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xvyEhu5yBmxt2saKfK22iE70M0+jbr/J+DoBTeIQMz4wjPOZSPyZV+uQ28rkzyafg1A7wlmlennpr0ARACgmZigWYSHnsRJYsMApsAJrUpqUJkqCyMFBYAVgUVp7vi6VHSkHcBJo1xrEkYiEHAOAE1Yepw4gAmvBDjYEke97pYrPgobpxDb0yn4sFhAbtcgvOWFopUplu7aMC/dedukVTx141q11phbnd197RdcZDTLNDjW7cE40Q8P3fVUpoVJxrGzY7TabYiyRMGvHpIKgPFUTR865UuBJHHebDTauJICxtUq5VA48VoDrRB1mfv3oW3Zpj1+pilLV6ZlkfcqZeHG6Njc7c8mV71BB6fmXXnzxxRdfd7QRxUaHVhycATuo5GwFCxNrzxMjpCngQPtet9WCMcY6CFgpT3uklacZkTXGgPxuqwNnyPe11uz7AOI41lpba9HrsHJtbOdoMjpLEOTHbQfTuJ4DL7itXFk35ZpZtu4LmsQnZuhxGt5zNOJHvOkOmlxTzSDN2E6iPMeRbWShp2FOxrcnN9dEVJR+vHzSL5PIzVc1q8aISls+e47OgHJC5G1Ow94z454xOY/kikjrcshP5WzQODwqHod6F3uxcccaFhGlTqiZbCfutz39lfRTJruNEt8UJAcGJXuFACR+rwwisCKlwNSL0KKTLcdQBNbQHmmPiJxzTMzM5ATkQI61x76v/FIsZE2kWQdBEAvEJtuSFUTgrGNiRUIEUoCAPVUKgnKtVK4mI3QQBFKrxY01eATFosirlCJRsGSNwcV7du5eevGJp7HewOzs7qsvfuH5A9Nzs44parSk0fGM1Yqchil7qJZnFvYEQdlGphtGZJz2FETAXK1W/aASxhEJT01NBUpF5TIiE1hX0f7i4ny9XmNmgY1MGFtbm509vrq80mxCs65UIidRFHrWTVPp8rk9V+8pRYBsnL8Q1C9eOv/osSMnjrxx6sTxlRPH0W7AOWhGEKBUCS3bThcuhpeYYjiZecI6J+jGJonKZ62BiSGAVkm9xHHkl0pEFLdD0jprKezZY0BEZH86e59JgVpx+gl9a35CaGKfoXGIUByhZDsckt+9JRuA08hKp4lvNoUg/ZQ0dis/dvAYUBhBM1m5RX4z2T1W2UeK4s30uG2x9JajZ7Hlo2dHFwz6/LxMDY9oOWiGAJEk3mu2ZJK7Pf5pYYwXxTk6DToDX5nt2VG2hkpvK44ZU2bkdR35Mv4GnrFGvS6st8WaiEhpACLOgYhUAjkgBO2x0mANImciS8ysSQHKwhlixcqLha21MOI0sR+oODZKg6DLZWsiIcVKKaWcdi62AGCcrkxVZ+eiyKytN10UAoaIUSpBDJSmoOT8wC03QD7O23XJ9de8/OhDcKq+94KZPUsvPPY4IrPODDFYX8dGJ0rcwpRDiVEOOifXUKmh08bqCmILBUAh8DAzjXKARgthBzNzYMLqGsIujEUYAYJySZcrnq+iqGsbDViBE1QqqFZReRPtNgRQ+ogOoptv2bF4Z7mOlbeOv/KDV9pRWCr5d3zg54yNRGzszPr66rETx9fX153BsddPNoQRhSrwlLjIGhgLSg6/FMdQnmalbOK96HtoNQEkDr/GmFKpxJ6ntY6iaLwq+4uG26ef9CH8p5WK3u6i2j3tesztc3IFbMIBxRBk3MQyjkiK9NnkWaT25kJrzXh6bJp+RLEzeS82h0rAmMvt5vFmTkvuCE7Nyc6Ieabo1jk6S1ToK7PNoj6t8X6z+DGTcxujFManHPtsiwwzhRkVzkRbGQVJLnM1he8uIzfZgAwiYkmPBJLejIpFJN1XTI4Uk7j0iADixEsGrEAGrFl7DAEzBI4gpERpOAI71p7SPlglNglrrViBOGGt/YC1F0EnR+UGM4tefXp9edlBoT5d0mTDTry+jkDBK9ugzOUpTPlY3HXFtde8fuhVtKg0s7gwt/PQg09jYRp769g5hyDAyhpOrcNYwIEMPKAcYGYB9Sl0OliegomTmMTwPcxOU60qjRbaTdSqEIcSIQphDZzAWWhttU8Q27IwPrwSltdgBZHztYssIzJAV+2a+u4TDz/5yktBubK2un5yecXFsVcrP/TAg83WGqIuaz1drSzMz1+8/6Ld+/bt/8w7Vjc2up12HIerJ0+88upLx954qxt21peXU18lzaQ1PB9+eXZmuiw7jhx+zRhHQZmVUl6gHYwxI600CWKjhN7+QDHnaDOavIv4YVuJhndsbpuYpCg+CsY8JLaaPY5jiNR4PJZYOA1WuR0iImAQZjgnCs6wlNzKGpixt6qC00c/idtAXnlmWfVyoYb2NGEIahTJ3VYuMmadSdHbFtGJztEYaaTxNwYxZPvtbzMnkm13KL3mwmDpv+DSb+4TLm+N0cQ9RpGs3KYm/RWmbMqhc4KSAyCTcFX9iG15ZsPedGHwcrKITef6BJcsEzMckXZEABMpMDMzyEFAThxEV8rGQqJOuRyUAi9qwUBA4lfLsXEuFq095QcwEekY1qJU8wI/jKKo2UJs/akZQhy3VzA1jYVZ1GtSn7ro0mud6JW1jbeOHG+9dawyOz9TqRx69BHUpi99502nPJo7b48StKdOor4W2Mjz2frKVXypVctzO2dm523YbawtsxiPFYN0uayD0uzijk4cRVE0P13zCeHamsBadspXmniqXF2oT1dIxa1Od3Xj/m9/95H7v4d2lwJV0T7H1CXySl632223m6srK+jGIAU/AKs4jL1SgBjoxs51V5dXV189/IMnni9NTYelqiFUp+v7z9uza3Fx/76LbrzmhoWFhaASNFrNlbW1yMRhbDaazaBc2bd7ac/0zF/8yZ+8fPBFEWssWQgpdlEa6SepSCISIiJySOLjpbF8Mm1j6KCr4ajKm22GPEeTUh8ouNN++Tels+VcmVVvm8MYbTpZ3/ZYmO6g3HzlK3OXidwmI/fYFSZymyGMoVz0e8JEKyVi09oTJnLiKD3gtve5ieabI5vNNcmFd1s8PriztdvT1oaZIntYkvdzdMakhRgEl570N1h7JaaByaEYZAwqagT3D1ZtEm8SFrh+ZNWMMWPI9WYzTfOisqa+MH0lBNQL+DZuPEynEqz619OEkrndfypZ0eQkAEzv6COXxM1Jz6kmImJiIgiRiHNiRUDJLiRmZqUUETGLc87EMZAcyyiA06RYKfaDKIrgRIlxzlEUOZRIK6V8NVX3fd90W3GnpRmGxMWRcyaGQbUyPV32bdg8dbx98jjiLoJSWA1RnoYXGGhV1a7bMM1TMKZUrbaWT1jNwWyFnW9jE7Ua0Bo79y597MNYnIGjcM2dePFwd7WBbmN+7y6F8Mgj92O2dtX7bjp89EhzNW691ZFulxpr0ck3pHUSbACHchmzO4KF/TqouLjdWT2B1ZMwMSxDGDOLamaG/MBEHd9TKg47x46gs4GyQ72Cag3l+tLsDtsJ0YniRjtutrHaxOyMiGw0G9NziwgjsSYOu7bTQbcLAKxhCJUpPTUlvu/XYZhccw3OgRVrZR0gmn2/1Wi/9IPDzz/7PFZWoDTCrlqcnduzuPfiC3fu3bO0f+8Ve/d0I1MreTur2HnB+S8fPsy+p8DtTqfEOgjKHMc2NiaKWbH22TIsidYeyJooltimR3AKMYSZHRmQsCRDmBOn0gjONFlXVbjAsX0WSJtnPp+zt5xbQEUn0p8l9mKRhA4pPpw4q4crzHDBdU79GEbFZuPGDo3s4/1SGulkSD3q7Q1I4G922Bv3DB2SkrEfDOmsRfpRqPrLK0kMb06a5ghXkcz2u176JA9Jn8kZiUPfBgNwH0kOtEpqZDABFknibPZ3WyFJSjz4BAsRwEjOA06+EwHsyA3GDjcIki6Zz6EYLUPtTYbVx/B+QwEgY3Fx0swJj1SKJBoko0HGwJK31pbsNk+eG27/NDIyZsqWBHAMAtQYw34FDbPqyRqCZVu8BgMWQ0/1bEUZ/km+trRijiQo6NyK+p+inmwkvYy1xa34KwTTA/WG8kXANtxyR2/R6MU0XGzCMlOsY5y3sMoUHkegAEIWvlCWW3ZpaXSzEo0Lzf5iApGApV/xScskAYHR22stAjgSIRGAJI3PK0kcWekRALAwcWKRciLOOeesc0ngbUNiSRwxgTzHpLQmERN1TNgRZyAOzOSXoRWIyMTodEyzJWHIzgoTtIJXVlwO2CNj2htrYjq6UoqabeMMJPYI7ARR6ExHTFy77Ta1d2nFt/Vdi9xxJ55+xnU2FudqbM2JZ56gXTsuv+Hag08/FS2vQ1dAmiOjnUG47sImXAgGvAC67LhirVjTlW4DYQcmRmzhLFgLkfM1NNmwa1ZOYH0Fgb7lE3fe9IE7Lr3iHa8fOrx6fCVqdFvL62GjHTY24Cl021CQarUbh5bYLwWdjVWYLlwMTdAMpeAHTinjxJpYTBc2gliQOMWG2flaPA7q5SvfcfnS0q5jx48Csu+aq9//oTteOvza4R8ceuXQoeWw+/qp5SPrq68dfuPkm6euuuzyj/7ix1qdzuuHX5uemWWm9npDKdbE4kQA7Wnte0IwJnbWwQlIsed72icmEVix6SImCCBxycJkEpRZJprjT+6yVhREoIDP2w5l3m7KdPG0WXZO10KfooBhMJDDufcjZ9EmL13/52gPuYXXDo1dyUs56O56Ieny19NHhab/91pvvj4ZCDXU02Z55rgeAhgqRsr/HFnBl8F+UB7L8eZ65tRFTjkXAMdxd5ZEq1555lJf6Zxrg9/ZMWhLjbeg8SgpW/QuBbfPXjdwxgbMs0SE+r78O9moo9uBMlKQnrzM5TOBMkVn5SQbcV2fw7BVZtCMxs9m6vmQD6i3rkBACmUSudyzLCE51ohAlIhMdEicSiEEJ5Q8SODs6y0iQsk/ABAmMBGUiAKgXQQxAMDK+WWn/N40xMAaFueIQR6UgtIIAiJPwpg7sbZOEMZsoBheUJve4evyxtqKiUNd0o6d63ShABjSgeKSEJMm4+zshz8yfc1ly340OzUd/eDkse8+xKzngtKpA6/C05e/4+IXDjyFZhN79110zU1r7TAol9jFEq65uKUIDiS6glItZE/7gWLLcYi4Q3Ca2ChqRLELtD9VV4qiTitcX53RwWUXnH/TjTcwqFqqP//0c1/5/f+M0Jam57vrq9B2/uILb3j3zTM7FmLH1WrdF6/bbsbKOhcpWE1MpByUoxKxtg7ElhHCxTCxiBC0aHYMRzbuhNPT07pSWm00Ty4vHz1yPCDvhmtvOPj8S08+8CAq1f03v1PKlT1z052DLxx98WA3Ct97663vvOGGp59++tv33ttttk2jgU5Xlyu+77fbDViLUgCAoZSAjLNiLAyYoD0ilsgpSeMPCQPiNIQEhvLnOZNCluLZSf578aOzyrzNNB69dPO+dFKfmDETWtFQkVzPO5ErE39Wxh7oV4AMjfBFJGPRigvjxiaRVDL6j8/XU1uDDH72o832WbFg5EqaMjUuuJG7RVGDRwRv72ey62fIcEKjaUfkDouTsWn/yEIS3Gj69Pq4rQXoleewDjKSsleDib2kAJhKnqoAY7T8c39m9c/lUwhoKL9eJo26W/weTbg6ts33sZ9s26/v2wBlhtGJFGySOutQpv+4o6xZcgIoI/010T6UycglST2K0pREPXtmuukumQk558BMRELc84IjVuRsrwsmAhGUIiJhBasAYYlYLIm1xE4F0D6E4ByYmEHOCisnCtYqFiHlyEcMxKQZjmPHFi6kUrkUVMUpFzulPeNRbGOQg+0ibEMEVIJS5ZmpjnU4b98VH769ct6UMdHGK8eOvnDIrK6b14/Bm7nq6msPPPMouhszF+7bf8klTz93EEQgwHbBBqWAgrI4AD75ZWGGAhM8chpQipSnrQejiQLPr5SZWeIIUbxjavaqyy49f9/uEut4Izx1dPn/+d9+D36AjQY83nvTNZddecWjTz2x/sJBVOqwgCU4h7KCl6z0KbCCKCAACMzwmD3na1JKCROTR4oFkVZQjkixlLyFPXv2X3pptWfoEI8AACAASURBVFT9uz/6U39q8YN3fLBUn/n2gw9tLJ8s7d0fr63Mt1prbxyOTAxx6HYvuemdt7//tub6xjOPPfbaK6+2jh6D9nTJt9aKNSBGV5iZIWCxXhL5iiACg0E0SAJBlDiQs5IfuGhiKFOYfjKrzM8clJmYRvlvAmWEcoeu7UGZXpozgTK9NJnBLOPIwuNh4lIFs5aJ0aFuxAU4Cx1GoFJWbsa3eOipEV4DsXnjcQ6UycuF5A3eo3oWyh2FYsmP7OLd8LYsh1FQeA7KZOmnEsrk4ZIzhzLpG1oMZYgoqbDkpMaklnrruBlQktfpjwvtvRD9k5sGT/UNnkNQpheGoAdlDBRTuvyZAqkU4iS1zgRSPSjjwTAgjJglHkAZ9vxyNTahWJNMI1h5ogKytiyRcTZEGeLBMayBZ6DgaVRq5fVuhMjNVBfBvBZ1/HIQtVvahBQ3yYnVviU9vbCw0epKI8TuRT2lTGMVp5qwQKmKcu2aa9/9zDPPIm7Wl+auuerK7//Ld3FyBTMzMBHEggWK4RiOIAGIQAbkYHuToeTgaEXQBE/DGHS7UD6cQPnV/bv/h//1f1KKuGmfe+TJf/iTv4Sv4dHiNVe8//bb7vmv32weP45OFzFQrSO28DRMp3cCNiDJ8ZUqRQ8sUAKyvWpTAGAjeBpGoAksMBEuuejW9773/KV9/+1b/+3ocwcuvu397//wh5985pknHnwEzRaaTYQhohBRyOctOQLW1s6/+prb339rwPrgs889+tDDndU1xAY2VpUqh8Y5ZxNzHTNEYC2cQ6AAlzidkaQuYsIE1z8bfIuGV0SDBplPP2NQpuiMmEm75kLaLpTp8S+AMuMcelCmwCpQxH+7UKbHp2CoLrDlcAEE4YL0I1aNjD9sOqBuBmhyhv8RPV0ulBlPOW6bGdVTcsZ7ZMpnPH2+2jlWrlEos1mOhvmfgzI/CVAmGzw7c1Hyr581KEMJVCKH/sz1tKBMujg1cHCjrFUmVbY3rZKE56DxJp/McCKWiIQUkUpnD0QAVHIAAjkBC1iYQQrkwQIElohgSZywsjqADkAM52AjiGNFICXQBOio5UCGy6AAViAWPjhgT3FoQng+vLLuMINQZkfOrDfJxuRCANCBc7Rw3u7VlYbtGIQdVAkQOAZrVa/tvfDS1946jtW12csuPO+8Pc9++16UPIiAFcIOfA++h8gislABVABnIHFvTxcBBMWABSxMCM+DwK/Wd+/ctbG6sfLGm6hXahfuetf73rX82vEnv31fUJsNV5fnb7j6/R++42t/9VewsV8uLS3sIkuNMEKlGokwgV3sG6MhELEOhjzHyvM8kGFn2FnnnAFZUiClnYtN2AlD1kqX/TCOwrirPe/666+/6eabn3n+4PcfeViXyp/8xY8jdPd+/Rsbr71OgSfra3D2qg9/8Lp33vjUU08duO8+WLP7yivf9673zE/PvPDsgSefeGLtxHHEBsaCFfllaC2GEMcgeCUvNh0gTiAdOwbYQYGQGJdym+426ccTyhTynxA6TGydKjge70cGZbL9W/r/mGPmMJTJUaxoLp9IKNAn31ZRMFQX2Ta47108DBQKbTN5UElE+lCmSLHsbwxDt0wCgZMiKJMvt9g2kyM9DUuakz7nAIfUQj8xlMmRew7KbM2nINkZQZkxkLEVlCk6TTrZonz6UCabiyyUGX7vE6vPwFdm+C2kjJTNoMyIAZ96p14LeLCDqaeVUN+TJgnsCALgIOIUJcFcFFhR/2RskuSE3oStKA+kHHsgBWMgTkkEcQJyXgCvDL+C5BTosAUXa8XinI0tWLMD64B0YK11cZdYtK/J9yOloTQW5lm0O7buQ5QfdxorMAwi0hqstQpiE+84b/epY6dcbABAO9g4RU5iMVVH5PylPQtz80eefQo+z1x9+b5LLzy5dmphbt60Ol4EnzQT6VJZ1cpdFqpVFHu+sSqKfYLnsWgYFyvFSlzd86666OK9i3P3fuPeL33xS9LpINAQB7+GKEaltufyS2945433/PM98drKTR/6wGyp/PLLr3qV2tSOXY0wsrGZKpURhYGNS1ppzY6VK1W4XBYRFudZo2B99pT24ZVJq2o56EaRqniiOO50PKVLWllxKPtdZ2KixZ07106tfOVP/2xux67Pfvozrx14/ptf/srFN1z3oZ//yDfvvWej2fjoRz/abre/+Y1/NK+/AcjsvvOvufrq666+ptvuPPD9+1888FzU7MARSlPKL1tnYQ3YwXTgYoiBS6CuhiiAAZt7OPN2oEBhZO2hcSCXzU8xlNmMf57FYsLN8AXbzYoir4zvkMoZbHq6AIOtB7kZGe+hxq0yqZTtgIaCM6Iz9gNLGSiz2WA8Choy+7GHReaoMa7J8LNZKEMCyYtfkGubwVZ2oLyTmFw2LxmrEo3rObKgltUcbrMhfHT5rE9uqEgxOZRJ9Rnjk94cf+gclDltKJM1yeCHCGUGoOS0oExyyMBQy2YCIOQlYIVlcHsAZRSDiIVERKyDOFbJOQWaWIE1ABJxzoizgFOkoRjahw5EaWZl2m0Swy62ELAHr4ygBq9SnZklSLhxKm6ueSTWdF1sya/oykxQqTHpdmvddNeJnO+XYi9wXmnP9Td0lV4+tjwHn1rry0dfQLcJ8SmoqZkZr1T32Gs2m/PzsycPv4ZuWJuZbZlYrC3NzlcW5rsBd50peXrPzh0vf+c+BMFFt77rVGd9PWpdceM1p44eP/nMC3j9aG/bpEMtQLWM2XmA0GhhdRVRCM3QgDOYriE2enHXZz/+qduuv+qer9/71b/4c/g+rIF1II2dO95xy7uuvOqqr/713fbEsfd/+hPHjx154f7vo9vB9DyUwvQCjMXyCrptmC4UgwXM8CuoVMEEGyNqIwoRC0iBArBCyaepipCguQ7jEMdodxFoLO147wfveOKpp33f/+THP9ntdv/pnm9tvPzKzR/84A3XXf/AA99/8cUXf/MLv6W1/trXvra0tHTzO2969umnH7jvX9yx4/BLCIIrL7/iAx/4wHSt+uCDDz/y6BPdWGZ27CiVK81uK2xtdE6egI3T3AnBUbr0mUY5KG54I23+HJTZlH5qoExumkmhzAiTrQdyDFmAJN155/q/x9JmBr+hYX6Qx3Eos7UmYzBo0LMKsu4+fSpc8BqDdJvfHfd9ydrOx4ERseRqviWUGZWbPnUOyrxdUEZxeZaIkg016R+N/QFjF5OTa3jMrsaUXuwv3CRHQKc7sfvfpcc5e3dEUJYShsLDWgG9E5R6e4aYE4HSv8oMJjBTcgDjEFhhYiZmVgpIwtHxUE4Us/KImZOzrHsqAgAr1pqUIqWINBODWERYMbMiz2ftK7+i/RKxci5ZlSImRZ6G9v1Kza9Oa8+zUcdnxCYCqdQeM7VD1+dL9VnWfhAEzpi43SJyipmDsje/u7p0npqbacYhXJvKnhES8MxN76KFXS2oPRdcVFtYfOvQK1g/ikBDGKVK/fIrZi+7XNXnGp1uMFXvLp+ANahUppf2dQzvvuxqW6+3a2U9X1tcqB969AEouuiWGw6/+QPlyRf+u3+9d/fSBbt3zszNHj74PIwBC5TDVAkLU9i9G3MzaLewsYI4TF1qFEMFmNvhphYPv3Hs3n/+3lOPPYVWB04ghGoFgXf1z3/kXe+/7Zv//K32a6994t/8m1eeO/Dyd+8HEepT4GBqcbfmkhaVWq5hejimhGq9VJtSpbI1MdotxDG0AiuQD69UX1jQSivlUVByjhAbQCCE0Nz2ntt+667PSCP+xt1/B8H7PvSBnZddcv8/f+uZFw9+9M473/3e93zly1/udDqf/+znmusbX/7LL73rlltuufmmY+vrzVbTr1ZPnjr1yCOPnTyxfO01N37+X3/+gisuaUk8u3dxx77dXq28vLoOYkDDEaIY1gTMvqLkhITknHPnHACttVKqqMuQ3Bew/xYU3M37Q/5fAVEBbdF/bJ+K9DwDPjl6ErJbhwGAi0Xn60MADRZMe3+pB9RYgebUYhKsIdPvZUmQeXocZgCSg1TzqjPt8miQ7bREMCxrkJJGenL0vf96emTKIb91JtsyiZJOnoiJOPUpTA4pG1IyCyAyavYFFYgABvs2BuWQ7BvNTU9DsV/G7+Y4XyNfzR40k94PEDi7PzzDeLS0h1pjdgAqGN2Sn+lu17FxsPAdJBni09cl3WuSqXEZzu/IO17wxo8my+qQ/75MNnXZbj/Qv75t9vkHF/wM0kiL6f0cIBsRSaIGJp5pYHK9IQPJy0cEgkgSVklA0CCATNK3CcGJJYuYINKJrdZCAmYWYwAFpaF9lOt+dUaCyvpqy6t4U35Jl0pRU7kodOIQG2eERUP7mJqGbcraCigILrxAzyw0HTqtcE21xQhKJVTrWD1B5ZqwXScQe6QVVOASjxaKrc+qXkesJKhvNNdjLyyVqkeXl+EHS5dfcvjVH5gTb134Cx/udFr7L91HPL9/775Th99aPnqiVp1yWncDLu/c5S/snJqa6u450jrylm9Ca02bXMhayvVSdWamOl3Rvl1fXV1cNM31kiYiMozp886rzMz89VfubqyufvA3/tV3vvOdjQcf3v++91x65RU7dy/tXtzjIul04sAv1+qlVnO101k3JgKgPL9cqZerdaX9VthoNtdN1BXrYOGrSq0+4xTFzoiIIom6YauxHoYdgrgwDoTXj659+s7bLr/k8r/6+lfvvvvuS6667Df+x3/70Pe+96f/+Q8/+PO/8B//47//xtfv/f3f+71PfeKT/+Hf/c4f/MEf7Ny1+KlP/9KBAwfu/5f7nFjyvIMvvPDKW0eXHv3+L971yc/8q0+EEazFU489tfz6sdWjJ9zKCQDl6pQ13TjqOoKzMSlfREQkATHGGNPpcBD8sJr2OfoRUJFVJkt0ZkcRbclk/NbQlW3gyNH0Ivm38h8cvbTZjHzs7mnna4tcj4nb/O6Wep42nZXaP0d90m7ieVFRuPC3I4z4gDYL2pmeSt1LOXx3EMeBEkyhBumIgIF9KCUmOCdpIN+erVMydqbk1BLWCUBxgGJFRE4sSDlxSJzXrBgtLJwc9QpWkN7R2QDYgw4Uu6iRhNZLzl1S2i/pUqXrGBaOPNIkys9myC2vt9TU1KUXVed168RxSMmf3VkKZkxoWSscWV5fadWWdqJUQcwgJVELCgi7kaNpeGx1KZb12EAJAqBeJfEcc3jsFGwLtHvH7v3ewnmnnnvRvPoW2G0sb0SsDq+3Ymu80LbC2LSMRFEch12tuNuNl9crizpeCTunupZja8PQOaM9bcowXa2nKoHqlrxgquRVrK+4u9paml9qr3cff/x70N7n7/rcE488vHHgpaXbbrvg8oueeuqpk3/7tem9F7ZaXXNk2V/aRT7C1ho21imMGEo8jXo9qNdr9amw25JWE8YQXAziqena3IIqB91u1xOqVcoMabY24m7HZ2Wa7V0Li08/8ZRonl7acccvfOj46uo9X//a848/eddn7vr4R+/8+69+7d/99r//tV/79Q/99u1f/OIXjTFf+MIXnn3+wF//zVc/9alPvfc9t37pS186/PwBzM7HPh033W/ef9/t9n03X7tHG+x453VXL57/x3/0h8dOLc9OT68ePQLN/uJcdPKEqs5oYmOMi2PjrIWAoKoVaydb+PiR7Tw6i4aZs03ptHP8+pmrXLCElzWmb97fFR2AkB3Ais7ZGTfZjy2ZD0nJTr+HwnvJaMpRdr1Tmoc0lJyE/fTjuvWO7xg7c4AUJG91tZ/rEViQTPEHBpEccgW5S7XtlXkyER0t5wJKLBl5XjWDu3CZ2iaXcSyijCYFdtZiucncdzPltkMFucseRfizQD9RVpme+QupQxal4KB3cdQWN0b5JsoCQekjGRHUf5cIYAahF1JmyDon0j85UpHScA4UsdK9cyUpsXpDaWJyrCFRcsxkGkgYcHEMsc45a2GtBTG0B2tgASVevVaenV87ZWAJukpc2mi0L5xdaHS6zVYLcRzsPa/jB9Y6BbYQGAvjjDgkUfuUBhFsbF1slXDJp8ADLGJbJr+12nCtZuuVQ9hooxYc/cHhQ6++dv7MO1AqPf/ic689/yzeOHkKJTgPnr/iHcLu/Sd27uguv4Vjh4EINoIAOkDXIai8sWOXmqrZxjJW3kLchAgqcy+dvB9+GdNTv/k//86zzz598L77Lr75lt0X7v3uvf+E48cwt9AMOyBGqRKtr5eXZqAYEBFrjUEcw0onNFB+p9HA2ipslNr+o6glFhA0mzD2RLVGJG5jFa0OiKD8E68egqen9++zx45896FHrr3mmt/+t7/z0GMP/O3/9Z8uveVdv/3f/+ajDz71l3/251dfffVv/MZvvHLoB3/0f/+Xm265+Vc++6v33HPP3MzsL/3SL71w7dX3fuc7rtkIK2Vr5OBLr9Yrs1Pa+//+6Z66r/+X//A7Tzz4wF/+2Z/7U3VVDjrHjqJatXFsnUCElFJKmSiCiCqXJ4Uy5+jHkAbx6rOUXNnUYoH+QEtEmzsAjfHv/97EDpEvq89wouFzE2NMnillSBwRYbP0o2WVlsY2VB0rt1xjDDLlPCRoS+WH04/nK/1yZkBEJl+fOUdFpFCa2VbCwRu1iXkkSTiytpfvFJyJqDeSPmeVNMG+WdSSSa/QW5TMmE6A4af65pD+tCJJn7jBILsDqkdMREzCnOxuSvkD6fK78hKjDoiTc5dEINZx4iZGCsojv6yUgjgRSzCABRwckmMRnV9mZnIWzooIWIE9v1Iv16YM4JpNeFRSEncbttNJ9keBPVSm5i693NTqzdVTCLuIjbUIFhb3XHTxsaNH4lMnaLoeVMu21eCNZWltlFgb+Fja58/vZItWp1mZrrZPHYXtoFItze1oG1eZqTVba/BVeX66Ilh+6SV0GlAONrKNtTdPnYyUPvzmW92ou9ptImDMVrAwjcVZzNWxbyFYqsdBBBWiDJQUggDlEqp11Op6bq5cr0YAnEAp6ABRjMUFaLrj1z/3+DPPHHzh+Wvf+56FnfP33/3XiKOF66+97Prrdu3cvWt+x66de887f/+O3YuVmWppbqq8Y6G2tLO+Z8/83gt27Dt/9759tblZNV/3d84GOxdKi/Ozu5f27N09PT/tTVfqszO7dizMzk15pSCoVqen55ura/B8PT0r2m+3O9hoL79+6uDjT195+WWf+Mxdb732+le/8rUbrr7207/06WPHj335b+6u1mu/+MlPPvH0Uw8+/PBv/dYXtNZ/+aUvXXHJpXd+9CPr7c7Jlw+HEdivvHDo8JMHn2/Hnccfeejb377nqisv/dznfnVlY/31Q6+V6vMmcoEfiAzcGJP/rLXJ2vZ2KPWPeZtpy2nATyyd6Vp+oVfBwIeg39skHIZ9REanT2P9JGEzKeMKDjQdJKBcB5e+YkRDWgzU7vu30MhTOekzQjPK9ztYyfVWoXTtfSx9LvV0yaYvapmJvSR7uEG2Omjk4oBbVlzm2YL0g9t92EHDeKgntOcnVJCzglzwpGCo0Ispn3+Rs8nZe9vfZiz2U+grM4xjUgw+aOgTF6jroR/KtHDqQfuBILDwsGuYCAjOud5hkylaH5okKQWlUj9ipUQpFzn0znqD55Ff9oNAKeVKFSvOyQZEAGEYn+102V+ZKlUD7ZGFdbAG1kL7qNZKe5a45HMpqO3azdLdcDEirpaqx19/EyZWu+ZmZmZ8oo1m03abJU9pUfCr1cWdczt3SbNbs1F9vn7y9VmYhq80i0MUWYloYQZgzy/5G2vUbkljFSWPKyVXLXvB9Mbxpjcz/fMf+7k7rrnhwOOPtdc2CFpX5+uLO0qL89XpWre90VpfkzDiOC7DC9iD6FK1NruwWKlRt4uNtfVOuylhWFbMmqzv3/PP32qsN99722179yx95Y/+EMQfvuuuRqe9tt644uIrTeSajWh2dtoP0GjPtjsNB3ieXwqmatXZSrVem56JXLfT3eiGGzaOxbmp8tTs7OzMzEw36sBJ4GlrbbfRct2uR/zAfQ889NBDzsU7atUw1CudOF5ZaWzwN7/812/edMMv3/WZU+889cd//Me79u75/K//2s3ve8///nv/xwNPPPapT32q0+n83u//nz/3gQ/+7u/+7pf+4osHDx789C//8nm7Lrj36/9waG2DduxkBXvqBDoheepLf/bF6268/q7P/MrlV171lS/drTwddruAY6VExEURtNa+b0x+3LxzdNpUDMUm67Qndl84gzGBzravzOYMadzqMHx7+1aKUVnJpK7IhpGXnsaNHJO4vxTpubXoLa01xaLTB4v02IpydTtHZ4sIM+dvL2G/6Y+vERdtxk5+q9y7/VXPsfR5Vpken2ziXpBdDxjZKyn9u8NWGQWALfWlJ1NeSrZT9XIiIs45IlLEpNiRHjiwOxJJAlIKWEMxWKeHuwJwDnHEbEHkdEC6pIKy1r4zsYu7rtuEi4nIag9+zZ+a1/U5Zt1trFLUjRvrcAZKqWq9Nr+kgjIrZU3kwtb6qRNYXwMD5ao3Mzd/2dVhdaa2tJ8lav3gubWjb5qWA5dVfXpu7xIqBOuwFp08+CyOHiiV2OepZn1+9kMfqV1wcWN1rdNo7Nuz+8Vv/SNeOVCen5+54LKjx5frF10gXuB7taAbr7/0XPvwSwQjURfV6tT5F3m79s1fcGE7iq+78uLDzzz67LfvQWMDysPUYrC0q7pnF0pe3Gh3V1bjk6tYXYNxcJaXdpTmpv35WV3ynbGdlfX41LrpdkizuKg2u9gx0btuve3Gm274/f/0++g2P3bnx5bfOvLgt76NMF44/2KrvVAHYmNsrEeNVdvpEIi8QIKyrsxwtWIFJEa6G6a5gW4bsYHneeWZqfqsgA1J7MJOp41OAyYGrFcpveemWz72kQ/vXdrlE773wMN/+ud/tt5ocKnsYrNjz97P/dqvXXLFpX//j//43EsvvOPG66+/5aa/+fuvHXruuZvvuOPd77z5wX+5/+TRY5//1c+99vqbX/36P9xy660XX3753XffvfHMc6jW4Qdl4s7ySuDpsLGGsverv/7Z/Zdc9I2v/8Oh517qrKyByC+VjDFEpJSKwpB4u75lPxwvmUIocJasNW93D16s5WSbRQtXagrUH/I1SbucUYnDJ04PDauDYbKIf4E+XJhGch/JDxlHDk422Yydv527V9rjt8gNLZvKcEi6LTYnA70D9HJ2dFNBUUwabyY/GnLvUlG8mZFtzCI58WZGPGaKym3kunKjW/fHHxzfTJ6917tOQM4m7aL4nJO2t+JN2mdpobyI/+bnfmforCww0XDCLNoA5S0wDXlvbQVlevv3OCexEBGkd+hS4mmS2IUTmAPh1DGlZ6YnCEhSL5u+lXhggxlQssBESYQ5SQ2oTiRpy4knDJhAjuHIWXEW4gTJodhaKU9rRckJSmIJrufTr0BKlBZQHHZc3BEXS9iFM3BOoti42Jguk2k1V9sbq2g2YGN4ASkNP2h0wkgkarVX3jwcN05qG5rVBo6flKjTDltwYXttpXHoEBqrnk82jhz5RpTM7+zE8cahF83KcrU2tX7wIFprQaWsylPt19+I4pYNQ9fsrr3+RnziTZguOUu+B88PHTqWltc2Nt5846UXXjjx/PNotVAugxikrHWdVrOzuhatr7lmG+sttCOQBjkx3ZhsN2q111c7G2tmbc2tLqPdQrcNQRTH73z3u6dmpv7q//1T7Xuf+OQnn3n88ee+/xC6EVWr7bXVrg1jdqa5Zhob0mojjkAQMAROnLWxc7Ezoeu2EXbTCrNwxnWshILYmShso9uBMcl6oCO55bqr77jh2pqLZwM9Va2+9PqhYysrzjpm3Vxdf/iRR44cOXr77bdfdNml/3TvPd/+7nduvf3WG959y/333ffk44/93Ad+7rKLL/mrL3+ZvOAXPv6JRx5/5MGHH/zkL35sdt95h196GVFk2h3lBfHaBlhVyuUnH37o5TcOf+quu668/IrGRmN5ZdlBBCLirFKwyRlRIuTSXpBEJWdhjG8E/qFQLpSRzawdP15UrOXZgVATlMLYKNHbSJxWJxElkyZki7e3LCKZz23qM1JHWy5LDV8WQHr68Ehx0abcxm+Nw3MaLMzkpB/NZbp5I9WESA2+J/tHhUGS/SzI16jmGSt7oTKgwRLd0MrdCKvkcyyr22kheeUpyVTZEShZK9iiwRakoF7JjVzO20m+TW23Rz8uC0yE6f2ZX9vJ4HAdDl5Flbk2BAlyI30Vz0pHrw8dQUBjSjIBqc86CZJodRAWVkKAJNIdsfTxfu9ca04ZggEorRN7DERAQkRJLBDAiXO1oGpidLuheJ4jBjuI0/Wq6XbgjFLaRrGvWJQfCwOesg4iFhYMaIAZsQG0JnbELnEl9nxoQFlEIVTgi5ZuZCESaIGFz4giALBMlqqlmgReq9PFwiKEESpV84jaZn2ZQiWhhVYIfAAQA2VgDSKkME6X1IXXWhIcexnO7bnuvW8deAato/U9S7Xdlx197jnEyxBBsAPdWHux6TZJNFdKOy+7dPflV7QDv6NI1yokthRFHlNsjFcptaMQ5DH7Aji0TRjq0AbsadJdG0clwNehtcZKWZesC2PT9jRPlWZXjp/as3vp1LGjr37zn1Au3/krn33uqacPP/TY0rXXXnfT9X61HLk4qJZiCW0UB0YHnu8EFhJGRinP84JOHMeaKNBh2AmUVmHkuhEzw/O7msOoU2YgNr72w9hoXek0m5Wodft1V9y4tFBnKVem3lhvfvGef/nqf/2WxEaiuL+fojQ3c8t73v2uD9z6wGMP3f/IAxdfeskdH/zAgWefe+A7911y6WUfu/PjDzzyyJPPPnvnnXeWy+W/+7u/u/b666+9+uq//crdy8+/AEtkieKYRKwzYKAc3PK+99750Z9/9tFHv/nNb3biSJyDaLBGNw6YhCPRpLTubjQ8VQn8bDMREAAAIABJREFUcisOtzTD9CJGOAAy6RZEzk+/CWTJ16co/YTWl9xQyNjEOjIhtJJNQ5mdDSrgnzPpz/7g0QfJAWARR2BB8gkksYiGZ4OSZ1dIplgpZ1DBLH8cZaUGBkcgB+GenSZTv6PWjvyTpQuuuLzgeAV2hUEql243FUq/p9F2BCCIG3wfjj+ZNc27TL0Xnq+UfB82aY19d5ulH7lFbvx20ZEFI2n6OR5NP1ae+TTs5NC74gbnBha/mMPWr+1Gl06/c66ek9Ok1tDxMjwjKDPkIZUPZYbOo85i29ODMjyGY3r9OsGpZMucg4CFVRJoWKh/zohLgwKT9E4aSPZFp/HxkuAfScq+d5uzMfP/z957h1l2VPeiv1VVO53Yabon59GMUBaSAAWUQSAQCAmUkBACG67TA8y7vvbz8+f0Lr5OOIADBmxMNiBAYjQSAiGE0oyyhGZGmqiJPdPxxJ2qar0/zunu093nzEyLkTFY6ztf9zm1115Vu/beVb9aa9VaQrEEBAllQQkABeW51hob1hsh/8lyxnXqmlk5gHSNENYYsCbDquFSQzBWCodJWlhIBeEACZRBqgHpsOLYEBFckQoNJUBAnAAy5+W05dhAZjPwPZ0wEpHL+RGX9PAhaMdVfmIT5fkuOyl0KkJAw/gkPIaB9NWCE0CsD29DFK0865Kdu3agPpibP1BYfOKBp59AeECBpZqnLRldghJCuJYEggx8D2kCWGQ8uAqxBREEw3NgDfwsYoaUcDUkQyhSvpQOSGjWIuMLVkSklKspZZX4nlsfDef3DURRffSpJ9HV/Za3ve2JjY8ffu75Fae/9sQTT/zxgz+p7X0JGQcm9fq741I53zWvMjIO3wNBuoEjJDFYqtQV7Kkgm42qtW4nENYIIaCkyAdxHOYEyGghXGuVG3RBp1kdvWHdssvPWNedUazcusw+vOPgM1t3bvjOd2yacmqoMUeEITJuYUH/ZVe+aWDJgnvvv2/788+/5V3vWrfuNT/+4X1PPrLxrddf19s/7+71dyVJcvXVVz/+1JM/feKJ6256b3l4dMOXv4Z6AkuIklxXVy2ssxJwJaS4/uqr165Zffv67zz3+JNwA0Sp5wY2itlqC2MFSUc55ESVCvxg5oqpzUqr8e8/A8p0DoLwKpRp0DFDmWZ56/jWem5DUT/bBDMrLUxbKDMlTWDCUN7SlglVRBvgMdu61FjOUStTa+0z6z0immnHPzu3+XSIMAf50zJpzxnKNETNED9limowzwJeR4EymHETjwXKtKFJw8osKNmef4JtRkWdoglPP/VVKPNzhDIz9g40TEtsASEYhgEhGc0AiM0AiaL1uRSYyKTNJNq4lRERLJG0Bq7rJWkMwPMcay0sCyW1tSbVECIIMpxqGOu6bjWuWY9AlEkdZciADFgrZmkdKRIdk5JCOakhEo6yXpomCACbSEvSwmorIEkqY611JUmYNIWUXibQhq0VuVyuUqo4KmCrmIyhMnTkkkeWU4aAdIwD2ETWWRiwlKRgoP0MD6wQEvbgTsTJmtdftm3LVtRH8wP9vStes/uZxxAdlLCKC4ZJI4JDgIDjgRUSA3JBAlbDcRDFEAxXwBGwhEwGtbgRcK+5G8JaGIZQSBjCAQtYCwfNBNqORGyQyyGJkfGv++Btd/3w3srO7WrJkkvPv/DR+x8obX8J9Tocz8/4Ub0GCRgDpfxC0RiTlmvQmvI51hqucAb6V61YufXRx8CEMIQrwQJSIomFK61tpEMSUD4AAe1Q+J6rLnnjxed2L1h0530PfOGbG8664MJLX3/uhu+tf/Ynj8Ba5WWNMZzGUARP9i6e//5f/aAG/81ffTLIFm644QbD/IVP/33vurU33XTTC5u33HPn965429uWLVv2uX/57Jlnnnn6yafc8Y1vDf50M4IsRsedfCGNNdg6fb1peXTlujW33nrjgb17vvDv/xrGKRnFCXLGV+SE0DFHQCwDz6Rm+mw3+Rp0eNXn6tbagf2XFsocJx+dTrV2TIzwikGZ5m7L2RMkTcaAmAllZnBOn9o7OcrM0kY0z22XlqHzz9n8bSDCtNn3KAKn/5wGZRrUmFvtLAjbKcFkJygz0doOWqi2Z/1XgzKA6Ah8W0/9ZYAy031ljmmYmA4mml+nWfU77GebVjiHHQeTxkmajNM9zcCMCZcXIsGwTMQTXjMN23TDT4YxGeq7+fg3gQ4JptnBmho2SQEoKJfRzJ0qrHGlY7WBciwRpE+QOknT1FhmIclKI5jJEjMspBWKBYhIEBubkhJWEluAXZDLhuAoMBzDgokFGZIGykIxuazheIHrebFJyfU8Pwjj2FWedH3NysYRdAzJRDK1VuZzmkiz0iQEWSVJEbE1xDCui94ediXKo5Cqb83akb37wcbJZIOueaWD+4CIHcewsNZ6Od/oFGmq8gWZzVvpItuNIIdCFxzfFdLPZjiXsa4LL4vGXkjfhXLAaCSIgBJwXLgBhAcrIAkKUAQmGCLfQ73udfW8/dprNtx7b+3QIXfp4jddfvmG794R7z8AzY7jScNpYgClXJ+JTjvllF95/wfmd/e+uOXFIJtNajXKBjBm8fIVZ514ytbnfirCmOOEPA9aK03KEBnrCglSUrqsIZiINYg379j27Q133/fY44vXrD3h9DMf2bjpwUd+8vZ3vP2Ek0/a/OI2U607mcAmCYQEI6zVHv7+vQmJW9//gage3Xn7t10/c+37bt62Y/sPvv71rkULL7rwok2Pbty5bdt173nP85s3v7h9+5uufGt2fv/eLZuRzdgogdEqW9Cjo/DcsWrlwQd/vHLVihvec+346PDwyFg21y29bDUxKYl8V5clNnFEpKgdHS8o04m9s9tvJznHx9recbNoBzoulb4M6lzxXEfwVknc4Xtr8eyaeVLKtD5pGt9p4mvHJs/0T2mWTvc1aecVgIn7NfNezPp5BH6aPfy33U51bPKBmdMdTXFRp/KWTqOZb9DMs+a0mXwiuUW7eudGHR6Ho0byn3XVc1xazHXz9iv8Rh67+JellZkJYhppIzG9sElMM/l5Fs9M+dNF2dbLoQmc0RrlhSwaHpNNPaUBNzKDNLxh5GSbiQjWCMA0vLtJQhBBsiC2dnq3WYBAgkSGrQEZSezGqQKRdMpJBC9AJgAcGO1YQKcph8oVjfj6BFfIwCinqc8wqbCJ5RSOhHJADty88LqEUDqqIAndWl3YNHYFKwduHkEejoew7rB2iOscQkmpXBPp/MCCMGGdSMQ1hCMwdbiuU8iZbNZCgrOoh6gMShO6ZLXWkEHqB3T6qU42mzy/GfVk2fmXvbRxE6LY7+vrWbziwPNPwI0o68nE0aUqwhrY5HsKK089zR9YWBbK5HsS6WYKOVutZkvjOSUSlxMBN9sVgv3ujHId1sJGoZOGrgJcZYQjRI5JKaWEA0uJgHGM9FOTs6mOIvK9/7j9u9HwSG7RogvPP2/97bfjwEGnWEjLVVKuVJ62UmZyZmQIjrnyqrfcdtvVhwf1//2Rj1crZWR8CEDwytNOO+eU07/22c8j0QAoG7DRSxYut9buP3gAOgEApRAaCDi+k8JAaJnxDAyMXn36OVe87connn3yke9//7Szz73i4sseuPf+xx9+1FGqPnS4kb3S758X1etQ8vpbbl26fMW/f+lLlVr5re94e76769vf/rZOkqve9vakWt2wYcPlb36Tn8t+e/2dZ5x++omr13z1X78QvnTA8zNxPczkc/VaDY6EEoiqa08+6crL3yyhHn7ymZ2lSgIxuvcgD40owCMOdTRjGGtGdD1O2oVOdNy0Mp2o46rrlTUA/dy0Mp3kdNDKMLfuz2+1lBxJKzNxbkMl0IA4YvKSZ+tm0FLSkhK7jcKgNWN269HW+9VOyX+s/O13Es3QkUzX5bTjtwRqWlonr7qT/qAhZ3a/TZS374dOO7Da5vqemIlaC/+TtDIAT0sUOtkbx0crM7NV//W0MnOHMm0MSUeEMmIm/7FDmQbPNIghaLK84b3bvH1TZlJu2mIbGIsACAgJyKZDnDWTzr+AgBANQMPMaHym6maQ8nPdYaUsFHkEEaWpMYkjkS+gWEC+gNRgtATNGdhElx1XxOWQhISboSDDQcaAEUao18gYydZIyY6LQpeT6wqCou9nRkZGbKXkDg/BREmg2Peoa2FuYHHQP686NBzt3IGwLALSnIJkrqff6eqTmW5L/tjBg3xwp0LqZj3ZU9Rdedndr4J51cPDeuvTKA950lqrtfC50NV7xaXFhQuGN28r7zk4b9WJQ089g9FKbuGCriWL9u3Z4Q1ku7uKIqKhXS9hdDgtjcKmKOaRy6K7DyvXobsPaYzRYWzfgbFhCAO2mL8E3QXM7wYIETBewtB+JCEUgYFcL7IFdOUhLCrDqNWQAKlBZUQuXGjKVeqed+KJJ1/+xou//LnPDW9+DmBUxlEsggS8fKbYV4+RDfza4PZCT/6D/+NDURT9w//353Dd3kULRmsVJ5ddt27dmSed/G//9BmUq253dxJWoNzrb76VLd13330jQ4dsnIAYUQKlYLkwvz+0Nt+dHz1wAIF79llnPbbp4Yvf9baVa1Y/eN9D+3bufv/1t8zv6bv969945plnLGuOY6QxsnnkMhDqtWed8+53v/PpZ567+wf3ZLoLb3/HO1588cUf3fP9s88864o3X37vD3+gwaefc9bjTz05NjT05ksu2/7s8/d99w7fcaJ6HZCwLLJ5m6YIa/PXrb3iqqtOPXfdrrLdsOHu4W0vje/Yi7Gqa61BytNf31ehzM9CrzSU6eQr03lT95ygDFEba2MTysw0ITXKJ8ZP5qkz2xqbjrR5mLk13l7rITHL92WG8egY+TtBhDbbv3lKCzWLv5lJu7X82KFM66FO5qcZU/vU0bZQpln9zwfKYBaUZOb/PlBm7gYmaoUasu07PlFOAGGmCqfN9xlnTzWXxFRqpUlbEgAhQTSRghtE1MyhSS0CGui4wUOigVoAoqYDfMPYJEFCCNnYdc2tTRJCSElCOL6fptp3HUlkiWIB9HUHb774rN/+tb6Lz8+uXj1mDEZH0iS0pqrTxKGMED5cj7IZFDLsOw09kdDSkYGGgyCfHVjkdfXC8dgNRFCAJUR1WGN8CS9QvfOLS5bnli6rpWkyPMxR1VFEYKWkm8/bfHdh2ercwiXjlRqPjWcch0nU4gjFHn/+4mDJqpSkPnQItaorAIImH73zek45yevr97LdITnMlBw4hMTmurqkIyv1SrBwYXH+Uj8/b3SsoseGYCPPd1kS53Ji6VKne8CQEkEm6/vJ4EGUSsjloQ2CDAbmI98FzRABQaFURbmOxCIBgjz8LPJ5AjBSwtA4xkJU6wgcPjyIgUUXXHDZCcvX3fnNO5LRcjFfqFVLFLhQBKUy3X2ZYo+gwJUqjSpRtWQz/kilvOelPTKTufmW9720Z68lXrBgwaqVqzY98QRgTz/nnNHxErnuCaedmu/r2fz81rBah07RQK6OA9chPyv9vOsVyPOZacXyJTffdut37rrz0Y0bL7rgoksvuWTDXXfv3LXrkksuWb32hO27diVaQyooCZL5rp5Ktbb+zg0nn7juxuvfNTxc+tbnPt+/fPnV77p68ODghrs2XHDeeQP9/T++/0fLly8bWDD/O3fe0TvQf+W7r961d2ctqhULRdKUDo57bl7NW1zNF8pLerfHyfyBnrNPW1P0g83PPS0BIcjYlMhOZGLmpgfShFb8Z6eOr/V/MQPT8aI5949oH+EXbcoa/Tm3z0Svzd7qMrH0mqir+ZnIddJC3GSnaZ034T1IrXzNebXt3Z1s0eyjRG0qaMgknlHTER4qNMfZaXu8J/jby29TPM1c1YYds6D/jA3IrS0UR3jOZxtriab2ubY9pX3htLmvJQrw5GMAYCLoQkd1X4fyFt+JtqdyS7uIZmf1PBodUX4DVE4rPz7DQOfmHDPny4cyE69xO5Z2rr4znvjOLwC1Ms0sbr5FYvL0pgsMQEJwMy4CAQzRnAaIJJGY0Mo0fWUmwu41/G8EEZEUQAs2JBJCCCFTw2w58FxmsJSagOWLTrv1pqHefFgsBNlCdXhE7z0Am0jFgi1paS0ZwdaR1pMQAolGknJsAWktw8tk+ufB9cvVehTFTs+8KI5MrWQ4bu6j9HuyvQNxxhs/PIjyuDSJtaGroAQq5UqsfJMr6my+VimjXAoE2TROk9Rqq4u9smegXot47x6ENVcZrY0VnurvUwP9o0lCfhZwdLmqd+2G5/Vm/ag+Fu7fk+TzyPdItzB+8BCqw0jqDiPVKYr53uVrjAjSQ2Oc6GI2K+qV7r7uCy88v2flyuLaE17zutepfN7L5dYuWLh2yaJMIed2F/uWLissWZqdv6B78YKlyxcv7u/vy2R7i70DAwNdA33B/L78yjVnn31eT77vu/9xR3W0UggyVif1ehnCnn/xG08588wDg4er5aSY7wurtbg8DGFWnXHaUGl8eN9+crxbbr7xgQcfqVSqvX29SxYvfeyB+6GcK95+5e7tu6rl0vwVy4vdfVuf36LjRAnFbBggJeH62sCSJ5SnE5uOj+55adfzm5+74aYb5vUN3PGd744Mj/7qh98fxfprX/9a97zea97z7lJYO3RgP4T0it0gEcepILy0c9eTjz993vnnXXbl2+69996nn3n2yivfesG5533+c59la2+68cYHHnhgy9atN9783p379nz/Rz/40Ic/LJXctX17Uq4FXja1InUz3Nvtr13OuWBsZBhResrKlRTWdRiWxkfYmFkvxis8WjSf919OKDNnosk/nQ7+7OLR2RViVnknKNOUNnOEnCyZOd4erVUd0M5sINQAUrNb1XLWLPDR+eFqD7Q6h+GfIZ9mQ5kGiY4SpgH62b00szGN7JTcnr+N/Fk7BI92QofyzjqUI58684peBpQ5ovyZ5f+FoEzQPbUCaBXRadXSWD00sfZsahxqA0fmAmUmkWFLqZREBCGnqYUYxNaTrgVbC+W6JCWzlcqBtQ10TkyAAE+47FiLhh+uaFX5EKbSQAIkSEophRCSyWEDAiBE2siQunrZojeeW8lny6nu9fPxoeH6c1tQqwiyph5JUsZYcApiOMpTDicp10JHODoOEbiU9Z1cLjWpPjQEJurrN2whUzgAAKtgvVqYOn09KuOmh/ZzZSQTKJPWw1oZIGgk2Vx22RIv8PThg+HQoA3rjnJMPbbK61m2KiyX7cG9qI5aHTqOzPf2dw3MP7xlS1ooLD/51NGh4fCBB+Gp1ywaGNr9QnVovxUMDW/Rcj/bVxo8hJFDHpENY/I8zOsr9C+OKlbvH4HhIJdVUkcmWrxq+YsH99me7prE4eH9aXmkzyHXRnVdSSl1PMeSdjwhHNs90J3LZ3qLRZB1M3JgyYLu7r4l85cWvO4nH3lyZNdBZqisn3CcJpV33XDtJRdecNYZp5+w/MQnHntKyUxUr+moCmkXrlmTWju0e2+QLVzxpou+d9e9SbW2YNGilctWbdz0GJjPveCCJx5/MhkbX3HiSYV84cXnt6bV0IYxWcs6FlIxEzRYBsrPAqSjCGElrNce3/jomtUnXPjGi3bt3PWDH/74tWe/7g0XXLDh3nsf3vToeRe+8cQzX7vv8OEwjv0gqFeqxjTSXdAjDz10ePDQNe+6JpfJrF//PZPqa9519fbt2+/dcM8Vb75i6bKl37tr/cCSRW94/Xlf+vevLVy45J3vetfOPbtKQ0Min2ElkM9LP+Nnc+O1yvjI0M6Nj111/hsvPu+c729YL4QiEsZaIaUQwtRq+e6eOI5Fm5xNDUB/nAwond5HMTMn0eyx4hgraPsREA2d6IxPyxJ22qeDcqQjzb2dE3+nr00bCm1uVXW8PKLpI1zLlTWWVZi69oZlYvbU2Jz+m30x0WeiMaZ1aJlo6Q2a/Z0mYoo2hvgphtYmNkS3qjua/XOErm7oSJpZ7CbkzJBP0+RPFE9WPtGwjv0JIqYJM9S0/p1IyzcVd7KVcRIYER0hbyVPVdTazBnvQmNmnHwfZ5mTpj2QNKX56/QotdELYrKrphI/tQd9rUpETMSP7fxGzKin9VtLO1v6quXBm2r/KzM+HC1g4BS9jBxMRwm4/nKGj+kC5sZurbGpNQyQ1hYw0NYwg5lITHqU28nsBBP3hbmZ1rr1Ts7cytTAQFZrhmBrSUAyalF5156sszrrB3KsFA6OQDq5Qh9H4zUREkGADUkI5ZKnoCwpSwQJeCRcUq5QDhFUqDyQA9cvLF/hDBRMaUjX6tWRCmoKEPMG5r90YCdciWwmjKuIUyGVNQZskQn8rvxQZUyHJeGrYjYoVWpwHDBbk9pGmCYlnKDY09UVsXPgmWewevnKE9bu3n+gPjrqr13dW6ts3/psUhpWCpAZwKZpWksTCAEoYwlQIJeE57pe4KgwMahHSZKw5uq+fffccQjlcVQjf0F/VBq6+rKLz122fHjf/j+/+7s8Moogj1oEYrDdtW5t0NUVvjSI4RHEFWgN4aHQN7Bw1ehIFQy4rvVc6WWFKcybP+D7vgCWLpvHZI1i7QgIhoVmS5BgMsYkFlYpGJtYFkQC0lqTGmsFwZGpNZbZglkJK0gYEEnFZFIDocCcaEPGgAmWkVoSzoZ/+kz3Sae+45pryXHv+/H9ynGuu/mmF7dt/erXvrLupFM/+D8+tGP7rocffqRSraNczvb3DR8+hGp16xNP7Xpx+zuufdfHP/LR9Xfd9Rd/9Ze3vPfmiy544xe/8uWunq7LL73sqS0/vWfrC9dcd+P4oaEvfv0b173v1r0vbL/jjvXQNQzuKQnDUZV8jI4M6W07att2/snvfvztb7789tvX5/sGWJtqWJeMrgULxg8NiSD42d6pV+kXio4ws05QO9eHWY6Q7ZJFT3mHzMpMdCS/otm2oCM3cvbRI/LTbEvIHOUfuf1HubqjUkt1bUQd8WLn1s/HlXjOs+kvMHVMXNAZkYgODFOeK61COmXA7ozi5Uxo1rBeN/xvZij/AGGtIGIiFgqyobMRJASsFQ1wLiQREQlBEtTwmgE3x4IGupRCCCElpgYInsCjQsiMgVCeS1LAaluPUCqPj5WX5rrdodLhJ346fN9PsP+QsIhqNWFhWFtmwIWbU5lu4QRas021jetIIyZjJGQmS9JPYgEn4y5eWpjXB9hamEjyrJaWvMyCRVjQUw4rGC+jXudayCmz8qBcdPcUTjkZfb3l0WFUy1LreqliEy16+7hYzK5aaRSlwwedQrbQ1XVofDwsVbrOPCNYseJQWKfu7p4FC6JDh8ef/6mJyyS08lzjOOjuV4uWU1CI6zWMH7ZJzAbWcTmT8/sWSjdbCxN0F/LFQlotpQcGMVyG8OAX5gX5q84995LVq1azOqmv++mHHx88NITxGIZgADcDN5vxC/FoHUbBAAZwfQgHmSw8XzsSkhLoOKrw0KFiT9eyJUsSbaua9o2P7SmPGwVEVYAH1qyyjMM797BUF15x6Y83btJp3L1g0brlqzc98oi1+uSzz3pm6/M2TRatW1vs69u8fVtkNVtr2ZAxUkrDBNdHNucEAYGtjkWSSGttFItid1gqP/3UU24me9XV7wxy2c/9y2eyhfy7b7h+cPDAt7/81ZPPOPOqd7xlvFQbHBqsJ1GQz3ien46NmzDa9sK2nbt2XHbZZaefceZ9P7p/1+5dt912W5wk3/rGN9eedNIbXnfu3ffck2hz/Q3vvfvu72uDq656e6VWGjl0AJzGu3bIchhu221r9YsuvuQNr1l+9mmnjFTT5x99OJFKKGlHR4PenthqtoaFmFjggkEtIcxeYa3Mz7oyOQpNXdT0D9BeL9OJf65yOn7a9MBMlcExUYdVZjsvmQb77LppQgUzo7h9DupJQW0nsFZlEk3xthPTopuZXi5A7RRSLbqZmXLaCEerbmam/LYd3aqb6SR/imcWA022sPGzjVapfY1Nme29edqc14G/Tc+0tPRIyqb21LH/28v5GQxMrfKPxeD1StCxa2WOI5SZpr+a/D5XKNMpuUXTnjUTylglBQliIVhICAUhQBNblAii6Q1DJBQ3MA0wpf5r+NI0ElhLgWlQhoiIQRaCrYVDJACbWqsRhlyqHNz0+MEtL5S3v4RSrZDLZ6RTr1SImG0KIkhf+EU326XcrCViNmxjwIAA5XjZfJDv8TJdqqvXyWSElPVKOapWXXId5WWLvT3LF9O8XLG7KOIoGh6lKBUWzEAur9aunb92rSwWHEG9gqLhYX14lAoFWcgtPf1UmtdHrlrQ3+MQHd6/Hwbda9apfD6UlJ3Xf+o551RKlfHnNy9bs6p0YBckmIizRbV81fwVq3OFoqsoLg/bpKaYrJJwpMzmCoUumQ0KPUWX9Pie3Tw+DpJuJmdS21/MrZjfd9Ky+Qt8xKX4Bz+8f3yspBML6fT09L3m1DNDbbOZLuVkBIu0XEVqYC2sNspTWR+BB99REsJqW6/s2r5tz0u7n3r22Y1PP7321NNK9ThOYpvGYLtwxUowDm3fxUJecsVlG598MiL0z+tfs2jpow89yFqfcs6ZW154waTJinUn9vX3Pf/Ci9oYWAutYS0RWSFlNuvkC07GUw4JaKlT0lq6jo4S5WcyhcLhoeGdu3b1Dwy849prNj36yH0//OEF551/yZvfdPvtt+/Y8dLV11x9+lln7xs5ODo8nB4chOsLoZg50emTTz6RGH3jjdcFQfDXf/Hn/fPn33jTezdu2nj/j+57363vy+fyX/3KV6962zsy2ez3vvPtX7nlvbfe/A6p7dbnnpO1UIcxUnPyySe/7oxlGzduP/nM04eiaHDwEACVz9fGxqCUEHLGErTlVZjbUDJTbd1Z7Tx5wpzkv+L0c2rOHO1aR2hlB4PCjBOntPSz8RCOqHhvSJk5Dk873uHXtO9tTydQa56mo/PP/Inpz207fszgn9F4vFx+muyaGTOIjoAbAAAgAElEQVRRJ+Et7ZwtnGY0furcmfyTPdPa6ql6qd09mhOUOSL/cYAyR6r2SAeOC/3nQhkWALW6O7WeezygTIv/zUz4xERsuREpU0IoSBcgJCkkETfQCIGIhWTRQOYWbCdi0BCISJIQRCC2jEZsx8ajRgTA9XwrCNIYm1jWUAJSgC2MQZwiTSQbx9o4rOk4cpRgxZBKyKzy8uRlrFQGltjYqA6TwFiwYNcXbpacjHTc8ZGhaHzM1KuOZqGtMcZ6kgNJOYU05pHRcPAwKnVhwI6LbC5YuEjlC06QcbVJ9u0df2kvDLu5nDuvp7hkURS4fX09OUGD+/YmQ+P5JSvi1JR37hg49cRlJ6weGR47sGs3SC5bMP/Q5mdgLZODQjG/YLEXZJKwYupjJhzW4ahtpLeU0hHke8pzGRzGo4fDl3ZzvQYds01Ix37GOTByONc3T7rZ29dvePixx0COdV2W6tpb33/Guefv3n+4Xo90PU4rVVWPRaLdBrB0hev7pIis5SjU42OIIoThyKGD1SgZK5WXrVi5cMGiHZu3qDgxRi9avkQSDrywHdaef/Glj2zcaJkLnr+yf8Gmhx5kidNf99oXt25NwtqSVau6urqff+55k2qkGmkjv5JgJYXnUuCygmFjdGy0NkazNq7jplpbS/l8MYrivfv2DB44+OZLLj1xzdrbv/WNnTt3f+yjvyld9wtf/lKQy7zlbW8dGx8dGhmGANdrnMZ+LieVqlarD/7koe6e3ltuve2xTY/fuX79lW+9cu3q1fds+C5set1112/auPHpTU+86aILTz5h2YIenP2aFS+9uH/37l2B69koGR0cOv31F33ib/9205NPfPBXf2Xr1i2lcsnEsZPP2zh2fN8Y21jKtoyBUy/fsX+OfWiYfFHneMJxoo5ajZ+Pm/Dx64WOUAYAMN1PiEQbI0vDraUzlGn+IWqZVTtCihkQavJ7++tt+J60ooHJKbyDbqDTlN85FNtcQtK1k99RQ9Fs4SwINUPrMHOeav+DpsqOAq069vPknZotZ+5QpmP/Hyco02bv0pHbc5zoP1kr01SbtD3xeECZKWAxqy8ZbC2bJo7xs/lCt+MFqdETu5QaKWnlZJwEYksNGyKjETqvoblh28jANNVzjZ/KlUJatoaNgTUwFoaRpFK5AnBBEtbEYZpGUpAla8mCSLADkDU6SSIb12xcVcII1oIhlMNSGQtj2MR1PbiXRwdtWNFRlAwd1qXhNA3rSaU6OljZ8mz94AFRrXG1KpjZFYBJE21BI4OHx154obp7B8ZHoY1hk0KPDg1Wx0e1SXY9+kiyYydUQJ4f79yBXLDohGXPPvTjsY2P23rEDJ+5tGM7wNAAZGJNaXio+tL2+uF9euwg2MAaVOtgSlIdhrXx8eHy+HB9eJAPD8OmcATXKpCiUi3V0mTXwQMHx8a+u2F9EqZCiJQtHMVBsPfw4ecef7I+MhKXx0y17BHi8qgQGlanUT3Rqa5WTRjacgWlcaTaDzwmSmtVhhgeG1u3dt2Lz//UVqtWJwPLlyml9r+4A9aec/65Dz70YBrWlaCVCxY98dhGuPLUs167dcuWpFLtX7wkn80/+8RTiBIkGomhhislgYktGxPHJqraOOYkZW1gLJFwvSDIBHGqyyMjtZGRsbHxR370o1Qn177rGs91/vkf/7m7v/em997w083Pf/0rX7r44osvuvTSQ0NDpbAuAj8aGkpA1fFxrfX2HTsee/yx8y584znnnPPAj38cx+FVb31zGtW/+oUvnLRm9Vsvv+zb3/rmG887lzTqZTz4wEP7DxxiwFHu2NjIGRec/6OHfzK8b//2nTv+5E9/d/OW7eVaqNMUgEkSCIXmkrJJDUfODilhOtIvDJTpSL/YUGbaLWyh1nqmjY0vD8o0v80cclunfEHT6pqhNqC2Jzb9BXhmI6cAysyKOk35raP8dH5uyz/tWqbrWmbwzwQ3M/mP5GY7xT/t1LZQbHrrpuTMhGIC7ft5op0dbkHbVgJHfv5fOSjTSf5/HSjzMtx+j50aOyzmGA2zjUaHWr5Pkw/YKXO4VJ6fyXV3wVidJHFVcyPyJdkmSgGD2VqrwAwmagSVImZBlk1L1M6phG0wUbUOR5EWDqRLjrVIDQwLmVobRylCCLbWAGykABHYwgoyVUpiW6tAAtICRnOCNAYDZFCyplKBMwwLYUM2KTsuhEScAoApycgzNsL4CDzfg9QiJiJjEoSChoRkYSol1MqQ1jOJNcxRqodriPMYHq5Vyti9EwboyvupicMISEovbMW2F6g4j1/cgmpcfOc7UQ99RqphkgrrFKSRhuS7THbt686Vwt288SmM11GuJ1EK1mrVklxP7/hIWSqx7OR1O599mlOgVE9iPSzcymteA0fCUXEcgSysTaXZv/MFVEYRR5BaCQgvI4pECoKQVusoN1ImOYJgrZUEGxtPOfWwLgKqjZV0miTjY4hiwCSckpRQjNTEUT0N66jXQs9PtYYAFCVWW2tgTZpEOopRrsGwskInVjjKwICBNIE2AIMFmGEZzMp10jgxJGJtOE0p4ys/kwyNgO3zjz72/ONPXnLlFR/52Efu+v69f/THf3zJpZf+X7/5W5/97GfrUXTDDTfE57/hrm/fERptozhXKFRLZYyOlRcv+PpnPjew9oRrr732pd3b//aTf3PJ+a//6z/43aefePLzn/o/Z73+vL/59KfPfd0F257e8vgTLzheJuFUuwaZgHLKCgtX7Ny1a+euQ//zdz76iU988uD+A9VSSfi+TV/ptIiv0pGIm1P5sfJ3XKq1kSAa/HPyBmVBmJ5AcbLmSTncWLQdyXe244zZ8BZEa1JGIj5yP9A079ejXZEgmhYr+cj8TXVQa2C66dVN48TEHmo+Ev/sGo/1LjR8QJmP8Xk4Qj/PrV5MoIlXDD/M8Kb6BaK5QRnusH2JWza2TSaplyCL5neQZUhqSBASAkwEC7Km8XhOBDU6yvao2URScMORzmiwcUAGSJIIZMF2CgDxhMOaZUNMRBCNXE3gBtBpvLRN0C0A21TrOFJIaZMU1pJQMFaAhFTErE2CNFXZwPX9KA1hNaQAGERELIwhjmBgHMBhwEJCsHRdNzIGiYa1YIuwLCVpoyA96fkEgaiqw3FwCgEvTaOwIqUjPFfA5rI5l2Vp7x7oFIEro7pJ00xQDFPtMNLxcfi5Ac/fmySAcH2VJlVSlqOwP5c/qJl37ES2iK4uXR2HMEo6klRqLYw2SJDETCa7fMVlb7uykC9WKuHep5+Hbq5RrEVXb+/41m1GmwVrVux84XlUa24ml1gCUwILq5HWiMjzvEgnMuOU6hUhyUZ1JyvSNKmGVSiZkieFBElXSG0MTEyWobVh42QDrSO/UIzCyM0X8pksTAprQWzBduruAMwwxlpLUkBJAFprWN1EvWygDawVDMExwQqwhYQFWIMZEBNPmtSpcVxfE7Ex5Dic6DQKISSRYG1g9H3fXb/pkccuecubz3vDues33PXo/Q+957rr6mn4la99ta+v7+bbbh1+af+d3/p2dfgQIL3Fy+LxMWTkoR3bP/3Hf3rWhRd89Dc+5kvu6/KvuPTcB3/8wHe/dTsbtfmZFwOZU34mSWpwLAL3mvfecODAgahWgeTf+LUPrVk+sGXztt/52G/93d/9/ZbN1TRJG1G2WTAxZCNFH4QlYCr6Z9sklATi1gnv56PTeJWOgVrns2Oc2xpp0RtjrJhc+c2Qg2kz7uRRO+uJOfrUzmIitK4ALJGckbfhyM0+NuggJoMgzxVqHAv/sYOVo++QauGZOjrRRUeoqHFoRhy/uTXv2IgmnhBqPiGw7Sr95aCOUGbiegXN1Ho1kiw2XWabpc3IAU3NY+OdsrAApJDWpiBLSinrpKmF4yFfQBzDWo4jDqteT0YbY8JUKmWaM81MhWfrGDzxzloA1qC5eYpNXBreUxoBc2PmAqyBmVDhETNgG/nrQY19UkwMBsMaCyEnMoBMuNGAAAVNBCWVtNpEnCjXJSAxKbEEGJ6vLekohXCEK4is4brjOWkkpHSz0jfGWLIsjHUIJFXiRmHi5zKxtGyN50g/KFqdMCknyCRAWI+RauUq7XgwBsYDs1ZKS5UNMkmclEvjjvAJFDBpJuFmQpaayBECsEjJGgHHAZHhWCjBAZN1hPFgArgGllEPsxkPea4mVZUWHOHoNLJkWAUQMs1mTcZTgWcpgdCARq2GYpdyc1HEgAWniSMhJFwniQ2EksonHSOtQFlXs64zXDclcnOZKNGe8uIkJS9gRdDGwrVRimxfEtYRCIJxjBBCGZKRiZVCihjShXDZWhgCJAjWQEqJRENQomMoQMnUGJYKwoFlaOsKt04cJZHyPaiG6bAuRV1r6/q5uB7Dy4AsSSXg2NQysyQiKQ2D2cBaZhZKkFCsjZSOVH6apiZJqwdG7vjif6x+zQnvedvVTz773L/97adOesM5H/uN37r9jjs/88m/e/255//O//v/fP6Lnz+wfWccxnADmAjVulL5x+954KlHNp3z+rPXrb5267bSgcE6h9L1Mmw5iitwpCxkTFj+wK/86uDg4Gf+7StI0uvfd90bLzqDR2pnn7AsMenv//Zv/Mkf/cXOPftqUZLr7alWSr4nTSlUJKzvprAmsUJMeqwLImIQGzPhwSDAthE0WIAtwZLomDJ4Jtmpl34WdVpodkx0MFfqNKC3TRt+BDquC8x2uR3npnXu1H6elcm5SbNCrgESmNY/dvIvtRfS8QbPVl1M77DW/AUTu+Zkc7FHxJZmoKHmbN4q0E7P8TTRFjsFiVpxlgWDp3URT0vvQNOPNJIEd9bNzCSCxdTmP7JTu6imDLU0lSZwVvtbsMtEybSjjf0mllr5m61vp0Cb1uAW89y09kxOSDRpgABPv4qZoiZC5zQOMDWiQzXaztya/GdW/BsB2SqkhQxae3UKw8mZzBOx2WZwTtDctMvHnjhlzgam2YqZ2ZXxxF8CmFlKaQSx1WnC1NOz+LwLTr7gvLGRkUe//k3s2++4XhrFliDkZLqDo2vkJkg0AS4RERNbIjLEDCbmhjcwN4NVE4MgREMH1FAP2gZCmjCHNm701FURQQgYNpYaiBbM1homMBhSQQgIdxKbW4BsCsNaMKwwQlVAzBIskVqkIYTU5FurIishJTzXKhknNUkusYjradQwmCnXMsFxIIWOBbsSGReCayBIiZ5iaohYpEKmdYNiHvlulCpJGqHYBT9n811ws0gTA/KlA2OZ5UjMyPbAkZAC1tUkoTVIaBI6YQQKQRbKA9l4z/4fPfhIn+vu37IFURXSRT4D5ciubvZdCAbBCtncQ6MU3Az8jDWA8mVg3ERoQAsnAmnXZS/DcGAjTmNI5ff1a2t0nCIGAHiKUxNXa66faSiQmS2TgHSE40jlwHcRGTQThDb6WBowCHAcKMc0/CJtk8DkZ7K1KARbL+N94g9/Z15P/uEHH/n3L38ztb50XXJUUq0awxAKqVZBkFjDBDBBCigllIKxhmGNTZPUcV0n6xuyaRpvf27rvxw8fNlb3nLzr/36d27/xl9/4s9v+OBt69accPfXvrl129Ybbr3+iUcf3/T9TQ5EGlWcTJCOVXM9vdU4fOT+hx9/4rk0ThBFTqaY1iMh4Ga8uDpupP/O294npXPPhrtRrl5/63sXLuz/sz/7yz/9nx/n2Azu3PfgAw/9ye//r207dj/0xJPf+8E9QsmwXs85niNlRGTidHLMaD65gojJytYdT6KRnedVA9UvLU3NcRMqjValQsNXunVCmTzaxrfiyNoIatpnAXDDQiQ64acOTT1yvBkx8zmlWc4tHexKxya/VbH0slQgR+mftu0/ktXsldaRTHqMNv9yu5v+S0FHgDJztvXMJgYsgQBpYYxFmrJkubg474w1NNyNuySkkVLCaLLWCimUhLbUEo0G02FNW4w26TrHRAxBza3YjVfOEhELIghiYm7AHaAJJBnU3IzNJLipsyE0sDtISIddRdKRxFanbAxgbcMHRzikpFQepGBmay2nKRuGzbLxoHLIdSOXBwkwEFdQd8Fs4cO3CDLIZBAUUpOkh/cjrgkWRGSZ4EjMH3D6er18nuM02jeEkSHk8ihmEPhQgE3hBSqldGQMxcwJF1/krlrx061b8fgzYsVqLhSzS5bRSIkHD0mbc1VQ0x6CYNzPiJNOsqYGAGO1UPjQWWHJIo98oBbm/IGe1PHiJMXB3Xv3HCjHCcYqyGQQZJyBxakfOIvnp0kEIWClFAoQcD2ZLZr8vPySZU6uGzJrqtWQXG0ZXi4iD4Vu9IWOFZpjWylBqjUnvSaqVrc98xwpN9s3ULU1mc8qT8fj41IJtkKAyfGMn3GzWaeQpa4iSnVOyTbWrEJASnJckS9aJ5WeD0dCCBCDBQsBx0lTUyh0FecveM87ruzu6pLEl15+2YGh0vd/uKkeGQjTs3RpvVyPxsaDQias16EakFRBuSrwlFI6jlC3wlM2DLVDqTAAo6coXC+2dv369cvXrLrpppuffvbZr37qH0686MKP/uEfrr/zO//4D/983TXvPvd/XfSNr3xt/55qGoZ+d0+1VKLunPK9NE1hGY6bJjGk8AOvFtZVd9db33lV//z5n/nUp0iqd99ya39//9/99d+TsI9s25/3/b/85Cc/fNNNy+fR/OyK1525Ymhw12Obt3OQD2tpbGWaJG6QMUY3oQyImcmAm3Z8O7kPxjaMq3NWmByHEeBVOlaag2/NyxFObdbZUwePJYZbRy+ZBrLpDGiO6rNytPZIZm4DxSe9RmYIPJr8SbIdrmjGlf6M7T/29nTyU5mrn1artP8+9Iq6/QIAERmjASulNJ5Crbb7qWe0EKWxcYyPgYU1xnGdWKds7AzP82PRzdCEHz6TbCrYWUBY2AYEFUyCWDQC5THbSeDcvNOChBBCSUA0nWYm5AoSJKWTyZMQEmxZmyRN07ThGSQcz81kXDcDIYQQWuuoXtUhBW7WSKWzPdn+RV6xQI5jpBOXx2o7QqQJIFHMZ5cu6Vq8pNC3DFF964P38qH9VmtIF8pBNhusWD1v9cpMUOA02V/YUR3ryqxdsfSUdYUFCzL5TODY6NDw6It7N296Oj08Ios9NK83SFeECXetWG19r2f5yrHDoyMsrbYsXHh5zOt3+gb6eora1L3AHTw0onM5iIASDdf3Fy/uPXGx6u2KSEHQIc+XQnAaww+cfFHku7pXrRYD83Uxl44ebjo0CQXhwbe5rm6xaFHfwgWFYhHkArIwf+HongOQstDT4xS7SyV2U3akGVcOolqmrzfRCSSE0NmcF+S6tbbVaJj8rNUhmK1Q0vWdbCHIF7xsLlsoGnbDWtmwtWQhSDiu8PxssTuUoeM4QkoogAVJATdAJt/d0+coz/H8TCZTD2M376eGzzj7HL9n2aannhurlpI0FoHKqu766LBwyVoDIYXjuUHGyeXIUaZWQZJYSxCCpYAElHK7ikGmYK0l4t07dn521+fOfv05H/i93/vcP/3jlmef++2Pf7w2fsGXvvDF+f2r3verH3jysQd+cNeGOEycQiFlWOEUe7vZpOWRERAFUtSqlVxX/p3Xvbtnwbx/+dfPw3FuueUWH/LT//BPRuOm2z6wa3DkS//yj74UJ7/2tUbDxrHveee97uyHnvwparV87+JqJaTAM5IsR2JKDQprbTOZr5gIRYaWQR+Aneuep1fpeFJHw9xxjwDbTn8wVdGkz9nsQ+1+HgEGzW58Ww+VaWxNi3+L3uiI/NRQq3fmn3E5M6HbEcDEBGfbCzli+4/UP7P5//tBi/9semUXXgRISCIJIeDIrOuryOLJF/d9eX3lq3fhYM3ToNRokBHC9XzWjDmuHi2BBfGkYgZNLQ1oaihvOPliCicJsJjwsCEiKYSSUk5mxqYGKpJKKEcbG2mbGDYsNWQj9AwgLEkrHM0itdBCsuNqoQAZRXEaJ0xkWYdj5crYeC2OIrbkurAGcR31Sq00Mjg+XgnTkVrKkChm0ZOBiOBKd+H8/PJlcSa3dd/BQcHqrHXd11/e947z0jOW7+vxdlCouzNrz1x19lln5KTCSLk+NL7/hR3hrj1YvrKeCcarVe06YtViLJ/POa8ugFwmM38g09UVWVOu1RG42eULMK8AVxowMo6/aJ7b11dK0rFaKJxsbv5SNyg4yoNQaZKmUo3HFpmczObJcRvuRA5cQMISM3u+I6XI+D6Ui3nz1r3uTOR82NhXIuM4WutqFEF5Kp+HQuRQ2SQgPTC/Z8G87qyfrVWjNGWpvIaJ3AhpvYzI5qAcawEIN8jDCUxjrCGyhMTCSpfcgKVrqTmKWQI5ngiyLJ04TcuV8N4fPeDnipkub/f+g5/9whd37jtw/S23XHj5ZTLwoGStVuI4JDmR+VZbWCLhSOXB8SE9GAsQ0hRMqtglvUypUq+UKpFmOJ4eLW16ZKOxWHnqaZTNfeHLXz7j1CX5bO7w+Oh/3HHHZVdcptkwG3acBUtXQnmlahizyPf1uUEQJrGTDT78m79uYP7ur/4yTpOPfvzjI8Oj//z3nzJpesuHfn24XP/S574IN/+x3/vdw+XS4fEqObIWm9eefdY5r3+91zPP1JOMn2U/a6xl6abkpFZoSIYg2Yj3SBOBWbllDBXt3/epsLiv0tyJ5hDUp6Pq5fjVOw0qTddn8+Rg2I5hkuykk1SLtMnvMyMpTxydXcuMdekMmhqoWy+nA+eE78ox8be26hj5Z18mjjYNWTR3Xbep65WhTq/pq69vg+YKZea+w8iwSxJEJjVGa8eyioxbriO2WQPPMhElOoVh3/WM1s2zZqlnOhGACVwippJQNpNHNnx7BZOYyAZHFsI2VItNVENNxzohZtcopdTacmoSYy2RkApCQkpIB9IFqVDrsB7GibZMIAUhWZLy3Ww+o1xZq4xFo6OpNYHvkdFCJ+AEaYjRw2Z4qB4nsQWM6Vm9ZvGZp6E7D2G97i6/2HN4pILdB0uHx20xWzhxeTgvu5frBzmqe/KlocFSPVm4sLhiyWJIZ9+WbaP3P4KR8UJvT1SvY9fu8fFRU8yivxs5P7Ea1jjZbJDPlYdG9O5dw6MjIuNYlwALwQikKmYj4vGDB5O9B2ph4uW74WWskoCF0W5Xd3R4ZGS81shHB6JGaggYQpiUy+Pl8mic1JrbNF3Vs3QxJEOStIZ0GpXKydhYmmo3G8Al61IqyV8w8P733fjrH/7gm970pp7eAdfP6VoIISEEpGOUy8pJLIdhWK/XyXEgFTM5QkIAaVqpRvUw1RCpZcMWMGBtjLEkyPEHDw/5QRbKGR4t/c2n//HXP/IHDz3xzLtvfN9Ytfq//+zPpO/8zu9/5DWnnQRXiu6iMQkkYI1JTBwmcWQSS1YoSKWkcoSCtrDsuj4JBwkBnjbWyxdksZsNXNethnU2RrM1KZIwMYQFy5ZUw1g4DgKfCaOlqmWBOI61gXKSNCr2913/3hte2LH9q//2b/D9D33oQ/v27fvehrsAvvKaa1Nt77n7HijnIx/72MMPP/pHf/qJex58NA7U+vsf+YP//X9uuvHm5UuXSdeJrAY0igUEHpSEEExkhYRQJKVSSoCJmCaNTGg6v881au2R3rtfZPpFua6X0c5pDO2m8BkDXfsTj8Z87O0/dvkzoNhc+Y96sXOFkk2H2WNuDx1D+1+lV4jaGpg64ZU54xjBkAzBMmYDaxJoQRIelEtekE3ikC0YggSx1jZJHQjDsNOd9mliNdCeGl70giad57mZMHhCAUNN/WGbrXTNvbtsmB2SRHIi0gzQSC0pBSBhGERCSggB48AwlHQzgev7JkqMsdL1XM9LLWurYZOU2HOYJEPXAeGyyUpJRAYmEsZKi4hRqYAMKyBfWDZ/YaY/u+/AHoxUrSTS4PEIJeOGWJjp7u3u2xaNamskJFuyMEy2twDXEYgTV3NYrrn9iwrSK8cG4xU3SkgxFMFVDV9n6bn5YgHVMiplE8U55/9n782jNDuqO8HfvRFv+dZcKmuv0q7SgiRKIJtFSEIsTQthdoMBGzB4oe0xQ9tun253T9MzffrMnJkxNozNGWwYDDQWIBax2SxCgCwhJJCEkJBUElKpVlVlVm7f9paIuHf+eF9mZVVmVlVqsdu27vlOVX7fixcvXryIFzfu/d3ftepyGEUExCwxByiKApmo80WUiGVEiljRqJ124QUP3fmQ5YhDhfpRqArTkPK4GAyKHvOCk9eVffUQjyQZSWMuAAWcCyEwCL4oxNl6uvm0LWeetb1RN6efvh0wZRlMeyRkR0AEMmpjxFEg8t6LE60ziIY1gAAtfNAAGKuLlEWiIYQAoiiOoqgMvpyd1zNOO+fii39hvPnd79/6/R/d+4Y3/dqZF17w1a9/5YEHznzDa167cd3E1z9/vWm1QukAhld1wQUFWDgCGxFEQBzFGsUE452gVGgQw8lIy/tp6fVGR1tsIkBHxsfSCMF5Z8LBqcObNicyM4u4lbbXU1pjUOZLaOgX2fj2bZc+6wKbxl+97muIzKtf99p+nl3/2c/C+Wve+KYd557/wb/4METf+s637nrg3tu+9/e1KNbxzX/5lVv+5rrPwsn1n/nSb//muz/2qb8p0+jh6UnUaujn6GXo9+CcqniogbKCh5vZJSG3CpyA5emJOeSfkadIVl3znhKExCI8cKWHTCthZRbji5bVxLpAbHFMbUsQM0vf1UM+mCqmRo+/3EqtXZlvpgo3oWWImdX4ZlbDndAyvuvliJnjFwsefl3e/hVj/FbszxVudBV5YjPwmen79DqYhAAmX2UOi6IoTRCR13IQiq7rF74UAzVUq9Vgo16/x0kkx46LU9Vql4U4VkFiR4+rVmiY4fdKWyIACKqLvtLjLk1E1jIMaIFcFSIIDt6HEEQkhIAQhiYfH+ACogSx7dWiXt2iEaEWcxRDKGJDEtQ7VDQ45H0Ebwnd3lUsPeoAACAASURBVKOP7t798G7M90BWlIIaSuqwJkoajVrTMnxRWo7SuOGzcqTWGMzNS0Cz3cD4SJQk6Odp5jgLEMDa2FjrBKVnYjYGjUaSJEkzBQvipFarTdQbI2zBpqJFFx+YGUkNJlIwR5bTWGMLA8Tc3rQewZNUea4YhkASiBBbJAaGERHXElFCmiJJJIqH1QoQKjqYo6H1zBxCODQ5OSgGhddAEDZRvRGyDCQgBciwiaLIRIaNQWRBBspUVUVaIZMoiiwbZmOqBKIEVoCMMabeqnsJvH7DVKf//Tvu/u7td17xsldcdsWVH//rv56ZmXnfe39vxzlnfvjDH4Y17/vj/7jj4ktgGBEQGURGrIGxygw2No5C8C74oBJCsCZGUoNyVGt0ZmetmqjR9CVCCGg1Z2ZmygEatZoxpj0yMjcHHh9HrVZ6Z02c9foQIEmjWnrFi6+66JJLPv6R/xcS3vqud23csvmTn/hEXK+/8rWvf97zXvChv/hzGQze/o63+XzwrW9+s3Dlr/7Gb8978zef+yqcBddu/dGdX/32t9/3/t/aeMHpO1/ygu3nn5WMj1CrhSSFIYCgEkIIIRy1xujih1f1MT0j//xk+ZtzdfvKSc08p174VBtzwgrX1J61ll+x8Fqv+GTa84w8HcILTlaz5LPUTMdLPsCxBs/qa1W+yslIzEsLKNOAJTeizACVLogqLIMCjCDhoCLAIMvBQDPJUao5ZhCoVvRoqiJD1Xix8gWH67ChFUhgIcG1eg8REIHZGGOttdbGcWytrcpUh6pTmNlLYGsojqoIchCpqnNOJVhrIsuhLMssgwgICF6D1+Ajy5wmrFIM+iEfIASUwGlnb7z6ygt/+TW15z4XzRFJaploJloGWE7Ypmg2zPYt3K7XN66Pzjprfrp78Mc/hY9h6kW/zCWMnnEazjldt07MlPl8t7Q2KfvOaNSIGs0Qb21O1Axy1S3PvqiT99Go97vd0M/HNm60z74QjaQJi06uzksIEDM7NROiCNs2Y+fFF16y00/O/eTr30FBEIvMhV6WJLXatu3YvqU+0up35wf9rgsejRrE5STYtiWJOUpsc7SJeg2RCYZgCKlFI0nGxzreJ6NjaIyiOdYbeAgDcV56R0C7iU0bvXA58IiaiVhWZNPT/9cH/qSTDz7+6c9kCKhZ1BIQWbYMw4Uv86LRaibNOiKy1sL7epKWWR7FMYytxUlibC2KE2MsmyrFEoNiGxVFEYIfGWlJcLXR0UzN1rN2XPelGyan537rd3+3253/xMf+anxs5Hd+5z0P7nr4Ix/7xPOvuvqd//Pvjp+5HSip3aiPj5QMqEe7IZYpidQwRZasccFHxibtkdhwLUrIi+tnEcMwoz8YbbWbdaRxQkRFnicpJBtAVINocLVmg9ptAIV3HNlekYEJIQhgrWVrg0qUJmAKeQ4pE8Mhy0JRIkp4dGSy04cmsCOQmEZGH5g9/PGvf/f1b3vJxZc/5/TzzzzngvM2n7YNSQRr0KijLI0xC/QERDBEtBTcwFxxVFafo4dMZE1k2Vha/B2qKsPpd8ofWkXW/GZaDWvCT81n1Uzaa67KnPqHyKy1f5YfraITeBU55o1NBlTNVUMwUAYMYJSMLhxdDftSXVRWieFfmiiqOvfoeKsupMvWC2ZlUq7+PMabv9SUIFR9WOnourPYHgUrVtDIdYkf9ejgWQYWQ2VVXslGUmEul65rK/T8sUigo+cuu8qy7lr1EVd9Ahw/cZa6gldoAxMWV1pm4uE6rkx6YhdytUbzqvsZXZAVb+Foexbn47Kjx9d/7PxdsWNPJEtPP6E87RFMFeXvkI+AoMSAB6qHzwIQaEiNUYXzrdVEVoVPk1Rv7wXKRQVk8eYXnk0AaOHvyuapqgvZDOywi5W50p1UlYIkSQTDrKiSMEFdlbxJXUlpWo8TJTBxWZYQD0PYsOEXXv9afe65tbHWy889777v3Lb77geNkwpPQyAbxXF7bKQ9miQJG2PXjc3NtfLuDHoDsOd2ZooibqNx8fkU6+MPPNjvT6179tmoNfJ80DDR5nrrtHE+0kE62jqzMXZw/34cOBCszScPj7ZOG9m0PiWZ27sfe/ZrKGvgbGo6b9QN/MiWDWPtls+zww/+HIenMRhENnbeh+kZ9DZtGR8t6i0ghMnDaaIxGEFRi9aNjbZb9TTiJOa42eB2S0rvvUcISON0rJ2kaZIkcZKgloJKay3A8N7ABOdGx0ZD7mpxMvn4fmSFukBeEELuQ9+F3OUCjRJLjdQHA+9rien35n0tuvTZl0BCu9GcOTIFw5OHHj//zF9w09PJpu0W2qqlg14WE8bGxkAEYgqSD3rIekXWN4bbExO9fqZcdPuDN77xjT+87ba//vjH/t0fvK/fn/r85z577o5nvfs3f/XHdz388Q9/eNvFO97+G+968J5d3/jajYP+3MjWrd7H/YMHfJ7V66mWuS9LX5QmiQyber3WzWelLEg8IEbBQVF6X5RxDEB84V1RQiqjt/iilODY2NhyGUhFSu+891CFsZUOTcNXdqVkYAGuWy1FHIhFCaoIktSaRd7zSZLVorsOD9ZtaaqeMW6aP95zCNYAhF6/tXFjd3o6TRJxXjWAqkg+EhBIKi6QYzjJFqbM4r+qujSxzZodHE95JM4/I6nWoZORpa0gtMRJUf39ZPt5wemz9BIniBmmlYJ0TjG6+4m1Z4Vfjr/EyWo49aMrVL7CWn6im1r0atEKfqgn2Z5n5FTEPoXImBWq0CosWoRMpc0wYDwIUtGpK0gqPUaBUI0EwVEO7uNlBeW3UmJEQVpp3FUGpWEOkepCqiJDz5WIHFeHiBARqVQfqAzp7gQaQqQBIiJBQqnioAFKgGiZh8JGBGIbxBWDAVyOOMKObTh3y1TTOM0u2FSrnbGB7nmwf2S6bo02G45BNkqd0qHpgXvIM3PRobJPrqTSCfswM5U/+kjc6SRJTaXMH7o3H4sj36+fsTWqNRvM1O3tm/M//OEPJ49Mj7XWI+9Ac5S96XvvctlMNJoWEubv3Y2fPwZyJRsMMkwdHhzc3z/y+ES8/fEDB6cf/Bl6cyBv2CDrF/v3zMXc3LI5C6Hb7ePIbNRqJFqiW8LWYlE/NzNXZqElIzEZYRETe0WFuy3y/tRko97q9aZRdJAaNgIVeOTdOVeGzuH9rbQxeWAvQoG5qVY9mTYGSgOn8/2sOzefF2TKoMGLBGtNkffA0bnnnKGu/O43vpHNzagj9Dp2w4bO3Fxjy5Z+tzvodopuJ+sNTJrs2bO7OdLuT8/2Ot3gcxiEIod3Zb8vgwzZkZ/8YPbA7kevuvLK5z7rkr/4fz505ZW/+N73/t4Xv/T1D37wr1501cv+zb//42/e+NU/+9Cfvvm1b/mD//hHX/67bz+yZy9IoiRJ1o31jkwhHyBOXLdDQilxMT9Xhi6FksUDwgpyDllWDgYKFK4s+7nLBiJApRD7Mu92uRYjNioOvijLIoTa0JAJIKj4oJAQgq8MfoZhTMkRKIIn6wCUQG6IQ+mjdrPfL2aCPPD4wZ0j51y4tZFsaszseqx3+GBnfh5MwfnK4ykEZQOlAMUwpd2Cn/VoXGu15ZVqghydZYLFHe5a7SnPhHqfVGTFLj3Z2nacHrFq0WUHTj0n0Wp8KrKk/Eptr7CMuvRcYNHpf7xZR9bcnmE9R9XuYXmDtfLNnGx4Ln00q3XxSftzsY+Wa0Or8d/oKtc98VxaDR9zTG0nrOGfkzz9vDIQ1oornQGQSmUirFL6egLAUKyNMvJ4CQtpsIdDQxGISElApAgkkIoWlnRIND7EwVVIOFXAez98oS8OL1UVn2X96i5Ig4QAVSiDCb4ser2yKIwxEiBlhiCICL150ULKLOvlvax94Oe75J67MN8buC6MIGL0fTkoMN/B/kOwDBQoelRmVlVIfG96/pGf4XAbRQnnMD2JBAe6R+j8s9vbtrsgU/sOdg8f7u57hE47Y8o9hsMHERn05hDyTncKNQvnMN3jspCyCCo1m2Td2d7+Pf7eu/fNz5ZTRzA1CZcjIR9yGwI65fwj5fyhfSBCUUI4qZ3eqKdI6lCdnZoc3H8frMlr6pupm+2i0AQRyCDrlfkAnf4R5SMbJjA/BW6APJggGvK8Mzst+x7ttUeR5TCAL5jBhgDTdzrXzXszc8h9UEKRmSQRX2jw6ybGL9hx1i3f+870Q7tQa6LvkMQA6s1Wf24eNo5IOzNTyF2nI+12uzc/B5GRkVar0Zh97JHZscaBvfvyuRl0u2jIq1/16rIsv3z9dWedteN3/6d/8/2bb/zj//z+t77lHc969mVfuuGrAeGqqy+/7LmXfuX6G7Zs3v6GN//KT+/f9a1vfctNH3HznfromDTSvNtHPlC2WZkXrkQMwKs4kBpSeIc8L/tZL0c/HyDLtfSsgDEQhqrrd+AjxAzyUC8hEBHYVrZA55wWBZTKsvShhAiYhAlkIAYSrAiLAxU2Sov+oEEj/em5waB8/PFJU5SdkfEzkomrr7j8zltvgSvJmEGvx1EC4iqpmFZYycWXJi2SwdKS6bLynhsL0+BJTMxnZC2yAM5d5eBTkJ3nuOde4QPWxI9CJxw2q574hOpf2XS0evtXNu1gSZceZ5M8gSy0ZLk97NTbs0L/POH2PCMnk6dXlVESP3QbGSiTAqRCrFjMEMFHVRkGILrGHCsLCZNUl5psFhXnygmqiqOOJ1SZPrAArVeAQN4DVTGtwj9ASipGyxLDPYcMaVMJrFYYKDItCm8tQFABM4LH/n2dH9+1+fmXchxl99w594Nb0Z1GFMH1YS04gQhMCe9QDkCMkIMUJB5QCIJHp4DvJBPri+l5RAEBuHeXPvjofL2JQU6Nps4ewVhznfCRhx/B9HwtbWVZQQg6mEcSwTlIIEMU+YiYQoG8iLMeDh/y7THqdNTn8AOk5F1pxNc4xaDnij6MARGEYtasciHZBEwIDllG3Z6oYuDgYMmCYwjgHMoBdbo+68J3oZZIYBRAGpuyP4/UXHT29jPPPPNnP9/18IPdXn+ulAAROMoLxWCAvARZSBkQkGfNtHH5Cy7b/fAD07seROmgfcDA6kte8uI7fvgDpMn2086opxbFAFl+5SuvefzwIXh/znnnjY6Ozk49jlb9Da957e233YH5GYrNOds2HPjZjy969s73vP0tt9157397//uvvOblv/N77/vSl748GOQvf9lLiehb3/1mbOy73v3On+965P/83/7LVS956Xve8bb77/vZD2767mBuDkScxuLFdedjU7MqvvCojB0EQIwqvA++7BbIXAlRWzlREYhiMNQ5FB5BQIrIREDEBGKQQhCch3NQhVT+IIEhYWMUEAGENDAC4H0o0nqSZ31IPPXAo8n65iVXXr77/l337L7pt9721gvPP+fO6SkNIUlT9UEVRGZRXQeWWleWvzors6hfswXmGXk65Nj1+MRL6anLCSJclq7HRDQM+VzlOsOsrkBloTluLV9u18HxSzmtWHK19uAUclAfV75q2wlKD5eHk/Wk0DD89fj+qXa5p96fT7PXVRetUP+y5Wm3ygzBhrKAlQEHUpCQYhEdZhRhqWFm7U9FVQlBoEQGkOFCAaiGYQIDxdFQmsrPqsda3yp7jC6k6ar0awAVgkmFhsABIpAgWDaeKr2HQWSIiciLYK7z8HXXY9eD4xPrZn50Fx7dZ2sjxuU02gST83EAIQJUUZZwJRCMEBnywUMBNSg9jBaHM4BrhlWksCkU0XzuvRB8MDU0xsr5nPqFZeMLz4rY5XmZQxsIHkYleFVhkxo2yIuIAKXYafDqBGADUTAhIATPZKyNggQNJcoy3dDmZhO9I6ByJuugyBE0Kr0VhokAI2ygAhGIgm1CEbhSJgOheqxa+BDXa2Tol9/0um3bxrfes/Xh3Q8TWzNEgUS+ApgjgBgQsmwbtbe89vWXX3XVF77//Z+mSZomxSDXOLrm2n+9f//+fXv2rNuw4RXXvOJLN3wRef+5V1zRbje//q2/HTv9jGuufdXnv3A9isEvveUtP7v3vkfufxBl8ZprrvmlV77gzNPG9hzu3HnvrvVjY+/9gz/61i03fejP/uzl1756/cT47TffPNuZe9VrX3Pw4OMf/fhHn3vJc37/D9/3ne985/rrfrJz585/++9+/6abbrrzRz+S/gDEEPXIoij2XobGDSZDVZYMYhVXYWcMRcSqgIiqsFAUm6ASNFQIGGMME0FBzAY0DJmuYrGqaH+CEoQEJNDAJAwCmQAFRL3jxMru3Vu2X37W2MYDd9332KOPfOQjH33H299+30/vDVkRXCAmV5ZsFiL4lr7c9RhIxAprwJLfn5GnQ07ct0efxbHaDE55aTxB/bric19y4tICdLLyOHYgPRlwzMlNOMusHadQ/jiy3WW6yymgVaqX/nLsy3Kj5onbQ0Q4znq0vD3PyJOTFU0gT2XEJqmwiNFqr87CLAZiOBgOBoFRZU4ygmEIxJMQVrBKNbBIj5IQ8JIcmKZKir0Aoj6KpjYLH1Y6Ci6oVJdjBFDWKnCYKY6tYYZWwQnsfQJCd4Bb7575xs2YnEVkucwI3hcDP8ht36d91AaoOVgCG4nFUIBSAkoghmChjKDoZynbJKl5NWoiTdIytlKPQxC01iEZ6cwMyKS2PuLiRBr1yNasTY1NYZLUJhFiQ4kHl2yQpj6KYSKKEsM1OGtRMz6KqAaymaIg8lwlOlFcdM7Oa6584dtee8lvvh0vvbxsRBBnrI2VDBhRjNgOIhq66ojBiXIqFA99IoERLDTOEJVpTct8XrUnmBnkcKGWNBMyoAgmDjaGqcBaHhTUuYTMtVe/uG35eTt3IuuVZamQ88/f0azXbr/tVvT7L3n5y+64447pvXtGN07sfPazvvPtv9NB/+qXvOSun9z9+P79Z190wcT42A++f8ug04P417/ipetTnt2/v83llc9/zoF9u//6k584/Zzz3vju3/zeLbd++9vfftEVL7jqihd88lOfnO133/lbv3Fo9vAH/uT/uGTnRb/6jrf++J4f/elf/vmOS5/19vf85vjZZ0B8PFa3DVv0ZtlWZEMMVmK1BFSZKGPAVsw7orxAqURqVCISWIatckUFkorZ2JAyaZX+yQ4D6xQQNoLAAaaAeoYO00TYeihFyhKDAdhcfv6zHr/noXtvvqOWJg//7O6p2emLn3MJLPkiAxEWglYYhoVZYZVsFbiix692SyfC8uACeorkyczrfzlyih11gn4+Affris/91Btz4vILMU3Hy4m4aNfWHj6OX+PE5Y9jt1t+uWVtWV0FfHr6c7X2PMPe+8TkaSaZWEJlseTxVGaPKn5swXeDRZiYArJIhrG0KhZmZdYFsjSAIIvjY4hQG36TJeAbxlGEACmREMxC1QpSWOWITQKOQbaKXx2eSAxRKKta0Yg0YlglK2ycKojVWs9GgpZOXFAJWvS67ALEIFNkAZwiqpVOfRHg1BITURmQKfmkIfW2s4lfSKEAGEQ1NFpotDGxOee4Z2LbblOSwBjUErRHecd5jUsuGTl3B+pNsVFGhpojI+u35rVGaI+HkVE0WqVJy8DKsSIuco+0qa0RNOs8MmJaDYh4JwhMSNTWJW2i1UajiZFxbNi486VXNLavnwlltK69dceO0S1bMdIO9ZQadbGMKmc0BEaRxKg3EMc+TmETcAJEVqPqicejI3OuRL320c9cd99De6+77nNwWjoPIQQZKpnWwsYVbzKKMDq2bnK+2yv9o4/tpXpDgtt42rarX/7Sr379ayj8L77k5bn3P73nHrtu/M1ve+sdd9zhnHv2818QQLfe9N2R9Rtf+a+uveGLN4CMer340ucMXOgN8sz5AOoPBm94wxsuvfTSb37phh/ecttb3vQr42MTn/jYXx08uP+33/NbB/bu+5vrrnv+Cy//tXe964Ybbvjs5z7z6+9+19UvffGnP/2pW3/841/99Xdc/qp/VZaDUjXeMEHMzNUu0VRDCmzZJJWiAvFBpMLKEDOYMx9yGHAMiqGMICIetEBPRAow2ILtcDwqQKLVBGGqNGSwRVHWx8cNs/R7FjJWS/btehj9bOOGTb/ynt/+7HXXve7Vr/bikEZegokq4DxXWclYKpgaFhX05a9dIlMFoy4m0R0GZS68WJ95vf6DylGs0tGo5qei1uWV8AmOnkx9OYW1/ISAgTXVv0r5tbT/hIN4zbrIU9P+tT7WNS7ZawRs/NMVi1XMy4tahK7wAIasADL00hx91Q3ddkuLa6yAZ0BFSaBDk/zwkZAKPDEBSjAKAyYoIq8AAiEQg0DCDY7FhShK+lIKANZanGSdeU4TIqNUvfRJCSE4iHDF4KCsBDJJZKzCivoQygogQypKDLYUN4xNVTUilTKXMhuGcLAxaRqiFFFEuWpWwnuBEytIGErcbqDWImLKg+/3pMyRpDBWQBCO1230E+Mi4mfmZHAQCiEtI3FuQBu3NrafmbXb0u/pvj04cjiUhWXj1ShF2HaG2XFee2LC9bq9Rx72e/fA2FYtdTB5bGXLpmLL5tCMMBIn/bPGHZV7j0wfPIT1m5pnbm+fvnWi3d59483dBx4Q7xFFoBjtCaxfR1e/cHRk3fzDgmaMfhAVVZZAaE/g3DMaWzfVbDzXO9I++wzUjOl76fY3tcZbnOKcc2yS2jM2hcLBGASpWQUJTIyJjXZ83aazz9qwfjPa6zEMSyOQ9op+bcO6rLcpao/NzWQYeBgTqthgBmJsGhupbdoKcDHbl04X5cx0p/y9//rfJrZsmZyZT+qj7fUb3/b2t3/tWzfmveyyF15x7tnnfvr/+xjS+mve8rZdj+3b9fPHnv/8F51z/gV//fGPY3Tdm9/+rq999e+6nZIabW2Zjk13d/KLdmzsducjJHP97AMf+vOLLvuF/+X9/+n6L3z5Ux/7xCte8bKLLzz7i5//zO0/uvtNb36rC/T5z37xgnPP+f33/eFtt9z6wT/5050vuOzf/tEfXvepz3/o//7Ar73zzVde9Z8/9akvzk7PuXAkMiwDJlFjyEmg5khSHxlPsK7eOpioMGtAbJKyELUpWutMUm8kcdadd9l8ynEcMWKoFKhFtfaIGR0lMEepMFVTSlWYI8QNEyGuNaI4Jms1oUEvi4xFmvq8TwiNZgLLab1x7lnnfeahj+y6995Xvu6X/vb6L7Ix6oKItZH1pWPDgRWqaWTyXt+mSYVKWxrLoRQqW06FSxjOXwMsg0EAWIVq5GhdK/5Ma3xl64mv8j+8rLrIHWtaWOH4Md9EwhK+5mPeq6u4J5bG+Cw56Wjsz1LHCqmCjoUnHgM3xLHjBARSWRwSy7luKy7gJTiSCmhiFtApC1BFDPthAcG45BJyXJz5wrVokX94aXsElYlzefur+1UAS7iGqcIPLPpel/TMkC94uetHAcjSblnCLFxVv6Q/Vx+1R4Ojljq2Fi56VHNdOMRD34IuYh6AhdllFsou3NfSezlaVjHc6hvgeP/X4lg4WtVw/67HFx5eZYnKeEw9x/CHL57IC0z6xx16UprV0jm1bPw/AazM8bmKTlz6aNeSDAHbujDCASUZMk8DS3Z/qoseSkDBSuxNlGc5xHCcAA6+FBHiyiFEAFcJI7XaX5JWYVIEEBkQgw3IQhjiAUOQCp3OUQyTCFtVYgMrwftCq/BsMqKUNpocp4gkYGCphJjCOjGIklrcHLXj68BxPj3viwIiZEUNIUrHWhtb28/ic87WKOrtenimRDSYNXBIDDcaE+ddOPasS3n7mYPu/OT3v5UFp7PzEoAkxmlnpM+9bPScc32gOkLcrM+I4NHd3UEXrRTjmzZcdAEmJtKxpBVHvb3799z09zi4D63RLTt3bvvFS2Sssfv+B7u9WdRrSaPWtMnsbIdqaXP9aGqyka1biu50L+KKYkcIaLYaZ56enLdD2y2GXbdpvIwS5XgkjRs605np1DaPNrafsW5irGhanZkDFCqqHkmEtFGb2FSfWFcbGRkdX0ebNqnLJYoABUmr1ciEs4mJ9sSGpNaCiQFNkkSZENt2vd5IE+WITBy1EqJYgKwzw+tGD+w/WN+0bf3E+Kte/rL7H9z18COPvPBlL59otT/9kY/GGyaueNnVnUF2249+/PwXXv7cS3Z+8ctfNnHyjne9+8bv3LR71yNxo9UYnSiCHJzt3PjDHzt+9hnbN0xOHbnxO991hb/1ppv3H5i65tpX7bzwsi/dcP2mjc33ve99t958x+f+8mMXPP9F733fH9zx97d87vovXHXllRdefNFnvvTZD/3ph3759W8bdDuf+uhHdz7veX/4H975la/85Gd333r4/l2ImvV6HUBarylHUVy3QGIsRRZRhbdlAsAWcYNrDRPHpixd0QsqXgJCiSgmayhJkkbLEKdJzdoYxkCDMhHHMDVSCcRCDOYhGmkBd0lDW2YoCx9FSRSnX/zCF37/f/1Pt91x++wje2Nbc8pKBuQUVMWUwXCt1SzcCqkLCEYX08WvhCFYapj5l0yOfuryFCE9nx5T2NFHfKxmgBWePq2RH+XY8rS0/pNUdWr1H/sLH69Yr4iAOeZHOu6WV2jVCW/qVMqfRI7p/zWp7Mc3/mSy7Pn+85W1p4dcsMes+ARO3c93wpLkmJ2pUisBYFjKQxdnbcK2CUEBAjgSH6yNCabitVQySgZstGKxBAtYiQUVvakltsQWYOJIyQiY2LCNObJCQ0wmR7biXlSCEFQQBo7KIOILKTLXKULPu77kg5AVeW8QnDAZJUA8NFdXwgO5zM729u5/fN/szHzw0y6oD2WJzHE2CMVcdmD3wft2/fznhw8fPDLdmZ5zMz2fq9g6Gi1s3pRsWdcJ+ZGHH8z6fbN5I7ZsQdRECUQG60ea68dNGs/Ozc3ng7LOqDMmajht3cj2DXunHt8zc7AXO5y1sb7znPXb1vcHs6R5yOd8yLKiS81EGxE0gCgxBBLUON44vr++KwAAIABJREFUEo80Z+fmJw8dKgI/tndm8lCfC7iZ/mP3PTD12N7+4UNZpzdWayTgKqzGkyJO0FpfH1nfne9PTk7Olxm1amgmmVEYgKNQeN/NUQozCxQSQBgu5GyTtAamvNfP85ysMbWagBFF0uuj0TDN2guuvvLRPY/96PY7duzYccEFF3z/+zcjyGWXXaaqP7z1B5s2bjz//PO//e1vDwb9N77+dT+776eP3n9fVKuV3W5/rtdKG+32+D0/vf8Df/LBf//H/+UvPviRW777g5BpxGlntve3X7/xyPTcm9/0tshEH/iv/3stSn79d97bHQw++alPXXzZc8+7+OKbvv+9n9z301/7tbdf9pxf+Mx/v+7AvoP/4T+/30t43x/8yYZtW659zS+d/7znQnUwGBSu9FCIy1xmALaWjQVRqLRzJmWjzkMU1kgcI469tWosohhKw+wXFe+qKnyAKMgS2LC1cRpFCZFRMNuIIktkpKJcYmYbgQ1AtWbqfUmGe3Nze/bsufbaaylKyrI0xoCkmhdkDFTzrCyH4XhHCUOXM72ewsytmFifkX9UOWV/k65sWV9a0xowJcvLr1bzquNqCRfw09ieZWfp6nSPRAQ+VbthNXfW6u97+uBiz2BrKnlSr6S1PZ5TsOUMM8UogxcfDwMAMzZPvPzdb3vxr7wOGycQxZaNy12lIFeOK1FohTipbDBDiAwrKCgJKAyNfNV6wGCzmOgAIUC9MpEhMqwEKIuSBnV5GbxXCLQU8kresjIolM71sl4/LwMCEawhZoSSQMQmiROIGKJ6PU3TGK0WGPVa0mw2TaNeNW3DuvHTt21bv25je92msY3bxzafxlu3n3b+BWecd16r1YprzW0bt5xx0cWbL75k9OwL2mfvWHfxzksuf1FtZGRy+kj39h/vv+9nINM471yz41wYeuBrXz103z1OwsaN68e3bR3Mze9/aJcrBiGU6HdmZ2cxNz/wzikDbMloACTAENWTQMDkFB6f1IJmpno/+uE9X/qbG27/xKc73715bmoSWckcMQx8BfWlQAAzojrZWjnI5+bm+q6geoIkGqhAAiS4ohz0+ugPQlCQgY3BkSpZEyNN0kadDZCXmjsxhMh472yUwtq03tz5i784XxR/95Uvq9Krr/2lG2+8cX56essF55977rk3f/d7o6Ojr3zlK2+77baHH374ec97HkRvv+m7iGI334mbTQlh9shstzcQtjDRoDOYmZ5PbL2Yy9K4OTvTmZrvfO+W2+68+ydveN0bbVK/9Xs3P7Z792te8xoy9sMf+UuTJC/719fc9ZOffvIT//2Vr3z5+eddeOff/+CGG77yjl9/y5nnX/DFr3x5UBYvfsnVtXYLsY3TpNvvYWG1EGiAeoUAgUjZAECeu7IsISFiRNZZVmNgUgTyuc/zsixL51xZllr6iiVSQMqGrGFjBRygRGQ4qkYmkYEZpsIGUBSFMaYcDNJms9PpXHTRRZdeeilUfShDFVGvwswmSYypdP1jcLgrI3OfQen+kxBaORH0irJ4aMVzlw+AE5veaVmBUzTVH3+hU6sfy76etD3Hn7VSX63YuNXKn6SRp1D/Wss/I2uSNTuYFo0xa3AzrcEhxZVPbokzt/oPGG1vPfccCYHXjclsT0QBY21ceo9FonewIAyNcEwYYmkrEmAVKESrfCEVJ0K1xxWEChyzmI5Vh7Z0HcJ6KFLjERE8cZXIm4xIFCgStTnYVT4swIrHYI45hSf4QudnQ7vuOrOYm7RUWp955wOAvIs0mt23O4eZP3TEzQzUCXo58tr09AxNTR/evQ933/uoUB3Z5IED2L0HM/MIA12/vlEfD3sPYqY3dt75W9dvLg9M9g8cwaOPYf0Y4vT0TVtkbv7xe36O+/eAgzDBRDBRPlCgoS4xoQZuh6I0xIgTRImxKVGE3KMrqbPJ6AR8vzc3QKePkXpiIwyKTr9oOxJhgGHEWUHEiKwaC6hXESaKDIoqmIcBIjJcaaWiWmWrUZWKdLmyBygQAkIIqkwAUcTGi27ZsmXz6du/8pUbqF6//PLLd+3aNTU1PbpxwxVXXPGNb3wjSZIXX3XVvT+559677/7FX3je2Wec+dnPXw8FnIch55yyjdI0bbR63QA1gNWg1iTUHulOzZnTtkatkezI7P5Dh8ZHGhFs1uuXWdlsNEpRVenmg3pWHx1bt/eRXQhwha+NjHS73TjFTHeea/FDj/z8FVde6cVDg43h1cOwkxAArwIJpYSg0GqTpwISkARGsFRpg94LCg+1VqskThaGiZktsbUCqJBAQWYxo0qly1SKiGqV8sxUAR2RYTbEsfUh7Lz4ottvv/0Vr7zmnrvvETUAKzOCBu+NMWSsgXoJS2fiElM5V/bW4/k5Vtt1rBX78kzo6dMka/RuEJ2QU25BKj8mLwIyjh8tx5ZE5c4/Wmbx7xV5bGmBrXgpc+5CqZXbs1jpqfLHrHK/FTjnxCw1a6C8W6E/19aefyz/zxKAzdNTPy25zD+IPBGrzPJd3YlLLz3x5JXrAsJIF5KVKKCC6dm//dKXb/r6N2RqGt4DSGpp6d3Sc4d7VmD4jKh63w7NM8NfmAFenGFD9cUQGIog4ofJmwgAgQ2iWI0VZrAFkYg6772XoIooQpz4KsU0WBXMnKapMhVlCYEL0nMSjIVh78tBWThXwDlAYajwwZMtTaRxBK78MuLKzJc5VJBYR0rBw5dgQmQRNC9DLgHj47jo4i1bt3dmO9OPPIaZeYysi89/1rbzLpyZ6/z854+FEnFz1Ng4jmtQC653uxlMfdApvTPUHleOxUQmjsGRuhIQNBto1EWk7PdMCA1roQTRsvSIk1prNJClpIY4rvibwQEUjAWMmjhiiM8KFEXEhDiGMU49RxbWAgiVqSYEVfXew/uiLEWAKEIcMwANJoqcK6B65ZVX3vuz+/LZmXPP27Fjx4677rprMDPz6le/eteuXYcPHNy4fsO60bFbvn8zVF/4whc+9thjh/YfIBvBBxijwcGQqSWeNBQlXABZw4kXLkNAqxWC7/R6jYnxQDAGZZanjUYURc5BVaNmI4rTZrPZ7/cbI2MASDQbDFqtFhsIlJkbrToROe8QwiD3cRxDxUsYpsNY8NoQmeFyH0cwRFVkdGVKEUIQChQRJxwnNoqiiCNL1oip7DvD6CEiqvI0MfNRMzINczZW4LAkSeKYyRoAzWbzzjvv7Of5i158NSDqS44sJTFCCGXhnXMuLGY0XDp/V92WPLNx/MeW1awsx2/x11jnan+f2GRyYmPMiSt/Ym07iQnnFNqzQjNOtmYdd/Sk691ab/MJdMszclJZs1Vmtcdw6u66VfMrVXo2AIgZbh1l6JX3QN9N3ngLiFA4RLGw9yLBlYjTireXGVVCThmCsBfw9UTEzMYQG1UNKgh+4WYEqoaVrBURRhDxijCkAzaE2NhGjdOYQJQ0yHkIefXKFiZGo4FGLa43mBB8aTSooxzqazW0NuG0s+wFl9j1GxrpaD+p4+F7fdGPSBFF2H52ct6zGtvPTRr1kTLvPnS/PPooEJASa5k00/ELzxls3riuUa+xn8q7kgqiOkZbXKvV1o2PrR9D3j+U9/uPH0KA2bJ164Yte/NuJ3cNMhNnnnHo0YPloI85J4WjwErxYH6AbVsGvVLJ1rds6s8ccb05qKDfK2Zna6dvpbM2oRu4ZcuZmQheQwkGao3GyCjWbxRmSmvJSBvNJjIPEoQc2azP2rX1Y6PjIyNJSv1cu4OaFxAgzqRxwmGQREkUN2p1NBrINbZRZbIREY5QG2kbmwig3htGWWTx+NjoaHvyhwe53kjTVFh7vV5j3brDk0ec98jzNK0F5zdt3px1ezB88ODBeqMxmJxEvR5z5Ig0omAREaftNlkJ/a4GVwSBAu1xtBtxuxlHSZDcCeqtevfQDABroUEStjGbeq2m4vv9rirSRopOx3ufAM16o9PpEJG1ttZouOBVlZnB3Gy0CbA2piiNooR5AT/LjNiyZWPICvs4rsdJzcbWWC01AidsYzIgYssaMSzgFthFEQCy1kRRxMyCAAjRYqSBEhGIut2OMQjOQeGcY2O+8vWv/fqvvvOuu37cPTwlbCmOK8Ibw+BqxA/VeoCORqMs7J+qvc1SaPBqmXQYAD85Iqh/xvKULVcn3NcutxOsCs0m0pUyKFVRF0tjYZbWuTxGaflFq6+Ltplll+UhYdVxbdOjKYSXNmsYBbJKe5bfliwrP7zTKunmKr2xPNfS0vILTTla8rgMoEQn4UReLitadJ6ArGZNWc0KcuKsUk/eNrNa9qilR/9hAgX+4bAyp1ZYlKpQaxgVIShJlTHAFkK5IAtQjq2B+CAuqteJlYmp0ligzGBm5qPmR4Iyc2TIGo6ssWyItArKqyj1mBHHJjbMUGiAyJBDj2EMsxGQZ6ghjtgyx+AIxnKaRGkcG6pFPNpotVstjmIn5BWIbVqvj46OtNIkIRpttcbHx5N16ymKvSgZu2HDxrO3nTaR1lOh7du3jk2MISbAIe+5Q4d0cmbE+20T62oU+Mg0TR+BZDYG8iKfPNLbf1A7nXLQLfu9zfXWpolNodPd+7P701ZzZGSkZpLN6zeeed55ja2bERk1ZGKqNZK20pntsY0wG+J048S4aaSwCjj0utnUlPQ6o7VorJlyKAaTB93sNKvAGqhGxq4fHx9Ja3HE9XodtRqMIQWKDL3ZsnukHnEzTSKFdnvoD6IqJyJrmsZpLTbMhjStJ7VaAmPEBw0CV5auCAFRbNggZFnZ74fgkEZl1jt48MBYrdlOEkGIGzUnoT8zA8MmjlCvz8zMEJErylqtFkKo1+uDubnGhg0oSiIyURQ10karnsSRZUKQsiwF4Ih5tIngkzRm0n6vs23r5jhFXmbRWDvLsiIrQpHXbdyIolAUxSADgveiJBgfiWObAEalVUvraa3VirPBwOe5MSaOU4prkbUEWGtrSRLbaJjingjEtSSuRTYhqjMbY1JrjCVm5ojjOE5ia42xhuLIRClzakFErAwlhSFYhjHEBtBAkAqPjsXUfayjY+2pqZnWyBhHsU3iLMv27N3/+KHJZ++8pD4xDkuVK5VIoSR6Sjv4Z3aN//RkjY91ueHhFE98AuWfyHBaY3tWLH8S+9DJ7D0nveha7U9P0l71jJxY7OIzO/H4W/wqyxyHQ81rmW53jIa7DGU93HoetcQsnKWqCEahqFQZqjxNce4qw31ptOyXIAHUuQKAsrKyCnmpIC8KrdKoVpkjDcS7Ig+VgUeENQCiEoKHFFBVuFK9Qh2HkjSoEhNUQygzzciVfVYm5+C8iAgppBTNpT+LUJbTU11lW+bB5SE4GI9eEI2yh3wxdQBJjfoDm3Xd1AG4AXxprc498kB3do7bm2As5d38yEHMTKMYwNqQPbx7NmufdVY6tm5+5lCxby9mJpHn3hEk9VNzM9Oz2DYxdtbW0chmD+4+dPcDONJBGuWHD+L8M+Jutuuu+9zex7O5DmJGHPuiDzfj9+zy3dmaadrS+anHo7IX2APKNvKTh47c81MeX19HNOgOcGhP7ZyzyRBEII6KbOqRhxE/toHPH4ljDAYoQ1rxEc8f6XX7vd6miHT+8BgOHkS7ZkOAK0Ao80G/N++njxw2dnpmKncl1NuIIwJEer3OfLfTz3oGcdnpYdBn8aCAJLGx6U1OdaanypFmL+/1Ox2IWMuVZ8oy54Os2+1aa/vZYHL6CKLIZSXIiIj3Bbli5tBBEyLjvbqMjYoEJcCVYC6OTMMwsTliQgAQQZwyAxJ6s3NI85DlfjDwRQ5XcsReHNSrhhxw/f70zJw7a3OWCRtjTAwBiWoQlxeDgO58p8wllG6I9mIDkaLfj7zPyzIgkMulLAVJScFEpu+zcQ793lxQH1yR9QdSlrARqYh3Ps9yaJnnvszzfs+IRfBJFHkfXFGApCxzYuOKslGrdbsdY6z33iaxevnmTTe+97d/47G9e6hb5nmhEBM0iECUQHqUBdWAQBpwHN+D8NHZP9wULN04H7XZyJJQphNbaFa15q6yVV3ru/6pqmetslr9SzONn0r5VYVX5qFZgrc49kDwWGmvvCLIo6rp6NclyJXKxVkdOuZOFmpWHIN9qS5xMtsMsABUWVgvllCeLMsofCySZhXbzDLmkrXK6naso38ObRtEWLDQDJ/v0n4efjn601JKmAXemhPlfzj+dwALGaBWaPZC5bS02iUExxXZpS6ZrUvvdNXlXlcgaziBHKv/gRbhSEsgUwt8tse257j7egJR+sfK056DaW2yMLkq28yQyouEBKRkhMEwKr4KWqo4a5RJRZVIPMholdkR4Cr3IxFBISrwIAvAEIZ5JkFQkeDhhpEjkCDB8zBqjyAKctKZheFAMQJYVEiq5AZwBQCoQPshIIiH5rAAhNRLfzbkOeanEEXqgxOPfEDioeoHGcIkZuZh9kINLFBkKEsYjti6rMDBg53MdSyj6KPfRShRAZa9ohewe3e9mQwefGh23x7sm8RsD2lzZMOW8TNP333LD3DwMEoHG7fG28lpp+X9md5jh/3cAFnf7TvQkwQuwA2YHFxBtVSKEn3CXE+mO71coAqXkXOIqvBg53tdPL4fGiZHjLTa6PcQ1HqFUwSBOhyeGrTag83zyPpoRKrVcyEr4vs5Zmfno2S229FsAOekwpx6L3ne6c6F2enAKQYeQUEKJpB47/1gIPPd+fnZ2fl5iEAFTGQYUBHx3jvnnHNZUYQQICIQqIYQoNDZeRgbNAmqFHIVD8OAosghAmdBpNb6BGUZnHgE8epFBHnW927QmW/Epsj60ODFOQkIriizLEPW7fjOfL/Xy/NcnAdQtQRZ1u1aX8CXpXfqvR82ShUaYiY3yFx/IOIRsqzXLUdSGA2MTMrOoDfozEFC1u1LWRBIgxPvgivhXSlSlrkvS/Wld4FoIZ+GMSQ0zHUAMaYakVS5UpHWRsbWscW1117z93f8dGrqyMzBfbG1/cGg0WoNivy4tWC56fvEvzxVpvJn5FTkf5SuXrbi0gnhxqcyqE5y6MRw5pWOnsK4PUkldDwm96nv/JNMn6fnov8S5ImqMqvEsq/w4wmRMatV7nnhXAKgwuJAngGmMGQJHrJJCglghvYhlaXKGlFlVoeIKAgkgAQoaaAqCzEAGIhATbXwDLMAEoMgGuCUEVg4UABYBGoBI1A1ITMagg9BFR4gQSyIEJs6i8IJce4iBjuCGg2tsREOSoAQ8jgOxgRVEhdZS0kNGhlDbCIXQgljE0jEiOtpq2EZCk9EMRlVlMHOPLALKFD24V37vO1b123qD8rdf/stJCksY2xs6/p15dzszMN71PfJshqFK+EH4IAQ4AphQZQqxWjV7Mi4Xb857/YQK2aOIDCpDYEgghBMMUB3BhpMfw6GEBxA7BQlIAyvcDn6RXC+wjI7WwUrwTrhvEBeBudLCUCABl1wZoM1qMI7gOEE4sV6WIIlYoUKvCu9KzVAAgBVJUNgCiGI91qWIHgJxAZsLEfBCLEhgpYlmIAAgg5xU0qiSjAuwKkywQq5GBAYVHoSM0P+f/beLFiz6zoP+9bae5/pH+58e26gMREjQREUKVKmKMmWJTEsjZRsSU4UyYqcpFKuSqpSlYorlbykUq5K8uB4SFIq23qIRUmmSImWGVukOIgEQRIEAZBAgw2gG0DP3Xf+hzPsvdfKw/nv7Ts2uikSFJ1eT/f+Z5+99zlnD2uv9a1viTY++kCiDGqtj8wAMxFBAB8RovdeFczGgA0xgxDF103wbZprlRhbqAkRqZIvq1g31tg0d9EkReJsQnACjTWFptXIVU0IJpKJEJpES0MjICITOAtZy9LqSZFTOx6VVdWEsgRAxsAwAGY2xqVFMXdoMcvwrnc9hLz/wgunv7Z02SiBqaUe2Jp67flJwCAQ4nZi3+1n7t0zd1J+7xnaACC5vVPdHfkOy5698CAUxZuiLnZvugfUvCMuaYvFcbP8fst8a49X7BpjuhentaOV/fuzrVfbn3T7093oRLsj7Ic/2t82s1+EkRy8o32boJBdL/Y2tZmDih743b/L+JW3Pnaplb9iVpktoTalzeYYZWpaRG+7XO747pswMqUJhzQx0Y7xq9jKjq2Y8PzKpDYiTFIgtVoP6+Sv1lGllpmpJafR2A5jiYgikQ1HZU8wgCpiayVqqsawEx+hiqaBigYNUQcR0QcLE6CaNUgsgiCKb9OwTaytBChgPLcM/6jbHNSxgmVYQAxKoCjgh5guTjz6cDUYnX7hOVCCKqDo3v3Qw6rx9eeewWCQ52m5PqAOJ2mq6nxUWAEbGAsRTjriXO/YXfmhI02RmYVDpxYOvfi1Z+TyBZAV8YCAQBKAAGNy52xryiKCMshABF5gnQExM5xBYsQyqOWhFSMMMsaYyC2pJbMxTBbMMK5FrU6SXwCTVYmZmY2xYFYy1loA4M3oSdUQgkaBSBsrZO3EQ8rMykRMSgmTFfDEltOqw6QJDIFVVaV1WTKzNTaJFkztLGAYYmuYLRGB2U6yVRtLbGliLGVqfzbM3EKAYS0zb2lpRMQMYwzBCNglqQgUJD7EWMemVh/gG7hMDNQAiYUnZmsEFhS2UoBRS0mtrSVZRaOPULVpZhJ3+dr1ctyknWmRtmMtRoyIiJO0NzWVd3DlSvn4O49lRffFZ75crg9clpZNfZMVZstNMHmOnSaBO8aY25LvJRhi0zex87f9+7Np9r+Ze+7Wg5P3K4+blN97483tInvrh+583lsYtzsq2fOubnGc7+4JgJtqMze3a+7zXHfkNuXbUmVu5T1/ex+jTT+5mYxmyyyDib5hoAxSCG0eGgDlNkpJJ5sWgYxCtgABChCzTvJcGLAAIGm3R55UrQSTIrYWhRpME7p3mBCCAUU2gOiWVqSqwgEsbGEsFNAGsYIAlCg7sINp00RHsAWpmETVEhuNHkRgCwiY4AMkwhhyVqV9cEIU2BSiEAUIahE2FTvjkKTFo/eliTn/0stYW0O3D+Kphx+emV649vqF8evnkRnOO+X6Sr8z4yVS7SJZItZIUHUokiwZBbELh8yR42tF1rBQmh17273Xr1y+urYa2tXKEAwaSJu6ObDxbSZIY6JjJBaW4T2IIyOQQgUisVVQSEXJGAeXAuyDIESAjHHMFiAECaIQAlvYSEKKAACCGDQQgymIxqBghahIkIn2KbbNMs0Mw2QNVJroVaChhSobIQMYqEJUmQ2IlAVKhGiMEiubRjQ2EA82TpWCKAiwVo31KtFHOGPAHAmiIhIUQbeyt1NUEoGAtM2KyoYYbA2M6jbPuhCqGJFYJRM1qLBRJFEQCVYohjgZthQEqobEisYYJbZwAuVJzJ0KEZNhQ0ys5fLKp//88+tX1xFLbfOjYsKgKqpRJYpYi1deffmu9O333j/Vn+quX19mZDHGybm0nRrbzmctWwFDdmDg2tJ3XEvfj3I7h/u9G/NNzu67VN6bx/K0/DTABG+1q6G9dh3o/qrGm9sStj3vrla2j9tNZOceXxKA/cpvqYD7jvx9FZqDO7jzvX1PvYdvjW3mrUxycnuqjNCtGY7+EkolwUAmL4KJoCKENjUBeMKyrpvquELBBLTpZngCkVNu9RfVOFGM2LKxQgyNEI9NRBuxZZOqzcEJlGAshYoiFF4JMJbYWtdhm4hBbCpEzyxqVNXYrDCuCEVPjdWm1GoEDUSqYgSMNMkWj6DfF2IHHV1fkmoMrcRYGGCqi0NHXNbr5Z3R1cv16hIGI4BgDDo5z8+l/WlmboZjf/06xqNUE0JoHKTTdQt3H37bA9eG11affx5NQHfWTs0s3nVyw9BrK9eRJjh+GNeXRMTOzG6Mxp18auQjpqY6RxeD5Xo4DCsbvgyI2p2ZN0XRJEyH+53czR/rT8/3rnbyaAxCBBSIplPg0GGkRWfxRM4WWYFQVqywBGfZZtJN06kpk+UwFmBVhTEgiLWu00F/uj811+n2kRaoGhHEFsiUJEXeQW/aIhGqqalFLBgaRIk5y1AULkmdc+25cQKyY7bEaZpmee6tyfM8K3KwIyVijYaQpGAL46AOvkKsACFiIgizJKkmTo2FQF3aoq8YpqVwQZa7PLNFbrOc80KkJgVUIbDs0gRppztqArX2JMAYw5aZGdYmSeIcjDGgGKEiaGOnYQzSBElqyRhfKbxTypUXiv5KNU7ASZKg16EgJs9BLCAyFmzJGiSpc+Ky3LgENlHPChWlxCbIOutr6yBOp+aZJ4RJrWavqk0dVtY3Ko/Lly++em39Rz/w/qNHj148+7ovG5dmIYS9J9c903d3tC1ueQm+XXjvHfnOym494Nt97Xu385vDUHZZX/aW36VqvOl42KNqHKip7NvErl5t3nJwE5uFDhrn3yk9fs+LfZP3fEduUb5jDqbbSANx05Kt0UTbhNhQJeZJpjtCZBADCjaqpBTbkyuMZTJgQzARupV8e2JBIbYuAdsWyItaABUVkLHE1qXkcnKpKknwMCqNShQFiC3ZPO0vkE2hXsbrGI9II6lGIiQ9MzVvFw5Fw35tJS5fRlVbjjFLxeaYPjTz4ON06DCleRL89XMvD994Getr1pBmHXf/PYuPPm77R4pOZ7R8/vKLz/sz52hUgkxyeLH3A49P33cvrBteuLT29DPx4sUeEFk0S3D46JEn3lenWbeercnhjUuZy7rsLl1exfH5hR96t8uzy6+e1dNnsLam0/mpxWOXXr1iO53iHffPPXgq2ji+tlSfeX1w5nWsjgAxKlPTne79RzInR46j0wOcBIqOFCSwnC0udO97MNi8f/JtHcKFueeweqUyERyQFXneT48v9o8eKvpTsBnUGGWwgWX0Os6Yzszs1Mzs3Nw8ZuexsiYRqgSX2E5venZuembeii3NKFRjP/KkEqMwGdfvoZntTc3M9GdapXWTI5iNMVmWFUXhrcmLIstzkzjTKFlSZk4zMzWjxiF3Aip/AAAgAElEQVSiGQ0wrig2HCHMkibazTHVhXWoBdYYRgInyoZMmhd2aroosqTbzfq9qZnZ9Y1lQzDClmySZIVDf3baG07zjK1r3VvGgIwxaVp0u2kOl6bcNDqJmSZrTOAE0324NEbvgpnr5Q/ee/+PPDHXo7/7v/+f/7RLaS/vdaZnEMX1u5QWsJYIbI2zSZIVKSEvijQv2KXMCZjCuNIQkWQAg40P4mxCRCBDxK0qA98sLy+PRjh79uzFDa9Cjzzy0DefecaX9eYqSdvhAlsRiLe+hlJr4MQ+iJn/32Sve4vk21MN9+67B52Sd0W17FZhb2cv33v7TVXeG3wzt9LP9p59+tOmWb21UbelK0ymwZ4Csp+VaF+5yXu7Xbnd93ybtQP4Ls7Kg97kWy9281GZtghwd/RrK7Dure5tq7FOMGSTxEytZ7SF0bSMwLrJ60vETGCFIkLa24XbS8xMba4lgdgE0cMQCQsYMMY4mKQtpmg0Nirc/svGmCSFzRBJ4RA1qrSQnBC9y1w6PRuN9WUNZTQhGKgfo5+g2ys73RCgCN1eLtPTeJ1RlhUUzmm33/SmhhKvra8Xc3NVXkACqhGihI1Uq4oBn/CyVrq2hCuXKstQQpYi6bx2/er8A/fNHT3ZLbJ1JGsvfKtaWkMn5ex4sJQdnuup96Pm8H2uqTfOPfs8KOdDJ+Yfujc/eWh1sEGq2bgZrKxhsFEOrrnY7xXTQCMxdhgWgnIYwlRODJPAZcX0bDY/N2pYrLFpik6OMmNrQAzDaafozk7n/W6WJIBBhApBADadTieCjHPGmMQ6k+bRlWADZ2CdMc4yGbYmSYyQSVxdDSDSApmQ2CTL89SlSQIwGKrEMK25wqVJkiSqykSGWVWDBgvbDpUkS9UlwQs1VolYEVvwExFbQ1mqSRYpKqkQNrG97BIkeUGpC8ZEY0yaOOeMAZGBsoN1AJJcU28TRwSR2ESEAIgYY9I0TYEkS6kSYp4AV5yDSU2nJ4ZRMnOA4ejyFDh27FTiuqlNLHGipEoObC0lLhv6cQv1JVJmA0Ot1zV4b0wGZ0QB48iwS3NisYnTlnWQSRSAgQ/lxtCPw6vfOot8+ouf+cxP/4P/Ls86ZSa+aW4E9+4HRBBlQBjQzXiPGznq91thtxYy3VwghHbzid2R76Xs/MpbG/CNzwqje5C22+7e+9F5G673NsKU9l7dv7DyVlT2zgKMbd7P3eVvCq+5SSX7VLirhm39uVmxreK3sFPe0nMdfPed48K+YsEKZVbDygxSEkGYZCACEyyUJ4mMWFrcJ7amxPYTgwKAbo9rp21Ft+q7Uf4G+8AO5cmiHSYyqdwAAIF4osVTe73No6QBopAYGW0oCxFiFEgQiGnJf6N47x1bwwYkUBJRBFUGG+vyQpO08hEhpEXuTOF9BRWVGBuNoLW1a9MLRybpc4xjVdEIS+Sqcv1S2Z8qpuaiBAAmyQgxGIZLcXgB/e7w9DnUJd71SPfI7Pi1KSyvAILp+e6p+9YF5VefxvyCPHo/jt6Nl17FYATyKMflc8/7NO09+oAawAJFChBqD8+4toxXXjT3Hc17s9fOrKy9eBrnL8MwnMi3Tq8WmL7r8Km7TqxfWHvtqa/oymWkBvcsPPzj7zp07PhffOmrzdXl3n33Fp1pZAn8oF5url9ofubnfuScb3xZNRUunruAathJyA8DNMHsyYQ6dV2XZ1/fmEmPnDg5d2h+eWM5aQyQYDRcCTIq7NTifCxrWIc0G41KqINaG3Wq03tlfS1n6+umk+YbqmRsYEGsjy4cMYLltVXAdOYXY8PIuxhtwEpdl3Xtm9E4lpUj4SyTJkoTiyRHiC7J2CZVEzaGg1DWlnlxcf7K62+k/U5z7Wra6Q6Wl4r5RcOkqA0DQGSwMVqOJHjjjOtbwwlYakIwZJXbXTvNs5Gvxz7AGptlaZqKIhpSJq5jo1gLXjsZESUMdoZb1E+QpqqGw6EHnEtiHMFYVjiX10FRpJZtXYY49iGxa4Q/eeqrL5xbvHD2ldJltdrUkKnrZnXQrYP1yJJk1FQMYgn1YGC7RQjaonlhEZsx5cnWWbaJVWIswHVQ001j1fwP/9M/lCAI8fXTZ/7o9/7YNi6UQ5faL3z683/9b/7kv/rIR5AygjIb8b5Nry0xkoKZY4xtlAhavA9hwpctsn0Gb5vdemPxVWwhatpE8TtQF9tBD/vIAfvoAcm3SfZfxw/OCXV76z7dJmvogVw6t+tkPwgPe7v71qaquqt53cZzo6Kb9bZKAMuN1jeZc1sDBu2oiCAqqjs4iLZ0CNn5f/vjDqzVXu7g1jazzcLRqtDblWGFYlNZoRvIm61GZSukY/OGSWyUbHVmu+XghkXmxvjdGt83XsLmtfaozJtPsbPpzf1Pt7O/TDLnQLeO2ZsvGjukfegJEY9iZ1wVAaLYvjNOJpEwJj6pPea6bd93u77Ywrpxg+Fm7wya+NS2devGS9atG/c4v9pzIMz2SrZJ3Kf/QJtZaG89N+b77npulU/cgoEWsgkQQQjCUBIo02YWpAnQltohxd9VLE8bI9T+/eZOKzLbHnWTH2/rdo1taiQS1fZk3h7uwbDMzGQTZg5k2kVcW0Y+w2BGywsiYhPHNjFEMAYQqEAjqC0QHdki6VBWjGwi3hhV1+3K3OL0ibtmjxxtLq6Mh5ju9/tFag4dW1ofMWn/rrunT90zVr7YO9/Ni0NHjtSd7PrLJyRG4xtked2dPjYzXywuuujXTt5t1E6rIkSyednNhzNTWQjD4fqgHKAuYQgWaEZQm2fuyOLcuRdfufzcMxiuo9fpnjy8+INPjENz/sKFZmkdV9fpqGazU6YotFPAl71e8cA9c9dOX3/9wtKF4shovQSTI5BNQAnUMFsWoB5X5TD4SnxAE00kqEFQsI8a67p2SYIYITHr9gBAtF90L2+sqIQKASKWGZ1eFRubWhiUTU0ESASpF20ZgtvJElRijIhRJTBIRABSJW5zYCmiSlSRxtdllVi7MVjnLB0M1nqHFwdrK5hZFFG1AHEUhhLYKBkHKFlWViVREmYwwKQCMBkCgBhjE7wXbe1xzGhHBQVBRBQ0pCEECdKCiFkBUYjEGDdHLStYBBIBJjKWrePUyrjyqgI6c+HCa6+9tv7Ga1kns4lBjBgPZhI+Mdef6yK1sAQiw6KAIEoMKkFpwo8Uta6RJAhRG2+LLsWQw0wZiqsrSPNmY4AocAm8//qz3/ReDaEZl8888/W//9/81//qD36fjZMYmFnM5oISgooiTSfzjW4soFsAyRuz7YCzo06OOHcOi997uaXDfeuUaTGI2qoHt8gbxLtVzzexguyRNym/ZxS15XVPu7v6s73aW+zSTYFEm7fc6M/Nw6/2Xro1Qwvf+lYNYBL1ckf2k4mDSa2oQEFKMgnrIVGBxmhlU71ibR0+evvpDvYui9t/P0g205gCE45F2u7L3KHEEoGNgifHiJbChXjiDuLWlENKJohCmChGJRaJMSp7xJaQRkBiGY0xIAEIZAKSSq2gTS3UINREbCLHAMBISVJTDAqNqiGQYG2IdOTXxutufby8ivFgsLSRdfLBtVV/ZQk2XD8yT0trw6BYXhuWVe/65bix0ly7ppeu+RjhMgzC5fNX544url9da66u49L1uq5BQRKLJMX1qfW5Q4cW5w+dPLnxyFo6GHcyXpdm1M/mjx+5evFyORphtouCF48t1DGc/fLT+f0PHr7vXnQ6iOvJWLSvURhDhdeNlfriuer0l78JQXUiRrKopa59IAfHcCQEEwS1F0NqOQVD2VGLwjawNoh40kojNMKRGAUFOKctttrJehizMzEGIESoiMDYxnuxAARsLbEiBIktLMYjtlY3EXF8Q+Wn1hQnQgoGTU/3m2pcFNkjjzz01Se/zKkrmwpZjogqtt1zIAt1EEMmiVKLMmhicnBK7IEmqDFEZBQ2wipRDIiBCW1GL2IVEpnwkwpLlBDbzrSnoi3v+yQzsDFtYLZqS1lHXiEMxBCTpCjycrhaD9eRIMZRN+eUK5L1e0+eev/bZ3LgrsPTqxsDEiJlQ5ZhEJUEjp34WtmhaUhVlRGFFVTX+bBOV+o+J3UI3hjbK5rBBrJkFMeUcYhBI50//8bSyuo73/HuZ556ilwyOUCrsuFoDBBhmET2XXlvYwX/S4BM78h3XW7567Q2mZvWdHvf/RYxNAfyx9wOGmarDSXCm+ZI2oUj2XwQvWk/t8u+GZpuUbbbiv4qyPf71OVNR4+AREk2Xy1PTskkIAEFUAAHsHzP3/1uwxoxmKllImEiImUC2zaQFQDIMFliQ4aNs2A7UXpUY4whBEhE6zIgRQybw9e0Yc9I8mAd24STxBprQKwG6lRT2K6zOcOYJEVewCUTNM+4atbWY1mj18GhReNMU5ehrkACFYzG9fKyaTxmprnXKUITr1/T1QGqAA9wgv6UZmkAgxyyDpBKIxJaFmNBnotxY6GR6jD45Uvn3zjz6vob58Py0vlXXl5bH8wtLL7tfe9dfNsD1y68tv7cc1gfHZ0/dOyuk73FeSzOZnkSy5p8BFlX9HVtfPqFl9dfPJO4Ip3umKk+OoVnG1tfQwxexaYOi/N5J1MSDR4qXgUSwIQ8c92imOqSYRQ5up0yeqjAcF3XIMJ0P53qGmerqoIKDEJTgTRQVANkzuZpzpxEUGxZfRVMaZpSkrDCWpu02RCBlj9IRIwxzjnvfZqmAH7qp37qB9/3XlEJVWlTi7zjXGqzHHkPSQGTgpyKATuyKbkEiYV1zrnUwLS2X1UJMIrUmtQlhlhVRQQKZQKRWjYGCRkHNsZYa9q4ISGwNeycc84Ctu1ukhC15DCkqtYyG0JijWUlUVUyXExPR3ae2ZPhouv6s8uK8wBcJq0jlck5Z11CRC3SGYBLLJzRGG1qKXMEmev3Hzx26NRs+uhdx5yvTYzNYAiXwlqkhaqIEpLE9Xof+9jHfunDvwDSliWPiCACwKUprN2K2tgXZLr9911ldt9yhw/jrRI6QG5+z40PtP3vzQr31n/z1nf9f1BVe8vv/fdN6z+oJzcrs9+4fZNqd76WN+3n3pK3WP6OfMfF2oaFRCYrs4FCyW7xuQCqJKR7vZy75eb5rm9dtmu4O2wwqkTUGoYiJr491ZaM1TBZIkNkdGIRVGxFYhuG4VYAJGkWmBFq1aiqDDFMGVsARlVikOChESA2RpKU+9OUFU4D1x3UhYh6IeEEJkun53szU2m3Y82UhvlKGxmuwQb4Mqyt0+Gqd/fhmNqY8fK1a9XqZcBDIjbWZGlp6t5+9+G7E2dkdTB87TIGDUwGEKamzP2nuqeOm6leETW/657B6jgGIJQwhG63+973pA/cOy5yX3eSw0ebKyu4eBXdFJ2Zo+941/2PPRiHevnrL1x7/TKurKPbP/LwYwloae06913ngaM2S0eXL+ryKhLrnQNMp5jCoWOd+TnqIT+6sFYf014Pa2PUHmU5qkexML17T8zO9mOoV8cb0BANwRl0O7bf7870iWhuahoz02i5VZxBXTahbtS7Tmd+etYQQmigYogcE7yvYh0I/Zk+amhZo2o4xIiWRY+yPMmStA3AdkniJ1gLAhC9N0SJtTFJ+v3+xYsXP/vZz/7Yj30ABl/9whfY2al+V9NMUxugcVw2XhChKjbJTJaYNIsmASX9lPMUqbUjxBhjrIEmFJkrEkesITShqUUACBmQIyY4oqjkjG3jrtsIbmNMkiRJllILH3YuSRLX5iMFNHj2VUpMvcIx1bUPdUDAWDSZOnS9ti5kdXH02UvD//H/+rOqrl/6xjlKXK0+QDlxlJjIGllNZm1lVZUSo1XDLoOBteaJdz32cx9853SCv//bv/4//x//9JWrq5rkofLgDCGAu4BXqE3smReeM1YefOxtZ751TiKcsx4kEo1hABJCu4LTwYfRXT6IzdXgL50Z+85y38oB7+FABNC3bQDb6Yu5uWtpy+j4prW2Gap5Exlzkxu38jRtL/PmDW1Hb+zs4Y6Y7R1wWmxH1E6sTXt4a+iAdresJkQ3uHP27eeWbYZaU9Cu52qhY99lo8et23jeesaXt0asiywM3zrKqQ1lYgUpA6SRdBKUQDIJI/oevYKtkdHCqXasqjwZOgJts3bRpoERQDt3RERjJCIRERFAIQJpfFPD1uAIJo1BmkqiByafmkilGdeIRoMNY0gQBGIbITAR1FTjZR820AwpVKwiEhNrmuDD1UuDPHdH50ncYGldL13A+hJEgYjV5cGrZxJH+cIcEVbPvlFfuISEu/lM6QP3ipnpTu40jAeF+s78tDu8uDHYiJWVLsnCXFakFEPYGGZBejOzawsL43GdTHWmTpw8NjVz4YXX1pfX/PJa0u8vvvu9HWtfP3/x8ulv4sFTCw8+kDhdvvra6OxruHoFTQMCHFlrUzbD60urqydLX06ygZOBKOq6XFkdXb2e9XIb4tpgrVlbRqwbCmACsYtSrQ+XrlzVI8ehQFlZNjAO4/FgMJCE/NpGzLriQ6wbrK1YQu4cQojRr22UWZKOhqNmvaYmIAqxKilEjRIkVJVvYrP50SMTQcR735L3d7vd0WgE4GvPfn1UlT/81/5aiPEbp1+OfuxDRGM5KmIkVlWaDGEI1Q2gypFsbghCElWENTS1H42MyVg0Nr4uy+i9Bii1sX2kgPgQm3pCcdMOJEzMNgAaIISg2qJ0wSBWRB+rtTVjkzxJWSiWNbwABoKm8tdXBt2ZBbjOeOxffv1q7T26s+qHXqKHRIZAa9/UvokaTWrrslIIKAISQp3n6b0P3GMT1IpT9/aPHTtyYb0sR3Xv0F2D1TUghSPUYI7VeJz2On/88X/9Cz/3s//r//aPmrIi55xzvg4iQsxb8wvbZ9nm5LkRxHjAtncrG95BJ9Q7LqnvgWzb8oU2QWq3plK86XffVWC7ovAdKb8vFAY3VU321c5v9iCTgJU9rWwf/HgT2T1Zvu8dOH/VpQVCglUitVFFbMQIWJSVBCbGCTxcKJpJNNNOuV27y7d313bMtqpuwp7bIRiVSGWTA19aULxMsm0IafQeqiECQFMjBsADAoHW4wYENmAmRAolaWSiKJAYqamwch5ADVEJXI81emVDUmss6426Lq9BBBRtFK29iV7rAGVcvlBvrNWXZpAZDAcYbiSIzmAUI+oRzr26vLpku93YeJBifWCcs6lLOVKzFs6/MhheKdloRCPOlysmhUsT5M6kzr/+BpZXvWENUcZN3ozSuaw72xtcvPDVF15E6ZHnxb0nFg/NjJ/95oXzbwAEx1iZal45O1hdk2tLWFtHqBMRgQbG6vLV+syZOrHXj02vfeMbqBVHU9OCwANwfa1+4ax00zWrVazBAb0ksKAqsc5xGJqmnOp1zp4+g5VVHFuo1wYoG8oKDrK2uopr193c4qU3XsdwAEUOuro+AHNRFBcvvDFYXfEbdRwpRSGN1Mb/1l7KuhqMNijUdd00jfgQY3TWAmiapizL8Xi8sdEsLS3Z1P7Mz3zo9//wD/Je/tc+8MMrg8GV62t1FQBrjEFZaoxwCViDRG2auB4bJbFpjX4d0FADNcSsMVbDEcGXg1FizWg0MhIVEBKQBIl1QD0cD8vReDxuGt3KeywidfBlXY0Fw/GorifpJFucucao40ooEJcSY4wRwSNEMoZFbePjYFAtLVMUA9KmRl3CSYjaeO9DUNX14WB9OBiWY9UoiCCBY4FAQ1mXL597eeW9d0vpS3GXVlfqGAEeb4wQCWRQNojREKKiyPJnn332V3/lV0+dOvWtl874EJxzICihNcyE0ELith/ZAQWR2RbecsMGs2m/4a3fDzLn3JG3Um7i1Djo6+yNn9/aqmnr3817W8reid6ju6/Kfif+XQOj/fegHNpt/ZDd5Q/iR1HizRZ1EvmB7QrNjfF5kLxJjqc9JScX9/AwvQkNwQH9v13ZngFqH5agW67nPzzbjG1Ma0bffWHiqKFJ8BeEbSSAAu+fIuwtkBvazNYYUlUIhAQBZFrSfyigES2VPoA2nCTGCSpIBBAyxjJLkAhFbBAZ0sYAekPERIKoIWpdgwmhiSSREaOPpMqs8JCAMmC8DgDGRmWNsCDV2EmzoKjLEVYCWFEOM2dNm+aQCS6BeCxf1411bRowI8Zo7RqvoQlgGq1egwXYomoqV8BH1BHOAAFXLDiFMa2ldCNE+BoW1y4a+AjXwfTi3ffcvTIYX3jlOQzGEEFijj/6cKfXf+Wll+XadRAQGwOiKFECbBZCg411ELipcO0aJOUZb4gRI0SobnRppRkYf2zGzRR0fBFE/V4HzKSiTY2BPzq/eObMyxgOjyy+fXB9BVXVP7G4MDP7zeefhY9H5hZe+dYZjMazD9zPotevX4e1073+tYuXyytXIYlBKsErqVGoIjY+jCsdjUfkq6b2TYUAFWk/vfe+ruvxeAwIO7u6dPXUvfd8+Jd/6WMf+1hWFL/4iz/7O//8X/iy0kDsWbxngsCAVFmjRi1HEgS2iP1MARiGKgwgsSnHUavxcCPNrK9qQ5urqqqX2DSoy1I2huVoWPsmxqhMAgSJ6ptRyWWNsq60RqvKkLTVKnwDgyZ6RMmyBGxrjblzUZV9iGXdjEtEccaqDwABLir7CPHSBB2N6+Go8uMGpJvJLoyCwC4Mhp/6/JN+dP2+u09+69XXn3/22d6Re1F7hCaxFhIiJCvyuhmlSb56bTmf7n/py8888sgjFy9eHm6MtoDJOonA2gVBa5e6lifjwLQ4d9SX7yM52Bez5+qbfdnbvfqdLX9wJbt/2WFHuYUgo9vq597CMsna8u0/xR35tsVK38ayQohZ1q3W1rKpWYB9VVubiipCA8OwBiEiSJqmihi3vJZ0gAuTNq9iN98D0SYV395PvBm3Akz4adp7J9Y/UVWNk0gWbEG6VNQ4E0WhgW0qMcJ7ckajuMSGEFQCGaOkkACbIAYYGFKCGCZAYwxgAwVaajyNGqMxNighCpOTyDAcmWNioALmllCYyCFGFSEYJQYkGOucrX2AqrVpqMZkpOOoGW8kSVpHBSxChGFEH4cbLskjJ6JAwkhT+DEaD6OoAY4gi1qhDGMQFewQqU1mCZsiCBoPyxPa1bw7f9/DvWTq4rdeb65cgdaYKYr7Fx5+9MEzp1+68Mw3mJNMjPcl5SaEKu90aolwiKwwhLqpBwNYg9LH4PtJAQF8U4eae7lsrM8I/ehP/OSLq5eNs++6+65nPj4V1lc84Z7HHz/36tmN0y/kjz6y0Ok//28+ibn5H/qRH/nsF57EoDz5+KNVVZ17+muYmnrn2x/78pNfKpeXjz384IkjR5/89GcwrrnIY+VdmkQNgLJoopQbhyiwaIJHjDBWWlWGSAC2VomMtWVZFkXxkY985G/9yi//3M/9zL/+g99PUvuf/73f/Cf/6B/7seRs1pfXZ44cWR1Xrlv4ukRdRpNQViiMqIIgSkiTqqqMMdZaYgJgrXWJNbKZxztNynJkHUgF1hpjiEjLcub4sVFZCYAYbeLIYG2wgWyuDt45NDG4rNBIyqzRQ5RAvg6kIDJlWSn4yae+LCJMJkQfrROAXSaMP/23nw5NA3GUZh/9o3/bNA1MBzEiaDv8YiSQRZpVvvncV771508+L4Li+H1l5aHeUB2aoTPGMryPAHkfbTEb1X70Y//vP/xf/vs/+vgfF928qipiItIgka3ZXHOpxQSoKoihbXy2buU7VdU2AGrnqZq3ZjD0u5AZe+f+cUCR2975Dmjrtqr57suBIMSDfr5l9NLkWAjsd9Zv36dsf6uTv2/8MnGaHGCb2dvfHSy6E0gAsNM2sw3XcqM/RLRJ0jL5DZgwr0xijiYX24BHASY8Om1a4M0th9q0d7pfZNP2Z7mFIO2bjbctXqXtb3LrbDDp+RYeedO7t8PnsHN8tqeOGz3drG3z6bC9/M7CN0LJt/qz6crQGzXs4MvZ70gD4AA+p5uEN96knr3g693evd0PMGnswH4CNtYjWANyRJR0ezE0QWKSOlE/yXfIBCJiBnwIDazBQfKXiGXY7n3c64nc4u2ZaELbcOnaLp2qUN0cNBNz0mZVwpCWfAASMMngJKTQdqq2pEYttcymJXPiwxKRRkEJKMYomw1FkIUYbQhCzI5MGlWgARq9F4iCrW3pp7x6CY1GVQlgwEIcLMMmcDFCrRGTZaYokiw3U1OkbE2ixnpVYRiyhjgFhBSOJEtLw5FsQo4lplGy1ESnNSDKly5fWVp6FU2Acd0TJ2eOTQdunv73/w5k4SHex8SKsI4rJLY2Fs04P3Hq+qWrGA97Dz+0vjqEGkwV/eneyhtXMSoX3v32pfVlkRqzxfv/xo+vr68axzML84OxvO/H3v/5f/mR4uRdvemps59/Bvfdc+r++57/1GeQF6cefPDC8rV6eTk/duTEiRNf/OhHsTB/312nlpeXB5cupfNzDz/0yJNPP43ao9NXUSh5CUSAYSPMUTUKAsiRMabl+JmoMm3ebOcEKl6IuW7CYDT83d/93V/91V/9rd/+e7/zO/93kOa/+K/+y9/9Z/9yfWntyIljly9fy+cWyuGIcqOckU00CpjUJCpgOAAiEiQGFQW3UKp2ZSGzybCiGiOCRKgwc1VVycz06vJynmcCBdRLNA6dbndY+aqpswJZkY/qmtIOlMhwu3oaASkMq5JpvB+XI8cmhBoaozSJQwgVwVI0DAgzJIYqIkZibucgb0ZXTRjPyFQ1d3rz4/F4PKgoSTrT09ZQNQ4Q384cFRUYkFVJFHj662d++qd/+pN//Cd5r1eVjYhARPkAegXazdC279n3TYEId86m3x9yU1PNPt9xT/mbV3UrKsLtdPbWbtnV7k6Kmv0r+Ut39c74f+vFAiAwIsrRqJsXja+YGpMiBs8wQpZgSQDWaGPQAMp0XyLO7VrnHrlxtNjStXd+4K2/thj4bthvNvUYMOmWhsQTrg8ym2mkWquIs44AACAASURBVMWTWbfCTdHmUqbN2lqgOXiix6iqEnGryYEIEhRBMKF/hRKYYRNjHWKp4pWMMUS+gUbPCZBYZ8AcAUgANQCh20cQCEdiiGFLYjzlmRcBpRwyiYoQkRh0+0KhqYc2VnE4rDZYhWNkaII0QZrAN6gqQjQaI4umQLePRlF0kVhUDdZLioGNRgDWod9Dwjg8c/L+B6ip33jpG7p6HcRwKXX6KuxzQvQYrDGxh0lP3nvP9JEXvvk85qZmH7j31S88hVHoP3jXmh9tLF1295zodrvnXnkRUy576N6NKTcaNelYjuSdc5eu8Il5HJ46+va3fevcOeTFqccevbC8hLX1zgMPHT91z1/86Z8C4Yd+9P2f+8xn0ZuCM/c/8tAnP/4xZOkT737P9aW10aWrMI7JqSqMwljV0CoKysTWwFnn1NoEYCgMO2sSsBVVNgYEMFdNDabWoPLxj3/8137t1/7GT/7kn/35p48cO/Hzv/Thj//hx5evLS0cOnL9+qrtdYIfwzKlTr3CJt5YFVi4RkVEgooaZmfRMgSDhWKrHkOVlZnANkEitW9UNU1TH4MSRqMRihxMoxJNDEg6IcaNIXyMLsuiMSITWLpEDSEYFWUwI02sRI8wRjMspntVNWBFCg2RrK8toKSkFH0QEWutSmx1LGtMkiTM7L0va6+wdaWGMi6yEMJoY4OsTVLTat6qFIkhDFBUjSJf/OIXf+s3/84nP/EJJRhnY4wgkOEWYTaZKZPp2GL8dRdokVrbzO3ELt0EwHFHvreyd6M9MFtTW3pvrq4DlvSDsCZ7/S9yQPntd+2HkgGIEHVXSdLd+Jh92t0xnmliydhC5Nx44P3fxl6Ffl85GIWzo4m/pNw66uXmebha+f6dqBaw6gGXQxGSTIHMcvBjopZr2bZwFGLAQUXAAj3gDLclt7Zy7TP0txvKbniuFADagykREe0ggSbSNnAazMwTdUdCG9BEZIgVbK110ZC0+I9JrarEZBKXFOQcyDR1qV4UAUQKhjGwBWxBRdfEPIY6jMsggcWTceDc9Ka7vSm2SdXUVblB9WrUgMoDhCzTbp+708Twg2sYroEZYImAyzBbmIU+eh0th3LpbBytwUcRFljkU8n0oWJ+gXr5cHXJv/Y6qrFKULSsM4oji8nRI1neqQeD+vxlvb4UJSJ16PcwO3v/D76n2+s//9RX45mXQBHO9Y+c2Li+wTOLxfxClcJII5cuNNeuwZiHHn/82a98HQhHP/ij6zFids4tHu73py88+zWQPPzEO5578UWMSzx06tTjj51+/fW3nTp19/zMYOC9j5//8lOz73liGKUajx/8wI8kU/1zT34RJ48/8gOPP/X0V1GOHvjgT7746stFtzNS/eCHfvpLX/giyvFD7/rBotN78otfRjEF0SLNYxQvNjQlPImqsBE2rtNxvX6eSeoSApE11lpmJuagsGmWdDrWWnKJgQbBT3zgx/7ic5/92Ef/6Nd/4z9tgv/Ex//kN//j/+yXf+Vv/+4//xelr3r9rjC5rKhi083SccIBKTMbA4lxEmZpXZIXSSfNi26SpGQ4Bh9aaxAosa5IUXQ7CG5mbkFExuNxlmUbG4P+9NT11atZnucFpmamV0JSNnVawFo7DkHJIitgHZEiRIzJhwYMtiwSALjEHTv+wId+9kNs4KRNGcBk2GxmAFPVGKOIkGhVVWVZeu9bZoGmaaqq+dKTTy8trVTDsvXSJ51pY7UcDiixABTUBom2YXwQee21s8Ph8KHHHjv9zW8W3amqqpTZGBMl7IOl2Fzed4V4EBHwJufOO4fR74ncXGvc94vsigBqy22/uhdQclD5vaFAtHPY3Kwq7GMF/PZlB4D9pkFPWx3b/iz7BTHdZvu3MP7pr55D8/tWLKgHDegtwCbVxgZqD0N5mo6bWkXAFpRoUKUAx8DBqcdu2F1uzKVbCVrbdcuNPE17xwG3fi5s5fVQAMxKxhjDbDDJHKneQyVEVQbBMBm2aWLJNE0T6yiqDFJiYWab2rSwWUdBXqExCAxUQMwu424/mTuW5H1H2tTl+sVLiI2YBKLoz/LiCTcz55I0EaTDlfLqa3H1WndmOuZpcfx4955TnVP3A3r1G8+svvKSGY3TQNyk3cVDnYfumnrovrg4Xy5fe+kP/0BXRkGEkgJJysdOzD/02Nz9948SCa+95qsxLS+bpmFWbw3mj8x/4P350WO5y7Sur7x2bvDiaQw2kCTTTzxy/xNPCNzpp74eV0dARgudBx996Or5qybrzT7w9s7x42MO7EfDqbRxmDl64uzVi6g2pp94+9TxxeHq2sI7HrtnZuHZz30a64OZtz/+6uVLGA1x6Og7H393WZYb64P0gYIYly5duXjxQuLN4ZOHX3nplUfe8+5Tp0598etfw8LiPffe/9K5s/6NC/2HH547tHju4nnH9Asf/sVry9fWNzb6d508cdepL3zhSQRxvcxaOzU1XTY+Ubu+FBFribVXDZZcr5PP9LsmJDbtFT0fta49mcokaZK5zpSZmZ+LMc4uzF+5dCGur1+9fOXv/sZvfeyjf/j7/8/v/eZv//pcf+bf/MknPvzhX/o7v/Hr/+wf/5OZztR4PCDV+X7n6MzMoIkXR8FIyAuAFQLLzM4mnbyY6nR63TRNjTFtyjEikxiXJUkGJJ08Rpvnea/XS5KkLMtOv7e8uprOzKRFngA+hCTv50keInwMZFOyNilSk2ZkXKibICOpCIYksTAGEkya0fTc+bFeu3ZFyjqWlbNght3MAZ6madM0g8FgbW2NmSWiVWW63W6n08mc/W//wW/8+Z8999nPfm59ec3YpKkahMBFR0Q20+JEEIFbD32M3n/m85/7iZ/466e/8VwUr4jGWZGwPT/2jpV9zzzcOvsSgSDbXfs37t2a/ne0me+OHIgZuuneuFt12Hnp1lWQveV1B77lwEZbpOO+aJXthfd28sCxtC9YE2gpXrfGJ1obv96C9WJL76EbCs0+9owD2t07/g/Ehn5H5XZtM//hqVAWMXGPvXP+h987d+L40tlXr3z5i/70M1IFWAMhuNzaIopXqUFvBujbqcTcUvtbxXaaZLB37DK1aIEbqCsi8IRBn6wjMiICMrBEKtoIEAVMxMqGjWPjQtRIHkSqQZUUFsbBpkhSZoumAiVQD2IwmTTLOj2w2xiXZI3Nezh0YmZhisdLy2fPYm6Wp/sjptg0vf5sUWT1eB0rS8PlJRguocsu680czYp8eWOMaysyHvjI8G4QA3LpTmXFzDQnne6RU8OrAx6NFEBUqaqlUeUAmpnx169ARXxo6hpJijQ3/UU7c2hDXVnFQ/OHjkz1G6bkwjVjqCFz5vz5ucVj/YX5uHho4dhR6+Lpr30NQlOPvD09vLDcNAM/7uZsjx4qEs6Mu/yVp3Fs4djdJ8689FI+M3v/I29/7fkX6otvdO9728LswpnPfAYnjj36Qz9kTXrm+W/Mz89fvbby1adePffqWWxs/MCP/vjXn3l6ZmHxkUce+eQnP8md/LF3PjFa39h45WUsLjz40MMvfvPFLMseuOe+4Xh8+psvdIrix973vi998anxhcuYniO2U7Nza+NRkuZVFTvT0+PrpYpRDpXGmFlKXcLswHVd16PyW6dfslkS1teD9kYlKh+csdYlUQlJ8sILL9x19PiHPvgffepTn/qzT/z7D33ob0qJT//Zpz748z/xK//Jr/zpxz7BFJzKex564IkH37G0Xj756huvX73gBZEjgZmsGhYmctalaZIkRETOsmFmttamLrGAMPkgQWK3a9M0rSUMBoNjx45dWKmUsFRianZmqTFs7cYI84sLl1bW0zSJSkzGOMsiYANMMFgwAFHqbPTNV77ypaWLF60xYThoKbaJ2VqbZ51ON/dNXFldkjqgze4eI4xLs8QYp7E+/dLzv/a3/vY73/347//eH734/AtZt9CIECMgEAIJyEAZBGImMbD2mWee+fmf+dmT99zzxhsXoGpTW9c1swWATV3kxuzbGQaCm+4xRLspyG6yd96Rv2qyN0IHN/2OB6mtt1J4l6p08/I3Kbb98r5GlH1UtJ0V7nPXrqpubqG5qcnnzvh/y8Ri/tgDH/rZ6p2PhsPz/QfuHSY0PPtibASssAbFVJrNBC2bck1DAMUDvUu36Q7Xg0ceNjND7vBfEhFN6Mh2uqXaHAtGwRIjMVljiSM4IErrqYwCkBFmIQITg1XayF8O0qbWpsQ5ZQcymPATENu0SLOyHsmwRKeLuVkcOvTw+95lB5c/9+/GMNYUNF5bwXpJaZpNT6HIkRgiq03A8gb08nh2tXMkt8EFk0HWwAIRHazitWZQ5OPFE5lLRoGQ9aj0Uo2REP4/9t40yrKjOhf89o6IM9wh7825smaVRiQkJMSMsUAMQmYwgwEbMdjgEePu95bttdxvtfu1f3i997qX5wGM/MzwzCSQAYFAIAZJRmieVapSSaWSaq6cM+9whhh2/7g3szKrMqskwAbc2uuumzfPiRMRJ05EnB1ffHvvmdlyz2PHatWtIxfGRDkEisGEyKDWGBzbbAp9dP9hTM/YM89IBpNi/7Hivj3IcwynfMlzh6LGyOhwfO62Q/fd5554HGUHQwNqsMrMrUPHMD9ndmzeeOYWOzaw59s3wXW3nH3JvscesVPHLnnz22yrffjgfow2x3ds3XfvLlQHeMum5tatO++81xdhfNPW2Vbn6PwCjk5Wzz13amoa1l9w3vm7H9ndOnRk8OwdZ+7Ycd01X0KUnn/xJceOHVs4fOysC58zOjH+7W/eQIudK1716unJqcm9e1GtwQWTViSKOscOdSQYSuJKWmrtgwo6ZOLaTtq2aBomkSSKy9ItLi5SpqC10tr5XixH6uYZM1eazQvOPeemm25665ve+K63v/1frvn8zd+46bJXvvKm2+/6wW23v/ilL56fe8mt3/n25S98/ptfedmFG2qLXYyNbvjsjZPdzDsqFVeJyAXftYUpi14E3hCCUr1A8FDEmhUD1ruuLeI4LgqUzlar1RtvvPGKN7/5yzfeuri4cM/9j7/y8lfdfN+jB546uP/g4Zf+3Mu//u2bcufEFV7HiSERIHg4C3JgMzY8NNqsm+ArqaKEt6YjqrSdFqJKvbSemaMoiqI4SWIRLDbTVqsdQrDWikiSVOLYdLv59HzryNSR//E3f/nC5136hre86TWvec2Xv3Dt/vsfGNy2rd0upO/dhqgXQtVzELDmvNO+/6EHL7v8VZ/95886a5cHHYCefcdqmxQCUd+vAVYefwbyLDzzUyVPBzlbM7pQbwZeD1M5gdh72od+suZ0KrOgXpo1TvTsO54Zc+uERbL0LPVO10V/aF8sJ7BznpV/C9EYHD/z4kvvbsZP5e3No83x889rV2qgAF9AsUnitFqxuXO5eO8AD9anzfT4zCj09HlE6wI5gmV7pKVkSx/hni2/iIg4+BCUEAL1Qgj1IkmJgpADGNoLeoxO1evNIggSXCicM+DjIe2DgCFEWmsqLbxAk4dH1gFEFDA7g5GKMQZEcGXBIonh2EAr5Z0nEq3AFClO4yhNaq24jriTsngWpJWyWVMDjVptgKE7ixlMwlHiS8vVgQBGu1N0OzbrhuDQ0648UAYonVar9UoN+UFMzbotEyZpIEoAhaBQYCBtbNgwMT099dSD9+PJvSBClKDREKUCA8FhdjZsGDJqfHJuDocOjl90CVt0H96F7VuTJDk0NwvG6EUXHXhyv52cwujwy37+FQ89uqtd2vrYxFw3m56ZKgOpHWduHZvYdd11w694RXNo5Pu33IaksmPHWUcPHQ3dbnXTpsFG85Hb76xPjJ115tmPPLrbHTm247wLtmzc9O1vfxtRDOeQxHFSXVjsIKqg1alvnSjmF5h1UDEYpfN5Xvput6yaQIGZ4zjObS7OQ7OXkOd56XxarbkAAM65yy+//MmtW774pX/58G//znvefdWf/T//78z8/Ht+4y3/fM23PvWJT/7yO955x83ffcHzL90yWgvzGG/gxRfVbn14xLCqJhVnmcHeicvzThGVvseQCYTgHazAE/cQC+ccyrKbd3SMpFJrdbq7dz+x5czH3vn2yz5/zY3f+MY33ve+91184XOzhdaXv/SV91911Utf+tJb7rjLZWVwNpAXzdILus7KML380kuvuPxsJjAjAQqgaKNeRe7RKaAUTAwmGICAAnAlrEfegfWoVFCrwQa0u7jtrp1333vPXXfedvcdd/78S172v/+nX3vq8amrr74aiiSICFh6Vky9Dk8AR2nlpu/d8kd/9Htf/fJXFudb4kmr2IpD36M3MVRPo6OeH0n0l55y8rtEGBRWvhtkOdjvsiO1pzsBPCtryLJK0fPAdurvU8j6igIThfU0m144AgqyFDQdECYKa/lr4ZVRAp5mHZ6Jjss/tCfGNcqFEvhe7122aVqDKwYsh4s/CX05TVDrp1nP9R7c8Qb/UeWHCL79I0cj+UmIRmHbk8fqI1vm8m7NVI8cPYQQkFuwg4JdnJputRILw4WQDb3FmayIubWEJ2PpDIDV09cJKA4dP7rCprr3IwBE5EV4ORgkej5merv9FPpWSBo9U2cPIs1KkXcQYW0UEVzB4ghOiIEYAJg8CMLKVL3tSigFQAikWHou8ky98K7vvy4vAQKr0lkyEaDhCmgVkytm5m/9h48in0UIqIZaXF8ILTSblbGheLBWn6kX6UBUtDhVLVaoEjeFBzhqNhAPRJG12RylsMajLEjAgSuVdK7awMHDJq1460IOJAbOgSUQrDCSGooZo6vBi3ce4grlYByqzDHSkdrQc3b4lkdWlhua6aaNx1qL01OTPWtwlF0QEGJdMkUMFDCyZeP4UFy///GDenzzxurQfbfcBQyMXXzpzrlpiKpNjFeFp/YfRTU547nnPfboLjGRbg6MbNq27547o7HR5nBlOI533fx9mth81lk7dj75BCI1cd6lrNI9O3fWNkyct2PHvbfejqy79dyzqBLvf+wJ1OrPf8lL7tu50xUlnId4cOpciEwtLwpUdN7pGlaZQ/AAqf379meiAF8ET6kSzT4XBfHwSmlfFqQNqdgFFmKlyCj5p3+6+jd+/devUObz//Kl977jlz744d+5+jOfuumOXVe+9nUf/8dPXfv5LztLKqqKglDuQiIKWmvjwVbDM8ASAkcRKfYSvHNpnJR5FwyrlNNKsTaAFiLWWmvrUPogHAPR3Xfu5Lj2/qte++d/uufaa77wmx/+9XL+/O/tP3Dtl657+7vePjk38/DDuzU5neosLz0zkgTZYkD0zetvvv0HDxyZmR7dvoljdtli9/DBRMVdiRzYe0eKK5VKtVoVkSzL2gsteIdAiCKdpDoyxFpR+VvvfsvLLr7gS1/91j133nfLTbfcc9udV7zmNX/8f/+XL33jm488urszOxes0wxtVJ6VZIw2SZF3pmcW9u2bfvVlr7r2c9dWmmPz7Q5iwBBIwxEVWgsERSAnamkk9r7CEk4DiEig/lBeWnQGZgIU+lvAWL6w96ev3PSyEjnFpLmKM7FykXM6jsXJZ9Y+vE4+a/pE+SFkXS7LD5V/T4tc+R2CoEfmXvkNnOxBl1c+uOXq9d20SJDjD3T53FKRy3UW0JIDGgGEV2MMgj4lpf/OX3Ftr4f0nbOv9LOyAo1ZURaBcDyMxordmR4OtHRrK7EcPh4fsK9rUQBAQeRE/SWgb5PYW7KyCEGIhOi4Te6KK0T6b7Wllu0hN8cHQ68+/Qz7ElZ26ZV8iRX3hSWHHxAgcMCKJ3N8F6Jv3PI0+/86/bznrkYt5X38KRz3ObKy6P5bnFemX8r9+BNZWasVV6+u2Yrdm5Pzefqy7jxwkjAOPHXf9V+T3bvPD1I8/PDMnXei1UFpERhB4ErYDDaT4ALoOCf3GclpLZ7WkaUe1wdjiIigQL2ow0RQxLoXooAhSpFhpUBBnHgv3iMIgoAUlFKKtI6U0YgMtOrtmRIRG0ako8jElTipVuM0hjFQPfax79oiqlXj8fF6YyD2Dt0u8hKFR+EiYSl8lFZRqXW73aPHDh85eNDOzHQ6ZauVwXkkUUfy6WK+bQhKlx3rut5mOcqiBwB5Wzhnt1x4HkaHumVeG6jDRABwzvbmpjFTT2vjw3rTJgw2HSA+oCzmZ2eCK/XIMM7YlFSjxaOHZ5/Yu3D08MLiXDY3O9ludaw1cVIdGkJjEDpBUJjvuG4egjOjzeGLzmkODRw5cgRFuWP7Gbsf3glLIxdejEpl3pWDY8PnnHPO5LFjyPOhM7Y7CcfuulOJnLF9+9EjR1BrDFRrabW6d+9jmJ0bGx5qLSzOzs5ieHhwfPzo4cNK1NDg4GN7dmcLixAMj47s3LOblDr7ORe02+0nD+zv+04zCkAI0GyiNIkqKavgfMmRASlYP3dsJp+di50bq6TF4qyikERRkiQQeG/BDCDWxhhlFCulrrrqqtHR0Y9//OMTmza85OUvufGm73rG+z74/ttuu3XfY/t++wPv6y5mLvAje/ZOLTjdSBa923O4WMg6tRjKsWbTC2idpmkaJ1Gk4ziqVCrGGM0gIg+x4j2QxHE1reg4Eu45q1MSdNaxhw4cevihw++56n2xST77v7788hed+cIXvvjo0SNf+9rXfuktr7rg7DMTRcqwiiMQIa6gOewLZ9La1HzbjI5P5bk3+syzzvq1q6668LxzlYCIlNJKKWbu9XpFzEpBGzDD2hCCMUZYsiz76N985PqvfOPKK177gQ++b8OG8Xandd311/31Rz568aXPf9dV795+7jkwWkXGQeJmlWMunTNRXLTa119//fOe/7xqs9rt5nEcgwKChXUIALMoJQQnDliiD/dXESfPACtZbhz6LhBWuFeg41P9CWS43iU/3OTw/yvp+zda8Y01v5/p9Pw0FaveQ6cldHyN57j6IR4/yycdWUPJO/WRJRj+VOlPL7R0Cz2oHuixL5fUkad3X8cTrNT8fgTl95SX/ji86j/TLH6GB6OOEeauv35u570Ya+LQYczNpiohI6U4H8AucPAcYJmhU3BAWFuvOlW7PxPAakln9/2Jk0ggtCQ9vab/fy8Ma3BBaSaQYhEKIj5AgkcICA4qEAcmUgQmp1g4MgFlEAmeoEQZRUYxgxgqUmI1GRZLIA8JC512ZbiZVKqp0Z2FebQy2EBWxIVyfr41PZkONpM4cd5nc/NYXESzCdYoHeKkMrKRG01U08qWCdvqhlYXCwTqopLqLWeMbNmCegIlerw2+pztrXyhbHVQTdGsRdtGks2DRUpmuFbbPj4/eVi681IGkG/NTI/nbtPwSDnWYJtnj+/HzidwdB71Ol2wrTZSr9frpE2+vUDHdttebAvBL85NhvmRiYkNW0aGi6z75NED6fhwO+tmU4crm88b3zpxxBX1emVooO7mF23wlfPOrjcGDh05hKIYbwzUg1StS6K4mJuNRmr+yAGEImnUDux7MmNVGx1ppOneA4cjkg0bR/c/tR8Li0hiBDnwxJPVKNk4vuHYkaMzk1Oa2JcOugIvvsjFmNiwKO9skZdt2AxKoCMpbFXo4rO3P++Msc2ReeEF593+wCOdThbFiQvexHGzpmpVk+ed2Kgu1IH9R6688o3/evNNX73uK+9973uzTvtfb7v9da+7/A2Xv/YH37sNjn/xbW/9xCc+fuN3b6Gi27zi5Q/tfezm/VOzttAaCWtLASxKUayV0ayIWSsTR1GmIoYGwYdOsDmgta4ooiDe9yJHGqW0tfbgwYPtztw733zlK15x2ffvuONP/uTv/48//NDk0WO7H33k2i/c8Lu/8fr/+dnbds5MxbUBPT7WPTwFRUiSBZuPb504ls03R2sVU77j9S98YYLXnrfxhnseL01MRMISx3EcxyRS5kWZF2VZdrvdrMhZGZPE1rusWzx87/37Dh790//+35570XN/68MfePKJfV/96teOTh/95Cc/vnnLtje+4U2HLjz01S991fnc9SGx4FnBmH379nVs53kvuvi2m+81XIHWEJCFSPAqeA2EANcb19RH15fMnLC0aF65hfTDIf8AnvmE+6w8A1kzLtLpZR0u48mytFo/xdNfWlgv1eQEMuzqP2vW5elW5nitelD+Kh+1CiJLO6VPP4cTm+Jnnfj1Hy/6Uk+UicccStguZo5ids6EEBMVRSlKCZhEWAIJHJMo03eMuxrqoeMK7+ojS/+sLG/51Frrsx4rhuU43YyWMqfjnx4yt2wfJwQm4QAgCAcJ4kuIhwggIMXEzJ77bsp8cCVCIB8kBNJaR0kQRUS+tLBWbO6KLpztUW0kSElgeMqL7vR06LYQHDsr8AieRAgItixm58PUEZTdDS+4aOuLXxyGhuAxNNCIqom4siKolT7p2npkEJuk0RjZtKE5NkSapewe2P1IuTBvWq1scQGVNN0wljbqg80mrFdZiXbWmZrG3Cy8QwgofMcGX5Z5ttA5fKjz6GNy6LByjiMd6kYQ7HyrmJzJZ+e7U7Nhdh7BA0FsUfhQazak0967e1d3cmqgVpu8/36IbDn/wunW4uzC5OjIsMwtPnb/Q2WrdeYZOw4/sbd86ilsmhhqDB14ZK8pnW+1Fw4eTAar3SefgISRrduO7n0quEAmGqzVD+7eU84vTGyZOLZvH7o5VavNDWNHn3pSm2i43pybmp49Ng1rgxdAwUO8EnEWmXd5UWbodiEBigHRkME4+eUrX/+iMzaPDQ5UhseOzs4dPnJU65RIseZLLr30rjtvF+/GRkdjHX39uuvGRide9MIX7Nv7+MMPPfCa173eOvutG75+2Ste4ay/5ovXXnjJJTt37m63O4ud9ot/7kXfvefumx95LK40Lj3nvJu+d2tBGNqwYfOWbfc/8AAImzdPGKV37Xo4lOXLXvaSBx9+bG5+plY1L3nJRd+8/RHnwlA93TC+4V9vvZMpMjoGw4nL8u6jO3ddygq74wAAIABJREFUccXLj00tzs0v3HPHw7/x628/fHR21yM7pw51X/8Ll+09Ojk9PevaeWS0X1zgOEm0mT9yJB1uEolk2WCU7tg09MgjTx2aaxVBvPelK621pc3zrJt1O3OzM+3FhazbEQnEQuLjSA8MNN78ttc8+tjByenDhw/su/32H2zfvv2Nb3hjJU337Nk1d/TIgQNHJidn2p0seAv2nBhxVumIhWJD8zNHXv3Kn7vz9nuEQEaRcOw1BQQG2IF8bxt4hZntCnRliRq80ihktYHIag3lOAlhxVpoiX+x1tS0tvxbaz0/rgBz6yIHPyG1bX3j51NUdcXG09LvZd9wJ1xFSwvMk65dwT7pp1w+sCp9P0zNirOrKnMSyYb6GxyrlIxVJfZ08NUvI5JV5dJSucsNtHa5q5uJTsh11ctv7YZeV3VYV/1bj2d2qsGyNmT69FIuHV+n/usef2byjDeYnnZ6nZuCDElYQF4oJuep5VxUqbnghUKAA4de2ASEXrxGeWY0olPK8X3OlbLUa1lCz4G8AEE8hb5bAAogEQiEhEgkeHHek0LfpVcAAGZIYAkQG5wvi8AueCcSHEGUIAi0EFkrPtjcgkhrxcGy88EF9JhhnQ5cmZs4BGOzNlSgmIMv2HtTeDPj7MJ8KSQQ+BwTjfNffrGdmJh9MGQP3XfoycfxcAW2gEq5BM12IqZMFZj0rakDeOg+JBFMjErNHj5iXY6qQXs+e7STHTyWP/ykF3hCgMfMMbTbzAoOoZMX9z1YHGhCO+QtLM4jzyJou5hh92J5aH8ZFAIRsbTbKNvQClawAOw/XFYGHp+cLGemMDbS7RSYmUe1boyZ3bMbvlOk9fkDR/KDhxBHxcTm7MgkXNDOHXv0sYUnjxmdQgT5Yu2CM6YQEHyktPLkJ+cKFS00p7DYQp6R92DAaIZ4Z9Fa7Oa2M9GyRRGKImR5FMWlDRDyIctDLkUBdpCej+UYIvBWK03W7dg4qjozMwdmB0c2b9288eGde3LrWCnDOmu3WwvzURTNTE3X0npcbVz/tRuy7s9d9Svv/ug//P01n/n8+3/tXQsLM//0vz5+1bve74k/9vcfaQ6Pm8bg/lZnCjjQyRfm29VGyAMsiWcKIt77sixtsJ1W2yjd7XbZOW+hBCTIvG0B7W7XFS7Lms72NlyCiBfAe7hgXbf713/7z//b77/n6OTs4b1PXn31F37rd97xF39+9KGd96aD1Te8+hV//2d/l8SNspsprVGUWasYH9/YmUdr6lh3sP6JT33rnz+VJ0nU9RQ0E3MvIgczI7hgXSWNbVE656jnItJ7KGXi9PYHtv3q+9705J6LvvC5T/m8/OInPnHD4Nhv/vZvXfbzL/uHq6/et3efius+cxRFkUHRnicTa+bcWgmhOHb0/IkN2zaPPnlkGk7pwAxFTC5YEQcdiCEBRApYiiuJPiqz1truxGlhxab8kp2InPT7Wfl3kXW5Oys4EOs6bllpzHw666cTyLOniEV6QnqhPmdnzfzX9bqLVceXqSEibs37XbPcE+q5Hu6yqj+vaLTjsXR+ltGaU8tPP5ajQVach8+YfBRFhMi6qHShR0oQ7T05CkyBjA9C8PxjW7icLEJr9IZeTxPve7w1WbYMFUA8M3lxcKHv9oYAFhD3YvoSAkS8tQjBMyMAXlgtxZzpbUcVDuLALEYJeSYJJAwy4MIWkEKyTokY3lMiBlyyE/Gu29IuJyoiFZPiPBQouDU7WahQLswg62B+1rjMliWEiI0WgLUhZ73FYob2HBjQMaqjcB5ZOxoaksjY2Q4kK+YzCQwJMIDLe9iTWAtH0ISpebg2JAdZo4gFLu9QosUuwgLOwxjAQwfWHEHltsRiO83t1OQMFlq1sYl8sQU2KEoPj6JAntF8yx6ZRe6BQpxH4WAMciucGyE7Oc1KA0UFBGXAOQVfId3KOmi3XbeLxUVEWjFQlFDaex9rg6JEKd66rNOFdcTK2UBEwsQhhNLBF2AHBlijLAANUQDysphdbIu4uFabLfJDR46V3iVJNe9mxOx90MzdVsuX1hsxUQUet/7gjoFa8qvv+bVPfvLTn//sV65482uOzRz75k3ffO3lV77wwNGdu/aWil2gqa7rigbUfKvNCYIhISbFROScg8A5H6yzpTchBA8iYmYvIQfysnBZETyYtVIKAu+9hwgzSLwNRT73Dx/7ylVX/eLXr/32E489/ulP3/CBD3zw6o/83fd/cNvBhe5//oPf/Yv/+peVSsWJs86nVdNuzXc6RXN8g5Mwctb4wuKkF1uBYmYo9MIUAAFBegEpdaKU8845EMiQ9862u088vOv/+qOH3/zGy/7w9//g1ltuvefuB0joz//bf3/xiy/+zXe/c+cjT3ztG99tx9rleZFlpppq0lm7E1dqG4dr73/jqyZqyRte/+p//My1hSjxKnjNwUcCKwjBCxFIL9tmr/TIcZytSbIUEu80o7sP4CxtTtEzQmOelZ+grFBBAoFxXO9ZUwc6QRVYlYZWKSsnpBdCEDCdJv9nWuf1MjlFPZd/n6CZAWvgQ6t1tWdc02flxyIagWEoiWsIZVGWBIcQS1FSHIE9yEN5YS/OGwcKHPhEzWzd0K3rHV/xvcQzWpk0gE5gCgeAlmPx9sIn9S5ikBKP4DwJSPeD+ApAUH1wJiCAQBKWw6aKIiZFgBURTcRCIQQGKEDIEwciIQmGlIUQvICIWYI33hrHZc/bcCSeHBQ0i/cOweHI1D1fvl6qVZlt4dgkvHc+IGYIfPA+UpCyF89Sg4ThKaAIlC02G4NzpMvZ+SgeAGmUhQSBMn2PMsyoJGSMBAZXwAmCwFswgZQwSMWKjS9D7wZALCKsAGLyTtDDPBwHMKugFKyPyDghiIRYI9IIUSVO2iAoDaY4jhEE1seK49h0JRBTQtQVVqWH99CR9wInUBrexwQEB2HuPabge7G9wAwQM5fOgslEUdnp6igBAC8kFKAlCELQmlhYBJaQlzZo+s599164bWxLZePDjz/x+BP7BMyRQVFkWfHII7ttGRAIbGqNZntmOqoPgMIXP/elxq8Pve897/+nT1z9jeu/fvmVr/7SV75yzVeufcdbf/nuh/5CJQ2ONWmtVARdcYWzCk4hAGANUn2YgEgxaxCREgJBMese3T2wAuCFQayUCr63g0k9ENSk1RDckUOH//KvPv6hD/7ade3OrvvuN8b86m9++K8++rGn9k3e8f09/+l3f+/v/vYvbSU2tcT6wmUtrsVMrd//8FX1Gtig12sJCIQQjq+EQoArgQBx6IUrJYIN8KXcf8eDDz5w13Wf+8KdP/jBW975Kxe/+LJvfPn6kOV777/30eHByy97XVLgc9d/t6O0qQ2UZVdrhgt6sHLOeedf+YrzyxyXvfyi7z/0yKGFbHExL2YzyiUOrEG5KB8CuGd+KzjOijj+WurHYabesF0xpz+NCb3P5j9x+C+d/Y+7wP23lRP3WPqypp+YEy99ep6aT7YfJjqVv5k1arJEdFw7AQBAVnBQe8xfWRsLBFa/a5bOMmiVc/pVnqxPkhMQl+PlnhRBqUe3+Vmhd615X1husVO25HpnfzqxGR2rqCyyvNuGYmNi70FB0mq1tNaLh3iEnkUCAyXI/FuTnJdZ+svttTIsNlboywxSJEq8QIKQ9HdnBWACiXjpGdsxixBYgRQUIXhRypMFxJFoItKKhUQkQIIERRCIJwGTEAtDSCuTBOHgyxAANhDRtYGysME7QMNLUm/mvghHpqHmUJQ6SUSJpwCjIYzS9gw7mBUH8T4ICHEEbVBQa3axsaGx2Gn5djE8Ot7SqtSKk9gYE4FD3q4ppzV3g6KoqkyltLadL/i8jfacK4s2BVAMzWADrUEEV5JY9mKLIijNlXqIKy2bBwqopYWIUYzAMFqE4DxYe1Cn6PbeLFaEOVI6qDQtCK7ooJKUFlCGAHRyaIY2lmRpjAREMcrMe480QWte16qBACb4YOIoShKE4CToNBHx6AX0ZFIqDaKDL9iTBjnvTJxabWyE6779nZtrSRLpLMdit1Rp2m0tmtqAzdo/uP1uIII2xNH03HwyPFrkXSGuDI195pp/ecdb3va7v/ehj1z9t/fccfvb3vbWf/7sF//xk5+K6/XcBbigCWXmIRybxHsoo0LeV2ACWFhIm4i0FkXMgRFYgZSCUoDSJmjj+g4JQIqJGKydJnjYQOhmqERB0TVfuPaKV73a2WL37kcqtfRX3vX2z3zuKz+45XvjCd769rdcc8MNtsyRAD4E7zOb/8WfXQ2f5XYhillcX6kWEWFaXgiysHMOwkopJ3DOmSiuprV3v/3dP/ei5339W9fddf+dH/3YP23avO2Kyy5/15uuHAqtbWPD1QrtHxpsz87Go6Ovef3l13/j+qwoTaPZ6XZVbDo5goOp4UWXvexfdz5WHp7t5lNSlJqg2DBCPxr80ottjddAf+37Izgoe1Z+onKKx3ES3nAq6AVYRc5d8+wpjpw6t9NWdV05KZMeqnTqck+L3zyjs8/Kv49ozouISDglUHBQYCEqXRb6Nms9fgwTIegAeOktQ09S8/oaRm/iXQKQAUjo6cV9H3dL4DKJ98swdX+/VinF7Pv4Xn+PqKebrFKKe/4CRHwIIFEsjMBsvBedaijtWm1RmpkA9t5pjogj0olKqzbLoMmygIAkBptcLExEKgEQfM5x4l0BMERyChLHUIBJfTSE0LLdruIEOkbZKb0gjpEOUlSVEnmnjeARRSgyRIkjRrWKgQqE0S7RWQAHxUY5IWMcM4YbanTYZ4UsOtfNFxbajYGGgGZnFqUSYWw46KjI8qKTo9PqFB1N8GTEGNQideaWi1532cBw/eD+p/be8yB2HQRHMBGGmhiooSwxN+cXFiIPDeNZl3mJZlLbvKkdq2J2Bo1aPjdHKhIPhQg+IIpooFo998zOsSMwulA6aB3EQpGPFcaasCCVortYBIGJwJIDZRQhEapUtdYwGtCiCBAksQteGY00gYodhCKNWiUszOtmo1xsqaTihUxSC8oEb+M4KRbnicQYU4iAIGxQH+qkUbt0REKR8c4iYutyRLGwIaUiY2YWOlpnHBnpBMskygSma755w4d3fOA3Pvjrf/03f7Zh46bt27ffed8uWwSVNH3ulMdgtV6v17XWeQcuK5ROoyTNunmlVmuXrWptICIjpS85QMMT5Z2stmEwBqA0TKST1AXxIXgI8qIxvnGhLEFk4sjHUSAbinxqdm6g2bjk0uff/J1v7nnw1re99WUPXnzm7j1P3HzXTX/6X3/z5p1bj03PAgWiCNFA1s4DVDWOjCsMURksmHs7o6xUzyNlCEECQWlm7YSc9ZxUKEq61n/sf3780ouf+0vvfPNFL7z401/82qEnnvzs4c9fdMamP/6ddwwYdLo444xt9XrVMY9PbGgMDS7MLFjrkNYeemzvd+4bfsmLnnPvvskzL956f7tTgZk5PA9jMu8ULOsIeW8Ngz5bbqVHEO6RQCHL61Q5vjrvG2j3/Q0D6NNFTxv/ZeU7BksI/wmnfqpkXc7sjyn9enKatjgJpei1Xt9rzgr/IutVZ43W7iUNguOxrI/n37/qpDX9eqjAKeyqVlk2rcY/aGVUo94RUSv0rbDi4uWS+4h+j4sDkWUb3BOwpeN9dWU/XKOC/a69sv4reumqHE+6bkVnpuOw5AkHsXzi5P5Px8nRa7haWZF+5VXr9pYVvhVIVm/DiSyP8RNyON7+8rTG6aoqndTmK9OsurZ/v+tV/UTRPUeHgRi9gFtgIARaciMUNMC93XJP/d2b1QPldEWtY0dHvYAyyw1FBMCfoLIE6cUAE4C0AhMTozezoz/hO2eFycQJQ0EbkEKcRkrDFcw9h70Rq4TSmkoqNgB5Gz2XTQRoJp3oqJrEVedclqmgLAIgCiA2GpGqbhjnZMC7tOsOoJj3ohAUOAUHMzJqhsdMMsBeL05P+2MHeioLiNBsqM1bzKZNHOD3Hiy6ouASpTy7nJk3bxw+95x4wwZmXth/eGHvHhxaLDxUHNFAXbaNjZyzo1lrLs7Nzxw+6vftw1TXBxGjUU0GL9j64isvH9s6kftiYvvGyGPXnimoGDu2D+zYVhkdpLIoDh5a2L1bjk6zopIVhkeqZ585eM4Z5Uh1cX6kYtI5WzoSQInRGB3B6ODgjjNcLQ0TI9mRI2IMvAcTpdVkuNnVlYGoms3MgxyZGKxhrQdUmgalxjZOjIyPpY2BbMH6vtXYkvOukeEBnQ6PjrjgJicnEarPff7FOx+4RwSIYlWpmbhalmUlYeetFD14hKE0ktQ06kSKXZB2LnnHOwIFaEU61lHFRKn3/ujUnLNtV+RgRhDHbCqVylDtmq9+7R1X/Px73/v+a796Xcna5jnimhDiJGoOYHCgEcL+PM+rKcSLSaOF+VaSJHGaRqONVpZvbg4MVOqLUsy1fDvPm6NjLisMMDY2NtfOAsQFCUriKBajFuZmUa8PTWwOhXW+dFQWDORlIBCRVlTVumaQpmmUxCQegJABIihGxDquJ5Xh52zb/rxzz2LVdrabkAohCJMyWpkYikMQEXEu3HP3fY88sqfoZjARooSTmAKPjtbveOjhe3bd9fa3/+L/+V8+dMv3HvzeN2/cuXPX179925WveulAHTd96648OJu1dz78wHOfe/6tt9wOaKW0J5ots9mAmVAKsO2MM44cnI5iUxJA4r33XvUtFpdG8fHV51qMh3VG/7NBs3/6ZJ05GT/yozkt0PKMgJk1q3qaGq5/a1gB9j8z8vLTKfeZV+Y/gvw08d10qUtPfX/qSwyVJf6sMMSoAAYJBWHfp2qvqUqfQqWhZaauOn6QV2rTTERCqt9dmJb34vucX+5tZjARBerrjz1VUFgJKaiEdGQlMLOKYoTexEtETIqVSUxSRyWFcyg6S2tJJtLGRHFaq9QGnJcyeO96QaYE1HOrF4VaLRkaDV3VPXYUgJHApS+gqVJrbNhim03PUWrqTZPMtOeQtSIOFqLTRnVoY3X7OUqZ+QVfLuZ+cVqi2KcxIqMuOI+f85yckzSN05FmJ3JuYS7PCwxW9fk74kvOxubxrqWaTMRnbTrajO0DXrpdDI3Wto2e/6LzL7n4DJe79mK2eePgwvDoLqV0ozF6wbm0dWNHi4ak9bSTtYu5tvYBieKtG+MzNpfDA066lUZ1wCRzU5OINETyVGF82Gzd6EebmetUBusZI0qT3k4zR5FK04jigaExMvGCLU1aQZTCi1FRWqsqG1cGatXGQFytZJ150QzFUApGx2lSrdVc4aBVc3Q4btSKUE7s2Pb43l2LCwtQhmJVaTYN0Y5tG1qTQ/seuN8HBhtdqUqS1OpDWdea2Ai6QvDdnorJzGySSlSppmk6Pzvjuh7MKjHeOaiIqlXUKtOtmVvuvvsdb3j9z7/0FV/7xo0IxMSsqVKpBUEU640bNx47cjRYhBA0kVJKmJSJCqKB4cF6pZ5lWRkLRYqMnp+bO/fMzQJEShtjAlhFLMG7YOMoKkMY3jDhQJwkSjSryKEUWwoFYq04MtokjDQZSpNmVAQA7AmkSSmTxNXKABx1PI7asvS26Lbr1qvSkdZJtaIj74IXISF17NjUk4cns06GKI6qdRMnWkdWMC2hsX1Hd/bJz3/6M9d/dehtb3rrn/zx7994/Xe++K0bb7n3vtmFztRCxzSaStH99975oQ996MF7HmnlbeXIWzebF7OCI1m3bPmzxpM7ZhdjH0pnoQ2cAwjawIdlD6QrFsu94UnSW4RIb53qgV7E7DVi6zyrvvykZGXM87WOr8F+fUa6wnI+a5RL1ENNVq61V74+es5l+xn31sXinyk35Xj65XhMSz12GUJckfw4e2aZP0REsoITs6zjLFvqLdd9jXtcjS397Pbz07BnfurvSYM8SMAM7u3loK92CACGcAAroT6wCFnNsHra6M+KlH0oyfvj//dCXhMTkZfQd3IqAILvzZW8hCuuRKoIAjJJtRQuKQoUg6wYjnQSykLKHk7oQwgC0kpDGRBDKQ6+bz8XKFixXpyw7oWT9NxHZYKID0I+63SSEUVGgwI0QUGHICqKKo20Otgqi6Lb0WPNdKCZDNTyrim7bQi5bt6aXsBiMdCoBDYcxZ6UY/hKgkYt3bElS6PFRw/TQHX8zLF484Sr1dCeAlM0MZpsHF3w1h44tmliy+DEhnx6ZuGJ/aUP3KiNbNsqA9XHD09GTiaGRw0jLzzSajIyEo2NLRAvPHUA1eqG4YYaHZVov81yVCrV8TEebE4XxdTsbGNgIKpVTTV1PdZwYtAcoGplNs9m5+aTSi0ZHtYq6tmvwYc8L23XFg1PSQUhWB8gBBc4SAgh73Y7eeZ6Dg2JhQmaoZiUqQ3USavO9Fy7yFiziiMwgmYyGraETkWhVebMKq5XyqxiGZrIg2uVahmZrLB5K5MKxVEspQYpBA+I91J4h+DhbVAEo1EGEUBHSGKVpiUpqtYffupA/N2b3viqVwaJv3j9t4RVkedSjfIcZZn3+5JCpdroWCsiWZbleW4G6qW1pbNRJW35thCAwJq7Re48iiyvViomjhba3fpwszU9W4hOBhqimNh02x1iMRGz0VaRhwiTJy6DdehTs4l7sJUHghhPsVGGs8I+tf/gkZnJNrd8sagXOsq6KE6rA3Uo3ckzESRJkmflYquDqALFpRdiTWyyooTROkZIqtwcMjr59Kc/PTo49K53/NILXvqCL1z7xZl8keJqFqx0u6amHnpk5/jIaGcuc3mWd1y7k+VB9k/OtJ0573ln2ENHpJMheFZpQAkBI/TC3vRdSS1/n3649xcbx/9dgqBXW4g83fnjWfnR5QRLImD1AzoJ7V955ISMVl57cilrsF5WlHsinreOhfaq0ldfdZoi1snztPVcp9ynRYVeN5P/6EbaPz3CkWcdCMv+xkNQvY8HhQAEITiG7wWgXkePOcUG8KpTFHBCiIre5jrYB4QQvAQQC6gX9iMQiSCEELyH9DbeJUAC9RkzApRWgDhQAhgEBAvHLEo7oiDigi99WTqbO1t4B+/hg/JiPHRgKoMrQp65bm7LQMETPOA1e8CLL0pkORZz44QVoAJM8No5VYryUZQYmDDbxdGZoigpjhExjEccwCKtlj8y7acXo7yMRDR8FLEXGzigngwMN1y7i0efkn3HEOCiCMbAGBCzFT1f2CcmccfOzq6n8mOz3em5cmYeM/NhZq7dyQ+27Q0P7P7ew4891bJPzGHP1DwqqYyPFibOFgvsmaKjGVPC1SZ0BcGAk6QyEEep7ZaYWaR2kUBVTQwEKIqSGGmqlMraGWZayJzW3Fcfg4Tc2VaOmVbWygIYzhfWAwxmDQpZifn5vCiK4BwCIEERwAgQIIqiHnMiiHTyzJIgiURxGQJA0UB1YuM4kfiyW/isU7QA58SL8w6kI5V3Wsg7ZZ7DkCgGaZBGIFjvbd613dwXAYGh4BFKDy9gEQ1i0y0kU2rnwcM33HTH+Recc/lll2s2YFlcnPcBUaQXuy0Y03FYtKUVmNS4shDv5hdmPVwRyq7N4mqiFMTaxOi51pxWsFlXShdCGBistOamqZb6UFZq1cyVnqjI89y6oJiMFqZAAQgObsHbRSAX7+FJcgYEOXSGWnAVm3OnRNeWGTmbCqugypIyiaxJS5O2AxYKu1j4rihOa6ZWR1rtNQWbNLCSshDrsm4xMDwe2MxNT+t6dbbV+qu//8j137rpbe94z3vf+6taayld0hiw87NxtVKv10kQSpt1u4vz7U4rf+qJA/v37Bu1qLe7Ku8guKgXTFUkeN/DNvuDfdneiPqrnpVM/JMXNitDFoRlJ1JEy8d/XMSRZ+Xpy+oJ+TTtT0vGFmueWy/bNS85IcHJ1Tj12z4QhI9ftZzDyj7WF2EIL79cTl2NXs6nDry1sizwGg3Sq8MJ1XhG7fwzIKtxhJ9C4QAF0QiMwAggAQk4gBBYesqHEwq9GF1LJK7jXeRUfX1JjidYupCISClWipUiIoSAEHrKCkKQ4L133rk+mYiX/aavgPt6P0hDNJojamwzj0ygMgCBC+KJAXgJAi8IgHhBgEAxevGCRSj0I2MDcKRLMNiQigHVM7HxQgiADd4hiIAE3ocQHAXL4pLEmyQ4QiFFUFbpQIB4xDHiGN4jhGDIGi6NFFI6De8dJCCOTCX2ElDmKK0ImShCnIIjWIodJ16hk6PwAIyJmZnBIAUBm6TgSmuqc/iBPbfdv2v3gbmpVgsiiDQiVfoSrbZ2HoAYhVgDAawksAvkfIAT7z0AFaleezIzVC8oUkAgEQmKHAcogYTgPDmBDfAQRYiiXgQraK0UgQJcKUykjCgNpUgZMIHJGCMiIQQQKaVKa0krxJENAjIw6UCtvmPb1kSTVpRojjTQ8whXr2a2YM3gAA1WTnQQTcxacUwUAQriABvXYtYEQCmjVQIAFKCZoCQAJg3V2kN7913/nbvOv+j8oiiNieGsy1wUG4l1xnj84OxFl7xARWbPnt0bNzYr1TQ2+tFHd205s9oYHVpcnN21a/fzn3eRVrTYbT12uHzRC19YdNqTk5PE2H7+c6TMoGDhKtVq4S1YgSCK2WiwCC9BicF5wEkI3Pdk3e/DwZPRFMdeqUBkdFxJKhCGiREnEicUJ2JiKAOjoXXuvM0L2BKKkcTaMJQAHr6IjJqbmoYnPTzhnLJeVSqNBx94+G/+9u+KovjDP/jPL3/Zi6943WsGtu5QUVoGCSxgWGvnF1oL8+2jTx5pHZ6sCkaqCXxOKpD4/gYrr0DmVxMS1x71q2eD9SaHZzWYn6ysp3Y8zcmcVveENU+tmRWtn35pTbuaiHq6TE5IsHb918rh5HqeNp/T6mo4iWXxbD//9xQuOHZkAAWhJQouB4YQAjnAg7ywFbYE34ufcUIWa+jFK+TkjrJ8vCc9xQJESusojk0c9y2Ke4xyIgKD+h9a+oCYeoEkuaqKAAAgAElEQVTB6s3a5jM2nXXulrPOrW3bjuYQVAxWYBIGMbRRbIxSirVGohErGApMwhSIoTWSVNWq3mikCScJjA7EMIoSgyhGVBEVi4mgI4g2QXnFULRIuhVVuD6IxpDUml6n7BWVSnVFeQIxBqvleH26odpDMYarIWIYBS8oQwmxFYUtw7yhUSIEJ1wKSkHHRZ3SGEPDA9jY7Awmiyq0iiyUJbyHLdtFqU0DtQmYhnVkg3jvkWXUWmQuogHCpkpjIg3U7to56C6Mg4jNbekDVyto1HykOrBZKKAA8cE5+DJATByjWkFsMriCPTQBQoqTJEGcJlESmFGJUYmhAYQCASyIjIkjHUcqMogjE0cgDcAY45wTETAbY0gr1goSCme1TsBGixppNLjIYzjjC+UdJFQGqudecAEIjrypMFKOUvZsA7wIOCgFBjMihURZn9lQhBBImHvHtbCmEABHqNQ7VtRA89Z77r75rgedQArbUHEsIj6UhpzL7t396Ate9rIt2zaH4G644aarrnqLNlytpnfe9+QvvPWNjdGhm2/67tjQ4Ite8Hxdjb/+rRsuPH/8+RddaK29+ft3/cIbrhicGNeVuNVZtMEnlQqSGFqDe+F3AwBm1kSDpJpAxIpMFOIYgJIa/AA6MYpKoAonTR9Xu6wKwIbQw8Ny58sg0NzTHQOC7nkAUgI42I4tWmK7YG8iqiYJdAxdc23v54sYCXvVGBzOut3PfvYzV3/sYxvHhl9w0YWtmRYFlXkrhpBQ0NT1tiyczLRU10IwNN70bKGkDB5MrDXU6om+NwyxSpuhJbPEE4b8aWeAnxnXHP8xhGm9Bl/zeT3TI8uo2wnJ1sMw1qtKr6qnLuvkUtYLWfo0sYQ1u+gPp4j8zKsvP/Xoy3qypCXIcc93gRDAoY+nHd956vXVEyhOz6QoWun4jrwT70QEYLCGTkPakNqwGtrMAxOoNGEqYCUhiHewJYiImYh6TBr00BpmBEniuFpLG8MDI8ODuhJBfMg6IAXW0LGKU2OU1og0RZHRcaxNDG0cszBBMRmTRnGidWS0iiNoBhOU0SZGnHC9kVYqtVoNtbpKmiaqk0pBCq25IthKvUbjo41m1UTMFpJ5WNEqQrUSbRgd2DAaDQ3UN28cOPssVFNdSQFCu4N21ojSZOvG8a3jaSjLo0fD5BGWAFe2pqZ8lg0PDtFzn9MYHQ42w+ICbAFFcIVtLRiS5tBgvGXHyNC47eZ+bg62iDW5vDvYrI8856wzzt5ekSAz08i7oICyQJHFjNHBRmNspFJJrbWtdhdGA2DrUXpDqFfTZKBWS2LJCmstQgCDI1OpVUwtrVeTWpKgVq3UKogUNLPRupKg2YjjOI6ixEQwkdYasoRdlTbWJq6kaZqmrMlalFYpVZYlSh+8b1YqCWMwTapJmiQVEDWazedecF4UaxVsorVir42Qd+L+P/beM1iy6zoX+9bae5/Qp7tvmHQxA0xCJEGABAEGZIIgCYpBpEiKpMT3LNGl5/DKZfv9cL3yL7tc5R/2H9tV79mvLEv1HiVbtCgJIikSIAAmkATACA6GCMRgAsKEG/ve7j5xh+Ufp++dOwmJUfKs2jXTt3uffcJO66z1rW9ZWA/n2YsisFJJpCPF/azb701rk3gQAsG6usi9hKlLdsKj15uyLqhO90c/fVwl2hijI5OmptPNst4Uprc+e+DgAw9+81Of/vDU1MzzLz1/8MAz/82//ERTlk889WTu3D//w8/0u51/+Ievvun669/+zpuPnzz57YefuOvOXTu3b3vhyKFHvvu9z/7Rp9K4o9nUZa7gtWYA4oO3Ac4DIE2GVcekCWAUawMVGQaYiQVx3JWG6ipoFcWxZvJBHIhaRjxpHLlgSCVRzMaAuc5zWAtM/O5xZNI4UUYzc9M0cEBpITrpzUZRUjV2XBZxL3O+XhzM/8N9/3Dq1LwCiQ1OWh1UwxC0MsagkRSGganZDpQnBe8cxAdx4tzZ0/yM+X+mT4DWV48LpPU5971W6LSZ6oy0z/9E5Td7dy9jV3g1ysq6vEIS7Ndqz3gddrvXcrVny8aQ/UXPe76+JIFMEm6/2gt77UPityt59cv75n6dwusuJZGWHR/cFpBBa60hhZZWlxVaI0oQBJmQRpz+kzcK1psTIfFBJjDL9pWOSTwFq0lCUwZnoRRUB9Nznb1vSvbd0Lnynen+G/Xc1TB9eIJixe3qLyxgUQRFrNfVfY/Glkun6tEpXyy4fInqMYo12BoAOBGV2RAxs4J1xapyVRbHzDoQCwLIcWr6SRJbHxV5R3zSiZFEkIAAozukk24ndk0uts46PerMVEgRYgRCOfLLJzhU27d02Q9WTxwqTs2nlKRJvwG4G6epSYU5dxn3tk1dopK+CwIJGA3zJ4/oU4PLdsx1Ddtnf+4OPw1UATlMVdq1cn5hC/Rc1otrO3/gKbx0CmlERqEq7bHnlo49kepqz+65ejQqTy1ifh4cKnG+tHY173a6W7P+2tMv0PEhVso4EIrR+KVjy8/9fNr7bZ2uHebj5TWpLJqApJtYwpGXxi+d2JKm2/tptbKCwy+lVkMIkUJHc0f1ZzPbjJEPe3EUswcEwQVFPjLU6ShiVxSx4qnpqUgbwIN0cBJcGAxWWateN4utr+aXUBa+LpgBTVmSGrAOrinHIcCkGeJ0VOT9SNnlBawOf+fWO67Zv0+H4Ee5qhxZF2uONKWE1PlZHSV1iILKulNOR0gSaIWiRlUJ+wau0+s142q0vBwzxNXe2VJ59FLfwbgoTCOdEOnu3MlTa//35+7/7B9/qtvvHTx44NkDJz5y97uTbv+Bb3574dTiPe95f0P0Z3/5+XfdecPbb7z1uSNH7v/as3/wsXfsmp164dDTX/ni1/7zP/mUCUaqPAp1P1Iz3ZRL60YVVDvkSJg5nRIgSC2hNNySMI9MVEpdSdko76ezKJWih7JaWtTiQYG0jpzUy2vGhszEcdItioqDR1UjEDzQ2GKcg1WUZMokZdmgaKBTONc4O6yKuJeJUbU4dGPL4hQqV3VijhjSOAjDN0GJD43YhknDeV/hTW+4wgUXCMysWMG5yGiRsJ5CAZMXbyIwRKF1ooGl9SqC1KSAA1hItStJAAlN1t/zvLtvOlBaR+r657MKwAC3qtKvrrxW2cBJnAZMTB7LeQoRQc6+r5cvE+THOYWgzl8uIBNw4TnX6QV+4oanIGiXKAi1/55zb+1RSojBalKUhmKIoAUiCG2+/gAJkLOsJtRSDq2Xl3+q7SHrf5xbzj0qtFFJp4viCXN6C44RaQuwjrkU2RgAZ8JBqR11k90xEEn7ucW/TwrJBGy6HgB1nnG7aXuk9hUaqsUOTMbG2SOnPYrUpLRTg9uHd/Zk2SBNOH2K9avbGACTmXsBCNHpWwat59c8oyaJItk8zHRbgLNn0Mt7ac6eEYrbsnlsTISZeNNj3HyzpM41+Onzwa0YbQjcL1k2mWSI4B0TmNmBoQ06/RB3a4kcJ5yadNqPxqtwY7gihNBSr0mYAH6BICKMQAFii/zES0fHyzAkZY4ih2OltQ8eIAiLg6tq773XEkIQJ7ZpgvcgQHyoy3xttR7l3JRempDF8BbiAXGNldAMXzo6WlvUUceurmGUC0Ncg1AjX60y3XAanEMvhW1QrCqjGmeFguRray8+t6YIjUcZIhJqHIIQiQxHq4cO4/ALi70uQk3DFcyfgKtgCFL45ZOLT0Zr80tb9+4Zrq2oxUFwDuORNA3iGKuj8Y9+ND764kk1Gwnvmk1RVghOrB09f7JZXVvudrZYWT74LBaWGWyrEpHySwte0cLslsHS8mhxMZrbjrKGB5xH1WCwBoOlo0eXjz2fL65gXKntDayHEtTNeHm5nF8FGQYFaexl2yEeJCRwZS1rw+HKYGVxYXFxwTrbNBWIoBQRubrBaFxW1fLy8nhlVfIcGoaYGRBpqno0Gq2tLgWm4WiUFxVaInOx7BuqalRVUxbVOLelC7UI+TiNxmtLV+7Zc/c976p9/aPHDz793LEqRD4oaANhEq1tqBYWaGpKbKN8kMo1wbNijoyzPm/8KMdwsLa6sFQ37K3lIC++cOLzn7//M5/52F987q++dO+XPvChD739prf+3d/+/Ve++sCnPvnpt9xw/TNPP/s//c+f+9f/+j9aKd3PnviJKlY+/uEP/rt//+dHDz338Dd3febTH/2bL39l/tAzvblL83ERvIe3mlVVVUTknYzyfBwwHK2MhyvMLZDeim/Ec0Sxz4fLS6eumo53TfUWxTx99HmVzdTOi9eBdTFYK+B9HKOq6vEIPihWPgRltK3LlRdfML2u1AWc0xD4IBpZmnqv89EqZxE8gRUUB4iOowDpd7OmrhECiEWcJwmKiDWEnEjaTZkZRKQgvo2BnUQbnQ7HENlsjDkjpINonQOKsJkf7VXIuYaZ305y9F9cfqvNTvRKgT9yHnJUAC0b2S/7Wn4FUc3nAdK82qCkM+OYzr7f9QqvrXfD+oB/taP9nPNelA3Rr/O4C2he5/2SBMLrih5EeDJhtI7A3LIKRpGKCTZYm49So2PNdWwaIngvIYjmABEJ8A5EQNjQp6EVqkqqHOTb7zHhXHcKWgMiVmrrDAdNQKich/NQPNE3beNC7rzAl4CHSuAqiAUYvkJVo3AoBs7EqCp4SWLtuamkRhrj1IsisWbjlsTEbHk8toFUTESoc5k/gbrGqIJToZshVGhqZWLngOEQ4xGUhreS5/BNG+YNW2N5OQxdZV566YmnIQ5KIs1+dsqPCzRCa41UAyxYyGrTSSxtR1PDOp1XaFabo8cw3StmdmBhgKoMHQWvFClflJgf2JeW7MmTWFwQHbOX4B0kkHUY54Crnz9pj5zCygBxkrYpmUX6ntyoLk8uASp4jxi2KBA8RJKApPT16qjod4bDoR0MAHEugDTIioi1FmWJMozWhkVRwFqQkhZoDNS2yfO8zHMwFUXRNE3rlgIgIq6VuinzCg0jgLLOSrPW7eDWG994y+Uzw1G1Pbp2cOLo4cUlmAwhhQ9Cqh4WSXe6WVgR2+heRjoW58Hk8gYmdmXwhSPr69GaDwoeSke9mZm11eFPfvzE++6553Of/+sHH/7eB+667da3XvfQd378V3/z5f/qX/6ztcWTT60s/bs//9qnfv+eFw/9/KcHDu7cOfehD3zob7/41Sef/Nm+y698xw03fu/7j43WlgmKLAyrJl/ZMpMdPHSUxPu6tiWqYlyOy8SQBUTi4GvfWLs2n0x1L5uK//UnP7hvBh2NR5+d/1++/O0jy3lwIkryygXv4B1so7Qi711V9qanRkvzne3bitpitNyxq9ddefkwrw8fedEGZZWrXIj7PescJGERIARbMae1R3d6tigKeIEmBKpFKiZvUKswti7uTpFSJCRGBReYWnqGADBROEObOWuC/6Pl0rgo5/bd61QlX1EN+gXrvwbZxI52/jOfwR/z6p/AxvdEJHL6Fi7UzkX5dcqv1vFGLUn0GYOCISwg10ZZh1Y7EROc8lUsjm0empHUY7gaYUPPZaIWigwEtMQcctps6RFCG/LCpBCcrUtYqyQYER0EPojz8AG+pckQArEiEEAB4iEOrUkgeIhnCoaDkgbSwDfsGu1q2Bq2EFdKqJgsbBHFSupC1VU3+Awh6Xco08KBSdjXXA2zYg3FKsq1pB4bW5Nz2gMCBaApMVrGYIVsYEQsBGsRHELQdc2jMU7OYy3H0sANhrGJOIlhXRSgx41erbBaYFRJZRE8gJTYFBaVm+SpVgpCcDViFiYEQWOjqlF5haJSdaND6zMkrRnEcE43Pmo8rEfjOHiEwF60BLYWpUXt0TSwbn0/gyKJCPDSUlq1oWcKk1izdcaqFtAdmLmFAwfnZd2u69fzm4sIY3LU2WZD60iEiSli8ZWJ1VQ/oTLoIn/Tnp07pruKLaQGHEcqSaI4inVjuaqZ2ddNlVcBrHUMk4C0a7wK3E1SlqAUwEFpWVtZqa37xsPfc0Sf+MN/vjS/8PC3vr1/z94bb3yb9/7f/tv/64MfuevSvXvmF5e/+Y0ffvxjH82y7GsPPjQeVb/zvnuaanz/V7+8Z/fcbe98h/KNFEOpKkXEWlVF0+/3g/OGoAkqAM5LUAwIx4FMlCRJt0Ouev+d77xyG3ZpbBV751U79u7YEmkj4sHrNl4J8EE8FGjrlqnRqePpTL+YP6nFz2r6N//tv/of/7NP/A//xWf+yz/+1FQs4grNVFeNBEOUKO5I0OKkCbAhcCeu6xoAwQDKCRoAimrxo7oOrDudTKTN/SR+ArRXZ4EiX879f1Zsy8vEOl2U3xo5Y9K9rPfhFRt6+XChV1n/vJ9/uXLWVb2aE52/zpm3cHGo/wbltasyrx3hzOtLGtDqERzAQeBdcNZCBN7aYuyKYRSqjD1XRZOvuTIHgjKGlQqnvWXr596I8RdLISgJ7Jw4T+KZJjkNlAQGRHwL2pmEH5MwQzNIAPEIDuLXCW9Cm0oS4taVMMAYb0xgA9Kt8xKkgop6l172O5/+5O7r31RLCCYa1XW1tibOK2OiyJAKgpq5UWQJtYbT4o1I0ir1wUIcxFGwsXDkQdazC5FQBsTiIxKdRKkmbQPGBaoqUUbHiQ8IMlEB2ntp3dWaAe8QBCJuwqfZkhlSYMAwlEIQCgKQUopIoCCagzpt3PTcAhdCi4EISjzBE9BmFdUagOcAA5BvSBwHkPfea8XQus2DDd/m6WJunaqYuDrbOT95cSHyEA+ZALfbJYC5dR0yQ+ARPElAcCyBSYJv9ExvXJePP3FwsDqOk+zUqWVm7mQZNEFcsGWdj3xdFflIax3HsWuaOI6JqBqNwKzjWGuGeFvXdZH3u5lmqhYXkiQmohBlf/X3X2mq8Sf/4NMvzQ/+7u/vu+Hq/Xe884a4N/W//u9/9QefufuSHTM/evTRI4eOfPTjH69s+PbD3/MufPB972vG4y984e+mpvt33fI2Ds3UzFRZ5rPdbjMex3EKVoSgCEwEUhASILAJSjtFTovVkvbjGGBUaFZ6QBYbktACKgIAJgRASLOy1g6WF5Kp1LtmettWctXvv+/uG7ZEuz2uSPD+t++b63d6aay1VjqVQIAKwiKEOKmUINU5rPOeSCvEFCLxEOch4l3I62Z1bbRlehsCCTxYhEyAFiIhah32E6wuCBvYF+INHMxkeTg3YmUy63+7fSv/v5czOu4X2ZIvAKelC42BFpuxXi7EH/OrFz5rNzyX+ujlr0T+ETDi/lOWXzkcer371wF/1G5dypiYtWHSIIK1zerKaPFUNVhq1paKwfxoaSGMVuGsYiJlgBYupKEjKANWGwAhgSfyRCJMokQUs1asFYOIyBMcwxmC0dA88eC3OK8Q4D28gAClQQBzaMFrgJfgALBGFCPORHeACJyy6ZLqQKKRDaPAI2WQZlW35+Mu4j5MJqI8KUeR6DikPZ92RaeOjBfWpBWxCJwwVIQkk7TTEHlImLCqUhMkJ1dpcRqlLV3EiHRZ1lrr3pYZpxFSE7oJujEiJazADKYm+BApGEI34U4L9xE2EawHA0YhUt6wM4xIieJJAooQnPdAgFZNrKpEIyKkxmmC4WDYabIRI9FQgFaACLXoOTTsrQIYgjDp4hA2rG9KKVJqotwECcEhBEAZNm1KBB+CDwGKQQjeB+/BilpCaZ5AFBURFLHRTgKCJ2ErdPDnR7/+2I9GwXzxgW8+9dyxsnKgSecaY+IsDXEUkriCh9FpN7V1qRPz9re9JbiSOAh5L67f7998883vfMc7pnfMee8FzEk3VM19Dz1gg/3gh39vNMy//KUvXrFv3/TsVgf6N//HFz7ysd+5+porf/CDx5479vxn/viPi7r60pe+NN3vv+vO20drg6988W/mZrfcdcftzpYIrs7zLVPTq6urpJQPwQWAFCgiackXAxhilLOVM/Tgo99bDAIktY5GQF7WzjloXs+QTZBASjVFMT09KxRE/J7LLrn73XdMZZ25mSllgcaFBh1genoaQDkakVYAi0dwnqBMmub5CIaqeiywzAxRLJoDwQcEbqfD8ZMndl5yKQsjOCgALNBCvGkWvyrbzGmMIX6xTfGi/HrlF1Eazjbt/ML1f0XmmbOafQXthM6utvH5bM3v4jj/TcsvTZV5Newy66ubEgKx1jpSSilmIKDMw2C5XDqZz7/kF09ivIqmamnTiIhYCSnWirUmrdFmNgATkXgfIF4REoNOKmnimJ3zYOUZjSGbGHRi9DKkXcSpoLWZq4kabgxlfTM9G0/PcNZXSRc6AkWeomBSJF30tmP20mxmFzpbwZ3aG2kiNAZL5cM/fHJQCC7bF7buwMwOUBdrEnJprIbqYXq33nsd9l6PmcvGqhd0V1QsMOBEZVswtx+7r8Ele8NUP6RGFAVW1kQ2i7FzK66/svv269V112DPXOgYKYpRWblIYSbDti72bMf+ndgx42MDpWCUTyKZ7eOKy7DvsmxuK3ZsQbfDjXAjcALDmM7inVv1ri2YyWqNxnlAQeCJkcaYnY52bac9O7B7B7K0VmjTWayx2CzBtlls30qz00haAl8FZq+U76aYznrTU91uB5EBEzODDRQTkdYacYJOmmVZkiQtER8CkQDMpJWOI91JOUm4tccQgrgQAjMrpbTWaRpnvS51YkSKOLaFNaY/DubrP3v24UPP2+62RneFU1YdtIAQloICtk41U6n0Mz3dzcshk7v1HTfcdMM1USJxIiZjlTIMTU/NJCYZDot+f7psbAAwO5tt2/7lBx/afenc5fv3DlbXvvSVr7z37rump3pref43X3rgDz75O500efixx1ZGxdVvuCZJki9/5b7LL7/8ujdeybA//uEP3nzdm/rddLqXNWUlQlESc6Qp6egYOsniOFGaAITgoCmIw+wUsu7hweiBx586Cvz1w08sAbkjUQpRjDhCJ0O/a9I06cRxt7+Wj2e2bZ3bueN3f+/DT//8KRK3mo+aGGWiqwhPL7r5PB95r6b6Li+IwRAjiMXrpj753LPK1qaulAQAEhSLjh2SRqJARhQUv3jyxK5dlzIpeA/i1qhDUBvhFW0kxWnbzEakw3qYxjmRuue34J6Xg+Si/GbkAnwzr8+K9vKawWTkXIgDZpNsjiY7L2f05lbPP8jaUK/zCJ9e/NcvYpM15ZwxfG7DtJ4W/qyLv6jN/Ebl9cJ+X5uEs8eHcGNdG4HFAmI4CnAVakEoQATD0EQyUYGZWYjAmpWW4AO3xncQGIbBTFqrOKFOzErZqnbIpbSe2DEQKaQpsixySlTlcg9HQiRCUDHSbjoz28mmpBpV9dgxXONADlqHuAtR6pLds7PbptgEHeULJ5wECgRW6GaqN9XffUnD7FSg0bAclsqJF4c4QS/rXLZv7s03RmvlIh/C8lKVL6nglQq619mxb6+fmVX9vhutzj/yMI+stwyEYAymZ+jqq3a/6dpenDVV+eLzR8qnHLwXF6qqUv1u76q9/Uv3WO6OllaM1CAGSRPpdO9cJ7s8iaId+/dOX3/N8NBhd+hwJ0qLiNDtRbt2zl19BbrJGD7KuuU4hzBDsTHo9bBj69SeS/NYrUx15fgiZx0YAwYnSbZlJtVZyvF4ZXUMq9ggMDwpNlG3U85Ob9m+bXp6mtJE6kbrCFoDKkCMMZzGsTZbZmaa5YGOImeda2wIAcxRFHU6nV6v55yLosjVDa8jaUgpHUVREnc7WW96amnswFryuhN3bV15rUK388gzz73nzrsOL6zalRVHQBBoo+PIKxVtm3WMlFQqoWzq3XsvvfqafQ8+dF+QSjikWTSzZarb70Zp3CpMo6I0nY5Lk6iXjFyzc+++7z/2vT/85Lv//PPh0NEjjz7y8Mc++sE//8Lfj2v382df+uxn/+Dff/nrX//Od/7Vn3z4y4374U+fvP/rD/4nf/zee+8NJ5aXnz1y+D/97O/+xee+uGq7TQgOYtJOQhJpxFkWd9K4CdyunExasSgVOlkIzfzYvTSUe7/x47233Clxh+KEIiUmMt1+qiOqClRVXg9NJ1JJ9JFPfuIL9947XBnc9a4746mZ/+eBH1975RWnivLeh7750spqiDKjE29rAWkNI8o7KUfDFw4dkrwwQkrEAQEySSErMCLM8LGeHw93Xj7NiuAFk/DI8y/rG45CnBHWcQH7+q8Q4HlRfglCr7IfX2OjOCv87dXVP+vCfulA2kAb+SNfVcsbaM3zHnIR6vvbI6/CKkObgtPPDEyffDOJg8fGN+vfMyk+bZ2bHEUTO4vRbeol55x3lhSRYcCDAziQbxgCCU3TeO9dEGWiSZpJImYGKRGy3oEAxVAa2tTOV86rNEUUBa0DE0yEOJ7atesDH/rI9NYdTlgCoLRrPGmNONVpv7t1LkRpyVE8va277VJwCpUhnkpm5jC1Ne30SuebXkKXzGBrFzOJTRQihV730ssv5yjOvetffmn3ikvRMd4wlIcvONbcidX2rdNX7sN0H+KAxptmzLnLPDIlmYl3z2W7d+q0470HCCYCRfEluy99w/XobVsqJPRm+9e9Eft2A4Si5MoajyybiqZn0l1ztGWm0AyjEHw8t62z71LePjtuaomi/hv2y+5tSJQ3JiITq6TT6epO2tmxzWydtSJxnEJUpGPSBvv3dfftKQkj77Jt29S+y+IsgxBIpTruJFkdJJuZnt6+DSalBggaQetAijSUieN4amqq3+8jMsE6pRScA9AqCr1eLzY6ihJXOzLJoWcO3Xnb7QiytraWZp19+/d3suzYsWPXXXedUgrAiVPHb3zbW3tT3cFg+c1vfnOaZEm/r5IOBMWo6HR7lfOUdgZ1/dTzR26/+91aa7HeRDGYrZDKeiaKiJCkESjs37/3wx9+z+OPP3706FFm7ve73jsdRzrWUQIbLBnmyNjgwNSdnkm2bMudt+V4LkN3ZiulWZWvXjkH0+9F/am8Gu3eAkqM6duoRLAAACAASURBVGZNgT2XXqr7/UFR7FGYm+37ODo5XLmmi55SKjINwFo13gUmCyTdLO11mEQBhpAqZbyfipMsyrLO9MzMLi9kaWbkYKEdiyRxMtNXcWQRoFXS78fd5PI3XvXB3/vI/3vvvYuD5Tvefdfs3PYvfevbPzmx8vTY/9kXH3jihXlK+qyTajSkKIKvs16WV2OOtdFxPirefN1bymEBIW/rtJvapkzSnnMhTnTaT8N0r+zG2/fMTU2nca/LykQmUsQAJjA18DoBhpJ1HgsCQyY8FpNpvkGggjNQwBszd2NBaNnFTy8XZ60eZ64nGwsIM2808ooOgt+IrGeHe21CF5CXYak5f3mt7bfXvInHZfM6v0FSss5ocr5+2XTXZ+eJvEDeR5zX6rPZTSMCWSekWQ8I2KgYzmS6I2r3lHUi+M1XCEVQZ2RlOoPVbWKD2bjf9ctYZ1MDYxJhIhvxCpsZes59yud52sy0acSet1/O7aDN2+7ph7Ppm7MaPLtPL1z//O2/FpFNctbZN059oft9+XbOlfM0sp6N8ayjfj3UgevDa/3v1s8gTMzcgrwotHivQOJYHIsnCCMAoc3X7pzz3ocQNvyXQi0EgaGMYiMBcEEETEqZGKDQhiyVzXB19PzRF0ZlJaxASpihtIgCmKOYVDSyvhiNGzJxdxqdGUDDkjdJNru9LO14dW1hOHSJxpY+NKGpAUEnNmmnzAs59vzCwiLHMU11J9mzxYfxeLwwf2plYdwUgIOtUOWoC9QjFIPF+RPzJ19aydeKYB1LaLEUKkbSS6e2mM7MsPSnDr1wanlUxQm2bUWvz6x9WVXLg+NHji0sLo0qWxE8MZRB0ulk3WS6vzwarZxcKIoydGK9ZRqdTl1WofH1YLR6YmFxsJp7a0kCK6NjeO+BEEVIkwZhWBZNXlW1RRSJUSAFK9W4XBsMq9FoXNWlE0BoMsk1u1CNSqysVlXR2MoG2ya3IigoRUq54JsiL8cjay0RkVJS10sLi6Ph8K033hhF0X333XfHu94JoBiNn/jpgffe/Z40Tb///e+/+c1v7nQ6Bw4cGAwGb77uWkPBsID8zNaZtdXV3vTMyjAXHR9+6cRykV997RuJ4eoi66QI/tK5HV2FKU2jpfmpLH3f+2751rcePfjU0xzF1ss4r5XShlVVVZV1pMT6xnOA0RzFgUzlfdlY5xu1DoJujdRKRzBKJDSABbHRaQStVNCakyQCyNtxVeS2AaDEgdgT2+C9uKopG6AR70Pw0njAey8ivq5Gg0FVlK7xBNUEjGuXZlBpCqUpjhxJFOvGFmura6zVtde98YabbrjvwQfm50996EMfSjrpvV/9Sjy79do73vPQ408+e2IxndoSZ/1qdWTSRJGHptJWutspnO3v2D6q3A033fyt7z6idZR2u8VoWaV65559x07Ne2neessNp+qie9nObZfEV1+zt9vrBU9N07SAsVcvr3lZ/O1TRC7Kr1pe1SB5RaDMb2jkvI6N/6L8euT1qjIEnI/XTzaVtn0ByRm+zLD+O4HWGQ+xHkAkE7ZAAIxwhlvUW/FWMIHHktJgDVYAiFkTswuooK3SFLGKEEdgZmJUVgbjY88esY2D0mgxttwq6crESVDK1haVDaxZJ9ARyEDHrCKjY+0ZeVkVuYdwC0AGoDRnWQihWSuwWqgamTJRFAEUeZgmYDTC6oofD0yoIjQoh2gq1VjUFrWzi0s4tUB1wWSh2sfGgAHH/c5MYrrlWo2FQVlY1gmiBKS1kAbBOiwNQmG154iMEYYDxHRV0o0zW5RYGUrZqIDYJNCp9qwtkJcYDuu6hlaITSBxJCBYhZBF6GZeqapskDdkRcUJoggqAjTX4sY1irr2wWoFrR2jZe9UgakJaGywzosjZig1ed1RDCYXPOraWgsRYVImglA9yp94/Ildu3YBGI5HBw48c/fdd+f56IVjR6IompubA/C9Rx+5/c7bq6b6zncf3j7dv+nqfbocpKnkzRrFNMrHEHYeVukDzzxz7Vuv7/XT6UTbteX333XbB9/1tj98320fv+22//hjH33PLbcMVqr5hZW3v/OOrL+NuTMe1eO1GoH7WVfEj6pxIAcWNhomFdZiq7ocl03wgFZesQMrARSzgnjvC8AikFZGg0FCitroMW9r610AAVqx0ZomufEk+FqAEHygIAwBODZRmrzrjlvShIOrva99qASwVDmg8Q28D14AVOOhH67O7thxxdVX3XjTNd946P610fAjH//9559//mtf+1pvy5bb77n1gUd+8KOf/gwq3rPvyt/70O9Ob5mFc76udKRrWzmtoM3yYHTdzXcsFu6FY6fysqrrko3fsmP6xttvevTAE/vfuO+Gm688traYbJ0mYPfu7XGWUZRGmiNNRNTOsgkmhtQ6OKFlIwVo8ootE8aNCR7i/ACGi3FMv/VyoT56fX13zo4AnGmbeZU5kjYQKpuu50LcN6+MngmbrFCviI9p5/JG2azNnME/fFF+o/KrtcqENs0KcQCfO+42zFwCPgfMNdFjhHgySkRAxBBF3MJ+25yR7T2ICAKRJyXMAQhCrKBYC8EF1C5fWlFQaOeD92i5TIgILGHChkJE4j1CgBCYacOqKQylNHEoazReRRFEjDEhBE+A0UaIakdBoBU8wxO8ABCCIw8GvAdpFg3HoAhBtSR/VgCjWsprsAImwTuVbaCNUkqD4AIaF0LwmhEzRDxxUMRaOUbLiCM+eOvADEXWBeccBWGQUkpEwAxiCkJEUAz4ylkYBsMqglEEpUUhgHygjQVCtZhsBqlAbEmguVGAeBBaKz+IBR4AawWt0IbngFzwjXeAN5q1ZhEPoA3YHq4Mnv7ZkzfccEOWZY8//ngcx7ffettode2R737v1ptvEcILx1/ykGuufePKysrjP/zhm6/YPzfVgauaahRliVQ1lCka70gvrKw88oPv337nbQqBXHPwJz/82t9/+ZtffuhvPvcX937+C9/42kMP3vfgNde+6bI9+4ej2geVxD2jY4H2oCef/vlTzzxt0kRYSE0gq5N4Ku8doBQZNUmfGNq07cE5P1nXQoCIUJAQQsvwzqw0tRnLPAURJ413ADgEEYTgwBQUCUBGx2n0gXdduWWmD3LakJPSU5AEaw6dTqrTFETTWS8W2+933/qW67dunf27v/7b0XD59z/xqWeeefbpg09u37rt7nve/9df+tYLS8vR9jl0s2PHX8x6nfFoIKFJYk0sLfkklAHUDTe+83uP/IjSXpR0NEOx+5N/8c++++hPh0Vx67vvOLkyclV52dy2FNi/85I07pg4gSIghHWkwLmz94wYpTMr0Fm/nhP6cfHt9qK8glwgrOmMkfPrGkVnnffi6P0tlNelyryWfjwTcz5J1MDrthmiSXKKc2w8LJspKyis08oQkdBGWgphsCKBgEix1tqwUgLyQZoGIfjgIIiU0cJpkoAYcdwutK29SKz1wUZZil5Xibf5kIOFNKjGvhqlCSPS6KRxt5elXbIeZQ0GxNZFBebO9i3YuqVp7HgwstZCGWtSq1JEGaZmfdatokSmpzG3C2nfUgJJoKdUdxZbLtGd6ZBk2LEV013EEYjg6lExaqTuznQxN5P04thaU1VAcAZWe8TA1p6bSmwESSOZ7qBjYKgMdWHLeKqL7dNNpiyC9z4E1yDULMhSTPXj2IDERFHc65pOBA3AN65G0yjBVJp1sk5MhLoOtoFrAK8jFXcTdCKOFQyhk6g0RqQQKTHEmqGV1ppI2GhEEbMWkZbJhxQQRXEcK6WIWt1Hg5SJ4yNHjhhjdu/erSLz2GOPXX311VPT02vDwXOHn73tttt8CE/87OBb3vKWmZmZ+VMnlk8ev/G6awkOEdVVpbbOAkwmtsK9LVuOvPg8KVy259KpXlaNxzHrqiyn+1Oj0YiI3vq2t0/PzP7DV+9TrIMnHXU4gpC2lp4/Pj9ezcnEAYIQ4Cx5SeNOP+t3YhUBDhRYGQkMkCcmHUU6VdCavffeQ4E0SLxzAEdxGsWRMR6oG+cbG6rKOUdEhlWrG7JWOlICNOKb4DMAYkEe7MlAYkEKy4hiVhJQNVLb7Vl609VX1UVx8PGfpDp8+qMf/uaDD71w+Pk9O+Zue9vb77v/gfnBAFnsE42OQaJEo9PvXHfdtXVTBuc4MkYZBFxz/VuPH31xdX45NQmDlKK333QDB3z3W9+85sort81t/8n3f5iM8/0mnQphR9aDs97bxjeO/Pr7BrcMCADWYQQbC8jZa8jZC31LU/S6MhxdlH8sckFsxGYbxoWYVzbbNn4TmgqAVxO79ApycXj/RuW1RTAJvQ4aoI3xcSY5tAiAQCIgMAAloIkz6mw4WNsMswTiM2FlUEqBiDxDacVKCRMFgfPwDpEmIo5NliZIstjoNI4RoqbRaDzEi3dNXaIYJWm3f8l2X+VrS6uoc6XF11W9ulhP92e37Fgpoo7WUXDGuqYqPDOMxvJysbjSnZ7RvSs0wnj+pF9aRpsdk2NM9zs752Znp5EkybZt1d49K+MBXA0RiqP+1Ey0c1c/m/Jsu1defXKYN82LKBvU9Xh1ZbYc75iZ6b7h8iyJmsGiPbmAqkDw0Abdfnrl/i3btqo4Dax07Ab9DCujajgy43K2k9W7d/Vnpo8fP14traK2UEyKo163v33bttktq6Ga7ffiDjcOp1pK+sZhPNZab+l2MbOlquuTq8tk0ravVBJ1pvrDxidJkiBx03nW68Colu5Fp4a7nW63Y5I4TpOo143jWCnjmZVSWZahk2itlVJxmnQ6nSqvbd24xupIHTx48M6773LiVuYXn3nmmXe/666vP/i1xx577Hc/8bG9l+8/uTB/6PBzH/jAB7799Yd++JMDt9xyyxVXXPXETw+gmxGzyjpZv5tkiavc7Oz0i8eP33XPPfMvnUizWW0MGer2uArIR14HfvSRH6ydOpVum1PKmDSOOkj7M+nUjHPU2SrFeAQBi0jjxDqTmjTLOqU1QOO8CEUQAwQXAI6MygCI9857D8WsmRAkAMyqkyaJiQgQYQVqaY2UMYkiw1AmMnEcu0S3YDfXWMB4P51k5BqW0EmVKC0Mwyoi4jgNZbV79+z7br/iP9z705XFhQ/efpOWZnn+VKSS//pffOBP//Ibg8HazNzOcVV751ipfr/rvb388v133Hrbzw8923hSxE1ekDGa6cc//EHwFoqcbzqxvue9d/+ff/qns0n0rptv/u53frB0YvHGy3buBUWDIqwU1WAo4kHeIRAZnBmstD712v+phWROAprOnOZ0PtTnRv11k+drXU8uym+1nNvpk4Hx6qHQRLS+QWx83tzsxIn5awkdOve8L3cjr2d/vCi/HPlVB2Ovq7oUhFrW1wABQckG1L9956MWH35ex2NLbOdE2HtPE2RNi7YBheBhgyKnI3LinOXgg22AwIa9CBtdw2lx+Xgs3oVgqc2G6BpSZIuRXVkyfZd0dFON65UlFHkn+KClGq0sv6h2RGlPmWZleXU8wHiI0CCIVokf58PnDvtdu7o7tnFV1osLWF1RJIEJioxOE2nCykotA15ZVU2uqPJcARA79NWqKmfKF18QClHddGDgEJx3Arcwv3T4ufSSHWmvbxeXR0cO47kXkDeJYuvAKu47pqVRvehNrOMuAIXa0sqwOXrSJsqkMVfN6rPHwpEX4XyiVa0Q4EkkNE0+GtSjPDNp8B7Oq4CoajA/KBqHnbt8U48GK1hY1GkPTGA48o68t3U+HmohNE6vBw8ICSmQJudcVVVV0wQCsQYAxcrolqnWWluWpYi0USfMzMLe+7qujx07dtVVVx0Yjo8ePrzntjt27979s58d/M53Hn7/hz947733HjlyZN+uXbt27/vJE0/Hz76wZ/8bjrw4XwS4ZhxNdX0om3EJWxZrK4fWlh//6YEkmWoqrdJ4em46r0fbtmwdr6yG1VExKrbtvGRxYSHZvtOkZlhhZTzOraxVAehAGogDBbG2KXID5YPztmmApmlCCBxAgKvq0ijXaA8URdHUIg0UiFhCcB5oKhvgnTgPiAODiIQFzFpILFBaW4eQAgqIBBKoB2SOdr/xDadOHC+XxjGgJaor+Ma6shDSVVlXK3KJwtL8KQohFCNmKO9jpWKAGrd1dsuwbKjxU0lWr1VoXL42TCPjvbN1Y+JuZS2CI22eOfSUtxXQ2OC73c4733bT0aMvlGtrb9x/+RTh21//3k3X7H/vvit3Jyovm8PDphmugU07cSUA2NBjuE3Cer5p3qrG59vJLso/RjlbL/2NSWjjk4IQUUtkTutKzsuOtAvcgEz2o9NfvNKdnn2iiyP8t1Jeuyrz+sxore2FZDNtUeuMmDRK3OJJATo7z2pbxYfATnkKLG2CbIAViXMOBBBDW2ngXWiYfdPAT+wuHnExHqCsgAFFWupSBR8ACYHEo6xgF2xZLEmdJhFCjSqvKaRphuAwWJp/5mDnkl3FcBXLi8poo42tx672vX6nHiw0w+XypT4CeDwGgSV4H+CDXV1eOcYINWqHtQqrayhyVAUYWC7XtMmLsTtkQAxfYXmA4TBSTIpkdWX4xBPDF3vo99HUOL6AwaATxYpRlbVfG84feBLd47Cstm29bM9WDEs0jmo3OHJ0VJeY6V+ijD90BEsDLeRDECY7HC6cOJ49e+iFxVN+NJRtO2RlCIECSW2xOC+jwcrWLcsnF/KTpxAcdjqwQnB1UcpgDSePj1YGcA4i1b7LEARQzkkxLv1odHJxqdPvDeeXEKiua+8dgmhFZZ6jKNa4GaytDsejoiqddUyk2YDCaHX4zDPPbN+6rd/Jnnr20Le/+fU77rhjYbC0MhgcOHDgtltv/863vv13f/vFT3zi0z955vBPnnjikkvee9ttt91///3R1m3NYNmmiRFpRsNeN2maBpRUea3SnnV+cXUI9sfnF6i2kQeAoij627bnTbW0lH/rkZ8dOfr8std10LDQUeRswczBWpsX3hFLMa7zMeBtYC9KIA6uKBsKec6FoForKHDwcOIaeCZVA1Ve5OOq061KoKmsc6GxUtpQ2hAHPyqwtraar67GTWWB4CcKeyiqd7xh9zPiFk4cKgoQdD3GdJZporxpYpLVpYUGqPNxCLYelT5KghMrdVkD3q2t5iNH7CUoXa2NeaavFaKYx/lqnEbWBXEunp6q81EgIITedG80WOHYfPADN/53//3/5oP7+Md/7z987i9j8FW793ZEJQFeR+JDXRUhSkFEcUcKu2nu02k95kJsMeuWWzqzwoU2ntbrxBe3hn+ysr6Yv0Z6ofMOmF+/ovzaeGjkwmmuhTfrTxflly6aJi/ZwIbhbqKstNrrxDw46R3BJJwBACYRC5NfRITWjW+nTc0TVnsKCBsWQSYEMBPQatmCIJA2/l4FF0RaGEtoMcFERMQCCi54WGLPrImIpHE+xFEUQC4IxjVDscDbBhRglNgGEuA8OIb3CCQ1KxbvHQJDG/FAqKlqMF6LY5XP58iyaEuvyatxcCbOrAjKxeJ4jjhB8L6GD8JTU6EpR6MlxAlUguEQTYiYVbdT1hUaT1qLa7BwithJnmOtATTKwkRxMCKaw/JJl48RFIRhCBIAF0Vpk4/gFOYbLM8jieACoGCiygcDREq7qgk6x/wCdl4267vHHn4Ua3l0yWWOMXrqKdq1o5PzgfsfxOoQde2AKE3E1vABiwvNYMWfWsbiEnW32iCIoxDrKEkgHvmqG602JxexsIqO1iaGByjq6lQV9WhpGbQMrRDFdW0hEbzO0lktL2FtPNo6bT1hKYdQCA6GEDM7ryGoCqdMCZtXZVPkpBLxVFZlOtW3aPKFlce//8N33XLL6ksnTs6fOHTs8Dtvvvkr99938PEndu3cu3f3lYcPPvvItx/96Afe/7m/+LNvfPP+P/qjz15xzdVHn39Bq8itDEKUKDLFyLXkwUFzcAUplsohOI5j33jrKMqmnPVoQqJN3fgDB4+OixydmLUJzrngoeAVcV3bQYUo1pEfe9sAWjQ3BCDViHzt88aHaW9hStHMysAZZ5XXBh4Yl+Oy8FUhESDwaTLVOOV0KlFqSxuAfLgU6gq5aECiyDZVA4ivTV1PGbtUrmUdFEWlCcE1zpZBR2VT1RE8UJaFJiZrMj0DiirnVAwvrlxbg06VjqtxDq8NJ4pD3QyzLGlcEyTiOKurqtPvFwtLnETehljre+6661tf/3Hp8rff+o75Mv/Z0cP9bvaOG65fE1CDfhcHjzztFZEy4ll77eDAYfLWEQRECO1LLWHTm0irvQDwQQBaX1UE62vHxKEs67N+srGFCdUHNvxTmOSvOFf+SVjvf+WIUVKvXGezrDOqT/7a2LAnuKhJvMV65TPeZM+geyF1+vDTlQKCnCbjmPwaNs7YWuXP2Ps3U/SeoQqH02ckbBwbwuYdZ9PVThrd7PrcJLTJ9j/Jl9LW5o27WL9C32ozpw9FIFp/EqcTHp/e3k4jPk8/zLY6k5zrn/LA6eCs086sCZU2Nl8/nR9tdCENqYVwXGC8bcrpfaa0RDubW2nn9kbqvzM4fi40I2VT/24+I0OdbueMI86w9Z7t1HsV8suMYNqw+63/TSDwBOTbjgZuM2O3P0+qbXT35Io0mIAJxpA3ApQmlUW8921gtnhF0jjrXMNBlIiSAGlTLgqcRRCiDZN4AMGoSQYoMLV5DhkhohCRZ3Em0iS+KXPEBkbZaoxurK+/qnPz9eZNV2DnVmiNgFDauN+HZtgadWF8Y1zZlKu2LqJIgZWAUDuUTVSUKBs0VgWJ2URkfN2EumYSthXGYyggAqYScDOWamqqn7GeNpFxAbVF3aCq4XwQeGIRCVWD5dXpvXsjjcWf/gRFddmVb+h0OouHDqfbt3dcqE8tyfLq/8femz9bdlVngt9aezjDnd6YkzI1IyEEQgxCsoQwkjAY02W7O8qucDvaUd3RUX9FR/QP3dERHRX9Q1d1RXRXORx2OWxHUZSxMTZgF56wwcZghBBoQFMqNWTmm+9wpr33Wv3DeS/zKQcMtiwJk99P95573rnnnXvO3mt/61vforoh6Q0GW2BfpJkJfFREJYEhRopJhbQXvXYc2iz1t9N+yQoMO1grQOgQOtQVmtaTAzsk0k44CtjAWgEhKJTYWRhFSiLRWwNiiCSRBO3bRqqqy/MYI+rGT8Y7mzuPP/6dd979HjX2m499K4jcesvtKvTY40+2UYti8MTjT5x5/rn77rvPGv7c537/3g98oMhyCyCJxKQJgCX2lp1jY4lYlaIAJtRBooqhTlJiFdJqMWfQzmI2Wl7SECmGFKrJ8tBm3gOZ6tDbwnlSjooEMDMbC6IEqIqqBtWovXI9ikJIACJRAqwaQ7aqmgYoMkaYjWxaNpLmW7GrjYFlQgysJgFNFxKBAWfZxtZTB0gCjHGaQND91RsRmFJfBKvK5IgskwXzoQAhaUxEROxiTCnFLrRk+pM31nufZdXWlh0MWNFUiyOra3ffdce3vvnY6vLkwQ8/+Mu/8RuuKD768ENf+9rX/vX/8/997akXzi7SU6efVzLWeFZOzSFKBhemN7l846ER5weVT/4gO19TVr65+IGv/xvjW/ZWwuX6CODvU8V+JVwzMvgeeGMaF1wEHVh89hnQK1COzP36r6/mFPQuM3oQ2BCgkJSI+yFbJYJMUiXS1Ae/SgSjwvtxNiCiIGJj437YLYAB7a8wWyYQt6GzZckppqrB2MExjh05/qEHbvjoQ7sGrLzz+LNnP/dlfeJpdE17dnewPKrrWkTFio4sYiKpWZMblUmNxBZJXfJRNAlyOFgmowgAkdgMSZHl43fcdtNH7ms1nP3md3a/9DfzRa2zOogmYwALYyysppi6LiZkPvPER07ddH5v0YUFTp1YGYzPnH0ZXbN08hR3qdqbxcUcMWRiYKg1UEOIQAJENAn3bjpJWIEEk9RERRQkRYLhvm0nK9JB/+5IUJAFFMYi8YCsYZusKw1lhpHlTCSqsL3nCIMIkgRqyEIZopRghPvYAJQsI6SGMxe7VlJ8/Nmns9Xld917/9f//C/+/E+/GlIE+ZfPn4Woj2myMnr8sW997OMfPfPCi2fOvPzCCy888OAHv/D5PzR5nvZNHmBUQWR7H8WktizaplUgH5aAhtgSpVbCYDQQJCKzPT3rqOjm1eqR1a2N884YQ2wDXGZrDV2ocooOUGeS52BNA3SWxVBiJIZkCJJUUyZUih10tgBKyknbvHAegGxnEo+5nXxcv2gXO3VMgCGIRMAmQUohzywD1poQW+cc93EeaxdhrNWemVBQb6PU2wsYElWh/eXavs0GIaYAUJZlZNhlPkUhIhFJmkBJNLjxONYtQ4bD4S/8wi987auPPv/Ms7/0r37p+aefm+3OVpdW737/+/7tv/4/Z+fO/fgHH4zGvPjyOcpWYteRqGFKenUq5LA8EyAyqvvLRmWCau/Ccale+LBM+IefZbmG7x/73P9V1CpXc4l5q0H347PLFWNvNbyegc9bPxH8jxbKXOYzcYGYuiJldOEjJhYQKEGNSgIp7Y/qpEQwBtoLbgTKSoAxYENgECszAAND0ERCUFJSVWVrrBd2GuNBSWAgNgoGMYyF80herEUXAELbIB8u3XX77Q/ddy7HVgpHltfX337j6Nzu02dOF5rVs526iyjGJsuTChxBurC72y4anhRqCN6BMl5d56pKcadRGCipks3VMazHeIATJ2996MG4OjTS3vb+93/10SdTbPKVrLHEkwGsRSdx1hAIeUaZLUdjeL8Z2s4Zf9PNIN3+7ovk89Xrb5hOp6GOCmBpjBjDdKEhqVU2VgQgwFszyPOlUVPVnYFC4C2RISIYi3xgh6VfFuxsgiEEcALpAp23GcZjY72CpK7b0KRqgbBYNHuzdoZu4Zz13mOUI2nS2E9OxHa