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2EUkrZ5k/lmwnLy41E6IIrCMA4r1wRCrCSDcaJo8+LG57f0pYGbi3oik3NJEIWhsUpbO9J2mOs440pAGMoolIakzuSwMP3URIFPlGxGvBWxImmkxo5TPxgUKijC0MVB0OwqS420wNGo/HeCcK6xsgSj9fzeR0cn7/lSH/B1333Lt/zl+UO3bF8VZy0Xz/du2t4XtN3KdEKssPyAmRjEKVsvH8Rntkq7Kid9xbl4UgNBDVvP1u3UIhdm67L57Iei5X3rq+veQKPVbggKGPAYE+vS1KSZPj80IWfNVhAnKmLaKxU1KC+KbKwvDFAFqt1oqMggcF64UDS0g9yMN7KMgWhESUNGRhahIRUJC5Rrt7mlOcokUs2YwZxJmFztpx994ty9azpQO+9c8GYA7YBQslBAHAS5C/O0GBWGZ0UschUFTckaYZBBnGb5+sirsQkFRAnNxQyTMKuOI3tj1u/nYZC3k6ARiW6kCi36BfWG2ll26MALzxf5iY2LvX/zr0a//V+GzcW7/sYPHn79O8i7CWY9z5Wj94Ur+RRW+b+SdwkgBzBpV+QqDeirtNfE7EDXkCw2oWkVtDkAzmUNBNdX8B//8R//JmVarIzeB/+l+crvRXuPtGzBfDEc5Vv9ogApAhFJEQYiENiKBHI2HhdbW9ogSo4iCJTgIcdYevRukBb9lBzIIFJBIAXzivskUExAOs4GWlsUElUgueBclt9CCLweDC9d2kKlYq5//XT6gfTAnFu/91S/5/mtC6rnnB4bBs44G6KLpVBRBMgzw/Lc5KYgQOVNU7EoViCjzPhxZgrjyetEQCOQrYRBoDTicAxpP/fGSE5N6WKOK8tLJ7Z6x09eeSJZ+Xxj13g08hefzu/9vQd/7eedyaxQC7v301elbZ5P4pExtnH6v1888XDY4d65aYt4vKoELUEcYKLAXaMeiabnjHjNP2RT3z30Jjn34hoLatj6c1OtysEkzz5633t+TO29VZFl6EQok1iEISucHo38ZuqAqaQVSimYoqbkIac8c5vjIssIGA9bIWMYSJFIkJyMLraGeWERhYzCCLjnRLESPELvbFrkqdbEg1AGJIVCFxSFQB9Yf3l9/ScH+wdqIZtfaFN25tzGU6k/1FXdSGgMQUM/1R4s8zYKfRJKFMozPkxNpp0njc6G0icRShVZi6nBwcjobAzORdK3hYzjAAIxNDhIfTpK280g88WxM2vHxNyZ7t5WwC+o1im5fImFPhukn//k5S99Mj1/PIji9t7DU88Eli/h+WO4Z4ytnfzvF048knS53y45wCmxEojGQKzKj8lh7AS5EL+6nJ2QmbHv3vBm2b2rxoIatv78SS1USq1+4Kfc6qksCEeDfKwxYFyqgEdhwFzMXQS+lxfDntGOGOfSoYi4lF6V3AvGGWz1CkIhhRBxIDhJzkMpmNPjUT4YgwcpVCAVMeSKZCg5FyI16SgtvMEwiEH4RjOGbPh/XQzvSW5lLCWAtLUnDNnowsX7Vkc72+FSCDyQnWZjlPlh7tPCkvMR+VhSmCiOKtWYa0i19t4GDBohJIligmsMejllQ+t1HhBFHLpJwENsNNt9jfc/dW4tmV89/KIlRh3FFmKZhGogw5Ny4Uy81B9lg/u/eP5TH77wyV9ttjq81Q2b3W0O4See1ec6bK2f/NiVpx+N24y8u+YwsXzpHtA4bCiIQ3B0DROlr3acYsW25o68TbRvq7Gghq1nAbY2Pvm+/kd+Ru49NIc+5OiQ+ikNCoOeQi6kUl5CO8RQSu38aJANc43IFOfcgxA+bvBYMJPT1jAbWeAiCKJYBFwKH5XSkmxhB2kxtEyiEoozjhwg4ZzHHJxZXVvdMNSKohNrGz+BrwDJmQBgAsnmyYJuzsX98188PcgEv6njBqlZbKpGrCzB2NA4N9oYDl4J3whFEIYEMjNYZEVeOOmdQtsIoa1CCKOUsJ+7ItV6PGwHfGGh8cDx8+d0qF/y2hbHSDAWhFwwJflcIOaVcIG6EnaeiuZPpUxvrZ76fz9y8dMfWDtz0ku1sO/wdp2Qn/REfO7C1uXjv3nx1OONDnPewTSldbXCx1J5h4gDKKnYMxtv4bVfMLQZdY98m2jdXGNBnZL/88lDxlw2Pv2bv6xlZ95LRJIBdAJsxEZbPkrzwQhjLpN2EAQChW5zhEazyItRP+uTi1TUmGtJ7hzZJjeJD7Tzw/XBJvBGO0jiQAVcei2ZSwA0+dGg32MsjJNWpBi6gCxAgQBBKLaeeuRnzQuhs4ubVU8xME1CoM5Nc+HCkdfsOPvlzzy2tjaae+fBhpDjwLN2KCLFNIV5CqvjLPQ2jLTiIpI8UuhtI0vNamq41yFniTRNzluRhFazl2tbaB1Ef/jIyYsa4FVviiXn6EoW6L13wjowthDGxtYsazvuRGudxue2FhG2jmxt7P61963f+9v3JfNv+Ge/sHzzS61+7s/UMrZEK0/kJzXRpQakCqpLaHLXFE1PSxG3Ie0aPz34ioDJTg0ENWz9eTPxADD8wq/Hq6fjnYfTzfVNa6IkbscNGSie6qgT5rnPDa31U0LZDbHJAuK5Rdudb1htM2dXe6m02GwEYSPgpuDgYhVa7caZXu1ryVijIeNGCIxEjqrJjKfCDtc3hWei0wiSRkepCEajT+at3198BVDqZYhExAN0OXCOuiATXj748vnGE8fOnP7ZUf7/v3VuhwrykekKxhXMxczyWFszHpu+M6EwsaRIqbl24CHIi2bq81GeRzSOMxHy0UIz7t6w+IePnn/q0jD81u9MmlJp40NVFQGTJ19isEXnvLEgbJ4Uti347qbaWuyc3Jh/EDeOXh7Prz/+lZe/ZuWm6JZ3fP+L/vr3x4srz8UW6VPC5ExJpFyVfcfKtyUrokkEjFNQTXSlayp7aFZHPbFHTFwRk0940KyB4PqKb66axInG0ZsXL/3kdzDVAdIicGmOWUaZTyUX3bARqdB7bWCMVubejzXY1EhB7VaoSmZClqGx3ueYpoUmiJphOw4UgnOFI3TWa4eDnJy3cRI2I6kE96S9tpahd5Stp+vaH9izbC4+8Z39F59cup3ZdU8MiJjzQI68BWtAF2AseYj75+dPPWQB/uoLlt5wsLO5XiQcJccgQBVyAuEBTMFzXXByceCiQIQoIRQEYkh+PBxxbffvXji31f/KyVX/ijeqvQfkeIhCIiu3GCewvnxW751z3llvnLPWGmMKbby2nnzf+S8/ceJH/9G76dRXjv/OB9c/81R7J/zYAxeDOH6OHTJOVgjn/J5f/+uP3/fxHXu5cxoZMFYSLYbEEBgHC7CkYCEC4/9IQc/VtQ8IbLzhb/6uP8D2nTUW1Gzr61yTiCiEOPfBf3v53OnG/tu7IiaRJYopxucgHLliYzyg8SAMVSnoQpGgaQooGmyYufWBEeSSRtSImRTOxF6EylvKtb68qlUkYhXFChjT3FEcojVqmBXr44wzkXSDJEyUL5gxhOZgJxydOfahXvPk/peBWQcRTbqfeG7BcfQcUABjwHNWFGlrV3pTa9fZBz786Or5gf/2w4lG0sTHmeepjkMdCUgC1YnDsfOF9YPMR2Ycj7mKZDdgnVaj055/8uKlh4+dC1/9RrbvoBiPhAoYYpWlQgLi3jPiJWxycsJz55xjgZBKWh2W+LWTWC/Gl932gp1v+VbzQ//0yY+9X9qxVBLgOdILmqDEbslw+7RhYCH34AAsImPkGXAEZOiRSvKF13R9uMZ9uj0Ttvq6+iEPKOIaCGrY+rqzWoQMzckvDj79voWjLyStN01mR9SMoijyyFUj91FbuaIYmPxymqtIdFSiAibJdCSQYrlh48Jsphhz1mkIHpQgJZlsxEKbvD8a9LZs2Iw7sZLoCW1HIrgw85T2ik1v2wlvRGGyJCLyZ071f2XxrwCniKQpsQNLReIZMV+yLcaQATJOyLHICVoXDty9dOkr9568eGGY/p07lloBxEloMhhqPcp0JHIVFLGSrSDMm8IUmA7teKxFz+zdG2xkm4+dOEe3vYTfeCsfbAnJOWcMGGG5vZCQeLXpvMfyMeSCece98NbxwElrFTLemetsrl1e3rmH6/zmN30b53zWjBCva7TyVOERwAyzaF3DihK7exe/vEXD/Zw7IzgIR5aBAxSIyIgckJwi1LQUcdsrT1OjKW2LRCFdjQQ1bH197L+8aXI89d5/0V7az4CJSIbcaOvT3niUMpXYSMqAWc7ZYtGwMRuT2RiOHdgkVI1ASnRhCIFCp2Gk3eW+5sCbIWtGAYJmXAYhQg4D4y/3hpxYM5GxavoSmMYxlzmQ62dnLvXay4udwelfwZsH3RsCcyEX4fSeTQyYZ96DQwdIJW5xKJUJZzz3hVvdfWcrbq2eefKff+bM99y288XL3hpYiCLP4n6R55kfF7qBeSNUgRKd+TAvgkAll9P8gSeeoEO3Ji+6Wwx6XIgStCqLEZv4kHDqN6ouUNXtzpNHJMa54mS8817KoNOKZbWzjTHXtXu+mg0HbJZB51j1e5D841fyJ1L7pSvZx548d/jAgbs2j3TPPdBymdgDos0kpxBJCjKsOlOcliZCZWub2OirPOFMHk4wy1e0SzX3pvXUnjq39fUF53zjE//pyZ/+YX7gzm4iGlGAeW6Z80raDGyqe1pzLtuJiKV0DCVyh6SzfFik48LEMmp3I0lILidH5DHzflAQaduIVNKIBBWOiBiCo6LwaV5oj3Ez7IQKEZgpzGgEUjHr7n3yxPce/YcgObeFY1jugXIfeQ8l2Sr/9IasJe/Qe7SajEZdgM68ZWK8tnL+QZ3Z1xyaf9O+2BXYQpjrxjmp1GuTFWh9wl1EdnHnXNDp/u5nH+01u523v5NlmQDPuOB80isQr03oTHcdTT8HIlcN+Zg8ImVw7syJd3z337/hlpdcv2l4SyBmL7rkTAyjKPy1s/mvruYbGo8PitHWmBcFBQWN01IWbj19y30///LV+/YvAd8HQUsm3AS8mp/PYSWCBQl6OjmRpqrwGu9W+R3HHLkbvvNRFu6ejA2uEaGGra8xz1oMjv3o67jgTLYzTuORDYmHoY94CVCmGlpgCjcunGOURFEjVALBaE3EyPu80H2TI2GroaJYCQ3WVdOGGfU1ZJlTgUgENiJ0jHz5l9AYyg2NsyIIw04UCp/PdVrn7r/3B+hl9x95K88uehZO79TTcycP5MGX0Mi8I2e9NegsWAumKJGrSL0lcLTrwn3Z+tatu5J33byEhQ2QukLEEQMVA2ODYR46bMzHT558+go14re+S3ovwLJSHE5Sy+yrBlpTZQ0hotmJGPrJhasohAzVpbMn3vyOv3vktpde16eHUuKWwYdzPJ7zXzzjjqcerANvlPOemzYZItsvipJzDi46L4G14PT9tzz83lesP7BnCdQB4C3ZCRwyv68B8yEUbnZ6OLuM06ZbpTxEsJyr1sobPhx27yByiHVrxlok/tmxs5p+eOy9P6XX1ppHX8jznEtsLMfjYbY1yLeA4hDaQSl9KGbzDV4doGXrlwcsDOOWDAXzhiLBlWkZZwejtNdPkzhuhg2F3vqsEfo25xnBIHebY5c0gm4YMG4FkeQiTAKeFWsXV12zjemVT+nm/Te+CfI1xyI2OZ3CimkhIYgqA+fAQ3lrRsYBHQB6AhFQycIUo8yHyYVg6ShsPbJuTn/q9N996Z59bXVqczyfyk7cDxSttLtzc/O/d9+xKwPbftdf5UCcNArJGGN8mnVmeFXMTKDTzzbfZO4Mnw6+ZlQpx0BwAHsdL0TBfzON/usaP7lhjg0cZB4Eckkgy1fOPKBj645HjiKhhs6iarJiCPaiP3DTI/t//pEzX775wV969Rcf2rPDbB2AVhc5uxa+cfsc0cPVdjfle4dCyDbUYyxr2PraqFbV6WH42OfHn/513dqfr42bLR7nhkEQByycS5ymQaovDHMVhl2luHIqkoHnoUxsYcebxRCLRpxwRYEscWaexeCpb/Rqf41znkRxHEiPJrI2TrhGXozzC6M84EGjrZKAMWs4c8sLqhkHx7704C/s/e5ywxjuGacJZpXgwCfp3BIuCEuc8M6DYwKQPHlfgodUE3QBzpngQwe7AkoNvufzZ99+pHP3/vbpdbck+JIznUX58FMnV/uD8K3fwcJY5JmQQXWuP9FGNCN3s8TWdt3KlDjgtOnBjD+UgI7onb5Ok1kMYC3T/8fHn7wQ3uCPtgWXgcqdJ+fL+4QDzMqrg5wxw5m1LkKpfeGRgXfMabBb/vDtjx35T4+dfuT2+3/mtfc8mB+lQ11ERuAm7bdmjPmarvI4RS8BU5JVA1cNW39WeeiRCy7E6q/+ZLLYaiWCLAwys5mB7Pfn5kPBkTG50A5yXeSmuDTUwVi1GrwRIkMPgjqNQFNhdD8dI2dho6N44E1hOiIBb3JPo3S8NaI4VO0oFijQZCpWTYappl7fbDDTCaREOd9t9M+e+BDsO7v8IsjWPJPEcZoZnh6h4zTD5KcMCBh5Isa444J8lXmSQblDuGRhDABD57uSLyj2u0/2Ngq6qxMUOZt/wY1PnL147uTZ5tvfxZZ2yOGIh5KxEq6mPABxUhI8mQhPsyZSuL3jqk3IZqf71f9Kuursdcy2EBisnnVrj/DhTe7w7fl8SBlgmjOPnHGiwFPhyFX8l4B7RbFm3JNDH5E3zDlkI3fH3Q+99nseunT22//dvpceIdFC0rTd9WG7nenVdhEEyAQyUaNWDVtfkzxkiHjut9+3+fjx+aM3S9I+MnMsLppUbBZXVgsWUKuhFIpAQMhVEnnL2LCfbQx9HMq2RCGAc6GYMBHYPOuta+AibgYiRF5gaFFFkfM0KvLLeS552ExUIhRaHQdFJMF7maX5eg6S2OmLG+898P2gC14i0WyxI6uW/OzwCSssc75EMU9InqMgRl54AGJEIASxwKp4jgOLhCPMgQ7M86cu9nWPvfHg/GZv7fz58/DK17Pd+8Wwz1SpDXEaMPsgIDblUuXzTC8Xm+jD8r8A16RsGJZ729nruKaHwLO4DRGZCyf8mROw5wa46cU0F7ocYJwz8hIC630p0sEbdE5CAMLyUCF3KjJceZQszfDKGbe075HRihteZBy9p2qWyOQt3IbIGevywEDUo3Zr2PrauBYg2s0Lww++m+1Yubw5anAIQgqbKMnIOZGYIE1Nb73wPEsi1YhRVJyk202s1trY1ZELhIiaLGaSuUJIxZW32oy3bD8MIg5hCCEKa1yXRQ649nprONxk424chmHIvY+cwSjYsaN1+cF7fz66HRaPiNFlp2TVipxVEMKqJT87iqJq7H4JVLYUFwy0IE6ceVEKRV7Vw4nW3PBKEpSULCqlDUrBdjWDTsiMgNNPPIl3viq89UVi0BOyxCzOpk+1feoPwKzRDB0yTg5BTE7YqhmAWCXjJ4yhOiOjmT3DuuvYfkRUTTxEgqQNVtO5J+HCCdx9FG6+iy2FOAJb5OCZ9OCAWRhD0tEiJs8yYyEbU75J2QiMKa+mVenGluEgJnz1ai5rRrhmLW5wMlWxzsTXsPW13WIBNx/54vqlCzu6Wbxn/1bGr2wN5KbuNKOoGRAZpTAIhctpnPvzoyIRYbvNQ+kJmWCy0xW60BubZt25RjNoKyZRg+DNBnikIs+3MpQiTpQE5Zl1iuFyW2mLRaY3RmlLBqGAuBnhYHjflvudO98Kac9LTlOSxSYT2ifppVKs4WSu1QSeqmy9E4DoJ0SplLweRHPfqU/dPD7Wn2+V1MDaBKkt2I6mWAgVbK5mB24J73i5HPa4kMiZmJi0qpTWpKSuOh7MF3fsgqDT6/ebge1trlXDlSuImkjISXKrOoGd7EgO/LoWiZ4mzhJPzpJHTNoEjs4+iheeov1H4abbYS6kAqwDMMCMhZGDbJ3GKbiMwDLgikknAwoEyMhYJzjM/KR4beMHekY/QSDG6wPEGra+JolY6qCdr/trG3Ju897fufCH/3VHt3lwoTmmcHNs1y6ljShMQsG4YwxaTZhnzXHhLq3nXgwHRS4t27cyLwUuNJW2PstH51IWyaATCRk4sl5GsulFoYeDMUEaJHEQxRGQ5d41wjAhpym/PEjnudg8+eTPLX8riGZgtjQTJe0paQ2vSj9mzWAmnvXtdnSTJD05IA5clmgiA9A59Ue7zn5FKViJmQpC68GkeYglJoX5Vr7vRnj1G0Xa50IwzjhjlUquvPA4qSCykZTJzjvuue/kp//gN4tCv/71b7jzyPx4uA5cbo94qABrmpnHKfMCb67vxg9+Yg+tLB3kPDAGSYfA0tMP4+ljeOPtbGWf622wwZbXOXcGq+tnhZQkJ6WHvMQ+Zp0FYzgHT7PpYzOsQoSrvWAnby9wRFGn5GvY+trCGnP05a+Bu19v/8HPfOnn/snGsS80Vp/c0VVuaXF1y/TXRg0Zxa0AvNnKMg1+qIsiZ0Imva1eEuuFZhAwDCQpGViiNPNXhmMk0W6qRBAaFyRBmzEsmM7NlZENgmC+1TTGKE4cxcHOHnvu0gf74fEXvQ7zLSP4RB5OQWTiVGSTZNJ0DPtUd2yjBQNyjKo8CuPSCbznprfsPvfQzt7assp3x2LnrpaXCi9d1N02e823htZxFBNb6cSnVZVMT02kkiHrHv63P/v+8yee2Hdw38EdKw9+4d4kefWNyyrXU+vr7BSxKr67WiCMxuvrWSSWNwhAX7V/n8JYqbiRQ3sRsjE8/WVIh4Isw/I+gaIEcQlQEEmYHBhWxyTlRbGMqJTyfjbFZ7sDBF79ckJTkXFkjGokqGHr68hwgbUC7Cv/l3+y0Ruc/r0Pn/n8f1tcO3VweXHL4ObAXrqcqiQoRZBlgotQQHlHTdA4e2kLAgmtMEgCyz02AxFzssYP+r0tiBpx0AYuAu8CCIJgAbi22ZXNIRdhUzVEiE0pvnTiqV/Y/73gDWPeMT7RhrP7cYlRzltvHUjBhSBXolQpD321uVilGBnOLIylZKTO0nl4sX36M665eG60vnhy47VzPm43xm/8qwkBJ8uEYJUs5JN0/zTjIpwZN1YO/Yf/+JHB6rm7X/3q8nnJxdHo+JPnj+zZTcUWYTCxkFXoWf1Fwu08F5nrWySSLRkWksMZpExfnSlIMhKJCGJFpYyclisC6eqqm+ovlAhurVAKnGOTDOQft9auZftkAUpyXY/AqGHra5aKOPV7l6AhVpYX9/3//v769/3wym2v/eXhqT1zjVRjK1GeQCnJAmbA57lJc5MELIm44qA1DAZZj0MrEXFshUPG2FK7aY1LbXohE0qwdsyVRIc2DKXgKEQ00GNMlT3z5EcaR9cX7uRmzQs+lYcMiVAiaiJIHTS70IwhL9xoCyJW/uuTg8RyF3BgFT1wDID5icOdMS+wIfniyrwO95y5uP44H9z9xtdQ3BIm54JXgFW5tK4KEyQyKg6Pnez5Ynzrnbf3ewOtM4YYJ42lHfPGiInimSpWms3QqhJdjggYu75T8hNx6ByQmK2K7TKcyeceoWocVD1uiSyCmKpAP0li+cnIC2uEA87hT6oXoGtyXcgUoqSabtWw9TVRLfJ+4rNELoQQg43V9KkHNx789H9ePrGYtDLjJafyOyBTKvJC51leuT/9aAxzLe65D0MKI+lIZOOsN4ZGIBoNVuKChAaXsWLa+/VUI4qWUhGnUDDGqZvIBoc/fOTSf73xb4EZE2dUKbbJHhHIdEEQhe+6feFAcYE2juNS54u7D37qiXMUC1/VKlfNnchPyBmwclsJIi5KMAoiAJDeSMCdc619L3qZXJhnvSFXosQszhi7Wrwz/aMkca0TTz1NQBtXrgQy2rmye3nXTpON1zf7xkVVBXXFqirj6zXH+SSq1JgxxXWKWIBVVfOk8Tv36LYbNFS+NaQSpUT5M6ZqZ0oTH8hsktjMzUCTPCQ4U6pFCd5V9rbZkwBcMz+x+rokrowhE7MJP3XUsPWnAlYlbbDy1YRheOXxBy5+6jeGTz20ev9nJeCuPYezAANO2sE4t5nW4KxAHympBI3GblzkF9dGCwvNhkQhvPCu01RElGZuY8MRunaTNVSpw0IvIiUKq/NsuGmo0+2EAEtJcuL+z/9i8nJq7BbZRRuoKmk0pVtWU7MZ/eRR+dH/599/4kuf2xkBWHjVK+9afNsPfvDR85hIX26yclNw4CXJKQkYA8+RS7IGeEBMSMYcR+c15COFc6ninOPMpEXXNNmsbI8Eo4xWL60uzM3vPrS/Ozd/7vTpixcvjjfX1zftq1++t8hhu96Qrpkaj0S2Olu017Vva1JdWUrF6hM/yRv6aqohw2kXLU8l25o2c6CpF+Say8Iq9ey9qFpHUJV2n/VJxCo1T888BABEUVVQ17BVw9b/YHVWreJhZjoSQvRPPd5KV+/9j+/ZOvNUvrGqWvNLR2+FMB5kY5sWaeG9KbgAJQVKIRg5zq0hKczRnfEIsTcaDz3Ggey0VOUh8EmCzRgLQ+MRDdKilVTjFJlVDEXE5hrBKDD9HOSV4X1Z+IWjbwO94ZSsPJuMqpSTMyRbzZ/ak777J/716pbpBpDsZNr5X/v0A9899xs33fXtTzx9iTUDb6mq83EVcvmqSQQiE8QMyIA4RwImmESFnKuAC83L55icG17VRlP7FSJlWbH70P6VncuPPfTQ+uXVyxfPqbDRaset+ab1xCYHXzCThjOF6CvygYTT4WPXbW7L0+wYsXqETXmUm7TqLl+m3y7OmQ4+pGdQqQn1QnC2ahlYNResukoQXVOUeJV5cSTPeO1+qGHrT7yXTruCTDCLMYYm7X3mw1tPPX760x/NNi7JZD6eW0pW9njjx0WWbq06xzz5UIKMBEcwQcAMGxcu01pbc3x9kEj+1qOt7mJwaWzXR8XFvm5CmDSDWIIny7mba0fO2nFG/ZFVCjuKBYHkgImnXbvmn/yD3/nlHe+EIAnsqsYAeXlzZ4iuXPfw4/vgp//NL65qnhxeCc9fYQZiJfYu28/87sdufclbnhAC3KRzOW2fKVZoVLm9GAMhHFaFKACCk3NVY8GqhVaV0JqNeJ+5SKu57oxz/vTxE1l/68qVC4s793TmFxaWdi4udS5cHJH1bnawOXVKTttBTA/GsJLb1/NSnNRLsQkeVfWE2xNZiZwFISpZSH9cdn12YcCTYGA0q/II4Gc9thC+emzP5DjFQ92spoatP5ZeuUkn0EmbB+uMH63e/6/+gTn/tF1bQ1M0dq2EN98xyv16XhTnNxhZBhgoLiQEslx7hngObNxzRZYitzzAMADB2H97uvfR04O33LDw7TfseumecMvY81vF2mAkSbabvBElnlnGsNUMmsyazK9vGpnIRuy6Khw/dfwTdsfx5b8EekMLWe2Wcp8IAJf7lx9cOPXpj5+50Ose2T/MC2JIzK9e8T0DnaC9oPsQht6ZyYua2FAnVAum5lRGjFsU6Cwx5gicJ5xg1rYzext+ZtoFARzadDSA5fn9+w/u3L3fez3qj08+dfzxh8+/6Ja3cmCTOTUl7F2DUFjNevCIJVu5jtkWeOvLPxgH57aNtSVmVUmrys37TLSiSUFmKQunxMt6VpJOUxJ5nCq/r/KawqxxDat6OoOQV5OEdTy/YataGM5KFTqHnPM8G28+/NnhI1+68Nu/VqxdCONu1GrEB3YzLrxnx84Pm4HZ02psORh4tBQQ+iAUVtOGtXlWANkAXJSwyrMDmYVuEr10j//ShdG/u+/Sf/zy5b98Q/e7XrD0kuVoROHZod4a5Ftp3krCRPCg2thSuuVFaTwMCyJtLj598hf3/QB4yzh5zqeQQmCAg6AXYe/9X3hQ7Fm21tnUrY2JDKwcfuFL73wJ612StgBKEOwsFV8iUrmxWInONKFTnFtE59ykCY0lYr5SgmxmfkT0FZbNFB86Imbhxptu2bdv55XLF/ubG+fOnc5H2Y5dK62lDmOMvKPKMjHRUjN7fOV2QgfIvCW4bo/EiKblhmDFM1yhU4+ah6u1mLNW8EhXc2JXT3gAtBHlbQjMNQl4esYBQBkOK7bFwxoFatiqjmcqJSgCZYwenT729G/8fH7sweLimWLTdnd12y+4RYSxyc2wMGmWZdqHetBNlFK4N2kB+mxkTqfs2OWBM7YT8UYoGQjGRbm3PSuM76fZ+kiTg5u70a7En87crz+5+atPbN5xeM8/OBK9dqfgncb5Ai72s+G4iKXqJHEgUQruvd63vDR88sEPNW9LF29l+SXPZYU8leMTkTwGiYLLx9ev9KFJQ72+c2X+rttef+Otd6bp4PHHHnvq3vvfsvcwNHdRP2NClkSqBK2JfwonhYWTLWWBIxFHxgRaXcnHSZ305BCQaNo2dcaR0AMX4Kw5e+LEmTOnm61Ws9men1/ctXc3yE1T6GBSWwTT9g+etidmQeUPR1YqYLxOkYu8R+8Aq/NCqpYQ206iT8ReBTNTmjnzdc1gbNtFSlMPIHBevTcz8Ju1j8erR5SVoQJEDVvPY9giq1XS0kUuleKcX/zKF4ZPPHz5k+9Pjz3CpAgD0dm9V74gMd6mGaUbfZt5Ri4QPFLgmJAiLLR3VguOScRuS+hQJzk/cJcHvqdNIxLoWVGYtDDgHDDXjqwH7gkLsrsIOjua4+Hwy2vme/rNlXD8fYfGf3138uql6AL5iwN3ob/Z4GGrwcJEsnTwB0+vf+COH4a8V2Xh2SQh5avCaGf9gQifvv8EgHnVS246unsHMv/UUxceeuA+InP61DlDnowWrMo1Vb21ACxUdMvhzDVfwZNj3DsvECQH6y2xyi4xbaMykXqTI/yJb7QUPEKyxx/5yo7FbrvdWVrZsbiwfHl19fTTx778wNMvu+O7qmIU2taFrGpwyibbmRxH4lI6ayc55quHlNdRSr5qZCo8WqyoJSFt92YoscuQ26aTOMUsujYhP0tZGSMq6chwG9C2e5rOOjNPLo8Hzhs1CjzPYIsmJb0opXSMAfnx+qWNe35r9XMfM2fPjE+cVgthd9dKkHRdBDr3/X6epxnPfRD6KEKOigUizTQQcCZK8oElcSmsN4w1hbxjORou2ksD98C5Xp67tmKNBCXgqGA5l5IjgCYkZI4RdGJxK/W+Mr/7YqHe/aR999P03Su979+HL12I0k7znHEXN0dhxoerx39j71+BxhIrznsWIPKqxQOfthpngEX+4iN7lLzbGsqdu+dT92ZZvrg0v7Q834rjW175sn0ry25UAJPVHvIlghBnpU6z09pC9BUOCk/l3vPe+ylZuGa2KOG1NSdVs0FC7w7ceMOePStk9Hg0fuyRh1avrB+6Yf+OXbs8ETk/dX5PwI98lbUGW822IYJ8OBJSVnzX4/WWqZlcR3DW+Yq8Ml4Srgm34oyqPv4lyXT0jDTVM/IS01J38oZX0O7cREFfc3Ays0tMinu8AyGTGgWeZ7BVWUSJKN1cvfTZj2098Acbn/0EQxSmSHaudP7SXSS4NXZzaEa9FK0LmG0GQrSEkIK4MJb3xtob3Qg5yqkrgqoJNUCQFpQ5HQDd2IR9R7obuX9kdbg5tsQ4aRrkPvVeICWiOux2mGEU52mYrufNZRVigep9G433Xbav6Ix/aL958wrecnj+/KXL778QfXHvm8Fc9iJAEJNSnpknmwGnDGFtY/13fuez7W7jjrtuHw634kAeumHf4RsP796z55EH7j99/OnOoX1bw7SEvKqjjWdVD06aliyWW1CiR07OsspToa2jiYqk6RCeil5VvqFZsdwExVqN+MqFCxurl8izheWl/ftbBw4dZMGatZpxdJP+EBXT2O7YWcIncgDK06EIo3F/EwiYELidAkLEb+oJr9Uv6ogqe2iF8VXD/soVR7PJ97OD26tutcnV99PmZOV3JZRvH5iCsYmEnJ1Fbk8OmRlIqregahkrWjUKPF9gq5qo6ZVSZz/78fOf+ECQrm/d/1lXuPbOXdF8F2NhAXtDPx4MvXWKQTfwQoIIYs4xRT7ScjDIyGrnXBM1ixql9BNisq4mJkIU5aZOc6M1lwHfmQQrNy5t5O7zJ9bOjlPHfCik5JwX5R3ZAKSeiPG96frxxk4NwJiFlvS+fY/p3POo7h4fvfvI+KYzJ375hr8NknHnPUaAkzw5r06tKsSxTAP3GtAXcby4vGPpVd/yyiCIrKWTJ84+9tDDl86ev/HAgUBw8I5AlXf/KlEsqjmjExVIEyDlAjAtZQjjzvlqdN92x6eKehFe695i5S/BTj59MuQ8jBt79x7oLLZPnzh19szZL33+4cN7395pM9LVkWXVSrWkqFVpgTVGeFcA06awuUbOrLZ+Vp84zbQxnKV2gJcqcrstPX4zJOMnVlCyBNYwBb7KllemGUIm0FeuNJrK6ll2z8tq8BqfDA7DyidBLkTItal8J2hm//Y1LpVJyXb1FnggcDJaoLq053kCW4Jzw9gXfvJ7ss/9NgtiStTum2/JFfeA6cCM1sZ5qgW5WDoZJmE16M4IVnjYSrUrUu4oFgAJZ4YAJC93uKzm/03qVqrEdqm0SEQCCZ2FAViG0Ob4d16+597T6/edWls17ErhjSbnyhtvwqBQckcxOpf3srgD4KsWvlqE3MTxlo3+3uae8Ja35GEMukc8BHCVNV3S7HYuELSEgYXmXPuVd79sz+EbFhbmHzxz4eK5Jz3YG4/eaHS2vHNu186ud+7qzq9SxW72adVBptyDXgZoJ+U4YJwFO4Gq7aQ5wrXVcUQMyLli1+793U4khFi/sra+fun0ybOHbji0d98+FYXG9ghkZfGaAl5h7Pr65mK3zRCYc8Za6+2k+mdaAgTT7s3kJnK+Mms4T9f0o590z5kWYxP9xc9YnJysZtqUms051GzSgcxXhtMJeRWemHXkfUjOTH7T8haDsqq/nhjgHFUVoSVx04KX/NU5tu2YmB4qEl41OpCPF306uNxwjpxBXhdUP6dhi7zznp/7/Q899FsfefWL77I8ABmM0+HahS1XeGldKKgTcAxCLgVjogAxzEzazwi1IuyEwnPgTFrvhzprhKraMw6rNnyV5iLmPFVlNtP+lKWY4954UOLMuc2PnB7IsPEtXXNmWDywQVeItGMNxTh5Q+Gu9NKJcB6wABIEgSFC5kVgXZLn87sg20LknlVD1yueBZOjwJIulZt8rPPl3SvD+x/64j1fOnxw3/lTp/LxKO60lldW2s1mrrUuvEBTdXqfbATCqQB0E7E36YdqgVvro1nG3c2ywZ4Ir+ZYKuMpTRPLzlOzGfY3t3q98mPnzr179+294ciNQXzZGCMCrqdpGfDOI6LzPmCCI7Nc8nZotzZcUYAQ5CtvalWPN+Ess2dj14wJnJblUfkTbjZdHibllhV+YWXAFd9QooXeC86llFBkxXhYcWyHnk8qPivnlq9yVsSIPJBGYtOGQSXnLKaV5cCmTWk8Fwy8Ddi0neIzUvZXj1m3XRCCq+4fmyqr47kFW0RcBATw5Ad/aW5ucZNxntvxYEDWtjgaVkRtJYKAc5EJkeYi64+MGYQcWiEiY4GUngtCNzIiy0aNAJiYVhZPOrLMthSWRKVKyVYdkcoVGSou0vSjpy//nDoKhj6cD78zGr11nxnnxRd68PCG7xeUKLnbDs8WW1o1kByRnbTcNcAwL9ANEQWhr/YEp+25EpUa4eUO8cDCS2tnnvjyQwXwwwf3dxcX5o8e3rl7T6ORnHjs8VMnzqYHd88dee0lN0WQyc7gJerNMMERMO+ltFqXL4NVRGFimIRrpjDMTu4rczdZT7EKzp09O+xtHTp0aM++/d1u99yZM6uXLzz68JMHV3ag0s4Jtt1i2HslIFxuGQyEMelD9+dzS/D/sfcnwJLd1Zkgfn7bXXPPfPv+at+0CySQEEggFiHaDW7R0G3cxoD/uMP/Dntm7J6ZCOzpxWO73dhu29gYG9p4ZccYJASSECCE1qpSlUq1vaq377nn3e9vmch7M997ZTsmYhqPYyAqQyE9ZeSrl/Xyd7/7nXO+833FAbG1klzo3QoIdgkh6hOXnWZ9P0Cjn7bYM0rttfOREKpnD9MjK0kXECcTjOTn/4/zsv6WYBeuKPXDEAA6+aIRtx1hAsmpxL+GKA5YCSCpGDWBGplmVfbFbqofbKFSER3vMiyAOMIk4V9yz+rmjjNzzyaoJ51AWIe+Y+X1x48sbEkhCKWL3/oSam19eT3+8XzjYCkLjGiM6LoRhRai2InBdbwwcEwFpoEyrHtTSzwZNI/zpsu5LzCJcRxiU8MksVtH6Q5a0mPtNSSSMEKEu9eUUBpFhpDPXVn71eINWi5f4d6Sb/1Xp2h63jtQ5x3D7l159/kmfqapWoAHvc0VPU9lEGMKiqj+dQLEVLGnEEl2R9BuhkT3kpVJF5gCD4HiieHBwuS+E7fcUNuurS0un3z2ucmZma2NjVhFtkHt5CpCpHu5IFAMgCcK7j0+NEgxI/KlUhIDkUJIKXFit3JtU2d3D5iACr1gdHyMTU1Ztl7d2PZ858IrFw4dPxaGyvNjmQHgUdLNF4jqQje55GpjHa0sdhYuD2oAr7je8pWh2X1rc5c109yNJFNop5HWv9hl32U97Rn17xXJ/UH1vajVzhChv90MQiSzPJRo+nfC0XqvxWkn/B9GKoX2/LYZY5gxqVT761+vfenLlbWl/I/9i/VQ4qiFRKAMS+oZ1KWJvJ8orWSiMekWuLhnx7UXt2Qyh8Rd5EIgAkb6bmj9ynBPX3ZXytW9eTFtb5jI9cePIGx1L0JKY6na3/vy5eWVLy+oV4/mbp4aAD9SEjte7Pmy7daYkjY18hQThpimc0Y4J44vHD8QSBAsiiYLlcAEdzGrNyaiCAmlBE7PmEzOYPdkCpUEC1tEv3rh8v9RuaU1cWA8aHCRnbSFzMT12PyMW/iM590D7XePtF9bdi65mUeX2itNJ85kMEmDwHYaSbLntpx2c1Kc7HmNp77ISAlsVcr3vPH1y/XOhZdf3lhebdU6AY+YRgeHhicPZA/ODl8VERCa0BnV30ZMtRDJzyDptZZQSAWAZbdMTa633Vpxj2/BzkhegjANs7q61sBQazoDg0OUGkyGdx82ylY9cCMFSGk6YiYPfTR/Pl6+qjU2BeBKsYCt3EzF/ev799/33588ctc91auXENPRLre7dmIJGO0k86dS/90aKtlVUj0P4503udfRdQegkzz//mIzKCFwz75aQe+DVQp43C2cDaM3xrEsSWn46KP8b/7G/+rDUXWrEwSbhLBvPP6/Euv3b72rPX4YeEjCQFo5RDQsuey+RYJ2FzB7GCNhF796ywLp4lQY0W612yXVfWeHPsNEOzCelvXdAmDvC64/fgRhCyHMGFs++dT2d7/+vCqbZPtvz2++ZiyrQG95gUBhjuKK4jRjY40SRiOduA51W66MPYpUxgSGAYgWII0EbYkZwpCUiIn8CKWrx30zqW4ll141PG/Y1fn5X7f3nz944/6wGVl5ImMphDKhEobYjDsB/baT+XZQOBx13pPzfmF//Ppo7U/c0bWgCBbrvnOiJ20cnBrHwK6gZ2fa1r01J0uJwuPxqWefX6l3Dhw5VKvXs6a5b3r/oWPHcvnC+fMXnnriavbf3A0qdefbbfZCf7Wwf/nj5KLpsgDFFZepOFSmugel+ibnvQUVxZU0NVpdX12rtsvlfNCql4as6ZtK09MFXa+0PF9QGyhF9bq/+KK+sRJ7Yc7WeL6c11mINd8NNyI0um/ipX/7eu1XPzPxwEP1hSu0tw2a2t+jJCV2x5axH3CatOn7hqJqb67NzpAvWSFIvSag9wK1h7307FXxDgKntKz77VYGSqVkfBDirS31hS/wp74bf+GLAkAwlrTVkE4pxbgNcL/w3vjcNz577vmPH3+NmD4MIsKaoVgWMQxUKGWjPXqJ/noA6u8m7hbESsSYpYVu/2+bAhva7UX2vl0qjLTrKPCjXyTqut789hdfrvHnneCwIZ81i0+08AM5RxR1pjQCjGaNMI59pfxmFIYhwpDRNGx360gETChwYhRLYArbFDBFOLFe6NL1pD3dZwUpAICS3GZ2vL72CV765m2vm+Ee1/XktTgRVktFKde4oemzRuhF5EKY/WUnKJDCz8q5365cbUvt97zx02o89aEjGACnfK5vFtMXBPXC6hFRuu5I7HU6RKOjYyOHjh72Xbfdal+5eH5p7upWtV7GUEwjXnEXj5I3o/rWfWqn8wRAOZdYKYpQzEXSPidxDw3SblKq1pV9riWjSEpJovrq0WOlwv7JYqGEKW4F0uOsi4AbK9HceaNdt5XgxCyNDvhRjAVaq4UZg2ezmckyiYRgOf25n3+3u7l0w/v/5635eSQjxSja2WjpN7l2QmR7zqFdAEHqGkXE3i7czp5Nb0kQwU4TX6WDvV7w2Z6tZW1gIAaIv/Nd/dOfhu8/jV95RQEI0v0QECEgpVCKSJkO8EyAVYQlqPe6jbc++7W/uPDC507cJab2g6GoNCWYqrfOlC5ToT0BrcnQGdLJSOKVLSKKd3xvduaHu7RRob45LMIoWe5JP4rrjx9N2GKMrS9fWfzrTz/PyhA0DAliZv/fEPxj0Uo2Y4ZCDwMZxHKr5mhIMIzLGiIawhpR1AoFdrzAd0PJkK4CTCHkzKJIJ4giiCmoZFyNSGLWLrpFolJIY6bpOH9Z5X9215vHUKxYYguD+nrL5NxjyiTTIl2jsT7JY7DpCh359ONX/4tvPbjfGpPu6SRATCkhRXpiuxUE7pkM4LSgS/sn3T84jjmmt73qNsfKl8rlzY31px57gur6jbfc4vmebZs3HZrZgLjLtngSkAzQK2h7d3XVW5fWWBcoJXAECneRi2h4h17137+ENPohCS9z/PjEgdwbTtxsmNlAIRdIzBWNnPD8Ob2+GtRq2XxW2KZpZwhXa5sNxjSkmfsqOtZ1hOJWLExGfE4n77zpxV/5X5auzN3/n//Q295SbkcxTfC0sZbuEQHq2zReEzGf/l77Us0dit3T+Cf8FHoRs2njaKczngJCfygahnR8ov3pT4/+5E/iRFUXJXsUXV6NsZQShCB7RnoYgAPklOIAVYSpkj/f2vzxp77w8cszT9x4Nx+dBRVjO5NORGXCkfqYu8f8r+ctiBSP+pOLXWFcfxlxVy3fv8Hg65PEH1nYSluqCKG5T/3aCsRn2tEIxNuVil20TxvZF9prtwSq2goZDjWCKzoQhrFmUGLEAG2B3FogIdBA5bIaITR0QowJZVgh5MWgUcW40jHhNNEwAO4eQCEZRXYcPbW08Vu3/ljGZDoPgKT2AMlwP7HnBUIUFogRIilwrXvwRTiQL+wfLw+fnfti7U6gCGxfKRukRFyApimZzJ2S+X5P4tTbtkGJKBQjI0ORfPappyfGRizDyOZzYSTGpybGxkcCIVbPniU3BICJAkWSnnYEkUISeLrxm2i2lQRMuVAisZwWUgguiYa4UqRnAZ9gVopeUkolleRUhEWDKi3fFBgjFa0s0NWlcGU5a+k+iKHZSaflU6qtbNUyRnaglGcMl3TTiSMSBxJQlqK2E7p+tN0Rldtvxo9+/GTYmPzIn8UdT2u3mJ1JqjGchmCL5CGljGNJiExskQlFsOP63KcnPQbTD/ZP/rcXm43Rbp7X3q62EoLnAc48+eTjAA8aRimOmRCuUpLzFK1Ef8SXYhZLYCNMnjeT6W8N4bISH92cv/CN+d+cOfHCjXfJXBkhimTYHxsA3nWm2bXc6mIXjynpKXqv8YHdOwHpvVsseZx6DV3Hgh9B2EqLh051Y+uRP3tGFGXoIaRq4/uySHm5zKPmoaNrL5YrFUEYSJTJVNwg9DF1XDfyPYog04UwjBEjutZwXIISp0BABGGS2hbEEFLJsNAwiigIwMAho5PL56/8n4ffEA4OT0TtmNKkU9NTR0Kv9aV6EfNKQRf1FBUkzOba2dJ+BgtoqwolJEiivJaKh8jKQCTQTms2uRbTixIlBoEKk1YcoZV1GcYijivTU/lCzrSzSKm5uYWFpUVV3b75AQRIasleWwSJ7kkqlES997edJRDCpVKCM8awQFxyIWWXGyoc8rD77nHio9+90HhaHRM7izQtbNTw+oo3dyXD3QjwwFgl4LxMzfXNBiWMh8H+sWHMBVfSBtRoNYuFjBPEQsDVamBrej5fKmS7KMQPHQ+f+9KTP37o4H95GDjvXD7JDItSShkzdd20LMM0NKJVKiUecZ44wwghVDL13Bl0JjGQieNOCjBq7zxOon7e/86YoW/V2n2dOTxYB/i8krNCvA7ATlDJSYySWULBRB+2FIDVhS0lAPHkeVsJkrg2vkmJd8+f/VBt6xMj+3CCqxqgQO1ohHfCK7s3IAJKEKKikOG+7qNfKiK0Z+06/dBl4mhLKOl+Uvw6FvxoFomGYZz6zV+pRvK0Iwal3yiU44GySdmogb4zdOTB+ZduodACMxKyEapGyxfSMxGxdV1DGAgG0+aANzuRLWPCCELQPZfdk4lJEvvU5VgKhwqpiEMcDRSya+fm/uvAiYv7T8z69ZiypA2GLIT85PRpCMtEUqBQFwES3EoSXIBrBuOG5kQwElWrehG6bCaxKYnjxFRgJ4cP9yU8qDduTERCnpIF25jcN3PbbbdKiM+cOtu4eLEyMNhuNA2N5UcGbU2BVBSQD6KLWYkTYG8fBWRKo/od4sRzq0uthMFIEMhIRO2Ow3RmU5w2thTVlG6ImMP2qn/1kl7bCsOoXBngRB/Ute1GhyGy6NWGi3lDx5ZOPN/hWCcM+0ArTD+/umkaJrIy06NZnQoUhqELCoRFqT98uHj10uWff/DtX3kJjhz3li86sWg2mwqgUa/7flAoFp548vGxsQnLsjSNZjJZwzCppjHGaM/0XnWhTCoBvMtEVUKDZRL5TFOZxI4VQ69rpnoFKKiYWwAlgAWARYDjAK8GyAP4AEHymyYJQsm+PExPOA8DMEHp3WdkqOASQseU0tqb0NhCtFt3R0rhRC4j+nnTpF8l4sRePuZhl5T3Zo29f/ZWhqq32Nm9WWBm/dMvBlx//NOxrebW+ta3vvSUKsRRSDnUx2ZMQkjGyBGynMk/Nnn8SPVcw+4eVozUcI6FLaJnMsBoiLAnwK2HcRxgFAsAzaQYMCZpYkGi9ExM1hkCQQE4sYt5d37xj/TRb9342hmvDpQiTBlGkotFiFkhZxJa6/i5kFsajRKFVyp3T2N0OKMsm0EYZomY5y0HhlF6O5UxUnFCJLpwKft506ivvEwfbYnvvfXm9itL5146rRt6c6suEZimaZlWoVKgzZb0AsDMl13MUjItD5MvpGBCgAwjxQBb3bKUMixiAyLJebXeTNwZOFZcVwRLEKalKJGtjrhyUSxfFZ2mThnK5YYrepuD4tFcvVHK6pTgE6VKO3CI1NoN3yrmlBBBqLygXUVsdmJMdZlX5MWe5wjdpFgpHomluo8RYbNHx72lp//5vv2//a3c/mP8zDNTk5OU0pjzyYmJp556Ko7lDTec2NzYAIS2NjbcwLdMi3PeZWWMZTIZQpBhWIZpMmowhinuFo1KduvfmPMkTmhHliWRVECoEumYofu7jgHyCGIFLwFcALgT4GDSgA8SzEoyRIAnX2QShIlAtQHWALzkswgTUGt3P7uoC0qcY9Zbu8dpHz25V6TbQIn7KwYRI5LkW3OU7m71tfHpjLS31ygVolRP97KUUgmV4/J6FMaPCGwlXVhN0y5++Q/Xa9GpwBwS9WZ5yB8YyFFMLBMZeol7395/07uWTk+WoEMZ1xRVlFp2W6hm2+d+iCC0MNg2KRBDKWgLSbDAhBJMCEEI4y7v6fIWJWOwLI3Wtv+0iv7i3jeOcj/J5yIYIU/FXi779oHxwZWt0HHiiZHHonar0cwxxpOdj96AC2MkEc5nbQbK0g91Gi/KoS5Oiah7cAlVMkxi1xOXGUA7YeuqN2qTPrEuv3Lx3Nn58kB+PDOGGDq0/9DBQwfrLefZF086K0t3HLodcEF0S6gez0qSqQMkUaQ0oGXgkY2bw2ET1avG6ISuR488+cKJA1NHZ0ZCoTK5LLUyURio7Q01dwnqm+B7NJ/NVsqK6jLi803PAKAYDg4XIxlRJDfaTcvKSC7MXKHVaAaEmpiWSuW8RdyOn2NQr7mWqUsDNdvcCSKis8HhokaJqeIGjBRx9fK7jpr/7jemH3hvs74ViXh4YPjbT377ueef+dAHP9xsNQYGBgnp/t6OjQ4phKLEsF8KuTC/YFmZRsON40BKSSklhJqGYZq6ruvZXC6bK/hhoBEiE1m9wBgioVCYbCCJtPUUJTqEXAJhTyg4A/A6gME9R6yTULA2KCf5QiWcK50wpkY1oidkT0pVRCjIOFHGp69Bu04aSTXLI0Z6JK43Ltjd/dwx7EIY8aBRp7mjcRwjTJXghFHPc3RdR9fHij/0sJWUCk3Hvfwnv3HGGPDr7gCQrbFJnVJiGtSyhGGYPLxaGvjWxLH31S7R8dEo4NthBG7Qdjs5SomOGNYNQ19yoi+dX/uxA+UbhuymiOsRkYpY3csFUL+RrZnE9IKvX6n93mseKmvYFBwww4RwwWU+90FWmfutP/nG4mVJ6biVf/cHf/Kz+UzQdkkiAkoPZsKjpK+biEI7ginVekl2uLIR6pIQhTUAvydJ6j4l+93mpMubqI2Q4IhSjGFifOLoiaOlgcrGdv3Fl84xgmDlYrjZYTS5m4NA3RcLIuMYQLICIAbxxg31V25sXhqNt7dLVnNhjlAqygO3H5s+vG/ME0QZFgtc9+XTdH0lXt+wiiZnRBsYE4FoxTx2XMy0UUPDjOQ0XPU6lq4FIZRy+XbLiRHy29uFUnkywwAjGsbNupO1te1QVCqFpeVqxFgma4yWypaJvY5DpOE67rStLjbd7UJusFnLZ6yGg8dLE9WtraeeevqDH3j/5uaGECKOooXFxdHR0WarBVJiSnPZ7MbW1uzsTLlc5lGskFJCuWHQbjYoYa1OxwuClbW1c2fO3n7LLZHk+sSYRfRMFBDLJBgMAKJpvJ9jmBTwXdQpJO2tLwAcArgdoA7QSIiV6NWJsLPQnPKetOcU95VxCmMhYpUE+WCULn/15okpcU7yFmOWBPVKUERdK3/YVWwkMxGhUaOACRGcE0rOv3JudW3t3nvvk9dJ1w83bCWkRNf1c5/8tS0fPcdhUPr1TMkfGMxRTE1T07TuySFoMPIfP3Dzm5ZeZg5vtz1dQ1kTGYJoliYxCQnbDmS9E9Sd8Je+u3TzoPXTxwduLRktU9uKIBJcSzqohKEsgpdeufybNzygSsVC2OLUoBhjUE1b+6A18Pyv/s45vzVz5AiRaGFj1f6rL73q//++x9rtfLcY6JtmpY2P5J1lGbWpdiLaOmWPKaEg9JMAUaGA9iSL/b7+7sgf42YQjk2Nv8ouEYDl1Y3zr1ziG8sDkyPh2pK/2Zk4MFu0GVRDkBEiWGJDkgIof6i19Or2Szc3L6vAEdSuKpbL2IxF7sKlTr2pv/1+ZmW9uTljdb6xsGAjFBBZnBkNw0BHtLrZoszACIbKWRtUOwwRlhsOVHLZtheAUnPLtULJNhkdGihSLp2QaxDHFBcz9kYrBITm2q2pySFGlfR8QHFnK7RpWJ27QPP5/3ZJ5u79Fx/4j/9NA769OJfN5TzP+cpX//Z9P/mvo0R+WigWFhcXJicnTdOOQr8LCIxdXVhQUlqZzPr6KkaYIAIEB0EwMDBEKB0ZGynkS3/8Bx+7541vGD1yvF7dlKdfCr/1rcsjI8ZrXxutr6/amc2t7aT1LvoOjN2PhwMYCEwFV/tPpgtBdLerv7fv33sygn4ZL4SkNNW9YtWTovWSRBKf6+4PEz6i6Z6o3OMjj/Ye6l70ERaovzdAkDh3/vw7Hnx74uqorhOuH2bYSuJ2mp6/9vnfOa0XOy23BGp+bEpnrEu1TAMxmijPcSmMFgfHnihMvKezYZSKiIMizNLNdhS1fR64PgVJgN88YlquOrPWeeDyyN1l+YuZzTvGCp6hrcYo5rxgWUvPn/3o5B1Lk/sOhI2YmYQghvF2HN09MLvxqS9eivyp4TG/2mQayRey2y33aMPHhoHEjl4dSYpCHtu2XSoYDgMO9iG/cyp2FaWJW3mXNSRGhLut2gS/VHLxqEiJSKIYkZcvXNGx2jdcCU8+Fygojwzvv+/BCb9dvTznbG9gbVxyTTIL/PqtzRde3zw74q1LgE1l0vwwj/l03qg7DtExirHlb7LzJzdPdejmSgfhYqkQClLO0tXtFkVaELqj5byJuSIIxfGWjMqG7rlQpGhhraqZOoCamSxRARxL12tq2MhR2gECrrrktHIlyyJsajhXb7cZtkACX56PI/+8svQ3/tStP/VL3//whz9wyx1j2H35zEt2vhhF0Z//+V+87W0PBL7baHaKpdLly5czmYxhWGHgU4q0bMZttzEhE1NToedh3VRAMFKtVtu2Tc/vBF40OTr8sT/5o313vubAkWPbjzycvXI1Ozu7+hM/cXh4uMSYO1jOVQba+VwIkAOS7jGqPkhx1f13NulwkR6T6nXl/06UYbpRINLX9Kxd+5QpUeClUt8dZX9vNsADxkAoga9RYyl0rTor+fhJrlQMXZfp+iOPPHzk8NHU8SIJHSZCiOsA8cNaJCKELn78P27UvdOxPSj8ZrboD4/kCVDT0Eyzy/xxkiytkBl5T9105zu/8xf2yHA9UI4X0kg0O67BtIEMBgLEpW0w8wpOFIUeNp6Yve+Jtc27z2//nL19f8UgQ5VLr8z9YW7meydePes3OTMQIQpDAArZ1vTi5hMvnRubGdMYUQRTShzXQUFEueIs7c/2zqRERFMyJtTMZHwFjoI84Yfj9QtsFvHE6QTIntF4L2EhVY7z7pMyoFq43cAvn/QA4so95ePHR48cGiiX64tXTn3j8Zc5vP2me2WmmFs580Bw7pbGxYxwm8jeohVJaYFKHkUEidXadtGwBMOTs8PK52L9ikJYlAYrBNX8CICvLzkjeYtgNFUpBUHoSkmlANDLurbZdFmoqgqGS3lCqM2w22wqy6QcqGlDIFc6ISBpMPPgbDnmIZVyc7s1WjTrqyuu11zTRs37/uWRdzw0NXNA594YDkwe+mGcLw+MjI786Sf/9G1ve+v0zNTmxkapVFxbXVVKmpq5vb2FQBmG0e64tVp1Znqm3W5jjAkhuoZd18vnswYhfhgdPnb8b599umRl3thsz//6r5N9++Chd53nXNZqttPeFFy6gT0GmqbHeyVTfWDayYCO+xou+Hto1VeR9L4leSUhKrW37inF0m0p1GfZSdJtUjTGnBqQhvOznn+8QnumienPkJxodoSE4Jx32g3dNA4dOriHkV2vE3+YYcsN460v/uFpOtCoBzOAFiemNUqxwZhhqS7VAoJpmnhV9r2LQ1PPZ8du3axWhW3pgJkaKxhScm6YXqSaftBsxCoWWs44sFF16qvt2Ynvx0Pf9YJjm2sfWb96RVmfufv+ydjFmqYISewskc9FxbLF2UVzdMS2sp1W03EcI6MfOHb86NCYsk3s1hUiO5w+cQJQPmaBwpUMszBDTN3hr1+AKSllF95Iz9a0rwhPle5pV1eAkiFQr1YNAY687YGjN920+Nx3Lz/65dOZwXx5uMXRmwczs8Hqv68+M7R42rDsDZzpsBwQWUBKhVG94zOQguB9gyXBhalpTrPNMrpFChwTHoYX62FFJ0RnR0dLYRxqFFVbDdPKW0oEEkWes96UJcvWynTSpFWno0vRaEe5ci52I1+R1paDmTZYMnTDNEHUWkHWABIFMwX99NVlb/x45a0/ffstdzAkoV07962vlcoVw8oGUbS1XTUM/ZOf+tOD+2b2zc5ubGyWSqVmva6bxtT4uO/7TGfJyrfY2FwaHR13PS9t8WBMg8AhEjKFgpPJTOzb/8L3nlr76Ec//J5/tUpA/9kPm9l8Z2UJrazP7ptxfb/Lj5mMALa2NilACOIavSfqwU36m+fXVoU74LUXv1SvJY+SxqNIOHFvNUn1rZd3F44SHRyl0CX3aX7irnP/tXmJAivdFsKPOTz88FcffMe7Uhv+VA8h5XXd/A8tbGma9uzv/Xvf1J+vk2EZNHN5d2A4TxE1M8w0BEBijYnT7S5Ncl3Krx6748S3vzB9uBh6kcRU04x60227otN2vY5byBCdkLon1aC9b2XhueGJGQjjsn1l+Kafrk3nx4fHsbQUEoQmFQVmQD2IbaoFV+brF6+I40dGxyfuPDBjZTKSoC984pOvetc7s4dLcctjlPZNwhHiUhlaPqNlNa1gGg0sC25nsrWwNDQNMk6ii6VIfOZIf0FQpnZ6qot5Xqs9fOPtr2XmysLFs17r+SeewgBjBfPVM5UHtfLgUE6uPbXd8J3C5HocDZh6pGQcRnXHQZgUDcY0kjNYs+1lNeJFXjmX7/hBR3EvcDTdPFjMSoYtGm+6rYKV8SJeKRaq1VZDYMVFNp85VCFxHDHM16vuQE7rhCprZlZXHcSozWBgtFBgzPWl8MN6HO3L6c2Ovxiqk465gMceeOtPv/Ytb9+8dNYwNE/xyuDwzL5Z3dClEFPTk3/8iU9kMpnDx4+dOXs2n88vXJ1vtFuzs7Nb21VMsM4Y1cna6vbM9D7GGFcRQpRI6Tq+bmWssTHPcwrzi4uf+NSj58984CMfuTQ6FsvY2NqO1tbXt7ZmDxzw45gQgilhBDdBmaaZgA5BqdnNbl9pF7NYX1yl/l6FuBfCZEK2EhFv34YiXUHsV/c9awdAIDhIQUl/aQeh3XiyXbOtRPVC4rAjNC3z9Pe+NjU1o2maEIJcz9b/oYYtJSXCOBSKv/SdJ9dDN+JF4Isj0xqjyCDMthQjCCOMiEIqDYrnmA167ZOTR86bhbtCz9Wzrh9ETlsEXEfSMJWlmx0FROAwimrMKq6s5Q43m6VSgYtZ5UUlS1KqAYjELUkCEIUxUpjijt+xZmbuZubMbcc8JJqO/4U/+sSr3vC6IyeOz0xMP+FXNYKTKk+lTV8GIiSM2RYKPS/EJmG0WLpjY3Ep5kngPY9oCZAOsQPcgd42s0AyzR8UiOLa2sITf/uNBsBNbzxy977RqRw6MIpVdImXjKovnJjlBgsyCEZttri9ZWlMCRjOFwwNSyKRHzQcPpA1O4GyNLm8WTd1jRI6NVBgwNsR1yRqBGo0U6i1PErQ3GIjW7CKGTBNy4yieqtjadSTMJzPrjY6COj2Vnt0JGvqmmaQMAwbcTDEmAG0w8Uji84FlPfypWK5PObWwoVzT39D1mo1jRmY4pHhYWtrkyA8ODRw8oWTuWzuoXc/tLq8MjE1HfpetVadmp7yPT+WgigUG/rK5UulYqnjOjHnjBATI5Gx44FCgenwzLNDV66Y4xN/hcT7P/nHFFNYWjOxQERbWF0dGR1xW03OhZSQyWWvzl8ZGx3N5nJRwmR3akN0LR7t1IDqWpIF19aVkLbkMQaRSORYondAvfbkjg1QLx4/0ZsmSfpix8U0VbvsyCSSZR4ahmJsavDUK+vLy0vve9+/llJex6wffraVfMAXP/fxzSsvn4JSMW629Kw7MJIjuEu1NE2AIkkujegyLZwstyJdRgTBY8dfc+OprzcGCQmFptOiRQMv8gFjx4983uY8EkAIqhT1wwtzTw/eDdwLMCFSYqR4oqhSSWhDer+kiLlhPHJ0Ypt2nj/94uPff+J97/2pt73jnWMHp0899njQbGgDLOS+gXC6WkaRStAH2gJlI5k3iaVhUSgPyObDjUVH/xegbCPe+oWthx8zDjxnnyDxNkY0MVJWuAt9IsRWA3IjGF53ZPyEvlw6YRpA1usdIExwQnWzrAW1Vh1h2GrxiUqRU8hRGoURDyNGlDKNiiJLzXYBUNVDU+UCEE4oCaPQRSKn0SAkGSTmVxsZW3eFODgzCHEQRhH3Wk1FyrZd91zg9Nx2daCQozqaGs85XhRCLL2gbDE9Qpfb4cl2/DLOhMzMUDKh4ebK8nzVH9kv7jlyTIhoeXlVSLm9teU4rmbqp06dOnXyzJvedN8TTzxh23Yum6nVarOzB3RTg6xCmOiatrqyMjoyVijm/FabSiUso2Fn/UtzlSuX3PXNaGR49F+953c///l73/lOywtrtTXTsiw7s7CwMD0zY2ftKAiFELquV+t1KWUZkXOtFtljkLrXanQvVMlrMevvxEennrAcUvsO6GVKot5NKmnJI7imCCUQxZRCknKE+o5iajcIFtKoMW5YUK1WLz9/8qfe//44Cq5PD3/4YUspRIjEdOvhT5329ZYbjYTR8sR+ZurEYMw0JKOAE+tRhFhqnk5wgnRswGk/c+CG5fPfPazjukGSAAgaUthquk7b54RkdZXJkJaPhF48UN8+1264lm0mNgwIg6Rd6EhGTr1cBsIFymWffuSZhZcu/ssPfjh/dKLAMouPP5sfHTSLRen7FOcC0YUcpEAAMkBhzjlglDVGsN/JME3KSKJCfvCBztpn2gu/tfXZG1ov70POIVl7jh4QSiLClYJuZUqyQDVoVccs9Zq7hoyy0WxX/VBf9p2hfK7jhzRDa9v1GLCpAdP145VSy/UyCjotJ2ubiGqBH4UNrxXxEdOkuj6SQR0/QkLFXDCq6xjVPaE8v4a06cGCoChDUb2xTZmudWtyPfKiuXpHxwhsdOzAKIeACNiqupUcyyXZ/yc3wu8H+iI3qG0VKDKjiPu8o1OioSj2P/fXn3v8scdfe9ddb3rT26b2zfqBV8jlvvfkk3Ym96/f+975+XlKqed558+dL1byzdOOUkrXtFKx4Iehxtjs5LSMZW52n9I0c3PT/fo3NN/lh4449795anrm0x/7vQOFwmShcHFuTtMMS6r5KwuarhOM280WIcTUdR5FYRAcPnhQBzAymR2Tvp3FH7yHdslrwSt9Mf6H6sSkt0UISi3O+u7Xff9VtLNOBElMuIww2TEz6/1316VC9RYYsaKdiL36tqNxFPx/JMTo+uMHg63kI3zl07/ZXF96ytfzym9hzRkdzWKJTUMzDYmgizGpmQBG/ZVWJDDKRtG6Vnx45pbpC0/SfQc6TtTwXAtAl2Fm0OYKe1I6ShqR2A6ljDqzC5fP3HiHEfvde6dIIgaBp1vWKlnWIITUXefW175+2BxwO+2tq/PDt79KP3jAXVhYPPXCYGUkq7G2AkP2TGNaiFYt27GyuXyBxI6MkBNLkKHU6UOW+Kkr/ymHYD0/9pigs7WVSXVhyZpSOBakBHFc6Sy9uXbqdue8iXiL6Y06N6gWCDlZyF9t1ExK1zecwWLWNHSsKJHOdm27lMv6QVzM5dfrLUowRCpnW5M55KqYQrRRDyt5OwwURbyx3eSEZExTHyxP61BvOBrQDS8YymUdN3QUa6+3lc2Gh7KGqWtKbtScsqUj6dw8PXhltf2NjvxehzRYwWC4bAGJAxFDKIWUSnIOCuWz+dxYxvH8r371q9/9zrePHDny5gfesW//wfXN9al9s57nmaY5Njq2vrF+1+vuymazvu9HUaQUbKytN7Y3B8ZGXlxeUEoZ33xsdGtzc32j8pb7zbe+CSN8sDLwna/97Whl8Mcfemh5eXl8fJRRfXt7GzM8OFxpNZoSA8LY9/3FxcXp6elarabGJ0UYqmvkUruQtBe2RJ9q4T1EDO3xupep6BR170m9Jcg9Bs+9VJGUcpGEMitJKchkY2hHtbXrnJ+ucFNw2rgyfvTAvjEe+teB4EekSAzDsPXsN09tB02UG/D89fEpmslhnTLDkIxBotUC1Fu3RQgn7m3dkxkxNuh3njtyy4MXnsrWW4EkJYNSJEyciTHmQDwvdP0wjKVJlBwYPLa2fPngcc501mVIKom76MWRJoUn4KT52oq9WnVj7OBBTNHiwpW4qqbvveXuXBZvVqmsmIoD0jco8yyrEnrv3Lzy2hcfztTqm4RgHJk60jFVFAHghlaZDxWrhR3RiRn8DD/9v6PjIOqvaj7z+tqpG8PNFg+Uba4HtIQN0CEMI6mic2vtSs6IFDsxPei4Po8iUAFXcqBc2K47OqGX16tjhaxBkJUniKOGCjOYhBhPFoylrQYB8IWslHIZBLFSmvTW6uGgZbihzNn2/EqbYioMXpkeGKCo6vrc9yKJTozYQSjWXPt/++bc9+bbw4cmxsYqQ4GLJYgIxUn/MZFGKhn6iGAuIPT9okGGjx7SDfvypStPfuvnDx86YuSyUspWqxWE4Zmzp3Vd1zSt1W5rjFmWgbjMlIrH7rtPuW1y+gw//ZIq5Jr3vM4cHPTDcOO550u54l8uPvriiy984APvf+KJbxiGVSqVMA7c0D1+7Fgcx/l8VgkglF65cmXfgRmdmR3PC7tHKCZpW0olWzl9bII9wCT/Xhtrb28rBZ104xp63UvcT+PpmZuqHaf4xAtfKQk80kji9C9BJg42O6Y2ak9fDbModLdlHF5HgR8F2BJxzHR9+VtfWHnxe8/JAdPveBLC2f2GktQwNNNMLKlSW5H0SKB0C7+3rIpwgUcX8gOPj574sPNKvTKiwjgWBJhotToBIorQASMT68Ljsg0047WnV66eP3Bj2fVBysTPBu0MgCQogpGIYmnoOtCFq3NZo3D8+E11vLlwceH57z5149gYMW+fs+IBwzzY2rr/6nM3Li5qbqdBIbLsrE51ojAQgVDIiSdkFMc6VpYuMxycbOnVqv7Otc8cbl05Qv3VQF02srliyQn8kQzd8jrSI3HgGwX7wETejVWRouXV1cpgHgKBMHAvWmwEJYNwBrdPDze9SCOixX2q2ICVa7qh8r1zfjiUzRDExk0ZCHAin4LyAjaWzaw0QxqGa7VwcihHKDI1HATBZsSHLJIzdEXwo5fr31psnNzwuYIKhnET6SIOpeLJ9rAUsgtbXCoCgcK6wooHBHAUikZjOaqtHrnnHUduOFYuDzYb9SiOisXi1StXLdMeHB4OgwBLqULh6GrFdSc1Vv2zT8PiIoyNWm97W1QqMM89pBuC8+KBA52O+8wLz/7yL/9KtVplLAwC/+rVhaWlxfGxke2NbUypoemlYqHTbtvZXDFf7vK1oaFBgLlsVkIyR0T/cNN9L2Cha4nYXvyKU0RDLNnl2QWsXrHXc8VHfSxUSMRE681ndlZO/06rPz20enYwAX6J/l/w27reLPsnhS2qaVLTFz71a3M0Vw1ZScRrgyMqlycUUdNAmKLEbybxt0zMlHHPzztZ5U9cRBEa8Nrfu+Wud33tfK5CN3jsOJ6OiWGTgmZwxbgAH8K4w1EYdLB2aGHu4swRiSiLBEra/OmxFJBGEXe5XTsORybGigcPfuGLn1xtr4UvBcfvuekN7/xnDFiVq3duXnjX8mJ5eyWU0LAtXinoFFmo+9eLFfIj5ccRwsSgkDERQSgGoutUKiowfSi+9BKiJ0N7bLBgddqxH+SVOLe0NVYsgoFGK2Uqac1zNASdmMxMDM2vbFmm0Wg5edOaGTIkVhmqVuv1imUHkSgaOc/3r65vmZQC1Y4NZ1zgBhb1NmcMLJ36UglPnllqD+QNWsjfVCStdoQ4+HE8YCKD4bVY++JL1Uev1jcDmUEwXQSKUcdRQSwxpB48CtLaUKmQAhUIOQ073FR4cqO2jcJoeGziNa//icrYpB8E1Wp1e3NzbGx8fWXNdb2p6e6TDGOcL7Byaet73z28vsn8MJydgnt+EuUyzsZWcPF8PltYEXU9kRN//BOf+FfvfW8UhYxp09Mztm3OLyzdcOIowazjdLzAU1Ktrq01283BytDCwiKlGAsxbtvV2hZLTAN3DJx7m4nJaZGp4eA1QLYTwrHbYie9k5AoFoCIneHhrpV8T3TXy/JQgog4yfFIFRKwk3Gy69Hc85cFozwO8I+Zf4Ex1g0dlIxjwTlPJvKqV9ruyRO5/vhHhi0RR1Yu/8LH/7OhkxcCrb60rWIR3XbYsAzD1JmuC0LSOIW0RIQ0DbR7S0S4b3EpMC76waVi6amR/feuXFH5YtE0QcmMbXqSS455pEIXE4ksQDifL9Q3D24sXBqaHowiuSe1K4mWUmmHC2zrwguntI1t6fKjN946duehLW9bXz5vbV796VUzu3K+bWbXS0XQmCHjHEICWCSF44swlIYOtk41DVEJIQAhuk1AU2puvfZo1YsNs2zQ2bK5vrlh22an3iqUy7dNTzVFbEvR8jmDuGyYrVgwwc8ub5d1MwjjAxMVxLnjh0xHW41gopTbaLYp0ReWa/m8PVbKIZNlpNx2owJB7SiqZIzNVtAUgofSzBo3TmuR1HWsNjbdgSzV4wiDPL2BXwjp11642AEYozCTBQYQRsAReCEowUGC4ElZqJAkiEchdl0t8pqOu+WAZp0bftXrb77rvmKpsLm19fST3yZUZrN508y0O87q2mrO1jbWVynRSDaTX1lu/tVfZsbG4MixcHoS2zZ1HNjcFHE8PDKmlGJCjo+PfOxjf/jWN7+5VCqtr69jisPQf/nlVcuyDctAosU0o1yqpGTozjvvjKJYCBkEAXc77ShyHAcAuOglg6XHI+1VSQyCgQp3qdC1CNYrGNFO5wsl35+Gf+/OGHftHHYiSEBIhDhOPfrxTqgb7E4b+8+oGDA2/3G5VX270W63R8cHQFGEFNVoWh6jJJytHz15nYL9Y8NWl65rmnfu6d//5sb3lfqJB9/wvnf/m5974elO5BumgRgLiNRQIubrmcT17n6sZxaHZNJVAIaycfj1I7ff+ejLQ2PDXtC9+QUcO74K40AwnM2hLNM832gEDo/s44uXXy6PxlgzEBFpYENSIsrE0cFA0AmcqdkDlbvvHGsdX6qfGj8/d6h5eaaoc81vt0kwNqZhI4dCDCKWmiuEH0ZIgmmonN6FKKGYwERT0SCS9Sh8seGeXKg1YzmuwUTeNoBf3FifKOQFwMEDY1Hb33KD4Qze7MSljNkO+OqWA1Jyig6Xs4IQA7NaYhloUhAhFHR2drFaymU9hY5MlUFjvu+RONyWasimdY9riJ5dqZfyWVtn+SFqKFSLeRYHwhPHimyxFXyvFp0v3eCPFBZOf3/AgAEENkOxVFHye3CEwgiEEDJWCpEYJI595HSMoM07vOOAVoSZ++4+esc9dr40d/r5089VQcQsP2yi0JCB77sYT0wdOlHj1PC2KQ9YNls9edJ+4UV06Oj25DhVii7OM6F8IbLFoohjLuXMzMxnP//Zo0eO3XrLLcsrK/l83jTN7e3tfD43MjbsthyhgPPID4KlxcWJiYnV1WUARCm2rKzAZHJopL1/3xJAlhAl0vz+hE3hLmbBnsoR9rTh4dploHSwKNJ41+6tqC9g2F2OVjtxZ6mLGiiBOddYor8X6Vns/QyF9pozd7/w2h3YSfX4wUALIRR4QWN1yxPRV04uzuwfyhZsY2AgB8okihAtaa3uqF7ldRvof0zYIghzzm/8n373LeU/+N2f+cXZiVHP8yZffuEKCKTrnOLEHQurfkBVmnScaviQ7GmVJUCMSdl3Lw9OniyOPuA7LVp2Ww0BPJMvGF0kAghljIQiIlDGMg4G6rXZ5vbq6NSIilQvhyF5f4pTYDXE5gsDB8bb9dPf2e8u3NK5Us6UI5s4IMEqFglHCHGQUYzcWArBdZAZA3SMgWIOQCTNxoER+1tce9ijG1cXglAcGi1kIDSzlh94NGPfPDHp+k7WpusrW6VyIY/lViSH84Wzi+v5rKnZZtEiOYI3IyeDSSNUA4P5Vt0NgDRdJ0L6/vEBgmhO41t+oMUBUBzHsqiZc1sNXWKfkCOTQwhFRKFWJGMpZnQUcr7F1e/N8XlaXKg133TXjQXZqZ+K2xjiGEKidi5og3YhjAOESkq3QSMfec3I6RKx4ph90zveMnHTXZ7bXly8uvrK1wxDN21TswzK2wJhIVWsQAQ+++RDlVse4ofu14vDzvYWec0dpfe8R33u8/Zv/Y68+Sbyhtc3EdKbLd7pbAsxPjX1la98pdVsP/C2t1+4dEnXNKazZrPZ6XRmZ2ejKDJtW0ppmtb8/PzMzKxpmkEQpqKqVrt1df7CDcf2Vet1lHgBhYmaivet41NL7dRzRvw9f4YdGcROkZhMEhEkwuZ0VpjaNqidZlaPVfVUFhQ4oQnS9SeJqGemhuSeMo1ziPxO8kfhHxCzIPHmX7m6DAh/5duXkZAHbpyunXrFW1nKnDhhjFTGZ82ikRc+RUIB1tP9IbUzAL3++AFhC1PKXdccnfiZj/wGALTbbUJIYWR8+dKZfXoxULGGiUqyc9ButFb32FCpOErTJJI0K1BESlPyb9z0utd85zNyGFt5jLhuYaWIHvmxE6FOJEOITZ3us8shhtuWri6OTUZJCyTNifARqpr5mOmzne27r750YusM6SxqdikqjPgUCCUZgoiUIceBACcMCSiTCjuvCU4EA6E0ncti5Ag/OCP1x/TR70jDdVsla/QXR50YKZatCKFmK3kaO61WJ6NDtdaenhzeWK1jU2+1Om0mj0+PODwyoziOxZYXDWb0pheXibp0ecM0LaLRwXKpbNFOK9AstVz3hnJGO+AklvVGuInCsbKtbDbL8HYr1ClCMjpo0FaAnq/x73eMy4piopcNNKqLMpEKaMQFIsAQxKJ7P7BwwhowMA3A83A0L9od1wNCYPTQ9P57HihPzLbbjRcf+0rstxnVS4MDlOBIiFgooUKJiANaM8RDSgywsPH1jy489wh5928FtdqMnfOuXiHv/OfaA2/Tn3mG/8HHBw4cpP/swdDUJpSaf/mV+fn5D37oQ6vr60opzkWz3lxeWpqendnaruHkE85m84uLVy3LzufyTuBgEymJNKqJDe/+2bdbyHrOtOoAOR1i3qVB6e5nL/EWdmMO/2/6PTssSHSvc4Jk3Pei6X9rYgGdeNumqeMYOMdCEtyPUdqVTvezKdO4Hgrc1cz8tGYYoe//gLWhImj10oKB0Ke++nTk4w///+4LLi23//vHmVLh6nJn5ZXSm242XnMit38/KQ8pHsQBFdz8+w376138H0gAoTgPeC8UgDF2w9T0Z86flIwwLhEhKol1TuRaO40JJFKT7j0pT5KyAbd1bmzfXG70Rhx5zAQh/KjL4NuxwhrkMgaWGGEkBWuPlCYvzJWWV6OxMTOSm8hwMlYB5L3VlbesXDq8ctWP/U4mJwtDEUK6pqgCKakfCCeOVASaSQoG6IlTaoQJYqjAue42Nn35BM09Yk5eMipAIO970yJcAO0v/ejnhjVFTZZBta01M5Mp26odwWAme3ZufbCQDUI4ODMOvld1O8O6semFOZMWTbK87lCC1yKxf3iAZGxNukKI7Wa7YGc7nWDYJnMrTUPPcMYHx/MFi3mRgJhveN6BnOE7gQv0s8vRmdjegoyl0yHEieJIEiElYMyIppRUgMNYpu4ukd7lCLZOLMZ42627YORg/92vPnDHvZnyyMrFs+e/+0h1ayubL+XKwxhBFAURT5IpiNaMqQFyyLn0GssZXj/7jMq8vLQtN668KvBGx4bbnkMIwmfPhPlCfMON5NiR0gunov/wn7Kvuq157+u/funSz37oQ26tZhkGSR7z8wsHDh8mhPA45lxqTFtfX+10OiOTmSvrcxa2BsjQKJtotx2Wa/1J+PsH+F2j9bE2wKYSpup51AgAkibcolQbvCu+gmu/3quY50kvDAgmAkkuFUtDs3HPxCP1h1T9XXiukBIIp9H/KVJeQ4z29EOSwcAP1iZPUWbrzMtWHDzywrxbd/7dh94katWzH/0PuVp78t/+krux6L7yyspfPbfy2edQWR+4ZV/+xoOFm07oA2XBsgQZIjIDJ6YME0KvJXDX0en/uW4L+tFTSqm8aUlGdIJjoD0TyZ4JPCLJYZHdGm3XjZ30EqtAR5Ii9OV9Nx08/VUxdaTuO5x7xZxdygHrwhWRQvOCyPGCAMFEvnzb5vznrDebVN8fdN48f+ruhfP5Rq0DbLXAiDVudQ+hIAiHEW+HwuURlSpjEt3qHlJBgFONxVHFdUMnfAUb38SD3ykPdDLFPCX7ZIwD38cqZhpoYV4zF9xaXodCHI+MlBuNdk2wyPVrihwbG/J5MDVgrm9uEtsq2Nq2z2eGS2eurnDECgVb04xX26ja8ZlbdxXTkCya+WqjDRK/3BJTgzmaNcqUeaFfbzpFg5lETWaNZzbclwLzZEgFy+V1NY4kqJgr4EkStpTdi0jTqAJEMWpz0DXIGlg3mC6F5/CGx8tj5okH7p+59bWh69RXF1/69iO6ZuimNTQyFnEex5GSsltgY8Yx1TvVG1htEteKBbztxCevNKs+6BPjA696x8TIgGkwipMEEkID1/Hr20ah5Nz7enLPPdqzz33mXe9+y7v++Vat6mYyrNnKY7y4tlocqFiGwSPOdB2yphv6vnCOHjma8fMVvRJAdAVd+O3oI18mn5tHlzc6AKfgxI25fX8Omi99AItAFndZpIJeYsXfWeXZK+ZC1wBZF5REYlIokLqmDbvjKaH2PCNjKpMAqYTi7cSOp5Y2uzxNgRSAmPmDs5vNi+f51tqphdrCpcVf+LG36O31Fz/2x7Ntj/74v2wsn1PPPM2GBvxIZZJ8vY3n1q8+/CIhf5aZHR26Zb9xYHRZvyk7vn+olDWwjCIvm7UTd97rtjn/o7DVj8tDk8UyZlQikoytU308gn5Tsecz2R8S4Z3Dh4BjWnGaL+4//spLTx3qVLOGpiMDADSJIs7dgDediGBlWSynYSsz9GC75S6fe/vm3A3z867vN3N2u1I2GMtpiIKQEoW+dCKfx8RiULSQ1mVXLEpacnYcGs3tKoev4sLXs7OvmGWwtEEih7vnU3KqVEyS4Aui6bQVEiH491ut+4YKnY0mUA1JPjJUzhK+7bWyhr2yuj0yMdqqt/4v9v47zq6rPBfHV9v19DZnzvSiKRq1sWSrWbZk2ZIbBjdcMCaYgAmXJIQ45LaEAD9ybwLhUkJCvwZCM8Y24C4ZY1u2ZcmSJVt1RmV6Ob2fXddav8/Z+5zRiJBckvvP9wb0xyBG1ozmnLWf9bzv+7zPo5nc0qoHi5XVnR0moKJWI6J+fq7aEfVUTBTAbCGv6dSUFFmJypd5calqC5oxb1YTihD34pJmHcjDp3L6uSqIxzwxL4HUqNPZhlMwa2bJAEotQnwMYk542Ae9ioiYUUoZNQASQ+1rd90Ybe8zjOqxfXv1YgES4gtGMSE2Y3V+xRiAoAoUERgRVu6onm9XdQJZtYD2Z9MFCGJrtq5cvztrybFI8K2jRzDBkqL4vV5VVQEH8USrJGCPbrT19nz7qHTNd789kEktfunLnu4+ftP1Z2wbEQHq5kJ1nosMiUJYi9ApvrP7RrtqH4Gv/aPv0b3ST47A2RoFPgNEK2Clii2Tza0pLT4CW34AVzwCWik3KQgQIDs70YxflID/z/HrV8ZETm8LuqfOWe1hjZSzpqd2w3CLA04dAus4BFIKCGlmV/ILk0Tu2M8zCjDysv+LfhYAwJyflbMpHAwXSmfvvXa9yrMf/8p3Lx/ZELjkGipY+PXXoa6B1CIBHMdauUcSRRIQe2vMphP52ZP7aSAkd5zTVPngYKeyZu1QV1+tVMvkCv0DPZIk/A6e/j2w1ehcMtsrSoDINmcAQQFAhqATIQ+XGhNOclZjyI0vRGpCBoFEqSmK+wbWb53Zr0U6TN2oWaZuKkXDFhCMRP1EshCjHCJASUTxf/jVZzmm06oHx0MigB5oE8hqGqhwbhkcUEuVsOJkRFmQ2DYUKI+YVbtcOo3UvVLnC8GWYp0hgQ5o1W9cBA0g1A+3bbu9FIggwkQDWLegwNFj5ws3tPr6oqoOmVbMzRNPSBJLutXVEh8/N60E/UbNiETiQ164kCm2KGLaqPEK6msLTierMhaOlWtdMW8s7JVNG0E6M5dLhFRI6dogfiulPaGJezNWkWG/iNsUFicmZZQ1chmbYm9gc0SWvPBryWwoINimVVg0iASGr9oydOkV4c72c8eOnnh5bz6b8QaD/nAUcm5Si1om58CCksWYQGBb9dxqtRqGZUMFuYy1UKhNBpnv8vXDw7f3dK/RypkhvzccCduOLTylzDTNVColimImmzENo7u370ePPOwhUsu6dWc7e/0f/4vgm28ZDz3U6QvGf++eoj8gLyzEcqGwFn0z/cbZ2LHv+j+/z//USZgzAYgaoM0CEiXU5ghzCzOAQTwFzQrPfJDndsPEl9DACabbwI+BjwEZNUaE9OJdn6WLcKkx7/7e6V1DkXEDuElBoAFeLh3j7kyRAgyBzSBk9QuWNtDqQm3otmJZIzORMwCJ9H+DWcXJs/5S+my2/NrB/ffctK0l6H9i3wFuiB1bdiTNssqp2jdgzkxJM1Pi/CIrFXmxgjTN1HTQ2ib09nbcemupbUWI6dOHD/z1lz6XrIJ4KPzHH/nwpk3rFUX6nTf0vx+2nHas3RUItwUDFuKIIWceBB1XSQ4gQo1T1VAuo6VOQeMlh4yQaLW4f+SSsTMH4xUtW+WMc69itSgICxAIkAGR1uyableNEidMag0KSAxCkwFbYGLZhIbJbIsjmYZUQRRFRrnNMANI1mueajVr8qfFyFOxgZNqlIlijNA+SDmHNhSduGEMuA0oJIAgwGwIIIIEIwsJFYNTAhSBPpks3iaCIOSSP6TUjIKOiFE6mikPdLaYltGzokXLlJMpK6oKczWrO9GanE2/OZGP+D26Im/pCJWZzQuFGhYk01zfFckXtTM2+eoYG9OVgm5GJdwjIwS4ZgEbYAQIgrbTSnbUSJhgInGAa6kFS9eAXynrQDItf5Rcsvv6Vdt2lAv5/OLs4VeeI5z5I7FIvJ1y07JtzqjBOECihUSpmhkSCn1GOujjixVtLGtUdbLQgjI7BlYPvn3Yt8Ynosz0adXjRWKkVKnJiiSIot+v6DUt0d4uElLTtNbW1hd/+YIoSDfccsvYqZPUMOcJtkLh/OrVK1LJ1B/+SXBoRdvtH3q194XHpa+9wvfN+C2ogRYTDJuAcczqLyyzOOUEmIBLHFIGzSBDJvTNAzvEZz8HFl9CvV/g/TUeAyAgAD9vjAsZuCjPH1yshGANX2bnG9S/VWOEzRv2//BCkD9A0CGtiDMkANbUbS0h19I169irOZ/B3n+39r1w+oSYWzyf1V96c2Jtfye2QaTTBLUAAIAASURBVLVWVBS/5Pd+6Uv/C+gmkaQVI6tsDGMdPZfdcBMsFrSjx8TFBd/OrdLg2i9+63vrShWPedbvUw9rpr9zZEXE397asmnj2rbuuFskNjIof4dc/w62Vb/06mWaaHBOMFp625Z3IgAHFEIKgbBkD7Ik6ILIa9rnIqEXulbfMXk01NXGocEs7hFkDZhaqVotmRYEkogCfoEIgADEGLU4qRq2oZkAUo8sKAqEArIRMSkUmBmuVkxDO8vFZ+X2F6IdOX9QwagD2iK3LYgoFCCEpBE0yhCs40SB6cg0BQA4YoKILU3QbdsCBAJMIHjwbPG+wVi4UF2gNqIU+AKbW5VkWQ/55IVzM8G2SBjCog18BB8aWxhIRAZasYgtHyKT89kA4YqitKt41hAeOpZ+drbyZlrze+WueHgwolAGbdOiHDp9l3qpa7m1tyRhiEy9Vs0s2qV8KQ8sXQcIt4fwttvfvWL95vnTR08dfm3y9VcivUPBYAgTQgFjpsEYpxAYUGWsFgOV9urEoMcAACyUy2fmTcJ9WufKwqUdnkTPlcJlXgsDYufLVUFSo7EWo/4sIVZjEmW5bE5RFMaBTc14vPXsmfqv+z90fzadH145LAgCYPDs2KmhFYP2ZRvSl2+mB45Vvv6V227/DNgFBgHoLwOJiianTtokN52meWNOA6EOOQEcaRD6OPdAwQSBHDeu5ONXwMXvoZXf5z0Wx8AN17lIt8Wb9ApebA7hyE0Rde5K4uQfOvOgpvKh/pI0ZKmAWhg2ABE5lIo31TqMLe+FYVg/HzL/NwIWhFCj3Dx3Wi0kqzU+OTXXK7DLB1orvPxf/vHh8flqIB6JhkKIIMM2T7z5Rk9Lq1bVSrG4NxqWr9stCAIUFVM37rnphuPHTy4WT9s+tHDw6KHDv3z/Pfc+8L53o4hftwwRC9BVGv0Oon4tbDHGYFM1+mvb8q6vdlwOnC1lPBKxL45AgRCyZhve0XA5oZvL8wYgYBjGquXnVm7YfvbQCsGuUqJzUKiZVd1m3FZ8ok9BjqcEsSCvaXalalvUFgQe9koiwRRxCxLMiVKrqVola7OnceApT89bvlbuUaMC7ee2sxKEKMSN7WsABc5sxiu2XbQtRIQ1wagH1d5ILfqggJzGvEahDZFjeANUCL98fOHODu+WjlBFQNCqzWRzLR4pldG7ezvnZzNZJJiaKfnVraM9uVI1IJF8ploqF9Z1R8oWW7TR/z6a2b9YSlXskAJWhAi39crCvJ6TPMGgJ+hHADKdAcagIGOBM4itYjGfWTRLZQ6ApIgeAsqlXKRj+473/VHQo+z57tcUAZhQiHR1yT6fqVVNUwcAU04qDPhEkqhOrVYKflADCp0plFNpKsjhwOorTvQy0hnaiEbbaFRDOgoL3EIixh1dHZZFFRFDhCVJ1LQqIYIsy7qme32+YrH02GOP/d67752dnrUskxBBUZTpmemW9nafx8eqesDj8957Ly1pyo8+E5kE5EoZDhk2MFkRulM8D4UmBCbiGAGIgeDchkjmUAWofqfVSzapBryQV/6cv/kRlP0UHH2CrW2qSfnFcodf0UVQ51wBQgDjkCPb0TtgZ5oBGpbMtI6B3HZKRwpdSQ4D9XsJw6akq+nd5X5pCjnkzAn7/Y0wyzU4sazFUhlPT7ZX84++tv/5Q28MDve9/dqrfAn5i9/8foX7P/XAH80vTpyZmFw5NDQ5N3WCne7t6smU8o8++nAum+nr6m1rTey84nLbpqKq7rhspadaWNE/PHbyXF9L4vdvuYnJSLNNUrZ0jAjGgvPLSeX4HXz929lWyOOHCBiUNjsSy/i889jzhvPRUpwmQHyZLRyCQVM/G44e6Boezk5npbillUWBhgMSh5IoCBQgy6CVmq2VbYAtVRFlSDCEJoE6EkXDDFpVVKmOAfEZb/tzoURe9ksC7EBcQjZFkCORI9QcAzS001nT0gCNBENXK4GuYiV78KTam3hdQNRmiFMuShpFOnTW+5wmSkAS/mm6GgrKqwW2CGFbQMjXeEjBB09MdUbDXolFuloETZ9N5WOqVKnV1vXFs8Xa0xP559Pa/pkCwSAg4RUhAWBsU4pEoU4KbKOYTmr5rBoMSl4fUb0Q8mo6Wc0tmjWTc0BkIkAkiqLFTYhxrZh95HNfuPpdt3HL8Hf0VWu6WcpDXWOUGVzkRAaV7Cqx0Kkvtih22Ybn0sVsDcihrs4rb8yvCh/n51eQtt5KN0B4Uc74gdeqmcVSsbu3x7KogEi9JBUlalsYC36fz7Jt1auGwsFvfONbt956m6jI1XyeECII0tTUlCRJBKFsNgsR5hqD1bOZZI5GgUVBbY/JTnL5KqAGualDYHAKIabAWy8KuYUAcrJ2kQKBzp1wN4AIwG0A+qDfAsZaNv415Gl37LecafWv7FHDZRSMuC15iABsuNC6G9QWWDJ7b8wIOXVSrqmBXbtI5lrA8UYrq3HNNvy3HJtUxLhArf+D2LRBsjjXDePswmyUljoDxa8+8fyLR08OjnTc9b57OjojqcWpI+OLt9/2ztFVPcEg8fvUhVz2+Zde+fiffFTC5EfPPBFuTTAE07VKZWJaM9i2q65MEFg6erhzZODvv/b1g0cP/eQLf6NGQoum5eGUIUESiFUvOpgtUUEQRCL8rlS8CLZcwe6/qIFoYBDzCIJl2/UagF7M6hsVo/O//CKez5e6qqh+rwXM6vPDl+1+7lik229jDKEgQsEisGIYlSqwa5agsIBfkAWRQmxixEwsm7qnms4b9rMk+LR/5RFPnHuUCAZ9wEIYcUyYY4LqGBbCZrI0txivAdjf1rlB8Zb2H6lMHx1XtBNjZ29YeReoMgYBFoDFkcaRTSl3EvU45wKAQYw+uX/+zy9NbAmJJzMmNCo51bdlVUelYik+NZvMKl55MOLBEilr5POvTL2aLo/ndJWAdj9WXHkRqD8OGDYuZ1EgSACIUiOfsYo5WCuY5ZKtU0GCqiIh6JgjwDpC4zrI0XrRAoA/GMtJgmXY1DR0DhCSqownBD1WOzvkKQgYljXr5EI5r4FAz+jQqrdbXdGpwOniwvgOvKnFn9B8OiI4RIKQw8XkYlu8zdDqVaSJEbYJqNUcq5lAtaYhBNva2n7wgx9t2bKpu7tjYSEpy7IoioVCAWOcSCRqtZogIA44EYXp6bnWaJucEzixsAewFCj/EFjrgWcTEGRglBtyPZk6yiwGuc0hB0IACCrkCqCM2ya0ikCb53wKgBJLNlFJv2gDBzQ9Zi7yQaX1Qyo4n7I5JEvNeFBnWBg07Znrp4DabqYA5bARjMFdQSrgS5E/TkhKHVhF8f88SXcOeDKzmCxWOoHe09ny08ePns1lr9y4+t7rt0YQnJsshFu6WlsSb751aGRFlwBJPN764b/6xNaR0UQkOjY5MTs7FwpFuzq63zh1TGptHZ+fKux57J6N68MqOXV+fr6c/4cH/jAWDjy/sNAaCDCBiKJMqSCKgpOQxyyuuQ7pvwOvfxvbMjnzEUFndiMUEy6lGUCwNOxoNuMvlI0XzEkgRShY02YTPQcivbtLGRRtMU07p1lmlVqw5pHFYFAgEqSQWFAUbcNXrrJyaQzKe8XEc7HWtCcgi6RTZBKzbYQAFoETvSHUv9eFdVoMoAm4yei9kbb5V/bPle29L73QOzJy9eW7fFKbhBFy3Ezq/z5CLIgQpRQz5xxDiwNEhA4/+x+vzb5nOHLrilBFCHoQn0kWgwG5mCwMtCimZo1V0M9fnX1poVCxQEgGfQEiYGjbnDZV4E0zH0cXArnToSDu4AwWS7KEkSS4k0QIbRduCWQCBpZeQ6KMATCMmmbYQZHkSiSueqTa/BawEIMmUO2ZnF4sG3kL2NH+tde93/R7jktHOTvRer57lbjD3x6osZqMZYEIkiynUgtdPT2yLHPHVwMKGEBWLpV9Pj/nvFotd3Z2Pfzww5IkDA0MjY+flWUZIVQul4vFQl/fCsM0BUGglPo8num5GX8gEI1G7ZMWV4EGgR0DogmsQyB3Dni2A7W9/k4bVc5wnVjVi3QLIBmYFJoVYE9zowDsav2OgBRIEWDWkAEYcRPDltWE8GLdg6tkdtpT2BlXm86JosvMaPCF9WjnLALKnQ6mE73TGBWxZoo+XLqMXZNVDAX+LyT0LPVwq+VCrpgvGHqXpQe4/pn/9mVq6P/tgx+YTWaAt/vE+YloR5+IlE/+5cceefixo2+8ONS/+pXj49du2P75T//XdKn4D9/85vCale+88e2vHD40NTmp2uSyS4fiXBepfiKZ/8YTz3zqD96/7pJLvvjy4XggkKdU9aiyTBmTL+y3EUIpRXWCCTEmCP0OuX4z2IIQtiiqRWnz1uLLydjS/13KG0AQLs2zGwcKQoExmerPDG/a8uKPkQ9kykyGZsiDGRegRwRc0QH3VmuBWj5jai/i0FP+dUfUKPV4ooj2QxthzhCmGCHs+OY4jMZ2+B5qCggpACbjiiBWTp15ds/em9793nf23mtUtXNj408889R999wnSsjSbLFOGTl1ENgGmHPGGhN0m0DY65e+eTwVDiq3JdC5ChuIeQC1YZD8Mi0/OZbeP5FTVBCRcJsHOnvjgFKOm/sCrhQXNp0wHFMfSJztfwQhFrEDaq72bZlQEtoiAozqbnQgg6INCCbKYO3AaDjoqc0ZyDMxn00WgNQSjV5xSzqrbN22K6OeO1P9xYC0Jl7eWFFqYgupVUsYwBqqKV7vwvSE3+/HABqaRiRJwIiIWKua0XCUiMQwjJ6eniNHjhiGcc8998zNzRFCmG1XbXt2drarqyudTiPH0d/j9cwlk4ahxVv7zJpOMAacIg6J6WS2xTjUee0pQPuBeDlUvPWfSOcQZ2AlC2iemwVg6UAkgAp1JilyCC1AdM5oM4m14S55AbN+xSyQN3QSsBE8bVlAFJq3YXMJmi0z4aIMObJCygCDHIELQonmHNHplzFocciQgjAE7Ne33i3LKBWLuUq+VNNWGsyP6AOf/gxU5F033vTxz37l2rff7RGiSVJJp8rCfMnnVW699c7C4tT5qbFy+vyGDQM/e/6JbK5SrFRnpucX5haOvv766pUrFQy3tIZHO1qyNfvBZ59/4O6bt65bu+3+92y5dlcwvD5VZGrNjgUpcvrxqLEBvGR1QTnnCCEikCUf6t/B1r/2a0UgYroeMsulzG4iyq/mzzU+30gibPAOaAtCuFIa7x3YfyB4fTmH/EHIbIow5DIBSK4WQb54FoqPq62/bEmkvGFZRO2AEmA6rt+ihZDo7EJiBK160QmXrtl6rdHsbxAIi0Yt1NV31933AUX+p69/rWqzd7377q2XbozFQmBqngLEOIcEGVAWWI02BuSs4TrhjJ0GgvL/75dTxctaPjwSGzfwT14794rpv/tTn1z8qw+3iyAUkg3Dps43RI4NdMMpsanhdi3I6jhV/9cCVP9gQ2QhQLBjZt1I92iyVAYwh4ahmxaWfBiU89mtnsVEcT7cSrOFxbGCXqhqMNErjIyGBzYfe308PAhfsr8rzQd3xm+VoJSVFwe6ephpYSIyACSRzM0tej0+n8dfrpUxIsAwFUWqZKuqqhqWoRlaLBabmpo6dPjw/R/4QD6fr/MsjCVBOD8xsWJgQBJFwzDcp6KczSwmk52DAwu64RMECur8VmG8jHjDQ1kBRAHWDLB+zMFmSBkwZrhd44wBCgGRAFaAyCAyGns2FudMaGz2YACWvErBxTs9yyHMdu8mBKDjuw3cj42QalfL0DBndiIxbOz8ocPJuLvn755CuDzrjHKCTGbZyNaZu1e4rFwAAOi6lkktlKjOqubOeG9q6twt9//hez70R4I/8twLL/3ePe8d6uk4d+jlS7q7oNev+v3ZSmV2soBxcGDt5Ws37UznS5NnxoCP/NH97/3Ro088+IPv33r7HQ/+4MF3bb4sIpHHDx545MVXP3zHnZfvvnr1mksDpolliQhm744twBsr5jHXDQ6Zw7Nc43PIGWPOj8AYt0yGEEdIQAj/doLXbwRbjLGQqkZkxXY9Hhqn4ELTnS0Tl/Cl1jhfrvKrP9+iRQmHr224cvfhnwltrZYmCHotbuWnsuWXxfDj4dWHAgkqK1FIe4ElQG5jDBBGEAoYUdgowCwIXLk0c8sxDmzIEGyEHDoHmpRrlR9998HedaO7dl8ne9WuzvbHf/ST3vYeNaDq+TJHiGNiYYghY40QPycIlHHYTPUciYnfPp46mdJO5bSFst3XamzsS7wcUM/O521WRytXT7t8BgtdnHI+5cATcGS5AAGrTscgQRBhRDCkzh+5P039b1kABlSALCtf1m/egDure4WgmslX3sjpNQpxx2rPldv8nYNrEv1P732MXzIXHOjrze2IquGF7EwynWxvaT97dlYJRhCv+RUxXzAFSejp7KHcJkKcUQYxLBYKfr9fEARd1yVJymazP/7xw3fdecf8/Ixh2KIoql7v5NRUwO9XJVHTDQEhKMtQUdKWNbJ1Ky3mycK0tKAhbtsi4Iw7jBUSACzGDQglP4cGKB8C3AMwBIrgdKEAJI7sitqc4/r7gzjEbmvKBkszFMYvsPJ/Xv80Vnic9pdj+s1deGoOBZtdVieEuv5Zy3SvDmbX31i4DAvdWtH18KrfNAIgamipGFx6I8vlYrVaocxKVoseA3QD5Xs/+N7TL7y0aecNWqVaODv1oesvn82UvrvvoXDAGw/5ZtPnfcEArNFE17CmmYIS0BQG0pWB9qFEe7ugwut2XLpvz35fa/sX/vyjI0Exmde/+dMv37575+WbL1/bNdgCwLUDq6NpSL63r/TiG56NG6ThnW2jayWV2HbNGYnarH5dCE6Wp3PUAXZyimkdlhGGv30eEr+BSp5SgrGkqG2qz4I25q6ZqbOIihqUx50suwzi4utzeXcf2oREKqXDPavefG3PzqnpIgPngfAdsWVP2+i8LyyKqI1QL7PM+uMuOavFiNUxyh11Q/d81emLKzRsuu4KHDZUr0731WbcAGD3ddf3bhgde+vY0z/7+badO0q2JYqCCJnFmReiKkAUE0urGqYJOcKQIVGEQHQaKQ2L4E6vOJarBGUhyABkZr3UUuqVoeQATaNAaZhSA2fBD9Q5PeT1IgUw1EAuiJ0aESNOEMCIYgiwkyuJ3WY8AAhxj1cKKdORg1+CMf9isZqZqmiC4l11dXxoixpv9elixK/8cM+35lpn7x65O5hNCEECZGqmakMDQ5goNctaOP6Uv2Uoa/lTc9MticSRo28IkihKki/gI4hgjKOxGOAgGAxHIqEHH3zw7rvvisViyWQSOfYuk+PjNmP+QCBZ07AsQwj9lM/u2xe0GTpyxKyW5c4VSu9KMi0BZtQfHVonwRqESv0h4s48DmIP5yqAJrCYu+jMTac5hTGES2p1t7Tmv6pyQMvkWssniY2MDMgBEgBn0GacLONkF3haHSA548Bq6Lao3cS05WxuCcKc2ERqc4sL2YUxiETmGFQqioohrejVs1OTl7b19Yfjn/v7L5xfnLvj5ncszE95atnduzcePn2ShNo+dPd9UlD543/6nGnTZHpaDYQK+ve/+p/++8zRIz959ufX797RnuhKz0/mEZXb+i7bvBGXpr1h4cT03Ne+/9j//O8fGd26Y9Oum1tHRu+66W2hlsSR117fvP2qQPuwnE2KY2P79v1C2nr5JaOjhFlKUPGFA9Vy2YkerlN83gRZVr9BHd7v1JS/PcyL/CaNLQBAXK0/scxkIoLNfgSHlAOMeDNfc9nWV2O/Gl48lGEIyZYJxOCrqzcKJw7/cNv1rxSYQVlYRr3cJphzhG0BO4V9HQ8wgYRio14lIMdqCfILw+yGwaW7K4PcPgd3OlWcyhHv/LGXn5s5W8qWCoa5YnBwt81CIT+uplijCcyrFpWwJ7JqoyRKhbnzenKCm1VRkiAi9WekXl3QqIwZRiYH1NAxJqrX76glIHew0xFMQodJuR+5Q6/qxMIJYEO4fhVCUufzgKA6SNU/OpUjQUBA9a/gkWDcKymqXzfN+YmJbA1Ibd1k02U9w5vUkF+xmFAjWkf5G2892OUb+Pim38+nCqanYnAhdX4+Gk8YpkENfPrR+7W5jO/mDymd12/eFMeCxBi3mU1tq1wuZwspQSTJhTkGYG9v3w++973+/v5wOJxPpYL1UtJfobZXFHt6e6vptDAzz5Kncak0Pz4ut7bRgYFkTxcP+0zsxziAZrjNGlRTcV5tC7vOMfUzQajjUdNYDnQTvt05H28u4IOGCpmypcBXDH5VrLX00Y2tpm7rvY6VVoMRN9MGwJIjs0Pn6o+0bRHnjDB+IQG7YSPYMABw5PEAcoMbFpydW+BGzeuTFFnmzD5z7szrbx3Pz87fu/Nqa2r6vV/840Ci/9prr0udP9MdCXe0Jl6ZSAb8PTvWr31y/95ZVlrdEjubz4b71yqSMhhInDjw5o8efmhD78qe9hWMKFSv9HX2Z5BSKud7W0NTeeN9H/3UZz/xwOjbrmhpXx8Odn74zz+W6OmRZE88mjhUrsCZk70rus5roR/95NHkj58aGhz63Je/aGbs0y8+s/Gm3YZWr2fxhaDuRiPGaY9Qd+yJCaI2+x1sNWDLJ8gEY8M0VIAQbLbhYdPQj4ML8pvmAKhh4bjkceTeEYQkStmXh0efGd5EBdjGpkmljASZIUlAiLq5/A0rOVClZpnZMiaKbSMsNO3cGF82BUDgQpfL4lysv4soUy6fniu3hltvuOOdq8fPIAT27H3OGtUCG/tpPo2QbFf16Mp1m+Kdp04dQYD1jKwxhtYUpsbzE+NWpSqIgIii43vIEadYAKYJqE1Vr48BIMI6d0LOz+Y6jzkTQ44hxW4l6PS2XGzCiAvIxSnkQFWdcwEIJQj8Mop4RSwIxXJt+ny6qAHSMRi84ioU6WaEEW6pWbsq1ua7JibOnVttbH33PR+YLyQ9vcGwICdT6f6RhMD1ml5L5qvlGveGQC49u2K1V6vNM2xCwESMERaJ4BleOxgIhdPpZEvIt3fvU9Fo+JL1o2+OjTFBRpUymJrNnj61IRGf2bdP0Q014PeMjJQ8Hs/WrYm+Pr1YpOWyVi6LvFyuVU3DREqD5RiOmShy5A4YARMCZEEoNx4jN/e+0SzkbvQ94s3t/AuLh07tRn5dNAZvIprp/pX6tUBcd3ZnscxVZ7ndCkYAo/UKgHJDdxM7KeU2X2bB7BK9ZnoiA1y3gMfrCQTVUgqcO3NqcnL65VcOdg4M71zXv+vmq04dOfPXX/rc8ObtPtl/7IW9t9x5RyFXePH0+e07r+qmrGIVXktPZqq1lliMiuqJ8+f+8YG/jgPfZ/7209defcPt193w3ImxNw++GOvsIG9NXL55U2s0oJVzChH2fPvrkY7WcHQDlH2jq4YPv7T/KqRIMd+WRBD6WEBVx2cmf/+jfzKyds23v/O1/q72hRNH9n7nu972ts13vYNWqg25EuTLRmKukh4yWr87GOVOIx9SSn+7Ycv56BEEGSGTUurs1C8JjyFvyKUay1NNNQKHS10wsKSPcPpQ9XPmtaifV7FtVwUCJAUoImSAOiUgcXpNBWpqELb4A1d5Aldbnmeqi6+UU61iwHL6s2CZB5yTSOZYUznlPuKcMdOWxOtvvM7u6MxMjz/6zJ4d+Ww78WwYHD5AqwhCxqikCIvHj81WqpnktOKLQWZg2ds+sr5t5JLs1NnM6Tesoo4FQGQCMAbIsk1AKfN4PCIAIsGQOSrXRqeYu8UgQU7MFQIIMYKggBFGsI5cEIgICNitDaEIYVCGAa/CEcwVKoVc2hJkq+NS3+DWtv7Vhlm2tGKb0mVh8AZ4mYRBIj08mFmxYXP/c898o5KdsrUiApbkja5Yd60vMcytuUq1pOMwKGSjNBcKhAxeAkT0BGNEDJQK6YFOv1nJvrrnB7vefvvpiex8nr7vg7+fzy6OtLWhhQWYK82Pj3XKCkaokEikvF6oKGatlhsfH+zsPL84L8uqpCiqVxaxRwDQK3tKdpWhpsGxs9JMIcCMY1QHKcyWvN8bPSgGLjjGNKaB/CKJKVrmHM8vzkx04wSs+u2EGkJmxJcGPRBQiBw5PAcWYIDSOv23TIzrt6Zmc2ezx+HqrsdGA7kaZJAiMD5+Nvn6ZDY17vOEelYOvy3eujomr17RdfiVg1/92rffccttqRpPpaZveddtx89P5Rfmrl9/Wf/wyo99/a+Kei2mxiJBIVmpXbtq81/e8uEnf/bo4z9/+GN/+GdD23d+/bvfh1WtB1VqmamN1913dGLu2T1Pb2mPb7795sr5sa233r9l846AR9Y4XDCrz76y54M37fLMZQPRyAtvHHn4+Re++j8/seOKbcWFhRf/4e8zi+maaYdj0WrJYJRz0rDCaEI8hEte0w69oJRDaLvTxwsv+29jS955OVREgoJoMQtw2XLGZw2cYpw42SyOGQhnbnvQLRBRYxF0uWzP1fw5DkqcQhGLNqN10uIuPevcztqUq+KQv20D9vRWjP0PPfXobLHlI3fa+RQQ+bJxUIPuYWemaDFKGRPca5QCJgtvvLzveLG6ddOG9cGWbZt3rN++87X81Ml0OijKpm1LUC5pum1biieABEJEkZlaJTkreHyJFau7Vl2Smzo7f+JweS4jibbqBSYGpqF7/X4RAAFz5HxX5/hAF4wIgKjOpBjGEENEMBQgJBDUf4PcvG3gE1FQxj6vanOWTGXSGdNQZWVkV2j1diUUVZClVRZl5ulrWTMBx84Lp4c8KwfZ5r0n9oT4/p//3UshCcREkKqAhUz9aT79zE+6NqwZvvZjkke1oI9roJI5B5iGJOKNdeemj5tTL8aUYqxGp2cmX/zKa/jkN9Lz6Vvv/0Tq2RfKZ074VElqjZ2jPHrd9Up7u2kafZRhxiBjZ86cHVy3rqppmqZni0U7k6HUymfLI4mVzLKQcNEaDuJAdNYknTsDEO5YczeNRJdJpVz7btQwxHWsDFEjMODXNOOX8vex0yADzkSWu5byrntDo6PFGsE8gAPbKZSMmuDYP1DadNdqLk4vfSfsfD0TgamZWX+4Z/dV22Egai+mRvsSU4uzH/iTT1Sq1S//3f/45TN7IPW//z/9aX5xOu5PXb/5hlOps//4lY9Fw13ZyqTgw6hc6w7Fd63f/sN/+MrB46/8wQceaOsZqi0sXrZqxKsnJ09U1157d4kDVpoJ2aivb9UT3/7xTX/2pw/c/6FcKd/R3uUH/LrNqyW9Uk0u/vDAobxm7th22Zf/4s8qudIzD353anJGkCWiymoiGrtkda2Qkwlxn58GYQAX8j+WLSDVP1LKXAk2Qogx/tvLtlTF47j92qixGwGW0NxsCgYx52j5DPtCkrlTHkD3RnAmfvWX14nlIYRjAyGoMVACdkDxbgtE1ugolMx/4+8/H+7pCnS1Dnf36gg79O0irQp3K0QGqFPZuz0UFwMrlZroa90+0n3ljbtPX5l5NHX+XD7FOA6ICqgTJRMgtc7yqA04ZKbOqAcSEUkSN83CwjlV9UU7OtoGR4qp2cUjB/Lnpy0T1CpVr98n1B82CyBAoOPdBQFB3CkJOakXg/VKkNSZFxDqDKteLhIAvRIKqaJHEatV7fzUbEEDQttg9Kad3t41AAmsnDVKCxYTPFbAjhtPgB/7qf9q+W1ey//ovifo6W8k5yuBOPD2rJk2ZMtTHl7jZ5Xp5MLi8b3H/MFvS5d8mBOfJICJ8cWB2XO+eM/5Az//xWf/qygBMQiOz4BeG/R3A62ce/+f/ZdYuy8TyB7q2CX3tqSS2TC1Iz6fNjNDGStAKErS1PRkNBYHBKmq5PcHEIKSIM0tzPZ3DwWAj59y1peXGxPwOp1EHFrMMTQDzkDWvasgWD6gaYoM6hiHOf+VJeolqOIXGZy6nm6OooFdeFKd2pI5zvBuiY4B5ZAZnBqAGnWoc6GK1c+3zaDtQCrkwHTzYiE0TZBO8xvu3gilyJFTRwfR1KhX/eGTj//szTfDra03XLOtlk9dseVSqHpSi2OWJYYSXbPFalvn6ruvCZ44c451r0gVF9+zZcdQcOCb3/6mFPf82c7/jIPRl8bOCunzox2RaOeK4MCVk8VKduy12zdvEMCmp954Ye9brz5w3/u3b9/91N4nw1b5XVdtXUzPn17IThVLrW2R912xo1KoPPLDx89NTUmyKPm8QsgfH13Vv3Y0EgpxakNRaAwNuWvJD5cxC97040GNbQFHWQMhc/ou2CmH+G+XAMKiFGAc9wQiomJDBpt6hwuNi8ZlBpfsJxs4BS8EPDUHdG5n1CkhmRNtTZAFuEfA13k7Rrh46skX//ezT22/7ZbuTeu37byqWi6e2vdG+/oRRFAzv4A53L9+EJ0MYqcrz1w0ZY7QAlWrlbvu//0XqukvnHx1UasGBDGmhhGzDeZqfgi0Wf2YGzrEiBEJOW7IwLYAIqKoMEYLi/NYTvm94bU3v6eWTb7+2HcrhZzg8WAAJEwYoxhCjLkAnSISNXvtTgNewJBgRzaKWVDCYZ+CBFwsV6fOpmxM7NbRBaG9Y9X6MuTG3KQoCb5gqN8zUrUqs61nNFi5xrqxxUrUpPK3fvzNvoE18cHBI/k3KibIHj0WW39LZPWVZ8+Nifk5uwYuvwpGWzuKRqWtb93smRctGyBiw5auyUeeUVUgxEByCoz2r8Qrbv7aU4/e2h1PbLsO5NOofEYvVHPnzrNarb2nt6rrit9vWZaqqvNz87FILBIK1Wo1TrmmlQRBmMnMAAz8qkNfEAGW5fbSadMbHnAgOnvMxOmEu+kUqKmjWuIGF7INWVOa4G5Q1E/hhUfqYnf55vXnSC4J4KZ7M3JnR9GRn0JnCAA4I6ZtGTo3DUyAxYBhNkgWZc66NVv6v8CmgJo8VwXj84sSylwpwGIy98mnnj50+uyG1UN/8KEPdHa2p/JZg0IkooAPcR1ommYaPDu1ECDS23bcwJFdM8wDr+z//Kf/bkXP8Lv+4D8tzidffPT7MVnWzIo30lZlXQcPvHZpW2DH268HpeJ3fvL9fEv4k5/+rJKZX8zM3fSJ94Nk4blXj3z90UdGR1e/98a3mZXq0z/bO3Z+goiSokhyOBJft7J77bp4NCQhJGBCRJUQt20FG49Zk2Y1boil0eoF0YezqWlySBh2NLhO4tlvj0re+djp8XHbhlhAjvqBNe2LmqtfjdlGo+VRR7Ml5bLrIcNBM0mROy0PVzAhQpzSqje09WrfevSvTx+78c47tt76jmt2X/fsk48ffGlfMjU/f2xq8PYbcP18U7DEkjmQODRBvd7nrjmRg4wUAC+G5wvFT+dfz3PDL3q6VBUYJrWZVa9Q6v85YTZlFlckDkggoBbOnrCVlUKgxbmeOLMsIklI9GAAC+mFajHX0ta7/u3voRATiFUBCAjZ2JYxd7pXwNFqNSaDAgHYSbuVIQsGhYBX4RykMtlc3tBFWVlzbcvq7ToXh0MqM6qaaWhUVCwPy4BHpn9o9le6Tg+sEzfYMjN7a8899fzundfYRD6ZXjG0YhxjQzfsysRjfOGxXgxUGygdIIna83rn1RtX7jezxANYDVinD9WOLhTnlEifEvOYDzzwx5033A5A4C8mtgYFLX/w5y/ue37PUdi39b3xsNTa2pYvVxBCtqarqpLJZCzb6m7vrlQqkiRZlqV4lEqlIkhCJBLhBsBO6hlwTDdN5pAs3OhwWUttTldWtaQ2aEaML2tMAeIQHwyAW1qSZXjFL7KSh+7yztJgjLqihsZhwwDYkFl1PuHk3dqmXafPlilAULOA5uw62qyhwmfUQSsKTLtezycz9PBpsL1c2Kh6Dh4d//mbb2mmduvWy29cNyzm0nOKhIwaFhRuGpbBsST7QioSUDnDK4vFWmpCxIIIcU9r20c/8tHu4RWFbE6NSDf83ruTE8lgLAEkbz4/1SNVPEDJLMx/4TvfuWzr+lt2vy2fzSAz19Md+8dvfvuNsVM33LjrW1/+m+L57OOPPXPqzIRHUQSC/bFw36aNwd6OSDSqEkEQBOLstBOMEHam0/ACYMFm+d1Eq+btsLzkxvUngzJn2IsaIvH/15nXbwRbBNevVeKJUsAw4G46Q53poAbjchLqGqp4l4kx4FIqh7426jtnBoSWFF5uQjrmTpN7qljctH7trVvXrx5Z+dC3v/fC88+9sHdPZ1fXFbt21gZWKRBQm3LiyvRZ/dAzYDlvhkvBnH8IY44nOwYAKbLNWAdWbZNTR3/ojggEp5zUsFyCUkT0icnTQFMDdhXMvMXzIZQYhuE4tAzOKEOWSCRV9UIIS/nk4tx0/4p+jy8sYiAiSxYgwYgsoZUzQcSo/qP6JBZSRI8iG5QtLqTyZZtH29iGjdGBTYFwODk70RIMMGZCVYmHEq1ibFGZOUIPXgpH1/JNpt9OGvM+v/LQDx6ORCKU0kxqSiMtL2eHu/Fb/X7uDQLTAiYEte6Vqdil3pbLdo2uqex/VXztKHJiyk699eram/9bJO4ZO+Klxr6jh392aN8/mVrVsq3x8/br4wC2Dd5w9wNdHfF4S0zXDcaYbpoCxqnFZDKd6unpSaaTGGKMMJGIYZmVSjneGnesyZAqyQAr1DKJMycFqI4FzizVmYrwiyIIecMNhDc9OZr1oIN3TkhFo1TkFynkl+BuuczLrX0Ac9az3bMDGWX1v+1eopTWub4Ngc2Z48JoORvW7piN1aHKsusVYv3sUHjiND0zD2699Z2BbPlzP3/uWDoVJvDuq3Zt7u/MQMMKBGXAICHAKwPLRrJkViuUcWTa2KQC8gsVzrlu1Kzujg4T0YJWpZ5IQPEoAunosSRcfXPsyKqursTaHeOnzv7tpz/u6xtRUcexY+cG/ZRTVGFyOJD4r3+4OyCQR7754zePjYuSFJAlNRpU+rsLAHi6W9euHKG0/j0EIhAnggQhd71iaW6BmowLNjfJG4uav1Z9yhsRIdyNzl6KifiPC1ucnVtM9b/yVf/ptyZbdqmsCDijAAuQQEaboZqNvZg6e4cXVhSXGl1LO/2NF9axLHGy7yB3aisDwqDPe/j1gxOnxw4cOrhy3erb7rgzGo+pIf8PH9l7df8KSZAcx/Q6TROd0Cm7uUzoDpfc0pHVQbKxFGlb1F2oRYwRyqqc1kSFQhoppUaSx2KljCBBAVY8bSHLNsu1lDWe4uF21DrAAi2IM9sGiBmYyKriUWSPaRihSEAVgCwIkFEBAbFOr+qEC9arJOaVYMSriLKo6ebE1HzRBDAxENx2nbdrlUCQwPW56TOxllbBI0OOorxFs4rPCj9LJud2e9/RLnSXUIGpdNPIhl++9NL60fU7r9k5NzvX1d6xcRQd6xtKG/acVq5ZZa2meZFnfUdPYGGurZhDb76hinLimt3nfrQ3nFvwRKGnf3BtEPs91y0sbvjM4z+o5VNd3Z2cBFes3nrb3Zs3bbw0n56rFx1EkKT666ZIChRwJpMZGh5y21LMZqZlWraxuLgYioYLhaxhmOFQtFDK13JFPQpsDgQGbABZ05+oXpk320num700amxsp/KmhbyLX3Qp5wIuk43C5TpU5mxoUke95TrRON0IypnzxvPGXcSwIwqjCCJcJ2eWiUhDOkEpsG1gOaJ4AQCzxs/O8ldO8FBX/I//6lOtmblXfvp0sauvqzeSGTtpMHvBq0Qvu1yUVK7rUMDcCaiCNvUlehGGZikvtjGpWDJmsrV0LRCNm5VqnX2KRLBLzCh5AwwHFEOvbd92CZDQSy8+9/LDz1+547qVV988OZ/Z84un3jQrd9x0XTAQ27Xxkmce2/P6gaOCLAd9HuLxto6ujq0aicfjAoRnxk898+yzl1wyOjQ4ZJhmpVxCSHAwCyxHJQgu7gUurWXCC4x1KdWB8yUuyynljS/2/2y+/78agcEoRHjx1GH2Tx+smAsjPDhqhM+1DvpEyW9XTVq/arEjXrahI1B3AJ0tC8BzjQOXh5g32rSwGVjAHE83BpkonJs4P3b6+DveeWdXR8f6TZsOvPTiz37yk+6BvlRyziOKolGnVxIEhEHEueUsZ9XxwuFZJnXJHIe8/ihRxkijn2ELtq0BlBck2bDbk5Odi5OtRtEjYTmqCETQDcOo5lVZCMVDmmmXi/P6yVkaaOXtQyTaA23ImU4tXRQF2zKxpCgyEAATCBRJnWc5onka9Yp+n49DlC/mc3OpMkXK0NbYmmt88R7CLaqXFUFeyBZjXV2hoN9r+UUsHEEHsvJiV3HoavUmKtqLdJFbtK21Y8/zz8/PzN55x+0zU1NIlr2tLQuF/CU7Lk0oqp0uoLkFlEubSDiXWZRGOk4hrCMRMHNqPhOJhd53i2+mDHIzE8GQuH3TKEVIlT0333a7rlnM1gHVEKQnjuzHWIjFYpVKWXS0aV6vd2JiIpFISKKkOas/nNg+xbswt5BobZM9cq1aFkWFU/7jhx4NjABkgyrkEgaEcepGgbmJEtDtOzrtAd6gRW4nqsm+nd84hTwCS8sVfNlzt6QMbRgfuQn71Kl0AAOEWzalYKnR73pDOKAEIYUQUiICZmEEKIeahTwEIMKABWbn+ZEZcKYESl6w6923fuDq++dffHL6xKHQhs0Hp88J1dp//uu/aOtuB4qMODfL5XrZb1FnlwH64i35qfMnvvfQ4Dtu8vX1w6CoBoJy1SiPT5tU5IqCtRoxzKDXX0vmp08d2HDV5rn5s3sefnn1up33/pe/nanyAwdfG4573nf5JuL3Ls7NP/Sd74ydHhMVJRT2iQG/d6AvMrSipa0t7AlIIlEVz403vK1YLJ0+fXJfct+qkdUdnV2FQl7XTUJcix54oTaE/J8xKw4ufkEv3lxaUqEwTiEkAGNEKfuPBVvOmbCpPTmXZK0tcY/ypaknnpoIP9mzZbZtRCHQZ1UBRLazvNGQOzvLq8wddjck7O4L7a4M1g80as4Y3VUzAIGASaFcGFiz2t8eH147+pm/+uTs/PzC5KTF6BU7r+lo7SSAIo4xpxKDJudm/bZ1AA84VQHjzCVbrH71WhxiZgnUthnTIK5hIaAVNmWnezOTPksTVVmNRQkElXK5lqumdD5pyINEUDM5v18JJiKWZZaK6eqZRT5/grcOgFgnFBXMc2alBLCoikAh9R+YcOAnMOjBAZ/foCyVTqdzlhBrrQ1dQbrXhdo7Ua2q5WYxEXzBcDpXCEZ83dEeyVCm1bNv4YM98op1+XdAGVG/zinw2Z5YS/jsmbPTi/P33P/+dCHHwiEFgLnDh/HcPKFs0jJxvAVGIurommRN8142Ggn6W8pVppvMrEY89NR06zN7nz9eJKtvnqppvtRi+tFHH73lppumxw4zZimqL+AP5vOFcDjW0dFeLBYxFnS9JgjC8ePHMUaWZVUqFVmSbQgURcll86Io+f3+qqFjLHZ0tn7vOz+49trde/VHCosTqhcWAfciKDgdA44a+aougXbSUBp7g81komWw5AAZZZA2NQnL9/CXodWFkESjgX/McrrrEDQZnbvnwgBnZmMj1aKAGj4JeCSALTaVBQsZcGIB5EOh9nUrr+1Yce2lV49Eu17+8v+aP/bW6m3b9x47LkdaP/ChuwZH+yiAZkW3GYaO8zhmAEmC5Auef+3wzAt7h3deGRoYtMpFhCQGRRLyBIdR5dVDPGcTUREkOffSL5Onz6xYu+6Ng5P7prKjl92b93rf3P/qNasGtu64FKjKyQP7Dzz707ETx6GoRmMtUBS8K7o6NqxPdHb4JIlZFAHu93qCkRBntiyLPT19r7z68szsbFd39+gl61taWvP5jG1ZRBAa9V7TAANeQKNlLhcQLvUJ/3npWH9viMu86tcAxsjdyf2PAFvIyZtkLQNzPTdOvfLIJYP+QKLzzmpl28QTL80cerpry1x7v4yQ36hRhOo/u/PKkDqUOCcSNeZA6AKZZXViBRuK6SVOhgA3KLcx/MXjT87MpSZSya3R7ddcd93kubPJ9PzXv/Wt99/9weAabzpVlCXR4AxS1jAg5NxmjLF65QAYr39NxgRq2RBkBZkbdiK3sC13vrs6r0BG/F5ZjQncrhSLmYKlyz7eN3rsXG7w0lHiVQqzJ/XUmK+a9vulREsQAlQs5KvT+2nyBO1ZB0S/wWwby3V6yc2YIsX9iqhIuqbPz83na5xGu6Nvv8mO9HkgjNcrBQ35VYj9iqouLs7JotinDkwVJk74DgmmvFN+u5CTqrCoBn2YixyzcCycsu3X5ubec+97tDPnpPk5T7GYTybVUDi6apUeDIiBAEVIIWRhZhYYhirI+UwaEAEhjDFSFUtovWRS6Lj8jrdv27QmGg59/vNfvO++98XjrZOTE7quM1CsVU8Xi8Xenu5cLu/3+wgRI5FwrVJpbW3t6OgoFAruojXnPJfLl0uleGs8lUlZ1Orp6n7iiSdCweDqtavTP0tyDAgFCgdVzmUARedyYk7nnDOAASeUYwA16Pp0Q/ePnHUJ7voZcSdBjCyDqiVfSX4xhNnNCAxHk+qIbZhRr904dByVHV5HKee266wBJBloxmISHHiTHp0Qc5Xec3253R/efpO6O50cM3Q6NZ0aOzPdvW1n785dqFbb0tPbumULUdUDh9OxiByNiERgzkojtBmmNhn/0SOsWrrsgx8W/R4jV8BY5FCAgNsVSwhG5c4OY3yWA23+4H5s2quuu/aNQi0N/Ws2DMmSTstz/aTS09958sjhPY89Oj15XpRV0RuwIWCJ6MiOHb0D/RLABCHJo4TDYYGQxcXFw4ePVCsVjFAkHL7lltt8Pt/Y6dP7Xngh1tKyYf16KRgsFAqO6JssX5u7EJbRqHWWZvYXGzD+C/yEUurYkqCmEu7/679goVD41/+Lqanzr5+c/MXTP1VO/2KzkhrsCnoDAVYuziVLT8LW5/u2FDsGRWgHLIM7IQUCgAQiCjkDyJWlQtfVFrkL/LBpSOX27jE1a9S0ahjdktJ/8dBD9/zpn7JKVaPm8eNvPf7Es++7810eUx7euPLLpcmyZUuOM2+jbq8/AIxyZjKGGJc4o8yuAqQRUapWelITa7NTnVZGkkTi9wmCzGpauZAv6bysxmDXeqVr5Im9z/cmWno7YzXNhh4fQgQsjguzx7wsG1QlfzDMGDUqZUM3KhSikavbd9xtP3hfTyICZE8+X0mnshULsI51vrXXxAbXGbWKVky3t7dZFsCiKGGsqGI2l/PzaEsiut/65QKfHuVbhuCqeX22oOcigZhNKWPUo6rY73/ys5+9a+MmFI1qoqhEoxVKC4h0rhwwdQuYNV7TFUEs5gtVy+zq6qpUKhzUaSYGQNd1wzAjLW1er5/btWAw8MMfPpRItFxzza5kMilJCiGIMXD+3LlEW2ul8atGqVWr1BaSyZ6eLgCAqqqKoni9XlmUM7l0LB4zTbNWq6ge7/jp8f2v7r/r7rtYit83dUcWlRMYU1wHpyoCANWRSxK4qTuOWj4omcB0KkNHQgxqjour4//gvOs64D2geAT2fIT9DQQGB9rF0dGsuT7tUq0YhLOcf6hzGN33l4wzpOvQXQiqX1Um5AQIhGOJUQqyKZBLDVfOrRYqcry3b0V/byKcmpx5IvXTYMB3Q/d7feFw9sDB0z//iZjoZF3duje4fv2qgBrgohTtDJXzMJ8uruiPefxEIize8v+n7j2A7DrP8+CvnX7O7f1u3wWw6B0gABIEmyiT6jFJqzJ24liSbSWynNiSPPrl5I9lWR4riahCy1QsS/oVSaQKZVliLwALCIAESPTt/fZy7unt++eeuwAXlCYZ//FvRXdnMIs7A8zdPec83/u87/M+T5SwuLGwGB0ccXXfd7zuz4EBwr1hHEtZCMyqeuacefosU2vK+657qVJZt2Fs42Budm5pZLCfyw5NvXz+kR/9aPLSJVEUMWEohqnxdX27diULWUe3YpI00N8nyrKhaYvLy8ulEgpAPBlPha9IJGLblmG4kiSYlnbxwqXFhcXRsdFdO3YElDabKsJ+t969Yj12pVF4JXrmDUoA+Av9NV6fSV51GbuyC0V/ZWHriqbw7CsvGqZ3cnLx0isn+clHd5PF8f44r8TdZmu+2j6mDD02eF05NRzFnuBaYaIGJrBblwBIe+YNweu2yVcHuKGSEGPPMqnrlR3jI/Fhb3bei0tf/YvPK5HY3W+9h+elwu7R51ulJ+YmAWZEhvQ6Z+FqRreqDdUPPgl8nwYqZu0AZDrNDeWJjc3ZDDW5qMzwCsbYUlvtltYJsJUcs/JbhcyQJAs/+/EPNo1tSKaT1Vq9N5P0g0BzKS8rrLrMzb8ccbT+DB+NRyWe5ayWyDNEUAzDrKlWuaKahBO23BzfdjOW4zwOmuWVjqb1DQwCAFlCMIslSYQGATbTGSy/5p/cALdto3soDupew7KsdCLpeZ7v+yzLChz7tQe/f3c2HTl3aSGqyLt3OcMjFd8bzWVprQltkyDMKpJlOfVGfWBg0DKd3nPNMNjzXFO3lWjE9x3TtPL5wtGjRyuVyt133zM3N0sIgQBxIjc3Pdc/UBQEsUt/wrEUQmh6crpQyFue21HbWkfTdD3w/ZWlpUg8KggSgTCWiAmy+OB3H3z/v3y/Z7lxmnzXi7fPeY08j30cIAzYsJY2CAAMwCpAEcDEAbSu9IBXXVyvPBxh94C6gCnS2qto+N/R/wypS0MOuAazrurjew4zCQDmAfjI0Eb8vk8C14euST0XIAQYHDAy9QPQqIHSHGrMbub0/cXMlh07I5mcF9iBqat6m/EZrW0ff/npAkytu+mGkXimPTflNSpLC4sLifS6HaPIsJNJb2w0mowVEU4ioWA6qFputaq1TE4eGE6LEuHTWd8LPEChHXhtFYCAZUUsoZlv/Y/63z/R/65bVgLP4Lmt69anYjIBlM2kF16befi7P5k+P4UJFkXR8r3cxvGRA/vihbzCcnE5HonKzVZtampW1zoQ41Qikc11XzzPe76ntTuNVtv3XYZhEUKcKOQzGcdyjp84Xi6Vdm7fvm58o2WZbVVFmCFojcHAakLptcUVXJum9QbAukYjt/rvIQ6HW7+y1VYPgxuN2sSFM8h3ixt2Pvn8S6eefpy79A/Xye3RYpyVZLfVKrX0J8TBx/r3VzNDUejxrgW6BRdiusUX8iHwr+ajXzkWelZTEGPfsqhj173gHk554FP/+YYjNw0r/Zt2b7UGY4+sTL20MOP5MKVEWLgqVwyVFCAIcYYEvk1RkyHI80ZaizvLE2NaSWEojMRYsUsStUazqdqmoNC+XcHAVsrFWOwrHP7h93+4Y+eusbGxVltjCMGIup5Xq9REmXEc36TY0C27MpfTLivaiiygwb6cjNCly4smBhqX5scPb/21f4GJGOhthHzLtBpNdWhoyHV9CKgDHBlFiM6cVc80x5aJyeyjh6NMwmA1ioFrOMlUqjd+RhAlU8lvfOMbB667bmjfnqqqyUtL/iuvlF9+OZ3N+aMjzoYNQSLRvSvbWnl6anhoCCAGMQgxmMUIQtzR9YgoAowM3c7lkpNTU888/ezv/M6/KZVKPaM0QeBnZhYURVCUiGmavWsqCMLs7Gw2m4koEd/3GY5FADECVy2XQ78y1KjVLNNieO5vH/jvN9x4fSIRs0xnNL7u96ffv2i1khyyUACY3q44ZDBFLHA6AMiAzXRhywOAXPHnW73eCOBwI5V6AA3Cxmk49NHgP0LgUuBceWDcnqv1mnhEE4ACBLMU/MHQZvzuj7G27SHqc1IQINBqg9I025jdADt7B9JbNqxP9A86SGyoTWp2MAFdNu97ekcNeFw7Nz1x7ASjiLFi38ievaMjG7KFDMeL9brrBLbnepa9IkDVt1rRiJjKxkeGx7/45e/Um/573/uvO+2KdvRRfvLV/Pb9A791t9I/1n1q1PorX/yG//zT8o1bXGZr0LCz0hzHeeO337lUV//+Oz+7eHYKY8QJIsuR5OhIYvN6KZkYLfbl0zkAaaVUXVxZtF03qsiKLEfj8Vw2y3NcuFBl2rbdpYGYhNNTyHMcL3Ke62OGiUViarv59DPPmJa1b//+4cFhU9d0Qw+79XCNCRAF10wdwet7i2snj/AXKDSvFG+r++//Z3LG/zVJ7CFXvdk4f/bMxrH1qXyx0mj97f94aP78y7HJR/bKjcG+lCgqXqs21zD/nh05OnaglRqIB44UWBZCDOxZHqCecipYtc3qfXX5ZGB5yHGWXOO9THJdyWd3DE1j+yczFy9XqjLLZxWFJ8RwnVDrQLt3vws93w5830CkA4nsaFsbc7sql4pumxcYFIkzDG/rWrNaa5vUjvfBwevQ4EbMcNTqMIEfTSQffOgHm8Y3rlu/rl6tYYIJQyCi7ZaeTMYwJqF+AkAMXcQ2Vd2vzDgXn423ljAC0ub9K+LQssPv27OTAy6mIJZMEsw22vX1Y+tRl7Z6QQBSKNf26j+ofzvSp+zzjuS9Yp3WXWBBD9cbzVgsegW80eBQ/0MPPjg6Onro0MHSwhIr8Ew8MVuv51IJrloLTp+hM3OQZ/GW8VlGyO3cQSmwSyXaLYsCluPaui5JAs9zvu8nEgnbdr71rW9++IO/a1iWZZkAdOFpeXkZAJrP53VdxxB7vscL/OL8IiYomU51Olqoh/IwQ9qq6nteob+AAFYkoVjM//gffppKpXbv3nXmtdfajSato49XfncFNZMmcsPIpgDTAENIqCAAVwdIBmweAA2ETg0hk7uyYn/VSAvZwBsF2hk08AfBp0OSaF2JHQjWbPn0TnobwBQEM5R+fN0u8O7/AEwbGDooz4Hq1FbQ3pLgtm7Z2j++2WMF3XZsyww8j4WABr5r2JhHHIPMjt4xOksTM62zE4hlAt+zHVtW5IF1G9ft2rnn5qF4KnX+1Y7W9AdG+lxfc4xaR620W+W4wBX6k1I8s3DBLj9yFK7MEzZiq6XicHGlWnGKQ/03HG4DCy1ERvVsoZA12402nn1h8ZnXTl1CLBR4LEqR1OhYdOPI8Ph4fzoDff/y1FS5VIYIJuPxXL6QSadkRSEE67qxvLJimqYsy106ibr3JMMwmLA08C3LCmjAIBIAqOsqJjibyaqqevyFFyiA1x24LpPJdVTVtk1CyNWYIrpGkwrXmHf+PFZdyyrX7laF9yjqFhee5/2KwVbv0EYITc1M9xf72DDsxNbb56fmfvrsi9MvHyuuPHcg2inm05jlfbU2UbWejq5/dnRfK1qMUYcL7ABhFCYYho0tEPQUEGh1MEs9n9pOy9T3Mko6HX9o6oJuOzlJSYgSpNQKuz/hLn+4Ne07Bg06hHN9UOhUd1QntqjTmcAkkSiOREEAjKaq1loaBGZmOx7dxxTGumBntoEfMISJpdPf++53N23csmPntnJ5pedFGgSwWa9FE1GMiOsFDAaIEAYjz7VFUYKcYmHC1GfS+bxQ3PTaiRd3bVpv6nqzrSKMDd1cWF7o6yuGDt84Jxaysdxl5cxZ5/Th1G3bwF7Na3doO1ycYxqNejSqIERc13Ucp1AoPPzwDzmWvfnmmxeXVjiO43l+aWEhk0zGUymHZZh4CkFKVpaWf/aztGUxnOj095Ed29xMjkGgPb+EbRNBaDs24ThFidz/lb/+9V//9Wgspukqx3CCKHU6WkdrDw8P6bqOAHQ8h2O5Wr3a0YyBgX7DMLqEkSCRFykElmEW8vm22q436+1mc3pq+ic/+dnY2PC5cxcbjZZjGqZl+xg6byuRDAU6pKh7oHEI2AQAiQY2YKNALEKvQzEBV7feg1UrotUVCd8GwTBsvQz6/z34T1dIIl1TYb1uIxhal+Vd/yIAH+8fBze9txi0ksvndhaiOzasi/UNBWKs1mxYpikLAiYY+YAQhAnqslcP2r7TPRYxaDVbyyvl2bl5oum0o/muwxBiGwYOwMD6/A133rD/1gMBl2jU/FLJsBpWvhBN5ETLblebran5kmngSCQuSREpnTNeOqH98Acm6wy/54NeXHj1gfuO5P7F+J7rLy+fvzC/OLsyV7OOi6yICZYHBsrAu+3OOw/u3VNdWbl46bKh69FIrMu7BSEWiyXicQBpp2NahkFpwLCMHwSmabIsH0vEOEJsJwDAx5h4nm8YmtFRKcLxeEyRFCdweMKLsjwzN3vm1ClRVg4e2CcrsU6rbTo2y5DVVc5rdbwQvi6IWNuiv5rDfA1mrcruun/Iihz41HEc13V/lWCr9/I8rze/6KFY94fz7OdOnnr2hbMr518oLDyxO6oVCnmWZ41qY1o1fypveH70gJnKR1yHozbtYkRvN2HV4c/vWSb5PtUtSAJVM1XbzEkRied8zwee79DAowGmFAc+8D0NogbmiOP0VxYONs5tMSsigUiJMYpCLa/TrDZapsXH6NA+OLyXTaahYwNHD2s7jBCTyWa+/a1vbdq8ceee3dXyCkJMF54QqlaqiWSCsEzg+Vdab9g0NVYQOIK7xQTBkXQRQHDs8Uevv+GgKMqhiY1IGGZubq5QzHuBBwyADeG8dfp58igpiTfh23lO1khblERFUhRFMQwrmUwIgkAp9Twvn88/89RTy6XSXXfftbiwGMZUcwsLCwzLRiMhmwsC4HliNFLSNDmfyyZS3twsc/ESmplBPG8WC3R8kzA24ngebbf647H7v/TlvQcObtg0vrK0hBChtHufLSwuDA4MOq4bBD5CSFREArGqqpu2bA6CgCDi+Z6mder11quvnW41WgvzCzMzc+VKKay8qChwlmGJkiTJIsFA80xPBZ7p1N41SThAdQhIOLgiEIpdsoeSgB8FvhaKIdCqwx8FXS7phJKYAAFiQjBE2ydR4T8E/wkCaw1s9QALhM4ZKAggACoATQCaIyPWv3jX+njC77SkTDY2NNrWukwK+h5G0HecjmlKophKxMJFVyoyBBHsum692ayslADwotG4KEc6htGoV9vzS63FJWBbHCKqaXkdK5lUDty5f/+b92/csf0vP//1J3568nd/59/HM0JRDphOzeKInstPnJmoLbeUWDJRGEkPDZz65teTF198y113nl+6+OJpt1WVCQLQNur4bH5s27pD+0a2bvEt60cP/rDWqO3cvuPAwQOyLLMsK4piAIJqpVqpVBiMlWicF1iCMMtxTJhErRlap6MLAifKsmM5htEBACmyJIgyAL5maJ7ji4LI8Ixru6IoEkwuXLhw8fJELpPef90+npeazXovc2at6+KVaAe4BqfoNbJVuDagsrf3jq4ERfSsiFAY8f9/hEL1HwFbvu83W61UMnnVMKQHXo7ROXlh8tGnniudOVYsH9ubsPr6cxgzVqV2sWU/Ett4fOyAEcvEgcV6HkA98ArN4MOQny6wq53QtgoRSKETuOGOYSj5cogf+IHfAEQlrKy31y1f3rlycYR2kkmRjcYR4SzVaNUrbTMwIwWy7rCwbjcUBGjp0HNCM2TS22Yo9BW+9tUHhoaGDh++fmWlzHM8yzKYYaqVSiqV6q3gdSsmCDHD2bbFEZbl2dB9zSUMSyB45tlnrzt4QBQkzehwjMiyZGFxoVgciClyzEurbPME/yzLMbnZofHEFp1t1fWmawemplqaWa/WGVFgJZbFDCZkZGRkdn724rmLH/rQhyq1ShdQOGF5aYVSmslkTNNEADme06V4i0uu42RSSd2ygCiDLqdA7vQMOPtqvNmAELsjQ6N33vmtZ4/Kkcgdhw7Onr1ACKGhk+rM7NzQyLDIcxBijmUQw7iOc+HCBYHla/Xqcqk0Ozs3NztXqpTqtabj2YHrY4hZ3P2QBGMMEREYXuAt03ZtG8eI73pe4FGDtYhafvMsYwLgrfqtYY7CAOAsEDYBt3PFq5qsquERCpXuBDJetygj/aB2CuY/QT/dhS1ohDPGcFkC4PC+0gEoA9CMRoOduyMH9/UPDRd4ruM45y/PxNJpnhUkgQ1g6LdBMMMRDLHR6XiApuMJhEmtWtU7LQqpLCmJeJQQYpp2L0VJM/Sq2q7Vqu2lmtts+K0WAtQPoFFvCQRvvm7THe/7tW3X7yzp5Oi3j1767Odj7dL6Xdet+/33Dd/55ma7ee7y5WNf/DvFcAaL8cL69adX2qfOn2J0eZDd5lFb2RCVt8WK69YHtruysMhgOLZhA4Px2fNnY9HYoUOHBF5YXlk2dZ3hOBZj3bJc143FYtFozA883/N7oxLbtiuVim7oyXgyl8+jsDakoHs7E4Jdz+5o3VON53lEu1/JRJwwzOXJ6amJiXQ2u2fXbt8PVLXtd8vPNTUU7Nl6QvhGhHrD8vEb6eQa2y4/TOD65W8F/SNgKwgCy7ZFQbjmTd9H4cbi4uzkk69MXJqcKp16Yl39hT0pL1fMEcxoleqUFjyW2vLC0F5dSSeoQQIvDLZACCACoIv8QNWDMASM9btg7oNwPuh5Ng2aRNB90NdZPlC6tLExmyKOHItxsXjg02a91m6oJkVwcDdcdz1NDbMchFYHehQxLOwty4cXu1Asfu2BBzZs2HD48OG5uXkUGtohjCvlciKZkEU5oJBlMcaYZ3nTMTmW4zi2J2lkWFYSxSeefGrf3j2pZLLdbnfxDjNTM9PpZFIURbfjnudfmWemb6Bvii7lImmBRAn2Mc+xGBE+JjtptnsH1Gy71mp1VI5nLl2++NRTT7/rne+ot1s+cOOJmOM4vu+v3zjmU8AwDMJU5MVao6Gq7UJ/0dTM7m+HUoZSy/M6gS/lC45p2VPT2eXlYw//sNxWP/ixP1hMpcR1GxTqSQy7vLySTqcgRrVyeaVUmpmbmZ1emJ6+vLhcUtV2q9UGFKKgex14zHJMCOEcy8msEFOi2Wgkl4j3JeP5NCexvu1MHL147PtPAhEQTALsk4ZQzy9V91SEJgwzKihgge8BvgjIFgg1iq5khPT+7CWArWbumAAPgvoJ2P9p8McI2BR6EPDhgEIHoAJAQ5a9ffvi+/ZkR0eTgsxblmMZPoSnp6bGN4xzmDSadVYQEokkDnGR6V45BmJQq9UXF+YDSPsKxWwmy3SxlwnPV9933Ea7rWkqRxiB5yFGuuM0VK21tNxYXPQqVRRKzDqNjmfaIxsHNhw6Mrj/cDadXJperLxyqvQ3f5MQUAWT3R/+/eTWDZULZ5Yq9bNnztvtlsh7CTSaHd4W2R3jUopaVZuVcjyZ2LR182BfEcCA7R5W5KUTJ46/dHyof+jAoQOKKKm6xmKGFXjPs+v1pud5sViMYZmO2nFdh+elaDTKMKjebGiqHovGlEgkoL5j26v+Ygh54eRYlKRkMoEB6nQ6uqa9eua1icmJ/mJx/fj4+JZNjuOrart7AMG1Haw3hlK+btuytotP107Q1mYa9baOYS986pfV8/pHwNb/pGffi28DIJi8PPHKpanTF6eqpx7b0Dq+K40y+RSiyKzXLmn0p8mtJ4b3u5F4zLMQ8GDPLgGioKOGAlRAAhj4DvVdNUAtwlHHHqlMXVe+tMOtJESC4gmWl2zTa9cqjabhyXE8dh1cd5BJ5JFvQdvsZanCMPV1NdMLoeHBgQe++tWh0bFb33Tr7MKihFlIIMdxS0tL8XhcEITu3eADH3YZom3amGBREiEALMPyHBeLxx9/8vEd23YUCoVGoxEu5HPTU9MRRYnFFGDjJtP4e/b/2YEOlOdKrmQK0Uhg+QyLGMRFElneYKv/9dnWxfOb/9072L2b4wHLUukHD/7g3nvvRS7otEwWklqlUZov53IFvaXRAHIYy4oCAdEMbdPGjQhjhsMIITfwLNes12qSJALoOBjkhodXVPW5F1746F13N559un3yldLyUmnfnsuVysrsXFNtTUxONJutTkf3XRdCzBPCMQIBkGG6MCVInJBQlGQkWkhEstFYIR1NKVxEZDkWAOQHgWvavuNBlklmok/d/8hj33xYKsg4wBRStiUsrLus9XdwG8KAdgEkgKRA2Z0AdMLtBQQYAhgIHLzqF7kqL9YAHITacTj0meCPEXAC0ACgCkBTEIO9u6P79xXWr08IAmtYvq553UeEVeLi7EpNSUQlnvcBEEVJ0zTbtpKJBMNzzWa71Wi4gR9R5FwmjxA2LZ3hmVg0YlmuZRmu7UIAeJ5nGKbLqT2fZVgIgWvbmmvXWu3l+YXm3ELQaPhmRwKS5XgdvRJNpscP3rDp5l9LDwzVX3ihOXk2sW0nQML5F5+em7rgdHSJJ4IgoEQis2UrkHnqOVEhMjw03DfQTzBUG80AQEHgHdc1TD0ZS/CSeOrky3Nzczt37dq1c4eh6ZqhhWIU3Gg0QoUd1z8wEI1EbNtzXQchxHKcZ7vVasl17YgcEQXBD+EVsUQSBF4U1U5namauXK+5ACZSifHNW5IIawA8d+KE0+ns2bIl3y3eDcvSIcSrDumvLzZeWRuFcM1OKFzj+AzfGCUIX9+KB8DnWQURqGnaryRsvUGt6zvW4tLSY8dOX56d1c89taF9cnsGpXNpEAC7Wr1g4YcTW04N7/MVJRa4rO+6mCG6STwPU8/0vCZiVQjjZnvbyqVd5Usj0IgnJBJJYMJ1mq1GudIwgJseEDffyo3uRLKCbB26dlhb9bIwEQztRntnw9Dw8Be/9KV8OvfOu961OL/AMRxhMcMwpZVSJBoJFX1Ol2lAEFIJEyHEsbznu6EgLIhEIk88+cTo8Njo6HC73eZ5XlGU2enpaDw+vHkLZQkiGDooS7LT5SkuJkQEcenyeR85HgSuo7Mb+r/3W/c+88iZNwMcu+9jzGg8KbMPfvvHb7vnrSgKOqomxQSfQrXdKvTl/MCjbuB6AfCAoRkzM5MxKW2rtmu5HGUlPjKQG0zHskN9QzExySDM+Jyqtr/w+c/fcP31l8tLZ2bnllcWaydOqLppE8wGGASAY3mOsBzDMISwAs9HOCEejebj8YFkLBePZBOiInAChxD0/e4ndizHdin1XOAHq/LFMHAwYEA6l/7+p7997sTLYlrsPjkU0Q6e3vIaID7QIeyWNZAUqXIdcA3go26FxUHgY0hDu6su7w8g8kO3mqGg/BIY/gz4AABVXrCv26fs2JXfsC6jKJxle0bHdQPIMKFvLOYl3jKtnzz19N4dOzZsHKcUuJ7Lsny9pS7MzTqOnc8X8sWcwApddhxmbXjQq65UNE2LKko8kSSEYEx6NQUKk8hN0wwCGgS+6zie52GITRA06s2Z6dfOLDyWLMvj/OYWrDdqJY4Ig+s37/v198SHBs8+8pOJ488GtskzAkbIk2RhsD+QpJgobli3bnzDuMBzbvifUti9eZqNhq6bkaiSSacxIa4bZDLJtto++uxzqtq+/vpDmUy2Wq1YppWMx2RF0VS1Wq4yLEqnsiwvWq7tBC4CGIu8G9CW2sE825/OiaEKd0lXZy9PtiuVOM9lY9GMKEkYe23VKlcwgBFZnDh9+vT0jHLHr+3ZuSOVTOu6ZhoGYZg1NdTPM8drJKlXoj/pNW+s3XqEYS4pR4AV+I7/KwlbV/tfGK9qnifOXzg1MfXyaxcbJ/9hq/HanjyTKGSxH2jV8mmNfTS/48TAXihLKeCyrZrm+DXMOi4YVEsHaud3aQsp7PHxOBZkP4CteqNRbeoQ4+G9zPiNbP8GjhBqaZC6CLEQ414mzGoSZhikSCnoH+j76/vvz2azb33LW6anphmOQIoZjimXy/FEPBVPub6Dw1kzy7GGYSCEBEHwAYBBgDFOpVKPP/54oVAcX7euVC4jAFhRnJ6cjGUyQ5s2Tx59evm556cuXjBXVvRWi0VkYOPm8TffsePd79FKJapbVJIqk5Pf/MDdEgBv+pNP2ht23rn70Ffuv/+Wm2/sL/ZXK41uUYPB7Px8YaCIMLV9GzAAEgo5uLA8PzA6wIgM4hAWoe3ZHaN1eWHKsu2l6szRE0dRHUcWEtOzc6FE3rZ1Q2A4jhW5SIKHiCDE8ESKSkJcjGYSSj4WLSQj2UQkoXAijxAGNPAcz3WCwDZ8D9Cek1lAQ6MB1FtO65G8oDeA8t18qv8vTv0pdz8ydRvIAYQAe4ztOVOjFwIHMBQgF8ABELuhC1su6tZZPgaBB5nQDoJD0EaBzwHLAJ0y2NJcd4N6u5hB6bH1GTkidmsSwwt8SBFice9C9iRhPGbOX768adPm2fmFUrU6vmE9waRSK/O82F8sMAyv6R3CkHgi7nmBZuqe5QNARUkgDNFUDROUSCQwxkEQQAh7ZNy2bdd1CSJKLMKGK022bfm+59vMkrP07bP3rxydeBM4Ek8oZadst3XGpvFcCnguJqxhOa7IKgNDQ9u3jI2ODBX6OI5tNJoYYSUapb5nGrrrB7zARWSFZblORzV0XVYiSkSyTJsgJIri5OTE8y+8GIvG73zbnbFYstxquJRKkkRYvmV06s0Wz3NxJQoCDxgWsm3epwpL7Hr18qmX2wC2Ssv8UqmvfzDdl0/KkU731PFcCC1ekOIxjWUcBORkLpdJzywunHju+UQydeDAAUkSW60WpQ6EzBrlKbyynr2WOcKfV0jA12UVV9ypwJU01CtJzv9sPa9/Ytham63frbxca2Fx6ZGjL5w7P2FdPLrdOL+ziOLZDBMAq1494zDfT+x6fnCP6Xpiu7Gzs3BD/cJmpxIXiB+NA0a2LbtRqrY6pidF/aFD0tYjcn4QB05gaQhQhLkuE7yCVL2Mst7fKKUjI2Nfe+CBTDb9G/fcMz07CzGmvssQdm5uTpTFWCRmWkYvS4YjxHJdRHBUlABCLMv6Ac1ls08+9WQymdy+bVutWiOEiKK4MDfHKJF8OvWVd77z3OysCMAuAEQAFgFYCYf3OgCHjhy+8777yhcvoULxmY98+MTJ079x6JD/G/ccPnzwwe98d8uOHfsPXF9u1BAvERhMXbiYjkUFVuh+DEYQeCGiRBqVRjKZYjHbqNaXllfmZ2ZmZ2bmFpaWFudrtYqmdnwKfOj62BM4nmM5Qrr/khU5KSbLGTlSTMeLiUg6JqfinMwSluuCj+11n1jHc1zf9ygBQbjJHOBweoXA64EiPT/fgIb0/UqSnOt5w9zAB8//prtsHnri+jlrhmd5Cinv8DVSn87MEgcQB/ADQDgMArNnjgUw7R4oHkNblOodwOhAWGCli/H33vT+wXS/lOeDACDLChwnQJjpwlWXC4NeuYwQgCAZjZ2em40NDI0lk0szs0sLC7MzU+lc7tbbbuNYvtVuMRyLESiXKo1WS5akeDzOMDzLwu5hBinDMJqm6R2dF3mO4xzHCfW3Ahe+PM/TNI3SQJYVCoBpGIahIx0JfPRZ+7GvH3tg5HL/DeJeFZeqrXI04FkG6QxPU4m9R27ctXN7VJTDAy5A4eJarVprNupKNNpX7OME0TdMn/qQEE4QAgibqqrbNi9JLMtSgvOJtALAa/Nzzz/77HCxcOTAAWQY5bk50GqzrueYurGw6HpeRpajhQJmmE5Hm6iU623N40hyfFMknewf7JdSWd33bRqwhAkAsDzHVNuGbkgsm4jEGIwsTZMkieX4iYmJ06dfKRSK+/fvJ4Q0m03f95jVyqtn7Xg1ppi+QQzxesP+qg/RNftCq7Hi8GqO1z8LeP3Tw9YvrLwunj19/NLc2dfO66cf3Wic3tcnRzIpIfBwq3qGiZ3gB/a3pgZ8DUqSJcgWZXybLi8v15oWKoxwm24TxvcROco4JnVMhAnE3Xqip/LpRdNfEbB2HzYfgA3r1n3xvvsqK+V7f/PecrUqR6KKLMbj8Xq5HktE4vGEruvhEq6PENB1y7UtThBc26bdjx1kMpnjx4+zPHvowMFms4kxlpVItVRS2+1tR276yh1vPnn6zHoIWZ5r7d/bsQMs8f2z03RypgVAC4D3ffE+vW9QPfPK0U99ahMAhT/4aPaWmxpNNVLoP3Lo4PSFc8QyOd8zOp3U4EhxeMjRDdu2Wu1muVw5+9qZcxfOVWu1ixfPtztqR+t4vg99wGIiciLL8F1Sx/G8JCixiJSJyJlYoi8Zz8elRJSPyizPIhD4HnWtLkh5jhs4bgAJhj4IuiUQBatB/1dN3uka56bVLEFAqR+4gKIAYAYBjBiWGYwPfODFd6isfvuPb5s7M89FSc95SHCVeW5qRa7yGkSDVLkJAgNgBngMtQnoWCBYAbE6O9ra8Kb1d5742ak333V7cWOm0ighCxAXEAYzpMu1UZjRCyEDGQZB7AMQ5fl5TStPXALPPf+K6nzgD38vmckoknz20qWJqanx8Y3Dw0PValXX9UgkIrC8amgwCOKpFMsw4ZVFrusGQeC6brPZhBAWi8VoNGpZVs+qpUceDUPXtA6GWJQlhGD4W/NSQdaP2H+9/N+OPXny4OIRxXUiWZjZvHV0545CNmu0247jdCGSMKau2Z6LCIkrCi+K7bbaJafRqDw2Anzf1U27owaaAVwbGYbXagsQRjExtQ7oqCk5ZgXu5VOvTFdL2Vzh4K23mBiptsNkMolinkRis5XScqmi2pZPaTweHxgYyAoSBsBw7FqtahlmTFEYiNtqy3dtjhVkRRYE3rIsTTcIQ6LRKAXUMC2GYW3XnZqYnJ9f2LB+3c5du3zfU9VWGOZ5BX/gWt0WvFZGT9cA2dWArquZNvD1nIBr+92/krB1bbce+K7VaDYvTC//+OHvuxePbaeTG9MkEk8VWE/29RZmK77QsqCmW7raOXGhjUdGNt7xr0luhBdYYGoocCFiECKrNjkQrEkMxaFryupxsW7d2H1fuE+SpPfe+4HTr5wNfMs0LejDldIKL/KZXI7BKBaLibwoRZXQKhopguCGUU2ubceT8WPPHnNs58DBA+VyGQSA5dl6o6622325Qt0Pnv/TP5k5dUYO8/tyt7/pXfd94aXjx1/+40/AxUUBgL0jI+Kf/PHgtq0vvfUtkyv1g3e/27/9SBBNNJ59Zrep108+P4odq9kxo/KK67eS+erWvQvt9szli7VaTVXVUGCFUYAETmAhx3EMw7KcyAsRUU4psWIqkotH8qloVhGjMuFJl9N5wHd813Y92+nSOw/4yEc0zD6DiPZilt64ukFXFzh61ZTfw3CAMYAMYQWOCAwmDMMgSqGt21ajY5ftb7701diM4iwEOq8yV7xDKKC8J0xwrzY8OzmMmDeBjhGYFggaQC7jYmP4SN+Rsdz41gObHnr4Rzbo3PGOO1Ym5jhOIHKcTcSpIIYwSjAKDWINgymVMQQBYZiAPtJs3P6l+0+dOXNpbMs9n/uU2upk01kpFqlUqydPnPApPXLkSDabbbRakFJRFE3TbDVbnMjJgmxYhu/5PM+LUvfLNM1arU4ITqezGGPbNl3XCwKfCV+u69q2I8uSJMmEMKrRai+17YZ30nv+x80fjNs7Pn3D52LjynJ9muoBL4kttVUtVwhD+opFJaIEQeB5HoRQEAXfC8qGwT72GC8InO2qliaJMieKUBCIwGkUmYHPpbNSLuUA5DAknS8ajvXM88+31Nabb7utX1B8AGYWF5fm53RVYwmSJLFYyBWzRc/zOppBURdwKQ1arVatVsOEFAqFeDzuuK5j2xACErYyVVVvq43AB6LE82z3s0SiCds0Xjh+vFqt7t69e2Rk1HEcVVXDscDVGePVBZa1frNXW2HXaifglXRBetXG6/W7K3TuDH4lYesXVl6U0ocfe+rJRx61zz6yn5kbyyqxbLahB1qzVWs0L9T8F6sgtuP23/7IvxVIYHdaJEzwAqjXu0KrgAXXTHVhGOkVPkLr163/0hfugwz+3Q9/eHp2VuB5FgJBlucXZhFkFUVpNuq6oRmabruuZVgU0ngi3mVnksRw3NDIyLmz50zDuO22W0orFZ9SjmXK5Vq1WioWCu1Wc7pUmj/72tCPf9JcmF9xXDcUf0sAsAAkMIEcO7Fly95P/JEwt/h3H/1U/75Dt9x1S3tgcOEvP5N89dRlAP1837IVlAkxswnN8QxdA2qX8PKEZxDHspglPCeygiILcVnJR1L5ZKSQjGTjciLKiRxkwgUiy3ddx7c93w3j43s3D+7t0PRsmK6ehHRtINUVK14QBD4IutUUQgziGJ4nWGBZjkMBtR3gNFW10qzOl2tz1drcSrPU0FuaY9oRLu5yQRBxMCRdeA1WJT0YYOCCic7lTp8BDgBmDmwLNl9XuClK5YH1A2yaZaJoZmLm0Yce/9jHP1JZLhPCC9lBW6uT4yfA2YtCtQFbLV8U/YEBfOQQPHTYr1TSAL742qu6LL3tk596sqMZ/+ZDY7ffSNqmbmmCLPUV+xOJ1Pz8/MuvnOofHLxuzx7H89rtNoTdYrneqHuuV+jrTyWitu16Xm/tnGVZ0mq1Go2GKIoxRQHh+dfrh0qSxDCsqjar1Xqr1VDbuqQI+Xx+c3Y7lyRfrP/5Y08/9i/p799+6K01dqVeq0e6BbximLaqtniOT6QS3eLOcV3X8Tqau2VL7MYj1fe/j/zGu7OeCyTFdmxACGQQi5Dnec1K1dS0uCwrkuRalsTz0WRarVW+990HO5Y+PDCczeWiEbnQ3xeNxl3fL5VWms1WRIrE4qJm2KbegRArsqJEIqbtNqpViGAymeAF0TJt0+iYpgEg4kUOQgKDgOM4wnGu47AMo0Rj7bb65JNPuI5z4OCh/v4+TdNM3WB5Dlyx74K/yOXmdej6RbbPlL5B+fX/b+X1zwRbVz99zxxDb5Sm50ovnJt48dmnhKknD/INx6QvNtxyfD1X2Hzw8OFfu/Vwo7RsezbCHFxttENwzS72lVy41beg53njGzd+8b77TN34tx/76PTkJMMwEABBFJdXljmOy2VzjuOE3twMxzOhBNliGNxutzVNa7VaSkS+cO5CpVK9+ZabHdOSFCWTSff8p8Y3bLAt27RMx7VfOnt26tFHsk8+zdQbPU+oDgAWAGoiuTQydPg977n1xuu/9MFPLM9U77x5s7Nry3KpcuZ735lPZkEk5nk2hlDACFsO1sN9ZIHjOU5IRmL5WCwXi+Ti0WJSiUdYmcUsAwPqen5ge67t+65Hu48gBiDAAIcOi7CXLvIGd9DVsURo2tjlez6m1IcU4ND0nuNYwrNdnEQE+r7VsdpVtblcr82t1GdXqkv1TqNldszA9xHBvMRzosDyLGGwQ20v8MNVq9AUedUXGYTWjyjQ6JbDWyVFyqO+4kimbjdaZiudS3mm21/o++Ln7//N336/IvDtjqFkCi8//p3S57/zTgA4AErh7zATHgA2AP7IiPLN/169ePHFyaltjrP1L//LSQDO/avfuuWOOxIMgxjiOR5FIBqRo8kkAui5o8/NL8zv2bunr1g0DAMTEovFHMetlssQwnQ22/MRC8mi34UuQtrttu/6qUxKiSoEEd/36/X60sqK1m5jhknEkwMDxXg85biOaraQTfK4/xX+2H2zfy69mPy9zZ/cfONovV23dYswDGGQqrbabV2ORDCGjg9Z6vFbd7BPPc1//W+rX/+69soLsXQyFomYhmXrvu86EGBBYAEmHVV1HCdfKLrUn5643FJVSRH1tja/OLd75549e3bXG01L1yGDGVZwXW9lebndVgu5bCaTISxnW5breSzDsSJrGHq1WoMASpKAMJZEieNECgEJmbJpmDRweUEAAdU7HUHkUql0rVJ5+dRJjMlNN93CcVy70/IcLxy5vtHTBqxxI3ydQ6428eFqZO+aB32VBl377P/TVl7/fLC19mc4ffpFQLFIsObSV2drK+eP+3ozSI2ODeRHMonCYH+1rlIYIMLQn8sEfz0mdg2QOZ63aXz9l770ZcMw//APP3b58qXeHgPP85VKBUJULBbsbgkNr07BXdeNRGIQrQZ8pNPpS5OXL5y/cPstty0uzbdaHd/1Go3G4uLS4FA/ACgUysiKIiXisVemZxt6R5uf95p1FyIYiRZGhiKJ1MaBof6B9Hf+6mvf/bMvvOmG/a/o8/Ede6dfe/VCvZWNRXnX5jDCCLkAeobbt3lo5Lp1UjqiZOKSIjAcEwSA+kHgBLZjebZLPQC7VU0oP0M+guSKHX+vb35Fv9yLfLtqnOS7IABBOKOAGDE8y/CE5VjIsIgC13KNmtooN2tz5cZsuTJfbpVrVscMC0fI8ISTRE4I9accEyZZAj9wgQ9CXx8vdMIKHOr3wk96CW+AQoxRvVq++fo3/fYHP3hx+YJp60szK+vH1nd0fWx08Mv/7f7xzZve8bY7JyYvWw7fMM48/bXP5U+A3wTgR7t3ncmk2ZVqNue89cmzyCE+8Lz9e0/dc49hm9sf+sHmky+f4/il+/7LkZH1vIAjiRgmqNkxGk1VIogVmVw236jVH3/kyXgsfvjGw4SQSqXOcawo8u12u1qtSpIUi8WuRj8ghGRZBgA0Gg3TNC3LqlarQeD39Q/mCllZUUAAmo2mbVgsy7M+4zkeQGCQH5Xz/N+Kf/XU0Sevn37nLe94EycKPqW267uO6zlOp92KxSKD6TQG0HNdN54U3vue1if/whdwdW7BdtxUKhcv8oIcwRjDIDAsEyLEC8Kpkyeq1frgwPC6sVEpEu1dxacefUxVtbe//S2UerbpdwwNuE4ylUKiWCstY4wJpZLMIYEFlgtM37IdjzrVegP4wWBfwaUBDXwIoKpZDEc4QnwY1Fsqz3cLNh8EnhNEFcXx4ezS/GOP/jRf6Hv7W+8kDBd6EHpXPQivGqqvwSt6RdAFX1dz/S92s1fb/P+E+f6/BNh6nTkGfrW0JPFCwPIUkurs+U5bV/J5x/ZAEAAMw57LGqNZeq3I9wpsuZ63edOm+7/85Wqj8cd/9EdT09M95ZYoiSulchB4xVyfYWj0Clf13MB1TSmiwNA52HPdVCq1sLj42quvvu1tb9c0DXTvCTmgwcLcXN/ggGM5jVbT0HVd023bnpme6S8W0rkcqyhSPBGLyplo3DZN3zACGqxUlj537x9R0775tt0PLkx95k//7K577kpFFFaKhjcEwgyrNduDIwPv+a8f1FTdNUy3W0w5lAa+70Mfdtk07hVNCF6toa7M+dbGHXQrHp92GV+4eIwwRmGeHs9xiGUQIa7lOC2zXW41FiqNhXJ5ttQqN9RG2zJsAAKGZTmJ5wS+W0xxBK6q2qlPfeoFP1fX06AXP0FpAKlH/V6wSS+LogelkxOX7v3Ave9593ufO/rC6MgIBX4qnTlx8sQLz7/wp//x//qHH/8MsTQqpc7MvbDw07/NvwzeDkCNEPKjh0rx+OOPP/L2v/jLgmbLADS2bz927/tTHNr00Y8nHPu5bVtjn/nzG3PJpVrjtedevTxbsXU30K26EwiBOTiUveVdtx28+bqjTx199bVXt23dsX3ndrVZb7U7PM/3Bmee52VSGUVWIOnCVrvTrpQq1VpV1VRBEDZu3DgyOOpYATV94ALX8z3XNj2jadU90SFxaDPmirvk+kER5Bk9y+oMWqjSCPHahqMbDPYjGLG22mw26o7N81w6mjRcS4PMxvbNPnQRop4b8Iif5n9sMl5sYNgy1YFMhiNYrdd8H/OsGJGTx55/TuBjnBxzXLprz/5vfONvxsfGEpkk8v3S9KXhwSFKvOeOPnXg+oOmaWzavfvYS88HLPLatlU1A1cTOWnvph0qtRtNm/CMi5FW7+zZue78XKljBIjFOuICIrVNByIWIkYScLGQVmKM6bYYzM7MTOfyfQcP7COEtFpN3w/WgNda3Ra8Nsry2ljLa4xy3kAYaW/wCiH636eN5JcCWL0PjRHOFQZ670xNXmhoZrrY7zkBgwjA4UNBrpl2/dzsq/uN67rbt2378pe+VC6VP/2f/+/piQmBEwCkAi8sl8rAD/qL/ZZtMSwLKSAsdqDrB05SToY9eOraTjQeXVxaPnny5Nvf9rZmR6W+zzCMbugTk5NDIyOe49LAT6XTbD7PCcLs5PTWHdsIIo163dT1peXlWcep1Gqu70qiHBsZfO3pVyab5vvedHAGav/2Y/9hqH/AtW0rEPkuIYNBQFkAOIGbPDs5++KkMpjQKk3Msb3uJgltoq4E2oSNqCupk6GwBtIg8AIIgIdDh0UIERfhGY4nPCYYBj50dEettVYWFyqzK7W5cnOp1q42DcOiQYAxYkSOF/hoNpFiyWqrsfsfduHoDXLBVS3Oanu15z8Dr960oU9t9wNqGAmug2HvDTA0NvaD7z/MYC6fz6/UKvGIrGnawz/6yX/89Cca9frMzEyhmLe0JQ4UYmjfevmioKmjnufd+fbcyNDuVododgBAJ198euPmRCLGHD8hOrYLQHXn7oStnT6+/HdffEgDYDQi7X7hoQ4AzfF1l/v2njsz+/wj/+nO997+b/7kQ/19/UePHT137rUjNx4ZGBhqNutBEAwODgJAa8263tQ921kulSzNymdyuzfuKWT7CGWWS4uzZ2eFpKDi5rI2bwAdiNSPu4QwxGJIh0uRzF7hcExMLs1MXzq1sO/Wg8999nPIc1nN5OSI1p+ZvDzVFJi24QWpxEvnjvdzQi6R6vjtT79jRy6SU50WYVmByifOdiZar1iPPRcw5EO/cbfRmHv22VcUOW1wjGqY6mKlb3CQFaWpSxcCDvvQP3HhjHESupD6wB+wGS4qnapYnRfPLFw4/duFFJJTjz3xVDTOiWLSE2m9XkLt+vpITIhFZBH5ACMfxpOJDQyz0jSAh0oBa0e5JFWIwDo+ddyAZuLQdbZv3SpJke3bt589e/Z733to88bxbTu2B0HQm6Ffkces8j/6OgekayaJb2yGrXEqWqurWJ3OrdKH/43i65dZbfX2HHvV+6XLl3iB9/3gjRyYvi4teV08AkMDcwAcx9myadMDD3zt0sSlj3/y45cvTcDu44xlSWk2Gx4NhgYGXNfrRWeHYO8YmiWIwtVU7Ugk0m63n376qTve8lbfcQzT7Jl/Tk1P9/X1SYLghhMiCoDA8/Pz85F4PBmLWZbFMSzGmOd5wzYNzeQJbrnuwukzz/7rjw6zxNy5bvrgnuvHt3Gi8Icf/ZgUicQiMmR56nkEI17kKwulRDLxW1/4vVa7y0aveCf2yB9dvUVW+0cw8AJMQhMWFgu8wAqhBARC27DNltleadTmy/W5kPGt1EzddE0XYURERhAFRuBI97MyPdVDl4a6/tWzbq0++n8iwet94/tdyhi6a7s+IXzgbWk0XyxkqaoCggGlhDCarmut9mf/7LNqpy2K4pe/cv+/+s1/ecON1z/z9LHnjh2TRXHL9q39o8MnTr0896OfKC+eTAIqhto3u+f3kEq8lEre9I53rbv1Fu33fn/fxUszALz455+5rpj5yf3fa9iRvr6EX6uozoSBJa4ysWFotMFsc6l1/tzcRz5974ZdO6KiXKqUX3zp+b5i8aabbyGYrdWqrXqzUWk1l5s84TZt2jq4blgP2lWnUndKVa+iQdXtXnrN9byN/ZuyqMgZgkAlgSoc4Xzq1dqVWrPKRcVGpfzck4/d/Tsfv/i9b0b7+86/eqEMQMm2krLMx5hBWaLL1Wpl4e1HDmdYlvK6vjAG/SJgLAAh7ZD0AVHZ5/iXqpj1LA09dOxMrtDn48C1nHq9XJSixKP9o4XyQmX+4sS2PZtnZuZiSiSVzq1UGjXbx9FYNEYsGwOOCBx7ZMcoaC0CSQTxAlCyALS1uVNzL7zcMn0QKMloYqSQYeX0dx/++8VWLdnft7lZVXzf8QKt3Yknco7ptfuH+NvfIlM9mZS9AHAsY2jG5cmJWq2+e/f2sbENjmV3zA6GpJcLu4YF0tc19nBtS/4XFGBrFhuvcfXq3VT/33pev2TY6j02HU1rqW3P896gVLvGXOPnvrNse9uWzQ888LULFy589nOfm52e7k2huTBKoKPrfYWCbpoo9NxgGAZjbLt2RIlh1K0UfM+TJMky7Z/97Kd3vuVOCKFlWQghjucnJyYymUwkEjEMw/d96vuCJM3NzcmimMpmLdvu9S0ZTCzbUjUtGUtYHZUUCs9/5rOnvnr/DAAb3/bWu/7kE5xpsaJ0z113ua4fT8YFgeuJE5lQrTR3YfbW991+6LdurUyvdIsl2PMC6pY+3UKIQYQhDMcQgSMsD1FAvcBumq1Ko7FQq8/VqvPL9aV6p9W2DQeAgOU5hmc5nudEjrCEIBKE8qtwjkavPQfWGF2+zrXfWLYHQdA7D1EvZiPsC/W+wQj5FDmmke0YCzFZN+qI4QKvSywZCMqV8sjQ6Pe/990vffErK8srH/ujjz139LmjzzyrRKOJSPTNd7xZ0zpYFBar9alzF6qXJrx2u1vXJBOb9u0vGVpMEEYKfZcaldx77x0D4KktW1sf/uCg402d+lF5NtJJFF3Lzg/lbr/75pdOn5r52TFPKDg+2m+3xt62nR1b3xdLjQyOxqX4S8dPvnzi5cH+ITkiudhmEkx6NKHi1uXaecM2ZFHmMe+2fClQcmyxIPcpRDE6ttpoi0lRjgm6Y3WMjmFrtmVBigAFiCHlhYVnHnr4dz71V42VJUsS4/nEcFbi7Y6gRAAUw9+iCRzD833XBTDq1p6qGs9DkgnTPXWAkjT+DgQ7OLBamhrwsSHT14iqmo26ZriVRhuaVjadtTEOMItcAByNi0jU1OqmLgTRWv3/Ze4/oOSqznxRfO+z98mhclXnKKmlbmWiJIIJJjtge3Acz7V9Pb7Y4+Xh7/uf5Zmxn9+MJz+PBxzGMOOEjcEYsAGRBEhIIIRQQBJSS527q7uruytXnZzPW3WOELLBnjvvXY/fAdaSqpvuWqf2+fbv+/Yv5IfXr2a4hEmjJi34ejPHEVI6IcS5nY89+sqBQ7LiZTsHA8RU6rWZ2TnTdVIsm0nn3nnVVXpcSr20P1etaRLplhU6myVIMn/wpf5//obSUCEy4vE4R9PJZCKdzVUqpYMHD9q2vWPHZblcm2EYmqZGRnvnTeQheKtzxBsMrvN4XBC+NdDxvFdbz9d/vmHEv/eapWparVEPg4IICN6AGOfdlLdrnYFpmps2b77vx/dOjk/eedddk1NTrbuCscgwjXqdIIhN69ebts1ynBcaXHmeJ8syzbKKIkenbwIvGKbx9FNP3XjTTQzDqIbBsAxNM9MTE21tbVIsZpkGSVE48DlBXFpYiMdiubY2XdNweAJAEoRhG816I5VNO44NSKwuLGz7wudGO3Lv7Ov5yM3v0ssVA5Jr1wx1dvecPPV6Kp3SNVMS47ZnRckr2Z7sSz/bvW7bxvT6jvp8EaCgVaVoTHEUQjiwPUu3tapSO7lQmS9V8yutIlVp6k3N8TySRhRN0xydyCUxJhGFotFn4Aee53mO54G3ReDnb5jwDVpzEHhAdyFPB+ef+YYR7mzopAICD/jhAnMcx/N9z3Vdx/YIWKEJrNQRAIReIQhIO4bH8D3tXSdPnvjMf//jVCb7sT/86PTk5OL8/FXXXDs6enLDlk0AEQEBsesM93RfNDJMCDwgaD9wk5JYXCgsTM/mBPFgcbG069n1AOgAmJs2rm7vgCvLTSuxzZ6DFWOGjtXH9J1/m08JfB+Q2ovLqeIovbqXSW3YPLCu2axOjU+umCty0HTWKU+VHorl4tsuukji2hYLecbjNpOXeDpQJ9S4mOzu6iEYbNqq7po1p0HGERuni4XFqaIuCSJJUiTENBNzoS/LDaPRPHZqtLiykk5WWS6oGeTyZI0x3dUDrKo1PU/xAUC4tY6j7GcEBHaVp79cgj7T+mxI4DahbQKEggCKNuk6tWXXUDKcwLdnliZeu3zdCDW46ZkDT7az3Nb+1b7rHRw7pHrBlp6h9ZnUzsljpUJjK4HasjHdte160SEl2QAdvPD8rqefe/rg1dfeunF47cCGdW6juXv/y+Md46VGfblWH508LbLofX/03/SNg95jEyzJOITn15Y9hkta9TXUFHHRmhdfqsWkGMMxlhcUFhdjsdgtt7y7UFja8/zzUjy+Y8f2dDoja03XenNa/6beJ/h1cHUeiv/1Z/dcOTsnYY6GFf/Z4oV/vzVL0/WG3CRbn/bZmfObNkBvWMed53Z21gnbdqzNW7Y88NP7X9z74j9/4xunTp0KoI8RJXB8o9kwdL1/cNAKs7MghCQmCExpltbR1YkA9EP/PIRIRMBf/uLh7Ve+ww/8