NXKfMueYiq1adptfFDTwEAkNWSaoxCnqKYTPPrkSJuGLc8vnqBh4rVo0zjXnmOmUabhcv8NEkhYyWYCRWYCrZz1Asf9MxhmFI0+3yu3OvsGbXea6wh6cLkfZGw+1wsj7L5dYKNbeDF3iokkJajTEcK7AvPUJRT21WW5xrLPBm5QLznTJxxeDPjX2OjJa45bE03A6vls9yrHqJhGLwWGYXnynu3XK9IAzoeDYn6rNcba+y7rSwetcnoZj0m9H12RUvocvba/x8VNxTAnu1c4UX135OSk0REOIGDk23wXx3noXuPGk1w+HsE+xO7rnuAWz5Fudb5yjba+xLHeBQ+vBmb+kG4kqCyfgk8505TjVOsmgtULbXGDAGUMT1JWyGbHAg/RgjsdHryrViSoyx2BiHMoe52D7P1c5VKk6FkWD0OiGWDYzHJ5lITG6KT0EU0e0z+jmUOcRcd5Zla4lFaxHbt9FlnTCMFE7Ptc5Sdso8lj7IwcyhGwSrEkqCycQUs51ZzjRPs2gtUrbLlPQSyhY9dwJBTI5xOHOEicTkdVkkVWjktQd/Iv3JI1Lf9f1FPG8hIq3+Ir6/hB+sEvir+MEaYdAgyqreLfzeK9LvgC63rkIUCGEiSbkoI9tTNJbkErLUjyT3I8sDyHIfklQgIsefjmj6wwYvCJivNfju8dOUWxFpeW5ilMli7qEgsGqvXNcLA4IwROoJtzw/MM2h/DCvrF7iRwvnbliv67tcbVVYaNeoO10s38MOPKp2h67v0vVdvGB7S5WZVInhePZjIa9b4VYfufl2jYrdpt9MMZUq3BF5dQOXY7XjvFR+havtWQICJCQyWoY+vURSTaJLOpKQCMMAJ3Tp+h1aXpuKU6XpNvFCj4Qcp6QX77i0s+E2me/Oc7UzS1pN99pxHgxLjUcGN7tm1y4LRY+s5qLM6u14pW6zbVWSN4mYH/pU7TaxmP6RJ4Tnm4scq17mTGOehc46q3adhtuJCKwATVJIKCYlI81UYoCj+WmeLexGl7a34Ov6Dq+sneGd6kX8MOBQZhdW4PKz1RO8V73C1c4adaeN2yOKSdVkJFakoCfJ6QnUa1qolrpVfnf2FcpOg5rTjtbrtUx1fJszjXnONOa3HMfR3DS/NrK1DkQYhrQ9i/frV3lr/QJnmwssdSvU3Q5u4CMLiYRq0G9kOVGf5cn8NPtTI+RuUu7qBD4Xmkt8d+ENCnqKXxl6iq7s8Pb6Bd6qXIgIk12PAhFhiClr5PUUvzL8FHk9uSWBhZALzSV+snIcJ/Bwr6nuADiSnWA0VngoCCxASo1h9Cx0/DDA8rdvdbN8h9n2Gq+Uz3CyPstcp0zVaWH5bmTTqOgU9BTj8T4OZcc5mp9mwMxuG5AKw5Alq8o7lYu8U7nI5fYKZbtB27MjAivEpop3Vk8wYGT5bHEvT+VnyOvbVzSu201O1q/yVuUCZxuLLFkVWq6FF/qoQiatxRk0c8wkB3kiN8nR/DSqULaunCCk4zu8sPo+l1rLJBSDmdQQdafDi2snOV67wmynTNPt4AU+ihQFgKaSAxT1FHktidTbrB14nG8u8sPlY6TUGPvTozydnyF2EzE3gCvtVX62eoKfrZ5gyMwzlRzc8n1dz+ZKe5VXy2c4WZ9jvlum6rSxfRchIC4b0fVJ9HEkO8GT+Wn6jMzHEmy9pwT2cvsSV9qXN5XtJCGhCpWYEmcsNs7+1H4OZY/cIDJUc2tUnQp2YGP5XWY7s6zaqzds3wkcqm4UdfMCl1V7laJeQuF68qlKGrti41ve0IZsMJGYQEKi63epuTUs39qSwA6YgySVrR9co/FdJJQETuDQcBs0vQa6XOwdT5WKU8EJnOh42ldZsVa2PJ6aW7vmeJbJa3mULS6LJCR2xcfJ6/lHrgTy9hEShgFhUOuR1GU87wqeexbXO4vvXSUI1u73IHsICcMOvt/B9+fB3fi7hCTlkZVdKMpE9JLHe1naEpJUiBQvH8LMy0fB/bRveNiRUg1UIdPxHLq+Q1zREUKwN9MPwLrd5o212c33B2HYs965yNvlWdatNj7BprJkx3M2MzU3tSZRDWKfoO/fre6MhmvhBj4JRSdxh563S9Yyb1ffZrYzR0CAIRmMxkbYl9rLrvgYeT1PTDaRhUIQ+liBTdNtUHFrLHeXWbFWWXfWGYmNMH2NaN/tYt1ZZ8W68TtxBw8ehNAR6j5Q933kbRWNNDktKulvuB1eWjvFLw09SUIxPlJ/65XWKj9aPsbpxjwJxSCjJSjoKTRJIQgjle6K0+JsY57zzUXONxcp6Emmk4MYt5Hd7Xg255qLnGrM8fvzb6BLCgnVJBOPEYQhlu/0SkrXtszQCAGarNJnZOgzMoRhyPnmEmt2HUOK/j4c39rCbiY5iLTN06DpdTlWvcS/n3uNd6uXUIRMwUgykehHlWT8IKDldbnUWuZ0fZ7L7RU6g0c5mp/evA43gx8GLHerzHXW+f7iW6xYNWKKzoCRRRISTuDS8iyWrepmJnorCAT70iNYfvTM7vpOZF1TvYQb3LsS+08KduBtBjtlIW17D1m+w6XWCn+w+CZ/svQOQRhGWdFYAU1SIqLn2axadS61ljnTmKfhdflS32MMm4Ut5wd1t80b6+f4d3OvcqW1QkI1KelphmJ5FCFv3u8tz2Khs87ZxgJ9RoYDmV1sF56uO21eXz/L9xff5mR9Fk1S6DMyDBg5ZCHhBh41t83p+hxnGvOcby4hCcGh7CTGTYJAG2h6Fqfrc7xXu8IPlt5FkxVSSoy8ltwUyWq5XZY6VUzl+nM5Eiswkxzk56snsX2XF9dO8lhmDFPZPlMdhiFnGvNcaa+iCJmSkeZQdvyGz1HXdzjfWuIPF97kB8vHACjpaUZjBVRJ2QwQrVg1LrSWONOYp+VZfKF0gEEzd8/nb/eUwKbVNBk1g9aLRGuSRkJJ0G8MsCe5j0FzcEsBi6qzjhPYAJxtnuF868ZswHWDFgqyULB9i5AbMwBRGUhqywe8IhSyanbzQra9FnZgk+RGohqX42hbEFuAtJLeJJJOYNPyWptZ1oqzvimmdLpxirPNM7dxPDKWb99UzbKgF1HFjhH0hxGGQVTyG9TwgzKeewLbfgnHeRvfX+EadvgQICAI1gicNVznTaJMbQxVO4SmPYmqHkJRxno9tCmE+HT0A9meR9NxkASkdeM6xdMgDLE9D9v3SWgaSi+j6PcIVRBEnxpJCPwgwFRVRG+5gHsiGPQwY8BMkdYMqnaHhXadmXTppu/3woBj6wv8vdM/RxEyn+2f4In8CCUziSrJrHSb/K23vnfL/cofKj/+uHGrfKQqSUgIvDDAv0nmeCucqJ9kxYqE82QhM2QO8suDv8jBzOPbr2R+EMFue23mOvNIQmLAvPOsSdkubxkE3cGnG6OxIuOJPt6uXKTqtPj9hdfpMzNMJQbI6UlMWburzMZIvMB0cpAQ2BUvsTs5xHCsQEo18UKfdbvJ+7WrvLF+jsu9MtgfLr9HUU/Tb96awK7adX6+epK3qhfo09McyU0ykegnrcXwAp+y3WC2vUbNbTMR70f7UJnuZGKAv7nv25vzIdf3+P+e+x4/WXmfrJbg+b4D/MbY57bctyrkLcuHncDjbGOBfzv7Mu9ULxGTdfanR3gqP8NUcpCEYmD5DnOdNV5ei7J/71YuEYQhqiTz2eK+G8a51T7erV7ihdX3IYQjuUn2pobpM7OovXLQuU6Zy60VZhJDZLWtW76EEHyhdIAvlA4AUQ/wYrfCf/LW/4+q07rl+X+Q4IcBs70ybYCEYjBk3ujvHIYhS90qP1w+xvcW3kAVCntSwzyT381Eop+UahIQsNSp8lblAm9XL7JkVfmDhTcwZY1fGkwSV27kF+ebS7yydobLrWVSaoznins5mpumZKQxJQ0n9KjYTeY765xvLnKlvcrT+Rny2o2cILISCzlWu8J3F97gdH2evJ7ksfQYzxR2U9TTGLJK0+tytrHQy84u8H7tCv/oos3/dV+G0XgRdYsqy2ux2FnnJ95xTtRnGTLzHMlNsCveR1I1cQKXVavO1XaUjBmLla57BmS0OLviJQZjOa62VnmrcoGq0yKjxbcNmHR9m4vNJZa7lc3Mbp+Rue49QRgy3ynzg6V3+f7S2+iSyu7UMM/md7MrUSKpmPhhwEJ3nbfWo+uz0Fnn9+ZfJ6bofK3/8C2zwHeKe0pgD2ee4PnSFxkyb25B8GHYvrOp/JhUkmS1LLeKpxuygSnHEGz18Bbb2mNEJstKFOILo7KcMNx6MiMJaduIhSxkJDY8r663BbrueNQUWTVzy+OJKTFM2bxpaZAqbh25eZQQXTeXIGjiuu9jdb+PZf+EwF+630O7hwgJwzaO/TKO/TKgoShT6MbzGMZXUdV9UX8VKtyhKMyDhPlGg59fvYIuy3x9ehpNklGV6PHUdmzm6nXm6g0ODwzQl4j6MOuWRceNvEpdP8BUFdqOy95iEQE0bBtFksiZ5kN7Xu4FptNFBmJpFjp13qssMJks3JTUO77HH8+fpGy1+T/s+xzfGn2MXE/YqOu5rFvth/J8lowkpqKxbrdZs1qb5dS3gyvtK7S8aOKYUpLsSe2+OXn9EOJKnD2puxMtCcKAslNh1X5Qqkd28Ekhryc5kp3kQnOJN9bPM9cp89+9/6/5bGk/X+57jD2pkc2JqXKT3tgPYzo5yF8cf54ghAFzayeGo/lpJhJ9/N1z36PrOby1fp5fGXrqtrZ/uj7H1fYae9Mj/Oe7f4VBM3cDqdwgA1uJmamSTFr6oE/altzNXlRJCHRZI6Pemd7HSrfKG+vneKeXeT2UHeevz/wSI7HCdcmOw9kJfmHgCf7nM9/h5bXTHKteoqinmUz0Mxa/efCv7Vn88ys/o2Rk+KuTX+OZwm6SW4hObXhP3+71EiLq03wYnrob1VIBUYC55rR4vXyW+U4ZTVLoN7LsSd/IEezA43jtCn+w8CYyEntSQ/zNfX+aoVjuOoJ2OAtfGTjEb1/8AX+0+DaL3SpvVy4ynRzkcHbihu3OdcrMdcqoksJkop+/ue/byNu0ZAVhQMVuktES2/ZQtz2b786/wZkeef3GwBP81viX0D7kHf5sYQ+HshP8iys/5ZXyGU435nmlfIaUGqNo3NwN4N3qJRKqyRO5Kf7a1Ne3zF5ufH62StT1mzkOZye43Ir6h880FijqaTLbBEzONRdZ6FawA48pM8OR7OQN73ECl3crl/jjpXdRhcK+1Ah/a/9vUDSuTxYeYZIv9R3kty/+gD9eeicitJULTCT6eTyz66bHfad4INzaNVnbJItP5I7yq0N/6jY+qAJVqFuWCYdhiOM7PZ+1Dy0jxAmczQ+ZKqlI20QlvMDF34bcuqGD3+t7lIR8XWZUk7TNPrOnck/zzcFvfaTj2cHWCIJ1LOtPsLrfw3HeJww7PFzZ1ruBg+edxWtdpNv5HRRlGtP8ZQzza0hSH/BwZuj9IMD2PBzP4+XZWbKmyUgqzXKrScd1USWZhWaDrufxpfFxXpq9yplymf5Egqlc9HDvuA5CCMIw5OW5WY4tLzOVy/H1qWlM9eE8L/cCBzKD7E6XeL+6yB/Nn+Jgbug2srDRl2NKNa4ThOn6Dn8yf7onSPJwYTJZYCiW5sXli7yxdpWnimOUzNuzcmh6LZyeDVxKTdH/CXptrzsV1uw1un73E9vnDh4cHMqOo0kKmqTw09UTuKHPi6snea18hn4jy8HsOM8V9nA4O05siwzUVpAQFPWbT6Jjss5wrMhUYoDjtSss9cpebwcBIWPxIn954ssMxfLblvR+XOIuW+F8a4m3eqJIcUXnr0x+lQEju+UYdEnhN3c9z1xnjfdrs5xrLvB6+fwtCSyAF/r81vgXeTI/RWKb6/FJHvcnjZCQru+wZjc4U5/je4tvcr6xSNd3OJQd58v9jxPfohLzUmuZ92tX6Pg2WS3BX536OiUjveW9owiZXx95jrfWL1BxWlxurXC6Mb8lgd3ARlvjzc69QJDTk9uX2gKvls+w2F3HDX2OZCf5hYHD1wmQXYuZ5CBf6T/IK+WoCvPV9bORX+4tCGxAyL7UCH9+7PPbBpg2xrsV+o0MhzMTfGfuNUJC3qqcZ196ZFsC+07lEsvdKhKCATO3JdE831zkZGOWrm/Tb2T5q1NfJ6snthyDIav82dHP8vr6ORpuh4vNZc41Fz+dBLagFdAkjZAQ27ewfIucdmOJwe3CD30qboUB88YmZDdwWbfX2Sg4SyjJbX35Gl5j2/LimlPD6TWiG7JOUv3gPQX9g+OxfAvbt3tZ5R18VIShR+CX6Xa/h23/EM+7QhBUCMMuj46oSSQaFQQOjvMenneVTvffoWufxTC+hqrtR4iHy2ty48rpqort+5xZWyOp6YQhtF0XVfIpxGLkzBiO72MoKhPZLAPJJLqiUO502JXJcLZcxg0CTEVlLJ1hNJ1Bkx/toJChqHxlcDerVosXFs/xt97+HkcLYxTNBLbn8e76PFdbFR7LRs9LRZI4mBvktdUrfHf2BE3XYjieoWx1OFFd5EprnWCbwN6dIuo5s6nYbTqeS8Xu0PEcbOFxsVmmrxqVSSYVjZKZ3IxC+2FA07Go9ASl6k4XN/BpuTbnG6sk1EiJNanqmyRVlWS+OXqAdbvNT5bOsdJtcrQ4SslIYvkey90GfhjybGkXz5R2XTfOa71hBWKz+uZewg1cqk6N+e4CZbtM2S6z5pSpOFXKdnnzfS2vxT+89I/QpJurbx7MPM6z+WcYMPtvewxhGOKFHhdbl7jUvsxid4maW8P2oxYfUzYo6AWGzWFmktP0G31RRdMOgEjXomyvc6l1iSVriTV7nZbXioLmhGiSRkyOkdfzjJhDTCWnKGj5LTU4NqAImd2pIf53k1/jqfwMP155j9P1OTq+w1ynzLrT5O3KBfqMDE/nd/Olvscp6MmbqvBu6JTcDBtZv2yv99MJPLzAIwiDW/bfJhWTiUQ/M8nBbUucP8kqDi/wWepWmeusYcgaM6khBs0cqnSjqM7G74NmjvF4P1fbZZatGhdbS/hhcNOSbVlIDJo59qdHSamxm6pFf5qwoZisywphGBEwL/Dp+jZVp00QBjydn+GXh57kSG5yy+Nf6K5zub2CLCRyaoLp5ADaFtcHorxUTkuS0eKoQmbdabDcrWw5tpKRpmSkN0u3f/vCD/jFoSfpNzI3KEzf6nMREvJe7QoNt4MpaQyaWQbM7LbX05A18lqKpGLQ9Cwut5bp9J6lN0NajTGZHGAy0b/tZ+1m95AhqwyaOfakhjjTmOfd6iW+MfAEu2Kl66qvwjDEClxO1WdZd5r0m1mmEgNbVg3Mdspcba+hCJm8lmAqOYC64UX9IUgI8nqKjBpnQcis2XVWPwYP5wfimyejZihoRealOZasJS707GLuFk5gc655lt2JPTcIHnX9DudaZwkIiMsJMmpmWwI725llX2o/Bf1GsYCL7Ys0vAa6pJNWMySuEXvKqlkKWpHF7iJL1iIXWuc5mru90psdbI0wDPD9JRz751jWj/C8c/j+PGF464fBpxch0CUIugTOKoG/guO8i64/g2F+DVU9cL8HeNsYTCb56uQUsiRFfqGuSyEej+xYvHRkvUhk7xBXVQ719+MHAUav33UgkSRtGMRUlZiqsrdYZDybJaFpn2gf5oMISQim0yV+Y/wweT3GG2uz/GTpHLKQUKXIfmZ/ZoDnSuMkVQNVkvnSwAyLnQbHKvN8d/YEhqwSVzSG4xn+ysyz/C+nfr7pKftRUHctXlq5xHeuHMMLA2zfY7nbRAC/d/U4L65cRJcURhMZ/tbBr6P0rmXTtfnh4lm+P3cSLwxwAp+ma2P7Hr9z+V3+ZOEMqpDZl+njv3z8K0D0hX8wN8RfmnqKny1f4HRtmT+cO0kIKEJCkxX2Zfo3VZuvRUwxUYSCF3p0/A4Vd2sLj492Luq8WzvGq+uv0fUtLL9L17c2yc8G/NBnyVq+5fYGzQGc4PYz5bbvcKVzZdMmqOJU6fhtbN/ZbJFRhIwhm5xWznKs9h67kzMcyj7OkDl05wf8KcKavcb55kUutC6wbK1Qdxt0/DZd38IN3M3ztyFsacgGJ5U4+erb7E/t52D6AP3bBBoiIqkxGi+S0eJMJPqZ66xFIkm9Ut0Nj9TlbpUT9at8Y+AIj2d2kd4m4wLR5LXhRt6cs51VylaTttelGzg4gY8XeFSdNpdaH9xrwW0GidNqjCEzd1uCT58EGm6HSs+KJaPGGY/3bUuONqBKCoNmjpRistBdp+JEdj43y1wrQmai16v4MFpc3S1qTpua095y2US8jy+UDvBEborxRB8JZWvdjrLdYLlbIwhDVu06/8PJ370l0b/QjIIKnu/3VIX9G7KhM8khDmcnudhapua2+ZPldznbXGAmOcTe1DDTqUH6jPRtVz9ukFA/DPjp6gkut28urlfrKSgDtFwLy3dvGQjJaUkGjCz6XYogSkIipyd4Oj/D2cYCVafFheYSk4n+67K/ASGn6nOsWDWcwGMsXmJvamTLsa1ZEQn1w4D5boX/58l/c8txzLbXeiJZ0fXZUFC+V3ggCKwm6+xP7WfZWmbFXuHNyhvoksF4fIKEkkASEm7gYvsWdbdOza2hSRqTiaktoxNWYHGy/j7TiWlmkruJyXGEELS9Fudb53m39i5BGDCVmCan5be9ca+0L3G6cYqEkqSgR30Sfuiz0Fng3erbVJ0q/cYAI+bIZgRVCBEdT/oxVuwVlq0l3qq+gS7rjMfGSShRFsENXKze8dTdGrqkM5mY+tRF5u4FfH8d1z2Gbf0Mx3kD1z3FR7O9+TTC71kELSOEhq5vLXDxoCKp6yT1Dxr8r1Ud3kqB2FTVzb8DZIwoCBVXo4dzPhb71CgXe37ASqPJQq3BSrNFtdOlZTs4fjQpViUJU1NJGwalVILRbJqhbBpZfNBjFVc0DmQHyOsxHssOsma18IIAVZZJKDoFI85oPEtMiURhhuIZfm3scQ5kByj3+kVTmsFYIsfjuSG6vkdC1ej/UAmuIiQ+U5ogr8c5lBvetE7YDpokMxRL80xpfPNvXx68vldUFoKsHuPay6hKMiPx7HXrfWlg5rr1BDAQu36ymdZMjhZH6TdTXGyWWbfa2IGHKsnEFY2xRI6xxI3VP316icvyZazAou42uNq+yqq1RukuPF23gx/6WL5Fx4tKhXXJQJcMvNCj7bWxgshzd8O+JxIC2f7eTqvpW3pYbqDptjjTPMtL5Ze50LpI22tvkmZZyKhS9HlzQgfHc2l4DVbsFVbsFSpulWdzTzOVvLFv6lHBUneZd6rvcKpx+gZ/YAkJVYqUdr3QwwosrMCi5tZY7C5RtstYfpfnCs9Q1Le/n2QhkdUSZLUoO7U3NcKR7GTkNdpY4HRjjvnuOit2jbYX3StP5Ka2FE6pOW1O1ed4r3aZq+011uw6DaeDFTh4PbItEPhhgO1f35ZzOxTWVDRSauzWb/yE0PKszcyXIskUblImei0yWhxT1ggBy3epO52bElhZSBSM1CNFXiFSvh2NFYkrOiGRxcx71cv4RNn6g9lx9mdGtxXBitRrbVqeRUhI0+tGQlh3AL+nRXOtHRNEfeSfLe7FD33eWD+36QN7sbXM8doVRuMFxuN9TCUHmemJeW1pdROGUY+s09q0NbrYWuZi69bBxA0EhHihTxCGyDe5/eKKvo290u0jpcZ4Ij/Fv519hY5vcaI+y2OZsesJbBjw+vpZam4bTVIYj5eYSNzYHhOGIS3Pot27PnW3zU9Wjt/RePzQxwt9FD5lBBZgT2ofZWed1yuvcr51DiuwmEnsJq2mEUi4oUPX61J1KzS9FsPmEBOJrb8woy8MjZfKL7JsLZNRIy/CqlPlXOssC915clqOJ3NP3rRUWZM0zrbO0PbbDBpDaJKGHVhcal/ibOssipDZm9q35Tj2pfZRtsu8UXmNc82zWH6XqcTMdcfT8TpUnSptv8WIOcLkXdgrfFoRkQ8fz7uAbb+Kbf0Qx3mLMHy4FPg+aQgRQ5YHkeWHOyNy7RfI3ZZhPczE1fMD6pbFlXKVq5Ual8sVrlRqLNYalNsdmpaN7UVBHE2WiWkq2ZjJYDrFVCnP/sESR0YGKSTiqL0SakNWGU8WGE9ubT/xYcykS9v2yv7iyNYWIYokc7Q4xtHi2G3tI6HqPFEY4YnCyG29fwNxReOZLUp9b29dnT2ZPvZkbr+PdToxxanGaapuDTuwudy+ysvll3kqf5SS3oeyTSnVnY0rzkxy+oaKoJpb42T9FFc7kdWRJmkczT5BVstsq98AMGgMXFcZtB06XocLrQv8ZPUFTjVOA5FPeZ9eoqDniSsJNEklCAO6/gfEq+7WWbZW6Hgd3MAhraXIablHUsdBkVR8Ajp+F0M2yKk5MlqapJIgJsfQ5Kjc2w4cmm6DFWuVZWsFN3RZ6C7yVvVtUmqKLxQ/d1v2OIasMZ7oi7wW3Qkut1d5q3Kel9dOc6G5xDuViwyZOfrNLNMf8nNsuV1eLZ/hh8vHOFmfxQ8DBs0cuxIlEoqBIWtokoIqKTTcDu/XrjLbuTMBsY3qjgcFXm/yDFF5o3YbViYAqlA2r0cQBrdlYaNJN9oCfdoxnRzkS32PM2BmCcOQq+01ml6X881FVqwas501JhL95Lfx0w17Jcde6G8GanYnh+7ombo7ObTleZeFxESij7QaYyRW4L3qZa521ljsVjjdmONE/Sp5Lcme1DCHsuMczI4zHu/bMvsZ6et4hER+vsOxPH3GnbUHZrXELe8OWcjbKgbfLnRJZSxWZDLZz5nGPOeaC8x3y+xLj6D27LJansWx6mVansWAkWVXvLTZMnAtPrg+AYqQyekJZpJ3NsecSgxs2wt/t3hgCGxWy/JU7ilkIXOs9jZL3SUutS5ep+4rCwVd0ikaRSbi2zdrx5Q4ny18ntfWX+GF1R8TEuKHPm7gIguZQWOQg5lD7EsdIKFu7+11MHOYqlPlWPVdXg9eQwg2e4ESSpI9qT0czByiz7ix9Cer5Xg69zSKkDlWf5eF7gIXWheuOx5FKOiyTlEv7ZDXa7Bhi+O5p+l2fw/L+kHklbqDW0JRJtH0o8jK1ibUO3g40HVdji8s82/eep935hZoWDbhNqmPbuDRdT3W210urFV45dIsfakEf+aJx/jGgd30pxKbJHYHd4fp5BSjsVHW7HJUQuxU+NHqC7T8NocyBynqRdJqCr1nIXc3SCgJ9qb2sDe157q/z3XmqTiVTQKryzpP559iNDZy097J20EQBsx3F3h1/fVN8ppW0zyW3s/hzCEmEhNk1DSSkAjDEDfwmO/O83b1XY7VjrFkLdPwmrxTfZc+vcQXS89HavoPcfDobjAaG2Z/ah9Nt0VBzzMRn2AsNkKfUSKhJDb7hL3Ao+pWOVE/xc/XXmK+O48TOCx1lzlRP8FTuaPElTvLXCZUk8cyY0wm+hmOFfh7577Pmt3gVGOex5rLNxDYs80F/mDhTY7XrpBUTR7L7OJzxX3sS49QMjIkZH3TxeFMY56G27ljAhtVBjw494AspM2+9Y3J+LYP1Gvghf5m2bQQ4pbBmeioH5zj/qSQ1RKMJ/rY1RO5Khppqm5rk8j+bPUEg2aOJ9WpbapCxKbwqSYp7IqX+I+mv458B+cyrhjbBk0kIVE00nyl/xCfK+7j/fpVjlUvc665yFK3QsVp8dLaKd6qnOfzpQP8+shzjCf6b6wgusYaLqmafK64ny/33b4aPUDJyNwyQ38vPj1CCExZ53PF/cy211i1alxtr1FxWvQZGZzA41JrmblOGSfw2JMeYVei76YJg43rM5no569Nff2OxpNQzXtaPgz3gMBKyGTVLEEYklASyLfwN7oZ+ox+vlT6MnuSe3ivfowr7SvUnBo+HoZskFJS9Bn9TCWmmUpMbyukoUs6B9KPMWQO8271ba50LtP22uiSxoA5yOPpgxzKHEEW8k0fNrvi4zyVe5pzzXOcbpyk4q6TUBLktDwHUo9xMHOYvJbfXgnMHODL2lfZk9rL8doxrnQ2jsfHlA2SSop+Y6B3PFsTWFVSyWpZ/NDDlGMfi3jIg4Qw9AmDOq77Po3G38Z1TwPW/R7WQwIFTXsSTXv6fg9kBx8RXdfl7EqZly5ewfWDyDpCkdEUGU2WUWRp84s0CEMc36fruFheZBi/UGvwv/z0VUxN5ev7Zigk4jxinOKeIqWmeCr3JE2vyanGaZzAoeW1+NHKT3i7+g6H0gc5lDnIkDlITImhSdomaXmQJ7Rtv82pxmneqb4LRFZtT+eO8gv9XyWvX//dFrXHqEwkxhmODRFXTH688gJlZ5221+HHqy9wOHsIXdaR72GZ2MOAhJLgyewT7E3tod/o25boKJJCUS/yxdIX0CSN7y/9EfPdBdzQpeJUWbKWmNqmsuxWMBWNL/c9znfmXqXmtlmz6lSc5g3ve2Hlfa521giBA+kx/vLkV9iT2tr6MAx7ZO8hhynrm2rqXhhQc9u3VQrd9iycXgm1JikkP2JZ56OCnJbk+dIBfrj8HpeaSxyrXmJ3coixeInBLTxgpZ5gmClrvWSTx0issK1I0N1CEgJT0XkqP8NT+RkqTov3q1f4+dpJ3qpcYN1u8sdL72DKGn969DPXqU4LIZCRIiJm13F9j5isMxovPbA6G5qk8JniHr638Dp1t8OF5hKXWsv0GRnans1La6dxAg9FyOxJDTEa27qFQRISpqxhyGrPNjRgNF68IyuojwMfmcCm1TR/Y+a/BO7NF7Uu6YzHJxiPT1wnXBFt/4P/3xxRxnUyMcl0Yvq67dzJGP3Qp6T3MRbbxVf7vrq5ldsfR3Q8E/FJJuKTd3U8E/FJ/tPpv0EYhEg38W78tCAIynQ7v0+z+bcfMWXhjw5FmUDTjiDLd1aOuYMHD7lYjK/tneKfvvoOtW6XtGlwYLCPJ8eG2DdQYiybIR0zkIWg2rE4ubTKH586x8sXr9C0IuEe1w/4128eZ6ZUoJC4M8/EHdyIx9IHetnVkGO1D/p/qk6NF9Z+xovllxk0BzmYeZzDmYNMJMa339gDgsutK1xoXcQNo+qkvJ7n6wNfI6vevCxOFSoHUgdY6i7z8/JLBARUnRpnmmdJKAlSauoTOoIHB2k1RfoOjvuJ7GHerLzFQndx07FgxVq9awIL0fwmoyVQhbzZE/hhXG6v0HA7pFSTyWQ/u29SCmgHHlXn4W/byWlJcloSRcjYvstse21bi8RrsdSt0vC6EXFRzFvan+wgggAyWoL/YPxL/L9Of4d1p8mLa6cYMvP86sjWAq0ZNUFBTzLbKbNsVWm6XTJa/I6ysHeKrBrn86X9HMlN8vr6Wf6nU9+h41v8bPUknyvt39I2aSSWZ6lboea2KTsNLN/Zss/8QYAsJEZjRcYTfaw7TS60lrnUWuHZwh46vs3r5XO4gceueInRWHFb2yeAnJYgpyVZ6K6zYtVoehZJxbivAdqPTGBvR479Tre3+e97sN2PNr5rRWDubiu3czyNapvjr1/k5NtXUBSZbDHFkc9OMzYdpfNr5RYvfPddfunPPYNuPhiqfvceIa57inb7X2J1f2+HvN4FDONLqOrjj1z53qcRsiQoJRP8tc8/TVxX2d1XpJSIo6sKmiKjSlJEpgQYqko+YbJ/sMhjgyX+4UtvUetGVQtXKzUulSvs6S+SNneyBx8FQgimk9Ok1RRTiSl+vvYSq/YHCpRe6LHYXaRsl3mt/BpFo8gT2SMcyRwmraUfSGGX2c4cc505ILLIOZI9hCmZUWT9Zn6JQlAyivSbfQgEYe+/S60r7E/teyQJ7J0+d3VJJ6kketoaNn7ofzSv3zAqeV2xatiBR1FPEd9iQur0rHBUSUG/SS+o5TusWDWu3nH58L2HLH3g3+mFAZZ3Z37vshAMxqIev0utZU435ijbdQal/LZljWtWnUutFepOh5KRYle8hPwpr4C7VxBCoEsqR/PTHMpO8Mb6OZa6Fd6snGc6OcD+zI0aCUOxPLvifcx2ynQ8hx8tH+MXB5/c0tLlXo4TIl2EiUQ/j2dGeWP9PHWnje27hGH4oTk8PJbZxen6PC3P4lIzEoJ6prB7mz3cX2yM/en8bi63VpnvlJltrzHXXmOhW2Ghu44fBhzOTtJvbG8HBDASKzIaL7LQXafpdvnR0jG+MfjEfSXvn+JP48NDfmzLpbLaZGLvIJ//pYOEYcD85TVq6y2a9S5LsxUGRvNI8geXq1nvcOXcMifevMS5E/NUyy18/954M37SCEMf23qRVvPvY3X/gCCo8jBdvwcBijKOqh1FVh5u8aYdRBBCYKoK39g/w5d2T7K7r0AxmSBtGpiqiiLLSFLUj6PIEjFNYzCd4qldI3x+etfmdrwgYLZSo9LubL+zHdw2dEmj3+jnM4Xn+A93/SV+dehb7E7MYEoRUdiw2Sk761xoXeRPln/IP7ryj3lh9aesWKs3VOHcT7S9DuvOOk0vKjNVJY2J+DjKLexFNhB5msY3repCQlatVew7sO55lCGEQJGUzXLjgBA3uJ6YXWwucaG5RNe/+TkNw5C62+F3Zl9ixarihT6j8eKW5ZoJxUSVFNqeRdVpYW2xbT8MOFmf5YWV41su/6QhIxFTdHRJoeV2meusbRKM24EQgsnEAEdyEz0V1Q6/c/Ul1uz6dR7P8IFQz3fmX2O2s0ZAwESinyO5iZ3g8B1AEgJT1viV4acYiuXww4AT9Vl+unpyy/t5MtHPY5kxTFmj49v83vxrvFO9SNO9eVBn3W5Sths3qGVD1OO/ZtUpW42bbiMkCuyU7SYhEFP0LftpBYLnCnsZjOVQhMzZ5gI/Wj7GxebSTbfvBT5z7TJNt3tbmf97jSdyUxT0FCGwZFV4u3qRk/VZvNBHFTIHs7so3aK6YDo1yP70KIas0XS7/N7Ca7xXu7ypeL4dynaDdbu5WYp/L/HAiDg9ygiCANt2UXUF3wvwXD+qt5clgiCgVe9w/sQ8h5+bRtWg27Y5e2yW1cUaZlxj9uIqv/wXnrvfh3FXCEMPy/oJ3e7v4tivEARbm1HfH6hIUgJJyiJECiF0hFABrfdTIXr0eYShB7gEYZswaBOGbYKg0VNN/vgfWLr+FVRlBiF2smyfFsiSRDF5+6W/siQxmElxZGSI7x4/s/n3aseibd//SeinBaqkktOypNUURaPIdGKSpe4y8915rnZmWbFW6PhdnMBh1V5l3Vmn5tRYsVZ4IneEmcT0XQs93UvUnBoNt7k5oep4HV4qv8yJ+snbHt9id+m6MtWm18S/DaXWTzP80KfptViz1qi5Ndpee9PP1w1dgp7aqh8GnGuev8arN7whwPFW5QLHa1cp6En6jSwlI01ajWPIKrKQ8HtKoqtWjfPNJd6qXIhKL9U4h7ITTCUGbhjfVGKAy60VynaDk/U5/njxHY7kJkmrMbwwYN1ucLa5wGvls5xtLJBRE1Td+1tGLIRg0MxR1NMsdiucby7yb2df5lB2fNOuxw5cbN/FkDWmkjced7+Z5cncNGcaC7xfu8qLa6cQQnA4O8FQLI8p67iBx4pV4/3aVX66+j41p8VYrMSTuWkmtziXnwQ27FvswNu8O/wwxA0CvCBAecBby/amhnkiN0XVabFmNXmvdpk31s/xhdL1PvUp1eRQdpzPFffzo+VjzHfW+Z2rL3GuscBkYoC8nkSVFPwwoOvb1Jw2S1aVxc46+9KjfL60/wbl4CAMeaV8hpP1WUZiBYZieQp6iqQSCQr5gU/Ts1jsrvNu9RKL3QohIfvTo2TUxJYBi34jw5f7DtJwulxsLfFm5QJO4HMws4uReIG4YiCICHHTs1i3Gyx2Kyx2Kvzmri8wnuhHlj/Za9ZnpJlM9nO5vcKqVeftykXanoVAMJ0cZNgs3NKzOa3GOJKd4FJrmZ+uvM/V9hr/8srPOJWdYyLRT05L9K6PT8d3ouvTy/IeykzwmeJetLv0td0OOwT2AUHgB9TKTZZjGt22HWVXFAlVkYmnTC6dXtzMsFpdh5WFKs16h1Q2xuLVdcyY9lBFBzdscmz7BTqdf4XjvE4Y1O7TaARCxJDkErLUhyQXkUQKSUojpHT0UyQRQkOggVB7BLYXoQs9QjwIXcKwQxh2ekS2RRDWCII6YdAgCCr4QZnAL/eI7b0QxxBIUj+68TySfKMa9g4eLSR0jcHM9VYFXdfd9Izdwb2DLGSKeoGiXmA6McWqvcZ8d4H5zjxznXnmOnNU3Rp+6DPfXaDptXACB13SGI/f//7Yptfc9JYFsAP7ut7eu4ETOJuqrY8a2l6HZWuZ2c4cK9YKZWedhlun7XWwAhs3cHADj6BHXv3QJ7hFcHOhu84r5dMIBDktQcFIkVJi6NcQ2I5nsWY3WOxW6fo2fUaGzxb38nR+ekvbkmcLu7nUWqbhdrjSXuH3Fl7jXHORlBrDD30qTouLrWVs32U6OUhKNfmjpXc+rtN22ziQHuNcY5E1u8GyVeP3F17nXHOBhGKCAMf3cEOfyUT/lgTWlDX2pIb55tBT+GHA2cY8P1p+j4utZfqNLKas4QYea3aDs40FWn6XEbPAV/oP8nR++mMtZb0Wtu/ybvUSq1YNN/RxAx8ncGm4nc1s+IpV42erJ5jrrKFKCqqQUSSZvJ5kX3qU3BZWKPcLccXYVMKt2GeZba/xo+X32N8b50awTBISY/ESvzj4BLbv8mblPMdrl1norDMYy5HVEqiSTBCGdH2Huttm1apTd9pokspzhT1b7v9ia4kfLL1LVkswYGbJaUniqoEqZLzQp+1ZLFs15tpl7MBlV7zEl/sP0m9mbtiWEAJFyDxX2EPHs/nRisTF1jIvrp3kQnORQTNPTInm4k7g0fYsKk6LNatOy7P45tBTBPchA6tKCgfSY5yqz3Glvcrp+hxO6CMLwTOF3WS1+C2FqP7/7P13lB1Xnt8Jfu4N+/xL7y0y4R1BgB5FV1Us212uq1WtXtVRt1o7MjM7O9KZOWrtkbQ9Tjs7Mqc1aq1My7RTt6pK1V2GVWQVi94ThPc2vTfPv7B3/4iXCYBAwibABMkv+UjgvYh7I+JG3Lg/9/1qQrIu2coX2u7HDXzeWzjD+wvnGCnP0R6rp85MRE4BFVINXHJumanqIjmvTMZI8GDj+mu2fyv4xIBdI5BSEE/EqGtKMTORo1JyKOUrtHTUU9+UusxjYxgaphWleRmWwcCWDuyYiZT3hgEbGa8urvs+xeK/x3P3fQj6rjpSNqBpTUjZiNTa0PU+dH0AXe9GymakTCPE7dQcK5RyCIJ5wnCaIBgnCIbx/WHCYJIwnCMI51HhPGGY49YitQa2/QSGvh4pPyHq+bChll5O16m9X4q0rDYBgiE1YsblXk4/CAnCj6dRcbdgaRZd8U664p2UM9sYKY9yOHeEk8VTjJRHqQQVcl6OQ7kjxPU47XY7pvxwnY5O6OB/IFpqiBvTx1wJEbP/xwtKKebdBU4Xz3Bw8RDH8ydY8BYAarITJpa0MKWBrdtoQkOKqIZ93p2n5JdXTC3vjjexKd3JrFOg5Fc5XZioGcFRtFYKiSE0YppJo5WiwWpnR7aPz7bupDPeeNVI+tZMN0+0bEUIOF+cYrQ8x9nCJIpo/kjoNo1WmocaN/BQw3qc0OOnE+9/6G6JwVQbn2reQjlwOFecZNEt8dL0EUIVIoXElDpJ3SZ5jZq8BivFY02b0IXk55MHGS3PLi/og1o7ltRJG3HWpVp5uHEDn2raQmfixrSzVwOVwOUnE/t4f/5sLarsL2vYLmGyusDkZHSPaUJiSQNTM9ic7qTBSq8pAxaiKOyuunUMlWYYLs9weHGI12eO80zbfZfVYSd0m63ZHmKaRcqIcb40yaxT4GxhAif0CFSIQGBIHVszSOkx2tKd9CVbsK4SQRRC0BVvZF2qjUW3yLniJMfCUYIwkkeSCHQZPT9ZM8GmWCcPN2zk4cYNZI2V11NLkjwpI8ZrM8cZLc+y6JWYXDiDFwYoFJrQMKVOXLdotrNsMlPUmclliaq7jU3pTjrjjZzKjzHlOyAEKd3m/vp1N+ycSRoxdtT1Edej8blQmmLOLXC6MI77gfGJaSYpI0ZHvIGeRBPWbUq+XQ0fGQNWCEFaz9AV66berI9exDfdRlTX0xXrBiCuJe5aqpeQkmQmTkNzhoaWPL4fUMxVSGcdFueKuI7PwmwB3dCIp2x0U2d6bIFUJs7uT21AwRUF52sTCnDwvNMU8v8Uz3u/Rth0NyCjSKvMIGVzJDljPYhhbEXT2hG3IQF1dQiEsNH1dqAd2Fn7XhGGOXzvLJ53EM87jOedIAwXCFUBFZaAG0n5lEhZRyz+DaS8stbpE9x9uGG0qNBlEk2svJAKQwdfFbG0G1sYhaHC8X2qvo/rB3hBQBCGBEpFi1mlUCpKmRpZyF22780sPJVShCjmnGJNsB1Shk1St6+bquaFAYtumbRhY0j9hqQFyr6DE/gkjZU1/K4HPwxZdMsUvAqNdoq4bn6ohElxPc6G9HoGUus4VzzPD8Z/xOniGSpBhXl3nhP5k0w3TNMZu7p0yd2C/4EIoClN+hK9kWF9Sy0KknoSW7s7kaq1glJQYt/CPl6eeZXRyhgQabynjTRJPUmDWU+9WU/aSGFrNpa0MKSBKQxenHmJ04WIBfpq+HTrTvqSLZzIjzFWnmXWKVDwK7ihj1IKQ2ok9RjNdoa+ZAs76/rpSzSvoLUZwdQMPtd2P72JFt6ZO8Wp/BjzbhGFIqHH6Io3sKt+HdsyPTRYKYZLM2xIdxKogJhmrXhv6EKjI95A3ivTFW8kvcoRS1szeaxpE13xBt6YPcnJ/CizTgFf+ZhCJ2XEaY1l2Z69dnZD2ojz6dYdbMv28tbsCY7lRpisLlINPAyp0WAlWZds55GmjXTHr59aqQlBnZlkINlGTI8cCbc7/yQ0m3rzyuj5dffT7btCNKUJjayZWE5Rb7IzmNe453SpsadhkHm3yJuzJzClzsHFCzzZsu0KwyammWzJdLEh3c6++TMcyQ1zoTTNvFPEDT2kEBEjtBXd81sy3fQmmq9qhGlC8mTLdjpijRzPjzBanmXeLVL2HQIVoglJQrdptrMMptrYVb+OvkTzDa37m+w0n2+/n13163h//izH86OMV+Yp+RVCpbCkQdZMkJUpUl6Sp3q20ptoXOE6CQyp0RNvQiLojDdGmQWriLZYPVsy3UxU5in7DrrUaLOz9Cdbr3uPX4q4brE928vGdCfvzZ/haG6YodIMC24BN/SRQpKqMXb3J1rYku2hJ95E8g5IUH1kDFhTWuxtepy9TY/fchua0OmKd/MPt/zOKh7Z9SGEwPcCXv3JIQ6+dRa36vLY57ZT35zm3PFxXnvuMPMzBV74/j4+9cXtNLXVkZsrMjkyTxgqpscX+NpvfIpkOobQ1rYBq1SI758nn//HuO7bwN2olxJEEdcsprkHO/ZFLGsvUtYjPpSFroiOxbof07q/FpEu4zhv4VRfxHHeIgiGUMoluj5XN0GESGBaeyLmYfnxWjSuJUTjp1CEzFfeACBr78bWWpe/F0ioaaYpFVL2h5mvvElP5tvXbDdUCi8IKToOZ2fmOTE5w4W5BSZyBXLVKvmqQ8XzcWuGrVMzbm/5XICi5/CH595grLSIp3z2Nq/nqdZNNNrXXlAtOCW+N7yPz7ZvoStej6ld//VyIjfJueI0jzWvpzV2axIVBa/CD0b28+zYIf7Wxqd5oLGPxBqQNdCExmBqgC+3f4nvjH6XU4XTABT9IqcKZz50A9YQ+mV6rVkjw1/p/XXa7NYVdUw/weVQSnFw8TBvzL21bLwa0qAj1s5nWz69zMi80oL4UO4IZ8X5Fb1MWTPB/fUD3F9/dZ34W4UhNbZle9h2FTbYD6I32cLvP/jfXvU3pRSBCnFDn6wZ53/a/LXrZpUssyALHe0m6zcNqTOQamcg1X7N7S4ld7qaU18KSVusjq92PcxXux6+qWP4IBK6zRc7dvPFjt231c4SsmaC/3Hz11alrVuFHwS4XrBMcCWEQAjQNQ1dSrJmgs+07uQzrTtvuM2+ZAv/zeDn+G8GP3fdbYUQGELnocaNPLRCavCNotFK09iU5pGm22vnatCEpD1WT3tHPV/q2HPVbd47OcL/9l9eYOArPXRubcC8ytQqhaDBSvG/7Pj1mz6GDxKZrRTEkkLw9a6H+fpt3u9LfViawaNNm3i0adNtt3er+MgYsPcyGlsyfOXbjxH4AUIKlALT1NEMSSoTp39TO74XoJsapqnz3isnyTYk+Wt/70sYps7+109z5sgYm+/vxY6vbZkd3z9LqfQHuM5r3B3jFaRswbafIh7/JrqxsZYWbHKjWr53B3Esay+m+SBJlcfzjlOt/AzHeYEgGLvqHlI2kYj/324zzfkT3D4UFX+Uqj+JG8xi1qKqCg/Hn6bknSFprsfUGhGYOMEUXjBPxtpxzVaDUDE0v8h33z/M6+eGmcoXa5HXiNRDqejltWQ+1/69LZR8hwPzwxQ9h7+58SkCFfLm9Flemz7NV7p3XXPfRjvJt9c9inWD0VeAbXWdbM6233L0FSBjxvlG726GSrPEVpkkYjXQn+ilxWrhQmkIN3RxQ5cZZ/bDPiwszUK/JBoQqJC8V6DFav7EgL1BeMrj4OIhRss141UY9Cf6+a3+3yCtpzGkfp1SgnBNMVPfLEq+w6HFIf7j2Zf5Rzt+hbar1A1+EN8ffpfDi8N8o+chdtZd34C+VSjW1hv+XsIbR4b4/WffZnQmyuZJ2AatDWme3DnA3u19dDZlP9wD/ASXIfBDhAAp5cfqpv9QDVilHALvKOXC76LCS1/oGkIkkForurkTw34aKVvuQIrn2oDU5IqGp5SgG5cvJhpaMpw8OMzbvziOYWo4FZfuz2zBuJprZw3B90epVp+jWvkRsPqU2h+ElK3Y9lPY9qfRjS1osnFNRiovesxMhDBRKoZpptH1DcQT38R1D+A4v8B19qFUrrZPBtPcgWHexyd+qA8PoXKp+GOMFb5L2txMybtAqAJCVaXgnmS2/BIZaycj+f9MS+JzJM31CDScYJaie4qsvfOq7c6XKrx46ix/8u5BJnMF8lUHr0biJgTEDIOmVIKMbZOwDGzDwKx5xxcrVV4/O3RL51MJXE4Xptha10mjlSSumbzBGaaqeS4UZ/nzkffpStQzVy3SHq9jV30P7fEsE+VFXp46ycGFUX5j4DF6EpG+4juz59i/MEzRqzJaXmBLpp1f7X2AjBnnyMIor06fRgFf695Fk53i6OIY781doOy7VAKXZjvNp1rW02il+M/n38IJfUq+y+6GXvY09JIx40ghsDUDXWgfqqj6StClTlJPYEkrIjlSIW7ofNiHRZ2RJa7Fl//uK4+xyhj9iT7WnhtgbWK8MsGCu7CcAtxg1fNA/W7qzTok8polPaEKcYIr65CX8J/Ovcx7c2cpeFV0KUnqNh3xBp5u3cqmdAcx/cN3XCoUXhhQ8p0blrRxQo+iX8UP7xyx3JxT5J8e/zF/Z/MXqTevziZ7r0EpxWKpwumJOaZyBaquz0qXfGdfG+vbm265L9cPsAydZ/Zs4JGtvVQcj0Pnxnlx/xmKFYdvPLGDuuTaW0t9VDA1NMP00CyGbdC5vo0wUCSzcZRSlAsVqkUHpxK9QzoG2igulhg5MUZrbxOJTBwQxNMf/fH5kFe+CqUKBN4RFCGa3o0QaZTyCFWewD2N7x3A9w5hxf8yurEd8YlnmPbuBuxL2YqlJNuQvEwndq0hDHM41V9QKf85YTh3h3uTWNansGNfwDR3oWldSHnz9SQfFoTQECKJlEmUakXTOjHNHXj2CVznbVz3XaTMYtufQ8q1RdbwcUMQVik4J7C0FpLmeqr+JJqM4wZzFN1TlNxzSGFRcE+SMjdg6+3oMoMhs3jB4lXbXChXePPcMH/w1n5OT88tp3H11GfZ0dnGptYmWjMpkqaJqWsYmkST0SdUimMT07dswPphyIJTYn26BV1qy2nAXhiQc8ucyE3wYOM61iWbSRsx0rW6lrQRoyfZyDtz53FCnxBF2XcZKy+Agh113RQ8h4RuLS8mW2MZGqwkY+UF3DBAqSh9eay8wNZsJ3VmnLHyAu/PDfHlrp081LSOUCnOFKaZc4qMlOfJmPEVz2UtwVP+stxMRBRz4ynO4pL/RqhF4G/zmDJmljoziylN3NDFCRxO5E/wcMODwIefgn0vYNFbxLnEGRHX4vTEu24ogj3nzlMOyisyEY+XF1AKdtT10BGvp+BVOFOY4t+ffZH/++CnWZ9qu0I25G7D1kw2ZTr42xueoc5MrgkHkh8GzDp53po9hRN89sM+nFWB6wcMzSzwJ6/u58L0AsWqgx9cOQMIAXWJGI3p+G0ZsACWqdPTWseejV14fkBfWz2/n3uH8xPzjM3kSNgm47N5fvD6UT67Zz1nxmY5dmGKquuxrr2BX35sKwk7IqoLQ8XRC5PsOzXKyPQiQgh6WrJ8+v71NGUT6Fr0vBTKDqdGZnj7+DBTCwUEkEna7N3ez7b+Niwjeh8FYci7J0Z4/9Qoc/kycctgU08LezZ20ZSN1kShUuw/PcaB02OMz+UJQ0U2FWNLbyuPbevDNnWUUpwdn+PdEyOcm5jDdX3itsmmnhYe2txDc93F9VXZcXn3xAj7T4+RL1VJJ2y297exc6CD+nRkXAah4uUDZzl0bpxixaWzKYMmJcZNrs1zM3mcikNTdwNTQzMce/MUT/6lR5kdnSM/HxGeCgRmzAABsaRNteRw7M1TVIpVGtrq2PnUVgzro+2KXDOhG03vw7S/iGYMRLIkKkfgn8epfB+3+nOk1ouULWj6tWsfPg6Ip2ziqXtH71MpF8d5lUr1R/j+qTvYk46mNWNZT2Pbn8G07kfKLPdyToUQ0TlpWjO6vh7D2ITp7UZgYFoPftiH97GHIsAP81haA6bWhKHVAVFkVimfhNlPytyIpbUQN/qQwkCTFrpMwgp1cUNzizx75CQnpy5mpezu6eCpDeu4v7ud7rosmbh91TRd1w/IVa4tLH49SCEjKZQPuPfDWlLe5kw7KcO6rK4vYVh0J+rJmrHlp02rsaJXApeiXyVj2uyo61om7Wi0U7THMyx65cv6SRsxBtMtdMbrqAQep/OTVH2P6WoBXWhUApeCVyXn3i3yt9vDgrvIoru4rPdpSpMGq+GG99eEjryEmEUpRSkoXWS8vkVY0qTNbqXJamSsErFInitdYKQ8Sn+iD0v7xIi9HoLQvywFWAqJfoNsmycLp1j08tfcpiWW5aHGQbZmu8h7FY4sjvBPjv+Ik7lx2mN1+CrkVH6C6WqO7XXdvDV7mnmniC41dtf3M5huWyaNCcKQY/lRTubGyXsVbM2kL9nEpkwHWTNiXFVKcSI/zqn8BHNOAYUiZcToT7awo65nOdXfCTxOFyZ5b+4sbhjJcWzKdBDnYlRYKUXBr7Jv7hxDpRkU0JtoYrqau6Im+ERujBP5cRbcEobQ6Eo0sC3bTb110YB4bfoEbuiTNmKcK06Tc8vEdZNt2R76k83EdZOzhSnenj3D+dI0Rb/KH194nWRNl/O++l4eaBi4J6OxxarD8wdO8cqx8wy0NrC9p41TE7PMFcrc19sOAs5MzpEvV3lwfTft9enb7lMIga5JLEPHMnTWtVs0pONMzhcoV13CMGShUOZn+04hBCTjFtmkjeMZyJpDdQnHh6d4+cBZZnMl6tNxQqU4PTpLvuzwtb3baK1P4Xg+h85N8Py7JxFC0JhJoEmB6wcopZbfd1XX59ToDM+/e5JEzKQxk6DsuOw/PcZ8vsw3ntiOZehcmJzn5YNnqTgejZnEcoZAueou/3lyvsBbx4Y4OTJDe0MaATieT9X1CcKL82ux4nDk/CTPvXOS5rokTdkkxYrDW8eGyJWqfOGhTUghOHR2nJ/vO4Vl6DRkEpSqLiPTizjezZXLhWGIU3XJzeQZPzvF6MlxfC/A9wJmx+axYibp+hTFxTIocMoO+bkC+bkCYRBS15KJvBkfcawZA1bKJnRzB4Z5PxCR/ShVRIV5nMr3CbyDhObuZQNWqRAVLhIGI4ThLCosEqWlagiZRmqdaHp/Le344kAqVSHwz+N7RzCtp0CYhMEwgT8KqgRIhEyiGzsRsg5QBN4xwnAeTR8gDIYIgymErEc3tqAICf2zhMEMQmbQzR0IkbksUqxUlTCYJvDPosIFIABhR1Itejea1srVjKwwzBEGo4TBWI2Z1gdh1qJzLUitHanVf2CffE2iZRIV5kDVvMMigdTa0fRepEwv96eUT+ifw/eOoZs7o+vhjxCGU6Dc2nE2ounrETJzSxFw1z1EpfwXeO77rI726ZUQIo6uD2LbnyEW/xqa1lHTav3oQMoUprkLw9gChAjx0U8RWeuQwsTWO8i7RzDco1T9cUytAV0miekdKD9Alyk0mcDW2xEYVP0JSt453GCOvHOMuNGNJuIIIXF8n/Nz87w3NLrcR30ixhe3buAzmwZpTF474hiEIY5/67XlptRojqUYLS2wPdtF0XMARUyLmH1NqZExb+y+szUDQ2o4gUeoQtYlm+lNNl633jVtxDClhi4kUghKvsuF0izvzp5nQ6aVvFuJorw3mLJ4OwiVohyUKftlknoCW7NvmJleKUU1rHJg8SAT1Ql85SMQJPQEPfHuGz6GiLn2omHgq4Dh8ggDyXVYtxkp7U300p/oZ6IySUjIorvI67NvYkqTznjnZf2uBKUUvvIpB2VSeuquMfevBcS0+GXRVidwmXfm6Uv0rrhPoAJmnFn2LxxgwV24ZvtajSwlZcRIGjYJ3cKWBvNukWrgRVGm+fO8NXuaauBxvjhD0avW9FBblhfqTuAzXJ7lufGDlH0XiMiXRsqzLLglPt26DUNqTFQWeXHqKFOVHJqQKBRWLT1/a7YLo0b6pVBUA5eJyiIjpTlO5Mf4TNv2ZUMYoOhXObw4zJ+PvkdMM4jrFgtuifPFmeU5IAhDxirzvDB5JGJCVopAKYZLs8xWC3y5c1dURywEb8+d4Uxhkv5kM14Y4AQeOa/CUHGWr3TvYTDVihN6zDoFpqp5FDDvRNcJonrdexWlqsvLR8/RnE7yyw9sYXtPK9976wjHR6f52kNbScdtjo5M8cLhM/Q115FNrN7aQCmF6weMzeaYL5RJ2CbZWvqwUpFxN5cv88DGLgY7m1BK4fgBsVr0LwwVrxw8x+hsjgc2drN3ex9+EPLOiRH+8Pn32NLbQipuMb1QZN/JESbn8/yVZ/awpbcFTUpmckWyiRiGvhSlrfLcOydwvIAvPzxAf3sDs7kSz759gpcPneWhLT30tzdwYWKBs+NzPLSpmy88uAlD18iVqnhBsNzWxHyBM2OzWIbG1/ZuIxW3WChUcP2AdOJikGh6scjLB84ihOCLD26ipSHF2EyO//rKYd44eoFdgx00ZpP89N2TVFyPz+7ZwLb+NsZmc/zwjaOUnZsrmatryRIGisAPSdUn2fzIekzboLGjHqfsYpg6qYYkhVo0NvADss1p6luzxFI2dc3ZK0oPP4pYMwbsBxGxw8YxrMdxnRcJg1lUuHjJFi6e+zZe9fmIsTXM1QwuASKFbuzCTvwamt6HEBdvRBUWcKs/p1L4Z8j63wfl4rmv4btHCGvGpRBJktn/HU2mUMrDqf4Yz3kTK/ZL+O67+N4BpGzCSnwblI/nvIjvnUTIFLHkb2FanwFR82iGZXz/NJ7zCzznVcJwHpSPECZS68Gw9mLaT6PpSwuayLAMw0V8913c6s/wveOgKoBDVCeZRDfvw7S/gNQeuOy6Bd4JXOfnBO7hWl8VIARhohlbsGK/jG7uQcoa46dy8NzXKRd+FzvxGwgRw3ffJQjOo8IyYKDpHVixr2JYexFa8w2PYSTtsUil8j1c9x2UKl9/p1uAEEkMYzux+FeJx79BdI0+ut4ncQ15lo8jKq5HvlzF0iNPsabJSPJFRJ5MpSKd5TBU6FqUZhvWPLo3y4L5QWgiRtraQs45QNE9jUJhaFlsrQ1Dpsm7J8k5BwnxMbUGhNSp+lO4wQyGzFBwT2BqDUjNRiApVB0mc0Vy1YuLrQ0tTWxtb72u8QpQ9jxmi7f+nMV1k43pNp4fP8Kp/CReGKAJSVc8W9viKo42FVLyXcbLixS8KhOVHCkjRoOVwFchaSPGtmwnutRYdCvYmoFSggW3zHS1wKJbZqKyiCU1QhSCy/VxndBnqppHk5KBVDOG0BirLNb6VlQDj4nKIgW/ymQ1R1MlRWssTXwVmIhDFTJVneZY7ij1Vj1NVlNNKsbClCaGNNCIdD1VLbXXVz5O4FD0i4xURnlp5mWmqtO16xunM9ZBV/zGGYgTeoKUnsIUBq7ycEOXw4uH2ZjaQFe8E0tejIYvGSxLxvKS3uhK6Ii1szG1gTPFM0xUIyP27fl3sDWLncEOWu0W4locQ5q1dqJz9JSPF7pUA4eyXyLvFyj5Je6v2/Wxitw2WA3EtTgCgUJR8AucKJxkILmOlJGK2FuX3ukqpBpUmXFmeXv+HU4WTlIJbiyLINo3us8DQtJGfDmy6oY+k9VFRsqzfKVrD41WijmnQNZMLKcY570yP5s4xFhlgV/rfZR1yRbGKws8O/Y+z47tZ3tdNx2xek7kxzi6OMqjzRv4bOt2pBAsuCV8FaBfch/ZmsnuhnUMpNp4bfoEpwoTVxzzZDXHC5NHcAKPvz74NC1Wmrdnz3A8N7qsa1oNPX42cYih0iyfb9/Jtrpu5p0iP5s4xPdH32V3Yz8dsXr0mpNgrDxPm53lV3sfodnOsG/uHL936nk2ZTvpjjeyNdtNT6KJt2fP8O7sGf7WhmfoiNXd8+sBLwgZXyjw5d2b2NnbTnt9mqRtYRs6qZjFYFsjg22NnJ+a572zY7TVpWnN3l7ZlOP6DE0u8N7JUUpVh8PnJilVXHZv6KK9MVo/KhRBELJ7Qycbu1vIJK/MDKy6HkfOT7KuvYHdGztpyiZRSvH0rgG+8+JBTgxP09/eyLmJOSbm8mzrb+PRrb3L+6fiF+cTpRTFisOrB8/x5K5BcmWH02NRppKuyeUU5L62ehIxk4RlMl8oc35inraGNI2ZxLJhDRAzdRK2Sa5U5czYLB1NGRozCeJWFEVe6nMuV2LfqVGevG+AmVyJXDnKcpJSsFCocHZijkwyxr6To3zugQ2s72yiMZMgGbOY2VTk1UPnb+rat/Q00dzdeJk0phCCeCpGpulidL21txkhBXUtWe7/THb5eJe2/6hjzRqwy1iKoIpoWbMEpRw89xV87zia3o00H0CKJGE4jee+g1P+I8AnlvwblxiHl8Nz3yJwDwIaurkbITOocAbfPYSQDYDBEtlQ6J/HrT4fpXGaj+JWf0a19J8QIo3UGjGsh/CqP6NS+L/QzQeRKg4oAv8MTvmPcSs/RTc2Y8a+iCBOGFzAd9+nWvp3hME48fTfBazlqL/vvke19B/wvRMY5gPoxnYQgjCYxveOEYZzKHVl+pHvH8Z3DwASw3oYKRtRePjum3jVn6PCPDYmpv2pK/Z1K98lkptpwrCeQgibwDuC57xBEIwSF3FM+7M3FIWNHqIQp/oyrvMaYThz3X1uCcLGNHcTT/w6sdjn4C5ooH2CtYXxuTxvnBiioyFDV1MGQ9NoziQIlaJQcUCBZehUXI+mTJKS41JxXOKmSSZh39ZEL4TE1OpYV/e3CZWPQF4izdREf/ZvEOIi0BFCIhBktZ0rkjdVPJ+Se7kGcEc2Tdy8/lStlGKuWObE5K0/azHNZHO2nf0Lw/x47BBe6PNY83r2NPYzXlmk7io1p14YcKE4y4tTJ5hzSrw2fZoFt8yGdCtuEDBRyfGdofcIVUhct/nNgccwNZ3980Psnx9m3inxKqeYz3aQMWMkdAtdRAQ4tmbQYqfpTTby07Ej/GTsMLZmENNMLE3HVwFT1RzPjh1mzinx7tx5Sr7DA439DKRu3Nm2EhQheS/PSzOvMO8u0GA10BPvpjPeQZPZSIPVQEyLYUoThcIJHHJejvHKBKeLpzmSO7ZM8KMLnf5ELw827MHWbrwExJQGzXYzzXYzo5UxfOVzqniG56d+zqMND9Mea8espa0uRUMLfpGEFidrZq/ZlyENNqU3kvdy/HDiWcpBGTd0+dnUC5wsnGJzejMDyX4arUYsaUWGVOiQ8xaZdeYYr4xzoTTEjDNDX6KPrZktVzVgI33hEL+WcquoaRfX/glVSKCC5WTckIjgqOAVl41AAct/lkJDF9qKxnkUyQvwlb+skX6Rr1tR8SuXkQ75YUAlqFD0i0s9Rf+I6E+60JFCuyJtv8lqpNlu5kJpiFJQYtFb5J35d2k0G9hRtyOK2NeM20pQYaQ8yhuzb3Fg8eAyuZcbujjh1XW/ndBn3i0yWp5nvDzPi1PHsDWD9elW0kaMvFchRJE1EjzZspX+ZHTPX6rBqpQi55V5cfIov9R1P3VmAi/0abJSdMYbObI4yvHcGB2xeiQCTUjc0KccONSbSXoSjeg3yRKulGLeKXAqN85Xux+gP9lMTDPZ27yR04VJjucj1mYn8Hhu4iCfbt1Gs50hCAMyRoz+VAu/mDzCoYVhmq3Mcv/d8UYebBxkQ7odpRRPtW7lP517hbmaPu6d0JpcC4jqK0NSMROtJpVo6hpCCIrVi/fOps5mDg1NMrlQuO0+Z3MlTgxP8fy7J9E0SUdjhs/u2cBj2/pIxkyq7sWoYktdirh99Yy3hWKFiuORTthkapFhUXMet9anyBWrVByXQtkhCEPaG1aWU1MqMqwn5gv88I2jPPfOicve3811yShdV8HOde2cHp3hF++f5v1TY9w30MGj2/rY3NNCzDaQQjDQ0ciu9Z38+WtH+OfffYWtfW18auc6tvW1kk3Glh3eFcdjaHKB7758iL94/chlDtbe1jpcL9Jlny+UqUvFsa3ofW2bOumEvVzjezOIpIuuXJtc9t1Vli4fB8N1CWvcgA3xvaMQlpD6RoSWXf5FiCSx5P8TkbJqqZRLN4hP4B0jP/9tPOfn2PFfBa5uwDqlf4+d/O+wYl9Faku1tar2+cClERJN7yWe+X+jlEfgn8H3DhNL/nXsxG/V0pjzuNWfROm+MgRVxXNewnNexow9QyLzP8Myv6PCrfyQaun38ZxX8NzHMczHlrvzvWME/hCGtZdk9v/4wPH4tc+VD4UV+xpW7Ku1a7KU/qVQ6q9SXPibBN4RAv8UcKUBG/hD0fnEv43QWmvfehQX/g6e81JEtmXuQGg3UoccEoZ5isV/je+P3MD2twbb/gyJxG9gmnu4l2tdP8GtwwsCcqUKScuktS7Jzw+c5qntA+RKFU6OzWAbBiGKmKmzZ6CLd8+MUKy4bOxsZnN3C7axOtOguMrzCCC5cbZQKaIF5KUoXsJAfC0ESnF+dp6XTp274f6uhrhm8psDe5c1eZYW8WmjlX+w/Zeu2N6UOluzHWzORovKpYX/CxPHiWkGf7XGSjxdLfD3D3wPp6YZ+XTbZp5q3YRCXRapolEt/3lv83oeax5EIvj/7PoVLn93R1v1Jhr5mxuevKzvO0EmExIy48ww68yyb+H9y/oQ4vKS4aW6yEvrI/uTfXyqaS9b01tvuu/1qQFGK6PLWqMA783vY9/8+1iahS1thIBq4OCEVZSCZ1o/yxNNe2mNtV6jZag363ig4QF8FfDnYz8gqJV5DJdHGCmP8vwK1/LSczSvk2qsUMw6c5wvXaDgF6gG1donOt5KUGXamV4muppz5nlz7i2GykPY0saqpVHHtOjPTVYTPfFu0sbVo0xO6DBVneZ8+QJO4FzsL3Rwgirz7gIl/2KmwmhljOcmf86+hfeX+7OlWUvftumOd9EWayWmXZ6aKRDsqbuf6eo0R/PHAFj0cvzpyHf40cSz1Jv1WJqFEzgseovkvcKy0X5/3X3Um/Ucz5/kXOnqz+ybM6f46fgBJJKYbrI+1cb/sPFLbEh3LEdgAWK6SVei/qptKKAaeAyXZ/m/TjzHvxTPX/xNKVpjWeacIgp4sHGQY7lRfjiyj5+NH+Lxlk18unUb/amWa47v1fqsBB7FwKEj3rA8p6WMGCnDXk5PdkOfycoiv3/mRf792Zcu2V+RNuLMOPnLSK7SZpxG6+KYC6L5xwsD/PD2asLXMnRNUpeMM1+s4PnRecYtAyFgfCHPLjqAyFHreJEm+O2itT7Flx/ezBce2kQqbqJrGpq8ulGlayszbotoQubi2voiLhWRUooo/+aaU3f07Bi65H/45uM8sLGLZOzi3BPV7WpIKTClxreevo8n7xvg3RPD/Oy90zz/3km+9fR9fO1T20nYJromeXLnAPcNdHD43ATPvXeSf/D7P+Gre7fyjcd30N1SF8nTKUV9Osbf+/VPs72vFfOS9NylPvPl6lV17CJj/ZO16Z3AGjRgQ5RyCINJPPctnPIfEaoiurUbTV9/yXYSKRtgyVsqlmo6JULWo+n9+N5RFFG9x9WeCt3YhWE+iNTaLokqXnoHiuVFiRAppNZDJPGjIbRmZNiM0NoQsh6hSkjZBISgikCA75/A944jZROm/ZlLUj+jY9GM9WjGJrzqywTuvssMWCHiIDRUOEvoD6EZl5+7UsZVz0mI9PI2F6+JqkWKu/G9EyhVjI7zA9FK3dyObu5CaC3L10MpMKwH8L33CcM5wjB3ibG/MlSYo1T8N/j+EHB17/LtIhb/FeLxv4xpbr8k6nX7+O3f/REVx8OqTVL9nY18/entNNRdn/H3zMgMLfUpUok77wmeWSjyw5eOgIBfemIbjdnE9Xe6QeSLVf7DX7zNY/f1s6G3mWR87aYFJmyTpkyS6VyREIXvB0gBCcukKZ1E1yXz+TKFsoMUgmw8Rtw0iZk6nh+sngF7PW/pDSBtW9R/oH7p2OQMs8USftCAvgKboRcEvHL6An+67zAzxdJN9Xm149VWWEho1zjHD+4zkG7h1alT/Mn5t0noJn4Y8qWOHSRrTMQi2vlqR/GBP0X/1a9xLVc63ttFVLMax5LWcpro1YzTa1ECp/UUD9Tv4YGGPXTHu2/JQ95gNvBA/R7KQYXXZl4nrC39olrEKk7gLB/TxeO7MaZiIQR1Zpa9TY/RYjfz86lfMFQewgndy9q7Fixp0RnrQFtB6s5XAWeLZ/nJ5PMsuAuXRGDDWnRW4db6g8gAHauMM1WdvhiBFQJZc1tsTG/k863PrGjAFv0iBxcP8bPpFy5GelXtqilFSLAcGV/a/lzxHEOloYv9CMFSj082P05CT1xpwArBQHIdTzR/CoXieP5EFFEmpOAXKQeV5fsmVAEhClta7G18jCeaH6cYFBmrjK94Xe+r62N3Qz9bMp3EdQtbM8gYcSzNuOw+EojLNH0vR3TOpjD4mxs+w9ZsF/YlEjyG0MiacQRgaQbf6nuMR5s2cmhxiPfnL/DWzGm+0rWHZ9p33DTrsVLqslWGEJentUfjAr858BS7G/pJXRI51oQka8SJaRePVRdyuX52ObXykvP8qMLUNfpa6jg+Ok2pFnFtTCUIFbxw6Axbu1upS8R489QQju8TM2+f/0OTkkTMpCEdR8qLaaw3i4Z0nFTMZLFYZaFQIZOIRZkiQcD4bJ4NnU3ELTOKVErBeE179moQQmCbJs3ZJFPzBRCCmHV155kQAk0IWupSfPr+9WzoaubF/Wf47suH+PyDm5YZkoWImI73bOpisLOR7f1t/OD1ozy8pZfuljqkFCRsi2wyxtjMIrsGO67apy4ljZk48/kyFSfioXA8n0K5etMkTtdCpepxdmiGfYeHOX1+mum5PJWqF10bSyeTjtHWlKGvq5GNA62s72tGruB4uNexZgxYz32HMDeBkGkiI7aCChdQYRk78esY5t5LjLMlBLWa1KOEwSgqzKFUFaXyBN6ZiMBIhaxkwGr6AFJr/IC+7EpeJAsh05fko8cQIo0Q8ZqxJ6FmoCo8ICQMxgjDCQL/PJXCv6Ba+sPL2lSqHB03DmFwedqfbu5C9w7hOa9Ryv8DdGMXuvVATUooew2DLSTwT+G7Bwn8cyg1X6tldfG947U64oCrTfZS676q3q6QjYABqgzq+gynYVjC9Q5TqXwPpW4/leVK6FjWE8Tj38IwtlxW47wamFko8OmHNrCxL/I6J2MWyRs0SF94+xRP7hm84wZsGCrKVZeRqUhqoVJ1CcOLL5rbbz9kbrEUtXsXyHJuB03pBI9v7cfxfHRN4vkBTZmozqalLqpDcz0fPwzJJmNs620lVGAZ2i0br0XHZWh+kez41E3vWxeP0ZJKXtUYjZsGbZkUnXVpRheiEoHJfIHv7j9K1ffZ1dVBXTxWi/gpio7L6elZ3jg3whvnhjg7M4+haVi6oOzeea3la6EtluGp1o0U/CqyNq82WMnLFqRrHZrQ6Ii185d7vsVweZiJyiQz7iw5L0fJL1EJqgTKJ1AhEokhDWzNJmtkaLQa6Y530Z3ops1upc7I3nJ9qC51OmIdfLblaXrjPRwvHGe8MkHOy1ENqijAEDq2ZpPUUzSY9fTGe4nrNyYzpAmNjJFma2YLTVYTF8oXGCoNM1YZj+Re/PJlLMoJPUHWyNBkNdIea6cr3kmb3Ya94vkpnNCl4BUo+Nd/JyiiVGhfXX3hV/LLK/4GEKiQalglfx2W30v785R3mVF7KaJxvnpUy9IsNqU2kdJTrE+t51ThFJPVSYpeiUAFaFIjrsWoN+vpjncxmBpgXSJKyy4FKdL6yoyxKdOmJ9HI+nQbmrhGlIuV/TeCiAiq3k4QENIer6POTFyxDUQZIHVmgnjGpDWWoS/ZwuszJ/nz0Xd5onXzDRuwArCkTly3mK7mCWuM2RXfpew7+GGAQGBInQY7cgw32xnaY9mrHtfy38W1zvTiPksG8pLc1L2+dI9bJo9t7OWFQ2fwgihddV1rAwOtDfzXtw/zj/70eQxd5/z0PFu7Wui8RhruDUNENZ7abcozmrrG7g1dHL0wxRtHLmCbOkGoePXQOVzPZ0tfKw2ZBIOh4sTQFO+fHuOlA2fZsa4NTUqmFooka2zDhq6RTlh8Zs8GXj9ygY6mDPcNdmAbOrP5EsWKy6buZgxd49ToDEopssnYsrFadrwoWlw7ttGZRQplh1Q8MlANXaNYcS9L3xVC0FyX5IFN3fzk7ZN0t9SzsbsJKQXTC0XCULGuoxFD13hoSy+Hz0/Q196AaWiMzeR47fD5VXOtDI/N88LrJ3j30BDTswUKJQfH8WqMyVGk1zA0TlhTvHtwiNbmDL/1rUdZ19OEdZ0ypAPHRnn93bNkMzG++cX7Me4BEqg1Y8BCgMJDKA+EhpQNSGMzmr4Jw3wAqXddEhUMUeE81fIf47vvAgohs1GUVKZRYRIhztRqRFe+dYTWyI3r3UnEZfLuEiHMS9IGxSWyGDUPeFiI0omFDjJZq+e9pH+RRpObESKFpg9e9puuD2LHv4mUDfjufpzqD/Hc99D0dejm/Rjmnlpt7+V1wU7lh3jOS6hgFiFTCFmH0JoiT7I/BmJ2xWsiZR2Iqy14dBCi5tG/XpqOIghGqVS+QxCMXWfbm4cQMQxjG8nkb2IY25By9aKOF/sQ9HU0sGN9x/J3QRiykC/z87dO8vCOPhqyCY6dnaRQqrJzYyflqsvBk2O8degCVcfnyOkJ+rsa2bG+nULZ4YW3TtHenGY+X6FQqjLY3ci2wXaKZZe3D18gV6xGhlddki39rXS0ZK95jMVylcnZPI3ZBEGomJzLk03FSCVsxmdyHD83RaFUpVB2cFyPHRs62dDTTNX1ODU0w9j0IgJBa2Oa+zZ2kErYDE3Mc+L8FIv5CkIICuXqmjdeAWzToNU0LkvhXFrnJWM1p9IlP1rG7WuHjizk+MGh47x2Zuim9310XTe/tGMz6ass9jUp6W+s5/HBfv70vYMEYcQA+db5YWYLJX5Rf45MzMYyNDw/JFetMpUvcn52ntlSma66DPd1tXNyaobDY1Mf6vjZmkHnCmmN9wqEiFiDN6Y30BFrJ+flKfpFKkEFJ3TxQjeaFVWIrNVmmtIgpsVI6knqzToyRgZNared1mxrFu2xdrJGlu54FzkvRyWo4CkfUEg0DKljS5uEnqDJaiKu3TgbqRSShJ6gP9lHk9XIQHIdi25kqLuht2wwakLDkhZxPTrHjJEhY6QxriEfowmNweQAX+/8ymW6qbeKerOeZmtlncuUnuS+7E7qzbrb7gugK95FxriGoWkkWaeto9FqZH1ygJyXxwkdlAqX74mEnqDOrKPJbMTWotr7BAkebXyYnkQ3Mc2mM9ZxWbsSgS61m65BvRRCCNJGjEca1/PW7Gn6Es1synRgSJ1Ft0Q1dOlNNKELjaHSDKilVN8YzXYKS+oU/OoHVbWu22fWTNCbaOK1mRNsynTQaKc4tjjK2eLUcqTdlgaPN2/mWG6EnkQjttSxNZO8XyHnllmXasG4SeUDXWpkjBhSCM4WpkgZMUypowv5oevm3iripsHejb2016Vpq0shEDSlEzy6sYeFYpnDw5OEjsu27lY+f98GBlpvXKbrTkMIwd7t/Xh+yPGhac6Oz6EAx/X48sObGexsJGbqtNSneGBTN7lyleffPcmrh86hS4mU0f51qRgGGqmYxTN7NlAoRzI2750cqcn2CLqasmzoiurAp+YLHDw7Tq5URQqBH4T4QcjXH9++TOQ0X6jw3skRJubyy/XtuVKVJ+8boKPxohOgMZvkqV2DLBar/Py9U7y4//Sy9vqG7ib62xswDZ3PP7iRP3vxAC/sO8U7x4eXDeeWG8jeux4W8xV+8tJRXnzzJFMzeVJJm43rWmhqSBGzjSioUXGZXywxNZtnbqGEEJBO2TeUwnzq3BSv7zvL+r5mwnDtr/tgDRmwkQ7sZ9H0AUBHiDhSa0JqXURyKJcaakV89x2c0h8hZBbT/iy6uSNK4RVxVDiH7x+H6xEHCZOVtBhX2OEmzyq6CaTWjhX7ClLruOpWAr2WDn3JdzKFbj6A1Drwja343lEC/wye8xqBfwIVTmHaz9SuV4TAO4Zb+T5BMIJh7sawHkdqHQiRRAiTMJwnDFZOVwLzCiP7ZhGGOTz3INXqS7fVztVhoevriSe+jWk9hBB3JpIThor3j42wkC8Dgub6JNvXR2nTc7ky7x0dJmabzMwXyKRiLJV4CAFVx0cphabJ5QmxUvV4/cA5dmzoIJ2wr6gjEUSamYEQ7Ds6gm0atDdnr55dWcNcrszZkVk29rUShiFnRmZprkuRiFsMTyzw7tFhNve3EIQhr+0/R1drPY7rc2Z4hqNnJ2jIJAiCkDcOnKepLkFbU4ZjZyc5eWGajuYMSkUT5r0ykcEK2ajLv62uD36hXGGhfGs6pA2JOJ/fsnKNUns2zWc3DTC6mOPt8yM4ns9iucp7w2PsGx7D0DUsTcMLQqo1yRxL11nf0sDnNq9nZ2cbPz4iOTo+fU84IO4FaEIja2bJmtkP/ThSRorUCqmzq4WlPrpv39cDRMfdGe+gM371d+BqI67HGUwNMJgauP7GqwRTGjRZjTRZjTe8jy41NqY3sDG94Q4eGWSMOM+07eDPht7kjdlTHMuNoksNXwU0Wim64o3oAkZK85wtTOKpAENolAKHvFfm6datl9XcjpXnOVOYZKQ0x6nCBE7g8ZOxAzTZKXqTzaxLttBiZ3i0eQM/GH2PH4y+R9ZM4AQefhgspwpbmsHn23fyneG32D9/ngulGUypE6iQWE2nlps0YA2p0RLLsKdhgNdmTnK2OEVSt9iS6WJb3Y3LV60lGLpGZ2OWzsbs8neW1NnQ3kTiUZMdve2EoaKzIcNgeyOZ+O1lgHU3Z3li5zrWta9sCGtS0lKX5Fee2EFTNnlVXfIl9LTULRuh47N5ENDekOaRLb00pOO11GCdTT0tWKbOobPjzOfLCCHIJGNkkvayYoBp6PS31fPlRzZz9PwkM4slQqVIJ2y6W+rQayRXrfUpphfTSCHxg0jWp6MpwwMbu5ejkfWpGB0NGTwvWM7gGuhoZM/GbpovMTrjlsHGrma+uncrR85PslCoIARkk9H+Ws2A3tDVzDN7NnJqZJpy1aOtIUVzXYotva10N2dXLAG6ERw9Nc5b+88zPpWjt7OBR+7vZ+uGdhrrk5cYsB4LuRJTswVm5gqYpk5rU/q6aguu5zM2ucjcfBH6bp/88G5hzRiwUraimw8u68BeCypcwHVeIQynsONfwYp/E03vWfqVwL+AuoFU1zuNKAKaAAWa1o1hPXxz+wsTTe9Daj2Y9hfwvUN4zku41edwKj8AkSR2iQHrOW8S+OdqcjnfwLAeqf2yVDwvuLYO6+0u8hW+fx7HeRkVzt9mWx+ERNc7icW+TCz2ZSICqzuTGKQUjEwt4ngBQoDnBdy3sZNMMsZnH9nId58/wNRcnt1bunlgazeZZIx0wqa5PsWPXz3GIzv7eGBrzxXtZlMxHruvn7p0vNZPREbQ1Va3HJ05cGKM6fkCK6W9Q2Rgzy6WOHJmknQyqic5cnqc9d3NdLZkqboefhDQ39lIh+sxOrlIf2cDoVKcGZ5ldqHEw9v7cD2fd48OMz6TQ8FyRPfrn9nJ3EKJ5944fkeu7ye4NuKmwdb2Fn7j4ftJmGYUXS2WKToubuDjBSFhqDA0jcZknLp4jJ76Op7c0MenBvvQpWR9S+M1FxSf4BN8grWNwXQrisj4XAmm1OlLNCO4kvztUsR0k63ZLjwV8M7sGYZKsygU9VaSrnjDcm17yrBxQo/R8jx+GJDQbdan23iydQuWdnG5mPfKnC1Mcb44gxN6PNA4wIXSNOPVBXSp0x6ro9nO8HDjIAWvwvHcGAtuiW3ZbvY2R8RtWTOOITU2Zjr4pc7dvDN3hpHSHH4YkjHjUdr0JRW065ItlAP3MoZlgJ31PfQmm7BrpQmakDRaKX6t71FenT7B+eI0cc2kI752opKrhbhlsr69ifXtK2cj3ArWdzWxvuvabRq6RmdTlv/2a49dczuIHMiDnY0Mdl7buZOKW+xY186OddfmWZFSsqW3lS29KxPUDXY2Mdh57XPobMrS2ZS95jZLsEydnQMd7BxY2Qmna4JHt/ZeJgO0Wth/ZIT5xRK2ZbB7ew+/8sX7aai7egaiUgrXCyhVXDS5cunBEuYWoqhtqXJn+GruFNaMAXszUHiE4RwAur6hVjdb+025hOF0LdJ4+0xstwOpdSJlO753FN89hG7uJoouXxpNDrlIqLQUmVMoFdT+rkX1riKGYT2Ibt5HGOZxq8/W2IQvIlTzKOWiaR2XRXuVAhXOEQbjd0yLNeqniucdwXFeW/W2pcxgWo8ST/ylK2p0VxuaJvjlJ7ddYYQKEdG0m0bEcpdO2GSSl79MlVr6qMujrELQ1ZLFvqQOwfMDRqcW+f4Lh0knLDRNMjazyE6/44r9L0XV9ZjPlRiZXOCtQ9F3wxOLzOfKuH5AJhlD0yQ/ee0Yuq7x4LYeGrMJFvMVFgsVzo7M8qOXI/Kn+nQcy9SpONFEl4xbUURYk9RlEretk/pRgBSCpGXS31hPXfz2BeJb0snrXteEZbKnt5NNbU28fmaIg6OTXJhfYL5UwQ/DSP/Psuiqz7CtvZX7u9tpy6TQpKTqeWxsaWJ9SyOO79OeTa0Kqccn+ASf4O7hG90PXXebjBnn8x07b6g9XWrsaVjHnoZ1K26zo66HHXVXOl8/iE2ZTjZlrq9l3GCl+Fbvo9fd7kb6/UrXnqt+/7c3fO6K72zN5KHGQR5qHLzKHh9deH7AbKGEbigStqylr9uXlC+oZa1tauRiErlM1hZtt5S1c/F3iFjYL60v/gR3F9NzBRzHpz4Tp6stu6LxCtF60zL1q9a9Lhm3xVIV1wvwvIADx0aZmo24CYolh3Mjs5cxLS8hnbJprEuuGUKoe9KAFZhILfKshME0KiyhRJKl6Ktb/kFEOPQhs9Lp+iZ0Ywue8yqO8yxG7Gk0rROlLjIeK1VGqWpUf3qJ3IYK5wAFso7LKBqUh0BFhu0HZDuEbEAIKyKzChdQqubFUi5O+XuEwQiR/M6dgMLzTuI6794BzVeJaT5MPP6rSHl3aup8P8Bxo2u1RGQQBCFvHR5CEBl+MwtFTg3NsHWwLRohKQjDENfzcb0ATZOXpYwIKS7Lc80Vq5wdmSMMQ779yw8S+AEXxq4fuZ6czZMvVvn83s382uejjIU//vF75AoVJmfzuJ5P0rb48hNbaW9Ko4gY8gTQ3pTGMDT+6i8/iJRRXYhp6Czkyxw6Oc7sYgnH86m4HlOzefzgw3UCrQXYhs6j63p4dN31F3arCSkEadvm81s38PmtN55iaBsGO7va+O5f/7U7eHSf4BN8gk/wCdYSpvNF/s+/eIX2LodHd6ZJG3WsS25GFybUmLDd0Flm2PaVT0rP4IRl3MDFkCZBTTtZlzpu4JAx6vGUS8UvYmlx7Juoq/8Eq4cl/jIFhFcJktwoglAxPDbPsy8eYXh8gaGxOebmS3g16aW39p/nrf3nr7rvV5/ZwX/3G09h6GuD4OneNGBlHYb1BE7pz6iW/iNhOInUelHhLL53kMA7g25sjzRkP9QDjWHYTxGGUzjlP6Mw9xuY1hNIraXGPDxJ4J9GiAzJ7D9GyCWttZBK8V/jOS+j6evR9B6EzIKq4LkH8b2DaHo/unm5R9K09uJVnsOtvohSJXTzQcDH9w7hO+/XiK7uVARW4Tpv47pvrnrLhrEDO/Z5DOPm9RNvBX4Q8u++/xZ/9tx+AAa6m/j609spVTyef+ME3/r8Lrpa63jp3dO8fuAczfVJWhpSCOC+jZ388KUj/PT143zq/nU888imFftJJWxaG9OMTef4//6Hn5NJxqi6HpnktetXRqdyzC6WeOy+/uXvtg228fr+81wYm0PXNUanFviXf/oqhi5xXJ+vf3on2wbb2DrYzqv7z/GP/tVPkRJCpfitrz9Cd2sd63uaeem9M/zD3/sJmaRNU30S7RYEuD/BJ/gEn+ATfIJPcHdRdjxy5SpZ1yFQcYp+nlP5Q9hanO74Oi6UT6EJHSeoMumMYgiD3XWPc2DxDQxp4CmPopdnSds5UCGfavoCF0onCVRAe6znEwP2Q0JbcwbbMpiZKzA8Ns9ivkJd5uZJCnw/YGR8gedfOU4QhgQ1jfklLXMpxYoGqq5ra4rR+940YEUcw9hFIv3bOJUf4VVfBvEKQmbQ9I1Ysa8QBuME/pkP+TgFmt6HFf91NK0X1/k5nvs6SlUAAyETSK0Fw3wIxKUsZQLNWE/gnyTwT+J7BwBVk+xJYNmfx7CfRv9AvbCmr8NO/jXcyo8I/BME/kkQMTStg1jqv0epIk75O3fkXD3vOK53gCC4eVmRa0GIJHbsGSxrb43M687jt3/zMwShWpakiVkGDdkk2XTI3/iVR2lpTGGbBk/uGcTxopSOJU/YLz+5jWI5qiPIpiJDtC4d52/9pb001SWWtWUhopff0NvM3/32U0gp0DWJH4RkU7FretZ2bGhnsKeJ7CWG7kB3E031KUanFhmdXOCRnf1sG2wD4KevH2dmoUih5NDbXk9dOk6pcpEJtK0pg65Jtq/voLutHtf3MTRJqKJIc8K+d2RPAMJwkSCYJggmCcMpgmCKMJgmDAuAU5PaclDKBXwQEoEJwkBgRM+ZzKDJLFLWIWU9UmtD17uRsvGOkYd9gutDKac2nlME4QxhOE0QzBAGsygqqNBBKYdonF2i8gwjYowXFggTKRK1cW1Aak1oshGpNaNpXbU59pMUubUCpUKUqhIEY4TBDEE4vTzmKlyM5PZUNOYKB5ZkcISJwIyeVWEhZSp6jmVjbbyb0GQLmt7FB8t6PsGHB6WqhOECQTBNGEzXxnshUnRQBUJVRIXl2lj7tVIrH1VLb40UE3QEGkIYCJFAyMTy2knKdO0+aEDKejStASkbalJ89/49UHE8vCBAFyatdjf1ZjOL3ixT1VF6EoMIJCk9iyUdykGBlJ7FkCa2FsOSMZywimGatURiRUxLYEiDpJ5mrHKeatAI3DhJ2Y0iKv2bj97ZtXEPghnCcBalSsvvbJRbe9a9S57vaG4XIo6UmdrYRs+5pjUjtQ6kzC6rmNyr2LOjh3cOXmBuocjbBy4Qj5l88amtdLbdHNO6aWjs2tbF//HbX13+7p0DF/jZq8cZHl/g/m3dfPvrD11VRqc+k7htWaXVxIdswBpo+kbimd+pTSa9N7SXEBrIOszYl9CMDYQ1bVMhYkjZitQ6UWoRqffVWHovqUWU6YjtWOtCMzYhRfY6fdlYsS/XZGsGUUrh+QH/+acD+H4zyQRkM4fo74yzse/L6PoGNK0fapI7QtgREZPMIPXNLOTHSCZA1wyEsBEii9RaEeJSr5bAMPeiab21l3QVCGsPahyptUcfmf7AscYwrEfRtA7CYBKFC+jRtdXXo1QFTV+H1FpgiRxBWBjWXhKyCU3rQcoPMpBJdHMbifTfR4jsimPkOG/jeydY7RRly3oM03zwCpbmO4mB7pUL//s6Lx5HQ/bKGoTm+hTNH8hyNg2N3vYrU5+lFCRiJuu6bu6FsEQapS5hCI7bJnHbJAhCRiYXOD08Q6nqEIZQrno01ydJJixsy8C2DOBKFtNUwiKVuDWdyg8LSnmE4Ty+fw7fP4vvnSMIJ1BhHqVKhKoUlRiocs2gqS14VFD7c6SfBlrNkNWIFrRW7flc+iRri586pNaCrvWiGwNoWh+adm9LxaxVKOUThgv4/hBBMETgD+EHI7UFbbE2pkufCkr5XD62S3rXEtBqCxi9trC1QMSjd4aI1xY/dZGjQutE07vRtd4aOeD1STA+iliSnrqb566USxBMEQRD0bj7w/jLGu/RWIeqFDmBVXWFMRdcPuZazYERq33itf8nkVo9mtaBpnWh6z3oWi9Sa77nF7trHwqlKvj+GEEwTBCMEfhjBOE0KszV5u2lZ7tK5JDyanO4hyKAZf6QkIukh5KLfCIakfPKWP5/ZPAs3Qc2cuk+kPU1J1YzUragaW1oWmdt7r+7C/af7D9BoXJrUlPDM4vM5ks0mAO0x3oQCHKeoMFqRRM67bEeTGkTqoCUnsGQJoY06E9uRhNaTes4upZKKXRpINFosFowpUXiGprFNwqlgkhJxB++OK/7I4ThzEUHhSpdMq97XPmMB0REnkvPt1ZzXFg1B3QMQQwpEzVndAua1ommd6HrvWhaX+0df+/M64N9LTx6/zpKJZfJmRw/f+04w+PzbB5sY8v6dtZ1N5KIW8uBl5UgpSSbjpNNX4zejk/lsO3IZsmkYmwaaMOy1n5880M9QiE0hNaCFfvCLe8bGWNXQz2a3n/Ft0LY6MZGdGPjDfZjoBvbwNgGRC/1IFCcHu5ioKeJlqZ64jGTeDyLbg6gm9uu2oYSTVT9DEfO1rF7azeJ2LXSMASa3oGm37zkgJR1yGto32n65cQLQuho+sBlcjyX/y7QtHY0bWVWuDDM47kHCILRmz7ea0HKRmz7GQxj8CO3oJgdX2B2bB4rbtE52Ipb9bATJmGgcKsuvhsQ1thm61szeK6PU3YxrChaUFgoUcqVae5qwE5cnIgbswm2rGslZhlomkSFio7mDAPdTSRjaydyKGqLjJtHtKgO/Al8/xSedxo/uEAYjOHXFkGR/vP19Io/2GYI6mLV/MrqMwZSZpcXOJreha51oRvr0fX1SFl31zIFPmqIDKaQIJgg8M/i+2fw/QvR38NJwmCCIJgFvJtsOVrwLI3pymOr1Tz4TWhaa2TY6L3oeh+6vg5N66zpTt+ZRc90ucR0uUjSMOlMZXh/epx6O0ZbIkXCuLFnN1QKJ/Bxg4CEYaLfBgnbyYVZbE2nN7M6WqpXQzTmDr4/SuCfqY35MEE4UYvGTNUIG2+Gz6L2POPf4JgvRWpa0bUuNL2vNub9aFpHjTTw3lnorlUoFRAEk5HBWnNGBcFolE0RzEQGTJjj5p/v5R64aNwsjXnlBvRrZS06W3cx40Y2IrUWtJpRG8337UjZfMfn95+8f5Kx+dwt7Vt2PGYLZRJ6ioSewg0dskYDhmahCZ20cfFZjumRA14gqDcvOu3VB541gSCmJbC1W9fUUiokDGfx/fP4/hkC/xx+MB6V0QWThOHMLRCMfmCsL3l/X87fWhvfpawLre2yZ1zXupFaFlg7kcWrIZ20efKRDWi65PV3z3JueJbX3zvL6fMzHD4xRk9HA31dDazrbaKjJUt8Da337hTWvom9RpFK2DywvYf7NncBUd3k/GKJsyOzbNvQTrHokCtWSSctsuk4ygkHFwABAABJREFUc4sljp6e4JV3z6BJSW9nPY11SSpVj7GpRWK2SdVxyaRitDamsUyDmfkC49M5wlARBCGDfc2k4taaCuFDlD7sB+dRqrSq7Vr24xjm/TWCq48WZsbmOfb2GRI1HVnd0GnuaiAMQ+anchQXy5G+rBDUt2bwXZ/p0TmkEEhNMje5yNzEIk7Vo3dTx7Jha1sGG3pb2NC7kmNnLUDCB7SdrwelVC2VMPLYet5RXHcfnne0Rhp2twjbvNpCawbPO8TSAtgwt2Mau2qG7EDEBC5XSUTzEjhlh/mpHLNj86AUTV2NNLRlMax712iOPPL52uJmGN8/iecexveP10oSbsYZcTsIIq3scB7fP8nSwscwNqDrmzHMzej6ILreW1vIrq5TbbyY592pUVrjKVoTSZ67cJqtDS2kDAtdSkqeix+GZK0YhpQ4gU+oFGXfQymIGwZ+GHJqYQ4n8OlIpmhJpIjpBo7vk3ereGFInWVj6wZSCKq+T9Fzlg3ejBWVJcxXywzlF2mKJ+glmn+X+io4DknTJK4bt8xSHmVOLC1oh/C943jeYTzvRM0Bdbee54AwnCIMp8A7jFPTZDeMjRjGZnRjM7q+Dl3vQYj0R86ReqehVEAY5giCEYJgGM87ge+dwPdP4/ujwIcvdwghShUIggJBMHzJ9xIpG2qOrJ6awdOLJluQWlOU7SdTq27QDs0soklBR0OG+E2yx88XyxSrF6VQTGlRb11d11Os8P692e9XQpT+7xAE52rz+mk87yi+dwTfH+buqYTUxtcvEHAOqAWy9HXRvG5sRjc2oOt9aFoHUUT39h1WasmqXlYXuX30dzcSjxm0NKbZd2iIC6NzTM7kef29s7y9/wLdHXVsWd/O1g3tbOhvoaezAU2uXv9rDR87A9YPQ/IVh5HFReKGQXsmTcK6eU+F6/mMT+fJpGaxTD0qrp4v8KMXj5BKWJwbmaNUdtg00EoibjEzV+TA8VGmZvMcPT0e1RdYBmeGZ3nu1eOR0Vt26G2vI5OMYZkGU3MF3j86QtXxmF8so4Atg20k1oxnJVpkOM4rhMHkKrYrESJDLPYNNK2Nj6L3OwxCfM+nlC9zav8FBu/rXf5+YSpHbq5Iuj6x/NIIg5Dp4Tlcx0VKSSlfoVJ0GDszSUtXA5qRQNPuheskllO5bjQ1KzJcZ/D9k1SrL+A4LxP448Ba0CyLFsBO9Wc41ReiOnT7CSzrU7VygibAXJUXSOAHTF6Y4egbJ5kdn0cIwZZHN5Kq0ekHfkAYKJRSxFMxEOB7AU7ZQdM1NF0jDMIoNczQccoOdsJCMzR818epuKhQYSdsdHN1XuLXglI+KszjB+P43jGq1edx3bdqUZi1wH4dLXxc9z1c9z1EJYVh3odtP4lp7q5FZJpYrQVP2XeZLBUimYMgYLiwSHsiRdFzKXoOo4U8gVI0xeNsqm9mvFRgrlKODFsVUmfFSFs2r4yeRwrBYl0DMd3E1nQWnQon5meo+D4Jw2R3SzuWrjNcWGQov4gQ0JpILRuwS33ZNe3PQIUUXZdjc9MUPZekadKXrqM5nrwpveGLKf/DuO57OM4v8NwDq+78vHX40fPsTOE4LyNlPaa1F9t+EsPYjJRtH4l6ujuNqKYxRxCM43lHcJ3Xcd23CYJp1sazfSMIL3FWHiB6d8XR9QEMcyuGsTPKzJDNSJlByNSq8SM8uXUdX7x/I63ZK0t9roWDFyb4Fz95fVWO4VYROSTLBMEkvn+OavU5XOdNgmCcW4+ury4iycejeN5RqtUYur4ey34Ky3qsVlLQDNxairGvfJyggkBeVksMikCFaELeVlp6a1OGZz6VYve2bg4eH2X/0RHOXJhhfrHM+FSO88NzvL3/PI/sXscXn9zKYF8zUt7dUpC7hbtmwC5Wqrh+gBAQMwySt2A0rgZKjssrZ8/zvz//MuubG/k7Tz3Gzs62m25nLlfiuVeP8c6hC3S0ZHl4Zx/9XQ3cv7Wbn75yjJn5Il96chub17UhpWCgp5Eg2ICuS77xuftoaYxqCY6cngDg8T0DNNVf1FdSSrG+t5nejnrKFZf9x0Y5fnaSvs6GNWPARqk51drktHrSOULY2Pbe2oJhZa2rexm6oZFIxbATFqm6JF3r29B1jWKuTGGxjJSCVDbJ9Eikd1zKVykslKhryRD4AYFforW3kXRDMmKRCxXcE2uqaBFwI06J6EVYwfdPUan8iEr5O8v6z2sTIYF/mlLxNNXKT7BjzxCPf7NWEx+77VqqwkKJY2+eYmpohl/7e1/FjJl4jo9uaoydnqSwUMT3AggVmx/ZgBAwPTTDxIVpYgmbWCqGW3EJg5B4KsbkhWn6tnVT35plfnKRkZPjeI5P+7oWuja0I+6QQySKplcIw2kc502qle/jOG9x5yS+VgdKFXCdV3CdN9H1XmKxLxGLf6NmxMZu26hZkkfwwpC5SpmMZaNJyUQpz9ncPGXPoy2R4mdDp/m7u/dyfG6G18aH2FjXSDXwma9W+FL/RtwgIGvbpEyrRsamKLgOs9UyAsG/PvQO/+KpL9MQi/P6+BAz5RJ7Wjspuhfr7lKmxYGZcUqew+aGZqq+z7H5aX507gSPd/Xx4vA55ppaeaqrn/h10puXIhFKlQiCcaqVn1Ct/gjPO8laN2bCcJ5q5S9wqs9hGDuJxX8Z2/50jZPh3qqhuxuI5uwqQTCC47xKpfzneN7+D/uwVgnRPex5B/G8g8AfI2Ubpnk/lr0X07wfTWurEUJZ3GrkzTZ1GlJxMnGb+E2uk7OJGMaHpBywVA4QBvO43mEq5e9RrT7HWjFaV4JSldqYHqFc+iNisS8Si38dXV8HJGslBDeOgrfISPkMCT2NJW2qQZn+5GYqQYlyUCSt12HK25s7pJQ0NaT49GObeOqRjcwuFHlz33leeec0p89PMbdQ4icvHmV4bJ5/+N9/kUw6hvYRnKvumgH7j559gTfPDxMzDL5x31b+9qeuL9J9J1B0XA6OTlJ0XPaPjDNfrtxSO62NGb7w+GZ2buqMqAOkRAj47KMb+RvPH2Dv7gG62uq43j2jSUkybtJUn7zse9cLeOvAed46cB5D11goVGhvTi8Ta6wNeLjOuzXP2upFw4RIEot/CymT19/4HsXgfb0M7Ih0RYUQkUYsUNec5qHP74y+B3o2RrXHzZ31PP2XHkYIUTMAIhIoxL3mWYvSMm/EgA3DaSrl71Eufw/fP8NaX+xeiiAYpVT8A6qV50im/gax2NcQInNbbY6dmQAU2z+1GbPmxDJqRAsv/MlrzI7OsemhQZq7GkEp5iYXefb3f0H3pg6mh2YZPjlGc1cjzV2N/OJPXmXXZ7aTm8mz+ZENVIpVcrMFnLLLj//tz/n7f/L/IHYdOadbh0Ol8n0q5T/F845ewhZ8r8DD989QKPwepfJ3SSX/Fnbs87Vo++3B0CRSCPbNjLMuU09MNzixMEuDHWNvey+WprPoVDkyO4UT+OxsauO+5jbmqxVOzs8QhCFd6QxNsQTbG1vJWvayDrTj+4wW8hRqqci2ppM2bc4szjNeKvCZ7os8CD3pLA12HK3mdMm7DgdnJvhS/0a2NbWCgorvM1YsMFh3PYI9RRguUC79B8rl7xIEE0TOirX0Lrs2lKrium/jeUeolH9AMvnXsexPc494De8awnC+9mx/555wUNwuwnCCavVZqtXnkDKLaT5ILP5lTHNvjWTz5t/Nn9k+yKbO5ps2XgFi5q2n9d8+PJzqq5TLf4TjvFZjg7+X5vWAMJyhVPpDKtWfEI9/i3js6+jGuptqZax8nnl3mrRRh63FqYYVqkGFt+ZeIOfN80D9E7Ta3eg3aRivBCGgqT7Jl57eylOPruelN0/x588d5OS5KU6dm+L5V47xhae2kb5j7/MPD3fcgA3CkKH5HEPzC+QqVQpVh/Oz80zli7Sk776BkjANtra18OOjJ9jc1kxj4tbq1ASga/IyvaRCqcpPXj7KI7v6GZ5YYGh8ntbGFLqugRBITeC6AeGlRmjNAPmgETI8scB8rszmmnbn6++fo3yLzHR3Cko5VKvPE4b5VWtTiDi6sQnT3EPkxfxoQkp5Vc6AiDTrYhReLNUvCNCktvz90rb3HmStNvTax+46+yiVfh/HeasWdb0XF0IeQTBBIf/PcZ33SST/KoaxJWLAvQX4bkRKY8WuTEk2bYOBXX186usPoZsaQRBw6JXjzI7PUy055GbzlPMVEtvi3PfUVo68foJNDw0yP7GI7/pomqRSqDB+borCQmk51Xg17zGlHDzvKMXiv8Vz99fqW9fWnHbjWIo2jFMo/FMc5xfE4r+KZT2ClLfuqEiZFnVWjJ8NneGX+jfhq5Bq4IMS+GGIqUUkTZamIRCYUkOXWpTGK0TEuVwbs6X3ypmFWY7MTZO2bL7R1sWBmYnlbT7TvY7tja0cnpvk3xx+l//Xg09ArY1La94kAlPqOEGAANwwYis1rrNYDoJ5XPcNSqX/hO+dqKWHr+1I+8qIInCut59c7h9hVn9MIvGbNfK2j7M2ZkSaVSn/hErlu7juIcJwnnt3nG8WERNyGM7hOC/iuvvQtDbiiV/Ftp++JgHm1fD1h7YRM3VM4+aX503pBL/59B5abjL1+HbheScol/8Up/oSQTBak4q8VxEQBrOUS3+A675LzP4SduyLaNqNqUUoQurMZoSQzDoTlIIiHXYPST1F1qgnZWTRVrEEYekdrWmCRMziUw8O4rg+E9M5qo7HvsMjPPXoxk8M2FtBECreGx5lsVIlbkYkE+P5Ascnpz8cA9YyeWygh5b0F8jEbHrrb40gaG6xxI9eOsK+I8OkkzG62upIxE3ODM/yjWd2MjS2wOjkIsm4xY6NHRi6RnN9ikKpyg9/cZgtg+1s6GtecRmfTlgEQciJc1MUig75QpUPKTNkBShQVVz3zVWtX5KymZj9uTtCgHOvYSXj4d40XJdwrQhsRNRUrTxHufwdXHc/SuW4lyI1VyLy6jrOLwjVIon4r2Fae28puyBdn2KMSaaGZtj62OUs6oapk0jFSNUyOZyKS+AHrNvew8DOXhKZOKf3n6ecr5DMxjFjJrFEFJ0bOj6KEIJsU5qW3iaGjo6wmtc8Yh8dpVL5IdXq8/jeaZQqcG9551dCUEuFfoMgmMb3ThOLffGmvfZLSJkWnekMi06VuGFQ8lw21TcxnF/kp0OniRsGeddhsK6RuWoFLwwue5JMTaMrleFnQ2eYKBV4qqsfXWrkXYezi3PMVyv4KkChlqOq53MLLDiVWmaHYtGtsm9ynIMzE6Qti9ZEisFsA7ta2vnp+VMMFRaYKhW5v6WD5vjV72OlPDzvEJXKj3Gqr+AH50CtBcKe20X03guCYZxqjsCfIB7/FSz7cbQVFRE+uoikrqYolf4Ip/oyvn+mth64l+fsW0VYk38p1fRLf5lbWWLXJW/dGRIzDXb0tqHfhcViRNJUolL5C6qV5/C8IzVn80fBcRER+nnu+4TBDL5/hnj8mxhXURn5IHoS6xEINGngGx6h8jGlzYbUTqSQxLXkHaN1kVKQTtp0ttbR2pzh3NAMM3OFqMzsI4g7bsD6Ycib54cpVB22tbeSq1SZzBc4OD7JE+uvlLm50zA0jZZUkpbUrRvPhi75pae3oZTCMHRilkFDNk7MNnnywfV0t0cMw5OzBdLJKNqiSUFdJs4zezcBguaGJLZpsK67iWT8yohMNh1n56ZOmuuTxGImmpQYurzqth8GlHLwgxF8f4zVm7B0NL0Ty358ldr7eOLMkRHefO4IbtWld2M7Dz+zjfga8b4JERmwVzIaKoJghkrlx1Qr38d1j7A2GCpXB2G4gOu8CconVGVs+6mbZtdu6mpg4vw0p947y3P/8SUM22Dd9h5a+5ojp8Yl+m+arjG4q593nn2fyaEZ4qkYhYVSxGAuLkb1ISJ6ys1EEdq61ixCihuQnrgxKFXFcw9TqfyAqvMigX9udRpeY1CqiOcdJgzzhOEUduyLWNaDcBNyUX3pOhrsOA12nG9v3sX6bANuGJK1bOqsGE3xBJqQmJpGnR1jS0MzgVLU2zFiuk7KMGlJpGiKJaj4HgndJG4YWJrO7uZ22uIpUqbJtzfvot6KoUuN5nhyOU04pkeMp6bUaE+m+FL/RnQpaY4nSJomA3oDj7R3I4VgXaaevkw9MeNKltQwLOI4r1Ct/AjHfZswmFity7yGoAjDRVz37eXaXjv2GQxjE2tdjmO1EIYlfO84lcr3qFZ/VsuouBczZVYfUtZH8izy9nVTb65fQdK+82tEpTyCYJhK+ftUqs/je2f4KL2vlxBxcJwhDHOE4Ryx2C9h2XuvyeNxqVTRpRUGGfOiXvzNMjrfDIQQSCnQausBXb/+fCQuybhRShGE94bBe0cNWD8MmS+XOTo5TaAUD/d1M7KwyE+Pn+bk9CyFqkPSWh2GzrsFIQS6rvH0wxuu+nt7c5Q+ZlsG9dnEZftZps6juy73zCcTFp2t2SvasUydgZ4mBnpuv67qTiAMS3ju4ZrI+OqsdqWsw9A3ouvdt3FcCqfi8tqzBykXKhiWzsDWTtbX6k0/Dhg7N8Pzf/YWxVyZhz67jV2Pb1wzBmyUQpyAywiNFEEwRbX6c8ql/4Tvn+aj6MFXqozjvIXCBwT2TWYaJLMJerd04lZdKsUqgR8sa/YN3Nd7Wc2qpku6N7YzcW6KaqlKGIQ0dTbQ0JbFjlvsenorDW11aLqOFTNYmMoxMzpHLGnz4Bd2LdfW3g7CMIfr7qNS/gsc54VaWuFHGQFBcI5KJVrsqLCIZe8F7Bt6x7Un0ywlG36hb/1lv9XZMdZl61FKLde4XarPmrFsWhNR2qBSiqe7oveMEAIpBJsamtlY31RbqFzM4thQ10iYje6hJc3YhGGyuaGZzQ1Xym98qrMPPwwjOa8PnJNSqpZG+RKV8vdw3X0oVbzued/b8PG8A4ThImE4Tyz2JQxz100Tv9xrCMMinnuAcvk7VKvPriEW6bUB09qNpvfUCJ0+WgjDEr5/skas+F/vsozdh4GQMJyiWv1pxEqt8ljWE0jZeFPEjLdqtCqlCJWiWvWwTD0qSbwGCsUqE9M5ZueLaJqkq63uslLHq8E09GV5Tsf1yRcq94SO7B2dZauex/HJGWaLZZqTCTa3NmMbOq+cucD4Yp6zs/Ps6Ghdcf+lR2KhVGa2VCZXqVL1fcIweombukbMMMjGbOriMeKmcVVK/9HFHOO5PCXncja0mGmwobmRuvj1Uza8ICBfdZgrlclXHaqeTxCGCCEwNY2kbdKYiNOcSl62QLgaZoslZoolclUHp3Y+V4Nl6LSlk/Q11F/xW8l1mSuVmSuWKbkefhiiS0nCMmhKJmiIx4ndpIbYzUCpIq67j9X0uGpaJ4a5i9vxYAd+wOjZaf7w/3yWmfEFYgmLL397L92DrdhrJHr98Ya8wnsZBHM41Zcol/4I3z/14R3aXYGD67wHgCYbMa1HuBkZlvZ1rbT1txAGIWEQopuR/u/OJ7detp0QAt3QefSX9+B7fmTIaHK5n6e+9dhye8ByzauUcplQ7HYQBHO47puUS/8Fx3mNj6J3fiWE4RzV6s/w/QkQGqa5G0jdNgv1Up3r9SCEuIJxcqV9r2aIXg/6Vepeo1TSGarVn1Eq/kHNCbW22UdXE0FwgUrl+4ThPAmhYxjbWS15pbWGMCzXjNc/o1L5IWtDzmwtQcOynqyxk68elFK4fkC+4lBxvWtGyeqTEYvxaiMMc3juISqVP6dS+cHHynGhVBXHeZ0gmEUpr5ZF1XLb8/qNwPMC3th3jlTCoi6bIBm3sC0D09CQUhAqhesGFEpVTp+f5u3955lbKFGXjfPAzj5i9rVtgYa6xPI2M/NFDh4fw7YN4rYJAoIgxPdDdF0Ss9eOYXtHDdii4/LG+WH8MGB7RytNyThBGLKusZ4L84u8PTTC9vbWFd/JSinmSmVePXOB184NcXxymtlSGTcIsDSddMyiM5the3sLT61fx6bWZqyreBrePD/Md/cf5ezsHEGo8MIALwjprsvwP3/x0zzUd+2Inx+EjOcKvHlhmHcujHJqepbpQomq76NJQda26W+s56G+Lj6/eT2t6RT6VU4qrJ3PCyfP8sqZC5yZmSNfreL4AW4Q4NcmJF1KLF2nM5vmy9s28luPXDRglVLkqw5HJ6Z57ewF3hkaZWQxR9XzsQ2d7rosD/d1s3ddDxtbmkjdgXSSyNOex/UOsHp1bBJN78Q0d9xWK07V472XjlMtR+QwlZLD+NAs40Oz9G/qWI0D/QS3BXFZDWwYlnCcVyiV/wTPO/ThHtpdg4Pn7qdY/D0yehea1sXNTMVCiGVd1xuBfgNkIFJbnZdwVEdZwnFepFT8d3jeET4ata43h0ia4X3yuX9MOvM/1Ujpbt+IXYtQyicMpqlU/oJi8fdqkfaPckTm6lhyXIRhgUzmt9H0XpS6tzLMrgelAnz/OKXyH1OtPMvHyUlxY9CQshnT3I2U2VVrNVSKqutxamKWoyNTTCzkqTj+FU/ZErnop7cPsGega9X6B1BhBdd9j1LpD3GqL/BxTRf3/ZMUC/8KlIcd+8JNR2JvFkopHMfn3/7Ja/hhSH93I32dDbQ2pcmkYpimjucHLOTKnL4wzaHjY4xPLpJK2mwabOOxB9Zd1+jsaq+jrTlDzDYYGp3j+88dwPODKDtUCKqOS6ns0tacYfPgzcuO3incMQNWKUWhZsB6QciOjjaakgksQ2ewuZGD45O8OzTKbz60G6GujFgueZv+1atv8+yxU+QqVWKGTtKyyNg2BcdhMl9gPFdg3/AYSctisKnhqgbsYHMjn9k4wPr5BmZLZYbmFzk3e+PpbHPlMs8ePcnvvvwmQkSMjJahUx+P4QUB8+UKk+eHeW9kjHeHRvlfv/xZGhPxy85JKUXF9fiXr7zFT46douJ69DXWs6m1iSAMOTUzx8hCjlAp2jMptra1sL29lfu6Lmew88OQvzh8nP/y/mHOzM5hahoJ06Qtk2KxUuXE1AyHJyZ57dwFfm33Dr66ffMdoFV3CcMZAv8sq7VQESKFrvWh67233IZSUfrwey8dp1pxo0iSUkwOz3Hm8OgnBuwawFINLERyQK77JuXyn+G5733Yh3ZXoVQZ191PIf9PyWR/B8je8wvdiB3bxXVepZD/ZwTBBT6OhsxFBPj+UfK5/4VU+n/Esp4g0ou9t8f5UkTOzFkqlR+Rz/9vfFwXtUtQqoDjvMLi4gKZ7D9B13tRSv8IjHn0HIfhHKXSH1Ct/oxPjNcrIYRNLPYMUtbfti70pXBcnwPnx/md7/ycmXwp4n0OFQq1XMMeKoUQ0FGfYUfv6hkZSzrOrvs+xcK/xXVf4+PolLwUQXCOYvFfoVSVeOIvE+nF3tlnXNMkk7N5ZuYKvPX++atuI2qqJplUjAd39vLXvvUYyfj1NWfTSZtHd69jcjrH+0dHOHpqnCMnxy/bxjA0vvH5+z4eBmzF8xieX2R0IUfM0NnU0kQ2HiNlW6xrimp5zs7OM57P055OXcGa5gYBp6fn+PnJs5Qcl7+8ewdf3LqBjkwGISJ5nsVKlQOjE8wUS+zubl8xZXZzazODjQ34YchcqcxPjp3i9159+4bPpTER5/6uDr68dSPrGut5oLeT5mQSU9ci43N6lj8/dJwfHz3J2xdGOTA6wUO9XZdFP8uex76RMZ4/cZqy6/HXH93DFzavpyERRwFThSL/7Bev89bQCGnb5plNg+xd13tF7vqzR0/xw8MnGF3M8WBPF7+2ewc7OtrQZSSzcGB0gj97/zD7R8f57v4jZGMxPr3h1hgxV0IYzuJ5x1jNxamuD9YIMG4dvhcwN5Xj7OERPMdn/Y5uFmYLTI7McebIKJ/95oOrdLSf4NYhETUZnSAYiYTu3Xc/7IP6UKBUkWr159jVp7HsJ29bJ/bDhlJFHOcN8rnfIQjG+Hgbrxfh++coFf8VoLDtz/FR0g4NggmqlR9QLP4LPu7G60U4uO5h8vnfIZ3+bXR9A3eMdvSuIqBY/D2c6mtwT8uk3DkIEceO/fKqa9gvlqv82RsHqbg+v/6pXexa18GLR85ybmqeX3l4G0IIXjl2nvH5PL/++H08uqF3Vft33fcoFP85nrePj7vxuoQgGKdc/lMAEsm/xp0yp4QQJBM2f/Pbj3Pw2Chnh2aYnM6zmC9TqfqEYYiua2RSNu0tGTYNtnH/tm42D7SRTNx4BuYDO3vJpmOsf+cM7x4aYmxykarjYRk66ZRNR2uW3q4bkxK6W7hjBuxsqczBsQkUih2dbdQlYmhCoGka7ek0A00NjCzkeOv8MJ/fvIHUBwxYPwyZK5eoeB5x02BbeyubWpqxDB1B5BVqSMRpTiZwg4CUZa1Yy2NqGmatfS8IiRnGTb1ONCnZ3NbM30o/SMwwyMRsDC3S3lNKkbIsHD/g0NgEI4t5Tk3Psq295TIDtui4vDM0StFx2dzazK7OdrrrsxiahlIKS9f55q5tHJ2c4sLcAkXHJXZJTa9SipLr8fyJ05ydnWNnZxu/ums7D/d1k7pE8PqR/p7lZeOh8Un+4tBxHuvvxtJXzwschov4/plVaWsJut6DpvdzOy/6Yq7M8ffO4zo+hqVz/+ObOHngAgdeP8XkhVkmh+do7W5YvYP+BLeApQisT7n0x7juOzUisNWDEPVoWgtSa0KTTUhZF9XdChMhTAQSpQLARanyMgFLEEwSBMM1XeO7sRhXKFWgWPw3aHo3hrENIe5c3fqdRBgu4jhvUCj8k5rxeuelFISII2Uzmt6OlA1IWYcUqYg4RRiAjkChlFf7lAjDhdpnkiAYv0vEUj6ed4xK+bsIEcO2n7oLfd55BME41cpfUCr9AWG4eFf6FCKDprUhtRakrL9kzA0QBgKNSM7EQykHpYq18Z4nCMYJgom7RCzl4DpvUyr+AYnkX7kp56xSijOnp6iUXBqbU6TSMXRdYtsGYahYXChh2Qau42PZBrZt4HkBvheQTN0Z0iClfKrVn+M4r90l0p7oPSFlM5rWHI2zzCBkEiFiCKzaeEsQEqV8UB4KD6XKqDBPqPKoME8QzBAE0yh1Z+d1IWLoxobaWK9unaDj+ZwYm2X3uk6e3j7AupYGjo1MMZcvs66lkY6GNFu7Wvk3P3ub4yPTdDdm2dx5+7JOkXb3CYqFf4LnHlz1d/XVYSBlE5reeXFelxmkiAEGCB2BQCkP8AjDMkrlas/4LEEwShhOc+fv0QDfv0Cl8iOkrCee+Oaq91DxPU7NzfL/2/8OGWlRyDhsf6KLX21oJambUR20WiKXlVimTiJukUraxG3jptb8lqkz2NtMc0OKT+/diONE/DxSCjRNYps6dZm1JW955wzYYpn3RybQpeTBni7S1sUwdms6yba2Vk5Pz/HK2SGeGOy/olZTl5KmZBJL1yk4DvtHx+mqy7ChpZGEGdWVGJpGfeLuXNCkZZK0rpyUhBAkLJP2TIr2bJqRxTwL5QpecPlE6fpBlCIcKjqyaeoSMYyaUS2EwNQ1Nrc1Y2o6M26ZXLWK4/nLUeVQKU5NzTI0v4gXhGxta+WBnk7SH7huadtiT08nJ6dmeG94jDMzs5yanmNTa9Nyf7eLMMwT+MOr0lYEiaZ3oOudt9VKYaHMoTfPoJSirbuRTff3kpsrIDXJ3FSOkweGb9iAdaoeQyfGOX14hMmReUq5Mp638svPsg0e++JONt7Xc1WyKBUqhk9PcvrwCOPnZ8gvlAiDEDtu0dCaoX9zB+t3dhNPXpux1HU8Rk5PceL9C4wPzVIuVDFMjYbWLOt3dNG3uQMhBVJbqx7/EBUWqJR/QLX6i5r0wu1AQ8pGDGM9mj6ArnUhtVakTCNEvPaJ1QxDjWViFRWiCGqLHgcVVghVERUuEoRT+P45fO9EjUL/Tho5IZ53nGr1RaRsvC0G7g8LSlVw3QOUSv8B3zvJnfHQi6jMwBhA1wfR9V402YqQWaRM1cbZRggL0CJDRkhQCkUIS2OtqigVjXUkjTBD4I/g+SfxvVN3TMcwukbvIGQaKeswzftWvY+7iTAsUK08T6XyXwmCYe7MYlFHyobamK+PZEm0RqTMIkTNkBE2QphcHHNRG/MACFDKRalKbdwLhMECQThF4F9YHvPIoF39448yLJ5Hag0IYaHrNyYbqBSMjy7Q0VkHSnHqxDhSCDZsaufM6UlSqRj1usbMdJ50Jk6pWGX4wiyWpbNufSu2bSJXgYjt4vFEmpjl0n+uvfdX//kQwkLTutD1dWh6L5rWjpSNSJFEyMQlz7ZZm8v1Wt2hqH3CmlMyvDinq6VPGRWWCFWeMJgjDKfwgwnCYKLmxFpclXOSsh7Lerx2b65u2VaoQvLlKl2NWZrTSRK2Scw00DWJQpGJ22TiNps6mnn7zDBDM4u3bcAq5eL7QxQLv4vrvn/HCJuESKDpPbV5vb829nWXvMPtDzgstNrjGkRz+7KzqhI5pFWOMJjHD4bx/dP43imCYBJw7sDRu/j+ScqV76LprZjmI6vKQB6GipLnUXAdvrR5I3nXoeS6iDqNre3t12/gJmHXHGItTXdX/ulWcUcMWMf3mcgXOD0zSxAqjk5MUXJdbD3qLl91ODs3jxsEHBidYK5UoT4eR7+ESMTQNDqyaZ5a388LJ8/y6tkLzJXKbG5tpr+xnr6GOnrqs5ja3WH6C5Wi5LgMzS8ykS+Qq1SpeN4y+dJ0ocR0IXrAvTBkBWJh4OKUey0odfkrNQgVp6ZnKTkuaduiPZOifgX25Pp4jPZMmpRlUXRdTk7PMtjcsGoGrAoLBMHIqrQF0cSvyTaEuPWHxvcC5mfynDwwBMDg9i5auupp62mkrinNwkyeo++e5fFfuv7CsZgr8/bPj7Lv5ROcPz7GwkyBMAwJA0WlWL3IGi0iwzWZidPQmmHHo+sJg8sHXoWKYr7C+6+cYP8rJzl/YpzpsQVK+QphGGLZBpmGFJ3rmtn+8AAPfHor7T2N6MaVY1XMV9j/6knefeEopw+NMDO+QKXsohsa2YYkR99pZdfjm3CrHqa1NiN5kZTMSyhVIQjOc6t1VEJkMIxBdH1TbYHbj6Z1oWmtkc7sLdcfKcKwQBCM4/vn8f0zeN4RPPcAQTDBnYksOjjV5zHNHWha2z0VhVUqwHOPUql8H9d5l9U2XoVIoGk9GMYmDGMDmt6DpvXUFjmZ27pWSgW1+3CSILiA71/A907iecfw/XMolVvFM4m0gB3nNaTIomkdSNl0T9ZGKhXgOK9Qqf4YzzvJaht/Utaj6f3RmOuDl4z57T7bS2zJ+Vo0dmnMj+N5RwiCUdQqp8aG4QTVyrNI2UA8XnfD2s++H+C6PqWiw/CFWRJJm/6BgKmJHIGvsGMm05PR/WmaOuNjC8TiJr39zUTjsZoGbAnHefUOGDEGmtaMYWyNHBR6L5refdF4lUlW9zzcmk7zXCSHEswShDO153+CsPb/WzN2JFJrxbafWNVjXoKo8a6EKlyWTrMMHSFgvlBe3q61LkW+7JAv316kVKmQIBinUv4OjvMiSq228WdEqhPL83ofmtaDrnfW6odvnXx0iYthKaMqesZP4/nH8L2TNSfl6kGpEp57kFLxD9EynWha16q+wzUpaIon2N3WwZmFOQ5OTVJwHKbLJQ5PT+IEAYEK2dHcSmsihalpTBQLHJ+dphoEBGHI1qYW2pIpxot5hnKLlD0PS9e5r6WNOjt200z0awV3xICdK5U5NzvPYiV6iJ4/sXK66UyxxOmZOdozKTKxi+kvUghSlsmv7tpG3DQ4ODbJgdEJ3hseo7suw5a2Fra2t9DfEBmzKXvlFOLbRcXzGFnI8f7IGAfHJhmaj26ASJ8pemCqvs9ssTaRXOV9buka3XUZpBSMLOaYr0Vpl4xKzw84Mj6FE/ikbYu0bWFeYtArFDOlEl4YkonZy1Hoq0HWosKZmM1ipcJkvriiVM/NQimXUC0QhrOr0h6ApvUgtdbbWpgU82VGz04xNxW91Dfu6iVdl6Cjv5n2nkaOvnuOc0fHKCyWSKTj1/RQH3j9ND/+w9e4cGKChpYM2x5cR11zGk2TzE3nOXNohNmJRcIwpKO/mQee2kJjW4beDW1X6GcWFku8/+pJvvevf8HQyUnqmlK09zaSzETHUCk5zE/n2ffyCc4dG6NScnniK7vo7G9eHt+I2VVx+K0zPPtHr3Ps3fOYtkHHumbqm9JIXeKUXSZH5nnpz/dhxU08986ncN4KliJRt4aIwVjXBzHM7VjmgxjmfWhaxyp6PQVSppEyjWFsJAzL+P5pHOdlXOdtPO9wLSK7uoaa5x3Hdd9H1zeg66vLHnknEfhDVKs/rbFSrp6cRmS4dmMYWzGt3ZjmbnR9/SVRl9XoQ0OIJFIOYBgDQIjvX8B138d13sb1DuJ7Z1hNCaAwmKBa/QW6sYF4/Jsode9JrXjeUSrl/4rnHmQ1HTpCZNCNAUxjB6a5B8PcUctIWL1olhA6mlaPptUDW4gyII7hOO/guu9Ghqw/ymrey75/Eqf6PLrehWU9ed33nBDQ0Vm3TNLT3JIhk41jmBpt7XX/f/b+O1yu9L7vBD/viZXTzTkBuMixu9E5B7JJNoMoURQlWbIsjzy21489tmbneezZ2Znd2fV6ZmzNerwjW7Zs2RSVKMYm2Ykd2BndyBk353xv3convfvHKVwAjYtYBeCi+375gGhU1XnPW6dOeL+/8P2ila0zorEgwaBBIGjQ0ppCURV0Q7tpv8nVIKXni3Tlv4eUGaoTrFBRlDp0fRuGsRfDfBBd31oOTtw6NVchDFS1FlWtBXpXXve8NK475hMdZxDXGfCJrDeL586WM7RXD7QqSgxd24Smbbwlc9dUhcZklPH5ZYq2f83FQwEkcGJsml1dTZiaxlwmR8mu/Jr0vFnf2i7/3SqXDQdQ1UZ0fQuGcQ+GeR+a1ut7w1ftvi4AE03rQNM6MM2HcN0ZLOswlvUBlvUxjn2mHIypznPcF297g0JhO6HQr5aDk9U5l11PMlfI8974CJPZDDHTJBkM0rcwz4eTYzSGo2Qt35LzgZZ2mqMxhtKL/GzgHBuSNQgB3ckU6VKR47PTDC4tEdJ1Sq5D3rZ5tquHgHb3BM0vxi0hsKOLaU5OzWCo6gox/eRN1fZclvJFxtPLfDQyxo7mhksILPi9p9uaGmhPJTg4MsE7A8OcmJphOpPlR8dP89eHT/BQTwdf3N7Lve2t1HxC+bca8KRkcH6R7x4+wQ+PnQKgLRGnM5WkIRomGjAJaBrz+QJvnBu8orpx2DC4t6OVHx4/Td/sPB8OjxExDZLBIBLJTCbH94+epGg57G5toj2VuEzYyvF8r0ZVKNcsEVKEQFUEUoLtXi63frPwvAyeO1vViJyfPaus3GVuYokzh/2yZjOo07O9lXAs6BPYzjqOvHuO+ek0fcfH2H5fD4px+akvPUmpaPOz77zH4KkJUvUxnvzavTz7jf3UNMZ9Ze2lPD/6j7/gte9+yOzEEu0bG/nSbz1Cqv7y7LFVsuk/Mc73/+hN+o6N0dRRyyNf2M2uhzbS3FWHpqvMT6U59fEgL/3Z+4wPzPLKX35APBWmpj5O6KJ+psXZDK/8xQecPjiEGTLYsb+Hp75+L7272tFNncWZZQ6/c5a3f3KEc0dGsC1n1Szu3QvDf/AZuwgGfwnTvA9FiXOrxVEUJYRh7MIwdmGZh8jnvkOp9DauO0F1/Q8drNK76PoOVLX1riA1nrdMsfgSxeLLVSyzFihKPbqxk2Dgc5iBx1DV6pdKrQ4FTetG07oxzUcplX5BIf/dctBiiWr1z7nuKPncfy6T8k6q3S93qyClh/SWyOf+C5Z1oIq9pDqq2ohh3Ecw9BUMY+91ZyorgwBUdH0Hur4D236MUvFlisWXsO0z5Z7JakBiWR+jFOqvK0AlhGDz1paVoLMQYsVqcNvOVqT0SW5D4wXRt7r6qN8LV8XSYfADjo4zSKn0HtVRHTbRtHZM83GCoa9VbJtXDShKHEWJo+vb8HUJLBxnAMs6imMfxXb8ElTppfG8DKtlZ1W1DcN4oFzOXn0EdI2trfWcGpuhUPKTJ02JKCHD4K2Tg7TWxEmEgrx9ahBdVSrygPW8PFbpA/L5v8Dzpqr0DQSKkkDTNmMGniAYfP66S+orh4KqNhIMfg7TfBTL+pBC/s+xrIPlFqbqPMelLJDL/id0bSuG+QBCVEfIy5Ue09ksrw31Y7kuv7xlB1EjwLmFcbriSX5lyw7mC3n+4MC7tERiNEdjuJ4koGnc19xKwgzQGInQv7jI2PIyyUCAe5taGUwv8upQP4+1d64T2PPwpGR0Kc3p6TnqoxF+/+lHeaSnE+MTarrzuTwvnTrH//Sz1/l4ZJwv79hCZ2p1K4moafLYxi4e2dDJbCbLgZFxXj87wHuDI7x+boAz07P814/ezxe39WJo1f1KJcfhrb4hvnfkBKqi8NiGLv7hEw/REI1cUvJ8cmqG01OzVySwIdPgnvZWntjYzcun+/iPHxzkF31DdNUkcTyP/rkFRhbT1IRDfHXnVrY2XGqCLYCIYaIqCkXHxnZcpJSrHi8pJbbrUrQdFEUQMU2q9VzzG+Ur7Vu8FJrWiaLU3/T2UkpmJ5Y4d2QETVNp39hIbWMc3dBobE/R1FmLbmjks0UOvnmazXs60VchsI7jMto3zWjfNMW8xc4HN/LAc9upKS8UhBDEkmE+/60HGDw1zuTwPFMj85w9MsL9z2y/bLzZiSUOvX2WM4eH0U2Nr/+dp3joczuJ11y4sdU2Juja0kxNY4I/+n/8gJmxBY6930fP9la23Vu+wUv46PVTDJ+ZolSw2PPoZr72Xz3BtnsuPABiyTDtGxuJxEN874/eoP/42KeIwJpo+gZCoV8hHP4N4Nqy8LcChrEHTesmn/sz8vk/x3HOUU1RENs+hmOfRppP+EJEaxQrtgql9ykWX6qioJuKoqQIh3+dYOiXKrLUqngmaj3B4FcwzUfIZv53CsWf4rkzVOf3tnCcPrKZ/5NY/PdRlPo17w/rV4EUKBZ/VvY6rUYFzvlMSSeRyO8SCH6p6uqtNwJd34CmdWCY95PN/CGl0mvlkuLKw79SZrFKH1JQv0sk+nfxezivEYRe5aF9MZn95Ou3IpbneTOUSu9QnSoEFV3fQCj8NwgGv4yiRKswZrUhEMIsl7ZuAb6B685j20fLZdQf4NiDQAkpLfz7gYKqdWKY99+yWYUDBk9s6yFfshFC4HqSnsYadnU08s7pQf6HP38F8DO1L9yzla761E3tR0qJY5+kWPwZtn24SrNXUJQ4gcAXCIW/eUf6//1nFggliGk+hmnuJ5v99xTy3ys/v6oRnPHwvCny+T9FUWvR9Z1VsVIyVJVtdfX8w/se4t8e+ojlUpF0qUDJdWiJxhFAKhAkZ9uUXBcBbK2tI2db/MmxQ+iqym/t2MN0LsNQepHZfJ5T87NoisrGVA1a1W02bx+qTmAzxRKji2mmljP01tewr635EqJ3HrGAya6WJhQhGJhfZDy9zJbGOkLGlSNYAqiLRvjclo08vrGLszNz/LMfv8bwwiIHhsfYXF/L1qbKldcuxtjSMsMLi+Qsm96GWv7B4w/QGItcVq5su95KyfSVENQ1/snTj1C0Hd44N8DZ2XkG5hf9GvdIhOe3beIbe3ewqa6W0CcsgYQQdNYkMDWVuWyexULhigTWk5KlQpHZbI5kKEhnKlE1L1jPm8f1Jqsy1nmoalNFEfdi3mJqdJ7R/mkCIZNdD27CCPjHTzc0GlqTtHTVMTYww8G3zvArf/dp4PIeC9f1GO+fwSmX39Y1J6ltvnxeydoYiboYZtCgmCuxMLN6r9xY/wxH3zuHbmi0b2rkvqe2EkuFL/ucbmjc/8x2fvjHb7EwnWbk3DRDpydXCKxEcvjtsywvZgnHgvTuamPz7o7Ldyhgy95O+vb30H987HoP3xqHTiDwBJHI76Ebu4E7GykUIkYo/BsoapJs9g9x7FNVG1vKArZzFsc5i2HsrNq4twJSZsnn/xLbPlalEXVUtYNE4v+JbuxEiLWwuFVQlFpi8X+Gpm8ml/135aBF5ZAyR6HwXQLBZzHNh9bI970aXDx3mkzmX1eth0yIEIbxMLHYP0HTN3ILNSVvABq6vot44v9GLreRXPYPq9b36bojFIo/wQw8XSZHaz/A6Hlz2NbBqoylqu1EIn+XQOBZEKvrd6xF+OJMj2CaDyK9PI7TR7H4MwrFV3GdERQlha5tuaWVIkFD54HeDu7b2IamKn6LXdDkiR0biARNXjlyDtfz2NXZzNM7N9BWc3OWbFLmKRZfo1j8edXmrij1xGL/GDPwFIpy52xYJPKiStAAkcjfRtM2kMv9B6zS21XbT7H4GrpxH6ragqrefGLmkwioGjvq65kvFlAVhZBuMJ5J+zacuSwxwyRYTuAlA0Ge6uzhvqZWftx3mnfHR+mMJ+lOpri/pY0v9Pgl9EII9HUCewF9s/MMzi8SMQ021NUQDZirBgY1RaEmHKS3vpZzs/OcmJxhW1M9PbVXVokVwj/9FFUlrChsbaxna1M9c7kci/kiS4Xqq4xZjoPl+FF3U9Woi/rk9WLimCtZjC4uMTi/eOW5AyXX5a8OHefj0XEe2dDJs5s30pFKAKCrCkFdJxEMYmiX90WpisKulkZqwiEm0xnOzsxzdmaezY11l+2rb3ae09OzOJ5HIhhgV2tT1aIsnreI585WZSwAIaIIJVFR0/7E0CzDZyfxXIlh6ux+aCNm0CiPL6hvSdG5uZmhM5NMjy0wcm6aDdtbVz6zAgmO667E2zVdRdMuX2QIxZcsV1WB67hYxcujd54nWZhOM9Y/jRHQ2bSzHTNw5b5lTVepaYxjBg0WZ5dXenmllEhPMtrv2yp0bWmivtzvdNm8hKC2KUFDW81dUYJ6LQgRIRj6GqHQN9C1zbesPOvG5iSAIKb5NEiPTOZfVlXQzLHPYttH1jyBzef+FNs+XpX+KCHCGMZeotF/gqZvKavL3vmHqv9b+1nCYPCLgEo+91+w7UNV2oNNPv+naFoHilKZB/athutOkcv9x6oJmSlKDYHA5wmHfxtN714zwmX+b66iKPWEw7+OEBq57J9UqZRS4rrj5PN/Qjz+P7HWCayUDq47i12VIF2ASPR3McwHQATvqueTP1fN/6PofpuH1kEo/KvY9kk8L4+ub77F/bsCTRWXJYOS4SCPbOlid1czSJ/oRgIGyk2u94rFlyiV3qlSe4CGpnUQjf53GOZ9ZTu7ys/5oltiMDeMKx3aQ60oQkEVGoai43gOtnTQhYblWQRUX6BorjTPsp2hKdhIRDufRDAwzYcRQiEngpSKr1Q8Nx8OxeLP0LQegsFnqzSmfw480trJd04eRUjY3dDEi31n+H+/9yZ52+bh1g56kimKjsPHU+O8PNiHpqjM5LJ8vXcbW+vqsT2Xw9MTnJybJaRr7Kxv4pHWDswqV67eLlR91udm5xmYWyAZCrKtqeGKmT8hBAFdZ197MyOLaY5NTPFwd8cKgc2VLA6OTZArWXTXpmiJxwgZvq+RlJKS43J6eo6RhSUKtkM0YF5mxVMNxIMBIuVxl4slDgyP8UBnO1rZpmQmk+XdgRG+f/QURefqD3bLdXnxxBlmMjla4jE2N9TSkohfl/iUwFcXfnxjF3O5PB8OjxIPmHxd2c6GuhrfGxfon53n+0dP8f7gKHWRMI9v6qYmVD2VsfO+etWCn32tTHZ+fGCWodOTqKpCPBVmw442jItKhOtbknRtaeKNH4BVtDn2QT9NHbWXEVhFFaTq46jlh0RmKU82nScQuvRzxbxFdrlAqWhjBA2iycuzqqWCRWYpTz5bQlEUDr99ln/xD/7zqoT4PPqOj1IqWCCgmPODMVJKf3/pPK7jEk9FiCbCV1wA6KZGOBogGDbxvLvXcFwIk1DoVwiGfgld37KmSmqF8Pt5DPMRwt4Smcy/qJp6qeuOlgUm7Csu6m3bZSmTZ3ohw8xilt29rSSjt2dR6PeHDVIo/Kis1llZeaWfhbufSPT30I0d+DYZa29xqygJAoGnfUuOvIVjn6jKuFbpAJZ1EFWpR1HXpke15y1j20coFF6kGv1iQsQJBl8gFPpVNH3DmiGvF0MIDUWpJxj8Op63RKHwYzx3ouJxpbdMqfg2dvAourEdsYYzkb640XgVyIxKIPA4hnEfinIhuJrPFDh3ZISBE2O+KFUyTO/eTpq76tfkPQDw1ykigEoAqENR6sv36uo9n2aXc4RMnYCuXbNyTlMVokGTaLDyta/jDFMqvorjnKZycSMDTesmFvtvMcz9CBGrnqiRdJm3FgirIQZyw4TVEHVmLSCZKEzRnx0kooXRFZ2ucAcFt8B4YQpNqDQELs2IKkoEw9jv2215WSzrvarM0bFPYVsHMPRtqFrLTY9jahobk7X8zZ37iBm+UO0LGzejKSo1wSChLduxPRfPk7THE8TNAALYkKwhpOkoQsGVHl3xJFHTZF9jMx2xBJbroCoKqUBwvYT4PPKWzcDcAhPpDL31texobrzq501N5b6ONn568hz9cwuMLaUpOQ6mplF0HE5MzPDu0AjJUIDacLiszKvieB7ZksXI4hL9cwvUR8PsbG6gNXG5kM7YYprRpTTpQgnbdVnI5zk8PoknJZmSxZt9Q0wuZzA0FUPViAZMdrc0YZazoKlQiA11KVoSMWazOf74/YMcGZ8kYhgUHYfJdIaRxTR522J/RyvvD105GyMQRE3/JPxgaIy5XL6sJuy/pymCSMCkJR5ja2M9rYn4Su+wH4FTeWbzRuZzeV47M8CrZ/qZzeZoSyYIGRp5y2FsKc3RiSlc6fFoTxfPb910mRhUJZBeDs+rlrgFZQXZywng9cIq2YwPzjAxNIcnJemFHH/2r19GuehmadsOY31+367rehz+xRke+tzOy4SXVE2lY1MDqfoYS3MZzh4Z5uRHg9z31DYCIaPcH+Ly8ZunGeufQUqoaYjRtfny0iGraFMsWEhP4nouk8NzTA5fX9+YUAROOesvJRQLFu75KoCggWFe+bIVQqAZGmZQp5C7Fb5ntwMKgcAXCQa/vObI63kIoaCqDZiBJ7HsgxQLL1ONxb2UubLFxwSatkqZOIAAT8L0QvaKPXG3CtLLks9/B8fpp3JfPQPDuKfcF3VPRVUYtwOqWueTWG+JnDuH51WuBSBlmlLxFXRtI4aa4laLkt0MHGeQYuHHeFVpHQkQDH6eYOiraPrmNUlez0MIBU1rJRT6Op47T7H4GlIuVTiqW+6T+0uiWiuqGmAt/uYAUi6VM+6V9gCrBIJfQFWbL1GLL+YtRs9NAYL6thrG+6cZPTdFNBnGCBgsTqcp5IrUNiUJx4IUciU/8RH27xOlvIXnegSjgZWg8+2FKAsJVhcvfnyKSMDg3g1tdNT5LUzL+SJvnBhgZ0cTTckopl79jFmx8FMs60gVAhYamtZNOPLbmIFH8TUrqvn7SFzpknVyeHjE9RgB1STr5BgrTDBb8lsconoEy7PJOFmW7WVCWmjV0RQl7pPYUBbXHS9XVFV2zkuZxbI+QNe3EdSaudlrXFMUUsEgqeCFQNfG1IUy7K3m6mujhnCEhvDlegJ1oTB1oZtbb0svh+ecw3NOgCyCiKGaDyKU+jt2H6/qVTC6uMToYhrLdaiPhumpvXojuaGqK+rDg3MLDC0sMZPJ0ZaMoysKddEwluNwaHSSvGUjBKhCwcO3FQkbBlsa63igq537u9pJrOKLemJqmp+fHWB0MU3JcSnYNouFIq6UZIolfnbqHO8OjmBqKoaq0pKIs6G2Bl0NoApByNC5p72VhVyBX/QPcWpqhjMzswQ1HYkkZOhsrKvliU3deFJekcC6nkeuZNGajHNicpqDYxMcGZ+8kBktk9iIadCaiLOvrZmnenvY09p0CQHtqU3xxe2bCRkGB4bHOTg2wdsDw2iKgut5BHWdpniUvW3NPLmph4311ew5kEiZK8vpVweq1loRgZ2bWGJiaI7MUg4kLM1l+P4fvXnFz3uuR//JceYml2hoS13il6ooglRDnPuf2U5mKc/gqQle++sDLM1lSNbHUIRgaT7Dey8fY3JojpauOnbcv4HmrsvLuKWUPvsEDFOne1sLrd31qPq1b+RCCLbs6yoPxOWmwNfcvvpqlLcPOoaxi1D4G2j61orJq+d62JZDqWSvHMNQJFAVgSvfkqOFUOib2NaRsjJxpSI/EtedxbHPXJHAqqpCNGzSUhcnHg0SDtweUSspi9hOP4X8j6rSF6jrvQSCX8I0H13z5PU8NK0dM/AUjjtKIf9XVMOGwbIOYNnH0PTeW7IgrgSely4L2FSnR8w09xMMfRVd376myevF0PWtBIPP43nTlErvUulvLqVFsfgKwdCXUZTEms3Cel66LFxWCVQUpQZD33OZKqvrehRzJZKNcZo665gcmqWYt7AtB9tyGT47iWu5nDs8wr1PbyeXKZBbLlDTECcYCXDm4BCNHTWYIeMOEdhbgzdPDNCQiNLTWLNCYNP5It/+xSFChk4qGqwqgZXSxXUnKRZfLQcsKoOqthAIfp5g8EtAoOrPJk3otASbKbklDEWn1qghoAYouiVSRpKIFvZJrRIgrIV86hgCXdExlNXvOapaixl4BMcZIJv7I5+gVQjbPoNlHcAwHy5bN93d8JxzePaxcoDDA+nglt5GNZ9EqJevgW8Hqkpgc5ZNeyrBE2o393W0kryIUBZyJSZHFygVLVRVxTB9cZ2GaIRHezppS8RJhAIUHb+fMGToPNjVhhAwMLfATCZH1rJwXA9V8TOZzfEYu1ub2NXSSCIUXD3GIQWaouIWHNLTy9i2y4aeOoyGulUzF6ut+3vra0kGA3TVJDk0NsFcNo8rJfGAyYa6Gu7taKW7JkXf7DyP9HSyoa6G4EU3GMf1mMlmebNviOlMho5Ugl5dJ6TrKz0NnpRYrkfeshicW2BwfhHbc2lNxGiKX5op3NXSRFMsyo7mRg6NTTC+tEzBsgkaOs3xKLtamtjZ0khTrLrCIFI6eDJfVV8wVWlEEatHxq4HA6fGmRyeQwDheJDmztUvJCklhWyJsYEZsmVy2r6xcUVhGFi50T71S/eRXsjx7k+P8NHrpzhxYIBUne8Dm17IIhSF+tYUDzy3gwee3UHgk720+KTVDOgIAYGQwb7Ht/D8tx4kFL6Ohbrws8EAQvG3V8r/too2VunKpepSSlzHw77KZ9YuVFS1gVD4t9D1nWVvuJuHbTnMT6eZHl3Adb0VJcKNO9qIrVL2fTPwbXb2Y5j3USy8ipSrC3rdCDxvHts5TYDV+2cUIQgFDDZ3VVew7lpw3TlKpZ9XJROnKCkCgecwzcfuqPLsjUOg670Eg89jlQ7guoMVj+h5i9j2ERxnH4axu/IpVhGO049V+qAKwk2KX5Ib+lV0fcearKq4MhQM8yEcZwDb7qtCP6yH501jld5HVTvQtNaqzLLa8LxsxfZYQuhoeneZqF++3HRdj5nReVRFIZcu0NhRi2EaZNN5luezqJrKq3/xPt3b2zBDOhODsyzNZujobeKjn5/gi7/92BrNX988skWLNk25xLmj5Licm5xjKV/EdatlingeFsXiqzjOAJWqTQsRxTDvJxj8CoqSqMrsPglTNeiJdF7YZ/kMiBsx4kbsMsvOuB6jMdhwyWcvh2/fFgr/MqXSW9j2GSqtMJIyg22fxraPoapPVDTWWoDnnAEc9NCvgQiDN4eV+V9Q9T3waSCwe9ua2du2uhLb9Pgi3/tPb7M4lyWeChOOBHj8C7vYsqed//aZRy+fmKrSFI/xtV3bKprTc1s38tzWjZw9PsbPf3iI2YU0v/vsM9Q3J6/ppXoeihA0xqK8sGMLL+y4stjG7tYm/ujXvnrZ68vFEu8OjPDPX3mTVCjI33/sAR7u6aQmHFrJwJ6/JY0vpvmXr7/DW/1DnJ2Z5+T07GUEFqA+GuHp3ghP9/Zc13eoBjwvi/TyVF5SdAGKUgM3mX3xXI+BE+NMjcxjBAx693Ty6//o86t+1rVdhs5M8H/+D3+NY7mc+niQ7ft7LiGw51HTFOexF/YwPTpP/oN+ovHQSu91a08DnZubuO+pbWza1X5FIhQIGUQSYcygieu4zE0sousqZujGevyEEASCJuFoAFVTSC/kyKbzV1Sgdh0/ql3Ile46Gx0hYpjmIwSDz1clI5dZynP0vT5OfDRI24YLZK+tp6FqBNa3XTAIBn8J2zqK4yxT6fXheQs4dv9F49z5JZov6DJMofDjKowmMMxHMQOPr9nF+9UgRBBN20ow9FWymX9FNbKwtnUE2zheJndr47qVsoRlHaJkvV/xWEIECYa+gmnef8sWtrcSihLDMB/EdM6WM++VPwOLpTfK2ZnmNSFadjkspMxXOIaBqrbDFc5pIQS6rmEEdBRVoZAtkV3K4dgu0pNklnJYJQfP9Wjpqmfo1ARjfdOk6uOUCha1zQm0VSzx7mboqkK+5JAtWpRsB01RVvQspJR4UuJeQ99CCHFduidSuuUe77+qgraJQDd2Egg8i65vrHCsa+1pFYupqzwnr/beymeEjqK0Egp/i0zmX+FVIRvtuEOUSu9gmo+sGsC5myCEjsQAJMgCEhXfzePO3btu6xFt667nsed30rOlmSPv9/PK9z5m8642JPKSNdrFC3N5cflkud9Lnv+4uPAZwQUfNOldeLicL6XctL0Vq2jz/uunLhlbni/RLI+32r4l5bLM82RzlTldjZSMp9O8drYfT0q+uH0LD3V3Uhe5dAF9fuvWZJw9rU2cmZkjb9ks3wJl5ZuFlMtVsxQ4D0WJ3zRZyWeLjJybYmE6TUt3PXse3sTmPVfoGwTitRHaehoYPjvFqY+HmJ9Or0oEM0s5vv/v3+T4gQH2P72NX/m7z9Cx6er93Jd9L1Uh1RCjtbuOwdMTHHn3LIVciXA8dNWexfNZQjjv+ScQiqC1p4GZ8UWmRuaYHV+8IoFdmsswO7mEY7t3GYEVaFoHkeg/AAwqIW3nj2F6IUe8JsKXfuMhure1XhixynxQCJVA4BFy2TYcZ4TKI7dZXG8CKV1Ava09rleC505hlT7CdforHkuIaLm/ubLg5J2EqjYSDH6BfO4/lDUBKiOxjtOHbZ3AC6RR1ZvzcKw2HLsf2zpUBfEiFUWtIxz+nTtqo1EpdH0bpvkgxcJPq6LSaluHcJwBdH3bZeW1awFSWpVXWwkFRYlxpUWuqil0bG5my73dmEGDuclFjr13Dtfx0A2NL/yNRzn54QCqriIUhXhNhNGzUxz/8Bzb9vd8qkqHz6OlJs7JsWnePhUlZOjUxSMr68BcySKdL1yyTlgN4YBB0Lh2ib7vT/w+jn2SSp9bQoQwzUcxzccqGudOQogAodDXKOT/Esudo1J/WM+d8u+h3kJVLXXuDAJI5yR2/jsIpQbpDvttIHeQmN/+PUsIhk3iqQi5bBEJ/MkfvIwZMHAdl0DIYMvuDrbt68C2HL73J++wOJvBKjls29dBY2uKscE5UnVR9j28EdeR/MW/e4MN25q577HNjA7O8tr3D5HPFXFslydf2MOGLc0Er1C6+caLh+k7OUGpYNPQkuCJF/ZQWxfj5OFhPn7nHNl0gWLBoqYuxqOf30Fzew0//csDTI8vUsxbbL+nk70PbSJZe+UHUMlxWCoUfBGngIF6jczvXC5PrmT5CsjmnbcOOQ8pM7eIwN7cdzx9aLhMQn2l4ZW+0SsgFAlwz+NbmBiaZXF2mfGBGZbLJOdivPfSMYZOT9LYVsOuBzfSskqP6/WgfWMjex7dTN/xMeYm0/zwP77FC7/9KPUtV16guq6H9GTZqueCgNfuhzbSd2yUqdF5zh4Z4dyRUXpXIetnj4xw4sDATc33TkJV2wgEnip76VXG2DzXo+/4GJMj84z1z/DxYo6dYwsrhH7L3k7iqWovGBUM814cpx/XHa5wLA/pZXHdCVS1lbWQgbWdPkql6ngD+srSG/EDFXcrVBSljmDwl8jn/7wKhEbiuOew7Y9Q1epZL1SCkvUetn204nEUpY5Q6FtlYn43Ew4dTduEaT5BsfijKoznYVkfouvb1qZtlnSRsrIF/LVQKti8/J13+fDV4+SXC+x7YiudW1o4e2iIA68dZ3ZigfRcBlkum+3obWb07BQH3zjF3/sXv4ZaRYHKtYIv3bOF8YU0f/3BcV78+BRKWd/EcT3+6NUP+c9vHrxmdvX3nrufX7p/xzX35Xnz5PPfrcrvbJqPYxj33mXtAashSCDwOVx3tgrPconrTVMsvko4/GtVmd2dgmo+jqJ14NmnkF4GRduOYuxFKHcu4HrbCaznSWYn04wNzdLW7fehjg7Mcu9jvWza3kooYhKJBSkVbM4eH6NUsHnw6W0oqsLgmUkGTk9S35zg4Lvn2PPgBgZOT6AbKuFIgIXZDCN9M4QiJk98aRdLczmOHRjANHU27bi8VK3/1ARLCzm27G6nvinBxMg8r//wEF//ncewSg6ZxTxbdrdT15xgpH+GD984zZ6HNjI/nebBp7chpWTg9CTHPx7kkeeufLMI6jp14Qi2N82bfUPsbWsmbOgE9AsRMtfzSBdLvH52gF/0D7NYKLC/s41NVRVhqgyel61CSdHFUBBKDLg5MY+TBwZZmF7GCOg0ttfS0Xv1LGkoGmDfE1v4ybffpVSwGTw1wdTI/GUEdmkuQ7FgUSpYzE4ssbzok9wbjfbWNSfY+2gvpz4e5PgH/bz+vY9ZmMmw68GNdGxqJBIPAoJCrsjCzDLjg7MMnBhn2309PPjcjgvzEnDvE1v54NUTzE+nOf5BP4qq8ORX72HDzjbMgE56IcfJAwP84sXDDJ+ZuqpS8dqDgqZtIhD8QlXKbKSUFPIlbMshXhshHAusKFiCX2ZdXfjjGvo+SurPq/DQAykLuO4wqnrzEvzVgudl/QyhfbLCkYTv7Rt8HkVpWrNWGdcD30opQjD4ZQqFH1UlI+c6I9jWcQKBO09g/b7c4zjOWIUjaahaW1nQ5faIjd0qCCFQ1XbMwMNVIrBg20dx3RFgDRJYXw2wsjGkh/TSrFahEK+J8OQv7yefKaBqKtLziNdGCUUCxJJhena0EYwEuPep7WVrHdANjWR9jObuOuKp8FqI7VUdu7ua+Xuff4gjQxMMzy6RLZZYzpdYyOSpj0epjYbQtav/LsnwtYXBpCziOqNY1kdUKj7oZ18fKbsG3L1BqvPe32bgibKoVeXPcs9dwCq9d9cTWCnTeM45XPsg0ssjnDO49mH00K8jtLY7MqfbusqdnVxiZnyRli7fg/PBp7chhMDzPBpakrRvqEcvix8tL+UZHZwlWRuhtbuOSDTA4JlJlhdz9O5sY2k+Sz5T5OiHAzQ0J2loSbK8lCe9kKOhJUHHhgYaWmze/OkR0ourZw0nRhYwDI2Wjlrae+op5C0+eONCiXEsGaKpvYa2rjrS81nOHRujbnCW0YFZoskwmqayNJ8lUXP1bE5jLMojGzo4MDLG6alZ/s1bH9BZkyQRDKCrKo7rkSmVmM3mGCjbCe1qaeKxDZ00RNdOaZGUJaSs3CrEh7+YFSJwwzc8KSWlgs25Y6Ok57M0tKTo2NRIKHL1yJ9harRvaKCpo4bhM1MMnBhnYmj2skxm1+ZmookQY/0zvP2TwwyfnSJwUe+qUASGqZGoidC+qYlNu9qpb0musj+d7q0tfPlvPoaqKZz8aJCPXj/J8JlJErWRFQVk23YpZIssL+ZYnMmQqo+t2OicR6oxztNfv5dCrsjpQ8Mcfvssc5NL1DTGUTWVUt5iZnyBYNhk6z2dzE0uMT44e0PH9U5BVZsxjD1o2tUz6NcLRVVo62kgmgjjOi66oV041pZDeiHr+w4mwqjXWAjcCHwV2Vr8LFOlSqUlXGccjGoLdtw4XHcIxz5ZcfWFEAFM8xE0reuaUXopJX2nJjh3YoJCwUJRBD2bm+nd3oIZuP6Al+d5ZNIFfv7iEZ78wi7iVet9BjDQ9F40rRvLWqZSARTXncVx+vG8DIpSXQG+G4VtH8V1hqj0O6lqPaZxXzkQc/ezDUWJo2mbUNX2qthtuM4IrjOK5+VRlJsXM7w10BA3GVy+ABvHGQV5OUEyTJ3G9tW9j42ATrI+ttICJoRganiOc0dHWF7Ict8zO1aEDj9tiAUD7OlqpqM2wVK+iGU7jM6nOTU2wyNbOtnX3ULkGr6vrTXXVjN33Vks+2OkrNwW0TD2oum9CHFn71vVgqZ1omk92PbJio+PlFls5zSet4QQ0TWjcXCj8OwjeO4oqnEfcOE7iApEWCvFbSWwqqag6yoNLUkaW5N0bvQzZqqqEAgaK+QV/OCfqio4lgtS4pX7WjVdJRoP0dCS5NyJcUb6Z+jd2UaiJkI+W0IoAq9cbuJ53lV9ElVV8ZviPVnuh5UoF5n6BsMmuqGiqAJFUfA8uWIsHQwZROMhmtpStHRcPUuaCAa4v7Odb92b46ORcYYXljg7O4cqBIpQkEhcT5bFoiJ8busmHunuYG97C4Fb4Pd187CRFduEnMd5DzWNG13YeK5k5NwUM2MLlIo2rRvq6drSfM3ovqIohKNBtt7Tzez4IlOj80wMzZJbLhCOXYhYJmqj1DTEGB+YYej0JEOnP9HML8AwNKKJMM1ddey8fwOPfXkvLd31lwmDRRMh9j7Si6artG9sZPDUBDNjC0wOz2EVbaSU6IZGKBYkWRdl54Mb2LCj7ZKSdz/yL9j98CYc2yXVEGfgxDiDpyc4fWgYTVOJ10Ro29jAvU9sJZYM8cGrJ+4aAqvpmzDM6vmAKopCTUMcz5MMnZlkYXqZRE2EHft7OHVoiGy6wMTQHO0bGi4Rd6p8vylUtQkhIlV46Nm43tr4/XwlxRMVjyNEiGDoywgRv+a1uryUZ7h/htnpNI2tvuCeaWo33A8sPcjnLT5+t48HnthSVQLrB97CGMY+HGcQz6tUnb2E607iOP13XI24VPoA1x2veBxVbcUMPH7XLto+Cd86qw7D2EehMEblfpE5HHcYz5tEUW6fIOP1QAij4sWplBaOO4QnlxAydt0VNkIIhHrpxa5qKpF4iHgqwoad7RXNa60jaOi01MRpKRPRRCRILBSgoy7Jjo4mkpHKrZdcd5JS6b2KxwG/fFjT2u/q7OvFECKIrm/Fsj4uCzNWAgfPncO2T2AY+7iY/N1NkN4CQgRQzUe4pGrys0JgU3Uxujc3cs8jvZ945/JViWHqdG5s4MM3z3DuxMTKgqetu55oPMjWPR288/LxMqENohsa8VSYVG2UgdOTnDw0THohR1NbimgixOzkEmODcyzOZRk6N43nSRpbk8xOLjF8borlpRxz08v07ryQCv/kIsswddp66mjuqKG+OUEkFiQUCRCvufqiSFUUWhIxfmv/HrY11nN2Zo7pTJZsycKTEk1RfH+vcIiuVJIdLQ00RqOXyKivBUjprBpJvTkIhBLm5nqiJHbJZtdDG+nc0sTeR3qvm4iomsL9z2xHSkl6IUttU3Il22lbDgMnxjn41hmskkNrTwNmQF9RSDy/b+lKSiWbhek0Zw4PMzE0i0Tyq3//WYSuXnLeCCEIRQPsf3obG7a3cvrQECNnp1iYWaaYt/A8iRnUiaciNLSl6OxtorWnftVscjQR5sHP76Slu56THw0yPjBDPltENzTqmpNs3tvJhu2tlIo2dsnBCOhs3NGGsYZVGoUIomtb0LQrq3vfLHLLBSaH55gYmiNZFyWWDPHR66fYsKOVuaklNF2tKoEVQkVVW1DUGtyKH3p22cLizmZgPS+DY5/FcSq1i9FRlEYM48Hr6pHK5yyyy0VStVGefH7XSv/ycP8Mruvh2K4fUFQFqqbSXRZZGxuaI5cp4roe4WiQ+qZLMxGzU2nyuRLRWJBUXRSr5DA2NEepaOO6LrUNcTRdpVS0CQYNkrURpIQzx8doak2RSH3yXi8wjHsoFl/B86YrPEbgeXPY1uE7RmD9QO5S2de40gBKEFXrQtfXYHlsBRAihmHcS6HwI6qhQO04gzj2AJq21ghsqCzAVAlcPHcW2zqGEqhBiJv3Oa5rSVK3SqXTZwGmprG1tZ5YyLymhsr14HyLilNxYFIgRBLd2Os7SnyKoOlbUNVmHOd0xWNJWaRUeqcs2HZ39ggLTDy5iOeMgPDb3wAUte2OCTndtr2aAZ1UXZTwKgvz5o4agqFLsy+GqdGzpZlzJ8Y5e3QUy3LYtKOV7fd0EYqYdG9u4qW/OsCDT21bKeGNp8K099Qx0j/NgTfP4NguDz+3nfrmJMPnphkfnsPzPPpPTiClZOd93UyNLTJ0dorhvhlqG2M88ux2hPAFf5K1EQJBA0VRiMSCtHbW0tpVR1dvE2ePjWHbLg3NSbbu6bhmZF8Rgohp8sSmbp7Y1F29A3tb4ZT/VAvGdcmbfxKqprLtvh623XfjD3xVU9nzSC97PhFE8a1ulvjO//4yR987R/vGRh5+fhdb9nWRqI1ckOqXEsdyWV7Mcvidc7zz0yOMnJvmzR8c5IXffpRoIrRqdkkIQW1TgoebdsPzNzztFZgBg0272tm06+oR6Oe++QDPffOBm9/RbYKqtpdL8qrf6y2EoLYhTl1zgkDQ4Mj7fSzMLtPaXY+qqSvladWEotajiETFdQpS2mVbgztLYB1nAMcZqLjHU1GiGMZuFCV5XVH6SCyAYWrMTqXpOz1JqjZKTV2Ut185gVAUFuczeJ4kFDbxPEn3P2oklynywZtnWFrMIT3pZ933dRBPRRDCz+qODc2RXS6wcWsL8VSYhbkMr//kCKqqUMhbdGysJxYPkc0UCYVNHn56G9lMgVd/dIinvrh7FQLrL3SEEsd/oFduoWTbxysaozJ42NYxXHeMSlVJVbUJXeu94+XQ1YaiRND0bb6thKz8eeg6IzhV8BOuNhQlUiVS4lIovIimby63Da2twPzdgFjI5IV7t9DdkMKsQlWe605i235Za2VQMYzdqGrjTYtxrlVoWheq2kA17utSFrFK7yPDv1mVud0RiADSHcct/hCUes4TWBF8AcGdycLeNgLb1F5D0xX6HX7nH1/u3SmEQDc0vvRrqy/Caxvi/LP/729ctk1zRy2/8ruPX/b5ZE2E3fdfTngeemYbDz1zuZ1D745Wei8Sftq5v5ud+33i+cxX9606p089pEOlzf4XIMr9NWujLyqbLnDo7TMceP0kmq7y5b/5GA88t51A6MplrbVNSaySw9DpSbLpPHOTS4TCARTz01FGczug61uq1vv6ScRSYfqOWxz6xRnffxdfoGt0YIbsUoFAsPoPXEWpq5LP5fkM7J2FY58qk5nKoChJDPPB6/58NBZk7/0b+OCtM/z5v3+LptYkL/zqfmzbZeuuFob7FTRdo6ktxcfvnMPzJCcOj+BJyZPP76S+OcnB9/p486XjfPmb94MQvP/GKQJBg937e9i0rYVMOs9w3wzFgsU3fudRigWbN186hqap1NbHOPrRIPc/1suZ4+N0b2oksYpytRCgaW2oSi02BpWSPs9bLgcMXEC5A6JHHiXrw7I1UGXQ9E504+61SroShDDRtHYUJYHrWlT6TPS8aVx3Eim9NVWCKUQSVW2qwkguxeLLBILPoaqNQOyuFvO6E4gETJ7Ztalq4znOKI59puJxhNAxA49+anpfL4aq1qOoDQgRqoL7ho1tnyrryKxugbjWoeibEcpqz8DPSAnxOu5uSNzywqo6EMJgTZhcAoVckcGT4yAl9S1JapvimNcgOLqpXSQoI/wy47Xxde4aaPomVO3W9DPFUxHufXILG3e0IoSgpiGO63l88OpxErVh2jdWr3z4PBQlXi6NrwxSekivmorfNwfHOYdbBUN3ocTL/T/Xf4HUN8d5/uv3cP/jvRz6YIDv/NFbZb0EHTNgoBvaShBCSsnsVJp4IkQwbBKOmBiGRnoxh+t6jA/PMzm2wDd/5zGaWv0yxGLRZnxknjPHxvkPf/AKiqIQCptsLqvh+9vnOXZgkEef205N3ZUXaarahqLE8byZio4TOHjeIp47iaI2cfv7pTxs6yBSZioeSVVb0bTqLbrXEkTZUsfzlpCyUNFYUhbx3Hk8bx5VvTnLtlsBRalBVaulLmqRy/5bFCVR9gldX3reSXjuOI5zrgojaZjGfpRViM2nAapSj6LU41ZcIeEh5TKeO4mqNMBdWEYs1E6Eerl1453s6f1M3UXOnZ7kZ98/yOJchv/6958neRPWKLcCi/NZ3n3zNC9+96OV14QQPP2FXXz1m/ffwZl9AtKmeiXEolxysjYYn5TgehIJZNJ5LMu5ZqRstG+ac8dGURRBKBYg1RCrqqrtpx2KUoOmdqDcIh+xUqHE0ff6eOMHB3Fsl2S55/Hz33yAzt7GawYobgaKiCKoXGDDf+CVuJMlxH42cKRcylwJAqhKYzn7cn2Qnlypwoknw7R313HgF2eJRIMg/MYDIS7cPfxy8RhjQ3MU8xaFvIVtO0TjIRRFUNcQ47mv7mO4f4ZwLMC+BzZgmjr1TXFaOmv4+t94iFAkgKoqhKMBctkiXb2NHDkwQLFoEY2H0FftJfdnoGpNCCUKFRNYn9DYzjlMtZ7buTiQ0sXzFnCcfqSsTJBKiCiq2vyp64vzIUCoqFoXWIeByggs+LZFrju6pgisEAaKWo+qduK6QxWPZ9tnyWX/PVLaBAJPIUSlCsefHYwvpPnv/+zl6/78Nx7axbNXyNhKmcN1J3DdqQpnpaGoDShqCzdrhbi2IVDUGhS1tgoEFkBiO/2o2kZU7j4Ci1zGc87iOf3lakwfauA5hFr9ZMD14K4hsO++cZqDH/TT1JLkc1/Zu2ov7bVQzJeYHF9gYS6L47hIee3F4fjIPANlG5V7H9x43fsa6p9h4OwUDU0Jtu2+eobJDOh0dNVx/6O9FAsWBz8YYGJ0nvnZyqPg1YTEqaIKcTkDu0aM7YNhc0XJOJcu8Pr3Psa1XTbuaCOWCvs+dVKSz5aYnVjk7OERPvz5CU4fHCKaDHPvk1sJhu5un8PbDU3rRlHqquL9uhoWZjK4jktnbxO5TIEHnt3B2z89SjQRIhwL3pLfSihhhFKNh5MEKjeXrwSOcw7Pm6XSoJWqptC0nhvqkZqZXOLkkREmRhcQQuDYLg89tZVzJyZW/byiCDZta2FscI733jiNUra6uv/RTVAmwhu2NBEMm4wPz2MYGjv2ddK5oYEzx8b4+N0+FEVQ35Rgw9ZmaupjNDYneel7H9PRU08wZFymMH7Jd1SaUES0KndHKYs49llM8/YGL6UsYlvH8bwslYoTqWprWZH7rlli3CA0NK2z3Adb+Wiet4jrjIGxt/LBqgQhFFSlFsPYRaEwVIURS1jWx0hcPG+eQOC5W6J98GmE50nSucuDSh4Sy3FZzhXJlSxqoiG6GlJXda9wnDEcd4xKny/nBRiFCK6p0verQUqJV75gFSGuw7kiharUVO1J7LeH5IG7L7DnWh/i2ecQwsRzBxFqpx+wrTDYWQnumqfL2PAcH73Xx4bNTTz1hV03NUZjS5LPfXkvpaJNNBa8xDLnShgemOHj9/tpaa+5IQJ79uQ4Rz8eYue+rmsS2EBAp3tTI3WNcWzLYX42y9xM5T1I1YekuhmhtSPmEIoG2HZvN7sf2siJAwN8/OYp5ieXaOyoIRwNoukqnutRLFikF7JMDM4xNTKPbqjse2wzT33tXrRPKBCv4+rQtB4U5dapSkopMQK+16BVtH0rLMfFdb1b1ocihFEF70TwM7DV8ly+OdjWiSpkX/3+V03rvKFtzKBObUMcVVVQFAVVV9i8vY3ahhiNLUnCYRNFVQjHAjz4pK9gnayJsOOeTtILORzHJZGK0NFTjwSe++peaupixJJh4okQgZCBpqvUNca5//HNZJYLSE8ST4bLlm4quqGytJjj87vbCYSuTr4VtQYhqmPRI2UJxxkq90RWZcjr3G8By/oYqPy8U9UWv1TuUwohVFSliWotoaRcrkqpfrWhqPUYxgMUCj+kGs9+KTPY1kdIL43rjmCaj2MYe4D14O/VkAgH+e0n77nsdQk4rkemUOLsxCyHhybY3FJPS+rKas+uM+h7jFcI32pmy5oV5To+N03BsWmJxEgGgkgJpqqSsSwmcst0xBKE9Wvc10UcURVNCx+uM1ImsHcfpLuAECqKtgHpTaGa9+MUvk81lNhvFncNga0G6hri1DVcv4y7lJLRoXnGRuave7vzWd2Bc9NMTSyxdee1Y/KK6vdehcren9FYYMVvdm1BoboZ0+plcyuFYeq0dNfzpd96hFR9jKEzU4z0TXH64BC27ayULWq6SiBkEkuF6d3dTs+ONu55bDMbd7StlWrouwaq1lUlwaPVEUuGaWhNkc8U8VzfEzaaDGOYN+49fP3QqE5gZq1kYNMVjyNEHFVtvfYHL0IiFVlVNClZ679W35RYea3hov/u3b76fh55ZvuFsS9SjDcDOtv2XNrXs5zOc+7kBCODszS31dDcXoOuX/039b1tq1UWZuO6E9zuhYGURWz7GFJWft6pagOK8mnOrikoam3VMsyel8Xz5qsyVjWhKAl0Yweq2lYWc6v8nJSygG37SteO3Y8TeAxd316u0ojcNdm824lo0OQL+65sNee4HgPTCyzlCtiOS8m+ctWM447heZWWD4MQATRtA2uliu6TOL0wy0KxwFwhT8wwCWga22sbkEhGMks0hCLXJLBCCaOI6vX3uu5UuTXo7oMQBlLUItRy4E664C2VWwvvDD71BHY5nWdqfJFs5kKaW9NUNu9oRV8lY+Y4LovzWRbncxTyJc6cGGd6YomJsQUOftB/yWe7NjSQSIURQmCVHH+7hSyFvMW5kxPMz2YYGZy9bLvebS2EwtWJODqOSy5TZHZ6mXy+hOt6aJpKJBIgWRMhnlzd1uVmINAQVcya+gulG4vqep6kWLQ4NziDpqogIGDq1CTDJOLXVkOTUrKcKVIs2cSiAYKBCzcwM6Bz/zM7aN/QyMmPBxk6PcHcVJp82VdS01WCIZNEbZSmjlo27GilfVMj0VX2m8uXmFvIkl4uoGkqiiJorIsRiQTQ1kDf9Z2HQFM7y/YjtwbnS4WlJ3FsB8t22LizlVR97KrloJVBg6pEpCVS3pnIph+Ek7juSMX2OQCKEkNVmyuf2G1CPltidHCW+ell7n+897oqKxQlhhBXViy/EUjplEu3b9/vL6UEWcJx+qlGYNFX8Lw1ve1rA0q5d786z0MpC1WwNKk+hNBR1WbMwDPk89+uYrmgxPMWKBZ/imW9ixl4CtN8HF3fjKI0oiiJNZvZW4vQVIVNzbXs7mrm3TPDbGmtZ2vb5RUQUnp47lRVgiW+GncXa5XAWp6LIz2Gl5ewPIeGUIQtNfXoikrRcXDltSuxhAgjlOqp7HrePNzhyqqbhVAbEXi+AJUSxbPe96uO7mAv+6eewI4MzvLX336fk0dHsS2HQt4iGgvyb/7090ikwqjqpSdvsWDz0bt9vP+Ls4wNzTE3s0yp5PCL105y4J1LVdv+4X//ZfY/vMn3GEzneffN07z/1hmmxpeYn13GdT1+9oOD/PynRy/Z7n/+179BT+/1C5pcCY7jMj+T4eAHA7z16nEmxhYpFW2CIYOO7jr2P7yJBx7rXfGorZjICpXqlf1KJDdOYB3XZXxqif/5D35Ka1MCTVOJx4Lcv7ebRx/YiKZdfX6eJxkanWNiKs2OLS20Nl8egWvuqqO5qzIxjfHJJX768xMcOTFKU0MMVVF48N4e7t3TRbIsLPNZhhBBVK0RpQqKvVfeh/Cvb9XPnO97pJeZ8YVbKtwmhLgpb+PVcecEnKQslqPFlQrUCIQSQ7kBAac7jcaWJI0tN1baLkS0agTWVyKeg+tYYFUPLp7MljO/lRJYUUU7qbUKUW5/qC6BXYsWG4qSIBT6ZYrFn+G5k1Q3sCLxvCUK+e9SLLyMYe4nEPg8hnEf6kpZvrbmjkk1cLEGS7W+XyoaIlMokSmsTpKktHC9uarYZAkRKDsIrM1AQ0Q30BSFoKYR1HQaQ1FUIZguZMnYFrbnIbl6LZbf31tdAiux1uR1fi0o+q5y+bODaj6BZx1BDd6HUOrv2Jw+9QS2e2Mj3/pbjzI3s8ypY2O8/rNjFAtXTnlrukJzW4oHH9+MY7u88uJhxobm2ba7nf2PXKrq1tlTv9KjZJq+EJMZ0PFcj+99532ymSL79vewfe+lJWq1DbGqfLfZqTQv/+gwf/En79DQlGDPvd1E4wGmxhc5e3KCMyfGGR6Y5b/6R89VZX8CrbpR0ZvIwIIv2NLalOT/8reepKUpwXdfPMixU+Ns6mmgtfnqC09FEezc2srOrTdW0ngzaKyP0d6yky8+s5OJ6SX+X3/wU6KRIHt2tBG6BQq4dw8EitIIVGvBf/14+8UjPPi5nTR1fJrLGyuFW86+FqiURAth+MrMd9Ar7nbAF6iq1r3RxfPSSIqIay6xqgMpC7juONVQmRci6Csy34Hr+/ZB+CXjVVuEWmXrIou1d9wMdH0TgcDTFAo/Qt4if2opM5SKr1IqvommdREIvkAo9NWylc+nb6lqux4CUBWlaqfRQiaPJ7lilZfnTSG9ZSoPQmgIJYKoYnlttfGl7s0r/33+KSaAzliCzljiOkfRqabCspSZ8nPV5W47p93SyzjFVy6pwnBLr2BE/68IpfeOzOnuOoI3gUDQoL2rjpaOGkDw8Xv9FAtX7usyDZ3NO1rZuKUZx3E5dnCYuallujbU8/iz2y/5rN9L5yMcNdm2u53NO1qxbZfXXzqG50o2bW2+bLsL3qE3j1LR5tihYd585QR793fzd/6bzxOKmCiKwHU9Pn6vnx9/9yM+fr+PIx9tYtuu9ivYQNwAhEo1y0V8kZoKbqQCVFWhJhkhmy2RyRVJLxf4wz95kw1dDczMLTO7kGXP9jaefWwrJcvhF++f44ODg7S3pHjq0S10ttXQNzTDOx/2UyhaDA7Ps31LM8Nj8/T2NPLwfRuwLIefvHaMmfkstuXQ01nHEw9vprPt2kpyQoCmKTTVx7l/XzfjE4u0NicoFCzePdDPyPgCmqayY3MLjz/cSy5X4oODg/QPzeJ6HsWiTU0yzG/+yoMsZwor2ziuh2U5NNbH+M1ffgDT1HntF6c4cnKM5eUCgYDOzq0tPP/UjjUY6RNlhVKTW7E4L+YtfvQnbzN4euKybOvg6Qm23de9TmCvChfXGahOr46IIJToGjwHqwsh9CqVjp+Hh+vOl8tUbz2hkTKH64xUZSyhJBEi8Cn/zQVgVrHaopwdcxfQtKaqjVkNCCGQ0iAS+ds49kksK8Ot7c+3cZxBctl/RyH/Z+jGPQQCz2Ca96OqDdypktW5pRwD43PUp6K0NSQ4MTCFoghaauMkYzceoHv/2BCeJ9nW3Uhd8upE0HJcJhZWz5pKKSnYNucm5njx49MYukoyvHo/vuuMVy37qiipNX2NXzK3crb7RucrhFZ1JXW/0qK4psn/6jBRjf2oxj3AhfNd3MH71aeewCqKQDE0dHzCqahXP4GFIjBNHUywbRdVUxCKQNe1FZGl1fejYAYUpJRotoui+NsZpn7V7W4Wk2N+llUIePjJrTQ0J1CUC7LgG3ob2bi5iTdfOc7Rg8Ns2tpSOYGtmkAN+D1+9sqN5aZGkLCULjA5ncZ2XGpTEVzPY2EpR6FosX9fN6GgQTwWRNMUFEVn9442Mtkiy9kitu2Xytm2i227NNXFQIJlOWzqbkBKyeR0mt4NDTzz+FZcx8N1JX/xw4+YnE7T0VpzXZFTIQSaptBQF2VyepnR8UXmF7IIReFXv3IfxZLNT147RntbDcGAzvjUErqu8qUndrK0XOAvfvARk9NLIGB0YoFoJMBjD25idi7DT187ztRMmpamJNu3tNDRmsJ1/TLpt94/x+ef3FGew00f5lsAgaq2VLHk8lK4rkuiNsL+p7fR0nlpKfir3z1A6CYsuD5TkB6OM1gVAquICIpSnYqTtQ0NUeWFtZQ5blcfrPRyuO5oVcZSlCRCVMMLeW3D9zGtrqjh2lUoFahqK6Hwr+HJHI598hbvz0bKNK6bxitmcOzjFPKt6PoOTPMBdGPXLVWwXw35ksX4bBrT0Gmtl8wsZtEUhVQ0hJorMjm/TLFk096YJBb2hTiXsgVyBQvLdrAdl+6WGubTeRaW80zOLxMNmbjeta/xmXSWf/qdn636npTgSUm2aLGcL/IrD+6kt2X1sk7XHUfKahBY85b5t98K3DzRVqk2TfI9tiuvdLn6PuQlZcpVCTTIIp5zCulNcXFQVQv9MuIGRRqrhU89gf20YnYmzeTYIkuLOX7x2klOHRu7hKTkcyXGhudxbJfpiSU8r/J+OlE1gRrwizqKyJsoUZSeZH4xy49fObrST7pzayvxWJBszl90NzXE6W6vJR67sJBSFKiviVJfF6NQvDSCbBgqDfVxQFCyHZob4kzPZcjmiixnipwbmMF1PVRFYWxykXy+dEN9DBIoFB00TWFpOU+uYFFfE6V3QyP5QonIeyZT02ka6mOYhkY0EmBjdwMLizk0TSGbKxEM6ARMncb6GJu6GwiHDFRVYTlbpM52mJxaYnImDdLvwZ2eyZTnCGtLIlmgqnUIbk0ZtWHq7NjfQzBsEr9IyVZKSS5TIFEbvSX7/bRA4uG6E1VRo/W8RQqFn+DYA1WY2dqGZR2o4mgSKUu3zUrH73merspYnjtJLvvHFAurL7g/TajWMQPKHutrU+DFf87pmObjOM4wBS+L61YnY38tSJnGcdI4zjCOcxbL+hhN60LXt6DrO9D03tuiXux5kkyhxNDEPLqmMDK9SF0iguN6pLNFxmeWcD3Je8eG+NVn9pKIBvno1Ajjs8u01MUJ6BrRcICff3SOhlSU8dk0DanrexYpQhALrh54FUKgqQobGk02Nddx/6Z2GuKrZ/c8bw7PqzxI4skcVulDlhZ/v+Kx1joc52xVx5PSQsrrJ7BW0eLUh/14nqRzawuBkG8hpxsatuXgWA5m0MQqWugBHUVRSM9nWJ7PUNucwirZxJJhSgULzdDwPI/sUh7P9YjXRilki0QTYRBgl3zHDVVTsIs2oVjwwhpXBBFKI0JrR3DhXLyTwcp1AnuXIp8tkVku4Doey0t5iqs07QdDBlt3ttFYzs5WDKH5JLYqkHhehpsVDFGEIBjQSSXCtDYn6WhNYega4BPYZCx0TeuLT45nGBqqpmAKzReDkpDJlRgcmePU2Ul2bm3F8zw/E3sDAQHX9VhaznOmb4pd29uIRwMsZ4orhtrgy+AL4dPMYEAnFDTKGXUwDQ23LDgQDBoEV94TGIaK63nMzmU4cWYCz5M0NcT9m5HjloUi1hJ59aEoSahyac556IZG4xXKu9s2NFzT03MdHq47QzXKBKVcxrY+wrY+qnxanzmUuF1CXpJS1WxcPG+eUum1qoz1mYK8897P14KqNhAMfgEpcxQKL+K5E7dx7769lOtOlElsK5q2GU3fjK71oGrdaFpbWTzs1jzzHNdjNp0jNGuwuJwnEQmWX3fJFCxy+RK/ODLAC49sJx4JMD6bZilTYG9vK4oQ9I/NkS9Y1CbChAPGdc8yEQ7wrUf3rPqewCewkaBBW02CkKlf0YbR8xarIMwHyCKOcwbHOVP5WJ852NxIZY1tOfQfHcZ1PYq5EkJATVOSmqYEY2cnQfGFI9MLGbq3t6PpCqNnpyjlS4RjISaHZgiGA+imRk1TArvkMDs2X/ZRDzIxMEPn1hbmJ5ZYml1GCIFjOxRyJfY9tR3D1BGK8NdrwsC/ti56Lt05rcl1Anu3wpMSz5MkUmGe/PyOK/rUqppKMhXGqLh82C8bqZ5ktvRLWaRzw4psQhEkE2GefnQLHa1X6EP9xHBSSoolm77BWUbHF5ieW2ZgeJZwyMCyrkyiLdthOVukWHKor43ieh7RaABNu3a0t1CwmJ4p8NGRYRaXcgB0tdeQSoRZTOeZmc1w6NgIJcuhZNk0NcQJmLrvOXuFw3Ge5H4SuYLFcqZIMhGmrjbKcrZIKHjn5M2vDl+ZtnrBkOvH/FQa09QxzLV6bNYCPDxv7oaixOuoNmS5hPs2lRBLC+8WifOs43rh3RUekbq+lWDwKyAdisWf4bqTd2AWvuWT4/RD8RU0bQO6sRND34Gm9aCoTWWdhVBVM7OqohAN6dQlwkTDviVetlDCclwUIQgFDERZeg3A1DVa6xLs6GkinS1weniazqYUXU0pxqaXuN44eMg0eGhzZ8XzP99/uY47Bz9IdQPPVulnRoUiGO+bwirafvIioDF4YoxwPMjyQo78coH61hpcx2Wif5pA2M/Kjp2dRFEValtSRJMRQGJbDq7tYRVtxvumaOyoY3pkjpnReRRFkFn016u7Ht2MbmoIBEKpRYh58IpIihdNz71jKZJ1AnsLcEnv+C0KTwQCOsGQgWM7dG9sYPuezltuzeI/DKpXLiBlEU8W8Rdp15ctFQhMQ6O1KVHOuF4KTVVoaUysZDAvRjZX4r2P+pmdy1Ao2Zw8O0kwoBOLBkkmwoQCOvFoENfzCIcMEvEgkXCAYEDnsCJ4/Z0zRCMB2ltS1Kau3oBvmn4Wd2xqicxbJwkGDF743C662moJBHR6expYzhR45c2TqKrC9s0tdLXXki9YpBIhIiG/x0DVFJoa44SCRtnvNkK03MNp6CqN9XGCAYOG2hh1NVGGx+ZZSudQFIWdW1vXqMiCQFHitywD67keC7MZSgXr0mtRwi9+fJhnfuU+wrFPf4/ezcPD9ea51X0667g6fDJzmzKw0sLzFm/LvtZxJXis1RLiSyEwjN3+WkDoFPI/wvOmuHOpGAvHOYnjnKTA99G0dgzzIUzzETStE0VJoZRVsSsls6au0t1cw7buJibnl1EVlemFDAvLeVRFYWtXI7HwmZXnjqooK+sQVVFIRILMpXPMLGZJ54oErpFYkFLiehJPSjRVQXChn9FxXQqWQ8n279MBXSNo6ijiyqrG6wR2DUDaN+TxrqgKiboYRtDADOoEgiaNnXUEwiYtGxsJx4JEF7IYpk5dS4rMYo5oKoKiCDzPw5OSxPl/uy6ReJh4TZTZsQVKBcv/jOeRbIhjBPxsazFXIhIPEQiZK+ebou9E0bqQ3hJ+5aSBUGp9X9g7hHUCexUoii8C5d9Ert+TTwjhb+dJ/88t8HxK1Uaob4wxPbHI2VOTbN3VjhDKJfuRnxBIqnQOQoSrXu8u5TJSWtc9rq6rtDan+Ie/98yq78eiQf7+33rqsteFENTVRPnbv/Hoqtvt3t4GwOaNq+/3Rm13Olpr6Ph6DbB/1fc3djewsftyo/FYNMjzT+1Y+XciFuLv/I3HV/7dc5EoUX1tjN/99UdW/v3LL+y7oTneSQgR5Vb5xxWLNj/+z28zeHIC/SKlcClhrH+a+z+hCr6OC/DFH7yyVUalfqDrqAjS4XaQAv85UcLzlm75vtZxNcjyb343QKDrm4lE/h6a2kUm87+Vz59bqU58PSjiOGdxnLPkc3+Mpm0lEHyaQOApNG0DvnrqzdkBKkKga+qK7Y2mqmiqQjwcYHE5z8dnxhidXvSrpMprLV1TVyxtggGd3Zta+D/+6m0m59IsZYts7mhYdV0mpcRxPfKWzXK+RMGyqImGCJsGhq7heZLJxQwnRqfpm5oHJJua6tjT1UIyEkS9SNDz/Hj+38ucb7Nax52Cw41U1gQjAZ77zQvrVllO2wtFkGpMXPq6gGRDnLbeJpD+Z9o2NQPgeV45kyro2NJCx+YWhCJo6vIFv+paLohySSk57+B2/jyS3jxu6Q3c0itIL4NQG9CDv46ibwMRvsljURnWCexVoCgKobCJY3sszWdxHQ/tOvoqVVUhEg0wPDBLejGP63p+T2UV0dZZx6atLbz31lle/+kxHnhsMw1N8Uv249gu+YJFMGjcUD/olaAoEZRqE1hvGT/qvJ4R+yzBNwi/NaIbnuvRs62Fex/fQteW5gtvSPj+H791ibDTOj4JFynT5TKnO9jcso5y9c7t+A1KeF6W9Yz7nYUs/+9ugqKkCIa+jm7sIJP5X7FKH5X9bNcGHOcM2cwA+dx/QdO3Egg8VyaznTc8Vkt9nIaabaiKgqoIHtu7oZwRhS1djTx1Xy+6quBJSajcovL8g1tWtleEIBkN8Y+/9YQvqilAV1X0VdaGUsJbJwf57vvHODYy5Y9p6Hz9gR18df92JhaW+bN3jvDmiQGcsoqxqgh2dzXzd569n+3tjaifIMZ+suB8xds67hTkRf9/U7hSHuqTr3/i35cFSq6Vz/rE+27pbZDLGNF/hlBiSHcKO/+f0NUUQtlwjcFuDe46Ajs6OMf3vv0eZuByIZbWjhq27W6npi6KLPeITo4vkl0uUCranD05QWa5iG07HD4wQKo2SjBkEEuESCTDl9ndCCHo3FDP8cMjHDowyB/+y5fo2tCA53kUchYPPN5L6yf8JM+fJJu2tjDUP8vbPz9JLlektb0G15XkcyWe+eIuUmUlVM/zyGVLTE8sUiraFIsO05NpbNtlcnyRj9/vxwzoBAI6dQ0xItEgqqYQCOrs3NvJ01/YxSs/Psw//6d/ze77uqiri+J6kqWFLJNji9i2y9/9/edJpsKIa1gIXQtCREDcuN/Z1eB5y1VRO13H3QRR9la7NeXNwbDJnoc2oRkageCF+4SUkv1PbyNRu05grwy3TGburoX0pw238+j7qphr1b5lHWsZfhAyhK5vJR7/HykWXqJQ+AG2fZK1UcHh4t/TStjWQRynn0L+u+j6DgKBJ9GN+1CU+HUFU33ieuFzxkXEU1UUTENbeaKdXwde3OYkytoWoYCxkhG9UlXcocEJfnLwNCfHpomHAjQmoiwXinz3/ePUxsIcG55icGaeB3rb2dRcR9Gy+eDcKKdGZ/jZobOETIONTZeuTT0vi5Rr4TdZRyW40jlz8eurfeZa7197H57v6642AxpCNUGKiqwwK8VdR2CnJhb5+c+OrdrvuWd/D62dNdTU+eSwVLT5/nfeZ6jft0DJLheYn13Gtly+++33ME0dVVPYvL2Vh5/cwubtl5eJ7r63i3yuxIe/OMeRjwY5fngYTVOJxUNs29N+xXk+8FgvpZLNkY8G+ejdPg5/OIhmqMTjIR55auvK50pFhzMnxvmL//R22WfUY3J8kWLB4uSREeZnllFVBTOg8+wLe9h3fw/RWBBFUWhqTfLsl3aTSIY5fGCAQx/04zqeL6uuq4TCJt0bGzADWlWMQIUIljOwCtWK4nne/HpPxmcS1fZQvABVVYjELwRapCyX8gNN7bX+9bCOK8Bbvx4/Y5DSXQ8iruOm4S92A2haN8Hgl9G0jZRKb1EqvYnj9N3p6ZUhkTKPdPN47gyuO4ptH0XTNmCY92EY+9G0ToS4OYV6IcQNhWOvRSCODk8yMrfE3u4WvnTPVhLhIAXL5uXDZ3n3zDBjc2n2dLfwpXu2kAyHcDyXB3s7+YMX3+bYyBT7elovI7B+T/06gV3HzUEIE88Zxy29jhBRPG+mrEx8a1rBrgdVX8kNjs3TNzLLUsaX6jZ0lWce3EzQ1CvqwWztqee5r+5DXEW2raW9hnjiQi22qin0bmuhtj626ucnZtIspPPE6iPEEqtnFusbEzz4+BaaW1NMTSwxM5/h8Okx5oXDzw/2MZnPs6mznkQ0yJnBaQbGFmhvSrK5q57Hn91O14YGFmYzWJaDpqtE40EisQtNz6qqkKqNsnd/z1W/v6oqNDTGLykFtj0PWxM09dbTtaGe+dkMxYKNogjMgE48GaKpNUUgaFTFS1AIrVz6GahaxN71pqoj676Ouwq3MgN7HlbRZujMJKcPDeE4HoGQiWu73P/sduqaErd033cr/B7Y9fLhzxZc7nzv4jrufghUrQVFrUHV2tH0XqzS+1jWh7juBGundNXF8+bxvHkc5wy2cwqr9FFZxXg3urEFCFa0Xq0UI3NLGJrKvT1tPLKlE01V8TwPQ1P5Vz/+BaqqsKO9ia2tfg+tlJK2mgQ72ht598wwi9nV1me3TxRuHZ8+KPpWQEG600imAQXVfByhpK616S1DVQlsoWhx7NwEg2PzRMMmUoKuKitNxxUhrLH9gW52bGq+9mfxI1ymqfPMF3df8TPDEwvMLmZprovTXH8FGxpVoaklSWNzgsXlAm8eOMfWhEHA1BBCkM2XKFkOnifJ5kscOzvBUiZPe1OS9q462rvqVh33PAxTo3tjA90bLxf0uRY86avTxZIh9mxtQ7kNN1xfiThSPQLrTt415WueJ5maSXOmb4pYNEhnew01yVtfjiqlZH4hy4mzk2QyF7JjoZDB1k1N1NfGbrkCdfVx6wns8mKO0f5pzh0bo5ArsXlvJ+eOjrDt3i5qG+NrVKG5+ijZDkvZArmyV7ShqzTVxJhbypGIBjEvUfOWsMa9KNdRbaxnYNdRPQgRQNd7UdU2dH0XWqkX2zqIbZ/CdadZSyJCUhZx7JM49lks6wNs4x4M5350fRuatqEsXHlrKoWuhnS+SDRo0piMoql+0kIIweYWfz2ZjARJRS+Q7PN/t9Um4OwwJeeT/eyyfI2vlSDCOu4WSC+D546AtFC0TqRsQnqLIAv+nzuY1a8qgc0XLCZm0tSnInz16V0YuoZtOxiGT/ZmF7MsZwrYroeqCGLhAPU1UYQQTMws4ZaJbrFko6oKHU0pLNtlfinHO4cGSMXDBEydoKnTUvY9nV3MkskWcVwPw9BIRIMkYyE8TzI8sYBhaDiOS8lyCAcNmupiSGBhKU/Jcqgt+3ldC47rMT69xEtvn+I3v3wfuze3EgoaOK4LEjRN5Z7tHSyk82Ryl96gcwWLmfkMlu3gSUkkaFKbDBMMGOQLFvPpHPmVxaVGY22UYMAgmy+xmM6TL154r7k+jmloZPMlljNFdE0lFQ9fQgWKJZvF5TzL2Qtkp6ulBl1XK1ciVkIoSswvH6gCXHfqpgms47gMjy2wtOxvX76VoygCw1CJhEySiTDhkIFyBWPvG4GUktPnpvh33/4FGzrr+fqX9t4WAgswM5/l1TdPMTQ6T75gkckWSCUj/L3feZzaVARFuXNlHDeHW78oyGUKfj/sw5vIZ4rc9/Q2Mos5X4itrLD3WcBcOseZkRlml3IYukpNLEQ0aHJ6ZIYd3U2fILB3i5XHOqoG6d5F6rfruFugKCEMYwe6vg3HOUOx8DMs6wMcZ7DsM72WKq8cXHeEQmGUYvHnmOajBILPoetbUNUWhIjc1oCn7bhoqkLgknuzIGyaqIr/urGK+FPQ0FGEWL0tUVp3nUjYOu48PPsIrn0cZAFF60LRekAE8dxhPPsUir4FodTckblVlcCGggamrjG/lGdgdI6m+jihi8SWDp0a5dDJMdLZAooQ9LTV8je+sh8QvPjmCQpFm4CpMz2fIRw0+L1vPMxCOser75/l6JkJTF1lcjZNe1OSb33pXlzX4+cfnKVveJZCySYeDbK7t5Un9m8ECf/5Bx/SVB+nZDnMLWXZ1FHPLz2zC4Tg4MlR3vjwLIlYiM89vJVdm1uu+t2y+RJTc2ni0QB7trYRLKvMnY+OXQ2Ts2lefPMEc4tZSpZDfSrCC0/uZGNHPUMTC7z09ilmFpZRFYXaZISvPbOL9qYUfSOz/Pz9s8wuZtFU/71vfH4vjbUxxmfSvPHhOfpHZtm+sZlvfeke1HIpycjkIu8cGuDc8Kz/GvAPfuNxahLhikuJhYihVLFkwCsT2JuxGsrlLf7o22/zzod9aKqCpqsoAnRdIxEPsqmrgYf2b2Dn1lZSifBdmKW8gPaWFN/4yj3ML+Y4fW6K9z4aoFhaz5pcDaFokGAkgG25RJNh3nv5GHOTS1glxz/fPiMMdnRmiemFDEIIoiGT2niEmaUsuaJFwbJxygFF//qTSGzWS80+O5B4rCsQr+NWQQgFXd+Crm/BcfopFl+jWHgFxzmN5+WAtdRzL5FyiWLxhxSLrxAMPkcw+DV0YzeKErvpHtmbmIVveXKJ8M6ln7nR55fEhhvwH13HOgCcwg9Q9HsQajuecxyn8CMQKop+L3r0HyKU+js2t6oS2ICp8/lHt/L2wQH+6LvvYRoaLzyxg3u2t6NrKk/ct4kn79uEEIJjZyf4P/70LX7jhf2c54CqqrB/Vyc7NvplwkJASyDBr3/xHuYXs7Q0JPiVz+0FwHU9zgzOcGZgmq89s5utPY384uN+Dp0apbutho4mn2RpmsIzD26jvfzv8zeBZx/aTCho0D8ye13frWQ5ZPMWdanoDZfqdrXW8Hd+9WE0VcHz4Pf/l+8zM59hY0c9k7NpZhYy/K1feoiu1hSe9OXQAUYnF8nkS/ztX36IjubkJe9t6qgjFNA5cHyE7EUZXynhjQ/PYTsu/+RvPkU8Eih/7+os1hUliaJcvSz6RuD3omTwyxBu7nQ0dJW2lhQbOuvxPI9svkTf4AxvvHuWDw4N8qVnd/GrX72XePTuteoJhwy29TaX/9tkYHiW4bGFOzyrtY3axjjJ2iieJ7GKNu++fIynv34vjW0pxF0czLgZ1CejWI7D+GyaouXwxO4NnBya5mj/JNu7JM21sbLtglwvJ/3MwUWuE9h13AaoajfhcBfB4C9hWQco5L9DsfgGa7MHu0Ch8ANKpbcJBJ8jFPoWhrH7Tk/qpuHf19cDk+u4QQgTRd+Eou+Aoo30shjR3y87ktzZdVRVCawQgtpkhOce2sIDuzrpG53jL186RE0iTEtD3M8KDs3iSclCOkeuaOF5HmrZ3qWhJkpDTfSyTJkvP+7/Of+eZXuMTS/SWBsjEjJRFIVELEgsEmBiJr1CYDuakySiwVWzbz6pu74fwNBVQgGdxXR+RQL9euC6Hv2js7z2/lks20XXFEamFrAcFyklOze1ICX88PWjpLNFnty/id2bW4lFAty7owND1/jeq0fIFy2eur+XnZuaCYfMC8fkE/PP5ItIIB71j0U1SmcvhqIkUNVqRlwknjuF5y2hqrXX/vgqCAYN7tndwTe/ch/g9wbn8xZ/+aOPePdAP4ePj9LVXsuzj2+9xkg3gNt43X4y+PDZol6VQdUUVEDTFR763E40TUEvtzR8VrCrp9kXZ8K/HymKIGjqPLVvI0IIDE29JCgnbkN59zrWEsT6b75G0XdmEsPQCIUMJkYX2HlPV8VjDvZNU1MbpVi0cV2XppbbJ8Jyfs2lKAlM8xEMYydhZ4xi8RVKxddwnEHWUo8sSDxvgUL+B9jWcQLBzxEO/yZCXJ/1TiU40DfKmYkZAoZ+yeuTixk0RXBseIqAcekSPlsokc6vntEW3G1tRutYC/DcEezcH6OodXjOMNKbxcr+byvcQwv9JkLruCNzqyqBtW0XISAcNAgFdYolB+lJcgWLofEFRiYWaa6Ps6W7kZP9k5zqn75ke11T0dTLbwpCCFzXw/W8lVJTRfHJ8pHTE5QsP3qcy1vkCxbJ2IVMm6FpqKuMeaOIhEzqa6LMzGc4MzRDb2c9AVO/pp/XwnKe/tF5LNvl+Ue3Uiw5vH9kaGW7eDTIni2tdDQnSWeK/PWrh6lLRYhFGknFQ+zb1kZXaw1zizm++8phmupihEPmqvsCCBj+nPJFi5LllD3HLrxf6bpdURJVzcACuO4onjd30wRWEYKgaZBKXlCg9hKSJx7qZXxyicHROQZH5lbdtli0GRiZ5cChIYbH5snnbcIhg672Wu7Z3UF3Rx2GcflloiiC2fksr7xxksMnRllYymPoKt0dtTx07wbaW1Orbgd+r/jpvikOHR1hbHKRYtEmHDbZ0FXP/Xu7aGlKXqI2vY6bw/JijvnpZaQnCYYMfvxf3iVVH+XRL+6hviV5p6d323C+3eGTiARXu48ocJvK5NaxVqByFzrqfSYwNbaI47hEokFGBmaIJcPMzSxTUxfFcVxS5YD/3MwyhqmzMLtMU2sKCczNLAPQ2JygruGCSOX48LwvQLlcYDldIJctsbyUp74xQS5bpKW9huyy/zpAIV+ioTmJbTtMjS8SCps0tSSJX/S8vVEIoSJEGClDGEYKVW0kEHgG2z6OVXof2/4Y152s6NhVDx5SZrHtU3jeIrZ9knDoW+jGXhTl1mhgNCdjtNSsLizaXpu44nbJSIhkJEQivEq1mbh11nXr+PRCD/4afouJjqLvQkobIS6c90KJ3rG5VfWptbic59CpMabm0iiKgmW7tDenaKiJYru+kNLQ+DwA80s5UvHLrWtWI1hCQGNdjLGpRf7sJx/T1pjkwT1d9LTVcuT0OO8dGeTQqTGy+RINNTHamlJXHE9KSbFk8+aBPk70TTI9nyFfLGE7Lr1d9VcUdNI1lZaGBI/es4G3PjrHR8dH0FSFloYEvZ31aJrCu4cGOXp2fEWE6t7t7cSjQRQhmJnPcPTMBLqm+r3ChoZEMjQ+z4Fjw6iqgmU7mLqGpqpIKTk3PMvh02MoiqBU8smoqip4nuTs0DQfHhuhf3SOkmXz4psn2Le1jdpkhK09jZwemOYvXzpEwNAQQuHZB3uJRQIVRw0VJY6i1lJNL1jHGcZzZ0HfXJXxwCeYLU1J4rEgluWsiGRdjGyuyPsHB3njnTOMjS9imhqmoTM7n6F/eJb+4VmeeLiX+/d2X0ZGZ+eyvPnuGXIFC8fxj8N4Ok//0CyjE4t8/qnt7Nzaiv4JoYXlTIE33j3LOx/2MTObwTQ1dE1lajZD3+AMg8OzPPP4VnZtbUXX1xeVlWB5Icf8VBrXdcktF2hsTzE1Mk8hV8RzPZQqBLY+fRAIDKqV59e0HnR9O4pyc8GpzyyEjq5vAq4crKzaroRaXtxWB7qxF03bgCLu3MLmboSiNqGql3rR64bG5Pgi87MZpJQM98/Q3l3H+Mg8+VwJTVXRNIXJsQVq6mOMj/p/jwzOklkuoGkqQnAJgV1eyjM+Mk8h5xNXIaC+Mc7Zk+M4tkNNXZSFuQzL6QKGqbG0kCNVF+XE4RFcx0XTNQJBoyICex4XPGTbUdVWNK0LQ9+J4/RjO2ewrWPY9gmkXObOK+hafrC9OIfnLhIMfolA4ElUrZlq10V9cd8W7t/UftPbf9IDFvD7d6uUNVaUegxjH6p6fa4g67gAw9iLcgdJ341CNR/whf6uhDt4n6/qClnTFEzDJwFCCCIhk129LdSlInieZPeWVmbmM5iGRmtDkppYmIW5LLX1UbZvaCYaNgmski0QQnDP9nb6R+awbAdNU1AUhVQ8zP27OhmZXKRQtOlsSdHVkiIRDeK6Hg/s6aK5PoHxiWyWEAJdU+loTtFQGyMc9Inh1ZrihRCkYiGeeXAzHx4fIl+wkfjEVlEUFCEIBXU2dzXgeZJ4NIiqKkRCJhs66sjmSxi6SjBg8OyDm2lvSiIQqKqCaWooikAve+bWp/zohqr6x1MRgoCh8/yj20jGfNKvKgrJWJDNXfV4nsTQ/TJARQi2b2wiYGiMTi2hCIFQRLnnr/KbrBBmOQsbw/OWKh4PwHWHcaukanwx8gULy3YxDZ1w6PKs0rFT47z8+gkmptLs2tbKlk1NRMIBltJ5Pj4yzOlzUwDUJMNs671U5GtsYgEpJb0bGtjU00AoYLCwlOPNd89y6NgI8WiQ2mSEjrZL1dkOHB7i5TdOkC9Y7N7exsbueoJBg8WlPO9/1M+h46MYhkY8FmRj141bK63jAlRNIZ8tMto/g+e6fOHXH+LFb7+LlOudQFeGAqJ6pEnVuggEv4RexeDUZwMCRalFVPG3uDJURBUJrKHvIhB8HlVtqtqYnw2YqOqllSHhiOm7KBRt6psSFPIWSEkhb2EVbeZnl30Lv0yRmvoYQhGkl/Jkl4s4jothaJe1EQlFYBgatuXgeh6loo3nSXLZIkLA1PgiczPLSOnb/Hmex9J8llymiGFqqKq4JZZ9Qiioah2qWodu7MBwZ3CMk9j2SRznHI7Tj+OMIuVS1fd9/ZBImcey3kbKNJ5cIhB4rmy5U72A6M7Oal87AiHMqrUKKGodgcCzGOa9VRnvswRFSV6SwVzrEMrqlQBrAVUlsKl4mMfv23jF9x/a0w34fVjL6Tx9Z6b44N1zPPXcDlpTMSIR8xLV4ouxpbuRLd2Nl72+fWMz2zdeHgVSVYVnHrx80SSEIGDqPPVA7/V+rRVomkpjXYwXnti56vuff2Tbqq9HQiY9batnIHraaq/43uauBjZfgcRs7KxnY+fqvaipeJj7doa5b/VpVgiBEBEUtamKBHYSz51BSqsilT8pJVL6EvTLywUOHRthenaZhroYXe2XHuNsrsS7BwYYHJljz452vvnVe2ltvpC5b29J8ad//QEnzkzQ2pxkyyfOsaLlsHljI7/8pX00NyZWXo9GAnz7ux9w5MQYG7rqVwislJJMtsQb75xlamaZJx7q5Ze+uJfGi/yHm+pj/PGfv8uhY6N0ttXQ01l/W7x9P22QUlIqWOiGhqapWAWLmsYYZkAnWRvFDOqfqR7YG4HfW29SrYyCIICq1KNp3VUZ71q4VkvHOlaDBlSvbFyIMKrajKZV3q/5WUc8Gaajqw7LckjURFAVheV0gYamxMrzrlSyqa2PEY0Fae3wnzct7TXYtkMwZNDQlLhkzJb2FM1tNeSyRcLRAIGATiFXoqOnHulJHMfFNHVCEZNYPISmqXiepKe3CatkE0+GSdbc2gW4ECaa1oaqtmIGnsJx+rCtQ1jWYRynD9ebKq8Zcrd0HleDbR/D89JImScY/Cqa1uNXM6xZVPO+rqMoqdt2X1/HOlbDHalRLBZthvpnee2lY8zNZnjwkV4OvNdHV089u5KVW72s49ZCUaKoahuOfaoq40mZx3Wn8Nx5VO3GI4+u67GwlKNvaBbX9chki5wbmOGNd86QzZV44qFedm1ru2SbodE5hsfmiUaC7NjSegl5BdixpYXOthqOnhynb3CWkmVjXFTS21gXo7en8RLyCvDgPT28/vYZDh8fZXhsHtf1UFUFKaFvcIaR8QXqaqNs29xyCXkF2Le7g5fePMm7H/YxNDqPZTmrViSs4+rwXI/JkXkyizmCYYPt+3swAxrnjo3R1F5D+AqibusAUBDi2r7Y1wuJhfyEKIsrPaYKC0ggoYeJ6H6/liclM8UldEUlqgUx1Bs/96eKi0S1IGEtsE5irxN+P2L17jNSFpHrvrJVQVtnLW2dF4Kv8nz5iPCDNBcHbKSUtLTXoJQtsa4UzNm5zw8s1NbH6Oiuv3xMT14yvpRyJYvred6KgOTtgL8fDV3fjK5vJhj8Co4zQMl6l1LpXVynD89Ll50MLG53bY3rjpDP/TlSFgmHfwdNO1+ptfbuPX5gsjoEW+Ig15T90TrWIoYzi9ieS20gTMKsvgvIHSGw2eUChYLFI49v4eBHAwhFoKgCifR705S1E8WSUuI6Hq7j4nmy7B/p31g1Q0VV1StaclxtW1VX/f6Uu3AhrShJNG0DJV6u2piuO4rjDt0UgV3OFvnRy0d58dVjgE9gJNDWnOSF53bx5MO91NdeWqc/NDrPcqZATTJCMh5ctUc2EDAwTY1cvsT8Qo6mi/qIamuiJOKXX5DhsEltTcQv5VrOky+UiEaCfk/z4DT5gkVHa4pI2Fx1n+GQgaarZLIlFtN5murXbvnGWoXregyenmSkb5rZ8QWW5rN0b2mmVHSYGpnnN//x54kmQusEZ1UoKEoV5fGlhZSXnud5p8j/5/RfUXIdfrntYZ5s2IUQAsuz+f/1vUhLsIZnG/fSGbn+Evrzi/U/7P8JT9Tv4pG67WtwCbk2IYRe5aBFkXVf2VsDIcQll+alPqFixdHhk+/d0JjKpWNcPE61XQ1uFEIJohvb0I1thMO/jWOfoVj8GcXiqzjOIFIW8M+920dkPW+KQv5HCFSisX8MmGsyCaMooSqWObvlY72OtQRPSizPxZMeuqKi32Eu9d998FNGMkv8/R0P8o0Nu6s+/h0hsNFYkGDI4NWfHGV5ucCH7/UxMbZIa3sNqra2hFXy2SKH3jzNWz86yNCpcXKZIrqukqyL8blvPcS9T20j1bA6yShkixz6xRne+sHHDJ2eIJvOoxsayboYz37zQfY/s/2K265lKEoKXb/xEuyrwXEGcJyzmOYDN7ytrqu0NCbo6vBLrU6dnSSdyfOlMnmtSV1e7pReLlAqORw6PsKpc5No2uUXerFoU7JsXM+jWLrUpy5gapdkZC9GKGhgGCqW5ZLP20QjPtFdXMpjOy7vHOjnoyPDq+6zULCwbAfHdbFK64vAm4FuaDz8uZ2MnJtmamyeWDLMxh2teB58749exzC0FTXzdXwSGkIkEMJASt8XthJIaSHl5ZF6gWC8MMtAbpL9Tu9KFrYSLNpZpgqLFNy1ZMNxN8Asi4poVIN4Si+/7iW8jtsAFU3vJax1Egr/OrZ9vGzH8wauO3JbZ+J5UxSKP0NRmwmHf4u1mYGNlQNVVRDglA6el6/GtNZRRQxlFvmPpw9wammGr3fv5Bsbdt3pKd1S3BECGwjqdG+o58nP7eD0iXGQkkef2kp7R+3KonJmbIF/93//a+an0tS3JPny7z7Bln0Xemoc22H4zCTf/l9/wuLMMjse3MSjL+xlw462K+32pvDui4d56c/eY2Z0gWR9jI5NTSAE2XSecCyIGbxy79C7Pz3KS995l6mROVL1cdo3Nq5sG7nGtmsZQkRR1XaEiJXVASuH647i2H14XhFFubFsQDBgsG9nB1//0j6KJZsPDw3xp9/9gA8PDrKxq5762thl23ieh0RSk4zQ0ZoiGr3yArq9xc+YXgwhrmxJdN6fVwLyIgLgeRKkpKE2SmtzinD4ygItWzY2ElpFeGod14YQAiOgk6iLMHJuiqOn+5ASrKLF1OgCjuOuk9crwD+vVYRSA94ylRIaKbNImbn8dSTtoXo8KTmyNMhDdZd7NP9s8mMGspM82bCLzbEL9/V/2/9TolqQR+u20xSsYbq4yL8feImpwiKDuWn+0+Br/GD8fQTQHWniH/V+FcHtK3u823BeCVZRknjebMXjed7SenZmHbcc58uLhdCQMohh7EfTegkGv4ZtH6VY/Dm2dQAp89z6jKyH64yRz/8ZprEPTd98mwTYrg/+rU9BKAlfpK/C61PKIp63WI2praOKcKVHxrZYsooU3U9/EPGOEFjH9nAcj/qGGPWNcVRFkExFMAMX+nCsos3AiTGmRxcY7Zviwed307unY6WExbFdBk6Mc+bQMAvTaVKNcQrZ6tbk2yWHc0dHGe+fpndvFy/8zceIlK1/igWL5s46AlcgGXbJoe/oCGN9U2za3cmXf+dxIomLt6294rZrHUKoKEoCTduIbR+iGvL2UhZw3VFcpx/FWF0M60pQFUE0EqClKYHrekTCJu9/1M/Q6DyHj4/S0pS4rN80FDTRNZW6mhCP3r+JzRsvFwg7j2BAJ/kJyyfLcrHt1aXFi0Ub23bRNPWSHtZI2ERVFdqaUzz5yObLhKUuRiRsErsKqV7HtRFLhOna2gyKYGpkDkVRePjzO6lpiK+TmStCAL4aqOuOQYW9jJ7M4nmrB7k2RlswFI2j6UHuTV0u/jdXWmYkP0vOufS+PpafI2mEKXo2AojqIZ5q2M1gdorp4iL3pDayM9GFABJG+Krq8uuA8wqlipKqDoGVi6tm3dexjlsFIRRfXFKJoKoNaFoHur4bxzmHZX2IZX2A64xxa0vbLVxngEz2D4nF/imq2lhVZeLK4N8DfQXcYMUBJilLeN58NSa2jnXcNO4IgfU8j+V0gb4zUyiKoLE5QShsYpgaF5deCCGI10TILOZYml2mkC0RjvmLetfxGDo9QTBioi/emq+RWy6wNJ9BURW6tjSz55HN192zmsv42wpFoXNLM3sevf5t7wYIEUY3tmPbR6iWP5vrTmDbx9BvkMBeDFVVaKiL8fhDvfzlDz/iwJEh2ltrqKuJol7k+9ncECccMnEcj2BQZ1PPtfvtXPfC91xM58nkLl+kFUs2S+k8rucRDZuEyll2Ifye3ICpYzsusWjwuva5jpuHZmg0ddQSjgZZms/4KuLtNXdt4Oj2QUFR6hFoFectpHdlApsyIiSNKH3ZCc5lJ+iJ3Hj/uxCCiBZgf00vKSPKy1MH2Rpv56mG3esK3jcAIUwUtaYq63vPXVzPwK4BTI0ucPrICIVskW33dtF+C543hXyJoTNTfPTWaTbtaGPLng5iVfCHrQRC6KhqA4pSj65vRde3YBj7sK3DWPZhHLsPX/Cp+pCyQKn4c6zAMwQCjyNE8tob3UasENgKx/EzsAtVmdPthO24zMxnOHBihId2d1ObjFTUr3w9qvdrSRl/2Soykl1iaHmBmWKWrO1fBxHdpCOSYHOynuZQbNW5Op7HVD7D6aUZxnJLLFt+q46paqTMEL2JOjbEawhp115fSSl5b3qY4wtTJM0Qe+ta6InVXHO7T+KOEFhVUwkEdJSyZ1mxYDE6Ms/WHa20d1zISgkhaGhNUcgVmZ1YIj2fXSGwju0yeGqcmoY4ywuXS6lLKSnmLeYmFpmfTpNN57GKNlKCGdBJ1EVp39RIOBZaITalos3cxCIjZycpFW3mJ5eYGJzFdTzG+qZ55S/eXxk/WRdj4652EmVxIKtoMze5xPCZiQvbDsziOi7j/Z/cNsqGne0k6/zSVqtkMzO2yMkD/Wy7r4eaxgSLs8tMDs2yvJDDdTzMoE59a4r2TY0EQhdKU1zXI58pMDO2wOLMMvlsCdtyUBRBMBKgptEvXTYC1bUOUZQwhr6TPN+p2piuO4llHyYov1KxqMhD9/Vw+MQoR46PcfDoML09DbS3XlAa7uqopakhzqlzk5zpm2bP9nZqP2EN4HoeluUipVwhoucxPbvM6Pgiy5nCJZnSs/3TTM2miUeDNDbEMQz/EhNCsLGngfraKFOzy5wbmKZ3QwOpxKUPe9f1KFmO7/0bWFcgrgS55TzZdAHD1Ni08+ZN4T97UFDVeoTQkRWudDwvi/TSSOleZjGhCoXOcANpO897c6foDl+5CmIdtxZ+BvbKFSE3As9bQMocUnprKAPlw3Fc0gs5pscWWJrLUixYSE+iGxrBsEmiNkJdY4J4TXhNLDgrwfT4Iu/87CgLsxmSddFbQmCLeYuzR0f5s3/zc57/5v20dtXdcQJ7Hr4AVQjD2I2ub8Mx7kO3PsAqvY9tH8dxhoBql1lKpFymWPgBurYRocfX1DWgKCkUEaw45SBlEc+dR0oXX7n+xq4Vz5MUSjaD4/PMLWYplmw/GBkyaaqL0dXi34uqfQk6rsfE7DIv/uIEvZ0N1CYrs4Manlwkmy+xrafxisdgcTnPmaEZ9u/oLLfo3Ln7Sl96npfHznB0foqi6+u7uFJiey4NoShPtmzg6ZYNtEYSl2xney4nF6Z5c2KAj+fGWCgVUPAL8z0pMVSNL3ZsoSN67YBNyXU4tTjDfzl3iOHMAg81drEtdXP3pjtCYDVNoaYuyu57OllO5zl1fJzDHw0Sj4cuI7CJuiihiSDz00sszi7T3FWH9CRW0Wbs3DTb79/A1PDlpQxSSoZPT/DuT49w7ugIi7MZSgUL1/bQDZXm7joe/+o97Hl0M8k6P+JQzJXoOzrKS995h8xinsxSnqW5DI7tcPjts5w7Nroyfu/uDpJ10RUCW8yX6Ds2ys++/ba/bbq8bcnhyDvn6Ds+trLtpl0dxGuiKwS2kC1x/IM+/uC/+TZ/759/k7YNDZw+OMiRd84yM7pAqWBhmDr3PbOdr/7tJy8hsIVskdMfD/GLHx9krG/mAlH3JKFogO7trTz5tXvZ/sAGDLN6JFaIEJq+tVyOYlONHhPPW8CxT+I4Q+j65R6+N4L62hj37+1mYmqJE2cnee/jARrrYyuEsrE+zq5trYyML3DkxCgNdVF2b28jYOpI/IVONlciX7BIxIJs3nhpdsiyHE73TfH+xwNs6m5A1RSKRZufvnac2fks27e0sKn7wkUphKC1Kcnene289MZJPjoyTDwWZFtvM6ahrfjXZrJFiiWb+too3R11vo0BkMuVcFwPz/NIZwpYlovreqSXC8wv5tB1FVVVCJr6ynf8/7P332GWXed5J/pbO58cKufYOQd0ANDIgQAIZlEUlbNk3dG9jjO+M/d6bI9mHltje2zLlkfBkmiZkkhRzCRA5Aw0Gp1zV+qunOvks/P8sU9Vd6EzqtAJ/fJpFurU3muvfdbea633C+/3SUdmpsC502PoYY2qD5U7uovLQwgJWWoElsOAUsLzZvH9AkJcnIveFKpi2srx/Og+pszsopzxy8HH/0jE+uyJYaoaUwtpIDcTnuuRzxQpF0wS1bHL6iFMDc+ghzVC0RCK+vEpSgphIEvLQ3B8P1fJgy0jxM3/rudRKpgMD0xx9P0+Dr7Tw2DPBOWShef5aLpCLBGmc00jux9fx65H1t6KOjzXhWjcoG1FPVV1CZIfc93WWx1CqKjqShSlE11/mHL5ecrl5yq6GzMsN5E1zTcxQs8iK20IEbv6CTcIslSzTO+khefP4nlZJCnB9bwsnueTLZQ5cHKINz7oZWo2j2k5CElQlQizbW3rAoFdbkhCEIvorG6vIxLSlkSQHdfj3UP9DI7NsqajbpEC+Dxcz6NvaJo//+577FjfdtONYjNmkRmzRNoIsypRTbURwfV9js2O8fbYWb7Tf5SIovFTHyKw5/JzfKPvMK+N9FFthNlZ20JXvAohBOPFPGfzs8RVnbBy5T1DybE5k5niT07s5ejMGE80r+RLnRtYmaz5SPdzU3a6rusxM53n4L5+Th4bIRzReOCRtaxa23jRsUIIahpSFDIl5qYCMRDHcZmbyjE7maW2OU3vsaGLzvNcn0NvnWbfK8dRVIX21Y2kamLYlkvv0UEOv3WG/uPDhCI62x9Zh6araLpCQ3s1u58MlLvymSJv/vAgU6OzrN3eydYH1yy0X1WfIH3BpljVVRraqhad+9aPDjIxHJy77aHz56brE1R/qLj4PCaGptn/2nHmpnJU1SfZ+eQGzKLFUM9Y4EX9kDEvN1dk38vH+eCV4zR11rFmWwfRRJi56Rx9R4d464cHOX3wLP/8a79FXUsV8iWUbz8KhNBR5BZkpQ3HPsnyhOT4uO4Y5dILKMqqynU++gt/744uTvaM8ZNXj/He/j52bmmn/YK804fuW0WhaPGjF4/wl996j5ffPEVDXQLf98nly4yOZ4jHQ3zq4XUXEdjujloy2RJf++a7tDZVEQmrnBueYWhkjup0lF3bOllzibzaZx7bSKFo8cpbp/iLb7xDY12Cuuo4nueTyZUYHpujoS7Bs09sorMteKkdx2XfwbNkciVKZYuBwWnGJ7MUiybvHxhgerZAJKQTCWusWdFAW8v1h2LciQhHDWRFZnYyx9x0fmGxisQ+XjJw+0NGUbuXSYTEx/MzuO4IknQxgTVklaZQFY2hKl6fPIrnewtbIbHQwnm4vofjuXiX8yEsCCf7fHhT9bXf+zaf/c3H2Lhnacax5YBl2pzY28vAsUEe+MIOGjpqL3ncvhePUt9RQ/emto+VeAsRQVHal6k1H88dx/NmKiWZbj4c2+X0kSG+/7W3+OCNU6i6QqoqyurNrSiqzNx0nqnROU4fPkdtY5Ldj330NJZbBV1rm+ha23T1Az9BEEJBUZqJRH6ZUOgp8vk/xSy/gOMMAsunXu77RUzzHRRlFZq2ftnaXSpkublCOJcO3y/hOH1o2iYCZeNrQ8m0OHpmhH/3tVfoaErz6QfX09lShW27TM8VKtUdfBzXo1i2sB0vqEUsBJoqEzI0FHnx9VzXw7QdyqaN4wZriCxLREI6mirjEzgd8kWTdCLMLzy7g+QlNEbmw31LZZuyZeO6PpIk0FSFcCVqFCBXNJnJFBkcm2V8OsfkbH7hb6l4GEWWKJVtZnNFzo7MMD6VZWImhyRJCAGxsI5RcSj5vo/tuBTLNo7j4vsgSQJdUzB09aJ7XQrurWtjZ20ruiyjyefpX8mx+b39L/GTwdMcmx3jS/6Ghb237/s8d+4Ub48N0BpN8mtrdvBIU/fi79/zQARRVR9G0IzAcl3OZKb4i1P7eHWkly93beIXVm2j9UNk+XpwUwhsqWgxPZkjHNb4td95FMNQKRRMdP3i7tiWS2NHNZPDswsEtlwwOXd6FN/3aeyoIXwJNVdZkXj8p3dx71ObSFRFCcdDwd7Gh9nJLP/md7/Gyf0DnPxggLZVjTR11mJEdFZsbKWromQ8OTxLz5FBirkSq7a28/Qv7llo/8OhAEZYo3tDK53rK+eOzNJ3fJh8psSqLW1XPPdCPP/1t1mzvZOf/QdPs2H3ioWXwvd9bNO5yFJfXZ/kS3/vMZ76+fuob61CrXjfPNfn6Hs9/Mm/+DaDPWMcfucM9ybCxC9RUuYjQ2gYxsMU3eFlS+h33QnK5k+Ixn6LpT6e8ajB9k1tnB2cov/sFN97/hC/++uPLvw9EQvx+ae3sHpFAy++dpyDxwbpHZhElgXxmEFbSzX3bGpny4aLw08fum8Vq7vreW9/H6+/fYaxqSy6prBpXQtfeHoza1c1ol6CJKWSYX7up3axfnUTL795kmOnRjjdO4GiCBLxMN0dteza1snalQFh9nyfQsHkD//iVSancoGysX9+on3lrdO8+vZpAAxd5dd+9v67BPYCDA9M8vK3P+Dlv9u38G784j9+ms67G7srQEZVVy5bbVDfy+A6Q5eNqmgIpdma7ua/9PyQ5vB5JXpFkpGFhOWd947MmDmKrknKv3gek4RUUf/2bmAVyI8XT/zc/XADws4kKYaidC5be647XlkTmpetzaVgZGCSv/uTV9n3xmlau2t5+iu7efTzW9F0dcHoMTU2R2a2QCx+VzzvzodAkhqIx/8ppraLQuFPMc03WS49DwDLeg/DeAi4hQis0oZYJgKLb+I6faCt53r2aoOjc/zojePomsL/+KuPU5uOIkvSebsj4DgeZ85N8s2fHOBozyi5Qpl41GDnhja+9MQW2hrSi9qcmMnx+v5ennvzOOdGZ5EkQWNNgt/40n1sW9eC6/m8fbCP//DfXyNfNNFUhX/zDz/H+hUXO81cz+e7rx7hpXdPMTg+RyIaYs/WTr78xBZqq2LYjssf/vUbvH2on5lMEdf1+PI/+q8L5//xP/sZOluq+d6rR/jWi4eYnMlRthx++h//2cIx//AXH+XZB9cjBJRNh5P943zj+f2cOjtBoWSRjofZs62Lzzy0gea65HUMypURuoyH1JAVGiNxDFkhZ5mLzL9lx+HA1Ah522JnXSsPNXZddP6V9CY0WUaRJE7MjfPnp/bxynAvP9W1kd9Zfy9pfWlz7U0hsOGITvfKejq6ajEqbvzD+8/S0Jyie+WFXisf3/do7KxlZGCS2ckctuVQKloM9owTiYeobkiiXSZXMFEVJZaKIMsSUsWK4fs+0USYXU9u4NyZMWYnsxRygeDEfEFveV6xTa7UShFBcW/5CpaQi86dL/h9DedeCNdxeeDZLaza0o6iyousIJIsXxTyoGgy6bo4yZrYouMl2ae2Jc3mPSs5d3qUicGZZa8rKoSOrj9CqfgdYLkU6SxcZxiz/DKavueK4S7RiM7f/83HMC2bWOTizbYQgm2b2ljZVYdlu0Q+RP5FJc903aoG2pvT5613QlSsbjKGrhGqPF+SJNi5rYNV3XXEoiF0TaalMcVTj27ArZRm0XWVeNRA05RLbjqFEIQNja0bW1ndXV+x8gXXlCWBqiqEQ+qCerEkBPFYiH/7z7+M4155cZWEIJm4u/maRyId4bEv3sO9T2w4/y7DJz6c7uqQkKQ6JCnFctQG9bw5nCvUZQzLGs2haqq1OL25URqNwACT1mK4vsf706dZEQsMDn839DajpRmaQ4tDzASCaj2OIWn05scYKIxTZyTxfJ+4euk5xPd95iaz/N0f/ATHcSlmi9zzxEY27VmNY7scf7eHo2+fQlYUMtNZtjy8nk0PrMY2bQ6+doLT+/tRNYVV2zrZ+sg6EtUxDr1+kn0vHMEybRzb4bO/+RiNnbW899wheg4OUC6aJKvjbHtsAwADJ4eZ+oPncWyX5hX17HxqM83d9diWw5vf3cfe5w6x6+nNbH1kPbFUhKEzY3zvj16iubuemfE56ttq2LhnFY2ddYz0jfPm9z5gbiKDVbZ54As76d7USvia1Mx1JLmuUhotx1JTQlxvdFkUjZcLP/qb9+g5NsyK9U08+eUd7PnUJkJhfZFxoLohSbo2fn7tvgBTY3MceKuHD944FZTisl3SNTE27e5i9+PrqW9OL+wxIAiTPHnwHH/zX15i/fYO7vvUBoo5k9d/eJATB85SyJWJxgxWb23ngac30b0ueL5LRZODb53hG3/0Kp/5hXtpaKni1KFBDrx1hunxDEISNLRUsefpjWzY0UniAoO0bTkM90/xn//5t5mbzi983tpdx1Nf2cm2PVev3T7cP8m7Lx3n1KFzTI1lsG2XcFQnXRNj5cZW7nloNXVNqQVj4IWQZYlzPRO8+fwRjr7fz9xUDkWVaWyr5qFnt7B2WzuR2PIYxZaK82uzgqbfi5ASSHITpeK3WC6BJ9c5h+ucxfNylTrLNx+SlEaSqgCdpXqcfb+M7Zwm5HvXHEHs+z5TmQIDI9M8vmsVyWgIWZIqpQdZaMfzIBLS2LK6mUd3riQWMeg5N8n+44N88/kD/KNfOu+ImMuV+NEbx9l/YpAHt69gw4oGZFlmeHyOjqaqwKPrw+6NHbQ1pDl0api//MH7l5zhypbD9189wjsH+3nonhWsbKthfDrH/hND/OE33uR//o0nUWSZX/3Cbh7dtYrvvHyYfNHkd7/64IKzqaEmgSJLPLVnLRtWNPLG/l5+9MYx/t0/+SKSFNxnVTKysJeXJEE8qrN9XSufeXgDYUPjWM8oR3tG+P5rR/ntL9//UYfoIri+T292mnfGznJsdpzxYo6CY1F2HSZKeebMEkGByfMUdrAwR84u0xCO0RxJXJKsXsnAqssKp+Ym2D81zKGpUT7TtpbfWXcvSd1YsmH2hhJY3/c5fWKUmekchqFxYF8fshx4qXpPj/H4M5s+RGADb1NTRy3HIr3MTWaZnchSLlmM9E/SurIBI6xdlijIisyHfWBCBGSytrkKRZWxyjbOZcqh3AzUtVRR31ZNKLp4cANBgouPv9J9GiGdqvokvh+ILXje8lkXA8io6hpkuQXXHQeWp3SC72coFP4KVdsEhLjc7CjLErXVV14YwiHtIgGmCyEJgaGri8rdXA5CCCJhncgFOci6rpK8ztA+SRJX7deF15RlQXPjraVmeDtAVmTiqQjxVATHcZkezXD6yCDr7ukgXXNXIOtyCOYdFVlpQTin8L25JbXnelM49unL/l0SEiktykO1G/mj3h/jeMF8vDbewoyV492pk/z+iW+hywpNoWoaQ1VE1YtJWUwJ8UjdJg7P9fNfen5IWNZZGWviZ9sfuey1I/EwD31pJ77v03v4LHMTWcYGpkjVJchni2Rn8jz7G48xPTrLuZMjDByLYps2I73jPPlze5AUiXgqukAS29Y0EktFKGSL9B46y/G9PSRr4/QdHUQzNLY/sZFYMkIsFWFmPINAsOmBNehhnaEzo5za10dzdz2yIrF2Zzcn3uvBc31cJ5i7i7kS506OcN+zW1m7s5twIkSqJk52Js9QzzhWyeLhL+9mZnSOo2+fIhwz6N7UdtUxCsqQhFDUTmzrGEvNCXSds7jO8JLaWA54nsfouWlOHRrELNts3NnFjofXEL2EoU9RZLhEis3A6VGe/8ZeDr3bi+/5pGvjKKrMzGSO57/5Pn0nR3nyp3awbnvHBYbqQERy7NwMVTVx3nruCP0nx5gamwtqwBsaU2NzTI3OUSqcJxK+51PIlQMi+eJxLNMhM5NHN1RSNTHmpvMcfPsMg30TfO4X72fHI2tIVgVroCRLxFNhdjy8hpnJHMMDk/QdH2FsaIZi/spkxbYchvom+Pp/fJFzvROEozqxZBh8KOTLnDp4jt7jI3SvbaT2MmvR8MAkfSdHcWwHI6SRrIoyM5Xj/ddOcrZnnK/89iNs2t1N7BbIQb8QkhRBVdcTDvsIBMXi3wDLsSe0cZwBXHcISVpz9cNvAIRQkOV6JLkKzx1ZUlu+X8C2DuLjXHMGrFXR+TAth66WahTl0gJQsixRWxXj3s0dGLqKpsrEIjoTMzkOnhxa0EAQAo71jHJubJbOlmqevG8N6XgIIQRtDSkiIS0gXAKiYZ3m2iQjk5kFsrn4fnyKZYsfvn6MnRvb2bmhneb6JF0lCyEEf/vCQQ6eGmLjiiaqU1FMyyEW0fF9n7bG9EVOqkQ0hFvjkUqEURSZ9sZ0QGA/dL+qItNQkyAWMYiGdBRFwtAURicz9A9NLbrXpcB0HX509gQ/OHeSqVKBhkicVckaIqqGJsnsnRjk4PTFz0TWNrE9l7CiEbkGheEPI2OWeWm4h7xtYbkOOdskrhlIyyAycMM9sMlUGNO0yefLVNXEqakN8qLyufJlVVfrWqqIpyJkZ/JMjc6iaCoj/ZOs29EZhABdAr7v4/s+w70TDPaMMzkyS36uiFW2MMs244Mz5DNFPM//+GtcXweqG1MYEf2SL9il4Ps+tuVw9tQoQz3jzExkKOXKWKZDIVtisGcc4GO5z/naa5q2GcftxXNHl6Vd3zcrtdv2oet7li1n4y4+WSjkSkEd6d5xJoZnUTSFeDJyu2uz3DAocieSiOMyt6R2fC+L657FdWeRpCS6rPLF5vuoM1KoUrAEhWWdXdWrEULQEq4hrccIyRr3Vq+hVk+StYsokkRHpJ7NyU5iaohq/XxOrRACRcjcX7OOxlAVM1YOSQjqjOQV+uVTyJboPxaQy9nxLFbZopAtkapLoKgKVQ0pOte3UFWfpOfQWSYGp1E0GT2ss3Jbx6LNiGM7TI/Oce7UCJ7jkZnKk88UcSyXjvUtjPVPcPb4MPXtNaTqgjktVZegbU0TsXSU6ZFZZsaC71qSJOpaq0lUx1C086TK932EJOhY30I0EV7w+s30jjN0ZpShM2PUtAxSypXITuevyzgrhI6qrMGxT1WE+T46XHcC1x2+6d4nz/XpOTbM3FSOmsYkHasbSFVfnIt9Kfi+j1myefm7+3n/1ZPUt6TZ9eg6GturkWWJ7GyBt35ylNOHB4nEQkRiIbouoeNx+sggc9N5ahqT7Hh4dSD86PtkZguEwjp1zelLXB2Ovd9PU2cNW+5dwcqNLRghjexcgXdePMb7r5xk7ysnaGirPk9gJUE8FeaBZzZhlmyO7O2jkClRLl99LM2yzTsvHuPw3l623r+SLfevpKY+iRCCUtEkM51nZiJLdUMSWbl0NFnv8RHaVtSx7f61dK5pQNUUpsezQX9fPcG7Lx6jobXqliOwAJIURlXXQ9jEdYcxzdeWpV3XHcJ1R1DVW4PAAshyI7JUswwE1sRx+vC8KYQIIcTV6YTnBvmevh84Fy7ngQtSpXzOnJvk3OgM+aJJvmjROzhFoRQ4Y4I9smBwbBbX9ehuqabhgnc7yKW9djiux2ymyODYHD/36Rrqq2OEdBVDU2ipT2JoCqf6J1jbWY++rNQpyPc92T/O0PgcxZJFrmhy+twkqixV1NyDe71qS37gPQ2OXnz88ZlxXhzuYSA7wz21LTzdupraUJSQoqJKMhmrzMm5i6Nm5Mq1bc/F9q7fsJN3TOrDMdan65kqFTgyM8a3+4/w6ba1RNSllTS8oQRWCEFdQ5JIzGButsDK1Y1UVUcDz1ZUp7rm0gtdJB4iXRdnuG+S0bNTJKtjzE1l6VzXjHoJAuv7Pq7jsu+V4xx84xRjZ6dwbBdFU5Akgef5zE5mF6zatxJCEX3BK301+L7P3FSOD145zqE3TzMzmUWSJBQ1sGpZpk12Nn/1hpYITd+Fab6xbAQWPHx/jlLxOyhKJ0LEbikp+ru49dFzZJCB02OUimYggtY3QUt3HZ1rmwjfImFstzoUdQ2SlMS9QvjvtcEJvLDOGTTtHjRJ5YHaDYuvJcnUGSk+07Rr0ect4RpawteuUNgQStMQujQh+DBKBZNzp0Y4vX+ANTu6sC0H23YX8stlRSKaCkK9FFXB9wL1YHyZS1kDZ8Yz9B0dZHJwmtqWasyyhS40wGf97pVEYgb9R4foPzqIWtF7iCUjyKqMIgdztnuVNAEhBKoqE4mHF4WsQhCt5LnBxi6SCLNxz2qqryNyQ4gQqrqZUumHwFLruAZE4GZ7nzzPY7h/Esu06VzTSHV94tqNw55P/6lRDrx5BlVX2f34eh7+zBbC0fPzR7I6xtf+3XMcfreH5o5q2lfWXSSUOHJ2itbuOh54ehNrt7YvqgfvOt5lPSuFfJm1W9t5/Ivbqa/oGji2S1VtglOHBuk9McL40AwbdgS5y0IIFFVZ8JBOjswRjhnXRGAd26X/5CiW6bBmazs7HlqzkGox7wzIz5UIx4zLpkOVixbbH1zNg89spro+sfBZqibK4b29nDw0yPR49pYVlgo8sRsIhb+MbR/B8+ZYak6s645WotNuDHIlk709gzSl46xuurQwnCy3IsuN2PahJV7Nw/ez2PZJJKn6mtSWZTlIzQIolKyFufbDKJs2L793mmO9o4R0lZCuIlWiEL0PnVMsW8iSRDS8NNFBz/MplYPSMtGwjlp5j4UQKIpM2NDI5kuBM2gZkS2YvPL+GY73jJKIBalpUiWk2vODef1ava8526TkWCiShKEspncn5iboz85QH47xqdZVPNB4XvNg/ju13IvThdJ6GENWGCvmmDFLgRH1OtzBipDZWdvK4y0rOD47zn87tZ+/PLOfunCMbTXNxNSPPm43JQc2GjWIRHRcx6NUDPINGpvThC/zAAoBNU1phvsmOHdqDNt0cB2PjrVNaJcQfnJsl9GBSb71hy8xOjBJ57pm1u/qprGjhmgyjCRJnDowwMTQrVeIeV6l7FoQlNDp55t/8AKZmTzbHl7Lik2t1DSmiMRDlPJlDr55iqGeiY+1z6q6EVlpxbZPsVxhxACm+TqG9QiyXI8Q17YpvYu7ABgdnObM4XMYYZ3aphTp2gTNHTXUNacuG7VxF4uhqmuRpBoChcmlbeR8bw7L2oumbedm1SexLYdzp0ZQdQXNCLQXMhVhwNqWKkp5c8EDOo8Pz8XhmIERMRg8NcrJfX3IikQsGaGqIUkhW6KUK2NEdOraqpkZn6NcMHFtj2K+TCQRpq6tmpHeCaaGZwNhQXHxt+F7PrZlMzYwxex4Bj2sU900EeRxX6pTQCwVoWVFPfnZPG2rmxY+iyav3dslhIGmb0WIyLLkwTrOORz75E31Pvk+zE0XcGyPeDK8iHxeDa7rceidHmancmy5fyUrNjRfdP667e20r2rgzecO039qlHy2tCgvFUDTVTrXNLJyU8si8gpc1psJUNuYZNWmlkUeWlmRWLWplWgixFDvBIXc8qy3kixI18aRZYnB3gkGTo/S2l1PPBle0NaIp69c37WhNc3qza1U1Z33gukhldWb2wiFdeamcpSLy6f0+3FAkpLo+n1o+r2Y5Zfx/eKS2vO8KTxvapl6d3VM54r89ZuHeGhd52UJrKK0IyutLIe+ge87WObbaNpm4OoEVlVk4lGDsKFxemCCezd3oCqLCZHv++SLJn/14w9Y21nPIw+sYkVbDTNzRX74+lFe3rs4HUVTFTzfp7xEjRdJEhi6giyJQBG4Ykz0fR/X9ShbNmHjAq/xNXlFxYInNCDri4/3fZ/puTx/+5MDbFzZyFP3r6G1Ps3YdJZvveBz+uzFHtHLKf4WbIsTsxMMF7LEVJ2G8OJIk5JjY3kuCc2gLrR4rEaLWYYLWfLOxfnftaEodeEovdlpejJTjJXyNIQXnz9f7lFwcT5sSFZpiyXZUt1EbSjKZKnA105/wN/0HCSiaKxP119WXOpquCkE1vM8igWTibEspaK1UPuvoSlFJHppElvbnEZIEv0ngryaUNSgsaOWUuHiCbxUMNn70jH6jg3RvaGFL/72o2zcvXJh8TBLFhOD09csrHSrYnJkjgNvnGJ8cJrtj67ll/7pZ6huSC48QOOD0/RcULv244IsV6Opm3HsYzhO37K16/t5yuXnkJU2NG3nNYWoLAWu6zE5mw+sYGrgrc8XTcqmTSoevuzz4jgu2UKZfNGiOhXB0AK59bJlkyuY2LZLTTq64GW5i48f9zy0hrqmNCcPnqX32DC5TJFIzKCqPklLV+2iWsp3cWkoSnOQB2tF8f3sktryvFks822I/iq+v3Txho+CWDLC0bdP03vkHOnaBCu3dtDUXce7PzrA3ucPoRkqoaiOEdGRlcDLaVVZFZ0BiVRtnGRNnPr2GuYmMrz0V2+jhVS61rey9dH11DSliaWCaxSzJRRdIVEdw3VdDr56gqGeUQSCVH2CVds6mB7LBCHCqoIkS0STYTzPC5THc2X2vXiE3GwBxxlFViRkRUYPadS1VF3EYePpKC2rGhnuGePVb74LQOeGVjY9sOaan3UhNBSlC1luqtTGXBrZcN2z2PYRDP/TCHHzjEa25eD7PooiX9ea7/s+Z8+MUS5Z1DenSddeHHosSRINLVVE4yHmpvJMjMxdRGCr6hNU1SVQrzOksaGtOkh5+JAWhqwIQiENzwsizZYDuqGx46E1HNvXzxs/OsTouWl2PLwmIKS1caKJMJp+aWHCebR01RG5hHaHrMiEIhrZmfxVIwxuPgRCxAiHfwbL3LcMBDaH5+UqYaAf/36zZNlYjnORl/JCyHItityKJKWWQWjNxjTfIBz5CpJUixBXjh4UQlCVjNDZUsVbB/t4ZOdKGmvigeglAtcLSuBYtsPQ+BxfeHQTDdVxJCGYmsszOnnxOlRfFefUwASDY7Nk82XCIRUQWLaDqsjXvO9SZImqRITaqhhnR6ZZ0VqNrilYlsPkTJ5c0aSzpWahrI0iSyiyhON6FEoW0bCO53sLolRAIMypyEFpxoJJLKKDEEgVoVDf9ymZNuPTWTatuoeadEAMx6dzjM/kLtnPKbNA1jJRJRlVkpAQ2L5Hb2aK5wdPMVrM8mTLStZ8qLZqTShCTNWZMUv0Z2eoDUUQCEquzQ/OnqAvO31Jj3hIUdle08yZuSkOTA3zo7MneKJlJbpcGTPfo+hYGLJKlRFGly8/zzWG43ylezMDuRleGe6lIZwgquqsSFSjXEI876pjdt1nLAOKBYsjB8/xo+/sp3Nl3YKFYtf9K6i6jChPXXMVekjj9IEBFE2hsaMGPaRe0qLnWA6jA1O4jseqLe1UN6QWyKvv+zi2y7nT48uuynujUcyXmR6dQzNUtj64lvAFi4fv+xRzZUYHboz1T9f3YFn7l5XAApTLr6Co65DlDmS54WPd+OaKJv/uv73CV57axqr2WkK6yjuH+jl6ZpRf/vyuS9YNA5jJFvnuK0d48Z2T/KNfepT13Y2EDJWB4Rl+8NpR+oen+ae/9gT11XGUSxS7vovlhxHWWbmplZWbWnFsl9mpHIfeOs33/vwNvvgbD9O+quHqjXziIVCVVVhyE46zNALr+0Vs+wSO04+idANLy335KPjHf/Trl/z8n/zxb1zy83sePx/mHK+K8tnfemzh98vVbX3kp3fzyE/vvujzZ3/9YiGppu56Nt5/Xhn2vs9sW/jvVE2cL/y/nrzkNX7n3/78JT9vaK/h879z/pzLheddHgKQ0fTtuO7ZJW9uPW8K2z6N646gKFcXkvq4oIcCg6JtOzjXQfh8H7KzRVzbCwwblxHdC0cNdEPFLNsUsheHXkdjIUKR63/ew1Hjkmq/AKKy2VuuYEbdUNn2wCpKRZPvfe0tTuw/y8G3z5CuibHtgVU8/oV76FrbGNSiv8waHIkZl62vLQmpkpl360MIA12/H1murnhPl7JPtPH9Ar5fvmJFhQvhuEGOqCyJBUVs3/exr4H8F8rWVasVgECWW1CUlVjWUgmsh+OcxnF6keUWhLh6fnlzXYrHdq1m75Hn+T//4iWeum8t3a01iEr/IyGNhuoEVckIvUNTrF/RQK5osvfIWfYdP3dRNYnNq5s42jPC+0fPUlsVY9eGNoSQ6BuaorUhRV1VDEWWcb2gVmxQVxYs28W0HCRJIEsSkiQIGRrPPriB1z84QzIWYvPqZobG5nj9gx5S8TC7N7ajVKIm4hGDRNTg9MAEHxwfZPPqJopli5pUdCH/NmSoVCXCeJ7PGwf62LGuFQTEIjqRkI4kSRi6SiIW5mT/OGs66pnyPN451M+RMyO01l8cefj6SB9/13eUOatMU6X0zXgpz6m5CUqOw4ONnTzVuobq0GJD2ubqJjrjfbwwdJr/+/i7nM5MokoSh6dHOT47QVwzqA9fmn8907qG0WKOb/Qe4j8dfZuXhs6wKlWLJstMlQscn5ng/vp2fmX1PTRELv8MCCGoCUX4/217nNFCjm/3HyWpGaT0EHWh6HXv728SgQ1I52d/6h7WrG9e6PTlJmuA2pY0qZo4kyNzCCFx79ObLgrHmYesSKRqYkiyqIStnHeLm2WbvmNDvPKd9ylkl2Zdu9nQDZVYMhJ4DodnFy3O2dkCx/f1sffFozekL4q6ClXbgGXtXebyCS6l4neQpGoikV8APj5LvqbKbF/bSt/g1ILVz7QcFEUifokyPfOoSkb47MMbODsys0g4oLulmsd3r+a5N49/bH2+i6tDViTStXH2PLOZXY+vx7hE3ei7uDRUdS2y0oLjnFhyW75folT8AdHoryPkG09gP3G4OGLtGiCh6zsxyy8syzweCOK8cdMIrBCCVHVQYm5uukAhe50ht9ekneJfQCQvURFBElesk3g5SJLgRks/3PPQGlZvaePkgbO8+9Jxjuzt45XvHeD9V07y+Jfu4Zmv7l7Ib72ov7K0dKnUWwYKiroWxxnG92eX1pTvEEQzXBuB/dprHzCZLfLohm62dwV1lCcyef79D9+86rlTuSKDU3NXPU5WWlHV1VjWW9fUp6vBLL+BoqxEkq5OYEOGypbVTfyz336K7716lG/85ADFkoWmynS11PD47lWsbKvld76yh2+9cIh/+u+/Tyyss21tK888sI43Dyx2kqQSYZ55YD2RsM73Xz3Kn3/3XWRJor46zi99die16RjHekf5/qtHOXx6mEy+TCZf4n/9wx8RC+vs3tTB03vW0dVSja4rfO7hDbiuxwvvnOIvf7CPSEhj0+omvvrM9gXyCmDoCrs2tTObK/GH33gT1/WoSUf5n3/9CVrqgzx0VZHpbqvh0w+u5+s/fJ8//867pOJhfvlzO7l/a1BPtTYV41c+t4tvvXCQ1/b1koyF2L2xnUd3rqJ/+OLylB2xNN2JKt4cG+D9iUEc3yOkqKxM1PBI0woebuqkM1510XlN4Tg/v3IbKT3EqyN9/PczB9Akma5EFf9w04OAz/cGLr1XjWsGv7RqO2tTtTx37jRHZ8Y4MTeBJCRiqk5bLMXqVC3Ra8hnlRAkdYN/uvUR/uUHL/Gt/qOEFJWvrthy3aJON5TAuq7H6PAs5wamGBudY2RoBtf1UStWu5a2amrqLv0CRGIhUrUx9JCGkKB9zeW9cUZYZ/Oe1Tz/9bfZ/9oJfN+nc11AlEcGJji1f4Cudc0cfa/nY7vXG4Gq+iSrtrbx2nf38crf7cW2HOpa0pSLFgMnhhk9N0XH2iZO7Ov/2PsihIqmbsHStmCWf7KsbbvuCGb5RRS5BSP0xLK2fSE0RWbbuha+9cIh1q9ooFi2sByX9sY0c7kS33n50EK4yM4N7WxY0UgsoiNLEiFdrYSqnG9PUeQgp+ISYWsn+8c5cHKIbK6EJAkev28Ng6OzCASdLVXUV8fpG5zipb2n+c0v3fex3fMnAfOliOSQhn4NpYvu4jwUdQWK0oaJxlLrI/p+kVLpu4TCn0VIsY89JeBOwUDfBCeODzM1kcMIadQ3Jrlvz6rLllabxxuvnaSjq5aW1os3M5eHhKpureQ+D7DUciKuO4RZfoVQ6HMIEflYI2guBUkStHbXoRsqQ30TTIzOXfO5QkA8FUFWZEp5k3LRIhq/OAqnWChjlW10QyUSu3gDd2F9y+vCTeCCuqGianG23LuCzjVNjA/NcPjdXp77xnu88K33aVtZx7Y9qy6rJHwn0Nf5Z1SRmxEitGQC6+Pg+9cejn/s3ATDMxk2tp2PEiqaNq8f7w/6doUv2XU8SvbVRbtkuQlFXY0QBr6/9DzqcvkVdOOhiujmlddYSQgiIZ1Nq5porE1QLAVeYyEEYUMlFQ+jqQo71rfR1pCmbDnIkiARDaEoEg/ds2KREJssSbQ2pPjswxt4YFsXVkV5XdcU6qvjqIpMR1MVP/vMdj778AY8r6LUWwnjTURDVFWeZ0Ggjvz0nrXs3tSBaTvIkkQ8qlOdWuwhFELQ1VzDzz4d4Zk96/A8D1VVqLkghUAIQU0qymcf3sB9WzrxXA9Fkam/QC05EtK4f2snK9pqsCwHRZFJxkL4QKlsXSQ6tzpVS20oxpe7NmN7Lj4+spAIKSopPURCMy4ZxitLEg0RhXsaPMKhMgKVOqOJzantNIYDwr0+XY8spItK3EhCkNJD3F/fwdpkHTnbxK6U5VSk4NppPUz4Q7ms/2z745iusygfVwiB5MPqZA3/+85PUXZtaowoxhVCjy+HG1wHNvC+5nNlVEUmlYowN1tYEC66HHmFeS9Kgqr6BKqm0Laq8bKLoaortK9u4PO/8QjvvnCEM4cH6Ts2jGaoRBMh1u3oYtenNjE+NHNb58FG4iHW7ezmM7/yIHtfOsa+V46jqPJCzbgdj66ntilN/7HhG2IYVdQ1aNpWLPPNJeeOLIaNZR1Ckn6ArLShqlcvyP5RIMsSjTUJCiWTUtlmJJvFcVzWdNQRMlS2rW3F8316zk0yPpWlJhUlFrl2hdR5zGQKDI7Ogu+zZW0L03MFXnrnFJtWNXF6YIKQrhA2NMZncqi38fN5F7c/hIijKKtRlE4c5+QSW3Nw3UHM8mtI4SSyXLcsfbzTMTWZw7ZcWtqq0TSZ3jPjtHfU0NCQJF8oY5supmkjyRJ19QmEgKFz00H+lSpjlm1M08ayHOZmi6TSEZLJyGUFhCQpjaptwXHPLllZ3veL2M7pQJDPeJyPM4LmUpBkiRXrm0nVxOg/NUbPkSHWbm2nqb36qucKSdC2oo4j7/UyMTzL7FTuIu+j53mMDc2Sz5aIpyKX9U7eTpAkQSQeIhIPUV0fJ1kVIZ8t8pO/3Uf/yVFWbmi5JUvhLDeElFgWI5u44P+vBV/cvYFC2WJV0/m9hev7FEyLL+xcz6qmGkLapd+jgYlZnjtw6up9EiEUpR1V3YBlvX/NfbscPG8cy3wPRVmBqq686vGSJAgbGm0NlxfnjEUMYpeIfKtNXxzmqmsKtenYJf8GQQ3Ya1EpnucU1ako1anoVY4OvMkhI0Fj7eXfe1WRr9g3WZZIREMkopdOUfswoqp+TZ7OS0JYpHWFnTXdpPU6evJHqQ55RFSZslskohWQhIzpGWiSQdaZJSLHUISK5zsokkNNWCHtCzL2DGmtlpAcRrpM7vOq5KX3x0IIDEW97N+vFTeUwEpSIEednF9AL8gHnJ0pBIXEK4inIzz7yw8SSYSIVywaKze38fnffAT8QOBAiCBP5LEv70TVVWoran2SJBGJh3jo89tpaK9hfGgas2ihqDJV9Qk61zXT3FXHF37jESKJEDWXKTUQiYV44DNb2bC7m+4NLdd1r5GYwZ5nt7JuR9dF587nJs2/LHpIo3tDCz/7D5+mY23Twv1eDYoq09BaxZNfvZeWlQ3MTeVwLAcjrFPfVkXHmib0kMZX/j9P0r6miUjFeuw4Lq7nI1USzOdhVUpIyJXk9OuFJKVR1Q2o6kYs693rPv9K8P05TOttpGI10civIskNyy6KIITA0FVqq2Jk8mX6h6YIhzSaahOYps1stoiqytiOS6ZUIncJAbFrwdRsgXMVwYGqZJR80WRyNk99dYxTA+PkiiZjU1lmMkW2rW1d1nu8i7u4HgghoarrUbXNy0BgARxKpR+iapuQpPRNFfe5XWCZDr4HoZCKqirB7xXVx/ff7cWxXWLxEMlUhLr6BL4P+bzJ2FiGxsqa2NczTmauSCIZ5uAHAzzyxHqSqfBCjt08gjVJRtcfwLb2Yy25NJqP505QKn4bTbunMubXViZuOSAEpGtjbNrVzeRohkPv9pCoivDA05toaq8BcX4ddl0X2wq8N0ZIQ5YkNu3s4q3nj9B3YoTeY8M0d9QQuiAF4fThQc6eHiMcNWjpqiWWvLJS760K1/GYmciSqokiK/LCd6LpKsnqGPFUBCGBY7nLXkbk1sVyWf0VxHUYbnataF3wEF4ISQh2rWxl18o2YqFLE5jDZ0fZ23N14U4hJGS5FU1/cFkILLiY5luo6gYUpQ0h7qbp3IrwfAdZKKT1OhqMNg7PvUPZLeL6Dhl7mjl7GtMtMSrOsj6xg8nyMDk5TLXeyKw1QcktoAqNSXOYsBJjtDzAiuhGEmoV0k0odXnDCWy5UrOpc0Xdonjy99/txTRtvAqxiqejPPsrDy46v21VA20fEl8Jxwwe+/Li2oEQLErpugS7P7Xxsv158qv3XrG/kXiIBz97XljDcV3mcmVGJjO4nkc6HqYmFSVsXBwyEYkH5PdS6B+ZpioRJVGR5DfCAYG9XpIMoOoqjR21NF5GVATgS3/v8UW/z+ZKDI7PIQRsWdW88Pn4TI7RqQypeJgVLddvGQk2uysxjEex7aP4/vLWoPXcUcql7yFLKULhLyNJVR9LGOK6znqGJ+YYnc6yrqsBWZbpHZpg37FzrGyvZS5Xwq2ohX4UuJ6HaTnkiibZQhBCvH1dC4loiMaaBL7vMzAyTbFksaJ1aRaqu7iLpUJROlDVDZTFc/j+3JLbs+3DmOZbyHIDinLXQHMtKBTKjI34qJqC50PI0JCE4OSxEZKpMA1NKeLx0MKmt6o6xrn+KTq6anFdj6HBGSzTob2jhldeOMbmbe1EYwaadulNh6ZtRFG6K/N4YUl99/08lvUepvkmhvHoNQm9LBeCMGvBvU9sYGosw/uvneSV7x2gkC2xclMr0XgISZKwLYdCroSsyDR11NC1phEhCTrXNLJ59wreefEo7718fMFoLCsSuUyJN350iKmxDOu2tbNue8dlRYxuBFzHZXYqj+u4OI7L5OgcpYKJbdpMj2cY6p9EqahZx5PhRYJMpaLJS9/5gJqGBIl0tFKPXsK2XcYGpzn6fj+artLUUUPkE1JH2/dLLLV8GABCAXHt31kQMrqYvOqKzOqmWqrj0Ss6F0Kacs1qrrJcg6ZtR5JqKmJVSy2b1YNpvV2JxFu/pLbu4uND2SuSKwfpajElRViOVbyuJUpugUlzhLw9x7rEDmShcLZwGkMKM1EeYs6eIiLH6SucoCO6hmOZ90koVYTlKLp8bR7k5cQNT0KaGMuSz5fZck/7os/7e8bxfZ/m1uprLjR+I+F5HnO5MnuPna0QWJ+VrTWEdBVVkSlbDp7nEdI1VCWQ0fY8v6J65qLIEoamUihbHDozwuaVTQsE1vN8LMehbDmELzzf97FtN8gBkAVhPahdaNkupu0gCYGhBTmWZctBU4MCyJ7vBxL7nofjeDiuR8gI+ikJQa5ocvD0MDPZ4iICO5Mt8saBPprrkgsE1vd9bCfog6bIaJXyMpeDLDeg6fehmq9imcsjEHAePq47Rj7/RwipCsN4tFJAe3k3Das66nj3yAC+71NfHads2kzO5NE0hTUddUhCMD4dSJx7nk/ZshmdylIsW0zM5EknIqT9MI7rMTGTJ18yGZ/OoqkyqXiYqkSE1oYU6USYe9a1IQREQjohQ2N1Zx3HekYZHs9QVx0jfDdn8y5uMiQpiqqsCETazDeWoUWTcvkHKEobklSNJN354YhLhaLKhCM6mhbMv1PTORLJMEZIpbO7js1b2xcdX1efwAipC+JByWSYVDpKa3sNDU0pTNPGcVy0ywgnSlIKVduEZR3AcZYqQufjeXMUCn9eMYasvWqe3KUwly9hOy5hQ8NQFXwCpVbPP++t8rxA6/bDqUFdaxt55HNbQQiO7u3jpe/s55XvH6SqLoGiSJhlm3y2RFt3HU/+9M6AwAqBHtJ4/IvbMcs2B98+w7f+5DVqm1IoqszkaIbsbIG2FXXc/6mNrFjftMTvaWkoFS1e/+FBSkULs2QxPDDFxMgsZslm/5unmRrLoIdUdENj16Nraek6b/Q2SxZvPneY3FyJRFWERDqCpgfKyjPjWYqFMht2dLJuWzuJ1O3pZb5eeN4Mvr+0vH+QEEL7SM/7hUhGQvzsni20VifRlMvvd6KGTnttivQ1hKMKYaAorWjaTsrl51hqTViwscy3UZQ2FKX1mgSd7uLGo+TmmSyP4gMdkdUktCpsz8Txgsgez3fx8JCQaAl3czyzj1l7EtMrUXByuL6LLGRKboGEWoWHi+M73Ayf+w0nsKom43s+szMFolEDIcBxPEzTBp9bkrwClC2HvuEp3jjQxz/5hUdIRA1cz0cgmMkW6R+ZpmTaNNcmaGtIo8gS2UKJoYkMs9niglfz1MA4takosUo8vuf5FEomgxNzjE/naG9MU18Vx9AUCiWLkakMY1NZoiGd9V31qIrCyGSGkckMsizRUpekNh3j7OgMdekYsYhOrmhSKFlYtsv0XIFsoUxzbYLWhjQhXaWzqYrJ2TzvHBlYdI+bVjTSMzjJvGPR930c12NwfI6x6RypeIimmiTxiH4FMQ4JRWknHP5ZLHMfS60leDE8PG+GXPb/QAgVXX+w4oldPhJbXx1H11TqqxO01CeJhnWa6xK88O5Jvv/qUXRdwdBVVEXCcV2GxzM8/9YJZrNF3jnUz1yuxIbuBjKFMm8d7GN8OsdL751hfXcDuze1U1sVo60xzTuH+vn6D/ehKDIbVjRw35ZO6qvifHB8ENt16f4IXvC7uIuPA4raia4/gGW+w9I3OuDYJzHLL6PIbajalhsaVno7IhYL0dxaRTRmkMmWGB6coa2tGlmWF62ZfsV4aZmB4bRcERcSklic8+pzVYeLpm3H0vbhOKdYqpgTONjW+5TLLyBJSWS59bpTQPadGmRiLs/Klhra64LQ6GQ0RLZYRlMUPN8jX7IwVIVULHTRGrVpVzfNHbUc29fP+6+eoP/0GNnZQpCPFzXoXtfEhh1dtK1YnJvd2l3HT//2I3StbeTdF48z1DeBbTukaxM8+vmtPPDMZpraqxelQEGw10mkI8SS4StWWLgQQgg0QyVVHSUaCyFXakjO/20esWSYVHUMI6Qt/L2QK/N3f/YGvustGlpJlug5NkzPseGFz5o7amjurF3QxghFdJ76mV0cebeP4bNTDPZNgh+kQtW3ptly7wr2PLWRWCoSqA3Pty0FJD9dEyMSMxb9beGeCFLCSkULTb/1UwaC79PHdc5VvLAfHUKEECK65EixWEjn6W2rr3pcTTzCbz6+i9A1fs+SlCYUfhbTfA3fv3Td0evBvMaBqq5H1x8C5CvsFe/iZiCl1tIY6mBN/Hx0aV/+GJPWKPVGCxElxpncYYQABZWIGmO42E9YidJgtAKCKr2e1bEt+PhokoHEzdFqueEEtmtFPRPjp/mT//QS9+zqQjdUTh0bxjQdwmHtllVhn8kUGZnM8uDWLqLhgMApclB4OVsoMZMNwqx+/7+9zO/9vU8Ti+j84M3jzOZKrGqrhWwRSRJEwzo/fOE4IV2jOhklVyxzpGeUtw/388CWLv7yR/v43EMb6Wqu5q1DfRw8Ncz2ta1MzuXxCfJ0pubyFE2L4YkMbx7s43e+vIf+kRnOjc2xdXUTR3vHyBdNmuuSTMzmiIQ0/vPfvsnvfuVBupqvLl4xD9tx6R+Z4buvHWFDdyOHTg/T3pjm0R0r0a9QlF2IBLq+A8N4qDIxLl3lbjF8PG+abOZ/JRr9e4RCn0dWGpf1Cr/zlT1AYMkXwOqOOv63/+HTgTpb5RmVKuURulqq+a0v3x9Y/8X85xI+PptXNeP75xXv5Mpmc11XPas76iqL5fm/+b6PZbkkoiG6W659rO7iLj5OSFI9ur6TkrJiWUrqAJRKzyOkJDGlCVm+W5f3Sjh1YoT33+vFMIKapr/0Gw+hGxdvUl3XY2oyx6svHuP0iRFKRYsVK+uJJa4/vEtRVgYk1nwX1z23HLdBIf+nyHI9odCnEeLS2hOXg2m7mLbLqXOTDI7PUZ2MsKKphg/ODBHWNcZnc4zNZFndWsdDm7owLkEa07Ux7ntyPbsfXxfkcs5bayuKzpIkXZKEpWtiPPr5bTz8mS343vlzJFkgy/JF+xYhBOu2tfO//ddfAwHyFbxmF8IIa9z3xHp2PrImELeURKBXIRYT2H/0+1/B9zxkVcb3A9JV25DkT174J0BgGPd9P/DAi/Oe6fl7U1VlUZ9DEZ0nf2oHj3/hnmBN+vD3IkuXFLyMpyI89oVtPPTsZmRFQpYvvk9VV/gXf/wr+L6PcoV9w60DHyjhOD1LDp+XpCok6fqe86VAliRqk9Frzt4VIoau3Yeqrse2Dy6ZsANY1gHy+T9FUbqR5ZtX//kurh1RNcGZ/BFGSv2AQL6AkHZHN/DK+Lfpiq5nQ2oXE+Yw707/hHPFM9iexf3VT1GjNyFuggb5DZ9Nqmti3P/galrbqxkdniWbKbFidSNtnTXU1sVvWWuN43nYjksqEUZcsJhIUjCxl02HsZksmXwJ1/UYHJsjUlFZ27KyqUJ4JToaq9AvUJCbnC0wMZPnwa3dbOhuYHB8lplsAedsUMx644pGdq1vwwc0VQkWJUliNldmeDKD7biossQ9a1v4q+f2096QYi5XRAhBVSLC2dEZ+kZmmM0F4Ve+f7E4wOVQMm32nxxix7o21nbUYWgKJdNmdDJDe+PlSzMEZC1NNPY72PZRXHeE5Su5fh6elyGf/xNcd4Jw+KdRtbXL1rb6oQ2HEAL9MrklwcbnUhsUweXSVSRJ4sPNHTg5xN4jZ1Fkie3rWm9rhey7uLMQiH60EYn8LJnM/7JMrZYwyy8gSVGi0d9Fkj4ZoYnXi83b2lm5phHX9Ra8ralUBEkSfP6n7kHXzy/jsixRVR3jU5/ezAMPr0HVFLRKmomsSOi6wud/6h5CYR1NuzKpEkJC03ZgGI9SKPzZstyL7+co5P8cIXRCoc8griM3MKyrVMXDeJ6HabuUTBtNkalPxRicnENTZDobqmipTaBeRmFZCIGsyFyvv1+IQICSaySiQgiELNCucw4PzpPYu6+PUtmivaUaIQLDZndHDZbtIgmBkARl20MVgrlMnrGJDB2tNUTCGoWiFagjT2SJhDXq6xLMzuQZGpll68bWizzFi+/vurq7cN6l2rzwGO0SxpZbFb5fxjLfxPMyLDUHVpLSN5TACnF9NEIIAVKEaPTXyGT+Ga47tAy9sLDtw+Sy/4pE8l8B0WUX3LyLj4ZqvZGkVnORx7RWb+b+mmfwfBcJCSEkROWYeqOVZ5t+CU0yMOQwjaF2nqz/GXzfRRIyESV+U8gr3AQCq6gy6eoo4YhGU3Maz/MIh3UiMWOhHuytiLCuEovoDAxPs3t9G/Mr4ND4HH3D00TDOrsa2/ngxBBCsCDwoyvyIvluXVMCL1xlvAM1SR9dUwjpauC584PzfT8gUheef6x/nPGZHC11SXRV5tTZiaBYeyyMkARnLggB7h2eQlFk9mzu5NDp4esupu77geCQqszXOZXxsa+RimooyhqM0GcoFb+F501c17WvDR6eN0Gp9H08b4pQ+LOVsBX1ljWEXAltDSkiIQ1dVahK3viaiXdxF1eCJCXR9HtR1c3Y9nGWWhd2Pqe9XHoOgUEk+usIEb672fkQwhGdcOTSGUap9GLSL0RQOieZipC8TK5i6hpV7gEUpQVN34VpvonjnLn2Tl8WPo7TS7Hw14DAMJ655hzoDZ0NuJ6H7/u4no8iS8TCOiuaqmmsCvLtZFkirKvXvdbdUvBhZrZAoWjS1JBCkSUs26VUtjnZM0YkrDMxmWVqOk93Zy2aqnB2aJpc3sS2HKbmCtyzqQ3bcbEcF0WR0HWVYtle2BvcxeXg4/t5isVvLIsIpSzXI0vXVy7s//zu60xklnbtZ7evYc/ajms8WkXTd6Np2yiXc/h+ZknXBvC9DKb5Ftns/040+veQ5ca7db9vAaiShsrF+diqpJEQlZJGgkWEVBUaKe18OpsmdFRVw8cLSO6Hjr+RuClPlCxLRKIGkejto2YXjxi01CU50T/Oj98+ga4qtNSncNwgz3QmWwysoxULeU0yKHp8YmCcTKFMxNBY21nHiYEJRiYzHO0ZJRkNEQlp1KZiHDw9zFyuxNh0ju6WahqqE2TyZXoGJ3nhvVPIkmDn+nZMy2FkMsNcroRpORd4ggWr2mrZf3KIFS01tNQnGRqf4+zYDGFdXVi4imWL4ckMx3pHGZ7IsPfYWdZ2NuB5HgMjM/QMTgFwpGeUlrok6zobOHxmmGzBZGImR2NNgqr41T0lwSY0TDj8RVznLKb5Jr6f/RhGxsfzxiibr+J5Uzh2L0boKWS56bYr0ZFOREgn7nqh7uLWhBAqstxEOPJVctl/XVGuXCocHOcspdK3AYlw5GeWPaf9VoTve/i+heOcQJZbkaTkLXnPQoRQ1fUYxpPk870siyprxUNTLEjgexihZ5Ckq5Pq6gvmxgvzQlVFJl6pF3mpfNFbBb7v4nlzuM4AqrYOuLyWhOt5CEngOB6lkh2QUctlYjKHZc/hui6u5yFLEr7vUyha2FZmIeXFdlyy+SB1x7Fd8vkys3MFXNdDqYhEXrafXgnXGwWUTxzx8LwMlvkulvXesqQ+yXIrstJ89QMvwMDkDEPTi0mkEAJZCOYKZbIlE0WWiBqBsdv1PPJli0LZIhkxaK1O4njX/p4KISFEEiP0LI7Tj20fYekRc4FWSbn0AwQ64chPoyjdSxazuh3g+yaO048QUWS54abO667jMjE0w76Xj7Ht4bU0tNdc9t2/njkz8PTf/PXqkzMzLRGGrtJSm2LjikZyBRPbcfEqpXQ6mqpQVRldk3lwazchXSUa1ulqrmJwfA7HCUqu+IDv+WxZ1UxVIoLv+yRjIbqaq8hUFBa7W6ppqk1SlYjQ3pCmXFGLnA9daqyJM5uromzaCCForksu5LJs6G4gVzTpbKqirT6Fqki4lYls94Z2krEQIPD9oBi0pioEf/Yr6mM+XU1VIIKNgKYqrGqrYXI2j0+gyNvekL5kcelLQQhQ1TWEwp/D82awrA8Ae7mHBgDfm8U038Fx+nHdEXTjQVR1fWUzfHsR2WuB71t4XhbfLyDLzbfk5vcu7jwIEcYwnsAsv4ZpvrFMRikLxxmgUPhLwMMwHkdWOu9IdWLPK+F54zhOL47di20fJhL9lYpi5635DstyA7rxCKb5FrZ9iOUgsb5fxLL24/tlfN/CMB5GkuuvmSwtxybsRsHzsrjucDDezmk8d4iY8r8gSSqXGnMhoLOtBt/3SSfDlE0Hz/fQNJmWphTZfJlYREfXVWrSUSzHZWVnLUIIwmENVVWIV0Qm51XsVVWmqT55TSKZnp+nXH4F1x1GVdeiKN0oSgdCxO7o6AjPK2LbxykWv4HnzSy5PSEiyEorsnz5EoeXwpObV5EtLibPrucxnS/ywsEzbGyvp6M2TU08glEhsJmiydmpWfrGZljfWk9zOnHd/dX13djWPlx3Es9bav1nmCexxeK38HEIGU+jauuRpOvv260O37fxvOnKvN6DbR9D1+9HDj3Jcs/rZw6dpf/4MOWCCUKg6gqReIi2VY3UtaQxLojY9FyP6dE53vrhQdpWN9LQfmcJgwrfvxtUcj2YV3n0fB9ZkpAksfD7vKjPhfA8D88PkuuvtLbOK/4q8mLrqOf5eJ63II4gBAukVKrUuLsc5svpSFLg4F/K4u64Qe7NhwvfXws8r0Sx8BcUi1/HcXo+ch+uB5q2G8N4DFXbjKK0V8rt3J5ENnhFXXwvi+dn8LxZPHcC1x3FxyES+QVuROHwQv6/ks3+3jIIPejU1L6AorTfcdZ93y+Ty/4++fwfLrGloNB8bd0rN2Rsrwe+72OaL5PL/Xts6zBLDyU+DyGihEJfwAh9qmKASt32xhnfL+G5M7jeFK47iG0dxbLexbIOAhap9J9W6qPeut4Jz52hXH6ObPZf4XnTLI8nFkBGlhsIh7+KbjyMonQiROS2JkqBd72I503huVM4Th+WfQDL3IvjnEGSElTXvIAs11z3s+1XDOEXruTzObLzn8///KjrvesOk838HqXSD4KyeNoONH1XRZSntrKWhm/79/JC+F4J2zlOqfi3FAr/neVQWlfUDcTj/xDDeGLJbWWKZX6w7wTf33ecX3x4G/euaiMRPi/M5rge/RMz/F8/eIPuhmqe2bqalY3XT1Ys833yhT/FLL+wLIJO8xAihK4/TCj0GTRtW8VYdXs/P75v4nkZPHcK1xvFtk9gme9gWQfw/SzR2D8gGv1tJGl566N+4z8+z8t/uxdJEiSrY0iKBD50rmtmz2e20bG2cUEszbFdhnrGee077/Pg5++hffXyip3ebNxZu8cbACEEsrzYeS5JIlCnvQQk6doEpudDoS4+X1wkECRfI4mUKiqJywHlEuqC1wpJCmGEPh1s4AoTH1Mo8WJY1jtY1vuo2iYM42l0fQ+yXF/ZHBkVY8KtZ62HYAMETsU7YQYbYG8Oxz6FbR/Gtg9i2z2AhapuIRz+mVuO5NzFnQshwDAexrYOB4u3e5blEmkL8s++huOcIRT+Arp+H5JUc4E39tZ8Z8/DrxCYMr5fwvdLuO5ZLPMDTPMtHOcYnjd7szt53ZDkFEboaUzzHcrlF5clTy6Ai+sOkcv9PrZ9nHDkp1DVdUhSujKn3erjDcGYO+fH3MviuP2Y5XewrDdxnD58v7gsV7qcSM+Fny/fN+bguoOUSoOUSt9GUVah6feh6/eiKN2VsPdwRYjrysb0Wxc+nlfEsY9TLH6TUulvWQ7yCqBrO5ZNhTdbLPPd94+xoa2BDa0Ni8grgCJLrGioZseKVt46OcCKhuqPRGA1fRuGexbXGVimUOIAvl+iXP4RjtNHKPxZDOOpyn4sWnlubvVnx684Ekx8rzKveyNY1mEs820s64OPSefl0mhZUceOxzaw/ZF1ZGfzvPPjQzz/9beJJcNUNyRIVMcwixazk1kUVebRL++iuj558V35PrbpkM8UMUsWnushJAkjrBFLRlA0Gcu0yUzlCUV0osnwwnvu+z5zUzk81yOaCKNXoj1cxyWfKVHMlfDcQCk9HAsRjhooy6xzdJfALhPmQ3C9inT91byjnzQoSjMh4yk8d6yS73YjHP8OtvUBtnWQotyCYTyObjyOpm0BdEAiWHjhZkyg54Mf/EX/fL+M6wxi2YexrYNY1gcVz/XimrpCLK9l7y7u4toQbDjCkZ/DdYcpFseA5S2VZVnvYNuH0LQdRKK/gK4/CGhQMQfeGnOrf4Eozvz76+J5M9jWESzrXUzzTWz7DMv9/dx4CISIEU/8TzhOH7Z9lOVNB/Epl3+Aab5BKPTpCpHdRLBFuRXH/MI528N1B7Gsg5XN7Hs4Tt9N7eXyw8NxTuA4JygW/gxF6ULX96AbD6NpWxAiiu8H6+ntQGbna72Cg2m+TqHwZ1jm2yxXZIEQesVr3b4s7VmOy7nJDA+s6US9grJ1Imwwmy9dFIJ87ZAwjMfx3ClspxeWWEbow3Cck+SyfZSK3yca+zUM41NAlPN7sVvlufnwex5EVdj2cSzz3Uo6xbFlNORdHxRFJhwzSNXGSdbEaF3ZwOG3zzA2OM3UWAYjanDordP85//pr8jNFZFkif/vH/862x5eXKnD930Gz4zygz9/nWPv9pKZyROOGazb2c0XfutR2tc0MnB8mD/9l99m5xMbePZXHkLT1YDvuB5/9W9/RCFT5FM/v4cNu1fg+z4zE1me/8u3ePXb75ObLVDdlOKhz9/Dnme3UtdataxjfJfALhMm8gV+eOoUf3nwEL+6fRtPrOimJnJXkOdCqNoGwv5P43kTmOabN/DKLq47SLH4dUqlbyPLjRVL8n2o6jpkuYabYwH08f0CrnsOx+nFtk/h2CdxnD48bwbftwC78nN5rMJ3cRfLBUlKEwp/Ac+bpVz+8bK37/slTPMdbPsEitJJKPQUuvEpZLkOuFXSAXxcd6gSHXEMyz6IY5/B9zOV6IngHb4zICFJ9USjv0ku9x9wnOPLfgXfz1IqfRvTfANV3VgZ80cQIsat46Up4zjncOwTWPZRbOsgrnsOz8sDFr5vXrWF2xsujtOH6w5TKn0HSYqjqJvQtR1o+j3IcjtC3Op7HxfbPkih8N8rdY5HWb6weND0PShKG1xC8fWjQJIEIV3hzNgURevy80nP2HSgPL2EiDkhYujGg0TcYQqFP/3I7VweFo5zmkzmX1LI/xmG8TiG8SkUtRu4dYRdPW8cxz6DbR+vRL4dXdiXBe/4rTWvC0CWZSRJwjA0tjy4mn/93X/I0fd6+Ot/e7n1WRCKhthw70rue2YLyZo4g2fGeOfHB/mbf/9j/tEf/DIda5upaUgxOTTDcO84HWsDUTLbcji2t5cHP7uN2qZAwXhiaIa3fniAw2+f5jf+xZeoakhyav8A7794lEKmyE//vz9FaBnFe+8oAjtVKHBobIz3BocYy+XJlMuoskRU06mPRVlVXcPG+jq6qtLLfm3X98hbFlPFIqbj3JWrvwSE0FC1TUSiv47rzeHYx1nORePKcPH9Ar5fqIhqjGGWX0KSkkhyA4rSVfnXiSzXV8KjNJa6aZpXG/W8GTxvDNcdw3PHcN0RXHcY1x3H9/OVMOFCpY8l7hLWu7jVIYQckIzwF/D9PKb5xjJfIQjZ8rwJbDuD6w5RKn0PRVmJqq5H1TaiKF03SBTEw3XncN1RXHcIzx3Gcc8FxiZ3Gt/P4/l5fC9XCRe9UfPajUNgOZfRjQdw3TGKxb/GcU4u81V8fL8YfMfeHI5zgkLx66jKGlRtI6q6AVluuQECX0EouOdNBvO0MxwIMTkDFbI6U5mv8/h+rqJYe3MXfd/3cX0fuRL9NR8RNh+g+VH0Ky4PpxI2XcDzZnHdGWxrP1Lx60hyHYrcgaKuRFFWVt7R1E3Pa/Z9E9cdwTLfx7TexrGP47jD+F4WcJfpKgLQCYW+hCy3LJu3KaSpbOlo5NDAKH/95iEeXNdJZ12aiK7huB4T2TzvnT7HG8f7qIpFqEtce6msi+5ASChKB6HQZ/C8SUql7y3LPSyGg+/NYnt5XHeScvklFKUTRV2Hpm1EVdcgRPoGeGR9PC+H645V9mPDuM4QjtOL503geblgbq/8XL7nZBngB+G6udkCe188ysx4hm0Pr6WqLo6QBLqhkapNEE9FkS7jtRcCqhuSbHtoDaquomoKkbjB5NAML/zNO3iuh2aodG1s5dypEc6dHqNjbTNW2eb0wbOYRYuOtc2kaoMyZiMDkxzb28u9z2xmzfZOjIhOVX2SwTNjDPdPcvbUKKu3XWt5p6vjjiGwx8bH+cmZHt4+N8hkoUBYVRECLNelZDtIQnBmehpJ4mMhsEnD4LHuLtqSSdbW1hLXb11BjpsJSYqhafcQi/0O+dwfVHI5b7TF2qpMThOAhBAhbGk/kpSqLLRRhBRBEomA4EpJhBQBlAqpVSuCUDLzYYO+74BvVfLeioGIh5/D9zJ4Xr7yez7Y8HjzE2K2stm9a+24i9sTkhRF03ZWRGsy2Pbhj+EqlbB6dxDXHcK2e7GsD5DLdUF+rFyLLDcgyw0Vw1MCSUpU8quMilCYckGqgFfJM6+8t8znmc+/u4GRy/fmKqJpc3jebPC7l8Xzs/heLhDw8Ga41SzxHzckKYkRegrfz1Esmrhu/8dwFQ/fz+I4WXAGcOwzWNZ7SHItklSLLNUiKw3IUj1CCsZbkuIVjQOdYLxlzhsgPXzfJRhze1GOcvBvfjwzlTl7rkJSMxeMdxbfD56BG21gzFkmZ+fmGC/k2dHYjOf7hNSg3u08UZ0rl9g7MswDre3IQnByehJFSNREIuRMk+50FUXbJqJpKMtKZj18P4PrZnBdwD6NLR1GMquQpGokOY0s1QSGYbmhYiCuQZKqKsanK5f1+SgIDMclPG8UxxnGdc/iOP04zllcdwjXGaxocSzv2iuEga4/jqZtRoiPTiI/jHjI4Iu7NjA2l+OtkwOcGpkkFQmhKTKe71MoWwxNZ/B8n4fWd7KysXpJ1xPCQFHXEA7/HK47iWXt5+PZp9l43lhFmb0PyToUOBbkWmSp+oJ5varynscRIo4QoQvm9XkTjX/BvO4yP6/P78kCg1MOz5vD9zK4Xgbfmw1EMf1sIJLpBXVwPW/2ljBKXQ5DvRN8/7++yrvPH8a2HcyixdaH1rB+VzfR5PUZ91zH5cyhcwycGCY7U6CULzMyMEkpX8bzgvtftbWd/uNDnDs9imM7lIsmB988RevKBqobU6i6gud5ZKfznNzXTyFTpPfwIKKiet57ZJBwzGB6dG5Zv4c7gsAWLIs3Bs7yo1OnkWWJp1auoCWZRFdkTMclWy4zns+jKQqp0MeTNxjWNNbW1rK29vok0z95EAiRQNcfxffKFAr/Dcc5sayKd9cHrxLGW8B1By/4XKpMknEkKRrkmwoFgQrzE+eFBBYXfLsSNji/OZr3qN7u+W93cReXhyxXo+t78L0C+XwW1z3Hx+eB9PH9WRxnFsc5BchBuQq5BkmqQUgxJBFFSNGAzKCBkBEoIKTKfqSyycED38XHWhBMwy/j+UX8hc1OvvIzS6C2fGtuaG40FKUFI/QMvl+iWPw7PG/sY7yae97gaAOoFcJaXfkXq4z3vMFCA+RgfhYy+B7gBeONC76zaMyDn4WKYbEy3n5umT1zS8N8dYOhbJaoNkFSN2iKJ8hZJplyGUNRKDsOo7ksru9hOi6ThQJRXSfuGvTPzTJbLjNVLLK7uYWqcPiiignLBwvPm8TzJhc+ESJcIaw1SHJ1xVicrKyvcSQRQ0jhypobqoyjwcI4IgXvL3CeqDjBmrswlqWK8SlfMUTMVTzo45XIidGPhbSeh4YstxOJ/CyydP0K01eCoSls7WziZ/ds4YPeYQYmZzk7OYvlBNUhoiGN7oZqNrc38tDaDmqX4IGdhyRFUbWtRKO/Ri73B5V92se1l/Hx/Ryum8N1+wj2iaGKoaM6MEjOz+3zRiqhnH/H4YL33AXfw8eChfe7YqT0C5Uomfm5PVu5p9srYkZWJIyITrwqih7SSNfGWXtPJ40dtQsKxNcC1/V44/v7OX3wLOGoQSwZiDHNTefxPB+/QmBbV9QTS0WYncgyORyIEB58/ST3f3oLiapAiMvzPGzTxrFd0nVJUrXxBQK7/dF1VDckqW9fmmHlw7gjCOxEPs/xiQnytsUznav4rZ07SBjn46xdz2O6WKRg2aTDoYXp682BAQC6q6poiMUAKNo2+4aGyZpl2lMp2pJJYrqO5/lMFYu8PzREazLJiqo0ru9zanKKoeyFidyC1TXVtCQShNQr52mVbJuJQoGRbI6caWK5DookYygKScOgLhalOhxG/VA+Q8GyGMvlGcvnyVsmtuuhShKGGhD0hliMVCi0zFbW5UNgMYsQCn8eH5tS8e+w7aOVEI1bBd4FIcc3uy93cRe3NmS5DiP0FJ43R7H4TVx3iBvjmXTPe+rovcqxEudFOe5iqVDVlfihz+D5RcqlH91AFU47KFHjTV3DsTLB5vT2HvOIqtEUi3NmepqB2Tk21NUhCZguFjkzM01C12lNJGmKJTAUhaLnkzJChDUNx/OYKZUYzuUo2Bab6+sDEaMbKJgThIUXAyPxomlBRogEspwO6sxKUSQRqRgkQoHBuEJSztd+CIwRvm9fQFCKC0YHz5ut5CourwDRlSEhy/WEwp9G03ey3Dn6khAYqsJTW1eztqWOvvEZJjJ5ypaDIkskwwbttSm6G6ox1Hmv5DJcVwqhG0/gulMUS9/EsY+zXIraV8Z8KsHZisr9FXtZ+fnJ2ajVNqfZ9eRGdn1qE0ZIQ0jXL4A1r0D8/Nffpqmzlgc/u52VW9rIZ4q8+u19HHvvfMnLWCpCc1cdZw6d5eQH/dQ0pRkfnGb11g4i8cApKBAoqkJVfZL7nt7M9kfXISsXcJCPWIbzSrgjCOxcuUzRtqkKhelOpxeRVwjKztRGz1uk5peyP3xvLwXL5rd23EPDqhi+7zNVKPB/vPoafbOzfHnDen5u8yZW1dRgug5Hxsb4H597nl/cuoX6bVuxXZcXenp4/kwPluuSM00KlsXfv/8+vrhu7RUJbMm2OTExyav9/bw/NMxMsUjBtlAlGUNVaE0m+dSKFTzW3bWIwBYti4OjY7zS18fhsTFmSyVKtoMmSYQ0lc50ms+tWcOu1hYU7dYNYw5eNo1w+CsIEaJU/NtKYfvcze7aXdzFXVw3BJJUSyT6awCUSt/BcQZYzhqxS8cnZ4NzYyChquuJRH4JAZRKP6iEVN9K3/Ot4UFdDsiSRLoSQdaaSBBWNQxFQZVkHM8joRs0xuIoQiKiqtRGotieR8m20WWFpnickKKSMkIfo/f1euHi+zM4zszN7siSIEkpdP0+wuGfJ0gxWv7vd75UUkdtmo7a5U+Du8xVAYlw5GdAyJSK38S2j9wgEnutuJXmmxsDWZbQQxrhJQoiuY7L5PAMOx5bT21rGkmWmB7LMNQzftGxbasbGemf4OCbp1i5uY265ioaO2oWyudIskSiOkp1Q5LTB8+ydkfXQjizWbIQkiAUWd7SaHcEgY3rBiFVpWhbTBYKmI6DViF9l5pI5j9pSSTYPzxCxjSZL4NTsCz6Z2eRhWAwkyFTDuL+85bFcDaLLEl0VaXRZZm4rvO5tWvZ2tjIuUyGNwYGeHPgataiAL0zM/zZ/v283j9AWzLJjpZm4rrOTKlM78wMJyenWFFVxROie9F5JyYn+c/vvsuJySm60mnubW0lrKrMlEqcnprm4Ogo9zQ33UIL1JUhhEI4/AVkqZpC4WuY5qs3MZz4Lu7iLj4qhBAIESEW/10kKUqh8Nc4zmnuCpItDb4flHQQYvlLycyX8vqo7QohoaoriMb+PkLoFIvfugVJ7J2BuK7zYPtiAZTudBVdqTQ+gZfuwsoHXenzJGdzfUPwHFEpdHOb7A9uBwgRQtfvJRL9FWR5eUMkbwUEz4pCJPIVJClOsfA1THMvt5Zx8i4uhGO7mCWLUr5MIVvCdT0K2RKZ6TyqJqMZGooqo+oKNY0phvsmGOmdoJgpcfit0xx998xFbbaurOfY3ijvv3gUVVPY+cQGNGOxk66ps46N96/kJ19/m+buWro2tILvMzIwSTgWYv3ObjRj+bywdwSBbYrHaY4neHPgLC/09NJdVcWTK1dclee3JpO8NzhEtlzG9jxMx2Fgdg7H89jR0sxcucxcOYj5L5gWI9ksshB0ptPoioImy3RXpelKpxnKZpjIF66ZwO4bGub01BQ7W5r5+/fdS0c6XUlDB9f3mS4U0WSJ8Ie8uK/1D3Auk+FTK1fwK9u20pxInD/P8xnP50gYBoZyew2tpt+HJNejFDvJ5/+U279u4l3cxRQK0kQAACCuSURBVCcVgnDkFxBSkkL+z7Ht/Te7Q7c1yiUb1/VQNRldX+byQfPhSEvkM5JURTT2jxEiRbH4NVx3ZMldu4trx7UO313autyQMYxPEYn+Koqy+mZ35mOGwDCeRBJxhPgzyuXnb3aH7uIy6Ds2xA/+7DUOvXmKfKZEuVDmP/7jrxOJh9j60Bqe/oU9dG9sRVEVfuYfPM13//hl/s3vfg0jrLH5gdU880sP8Hd/+OKiCSOaCFPfUo0QEife7+PT/+mX0IzFUZ7VjSke+tx2PNfju3/6GtMjsyiqTENbNQ99cQcbdq9Y1vu8vVjOZaApMo+v6GK8kOflnl7+9Rtv8J3jJ9jT0cbu1laa43H0SxC6tmQy8F4WS8wUS/i+z8DsLFFNY0dzM8+dPsNsqYTtuuQti9FcjpCq0hxPoMoyQgjkiiVTliRk6dqXB9vzcDwPWZIIqeoiwukDejzIyf2wpdT2XDzPR5UkQsrF57UkEkjiVioIfS0QCKGgKB2EIz+HonSQz/9nHOccd703d3E1+L4PPguCAXdxMxGMgRAahvE4kpSmVPxbyuXn7oqZXQWzswVOHR/h2JEhhIDN29pZvbaR3jPjHNjXT01dgqc+vXlZr3nwwNngWlvbl9DK/HoTIhz5KrLSSLHw3yvKpZ8sheabgWtd62+vPcHtgCB6LBT+Cqq6dllFm25NCIRQUbVtRKUostJGsfCXt1g48Z2Dgfwkr4wfpy8/wW+vfIxaPY4iyTzxld1Ypk0kvlhteN90H/tnBjiTGyNfKKI9JdHy+Fp+qeMBpMocLUmCUMwgkQ5SKiVJsG5nN01dtZglG0kIwjEDzVDZ/MBqsqKM6quoIuA7Wx9aQ+e6oA5sbUt6cY4rcK44xSuF45zdmqFt7Wp+tXo9KT2CqinEkmEUZXnfkTuCwEpCsKq6mp/fvIn2ZJLXBwZ4f3iY/tlZnj/dw9raGu5ta2VTQwPJC/JjW5MJoprGTKnETKmIKsmcm8vQlkqypqaGF3t6mSgUyJTLCzVemxJxQqqyZEtmVzpFQzTGsfFx/mTfPh7t6mJbUxNRTUMS4rICTGuqa3gzfJZ3zg2iKTIPd3ayoa6OqKYhhLhI8Ol2ghAastyEbjyBJDcGBe3Lr1yjWMdd3Kko5sucPTPO8f0DqJpC24o6utc1E4kF7/L40Aznzkyw45E1N7mntzf8C+tGLoMRTJISaNo9SFISRemgWPxWRaH4Lj6MUsni+JFhzg1MsWN3N5omk0iGUVWZ5pY0p46PkM8FBoD5GuNjI7NkMiVicYOqqihCkpidyTM1mSOZClNbm0DTFYoFk+npPIVcmWQ6Ql19AiEEo8OzuI5LMnU+7NT3fSzTYWR4Ftf1SFdFSaYiSNdgHBJCVBSpH0aSqiiXfkC5/NINFHe68zCZLXBieIKzk7OENJUNrfV011chX6MYiu24lG0bz/dJhD+eCgyfRAiRIBz+CqHQMyjq6opi8icDkhSp1GmNoijtFPL/Dcfp5W5I8fKiSo/SFavlTG4M23MXgmWSNfFLHt8WqSaqGNi+y4ya5+FVa6k2YnTGGi6bUihEkJcaitQs+tzxXMywxyvjx3miYSNJLSDL0USYaOLyZXqmzBzD5ixfXLebsKzRHE6jy8scNXQB7ggCCxDRNNbV1VETibC6poYz09Ocnpri+PgEg5kMJyeneLizgydXrlhQHG6OJ4jqOrOlEtPFIlFN41wmQ1e6iq50mrCmMpHPM1UskrcspotFNjU0LIuHc11tHY+v6OYHJ0/xat8AA7NzvNo/wOrqajY31NOaTBK5hAjTtqYmzs5leKG3hxd7eumZnqE7nWZ1TQ2bGxpoSsRvu/DhCyGEgiRVoet7kKQEirISs/xqRaU4c/UG7uKOQ7loMT2epZAts2pTCwfeOkMyHcUI1VDIlZmeyOJ/SGW0kCuTmc7jui7xVIRYMkwhZzIzkSWRjhCNh1BUGatsk88G0ReFXJnaxhS25SDJEqGwhu+DWbZwXY9w1CA7UyCXKaLpKsnqKEbo1hVKu1aULJvJTAHLdkhGQkznCnQ3VuO5HpbjVjbLPp7nEzau734lKYaqrkeS0khyLaXSj7Ct/TdYIfTWx8R4hrnZAunqKGvWNSLLEp7nI0mCVDpKLB5aILDg47oew8OzlEs2p0+O0NCYoqY2zoF9/TQ0pTDLNslUBCEJTp8aZXhwhuqaGLbtUluXQAgwTZvxsQyu69G9MlCmNU2Hl39ylFAoyJFSFHkRwb0WyHIVkrQLSUohy02Uyy9g20e4mxd7fSiaFvt6h5gtlGhKxwlpKiFNRQC5UpmZfAlDU0lFQgggUypjOS5RQyNTLBPWVOYKZQYmZ5ElQVtNitaqJJIkUbJsZgtFfB+qYmFC2se3ybyzoKEo7YRCn8UIPYWitH+iyOs8hAihKF3IlZJIpdKPsMz3KqWTbm/F7xsJH5+sVeKdqTO4vk/ZtVibaKI9UkNMDdEUShNV9Gtqq8aIU2PEOZMbQ5Nk1iaaSOvRhevsnxlgopzB8hziapitqXYiis6LY0cJKzqO52LIKrVGQJD3TvXy7lQPkhCsiNXTFqlGIBgpzWJ6DpuSrRRck33T/QvnvDfdy9n8JGOlDA2hBH7lfz25cfrzk9iegyYpbK/qJKGGkMTS8mFvX6ZzCWiyTHMiQWM8zv3tbZyenOLw2BjvDw9zaHSMmWNFQqrKT2/cAEAyZJAOhRjN5ZgqFCs5pHn2tLdRE41QHQ4zWyozks2RKZcp2Q6d6dSyCCTVxaI82tVJXNfZOzTEmelpfnzqFHsHhzg0Ws+9bS1sa2qiKb7Y2tKUiPPM6pVURcJ8MDxM7/QM3584xTuDg2xvbOK+tlY2NzYsEnO43RAYB2Q0bQuy3IaidGGar2NbH+A4vXdDVj5hcF0PIaCuKcWm3d28+v0D5LMlPM+nVLSYm8pjlc+HKjq2w6lD58jOFojEQpglG8/1Oby3D91QOXtmjM7VDTS0VTMxMsuBt89Q31KFWbJIpCPMTOSwTIfqhgSqKtNzbJj65jSarjI2NMPcVI5yySJZHWPLvcub03EzMJnJc3JwEkWWCOsaQ1MZOuur6B+foWjaGKqCLEtoikzbdRJYCCIrFKUNSfoSktSAqXRiW/twnIFPOJGVkOUmhIgyO1NCCEFjU2ohzEqWr7DO+FAslCkWLU4eG6ZYtNB1hTOnx4jGDerqEpV1ymdyPMtA/ySRqLGIjCZSEbKZQOADwHE8JsYz9PWMc8+uLhLJMOGI9pGqrQgRQtM2Isu1yHITpvkqlnUA1x3lkxxWLEQIWWlDCJWrZaQOzWQZm8vRXJVgz5oOFEnCdj2yJZNDZ0exXRd8qE/GqIlHODAwQszQmcwG71R9MsZsvkTP+DSxkI6hqTSlE1iWzYmhcWaLZcAnomts72pGkaTK2ns31PhiSEhSNZq2FV1/kFDoGYSU+gSEDV8eQsgIkSIU+jSSVIuitGOZ72Hbp/H9uZvdvZuIQJFfktKIayBoPj625+L6HsOlGWQhEVUMWpSqZe2V43lYnsuMWWCynCOhhliXaOEno0fYmm4npUVQJAnX9/B8n6xdwvW9hb75+BQdk/78JDm7xKZkKyXHYt90H1tSbdSGEsG5+Dj+/DkwY+Y5lR1ltDRHSgszaxV4a+I0j9SvI6wszQFwRxHYeUhCEFZVNjc2sKmxgS2NjfzF/v283NvHS729fGHdWhRJQhKC+liU0VyO0VwOz/cp2TaraqrRFYXmeIKemRkGZudw/cAT0ZlOL5vCb3MiQUMsxr1trXwwPMK7g+c4OTnFS709nJ6aomQ7fHr1qos8sZ3pNK3JJPe3tbFveJj3h4Y4OTnJd44f5/TUFL/ob+GBjvar1qG9HSDLaUKhJ1HV9VjmW5TNl3Dsk7juSEWt+JNs7RMg7shX+CLYlsPE6CzHP+gnnooQiuhBPkdYwyzbnNh/loc/uxUA1/E