/G4PNCptXvOi393aI1lqxpqvn+dpas5h9/Zt/+9DDP7G04qaIDQAAIABJREFUft1sb2qMBROEzGTEbbc9nRHkG996/NRNt25M51/7xqMP/PiHx+vri6pxbIG+LjolSY4NgxOrEuA4Hww0hXY+J2fyPLOEajadTMbTui0yX7o8X87uvPudq0sTAgY+L8ktgjx5fuPF00+nrSYAkVUcR0YC4Ix3We5y74DCiiARAM5grVoDuCwnO7feJGCQ6SMP3P9Tt18nuO7mYf7vf+9PVGGtdc6lTkK6aGzvnEspDcvCe9tfnDZGX/i+fJ2N6RsHWL/vYBQgFx4iZjaZjzanRFp3bYp1G5o2tiECbIwNnYyKcrq3GRIggRgffPD+pqk+/1+/8KGHPnxkbe1Xfu03NNJ/+7M//7u/9/mNneknfuoTVA7mEbM2UGG0iYaMJU568RG9mD6+rKLkIidPuHz7Ja+vNkpcwzVcw5uLq2p93vL4R2ZlDhZecqC5ucDK7OvADxk47oc4bKhPoIKU97Or+zpJa5kdsaqqSISoCtgaMkzWq7FgVtUUE6XkfC5dqwpjvLWZGwwSXNUsEBIEQFJRkIH3PJy4wdgY0+5sS5wPimLRVdA4PHUco2IlM8veTgrWgR2+55anf3deJ4IhEdCxk5MT19ddil1DdR26l5F2pG5p7LC8gtWV4u4703wevvPdtLmRqkAQY7NoGGurxZ13DG69afCO2wbO7L70kicD65FLMyhxdJKdPFoOR4tXNprHn9camAy0tLPcRTJYWrvx7nfVNp578ons6PogYW97J7SNO3Y8O7rGq6Nqdzd+51lsbVF/bb3BeMjH1iY3nWyHg10JTWEkGlgTLMXMYFxiKRueOh7sFDvn0dWJBA5gbR3Z5RFdf3LgBwCm85kOc/iAAXW5+CODyq2OxsVkaZQdW20XVerTVYaVidkiH4zy4Xg4WWwv9n99w6kTWNul6E8cdcPSro6bRfPUs8/dcced3/7bxxaL2pRFKm3yuVj2g1yq5ttPPf32u9/DxaBp2r/4yldPnDgZZSvP8wR0XVc3NWIMhoitkhTDfJSPLZutzU2b5d64anta5v6RDz5YFtnXv/bokfW1I8cGJ08dk4SyLENMRNx2YadabNWz1uqgzCwQSYX7Qjn4rCiyfOTyEvDDso2tOE4ENRbWCWCHg3y5y0ZFBhxZHv6z++45BnDS9dtu/uKJp51D3zsTzTQojCGJHQNFmVnCcDLJ89wCee5VNS8L411mvCM7GtoM8IOibVs4kwhk2BAMYDOfDwfGl6lLbUzG2qwcgG2eDYm4DxpC6HxZdrPd0dLEm/Sud7zjP3/yN7Px4MGHPvyFz312ur17//0fdbb48pf+6m233nLn3e976blnMvaJvaohS45t09YwhywY9iVw+6vSCy4i/eLgQiKYiF5LxJAefHCBp7lW6HQNl+B7T6LypjI3V/v2twaf1D+Pr5um+Ic0mvnHCWUOGX1e1hR730xJL9//4FMBiEmFCAZI+y7pbEBE+40OQIkSEiSqiGHL1tk8V2NjjCKNhkDOC5IlNi7jLM+Hk2izsEcpBUkBmgAFWXZZMVwarqyqUjudM1lnLEKCks0Gvijy0qqmIxnH0aA5twWNJhukVrC8nh85Pjx+I7fJkNqu2egkhA7NduEzGS/7k6fW7rwzm0+rrV3d2wNXShyJ4XN/w0033fOBelScXyzec8v43NPh+cefwrxFMcBktPqOtx+745YA+MHo/OntFKfIc6QuVjWy7AM/9ZPnQ5t0ceKud8pjz579xuNoOxxZWz1xorjxJJ1Y3Xz5zPTFVzGdGolJEvLMjAbLx46snzzROX9mZ0utg7fQBFE2DuXALY8Ga8tRPU5PsFHzvsE+ZUU+WltmdRl7VV2ZDP3AwQpM0DQb5KRLg9XRKM/9cG2C3BlLIAWTgrzLh8VwMhyNBsMdnxFBSaJaYp8AuzxaObK2tXl2XBbDcrB39tXvvLQ5rxY2K5FnVJbFyqDr2ljNFUBRPvnUU+QcmDuSzcV0uLYsqs5Y7roOkhoFG7ZWjfphUY7L2HYud5Y4zKui8O9/z13ve/8Nn/n0H588un7rzdefuHH9t3/7U/PpvKqDWGt9MSxKN1ma5oNsWORpkIAkqiBVZcDnWZEXGVsGfFGmZMhRBMBElgxgM1uW5Xg8doA1uQNmgGvhSxTFAEBRFGVZBFexhfdeYwtgMpn4zJa5Z4IHRpMxGfZFbq1VYweZHQ69B/KiaCRS5uAMW+OYDeCcc2VOWdGZgHlDahN0Y3OHjQ1RVNV7X0+n2WRQjMd1M/vIxz/6xJPfPn369Ec/8fFvP/PUN7/5zWE5+cgj9/67f/efhoPJAw88+Njj357v7nhbZKPlulf3M5QuUw323AxBiXp97qXUy0Xt2yX9lv/hw8o1/BNHv2T9YZxHr+FNxxshy7pauHhJpmlfUnmQQ1WCAMqkvUrXGJBR8L4p8L4JqUFSEUhSBavxajzYASYKKQjWwtgA25JNJoPPjVgnhsiCCGARaCIVp8iEPDsfQoRxELN3dhPBJsr2ao1AFP7Wk0+jzIeTZRjPSyt5MWybtLW12woVR9ZkdQi0gFAIYdbMpk0VSdyA2CMksEXfN2BleXzTDXa0tHVu75Xnzz7+zOz5c+d3zu7ADrPxCrHdfmVz49XNijR6p8QOrrQZWsGiA/zLL74awG403Kmrje0tGEN5iSrMdvaqWdMGjWoAQoKkpE0HQWpiU9dd11lrsyzbr5KNiqQmCOouNm3qgrEOzkFNloCoEBrabJKPDLvpvNnZWwCg0JKmYZkdI73Om2PW5KJtVUdSPywza/sWyanvJCF9BZshw2wNoAJJeYHRcr52pGrb48XwbcPlm31+xFDX7kmuMdMOIXZdamLu86XhBAmZ9Qiiixp5Nlhd7hyFjGZoOwcqvS08DBPUqxqlaWgXQHAOg2wx3+Q83v/jd737ntt+8z9/ron19Tcu3X7H+uf+8E87yfeqUEwmfjRsSbbraruuFkjirHWegaQEpj5udrmz3kEkJFiX+2zIBkogiCM4wEO1bmLVLYBZtF/eaF4FzpX0lV28tDttOlhDAOCMci8W0AQMh6UxFEK3WCwYGA4HQuIyL1AJvUkNFIAzwgRrlAnMfRlEUlFChGZ5AaZYVy+eefmrX//bnb1F00XjMuecK8u2aZq2doZvufmGL/3Zn2ZFfsPbbvnzv/5rsu7dd73na1974fy5zaWlleWl1a/89VdHS6t7TVBfRJASGo0m83qgAr/M7ImVzAWH7t6E+/JHm4gOu3hf613wo4dLZeD798OhjkiX4FB3pCt/9Mbi4vlf4kd/le1vEq5+Pf8+B6Mr/wpyle1vBbxurMwlNVpXxL6z9dW1MrhQvqUAmEiU+8bzTGyVICoibEBK3LsmQUijxigUhYwK9cO9TaIgyyZLsG3S2EVYTQIjapSYWQyrAEGqqpOsHq6sqHPCXNddWQ4qwc7jz5x99+m1m2+mVnfqtHvm5ae//gSC3Ys1mJGXfjiMUXH27B7CsWPjYpzPnSIwYkqLKcz5ve1pVhbGeMkKpIDUwhgsLxWrq1uz2fTp57G2XN1wHGW5NF46D9uFpNbhlbOzU8eWzJHALF2khIx9FRWzBrbe3dhZhGZw48pwaTLLs1TPNRoU5eLVzbi0NDq2bMnC5XDOiSQgBsHO7vwV2jh/PsIQEUS1iyBmELqI2UJ9nO7uQXMIwThSRhRYdWDPpq27dmML1mTj9ZVB8a4bT73jyPrD970/Nt35ef3ZLz863dpZVFUxHFhjDCEqVLXrurbpKlN3XZegytoXVaGt7cpEU9S6+si99777hlM24vN//MWqe0FVInXKliO45oxyDd3Ym3o6HxRZB/GOUzU9cfLU+e2t2LbKmhmfEEUDkrBGo+SS5aYxjIHl1uCud91x+x23ffb3Pruzvf3Afffecdctn/+vfxljfOedd+bvu3vRNY9/95l2vtAOUTkJkjBkv1+GklFiBshZsWg0NAFM1hLrviJE9/XrGppqEbuJAyp1//a3fueGG24pjX/yyScbjjGCycYYo0oE2roxGgPgvQdQ1/VstqeAy3xSscZKTG1oncTQcOjTNwQlXJDZJCCE0HRd2yuqrUGRa2p3pvOl0TAJRdEYurwYhLozxjz08AN/9ZW/nM33Hnr44dMvv7I13T0ymdxzzz2f/PSfZIPhj/3YvX/55S/Nq8WNt77tiSe+u7No4HJjbapq5P4K6sZL6ZaL2aLeTu/7X1VfSzNdwzVcw+sCSxf8AwAcrKKA79fC4TC3fKHiHweamMO4uOWw98zBsCiy7zABAxAxs/SOAULERpnYmrQ/UkKJGCxCKmTYpyRoY7DBWE+2b3kNWI/QNF0HD+QZl4XmOeo6CTmfJSSNwtYKMaI0KWXGwNmkan2WgsAxNHv5q09/6wuP6uaOOXFkZZD5Ou9CBudQGHGEwlebM3Rz2GWgycNiHlpIsD7HIiGQzjtflJ5tSAIQ2MFnUFVjmS3UYG8eHNcsWeYA0cz3NeQrK2tdm1QJbFOcT8rhztlXjM9TSGHRYJAzc4A0KcA7cpnGhLoKi9oLMdtp06FuNCMyzCAxFlVDomWR9zXMhjklMQIWhSjmc6Ngs+/V20kCQMYQYGCcMYgBJI4lV/3we9+71iyWp7XtwrIrq/d94E+e+26SoKopREcmpgQAZFLb8NIkSIqIYDUQAsRo1k3HWg4Le/2wPOagQX7mwx/8AO6dMrUCgXXOOcfOgAGJaOuWjLHeJlWfU5ahDQDgCxChrRAjLANASHAFigFIsbOHpMgK/Mqv/UFoqg/fe9+tt932Hz/1RW/d+991+zvffmqvQxXx149925o8aNBIEPLkukVrgdQlVRWoALVGZTtNTTDoukiGGRBJSSNMSkCTOtG4tbUDoOVsz+BPvvPM6tKRAM8ajUPXNN57KvJ5A1W1DAPE2MUueO8gYgDrvQoBcGwy7wkSY7SAcw6Gq6Z260f6wqUEJNWmafxgHEMyWZa6hfM+tJg34dkXTrus0MApJedc5uknH3n7//G//dnqePKzP3P/v/nV309Rf+zBH2tD99LGuXwweOgjb/9f/5c/iJpWjq6e/tMvcVmkxBKCHxQxdugdnKVvTpn2X19YjigDqn2qSRR9fxHVfhDRXhaDS+OVnne9pC6g33JxRXTY2aKPlno3EbmU5H0jbez/4Th8nm9BXuqqBjMXdjh4cam/yOXXv9/CBIDUHPpjwcHvuL99/9By+AsuKCkPmRPtn8y+gdm+7vLwFx6yYLnsJK98h/xdt83B3cWH/vMDurT39bh4thfvTLrEUgW4YIZzyflccg9c/DRdLZlx2PflYh3AwVGMqtLh6rDXOL3h8CLk4g6vsZG5vKaYgCvkVvbpWLz2HjhkQXRFXO05veRe+rtpkQN3jwvhxBtdjN1D6FK6+jXQdGGNh4uXzBAriImICRDRfmmsChgwE/UTzEEJaNehyIy1KSTEqGTYOhiGdx1E1EKjRAEllOAiq7WDAQzHoBBYYSNUdJovpF4ErpPLKE+mCwBiv6cgqQUyC6eQaBPAFnnZaoJEWDLewJA6A2YoECKiQJQkudxhXALJGJO6IF1AEoQAB2Q+OWOLPBU5VkeYzSoJ6HsYZrnJHSySqrMGuQOgKYItDLNjQQIrjUodllE6gookkEORsXeJYDJPyqGq0BcYE2ANPCdCCB2MgrSDgFkNC3EnSVXhGEYWzUJZnTMjlFLVLql1g8KaSTl0ZBCDZTjnauK+ZTQyrwRlLYZFXliZV+iioO2siNpFCC9snDuyshra+m+++Y3Hzp5ZEGJSGM7zPM997p0h3t2ZVVUFMFsCkA/KwWCQVJqmGQwGzmZVVS0WixBCjNLGdjAeTSYTAuq6ns4WVVVnxeD2m256++1v+/znvtDW7e133fXud5/61Cf/6LmXXqmFkA+bGLwajUJCpJTnuQBMxMoiAsA5ZzMHT8KwhvZdqQFilb4o01ty/siJEwCE7bSdDo8crapYlKNxXhY5qkWtitHycpajLMsw2yGAiFJKhc2ZbG+dFSVlsBfGuAQNQEqJFX2T8wTtT4mZXQ+iukkpxsgM8MbmbkrPLqqFdSMlgjDvdwNJqsqK1CVnbO58URSmyMixzeA88oi8pI3d7S50xpZIahX9QHGRTL5YxAQ9mGO+Hx3MRZn/NfyTxmtotit6bbyu0O89j1zDjxLehFBGvmfcRpKw38TmwGjw4HlgZmJDRCqpr2ICQIaNGrHWsmEQKYRZ2cACEpUsDUpkZZZlvij8+tp8viddRCsAE1u1DsM8nwwwyLp2gGpgoNxEwHCXQj23pWI9Hyy7RCHECohMTmJEvdAUhyuTJl5XTkYhoWsBtUhNQ8BSyceWaWRSSebIGKeO4fkWXUAbMK/CfFZef3R041oTUw6hvTm3EWwBxqjA2jA/urIgwTDHTUfRzOZe4AhgMynHx1fbkn3uhmTDyvKsyDDrkBscWxkcX+KRS50OTq7Nq6mc3wAAAww9VpfsqGwT3Kgkl7XVDI6T51hYLA2w7LOlUdsgX19pYmfGJXIPaxqmDoKMeG2cF1mgrvX0ajVbHgx2YgCZ2LVVnUaD8cD6tulw4LzJRMZZXxTGOTIApxTbTLqiLGJW7Grs8jyK+8yTT33lpbPDcnDmpbMb80ZTgkR4kw3DAHmmLmM/q7tFFWLSxKJMAzIDl4GpCqmoO6K0mM2b+YJEPXtxWWVpbtUbns+boiAb0rtvPfnB++/57B9+8fzZlx6+576H77/xVz/3jT0UoDz3PIsCosnKcqccrbeg4XCcAc5YA5KYAFiQZ5M7aw0sIUgnESARcCLqgM4yymzaVHsAsxa5NanLYrhu+cg//xf3PvPcYmlpxXj3oQdu/6uvv1S3TelzAZitJgzLgfe+zyK1bbsyLJy1Qs5bct4YwFo7KMrM+f0FIhMBxpgiy5zzTQzMDEMqav0ghnp3b2HyoicvLohwSZNRoQSbyAqntlNVMRo4hIgY9rxHUaBua1uUGXxTz0MSVSXLCgUTFBCGKmg/lNN+zaxKfQ8m7okZOXh2cYmvzD4vS9S7BqvqPw0D32u4Ki4s/Q9FHPu+Mj03c4Xtl2Y0f6AsweuOy79W+/zzlTuR4XWsJPo+cLH32WvwGtHM6+R/86b+Ct8P3hxWBleLYxSaBKzcm/H0g6YaUJ+BOijOTiIigICViGDJW2YGqYqqlRRJyJCEViL7PHN5nhkm1dy4djIIswaBkNQYR0XBuTUIqpQ7prJMTadBVUQXi+nGeTsZTMbZuLCzjc16exspDUw+Cw02t+Jsd3RsOBgPuAuzsxuznV0EgTGmKPJ8XC4PCqMWaWkyyI6u753bik2nKWG6OztzZnRidTzKR0Th7GZ4ZSPOBQxr2Q4GYWUyLEuZTh374uixl89tW2Y4hiSX+xPH1ttulqlOysKur8+XJtpOTZ6P15cmK2OyxAH58sSsLM335koszvnxKF9aKoqinddZluXDYVMULSlI1ACZNaOB9z7U3bAo2/GwLEuQQUhN09TNgkUmwwKs81m9PVucPffq8Pob/HipzIudqnvhlXO6vGISharpui7EFqLGGGYWAltjLCXI0rh81823nFo/Mk3h0eeePT/bzpaXrSs26nZz0Uy7qMaBmJScI2+NYzIgiDrnCo8mBkZSQwyCqgGvTJYAtE2QGJHAZKy1lJnRZKWRriiKcm197+wrd7/7rvvf877f/Z3PnN3aeeSRR378vTf+8Z89sbczfeCBDy4Pzd4idt5GwdDhqec3X57XzWyn7uoWMCDHBikIEBeNEHM5dEAKMbSddKAonFRiikBKurZ2ZL6oloDdve0EWcyqd19/2z//2Q/84R8/vjXfk7z4xUfe+Qd//fxj3/o2gb3lDiCiEEJROOe8AEmpqmq7vuTAgZmdMYYVMMy585YNRIn3pchEZJVI1AjK3PN43E7nIGS2DF1FxHpgoL7P8UAgyoIUhATOuZX1/NjJY3UzR0Ah3WQyabYXXbVQM1YR771KMM7Vobu41O5npn03hStxxXzB37zf5RoT8yOHa/qna3hT8AaFMvvGMN/PHa5KSgQlSJ9E6jdKShcHz5RUpZfnRASGkjASWJJCkQJLSiGQYyB11SwKqSqqtq4X3kU2JM4BMYpYCWk+n8dOLZCiqWoJQTVBOsz3AgIWmS8Gvm4XZzewtWsk5XmqupC2t+rTp7VtF/MaVd3EGLY3oBHsOaGrFuHsy/WTppiMdXdRnd9GTBCFCObzxXPPbZeZri0Zm4eNKZ57sXYDpC7GDl2TdnYXL7y8+8yLZYCtgt+cueUhrEVou9jGna3puVeQUb62nmYL7QSho9a1m9vTwsaN8yGEog7dvEoxGmWKolXb7U2nZ89vbu/u7s1XjpC0DaAQpSSo27RH8/MbW2fnextbGjtqWgQBI9Vtvbk7O3cut844Dnu7e2vVM48/tfncq0e8X7K0uTszR65b977am4W6CikmESiQREhi7KpqvjebtfXsuvW1D733PSeHA0h45O7bvzufBVdmfikGVViboWpaRXSanCWfWeecVQIYlkKHlPYTK84b70GEEKCACKIogayFMRCCsWAGAW2Lcoi6xid/53PnX916zzvvuO22G//DZ/7y5Zdf/rlP/Dc332AefWLDDAaWEFOYz2S2cbaqQ9fO5zIPQJc6YmUiAmRed0Fk4AnoqkUMQbrAES4St6JAXHTTvSovyjkg2i1S97Z33fnQhz7w2S8++tJLL5+66eZPPHLHf/riEy+99EKQ5G3etlUXAfBiUUPRNaEBRFA1HRJSiEECsywaWQCL2TxK0pRiT6UQAMSu65qWZoumDRCwqqrGoJxZNiaq7FsdgojAACclBSvyvPTlwOaZEub1YrF73iyqd588dte77h7HbiXPzu8uWrgyHzRdJ9r3jbngNKH7be11n3252MnltdmEy83xLmTW9bDb7zX8U8HfbYf4d+GH0c7ksN3Sm4c3ng16K+JNY2WuAu092y+2muqbVDJBBL3LBwQKaCIisCIlUZWonBL3QzpENUEisWXiJCr1ompakAdTSw20ZWMkEUIba0UM2NPeECUpkBIYIEJssVAs9jqlze1dXTQUkoNK7CS1CGhfPdPO9lC3aFW8RdciCepIvutig52NbndjPhgiGixazHt5KpAiXn51KwSMR7AuR4bdKa1k0AiJcW+K3fNn2dePPTNtAkdIW0t5AxyhgSzm557+rjz7DFhfvv6UqTrs7qHuCKY681I12wYRYqzJoOowb0GkjLAzC9Kdf+HM2Y1tnU73lNK8hhBETFIsKqT53ivnt1/aDec34SmeqkAE5zy5WFXp7PlFP/GkkN3hIcWrZ3dfDXVB2kpczUZZVVc7u4hBALUMQxKTqqJrpzFsbW801fToIDs5mYzajjR4w88+9s2nz+84M1bxddWCVb0SSwZxIENGiUUpoq/rViRRJAbxQQdT732CivYVRKyqXUx9wfmwHMynM5+7pFJ1bRvDO97xzo89fM+X/+bb23u79z/4wTJz/+b/+lUtig5CxsYuZMiaRFO2vnAzmS6AJnYAegpEojap7dq2C2jbEGNMfdNTAMoBaJpmd2t2/Hhugbaqr7vh+g9++L4//bO/efJb337koZ94593X/d+/8sU6djvz7fW1o9tbswFRCFDV+XzetpjNZgtAwTFGJjCQREKQug2zFvV8EUKQNrS2SSklSx1QVVW7aGLiGAUxMhkGCSHGSGwMcwrUx/rYrwbcH3eVyWSenJ1XcW9vp0jxzvXybb/436+OV3aSefA97//8Xz1aVzFoDJKwL6s85PPLUFHCxZ4D+2mj7zmNXZWhoWsZpn86OKy//vtpsd8ajnPX8EOJt1IoQ/uBef8Q7A99hyWHOKSvvvCoGPTzncQoyn21MUGUVVKrmnxRqmpXV0DMh2XT1JCg3pM1mhJSNKKUIgA4o4YTEZyFIahSaDQ2AKm1CKmAWqOdE40JhlHNuIvSgRNKlFGlFjIJAzUJlCRgNsN0ASpzkycywVmYCBDqBmfOId9DXsTlVTA7Z0GaKcU2ptlMRjuIgiaiTbBAxmABBHWD6QJbu3DWL7ep6RA1Jzcgt9W12J2ht3MhA7UZOydQoA0xzmGrYOsQphVmTalmnuASChi0EaGlurF12y1qJNqvJjPMzJQEdYOY+gafQwyKbKWeK4yppaO8qAQBghAggt4e1xgmdF2HGEHCqkxo5vN2Ol01LsuGL50/99hzL56pOpMqjcYIu8w2NjJLKTAKJBeIK+uTIaORSUgUJMwM0X5Zb0wjUIIha4gNACEWNaFuS+YmtrS3WB4OROKdb7vl44/c86lPfX4627vv7vf89F0n/vf/8OlFCLmz2nZI0SuuW5+M1o9/4/mXUuyiNh0gJMIaSWNvcSQIRG1CjKJKYkxiDs6w5xaokYx3s9miA0Z+dObVrd/7L1/ozu385Ec+cdMt67/8y58NMHVbL01W9mZzgK01McKwq7uKBF0XY4S1lpUNgZkJlDQlEVWklCQlTSIp6QELEmOUmKQL1jjymXaRDDFc6BpoC8eg7NCDBRzMEOKcGZSuzJvQhbrKY62zdL3LJ2ys4J63v+NLX/82CtsZgsngDJoGoq+ZmQ754BGT6v67w+0LDj2mr9HKQPe3XEtD/Cjgir/ywSr1qlzCDwk98yPJhbyFFTMWeN3sbvQgk37JMfd1UgAOaX6viH591xfa9XVWrH1dtlGjSgzSvnBEIYAAQtbvW20QGRDpgVuXirUcNXV1xRbWeUkkTb00GM6qaRKy3qTMI4mHqkjvO5bUCJNaA1XDQkQxdJyX3tmm7mLsjHfwjKAgRYoWGlU1JOoijFqCY2NiIomw1hRlagLqpuG0urQ8q6iTlhFNCioiCmusdh0MkwEkGSIFpcQ5+bZLEIGoYau9BMFm1jqrBDJw2cD4RWohqoSQkmWKXYIxYI+UQOSNVYkppQhFTFbgwaErzRUUAAAgAElEQVSLUjfE1Jvg9ZIHRIFawwwQ+taMMaKRqm28WigRrLe+jSlVTbuoUDU0GmoDbYIaY6yD8aDAogiJQd57VoAYZPK8mM92nnrm9LeWTpkbb66nW3/7wotnzu/w6nroAFXrTWANGvtumyosmiI4EUCUNJkD40RmBgNIbExKIGIYA6IkMSmYma3JfTZdTMdl4cjPF7Nbrz/18Y//2Kd/+wsvvvTixx55+OF7b/1/f+uPSPDzP/cLRZ4bkGeCyMkTlgmvfCq+unn2oEDNgU3QmIAIJQM6aE9PpEQkxsAwrFFAQIF0XOQZIF0YOxf3Fu973z3Hrlv/9d/6/aoLdQyj5dWNzbOrS0v3v/+9L37nMRZYx8RsPECOGM4boZgSJCEhkaakRgnKJKpkLMFZ03fshPS3u0GcT/PxcpuSppR7J9YmOLZGkoAYTL1MuNfuCiDQLsWmaev5whGPBkNhI76oggThoijm0xmyQUoCI+jiVR5UurzEun8jYEB6agnEUBH6frvQ6cEQwXrl+Yyusv0a3jAIiL/nVPYGB6k/XPfDP/xsL3fPv4Ye9jXOffuLPj7sEIMDSh+9VReAC1XgdOj1/h6sdFDwf1CE1M8BPc1NF40BLlAs1M+g1KtpUjTWhtgZayXGsigQNIk0JPAEy0iJ2qAaNAd7J7PaZCWTiyoq2tdMsHUxBMMWISgzK4Bk2UhM1bzzWRGZQwwwxlnbdo0aEKmGYMXm1msUsdo5AQg2k5AARpLOACQeCSE5VXV5l1nEFipqjPGmDV2XusLkIgkxkpAvh8GrVu3efObYFNmoaarAACtiZ72xzKGqyVtYS1nWhQCXLZJCFC53kE6CFwt4NE0s8kUCXIE+zUYKAxpnizYmGBgPy2gbWIeklLs6tWotGMgzAYXQQuJoXDSLCsZxUc5iCyT4ojWuEkGWQdSIRZ4jtr4sjDGwpXZt27Z2XIK6wdDOtlpNGaIBW1aboNjb8ydOZJFHrtyVSqCcEtghhKZG7lcrGfz6X3z9848++RMf+5CeOP5z/8MvJue6LlrrC5+F0DIzk2ZkOCkB1mWwrovBIhl+jbliVMQYmfdbWDCztVaFUkpEBHCMsXC2Dd1oZZwP8Okv/PGZzVfve+Ce++699Tf/4CvfPXPmZz72M+uD4fY0tRKN4yBh56w5N9s7G5rdEI/x0AtyU0zbmctLARLa3GWIwQkMp5SSITawrFCRXgsMaxbStkBmSerqIz/90YUtfuMzv99JEwyTn+x1lA+OlWR+6d71X3086Hx3MHTPP7+dGG3HxKhjNR4XCRBiQxwlEWfsEFXUUBNQ5s4qcUiuV4tZTiSU501VgQlkU9tYZjY+xBbMqhHGi3b9I9xJSgQAoWr2NndX8rEns1fVOzkee+y5t11/02SVf/mT/yky59ZJW8MQEKGG9cAycN8l4TLziQt+IfuGMn0TqP1dAb3Qgo0PrAUv7sCEPkbcT9gpANHedqF/ua+q4QvZLrpC5uLCgPUWJ3t+UC+Zf2xb1QsB+uXffKWdBapyMO73G3n/d9Mr/ukBmX6lsLQ/JF57O/UTBA5LqQTYXwRfcIu5mJrk/TnpEvVVX8HHh9xTDgoAex7F7J8CHZyCRABKBvqaS059pdJlLiwH3i2ES766P5nL2IsD1+xDmVkQekO1K6HvoHepOY3iUGr3MkuYS7/ziu8vHuwq26/8B3ThP73wqB1k2K9yxB+scuq1F/3Qm4PuKJd8qheCh4NP7Ov7qPSky2F6BsqgS0iaw+d0yYUjl+UpJeuccTbVddVQTj7EiJwRGapg4w2poJEoSeC8sCE17IwxjkhTihEJNhNmsMBYco7IqXCU5MB9+ROiAgjGwTNIOHeyqNEkgkEiYcAYZA7ZENaV68ebZDDd7fqRl21Qly+vpLVlXXSYdZ3NUqiFLTKSPFNLgMQyhy/gHEeKL50Xopw9FaS2RMbIXb6+nqYNmFQIoouYwAZHVpZuObUJMU1Kewvd2+2iQIGscKNhdnQN2oLBy0Nt5uDUwqklFJldWbaTUdu2uqiwuTWLtVqCJhQFSm+Wh+ujG1620hhVZ5DlbReTIQwLkCnX1lLEVgyAaVRgCCl1sc3LMbwnX5oUQZEzO29mGBeoO7YlOT8YTVJUuu5Et7PnrZcuGGeZuchyqLrB8OjxE0995yk7WF5I1SX9kyee6mIDw857ssYYEpGuaQwxKwz2papZlllrRYS6DqJJBQBZY60ltv2trKopJVV1xoqIiJDhBBoOh8aYze2NoNI01Xxn62OPPPz2W2755f/4ezHpv/wf/6fZrP313/7sbtNZl4cQmtDmy46Hg/OLNBiNS4mOAYExdt8VziDPfZZl3iIrfBcDiFiZlA2IAJ9nVsQVXgFVOTIcvevW1X//+a/NIZx7cEZ2QGpTCKKdA0qKhBAQitHQZfDlEMCwzHLLMFAA1nhjVDUKXNmfZHQuI8MxhXlAXgwMtYjqMx8p9SEGReGeFzkYX5QucKIiBGE0Xd3FYNhZ6w2zKcvTO/prn/ksAT//i/+yccaWuTJDybEJsJSuNLQdGpFfA1ak3gwtgQwkgS4sXL4/9PsyQa8tQN9yUDC9XsW9rx9+dIiZaz463wOWDkeRV8E/4srgtUGlkgmwCmJVowZFrsYKrPXWwMbEul+qokKWUlAInFPjIjlmJpcxcwqtdjWElABDyHIUBbtChbGoQjd3UIZJZOCG2WgSfWaKTFUTbYa0QFLVGJWQlVia2PERk5XjI8ebfFS9cBrtIlAON4B3dNMtSydOzuvWtNE2oT19GlUDw7vsMBz7U8ezW05K4bFo61c2EVT2FlUKdjwY33SdP7bWDfNBnm8//hTObwKEvjPPeIibjo/vvNVdf4wW9eLFV/eefqbtxcLQ5M3aHTfvnRgxk7dWFzOwURCKzN9yanLrDWZlErumfWVj9mTQrV0QgR2WJ/bUeva2Uz7JYGBdlldnt6BKzGIZkyGGw7UbTpjMLjg2s3ksLSjBG/LshhmfWp/Ykqpme/Pc9myWlYOmDaBoSufL0jheP3rE+ixYc35jYzgaT3d3Q4wikuf58uraYrE4efLEmRdfKMt8Pp89/cxpZN7nWZY5a20IYTGba9eZLANAMGyNLbIsV5N5w9zuNakLIQRVNc56732W9Qa4bdsuFouu6ywxMxtjbObtsCiCdF3gwXjj7EurNvupD3/ilqMnPv3J3wXw3/2zn1aLP3/i0fOFzgneYnmwnOpZZ6J3bnltbeQ87748D1BiYiscAuBdXgyGPi/Jww+HEjphKDMTMSgBblAMLBc+T4Ax1hh31OCFV19NWYbMsy/YDVURIlVN2wAYuJiZbDQoy5kDJssjAqRqk2lJIUaToazIvOmcxWBtOXXBWihCZ9i5PBGyvMysFzKuUaBJMfaJGQKpHnIdVdChdL4QUm671LbSJWjddKNhnmW0vah3p3vnm2py4sizm3tGIlRJ4Ygjkl4sxmYQIAnopfi4uPa6JLg5XNB0Nas0MlB93XwvruFNwpvXs/otLNy4hjccbyXZL4A+neUKbeuu65B5iLRtm+XGW09CyTgCqaaUyLARcgKGcXAZ2CZjiY1hE0XRtejHdHbsClcO2WSROe5UCeIpRZBjyvMyDAZ2stSFEBYNTJckiDKMwWDk1o4XkyMRxq6sDrvYTudpIyksTIbxpDxxcrh6vNmdZmt5Iand3QubW0iCYsRrx4Ynb0zjUUPJF5OC83p3IbMF5rM4sHayUlx3yq6NGcpnXkXmVQnGYjgw1x0d3nDKrU0WlIqlkVjem+8wDKwF1Axyv7o0GrHLnK8Dew+yMAbjwfCmk/l1RwNpmbslNr5rtkKLvTnKgVkart56o1ub7G7sjI8dzUeDeneGFMrhCpzB8mR89Fg+KJMz42PrbW7spAASLA0zlzkeHVkZ+mHcW2B74/TzL548deL5zU1khgz53CYomFbW1s5t755+6czJ4ydeOXN6XlevnH311ttu297dff70C+9/7/uWV1bmu3tghjHsczLO+nx5NJKYcrbShbZtSQEm57IsK23mYa0z1mQxKPX2wURsiK0x3jk2BkmCsTBimPs0k/V+tLI875pAMsrzY6vr77319uuPH/vMZz43XTT/87/6FyL4o6988/TeTpV5Pxg2u9Vu0wAMssbZfFAOsyILw4FDpJg4JREG8rLIsiwxEmCGhW1Mb+JIB0kun+dICYYI/z97b/5kyXWdiX3nLrm9pdau6qruBnpBYyEIEAQJEgRBEgR3iqQ0GsmSJY1NS8GwwmE7/IvDDv8BdtgRDvsHS+OYRSGFNNJMmKJIUZTI4VCkuIALCBDE0gB6qV6qq2t9r96a2733HP/wqhc00BQpkRpqWN8PVa+yMvPly5d578lzvvN9aDWnfF5XgEkziWPTbCkdMYwmjhSDdAB0Gg/H46yRkDgNxM3IAqF2sbFVQNxqaIpNliSRZgBEyhptdV7lrKgClMGeYlIUE6q6rhEUSETRXrqa9KSyM7mnrnqETDqNBCRaa621Z7BoG0Hp2LamLq5vHD169HvPnLaRlRjBV8HXRBGu7oWuJupvUt+/3sEk6maqhKgJsWfvxfU9XSsvXy0xTCKen7xE7D72sY//9PC3hDJX1SB+wqA9CAFaCXtrFUQF5yR4rUxwtVc2GA2jAOLgmZ0mZY2pAgPWmkRIec/QZHRkjPcuIDgEwElwAjEwEWsLbZgrRVC+kEL5vJezj621WQIFcJik5MVYNKezuYP5wLnxsNdswSiVxkETAaI02jONrO37dXFpszhyoHVgNm1nzih4iVvT6aHbbGt2fGXLFWN94sjUzJzEWWEiUYxivNPtdrfbyXSjHUcwMWC9Y5DCVLuxtNScnqmLevP0uZnlpaSZYHZa5R4EILjgtro7A/Izpi0hwAmEkFjMz9qD8yX7/srl5ax1z22324cPPpHqjTOn4axqN22j2e31O9udRqMRCcWtVmVVDfa+bs7NptbsbG8Oxn0o1Z6bqkNNWSTDgR6NdaMxKnMTpbAaNnYb240jR9P5hWJ3t84HSRKFkJ05d/7u192zuXK+PxwsLCwsHLlta/3KxbXL737P41/72teG4+Hq6qU3PvjAX3/hi8oqdo7HXBl1YKp93113LS/Ooa6Vd4mxWliB0jTNmhQELkBbZAmKAkUFEYiCUjAWcQzS8B5VBRFoDWYoQCeQBCOHhkXlMKNQ5viLL3/jTHf1t37t4+u98NdPf3fI0lo41F1d97mPTMYs2kYh4opV4DINiD0pIKja6coxaSDNrKiQe58DEtlrc7IymiKjAKPUqOwzZymQRqmDLgDdaARSSBqeEcalNiFq6kgHARQJSSBwqMcKKDgPACudl+7U2dOVEtGaIqNTlAJX13Ec28x4hCiKiqIiIPc124y0IeWEAOHJgzGDBQSia/q7RESyRxrQhMiFxEZWBMGbJMnaU2kMDuS8vPj86V/+p//5F/jf+8opIoIiazkQYy/CmDDRrlbLrzUrCe0ZoejrC2+4tW94bN7j3Fz9KbcsVO1jH9fx2irA+9jHjfiPlpWha/SuGyFQBC7GKstYW3YOsfVZE4MRpIY30Iq0CWbCnmArAtKA0tr6SYsqiTXGKgMYzwE+oApOV4grY2MODKPhSSlAvNRjLlIEVGkapRGEgaDgJ90yiJM4a/a3ttDtdefa7STSkXYaChKMQZpAmWKQY6uD+UySRZtFUIBWSRKrNK45VJubGPbKpZlGo2WzKKSmyoG6xG6fG53y8EJqrQsMoSowIMhS20jVZLeX1satqcbifNxqWCkBBeeQF1udHR+pKksiLzoQREMb3W7HU61ut1tfvhIOHlmemz92Aj25t1ON3eXdZHra+7CzuuXLOjJ2uNtrNZqDdsP1uv1O98CR5eFweOnCRbJaRdbaeH1zozXVHuxs9TY25tI05PkgaUasESeA3r6ydfj2w2fKAv1OXozKkZXYDMejY6+75/wzz253dk6cODEscxf8qMiXDh967rnn1tav3H777QtLB9fWrtg0C0Ls8l63e+bMmcuXVka7HVfkxMFANKjVaDab7crVZVlqa5MsHRWjsiwZIiKB2RhjkzhN09q7oiiYJ41YiG2UNFs1UHoXhCPgtqn58ysrvaL41V/+dbLRF7/69W0t6dxcWfgkbpW7I2Wt18KEUAdQXWnVMjpAAhDgCEEFTYCyhjVVde0EXlgZzYzADA1j1ISn6lzlldFAURRps0VA7TlqNkRZ7+qqKBIjSRZTCA5QgZo6cr2+Go8ABK4cELQ5u7rWH49HjkOzyQQvnJcIwYkYGxvUHIKLtApAXVYD12vHiWhhYhCBiK4GWUpp2svKTLhHgChSXjESIu0YdU1EKjIlBwayZqs3ku3O7sIUTSfJWnds04ysVkyBea9ZaY+XL5g84SiamEjedFdfv6EnNO3r1P4fGLPcmJK5SXZhHz9N+MdFTNnHzw72QplXuI8CuLmb+if2/q/kyhhIFKQkHbz4Ror7T2K+BdLY7mFlHaVH8IoQzERbQ5z3iBrX6c3XZC2YSEizCqLBgPMhBCUEUiAFIdKTygAUARwikGIGCSiIYhEGXXXqNgaKRCuJIFZggpi9aGcPWsEosgILaIfAkEpUUHFAw0Nhuh0nVnZMWVEBn8MrjAPGPmXWwTEExrBWUBpEIQicWBAqrz3TpIFjUmCyFtogMIS01tbqRNuhEDwlykrtJ2xmiszYVUUV+9GY8wLaWhsZT36Q2zieTptFkfvK2wPzrn9usL5+/PhRb/TOZre5MN9uNUbjIu90FxrZsNkM/d54d7e5sOQEMAqkECU7O91ocWrx0PLmsPDOoaqSRvbiy2fuv/vuS9MXe7v94Wh05Lbbdnvd8+fPHz68PDs7zewvrV44efJEp9MpR05HVkcNUbY7zkPuB6NhXZdSusnkN4VoyoSy9PnYRwm1IhpUnBc+THi+wtZSoqSdmNL5fhF8XUMESmUNM50SaeUp9Efjhdm5s/mYo+wXH3rvkUbjD77w9fPDvp9paooDEcEuTM2X4zJY7clbJhJiMspotlIC2ksKrUU8wAQ2SiohBoWgtA6COniQWE0A4F1MFGvlAcdiQj25IpUyJKSZlPeaKGIljj2gyUSjet6aZH5+KsjhuekxMHC8PixEiKIENhJtnK9FYGPjfZ0Pxofml0gCIP0e5tvtzd7I17VVIA1oBSgVWMgD+pp2C4EUgQCa9G4TdBAUlYE0G6lndHv9/gijvBz7UIRyd7s+vLDYGV0Wqzyzq0qYCHsmS3uWSUQTtd9AN7ZjXK0rCe3519+s/Xq1qYNoL4nz6nbuffyjwGtGMxPGzA/Zdf93ws+kjss+fmj8dHFlCOCyaDTaQ+8xv3jnf/YrjXuOD0ejxrj6/v/1L7Ddx3isBaSUJwURBFF6IuwhAYAiaEVEfiI8Q0xK7dETlVJWIdIYEYQYalKzUVAQzbAQC2VgFHsHEcBr7xC80pqtMcYoIvIeLArCImBhBhsDGwEUvEdgEKC4KkaZH9997OT0PQe1ocb8/MWVy1vioAMsQWlrtCOKLZnYsCLEhoyF1ggildOMKEswPZ1lmRHyRVWVNbyHZ4SgyIAg2pARGAsCfFAsXPrYRvnCvJ5treW94nxy/uUz4eIaGgtc+HhWk2gRabfb5PzlC2sLBw5c2djEcGSF2u02VjkgTE1P93tDQFtrpw/M7W5cGQ9H973ptktbHaIY7KERQli9dOnNDz802uyMx8O42RiNRsy8unbl5MmTL337O2fPnn3HO97RHw+Gw2Fnt/v617/+hWef29zcfOC++2dnZzeKTWYW8RTIIahIJa3UBjNRyNeC6db0THumLIokipIkyZopidMMLx5QjJAkWdpoxHFstdGkau/YhyCcxUkaW2bmOiy027FW0PzwY+88ebDxu7/z57sNK0ncmJ0JpKAMgEGvF0epC54pJEoZbQIpBWL2VQCJGKVNcAJIYENGKVYCck4rciyevWJnkE5WiJ1kDVUAyXSryscBSGw0zIt23CStrNJGkyFTii4BRHEK/bY7jtKh22JNJ+YXt3byi+s7Y7aRgWMEFzgSRNFUhiNLB/PhqD8cP/rw1KED853NrXHf3XfvvePzl3erSitRCtCaJiQWMKCvqvvyJHTA1SSomrh8G6M1ee9DVdpGq92EWGOyNhFtdrbmF2fU2nougUkoTcTf1Ct7/a89f3t5pZTIDya7vGoa3NfK28c+9vH3x/VQZqIsR3S1c3qS4ZhI4rIoYziEH2Cbsjdi7g1u1+vlk+zHTXkdei2JLQAiopUtGGi0Dn/4I+HOe08b0nOtdly8/r/8ref/z/8bjabxwQ/HCazYqCrGXOVIY5imtbbWAgXPvg4VEEAMo1RiTLuls1iIs6lmPh5Zo8pQiLaIGpK2TWPBJDNJa9a5UIfK93dQFAgVlSMZj7NGWjduX5ydH++su36OKsRJ5oNH6arKpTMzvRPH41Yio7ocjFBVMCTaLbYabz42Oy3IBwOrcfDo4TNRvJ2PYQ1s1Dp8MD5xex5J3E7mbz/UHVdqt/QMkzvf6dHCAZMlyV13pNPtKs+5PwgeYMBEiLOlpaVt61mRbaVTywvb51bAUvZH6biIptNoaX7mnmNnVi/Ml3b12efhCEZCb+TnirnFOUdC1rTSzG93a1GtAweH3a2VlZUHHn14fbhbU2BCM22OBsO17c49d5/c9SWsHXeHdxw6emljM11aLPIRQNjtXDy/cvy+e06vnGu0mgGSxcloMDx04MCBY8e219dOnz599+vuPn3mzPr6+u1Hbjt+4ujq+YtPPPH1h9785rUra3EWa6tNTFEU0jR6/UMP3ra8HIqCvItEmlHSjBILNRGZnczAZQkTQxFcQJzucZ0n0WzlEWowIbGwEQA4IAIGHspgI8e/+Mv/0J/lKrikkejYBO2jiKKpzHvvShfriIzx9TCyUWJtwki1ndbQkR4M8rTV7ncwHaXCkiQJgLk029zeyNoUNeJQFHG7JQCDWgGHmjMDYNeXTeEMMMyaSImjUDfaDaupOxi2stYnv3nugIkaSbsBWFEK+OhDD/yvf/6NJJ3Og/JcR9YKjBE9cOGTn//eXXeduP0t7c9+8q//5JNf/aUPvfPi6bXf/71/9c7H3/Pr/+Stn/qr73WHu1kUeRfEeZDn4MhaH4IxShiBg4rIAgBZqyMDNqpAiFppGWpESRJnJSNuNXQjitJoxw+P3X/Xl8+c5t1RPN2uen2o6KpJJGGipTGR6Jjc76KFRBBAe0IbggACWIggiiAQ0YBACcDMExenvZv9pkHhNV2c9iycrmoKvwZxT67Tjn96cGNS6ifyBrfa7Y/pRNx42DexvPFKYsBkoL8mGPYKHaCra15XJnvVablZHEUUrn3JxDdfJK92z75ajnz1W7wm9uajyRX1Wp93UpT9W8PrG9/ulV/xdT2YV1znr/iMN5/PWx32D/mhrq0przoPN3+PV/+8dmx/y/o/Cl5xnbz6+H/owvGr9/PD4JZZGaU1hyDMpDWuKvkopW6h6PPjgQBe2Psa6bxtz5QmHbO3URIrzCweRKuFbl8zrI187TmIjpOgJGk1iFC7Ms1SAMVuD76CBBATiVIgBOGagyYmq7QS7cXChEnVxmoTaePLgiQoa1RsOZRA8KNRsb2J1rxRVuVDDAY0LuA8OY+yRGc39HqtpemG1m63P9rO624PSoFDYD8z2y4rUMKHjrZXdsJGp7+wdHD75QbyGlGUNVpRnHAd1LiYbjZ9u+WKUAYf+sMx18PZKRubxelpqevBxhZ2epwkECahiHQ7jrtVjbI0UabTSDeyMBy6brfu9tpZ3EpTItrodiiO4ViphEdludMt59rRXFMxhjsdDIo4S3fOrtz1hgde7m4Pev2VM2cPH1w+d+XC+uW1tm7ARBiMVi6tHrvnvvMXLqByieepNBnsbqu5Jnd30Gptr2+25ucOLC0Px6M4ThWZYVFeOLdy8ODB7fW19YsX73zdXc1msz9wL7300iMPv+XF514gCZ1O5w0PPPD97z/bTmcSaw2ZvJefef7MqSe/74vcsliR1JpWlibaArDKOhf6/SGAyrkg3JxqJ0libKSMFpGy9kVRFEURQtBaG2Pa7TYUwZJKohcvnNNpXAiVCK2puRDHpSuMhialqNbGAbWF3h2Mjhxf7g27Bxop8vF7Hnt7rwSX+fxM+12PPT4aFr219fmlxY+8+/4nXtged7Y/8K7H0gQvvnzq4PzcW9/y0Ajo7HTvuu34Pa8/sdoJ/Spfas06IFR1kmZuPNaRjZUGoILaGQ7zXn962PvwfXcToDNVARrIshYGhYmS8bAS9kQStK6JdqvR+TNne1vt93/48b/89Jc++8m/euxtD3/iv/rNLz/x9dU/2/nYxx75wpefXu/26ory4ejAgZnN8goDaZo6V+EaSRdQkxQN4EOAoiCBESbCwVohbmQHDh1YPrLMrr796MLs7DSljWGnA2tek20pmNSHbnkj3yLXMpEAngRGP2Whxz72sY9/zHiNUOaaGfVe2wLvqTvuOez8hPuZKKIojeuyyK9szATMZ5mrXNtjvHIZgwqjPChmklq8SCCjUeQRpowhLoIfjn2okY/g6okWLhGJq+t8LN7DFBCg8gZBNKA1FKQqqmq7Gg8pUsGPUPfgSyBAgPEw397g7hDaFqOpYmsz7HZM8JZruAr93fG5c2GQ52Up+ZCllt7uRGPSl2WnHKwMtzpMWdx8segEo7sUgAjBoDb5dr9fVEXsZHF22NktdnpxBQARc7Xd7Z1f1bXr9/shz91OF9u78aHlipTUtc/L0drWcOMyDGUHl2lrJ0zkevNh79SZsttjo0Zi/Pk1PnQI0AhA8G67s6Ul2W2xl553SRzPNtrrxaUrly7ece+9Z59/9nxV3fumN1Lu+1tXxpQdWlxe29ys+gNXuQOzCxvnLoZBMX/08FZdVdZgVEYAACAASURBVN0dDHomy3zlh93e8tGjly9dIq2ldho0HhVLiwdfd999p5566plnnnnDG96wvrG2tbE5O92+4447vv/Ud0+/ePpdj31w4/Lubr+nQzQeVCJcj4piPPZ1HRkrEpSSrBnpSImI1lznw7wYJXFmIrs77JvSz8zM1GEcxTEzu8B1XdfBA9AscUB7FyaJxjb4vAhqOgxKCWzjZDwqwVT4OnG1eI9Rfxoqi3XhKu3q9a3NA3Pt7vlzv/6LHxyN5bN/9eVWpH7jl9/z7W+ff+6ZZw8uzn/8/Q9+5hsvb25vvfttb2839R//8Wfhws996EPjUf7//t6nl5aWHnrTiUsb7ktPf62fj6ukKUAE2ukPKLZxHHsvJKIcUY2B8jD0jQvn5+fuvsNiDLw4xnpnMOi7MiIwOQ6AVMoJOJRlOdpdJTl/YevnPvieSy+e+dSn//Q973jXux559LunXvh3/+YvHv/A+3Z2B99++iU/Hq++/MLC4SO7o1BVFfRE+UUmepl7LYKA8myEmFkkAMzMGmg2m1OLB+85cSJdW11qYa7Z2B1uQ+vmdGu03cf10ORGJQ8lBKFJPkaLCCjgKgNmz59E1N7KBIgHJjybsDfKqD0pzx8Nk+fyV/R7/+Cd7LMrfrK4lQfkj0FvRhRE9nKBNy2/hlf/dx8/k7hlVkZC0NYGDpiEMlqT1uIc6CdIrxGSsh6SJQzGm089lR4/snTXnQpkOrvf//wX0N0BhdrXooiJEZyUgNU+z1tpU9toe2cbIdjIsmZxNYHBSjwLC6oKKgIAr7wRxAQJCC6UAy56AGAVqIbUYD+RaoP3nI+Rd6HMsJ4udndR56QhKkB5iKs2r1TDEWoHVyGLNLsARpIiyKUXz2TTzXqpUdbD3cK7sVu/dBlFDdbEGG5uubUaqHcPzrt+F2AXN6A4UqjGI395bXM4wKW1iVsCgosACCO4UFb9S+s4fx5aurlDf4DxACAw4cpW2e3BmH6SYqtn5w8DEaqgDHFdy8ZWsduBJzhXT7fT5aXGwYXh1ub08uLU0nL/wsUXvvXk0TfcO9o56web6QMPmtkDvte5fHblwOxCvrZxqdsPXM7FuihHUWLHwx5UtH3x0uzU7PLCwStnTiNwMBGcO7+y8tBDb7o4P9/f2Owf7c9MTY96/Wef+f6jj7y93W4P+oMz584eO3F888nvUFV5xzbSo36plNFKGRuH4OvgpfLsggs1kUBYR0mpNAnSufnAvJkXURwPi2qSzRZtyURKGTKGSQ/FDPr9dL456A3mk8by7EJqI7Imh9hWVvg6ijRQzSwtvOveu4428buf/t7s4tzKlUvFdvWJX/nwzsbwU5/73MmTJz72wYe+9KXvba13lhYXf+kXHv69T381hHD82LGZlv30n35u2O/915/4ZxdXut/+5rehzCOPvvWZU5tPPfvcVtFdmJ4b5AUBzKwFwqEcj6RmxbBsrdJsYhfxl55/vrO7/cAdR0dl+d2zqy+tbanGYQsYrVzwEthVBIIE4eABeD/8oz/5i59/36M/97GP/vvPfu7Qxdvf8tg7+uX4T//k37zz8fc99sgjf/Hnn5u//bZutxvQ0Eka2BERK+IJx1YUiShAi4rJTKhlmgiBCUjiyGibAEdmZqzD/cePrV3ZRiMbbm3jmq7MtarxKx0ir2dfFF1TGX81deaGhep6gUDRfqSxj33s4++PW8clNHH5oTjLRFFdVf8A7DxWrNKE6xF0gjMvXPjnV9Ttx9rNdu/sWWzvgEtqxt4LQlCKIRAOhDjvdFxea21QFBBhF4J3kQEgIGFg4vUszCBSigAwCwJrqY0Knj0Ti2eoPVUxjQhkBJqdg1QIBZwFKlg48gUqWAcYjdpKVXoHV1kosWoSK6AIxbNnd2yGt9yZzE01tsuVU6exsQPnFVREZMQ7CSic7o6DQIiUc8weYAjDBTvIXe6UNgSE2qH2UASjoCQNhDxAc1z74AN8IFHivIlSXzik0J4wqnXFuqKIdeFrlWUMwahKYB1LGIx29ebh40dXRv3VU6fuf+SRZy9tYDcvtvvJ1Fw52lo9d275+OFLoz4G5fbOBdIartxaOf2R9z321plm7cpnL11c3epVnUH30pWTb7z3yupFVDV8DZHhbvfyxUt3HD/x7AvPn3np5be97a3bG5tVPr64cv6B++7/+je/dXb1zMOPvvPQHYfWLlwwUUIa0J6EH33k4SSyWjhwZa0mA1FM2hqbCFRd1zu73fbUTB389555Ni/KKE54QpIAIIIQ2PsC4mOdzsWDtXOHjhxaTBrvetubzl+4MruwmPtgbDQajALX47KXCKcBKbCwOPvMCy8da2Yf+bnHN7fyr3zjaydPHHn8sYe++Z0LqxeuHF84+L4PvelPvvjk5nj41jvveui+o3/wJ59ThP/+t//Z6trwG9/8loni3/yN93z/pcHXn33Wi2TtGeFQeyZgXJYmTqu6Eh9iHVutJx6PgVEH6Qj9zflLz21vl7Xr5I7SdppYV9SkvWImIfGhJq8hirXSelB7o/D5L37lrfe97lc//vE//4vP/sEf//Fv//bHn/yW+cJn/uzEPQ//5n/xsf/wuS8O+l2yljUci1KEwCI3pD4YkZiINAAhJoEEFoB9rcoqqcJSlrrt8eHGtBrmrAikgYlEjdzocY1rFJZJ+XsvUaug+KpDz14i6Pqg8Up1YLrBXnsf+/hBeDU/Zh/7eBVuHcpoLSFAqXa7LYp2trfBDK1/sk9RopgDRLLIuCp3V0bc2+3FCTa20GyBIM5jLxaBUUpFCopEW1eMnbZZ2gje13WVprHzFQAhFigIg9Vk8CTlibQm5UgpkAaFyU2iJk+QRNAEQ2J8AMAQDxJwIICUYmhRGsaiLENkU2bUDs4Rp0IEH5BmhsX3R6vfeGp17SLmZ3FpA1fWsXgAEMUuSBA/AgAJ7Gtix6EOziCwJgaJ0gJ2kxZgsEBYKYLV0EAICAzvwCFUlWMHciTWEHRgzwzWUARmrSxDQQFcK2J2AVXNBlpRqF3d6+eu0nMzYWtn7dLqodtuW7t4cXPl/JGTJzvDPO9sVfXC9PJy7/QlCKlEk/Ch+dkTi3N33Hlsc3tj7raD33r25TMvrnU2tpqXp48cPrL64kvAHhX0ytrag296U6vZHPT7Gxtbd5288/tPfntjff32Q8tLCwcud3Yur66cOH5k7dJ573JfcqPZokBbna2IKDaTMqZTSrQhATyMhw4hJGm6s7mWZNmb33jfk099j9kTgUgTGEJMgYRqEmguy8H8wmws/tF3vPVLX/rKTrdfBZgohY6k9nFkPJdSj9dPr2RZ0ndB6vH7PvzzW1e2v/z1r88tzn/ow2//0pefOn/60uvvvPvdD9/zqc98dWvcf+dj7zh5YPoP//DTaav9kY89/r2nzp2/eKnZnvrwR9/+tac3v/PMMxIncZb2djsU/Ly1AZj0bCOARDGk9rXygZQSEWvimqGyRje40gNTU0xmMB5SYE2kwMYkIQThIAhVCCQUoFrtqcHG+je++/Th24889t73ffYLn/9/fvdf/s7/8omLZy9srq4++c3mu97x6JV/d7nSVLgagCiB0I38RmZAUVDKkUCrvYsQGI96w82oPnRwbm52ejqqqHH7wtK3Xzw1vbQ0GOavcZ9ebfa+vutbkHZveo2rpa79rqV97GMfP0bcOpQRwZ6QHCNcpc78xEEoAWXJOXG1nW44KlHkaFgdHJH2jlSUCZxwzZoYAbVDrGE0QC545x041OSDmmjDACDgqpwMCwdvNIhiUCSkg9LeEDQhjeA9XBAmD01BQytlLIcC1hqTIDgVBFoIEVBDEUxMKtJwQafEETPDax2QKNRZUteM7QJlH91KNWa5KJXUSkpWujIlrEGkMZWFgUO/Ih9ASgsjOI4ISYQqDaRQe+Raa21IeSIocUrQzGChWomXHMozKYqs8wxr0IhDbBBFlMUSm8IFKCUSYAlxKnHsnENRQ2Pj8uriXXdslq6zevn1D79trbMFY8Zlkcy18nq4ubN97Nix3uUtHUfBVUbkgfvvtlRLNUy0HFo4cFfpz59dL3N/5cqVN7zpgcFOZ9DrSR3gQznOz6+sHLv96NmVc6dOnfrIhz+0tbk+3O2dfvnle+68a/eJ7cunnl+enrr3rpNnz12sqtpx8ME/e/rlSKnZdqOdxd6VJE4bVdd1ZOMAKcuyPT3TG4yiJL7/DW+88/jtZ86tBJ74LSoiTVpppVNFIa8bzTSN9Ps/+N4vfu3rw1Dtsms0Z8aeQpBgrAEZHSGWixLSYmzG1W//1i8++cLF7z/13ZMLB9//gbf/0We+0usP3/LgG9784PF/+6mv1nX9ax/5aDEe/9Eff7LZar33g49/5zsvrJ6/qHX8q7/+9r/6ysqpCyu1MbXn8biAjqI4jsECJFmjCJwgUYpyX9dSzc62JLDr9JI4czWidkt5DlRXylTeaYRWK/N1XrOHEiUwRFrbnOBJRdaMipx1lMWmhozHo15eZc2pAoiAUJUHpqclOBbvvY+irK6qCbUlXFOpY/FApdVYcaWENRTEQAQYjAZbo9Hygan8yHS9W7Kkj7/rXZ28OHv5sk4zeS0a7yv0fCeqM9c6WSTcZARJV41jf0CIs49/1LgVY2Yf+/iHwc2hzHUXW+9NmnpXD4fDSW5ZWxvKEsreal8/lnxNlLZ8WdgoKuvKj4aIjLaZiITap2mmjSYb1SAJEARUNRopQLrZMMZWeYUosknsqnJP0k0paKtMSioi0cTiq0KIhD2YvYkQZ2g0KI5VbENZYlTCBQQWImWtyRp1lqg00XFTxl6KSgkzK7iAKItmZ5PWAjeKcjRmJXAeOgkhGB0VOmA2U8tLWZIVlYTuVtqORbwHVDNtLM/VzaajaGp6tnduxY367GqlrACIbXZoeerggWI89uMy39rhza1anHc1NKHVaBycs5EkU+nC4tzGRT3aWkfBQSm7OEvTzdBI2wemN2KjFSAB4qGhDDWmpqbn50wc7WzuDLY6WukwHivvG63GuBqv590H3v3IM089PZZ6+dBCt7cJ5/L+ePn48SsrK+n8lIVfuv0IURjWrjE1NXCYm5svXD1zcLEyqJx/wxsfeOKrXxPFRkcisnr+0h1339Pa2Y6S6NRLL957771PfPVrvd2BUuqO48fOXTi/urr6yDvf0e11QLY7GJExUlc2SZeOnTh67LbEmiTWWRI7V42KMo5j51yn05mfn+90Oi+efvnd73730vJB531du9LVfmIGoIxSyhqlIHcfP3r+7IUrWzv9qpw7sDgYFdq0yCZpI2XiUJbKG5aRNubXfuMXvvWtF5956bm77zr5kcfe+OW/OYXK33vXPXe+/vgf/n9fZOb3vuc9a+tbzzz91MG5Ax/5hXd9/buXnj+9cmi29YEPPPaXn392ZWMbcZw2G1bAPlhIub3pYqWBOtRlzZFSrWaDHaam0/vvuMMV5fPPvSxk47TpAKVNnDXFQIWaUOd53syiIMJaAthaG6cxOxbQOB9ZrSmKWCSzcdRSupGlzZSBLImiwq9tXbn73jfX3ptUj6tqr6uUIABPKpdEIgiEAAmYyEXKxCykKipW0W6nt7K28fu/888raf3ax/+b97/3Pd1PfXpUl0x7vdR7UAoiEJmUoEEsN/qb3Nxh/WquzCsgN1Jn9rEPALfWFP5RtYb3DEbwo7Wr3EhYvtWGTLfSA1SvPRPe8iOpH565vB8p/gCYG76pidb51VHJWu89SPmqBkCk2HnS19e/1jK+Z2AHtbcpEd2gK8M3esgpDYCECCQTl92ro5uQghIF7T0rk+SBddIUYfYSQoDSKk2CVdCKxWtjAGEvyBpggdZBG5M1oqzlq1pGFTiAQdaIczCNaGrexBmzqka5iHhxSgs8Qyk7Nx+12rVCkiTwXO/s1lsbcAPEsW2kdvYApqZb83OZ0r3hOJSFVmKNckpBRfNHjs/ccXdnd+BXLtXdTqjHgMDzWGl7YKZ521Jy7x2RiYp6jNEuyDpXBpPAmMbSkekTt7m0GQm4t4OtTUhgsGok0O3Zk3dky0u2rpT347X17aerztZ2OtUuOCCyMt8+cnJ5wNUIMnfs9vFOX86vm6wVHVuau/MogJmpacTRue9+N1Jci1NR3JhuLiwuvP/972+1zGc/86UX1jet0eyx/tyLJx96cGu2UTcNz2eH3nhPVeRdrmbvOtF98tTO6saRo8daiwdnDs9rgwvdvj1wQClfVMor9fzzp6J2S7ez6XZ7dzyenZ56+9vf9vW//opwgGhj4qefeub9H37/t576VlEXZV3d87rXP/fdZ7/3zKm3PfrgdjneHI7WOjtvevP9Tz/9zNTc7G5vFLwvxD9z+vSzK+ebrVbWSDRBCKUPnV7fKnJVrYXf/Mb773vwzd944ol7X3enjXQcw5RS1l5HSRAt2uhGZNgbLfOtKa717PShvBqxhoqirDUlqdhYV+OYBn46d//jb3z4U0+snD134cHjx3/psTf+7t8875x754MP3n3H7P/xqa/lLP/tP/nA5cs73zp9Opud/+9+7qGvPL12fmV7fvn4Jz507+995q8uXBnc+5ZHz3U6lTZZkoZx7re7TWuUdgowRkdWZ1OJZ99M0sz7N942t3px84y2YyG2gshESaajKPYc+iISjMkqH3TaMLENBAaj0WiIlgAm+LLUWqdKxVUdxSqbna3JR0Cj3VL1KJmbGjFUkniuIm18EAXtYIUgQFDMQsIwEPEiTgWHgNILlxWoMr6uUJteHfdVY1TWf/Tpf/v4uz/0K7/yT//g9/+1sla0FW3yQd80WwTrioJQ79luCwuI9xyfIASCvlH5dzIAC8nVbiYSEEAiokgYTGJEZE9NGIJr2xIxq73h6NpYweHGwecGvYqfrniIoPdeyc3/+KE2v/YBf9RU+I+YO78VY+nGWfMVuinyiiU35+pufHPZswC7vuBGCZYJYUvk5hNy9XsUeWUp4OquhW/SoZm81Q2C7686A8wCtVcMpavZwev61DddOXub7ylZU7geZ7xCAkcUX1eiubrpnim9ur6PvQ+4R76H3LzBnlDrjYv3ft98Pf944xh+9WUyUaa5RVOh0A0HeXXSnuzpFusDr3XMP7xSzo+K63ng60f0d6oj/bjMDUSEQYEMwxAsQQMGYkA6KDjhMGEgkgIURKANjAVpVhpxZOJYa2N1pEVrBsgY0izExuikoZLk+hWiBHGi0pZEqRczdkQ6ixpTyNqII1gFwNW+9qh1GpIEaQTiEGoONUjB2HFed7wbR6aKTDBKR5oiC2NhbM08KqtcQkgMRxG0qZmELJSFTipBKZpaLdNua5uADAQAcxZh6aA9MJ8bszYcVcZOLS3Gy0swqshLNNKpO++oNJxVtaZCsTSas7cdiRcOeIgkdlQVSRw34pjA1W5HcUitYedqF6I0gdVRGwcOLcWtRlkWWhiV6w1688uLSbu50d1pzbYRm5IYcdw+dEQCttY3a+eHVVWLnFq5cPrylU4lm6Pqey+effnCqtM6m50d1XV3t9/p7dokbjabWmuljFKmrN3G9paNtAu+s9s1cSSkyqpe395pTk2rKF7f3I6stlYLwaSxjixIs4mQpJxkY9JDoRxaZS2KkjqQkGFlO71hzXLXvfdubG3vdHZ7u4Nut7uz0924srmxtrG6duW5l1++cPlSNc6l9lZlQZSra2MMQTMBSrTVKraRtW1NLcBzCISExQIjlhELVbUF2BinNTHg/FgwFgagPCsdm6QBgN14fqZ93+sPnThxwkRR7b0QFEiCr1xZAsw+gTSr8R3N5HiWpONBw/vpyBgFUVqIAthTEDPpDiQ2YKucgJUqnKPENudnSoRRWXmIiiyMFiIiiZUkBNIqkLJAZBMdJ2SNKLC2ID2xeFQCEqUxkcOGEAIBgCFlyBC0UhAlGtDQDZNpsp7iUVCmmc4tL37j20/Mzk2/4+1vy0f9uiqKfGQbaQjBDfo6jic30NWBiffGjx88CLx6jgFdHzr+IerX+/iHxd/nK73Vtn+HfV4NLH6kbYV+5Ent6nz/atxi+U/QEuhnDj9dxgVCmPjsEpGahLCiAA2aOGbvpY3YKGiFQGCgrsEMEafEtFssjn1JoRIJxCAhr4FcSURIyPkC3hsDIYJSiOI4bVZQGBXiXZiJrIlhFCAITorS8xCseWY2kAE8dAAzwUMTimqwvS29QzrNDClPLOLElSBDHpznkF21k7eiqb6YMbTSBjbi2qMK+WY3t3HbpEpIDzwKIGhYGidWzc9QIxsOR+HsyuDIkZnpditrVQyUORbn2kuLjZnpl1bOAbjt2JHaV304PdvGSOdnV/2wfOCRY3fdNrt6z71neiOfez8e23Yr748ubWx8+9TzC6NDHRVkuol8zMbA1bv9wWJ6EpE+d34lZdO0yagY53kxPzs33B7kuz3MzYTBOE1jZvXEd558YjgGS7+qYW3z8KGgtQ+SDwadbnR0eTlrNspyVyCOg/L11s521mwOxoPdfn958RBZUwff3x1MzU93+2V3u5u+7gR7CeKzpBHKunYlICaOyJrSlT641JoUum1UWTMUG61Gw97WTrq8tDi7eCiOY02qKmrnAgIguuT6yZVT3ujUJI68tqYOISKbKjsua2dLEBurtRPlvfKsAXHBgIzSAHxd+qqW4AjQpDQIDOerypVWiwe8uMCVNU0LVOPci4oIyvGwO4imWqINxxF7y74UwNp4Rkf/w/veeDTCxTH+bHNVFUNCVbGDiSiI4hApnxjvSWDqylYIbMUAipQy2nrh/mBEohEZSXSoiBUHNdHaFR0Ch6ABizgip8QAgLJMJkzEOEQbYS2kJk8IioICE5RS19wMlFJMYIILfiIzKMxaRx/96Ed//1/90ec///lf+PkPXdnaXl3f7A1HVsWuKBDZUI1BxBPtPRLZS6XwK6aK1xb3fO1nuB/B+mAf/1jwj0bxZb9D6j8F/HSFMsCEbjxpFtV7qheYBDR0NQs9GYU1oMCsA7RCXQdYT0SkFYiF2FoDDiLEwuxqx04TAwFKlFJhr/CltbLwQFFDlIa2ei/zCRAJi6shnCilJ5lADVgtInAezktehNrpjEQRJrOCCHRkherCA2OflyqQ1hbaBJAmjQC4gGGB3ogdB6Gin8PB6thFCsbqNCat8tEYRcm1A6C1hrIIJYp6PMqzxXmjjV+/Mmg1Dh5a3taq6ncpbQqiVMWxivIcNsugNTS0jhVZhHrQH57f2hpkdhwqOztVbXeYDAxCkF5vcODYEYzGF196+b4HHxzl453uZh3PRVlWFQ4++Dyvw7S1Nq+DKyoTRZSmttXWNu2NxiKCEMqyTNJUGROYJ2yrwKEsy6w1RUrVVR2niShiZlakjCUbubowUZLneYiiJDUEwAs0G62N1lz4UNdQkNpHilgrL0EpxZCNbnd7OOz3+1nWjLTxlaPKV3kRCYa+4tkGM4JjYa68Kz0vzU0PBgPASPDEooQtkSFMaCJgT0RWkwbgAjsvgRUQKbKKSIHZe18zawYYofRFCLUCIqHKOa4A5wwIUJ6ItSatWbQGYhO1tb0/wgGg0cB30jgWrw0YIRDYB0hg7TiJoITJg2oArHReVI2ZlrXWOU9KxSbWka1r7ylA2DN78UY0Bc+udgC80yEo7ylAgbS2HEQUiex1eAWBCE2S8J4nNpDEV3W7RSAicRyLSF3XcRzneT41hfn5+VMvvJimycd/8xf+t//9X05PNXq7/ShtKGVcHSZlX5k0ZEu4ad66Fb138vrVP28eBG502N7HPvaxj78Nt0qI/cfDVaUKoVfX8xREQZSasBBFAKgg5loZFXs+1VBESkFppZRSCkqIBIYQaRgl+rpnsAKRVjAWcUxGK4EOAcFDWIiFGLXTMinsCQQgzZNsutIwEUNV3gUJUGBSIIMgVIVJs7hXqiQulUDvNYNDAFFQBsaQ0dAG2kBFEgAHVM56MV4sC5RSSoUQgvMqAIjgKYxLV1bNdgutVpkXIS9n0yZ0LBtbOmvoZmO93/v2M2curW0irz1T1m47F2AjKCqNGkjYcaVuNWEMBNCWmIpB3kib0BGGRcPGbZ2i8HleZq0msgRgKOMFdWDTaCJreBYpqzovS+ezOBURiqJJ7blyNRPIaGUNQlDG1N5Npisv7ENwAp2mObO3SqdJABxzFKfehcnJIaFIlGWKSUdEVlljksDGBaqDlBw4in0UXRkPuxx2iQeaelx1ymF3uC1UZA0FoiDkXbBREmdpo9164L77ExupPetxIiKjYIigJlaJk0tKAaCJY7pSADTIKHU1qiUymgBGSDKrNAPQpNMoTg1iZSwpkPagAlwBDAXAe/Zl0QAGuTSBhbl2lmqlwFpYcdDkDdUKjkQIWmtoDZCJrEBEkfc+UloLXJEbCIhBDCWiSCavwQArgOA055pLzcGIsyKalCZiIJDympxAoIxoBbAX3hPx3jO4CQwfggshThJm9t5rrft9kNFk7HefeeYzn/3G//Q/fwLiG43EkK/zvtUCgEECDVJQ+uq9CSL96jT7VanhV93rN1hdXlv09xo99rGPn0nwa0yXP1v4KQxl9lzrRMJeGga47itGsucux35SiwKUsFLKQluZiL7oGNo4mVQERJSGjZW2SikdRYis6L0cPYHhnVJAM0Yj1iSuLENVQRjgoAALpJGOjTIRkgaiFCryTDAx4sS02yZLbBKns9O23WYdgYwSOF8jNZifNtOZNGIz1cDsNMWWNYEIVqPdULPTKrIc6dbhZcxNeVJglspR5aPAc60WpttJYoK4vByzqwBBEFV7pVTt66mlg4cOHcp7vbw3aExPYXYmVAUndkTcr0uKLdpTAGoBa60aTTRbNs1gbGBkjRayBnyAZ669ChKRjpK0tbxsRPtRgaCC88oaaIU4imemrYk8SMcJrAER4gZMrAIsKatts9mMoshzKMsSQJhw36xpN5sASKtPWAAAIABJREFUjDE2ifUEhpiZmcWH/5+9N2u2JTvOw77MNdSw9z7znbobPQBsgpBIQGyCIEgKBEmDBAlZjJA8yUHIshyWHeGQIvwL/OhHv/hBLw6HFZIpyTQHiSHKDE4CMbOBxtyNbvTcfccz7bOnqlprZaYf6tzbtxtoBEECEEDeL06c2HF27Tq1V9ValZX5fV8650y0qeq6qvIwsCEwOWaoMjR639QxBAfvk6qy4xjGRIqr6ma63WzvtVu7sZl6F4Pn3a32vT/6w//Fr3xoazZxIXYl9SUXK8S4fHmqWjiAPbkxinVM0Su7DBiTEQSmgHMuxEiOxUYe9jlD0HsfQiBAyUKkGL0DzEhECHCmmkr0wcfKVdHFyvtAgIh0OZ0Af/rUF1/pkbT0fV9SdsSxqSl6qmuqojhHzle+abn1LhrBN5GIZEjBLBTZbaaPPvCQF/PE5L0LDKbRyTFUkYDRgZo4szNAzzsMMBtBmYzdqFpyRM4gIjA2M5NzybQqBNSnoaiFWJdSgq9ihKp2Q+9D86efefxLX3j+f/xH/9BZSet1HdCv5yAFGHCwcykAg+7uLPjadH7tJeEbhi933r0Xx9zDPdzDnwvfg6HM7RLSeW9VhQkMEGEyxziXGYuODY+cr+ArX09iPXGu8q4KzYSrFrFGiOY8h1i1kxhrD1eHippGPSsAiPbrbnWqeeMD2ppIuzIs2QYE7yqPyKjD1oMX692Jq0K1tYfdy6i2TB2IsDcLexOrmeqwc/Gg3dtHiKgb39bFCWaRr+zoXtNPEPenzZV9ql1xigpoXH15Z+eBg9A4RJ4+fMXft4/tCoG17zdHh2kxb5uw98iVeqveDKtNvzzv8r1Zz28dLs/OmmnjaoaTyLS+eXN9dHjfWx9uHrxcXdwtbXDbk9neTrh4gMoNUMTYTGf7l++7sHtwYWvnYGtn1rShbsEORdCnfrGSbmh83NnZKSlvjk7RZcvFxu4nlW/rhonqup5uzarpFqoaquiTbQbdDLUPTV075/KQUkreeylJtVSxqus6+hA8e0KMPtaBQZRlSiEvNyFpG8KsrqXrNGeV7Jwjsi71m9xTIKqcOFulfjAzZ957Zh66vnRDw9XE166wdZm61Jayz+7Ri/uPHfDlg9121vomqrNQsfeoG/joXHShDVVVeRc5BtfUrmkUEFA2JNECcIi+qskFEZSiNo4BuXAnlIGpqo4kLheJPRh1rPa2ZqTk2cUY6zpWPoyk9OzpCHhyszl2sMlWqFrPQfqSNz0cuxgkhgwW9c6axiaVNCKiLNBcEXzK90233vfOxx7cm3KXnZh3jogElg3inFVVBtamGyuZNZNlssJszoOdEpszdSZ2O/lkUDPw63IkCqhZ1dSrzSbnXNd16oe+1yHldms7qZELv/7r/3qzWv3tv/Uh72xax8rf2QPf9QN+jSzDRA53uiXTa3lWpW9AqLyXmPm+gfEbf77P8Cbs3++T73L3bHqzd/9q4nvv5BGN1F42NROMElIoTNjMjxwZA0xH2d4gZRBVNSiVlCUrFdWipmqmJppVVCFDktVgQ658BXYghWT06251IqvTmNc8LNLyqF8ea+qALCiqCi3Iw+r41tm1W7QpW2HS+BbiYBxjJM390a3u8AY2a3Q9ugSD8yalQxmoDLJcpuO5LVexyLSO7BlUIIODxpL15snw6k0uRckAQU7l6Mieff7ohRfLyfGkaH/rxtHzL+D6dZQhOELX2+HR2QsvxmUXl93ipWvzl6/hdIHjk2vPPrdfTXg9rG8e6WrDQ2m9BxFMgVJK8sWGk3k6Xbq+2JAdcQgBahBdHh4Oy3Ug9JuupD5vevSdlmzjsBM0DWXT9ct1v+pplIyBYURD6c6WJAI1Fck5m1mMcaQNkUFLZsCK5JSY2TGLFAeLwQ1dR2ptVXni9eKMVCwnViEppV9Lt3aSK1MqaVjNWQZKnfVrlzrqOlmuOGcnJH3p130aypBlvlxcv3nj+TNbnp2uFvM+930Z6sCONPcgQGSAFhK1XIpYgWXDAAhBTHsTAcwxe6eOskGymsAUBMfkAwcCxFC3rShvAHUVYlSDlORAw3qlKTszbzQG2QYVRxvgRLFWJHOOq9pVEVxxZDEGOTgTWDFn7C2wUltVlpOlLhLS2WLqw1v28fQTX7Mhc4ZTmFERG0TVnHHYAJuiwzCUUqBiZgVkHIRg43PBeWZTwaYEITkPZYz4TuWHqajWdb1er6Woc24y4WI6pGLEPlZbW7PPPP7pRx999Jc/+MH58fWtacWmXzdtgW9FMvpm6Zl7uId7uIdvFf4uof/o6vDnhd02jbibyndHjEB0p1o09s61u/T9er4Dg2WYgmjcimmU5quJGZDTHVsCJtOx16CvKyMqpWC5whog0qLeUCQjEBjou7xaYdOltGDvtWQEAQTOoCkfXc9nJ/CembXfIGVuYtXWXRpsvQFXyxefX2wGhNlWnKXjRe42ceLEU3r11XR8hNl2CeF6D/TZG9euWs1vwBl7L197QW7cOplu49Wb6HNsg5eUVGG6fuGl9TMvQBoMZWmC3KPboCScDJhNV197vpycnB0eYtig69D1SMXYOLa6XA9Pv3zz5evkWIYe/YAiUODa4fX1E+a8egYTsiAnLqZEGPphtbj5woCXdfuBK8v1em9re+jWljp4wtDDlaNr146eew5Gu1V9dnIC4rptiAhSMJ+frdbkmdQ0l/N8WDYmVaR1SZ2klLrpJJpqKQU6VCEqaOh6TXnTr25cvz5rJ6UfSsmxrkIbOx0K5Mb8qEv9fDkfhMdiCuXUVvV6tdZS0mbhPcwsiFVkzsyZEhkV6Jq6IVk964oBECLEKrb+E197tnnkkZOj4xDCfDnPw2D9pgoxMtiUiRYnJ21set/3zC1K6PoKgPPmPJzPwM2j4+D9Zn9Xga4fvPeqMEMZyuHhcQHm88WNk80jP3hhDlg9OzubC0OsLM6Oe65Lt2HpdH12wWMAhrxRBAE26yIFm9XQrfp+2UcL63WKVRiGrtDaOScKVtIiOQ+Q1AbyUtZHR5XoO9/zk1/63MuvvviCBnKVz0lzES3IIouztcQyAOuNUsK2r23dO6Hi3GCAqPceJpRz5eCcG0rKDBCJyB3yinOBPOC4iPgYvPcmAs+mGDVKUhR9fuAtex/4wM//n//XP/sf/rt/mDenH/noRxVTcIjEIiIwFCUix6GU8ppZyNgZ+3x+n/vBnNPOzoWob9qI6TzQ4bGc/M1M9r7XQK/35/jzxWqv03N9P+D8W7/O1uUNJirftv2/zrzmm14/X3dI5x+4Q0N/7TjP3+XXNiN9w35e55Hz2mZ3Wdp86/gG4/ZN9/O678WEu6z8xrcxzqxxoOzbNv53/j1eP2qEbzo+3zG8Nm63A4zvpILpzzYJX38W73xUAbCNNjMAGZsqYHAAGGoQNlVCLglcYBHm4QgGzqJaKMLUMAq4hw7G6KE+QoqbBRc4EQCBGvoCBshDpG4aOOlOzxCCr7fKurfNEi7QgHXeVOomVcwYyjBAeg6GlWkxFBdcJLJOB3IwU1uvsRmwkaqnYd5BSz2tBjGUjMSuT7JObt0jqVABGVDIBAW0GcrJfBgGnJxAM1SdKZjglCyjwA5PLXi4saGOwDE4EDHPV0ROnVdnCoMWHc2d2gqpoM8Inrusq83GB1fF4ghuNFqloIo+oeSuW4MIKlkKWYEUiCHLaHAGM3ZwxMbnWxUBOoKkHCnlbCIKN9q/mkpJueRBh2yVmpnkIpphwqZMIJNSiqRMGokQgtuezB577N0XLl0CqcgQPaXcm/PecwRY1REbuXXS+SD/4dNPUDZzBKKsuswSYkBVk2E1PxtyurC/j6eelXXvFVtts96st6tJXg0UrNdS125nsn0GWBEiGkreAKo6pLTYbIYC7+PI/xUxVTgjAVRhSs88/+K/HYaXD0+2Zi0YKrnbrKkNnhlZau8AsZGFY8KASckZuQjBMXsix2plSIZCRFZKUfHsKucr71VKHoaSy7SuLu7tLxbz4+PjopKVkLmUImIlQ9kkq2hiQNZnW/36bSE8Opk96MMLm00KaixwnlSd0DmljFgZwtBx3bk9AdVgBDivdDtLM85HJmKumyZ1i/nZyaX7pi+/8NKv/8Zv/d1f/plchj/41JPO+6BqmkULIDDng89S/uzL2Jt6sN5TMN3DPfyF8a2aI39f47sixr6dSbYxmPr6wT2PQ+/YHZrB+DzYFIBh4+PbeZCpdIesrWAFOTIimBo5Q1AlK8VGhYYZO7MMIVYKwoNZBHMxGQTMxCQwMtM8VFXVej/kAkV0wVMoaY2QEZ0NGxmgky2reChZeXBt8KSSNlbIc3CeCkSshxN23hUqRZCkylYMSqakZbysxLxAslpfKAk7owAEM2ZnIFXth6yGoYAUZEpqkUAKy+AAIohB9bYF6rn/pRVlMoziWAKIzg1ycoEZiH30EYCWTdq4yGAFM4RgGA1UIMojKzlDnfnAYIaIL2qqzjk1I3NE8MTKpDRagiuYz2+BzOwdKQG3CbZ6m3sKc0RQREEsVguBBClZyQRP5IiwWC+eeuapp5//qmphpraipJKCY++9ihc0IVRVPWRadMOF/a2mG0b7uOAjAznL1Veu/r2/86H/51/925dffvntb/uB9/3Yjz/++ONf/MxXPvSBv/mxz33lS195erfer+pJvzmLRfJ6eR/gTINnByMgxjiUPJQsfG6+IgZVNYUxyeg0y6TEz127KaEh50uCJ/KQTe4dV6QS2aWS5NzgUxlgS0WSSClkQoDjUsgFR8TMZjKk0quIOs+mrmgT4/0PPfz2t7+9jv5PP/3E0y+/GKdTdTBHJN7BHIjMeThSBXDR93//73zoh+9/4Mq2u/Jf/mf/9COf/ehLL6eKzKkDE48dxJk4gmBgMI3yJaLxBcbHT1UlIkdsRKogImPKKgoLVX39xmrn0uXrN2498cRn/tYvffCVo+HJzz/lqlldxZyHUNd50fWq58aV34qnCN1tJHPPVOZ7HWPe4q7zO2YyXnfP/P46g1+XmxnxrbQU+I8Ie5PD/6uGb2so83qFwt0pr6/bkECvpY7Pt7n7N+68xwYDkdE5tRAEEJupEgPqnDMzg5opGbGRZ4JBYWZKBGMYmRK884B6H5zCpHBR8qxwZmLBI8DXzWYz5DxsTWcWwnKzgWM09fnaWnl47aVTG1AHF/3QD1CKoQkhZM3FErhAlRyrksGxD8YsnmDcmxE772IRUwWcVy7kiZ0w6yhEdkqiWpJAM7yDESzbGLKoQgXOgz0cwwWoQA06hndaTMWzBQYBKo4IjsAsQwZ7RywigxYwQYXqeM7UzAoZG4MTXHA+ggnsXAg+xvG+QsTsnAHGDmpkGD3ZhRXOARjluAqDY+bzzp1sNgYhzHznRDvnHDHUzMyP4Y9jApvYYrN89JGHr1y5tNmsQggmyaSfTNqVooBYi1kuRZiFjdsYCqyetU30InWIJGm4de3q43/6xLWrhx/+8K/87r//yP/7m7/5n3/wb//C+3/uY5/91B/94elP/PRPzUL97Fdf7k9PmkiVaSTqASeCNGiOBMAksAsxen87SQuIKTM7x6Nhrhopu8Wmm7QTloELnGbPCi0ivnUslpKpYjxsAcCejM2cJc295ESmngtUNJGKZ51ttZcPdu+7eGGrbh+5b3uzxo3j9XMvv5hSeu7aKzxteFJrSUpwjkf9EgOeCdAt4G33Hbz74fsfrl0/T+/aj//JYz/yleuvHiEVmBIrqYyXCfEYoDCziJmZA6mqjGxBoOhrLmEiEJgZjaY0qrq7O133WYje//73/9Ef/v4v/9IHctKXnnqWLfsIIoNjojF5+udbPG5njO/hHr6H8CY9le7hewzfvlDmTUKWO3XuN/y++927jO/oNquG9VySbURsYIz3SHJ3dm1QMicyBjyj5IlFUVSgZm6MpViNjAghFPKahCGicETex2yuCKFuMGm5rruhaB7IPLFPWSCE6QyVgxQ0DEOfe6igAphT18M3rp14X6Uh5WED7eEYcJINuaBtdXeWtrZQEtbLQi4SE6WS+hI8mgY1GE5Wp5I7zurNSAEmBEb0qFtIQbeCkjMnUqBAcAiMtoEjDJlWiRVCjODgzaYtqgpDwqqTXEhhjnxdl1LEEypvTQS1MKHoYQy5HYV4j6aBiDgHBFgGWMRQEmBoJuxcnwZyAUW0KBPEVFXgHZjhyAhZBSrZFAo2VhUiqnyofCAiESlWGCAfCrN44hC5rkPTOq36vt/b2zs4OLhy4WJJ21vtxEMdctU0x1028p4QHEOllEJEXFVZpW4nq6HLaiJyujjTlLtuODxd/M7vPf6TP/P+xa2bv/Fvfvvn3vczP/vzP/cnn/joZz/5iR9/90/szfY//6WnkPspg6UPwDT4xrRmVICHqHPOcxLgPJcBI/Lex+ABsKdQhw0MMTZ1xOlSlr0O67YKXWw2Wcm7odc6hAI4X2mxAnAVfBVddIWkk6E4NTaKXkWgYqYJuknD0cnJmZ196k8+y+Q2yPPNaro9TdHPpjsn8zNUDDE+7+6ezRHYDLYAdva2t1ofgZQ2s62421TexJEUVUUoKEmhIG/QMp4cEpFzvx9VMRhBgZG4PU66nG9HFapbsx2j4fSsj9Vs0sS2bb/85S82Bw/9zPv+5h8fn7567WpotpNm5z2zl9QDdzgHX++g+o2r929mqXcP36v4utzMOe6duHv4j4Nvc4Hpm1tHfP2WuOs5bHT8ADkau3DdblNpANiR80YOd9y3TGFiY7rCh1FGqyGgiFlXssFoLCDBB9STqp059d1yZSmNViEKFHGYzMLl+9rdHTPbnM6Ja5e7VVmKatze5e2tMp34UIVB1kfHOgxwzM6rKOKkvu+hg4P78lBOrr+CYQ1jTxBjA2FrGh9+sLn/Le3W1tn17c31Gxg2SYSNEev68sVw4YCym1XVyfWX+sMjvXmYh0Q++MmseuBKfemAqrA+Pd288grOzljAFkqMvLUd7r/sd6ZM1h0e4+Uj63piZ941D1yqDva4jv18kV+6URYrswJwgSG4nSuXJhf3dh++v10vbt686ZQyAANCDJNJs709uXSpkxynUwKbgHRsEOgxbWf7++SdywVAP1/KclPGnoFVVc1m8MEHZhL2Do6NYCIKOO+qqgqB2tm0qqqqqpxzIgLHRs6HWNWVH3+09mK5lCeffPL08Khi70wjWRu5lLJIosYhhBC8iOQyuBCqaa2gLqdXrt8wZh9bcj4LQpxwM+lIPv7ZJ370HT/0cx/84OOf+mT10vSXfulnH//Ynz7x2U//yGPv/eCHfvJzn33q9NpLUroJkJdnLqegpQK22hbex+hx3v7ZiDH2OojReyDUlawXhdjVMaUUh2G3inJw4YWbN2W2lVad5V5BqJubAPmWsjIQXRs9uViRY4WwhzGKplD5qm4ik1dLRq8enq1Pz2qqVASTqm2jOgLAbduCNsMGZp6ch4GNPPsmmqs+/uQ1O8tPL7uhjtP9nSXjqavX1QVnRiIGNTMxKEA01qNA7LOOoYxTKXfCBrntNGOKLMUAhU0ns8XprVnbbO3Um1TIEBus1+vf+Z1/8+G/9w8+8HPv+8M//uPnjm40O/udmuQM+tY6XX/DwOUbl6Hv4R7u4R7eBN8NrszdJac7i9TrlrDRsG5keLAf9U3E56RoI4AcuUDExo7OK1MCJZiBKwrRtROuG0RnqWh2amxjgp8DQsWTrfbCJVhIfKRnx2oleR4ogCpcurL9loepatbr9XSyo6vV4tarWK5QRd9U4qvm4lumu3t6Ml8uNggbz+OdmrF32V94i7//IXQDDRnrNTY5mAoYscKVy9N3vI0vXaam3Zo03NSra9dweqbM2N6aPPKgv3xh6BQxbl1q8zNf025jZzSESLvTyUNX2rc9EuqqvPLK5uQE85XTMbEUZdpsP3Tf5NKBI5q7sDpclZTgHaKvLh/M7rsctybL6zdXZ6vc91CDY5DNLl96+Id/yGa139na256WrLrY9OZJEEP0VRPayeTCAUPcpA0ckjqn5DlgOm339+oL+53kEDyKZjMZErISwXnftm1sJ+xJSu9CwGj74wxGk8kk1nUR1HXdtk07bUKMSImUondN1VZ19J5diBW3am59enO/nTz6gz/00AP3tREVYRqRMrIhlXHIqZgKIdY+TqCGk2UJX3jy1snZapOHwuwnFCvanjoufeo+/eUvvucHfuinf/7nv/TcU//yN3/77/7iB+fz+ee++uW3Pfroe3/iHV/61Pr60y9n4Mr+Xsu0U4GAaYxaRceYRIToRcQYompsvnIMxDo0s3aVSAVm8tDFS48d8Opg78lru2v2ddvIckCMg+nnvjoX460wq4FGGi+IFKOvQvTBczBzDsG7ygUAhaAchiaWgYdc6rougdSp93ywt0/FzRcLx44gkXwkY88WXJhWxbcf/crNeg1+/JlfeM8PX5ji419+9Y9fujGnlhBC6QFwUTKQu8Olp9sUmXOujN7hypiN5SfFWBBkIlqtVnXb1nW1XJsp15NZGuA8oy/LxenfeOStQ/+eW//hj/pSiIMpXhfH0Dd+Sj/vmgsFDMZ3c2Vex5u5h3v47uH7g3LyZh2n78FjdOEHgL+ou84bfLfo9X+8zU4a1WLM55JFIlIDj4sa6K6dGN+WaTuAmR2IjBnkYILz6pPSubyJhNjghUHOGRGEQDQWOokjxVYtFBehlrWweTiHqq52tuHiyclCsl56+IEzZrw6gAKH0Pe9UmjrJk531vMeYMSKZLC+R1UB1G2GxTp5H1C3IELOwszBS1Vhu82TNqXiKYV24i4eVDkPXULsMa1pd2uow+J43vf9hcs7ttWiauAziEzKKnVOhmnVDCPxlhw5BzOTgpTmZwvs7zRNlb0rTDACM9h1602URL4dAvWkMB3bd9PW1sHbHt59+C0v3bq6uHV9e3vWbE1J+ZhhWVVVRLKKeWYmVfXs0tgp0DGq6NpqYDtbrRs3DcwcPFWRLGtOeRjcsHFNY+AMzXautmVmZh+ripklZVXxxMF7x5xFzYS4clX0VfQEbxSCk6bWYdaJfPJzX/jYpz691VaTuqoZpHkxP1ZVMSLnQqx9E0IdXQyrYb2zd7BYrCybZGOLkQIpb/pkTme7W7dePfv9j3/ihx9923ve9dgXDb/3kT/52Z//2XdfvPClL3xxdWv22Dv/+mbirgD/00//0M2Mo6tXd4BA2KTSr1ce8NFpIQBiYgQjckAMruScegOcwC1KeRFgYJ5KChLqIBt2Pq7S5otf/WqWEOrWAQ5kBqUg9USrDYWIpNH5uq6rus5qaShG1JCnWImULm/SIM2scWIY+rTqdqNfL1ekxsEpJKkyAHLwkabtK4eni6+9/NGnn4qMJP6oC5PZnqWOikKFEcCAYxCXkeHERips55x5AUDKZAAbE8g5CBnDMTFXkyZtFut1qWsCUExDAzX27Far1VPPP//Ox37sMK1/9/d+v51e3MhoAQWMkYrC7tQgXpv+dnshZkCUwKOEyv5MpaW/UqKMvwS4c3P5XsC3fOUYKyl/b1xvf8678/cJefkvDq8AaGQF3v7buWD8vCvNnU3H13p7XAxGcLjzOQPdHm7FbW91AFIAgB2MCGQMgI2cjlLrc+sYgqmKQcHI509mZjoGRExgYoMSVBVQ770CUghKgU1yX6AgRTslA5GBEFnT0AGBay99t5ovrN4GB7BjDq6gMOD9bDbBkOV4hZJx3+WUO0SPZQ4chr5Dl09fvVpVW7kY1HGsynLB1HuzUPr1Sy8vLexfvkJSYIYqAiZ9j4Pd+sqlbDRcOx3YYX/WbE1xUp/rrSaNNiGlIs+8YrMtbE/2dy/eolesANFhSHj22bM21JNINK4EXuC0ZBLYYqUvvNq1k+qBy+bcOHQxWZJ+eOnVRWS/3XAT1ApIEBqEcP873lFdufjs4Y1Xrl/FsCnlysH2XrfeIADFxDRvlqfz46Ffxbbxail3QBavAxVULjYx94MenvTrtL2zT3BFzUpWK/BuM6yti7Nq2xwLMciRgESbyqsVF1i7nDZrnkwa9oGdRq+kVLtcO8dYnq6cipSsYIS6UEmi2VAM6iJi8/CVg93lrdyvTR0RQWwybdu2XnWLnWlQlN39HYVfJVKrWeKyT0dd53cnnYg1TU767AtX/8bFh96698DTx/N/98lP/y//6c8cNuHW8uz0ePrh9/7oWwAB7gumD195AZAub5TqrcYBvm7WqyWAKnJSW206A1wqExcnATnBvL+p8r/++ifUA02kAFYhuCF3sZpkYlRkYRiATN3RsltY0qwvdtIbO3bdJjWTAPB8fug9V4Z9q0rujvLS1XHKQVado1wHN1XVQSaZ1rmY5z6nQpVqODtZN5dm3TD3W+6spzrOKrUsGmNQycZOfdSU6ujSAFDodBgcMkpGqepYSskKqppiYCcsYPad5MIchJ1y3/fqqJcCRzlJ6uCirdfLVYdiMaXkY3zm+uFHPvPP/8k/+tXjqy8/8cVnptv760TkPeUc4UvpBRKaetis4SMMUHPnvh6qZgCbjSEweLSQGReUO8405xY046oCAOC7ak9qd1yH73zgLwfP5rUnwDfb4ttlTfsm97k3M/yhu27sZndTJG/7jLxh+9v6jzf4u5C90bXl/O93n8fX+cHc9fLuf/IGz5g3d2e5O475ereYUTE7dph9w7/W20/6r9/Pt9hJ+7Yjyzf0uXn9lgBgb6RnEAA2qOo3DsjecD3cbvhDRKNM427/BZxLg/H1w/XN/XjOX97OUmC0BPmGUMbrT9rtw7Q37vyclOju/N/Xf/PXjbN93XHegb+dPHmTA/rW8YZxZje2ub79xcxAjPP09Xhso925w+1nepyXn5SZxgfiURd63jjGOe89gCQwTZrPqx0AyMAGMYVkZoTgObhiAsk2qpRHMdR4aYoASo5KVqQBquTgAsMTnK9hISXTAAAgAElEQVR8GJyAGSZqRYlhUDUQCKoy6NBBhEQcw3sevANcQfHNBKH23pOL/XqOojjYiXWVQPABIJDVdZ2HjH7QmDUrja2j4GCKPAAVJFdV6CuPOiKEwk4dgLFXAyMbAAoejuBHqXIqQ+YiRXLRgiZiZ+vKzuWtg4Onb1w72K5D7cGMlPN6XWKjKY2aKUDHC5PcqA7TWIeyZnNsTDAlJeccfGhDFb3P3imr8y4QJynYdDbbEVUjnLcHBzERg0IIBsk55zyUnLXIKFwyKWYiBGEAMCmqBS4ul8u2nhQRCtHFmMmmB3sPvvXhutvaqiLMbYZCk8lshz2hz+igWaXxYb3KR/O149qyv3m6OHz1BRFROAm+hAiVBi5TMK6MXQ1Uue/zkKSQIRIAFKgCNWDkU5FSyp3ZQAZTNQU57wAyyirFnJhmw4pdRoQzBtcggmWigdzYHdzYRnnYUV5d8DhJwzPPPneaTEJQ6HQ6hRgZZk27mB//9R/4gfe+7YHNQgZvFDwV6IDgtanZgGHV92c51tPDvHz66aeTYzUmIobAmzkzH/NYn3XKYGZWAdgRsTEVnOcozSCjtE9TMRFENVWDIStVYlYEZnTO9x1ti89XYB7zoACEoPCTSXChKi6erja/9i/+xX/74f96tf5nX33harO9t5kvvKvHDKvnmFIBh/Hqustw4bzIdGeJ1D9zuuVeYuYevqt4kzrpdx9/9jlyDvsrJL/6LnFliMcOjszMRgG3uTL2+ljSCAKlc8vg166fcy3JGGi683jQ2IyojLERE5idIzYosxFnzey8spPxfm3mIYUAF4lZSoIIUIytVISWoZwqVxYZpQckkwCA83BUGMoG75AdyCkFBeArcEhkiUSdwTGMVZ2a0Ka3RbdzIfazSV4unGS37qtUxDgN5nptxAYm1KyNK4xeC1AwibjvANMK08rvzIypqhq/u1u6bAJms0AWPOrGHAvBokPl4dBBTEvcOohVnYYM5r0HHwr7KR2tnv7aM2h8Wi0rntRZktKVerLr61cW11BGwZSMBbrMalR65Mxjj6tAroYEZKLo4MNy2PAQhVUZauqNgBAmzZ1o2oGgplATKkVbDgRHxOwDyJ33DRF4hVc4ZWZPPsB5MDHzzvZM+1Ty0PhKu9WQaXUWP/upqw8iXZ5Nzs6GJ2/cejWw29mdxqn3/qg7c9HttbN+vVmdrs1Iybtmot55NTYmouxpyJohZIpNj1Ggpa7yFSbNEaHP2A1YqTMpfUDyoWQp4wMjlNRYjIW8coXgAGWXvBvApFCoGTkiIwfn1HlhzVUYSlAjD/JKMHcL2LQtdhu07eLwZGNuzVIgPptkcZPgVC810/e+64EvfuHq6dnJINqGSSOB1JqmbnYmg9duM7iEidjRar5YLFapRyxwSTmZwohYx/5HpDDlkbtr3kgBMxGGsTozNzY0MyokChNjU7YCMytjhJw9FV9cGljMCOf7As4fv5SYTWFgVTs9m892tk+OT1aTXQX96t//B//b//5Pj44Oq2ZmpqmYumAiIGIfVQaGAQIyG5/UjWHjs833xo3iHu7hHr5v8R0PZVQEt6OY28YxBrCOfWremPTTOx4kdifHSWM22lzwDmQEERERFSEm8s7YwXsXXOUDAeZ9jkEzwDB28BWqqo7eOeKqSlXljYvmc/u4lCjW2J0B7ANrv0YegKyqIELb1Pt7YVJh0PXOrB6ozwuoAzFvb8PXk/2telIjT/vdbcsD1huomZgtu4uz6aVLV169dUtMbbmWda9dQhGkout+sjVZPHBxe2uLAqWUUDK2Jm9/7J1b913sPJY51aGZbPvmgQdOC3WnC7VStdHNptjdnlzcrdsKeZIv7uW+sy6jbTW6um12Ll5sZtPu2smLX3iyv34VUuKVh7HptmazR9769gZ25cLBet09e3TCpRAxHKNyoa1DP3jv6rqumyb7oMymhATOFJpYzya729upX62XS5TexyoYWdbaxaqqQwgA1bGCc6RkZqWUUorCqrpuJpMQgoLNSGHkHYNM1Ru1VVVX1cpIRFM/5G4zayc/+Pa3VUFv3bjmLb/lYPu/+rEf2WPKGZ9+6ea//Pznb+Vy4WDnaH6GyQ7aai6yzKWqazM73WxmoW3rKsRozkcfcigYMkiJbH9rxzubAtEHiK1FPnv19LnPf/7Btz66WZztBHrksb9mvprMQmwjAWMaiYy8USQX/Wg4yDHWlSOTxGowMHkXA8XgQyAyX4lXoSwq5tgVpn//9NVbxV6Yp5NBfJz6lMuQnHMxVNMqoh8a1Q+8792vPr+6evWV07M51BpX1eqdwXtPN/0qWJ/y+mTpQew9VDNREvFN5WMw1dpV4sEqZgYfzFBAloXu9G40MMgbeYDUiMjgxcDMYmo2+ua5ke1rxPDeiF/L8xIRkdg5U1gESkj9sDXbeeVwAZHlJi274XNf+dqHf/VX//X//c/Pzha9eWNv6uE8DGTqbDTpViEzonFyjlme7/QSdA/3cA9/6fGdz8qMcYmqmOF8GRWMaxnuimXueKV7BzYyNjMbW+GRwZGqsp0XEAVFtMCEiOGC8XmSB5bJ2DOUPddetcCHUNdhOqkcwRKzGNjDw7iAMYicnpYpuRC897pZytkSqYDYkTnvqGna7cackNd21lYR/SIgBcRoTRMms2bahIoxCbPdrfV6I12GN8A2R0fp+PjChf2dWXV87WZ/eDIsl6Xr4FhU5rduTeLlnYPtuq6Pj082h8dYbeCzEFwdA5GsVoLOZWOwlJLPztB1XYzY6S4+eH+YVFokgPf29m7O58CS9ncuPPjAQ29/tNnbPz49Ob51PIjS/Rd3p1shutXJ6fbewXsfengvIjq81KPNuhIrnlB5tFVook+uIjcLcWt7ujxbmONSFJtcVl3Vht22kdVpTWqsZ6yl34hyU013dnbEsTMj5+oQYwheSIMxc9/3pagLMVS1GIacipqCLbhkkje9sWsNrTmkUlR8YEaduNTT+OM/8shXv4BrL73yY297164N9aI/aHceu3zpo+yWq9UDO/tbO3vPn94snhPLknSQHGOk7TpHcFuxd0zcuMp8rrzznjW6XAYDCpDZfIztzu5A+vTp4nNPPGmpvOPiziWAqjpA2HMP5JxJjQmeQ3COyY+NC1qLM7UeWUyVQd6F4L1zQeHOvWgow4y0UFmb+8yr14KL8sqtk9NNpKp00mYixamcBb990cf3vvuvNQH/3yc/tj3b2jaiUjgtjTVW1dTXzseKaai03eYhCzs+Olk98ZWn3XSiYeL8hDE4Z9mbShkbd8MgoETi2MCIZs4QzOI42y0zM6E2RHaiKmog1A6RwcrFvDLiSGoDAyYEU7DIOa272NhXidiH9WbT7O4vVvNVP/zGb/72P/nH//hXfukXf+u3fmszX6OdgQkhYt2B4HQ0VhjdIN15ovUe7uEe7uHbge94KEOjIeztAhMRGY2C6m9AEAMTGGBmODPTcx8Mp0wqogmAgYl9YGYlve3SD0guqSMzZracQUbEQgxVFeGSuvWZZUOxkjMRMciDy5DS8XyzyqhrcW6+Ok23DpEyj2pVVSv96uiwzOe0yK5PFQFZYAwOed0h60nOq0NPucvLlQwJY4uvPODG4Quf+8J8OT/rutXhiS02WPcoCc5hGE6efWG+nEfnN21rTcVbjT5wH27evH716iJ1HHzXpw15DEXW/XrTQRXsoYYiJzduumENUco5DIKUIcVMd6az5dHJS6+8ulguJ65+y5X7Nqv16mzRP/sy2M339g4PT7RqKu8Oj28dnp4o89iNDKq565dHJwO4923pO1g2E4Yi5/7kGNr5xt5yeffRRx6qiK5du/H01158+aWrA/t+6NJQKDkX3TAMIuLhmVmN+pTXfcdIwzAga9+lImbMBaZSuq6bskOC9iV1QwcrKXvP3fLsic9/9v7t5tEHH5wB/fzMzw4qh816E3z7Uz/2rvzU065f/vJPPfzbn1o9c/3lRe6bUBdLQ0rt/g7Yd2lIKcUhqCTKBaJKMMfwDgEKbFJebHIWw6TuQz2X2kX0YUJAt+4L0qaJ64JU8rmvDDMZDDIAmzRIclDAsqAonDPKpQBAFlU1zSRqAigglrW4SSPsjk43Ym7YJOnLNFbrzbKqabOc7973Fk72Bx/5+E/++I8/+oP7acClCgUYAA+0AAM90AMEbICjBf7k45+7eu3EmbPGHd88nHJtQyk5i4hCi6oawwrMhOCYVM0EPBLEFGRGBjImI2IjM1WQsmMmNaWRw+ZMgVFZWM6nZxaBCXMoCjMj5+fLRdu2Sti9cOH4ZD7d3vm1X/u1//m//28+9Iu/8Ou/9wfrVe92Z7LqQUx3lZDsTgQzdu3+Ti0893AP9/BXCN/xUGak8YLPLe2Z2WiksL7mUz7yfols9A6BwSBmRjp6x4wPdAJSGEFJR82VioGZCKrICVZyKuwcG6BIkuAcFOj7YezIOGRkAXmKkUSoqDfQZo3lCt6BqasqLFYhsJFBVaVgeZpeKlCHtRicTWpseoiQBy83kuZaUu8AyiiKzDBGZIiSaXrp6quHt+AIQiiGPgHOe1dKwdVbeuN6Hyu6//L9739PuXiwvXfx6U893p3O+8WiCjFwqJutlLMPYX9/XyfTipyH+bbuvefsiDyc46CzS/dpkTpWz3zy81IS2mrngfvaqZtfP5xfP0TqQUqXDtbevnzz5d3JJITw4vG1RAmxhYGTGPJwskpXbyTBqposTm6hW5ptEUJAtkW3SfPZxP3g2x9+uKmclosPXKmgy9XZ6SodntyAC7Sm2Ma+62Qo8F4VogLPXd+LbJardQnNULLClJDJPJtJLkNKw5C6PvVJQyT2sW1cG1fzo0987OM/+c533n/h0jA/OT5b17s7A/mnXn0FTf3Wt1558avPXfvy5Bfe+vBOt378a0+7FjFOFkM/zPs+53Y2Y7FCjoGC4nPJmhPKShKZzIHFZn16slocnx24C8Wi+hZqKdsALI+OOLq04weBKm6L+yhLsowVcJZWZ4PvnBcalFVIzMgSl2TBTLUoQ42pwKmLRhhEUxYkJee8H1LybEzWKHaqdn56+J73PPTRP/xCUH3o/v3/41/9bjEv69g2E19plmxi7J2RK5IcJe89+/rGzRMVV3tlaOk2K84QtZIhWkiNAGOoAhSIRhm4EDLUqYoARZ2paVLNbIAZCchkbErPxmRMyKpKUpxCoYCCvd7WKoymT8Q0aae3Tm4Oi0VXbe0cXBTV+Wr53HPPvetd77o2X37i8c9lyVIy+zCS+AFTeNxuIknjGnAvlrmHe7iHvzC+46GM3S4wqSrOdUwMYNQcjQUmvSuUUdgo+Ga7I/Qau5kbgofzUMWQMFJwfGAHFTMT5IKi6jxzYCJR5RiVDKUgJXKMPFjO4CCBSaSoemKnQO7QZQQPTIgphFBUnHPQjLxB75AJq6xg8+IsA8Q51cAyZfQbRDgPydll76o2FYHJznRHJOfrx2jr0E5LKdbnMJmQJ2ZoKlifoa627r+MlAD6/9l7ryDJjixL7F4XT4TKSK0qM0tkVaEUUCgABa0aQGMAtMB0N7p3ela1cdlG7gf3gzTSuB+c4RfJpdl+k8ZZcsi1Nc5ybHZHtwTQ0LIUSmdlpdYq9FPufi8/IrNQENXTmO6Z5gzrWlhGpD8Pd38RL/wdv37vOYGnIY6pVoMkjRhABzUnwBGUCwAMcQbAQA5yATgHAkBpcARpAtoDpSBNgbk8OlLq6ao3GjOTcxDHIHzw9fDBcYs0Pzc3f/1aoVwmoqhRB98HJ5EAHaAhSBw0UkAhLLE1gA7ZoLOCWLhMGC4qb3dvd1lgq9Hq6ujo6SgiOCADoME5BiArkdtSygKlIGZAtNamSZZlmSe8NmdJO562DWuZnbE2Npklh0pKhqgRoeaeju6oun76g9P+vScP7hr+wTuv7RkeHNl/8Idvv7OJ7sVvfSdn4MybP3v8qS/fP34gqzWn1zZEARRrAO0X84SWyAJjm3AHBbMAkgAC2xJDWkjFEhyTQ4cKtEJHDhgBhLXaVwQYpewYBEiH4ICdNSwoBYjTNE3IegIlCEEkBLWjiq3RRNJZqVEjEoEEDIjBOraClcgoZSdyzmgEaDRygj2D3/+Hz/zZn77lI7zw3GMvv/aBYmlM5jONdnR2FoLNWnW+1mwYtEJZa0MJaa0hZArCC/x8q9UQkSkUwjg1AIIRQIAHEgAcgmMhADQKQoEM7IAYM4CMAMgKckgO2QE7cMSOwFnETJBBYiQmNEAGyAmmG0ID3P4gd5hfhBDGGCkleJ51XGu0hNLKD2YXl8rl8mMPPbi2tv7RR1fLnZ3VSh21AgBGZJYAtEMtYwWDux3ze9tu2237pU19Itf/ptd/jQlmhxaizQqzUyjaQtfb82FbZQkAoB1Xix+TFzIxOL6JWQAlIghElATAEtlYaMfZKKW0h4hIbLNMedo4AufQEwqBTAYoC2E+thkAgdaAzGkCWQpCgHAsiCX6Op+aLGWntLDOghAKrREUxbEMAikRjAEBYBOtAiNJAEemQZ6Ttq2obaR0LJjTFFBIJZxzBBkwFPv6TJLErZby/EKxVDcJp7EoFISG1GWQpiAk+B4S1Kbnug4d8HxvY2oemhGkBnwfDEHqsJ3B3myBRBCq/fVAloKUkBkABc6BYwADzkAYDIzvM8asLa8l1SokGRQL5V1Dvd1d6/Pz1bVVQAIBzcrSDiGvDylp4QExCpHTIYCE1GpUNs0AUUsFxOycEpIprVWq9Uq1d2TQU5odNesNIBZakxBCKMpiBJ+IfN/PkkwJX0qZsrNMUivlewScGUNEEoUAAnbOWCOc1cL6aFiAkCZKOksdrUYlTlpHhvfNT028e/ZcCnTkkaf+/D/80Uld/Op3vv17f/hHf/mDH333+edC5f/g1R8/9cxzX3/s/v/wF29N1JteuSvLLGrB7HwtpRGZNTJU1jjhS2OIUlMq5jVAFsWB1BKVCsA6NlksgNghM2iFWZo6lMpHqQKbJQAghGBnBZMF0FIGogDgOTZR2gxKJUdCCyWt8Uy8u6+7t68LpIuTxNlMG4OOPB0apgzIWqsSjcY4Fg7hoWceeu3V9+urS//ouy+99fqZtZX1uw8fg6y+u5i+cPdduwA2Gf7g3MQfn7nQ8jszYmcokDnLAqVKnVGeDpBs1ALhG3ba1zaJwVDBC2xsrHWsddSslzs6kMkZABDkyWqSBZ52cdyq1e4Y32/SyFM+G9sR5LYsBUoWwiCO6qVS3po01EojAQjlKa21UgrI5QJfIgghjHV+4MVRs1AsRq1qPp8nIgYIOzrPX70Wf3j2n3/vu7/zO/9j1Kp5kqUvk8xx4gBRhXmXRWzTwPeMMdhe3nyKXQNxh16jPWNsTy+fK/T2/zXbOZfPH+rNZ/rzpV3+duxzVDx/CWKez/LE/GrtF2//xnndxLn6iw7pk/fB7YvvczXbP8vC8on33tT7z+/8c9hcbiP8L2h/816ZbdfKztKuTa2H2BZV3tE0+Phb154PbT6PnS9/h8MIthMfBLb3f5CYiAQiOAIAkG3ZQhbMgee1mnVQGiSCSQGFkgoVG5MCglLCOk6zFFAYZLIGBAEQtIN1GLhNgeEI2IJU3I4z1kp5EjLDWUrA1iTOph4wKEXYFhJ2TJjr6G5Ua2CyXD5HmanX6yRY5HKhlEQOXAZM4ACMZUeAOP2nP4YwhMhAPQKpEKzQqpAvIQqWKlPSoRMMCkFIZClt4GWWCkJSaiw5LxfqXOAX880kri0uwkYNOjq69+3zcmEjia5dn4T1TSzk82GQRC1rAFmyJXDkKV+ickgsJUoBIMFZlzktA2sSdm2UiyiVFRYFfzQx2dnTySg219avz87Xqk3hdwghgYGEEEKIHT0KIYQjEkIgoiPLzEK2o5sEsZEotJBKKUTM2KTsrEAtBaLIskyAHB0auuvY0WatPre1SlcnBneNnnjiqXffeBOC8BsvfvPP/uxPX3n9nUceefBe9eAPfvrD77zwraeffnjjp2+1iILO0mplQwdAJBmlEAIBmNhay44US0hp+9Iia4zJLDhgraUgR+1bp2DthwbkegOIUAhlGay1oVYIoAAkihySqWz1htnR3u6msUvVWgyJ9nMgOCz4w/095ZIGAVKDJ0ACcDtsBkAD5AA0tDOS4ZWzV2dmpr7x4otnz15eX1//7e88f2WqWi57D+z19wDkTNKpg2N7Rn54/sJWGgudVyA0SAZIiByzEI6JBHJey5SIyCmJAQiXJpTZYqG00WqEpWKSJJhlSYvuOXHfh2ffrVQqL77w3L/7/f+rlaaHjvaOvFJeWVnzPHXoyLE33/9wdX5+7IUvHbpj/9Ly8vz87Feff/Z//Tf/e7HYtbW1QTEoIZhcbXOT3fZvs9FodHYUr16u95Q6jHGtei0Iw3qSFXP5d19789Xe7n/5X/6L/+53//ue7p7lSlUGBatykKFtxUKi1l6aRH4YmMwR3o7/vW237bb9UvY3A2V2sq4BQAgNN5ggURACggSBQtyMYdv1CVhsq8OwuAnKOAIQqh0svJ2lzczbgQHb/m4JjETtKAHBzEIIEODapOgCQSpwCOBQCF8qRkdgQUlWgg2AVEBAKEBIcIQAklEQEgsJUqG0KAGl0p6jVAAJsBLbCVkKgC0TCJSB8mUYOkyaKflKh2FkLGUWAq2VokaMliQ7llIKNg4BPc2+aThoRMCE2tcoSAoroEZpoAOBTiqlSIKxQoCS0gJKy+godgwCvSAntG5tNTYWV6FVg2Ih3L+vXC4b5zY210ylCtbIPbs6OssiTlvzETJqFoZYB34WWxAMEkEqKxAkgtKBDoQMIDHkBCrlhEqJEqFQ4BuXLqc5XV3frNebzdhKVcj5HVGWGaI2GRoRETkAQmRCaEMZInLOoiKJQmwHQAELlG2g4xwRAQgCJ7QUQhghQSrPDx3LXEf37jsO/eXLP3npuecevOe+j9794JGOp5958kt/8pd/Zj9U995733j90A/OvvXggw9/4ysP/+Dld1Zqy4HHpIUDAGYEoQgEoTAOLXlSSQYA8DyPstgJQoQ2unJgnJIEkLKz1lyZW5ha3rIONQhnIU4TJYSzRgCgMaa68FuPPf70UGkkB8s1eHtm7f+5dHU5iZwHV7Y2ltYrkJjMgFGYKpcBKfABBLNDIOkYwDlpBThpspMPPlIz5qOJiacffPzyxOrLZz7o8GlX7sjh0bLCQABYHTqdw0ykqROGwNNGsGGLWmqlKU3ZWm42mSkMQ0C0ZJUWLL26jVQuSKzRAj1Pv/rGm9/+2mMry/Nvv/L6S9/45sMn7zl/beL//g9v/lf/7Bv/+l///r/793/w/f/su6trW0szM++/9f7TTz/xv/2f//bs2Y/279vz2AMnf/yjl4f3jdfW12qV6mBvz9rKUjEPaZyUiuVmrY5SCedsZsCR53nkXJyZQphLkuSNV1+979jR3/mX//Xv/k//CizZOALUKHMiV5A2Q0q11m2m39t2227bbfsl7Ve8HvrYvbbjbSMiImLn2DkiB0TMxM45Y5wxNsu2H8ZYY6y11hpnrXXWkaN2aikAAHCbMQ933H0AUkqlNQOwlEJqEBpQoheg0FGahbmCEALYgUKQ2hIakKACZtlotEyaABNIQETQHkgflE+izX1MbdeCFr4QgROBDQoQFABVBOhQkEBQ0ioFSqXKT3UA0gcvwCDwwqBVq+fCUOfzdesMCCh3Qa5oMgJiBaikBGbjCIQCTxsAiC1KCbmAPZ1lmQUAXwNnSdKImvVmZatRrTQqlVqtulmr1LbWm6tLtlnPklZWrzTXlitLC3GtCkww2Jfft7vU37Neq2xcuWyWlmQu37t/f6HY0WhGG1tbbJ3SvtSKhWQQqBUoCVKDwBQIkEFCzEw7bDwolUVOBLP2yNNeb19LhQuteC0xBnxrRZa6tuJgOz3NAdu2e0MgABFZKaWUN+ioHbMTQljacaIBCiGklEKAcybLkjbbsiGXOoqNDTs6+oeGjx059OpPfjiyd9d9jzzwR3/8h3Ecfe8f/9b8/PyHH354//1H+ge6X37lR34ATz12MmeaXZJCJKmwHb2KxIHSSrTpoRFQIoDUggSAFiBAofB9HyVkAC0AoyQEQYoyMk4oz/d91EACCQCUbqfWjXaHXxov3ZuDAwT3F+Crh/tODPUN5oOcYmfjOKpncYIsfOEJ1EjI1kljfcOeYUGEzG3mvfE94wP9Q6//7I0T995X6iu/d/6jGEWiglfPTl6xsKngTARvXp7djIz284Ugr/2QpSCyjkwhp8dGBu84cPDY0aPHDh3syeWwUedGow3RIJBWA0gU264bObu8/O6HV7/y5WcrS+s/+/FPn3nynixJpyavX7u88sC99xhjfvSjd7/27LMD3V0/ff1npMV9dx83rdZ//MM/evLxB3bvH99aXwOAPaO9oVaBp20Gd991ZxK3gNwd4+OlfD5q1ru7O/eOjgimRqMxfvDA0NBQK4nJmt6uzt/6zrd7yh1KK+l5nMUuS51z5Fh5Om1G9OvfY7ltt+22/Z03CcXBG/98Yu/2Mzt/2/s8N4Xp4SeQ0MeeGEDRptUC2F6dgxAoBEiJQgAKFOKmQBlsp4qiECAkKo1SCyFRSikUSiWkEkoRIiollRJSAqLAdlQnopQOkJUHfijCotKBQNneSLDgAAiURi+QXggYgJBBoeSYROgxMgihvRx7IcsAw5KnPZuk4BwqpbR2DtHPc3ev6uxxMgQhQTmwGZN1Upp8Efw8FHug3AmdHZALKcuSNLOGg3I59jR4Cvq6vcEBBqQ44cwCshFMAqBYUEOD1N8PpQKUigOH9xf3j6mectKoAzJ0FaGYh95uUBqIwREoBI3gC/AQujpgoBc7S5ALQEkAhnwu6O/uObrfeKJSrVCzAYVcaXio1NlhjKutb9JmBYTs7O4uljt0LkwBbJZo5WGbHc33BgdG6V8AACAASURBVPfv3khbEIhCb1dto0JppssdUuu0WoNAiY68KhVByVJP78pmlUl4XsEYQqW8XKhDH7TMFcKBgf7FuXl2rIR2RCR4eHRQa4ib9bwXjo6MXZ+aQcSegS4lvc21Rt4L7r5j39LyQiyEZZCAWkpLtpgLD+3bNT03W2ebps3nTh5Ka9HLH7596P67uoZ73nzr9QODoyfvOv7+2VOtWvzEXUchyn74zuuHjx584ujhhctXUwHgBVIXlFCawQceHx0kKS7Pr2lfP3Zw6PL84rpJu/oGyrnC5ORCApyRDbTYtX/swmxFhAUCQKHZgkbaMzKwvLG20kxEEB4+MDg3s3xsoOv+ob4+iFTWbCVpPgzyYz2HD+665+DuBw7ueeDo3uNH9hw5sOvQvqG79u26b8/YY3eMPLp/5JG9I/fvGb1z965juwaPD+86OjY6OND/F3/+53cdOLpn78gfv/HuSpwWOnoarWyjbs5Nrb9yYf3liaVzlVrm5QIdpK0WADiySkHg4VB/94HdI8N9/X3d3YP9PY/cs2+8b7dpNBpZkkpox/5A6vLKT+NUeZoELS4u3bP/6P7RsQ/Pnk4t3n/X8clLl2cXV5998UvnL11bmLp+8sAh3w8mNtfOXrj4L/7xi1dOX15YX5Nh/uT9D5758BQCRc34K88/d+706emp2e/+1m9eOH9pY3XtoQdOKonra6txFD3zpSc/fP+9ja3qPcePdxZL1yYuz8/N7hvft3vfXuvs1PVpaOtjIJM1RA4YUUq+aXdpR60Hbjx9suSm8o9ffkKs6ddgt4x32Yn4+1SFT0yxt8Rxn1bhvXX7v6R90XCdW5/tLTSbvnD9L1Z+qyCUz+mXb1F+0+HPO8o3/nyy5c+vj7cMirlF/Vtet7c4L7rV5/D5xV+4/VvUvqXd6nxvNZ5bdIC37PkW4/z0zwr/BqAMIqLAnf0jz/OklEIIpZRor9OVllJqrdv/CSmlEu24Qqk0KoVCtqODGbH9VRAzKimUUloLKRmAyDExEanQJ0fghX6p08sVUHpSSq1VEtVBS9AKlAoKneXufh2WUha2tgV5X5fzLqqD40K5O9czHHT1CT8vlWebDaaUNZJWzB4WunoPH9GdfcovURiSZMhiMBmwgP5h6B4s9Y/l+4bywwMiF2TVOjQafr4EOjCBDvbu7T16pNTTHVfqrt7kNHPgWALkgvyuXYNHjpT379d9fd2jQ6NHD5bGR7tGBgVjvrdn6M7De+45MXboYLGnV3mBnysUurtyPV1+X29+z9i+e+8ZPbh/eM/YyP59PbuGRRgGnq9zYV06VQgH+wcGh0e6ymWybmNjI15dARayVBod39M11Od1FMKeTtVRsFoKaO/lkch5Y8fuiBVB6HWWO+ormy5JdbmgPJW0GrqcL3aXc4V8R0fHruGR1fUN10q1ChCF8JQIVEe5JLXMF8LB4cGFuXmyTqIiZqHl2N7RQMs4ahbC/Ojo6OT1aWYa6O/TOqhuRYVccOzQ7vnVhUgIAuEjAjkB0FUsHtk7NDW3ZAo6dVlzbeuRh44vrKydv/DRo4/eX/TzZ19/59jBQyN791z46EKy1Xr4oWNLm9XJK5fv3jc+NDC4XqsJHWJQUH6ITM4lfX2l2NmZjXohzD0+3je9tl5BLvf2Fb383OyyUZ7UqqOQ7xkcWG0B+jljCYgkKo2uqzPcbNYiqb1iuW+kc2st6sp5R/q6yoCkLPjlOsBb89W3r8yst6LFjc2FpfWFpc3ltUalEkXNKI3iRqPRjKIkNa00aSZJbFIwlhlPn//ID3KPPnH0jQ+vbwFTmAdUSWZz3X2bqWmxjsN8EihdCH0hgEn6GpnyEihupvVKVK8sz85OTV5bmp+fnVocHRy69759kYGtymYQBpwBZiwJEVH7XpqlOc9fnpt76qk7r08vRq3mnUeORs1oq1mvNbJnnnr64plzl8999OI3n7u2sLqxsebq5umnnrw6PX3h0qUnHnusVqvWK9WZmelDB8Y7SoWJq9dMRifvO/nB++9dvnTxv/jnL733zrmF+bnB3t7x/ePXr09dvnDx2y99a2Li6uLCQpplu8d2D/UPxnFy/do1QMwX8lkatwN7PeW5m/aYbkOZzx66DWV+fvltKPNzq9+GMjt9/uJQhhm2s5OwTUG37ZWhm42ZmalN2gLsiJxzxMTk2geJgVE6xvY2FNMNHQMGpYRSUikGcNTepWCQgqQC5WGxJIOcAw+lUkISOJvF0NaGVJ5XLIelbpZBZBnyweFHHxw9Mra4tgiJCTp6KNfp/KLOdZBzaW0DKIVAUBiSV9Q9A+W9+2OSQLqYL4Nk02q2GWI69h0KuodV2MNePuwse1IllQrFmdJ+Yi2E+fKevcWREeH5yfJmtralpQCJrCXkAi6Xw4FB0dtr80ElblExWHGtehStT87XqzVbCLCYTyVsbFXWp2bjxeWo1oy2qkma6ND3i8WmTbea9cyZVrO1NDMXrazHSQKlcGh0ZN/YqLQ0d316Y+I6NyMIQpCiMDpUGuxtCpuFKr+rx+8pe6UCCyE9RQKcFn37RpomYqSOIGyurJuo5XXkdU7FWZTvLIb5wJmMUzs2NLy5vhVVay6zzlldznX0diEKBgpy/tDg4MLsnMusBAQBYSEc2T3MLqtWNnJBMLZrbOLqdUc02NOX8/L1auQH/vj40OTSbCwFoBju7LbNyEPsK3eMjw5cnZmh7kLqwermeq0SfeuJ+7emN95798MvP/bQrvLgmfdOHzu2u9Q58P7lSzGpp+87tjlfefvUmccfPub5XTbjhkUOA6ewmTTQs2vVrXqmClo/vq93cW2rwqwLJUXe/OJKwtIR2CRqprgRW5I+ACFA3GxmaTNJquutaporZMqv1HGj2Vqpt/r3DpRBNCGoAZxO4Cczm5ea6bK1K3GyEWXrrXSlkS1W6/NbW9c2NybjeCJOr7ai81uVM6trF5