wuIiwlgQxZmp04l4PJVIWpbJUrTnupwgFVZWoO+3d3Ybpt66ywGgKSYAQbVazXW0e2EX6bpeb2/vK68eXJ9OfuiGW2am847vwCAorCz1dHW9evBgBF4c2yBZ2jJt7HmCIOiC/tidD97yhQ9IHTGACFM1K/nlSr5cypca85V6qarKqmtavh9QHE3zLCMwfCZORjbXYUpB6Knv++Z/gLTfWC5vzkf9sMDBMDWVRf7ajDlTEjFyIq5J5D5Gkr5lWa4fuK7jhqxV7JgAEyRJczzveS7juYimGQopPWtJ4C2vu1aw1S2Tz3X33fjoIw/93V/9TTweLxaXr7zyco4XYpIgxSVD10lMBi2ILatNOSLgYYL2YuxXv/xXmy/acs22y+KDqxJLRSb0sUHbL+5JJeg4Z3Bw188eybz4UpemdwHAhlIqDwAVgCMjiez1N20dLO2aO2OUvXlihkngLT1bL+q55ePMJ2dP5F+791h6NbN908Vys1nXmvEk397RXqmVTq+cSCdTghSjEHRcX1dswzI5TqQoVpEVjHBMEjXD1GXV1EylqZAk2VQ0gdYzuZQ3rVeBqWuurmfCfTBAmACeDzwnFG340HbJFIYMAdwAkgSgg6AeYJMmEwGEwNK0pmyRNBfrzJ5amvz8/Xf9X3/8tZcf+JdTM693xtOJoy+lMx2PvPrCBf2DPz28890bL77rmSev3LLj///MPX+09dprr7opbjll2dFdDZix5dnZj334g6sHR2YKS/f867dK1RoiqVhMdKoO9uzenq6ZfP6ZR3du2roeyTKqLkCWMww30dXrAa52arJv/Y5csiSrNiYaPC9IsZhmGIVCgReEW9//gaWlpWeffT6dSe+4bIfE07Isu65LktT5EZfn16c3A9AA+HUDiV+1GDwfryFEhB4hgf+/ljWLvvSlL/2+apau681mgzjrIBtZy4USw4hCGqkMASAAPGtwE7qUWqa1cWTDzx64f/+LL97z3XsWlxZ8xw18H0GiXC6VSqX29pyu6q7vIogwwjTCpmmILI8J5Hoe8AOaJiUp9tSTT151zbV9PT22aQm8kIjF5ubmGZJMJBK1atV2XEPTMEmVymVN0Qb6+lzPwQTGBCJQ6w1XK1VJElH4rj3P6+7uGh0dPXrk8Ac/9NH5xQXTMkmSLJfLjWazo6PjkUceSSQSUYIWg0PzwvDz4yWhUakd3fnK2L6Trz9z5PAjLx975tD0kYni3JJlGgSNxbgYyyWSuTSfEJizKuggpCL4v3GDertoqXNzhEi0ELZ9iGEYlmUw4khktsWUoibBwIIwGvcj27bCLAYfOjryPM41JE9DYpIVeN9zw6AGu2/N0CqBnh65cX7rewbK41QAtUxv19JJSkhDBJ558qkdO3bkcrlKrW7btiSIXqtJRgTEBBGa+JMUohkConQ2vXvfS9dcezWEwb7Dh+NxafV3744b5szwiHb9Tb0WkWLa1rUP7bjqeu/yK2eGug53kMdHYnNXDuVv2mTffmPvx25KJ3irqvRz67YP77hp5F3vSF+f0Nv8YuDKXiqVaO9rPzbx2ivHD3St6upbN6AasqkZiXgyKSUbzWatWg3NYB3PcziW4zhOEARJimuqtlwqYoQokjINQ1Fk3XXGjr1+zTu2ieku32W7uvsYlnYdg8bQxySEGJI0aDXodEBQBEGZ2NJPacghIBlOd0xIJV2bNh0z8NSGb5p+jA9cwDLSsfn5BbM+ICXmSisDa4affv34xesv0hDs7+5/+dBRsqdXgkjTa36m/dTCxPWX3GzJsk9SFrASEl0r1p/Yd2R2rjA6dmZyciYIgu62zg2rhwjfEjrnZQAAgABJREFUA46f4uMbR9YHttO5Yb1omUFTo3vaSAITkhRQpFmrddz4DqtRqRowGRc13SoWV0zTTGezPMfZtp3JZTZu2Kiq+v6X9huG2d/fy7KcrmthfstvTGGMtkkf+I7pOK5DtB5mAoDfFHp2Ng+ntfBIFHjB/4bZViSd/y2D2/9nl2EYiiyDNzrht/XAeas229CNkfUjv3zssRdeeOE73/rW8vKya9s+ACRJys3mSqXc392j6Xqrl/Fc3/MhIGRV5gSOwlRrUREkxjiTSj/0y19cuHVrX29vuVKBEHIsmy8sEgHo7e7WTTOyCqBput5oFEsrPV09UeaND3yMMckwcq0Rj8UplnJd37XtZCJRKCw9+ugvP/OZz8jNpmqaNEkapr6YX2jv7Mxksx//2MemZmZ6Ojv9IKAoiueFsAgiAqGwZwgcw/B8n6JoRGMcjpHCtFQnzNr+D2DUr8ylgjeYNeeJxEKLZBRdkCCCsHhFepSokFmur9pIYH0MycAxIEGEvQD0A8CgAPDxGLCMWGezfZXjg80rxwJOnCssFJdWSJrZuHaN41mLUr/ugdzEyw1Vb7qBbZoYw4ZSS/GJ7/343lq9HgQ+CtWHCGGEwmABAhEEdAOQSIiTU+NzM/nrrr4eOO7oYvnQPd/vfPyBOQDEm9634X9+FhkrK6DEtGFKomKxWDyelciM5xP2kq/MVbVCM83H1qwZlpIx3VdUU7cdywkcx3Ub1YZh6GIs0dnWlslk8nNze/ftSyVTV19zNUJoaWkp8hk3Ql1iMkwnbBVjyw4TmgOSxIap52cXNEMTOM7U9OVm7bF7f/ann3zXjZ/41Oj4VGHWEhh2zXCaY7DPsEvHjnvFsgug7ZpMKhksL+qEIja2skYXYEMXdh3ZiTq/Bdo1HyYBgTHDA4hiWlWemZ80POO6vgtOVBvTStNqqrdsvaJYXRmvz1fKtXdceLkfEK/ue8ojvTW9nZZNd63uwzzRbBgsS3uG/PADj19z1XU7brp+32O7FufzwyPrZ/L5mYU8wPjIoUObt2x6z/U3L3kOffwo+W8/IgYykGAChrXJwCrMXvXIN30q9uBjR7PZdFs2k0mmAgKGHQlieTYMt4TxRMJxrL1795VKpUsvvXRwcBUAoN6oozdd1t8GmkiSdO6viiK/lYb66zOyN6k2oeAMBP+v0Nb/xpoVFSnTNFVVO0f/O1cWz71y7sVz30AQhG3ZwxuGdz2z64c/+MEX7/ji+MR4vVn3PJ9hKMMwG4oysnYdgJCkSKZ1sRzPu54bi8V4joU+9D3fCcfwDz/y0ED/YF9fX6lUwhgJglCpVDzPH+jv13Q9ZCMFLM3otl2rVtetWYsxpsOAChJhhmaqpXKrCASBLCuapjIMrev6T39636c+9SnXcTTDokjkum5+Nt/e3q4qim1ZvT09jz32mMTzraoRQkiaoiO5bxStjJmQcE0QwA881/NcLywu/znLDeAAwPgReeLc8rAsK6y0TBRhYIeBVpqm2aHVlOd5wDaR76a9hmBrjKuRYpKgWuDCte1UNjvclavmBvbe9JV6brCWGeQtWWETaaOm66ZpWoHvzS0U5FpNf/0Va/p41fZ02/Bcm6IoDuNstmNmcmp2ZuYPPvDhWr2Gzore33B4CLdfhuRJG58+NH7LNe92PLfYKOKkp7a7D4vNl7eIPX+wecv2Tod1kvG0ZKe73f7VcD2cQgsvzVeOFP2a2ZXJrFq/iohThfqC5mkBAVRVlRXNNh0EiGw2m8u1eb4vNxqmaWaz2QsvvLBcLT/z9NO27fX1dcMQOmdaj2pKVdWVlRWapjlBcBzbdW1NNwLXi0sxhIhKtep5XiKZWF4sUZp8yfU3/uzB7//84Wd3XH1jvDshCaStmbGOdmlwKLlmMDXQk+jpTa3bmNm00bOhP+lAkWh9KOFhK25r4MDDpmcbCOgOVOS7vnEnobi1U4sPP/f8hz/66VcfeZQsVo6Mjz77059wHsV5wdpkZucjD6fSSadpmAvlnU880DHQl6FEoyTTLpngmYCA9/z4wZ2/fOzk+JgNwXh+9uTkmA2C2ekZgkQnTpxoa8slkwmYSGeuvJTWTXupCBtNE0JrIZ8c6RTWrW9WGxyfisV4z2+tSoahW4vHdCEIPN8LGbn+hg0bN27cuO+lFx9+6OGuzvaOjk6CAIZhYkycAzYRkZumaUEQlpeXjx8/vnPnznw+v2XLVsM2iHPS4bOBjPAtQC2CLER0dvmbjrx/PyeJ1Wo19HUKfttDeLYbbv2DEFIUZf369U8+8eTPH3n4h//+/fxCvt6ombrl+V6z2VxZKfX19RCIYCgm1MGzPM+btslgBpGk61qhvZzbls3+4pePtrXlNm7evFwo+C2AQ1XKFVmVVw2uMk2ToikCQobjfddbKMwP9A24rhsFlrS+mSTrskxhLEqSY5qu7wMIMUV9+85vvv8Dt7IcXyuXCRJhTM7MzLbl2iiKVBRF1/VYLHb33Xe/9NJLXV1drcKKscALEQXmnFTzN20ZIOR6vBV7hpMpGNE1WmuGgrTCljblOStGnKB93oFnxdKE7TmuFwDfieKjMIKh+QHyfR9DQLGMCIGSW73QMcKIwkXHf+Hx6VK1vLiwaFrWunVrU8gdo3Ox4qStqjU30Ew7sEyfIBMxkSTJMNCDsH2XoTGDMWRoARO8Y3k0x1o24NmdTz77qc/e/ok//vT0xATL8aGdJ0YtoIk8ADvSuYf2PUBkgmRPsmysUBSdAPEM3znYMSy53K6Hnolz6fe8+90AgLHJyROjry1VCplsdmTDSCqVaP0Ez/WdABGEpukLC/nAh+2dHZIohcl2MMKSDMM4jlOtVh3HYRguk0o5nnPgwAHXda+++up4PB7qKCDHsaZpFQqLgQfS2XSrTbbs1iYCIEuSpm2+fuz4ydHTR147fkkm/Xc/vcv15MUVdurgaDZLDV2+0VZ9ECAAEUA+bmFlELgACqQ8WVIfrFPtPPA8EEDPDtiLazzCgQeNBOtptgggwfqLc0U627lSLGHMatVmsrvXZ7MdhINddQEKjmt65XkSwpphbNy4gfbMRq1AktgnWCCyIoENw/7Dz/5PMRn77Mf/W6FY5GPiA488bHhOZ64dQYpyiauv3tbf3YtjkiklXjl2DE+OX8UJPvSXf/lLOJK+4Bt/N3542vBRNp2MPJoQIoPAM03Ttm1RFNrbO1zXOfn66EqxkMlkJSmez89omnnFFVckkwnbtkzTchwXAJBKJaP1eeDAgX/4h7/fufMJAEC5XE6l0qqqvCnGe5NE/dYc7XO8/CBsCAKO40zT/L1pElvNl2Mbqhn6XAW/vWC9KTKHwND1dcPDe/bs+fGPf/zDH/5weXnZsqwoocs0zaWlpe7ubtM0WzVC0zXd8DxHVWVE0zFBpEiK4xiSpPv6+p597rlkInHZZZeF6YGt1rJSqVTKlcFVg5qmRX0TQUDHcWdmZvr7+0hM+q0dEofLmpNlOQgCURQtywqnREE2k/vmnXdde+21A4MDy4WlCC6Oj48xDIsQqtVqtm3JTdmyrHK5fN9990mSRFMU2fqXFAXedX0UGVq8cX/O74h/7YZEnd25TOnoD2+eIofnh47uQAIi+uzP9DwAXB15HuFagBUoBGhGtAgkhxgwns5uzvE717y71L1VaCxrnMj57sjpZ/tqUzXTyc/PUZiq1eoMwyBDNn0fEBgRiGI5EkGGwgEreqbpuL7n2n19vaIkWY4nNgsa5srZNe0zB5c6NtK2yTQLTz+95zv3fm/L1gsW5+fDRwJhjH3fTyczs3Nzh5ZfvnzbZX4FUC6blbI8I/qBF0BPSAhiUnzk8V/u3vP80NDarZu2JJKJ0LPYsgwzHk8QiNA1zbAs13UZmpZikm3YtWaNE7hUPOW/0Qtb4QUBsGwbABCLxZLJJM/zY2NjL7/88uDg4PYdO0zDKJXKDMNSFNlsNur1ejKZzGazPgDVcml6emZ6atayLIrCc4uz1aOjf/7d+yxDLvz8/soTTw50pPktI71f/BwjZlzTQCSCrh8iBkzFuLpcl+9ZoiTmbBikhoPNKp+CsOYZSAOZtImTJOHQFChXtcNPPO2bumcb8bTUyJdvu+22eC7x0vP7ZucWxJjYqNRKmo46ejZv3HzhpkGNJGs2ZJRKHMGjB4/XISkk43/3j1/rau+95orLFvILFbUJSUiRzOUX77hoy8aJ8bFsW5YEzF/feeef/sPf+gYeWZ3hdu8dve8Xub/+ar60srBwYs2atYlEwrNtN2zxstksxnh+Pn/i2OuKoQz0DgyPDEuCGKUFlkqlXc88m0y1HqhY7KwsqVQq7tz5xKuvvtLV1fWnf/pFSRJN02QYptFonH8K+UagWXBeL/croMtxHJLEPC9Ef63VaqHvRfBfWrbO1izbjoZE8FdHWr8FdkEIDcsaWr1679693/nud3/8ox81m83IiZSiacs0FwqFVQMDEWktAhEYY1VVw1eCRqMZNqRqIpHa99I+BOHVV1/dbDYFQUilUrbrlovFtWvXmqbpuu45pff09HRbW1v0c6J8MJIkZVl2HCeVSjmOQ1EUgLCvp/df775744b1F1584fz0fEAENEXNzs0RBIwnktVG1TGdRr2uaMrc7Hy1UVuaXzh69Ggmk8EYkxTJhp3s+d4YbwM236horutyHBeZ20UuM9FkynUDL3ADiFpYHiACAgZBAqEgTIVjgB9I6USg13s3ta9M1TO9rGelXaNuGDPT06phDPd1s8nUNJHI5o9qPoFrKyVS1FvFzo7xPEXTJEm22klIUCQWSSJmyrxna22DgRDjTMUTk8vzcwsLC7FEbN26Ya1cLmaHpnPrisn+tuKk7zvdc0fXuMXFsjwxMfazR3+JEVJkmWaY6EAAALhn955brn0XJkif9HzoGaaOaSzGJF3RpqamKuVaZ0dnJp08evQYx3M333QjRVOVSrVRrS6F5uvZTIZsNe+IQITnehRFhSHqNcu0YvFYEAS6rpNkNDRgKIq0LKtWawAQSHEpnc74rrdn75783Pzll+0YGlpXqVRc1wm9Bsn5xfnp6bymKI5lkAyTzbQxNNOs187MTb/64M+/+HffZrt6zEZ54YGH++u1zvddlrvpfbZvQ4I6FzpoARA4iu6VKj+ss54IaKfVEumY6GryawOMc54vQ7vuubzDiDCwdZ8p1w3oAQIEtunAuiYION7bXV8uleqlWCrNipmmogGMITK7Y0iMx+jAZhkbmDYgkCnbhbklwjVc3fMCu7e7jZXSL+zfk4gnO7NtlcWVnnWrZkulkaGN06XKv/zk3nhbD6DjC6dPLL269y+++93Vay84fmy/7VixRGJ4ZCSXzdbr9fHx8eXlFUkSVq9a3dXT47caQA8hqGlqsy5TDE4m08ViaWpqcu3adZXK8gMPPGSa+qWX7vjAB95P03Q0oyBJLMsKSZLRlPwt0sU36V1B0NqA/TCUKFweYGVl6bXXjsuy3N3dPTw8fG6f/q8oW+dqVoT0fg1W/JYBMwRQN/ShoaH9r+y/+zt3f+9736tUKs1ms1WbSNJ13MLy4mD/IMY4cjuKAJSiKBgTNM1CABFGQeC3t3fs2/eiLCvXXnv11NSUYRie5zUajYXFxVWDg61lTTMcx3IsywlCfn6+o71dEATDMKK3TbR6EE1RlEQiEW3duq739fXdf//9vCi++5Zb8gsLFIkZliuVioZuZNLZVgNrmrquWaZVqVQWFhdYmpYVdffu3fV6PXKYo2hKFMUodPJ8wPX2wPONV2zbjv4cdtkQBQ72XeR5kOW4wEKMqEBS13Xf90mOu3Ro1RRgd23/DO1aa+aPxZrzMbWSdOSG6cxOTTEsI8sKEbiUqZuYBiDwKTZmqUzg+Kl2wEm+Y9u27dh2d19fSpLsRvGxzz1EyxV+ZXrda4/2zb3i0aKb7pqaW8zPzQyuGlzT03kCJHh5hSZRqWfL9kP36ckek+JYgdv1xNNd/X0/vv+B/NwsQbR622Qy+drR1zo7O3PZpOO6AsuTrbLiTE3P5GdmaJoaWDXU0dFBsRTHsrFY7PCRo3v2vLBpy9YtmzZphuK7wHQt1zTT6TTNMLZphZRdaNsu8PyG0qjV64l4vKe7lyCQbqjRZo4xhTFWlEa91iQxEU8mcrlcsVjcvfuFZDJx4403UBQeHZ3Iz00rqkoimuHozva2TDZdrTWXl5fUplLWGs//5Kcf/8Tn11/+ztkXd03++w/aKVJH7sCnP9K54wrzzCTEAPoGY3lms8ym0vLiGWthKNd9heWXEISeJwbePHCfI7v6me50orMrcBxPNkiKqTTNsUIt3dbPcMLp8VlJymLf6+5sb6qaaVm60ky35Z559kkGBH9wzWVJiYDYMVz4yK7nXz/92pYLRiQPUp640iyfPHVq8/qNHISsEBufy1frat/qPh4RBKSOTU/ajodZ4dE9+3yENq7b7DP8iwde+fQffvSrX77j8NHXMkmGwuz45PjK8grHcWuHhjq7upPJeDjnsizbrNUajUYdY9wWnnJEd3V5ufAv//JNXVfvuOP/Nzg4GGGlRqMRcbwjluKvPubRYiZ+1dvKQYjkeT5a5GNjYxMT46ZpXXDBBZlMhqKoXyFP/K5tAqNi5LpuhLPeWs7eSuWIXmktL1VZs3rNoUOH7rzzznvvvVdRFFVVIYQkTXqONzMz193d6ft+xG73fZ+iaUM3SRIKQqhbRthyrY629ldeOViqlG59963FUhEhxHFcEAQTUxP9vQOWabR+qKbqhg4DmJ+bi8fj2VwOACCKIkPTXHgfm001m02Zpun5nqEbvT29jz3+WLlc/tjHPj4+fsbxPEQQ5XKpUW92dncqigxBYNme53q2bU7NzAo8rypyvVGvlqrPPf8cSVKSJJAkSdMMz/NR5frV89Nzg0oEUAtOhaGDZ/FghM5anREmgJAQCF9uX51uLJY7RkS92e7WC8VyqVRWVaWrt3cwxo4RKbE4qQUQy7VqgFU38E09Lok0w2CMHC9wfR+TmCXJhK1ZqU55cEvf+H6MCTveVi0uz8xM84K4cdMmN3/mscs/31j3TmH+aJDIaOs2rTrw6BW77jSyfQuz04VCYePGDd0xzgyH/6am1QKsNlVNaSqWKVH01MTUhz/1R1/9y784depMe0ducX5xenb6lptvxvGEaprLKyvLy8uuabW1Zft6W+25a9oMgxEEmqoYmimJku+5zz//vGNa7731vZlMplGrqYZRr1d5iounE5qq67oOYcDRDC9JNE1XyuV6s5lOpaRY3LUdNwg8x/Z8F5EUAljRZdd2Y4lYT08PQeD9+1986qld2Vx608ZNqUw6LkmxeNwyrYWlQq1c5gUBEGBpbn6hWN7/1GPvu+bWaz/zJ7W5GW1+0idpO18mMzHQLsnPvpACauAHlOYg1u/efpVVHjUbHUzsFtsrYYgJgnZt1YXPsu1pLpdhe9oDy4WYBAEGBAIECQyqPD9XazQGBlaTouA3NQJDQJP1paJhhwkith1vS9EMDqhgfn620VQZgXKBx/G8Oq9YpkZQjKOaUoL3gRPYwAFEgKDIk7ZqGQEBScrxASNKiUTKDVqdv+ejhtxgaK6h1Gcri4HnZbPZ7u7uMMAxxouiY9nNZk3TTc/zBJ5Lp5MsK4QT6srBg6+OjY21tbXdcON1qWQmPDFUwlOg1vVrvPFfy2Q8H15FwbdhBVx+7fhrBCAkSVq7di1FUW+r/vmvQFuu676Js0J54VkzsrdriM5duq6vXr36+PHjX//61++77z5VURvNRnSk6Hve3Px8b08PxthxnOgxJghC1uTADSiKajVQYc3u6OgYPXVyZnrmtttuq1VqQegvHgTB+OT4QN8ASZKu61JhRCLHcTOzMxjhWCxWq9X08HJc13PdRqPR3t7OMAzHsgRCA6tWHTp48MSJE5+7/fa5+XkvFBnLirw0v9jZ1aMoTcsydcMwddOyrZnpGT4m+Y6jaZqqqqZh5ufz+/fvj8ViUUwxy7JR0x5hrihdwAMYBI7rBoRjBmHyKXYtyPDwjfZe07Tu7t6etvSiau696g7fsTuWT/qMIKjVS+deyOve8sICx3HlchljinE1w4OuH2CSlGyFxdBLd0GW9x3HcWzfD9qy2URnNzM3emjHR1/7408Tu84EG9d94o7L/VQSSNmF/PTszFxHd/fmdWu0/DRdnjuz7cNzicFq7yYB2O979h/9dOfk+HgjPK2jGNZ0bENrlQ/kOQgzHM8mEIrziHL8pw4d++o/f+OD77t1Zi6/srw8Mrz2yNi4OXaaoegEiTs6uzJ9fRTLW5YdENgmvLqquTDgBIlkGERTgiS0JbOnZqd37dq1fsOGy3dc7oGgXq8tr5RkudnV05POZFq9ouN6YTIPRZO+5a6sLMMgSCRTIPCAG2Jb3yUxlkKou7i4uFIoVCq1eDyWy2RmZmcd237HVe8QRH6lsOwFPk2z9UZ9Yny8qSgCKzjQf+3Qq2BqcdXQkGrppJgICJfwfFc3PdsiGDbAKILC0PUtRfegzsIUbfR6wIjGOQjSGj0OCcKzXUvVYOu59DFArXcc+AhCmmUwhIamOo5DwNau5XhOGJFOotYThEMMbAUe4Hie4/nA8XwCeJ4fAEBSlB+4Pggcq7WivNbPBX4AHMdGCMEAhE6byA8c2zABEcDADzEPsuQGGY+v/W8fG+of6Ojo4FnWcpxSpVKvVhmGkWIxSRRDeNW6Tpx4/dVXD9q2vXXr1gsuuICmaT8IGtUq0SpW6K0crrcT97QeT4zJyETIdd3R0dGxsTGM8bp1a3t6es8JrX+NDvU7L1tBEJiWRSJkO845gfCbrdCv6nTPx1kRQ2JwcPD0mTN/+Rd/8e1vf9sPfFmWOZajaRqTOD+X7+rqoklSb1VDGAQ+xtjSLQ94oihGxALLsrLZ7JkzZ46/dvx9H3hfqVRyXReFw4/p2dm+vj4uZNNFv5fn+PmFeUzgju4OXdOjvpoiSTfwVpaLHMvquq6qqizLiURydnbmueee+9znPqcpCkVhVoojAi4WCv29/Y6lG4ZtmIZlWJCEE+MT4S9E1UZNa8q6rtdqNU3TpqamxsbGEokEz/MM3YJcrVUeXrbrUYZMBw4KAIehJyUZlmMRVMT2Sn7Sdj0c5s5GRlGbN28VPW0WxXBlybV00KzIDmj6MPCDuCQiijo7mfIhSWMeQx6BZtd6O9ExOLUfMFzAiZWl5dn5PMMwGzdv9dVqpdp88k8fEGfHlEsuFxsrt/71zWbfMAXhfD6/tLS0Zs2a7oEBpCk1SBIrc97STMNwK7RU0wyKolu/ESHXtklMCTFBwIDiRNazeLOpsqkanzLaVzHHnt/38stPPr/n5PHXZFnJxGLkmlX9zzwXe+Ip9OAD8dOnNYKwLTOoN81GkxFZghMcRaFNmxNFlIzZhmbJSluujY7FH3r0cQ6Avv7e7vUjHkbQ9UuFAsmL8d5ugmIsy8SY9Cjax5BAhKw3G6qa6+xMpNMQIUoULSdYXC4uFld0y6IZpr0919c3KBCIBGCyVt6z+4W+3p5LLr60Vi2tFJZJkuRZVmnW56dnNF0vlFfqew8ICDmBDwKAQsVhgNCbOXdBSCEPfECQHjBF2BbHazyghy+TvmeX/eOIYAgQyjshDkDI6gIkAbwgCCksmKAhDk1eomed8MLU4+jRR5AEkZG277lBQERZFAREEIfIHCCIW98SJpG2voYAQjgIbQrCFLfwSNmHEdMgfJ+ErWkkL7zjs5/mQuDvO67jezRNsSxHQJhKJjlBKBaLL720v1BYzOVyV1xxWUdHVzS90jQNQEiR5K/zTuF5Sf7nNWCu53AsQ1EsAGBpqTA6OlqvN3ie37JlS9QkRgf35yZZ/6Voq95oQgJyDNmqm2+M3s4Wq8hBOTopPM/ePLJHMU2zv39gYmLiy1/5yr0/+pFjO8VKEQPs+q4fBDNT051dnRzHeZ7HMAyEkGFoyzC9MLLJDk+Lotl5Pp9/9eDBD3/kI4qiOLaDUWu7Gjt9OtvWJgiCoiitx8x1BUEol8uu7w70DRiWAfyz7wdCWC6Xk8kkTdORTC+dTjebzbvvvvv2228vlUrlctlsbXvW7NxMR0cnRdEEhAzLEJAQeGG+sAAgFHmxXq9bli7LqqoosiyvrKxYlrW4uDgzM4MQkiQpnBazEHpuABIYOgObYACXhi5WUByLMTcI/NIK7BrcfuCeuaOvIjFBgNYnqiiKbrTqBdAbXoAgCYEH6Ba8jjMUizCKaFlBEHS0twuZHDN7/Kk/+tbi5guBCXpGD7/3rg8V1+9gCbywMD87O9ve3j68cQvMn1qyyCf++Ee028w25m7cfbefyJAkOT0zU6vVoiMh23UtWbZ8H1IMxYscx6QZSmIpD9HlWqVRq3d0dAz1dRccMrN4rBHreunKz6FmFQKPrha2Lb0yMz09vVT7m699ZWCgl+OEuCCAkQ3eJ/5oZXid94XPJ2fyAU37IQGYIUlEEAgSlmMZikpjJAmiqWn1SgW4bndHp95sPv7QQ12dnddfd71v6M3lUmCZK9WiKEgCQTj1JkFAQAbAFxg6FQBaMS3fdpBhzE4cr8bcmNrsTaR72zvIREKGQXFxkfYBH4tJne2QZp564ol8fvGG9727a2hIU7VKpWRh0gzA/OLCa+Nn0OzCQDztEhiE8nKAQtwSeX1CIhretMoMJAJsCl4X7w86QPc9myQFhO26+YrvID8UVwEI/bMihtZ/LMvYlmNZJiIwCLyIvAcBwAiF2N8GrTqFCITDc5owog0DRBCm44AAEK0aGniOBwiEMXTCt+R7nu7aGBIs36pBiq67visJoucFsqwgRBAkBUyLb8+1XbE9F4tnslmGppOpVEdHFwoL3969ew8fPixJ0qZNmy688KIQUgWy3HBdSJLo3KjnNxE8o37QcRyMsSiIALbQ35kzZ06cOClJYn9/f29v7/nzot9uTPI7LFulRp1GWOIYy/XO4ayojw35SgT+Vb7SGzWrhbP6+vrm5ua+9Od//oN///cAwkqlwrJsdOQ/MzPT1tbGMJSiaG7geqYTQGgZpu17UjyGAp8k6QAEmVS6Uq3t3r37wx/9iKYoUYvKMMzk5GQmk0kmEyFHDke8uFqtVq/Vu3u6W5tGeMuIMHasVqtJksRxXBQAw7IshPCub951+/+4nWXZRqPBcRxFUWNjY6lU2oOgVq1qsqwpihcEKysrpml2dnY6tg0RCnzg+45tO6VSKZpWViqVQqEwNzcnCALD0OFBF9PUjHYOxS+95ufrP7568ZBOsH0zB0mzAZs1rlFY0T0bQJGh/LMyrpBe7zih4T0looBlWEeIS6YSCCLEDIXRYqGwsrKSiMUGVq+1m8u1lcZT33mRPXPaWD+85sDTO+7/K717Lem7M7OzxXJ53Zqhzr4+pNRcuaaYTlAvF4V0TTXURhMDJ5FIRXcM41YfzXCCiIKkUUWOrfIZk+Yggq7nnzk9Vm7ULxzsnrry01WfWNxyU8/Yvpvv/6yS7ic9W4tls5nM7hf2b9q27e5v3nnq9GkMgI8pt609fd07l/7mq0ZXTx8kEm2dmqkD3yUwwgSLKML3vUq1rumqGIslkglEkp7rsaIYF2PPvbD39JlTN9z8ruHe3iVVtQytVq36CLV3d7Ak7yACeMBaesErHaScaqlWPHAkf+E1X1i77f0MARylrisacB0SYRKAWqFQXCwgjDvachleMuXG4QMHlovlq3dskzC5slSou54mK2dOnjxZKc0US57jESTyNMMDASYgdAERuIHnW6Zpe47IccALVEsVmDQFeQJ6fDLplysLWnNZCNKswMcEz/ZDkQz0Q82Z2sLjlZ6uPgIjzdBCSh6BMTJs27EdSRIRxq7nu45l2R6EPokpgsC6qVMkFkTJtU0vtOnALdQYRmH6rm05kiSls2nHNOcXC81aI5FJURQ9Ozu7erB/YNVAvak4ps3w3I5t20dGhoMgyGQyW7ZsAQDMzk4fPHioUCisWrXq8ssvT6VSAABTV3TTJgA6K+D/jXOes0eEntda/KE4pIWkCgsLp06fbjblWEy64MILqXBo879YrX63ZWulWmVIMiYItuOc6/5ITMYT8Yh5ZNp2uViMcCBBkuf8q03T7O3tXVxc/PznP3/vvfdyPF8ulWiaDvctYnpyOpPLiCJvmQ6AIFKrRGoMhqUt23Edx7IsnuVVXX366ac+eNuHCQQj+oIoipOTkzFJSmdyqirDkIvIMIwsy5VKZdWqVbZhAIwDx3FDolapVCIIzHOMGVo5kySZy2X/6Z/++bbbPtDf21sslxGEFE1PzUwJvJhOJjXDwJhwXZ9iqHqlVlhZyaRSlUpFldWGXNd1w7GsaqMRBX7puq4oCoTw8OHDYeOZCKNZeYhhpVrt5CkcT+PyvOp6uuNbgHAdz0SIB4HIMg4rhmj8LG61bburbyAHzDN92w5uuNV4x6qLv/9vW/b9oNk1jF2LpOnR0dHl5eWhoaGBNev80wcPj1w7duV/h4q8Zm7fFWd2m6kOGhHjE5MRG5MNE4xMVfVcFyCUMOpxGtPt3SDXSzhOo9lQFCWbzQ6uXq2WSxZE+2784tr9P3vh9n8aevwHVz359/LIFbbcGJ+YNEzjHWsHHFYaG9jRXzget+qakAaOadmWY1j1SnXvgVe//NWvfPIPP3Ls5CmBJNmBVbyhZP/H542X9i69dhQ5fmdPN00i1bBso/V/QQha9Z2iNcvEIGBohiAI1/MAJNrb2paXCr94+BFBFN9763tYmm00m45lNhWNSg7jM39mHv+OZwBEgZgAJA7ERGDpoEh30dfuN4gUhw3Dsi3LcFyXkSRBEEzTMi2T43k2FpMgmigUXjn0cm/PwPD64Uap0qjVREFSGvXZqamlQqGFStJpPwh81/a8wLb0wHYohnEdV23KKV5IxCUCQxg4zWJldnqqVphPrhlp37iFAYjl6Tf0Aq1N3DIt23by8/mF/PzWCzZt3Lil2WzKisqQJM1ShmmpikbRJImxY7uIRALHAQJHOYX1etMHXls2SzO0Zdmqari+xTFcKpkIiVfzc3Ozrhuk0qmOtjZBFBmG1jTj0KFDAAQXXXQhx7CaYWZTqa7ubpZhms3m448/Xq1WY7HYxo2bLrhgazR+ChctiJiGv0nCcX7dibhXgiBGkOXUqddPnDjJcUJPT9fw8PC56dVv7wf/i8pWuVajSSouCmbYr4XdX9CCuLY7Mzcz0D9w5swZnucvvPDC6B3rqq6buuu6tm339fYuLS/fcccdX//61ymKWlleoRgGkQRDs/Pz892dnYl4PHQ9P1uYLcsyDCOiEUTUW0mKua774IMPvuc97wEEbNYbUS85MzPDMExXV49lGZFXL8MwlmUtLS319/eHShcvsmyhSLLRbBIEIUlSRJJyHKerq+sf//Eft2/fvmXTpvz8AkFinmcLi0sURbV3dGhv9JsEQei6vjA/39s3YNq653rAB3aY6FIql9Vm0zCscq1crdQB8E+fPl2r1UK5shePx2maYhgWeKCpN33TdFogLZA8U/AdO9ORtDU/1QmkGFdcmHUheoMzoapKMple3d9bNYzY8f33/vVu0N7xiS9c1FyzxXcsmmJc152YmGg2m1u3bk1nMlR5wbQMRzdM11sk46rc1OQGDDm0bDjCwwhJ8bjIURJJl/svWOrbPHB6b9vcITW32rOM0dHRarW6eevWVCym1qtn2rec/D/+CswAsFzdcPS+ra/vpHvXlQrzY5MTJMlsGuwltUo1YKuhY5Omarqh+wEhCQxNsyem8v/2zX+65uqripUaaTlg03rq3vv4Z5/Fjz9eO3pkpVwSJVES4wgBkqRDrUnItsWkFQZxM0yY2GpZhm7GErFMJnfglZcPHnzlhutv3Lx5S72+fGLG8V7/ct/CTwwJZHM0hfipvH1yWiuUKSbO3XpxffDqr02Z2ytWJikCkmTZkE0WuuMDz/UUVSERYmgaU5SuaQcOHKjW6tded93gwMDi0pJjOxzPExDVahVZVUVeIELxOaZICjMAtXo6kmE0wzQsU9e0xcKSYdi9fd3dff0cQp4qq6bp2jZN0gAThqZruoYJguP5XGjotmf3bl3Xrrzqqp6unkqtomla4Pm6qddqDZqmu7u7WZbWVN33fEBAjDFNUaqiLBeLAIBkIplMxDmOs2xreiY/Pz8HEWxPZ6VY6ws8z7m+r8iyHz5BY6fHTo+Nbt++4+qrrw6C4MSJE0ePHqtWK5dccvFFF10Une7JYVQHQogkMXjrSVpECT8v7SdsqgDLYobhw+nV8sTEeLlcbm9v6+7uTiSS54T9/9lq9bsqW+VanaVIgedDnAVCOW5k6IsIAvzLXXf97de+BgD4k8//SX9vf64tNzg42NvTk83lotPAWqPxudtv/9a3vpVOpxcXFykau46PMJ6aGIvHk7wgaKraAs0EQTMkCFDg+kJCJPxWX+fYNsMwCMKfPvjgu26+WZIkRVFIkhQEIZ/Pu57b3dVdq9WCILAdhwi9WRaXFtesWhOdTpIkCRFkabbZaDiOE08kIsaG4zj9/f333HNPT0/XjTfePDc3BzHmaLpQKKiq2tne2dSawAfRiSSAYG5mrre3xw8Cy7LcILBNE7p+U1cUVWNo2jA0palCAs7Ozh48eHB4eDifz588eVIURUHgaY6mIA0x4QWAxYRIU3qqp7Ju+9anv3PoY186tuqWQMBbR1/ceucnj1IZgSYjCszS0sr69evjmSw7fuS5Kz859ZHPffiOd7LZNkiyBAGiNlaWZdM0eZ43Xd9UG44fUFoz5uoCLwRdA2y6jXCceqNhGEZ7Lte5ag2YHj0xdPkrX/4yeGgUdPXcds/HWEcPcj1KozY1NW2a5qXbL+MCG5QXd19x+6X7vk9wopXMcWpFD4hyuWwYhu/7DVlzUWApKosRy/OcGOc5lichSwQ8RjM1/djY5E+/9922rk4EIWE57iUX0x/6sD3Qb3/lK9zx1xuBw5B0W1snQr5pWm/o1BDGyDT1KOBPkiQyDHPFmOjo6HEc69v/+u3icnHD8BoyswVM/tlF9h4yCfcuXv+tXzRWFA8zYoat3rbhxB9c29N95f9ZnD7zinHLptXZWDJj6nrokoUwpgDwLdsuFouGYSTi8bb29mQiVSjMP/vscxwnXnfddSSJi8Via1eD0DLUaq0ucEKuPYcgDkDAMVwAg5WVlbmZmUKhQJLkyPr1I8PrLMtSZAUAgEMqpq7r1WrVCqM504kUwsh2XNdxKJJOZ9Pzc3O797wQS8S2XXQpRFBTNEEQxJikKPJKaYUm6UwmQ1KUa1mO52m6DkELkEZ7uWmaK8ViqViKicKq1avb29tJjG3HKVeqtVoFAEhiTBA4mUp0dXUBAHbt2rVz5861a9cKAr/tkkuH1q2LVLq5XDaip58nygG/6v/3RrWCIPCDEF6RoiiEvaF/6tTJ0dFRiiI7O7s3b94UzZ3/s/3g77ZsBUFQqddYmuEFwbGst34VY5xMJvfu3TsyMpLJZF588cVCoZDP5yuViuU4DMNsHBl58plnvnnnndlsVlZlmqS9IEAQjE9OxSUpkUjomhYgFIQIyDbNuqLwLNuCmqEgOQpoefDBB2+44YZstnW7EUI0TS8uLlqWtWpglW7oCIe+oZjyPWdierq7p8u1Xcuw/MBzXJcmacPSTdvpaGv3fY8Ko+W6u7t/8Ytf2Lb9oQ99aHZ2FgFEc3RTbtaqtdWrVxt6ZGXpgDCpcXxyIp3JtDCXpkEEPdsLQGC3QLvKCZyutjZV3/Mbjcbjjz++7dJt1VpV1/Xx8fF8Pp/L5ViGIUOet6abm0eGPKVx79d2AgH0PLdXOPXy6S/85dY/+8zUO//giqXXwZP3LhGcxJBe0KqYsixffOmlNGg93qoVsISlOkFDt2S1qYeVOh6PUxSlqmoEphIYmoOb9UzfUvvai579rmg39bY+S5YnpqZqtdqWLRdIMV47/urP//wxkEuB5TrYMXjbH15OZDtjiUSlXD5z5kz4KG4Avo+XJmskpyl6U240XRC4Hhuav5AkFQQ+hXBnVwfAmAl83qi6ANoAy0y8wmX6nNqhI8cpgf3+v36rsLQcFyUfADiyLnfNDeCur6Ptl1P5uaZlViq1bDadyaR13XQtx4MeBC38RVFUqJizUqlMdKhy5syZer3a3t7WkJWx10+uGt40enr8g10/6aVeP7UITpZyg/3ZvngxTVdMxff53vR1fzl9MD/DfiBGqQSm+vv6MEXJjYam667vszSdSCQIAteaVcd0BIFPJlIMyx05cuTw4UObNm0aHh5WFNWyWmCfJHGzKbuum8lkPdcbGx+bm5tjaXb10OqBkM+8uDCvKGommxUFQQ4v1/UoipTicZokNUNzTZdiKJqmCAIEPmzBXowhQZw4fnxicvKyyy7bunWroijVaoXjeJoi60qzUW2wLMvQNIRQEEKfLE2bn5+fnJyUZbmjo+OCCy5IpVKGaRq6bhiGbugtGBTOl1OpVHt7OwDg6NGjx48fpyhqZGRk69ZWMyg3GwcOHuzq7Dxw4JWOjrbrr78xGg3/dk1LNGFgWTZin54+PT47O7NmzZq1a9cwDHcOXkUB8v8fSu6xbDtwXZblLMd+W0kdAMB27c72TkM3bMc+J2IKR1pGuVg5dfoUhFAUxWKxyIuiwHDpbEZVlWwu19XRYZk2gEELf7a6NktVdZ7nI9VLdF4mCMJ999136aWXdnV1VSoVmqYjBpZhGENDQxHl/Zx1/dTUVHd3N0PTVoh+gyAgSdLUzXqzFo8nDN1wA9cy7d7u7hf27p2amPjCHXcsLi5GMhHLsgqFQn9/fwtThLI3z/NYlp2ZnZVEMZFMyLIc+EHU/VmWVSlXGJaxDEOzjMALaIr+8X0/vuTiSxBGK8srET3vyJEjrm2ns9noV9RrNUpKbiPkn9zwpeq29wIB0YW8JcsgtxokmO79j39g73cONhzCMomQf2wYhuO68VjC0GWzUdN8GDhu2qwJLO/1rRNEwXUcRVFc1+3r7U20dcLxww9/4O8r778cfG8Ps2HovT/4AmWqINtVL5emp6cRQhdeup1Wyo1687kP/z3jErHSmW2vP+5xomVZpfC0IZJMYYxbXZ/vYYyjGCCe45iw2zJNs1qumI619YKLoamXXbC4aodBcgZBBp5LG83h8T25ZPyXz+95/0033v7Z/3Hy5Ml1g2uSawfNhszc9C73lf1Q0znP9Qliaangum42mxMEznG8yCKEC32xFLXx+omT8/NzNMd0tnV193RKAs9zvNJs/uLxR+574PHNg+lPbq8OMHsTEgAOMC2gB8CxAKB7T6z0PFcYftcHP71usLVgVlaWWJbvyGVphkU0jUI8DkCAKcoxHEVvtmolSfG8oGnaiy++aJrW9de/M5fLNeQGTdIEgScmxg4fOWJZ1vqR9Zs3b2ZZLqwYOkKAZQVFUWZn857nhH7fAqbIMKLR9zwXYex7wLYNP/BCdosdBB7D0JwgZVIpUzef3vV0rVa74YbrOzu7ZLkhN1UCERGFWxCETCZTLpdHR0frjUZbLjc0NNTW1mbb9uLiouM5AicYhmkYOs2y6WTrCkUzK7v37KmUy/F4/OKLL163bt3CwsLy8vLhw0dM00ym4jEp/s53XlcqLbe3d54bQsFzB/9vPODREY0gCEQYW3vy5MmJiQmWZbq6ulatWn2+7P8/sgz4PZUtRBBeGKH5thrgc7U6okpFZ1JRAW71zDQp8mIkYmo2m47jyOddBISu5/E8L/JCPCnxfBzCIJlMRsx423URDHhevP/+/5u9PwGv6yzvRfFvzdOe562t2Rot2ZYtO3ZiZyAhJgTIQEII4yEdaKEHWkr7Py1P/73PPbT0XnraBnp6y6UM4ZRCIKRAIIGEOKPteLZlW/PWLO15XPO87rPXJ23LQ0JOoRRolp7HUWRpe2ut73u/3/u+v/f3+8bIyMjAwEAulwMAUBSVz+dhE0RVVQxBMNeYmOO4xcXFlpYWj8cjKYo7b2EBDLN0vVarJRIJWCbTNK2lpeX0mTPPPP30Jz/5yYWFBcj9tW17ZWWlu7sbImc4c8Cy7MLSAo7ikUhEEAQ4M2i7WlnVeo3lON00dFGVVdnv93/xi1/s6e5JpBJLi0u6YdRqVdMwl5aWJicnI9EoxzIkQxEAWytVRpMBq2Po2x/7x75H/ofTtaX/xJM0jgJZ8deXeX9sYWERmpXCQGwYhixJKI57AkE/SQRIbHX32zVvYOTQF8VQG0WSa+6FYdjg0DCiq+jy5Pcf+CvSVAs3vRMQ4Dc+vlfu6PMwzJrLzAgEAoND2zBNQJbSFVWp82IJo2RZkxHUz7J+nw9BcYLALPe5wDIT4RY/mg+dIIh8LjczM0sz9O5tQ6sqkvWn4vU1r1bhTJXEEMUTIWiWJKivffMbX/5/