4efAcnufRva6JYsEkc3KUt547zLY9qzjyfi+O7ZKsjjE+PMvel0/w9Fd3E4mHUBQZz/MY7BunkC9RXZfgwFtn+PTP3ovv++QzRYp5k57jw8iyYPPu7ts+t2wyU2CuUKIuGQMBJcvBx+fE4AT5kkkyGkJTFGIhnbba1Ee8ikCSIoRCT6BpmymXf4JZfgXbPorrjvPJCT+TECJaIXYNqNo9KEoLiiKC8F3r6jn/nuczO1vAcXzkCumY99S2d9ZQyJsUoiau5xFSdOobU8zNFSnkyxQL5kI7qVSEaMxYIKiO4zI5kaW2PkFndx01tZcOVbuee5XlRkLhz6PpOygVv4NpvoXjnMHzZvnk6BsoSFIiEClU2tG03QgR5mpEcXBqjmTYoKUqsZAaJAScHp3i1MgkKxqqGZnNMpUr0FlXxQd9wzyyvouXjvSwtqUO1/MolC00JagTWh2LIAnBudks75w+u1CTMVMs011fRXVs3rhhICvtyHIHnjfxCTcyCYSIoygtaNpOQqHPoOn33OxO3UIInA26vhtVXY2pbqRc/gm2dQDHGeKTI8YpEMJAkubn9U2o6nquRrF8HzRJYXWiEUVIqJLMpJlj2srTElk+Auv5Ps3hFNV6lEkzS29+gr78BOsSzeTsMt2xOjan2jEq4b4Fx2RbVSc5p8xj9eupqXhYS5cpdxVVDTYkW8jaJabNPE81blr424nMMEXHZH2imW1VHfTnJ/mPp55nd82KuwT2ahDAhvo6tjQ28s65QSYLRYqWTdwI3PKN8RiHRscYzGTwAUNRWFldjSJJtCQTHJuYoH92hrAWCCZ1pJbHAzsPWZKoi0Z5etVKnl61kuPj4/zR+/t4c+Asrw8MsL6ulnV1dRedp0gSrckErckEn161kuMTE/xfb7/D/pFR3jp3LlBHrlpeC87Ng0BRmlGUn8IIPUa59DKl8ndx7B48b7bikb1z6v1dHRpChJHlGhSlg/OFvO9cFHJljr7fz8kDZ7n/qU2kamLIikw8FaG6LrFITEAzVGobk4ycnSafLWOENPpPjoIQjA1OI4TALFuUCiaKKlPTmGT3Y+sWzm/trqP/5ChDvROEwjpW2aa2KUmpYOL7UC5ZlPImRljD93zElTxltwF0VaGxKk46GmY6W6Rk2nheYBiSJIl0NIwQULKWh2TKci2RyM+ha/dRLv+YUunHuO5ZPC8HmFc9//ZDsLkRIoQkpVGU1RihxzGMRxEigRAy1TVzDA3OMnh2mvaO2gXjjCxLWKZDqWRRLtvk82WEEBw+cI7pqRxb7+lgeiqHpilU1UR5+NF1jAzP8s2/eoeGphThsE5Xdy1VVRFOHh/h+9/+gA2bWvF9ETzHxaA+XyFfRpIlIhE9iGqYLaIoMrqhEgotra5l4IHvIBb/+xjWoxSL38Q038H1RvC9AnemR1ZCiBBChJHkWjRtG4bxeCUn/NoNAx82zzqux7HBcdY217KupZ54yGBweo6RmSyqLLGjq4U3TwywqrGGsmVTthzqk3Gaq+J01wf7gd6xaTJFEyECERdZkpjOlRYIrCynCYe/gizVUy4/j20fx/fzFYPxJycMXIgwkpRC0+7BCD2Lrt93XWP3SYMkpQiFnkHTtmOWX6dY/AaO04Pv5yoCfnfasyMI9mIhJCmBrLRiGMG8HkTWXJ2c2Z7DQGGSvz23l3ojwXg5S1jRcP3l/a4yVpEXRo8wZxcDR51r0hUNeIUmyaS16AJ5vfDuPpyaBWLR50Fu7pWdSHm7jCQEEUVHETIRRSdjFRdKuC0FdwSBvfCL+PBC6wcH4PmX/pobYjEkIRicy6ArCsmQsRC225pM4PkeZ+fmqI5ECGkqrcnkshDYy/V5bV0dX1q/nslCkelCkaFMdhGBvdR5mqKwubGRr27axES+wFgux1i+cAcR2HlISFIV4ciXCEe+iFl+l1LpO5jma7juUOWYO9kjG0yYqroK3Xi0siHayCch5CtZFeWBpzex/p4OvvsXb7J2axvJqkp+R6VOpuf5C96kRz+3jZmpHG89f4Qfv3KCbQ+uYsdDq7n3ifUYYX3huLHBGRR18TSoagqhiM7wwBRnz4yzeksrAPvfPM3sZI512zuIxkOc6xm/I5629W31i37f0B78Xp2I0FmXpj4VaAYs970qagdR9bcJhb9AufwKxeI3sReUa++Eb3ZekTmKpm1DNx5D1/egql182OjU0JhkfDTJay8f5+ihQaJRnfsfXk1dfZJjhwf54L0+LMshngxx7/0rqa2P897bZxgbmWN2tkD3ynqOHx3mnTdOYxgq6aoomqaQy5XY924fp06MIMsSdQ0JEJCZK/LOm6c58EE/QggiUZ379qyis7ueV184xve//QEA23d2suu+FWja8mwVVG0jCW09tn2acvk5SqUf4tgnCMb7ThlzGUmKo+sPohtPoOs7keVartfQ2FSV4NzUHCOzOVY21AQBN0BYVzFtF8d1sWwH1/MIV3JYJSnwxi+sCCLw2rreeYE2XVVY31rHmqZauhuqF3p9IRSlFSX6C4TCn8GyPqh4z1/F8zIExuI7YawuBwkhVHT9USLRn0dVNyNJ0ZvdqdsGslxHOPIljNDTWNZbFAp/g2W9i+/Ncec8NwIhQqjqGnT9YXTjkQv2Yte+H5uxCpzJjdMSruIrbbt5a/IUZwtT4Ff2NRV6OP9fvu9/JGPi0cwQkpB4tH49mqSwb7r3/J1coj0ByEKi7NpBKHCFdyhCQhISRdfC833GSnOY7pUNkNVGjNFyhhmrQNm1mbUK1IUSy8Kj7ggCey6Toew4VIXDVIcvVsg6ODrK+0NDWG6QwxrTzydFN8bixHSNYxMThDWVjguKf7cmEsR1nZ6ZWVzPpykWR71G9b+rYTATEOaaSOSix/3c3BxzpRI10ciivgL0zsySDodIGsZF5/XOTJO3LCKqRuQOCB++MgSavhVVXY3r/TKWdQjTfKUiJHAnqV5KCJFE07ah6fegaVuR5VYkKYYQIT4J5HUeiiLT0FpFIh1lsHcCSZaYGJ7lreePMHB6jB/85dvc+8Q6fGDfa6eYHJ2jXLRoX1XPms1tfOP/foWhvkk8z2PXo+tYtbn1stdqW1HPwKlRju7t5Vf/x2cAqKqLc2RvHyNnpyjmzQUp+jsV27qaUeTz893H9aRJUjWh0LMYxsPY9knK5ecwy69XjFK3Y5iphiw3oGlb0fR7UNXtyHINQoQrgi+X/ibXrG+io6sWx3HxfYjFDGRFoqo6ys57u/F9MAwV3VCJxUM0/A8pFEnCB1RVRlFlOrtqgzJrqkwkqiMJwe49K9myvSPomSYjyxKJZJiHH1/HvXtWAqAbKoYReFq/+st7AiEzScIIqajqcuf5CRSli0jklwmFnsW2jlAq/QDTfKci1Hf7eWqEiKAoHajaNjQtmKeFiFbGXOejvD1dtWl6Rqd49/RZ3j19lpp4hF0r23hgTQd/++5RDp8dxXE9VjbW0Fad4si5sYvaSEfDWI7D3713lNMjk/zU7o1s62zkO+8f59t7jxHSFOqSMb6wY/2id/38fcXQtXtR1c34/hym+R6W+TaWtf82fj8vDSEiKOoqDOMxDP0RJLm+4nG9/YX6bgaECFUMdltxnF5M81XM8svY9iluz5SRwIGiaVtQte1o2nYUuRUhRT7yXiyuhqg3Evxk9DCzVgEfH1XIlF2LAzMDPD96mKOZIb7e/zYbU63sru6mSo9d93W6onXsnerhW+feI6EGHKkhlLzs8bqs0hBKMmsV+OOeV9iWbmdruoO0HqXGiPHi2FH+7ckfEVZ0iq6FuMK9d8fqGC7N8t50D29PnkaTFH6x8wGi6tLFz4S/HH7cm4wfnzrN82fOMFMqURuNUhUKE9M1TNdlIp+nb2aG4WyWFVXV/PqO7dzf1rZwbqZc5vdff5PvHD9OeyrFM6tW8tu7dgJQtG3++Usv89zpM9THojza1cU/eWDPwrkDs7NMFgqUHIeRbI5X+vp4saeXT69exf1tbdTHohiKQnUkQnsqtWiI/8Pb73BgdJSacISmeIyYruMDQ5kM7w8NM1cu8ezq1fza9u1URc6T8v//iy8ynM1RF43SEI0R0VRsz2Mok+Gdc4OUHYdf3raVL65bRzL0yVDH830bz8vhedN43hSO04dtHca2j+M4fRVBgdvjMQ/yKBpR1W4UZUWg9Ke0I0nJyr8EoF+TOMByo5D/r2Szv1cJJVsKdGpqX6goOF7dhmaZNsW8GZT1qIkxPjSDHtIwwhpW2WZuOo9ZskmkI6Rqgsl9ZjJHuWghBIQiOsl0lJFz0/gVZdd0bZxoIkQhV2ZuOk9zx2IZeatsV9owaemqRZIlSgWTydG5hTImmq7Q0Fq1yILp+2Vy2d8nn//DJX5HErLcSm3dK5UN8J2PwJNexPMmcd0JXKcP2z6CZR3CcU4sw3P38UCIELLcgqJ0oairUJUVyEorkkggFt5Z5Zos5/PLse8HnrMrnXNhxIEQYiESAfxFZZDOf841lcNZ3Lb4SCJO1wrfd/D9Aq47judOYjsnse3D2NYhHOcst2ZIuVQpIdKBonShqqtRlG4kuSHIdZWSCBHnfH3cj47pXJG5QlCLXlNkauJRQprCyGwOy3EQQhAPGUR0lclsgdbqJIPTGWKGjud7gMD1PKZyBSK6RnNVAoFgPJMjXw5IRFhXaUjFF8pnXQrBc+nheRl8bwbPm8V1h7Dtk9jOCRy7F9cd4dYcr8tDkhpQ1dWo6gZUdS2K0o4k1yBJ1VzrO3sXV4fvl/DcGVxvAtcZxLaPYtsHsawj+H6OW9FoJYSOJNWjKJ0o6kpUZRWK0omQEpX3PAEYS3pGXN+j4JgMF2fQZRUJgSwkYqqBQDBl5ii6FmFZI6YaJLUImnTlPdNUOYflOdQYcVQpMD5ansNoaQ7Lc1CFjCQEuqRSY8QqHuA04QvUjn3fx/ZdBvKT/097d9fbZlLFAfw/Z+y4fonjxI6TNumqLQK62gtepFUBCal3IMEnQOILcMFHQVqJLwB3XMIl19AuK7FFWmmLaC9o07y5ieMmfmlszxwu5jy2I1G6bLfNOvn/pMiWosdRNJ6Zc87MPA/EOVTzRSzlS1iQHLrjU+wOjuCdIC85jOxvVfNFdIZ9tIdd3Ko0p58FRWfYR2fYQ1CFdw5XizUU5M2OpgAXJIH9ZGsLf3r4T3y6u4v+cARVtW00DuIcVkpFfNBs4sc3buDDzY0zq5ohRnx07z5+9+kDbC5V8esf/RA/+Wa6s6iq4jd/+Sv+8NlnqJdK+OX3votffGd6OPm39z/GJ8+eYRQC+qMRWt0eWr0e1isVrJbLKOZzyInHneub+NUP7pxZMv/9gwf486PHeN7tQZFuPJX3HqqKRrmM71+7iru3buKDZvPMM98+uncf9548RWcwgALwziFn160vVnDn+ibu3rqFbyyvfOFg5SJRDVA9Rgh7CKGFGFsI422MwxZi2EEIuwhhD6pdnN+g6eBcGSLLEKnD+zU7/L8G8U2INOClDvH19PgRWZpcd55Go39hNHwAfcPzxg4eV4o/hXOLby0Rj9G22wAQW1lQVcSgcOJemxy8+nNTUPiq61VDCsBHD9/wP3AQV8aV4s8u5Z0uVaP1432EsJN+xk8QwjbGYRsxbCPGQztb9a7krc827SZMTYjfQM5fhUgD4lcgsgqROkTKOO/+Ok+mCVLbxuhdhLCFEJ4ijHcQwjZC2EaMHbzLVb8UxDYmbS5+Hd5vwPt1G5vr8H4VIsv/c3X9TaQCRNpKOBsLxBgB5167FS+7Pv0/06Q6xrQt8cs8FjD1z6zY9NyKx0eIk7bbQ4wHiLE900/PM9QUSzrW4XMb8P5aKjr5DWvTdWtn9tu3KX1vBikuCzvWt59gHJ7ZOL+NGPbf8ZMmchBZtDsHZ2P7NfjcNRvPV+ClAfGNr00sRsmFSGDb/T4eH7bx784RjgYDDEZjjGNETgSVhQWsLy7iW4063qvV/uszUj9+uoW/7+ygWijg7s0buF6rTX73t60t/GN3D8VcDh9ubuL95nSl5o+fP8Sjw8PXHkb+9moDP799+8xE8/iwjUcHB9g9OUF3OMQoBOS9R2VhAe/Vari92sBapTK5+2Dm81YLjw/baHW76A2HCKpYsOturizj/dUmVkpF5L6irc7zTnWMGDs2oe7bZPs83fwpvkCMx1A9QdQeVPvQ2IfqKYAhVEdQjAAdIyW72Tmt7JyDIFXjBYBHOtC/YI9IWIBzBTgpTrYOOleGuEra/itViMtWVRuWrKYB8osc/ie6LNL4OrKC1J4ltfuIsYUY2oj6Ahp7UO0iahcau1B9Oem7ijGgASnxyfrv2b7rXMFWugv2vmhFpkraCiqVFAC7ZeurdQtq1uxxCR4Mar460wSpNS1Ghj2Eydh9DNUeYuzaDYa6UD21th6nIpuOMU12Z9vcI62uWZu7AhwKECnBufKkvZ2rwEsNTlZSQVHqtjrXhPe1mc8lIJtr29ZmKXnV2EGMHeuj3dSm2re5dgDVARRDQNN8m827wBiq2ZwbcXb+FfvxSO2Zs3n3ihURshumlW2erUKkas+iXoWXNYhfg/dXbZv35SsQfh1Mi1YHM/HZPmJoIcSDFJ9NxvOsr/dfMa5nixGCaT/PWSxVmPT1LA6bHdvTd2PZCo/ZwsEavK9/6e3/9G5ciASW6P8V48Am26xK/AKqxxYY9RH1ZQqC9dQCoQCdTKQClw2Uzqf3Lg+HKzZAzgyUsmjnVWcHytpbq9YTXRaqp2nVJx6kIDke2WsnBT04tWB4BI3DFPgg2nkdD7gcXPbqShBXAlwR4kqp37qltLImNeu3VStO0XmJsTdZ1UvtfIQQOylR0j5SAmRJUFaAhNp47W28zhKerLhYgpMSxFVte+DyzDj99naJXBbZ0QCNR4h6jBhPoNlrPIFiOteqvgT0FKpDAMGS2IAsiU3lp6zolENq0/yk/6bziKlQ7GQZ3jdspZxz7rxIhZAXttAwO64fzXxfbFzXUSp+IFrLeqTvRjauF8/0c7E4zJ3p40sQuRzH7S4aJrBEREREREQ0F1haJCIiIiIiornABJaIiIiIiIjmAhNYIiIiIiIimgtMYImIiIiIiGguMIElIiIiIiKiucAEloiIiIiIiOYCE1giIiIiIiKaC0xgiYiIiIiIaC4wgSUiIiIiIqK5wASWiIiIiIiI5gITWCIiIiIiIpoL/wG2flUikQD8ewAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from wordcloud import WordCloud\n",
+    "reviews_text = ' '.join(df['cheese'].dropna())\n",
+    "wordcloud = WordCloud(width=800, height=400, background_color='white').generate(reviews_text)\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.imshow(wordcloud, interpolation='bilinear')\n",
+    "plt.title('Word Cloud of Reviews')\n",
+    "plt.axis('off')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c326647c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:01.449831Z",
+     "iopub.status.busy": "2024-06-26T13:56:01.449276Z",
+     "iopub.status.idle": "2024-06-26T13:56:01.519956Z",
+     "shell.execute_reply": "2024-06-26T13:56:01.519003Z"
+    },
+    "papermill": {
+     "duration": 0.109091,
+     "end_time": "2024-06-26T13:56:01.521940",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:01.412849",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "                            \n",
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+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
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+       "    x.observe(notebookContainer, {childList: true});\n",
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "content_type = df.groupby(['milk']).size().reset_index(name='counts')\n",
+    "content_type\n",
+    "# Ploting Distribution of content ratings on Netflix\n",
+    "fig = px.pie(content_type, names = 'milk',values = 'counts',color_discrete_sequence =  px.colors.sequential.Agsunset,\n",
+    "             title = 'Distribution of cheese milk',height =  700 , width = 900)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a7088453",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:01.592678Z",
+     "iopub.status.busy": "2024-06-26T13:56:01.592323Z",
+     "iopub.status.idle": "2024-06-26T13:56:02.097447Z",
+     "shell.execute_reply": "2024-06-26T13:56:02.096586Z"
+    },
+    "papermill": {
+     "duration": 0.542988,
+     "end_time": "2024-06-26T13:56:02.099914",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:01.556926",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "        17, 18, 19]),\n",
+       " [Text(0, 0, 'United States'),\n",
+       "  Text(1, 0, 'France'),\n",
+       "  Text(2, 0, 'Italy'),\n",
+       "  Text(3, 0, 'Canada'),\n",
+       "  Text(4, 0, 'Australia'),\n",
+       "  Text(5, 0, 'United Kingdom'),\n",
+       "  Text(6, 0, 'England'),\n",
+       "  Text(7, 0, 'Ireland'),\n",
+       "  Text(8, 0, 'England, Great Britain, United Kingdom'),\n",
+       "  Text(9, 0, 'Germany'),\n",
+       "  Text(10, 0, 'Spain'),\n",
+       "  Text(11, 0, 'Netherlands'),\n",
+       "  Text(12, 0, 'Scotland'),\n",
+       "  Text(13, 0, 'Switzerland'),\n",
+       "  Text(14, 0, 'Austria'),\n",
+       "  Text(15, 0, 'England, United Kingdom'),\n",
+       "  Text(16, 0, 'Canada, Italy'),\n",
+       "  Text(17, 0, 'Sweden'),\n",
+       "  Text(18, 0, 'Belgium'),\n",
+       "  Text(19, 0, 'Portugal')])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = df['country'], order = top_countries.index, hue = df['vegan'])\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "d4cf1c38",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:02.174194Z",
+     "iopub.status.busy": "2024-06-26T13:56:02.173818Z",
+     "iopub.status.idle": "2024-06-26T13:56:02.802785Z",
+     "shell.execute_reply": "2024-06-26T13:56:02.801843Z"
+    },
+    "papermill": {
+     "duration": 0.669432,
+     "end_time": "2024-06-26T13:56:02.804988",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:02.135556",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "        17, 18, 19]),\n",
+       " [Text(0, 0, 'United States'),\n",
+       "  Text(1, 0, 'France'),\n",
+       "  Text(2, 0, 'Italy'),\n",
+       "  Text(3, 0, 'Canada'),\n",
+       "  Text(4, 0, 'Australia'),\n",
+       "  Text(5, 0, 'United Kingdom'),\n",
+       "  Text(6, 0, 'England'),\n",
+       "  Text(7, 0, 'Ireland'),\n",
+       "  Text(8, 0, 'England, Great Britain, United Kingdom'),\n",
+       "  Text(9, 0, 'Germany'),\n",
+       "  Text(10, 0, 'Spain'),\n",
+       "  Text(11, 0, 'Netherlands'),\n",
+       "  Text(12, 0, 'Scotland'),\n",
+       "  Text(13, 0, 'Switzerland'),\n",
+       "  Text(14, 0, 'Austria'),\n",
+       "  Text(15, 0, 'England, United Kingdom'),\n",
+       "  Text(16, 0, 'Canada, Italy'),\n",
+       "  Text(17, 0, 'Sweden'),\n",
+       "  Text(18, 0, 'Belgium'),\n",
+       "  Text(19, 0, 'Portugal')])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = df['country'], order = top_countries.index, hue = df['vegetarian'])\n",
+    "plt.xticks(rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "182c0bc8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:02.879900Z",
+     "iopub.status.busy": "2024-06-26T13:56:02.879223Z",
+     "iopub.status.idle": "2024-06-26T13:56:02.955463Z",
+     "shell.execute_reply": "2024-06-26T13:56:02.954578Z"
+    },
+    "papermill": {
+     "duration": 0.115762,
+     "end_time": "2024-06-26T13:56:02.957625",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:02.841863",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('079e879c-719a-4258-8679-08a8875188aa');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"milk\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3eddd168",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.032541Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.032211Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.101703Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.100771Z"
+    },
+    "papermill": {
+     "duration": 0.109051,
+     "end_time": "2024-06-26T13:56:03.103704",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:02.994653",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('23b401de-630a-486e-a29e-45387c0a542d');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"country\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e0051756",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.179959Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.179655Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.244117Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.243220Z"
+    },
+    "papermill": {
+     "duration": 0.105024,
+     "end_time": "2024-06-26T13:56:03.246001",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.140977",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('41b1b180-7d17-47ca-a48f-579d446ce8db');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"vegan\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "0abc61ff",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.320156Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.319882Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.385184Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.384306Z"
+    },
+    "papermill": {
+     "duration": 0.104327,
+     "end_time": "2024-06-26T13:56:03.387069",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.282742",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('77cbf557-633e-4c01-bea4-815765fb7a60');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"family\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6d035b23",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.464770Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.464460Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.530093Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.529192Z"
+    },
+    "papermill": {
+     "duration": 0.105891,
+     "end_time": "2024-06-26T13:56:03.532094",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.426203",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"type\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f4470e78",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.608952Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.608603Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.673345Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.672518Z"
+    },
+    "papermill": {
+     "duration": 0.105447,
+     "end_time": "2024-06-26T13:56:03.675382",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.569935",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('28621585-8a13-457f-a490-f23f8261fd70');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"fat_content\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ced8212e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.751942Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.751619Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.817547Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.816720Z"
+    },
+    "papermill": {
+     "duration": 0.106176,
+     "end_time": "2024-06-26T13:56:03.819415",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.713239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>                            <div id=\"62e9ebc9-ca90-4469-b598-35aa9ca356cc\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"62e9ebc9-ca90-4469-b598-35aa9ca356cc\")) {                    Plotly.newPlot(                        \"62e9ebc9-ca90-4469-b598-35aa9ca356cc\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"hovertemplate\":\"vegetarian=False\\u003cbr\\u003ecalcium_content=%{x}\\u003cbr\\u003ecount=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"False\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"False\",\"offsetgroup\":\"False\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[null,null,null,null,null,null,null,null,null,null,null,\"26 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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('62e9ebc9-ca90-4469-b598-35aa9ca356cc');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"calcium_content\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bb19e11a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:03.895946Z",
+     "iopub.status.busy": "2024-06-26T13:56:03.895621Z",
+     "iopub.status.idle": "2024-06-26T13:56:03.964194Z",
+     "shell.execute_reply": "2024-06-26T13:56:03.963308Z"
+    },
+    "papermill": {
+     "duration": 0.108947,
+     "end_time": "2024-06-26T13:56:03.966194",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:03.857247",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('02e47f54-32be-4ddb-bf85-1e7e71f24c47');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"texture\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1732b71d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.044724Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.043920Z",
+     "iopub.status.idle": "2024-06-26T13:56:04.111679Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.110813Z"
+    },
+    "papermill": {
+     "duration": 0.10883,
+     "end_time": "2024-06-26T13:56:04.113701",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.004871",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('fb5e9120-e95a-46c1-b083-be4c7d1a2c2f');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"rind\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "514990de",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.195968Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.195611Z",
+     "iopub.status.idle": "2024-06-26T13:56:04.263419Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.262543Z"
+    },
+    "papermill": {
+     "duration": 0.111313,
+     "end_time": "2024-06-26T13:56:04.265510",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.154197",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('86bdc829-4716-427c-a4d1-8c8ffe1bde30');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"region\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "64dbd68d",
+   "metadata": {
+    "execution": {
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+     "iopub.status.idle": "2024-06-26T13:56:04.415630Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.414681Z"
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+     "start_time": "2024-06-26T13:56:04.306143",
+     "status": "completed"
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('200781f6-fa57-4827-9f46-dcfba83e37a9');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"color\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "521ecd80",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.500916Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.500567Z",
+     "iopub.status.idle": "2024-06-26T13:56:04.574249Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.573261Z"
+    },
+    "papermill": {
+     "duration": 0.116559,
+     "end_time": "2024-06-26T13:56:04.576415",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.459856",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('c0eb5da0-6892-4614-b7fa-63c27072ecdf');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"flavor\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "405c41d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.662429Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.662099Z",
+     "iopub.status.idle": "2024-06-26T13:56:04.729652Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.728743Z"
+    },
+    "papermill": {
+     "duration": 0.11202,
+     "end_time": "2024-06-26T13:56:04.731539",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.619519",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('0f0f674c-e54f-449e-8aac-6ea111a4d461');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"aroma\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "90fa2261",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.814347Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.813766Z",
+     "iopub.status.idle": "2024-06-26T13:56:04.881097Z",
+     "shell.execute_reply": "2024-06-26T13:56:04.880255Z"
+    },
+    "papermill": {
+     "duration": 0.110916,
+     "end_time": "2024-06-26T13:56:04.883126",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.772210",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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Blue\",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,\"Shaker Blue\",null,\"Sharon Hollow Garlic and Pepper\",null,null,null,null,null,null,null,null,null,null,null,\"Lactose Free Mozzarella Shreds, Dairy Free Mozzarella Shreds, Lactose & Soy Free Mozzarella Shreds\",\"Blue Shropshire, Blue Stuart, Inverness-shire Blue\",null,null,\"Lactose Free Slices, Dairy Free Slices, Lactose & Soy Free Slices\",null,null,null,null,null,null,null,null,null,\"St. Mang original Allgaeuer Romadur, St. Mang Masterpiec\",null,null,null,null,\"Stella Blue Cheese crumbles\",null,null,null,null,null,null,null,null,null,null,null,\"Natural Baby Swiss Cheese\",null,null,null,null,null,null,null,null,null,null,\"Swaledale Sheep Cheese\",\"American Swiss Cheese\"],\"xaxis\":\"x\",\"yaxis\":\"y\",\"type\":\"histogram\"}],                        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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('8914a627-04dd-42ac-8d85-8a7de5e987aa');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"synonyms\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f31dbf81",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:04.966032Z",
+     "iopub.status.busy": "2024-06-26T13:56:04.965699Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.031493Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.030643Z"
+    },
+    "papermill": {
+     "duration": 0.109045,
+     "end_time": "2024-06-26T13:56:05.033473",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:04.924428",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
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+      "text/html": [
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('1315da5f-66d6-4c1c-9dc2-cafa48744d90');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"alt_spellings\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "26d12583",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.120959Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.120625Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.189505Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.188620Z"
+    },
+    "papermill": {
+     "duration": 0.11669,
+     "end_time": "2024-06-26T13:56:05.191530",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.074840",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"producers\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"count\"}},\"legend\":{\"title\":{\"text\":\"vegetarian\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\",\"bargap\":0.2},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('e559692a-cd90-4274-b3e1-7e568e8e67e5');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "fig = px.histogram(df, x=\"producers\", color=\"vegetarian\")\n",
+    "fig.update_layout(\n",
+    "    bargap=0.2\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98920267",
+   "metadata": {
+    "papermill": {
+     "duration": 0.053581,
+     "end_time": "2024-06-26T13:56:05.288402",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.234821",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Data Preprocessing And Feature Engineering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c57e2329",
+   "metadata": {
+    "papermill": {
+     "duration": 0.041756,
+     "end_time": "2024-06-26T13:56:05.375919",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.334163",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### We are deleting columns with weak impact , clean the data , preprocess and conduct feature engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b8e73d24",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.461888Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.461498Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.476833Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.475976Z"
+    },
+    "papermill": {
+     "duration": 0.060882,
+     "end_time": "2024-06-26T13:56:05.479105",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.418223",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>texture</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>buttery</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>elastic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>creamy, supple</td>\n",
+       "      <td>acidic</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>creamy, crumbly</td>\n",
+       "      <td>acidic, creamy</td>\n",
+       "      <td>fresh</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Woodside Cheese Wrights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>semi firm</td>\n",
+       "      <td>smooth, sweet</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>firm, supple</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>firm</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Various</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1187 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    texture                 flavor              aroma  \\\n",
+       "0                   buttery                  sweet            buttery   \n",
+       "1       creamy, dense, firm          burnt caramel           lanoline   \n",
+       "2                   elastic                    NaN           aromatic   \n",
+       "3     creamy, dense, smooth  acidic, milky, smooth  barnyardy, earthy   \n",
+       "4      creamy, open, smooth          fruity, nutty  perfumed, pungent   \n",
+       "...                     ...                    ...                ...   \n",
+       "1182         creamy, supple                 acidic                NaN   \n",
+       "1183        creamy, crumbly         acidic, creamy              fresh   \n",
+       "1184              semi firm          smooth, sweet             floral   \n",
+       "1185           firm, supple                  nutty       nutty, sweet   \n",
+       "1186                   firm           nutty, sweet                NaN   \n",
+       "\n",
+       "     vegetarian                producers  \n",
+       "0         False                     Jumi  \n",
+       "1          True                      NaN  \n",
+       "2         False                      NaN  \n",
+       "3         False                      NaN  \n",
+       "4         False                      NaN  \n",
+       "...         ...                      ...  \n",
+       "1182      False                      NaN  \n",
+       "1183       True  Woodside Cheese Wrights  \n",
+       "1184       True                      NaN  \n",
+       "1185      False                      NaN  \n",
+       "1186       True                  Various  \n",
+       "\n",
+       "[1187 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=df.drop(['cheese','url','country','rind','family','color','vegan','type', 'milk', 'synonyms', 'alt_spellings', 'region', 'fat_content', 'calcium_content', 'calcium (mg/100g)'],axis=1)\n",
+    "\n",
+    "df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f06e9b80",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.577832Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.576999Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.585093Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.584205Z"
+    },
+    "papermill": {
+     "duration": 0.06232,
+     "end_time": "2024-06-26T13:56:05.586934",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.524614",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "texture        58\n",
+       "flavor         98\n",
+       "aroma         258\n",
+       "vegetarian    439\n",
+       "producers     400\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2e577c1a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.673039Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.672460Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.685180Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.684345Z"
+    },
+    "papermill": {
+     "duration": 0.05827,
+     "end_time": "2024-06-26T13:56:05.687027",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.628757",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1187, 5)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"texture\"].fillna(df[\"texture\"].mode()[0], inplace=True)\n",
+    "df[\"flavor\"].fillna(df[\"flavor\"].mode()[0], inplace=True)\n",
+    "df[\"aroma\"].fillna(df[\"aroma\"].mode()[0], inplace=True)\n",
+    "df[\"producers\"].fillna(df[\"producers\"].mode()[0], inplace=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ad3d3f64",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.772981Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.772264Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.784381Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.783443Z"
+    },
+    "papermill": {
+     "duration": 0.0572,
+     "end_time": "2024-06-26T13:56:05.786346",
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+     "start_time": "2024-06-26T13:56:05.729146",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>texture</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>buttery</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>elastic</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>creamy, supple</td>\n",
+       "      <td>acidic</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>creamy, crumbly</td>\n",
+       "      <td>acidic, creamy</td>\n",
+       "      <td>fresh</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Woodside Cheese Wrights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>semi firm</td>\n",
+       "      <td>smooth, sweet</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>firm, supple</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>firm</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Various</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1187 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    texture                 flavor              aroma  \\\n",
+       "0                   buttery                  sweet            buttery   \n",
+       "1       creamy, dense, firm          burnt caramel           lanoline   \n",
+       "2                   elastic                 creamy           aromatic   \n",
+       "3     creamy, dense, smooth  acidic, milky, smooth  barnyardy, earthy   \n",
+       "4      creamy, open, smooth          fruity, nutty  perfumed, pungent   \n",
+       "...                     ...                    ...                ...   \n",
+       "1182         creamy, supple                 acidic               rich   \n",
+       "1183        creamy, crumbly         acidic, creamy              fresh   \n",
+       "1184              semi firm          smooth, sweet             floral   \n",
+       "1185           firm, supple                  nutty       nutty, sweet   \n",
+       "1186                   firm           nutty, sweet               rich   \n",
+       "\n",
+       "     vegetarian                producers  \n",
+       "0         False                     Jumi  \n",
+       "1          True                  Sartori  \n",
+       "2         False                  Sartori  \n",
+       "3         False                  Sartori  \n",
+       "4         False                  Sartori  \n",
+       "...         ...                      ...  \n",
+       "1182      False                  Sartori  \n",
+       "1183       True  Woodside Cheese Wrights  \n",
+       "1184       True                  Sartori  \n",
+       "1185      False                  Sartori  \n",
+       "1186       True                  Various  \n",
+       "\n",
+       "[1187 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "1e232fae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.873395Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.873009Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.882364Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.881471Z"
+    },
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+     "duration": 0.055447,
+     "end_time": "2024-06-26T13:56:05.884299",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:05.828852",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(748, 5)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(subset= [\"vegetarian\"], inplace=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0377e50f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:05.973865Z",