aWri6vTS2taPC//NzJd69snJ2bLQ4NtrJ0a7PiF8IWx6bNSR0oXfS8QBOaZtRqxgkYO5wvdCCjjfK+ygkZKO35uhbVzl2/1DB88vjh4/vvwHq2PrMU+DnDJADRUEH7WRw1wdRT9xtfeuT1V14nFM999cE33z61trp8ePcdng4uLczElr/y+JOXT59di5qdu4ZHBofmrl+fmZ775je/fvXqtSRpzU5NfvPFr09PTZ07e+aF55+fn5uLm63VlfrXvva1d999d3Fu5sWvfe3UqVNKipnZmRe++pVz584tLy3v3b2nsrF57PDhOImWVpbTLAalAIUUfhanKD6exG5Dmc8eug1lfn75bSjzc6vfhjI7fX4xr4zAbTQjBO5AGb6RvITtiN3t9DZmBqKdBzAQs6C2XAwz3PjC2htJUrS3qBiEI2bjgB0oKZTP1niljrBYTq1zSSq0pxQmUQMlsDVgM5AK/RwJ1cosmQwqm0cfOTnc13nxww9gq4b5UirzTnpePm+yyG6tASXgKwhDCDtzPQOZ9qvrlTQxhWIO0caNGhgDQnYcOIy5YhTZZqNhfQAkqjakoyxOIDOgtCvlahIJGDaq6dIKZRlTBgJBIDFGvp/lQiMxnpsJijlnnak1k4vXYH0zFRhpEZY7Gs1mtrwGtSYwgHUQ6MJgb663a6NZ29xYR6VyQRg3mnarBq2oODxULJec45Xlpc3FNU4zdACphWKuODKAea8SN7ycV+gsp0yxTdtRSETkbJbf1deIm1rJvo7yxuyCiaOgI+/nw5bJcsWCRVGt1jwnRgeGt7a2WlEc+gELEEU/VypkcWJN6vlqaLB/cXouSzMQEqXIl/LDQwNx1NpYXS363tjI2KVr153lge4u3w82Gi3pyd2jg5MzUwlKRPnlhx6orKxWK5s5X40Oj12cnnR5vxK3oiRJoihvc/ffeaRSaUxcunZ0/x3ljvKbb586ft++fPfgO+++qzl85OHD1cSdvzb3zJ1j6w2zvFFDrVFjrVklZ5fXtkj6PvKD+wenlldXrXMqtCnML62CDlBJYkqcYz/UYUDWxHEUKCmQ46xhhSj1D7H2NtY3wfNFmJtb2dpIszXLl+rmh2evTMVOd/XK0Cvki6VCKfBy7IDJ+UqEhTATqH1faQ9RWMqAnBKstffUl+5/69TcmWuTfXvHqq2mtU6xduRIu2KpSIYcMKGLsxgFokQgMdzf9/SDd91/7+7xQ3ccuXPP3gNjo3tHB0dHCp3dztrrk5MXT18oevn779m/Z+zw9OJ8qxVJFOyYjFW+TsCtra8OdnYfPnjghz99BTD31NOPn37/w9mZxa9+/UvLq9XJyYm+Uucdhw6+febM7MLC0088uba2Pjc3j0I+9fSTE1evVLcqaZZ+9YWvnTlzdnJq+p9+77tv/+z15cWFPfv2FIv5yYlrRPDSt77105d/GkXNw4cO+UF+emqmVq3dd//J06c/ePLJx9bXVtZW18od3UkjRulJrYg/lsu+CbjQTdPKDqfeTVPbbShzG8psW/u+8ov0u4NJbjGkXw2Ugc/S2P/c+l8IarQ5LX/h6l+4ffh7BmUYYZuN83OhDLQPIwCCkO3sW0CxPZLtCJibppq2n0ZKlFIILaRqR88IRIFCCCmkBCUBRVvzWngeSIUCEGFHNVcAIljHILid+yQVCwGA7Ej7gbOEiCyQrCGwWgtPi7RZF+yU1OTItRKnpM6FllKIGzMXzl187Q2IUpDKgaTMiTBQgXZpZGubIAEcg58Hv1Dq7jcA2do62CwsBr5HjfVliBNQnrd3DwdBc2kN6luiLw+K0s2Gzsi1aojEWep85cphWMzZxbWsWkNrPKXBEqMEY9lTuV2DvhTR9dnWu6eT81fSxXXIAIIQAMuDA0F3BwuMGhEkGeRCyAfQXy7vHZHlfC1q2jQtlzuLhZKJ00B4/UO7Wo1mvd4Qgc8CawurUGvmRWCsg3Kpa2hA5L1mq1EOcjmlhZLEXKs3PS/QSif1mj/U00iigvbynr8yN0fWFAslrYI4tX6pnCC6WkOQHBkYilrNerMWR02ZC2TOD3KhieN6dStf8PeNjc1PzZgkdUqrQAeh6O/radWjytp6Zy4cHRm9ePU6Aw71d6qcvxo1WcPYYN/MzJyRHrA4vndk8sply6bc0zO0a/jq9BxJ6Qdhak0cp3OzC7lCx3333LE0u3bho/NPfelIRsH7H144cnDPwMDQD994WQSlB4+Pb61vXV5cfuCuvYHsbG5VMoiSNNNcdEaDwizeeOrI+OTm2nQjbTpvenpJ5woOiNFZ5JRI+irwFAKRSaxJ2znqKshrFSADsbPWoPIiR9dWt96+NnN6YbWpfJEL2KQnDo7eOZQ/0ZM/OlC8e3f3Q/sHHh0ffGB3/2O7B57c1f0bQ51PjnQ/uXfoyQNjj92x+/GDIy9f27i8uMaghFAsmY0LXUAJKSGMyYyzqTUsMQxzTJBEaRanUqjFla0Lk6tTq9W5rXRiuX5+cuHa9MJw3+Bv3LNbcWl5aXlhbe2DK5Oi4D/37PFAFRcXF4110guaSUJCBFKvTM/ce++JVhKdv3hu98hIZ1f3xPzc3NzaP/rNL390+sJH09fueeQBkWaVtfXz165/93vfPHP60vT163vGRoaHh67NzczMLe7dtffwoaOvv/9OmM8/duK+ix+duTx99Rvffml5YeX0qTO7x/eOjAxPXr04cXXiq1/7ZrXRmpqdS1129K5Db77xyosvPD8zMbmxUgPpy8AzaQxSbC9xCIAIiAFICMHMgG2UwwiCb8xpuDMdbk8423RWAm8wO2wb4F9jYv7idvOUyuLG3PjxMAAAbioX8MlDX6T9Tx74JQf+VzT/BXv9m4Yy258e79x62i+2lS/wEw9uXyKfaYc/fr7xuDHC9m1qG7d8QuX4JijTrnSz1/BzxsmfevVXQB/kTw5/e18DmD5VUewA909f55/u9pPN/5qgDH7qJ3Bjx+aWkPSWHXx++7+QV2ZnOfGpN9zwstz0fvGJOh+H8d5U3iZr+bw2P0633v6pAwCAlMAotq8p2vmksH0IhUQhiRwQt79sBqG11kHeAlCWgBTa84CciVvcZrEhABCYC1F7LkshaoA1OrPSEaECPwfC5zCPvuespUoFnAUQEOTBC3PlTitU1owAISjlFGSN1RVIDfhBbnwcpE42a+ASOVDWWrmtFrYSmcbsLKPA/h7oK+pinjcbWaUO1npKITELyUpAR9Eb6ve0ak3NwNomJJzzcgIlSQW5oDDQkx/uTdM0W6tQknp+gJ4KO4pdg/2F7s563EqTtNzVWSwU65tbWwurta2KabUgDLzejoQo3tiCVqJZCFSU8/J9XZjzW0mrqL1iPo+eipIkTa0gcJFJG7XCrqFW1NBClMLcxsqSixM/KPh+Ls1cWChlCLbV6srld/V0bq6t1KsVS06FnvACJbVLUmOyYim/a2hwfnIqyyxLRQhhoPr7e9KWqW1tlUJ/167hCxOzxNDdXVKBWq43lBJ3jIzMz8zGwkOiYyOjc5MTNZt4+XB4cGxyaiqXz/f3DlQqNU9ogWJmZsYPinfeeSjJzJXJpRMn9zr2Xnv19RMnjtxx6PDLP/lxoVB++Nj4aq12ZWL+3juHFRZXNuaLHaWl+UouKBRDVXTRw4f2tqpba02z1TQggjgxQjEKJoEohfa0pwRZkySxM0YgMgqlPC/wkTnJEuNcGOYsiBZjg0WmNQa+1FIjXvro8uLi5vTC5rnLs6fPXj59/trpi5MffnTl9KWJs1dmrszUTl9ZePvCpVNXr350ceLdifmZWhwTe461y3zOVJaoVpJDUi7z0XrOeeAKShak8B0rYzxnNTtKk7gVmSwzmanXG5XKhgBbXVubvLJ6cO/B++490ojjzcr6/MLMxOW5tdV1RmUYnRRBoSClQCIEvjZ57bd/6zc+eO/M1fOXv/riC9emFhYWF3Mif+jI4Y9mr01OX//tF7/+1mtvplJGGd515Oji7Nz7p9599oXnl1dW6vVocuL6vSdPGqY3XnvtkXtPhLlwcnZqZnb2pd/8zoWLl06dOfX8C8/GrebExOTi0up3fuu333z7rYXFub37RwJPL07PPvLQoxPX5ixBkjQhFwAT0LbnBZmBeed+sRNDg4Dthe72THDTDHTT9tMXvSX/yuwTHX96tvxs+Re+V9yGMj+vtc93yXx+Ox9D4Vu3+dnGboYyNxX8PCjzSQ/DXwFlPtPlzt3x01DmZk/kL9DMjXf9uqDMFxvPrfv924cyn/grbiDHmweD7ZRqZAAG3qZAZ2ZgQmBkbufnMqBA2AYrzIBtrw+C0lp7Sor2UgxoW2VXeKEIQtvOg/JDKT2TppSmQMztVCkvVIUO5eeBgdII2IGxDIJVCKUuCDtFqUcXSr7vp7UaZBkIAUEIQgbFoip3xUSgVS702STRyipkDrQX7N7LXuAyR77n95bQuXStiq0Yssw6C2Eod+/irpLNhbYScbUOcWIdOWNZKyjkoa83HBqWnk5bEbciaKQhSROljgyUfNNbxGLYrNbT+VXYrLlGi2oNG2ehlBiozUbVJbHwfbC0sbRCK+uQJhB43kC311t2ApI0hjhxiSEGVqg6S1AIMrA5z8uV8k6Ker2hDECcumozi9Ph8T1xFntKdhZLtY1q1oq1X/D8nEkoDAMhmWycN+lYT0dc34hb9cRkXhD6QTHnh2AJ2RU68gOD/bPXp50hCwKAAl8N9Q8YS7V6tZjzh3ftunRtxgF093RIX69XGjntHRsdXlhYbAhBTMeHR+emJ7dc6hcL46Njs1MzpVJx79jugsxtLK15fmCRr05P9g0PHT0yMnF9equWHD822rTu/Kmzj+4f7+vo++mH73fu2Tc+1Dc9OVWL9ZH9HSYz1y5fDIqdzhjZaD2xZ+83hst78p0cBJPzC6DzTkjBLIRAIZXSfhB62gNGcOyMEyCwLX3teYiivecZ+iE5Z7LMbStIKKUVammAZeAbIVrsWkSpkkbryAu2cuFmmGt19m4F+XUnY8wnFFYjsgBdxVyvz8N5tSenuyjTpt4TQhlNj4QeAb2C+wT0IpedKdq0G6HAxs+SPLmywi4lykAFzthWAV2S6UuXrtnEPvfk0YPDIwvXJzZrDSuDzUYm/BxrEVe3hJRaqaZJG61mh1fePzK2sbZZrzbvvf/ByevTMwtzJx97ZGF5oVGpeCzuPn73hatX11ZXn3rksbnZmQzsxLWJb371xVPvn2pmZnFp+Vtf/+rc5PVLly+89A9empqamp2ZLxbLe/buu3pt4trExIsvfu3CxYsb6xVkfPjhB0+d/mBtdeWZ33j63XfeG9m9r2tgeHJq2nnI7KCtWwmMiAKgTTyEiHzzHtPfFSiD7cnt0zPnjgvg5tn1r9X+Jw98wYa+YPNfsNdfE5SBLwxlfn6bvxIoA7ehzN9pKHOjEt7kW2qfafs4bzuJcVunAG8SNNhpHwUj4nZAzs0uGQQhpOf5XqC0BkQGYrbAANJjqVFqIRWqQHs+WGuiJhABOxACPV8FubBQ8vxQCWHTpiQLjkFI9gO/3Kvy5Vy5Myjk80GY1GsuSQABPQXWYeB3DgyREj4KQca16sn6OqQpgIDuPlRezgsKpbwKZWtzk5Y2ROZcEjE76OzqP3SAujuLxVKJpWJI6i3BxMxQysuhgc7do939g76nFRClmau20DhjMlEuhnuHOseGg2K+qPxcy0KUZmkK1oAj3/c7uru8IPCKxcDzk0qjOb8MrRSULo0Od48M+p1FQPQZXWJcPWJrIPB0MR905MJckPO8nB86x81a3TXStNpw1RYBjx85lKSREqqns6u2UYlaqR8WPe0n9dhXUmnMCdcp6ci+0YJGdrZSq8WZZdASRCHwESgo5rq7umcnZ4xxTJKBfQ8HBvotQytq5QM1ODx08eqsZeru7dC+v1Fp5JV3556h2bnFFgE7OrZ7bH52uk4mXyodGR+dvT4fet6e0SEwcm11o5HEuVIxNlmtXusdGL73zrGrE1Npxvcc3Ts3vzR1Yer++49AruOD06d2D48ePTh2/tI1P99x72jvwtL6ejUu5vJjpcI/eXz/QYCCFMWO/MXVapSBadNGC0Spta+1F2hfI0hAsKYdno4oAKSWWirl+56PzG0OJEe07XKUApRGqUFrCxBlLmbIUFipY+VBuTsLCi6fy6S06DkSqXHa2UO9HQ/uHnry7n2P7Os/PNJ3eN/IoSPjhw7uPnFo74k79pw4tOfuw3uOHxw7Pr7rrgO77jy0+8SRPUeP7j1y1/ix4+PHj+y+88DwXQeH7zy898jRQ2MH99c3m+vLK42tjQtnr1iXfeelZw0XVlc3mNARZSYpdHX6SrbiptPS94Kp8xeeefTJclfnX/zgRw8+8HCx3DG5MLdSXf/Gi89NXrqyOD395WeftgBz0/OV1Y2nn3n6+uJMM4oKIjhx/MSl6anNar3khbtHRl5/63Un+PHHHz935tzU9MILX/nK5OS1SnVLe/LO48cnJyZnZmdOnjxpjbl47nQzjl/42m/+3v/xb5/72ovCk/VGpdWsA0pgAKYbUAb+7kKZnSL47FS7U34byvxV9W9DmXbxbSiz3cHn1/8MlPkbFy4A3mG5a2+Tifb9AQEc3IAyCADILIEZHHFbj/DTqEkAABGxAyAHxAjIbbBk0iyRXiCUFBwnJosgS4AMsAUWCFIgQ5ZZaCE5yeTaoEpIABRMwBnHjYQTHQZIDiQCo2SycRRtbcbVNUGCorjVqnHWBJsBM2RZsrySRmlHruh35NJKwy4sQWVTeAFIIJToyQCxWW+5xGFifJSBUCBUKl07i1la5zaqhISJCXKFOO8lrQSkkKEKfE1RHG9SlwrBUcQAQkDog0m2tjYKK51Bd4cIvbTRai2tQrUO1oIkpSQ6Z+stzIxvIRPKaAmkwFK8WcW8Dou5BHirHkXW1ja3bC11zVjFTnlSESTNaBuqsgShUQeA2rTiBMhHHarsyN6RfUO9STHoLOZT5yaXKjY17BNIZmZjTJTEkUmJCEBJwCzL0jTNSBimxJrEmu0kNgTjnE0zqzwiAONk6ojJSUiVQGJFoBQIYNuKIGZmBqVNCpV6q7uzt5nGP3z7nW9/5bEnnrjvjZ+8EzXqzz7zyE9eO/Xvf/rWN154OI/hG29/8MQTD3z98RM/+cm7NDL00rOP/sFPrs7MLmJX2AGQMViCQEJeBXmKLWMdwAohpHAoDDNbR8QOEJRyQI6JmF2WOYnakwLBpcYlGWdOADCAtS5LHQqSUhsA6zhFzwgQAlgIAhDs+1rIJCJjlRRGJFLH4yX/Xz51bATAALxbTX//zfcXAbNSsR6luul80KCxfeVqicoBOZskiXPOgG1HAkgBbW6DzLqR7vLzd9/52OE9P/nxa+vN+FrTvfG//MlvPv7Ef/7t8fc/vHTm0uUaYLO67oW+CwSTSDLqzZWuT82W+zs7ursufnT+6N13GeTz587945eeCqReajZOnTs/vnff+2+9tzA309X1vOf70dpavVYb37tfaC9Q+mevvf797/2Thx579JWfvTo+fuDAwSNXJq7/+z/8o+997z/5V//z//DGm29///vf3zU2MnH56is/ffm5556dmp64PDE9dmDmyReef/ODt1/4ypdQ2w8/MFuVyLlPz9p/T619mr8uzHXbbtvfZ/vVe2Xa5Tc2mD4OoUIEQAQU216ZnfK2gBYigLipawIAYAfA7aWvkJIRHDljnbWGt3eXGIiB2+ndTFlm44izBKGtdmSALDORY2uyNEqypEUmpjRBamsdOJDCJGka1bP6pkmjdGsTsgSQtVYuaoEzUWayVivd3HDVDWcjSGMAAAKQPlRrSaPh0iiubdLKIrRaQeCzTRw5YIeeV59bShaWGstrreVlV6+DTZkMU2azNKo3qusbteW15lbVY0irVbAO2BFQnCWt1dVWFKvE1WeW6psV6cuwlDdxzMY2KrVmtba+sdFYXLFLyx3aVAAAIABJREFUm9BKgRHIxWRrjUa0UY02q+lGJd6scpwCIhiiLE7jVhanrdWNxvpWdWXNrm9SK4HEtFP7Ood7p6em4jjqLXcvzS3bZuSVOnypm6ubYBMHCZnozvHd3TkfTep54XqttbpVj2MHjOBclMVWUKmjvDAzDztUhuSyrp7OVmbWN9eAs/6+wavX5xigp7uMUiysrCvAIyO7r09cq0aZAz5yaN/U1PVWEuUCf//YnqsXr1Ka9fX31zOzvLHJQijlJUlmASITT08vjPaNHNg9dv3qlVorvevksZW16tSlpbtP7IkdvPvee3uH95WL5ZffeLXYNXTirl1L0xtJa2t4995SKOoC3mnCmxevtTJOHVglWCAIZGDrbGYym2XGGAR2zrWj19pUAY5cmqRonDUpOdpxMzIBA0Mcp0mSJklqMkMEDEKABBRpbJGI48i0GtZRlkZa2KN95W8M9vUBCIAGylMzC8uRqTtqxbE0zM4adhk542yWpanNMpOlSULOARMyEFlyjplBoLUuq1Vnzn2oKPvy848XeoZOn78qhTd/+erM5OxdJ47uP3RgYXU1cc7P59MoBeVTlBYEDg32d/R2nr90sTNfGN01+s65s6Vy556+URNHM/Pzu3aPDgz0Xzh/HgkefPi+d06dqVfrXfniHYcOv/L225klyOzaytKjTzxcqVTef//UP/juP5yfX5qdmQWmJ558/Pylix+d/+j7/+w/nZ2ZvT45lc8Xjt991+Ly6vTS8jNf+Urg+zOz8489dNKk6ezsomsvTgAEABG1PTR/H70yt6r/Rdv/uJdf3m57ZW57ZT6nl1/c/h56ZW5oh24v7nkbx9yQDN0ZFjMj37x3hDuTETEJAAJu70ER8k6gOiIzs3MfK24jtqsCZSgVO2ZnELUgJjYCHYMFsMQWwDA5F8cAGpQEYcFaQK0EG2cxa7KpADmQkJkOaLQQJYMU7AETJAmvrbDUkKTgUt0RWjSamEj4adyKMuBq0gpIptCoAggPkszGQATNzK6swmYDrAVPQZpIaxSSFJymGdQstCIgAcoHIXlwEBIHREooigxt1IENNJJGn20tr0KcYL478LzIkiRWlGVZFYCACFJCEDqnMyYwBlZWbUYA0lli50AgSOkJ6TJyW02KHcQZKglkAQh8DeCILFuXxgnU6oDcqjdskoK1zAyCBVi2No4pSeJGK4oN+ayYwDkWQmipKLNNmxlhyGDGjgUKRAAQQljrssymlmKbeYIzsgAgALWQEtE6l5jMGmPSLHVOSM0EglgJCcQuZZcmwEQ23axugXCcGp3LxxoTMnmvWGk2/uTVn33n2afvu/fBH/3Fn+dypUceuPPP/vy1Nz64+OhDRyrrc//xBz/++je+fNeXnvjJWz/76kMvfOXJ+/7klVd/76cv/3B3N+eCyfVkyblQdSBKxEQwgWMmYGbH3BYoQCGkc0IIQHDOckpkjLEpCuWcY0AUQgJbIrDs2pc0ERKpbS4kdoCIElGxdUyGHYFjQcIqWY2TCkAeIAXo9/E3H37gSSkzBV4AmQMgkG18DmB2eAkkgrAgLIADy2AIUsFOEABf+ujctYtrb5z66L1r1x5/5PH/9p8+/7NXL79z5nwm3R/85OWRA7sH9u9aOHM5Wah5nX2aqZVsxpL8YgB5v5Y2fU9KIGHJxWm5UKxsbOpc4BAZRBzHLjLMMNg/NDc9QxJByUJQSDMLvrg0NfVM9tjRw0f+aOov3zn1wfF7TszPzr311ltfeuYxYqxU62fOnrrnxPH5+aV33n3/d373v3njvfcbArYazc6B7iiqTFy79OQTj772+odZym3ZrO0Vzt+MqPLfnmH7JiR/zcO4bbft/0/2K91g+lignD+F9m+OAubtWeszvpyPjXYwD9wIuyHeWYK1NRCAgADZoUAp2FlDJLSWylM2NVmWeL5gcBYI2AJ55DJgBuW1sZAQQgk0RIKNptQ4A76vXZJRJqUmAgEECIBOgQPH1mYgONAiTh04J53KAadMlklmKUEMzkqlJTrfE5JFYllbJ5PUZRaYwBmnAQEEsGBgIRkYLIEzEHqh8BoEQIxIRBlYAkRIU2UIrAMAVBoAILWe9tM4JatAI0il2KIlMA4E9w0NrS0tQbPpAQrHKJX1JBEpQAkizgxChsZ5QjGhk7KUL0RJbONkZ/WLYC2lDqmtDG5ROCEZESwTOPfmh2cC6fcEha16Y3Z+OY5S5XnUJjkEIBRCSakVEIODtthnmwiR28Hebc4hEFoqhRIBmNkCW7CErIRAy/8ve+/ZZMl5nQk+57wmM68p2xYN1yBBgvANbxoN70iIRpQ4FIfSxE7EbMTsh/my+yP2F+xG7MxESKvRSCJF0YKw7EaDABqu4dhoEL5h2leXuybN+77n7Ie81WgaKWYlciRK9URF1e3Om7fy3kpz8pzHMMh779iYpBwjCKA41y/eeW/ZsFcgGWMzX41G09nU8eHiD5946t/cufOW2+966LFHb9Sb/+DLt/75Xz04PWXu2XXTg4+/8uPH999139XHL7r4wUd//PWvfvme+27/9qOPHjx6vMr8qdrZ/uxonAzW+OZJAUhLM2cmAqJAlJkYLJpYQQArx/Y9Ebd1tiFSaUsgncRvEINAqkTEzERomoo0WiUkQyhiNXrn2PKeQyeuPX/T0dWVNw4d/mgp2am55eHIeMOeUkokbTGkAo3tkRISBeE6koIMR0MNayOJjW7btm3Xzquff/7gy6+8sHfPE2/u33zrrrsvuuZzj+x96a0Pfv7WW2+OY6PiO7Mby1EZU9PvdDpcLyyf8HNF7m2RmaYaF9YPy/HsNAVJZKwvOivDgbW26DlJKEdj53OwKmG4smqyjstzrnxsAiWBM8la731d194yM4zznU6v2+2Wq2MVCiHUdSJjNm3atLCytGF2myIOVgaQ2Kaqp8n543e8iFnHOtbxT4T/36XMmY0dPfOfv1S+TFx6AZno5idOD0BrIjThvvziGpMmDdEnJRGgIopkvEtAm3+pIoCoKEiNMapKIMsGSap6mCQ4Z0WidUYVMQmgbK2qJWWhABKRmBIgQkTGmEiqKamqNSwixjEpiFhDgxxkAE0gxNgQqUhkEJhFFSrCZK2LYplYgTpUUQ3ATYrOZamJ8AxY7dgQalQRCuddGDdwHSiBuWkaSqqhCZkDA5mCCd4wM1ThHAxXMTFbVYX3oARrEGMKMbdODENjMdXBCcBCAU1JRSz7RkRIlGA4U5BxpolJrSmKYn5+dvXQMhxT5lIKsIw6WsssCRKJBUaFYkICEdgdW62/++iTXWFVKi2zdaKNwIBZgRijiMQYWcgbk1LyzgHQmDS11Nm1P7EoKTsFJJbaNJaMMouGurnwws/u3f/c7JSxgvO2nPXO4ffrZnze/Ma3vD1ZV7BFHcQqplxPUvDTm95fWfyrx3/65btuufW+e55/5pm6vuibX//CD37wZFP6O2678gcPP//Q3lceuPVKi+6f/+DBXbfc8MdfuOfHP37k4+XR5vMv/Hg4JCMdl5EQ0mTzGKRQBpOyda5twKQoTBaK1IgqEVtiSimpqnOOCMrKzJoEBAgZnGGmIFFROU4iIDgSYvVM2TBV//czP/8vz7wWKUUwIcPRQRSklCBCxsByFEkqIhEqRsUppVB7tkWRLQ+XYTgR2Bqofe7g+585d9vdu268YvvnHnzssf2Hj779yIPnb9p8/53X3HDqgocff8wkI9bFatD3LjaaW27Go6lOPuV9YTiGio1ISpl1ZQ02pgmpiWLYAUgpMUME3vsmxSY1naKoko7qipwhVY1JiZVNE4IzBEhbyoxGI41JVYUgiiwz1bg++fGR7Rd+OkkYrS71rD958uRoNEqJwYyYRKQdQ4vI7w6l5HR7eYI1zlkC0Jbw/ySb9T+IX1M+nnHu/VWcaaXxC2/td7wKbT+H9v380jUOZzIl1qvtf25Y+2P95royv8qYwaS+oV/Xofl1L0DarkvmTI5NCxHBWgglAQAzCYhjkpaBY9iCDAAkjRCaiL2NkjHWsiliMqJCbFUiEzMzYJWdWKsq8AbOR1JAWJVEiVSNkTxj7xEBjcIWxrNjFTtQlTyDd3a6SCgRytBInUDs2diU5eh1q0joZPm5m1w/T9RoNW6OLaRTS4ENZmZc1g1CEGiRK4vbumHjhVtnt21aqkZH3v/Imo7vFOjkqEMAijzTzEZicQa9LjoFmgYYNk1KKcJgLBH9Dow1QcNgLCFSipJial0OvHedwjgnIUjTlBo5c9AIUU0wKlmvX3tvrfXGVqQiURDEKJFB5sAZEoVGyhoEJJWkrakvmK2SYWt5AmJmKJoUjTF5brt54a0YqGgi2JSSE/VsvHWcOfU2xaQie/Y+ef/dd16/Q15+8YVXD7x+7c4by6ea11955bbb7rjl+queeuXAx8uLU9ObopAS1UpV2fSn599dPvk3e575k8/fdOtttz/52OM94790766//vbu2ZlNv//Adf/Ptx7/4e6X77tjx7gpn3vuucs+e+HXv3rvg0+98vyRj+anZjLH1XA59wVrW7mu+appQkJTD62qqrbx3cYYIigjhgZAjBGAkdie49jAGKOpDYGXyWlRFYjGThJBpU3ngFWFMkoywRRghaSWdMaSkkRSlhijQABmzowlEdKUQuzNTC+urvRm5885d1PTNCyakhqbH/vww+NHT/y3P/3rz2y/8A++cc87i/jb73/v3bcPLhx66/LLLv0P/8sf/pe/eOjYqWGRF8Ph2DJVoelYlIPlXM6W4cgZdblXtB2q9otx2sibBGhNFBhQnsy7SNlMLL1JFZzWjBWodbNrE9LbvhRbIlbFYDCoi7wuR+Vg5f333rnw3AtWVgYQmfijTl5Lfw0nbx3rWMc6/l78RgdMazLsXxRYn/GzPWfxr5QyBBCpMk0iJNunm09ssgAwETOxITIqcTJYJwYIbGE8WS/sIYLoVIKGaqKWdc7mfeN7mjg2Y9UxiFQ1gmBzFNOwhfM+Oo4xYHASqYqgBDXGSKdTbDvHFJ3VY4tYXqo1oR08GRumesgK35/uz/fqOB4Yi5MrkbImNSBC3sm2bTPnOfQ6bsN0o3U9XCliZLGpjJid623Z2uvPgcxqXcMTVoqzbrysc9ZUPtU7l/i8Cy8cLVccqKrTytGj0BQMq9HoiC84N5ubyrsdjMv6g+PjhSXEGt5MbZqX2Y6GZKNUJ5fLxZU4HKMhWIPCu5mZ3uycL/KqqlaXF3U4To7BlOWFz3PP3Cmy7lR3arqX537VGEBAyt4Y53mqSz5DJTJs6lgbFSUBEhnyee58x5Iip0kEOpCgLRs8y5yxvtctujZ659omU3ut8sZ2srxb5FmWSQxNSCt1/cSevXfu3OWvwIGj7+qHb3/5S7c+ufuF5/bvu/6mXbdfdvnTL7w8qGrp91ZS6s5usBJCrHVuwwejle8//vKdO3bcvuvW5/Y+tSWf/4/fvONbP3ldsu3/69fu+vNvPfzjPc/df/v1BzL/4dHD8n6xa+eV3Tff8ZrJUuTOPGVJKTHITeoVSimFlAQTygsRTXJQiYgoRZGoSYKqkiFjjBIRqzGmlWillEQEa6W5kDBbVhOER4iNJKPCRNz4zLoMEErRCJSlltA0bFyTYkyJma0xlhhCAh1K+PmHh2amimuvvaTX7Ziomzs0GKAinH3nJW/+bPGFl/YfOPrhwb84cu1ll/3vf/zlN197++mn9r36+hvPvvaW687abIo6/RjFZxZVDMOm52xPsCnP62qUKASDJJwIrXM2n5FYrW2kK4Fbnj0hEQyIACFZa6xySxBao8GxKk16qJYBVoWFDMfVwpEjzZb+kfcPfeacTx/++FhKLT3pk/ufta7Gb+ac9D8LZ27umTd1csZ93ZkxL+tYxzp+k/itdGV+Qff0y88iXevXCf3KIjbEtq2GiEUEqgo2IGPYwjpjnKqm9v5XRGFgLKwzPmebg6wBSF1qSo0JkmANm8z4TtabsuTLyoWlEVRVooqFd5z3bXcmn54rkZrhINoBmhoiAKzPqNPrbznL9WdScqNxBakgEXCw1mzcSNOzeafrpjrGxTEo1YdTSHABEBS9zqZNtGke071xCqvHj0Ap2tx2p8LsPJ+9dXb7p3LXc1lm6rGiXjn8tt84I56nZ6e2FDNF5o+eGAxHzcy4Wkk16oaMQ+YwP7P1ok9LxxtmF2WoppIoSwm9fHbTBg1l2dSZdb5TREgYDWEZhfMbN0xv3VJM9+GsiTHmdnTsWK2hs2H26ssu27hxY29upnollDE677NOwd6hLROtNVnue33f6cZhU4ZhrBOpWhKBkrGcOd8pLGvyE5a3ikKTGi0KXxRFrZyx7XjbyzLnjKS1isFZm/ksQ5FlvoYkMpn5aOHkE08/+3u/f/PogJ48fuxnr2f33Hnt3zy496V9+265/pa7b7npx088NSyx+fxzByn2/dTq6uq4iVO9+Z+9d8ik9NW7r7lh1y2v7N8/u/mGXTdf8vTzL52zYcdXvnzff/32t598Lr//+iueaOrDR48lq5//7KcXgK6gzxgAAAzgAQ8wEIASAJDWfM0cYAG3tjQCCRCAAAO4tccABIhA/MQQDby2bgOsANUZL1UAORCBek2nWwMKBAEAw7BnaHQCcBuufvPjwXNP7huPRlO9vgnIu52aRGK69/qdf/wndz/09JsHDx74+Uv7P3rtlVuuu+7/+E9/8p0fPvXam++qySXh1NFj3S2bq2bUKwqMecflV5w9nd1w9bXHVo9HlbZAOX1I0qT3Im08XmIogSAEFeJEZNtqhibqImqFhKSKtrMJFgNRJcT2dYBu5lcGZT0Yohovn1joFr1TC6dEJqYyON1vRest9Q8/Fa1jHev414bfcFeGfqkrM1EaQX5xEklE0sZGtv/bElP4jFEUU8v5bVcAkxAMETMLFMlgrWFDILBh44QpqhjvLFg1JeOQFEIqLMpqPNkcoQExmDmJaoKkpBJBxmRwrHUAm3YrBZqEIkyZ1LhCsy6MhVoYB0vIPE/3MdMbV6kajjqbetn83PjIKJxagihYEeOoHAeZchYKtr5Iw8oEiYnQ6/mtm9Lc9PHjyz6E7pYZRUAns2x64C023xA5RWliaDJjNs3a1fl0fNkkwPj+/AbT664043o43jQ9beemsTKFuvRz8/1ub/HUaHmwvHHjxu78zMriEjKDzhRSKman8qneMIWyqYois/0OVjLX6+Tj7tymuY2bZpJiNFgNRN65rNthn5GxShyUKYlJsMSwrCRCQYlYiIVEIErCBMfKSURIAWpTKKgoCudMPY6pbpz33jnHJqTEIAYCacMKhgP3EpNynUSmuh9T/bePv/jvf++aF/fhxVdedpn9yr23fv97u/c8u/e2e+7ZufOqPc/vHy8dzadmOaYpX8RhWcZ65pxz3x2sfvenr/2bWy8vzWce2bf73ttvue7Sc554+Ymrbtj5+/d+4ZFHHn9oHL94+9UH3l/+4Mjx//P1J8kX9WpjjAsgMswgb22R5dbaEMKoHA+rEgAMM3NmXeZ84bxxdnk4qkKjEwcB8d5nmdM2CYgIQEixrusYoyoxdNrlucky+JBkUI+qULvWX9j6PM8Nay1VZRs40kBNFetRMmS899a7hrRJkRi59Tyq84j7b7rmm/fe+eKrxz86dWLFx4+bcdbtFN5+7+lnLty8+Us3fPa2Cz/7w8cfWVlZ3r1791sHNt12w/XXX3nxtx7/CWnWm95yaljFGCsr8/3p2Y3ZPOPsc88fHCqtcwJey8Fr+51t/aEMwRqnjRSskJb6piBlQAAlZVIChFRoTUSqSqqUlKJK674gTelSmPFZGpWF8zPT8z9/+9i/opJlXdm0jnX81vCb73n+PTKEX0cxa79P9E1rMADa721MHIgkpSgpqbQ9f2JLZAxIJVJKihQlIVRJghIJAZZABFFNqaqqpmlqiSEFkDHGWUMGilQ341FYXlldXU4pqSYggQBSkRhC1LJaWRrUZaMJgJmQdURB5LuZcTYOxs2JhUFdc9ZxWcHGQRWhxvLy4pEjg8XF0WgUVoZ2UHeGQY4v14srAHiqFzyvHju2cORw8jYZQpRyYbkQ3tyb6npb1vU7hz9caMo6d7bbtdZrVLDxnW6KOlxaDUdPlGVtisJ0C2TZ1NRU12U6qsqTp9DEXqdb9Lrod7dsPxdFnhVdIl5aWiqPH6+qxvvMFnnV1MvD5ROLp1bLUgSxKsvBsCkrlxewRmGTMII0ZVOOxk1VQ6NKLdKINCoNkoSQqjqMmxBVRKJqajnUxhjjDRlDRKGqq+FYYzICVY0qAg0phRBiE1KASoQAQmVTc7ezGuuadO/edy+9ePuOHTtee+21Y4eHt99621Svs2f3o3MzxZ07r8maMi4t5pBQVr28CzWrdayz4t3l5QcPvP+ZrXOzZ2966qmnzp3beN7Z2/c8vnt+rnP7rbctnDz+7YefvWz7zHlnnz/bnw8hRYRBPQisESSgKEgCkAEZAZN15DyxBZkESgpRErAwJagyg6kNNzXWg20TRZWIDMMxHIkjWCJXxVBL02gKKUlMCBKjpKQJGqENUMXUBEmBVVjFqAJk2DhYJ8RNkqZtPzrfjOsf/3DvC89+fOVlm++447LzzjuvCWE0LkcxjJgPvPf+f/6vDx49dPg//NG9X7/v3m9+4QsXnbV172M/fvHF5/7tv/vDTds2D1YWPeDybhMowex+6uBPf7760oGDUcUaAzApGQG32Y6kDGGV0yHVQtKWNe1NiSrpaWo/QERmrTPVMqFJeWKlwEYtE2mqa6vqAVTNtVfsYLHvv//Rae+GdfnSOtaxjn8wfsNi7F/QNLWgM7KW2AiIJ+GRrT1525hp8wq4FV0rdEKnYTpjZn6au/BJS0YkkirbxqJIqdEUIMZYIxoVkjQySFJAqFKsSD00ASAiVqVUgwT1AMpoeiZlQKy5ATWGW/VQQh0xrlwjXrVstyRFxBrJZYYToaorVCVSZHaWjXU2dHysA5qA4YjqmleHow8X9PDxThXDYBUUsHXG5YYsYTxGVoyrsucdVqtDz+zvXHOR3/HZ4PH2B0cOHf1gZqZv1QVRBoMZRe66HUfWBErjaBtwYeAtGMYYHzQbRyyX3Ur6HeIYocFPdXHCWmYrsLWGMrmohTMDJTIkywuvHHzt1PLJ7eecX43HqsYSK1MyRokJFmoQkjaJU7RIlprADYSMOlFVMU1UI8mSJSKIWmImhLaGhBIRQShFTi2jVIQpWAKrTZqFZAVElAzVAmuy8WA8MzVd1+EdXorvHLr54vN91BffeP2KK67Yed0Ne/fufum5p3fedvsDN1771IsHHMnJatQkdkWnLEdJ1U713jy1+N039csXXbq/+fDhJw48cNulZtz5s//+3a9+/St33HHHI4/u/qvHX3ngrivPPusSEKqAKGgaccxGYBhFhsIiAsMSwlBCAkRgBI6RO1iDSjGuQARDSAmdHAVDgHEN52AZIqgrxAgikMNQkstMh6AR4wp13erpjPHIcwAYV1BF7uEj6hplRCKYDMYhRlSVsHKWodvFq69+dOijD1/94KU3j/98x44duy6ZP3v6+r0vPMfCwbtTkoYsP3nlxWbh8H/6wnUEVNtnVpry0YNvvvTff7T57O3WnSJJjjsl5YN68Ni+fS/m/fHiwpWXfFqjGiESMgpWJBYlAEptHbN2QAswadAoRJXX2DNrBzoZUm4T1ia0X8PGkHeG1VjKMxdFTIrz/f7slVe98OqBY4ePwmfAmXZRBEVLNvqdxXpNto51/M/Gbze4oHVag64Jrdea8GtWJm1aJFpNcjuZUjABCgYJlEEAGxCBLBEJgVpJhURNCRLZGma2hiI0pkQqjiUR2pw6Ip6EozMZY2AtlACOKiRgZiVWZ0zXiQOcwpi2A8zMMIQJrZiUDNhgYvFKYDIChQE5kLfqNCGmxgKxrKAEy85Y8mShOh5gdWV8aoAYqJtb2AKGoEBC4a01OTOMw/ETb735/p59r6GXf7C6CGfyfpeHRFFijAwBpG6anmpmeExt6LskCCTVJOqs9a7D2ZQpcraICU3DSRGiNJFy9cYGa3MYShKrupifAbuyrMsqcpb7fjeUiYggUSUqgZlbebIxxlrLomwNWeIodsKZCA2YSY0xDBgYIgJUJHJiEWG2xhBbI4ZBlpk9iFqZExGzdQbGOLGqys5ZIgyHIzM9VXv/8yNHVxcWv3T3VRu2nfPoww9ddfllf/Tle77/2JOP/Oj7v3f/V752zw0/ffm9qpMtNRjWwTITcyXodbvvL69+940Pfv/y81597dRPnvno2pvOOREu/96PHv783ffdc9/n9728/9GnXr/o/O0njh+OTpUAYceOFKxS5Hmn04kpDcajOjRtQa2qBuqd67jMez9uwqgcM4iZQ6w7Wd4tOiGE2CTnnDFOQqyqJjSRiJCZsa1d5grjNXJVNU3TCLSlERdFIYRqNNak3azjk6vretiMq1TDcpZlAJqqBuC9r5p6Zn7mi1+6+eevH37jwOtv/+zlUyc2TG/Y8L/9/m3PHxq8/NrPpnNfqjYhzM7NDwRTCmOwadv25uChwJ033/uwT9JUK1XZdLvdhLwh+WBheUN/OrgikMOEGQOgnRxNBE0EkMIIGWGFFWJWgRJUVGMkEQqgwCQEMByrWuUJU9hoAmtuxQCWOr1irCHr9rZsPXuwMtzzk//mi+lEmiCtZ/fEGnNyT8STicx6YbCO3yD+le1Op4+pf/FYK2XOEBidCf2Vx3TGTIomdUh7xvmEJbMmbWWQSpL2uqgEJQOCkABt+DUwKXdavQgBBu1t3cSHhtruzUQUwWytJSIoiUSVpMRCJipTk6xzvj/NzKmsSTTFADaJCd67Xt96J6rdvBvyPjUlbALYmML35kfTveTU9vO84+sjJ7AygvdqPFLAVI/7vWx2+tRwhA3zJmWpHGbO1VGaURBve1vpfGXpAAAgAElEQVTPV4KzXC2dCKGSUHWoMx6vYOPM7AXnjqa6MdPOtrnxaIiqQinaBFkdy8Lq1PRM/rlPwxprqF5ZQj1GlsVgFhrbJJtmNphOp7BexmMqGxhY7+pKUjVWaeY2TItLMZfpDdMbN13+TuGXj544KVXsd8459/xe1o1RSBTsTVlnkeKodJs2fPbiz77/7rv9Ih8PBynEZtCAchZXlmEkyv0p6xsh1dg4CSq1JGeRfJZRJ2+MzZjz7lRZjkKo5+em+1lGHffOsWPezadG8sxNd6aIqEnBu8IyG/JR4HNnC89FtxbSyBoaU0cio9ZKnpEHG2ctMnKkdYzJ93rR2KZhm08fifGvnjjwb++49K6dOx99Zt9SWd9/967nnnlt96MPfelLn/+D6y74wZMHPySjSVZXR73pXpQ0jKEqsteXTmw9MnPr5fMPPn3s2w/vu+e+G6sYvvfo7gceuOPuO65++eX3XnrnzZHownik1oeGiKy3Ji+yrFAzaGJKdQzjwTCqpJTIwDvTyXzXZc7YlbIOKTZ1TCkxc+bLPC+dcyEEZlahpq6rqgpNZGb2Rj0bzxl7TVLXoa7rEKOqujzrdDrW2rquSTS3Y2nScDguq8jW+MLneWDmWpqIZJkwLnHooyOHNt1742W3fWrbk8///MWT7+l4+eDHR++86dorH7hp755X3/7guCS7/6Nj19/4KQaWgRcPvMd2brAcXOEiys60kyrVcWQ4i4nE8QiudL2VRMkbqkN73BkFRQ2qyRhWhYJrMdHGxOBMJbKSN6QqDYeGA7R0nDznTguO5BRquDZN1u+cGi1n523ZND/nuth2wQXLh08uiGw8Z/P/+2d/NdUrRqmRiamPYmJ7qe3BD6xJfmhNzsT0yWnonxF++aT5L3tQduYc8MzH/6zNc34RZ5Iq9AznGJxuD0prpvDJs38hfud37Q+sv/a4+S2/iV+eF3/yAf7655/pV/QP+42/xa7MWgsGgOATUuHkk50QJTW1sZDtWWvtOe1C4bYDM/HNE1XVmEDasnW1NQUWEWhKyRKDVYlSiiKhtW2FYVhD1lhr4YxRqLUSKACqySh5WzR5Jp3CEgpr66JfuZWUqjoQDOfdzsz8jLXSL/JRr8chjYusGS1ZduOFkyqm298wNTMNqleXFrG6yGxUlXzhpvq9Xgc+S9aY3NNMb3RyAZERklSNLg87SWf7Pc59tbxCwxHqCjGgiiurZYRy3581s7FItHDsVDh+ElDOe2iq0anFqQ1znQ3zm+ZmjaGuMYV3HetXNK3WZcNadLs2L1aHKysrA1S1U5KqCaNSUyo6eTfLpGnKwUjLkFsHMVLFpk5kre0URGStrcbDGGpLaizlzmZ5lvKMvU1VQ2R6nX7h/M5rrt081XUde/Cjwy9+eCo47yw7a9lZjkk1adTQRBHAsFo0QCMAyLMprGfnOXNkjTKI2BhjWbRSIsSUQlUznMt6tbelo4f3vnHr1Z+75eabn3jiiQ5j502Xv/3exqeffOama2/68q6L//qJNxYHC586a9vHiwshojs/M65Tf2rL/jff3dC7aufNlzz82L7H97x46+3XlJEeeeTJL96964arLnh038+bJPOd3rHFxawoGOQsZ5nLnDXGWMvOWsQkabKHeee8Na32qsjVRms5pJSIjLXWWrum1KbWOUY0s84ZY4yzCckaa4mVlEQNNFnXyvcsyEJhWFlhoEbIIbNWBCQJEsGWmQBWUpdnyydOnmyO/egHi58679zrdl00O7po73PP6Vgf+v5jF2/fdv3VV5xzQf3CC/s/OnXy//r2E2fN9I8vDl5890TqzXeLns0RxqMyJpflTS0xKqmFzaqk7x05MUqvCYGpneuImQiZ+JMxjzLAApOIATaiqWlIRARQ9mwQUl3WdRBKVCcoCVhjquZm+xu3bJyfmXEWGzduPL83t2Xrtqf2v/XRqVOjkMhmSEK/5rzKp1u361jHbxDrgvh/qfiNlTK/WFVNQpVOz8AnSQWt7kNoYkIx8caaLG2tYNdazAKSVthE0iZVq0pMAQSklJBavQxUE2KtSklVJmyblFJoXW8hihhiNRorsatIEWIlqJRrwBhKVJc41aCqK17s9Pq2qsEKFtUAtYhNWlpYWD5VrzYphmADQtCyND4zq0vj8WC1OGLm5whJjhzCeJl6/WijQjVU5amF1cFCIuS1yPIKmhopAKrl4OSRj7LpzqCslDBaXe2TQVlBgdCMjp9sjibX891zt45BS4c+wsIiZnpWBINxqpuFLJ+pomgMVZmOLHby/ORbh3Di2GBh8eTSciHM1iydPDFeWEBK1rsU4urKUrZwstPvDJdXNabVlRVUDUUBgLoZD0fj4Wi4OpCqGY1Gg9WR1pMU65AihdCUpYS6bxyqxjVi63h2f8PZUz4oDgVbD0ZjH1Jum6ZpKQ6qGiFxNCrLUhFCk8pxNSrHdWgMcYIC2mYwlSXKctw0omRSiJaYjAlRMsP9TlEuL6w0sjooTz327Jd23fDNLz3wgyceK3V893VXdFzx6LMvXn755d+47XOPPiN733unu2nr6jidOLS0aePZTU2DpvejZw58c+eld11/4w92P/nss2/cdcPnnt6fnt534IorLv36jRcxMAQU8EA7U8wAv6asTkC5di3ltSecvty2Yuy0tvT0l66t26q4zZpUe0INO2NRK65uf3VaWyusSbXrNQU4AwlogAAsAR5Xvfz4K2++9c57Mti78OGOz136R3dc/+YbS/v2Pb33pf0Hjn586SVX3nHfTccPvnnwwBtHVkYnRinMbGyEdLDog402SyQck8QkUS3YGKMpLCwsLC8vq/6a+6HWMU8YiUVIEqsQoEyKji0o2r5OjeJyprllb5yH92CkDCqhx0TlaG56w3kz/bM2zfsam2em3NaZmS1nP/6db50og7S1HMK68Hod61jHPxK/3a4MoBM2zK8u+6XTl7QdPV1LBBWAiASTzk5SIY1JAVFViVBlgIgSEhIlbVJKYDNZVxLWKh2ERpJKWYMdAFCE1uAEZk0hjAZhpQEzyKxOTYXVAVJjMmMcN3VTLS/G1EQFyhqGMVOgm2G2X8eaxstoKlVdPNUFEQYDJEX0iQIQw+ry0gcfV80IMQY1qAJEUJdwDinhxPFj1sbFFcQEy9XUFJrkrWlEdVTKiYXa6uHlpal+P5xYQBWgSikiJESp3vvo2PElaAJRNd3L8zweOwU15cLy8KOPhklSWZcrKygbONMa8+hgtHzyVNHpLB8/gZhQ1QANF5fb62qs65Wl1aXjxxHSylnb6rpBUgtDUVLd1AkNB6S4yMY2waUmIz51amlzf/NgUA8Go8Wl5TL3ofHj8bj1wJ2YBklqS5m6rseuapqYpHW0TUgphFDX9Xgs4/G4rFWsZwgRMRSaOt7NdbtHj388LEuKTIl/9Nie22+57v4v3rP78b0PPvjUA1/Yqf2rntz9uL3owi/cdEmY7u557U3r54rO9OrJU1mnEIPj49HfPvb0nTdcc+f9ux585CfPvxiv3XHZ/gOHH3ro8ddmZzQ1g3rUn+qMh4tGhVUNyAlIiYQSMbtCiEiIFK02JxhRUmtINYlAJJ6+xyMiglEkSVAkFZoY3RLFukHLOzkDrBAR5kkot0CDSpAUJDlPKUQ0wspgl4ijoiEk6Ozs/F237brs5iu/u/fZ48ePv/3awUMvyk3XXvsf//0De156/8kXnt39xE82TE/tuPAzu+6774nnfzYqT4o1CZqzrZuRSEHGxLqCoP2k262EUkqJ2LVGdr90ZGprJkOTBAwASqzgIGzEENkYJYQAiczaxDGb6BwQa0/Jx9BV3UDW10EMbL+T2C0OV9/54CNxGdRADBB+c6ecdaxjHf9K8Y8qZVqt5eTxL/6/TsaOOiH7Ym1admbkJCZVzidWNKrQCJjWbb/NvlYRpdb7V0XbFQUKYiIIQ0USFCCZmKpDQa3LlkIBTiqACNo4oYyg7T2wJrUqwYYkpCDY0biux0AjypYySDJVZaMIqaQGW+Zx7mZM91A2+PADDQHDAUQ6RhpQrEqQd6ENLUpoajcaV+MSTYNESLHTycexYUvCFk1Nq0Msj6FA4bO+sgIhIaVCUY9qaK0aUwKqBiqsQprYWk+mGgVUA5BSntk+W2EKpM5l44ilFQSR/pxPXJIHtGoiA0kTYtC6RlUiCUIA29WFRUoCmIkQuQpIUVU1CWA7ZDswY2EkCU3U2FjjTUqxSU3GT+7fv9pccvj44Vfee7d2GVJIiZWk9fNtIyNc5gXaNE1IE7aU8Y4VZBhMxFBNIjGlQLAMsmySSmpCqmtWyaxBVSJGYwrNO+8snjz66BNfufe2+2+79bmnXv7Wdx67995dD9yz84mfPPHx6so9N1w5093w6IuvV3Fkpni1PuqE+n3/s2NLC/tf+Pzt19z5wB3f+vPvVY275saLtmyefX7vs6mufScuLixbN93AMoRJwUIKWAOiOkbFWoAOiWpCTFC25Ntpy5qh7QQibbYYq675CACAACZBFUiT7LE2lhLEIFEk1YQEROIEIs7C2FgYqyIqQVMgBMOq4EYXF8o/ffDxz1382T+574aTH8tjjz+0NBo99PAjM/um7rj1hou/8od/u3vvh8dPjJt3Ry8e7PWmjFXhWiVJh0SNEdEqgY1hghAlVREmYrZAG7fwy90RItM2pyZuQSqtqCkyQgiEWDmKDqYgm0veST5rEBsKSM3IIWVJphIu3XL2pk9vWCKczM17JxZOHD5Vj+ocrlEiMqIMSljHOtaxjn8EfjsZTLpGEf4fWQ9mjXXcdmWopRYTBCBWpHb+JLLG/01tkdR+MUhIWl0SSACecGvaxGNABTDSiqRAyjEJAlhAqixKAlIjUEoEMLMQq0iKDZIqxFiS0Sq2Tm+/64bNO3Y0ecbkw9Lih/ueWnrpJRw7ZfLCsokwlqw1XjlBgBBRRxMhanIlQ6ohQBJUwIBhBw0pQU0r1jKgGCMUDoyUoIJQaxKEiNZRjagNzjbWKRnVpHUTQiiKQlNCCB4MIQhMm5CUBMYY45iR2DBIU0KMraUZKzXjkoEkgmSttWBGaoOuDHjC2SYFC5ygAShGBmANvH/v1ImN5XmHhoMliJiJbPdMixEAxrgYpEkxikSV9lZeNQmYWrnLL+8+RCJMxjBbwMQQRoM8L0zmjp46tXXb2WG08pffffhrn7/njjt2/GT3cz/6zre+8Udfu+PeO3/ww4dTNLt2Xuby67/30yeDBXHKbLE6LrMtWxZD+d3de75yx+1f+9pXHn/4p3seXbrplhsf+IM7qgq5w2DckPN6xp76CeWUAIactpsXGIXB30mXEznTNwAgqEABQxAgESJNxM3caoISSCYavnbwlAgExBoZwyliQiVoSAIriXaS/fDjjw4efu/NV352/KXXd15z9b/7xhf2v/z+/pd/NhgO/+bb37ns6qsv33Ht6iuvnVhcci6r6oZj8E3Zd2RTqprQhMz3p+tYq6qoTvK9W1aaatuPWcsc0E9UD8pYc/41Kqad/BJsr8MJRNLJePvG6S0ZNrv6ym1zhz48LIPBXDdHki1nbT1n+/YDh5eeffZNP5+9c+TYoSODI29/YNhxEE7KLLLOiVnHOtbxj8ZvU4w9Ic1MFK2fcGXWhNmgthTBxP58whifjM7XLo3SZu2159qJOGrtF7Q/WCHMYAPmyZVn0s9JECXDa8R0hRKaaFiTa827qGaNUBiFUEMk1oIVCBBisJBxGzehzsy1n9pw7cV2w/ypYVVF7czMXLBz5/6TC1haLUFEOSxHW9TOSVOCAFAiVWsVrWzVJA0gAis0wVi1DsyIAtJGVImVCYZj66ia5ejllGewBCU1nAhK2kiEczAEskCqmLrOwfm23IHPQME70zQRKUHYtclHpIa0LTlakr4BqcIYk1JSSYYIxGCTiIMxYI6gCGooGrCQBTOBVYXYjVKK3i8bWjaU8hyfZPWstd/awV9KIkJEbI0SokiCKjSpkLSXzoS2FwJVTe2V1DlnQJ7IiuSGtp616f2Pj2/ZuvHk4kqWZdNnf+bBF15bqi/44h3XP/MsPfj9H950+z3f+MMv/fXfPPbwHr3v9sv1xqu/88yeqU1bqjGDnPpcGOOhPr731T/5/PV33XrD7j379u7Zv+3yz5UpWXUW5ExgpHZjPtmzpG3RkLJJraxY4IkMtMbanKUVDK+939N7rKpCSSFQYlGnhogiSyCNpBE66XA0akF2YjRHERpJA2KDQEQuIjYpCatFVImxQtNcfuGnrr/knOf3vfT6h4eeeOWFtz7cfM2Oqy69dPuju5//6Jjb/9GxU28dqUizbtcR0rhJqyu7Ljz/D+7YNev15bc++LMnX12sg7KmlFQmjSOVNnJATpN8f6kkFSIlkLIVeJEsRSsxqYnNKOPktf7cWRt3zN98zac/fU6vf9uG+b/+8e4Zkft37upt3DD/6Qt+8tLrj+57OWWZ69mV4aAaEJaqjG2SMTH9QsjaOtaxjnX8Q/Hb9ZX5O3HGXfna9YNBbdCLAYnIxJlCaC1rUnXi/Asog5WUdGKzB4AYhsEOrekFoEikpIAhA3bJGGVrQFal5eMoGETJWcDB5iCj3kMFKoCw8eoAky9XJbZtOu+qS81091Q1THkexY9j2rB1m992TrOwak1eNUAcgbiKAlEwI/dmatqoyLipRiXFxnnPRpUZInCWp7qoFTHCgKxLjgELMpEYhcd8L5uf7vSnhseWMaySIoFgDAD0MrBBZgHlXicyQQKYkzemk0OzYrqvhpFZpBRjjCogOOc6nQ66HVQ1QlQRx8Z6X2sCszEGxoApK/LEALSdhqhhYabMGSaKURKMMWU5LqangnPRe+QZYEi0VfEwMwGkE5oIWZNxlue5sxmZ9glmLUCbjTHOkDGGE8CsKYnIpDkg0RvdMNW7+NOfWlgarAyHMC6QGRkfE5458Hrh9e4brtsded9Tz9x2191f/fLde/fuf+ap127cebm5Zeeel15touv2ZpbLEsrG9JfH4//8Fw/+4efve+CBXU8/98arzz9DvpBgJURvhNGwqmEYY8hwO42UkIhM0taRXw2pJ2LmmpIgqdJpG7e1LpRRVV1LoWwXGQU1ysxiKFCqVRPSJIg7JE/OTWI9KJLWKVbaJK8g5Sq1E8UsywSpCpU39r1XX77y3E/dfus1n6137PnpUx8fOby4cGLD1vmbb79uscKje19M45VOnkls6nrsEs7ZMH/bJRfdsIGc0vwl259++/gLxxZVBAAzUyKRoDGCBMao0mmR4Zk9MwVjMk6DFbWiRgVEWafT99LLs/Pmps71mzeR0InjZ2XZV++/TzdO529nTz7zzHNvvnFyWB4+sQibidaoGyTPjSRp6hiMtQCQ1jm/61jHOv6x+DtLGWbG3yGIbJsopwOTZMJKWfN+ICLi1v0F1IqSiIiFADo9/VlLLGgplKDWGrT1iQcJKaBJJLYvS0RqDBlmMgBEkyQBYJhEocTW55xliS0Z1oRUlUiBnU9NUDbd3rQpisi2GY8QxQAxNZoEude8m22cI+e63b5jc+zwYZw8iixrEmAsb9iQXbg9zhVnbz07zztVE5J35ajJjasZ0+duX2xoynfmkjny9ns4dspleZAK4xIb5uc+tT0WRV3Xgw8P10cO16Eh71UVmTHbtmy99JLllXK0OiqPnVCmYAxCBLOfnfLzn53dPDc3O93J8lL84O33IQlkwIxOMbX9HD8/C2uXl5fjqBrVFTKHzPNMd2v/U454w8aNJ06ewomjGDUETjGaPNuyZUt/eurc888brKwuHz6JpvXRD2B1he90Oudt317FqtfrzXT7i6slQqKMbCc3uTfTGTlLIVUrg7IK8FlZ1cJGlDqdflF0y/FwqtcRkRBqCTIzNTccDjt5tyiKTre/sHgyz3JrLZhiHdpdyzk3MzPd6WSdTp4RRiEaIudcjNESzfSnZqenO95tnO3mWVZqClEE9TjB9Io6y184dLwcyFd2XvP8Gx989y//8uvf+OYX77z6uz/a/cLT+79689UX3HbzD994/d3huGc6TUWZ9WpZep1H9h+4Z8dl9+z63Nkv2xPHF+xMZzAeOZ+xZrZqNs/OlqEspnsjCeOqlCgUYaK0PB6XWesMGztuQhMlpXS6E9NaCLZibIBjjCGElBIAy8itYWb2brUcNFGINVaxm+Wpbowa7z2xtXmxUo7GoalTVKVOljvLw9VVA/LeW+tWx2UTdDiTHzx69K3vPnLZpRf/0ZduOX6k+ekzTx1ZOvmn3/rOpRdf/cdfvea5F1cPHDw4aMbETlPTjMtOzgnoADnDQ7vOA4gqKaiqMrM62/bDrLW+UwjQVqWta46IaAIpjDFQ1ZgsteGuJkrKu92maSx3pjrdtLJorY3gfGb6mYNHHtnz0zc+/rjYsmFcqw4ETqENFCY0LJIgbEw67ZT5a09Ap6l1pxtFf6/hxK8LRTk9tv7dwyeC0H/a7VgHgDUpA/C7ujv9S8YkyZH+iboyvwCaTIUmJqEKMJNAoWCwqE7EFkQ0MWCV1sOXREFsrXXGZ2IdwZAxTKrOSdOIEtga69UYspn1PiYNZS0hgg2cRd613b7p9wOZ6PP+1HS/joPBClIDZ5D3qNffuO2c5TQ0AZs6eXR2PBhyk4yf6nRgij53ZzTrGJt1R9WoSWgakEXed72en5l1c9PU1MPhCEtLCEGjkjHqClcU2fx8f4bcoCxXhtw0k+3Jss7cHHesmy4Ck+v1ps7aMlgdYjgEGRhPPp/ZvNlvmI6GV1NNTWIIiFC47kzPUB7L2hRuZttGc3RjOnxCRLJux1rOu51er5f3hqOyJMOJEyUlVXK26Ha8922PIc/zoiiM85aYJ0QOtca53AsasoYotgEUxJZhWj1Zp9Npk5da2U5d18xMKpkzROSca9mtMUZrJiWyNzZUpTXIvMP/x96bdtlxXVli+9whhjfly0RiTMzERIAECc6DJLIkSiqVVNXdq6t6KNfyWvayV/sn+JfYX/zBbXe7u91VZXe7qlWaKIqiOAPEQEzElJnIBJDI8U0x3XvP8YfIBEGKrJaqSra6O/fCSiTi3Yj3EPHixrnn7LN3UYYQtFJESgTa6DiOrTGKoBRslGiutFRkIDqQUqySKjILAf/3pftfOb6PiP7tv/k/vvWtb/3x977+Vz977+0PPn7x2ce+8cTJ6MqNmbVwtxiVJDqya8F7yIe3rkfpkRdOHdY4XAIOsMAI2AVkwDYgByIgBwjrvBa90aT9oFxa71iXRup2awuUG03XvOGMXTdjP+joLjd2Dw+1YdPGodY2PkyNHGgCDkiBCiAgB+YLzK3kP/yrH1w8d/72J9de/NrLf/SHX3//yu2PL1394J23F+buv/jCc48fe+Gtt69e+eRqpG2Vy+zd+8UjW/trS93xyRF8v8pVrIWFBEzQ65MAAWChz0UDteSlJnkguitKMVEAsVAAeei03enl1aLKOybtTnaWV4sfn774pz95a/b+arM7OVgbwSM23eCDJ67vZ81gglcbKqRfpCqziU1sYhO/Fn7tUObhrqX/6MgvGqg+O3V98UzGgFJEQrLeLbJ+RCItxABtCO1pEVI2UiYKgBc2sEZDtGalRGrTA1MFYiFt4kq5oC18SWSVSdg2o7Sj4qYrimHpaVQE0hifRDVEPkQnnjqwvz97vz9YWOjGB/bs2JGaUjFFZBTPXe/1F1fdSraWSLOrVasZNRM/HOiarTuslns902mRjSpjAQWdKO8Vi899sTpa6fXLuFGryLFjBK5dM6NWSzrxyI9GvbzdGrOdNjodZDl7hhMU1fLc3baSaEtXpNb+5wrUara2jHXuLN9fWbzXSJPGRFcnJnhHWpWuLKuw1us1J7piVFBgChBPCkxs4yRtNq3VVZ4V4pVSSZIYqwwpLQhBlPfCHJEVBU9WxAfmoDeefz4wc5zGnp0QEZGNbPCubuEJIVCQRpIORkOANdW0KPLeiwTnQhQBdWLDRBy80RoQbYzW2rnSuRIEjhUHDQ/FTFRpibQ3ITSuD/vzQ1dd4t85vpfppXfe/YV9+dVvv/L8f/jZ2b945/I3X3x07PCh//Otj6hte9os9IZjrTHT6n4yWJ4/d7HTSNYGa72sJCKT+2azUdkwOdaOhlmrcLtMSs5l4guunDhD1LTWWFWxC0Gsitmzc845B8Baa63VWnvvFRkics5VVeW9V0pRZHSaBmatlHOOSxdCcC4oa4Z5YVpN3WqOhAujVoqsIAiT8SZ4okZKWk222gt35wFud1uDYtiMzdefef6f/bd//+LbV2/cuvmj99/uTm555emTe7bu+ujMpUuXP/nBDxb3H9z/xOOHjx3b9/pPPiwyf3lu+dzC8M0f/bvX/vF/dS3vhW4zFDnWSb61iCWBNBB4vTq2cVs+cHitpWQksKagdEna1UJ5hVsZZE4lC4OVhXvLTz9x/OL8yq3b9/+ftz64NHuPrTUOcJRoG5Wlh/goQMGJEk0QpURYdO1AvxnLbGITm/hb4jfswfT5sOev0Vp8EK8IFNF6qmbDzU7kU3maB9uZ6tVlbXzgfQBz0EppA4CUEh8AOA5SOe9YB4QgUBrKwgjXtS5joTRGZXCZtDrNVjdbXUFegjTSxAlrzxi5Ty5fp4aa3L6l0Wp0Kv/JjYu9lQyFwcrQyWgUmyQyEhtKNLKAIBgMVhaXbXes0W4x1mM6xaIVfOnQ7/d6A+5aEsAYUQStoQ2SyKaNMqK1tZGs9bZsmYSKojSprDVKA5C8GC0sRuOdxuRk7UhVR3SdRrNlk2owWF1YmOiONbpjkTGVMcpoiKAseoP+RFWyIjIa1qD0643BRkeJBUs2GuXBVb4UA1ZUM5W8MFeCsvKJi0GGIQEVS9CkBcozV46T2HsvwdVU3yRJOHgRZglVkQuUsZqDVyTGqlBUrqwSm7APQYQEIQStNQUwAVoJkbI2QK/cymwAACAASURBVPKyKKu8YgStg/EKUBxqxi2Ly22lxtolyp9fvlJU+R8+ecBE9vXXX/+DP/j7/+CVJ//F99/+8Y/PfPe1p/7+y6f+9U/fvlf2ut0teZatgckkOm0MXLZWhRBbn7uOTthrscaZxtap7d/cP9EBxoAhkAMlYIAmYAEHlEDjIUU7ALa27NpQtzMbonZ1xoWAAQAg3UjDyMZ2VydagI+uL95fXesFmwVXurKhbFbm1phOp3Pr/kqatCOrBmVeAVbkRz/88YsnTp44dfT4k0f/3Ru/WFvr/+VfvbFlrPvyV588cOjAR+fPXbx64fbdmUZjIu20fFFmJhraxqWF1edMUmgKICKoB6KU60Q0AhkOdX/dZ2i/RFJn1gMhkAqkmMjXOglpzByKwg372bWLV/ccPf4//en37y6tLS1ljS07nSuLQS9KGxYosqGNaydXUut3LTEUpDZdC/8pid5vYhOb+K3E3yqU+QyT5jNUwb8p5ItiHXkQzdQdpAQW1AIzIqAN5WBhMAuz0syqZgoHCNWiMoFItIHWdSlHKQ71M0UZiILzcEHVnAAiBIYlaE3A9rGJ5btzfm14d3Gwcmvu7uUryEpAYf+R5rY9mUQyHEZMFJFTDolWEsAMFjCv81jXtUlEkURKlSRwgV2lBTaOczBrARhkoFRtNCPeoSwq54y2VsjXXUIhIHgENkIWCqKExQmgVCtKTOVVURrnNQdd+VTrYRwVroSxCBFDkTaBULKvH1g1e0mI1zlP3ot3IXhvyFkuDUcqMBigUK+cAyxrYhIvXnsdAjmHqgJLFXyitdaamauqsqQBiA9xHFc+c0XeTFIN8mVVU56sVlWRJc1EBMQCoAreKhNIeYCsUZERgIhcCF4hkFIQyyRiAsSZISTEMpFVKJrNn9+4Xir61sn9rzW/8/5bP3/ulRf/wXdeOn3h0g8++OiVZ0/906+99K9Pn7+xtjrR2jJylbKNJG7DB8PKRDpNFC8XqTEnd+w/tLer2vjn717NLKBI5UHK4B1ba20jiUijqMoyV+3UUaiqqqoqAFEURVFCRMPhsM7QMHNZllVVQRRrClpMHLVNTIHzlaErS0XGiRTMutGgJC18MEnThzFUjoqhidVEDCr9LjL7jx3ZMdFRHkZjlJVnz5/LOf3pW++/m6bPPPnkH//eyzdnqx+c/nD69p0b9+89fvToqeefH71La73RytKaDxyqwifdkKqRjFUO2gv8SPN6qFJ3YImSjbLYg7uNNoRz6m8vM8STsKKgVBAQkdKGy9LD95dW/MpajGRpIZ+bW+s7hkm9D2FUxXHCCoMyQ0MHBRVALJqZIV4ThMhDswqbccwmNrGJvzV+G7gy2EjJ1O5xVC/XCCLr0u0gaGEhDbVuNqcF6x6WMFpIAURaiyKlCRAlDA6kUOuYII50nKo4YWYUGcGQuDqSEYLRGo0GyLAPVai01sFosAMHY/St+Xl0utjVRYgG8z3cH5ALEkcoXaotJekwIE6SikfICzBDESjAaCQxDFiTijQ3ExQFDAUwDCGxxhgBkwbSSCEGM1wJZufKkEaq2eCqMGmkCqHKiw9BEUSQWMTGW+WJdGyRRqHyUCrLhsYqa3XcStJmokhcWSA4YwycA1CzWOqmaBgDG9gzUDf6eg2kcQJDVhsSXrecqFvGIkOxRWRc4WGUsgYSGCHQBn2JSCmVJDERxXFcZbkyaR3WNNNYObe8UnTaXUFgDomxrUbDcyjLsr1jq1EwxlSDURQlzOw5eAFpw1BkdBTHQN1JpaC0qKBBAFMkyqIsSygjUZqm6QeXrvte/l9/9dFnX37p9Td++uxXnj/5+PGzN2+9/v755587+Q9fOfnnb16Yub8QNdvGtEdFDoZWkR8MYzKNJH7u2PFTB+KZ6eqTC7cRSs8hajSLfMRlIK/Ei5ASbVTpvUOqdemDc6Es6xOrjeHIJkZHWhmI4sDesasCsxNFgWAYElsLpZTSNobVeZaFNBmxJ+/iRruRtkNvZINEpqGLAcrR1rHO7lZ7z5bOtWu3RoPh1snJxEYvPv1cmmBQ4Ec/++kb7757b35/OtadevR478rl+TvTC/ff2b1nKauwOMxbY51s2I80WKisUBWoCiRRGiJTDgZYb8uHbLD1a8nCB3fjw8LEVEsVQ8n6mgFQomKiVqPbMI2xxrBPjx4//u57H1QBpWNlTFkV7PNG1PB1LTg2wkKeNEMJ+NMKNW/mYzaxiU38neC3JJT5AtSWTFT726w3TGmlTF29D6S4ZryKYmYlYqwJIloRJAR2gqAUBSYoZZKGaSSIYmZWcUKFC66EAojzKkeSxu2mVbbVbOb9fsgyFDlio8uwuLg4cfTIQm8YnOR3ctdzKEmyHEIoXJFXptNubemqZpLfXULfNUm71Fbao9tobh0LnaZJo3RLuxxN+GG/Eq68Q5xgcmx863iRNEnZxtS2zqh57+Y1FCUERVH4lm52W2hF6Vh7tLZW9QcqBBPFaBg0E71/p9re9c0o8d1gYzXICqsXV1fWsmGybbypC9+0QbGCoHK+rOJut3RFFCVRnDZJxsaLomKnhsVqH0TsfVEUBLTTRHsVM+lhGeVVaiUygDFIYqSJi60HISTgUCoPxS4CEq3ZKkPWWlLKObdt++TC3D0OHEeRkMrzvDvZHQ77gdl7v3PHtv7qWr+/Nj65ZfvWbbG1vV65bfvkXKmIbJZlAoiiyrm1fi9GrK1JIq1LqAolSzCIqYKGihIbJzZNA5OSjh8MOs3xW3N3/tUb1R+++sRXvv3Nf/eXrz/y5ONPHDzwb+58+Oaf/eWffPe7//Rrj//Z62eWCVnsvNGm0Rwzxt92Y0a9/MKpsSbePHe1zEZ+lL1y4rFuqxlHdrU3qlg4QNsoiSJjIALPYi1VlS+K4gFXJkmSKDJZVtUt6CEE51ytqaO1VaDYRobAhGFWjELow91YWphdW4UwIqXVUIEiyWLxGkEpjmJzdNfuw4e3n74yN9tbHpKfXsloVPLp4Y6k+4/+6KXJ1rhYO2MwGq645eVG3G6O7YPj6XsD00wa27cOixHbWIkaLa+pDE24VgQfMHBlrA2Cry3nH6Q569whkf5UskmkzilKrQMliuoFAlgMqKGjbtLsNuKtEfn27j17/uwH34cSaGbJVUyaVRFyTRFsLI4VKQYFQiBmBBAgLCrwg87HTWxiE5v4W+DXNgolWf/z+Z3l058P1lobHdcK657pjId2xwMFC2IIQJ7ACqQEBJAoBZCsa5c9lAMw9TNDrROCA3snIWjAaqVBEjg4D1FKW2gFksiqmMSEMgo+jgyR1IaUCJWMBlSM2lYnmocr93rLd4ASsYYrOB8kHPL+csSuHVxYXvUrfXgGtNYGa71ieSkinhxr6rIM9xfQG+jKs/NgRhp1O53xJG0a1dK6abWOtTJUr0QbcdwyUVyWajiYbDfHuh0yEZxHkZWrK2F5Jcn9RKOtg4wW71f3F0OeaQGCgGi8242NpaKMmFtGdRqpabe8UueuXImbrfEt2wb9bNQfWW1QVrMzt6amdk50x621Vul2lI41WkkUf1r2ct5VlfhgTGR1JMzlKAtFhcAKZLSJjI1Ik+fa5xmRIaNBpJTSkbVJbI2JlBbny7LctnVHs9URRSCKouj+0kq7NbZly9ZsNGLv9+/fS0qu3brZ3TJx7NAj5TCbnbuz78Aje3dsNb4keKVglXYuLCyv3F/teeg4QagcXAUn4oNHYEXaGm1NWea+qojI+xBYKI5vLC/++7O3Io2px565cmftf379zH1RRbvzlz99y3t85+tPTXWaHS7HUMb5MKnyR3dP/eHvnoo13n3vg5npG6Dw2re+sneq29ImDDJLpDUhNjBaaxgDHSNKSQKUILFRp9lqt1qxTUiUBHRaUTM1sdVGWUs20iaxUWJNbCOlIAINRFGUNGJrbVVV+SjTZGKbKBbyZaok1UG7keTD5546tWfP9vfevz5z715JGklSQOWkfBQvDbOZaTxx+FjE1PfOJ7FN0hBYoFXUFMQ2ahS+Iq3EuUajERnVaqAmiYlQI20RNJEWUg+o85BAYE1QwtiwkBTCui4l1wLAUKJUrSRoFEVq176dO7aNT6Rm13jHalrt93rZAGBYxfmIrNXWuKoCkzAr2ehPrI9cTwW0GcVs4m8H+m2UiqbNr/X/H/jCrIzCF+o0QJGglmcFEYust2mqmtRaT3nELKB15XMBNKkNXwKQ1AxdMFArxAgCFAFSW8KAmUiDicEkHmCAQSQS6s4X0iYE512pDJQVzkZIWgT4YuSrQkhviEkoiHLMZI0iuMFqKAtSRhTleQ54ZQ0ALkZY4TwblVFK1jifoxihKsAOVkk+XL1xBTMzabuTBe97qyhLIharQ+nRG3k3PVrp00p3KAXm5+ELgqXSQ1sM897s3eH8HZSZzvM0y+L+MDjn0zSQLpb72eyd3v2VwlWNbsdrLZWHthgNwtLSaGZGNxPsm9KN5uLlT9AbxN2xmEUXzuqqmL4zvLsQq9oqe4SytK3Wlp177t6cKRnbdmxfu9Of781tG5sY3713dWZ2TtHho0fyPD/zzjtbd+5oNZoDXidRN1qtrNcbrQ2XFlcJZmlxod8blRJCqDg2KrJK4PujKKhABFJlURhjQuXiZjNfGTRsOlhdk6JyWmtSV+4u7tu79/CxY9euXMvzkavYL69++MG5E48dndq19+KFC9u2bn/5ay+88fZbb77/9qvPvby10Xj7rdefKMqXnjg2XFkuyv6orNKkG5z0wdXKYGurtbSGUT5UopsEsKoEXlSel3nphMjoiCLDpqrYefE2ij6auX0faWTTpRKVxMMi0zC3XPa//MVP/snvfeO7Tx/5yYcXF+ZnjbInTjx+Yqrzi4/nr9y+VfSHrz7+1GPHd/x87u7lG9d0FgxFw4CcKBcisokykTbGGE2qyga1bXgQZgIpA20U6djEIuIr53ypRFgqItFJ4rQlpWOQZh4Me9pEFZAHCejwyBBbsA9GstXlbhSBy+9+77X5+cWLl68t9vtlEimBGTqq3Mh5RTZX4dy1a3/yyuFLp2X+3pLaOpkNxYqwGwBGB131ckTsfWZI8uGqTHZ7XgakvIaUjgsp8zyKImZRJmJfESRUpSKJ4tS7wouIghMOQoqMEgWWiEiJYs+AIqVERUK2yrLDU/ue6nQkJD89fzVJO8oGdgEVm7TrSw/noU0IASLQCr4C1sUX1y1j1/3ZRK2rfwtY8Nk8DT20QNrYLJ/bviHw8/nZiuTBkN8gvuy5Jb/p2tmXPcW/kG74nz4IDGzItuNz7SQPTsVDK+lf+cKvD5eHvlfr7Mb1F9Z/fFHg/Zmn5MYRfq0rT5+Rtv+lYz4ErrXiH9wGnxv2+e+DPPj74Zvll17+jeHLPs/feVF5I5P8axeYNiYIwUPJaBCpjY9aN1uTYEPfHXUX9cZiTKBoo4fiwX1X7xxEAGHh2rcXRAKQgnDdtMnBF0EI8E6UIkMUWxJmMERovbNUARBFMIa0ApjZc8GqqgAKIBgNCIcACeQDvGcXxOTCAarWBAkQJi8SRvAEnYViFBSjyrSvp16C0Sgy5FWWjWSwUqmAQQ/CLlhtyHmPtWF27z4qhyoLocx9FZXOex9qrslqf/Xm7ODefXDopZEbH0fhoC201sMs3Fl0xlSF8xPjGIzgfHC+FvkoVnooC1hVGktawTPAznEPNL5158rlW6PFwf59+65O352fvbf/scejw4cWrl29rtXU1BRYbrz/4Y7Dh8fHugufXIM22XAIpazSS6trPgTv+fLVK2Pj442tk/eXFo0xO3fuvDs3n62uKqUsWR3r2ufZjfJyMBrfMnF3/nbJwZVVFMVRFN24fmtqaurIsWPXrl1bW1lSqrGysvaLt9579ulTE+Nb7t6Zr7h84YUX3nn77R//6IcvPff8008/e+6jM/dv3/36N3/nL958e2FtEKo8K6stk5OVK2/f673YgIVXHCIOIpJXgYNXqIg0ETnJEJmEKInigqVUmuLoyu35NGmiKCJL5OF8JSLK2l/87L3f+9rzv//Mifc+unb0ycOrff8v//SHQxfsePcP/+h3TR//4k9/vJKIt+iodHU45LjpQDVBxHsvPlRFqQSq7uiR2ocBIp4EAVLmldYWIkwIwoFFEFAWHHFVeVV6rRQZba0ZDQYRVJeN8kVH65XeSr8YdsZaTYMXXn319vT87J2FhdW1Slkn2o+qtCytNlrFDHKQ23fvFPnhiUZq1/q9hSU4o5LEl0MOpMNYkqS55N5XMRl4V/gyD85rm1XQQVAVWoh9gA9MBAlKa2O1JsoGw4YlZnbOEREUEWljrC89M4Tqtq26c5A5hJj0ycMH83uDQzvaa8uD/jBnpUEEXu9OgiilFRS48lVZqo257LPqC5tkmU38LfHblZj5jX6h+Yv1Tf46/JeTIvqb6Mp85uw8CF3pl07yuvTLegADgIgBiGzQez8PBRERhw1RLk2q9iUgDnFsvIBIWMgrQoAvSURDr5tMijDIg3TdQ6q1riXXA7OIBBaAwLVnHyswSKg2n/HMvlTKgATCYE+1fpioEJhEXOFEMSBGQhBmIhAZFYcqM7knxYoE3sGQIydKIIKK41GZZwXKEVQI5CuFYAgQBMGoqOwAgyG0gdbGy7ppn0BKL8NKtAsrg4oNcgcWH3wenI8UApncSyainRgFIYBRSjEqm1O6M7alf3dxelgcOfDI9MzM9Plzh597jo8eXrxwYYbDvn37+sPBvdu3zT69++jRe3fu+n4fjUbpqps3r+09eHDPgT03rl8nrXdP7Z2+eXNpaenYkaNgPzMzoxTlZaZEQVSURlVVzdy8HmT/iRMnZmemV5aWnatC8FqbXn9VaRw/cezixYtFkWfDbGys/d6HHzz//LONZvrJlavbu1u+8dLLpz86c/rCR9/+6qsnH33i9NmPzp699O2XXvrBmz9fLrPuRGf1/l0SNbl1+w9/9OFXTj4eeRcFhohXIIFhaIHS8ahyVRwhTp3QpemZuWFftI4jCdVAs989uWfX5GQC4mJofRn6qwuzS4/smfydU4f/7dnpW3fmNKuTR48ce2r/5ZsrF8+fy1y2b9e+1156og0kQLYhc2c2tO8A+I1InR/01QEAwrrr1/pG/dAuAhggBUrgDnB1De+984tWmf/jkye+cnBiucC1+/f//MP348n2Y8dOrfayT65Pj1zwAY7Fl5qUDUp8CJV3WmsDDEeDT26v7Dt+6N13FjU0NCkExdJpNDQnq72lPKyObW37rNKktI5Lx+1WRwSRIuVdrJVnT5aUkuDYsVfBBQBExmh2nplBXGSjfDQoy1IDQdihCrpiZIpz5UrK7MLM3Z/88L3tJ06cubz85LPPfv/D8yr4tf4aatVNDjULR2vtg2fv605D2pSQ2cQmNvEbwJeGMutp289tA1BX0+u0ypeEiOt9Cr+EdUmujd8/9xLq9yO/of9LWJfAEBF2ZcUUFBlRGuutzZpJi6wXvAAAvC7bwWBhEiWKRAikyFiBQmBiVrWnAhFqhVmAWGJrmMGhCswkgBYFgjJBbfCD6oYOqXus2BAExFYhMuBQnx5FxlMtAys2wEF50kqBlFDtIlh/PNJKKcQxmAFNHhCCZ8RaWRM104oDtF0/5VrD6DIwADQS7UGBvYIoIqYgJI7BxfL83YMnT3Q6nbkrl+et2r17900J186d3XfyxMTJx1du3567c+fIsWPTN2/OXbl8+NRT+w7sv3XjJuc5tEIIC8uLW7dv371v39zNm+1mc//+/deufnLx8qWjRw7tO7hv5tYtExtoxc4Xo6I5NhbYzs3ejozdu3tPVZRFUQDQWo9Go6qqxsfHH3vssQ8//CCO4ywrGkl88eLlF198Ph9lH5+98Myzzx4/fvzShfNnP/zgqSdPxa2X33zzzU7a/P1vfvXnZ86cu3Jp28QOhu31ekYTFIlWRFqD6mplEsQwGiZqGTsk6vsqYyp9WQUXkREwizckIbjSl1prG1vRMr5r9/S9uytZee7q1VVilSS/+83X9o7jJ+/fuD47PSiGr7766sndYx9dnF+8OR1F0Uh7KElZaYFmsAQREVYemkFQTPAKAeRFhAmitAsUmDgoElLGMuCrItIenjXHmehhIxm6YRgsfvvUk987OJEU4XCiD+zd1p78+lrceOejW+9f+CQDUZQo29CsvWgAonTlK23qQDqoSL978cw//KPX3C8CEYioGA0TwmPHjqZ2y5kzH3aj7vf+4DvvvfPhJxcvK22HhSu8uAqdRrKyshIUlc6btKW1Cmoj6AcirUOonHPW2jSK6ygkiiJfOSfwkKCCoFRSGF9JUebOvb947unWOI+Kvu3/3ve+9y//9/81IQmKRAFaMwIzK9C6O1X47Vo9b2ITm/jPCX/DDqbacEZAEIAYVIcdn9aVHmDjnzV1pk6Q1ZOafvh4G0NAWoSl5gcGIaq5hyCjoZSWdc8mAyGI0Vp7LqFAStX5GAB1JV5YACWsQAqaQBpC4ECKhJip9jfeSJsrk5cOoSR4XWu1gYJAFMFGMIIogvdVVRgiQCNQGVhIIY5HzRTOIy/hKKE0gwvwYC4VeaNBllnADBGjNDswAS1LzQQ+QpYB4r2HAhTBIDeqiAh5iYblVoxIo3I6jpQ1IIC51IxIQyuwkCMtikg7DYTq5tz01v17t5w8vnzhwnRwjx4/fvnq5ZlPru4+csTvnurfvn173uzYtWstiq9dvrzvwIF9B/YvLCxka2tI43J1da4sdu/fv3vfwdu3Zg4eOnjkyJGrlz6evzs/NTW1fWrnwvxcnDTGxydXV1edq/bu3Zvnxc3r1w4fOXTk0MGZmZleryeinXNKqcuXLz/yyMEXX3zxnXd+oa3Ny0Jb9e57Hzx18glD0enTZ5579smvPvPsmz/80UWlnv3ay88888xHZ09HMZ069Ai5YmZxSXRsxAjsX7z/gRI0vNagwirSFIukITQLt3vXtlzLjaW7rhFXpBqJtcY5JyBdendr4d7NtXswEkcRSp9WQsEvLp07fGD/kVbn2WcenV7E//bDc37Un+x0/9EffGNQ4l+9fra/1utqmy2vSMsySexZCwIBSjNAZNgTABJfhzIENiAARZ6RjnSUOqHMMVvRka0QkQtKWcRR4YNzJeX5nrT5/OFDDaBtVMjzLWm6t9H499//yZn7g6Ix5rz2rA20tokRVVYZozSRhibHTtip1M6sLfQJ2/dMzc7fN0o5YUPotJqpMnl/hWy+tYNiNEySxvy95d/ptMtAP3vr9Ctfe2n3jVu3Zmem7ywodsSGlNIgYgRfxVFcZVlNA/Le1xT7EAQSeVZeIMQCr9hH3jt2ThsHWXNSluUPfvb2f/8//LMTh/ddvHxJIhVEW629UHDeBa+IHlimf3YBs5mh2cQmNvF3g18vlGGCroORuhNhnR71meCFNiR+f9mSgB4auZH1+VRatCZNqVoFlCFMEIgoKIKSSthEiRCJ90pIWLMXsIdR0KR1pIxWIGYOIQizUqitAEQpHacmaTohLovgC0UEIhBgFExEtml1Ah98Bq6CEEgoCNZNmjrjPlKIBEVGoS/OESkREhur9hhvG9fjXe25onvo5cIGomAU4jTZsdVYVZRZ6C1jzRmGESq8ICK0m+nUdr9zvMyLRkmmYgxG4AoilaV412SJydaObWmc5Es9LC4HH7TWSFOUBXZNqkaiQb4/kuVRyJ2uTcW3TyJRSy7bu3Oq2X5+9vLVy1cuHzxyeHZ+dm5mete+fTuef25u9vYwG+3YtVNrPXP79t69e3dOTS030kGeKaNVbOfv3T2y95GdU1P37t3buXP7iScen5m5tbR0/8ixw61WsrCwkDajnVOPfnLl6sLCwvHjJxqNdHlxYWrn9kceeWR6ero3GNWKMiGE6elpa+1rr712/vx5V5YLC3fHxuTjK1dPPfGUMWZudvb4I/v/3ve+8/HHH7/1xhtPPfvseDM9c+b04WP7v/G1lz48+/Glqzc7Y2POWISCBGlFJIojIkOWOGH9nd/56rFt6nqOez9dvVfmqtHWbIqsNI00bsUpmaoqczcqqtyLFMNhm3WnnR564sShfXtObRn72fuXby/1htno8SOHnjq57+zpmVvTc+lE9+iTzzZM1GybxTIP4iMfSEDakDJQBiAWHULg4MCOhLWCVToiG5lYgMJjdnHpyu2ZXlUScdCI46aNI1IxB6l8RS4PStaGWRk3Yhcim5TAyGN5WI1YV8rAGkVKCYHYkLJRwhDmKitKRTpJrLAnba58fOupR470760V3us4MkFcWTUTacZGFKiCUVqbqNUau7OUOzZXr9+yip56+lS7nfZHw7VR6V0pqLllIQRxgYm0iSPvvQuitXVBCBYgDggggaqzkooVBWHnRlVBabq8eM9p/vkv3vj9737zztzNe6PSByNK04bLrKgv1L3cjGM2sYlN/J3hb56Vwad174fzK/W0tdFG9IDG/FAQU1tC0hcy8GvPbQiolqgXKAUdwWoQhWZLEFDkzKQYxE4kCGlSSttIR1aDQghSFkFkPXOkFIyN0kbcalvoqrDVWiVggYISaIUkSZqdJG5ZpQeLi8Wqk9rvQAGNJBqfbE7urJQKiVCeBXfXrfYEABnT7jb27PE7JnS3GyuzqtNw465jdqJgLSbG0kd2yVhTjYajW8RFrnKv66RUq4lt47Rve7cRWyFe7A/n76MfwQl84bXedXDP2I6tAwluWCQT40VeYJhVNuq2O+XEWPLITjvR1ozB3MJwNC9ZKRBoNTG1Q20ds820NxiORWbr7qnF6zfnZm/v2LMzLwql9Y4dO3bt2nXmw9PXbtw4fuyYiNxduDc21t6zb3dWlVlZkNX37y1evXzpiSdPeV/NzMwcOnJo/8EDeT7oDVePP340adrVlR7AU1NTd+/eXb5/f/eeXe00und3fmr33uPHj12+eq3f7ytjiIghplBvoQAAIABJREFUt2amx8a7X3/txV/8/EzhirIs5+8urA3e/t63X12bu33544uvvvjca1976Z3TH108c/rJp5559ZvfeOv9t91p9fsvPLYv3XJ+dvr408d3b08NkDqQoIiggRhoAA1gJ7BAMI6JY/ZpgnbcSHhMVAwJqLKhGeFgd/upJx/dOb5uDGmBPvB/nb7QX10aQ/juN19SrcaP3vowz9zuPbueP3qg1cBahblVbjVTB1YcYtJNpaJ1SitKQhm0K8l7FRSMMYlVsYZ2YEalwEY5i6DINgwCBWOSqKmcDgRHxGl7WYoP5u6+tOURHRmnMQe8N7dcdnd1W+mq95VfjRJoRqh8ZNNGo+ED9wcVKaNVZMggH2yhltztn9x94KZuzKuilCCZ896TCnGkghNXBOfcsCh7wyX+6LxtjUmZX7169djRQ91Omziw92w0yDCRtWms47IoIlLWxkQqipLKhdW1XqPVHvVKYSg2JJpEaWglhCBRo8laza8sqla8OurdWbwj9sRXXn3x+z/7YG3ogg/GmNpbe2MBU9dnlYj8F8RF3MQmNvH/CX79DiaiT3kwX0Dd/RS1NEWdjv+0E4se9Lt9Jiuzvmm90KQUwOB1AqWJEaU7H3tMJVGvtzq8N48s4+CteE22CCxKk7baGCJNVJE3CCHwuvQ6aSJllI4FisnVFGEwgwFh2BBIsbVaW9FGhEgU1fX+KDGthkpSMEeR1kplcUMwBAusCXGiWg0dN0Rp3WxG3W7e6IWqBBSYAYXISqcJI5QmIOMrZ0iRjaTTxljTxSY2RikTbZsYDIbUbqKKZSnj/qAKXseJQeit9uI41mMTozvzo5W1bVO7Oju6I6NNElulKYmgCSJWaxvFUnkSieO4GI5mLl3utMcfffrpy1evzN24cfK555h5ZnZ27549R48ePf/R2Ytnzz7xzDOzs7NLK8uNsfbU7t29bLjaW4uiyGN04cKF48ePt8das7OzJ554dGrvznPnPhoM+ydOnAhXr3186cJTTz7d7XavXLzSGWsdOHBgeubmjRs3jh07duLEidOnTw9GWRRFaZqmaXrjxg2l1DPPPHX69Ed37twhi5L9f/jhG//wW68eO7j73dd/fPTwga+/+syPfvre22+9+bWvf+OVr7z63s/fOvP+ra8+d6A9OXb64tmP3x0Ks/YapENiosSmkW4q0/C8ffv2+eHICXW742sliiJ0WpFDIIZWJk06nA9XFnpn3z73oR8mDYG4dtKKGs1slO/bt++bxw7eml14+62frQ7zx089/fKRfXduluffn80lLI16Q5cxHBESG3WiRmIaloxHcMYXocrz3LkykDI2aiTN2MRcCZEWa/vVSOf9TmyMVMOqSCQZi5uEULB3Pg+hKsvswo3r/3ww+tbzJ3OHc3OLf3nx4rCxBZ1ufn/BRrGrCpYQR3GaxAAjsCZj4zhUoXLVNmMPNaJvHjv05DgW9m7/q/nbg6IkF4yJ1iWkOUSRTput0i3aJF3pD/Osmmg1qSyzfGijBBKiKGITFVXgsiqM1lqZOCbPLOSDlJW/OTM7P8NLa72GSteln6EgGqKFLEM5F9I4ihqJq4Inmdi+9ZPZ2ee/9rX3L9/y0suyDEBdShYRCWGzmrSJTWziNweN9k7gYVU7+jSmwGeDlfWm/Zr5SvhcQCOitYaAmbHhpwutBCIh1NL5IixESut1gdGH33fdZwkCGBMRkwiUiYWsndi2/egJjE0WNs2hqdnifg9KlMvZl0waXLdQg0G87t3sbZSEEEhHooxzviIDG3nvVVXGBB2EmKGsBA4wKkmZqRxmkhU6MEBiDXW7ycRE6Ti7d7901US7pUPIB2uQgGYSb9uSjo+trfWrlTVqpMbocpQp8cZXoXSoxMWNQikFpXqZX16JWbvKq1aLW43x40dVnCydu1KWrr1jmyfOPrmFrGpObHF5MeoPqJmOb530WbF0+Ybkbuu27aNBf9RblWZik6jm+bhBVi72MCoiMqRo4ApjTbPRQOmH95fLxaVKZN++fcv9tYW520mzuWvnzoWFhcq5rVu3Lq6sLMzeOnLieKc7duv69cxVU3v3CNHy0krwLGW1tLo6uXVi955dWZ45V23ZumXx/v2yLHds3wGhmemZse74oUOH82y0sDC/a9eO8YmJmzdvQNGevbuHw6FzlQuucpVSZjQa+cA7d+1QWq/1e0xg9sPVlantO/fsnbp87crdxaWXvvLcaJCd+/CjbZ0tzz994tLVG/cW+88+urPT3vLx9eke0ZLRd9ivkHGNsUqna6UsuTCf5asQZ+3IV4VznkpWEvKwtTkpBVzmHAfROsAUrAfB2HRLmRctbb7x7KkD28ffPzt/5uMrSvx3vv3a7l1b33jv/I35W0k7RTYyxagl3Ayhya6lkMTa6qCrHOWoGcH4glzejrQtsmYIY0RtQ8VoYMhZcbGvWt4lZZGU2bigVbqWrzpStTlrF7120RtD0Ai3+ytv377z9t3FX8zPr0XJSFMVq6CFc0mQ+lKCVyBtdBQbU+TVqHTNRiNib/oL/+M//vbzW6JdwI493bfn7i/kFTzv3r7TWnPrxrU4Uo+eeOzMlVsDxyKqKCoTN7WC+Hz3zm1RFH9y/UYF7UUzAyYCBxHEUSq+eunUiUuXrgxKXlntFUVpjeaqeOHF5y5cvVqVo5j94489+cGZSyMv0Mpz+ejRvVk2nJ1b2LJtRyCcvXDhj//pPzl39vxoOLTahsBKaWLRym4oNtTsfl5fUaw7qT1g/T+kKFOLVYHqOQfrWdZ1u6iHCHgbOpz0mfo1vlyf4+8W9GXLOfqSAfRr6r78tcvFv+6Nf9Xhv95Z+rKj0wMrjM/hl5RXfpXj/Orbv7RS+Uv6K/TQzy/TcflVGF30Jds3Nn+m2vDQoQRf9L/+8p6+LzkP/GuO/9LL+/k7bmPzF1+vL8WXjvmy8/MlO37mbR++Ip8Z9kAp9/MfgOhvWGD6VC4Wn3Zir8c3VAuyqiBcL+bIkDEGgAu+HvlQ2vmXYQSKEESEQoABtGEdeRWFKFJNTcr4KEXlodb1gKEUAF63bQKDQLquMQlApEUZpbXW2hjjqiqIJ+cVwCqAFbwvKp922iqyMJEiMAUoDa2U1swAM5xHgGgFreEYpCSJTbNhCuf7vTzPx9od1Ujc2goGA2UMi6mW+9JpplvHMwKUKuAotqKUSlIA5ahAb1SZuAy+0e0Mtm2rbsyNFldMq+FJ3Zufn9y/p9luRWOdavr2KofG2FjGRX92Rml/cPujcTNd9fdAohqpVFTkObR3eSk+6PrSF2X/3v0iyw8c2H936f7c7O1Oq7179+5r164N+4Pjjz82e+vm+bfe2n748PGTJ6dvz967d2/v/v2jweD2yk04x4Zu3rx54ND+qd07FxbvZSvDR48dn56enh5O79q1WwJu377daLS6ExPTN5cuXbl87Ojxo8cfPXv2fLvTefrpp69cuTJ/906apsYYEbl27drWrVv3798L4Mat681WU6x968xH+/dsP/n8S0t359975/Tzzz0tAR+9/Z5kTz7zwjM3rl59561zTz/zxB+89u0/e+PNO6Nee+eOtDk23plo6agcDMQVzUZijE7KohVCIJU77z20p61Wi0kGFPICviw0KWZtorgsBiceOfTSkYkqw+v/4b07q8N9B/Z94yuHLlyduXzx8uLq2qmXX/j6sf0J1stYjPXEoAZow/I6BQQogAAYYAuggRHQA7BhlC2AB/zGXjFggFqzyG388kmF7//8LdNuDlhW14Y79mx11pRr/bGoGfLSe45jareb1tBwaQUug2cORpRvNKNWghhYGvSb7Y4oEW0AYogoUkYLh8qBbBSEFKHT7Q6GVaWQQJkk5Qfte4KHvM8kzwYJpCwrpZQLXkUJaSgGdNDKg4syy1u7tgVBIaKimCFKKa2V1roq3NXrt4V8mQ0OPXL97/293//zf/vnq/2BUuRdFZm4qvzG/b7uqvYrzTCb2MQmNvGr4a8LZepi0IaYpmBdwOWLIAJRwsQiwhyEaV3Sc33B5ergsE7Y1Mf7gkiGAAXSD8Q71o15iVl8npcgHZgCC0iBWClEsXJeCdZJNlyv4rSAed3eRUSYUXsZVJX3FUQcByVMdepIKUCJsBdmRV4TAq9LhCgRQhBACIzShYpR6wiDjE4aFWkvGkoHwIXAzAhBpWlKqgzw/ZH2YoxxEtCMwSxQKDIolahICY+EOC+dc1o4ieJKafQGIbZxZ7Kxc/vqaKAE0XizWkyqqlI2TpJ2UYIHa7n4VqfZ3NId3brDFAqthTQ0iaYAqcCwBlqjKCvn75Ds2r93NBr1V9cmJyfHxsbu3p3/5JMrBw8enOGwMD9feLdzaqdzbubWzd27ppTDzPUbKApvaH5+vt1p7ty+69Klj2/enH7iiSduT8+cP//xoUOHOt3xW9PTx44ePn78+JWrly5dunTi5OOPP37i7Plz5871Tzz+WKvTnp6ebjaZlHh2c3duO+dOHj+6d8vY5atX7vd6yVh77fZdtvHxbXvnRuX3X//pa6/8ziNbd7/91ju6HT311NHr71+88N6FvQeO/TffeuXf/OK9hX7v5NHDO7aPTxoQxlyBdoIYKIBBCQaCR14gsuh0EICqaojbGgJI1bqMKH1pG/HF+9nNc5fjyHz3tZf37Ex+cvr6zWvXp9Lu9/7B7/oOfjK7NDdaXV3ttTJQQGZIJ1HLNpTRw1BUVbU1aZHI0A19b3WfSV84+ijKcnr1/pV8tWjEiiKqYAahqvzICLRSoTBxpG0zeFTDymWVBPbae8vRWCPkRRR4e6vL/YJi1aTUV2Wer3XHGnnRT9LuK88evj/XefNnb6U2BorclSOiD24udg5uTdqd64I1J2Vg431ZOhcqJrDAea6ch3ASx48ePXzu/FWtxVVwggiq/g5zrQsABQkQGGMiCgS2moRhbFSWReWyjnFFuercSNhB0agsS+8RxSjLQKX3AaKBRpaB46Q11rjyyfX/7k9+987ssz99483cu9hGRCpJkrJwv50y85vYxCb+M8B/RFfmQe6EiGoqLTZkY2qRmM9k4mrnXEUAhBk2StOUBFk2RAi19eN6gPLXQKj2XFIKQf5f9t4zyI4rOxM855o0z9crA1QVULCFKniSAAESoAHJZpNNim2kbkktaXY0sTOriI1QzP7b2L8bG/trInZ+zcbOxqyZ6RmZVasptmGz6UACIEEAhHcFFArlvXku7TVnf7wqELabUDdHrRmcAF69d/PmyZuZ15x7vmMsWG2SIKpX891ly1hgjUkisAaMNsZoMGg5CbkcT48BIeOWG+Kg9IquqKmfMWRTjka7ApRBtIiIgpMnwfeldBgDLoXxODV9p8GQUSZNXFnUxRYpEAC1tgAMUAAJa0Fb8LOZyJGu5xmlIEnAGCZZnGpEIT3PzXooGZZyxa5dW9Z0VyfnBz4+YeM4CsJMqZDt7uSeiOO4NjcLS4teIUecJVYnKu0qF5VgURCR5E57a1qpxdVFID/bvTpgqppGeavbujurY7NJIySlwfehlPNyWXQEWuHlsnE+hnoMqU4mp+ek7O3bwl1nZGSkUCru3r377KlTg0M3+nq3TExMVCcn2zva1qxZMzU1NTIy3FYsb+7rGxy4DFoL4YyPTaaJ3rypv7I4P3pztLW1o1YLL1++2t/f39/fPzM75XDo27q10QgvXr7U1dW1devWS5evnjlzZvfux1taWkZHR9M07ejomJqaGhsb40Y93r9p5/ZtVyema1pZhNPXBr3U7NzeN3ui9rOf/fy1Z19+/muH3v7gnZx8/rWD2z84dvXcp8cfO/D0t57Z/8MPPjj+/vtb+vqIIA0U18gQJeOJVouVBZuodi8jGMksiazQKBJldURpojQSkWWQZgu5FNCRmV3btm7szk6OL/3NW8eXArNr+56929onZ/T5KwOLXE+jSjkLjLaKBQSCsQITQgjNUAvJuKfTqKENM7B9dfuutYU8QA+0L169FnoSSLAUPInWYuQKkOgy7TgOOhlLUkekE01WAxq/JB1P5PJeoxbEoQoCNTG7WLNpQ+tMS6lam5cOzMxMnLuIj2/ZuGd3/4lzV8iAdGUYRn9z9OiNqc1o5UQY1wxyzh3hgjXGGEuEIBljqbaALOeKLRtWX754pRlFxqxop5uZJIHscoolsMxCqgKXM6NTBLLWWmMllyhiTQYRM5lMFEVEhI6wCMCAERpjkiQBg1Yxmc+2d5Rdl8URHDy4f2Ji6vTnZx03E9XqTjYP0Iztbe748qtoOSb4bZ+P6BE9okd0Lz18tN87w9zdKesQMWRcAOMWWCafa2tr44BzM7ONer2JEZNVAIScE90+lzUV3U3NDRkgzpBzbq0hE0NUCeYnXNdVXKRhbBsVSELQ2lrQREQGrWpa2iBi02mUMWabZjkInCGTggmGAkCIxJMWtEEFQMAsSuYIITnzBIeMR6mbQgKJATQQxWm1XlhV5i1Fh5NViU5SAAQmgSBZrHjl1mKx6GptkaFKPWXiJNW6AYK7hXKmswQF33qsfX33Y7u2qTiMyYDLwKRJFHitea+zhSRnvgupgqAek8iX8sIVtph3fa+1o71WqS5WAszEjqVGEkEj1Knq3LWZFTJhmhSlt65307TF2tA4SF5oLedKRSeXQSmo3CJirUOTBKksFeNqENUbfT3bcrnc1PysdMWOJx6/ePbswPVrmzZuWN25am5urqWlZcf27RcvXhybnOhe3bmpv//G5cv1esP325fml3zH37Rhy5WrV28s3Vy9enWizLXBoS1bxOqurrmpyenp2W3btlmgM2fO9G/dunv37suXL1+6dOHgwYPlcvnKlStBvVEutYRhODw6EjWWnnvm2YMdnUdPnalQKvO5C2Njyujn9z/5i59//P+99/PvfOv17377WyeOHrPRhmcO9l8d6Djy8fv7X33pn77x4r//+dHrw+MdPRu062UyWZWkWqcp2QaD7lJ2f9f6oicvjF9TxBzf5y7VbWi1ZQpAONbxU5Ltfmbvzp3rSnBiZP7YqdMZkXvl4IGO1fCLc2OjMyMd5ZaC6zlOfqZSj5ArxsEKbZwGORlwPERXqGocRGmorc5yNAh2BWb6Xv+WcAXw9QEAIL0z+K8CSABicDQAAFgAB0ABgJ/NAliAc5eid45ecLKrDMvlM3lFscP5/NzisMMO7Oh1hPvJZ6eM8LMtxZHqTLS0lMZS5kupw7IAWYO+dHjTtAQYZ8CBe4JLk5YcSIMqz+Q4l4wJImgGj8GmtopbRoaj9QTXUeoJBjoVCLG1IITnOUm9JmRGGzQEjShyvAwSQJoyRCmEEEKTZYBWa52aJEpl0a8thY1GfODAU5cvXU1TBUJYez99TDMk1SN6RI/oEf3a9EBRZlnpsiKsNOPUATV9pQkJgDUj/zfhnVt1mmnhENBa20SImuYmQF9YvD3QMA3BkiUiaxgIxsAqIAtJnSp2oV4BJwPGgNYABpEQOQKiIbAGrCGrrUZCBtZaawCIIQJZMqk1SAmQVYjEyRirgTQgAhkwmilFQZRoq1RsrAIwgBoMYBgauxSILM/4ilHSaOhGFawBxgA1VJdq0xNOS4cjeBykweKCXpiFtAGoAIGYNkrFCwuovLwrG7NzwhfGamAMakE0PJqYNL+2vVAqCsfja9cu1FI9MlFvVFtbe3g2FyxU0khlfN8lNr+0JJTJFUtRFCSLlXSh1lVunZ2dnWhMtXvFtnJrbWQaEIUFG8aN+aVUK5MkZCxTVhKaKGkpdVTmFm7eGFqzvsf3/bGJUWDYs3Hj2NjY2Pj4hvXr13R2TY1PNKq13o2bzl+6OHhzqKura/P27YNXr1YWljpXrR68NlirNNb1rLt+/fq1a9c3921hgl++euXx3bs2bdo0eH3g1OnPe3p6+vr7BwYGOjqqe/bsOXfu3IULF7Zv39nX13d94FqtVlNKFVpKi7Xaqc9PP7ln3/4ndn8+cKXaqBpgn5y7oEL96hvPHT555RcfHv7nrx966sm97330oZdxe3s7ttH2I0cO79y///deeeadTy5P1gIn3xoYSAkjYy1pnnV7N6772tZ1RYBdO7uGAWoACYACMAooBgLQLjgM1gswBO+euDo8M71hS/+G9T0lF97/ZOjyzPXHHtv2Wt9aCXBqeFaNzQRWhtY6loy1DKzQcQaAMxJWu9oobViYDF29+rfzi26SRGkSuSy2ViWWwlTECMASTwBjeSkEswZZSipSOjaJNgRkTKqymQwDaCkXg6C+Zk3X7t27pZQ/O34xTkUMRvouZ26tWr9YuTk9NvO1A0971j187NNamlA+O0uGuwXQmKjUoWZKKIPWGqO0WZZXGFhMYscQS2KDHJlWSmnEJuQKxJppSJAsI40WBdj60rzViqzm0gNgcZSg4aniysokNuQLqzQHboBAGyJlwVprGSfGGRiVBLWpiQUh97333i/+2Z/+YSaTCYLFQrElDON7J5jlIf9Aq8EvFEj4SCXziB7RI/ql9NAeTOw20/w7/I+QLfslWQvIAEkbilMVNaIwDMCY5Ti/K1a6d1o4EwA1wStkEqiZIddYMoAakMBqSCIBhhljVcKBwGhrDAFwIEQyuGyVQ1aT1qAN2KbxjrHGGGuVSkwS6TQWxtgoJrRN22K0wBWzQRLV60lUpzQEFYExYMFLARKVprFKI11fUrUliAMEDcwCt0AGghBqVazX0oW5dGYCavNAChhAok1ijLKqVjcL1XR8ZmZwaOzG4Mz1YZiqSJJWKUpjUchlSwUCcqXTViora5PpmdSaqBFWbgwvjc/oSqCWavHIhG0E2UyOE6X1WjSzWEBnaWSyenM0WKoLQ8FCFRJtkjSqB5XZ+WhmLl6qqmoDwkQAploZokQlU9PTIxPj+Xw+U8iODQwkAGvXrFlaXJydmenq7EyiePDqldSoUktpsVKZn5/r7uxuKZfnZuejMBZMLC1WZudm16xZa8kMj4ysXdvT2tF28cL5KAw2926am5sbHBzs7u7OZrOTkxONRr2/f+vi4uLw8M1Vqzpay21TU1NRFBllfC9fqdYXl2Z7ulevzuWmR0eqUYxedmR6th6LJ3Zuxsh+9tnpTVs27Xys//2jH083or7dvUGqPj9xYkv35h2bVo0PzSzOVazFQMUhKBJKYrS5vfhsuSUDcGQ8Onp17NT1sUsj84PjSzcnq9duTg+OzgyNTg/dnBoYXTh7YahSbTz2+BObt7TPNNRPj56cqs1lPPt836anMl4fQHcpO3z+EtRqfpp6Ks7oKKeqxXgxH87nwkq20sjXw3wUtXPmGk06jtJIkdFkyCg0mqFlLkEWRBYdH3wHhUPMReaBzAg/6+RzfksuvypXbsuUulo7wXKFYq4RXx4d275nOxqanhizjBHIICSybrUexXVVGa3s2dIrtFtZXCSf18JYNxA0hVHdxBFvRB3FvJdxx4aHKTVb+ndcGhpRSeTrxuPbt128NGCMNcas6eqSjK5fv66BE+cWgCNym3JSUaO+cc3q7Zt70igZmphF7gMynaS+zGQyLcOT8wTMRch62ZGxaUd63Gpjog2bumv16uTINBlDpExSxzTc98RjZ86e37x528dHjiRRglykUYyM3xrpzbyyAABId+hl7sxlcmtauit++BcldzhB/sPIOo88mFbKf5XHyq/H52E9mO69rfv1pS/TqofzYMIHyty/lR5M9yl+yHH0sB5MD6CH7ld313t4D6bbfKmacXG/+NmEhJrx51AKsiatVVLigMhcCQDWKGCMIbNN/QTYlTR8tz6BcTCmmZ6AgAgAGQKCNUnMpGAorDFA3BoDCEwKtARkwRhoZmoEBkRgDTAOSGStBWAEoJvYEyNCaxRwxpEbsqSN1nWtLHAGkoG0gATIOCEYa3UCaQygDRkwxuGMEBSmYA0wyYzR1SiKEzKKOwIEM0oJcDQRaCuJdCOEEMmaeC6BggdKeczNSm8hTWCpQmEkGSqghXq1I1do7VldGxo2jRBTA40AhFmsRsJzl7NsKk1aQ2xBpwtDY3HQgCSO66nqkoVMtl6tpdUGRDE047JKAZoQkTvMB6bSRKsYGCRhcE2pjrXda3v7x65dr3C5Yc262bmZs6fPbNiwoXfb1utnThfXr1u/oWd6cubC6VPbd+ze+8Sec2fOVpaqructzc4PaL1+4wYmxfj4eLm9rW/b9hvXr7AbrGvtmsTogYGBrVu39vf3X7l6yXHF2p7uibHxT4599Njuvbt37Th39kIapTq1jvSnFitHT5x4bu+Tu7bt+vDMmYTIOvL80GDcqL72/K5hh//8nXeee+n551586e3334+B9u3dXcqXDr/z8wP7D37/1cff/PDK0GJFkwWXC8HqQXRzdPJivjUnsmeuXBucX0oQwXEMMuTMWus4ArTSRi/N1vs3bH7+YO/kLPzlj4/WdSKyOetxQgIQHKAG4AH8/u++2lTq6NtAIheAwbKhcQDgATShIrbilmNX/jWHSbNDcwALkDZVRCv1BYAgEAhBAoog70EV4MfvHf/Rj376h9993c94n5y+0mjEUhQVWSfbUl2cH65Mv/P2se/+zsFi1vnh8XedfBGYDSqLkGXK6ESlRpNAxhCN0Wmisgw1abKKQDmSp6FmaB2GxhhrwSJD4IIsx6YaFEv53NdeOHSgt6W91PbpxZvVJOTSEUIaa499ciJIUy/nLywufnLi8zCKfZ8hB62MIUQupeTWkDGJ0kk1Tm4Oj/b29l6+OlCr1bxMLg5D4XrGmOXn0sSV6N5g4F+GDDz04vuIHtF/IcTo4cbMVzpSmsx/e8YiQvfjt/26TTZi95dyEPldJU1phjU9ogEAWNPyd5k/MPjCnoaI4XKyyWZgCcYACJCtLBnAUAAw25z2sQlvNfU9CIjNqzfzJyEiMrJWu66riFkikB5YC9aClKAM52itpWZyg6Y6iXFOBhENIjAOTCDypuerK+4AAAAgAElEQVQRCgGIxACAALkQrmTcgkjIctcDAGOVIDSMSBI4HJSBVDVhM2Y5WGBCcikNWe56zPMipQAMeD4DZomAjJPJUyNVSQp5D3xs37dr/ZO7JoNaLQ6zfgbn61M/PixIOsINq1UActvKCSMIAkjTTKGU8zKz0xOQkcWuVbX5ChkD1q5ev2lhclYFIXJOZMGXQARKCYaAyJA4ATKWGAuuYI6rkhQK2fWbN+V87+LZs34x17d1y7Ub18Mo3LJzay2oTV8b8gvFrX3bJsfGp4eH+3c9ls/lLl+6GtRr3PdMFGXK5R27di4uLk5OTvZt6zOoL129lM9mH9ux6+bVa5WlhSef3BMnwfmL59Z0dfb39d0YvDk0NLrvif1txfaTn55KQmU41qUBAQXueK6rgZYaNWBUKuRVvbKurfX3ntk/PTb12fnTB597tpQvHP7o40yp8OKBx2auzly5cH7HnsfXbWz7myPnRyvVJWMtF23tpU2trd1BhKkJ/JxGwdJUKaU5goAEdGJS7joqjXu71m9enfns2vznV64EBOj7WmvBAJJkfUvrgW07Sj5cGZocGh9XnGnGiHHbjHFkjDAGgEUpWRCIuGySBWStNUYxJpaTZjRh1qbJOSmwiXS8mPtxaiCKXWAecJVoLjyQohbXubDMhjs397x6cNfZ0xc/P3f+W9/+XqNi3//o5FQlEuXyEotM2ljlSFGptIP4p3/8rYHR+R9+8EHU1tZIEuDEpScrQQsXr3/9BWb12z97K5/Pf+Mbb5w4ffLi1RP79u/f2rf3rR/9Ymrk5re/881UqZ+++yG5pSQhSINMJhPGgZRUcPmf/fH3DnXL8RD+53/9g5lapAgcz1dRrLUmIYA0kAHGQAMwBmnq+XLvnt2Nem3wyqAQIkkSo2Iw8SsvvbRt2/Yf//inQzeG01Rbg4yxW+gz0opzIlkAWMm+uhxMDwCADKzsPhHssjNBcx6nuwtXJpZlB4QHxQv56uiBWhn2IG3N3fPnLyd6EJ/fED14l/8AuiNR8Bc+HMia7/ce4ydLt9RQ976R+7wja24/DLfW46b67p7WktV3cbx9/Ua6+xJIdKuQ8LY23PZ5x1m4sj25dad0z1m3t9beYc/+BR9aEeXvPOs+8XiaPZ/uwwfp7pJbhc36d/V/vNMc7VdE/Vm+ur3v0XvH1/IIXWnT3fmh79x13DrK7im5xe6+7XyQNHbvvXAodN7G7nbE6P6KULy3fDkRUzN6LzR7CDYTQgItM8UmhIQrMa9uxdvDLz6XkS0GQIS4AnUxgJX81YisqaZm0FwsGGNEFgUnYM0YXM2JDpABWEQkMIAAjAHjzShavNl9kQNwao4/bLpeIyAgMkAOTFgEDWiIQEjkotndOCLnzCwDXgoIQDLgDhGC5cgd5rgopPT9RFuyBnIZMIai2M3k0QoVp0xImXHBEURaFPNuLmvISCl0FFVGJ8zN6ZyXtYwplQADls1YspAkwHgulxeODJIImJWlgkkUEYKFQrmcBJG1RAjNWwAisMQ5A86AobWWSWERCRjj0gJBklaqNc7Fuo0bZufmZxcW+rb2RzqdHBps7Vi9qrNrZnxqcWlp3fr1ju+PjI4gsk2bN9WjIA4a6Eij0+rS0pqu1eXW8oULZ9s728ttLQuz85WFxf4tW602l69c7u7q6uhoGxy8HjTqu3bsEMiHB4eybmbzxo1zs7OJSZVgzHO1wTBVhlBKxxqbqhQdEQShjtWG9Rt8P3P21KmN69b392787LOToyPTrx7Ybph//PiRTKF13+5NI1NzURgnaeJ7+U3rNmYyJfKzxvf8Qq6lkC1m/VwuU8xmS362xc+VW4pZ11G1yvDY7OWBK3ES+54P2rqIGc65gnqlOjoxc2VofHxmoZEmkaFYpZFOkzRRsdJpqhWlyljGNGlltDZGG7OypqLW2hhjNFlD1hJYIEtIaI22hEGilLGun0fppZpp4QXIlrRihYzl6PnO5PDNueGpV196mjP32MdHD+7dWcq3zc8vRDpumLjU0bK0MPP6yy/3dq/54V/+7VP7ntq4pe/ExfPGlYCcwtgDF7QZG5solgp927fMTE9fPnvp9VdfinR4Y2ho+MbkH/zuK5MjU41aNdFpJQwbgXL8TN736/UKcMEFT6PApunMfGPg6vCl68PAXWIsVinj3AItD5vmLgXBc6VWiU5UtVafmpqNGo0UUQEZsCaJydpt23YeOXo0CtNcLqeVcRynqZVZmTvo1uetmfD2aepW+e3q5RWA4D6WwvhAgeIrpwcDTA8q/6oBpodk/7Dy3h1RNG6XJu8uaRJ7aP4PAHQepAS4R3jCB3y/T3t+BTZ4a32l+x+977kPCbjch9s9PX+F8wOuCL+0/sO+4AcBdrd9Z3R3fNz7MoL73d2D7WS/9PN5AD1QlGHI7qsC/hKizF3Dm90KDXx7nL4VkRJvfS5zQEa3t2JZ2kBEjticSRGINTfFgAyIgEsLCMBQyNsu3XyUDBhDIbhwiEsgQFz+D8iQcWAIXIDjAXBABMaQM8Y4IQfkwCVwjxwXpQAAZiwYC5ZIE1gE7oDwwPGYmyEpCJllTGYzUWWJHNcvt2oykMl6pXJSbWSZ56BQkhRo5nukdRKGiaGcdLzEpiPzwcC4natlMtk6tyAJysVsuQTGmloAxPxMNnYgVSF43G1rI4uWLChdLLWEjdAgkUAoZCDngcMBwVoD2jJgClEjWgTiglwpXBcZozRt6KRjbVeuWFioLAQq7ljVIR1/anSita2du1690QCC1vb2VOtKrcIkby0X4zAEk6JJ4zCwNvVdp9xWGBka7Olo7yiU5qbmUqW7u3sYiZHhke7Ota3ltvHJqTBM+tZvMnE6OzPV0l7q37FleOpmalLf9Yy1hFwBciYzPKMClc3mIp2OTk9lfW/bmnU9rR1HPz28Yd26XX19wzduXJua69+9tdyy+vTnZ2WuvH/3prqi0eGbgsT05PzAyMTw/NLg/PTQzMTV8ZGLwzduDI9Mjs0sDE3P3picG5+bHBmdmRxNoxpC6iPkFWUTxdMoY02BoGCNF6euTvKABclkkrgm9bTKaJ03Nk82b6lA1k3rGR3kbZy3cRHSAiU5k+RM3CahhZmyMGVuWqVtc6jDww5XrCmU8lpvKog8V/U4io3VxkrPNUwLT5g0VVHsI3OZwxW/PjD67L49AvH06YvPPtdL6I9OjBbKherSYs7zJ0cntmzctGb9hr/56Y97Nm9Y09U5Nz4hEnJIUpCShUYUzlUWM8ViX/+2m9dHL5w7/8KzByQTYzenKovxN149dOHS+eGx4Wqtms2XgCiJ6kJw7kgVJ4BsYXZ2bHh8ZHK6FgE6WZSeUtoSA0JEiYBgLCoDUQJKe9JFjmGtYYyGQpYKeV4u+sV8ay5bymY2b+49fPiwSg0iqlRrrfkdk8KvI8rcZ8p7JMr8vemrFmUenv9XK8rgbbvtuxjd5yykuw7dl+ed/L8yUeZBV4T7119ZjH/zoswd339rRJkHAkyMCYD77IC+BMB0x/Am4LgcFu8OUcbeboV1eyjiO4CtL2RoxgSw5WpksckBEckSOhKZIGScc0Q0xlhjlrs4QxSSC8EdlxhTSQo6JasZAQpO3CEhmJcTnmet1UlCOgawgByQgcyAl+H5ovB8IcAGjXRu3gTBsvQjuCy3O6UC933LMWmEaqkGaQJGY6lIAFAoljevQy6D+QVWi+RCo1ZdYq15AwZ812tvE4VcpBMzOwNxCtUYggSQt/duiFcVqeBzzjvypfrU3PSlAYjSbFtrlGU2qsOq9p71PUk1rMzOJ9MLHWvWzk7NAFlWLhRXt6PvKJXqSiOamYNGzJBZjuB72ZaWfEuBGKo4iar1qFoFzwFS67f1O1l/ZGK0a/XqNe2rR0dHR6cm1q7r8Rz32qVLMpPZ+8SesN64OXSjf8vmOAxmJycW5meFEMaCm8vs2LVdq3B+dm7t6h7GndPnr7S1d2zr3zpw9Uq9Xt2zdzeRuXLp4pqOjg3r1t+4cePmyPDefXvypeL5c5fnqjW/pb2RGGMxjUzBy4RhA7gVDkrX5hF2tnccfKx3cb5y9MhHr73wYueq/L9/6718qeW15/YsVuHjT471bd/W0dNy+drU8ZMXU3BYsb2qE55zLDeuMCXPXdvasam1rZuDayFm4GXBKGAIIEAiMIDAAOeQAkiCDC6H5SWAaMXGpRnt11mJ2AsAejl+4nLn/UJ53uzVAGbFJqbpgx0DCAAfQAO8PTj74YmzTBQCZYzDM9mcMcblnKlkS9fqvb3dx947Cqr+R9/7xs1rU6fOnvveH716Zqj6wWcnA7IxoYnT1TL7B98+NDeXHDny0Qtfe35gaPjG+Hy1FlNqiKFhlKhQcNjY2fXqwX1H3js2MXF19+OPbd36xI9+9HcM9e9+91tXhoY+P3uhuqSCMOaSCSk1E2TRk0IFVR+0jrUF16DAjEzjCBgHpQEspAkH9CUDFaMxVhvHc2thIDK+WyyKnF9a3eVx8MKwv2dtFCZvvvmWYFJrnc3m4igls6wAp18PYMJbeZ0eAUy/CfqqASZmf9kb+c8PMDXbY+GeVjVbTneetayV+bIAE9IKkHTvDf7aANNda/2vBJiWn9VXADCx277/9gBMCF2PN/8C/MOLMohIyO/RSjJAQMYYYwi8GZ1v2Tm8iUw5rpSSkC83w2qlFBkNRCgkSsGE62ayxEUSxyaqg1YImgkHhAtORhTKfq7EBAa1ahoskkqBCLjDcyVWas2uXZsydJjFRiMeGYvm5oEQpIBSMbN5c7GzkzxpgILFxWh4nJaqIBmoBHs3t2/ZXCWbLZbKmezEuUv8xjiPkgbXRkWZ3t72Tesi3wOtZk+dhpkFSAEYBynadvWJ3i6VkYJYd1tHPLt0+fgpmJnnpZLJOkBxfvOGjZs3x0E8OTxavzZcWt1ZmZwByUu9GwqdrQkjIgthsnhzVE/MgwaQwim3lNd2uoUMGW2UDmvVpakZiFII6tBa2rSzf9Wqzrm5OaXU5i2bhkdHB68P9KzbwBFvXruxurtz88ZNZPTQ4PXWlqKUfGx0dH5+3vUynudJwZ7a/0Sj0Rifms7mS8VM6erVa9rYPXueqFWX5qYmn316XzbnHvn002whv2Ftz+TY6NWBSweeenpdR9eF6zcHK0vcy6V1Xa0FIUc/mzFBKDmsXrvKUpIGld5VHd/etXFyePH0ic8e371rW1/3+8fOhcp849ATDsBbPznWta77iZ3rP7kwc+Tc5ThXDl0/s2pVginaustszuE5cpwAdRDzjMy40Coo7zthqkYXqlXraSGZAK2ijOPmXF8AIqJSqtqoRqnSpIGhlNJzXF86gpAIFVKc6jAMlVIAIKV0HKeZnwEAjKEkSZIk0VozxohLcHKFfBbjWmdBPrtj4/xi8JOPTy4qZIW2xSDIlrO5rAdh4CTB3t712zd1/vyn73rK/LevvTo5Pn9m+Np3Xjnw5tnhs8PDM9omqW3V0kn0t19/iXF45713WC47FevFemISK5l0My7nLAkaUpOn2fe/9fWbNy5+fvpkW8f6V1554eMPPxifHDv40itRbK6cuTY8Nupm3UaSoPTdTAat1SrxBSRBCClLkhiznFQMjIPVCCTArlnV1r9uXWvWF2Rd4JLzehBI1+Ou5+bzHZ1riMHU+OTWrV3/5t/8xY0bNxgTcRhK6aoo4sIFACJDK8YKBI9EmV9Nv62izPLKcI8oc7ca4x+jKLN8aXo4UWb5ST4SZZo//wFsZW53xr5tWDaBpHv5/KYAplsqli8ONKUZJrGZGu5WZUQgQsYQEdhtqSibu2LGQUghHUS01nLOBWfWWiALZEEIZMIg566H0tXGUKrAagTLGRKTVrjML4hcUbiZJE1MFIJRAADScQutfks7yxXrQRzbVHAGSaqiCICB67B1G9yOjpTJWqptLiNd1zYiU6tDmsLqdugoY2tZZArVKErItJVaoFqvzE