Hr7jre94/sXngiTV+rY76e99z/7cF5SXngdjpxGc8XoZSZJLpVIsFk4m21AUVRQlm83NzaUlUWzr7u/p2hplKJIjTQDKKqgJwEZAMADOjC392Z/95dri3J4Oy6NPs06FpVALZUQnkpV9pq/7fQ99ePe2HkHgPT6f1xOQZdG07GSYZShH1023KUc4LmFK0dRKqeIAJxKJ+v1Br5dbXl46ceJkR0fHwED/+fMX0unZZDK1Y2TY5/GXK2XDMF2Ei2uaAaXlEAzx+wKapquq4vF6PBwHtwBJUpZlq6qiKqooCQiCRKNRj8drm7ZmaJpmsCwdDAbX1rLPPPO0x+e56cCNPp9P1w2/36tp+unTpy9evMhx3N69e7u7ulEMVVVVaRyQliyrxWK+XpdaWmJ9fQNwL54+ffbkyRO6ru/csWPn6G6Px+3ura0VCoWpqSlFUfr6+np6ehKJRLVaDwb9giBcjULc5e0wDL0Br7JTU1PLy8sDAwPRaDQWi8FI99qsiP/4sNXYqKbpvIp0wav8SNPAFjRBE5wJcNc/gREYhmDw3JYkief5unvJsuw4DpzF9Xq9cFL/Jz/5yfbt23t6euDwM0EQ1Wp1bW0NjkkbhmE7jm0YGEHMTs/6Ar5AIOAOeTYCGUVRGIbVarVQKAQ1AxoHTihUKBS+9rWvfepTnxJcCgVBEAiCpGfSiZZGaIODjZZlsSxbKpV0Xe/q6mp8G4ZBqzAUQfK1Mg5QDAWqZkiylEqmHvv2tx3Huemmm9LptKqq1WpVU7WVlRVREldXV7PZbCwWc0VHGV3XhXp9x0AvqcmELmv1eolgeF6oq4aI4KZpBv1+GscwkkTXLfIbR9+2oSFAUEg1W2XC3/nklwAH9n/+0yOvfL3Udz2LowuLC/lcLhQOb92+E8kvZXHPobv/OFQqd2ROb186qbF+yzSXV1Ya2M0wNEWxEETWDAw4MaXiCQS5QMRLoXqyG3caeW6xmLdte/v2ETi5CVfz5hyCpunllZWlxcVEomXncL9dKdokaeGUgRKKYamSIImCqohrhVoht/b0D7/X2d03MTWtVIqRd9yV/JM/1RxgfO5z9IUxhOW8jSzIm13LZNeysqryfJUgyK6uzu7e7erKy89e+PaElTIcBM0s7ci9cGAfp6umrkpeP5eMhc6cPe2gmG5ZUDfTsAlF4tsGbh6892+AtgryJUDSgCKADQCqATM6ZRxAjUoi6jUMB0JUHEcomvGwrKbpdV7w+bhgMOg46OzszL/+678Wi8V33P2ON9/6ZjjvgqIYjhP1erVcrpIkRlEcsE2GYxmWQ1zCnWlahUIJQ5xILIZhaLlcVVWNIDCO4zwenw0ckRdNU28kgDQFEKC7tSqa5liOnpqYmptf2L59KJFInTt3dm1trbe3d3h4O4I4xWKBxEiWY0VZqNXqjZXp8aSSCZKkTdOampqca1zziUR837593d3dbnFNkhXJsQFJkn5/YGlp0bLs7u5u09QVRUNcdhVBUJe7N1nQHxc6hp09e3Z5eVlV1e7u7h07LlWvfu7w6n8jbEE487pi1ibLs80Vu83k+Eu0s9ejeLe+BxpwjCAIKNQHpVpsF9PV63Vd12VZLhQKlmWtrKwMDAwoihIMBkPhMIlhpUpleHgYNigd1zqVoul0Ok3TdDQalSQJvm2I/jKZTCgUgvoBGIZFIhHHcR5++OFPfOITKIrWajUEQSiKgrQvv98Poxgsw9dqtVwu19bWVqvVoCgD7F0KooDThIfxWLqhaloimXz+0KHJycl3P/jg9PS0YRqSKKu6Vi4UMtks7Xadjx49iqJoNBol3EtRFF4UCcSpq7qDkRywgoTt9fo5r8/LcSQCqhi3uryIuL0k2PHxejx9Q8NaflUl2cc/9m2Qy4K7k8P/5+d3n/qO1rKFAmBhYWFtbW1Ld3dr1xayVjAzi7yuioazivnqkqiLvI+mA6EQnNcncDze2uqrZo988P+e69ytk+CW7/7tjucfyfXs9eD2ysra0tKSz+cbGRnhef6yki2CANsGOIIBZG5usVwuxeLxUCQqi2K1VlNlSVUb+bKJEgRDRkKxlbVVH2k98cSTiqSIslQpFdkDB/ruvR/9s08hB99Cra3UNGNpYaFcKZmGxXDc8NDWrq6OSqUiqcbyxLnKxX8C9vMY7XEqxahgpVqBpgHbApYJJAMQDNBt4CCAxIGfAxEvkEXgUMAT6lVqJQzHEABURXYQjMQRIPNVJKrccqJSWoiFQslUynFQ27ag4oiX4wzTGDt3/uL4BRRFBgeH9uzZY1nWj370lKJob33rHR6Pp1DIy7LkOnhasizRNJ1KJTEMd1mBCOo+LJIkC4XiyuoySVAdHW0s50Fs23IQ09Rd/EValqkoKhTUNVWTZulAIEDgBIKip0+f/t73vqeq6vve976RkRH43EkCUzV9cXGxWqlGY7H29vYNZVH7+PGT58+f1zRtdHR0584RmmZsG1QqJTfpwZoa3m5C48FxvF6vQX+mzTjJPaDtTdWr7MTEeD5f2Lt3L0QPVxhb/HvPC75W2PopuGmjh3C1qLnTHKGEKps2uFJfFQHAeb1j2FdERhjFXdIDyGRWDcs6ffLknXfeadt2Pp/neV4UxXK5zLKse17RfvcKhUJld7oqkUhIkgSrTqirPlwsFj0eD0EQDXStKARBMAzz2c9+9gMf+EA8Hud5nmEYr9e7srLi83ChSFwQ6hDduILr+srKSm9vL7wJcN7CsR2e5yWpsWQlUZQVJRwOr2VWvv/9Jz/6ex8t5fMNLKPruqbxkjA7M8exTLlclhQls7p67ty5WCzMcT5YIXJXix2JRIIEIEh6uW2U1kVGrjqqeKHrwL7pH/O8lM5kOZcKC4WGh4aGwuGwlFua2nqQK6zO7Lu7c+7U3qUjkj+BmPrs3JxhGIIgYYij2I6qaQhAgo6ecCTG68daujB/GDhOsVgslUo+n294ZERZmTvdf4tSk+b33u3s2nXP5z/iLy1o/igBwPzCQjab7enp6ejogEy0jcnTdQ91hiR5UVxaWjQM07IMTTNImuY4xsN6WZrwIrYX6BZK2vHuQ8/+ZHhr1z9/7ZsLy0semlJpSjOM2N33r3z3sYWpSadSa+3s6OnpTsSTumXNp9OyLMdiCYYizk3Of+df/pma+aqqApsEIsbysqMpqoZEJSTCOmJrSIv6bBo16wo1vmJXzXDUa2BKGtiAZhsBjsAA46Vso/GGOZbat3fk7o98xeOhK+UigmDt7R1uUqavZjJzs9OlUjmeSPb19rEs5T5z2+fz0jQ7MTH+4osv9vT0jI7utgzDsEyfz0uSVKVSLRYLHo8n1dJiO2a9LiqKpOsm6/EGvF5ZEet1wetehm44oJEoNM5S90DVNJVhWFeiz56ZmUzPpXVV3zo4tHV4q2kaTz31FI7jN958E4ERCwvzbuUk4vMHoI5VoVA4cvjwWibX3t66f//+VCrl1kDhtDnAcXKzJxjMe+Aug/urOUhoWTaGoW6PEjVN4+LF8eXl5Xq93tvbt3XrYHPj/yxshl9QknipeYkglm0DF5E5jvMaJtuXbs+m4Z71QH95eLpm0Lz6b1EU5Xn+H/7xH26/7XbDMAYGBtra2iDrHYYPKDVTr9d5nhcEwXCHmePxOEmScLQdMpVqtRqcPldV1bZtWF/87Gc/+5a3vGV4eHhpaQlW4paXlx3HaUm16JoO01hIml9eXu7s7HRcugNMMGFXRVGUgM9nbAjUqqr68N89/Nu//ZvVer1eremWaeg6cMDc3Bxkh5XLZVEUdV0/ffp0pVKJRCIMw8BegWnbI1sHBFl8eu9H+k98G/N4JMKLmxJp2Y7Ht7UwPr2aFUWR4zg4iVmv13fv2cORJF3JmprMl4tVC1slPDJf1yWJYRg4TgR1FoOhiN/WlPb+9La3knK99+TjtmWhFI0CZ3JyKpfL9XR3dwz0e0/85Pvv/auF4buAjySziw99/gG+c7sHQzRXYKRYLF533XUsy8K+x+ZVYgGM9Xryy0uVShnH8VQqxbnyG6xcNy29xkQLrUOGafedfRx0jn7nO9/4g9//yKc+9edTU9N6vT6lG2tP/2jb49/fMjeTFEXbMBVV1XUVPqN6tTa3MA8AiMdjim6fm83LiobaNuo4OGGi9BaxNtlV/eu+wGzUq9GUQ1MAR4maZDxxjFhL/PXQgfdyTo6v8QRN5XN5w9KTsQRJUiTNtrUkfIRK02QoHFRUJT2bLhaLgiASGNHW2balewtL01IjjzZN0xAEEcMcxMYojkIAcvzYiRpfu/Otd4ZCoUqlZBh6wBdAUXQtnynliqzX5/f5CAKnaQpFcds2UJSwLLNUKto2EotHUASr1WqGqfu9vlAojKBoPl84c+ZUuVxubW3ftm0wHI4AgAiCwDAUjlPPP//CiRPHBwcH73IFYCGGOHHqxOGXjkQi4X379g0MDLrA16zVeJc8gm3y/gLXVHZvJoMAmAzDUVQDXuXzhbGxc4310NMbjUZaWlpM0/y5UBl+obWtRrLm2Kqm2raDOgjAAI7iV7P7N4UyiLvWs0bkVRLDy7Slr7B63XRDA8HQxMT4saNHR3bteurJJz/96U8LgiBJUlN9EMMwDIfeMK5AO4rabrZYrVZrtRpEZIZhQFpGIpHwer2s2wKLRCJf+9rXtm/fvnPnzkKhgGEYSdPlxqoVOjs763Xeti0Y4KBzddIdb7QsiyRJzBVzsp0G1OI4TjdNyzAsy4rFY5/5zGfuv+/+ZDKZyWYRl7CDOMiF8Qs4jju2XSqXYfFOURRBEI4fP+71ev1+PxxpqlQqfr+/r6M17TCVjp10OacRdNjkl8NbEMu4Y+l5NRAfO3UK6li40VxUVZnl2LqiK4qC4wRFogmxTEdjaKLTS5GqK7Uqy0pnZ0eivcMeP/31P/mmNZ3WD7xpy5Hvvu3Lf5jZdgtnm4qup9Npnud3ju4OB7zVUmWh5waWL7Znz5PeoGQ5mixX6nXNvRuiKO7bt09RFGhr5iAooau0UDZJGjM1O9KaXlrJZNZaW1t7B7ZKM2MvHfyD1ZG3YULNYnyA4bY/9dltU8+KXOTFp5/467/5Gz8TLPClbQM9nbcc9P7lX65dHLe++Y3Y3IzNeB3bdGVsRYLAWdYjy6JhmB3trUEfp2k6gjUWkI4E5MJF75FtugoAAUgamAZaqtrlIiC83ECHopXtJ5XfGXrbp7f3+PhK3XapfJosRaIRxsPZhomSHkWR5+YWlpcXdVXlvGxra0d//yCKItVK2XXHIGB9U1WVel1AMCQWiYbDYZKkcpnsj595uqWl5eDBgwCA1dUVXdVxhkABKokNAB4Oh1EUVVUVIMCd3WmcgtVSaTWb83Bcf18/RmCSIM3MzS7MzxMEOTQ02NqagoZ1GEYoiiqLtbrQSDmhIsDk5GQ6nU4mk3Nz86urK8PDw29+862maUuyHAz4eV6A1QxnY4auSQ1tekpvKjdfVr0yTfP8+fPLy8vVanXnzp09PT0/C7X9PzhsiYJgI0hrKgU2BVpFVlRNdfUGbKQRKFACI5AN1ejNIOuaJsibjYuahvmXvueKohiCcB7ONC2OYeqCgLh6xE0fjc1pdrM2BycSaJqGlTKo1StJkizLxWJRkiRVVf1+fzabXV5evv/++3meD4fDgUBAUZRyudzV1SWKIuwbwDJ8Op32+XwBn0+QJMO9TMtCHIcXBL/fD+v3AMO62tr/9u/+dmRkZO/evel0GvYoMQxbXl42DMMfDNZckRzYbYBthEqlMjMzE4vFOI4jaBoHIJMv7uvrVHpHcoI6XJqWvWGFDVEK76uuFKhQtVKGSTFcRijagL9ua5z1+wIUTUX1enrPO7nsXCJzse5L0hhSrVZmZmY1TRvdvZvAEHNlwUCQx/75cNdX/mX73HMembcJMuD3F4vFmZkZAOyevgEvAqz8Yk3WqgAvS5qqyLZtez0er0v4LJfLHMft2rWLr9dtgk6kjx2++XeWrn/73f/wW9N77+tIH8UwZHJ6Pl/IDQ0PtwbYhSL/7Hv/HthaNJtmTD5VXOxcO+dt65ibmj57/OVDR18aDA/XcvWCsWbv3hG4/bbsHW/T3/+e8NqqTTHAcjgvxzI0JIcKsljIZrxeXywaEWUln1kUsA7hyMcHpSdBDKjorufP4i+dXp0p0DkeDCTRv/4v5U5PlYr3fmnhj22z/KEPvD8WjdRqdQRBeEEkCdwB6OzcTG4tF41Fe3t7W1tbMIysVEqZtVWf15dIJGRZKVbLlmEwFMN6uEYKaWjFXNlB7EAgEA5HAQCvHDly4tSpXbt3je4chSXUxiGEoJV6uVquhkKBUChmmg1ULrs011Ao5La2pYvnL7qDZWg0FtvS1R2OhN3kTnUcu+xeGIb6/IFQMOSyOoGq6jMzU08//TTLsvfcc08qlapUKoVC4ZkfP1OpVwNe3x/84Seq1TqGI8jlroXQR6cJEmA1nWFIqKWVz2fHxi5ks9lUKtXa1tqSbPk3TA7+coWtTCZz6NCh2enpREtLOByOR6Od3d1dXV2b/YVsN01TFcU0DdA4VBACRQGGbcT39TC0SQe/iciadq7OVbUwZ/NsAeqOziPXtCxrFtM2Vdhg8gglxmEUawQwN4qRJKnr+szMzMLCwuDgoCzLuVwOTlCXSqW+vj44U+2OPdNerzeTySAIEolEoNul48oYYRhWKpWg5zikNXV2dj766KOWZX3gAx9YWFokKMLWGyGvUCiUi8VES0upVIILVxRFVVXn5ubgafbyyy8rihKNRjmOcytoiiCqO/q6GcQu2ZhYraiVSk3TeAdFVIVhmEAgsC5d4hYmYFAeGRmxEAQrZU933HTmv/4RMMBvfWC71talUx6WprLZ7Pz8PE2R1918K7M8cWrg4Cv3/XHy3NFEefb68R+JOK1rei6fl1UFd2WOLduWTZskST9Dez0My3o4joMMDF3Xs9m1TCY30NebaO/S1+bmtt5+9NaPg2ESSIB45pU7Xng4RCGC6cxNT/M8v+f66xOInjcwVpMoQ0QtkwdkGZCyLFZkVcnV8GHtff98f29m53X2DV6uLeMTmZt2rX75qxrN9Xk84WQLL/GODhAcIDjCkJTjgPn5+VK5HIlEQj5PSbDOnDj6JvCxFGcezsT+/setwVhwVze2Nbo6HJwgCVAqguGRPZmBJ+aWFo69/Nytbz44OrpHVqSLFyYmpiY0Rdm5c+fu3XtcVVtR01QAAEuzDnDS8/PFQjmZjCVaWtz0wrEs07QNHMEYhpZkuVqtA3ckJBwKWZZ5+PDLlmUdPHgHx3EwGHk8HALAWjZXc0Vug8Gg1+uFjezp6el8Ps9wTCQUTibjiUQKACAJvKxpqjscRRC018sxHOPlvACA6empY8eOF4vF4eHhXbt2xWKxY8eOLS4urqyspFIphmFuvPFG23FcywITXKMks67CYtmb4JVhTExOzs3N1WrVffuuD4fDNE3/7LM4vxRhC/7SpVJpZWWlVCpBgjKsH/t8vs7Ozp6envaOzlRrksTJS3xUw5AVxdA0C95EFCU2hgY2LP6QS/WvV28FOJtqZFfadV9b0RhivGs0MdeldGwnFot96Utfgomhqqr33nsvVEyFsla1Wq3s5nGSJLmMM9OyzMHBQZqmPR5PE+ZAXj6MgIZhdHR0/OQnPzlx6tQnP/H7ExPTtmMrmoLYiKzIK8vLW7ZsUV3fbMMwVFW1LKupFl2pNODT0aNH4ZqmKArHcU1RKgJvWUBTZIplfBwXxB0uEKQ9forl0Mbt1ZeXVzbGXJ16vc6y7PDwsMgLmC4oK6v/+p0xz8T8Oz/zLq29H3NpvQsLS/Pz6Xgs0T/YZ9bKZnZVl6tlA8sQXk0UHEPzorYnGgcUy+AoAGgsEiEabwZDXENetyBtuVRbShTlxlqvVndff73XlCe4NkfkHU195UP/8/3/4zYm0a5y4YDPs7i4uDA/b9l2a0cXgxi8INU1U9YNxzYcw0RxkiBJlvNkx6fu+uA9N//5nh/NPNkldLxj26d6Z1T6Ux9ZfPkHK8dfZB28Y0tvI5oritqAzIJtIx6PF8eRfK5IEChJY1//1jMp5+J7dp7yG1M45ZqvG0DVgaACgAKO8h4pv72Y+PB77rkBAcTnPv+59Mzs/v379+y+LhqN+gL+bHalVqsnEkmOYxVFE0VFlgXbtqGKUalYthEnFgqhOGoYFokRALUlSdF03TIMQVFoio5Gwm48wtPp2ZdeerG/b+v+AzfYtpPNrhmWyTEcRZG6biAIUq/XZ2dnFUlq72jv7RuAGKpWq+fzWQcgDE2ZhoUTmMvp4yC1+Nlnn52ZmQlFwvt27x4cGgYATExMTE9PptPzqVQqGAzedttt0HzANE1VUSAEQJonubtNTN10gM0yLOU2B4vF4pkzZwzDCAaDLS0t0Wj0lyQf/PmEravbi/BXqlarEIZMTU1dvHhxbW2NpmmO42KxWDgc7u3t7e7ujrtix81LaVyyphkAQKtkFIVw7HJBQeeqPuSlgwMmoVf3MS8fRm82Ja/4pGlC+eSTT6bT6ZWVlQcffPCGG26ArnDwHcGkEtbI9MalCoII2Y/QTMxx54ogkTWZTEIA4vf75+bmHn/88T/5kz/JZDKGYQB3CMlBkPELF1pSKegzBClpCILk8/larRYMBMuV9dr8+Ph4Op2OxWLwGIQzZQSOJ1taaBxHUEz2x/yZaVSSME0wvV4z1F6uVRYXl6A8CHzNvoGBZDwuyHKkOHey7/Zj9/3Ze7/0WwHCUVDS0vW5+XnYuzAMHQBHNSzMtnw67yNwJp7yI8Du2OoRCvVaLa0BxNB2usMfiqq6q950LhFzHIZhs9ns4uIijuP79u+nhCphKnYDC+p6MGHoel0UXfZDlSIpBwBFkgwH4IhDkHTjjmG45bbcNcNqQFeg52fmP/3Jv7vvN+/6sfbEK7UfhLbddfAvT4ycte3vfG7h/MVMtRRo4F/OtC2OYWjWA2wDcRyAIZmVNVGUTMf5X994TFk6fM+OytZYkcN1gAMUwXWLkNDOJy/4svQN9971dlmo1Xl+544dFM0cPnp0aOvg3r3X67rqPmd9eWUFxVCfL4hiCEPTDMs4G1Cd5/larR4OR3weplznJUHAcZzjOI7hCJqSebFSL5Mk5fN5/P4QAODZZ5+bnZ2+8cYbh4eHYenChVez6fQMQRCjo6O9vb2Ne6sppmFpiiuMq+miWAMAHRzcCiH8xYsXX3nlFVEUR0ZGrrvuOvigBYHXVS3s6o4+/fSPbrzxFpZlYLtQVbX1cULX/8zZOMBd6V/UH+BQBNNNY3J8YnV11TCMXbt2bZ5d+WVIBn+eYatZM2ryAK7+NhzHeZHPrGay2WyhUBgfH4c1HZIkW1paenp7erf0tLa1QUnW5m2SZVlTNM0ygG0DFMXc4vrV985Zl3lsJpygqVr9U5muV3cnGYYpFotzc3Pt7e0cx0E/MWSDw9l0+moWyEiSXB+9dgtVmqa5i7gGOf3Q16ejo+PJJ59861vfmkwmTdOkKArOA4yPj4dCIZwkRZ6HNV0EQarV6uzsbCQWKeQL9cY+4iEr7eTJk3B8DC5QKD89NDQkKAqemf/Wb35Z29NHr8hanKVOTLzpsf/WEg7MLK2pssgwDGTw1qrVnbt2MQxnOBZWymhry3k2WNIsRZZUVfeQmN/vpyjKMU2EosI048Gc0sCNViS55YVv/vC/fS1TNY2elvd98Y+0l394TiP8FLFzdFQSRcexAIYDw2j86Z5bcNPOzs4uLy/7/P7Wzi7NMCWhrkqCIoiaaTg2IHDCH/TjrlMFThC2bdk2cFDEMizHNSK23dqCe8sxB9TT6dmPfOgPfuee32lpaX1FfOrEsOz5rx/ecedfh67/gJm+UJSVoM/X2dVhWY6iSOtW7DhKEmSlUsmursq6eXJs6sTLz0q5Cx4ngzs67Y0Rwd66EwNU8IY92wZ6Ozs7Oju7u0iapAgaAOTHP35qbn7u7nfcnWpJLi8vWQDVVA1FkFRLkqAYWRY3GNEEhuGyLM25bJKOjo5IKGTajm3rtgUcy0Ebb4MSJF6SZBRDSByPxZOqqh45cgRFka6unmx2rVgsdnV1j4xsAwAtFouiKAQCfsuya5WKyxTlIpEow7AAgMnJyfHxC2tr2UAgcODAAegxoWmaKIoAabw4giK23bhrPp9P0xVZUjBYykKaLkKOux4afzAkybhKMvl8fmzsbK1Wp2lm165dXq/3UoXnlw9e/dxY8hRFaZtGpq+2+bs09+PGNVmW4fTMzMzM5OTkxYsXZVkkCCIajUPJ/UZq6crDX5rPNA1JlDRNc5+KA7fHRpUf+amE1demg13ihTkO5ZbqG1mhqmiqdk1z/6vptTBkw04QPA9hzKpUKkePHg2Hwx6PB5beUQzzer2SIASCwZ6eHtU0CdcWBKrsT0xMxKIxSZHEer3GS7VapV6vC4IwPz8/OTnp8/kgFRbDsFwu15ZKpbq2mLmFal34wReOAC8IfP27Nx7/eoQAmD+MAnDWnYyFXUVYNXPFlWRF1wFJ+TUhbIhej5cJhKzWfssyyuUyX6+HkqnrPc7XHvqHtVQ3SGc4XLKrPAgz+OS08K57f/P/d28Di1aELd2d/V0dBdk4OfrO288+ZhMUgmCWZcCeRiaTgSxZWZKw9eEn2m3V41AcTddN4FjrnD6Yt1w6GlAbrDtx2wBBASKL2Uio5cH3vU+15XcevCcn5p+MjY1/43d+7+D3d/XepVXXiuV6sVhsbW2NREKaamiWriumokg0TTIMI4qiyzEO8LqdK9RqtWqxWBL4Yls8cv2eoWAg6KAoTVCG41iGgWAAtdFAKDg1OfX4d/+1rbX1Ax/8IAIQSZYlRcyt5Tkv19rSijhOXeBFWTY0jSbJcDSi60apVCIIwqUoA9PUoTMAbNLAXk00GvF6vfW6OD+fPnz4cDabvfPOOw8cOOCOstUgzSWXy62trnl93t6+Hp83ABfaqVOnT58+ZZr28PDQvn374PRbtVoFwMZwAjiXaiSwf+W2Gl2vXxitkM3UduDxBFAUcSxrYmpqfHzcNM3h4eFYLAYdYa65c38Nw9broXe9BhxzAx+5uroyPj65tLTE83w2u0aSNIZhHq+3r6enx80rI5HIVXmlYmiavZGf4pcXyH56IHMVNq7OGd0hbZtACIBdIyN+NSuw5u8IjQs72jvm5+ZfOvzim998+6OPPnrfffe1tbWVSiVNVau1WrFYhLwqyzSBiziCgUA2m21paQkEAo2csQHchGqlzPP8zEwjfTh79uzq6moymWQYBlLS8oXCntFRkvOQq1MXem7yKOKe9PO1tsGqTaiy6NpBVTmWQzEU3nk4q8SQJBcI+ICDhONTu+8rpQb6pp8deP5rcttWXZXHxyfKdeHGLclCcutP/vsX+r7w8My7fj/+0hP5rddhNcPa0b7vy5/dOfXc8bIoFgt9/f3RoE+s84qu1yVFFEVFUaDWmN/v5zgOafZ014G52cAgG4VEcJmueFOeu4G7AHAstyRgu08IwazaUu7G9980/EDfs99/6W3XveV9d3zSOZLWf+P++vQZZHXVS5IOgqwsr5imHY3GUNSxHYslPShqm47j8XAYhlWrZVmRNVkpl0sMzfT294XCUU1zBdYMQ5UlB8VoipAkTZQlBID2jnaWYU6cOH7kyJG33nFwYHAYMvJKxWImm/U1Lj+OoxTDEgiimyYE4KVSSVXVRCJBUVStVlVVnaZJyIfQNOPUqRMzMzNer3fXrl1wquall17K5XK33nqLPxCcn5s3bTMajsZiMSigVipVTpw4vry61JJoufHGG9va2l1NUVVSJIA6br0Y2cTqBU1HnCbJCBKJLNuyLIck14X6CoXcmTPnDF0nKGrH9u1QieCXPx/8RYet1w4flxs9NpJB27IKxaKsSJnVtfHJyVKxVCjkWJr1BYOplpZkMtnT09PW1nZF+i1Jklty0h3bRl2Isdnn+aopSPBT6PmbivyvYRO9+ZXh56Zp+v3+9Ez62eeeLZfLCIJ86EMf8vv9MN9szlpC/oQkSaIg1Gu1crUKZUUbgN9tU2AYjmGgWChZLlW1VCq9+OKLsEHOsiyO4wLPW5a1dXDQQXE7tyRKwhrC6sWiokgOAB6G4cJhx51kbqbzmqYNDg7inJ9cHn9l+z1j/8fHwp/+avmjDx38iz/qmDmmdQ3xhdz84qLhIG9Lsk+Ovnfyjt8FNX7r7A/3/uCf5m95b3D2VFjK1TF6cXkZ2twiCGI7tmVarmda44LTSPC3e9X5inWQ5Ww2FtjQyXQ2M1dseLI4KIro6YnZx7/x/et37//8P/7PgId42x9+hHr0Uf5f/gX84Af29LTRSNjQer0uimKqvT3o9RqmTTMEQzPVam1+fmFpZUkWxVAotHfv3lgsIYiCLKkE0XgitmUqqlar1iRFdhviSQxFXcVKPRqNK4r8ne88ThLYO+66y7SdUr5IUqQkSSRJJpNJ27ZlWXNPTARKeEN0TJLkwMCAa6HizM8vTExMapq6xb1wFFU0BScJlmRwkjx37tyhQ8+3taUeeOCBDaaoPTZ25oUXXiZJ4pZbbu7v7yMIynHHP92UpakBDFvnV2Ycl8uKrpviQGr7zMzMmTPnvF5u9+49HMsiGwngL2wW51c4bL1aLINFn1eroymyvLC0NHHhwoory6fIitfvJTAimUr29/X39PSkWlqwzXmly4bXdd3NzxuPG0EALK5fMSG5GWpt2KA199MmJgWy/pUrQuEV29I0Ta/XOzk5Cf3Hjx07dvvtt2vusQ7hGDSRvsTecC/YM1VUuV4Xa7VqqVwqFMuVcrlSKUOpmQa8yuenp6cjkQgs/JMkCXn/lmXJhkWQpJciqi0tdCjEeDxBFGsXG6htZWWlGblc0yNm67ZtkigSM2f++c+eYr0eM1vW3zT4vg9eb8U6WNaby2UWFhZ8Af/1LcGqiZuGhgo1gaS1wppggQqgYB8Njm1Cuf0rOMCbAenVYesyZ6Z1/RME3t3Nj8Re57jYjR1pWwCxJamuaMgPf/h9v4978eUjE8dPvuUv/2rX7//BSiwm/Pmf+y+OEwEfyzKGYZZLJb/rNb28sHBhYsLV8EgNbt0ai8Z0XZ+bSzsO0tHRjqKNNe/2Hy2SZELBAEExfJ3XNJVlOZomAEB1XSdJiuPY5547dPTokdtuu/2GG26AQjE8z5fL5Wjjiqiqpqmqpuuapnu9nkgkYhrG5NRUqVRyc3O6r6+vo6PDXZYmAFalUs1mc6ape72B9vY2hmGWlpbOnz+fSqXS6fTS0lJ/fz/ku1uWg2FIsVjCcRTHr3LE2YjwyCWUhcBwZpoWSWDcevUqd/LkKVEUCYLauXMHVILf0L1CfhWj1S9L2LoajjW3xJXCOOsUcB7KRZ47d+706dP5fB7H8Xg8nkwkOrq6WlOprq6uRCJxmciXrrvNMsN0IT3YhETWtxY8vK4FzK5kgb3aqJO7dGzbgcDbMHSSZCRJaA5AwIo+DFtQ08ZyPwzHNlTTsRvvza1G24auiaIs8Hy+UMjlcvV6PZ/Pz87OyrLs8/m8Xi8UtoevHItEcIYp+QLvnxy/eWIiLMvP9HT/0+ieTsfJlcvLy8sejwemitlsdsuWLS3t7WZuWVStQ+/+76iJdswfvm7piMUFUBRfXl4sFkuGYegA8aC2IisaiuMogdMUReI0RpIk1izqXbNJcoWx9rXx9aa4ZW8e8QKXaHzr4Wu9juxQDDk7Pf++D7z/Y5/8Y0ni+Urtez94ouWmmz/8F38h/OM/lLds8a6usf4ASeG1Wv3lF19KL8z39vTu27c3EomgKN44wxSFYGmW5rLZzPLyqs/niUTCrq4ZhWEEPFc2WO9iA/cDTFREzdAZimlvbyRo3/72Y/V67d3vfrfP53N7xyCXy0qS5Pf7GZblWJbjOFf8enppadmyLC/LtXa0w9q5KEq2bUmiJIgCgiDQFtPjXpqmzs6mn3rqx5ZlPfTQf4Hr9siRw8eOHc/n8zfccP0999xbrVbcsHU1NdvZuHXohmo7AcvqhmFMTI6PX5yUVXXHtu1dXR3EJkGhX7l88Jc9bL3a1SSIXvP01lydrJnpaTj+nslkKbes4PF4urq64JTv5i6J4ziCJGiKZhgGhDzIutwvcq0q/7pW2BVVgKuTRPiW7PWacrPOvLn85VwRueDVSAZN0zQM3dA1WdMMTdm4LMu6cOGCIAjpdHpycjKRSMAJJEjFoBhmtK/vMIL81aHnbp2d2f7Od96cy+s4tti7JSDKHo6bn58XRRFO52uaVq3VrhsdZbx+Qq0TuRXB1DVVzaL+uizLsggAEgwGIenMth2UwAkUIO6wjOuUar92yfaK2t9Pg13Oa9Ly1utdGIIDFMEwvFQuUST+/372s0VJaevoTLUkDs2mx1566Te/8pXU+PhqpTJ37uxaPo8iyPC27d2dXaVyyTD0SCSGIK7pqeO41roiTVMsy9TrAk7iiVjCti1o4ERRFEzJJUmp1ysAIFCOzTAMWZFpnPYGvHNzcz/84Q/37Nlzww038HxNEEUEoAgAgWBAVbWLFy6sZTKJRGzr1qFYLN6IVoqyurRMkgTD0JIk2jYaDPqDwSDsOE1PTr9w+AWRF/r6+m+99VaO42ZmpldWVh9++OG77767VCo99NBvlMuFgYGher2GY5iz7oC3+ca5INdBgG3gOAmrV7lc7sKFC/V6HcWw63bv9vp8Vxdnfz2uX4Gw9fqTSuhmmi8UlpeWKpXKxYsX6/U6XCg+nw/asbW3t7d3tOMYvjm/k2VZUTTbNuG+gnDMXSXra+Wa1LDm5tw8SX91tX5zwNpcwndsxy2aWpZtwblcXTegvwMc+Lg4Pv7Uk08ODQ3V6/VMJrO0tBSNRl29YwICqO19PeW2rlt+9KP//8Lcx97znpdTyb//3hNtS0vvfe97BxwLpZiJixdhsQ92FSVJCgUCoqbLmgEc00JQGjgUSVCu4dnmRO/a46LXqoZcDa9e7RUux1ybZ64vM1BAUdRyAAYcvQFHTcQGeb4SQPFvfeQjlRqP2bbXz/Z296xynqfe/W5l69bU//WZbgcNJpNtrSndNCRBhBPyRVe6E8cxSZIoinJV5ylXkQUtloq1Wi0eT0ajQXfeXtR1DUVRjuP8Pp9l2263rrFmUAQ1LdN27FAwpOv6t771rWKx+NBDDwWDQdu2zp+/cPbsOds2992wb2hwGBaVXOUGvYFbdS2TyTiOPTy8HRJ9ZFl++eXD4+Pn/f7g9ddfv3XrVgDAc889t7i4uLa21tnZIYrSfffdGwiEoHaNJIkbuRy8QcjGRO+GrKhrvWHb9vjU5PmzYz6ft6Ojo7u7u0nZ+fULWD/nsAUzu9ejz/WLaVk2H5Usy16v9/z58+fOnUun05ZlmYYZCAYioVA4Gu3p6XkVHqwClW3W5/wuz1tfVZoCbsFNe7VJcLsibMGvNBNG022WQ3UwVVNRBFmYnz92/EQymTxx4kRLSwvHcU8//bSmabDIRRCEpmn1anXvvn3jFHXw6NHfHhszaEZi6L+/+eZaS4ITJEHRlpYWKIqCbx7DMMjIh6QzAkdxDHcQFILE9fmPy9/85sGpV0sDXw18vXYuedXicTYqgKhtG/DOQASEYli5VEi2df3dV/+JY7igL1Au5WenZrRabcvePcW1tZUL59/7nvdHopFMJkcQqNu803TdkCQxl8v5/b7+/gEMQ0VRajIKGYbRDWN+bk6ShHg8yXFeggAkSdk24s6iAdfITjcMnWFYBEFEWdBkNRIN+/1BWRYffvhhluWGh7bGEm1dna1er3dtLYuiaCgUUBSlUimrqu4KjfgjkShkSK2trZ0/fx4O4hw4cIPX63eZooIsy/F4fGFh4dSpU7e+6dZwJOw4DQxomiaCWMDtam9wE+GormUYNkk24dXqmTMXeL4ei0a6urvD4civbnPw1xlt/YysC0gWLbhlo4J7jY2Nuc1w0+/3tbe3d3dv6ezsvCKvNE0TFtehQmGz9Xlt+elXeT9XAC4bfmzkiZZluywuo3FKa7qmG4qmII6jqurRo0f9fj+csj58+DCUD4MlknKlwtD0rh07Fj1cQdNAtVq0bE+pRBQKqmHYlhEMhiHZp7mI4W2x3MnzV0vVrs74Nqd7P3UzXDPAXX3INW8FbFZsTpzdTbtO9yVJMpvN7ty58zOf/vShQ8/ZDgiHA929vV1beoIc6w8G1zK5xx77dmdn9z333FWr1ZeXl0iSpGnaFwj4XNG+TCYbj8ej0ahhmAgCdF3ned4yTZbjLMsSRSEYiQS8fkWRHMdCEALDUDhkyvO1UqXq83ja29pRlxl/9uw5TZOj0eTS0kK+kHvwwff6XSkuTVNXVlYLhUIkEk4mk16vlyTXOVbHjh2D7If9+/f39/e7xVZVEEQ4FOKeKI2bQRA4huOSKAJgAnBJg2C9Eug4hmkRBOA4l3tlmxcuToyNjcG25q5duzbrdP5nuH7dwtar7SK4B64ATfB/BVezAaodnD51anFpAccpAKxEouXmm2+2LCuRSMTjcZ/P16TC6roO4dgGpRhBUezygv7lxa9GnLpGqgi3q6u9akPmFyQoqhupoiRJtm3ncjno4gUBIyxyNcmHpmkyAFEMHWAoDVCUInGKgsL5V6/mn6Xn/bOaRG3w4xDXXaIZsuEnV4T45icEQaTT6V27d91/3/0sy7R1tMUj8UQi0cjddBsjsWAoxNDcY489duHC2QceeHDHjh1wSbu31GaYBthcXV21gR0OhqF5hztJ6sEwBJI2VldXAEBaW9tIkhBF2Whk6iqCgHA4xLIeWVUmxifW1lYRG2ltb+/q6ggGgzD6fOUrjwQCgYMH79B1zbJMhqFdcmkMRdGxsbEjR46Iojg6OnrDDTdAUVDXJ8EiCGx9HbpcEBgiHQeYjo0j6GbJOphPG4ZJksQGvCpeHD+fyWQi4VB/f380Gtu4VzYcFH0jbP3aRrHNLIRXK5DNz82dOn2aoIgd23dMz0wXcgV4hgeDwVgslkgkotFoE5FBrVT31NXgDlwv828aDt+8Ly+DXc1UcSMtchGXAVWAJEkWRYHn+Wq1WqlUBEF48cUXLctqTvzA1M92HMIlhiEoZgMHuK/2c1sil8sBNSMX8tOmqaBgE/wRqPmzuSPhXH43rnhG8GdN04Qz4R/72O+P7hrx+T0sy6mqpqpaNBr1e301XhAEgSTxoaGt9Tr/6KOPtra23nXXXZCpwLKc41iqppmGWSwW6/V6d3d3S0uLoigbqucNeMVxnnq9msmsURQZCIRwHA8EAhiGlYrli+MXqtVa0O9LtrW3pZLwBWVZ5XkeOgS/+OKL586de//739/Z2dk4AiXhO9/+9srKWn9/75ve9OZYLOrKgWiwSnVpAmTDE7WpDLBRG13/mt0AoQZBEK76Y+NEHBs7d/HiOMuysVhsdHT06gGVN9AW+M8WyDY3XJqxDMfxC+MTmizdfMstcEReEIS8e5XLZehk5/V6I5FIKpUKhYKhUBgeqhCOwXHF9e3hLiv4ylfXuTZHLgi4YJFLUzVFde2cRbFer5fLZUEQFhcXjx8/DmvzsG6FvI5hzNdTkHptlPR6vu44Do7hFrAcy2niqQ1E6TT/94oktNlmQVwXJQg24R3Acby3t3f//v0jO7YHg2F/wO/zBTiOMU1tdTXrOE5nR0coHEQQVNcNj8fDcdyPfvTk7Gz6Pe95TzQaW1xccM0jaIZhA4GA49gLC4uO47S1tUGvIxzHLMuRZckVtiUNQ2UYLhjwzs0vXrgwhqJ4X19fd3cnTa+bM9frdQwDum5Bl2ZXXSsMAPj617/urgeiVCq+5S0Hd+wYAQAsLS0KgjA8vI3n6+us2vUuD2jC8eYR4E40Icg698qkCZJxq/iFXG7swsWFhfnu7u59+/b92tfaf93CVrMa8gtDZDDKzM7MXBwfv+uuu0KhkCBJXhfjwIJXtV4vutXWarVaKBQMw2YY0u/1x5ONLCYSiUBT1SZVHV4QgGzeus3IBS/TtNzQZbhhT1NkWZRkwQVc5XJZUZRXXnklk8nE43GGYZoctFeDP69/cb9aaLu6znUFLGoGYsNVc3UbgEYzNG8uYzV3HY7jTfV9+EVd1+EAOUmSoVAoEAgkEolwOByPx0dHR1tTKZKmcAw3Td00bY/HG4/HbNsRhJrrCcJZhoUgAMOJYCCwuLzwvx55pLu75z0PPggchxd4nCBdg0XAMAy8jbFYjOW4mptie72BcDgAAChXqsdeObq4uNjV1XXzzTe7LLxGGNU0A5YFFEUVZYnjmC1dW2CVcGFhfmzsfK1W8/v9Bw8ehBD47NmzExMTzzzzDE3Tb3/7O+58+1vFWh29JIiMbHKuWr/HjmOblkUgCOfzoijuOGDs3LnJqclAINDR0dHZ2QmLqr8AU5w3wlYz4jRypp8xbblszga6yMOttT7U5rxOo7PXb4nWHIWXJOmZZ565ft++/oGBbC6Hue4rmDtMDsWw4DdLklgqlfP5fKVSgerPlmVRFLUBx0LxeBzancMo5poq67Ah0Lw58LCFgQumiqqqyo1UURQEAUpx1ev1Q4cOQVF8lmVfZ9b22sz1K4LXNZEUzHybgaZZSocQ6VKccj+ukJltvgGoYtYsC0LlT1mWWZbt6OiIuxcUO4pEIolEAsfxcrns9/sDgQCCoMFgA3BRFOm+bONkqddqOEnGolFN0+vViqyqUbcCf+jQocmJqXc/+K5oNFYpFoCrmW0ZjSciSRL0cBoZGUEQRJKEiQnXR5phhoaGOju7eL5eq1Wj0agLoEqyLNu2TVFUOBTyuJWBUqk0Pj5+9uw5HEf27Nm7d+9eVVVXV1cPHz6CYcgXv/hPf/qnf8rz/N133yM3whznSgNdWrabHgGAt7HZHCyVyhfGxjK5zNbBIY/X8x+u2v4G2vq5lVqgRxsc4nM2MPNrwLHNzmlwT75+ogaMXBRF/eCJJ/r6+naMjFSrVbiAmukPbCwSBE6SFEkQqHsYmqYpSVKxWKxUKqurq8eOHfP7/bZt9/X1cRyXSCRCoVAwEGBd64pGTqTqYiM6iQ3AZVu6aQLLakQu95IkyW2uV+DgyNTU1JkzZ+D4PuXaqV+hSX11eHq1v3rtCI6iiDt4DlyZGdvQDNsyoY9vE2Qhtg1QDCCXVf03d0Jg2guRpizLHMc1fXA5jovH4x0dHX6/PxwOd3Z2wjlHqPnZjKGKooQCod7BHgzBFEWFPRCSJAgCRwCey2dzuXwoFHSHmUnbdGxgB4PBbDb3zUe/uW146+23v8UwjHw+Dx9oJOLW2hX5+PHjtWrNlSGJ9ff3wURS00wMB8VCcWFhAcPwtrY2iqYDfj88n+bm5k6ePL66mu3oaL3lllujrtBVpVo5dfJUNrtWKJSGhobi8fiuXbvcFWK5BC69yUprzmVuICfU5/O469M5d+7c9PS6weqePXughf0b0eqXKGz9NKr0vx0uXdPbEUERqPrwb7ua8kPLS0v5QuG2226T3auJs5pbtBHFNlYliqE4hlMulRMuO1VVYdzJ5/OFQoGv1w3bJjDM4/FEopFIONLS0pJIJBr72XYUXZMEQXSlZyRJlGUFKgvWarVSqQT93KvVKvT42ZwqXg2mXiNsXXEz18ck1zuetmk7tmk0sKCuG7Zlm5azPgkNYE2t2QEEGAKsy0rsMO9bV9k3TRzHoQIPy7LLy8t+v3/37t3BYDAabWAjmqZJkhRF0bbtrq4uKP4DbeX8fn8wGPR4uGKxUCwWWlvbI5GoO+LnuG4AomlagYCfYVie51EUbYQeyzEd07asUCiMougTTzwxMTHx7gcf6OrshkMwPM+PjY1Vqw0Qx7JsPJ5sb293HCCKgm3bgiTIvISTOMtyrnYrEwqFVFV7+eWXzp8/7/F49+zZvctVTDRNvV4XYTIRCPgnJqYqlRJUpFFkRTV1BNioOxW7rncJIIy1LcskCIbj1mVFx8bG5ubmenr6Bgf7XSe9N6pXv6Rhq7mpAPhZAtjl2+9/C0b923r/Kysrk5MTd911F0GQlXIFJ3CnSaWH37kJb8BPYVUCx3EURWmahlqpCIIYuq64xtS5XC6Tyaiq+txzz+m6fv/995dKpXg8Hg6HPRxHUpRl2Yqq1BqZZ7VSKuYLpXK5XCgUXnjhBYZhoHna1Uv89WSFbphCN/OnLo0cGVYjcDWCkI0iqLMh5H/FbAD8V2CSCCMUzHkhO98NCvFkMhkKhRAEgba47e3thULBtu0HHniAJMlyuQyl+uHny8vLkUikt7cXOkXC17dt0+fzA4BmMhkURYLBgCwrjgN8Pq9bgWq8I4IgKpWKKEqxWMTj8YpuyLcsq729vVwuP/XUU319fYODg6dOnSoUClu3bu3r64MtlEqttLy4Eg6HMYwShAb4grESnlVjY2NPP/00hmF9fT0HDtwcDAZcDjMvSSoU9oC3Uzd1L+elKEqWZaiI6RYwkEt1jfUWauM94zhuA3t87MKMq8S9ZcuWnp4eWAb9lVDp+8+eJP4iC+0/l8jleiOunDx54s4774zH46VSaTPmuszUYzOBqxnUNvnUwqUJRR2gih6O41/4whfm5uYefPDBhYX5ubl507QxFMFwzO/1etzxFIrC63U+m82KonjixInTp08nEgmapmGR66clfajLqEQaeMQ23XDg2PZlbb4mVjI3xKQukarc/8AMvckLhakf/CmKojj3gi22ZDLZNNeCZrft7e2tra2wUDUxMfHiiy/ec889e/funZ2ddS0eiAYiCwartZphGKlUCsMw0zRJgsDd6XGX8WsViyVdN3p7e4JBvyCIru4gDkdKKYp2HHNlZdUwTOiF43Pn8iRJmpycfPzxx+v1+ic+8Yne3l53cFpGEExVZXcaUVtZWWQYenR0FHMHv0RReOmll8fHxyORyP79+/v6+lxNUV2SeARBMQx1PVguaetuBCYHwxDIw9qYWEIb8Mo0CJLgXOuKcrl86tSpcrk8NLSNJPGOjo5muf0NePUGAeLfWjhzVbFe2/Sf5/nnDj23a3TXtm3b4Pza1UDmSomby8OJs6m8AeEJ1CFIpVKHDx8+duzE7/3e7zIMm06nJUUp5vPZfK6Qyy8vLVWq1Xg8blkW1K45deqULMsej4eiKIgdoDQgfH14Kmz07NaD0vofFgxPhuMgpmPD/jz0D2/miW7NrpHgmOY60woqAm42QIIoye/3Q0lrjuNSqVQ0GiUIAlqHKIoSCoWGh4fj8XiT/UCSZGtrK8/zX/jCF4LB4Ac/+EEcxzVNoWkOQRyKomu1erlcSKXawuFwxfVkMwyDYRiWZYPBoKZpi4uLLMMkky1wrtO1JtHdwVKDphmCIF24hywtrUxOTiIIMjQ01N/fLwjCN77xjcHBwZtuusmN/hkEQUmS8Pt9oVAYALC8vFgolM6PnStXqtu2bTtw4AAcJ+R5XtN0V44Z2YxcN1mlNsPXpZQchvcNorJz9tzZhbnFfCG/e/fu4eHhDe7YG9HqjbD173Nh7hS+7QKlZgf6e9/9XrIlefDgQUEQNE2DarxXezVeUai++iubeQCaprW2tc5MzXzlka/ce8+9o6Oj6YW0YzoAAZZp2Y69ML8wcXGC4RhBEAqFwvz8/NmzZ8PhMEVRTS3mZi0cRqaNiGWYJuI4luPYiJsbriND23aA7WzoYqIo7vJV14vuum7o+rrhtmEYfr+fdCvhEDEFg8FQKAQRDY7j/f39fr+/XC5nMhlBEILB4JYtW9rb22H6RhAElECAgqs8z3s8nt7e3hdeeOHMmXMPPfTB9vbOxcUFiiIJkuRYTtfVqakZXdc7Ozuh53aTNuGm22wun5UlqbWtDXFtkxAMDQYCPq8PeuKdP3dueXW1ra1t//79Pp/PcRxJ4D2NTBM88sgjq6urDzzwrr6+fl3X4CBOqVR65ZVXpqcnfb7Azbfe1t9zSbJ9Q3sSbLLH26wXsvmhb6a2kzDklUqlM2fOra2tRKPhnp7+VKqlWcp4Ix98I2z9O144jjf70M3VhmEYHOC47777DMOo1mrk5cJG4Arp3KuC19WRy5Xi0aLhqG1bDz/8cLKl5d3vetfC8rKmqrC47eE4TVPPnDlbrVQzhUwpX6pUKpOTk8FgsBmzGnsbcVDgQi3HVTu+VG5DoNHEhuMk6iCOy3G0DaeR7JmaDutTEExBonY8HodghyCI1tZWWER3J+9IWLEOhULVavXChQuapoXD4ZaWFuibCecHSJJkWRY2QxsA0DRJHA9EIhRN26adakstLi5++ctf3rdv79vf/g6eFyqVvOvnhAaDQch3T7a0eFhWkmXYR3Z/FYSmiUqlOr8wn4wlevp64fTM1NTU0tISy7JDw8NdnZ1u+7UWCHhQFOMFoVqpAuAMDAwuLS0999yzt932lvb21lOnTr7yyiuqqtx0062jo6M4Dp2o6rZtEAT5WjjITQw3QlgjXJmWuS4dgWKO44yPX5yZmalWqyMjO5LJVLPc/ga8eiNs/ceU4Zorb25ubnZ29o6Dd/iD/lKhgJOks8l0dlNRC2wueF0ZudyP5prWdZ2i6Xgs9pUvfSmTy330ox+V3fKQbZozMzPpubl6rVatVlmGDYVDuVzupZdeyufz4XC4mR5CVePGP+aWftatPB0HYABxa/BuYGogMMM0NRinGlkhRpKk18v5vL5QOARlyzo6OorFoqqqXV1dNE2LohgIBJLJJFSDwTBsbW1tdXUVANDS0kLTdDweb2trg6YYDMOQJAlVp+E/iuN4rK2VCUUMnrdECSVQ0zChUdtXv/rVxcXFhx56KBIOV6o1n88LDwzDNJcWF1mW7ersNC1LURTDMGAEjMfiFE2VSuWJiYuiKNu2FYvFBgYHfF6fbZqarmMExtf4xaVFAEA0GuM4zu/3EQQJdT5+98MfTrW17dq965YDN0fcQZxHHnmE5/mPf/zj9Xp9QxAfubot27TphEOFbpA3XEdn1q1elU6cOMHzAoqiw8PD7e3tmwUj34BXb4St/4+9Lw9vqz6zvquke7XvXuJNsuN4iZcsTpzEiQPYJIG0kLAkAQpdaIcBhnYG5uvM89FOoU/70YcC09ICfYBC6QLTkpCErNCEkM1ZvBDbsi3vm2RZu3SlK91F93tyf7ai2A60M9MFuIc/8CJZsa50/L7v77zn/FXHXvOn3eBVGAwGT5482dDQUFhYmB7Sz1PACnNOToVMc/vZj6/IzVkWxfG8vLyOjo4333zznrvvmfZ5z7W0GAwGi8Wq1WtgAe7u7u7v7wfSgRMnToRCIYPBAH5IChZQcRgspCAEEpIsm0rxAgSJfoScOETnIYYVdFqlXGEgCJ3BYDYadWLHp1aqMQWmUMhlmDwrK4vjuPb29lWrVoGFTZVKBSzVVSrV0NCQy+UyGo2lxcU5BQVKguBTKdfkJBWLFRcXy0UdQyxOITAKcnBJlYqX4wGvXzk6ApsNZGEJSsdTCBIKBhOJhN1u7+7u3rNnz/bt22tqaoBoHkVRGYYpSHJ8fDwozvWA8Q7IRvH6/T3dXYFAkCDkKpWupqZKqVQCEW8qlaIoKhKJ4BiuUqtAhwvWSFtaWk6dOkUQxI4dO8B2jt/vP3Xq1PDwUGGhrbCwIBKh1q9fFwwGM3Ok0tMsscICYR4Cx6cgKCWWVxjPpxw9XX29fT6fd+PG641GY9pW9NPr2i7R1mcT6YZx9+7dVVVVNUCPygswOv81KszXfMxvGFEU1Wq1giBEIpHh4WGO42w2249+9KOioqKvfvWrExMTYnpGJBaNQQjk8/k6OjrACObs2bPRaBTEwc4ERvMp0Y2QB169PJ8SzyvRywQikymWlKndLqyrG7uxyaLV4ThuNBoNBoPeoCsw2rQaXUrF8gz/zt53Nm3ahKKoXC7XarU0Tbtcrs7OzlgsVldXV1tbC/L0mXg8JW4FqNTqUDDY09NDkmSezXaZ/nAMRnA2EWcNRnT/fuOJ47TH7XnwQU3dmqTbzaRSJEmCBgqoN59//nkcx7/xjW8AQgEzJtEDK5lIJAoLC2UyWXd3d19fHwRBJSUldrudJElxDXCYYTiTyZRIxMJhCpwVgL1oCIIGBgaOHz8WDkdycnKam5tNJlN/X18wHD59+rRWq3vjjde/9+ST066p2+64PRqNXn3V5ihLgLRdIMT6CpRXbW3tPp9PoVAsWbIkNzdXyPg7JJVXEm39PSLdO7S3t3E8f8P1NyToBBWjrs4Qmut9Oktj4L+r/BU6OjoWLVr0xhtv3HfffceOHZuYmHjyySefe+45mUx25513ejweKkKxAuvz+iLBSDAcHBgYAIaoly5dmpqaAkeGYAwkjsAv85fRaNTpdEDnpZTJ1MtXalvOajva5A8+zMAQwnFgX88X9JtQyyjeF4WpZuNNjCo5NjxWV1dXX1/f19fX2dnJMIzVaq2urtbpdGNjYyiKWvR6FkUQrYZH8XAkHI8nVCq11qgPTk5C0Ui+xcJCUCJOwxAfVKj1z/8ki4q6IOjQspXl9kKr0ZSdnQ0Wm8EKukqlUqvV586eOXTk6N13311cXDw6OqpWq8BJXyAQOHz4iNc7vWLFiqqqKlA9JZO06IKR4jhmbGzc5/MWFtqKiooAXzAM09LScv78eZIkV62qX768FlyCkZERl8vldDqj0WhFRYXoC1oEw0g0GgNHGgtuHQD9ilheISB7tb29PZlkqqqWAqetKxJcqbySaOvTwlx+v//0qVN33nknjsn8QT+O4/P1XFff74q8K5VKKRSK0dFRj8dz4sSJYDDY3NxcXFwMQRAgnT179nR3d3/ta1+LhCNDw0M4LgOnhQzDOByOoaEhgiD8fj9YhwR/58HqImidsnNzLQYDJMCyZVWq115Xjo5A//E9mGERioqKmf42W7FCiy9JVt2HNS9PbLiN/nIiN2LWWH/xi1/09/dv3ry5trYWtHsgHklBENNTrrhabzHr6c4eZmLYgmJKnmNjCVirRUtKPNnZXrergFRqtHp3ODAx6arZt99MyC/E6Jb6lXeubaASSVgQtHo9jKJQKiXDsBQExWnaaslyuydfeeWVurq6TZs2QRB05swZl8slk+G1tbVms8nr9Ws0Gq1WSwFdKRVlGBbHcYvFqlZfbmOBt/rJkyc9Hs/ixYsbGxuBTxZN07F4HEUwuQwnlWRLSwtBkNXVVRAkxOk4wzHoTFJmOttm5gLyPCe6ts+UVxcvXuC4FI7jlZWV4PBBKq8k2vq0zr8IgpiYnGxrbd2wYYPJZJqenk6rn+d0HfPbw3S4v1wudzqdDMPU1NSA0ONoNApy29va2l588cUvfelLtbW1AwNDqVQyHr/cOqEoCpwOCYJIJBLhcJggCEAx4LwPRVGtqvCEAAAc30lEQVTP5CRpNBZcdx327f+TEmDuP77LjYyhTIIQd2h4nvf5fNmmnKIs212Gdb8Lnx2a6O/o62ASTH19PY7jFy6cX7myrqCgYHp6GsQ+Jmg6plIF392f19pqgGE1DMd6ehQGE1pSFPD6uBitsNtDO3c5ohHI7RayzXj/0KqWFjWp3pPik5uaGoqKYFwuCBCXTKhUKplcHqdplmFgGJbL5SYxyebFF188derULbfcUldXRxCE2WwGo30IEgYGBqPRqNlsBkIwvV4PlGs0Tbe0nD158pTVaq2vr6+qqppNoqNgCEZmlFeCAMEcJ2i1JIJg0WhEZCVs1vkKpDqLh4MMAyGIWq1CUUwQ+N5eZ1vbhXg8uXTp0oqKisxFHKm8kmjr0w2CIF588aWmphvKysoCwcDldwsMC1eXZgsaOqcFXOAMLhAIAKET+DpN01lZWclE4j9/+tOqysqbbr55YGAA6JjisViSYUOhYH9/v9FoLCoqomn6SreIYTjHKW02Vywm++qXNdc38f/yL3Bfn4okZSCdOJXSarWkhhBc6Pu+gz+Lfv//jr0YznYvr1yZnZOTFJdyGIZ99939Vqv1hhtuGB8f97rdvNlq7GjN27uXzclRm02P//jZcx6PEYLu33rz9Y2N3igFe9x4CnL+w/3hQNDHshWuSXvbRzipfJVU2LduLjdZUQHGNDqWJHzhUAqGzQYDhqJKCEoIfNeJExPjk0tranAcP3jw4MaNG2tra5PJJBWLcUkuGovgOA6WQ6uqqsDw2+FwnD59Ouj3l5aX33D9DUolCdxraTohk2EoisHpoJIZCTvYJOUyR+8goYOH+BQnyHBUpdKI5VXg0qWP3G43giCrVq02GPRzqmwJEm19FqBWq998802T0dR8Y3OUijJJJn3COJ+tZj/OmHWJmisYvSrrDOhRSZXKqNe/9NJLyWTygQcemJ6eHhkZgVEEEWC1Rk0QxMDAAFBpAgWWjCBQmmYL8xmvP+sf/3F6++3jzU3lTFKl18fjcblMphRF9t5p70f9H5ldi57UPNCc88VHy7+T1CcpbzSeoHEcQxAUx+UkqXj33Xf7+/u337rdmmXpCwatzz5jUij8cnkegu384PgZjaqYFzyXPvpxdU3z6lWTNG3wBYbsRcKOHUNDQ4vfe7+UToYw+LXcnOubm9QKkjWaU7GofHxcF4sjkVAqFL3Mzjj+YTBo+8IXyvMWqVgWEcvVX/3q9UAgeN999wmp1JTHY9Bd/g84U3d3d3d1dY2Pjmr1+nXr1pWVlYkjLRCABM9OGK9y0Em//jMqYAFkDnIcA0GYSkWC69XT23vh/Hm9XpeXV5CXt0gmk0mrzhJtfWYBvB9279mdTCR37twJDvswHIcWOkOcY9IgXE1gcwo0oBTPzs4+fvz4vn37vvKVr1xu3LxeQi5nxGVmgiDcbncwGFxcWophWGhyMlFRgVy4oP3GP0BvvCarWy309g1MufMXLbLb7X6/v6+vb3h4mOM5u83eUNxwd/b6H0ReTw1hvI7JMWcxHCdaUMToWBLBkJKSkqmpqf3vHqirrcGL8h3/+NAOmz2g1cliUY1KfaCkxJGkD5473zgw+N2163w4ZgqFjhNK1/p1BlhYfeg9HZTqFVJHyys3rlzGaNSW02dVZ1pISFBEIqH+fpW9GMvK8vqndXICWbo0cPfdgkKe8gd4IWXUG06dPn3o0KF7770X9H0QBF1sbTvX0kLT8eXLlzc2NoKnUVxaYGQyBEGwq7kp89nOPB+cuRSiD/LM9CoY9Le1tU9NTSuVZH39apJUSmwl0dbnZdQFw3B3d7dzwHnHbXcoFAqv1yuXy6+lfsi843w9/ZzFIIZhcnNzx8bGXn755VtvvbW2tnZsZAQSjw4FQSAJwh8IOBwOq1Zr3rAB37+f/OFT9Nt/ELKssvFxXXY2jiAnPvzQ6XRqtVqTyVRiL7bmZyk4RR/V9aj8nv2JdkpBTU1NJZNJhULBMAxJkmC3jo7H5UpSYLh9b7/tg7hk/8A3/EGt1cKYLRjL4hAUtlgYljXzHBSJCmOjp3scB6qqq2/eqvN41p4+rdbp9kWpU7lZX/7Sl4yHjyiPvM8W5CGE/F9/+NQEw+gh6PGvfW1xZaXb48HHJwSzYfqRb7HRqByGFUpVUWEBBEFvvPEGgiB6vb7tYlteYcHGxg0gOzoYDOrF+pHnuRkT5Nkn8+MvE9CCqlQqsDnY29vb2tomSsPMVVVLM7VXEltJtPU5wsjIyNmzZzdt2lRYWAjGVQsuXS/46ZxvZfJXMpm0Wq0wDD/11FPFxcV33HFHOByORqOpVCoWjxO4giBkI1lZlqeespw+G3t3nwaCZLHYdDTa1dU1MTEBnKMJgigrKxN4aNw/Usjbnjc+SSPRx6Z+1MVeIiFVnI5jGAbM14HJJzgsk2G4Rqs+dPTQ9597fkUy8eUEo2YS2uwcnFAqmaRAxdw+n9PjPs8w54rt6++6y7Z8uf6D4w3BsIwkdmvV6LqGQHd38/vHdUtKaQgysuyNJ09FlxTjzkEjRf1oxYqCrKwgJBDDo56bN6ObthTKZIhCEY/Hnc7+rq7O1tbWXbvuXLlytVhbUX6/7+mnnwYmDbt27VKr1cBNVCSaK9VWhtMONLs5yKZDcXz+QHdXl9frLSjI1+l0IDoTNObSyaBEW59HoCgqk8n27t1rsxUuX74yHA4D0wIx0Af+GM6a7+A+p7VkWVahUGi12rfeemt0dPSBBx5gkslINKolCF6hgHQ67eOPU2536OWXcyBosK2tc2BA4PmioqKysjIgavX7/YODgzk5OZZci9wnv029+v6Jf1udaorovDrCoCAUkUgEbA6pVCp+VrkaDoVCkUiWxRiORr/z9HOuc+eW0dHw4AguzoeiEBTHUG7x4qSt8KYtN21as7Y/FrO+8evVRmOUor7lmcpfvtIT8N0xOrqhosJNJ3QchyiVLfX1XgXxg1+8sGPC/a8bGz0IZHV7+jes1992W7in9+T5C1OTrqwcy6ZNW7LEQ8b21vahkaFXXnn5/vu/fvTo0XvvvVen0+Xm5oKN6/mEP7PnDEE8y6MISiiJmelVd0/HpQ4UQUxmy5o19Z/zUByJtiTMAHiuymSyDz44gWFoU1MTTdMMTcM4JqSEPyUm9mO+Duovo9HY1tb21ltvPfrYY2aTyc2yShzXPPzNVLFt6pF/Ov32287OzuWrL0Or0cAIIjqxJFHRW/UyBVxqR2KYNc90/6LmI9E+WkGzKZbneGCHj6IoWFpSKBShUAj0jCqVKpVKkXKCVCl/ufv3Fxy9SyvLA56pMBXVG01Gg6kw27okO5cOBRBcPklFrS+8VGazDfb2/thi+sodO/snJwcOHfhnASJyFiV0GoJhYAga1+kgQchjmRQdF8Zd4wHvdzB5li3frCBW1Tesrq+TyeRer3fvnj0wDI9MTFSUlQ0Pj+zadWdOziLQzYn+WfwcrgGb49DVxgx+v9/h6HY4em32oqqqKpIgJaWoRFsSFobD0TM15b711luB5hvHMOFP9ned736TVnLTNJ2Tk8Pz/PefeGLjTTetr6sLNjd3rVo5fs+98ra25StWFBYXDw8Pi7k4xkSCBm6r8XgcKMJUBiI7lP+y7JmRgq4fRF+d5CaUkApGYJm4GQ62aqampniet9vtBEEAI1NBEBieR4SUxWIdcQ60d1/asnmLXKHwTU2pVCqFkhBSqelJ176TpzqPH/+eQqY3W4999NHAfffeWFISisdf+f3vEwcO7mKTSwxmwaBXarVqnocTCa/PO+pyOWHk0KJc04YND959V/HiJRAEBSMRnmEEQTCbzW+99ZZWp6utqQHdXDKZpGkaglIois8aYF3pDVmWR5D09ApyOBzd3d0yGZafX2C32YTZW0qhOBJtSViAdADLBAKBkyc/3LRps8Vi8fv9ONCjzmGudFdz7SIrzVwYhimVymg0Go/HrVbrawcOwM89V7lzp3DPPcVeny4nm+E4OhaTy/GxsXEIgoqKCgOBUCgUAqUHSZIMQusF07eIHZV9a75K/BNTkORjKS7FURQVi8UQBCFJUrRUT3i9XpDmz4i6UNELlKdpRqVWjo+OHTp48IYbmiqrltKxeLfD0TfghOLxrJoa4kJbdXenUqP+r0mX6uEHl+CyMJ3wxaIHPjjevf+A+aOPDAKUD0EBCBqHoMv0U7pYt3791ju2f2HDRoZmo4k4DAlymVzcWIJxXObz+YxGI0EQolI0lVY5XLHEmnWLTpdXoVDo0qXOgcGBYpvNZDYXFBRIoTgSbUn4M8Dz/IEDB+pW1ZUuLk17pMxckszdxYVYL5O2gC29XC5vbW2lKGrRokXPPvvMz3/+wune3vMXL37r7ruBZ55SqZTL5RzHi+LygakpV03NMpPJBKiHYRmZICN4YpuhZjfXMT40EWaCRqMpkUiA6Aq5XJ62aeZ53uv1kiRpMplEVUdMnH9DBEEAV4bXX399YmJi2fJlGrXWkm3O1eonUER/+Ih5fAyacH/H713+Dw/ky3BYjmeZzCardTpBn+/s2vP222c+OGHKsiyrX7N0aVVlaUl5Tg4By8PRMIZjGIalo4mALZ9CoeA4LpFIgm9lrBCCRRweSOdA9ST6XvUHg4GC/Py16xrS0ytp3C7RloQ/e+D15u/eXFK2pKGhAaRvpYdcM7Ki9Kdz3btmAJL0jxw5kkwmp6enY7HYQw89dOTQkVX1q+x2e1939+u/+c39999fVFTk9XopimJZVi6XW61WQUj19w+AlI1YNMZirAkztSKnXoCfeNlzdBQbCXnCLMcuXboUx3GKojJ5AUR4gDhIvV5PkqROp0MQhGXYto62seGRfJstFo1OTEzu3LkDx/GhkcEwhBA7d9Hdjo+ikcF/+3ZNY0Pd4rK8wiIIgvzT3rbW1oHBAVNW1pq1a3Ozsy//YhyfTCSTbJIVaQV4H876XqVzBi8/GXNIJyNzEGivgl1dXd3dXbm5uaWlpdnZOdL0SqItCf/ThhGG4WPHjvE8v337dpZnqRCF4mjm0OoTwTCM0Wh85513wE4vx3F2u53neRC8zHLsU//vKZvNtmvXLp/PBxSwyWQSxzFBgJxOJ0mSxcXFYSak9ej+XXd/HmN/IPZtn9FnUpoikcjg4GBubq7JZIqJi9ai3XsS5DYSBKFQKEBF43a729vbo5Fofn5+RWWFWq0Gmbhvvvlmqa24sq7mw6Pvn952+yBBEMtrHv7Zz+uqqvt6ej0+b4+jx+/3VlVVr1m71qDXCxwfikQ4XpgtreAMi7657jGZr2FQXmW4tkM9vT39oq1osb24WAwBmimveB6RplcSbUn4nzNXOBy+cOHC1q1bcRz3+/3p1euPGbhkzrwIgujr68vLywOxY+FwGAx6OIZTa9UYhv3yl79kWfbrX/86BAk+X0ChkEEQDBKD+p39bre7yFaUh+d9wVD5auqoSciJ8CFYQER7CWF4eFilUuXl5QFFGJBlpq0UPhJhMBjWrFmTm5sLaBQEmoqupOzuP+ymE4k77to1HPCxMFRrXxzzeE6dODkwPKjRaDZs2FBeXg5+FE3TEAJhCPYxObXznwQwvSJkMmI2qKKtrW1qylNoy1eRapvNlg7FkfpBibYk/C8jEon88Y9/bGpqys3NzTTV/MSgafCGVKlUwNJT/BaSHo4B4lOr1WfOnDl8+PCjjz6q0WgikQgMw2DWrtFoEmxcmMYhe/Jxzb2/CpwM4SEURuHZVT4Mw/r6+sLhcElJCfDwgyCov7/f4XDQNF1aWlpdXc2ybCQS0el0wDk+Go1SVAyCBK1Wm52dPTk56XA4rmtsHBsZPXzwUDhOrVxdd/3G69O/OBDiz5MsCJmdaeZAL52km1ledfV0DToHXC53ZWXFihUrGEZiK4m2JPzlyy6GYY4cOVJWVrZ8+fL0qGvBamv+JlA60zDzBulPGYYxGAw+n+8nP/nJLbfcsmzZssHBQYVCodPpMBTj5TwZIR8PPwhpuScVLwVTQTkvF2CBYZhoNIqiqMViQRDE4/GgKDo0NDQyMmIymZcurQSaT5qmgSv02NiYXC4H4imdTqvXG8CjJ5PJV1999cMTJ7Z+8YtNTU1mcWx/7I/H9ryz57vf/a5arQbHAtcirKtGfpd/0xQEcSiKA4PAcDjc0dExODis1WlshYXFYgCiRFgSbUn46zEXjuPvvfeeRqPZuHEjGCFdZahyDf76xCkYDMPJZBLYzP/whz/Mz8+/6667eJ6Pi0E4CS5hwkxfV23aMfpIDdWAFwtsjKUoSiZTWK1mwKdDQ0MOh8PlctXW1q5du1YkBY5leRDyGAwGgeKJouImk2F2LYY/e/ZcS0sLz/Pbtm2z2+1AC3r06PuCIJw/f37r1q3Hjx9/+OGHxYhDNrPvy/ylMgMlU6mUVqsB8au9zt7e7r4pj3vFiuVWa5ZOp5NCcSTakvC3YS4Mw06ePElR1G233QYUXvNdBudPvuZ7SMx/64I1IIIg9u7d63Q6H3vsMQiCpgPTFpklBsd2ECv20z3O8X7X1OSKFSvASdzY2KTD0RmJRLKysqqrq7VardfrBXNulmMDgQBIe1UoFBqNRqfTiXQGDQz0XLzYNjIylp+f19TUZLVaJycnYzT9zu7d+fn5TzzxxDPPPDM9Pb1z506KolQqVSKR+Bii4UTlRYb2KtzaenFqakoQ+KVV1cX2YikUR6ItCX975gJKq9bW1ptvvlmn06WZKyPkCs78eD5zfcxXBEHQaDTd3d2/+93vvvnNb5pMJsgN/Rfx8gns8M+if4BE+UFnZ2cgEAiHwxAElZeX2+120IcyTIIkVS6Xq6evL9uabTTqYRjWaDTgnI5hmLNnz168eBHH8fr61StWrASPDsywEokETdP5+fl6vb66upokSSCyTyaT4lbzAnXkTHml0aBiydnX19fd3R0IBJYtq83KylIqVX9WySlBoi0Jf0GkPeAPHTpUWVm5ePHiSCQy952JwJAAzU9Amz/Cn/9+BgmsoUjo2R8/u2rdqi1NW/6Z33kn+pVVUFOXs2tiYILlWY7jysvLS0tLwfQKhmGW52PRSCRCkYQCEQ2gi4rsGo0KEMrp06enpqaAZTsQnVIUxTAMgiBgpea3v/1tVVVVZWUlqPsSicS1/nnAcxkkmwHzrIsXL05PTwuCUF5eXlJSks6gl8oribYk/N0BRdFf//rXNTXVq1fXR6PRWdMIYSac4ergsjljbOgacnsYhlOpFI7jwHb9lZ//ElIKZ+/9zT+1P9PT4dQt0pSVlgH7qqmpKa/XW1paynGcy+UCO9UajQYI7iEIcjqdPT09fX19JEmuW7eupqZGpBIhEgmDh0hTEs/zCoVCLpdTFDXn9CAT6cNBIG0fHBxsbW2NRqPl5eWLFi3KnF5J5ZVEWxJt/f3WXDAMv/vuQYVCtm3bNo7jKIpKD+kzo4Dm6Olnr+3lG2XSGViIAW6FHo8H1D5HTxzdffatf7/xifzaXHA/mqYFQZDL5ZOTk52dnfki0l4RYqxZ57lzLdFo1G63X3fddeBQTyyvWDHiDJ3PlaA/vWqHabY3BGyVLq/C4XBrays4LqioqABR21J5JUGirU/TqAuG4fb29omJidtvv10mkwGx+/wTt7mToavrL2ASHQyHLSbTT3/60+3bt3d2du7fv//555+/zBRj1PO//s8dd+6w2+3hMMWyCYqiEomEwaAnCNLlchUUFCgUikgkcvz48a6uLovFvG5dA7Bs53meoijAO3/K2C7jpQfxHAtBiFqtQRAYlG8dHR0ej6e8vLympuYKQUvllQSJtj6NzOXz+d47+v6tt37RYrWGQqGPF0Ys1H8JWq36hRde8Hq9ubm5KIpu2bLlxIkT27dvBxH8PM8//fTTS5aUbt68xe12y+VytVatJJTg7gcPHuzt7eU4vrJq6fXXNcplCrG8ApbtsllCudYO+AJL4JnTq0gkdO7chenp6VQqtWrVKqCfkGorCRJtfRZAUdSRI0dqRVAUNbcl/OSuk9dotG+//fb4+PhDDz3k9Xqzs7NpmuZ5nmMZjVaNIPhrr70WDocfeeQRcJdAwP/++3/s7+8vKCior68H2is2maTiCRiGMBSB/uQKKL2IIw7plUB75XQ629s7IpHgkiXlRUVFGo1mwVgQCRIk2vp0l11Hjhwxm82NjY10MplM0BiKfRJfQBlp/pBKpeI4DsMwcVJGYxgMfGDA7Emt1pw7d+7o0aPNzc3nz5+Px2N1das2NDQgYnEHYtPAPD5NRtfaHczUkQkztqLp8irS1nZxfHwSgZD6tWsMBj2CIJkBRdLlliDR1meKuTAMa2lp8Xg8u3btEiAhHApnRst80k+43C8iCAaG3PMIQmAY1mAwTE9P79u3r7m5GRwpJpPJeDyOIAg4AUwfU8IzlgzC1RkTs62i+H9QXpEkCf6RTqfz4sWLJElmZWXZ7cUEoZAy6CVItPV5qbkcDsfIyMjmzZu1Wm0wGPz4cXhGzTXv66LFemaJxLKsRqMBDBIKhYDuIUMRBs3c44rt8ZWo1NmknMs3Y1k2U3t17ty5UCikVquXLVtGEMS8iAoJEiTa+qwzF5ApHD50qGH9evH4L3xVKbTQXCnNUAsovOY9BMdxoLJb6MgSGPUJaeITg+mBIGOmvFIqleDQwOl0tra2siybnZ29Zs2a9CKO1A9KkGjrcwfQUikUitd/9VrV0urVq1dTsRh0xSxhrqle5hDqytQJmom+vhZDgnwu4eqSbYasZthrplMUp1dMOtI5Fou1tbX19/eTJLm0qio3JwdMryQpgwSJtiTmutzKtbW1IQiyYcMGUUIVQTF8QXH8nArrWtXTvA2hubKGOaQDCihCqZSJ5VV/P5heKfU6Xe2yZZlhtFJ5JUGiLQlQJhMdO3Zs69abcVwWDocwfAHmmr3xFae9BThqIWKa82PATYCaQSyvlKI4I9LRcamnp8doNIJdnPT0SqqwJEi0JeGaFPbOO+9s2bLFYrEEg8G0aUTmwCtjULXAK2G2tRQyO8H5AAvSBEGAh3A6nV1dnbFY3GQyrVixIm2zI2UOSpBoS8InXUgYjkaj+/bta2xsLCsrm69HvUa9A+RSC393jvWzKG2XKZUkBEHxeLyjo+PSpUtms7mkpMRut0vjdgkSbUn4b2Lfvn0Wi/XGG5sTiUQymVxwDegTaSWT74AxVjrS2ensuXSpJxQK5OXlrVmzJr3nLLGVBIm2JPzZAEN6FEWHh0fc7snm5huBocK1FhjT5jcL9oPpSGelUgkOB9vb2zs7OwsLCysqKsAaY+bjSs+/BIm2JPx3kK53xsfHOzo6tm3bRpJketQ1h7MgeLZFhK6aZPE8LwhCpvbK4XBEIhG73W6z2ZRKJSjEJLaSINGWhP9l5goGgx988MH69Q02mz2j5gK11cwaYiZhpY0ZQHkVj8cvXrw4MDCgUqkKCgqqqqrStqISJEi0JeEv2DDu3bunpKS0rq5OHNLzMLxAw8hxPARdKa8GBwe7urrGxsaqq6srKytnLFWl0ZUEibYk/HWYC8fxw4cPa7Wa66+/gWWZeDwuuo9eLrg4LpVKpWQyLD296ujoGBoa0OuNeXl5IJQ/80dJT6kEibYk/PUaxrGx8f5+57Ztt4pXPIgjCCOkNCoViuIQBA0PD1+6dMnj8VRXV2dlZRmNRkBYklJUgkRbEv6WzBUOh48fP75161aQ8yqGrSbOnDkzPj4uCEJFRUVlZaUUiiNBoi0Jf1+IxaiDBw81NDRotdozZ86GIuHrGhtBcmp6eiWVVxIk2pLw91V2MUzS4egJhUL5+XnFxSVSKI4EibYkfAqYCxGR3sWRzgclfOqASU/B5+hvlEhPKRFzvihBwqcI/z8AAP//EcHMAzzn/ewAAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"# **Table of Contents**\n\n* Importing important libraries\n\n* Import train and test data\n\n* Understanding the Dataset\n\n* Handling the null values\n\n* Concatenating Train and Test dataset\n\n* Data Preprocessing for the Model\n\n* Prediction using Random Forest Classifier","metadata":{}},{"cell_type":"markdown","source":"# **Importing important libraries**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport sklearn.ensemble as RandomForestClassifier","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:07.999532Z","iopub.execute_input":"2022-07-30T10:16:08.000061Z","iopub.status.idle":"2022-07-30T10:16:09.029903Z","shell.execute_reply.started":"2022-07-30T10:16:07.999966Z","shell.execute_reply":"2022-07-30T10:16:09.028681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Import train and test data**","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')\ntest_df = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')\n\n\n","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:10.669710Z","iopub.execute_input":"2022-07-30T10:16:10.670105Z","iopub.status.idle":"2022-07-30T10:16:10.748981Z","shell.execute_reply.started":"2022-07-30T10:16:10.670072Z","shell.execute_reply":"2022-07-30T10:16:10.747822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Understanding the Dataset**","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:11.620250Z","iopub.execute_input":"2022-07-30T10:16:11.620645Z","iopub.status.idle":"2022-07-30T10:16:11.659102Z","shell.execute_reply.started":"2022-07-30T10:16:11.620610Z","shell.execute_reply":"2022-07-30T10:16:11.657884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# number of rows and columns present in train dataset\ntrain_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:11.883486Z","iopub.execute_input":"2022-07-30T10:16:11.884174Z","iopub.status.idle":"2022-07-30T10:16:11.890129Z","shell.execute_reply.started":"2022-07-30T10:16:11.884136Z","shell.execute_reply":"2022-07-30T10:16:11.889130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.describe()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:12.884151Z","iopub.execute_input":"2022-07-30T10:16:12.884957Z","iopub.status.idle":"2022-07-30T10:16:13.001107Z","shell.execute_reply.started":"2022-07-30T10:16:12.884889Z","shell.execute_reply":"2022-07-30T10:16:13.000180Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:13.148749Z","iopub.execute_input":"2022-07-30T10:16:13.149718Z","iopub.status.idle":"2022-07-30T10:16:13.179609Z","shell.execute_reply.started":"2022-07-30T10:16:13.149680Z","shell.execute_reply":"2022-07-30T10:16:13.178280Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Handling the null values**","metadata":{}},{"cell_type":"code","source":"# percentages of missing values in each columns\npd.set_option('display.max_rows', None, 'display.max_columns', None)\n(train_df.isnull().sum()/len(train_df))*100","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:14.418054Z","iopub.execute_input":"2022-07-30T10:16:14.418419Z","iopub.status.idle":"2022-07-30T10:16:14.439929Z","shell.execute_reply.started":"2022-07-30T10:16:14.418389Z","shell.execute_reply":"2022-07-30T10:16:14.438370Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualization of the positions of null values\nplt.figure(figsize=(16,16))\nsns.heatmap(train_df.isnull(),cbar=False,cmap='Blues')","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:14.669303Z","iopub.execute_input":"2022-07-30T10:16:14.670360Z","iopub.status.idle":"2022-07-30T10:16:16.998862Z","shell.execute_reply.started":"2022-07-30T10:16:14.670310Z","shell.execute_reply":"2022-07-30T10:16:16.997601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# columns having more than 50% of null values are dropped\ntrain_df.drop(columns=[ 'Alley','PoolQC', 'Fence', 'MiscFeature'],inplace=True)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:17.000634Z","iopub.execute_input":"2022-07-30T10:16:17.000956Z","iopub.status.idle":"2022-07-30T10:16:17.007759Z","shell.execute_reply.started":"2022-07-30T10:16:17.000921Z","shell.execute_reply":"2022-07-30T10:16:17.006925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Replacing all missing categorical variables by the mode of there respective columns\ntrain_df['BsmtCond']=train_df['BsmtCond'].fillna(train_df['BsmtCond'].mode()[0])\ntrain_df['BsmtQual']=train_df['BsmtQual'].fillna(train_df['BsmtQual'].mode()[0])\ntrain_df['FireplaceQu']=train_df['FireplaceQu'].fillna(train_df['FireplaceQu'].mode()[0])\ntrain_df['GarageType']=train_df['GarageType'].fillna(train_df['GarageType'].mode()[0])\ntrain_df['GarageFinish']=train_df['GarageFinish'].fillna(train_df['GarageFinish'].mode()[0])\ntrain_df['GarageQual']=train_df['GarageQual'].fillna(train_df['GarageQual'].mode()[0])\ntrain_df['GarageCond']=train_df['GarageCond'].fillna(train_df['GarageCond'].mode()[0])\ntrain_df['BsmtFinType2']=train_df['BsmtFinType2'].fillna(train_df['BsmtFinType2'].mode()[0])\ntrain_df['BsmtExposure']=train_df['BsmtExposure'].fillna(train_df['BsmtExposure'].mode()[0])\ntrain_df['MasVnrType']=train_df['MasVnrType'].fillna(train_df['MasVnrType'].mode()[0])\ntrain_df['MasVnrArea']=train_df['MasVnrArea'].fillna(train_df['MasVnrArea'].mode()[0])\ntrain_df['BsmtExposure']=train_df['BsmtExposure'].fillna(train_df['BsmtExposure'].mode()[0])\ntrain_df['BsmtFinType1']=train_df['BsmtFinType1'].fillna(train_df['BsmtFinType1'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:17.008995Z","iopub.execute_input":"2022-07-30T10:16:17.009572Z","iopub.status.idle":"2022-07-30T10:16:17.038276Z","shell.execute_reply.started":"2022-07-30T10:16:17.009536Z","shell.execute_reply":"2022-07-30T10:16:17.037115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Replacing