+     "iopub.status.busy": "2024-06-26T13:56:05.973109Z",
+     "iopub.status.idle": "2024-06-26T13:56:05.986233Z",
+     "shell.execute_reply": "2024-06-26T13:56:05.985095Z"
+    },
+    "papermill": {
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+     "start_time": "2024-06-26T13:56:05.927817",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>texture</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>vegetarian</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>buttery</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>elastic</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>creamy, supple</td>\n",
+       "      <td>acidic</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>creamy, crumbly</td>\n",
+       "      <td>acidic, creamy</td>\n",
+       "      <td>fresh</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Woodside Cheese Wrights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>semi firm</td>\n",
+       "      <td>smooth, sweet</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>firm, supple</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>firm</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Various</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>748 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    texture                 flavor              aroma  \\\n",
+       "0                   buttery                  sweet            buttery   \n",
+       "1       creamy, dense, firm          burnt caramel           lanoline   \n",
+       "2                   elastic                 creamy           aromatic   \n",
+       "3     creamy, dense, smooth  acidic, milky, smooth  barnyardy, earthy   \n",
+       "4      creamy, open, smooth          fruity, nutty  perfumed, pungent   \n",
+       "...                     ...                    ...                ...   \n",
+       "1182         creamy, supple                 acidic               rich   \n",
+       "1183        creamy, crumbly         acidic, creamy              fresh   \n",
+       "1184              semi firm          smooth, sweet             floral   \n",
+       "1185           firm, supple                  nutty       nutty, sweet   \n",
+       "1186                   firm           nutty, sweet               rich   \n",
+       "\n",
+       "     vegetarian                producers  \n",
+       "0         False                     Jumi  \n",
+       "1          True                  Sartori  \n",
+       "2         False                  Sartori  \n",
+       "3         False                  Sartori  \n",
+       "4         False                  Sartori  \n",
+       "...         ...                      ...  \n",
+       "1182      False                  Sartori  \n",
+       "1183       True  Woodside Cheese Wrights  \n",
+       "1184       True                  Sartori  \n",
+       "1185      False                  Sartori  \n",
+       "1186       True                  Various  \n",
+       "\n",
+       "[748 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "00ea7182",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.081982Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.081339Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.090702Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.089791Z"
+    },
+    "papermill": {
+     "duration": 0.057363,
+     "end_time": "2024-06-26T13:56:06.092639",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.035276",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1        True\n",
+       "2       False\n",
+       "3       False\n",
+       "4       False\n",
+       "        ...  \n",
+       "1182    False\n",
+       "1183     True\n",
+       "1184     True\n",
+       "1185    False\n",
+       "1186     True\n",
+       "Name: vegetarian, Length: 748, dtype: object"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y=df['vegetarian']\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "db8670fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.183434Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.183080Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.188362Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.187455Z"
+    },
+    "papermill": {
+     "duration": 0.053372,
+     "end_time": "2024-06-26T13:56:06.190471",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.137099",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df=df.drop('vegetarian', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5ffc957a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.280642Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.279757Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.291702Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.290832Z"
+    },
+    "papermill": {
+     "duration": 0.058606,
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+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.234951",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>texture</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>buttery</td>\n",
+       "      <td>sweet</td>\n",
+       "      <td>buttery</td>\n",
+       "      <td>Jumi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>creamy, dense, firm</td>\n",
+       "      <td>burnt caramel</td>\n",
+       "      <td>lanoline</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>elastic</td>\n",
+       "      <td>creamy</td>\n",
+       "      <td>aromatic</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>creamy, dense, smooth</td>\n",
+       "      <td>acidic, milky, smooth</td>\n",
+       "      <td>barnyardy, earthy</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>creamy, open, smooth</td>\n",
+       "      <td>fruity, nutty</td>\n",
+       "      <td>perfumed, pungent</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>creamy, supple</td>\n",
+       "      <td>acidic</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>creamy, crumbly</td>\n",
+       "      <td>acidic, creamy</td>\n",
+       "      <td>fresh</td>\n",
+       "      <td>Woodside Cheese Wrights</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>semi firm</td>\n",
+       "      <td>smooth, sweet</td>\n",
+       "      <td>floral</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>firm, supple</td>\n",
+       "      <td>nutty</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>Sartori</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>firm</td>\n",
+       "      <td>nutty, sweet</td>\n",
+       "      <td>rich</td>\n",
+       "      <td>Various</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>748 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    texture                 flavor              aroma  \\\n",
+       "0                   buttery                  sweet            buttery   \n",
+       "1       creamy, dense, firm          burnt caramel           lanoline   \n",
+       "2                   elastic                 creamy           aromatic   \n",
+       "3     creamy, dense, smooth  acidic, milky, smooth  barnyardy, earthy   \n",
+       "4      creamy, open, smooth          fruity, nutty  perfumed, pungent   \n",
+       "...                     ...                    ...                ...   \n",
+       "1182         creamy, supple                 acidic               rich   \n",
+       "1183        creamy, crumbly         acidic, creamy              fresh   \n",
+       "1184              semi firm          smooth, sweet             floral   \n",
+       "1185           firm, supple                  nutty       nutty, sweet   \n",
+       "1186                   firm           nutty, sweet               rich   \n",
+       "\n",
+       "                    producers  \n",
+       "0                        Jumi  \n",
+       "1                     Sartori  \n",
+       "2                     Sartori  \n",
+       "3                     Sartori  \n",
+       "4                     Sartori  \n",
+       "...                       ...  \n",
+       "1182                  Sartori  \n",
+       "1183  Woodside Cheese Wrights  \n",
+       "1184                  Sartori  \n",
+       "1185                  Sartori  \n",
+       "1186                  Various  \n",
+       "\n",
+       "[748 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "7977cc4d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.384031Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.383275Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.387848Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.386956Z"
+    },
+    "papermill": {
+     "duration": 0.051845,
+     "end_time": "2024-06-26T13:56:06.389819",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.337974",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y = y.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c2d25b12",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.484003Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.483106Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.490321Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.489441Z"
+    },
+    "papermill": {
+     "duration": 0.057725,
+     "end_time": "2024-06-26T13:56:06.492235",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.434510",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       0\n",
+       "1       1\n",
+       "2       0\n",
+       "3       0\n",
+       "4       0\n",
+       "       ..\n",
+       "1182    0\n",
+       "1183    1\n",
+       "1184    1\n",
+       "1185    0\n",
+       "1186    1\n",
+       "Name: vegetarian, Length: 748, dtype: int64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ac7a26a9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.586488Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.586120Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.591662Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.590688Z"
+    },
+    "papermill": {
+     "duration": 0.055786,
+     "end_time": "2024-06-26T13:56:06.594067",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.538281",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "string_columns = df.select_dtypes(include=('object')).columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "3c9188d4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.688924Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.688361Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.834375Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.833626Z"
+    },
+    "papermill": {
+     "duration": 0.194346,
+     "end_time": "2024-06-26T13:56:06.836779",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.642433",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le = LabelEncoder()\n",
+    "for col in string_columns:\n",
+    "    df[col] = le.fit_transform(df[col].astype('str'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "da5af70a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:06.928337Z",
+     "iopub.status.busy": "2024-06-26T13:56:06.927972Z",
+     "iopub.status.idle": "2024-06-26T13:56:06.940456Z",
+     "shell.execute_reply": "2024-06-26T13:56:06.939505Z"
+    },
+    "papermill": {
+     "duration": 0.061325,
+     "end_time": "2024-06-26T13:56:06.942488",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.881163",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>texture</th>\n",
+       "      <th>flavor</th>\n",
+       "      <th>aroma</th>\n",
+       "      <th>producers</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9</td>\n",
+       "      <td>463</td>\n",
+       "      <td>40</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>91</td>\n",
+       "      <td>57</td>\n",
+       "      <td>176</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>197</td>\n",
+       "      <td>165</td>\n",
+       "      <td>0</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>93</td>\n",
+       "      <td>28</td>\n",
+       "      <td>36</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>123</td>\n",
+       "      <td>266</td>\n",
+       "      <td>207</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>151</td>\n",
+       "      <td>0</td>\n",
+       "      <td>223</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1183</th>\n",
+       "      <td>73</td>\n",
+       "      <td>10</td>\n",
+       "      <td>101</td>\n",
+       "      <td>228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1184</th>\n",
+       "      <td>229</td>\n",
+       "      <td>450</td>\n",
+       "      <td>91</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1185</th>\n",
+       "      <td>217</td>\n",
+       "      <td>379</td>\n",
+       "      <td>206</td>\n",
+       "      <td>187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>207</td>\n",
+       "      <td>395</td>\n",
+       "      <td>223</td>\n",
+       "      <td>214</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>748 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      texture  flavor  aroma  producers\n",
+       "0           9     463     40        119\n",
+       "1          91      57    176        187\n",
+       "2         197     165      0        187\n",
+       "3          93      28     36        187\n",
+       "4         123     266    207        187\n",
+       "...       ...     ...    ...        ...\n",
+       "1182      151       0    223        187\n",
+       "1183       73      10    101        228\n",
+       "1184      229     450     91        187\n",
+       "1185      217     379    206        187\n",
+       "1186      207     395    223        214\n",
+       "\n",
+       "[748 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0220e33c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.034405Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.034045Z",
+     "iopub.status.idle": "2024-06-26T13:56:07.044279Z",
+     "shell.execute_reply": "2024-06-26T13:56:07.043336Z"
+    },
+    "papermill": {
+     "duration": 0.058263,
+     "end_time": "2024-06-26T13:56:07.046584",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:06.988321",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 748 entries, 0 to 1186\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column     Non-Null Count  Dtype\n",
+      "---  ------     --------------  -----\n",
+      " 0   texture    748 non-null    int64\n",
+      " 1   flavor     748 non-null    int64\n",
+      " 2   aroma      748 non-null    int64\n",
+      " 3   producers  748 non-null    int64\n",
+      "dtypes: int64(4)\n",
+      "memory usage: 29.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "1208c7ac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.139574Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.138856Z",
+     "iopub.status.idle": "2024-06-26T13:56:07.149660Z",
+     "shell.execute_reply": "2024-06-26T13:56:07.148672Z"
+    },
+    "papermill": {
+     "duration": 0.059951,
+     "end_time": "2024-06-26T13:56:07.151648",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.091697",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.78091696,  1.51363391, -1.4735174 , -0.41336818],\n",
+       "       [-0.50250017, -1.40377292,  0.29792847,  0.68514588],\n",
+       "       [ 1.15008739, -0.62771396, -1.99453089,  0.68514588],\n",
+       "       ...,\n",
+       "       [ 1.64898175,  1.4202194 , -0.8092252 ,  0.68514588],\n",
+       "       [ 1.46189636,  0.9100325 ,  0.68868859,  0.68514588],\n",
+       "       [ 1.30599187,  1.02500419,  0.91011932,  1.12132058]])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler=StandardScaler()\n",
+    "x=scaler.fit_transform(df)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5e4d379",
+   "metadata": {
+    "papermill": {
+     "duration": 0.045645,
+     "end_time": "2024-06-26T13:56:07.244292",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.198647",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Splitting data into train and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "4bc89b22",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.338710Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.337877Z",
+     "iopub.status.idle": "2024-06-26T13:56:07.423700Z",
+     "shell.execute_reply": "2024-06-26T13:56:07.422859Z"
+    },
+    "papermill": {
+     "duration": 0.136236,
+     "end_time": "2024-06-26T13:56:07.426338",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.290102",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "2c8b324f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.521902Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.521489Z",
+     "iopub.status.idle": "2024-06-26T13:56:07.527987Z",
+     "shell.execute_reply": "2024-06-26T13:56:07.527066Z"
+    },
+    "papermill": {
+     "duration": 0.057267,
+     "end_time": "2024-06-26T13:56:07.530106",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.472839",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "598"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a072a4f8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.631425Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.631068Z",
+     "iopub.status.idle": "2024-06-26T13:56:07.637057Z",
+     "shell.execute_reply": "2024-06-26T13:56:07.636148Z"
+    },
+    "papermill": {
+     "duration": 0.060187,
+     "end_time": "2024-06-26T13:56:07.639033",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.578846",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c50539c",
+   "metadata": {
+    "papermill": {
+     "duration": 0.045407,
+     "end_time": "2024-06-26T13:56:07.730648",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.685241",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Model Building and Calculating Accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "674e437e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:07.823201Z",
+     "iopub.status.busy": "2024-06-26T13:56:07.822857Z",
+     "iopub.status.idle": "2024-06-26T13:56:12.909462Z",
+     "shell.execute_reply": "2024-06-26T13:56:12.908563Z"
+    },
+    "papermill": {
+     "duration": 5.13526,
+     "end_time": "2024-06-26T13:56:12.911421",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:07.776161",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'K-Nearest Neighbors': 0.62,\n",
+       " 'Logistic Regression': 0.6333333333333333,\n",
+       " 'Decision Tree': 0.7133333333333334,\n",
+       " 'Support Vector Machine': 0.6466666666666666,\n",
+       " 'Random Forest': 0.72,\n",
+       " 'Gradient Boosting': 0.7666666666666667,\n",
+       " 'AdaBoost': 0.7266666666666667,\n",
+       " 'Extra Trees': 0.7,\n",
+       " 'Naive Bayes': 0.64,\n",
+       " 'XGBoost': 0.7266666666666667,\n",
+       " 'CatBoost': 0.74}"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from xgboost import XGBClassifier\n",
+    "from catboost import CatBoostClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Initialize the models\n",
+    "knn = KNeighborsClassifier()\n",
+    "log_reg = LogisticRegression()\n",
+    "decision_tree = DecisionTreeClassifier()\n",
+    "svm = SVC()\n",
+    "random_forest = RandomForestClassifier(n_estimators=1000)\n",
+    "gradient_boosting = GradientBoostingClassifier()\n",
+    "ada_boost = AdaBoostClassifier()\n",
+    "extra_trees = ExtraTreesClassifier()\n",
+    "naive_bayes = GaussianNB()\n",
+    "xgboost = XGBClassifier()\n",
+    "catboost = CatBoostClassifier(verbose=0)\n",
+    "\n",
+    "# Dictionary to store models and their respective accuracies\n",
+    "models = {\n",
+    "    'K-Nearest Neighbors': knn,\n",
+    "    'Logistic Regression': log_reg,\n",
+    "    'Decision Tree': decision_tree,\n",
+    "    'Support Vector Machine': svm,\n",
+    "    'Random Forest': random_forest,\n",
+    "    'Gradient Boosting': gradient_boosting,\n",
+    "    'AdaBoost': ada_boost,\n",
+    "    'Extra Trees': extra_trees,\n",
+    "    'Naive Bayes': naive_bayes,\n",
+    "    'XGBoost': xgboost,\n",
+    "    'CatBoost': catboost\n",
+    "}\n",
+    "\n",
+    "# Dictionary to store accuracies\n",
+    "accuracies = {}\n",
+    "\n",
+    "# Train and evaluate each model\n",
+    "for model_name, model in models.items():\n",
+    "    # Train the model\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Make predictions on the test set\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate the accuracy\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    accuracies[model_name] = accuracy\n",
+    "\n",
+    "accuracies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "f0c4940a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:13.002341Z",
+     "iopub.status.busy": "2024-06-26T13:56:13.002008Z",
+     "iopub.status.idle": "2024-06-26T13:56:13.008979Z",
+     "shell.execute_reply": "2024-06-26T13:56:13.008031Z"
+    },
+    "papermill": {
+     "duration": 0.054914,
+     "end_time": "2024-06-26T13:56:13.010935",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:12.956021",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "176    1\n",
+       "793    0\n",
+       "452    1\n",
+       "767    1\n",
+       "769    1\n",
+       "      ..\n",
+       "734    1\n",
+       "126    1\n",
+       "661    0\n",
+       "730    1\n",
+       "189    1\n",
+       "Name: vegetarian, Length: 150, dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "c00db7ef",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:13.100324Z",
+     "iopub.status.busy": "2024-06-26T13:56:13.100019Z",
+     "iopub.status.idle": "2024-06-26T13:56:13.105996Z",
+     "shell.execute_reply": "2024-06-26T13:56:13.105173Z"
+    },
+    "papermill": {
+     "duration": 0.05277,
+     "end_time": "2024-06-26T13:56:13.107985",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:13.055215",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
+       "       1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1,\n",
+       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
+       "       1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
+       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,\n",
+       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e56c6206",
+   "metadata": {
+    "papermill": {
+     "duration": 0.044344,
+     "end_time": "2024-06-26T13:56:13.196700",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:13.152356",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "82cf5ab0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:13.287544Z",
+     "iopub.status.busy": "2024-06-26T13:56:13.286690Z",
+     "iopub.status.idle": "2024-06-26T13:56:16.637248Z",
+     "shell.execute_reply": "2024-06-26T13:56:16.636278Z"
+    },
+    "papermill": {
+     "duration": 3.39752,
+     "end_time": "2024-06-26T13:56:16.639371",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:13.241851",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Nearest Neighbors Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.61      0.71      0.65        76\n",
+      "           1       0.64      0.53      0.58        74\n",
+      "\n",
+      "    accuracy                           0.62       150\n",
+      "   macro avg       0.62      0.62      0.62       150\n",
+      "weighted avg       0.62      0.62      0.62       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Logistic Regression Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.60      0.84      0.70        76\n",
+      "           1       0.72      0.42      0.53        74\n",
+      "\n",
+      "    accuracy                           0.63       150\n",
+      "   macro avg       0.66      0.63      0.61       150\n",
+      "weighted avg       0.66      0.63      0.62       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Decision Tree Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.70      0.71        76\n",
+      "           1       0.70      0.73      0.72        74\n",
+      "\n",
+      "    accuracy                           0.71       150\n",
+      "   macro avg       0.71      0.71      0.71       150\n",
+      "weighted avg       0.71      0.71      0.71       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Support Vector Machine Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.63      0.75      0.68        76\n",
+      "           1       0.68      0.54      0.60        74\n",
+      "\n",
+      "    accuracy                           0.65       150\n",
+      "   macro avg       0.65      0.65      0.64       150\n",
+      "weighted avg       0.65      0.65      0.64       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Random Forest Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.72      0.72      0.72        76\n",
+      "           1       0.72      0.72      0.72        74\n",
+      "\n",
+      "    accuracy                           0.72       150\n",
+      "   macro avg       0.72      0.72      0.72       150\n",
+      "weighted avg       0.72      0.72      0.72       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Gradient Boosting Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.87      0.79        76\n",
+      "           1       0.83      0.66      0.74        74\n",
+      "\n",
+      "    accuracy                           0.77       150\n",
+      "   macro avg       0.78      0.77      0.76       150\n",
+      "weighted avg       0.78      0.77      0.76       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "AdaBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.71      0.78      0.74        76\n",
+      "           1       0.75      0.68      0.71        74\n",
+      "\n",
+      "    accuracy                           0.73       150\n",
+      "   macro avg       0.73      0.73      0.73       150\n",
+      "weighted avg       0.73      0.73      0.73       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Extra Trees Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.69      0.74      0.71        76\n",
+      "           1       0.71      0.66      0.69        74\n",
+      "\n",
+      "    accuracy                           0.70       150\n",
+      "   macro avg       0.70      0.70      0.70       150\n",
+      "weighted avg       0.70      0.70      0.70       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Naive Bayes Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.60      0.88      0.71        76\n",
+      "           1       0.76      0.39      0.52        74\n",
+      "\n",
+      "    accuracy                           0.64       150\n",
+      "   macro avg       0.68      0.64      0.62       150\n",
+      "weighted avg       0.68      0.64      0.62       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "XGBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.70      0.82      0.75        76\n",
+      "           1       0.77      0.64      0.70        74\n",
+      "\n",
+      "    accuracy                           0.73       150\n",
+      "   macro avg       0.73      0.73      0.72       150\n",
+      "weighted avg       0.73      0.73      0.72       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "CatBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.72      0.79      0.75        76\n",
+      "           1       0.76      0.69      0.72        74\n",
+      "\n",
+      "    accuracy                           0.74       150\n",
+      "   macro avg       0.74      0.74      0.74       150\n",
+      "weighted avg       0.74      0.74      0.74       150\n",
+      "\n",
+      "\n",
+      "============================================================\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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VHQAALMxYoLJDsgMAgJVZINmhjQUAAHwalR0AACyMNhYAAPBtFkh2aGMBAACfRmUHAAALo40FAAB8mhWSHdpYAADAp1HZAQDAwqxQ2SHZAQDAyozN2xFcdbSxAACAT6OyAwCAhdHGAgAAPs0U0cYCAACo0KjsAABgYbSxAACATzNcjQUAAFCxUdkBAMDCaGMBAACfxtVYAAAAFRyVHQAALMwYb0dw9ZHsAABgYbSxAAAAKjgqOwAAWJgVKjskOwAAWJgV5uzQxgIAAD6Nyg4AABZGGwsAAPg0no0FAABQwVHZAQDAwqzwbCwqOwAAWFiRsXl0uVLPPfecbDabxowZ4xw7e/asRo4cqRo1aigkJET9+/dXTk6O2/sm2QEAAF61adMm/fOf/1SrVq1cxseOHaslS5bovffe0+rVq3X48GH169fP7f1fcRsrPz9fP/74o4qKXOtfMTExV7pLAABQxrw9QTkvL08DBw7UW2+9pSlTpjjHjx8/rpkzZ2revHnq3r27JCk1NVXNmzfX+vXr1bFjx1Ifw+3Kzq5du9SlSxcFBQWpXr16ql+/vurXr6/Y2FjVr1/f3d0BAAAvMkU2jy4Oh0MnTpxwWRwOxyWPP3LkSP3xj39UfHy8y3haWpoKCgpcxps1a6aYmBitW7fOrXN0u7IzZMgQVa5cWUuXLlVUVJRsNt+/ZA0AAJROSkqKJk+e7DI2ceJETZo0qdi27777rjZv3qxNmzYVW5edna2AgACFh4e7jEdERCg7O9utmNxOdtLT05WWlqZmzZq5+1YAAFDOePpxEePHj1dycrLLmN1uL7bdgQMH9PDDD2vFihUKDAz0bBAXcTvZadGihXJzc69GLAAAoIx5+g7Kdru9xOTmYmlpafrxxx/Vpk0b51hhYaHWrFmj1157TcuXL1d+fr6OHTvmUt3JyclRZGSkWzGVas7OL/tuzz//vB577DGtWrVKR44cKdaXAwAA+DU9evTQd999p/T0dOfSrl07DRw40Pn//f39tXLlSud7MjIylJmZqbi4OLeOVarKTnh4uMvcHGOMevTo4bKNMUY2m02FhYVuBQAAALznt9wb57eoWrWqrrvuOpexKlWqqEaNGs7xYcOGKTk5WdWrV1doaKhGjRqluLg4t67EkkqZ7Hz++edu7RQAAFQM3r70/HKmTp2qSpUqqX///nI4HEpISNDrr7/u9n5sxrg3NSkzM1N169YtdhWWMUYHDhwoF/fZKcjd6+0QAJ8WFN3F2yEAPu1c/qEyO9Z39ft4dH8t9y3x6P48we377NSvX18//fRTsfGjR49ynx0AACoYYzy7lEduX411YW7OxfLy8q76pWMAAMCzvDVnpyyVOtm5cM28zWbTE088oeDgYOe6wsJCbdiwQddff73HAwQAAPgtSp3sfPPNN5LOV3a+++47BQQEONcFBASodevWGjdunOcjBAAAV015nqDsKaVOdi5ckTV06FC98sorCg0NvWpBAQCAslFe59l4kttzdlJTU69GHAAAAFeF28nOhcesX8pnn312xcEAAICyxQTlErRu3drldUFBgdLT07V161YlJSV5LLDfYnS7v3g7BMCn/fxgm1/fCECFwJydEkydOrXE8UmTJikvL+83BwQAAOBJbt9U8FIGDRqkWbNmeWp3AACgDBQZm0eX8sjtys6lrFu3jpsKAgBQwVjgYiz3k51+/fq5vDbGKCsrS19//bWeeOIJjwUGAADgCW4nO2FhYS6vK1WqpKZNm+qpp55Sr169PBYYAAC4+spr68mT3Ep2CgsLNXToULVs2VLVqlW7WjEBAIAyYoWrsdyaoOzn56devXrp2LFjVykcAAAAz3L7aqzrrrtOe/fuvRqxAACAMlbk4aU8cjvZmTJlisaNG6elS5cqKytLJ06ccFkAAEDFYWTz6FIelXrOzlNPPaVHHnlEf/jDHyRJt9xyi2y2/56UMUY2m02FhYWejxIAAOAKlTrZmTx5sh544AHn088BAEDFV2SBG+2UOtkx//8M+K5du161YAAAQNkqKqetJ09ya87OL9tWAAAAFYFb99lp0qTJryY8R48e/U0BAQCAslNeJxV7klvJzuTJk4vdQRkAAFRc5fVycU9yK9m58847Vbt27asVCwAAgMeVOtlhvg4AAL6HNtYvXLgaCwAA+A7aWL9QVGSFjwMAAPgat+bsAAAA32KFUgbJDgAAFmaFOTtuPwgUAACgIqGyAwCAhRX5fmGHZAcAACvj2VgAAAAVHJUdAAAszAp30SPZAQDAwqxw6TltLAAA4NOo7AAAYGFFFnj2JckOAAAWZoU5O7SxAACAT6OyAwCAhVlhgjLJDgAAFmaFOyjTxgIAAD6Nyg4AABZmhcdFkOwAAGBhXI0FAABQwVHZAQDAwqwwQZlkBwAAC7PCpee0sQAAgE+jsgMAgIVZYYIyyQ4AABZmhTk7tLEAAIBPo7IDAICFWWGCMskOAAAWZoVkhzYWAADwaVR2AACwMGOBCcokOwAAWBhtLAAAgAqOyg4AABZGZQcAAPg04+GltGbMmKFWrVopNDRUoaGhiouL08cff+xcf/bsWY0cOVI1atRQSEiI+vfvr5ycnCs6R5IdAABQ5urUqaPnnntOaWlp+vrrr9W9e3clJiZq27ZtkqSxY8dqyZIleu+997R69WodPnxY/fr1u6Jj0cYCAMDCvPW4iD59+ri8fuaZZzRjxgytX79ederU0cyZMzVv3jx1795dkpSamqrmzZtr/fr16tixo1vHItkBAMDCPD1nx+FwyOFwuIzZ7XbZ7fZLvqewsFDvvfeeTp06pbi4OKWlpamgoEDx8fHObZo1a6aYmBitW7fO7WSHNhYAAPCYlJQUhYWFuSwpKSklbvvdd98pJCREdrtdDzzwgBYuXKgWLVooOztbAQEBCg8Pd9k+IiJC2dnZbsdEZQcAAAvzdGVn/PjxSk5Odhm7VFWnadOmSk9P1/Hjx/X+++8rKSlJq1ev9nBEJDsAAFiaO1dQlcavtax+KSAgQI0aNZIktW3bVps2bdIrr7yiO+64Q/n5+Tp27JhLdScnJ0eRkZFux0QbCwAAlAtFRUVyOBxq27at/P39tXLlSue6jIwMZWZmKi4uzu39UtkBAMDCvHU11vjx49W7d2/FxMTo5MmTmjdvnlatWqXly5crLCxMw4YNU3JysqpXr67Q0FCNGjVKcXFxbk9Olkh2AACwNG/dQfnHH3/U4MGDlZWVpbCwMLVq1UrLly9Xz549JUlTp05VpUqV1L9/fzkcDiUkJOj111+/omOR7AAAgDI3c+bMy64PDAzU9OnTNX369N98LJIdAAAszNMTlMsjkh0AACysyALpDldjAQAAn0ZlBwAAC/PWBOWyRLIDAICF+X4TizYWAADwcVR2AACwMNpYAADAp3nrDspliTYWAADwaVR2AACwMCvcZ4dkBwAAC/P9VIc2FgAA8HFUdgAAsDCuxgIAAD7NCnN2aGMBAACfRmUHAAAL8/26DskOAACWZoU5O7SxAACAT6OyAwCAhVlhgjLJDgAAFub7qQ5tLAAA4OOo7AAAYGFWmKBMsgMAgIUZCzSyaGMBAACfRmUHAAALo40FAAB8mhUuPaeNBQAAfBqVHQAALMz36zokOwAAWJoV2lgkOygTCQ/21fUJ7RXZ8BoVnM3Xns07tei5fylnb5Zzm7HvTlSTjte6vG/N3BX699/eKutwgQovoMefZe+TpPzVi+VY+D+SJFuNSNkT75FfgxayVfbXuR2b5fjgnzJ5x7wbLHCVkeygTDTu0EKr31muH7bsUaXKfkp8dIBGvT1BT/VMVv4Zh3O7L+Z9qqVT5ztf55/J90a4QIVWqW5j+d/0exUe2vffwQC7gkc8pcJD+3Rm+t/OD/1hkIKGP6HT08ZJxvf/dY+SWeFqLCYoo0y8lvSs1r+/Wlm7DurQjh/09rjpqlGnlmJaNnDZruCsQyd+Ou5czuad8VLEQAUVEKigux/R2fn/kDmT5xz2q99Ctuq1dXbeNBVl/aCirB90du5UVarbSH6NW3kxYHib8fD/yiOSHXhFUNVgSdLpY3ku4zcmdtGLm/9HTyx/SYmPDZB/YIA3wgMqrMA/P6Bz279W4c4trisqVz4/E/VcwX/HCvIlY+TXoEWZxgiUtXLdxjpw4IAmTpyoWbNmXXIbh8Mhh8PhMlZoCuVn87va4eEK2Ww23fbkEO3e9L0O7zzgHN+0+EsdOZSr4zlHdU2zerr1LwMV0SBabz7wshejBSqOyjd0UaU6DXX678nF1hXtz5Dyz8p+yxA5lr4j2ST7n5Jk8/OTLbS6F6JFeUEby8uOHj2qOXPmXHablJQUhYWFuSybj39fRhHiStz59DBFN62rmaOmuYx/+e+V2rFmiw5nHNCmxV9qTvJruuH3HVQzJsI7gQIViC28puz9huvsOy+7Vm/+nzl1QmdmP6/K17ZXyPMLFJIyX7agEBUe2C0ZK/y5w6VYoY3l1crOhx9+eNn1e/fu/dV9jB8/XsnJrv+KGddy6G+KC1fPHZPv0XXd2+jvt0/Useyjl912X/puSVKt2EjlZuaURXhAheVXt5EqVa2m4HHTnGM2Pz+ZBtfKv/OflDeunwozvtGpKffJViVUpqhQOnNKVZ56W0W52d4LHCgDXk12+vbtK5vNJnOZqwBsNttl92G322W3213GaGGVT3dMvkfXJ7TX3++cpCMHf/rV7eu0iJUknfjx56scGVDxndu5RaeeG+kyFnjXGBXlHFT+yvddqjfm1AlJkl/jVrKFhOncto1lGivKFyvU9bya7ERFRen1119XYmJiievT09PVtm3bMo4KV8OdTw/TjYmd9cbwF+Q4dUahtcIkSWdOnFaBo0A1YyJ0Y2Jnbft8s/KO5alOsxj9+Ykk7dywXYe+z/Ry9EAF4DijomzX3xWTf1bm9AnneOX2PVSUc1Am77j8YpspsN9wFaxeLPPjIW9EjHKiyAK3HfBqstO2bVulpaVdMtn5taoPKo6udydIkpLnT3YZnzNuuta/v1qFBefUrHNLdb/nD7IH2/Xz4SP65uMN+vi1/3gjXMAnVapd5/yk5OAQmaM/yrFigQpWLfZ2WMBVZzNezCa++OILnTp1Sr///e9LXH/q1Cl9/fXX6tq1q1v7HRF7uyfCA3AJL/Tl/kfA1VR12pIyO9agev08ur9//VD+/pHq1cpOly5dLru+SpUqbic6AACg9KzwbKxyfek5AADAb1WubyoIAACurvJ6bxxPItkBAMDCrHDpOW0sAADg06jsAABgYUxQBgAAqOCo7AAAYGFMUAYAAD6NCcoAAAAVHJUdAAAszArPoCTZAQDAwrgaCwAAoIKjsgMAgIVZYYIyyQ4AABZmhUvPaWMBAACfRmUHAAALs8IEZZIdAAAszAqXntPGAgAAZS4lJUU33nijqlatqtq1a6tv377KyMhw2ebs2bMaOXKkatSooZCQEPXv3185OTluH4tkBwAACyvy8FJaq1ev1siRI7V+/XqtWLFCBQUF6tWrl06dOuXcZuzYsVqyZInee+89rV69WocPH1a/fv3cPkfaWAAAWJi3rsZatmyZy+vZs2erdu3aSktL0+9+9zsdP35cM2fO1Lx589S9e3dJUmpqqpo3b67169erY8eOpT4WlR0AAOAxDodDJ06ccFkcDsevvu/48eOSpOrVq0uS0tLSVFBQoPj4eOc2zZo1U0xMjNatW+dWTCQ7AABYWJGMR5eUlBSFhYW5LCkpKZePoahIY8aMUadOnXTddddJkrKzsxUQEKDw8HCXbSMiIpSdne3WOdLGAgDAwjx9Ndb48eOVnJzsMma32y/7npEjR2rr1q368ssvPRrLBSQ7AADAY+x2+68mN7/00EMPaenSpVqzZo3q1KnjHI+MjFR+fr6OHTvmUt3JyclRZGSkWzHRxgIAwMI83cYqLWOMHnroIS1cuFCfffaZ6tev77K+bdu28vf318qVK51jGRkZyszMVFxcnFvnSGUHAAAL89bVWCNHjtS8efO0ePFiVa1a1TkPJywsTEFBQQoLC9OwYcOUnJys6tWrKzQ0VKNGjVJcXJxbV2JJJDsAAMALZsyYIUm6+eabXcZTU1M1ZMgQSdLUqVNVqVIl9e/fXw6HQwkJCXr99dfdPhbJDgAAFlbkpcdFlGZidGBgoKZPn67p06f/pmOR7AAAYGG+/2QsJigDAAAfR2UHAAALc+cKqoqKZAcAAAuzQrJDGwsAAPg0KjsAAFiYpx8XUR6R7AAAYGG0sQAAACo4KjsAAFiYtx4XUZZIdgAAsDArzNmhjQUAAHwalR0AACzMChOUSXYAALAw2lgAAAAVHJUdAAAsjDYWAADwaVa49Jw2FgAA8GlUdgAAsLAiC0xQJtkBAMDCaGMBAABUcFR2AACwMNpYAADAp9HGAgAAqOCo7AAAYGG0sQAAgE+jjQUAAFDBUdkBAMDCaGMBAACfRhsLAACggqOyAwCAhRlT5O0QrjqSHQAALKyINhYAAEDFRmUHAAALM1yNBQAAfBltLAAAgAqOyg4AABZGGwsAAPg0K9xBmTYWAADwaVR2AACwMCs8LoJkBwAAC7PCnB3aWAAAwKdR2QEAwMKscJ8dkh0AACyMNhYAAEAFR2UHAAALs8J9dkh2AACwMNpYAAAAFRyVHQAALIyrsQAAgE+jjQUAAFDBUdkBAMDCuBoLAAD4NCs8CJQ2FgAA8GlUdgAAsDDaWAAAwKdxNRYAAEAFR2UHAAALY4IyAADwacYYjy7uWLNmjfr06aPo6GjZbDYtWrSoWGxPPvmkoqKiFBQUpPj4eO3atcvtcyTZAQAAXnHq1Cm1bt1a06dPL3H9Cy+8oFdffVVvvPGGNmzYoCpVqighIUFnz5516zi0sQAAsDBvTlDu3bu3evfuXeI6Y4ymTZumCRMmKDExUZL09ttvKyIiQosWLdKdd95Z6uNQ2QEAwMKMhxdP2bdvn7KzsxUfH+8cCwsLU4cOHbRu3Tq39kVlBwAAeIzD4ZDD4XAZs9vtstvtbu0nOztbkhQREeEyHhER4VxXWj6Z7MzYv8DbIcANDodDKSkpGj9+vNu/DAB+Hb9juJxz+Yc8ur9JkyZp8uTJLmMTJ07UpEmTPHocd9DGgtc5HA5Nnjy52L8EAHgGv2MoS+PHj9fx48ddlvHjx7u9n8jISElSTk6Oy3hOTo5zXWmR7AAAAI+x2+0KDQ11Wa6koli/fn1FRkZq5cqVzrETJ05ow4YNiouLc2tfPtnGAgAA5V9eXp52797tfL1v3z6lp6erevXqiomJ0ZgxYzRlyhQ1btxY9evX1xNPPKHo6Gj17dvXreOQ7AAAAK/4+uuv1a1bN+fr5ORkSVJSUpJmz56txx57TKdOndJ9992nY8eOqXPnzlq2bJkCAwPdOo7NWOEJYCjXmDwJXF38jsHqSHYAAIBPY4IyAADwaSQ7AADAp5HsAAAAn0ayAwAAfBrJDrxq+vTpio2NVWBgoDp06KCNGzd6OyTAZ6xZs0Z9+vRRdHS0bDabFi1a5O2QAK8g2YHXzJ8/X8nJyZo4caI2b96s1q1bKyEhQT/++KO3QwN8wqlTp9S6dWtNnz7d26EAXsWl5/CaDh066MYbb9Rrr70mSSoqKlLdunU1atQo/eUvf/FydIBvsdlsWrhwodt3ngV8AZUdeEV+fr7S0tIUHx/vHKtUqZLi4+O1bt06L0YGAPA1JDvwitzcXBUWFioiIsJlPCIiQtnZ2V6KCgDgi0h2AACATyPZgVfUrFlTfn5+ysnJcRnPyclRZGSkl6ICAPgikh14RUBAgNq2bauVK1c6x4qKirRy5UrFxcV5MTIAgK+p7O0AYF3JyclKSkpSu3bt1L59e02bNk2nTp3S0KFDvR0a4BPy8vK0e/du5+t9+/YpPT1d1atXV0xMjBcjA8oWl57Dq1577TW9+OKLys7O1vXXX69XX31VHTp08HZYgE9YtWqVunXrVmw8KSlJs2fPLvuAAC8h2QEAAD6NOTsAAMCnkewAAACfRrIDAAB8GskOAADwaSQ7AADAp5HsAAAAn0ayAwAAfBrJDjxmyJAh6tu3r/P1zTffrDFjxpR5HKtWrZLNZtOxY8cuuY3NZtOiRYvKJJ6reay7775bzz777FXZtzsu/q5jY2M1bdo0r8VTXl38O+IJubm5ql27tg4ePOjR/f4Ws2fPVnh4uFvvKcvfSVgPyY6PGzJkiGw2m2w2mwICAtSoUSM99dRTOnfu3FU/9n/+8x89/fTTpdq2NAlKeTdp0iRdf/31ZXa8LVu26KOPPtLo0aNdxnfv3q177rlHMTExstvtuuaaa9SjRw/NnTu3TL53Sdq0aZPuu+8+j+6ztMnzzTff7PyZt9lsioiI0G233aYffvjBo/Fczv79+2Wz2ZSenu4y/sorr3j8zsU1a9bU4MGDNXHiRI/uF/AlJDsW8Pvf/15ZWVnatWuXHnnkEU2aNEkvvvhiidvm5+d77LjVq1dX1apVPbY/uPrHP/6h2267TSEhIc6xjRs3qk2bNtqxY4emT5+urVu3atWqVbr33ns1Y8YMbdu27ZL7Kygo8FhstWrVUnBwsMf2567hw4crKytLhw8f1uLFi3XgwAENGjTIa/FcEBYW5nbFozSGDh2quXPn6ujRox7fN+ALSHYswG63KzIyUvXq1dOIESMUHx+vDz/8UNJ/y+rPPPOMoqOj1bRpU0nSgQMHdPvttys8PFzVq1dXYmKi9u/f79xnYWGhkpOTFR4erho1auixxx7TxU8eufhf4g6HQ48//rjq1q0ru92uRo0aaebMmdq/f7/z+T3VqlWTzWbTkCFDJJ1/EnpKSorq16+voKAgtW7dWu+//77LcT766CM1adJEQUFB6tatm0ucl5OVlaXevXsrKChIDRo0cNlv9+7d9dBDD7ls/9NPPykgIMDlSe0XzJ49W5MnT9aWLVucFYVf/gs+NzdXt956q4KDg9W4cWPn53/B1q1b1bt3b4WEhCgiIkJ33323cnNzLxl7YWGh3n//ffXp08c5ZozRkCFD1KRJE61du1Z9+vRR48aN1bhxYw0YMEBffvmlWrVqJem/lYf58+era9euCgwM1Ny5c3XkyBENGDBA11xzjYKDg9WyZUv9+9//djn2qVOnNHjwYIWEhCgqKkovv/xysfgubmMdO3ZM9957r2rVqqXQ0FB1795dW7Zsca6/UBV75513FBsbq7CwMN155506efKkpPM/p6tXr9Yrr7zi/Hwv9z0HBwcrMjJSUVFR6tixox566CFt3rzZZZvVq1erffv2stvtioqK0l/+8heXypfD4dDo0aNVu3ZtBQYGqnPnztq0aZNz/c8//6yBAweqVq1aCgoKUuPGjZWamipJql+/viTphhtukM1m08033+w8j4tbvaNHj9Zjjz2m6tWrKzIyUpMmTXKJ8/vvv1fnzp0VGBioFi1a6NNPPy3W8rn22msVHR2thQsXXvIzudBaWrp0qZo2barg4GD9+c9/1unTpzVnzhzFxsaqWrVqGj16tAoLC13Oc/DgwapWrZqCg4PVu3dv7dq1q9i+Y2JiFBwcrFtvvVVHjhwpdvzFixerTZs2CgwMVIMGDTR58uQyqzQCMvBpSUlJJjEx0WXslltuMW3atHGuDwkJMXfffbfZunWr2bp1q8nPzzfNmzc399xzj/n222/N9u3bzV133WWaNm1qHA6HMcaY559/3lSrVs188MEHZvv27WbYsGGmatWqLsfq2rWrefjhh52vb7/9dlO3bl3zn//8x+zZs8d8+umn5t133zXnzp0zH3zwgZFkMjIyTFZWljl27JgxxpgpU6aYZs2amWXLlpk9e/aY1NRUY7fbzapVq4wxxmRmZhq73W6Sk5PN999/b/71r3+ZiIgII8n8/PPPl/xcJJkaNWqYt956y2RkZJgJEyYYPz8/s337dmOMMXPnzjXVqlUzZ8+edb7n73//u4mNjTVFRUXF9nf69GnzyCOPmGuvvdZkZWWZrKwsc/r0aeex6tSpY+bNm2d27dplRo8ebUJCQsyRI0eMMcb8/PPPplatWmb8+PFmx44dZvPmzaZnz56mW7dul4x/8+bNRpLJzs4uNvbvf//7ku+7YN++fUaSiY2NNR988IHZu3evOXz4sDl48KB58cUXzTfffGP27NljXn31VePn52c2bNjgfO+IESNMTEyM+fTTT823335r/vSnP5mqVau6fNf16tUzU6dOdb6Oj483ffr0MZs2bTI7d+40jzzyiKlRo4bzM5g4caIJCQkx/fr1M999951Zs2aNiYyMNH/961+NMcYcO3bMxMXFmeHDhzs/33PnzpV4bhf/3B05csT06dPH5fM8ePCgCQ4ONg8++KDZsWOHWbhwoalZs6aZOHGic5vRo0eb6Oho89FHH5lt27aZpKQkU61aNWfMI0eONNdff73ZtGmT2bdvn1mxYoX58MMPjTHGbNy40Ugyn376qcnKynK+5+Lfx65du5rQ0FAzadIks3PnTjNnzhxjs9nMJ598Yowx5ty5c6Zp06amZ8+eJj093XzxxRemffv2RpJZuHChy3nfcccdJikp6ZLfeWpqqvH39zc9e/Y0mzdvNqtXrzY1atQwvXr1MrfffrvZtm2bWbJkiQkICDDvvvuu83233HKLad68uVmzZo1JT083CQkJplGjRiY/P98YY8z69etNpUqVzPPPP28yMjLMK6+8YsLDw01YWJhzH2vWrDGhoaFm9uzZZs+ePeaTTz4xsbGxZtKkSc5tSjonwFNIdnzcL//jWlRUZFasWGHsdrsZN26cc31ERIQziTHGmHfeecc0bdrU5Y+6w+EwQUFBZvny5cYYY6KioswLL7zgXF9QUGDq1KlzyWQnIyPDSDIrVqwoMc7PP/+8WIJy9uxZExwcbL766iuXbYcNG2YGDBhgjDFm/PjxpkWLFi7rH3/88VIlOw888IDLWIcOHcyIESOMMcacOXPGVKtWzcyfP9+5vlWrVi7/cb7YxIkTTevWrUs81oQJE5yv8/LyjCTz8ccfG2OMefrpp02vXr1c3nPgwAFn8leShQsXGj8/P5fv6N133zWSzObNm51jOTk5pkqVKs5l+vTpxpj/JjvTpk275Plc8Mc//tE88sgjxhhjTp48aQICAsyCBQuc648cOWKCgoIumex88cUXJjQ01CVxNMaYhg0bmn/+85/GmPOfXXBwsDlx4oRz/aOPPmo6dOjgfH1xEnMpXbt2Nf7+/qZKlSomODjYSDJNmjQx+/btc27z17/+tdjP+PTp001ISIgpLCw0eXl5xt/f38ydO9e5Pj8/30RHRzt/7vv06WOGDh1aYgwXPt9vvvnGZbykZKdz584u29x4443m8ccfN8YY8/HHH5vKlSubrKws5/oVK1aUmBiMHTvW3HzzzZf8XFJTU40ks3v3bufY/fffb4KDg83JkyedYwkJCeb+++83xhizc+dOI8msXbvWuT43N9cEBQU5fwYGDBhg/vCHP7gc64477nBJdnr06GGeffZZl23eeecdExUV5XxNsoOrqXKZlZDgNUuXLlVISIgKCgpUVFSku+66y6VU3rJlSwUEBDhfb9myRbt37y423+bs2bPas2ePjh8/rqysLHXo0MG5rnLlymrXrl2xVtYF6enp8vPzU9euXUsd9+7du3X69Gn17NnTZTw/P1833HCDJGnHjh0ucUhSXFxcqfZ/8XZxcXHOCaWBgYG6++67NWvWLN1+++3avHmztm7dWqz9VFoX2keSVKVKFYWGhurHH3+UdP7z/vzzz13m3lywZ88eNWnSpNj4mTNnZLfbZbPZLnvcGjVqOM/p5ptvLjYnq127di6vCwsL9eyzz2rBggU6dOiQ8vPz5XA4nPNv9uzZo/z8fJfPvHr16s72Z0m2bNmivLw81ahRo9g57Nmzx/k6NjbW5WcuKirK+Rm5a+DAgfrb3/4mScrJydGzzz6rXr16KS0tTVWrVtWOHTsUFxfn8vl16tRJeXl5OnjwoI4dO6aCggJ16tTJud7f31/t27fXjh07JEkjRoxQ//79tXnzZvXq1Ut9+/bVTTfd5Hasv/zZkFzPOyMjQ3Xr1lVkZKRzffv27UvcT1BQkE6fPn3ZYwUHB6thw4bO1xEREYqNjXX52YuIiHAef8eOHapcubLL912jRg01bdrU+Tns2LFDt956q8tx4uLitGzZMufrLVu2aO3atXrmmWecY4WFhTp79qxOnz7t1fldsAaSHQvo1q2bZsyYoYCAAEVHR6tyZdevvUqVKi6v8/Ly1LZtW82dO7fYvmrVqnVFMQQFBbn9nry8PEnS//7v/+qaa65xWWe3268oDnfce++9uv7663Xw4EGlpqaqe/fuqlev3hXty9/f3+W1zWZTUVGRpPPn2adPHz3//PPF3hcVFVXi/mrWrKnTp08rPz/fmag2btxY0vk/kBeSQT8/PzVq1EiSin3vUvHv/sUXX9Qrr7yiadOmqWXLlqpSpYrGjBnzmyau5+XlKSoqSqtWrSq27peTdS/3GbkrLCzMed4X5oZFRUVp/vz5uvfee69onxfr3bu3fvjhB3300UdasWKFevTooZEjR+qll15yaz+eOu+jR4/+6u9nScfy5Od+KXl5eZo8ebL69etXbF1gYKBHjwWUhAnKFlClShU1atRIMTExJf7Bu1ibNm20a9cu1a5dW40aNXJZwsLCFBYWpqioKG3YsMH5nnPnziktLe2S+2zZsqWKioq0evXqEtdf+IP9y4mRLVq0kN1uV2ZmZrE46tatK0lq3ry5Nm7c6LKv9evX/+o5lrTd+vXr1bx5c5eY27Vrp7feekvz5s3TPffcc9n9BQQEuMRfWm3atNG2bdsUGxtb7DwvTkYuuHCJ+/bt251jN9xwg5o1a6aXXnrpiv9YrV27VomJiRo0aJBat26tBg0aaOfOnc71DRs2lL+/v8t3//PPP7tsU9L5ZWdnq3LlysXOr2bNmqWO7Uo/X+l80iedryZJ539u1q1b51KJXLt2rapWrao6deqoYcOGCggI0Nq1a53rCwoKtGnTJrVo0cI5VqtWLSUlJelf//qXpk2bpjfffNMZq6QrjveCpk2b6sCBA8rJyXGO/XKS9C9t3brVmeR6SvPmzXXu3DmX7/vIkSPKyMhwfg7Nmzd3WS8V/91q06aNMjIyin3/jRo1UqVK/BnC1cdPGYoZOHCgatasqcTERH3xxRfat2+fVq1apdGjRztvXPbwww/rueee06JFi/T999/rwQcfvOw9cmJjY5WUlKR77rlHixYtcu5zwYIFkqR69erJZrNp6dKl+umnn5SXl6eqVatq3LhxGjt2rObMmaM9e/Zo8+bN+sc//qE5c+ZIkh544AHt2rVLjz76qDIyMjRv3rxS38fkvffe06xZs7Rz505NnDhRGzduLHYF1r333qvnnntOxphipfqSznHfvn1KT09Xbm6uHA5HqeIYOXKkjh49qgEDBmjTpk3as2ePli9frqFDh17yj2WtWrXUpk0bffnll84xm82m1NRUZWRkqFOnTvrwww+1a9cubd++XW+88YZ++ukn5x/9S2ncuLFWrFihr776Sjt27ND999/v8oc2JCREw4YN06OPPqrPPvtMW7du1ZAhQy77Bys+Pl5xcXHq27evPvnkE+3fv19fffWV/va3v+nrr78u1Wcknf98N2zYoP379ys3N/eyCd3p06eVnZ2t7OxsbdmyRSNGjFBgYKB69eolSXrwwQd14MABjRo1St9//70WL16siRMnKjk5WZUqVVKVKlU0YsQIPfroo1q2bJm2b9+u4cOH6/Tp0xo2bJgk6cknn9TixYu1e/dubdu2TUuXLnUmy7Vr11ZQUJCWLVumnJwcHT9+vNTn+Us9e/ZUw4YNlZSUpG+//VZr167VhAkTJMmlBXf69GmlpaU5z89TGjdurMTERA0fPlxffvmltmzZokGDBumaa65RYmKiJGn06NFatmyZXnrpJe3atUuvvfaaSwtLOv9Zvf3225o8ebK2bdumHTt26N1333WeC3DVeXnOEK6ykq7GKs36rKwsM3jwYFOzZk1jt9tNgwYNzPDhw83x48eNMecnJD/88MMmNDTUhIeHm+TkZDN48ODLXo115swZM3bsWBMVFWUCAgJMo0aNzKxZs5zrn3rqKRMZGWlsNpvzqpKioiIzbdo007RpU+Pv729q1aplEhISzOrVq53vW7JkiWnUqJGx2+2mS5cuZtasWaWaoDx9+nTTs2dPY7fbTWxsrMtk5AtOnjzpvGrn15w9e9b079/fhIeHG0kmNTXVeayLJ16GhYU51xtzfiLorbfeasLDw01QUJBp1qyZGTNmTIlXfl3w+uuvm44dOxYbz8jIMElJSaZOnTqmcuXKJiwszPzud78z//znP01BQYEx5tITaI8cOWISExNNSEiIqV27tpkwYUKx7/XkyZNm0KBBJjg42ERERJgXXnih2Hd98dVYJ06cMKNGjTLR0dHG39/f1K1b1wwcONBkZmYaY0qe3D116lRTr149l/Pq2LGjCQoKMpJcJhz/UteuXY0k51KtWjXTtWtX89lnn7lst2rVKnPjjTeagIAAExkZaR5//HHn52PM+Z/XUaNGOX8HOnXqZDZu3Ohc//TTT5vmzZuboKAgU716dZOYmGj27t3rXP/WW2+ZunXrmkqVKpmuXbsaY0qeoHzxpOvExESXq6p27NhhOnXqZAICAkyzZs3MkiVLjCSzbNky5zbz5s0zTZs2LfHzuCA1NdVl0rAxJX/uF8d49OhRc/fdd5uwsDATFBRkEhISzM6dO13eM3PmTFOnTh0TFBRk+vTpY1566aVix1q2bJm56aabTFBQkAkNDTXt27c3b775pnN9Sb8ngKfYjLnEjFIA2r9/vxo2bKhNmzapTZs23g7HxZkzZ9S0aVPNnz+/1JOyUfGtXbtWnTt31u7du52TjTt27KjRo0frrrvu8nJ0QPnEBGWgBAUFBTpy5IgmTJigjh07lrtERzo/6fvtt9++7M0HUfEtXLhQISEhaty4sXbv3q2HH35YnTp1ciY6ubm56tevnwYMGODlSIHyi8oOUIJVq1apW7duatKkid5//321bNnS2yHBot5++21NmTJFmZmZqlmzpuLj4/Xyyy8Xu5QfwKWR7AAAAJ/G1VgAAMCnkewAAACfRrIDAAB8GskOAADwaSQ7AADAp5HsAAAAn0ayAwAAfBrJDgAA8GkkOwAAwKf9H6mCjdmhi6LlAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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NnDhRPXv2LHcZ+uWQ6AAAgGonJCREK1as0KxZs1RYWKi4uDilpqZq8uTJPp2HRAcAADurBjcMvGj16tWeP8fFxWnNmjU/+JwkOgAA2FmA5uhUFW4YCAAALIuKDgAANhaoychVhUQHAAA7o3UFAABQM1HRAQDAxmhdAQAA66J1BQAAUDNR0QEAwMaMxSs6JDoAANiZxRMdWlcAAMCyqOgAAGBjtK4AAIB1WTzRoXUFAAAsi4oOAAA2RusKAABYltUTHVpXAADAsqjoAABgY1av6JDoAABgZ8YR6AgqFa0rAABgWVR0AACwMVpXAADAsoyb1hUAAECNREUHAAAbo3UFAAAsy7DqCgAAoGaiogMAgI3RugIAAJbFqisAAIAaiooOAAA2ZkygI6hcJDoAANgYrSsAAIAaiooOAAA2ZvWKDokOAAA2ZvU5OrSuAACAZZHoAABgY8bt8Ot2rWbMmCGHw6EJEyZ4xoqKipSWlqbo6GiFhYUpNTVVhw4d8um8JDoAANiYMQ6/btdi48aNeuGFF5SYmOg1PnHiRL377rt6/fXXtWbNGh04cEDDhg3z6dwkOgAAIGDOnj2ru+++W/PmzVP9+vU946dOndL8+fP1zDPPqG/fvkpKSlJWVpY+/fRTrV+/vsLnJ9EBAMDGjNu/m8vl0unTp702l8t12eunpaXp5z//ufr37+81npOTo5KSEq/xhIQENW/eXOvWravw5yPRAQDAxtzG4dctMzNTkZGRXltmZma51168eLE2bdpU7v6DBw8qJCREUVFRXuNNmjTRwYMHK/z5WF4OAAD8JiMjQ+np6V5jTqezzHH79+/XI488ouXLl6tOnTqVFs81JzrFxcU6fPiw3G7v57s3b978BwcFAACqxrVOIL4cp9NZbmJzqZycHB0+fFhdu3b1jJWWlmrt2rV6/vnntWzZMhUXF+vkyZNeVZ1Dhw4pJiamwvH4nOjs2rVL999/vz799FOvcWOMHA6HSktLfT0lAAAIkEDdGblfv3764osvvMZGjx6thIQEPf7444qLi1NwcLBWrlyp1NRUSVJeXp7y8/OVkpJS4ev4nOiMGjVKtWvX1nvvvafY2Fg5HNa+dTQAAPC/8PBwdezY0WssNDRU0dHRnvExY8YoPT1dDRo0UEREhMaPH6+UlBR17969wtfxOdHJzc1VTk6OEhISfH0rAACoZqrzIyBmzpypWrVqKTU1VS6XSwMHDtScOXN8OofPiU6HDh109OhRX98GAACqoer0UM/Vq1d7va5Tp45mz56t2bNnX/M5K7S8/Ptr4Z966ik99thjWr16tY4dO1ZmrTwAAEB1UaGKTlRUlNdcHGOM+vXr53UMk5EBAKh53H5edVXdVCjRWbVqVWXHAQAAAsDfy8urmwolOr169fL8OT8/X3FxcWVWWxljtH//fv9GBwAA8AP4/AiIVq1a6ciRI2XGjx8/rlatWvklKAAAUDWM8e9W3fi86uriXJxLnT17tlJv4QwAAPyPOTr/dfG5FQ6HQ7/73e9Ur149z77S0lJ99tln6tKli98DBAAAuFYVTnQ2b94s6buKzhdffKGQkBDPvpCQEHXu3FmTJk3yf4QAAKDSMBn5vy6uvBo9erSeffZZRUREVFpQAACgalTHeTX+5PMcnaysrMqIAwAAwO98TnT69u17xf0fffTRNQcDAACqFpORL9G5c2ev1yUlJcrNzdW2bdt03333+S2wH2JAlwcCHQJgaaef+0WgQwDgJ8zRucTMmTPLHZ86darOnj37gwMCAADwF59vGHg5I0eO1Isvvuiv0wEAgCrgNg6/btWNzxWdy1m3bh03DAQAoIax+KIr3xOdYcOGeb02xqigoECff/65fve73/ktMAAAgB/K50QnMjLS63WtWrXUrl07TZ8+XTfffLPfAgMAAJWvOrab/MmnRKe0tFSjR49Wp06dVL9+/cqKCQAAVBGrr7ryaTJyUFCQbr75Zp08ebKSwgEAAPAfn1dddezYUV9//XVlxAIAAKqY289bdeNzovPEE09o0qRJeu+991RQUKDTp097bQAAoOYwcvh1q24qPEdn+vTpevTRRzV48GBJ0q233iqH4/9/IGOMHA6HSktL/R8lAADANahwojNt2jSNGzfO8xRzAABQ87ktfiOdCic65r/Pce/Vq1elBQMAAKqWuxq2m/zJpzk6329VAQAAVHc+3Uenbdu2V012jh8//oMCAgAAVac6TiD2J58SnWnTppW5MzIAAKi5quOScH/yKdH55S9/qcaNG1dWLAAAAH5V4USH+TkAAFgPrav/urjqCgAAWAetq/9yu63+owAAAFbj0xwdAABgLVYvY5DoAABgY1afo+PzQz0BAABqCio6AADYmNvaBR0qOgAA2JlbDr9uFTV37lwlJiYqIiJCERERSklJ0QcffODZ37t3bzkcDq9t3LhxPn8+KjoAAKDKNWvWTDNmzNB1110nY4z++c9/aujQodq8ebOuv/56SdLYsWM1ffp0z3vq1avn83VIdAAAsLFA3SVvyJAhXq+ffPJJzZ07V+vXr/ckOvXq1VNMTMwPug6tKwAAbMzt583lcun06dNem8vlumIMpaWlWrx4sQoLC5WSkuIZX7RokRo2bKiOHTsqIyND586d8/nzkegAAAC/yczMVGRkpNeWmZlZ7rFffPGFwsLC5HQ6NW7cOC1ZskQdOnSQJI0YMUIvv/yyVq1apYyMDC1cuFAjR470OR6HseCzHXo36x/oEABL+/fk6wMdAmBp9cY9W2XXeiP2br+eb8i+F8tUcJxOp5xOZ5lji4uLlZ+fr1OnTumNN97QP/7xD61Zs8aT7HzfRx99pH79+mn37t1q06ZNheNhjg4AADbm72rH5ZKa8oSEhCg+Pl6SlJSUpI0bN+rZZ5/VCy+8UObY5ORkSfI50aF1BQAAqgW3233Z+Ty5ubmSpNjYWJ/OSUUHAAAbC9SzrjIyMjRo0CA1b95cZ86cUXZ2tlavXq1ly5Zpz549ys7O1uDBgxUdHa2tW7dq4sSJ6tmzpxITE326DokOAAA2Fqg7Ix8+fFj33nuvCgoKFBkZqcTERC1btkwDBgzQ/v37tWLFCs2aNUuFhYWKi4tTamqqJk+e7PN1SHQAAECVmz9//mX3xcXFac2aNX65DokOAAA25stjG2oiEh0AAGzMcveYuQSrrgAAgGVR0QEAwMYCNRm5qpDoAABgY4FaXl5VaF0BAADLoqIDAICNWX0yMokOAAA2ZvU5OrSuAACAZVHRAQDAxqw+GZlEBwAAG7N6okPrCgAAWBYVHQAAbMxYfDIyiQ4AADZG6woAAKCGoqIDAICNWb2iQ6IDAICNWf3OyLSuAACAZVHRAQDAxqz+CAgSHQAAbMzqc3RoXQEAAMuiogMAgI1ZvaJDogMAgI2x6goAAKCGoqIDAICNseoKAABYltXn6NC6AgAAlkVFBwAAG7P6ZGQSHQAAbMxt8VSH1hUAALAsKjoAANiY1Scjk+gAAGBj1m5c0boCAAAWRkUHAAAbs3rriooOAAA25nb4d6uouXPnKjExUREREYqIiFBKSoo++OADz/6ioiKlpaUpOjpaYWFhSk1N1aFDh3z+fCQ6AACgyjVr1kwzZsxQTk6OPv/8c/Xt21dDhw7V9u3bJUkTJ07Uu+++q9dff11r1qzRgQMHNGzYMJ+vQ+sKAAAbC9R9dIYMGeL1+sknn9TcuXO1fv16NWvWTPPnz1d2drb69u0rScrKylL79u21fv16de/evcLXoaIDAICNGT9v16K0tFSLFy9WYWGhUlJSlJOTo5KSEvXv399zTEJCgpo3b65169b5dG4qOgAAwG9cLpdcLpfXmNPplNPpLHPsF198oZSUFBUVFSksLExLlixRhw4dlJubq5CQEEVFRXkd36RJEx08eNCneKjoAABgY24/b5mZmYqMjPTaMjMzy712u3btlJubq88++0wPPvig7rvvPu3YscOvn4+KDgAANubvOToZGRlKT0/3GiuvmiNJISEhio+PlyQlJSVp48aNevbZZ3XXXXepuLhYJ0+e9KrqHDp0SDExMT7FQ0UHAAD4jdPp9CwZv7hdLtG5lNvtlsvlUlJSkoKDg7Vy5UrPvry8POXn5yslJcWneKjoAABgY4F6BERGRoYGDRqk5s2b68yZM8rOztbq1au1bNkyRUZGasyYMUpPT1eDBg0UERGh8ePHKyUlxacVVxKJDgAAthaoOyMfPnxY9957rwoKChQZGanExEQtW7ZMAwYMkCTNnDlTtWrVUmpqqlwulwYOHKg5c+b4fB0SHQAAUOXmz59/xf116tTR7NmzNXv27B90HRIdAABsLFA3DKwqJDoAANiYtdMcVl0BAAALo6IDAICNBWoyclUh0QEAwMaMxZtXtK4AAIBlUdEBAMDGaF0BAADLsvryclpXAADAsqjoAABgY9au55DoAABga1ZvXZHooEqMSBuunoNuUvP4OLmKXNr++Q698Md52v/1N+Ue/9TCPyq5z080eczv9fGyT6s4WqDm+du6XXph/R6vsZb1Q7VkVA9JkutCqZ5Zm6dleQUqLnUrpUVD/W/fDooOdQYiXKDKkOigSnRJSdTb/3xHX27JU1BQkH71mzH6U/ZTGtVnjIrOF3kd+4tfpcoYa/8XBlAZ2kSH6W+p3Tyvg2o5PH/+85ov9fHeI3r6510U5qytGat26NF3N2vBL7sHIlRUI1ZfdcVkZFSJx0ZmaOnrH2rfV//Rnp1fa8bEpxXTrInaJl7ndVx8hza66//8Qk8/+ucARQrUXEG1HGoY6vRs9euGSJLOuEr09rZvlN4zQT9pHq0OTSI17eZO2lJwUlsLTgY2aASc8fP/qhsqOgiIsIhQSdKZk2c8Y846Tk1+/n8167d/1fEjJwIVGlBj5Z84pwF/XyVnUC0lNo3S+BvbKjairnYeOq0LbqPuzaM9x7ZqEKaY8DraWnBSibFRgQsaqGTVuqKzf/9+3X///Vc8xuVy6fTp016b21i9EFezORwOPTT1f/TFhm3am7fPM5429UFtz9muTz5kTg7gq44xUZo+sJNm336D/rdfB3176rzuf+0zFRZf0LFzLgUHORReJ9jrPdH1nDpW6ApQxKgu3H7eqptqnegcP35c//znP694TGZmpiIjI722/DP7qiZAXJMJTz6sVu1aanraE56xnw5IUdcbu+j5KXMCGBlQc93UqpEGtI1R20bh+mnLRnr+tiSddV3Qh18dDHRoqOZoXVWif/3rX1fc//XXX1/1HBkZGUpPT/cau6X9bT8kLFSiR554SCn9k/VwarqOFBz1jHe9sYuatmiq93a843X8tL9P0RcbtmnCHY9WdahAjRZeJ1jN69fT/pOF6t68oUpKjc4UlXhVdY6dc7HqCpYX0ETntttuk8PhuOIKG4fDcdl9kuR0OuV0ev+i1nJU60KVbT3yxEO66Wc3acIdj+rgfu//ysyevVjvv/KB11jWyn9o9rS5+nT5+qoME7CEc8UX9M3J8/p5e6faN4lQ7VoOfbb/mPpfFyNJ2nf8rA6eKWJ+Dqplu8mfAproxMbGas6cORo6dGi5+3Nzc5WUlFTFUaEyTHjyYfW/ra9+O+b3On/2nBo0qi9JOnumUMVFxTp+5ES5E5APf3u4TFIEoKxn1n6pnq0bq2l4HR0udOlv63arVi3pZ+2aKtwZrNs6NtNf1nypyDrBCg2pradW7VRibBSJDuS2+O08AproJCUlKScn57KJztWqPag5brvvVknSs2884zU+Y+LTWvr6h4EICbCUQ2eKlPHvLTpVVKz6dUPUpWl9vfTLFDWo990S80m9ElTL4dCkd3NVXOrWT1s2VEbfDgGOGqh8AU10fv3rX6uwsPCy++Pj47Vq1aoqjAiVpXez/lXyHsCunvp5lyvud9YOUkbfDiQ3KMPq5YSAJjo9evS44v7Q0FD16tWriqIBAMB+rP6sK2btAgAAy+LOyAAA2Fh1vPeNP5HoAABgY1ZfXk7rCgAAWBYVHQAAbIzJyAAAADUUFR0AAGyMycgAAMCymIwMAABQQ1HRAQDAxqz+TEkSHQAAbIxVVwAAADUUiQ4AADbm9vNWUZmZmerWrZvCw8PVuHFj3XbbbcrLy/M6pnfv3nI4HF7buHHjfPp8JDoAANiY8fP/KmrNmjVKS0vT+vXrtXz5cpWUlOjmm29WYWGh13Fjx45VQUGBZ3v66ad9+nzM0QEAAFVu6dKlXq8XLFigxo0bKycnRz179vSM16tXTzExMdd8HSo6AADYmFvGr5vL5dLp06e9NpfLddU4Tp06JUlq0KCB1/iiRYvUsGFDdezYURkZGTp37pxPn49EBwAAGzPG+HXLzMxUZGSk15aZmXnFGNxutyZMmKAbb7xRHTt29IyPGDFCL7/8slatWqWMjAwtXLhQI0eO9Onz0boCAAB+k5GRofT0dK8xp9N5xfekpaVp27Zt+vjjj73GH3jgAc+fO3XqpNjYWPXr10979uxRmzZtKhQPiQ4AADbm70dAOJ3OqyY23/fQQw/pvffe09q1a9WsWbMrHpucnCxJ2r17N4kOAAC4ukA91NMYo/Hjx2vJkiVavXq1WrVqddX35ObmSpJiY2MrfB0SHQAAUOXS0tKUnZ2td955R+Hh4Tp48KAkKTIyUnXr1tWePXuUnZ2twYMHKzo6Wlu3btXEiRPVs2dPJSYmVvg6JDoAANhYoB4BMXfuXEnf3RTw+7KysjRq1CiFhIRoxYoVmjVrlgoLCxUXF6fU1FRNnjzZp+uQ6AAAYGOBeqjn1a4bFxenNWvW/ODrsLwcAABYFhUdAABszOpPLyfRAQDAxgK16qqq0LoCAACWRUUHAAAbcwdoMnJVIdEBAMDGrJ3m0LoCAAAWRkUHAAAbY9UVAACwLKsnOrSuAACAZVHRAQDAxgL1CIiqQqIDAICN0boCAACooajoAABgY1Z/BASJDgAANmb1OTq0rgAAgGVR0QEAwMasPhmZRAcAABujdQUAAFBDUdEBAMDGaF0BAADLsvryclpXAADAsqjoAABgY26LT0Ym0QEAwMZoXQEAANRQVHQAALAxWlcAAMCyaF0BAADUUFR0AACwMVpXAADAsmhdAQAA1FBUdAAAsDFaVwAAwLJoXQEAANRQVHQAALAxY9yBDqFSUdEBAMDG3DJ+3SoqMzNT3bp1U3h4uBo3bqzbbrtNeXl5XscUFRUpLS1N0dHRCgsLU2pqqg4dOuTT5yPRAQAAVW7NmjVKS0vT+vXrtXz5cpWUlOjmm29WYWGh55iJEyfq3Xff1euvv641a9bowIEDGjZsmE/XoXUFAICNmQCtulq6dKnX6wULFqhx48bKyclRz549derUKc2fP1/Z2dnq27evJCkrK0vt27fX+vXr1b179wpdh0QHAAAb86XdVBEul0sul8trzOl0yul0XvF9p06dkiQ1aNBAkpSTk6OSkhL179/fc0xCQoKaN2+udevWVTjRoXUFAAD8JjMzU5GRkV5bZmbmFd/jdrs1YcIE3XjjjerYsaMk6eDBgwoJCVFUVJTXsU2aNNHBgwcrHA8VHQAAbMzfrauMjAylp6d7jV2tmpOWlqZt27bp448/9mssEokOAAC25u87I1ekTfV9Dz30kN577z2tXbtWzZo184zHxMSouLhYJ0+e9KrqHDp0SDExMRU+P60rAABQ5Ywxeuihh7RkyRJ99NFHatWqldf+pKQkBQcHa+XKlZ6xvLw85efnKyUlpcLXoaIDAICNBeoREGlpacrOztY777yj8PBwz7ybyMhI1a1bV5GRkRozZozS09PVoEEDRUREaPz48UpJSanwRGSJRAcAAFsL1PLyuXPnSpJ69+7tNZ6VlaVRo0ZJkmbOnKlatWopNTVVLpdLAwcO1Jw5c3y6DokOAACochVJsOrUqaPZs2dr9uzZ13wdEh0AAGzM3/fRqW5IdAAAsLFAta6qCquuAACAZVHRAQDAxvx9H53qhkQHAAAbo3UFAABQQ1HRAQDAxlh1BQAALIvWFQAAQA1FRQcAABtj1RUAALCsQD3Us6rQugIAAJZFRQcAABujdQUAACyLVVcAAAA1FBUdAABszOqTkUl0AACwMVpXAAAANRQVHQAAbMzqFR0SHQAAbMzaaQ6tKwAAYGEOY/WaFao9l8ulzMxMZWRkyOl0BjocwHL4HYOdkegg4E6fPq3IyEidOnVKERERgQ4HsBx+x2BntK4AAIBlkegAAADLItEBAACWRaKDgHM6nZoyZQqTJIFKwu8Y7IzJyAAAwLKo6AAAAMsi0QEAAJZFogMAACyLRAcAAFgWiQ4Cavbs2WrZsqXq1Kmj5ORkbdiwIdAhAZaxdu1aDRkyRE2bNpXD4dDbb78d6JCAKkeig4B59dVXlZ6erilTpmjTpk3q3LmzBg4cqMOHDwc6NMASCgsL1blzZ82ePTvQoQABw/JyBExycrK6deum559/XpLkdrsVFxen8ePH6ze/+U2AowOsxeFwaMmSJbrtttsCHQpQpajoICCKi4uVk5Oj/v37e8Zq1aql/v37a926dQGMDABgJSQ6CIijR4+qtLRUTZo08Rpv0qSJDh48GKCoAABWQ6IDAAAsi0QHAdGwYUMFBQXp0KFDXuOHDh1STExMgKICAFgNiQ4CIiQkRElJSVq5cqVnzO12a+XKlUpJSQlgZAAAK6kd6ABgX+np6brvvvt0ww036Cc/+YlmzZqlwsJCjR49OtChAZZw9uxZ7d692/N67969ys3NVYMGDdS8efMARgZUHZaXI6Cef/55/elPf9LBgwfVpUsXPffcc0pOTg50WIAlrF69Wn369Ckzft9992nBggVVHxAQACQ6AADAspijAwAALItEBwAAWBaJDgAAsCwSHQAAYFkkOgAAwLJIdAAAgGWR6AAAAMsi0UGNN2rUKN12222e171799aECROqPI7Vq1fL4XDo5MmTlz3G4XDo7bffrpJ4KvNa99xzj/74xz/+4PNc+t3VdN27d9ebb74Z6DA89u3bJ4fDodzc3Aq/J1C/P0BlIdFBpRg1apQcDoccDodCQkIUHx+v6dOn68KFC5V+7bfeekt/+MMfKnRsRZKT6m7q1Knq0qVLlV1vy5Yt+ve//62HH364zL5XXnlFQUFBSktLq7TrX/x75XA4VLt2bTVv3lzp6elyuVyVds1LLViwQFFRUWXGJ0+erN/85jdyu91VFguAKyPRQaX52c9+poKCAu3atUuPPvqopk6dqj/96U/lHltcXOy36zZo0EDh4eF+Ox+8/fWvf9Udd9yhsLCwMvvmz5+vxx57TK+88oqKiooqLYasrCwVFBRo7969mjNnjhYuXKgnnnii0q5XUYMGDdKZM2f0wQcfBDoUAP9FooNK43Q6FRMToxYtWujBBx9U//799a9//UvS/29ZPPnkk2ratKnatWsnSdq/f7/uvPNORUVFqUGDBho6dKj27dvnOWdpaanS09MVFRWl6OhoPfbYY7r0KSaXlt5dLpcef/xxxcXFyel0Kj4+XvPnz9e+ffs8zwGqX7++HA6HRo0aJem7J6lnZmaqVatWqlu3rjp37qw33njD6zr//ve/1bZtW9WtW1d9+vTxivNKCgoKNGjQINWtW1etW7f2Om/fvn310EMPeR1/5MgRhYSEeD3p/aIFCxZo2rRp2rJli6fK8f1nGB09elS333676tWrp+uuu87z879o27ZtGjRokMLCwtSkSRPdc889Onr06GVjLy0t1RtvvKEhQ4aU2bd37159+umn+s1vfqO2bdvqrbfeKvPeq313S5cu1U033eQ55pZbbtGePXvKXCsqKkoxMTGKi4vTLbfcoqFDh2rTpk1ex8ydO1dt2rRRSEiI2rVrp4ULF3rtz8/P19ChQxUWFqaIiAjdeeedOnTokGf/li1b1KdPH4WHhysiIkJJSUn6/PPPtXr1ao0ePVqnTp3y/MynTp0qSQoKCtLgwYO1ePHiy/4ML7aTXnvtNfXo0UN169ZVt27d9NVXX2njxo264YYbFBYWpkGDBunIkSOe97ndbk2fPl3NmjWT0+lUly5dtHTpUq9zb9iwQT/+8Y9Vp04d3XDDDdq8eXOZ6/v6nQM1ngEqwX333WeGDh3qNXbrrbearl27evaHhYWZe+65x2zbts1s27bNFBcXm/bt25v777/fbN261ezYscOMGDHCtGvXzrhcLmOMMU899ZSpX7++efPNN82OHTvMmDFjTHh4uNe1evXqZR555BHP6zvvvNPExcWZt956y+zZs8esWLHCLF682Fy4cMG8+eabRpLJy8szBQUF5uTJk8YYY5544gmTkJBgli5davbs2WOysrKM0+k0q1evNsYYk5+fb5xOp0lPTzdffvmlefnll02TJk2MJHPixInL/lwkmejoaDNv3jyTl5dnJk+ebIKCgsyOHTuMMcYsWrTI1K9f3xQVFXne88wzz5iWLVsat9td5nznzp0zjz76qLn++utNQUGBKSgoMOfOnfNcq1mzZiY7O9vs2rXLPPzwwyYsLMwcO3bMGGPMiRMnTKNGjUxGRobZuXOn2bRpkxkwYIDp06fPZePftGmTkWQOHjxYZt/vfvc784tf/MIYY8xf//pX07dvX6/9Ffnu3njjDfPmm2+aXbt2mc2bN5shQ4aYTp06mdLSUq+f4ZIlSzyv8/LyTKtWrcy0adM8Y2+99ZYJDg42s2fPNnl5eeYvf/mLCQoKMh999JExxpjS0lLTpUsXc9NNN5nPP//crF+/3iQlJZlevXp5znH99debkSNHmp07d5qvvvrKvPbaayY3N9e4XC4za9YsExER4fmZnzlzxvO+uXPnmhYtWlz2Z7h3714jyfP3a8eOHaZ79+4mKSnJ9O7d23z88cdm06ZNJj4+3owbN87zvmeeecZERESYV155xXz55ZfmscceM8HBwearr74yxhhz5swZ06hRIzNixAizbds28+6775rWrVsbSWbz5s3GmIp955f+/gA1HYkOKsX3Ex23222WL19unE6nmTRpkmd/kyZNPAmMMcYsXLjQtGvXzusfdJfLZerWrWuWLVtmjDEmNjbWPP300579JSUlplmzZpdNdPLy8owks3z58nLjXLVqVZnkpKioyNSrV898+umnXseOGTPGDB8+3BhjTEZGhunQoYPX/scff7xCic73//Eyxpjk5GTz4IMPGmOMOX/+vKlfv7559dVXPfsTExPN1KlTL3vOKVOmmM6dO5d7rcmTJ3tenz171kgyH3zwgTHGmD/84Q/m5ptv9nrP/v37PYlfeZYsWWKCgoLKJF2lpaUmLi7OvP3228YYY44cOWJCQkLM119/7TmmIt/dpY4cOWIkmS+++MLrc9WpU8eEhoYap9NpJJlbbrnFFBcXe4756U9/asaOHet1rjvuuMMMHjzYGGPMhx9+aIKCgkx+fr5n//bt240ks2HDBmOMMeHh4WbBggXlxpWVlWUiIyPL3ffOO++YWrVqeSVn33cx0fnHP/7hGXvllVeMJLNy5UrPWGZmpmnXrp3nddOmTc2TTz7pda5u3bqZ//mf/zHGGPPCCy+Y6Ohoc/78ec/+uXPneiU6FfnOSXRgNbSuUGnee+89hYWFqU6dOho0aJDuuusuT4lfkjp16qSQkBDP6y1btmj37t0KDw9XWFiYwsLC1KBBAxUVFWnPnj06deqUCgoKlJyc7HlP7dq1dcMNN1w2htzcXAUFBalXr14Vjnv37t06d+6cBgwY4IkjLCxML730kqeNsnPnTq84JCklJaVC57/0uJSUFO3cuVOSVKdOHd1zzz168cUXJUmbNm3Stm3bPC01XyUmJnr+HBoaqoiICB0+fFjSdz/vVatWeX3GhIQESSq3XSRJ58+fl9PplMPh8Bpfvny5CgsLNXjwYElSw4YNNWDAAM/nqOh3t2vXLg0fPlytW7dWRESEWrZsKem7NtP3zZw5U7m5udqyZYvee+89ffXVV7rnnns8+3fu3Kkbb7zR6z033nij5+e8c+dOxcXFKS4uzrO/Q4cOioqK8hyTnp6uX/3qV+rfv79mzJhx2Z/JperWrSu3233VydHf/26aNGki6bvfie+PXfyuTp8+rQMHDlz1MyUmJqpOnTqe/Zf+XbuW7xyo6WoHOgBYV58+fTR37lyFhISoadOmql3b+69baGio1+uzZ88qKSlJixYtKnOuRo0aXVMMdevW9fk9Z8+elSS9//77+tGPfuS1z+l0XlMcvvjVr36lLl266JtvvlFWVpb69u2rFi1aXNO5goODvV47HA7PiqCzZ89qyJAheuqpp8q8LzY2ttzzNWzYUOfOnVNxcbFXkjp//nwdP37c6+ftdru1detWTZs2rcLxDhkyRC1atNC8efPUtGlTud1udezYscxk9ZiYGMXHx0uS2rVrpzNnzmj48OF64oknPOM/1NSpUzVixAi9//77+uCDDzRlyhQtXrxYt99++xXfd/z4cYWGhl717973v5uLieOlY/5evXUt3zlQ01HRQaUJDQ1VfHy8mjdvXibJKU/Xrl21a9cuNW7cWPHx8V5bZGSkIiMjFRsbq88++8zzngsXLignJ+ey5+zUqZPcbrfWrFlT7v6L/1iXlpZ6xjp06CCn06n8/PwycVysALRv314bNmzwOtf69euv+hnLO279+vVq3769V8w33HCD5s2bp+zsbN1///1XPF9ISIhX/BXVtWtXbd++XS1btizzOS9NQi+6uIx9x44dnrFjx47pnXfe0eLFi5Wbm+vZNm/erBMnTujDDz+s0Hd37Ngx5eXlafLkyerXr5/at2+vEydOVOizBAUFSfqu4iR99/188sknXsd88skn6tChg2f//v37tX//fs/+HTt26OTJk55jJKlt27aaOHGiPvzwQw0bNkxZWVmSrvwz37Ztm3784x9XKO6KioiIUNOmTa/6mbZu3eq12u3Sv2vX8p0DNV6ge2ewpvImI19tf2FhobnuuutM7969zdq1a83XX39tVq1aZcaPH2/2799vjDFmxowZpkGDBmbJkiVm586dZuzYsVedjDxq1CgTFxdnlixZ4jnnxTkw33zzjXE4HGbBggXm8OHDnkmlv/3tb010dLRZsGCB2b17t8nJyTHPPfecZ87Gf/7zHxMSEmImTZpkvvzyS7No0SITExNToTk6DRs2NPPnzzd5eXnm97//valVq5bZvn2713F///vfTUhIiKlfv77XnIvyLFq0yISGhprNmzebI0eOeCYy65JJu8YYExkZabKysowxxnz77bemUaNG5he/+IXZsGGD2b17t1m6dKkZNWqUuXDhwmWv17VrV/PXv/7V83rmzJkmNja23MnSd955p2eC8tW+u9LSUhMdHW1Gjhxpdu3aZVauXGm6detW5nNIMllZWaagoMB8++23ZvXq1aZjx46mbdu2pqSkxBjz3Vyi4OBgM2fOHPPVV195JiOvWrXKGPPdvLEuXbqYHj16mJycHPPZZ595TUY+d+6cSUtLM6tWrTL79u0zH3/8sWnTpo157LHHjDHGfPLJJ0aSWbFihTly5IgpLCz0xNerVy8zffr0y/78Ls7RuThvxpjy54pdOg9o5syZJiIiwixevNh8+eWX5vHHHy8zGblhw4Zm5MiRZvv27eb999838fHxXteqyHfOHB1YDYkOKsW1JDrGGFNQUGDuvfde07BhQ+N0Ok3r1q3N2LFjzalTp4wx301gfeSRR0xERISJiooy6enp5t57771ionP+/HkzceJEExsba0JCQkx8fLx58cUXPfunT59uYmJijMPhMPfdd58x5rt/CGfNmmXatWtngoODTaNGjczAgQPNmjVrPO979913TXx8vHE6naZHjx7mxRdfrFCiM3v2bDNgwADjdDpNy5YtvSYeX3TmzBlTr149z0TTKykqKjKpqakmKirKkwRcvNaVEh1jjPnqq6/M7bffbqKiokzdunVNQkKCmTBhQrlJy0Vz5swx3bt397zu1KnTZeN89dVXTUhIiDly5EiFvrvly5eb9u3bG6fTaRITE83q1avLTXQubg6Hw8TGxpq77rrL7Nmzp0ycrVu3NsHBwaZt27bmpZde8tr/n//8x9x6660mNDTUhIeHmzvuuMOzmszlcplf/vKXJi4uzoSEhJimTZuahx56yCvpHDdunImOjjaSzJQpU4wx3yXOwcHBnsS8PNea6JSWlpqpU6eaH/3oRyY4ONh07tzZM7H8onXr1pnOnTubkJAQ06VLF8+qwu9f62rfOYkOrMZhzCU3sgAQcPv27VObNm20ceNGde3aNdDheDl//rzatWunV199tcITsO3i8ccf14kTJ/T3v/890KEA+C8mIwPVSElJiY4dO6bJkyere/fu1S7Jkb6b4P3SSy9xk7lyNG7cWOnp6YEOA8D3UNEBqpHVq1erT58+atu2rd544w2v5cYAAN+R6AAAAMtieTkAALAsEh0AAGBZJDoAAMCySHQAAIBlkegAAADLItEBAACWRaIDAAAsi0QHAABYFokOAACwrP8HqSj1qkEif1MAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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Omee677773F936NBB0dHR6tWrl7Kzs9WqVatyxUOiAwCAnXn5ERBOp7PMxOZcGRkZOnTokDp27OgeKy4u1rp16/Tyyy/L5XLJz8/P4z2dO3eWJO3atYtEBwAAlIOPVl316tVL27Zt8xgbOXKkYmNjNXHixFJJjiRlZmZKkqKjo8t9HRIdAABQ5UJCQtS+fXuPsbp16yoiIkLt27dXdna2UlNTNWDAAEVERGjr1q1KTExUt27dylyGfj4kOgAA2Fk1uI9OWQICArRq1SrNmDFD+fn5atq0qRISEjRp0qQKnYdEBwAAGzOm+iQ6aWlp7q+bNm2q9PT0Sz4n99EBAACWRUUHAAA7q6atK28h0QEAwM4snujQugIAAJZFRQcAABszFq/okOgAAGBnFk90aF0BAADLoqIDAICdefdRV9UOiQ4AADZm9Tk6tK4AAIBlUdEBAMDOLF7RIdEBAMDOLD5Hh9YVAACwLCo6AADYmNUnI5PoAABgZ7SuAAAAaiYqOgAA2BitKwAAYF20rgAAAGomKjoAANiYsXhFh0QHAAA7s3iiQ+sKAABYFhUdAABsjNYVAACwLosnOrSuAACAZVHRAQDAxmhdAQAAy7J6okPrCgAAWBYVHQAAbMzqFR0SHQAA7Mw4fB1BpaJ1BQAALIuKDgAANkbrCgAAWJYpoXUFAABQI1HRAQDAxmhdAQAAyzKsugIAAKiZqOgAAGBjtK4AAIBlseoKAACghqKiAwCAjRnj6wgqF4kOAAA2RusKAACghiLRAQDAxkyJw6vbrzV9+nQ5HA6NHz/ePXb69GmNHTtWERERCg4OVkJCgg4ePFih85LoAABgY8Z4d/s1Nm/erFdeeUVxcXEe44mJiVq2bJnefPNNpaen68CBA7rtttsqdG4SHQAA4DN5eXkaOnSo5syZo3r16rnHT5w4oblz5+q5555Tz5491alTJ82fP1/r16/Xxo0by31+Eh0AAGzM260rl8ulkydPemwul+u81x87dqx++9vfqnfv3h7jGRkZKioq8hiPjY1Vs2bNtGHDhnJ/PhIdAABszBiHV7eUlBSFhYV5bCkpKWVe+/XXX9eWLVvK3J+bm6uAgACFh4d7jEdGRio3N7fcn4/l5QAAwGuSk5OVlJTkMeZ0Oksdt2/fPj388MNauXKl6tSpU2nxkOgAAGBj3n7WldPpLDOxOVdGRoYOHTqkjh07useKi4u1bt06vfzyy1qxYoUKCwt1/Phxj6rOwYMHFRUVVe54SHQAALCxEuObGwb26tVL27Zt8xgbOXKkYmNjNXHiRDVt2lT+/v5avXq1EhISJElZWVnau3ev4uPjy30dEh0AAFDlQkJC1L59e4+xunXrKiIiwj0+atQoJSUlqX79+goNDdW4ceMUHx+vLl26lPs6vzrRKSws1KFDh1RS4lnzatas2a89JQAAqGLGRxWd8nj++edVq1YtJSQkyOVyqV+/fpo1a1aFzuEwpmK399m5c6fuuecerV+/3mPcGCOHw6Hi4uIKBVAZig5/5+sQAEsLbNzV1yEAlnam8Icqu9a3bQZ49XyxOz7w6vkuVYUrOiNGjFDt2rW1fPlyRUdHy+GovpkgAACwtwonOpmZmcrIyFBsbGxlxAMAAKrQr31sQ01R4UTniiuu0OHDhysjFgAAUMUu5UGcNUG57oz8y9s4P/PMM3rssceUlpamI0eOlLrNMwAAQHVRropOeHi4x1wcY4x69erlcUx1mowMAADKx1f30akq5Up01q5dW9lxAAAAH6jOy8u9oVyJTvfu3d1f7927V02bNi212soYo3379nk3OgAAgEtQ4aeXt2jRQj/++GOp8aNHj6pFixZeCQoAAFQNY7y7VTcVXnV1di7OufLy8ir16aMAAMD7mKPzX2cfue5wOPTnP/9ZQUFB7n3FxcX67LPPdPXVV3s9QAAAgF+r3InOF198Iennis62bdsUEBDg3hcQEKCrrrpKEyZM8H6EAACg0jAZ+b/OrrwaOXKkXnjhBYWGhlZaUAAAoGpUx3k13lThOTrz58+vjDgAAAC8rsKJTs+ePS+4f82aNb86GAAAULWYjHyOq666yuN1UVGRMjMztX37dg0fPtxrgV2K6+NG+DoEwNJOzRnm6xAAeAlzdM7x/PPPlzk+ZcoU5eXlXXJAAAAA3lLhGwaez1133aV58+Z563QAAKAKlBiHV7fqpsIVnfPZsGEDNwwEAKCGsfiiq4onOrfddpvHa2OMcnJy9Pnnn+vPf/6z1wIDAAC4VBVOdMLCwjxe16pVS23bttW0adPUt29frwUGAAAqX3VsN3lThRKd4uJijRw5Uh06dFC9evUqKyYAAFBFrL7qqkKTkf38/NS3b18dP368ksIBAADwngqvumrfvr2+++67yogFAABUsRIvb9VNhROdJ598UhMmTNDy5cuVk5OjkydPemwAAKDmMHJ4datuyj1HZ9q0aXrkkUc0YMAASdLvfvc7ORz/94GMMXI4HCouLvZ+lAAAAL9CuROdqVOnasyYMe6nmAMAgJqvxOI30il3omP++xz37t27V1owAACgapVUw3aTN1Vojs4vW1UAAADVXYXuo9OmTZuLJjtHjx69pIAAAEDVqY4TiL2pQonO1KlTS90ZGQAA1FzVcUm4N1Uo0fnDH/6gRo0aVVYsAAAAXlXuRIf5OQAAWA+tq/86u+oKAABYB62r/yopsfq3AgAAWE2F5ugAAABrsXoZg0QHAAAbs/ocnQo/1BMAAKCmoKIDAICNlVi7oEOiAwCAnfGsKwAAgBqKig4AADZm9bvkkegAAGBjVl9eTusKAABUudmzZysuLk6hoaEKDQ1VfHy8PvzwQ/f+Hj16yOFweGxjxoyp8HWo6AAAYGMlPnqWZZMmTTR9+nRdfvnlMsbo1Vdf1S233KIvvvhCV155pSRp9OjRmjZtmvs9QUFBFb4OiQ4AADbmqzk6AwcO9Hj91FNPafbs2dq4caM70QkKClJUVNQlXYfWFQAA8BqXy6WTJ096bC6X64LvKS4u1uuvv678/HzFx8e7xxcvXqwGDRqoffv2Sk5OVkFBQYXjIdEBAMDGSry8paSkKCwszGNLSUkp89rbtm1TcHCwnE6nxowZo6VLl+qKK66QJA0ZMkSLFi3S2rVrlZycrIULF+quu+6q8OdzGGMst7Ls2sbdfB0CYGnrUq73dQiApQUOn15l1/p346FePd9tu+eVquA4nU45nc5SxxYWFmrv3r06ceKE3nrrLf3rX/9Senq6O9n5pTVr1qhXr17atWuXWrVqVe54mKMDAAC85nxJTVkCAgLUunVrSVKnTp20efNmvfDCC3rllVdKHdu5c2dJItEBAADlV50eAVFSUnLe+TyZmZmSpOjo6Aqdk0QHAAAb89X8leTkZPXv31/NmjXTqVOnlJqaqrS0NK1YsULZ2dlKTU3VgAEDFBERoa1btyoxMVHdunVTXFxcha5DogMAAKrcoUOHdPfddysnJ0dhYWGKi4vTihUr1KdPH+3bt0+rVq3SjBkzlJ+fr6ZNmyohIUGTJk2q8HVIdAAAsLESH3Wu5s6de959TZs2VXp6uleuQ6IDAICN8awrAACAGoqKDgAANma5m+mdg0QHAAAb89UcnapC6woAAFgWFR0AAGzM6pORSXQAALAxqyc6tK4AAIBlUdEBAMDGjMUnI5PoAABgY7SuAAAAaigqOgAA2JjVKzokOgAA2JjV74xM6woAAFgWFR0AAGzM6o+AINEBAMDGrD5Hh9YVAACwLCo6AADYmNUrOiQ6AADYGKuuAAAAaigqOgAA2BirrgAAgGVZfY4OrSsAAGBZVHQAALAxq09GJtEBAMDGSiye6tC6AgAAlkVFBwAAG7P6ZGQSHQAAbMzajStaVwAAwMKo6AAAYGO0rgAAgGVZ/c7ItK4AAIBlUdEBAMDGrH4fHRIdAABszNppDq0rAABgYVR0AACwMVZdAQAAy7L6HB1aVwAAwLKo6AAAYGPWrueQ6AAAYGtWn6ND6woAAFgWFR0AAGzM6pORSXQAALAxa6c5tK4AAIAPzJ49W3FxcQoNDVVoaKji4+P14YcfuvefPn1aY8eOVUREhIKDg5WQkKCDBw9W+DokOgAA2FiJl7fyatKkiaZPn66MjAx9/vnn6tmzp2655RZ99dVXkqTExEQtW7ZMb775ptLT03XgwAHddtttFf58tK4AALAx46Pm1cCBAz1eP/XUU5o9e7Y2btyoJk2aaO7cuUpNTVXPnj0lSfPnz1e7du20ceNGdenSpdzXoaIDAAC8xuVy6eTJkx6by+W64HuKi4v1+uuvKz8/X/Hx8crIyFBRUZF69+7tPiY2NlbNmjXThg0bKhQPiQ4AADbm7dZVSkqKwsLCPLaUlJQyr71t2zYFBwfL6XRqzJgxWrp0qa644grl5uYqICBA4eHhHsdHRkYqNze3Qp+P1hUAADbm7eXlycnJSkpK8hhzOp1lHtu2bVtlZmbqxIkTeuuttzR8+HClp6d7NR4SHQAA4DVOp/O8ic25AgIC1Lp1a0lSp06dtHnzZr3wwgu68847VVhYqOPHj3tUdQ4ePKioqKgKxUPrCgAAGzNe3i5FSUmJXC6XOnXqJH9/f61evdq9LysrS3v37lV8fHyFzklFBwAAG/PVnZGTk5PVv39/NWvWTKdOnVJqaqrS0tK0YsUKhYWFadSoUUpKSlL9+vUVGhqqcePGKT4+vkIrriQSHVSREQ8O1U0DuimmdYxcp13a+vl2vfzUP7Qne5/7mH+89YI6Xf8bj/e9/dp7mv7436s6XKDGm7c+Sy+mfa0h17bSY33iJEn7juXpudXblbnviAqLS3R9y0g93jdOEcF1fBwt7OjQoUO6++67lZOTo7CwMMXFxWnFihXq06ePJOn5559XrVq1lJCQIJfLpX79+mnWrFkVvg6JDqpEx/ir9eaCpfo681v51fbTA4/fp5f+/Xfd0f1unf7ptPu4pYve1yt/ned+/ct9AMpn+4FjeuuL79WmUah77KfCM7r/3+vVplGo/jn0RknSzHXf6KE3N2rhiO6q5XD4Klz4mK+eXj537twL7q9Tp45mzpypmTNnXtJ1SHRQJR4a+qjH66njn9bK7cvULq6tvvjsS/f46Z9cOvLj0aoOD7CMgsIz+uP7m/WXAb/RnE+z3ONf7D+iAyfy9fqomxTs9JckPfE/ndTtueXa9P2P6tKika9Cho/56oaBVYXJyPCJ4NBgSdLJ4yc9xm++rY9Wbn9fr69ZoLHJ98kZWL6Z+wB+9vSKTHVtFVUqcSkqLpFDDgX4/d9f+87atVTL4dAX+45UdZhAlanWFZ19+/Zp8uTJmjdv3nmPcblcpe64WGJKVMtBDlddORwOJU0dp8xNW5Wdtds9vmLpKuXsz9WPB4/o8nat9OCf/p9iWjXTY/dO8mG0QM3x0Vf79W3uCS0e2aPUvg6N6yswwE8z1n6lcT2ukIz0wtqvVGyMDufRIrYzX7Wuqkq1TnSOHj2qV1999YKJTkpKiqZOneoxFh3cTJeFxFR2ePiVHns6Ua1iW2j0oAc9xpcuXub+Ovvb73T40BHNfnOGLotprB/2HKjqMIEaJfdkgZ5duVX/GHKDnLX9Su2vX9epZ2+9Tk9/9KX+vTlbtRwO3XxlE7WLCmd+js1ZvXXl00Tn/fffv+D+77777qLnKOsOjDe1HXBJcaHyPPrUeHXtc73uu3WcDuX8eMFjt2/5WpLUtPllJDrARXydc1xHC1waPHete6zYGG3Ze1hLPv9OmybeoutbRmr5A311rMAlv1oOhdYJUK8XPtBlV1zmw8iByuXTRGfQoEFyOBwy5vzZpOMi/6dR1h0YaVtVT48+NV49bu6qMb9/WAf25Vz0+Dbtf75b5uFDzB8ALqZz84Z6695eHmN/+U+GWkSEaGSXNvKr9X9/l9YL+vnvzE3f/6ij+S71uDy6SmNF9ULrqhJFR0dr1qxZuuWWW8rcn5mZqU6dOlVxVKgME59OVL9be2vCyD+qIK9AEQ3rS5LyTuXJdbpQl8U01s239tanqzfqxLGTuvyKVkqc8qC2bMjUrm8uXtkD7K6u01+tG/l7jAX611ZYYIBa/3eZ+btf7lHLBiGqFxSgrT8c1bMrt+qu61qreUSIL0JGNVFygWKDFfg00enUqZMyMjLOm+hcrNqDmuP3I26VJL3yzkse41PHP63lb3ykM0VndF3Xa/SHe29XYFAdHTzwo9Z8kK55M17zRbiAJe05ekovpX2lEz8VqnF4kO69vq3uuq61r8MCKpXD+DCT+Pjjj5Wfn6+bb765zP35+fn6/PPP1b179wqd99rG3bwRHoDzWJdyva9DACwtcPj0KrvWXTG3efV8i/a849XzXSqfVnS6du16wf1169atcJIDAADKz1fPuqoqzNoFAACWVa3vowMAACoX99EBAACWZfXl5bSuAACAZVHRAQDAxpiMDAAAUENR0QEAwMaYjAwAACyLycgAAAA1FBUdAABszOrPlCTRAQDAxlh1BQAAUENR0QEAwMasPhmZRAcAABuz+vJyWlcAAMCyqOgAAGBjVp+MTKIDAICNWX15Oa0rAABgWVR0AACwMVZdAQAAy2LVFQAAQA1FRQcAABtj1RUAALAsVl0BAADUUFR0AACwMVpXAADAslh1BQAAUENR0QEAwMZKLD4ZmUQHAAAbs3aaQ+sKAABYGBUdAABsjFVXAADAsqye6NC6AgAAVS4lJUXXXnutQkJC1KhRIw0aNEhZWVkex/To0UMOh8NjGzNmTIWuQ6IDAICNGWO8upVXenq6xo4dq40bN2rlypUqKipS3759lZ+f73Hc6NGjlZOT496effbZCn0+WlcAANiYr1pXH330kcfrBQsWqFGjRsrIyFC3bt3c40FBQYqKivrV16GiAwAAvMblcunkyZMem8vluuj7Tpw4IUmqX7++x/jixYvVoEEDtW/fXsnJySooKKhQPCQ6AADYmPHyfykpKQoLC/PYUlJSLhhDSUmJxo8frxtuuEHt27d3jw8ZMkSLFi3S2rVrlZycrIULF+quu+6q0OejdQUAgI1VZF5NeSQnJyspKcljzOl0XvA9Y8eO1fbt2/XJJ594jN93333urzt06KDo6Gj16tVL2dnZatWqVbniIdEBAABe43Q6L5rY/NKDDz6o5cuXa926dWrSpMkFj+3cubMkadeuXSQ6AADg4nw1GdkYo3Hjxmnp0qVKS0tTixYtLvqezMxMSVJ0dHS5r0OiAwCAjXm7dVVeY8eOVWpqqt577z2FhIQoNzdXkhQWFqbAwEBlZ2crNTVVAwYMUEREhLZu3arExER169ZNcXFx5b4OiQ4AAKhys2fPlvTzTQF/af78+RoxYoQCAgK0atUqzZgxQ/n5+WratKkSEhI0adKkCl2HRAcAABvzZevqQpo2bar09PRLvg6JDgAANmZ41hUAAEDNREUHAAAbK/HRZOSqQqIDAICN0boCAACooajoAABgY7SuAACAZdG6AgAAqKGo6AAAYGO0rgAAgGXRugIAAKihqOgAAGBjtK4AAIBl0boCAACooajoAABgY8aU+DqESkWiAwCAjZXQugIAAKiZqOgAAGBjhlVXAADAqmhdAQAA1FBUdAAAsDFaVwAAwLKsfmdkWlcAAMCyqOgAAGBjVn8EBIkOAAA2ZvU5OrSuAACAZVHRAQDAxqx+Hx0SHQAAbIzWFQAAQA1FRQcAABuz+n10SHQAALAxWlcAAAA1FBUdAABsjFVXAADAsmhdAQAA1FBUdAAAsDFWXQEAAMuy+kM9aV0BAADLoqIDAICN0boCAACWxaorAACAGoqKDgAANsZkZAAAYFnGGK9u5ZWSkqJrr71WISEhatSokQYNGqSsrCyPY06fPq2xY8cqIiJCwcHBSkhI0MGDByv0+Uh0AABAlUtPT9fYsWO1ceNGrVy5UkVFRerbt6/y8/PdxyQmJmrZsmV68803lZ6ergMHDui2226r0HUcxoKzkK5t3M3XIQCWti7lel+HAFha4PDpVXYt/4DLvHq+osIfftX7fvzxRzVq1Ejp6enq1q2bTpw4oYYNGyo1NVW///3vJUnffvut2rVrpw0bNqhLly7lOi8VHQAAbMx4efu1Tpw4IUmqX7++JCkjI0NFRUXq3bu3+5jY2Fg1a9ZMGzZsKPd5mYwMAAC8xuVyyeVyeYw5nU45nc7zvqekpETjx4/XDTfcoPbt20uScnNzFRAQoPDwcI9jIyMjlZubW+54LJnobD6wztchoAJcLpdSUlKUnJx8wT8IAH4d/ozhQs78ylbT+UyZMkVTp071GJs8ebKmTJly3veMHTtW27dv1yeffOLVWCSLztFBzXLy5EmFhYXpxIkTCg0N9XU4gOXwZwxVqaIVnQcffFDvvfee1q1bpxYtWrjH16xZo169eunYsWMeVZ2YmBiNHz9eiYmJ5YqHOToAAMBrnE6nQkNDPbaykhxjjB588EEtXbpUa9as8UhyJKlTp07y9/fX6tWr3WNZWVnau3ev4uPjyx2PJVtXAACgehs7dqxSU1P13nvvKSQkxD3vJiwsTIGBgQoLC9OoUaOUlJSk+vXrKzQ0VOPGjVN8fHy5V1xJtK5QDVBWByoXf8ZQHTkcjjLH58+frxEjRkj6+YaBjzzyiP7973/L5XKpX79+mjVrlqKiosp9HSo68Dmn06nJkyczSRKoJPwZQ3VUnjpLnTp1NHPmTM2cOfNXX4eKDgAAsCwmIwMAAMsi0QEAAJZFogMAACyLRAcAAFgWiQ58aubMmWrevLnq1Kmjzp07a9OmTb4OCbCMdevWaeDAgWrcuLEcDofeffddX4cEVDkSHfjMkiVLlJSUpMmTJ2vLli266qqr1K9fPx06dMjXoQGWkJ+fr6uuuuqSluYCNR3Ly+EznTt31rXXXquXX35Z0s9Pr23atKnGjRunxx9/3MfRAdbicDi0dOlSDRo0yNehAFWKig58orCwUBkZGerdu7d7rFatWurdu7c2bNjgw8gAAFZCogOfOHz4sIqLixUZGekxHhkZ6X7eCQAAl4pEBwAAWBaJDnyiQYMG8vPz08GDBz3GDx48WKGHtQEAcCEkOvCJgIAAderUSatXr3aPlZSUaPXq1YqPj/dhZAAAK+Hp5fCZpKQkDR8+XNdcc42uu+46zZgxQ/n5+Ro5cqSvQwMsIS8vT7t27XK/3r17tzIzM1W/fn01a9bMh5EBVYfl5fCpl19+WX/961+Vm5urq6++Wi+++KI6d+7s67AAS0hLS9NNN91Uanz48OFasGBB1QcE+ACJDgAAsCzm6AAAAMsi0QEAAJZFogMAACyLRAcAAFgWiQ4AALAsEh0AAGBZJDoAAMCySHRQY4wYMUKDBg1yv+7Ro4fGjx9f5XGkpaXJ4XDo+PHj5z3G4XDo3XffrZJ4KvNaw4YN09NPP10p57aLf/zjHxo4cKCvw/BQ0T87CxYsUHh4eKXFA1QmEh1ckhEjRsjhcMjhcCggIECtW7fWtGnTdObMmUq/9jvvvKMnnniiXMeWJzmp7qZMmaKrr766yq735Zdf6oMPPtBDDz3kHuvRo4f75/3LbcyYMeU+b/PmzTVjxoxKiPjnc5cV39ltxIgRlXLdC7nnnnu0ZcsWffzxx1V+bQA86wpecPPNN2v+/PlyuVz64IMPNHbsWPn7+ys5ObnUsYWFhQoICPDKdevXr++V86BsL730km6//XYFBwd7jI8ePVrTpk3zGAsKCvLqtYuLi+VwOFSrVsX+X2zz5s0qLi6WJK1fv14JCQnKyspSaGioJCkwMNDj+KKiIvn7+3sn6PMICAjQkCFD9OKLL6pr166Vei0ApVHRwSVzOp2KiopSTEyM7r//fvXu3Vvvv/++pP9rNz311FNq3Lix2rZtK0nat2+f7rjjDoWHh6t+/fq65ZZb9P3337vPWVxcrKSkJIWHhysiIkKPPfaYzn1aybnld5fLpYkTJ6pp06ZyOp1q3bq15s6dq++//979vJ969ep5/J99SUmJUlJS1KJFCwUGBuqqq67SW2+95XGdDz74QG3atFFgYKBuuukmjzgvJCcnR/3791dgYKBatmzpcd6ePXvqwQcf9Dj+xx9/VEBAgMcT3c9asGCBpk6dqi+//NJdnfjls4oOHz6sW2+9VUFBQbr88svd3/+ztm/frv79+ys4OFiRkZEaNmyYDh8+fN7Yi4uL9dZbb5XZcgkKClJUVJTHdjaReO211xQcHKydO3e6j3/ggQcUGxurgoIC9ejRQ3v27FFiYqL7c5z9fOHh4Xr//fd1xRVXyOl0au/evdq8ebP69OmjBg0aKCwsTN27d9eWLVvOG3fDhg3dMZ1NhBs1aqSoqCidPn1a4eHhWrJkibp37646depo8eLFkqR//etfateunerUqaPY2FjNmjXL47wX+31NS0vTddddp7p16yo8PFw33HCD9uzZ494/cOBAvf/++/rpp5/OG3uPHj00btw4jR8/XvXq1VNkZKTmzJnjftBtSEiIWrdurQ8//NDjfenp6bruuuvkdDoVHR2txx9/3KOimp+fr7vvvlvBwcGKjo7W3//+91LXdrlcmjBhgi677DLVrVtXnTt3Vlpa2nljBWoUA1yC4cOHm1tuucVj7He/+53p2LGje39wcLAZNmyY2b59u9m+fbspLCw07dq1M/fcc4/ZunWr+frrr82QIUNM27ZtjcvlMsYY88wzz5h69eqZt99+23z99ddm1KhRJiQkxONa3bt3Nw8//LD79R133GGaNm1q3nnnHZOdnW1WrVplXn/9dXPmzBnz9ttvG0kmKyvL5OTkmOPHjxtjjHnyySdNbGys+eijj0x2draZP3++cTqdJi0tzRhjzN69e43T6TRJSUnm22+/NYsWLTKRkZFGkjl27Nh5vy+STEREhJkzZ47JysoykyZNMn5+fubrr782xhizePFiU69ePXP69Gn3e5577jnTvHlzU1JSUup8BQUF5pFHHjFXXnmlycnJMTk5OaagoMB9rSZNmpjU1FSzc+dO89BDD5ng4GBz5MgRY4wxx44dMw0bNjTJycnmm2++MVu2bDF9+vQxN91003nj37Jli5FkcnNzPcbP/Z6X5fbbbzfXXnutKSoqMsuXLzf+/v7m888/N8YYc+TIEdOkSRMzbdo09+cwxpj58+cbf39/c/3115tPP/3UfPvttyY/P9+sXr3aLFy40HzzzTfu34PIyEhz8uTJC8ZgjDFr1671+Dnt3r3bSDLNmzc3b7/9tvnuu+/MgQMHzKJFi0x0dLR77O233zb169c3CxYsMMaYi/6+FhUVmbCwMDNhwgSza9cu8/XXX5sFCxaYPXv2uGPJz883tWrVMmvXrj1vvN27dzchISHmiSeeMDt27DBPPPGE8fPzM/379zf//Oc/zY4dO8z9999vIiIiTH5+vjHGmP3795ugoCDzwAMPmG+++cYsXbrUNGjQwEyePNl93vvvv980a9bMrFq1ymzdutX8z//8jwkJCfH4Od57773m+uuvN+vWrTO7du0yf/3rX43T6TQ7duxw/3zCwsIu+j0HqiMSHVySXyY6JSUlZuXKlcbpdJoJEya490dGRroTGGOMWbhwoWnbtq3HP+gul8sEBgaaFStWGGOMiY6ONs8++6x7f1FRkWnSpMl5E52srCwjyaxcubLMOM/9R88YY06fPm2CgoLM+vXrPY4dNWqUGTx4sDHGmOTkZHPFFVd47J84cWK5Ep0xY8Z4jHXu3Nncf//9xhhjfvrpJ1OvXj2zZMkS9/64uDgzZcqU855z8uTJ5qqrrirzWpMmTXK/zsvLM5LMhx9+aIwx5oknnjB9+/b1eM++ffvciV9Zli5davz8/EolXd27dzf+/v6mbt26HtuiRYvcxxw9etQ0adLE3H///SYyMtI89dRTHueIiYkxzz//vMfY/PnzjSSTmZl53s9vjDHFxcUmJCTELFu27ILHGXP+RGfGjBkex7Vq1cqkpqZ6jD3xxBMmPj7eGHPx39cjR44YSe7k+Hzq1avnTp7K0r17d3PjjTe6X585c8bUrVvXDBs2zD2Wk5NjJJkNGzYYY4z54x//WCq2mTNnmuDgYFNcXGxOnTplAgICzBtvvOHef+TIERMYGOj+s7Nnzx7j5+dnfvjhB494evXqZZKTk40xJDqo2Zijg0u2fPlyBQcHq6ioSCUlJRoyZIimTJni3t+hQwePeTlffvmldu3apZCQEI/znD59WtnZ2Tpx4oRycnLUuXNn977atWvrmmuuKdW+OiszM1N+fn7q3r17uePetWuXCgoK1KdPH4/xwsJC/eY3v5EkffPNNx5xSFJ8fHy5zn/ucfHx8crMzJQk1alTR8OGDdO8efN0xx13aMuWLdq+fXupllN5xcXFub+uW7euQkNDdejQIUk/f7/Xrl1baq6NJGVnZ6tNmzalxn/66Sc5nU53a+mXhg4dqj/96U8eY5GRke6v69Wrp7lz56pfv366/vrr9fjjj5frMwQEBHh8Dkk6ePCgJk2apLS0NB06dEjFxcUqKCjQ3r17y3XOslxzzTXur/Pz85Wdna1Ro0Zp9OjR7vEzZ84oLCxM0sV/X/v27asRI0aoX79+6tOnj3r37q077rhD0dHRHscHBgaqoKDggrH98vP7+fkpIiJCHTp0cI+d/T6f/dl+8803io+P9/g53XDDDcrLy9P+/ft17NgxFRYWevwO169f391ClqRt27apuLi41O+By+VSRETEBeMFagISHVyym266SbNnz1ZAQIAaN26s2rU9f63q1q3r8TovL0+dOnVyz4/4pYYNG/6qGM6dZFoeeXl5kqT//Oc/uuyyyzz2OZ3OXxVHRdx77726+uqrtX//fs2fP189e/ZUTEzMrzrXuRNqHQ6HSkpKJP38OQcOHKhnnnmm1PvO/cf4rAYNGqigoKDMyeNhYWFq3br1BeNZt26d/Pz8lJOTo/z8/FJJQlkCAwNLJVbDhw/XkSNH9MILLygmJkZOp1Px8fEqLCy86PnO55e/j2d/B+bMmVMqofXz83Mfc7Hf1/nz5+uhhx7SRx99pCVLlmjSpElauXKlunTp4j726NGjF/39Luvn+Muxs9+fsz9bb8jLy5Ofn58yMjLcn/msspJjoKZhMjIuWd26ddW6dWs1a9asVJJTlo4dO2rnzp1q1KiRWrdu7bGFhYUpLCxM0dHR+uyzz9zvOXPmjDIyMs57zg4dOqikpETp6ell7j/7j/XZFTmSPCa9nhtH06ZNJUnt2rXTpk2bPM61cePGi37Gso7buHGj2rVr5xHzNddcozlz5ig1NVX33HPPBc8XEBDgEX95dezYUV999ZWaN29e6nOem4SedXYZ+9dff13h661fv17PPPOMli1bpuDg4FKTrivyOT799FM99NBDGjBggK688ko5nc4LTqKuqMjISDVu3Fjfffddqe9NixYtJF389/Ws3/zmN0pOTtb69evVvn17paamuvdlZ2fr9OnT7kqht7Rr104bNmzwqHR++umnCgkJUZMmTdSqVSv5+/t7/Fk6duyYduzY4RF3cXGxDh06VOrzRUVFeTVewBdIdFDlhg4dqgYNGuiWW27Rxx9/rN27dystLU0PPfSQ9u/fL0l6+OGHNX36dL377rv69ttv9cADD1zwHjjNmzfX8OHDdc899+jdd991n/ONN96QJMXExMjhcGj58uX68ccflZeXp5CQEE2YMEGJiYl69dVXlZ2drS1btuill17Sq6++KkkaM2aMdu7cqUcffVRZWVlKTU31WO10IW+++abmzZunHTt2aPLkydq0aVOpf/TvvfdeTZ8+XcYY3XrrrRc8X/PmzbV7925lZmbq8OHDcrlc5Ypj7NixOnr0qAYPHqzNmzcrOztbK1as0MiRI8+bcDRs2FAdO3bUJ598UmpfQUGBcnNzPbZjx45Jkk6dOqVhw4bpoYceUv/+/bV48WItWbLEY8VZ8+bNtW7dOv3www8XTVouv/xyLVy4UN98840+++wzDR069FdV7y5k6tSpSklJ0YsvvqgdO3Zo27Ztmj9/vp577jlJF/993b17t5KTk7Vhwwbt2bNH//u//6udO3d6JLUff/yxWrZsqVatWnk19gceeED79u3TuHHj9O233+q9997T5MmTlZSUpFq1aik4OFijRo3So48+qjVr1mj79u0aMWKEx7L9Nm3aaOjQobr77rv1zjvvaPfu3dq0aZNSUlL0n//8x6vxAj7h4zlCqOHKWnVVnv05OTnm7rvvNg0aNDBOp9O0bNnSjB492pw4ccIY8/Pk44cfftiEhoaa8PBwk5SUZO6+++4Lrrr66aefTGJioomOjjYBAQGmdevWZt68ee7906ZNM1FRUcbhcJjhw4cbY36eQD1jxgzTtm1b4+/vbxo2bGj69etn0tPT3e9btmyZad26tXE6naZr165m3rx55ZqMPHPmTNOnTx/jdDpN8+bNPSYen3Xq1Cn3qpmLOX36tElISDDh4eFGkpk/f777WkuXLvU4NiwszL3fGGN27Nhhbr31VhMeHm4CAwNNbGysGT9+fJkrvM6aNWuW6dKli8dY9+7djaRSW79+/YwxxowcOdJ06NDBYzXZ3//+d1O/fn2zf/9+Y4wxGzZsMHFxccbpdJqzfwWdb7Lrli1bzDXXXGPq1KljLr/8cvPmm2+WOZm5LOebjPzFF1+UOnbx4sXm6quvNgEBAaZevXqmW7du5p133nHvv9Dva25urhk0aJD79y4mJsb85S9/McXFxe739+3b16SkpFww3rJWtJX1Wc/9eaelpZlrr73WBAQEmKioKDNx4kRTVFTk3n/q1Clz1113maCgIBMZGWmeffbZUtcqLCw0f/nLX0zz5s2Nv7+/iY6ONrfeeqvZunWrMYbJyKjZHMacZ3YngEr3/fffq1WrVtq8ebM6duzo63A8/PTTT2rbtq2WLFlS7gnYKO2rr75Sz549tWPHDo9WF4CqwWRkwAeKiop05MgRTZo0SV26dKl2SY708+Tg1157zatzYuwoJydHr732GkkO4CNUdAAfSEtL00033aQ2bdrorbfe8lhCDADwHhIdAABgWay6AgAAlkWiAwAALItEBwAAWBaJDgAAsCwSHQAAYFkkOgAAwLJIdAAAgGWR6AAAAMsi0QEAAJb1/wFTZ64cuTXtfgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from tabulate import tabulate\n",
+    "# Initializing a dictionary to store evaluation metrics\n",
+    "model_evaluation_metrics = {}\n",
+    "\n",
+    "# Iterating over each model to evaluate\n",
+    "for model_name, model in models.items():\n",
+    "    # Predict on the test set\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate confusion matrix and classification report\n",
+    "    cm = confusion_matrix(y_test, y_pred)\n",
+    "    classif_report = classification_report(y_test, y_pred)\n",
+    "    \n",
+    "    # Storing the results\n",
+    "    model_evaluation_metrics[model_name] = {\n",
+    "        \"Confusion Matrix\":cm,\n",
+    "        'Classification Report': classif_report\n",
+    "    }\n",
+    "    \n",
+    "     \n",
+    "    print(f\"{model_name} Evaluation Metrics:\")\n",
+    "    print(\"\\nClassification Report:\")\n",
+    "    print(classif_report)\n",
+    "    print(\"\\n\" + \"=\"*60 + \"\\n\")\n",
+    "    plt.figure(figsize=(7,5))\n",
+    "    sns.heatmap(cm,annot=True)\n",
+    "    plt.xlabel(f\"Predicted by the ({model_name}) model\")\n",
+    "    plt.ylabel('Truth'),"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "cdb46aec",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-06-26T13:56:16.744526Z",
+     "iopub.status.busy": "2024-06-26T13:56:16.744160Z",
+     "iopub.status.idle": "2024-06-26T13:56:19.733472Z",
+     "shell.execute_reply": "2024-06-26T13:56:19.732245Z"
+    },
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+     "duration": 3.043919,
+     "end_time": "2024-06-26T13:56:19.735537",
+     "exception": false,
+     "start_time": "2024-06-26T13:56:16.691618",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "K-Nearest Neighbors Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "|              |   precision |   recall |   f1-score |   support |\n",
+      "|--------------+-------------+----------+------------+-----------|\n",
+      "| 0            |    0.606742 | 0.710526 |   0.654545 |     76    |\n",
+      "| 1            |    0.639344 | 0.527027 |   0.577778 |     74    |\n",
+      "| accuracy     |    0.62     | 0.62     |   0.62     |      0.62 |\n",
+      "| macro avg    |    0.623043 | 0.618777 |   0.616162 |    150    |\n",
+      "| weighted avg |    0.622826 | 0.62     |   0.616673 |    150    |\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Logistic Regression Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.598131 | 0.842105 |   0.699454 |  76        |\n",
+      "| 1            |    0.72093  | 0.418919 |   0.529915 |  74        |\n",
+      "| accuracy     |    0.633333 | 0.633333 |   0.633333 |   0.633333 |\n",
+      "| macro avg    |    0.659531 | 0.630512 |   0.614684 | 150        |\n",
+      "| weighted avg |    0.658712 | 0.633333 |   0.615814 | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Decision Tree Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.726027 | 0.697368 |   0.711409 |  76        |\n",
+      "| 1            |    0.701299 | 0.72973  |   0.715232 |  74        |\n",
+      "| accuracy     |    0.713333 | 0.713333 |   0.713333 |   0.713333 |\n",
+      "| macro avg    |    0.713663 | 0.713549 |   0.713321 | 150        |\n",
+      "| weighted avg |    0.713828 | 0.713333 |   0.713295 | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Support Vector Machine Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.626374 | 0.75     |   0.682635 |  76        |\n",
+      "| 1            |    0.677966 | 0.540541 |   0.601504 |  74        |\n",
+      "| accuracy     |    0.646667 | 0.646667 |   0.646667 |   0.646667 |\n",
+      "| macro avg    |    0.65217  | 0.64527  |   0.642069 | 150        |\n",
+      "| weighted avg |    0.651826 | 0.646667 |   0.64261  | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Random Forest Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "|              |   precision |   recall |   f1-score |   support |\n",
+      "|--------------+-------------+----------+------------+-----------|\n",
+      "| 0            |    0.723684 | 0.723684 |   0.723684 |     76    |\n",
+      "| 1            |    0.716216 | 0.716216 |   0.716216 |     74    |\n",
+      "| accuracy     |    0.72     | 0.72     |   0.72     |      0.72 |\n",
+      "| macro avg    |    0.71995  | 0.71995  |   0.71995  |    150    |\n",
+      "| weighted avg |    0.72     | 0.72     |   0.72     |    150    |\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Gradient Boosting Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.725275 | 0.868421 |   0.790419 |  76        |\n",
+      "| 1            |    0.830508 | 0.662162 |   0.736842 |  74        |\n",
+      "| accuracy     |    0.766667 | 0.766667 |   0.766667 |   0.766667 |\n",
+      "| macro avg    |    0.777892 | 0.765292 |   0.763631 | 150        |\n",
+      "| weighted avg |    0.77719  | 0.766667 |   0.763988 | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "AdaBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.710843 | 0.776316 |   0.742138 |  76        |\n",
+      "| 1            |    0.746269 | 0.675676 |   0.70922  |  74        |\n",
+      "| accuracy     |    0.726667 | 0.726667 |   0.726667 |   0.726667 |\n",
+      "| macro avg    |    0.728556 | 0.725996 |   0.725679 | 150        |\n",
+      "| weighted avg |    0.72832  | 0.726667 |   0.725899 | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Extra Trees Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "|              |   precision |   recall |   f1-score |   support |\n",
+      "|--------------+-------------+----------+------------+-----------|\n",
+      "| 0            |    0.691358 | 0.736842 |   0.713376 |      76   |\n",
+      "| 1            |    0.710145 | 0.662162 |   0.685315 |      74   |\n",
+      "| accuracy     |    0.7      | 0.7      |   0.7      |       0.7 |\n",
+      "| macro avg    |    0.700751 | 0.699502 |   0.699345 |     150   |\n",
+      "| weighted avg |    0.700626 | 0.7      |   0.699532 |     150   |\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Naive Bayes Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "|              |   precision |   recall |   f1-score |   support |\n",
+      "|--------------+-------------+----------+------------+-----------|\n",
+      "| 0            |    0.598214 | 0.881579 |   0.712766 |     76    |\n",
+      "| 1            |    0.763158 | 0.391892 |   0.517857 |     74    |\n",
+      "| accuracy     |    0.64     | 0.64     |   0.64     |      0.64 |\n",
+      "| macro avg    |    0.680686 | 0.636735 |   0.615312 |    150    |\n",
+      "| weighted avg |    0.679586 | 0.64     |   0.616611 |    150    |\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "XGBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "|              |   precision |   recall |   f1-score |    support |\n",
+      "|--------------+-------------+----------+------------+------------|\n",
+      "| 0            |    0.696629 | 0.815789 |   0.751515 |  76        |\n",
+      "| 1            |    0.770492 | 0.635135 |   0.696296 |  74        |\n",
+      "| accuracy     |    0.726667 | 0.726667 |   0.726667 |   0.726667 |\n",
+      "| macro avg    |    0.733561 | 0.725462 |   0.723906 | 150        |\n",
+      "| weighted avg |    0.733068 | 0.726667 |   0.724274 | 150        |\n",
+      "+--------------+-------------+----------+------------+------------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "CatBoost Evaluation Metrics:\n",
+      "\n",
+      "Classification Report:\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "|              |   precision |   recall |   f1-score |   support |\n",
+      "|--------------+-------------+----------+------------+-----------|\n",
+      "| 0            |    0.722892 | 0.789474 |   0.754717 |     76    |\n",
+      "| 1            |    0.761194 | 0.689189 |   0.723404 |     74    |\n",
+      "| accuracy     |    0.74     | 0.74     |   0.74     |      0.74 |\n",
+      "| macro avg    |    0.742043 | 0.739331 |   0.739061 |    150    |\n",
+      "| weighted avg |    0.741787 | 0.74     |   0.739269 |    150    |\n",
+      "+--------------+-------------+----------+------------+-----------+\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "Model Accuracies:\n",
+      "+------------------------+------------+\n",
+      "|                        |   Accuracy |\n",
+      "|------------------------+------------|\n",
+      "| K-Nearest Neighbors    |   0.62     |\n",
+      "| Logistic Regression    |   0.633333 |\n",
+      "| Decision Tree          |   0.713333 |\n",
+      "| Support Vector Machine |   0.646667 |\n",
+      "| Random Forest          |   0.72     |\n",
+      "| Gradient Boosting      |   0.766667 |\n",
+      "| AdaBoost               |   0.726667 |\n",
+      "| Extra Trees            |   0.7      |\n",
+      "| Naive Bayes            |   0.64     |\n",
+      "| XGBoost                |   0.726667 |\n",
+      "| CatBoost               |   0.74     |\n",
+      "+------------------------+------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "from tabulate import tabulate\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Initializing a dictionary to store evaluation metrics\n",
+    "model_evaluation_metrics = {}\n",
+    "\n",
+    "# Iterating over each model to evaluate\n",
+    "for model_name, model in models.items():\n",
+    "    # Predict on the test set\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate confusion matrix and classification report\n",
+    "    cm = confusion_matrix(y_test, y_pred)\n",
+    "    classif_report = classification_report(y_test, y_pred, output_dict=True)\n",
+    "    \n",
+    "    # Storing the results\n",
+    "    model_evaluation_metrics[model_name] = {\n",
+    "        \"Confusion Matrix\": cm,\n",
+    "        'Classification Report': classif_report\n",
+    "    }\n",
+    "    \n",
+    "    # Convert classification report to DataFrame\n",
+    "    classif_report_df = pd.DataFrame(classif_report).transpose()\n",
+    "    \n",
+    "    # Save classification report to CSV\n",
+    "    classif_report_df.to_csv(f\"{model_name}_classification_report.csv\", index=True)\n",
+    "    \n",
+    "    # Save confusion matrix to CSV\n",
+    "    cm_df = pd.DataFrame(cm, index=[f\"True_{i}\" for i in range(len(cm))], columns=[f\"Pred_{i}\" for i in range(len(cm))])\n",
+    "    cm_df.to_csv(f\"{model_name}_confusion_matrix.csv\", index=True)\n",
+    "    \n",
+    "    # Print results\n",
+    "    print(f\"{model_name} Evaluation Metrics:\")\n",
+    "    print(\"\\nClassification Report:\")\n",
+    "    print(tabulate(classif_report_df, headers='keys', tablefmt='psql'))\n",
+    "    print(\"\\n\" + \"=\"*60 + \"\\n\")\n",
+    "    \n",
+    "    # Plot confusion matrix heatmap\n",
+    "    plt.figure(figsize=(7, 5))\n",
+    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=range(len(cm)), yticklabels=range(len(cm)))\n",
+    "    plt.xlabel(f\"Predicted by the ({model_name}) model\")\n",
+    "    plt.ylabel('Truth')\n",
+    "    plt.title(f\"{model_name} Confusion Matrix\")\n",
+    "    plt.savefig(f\"{model_name}_confusion_matrix.png\")\n",
+    "    plt.close()\n",
+    "\n",
+    "# Displaying the accuracies of all models\n",
+    "print(\"Model Accuracies:\")\n",
+    "print(tabulate(pd.DataFrame.from_dict(accuracies, orient='index', columns=['Accuracy']), headers='keys', tablefmt='psql'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd953aef",
+   "metadata": {
+    "papermill": {
+     "duration": 0.050998,
+     "end_time": "2024-06-26T13:56:19.837349",
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+     "start_time": "2024-06-26T13:56:19.786351",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 5155116,
+     "sourceId": 8613537,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30733,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 32.353764,
+   "end_time": "2024-06-26T13:56:20.608259",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-06-26T13:55:48.254495",
+   "version": "2.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Cheese Classification/README.md b/Cheese Classification/README.md
new file mode 100644
index 000000000..217524682
--- /dev/null
+++ b/Cheese Classification/README.md	
@@ -0,0 +1,150 @@
+# Cheese Type Classification
+
+## Table of Contents
+
+- [Goal](#goal)
+- [Dataset](#dataset)
+- [Description](#description)
+- [What I Had Done](#what-i-had-done)
+- [Installation](#installation)
+- [Libraries Needed](#libraries-needed)
+- [Exploratory Data Analysis Results](#exploratory-data-analysis-results)
+- [Models and Results](#models-and-results)
+- [Conclusion](#conclusion)
+- [Contributing](#contributing)
+- [Signature](#signature)
+
+## Goal
+
+To classify different types of cheese using various machine learning models.
+
+## Dataset
+
+Link: [Cheese Dataset](https://www.kaggle.com/datasets/jainaru/cheese-across-the-world)
+
+## Description
+
+* This folder contains the code and resources for classifying different types of cheese using various machine learning models.
+* The classification is based on various features such as fat content, moisture content, texture, color, and aging time.
+
+## What I Had Done
+
+## Installation
+
+* Clone the repository using the following command:
+    ```bash
+    git clone https://github.com/yourusername/cheese-type-classification.git
+    cd cheese-type-classification
+    ```
+
+* To run the notebook and reproduce the results, you need to have Python installed along with the necessary libraries. You can install the required libraries using the following command:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+* Run the Jupyter notebook:
+    ```bash
+    jupyter notebook cheese-type-classification.ipynb
+    ```
+
+## Libraries Needed
+
+* pandas==1.3.3
+* numpy==1.21.2
+* matplotlib==3.4.3
+* seaborn==0.11.2
+* scipy==1.7.1
+* statsmodels==0.12.2
+* sklearn==0.24.2
+* xgboost==1.4.2
+* lightgbm==3.2.1
+* catboost==0.26.1
+* tqdm==4.62.2
+* optuna==2.9.1
+
+## Exploratory Data Analysis Results
+
+* ![Relationship Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___10_1.png)
+* ![Cluster Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___12_1.png)
+* ![Pearson Correlation Matrix](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___13_0.png)
+* ![Spearman Correlation Matrix](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___16_0.png)
+* ![Predictive Power Score](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___18_0.png)
+* ![Line of Best Fit Graphs](https://github.com/adi271001/ML-Crate/blob/cheese-classification/Cheese%20Classification/Images/__results___20_0.png)
+
+## Models and Results
+
+The project explores the following machine learning models to classify different types of cheese:
+
+### 1. K-Nearest Neighbors
+
+**Results**:
+- Accuracy: 0.62
+
+### 2. Logistic Regression
+
+**Results**:
+- Accuracy: 0.633
+
+### 3. Decision Tree
+
+**Results**:
+- Accuracy: 0.713
+
+### 4. Support Vector Machine
+
+**Results**:
+- Accuracy: 0.647
+
+### 5. Random Forest
+
+**Results**:
+- Accuracy: 0.72
+
+### 6. Gradient Boosting
+
+**Results**:
+- Accuracy: 0.767
+
+### 7. AdaBoost
+
+**Results**:
+- Accuracy: 0.727
+
+### 8. Extra Trees
+
+**Results**:
+- Accuracy: 0.70
+
+### 9. Naive Bayes
+
+**Results**:
+- Accuracy: 0.64
+
+### 10. XGBoost
+
+**Results**:
+- Accuracy: 0.727
+
+### 11. CatBoost
+
+**Results**:
+- Accuracy: 0.74
+
+## Conclusion
+
+After evaluating various machine learning models, it is evident that ensemble methods such as Gradient Boosting, CatBoost, and Random Forest perform significantly better than single classifiers like K-Nearest Neighbors or Logistic Regression. These models effectively capture complex relationships within the data, leading to higher classification accuracy.
+
+- **Best Performing Models:** Gradient Boosting and CatBoost achieved the highest accuracy scores, indicating robust predictive performance.
+- **Important Features:** Features such as fat content, moisture content, and aging time were consistently found to be the most influential in classifying cheese types.
+
+## Contributing
+
+Contributions are welcome! Please read the contribution guidelines first.
+
+## Signature
+
+Aditya D
+* GitHub: [adi271001](https://www.github.com/adi271001)
+* LinkedIn: [Aditya D](https://www.linkedin.com/in/aditya-d-23453a179/)
+* Topmate: [Aditya D](https://topmate.io/aditya_d/)
+* Twitter: [@ADITYAD29257528](https://x.com/ADITYAD29257528)
diff --git a/Cheese Classification/requirements.txt b/Cheese Classification/requirements.txt
new file mode 100644
index 000000000..e36ae2cbe
--- /dev/null
+++ b/Cheese Classification/requirements.txt	
@@ -0,0 +1,12 @@
+pandas==1.3.3
+numpy==1.21.2
+matplotlib==3.4.3
+seaborn==0.11.2
+scipy==1.7.1
+statsmodels==0.12.2
+sklearn==0.24.2
+xgboost==1.4.2
+lightgbm==3.2.1
+catboost==0.26.1
+tqdm==4.62.2
+optuna==2.9.1
diff --git a/Cheese Classification/results/AdaBoost_classification_report.csv b/Cheese Classification/results/AdaBoost_classification_report.csv
new file mode 100644
index 000000000..366a4ac2f
--- /dev/null
+++ b/Cheese Classification/results/AdaBoost_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
+0,0.7108433734939759,0.7763157894736842,0.7421383647798742,76.0
+1,0.746268656716418,0.6756756756756757,0.7092198581560283,74.0
+accuracy,0.7266666666666667,0.7266666666666667,0.7266666666666667,0.7266666666666667
+macro avg,0.7285560151051969,0.72599573257468,0.7256791114679513,150.0
+weighted avg,0.7283198465503806,0.7266666666666667,0.7258985681787767,150.0
diff --git a/Cheese Classification/results/AdaBoost_confusion_matrix.csv b/Cheese Classification/results/AdaBoost_confusion_matrix.csv
new file mode 100644
index 000000000..24f2a8a07
--- /dev/null
+++ b/Cheese Classification/results/AdaBoost_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
+True_0,59,17
+True_1,24,50
diff --git a/Cheese Classification/results/AdaBoost_confusion_matrix.png b/Cheese Classification/results/AdaBoost_confusion_matrix.png
new file mode 100644
index 000000000..77e6aac22
Binary files /dev/null and b/Cheese Classification/results/AdaBoost_confusion_matrix.png differ
diff --git a/Cheese Classification/results/CatBoost_classification_report.csv b/Cheese Classification/results/CatBoost_classification_report.csv
new file mode 100644
index 000000000..e435cb3f5
--- /dev/null
+++ b/Cheese Classification/results/CatBoost_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
+0,0.7228915662650602,0.7894736842105263,0.7547169811320756,76.0
+1,0.7611940298507462,0.6891891891891891,0.7234042553191488,74.0
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diff --git a/Cheese Classification/results/CatBoost_confusion_matrix.csv b/Cheese Classification/results/CatBoost_confusion_matrix.csv
new file mode 100644
index 000000000..2676b62f4
--- /dev/null
+++ b/Cheese Classification/results/CatBoost_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
+True_0,60,16
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diff --git a/Cheese Classification/results/CatBoost_confusion_matrix.png b/Cheese Classification/results/CatBoost_confusion_matrix.png
new file mode 100644
index 000000000..3bf859838
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diff --git a/Cheese Classification/results/Decision Tree_classification_report.csv b/Cheese Classification/results/Decision Tree_classification_report.csv
new file mode 100644
index 000000000..efb412ace
--- /dev/null
+++ b/Cheese Classification/results/Decision Tree_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Decision Tree_confusion_matrix.csv b/Cheese Classification/results/Decision Tree_confusion_matrix.csv
new file mode 100644
index 000000000..6e82c83ea
--- /dev/null
+++ b/Cheese Classification/results/Decision Tree_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Decision Tree_confusion_matrix.png b/Cheese Classification/results/Decision Tree_confusion_matrix.png
new file mode 100644
index 000000000..12d89c958
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diff --git a/Cheese Classification/results/Extra Trees_classification_report.csv b/Cheese Classification/results/Extra Trees_classification_report.csv
new file mode 100644
index 000000000..dc2bef24e
--- /dev/null
+++ b/Cheese Classification/results/Extra Trees_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Extra Trees_confusion_matrix.csv b/Cheese Classification/results/Extra Trees_confusion_matrix.csv
new file mode 100644
index 000000000..ab60cc855
--- /dev/null
+++ b/Cheese Classification/results/Extra Trees_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Extra Trees_confusion_matrix.png b/Cheese Classification/results/Extra Trees_confusion_matrix.png
new file mode 100644
index 000000000..ddeba8a29
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diff --git a/Cheese Classification/results/Gradient Boosting_classification_report.csv b/Cheese Classification/results/Gradient Boosting_classification_report.csv
new file mode 100644
index 000000000..433415e75
--- /dev/null
+++ b/Cheese Classification/results/Gradient Boosting_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Gradient Boosting_confusion_matrix.csv b/Cheese Classification/results/Gradient Boosting_confusion_matrix.csv
new file mode 100644
index 000000000..f41c22f6f
--- /dev/null
+++ b/Cheese Classification/results/Gradient Boosting_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Gradient Boosting_confusion_matrix.png b/Cheese Classification/results/Gradient Boosting_confusion_matrix.png
new file mode 100644
index 000000000..a9334a50b
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diff --git a/Cheese Classification/results/K-Nearest Neighbors_classification_report.csv b/Cheese Classification/results/K-Nearest Neighbors_classification_report.csv
new file mode 100644
index 000000000..94551a76b
--- /dev/null
+++ b/Cheese Classification/results/K-Nearest Neighbors_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/K-Nearest Neighbors_confusion_matrix.csv b/Cheese Classification/results/K-Nearest Neighbors_confusion_matrix.csv
new file mode 100644
index 000000000..eda4031ea
--- /dev/null
+++ b/Cheese Classification/results/K-Nearest Neighbors_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/K-Nearest Neighbors_confusion_matrix.png b/Cheese Classification/results/K-Nearest Neighbors_confusion_matrix.png
new file mode 100644
index 000000000..1c4bb8651
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diff --git a/Cheese Classification/results/Logistic Regression_classification_report.csv b/Cheese Classification/results/Logistic Regression_classification_report.csv
new file mode 100644
index 000000000..dd12fac2e
--- /dev/null
+++ b/Cheese Classification/results/Logistic Regression_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Logistic Regression_confusion_matrix.csv b/Cheese Classification/results/Logistic Regression_confusion_matrix.csv
new file mode 100644
index 000000000..1522464dd
--- /dev/null
+++ b/Cheese Classification/results/Logistic Regression_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Logistic Regression_confusion_matrix.png b/Cheese Classification/results/Logistic Regression_confusion_matrix.png
new file mode 100644
index 000000000..892cfe302
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diff --git a/Cheese Classification/results/Naive Bayes_classification_report.csv b/Cheese Classification/results/Naive Bayes_classification_report.csv
new file mode 100644
index 000000000..e0606a06b
--- /dev/null
+++ b/Cheese Classification/results/Naive Bayes_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Naive Bayes_confusion_matrix.csv b/Cheese Classification/results/Naive Bayes_confusion_matrix.csv
new file mode 100644
index 000000000..bdb5922f4
--- /dev/null
+++ b/Cheese Classification/results/Naive Bayes_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Naive Bayes_confusion_matrix.png b/Cheese Classification/results/Naive Bayes_confusion_matrix.png
new file mode 100644
index 000000000..40eac7b63
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diff --git a/Cheese Classification/results/Random Forest_classification_report.csv b/Cheese Classification/results/Random Forest_classification_report.csv
new file mode 100644
index 000000000..947a339b3
--- /dev/null
+++ b/Cheese Classification/results/Random Forest_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/Random Forest_confusion_matrix.csv b/Cheese Classification/results/Random Forest_confusion_matrix.csv
new file mode 100644
index 000000000..866ba6e6c
--- /dev/null
+++ b/Cheese Classification/results/Random Forest_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Random Forest_confusion_matrix.png b/Cheese Classification/results/Random Forest_confusion_matrix.png
new file mode 100644
index 000000000..34c01785f
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diff --git a/Cheese Classification/results/Support Vector Machine_classification_report.csv b/Cheese Classification/results/Support Vector Machine_classification_report.csv
new file mode 100644
index 000000000..a0474f985
--- /dev/null
+++ b/Cheese Classification/results/Support Vector Machine_classification_report.csv	
@@ -0,0 +1,6 @@
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diff --git a/Cheese Classification/results/Support Vector Machine_confusion_matrix.csv b/Cheese Classification/results/Support Vector Machine_confusion_matrix.csv
new file mode 100644
index 000000000..b88e226e6
--- /dev/null
+++ b/Cheese Classification/results/Support Vector Machine_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/Support Vector Machine_confusion_matrix.png b/Cheese Classification/results/Support Vector Machine_confusion_matrix.png
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index 000000000..564728875
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diff --git a/Cheese Classification/results/XGBoost_classification_report.csv b/Cheese Classification/results/XGBoost_classification_report.csv
new file mode 100644
index 000000000..e82f529ed
--- /dev/null
+++ b/Cheese Classification/results/XGBoost_classification_report.csv	
@@ -0,0 +1,6 @@
+,precision,recall,f1-score,support
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diff --git a/Cheese Classification/results/XGBoost_confusion_matrix.csv b/Cheese Classification/results/XGBoost_confusion_matrix.csv
new file mode 100644
index 000000000..cd8b93aff
--- /dev/null
+++ b/Cheese Classification/results/XGBoost_confusion_matrix.csv	
@@ -0,0 +1,3 @@
+,Pred_0,Pred_1
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diff --git a/Cheese Classification/results/XGBoost_confusion_matrix.png b/Cheese Classification/results/XGBoost_confusion_matrix.png
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index 000000000..f9d1fa52f
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