5bq8CRiiEW8rKjQ6UqGp+GhQoDSUDAyd+yrrCxu6qTSq2WK+Rdx1lYXLTVOroucAaWrO+UVrWREJVaVc0vykIhTVMo5Fq7V4PrzNeXCDGby6owUZU6xAoc12tvFcVMJWrUo8D1HSlYGoSmsoS+BzZNBetY1e44br1er9YqPet7avX69PRMS7nc0bF6dnYuboTdnZ1EdnhsTCO0rV6dWghqAYJwpLe4sLClv597/vXBoWwm27l61VJlsVqt7NjWn/fkxOioK+Xa9T3T0xOLCwubezflCvkLZ8+tLpR39q+LY12vVG2UGKSYWYPgMeFISZyTK8l1a2E0Prb4/K6eTKnr448P5/Ll3bs3z83Xzl641r95bc/anvNnzi0sNp7Zs9HJtd+YGs+1tmnGiYBxYoxipRZrtYWl2nzUqKh6o7HU296+v3d1a6F4c3hqolZLGSwszARBVafK6CSOY63iKA5q9VqSRIlKtVIAViCC1SZN4zTSmpIkTZNEpSkQccYE55yxJI7JWrBgjbHGIIAUwnUc380kUcCRanNTPKpv7ulyPWehskjoWCGEJ7VROc+vLi5MTU7k88UXDuw6ffJCY6Fy8Kk+i+yT8xe+9dSu2UDXwjBVhjNBjH1+7vyePX3r120cHhuuknZy2SROiDHmynq9AcBymRwj/smxT3fv6Htq/77PTp0ZGR195WuHGMOPjn/m+7nHtm5Lo2iuOi88j4ADcKWtIVII2gKzHJERt0CqmceMM+KctRQLHR1tWd/lDLXRYRgmaaItIeNcSIZowa5fX3YcePPN99I0tZY831epkq5L5otFbmXY37ZQPQKYHnyBh6v/kPT3BZju2RM/6HYfmv+9u+3bGN3L7SEBpmbh/Rp1vzKke7fzDwR67tOTfxX/RwDTFwe+9PN5AK2gOfTFabeDobcPu/8MgTmbmytauTJ9IR0BAbMrEWmWW0oEDJr2vIRIREYbxpiUXDCuUDfPtJYArLIkm3IzckCOYJqiD2mVKoMaSPDUMiAAxptskXFXeI35BiwsQA5Ve976AhwGnEGukCuWGfMqcw0IA82xpZAXnp8CA+lAnPBc1s/k566MgkrF9t6GCitxw4KFegMSCY5XmZ7PtpRz0nWll3DpMBmDhozjFLLS9eK5GZqYrOaKbrFFOo5mXBjkoY6iQBPE3XWezxiyyzsYIbJ+xkGeBLGanLXFQmtXVjDOLLMWgDGZ9aXjxGHVBrWQQwuTec/R3e2xVqlE4Tk3J8dzfqa9VF6Ynx0evL5uTXeSJDevX9+0acumDRvri5Wbgzd2PrZ7ZnFxYm6BHH/VmvVai2BuKeM7obIXrt7s6elZ3bXm8o2BHdv69+977MzJk4MXz7z0/FMnT8ydPH705a9/fXfftuMnPvvss5N7n9q/MTGffX4q5zzz9d51H83XR1jFgEHHrTQaBDKfa0cm43qiySZaLxSz/+7Itd97dssLr7x++Oi7AX/8yb19J09efPeTM88eeHzPU3vffvtt6Wd27N44r9fdnK9BrK22JDS5TJFpKKXRMB+RKR9te87fDIAusG2b9rg84KyUc1VCoC0H8F2edQAAggSMBQvAGLguZFbyQSqCLIJZgZk4gASQAAwgBBAAHMA0Y/gCSAAfoACQAABAHfrfOvbpsZMfPfvcIU3h4TPXMn5LUlcAiAXHy7ToKPng2CWo6D//0+/8b//hb8zJM68/+Xh1Ye6z8wNv7OnTlWCgPjcUVHjGz7YV/8+/+vF//703Xn72+b87fuTmzBRY4XgiDAPhu75wFpbqOXQzxdIv3n//z/7Ff7Nu44YrFy+cPX3ytTcOfnLxwrlLp/Zv69vY03p9asDNZJNqzB3HKgLkNjXAskDakzKx1jKPo2zGukyC8PrNmeHpJZOENkkAEVLFHAc5K5ZKuVKp0FLauXVbsS1z7vQ4EQrhRPUACG0Uad+/MwgVATykX+kj+m2he9aB5f30vYvdb+kLvicOwAOEpHtCUd+h7/nizBUtxW+uhY/o70F/z8zYXxHZ2xAn+DKzHREga2ayNMaCMdZyBg5rqnCaFYBAa6MNWrucVxChaVpM1kKqQekk1Y7Plv3GlwP42dRolaQ2Qmgk4EsppXZcxRggx2wmSlQBuMvcRDcg0anRjDXlmMDNF1G4YSOAMAWl0jBywaIUIAV4PggGYQD1hkChiRRYMCrVCALBdZFzpZQOQ4iiJEkYY67jRFx6TJBSEMTgO8yQIwTnHBCJITCUnLtcpKmGRIEygjHJucOFchzjulJKQGtVCnFoc5mO9vYt3Z31OPSL+RsTo0tBGKbRwsLChvKqbb19w1MjE6NjPZ3dc8K9cfFSz8ZNHa2tS0sLZ06d6l2/Puu5Q9cG1qzd+Ni2HaM3R8fGxlo6yo0wOfn52fUbe7bv3nnp0rmlltZ9Tz4+Ojh44rNTW/t6S7nMqY+P7t2/7+Vnnvm7n799/MhnLzz//Con98lHRwrPPP/qoR1/e+R0ZW5WBWHBz6vUxiopF/PK6DiOM/m8cb2Z+bm/e//Ct1/c+eyhl05/9nFeyENP7jh9/eYvPjj68nPP/P4ffPeHP3xTMfXSzr4TBf/S1SlMKTJGeFkQnuMYq0JGlhhNzy+OLCzNdRWtgZHJ8XlJCWfTGjkyQYwzzLi+48g4TqrVigEiIsaY68qMm2FIKlZpHHmep5SK41hrDcCEEFJKznkQBM3072maRlGsteacewxbJCOimWq9de2alw4+/fnV63/9gx/83vf/yM8WDp+4EFRrxfZOjixKTWKYJ7zPzl/RpP/ZP/num+98cOzE2Zf3Pfb24U+GBsa//9Luf/ujT6oC6oiL83PlfMtfvvnh8wee/ObTz/zwrTcbYIlsQkprqgcNL5c3qUgalTLjhSw0GjUheRonHoAhYFyi0VIAIiYq5c3EAmSRS7LEHWnrETBrdeB4PA4jsCiktIyB1TpKKLXIJGNADnNdJwqDRlAjRn7G2byx8913Txw/+klQr2cyGeBcay2yWc65TvQvGceP6BE9okf069B9RJl7Uefl8jv+PAStmFDhnfD2fQAsIlqRQFbURM103IhAy/HuEJfDVCwndiECxshashaEQMQ0Ta01xhhgDKwFZsD1kaHWGoSAGIGQLFKTrRDAOQDEcQwAwBgkGrQBl6ElslZwB6QALiXjXDqxn4EopUrV7VonuSMgSYTkmazLRBglkCYAoILAV6ro+AEwCGMTRYZZ0BqaCRM0AwJwpdGpkK6VBHnH1g3j0irFrQFrgDPwXOE6qC3FCixwAkUA0gFrHSHSNGWMgesYY5AjgDVGIRkQUgpBxiIi5zxOFWglrKUklQyVI10h8tlM1nGUSRnZrq7VanraAKZhfP369bZSaWPPurmp6fHrQz0967Ib/OmpaYZ29/ZtNwYuX/v8s951G1o3brw5PDZWr23f/QSBSgCUBuTs6rVr63t7tmzbOnrj+sDN6/ufeGzo6tWTJz/bsaW/fduW4Yvnym1tv/Pc8+99fOKzD44c2revfPDAp9fO9YotL7/0RP3d47rWmG9U/GILy/JaVNNkjLSWG5VErutMzs781Y8++s6Lz7984NAnJ47FQeXZvr4Thn745k++8Y3X//n3v/3Ddz483Fj8xtNPrxL5D0+crqo021k2yqp6lI1NxnUCy6il4/js/MDRQGoThPVQhQQI5IAFAOZwlvVznucoZcKwoZQyxiCi57sZLxaISZTESVQu5bROoyiK45gx5vu+52U457VaXQjBGGsKOkopxpjkLOc7HucJ4cLIzFLCDz3bxxP/L/7dX//Bn/xh9pn97x4/F5HSSOi6cVIHBmHe//jCBSfn/ZNXXvzJW++/d+zz1w4deP/wySsWf+fVA43Dnw7NzDHpplESFVqPnDj9xrbNf37o60cuXj0+eKNYyC/FIfheDFoSuY7D0hBTcBgHayRHA0CQ0UoJcCHlgvtBTII7HBlyo2wCaE0YrW7Lf/f1r7d0uEoHLvMYccdBx2kqpkAAcAKXg06XB6i2xs1yZDBwY+Lj935hSADjQRTfGqdNUYmIYNlSwQIA2qa9y/0Bl1tWF7eMEujOeenuZCn37Hpur/CPmB7U/ocFnr7i53CbUciXutAD38sDypctM770C13pLndfC784fqfty/IPe1s9BAAwX8Cdd1z6NvutX+fJ/lrPbbnzr7T3H4IeaJ32gAZ9dePxK9HK0G1pJn95tbtu+O5Z7Y5Hc4cBDeKtaDTUtKhizVjBiGSNtXrZbosDcM654JIzKcCSkpyIN4Pkcc6NK5nruJ7jeV5kAh0LrTgwDcYarZRK0HFYueC1uA6wOIrRWiZc6/gqilQaOr6kTFm4ThoGOo6QM24QEm2qdVaKS6Vc5Iuc69qwTlEMSgMZ8BwoFAvlUj6bZZ6IuzuCOISoYpUGpU0QC03FYimQbqFQoFQnQQhpah0fHAHWcVa1Oxnfcspms41Syc9molq9FjTKQH42W1jV7nqe1joIgjhNgCzTOqlWW/LtrcXiEqlCPuv6Xr3RKBZyCimMVblYmltYYtZ0rO6YnJxcWpj3uVyYnxtsJJv7+zZvXF+LahOTN/fu6r8S1tpNsn/vnvneLT/76IOr549v2bFrsZLUwkhJTNPoyoWL2/r7nnh87/WLF8+cu/j8/n3XL1wcvjZ44InHO7ZurS8t5ix9/dChD44eO3Ph/HPPPr2nZc+JUycXa/XXXn7qR++fDs18nCS+m5S8HEeMSNso8KWTNBq+7y/MzH74zuFXnnv64N59N29evzB448n+Ta50D7/z7u49Tzz/3AsDVy8dPXnumSd3Oy/u/2hwdKRaz2QKrZ1rvErNqLghObiymCsVckVBWAhrUbiklBIyh8ARuZQ8l816nmeMiePYGGOtBmAZ18n6GcdxyFqlUteV2qRKqabexXVdR3qMYbMHIoLWcMsiWErI5GFxJhLGusCuXBz4yU9OvnToyYz30vs/eTSDMVgAACAASURBVPvbv/+a//zBtz850UgCJFtuzVcqi3Xk2Xzx2PEznnrsm9986Z0fHz559vorh578i5992F8ufP/Vp//3//etiEvy8iFAznVbHe/g+mxX154E2amZCUALVgPjiiwHjdYiAQABa0YgAIaCoWUIQIYMEIFFy9CoNAStwc+g5LMzUz/76d/Fal6bSJIrmLQWkiQBjYJzCQK1dRlG9ZrniiSJVBLmS/l/+S///Ic/+E/Mkl6xZPyHml4f0SN6RP+10QNFmS/8778cfUnx5fb69+WwvM2C2+y3kUEzeeUXpk0EAAzREjZzSQICQ1z2orLWGANEwAiAIRBD4mTBWkZgGViGZAiII6JgXDBw0UhShqxFQCSyBKApiaMkKHa057jvucwEVTVf5dUIkZs4TRwpS7lsoSU1JoqqLAxRpxTHDFBHUTw9H2Ry3Ms4vmtVGC/MY73OlTaM0JW5lkIxn3cYAEChlDct+Xi2BrEGrXWtrqq1nC/clpJg2FiqxNUa6CQRhI4AYi1rO2UhGydRJpNpKbcU/OzizIyNw1rUyGdasi0FIgqiMGw0wGgQXCqjlipR0XULfmuxpOJkfG5mTXv72s62ubl6zSYt2Tyk1gPmu5nq/GK0GFZnFoQCADU+PLKud33Php76wtTAuVPf+9qzxSAJZ6d3rGpb9frLb50/NTJ4/uD+r0/PLAyOjbS4Xh756OXBpBLs2vnk2I0bVy/f3NO/a/DC+Stnzr/x8vNt69tn6vDO5cGvfeuFj44ef+foh//Dt15o23/oZ8ePXsjkwLK8yBV8b8emvj1rsu0+CIBIAUkYn1VeTqICNVnzhbuu1e1rfeKTC5dOn7v83O5tWeO88+nJQ197ce/u7Z9/fu39k5d3P7mtu71zZm5g26Y1j6+CFui85TTCm5o+AIKshU4D0FhxrkYAsZKCIIUMNrMWWGAMHAAOAMAJ/AQgVUKpZV0h5yAlcAZpCtaCUhBbnRplrRVkGAkbiNayLxFyElr9vp/+9Kdvv197442XXGf/f/g//tO/+LM/wid3vHPqopfLTdQXnQyPYstRWmCfnr20cfWql1469P4HR0Qh++prL/xf/89fvvTiN/6nP/3m//Jv32KSa+lyBn6pJAA8B7TWOkpymYxmLNXWWqOtao4jy7Hpd2IJkCnkKbKUILaUguWWaa1NSz4XRZHlrkCmIKwshVESOgIjpRxHWMJYIZBoBjtAbTnZjJOfX6pwxhhm3nj5O2cvD0/P1TV3CZEIqBkgE/AO64r7GW/S7Qkj/7ErUf6Lp7stSP6Rva+maL/i9XPbgQcnab/LIPW32g7ov2L6TWplbgFJt75/eeHmlhxDYG7PdQCwvM4sMyS6FQd7WeYhAp1YksibyXDAGkXWAhIQgCWyWqvUAhEmjACMttqQtcQQrAWVqrgBADGASRsmjsDYphMoqNhG9UZ13jKMSdt6Has1YQwyVFaDShqjI3G56hfz1EiSqWmYr7icmzQhk8L0bJWBLJeM4BQnZmZWBjGz1nBLOtVxHCwsNOIYJddpncKmlSmBoWihojnXGal917qenalCLQBjTTMUPtlKpZKdm6/VamkUp9VGDiXEKYTB/PhkHMckORgb1xpQqYMxDNAaFTfqdhZlmEHJ0jROg8aqUnlyujY9PR0DLdVrCwsL3d1ri7lCZXIurDbAgCBuUhtWg8nxyQ6pOlqKSX2hMT+7sdjRUW6ZXlgsd7X3926oXb9x/MSx3r6dvb2bLly4kEQRZ3JsYsbxcqvauyanJvzh8a6166NFf2RqJt+zijtw7sqlsJDbd+CpiYFrP/jrDw/uf+q1V5/5jz/+qJ6aRpyGixU3sfpGJmts1mNKhzUVJwCpcDg4vJFcsOn6znJ3V+uBndsPn7rw88Of73t6z7P55z84fGTHht7nntzy0cUbn5y4OFJJGMozp86eSaJVaMPqgpbg+9k8L3DDLSNr00RVorghpEvIGGNc8qbVCxGRtY1Gg4jQEmPgOI6UUgoBiFovu1vDiuOdEEIKlzGmtVZK3UKmhBAgZCUJS+WWKGyUfP/p3Tt//w+++x9/9OO//Kuf/snvvv7iC197869/8se//zuza7uOXxt0EDUTpIkkA+4FSv3t2x/8ye+9uGPv/vfe+1lKe9544433fvLu3qef/R//u2/+r//3z62brZE+PXQ9TDuXFE0uLiplbKoAmFUKrKCmPycDi6AJjCHS0IzvAmjRElhq2plZa9IwBGPSOEiV4dbmCuWM5VbHAl3BXUIwyITrAHJgKDjaRDkMN/ib0zDI+37v9r5/9a/+dWIA2HJ2+4ebNO4p/ZKnP6JH9IgeEQCIh9W+/GpqbleXv94NId1Wa2W2uo+4s+z2j8jp1k+LFmklFQFfbjMRMARriAwBByua6xAQIUMiAjKokSA2RgEwS8gRLWkEBsCIiNIYDJn/n703jbKjutJE9z5DTPfevDlJKSk1IjQAEqOYDAaMmEEGl7ENFMbUYHdVud2vutd6P996763VP9771b1eV5fd5dkGgw12yQZssA0GzDyPGtA8ZaYy82beMaZzzt7vR9xMpSTkgiqXC1PsH7kiI06ciBtxIs4Xe/i+pA3WAROgQZwpmHQWktiMHgJmBw5yI431hWABCthOTytrRJ61xkaBLRAJoixuo5SIyJ02HbBZvQ4CIDcQJzKngnoB0jSZmEymmoAKFAIbCQB5DszoyE2300ZsBEEgbV+fbluIMwBAR8pQnuTZjj0ThO16g9sdyDOSnsgtpQYOjrVrdVACACFzkOTggBhIEqQOGqJTb4DE/qH5gdVvv/726MH9y0871TkeHR0dHBzUIPdu3XH4wCGBWPIiC47IaenVJqbq8VQ+PO/0+cONVjLJjaFKDwnZbnbyjAaHhl/bue/Q68+ftmbt4pVLX3njdesoiirbdu+SUak0vOjJHdsW9lXOW7Vyf7u59dmX9k9MNJgf/uWvrrn4ojNWr371cO2Jl59f68688Nzzfvb4E1G5vLB3YNXy5Yui0CNXKktiR0QkZOoQJUoAl7nmxNjTb2+rdTpnnr3+lTd23PvokxuvuOSSSy557De/zdBeevbK517b/k5rrOOVmiBTQwYl6n4VBbnnNazIbMZgIg2+8kEROUJgRnJELjMEUKBlLQuqGAYAZ5hdUeeGSkqRsbaiyNlCROmkFhIIBEkJ4CuFumBBEk5i3/zBOE36qwuMsfdufvSaa6656tJrn3/+2R/85OEv3XqNxgvuuuvBL9x+Q238cGus1UzzoBS0603hhJE4kmb/876H7/zUNZd+4qrNP73vC7feunHjxgcfePji8Ia//Nw1X//hQ5O12j8e2tXb0+NITTUTUopjE3p+lhHL7tekAzDMhsk6thZshkQSnACQQD6gBpDMhlyugBUCenLpkuUXbDhz0aJeXwEl1pPKIudsWArhSQIAsp7Szaka5NSebpx71tpfPPRkuzMdhn5smFECF1qwPJv9NteOr/g4yiXzEY75yP7g9i+fBz9o8or/3uxfp4JpDpp5742PpVdC7LpqZhoid2uamGnG30PACMwgCAgZLc+Q9B1J5ALCLvEPAyIQCAZQEoViRmALzoEpCGxYKCmFBImWAJyFPAVriiohICeAc2bHBFKUNHamayBBlCIJaOKEHGnfd84JFJLJJm12MTADgScUIBdi3UAISQqUAiBICXkGUQQAqJVSShOZOAWTgyQL2s8QLIPEruxFbiHLZJyLduamW8BWOA6EikFCThC3QAkQAgxKKaWUBixbA7427Q4ghAvmlaNy2u6MT4xXFy9lqQ/s3O173mB18ODeg1MHDkWlcprGSZJEQUUzNjspBoIz3rllB47X1mz8BEQ9W8fGVKkyncObb+8dybPVZ5z+1rbtr7/++obTT9+wfv1rr73hWu1qpeftN96Mentik0zX6+1687z1p4bDy0Z27EVdGV7U/8rrb7anapecf97Inj3PP/Lr9eefr4BbaTueMHGnvZMAXC4DEAJszsBIwCQxMVng+6efvPLCqy975pGncpannbmq7tyDv/jNddd94pobNj7y8C8VZ6efcyoNLf3pk8/nyouqfUknK5V661lClmxuiO1gf++ylUtOWVwa9AC5kEI/EntCAAmQzYSc5Iz2deEN1AA8E5CaO8SLlbNEwMW/BsAAZAADAAnAS2+seuLp5047efmmTR9/6FfPf/2uB2++8roz1p/584efuumai+NH3sgOjLZbVjF4ntdOY9TY7nT+8Ze/vfn6j1993Y3f/u7df3bzZ2/81Ce/e889n7vzjtuuvvzBxx4babSnDAILGVUESpvlaIEzIwNPMgjHSMDMltkyEwEZ12WoIwQQgBoAgdHmqRBIlil3uzrxodGDTIlNOlEhYILWOKNCOX/RvP7+XgHUbrZMku7ZvvdTN35q9+6xxx7/DSNKIel3U3J9ZB/ZR/aR/StYF8rMMCoeu/m9Y9X3mytzBO4cs1fBUSiKwiWCI+XRBABd6WqkInaFiEIQETFKgQVjqAMQKCWwE9xlsBDEAA4ECVDUBTuCZ4ofChIwIQrtyi4VqQAGFIKNSVP0/eK4oAUBOAWguGMTXdbGWerUgUUgPFbCmZwRLJNSCMwABGwBAJWyDAysCRGFAGSJmSrUH6VAdoJYKRIIQigURgoA9kEoZ6V1TgohAJHBGRAiIE6cc+QAyAkgIrAukDo1xkOphSY2QOw8QBYshBCKyIYD/UPzF7aSznTcKc+bt3hoyba3t0R+tGr5SRMj41MHRlH7cZIoKTyprHOWUGsfASh3HpbHJpqPPP3ihvXrJcjaocm3d+3dOTqBPeXtb+0+c936bS+/+uqTz17xsYtuv+zKd3bsem3Llnm9/Vk78RxVq4OTU41nXtpywdlnrlt/0Vtbdk1ONsOB0taxQ/Cb+p9dc+V1J5385u6REnIdDKFq25yckIxxJyVGzSEigsjI5lZyK66/8Nbbhw4evvLKi1959q3DT7116cXrXsiz++7/6Y2fvvGyqzfedd/9tah37ZpF559+zotbd5gkY4TptBOj9aTyQsEOk7yz5+D+iVEBjsgJ48gCowTLZK3t1mArDcRAxMyWiNgSMDO7/FieUyJwzhXkK8VTMFs7I8hJk5U9T1jXW61cfeWFIM545tnfWmmuv/r8++5+8LEnHr/1s5ePTk795rFX7rj67Hsebr2wfbxneMV4fYoDZbN4sH9g66GRHz74xKc3Xrrx8qvu/vFPPvPpm/7iztu+951v/vWXv8jnn3fPr56pN5KwVBKeb8lZMtZSOSxnjqRjaUgaQAtIyAVXExlEFuCIDTgCZAAJREopJaQPnlUKQeYuJ/aUrxxz1mn7vt/XV41Ctah3cMnQ/FCruN3utOJB1XP+6ed85/s/EhCC1pmxCJKBYU6u2z/9ZvjIJfOhNBa/IwHlg2Nd/uj31PKIvW+ZzI/sX9kkVt5dTnKW1fdYHHMCtt/je+guCwTAbtXm3OyZYg0eB2VgDr7pzgrIAFLKLnGwQATJQggQKAARqZC8lopBMBGgUEoVaTRU7I7AjAySELsU6V3ZLwJEUFIo1aXaAyTuOn9QSCG1YyE9TSCYiYWgQhbbGdBaSEnIRWk3OQcOPM9jZ8k5kgieAi1AFLR7AlE4ZwvtGUa0WoASQISomIFNXhD4kVY5MAgBWqlqFXOTpgkoGVTKSsq00QIhg4GBJM3IWlCysmAobcY2SYiYjRFhIJVyWW6dM8gMhJ5mIFEtL1q2xDgaHz0cVirDw4u3Pf8Car1mzZra1OTIjnfA91ALoVU5LCdpmqc5Kqk8mTtD5ILAR6XG69OTjc5zb2zZcWi8ZUCXezLnLODUxAQlGWR2MCp/7OzlQ4MLRw5NZCZP01wpneWZ9sNOZkbHxs846/SF85aMTIyk0gHlfSDPW33ykh7onV95ZWSyyeRQ57kj41jKBJk8XwWlTpIIJYSnDDjp+WzFdKO1b//kFVefPTrW3LPrwAUXnNFJ7RNPPS0q1dHMvrFzj20KmeXJ5KTnrBIsFAHkkcSSNV6eokk4T/JOs9VoWGM6SSfLMmNzm6cmSykz6LiTdpIsyZMkzpLEJrnLIM/ZupwsMziXk8mYrBAO2Dk2QhX01YwokIrgikN2kdI2jk2Wxs3pHTt2fvzj5w4MDT3zzFNI6nPXXvzKq9v3761dc+05W7btGxs7sGnjx/btGW9MNzuYZ8hhVKnV6lG1d3q6fmjfgT+5+qzUqUce+cVFHztv3WmnfOtrX7/yyssGh5aMjY7HnSRLDaIUSuZ5Ugx4NNan7Pzzznl15956vbGsv+e0U056/OlXBfKG008ZH6/tGBl3KAERXCbytrQpG4cOAIW1BoiInCELWm04Z8OnPnPTZVeeve6staesXLJ6+fBZK1acvuzkDaecum3rtqdeernjMM3BCd0N+0JxBgKZAbkgtZ/FKQzAOEeq71iizyOe+uPZfk806Xx42X5PeOD318/77P7E9t7ncPznRGxOyN7L8O5ne+z5/BNsv13/+5GtR/HkHv8xT++Oxo4npz3R+EQGQHrXy4YAyDPSlkfnIM/2XxQeAs9ZmHUJz7QR73bcPwzb7wnHA777/Trh6fzL2X67JZNdBpe5J0gFkjgS6z5edaJAJ8WKGa2Quc82zTlLPOZHF6LWM5WicxQli4p+idgtu2AQwOwco5BCiCKgBIVwGQtAACFAoAWBAlF5TNaaTCpFAlEggwDpSc8HqVyeMxugXAKjAssChAK/TDpQnmZnXZ6AzQQgFST0qCCKyA8QiE3CLgMiUJ4IfEoziwK8CoRKOKZ2QnnuGZa5UZ7IfM2BgmoPSA1TTRcbzewrnSkgk1F/D2gfPA3aF+MtF3dA+0AOmMhTWO5hZKxUwp5K5tWgMwWCkQkNg9TQ3xMsGCyXg3o9hCzLHRlmEIKUVEOLZOgnnYRdznEGhDIMnUYIvUVrV5GSh/fuXbRkUX+5Z+ubb0DkrTr15NGpkcnRQzBYAXZ+KVy+9CSVq13bdhiRKYVZ3paBUoFvEaQMM+bd4/XQi4RQKTtnYwi1QzYM0vMhojrBWAyphTY5g9gz0NsxeVgqG3LWOdVTfey5FzesX7fxiosfe+5Jthp1mGhoCKg5aMUGnRYAWge+J1ErHVQM2Nyl7LkMIGBPYGRz0oEf9XhJlj3wm7cuv2Dd9rd23f2Txy66+ELRM/D0628n1Wp5/sKpsdGPbTj3U+uXTTSSB158umntPO17ua0YLvlerqVB9rzQknOGpFKWAYBKoRcoXxqVEzUxlkpJUiAFlMGXUDao0OuQMuQkO81OgpMSpVYWkJXnWLrUIaGvtJDSCRJKhNITRKbT8UPv0Wee+OGP7rvppptu/OSmRx55JNDezTdd89DmR19+ZfTKay+4+96HKm/s//MbL/3BTx/P4nzMWlcZKC3s6TSbnlcZz/Kvb37qr2662CSd//e//fevfOUrn/mzv/jmd+++7U9vuf36C+/Z/Fg9w5QgM67SX27UatrvJ+Wmc5eFYJ0uqVBSFmoQNveiiiGBfuiYhEaiXPk86IsL1q+fnqjv3DM2lVmpPEDMkS3lrNTb23YcqE31nTy8YNn8wR49lCWX981fqnTu8T7PkcqsVwkqi9N6G2wMzkKceFGUTzf8MEDBadxChYwzLwtC4CLci4wzyq8zoblZiMPMhXJs19fVXS9gjksMYbbm8Xc5AI5Shfu9On5ONFf/q0OrP6D76nhelhNdQ+S550XHt5zD5nKsd/OYfma38swSQHciP56/BI9XeqIjU+lRu8zlQen+mMLT72b3mtvyGE6BOQNvdrzNnGBBaF0QHXQVlKgAKwx8tGL2zFzZBSjEeDQfLNPs0Wd36FK3zlWPmk2i4COr4OjF3z1K8Kifeyz4OHrfIw/pjKIowwkRyPsXrJi771FFP++1o38iV+Zf/rgg/55yirEAN1goOnXJtYqXI2KRbCtYsmBi5K6vHwRIUIqFEjogIcEVxduCmck5EAK9QJYqrHxUWgILIWyGbAwKAcqTXhQMDqOvbdpOOg6KPANrqe1EparKPUFPlQWmjQZlFqzJXR6GgZGgwhLOG1DDCxFlCmOOatK6NGlBoKGnF6o9lYWLKwuHTaPdSt9BawgdEYOSwUBftHSJ84MoinrLpemRKG5NQyMmIuFL6C1Hyxb3LV8Spqk3Xa/VJjjwQEuIwsFlw1gK/Go5bXfqO/YW1Kqu3YJ5/WvPOiOTUG82Tlu/DpJ83/Z3kGHVaWvGpyenx0Z14PdGUU9f7+DihQTqwK7RhC04h+iUElEpUpUIlLKWjEMQhCitcxZIBb4uRX5YkoieUFmzuePQodpDTanEodpkpa8XtOzvHbAAcaMpJKRZ5rL0rS1vDi+e11sqiSDI2tkzr+0vKb8WdzJHQKwRfYmh9oSnVagJpTScGUGZg9wioKe01lp6WgeesfaJF946ddWqtnWPPvvSORect/LMM7ZN1HyilYODZ50UDgPYnrC8+Io2AgIEAL0APkADoAMgAHLqes0cgATwZ55kAmgBOACRASOwBxogIkAHNQOpBe3YQ0JwSinPF6zAIeQGkja4lDQKRLCCUWFsUl/Lwcpgo0Gf/cwtv37s0ft+9ONPffLGTdds+u2jjy7tG7r51o3f+/5DoM/621uu/3++cZ+Xprd/+rKvPfgkx2Y6T4K+Sn+lbJqNrDO15cChr93/2Jduvry3v++u7939+c/f8Sc3ffLJX//8+hs33XTdJ+66/5FqWDYyaCfTXqlEhgFAlgLyAYWnUJd8XxBoX6d5lmcWUQrZjTv1SPlfv/yVhSHM8+Dv7n1q87OvYrk0PTkBPorQQyKbtWuHTV3mHdtKBksCGXoHBGEEOL+nNDw0yE5hJVA9paw+aRp1IGWdAyGMMSg4iEpZHgPSzIsJAQR2p6V3edY/4qT5yD4g9r7GIXfbd1HOLPgQPAu139UnJ+a4LIEEQUEDBV18cTQs4OOXZ58W7ibsCQBiEH8QtaEPin2whAt+h4kZ/aU5uQiF244BuxTAWASYeAYKY5dJT0gppRRSsSOyEoQitMAIKJQfKD+0KIVQvhYExpmUmABRSYmeCqsVFQW2A3k65VptJqsAHUiKYwhKURQJz3dpagSDsJZcW/osJGAY+b1RdQgRTRA76KRZHXqqcPKw7usFJ7DS6/fNSxNyQlkGKSQoBE96/T3RoqEMhEnzMIpgwbzxQ72mnRni3JcQ9ZSXLuCe0LhcVkuBR0poGpuANLECqv1V7C0RW1EKoNkCADnYv2Llyk6t3vZg/qIF1VJlz+EdiTPzVywZbU7F7faiBYtW9A8t7h1UgX8grk1mnY5LwRfggBx4UmmptdQyDNLcILYFk2BweS4U+9qLopJUfpIknq/DSk+n0zlYmwwCz+uteNUoNmnglct+iExpkpkkcVk6GteZUunryPd8goPjIz7KVpY4ypGsEMoXXkUiAGmbIrLHmBHmjgkcS+UHXlAuCa2IrAWLSr+xY9vhifHcuEdffkmWQwh05EshOQZIAfa04afPPb+t2TBRRUXlQAW+0p00zZtJZDWbPGNjBYlSFFUjpS0ZUhT5ntdqT5okdR2BDF4JS4EuYSi1NxZ3OmlH5JlAtkyMMggCX+tSoLMsq7faaWIlCIUCgdGzsk9adB4GMuZzFp50840bv3v/Yw/94te3Xrdp08brH3z855fJC++4/fr77tvcjJd/6S8/c+99D0QL59925SU/feKlw76/uznlwijsjwxnWsCuqeb9j75+4/lnVMl79MGHP/3Za84/75If3L351ttvuumGTzzw8LO+rqROSITEZM4kIsA0BQecGVd8u2Lg245RQvtKF0EKLXBABGsk9DVBB3DFaef86pWth9oNHKz2Bf5p84eqytNBCUKPy0FU8SuRrIIZb7dVGM6r9KxZvfoOHe237u1Gc9fI4SZ4jRaBFpQYCH1kcklckA0icfGZLQmBWDIWL3CeRTRHf4l+ZH9E9i/5Cv9w2Sx6oKO1qd4FxDDCbNBjjkukqM8tYmBH8nhmnZVzOziuR55zrFkY1nWjfLhv0R8NlCns6MxKBHbduqaj6cxnW1NBPFNk4AKAQBAoUDIDM4EUQklGsNYyOKUDqTyQsrjnjgEJJprNEEg7g8hCMOQ5ECJ6LNjkBMIrlXvSNE6ThlYWHBn0WXhKhQhKoAqikq302UqHtFh2xql961bmGsf3jdemmn6SRj2VKQYgYInAAIiiFEHgT09PZ3sOlj0vinyvXDaiZpHZl9ATidBvJJ2J2oTWWoZ+wWhC1tXHJ6Ac+CWdscvzHIj8gf4lS5bkneTA4ZH+1csrlcqhsbHUmfmLF7WzpD0x3rNgQV/Yu3J46fqlpbe2T+/eviuNFPgaIg9yw9YKFi61VmVB4EspGYHIAkqJLKXSWkutMmPiLAcQpSiIeqroCRZM7GrtRk9PT29/32D/gGCwiXG5UUoR2SDyo3IYeLICgtK0EkaJM8PtVsospewJy72+ZgbDICQEErKOTTppnueshBeFGHoGbLPZjKIokLpeb65YsbLWar35zk6IrZJeymJXbfInT8aDntdIkj2TTROFHZaQOyMJfQ+YLaQut2BIhh5J0tpXKMmmuckJPOmky1KX5mS1QgEZgxayHOgwNO26Mzkai0pZUBYQLAgmQod5Ds4CGyEDhdIXIKXotNthJeytVDtxc+uWd1qTzc9/9vL773/qxz/+6aYbrr7hhk1PP/WbgWDDVVdc+b177+0tVW77zKZ/+MY9m67a9Gc3bPjaL14aBHOoPhEMDintx624Wul/5q3tPSr85KWrE86/+Y177rjj1quuvO7uH/z01ttuTNJzH33iBW2c9IOcM+mJIAhabfB9n5Ey4wghJWu0sL40zMZYACMRrKKRGgwMQeqgb0E4ODg4OTWVNabLA8vXrTktQkUCY7Y5WiB2bdty9sV9u0tCTIqwAAAAIABJREFUelpHA72iv+/k4V6vnoWePshy29Q0p9Y2WwASlEbtA1kgBygEA7BDAMEkWHBRe/iRfWQfNnuvo5qPQJx3yxibgxDfA8IvsmmKDsWxsOfDbn80UGaOJkvXocbdgCIDAhMRokDBzMACiFkCMDARIDFZ51wh+gLOETCiACEKXhAi4twAsQu0LMCQEMDMROzc+nPPbyadqYM7rDWQ5+icQO3pIAbNiZluxM4L23lOeZ7lKRQMpwjWNa0EN9gjpUR2bA048hF7wiATPBbHPDHR6utTYSiUJKGYCZwF9AM/Ukpl7RRqjSRJor6IfAVMTCg9P+rtRwATp2m7JUslLyx7KLVjmxO009bYZDCvr+QHsaWo2rdowSKbmf1btg6dstovl/fu3x9qtWDp4trYeH3f3vKqlaHnHTp4cO38xVP10htvvzXVaoTlQeEJEFxkmQFhllgnhNcDoAUqCUDMoLVk30MpLGAnyy0gS8FSsBIGyOY5kQOkhYsWVSqlZrPeaTTjViwZlVIgVVCK+mxVsJlwjvK8v78XBDazhBg9IZ3ynNRgDecGtBJVTwYqRC8i6fteT4/UGnLQ7aCnp6SRAfr7M4I3d0BZllsp5ypqep4ql7Zn7ZcPHkREDqtMXMpBk1tS9U49aVFPCWwdSilIgtgjJ7ESYhSAc0AEkQc+gqGl1oKQIEUhMgWeAAWQwbzcQWjA9yAVYAEUQAQwAOAAWgA5gATwAHwAH0AC5AAGQAN8/eevvrNvp/i1vOXmi+++99c/feTBL91+87lrznr8qVevvfqSWzfdcv999/QHlT+/49b7f/Lgpf7ld1y54Rs/+qVDyFotCYF1cnSiNbxw6a92vZP0+J86d9Wuffu/e9fmr3zxposvOP/r//D9L3/p8wgbXntl+76RcamYFbRa7d8+89z+g4eTuB3HEQlIgSkKxykbzxMBGCoPyDU4+68P3f0fvnDbj+66F2R0+52f+tb3Hzw8zfu37fr6njHBAozJKQcPwPc0CC+jCklGqvvsLehbvO7k8/UZq0ulBavWbt4zKshmJgWF4JCZtdbOEJErnl4sEAwAgyMEweDgiEvmowqmj+xYK8qg/mjGxbHc1u8CamZTemdazUSI6F0avddlAKCjHp85eTbH24dPzfuPCcrMelwK38zcbcWqd7lpzEAERAwOgZgdOAfILATgjHuHi3IAR0QsnBDopJzxDvI7u3bnQBwnICRoDxwxSwIpvdAJnVlOjDGMwAjEAIgMbHNwDIlKTRK7NMsTTtqQpe+8/Vbbt6pSnhobBeQg8mObkwBA5uJwQnpSCQKwDqRkACqqYYrwmWNBDhwRFNStrFAoKYEYsgxQCcQ8z5G5b3AgcthOk8nJyXnLlkVRtGf/fq9aXn7GusnD46OHDkTLlkshJsbGh/v6G1n81OtbRlqNXIhQaQQGckgsQZJjB+SICTDPDCMRU+4seL4UmBNrJsdEzISAUhC4PM8ROYqiJYsXlcOoNjHZbjazJM3ihKxjRhIyioKs03Q2zdNECphslAh4cnKSHUnAnlK5v1RxSeKSzPN9LpfjNDGdFuTGV1AqBcrHPE+ttVqHg30LxibqU63Mq84DwwIUC08HpVxTknXyUgmZUKgsTSthwLmJO51OswNOJ1NTnY4RIFNfOmATRLGSrbie57nvhZ7WWZJmWSY9XwhIk44BJ6sl3w+yqXbWiGXuhNRG61wKRJZMIRh0rpMkmTFMElkoggAlGGLmnr5qgrzxurNef2vJ28+//NvfuFtuueK+Hz3y7W/+8G+//LmWOe3+zb/6y89eef31Nz704M+vv/Haz958w4/v33zdxmvu/ORV39z8cNO4BmVRuWSJR6abYbX04p7dWvC1N2/8xQMvfP8nT/7JjZdccM7Z37n7J3f+6Z+ojNO4PdJqEAsiuX3bjpxlqRT6vh9pQBbsaOf+/YdHDyGilsIix+zenqo9e2hXZe2KV5959atf/f7f/NXnv/qNH2RRmkufEIUEReg0IKLNnImN9kokFPmeCqsmp7H9B/uj6mBYWTE0NDFvbOfYFqECJRVZdrlxmUElgIlQCCBgcDOFuu6EbL8fZct8ZB9+Q6CiwHeWNK274ShkT0XTWWa1o9p0OyI4uhqLuzGrfy9eTzUHCb5L5RR0VRsLY+D3WUn4zzeak7VdnAuhEEXy7xH/jEDoVkKgEAKk6GIXLsZGtzZq1gEjhXBaA5mif2RmZ4UmoSRJFExkLFsH7KDIn2LIalNQrUgdOBUBN5k1yUB5JZdZqJal56PUKghNWIY8A2uYLBBDoKHiQ0nn2qWeg7IPwsDE2MjLHdAaDMCSxRAINgKrITc1pAQGwbGLUy+zoe8lA1UMtDEmyzJgVhJdliaNVtTboyMtoxA8Zcgak7siyubpnsF+kohCDiwbro8cnhgb86JgaOnwO3t3A9pFp6wGxFp9GoJgeOGiHW++JbWqzh8czTtTybSd18NpxwHLnLQhACQEFBJ9X4cRKOUBqmpPkrm03WZ2gixbp4TwSj7HiWObZakxBojLYTDQ219S3uGDIwCglBaRUtpPkoTJRp6OPFUSLHzVyF1U8gVal+cVTwongKiiIfTBOeacfE8kNkeTacsA7KPyUSqQKFmyVELVp6bZEiPUpiexVJGejLPpUq8AyYlro3Z5agSjCkLn+akSI+TyqXGYtK2Jmp86IJjOU3Iw1Nvb19fXyOI4S4PAC4OgXWt3mi0UVmvJeUY+KlMKdMmf4nQ6aSepASIF5CFIcM7NHxgUjvPYpHHmHDsCYOlpLa3r6es1aT4xOXbgabzqorXTYwO79mwR0v35Z6/e/PAzdz/05G3XX5KkJ33tngduvGHTxRs3PvnEU5/42AV/cctNd93/4IUXXfKVT19z1wPPvENuvFGPyv1x0s4MJr7+7dadpMPrNp332C9efvTh566+6oJabfJH9z1w7cZNWvLm3zw53k7CoJxZE1Y8H2ySJFkMkVONPN+zdff05AQQtLKEkcKS58ny97999//xn/930YQXnnjlru9t/usv3XZoIo76IwCQFshlAsFXWgjhLEiEPAcpIQwhDEAQ+AhCQgJDCs7c8+ZWTlMGDSyZCESXXBsQugQ37ApdK7DFwz0nS6aoIjnubXSkfvsj+yDZiW7K7+G7H+ndO/iA09Ugn/h3HzusxWyF1lzZwdl/jwYrCCDmJMTP3WWGa/jovJpj2n+oH5/36ZVB6irr/cGtK4otilLM7pojCpOF30KIIpWG5zQCAUKiRAEChAAZeNYAuwyKCm9nBJMUKFEiEJvcmgxcoRZYxKisB8JTXqp9G/WgzNlCkhtQYdTbMzA0GPb15GlUA+4gYpIEzrJAHBz0ly6KFi/xotCCz9Xexr79SegZIEhzmDe/f8GQpyRLEQwvnOq0ebLuTAZZatrtHucGe6tNzwvLIeYG4g7YXCKjJZekkly1MkALyFqbttsdmznnoBxVFy+Keiqitzcql2Qrbh04AL5asnLFW6+/BoG/5vxz+ubPOzB6cMGCBUvmL9zy0itC6ZNPXrVz3x6WYmjxoqTV6on6fKlMoy4y65gJkZUMS6GulHzfL5dDjVBnOUlEDoxjaUky+kKKQHsoBBlwtuR78wcGlixevH37Vs/zzj3/vHKPnqrnJDDLMhRMmekvlxf0etUK1FpQrXRvoMtBOGCGcgS93QkOygAE0AGgmahNCCABLEAC4AFYAAJIAZ7dlTz7+pugRbVaIbJhEPZVq51GkwR7XtRqJzKM2AuhJDPtUwa58tgDcISAkBvpR0F1IE3CJrY6oaIwbLVtW8YouE/rCpEvhHaC0VFQsiGmgOhML3CP72lfJAI6qFIlc42xBiecY5Qicp4HZJ3vkxIDa1a/ueudemPytusufue1vtefeznS4pPXfOyhR5/96RPPX3/p+Z3M/Ozhh2+/+Zo8Tp787dPVvus/+akbHnzwkYvOuuiOz37sf/zshXqWNFs19MKyCoSVMbunXt+iDV117TmP/eOzP7r7gdvu3PTcC6/98sHNN//pTaPt0x9/8XUgv2OMMAZdApWgP4J5Pb3cjvNGG1KnVUDW+IE3NNB3/bWf3nzvXf/wd1/90he+tKh3+Lvfuev/+r936jAwzkqJgOTylE2O5ICkI0alwYHPwgcIPFQe2MDl5M4+47zhRcs1kXUAQEpKAiW0l9q0eFC7zGkFN8P7fLd+hGY+sg+0Ib3PAdrN7Z0DfujIlmOQXNdnc0xxH8/wnCAACwbC4u+/OxI/dYR1l3kOuUuXJ+YIJoAZUqZ/1gWaGxvC2cPNHHR2E8xWw3YDLgCIgFQIKhERABX+lW6MyREogUIIKYu9CqpWUIpMBgDAFtFXRQ6NdYLBmFwwCQTH7CjPs1hJTwURIFCeUBqDScHkBfxB5EiC6zQYKVDaBqXUEhsLTJW+CrJpTI7E7RqxNa0OJQbaSQzEWkKSmEY7339Y+R7GOXY6zckpyDNwFnzPE14ASsaOkVgKvxzENQZkkHJ6cgLKZT2/PygH1uaN/YdgfAqypDNVgyyHxIv7enp6ewKlJuv11ugESp+sA6W8UkjAJV9rJfaOjUDJX7xw1a4duwDcmrPO0Ert2Lp98fLFgedve+MtLwjWrFr95huve5XykuXLp5p1a6nkB53atIpzl2TWEEgNHuSamYxn9YolSw+PHKrXmwwit8YPfJtaTjKl2ZOgBMSduNOYPmX1mv6+6isvvVit9p177vlTtelnn98pPI1SNONOqxNLHXpSV6VXCnzhSwM2zjObZpBYiYKERE8FpaBcKvmWcpMk1DFkISVwIFhoPyz3VEqlkpRI1rGxrU7bAMbOtskoEUhUQNCpJ4pUCSMgkbetr8utjhlYOCAUteotbVxFluKsVdJeRUOnFfP49IYLTtk/VjateNqpWivN4pb2pUZ0E5Ofv+yKs4ZKgYQxgG+/smNcGRammib/5w1Xr6qAp2B3At96aeuWViykBE/ZNCMHvmAT50IhMKLL263Yr1THO/E//OBXf3Hzlb1e8Mzjj/ZU/cs/ceHDjzz1y2e23PCxU39W7/zgez/767/4JHvq2z/+8e23fO6iSy/77bPPrfPO/ey15/2Pb/00RZCCeLpd6Z9XM1YEwfMvvTJPnXfttRfe9a37v/2dH/3NnZ/1ctp83wM33LJppNbYvv2wrwKTZyVP9Vd7DIAf+dxoqoT6ZYCsOkkcaj21b2TH869++Y4vfuN/fvV//f1Xv/w3/+XOL/75/fffnyapFJocGARLqpBUUgwoBBBakyNK0iKxhJ4ndInITtbr55w+KAFAIoBExNwaPwi6XJezbw8W0E2SQXEUVwYf9TaYUS85svK4T8uZesbf9f7591MV9fuCesdesSOzwLFHmdtSzOF9mZup8Z6u/5xgSndE8LFbj7AHHdfh+7rFczW/jrhFTtDzsZ0fV2c3Z3m2xEQcez5FBW7BkDozaAUIlsyM3ZE9ZzaUQjK7LvkdMyB1ExhwJtWsKNNmV4AYrT1mV9SrFCkzKAslQeLjTrh4rN71uv1LnpSja26O6+0993w8j9G7bjrGJJQXdFl3AY6CMjhDnTfnTAFgbtDnqFOfWT6KjQoAUXSLoo/Za+6B5oSx8Kigkig24czfmXSY2X0LMOSYsCjdpyJdlxmL0BIDgiAAR2StcSYXDAgOiZhdkaFChNY5myUuy9hm6CwAgRBFErFJM5vEJo1dnrrcsDUADBLyPMtNYkycxe2k2bCtJsQpWAeUA1mw5BKTt+K01konakmtBnnWJf9jcMa02nF7bKoxMtquTeaTk9BuA1mQwiLGSVafqHWajbTZ4sO1fHIKrAFGyAx0kjTPPd9vNVvNg6NwaFyxVDmZdjtut+YtXWzybPurr0ml+qt9h3buhnp9/tq1jVbzwO7dfQMDnpSvvfiSSbPVq1dv3b6t2tcXlqLpWi2eblCa580OxXk81RQoOPC9vnLUX02yGJhWr1wxsnf/+IHRdquNjOQACASBS7NAcdputKanJOBJK5b7vvf2lre11utPP+ONN988fHjcMXWSzFibpRkxWmKwThpDzhiXG2upiMcYECCYMSOXOptZmxuTmTyzGRmDBAKFBE0MSZI2m61mo5WmadzuTE1P5cY6wY5J+b5zaHMDeU65tbnJc0MsUUgv0ABskha1O4t7+od6+ydGD5+6etXK4fkje3dJlp7sWbm4tGXbnoZlk5vhSjS1b4+w7pzhJbeduWS1giUCKgJ4cOCNHbvzuHX7xssu79NrAaIm9/i4p5Hsn5pu51lmUmES7dizUhFpSe3ONJFttlpKB3EnM7nbsWX3pRecNVDte/KJJ05Zs35o3qKtb2+t1fLLLl0/dmD6+Ve2X3zl2ZlTjz/6+Nkbzoz6+x76xc9XLFxx/tnrX3zhJWEpksGBnTtFVGJr80593/atAYdXXfHxZ196ZcuOXddf/glrzMs79mzduTttkxC+F/pZqz4YyY+ffcqvXnpncrLeX+n5/Oduue6qMy84d8P6088Asq+9/GKrWb/+uhteeOHll1999ayzzx5evIgB+/sHBweHBhfMH1owtGh4eMniZUuXLVu2dOmpp52yfOWylStXLF6xbMnJJ60664xVG85as27duhUrS7733AsvEWCaO+37xhhncxQCgEWRp198qHSnk99JW3s89+sJX2X/RmDlBKd/Yhbg3xPb7++n+fs2/J11ve9y9BNSCJ4AKp2o65myj/d63HePUv4OO0HPJyIDfPfIV0GFd+JML+YifsquK5XL1IXnBXIXRZEtArlu/mfBBdstKAEUcGSeLeIUAMBCOEtFUihzEcEVRW2UONKyO7nPxTHv4zq89yv/T/RzAvuXs/2+55Z/KMOZOwcwkzEjYfbV0P1YO7pinxmYZlFtcZuEQMKCeYjBWrAEiMDMWjNTMVIQgK0DysC6bkdsCx4+ACZrwIHIDAhBEp0UgEUgn5ERKAdAZkVCgmMk4YFSnmDlGyYLIJI0SFAIxUgOjANmsETOkYNmDu2YMRSIJBk4B2KQEpQEYqi1IImh5GWViuzEmOQMQhKLLDcs4MB4Vu2vJy2YrkMrsx0gm4PNg/6qm27u27UTkAfnLRrbuQ/GJheuWZNlaWPfnmjJEjb5lqdfEKXSouHh3Xv2kZDKD0wniUcmMbVEbBCCKBRCGZvJsr905ZLDtUmg9KQlSycO7q+NTCStWCmFIHyWaNFTMm120KPekh8DLFi0kJB37N6hAn/12rVbd+1otpvLli4fGlpIBFprQgCXe9Jqch6RRmApSDKDEAwBeUop1sogZ4qFEApBMtg0QUsiZ+FQCym0QikAyDlnjDHGEC5hzxup17cd2JdM1ZSfM0OGBMIZJqdAICop0k7LS7E/8vN2p7fXrlvc+9ZL6cjo/g2fWP+0Tx0y2w+8c/bpF4SUcgfRuVuvPPep1vTIVOuMZct7AdJG4kUhOVgtYb3y3uw01i4MvdQFWmroMJQ3nbXU9fU1PV8EnuLcdvK4brM8KfXpA7XxF9/ZlzuZ1kYr/YM2NfUsv3fzkzdff8knLrvme1/71l/95RcvPfOcBx78ddqyl19xwY9+8quHHnp20w0XYk7f++4PP/npGz953bWbf3TfHbd/4b/cfPP/uuveuCLKSwYFc31sckFfaWqq9uzzz1zysdW3fuHz3//29zdvfvAzt2za8stft+qH2fSj0p16Xg2jLElygE6jLrSYihsP/ebXkfLSuO2F3kWXXVTrTD6/9S0RRH/1n/7j1//+69/97nfPvuiC5aeu8bXPjOCstZaMcWSYHaPLTSoD9IRkRBFE5Xl9qr9fMkGclnqqwtNZ2hG+7xBUGAgGk+dIjNDlcGdiACG4qGk6EtyfrVMUx71w//14Vj7ghu9W5PvPC2e8n3v6wc6POcqOQavFzEVQkLoKxC7RCzKzAwVz0CEDddlfgWcZ9GeuEgJ0k4SP8gkBA4DUupgziWim2AWYkXEGBM00p2Ov+YfksfrgQZl/0vgIXzMisuAuYp3JFizgJ5GFLmkicTEqEAGRiOSM71oCWkAAAldkJhbIhwUwd+vyrVYIQJaEQwczQg0IBIhsidlCQedIwpFgieADoQB26IjJIipWwIq8QOTkkG0XnzmrbIqE4CknCixGwKwAPYdxRkCZlqHKncydRVZahqCsVG12mgDqbYiNQJ3V6uDJ+UuHowWDe9/eAkotO/nkg+/sdtPTi1evYYapPXuqJ6+Iomh01y7Q/vCChfV6PW23FyxfNjU+kU9MY2I9yxLQKDRJBkqoUnjKqWsacbNdG12+ZLFMG5N794MR0pGn/TSzUkog9qVggBuuvnbxMnhj2/S2Pbv2HjioQn/JiuXj07XxqdrqVavyJH/jjTeAmIg8L1AaAuk8sB4IiSAUghQolWL0UWvpsZZOYi4JBCpgSdBb7hXEaBgt5RK10zJQUkrhgac8P9CoghwYGy2FvifYZ0AGAuuYbEEcTjmC1ZRHnh7uLx9u1NLaxLLKiiXD8/Yc2Bvb9fPnDewba3aSeGI0Pnnp0kO7xkXuDu9prlm+fP+hl4xxAKAqoQMQAuZJOP+009q20W42RU/PoWYzErINsGsy3bLlrXFLGbPPHAglWEklgiZvuGBDp8Pb9oyqvvk2zU3GIiwfmG7c/cBjn7/x8osv3/jNb37zzju+cMunb/ze3ff4kbr1jivvvfvRH9/z6J/fujHP7bf/4Vtf/o//4Yt/dsd3vv6tz3/m9v/tzj/9b/fdHzebEsKyryanpvvCcjNuGAEYwJ/e+fmHv/t9KeDaa67YN96ojdrDE62e3v5O/VB50YKUwAu8dKIWlSt7Du5VufOVbLebI4f333LHLYPzBp578im29Lf/+T/9f3/3948/+UQQRXmSI4C0zM4554isExaAEawnUACAEJV58xesOKm6eHGoFUzWTrryUi+KXL0dlIJ0qg5Co0BmEkCF4sxs0WjB6nusA/x3OfA/sn9j+1cJYH1o7m8xdZzIwcAACAgkuEAjjCAcmZnYU1fvCZmZnZaKGQRQkUgB0L1K3XAqHHlGCtUDZ3Io9NRwNpmVoQuM+Pin6f1pP/8x2AcvwNT1vuCcTQIRZxyzDMDd5YLkt4sqBWCxl5jJ8JnR+IDCLSe6BVnFWRVCk4CEAkACipkdqUvAiAioAAqpbHTCFa5wJVAjKiGV1iAEFT2jQkBFTM6QZgcEBERsiHOwOeeW8pwy53JiAwJBCkBBDI5JCIkMCpAEgtZKedqKPLFAFEShAorjDljngVIkHaLRGAz2xlkCWcaOQInq/MG+RUN7Du4FXw+vPGlqejobGR086aSoVNq/Y3uwcGje0PyD27cDwKq1p46MjMSTEyvWrq3XppLDk9DMNEjNwvN9UCpPk7Cvd+XqlS5vH9ixddWCwRWDAwff2d6ZnJSMQMCorCMpJTMhMKAbGhqcmEhffv21Hbt29c0fXLJ82dj4+KGRkXWnrQt8f2x0tNloCODc5tZa62i6k9cT24rddGJqiZnqZFNtM9029XY2NtUem2qOTTXGG+3x6Xat1jpcb+wdO7xvbHT/+MT+6dr+qakdE4ffHjmw5eCB7aOj7xw4+Pbug1v3Hth1cGJsqpOhr3XgWm2VZ5INshVsPYEhQMAk2GiwlVA2G+NZqzl80inTWdxJ2iql1ctX7dp5QDgQaXrmmatf3XaAmDpThz9+8anPbd2bpPGKZcNVBQQwbeCXOyanFZYGerZu27p4xXLn++h77xD84JkXXjpwcMpxK3Pt2DWTbDJujzfrk1PNpGkvO2dd53CnM9nUqMkiAetqaaQ2Nnp48oqrzoozfuWl584/fe2aNcvv+/UvhKzefNmGN59569Vt+zbdeLFN8ec/e+DUVWvWnbL2nh/eveGcM09dvnxsz648b2U2C0pVk6a9Pn7842f/92/8bHDeokvOPmOgLF/Yvu/ST1y4a8ehqcmaZdfXE8m0cdXHz3n4je2N3ChiSPKTlizZcNaZ69afMj4++tsnHr/w7HMXL1j05G+emBgf/8sv3rFr5/7Dhw9rRMXgFUJoCCCFVNKXUmU2IPQsapS+H/jaU0SY5SJzyPr5F1/OjdV+YHMjA5+IgVgAi8L9zYhceFaR54SQ3i1GflxmzEcBpt9n8/dtJ+r/xIGDE6w/GrnO3XBcU5rjr3vPB/k3CTB1288KF+KcUyjI0BwSs7PAThAhO6mkRFboNDsFJMEqdorJR9JAmq1iJ8lJctIZ4YxiJ6lo4ySwApJFxitbiSCRJbIEAnCCHQJJcew1wLmz/Hu2D2WAiU4gJPGHsjm4h5mLVyHOApoZEFr4rhm7onUADorC/C4EFsSOAUFgF+UAAEpgh10gIxkRhHIoQSAAARh2zrETQgNAmlpABK1AIDAyECEjInV9hwI8DTpgKYAcuBwkA0ogBiawBJ6AIAShjLFgyHcYgE4ZnaHEWZAMYUS+tpbQ92XuEDFDyhBAi47NlO9ZKYC5d948v1LaNXoA8mThurPGxw6bsbGhNWuYefeuHWr+YN/gwN433oTAnzc8fOjwGAkcWLasPlFrHzgEtpueZiQmNmOU/kDf0PCiOGmP7NqyYe3Jl244M++0Vvz/7L1plGRXdS747XPOHWLMOSuz5qx51FAFEmgCgZiEJDSBEIPABhqen+3uXsuv3+rVP/p1/+rl9/zsdtusxmBjkNAMEhJCEkgghOZ5qCrVXJVDVc5DZEx3OGfv/nEjs1JSySBWgzHWXrGqIiIj7hjn3H2//e3vy58zO9945uUD0400Ydfe1RUlcZomiSS5gv+zX/4ilbRZq/euX9PT23didKxWq23etMmm6YG9+zxtirlcs9nUvgfNVqxSGmKYmEiUgmjAwUEchMAK8LQRRcyWXarFduaDtJnEqThlEm1irVyQo8C3jsVx6Ju8F0SN2MVpsZAvhuGKFf2eBiiJXBxxKk48S+LYLwSMfsvjAAAgAElEQVSkuK0931kqpHO151586cDUjHPu2NHh9+xaXyoUK7OzJydnigW05f25WnWmHtWAnmW9h48duvspNbN58/ZlpSf2Hb9v74G2NSsuu3DHP/3ikVvkyRW9ywv59uf3HzowX20aj73QWaWgE2YbqDSVwJnBk1Pe0weu/PjOf/ynhyq16WK5s2rjibm5nv6VRyYn/vkHj//Z1effc1/tG9/9xvWf+cy1n7zqphtv6yX/K1+94r994657fvj49Ved75P+3k23/MV/+tqVV3/inh/c/qkrrv6Lz37ytnvvOTg+N96MRHmirQFUkLvp9jv/w2WXdPeseubpl6wqfuZTH/n7r98ageYmRzetW94AkiTRnolmqipNu5d1r1iz0gTqmoFP3vjtf7rz5lu/8qUvv+/8C372yM/vvNN84YbPTE7NJc4aUoEoTSRaQStjjAeVI8r7BhpMYA+kEfpoK6BewY/u/QVZ+F7YnJ2FMgBQryPwM5C95VJPmVMkWn4xb3Fhe0vy6TvxBxD/rs4mC2W31CSkSYmmbPLlSMQJW7ZOrGPn4BxEonoNyEoQboHI64AF+nDrIqgWYZiwrT1r28lGnCHKGrEFOjMufIMY2x8e0mla3uFvTiJPH78rVKrFhjnV+N1qZFigAGMRKBNAkSIligRaWrwqd6pPSpHSmrRyoiUjZJGGImop1yjyffICIU1iiYWtzejASnvi+abYLlDWNRA3kTSFybEIaQpClS/qQhGKXNyUpOmnqVbkiKz2JMir9nbd0U6B5+LENeqoVrVNXZykcYwgQFcn2tvhG1TnMTOXTDeC2ILE6QBtZfR2mK4OK2k0MwVPEYOhosAgF6CrUOzrRpROM3ukUQinohriRvfusyIlKXHX9i0BzNDhwyjn23u7R0eG4XlrN21yzLU4GRhYE83Xhl57CakyUE4BOZ/zASvkisX+3l6l1OD+g2cObLhox86OxKZx0l3Mr+3pAwVP7js0m/CyVcuPDw0mzIGn893tfb3L2jrKcWKd4gOHj1QqldWrVxeDwoG9e3ra2nbs2NHW1tZIYj8MIhcnUdyVb/MV+T75OYQ5GAIBSuBiGCDQCL3WWddAB+BV06LSlFdzgj3j7tljQ/tnZqtEXiFga5HEjWbVVitr+3rfvXtzqS2Yq4MCKI3UwTJChbLACMSAFXIFMJBU8ejTe2PJKaPmm/XJClZtWD23dz7Nh8encfbWdS++8nKc6Jf2TJy5dfPUyLGnjx+fimN53/n37H11mLyh4RMfkh1rV29+5vCwjFTFL1biSBXzui1vrQaTpnzims43MbGTXL3ebLONV47OfPqGS+665/6hqcFEh53dK8cnKj0dq+upvvXh/R/+0Pt+mlR+8NMHr7ry09deecW9D//Mtl36P/zHq2698eHbv//4tdecb3L+//l//c3//D/96bnn7JoaOXbhru3//QvX/PLQ9P9+189tEDSr1SYgxsTifvHLR8464/Ptbcvuu+fBqy679M/+9NNf//btHpdd6lhQDAonGlPr1qxd1d/33AvPPLvneUtux+bNX/rSVx68+947b7z5a1/7Wi7wf3jvjyZGJ8JiiXMeoMgyEiupZcsiQtCWnXgUKZfYGM6WjLdt+cqdm7Z0dvTsf2Wfa8ZCBMc6DJRSrlCAS1mECK0GUwG0gvCb21dbJac3kxP/4CbfP4z4DZt+/384m6cYJL//QVkPChFEiIWZxVqSNK5Ngi2sAzNsCufgLFig1KI/9uvJ7xmaqYlIMoRPaQDRaAVaQRkYzxijPKOVB21S8oU8KMpMDGnJpfMPLBZQmbfmXJ8+MuJ0Cx7JKnwZrQlMxEotQh1vddB+hWO2tEpIb/wWkVJKFqc5EaWUUkq0VsoIKRERx8LSKkaSJqW158N4IAjDxXGrYqWNKK39UOWKJsgb4zsb2Ugx18RaKBgF5fthW0cqoNRYgVhWzpEiaKW8ICi1+e0dKbg+C6nVG0liSDnR4mnktJ8veh1dEgS2XrOO5cRE6iychR/me/vLA6uptysxVJ2fSY4PozHumjFB+7lCaWV/sLzX5byZyQlMTSBJUwtLjEChoNtX96/bui6er1tYX8z4+CTqla4dW8tdHbP16q4Lz0trzVcfe0IVw86+vqnJCTSbG855txAU0VmbN85MTY9PTFAub9hK6rRndC7wu9vberra2tqM0i8/9eTKZcs2rllTEupkLrW3s/GrSp+7a+uh8an52Wq5qyM6cghxTLl8vlyIIaNTMwCGTozUGvNbt++QlA+8tr9UDHe/a2e9Xn/u5YOOTDOO6vWqpEng2NO6kAtyxZwfBgCnccKJi2sNLfC08j0TGioE3rK20pq897ldZwSQqkNjJp45dnx8eJQLhZ7+FbVGM++XqDIXN6rdXW27dmwqGHnkwYd1sUM8E9ukXq83mk3tUHDGMIJ8DkbCUi5JkmKuPDw8rNs761Gc8/wDR0bWD6wYGTrqldoPnZj4yBm9Rw/ok2kyOHriXTvO1trYto6hKDE+Vm/dcmzwpEvSiRONgYGNTw9NodiRer5pbzMqzQvPT9UkVZ6SBFBGMzy/0Fbs6xubn2wc2Ku9rVdf9bFbbr17LpZqrda1fKUTVGw83kiOjDWvueqK795y2733/vATV34ipYvvuf8+cZdfdfUHf/qTJ+/4/qMfu/SimcrcTbfc8T9+6fo1CmWAYpy5sWtlf9++0fHOYtEBzTRp7+yamBo2CqPjM1PTlR/f+6NLPvT+Gz7zqTtuuV0xdxCUUqGX62svX3PFuw4fPXRydhIK+w8fC+SJyz9x1Y/uvPMv//Ivv/Znf/KFL9xw+513urmpWERIM0Sx6JSNExJFyms6Rk477cAx0oTDwKxc2dfb/7f/z9cnp2cdKetsUCgm1rn6PHI+0KLJLNHk5lZHaTbif1U79R8KPfHfdPDvW+qwNA/OrikLYnG/XshbPF9cciaDJOpNunyn951upRfgjMegJCPHsBJWEOWcSxNO0iRqIo5hm0jrkBTIrpQEsAIpLc7G2ToWqB8Ly29tFbdkTE/xiUlEIU2RNmwLrSEoD2EZ2iNjYIzWrQQoYxYL4U2J6JI60pIj+29CzElToY8EmYw/0KK3AFjgXS+wU0AkIIEiJdDQWatzdv9MBJfztRJLzrIi8TUCH4EP4yGJobQxhtMUItoYZjZat34Gb+DNAFp5RIpIEykhlT0HiFo6NyqDqFukbBFwKiCnDGuPlRaCSAp2WRMatA/tO+1RGOjQ56x1IrXsmJUSHap8kcJyQl7kWPm+72thx9aSEqOUUzoxnl8sKlDaTANLmiUVC1+pwI9hpb3kl4pJM5ZGEwLJbll9D33L8n3L4piT2PrloueHaaOJRgQAhVzHwJqwp3uiXlee19ZWhuNkfJqZRCkXel5nybQXg85StTLLE7OoxYhjP59zYlEKyt3tpe6y5FSuVDz+2kHMVNrXrUuSeLo6u2LNqnJ75+DwIBOX20vT49OIonXvPnu6Nm/Khc7+3hPj4416vbPYXpmadUnKNtWFXEqSK+c3btlkwa/sfbWzt6erXCpzuioXrM2FeWs9gRVCzj8yOTfWjDr6+qeGTxpQLvQbcRyLkJebq9RmK7Obdu+Yb9aOv3a8kM+dd8GZ41NDI2MnYmHWISlfJS6pN5TW2teFYgFGNdKYjdZevt5IFPm1RpKAYmZo8RWvKhcve/dZZTtfIqfF8/Om6ocvnxhUPR01ToJAey7Ng0taffwj5zUb7uc//7k2JuepvKYQoMQiick5QcpkQXHep6JvCsZLmg0BIo61FieuWZm9dvfAkSNjdb84OTt3/oZllaobt7WJytx7tm+YmEgmUusgy4ud61Ytf/XoUH1+3k5NXHTBtkdfG2kGxuk0CFwIe86Wzds7emqjJ2MXsXFOWS+X84qlJtsYkjLNTdWLUj5nYMvkiYlaEEY5o4qh9SWxbnzsZFytf/oj73v2tf179+3/1MXn9netvfcnDw9s3n7OGauef/rFkaGpj116wfGTtScfffqiTdsCgAMci/GjF/dFlk2j+uGLzn5879Dc7FyR8YGLdv3sxaNztao0q1de9iEY7D5r++C+A7t2b3vg6YPNZjRQzJ2/e/0vnjk0W21Aewp6amJmfGzimmuvPHFy5N77fnT2rl273/uuyvzcut7+Fct6u1cu61vZv35539qVy9esWbl2zeptm7ZsXrt2++aBVb3tu7eue+/us1atWvvXf/v1mXpklWaCaHLOCTtolV0BSdBqp5CFKVkkE25v3bAiG+inaHMZwS2bf1Srxs+nZqUlj6Xtpr/LyCDiNz9OXRFev0mnc1c5zceWLP/tPd52tJTN3/SQ0x7nbApueeBR1nBB2RVwycde9/Kt1ovF47TgkL6I1zGJtDCJU++f7phldjGtdWaE2dbln7DIKF9YbksIYFH65fW7mZU9MwqgcLZ32Y4qEElLV/7U6c2ONYNIGa2UUq1WWRO2xGOEPWEPNgcXSOrFDTTm08pkPD3u5ibQmEVSg4vBdmE3GcwQB2FhVq8/mw7g1z8Wd4wAae2IW0I6zhZokTSR1BFVJaq7pEGcGLDWSoNpkT+aNXUrOlUGkeza2oIrlACZ3olasnKSU0OaBLTwhWw8vsVppwyGffODT/1kWhCInJoW3kwSp2y9S36lr+fK/CpshgRKAGLO8j6wlgwj5jRlgJUfsAADA+s+cEnYtezwCy8kjzyKWtWKkDGtyUjEOUdvSbhZpPe++SicJjtUAmJhxwsWu5INNljnLIsi+IDxAeWcc0kCXjg/pKBISKUiwg4iDC2UPYCsgGkTJE2bFjllpM6lrDJ6CTtbn4cUUpeEWvm+HxmD1IJAnkflUqmnJyyWmqOzNk1ybaVCuexKbVG1Bq/gl4sUeFESJ1NTyfxcsHmNyvnI55A4MKNaq5wYaeZNT1fBC4MUCkrD+Klj+IQonjh0JNfX076sc2RyBEKFNQOI0vrMqLeyB4mdnJwsdbQXQjO4Zx/mk2UbNg0dH+5ZtbxQKI2NToweHywGxXKpo1wqVRqx8vJpEsELurq65ufnJqenBgbWxKk9Pnh8+fL+7v5lrtGo1eNcez5xdni8GjFL6LGiTMGgGUcuicJyW7VadUnqmeDw/oPspG/5qg1rV+/Zty/M6WJ7R2ehI1fqNeKp2rzYJHZxvuC3l8ukZD5qeEGuGBRtbONKlCQJeQqclHLo8dUqzwRAhylQXCdxhdCUi8VcLteAS1IbVeZ6g3xR6es/9u77H3/t5MjI+o2bt2zbYALkPfhADDQTKA3SC62NggKhAFjgyDye3X/s0OBgKjZ2HDG2rB14aGgycTgxi5XL+54dG9RaHz5Y3blt557HHxHDR46PbFqxzdOmkMtPzzfqDr29vUPzc4lLw0LhwjN3Dr746kd3n3vuZy++9cEX9p4c6urqmUqaSWNe5/IpS6ncMzsy+fwr+6++YPd7zjl35PmnmrFVBZVYOzVfYU1Dk7UHnzl0zSev/OHt9/3dP9z5R1+89qpPXPHDe+665MLzvvjlK2+5+aEHfvzMVVe+9/47H/1vf//1P//al2vs/eO9D1pSrQs5AMAxp1ZiwM/l63GUJ5fEeOKFvRrOudQBRKIhnsQBQCSAgpVUwcVudHLmtrvuue6zn73zjjv/4ZvfPPei88/ceQbPR6zUrJEU7CcxOTZhrtjeWSh0Bb6Znxjp7SpuXNs/emLsxptuddCOmDM/0qzMvzCUM2uC1kAGC0S9wVDm1Ez1RrrM7xcO8O871G8VHPuNHAmWWku/LRbn0q7y16E7S7dHFAMZSV0t6PcBSi3wgcWl1oEyY5wgsHETpDwNjxRx6pr1RrPGUZOTOlkrLoa1mTl8lkS4U8WjU9yxpbFwVXv9lr2pJovWJf5N1puZ+AELWIG1tbGNY+ianyuJMqSM0Sa7dooVwLXIbUsW8jrcJlO2XFwy8Dv0MvoV8fZovyQI2LFyTRBEBY60cGLEElIR8kKxhOX9y6/5VPH885p+acPmHfuOHddHjrgkkSXWcadNShbj163kkWiIBqGlvObIgISVc8q6wPNEk1N+JJCoacFBaJQxSZQgm8RFSNhALLNLLBQB3gLDBoC2TLAMR0YISicLMosEEscwHkSbFCYR5wBBJpvBBHFOHJNl22jAOk8op704CCPfV542vhcGQSO1qNSwahk8Ys3GUwBI61QY87U0jtI4IctIUihDoRZO4ATzDXRvZM7V5jmNSBe740ZUHzuJctibL00cH+xdO5DP5/bu34eUO9esi5uptqo716YTGh6ewFQ1v6zspyArSFNmC1/1rFluxc5MTazoXSaEVw4eDn1/Nk2ePXp8fSnf191Z94PDE5NPHRmciBMJAquQAkppaNK+jmrzne2dKam4jvpsrW/5iu5yz/RkrV6nvhVroP2xqZn5+kzeDzBfiaO601R2RaNDEVdp1LzQmbLvq9BocQqeMUoHRjtx7BdKZQBOIy2wrxMgTeF7Ra0Ck8TScG2ef93FW194daIyOR363s4zNvzkseembBIW2gLymrVmfb7JEGdEhLVCaHRbvthe7iBlZuqNyfn5WAXiJLLx0MnawIbe9MBg4vjg0cELdq8xTgUwB1/b/6kr3+07KKKD4ycvwrZlfT21I9FUovYPu7M3rj/5yM9SP6cTvTLMv/9D5953z5NJ6r58zQXPjmy57YF7e/r64hD1ZtXUbHV+clVf38SJ4f/3vh/dcMNlm8e7hydGo+o0iUqSiMPiyaaM7hvuLPRc/tGP33zPA/98z8NfufaDtd3bHnr20ST3/g9dccl3/+mmxx4JL7/6oh/ePP3f777r0k9dN9aQiOGcyxh9DHHOWWEHOBFRRGTCHIZOnJybmejPkQUEFpIIEmRV4szkw6Hc3jE3V2k2avf95OfXXnetI7XnlX3PPPFsThkdhA3fODDEgRx6u3vWrlXlmf6Ojnh87Ow1q+as+s7NP5gZrTkmFQT/Qp/taZpC37IcsKSzKRvuv/7M8E78G4jTZy2/V7UMAVomG9z6mWpB5oxDwkop0RAxTljgBETCeQ9wCSepTZpp1JRmHVEdSQR2InYBdlooKrTMAnkxRVnMC3jpNmBpEqMW/06n/YpgYXOz72XVDEEGRTEjTaG8xDkoH56vjKeMr7UyWjMUg1ud4q1MrSUhDFIAK4GIal3qROH36Xy9vVRGLa0QEoNoca4i42nPt40IJuxZu3FeBSfnm9u6enJtnRSMNJ1r4W9aU1axe4tDcDpdrFPK5W+GYUWRCAschBVrTUIiLBLHccokHmB8FQReEChxURRnSjMkLJwoGEWsyDnijD2lW2dGgQhKZ3RxApTWymjRCkREutUYxZI0oqauJY0GoliSpiggF4BtM24gzsEmiOP5+bk0qVenxlGbZ4WGFqUUBAj8VesGtr97zeDL3tSxyagaOWZdzEdtYU9PT0e5zbPC3d0J5jSJg1M54xfzQZALKnXPJgMdXTPR7PjUlGor9vZ3TU5NoxAU2trnalU0o7CnxyM1fnJ0zdaNSKnerKeNFDoMgsAJJzaFb2Btz4aBtrbS4SOHV61a2dfZ9cQzzygnXT29J8ZOHj+w710bBrZsXD/XqO8/OTZSbUZBgX3DOmPqUlAqdXR3VKZndBDO12vWoq+9c2133+jU3MjY+K7d28ulzmMnThw9crxcbu9qK0czU7X5GVKmWS6mlTl2yVy9CmC6UAp1rjpbTaOUDAHWk7SkUF/W27V18zm9xXJei8J4HcODY1KP8tpPm7YtX77grK1HjsbHDx4ulvIf//Cum+76+ay1ke8jcIo0Wwe2YBYhJw6GInYkNWuZHaqNRqOZpOK8nB/b5ODw0AdXbmvLh+Mzs6PTkx7WLOvoOjkyNlWdaQJrunrG5iZril6t2B2bNh7cczANSs/s2//5S7f/Ei5h5WI5vv9Q5JtrrnjvnkPT//CPd134oQ/8H1++7o4nXzxZqSO21ss1G8nY3GRhWXtl3n7vjnv/4rOXT46vfWViat/waBwY5HN1FSa2ec8Dj1531ceu+9xHv/Wt226784Ebrv3oDKLbf3DrF67+7JXXXHXLTd/LBebyz131j7f88G9uuafGXkTMcNmEIyKZF4ACmnEU5MKkUfNyKLSVR0aOWz90gHPOceqcE4CgWpCyUKOZWtF+GD718iujU1NfuuGTt95y1+Dxoy5KGEpSAgNaw1cEcnA9XR1xav0gZ4LCd268fXqublRYLhQrUfU0yO4bMpjT5TdLteSXfPL008I78U78jmJBNGARfqBM7oUFcEoIWkhBRDGcZqtc4pr1uFGXRh1xBJdAGMKZ9C6xiHDrVy2KiBgWS5L102Ewasm/iwhM9g/jV0oHtm6/ARLJyhEQiEXkgAhas/HZD03ge36otElYmJQIcVaue91gzrbBQZRa1Dr5vYFM314q4wiJ0qlS2QSYKNECRwxA4sSmgPIwV594dV9/qaNU7LCDw82JSR1FmXGSBbTWAJxzpJbYUr6psMWnTi7QAplJREiEiBa0EuFEWEFAUCpTh8ka0lhBGY9Ii8kBmhkxc+BrLwxclMBxNucLW8XOI3YeCUPBimO4rAJlYDwoAxJmK1qJr8QTR0pIt7y1jQo8HfieKRbrcdMpBhziBjxYYsoZdJaQ+pL3LDG0QAOBB0NRGutyET1tCdn5Sjo9OlqvzLkkitOYih5yQQyO41hrXexom5yacqEprVjevqJnw7LezSbfrnXqq31Dw8NjQ6C0vb93JomSNF61cUdiVCVu9m3dmEzOjb82uLJvVVSPmjKt86Hf3pYEXt1QIjbyCOBV27d7vj68/0Bf37I1y5cPHT3S1da2vNwxMjJSGZ9Ekrx4YmwoisXXsTKqswNMqWNmR0oRswnCUlfP9HxzJuHEy6tcEs9PH9v3Wl3lBjasLZfLMzMzYyPDPvGKnlI+1FP1pFxQeTJln9qM6CDoMiIiWmtDzlOpCySxaWqboVHtgZ82qi/se9HzNoRK1Wu8/9jo/hMTnqOBQlsaFna9e0tl3h49uH/j5g2rNvTeeOfPfeOduWGd6ijmi4UiBUmtEVeb1lomWHIpp8aoUr5QyBU1dL3enK/U55L0pZFhS2Zkeq4m2LpudVyfbwoGY5y9ecvY4eOxkwOHauds3frTJya5o+OVY0duOGtzb5gb4WBkcloLtg0sf+xELRU6710bJ4anvn7TvWftPONrX7zqZ488u+eV6hWXfqDWwP2PPXU0klxv2Tk37+oNm5go6RPsXtkWFtoODZ2MjSCnYY2EhdlG/bb7H7jsI+d/7Y+vu+Pm+2/60aPXX3aRSaNbbrv5k9dc/0df/PKNN32nLvGnr//Ef/3+4ylrasRBEPnsK7S6/JTRyPyNSDeiKEqhjZewiDYASLSyBuJZBrMCGwAiKkrhF4s114DWx0bH/u7bN//JH39mamy2rVyGqNQSCfIGyoNXRtgGBh5/amjtzvU/uP220ZOT2uTz4s/PVJA3AJM4LM7IIktG8YJDXmtInz5HWaA4LH35+hnhnfg3G0txuN+fe/rTxgLnadHWMWtGYQi01s6lAqcdjCEicS4lm9TGh2EjJClcCmeznzhIlFAric9+8Itqau5UgWYhg1FL1r+40oU3sxxiicabvKG/aQEvWdzUjHYjJEQsAAsJLWBD1sKlsJFNA/KaMB4FAZQmWSCqQQll5FS1eLYIUNLCNPj3Jpt5e6mMkLKkmNRCVkiSHTuwH4ZixfP9RmV29Id3jx49Enb1R/v2YHzMxTF5XsaSwVvdYJ3KZpbo1sjrzCZPMYDe4IalKFPiy8wnmMCKnMpYJgbahzIIvFgBTNowMWcoGbFjl2jOeaRSMGzKLhG2LViOFLSXC0JtQEppX1tfOWaQQBhK5UK/lC/mC3n4gZGkTkkSN0U0jOeJFAO/tHo5g0xgbLXmQj/ROvADAipTk12lfM+y3katemDf3umh46jMeQAUIVBBuWhCY8UZo71SAe3F8sredVvWFdpLfYVgjaLVxVAXEKh+x7J/ZHxoegIiA7vONmE+SV1/f388N5lOzC7v7zEkI0cHvc6OZRvWtHd1qkbgiOJajdnmVyzXvnds8FgulxtYv46Z/XxueXfv4NCJysQUEodcuSF+XIvb+3pWrB2Yb0S1ycmkURPrAq1ZoASkjMnnUqUK5WIaN3Ozac5g1apVyOmXX3ne5LwwDFeu7N+0aU3eN43+Hk/IF1MMc+WCLuThLWh2M6AYqQMUfI0CUABygAIskAC5HmwZ6F8ryLhPDPzitbkjx4+sWNHfvar3xz95zAlf/MELTk7MTkUNVgBZZR2QqdeQp5QXhvlioVQo+ppgoVUhH+baRL00OAgys3E6OJ6csbFtbCg/w/Ty/tFPnNn/uG9GG+n+o0c/+5Ezns2Fk8rMVGoEbFu/dvDAKGl1+ODw2WdsfXr8OWh8+66fX/nBC7/0ucufevnY7bc/cPGFF+hyeP9dD6xfs/YLH33PI8PNh557Xnu63qi7OArD3OGhitnQ1mhy6OXbjR85qVVrSJLQ0/Np8+4fP/DVz3/mM9d/7Ad3PPTAI09c9L7zCn77vT+4+5qrr//U9V/44X13VuEvX7V2z9Gh0DNe4AUc0MIYyaq3yug0TT2tWNCImol1xvccQNBEnrAWhrMZUY80KVaUxDEQeV2ltFIZGzz6X/63/7K8t9s550QUcgbaNhpxVFeB83J+WGzr6+tf/77uYi6f1Bv5UnutUgvzQRPpqYH5q3QsFrsYl0Iy7/gV/PuO3y+DAlqCFTIxSBFDiEGZfKpoiLLWJlFcryGqYX4aLgHQuh9XLdMlXigpKEWA4kzmRfhX72/WPyW8JItvtUcBpyvNvsVwIQbTIstDkVICB1IQB+fgkjQxIKXKbaK0UoaUIVIiWjLhWVqohr1F69a/evwmxgVKwDaDrAjQxFCAEbKcao48jXT4CMaHIoY6XhQAACAASURBVL+IOIK09P7ZWog4awFoY1o2A9npWAKaARBxr8fWsmZvDRFRLkNdFr4hBMCJYlGKFTGIiSHKQBn4OXR2q2XLO3t6fd9vzM7MnRhxU9OglJQmUg4iSWJ0Q7QPxyknjlOwbZHEHUM5YWujpmjjXIrWXy3YQRKxaVSdT5NERJJGPU6aiOqARmqTo8fHmnG+2MGabBLH0zOYr2C+llRrCD1pNEPt5VesMAXjF/LzHR2pnkoTCwXhliJOI2qS41SS0sreddvWd7S36SSNZibDDcuaqNoaurpyZ+Q2HRgewWylc8OmkM30ycm2FSt8L5iamQtyYV7h8DOvwBknFJUKVM7DILZx1GioYqF/+Yojr76qCuHmnTuOnRgxRm/dunX/a/snB8d8i0QFqNtUBZZorhL3WEpTV63Mo1ZX1hpmx45YlLA2SKJao1rvI/nA7p3Lu8q2o/uf77+/zmku35MLyzO16uPPvZDW67YasRVL2gR+YHSYC8JAM3OSJM65WrXhBNrzi+Wi72kDKZnA98Da1Zu15nwUx9YPyoVS2fP99lIxmpvp7Onq27n8vgcemxo/8blPX/fa4aNHRseiYsFq7bNGYtNao9lsps4po+GRF/i+0UTaIwVHUT2qNhqiQkDmHfYdPXZW3+auXDA6lx4eHI3P7F+7su/4oZGTM3M2xboV/YMTEwbu4JHRnZvW/nT/cRDtO7Dvms0f6SgG8zYx3d13/OzR3nLx6g+cs2nFmsce+UXH8mVXX/PR557d9+CDT+1673s2fPSC2+7/eXVmqi3fPteo/8PPHmp7sWs2clZ0LpejpCGpOKSOGnUkftj57Vvv++rVH7/245d848abSHkfv+Dd40fHvnXTLf/pf/nSH133qX++/UcnTOgbL40TzSyOUyBJoswc3gLWWudcKQyhkFgWUOrYAdaRdeIElsGMzIDCN5oV1WvzpjNIJ4dgbXspVxY10FuOxB4eHp2pzXsmjzhlZ/OG2LnpuenRyYkrr77i4KEjKBQajboOxEoK4VPFY0GrE2RhdP/LcvULg/otJkpivBHxfif+LcZbsGQWsfg3vg+85QX6tx5akGUe3OoQY1FZqw48w9o5iuO0Vo1r82jUkUQwThEz86kbcwAtZTuhjKQp3OrZAbCEKNOKpTr1rf9azgYgPoVjCYDTcGsWXy0imW8gnWXsnEw7jbLBmdHzrQUprgEmYONpPyDlc1ZCAwCdCa5kXGmm1srlN2Jq/zbi7aYynGkyE2elPmChf0sc2ziBs17g2zQm+B67OLWkPVGZjotGNpc5p7Vm6/6l9SyNBRhmEZU5RQAkgCXDezLGjLR8BzRgUO7Ufav8ts6qM6op2iu0L1s1N1uBaCFHmiDO2QSxRx7EuRaxEY6IhR3YgW00Mwll4HvgFHEESqEUHMMwR0k9mXE2oww7uAbYZVCV1OruxGg1rLUoY80aksj3TRI1AEfGmzlwaHa20r9tU9fqfu7umylP+X7KvpcGBo0kHZu1SpFWSnjNwOqBLSujyOaR7813Ho3GpDKjI1m5ekt9PpqcnfO7uzxtXnv2+dVbttROTBx68ljHsvbAM4dffQ2AIYV6c+rYIHIGxRAQGG/thnVHXtsPz6xdt/7w8cFatbJpw8ZXX3517PiQn5CkQpYERjkd1ZLY1mqz9flqxVbqsLGBkLOSWKROsZs9MQyLnORWr197/lkbdJxyt1lWyk3ONWdmZnI61tqL0gYnaU4C3/ebLrUiqbM2kRTGuTSJYhFhTb4fQqlaoyHO5f0g9SWOm4lreqFHOoi1stZGlbk4TSrFwtrlfdvPXHvfjx9L0/jzX7zu6KGT+1872GCJm2mqlU+eIcWJTdNUSBORjayCTixb29REnvJZrBIhUMLUSPnI8AmLzYGSJHHVen1oqLpi9Qo5OlZtNk6ONdeuWpUcOEah3nvg4I5L+wse5qqVKWct0F3wq2PTB+ZmvnrDpYdeOvZ337jtI++58H2XXPz0K3tu+/5DH7z4og0b1EM/fXj1hg1fvfriHz38wssvveaHuYpWU5UawRQDvzHfSKNYGdWozRfKQc1iphHHrL972/2fuvzDf/ylz/3ff/13XE0vv+J987rwN3/zvT++9urrrr7sr266u5EkojWJS6I4cUjjxLnMbxdxMyHSJNxotBRdms2YgMhGiUtSlzgHQUrKBkbnfQVFPWv6Jysn+lYv041mG+gLl19+9o6B6QQ3333/Q4+9WI0iYqNI1xKbNuso58FsNDx4zWYM51zcyJfa0ti9+bLzui6k12Uzrb++9dD/l1Kfd+Kd+K2GksVizdK2HQZYgcGW40YyX+HZWTSbBHgazjnJqrYLVaCM8aUEInCtBEPQKjHRm6zfgdP0YXGrVHG6Vv7F0tdpc4qluf9CUYsAcGbQvTSRylrokwjWIQgVEXlE5Cs4l/Gfs915e7o9v7t42x1MJHaBGqtaN1CiALZwKvAcMbMlrQGkNsm4sQQljgFIdhyNSdP0rbxI2Lkl9kzAAi5HqiV/12IDtrrhIbRQR8zel4VfUKHUsWx17LU3akyeUkoF4kNc94qBqUP7kCailbCDUs45pA0YI5JCnAJrwBHExnAWKSPwEccAw9dwoCQJfD+NE2frjixIwWjAKjitVUbTUTp0Vly1JgTNgLh8sRDFtVJ3udpsSm0O+bKcHD9Zrc7VKo0TIxgcr0PBeCrweWQCyoAAj8Bx28YNw6NTaQ4K7mi1gbi6pqOrMwxeGByvxGbz2We/duDg+Ksv5VasSebmx8YnS+3tuaYb3nsECSFySjkiK4k4TlCbbx8Y6Fm27NCrr4Hdtt1nHTpyOJ2eXrtj+/To5PS+A7pQckkcaEPMIiSNxFOUuMRWm5QImg34KjDapjEEnqakUlPVOCAtUS1gEqDc5Z2oI21GCopitlFT+6IVFHmeDs7ceWbQZiwiYhdoHRjNNknjyNMaohS0JmNIQQSOASiSJK6TUqxJmLQoTUop5TR19Pfe+J071vat+NDHPvjIEy8dOXJs18YdW7dshEaYh2MQgRjOIRcAQJIgSQAFMhALUtAaSYrDQ9Wn975WRdq06TPPv3beOdsf+OYDKgyfevG5P/rExXhUCOqFPa9c/7Fz2+DVKvUjM3OTjLO2bnj88SerYvYdmLxk19nfvOnWyCvfce8Ln//grnevHfjZ/Y/sOXTk3A9csDrd8MO7fnzW2Wec/8ELX3j5pUN3Hnjfe9534Zqtdz740+OJWKPI2fm0KqQJLojTkjbUFMNaeX5MOFpp3PLgwx9679b//J//9K/++jtpvu2yj7yrMjn+7Vu/99nP33DRmWf95Be/rPt+5KIkTYwGp0ycmWiAmYlIWDydwahiyAiQuiix8wnnwxxsWoNE7929+5L3vYstoEAhfEp1rZFLojNWLOMmjI/dAxuffPyVmAhhMYkTzvkwDN/CD9nBi6hgfeckgSZkeRQtKIWcKhiJvOnWJSvZu5ahL06b07zuno+AzFL731K8caf+lUClt8oXf/Xm/KoetF+xltY7DLQaaxYbK0QkO7906u3TJK9vWP3C5yGvo4u/eQNOt8A3LFmWPpfFby3eKjtAQTEzsNCUzA4KvoJtNm1tDpUKGjXEiYLzIOIcWmofpzZBLTkMGcligYIjLQ4xkHFJszGilFKg1KZYktOc0spbeIcWqhkZtXPh3l4DS4k1DnIK5lxgoMmS5bzu4BAcSItLpWGTKIHvKz+ng7znKQYvtI5nYMzCLr3h8J56+ev8sk59960+fbo5IUM03pi5/YbO2E6d6jfPfDsX2pgVt7T0CFAQ9fbt037VIWhJUmFh7RniJYvdVA4AjM4VlMkp8omMsOdStoART5kAWiFVAmpZiWaMHJsCDFhabNInBUAbowipAwCCkFIkLGni65wTOBZowDLIgjJ3JcMu8Z3SMESGiLQICJzEIpymMSDwfSQJxITldu2E8mUJ5pAIgbgZI7YIoYzhZh1t+dCYBBwncSHwmmnS27sicjw923Axt3UtPzlVi0ZGvBUr2wul8ZGTXd29njLjh48iikEGxnieSdPY2SYcda1f29bWdvTgQaTp5p07jx084thuPHvX9NjEzP5DKiy5RqSEXcafEGUcLISEyYlwpuBEUNBaO7iMlRSIkaYjNqOT0/tG2rs78kNT1Zlq3GQultpzol3KViEIAxKcPDHMo6loZwiBb/KeJnaKyPd9ATQZTcpTnq+0Ukq0UkrlgqJSIK2EoFhpEg0jBj++955Vy1d8+P3n/eynTw5PTq7qX7Xr3I333f3LNLbKUOqs9g2ANE09KJumnR1dzEyatNdygPKNEfIakSepZU/Vk+TQ8ZFt27ZuXLVyZGJislofdFi9asX4ydGhybFJh22r1+55bX9C4UsHTm4dWL33+RerTXXw2PjuTT2rOjunK1yP3Y3//IMLd27/4vXv/+lTx+7+/g+2b9/+5T+68vkXjj388MPnnnduaUfxsYd/uaK9+z/+8RW3PnHkyb17tBJfB5VqzfNCX5sodixQKpdYVkopLzhyctx/rpka+cqffeFv/uu3onrjT7788Rvv/NnNt31v585zotQmC2M4m3/ZihUmBjODhVrnjcGSdS0xOwqMMUoAUk6S5hNP/vKlZ59WLFFUi7imOQqtHejo/OIVV5/7rh2NGENDI3GaJCkDqSqVWLNpyxU7ct35Qs5DqVSKJiMirT1/vt44pUvxa8Q7PJh/t/GbnPrTZVG/bVUTBrTnsVixqdLiGyO2mdSqMjeDpIFmE2kEOA2X+f9lTLEF0mZrCQA0wPQ6XKSV2yjKLqXsWtfmLHMKPB9ouWSDZcEK+1TqkdWQFln02vhZ4erUUcrUft0iSvprVYIMRAAHCCdImbPtST0/zAmQzf8t+yf+FR6fv+N4u6mMYighxYqhZSEjXBDuZWUcATpVZqFxG4AokaX1wGzvX9fjtXBA+A2ZZ0snWy88X0TLsq56khY/N2tvEksLlGFCkMsLlGUi42kTsnNsE2fF+B6MOaUyzIASKNUyeoSIzipZggXsZ6FKChFSUETaWRZNCVuQt2BXSSwEJ4CD5Zgd6UCMD5CyrGCtayLwmC1Y4BmkKdpKG7ZsDlf1zs/MDs40pNoMgoCZrbV+Puc81YgNSrlmEpe9NmNtr86popc2Ys+5IEk7O5fNNqPRoUEU2nvKneMnxsMwxw4zs9OuWgcYngK43qiYcgFxXff1ty/vGRwecVFty653jZ042axWB9ZvmB+fnjlwBH4OKSsyLEkKa6BJZT0xQkoJCWdSksIWYA3LnMJZiCNjgtAzhT3DY0MTx0o5L7beVFOkXK43bTxXL+cL+VwucQnH8cxE2nBOlFYKns7u3R2IyNc2Q9aYhaEErHWioIDQkSeilQLEkSNhnxAQtRc6PnDheY8888rJqaldG7dfcMH6v/3eAzpfaETgFBbkkRZADPmkGXqmUgOQgqFAhkicOGvEQ0yRFeXljB8Mzc2NzUZnbhoYPzFUZX718OTundsfGD4+UZ/fc3jy3dtXHNqzL/Lzew4OvX/r8v723plm/fDxcRvv2Lpu3d49g5W56ic/+rET+179q7+/47LLL9+y5Zrnnn7uu9+4973vfc+lF13yi6cfC9s7zvvYB4+8uuemO3/8gcsvPWv7+gd/+sDQiZPd+W7HBC9wikiHIMBWmdOin0ucv2diYvyXj302/9E//w9f/ua3biKiG679wI3ff/C+p57Qbd3caCwCHkTkhJ1zkhXkmUWELYSZRNg6SJbWEFrzpgJQjeJq0gh8XylDftEzRVur7x2e/u6PHn70lUOxkqf37Jm3Sa6trRkxc6R6imvW9m3u6uwJ82AUeoLRholTkC6gLqCM05Ld+ckbr1itOzl36jmdniHx+0ktfCfefrw182kh3uru/K3wmN9paHIKikUp8cTpOE5rszI7hWoFYsEW4hQEtKCaLKd6pxeYKgrgrGObl2A/aKEAC6WhTHJ3ARRIHSPj6RIbpRQpTYqIoiRBq/EkW0Irl+OMSCa2tdmKSBYMulso0JKsj/g0YEfrmp1dRJmhxFlwEzaF9ixYtDHGF8At2Da/Ttb6XzvedioD8YQUyIIAsVALFN0FlWeSVhNXVmVUbwaWf814fe9Sa3Jc8iT728InFzLejGKthEVYLFNGHXAw8HyPUi1Nh0wZjECLQ0NrEMG1VGyQqVMrglYuY/koAjOIHUNDiSKrCMZXYdH4uYQTcAIXI82yIsB4Ksy5fAgRbiaSsgoKxiNmZrZQhFyAUtjUEgI2ja1LCK7JMUPpvN/0SZVCJT5zNDYxvmvz6pILPGt9x2g2C0aV8sVyuXh87GS9Xs8XiydHTsD3O1f2T85UkmZN5XMqiS07KPI6Sml1zqxZObBt88jEhFV2+Y6tE7OTc9PTW7Zuq1ar40eOQJMKfU/IJrGCRywsIGIRkG90YEzO81SAIIRmJ0hFrHOpMHk6AZOnjG9c6s1xM04BNrm2ctyc61u9um1ZQIKmsg5pgRRb1xWGAkWGDCk4Fpcm7GJOmi4lghYYIaMMfD82sMzSSIkdCVN2vsQZpbSnP3H1hffc8+TU3MwZW3e854yBf/rW/X7RD/L5jvayBbEWyzaKGuxcXodK4FLnBX7CLhXreZoktVFTUuno6RkZn6mlzsuHrIpDU1Pv2bby55zWg/DgscELN/c86lE9CF49cPCSzT1d5UIl5blqMh9h3dq1+8YP1tN474ETG7ds8PYdTxI7Pjv7icvO2XN46/fvvuvM7Vs+cOG7hlZVnnvu2a6J9ssuvfiFfcMPPviT88999xm7dtx6072btmz7889/9NHnDz/00ydJF2YqVb/UmdhUIPA9JSpNEq9QsJ6ZrFfufeChG6669tOf/tRN3/5mm65fd81HJvMvvHBoGBJ4TkucMgBtyGi2qZPMdzpzfQFl0yuzBnwTSiJpzEbgrCDIb16/fseWbXAwnmomDV9JTnsqTYueHxgvZdezbafztIV65eXX9hw7VOgsrFjeec157yoRaozulZ2jUT2enJOEoQO4dEFy9B2ayzvxW4nfLZjHsAkJ+8SIoubsFFdmENfh0qxTVisREaalkMliNsOnnisSfqO69SI5Agu1JxZZMDPWEMUQ4UzG3i0UhjRwqv8546sSkXO2tbUtdvLiK3U6fs3pqTXcOraZifdCpYwF4riRIsiTJoVA2DEDiqDMwk3Rv3687QKTZqOgUgKyVq5TrdPUqn5n4JlkHe2ZjB6UvJmo/cbIyoai6FQdDlmhp0UbbJmLZmvJgkWTgagFOTsGkVMMpaL6HNlOVWiDZhhLoa8D8iJTm5pF0gSncEppLVpD+V6YZ2YXZ7VFK4CQIm3ID9gPSBmjtYjYuIEkckpDK4QBFdsKxW7yQrg4iapoZvtAIKVzRa+ry3S3O2aamU8rFS2cxg0RQRDo7k7T3R2XioP12SPPHMXENCZmEOZhHYoF09XplwoSeEm9hqGJo8+9XOrq6Ogte74qJo3dhfKKYiEs5Y/Nu9RFHKjG6BQKhbVn7KxFsVfw4wA4cUIlQknq53JxmqKrc9tZZ8W+kmq4dv0Ape7kkSOrN6ybq8yMHT2KQr6zq6vWbHhGBcjXJueYnZBzBONJUPL9YiHXXuDYU+0dnMYQxQ4AGWO8XNixukezShucVwGSgFNO48RaybXl+pd3d7R3Ke2zZzixRScuaUA7baRYLLeXvIKH0EAEjRgw8Ah5IE8ohi3ngQZgFxh3GggWMDoFfOfpvXXNm9euP2vjwB13/SLMFT54/oXQpHLQAUwAx7AxlCAAPI00QakdOkOMs98bA4TJBI8+f/LQ4FBiOSx1HJuZvtSsXNHXNTVTm56tWmDT6tWVo8dnarU6sHXHlsG9h0Tcnr0j27auVM8dyIXhy4cP7zxzxdYNa05MTD+5d89zLz//yU9c/rU/v/6n9zx1+/cffO/7L/7kDZc8++LRm266+4MXXbzp4ksee+apo8Z84dOXHzkyfPvNd7//wx/e9Cefv/mOH4fFUiW2lAvjJA69IO97zjkhFypjQRMR//13bv5fv/q5r3z2+rvuuLWhc6tX/X/svWe0Hdd1Jrj3PudUuuklPOREggDBnCQxWxIlWhJJZcsSLdtKI6fuNTOrZ9b8mr89PWvW8kzLbTm0Lbtty25JVpZNBYuiJEYwiQEEiBwfgJdvrKoT9p4fdR/wAAIMsqS2u7kX+dbFvbfqVjxn195f2LD31Iwvo6i04vsWALTSxoCzQaqBBlFwKdtHZiaEiLKI6uQ1BGCHIJrRQJT43Bkdk4TSWSYE1PODHFwPrDURFlLmZZEPFjWFek1PjjRHNNQVTM10c+mxYdAIfYhVYr1DFsRlAzsvu5fhtNnv68nN/0ix7Gzj8rL8q0Y+4ZmL6BeZIldeSF6xE1vaxTmenwGbg3gDoiuvJpaAwFX/oPIIO99GDbF/FcJzaCVGKCwclpU2kEgJIqLiEIZP1MMURgMqAhwCd87AXwQFBFkpRGAaAjsAAFAgIJTMywWCXyaqlocHpCV5YwQA8ABKQMB5AGEAiljpWBEFQfZuiM75FxCvOZUJ5EEIwQ8xRAhLV6UCIU9D5jWAEJ6eg4aBpxtErxjnQ8ycSWiGysqCQgpJEAVRkBEBiKli9XUXyvZiZGq1Vpy2srieOt/vz7X7C9NQ5sAOiURAYwRax3HsfWD2wj5UvlRIoGMVp3Gt5gUSZRCxCyLWARIgQZzE9ZZKax6ATKIh+DyHipalTUCK4gibdQRw/QJc8N5CUQISGEUqakyuaE6u6Nsi8nZw4gRQlccJNWsjq1bUVq2gem3mxMlOdwBz8zt3PL1p2+b1Wze2mhOtWs0omOr4I+32nHeDwJDVVm3a5DnoLMmSOIlocW7Gzs1rlZRlCbXmJddew6kpnLvo0q3CvDB1avNll/Wn56f37Qdvk5GWVrh+9aSILMzMAg8BVaKZI52M1HUjoyQyRPVGMy8Lo2IFhkjiOEnrNRUnWkWovKknvYW8sIMkyZrGUBj4k0en5k/kHk3W7Hb7rr0o3hIWSlGj0ciSzHhRSIF9p9de7C5GhLVAGVAzqaWNemmw612vnzsOghwrSgMQkWrUaLTutN62bdvm+sTDP3pqZmburb/8dgvwxNNPQ0SMjArBO7Zl3m4XnUESpWSirNlIWnVmP1icL9qLDaBsZOyYk6lOYUwjadazieagWGwDbN607okTzyplXnzu+DWXX/7MsZO1Wm3X/vnLrhp78Hh9sdN54dCeN16/bnysNijLubzfAVgz1pqbm+vVamKaf/KlL21ds+bed9/Sn4enn3/hRztO3vXLb33DtRd9+e++raP45re+pdcbfP2r/3DD9dd84t73fvfBx3V95Lc+8a6vf+uZ5/cccDaPtSGPoqN0dFyUdDtzXmyR52m98dm//Nvf++hHfuMjv/nFb34jKn0LQ89EhpTKIxYQIDIRUrl8hGcGAK5SGQRwzhMqFBIPKADMe/fuPnhgTwQ60lQW/RAcBPbC7J0ykbLBu1JHYIPTcd3rqHO8Nz26eHi1HU81IomzNh+AMOS5R0Vwhq30yrPO6wnN/6ix1IJ8zYuc/sfPeIPOFyRggjfAoSzzhWloz4PLAXxEDFzpa1eVjGpPNKAABzzNhQY4SyMNAIkAhjzXavrUJg4Vy5llCZWhhDRQheHFYa4gS6WWStC7IjWAAFStoqp5XGmFBAQeZjPD3361hRMZFiGGv7eEs/AAgKTFlcH5kIYoU8rEwMyBUal/lRJ5TB6UBxAVQDEyoCBgqNAzShCHrqniFQzTRUBEoddw3eGZLv4wlgnlDb8iS0cdwaEIEBADISIPf0wCO+aZmbKAdODROuoZHrT7Rw9AtwPiQFV0NBdIk05EQFAFIWAaGjKQQh1RFCtlirz0DGkSRzotKQfvQFCbxMRxYV3hOG1mUZIF6ggYCAws0Le2V+jc6ygKjkLhQQAoIkF24Dr9zky70ZqoZdmlb7huL4TB4Wl0YcCSpZnjoCJjmrVGOa5X5iXF5eJg8cBJw6p5yebnbTQ/P/vi9LEiS7uUwuhYCoPO8VODYrBq+yUqNh6BYgOk2MSUJhuuu7yoRf0iz5qtfnfA1q0am+jNzE/v3UNZjXO2vY4FWbtm9YZVa3586qG+A8sACkEBxIQ1EyLVd3lwpLWOQ2ZAayFEE5NOozTvM4tTSulGEmwq3tr+wAzs+2+9dtuW9TNl8cDjT3XLQcNEi6osbJ+tS+MERIUgtvRGaWU0pllwpQPJXRAAZQhTUyaKrbKdPpdBhK0RJwzIkVZZnFxz7VWrxqPvfPnHoW8/+Rvvf+H44tce/E48ucKY1LlAIokxickGA7FRNLFu3cn5uVN9l2rfajWgqY2pr1+xYvOWi576+tdYZ2mtzmhmu90RxTv3nrj66k3f+8n+4MPeXXtuuHLt5Ng4aty5b9fWi29prR5ZCN3phfbhjr/mqi0PPfq4amS793du3T6587nn86SxqKio158+ceL45+676crL77j9ssJe9t1vP9BqNT78kXc89tyhv/3CV66/9oa733/Xc88+v/tbe265/ZZTMyd/8K37b736ppuvu/Ir3/lmJy8G3ucl9fOazjJMmuQDdMuSaX/X/v0PHnr/1Zf/rx/58KyTLz/06I5TC5AlFBlGCMIsUikKCEIlhhGGSunM7AHAeu+4MmgCYAdYZkk0Vk9TjFRwrExkYkNKJVHBPkpqEcbehzihvg2zPTk2tZBPwS53or1YTIyYW7ZdqrsYFnrKURBrKHbncJGqSszye/mcv+cbZl79aPF6/EuPl57koQ3ha1zNf4usl4TJO5/3iu48dBbBloCsSYADwFkO1YAGQAHzafneJSzL8umMWJZguUhaazGJZwNJprJalNRNkkRJ3aQ1EyW11pjSkUmSOMmSJNMm1loTUbCl99YVZX/QHfR6g16nHHSDKwfdeWEnI7QSigAAIABJREFUrgjlwBWDYAuwOQQLQ8+Rc+P8RxPP4FGX6E7DtxG8VNCRsvCgJQZSWpP6aeEjP/s4XyojQ0jtebItlCViPSEgIKGgqoDMiGEJHnum+rKcZnd6HT8VVOi0rszZ7wYZChHSUtUGRIAqLPegK7kfzM0O9hPEBgxAvw3oQVFE5L3nwAF9wAJcTCoanj1GUASgiEiR8Y6lX+Q6GK2RCJQhFBaMkrrRcZk7KKwZbSZZgv1e6T04y8zMwqXDwKmO4qy2WKvzwjwgUhABEBtsu724uNhUrZ88+VT3xT0wn4MgELaLHE4cW/TFKr2102n3jxyHdg+8nV1YnD10YLHXGxubmOu2XUpu4Oorxtet33js4KMwdQoa9VMHj6wdubIsS+sY6nW2suaije2yzBeKi7dvm1ucO7l778WXXdZoNnc//SxEKc8tYL3OvQLj7OINF61bq72/6atfvY+IqrqqoAJUNoR2fwCsPIclSL2oqlIqBF5KW0a1tAx+UJYoYrSMxvFN12/bmMEJyOZ6l0JjHLJ0ursgCOgkS2qjozEA9NqeiLKMLEOnNwAJUJRSlvU4rY00bYzW+tC1UjrgoAiCeMfOpFm90arXzX3ffBCQ3vWBO57aefzRF5+jVc2+IqOi3JbKWzGGIMq9iVOzevMWNbri4NEj+cAlSbCOpQyFiIogiZsuSG77ANoNeMV467m9+2+8ZPWq0db0ien5hYEDWLty5cnZ6aMzc3MAK0ab83O6uygv7Nl7+w3bf/zjAaUrXjhw+OaLr1zTaCyauB/Ea6OS2kIRfvz0czue3PHWW2+8+91v3vHsgf/wh3/x5tve/Ju/8f7779/xpS8988533blqw4YfPfToNdu33XHLrQ89+nRA/De/8Z4v/8OPd+49RlHaLftiVGKUtT7Ksm6RN1atefgnz451Fv73T94zAfjBO29+6E+/aLKElKmylRCGvLpKYakC/zKEAFKVpiONLM5hgREELhXiNVdd/c63354G0AxSWgLxtjRG9fqLTiD3GlVkbWkDPb378LFDj8fGzB6dmS9nGxneePH2RlI3HkNZErOOyfmX3LrnuZnP/P0XLl3/epwVr/lk/Xyy0uUajP+8oCUWzrBGhFRZJikRJWzYqbLfnZ+G9jxAAMXEzMHjaVo1giAoNALEPAT94lkX+NIPEQmQiAZE0BEk9ag5omqtyY2XRLVmvdlKsrrSsSitdAykGRBJCxKQEsFBkBACe450jApULPUW1MULe2Av7DBYDtblvUFvsbOwsDg/22nPyaADcydEHIgHEBB/Fpf7vEcEz3p9+l/CgFg5Zjoe9NjZtN5Ia81eWQYgABoaTJ7m3iPQEg+czwIP/bxCyzDbOL1fS2mjLIMLUaUBUDGKVCWBOGQtI3msyNABhCBU9ITK+4EqccPT8gC4zHBg+CaeeXP4v9BLEhYGAcSh2u8Swrc6GYRKSeX+yAgVNgARAZTCEEpAC0KgDJQGPAExlCUgexZhQiBBBRp9KDThkMo0lFYURDAK85IrRCQqKCEAukpKJwR2pQdrwZbFIAeMrAdfWvA5aQWoxRZ+MAhx5NlxKAFEA2gEVOjQgyGTJbYo+4enoe2ACZQCAnAlpI3IByPiXQnI4B0AADO0WqVIvGJFsOXc/v0rtm5Z2WjsPbgTOl0QhH6ZsCm6/VrW6OsYQm9880YwauHFPVe88w5CODl9orZxraqnuw7tZ03gGaK6dJ1JklWr1/lIH+rC3vlTJQUoPepYQtAqDaIZNOi4nw8wMZFnKzYkCEghhEQrECfeKYkiMHVdG9guhjDgcCT3PtPPHu09eeCYavRMo7nQ7ljnQEKrVpvsjDL7+X63Xq+vcCO2cL7IvXUiUs+SrnW+2x1tjCqNvRBAo2aOFQE7L5FOkqxu7n/wMd2q3fiGa3/09P59+w9InHGJI6OjA4mcLVAKFl/LRjolMoUsMuMjE3uf37W6npiiDM3W8cX5rK7yfhn7mgUItJgXxWTzIl9CN8hUgJuu3H7/qZOW1VO7566+fN2h+452IX3qxe412xqze6jjzfO79911w/a16yZnUNuSF9uwfePm3Qf2NRrNwSAklJZ5L25kbZavPP7cC53eu2++9KpLL/r6335j5zPP/8qv3HPy+PyXv/rVNVsuvvOdt+/ecWD/7mO33famqZNzf/25L3z013/1yksXv/Ct7zYUpDrp9vM4SUrbVrGxgz5pPGHzF+bCRePqVLsnEELwQigC4p1iF4IlA4SGTOYGuTKqsNZ6q3XqA7DrA5beUJ8h0goG4eEHH3/4ocdrwDoIO1JKSShCubi+mfb6/QWMczBY6WnrDD33e4sq0aaNi0dnH37wR7fdcssjjz/pe4M0rlcW3XJaybS62ZdIqZUbfXVDV0MNVhjD02PP8MVZFVk5PezKSyeIV47XUhBetrGvOi5YLbiQqMQyutnZP3wBWvFLh8NX2KALPCdfUFfm/OsXfkktrXp57lqHb1Rab3j2m0uxdL5fcu6GsMiXbNp5HqKXs0CWPpelmshL1W5OX1R4oXMkjCw09EcCJs3BgUFAEmsNSC0EyDtzB3aC2KEEjGM+q3kES5ObO33RGiLPHMWZDT6wBQLwGgGJ0AcAMDSycnTdpuaqjcnEuqw1PjI6HkACCzMHUkTEiMhL1D8B8b5iDaM2w0f0YZmFkRVoA8CVgyAGH9cn4hXc2hBWFHm/07V5e+bI3sHcST91GEJOOmgpg/U0nGmHCB5SSkSEPVZOREt7eM6ZQQAEHlpse2v71rtB1hwbOPYcgDQgAQiwoFLEoRISZKmQc/o0q+u8VaILn+5XW/e5QINp6YHppYWZig2x1EkDWBI/QlAVRmWpcUjDfReCZQPE+Ys9p3+2SuVe3c07NKIQRgRhGnYWBavyH0oYckGFSAQJEDUiOYUEBGHoCIWISEQKiXjImF86gQgMEhARjCICkSASYCiaIyGEEAIhAKGA91a898AMiBw8hAKc9t654D0wkII01UFUEIhiV4uh1UhrtYgUjq0o8qALi4ilQTZkWs3WyFhUS+sjLVm50opSpRWUuDZWb430ISwGC62RsZHRotPuzc1CYFAEWqvIjE6smJo5ASGMb7sECKZ27Wxuv0Qx9DvtidaolTB9Ykqz2NyCdSaukY5Wr11rhb7/yKPRSDJ17DhGJFaEGRCRSYNhpZil0Wgszi+4fJCkUSD2hR0dGXGlJ2EiIBQMTKwIYyRfIv/Nfd9fuWbNiYX21Oz8aNBxKe25BWstQujo+d7cjFLYzrsAcCJuRIKLs/PWWichjk0EkNXikdFaXpbtXuFKr53XCMCeUUwtS1utiZVr12266MCx+RcPHYQ4YiJXBOnljShY241bupY2ul0nSnvXF1dmOm6QnygXL9t86WHneq507U5r46Z+t/BabdgwfuD4AS6KuDXu0by4b+a2LSseEW5LOHTy1I2Xjo9njblcjhw/cde2RhMoSWr9vHu0y5s3bz55aC4PvO9Ed93Giezk1NGjxydHxjrTC6tXTEydmk7HR3MIe6dm/+iLP3zz1m0f+vV3v7Dr+J997vN3vOUtn/zkh7/34ye+8Hf3/drd75ybGXzlS9/adsVlv/vpX/2zv/jm+ou2/J//7kNff2DXjud3rli98fiJGa1i31kslE5rE3tm+5//wcPrV47/cMfjGDfU0MMW1NAYS4ggBOEhfb4iJBAjEAGCkYCW0Qt4RyFnUBTVU/IFiMcoLawN4q/cvvVj99y5Zs2af/+nn39q90GFKstqg16ulTK1yIecBg69e2LHY+97z7ub9Ubet81m89Sx41RPz3u34rIazBD3efaUc858s0SLPKMM9nr55l9nLM1br7FDdH7TrtfeZnqZRc6UKCpbU61BAfgSglPCod8fzB4HtkOxmOVAnfOsbJjJVSIl1oXAHjSgMhJIQPkQMGuOrty6bsuVI5su4nTEx62o1pjvtLU2ShsiAgDPXDGGCAWFq0xekASIq27HkLoLMvR3rBARFXFWIzAiokETNWppK7UTWXPU9xdnjuyfPbybF6asKwEgjpS1Z1KEYcejsm97BSD2ElVLAAKzE1fERJEmzVCpYwLS0Dd7yOOBIRRkmBWcV974ZxE/pUTezyDOArKfiZcoUZz5ZHlifnpRZJEKt40MoIBlSVlZTqscEhFW/6FQdcnwEC6FiAoRiZRSWvmglPhQ8a69dWVZoEkp1loRhxLLQoIHFgDxodToKFVxnKAWcQWVRajSICRIUxgdTVojOquFIkCaQvCFBGJAQohrcdo0JiGlkvEJ1xv4cjGUVpggTkyjqUdHyjhWrVZtLFcd6/NFZwvpuf5C36206ZqJsbFR0mphZg76PdAArGhyrL5mvB+KpNVYffMbZ48eW9y3F9asGh0fO3jgwIYtF002x6emphJtZGAzMgPAgLBx25ZCwvSg3ZocLRFrzZYvqW8X2QUwWiHaPNdxY93K1VOnTnBhR7J6Z25Buv2s1fKlLwd5kjUhCkYbH9A68ZwMEAHYh8zlmMeNdFVqag2XO9f3Ulrr2uOTY6NxpAAaSc2QItDWWktktBZRsTFGqWZiGqCMwwQyy565IOEAKJoizFLMrrtiy9GTgwP7DiqluoOBStOsUTeud9lI/USeH7TcrdXaeTeOqQbeqLwzmF+R2XtvvObG9SuPAXwfQ58x9CGrp0ms3/aG677b60oWrV03OVhQR44cW71txdqVK/afmjowdSSBy7atWDlV2MWFWYStG1dMPnfylAPauXPvjTdu++GeEyHSjxw7ct3k5b0S14yvGdH6xm3bHn/gR2ub9YXCTYyPx1msxN33xDOP73rhzttu/uinfu173/nBzhefe8PV1266+YYf/NMjWb316U/c/eCOo5/9029+8MP3HDxy6ktf/sGbbr/5mmsu/d73ftwQ3+3aerZqYMPA2rix4rkFerY9jRObpMwlsPdeBLASrRJCBPYWQliidlYjIBGCphahd05ZB7ZQUTa25bKt1161PcPCQGi2VqDGQT63sqmv2Dg58PC+D7x7z2c+1zs1A2mtkhJATSEEZpum6U033fTMM8+89Y63fPELX56dm0lHW6Urh6KupwExrx0bcf5u8uvxc4hfDAaFzj7/y2o5r5zh0JJ9uogsb0f+lCJ7y64rAWActj1EBAIAMrEQcyh6RWchdNtDBdZXufphRqOZHQBrowIDoIa4GY20Nlxy5YYt1yejK71Wg4AC4GyZZRkzB+YQQhAUGXIOjVKVuMjQThLPOminITqnD8XS3SYiUmkHR1GERrdqmeLJsZGRsZHm9OE9i1MHob+QCwoCDbeXhZmUQiLhlyuBnHPAmVmsy6Gn0wZFWpgBAoKu8iGs4HqwbDMFfr4Npp/fqn8xISLIAopBFCLw0HOOpaJ9DjWAA3EQoQp/PHQ4GNK8gwhjYIpQIyFW2HQBFxjLAilqGq0AwPvCSjkAW4AnAA/9bml0nGRKa/bWDQZc9sGVSZayNqrVSkfHTL2pjDFJqsbGev0uoDABIQApE0B6hScEAFHkOYCzABrCkGyS9wZsWRgcB186GBQFzxXkJtaPj60YVW7QPTFTzM3BQh/6BWTZaKtVr9U80PjEJA2KA50O1OuTK1cefn7nyosuUjYgc4OVFD7kbnD8RDQ6Ojk5Od1e6JZ5fbwFpIlUHKcm9Tl1KNbeu8H8/PjqlaMTkzPHT+S9zuqJSR4Mdj33vI6Tjes3LMzNz3e6OqnFCCDODUqfl+KFSQuYMkTtgnWqtVZlkeeLXV/kisPmdRuuvGLrli2jzQhqABogB+jZocRTpCCNQAMkAGmlvgxgAbwHqUY9AwYhAvjOswtTs7NFaZWJyTP7YAf5e95+63tX4EnY/plHnp5hVIAMjrmwtpNmiaFw+fqVDYANACub8bykOoEilFKW2xrwGJgC/MqJujTr+05MT/Xg6muufOj7J3q93sFD5faLVzx28rg42LV7fvvWNf/1kUd0lu3de/DuG7eNJ0nb+9ki/84jzwSn50+e7FH55qu3Xfubd//T/U+ZXlE625vpYaQbK1efmD7xl1/6xnXbt77t7W8JPfvkAz88dXD0A3fd9Mhzx/+/P/i7m25786/86j3/9P3HVq4au+PNtxw8dCCr1T9w5+1P/2TvEz/Z1xn0wSEAWdEgkQtgFAGX1g5UUbIHYJFQ9XFAAgMHFGBmFEBE7z0zMIsiQ6i0ghDYubLX6czOzspgzubdwPuKcuBlsGXt2JradaOjo7v37ittDohlmRMBonKuMBrtIP/or3/sumuv/v3f//3f/a1/+81vfKtT9JQiKM/rE7M0FS1PcZbdwud9/Xr8a4zzehfIy3z68qtYNkm/3E+84pacs8iwtyBQkaErXIW3xBBjCDYvFuaguwjIr3XyZUBFxIGJiAS8D0BRtnLD1uveOLb24qixtu8Vg1MJYQBbDFSkxLEAkom0NkFwaKMGFRYVqlyuwvLQ6QLSaZG9pdcVEq6680KoFNIYWUARMUWN8Q1ba2MTk0dbY1P7X5DFGQBhYKJqNhThpT7Fqzikp80QAQLYIiiNiKgiwlC1PxiBhIb8p2FX+eeO5f/5pzIvZST9tHE691yWWS8T9an8E5auewSoqBuIKIE9eEEEZBQvzJVosQCISHC+spUKzkMQZEZhEQbvoEQ30KQUQAg2B2fBO8XEpKTXFYTCWVKG2UF/AP0cA/ses9YucOG9dPqklfaigwOlgIOqcDzeYrdbegggCgJ4D5FGnRFp1JpK35+ed7YUQV0WSmtopBiRQXBW8iOn/MyiWhzIYtd1OnByEbxElPRPzonIys2bOvPHDj/3bNoaWbf5kr3PPAXNRkJ6as+BvD9oZI2Fufmi3THNkVVrVp+cnrZFnraaPrfTvSlUxLbMRAlCyAuIzOiKFUrg1PHjnWIwOjoao9qz/yAX9tKt2wbt3tTx4zpN4lZTRLQPIS9x4GMmUlFA9N2i4GAKIRXAOyltpEUhHjpy9NTC3Hef0EqphCg454STKC17OTHWlNaRdhFgRJEipZSm2HsOYYjDJyKjYx2ZU9Nzaa2RA+ZlYYwxpAa9bj1gDWArgDnatkW7Fo3k+SDSqLQuhGZ6+dMnT8SrVh8H2LVwEuurRwkKybnvUoByoTMYdENe1KJksd1/9Cd777z1kqbR7bL/2JOPf+oDt0YgA5s/+exTV136tomR0dlOfz4vTvbhjZs23vfjH7tGK/eBctU09Qj8t+/75rqxsQ+++5Zn93Qee+pZoiBKH5s66YrSYLbjuQMv7j7w9ltu+uAH3/7EjoP//jN//e73vft/+bcf+c9//c3nd2V33XXH7l17f/yd773tzbctLCzs+N53brn9jvUrxr923/eJ2TRG5xZOlUWcNutFd0BkocxNaSEAh4AszJUksyCKAgwhsHgQsS54BmvbXjq5VaAgSkLflkf27T5ycHcijgBIpYV13ufP7Ciee/TZidGRHS/s86Cjet2WNokiESmLHOrJJ3/rty7esvFzn/tclmWDQW/bpVsP7Dk43+4MlTCGKJkz6Jbl88rSMzrL2d341/OYf71xoXOHrzUF+Rkxri+cx+AQY1M1aiqBXQEE1ogmWCwHZXseOvMQSjAI5Wuag4kZSBEhIYhzAcCYyXXrL7t+8pLrBlhr2zQAJrGO0cdosxgQ2argGAVZ2AdBRIqixHtLQFz5Vlarfsl+nbWPLIiIQw+mUFVhBYUFfCAPURzH9ZXpGopsgJk9z0F/EaQEAk2BvYBUedMFDzjRkhfV6eM4hLqxlIUD1AkprVg4CCIiQ1U3IMHhvFx5aL8aoMxPF/+aqzJLeQ1AQEapMswqc0IB4TOAYqlYHRZAhBiHcB4GRAgo6AHY2gDMwKQACTEIAiJwkE4vRCaIA7YoQiKKxCgqWCAvIAgTQnDgvALRhAjMHJwtpCPQzxnAogZDgBbAEmBwjjvAjgPPAiFpBLZQWlJaMYe+LYR9ryunNJCyHlThCbwxFAOg9/0XD4J3INpY7xbbCjBOank/l7wsymJxZoFdrptNQ7Rv1wsqyTat3XBoz/7Q7oIP/SgVEVC0cfOmk9PTdjCYWLXKFkVn6hRoDSFglvSKLgiYWjYxMRFl6dSx4xZ4w8WbvQ+7Hn+y1WxOrFo7deRoe3Y2HWvl/Z63JShlRMgGDIBgQKp9wdB3obBIgVCUCDh2DJFJWFTuhFgwoiCYl6FfDCI0yEJeXDEoNENMABBFkfAggAyN1ipPL1IKNZHutbsu+EibEIJijpX+4Q8frG/bsnHtqhrF1FswzVqelz71QWhQhpzV3933/davf+QfHn702b2Ht22rNxnqtdhWddDSFWXRnZ2Nx9Z1u/1n9+37pVsvWTuxwvUGe44emoFbJ5vNqQP7Tog/lsMNV1z53ft/aDk8+fT+d9168ff/ca5TFC5q+gElhIVf/J9+91cPPL/vs5/9q3fe89733X3rgw8/88Tzu1SUqjglQR9kkPt/vO9HLzwz8Z577njn+vVf/NoXxkZaH/zQPTufO/q5P/vb226/+a53vOuBf/ru+NjI7Tff9PCDDzQnVvzGh+759vd/+OKBw42k2Z2bywd1kyUu5GKL4B0HEB8ksAjaElACcajy+ApF6Bx7D4FzYFuUUloQ8UmqnFCwJSnlijKIZcQoHbX99sGjc8dOzPvAJjHsAyA7V4ayXLF29W2337Tt0kv+6q8+hyC/93u/O3Vs6uabb3zisR3KJH7JcO4Ct6yc8/ecj16P/17jNZ/fV4OVkfOVAF95PVU2QwBDqXoBViHUtILS9ebnuLMAvgR0wHhhY40LBleSCNXG1hobLrls3SVXDCgtdcPrGkpA7IVywL05cLlSKiBSVFMkDjR7DFg98BIjo4gI0rIazBnI89n7RUQVXbHaHVxSAWZBNElg7toyVlFj5eY1Plhr2wdegDyIlEP/GADEauK7YKqxfD7FYRALQLBQgiiFoIadK6UBgJFIqu4YA77csPAzibNSGVx2YfysdW+GPk0AcPYunZMvn5NNL3tJ58fKgDAgiQgMG3RDUeAhuf+cRSCcRufg0L43VCaSULUOpWqeAiGwMDGwYwUQpAR2aIZyfxogBQxWgriAAMwKKVJCws6XgIoCSxkYDATWLIEEEgRyQWm2HmwZ8gE4BB0zO62Igw8aBZBDwDQTBOAApERpjwLMFmQwLDlpEIY4cc5BCEmtbqJoMOhCkkBpud2BsZGNGzcemzom09MXv/HGA3v3hl4Og1LHmW/39Whr9ZZNh44d9YP+xosv6U7PdY6f0BT59gBADBobhLJ4zaq1iHj02DGdxmtWr16cX+jMzo5NrBitNY4fOqI0gjF5p6eyNBROKcSqQokCyIIFakOIHIL4SjbZIAoAqMqNJBAE8d7mUBqtU6UJNbMmRCDxhTVkEp2Whfc2iAbBYQsXERQAerHimR0pBYLOhzSK+t3FVJnpfvG3T+/cPLswvnlD1u2WttRIwWnvIyVKm9bUbGeB1DNHB12XJKBaDFDkINwBCAzEzP3CNAGATnR7Tx/t37Bt+6lDh+e0+qefTF225eLnH9/RA3lq5+E3Xr7xqYfiU9btOrzrvbde/Kat636490g7BFIjvbIcrde+/PXvvmn7pR/76L1f+Mrfj4yM3HrTm6/csvG+Bx6ampv3aNiiZQOkD88V/9dn/+aGG6/69d/5tUMvTv8/v/+nb7n1jk98/N4HH3nywYceufud7yjz7jf/8XvX3nBdvd544LvffstNt7zp6qu//t1/SidbeRm6nTnQxMFXrTj2AZglsPeAIVRNuyGrSMQHHwSARSlVkRGttUUZgAx47wW99yrKENGzAGowqaDXSLbMEZTWJli3dtO6t/zS7Zduv+Q//sf/d+OGdR+9994nHn/i+ed3/soHf/X666/d8dgTZNKquD2cZk7jG6pyKb7E0+CMF885j9H0kvHh9VznZx//bTLIZVPyq/u6LDVWzgT+FFt+TupTzbIACIwQwBa2Pe8X58HlSCzi4TWrpjAiCgSpWC8EY6s2rN20zdTHehI7NCGAFlZouTe7ePCF2ROHB4NCxcnIyjWrN15SG19LUVIE8N4CaZGzJ2FmhPOQu4ZHhoZAGZHKJ3uYcyhAUAa1YcYcQBvVXLF+/cVFvjBr/UDK8gz+SEgunLedbi2d/RwSCBULALtQ5gyEJiEyIFwliTxs3lV/f749pp+3segvJIQBwmmjznPsvwmkonXAadXoJe1oktNqewJEQyHhoXqOAAcJISI0ywpvIlL1MsEF8AFcwFBZEIOIWO9QI2Ng8SIMxNpgjBgLg83BOWEHYkEciAVgpQFcyewBPaEQeIDgg4VyUOEdhiJM7IBLIQ9UGSNo8A4U4Eh9QLw46EIWgwLotlvr16/bsGH/44+U3fbWG286sHuPX2hDt4c69t6DVh746JHDvt/bdOmlh/fvWzg1Q4LKuVatoSl2A0tZunrDuoVe59DBAyMjI2vWrOl0Op0jR9dv2pwkycGDB7XWF23cBACgVPBiKNEYgSgWJJQIXV3lY7qsh149lHFwmiF4Lgu2PhCRUsp5qwTSNNXaiHfNtLZl02aQQFpRbJTRjXo2OTbeaDSUMlrIsNZBUUDFGkUTaBBldIyosqxelqX33pjYGNOx7mBePnXq5MZL146M1oMv0ygR1uzRoOkudqO02WfoQdwP2hWlAWDvtNZ5AFGESnsbuAzBhTzQMy/s3XbRCCmgeu1HTzyxbQKatXoR8MCh45MprBsdRXTlYGFw7OQ911119zWXrSSnbN9oAKEbbrhxz55999//wMc/9uFLNm/63je+Onv4wCfvveu973jzeIwGLCn2ChaLgpPa4zv3/Yf/9JWjJxf/t//j053cfu6vv7zp4ovu/fBHHv7BD2aPTn3gPe/pzC/seOTRt7/9l/ftO7DrxRfvvffeq6+72ha9LI6AA8nwIex0VYYDSPBziFseAAAgAElEQVTEAaVK1qUaXwEAUQGj84AAShltjDGRiiKllDCTAgZga4GQorgM7L03xsSJ8T5vtup33fXOrdu2/Oc//pPVK1e9733v/fZ3/vHRxx6+++67BMKdv/y2NRvWvQx58mVmzZeDNbwe//rjNZ/fV2xLvcqL5Hy/WzFrls18jMKRBNvrFvNz4PoJQUJ4hj73WkIhADAhgTIQ15qTa+vjqworonRgp8ApzjsnDu7a8cALj3xv+vnHeod3tl944vDjD+599vGFEweh7CUKUqPoTGup0pw8a1J7aZtpiJVB1FobYyo9PQVoFElwIqjiOKDqWxGVja5aWx8ZgziD01kPgAjyhZONM6DjZdUBEQEOqtLZ91ZcQewIGZBJ+BesdTmsyrwqoarquQrOys4AzlVQOGdXzzARziwSAEAqQ4Nl68HlOae8ZG3V6+UuknLWTyNJ1RQSqeTAGICJdAWlOXMZey8AVF1yMAQ9DdV0mIUBSRFWSHIgRdayQq4QVyxAmgDRAyApUsqREmEgFE1BgDUFxcwMkQKtwUSICtErVJnRrIQVQL1mSYNnUFnQGtIGAyuVRqTzPKdIc6ShlgKj0iYUHsSDzoAEFAJjpGqBUTIDJFwWhJSpuGAfjKlPjCHDsaMHYdWKqNbYt3sPd7oQAigtzIAMkQFEzJL169cf2r0b+gPxKC440iABYyMKtm7f/uLe3dDvj65cOTLWOnj0EHfal9x84+EDB22nC0HYaNKR0lHgAMEzKtcfQGQajXo+e/zyi9a/7603XbKhMTPPP3zyme8+vtMpDSYJShQEIRkU3Uaz2e4XYDBQGSVoub9py9pd+/eCYF4WSM6W7tLtb3hy5+4wYFVwhAqCaK0QyAUvKHEUWxZB7PfzKEoAEAhzV0KkgbmU8JWvf+Ntt7/5+D/scDYkKlEePfczwxwKEeBAJkoH3gYUALDWBoFANMiZUSOJRnJsDh6eyuGaVRtXHz0yL6L2nJTrr7n+xI6njxw+mS/CdVsufvHUvsvWrL1t3ao6wxUrr7/16uv/7y9990g35xKf/cmLH3zHW48fnf6TP//iZVsv+eivfuTFF1788z/+szfdcsv//Mn3P/jIzoeefaHdL5MoRaVKhXkvf+ixnU88+vw7737bB96w/eEfPJCy/7W77qwbfeDooRVJtu0td97/4I6dew92rX/q+OxlV1w+tmbN3KnpWpbl7bbWxmgQ9sBijAkBMHjioAgUUfCO2StlvANXlOyw3hrp9UFAKeCyP9AmKr2Lk6Qs+wAatAHmwlmjNXgm4aI3GBkd/fSnPqU0/vEf/uHqNSs/9rGPfv7zf3Pq5NSnP/1pl7vP/9Vf/87v/PZpF7vzdN2REYGkUqjC0yNDZWp/VsG1sj65wLD6WnVi/qXFa2ZmvWqXoipeA9/mp4qzRv4LbsSZLyCee34vVI/BJSpGtRic5i5Vafqy62E5H+oV4MDLjzYiAGitfF4AERFBcAigtKD3Pu+XM1OQdxJA4cKzQ3rNSTUCoAQQYBBgAkrqIytFZwE0SEDnagYTKB9/7rHugZ3Qm9VceC9AGnKY2/UTEbl2bCK4CLQoIa7oy4giQsMLh5eXlc5hCFXBzBgEKmESEAhWA1n2zEqRAXEeIIuy9Rsu6pw85JEEFaEPVR6D6mV4Ruc/78PLIYAIOOsBUUCZWFCRECMAIlWemRyG7bIzJ2TZ2XnFdvM5Nbkz0ONhvEJVZrkaxC8gXrFm+PID2ekG4dnZFSEigoJz3wdEVEqRMRhFoCpDyqVks1oPkUpjlaYQG9AaCFmQBT2gJQyxUfUaNhrUqEMcBaIQAMgAaVAKjIYkdUlSKMwDBwcQFDuwjkEbGBvDyUm1chLGWlBLg/V5EFWrc1aDOFKjI62N6+urVqmxUUhSVJHWCUUJZKlqNerrV45fvLG5eb1ZvUKNNfRIPRsbq69fxYlxhuob1jXXrmVmbnchLzVpndUgi3ByHCZG49UrVq9efeTgQegPABAjBZmhkYyama9FUE96toA8B5AAstBpc1FAlqZpGirJIMKqKRuEAQGiWDcyM9HCLPEcRrLkHTdef9VEvdkZXDFG77z+iqu2bo5jVbBnTXEa+1BojeMrxqMsRa0wjSiLoixiCCw2sFUKtYKIpJnF9VZdZwkkkQXwHKqigjFGxQlEOslSHUVEVJF0gpcgwgrjWj2r12qNWlbPICIH7Dkws9GUaMoikxiIoghIO2APyAwiohREaWIaraTWTIw2AlHS6Pfd08/uu+6Nb8gZGNTM3PzWS9doFRsTPfbY7m2XrM7Yv+vGGycARgOsAri4CTdffmkkUgzKg4en/urz/3h8ev63P/kh0dnfffHrzbGJ3/mdTx07cvTLn//yVdsv/vVfe99l2zbEOgTXHxlt6jgVirul/9I3/+HbDzx8yy/d/va3vuXhH9y/cWXyy9dtWb1y8mtf/+YV19/wpre8LVdxd77z2NPPdwYWUFlrm816URTT09xqNKNYt9vtEIBQYkW9XocIrC2MUnmeGwPClsuOcy4EKMuc2as4FhFFJgBqFZFS4BmEQLRzIUkSZo6T5Pf+ze/WG+kf/dFnN23e8IlPfPRLX/rSoUOHPvWpTx0/fvwzn/lMu714+PARWwzOOw5eaPI7fa+9Hv9y458HaXr57/9zPv3pIoQldggHAFbgdfDki7IzD2UB4BAsgq8kxpBempK/QuAZe2UFYDwaBg1AJJJqNpL3Z493j++DxVMm9GN2NfTIBbgc8vb8iaO9hRmNDrwj4ap0JEv07NPSNS+fvb2kDcckrIQJRIYNMxKAOKtpEwEQA4alRtZPoYAwxHrI6e6Bx2AlOISwZAxw9pT6cwt9oTTlHJHhl5e2+6miWv35fDXPn/ydfy3D7KyS91n+ECAAQLysRkhEjARhqL5fdZmUMqAj0CoIBxbAyrAGBAEVgSZIUjYGPEIZKrtvqfy5Y83NBqY1pYgI0FrX7oB1WieWBeIM6g1otgwpDsa321DkkUcmAlCQNPWq1Vk2RkZ3+50wdQKCBVAhH0A9hfWrR1avbdaa1toc5kK3kIWOtw4UQmrMVRtprKGaaTnou/kSpqfbgwBaAayqr1uzbnRlrZaenJvp7D0I3aIV19qugMTUN6xXI42JNWvGGq1Dzz4P7R7kFhSKJsgis3I0aTaIMcuyerMGRoMXE0dEZOr12khzZHTURFFQBIGElCAAM0RR3KjpLFbG9Dr9fH52hMOV68a3JGAXinLRbWy1Vq1oHg4cZc2s3khcfmxfhyDatGFT1x4tEDCBiEKqqfKIAA+ATiPUo2hiBBqjY7qXh5T7ix2xgRE9iTJkaomOE0SUwoUQxHthFmYg0mSyeq0eQV1TraExiwufQ3CF2BGTRTrOtE4AkiTK875ncMOBQghBJ3GcpFGrmWowCAuFrTcaswsL267a0mqNKkr2Hjx0++Xjo83Rk/3jT+7bdfcvX7pxfHxjq1FaoNzbGMcT1TA6IcVZ2i/yFxenc4IXjk+99z1vfeP12771tR+rnfrOd9w5N9X+6pe/tnrzhg/dc/vMqcF3vv9ALk7V4oWT7SSOSiiefmHnkf173nbdtb/ywXcrgAHA5Lqx2+6841s/+v7MQqGymGor7EIbdW3FRMv6Nrk8mRxft45mZk9NjI5NNhpxDN7mg35v8/oNY620zPsAvGXLlsCQxIyKLtm2QceAytvSx5EuCwekAJTW2vcKUGmcpnbQjSLTa89s3Lzu4x//eJ73//xP/vhNb7j2zre9/Zvf+Na+fXs/8YlPHDs69dk/+IObbrr5lltuGRlplWUJy0fbYcP3LDGu03+B5Zze/+tpzb/0kLN1WV71+VpOLF2++IV0Ymip7H5m2er9177J54QEN8THCGsIRgRswf0OdObB9wkCQGAQPvt3X3sQAAFqAV3hNkkch1IcF+1T0GuDuJgIAmsCw0CJLmyA9sKg22muFAmW0Ax5Vkt1GBIIIADhZebhs1MvQqkMoRiFERkFUISAESDNsihJC1IsDEvYi3/e7ccACMGKBwBRRAEFQQmECypf/0zj5RhMVTbzGuysf85RbQnJecBYiMiVh/rp5AYAYAkPhYCoEBWR8NDuE1iwMo4EQlKkBJkCIJ9ZuPKa0hqNVhiC10MPAUAghDSFWgZRLAg6jikObB2XNngAIQURmkw3R4wxec9DN9coihG1snFCY5PZyISiDLWqpVnH85rVF080G8/v2yP1ePXWbTpLFJmaMtJozajI8QmVl0kSmcmRsYs3DWLUmlrNGsVmkRTMD6CWTm7fOrFqJRah9BwQVLOhanlRlFSP6+tWqbFmc9WquNVa6PaBtEpTsI7Fi1HZxOjouslSKdcvJYvSZh20hrIARCJiBB1HaZrW6/Wi14OBX8JcIxCZOEpaTYy0oBl0er4cHDk2e9HGibHxMQVwwkPH+wLEZOno+BgvzFhbIKpmazzgCSBKYoOca0TnXAgBgDSCUZjEkSYIpQUAU0s5H5AEthYBgxdFYmoGBEMICiWIgKAwUByZNIuTFLjQoBIFJkmxcL4sy+ABlUhlcwEqNp7AVqkroFTkZWQxiTZRpAEFSKu+LR59dOdCt33Rxi29gZs5eaQDcMPVl3/9yP459D+ZLi6/8oqfPL9n7ZVbJ1vaAZwAOLowK7H2TJjGE6MbprsLdYX/5b/+wxVbL7vnQ7cdOsR/+NkvbFq/5hOf/vAzT+/7yz/5qyuvuubffeJdX3lg9/7Dx7O1E/OdLlodnDYTK76345n543M3Xnf96i2t//QXX8m13nfw6MjYGt8bgPXZ5NpMomLQzpJkzdrJD7/71q99Y4cxptlsfOze999//yODXnf7pds+9tEPPLbj2Xa7fcVll3/4Pfd84QvfLoveG2980y/d/tY//dO/zrLEDuattWmtXgxKECn7/Vqj1e/Zst1pjjU7p46uuWj9b//2b83MnPrjP/ns2996x8033/i3f/c309PTH//4x48dO/aXf/65d91zz2033/L3f//3H/zgB1/PRf67ip8ngOkVGkOv4gsv8/1X/i4EENYA5L0fdG17EYo+iEf0QZiX8JMvgx25UJxdAqAl1XuItPHitQGtNSQx9MELAIAPwMNakQFSVaNAieKXHAERobONNc7Z5eUVziGABgCEGJkEgEUNVetERIyJFMUgSsQDEC7JI7zW/a3AmwCMwCAMzOJFhMkYQFOpqJ3pJuNLvSt+ZvELIGP/zHRlLhTDjtKwPDN8c+mIndOZI0AEQOHK8QAhBGQGZiIFRFJZZuGQgwHCgatCnwqytFZBAAVRDEr//+y9abRlVZUm+s251tr7tLeLvieCvpVOAkUQpVFUGkHAHptMzTSbMisz6433542qUe+NzOcwqyozMRs7xBZBQRFRbFBQTMGULkMCgj6CaG9/T7f3XmvN+X7sc27c6BBIqVdZ6RoxRpy7z9r7rL12M7815ze/GXyERl+pVNKEkyq4F/MMSsKFuhwj3tg0gBGEycbgxTmT1Exa7Xnx01MwPHb4ysrY4qG0Wa/XK63FPFSrLhprdXvjeyZGlyy2i4aTTkfG5wy4OtQYXb2ax4ZanWlpZ8sWja1atzaF8fXZoUWjo+sOaxfZs08/U01cY7Q5snLZ1O4Z7c6iOkzN6uKVKwrDmx9/zEUsrdeGhodbnY5mWeIqI42GBj8xMwUf8npKRGBTLgV8CHF2tlV1gpimKYgQgqoyG4ARY575fK5r0gp5imoyU/3O/ZsqzdPWrGqOZ7h/664ts8V4V5rtuHpFNVdTCCkUVW7nkZwZUgMP45jZRmIiZpfYlCqVikaEds9FNmwSVYoRRRgaauRSFL22rbpqta6IOuBcgwmJM/VqJI69UFCwitQ651wICJAolHnqpaYDeGe94aLUezasMfiAoig8nEhUtSAhR8cff3yxTZ57ZmuVx7btnjxyrPHw43OnHzN0++2h58ydDz90zfkbb/zC1/zYssWLhxGLezdtfirEfKTRnpwD03Sem6Ta9ZIkyaZtzz103dOnbDj6vR+8evOjOz723770lnPP+ZPff+9377r/f3zmlrdc9OaTjjvmK7d801jNxnuNkWXbp3tLVx2xqQg/uf2HndBbtnpV0e2Z6tjM7ulaY8g36tba2dm2lXDYiqXXXHb6F7/w4y2bH127ds0f/cGlN3zhrgfv/+XqVSve/e4rbr75tnvu+ekpr3nVFVdc/NUv3vzwQw+cd8F5rzr77M9d9+XxienO+K7qshVFR72PqiCIS6koZlOb5D5vzfSOOP6I97736s2bN9926zfeeMGFZ555xvWf/1zW7X34wx/a/KvNX/rSl95+1dvPOOOM66+/fuvWrc65oijKNxcAHJB1ogOJvAErQhZ+9fK9H37bXkp7Xhzzoq7XgclHz9NKfsw+Jvl5d32hR17A7GRmFSEVA9EiK1qzaE1DC6IgZRpsSbhUvIQKiNI3En2FaxpsyH0hoqlNK82xZHhRMbU7i6gnFZFgrM0Lgkns4hWVxogXEnAEKfVzUF4I1GMtCxPuW5ceHAmqFEkZIiWVp49XuKw4W5pMIurryr648+Uy9MHgkgIL7SdOIhTKADER+mo3YJ23oS9D+7esKwOg73fpe44E2k/GBjAQLxz4zwY70N4/pATeMUZDjg0GsFGJpAw0xQjvjXUk8CVQBzGRliUtRdHLUQRfqSS1Wol12FrxQX1Atxt6ua1UwQqGKEIUQJA6U018VEzPwpquX5RF/+gjj8B7IIdd4afH826GJ5/p+bzJyzvtWUxNxLm5iTic9RpurtnZthWTU1tXrVq8aHTPtm3Ytme2tTQfqo7PTofnnjOr13CSZhA10R21DnU3tHzJ0KLRhx/ZrNt3pevWqSI6DhzZ8vrVq9au37B1bgaT07COFNW0wtaKCBFZZojkeTYzMyMi6C9XyllS5Llvt6sjQ9VqFb7IhDMxd296VEcqlacqe9rtPW0/0TMaDYpQtUYrzqU2eLGMouiRigZrY7QpVVJjGHm3XUXinJHQJYERzz6vmHrinISuY16/cuVM1np2ZjzL20m1IhKh0SiIKIKFjTinpHnWyxFIYWEZZJURREVynxXB5oAyCSGolOFNUQ0Rhc+8wntfJFZJirxFiJe99S0PPvirXzwzXmeza2rqV09sPufIjYctX/TgxMTWnXuqQKw2v/7gJnVJMT0+Nd2qrz08JlXYNgSxk2m1WmRZGEq14tJGcu8DDzz9+JZTX3nKpW+7/Jc/+vEz2555w6VnV+5v3PiVG07feOaff/jS6795Xz61lZXTkZGQJNOzXU+JWTY2o7bI0RxaUnTGi24ujtxQPY/tNauXXXHZ6Z+76e727OzIyMjv/d6lH/v4DdO79oyMjHzkI++/5eu3PfXkk0cfe+xVV136yU9eP7F112tec86555z75a/ctG3r7tjLmyvXtiYmKvUl2VwLbIyjNLXtyQkgbTQbh61fc8XbLt2y5bHbbrv10ovffMwxR/3d339i8eLF737nux5++OFbvn7L1VdfferJp/zFX/xFtVr98Ic/XK83cOh8k4O+wH6LYP4NtH8dV6ZfIOaQx9YD/n5xzpgX1PYeR1iFqe9z0eBDr63tFoqcSLis2wfAEJQRiIlFw0vNxJF+ZEcBglcE5ZQT1xxZseHYZ9tz2LO9U+QgBluQwejytYcfPbRoaSQbyc7LiEipTLzP2bxQrowShzJIRSxgVWUVGvBXBApiDIp5MwTzr/gXfarK/VLYiKKgEIocjogtWbPPwF62CM8+szQolfgy8Hz7IjkvePtgcPP/Dn3kBd33wZgMMmXcvu+e00G8VVnJEBmQAXjek6il662sikmACkRNEdOoqeg874ZBFsRRK2qMB4poIhkpH0NRR5yys2CCJa1YTtMEFYeqRc2g4WjIJcO1xnDdVKquXh8ZaY6MNS2EigKVtLZopDE2ZIcrWDw8Mjq0dNHIyNgwLx7B6sU4bFFt3dJms26ao6iPLKsPj9WbzA6KqksqxiVqETUW3nvfI0EjWXrSURtOO3HdMUeOj4/7PZMAh7zgStL2OQw1RofPOPHk809fd9ySVVVYBIntXsJGvEcIAGL0JfFs165dQSKIUDKDAIiUYeVeZ9ZET71OjeGc03pzG9G/5MW/dPOZpA5yzqOa5WjNZbHltWti10YgZBp6vuhAC4Y36uE7VSMnr1998oa1VRQhzx376LuigRkskpJZs2zZkrGxfgFR9YpIIoz+Kt+rRKiQRC0iR2YYhcnJeVSEjHrELqQHICV1qqTRAFFERFRBEjkEibkneMZYrfrkr/7lSzffetjRR7/9TecurlZdvRoTmspw2jFHuEDZbP7k1u5pp581SbVtPt0+za6+IvdpLJSiki9cra6ByY2oVonTPMRAxbM7nr77ru9t37H1ivdc1IV+/Zs/WbnisCuvevejm5/49E13nX32Gddc8zZGN/SmfWdyyVDiTIwz06GILmnmBQQJoqQckU28YsPIey/feMOXv7dt2zZr7R/+h7d/4hNfm5jYMzw2+vu//4Fvfet7997787Gx0T/5k2v+7m8/OTU1tXHjxre+9YKvfvWmx7c8pcKm2syyAHJZp+PqdTJGNLRnJ4dHh2pVc8yRh115+Zt379z69Zu++rbLLz/hhBM+9alPjQwNX3nF5Xfd9aObb775sssuO/HEE6+99u+SpPI7v/OhHTt27Nq1yznXfxb3smQEB7x5FREkv8Ux//bab+6SkR4Qzvifcj/0k5ZZWSUWedFqIetBlST2aSlU6l8RQMa86KW+zsunKIAwn1OdVBu21swpiUlz1RHHH37SGfU1R6I2ClNDddgsXXPYsSctW7eBK41Ijl0qYCk1QRce/EXS5FW1D4wGhanLNqh/TX2yBMAl6nqxZwuUub0olU0ALr0jKvB56cKngXOhb1hftlZKq9DBwJIC+0CbvR8XzK/uxSL7I5KXiIcOhmyk/1sH66+le40xL8WjtPchGXwoBzMYNxOREoHns7thBuzqhYxFCJGCBtqG6LscGSCnlBInxoI5JXLEXEKhEARQw7DGWuuc4dQgZbEMR2DVEI0gZaaEXWqRZbE1x0WOIsf0VN6ec0YjIlILjdTpYG5Oui10e+jMzbVmraCRVGqNZqNSrURCL0eWq49ZJ0vTFM0hA+IgdZsgscONZrM+tO2ZbT7LbbOJNKmmlRgjO4sQQhByCTnkIQDM9Xq9VosxQCKq1UqlUvgIAZmkmxdKBjZB4ohIyprLTHCGmJLUeMlh0CtyrVWmwG2Xek5MrR5UXEJDw7WyTAcRWWJngIThYK1N09Ra6ww5DcetWfOei0555zknrFm2zDBqtbRSdVmngxB9LIrQSxNxHLz3SkxEDC3/A0WRgBA1REvMTCYpr6oPsdDAUGNBhH46fMLsmJSMmPl6JSAyUFZib6BGJycnL7nkkjPPfPUNX/zSs5u3feSqV134urM7s5PPbtly1NqVS+vVVPVXjzy69rAGXNVzwrWmRyJqmC1F0dxr7tWL9nKX1gQcFVxJteImW+17fv5P3/7uT9531TnN4bFbvvGtPbv2vO+aN7Vm5j776S/OTk6+4+rL6hVngU6rnZgEWbF4aER9kbfnmFGppkXWWb96+aVvPOe2r92++9lnjzns8Pe/582f+eQtz23dunzp0muuec/377zr3l/88+vOO/99H3jv//jbL0xOTp1+yumvf/251/7tFzdtftwrjLO1Ws1PdyvVJpR9LyeoRG8ryezsxEknHX/VlZdv37b1C5//3Ac+8L5Va1d97GN/OTw8/MHf/Z1vffu2H9991xVXvPXkE0/65D9+NqkPf+jP/vyJ9tztv/znpNEIIby0UPLCTMmXZSn12/bS2gulyxx8OfoiruO/KoZ1cNNTliOgMmgyb1ZKKKPK4uF7yNqIRep4XlEXBiBTdmPmfU3j/vf2fhZJ9xlK358iBCG021nhNct9r4jNRSsOP+G041517lGvOn/9mecds/G8k846/+jTXtUcXd7JQqeXiwhr6eSIgzmU/WKyB5mifZcHg88CiJKoRlawMsAKZrbMpUJgJBJA+EAQQAv+7edY6G8XUCxhqdBedRQGQQUxsHom4fnSJS8nWrUoQyqKEiqoKpeygtrnvxL1q98AgBLBDrQgZG9kkGMZBy+7zUdwSAf865KBAsU88aefa1521L0CM6oYcJ5lb4JS+ZsRfV4uAKCsGaEMUSWBEhEr9eHL4Kajsn55WRqbuFzDlwBHQcJEzMSGEWNiDFIKIcQQQUzWGpvYStWDwMZVG7EwyIsgiuhjL+Okk9ZrlZG6Wu7NzkiWg21ZazUYa4YalKadvAggag7Jrj0QoOcx1Zl78rna0qW15WMxRm5lnad2yOQ4fIHUxO07xofrw8uX5UvNWGOku21n+/FnMDOLPEfIMto2w82kWnUjQz4Wu57dLXvGodqZmkq6K2pLFrk1a51q1WPiuV3cyvInt2pzeK7TllpldOniMOINqDs3GyZnEGx3rveLx7Y8uKsx1e0016wyUJMmIQRIRAiimtbqrdGx2tCIS6rGmJHRsZlsd6VeiwQgIE1qjUo6MtINRbpsxFcdqNBQiJKDRVbAdBcvHZmbnW1xKKzhWJU2MkuZyNBQ3StZV4nqOTHVas2R64xPHQFs64A8atUUzjBLGkOr0zb1JOvOhjBt0K01hm3S1KCLhoeyglvTMyFmbNiGaDqFs+nI0IhBW4GZYjqmEr2d68WlY3a0MTJUSVMAoGqjHk3oKYjFGnBExTV4eHErj2kFtaFmFelt3/nhhWce/9Hfu+aBe5+48YY7z77o7N+//KKtD285fHF17VD10We2Prnj2TNx6rLRuu3lna511uYSsiwzYMeJUWJHZmQIToiMdWlRbSBo6GbdPHv28ccrOLveGKo1h3Zve/qNr1yedzwidu/ceuxxi3pz7XS44txQYpmSWmdynLA72iYAACAASURBVPNuNZHezGRSdaed+oo3veG022750fand5161CmXvvH0z3zuls2/evDEk099x7su/cIXv/7kE0+feMrpZ5937n/9fz+WZdnrXnf+K0991d9fe93k7FywVD5UrZkpY5KkYIKJiMZRr1cI0UWXXXL+Oef+4NvffeiX9//pH390stX620/8zdlnv+ass8667guff/LJx9//oQ8sG1ly7X//xLEnnPaqi97w8OTUHc9uSU48HM16I62CkBcBMYKI2YqU0hKk0WPeti1YePRLUihiyaGhgR36Db7kXkw7lNzDi12xHsqKH5r6cXBpwUPlrx7S0r9I5iYdQnut1FyYzz3Z+3lh8eSFYyCC7k2uGbjfSqmVfY58UC2Q/QbdVy49YFQHtNIJIv2FqpbRFCgUon0iKgYsrb5YuhMJKWLIO/n4c+hNMStCBKClFLwwfInItSgyBko6CNDXkF+oWtuvqr2QigvLbEVJlQAOwl4hTM4mqmBWR+h6b1y1sfaYxtpjoKU0MGdggSHHVbUiQirMHIIHkTUkEtSSqrLIvEjxwnuptNYlA3f+jEttdQACVoIRy8IgJxolqsaImDnq7XWQ9Wsk7HXZDM6KATN/uiDps5l17/0gZXRpcJGt4eAzaQvXYdJaLAEd86HS1g7UiRncXQe/9AfeErY/6AXbpY8iTPn0ah+RlXdC+YEAXZCeffDyCqwQeqlZ3AufogW7l2yYfYXySna0EIyqMkz/5dhHUOUBRBEhvPfxGvRQVYoCimAFWKQEtgooRMs/gwZS7t9BxMSkRFLkIetEqHpmZi2CFj34IrU2sgmOiZS8Z4ZTNQZZYsmrBkW7Y+fapl5XZ53hzo7dNNtBCJCAIqLd46l2dFUX4LPpYudEnJyhTlcJyAKm2zTTtZwE+NZsJ+wZR7eDbg5L01PjsZrWrJFOb3bX7rBzSqYnntw5YUYWxcSlK5ckQAqjwcdOhiLAexBpkjy7e1chWm02E+vymVavkiJ69KOmRGyIjGHX6/aYLQjtdmfRaIBzkJhYR7mPMXqiQmL5vrMgK8D0XK02vH7tms1P9FpZe3J2xnoYW4/IRZmiIGi0gTVq3aoBpWnHFw/sxg/vuOPJmakVJ5+i1kbh7kzXd6MYNYp6NfUxpKZlAjdYpN2JRVm+IJEg2hNvokkKE0NtuNIWxF4n7wpJLZCNDC8x98ELQl547wVkTFkwRUUQYHpestyDIDHU681XnvKKp598cNPP73n3u972yjOP+OFP7n3a4Y0bNwpw7Ib123ft3DU5PtXDsevWfOfOu4aHh3u9vNvu+W6PvZCCDVJrIgIJ2LOKxCARDDiFNwIAIYTCe41ByxxQJY25esTCSx4LFkBFvVFfMTK7e/uadRvyonfe60+74ct3+NnW0Ucdf9H5p1977de6+cyGI9ZfdtklH/+rv7PE69atO+/151/7t582iTvxyCNfeeoZ133mc+O7JtQwHIMp5JlLEw7U6fSMIVEvPc9Mb3rDha85a+Pf//0nsnbnP/7Zn27atOmOH/3wkovevHbdmn/4u0+0s+xDv/+RLGv/5//yn6+8+O3HnXzmQ1t3Ptqbrq5dMWo4L8LI0GhvYqbv8py3Zwt1Mve+ZPe+JRZ++LeugPe/TXvR4b9DqDzvB+l+LYP1hfz6vt8OgIXGfcwQCbRcksv8Grvc2RLD+9BrwfcAMRr3wj+dN4j7hRr23pgKXrC4329kLApCJCp5I8IMUzr8VaFgjUCM4IhEyApBQ8QCzECifQnZ4NUaA4oac6+KaGCwwE1wiCnaj/Y7L6MXeSC8ooAQ+sv+wQlKGZmAKYv47HPovbEI9NFMySE+AIf24dDe4SkkaghqA3EC5r7r4eVRAbbQ+QDWwMgrK0GJ530rA8xXTuQAoc67ZLDXObfP2fwmamIvXBks3LrgZ7AXfBAdqJvep8mIoCR1aRz4nwAAohKUNCqRsgGgKtAIBYg1oOi2YRhEINEYIIJYYl/xOWnJJmEDUfgC4hEDKXEWdU5CkZM1JErBV2LBiEoSRbg9G/YgdrpgCnMdKrJaJSHYXKMWnndNxVbRbXV7qjo9q9OziIGdlR7gpyazhzA6jETQnUM3Q6+HWn3xkRtkzfJoMfHc7rBzkqdmZXYWEFgbp6eR1qJSZ6qNqDGG3OfotpE4OALJ7NbnACPNnjGul3dXNero5QiKKBRF290u2+Hh4annnkNUYjPcHLIg+IC8W9Sbca5LxsCQ+IBuDss2souMTh7mOsOu0puYyYosa4wMJ3VLHANThGS+6BTtbrQIVVefzf2U70i9NpngV3t2FknSBeZ6sme6w0WFtJEoaYh5V2fGp/3E9OKVw6esWPerf9k0M9vNRL1agXFUE2/nptr1SlG0orSgU92GbRZpknMsqGjlLe+TmTbaraw31x2usRWQsoC6AZkPc3NzjYrzvcWd2ZlO7P3Tz376oavfmLdbn/jsjcccfdwF5218+vEdn/78Da89+5zzNh5N9fot3/3+P/zt9c1lK3qzrXy2rbBQmBBFtQi+CJ6KjEPFGCOUASKaw3sEYdFQxByY63VmOq25umkDHZ/3ijzGSBFS5Hm7GzVACFoAHr3Z6sjw6uXLzjv/jGv/5ro8651+3LHnnrvxr//xxvGp8eWrxt7xzrdf97nPd7vdk0886bzXnf+pT36uMz17/oWvee1rN/7f//Wv80x8rze6YvlMe0pDADhq8CHACCfOslHxH/7g7yxdPHztx//bunXrLv/d3/3hnT/65/t+efUVb0sS+4XPfCaofPh9H7rvl7/8wV3fu/CSt6w9cv2dv/zZrmqtunbZ4jRfbm3duVqlTjTLVBbDpX5K66Et036w5rftf832QmDNwuv4YqHJPsc5IGHn1+27n2nc21kIrIAyVPtBAAUkOoOQdePcHELRD0qU/fc/hmCv02XBT2jpnlmQqa17k64tgpbIiQihp6FHvhdFKKkrWFUFRDAKjmRQyscvAErzyC+qEhtrLWkoioKI2FhVlSh8AAdDD62hzGJVlblU2wZIBLGUehUCyEbyqqIE1ZSVGKF/1rrPeREEpYYsWGD7hhQCeEAG+GXBkGIACBpjKBBTGEUp6vayNbs/Qb3vieH++IG9KnZ9P4dQ6dwgHTi1IERQ3ncyywhlP/70UoY2D2L6vsr5q017O5Qj6m8pufKDSV0gkKyqKkEYiFRCGZChAcqJqmWWGDgSmf4PqaoEqCJ4kCmzkPppUkIAwZIGDwlQARswQ8SwhpgrkeQBoYhz7cHYlKK3hogoRipmc/S6MOMDl5wqCVMMEGRZnM1hEsTgmVGW5TIqpkyP9uhmyDIkQMgQI2KojqVHrFvnVyxq+bBl81PYuk3JuUhe1FZckIgQ4vh0t7y0LLAMC1hUR4ZbUzNodxAp7xRQosSZqAiKgFSZVDHXkhDd2rVotQFetv6wscZQa2oGXsEum21JISCGYwAoIoiswAmg3JueozxkE9Mwfd3xIgYQqyAWEju9HIWXIm9IyHIQxLlkCD0VItudk04r950cmUdkk1QTNc6lDB6pVI9bs+aS45u9baOTM3mmwmQA0hjzTjvYXDrtqYomBOl5UxXvuyEWAbEbs1hot4esm+edjKxLFAasTF6QRd/p5e1KIllRdDuuksYi//JNN5147FEf+shV99yz5eP/7bpL3nDB1e95+623fu++TY8cd+IpH/7w+7/+zR88/tRT9cZIt8gCDGIQEcDAWCpr73Z6EWSQEZFwABSRjEJUoqLby1u9bjev9IAs5qLBEgjgqNHnCBFBAQ94YzhkvROOP/rpx7flnU6vPXfKiSdseeypp3dtTyrphmOO2bFn93PP7TDEJx5/wqOPbJ7ctUdVX/Oqjffcfe/MzAxzZfVRRzz32Bak7CqJMod2C2mNk8S325Xhxoc/8HtjQ42/+vhfrl216qq3ve2Wb31z06OPXvzWS6vDwx/7L//Xkces/7M/+pMf/ODee370s4svueSEk0+6/rovtK1dfMrpc1O7D181tlQ5b/UazRHVnfs/pAM36sL0lN9yfv+dtOdPtzlIhxfZn4ABt2FBo0FoqXzJ93EMk4rVSKHw3RZ63VKkMe6vLId+8tF+P9x31fAgiMHz/n3qc15tqVYG1cEGTh3VKhzFZpAIMsoD+8RERglSeFqAihbE+zQGbziRGEUCM4cQVNURP/+U7uOSkb4bYlDFNQIAi0CU+6FfhdU+DdjMh3cXKuKUp85QwJcmhAHASr+YVTy4wHffucCIXkLBNjVcrnBeLu6vXRj5UzLAPLuFBmhgIRDpu91I6SDrqT6IG/isDuhAGITy5gOotPerg962L0AoUPaVDF4YkZVSoqf/twiISIWof8eoli5ISL8gH0H7xRMUSlLSrw0Q531ShsgwCSGqqEYVQRSwgbFGQRSDUxgiQGWQYc8MImOIDEQlxrJocIGI+ZJRMWZKgGUww3uwwlloBCscgRiWYCx8RBGQ57ACSynBC7Tb605MpbXKaKUy5NI5FS18SWwO7QyOIWS8WDXWWi+c+xLM6cjI0PSuXYBFiBYIITQazdApYCxAkhX9qpmFd8YClC5eXKtUt297bnbHTjBzmkgvT+AAFZRKPYAyE5GoUWjua8YxG4GoahELhYDJM1QJEZBoobVADdiEK6ZQEyA9cSqJp1qw1AuVqokh51xC0dEo3vuJXXueso/IyRsnntk6MzHbq1RMJTGGKHZEQlWd72XrjjiqCKjVGm2v4nuOFIwAtTFKQIwRPiIap9AoyjYogpco3qg4cKLc7vaqzdrFb73sV5se/sz13377FW8+7fijbr35lseeePK8iy6c3DP5tW9864RTNr7zyvOfeWLq9u/9IISIKJbFJC6HLXKv0UdmW9KnRIFyIaSkFkKJs2UZsBhjyV1mEBlLUKuwoKAQFRgGmMEx66WOjYoUBWKoJC7vdEIohCkLRavbGRoaIqJQFKHwqbH1Wq3b7UoAM7Mx0ec7JnahWa0Sh1BEiG3UQ/QyN7N8w2EXnndupZb8xV/+18PXrrvmmmu++tWv3v/wg+95//urwyMf+/tPnPj6s9/xhgvu/sGd373lx297x7vXHrn2b/7mE3Mz0xgaMju2HXn4htV5XFFrUNCnduyK2JtkMc9+eyEm6oBH/+B9Xlbt8/+d20vCji8WcT5P/4N8tWDTr/2hUsL/gP5l2s28FRmQPAYRAym9I/08FTIqKUXptKTdQixAqlEAGMMhHsQel7v1j7XAVVOaq4Fp4UEfAQTGxlhAuYyn5HnW68wFeLI1Q3aAVMrHXQhIWfsqZuUX81CCuSi8AUGlwjDG+BisTUII+81ff4+DcX4JDC1N4LxgjAgpQfteGbCSgWpJay1njPuMkb1MGeyjNyNAAPJ9ok6DMQB9whKVtNhSNM8HJAFkAXop9cxfWLOsAkAGPoy9AGWAZAd/ysDvIiU1nBRKQmCAWBll7aj51o9Qvuj3zv7cmn4yOs3zfw/Wyjxpc8DG/gfVktBUOsWItNTx6l96Yt6H3db/dCA9XRUQjawGZFQVUrpqtPSkqUK1jCCqEsM4ZgPAEBOLtSDWKCKsQo5chdgJiA0sRQ6wDE5ShSkCR060WoniHYk1EAt2FmSi14atF5nv+o4WGQdximy2s/mBh6u7d5p6s+jlGB3FxHSe5TZJwBqYoFFERdUre1JEDyI0ao16fffsMy7Ah4iEoOj0utt37oAolKb2jCsxiGEsEaEoarVaa3Zudtt2xEiVinR7YEfcn0oFVNUsuFGjSOaLUmkQhqNKXxmyvDokykYFEKIg0smNTZ1DkiQK1KqUEsP3gtHEJM5RjKZarQ+PjgX39HNTOz3Q83lSSWOSKJSiqPYYokWxcvHS4096xeYtz+RKYK2lzvhMihwharSqCBKLWGgBKaAxiCgigs85kmH2PoYgNrV79uy59fbvXPzmi44++hVfvfFb65YsufLKt25+auu1//jZV73y9I9+9H3f+9GvPvvJm19z+sn/6Q+v+so37392+87JyZ3inakNm9SKsGPj84IVBKuqUj5rKuXNwwBEORIrDMCiKpGCQBFjVC7TmBUQUUnTtNee1lhEX2iMzthaNfUSjTFRpFZt5L18pNEc376zkiQxD+12WyQag0qlkqS2sBwNgckam/d6JnHWUJicHjty/ZvefKFj/vjHP3b04YdffeWVX/zKl598+qn/9Of/x+NPP/Opf/z06pNOfMull916040/v/MnH/zQny9auurav/77ufYUKmxcMrn16TOPWP+qI4+e2zn55Ztu7mZZDColgj80X/W3YOR//fYbATH7EX732+F59t1/y75/7/vXfqvgQ/Iwysw4E3zenkVvbrCXAFAmif2w0fxGHhy9dNTvJ7iyD4FzMC6Uur1ltMISHEne7k1tj7YRyekCoKLgkk+WhUAIwDwfo69l54zJimCM8SEa45htDJouWqzkItkDp+jQrhoebJnPuClJElDCQFemDBLlNAgkLeC/llZ9QYI5UCYr9V1fygsg374TjhK6CCRK7jkxhu3LQpMBANiDu0PmR42BmkqfCi7zqUV7RzzYn/UAPATdz/vyPG1+JPMH1PkInA4KaR/kaHLQQk4DsqH0U68HW4l54E8j7ZeeYKI+JMF87nufukjo52yXsFukT9UWEJNhZYYxYAcoyrTeUlGJ2ZjEckpgqEgs8iKHAViFmGzC5CISSBTJCxM5+EBqQQVIKEVqYAhEoQixCIY0EoFNhJlJjVgDYkT0et4BiXPF1JyfmMLIEJYuaa5e3huuh23bQ6+AcHnhxJngnDqnpFALkuHFi3230CyADdIKahVUVXw+PTmBGCFoT81wkoANUpekFomxlvNOF85VGg1EyWKR1KoQRIGwlD6ZCBXVCI1ErlLpSYAlROXE9b2oqhYwpGAO1kClMAiiEHJks4CeBi8xOCi6w8PpTGcaEawNFesLdIP6BLNV3QWMSyejSlEEQ8wOxOwSUyP7ylNO3jU+88CjT0x2O0m1UjGkRafqh6rBVChJDCRRTUCq5NVEH01MCVak6tJaWguikUzPh1XrDluyYvTGr928fuVh73nHxbM7Z77ypRtP3HjmR//jB378w1/+1X+//uq3v+vVpx9/+ze+++D9D1z53iueea636eH7Nz/+xMzMBNIqWVP4Aq4aLZMoqUKhIlCoxhACFEZhQQnYRpgiliBAFSqkhH5FOxWI5r1uo1631rqK9d4bcp1OKxZiFDFq7OVO0J6Zc9bFGAvvXZIYy+1uBsO92ZlkdGmMCuJWL6skiU2S9uTk0KoVl150EWK8/nPXrVyz+r0ffP+nP/mpZ5555iMf/r2HNv3qxz+6e/W6w9//zndf99lP7358yzV/+MehWv/012+cnZxz9SFftONs+/w3vWF1s7F9yxOP/svj257a2pqag0EZES91Pge5JQe3MP8Ow0wvNs/8kC/OQxznf/6EHqpW0WB7PMQ5LNztkFYZ+PU1hgdOEiEtE4/3dedrvwcBVgQ+k+4cfA4NpFJ6ECRifxbEwO9iBpEE6nsn9jHb8xnefdMOWOdUEYMg76Eonnj4vmceeySCYz+JZt7IoVQ4Yx4caZARTBAlGLIhRmYTfYStAExsVh95wobTzy2hDPbeS7Ifs35f7rwIVEj620l1voqTUt91hYIAQ+C9yWCQgz2yfebQ/D89VOLdwj0IMWgoxFjLVn/zxRz7zc5zXEpDvrdpH5mW62ghhYqWk0Ggg6GTf33JyZe6d4lmBqhlv1ufym/LjSyEAXV73i1pyBgAAzMSAS6TuBVkk1T6aeOCGEgBGMNGQGQcnIUq2LBCY4EIMIEYZKJxZCtEJL6IoYw2RgSFJXUp1RvOVaOItCdBIiISQxAgrWC4ni5dVqnXfZZls9MyMy2dLkJEkqJWR7Nmm41gIzo92rrHt3opWcSAJcvqK5dKLTXN+uiysSln4lPPofAAo5o2mkPVRrNLsdPtYK6HLK/Xm+Pj4+pjrCWVxWNmuJnWq525GUxMgImUVSODxFibJI2hJhoNV0lDuwfVrNVi62ylQoaTZg2iUUMofHkFlBAJkFAdblbHhtNFo/nsNFkTewUkMpODGiZULFeqIMuNOlXrLqkw27kM3sUkTYebqFOxsp4Mc3WmVRS+x6WzVIA0HVk0UgGSapIWacjhiK1K9DlCOOHUV576itG/+fyPPFtUnJK4GEbTtGIrjbSZcrVeQ7Oahop1SeocORZDVElRSWwtaSwaGrHVmh0aNkLbdu484ojVf/C7V99775ZPfvKLV77pDX/8e1fdfOdDn//iprde/KbTTzvtxhu/vmzR4ne9+417dscvff7rrzvvgssuPWvNQ2t/9NOfT8zNOVMNiTPVFGQogqKEskD64CExhMS6xLqqS1KGU4pa1iYFWWdcSsTWWgDqXaWWzu7cGjSwtbXGUKc1u3T5st3j09W05ouOI64kaSwiMxtjlMlLjEUABFHS5lDe6SKpQITZBolSFLZauej8C4eb9Ru+/OWR4eE//MOPXH/99Tt377ribVfNtNrfvu079Ur9g9e877u33Pbc489ccMFFY4et/+TXbu7MTdTXru1MTwGVDRs2nLh+3ZOPbKqtWPODH/xwpmsAB/HPE8v4tcyJ37Z/b+3X3Aa/3mHD+0uRHbALAaRgVYNIRQ8+h3gMlrtMLCK6n/HRvlEZsGEAsPQpMADMAEfMm5G+GQ1eYRxYYS28SJ4V3d7eUe0/eJJS1F+lVGcZYBqIKshEm6AIYAclTasVCmZAjTjUhOzr9ZRBrRD0cd58TKpk7WgZ8AcTHOarD/Qhy15XwmBCSqyzIHSD529ULuAQETxsICsv3cj/umbBZkANGqAZVQCGyBCTqogoEUkQJYBFI6xVolI20RgyYFUq4yt9N1bpGCeF9rFiKRVdTsFgfhagcl6IP3QwC/ucc8l02bt9/s4w/Y1EJeVgb4d+hL4sumFYtUzGVlEZYHY2SWKTShkhcYZjjDEUUIJENewq1aTSjARmDgih19MYRTlEwLlKvSlJ4iGpdeJDz8+h8EgcmJHWk6GR+tBiEM9MT2EyhyEwQwW1lIeblaXLqiNLfC+be6InrQwhwlkYg9Hh+nFH2rFFtaTqDO/etT1/ysMoMg8FGtWh9SuH16xqc5x64mnd07LR5XmB4aHhDYc11izjSiKWW91WfeWSGKS3Y0JCaFSay1euciPDbSN+YrzoPIe0MjK6aMdTT6FRMyND6eplbvHoRKs1tHjdHATTbZ3tlNGNkUVj9RVLGs1mfdmSIoYgAYYQyFprK1VXr6aLRmoumZ2eCVOTkIgAIiJr0KxXR4dsE+lQvTAQEaeAko2hbqlZTWytmlVcUWTarM1mWa/wXuFqSBvpktEh6vROWjZ6xtJFUYtNW3fecf/To0tXMsQGXd1YPGpri4AllcZMHmKR1+pV5O2mZCtXLX3ja4+66c5HRteune52a4im3dowsuiNrz7lK9++Mx0avviCC4aqcHl3SaN61ZVv+P5t/xR6rcuvurLIxDKdeORR55+1/Js/fc6MLWpPzb369edxZ+5Ln7/9rNe+5o/+w7sfue+RW297bOPZrznt9FfcccePVOKHfueKBx944pOfu+Wss8766B9c8fP7t37zWw8cf+Ir/s8/vfKmb9x3/0Ob0loNFrWhBgpY4t1PbXHDo94XzCAGA2R4eHi4Xq0hwAiyoii8DwRlttbZxDEzRE2aZLPjyfAI2BTBZ3lRbw5nRag3GszsnEuM9XlhjBEJXkUYAo0xWmNClodOQZyoB4QlLyr1Wp53LnvrZUcedfi11/7NkkVj777m3V/96k2bNj3ynve8x5L97KevW7ly1fve896f3fXTf777p1defeXa40644xf/FJc2lxy9fvqJcVev+j07a9W6Qzxn48a//H/+arbrXbVR9HIAYIZoP8W1n4g6EJMoH+G9TtM+H7z/jP8rODS/qfab4uL8prg+L/58D9H/wKj9YDwHjw0dUucDGKz7F37ZP68FqjZ9q3moStqDtzQdENyf30ILnCvlt/uwQ8rt/Z4AhCGxdAIpgwgRbJOSV2uZQ56lDpPju0zIQYQYyqxkUVGotS6EQMzMHENBBGsgYXC65MBW55OhmPtqH/twKhgAwoBMU9KJy3wgRWmn9o5aywBNSdAceHr6pI6BZSxZK0SwCYKATSz9nTpIyaaD+GNU52NV5XTF/UirLAyCRljiwlIiwRmce9Yy1hmSXMnGoFEjDBlnRSTLNc9kzzgmJlB4BOlzgmIkWGe4X0CSiMgYibEMZpgyXWmeMQMhiaWyvirPO6P2u7L7tIF67QtsFgsh2LyfQoEYFQLSsuQQsSNDYIpRwCx9vqqqlmo7hLKGkQ5YVy9b+vjBWll70+gBD4aqQssU64PsBTZalhxASXghJQYbRAEZIiKwj+pVTUJELOwQSVVBRNaRSwQUglhnjWPjqlEVsSjNFCdp0qjZNI2sc1qgM0MWCkGamopLmrXKktFmUJ6Y8Co9rZrUkuH66jW19euKSiV0iqGR4cOWDu9pVLOntia9kCZVatbrS5akY82kYmO7M/vI9lBkSBIMDdeXL+8Y7s3NNYaalKa92TmjtGjJ4vGduzq9bKLbGVo8rM1a7CRwBtbtGh9HVqBet0MNsdzzAWANOjw21s5DbHUBhBiyLDPBq2WTJmQsmKAKkRijeh+8ZSKTGHJlFQiCUvTCIcL7XpEHANYQESuMwMYYoi9iKIqsME5NSoktnzRrnGEuAojUSFhcTzccv3aVAQGnH71hy3jr2akpQBI2vtVzjWYKRB9U1VZctzW5vGovOevVazaseWzr7LapXTNSGR4eWTo0mrU7V7z59T++82cTE5OvOH3jyFLcfsfDTdBFb37DD+986KFHH3nt2a9pJO7WW25et+6Ic89a/o0fbd86PY5Kgyv+lw9uuvy1Z511xjG/uP9XxXfOCgAAIABJREFU25/MTzvu6KOOPvae+37pqvVzzjnHMP3DP37hzDPPfN8H3/r0M7NfvfkHx51w/OWXv/aO7z/4k7uefv3rz3/VGWfc+v0fPj057vKkatLZiYnR5cumn9lWX746mxqnCgGIKrGMOTFgmIjYGjUg49RYIUSJKGWS2Ahx1LKGCvkQCx+jKhtHplA2pU2K0KjixYsBC1nihE2FjJCJZISpvnhodmryqqvftnzZ4s9//vPWmksvvfg7t3374YcePv9151dc5eabv7FkydKrr776nn+696d333XRWy5ad8QR3/3ZPVMJ1p16wkw3O2zp2unHt86GvJ7WVows+tJ1n8kzEU+FLYAIEA5h/xZs+60/5v+/dmiOxQEdn+86HpTgQs/77fOM5PmP9muHOt9hr9VRBqmUqR4irOJIQtaDzxEKxIAFOIRAJZ12YD5YVUQpQsGpKCsMlOGqqDYrQ6OVeq3eaPZF6EoyCkFgGeLANCBRljEvgrKKaBBCKXRSElBYA8DQpLRUKLMDSgowCRunqirGizqb+CjMdmTFYXFB2cQXMMkqVGoFstHScgEAKYtSKDx84QyOPyJ919vOW7PCWu5aa2JJ5jNMzKrIvRYezz69Z/Nj27dtb29+dMcTTwMmuupQq5eJhhI7sGEwSYyqMAZ7M7H6EC1AvEphOIn7ovmDgOaX1Oy+KeH93yfAMqsIw4LIiwiDyKoo+prwfceGAgpViNmb0/Qb8yDtw4zRvVEtms+HUGCQUT9/nfZt+0ZA93r5FqaOUxBFn2BN8wcEAKYgolECqU0cGatBynO3SRWc5EWGoghp3VrHxoGNdgvxHgEZLJvUVavtmWlMTkILZYF4dLq+V0wpdcAVSrpTs35mDjYGbwA2WRHa3dndU3hmx9TKFcNLm9O7J7Hl2V4vJrZaOMLu50ZPOnpoyRKdaUMEllFv1pctkXq11Z2L23epD80lI9GHYnLaGrdk1erx2amZ2YnZVq3RXBITh6VL14wt3v7sVlSrMKZSqdRq9e07xjHbqa9fF42NeY4Q0npDCVm7lc0kQWMgSRgRWqZlxRhjnsPAt1tN54oY4AMAA7JKjiyyPOt0fQ6IaowI0QlURBB7GuZ6nYxNtVk1qiYX45VzMcIsqCVpEsXlfjhJlwBDQK2KOnJfzJATYZrtdpvtngcK0UwLGKoYPfWIw9776iMDcOsd9xfWZDPtJcNjNNv+6PsuefCfH/35A79ce/xJJ5669gvf+Gmqcsm55048u+u+++5bvX7dK04/4Ybrb6hS8qbzT77v0eyBJ7eYNK0Pj1RcpdPtfvVb3z5mzYp3Xny2Rnzne3fUGtW3XnTOfQ9su+073100OvTOd75z85ZHb7jlx8cdc+x5F5x39913P/zY0xde8Oo92zfcfvu3xxatuPwt522bbt/+vR8ayyO12p7x3cnwUG+ubUAxRg/ksciCzzV6RmAEHuT9MYFN1OCLAtGQs8w29PnVKhGFxiIEIXjSgjQShBEJERpIAvo9jYKimkiIiKIRcXZ6GqwrVq94ZNNDW5947MRXnj48NvrzH99lK5VXn37m1792855HH994/oW1auPn9/2T77VHD1tx9+YHJy3ZZYuri0dqaTq5u9NYs0x27Tli1dp/vuuexx56NJdaWh/Ksw6Y9rIBDpLj+tv2v1jbl539AsN/B8oYqvbVbV/E7x66la/shT3mczQOwjQq2fELY0wlZaJ8yYswKamQimNk3Tn4XKKnQU6Sol+VZq/SXj8ZxJQaHNGkpT1EbaS2Ys2qw45YtGx1WqnBmFKlV8HoUyktQVwpVUKlIK6A1GgARESUOBLPs20MAitHNQNhfSlTcEpfi3Mu917ViKoxtqx6XGuMhD4B9IBpOFi1eSEol24iW7p8St1aAI6NM7aAIGK0yccfvfTI9QnJpPqOQlSMMpVp1yHGILTxhCN2bVzZ7tj77n/2hhvvuv8hDd25KiEfxKRijDSwtgSWAZahMmlJo2jhvTWVhPoxmr6voZx/1X+t48PiQNYLCRTGuCikZc1hWLBRk/SvfgxQgBimr/6rQlAhRZnBP4DGB+Zyv0xtnkS98AmcB+i8nyNUAWgEWfQnUSEC5r48QZmWpkJk+3hHyqoIxMwR/eewj99DQB5CCGps6aGyTAINElAUWVGYNGFmISDL4AiIUKDI0OvkecaWIgilT4stQNYmRAbtDrJCfIQyGyNpFVm36GWAaSTmqJFm6pK8m80hwgGIhQg5R8zo5EW7W1m1rFFrzIbYmunaSnXp6tW0fKy2YnF90aLpan371KPbHt+CqRk0G0sOP2z14eun2m30OiCW3K9cv6rXarVnOnmrBRVUHaC9IhcoDLMxMAznDJuoChFmttY656IxpbxSP7THrFFI4YiNkiFODBl2MYEmSQFBUFZY5USRkgmhEJEIRIF1VUrSXV3UG4iCPT20u90kqcQidrNeJO6EYIA0TZ3XPA8hahFkCiiAyTyfmussbizpbd/17recOzOD7//gzg2HH33xFa/94tfu6vU6R59wfLXBP7n1nrVr1rzj7Rfe+vXvS9QLL37T9nHc//BDrV7XADVjnXOIMJzu2jX1D9d989Vnnvb6N7/hkc1P/MP13zrzlRs/9L43f/aL3/nMdded+/rXXf3Wc2/59s8e/MXPLr744h3jrU996qbDVi1/19vffu8vHvz0P1x37Gmn/NHvXnrfPU9ueWSL8cEZV4apQ/AeKGLIYl5ILHUbg4YgvqxBTqoSAvICYiKTgVFVRSxLpWqMGkUNZdGrxgFXXVhLHzmriC5oQuohKgLHxlJaTXfs3gFnE2e6s63/j703DbbkuM7EvnMys5a7vP293jesXECCJLiAEjXaTI1kkZY5WqiRrJFkjUbyTCgm/Mf+51/+44iJscMjW44ZjUJBawsztFEiJUqkJIIUN5EEQRDEjm6g0Y3eXr/lblWVmef4R9Z9++sGQGhGdiADeH1v3aqsqqzKzJPf+c53QORMVmbl5vogm18iIut4Mhza+Znu0szgxQtmbi6f7Q3HwwxiGWTo3W9/23Kn8zu/8xEW29RSFgwI5ZnW2zzAA6fDPWv3vy/P+f/fy6tZxd4OLXs5B7Le3uLZz9649Xl37sAv26LaLi17NSZTgCklBCIYQoxkGOopCmushxuIAYk6stvMTuO5iCS0Hmwi0vLWYvHo7PHTS8dOLR07OzO/QrasGzHOSZp3lJWYW/BBKVaARLbJZGGIaEhhQpFI2SpYwYQADQKomq0gI5maMgAaQLJIMMxGASZCRE3CEukguszuNt7daKSYsj+2dogxMrO1zoSJxop1rH6A8JIzQuqFLZQRoYSMRC0Yw5NLuTm2dOLIW04sLfzxx7/82YdevLwOhzYyIapKiMRWVWLcIQOnICQN5CBcG+puhem8tuis3Rde0H4OEkWhkcQ67vSo7FCegylWQ61G8E2alRURugMwaa+RD4RmaMeOybSXKcqC/RbVyy17TKVtkEl3+sx23eA03kzjdDBNEU0pYBttzJSqQri9yPSmR6gk9xmpUErawSCNGrxKkNCQYWKCzZE7KhwXufVl6HRAPrcApLaMrINeN+/3OlmnKTIUBdgDBOLM5C7vrPf7mK17i/Pdfm/S64aVRc8MoXvvPPNDP/ieO86tEPB5MX92c219VHPRLzuF+pCBAxkSDd4jRDQB43rt6fO42sHqQmcy7q1trF6/ges3UTe8tHTqzjNV6Z5fuxqtwUzOpnP83rNR6qqu4RtkGRErAkRsnkVVZiYipAXC1OaTGIP3yTIXlSSgqNom3zIpABLJChQfAyKCkqiBqlEYiZmqIeHchczEDJtN7AR5bjT502880gd9z1vfVsOt1lnuctZMxWjhokMDNE1DAZYLdvTQNy88d32D5zvrs134Tfvi1X/yj39obhn/5t985MyJs+//L37gz//qG88//+x9Z+/8wXed+a0/+9LVevzf/+yP/tWffvXCN88/8L73dk8Vn/zMU88/f3Fp5djYmdXBxmRj07AxVd0YXrjz2Gcef+Izjzzy4z/yw/fedddHf/f3//bL+Y/+6Aee+Objf/nXDz353Iv/7EPf/anPfvPX/q9f/+n/5uf/x1/58f/5f/nNixcvffC/+q8Xjq586rN/8+yTT/7cP/3w2ZUjH/vYxzYGIyCPodG0fuIplklTHp4EUkAiRClFNUOSHpFGIVHWhChSkqDeZhgojEDaA5VSPnGwsvGGGzaaWagAPoYJ58Y5k5c5xeAn407ZrSaVMYatyYp8bW1VQ2BLYbDRVFWW2xdfeuFIxx3rL+uotpWsX75SdBY+87kvDEdV3cRyZmEy2mSn7JsASiJae4ZaehVT1OvlVZSX07Avx/tz4M6758zb2zFpILjlte3d/5Ap+cDIL9XEyNxWaWvhoZSuWRKWL9wuEyqMh4kfA94SF0tMDjCTSIKpFOSQBMfIYuH48j1vvesNb+7NL4spK9goNrCyGtlaq0sbuM2INggoeGEhStOM1RRahUg2ECUuCU8Ds0QREdNsSDsMjiieiIwBpJWestbGEJn29qOWinxQGmYAUCaNWyTedjkERGhi3khCsLQGgrMpx5MxMFO2r8IAFH01MuSir2eLpe9/3x2nji6szH769/7w/LUxvCDLufYSpY1qUiXawb9t45hUEPgQ58lrULaSK2FLBiflWlIlYhth1JbZzHxnfsXNLJdzK53+gil6MBZsABWJSY5wm4D97ZVXkxQ30Xt3oS87klO2QFabw3yq0ZjcYpE0pmxDjORajZCQ+oZqRAwMSRHZGoNGP+02QhoRvLWMMmdSlcAqUK2jeFVYC1cY65QMjEGWk8vBNqWpRFZQ0cuzrsnK7tGVbHkeWYZESIriwPPLK3Tk2NzcHIlWg5FvGkiwc723vvu+u1cWVgT3zOAdbzw7f2IOsyYrzGQ09L7JsswdX1lYWVbV4XgECHJHs30YAqJzzio5kXxhbunkqTfccceNK1evPv7YzbXrxpmlc6eOnjs5gX/86afCaGgWF06ePr28vIxOx+R5WWYuz7KycM5BU/8TgEyW5TYnUWZGXoAtmMgaAUCUW+cYlthkzuWZMKkCHohkXE55YYgdU+aMcyb4qoqeAMd5v1yYWSgvR3zt5tpDFy7cUJ406ifBknXOqTOc5zlQuowUzXDCroNy5vlJ2LQd5W5hOu95wxsfeIP77d/7dDE/9473vO+l6+vfeuzRc2dP/fSHv/tPHnpsdW39l//lz3zuc4899s1vvu3+d733vW/57T/44lMvvnju3nuDRFHtdct+p2TRuvZlb3YgVLkC3YXf/oNPPPncS7/0yz86Oz//v//qf3Bl/7/9hZ+37H711z924uS5f/6Lv/xnH//Eb/zWp//lv/q502fOfeQjHxkMBh/+8R+7+66zf/jRj8Wm/qmf/PA9d92h0XdczpSC0jlZ/QYJLuEEWDNAMRCELIOJIdE3iAKNLGoUjtiCEn4OkIVakIlqgsBHidEYQ0QBWmmsjErG6BaYKdCx6FmBr5sqjsfWSwa2IAmhkTCYjCe+mpmfVUQTIzEXon71ZrE+sasbdnV03JT3zC7ed+rEZx/664e/+iXOu3m3z8aCkBdZqKs2P/y0K265Bl63Y/6hlH2QyaGPQ3V7552fpwfu231HVfv2P8COueX+t35J2nPRdrW7rGdlKLey7FGYFCLNZAg/AaU4VzqgtnaJm2TGCCbD3NJd7/iO03ff31k4EUx/6M3E22BK152PplCTqcmILRkmw2SIGZw7znLKc2Q5ZyVnGWelzXK2Lv1nTG6Ms+wsu4wNM4wxxpgEpm59dsYWWe6MdTaxUKI1asyuzDy3eQQAiUIIapO7ZCusXUmyzEaVIBGtM4001lFqEIEMiKaJR4CoEHW5dUZyW7PelHDpjnPmgx948wc+sDDTBQMaJbetgD7BMNmpq2Qq4JZCzTXE6FPA0GuudWlZZa+0nbICAjKuQKMg5zpzVPQ9DDnKOzHWk1iNEUJrEBxGUknt9rJtk61Y7lcW1D1192r74ZZ0YyFQSpuuqgKwRjEm+VMVGlVCsmxAQgqJHhwAkHoJqt4jRpO0gZqqNjBlwdaJxCAN1EMFxCAHW3DezbN+nvWcmszL5mgYJcZIJnMm7xVFv8g6aqzMlohd1AOMPZMO1tbH167blcX5+XlEaW5u6I11VBUg0arNzYLVeRNy2KyH/FgXfhCv18EHYs3ne91+p1Pm66MNZBZHFrKAJviZk0fzkyvlwmzP5ceOrmBUr55/4YmHvyHNGD1bWHfH8ePDqr7wwsXBhhHo3MmT3aEf31gfjUb9xZl8fr6qPDPneS5FgTznSJaNZLbT7XZ6M2KtUVAvjiYVNASGSkCelWWZ8jXazNki16jICwgVhG7Ra1rhHslzk3ccOhkRyQi67mudFIKZpdNrdaxE8hxWom9CHWrPkY3LXCHATNk9ks+OsBqaBiIqFIbNjOsdWzr5/T949+/90efH49E73v7OmGWf+NjHOjl/6Ife/6mvPHHx+uXv+47vHl/VL3/l75ZWlh/8rrd/9I++/NLGzWxxds1PxkYX5mcGN9fHN27GjYEr+zfrSbxWzx8/iqwITfjKo49/65uPft93/6N3vvPd//4//MfjK0f+xc996NKLG3/xyb8+c+bMz/70hz73+Uf/1//tNx/8zu/4pX/1L/7dr/4fi48vffCDH+w/8J7f+sjvzM/P/8SHf+hLX3nxT/7g/1npKifUKgpCZIGJahQkkVTU+2Br4kAQUieNhw8k0YhK8BqFDZEoNHJUFjihLCKL0KAmKkdFlKjSQGoLdDKan6PFRVPYUib1zWtqaa5X9qzrG1sq6sGg059pJM4dW7l+5aIwSfBaV4v97qms+31n3/Q99/aPnr3jpc31F164eG1j7eGvfm0wuIITS6ONul/2Nzc3TWGUIxdWfaQdzMStAeB14+UfYtnNldn708sr1K71b7G/7vh7WOVbWw64nts7InfVlyi3lGgDEj0RMcJ4MgRCAoqSjvuWJUREWxGyAKAEtrMrR4/fdf/pu+8P5QLZQpTZkZINUap6ZK0hCEN0GkfNCiHxCgU3mkT9wcqkPplJClGSJIjHEow2hJgCcNP/rTCbJCwBVsT7GoatxiiBWCgE2sGm2N9Ku75LOyGS0HQ63gq1FoHGaR4G44zLM7KGwEpEW3HYDCghgTpkJVaCihxUx65Tvfn+/J/wO59f/cZfffbKuILLBADBpJDhGFvXBnZpGKrGiCQpPI2ee60WNtuDTjIgtmCqGCNMegKBLSkjeg/mzHBNCtHk/wIxWK21OhV+nvK3UzO9sjjyaQDUKylbbUWtWw5IMfA76kx0J7QoHEMYRgUCZRNJHREpRJVIVCUm6RxVEQkInohZSFXUe0CInYH6piJo4RznXIeGYkAM0GjLUshoFFNXcbA+qSfiqzAeGgJFRRSJkb2Pw43mypU6BsfehAq+gQhTmKzeMBeLWdXM5pNqMl6/ic1BBhPYyfrmi89cWD82W9ruC5fGD184P9pYdz7qZDLTm6uuXemRsLX1eDxavUqjcVHmLmjHlJbAVWMnzXBjdGNz6DeGa8+ex3Bz+eyp/qmj66F++HOfj8ycF725+cWlpevnL146fwVVYOdQNWE08qOJv7EJ7mjVoKklei8MAjU6Wl0NUFbWENF4WFULNQrvQ+MnAx2uDT1iXfvoG0iAV2mAJkjwE197aSa9sgqBQFZJPRBlsD7Y3ACiXb2xcdXy9fVxaOpedzYrnDVZBvajkQdWb15fk1x80CiQwNZt3li1Vf0L//x7P/XXjz/94sWT5+646/TxP/3DT42Ho5/40I8/8+QzX//6V99075uOLRe//ZufOHJk+QM/8sOf/cLjzzx/vuk6qrNmPFlaWNx86fp4c9PEJut3JnWFxkGztUurWb/ngMFkdHX18mfE/9zP/vDcwsLq2vrDf/f4Aw+8cXNYPfLok2+5595zp07/5d987m8//8X3vveeu+6588nnn//8l778Ux/4nhy4fPHSn3/ii9/7vQ9+8dNdW69mgm6z2R2tFSPnJiDvNSJEioEkeFQ122jIxhDFR5JIQqqsSpAA4giNUch78l5EAlSZyFjLbNlo8AiBAJflbqHfPbnSO3Wq0y9zv+k25lZmi/e/553/7Lv+0dLM3MZ4OFu69WbsQzMabNiZmaNLy/Pzi7/yr//10cXFa5deunrh8vkLly/f+P3VyTgYEQkaa0BhrYpurq+XvV4kXw2HsJx0tkhZSSC0Qzw0DQ57Btz/ZEGOrxcAr5Yuk4Z6Rfu8SFRoqv8uKvuIqNs1y74tOz7r/v23NhyI4emON0insXLJFSCqSttOBtoSylONhqKFal1hW1sleXQIrNSmTUrTFSkIalD250/cc8db3hPLuUBZaNRLUGPJwlprDMfQKAQqSi1HJyVmjiBBEqoRUgiCKKBCZChJg6oSxNCWm3hLvoRbMjIpt+SWGLw3YGsNKbMK61ZuS9Y2E/jWPLu3Adv4ATFC6REmzGJ7mk7utyaiaWKMUVWZbZSUY1FaI69lmUQi4k6XmyZKk2WEuJ4Ze9fZ7g9//33PPXPlyfOQVlSfIBDZCspP9g2mfjOBBHAgGCKOqdXT5dxOa++2xQZFCpwiItOKH0aBsrURFbIM1m9sXOnSUj8vWCLqIdVDhArRI8nmMpFQAo1AUNUUxZbe/l0JnGiH89S0EnxILbr1DNp/d6gdJGM5CYdOYcgWniJQq0wRIAIiSUxe2hlWp+o9uG2pyCBwFokVqhSaWGMC2wa/Jy4qQYWhjCgBMgE4NAJSUhBRTGa+y6KfjFcrJEq5KBMbw8E3ANOk8cOBv3kZNikQKHmRGJSg9VjWXwqbN8byNII0hKgBKlDyZGBcfHxw86lnKHNaN8mAbXxEFBj7xU8+9OKTT3a75eb6+uUbqyF3iIKJbJQ3cfH8MM/BDqFB00BCiKiU2DhpAjKHfgfOwDcAwdn73vvu9Y21C49+S4YDlDmOH186sWhG1YVvfh11dHURamVn6rVNSFVf36ifvOzXQRwIqlorLBreeOka5yAip7n3kTOSpianjTQI9eDmZtz040vrUK8nog8eWsMZl6HaWG/GFRTE4Wo/P16NdVLbXrAO4zgOoVkfYePq1fG1m4PZfgV2uRtXAw+ZjMfjl25k3WwDuHTjcuNWOLCIsCWNUf3oF3/5g5/6sy8+/vj50+fu+N73ves3fv13h2uDn/mJfzqpwmf/6osnTy7+wHe+/d/9nx9fX1//n/6Hn37ooYe/+sgj4iykrldrE/LV60MIOfKRqwoRbFjETYxWtR+OG6obTIyinowyYDQahuBjqA1jPAnjUTVcH8zMdhkg0qUCg/Ew5mVDbjmDjgfRU553S4KtB30O3Sr80g+8K//H766vXO7VdT1c95yPgp145RBIxqhZVDUqM7MiBlPXVJazqt4LRr6OPnBVkfcbk8laCD53EHtjbdM67hgaTWodN6XLXFmce+Mdsdedm+9mE7a26YfJA288U2x6A73cyPvefd8NL/CjX/mFX/BVXL958y8+/hfnL71w/vz5UHlWhpASxzaMMAIMVVQNDBm2ja9UlblsEc1tsfOtSS5t30saxRbdoe3pu7cfVP6+czAdhi4czP7bqbT5Mut/hff17Ud27DnNns+U1E9v6f5LY/FWrp+WbjKdv6cJhVtoYScZQjVxIulAS2Zb1X3Xr3t1hrZh/anx1K6502fZjspRAiVgQ1vmPDSCIhttRmOMBm3exh0zOqYbysxUTTQmCyaHFJ2z9x196z9q+idqsSEFuJjEC27S/pZBmua+qZAeGVVlSrIzAIIm3gIIbLRFXFoORqI1kJqdqTC3hObTXBpUsjxv8/0R+whQqjn5+LXtViQHvSSJnmagGSAwo4hWGY7FsgiLzywCUaJPSPQUic2W/u9WMFLKNWVUFbUHiGHalCoxLpf6I99z7PGH5154YT1mpg4qCkPEINEIClPuzNQSVkE1cj1LkYIa6woBxRCUuM0isH0jpKopon7frR1cbPskW5uDCaLKIIj3MNLaqc1YqwE0qITJeCM2Y4gHlIiVhNpAvF2oF21DIwdkFdjX6lO9rFc+TKkm+eVEIScGKcxOdGe6nJBk4qQUTEpQSV79RPVtx15WmLY1NSarX0PboESc0BoiEEuMyXiCMZwIWxIlGbNqnBBJ9MnMShiicpuoKURQBIINcAIv0RiOzqZgMiCaKpBWTLbRiMzBJc+lmhijmvPPXMJMD3VArQgEAEHQNPCMRkAe0qaaAgNMImJyF5WwugZDmJ09euLk8vLio49/A0zY3ESnd+oN92Zl5+LzLzUXnwfVqIJhx2Df1CgY3t947jwG1ZBWx1zpZAgIW6eREMQZ0hjT2GJgG9XQeCVBDCpBmwAfIYF8Q8EjNGCOUWNTo/EZO1H1QXwM8EGDjxGNBAU1GskHDqLCoY370igiIhwEPhIQCQjCMS07wApDKAmba9cGVRVhwgSofWk5c/T8xdXJxNsgfoTBZBIMk2Jjfb3RGMQgAFD2hMgCFVYxUclTVBZjRKEiNgqUyDLluS1ygK2Tpql9Na4wbrzrdGC4qschNDE4Bu6487SLdnFhJQNm5meuXRmGGLMMoR6944E33tuxdTnrfD1zz5034WJdUbcbVMkxKEjUzBbee4AlBiKNiHUU4hi0IUijUTTWk1FovC1zLopRPbFgawiNr9dX33rP3ecW+6EZnVjsllqXRXcuI1ZjrMuqKiNHw80IOXtq+Sd+7ENfPf/8pRdf+MJnfv/FC5dEUPlGCgffwBQiBE7zVhpFt4W8SNOInIabNpyxXSjLdO2bRsbDRqRXQ457vbw2hbappm3ZY8dsmZ477JitIRe7EI6DDt+/cSdL/RaHHHSt24HK7cEkpNvcXQVIecfAn15RYQY0xtAgaivVf9AsE2PZbIeyAAAgAElEQVQkgAwjKJaPnbjrzXZmpXG9xvstWFE17jm0TcyxPfe1tZs9fhNtY5S2Yr5VNdH3edqwu1qpzVDN0jb4VvzK1uEyPR2wz/je3aTEQNQIDpEstzFe0BBbcR2CZTAMSCDKW0Ye7RI83HXXW08A9XK/et87T33hkfWvPh3JsIqqKkxCuNqrTvcYWwgmQiKmuvy6ncIJ06gyaWUclA88+2HFbmUTnVbQntuWpcJ4hQowmoybCGMRg6EYmwoiZAwzxynKslPJ9+WULfPltuXW8U3bubW3xPESVKWm/ba1PbUsCYiqZGCoIuWJiUqiBNitN2fbUdYalqRQbq95iglNHwMTgRMMS9YxyEUYkAMiKIKE4IwRgmETCUKwavLkUzQqlp01CsCwgzHBkCpneQHAsFpEYjLswGpcNM7lOUNIpcgyMDcAGc5dQcqWDAOGVAx5q2I5MMgaB0tEwkaaulrbfOKJJyABIr277ziysnL98rXNK0+DHAJny8vNjZvMxoLrOMQkotErTQPEm6M1GAEZJHaqaIgqNUWCsIcKRYL3XCNzBpEgIUoFrUACbcqUYFzEGBJDbTyYiIoY4law2UCZIEAyW0QSxTi5VDVtUvUSk2njEbwwMzRKFCWVLMNgMBg3Nayra2mqmg0JdFJXEWrZaETlGyVExXA0EXSAnIOQchTnmduksBpImCNBuBaJpJAAqKIIDSYjHgONN0EtucwWiByJmmg8O2cyLkpmoDvTne3Omm5/CBRHF/3aZCM0gZF1uu//vh8cTLDA6CEfrm64+dn73/TWzz93AQ6eI3LVycQzC3mwA4lIRBYmOmQFCoBo5McWxuSZGNd4PxhsuMxmUUZrq2G0udLNv+ft971pGR1aevHkkQb10U62vDjfDJ0GR+MmUlhYWByOB08++8LHP//53/3kX0bb0TGsLQubG1YNUJsjToHQ1NNflWP79lyH18trVA5r54ONDBz6QA80Pacck0NPQbu/HnbeQ8urfbvSrKnJs6kKEE1zMBtmDeKrGhKYICQHWjMhgowNYJBdPnnmjjvv9XmniVvkkr2F993aXgxp7xVOw3v2lIMij1R3ipsdVNvhX/dUThqlhVFte9uinLK86RQ0VeZkFNI07fYhVe65O9UI8W9/+73vfNfoa08+x0Za/g0wrblFY9stChBEgmE3nUNfM9DRtheoSdVnOyVkDApESqkXBYgtKyrCQ0Jrvkw5U6/u3DsP16nf6FXUsnUUEckhFWxnlETbYWJruAnIpHzOrG32y6ntLYABEWA4xWelMCjDqqSiIE7YqoiCtZ2NYxChStSk7gQWNUpSi6hlNZrco15DSClXY0BIr1QEm0AGmkMJPoCBEIEIZ2AY5GAcBK3ovUQEBSkyB2JEBRHIghlRAEEGOIb6NrwwcyCDxqOOIKHjS2fuOru5Pnj2sW9h0qAW7nXO3vdW4XhhczwejkyRZ7mLRDFWcbwJ00OMAMPlHBoNUREYHIQos7AeITma2agVIcQYRBr1sJJSTDoSJhFYY6CGwYgSE3HVpqg9IjCUsGW3ICZ6fLtFI1RImYRaD7nXJCxtVHyUQBSZIUpsDVub5LFjjF4lK1x6qNZCmULThADLjtQiGkSR5BglAgzAIMeSkNLkLk14NWtk9VBhBzjjapGqaqoKMfoYAjHbIvcQZBmA5WPHJ6orK7MEuAxzc52Zjlu9MfGTcTfPlksUDeKwObky+4JHx1GvcJur15598kmrQTNb5FYzZ4whRX+mQ6SOtcjc/W9+ExHN9foS9MEHHzQuWz5+fHl5+Sd/8ie7zp06doRC/UPf//4TC2WlyAv7jre/e+w4OjcZx25vtqnck+dfXOPBW06fDeqeunzjc195JKIA5eVMJ3qpfGRjDasS4nTVu/fvjl5Muz/f2lvxejmw/ENoqFvMkdPx+dD9X85Dv9UcfEu6zC2q3VNJMgJYoUzTKGuEEGJVQ8FEhwANKXue1aDozx87eSbv9CdegwZMJfb33ODOam7fbre8kVvv//LPe0htnhIlGURqpr8RkSEYiNJ0faJIWuJoLY9b34W2FkrTjI+sHP+OB+/9o08+d/UmIiQzrolh96vC7avTmjLCUzM3WQAHmnj7b+3WxbZB1MkHMvVyQ5lCgJIQM1mlmHJII8Ew2CGwq6pMYHrVrB3dYYtAD7f5b1lSHmvZYwkl91uSFGuRLqJGiEiNxAS9MIE0xfVHTP37JNiSgLSGYEwrpCdpjlXDCBHWwNqWKi8qUEgkohSPFq1lZ8EkQSERIARBFBDDOVineQ5jIYLg4SuotD4Sk8FmMAwVDEfwERB4ggY4RbeEM8gyNAEbQ8QkuRTBhMLBZQiCYQ3vERTOIMsBhUm2TkBuzfLy4tysMebCw49hbRO5w/zM/P13HestXHz6ucHqDbDDyXlXZOW4GlQDdEoUHVQZhoFrybxGkEhUp5SZLC+RQRGaqmGYaMmarJIAGLYmOqBgeFQiRkSCgMR7AILMMmVWuLCOoyZboBEASkSG2BlrnWNQQmXSq8LMnDtTuCSRN4oWDdDS08CGlZB3yqwjwrAWmXOTpiJrlFp6vjFgZ+HrCDjLpm6UVCmSI00k+CAI3hhWZUmYMhMswYDBPAlsNLPkABPFhViw7eXo5h0FTaoo7G1nzme9pxuMm9jRpthYM0vz73vnfW++W+qRz/3k5z/8o7Olk9TzrK5PauT5z//UD/9ABWuxNI8H731TnrVJ2ZsGKqgqjCdj50xo6sW3vWc8HsvQjzYGxxcWquAvPvvsc089WY3r8WC4sbHRTMZSRxYti463ZlCY3rFjiydO9ubmawnj66vXH32Mbt5czlyoJpN6cuPS9fzoMR9JxDa+QghZpxSKEpLmIbbxmG0HgewcWLc+vm7H/H+07LMJDvnpkP1vu6Dd6VrSfZP5gdXSIddwe58kCbXSMkoM8QFN1a5GDiqJKxqVQG759B0rJ86Om1gHpsxOnWj7L+P25tqB17zPNNm7/5Yv75V2pUP2jy3yoRbKKXp351HSCqgpq1KK/bntebc3SmZtzfU733HmgbeVf/nXE4Ews8bpDsKJFZucFwCgBGlTR03vkF71vL+zWOz0jaXYKWUAxmWISTwUqqqhUVUyzM5sMd22Jphvd8zasmYOgv5eZmn1p/csIFoUi6Yct/SCJ18QT0ne1Ob62m5QTh4lELExTDaxzDj4kIxRAYxDVuRlwcwikvJQavDOWDBpWbhu15R5VNTjSZxUTAksYcoL1++Z2VnudznLq8HIr93UtVUSFWfQm+nMHSsXl2O3rIebuHhJNzbYkrBRk2NmpnfHafRLm7l6dT2+cJUqr71cirx/ZClfmEUnrwej+OL1sDEKFIlUveQzvc7KUjbXj4VrRpPqxvpobWN08SKsgctm77r7zANv2RxPnnjiOfE1Ypg/epTne6PBYNwMxDoszecLS+WQZK0KNwdhMlEiWIcyM51Od37WwXcFg81mbTSJDQQEtSi7vV6vnO1jfh6jWth5TQAjxRiRWRTljCkLxLmZWWcsFESkDFhbZmW/b2dnZ+dmZi2xqoq2j9LazHU6WWkLoNfvT3ynFg9RZmtAeW5zYGZ2vtNYIWgEEQWVpETX7fVmuj3nkBV5o5F5So0y0Jy5k2ueq1IYeZkghkASSdSwpYyQMZy1AGLIDLOlGmiaBpzBFD4ieC3KzhOPPzNpJvXaZj0Zf/qTX+gU5s1vfvPZpW4XcFkJrTc3Nq+9cLmM9eoLL3zo/d97cq4oinxj0nz0j//8T77ydJ31BoONEAKRITJN7Y0xdVVpy0iQ6GsA3W5fVX0TCXDOTbxvYoRlsCNmjREizmZ+WI2CDRLh6KbtbVQXyV2HstSVDAMCTyZjGQ3LTtE/dedgYxNRfUbW5WqMj0E12THTrrH77ytder5e/tOUV9Hs3xY6cmtnx34f0/654tt+T9pgpVSSmIhuqdQIqcbQICRoVQkHcBUUACyU0Z05ce7u7vzS6iSSKazNvK+35v7bAkU7HUO3xVduW9Wr23n3V9miNyX6buL9qGoMjUSf8j0zg9gDbWAODr+FtHXHr5EIod5Ynp9/x/2nH/rsk95jp2LhlhC/7HDSaYqWItIEkTEd5s8CcEDm80PK1JRJfiVioI0nChI1RlIwO8Ot35EtNaJtUtAdXhuZprfd+ZbcmrSjtzTk9/+wUx34gNqYlGmaWWN3QjtqqboAS2p7IoMUZYLE4W1dRTqVyWlhLyY2zEzccpQEoBg1IVRZ4bLcuMwYAyBETzV5kSCRwCowxlBZqmqMAVWdqOtMbFxme303Ny/9Llk3218cKIfRUOoAIjibzy/0Tp6Q+Vm9eqW6cT0OYA0RyNo86810Fufc0QXkptHGX4FWntSwddzLypV5LPbqa9eam9ebQaMxgMl0Ozzbz5bm84WZkfeD1dXJpav+2ipCsMvLx8+dyef6L126HIzpnlwZaDh1/Cg2J1fOX/ZrG8hs/8wJc2RhAjULZZ6Fimhyo9HIyBjdIlvoUodPzS28Z/nOMJ787dPnHz1/OYRAJgNM7grb7WQzs43UbIqoCnJgw8y237MuzzxnEnplp5PlZC0ME8M4m5msU6Isy7IsqXVrJuIcG2fzXifvOALyTpmFEpXCB0gLmuZAlmVZt7TOea/ig4gEiRFaFEWe57lFVhYmRgESyhqJ1ZKUhrpZ+9pKxFhVxEVYUuYYLTVWgkbrgs1ZclsDwRWNmKdeWL0xfEy56xt+8vFnQj3o5+7s2RNvP7n8lvvuGgVcel4//dQjj33ridVVX41CDukYSLX5N49duPOOs0vLs9964tGLl1c9LQ7XJ1meT5omc1kIosoWBlmHDbyv8zyn4L2PIzExqKopi07dSPCOnFEAjVcS5owQuQ4koQiTRmIjRDcux6sCr66YQ9VgPIA0TGKNaSbj0MTe7Ox4MpG6Fueccxq9RobEbQf/th2zPbLsT5dzWGd/9WuU18vfZ9k/XR2GeRCgciu747Z2zHbuvH01b9Vwa8RlH1ejlbA9YCOEWEkBUQ0REojBEg91HrABbGflxOzyCaFMWNi4cNDufJhlIy3Hct/d7cIvdx5wyN0dkO96q679572d8Zqwj0SUsdAU74K6GnlfJ7zeZjBWUj7ow/rp/pZPH4KvWQlh9Y13Li/OPDmZJAL1zqBEBsJ0kBCAoJGgrDuilF8bVGbnILPjszEmpOVwG2YeoKpRlA2mtBAi2vK5EN/adDmg7HpgO91Mr7DQFuiyJ2H9vgycwgATmKISIoyClaJSyz5qrRkghSsRE4wCSmAmEoAIhgEDMmxMhEqIpGoIqvAQCEQUElF7Xwf1qbkM1KBuEEQs+SAQbXzwg4li0p9fmoiiiVR7CCCba/lq1ZnhzI2GI2yMMBwFm3LHj6Fys0TPRjPX2ZhsYrCG1TVsZhiW49BsWDG8tHnzGq5cxpUbkKQgb0fXrl2bDBbvOB2d3RyOUFVg84Z3vbuBvHDxxfD0EMvL82dOLh89fvro8Sf/6rPhxjoGYxRlPreQzc5FY+vVG3nhiyyzhaoJiAFwyDPT70388OjRs//lg3f2AO3PPbO60YaujCax8TGm2DhO0rWIihgBZEVObEeboxgaibGFXkRiRAwhaPABIYQm+K3QtkS9J2NsntkyCwBbw5zDNeRVNURR30QF6hCMzWyeJ5Yws1HVuq7rum6qOkYQ2xhjCLDWBonRN9oQGlBhQU5j8pw7MIsGNRwti1GJkCiZc15kYzi4voGmqQFcW7327LPP2rxvyZ47dfKB+99075uOqeBrX/q7//jQ747qvIJZazZg3aRWBJaiszYZajCjkVz45lPGUSOVc71mU3LXrf0YplSTiXhjTAhRFagDiKvaAwxOStyGmEMkQMmSakQUMkZjlMmYHBFpxojeG5ICRoZjr2ptxpvrcdy4DGzJNyFCABTdYjgcGOtgCKree4TIhpVJ5aDB/1ZLN9mz8XV45h9COTTYe9eXVwYk3ApQeZlA3bfxnmzvv2sK27YzDJGIKifhU2UQpVxIB1bHBnD9haNc9MZe2RaRTVVVeWZfzuXpQZTnww58RYCN7qXM38aO2Xs4tUHbU3orkTIBvh5InADsDJyFzXybSvlwCCpt3fmNAIiWvcw3g5NHe0cWcX0VVYjGWNk1buhU/qdl4agqcZLLs3uzYb3aYk07iyd3CrWGJSHECCJYeBWCkFFWREpSP6pQkamWQGKP05SxnG6yJZDzrqZ/GcbK1FO4d/vUbXRADapKUUAkPPVSAZAIpi1sSAEgghn33mGKPH79ce727djDR0dmPJ6YXieqIKQINGJDlFm1VkASArEaYiVoFIgAMea5s8ZXNUSo12HDUEVmEQlRUYW4PjK2Y7uuiYwqQC2aCUymxFmn65X14hWUnWF31nb7tuj44aak3AvXNyfZ6uzKistKn+S32cIRKMNoghevDudnim4Ga2EYzsJlCMC1tVGnyPodmKwlN7gMXuAjLPDiS6udfOHUSSrz/PTRE525i89dGF2/gabB4oztdpaOrMTKP/bwI7h2BWThLBzXWs903MZwA5s3aTxYPrKy5upVGsI5FBlcoZyPxtdjjHXEisFMp6sh1qNxb7ZE3QxvrktTs0R4YaSsaAYh5DlZa0fjKm5sRIiq5rlByqlEgPfDyXA0QpLej1Cyhoi0qZXQ6XVh+Oba2gQYVRMuF12e+c0xJMLyuK7GLUbI48kky0xVVVm/E0Jwzg0Gg9FoZC1ijBAxDGZmigja5XK0NlRBb355XNUSHSYN92caqaRjzVwJZWzUOhRf2MpX49Hg3/7b30AIhqIL1d0n5t7+zvfefe5EafDIw8/82q999Pq1VUjQKMYWnlFZDzKQDMzNZAwV43LRGMHBB4CbKBkRNQ1DQRS9Z0CawJrMbFJhgrSQrBIJqWqgynBF5FPyPFUFA5kC6qMSQ0SgUB+FWEHeN1AgE0mp05yksIIq1DCIGsAElVYSLSoDcdeqest/T7sXLtshpof18C3gdm/nfYXj2Cud8F4rHZrDznuY3sxrVV5p+xDzgfPijol/34fpun//Le6antMWahfuOxeiezgGvAN92eVwob3mL+/Y0tZ22KSeMJ59V56kXdqcP6pbPAMCRIQIGqKMxilwZrcdsFsXPggW5or+PGUdsVldibLPs2xLZEW326C9Wpp6JRRx2ipTx0SaVHce05ZU2y6karuV6AA8RnU7zGfqaH65RVWJmB05zn3QUItjZxmhrjY3r2kzcNZqxMnT3YWlcrx5vZtbQtgDF9FOUHbndgCANRl87RhHl+bOnJx/9FtrsptdBIgioE3gmVCZlpLB7KKIIhInxb+toIEdc/3Lvl2LHb4bVd2GhmgrskkUYGrjenZK+tzaSZRu4+XoyuwvhwI2rwi8kVYtZ/uSmX7mv/vFa2vrn7y+Kc9fDV6zCOXY6XabRI5RQImViTmBnRKn+jLpdWZKzJuUKQOWoYBhAsgYJYWxFGCIrS2dyZzJQyZNUfbZNsaabmFnZrqzM9EV1ypF5pYWFoNh25+J47EYbUyO3kw2M7s0tzA0ZnNlJYIKB1UF595murRw/NiZ7MTydZdv3myoOyiNy1yOLJu781TvrnO1b9Yq5xdWu2RzBbxKZsJSf/Gu09rv9jrFjScvPPutb2DUgPn4Pfd0VhYuUf3i8y8U5BADioysVT8CRZrtiBGD5txdJ37sgXfdebTztSde+INPrF65NkQo0YiBg6cvf+OxM0Rvu+fuL379kWpSH5mdjRJS/g1AYgxQD4oaIyS2aYe0NQoFIiLKcM45NlNqU6udrQQfg49BY0zbY2yFKRsghBBiozGJZCkkQLVR+CAxErT1BGKLaSgSQtB0alURRMRaPDjTGBksw0lNg5KyUWzQ7bj5flnO2JK5n2vQUbM5HowpeKdNXY0hzR1nzzz4nnffd9exTolPfurrf/yNR69cvDSZ1EGzoBlx7hxF37QxiAnwI0la0ukrMauapLQtias4FYnaNR8kYBQACSm39WhyvQXWyOAoKW1Ka+sItdJhpEwgFp6S4kPbJQjYEkpPSKRiKjS+3RH39Mr9/XTn13bsfh2S+c9XXm6DvzyogA759bYQxSu6qlfNtUoCJ0oAGFvJiZJ6HtDyEdrkgFtRNHtLi9MYC5O7oiemEFgYnUZJ7kUZD7hOOnTLrVv1gKput+WV7h+hoaqBhslazi0HagI11Wj9GvyQ1BnG6VMLoRl0yxKxejmW+d4OrsqIhZMzx5cMrZFCsLNNtg2j1tjbWVc7KL0GIdkWQlvcl2ntun3a6S9bkU3YF/J+2JM5DF95OWVLvaCtfJ8Fozql7KgQKBlMU9lH2T5SAQIRMRERBaKvfOFLjSiGY4ALy9DoY9TIalkJ3PJnSAmapjtiEDGn1AZxCq2T+BCNBRSqiDGotskmRTSSiIsqJBqjb2KAyGA8RF0hVoBUqwWVPYzHaNxoba1eX+X1dR0NBVGMBbnxTIduXBsON/1whMGgjg00gh3yDibV2tJyTrp+9Vq8tIqrq00D5DksjxSlMVWoRucv4fnLN8Y+LZ8wk+NqeXm0Vhxb2rx+Ay9cQuNRZsvnTtu8eO7SRYm+c+IEuoaLTEKjm+udN9195xvvXR0Oo/pTJ+a+665z7znRWXKwZ449c/ro6tWnvI9dKnpUbCC7ceWlv+g+2ywvfePyS1Amg0lTwYrPAFaQwAawB2qEGtYJQKKOOLjMSSBmMFrJGJ8UHrbo2pSwmTTyCkmQiCiFyxhgZgoSvW/ja2K01ghgjKEGBmQdlAlRJMSMDINijETgBLuleTs3AhqPh9zp2LKIjQ8BcMByx5d67vTiiWML5PDixZeG1+q8T/nG9SMz5f0PfscD73pr1sE3Hr32f//OJ164eIlcMZlUsfFsDJOJiAQCccoyitbOiElukVRYYVLWL4InA0JgDeShkSAtZ6BdpkYmadPIEVQJzCpJU1QAJzAcicEy1Y2AgjRONQXUKkNgkuIWWYVADCBISVZFjQpJqzQhaDmCQq1GFk2tq9Sht+XAdvNm0s6v2y7/ecueqJ89o+8upwyAQxiNukOUbX/9e1Lk7cFjgL0L6QPNoJdPR2hF/A6fR9Igt/Mce3MuqqZuNd20D1UEQMQuy3p9dZlXIiaKwpAEOe2//h2agbvmdUoyegd0gp3ozo79D4EdWnHCHYzX2/asg9uZ2TkHADGy1lbYT8bDm1dWL18gqhHrsoc3v/EU0FhXqhhoTNqDe6re+W37USgns9GolIbe8ua7mZ+WiJ0AGrUcop3tQCRxp0DRazJo2O2KiNplme4kFW+FK23jGzt9Pbfw+3ybpQXx9qeW3DJr0is1XXYjoUqtjPZ0N1VS2sZ4BI9/9I9QdrA6cllHJh4QW5ZjbcAZRIRAnNKKsKS1akpYSkiacggBxGCr0UswKd+MRK9REDwkQCLUKRklEkQQyBotMytRxUQV+Co2k7zTyftdWBercRwNom/AIMMoHM0WnbmOMcidjTM9GW5i2MBHkIIzLM64bumc65W94cxMvLmByQTCWDjSmZ91ZREDZ7OzTXcdg5sIAWiwUGCmW/a7WbfjJp0wP9dfWDy6snLx+QvXb74ACPr9XqcwvWJtAzNvuGdpbvbmxuqjn/ssrCmW5pZPH/3OO4+aMURQZpjplAxF7f1w3AzG80dPrhV0lcwNk08oQ+Y3xpuwBk65k2VZljsbi7wonTQAK2LjPdSHDNzpdJ0GZWoCovcaJfF78iw3GdgadrbodLIsg2oydqJvSKST5yWQZdnEC5qmlQMnuMw6QpkXtmoM2SxDii8TH4osy4wF4ABDyI0FwMzRRy570gBiZrJ+0/jae5tnMcNMRx88c/QNJ2YVct7Ik66wyt/1hrsKwdUrq3/+8c888cyF0cTDWEE+WR/bTlnOdLz34gNYRX018eQyVUUEKMXkTwdZiVCQKoOYIQxQBCnET4EVbkcQkin/f+r6aYGc1mRXNUn4FNBpD0WbBy3pJEGZKGoE1MBDYtu/lLUVIoC0qWuQHOZJCpuALZTo1u58HDTzvW7W/MMp+501B/50W4Dk1pDDATvrwT8dtj/tu9S9B+3+zkAUUprOU6KJ2aCqnJa1kuRlgb2kVsY0Z1/6MS/KvDsj7JqozJakIejOefig114P/Omw/fWQn/a3g77CdjvsJxZmA6hEjRlFq7q58dLl849jY3WmT36kb7gLd5476uykqSbZfsNq/6n3nVdVmTRjvPHes4x2Ip7uIlM4uW2qLWaMagRb6EFTfPuUbpkZel+xSizTkxJPwRfZKZ6LFpzbiZOkdTBvv9SvlTVDoltKd4c6sHa7XYlIEUmJYPY1iqgqq23zMQRV4zAM8MpogkbKDFsABA2AgNvHICTQ5NoTTg9HRGNr3gBKEikGYwwZZtWoQVVEFcQwsNaZwlJuua2/iJMRrAAqCNzUTkJRFpzlg/EmqhHQIIPmBrNleWS2t9K3BRnKi5XlUfQTBzQjyyQznd49J/hoj7q2zzNFPHJdvawP0SlmTp/ITy1ioeOC6cYVhfe5g2/Qs+gXs6dOdBbnJDNHT57I+ov1letPPfwwJhXG3izMlkcWK4c8M2997zt1MH7qa1+tL11CXWGmf8fSifuP37F6E3kG6uCpK6vffP6CV5C1zcZaWJ4/fuoNI9MMq+FGpCYSQHX0ZdcgNy7LGAZemchkhjMHZ9H4OsRmODbCDpaJqtBsjsdQtaCMYYzLsiyl1FZVa22KEYNIjLGp6ziuOGMDaBNiVWvtKbaOdKuJnG3+X/be/NmS5CoT/M5xj4i7vfu23NfadyRUJQoQWhCqQgKEBIOQkDC6Z8Z6ZqznpzGb/mX+gLHuHuuxMcZg6GUEtGiaTdB0i5FQg9Bekkql2lV7ZWXl9jLz7e9uEeHu58wPHnd7W2aWCpqxKZcsK15EXA8PD1/O+c453wm5E+eNgQLqgxks7l0AACAASURBVDqfcCQldwTAByMQDwpAp0SQRm0uBOmvD9I0zcAuuMT17ps/+smbZm8CLLh76vDXTh1+aSX/8y/89dWLK8tXVoKnJEkJxudeRFqNelEMBr2SGDCsGiACwwgyDvtHHFlRX6iyloWhP17FBhjZSEFAqHTHyvU+CtbVmKykFmFoIGFSYhUxqMxYkUyCBCwQVR5lbvOkuUHJyqokYE8IPBH0p4CyEU4UQ4STI332EB4drVJjb4BdFct99s7/P5cbFu/e0LK631N+ADlmiEnvUniaLWafBvBu94yOp34ouw+hbchT3PF0wqOXFKJTWn9MJFldBTRycQ7/BACRerOVpJlodL0BIUCUiHU3CEkRbkikY1yXtU41JgHEPhHIN2TA9d5LUViSzGgCX3a3VpdeXj37IoJioHN1/PzPvu3QfCPl3LscJiEWTHzFbc/d8QoEJlUhVag7dnS+Os26XQipauKRo9VU498M+cFOPjGKBVFLo7FRCYgCzM5I6DdPgpmuVXUIt0web380gNjaKBZGGVkBpug7sX2oKdgLOa0laQ5yRZE2656lLDpcq0lwFTJfefKFyvnbaTCGSEkU6rlK5AQNPhQKm7KJkEFQ7xEcW8swihB8Kb0eOacaqMwplKpllfW02+mDkywnm4R+D4MtqJK1apQk+P5m54qlza1QalqK5CV7YYIxgTmEvLN5+WJqOBHhXlmjUNRMkhnyebmx5suuqprctxKr7YZKrbSqaVoPxJ3cI+/mg/7l1WJ9A9CkXm8229ysgak906IkuXDm7Nr3n0NZQHD4pttO33Tr0tKVP/7e55666fiPvv22Mu+8dObsa68vi8sIAi5c2UmMlssryIt8q0S3j+CTlEo3QOn8oMzXu72rG5BQHClRegQF2xBC3umRU6XEkN/YqnX6PYQAiUTYvjRlp4u1zY31zbXB4qz3ZfVpJJSDfn9jqyYuBzqbG0UpKEJMbsuAd67fQ7/f72xstpvtEKBKKkGDIGgIIc9zB+T9gfdSlqLB33rrza+euZh7V2vN9Pv9sigaZFGWMgjY2poVtBSpQQ48+8jjf/T17/Y6KsEq1dSSDzFvqbHW5oOBwFtjTcIhOO88DCdp3RcBYAoRsveAUoxCVI1BXVCtArQkxDQow/k2XP10gnwz6pdagZBAAIJSULUaJe/KNCVKvpJ+CKgYYhQQJQkEDYrI60CkmLYYKxgCJVKKzMv7G/63/blTq77B2f9Wua6y5x62T3/v+y2u8XH33n2rv/dFEfbZvHe9/5qtjWXKDqLTRihVQIlUEVA5BkxendpQCFDCzOws29QFAVtRT6QkQYl3bTBd36S4oa64vptv7P7Esghl1mTsO2tXL77y4tJrLyHfTFRlgHvfgYd/6u2GCriQWgP1k7vmNZ+iJEqs3lsWiK9lXGlhAMBQnpZGYwpOifhCtbDQyJtWJv59IyUmLiAMjY08xNwIZBTQSqFUocqNE2F38WVC7tknT8d1FlZMyk6TQ2eqDLWEKm12BMlj5lTiiU+hqnFhFuMAJWtNHlyuJVQQvEFGpa+araqGREkI0FB1RoW4KEU5qXLS9sGHYBgSc1rEnUZVVD20EO0VAAMO4kAOoQQJ1CP3WN10tgEw1COUhsFG/MBrp1uud0u+CKrDUW7rKHO4rqTBmxK9bnllFUgHsaNF4UoIBWNza5EmYAMSsEEQlA4wMATCZRiQolaDBLhQO3ygfuhgq93SwjnnjdPlZ17uLa9DBepwaP7wqZOUy3e//F0dlDh17MW+XH70+ZXzZ9HN66ZlAoeijyZ3N6/0O+tYugSX1NYKFIrgMpbc5ej0qF74lQEud0EIW4U6oARSUlUtS+0UPTUwvpk3fQiRAlIctCgGajudzsbGRr611e12XVFWxJCq3rmw1e2L8x79rW4ZEjgDAzJQkTIv+n0U/UFnY7PfnncCVUUQFhUfQukGAxQlil4fZEIIbPSnfvJ+Y91LZ17viyCz0s+D1IAQOnT2wpXzA7SaUODlVX/u1V65YsU7JErEIhpEiAE2qp6MAuxFfeEQFJwxUimHyUlBUXWssqophLSiBgBQqg0wooB65kBc+TxjmAcXBNFhrOBQ1ok5PCEgjV7pITpAiiL2ZmU6CjYwiyrgmUJSj5OChYZzW4cmf4UIiQYFg5RVCNgj8gjTWhp2alpvlb9X5bq/C22XTHbWtF2OudaTr0Nw2T3eZ+IRe+wo8VcVIDNW8rWiMBku/yCZgFa4ijxTjXy0AQDz7OwsW+OhzCwhMKlqAJLtDd6DP2b4rJ2nr3cG7VLbjufu84Nd9QqCMKkvy83NyxdfeXb1zPPobEALC5w6jJ/70L3HD1sbuiTC1sCHbV9gVOfejx2GIEkQn1dujipDJpuxkYgxXNAQaEIM1Uro3FcKv45iAUR70vj9xygSV87hw0ZXFq9JMCYKE/u0Y+hPLtO6X7SQVf+Of80ylEFCvE5TzjpDC1hETHiS3TmSyowjVghjmkANAKsqKZIk6fe7Wbtp0loQB7ZIrev2mJMq0diolUHBBKpi51TD0BMYCuU0E68VdCkCUjasChICqQFUPYIqV4nDOUnEgoiSJHOliCsBC6JECKoGquopODUELSCemLWUtEYqTkgTYwoBFU6Eon8OAQKlxGpi4DxACKHa6tjARGbAAE0wKG0j9U7Q72CmiSTL19bz1cvrCVfkckkNShBCo3HTfW+3mX31tVf0whKknh46NnviuHBYee5p9AJyNvUkS2zfO0ANEYoCooB4VzQ47RddDpolKQLbKEmoh1IiRpRBFq6I+TvhPMSDfAiBbAJLbA0ToOwgpYbIT1W4UgQkASKkQBC43JVgQggC70giYmMhKhLUI3hoWTpXkAIaVMkLiwQR+CIEB3FChojIJHzHHUdgHlzeWltfXjWzBwIFIzlBtdTOuj+/pZsdeeWVV55+4ex3nj1nbJ1IvBOQMls2JCKiARqIqaL558QwhRBUiA0NWcMZWlE0xTErkY0SQ3pNFSMAIWjko6zMuMPFbqRsDENtVSt3yJjXjqQyIWkl+ERgUiWiLFF4EqgCFqpDtYQqa1Q14cZBU0KiVClVPFwZJqbv9tX5DSvWb5X9yt962vBrO0LFUxNNkqml/lrAwH5V7XK/Vo8QqjaO0fF+JabLFoy9ZatzTCCB0yoOmDF0EMA4T2Q1RTittWbVpEHJMotTYqNwO1u7U3yfOK7CDKE8nqrbfq4AhDXuSkpDB+ThzcP6VbZVXqnmihicGKakmmjt3f4RCU773XKw2Vldv3zuTOf8KxhskBY1+BmDH3vn8Z956O2uXG03CQHwARKZ1PabuTo9GIgoQECJCpwrq1VtGK0zXigUI67hHU4wk8eTjtHDzry+Yoc6WSVoCCFCakQ0IcRAJ8OCJs6OwZhJIJrGtDeM6LcIEJQMJqZnRNoFCjIRWwERNBUAkeCFh/b+arlmI2zAAg6EYCL0FxgErtIqAyqRGDYyYkStl4f+XkyOgmR2UBawBmwRCMbYVt2XQZwDszEsIhoCGwMl8T4Yw5F8L06OKFM5ByIEgTIZC0jwJTGLcMopK4XghSNbpMCaECEuL857GAsW+D6SRMQkzEGLIM5aFmO8KIhVChgtfZmkNvhByENaayColi7LbKlUVqp9wGwLqigEIcAMXSuSREnBBkSoNbzzsIS0DgR4DwNA4QIUSCwQ0Gy1b7r58PyBlVfPrb/yCmbraLcXT920sLCwdP5898J5FAOT1pgk+HxQlsl8W5znQbBkQQD69nA7DHL0vTFpERySWpkXvkXIPLb6DOeDhwC1huQ+M6ZwOWwChqE0H3hQ6LsBAVCLwiczMwOfw5VI09yHzCaDXs8qIXgYlyVZpweiug8Fsw1BREPCqQoXOVQSWPYuNwIWCabRd6SahBLeICWg8JRRv+hzwr/xm5/+tV/7hx/78Ef/3e//ofS6iaIsBjaz0pFf+rVfu7he/m+/9+9LHzjNOizedZmZFQKV4DARWyEuVGMs+DDEJ7wMp2Kc/0ER83tBK7mC4pLKQlJYkHIVYw0ZCqtDMRqABhpNOB3GTQeCskRCjbh4kMQFnUSVwMpBNTAYGmk2gMpQJcPg6ThvqrjT8Syurku1muikIkUKGqoZk4ldpjff8XKh2IHZ6FCh/tssN4oS3SgPzQ6ngGs+4MZuB7CHWn/dWEg8iHznOn2papJM7YIRBpxgMZioE7R9Lx/WoePfT7aBpt94W6NpQnoe3i9QATGgYFUVMKOiBNnxatVwFVLlqpeG44ktxHOUAHw1jzxigloQhhmRo1BDCTy3F4/2TI04RfAQz2mSl0ViULGtjfT8ifeZtnABJEKeSKEMtSQ0znBsYECkhiQgKKtnKVU1eCYyRKqqol41MBMzq/iJqocvpYQq262aqYEkzjkDJSImVVWXF/1+vyzWuxtnehvLa+fX0BlAYYAMfjbBhz6w8A8+9eCxI6ZhCaHwvrTcgAjIT0oP20erbndU8uyMoWIQslq7cBSjF9hkovl0vBYLhCvih6g4C3MFKaMSGH4gbxW7U+oZASS7zTrd/naTJ3ZcjGUynmzU2skk8jsxHaMAVAQ6dEquRBGtbE/KHDSupFMdpkyQ3VA4Eopprq2xTFVqitIjMXOLh44eP3Hx8tLW5iaKMkSpLgCk1nA5ZjbetkpTzF0BimmqYnIEJsMi8OIBIWPZkCjgXcVcJ0YBJgUzGDAURIiZCRasTF4Epo60BmIEh+CcFmACJaqkymKor0FiFqQ0nbv15lP33snGvvzMczZNtJaQUM3DgBwzDJs0CSpQTtM0yzIJweWFik+ANLNeEEgLRQl0y+Ll7z+D9T7a7WN33GpnalfXl1cffx5FCdH6wpzv9C1TcGVaz8peF0wmnQEAQ2jUVwZbQVy90er3cs0svJSuREKgEo0s93nKDBH0ikajUeQ5rEFwYFPkJbOB+NKXIQCcIu+U/YH4AGOdC41Wc7C1nmZpMRjMzs5AysI7YahJveuY1KRZTZUYbEytikgW8eJUQaQK8iA26imULpQlQu5gmIiCSq3Z+OM/+pNf+fgnP/4Lv/DHf/CHCplfmFnfWn/4Aw8fWjzwW//yM/1c2GTF1gAJUQKNWbgmtoS9KL2HQswUdWQlmgwPhnJOGGekHdVMlTl5SIu3C0kpabT8EqZ8wyKdDKBktFo4AAiikZinLEHjXWFimk7BpzJ92/T5ba/8Vnnzyw9GuXFN1CTSsu1xdf8ve01fjRu+fwz5RPV1jJ3s+pMoaUcBqMqEiKBkR9coJlCkKO+MpypXfvXxRq4AA5uQsarKSqQiSkRGd3vu6MzOfotUqSCjFTOIWhhr1LsOQdSV5SB3/b7PB3m+lfcHhKSaoxEnhYj6qPuPAd1h75AihKkJWxHBkaZpShJEvXjnyzAY9Pr9vi83wvo5+ByaNdKUipxR3nQE73rw0H/1C2+/7+56ylvebyQAG4GNetHUG2575Z1nqIJbqiZR5QBjp+8aZWKqPstkjW9Mut9Z7M5T+1c84RJzXUUnrVGV/XKnIhYHqwEpUBpVq8IiquQBT5FCwygjCBGYFEYlqIiCYgYlADHT0tijQCtBhwiR1Y4IQDkYQBVJStZqkmQzzWPHjt12+63r6+t97vrgQVN9QsxkuOKmqTQDA4ISsUm0uhLFrkrA9BKgSmli6zUkpnQOZQHV4dgzxhhbq7nEiCEMBl48CYyienq9ZuYWOKv74LSziV4HXkjIATAJ5ppIE1iLskBZqjE1U3OK3mbHLixmnMH77ka33Oj4siDDwgTLaDay+dm0WfeDYrC6jvWthIyFOlHbqIVm3VmCZRCy204eO3BweXm5e/YVUIB3uOXk3bfftvbahSsvvcqJcc61qF4WBeYapVWkFvU6Mi6KwpMaQ+q8Z4Xl+ly7zB0WDmF9sznb7q1sggjWlhKQWTib1lrlYHDq+InexhZCuPXWW4t+wFZn8aaT82njSHt+BRduPX3qytLlmcVFljA7P//8mVfIpHfccVcvR32mnRchZs5wpYMLIMkStOo1atYlSz2jhHgtg5aeQ7CesiRtwaRplImD1//xH3/yN3/zj37v9//9//JPPjXIP/LFL35xZX3tnnvued/7HvjM7/1Fng9Uqej3kNg0teXmJmU1TAsxlWSwbU7stJpPWJ1HWPTk+kiiOnGbqk6w605GY2g0L07Onb38G3YaDiqdW0cy07BtY3xll1r2qnM//4a3yptR3piYuPNXvFs1qkp7u2vsY0y5nofuf3/0fN9+/25N2ZWrZvyba7VqSCIcwZhdqh+JSgkby0a9AjRyV9i1c/Z8qLKVNKh4sSCK1gJWMpJb6fY2L2xcvrhyeWl9dUsHZeXAEEr4HJGTjFHh5RpgzKjO0atAATJTst0wCyLKchxPqFRRhwRNbcKwEnKEvGZx1x34uZ++/eEP3Hf7rY1WPXeDNUZB1pIKqC/Bs0n3eLXd1gECwNGWXTEKTninTGg7I8DszZFadi27iDJveqkSIw3JYEYeLbsVAUkgicIwg4ywJ64kEuUwJE8TEmEFRdOhmURNqvm34wnxobVGw4egSkEV4otO99y5c71Bv9Pp+LIcNhjKHDNMsbEgE1NqK0kEOpUYZMgaZgZgmAGnMEoKDSAgyWytbmsNTixnUpa5dDvRu8cYSpIsnZmRZtOR9sNl5J5USAhkUGumBw7NHD6KWiOoL9bXBktL2OyRBARGo3Hwtrt4rs2JXV665F4/t3lx5Tn/fMmExQP1o0fr7baWvs9XXbeDzVxVkVkcXKgdXqzPzXGWUFEWzsvmlhsMnHMQ+CypN+rNxfn548fmFhdcb+vZbz6CpUtIE9SSow/cf/qu28++8nKSGKgUeRlnedpq6dyMmW3NHzyA2VlIUavVkJhi0D9QbxQW3UbG9Wxupp02mqWoJkl7dhZBAWFrkkYdaZoS3fu2Hzp48ODffPnL2eHD9//wOz772c8mM+13vfPBtYuXextbd91197133/7pf/Vp1+l88OEPXr58+ftPPn3LPXf/8sd/4s///ImVrS0VQRE0MUmaUj2pp0gTNJppvdnKag1OIErBlyRFliZJIsKBLUxiyzKHULPR+Mu/fOwf/aNPfPpff/af/Ys/+if/8ye891/+8pc/9amPfulvvvfc40+ls4s+uLTWCCrBCZKxD+Cki181q7ctcEMxYdsZnbo69SudjACKy/Tk46bqnvr5VBUTcAtN3DylQY6EsGtuPzuEoR3Xt7f/rfJ3UfbYTadO7/1xr+dT3tjNuvulve+/4cbsYp3c+ydEpDJsFlnQvumXETm4hRnRjGuINMgoivY6mxr91kQgQcmQTYJB8J2837169fXHVy++kF8+B+/AFl4hAeTBwVAVQhVjrapSVniFTisYlXhAY5eTSo+nmAAZGg0XBMNgRihdI8HMAm46jXf/+M3ve9/dd9w612oWBivFYKAuT2sphrlRBNFONxEyOXxB2v37MlfmSw/4obl82Bk6aVGRIV/+jvImLRn7iDLTpsfpwhN3VJrZJFyza+MmUmqr7gorCUhhAiAlwQjbaBoUDkogi8hkTwQSsMIIFBArgpEFk2hMFLyt9ojF5b0eiEAGhmEsjOn1eoPBIJQlVGEtgJgd0xgjCrDR6GpTcelXRiUQDWVugJjYwgSEAAQYa+uZqdU9GQZTktSs6ecDSGlAJqJHNrP1FkH7bKFqHZjZWUsz7fr8oaS92HEha7RaSZb3cu1640oBW64HlzbqB6TOuryGAWNlvZsrThyd+aG7aWG2IJMamx5d7M1nOHsJ3R5ajblbbpo/edQRlRpaNm0uLlxpZLK1CWtBVF+cXzh0OKnXjUn7/f5LTz2DvMBMiw4fuOXkyUDy0rPP3Xzk6N13//CrzflHH300FGU/FF6B1dXmgXmGUKOmtu6cg3jUslqtkbsBmGcPHhSvWgrqjaTZ8PkWVNFsee9dUdTrjfmZmQd/7G1/9Vff8JcuPfTf/sNHHnlka3X11MnTP3zv6U//zmdzo7/6kYe++cijrj84dfPN99x39//xL379wNHjD9z/o089s7W0vExpDWrUF+q8cw61kCYJDBRefWBwliK1qZSF8WVSuro6dZ6BtJ6W5WDQ64fS/ee//urBQ8c+8cmPffrTn/m/f/sLS5eufPQXfvHlV1b++q++0phf7He2svZcUfTrjdZgfdO2277MR0lJhoNdCCMz0E5pZsf6q+PjSV+E4ekwPln9O14gaJK9Y5tpWHW8dmDowLLTw0CryKjtctIEzjT5k6klbFzxEPicFObeKn+vyg8gx2AHpfs+VV0zXvWaj96/JSPBfvLkrr+i8TZU7S9KiPmDQdDo/DUasdt3CBFXkgYGqXLlKgSjurshlXYb+UFVySkTa2YMUtNx/Y21pYsr519Zf+FbKDYRfGZgfBECYJAmmMnQbmNhDo0WDEMEqiCGJYxyj/DEtDYjt5mRoAMAiNmcSSFSLRiGYAxuv/Xk0SOLd9xx5Oab5hfmQ61WWF5lKllEA9i0OCTiVURskpgkUrxe7/ciBakFCUOZ3BguilHEkfFn5HsHobHTD0NpnASpchTBDxyM/XdWpoxNVeHtPNgAMQiBiKjyjDEaDYkMQhXqTYBKFRgyUWRv21tceTlJjDEg4yWoKFg0uFBGOdawMRICRMgYZo5J7VVIo1cxqVSWAwKxUhx5yjYyXRtliaRiZA2xKfMAL0mdLWCslaKMlPSFc6F0xkcDLyOQCLwKMqO1ple7ttEvN7YGi3OzMynbWhB1ogguFK64eMW350zW9NYgsSBmkx48fCQ5sLCu0ltem5lrtw/Oojgctrpd76SWNWfbaaO5sr7S7Q+OLh6cac0O2nNbpWcD1NKFI0eyZmvpytXBRsdYM7cwvyVy+OCiL4tXv/cEBr3GLTc/8PAH7z2FGfuOx7/3ZCj6UrNQoFY/dvzE9556MqvV5ufnz555DaE8eOddebe3eu4sZlonb735+eefd0tLhx98oNFsfvubj6JRv+/BB5964kmsbQzq5Tvf//6zr1898+qrd77nPRvLqxfPXwD0I7/0c3/xpW87xoMPPrix3r986eKhwwd/7ud/9rf/7WdsWluYXWi1Zj77uc8lzboYA5/DGiZIWYI5aaTCKHyR9/ranE8JCdnSdW2Zz9XT07PtlbW1jQ14CTCsIZBHmtR+//f/4Jd+8Rf+u//+H/yz//V//5F3/uiJ40d+/df/TZLUy7Ksz8567yBBNSBN/aAE0djTc9K0tBvKssuZ0fY/aU0a3yCYNDOpbvv5+HAkJG172jSKsxcqo7tqupMzZd8zOi2ivSXH7Cz/Jftk30df4+Puj9JNwy/XHCfb/r6u+yfF8V1fZPdKpoVyijotA4HIVJDFHmW0nxZ5PzRKRkoaFErEeuP8MYEA5dSkFmXorW1cennp5Ze7515BuWXJpwTjMZPh9G24557FEyfaP/bOe+Zma/Pz881mgxmKYAhsqCzcVJTvsEjYHi9dvYXoCENiImZOOBK4wvlumvpa5hRdlT5JqUHSZEYZ0FQDQvBERrSK+qHJztz7lQGoRD4v0ZiEkiYMTCPRZLwhR71qChz5gWlbxsXGBCvDLzZ+zBDZ2A+buZ6ymzmp8ozZBTwRZuHRHYEofsqh4C8S/XcpZuWrYrwruARKVNkXJ3NeQjXy1MQwaQkhdrpq5a4Am8ByxXAfolRMY5x/Mowr+mBOBK6LBKgKV2mcK4YeCRFmjPFNPigTmGxQFRGQCa4MziEIGUJgBPLKMIR6PZmbS9JGv5NjvS9pg2dbJstCmkAjPa1DZ3Oru9k6miIVcAHpY7Dl15b9xVZvYwOvXugcPpjccqh/ecm98opcXsHBo/2ZxaIInYvnsNVdPtE3tpm/dlkuXhLnMFPvBbtVzwavvQZja7ffevTowcWZ9tKLL/fOnEEjBZv+uQvLS8sXGweXVleBGFCtyOzi0aMXVq/2l68e/ZEHksLlr72Gwwt2sX3lzEWsbh17x9tXet3LFy5CcPrkqfPnz+vmxsGbb87qtWe//lUU4cTxQ/Va7W++/tUDp0/cdPutX/n8X8rK2sO//Evfeea5s2tXTtxx8+Hjx/7yLz7nyv5HPvLzjz/+eD8fpGn6oQ994LN//oXO2uZCq22TzEsPShTZqVlLCQMKXhy7kJRgBypDTehAmv0PD73j7P1v++r3Hp2po3A5VEII4kPeG7zv4Ye/9OW/+daj33jbO9724Y+++//6P/+01xuoQMULifT7s4cPby6vZzMHi0EBmmSBE2DIaTQ5yWkCP58ah/GijP+a2kWG9HeR0mgkx1SKi2CEpkzUPF76x/VPXh1jLTpMu0cTvjKj22jod7zNSKQTT5z0DXqr/D0tO77OXgHdQ/+qnZJ3dXmnnLF/zZN2Fuw9TkbRUjvqH6OP+2Tq3i5q7T0ax2/HhIq0dw+lXwXQQb9r284mmUhg7OUnM42MbquGWCiFMMQan3euXlx68anu66+j12F2HNBu4J47+L3vOv3uH7/9h+45duhgKx/0LMfAkU4IIcR4EVaTmnE4uk7sv7u5/2PbKhRnfXQFgId1xgC+LF0/sYZNU7yHEgIDokzWWlUJrgSJ3emYsRv+FJ9IoCrsfOjRO1xXRoavYZ9PfTmORg7S/eWKG0No/i5QGZ0EY4bQ3x7DhEXN8P3HoR9KoqxadQERhJQSYaHYd1ORXHuZ66PwbhMTP4lhClT1fRREJGbzYUPMQEzZHEdtzDG5ramkGhMyi9ph+HpshiqCiPHxZQ1ZZg8FgqhGj81oKFVlAyGQhQFSg/n59tFjM42DJW32+yXXa42Zlp9rSaueZHbgSiQJ5g+3FufnDh1Q6XebGbqWNAzWtw6wWWcjQAYzIxSc4+AHDJRiy2CchxcEp0EEITgHEIwFyJeOaxlmZ83C4p133rm6dP71p5/G2fNotOaOn5pdnL148fyXv/61TVX9ygAAIABJREFUbzxWX37lVax3OauJBm43j9x8+vvf+ibuvOXg4oEXvvsE8mLxjjs6nY6/eiU5eeLUiRPf/tYj2Nq67V0/vrW2fuHsWbTbd99999e++AUUjurNn3jPu7/1rW8ZYx64//5nnnyqWF6++R1vr8+0vv7Yd+YWZt/zkw/86R/8P2ubGx/+0EMAHn30Ue3n/80//p++9KWvX3r9fH3xwGCQCwjE8GUoBZastS6U/SJ3EoySBSEgOMnS5MjsnFecXrAnjhyxDIUiS6y1RJQ1mtamt91225WrFz/2sYd/7zOff/3s2TRtmDQpigGRtA8d2FxeprRZbHVso+ldvs1uvOtQGx7scs+U8WjnT0i23xP/3W1U77WO6w7W3d1glf2Amb3OX1uxfqu8qWWvDfWNdfs1bD371Lnbd99Hob7hcXJ99+9vZqqG/fRJqWhnd8pBUz+Fii8K4x2ncVnH5Oa4f79NnBFm40sH3xO33lu5uHHuVXR7zYy0wPEj+Mn3nv7lX/yJd73zWDPrlt2VYmO5NtOCiIZAEGMYNm5oVV6TodtE1UYAEf8g5TEr26TGXfFXDZslAeQ1OKIMxmSSAQSxkS5KRZhBxDCGVIL4xNgRJeb+32vn15fI9DZVuMJsdOfy9eZzMFRfaxibE4hIdjyG4vI60YIRZE1UBV7QyDY57T1DFekERY9uDCtSDaqaZXXnvQ+e00REEDzqbQSBTVgk8blhdVIGiDIqJxUlBYIipgsjY8cm0oopaBTTWrW+ikyNIXkSt4qY85fBBJXgSmstWyNCEFGBElgNGROImJigIjElRwy6riK0YSMOpCEEEYF4hJKTlIJXFyipK3Pw3nAoej02xqg69bApDAOCJEWzCVX0u1hYWPyhuxZvurnsE4cAyZuH5/taDnwegqd80EztILFCmszPixpOa81bbumFs2GtL6KDgUvqzeLgQTPbIuXQLwd5CVEYDHrdujQxPwMrdrY+u3i43q5tXL5MRGhm80ePZrPtziDvbPVefPHFwepV+MB33H768GHdGiy9fDbUrdTSLgSNmp0xfmUFtxw79rZ7vv/qq/D+h+6+99yZV8u8x6dPHmnOfv+R76Jeu+uBtz//xNM20MLdd88tLjzxxBOJSW679/YXnnsenT6A93z4g19/9onNtdUPfuChztrGpWee42OHf/Kj7/+d3/gdsPn4r33ssW89d3V1ZX5x/p0Pnvrn//R3ADz04Y+uLq99/4knuTEDgJlZWdI0JtokIuccsxBRmmXOuRCCzZD70heD0zffZAkOcCEIIroX4+HJe//yiy9JyH/547/0ta8+88yTTyVpOwT13gFQH7pbW5ykoorEeleShu0ZFickjW0sIxOq0nbsZCim7Ah1nmTXjbOSZPhPhZEMvVUUWrFYVkJJhBlFx+N/8ri6Z7sybSam5S7rl+y3Y00VHarz4xP7bpy0u0/bf9myJ5Zwg5wXN3r/ng2QPdoz7GfdNqLi1QlNWqd+NdXEyXM68o+hCHZMKtHjj7QXf8zozt2LbM96M9xBhlNpWN1ekVbT+OXkTbthLQqRkKapFD1VtfW6762TTdUNov+vjH87ZApmXVleOnLoVpYZUhFQCGGbx9Be8XqjNgsJs5Z+rV5vnD/zvRcf/xr6mwmYC3fbLcknf+VdP/sz9xw56ImW1IXUZsjqcH3EqO0YMhKBpOmE29Urx8VAKyIR0nGTxn0yPRiICGqJLAIjAJrFa2wsNLCVirnKQwHLUPU0ZBjf1c93khNu1NUqElTUB5M0fIBNUZQeSLZ3U/QJIQtmMsaYRCm6aACcQDwmhsG1y455el2ozISRSIYZE67z/ulnj0hlAGstAO99CCGxiRNBCDh85MRPfYCzxrlHviOvvlY4By2bs03vc5AOoTYzTHRHI0EKGMIkwEhqmXrnyo0LQyrhYcSJKscDGc7eUe4nVRVRCFiVKrMRVCta+eABS2YodaqIeKgyMSnghVnIBGVSBPGOLLMIBGxYrEWamlZrZnEhm5vtri73Lp6Hcxtr69y4WgzQ3RyAtex2Bhsdf3kJG6uuLH2WqlecnOl0Op6KkJfOS9KepdLmV9fqnX5rcT49crSdWu32i7UuygAA/b7rbDWL+QOtZqeWpqpL58+un19CUSA4ZGnP5bY10y0G4om8n2+0GrfMhNxfPPN6eXm1NtdeODonJhFS22prfxMHDx48dXplbR3LK0fe9RNlXhS9/tyRo6ePHn/u649ipn343ruXLlzcPHd+/uTJe2+/89nnvp+wOXhgcabeeP7VMyjd3O03a2KurK/ee/ttR4+2vvW1byDLHvrgT/+H//CXyPNb7rmv15Fnnnp6bqb9ix/+yOc//3i/3z9w4NAtN9/8B//2D+szs0XpszStmJ+N9WyIPaloEHWewZaTWqOee9dzUMOmVnv+zJmTR2/d6smzr7xkTtyc1Otuq9sbdIUQQjh35rVf+dVP9LqDv/iTP03as+o0OE8mifs+Km/C6IsbJobVbljLhEPKLuaYHxjAuIZwsOPuncd7wcXXU/l+T3wLrXkzyhvvtBv9Xjdy541CLDunxvX7BV9P/bsV1rHlRWLEq0ARdyLD1e4QgzZ2sVwIxPt8oBK4MgjwcBfT/efF5NuxcugX7Vodfn318ssouglJinD7ydZ//avvfve7j9x6kya8hZCTZ0haOcKO/ffHqsvEu4xQgfEtOyhnhiWe3yY3TsJRI3GMIhVhtUgNZbaRW+6+/T+9sJAxHGygpCyZTeQBnQ5BmGzJLs1+cxCaXUQZ1kjnzNvEkaH0DmAXI85O8YWGa7sSxjFkAIMBOOfSNFX1Kg7EkNCYnT38zrcd/shPmcV5Wmi8/qddrK4nkvQGHaSmqk7YCoxCwJ6H+TdHzAOxj3gs3kAj1a8Zqa4jeUW1ojlWBFWK6np8sSoDlMS46wA1ECGoiigJgkINCMoVlaPqMH2BKAEUROGISiYWCaLeBY9i4DVmH0xAZK2Zazfmjx1Gq8brc33N9bWz4ennl1+7jGw2q82kaSq9Ttlfw/omvCPDsIzEmNzJ6qYvEwo590stvVEqrdk8f2nGS2JtKbJ19bK7tIzSW7K+3y9XV7v1pLY4WyPOB4Pi6iqurmKQg4CE+xsbWFxEuzmzeLBVm0mKYvW1c/0LKygKwyyu7G50yXlmhkdgHLv9tizY5ZdexpGF2cWFV15+MfT6d9x/y+Vz59zK1fmbbpk3tReefBxanrz5PRdeemWwvGbnmgeOHnz8K9/B8lUcO/yun3zv5z//ufr8/H1vv+/P/+IrSyuX73vwnf31zY3nXwboQw+957f+ze/Oz7TvOnXLnDWPfftRnxc/9zM/+5W//kpnZYWSeqs9V/YGRkSdI+e5SjStKL1KyLf6LncC2hhsFYDj4PqDP/zc5164cHs/7zx/9tX7PvCzCdjlJVsjTPD+/gcevOPW07/xG//K1JvqNYRgUxtcAIYkRUAVZg+MpugIxB3qttUgnNRgVJUmDUaxVHr27lzjU/FN8bBKEy+jMxHsqVaf4fntRqU9tPldS5wZe13dw8Ix0ebrqv//2+Vv+xVorIndQNn5k308Y3Y/r9tFjW14DHaMjF2F+OvHY4bt2dMTYocPzS5mo2sUDcR2qHmStdYbrnhcdq9IIKHf68IXLF6EksQUXtgaFd2rS1HlSZj4OxjkSatdX7vy6srFs3Aqoo0a3vve2x56/6lTJ4qauVCWmywJqCGGiUzcwniqWVw5NYwtDBNX4xY6PgZVsS8TaGhMjkBxK3cYO1+MXndCcFGeWisqGvKJvoxr0V6dQAJmEuM8X77aU0YIACuCjBo6pPgdrV9EYOhEYofpJ1a5424Q1twPldERSDV9vP/9mL5/hFvGhXbUqSISQohuNCIC1dTaNE1h4YIjUpQ58gFZA++5mUlwiN2hSjG3wjjhxbgFlatKLEPubahW1tJxtu3hVlEZFodb0MhxOIIy0TQQQZuY1SOmBYiE9KHCJlVExQPCpBIUCJBCgMhLowgQR8ZSCIZYFCgKv7WVb6z35mZyaRhD7bm5zZkZdLtYWaO0rM+pTVPnB1nZA3HSbBlDIUtDVrNFvyG1sp+XbmDdoLuxik7eWjzQvXR+c30ZzFCgt4XuBlIDMCRob7N3vuyt1MAWxQD9EiIw3Jxtl5YcFP1OVktqg8J3e0tnXsP6JjpFahJNTJnnWHbYzMha7WzSqRNJu/na489C6MSJ0y8+9QwuXjz2wP1Xz56/8syzaDaOnz757CPfw8ZW+0ffsbK8fOnJp5GYB3/k7U8//oR//TUszr/3/e9/7LHHoHrnnXc+/cTTF59+evbO20+ePvWF//if4MJHf+WTX/7P39Ai54WF++++789+/0+LvHzfQz997rVzLz//AgqnbItyUAah3GgQOIV30AAFIFDprnb9wIuIy3sOgCFuNFfWNr/6+NNusI5aAqb+6jqAWrPWGXROnT79qU899M//6W93u4NQanOm2dtYQXtumCsAEwtHVPu2bzkjLHD7mW2b/XaMZA9V71oqICakqG3He+6Fuze4Ot75o8lTtNvJ7aLPvvXv3qS3ynTZq6OuqwOvCcnsU8uNQHHbDD3Xc/8196Pt7dxR//Yaro0mVn+KSARV2VgYAyd7sctEUWnQ7XhX1CCGmJkn3BV2dx+ZbGw8MkqWEs3LpXOvorsef3z7rfzTD99+6iQ1a134LnxBlCmzsgSIUQGpbHsExe1clFgjUdnwavSSiGY1UtYIFdN4+1cdbmmEoIFJaSiqRD8LAJGkLXrsjtciGobn7vppdut/1UBgDV7ZlAU9//zrzg2zy1HARNdNdTtR5GMDMOSwfRPKtXllJuGWsQ4al2mKbRnfHUWNSf6YKM0oQKPYD1UiqtVqRVEQkbGRZ1o31lY2vv3tO0+cpKx29avfwVaRqpF+kTWaRb9AEmPqYrSbgqP3zPCJUWwaORQgfq3KFZeG7RvR6LFGmCW2PYAq0ssYA14xyFcbkoeQknIUd3TkaMUQXw3BEKABxGRJVEEgUXgfpA8TA8hJJdLGE4DgHDqb3cuX1XDerJEoLl7C1haXzqhzvWKrs6XGxvzahkhC8KRiLdlMB5e6jZqkAQmQ1SAemThs1Yzka5vkSptZz6p1Dxu8V1gDePS66HWJEo3psFJWi9pifX5hVtgUpS8HxebVV8vlZQwKztJareZDcKGEB0om4zUEwB06dOD1pXPodW774fsHa1088yJOHGk3mi988ysgOnD3Ta++fhZXLtkjx+44cvyxR74OXxy/697OhaX8uRfRbNx2773d9c2rz3y/de8daZo++YUvQvDgOx745te/gUtL93/g4Xyz+/wzz8J13/3ud3/1S3/z2ouvnLz/nvb8whf/7D+p8zCGicuVZcy0dTCARtF95CEvCPjsv/ushypZNNJaCkDY1KTeduKRNpFyWYolI0wbq2vW2o9/4mN/9tmvb61vlXnJnPQ2Okl71hV59Mqa5ORGJf4ybZ/kqtP8K3FAjla58byYmDvV8iQ7RJNhBrGJiRhri3jhdpFldLwnHrN9q5j6C0Pth6dPjo5pj8ftXv9bcszffdnfOvNGPwFPD5Z9PuVeI2efyneOq2u3cw/8aTveObHrQzUKIsYY2GSYimeaxm0MDmk+6JWDAWmkpJdRO3d9O9bdpHwSY2UwWL18+QykRxTmZvDe9xy9//52ajbgC0iaICNqQROCJxIgbBOVRp43GjO8DmHdCf4eIUXMnDWWAmi7aKWojGyjTzQMjGaAGRxTr4GESFRj6iWmqUVvz08wfhJr8IGz1Dnz7DOvBgVbBD+p6kxb9FSZLZikSqRLsenjT/JG14wb9ZW5vjKBvkxWMhnGHEJQ722tRoadc4gJepaWX/zt34NJ0e2DyBoGW2brOVRZaqIBJwahjBGr7fXv8mgFeEqf5iqsG8DIv3JkLRpPYUggZiJE7iACKLL6QiBhbHTUQASGFcMEQzEVqwoCITHMLHkpYBg2RB6q3qO7VVzhbP5g2e+HlVV0O6ya2ATEriyrWHVWMBxEvQJGKTQs5/0uwPBAr6BWC41asb6+sHiI87wog5ReDcASiathMyDAC0RTViLyEnwIaGSry5dx9SIUcJGsOINIrZ55VwyKIsnS1CZl4ZAXyAjBHbnvnnLQx4VLM7fdVG+3Xnn8SdjayZtveeGJp+H84bvuqpl05ZnvYmHh1jtuP/fCy1jdnHn7XYePHX78838NNWAcWJz/9l99FWTbrZnvffcxbHbswcO99c2tl15Go3H06NHHvvZNbGzixGJ/MHjmqadsrXbk2PFHvvUdA4OtTn3x0KDbQaOBfg/GklRckzGQDQqoACarpbkqQL4P5LkPDGOx1cVsA/mAPM+32svnzi4tLd3/wNuWLl547DvfdiW1WrPdTtfYpKJqhIJ4wqwr1UAZyh6T6/x4mE1OgcmrOj45kj0wLcRMVULbKpm+OnG8vYa9tofRU2m3PWCvZuy/P11ru9rn6lvlusr+fXgdYMx1P2f7l9V9ru6GT+xX226X9m/n9VcIAFWYTMUyMMGSByLyojVjyNq9qohbdyDVvN/vdRZUQQghGJO64A1NUZddqysE5PuDzbK7CQnG45bj+Kn33bE4X7pBVzVYSjlavsQTYImGgMFYbJqY10wAlFXDaGMb5TyikQFGhur1kIGukmMUANNUjIIBMBQjtWpwdYmGa92EGj/xjnuw/UI1KCtx2s/1pZcvAiAeMuPpjpBsZYCJiJkFYwBpVN6wHINxOskdDkSRcucG7FV7SxI7igBwZQCRtVaiUsvGpimRCbkPblBrNZy4vsvJGO33aklqHZQoUsj4GMoepcwJN+RJzwOqsJg4oCfRmrEJqfKViek5KjVhnKojmp+UoKoxrEsRswSTIRsUoFAJVMO9ISJOWmWb8AzokEaEE2vIGhgANogTh86mz0t/bg1BmAsmVS09KScZnKOsoaogL8xKCdSAM5hkEFTZIK2BGb1Ci4TSOup2o+fVZNpKEEqIgjliVxBHWrISlMQ7YgDKGtAX5CRQk9UEpBogDkR5mdsstZZKV1gfDCEkpFZxYK55eP7y8y/DpouHFl848xK6mwduvRX9Aleu4vCB+cMHzz71Amy9ffK41JKrL70Cm91x+pannngSROj3kBkMCmx1udVqpbVLa+uwmS2DDAqwAVQIV5aWkGVI7KXlK0htAJxKUbjB2kYyuzDodMHUaNT7va5ly6oMhCq+HaRgsCsKAZhMPU0411rW8H0jJYmZyQKcL9KAsl8gzQ4eOBC8/9znPld2uzPzRzqXrzYPHXG+KFdXkvm29x7KiuHUICZ1iMmmx+MNI0B7cnJPjvNJIWZYAhAhyqlFgaYij8ZjMProDJeSaFnVaoSTQHS0qg1r2847vq2MAzgn2iwTotV1lRuSVG5gcXirDMuNyzFv9Dk3IKHuvH8vOWNvr51RPOw16t8p6O9b86Q5Q6FCZpxKyVrrIpy/l48OEZzr9XoQD6YQ2KSmcGVUY6+zBARjfKe/gSAAZaJ3nDxy3x1H4LfqtTaCggFyCH14kGTqiY0BMbTavKbsHuN5OrnmDHdq0aHzw0Q00zD568R2zkOZpqo/2i6EPCBcYcAM2CokWP0UJBwlgT3fWJz6JM0KF1bXNs9f6BDBu0miPBC2aYPD4N83u1zLV2bKShdp+3XPFW9ywdp18Zo4yQmrRtuTsrVQuDyHohZMM023tta5VafMqPPNVqvsFVYZikAsMffxqBXXMaNHqFLcPjA09Gj13yqLxSghvYpyFXE9FhMFQ+eb0XtFv5nK7DfcmYbBvsSVsAv1EgAhhgYVIiZi4kSVUPpMWZ3jRNRooaLWpI0aXJY2WyEERSBSZqtC0ETIJvNNUVKmhI3WwmBzQ7fy5MC8W16j+bnGXJshdQ01tqF0EtzlC2eVrWYJEfnCURBjODWJK3PD1gWR0muSwtZgEziHtCYiUpYEspkpRABGlsyeOn72ymX0+/NHjvY2N9zrLyNpHDl29NnvfAeNVrIw/8KZM1heBvHhw4dffuE5qB46fvzc2fP+ygo2t9CeQb+fZZmxSej02vUmRJDW0kY9hADnkFkw1VqtfG0Vys1WG0mqvX691Ywyvi9yY60y9dfWqdGS0muQOOcYFCP9iYhsysQi6jr9K2cvFZs9wqwqpdaWvV5SZ2LtDzbf9vYfuueue//1v/6XWytr9Zn5TqfTPHiwt7VFCZt2m4hiHnWqRqygYhOIVIx7ABu7/qlDVGZkDB2O3j0V06njSTlmNBJpZ0L7/cp0wyazJUxe3QmYk44Tbt8QyvKm2Dj+XpW/jcV3suwxlt44j/v1dfvOMOZrACQ3eH/cYyuqNxWacm28jnZec9RV+7coQSpZRgPA0EBkwKRehZhNArVgP23vqIQqASABvtTBlgkDInJi1BinU7mFR41hjJWHyVnDEGNpkHcgMGws+4ML9fl2FsqrJqm5ADIwRqHKJGClCk+miWCCMSHeaJGhqWxLseEjpSVMJW5Ujq9PFPMEDWvb3o1D2rb4UWCq0GAVnUJTQLItrRKrjhYfASkFR3amv6VXV8qVdUjc/FKGGwEzGDmrxP8HIsNmGFsOcOS/qfIp/gAGpmk8RlUriGkXQSSex7QKOpR3hvePHWvivzThSxOX8cg0QwCh9MUwVIphElJ4hldvkiy4KDxwf1BELKFKdUPxf4j+RczRs2va0jSmwyCiKKMMD+ABYx3ApmRGFIpBrCQkahSqcIj8sSAmZrLEiiBeNcDYEL20rTWckrGRSy8aHiTAKhOMpgaWJEG8SiLwXiFZ2sqdeGuSZqskk6Rp2NriRCChcA7NhgSTUyOdb9t6Hd4Xgy2sr8IFGAtVpI1gsuTgQZNw4cqAAZp1+MKtrLRvv7OTu74a5J3uxhJcDkcQggMadTmwYNNkFra/ulb2uh6B01RTA7Hqg104YBozktSlyHl9y22uG7AS5RLQaJisSc1We/7I5upLKHIRv3rxEmwKI4UFzy6I4tiJm1///tOAh3IGxsomOJhWfeAVtTbmLAYdZElOEiggM8F7np2VJE8OLCSzbRAgIa3V8iIHod5spjZNak2xSa2WBhkgFEnSKPt9ZDXbbHsvrZmZcpAXRcGk5F0tsdboYNBPbY3ImiTtdjZ/97d+J0trucCmKRtRRdldK23RXKj94ic++Luf+ZO1C+swzUHuCabf75EhiIhAwshnTQE/ngJRsRvqNzsmyBiGHYqx1XpRQX7VlAArVLZBKRHbGa6VOhX3RJCJx2lFKl1d2+GTSDtioyZ3D6kk9m1Q/zAd63aeGyICSZzle1mmpjJJjQIXJztt+H67lBuJtHojhd8cEeRNs5fJHmytE8b30SMxqWpfXxkl7Z3yGpmWhqewwCFMUl0afqbthCXDH072Au/4qJF7ejpvV4AKRZgEHMMixjVMS9WTNtNthUfOZdNSMgEcc0EO6UIkUo8RhJWg6oSSJKvNFLQGMUARfTMIEl9cKlgCCIP117///7L3Zs2SJNeZ2HeOeyyZeZdauqt3gOwBmgCEZbiBgEgONUZpTJLJRjKZpJFJTzLp7+hRepRkEk2akWmMNhTJIUUBGGLHAMRKAN1Ao7vRXdXVtd4tM2NxP+fowSMyI7db9/ZCcqB2K8vKG+nh7uHhy+dn+Q4+9pHJ4798fFb7sRc/htW8QX5DpozOVGUB1tJQ09iUzqNuESPnePb5G20dDvb2RFuwNyOJLrMcFmENiGGu9/BVkEIFnU8LASBbDN8FBFyZX5YCwA3djnqcQam7sNKnBmNbAIuFy0tCNmoQIICSKoEpeWun/C4P8ybz+0QMUziLYeozIqi2lhXPfOPbn5sGBANchiAgBqkaGMoJOygpchRj86NozjpJsKSjGXoHnPMUQefOwXef7fcyhjWmRIBaJwSB0VK2tfTlBizFtgCUu/ey3DOSWfqlrHlMGM4sYVjq0ZYtV3yDS6ql7m9WGthndltBz/ALR2RkzjpvJurWBnZgAhvHAAMxk/I8xghCltv+IXwRYgvvKMQCfSDMclTuXfXjQ5fnGcONinmIODmlqMaELD987gO0v2+kOUI1mwc8QC0oitHB1SdeeDowv/7aS1rdQxVQtRgdYLKPK4fZEzfy8WjsMimy9pbZ8QNhEyLkOe1fnTz2RMiKPN8rCPN4U9uG2oZY1DMXRTE5KA6vlKN9uBzkHMiFqDCUuR+X+/uHVRuKyR729zHO0YoDQRXjUTYZAf50vEeAtDUY6hlQsMuyzGWZZlHyLBuNcXiI6WlUAROyLM+KLCs4y9kTEeW+qJwD4EcT8Q4+y3P2XLDLKPdtXamGWV1lpFmRu8wbWEDEJfucyxELkXP18dH4+qjOrr55+85/+9/9N5//f7/6+qs3QTlsyJO0GAA8vDKAyV3EsE2pw+6tQrG6XtOQK2CjnM1DCWEIa5afHfnkZdQBm5H/BgKYbU3qmru+2byf3vO0vjNd6tbz3tfGFb1M5vN+3ZJ5KYNJY3gdxl2q/PPyk7LB0n7S7YspGBMZkxiBHLISTT2Uyqw4rKiCYpg+PL792ujwiSIbNypZlqGtyZIn0ZaqFzhGwSDmxUkgMUQw2AmDOv9KUphjJZgDtD8jLdef5KzUW7fQusEsLehCBno0LLHLMqOpUfe5cn1xvh/05/IAA13YRFPn3NTrKaDS1EQMRxAxU5CoiSrYZdHKW3fit777ZtWAfAYR5B4xLHFuiouZDGjYgUj7xetdVD1fBsp0XbZBkbfikrFy+Vxqa6w+g+6MyLCr3tW6COdWBwDg3pkaAKm5CKhlZESkADGiAEYZLM0FUSHmFJ50+0OsQigjCJLvHDPA7FP0J4MImBybMSZ77saT46efwf5hO5tWP5pZNTUiMIMZZVlO9kBcV20+KfLxpBlP5HRmJsgLd7Dnx+PTEIKGyaTw5SjAEAVFfufmG3FZKge3AAAgAElEQVRv7/C55w6feeosHlOZu8qyfFznmV27Mnr2yf3rV68Vk/La1awcPfy5w9GpN2YUo70rfjw+mVZozmg8sYOJzB92ZvVqGkKMsQDyPAcIKWCVEOBQjHw5ijG2bROgGGXwDkGMAFWYBtNIZo6JCSqIHeMgcpcXhcvyEGYxqvc5ygnm8ygKI/jM54URYoyZd96VbB6Wt9MWWYasyEbjpq0abUVC0KAs2WgkwbUhmMsMZiqsXvKRjPKQEWaVhvr6tStHR3f99fLNV+7tfeDwS3/2OZSZG49k2mCbxGD1ZLnyxgcDc/tSOxS3GJYAZdcBdN1ycPUkvcmA2bdtHQZttnztrjXTwpX827bMhSXQOWW+n97FdHGkuDPnJpw4p0jbwLa7q3gU7NgOuR5R+4XLX5fHYEW5A0AXnqWdrQdB1QjETsxclqEcoa16wzbdstk4Z1V1642fX/nAR0cH+3U798VkKLJa8OATrJcTsRHEyMBGxJYwFcHIFMzw3rs+2CCZrh6cuD8FUQ+rfOed1LkXLQjrUq1J9LLoBEXyzR5KaftZ3EGWdWd4W/nfVlUs6U9Nyia/FCNQBFQ5uMyB21ZadvAOGRv5fF6hqrOvfP2lH/4YAch8Uct8IbpeqxaIIFvYadhQBf+OAc27J5UZwKvhBn8eoBk8Q1o0355C2nqXbzySZoqUo1FvkctQMscGsBqZkXEHdMyYhGDEBLF11NXXupU1wXXmNmpKZiQMNTWDiPkMeYbrj/sbT+LGE+Onnx7P59XN1zA9lhgJzsjBewCz6TxMZwGHo4ORsoMCYvCu3NszQjg+QZhJ/vg4yysD2sBwyu2Dmzdlf2zOYqhZpERePTyj/f2Ik9OM5xKnk/rs+KG2DVRAFI0RtcyyCGA+Q/DmC8scmJFCgEdF1bSu4rqNBkSFUQbvwQ0YPjOm2XSK+bxuKzCBe7c6M9TVrJpLVppEEUGIAJsSwGBHzpsZqmo+n7cSAYLzAgIx2MysCSLzWnI/LkaOc5eNpDIUY2S5y1lPZvN67ifjvPD1bB7UUIwwKkPbwBOMRXOMRrg+ptIdhuIaNN69y2N39alnP/2rv/E//Pf/48HB1dOju7KDZwLnyDIfuf6uAJZ0ylHr9NlsS912bz3Qs0Sk89aqmGZFmLyofaiSWm9orwNYp1yzxbqx88j+SOnL+yKZ9y69F9ZF52GRcwUeS2XotnI2S7jIwDin9kcPNrOtgzOdBjrLjqF1atqMFMTM5EWDZ5+NJ2F2jIiedBIDNS0WiODBvbunD+5dP3wyk0YaY0o08B1S688n3NN6LHGMgtGxmBGApPJyziWKmkQNwh3nh/abPQ+aTL01CXr19DDJ6omq1yili4sz1RLnbFmXlnlW3++28ZZ066xp1TJzWU5kooFYnWeYqqpFouz6rdv6L//i2/MGRAgqnGUqba8HcwQ2mCDx5gt4gCGJ3q2hjh1QZkEN9Gg5B5Znx01ogt5kci0/LUs2xuCtXU5PNLhliWaW1eyU5UgacgyoOVIyA3NgGBmDjFTY1Cjd2lEKdTpHWw4gU2PumAx0Ie1JVsAKEEwlKjOTkQmDPMhhdJBdvR7Gk8b5kBd73rurV+XeHW2isoEJPuNkAd20mvQ4CSIQw3uX+YIITYXZib96sDemWkM1P0M1L8eTuq2q4wdNfYI7t3XeRBn7s4iTJjDh6DTOqpNrhwgtDvfgn8Wtu2CHtoneGxOCookuSCxSdcQMpUTdA3VUSQA0jWCJ1pmAdYJJMSZyzpzvCcINkIAIVZiRweBAzjEDBlUlqBFC1DaKCFQ7DgRmRBUjVUUIAJ/NZ9FU2gaMp57/pdv3bk8f3oW3T/3Wpz747FOjUfHij176yY9+Wp1Maf+qiSArQQz2OCzx3PUbT04+fbX8xJXJJ5945scvvXzw/Ef/13/2R/VsWs+qyeOPzU5P4LJzLMe3nCkHCpftQpM1lQ2pmVinkF6EWUnfFyDGhmZ9S9OTldCSA3+orhZbybzeiqU05SK4ZKfJgm3L/z6mee/Se4Bj1gfA8P1u5NzUQg4Tn1syrdezkz/mHGnNujXMuf2RFmWyzhzAOishSpa/pqbEahSZ3WgUihJxlkQya89IzCYRxnF6cueNVw+eeHYyuVHFurNcAdJ5N9031PkyDFBRYrBDNFuAHhCBXeaJYcamtnCN7mY3dw+wJO/mfuotdsRU2GpMhgR0tu2ztDTypeUtG+8XWAiEN3tUgdjtngSDwZwxJIIZMHXOkaoEgVJAbpMbX//Ot7/zQyhg5Oq2ycdF2z1RigztFA6IoAZQdtC1Lf5d0jG9B5Gxhy27YCvf2cNcAgAZa/Kgdp3Ji5gRk8GMFAQ1SbgatAKZ+9v71poNPVCosylOwj4FMSipqIzUoERwluDIZFLsHUzzEkRzldxxORnPMq+wtJ2TKYNy59u88M51x3rvYYD3YA5pTDlvItK0EqK1wTTUZ2eHzzydHxzcD1ObHKI5rs7aEq5gHz3FPMdoQoeHyP2zjz1WNPHl2bcOsvz0wQMFSYxgg0qoGpcTTCFiImCDcyAyUiEg82BqYxAxCKAKVYYpOzaYCMzQAR2A2TmnRDA4RSSGQaLBABFVJSKw85n33gOSwigSs5mZmc8zOA9PzDTZnxxDMSl//bO/+aWvf3F+Iv/wdz7ze5/+xN4IewU++8kXvv7l7/zzP/zTMJ8hLyEKl6Fwo8cP8w8+/txzV37r2au/OyofA174zEe/8Eb9vW/8a1heTibt2SwjH/VCeL1LCxxz/r6+kJ0kNToW4hlZfqfhdeu+GzoP8K6Y3UerRY5HbXzv0ABi/cr7IOa9S++SJGyrNG5XLZczrFn9+5y2LQSKK/Yxlyz/IvkBAJqsE6SH70wwkJGDRrATZjbjLMdohPnW/RtEZKJgQlvdu/Xza0899/jzE6NC4IW8gnt1zsCxyJR7TJNsABliZkQOS4dZAIAoGAQFac+cwJ1QfwA7OhjTQQ3rpQB9B/ZrxeKGLe+LusYsfqWB7GlL/nVbwBSmqmMyMbOEt8iYYdBk6SwSjZBzMXbh4Ovfu/WHf/Kdh3MIiLISTRVigxQ+2tDHG0pEGYDjBeF+t2mmrf9dgTIr0uuVnxQAm8O6UfHS/nmYepayC7cpRZneqLHv2A0YQdvrBbTrelqR93QDjhM+SEG0sSyhw+Up6hgvzwgdiww5gzKD3CJwcPf7QgQFMKVQ3aRERMTJyK3bqDyIHTEn1xBHLRHM8jzPM1/mXkYlGM5iKwpKqhmCRKtrK9sic0DhAKtraxpA04uKUUMkHFzDeGR5OZPYGFCUIIaalpP9azfyg33b3w937sX79Yj9eOKvTwq5ca18+gYdHpjZE+V+dfc+pnN/kKGuw9mURjn2RsiYcrJ5jap1QZ3E1gMwsCmpOsO4RO5b6gmbWrE2ZsSNZwoBsxp1QBCNAsdgY4YDWLUwipQBJmIgQhSIekdtnpd54YnRhuTZQUQWW9WY+ww+yxhtbII2iDUcjQ4L5Hr98b3f/ewnJh5UV01tH7kyHn/mV7/yF59/4+7pKBvVtVhpYPNjP94b7xXZk8bPADxt9vaKx8sSniZXrsxu3xtnYwaURHbYaC3f9PBPs6WgcfV0i81dv5sTNszfOfxt3UJ6M72VcnQHeOoc89Y3q/OtE4bSmkUFy3tXcq9bJ1y0lvfTuWln8Oet6ZL9nKDDUuKyuiSzrUgR1w6BmzKV7q7zW7jLmmqHwfJOA/ON8vuc6z56/UbemcTs6iICmSRZu4sWM+f8aBw3dpAOOdDCNMTqB3fe/NmLk8Or4+tPV+T66nlhk2ImBGMIm/LKNFFJhzRy6EUqZgYSgNh40ftmlkw3adGExEi2ZJNba2QfvmDgsbyahfuc1ocvoN5PZvnTsvuwyDP4Ew7IDCATQCmRGqsHKZxBWrEAY1Pvsgna0atvzP/ZH37tm99HAFpknhwIJp2jAy1qhAAGdnCZJanS3zCvzNa0e+QM0qKt52btZIFrt7zdZMvwno/Myl2IVEdgWli9J4E/qTk1TsrWJUlR0pIsU6qLOrofIjZTMKCicEQMT/Ddb6RGJGYhehMXmtLKPPdRYhFj3dRQ7SglY8S8CsWMuBjnmZq1VYNqjtAAQOPa+YxHsSxGrvA5xfakQlUDAmZ37Xo9nc3uHxHbyZv3q7fewlF9FCK4xvV9am6UzZTLsq2aN1s7e+llhEi6h7p18zoPUbLMEeew6dkMTetEPAPeoch9nnOWeRgKj9zDM3tSZQRBG8siF2hBjBgRFQpO5nGOMu+8ugA34ixmRePVOQcQTMzEEYMpZ2IVNBVSECuYhigaHJmDsWqUtmrmyB0Yp/Ozup3duHawP0E4PrsycvPTk4jx9X1cP9y/ezSNdVX6olVICAiBgkij82DHwP6kuFfj1dfvgvPZ2SnGhQUxIdo9A7bt1roc0wMcs2WVX8SMXMMxy+87x+ojpCBrv9Iystilizo3/6NlQu+nSyaydw0C7pJDr0lkNn7d+X7t3HhJl5LtPdK25kK/Xlx2SGmrX5rUdl4gid7dUhQickSc5SBOVOhrlgdppXak5BDb2f3brz1177nJ3gEXOZOTRO7SBxUhYAiwFoy6IKNE2dJLZZSSt3Sa72omZMleOHGK9JQw3VqysMew3uMpfelXHWN0UDhFAufkLmOk1DP3EBm6AJML1KZkbJCuOmOQkrKRkLKxwnobPsBYOmoZNU4bZdLdkUBbg5AriCZih2/eab74jde+8s2TWtCCIii2DYio1yWkHhFIagqxN85SKKG17f7txVJdS141Lt9lsjla/tljfGMigvVwL/XOUqaycEvuZTPppSz4TBeN7EIHpPy8YP2zwVrcl9+f0Q02pFLufbgAJOTbdU0HnDYkN+kYmqoVwDhnF8XgWB1hz6MoULWIgkikBDVn5JUMEGKDEYGjSO9lZyrd2ZyUhIIFIjKImZhZB0PJEMQ0tiIEl2y4TAzM8/tvcc5XRpk7OZqeHVez6eyN1xFaQ4RGtAqxKe7no0Mwk3P17AihgmOoIsZ4fEK4d3B4XeqzqjqrHz5EW5FGM6XZKdidBpJQudmDUROr0KKukQVUZEe+qmr4HELhrEJVwYQsoppDIQzL82J0MJ2etsf3wEROm9BSlucwq+rpm2+Nb1zHfA4JzCA2hIDg6+PTdj6LocW8wekcsxrgIsuhESE281mRZdXxCeomL/LGpK5rkEGtrqazsyPMpmivTk9PEBrExlRiXUFiCO1sdibtXGBtbNoYEAKiwVkT2wcPHhDw7PX9V2/d3C8LAu4+rO8fHylTEAkUEdqscPLwbHrz6Lbijx8+mH/qo6No//rL3/7LL30fWkBqILbgPM9V407+z61Xe6lMr2laXFzJsxTzLQfjcLrSUDqyKvQckm4NWrZ1qhMlxdyikDURzdCyYVUes37STXQIttkXm0fnfoFYbUi3HGwwoJ3b/r+BdGnemnfASveoNFi8Lpo2cerqOrmRf1c5G/1AA3VJ37iVKrBNBt5nGLr+Dm7pbSxgtjkQdsljhsfjXmS1nQJg4E+0dTookIx6yVKIRp8DqmZgalWLLM+uXw8P71qsvSdTEwUzq1EMwTkyiWYGX9jZ0Ss//h58sfe0p+KwzPMItDEqzDnnnTcx0SRk9mDttTmRoCYBEhIRWNu2ZgbXRW2khFEMwILVQXoiOx70t8JBmsZ5n5wwVFXBmc8spP2aqM/f2cdIr5cgghF1K4zrDTf7/AlWWNrAmYxNjIyIGHDGETwnKJSYGOp7LBbAESxKVCmBD45O8j//yov/8//54ss3UXcntsQ8bhYAAjNDoQATCbUAwJOyOGh108rnPFh88flyOanMOW7Jl03DCbk6rt9OBUNJ6RbTmR4DMpS5yBl1I9jPnv6df/sTv/3pN158+Ud/9CcIwkIgYwMbhDpA1onkCVghRUgQm9lM0YMYpACXPZMIIswJ9SK+GGCGk3Yq7Wx6bOUIYqjmmE4RW5gQGzQQSOuqMbIo8BmqU5gid4BBHWKwe7fPqhmqKYcG8zMgOIrmvFRHdnoa+C1IhM6ZFIHhCB6YTtEEFAW4QDS0AlIcjDhDnnmNdTsHGlfHGiqYFJieNNr6jKJFmZ6RUzA3D45xcob5LLR1bAPaGrAwm1YP7kNjc3yCszmaQD5Lpr4wpdicnt2zs6MAN59PwXr16iFCCwlt3WhdYT4L1byp52hqFM575mSGLSGGFnUDmCqYPVwOsahA1If3Hn7t69//x7/9Sc1HldhdwUtv3LxfzYIr/GQidbS6tdDW9x4oUTw+/vi/8xtfe+nm5/7kT6Z3TnA/gJzLc1WoWXCqO3au3UqnDWfpnYLx9SG6PiZX5+k755O97EF581x+kaLeT3/D6e29l0u9wUuZTz265HciYrl8e5ZyUizkHxtzjQBjBRPnyEpIDKIdvkhORczJTsFMnYlIPX1w5/WfvfTJGx+gPHdqplSQoywPYvN5NSoKMLQLYOkSnGDFKLcsmd5qooL3zB7kYHGJ800Hlr26IndJiRQxOkdgdGw0PoNAxBwt7HWp+yQjEFjT9+46JWJOVR3Y52Hw2RHVKgzamQBGaIzWEoThmDI4AzuoA5yGxvKRL/frWL70yvwvPv/tP//cay/dQgAEvuOkoYF2rANSfdBvB+JMzC/kDu96WoMyCoC2LOM6qH9wzASwPMkNYyH1ChtaZBoYTC3Pj5Y0NH2UAEJvurXZqu1WMn29Q14ZSpq6jt93UCOghLquy9EIjkFUXDt44pc/cHx8nMaToJtyRE5oIfDTFBunP16n0w3DzDQuzraAEoNAbICZmKp1fMzJYDvPvYgIBPXM7tZgD1WECHZgAxmpOjWYKLUmAkpebEak7LyokpqLbZS5zmtMp2SBYu3ZiIPC29EMxSHGh7C2lBnD5o7gPIJBBAFoFYgIBjV4wIXTg7LliHYOYjz71PjDH3ruuefs9Oy1736vfflnsamhMAHBwWCnU0znaFqvloMaABK1jWhamFoTEBRGDqYaoRExaNuEuaCtAmeAgRZxQ00tQgQxIsYUpzP5TDmYqohI29YILYA2QszBPFRCJFCB4+qrX//+hz/2b/3gR6+qhF96Ln7jxz+pBdc/+sKnf/0z//IP/ikVeZweQ1uIxNnZR67+/h/8L382vXWEWhwcK0JTQxsYovcg3i7z37WWDvRKQ97E/sfllX7BXQpjVryNhiBmQ7RjA/nwFrg/vGsjDefJti1hw6Owy7mdhXatrmXzV2Q8WLbwvVmnfuHT+ZYo7yStMeo+surh2NuWeV0e0/8xlCCeV/vwT1rPjq38MViRx6w1aJlHF1t5f70LgIdu0zcil5e+HMemFm0Tx7uZEXuYqBrByMyRihnmp8evv/z1qn3m+Y988MO/cnBwpY42r1smP8q8docgMiMjmLGCcqjJHNLAAhnYwXsvpi4E9rSrU7fERNMUrckBptHaqGrMlDODSECR+vApRp1URgf9s/ChBcDZli4GoLKc77ZIhCLfj7FVCSGGFMvAEkF7+Xhj+c9fr77941tf/uarX/jqnVdvwhhtije4UJMtDHFAyWHdIHCAJ+Ki5wZ8T9J74MG0mnbyyhAtFEzvSnyTNSMmLOZAT+WdKoWGOjhmr2I///4P62r28NZbqAMEMAWTgFzyeVPrHN+XAiTtm9rFtjDt/VCIzJJKbFF3wjzJswnKkQlEUGs1OFAEMZxLjkuOCKakgLduHJLjFDiBqKNIUDFlMDFEpXUMZjgGyAyKcnL41JNXDh9/eOf29PZdgiArQIQQ4EoiZwqIkiEztKa8f5Ax1Wwoczz/3PVPfhRXr1R75UdfeD5K/crxA7x5O4WG5mjqvMWQIoFn4MhoiEFMHd5iBgFKzJ1+WhUmFGI+LtorE5ycwQBftm0L56A88B7stNRQEZHuXlWz5N9OptDkMBVhAvgSrnzwcPbdH73yf//x/4O2/b3/8D96c1ohy89UUWaoa/N+9MT1qprj7OELH3ru3it3b37/B8hzRLCqYzCz+FEkhRnt2MEvsvrvMpTZeoxe0bQOfl6Mzcu24SKVnlPOWv7h1HlfJPN3M13wvaxk6zWOO+9dzXt+aRdqwHrtj7h9o7WXyw8MdlCzpc8H6cK0pc+sakb5yI8O4nyOVixFXuh2BlpY4pOJ9y6Kopm1d167GSoXpx/4ex8przwOokZauBzskuWDEilIASPOVDKLXsVzFzAjWYGoES/57pK8QA0rIa8HT6QLggZVM8qc92x5nu8BXHcsfxh88qLYLSeluH2B63xIB11qMCi3TQ41Q3ReM3ZmPpJvpdD28Nt//fof/vnXvvDVt27ex1whHklY3pW4KQsbfGdfOi4UXhHfIzSzm1cmBQS/QBGXgSFJtwcAxH5RV48JBWZw2SXKwzmqpcGZeHhxlKGtSTKo0x/97PZLP0cb0Nm4KJiSxtMbXOAIVceL6dFLfZalLzuoE2x27nDdztB5OBlAMQRi6nylUoxGl5HnhfTIAE2wJ90rohIgrZBlwiZizAAhmvoMlkc1kxSog5Q8j6/uXX3q6hNPsytcqMJsPgsRjfpyL4paGtDemTJBvcBmDbxHLch48thjjz377P0Y7s+md+oZHezlhwftnXvJ4zqqoCjgElzRGGOMClE4b44gAiCqQMXIhEw1AoTQfPCZJ68+81zV6pu3bt1+6Sc4OmUHxBYxqmri/VM1iQoRKIupaseCYF0/MHunMIgCZjFADX7ElBuVOK4hotkkmxyiGLdtVcUW44JUf+8/+Edf/ebXZndu//Zv/+oXPvdnPGKNLciUoEJsTpVhERrN5efog3bFNhuCmE2pzPD74rBKa6vwmu1C/xPvsI3oCtnRVB40aeWoPGD43Vrg5naxuPDI0/wvZvq7+shbtvCt2XChbLiAVG/rINw5WdbsYx7VjbQ2VnfnH5iObXs0Wyg1bOkACCaDQRYcOdZhFjZXUC4oRgiNWqTeENM5ZhBE1RCjKBPA0BathNtnLx/dPr7zxpMf/PD+tSfKvYN8vB9aTeJ2TWbFYICdtRpOSdqcLRo6M1Ry3ntATZdoydZONl2gAFs8sIqw9x06M+/d6OykevFnd8/qUlAMV50VNbdtBFjYoUFfaNYT032fPAKIPDGIo0o9n89PT+uzuX3xyz/46WvV62+hAoRgvlA2hJ6BJvHDAVh2OBPIkg0yM9HIyJ9rWf5O03suldmVOgFXZybWy2bW3bMvlIbSlxVJzCDHwqPKOVZmJkBE5sZOIHC5CxbBabing3qKX8qdg/jSDW8J41OLsXx9HZY2gnHH9djVS0bsGCBiB6R5Qs6TcxFqCktMx4xIBlJwBuc4zzTWKVolJZTmPLLMjceipk2NqMGUQWKMs+rWa2/cejBFPaNG9srR9f1xMRqfVbOzeQVSmuxRWWrThtNaRTPD2JVTdmao1OYKc1lWOioKlxdgh6AAw3tQjqLIDvZRz3B6qrCoChgcUeaR5yDizIMJ3kAWTWEKn3/mt35z75mnHlTh2Y/9vS+W7uhL3zBLIlEm5+EzeO+cc86loJ5ELnEBg6mjVyLAkTFAAibViLaGKJRgHq5AmVetTac15jPQtYO9PbS1EWVXD2WSP/6BpxptfvK972XjfYvRjAQGMw2OPXGRMxexbrZPsB3ykl1mmJsDcmeGc/ak1ayba/fWY+p59W7kP2dH3Hqafz+9u+ltdOsFQczmbW+zikcO4K0/ndvIzSmwVsEF2rMVx3Tu5WarGi5SW8YPQqcUIwAcDd7lrhhLPUeIZh3pnIgazBNSLD3TmBguvI/EFML0/s9+eP/119z+4f6Vx4rJ/mgyHq4bYpYUOw5h/uCt0FSeFmdSEQfqrWPIgF4Ys+SJ7XDMgjDTzAyZpwgJ6eTrfv7G3f/rn3/5n/4J2kHPLM5aQyq37oqtXN/o4WEpfU6DtCBKzGUgQlS0LZqIosC0geO8KPbPqlZDRA7kQNP2kiEaUPmBiTXBR5cT5UwjwJ9ngviOk1+zJnkHaZdFC4A1PRMDyIs8UR8neEhEzMTMQXZNm13svVvq7WbC8vV2ljRk5kIkEngWMk8uT4d+U03U0JrMu7l1APfRsVf9TRZCGk7vPKEeSxOqQ1IgIufBHslPCypBJcXXcEzOO5eR82BHpoiBIwwkBGOiPON8VI7GzDw1sWYOAMYgZ47za9f2Dg7aUdGenrZBzJL2JeMnn9HJPsYTzMn07Gxe4aSCP80ODvKr17LR2Io85EwhIj/ByUkwC6LWCA7HxeEhj8a5IIRQZuWth8ft/ROIgTkvRlzu10VWXLuKZoaTE828JVCV567M/cEBMcq9EoWHJ6hGjYC68egDzz51OsbDs5nby/afvHY0KjsJmcuzLKOytNGkLMe+KJFlIBEjTR6P7Ml7sAdpsGCooRXgiQJig9BoGzLvEQ0Ozawa+RzOoa2b0wfQgKK4eXoiB4e/8qHnv/Kt74AnVDsOBXsXcyY4rklCazJVinDl2kkzrQ660LKvpRRGdDjMVgajrf26ElhMty3Ha983eWWsQ+Fb0861QS8+p3WtCWwX2AeTBdlFq3g/PSrt6PFtWslHl5U+z/EYWv5n636wu4DLzlPm0p14R2vOgfIXyL/Tiivl0iTJHphq2TAHrQ1mIzbOsnKizdRC3YUEUjMzRYrknIohI4IJYvQEoAnmIa2E2fHxPVjiQU2bdU+DCQCMcg8W0TZF8j1KIIltA1IsLH87Vv10YkMKX0CaLGwM3nFOKLJswlTevo+jFvWglKWn5IpwGOi5ZzaFMp3Ohfu29/kpXfdQhbawptdrMMCYBQgB2qI6Y/YZO41B2s5UBkYEj+Sf1bUnOVca2Ds/ISvVVina3u303kpl2JY2Tb38qXMh2zu8EmNs2zq2dYzRNJpxj213pt5oGz2CXaZNWxkMrix+dc6FpjEA7IlMRCRG5xx5414gr1iQ9qp1tDCWbFKSAx2QzPyV/F4AACAASURBVEiIOQVMYNXeXtlSqAEHx8TMXcgJBkVoctJll+XsM3aZEkqXxboyjRYVjsHeF3k5nmRZqQCzF3OqZqpmQmSTyWTv4LAyxKaFIwioM5nBeH8/Ozg8kQA1qEAFwUI9P3zqxvixx86aRkXGxR6rOwkRJnmeo2Rk3M6rk1v3ci738uKtH/7s7vd+hDv34QvyOfuymEwkz3hUIsvgvCajdI3JcsgXHuzYlyAHJcQoolCWYLPaQkFlVhoRCWAQke79sSPvLMudz9k5MENiR3kMgNl7jySVbRpERWzhADY4gBwpwQhBkJu2jSNBXaFtkNagNgqb5dkzz33gL/+nPyAeWQCTD6LQaAQyx8zqc/KZRkOKNcK2S520OQi3pqEqZyiQ7wz0dmGXte+JpHFxFxYrcRrvm5/rNZ6bdDmDLnLUfj/9LafVjWjXe1msiYtoggPQfLm3+QgBSToidtwkpoRNHrzzq9v8dUV/umQW2DlK167Qgohl5f9B822QDULkXJ6Vk1iNI59BhZwzjT7LYggKTfIfSuwj0IwgBgO8EwVprEE1upA1iY5OqQuDDQGjCnCeSIxSkBJjZl8UmrjBQINVYmXyEgzEsGR4g7woYhAxcQ4S29jMPMf9Ma5MUPdHmyFs6xwRVp4V2nH1rcDQtGpUFaKhhY+p4cmLiqwL1w2AHcyBCBRSHGbvKUZA1TlSCaqhyH0bovU6sZWzGlMHmsgR50qsfXCJ9yj5Rb8uB03vnjHM1wv4Oqq+lXDQ3X9LG5Ih440zQ1Iomhm7FN8H5A6vPd627dGDe1Eq55yJaQzkHfMyAGmv/wMATrY1CT+mzlOjzkqbEivRUBNJ5MD9u+4Ji4yoDhGcpwYHE5c8+ElgPqF7ShNIDSYgBwq6oFhO1loEpIBBEgHnMk8J5ZgxOyO2FKjCuSiqMTrnnXMwWxLhJCc5DQLKcl8Uo7ptJAZihkiYzowKVRqP9iAetVGZGbVq7chNju68Rcicy4w8mKHqPIm2Op3O7z24UYwPOT+JHsreSTSFNLO6Orx6WN++L7ce5Ncfs8gO7NiHEJAFyKncO6pqP3tY37137+Dpq/jJG2gjVF0xKSb7IhSOTqvjU+cyKcZk7FjFWqi009N6Ps+vXI2WQ3M0EZOyM85l99W/+vHzH/+YnYW3Xr9569svQ3KDT7bVUSOIMJ/P2hpAsgU2UQAQgcTYVsSwVg4sL1uqizEkTKMiK8GzNtps1iDPcXZ2WGQPTo8wKeGzOphVUuyXiM2nf/2Tf/4v/phGB3bvxHwRKYBAArLWwEZqCmv7k6stB09KvAKplz+pLuO6rVixEK38vRyK3cKzptzevsEQQZVIl1Cl9yc0037tW/lUWz8TD5etTWjFBiVds4NZNTJbWW/WVp9FLt11SL+0Sd/GmXuj4F8kZmGjztp9PQ3W4U6EvdgXdtDmLhKjPyUlI/q1l7YFDAzRg6wNku4svmCdWdOlksKULO3ibLry+jbH1eDJVtqyiz+GgAQOunV+C7Ra7FCLxwY6PJG2oZVvK2JRYyjMM+Czcizl2KpoUdhRDAKwpJ0/PZ21BLS9g66JAaEjZRHpH0b7nTFlU0iANmBrDWPAKTGgTUsEo86nt2OXoQiYKTN6bhioJQMDgIyIyIOhocwywvzKfvwv/5MXPvax04g9WK4wpZhY+EjFrPLE3kZsbNaYCZiUSR2FVnJ2DKqbuYNMikJtfDK//n/84TdefCtyNlYReAIpYr/TG4AULQ6gzjsqdgoTbUPVfWmVOfmrSIeHut4m0RbOwRe+KOGgGsx0PfrSBdKuub+5fl5WKqPo7aQu4nZEtnBYHcRZMALZ6ek0xjaEAKNel9lbxT6qTKw94aY8pmusdXbkw7yrOXuaHCN0ZMFLXZLBWIEdYrEE0ZKdlVlyZUrfAFONbAxNqqf+ZNC3M2miUsBsM4tKiZImBRkELMaYZyRiFgjmHTlymVjbtnMoQtOKz5oQoYou9JNiPkNWhrN5rGs0LdooGkFAth+J7p5ONc8x2ptXofCO85HUtQmgAcFRLQ7ip22Yx7KS01ogaVF0ZiQqiDE2LUXAyKKZKNLFukEMbZAogDBEEAUCGMHlL79684c/eeXo9ZsM0qYFOefSGaGHwhJDCF00gyVytWQeZBqhGoMyHNqIIqvrGkWBWe33HiMi5DlYp2cne6MRmgZZIcTIsubstJ6ePeOfffDKz3Eydc7H2KLgpFl3isgDgfByRPFwz9hyGB0Mv23DYfsPl9yGlUzQ8ywuPgdt2EQzj06pbYvN43x73vdFMn+LiZYb44VwTEdgPxAH0jYwMUyXer/rmZeNScvdOgw9v/CLNGYpmNk2Sjfak6bHQgoxmKdbG0OA46DCRuwzX45D2yDUHck+OoHMcL5vAO2dr6ODgIhJeK9JiNzxO2t3DF54G6UFj8BpS10Ejh3sNc45gDWoSONzu3HjIC/c3//URwyFWqYwTbIfOJhkXkgUMSM1UMNMYAM7IahagQxAlAYUvcZZVb714Nr3f/jKzaN7U4WaLRVRixG46Nj1XhwskoCpS4+7BK1LQxKCzzr31g7EbxPjvUvpElBmKXXZuL72DoZpAXqIaIGgYTh+eB9QCQGabMBThsu2f3uNW693usMdd3UnAF436kw/JeNes6FOMqHwDq8kac3CPK2DL9ZtRJYyQNP9pGZkqoYUJZoGu3j6FEHnitfLwAxQlSag6JVWad6KRihaQTkGKLlbw3sEx6QCQgTIC/unP/TBcP3s6OZbbaiCCNSpOLQezBx9DKbRQLykpyUmos7VSM2imESICot2kSMttJLOKKqS1h6odp5oo5H3/vj0BEdHOtlD7hGtUzCBGAwliHYO2ERIoTeBvpDUCTp1quMcMUJknGeoKphq2zAI0ymu7jcSmxjRNJjNfZljL0O00mevfOevcfcBcemYVNUSfbb0rxLLGWvYou/vx8DGUrs5xtYNszZW2/PS5eDIth8uWMDOoh4Fa/4Gkc0vEIjaCW3f1l2baZcy9B2qkxZpE0mkRf684XzhcQVgF3/MOWmZwfrqtvXmTju2RRIRE5/l+XiibS0aTcOiksSssZRrXXpMMnqp0lrr17PZgoqXNzd4CdE5B0fMLCJQhfdXrk6itoomBWmwtMLriI0JgRzYE0wMjSGm3hFzTJwhmpnzmuXkwOT9oebgqmoQaQ4iUm9GzKwWLiVZtXWwl8j3GUZw3nGWFBpEyRLoEkEaL5velq3MqhTkvKUwhZ5G916JkgCGYJBq2hdi0FQim4LcDh6hbbUY1raQDRmMWd+V3edy4zHD4uIg5/B2toQlB7bGPQ/KxvmAsCjBDCZsav2rYyy0AyAVY4YRUxeM2zq+YEANPrnzJPcm5zMOLamqLLCCWZBWogMTsgJSqQApbKVzIHOOXFlImEtI/C5Ao3Fex9AYm3hBq4CBzJQgDC6KrKQsazNC6X2Rw3sEAKaqkMjs4F2RZ3VoRZQ5haNisM+yDD4DyBEjy8AOi1PI2ZlFyZyvPcMUURCjSUx4q3MOd86zIwOiwSxRHaSjHiOp5KCF18IjRnh3cvutq48/fsS36mpaxxrXruDOm83sl64+8fgboxJ375sonGJv8uS1xz73R38K89aEaHCZjyKpbQmybqxUG6NrB5RZFdZsAJqNP8+BCVuWjPdgL984o+88sp9/mn8/vSvp/I599DZ8boG2GofuPKzwKIHHIwfDOaVdJP+lyl/PcNn8yx+AnkAMnLti5Cd7EiPqFPp6EdzxbSP4RfAB7bDmkich6YoXMQoMYDIsvGE2Fb4iQprWdTIza1uRABf6SD0GE28OChirVp6ZOQOpoY4WmTIYe4pkzEKqCorSJsEBtXIqqMjDEaBsQgCzOkXAZVKn/gZ1J/luADKYyZfOebVkMWvEqYPfq4VlG26wJIHYnpa51oeLbkdz1kXhYhhUAIUlgtdk+G0L/2uj8+fwrvJtMfaGLRwWRcM1YscoNUiCFOnGlXJMYEID8wAypJDXjoyhHVLpOP8sZaCeMYbTjWmDN0nUKWxgZuecQUSCLdEhgR07AtQxisyz5xgDQgM1MPvDSTYufZn7ySg72EOemSPkGUZ5PsrhyZU+nxQ8ypE7EBCUTmd8cnb889eP7r4p7aydnaCtQGSeQQ7eF6PSjRwyuIKLUQnvk9GGxDaG1plkGY8y70URApM58jCGy8bjPZ9lnpAzcTpDkBEnGRU7WOkdilHGPr3rLGFzkRgCmcG7MssBIASomepSvJx6gwnet22Ao9Fo/MrXvvHxD/5yfuNxtLMXb736y7/x9xHtte//9Y0PPpt9+HkYv/LiT5/7/X9w49c+MX94Im/ed8bcRCKKbQs11sEwXv4TmEBt/Z9h67+0DpEZLdw9zdB/X/w5GLa2tMIe/jPFYlQs/tGgiRdLiwG5+LcY84sNg60rePfk+tvDMcPu3UjDJ/rFTosnvXjnry1352ORXchjc2G/VL2L0X7Om1rLT2rnv9Pzn+uR5e/KzwnHdETqFMyEC1fuuXIM75M0mrqINYst6e3IEDaNzpIB0Mr+ZQTzloQrlqIbMoxNu0My57lzTlVFhF1iKVHnnOfCc+7ZZcQ5U06cg3Ky0jtHQWUuOo8IYDJ4gB0bo4bNmZsyF+fEtAEFduIz8VnHTkowB/bmtnH9X/h5uzBPDnBwpeOcyJmRCgRGRHRBx4q3lS7v5530o/1+f8E5kHKSWbeIQ5wDSJiVuWP1SSwtq9vM4N+5pW89E9vG51qGtadYgS9rTV9MeLXkudeBln7r6eKhm3a0AaYwgYqJmqhqpBRtK1kTSzSNyfrdYtDYwgSdBsuYDCahbWJbUQo2JgExyf3UO2KYSSS1jEDMbJoR5S7LiLSptW28mSNwMrKu5zSfF/OZ3r+rR/eLZo6Th5ifgcRIYQKIWdC20vYMUrukHoLCxGIb65m0jTNDW4d6hqZmMd9borrEPNzU2gZOp4PUAyqoq1g3OTnOvIWA6Qxns+npMWAIbWhaCTH1homgiUnfxMkBUsAGiAKwEFHXGI2q2QwPTu+8/PKvffbXkOvt7/zVE4/fuPaxX8F8+q8+9/nP/uZncXjtje9+99ozT37kkx//0V99F9PatTIqSkfMxE7hDBgsJynY1iMG1ca/Xevs+pbwyHHb/Spby99M55dz/i2bt5+T4YIz+u9aumy/Xbqf3+NysPFSFld3jcPhjbvK2VLm20Yt/d/nFPXu5t/ZGxcufy1nEhgzA+SCcTAyP3LlHsoJ+ujGvOjjt4VjePGxuLIENNoZZ1qCdIDxkJxm5VliBBEzd24iFtUiSCEKgUUzMSSq0tgitohztQA2czn8vrkrRo8ZrqkWMIYEjRUkUu+hJLGNgrZFjADg4TKQJdPdy6Tu/M3WS6QI7MC58wWREyMDg7tYhu8Krf+utFPBtJNvxkAdZwUt39lCTbPJ/sIL07S0T3d2M44g2h9MzToilhixErByGam7T+ey1/SN2f5EXfOtfwok7VQnVezUT10k8OUNatSbLC8s0AlkAk1IU6ibkCRszlQhqqwQUQVShFZj6mxl0k4tAJECjk0VUUgVoqYKNo0OZnWrFFXqBrElUyNABU1b37nD4uaNYjY1aV0bXYjOOJydBXUzPSYCyzxWZx5mIXrAqpP4VgjejFUJblaJEKwWymE12tjMHjZt1Ht3CRZmj6GuIREwxDaaIYoUfnr0IJ6doJ5jb0IqMEGI8/k8TmcQnU/PYtMiRiNiVYiiqaqzU+ecnpzpdA4yjIrS55DYKZjagHreVvPQtIgCThIZhSpERAQhQB2qCMpAhLqe7E1+8tVv/uo/+cfZxz8cvvvi9770lV//nd/68ltvxB+/cvvqMx/95Cd//NUvvvjii88//eTDV1/L0ilFQiTN8lya1gClziR/Y1xtGUa7fliYEQzVAWss4IO0wty1o/zh9e00kRfZHTcnazf3zi3qvJLNLstrctlz13m2a/8mpF1r88XRzGWFMbt6pue8uEzHbat0FxtN57x58fwbhfc5dS1/P4/OiwKWcqw4Qw3K32XHycu29Tl54R5LLdjDuBgVOGzayky6YIPW30MOJJecAVgf1GQDlYIl4Wsqk7H4P6Xl7tO0dVEUKewBQGZiMVpi9zAmZKBeuUEBpLCas1Fr5YOz+NaD47MzsMmk8E9c5SeuHrrJHoepsRDIE4zyMp94gmdEBTsAEiUa7LJQxjpj2e5RQQ6Ug3PHhSoZExGnU7qpgC8vOrlweju2MmY7PIa2pWQ+Yr0QEqzUiV+AGOAyeAKUyL0NyLZCvHeBZlNvInNehtUrvU17n8Fg3Hubmxn3xrmqxmTMlhQWomCBWHpoScKVrhSFRpgZKySRF0QzYqiYIRq4hTCsqSVaE2DiEnOSqYY228/2ykLaumnaKJWLrbaNNG0smYytCUamrJAaWebZTILERk4rV2TCYqHlVikvY6ygJbRBXbWnRzqrcf+O+SxOz1DXsMieVCNijG2LkJ0dPcSsQogcI6lAFW1TzWaYTRG0ns5QN2hb44yS5ClzCNLWLWYz1DVGYxCHpkoKHRMgRDRNVVWxaSGAMczYADWImhhiC81ctL1yPD2bTq5cae4+cAej7/yrL/yD/+I/++Krd6qfvfLqc098+Hd++6d/+vmffvNbn/2P/9P8hQ998sMfefGvvoWTaebH6tDGwHnWzOfeeSTYSqCNSLQXHW8X8BBZHVJrq/Mj6lw6sLx76fwWngdr/s0Uz/z/LZ3zfrdLNbZ+v0A1j77yDvJvNvVS4/b8/MOfHMEMogpmGEVRx1k5mjTkQM4QBqXw5afAjjAB6HxTu8NM8mre3GsGyTkHIhUREee9yzKXYvaF0LfNd7L89CeNgozffOi/+u3XP/+lF19+GQ64cYX+3X/4wm9+6qkXfumKo2A690QM9sbOPJtXiSCoQiwy4DKSy5nK9J4syX4yCWY4I/ZglzZGx8REyVPEzB5l+P72k19hIB3U0gso1x2L+uiMaWgKESnx0Ax2IdtIeLNzEOmEMZr0ZQoRMXYOgKqCSFU1LHlOl1EbASYHQIcYfOV0m9zbesEjLaQtAzv0pdBoDcckqSMBsOS7pL2ba//YTK7nROyNl5VNxXrxTKqXGGbJ7JyQZQpW7RF+R73QP1e6g/o2sxLBqbIZEyKZaUymYdZWjh2rSWzZExGphsxDtfUFN7lDbQplR1meR2ktciduyhnRxVC7PDcHUqciGhrOCLDMTEyjWea7OKUjsaZVCcptI/MpcnaBc6YqtlADeThwjGhqEFgFUaCK3MeqghG8r6dnkAAzjVElgB0UFkKoahjBZWgbZOTSwE+jJVqP1jvWAjKoBJggGe2LAOQMUkc4rqdno0lZS8TR/Cv/4s9+9x/9e1/63/+3W3/5ld//r/7r+7/ywtEPfvC1r/zlv/9P/vO7t14/++nrcJmZNSbmSKuK89y0M+pLgsAEgm050Adpp0BgC8HtwOh82yLWcR0NHB0HI68bacPhCNBlp/oQVwEYOOiul77ZiJViNq53M/1yq/mjz9ZrlewsZ4ds9b2UUZ9T76506dasgInFx0paIYhOtZwjiVkdVG4HP9D5+Pv/Y+/Nn21JjvOwLzOrus+5y3uzLxjMYEhsBAkRBCSSMimaocVhR1iWIxihsBVh+yf/V3aEHaIjZP9gSrIkUyusEEQZ3CkSIJYhMABmf/u7956luyoz/UN1n9Nnvfe8d9/MkEbGeeed211dVV1dXZX1VeaXQ2TIN45jRy/Ywya8+HsXf0yfctFDL1GMdrVz8RXavKPCOLA4ulhL9xGkQ3dLHNw0OR89+3x7djdPElyJ0JG/DDyZrihMUDdhRCl7RBQCt21b1wSAPAOhbziGl5iOhi5q39J/VkTcTIglMBb6hSqIAXEXoDzqDAg8KqqL+c3/4zd+6//8ze+/8YMyPCMl/8Yb3/mv/vM7/+N//9efvXFcVw5q8ryRGp4yZZTmYGZmUlPNB69eCs7iJHAChEIlsZZQmXFhOMoGkKHQ5u+Rx144PUHAB8DqbtElKYd/HLDwvZqsgZnbs72mglwNap2T9uKzVpB1liKFb4o7u1Enc2jHpVtcvYszFMxKttOHD8/u3p08uI/5FCmjTJfaOxmX75SQE3LS1FpqTSiGCmBNZi4ugSBB4uTsHMRhfNxeTNNkWnPFIFiGZk1zSy3gYCldkB1QL87eTAVbMoeWG+HCmtCT68AcmmFlP3Fh3woWKnphFzbS3UsISTOgI+iBG7Q3lXV1NdMEwIk8KyXF2VxvPbx///7Lf/WXME3/7l9/9S//4i/ghedw5/3pdPrOn34H985I3VydHIHAzN02uYMWg+sjPmvfIbuveFI8Co8v1/Jm/Vg+QjkMkrnGrD46iGXz7NbEV7p9L6GtgxMjjuLxaTg6gVQO7gIIOK1GHrlczI0IqpgnEEFVm6QD/buYJy7N/HcpecM7Wf1bHcnIjAycAS3E9HMd/dZvv/GPf/P73/weLhTnHh+mI4rjN2/hn3717m9+9T/OrDZI26YwqjW1gSWwiICEzUUL9dtmWKDLxMHmVIIPQSJJIGYHGcHBH+bYt/qcduDbm/4RK1m48bYlKW386LsOUELtbFOkhmX1JZq7bk+9x3Ke1reNd3duB4rR7o7z6zJwOdk4vpzYVjUYAQloZdrrtRwv3REgIl6sA8iJQW7u6u5UNCQHpk177wHOpkgGQ1CKRpxUtHQogkPUJbs41whwgVL2YKghI6dqhjBPyK2N6mNIladTUw7jG7ket4u+rDlrC/dO6zYLvVsTFcUFBlNmhgPEMcaSDO6dmpKyMMdOExosbqi8wF0EMyLvWXMcat4blbsTes95NQPD2Dyn2BrNFQ+m3/j6733+Jz+PFz6R3rv1rR+8+fqv/jKeeYoZH3znDZxPA5CtADwOIeqervX71oVS00ELzyNffla6nw0+xXNt+6fzadry4cFns3szOS+8n2jQK9Y+wzTDT3n7hp/V/rn5+Whkpwo4+FyiGlK3/j5IlTxc9TxULm3w1Y9j+VmtKPo+M5Rd41vxOVi7011V3HNqe/67yy3ecMOzw8xpONp3YmtAXUlPMNrRG9dqSxuQzPAN2ry1TXP+HfqBL6MZQBCqMD4NR0+hGgMRJZztI0kIUvam6hpEkSiIxDL3OZU9pt5dEZ1v7GYmZWRYr7RnoAE3zjPnmfPUZQ5unCybf+u7b33vLSiDxzdcahV9mGYG+cE7+N0/etNp5Czzdg4yQnZLoMwMggACD12rHAoK99FkwMIhhqr2EJRRaIifPFaylCdb0mIvqvzpVO6cHexdX6Zd1i7XMtysD1urLqmHrhUOEgYNN7MWNSEiBoECRMAC18GwTgSREBAjQogxgskWPMJERKGqahHhqpKjE4mVE9QpAxxCrKt6PA5VVWzxRSKRkFTwwKGuTp7C8dMY3cD4FEen4GpyPkdSPj49vfEUhZjqevTyy9P5HKZE1NE0B+FYoRqPj08xPgIHLfO/A0yhqlBF1NXoaIwqls0jNSvRCWKMoa4QKq4igmCh4QHMDIkIHGOU0g6F44dRoruxo4/KWeIGKczNTEQqqWCMD+598xvf+sKXfg5VfOdP/jiE8JW/87e/9Sd/jItJRUEIqtr5B8EY5K6dA11P5OPFxcwP838+tBt83PIZZjj845Hz+f+L7FAxr/25bBR7eRF7/tyszGbdVtJsQMhr/WRPudeefgue/diAfVmVdDplWXSBy2Iwc0XVMY1PUY1BAhLavyeyTQrDF4vA4YScnCCaizt2b4bRGTtcxpU8uP1B0/Hiu3c4Kioy3bn3IDu4Cm0zAxqp3YFYnxhw53Y+Pz9317qOsCxVlS1ZAcRLRbvo3AffbqeXSkAYSQzEAUSKVWzD+xidT1K2mf36o5W73beIOlKgorEUg8vhaR7Eb+0cSxbWOb5ih3sF36W9wt5vd3X9abkx2XP3YfO2fQiSLW+kP1B2AVfV58KhR0S8tJbvLUIWDlwiJIHIU8pwVScBEQuHSFFYGFGCcLbGvKf7deaqGp2ctB5Qn4BDEssXswwiCfXpMY+OJFSpbednGZrQxSbleDQ6vvEcqpoIiFLDU9POzqd+foGj8emNpxvV6uZTRy8/N5+en737Htokhb6PJVRHwhVG9fjkZpw0KbbmpCXwSJDx8dHZeERcjY7GPKqtChBqizFalDiquWUKIRC1mUBIpZHZRQRRECVEDkV1g7q7gJQBMBFBBFATcgIMbG6MzGwlaPbR+Pa3v/PMf/E3qi/+VPvm9//s9/7w1/6H/+4P/tdfBzGLaoFkiGEZpgZIiRbTLztKT2DnHfYy/bCyuTLdCe3tOLGydF52nRWKhUH/OdRjyH07S0fX07e9xYcpMR+ilrPLD+XPpRzYbtc1zO+a6XfN+ns6w5ZTu2tJZZTbkdVayn1F7+4EpasP3QZ3yVCTW5Y7uIIcPU1tsfVgJZhDQh2PbuQSU6adX0KAs1WIkmaXAKCZ4/69c81MxOalVIZzr8Rs2lQs5wvytVMAiFzgkRCIxGBAJjd4hIkwj8aBBUkzpAyYOKnJ0lkEnroBplbYx8c1zhuQiggJgxU5A7Gw0AOP8PoJJCJWIVbOQRm67CqbNoJPUJt5spGxOyFbmezXVV3efKje2/cNfx8mvhoWGyjtuHR6Wk2wXsTg7KNJ4Z0Zcg13uRaooeMM6BRtB9SJjMjZKUBYQmQhR4KbUzE8J5bIIbqxgkIIMh5rO0JS1KFlqkKI4xGRgwUcQFysv8KoprpuKXgVn3rmadL24r13QSRHx3WcTWcXYXz83LPPztkfvvc22hYAOACKEClGR3COLjGMjlM1VZJc0NcQ4qhmEXWQCMWAGCBIJcJIiByDpuwEYkYQQHM5VXSzvnmJqMTF7FcehIVpiyOXyAbm7DCWVFDZUGN8hHH84b07P/XzX/njZvbSM8/8x699HW2G5tasUD6HugAAIABJREFUUxxL9Gw1tRycmKC0HCv74cy3bof3febKqsyBcl0L9z28ZLi0sk9ATblGQOIvplxr+2xZx+8kBbg8r50574dSNv++LL27r/TN7RtA+y/fIb0+tTtNoTEzdBOPlWgtzsE0k3CgEMfaptZzs7aFdxUpO4QlAI4Z3vvgzvnF/OWXnrb2IdBZBNL6JcvW2I5aLS8omhARhNgJAnMoEawK+qUv/uRrn/rD+98DBHUknTrBBXjuKXz55z7x4rNjzw9UG3HJympsYLCCCGX3nJy4kHldXRgUEOoQI0l04ry4fE0l3Rac4XrlcFWmI+VYH/27CEcbs/9G5KNVo5eBXyz1/wF9kMTuN+AO2adYbPfKXt368+4934fudJuvG0Eol7Xdeu0g0HwfidVLcYWIZhioopuwi02JMIhB5kQAqzPcC7uem4uIllZhdmIHRRZTpKzWts71uD5Oo4nbXEKdZ808jOKIyiYNwMTBoahoHhXiCZ7dZprZzHKqR3U7uTdtHtajozHT/P7ZvWaKDJBAxC0ZiCQSV21ynzaTJjsEIRIHA4MFJCwxZYWmRs2IEbgEn0AQMBlza4actJD1DUCCLvBvTimlLsKIlXhMJbqTdQY3lnNqYYZsZI4g7o4guHHEr7zAN09T4Mb9y7/yn7x+89n/+3/6+2gJZsYKdyE2VXcDE9wYBIcW4iPqvCzIebdrxIH8CjuGzp49cyO3jrto/fj1qgIra7sni+9eJrtQgV3pdy0kdrXPdaX/iOTqz/3qPC6PkDmAzjt1d/tcvdyt6Xf6uC3Wm1dTXDZnHN7BSbmJcBQDwOLBYF4wUgEjQ5haiaN6fJw1azvxXe7VO8SKT7JzyfmNN9787nc+8fJznwAECA44uTg6jrGixBQvir6CvNvQ2F2pYN5E7E7FAxoKNKDzn/3SJ/7m3/zgzuzdt98HqwegBp5/Gr/6K8/8tV/4jNDEmmnyLHwzyNEHtx+cnSfNWHCriRv5wmv8qkJVTSESSa8jFribBk+zS8mdpeyTUmieOCpja9oZABT1hXdSlXm/9XPIcLOuzXivdpD3wM86/LP5xl7DRGLmHABXOPWbnX1oCoc7jAwZRtSpcZ2pvIO1dCV1uFmEO0y7u+ogAiInNstISS1LPA7VKM0rl4BpgnqGmAuyIXvvXiTukHEtMrq4mJzfuzsaj06efvrivQ9wdlY9Papjfe+D9+W4kRtHHeeSWdlCYg4hhKZNSLmZNWaAsAeGhCW01rZIllJyK9ibkgNUjMg854yUtIRQKHbtYDhnz9CENqUm52QlcmRxZip72EXbK/oNvFgEEwWCMGIMN27Wzz5z9IkXaRzOU/rSz/zM5IdvYd5iMkMNiAQCOVQzzKSqoFbWVwWg7lTkZbexrjeufD/Ks1/taSWG/PaB+1qcih+LB3zPFkDprw7QAe9DT9H2xORDhHvKCDz83iFXG5cvy+UaZOVBLSA5Wvu9/c7IYNRj5wYv4YulP6IAg4y63djlLdPWAbOQb1Efi22Rcnf61T/72nYDuq3VuYvit9gq3hnN/kCxQoPrycFSxaMbAPTCMZ8sJ461aXi9tJLMuWIzAkKj+U+/k3/nj37wk59+/sUXjhkTeKZi4OKAF6PJsvXETiAjAg2WytaF7HYukUeoKwVd9N3OXMNASdvmlRde+m//zs/fvPGtf/4vvvvwNk5kdFLTV77ywq/911/6Kz97On343aOKyY5MR9Nc/eD9W7ceogEgxS1DYdhKKLEhS2MdkLAE52AgN9iCCYVohWzwQ5Gwa/VJ3jmzrKgHHd/tmgKw8EtalWJZ4gAvyDKcYbaE08oKeXHlYsdBFkrJgES1pHAispUuO3R163MbnKfygIq/2KLERQLqu85iP7L3Iep2ObYg+AoAJHCH9oQARAQ4mETcqbPYIqcyiTu7KVDUaHczeIYTd6EMvLNrcepmJ+bgIhRIuJDjwRRCRNQqIBVOjulo3LZtjHVyUmOEChQbByiCRxADtSADqpPnXzu9+fSt23dx7+5zP/OF2vydH7wJZDz3jOZ09mCKWFE0yw0Je56DHSIgJldrG6GgZpTN2gbWzlOrpuAII2RHqJATG3Us2oColz2vJrVeiBCsc3jweYYKLBkyOME4ZBIS5Aw2bVunABcYNU1T2oGIicVJNCdWh3GsZSwjrkZHzz/LJ3V79uC0pj/4g2+0TQaXcFjWcVBBwGLZ0esm3btauoIDsH5l4yVK1PJ7F4rAsuy6q6vHrhCy4SC+a7vdV/4bdFe7Ei9LGbVt9wbTpvSqxo7harj67CeO/jXsJ6S9ZV2imu3mpTwst13tebVheJD/3vpsTPqL6Xn43aW/bKG5VGyx2xbqCtPwcNOQV5vIl2msnwJXFGsaqOm+UFyocCJY4TiBOy1+A927ULyFyxtBq+GsbRkre8EE1s24vlRcqI8U3eHrm/fdrSpKhsM6sPtK/d2XNTcwyHgHZSWDgC2nelo0AmQJhvRKQRkUWpByjaNnxuMb09vvIc06dgzSbvCXXrcpqEpZjoKd3MVNlTkwRSa+P2v/yb/64Ss/8RP/6S+/8uxTR2n+QfDmKI6syUw16lrnF2ASYhIGuAOkl+1JAIGMwFT2wiSWaIVdSzuIsrlXlSDf/9zzo2f/1mt//fM3bn8wERsdn4w+/dlnnn/GuPngpGKdzZwj10/dn9Vf/d03fnCvvOAMOAf3FuIQIXMpbrPDHkZceNqCFeczqhBiqGqEuusLwyeqq8saGiQ4UN28unp6OSqzZkdSXu0txiU70vvqmOFuTIyySdnd71pWK/DaZkH70ZqN9OaOzr9964hjvu6Qvna/hxAK929veV3KuGMLkIkGSiuBhEFScBCGF/PhohW5EEqEByHOIugsO8w1M0Ax+lgkOCUj7a2JKdRHR2E8ntm0RKjuCAwyGJJmc9OE41GAXzy4j8k5yNCati3UUEOCE9SRVXo9zNVzIs3CUUfVKITcNtlakKBsCeUMU2LxoOLG5mYOKGeDZiBQCdLEEgjJAXMyh1HZ3AUc4EiBzUEC5H7NwVjAAe65TZ4yir0RGcCWtZlPj4jatp2ezz/90gvn95qHt28zueZEHLd3jB1/9K+KbnzvkIENzcpbRsM+Nvy+/o2MJ2ePstATqAex/AqA8Mdrq+bxZLMF9nwDl2xELmCzx4+jt+ehD055/2c5MuiHff907x2pN/oq+Wbv7SdMrNzpIiDGumLZG4Isl6eLALHbHKfXj9BC8y/7LwPz2M2ak23JYUfmW4tePWSLyjs4wcHszqPTp9NUdHbRUesWEofBM1/t/EyinmDqplb2Hb73Fv7Zv/zTF19+7nOfqV986sWKp95cgB0MzCdSxzKjwoqHNnHgZd7ucDJ3s+zu7kQorHpgEDGDIihwUTS9OaF09DS/fHKcP3PCUtV1UD0jtDpt3T3ITRy//KMP/J/82z/8vT+9deesqzNc3SRAQ0DKKHwAyxvsn6h1Wm+EVJBKpAKFDmrHwTrKtcvjbjC574661VuWrCs3bp1D8g581vtQ9dv8mHbVgbBcd66nX9tVXTO82pV/CTXVa77L+nRnB+sg5wXZsfe3xV3O1s3KhB59AYh7rcbBZRlCZSvK4SwITE6mMBIqM7wDBnNPcw9NXVUiAjNrZ8gZZkJukW8eH8nREc1bG9VJp1nJOcBz0Jwu2pByvHE6Yrp39gDtDMcjzBPaBAl1iCNmwIWojWJtVssgstSqtFLXdRxVTNAWKREUuS0sfNbOAzQRUc7ICaqwRJqhipxJcwCYiR3QDFekhJyRc0gZpmAEJk8JzFB3V7LeXAYo0TctpxJQnQVghgSLSMgP7tzmyqtnj26+/sk/+ndfv/3225WqHB+ldr6rl2w/fqiNxU5UYzBurhZwYP4fjVyCuGyc7RfcwIeKIv+5EdpCDX09wnuXc0/U7NpLaJHtYpvj6qW59b+6v7e+K7SW+CoZHtoOJdTx5mEgHo9Bqm5oZ1CDd4uutZo6FQVHkMZQBQPIICanBxP/2m/dGlX/79/9tV/kL96oaHoyrsbH7NpmmlH2LmYk9Rs37iU+NhGTF/IOlDhJIAdlQOEBRrBOhyFnIs+WQCpCsQYHVdV5SnUkInGqsh5N7MbZvfqrv/39/+03/uRbbyJnUBi7MkzIyeGayZGHe+JLNvPOiIbARCFIiMRiHyeDs52qzHCC3zrZLw4uO83u9D24R7v+3Jp+mKEP0u+ycTk0/0vTLyCZtfqsX7I8W6Cmoo5Tt7/syzwJHbwCMssKNnKDO5uZOZwZ5qYwymkOim65XFB0ds2tzydVDJQkJ9PpRQCgKVhtEEqNzibazo5iAEE1UxCznC7O2jY7gXOw+cRnE5hVoNYMniNIVK1pAJBlMZgZTEFEqrmZMyIR5zRLswnaGckInqAt0jxNJ5bmUORm6s0c2gKZtIVmNMjzhlNGTqYOyyCCKUyhahcTzBqouWWdt8glQHchO+7Cj5dPlCBCSoTUqrmzIpNDMJ3p7OJTr/7VT55U/+Gb38L0or2Y8eZa6yOX667Pyg0+wu0+4cHnutp/dz5PNv8nPTY/QrnrT3z32f2Nf8mj2QuZXAqoXFqNfQkOTX+g7Knq/qKN0BLRaBQJaSaYTZAbOLjEEwYAt45YszfNVAckMLIlhYaqym1z7xz/6J++894H/+zv/d1f+lt/4ws8ai7aW7ALIFRsLIXv3QFQMY4lsIRu8W2LSjKg8AwUmngBQMW5lcg1hUBggwBIRC7igtC2KnE81bFULz84P/5H//L3/8Fv/P6338QsgQoKpgYwUzBXW1unLVRNAgoPHgWEKCFKDAaG0xbDko9I9qEyw9l6cy7Hcsov4CR3cMcqX0uHlGy74WGea8rEpfXBtvrsr3/RcG03M00HzBZ9Y1H/sroqPk02rED5KprqAj4CFjYT3hGioHMCNwJ1+8XGDoeZERWiZHJHMSrRTOTmpuTmLVkJcWAgF4N7zjolZibL2XQ6lRigiTW1zezhXUnyUNt2XI9zM7dmxqjhPHt4P6cEpotmImmWJhdo5hgJ5QY5syNNJ7P5hCDCQE6mCW5gdsvWzFUZ5s3ZuU4ukOaxjnBLrkhNujjT2QTOaTLx+Qya4VmKAj+fNZMLadXmjZkCBg7QLG5q1tx/gPMLpNbaxtoGqUVwd2V1NYc59wqNu5oZHFEh6i5mrvlsjpbA9585b+SDSfP225jPYK1lg1yTJfsBo6etpadLhkrgsq6+o0ZXyPcKuRyU/BGYNT4aORR125nNgTf82EP50ubmUtmrPQxN2teqtqbxrjzTHf7GK6jbFbSKK6pih4Irey4YWsn4jrdvS26+cg7odw98S3p3NHBh4dE4kijE5lNKjXmWEm2prF27bS8UPYM6inNTt0adibMbE/7Dbzff+bP/5+u/952/99/86uc/9yn4vfG4sTQFA66pbWGZmTtXXS1U7z7cTQChmLd6oUp2L+EICQQmZzH33KgZEUXmGlyF+vR8Jhaf/50/ufU///3f+NrvzCYJrQME9spTIriwgE0tBxFSX0yw3rVQZ+ELCYgVS+USlBjO9nEaHQ60ldlmWYK1cXkAmbg7VtGd9cSb6XFJ+hVwdQO8Gf65Wf+BsjJMv37DW+ClVa1rO4aELm/v/4MXZ+qNoN+dqW/ZSTZGsZwxgB3qSooW7upCXOIZGRUDEs1Ak+ZE5K6KtgXXJRC3p3Z+/hChhpqHylxhzmYemM1Y1bJhPp0LIbXQHOBeuG3guW3c1RERBZbI+4jeJeq9NTCfTS8wu0DOjMymgENzO5tgPgPFtpmhbcpOkJSXS3Nq5toY2gZqYKBitg5/mty+i/MJ2rZtW9OE3CJ0xO0FlXHvVRlVz1o4kc2NRLIZ8gxEeH/6zje/NXn3Ldy5A9Xx8XFCm7uIaxty8EbSDtmS/lHe58dCHZ4A8vSkUZC/sPKhoYAHoiCXnrpysQfksAX/2F/PDXhmi6qxq6TLVMBLVP/9ONPyd1CYOUKo6mNBqNPkPDdTgzEAMJCXV5FLyO5ZczetqJdoMhRCbLR9/y5+/X9/999//R/87f/yc7/y177w0vPhJoenTuR4NCbKTi3Y1ZOlNkQqTCVMnZ7aUUiQgAzMQIlswO4Op1mTiMRpRAghjohiam06xVsfTG8/aL/2u//6H//zN7/3NlpAHYRInVmiGYygbXYwsmiJ3OIL63QSEIMCJFKIEiI4OLG6F0P7Jx3e9eryKEvYpbK/+LFQDzf4cHtns2Wgqt6WZYlnlOQ9tmXe26Ys3DvX0J2hJQ3KFs6wQc0XfMHbK9/rIl2JplTogZayopiv1bMcXRgtr9k4u+uSOXGZEzFKVCExB3d0M4NaOWjBTrPoRwuT3pKmo/Z319aT9BZ+DrUlBzYxvAQOAIsQjOFVYGvdNIHBcKgBFFic2N0IIkwZBIepisOdQeoF9Cw4k2XLCe6AQbMX2AbwEr9a2FILzZ0TRGm1wnqnBiueTQSATGEqbrM799DO4e45CQFqMJRIAlh8u8ItkhDInRpXJ0U9Rh3RdsbBo3H1Z9/+UzCBBE3OswYhbEcSdvWHg2wOaAtMsbmqXhuetxe8Q66yzhlW7VCy8U1Ln11O1Hvw1EUlfqzmbMoulKV7srRxZEM2I2PzalMPd8CBlXPdnLejVmvieyJObz267aVYhBDa0GMu88XbbVvTDe0bVywsdba/nTvQHNrBN9NFvluNoQ0AQ3YVIji7Q4mjBBkFODlFnV84GGhB7GTDygZCiWTNQgAb1IkmbRsEHGNq0hs/xP/y69/9F//mu1/4iRv/2S98/tUXT156MZ6cVuN6VI8QQlabWkpCmcUBB2e4mpmbG0LHMAoC3EzN3JSlOjUNbcvzGc2m/PDB7L337r97e/pvf/97f/bO2Zvv4MEMXEFI8tyPRkdpPi2BkgJ5C4ABgReSnUXzUgQxcQBFqeqOeZVLDK7thk2PKVcnV9+UK6kymzjEpRfsgUaukv+h6TdLfJz8d+W29arFW1D0rYWvZk+Q0KluPcJUvJeKa12hEvLiHO6L8Yp7JlwiAvvC/gYMsIgoMVFRami5t2EOZ4ggpUbVVAG37LDGXXNOaFscjTgIVOFmZq1mz0pkSqGYmyG7CZiosGs7iLiEtSQhykQgMjPLxTKXii8iekWzbOCh3DdHoeCUABnyFbhluOrkAgUUdQ1cmLyJiovT6kxgKXtWwKUe4Tje+MRL9OzzahwyVUy/8Je/8p1v/AHcQqhmZ5NAbFfl+ngk2cDwr3DJjpfxUVczHztjoB/Lk5c9GMx6f7gyz8oj7PVcmn5P5odW5hqhpv1o0CVYkQPEEHa1uVsFoXpchzht5g50E6gDpB3cr5AKpsgAObS4eTNJLbnN8+wcxJLeP8eD7+JHb5597d/87uuv4LVXR88+d+OZp0+ee/706afqEPXZZ09HlRyN49FRNR7VVRUCs5OkrAY3s7bN8+lsMp3PZ/OmtXt332kSzh+2d+/O7t+bf3Dr4p2327c/wP0WU4ArWECbQWDALef+Pt0dXEJnBiABS7WPAQJXxAEcJYyM2NxtObt9vCSsPLa92kD/S/u0RF1Qq6F02MwSuSk/yYHFSoI7H2VaaRIeqHlrs9Gy9FU36WWCzfSDWE6Xirsv2WJWpeMn2KhJZzyzpZLmxa2aiGixtdT5DZplZ3LnTl9xh7OaMVHJQCRKDEkLMYSaKUQoiqc2Q1mCwl2zawUhSEiWEatU3reqklhr1syMEJAZKcFNQSDB+AhMxAFMAGdTc++UKDiCwBlEbqqmnQcWCiWMIUpBWZAzc4ixzs0EKYsIqgqhDqGCE9rMdZVzhhEImr2O9awDghkQVY1VpblF22I8RjsHeUotQgBUS4wCd5QfmlFXroARnPL8Ijz9wms/9fn58RHHcWxxs65feukF3L0DGMwEFCW2Ozqx7+Ig34PebWZCA8xscbCk3/lu79rY2pV8+4nBK7B6/FBu9X4lush2eP1K3ntbZheKc11ybRteh2p+B7ORFrhx+9S42Sb7W2mBxywXSNieMwYeUmu7MsOyltYkg1yWph2+srYe2sdcJss11CpcpEBPRLltu39Zhx35FlRp8+nzKnayHId38Mcsc1vrMbSwTQQ2zpYF5mrZAnM4nISEWnMhJqHjV17J82kzfYBmBrMOd3fA0TYggKXUpCDjUGQA2TJ62IMcDxucAe+/id9+cw7MGbc69p3CL7bg7OnutAOLfFlbeP+NfjLywT2UWc0BbbtcHAlAmy0UM15EhaVs4H5Y4+Lry2ABRw6VhJq4yoY+mgz6DRXHet97dNmCZe7pP9vkQ4nB1MseLOSqwMke9OVwYGZL+r2ZXFG6t3GjAosSmMiJQAWc6dzZDSiWwgYmcRAoiBGQ1S1DAoOIPIzG2a2qj5womVtuoIoQMIr16alUR3MJ45P6fHYGbwAGRalHYkRRKIbTp5+e3H/o86mxFJhIYqTx2CUQiYDa6cQ73kcGhKSiOPIYR6NREyNacfeu6YKEEEMILsJBwIxCX21UbnI0GlVghIo5FAMxVZ210wgTCULUcogSvEOhXNwIjrZFVeWcAUdqyV2IFMD4JJ9P/uhr/x7TGcZHaE1e/aRPHyIZJLq5SDgYPjz4ydpHTP9/rfJjgOd65GpmK4fk91j4x75ThyTeD8lsbk4dAB3tLXpr4ive5pXQnUPSc2HSBAAYGORORgjumatY8U0LIc8JbWNQAFIIY6HeD+zdJvDAosEZaiAEhfQcNY4uxvVy6cwregwXejQdmuasbkV2emi5YPFsLHR1H6y7dblvSSgUNw5o4TYzQBCCSCSpWSriyondFnSCj6DufxjymKpMafpNK0tbtKU7r88uRS/uJ7zF4cI3Uy7smTGBtT37HuFfm7SGj8+WGa7w823hmxmm90XdOvWViICdMXo6kt/N490XMQrTAC04ZjCwzKCiyhBBxKHmhe23O8VEBpIYA3uyxjOBWESMTKpaTYUjVcHMDQpQqGoNXB+fgKtawvHN4/P7tzAjSARXcTQyEmMKo3p8eqM6Ompy8i74AIdqFMYjJXanwGJto9lMteNrYg4xYnRUj48QI4Dc2cMIKLBEDhUkUBBIANg64kwCuBqNJRmYo0hjGZ6TKdyzqpBEkdadHFkVwjC3lIkIQnDLbYMgaOeACbGqAoApLhxqiI66evapp9/8xrdhBLimXMWanZM2W5/YLg3k4IBxvn1l7b7styv5H5j9TtThWlc/W47vv+qA3K5rmPto1Cw6uEPsyOfAbHb6VR6U85r1yTD/DYa6LdY8O04NYxvRtufrxYput0XOSim7rIgWiVd724KOb2uGu1Sc7a20gz8GO+6LzHtDAO7cnKjDRQwUQhWCMDMzJ556aqCmHXpSEHdbWgusFUtwzz0D2QqN7AIZKz1xOEk5BkspxyrnM6/oK0vsNC+NHbppkgFox3G/yI3ZxQEPDGYJlcSKpTKwO/eBEhmwS0aKw2UXctnb61w1nw8DlRnO4msz+qV/brdNGfqHr3o8XYE/5hK+HKzkr7TVHWaXDGL32ALCXZj+Fu1nlZyGCJ2709C8F25mAhCRiGTK5cFKqMxdszXaRBauA6yCJQ5VsRFu5q0TuKohoWNqqiKHCsmb1LaS1MEhQkIJAAZhEjZw0mxKFBBCcMumpSu5u6t7IGLmnp3Lu+HGkUwLzSQwWBAMJKUEVQSG584QuQqeGlUl9QjmksYd5pKMHDg6QtsyqOyRuWW3DGJoxnj0wuc+04bqYWpHN07+0s/97Nf/4f+FnkQ+m1rOu+aEnau6Q1GcA+eojbhfH5LsHOJpW4LLcIAfozdXkWuHZHxAQPoIqMnKH7uf9qWaweOkv9TaZojuPGYD7tPVDryvjfR9vLwFIOGc3cw9hBiOTiXWuZ3n1HqT4NpR9xYFiQpXO3qv5qG2Yf2ydwuhi3cFY13RXdtY2j/GUL/+B+C89O6UwnhGxciSQEAgkNQjiDAHZnaSEupJu8gMGyP8x2lcuBZVpmAzgm23tmDaHeoN3SbiDtmj+ixS7GKr27NBu0B3Lk+/f6+KgK14DHX9ZvFyereZBADMDIBI3AnEpvCijpPCjJ2tQH0wh7o7zHLrMUYiAjNSNssVBw+CNuXUUohyNEIIaHPBMHOy5myGqu+sDpjBnUN0JLTZU5rNZqoK7hiZwMzMrqYpwdmYI5caGwCYWc6Wkqcm5xaa4E5ERgJzJGvmKecMIi2BrH0wZDKb5TSfIbeQCM2IdQih7Cu7oZ21iFKFCABtAuM41pYzQHA6qmrM5xA5rkbazJEyXKmuXv/JT50+/9I5+/vz81e++JnmHybMpyWUrIKCxGxpx3ps12rsMPEOCl4OLr1uU1Y+NDy+q6tclv+TlUuH+GvL/Ary8XHmfEzZozrsl037mOHvXRlteYj9KnZpR7ItVgDWtfF9msS2CEfb1ZSh9dVKxXgbJtGnpP6FueIO1NYS105t8Ui6RI+xTaVm+bu3CCEvSHx/igjm5pLJISIcWOpQtUnOYRnJkLULHYO0jG0MgUfAAB2EX1hVRFaANR5AM+gdFkuYzy4FSnxK2HIxucRaCqH8Qh/i5ZBkBibAIQwKIGJUTsSxNrCRmANOWsAYxpZQk09gr/1x7O0+MluZDc1mH3/M8PfiNACsIxy09eyu/PfQBy9y6AOxbsjW51gCyS5Gk8IJUEpf7rj1LAFeJr8OceWOjgbcxTLKDstEQkwOd4WRu1cS2hAsq5kFInAoEcioFJQSOFhRLEQ6ApuiSLnDTYtRr3m3ZTO8625/zjtkswQQUCAnzVlL3KVh+5TIZ6awRYRFWwKiAsDMM9y8kOaRa2rggDApUkoMD9QrVU6UlByYzVB8o5oWx9Et18wzkcic7t39nX/1VahDgBdOfy9QvncXAEYVO9s0t9k5PNGp8cB5+pAAXpeVvGtKOzD98BauW4+5TjkURfvo7uVaVMOrbJrsuvLq+fQgQB0WAAAgAElEQVRj0fLPPajPUCO4BBPaPLJ2dg/+sf9++zrsSXOJEnl5PXdW1cnNi7bRBYYbNi5zBODQVg3OEiVUQRieGxNDysgJlkqYvbJltDDe7Yf+QeFLrOWybeplgkW25fjgTmyR1fBa6woqmTjAAolcPixOUZ20ZNztKTELE2C67lq/Fg7oI5eDVZlL2CY2pGgMW257wDezGmtpqBFj++9l7luOLxXTDfBmP/vwQJYo4tZ9EyzhnNXKdEeG+5d9jmZE5G6re1rUhdwo3tpO7F7IgJUd5DDNngtXDQOWcs6ZmZlBDisxHbWjHGAiBAazZ4UWehuHlVfJwAxmEQEAVXcp5HuWEwlTUag0m6ta7kLedwb0xERCDGYQERGXFYBAAhWTmqXpD8wKPbEaEwUWkMMy3JDTnTt3wulpfvCQyIVFs108OLfnZjg+wdmDH33/zZsnp/N793F+8fDOPTxzE7OLH/3oB8fjeqa5clKI3TsD1xhHhNHDt95h4+iUkoIdwoGjatqqb+x64AeiIH0Y7S7k5LoH36ZX3cEz28Fz4S5seVc+1zP2fJx4Pq9TrmGT6JpaZnOk2tnmy1V4h3Js2peghxaGf+4s+7BGsEtQqKspLptzxNBGZyhXvYu1qzo95+q3VgB2p4GByILYxgzOTlTC6AHkSqwkJCdMdRS12CInpAaZ2XLOMwaAbIvKgHsU1wD2zu0Lg1ljAdtsg1tKfTaqjAUG450XqnaYTZ+PA44QAnEEVyU2pIagzA7uBjbqv4msmI0WgKZYDpVp4RrXaduE/TA96RBDkEeVPQuFSzrilbrpXh6Fq+GWg0M7quGr3wB3nWP1s2baPdT4zdysOCu6K6hoAD50QnN3uLOD4EQcWJjcNUEzd47SqW0aqBE5XL1t0bZQEwNyDvAQQiVEuUHTFF2o6BCWWpiCqRIWONTIDTBYdlXVRK6wNqfGNZvl4n8IIjCzUBSOQswCYnfXspfGNAoCYmaqBJ3jlhGbQlu0MzELbIB3iE7TTG7dfv6ZZ0FiqnVdE/nk7q3Z2X05OUZj73z7jdOnn8bRCHfev3/n/Rc/+SpA8x/88JkbN1DLJGlFUTKOPeBi9twLL57ff9iZhzcN5yGJtm9+u7u7bXxfaVwjX35fZvria+UWfPrq31epz0C6ytDG9y5Zpjlkuhq2APlfWD3mceU6kKFHh2QOGVr7ldg2drt9MMaV0q/WqvvXd5qFw05/zodHcHVenC2ytyZXmh223ddiwl7f0lK1rHBAAkIFELI7MXGFEEM9DqOxjI5kdELVkSMqWEvApqLHIALRwQsFyTvysIEsTXO6dXt5+7brMb0wuD9LgAARhTls+fJzVR+F6iiEI5axSyzmvTBnERFhZipBLqEwc203R6Yn7ctZ9JirDzVC9VPUm2xyz1NLxWABZTPT0dGkgQhOtHWw9LKPUPxp4XCDL2yaesOUUrne+rUs5tE7Jpckw6GYhnfSdwFCoYvp0RIiYCWHrp68uJNl5UFeMNWuR9Hy9+Kzend9wGqgMwkiXzL/+qAQoEvpQDFz79uBnMj7aNhc7hyAkzs5VJWc4Fw2sszMrPOEdjPNGVmLjlOaOMa6rmoiqKkV4l1mAlnW0+Mj1ySeMZ/Ozx/6dFrIncDsqYXl09NjgV/cuePTCxnVpoqsbkbMsQ51kECYzyfI7ejkJCcFhENkCmbmWcd1NZ81mnPgYG1LEo5Ho3lqTTM0s2V98DCEioibnJBz0uYkVpPJheVGquhnEzioqk9Pb6ZpM59NR6ejPH3Ymn361dfvPZiiyfbU0ckzN6fvvp3dXv/il26fT/HgXgJe+/JX7n3wfn44O40jBhqxz/ziX/nRN/7EUtamoZzGLEQxq3lRKB1r30RM7ARa+ab+MXUPa/unRDWnJSQz6B5bP2ulo6hZV/ve6JBdt3TbVlb/1ruXHrX47oLSbXx78U0z23O/m58Sp3PxvbWGfT19MIYsP0y09fjOzy6BbS+6HwHWPosR7Kqffqq44qd7Cr7aL3rhzfQb7bMiZmvHhym77aGuLB10A0PfDgzp9waGg3P3J5H1qminkBIRYOxrT3D1mS5/ox8Ju57WvxQrsnqDTijh54xQfH0JMHIiMi/vCAhkDnI2diymnVLzrrIrw2ypRYmKs1LP7r4W05avn132I6BnLGV4790xmEi6GaObVdh9JethSpgVq0QArOpQOMzdmEgCxZqqUXVyQ8bHFMbGEQjwXr3oZpy+KKJB9p0Pbc+rCkJBxruadu1bZk1aYCXcd0bvFSYBV4gjxCOuT6Q+pfGpyci4Ug7WxSUsVjjkxWbZzRfGy77oc0vdbq1DbJHdauja+7XSRdeGgGXTbHmP1sZCofqplWLWDEpo8/ie6m/m4AvSuSFh3Xop+/On9eNdT6Tuj808N8nxBmYxtPxNWypDvsyzS1+GxNJtimqMXgcaTDxO5YVZ6MpdT/cCv5Tts+45LOrf0Qr0b5KXixhkhVPRe12KiEFOZE6A55RUM7uZGooZb9NIFZrpzHIbPTfTCdpUblCqmNu5NjMXDmST+w/RzLmOllu02d2M3C1rSinNAYdZjDE3qWiipQ6a1c1yajFvKARi9vncAQTW+SznXIPMXVMieFVVqZmq5vF4BPc0mXi5gza3yZ5/6cWLs3MO1D68G566YXfuhvHxSy9/6v47b8+9ff0zn749uY+zh7cTXn3tJ85uf9D+8Iev/PIv3Tqb4GzqKWcyrcOrX/j8u29+H9MpchL3CE5eNNN+tOmeJfohG2tH+mT7+vPiZVt+X2WZMCy33z696veBZrDUbcOv13PRy9e+D13p8mYLPJJcn3nvYfU/KPW1A04H3bPvJo7DUB3pD2wtq4wkvmmk2aVxrPYBKqu7jVIWeW50GBvWZCvUMRw9+28f1MoX5W6+p5tT4PY2WSpn22u+VpOBMkdrife25yW9fkW16vpPuakCgbAyOdiIyhY4cSWxFqlJKpLoXNybe8bU4STo/ZQ9UGUBdCwfi9U5EZEQsbk7xLuQSUwsxME5cH2CUHOoONYURiS1SwUKCjFiJ3GiQdA6o6JcDpTd630lDstsO2yyXQTVzTUlYHj6sVUZDFWZ7oFs5EmHqzLd37RdlUHRTrFNeermi6W65xvXcgf1LMtd8aAGlnr94N3ocnRan42G7tydRl18+ksAResMgBeIFHWqT3n7yxkuB4ncDOSaE3LGgnyWCfO5R0mzmWoSWDufwXJR+jnG3DSYz7X4Qj08hyauKzNFVuQMV9dkOXnOJAKzWFW5SXAjkJu6qc1nairE3sw5hlFVpekk5TaeHOVmjuk8hCh1lWfT3LQnx6dqapOLFGR8fDQ/n2DecF15VqQc6vrouafOH96GKiBumMzaZ155uR2F9P4tHtc3PvnyxVs/wkyff/kTAp9eTG7dv/+lL335gze+51FyBG4cv/Laq+9+97u4mMJcWAzIPSJ22OuyY/qgLR2xnDjsxT54Aj9UlTl4T+pAuY5Nk2uVg9v/6p/Ds9+d/tB2s8sNA1b8fTb6Ya/KrOsxCyxnqyLOe/WntVPeQ9SLmqzntpF+3fxxMeruaJ+VCm4QUm5etf11GfhGbaRfGu9S56Wsm95QuOzN3T4+UDf/O3GHJRE64MhATCKBOYhEDoFZJARjohBJKnAEC0gKZRdEwAIQqBzk8sOdHOzODnGwg53EwYVCDBxIIksloUYcSahjNQ5VLSFSCExCvb1jDw52N9zPPn7V1dqjyqHj4SGqTH2zu2abQnONqszi1C68pFcpdqgyq2DJUhcZbHhtX/mtXeUbt7nt8gGuc0n6lUJ9gMoMccD1iq1d0m9rDaGDteUREVEfjZ4BVZBLECJyIIRobSNVtHkDWCBobhnena1rTRmpRZAgkiczkMdR7ZYlqxePweKvZBbr2syqEFPbdMuaEt9RFbAQgzO7W5QIkKXko8qZkXJWiyVqQdO4yPHJ8Xw+s9TWJycCShcTZ0YQcprOJicvPTvRFm3rs+bkxjPt2dm5t5/7/GfvPXx4/ub3b3z20+fMuHXvIufXP/XarbOHuHs/np6c3HzmbHoGa2++/loA3f3BD9FmMAmJebc3s5OJcqeKsFOV2ZH8yaoyh44iT1qVOVgVe+JyICpzoEpxvfe7mdvOKXx3JttRkEU/XEdrtpfVD2ar9mG7Azqu/Tn4vZzm1+9lW/qFKnMJlrOo5/DMSk1X/limp+2n9tzXeuIdb/Slqsz2bBfNs0RZFmN70RvdQe5ELCTCMXAoXKORQoUQOESKlWcHMSAg6r8JYMQRuIJEcCwWu+AIjlKNOdQSB58w4hCJgzO5FzOHsm4ufWDz/ghwesJMvh+GKoNt2szjqzIO3qEirF/Sv2zrxky9drOsykpuAGG3NkPrl+xRTdZy6HCdbWgQ9XmuoDUdgtKfL7WifqN2pXTvtRfvrBa4RKHstWOinoaW4GWHqgdyABEuV4Ug7DD3EERTlhgtJ2IScm0bpu7qejSylFwzQohVyNM54KGObpmTuvV0jgCIYj1SQxVDalo4hAsNsYEByyJyNBo1s5mmfHJy3Gjydl6fHBkcsykx13WVTbWdV8fHXNd5Os1Epycn85yRknBgNXOdpOlLn3r14s59OGvKDth0Mj45Pnn2mbP33juft5/94pfuvfW2PbiHk+PTGzcuJrOzd9577ed/7vbdD5Dzqz/9hVtvvdXcuc0slYOM1OFS9uXW91CXm7zbjx/4cu1KvnO4PCz7nSrX/uH4yrJpvbH/8/GTPx8o1KGttzXxOn6wsNWlFd+eXQjESv5kPrRY35jy1xQX2nGKhqRZO3Jb3cCiFZRod/MWK5nuD9tigrPMYVXj2pr51sqXsRRrLUbrOfRn9/aEne9j/+osV8JllinmvSVmbgexgwVSAJVAIUACScWhhoT6+CSMj8PoKIyPZHQcxkcyOpLxMcURVyOuRlKPpRpJNZZqxNWIQw2JkEgSwQEcnAKYc+dexsVIyoZV2qg6Pn5Lo6unX9EbrsERcZsMXUV644/1BAdlt3bhMM/tbik7NPqtidfXIpelX5f9GwS71mSFAGbt5HAfuBe4wdzdYe5qatnNXHNXWwLDrbhKaQlhra69R5IbmaP8qRnaEdKUs715XaE67IgpCUYwcmVyuGozL1yW1s4IqOsas7lnlSqCKbVtFeP45ATw88nF8fExYq0Pz9Xt9KmbVNeaUocAnZ9PHj585pOvwaGTCZFD9YdvfGt0eoTXX8eP3r+4e/baz/w02ubWn33/xRdehju0/c4b3735mc/gaHT8/HMPb99C0oo7Xy6yMhaukx9cu/x5d97xA+Wjru/HXgbG0Sufx811vfEPehbrl2/8uSvxVcpdSbNxs/su33MLfZ77et3+hj2knpvtcy1PbbX44UTAAIMYJJDAIs6ixMmRjbNxhmRnhYCCc4DU2Tk7Z3BGUJKMoBSUAsWaYk1hjFBBRh0wEyrjyjgaxwzJkOScjJOSga2YDjOBiXu5zjv9eIigurG5cbP4cQ0bTL22tGIZ0yEOW8rdRGX6q1ZSY+N3b3A+hG8WVt4Du5nejWqZg6/gOhu3sJG+y7lDTjfsyHiQppTr1LElek9EwoO6WW8830M1zEQoLk9EJZZHh+B0ukYIXHwWWMwNDhYxtVBV5h5EANfUMMjdwVzXI7VsmqmuY4xpOgMQR5WrsZq7uxCJlNvh0UizhSC5nQPOxAzv+beL1oMglYHMUdV1a0k1UxQK4m3LRHEUG1PMG65rUtPZvCWMxuNA3M6nFNhz4vG4OTt/8bWfePDgIUDQXMO0mTanJ0fPPtfcu7i4++CzX/zsu/fuYNpO3Z569cXJ/Tv23t3Xfvov3Yv29Guv3PnDP8R0HoVL1HlzRywENmW9swEwbPNW6R/vVrhm/cNeYNptmePawIJFN75i/rxcBq6l33H8QDlUm9ll3nt9WtGTRmUeN4PSzle/X9phrdL5avlw3LOFbezQDWThUlQGpPWiXbfe1S7sYV8/2T3fb7+qQ0F8V57s6917J8K0M7L9Rv2H6M5KZUo9C85ty1FggMTsd8rp8t9xL+vl7s5o6VJNffSAbgKihc8wQTq3v2LGS50y1K95O6vpjgXEnEoI4qF7UPHSZBQb4Q23vM1oSoYr3P5jyhNGZbbviV6XbKMi6A89YrnbEl+Or1yWw77KXA1JWju432uDqHOg617GwbtKvWy5jFlEhCNz0YeYQESF34VDCCEEEQEJBaHCD8AsoBKjQEQKV16JstFL+S0EKf6Vit6JclknExEQpfm8ruujo6N2OhWR8fFJAWaOj49BNJtNzayuR3C/ePAQAEKws/PUtKPRiGJ0MqqCz+eRwv1bd1547TWMaghRAMgn3//eaTWWF17GxeStH/7oJz/7Wbifv/vO86+8jOefA8kb3/7Oq1/+8v3JOQCYtpoTzMEFpir+mVdHFw6FWPjaX44fy49lQ/YPZYeBB1cYyh693Cul3z3+7y73YNk/lRyC5TxGFRZZFZfyjohhWZAazKBqqm6F2oNAAhYwgQTOHX4DVoc61JafsjPVWQFT53SNon0wOZOzOxOEuw8zM5uTFRKtQmzWf67rrj8+IjR6mlZlS6odtiZb06x7EoEGPv1YrnEL+8EmqcyAIGCz3IUjD/r5fhNNGWIzw8p0rDnEazl0aUoYbVrJqq8/CItgSqvipQv7wgSYnBdacbfsL9o6D3Cl4i8J87J0pgLBsBfSgOJOaR0A4O4giDAxuRtJ4CAA1HwBPKk7iEWiqgkLgXJq4O5mII51rckgAcIAcpOJiYOknD07CBA2N7g7URwfa5M4BGtzeWSas0hwkOYMACxtqxwkxHry8MHps081qn5+wRLGN07b87N2Mnn+hRdnTeOaNLVwLQZTsQr1UT2bX8DtJFbz+w8zx+dfeMFzbtpp1gZVwEU7S/jU5z9///YHk3u3XnrllYlTfnDv9tmDn/v5X3j/m29A8/M/+4W3vv1N/Oi96FAuvUqEqISM2LVq2qVW7qIXWeVYGuRou5hXtqMphy5zeBfjybbMCfBLKPseV3YVzbR4cdc+23lfsMFW8qifnU2xHYXyHbZTOz6H2hINx4Q96/VBL+pgla2bGtQzxAxC3hSw1rFq6dIPVQas7ZUsL1rBY4jQ2aN0eA/gS57xvv6L+qwYOC5RYXSV6RsXPb+Euy/LKr+9K7Y/uyIrljGDBLRxpLTSzhmKhpjKevuv1hwAiH31ofXHd8BxpRq02jhY/B52HgCAwJmcHERGDifro8f0Xbd/T5iwfvHmGEWFU2bQULTEZf6/9q6sSZLbRn8As67u0Rxa2wqvPJIdIcfugyP8sP//L2xsxMY+e2VbkqWZ6Zk+6sgkAfiBzErmweqqnm5NjzWIiurqTJAEM5lI8AMI9tA4NUCg+4xumnkVqQ0KbMfekLR/706hR8N/nM/sRDft8cwj/ollhKUjt8wh7sR/N+Y78OeMiM8jKZsigwpSDa0LOR1UVTGNqfQsgRAQ3bNZjFGPdiEAcIQlJAQzc86RYxONaahjPhxRTWvJybUAdnxeUoqptGsIO8DFfDep9aq6end5tlxVy9Xu6roJ3j17CtDl23dPzs4BgvfxaWx2u7quV6sVzCB+d71eLs7k6urVP/7x/N8+BxlMmBlu7l+9vrp4/dkfvsLV9bfffvvlVy9xfo5Xr9789bvf/defsbnxl1f46S2i8RUjgCBmxmKsvdTsn+gTfYx0Eq5wkr69sw5sfx8VwHtME8eKfYzAp6D7d0aAjiuoBIXaPkELqbXhhhPcQ8vliCYIyPahzL6pTTfffUK3zeQvgBzmzyZPDFf3dCGohdltb1ba/UaGlOQWdY6g5HlcLEthF6tC918qOJwSTcb6pNbH03HqxdD0sZZccku1dcV5v7JwqhftFIUHtcUKerlq8jgGTUljkNLLEFHcEUko7lxqCjOmbtFhyi6jusdvIAp27CoRibsdBL8DAFWQm1ezELyKt6qaz+fSNNbUmM/MjMRgZskqZ4Dmy6Wva3ZOmwbAfjdNA3NVGQFqIDYQGTlXhZvr86fPiNlfvpOqevb02a6uw3rz/MVz8T74BgAMCF4IZ2cLYvO+0c12uTxrdr4J4cVvf6VzV7/5ibiam5NdvWF89fXXF5dv/bu3y3//gpfz+i/frWv/m//85qLZfPnly1f//T+oGzNLIIYRmbiU/TOGRY1QgVM/VD4+HXcz/TkVdThZzoemQgtl5+njsiVPRllOfNX1EKDx2XHkR8nXmZCSKdojFtZhA0TDu9+eykZpRpz0Szf/zvGGUt9H8ltsqUV3bkdBxuOBR9ckb5eyI3kXx+MtQtvFSKNSzE12WWK+0VJ8zL530/VM36uYa7vNhpzASGpbmX7SB0c4Sy8+QCKjvdLla+00QMSBYgRVVmHaCHOgK+LmvtT93nf4Z6BT1cPR/L3F2DlNLlQGlZ7ZrMjg9+TBKf72NT8MEx6bFLm9kg3MydbH7dKAx0qC9WynTE4MKWewEf/UNcy6EJdZJhCRCJT2zYYRd5FMMeY87itJTKYKM0dpZyaowlWuchqUmYAgdR2VE2DsGAqFYcYAqQ+mao5hEa1Ua9FgEKr5LDSeoZpWG1nyEBJVs5mCIQJYzGc9n89CCALMl4u6aSBhcXZGoFDXrqocMQziA8xAamo+1KvVotnuEIyVZvOFwG7C9suvX15cvLHas4Gqyjbr6sn557/57dWPP1z73e9fvnz704Xu6ndz+91/fPOM3A//+38IccJhQNzbzdjASoMIn8l7dBSdGL56cv0PTPcV9HZ6rx6XKfPQd+XwdZ5qvWTK9CyMfe2Dxjr+Qj6Vctnuz9h0KFFuWGS1FFdKT9hDhVR+RSp0KneE9U9ZduyIfuWmTK++4n2cPlUyZbqc79Zf0n4L5S62Ycxl92NgoY7efn3DPN9lpyXtF8hq28/gH5QeiykD3OqTGr3Ii6bM4GCbx4UHp6KZb+OaAex7mm7kyCKhIf/YJCqZJq0hPDR9aIzl5IKNVmwltIbSHIixX0RFQNrZCeS6iUSMlhFLTlI1kKZ05AYQXIJo1DERyGLCCGbnnAZhAlkQ3zDDVGBksCqiPgyVYKJwHHPiOYtzE4t7PsF0NnOh2ZLBJBAMqmAACnLVrNJ2o22oEmzmqsVitXt7SatVdX4erq6D2tn5SoDdZlsRcQjqvVUENgSRZqe+8fWuoiqIrZarRoNeXfBnnz1bnN+8u1RSYjIf1uvtF1/94eLyCpcXi7OzarHaNlt79/abP/3p9Xd/v/zue26UjYwBljiDYSPKNiGbiDUpQyppKUN+T3FAY0/QYzNlSnQg7GbyczoVdPz9xVeeRB/qrhRRgbIpc8A06demw0oO2zSp/lGpdDwWKuIxfam0OCr2sSPjikYtdu7y3PjI+lhchZTO5jjKtAlYTHkf9fBBT3TbRz00bEszmjgPBVsyaAggs96raZB6JgqTGp1qr9fZ1MD+PlDaSDD77APX4vl2lZaRxQx4FufOHQqY39SP1pS5//Xlh8NTTuIf/5s/A7d6lFPAR5m/pDtuZc5r7vNziX/PU/Icl6+DdEdMoEKmlAK7BCYwBRlUyAQWv+Ou1xJ51NeJxxRNDZPWoSsGYbN2y1SDNAgNxJuvoSHinI44rq4SETaAGAaYNw2hqRezOUR22+1yNsd8Juu1iKyWSwthc3Ndb3ccC4gCSsz1ZuuIlWAVrrc3FmrMl6///v2Tpy+gDJhub5Zzxs3VxcU79/Q5fPj+///y/ItfQzzqnWy3b/72V/iQNL/FVUsBqgILH+zN9YkeKdmJ9DMINPk5DLEMZTvCdik0PqznQK97p/oa7+6UqcHi1T58Iw5fnFvlvK+OFEiJ1cjMonth/Gqwvk1TVvuTxKMPJr5t/839Ofe/4MKlPVWl+zpAQe6RRsDWoGHD0e2me78P8R6XS+6bnN8G/YpDQEdNmw05D4tq1qaAMC4NmrzO+Dt+816ArhuSj3FAzVoxwTCDCcSMDQoQU4o4ExgIAhVyxGSAqYgGL2TkZsZUEQXvMauiUwYmRM6RqampmG9IQgw3ccSZwCKi7BxXpGpQc6bi611Tu2cvZLPZzNezJ+f+6mq9Xp8vlnPnmsYHs6piDjAAFRFh5pjZNYCxQ10DbrV4st16YcbMwTcgIDQ0O2vqMK8W20ZBW17NIQ2WlTOtX7+loGxO4kVWkMKZCmbGES+buPjHvKVSyPMUwjfgmby104dP9Wudqmb5k/V2n/RzWDNDuu0Fc8DN1FOkMfZ/lC29e8uXsZyJNk+zkI6ZPXPBiuj7TA5VxP0Xh6XYoIE0sdLb5RmXbQsduiNkBy+OxVDCYRMd5JQpGTLkm29NPfs8lHJwrQgGBoGsM2UsZipr3ypkPUCOcy3X69jDQzIPSXxA+js/1acW7PGTAtKGZI/dtFN+xM4kn7bZDxy58+O9t0cm+Fo75sC0Y1xtjOBFwktSd9vUAdr9lsRAaqoKCQaJe1KSKkPYEnZKFKNzCYBaQGjIUFWuYofGp6YhCgOU2xAfE4VaTBCc6omOFjV4gVqKHyQCTETWm5vPXzxDaJq3F2fLFeYz22yaXT2vZq6qaMZKEBEHc8TqAwHe+5h3EosFiOubjSNWMywXAM7Oz3frNTPOVjPzOzCWq2VdbzGfY76YORcu380cK4lxu+DTALCRxs25DWJm6fv9Zttkve87LI/6sCuq3l/+T3QMFa6zFb4P0gTYPH12GFTRd9b0Jem9m0uOjONEul3BThIXLICTxuRIdevg9C3l7wOWOEGZHOQcdvzYanMMJgdgMAXSTJCOowmPHpuPmSpGbx/VLqw1XlntnQJgJPmh7rf1rl2G+ElXcw/scpP87RJ4Qhv3kKW77DVB+xLUjQMD9cZEdB32ss9FYCOLtkkv7JH9G4GToRaIFwHddDgHb0xi4v8UsYEOE4pRzRbNgCzIL5kaGgBwFBIc64wAAAYBSURBVE4BEyaGaRuRTlAjEBnMOREBAI4wpgEwGFRVuHJEUAlx8REZMcycm8f+6manRFpVs7NlEDUAXEXRNea4cbMmCNgBILggZkzeewAQJWYXI2e4Ct4LmXMsvgnNGnHr1tDAe0ggk9lyta131gRUFRyCBJgyc+NruMpUETxEQQ4zp2QOgqZxi9Vms6vOnwUxCuulk535XfDPF2d/u7qZ//GPr354BceNbImJAFMGsYKVY95LacdZnKFqe7OKz+kYeGsd2O3tjb5muosdoGUE8nTIs6CFtaCzzIBO8k7+gjYvyXNfKMVDY0f3JufBlVnjGXn619pJbT61JY0ArSntf5feWFyYHJlJF8bQN1zy4BLr8JgYfDcxr+tUmRpG43nQr3xU9SCXUdbdNMZL46rlNx0IM11hG+ExFIna98hwKmvZ1DHNbOLvxN2TJk7JJu9BUqRD6YpZhg9YUVNzhhj22OuCDQvmSMmkDdpKpu2p28e8mfVjYYAUPNPf4+W+7ZiStnw4e6kCwDa96GNAuVp8QErjMho6+e/474TizkXi2+HaRLf7j1o29OyeuBUYFENXUc6P/N9oAo5cS5NtMUgTHEWk3XoDtqhFjY2mbpYCDItoRD9eJ7qgot0TnwONxhwBkIRAtj5d61YmGsz6zgtTVZFuLmhmZuSYTGMwMos5I2HHzE3TiHgABiEARIyYxylm5lUYYA4ENYOq1jUAFUAsGAD1u7VpQFU50OWb16gW59Vye7PGfA4xeCGLpdli+A4JQVuTOp9ovNd45RweO73sMU/WQ9Be4PeR/xPtqff6yWivsnNgBmj1GKlZ+2Yr2zHDVrLfJTuGs1E9gRZj/L7unvS9nHtFNIHuDBTarTIfHOdUckDpqL9Ht9tWfSrQ8gBPwggPm6KeHfMe+Khm33ck+8h9SZPkaP4UmT04yqXY/duleKGJs8WxPFoB1B4vzCbTSh/upOpx0r4qyo4gk39o/JfsleF6KBofH5XIbVuaPJ6jO/0VUtNlpyvP+AfajZj3yWl67AYQiYiZaZua2lT3s6a8XVNNAJEBZnFpN/rKetTZuMYizV1S/QBUDBa2OzMzgt9uIKJmIsFEkttKlHm/nDIuNo8X3KXKzbwGf3MVU1mywRgBtrm5Ql2bURDzKp//6teXF6/8zTVCIFFOKjKBX3GXKxobu3cM+TqtVBHVuDedUcSVCuwl/tPkKb1R7iuQ7kPEphyiw/JMGBbHXOdj+thjz/+ZzkpHoLHdM6xofNwGBwcyFOXMUPNCDaWXs/a0yqDCYm0jkUpJd4rJeG6r8Gj+8okp715x5VTP+XH7sC89XSf3t0gP+9zd2wzuaDGr+2rx4yMz9GCSo9gwpcRLiMsAoZmsM6+kV/mIp+QX3x8REYhI3P1A1YigGr1FUANTC4AbzODi8aJ1b7kA0YRSRQR1ciEdi0hc/Zd2NiACICGAFIheRU0Wnllai64AjAEiEjVA1q9fIQSjasbORKHwl+/gPRxhu1m/eQ1Uu5vrzas38A1MGUrWYg35tPADoSCf6BdCJ9tep/AfCt2bdGyVRSrJOX183G4BHSkWv9NzN9HfXLc8ZjqsjQ+Ve9z9+mjpX3Cz7xPoyFFVenoL2qT4tE+O/pIM4xiOPRR8xCRmX3yf5SYdybL4pGSag89kbWhTBA3qJ2ICVFL2BBMwENPUkQHkkPxKZGAYwxxRapr2G7Ya1MAMx6SC4BG8i1H3lZuzg/doPG6u3/74I9Y3aHwFJgMPvTc0CNh6H7IC3Vf9n+gXQYNH/qTxU3pZljXPrVUNp0PHmDWHT53aqQN0UruPh95Htsfcr4+NftmmzJEg+SRbOfvfNPbe5+9+HycD9Q2RCWJmZjhH8QcRM4MZgCOiveNNE1CUU1HycXN5kRiwLBJCgAh8EBGoIgRVdc4RdbGFA8yJqFuwGEPwXTWL2ZxitI4jI+eoYhNBCPPlCgyrGxAhiIam53Q0oA0Hua9FOlSg+6n9E320dNowGDzyJ42fgjv7gOa5taqh17ikqY4R6XAld6CT2n089D6yPeZ+fWz0T8kYtRC07NNqAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "kniAbPo4hE_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Recap\n",
        "\n",
        "\n",
        "- **Session 1**: Quick Introduction to Python\n",
        "KNN Classification of iris Dataset\n",
        "\n",
        "- **Session 2**: Pandas, Decision Tree Classification, Support Vector Classification\n",
        "\n",
        "- **Session 3**: SVM Regression with different kernels, Decision Tree Regression, Gaussian Naive Bayes Classification, LDA\n",
        "\n",
        "- **Session 4**: PCA, Logistic Regression, KFold Cross Validation, Ensemble Learning, Introduction to Neural Networks using PyTorch (MLP, Feed-forward, Back-propagation, Activation Function, Cross-Entropy Loss)"
      ],
      "metadata": {
        "id": "ZJIU1L9oqx_O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Built-in datasets: https://pytorch.org/vision/stable/datasets.html"
      ],
      "metadata": {
        "id": "nyCidEj6qikF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from torch import nn\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import transforms\n",
        "\n",
        "class MLP(nn.Module):\n",
        "  '''\n",
        "    Multilayer Perceptron.\n",
        "  '''\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.layers = nn.Sequential(\n",
        "      nn.Flatten(),\n",
        "      nn.Linear(32 * 32 * 3, 64),\n",
        "      nn.ReLU(),\n",
        "      nn.Linear(64, 32),\n",
        "      nn.ReLU(),\n",
        "      nn.Linear(32, 10)\n",
        "    )\n",
        "  def forward(self, x):\n",
        "    '''Forward pass'''\n",
        "    return self.layers(x)\n",
        "\n",
        "# Set fixed random number seed\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Prepare CIFAR-10 dataset\n",
        "dataset = CIFAR10(os.getcwd(), download=True, transform=transforms.ToTensor())\n",
        "trainloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "45d114d6d20a403484e661eef3358a6c",