all missing numerical variables by the mean of there respective columns\ntrain_df['LotFrontage']=train_df['LotFrontage'].fillna(train_df['LotFrontage'].mean())\ntrain_df.GarageYrBlt = train_df.GarageYrBlt.fillna(train_df.GarageYrBlt.mean()) ","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:17.040575Z","iopub.execute_input":"2022-07-30T10:16:17.041398Z","iopub.status.idle":"2022-07-30T10:16:17.047595Z","shell.execute_reply.started":"2022-07-30T10:16:17.041366Z","shell.execute_reply":"2022-07-30T10:16:17.046566Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(8,8))\nsns.heatmap(train_df.isnull(),cbar=False,cmap='Blues')","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:17.049103Z","iopub.execute_input":"2022-07-30T10:16:17.049748Z","iopub.status.idle":"2022-07-30T10:16:17.903535Z","shell.execute_reply.started":"2022-07-30T10:16:17.049716Z","shell.execute_reply":"2022-07-30T10:16:17.902542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All the missing values are filled","metadata":{}},{"cell_type":"code","source":"train_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:17.904834Z","iopub.execute_input":"2022-07-30T10:16:17.905171Z","iopub.status.idle":"2022-07-30T10:16:17.912016Z","shell.execute_reply.started":"2022-07-30T10:16:17.905140Z","shell.execute_reply":"2022-07-30T10:16:17.910903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Handling the missing values of the test dataset**","metadata":{}},{"cell_type":"code","source":"test_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:19.161034Z","iopub.execute_input":"2022-07-30T10:16:19.161423Z","iopub.status.idle":"2022-07-30T10:16:19.169038Z","shell.execute_reply.started":"2022-07-30T10:16:19.161393Z","shell.execute_reply":"2022-07-30T10:16:19.167822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:19.439851Z","iopub.execute_input":"2022-07-30T10:16:19.440315Z","iopub.status.idle":"2022-07-30T10:16:19.458059Z","shell.execute_reply.started":"2022-07-30T10:16:19.440278Z","shell.execute_reply":"2022-07-30T10:16:19.457109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# columns having more than 50% of null values are dropped\ntest_df.drop(columns=[ 'Alley','PoolQC', 'Fence', 'MiscFeature'],inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:20.137363Z","iopub.execute_input":"2022-07-30T10:16:20.137840Z","iopub.status.idle":"2022-07-30T10:16:20.145766Z","shell.execute_reply.started":"2022-07-30T10:16:20.137807Z","shell.execute_reply":"2022-07-30T10:16:20.144740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Replacing all missing categorical variables by the mode of there respective columns\ntest_df['BsmtCond']=test_df['BsmtCond'].fillna(test_df['BsmtCond'].mode()[0])\ntest_df['BsmtQual']=test_df['BsmtQual'].fillna(test_df['BsmtQual'].mode()[0])\ntest_df['FireplaceQu']=test_df['FireplaceQu'].fillna(test_df['FireplaceQu'].mode()[0])\ntest_df['GarageType']=test_df['GarageType'].fillna(test_df['GarageType'].mode()[0])\ntest_df['GarageFinish']=test_df['GarageFinish'].fillna(test_df['GarageFinish'].mode()[0])\ntest_df['GarageQual']=test_df['GarageQual'].fillna(test_df['GarageQual'].mode()[0])\ntest_df['GarageCond']=test_df['GarageCond'].fillna(test_df['GarageCond'].mode()[0])\ntest_df['BsmtFinType2']=test_df['BsmtFinType2'].fillna(test_df['BsmtFinType2'].mode()[0])\ntest_df['BsmtExposure']=test_df['BsmtExposure'].fillna(test_df['BsmtExposure'].mode()[0])\ntest_df['MasVnrType']=test_df['MasVnrType'].fillna(test_df['MasVnrType'].mode()[0])\ntest_df['MasVnrArea']=test_df['MasVnrArea'].fillna(test_df['MasVnrArea'].mode()[0])\ntest_df['BsmtExposure']=test_df['BsmtExposure'].fillna(test_df['BsmtExposure'].mode()[0])\ntest_df['BsmtFinType1']=test_df['BsmtFinType1'].fillna(test_df['BsmtFinType1'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:20.558321Z","iopub.execute_input":"2022-07-30T10:16:20.559233Z","iopub.status.idle":"2022-07-30T10:16:20.585714Z","shell.execute_reply.started":"2022-07-30T10:16:20.559194Z","shell.execute_reply":"2022-07-30T10:16:20.584567Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Replacing all missing numerical variables by the mean of there respective columns\ntest_df['LotFrontage']=test_df['LotFrontage'].fillna(test_df['LotFrontage'].mean())\ntest_df.GarageYrBlt = test_df.GarageYrBlt.fillna(test_df.GarageYrBlt.mean()) ","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:20.866051Z","iopub.execute_input":"2022-07-30T10:16:20.867184Z","iopub.status.idle":"2022-07-30T10:16:20.875205Z","shell.execute_reply.started":"2022-07-30T10:16:20.867146Z","shell.execute_reply":"2022-07-30T10:16:20.874309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df['MSZoning']=test_df['MSZoning'].fillna(test_df['MSZoning'].mode()[0])\ntest_df['Utilities']=test_df['Utilities'].fillna(test_df['Utilities'].mode()[0])\ntest_df['Exterior1st']=test_df['Exterior1st'].fillna(test_df['Exterior1st'].mode()[0])\ntest_df['Exterior2nd']=test_df['Exterior2nd'].fillna(test_df['Exterior2nd'].mode()[0])\ntest_df['BsmtFinSF1']=test_df['BsmtFinSF1'].fillna(test_df['BsmtFinSF1'].mean())\ntest_df['BsmtFinSF2']=test_df['BsmtFinSF2'].fillna(test_df['BsmtFinSF2'].mean())\ntest_df['BsmtUnfSF']=test_df['BsmtUnfSF'].fillna(test_df['BsmtUnfSF'].mean())\ntest_df['TotalBsmtSF']=test_df['TotalBsmtSF'].fillna(test_df['TotalBsmtSF'].mean())\ntest_df['BsmtFullBath']=test_df['BsmtFullBath'].fillna(test_df['BsmtFullBath'].mean())\ntest_df['BsmtHalfBath']=test_df['BsmtHalfBath'].fillna(test_df['BsmtHalfBath'].mean())\ntest_df['KitchenQual']=test_df['KitchenQual'].fillna(test_df['KitchenQual'].mode()[0])\ntest_df['Functional']=test_df['Functional'].fillna(test_df['Functional'].mode()[0])\ntest_df['GarageCars']=test_df['GarageCars'].fillna(test_df['GarageCars'].mean())\ntest_df['GarageArea']=test_df['GarageArea'].fillna(test_df['GarageArea'].mean())\ntest_df['SaleType']=test_df['SaleType'].fillna(test_df['SaleType'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:21.314340Z","iopub.execute_input":"2022-07-30T10:16:21.314788Z","iopub.status.idle":"2022-07-30T10:16:21.340483Z","shell.execute_reply.started":"2022-07-30T10:16:21.314753Z","shell.execute_reply":"2022-07-30T10:16:21.339356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:21.780313Z","iopub.execute_input":"2022-07-30T10:16:21.781258Z","iopub.status.idle":"2022-07-30T10:16:21.798296Z","shell.execute_reply.started":"2022-07-30T10:16:21.781214Z","shell.execute_reply":"2022-07-30T10:16:21.797063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:22.091251Z","iopub.execute_input":"2022-07-30T10:16:22.091663Z","iopub.status.idle":"2022-07-30T10:16:22.100047Z","shell.execute_reply.started":"2022-07-30T10:16:22.091622Z","shell.execute_reply":"2022-07-30T10:16:22.098606Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:22.524497Z","iopub.execute_input":"2022-07-30T10:16:22.525482Z","iopub.status.idle":"2022-07-30T10:16:22.533259Z","shell.execute_reply.started":"2022-07-30T10:16:22.525442Z","shell.execute_reply":"2022-07-30T10:16:22.531978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"All the missing values of test and train data sets are handled","metadata":{}},{"cell_type":"markdown","source":"# **Concatenating Train and Test dataset**","metadata":{}},{"cell_type":"code","source":"# creating a copy of train data\nmain_train_df = train_df.copy()","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:23.629894Z","iopub.execute_input":"2022-07-30T10:16:23.630477Z","iopub.status.idle":"2022-07-30T10:16:23.635438Z","shell.execute_reply.started":"2022-07-30T10:16:23.630430Z","shell.execute_reply":"2022-07-30T10:16:23.634676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#concating train and test dataset \nfinal_df=pd.concat([train_df,test_df],axis=0)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:23.896413Z","iopub.execute_input":"2022-07-30T10:16:23.897228Z","iopub.status.idle":"2022-07-30T10:16:23.914855Z","shell.execute_reply.started":"2022-07-30T10:16:23.897180Z","shell.execute_reply":"2022-07-30T10:16:23.913759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:24.348792Z","iopub.execute_input":"2022-07-30T10:16:24.349813Z","iopub.status.idle":"2022-07-30T10:16:24.356369Z","shell.execute_reply.started":"2022-07-30T10:16:24.349768Z","shell.execute_reply":"2022-07-30T10:16:24.355307Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Data Preprocessing for the Model**","metadata":{}},{"cell_type":"code","source":"# List of all the categorical feature\ncolumns=['MSZoning','Street','LotShape','LandContour','Utilities','LotConfig','LandSlope','Neighborhood',\n         'Condition2','BldgType','Condition1','HouseStyle','SaleType',\n        'SaleCondition','ExterCond',\n         'ExterQual','Foundation','BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2',\n        'RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','Heating','HeatingQC',\n         'CentralAir',\n         'Electrical','KitchenQual','Functional',\n         'FireplaceQu','GarageType','GarageFinish','GarageQual','GarageCond','PavedDrive']","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:25.163415Z","iopub.execute_input":"2022-07-30T10:16:25.163867Z","iopub.status.idle":"2022-07-30T10:16:25.172718Z","shell.execute_reply.started":"2022-07-30T10:16:25.163834Z","shell.execute_reply":"2022-07-30T10:16:25.170996Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# creating a funtion that can change categorical features into dummy variables\ndef category_onehot_multcols(multcolumns):\n    df_final=final_df\n    i=0\n    for fields in multcolumns:\n        \n        print(fields)\n        df1=pd.get_dummies(final_df[fields],drop_first=True)\n        \n        final_df.drop([fields],axis=1,inplace=True)\n        if i==0:\n            df_final=df1.copy()\n        else:\n            \n            df_final=pd.concat([df_final,df1],axis=1)\n        i=i+1\n       \n        \n    df_final=pd.concat([final_df,df_final],axis=1)\n        \n    return df_final","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:25.419213Z","iopub.execute_input":"2022-07-30T10:16:25.420826Z","iopub.status.idle":"2022-07-30T10:16:25.429584Z","shell.execute_reply.started":"2022-07-30T10:16:25.420773Z","shell.execute_reply":"2022-07-30T10:16:25.428326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# getting the dummy variables\nfinal_df=category_onehot_multcols(columns)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:25.882060Z","iopub.execute_input":"2022-07-30T10:16:25.882645Z","iopub.status.idle":"2022-07-30T10:16:26.015754Z","shell.execute_reply.started":"2022-07-30T10:16:25.882611Z","shell.execute_reply":"2022-07-30T10:16:26.014299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:26.153671Z","iopub.execute_input":"2022-07-30T10:16:26.154716Z","iopub.status.idle":"2022-07-30T10:16:26.160448Z","shell.execute_reply.started":"2022-07-30T10:16:26.154679Z","shell.execute_reply":"2022-07-30T10:16:26.159338Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# droping the duplicate columns\nfinal_df =final_df.loc[:,~final_df.columns.duplicated()]","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:26.646711Z","iopub.execute_input":"2022-07-30T10:16:26.647076Z","iopub.status.idle":"2022-07-30T10:16:26.654855Z","shell.execute_reply.started":"2022-07-30T10:16:26.647047Z","shell.execute_reply":"2022-07-30T10:16:26.653875Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:29.397490Z","iopub.execute_input":"2022-07-30T10:16:29.397873Z","iopub.status.idle":"2022-07-30T10:16:29.405688Z","shell.execute_reply.started":"2022-07-30T10:16:29.397844Z","shell.execute_reply":"2022-07-30T10:16:29.404450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Correlation among the independent variables**","metadata":{}},{"cell_type":"code","source":"\n#Using Pearson Correlation\nfinal_df.corr()\n","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:30.485802Z","iopub.execute_input":"2022-07-30T10:16:30.486647Z","iopub.status.idle":"2022-07-30T10:16:31.975344Z","shell.execute_reply.started":"2022-07-30T10:16:30.486608Z","shell.execute_reply":"2022-07-30T10:16:31.974064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# with the following function we can select highly correlated features\n# it will remove the first feature that is correlated with anything other feature\n\ndef correlation(dataset, threshold):\n    col_corr = set()  # Set of all the names of correlated columns\n    corr_matrix = dataset.corr()\n    for i in range(len(corr_matrix.columns)):\n        for j in range(i):\n            if abs(corr_matrix.iloc[i, j]) > threshold: # we are interested in absolute coeff value\n                colname = corr_matrix.columns[i]  # getting the name of column\n                col_corr.add(colname)\n    return col_corr","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:31.978254Z","iopub.execute_input":"2022-07-30T10:16:31.978677Z","iopub.status.idle":"2022-07-30T10:16:31.985496Z","shell.execute_reply.started":"2022-07-30T10:16:31.978644Z","shell.execute_reply":"2022-07-30T10:16:31.984529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# removing all the independent features having more than .7 pearson correlation value\ncorr_features = correlation(final_df, 0.7)\nlen(set(corr_features))","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:31.986821Z","iopub.execute_input":"2022-07-30T10:16:31.987124Z","iopub.status.idle":"2022-07-30T10:16:32.782761Z","shell.execute_reply.started":"2022-07-30T10:16:31.987096Z","shell.execute_reply":"2022-07-30T10:16:32.781561Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr_features-{'SalePrice'}","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:32.785674Z","iopub.execute_input":"2022-07-30T10:16:32.786489Z","iopub.status.idle":"2022-07-30T10:16:32.794588Z","shell.execute_reply.started":"2022-07-30T10:16:32.786444Z","shell.execute_reply":"2022-07-30T10:16:32.793212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"final_df.drop(corr_features-{'SalePrice'},axis=1,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:32.796218Z","iopub.execute_input":"2022-07-30T10:16:32.796767Z","iopub.status.idle":"2022-07-30T10:16:32.804322Z","shell.execute_reply.started":"2022-07-30T10:16:32.796722Z","shell.execute_reply":"2022-07-30T10:16:32.803513Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#dividing into train and test datasets\ndf_Train=final_df.iloc[:1460,:]\ndf_Test=final_df.iloc[1460:,:]","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:32.805798Z","iopub.execute_input":"2022-07-30T10:16:32.806289Z","iopub.status.idle":"2022-07-30T10:16:32.816693Z","shell.execute_reply.started":"2022-07-30T10:16:32.806260Z","shell.execute_reply":"2022-07-30T10:16:32.815558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_Train.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:33.028282Z","iopub.execute_input":"2022-07-30T10:16:33.028911Z","iopub.status.idle":"2022-07-30T10:16:33.097545Z","shell.execute_reply.started":"2022-07-30T10:16:33.028865Z","shell.execute_reply":"2022-07-30T10:16:33.096377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_Test.head(3)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:33.525803Z","iopub.execute_input":"2022-07-30T10:16:33.526886Z","iopub.status.idle":"2022-07-30T10:16:33.594726Z","shell.execute_reply.started":"2022-07-30T10:16:33.526846Z","shell.execute_reply":"2022-07-30T10:16:33.593633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_Train.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:34.040488Z","iopub.execute_input":"2022-07-30T10:16:34.040880Z","iopub.status.idle":"2022-07-30T10:16:34.047765Z","shell.execute_reply.started":"2022-07-30T10:16:34.040849Z","shell.execute_reply":"2022-07-30T10:16:34.046531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_Test.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:34.322679Z","iopub.execute_input":"2022-07-30T10:16:34.323477Z","iopub.status.idle":"2022-07-30T10:16:34.331130Z","shell.execute_reply.started":"2022-07-30T10:16:34.323431Z","shell.execute_reply":"2022-07-30T10:16:34.330183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#drop the sales price column from the test dataset\ndf_Test.drop(['SalePrice'],axis=1,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:34.700640Z","iopub.execute_input":"2022-07-30T10:16:34.701775Z","iopub.status.idle":"2022-07-30T10:16:34.714014Z","shell.execute_reply.started":"2022-07-30T10:16:34.701735Z","shell.execute_reply":"2022-07-30T10:16:34.713084Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_Test.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:35.128772Z","iopub.execute_input":"2022-07-30T10:16:35.130209Z","iopub.status.idle":"2022-07-30T10:16:35.137431Z","shell.execute_reply.started":"2022-07-30T10:16:35.130171Z","shell.execute_reply":"2022-07-30T10:16:35.136121Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train=df_Train.drop(['SalePrice'],axis=1)\ny_train=df_Train['SalePrice']","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:35.547274Z","iopub.execute_input":"2022-07-30T10:16:35.548011Z","iopub.status.idle":"2022-07-30T10:16:35.554246Z","shell.execute_reply.started":"2022-07-30T10:16:35.547975Z","shell.execute_reply":"2022-07-30T10:16:35.553394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Prediction using Random Forest Classifier**","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score\nrf = RandomForestClassifier(random_state = 1)\ncv = cross_val_score(rf,X_train,y_train,cv=5)\nprint(cv)\nprint(cv.mean())","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:16:38.339992Z","iopub.execute_input":"2022-07-30T10:16:38.340377Z","iopub.status.idle":"2022-07-30T10:16:53.259865Z","shell.execute_reply.started":"2022-07-30T10:16:38.340346Z","shell.execute_reply":"2022-07-30T10:16:53.258795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rf.fit(X_train,y_train)\nrf.predict(df_Test)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:17:03.328277Z","iopub.execute_input":"2022-07-30T10:17:03.329031Z","iopub.status.idle":"2022-07-30T10:17:07.540421Z","shell.execute_reply.started":"2022-07-30T10:17:03.328982Z","shell.execute_reply":"2022-07-30T10:17:07.539327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred = rf.predict(df_Test)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:17:10.744627Z","iopub.execute_input":"2022-07-30T10:17:10.745616Z","iopub.status.idle":"2022-07-30T10:17:11.194371Z","shell.execute_reply.started":"2022-07-30T10:17:10.745575Z","shell.execute_reply":"2022-07-30T10:17:11.193050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#convert output to dataframe \nfinal_data = {'Id': df_Test.Id, 'SalePrice': pred}\nsubmission = pd.DataFrame(data=final_data)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:19:07.376214Z","iopub.execute_input":"2022-07-30T10:19:07.376611Z","iopub.status.idle":"2022-07-30T10:19:07.383726Z","shell.execute_reply.started":"2022-07-30T10:19:07.376581Z","shell.execute_reply":"2022-07-30T10:19:07.382469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.set_index(['Id'],inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:19:38.581598Z","iopub.execute_input":"2022-07-30T10:19:38.581995Z","iopub.status.idle":"2022-07-30T10:19:38.588318Z","shell.execute_reply.started":"2022-07-30T10:19:38.581957Z","shell.execute_reply":"2022-07-30T10:19:38.587517Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission.csv')","metadata":{"execution":{"iopub.status.busy":"2022-07-30T10:21:34.376604Z","iopub.execute_input":"2022-07-30T10:21:34.377063Z","iopub.status.idle":"2022-07-30T10:21:34.388677Z","shell.execute_reply.started":"2022-07-30T10:21:34.377030Z","shell.execute_reply":"2022-07-30T10:21:34.387689Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}