            "eaf96b06fe8e48edaaa80058fd9ed860",
            "a30bef9e62074fc28c2d483455c1e988",
            "3ab1865309f04371811491d07b39856f",
            "c50f5a84983041ca8c96a02719cad681",
            "21fca682111b4c639a93d5095f6fb2a8",
            "b1377a599c5e4b03913cb16bc68ebb47",
            "a8a6555bb4644d6baa382955f0d1e879",
            "acb59df092124ceba31eb0fc0dbebe55",
            "0439951b46fd4e89baee0caf320ecb2d",
            "f20b5119005d4de193d8f17085b2d9f0"
          ]
        },
        "id": "N2gLmXzqhEag",
        "outputId": "fee90b76-e2a9-4fcd-f6b5-9b55830ac786"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /content/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/170498071 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45d114d6d20a403484e661eef3358a6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting /content/cifar-10-python.tar.gz to /content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.getcwd()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "X1TCGSrR9zKy",
        "outputId": "969493f1-9911-44db-c1ff-7e1615fefad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "torch.optim options:\n",
        "\n",
        "https://pytorch.org/docs/stable/optim.html\n",
        "\n",
        "\n",
        "https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6"
      ],
      "metadata": {
        "id": "_X0O0-5upYEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the MLP\n",
        "mlp = MLP()\n",
        "\n",
        "# Define the loss function and optimizer\n",
        "loss_function = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(mlp.parameters(), lr=1e-4)"
      ],
      "metadata": {
        "id": "jIqT8c-BhEcl"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the training loop\n",
        "for epoch in range(0, 5): # 5 epochs at maximum\n",
        "  \n",
        "  # Print epoch\n",
        "  print(f'Starting epoch {epoch+1}')\n",
        "  \n",
        "  # Set current loss value\n",
        "  current_loss = 0.0\n",
        "  \n",
        "  # Iterate over the DataLoader for training data\n",
        "  for i, data in enumerate(trainloader, 0):\n",
        "    \n",
        "    # Get inputs\n",
        "    inputs, targets = data\n",
        "    \n",
        "    # Zero the gradients\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    # Perform forward pass\n",
        "    outputs = mlp(inputs)\n",
        "    \n",
        "    # Compute loss\n",
        "    loss = loss_function(outputs, targets)\n",
        "    \n",
        "    # Perform backward pass\n",
        "    loss.backward()\n",
        "    \n",
        "    # Perform optimization\n",
        "    optimizer.step()\n",
        "    \n",
        "    # Print statistics\n",
        "    current_loss += loss.item()\n",
        "    if i % 500 == 499:\n",
        "        print('Loss after mini-batch %5d: %.3f' %\n",
        "              (i + 1, current_loss / 500))\n",
        "        current_loss = 0.0\n",
        "\n",
        "# Process is complete.\n",
        "print('Training process has finished.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zNCMeWQhEf7",
        "outputId": "8309bda8-cef6-42a3-fc62-02aa0558249b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting epoch 1\n",
            "Loss after mini-batch   500: 2.237\n",
            "Loss after mini-batch  1000: 2.099\n",
            "Loss after mini-batch  1500: 2.031\n",
            "Loss after mini-batch  2000: 2.000\n",
            "Loss after mini-batch  2500: 1.938\n",
            "Loss after mini-batch  3000: 1.945\n",
            "Loss after mini-batch  3500: 1.916\n",
            "Loss after mini-batch  4000: 1.903\n",
            "Loss after mini-batch  4500: 1.870\n",
            "Loss after mini-batch  5000: 1.856\n",
            "Starting epoch 2\n",
            "Loss after mini-batch   500: 1.831\n",
            "Loss after mini-batch  1000: 1.832\n",
            "Loss after mini-batch  1500: 1.825\n",
            "Loss after mini-batch  2000: 1.823\n",
            "Loss after mini-batch  2500: 1.816\n",
            "Loss after mini-batch  3000: 1.811\n",
            "Loss after mini-batch  3500: 1.802\n",
            "Loss after mini-batch  4000: 1.772\n",
            "Loss after mini-batch  4500: 1.774\n",
            "Loss after mini-batch  5000: 1.762\n",
            "Starting epoch 3\n",
            "Loss after mini-batch   500: 1.754\n",
            "Loss after mini-batch  1000: 1.764\n",
            "Loss after mini-batch  1500: 1.747\n",
            "Loss after mini-batch  2000: 1.754\n",
            "Loss after mini-batch  2500: 1.725\n",
            "Loss after mini-batch  3000: 1.726\n",
            "Loss after mini-batch  3500: 1.732\n",
            "Loss after mini-batch  4000: 1.710\n",
            "Loss after mini-batch  4500: 1.715\n",
            "Loss after mini-batch  5000: 1.686\n",
            "Starting epoch 4\n",
            "Loss after mini-batch   500: 1.717\n",
            "Loss after mini-batch  1000: 1.693\n",
            "Loss after mini-batch  1500: 1.694\n",
            "Loss after mini-batch  2000: 1.688\n",
            "Loss after mini-batch  2500: 1.679\n",
            "Loss after mini-batch  3000: 1.678\n",
            "Loss after mini-batch  3500: 1.682\n",
            "Loss after mini-batch  4000: 1.673\n",
            "Loss after mini-batch  4500: 1.665\n",
            "Loss after mini-batch  5000: 1.676\n",
            "Starting epoch 5\n",
            "Loss after mini-batch   500: 1.671\n",
            "Loss after mini-batch  1000: 1.656\n",
            "Loss after mini-batch  1500: 1.653\n",
            "Loss after mini-batch  2000: 1.633\n",
            "Loss after mini-batch  2500: 1.644\n",
            "Loss after mini-batch  3000: 1.665\n",
            "Loss after mini-batch  3500: 1.648\n",
            "Loss after mini-batch  4000: 1.622\n",
            "Loss after mini-batch  4500: 1.645\n",
            "Loss after mini-batch  5000: 1.643\n",
            "Training process has finished.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tensorflow"
      ],
      "metadata": {
        "id": "TZPrU4YxptLJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "w0 = 0.25\n",
        "b0 = 2.\n",
        "x_range = [-20, 60]\n",
        "\n",
        "def load_dataset(n=200, n_tst=200):\n",
        "    np.random.seed(4)\n",
        "    def s(x):\n",
        "        g = (x - x_range[0]) / (x_range[1] - x_range[0])\n",
        "        return 3 * (0.25 + g**2.)\n",
        "    x = (x_range[1] - x_range[0]) * np.random.rand(n) + x_range[0]\n",
        "    eps = np.random.randn(n) * s(x)\n",
        "    y = (w0 * x * (1. + np.sin(x)) + b0) + eps\n",
        "    x = x[..., np.newaxis]\n",
        "    x_tst = np.linspace(*x_range, num=n_tst).astype(np.float32)\n",
        "    x_tst = x_tst[..., np.newaxis]\n",
        "    return y, x, x_tst\n",
        "\n",
        "y, x, x_tst = load_dataset()"
      ],
      "metadata": {
        "id": "F833A0Mcpn0t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=[6, 4])  # inches\n",
        "plt.scatter(x, y, label='generated data')\n",
        "plt.xlabel('data')\n",
        "plt.ylabel('target')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Ec8bM-19pv4T",
        "outputId": "23c51922-b7b6-4d72-b776-151cf5bb36d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Activation\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(10, kernel_initializer='normal', activation = 'relu'))\n",
        "model.add(Dense(1, input_shape=(1,),  kernel_initializer='normal'))\n",
        "model.compile(optimizer='adam', loss='mse', metrics=[\"accuracy\"])\n",
        "model.fit(x, y, epochs=200, batch_size=10, verbose=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fr2fV_Xp0q4",
        "outputId": "f189362c-7864-4e65-b729-fbdae04de40b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f30e3283990>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_tst)\n",
        "plt.figure(figsize=[6, 4])  # inches\n",
        "plt.scatter(x, y, label='generated data')\n",
        "plt.plot(x_tst, y_pred, color='red', label='MLP')\n",
        "plt.xlabel('data')\n",
        "plt.ylabel('target')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "5xujrpcVp4Xo",
        "outputId": "3711cafd-ddf8-445f-b3fd-2d2595a45614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "mWcCeWGPp8Kg",
        "outputId": "c436a703-6be5-4ca5-ca07-3db11e81560c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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OAeaLT0u+qqGhocNCe8x8mGKOLOFz8P7772PHjh1ChyGYDz74ALGxsTpXFZorni/Tzxcnt1dkZGSgoqJC6DBYF0wxR/w5YMzymV1y62nJhj179kAmk8HV1RURERHw8PCATCZDYGCg1jprkZGRsLW1hbu7u2bbqlWrYG9vD4lEgidPngAAoqKisG7dOhQXF0MikcDPz69X8f/3f/83Ro0aBQcHB8hkMowZMwZ///vfAQDLly/X/H7n6+uruVFz2bJlUCgUcHBwwLFjxwB0XY7iyy+/hEKhgFKpREVFBdatWwdPT0/cvn27VzEbE/WgTEdf5shUn4Pvv/8eKpUKycnJRh0vxpiB6HEO0yg6+s0tLi6OANCZM2fo+fPnVFFRQVOnTiV7e3tqamrStAsPDyd7e3sqKiqixsZGunnzJk2cOJGUSiXdv39f027x4sXk5uam1W9KSgoBoMrKSs22kJAQ8vX11YlRn9/ccnJyKDExkZ4+fUpVVVUUEBCgdXlrSEgIWVtb06NHj7Ret2jRIjp27Jjm3+vXryc7Ozs6dOgQVVdX08aNG8nKyoouX76sNUarV6+mr776iubPn0//93//16MYe6s3v7klJCSQra0t7d+/n549e0aFhYU0fvx4cnFxofLyck27vsyRKT4HJ06cIKVSSVu2bNHr/fOtNZaF58uyWOxvbl2VbGgnlUo1RwWjRo1Ceno6ampqjF62oTOhoaH485//DCcnJzg7O2POnDmoqqpCZWUlAGDlypVobW3Vik+tVuPy5cuYNWsWAP3KUezYsQOff/45cnNzMXLkSNO90R7oTZmO3jL25yA4OBhqtRrx8fEG2R9jzLjMOrm96vWSDZ2ZMGECFAqF0cs29FT7Ja/tC6j+8z//M4YPH47//M//1FSiPXjwIMLCwjQ/tgpZjsKQ+lqmoy/M7XPAGDMti0lu+rCzs9McKZnayZMn8c4772DQoEGws7PDv/7rv2o9L5FIEBERgZ9++glnzpwBAPzXf/0X/uVf/kXT5tVyFO2/0UkkEpSWlqK+vt50b6aPDF2mQ19Cfg4YY8ISXXJrbm7uVQmI3jp37pymztn9+/cxb948uLu749KlS3j+/LlOXSYAWLp0KWQyGfbt24fbt29DpVJh6NChmudfLUdBRFqPgoICk7wvQzB0mQ59mPpzwBgzL53Wc7NUZ8+eBREhICBAs00qlXZ7OrO3/vd//xf29vYAXpZNb25uxmeffQYfHx8AHVetdnJywsKFC3Hw4EEolUqsWLFC63khy1EYkj5lOgw9R6b+HDDGzIvFH7m1tbWhuroaLS0tKCwsRFRUFLy8vLB06VJNGz8/Pzx9+hRHjhxBc3MzKisrUVpaqrMvZ2dnlJWVoaSkBDU1NV1+ETY3N+Px48c4e/asJrm1Fxc8ffo0Ghsbcffu3U5/V1q5ciVevHiBEydOYPbs2VrP9aQchSXQp0xHX+fI2J+DvLw8vhWAMUuix6WVBnXx4kXy9/cnKysrTYmE5ORkvUo2hIeHk42NDXl6epJUKiWVSkVz586l4uJirb6qqqpo+vTpJJPJyNvbm7744guKjo4mAOTn56e5XPzKlSs0dOhQksvlFBQURP/+7/+uKU/R1ePw4cOavmJiYsjZ2ZkcHR1pwYIF9PXXXxMA8vX11bosnYjo7bffptjY2A7Hp6tyFDt37iS5XE4AaMiQISZbdbw3twL0pEwHUe/nqLy83Oifg/Lycjp16hQplUpKSkrS6/3zpeWWhefLsoi25E14eDg5OzsLHUavzZo1i3766Sehw+gxc11b0pw/B5b098R4viyNxd7n1hPtl9hbgldPcxYWFkImk8Hb21vAiMTDkj4HjDHjE90FJeYsJiYGK1euBBFh2bJl2L9/v9AhMcaYKFnskdvGjRuRmZmJ58+fw9vbG4cOHRI6pG4pFAqMHDkS7777LhITEzFq1CihQ7J4lvg5YIwZn8Umt23btuHFixcgIvz8888IDQ0VOqRuJSUlobW1Fffv39e5QpL1jiV+DhhjxmexyY0xxhjrDCc3xhhjosPJjTHGmOhwcmOMMSY6nd4KkJ2dbco4mAV4+PAhAP5s6KN9oWseM8vA82VZulpIXkL0/4uK/X/Z2dlYuHCh0YNijDHGDOG1NAYAOTrJjTFmeO3/aeQ/N8ZMIod/c2OMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6nNwYY4yJDic3xhhjosPJjTHGmOhwcmOMMSY6UqEDYExsHj58iD/+8Y9obW3VbKuuroZSqcQ777yj1XbEiBH4j//4DxNHyJj4cXJjzMAGDx6M0tJSFBcX6zyXn5+v9e9/+qd/MlVYjPUrfFqSMSP45JNPYGNj0227sLAwE0TDWP/DyY0xI1i8eDFaWlq6bOPv749Ro0aZKCLG+hdObowZga+vL8aOHQuJRNLh8zY2NvjjH/9o4qgY6z84uTFmJJ988gmsra07fK6lpQULFiwwcUSM9R+c3Bgzko8++ghtbW06262srBAQEIDf/OY3pg+KsX6CkxtjRuLh4YEpU6bAykr7z8zKygqffPKJQFEx1j9wcmPMiD7++GOdbUSE+fPnCxANY/0HJzfGjCg0NFTrdzdra2u8++67cHV1FTAqxsSPkxtjRuTk5IT33ntPk+CICEuWLBE4KsbEj5MbY0a2ZMkSzYUlNjY2mDt3rsARMSZ+nNwYM7I5c+bAzs4OADB79mwMGDBA4IgYEz9ObowZmb29veZojU9JMmYaEiIioYN4VWcrOjDGGDNPoaGhyMnJETqMV+WYZVWAqKgoTJ48WegwRCM1NRUAsGbNGoEjsRwFBQVIS0tDVlaWQfbX2tqKrKwsLFq0yCD7EztDjz8znvbvF3Njlslt8uTJ+PDDD4UOQzTa/0fFY6qftLQ0g47ZvHnzIJPJDLY/sTP0+DPjMLMjNg3+zY0xE+HExpjpcHJjjDEmOpzcGGOMiQ4nN8YYY6LDyY0xxpjoiC65LV++HEqlEhKJBNeuXRM6HINobGzEyJEjsWnTJkHjOHXqFBwcHHD8+HFB42CMse6ILrnt27cPf/nLX4QOw6Di4uJw+/ZtocOAmd3vzxhjnTLL+9zYry5cuIAff/xR6DAAAMHBwXj+/LnQYQAAGhoaMGPGDFy4cEHoUBhjZkh0R26AeJbwamhoQHR0NNLS0oQOxexkZGSgoqJC6DAYY2bK4pMbESElJQUjRoyAnZ0dHBwcEB0drdOutbUVCQkJ8PLyglwux9ixYzVL+6Snp8Pe3h4KhQJHjx7FzJkzoVKpMHjwYBw4cEBrP/n5+Zg0aRIUCgVUKhXGjBkDtVrdbR+9ERcXh1WrVmHQoEG93oehnD9/Hl5eXpBIJPj6668B9Hzc9uzZA5lMBldXV0RERMDDwwMymQyBgYG4dOmSpl1kZCRsbW3h7u6u2bZq1SrY29tDIpHgyZMnAF4uz7Zu3ToUFxdDIpHAz88PAPD9999DpVIhOTnZFEPCGDNjFp/c4uPjERMTg/DwcDx+/Bjl5eXYsGGDTrsNGzbgyy+/RGpqKn755RfMnj0bixYtwj/+8Q989tlnWLNmDRoaGqBUKpGVlYXi4mL4+PhgxYoVaG5uBgDU1dVhzpw5CA0NxdOnT3H37l0MHz4cTU1N3fahr//5n/9BcXGx2axFGBQUpHMKsKfjFhkZiaVLl6K+vh6rV69GSUkJrly5gpaWFrz33nt48OABgJdJ8PXllvbu3YvNmzdrbUtLS8Ps2bPh6+sLIsK9e/cAvPzPBQBN7TTGWP9l0cmtoaEBqampePfdd7F27Vo4OjpCLpfD2dlZq11jYyPS09Mxb948hISEwNHREZs2bYKNjQ0yMzO12gYGBkKlUmHQoEEICwtDXV0d7t+/DwAoKSmBWq2Gv78/ZDIZ3NzckJubCxcXF7366Mn7ioqKQnp6et8GyIS6Grd2UqkUb775Juzs7DBq1Cikp6ejpqZG7/HpTHBwMNRqNeLj4w2yP8aY5bLo5Hbv3j3U19djxowZXba7ffs26uvrMXr0aM02uVwOd3d33Lp1q9PX2draAoDmCMTHxweurq5YsmQJEhMTUVJS0uc+OrJx40b86U9/gqenp16vMxevj1tnJkyYAIVCoff4MMZYdyw6uT18+BAAuv1Nqq6uDgCwadMmSCQSzaO0tBT19fU97k8ul+OHH35AUFAQkpOT4ePjg7CwMDQ0NBisj/Pnz+PGjRtYvnx5j19jyezs7FBZWSl0GIwxkbHo5Na+yvqLFy+6bNee/FJTU0FEWo+CggK9+vT398fx48dRVlaGmJgYZGVlYdeuXQbrIyMjA2fOnIGVlZUmQbbvOzk5GRKJpFe/4Zmj5uZmPHv2DIMHDxY6FMaYyFh0chs9ejSsrKyQn5/fZbshQ4ZAJpP1ecWSsrIyFBUVAXiZMLdv347x48ejqKjIYH1kZmbqJMf2I5u4uDgQESZMmNCnPszF2bNnQUQICAjQbJNKpd2ezmSMse5YdHIbNGgQQkJCcOjQIWRkZECtVqOwsBDffvutVjuZTIZly5bhwIEDSE9Ph1qtRmtrKx4+fIhffvmlx/2VlZUhIiICt27dQlNTE65evYrS0lIEBAQYrA8xa2trQ3V1NVpaWlBYWIioqCh4eXlh6dKlmjZ+fn54+vQpjhw5gubmZlRWVqK0tFRnX87OzigrK0NJSQlqamrQ3NyMvLw8vhWAMfYSmRkAlJWV1eP2NTU1tHz5cho4cCANGDCAgoKCKCEhgQDQ4MGD6fr160RE9OLFC4qJiSEvLy+SSqU0aNAgCgkJoZs3b9LevXtJoVAQABo2bBgVFxfTt99+SyqVigDQ0KFD6c6dO1RSUkKBgYHk5ORE1tbW9MYbb1BcXBy1tLR020dfVFZWEgCKi4vr1etDQ0MpNDS0TzF89dVX5O7uTgBIoVDQnDlzejxuRETh4eFkY2NDnp6eJJVKSaVS0dy5c6m4uFirn6qqKpo+fTrJZDLy9vamL774gqKjowkA+fn50f3794mI6MqVKzR06FCSy+UUFBRE5eXldOrUKVIqlZSUlNSn90pElJWVRWb459Fv8PhbDkN8vxhBtoTIvBYMlEgkyMrK4vLyBrRgwQIAwpaDj4iIQE5ODqqqqgSLQR/Z2dlYuHAhr6cpEB5/y2EO3y8dyLHo05LMsrTfZM0YY8bGyc0Ebt26pXV7QGePsLAwoUNlBnL69GnExsYiNzcXPj4+mjn++OOPddq+//77UCqVsLa2hr+/P65cuSJAxPpra2tDamoqAgMDdZ47duwYdu7cKdh/aPr7+Lc7f/48pkyZAoVCAQ8PD8TExGhdXS70PBmVsKdFdUHP39xY94Q+Jx4bG0u2trYEgH7zm99QTk6OYLH0VF9+80lISKDZs2eTWq3WbPP19aWBAwcSADpx4oTOa/Ly8uiDDz7odbymdufOHZoyZQoBoHHjxnXYJi0tjaZNm0bV1dV675/Hv2s9Gf8ff/yR5HI5xcfHU21tLV24cIFcXFxo2bJlWu36Mk9Ewn+/dCKbj9yY0W3btg0vXrwAEeHnn39GaGio0CEZzY4dO3Dw4EFkZ2dDqVRqPbdnzx5YWVkhPDzcbEoH9cb169exYcMGrFy5Em+99Van7VavXo1x48Zh1qxZaGlpMUlsPP6/2rp1K9zd3bF582bY29tj8uTJiImJwXfffae1KpAQ82QKnNwYM5B79+4hPj4emzdv1iww8KrAwEBERUXh0aNHWL9+vQARGsa4ceOQm5uLxYsXw87Orsu2iYmJuHbtmim0uTYAACAASURBVEnKNvH4/6qlpQUnT57EtGnTtEqAzZw5E0SEo0eParU35TyZCic3xgxkz549ICLMmTOn0zZJSUkYPnw49u3bh9OnT3e5PyLC7t27NYtNOzk5Ye7cuVr/69anXJOhSzL1hJOTE6ZNm4a0tDSjX/nI4/+rn376CbW1tfDy8tLa7uvrCwAoLCzU2m7KeTIVTm6MGcjJkycxYsQIKBSKTtvI5XJ89913sLKywooVKzRrknYkMTERsbGxiIuLQ0VFBc6dO4cHDx5g6tSpePz4MYCelx0CDFuSSR9vv/02Hj16hOvXrxu1Hx7/X5WXlwOAzqlZmUwGuVyuif9VpponU+HkxpgB1NXV4eeff9b8z7grkydPxpo1a1BSUtJh7UHgZdmj3bt3Y/78+ViyZAkcHBwwZswYfPPNN3jy5InOKjxA12WHDFmSSV/Dhg0DANy4ccNoffD4a2u/ItLa2lrnORsbGzQ0NOhsN8U8mZJU6AA6ou9ixqxr7dUTsrOzBY7Ecuj7GayoqAARdXnU8KqkpCScOHECe/fuxcKFC3Wev3nzJmpra3XWEZ04cSJsbW21Kph35PWyQ4YsyaSv9jHp6GjBUHj8tbX/5tjRBSJNTU2Qy+U6200xT6ZklsktLS1NVD9smouO/oiZYTQ2NgJAtxdYtJPJZMjMzERQUBA+/fRT7Ny5U+v5Z8+eAQAGDBig81pHR0fU1NToFd+rJZk2bdqk9ZyHh4de+9JX+xdp+xgZA4+/Nnd3dwCAWq3W2l5fX4/GxsYO+zTFPJmSWZ6WzMrK0lkZnx+9f4SGhiI0NFTwOCzpoe8P/e1fDPrcDDt58mSsXbsWd+/exdatW7Wec3R0BIAOv0R7UybIkGWf9NXU1AQAHR4tGAqPvzZvb28olUqdRcfv3bsHABg7dqzOa0wxT6ZklsmNMUvj6uoKiUSi9/1TW7duxciRI3H16lWt7aNHj8aAAQN0Lja4dOkSmpqa8Nvf/lavfgxVkqk32sfEzc3NaH3w+GuTSqWYNWsWzp07h7a2Ns32vLw8SCSSDq8oNcU8mRInN8YMQKFQwMfHR/P7Zk+1nx57/Yd/mUyGdevW4fDhw/jrX/8KtVqNGzduYOXKlfDw8EB4eLje/XRXkiksLAxubm4GX36qfUzGjBlj0P2+isdfV3x8PB4/fow///nPqKurQ0FBAVJSUrB06VKMGDFCp70p5smkyMyAl98yODNdHses9Wb5p8jISLKxsaH6+nrNtsOHD5Ovry8BIBcXF/r88887fG10dLTO8k9tbW2UkpJCw4YNIxsbG3JycqJ58+bR7du3NW30KTvUXUmmefPmEQBKSEjo8n0WFBTQlClTyMPDgwAQAHJ3d6fAwEDKz8/XaR8cHEyenp7U1tbWs4EkHv+u6DP++fn5NGnSJLKzsyMPDw+Kjo6mxsbGDvfbm3kiMtvvl2xObv2AmX74zFpvvlzv3r1LUqmU9u/fb6SojKu1tZWmTp1KGRkZBtvnkydPSCaT0a5du/R6HY+/afV2nojM9vuF15ZkzFD8/PywZcsWbNmyBbW1tUKHo5fW1lYcOXIENTU1Bq1OkZiYiLfeeguRkZEG22dnePx7z5TzZCqc3BgzoNjYWCxYsABhYWEWtTjv2bNnkZubi7y8vB7fK9ad3bt349q1azh16hRsbGwMss/u8PjrT4h5MgVRJ7fXazm1P2xtbeHq6op33nkHKSkpqK6uFjpUJiLJycmIjIzE9u3bhQ6lx2bMmIG//e1vmvuj+uro0aN48eIFzp49CycnJ4Pss6d4/HtOyHkyNlEnt5CQEPz000/w9fWFg4MDiAhtbW2oqKhAdnY2vL29ERMTA39/f6Ovr8f6l/fffx87duwQOgzBfPDBB4iNje1w+SdT6O/j31NCz5MxiTq5dUQikcDR0RHvvPMOMjMzkZ2djcePHyM4ONiiTmNYmoaGhi4rBltKH4wxy9DvktvrQkNDsXTpUlRUVOCbb74ROhzRysjIQEVFhcX3wRizDP0+uQHA0qVLAby8e79dV7WX9KnhlJ+fj0mTJkGhUEClUmHMmDGa9d6EqK/VU0Td17KKjIyEra2t1u8Eq1atgr29PSQSCZ48eQIAiIqKwrp161BcXAyJRAI/Pz/s2bMHMpkMrq6uiIiIgIeHB2QyGQIDA7UWpe1LHwDw/fffQ6VSITk52ajjxRgzM0LfjPA6GOE+N19fX3JwcOj0ebVaTQBoyJAhmm3r168nOzs7OnToEFVXV9PGjRvJysqKLl++TEREcXFxBIDOnDlDz58/p4qKCpo6dSrZ29tTU1MTERHV1taSSqWinTt3UkNDA5WXl9P8+fOpsrKyR30YSm/uQ0lISCBbW1vav38/PXv2jAoLC2n8+PHk4uJC5eXlmnaLFy8mNzc3rdempKQQAM37JCIKCQkhX19frXbh4eFkb29PRUVF1NjYSDdv3qSJEyeSUqmk+/fvG6SPEydOkFKppC1btuj1/ntznxUzHB5/y8H3uZkxpVIJiUSiWSRVn9pLXdVwKikpgVqthr+/P2QyGdzc3JCbmwsXFxdB62t1pze1rHpLKpVqjg5HjRqF9PR01NTUGGwMgoODoVarER8fb5D9McYsAyc3vCxHQURQqVQAel976fUaTj4+PnB1dcWSJUuQmJiIkpISTVsh62t1p6+1rPpiwoQJUCgUgo8BY8yycXIDcOfOHQDAyJEjAWjXXnr1/rjS0lLU19f3eL9yuRw//PADgoKCkJycDB8fH4SFhaGhocFgfRiDoWtZ6cvOzg6VlZVG7YMxJm6c3PDyogMAmDlzJgDD1l7y9/fH8ePHUVZWhpiYGGRlZWHXrl2C1tfqjqFrWemjubnZ6H0wxsSv3ye38vJypKamYvDgwfj0008BGK72UllZGYqKigC8TJjbt2/H+PHjUVRUJGh9re7oU8tKKpVqTsMawtmzZ0FECAgIMFofjDHx6zfJjYhQW1uLtrY2EBEqKyuRlZWFKVOmwNraGkeOHNH85taT2ks9UVZWhoiICNy6dQtNTU24evUqSktLERAQYLA+jEGfWlZ+fn54+vQpjhw5gubmZlRWVupU/wUAZ2dnlJWVoaSkBDU1NZpk1dbWhurqarS0tKCwsBBRUVHw8vLS3J7R1z7y8vL4VgDG+iNhrtLsHAx4K8CxY8do7NixpFAoyNbWlqysrAgASSQScnR0pEmTJtGWLVuoqqpK57Vd1V7qaQ2nkpISCgwMJCcnJ7K2tqY33niD4uLiqKWlpds+DKk3l+r2pJYVEVFVVRVNnz6dZDIZeXt70xdffEHR0dEEgPz8/DSX9F+5coWGDh1KcrmcgoKCqLy8nMLDw8nGxoY8PT1JKpWSSqWiuXPnUnFxscH6OHXqFCmVSkpKStLr/fOl6MLi8bcc5norgISISLjUqksikSArKwsffvih0KGIxoIFCwAAOTk5AkeiLSIiAjk5OaiqqhI6FB3Z2dlYuHAhzOzPo9/g8bccZvr9ktNvTksy89Ta2ip0CIwxEeLkxhhjTHQ4uTFBbNy4EZmZmXj+/Dm8vb1x6NAhoUNijImIVOgAWP+0bds2bNu2TegwGGMixUdujDHGRIeTG2OMMdHh5MYYY0x0OLkxxhgTHbO8oCQ1NdXcbgi0aBcvXgTw682WrHsPHz4EwGMmFB5/y3Hx4kWttWDNhdmtUMIfZiZG5eXluHr1qqbyBGNiMnnyZKxdu1boMF6VY3bJjTEx4uWkGDMpXn6LMcaY+HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiw8mNMcaY6HByY4wxJjqc3BhjjIkOJzfGGGOiIxU6AMbEprm5GbW1tVrb6urqAADV1dVa2yUSCRwdHU0WG2P9BSc3xgzs6dOn8PT0RGtrq85zzs7OWv+ePn06fvjhB1OFxli/waclGTMwNzc3/NM//ROsrLr+85JIJPjoo49MFBVj/QsnN8aM4OOPP+62jbW1NebPn2+CaBjrfzi5MWYEISEhkEo7P+tvbW2N3//+9xg4cKAJo2Ks/+DkxpgRqFQqzJw5s9MER0RYsmSJiaNirP/g5MaYkSxZsqTDi0oAwNbWFn/4wx9MHBFj/QcnN8aM5A9/+AMUCoXOdhsbG8ybNw/29vYCRMVY/8DJjTEjkclkmD9/PmxsbLS2Nzc3Y/HixQJFxVj/wMmNMSNatGgRmpubtbapVCq89957AkXEWP/AyY0xI3r33Xe1bty2sbHBRx99BFtbWwGjYkz8OLkxZkRSqRQfffSR5tRkc3MzFi1aJHBUjIkfJzfGjOyjjz7SnJp0c3NDUFCQwBExJn6c3BgzssDAQHh6egIAPvnkk26X5WKM9Z3ZLZycnZ0tdAiMGdzEiRPx6NEjDBw4kD/jTHSGDBmCyZMnCx2GFgkRkdBBvEoikQgdAmOMMT2EhoYiJydH6DBelWN2R24AkJWVhQ8//FDoMERjwYIFAGBuHz6zlp2djYULF8KQ//c7dOgQQkNDDbY/9hJ/voXVPv7mhk/+M2YinNgYMx1ObowxxkSHkxtjjDHR4eTGGGNMdDi5McYYEx1ObowxxkRHdMlt+fLlUCqVkEgkuHbtmtDh9EpSUhIkEonOY/To0YLGderUKTg4OOD48eOCxsEYY90RXXLbt28f/vKXvwgdhiiZ2f3+jDHWKdElN7HYv38/iEjr8eOPPwoaU3BwMJ4/f47Zs2cLGgcANDQ0IDAwUOgwGGNmSpTJjZfwEr+MjAxUVFQIHQZjzExZfHIjIqSkpGDEiBGws7ODg4MDoqOjddq1trYiISEBXl5ekMvlGDt2LLKysgAA6enpsLe3h0KhwNGjRzFz5kyoVCoMHjwYBw4c0NpPfn4+Jk2aBIVCAZVKhTFjxkCtVnfbh6U7f/48vLy8IJFI8PXXXwPo+bjt2bMHMpkMrq6uiIiIgIeHB2QyGQIDA3Hp0iVNu8jISNja2sLd3V2zbdWqVbC3t4dEIsGTJ08AAFFRUVi3bh2Ki4shkUjg5+cHAPj++++hUqmQnJxsiiFhjJkxi09u8fHxiImJQXh4OB4/fozy8nJs2LBBp92GDRvw5ZdfIjU1Fb/88gtmz56NRYsW4R//+Ac+++wzrFmzBg0NDVAqlcjKykJxcTF8fHywYsUKTS2uuro6zJkzB6GhoXj69Cnu3r2L4cOHo6mpqds+9BUbGwsnJyfY2trC29sbc+fOxeXLl/s2WH0QFBSECxcuaG3r6bhFRkZi6dKlqK+vx+rVq1FSUoIrV66gpaUF7733Hh48eADgZRJ8fU3RvXv3YvPmzVrb0tLSMHv2bPj6+oKIcO/ePQAv/3MBAG1tbUYZA8aY5bDo5NbQ0IDU1FS8++67WLt2LRwdHSGXy+Hs7KzVrrGxEenp6Zg3bx5CQkLg6OiITZs2wcbGBpmZmVptAwMDoVKpMGjQIISFhaGurg73798HAJSUlECtVsPf3x8ymQxubm7Izc2Fi4uLXn10549//COOHTuGBw8eoLa2FgcOHMD9+/cxbdo03Lx5s2+DZiRdjVs7qVSKN998E3Z2dhg1ahTS09NRU1Oj9/h0Jjg4GGq1GvHx8QbZH2PMcll0crt37x7q6+sxY8aMLtvdvn0b9fX1WpfSy+VyuLu749atW52+ztbWFgA0RyA+Pj5wdXXFkiVLkJiYiJKSkj730ZEhQ4bg7bffxoABA2Bra4uAgABkZmaioaEBe/fu1WtfQnh93DozYcIEKBQKvceHMca6Y9HJ7eHDhwCAQYMGddmurq4OALBp0yat+8ZKS0tRX1/f4/7kcjl++OEHBAUFITk5GT4+PggLC0NDQ4PB+ujMmDFjYG1tjTt37vR5X+bEzs4OlZWVQofBGBMZi05uMpkMAPDixYsu27Unv9TUVJ3L6wsKCvTq09/fH8ePH0dZWRliYmKQlZWFXbt2GbSPjrS1taGtrQ12dnZ93pe5aG5uxrNnzzB48GChQ2GMiYxFJ7fRo0fDysoK+fn5XbYbMmQIZDJZn1csKSsrQ1FREYCXCXP79u0YP348ioqKDNYHAPzud7/T2Xb58mUQkdmVcu+Ls2fPgogQEBCg2SaVSrs9nckYY92x6OQ2aNAghISE4NChQ8jIyIBarUZhYSG+/fZbrXYymQzLli3DgQMHkJ6eDrVajdbWVjx8+BC//PJLj/srKytDREQEbt26haamJly9ehWlpaUICAgwWB8A8OjRIxw8eBDPnj1Dc3MzCgoKsHz5cnh5eWHlypV67cuctLW1obq6Gi0tLSgsLERUVBS8vLywdOlSTRs/Pz88ffoUR44cQXNzMyorK1FaWqqzL2dnZ5SVlaGkpAQ1NTVobm5GXl4e3wrAGHuJzAwAysrK6nH7mpoaWr58OQ0cOJAGDBhAQUFBlJCQQABo8ODBdP36dSIievHiBcXExJCXlxdJpVIaNGgQhYSE0M2bN2nv3r2kUCgIAA0bNoyKi4vp22+/JZVKRQBo6NChdOfOHSopKaHAwEBycnIia2treuONNyguLo5aWlq67UMf69atI19fX7K3tyepVEqDBw+mFStWUFlZmV77aRcaGkqhoaG9em27r776itzd3QkAKRQKmjNnTo/HjYgoPDycbGxsyNPTk6RSKalUKpo7dy4VFxdr9VNVVUXTp08nmUxG3t7e9MUXX1B0dDQBID8/P7p//z4REV25coWGDh1KcrmcgoKCqLy8nE6dOkVKpZKSkpL69F6JiLKyssgM/zxYBwzx+Wa9Z6bjny0hMq8FAyUSCbKysnTud2K9t2DBAgBATk6OYDFEREQgJycHVVVVgsWgj+zsbCxcuJDX07QA5vD57s/MdPxzLPq0JLMs7TdZM8aYsXFyM4Fbt251WMLm9UdYWJjQoTIDOX36NGJjY5GbmwsfHx/NHH/88cc6bd9//30olUpYW1vD398fV65cESBi/bW1tSE1NbXLBazPnz+PKVOmQKFQwMPDAzExMVpXNx87dgw7d+4U9D8+/X2uzGEOjELg86I6oOdvbqx7Qp8Tj42NJVtbWwJAv/nNbygnJ0ewWHqqL7+5JSQk0OzZs0mtVmu2+fr60sCBAwkAnThxQuc1eXl59MEHH/Q6XlO7c+cOTZkyhQDQuHHjOmzz448/klwup/j4eKqtraULFy6Qi4sLLVu2TKtdWloaTZs2jaqrq3sVS18+3zxXL/VlDoT+fulENh+5MaPbtm0bXrx4ASLCzz//jNDQUKFDMpodO3bg4MGDyM7OhlKp1Hpuz549sLKyQnh4OJ4/fy5QhH13/fp1bNiwAStXrsRbb73VabutW7fC3d0dmzdvhr29PSZPnoyYmBh89913WqvSrF69GuPGjcOsWbPQ0tJiircAgOfqVULNgTFxcmPMQO7du4f4+Hhs3rxZs8DAqwIDAxEVFYVHjx5h/fr1AkRoGOPGjUNubi4WL17c6aICLS0tOHnyJKZNm6ZVgmrmzJkgIhw9elSrfWJiIq5du4a0tDSjxt6O50qXqefA2Di5MWYge/bsARFhzpw5nbZJSkrC8OHDsW/fPpw+fbrL/RERdu/erVls2snJCXPnztU66tGnXJMpSzL99NNPqK2thZeXl9Z2X19fAEBhYaHWdicnJ0ybNg1paWkmuUKV50qXqefA2Di5MWYgJ0+exIgRI6BQKDptI5fL8d1338HKygorVqzQrEnakcTERMTGxiIuLg4VFRU4d+4cHjx4gKlTp+Lx48cAel52CDBsSabulJeXA4DO6T6ZTAa5XK6J/1Vvv/02Hj16hOvXrxs8ntfxXHXMlHNgbJzcGDOAuro6/Pzzz5ojk65MnjwZa9asQUlJSYe1B4GX5Zx2796N+fPnY8mSJXBwcMCYMWPwzTff4MmTJzqr8ABdlx0yZEmmnmi/ItLa2lrnORsbGzQ0NOhsHzZsGADgxo0bBo/nVTxXnTPVHJiCVOgAOpKammpuNwRatIsXLwL49WZL1r32ihM9VVFRASLq8kjgVUlJSThx4gT27t2LhQsX6jx/8+ZN1NbWYsKECVrbJ06cCFtbW60K5h15veyQIUsy9UT771gdXZzQ1NQEuVyus7197Do6qjMknqvOmWoOTIGP3BgzgMbGRgDocdUGmUyGzMxMSCQSfPrppzpHMs+ePQMADBgwQOe1jo6OqKmp0Ss+Y5dkep27uzsAQK1Wa22vr69HY2MjPDw8dF7TnvDax9JYeK46Z6o5MAWzPHJbs2YNL79lQGa6PI5Za19+q6favxT0uRF28uTJWLt2LXbt2oWtW7dqXXzh6OgIAB1+MfamTNCrJZmioqL0em1veHt7Q6lU6ix6fe/ePQDA2LFjdV7T1NQEAB0e1RkSz1XnTDUHpsBHbowZgKurKyQSid73RG3duhUjR47E1atXtbaPHj0aAwYM0LmA4NKlS2hqasJvf/tbvfoxZEmmnpBKpZg1axbOnTuHtrY2zfa8vDxIJJIOr1JsHzs3NzejxsZz1TlTzYEpcHJjzAAUCgV8fHz0/q2u/ZTX6xdeyGQyrFu3DocPH8Zf//pXqNVq3LhxAytXroSHhwfCw8P17qe7kkxhYWFwc3Mz2JJS8fHxePz4Mf785z+jrq4OBQUFSElJwdKlSzFixAid9u1jN2bMGIP03xmeq86Zag5MQqi1UToDXn7L4Mx0eRyz1pvltyIjI8nGxobq6+s12w4fPky+vr4EgFxcXOjzzz/v8LXR0dE6Szq1tbVRSkoKDRs2jGxsbMjJyYnmzZtHt2/f1rTRp+xQdyWZ5s2bRwAoISGhy/dZUFBAU6ZMIQ8PDwJAAMjd3Z0CAwMpPz9fq21+fj5NmjSJ7OzsyMPDg6Kjo6mxsbHD/QYHB5Onpye1tbV12f/revP55rnSnSui3s2BmX6/ZHNy6wfM9MNn1nqT3O7evUtSqZT2799vpKiMq7W1laZOnUoZGRkm7/vJkyckk8lo165der+2N59vnitdvZ0DM/1+4bUlGTMUPz8/bNmyBVu2bEFtba3Q4eiltbUVR44cQU1NjSDVKRITE/HWW28hMjLSJP3xXOky9RwYm6iT2+slLNoftra2cHV1xTvvvIOUlBRUV1cLHSoTidjYWCxYsABhYWEWteDu2bNnkZubi7y8vB7f/2Uou3fvxrVr13Dq1CnY2NiYrF+eq18JNQfGJOrkFhISgp9++gm+vr5wcHAAEaGtrQ0VFRXIzs6Gt7c3YmJi4O/vb/RlbVj/kZycjMjISGzfvl3oUHpsxowZ+Nvf/qa5P81Ujh49ihcvXuDs2bNwcnIyad8AzxUg/BwYi6iTW0ckEgkcHR3xzjvvIDMzE9nZ2Xj8+DGCg4Mt6n9vlqahoaHLopaW0kdPvf/++9ixY4fQYZi9Dz74ALGxsR0u02Uq/X2uzGEOjKHfJbfXhYaGYunSpaioqMA333wjdDiilZGRgYqKCovvgzFmGfp9cgOApUuXAnh5g2m7rkpO6FO6Ij8/H5MmTYJCoYBKpcKYMWM0SxIJUdaip6gHJTwiIyNha2urdXpk1apVsLe3h0QiwZMnTwAAUVFRWLduHYqLiyGRSODn54c9e/ZAJpPB1dUVERER8PDwgEwmQ2BgoNZafH3pAwC+//57qFQqJCcnG3W8GGNmRujrNV8HI9wK4OvrSw4ODp0+r1arCQANGTJEs239+vVkZ2dHhw4dourqatq4cSNZWVnR5cuXiYgoLi6OANCZM2fo+fPnVFFRQVOnTiV7e3tqamoiIqLa2lpSqVS0c+dOamhooPLycpo/fz5VVlb2qA9D6c2lugkJCWRra0v79++nZ8+eUWFhIY0fP55cXFyovLxc027x4sXk5uam9dqUlBQCoHmfREQhISHk6+ur1S48PJzs7e2pqKiIGhsb6ebNmzRx4kRSKpV0//59g/Rx4sQJUiqVtGXLFr3ef29uBWDCMNNL0fsNMx1/vhUAeFlzSiKRaNaG06fkRFelK0pKSqBWq+Hv7w+ZTAY3Nzfk5ubCxcVF0LIW3elNCY/ekkqlmqPDUaNGIT09HTU1NQYbg+DgYKjVasTHxxtkf4wxy8DJDS9X4SYiqFQqAL0vOfF66QofHx+4urpiyZIlSExMRElJiaatkGUtutPXEh59MWHCBCgUCsHHgDFm2Ti5Abhz5w4AYOTIkQAMV3JCLpfjhx9+QFBQEJKTk+Hj44OwsDA0NDQIWtaiO4Yu4aEvOzs7VFZWGrUPxpi4cXLDy4sOAGDmzJkAtEtOEJHWo6CgQK99+/v74/jx4ygrK0NMTAyysrKwa9cug/ZhaIYu4aGP5uZmo/fBGBO/fp/cysvLkZqaisGDB+PTTz8FYLiSE2VlZSgqKgLwMmFu374d48ePR1FRkaBlLbqjTwkPqVSqOQ1rCGfPngURISAgwGh9MMbEr98kNyJCbW0t2traQESorKxEVlYWpkyZAmtraxw5ckTzm1tPSk70RFlZGSIiInDr1i00NTXh6tWrKC0tRUBAgMH6MAZ9Snj4+fnh6dOnOHLkCJqbm1FZWalToBIAnJ2dUVZWhpKSEtTU1GiSVVtbG6qrq9HS0oLCwkJERUXBy8tLc3tGX/vIy8vjWwEY64+EuUqzczDgrQDHjh2jsWPHkkKhIFtbW7KysiIAJJFIyNHRkSZNmkRbtmyhqqoqndd2VXKip6UrSkpKKDAwkJycnMja2preeOMNiouLo5aWlm77MKTeXKrbkxIeRERVVVU0ffp0kslk5O3tTV988QVFR0cTAPLz89Nc3t8UcQAAAUZJREFU0n/lyhUaOnQoyeVyCgoKovLycgoPDycbGxvy9PQkqVRKKpWK5s6dS8XFxQbr49SpU6RUKikpKUmv98+3AlgOM70Uvd8w0/HPlhARCZhbdUgkEmRlZeHDDz8UOhTRWLBgAQAgJydH4Ei0RUREICcnB1VVVUKHoiM7OxsLFy6Emf15sA6Y6+e7vzDT8c/pN6clmXlqbW0VOgTGmAhxcmOMMSY6nNyYIDZu3IjMzEw8f/4c3t7eOHTokNAhMcZERCp0AKx/2rZtG7Zt2yZ0GIwxkeIjN8YYY6LDyY0xxpjocHJjjDEmOpzcGGOMiQ4nN8YYY6JjliuUMMYYsxyhoaFmt0KJ2d0KkJWVJXQIjDHG9DBkyBChQ9BhdkdujDHGWB/x2pKMMcbEh5MbY4wx0eHkxhhjTHSkAMzqEhfGGGOsjy7+P5OaFS6E9ByeAAAAAElFTkSuQmCC\n"
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyiRNj4Ap88x",
        "outputId": "2898bc2f-d432-4634-ccfe-c66be6b7797a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_4 (Dense)             (None, 10)                20        \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31\n",
            "Trainable params: 31\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the results:"
      ],
      "metadata": {
        "id": "kpXZhf0_qKxq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.savetxt('y_pred.txt', y_pred, fmt='%s')"
      ],
      "metadata": {
        "id": "hzALi3xWqBrB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install xlsxwriter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjSTn7NKqEI7",
        "outputId": "100bc434-d9e8-4620-be36-c100768254fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xlsxwriter in /usr/local/lib/python3.7/dist-packages (3.0.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xlsxwriter\n",
        "workbook = xlsxwriter.Workbook('y_pred.xlsx')\n",
        "worksheet = workbook.add_worksheet('y_pred')\n",
        "for i in range(len(y_pred)):\n",
        "    worksheet.write('A'+str(i+2), str(y_pred[i]))\n",
        "workbook.close()"
      ],
      "metadata": {
        "id": "WN4TxPZvqGeN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "91wg3_W6-6f7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###PyTorch: simple MLP"
      ],
      "metadata": {
        "id": "1_M0zqak-6iv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision"
      ],
      "metadata": {
        "id": "7IaaEyQw-7Ek"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preparing the Dataset:"
      ],
      "metadata": {
        "id": "c-pmpaNCBd5w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_epochs = 3\n",
        "batch_size_train = 64\n",
        "batch_size_test = 64\n",
        "learning_rate = 0.01\n",
        "momentum = 0.5\n",
        "log_interval = 10\n",
        "\n",
        "random_seed = 1\n",
        "torch.backends.cudnn.enabled = False\n",
        "torch.manual_seed(random_seed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kP5PlFVzpv6q",
        "outputId": "01ede473-48b7-4225-9760-f7b18028c6df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f30ee2a21f0>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TorchVision offers a lot of handy transformations, such as cropping or normalization."
      ],
      "metadata": {
        "id": "vJclcdewBkwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('/files/', train=True, download=True,\n",
        "                             transform=torchvision.transforms.Compose([\n",
        "                               torchvision.transforms.ToTensor(),\n",
        "                               torchvision.transforms.Normalize(\n",
        "                                 (0.1307,), (0.3081,))])),\n",
        "  batch_size=batch_size_train, shuffle=True)\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('/files/', train=False, download=True,\n",
        "                             transform=torchvision.transforms.Compose([\n",
        "                               torchvision.transforms.ToTensor(),\n",
        "                               torchvision.transforms.Normalize(\n",
        "                                 (0.1307,), (0.3081,))])),\n",
        "  batch_size=batch_size_test, shuffle=True)"
      ],
      "metadata": {
        "id": "218iSPOZ_WP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PyTorch's DataLoader contain a few interesting options other than the dataset and batch size. For example we could use num_workers > 1 to use subprocesses to asynchronously load data or using pinned RAM (via pin_memory) to speed up RAM to GPU transfers. But since these mostly matter when we're using a GPU we can omit them here.\n",
        "\n",
        "Now let's take a look at some examples. We'll use the test_loader for this."
      ],
      "metadata": {
        "id": "Ez5lffIvCyzg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "examples = enumerate(test_loader)\n",
        "batch_idx, (example_data, example_targets) = next(examples)"
      ],
      "metadata": {
        "id": "-b0xOacnChOg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJhQIVo8pv91",
        "outputId": "c1b968a5-ae88-4a90-af7c-a6cbca270917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([64, 1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure()\n",
        "for i in range(6):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  plt.tight_layout()\n",
        "  plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
        "  plt.title(\"Ground Truth: {}\".format(example_targets[i]))\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "fig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "SH9djLpyC7jx",
        "outputId": "2f5bf420-cac7-4f92-99f4-34e174f87a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Network:"
      ],
      "metadata": {
        "id": "3wPVFh7yDLjN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "7GIMqMy8DB5-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.flat = nn.Flatten()\n",
        "        self.fc1 = nn.Linear(28*28*1, 64)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(self.flat(x)))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x)"
      ],
      "metadata": {
        "id": "Q6IOe2r1DTJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "network = Net()\n",
        "optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum)"
      ],
      "metadata": {
        "id": "rwB0xD_lDj-1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_losses = []\n",
        "train_counter = []\n",
        "test_losses = []\n",
        "test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)]"
      ],
      "metadata": {
        "id": "ADEy3Md5DneX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.train() tells your model that you are training the model. So effectively layers like dropout, batchnorm etc. which behave different on the train and test procedures know what is going on and hence can behave accordingly."
      ],
      "metadata": {
        "id": "BbRRsk2EnpHr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(epoch):\n",
        "  network.train()\n",
        "\n",
        "  for batch_idx, (data, target) in enumerate(train_loader):\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    output = network(data)\n",
        "    loss = F.nll_loss(output, target)   # negative log likelihood\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    if batch_idx % log_interval == 0:\n",
        "\n",
        "      print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "        epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "        100. * batch_idx / len(train_loader), loss.item()))\n",
        "      \n",
        "      train_losses.append(loss.item())\n",
        "      train_counter.append((batch_idx*64) + ((epoch-1)*len(train_loader.dataset)))\n",
        "      \n",
        "      torch.save(network.state_dict(), 'model.pth')  # Saves the state of the model as a dict\n",
        "      torch.save(optimizer.state_dict(), 'optimizer.pth')  # Saves the state of the optimizer as a dict"
      ],
      "metadata": {
        "id": "cVgHg9xmDr8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can call either model.eval() or model.train(mode=False) to tell that you are testing. It is somewhat intuitive to expect train function to train model but it does not do that. It just sets the mode."
      ],
      "metadata": {
        "id": "08NqS2QCntb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def test():\n",
        "\n",
        "  network.eval()\n",
        "  test_loss = 0\n",
        "  correct = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for data, target in test_loader:\n",
        "      output = network(data)\n",
        "      test_loss += F.nll_loss(output, target, size_average=False).item()\n",
        "      pred = output.data.max(1, keepdim=True)[1]\n",
        "      correct += pred.eq(target.data.view_as(pred)).sum()\n",
        "      \n",
        "  test_loss /= len(test_loader.dataset)\n",
        "  test_losses.append(test_loss)\n",
        "  print('\\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
        "    test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset)))"
      ],
      "metadata": {
        "id": "O6qEzYSJD4EW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcCnh2SaEAn1",
        "outputId": "c7941977-f20f-4395-e7fa-ffe14545f9a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Avg. loss: 2.3226, Accuracy: 1101/10000 (11%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(1, n_epochs + 1):\n",
        "  train(epoch)\n",
        "  test()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zYW0a-jD7r2",
        "outputId": "c2fb647d-35d7-49f9-d125-98eff2aedaa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  if sys.path[0] == '':\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.275308\n",
            "Train Epoch: 1 [640/60000 (1%)]\tLoss: 2.017191\n",
            "Train Epoch: 1 [1280/60000 (2%)]\tLoss: 1.719950\n",
            "Train Epoch: 1 [1920/60000 (3%)]\tLoss: 1.624231\n",
            "Train Epoch: 1 [2560/60000 (4%)]\tLoss: 1.490757\n",
            "Train Epoch: 1 [3200/60000 (5%)]\tLoss: 1.240576\n",
            "Train Epoch: 1 [3840/60000 (6%)]\tLoss: 1.083202\n",
            "Train Epoch: 1 [4480/60000 (7%)]\tLoss: 1.095552\n",
            "Train Epoch: 1 [5120/60000 (9%)]\tLoss: 1.003835\n",
            "Train Epoch: 1 [5760/60000 (10%)]\tLoss: 1.124319\n",
            "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.957213\n",
            "Train Epoch: 1 [7040/60000 (12%)]\tLoss: 0.870344\n",
            "Train Epoch: 1 [7680/60000 (13%)]\tLoss: 0.769392\n",
            "Train Epoch: 1 [8320/60000 (14%)]\tLoss: 0.759169\n",
            "Train Epoch: 1 [8960/60000 (15%)]\tLoss: 0.968829\n",
            "Train Epoch: 1 [9600/60000 (16%)]\tLoss: 0.779855\n",
            "Train Epoch: 1 [10240/60000 (17%)]\tLoss: 0.634936\n",
            "Train Epoch: 1 [10880/60000 (18%)]\tLoss: 0.697694\n",
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            "Train Epoch: 1 [12160/60000 (20%)]\tLoss: 1.000870\n",
            "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.582267\n",
            "Train Epoch: 1 [13440/60000 (22%)]\tLoss: 0.829611\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Avg. loss: 0.2861, Accuracy: 9170/10000 (92%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.309589\n",
            "Train Epoch: 2 [640/60000 (1%)]\tLoss: 0.384454\n",
            "Train Epoch: 2 [1280/60000 (2%)]\tLoss: 0.565612\n",
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            "Train Epoch: 2 [55680/60000 (93%)]\tLoss: 0.281290\n",
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            "Train Epoch: 2 [58880/60000 (98%)]\tLoss: 0.316122\n",
            "Train Epoch: 2 [59520/60000 (99%)]\tLoss: 0.634084\n",
            "\n",
            "Test set: Avg. loss: 0.2279, Accuracy: 9327/10000 (93%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.236278\n",
            "Train Epoch: 3 [640/60000 (1%)]\tLoss: 0.202497\n",
            "Train Epoch: 3 [1280/60000 (2%)]\tLoss: 0.337368\n",
            "Train Epoch: 3 [1920/60000 (3%)]\tLoss: 0.276965\n",
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            "Train Epoch: 3 [5760/60000 (10%)]\tLoss: 0.438483\n",
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            "Train Epoch: 3 [59520/60000 (99%)]\tLoss: 0.189303\n",
            "\n",
            "Test set: Avg. loss: 0.1988, Accuracy: 9408/10000 (94%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TEXT CLASSIFICATION WITH THE TORCHTEXT LIBRARY**\n"
      ],
      "metadata": {
        "id": "zaCoDU7tuwGc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TORCHTEXT.DATASETS: https://pytorch.org/text/stable/datasets.html"
      ],
      "metadata": {
        "id": "ChkdxgjhrVLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torchtext.datasets import AG_NEWS\n",
        "train_iter = iter(AG_NEWS(split='train'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwjlfMjPrVZL",
        "outputId": "06b8984d-00f2-4aca-e5e1-5f1cec32fe38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "29.5MB [00:00, 74.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(train_iter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9r2E3GKrjcL",
        "outputId": "ab9421b4-0e89-4845-d989-fe485ecb62e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,\n",
              " \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\")"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(train_iter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-Vzx-FPrjfR",
        "outputId": "b3acd34f-71b3-4189-b7b3-e28859b078b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3,\n",
              " 'Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private investment firm Carlyle Group,\\\\which has a reputation for making well-timed and occasionally\\\\controversial plays in the defense industry, has quietly placed\\\\its bets on another part of the market.')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepare data processing pipelines\n",
        "\n",
        "torchtext.data.utils: https://pytorch.org/text/stable/data_utils.html"
      ],
      "metadata": {
        "id": "xXh6_hnGrvuH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torchtext\n",
        "from torchtext.data import get_tokenizer\n",
        "tokenizer = get_tokenizer(\"basic_english\")\n",
        "tokens = tokenizer(\"This is an example of get_tokenizer!\")\n",
        "tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZqWRuW-qU0-",
        "outputId": "ca7d9a4c-9849-4d9f-b1a8-0b1a0c8c28a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['this', 'is', 'an', 'example', 'of', 'get_tokenizer', '!']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "torchtext.vocab: https://pytorch.org/text/stable/vocab.html"
      ],
      "metadata": {
        "id": "Cr63tnTzq_AV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torchtext.data.utils import get_tokenizer\n",
        "from torchtext.vocab import build_vocab_from_iterator\n",
        "\n",
        "tokenizer = get_tokenizer('basic_english')\n",
        "train_iter = AG_NEWS(split='train')\n",
        "\n",
        "def yield_tokens(data_iter):\n",
        "    for _, text in data_iter:\n",
        "        yield tokenizer(text)   # used to return from a function without destroying the states of its local variable\n",
        "\n",
        "vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=[\"<unk>\"])\n",
        "vocab.set_default_index(vocab[\"<unk>\"])   # Value of default index. This index will be returned when OOV token is queried."
      ],
      "metadata": {
        "id": "RA-6U8Qcrjiw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The vocabulary block converts a list of tokens into integers.\n",
        "\n"
      ],
      "metadata": {
        "id": "rLk82BQ7r_0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vocab(['here', 'is', 'an', 'example'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2He8nxAdr63H",
        "outputId": "268a2908-23cf-46fb-9e3a-91ce722baf52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[475, 21, 30, 5297]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_pipeline = lambda x: vocab(tokenizer(x))\n",
        "label_pipeline = lambda x: int(x) - 1"
      ],
      "metadata": {
        "id": "GXe8z_vgsFMI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The text pipeline converts a text string into a list of integers based on the lookup table defined in the vocabulary. The label pipeline converts the label into integers."
      ],
      "metadata": {
        "id": "uPNy2BYasRDx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_pipeline('here is the an example')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8xMBwVKsGZp",
        "outputId": "74175c96-b56c-4fd0-ae08-5216bbb6444f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[475, 21, 2, 30, 5297]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_pipeline('10')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pni-sGA8sLDm",
        "outputId": "fccc2f15-7dfc-4848-ea39-fecc3e4a6356"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "def collate_batch(batch):\n",
        "\n",
        "    label_list, text_list, offsets = [], [], [0]\n",
        "    for (_label, _text) in batch:\n",
        "         label_list.append(label_pipeline(_label))\n",
        "         processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)\n",
        "         text_list.append(processed_text)\n",
        "         offsets.append(processed_text.size(0))\n",
        "\n",
        "    label_list = torch.tensor(label_list, dtype=torch.int64)\n",
        "    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)  # cumsum: compute the cumulative sum of array elements over a given axis\n",
        "    text_list = torch.cat(text_list)  # concatenate\n",
        "\n",
        "    return label_list.to(device), text_list.to(device), offsets.to(device)\n",
        "\n",
        "train_iter = AG_NEWS(split='train')\n",
        "dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)"
      ],
      "metadata": {
        "id": "2z2O86Y0r9xZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Define the model:**\n",
        "\n",
        "The model is composed of the nn.EmbeddingBag layer plus a linear layer for the classification purpose. nn.EmbeddingBag with the default mode of “mean” computes the mean value of a “bag” of embeddings. Although the text entries here have different lengths, nn.EmbeddingBag module requires no padding here since the text lengths are saved in offsets.\n",
        "\n",
        "Additionally, since nn.EmbeddingBag accumulates the average across the embeddings on the fly, nn.EmbeddingBag can enhance the performance and memory efficiency to process a sequence of tensors."
      ],
      "metadata": {
        "id": "w6Em0EJatBEB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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qFGS5ubmNDk5qYGBAc3OzsrhcKiurk61tbVKJBIaHhlWdD6q0tJSVVVVqbCwUF6vVy6XS5IUj8c1OTmp0dFRpdNpFRYWqrKyUuXl5bmQDQDAesNILABgXVraV9Xv96u2tlaxWEwLCwtyuVzy+XzyeDwfeq7T6VRDfb0qysu1adMmeTweeb1eFRQUyDRNNTc3q6amRqWlpXK5XLkuv0vsdrtqa2tVWVmpTCYjl8uVGwH94GsVFxerpaVF9913n+bn52W321VQUCB/YLETsSHjhvOkxRFjn8+nqqoqLSwsyG63q6ioSDabLbcO12azqaCg4Kb3W1BQoPr6etXU1CibzcrhcMjh4NEPAFjfeJIBANY9h8OhwsJCFRYW3tZ5hmGooKBAtbW1y75us9lUVFSkoqKij/TaNwuG1090sixLDodDwWBQweDtTeO12+25sH49j8fzoUF9yVKHYqfTeVuvCwDAJxVb7AAAsAosy8pteZNOp9mXFQCAPCHEAgCQR0vb4yytlzVNM9eUKZVKiVYUAACsDCEWAIA8SafTmpmZ0bvvvquBgQGl02lVVVUplUqpv79fra2tCofDymQya10qAADrFt2JAQDIk3A4rAsXLugHP/iBEonEDaOuZWVl+vZ3vq0tLVtue20sAABYRIgFACBP0um05ubmNDQ0dNM1sC6XS/X19fL7/XQJBgDgYyLEAgAAAADWDdbEAgAAAADWDUIsAAAAAGDdIMQCAAAAANYNQiwAAAAAYN0gxAIAAAAA1g1CLAAAAABg3SDEAgAAAADWDUIsAAAAAGDdIMQCAAAAANYNQiwAAAAAYN1wrHUBAACsd5ZlKZvNyrIsGYYhh4PHKwAAq4WnLAAAK5RKpTQwMKBkMimfz6cNGzbIZmOyEwAAq4EQCwDACsViMZ04cUKzs7Oqq69TXV2dXC7XWpcFAMBdiY+JAQBYgXQ6rampKb322mt64YUX9OaJNzU3N6dMJrPWpQEAcFdiJBYAgBUIhUK6cuWKzp8/r6GhIZmmqfb2du3cuVOlpaVrXR4AAHcdRmIBAFiBnp4evfbaa5qdnVUikdDIyIh+9atfaXh4eK1LAwDgrkSIBQDgY7AsS7FYTJcvX9aJEycUi8UkSbOzszp69KiuXr2qaDS6xlUCAHD3IcQCAPAxmKap0dFRXbp0SRcvXlQikZAkRaNRtbW16dKlSxoaGlrjKgEAuPsQYgEA+BjS6bSOHTum1tZWWZa17HuWZenUqVM6ffr0GlUHAMDdixALAMBtSiaTmpqa0unTp9XV1XXTYzo6OnT+/HmNj48rlUrd4QoBALh7EWIBALhNc3Nz6uzs1IULF27ZwGlsbEzt7e169913NTc3d4crBADg7kWIBQDgNg0ODur555/X1NTUhx7X39+vn/70p5qYmLhDlQEAcPcjxAIAcBvm5ubU1dWlY8eOKRwOf+ixMzMzeuutt3TlyhXNzMzcoQoBALi7Oda6AAAA1pPp6WkNDw9rampKXq9XXq9X2WxWkUhE2WxWTqdTwWBQNptNhmEoHo9rYGBAU1NTKi0tXevyAQBY9wixAADchlAoJL/fr2eeeUaSZBiGIpGIfvvb3yoSiai0tFSPPvqoAoGAHA6HHA6HvF6v4vH4GlcOAMDdgRALAMBtqKuvl8fj0e7du3NfGxoa0rlz5xSNRlVZWalvfOMbKikpkcPhkGEYCgQCKi8vX8OqAQC4exBiAQC4DZUVFaqsqFj2ta6uLgUCAdlsNhUWFuree+9VWVmZHA4eswAA5BuNnQAAyAPDMGQYhizLkqTc7wAAIL8IsQAA5IFhGLk/E2ABAFg9hFgAAFZoaRR2KcgSYgEAWD2EWAAA8uCDIZYgCwDA6iDEAgCQB7kAe+0XAABYHYRYAADy4Po1sWRYAABWDyEWAIA8yE0nJsACALCqCLEAAKzQBxs7mabJmlgAAFYJIRYAgDyw2d5/pBJgAQBYPYRYAADy4PqRWAAAsHoIsQAA5BHb6wAAsLoIsQAA5IHNZmOfWAAA7gBCLAAAK/TBxk4EWAAAVg8hFgCAPLh+PazFPjsAAKwaQiwAAPlGhgUAYNUQYgEAyIOlNbFL62GZUgwAwOogxAIAkAesiQUA4M4gxAIAkAfL1sQSYgEAWDWEWAAAVuj6ALuEIAsAwOogxAIAkAdL04lZDwsAwOoixAIAkAdLjZ0kRmEBAFhNhFgAAPLgg2ti2SsWAIDVQYgFACAPlnUnlsVesQAArBJCLAAAecZsYgAAVg8hFgCAFTIMI7cm1rIsUiwAAKuIEAsAQB7csCaWIAsAwKogxAIAkAfL1sQSYAEAWDWEWAAAAADAukGIBQAgDz64TyyjsQAArA5CLAAAK/TBqcQEWAAAVg8hFgCAPGBNLAAAdwYhFgCAPLNEiAUAYLUQYgEAyIPrt9iRRZAFAGC1EGIBAMiDZdOJF1MsAABYBYRYAADyYCnEWpYlyyTBAgCwWgixAACs0PWjsAAAYHURYgEAyAO22QEA4M4gxAIAkAfXj8QSYAEAWD2EWAAA8oAQCwDAnUGIBQAgD5Y1dmI6MQAAq4YQCwBAnhFgAQBYPYRYAABWyqBDMQAAdwohFgCAFTJkyLCxJhYAgDuBEAsAQB7YDBtb7AAAcAcQYgEAyCPCKwAAq4sQCwBAHly/JpYgCwDA6iHEAgCQB+wTCwDAnUGIBQBghZZGYVkTCwDA6iPEAgCQBwRYAADuDEIsAAB5QpAFAGD1EWIBAMgDm41HKgAAdwJPXAAA8uD6NbGmaa5xNQAA3L0IsQAA5EFuKvF1vwAAQP4RYgEAWKGlAHv9NjsAAGB1EGIBAMiDXIC1ru0Ty0AsAACrghALAEAeXD8KS2diAABWDyEWAIA8uL6xU240FgAA5B0hFgCAPGAkFgCAO4MQCwBAnhiGIcuyCLEAAKwiQiwAAHnASCwAAHcGIRYAgBUyDEM2m+39vWIZjQUAYNUQYgEAyANGYgEAuDMIsQAA5MnSmlgAALB6CLEAAOTBB0diCbMAAKwOQiwAAHlGgAUAYPUQYgEAyAPDMN5v7CRCLAAAq4UQCwDACi11J5YWA6xlWQRZAABWCSEWAIA8uX6LHTIsAACrgxALAEAe5Bo7EV4BAFhVhFgAAPLg+u7EBFkAAFYPIRYAgDy4vrGTaZp0KAYAYJUQYgEAAAAA6wYhFgCAPFgaibUsi1FYAABWESEWAIAVun4qsSRCLAAAq4gQCwBAHlwfZBmNBQBg9RBiAQDIA0ZiAQC4MwixAADkAWtiAQC4MwixAADkwbJ9YsVoLAAAq4UQCwBAHnxwTSwAAFgdhFgAAFboZt2JCbIAAKwOQiwAAHlEgAUAYHURYgEAyINl04lFiAUAYLUQYgEAyINljZ3IsAAArBpCLAAAeWCz2WSz2XLb7AAAgNXhWOsCAABY7+x2u4LBoKqqqmSaprxer+x2+1qXBQDAXcmw+LgYWHcSiYRefPFFjY2NrXUpACTFYjGdP39eXV1dsixLhw4dUkNDg4LB4FqXBuA6e/bs0cMPP7zWZQBYIUIssA5NT0/r6aefVlt7u4LBorUuBwCAT7zp6Un92b/7d/re97631qUAWCGmEwPr2FNf+Ir+6Dt/sdZlAADwiZZOp/Vf/ve/XesyAOQJIRZYx0pKy9WydcdalwEAwCdaKpWUz+db6zIA5AndiQEAAAAA6wYhFgAAAACwbhBiAQAAAADrBiEWAAAAALBuEGIBAAAAAOsGIRYAAAAAsG4QYgEAAAAA6wYhFgAAAACwbhBiAQAAAADrBiEWAAAAALBuEGIBAAAAAOsGIRYAAAAAsG4QYgEAAAAA64ZjrQsAANx9LMuSaWYVCYeVTqfl8/vk8XjlcPDYAQAAK8PfJgDgU86yrGW/34phGDIM4yNd0zRNJeJxdbRf1FwkrE3NLaqr3yC/P7Dietc7y7J+73stSTYbk6UAALgZQiwAfMr19VxV+6UL6rzcplQyeUPAstls8vkDuvfQA9p34DNyuz2/N8xGo3O6cP6sXv71zzU40KeGjZv07f/pP2jrtp2reSufePGFBV1qfVeX2y9pZHhQZjZ7wzFut0cVVdX67Oe+oIqKKjmdrjWoFACATy5CLAB8yrlcLvn8AQWChbr43lkN9PUqFovKbrdr46Zm7dy9X8XFJfJ6fR95JFaWZFqm5uYiCoVmVFldKzNrru6NrAOGzVBBgVf+QECmmdV7757VXCSsTDolp8utAwfvU1l5hYqKS+R0OD/6+w0AwKcIIRYAPuXqGjaoqqZO99x7SLHYvK52XdbY6LB8Pp8OH3lc//Zb31FpWYWcTudHvqbHU6CNm5q1dfsuBYuKdc89h1RcUrKKd7E+eDwF2rVnvza3bFN9w0Z1Xm7T0GCvYtGoiktK9fCRz+rQg4+oqIj3CgCAWyHEAgBkt9vl8wdUU9ugyqoaDQ/2q7qmXnUNG1RaVn7bDZmcLpeqa+r0jT/4k8XGTj6/AoHgKlW//rjcbpWUlmpjU4vGRoZkyNDGphZVVdfxPgEA8HsQYgEAMgxDdrtdXq9PXq9XklTg9crr9X2sNZk2m01ut0dV1bX5LvWuYLPZ5HK5FQwG5XS65HA6FQgE5fEUyG7n0QwAwIfhSQkAyLHbbbLZ7Nf+bJfNbl/R9dLptLKZjEzTlNvjkf0D1zNNU+lUSlkzK5vNLo/HI8uylE6nlUolZbPZ5HQ65XQ4pQ9ZH2qapjKZtNKptDLZjBx2u5wul5xO14euK7VMU5lMRolEQoYhORxOuVwuGTbbLc9Lp1PKZDKyTEtuj0fSYr3ZbFYup0tO10cL/YZhyGF3yLAZshmGHA5HXtbAptMppVMppTMZuVwuOZ1OORzLp4IvboFk3rRLsu3avS/VsnTc0rFL3/tg9+RMJqN0Oq10OinDsMnhcMjldMlmt994X5Yl07KUiMclY3Gada72dFoul1sOh4MOzQCAmyLEAgBuyjAMrTRSRcIhhWdnlEgk1LixSYHg8qmy6XRK42OjisWi8nq92rCpWdlsVrMzUxoeHlCBp0DlFVUqL6+8ZcCzLEupVFKz09OanBxXJBJSsLBQFRVVqqquy4Wym0mn05qdnVZ/T4/sTruKi0tVU1snt6fghsC9JDQ7q3B4VslUUps2LtY7PT2p6Py8qqpqVF5RKeN2w5dxLaOv8A23LEuh2RlNTIxpZnpKVdW1qqioUlFxSS4QWpalTCatZCKhTCazvAzDkNvjkdPpyt1/OpVSKpVU9lonZbvdLpfbLafTteya0eicpiYnND4+KqfDqeLiElVV18rnD9wwHd20LKVTKfX1XpXNZlfjxk2y2+0aHxvV9NSUauvrVVRUnAu3AABcjxALAMirifFR9fZ06crldvX1XlUqmVJdfaO+GPyqAsGg0um0+nuvquvKZV3tuqyBvh6VlVdq85ZtSmfSev3Vl9XV2aG5ubAcdocaNzZp7/57dfjIEyq4NtV5SSg0q77uLh1/43cKh0OSpEgkpEw6o0AwqE1NLXrw8GPauKk5F4gSiYQmx0d1+uRxjY+NKJlMqKDAq+HhASXiCwoWFunBhx/Tzt37VFffKEkaGx1WT/cVXbncrv7ebpmWqdq6Derv6dboyJDaLr6rdDqlg/c9qPsfPKKWrdtvGYJv7eMn2IH+XnW0XVR720U57PbFDwJC04ovLMjn9WvDpmY9+flnVFVdo3Q6reHBAZ088Zq6rnQonUpJkvyBoGrrGvTAw4+qoWGD/NfW5l7uuKR3z72ljraLcrpc2rJ1hx54+FHV12+Q3WHX1OSEzpw6rq4rHYqEQ/L6/JqenFA2m1FpabkeeuRx7di1V5VV1YpF59Xb06XLHW263Naq6ekJNTVv1aOPP6XTJ4+pt6dL0fk5lZaW64tf/pruu/+h3MwAAACWEGIBAHkVDofU3dWp06eOq+PSBUnSgYOHFItFJUnZbEZjYyO6dPFdvXPmpK5caVNdfaOmpyYUi86r9eK7GhsZUnxhQaMjQ7rccUmzMzPaum2nqmvr5RUqgjUAACAASURBVHa7JUnR+TldudymY6+9ot6eLvl8AZWVV8jr9amzo01T743r0sV3Zbfb5XA4tLllmyRpfGxE75w5qV8//zM5nU7V1jeovKJSNpuhvr5u9XVfVTg0K7vdruqaOtntNoVmZ9R1pUOnTr6hy22tkqTmlm0aGmjU1OS4OjtaNTs7I0+BV80tW7V5y7Y78l6bpqmFWFQX3zuno797SX09V7Vrzz5V19Qp4A/qyuV2TU9OqK31PdXWN8jtdsvvDyqbzSoSCavzcpvaW9+TJG1satFDhx9TOpXS9ZOMk8mE+vt69MIvf6Kt23epvKJKlmnKkqXRkSG9d/4dnXrzDcXjC/L7Fxt4jQ0Pqvtqp6YmJzU3PyfLMuX1Pqjo/LxGhod04d139Jtf/0KpVFIjw4OSpLZL72mgr0eh2Rl5vT7t3X9Q1qEH78j7CABYXwixAID8siwZNkM+n19Op1PpdPqmhzldLhV4C2QzDA0N9CscCqmnp0tPPPW0jnz2KU1NjOu5n/xQI8MDuvDeO7pypV1ev18VFVWSpKGhAR177RU995Mf6n/+X/+zDh95Qg2NG5XNZvTDH/y/+uVz/6Irl9v0ykvPy+V250Js+6UL+uXP/kXtbRf0re/8ub7xB99WfcMGzcxM6Ve/+Ff9X//nf9bvfvOCamrr9eDhx+R2e2RalmzGYgdnh9OhqYlxXXzvrBZiMVVX12rfPZ/RmVMn5Ha75XZ7VjwN+6PKZNIaHhrQqROv6czJN7T3wL360pe/rkMPHFY2m9UPf/Df9ZsXf6nurg6dPnFMpSXlOnjfA9q6faeKiotVUlKqy20XZVmWGhs36PEnv6im5pbcKKwkbd+xW6MjQ/rXf/6+PveFL+vJzz+jpuYtsixLZ98+rV8+9yN5PAX6xh/8iR4+8oQ8Ho862i7qVz//sX7wP/5eL7/wnHw+n+oaNiw2D/P5VFRUokwmLdM01dnRpqGBfj31pX8jvz+gzo5L19Ymu2Vjn1wAwE0QYgEAeVVb37jY7dhm1/Bgv8ZGh5d93+VyaeeufTIMQ4n4gi63t8oX8OjAPffp3/7hn6qmrl4FBT6FQzPqvtq52Egpndb01KTiCwu565w6/ro6L7erqXmLtm7dofLyClmWJcMw1LJ1u3bvPaCuzg719nSp5+oVLcSicnsKND8/p4nJMWWzGQUChSoqXtyTNRgsUnFxqbw+vxKJuNLplBYWYnK53GrcsEk2m00Op0ND/b2amZqUz+/Xl778Ve3as19ut0df/eYfqbyiUjU19StuiPVRZbNZhWZntbAQk9vtVm1tg3x+v6TF9a01tXUqr6hQV2eb5qNzSiQTuXOLS0q1ZdtOHfnsU7r43llNTU6o9cI5bWzavCzEDg70aXhoQFXVdWpq3qKq6lql0+nFacHtrYqEZvXo1/5AGzY2yeGwyzJN1dU3alNzi8orqhQOzWp0eEhXLrfpwYcfVVlZhRLxuF55+XnNzkypsrJahx58RF94+qvy+wOan5+TIUMbNjbJMGjsBAC4ESEWAJBXfn9AlVU1qq6pk9fru+H7NptdJaVlqqquVVl5hWx2uwoLi7SxabMO3vdArmGQ3+/Xxk3N6rnaqdmZaUXCISWTCZlmVqlUSl1X2tXX06VgYZEuXjin8fGx3GuMj40oGp2XPxBYnHK7EFM0Oi+n06Wamjrdd/9hxaJzatmyTW63W/H4giYnxjQ5Mb7YhddaDIjpVEqWZSkQCKqqqkbVNbXyFBTI6/Wpvn6Ddu7ep1179svlcss0szKMWzeRWg12u13FJaXasXOvfL6A7rn3fpWWliseX9DszLSmJicUi0YXm18lE8pk3h8Vd7s9qm/YoEcf/7xGhgY0MTGm82ff0gMPP6ai4lK5rnVZ7r7aqZHhIe3df1C1dQ3y+fyKxxfU13NVQ4P9CoVmND42qrNvn1Ln5fbc9ft6e+Qp8Mp3rdP0zPSU3G6PgoVFKq+olM8f0Fw4pMqqmtw64uLi0sWTLWux0xUjsQCAmyDEAgDyzjAMORzOD90ixWZb3IbFkBQIFKq4uFRut+f979vtKiurkD8Q1PTUpGLR6LUtWDKKRMKanp7U1OS4YrF5vfjCz1VQ8H7Tp6XtYJqvTSEur6hUMpGUaZnas+8e1dU3Kh6PqbCoROFQSDMzU+pou6CO9otKJhK5bWVM05T0/tYydrtDhmFTIFioTc1bVFxSKpfLfe1+7nwDIqfTpeaWLfJ4PAqFZlVTUyfDZmhooE893V1qvXheIyNDkiQzm5VlmsvOLy0t14MPPapXf/OCenuu6uKFc+rv71FlVY1KSsuUTqfUdaVDkxNj+uLTX1VpWYUkKZvJaGhwQLMz04pG53X27VNqvXh+2cipZVkqKytXeUWlKmtq5fYU5AK+YdjkdDhl2GwqKirRlq075C3wvf8BAOEVAPAhCLEAgPwzjMVA8yFZxFg65kO+73K75bA7JFmyLFOypHQqqdnpSaVSKdXWNejQg4/oyc9/WRWVVbe8lt8fUGlpuRxOpxwOh+x2uyYnxvX26Td18cI59fV2a9v2XXJe2yc2lUrevCatfNuhfFoK1lXVNXI4HXrvvXf09uk3NTExpuLiUhkyVBgs0uT46E3Pd7pcKi0r1579BzUxMaahgT69ffpNlZdXyuvzabC/VyPDQ7LbbDp43wMqKi6WJGVNU7MzU4pGF7cV+g9//Z9UU9twyz1yXS63fD6/vL6lqc6SYfskvZMAgPWEEAsAWLFUKqlkMqmCggLZ7dceLYZuL/LdZPTNdpPpuaZpKpFILo4sanEEtL5hgxoaN970skujskvXiUQi6uvp1uuvvqzJiXHZHQ498NARNbdsVXfXZZ0+eUzZSOam17qTU4U/zEIsJsNmyO12yzRN9XR36fw7p3Xu7Fvy+nzaum2nmjZv0cz0lObmwrra1XHT69hsNjldLu3df68G+nvV3XVZ5945rZYt21RVXat3z70twzC0cdNmVVRWyem8FlIta3Gf2XRaLrdLhYXFqmvYoGCw8OYFW5YsWcs/tPiEvJcAgPWHjgkAgBWxLFPTU5O6crlN8YWYtGyDlo/oo+SZ3FRUQw7n4lTU6PycBgf6NBcJ37IL8vTUhKYmx5VMJJROp9Tf26PXX31Jz/3kh5qemtTefQf1rW//ex157HNq2rxVBT7fHWvMdLssy1Imk1Ff71WNjgwpk05pYnxMp958Qy++8HNdvdKhzS3b9MxXvqHPf/Er2r5zt4pLSj/0moZhaOu2ndqydYc8BV719XTpcsclXe64pDOnjysQCGrH7n1yuz3LpofbHQ7Z7HYlEwn19lxVJDx703pTqaRmZqY1PjaqTObmHw4AAHA7CLEAgBVJp9O68O5Z/dM//DfNTE+t+uu53G6Vl1fI7XIrHJpVZ8cl9fZ0KRyauenxrx/9jX77mxc0OjKk0OyMTp54TT/+539QKp3Sg4cf1VNf/Iq8Xq/sHwyulnXtn1W/pY/MNE3F4zH96hc/0ZvHjmphYUHHXntFbx57VeHQjL7wzNd0/4NHVFvfeMO5H3YbxSUlatq8Vbv33KOCAp862i7q17/8qd46dVyFhUXavnP3suNtdrvKyisUCAQUiYT1xmuvaHCg76b1Tk5O6M3jr+lXv/iJYrH5lb4FAAAQYgEA7zNNS6a12PwnnUr93pEzyzLVeuG8Bvp7FQwWyuFwLk4hthbXsFrXppFmrzVKWn6uJdNcnBJsmZYs88aYZS1FL0uyrl3D4XCqsKhYpWXl8vn8mp+L6KUXfq6TJ17X9PSkstmsLMvSXCSsi++d03vnz2pibFTBomKFwyFNT00qOj8nWVJBgVc+n1eGYVMmk1YymbjWAXnxtbLZrBKJeO59WLoH0zSVyWRuuKfbYVnvvy/ZrKlkMnFD46UPmpqc0JvHjiqZTMrr9SmTyaqvt1sTE2PKmqZ8Pr88BQVyOByyLCm+EFcykZClxSZVWTOrbDajVCqpbDabu67NZldD40Y9dOSzCgSDGhrsV0fbRfl8AdXVb1B1de2yOhwOh+obN6iktFzxhQW1X7qgE8eO6vzZM8pmM7kR49DszGLTqO4uFZeU5ppfWZJ07edtWubiz+yT9GkBAOATjTWxAABJS6N8C4ovLCyGwLmwZmemFA7NLk7fvTaddymgLMSimpme0rHXfqu5SFhNm7fKU+CVDGNx3Wo8rkw2o2wmq4WF6LLQJEmpZFIL8cXXSqdSSiWXN1Na+nomm7lW22KYtNvt8ngK1LJlh3q6u9TedkHn3jkth8OhdCqVG4WcnprU5fZWzc9F1NTcomCwUOHZ6WsBW8qk0xodGdKVKx3yev2aGB9VZ/slxWMxmVlT4XBI3VevyO3xaOOmzXK5XEomF8NfKp3SXCSkdDqd25v2dliWpUw6rfn5OaXTaaWSi1vQzM7OaC4Slv1a1+alDsmZTEah0Kw6Ll3Q0d+9pOrqOlVW1cgwjMUPACxLyURc/f096u/rkSxL8URcba3vamR4UGbWVCQcUl9PtzprLqm4tFzFxSXLOjpXVFRp/z2f0dFXfq32touKRELaf88h1TU0Lts3VpIcDqc2NbWovnGD3Gc9mpoc11unjiubySgSCclb4FMqldL42IhOv3lMTZu3qGXLNrlcrsV7ymaVTCZlmqbS6ZTm5+eUzWQFAMBHQYgFAMiyTGUyac1MT2p2elqWZWlsdETdVzvVdeWygsFCOZ1OSZbSmYyi0Tn1dl/V6RNv6Ow7p7Rz1z59/umvylNQIEnXRuFmlUwklEomNT05eUPH32h0XrPTUzLNrGKxec3PR5bXZFqKRueVTCyG4dDstJKJuKTFdZwPHfmsZmam1Nb6rubnInr1lV/rxBuvqqa2QYlEQvNzYUWjc/rTP/9fdN8DD8vhcKi6pk7FJaWyLEsLC1GdfPN1hSNhNTRs0JvHjqqz49LiKK2k3p4uvfD8T5WMx/XsN76lnbv3KhIJKZ1OaSEa1dBgv+ILMVmWKcO4vTW0ppnVwkJMo8ODWogt7mHb292lrisd8vsD8vn8stlsymYzSiYTmpuL6J23TumtUyfUeuGc/uo//m+qrVvsBrypuUUd7a26+N5ZvfLiLxVfiKm2rkHjY6M6/ebrGh8bkWGzqb+vR7958Zca6O/V5774ZW3btmtZiPUHAtqwoUlbd+zR1OSEstmMHnjoiGpq62+o3+l0asvWHdqxc6/Ov/OWeq5e1uX2VnV1tuu5f/0nVVbVKplKamJ8RHX1jXrwkce0a89+2e0OWZapVDKpuXBI6VRKC7GYRkeHtWFjk6TAbb2PAIBPJ0IsAHzK9fV26+J753Tm1HG1X3pPY6OL+4pmsxmde+eMxsZGFvf0vMlI7OzsjObn51RSWqam5hZ53B61t13QyeOv6Y2jr6i/r1uJxIJGhvr14x9+X5MT4zpw8D69e+5tHX/jd3rnzJtKJBKanprU6ZPH9H//3X/RQ4c/q3QqpbfOHNfpN9/QQF+PFhZiOn/2jFyuxf1Qn/jcl1RX16DHHv+8DMPQ737zgsZGh5VOpzQxPiLTtFRRWaVnnv2mHnn0SW3Y0CTDMOT2eLRz9349/rkv6e3TJzTU36f5uTn1VlVr776D2r3vgC63terie+cUi0aVSaf1pa98XQsLMf3on76vk2++rsGBPqVSSU2Mj+r7//17euTRJ3XogcOqrK69FvRvLR5fUEfbRb3z1imde+e0Lrdd1PxcRJa1uNb1uR//o3738q/kcDgkGdemAWeVSWcUDs0qHJ5dnMrbsEEVldVyuz3au/+gBgf6NDExqunJCb116rgaGjdqy7ad+uM//Qu1t17QS7/+uWKxqIqLS7Vz115tbtmmYOGNnYSdLpd27d6r/t6rmpme1O69+1VWXnHTezEMQwc/84DsNrte+vUv1NF2QdNTE0omE5oYH5E/ENSOnXv19L/5hg49cFjZTFZjo8M6eeL1xdH7ubCy2Yy6r3bqn3/w39TX3aWHjzyuPfvuWdl/0ACAux4hFgA+5ZxOp/x+v8rLK3Tw3vu1b/+9t3W+zWbTPZ95QMUlpbLb7XK53Corq9CuvfvVsnX74lpXQ6qsrJHX55Pd7pDX59PGTc3vhz5L8vp8Kioqkcfjkd1uV2lphfYf+Iy27did60pUU1cvr9cnaXHv15at2+X2eOT3BzQ01K9YdF5mNitPgVcNjZt0/0OPqLllmwLXtn6x2x1q2bpdX3zmq6qprVMkHFp8rbIKHXrwEfn9fjU3b1V940bZDJuaNm/RvgP3amx0RHORsHbvOaAdO3YvTiG2GSosLJY/ELw23fr3v1eGYZPb7VFhUbE2NW1eHOW8jXW1hmHI6XSpZet2+f1+yTBU37hRDx5+TD6fX0ODfTJshiorq7Vj1161bNmuhsZNcrhcstls2r5jj/bsu0elpeW3DNyWaaq0tEzFxSWqqWuQz+e7ZT01tfVyu92y2e1q2bJNY6PDWliIye12q7SsQhubNuuhhx9TdW2dbDabHA6nAoFCbW7Ztrivr/X+PZWVV8jlcn/k9wIA8OllWCvpSgFgTUxPT+vpp5/WngMP6G/+0/+x1uUAa840MwqHQpqdmVE6nVRJabmKS0pvGYpM01QqldTCQkyypKLiktz2MWY2q3AoJKfLpUAweNPzP4lSqZRCs9PyeApU4PXm7j2VSiqRiMtud8jlct86vFqmZmdn9P/91/9HC7Gotm7bpWee/aZ8Pv9Hev2FhZgi4ZAmpyZUGCxSUXGxCguLPzF76+LTLZVK6s+//XXt27ND3/ve99a6HAArxEgsAGDds9nsChYWyevzyzJNOZzOa1Nyb3W8TS6XWw6HM/fvSwybTcGiQhnG+mrg73Q6VVJaJpvNtux+nE6n7Ha7DMP40HtaiMU0MjSoC+ff0bYdu7V330G5XK6P/PoeT4EcZU4VFhbL7rDLbncQYAEAq4IQCwC4CxhyOJy5UPpRfDDs5a5kGLd1nU+KpWm5N37dJrv9xvscHRnW+NiIQrPTKiwq1uzsjDraLsrvD2hT02bVNTTKbv/of01Y/GDAdVvBFwCAj4MQCwDAp1BnxyW9dfqEhgZ6VVVTp7lIWMNDg7rv/oe0c/d+BQuL1rpEAABuihALAMCn0Lmzp/XrX/1MC9F52e12+fwBbdjQrMNHntDWbTvWujwAAG6JEAsAwKfQzt37NDM1patdl1VVXaum5hbt2LVXGzY1y+MpWOvyAAC4JUIsAACfQnv23iO/L6grl9tU17BBm5qatWFjs5wul4ybrBUGAOCTghALAMCnUG1dg2pq6/XQI49JEp2EAQDrBiEWAIBPKYIrAGA9Yr4QAAAAAGDdIMQCAAAAANYNQiwAAAAAYN0gxAIAAAAA1g1CLAAAAABg3SDEAgAAAADWDbbYAdaxN4+9qkgkvNZlAADwiWaaWXVfvaJ9e3asdSkA8oAQC6xDLpdLhw4dUmdnpyIzo2tdDgAAn3j79+3R9u3b17oMAHlgWJZlrXURAG6faZrif18AAD46wzBks7GaDljvCLEAAORJZ2enwuGwdu/ZLY/bw1+WAQBYBUwnBgBghSzLUiqV0tmzZ3XlyhU5nU41NTWpqKhorUsDAOCuQ4gFAGCFMpmMZmZmdOrUKR0/flx+v18+n48QCwDAKmCeEwAAKxSNRnX06FF1dXVpdHRUzz33nHp7e5XJZNa6NAAA7jqEWAAAViCdTmtiYkK//e1v1d3drVgspq6uLrW3t2toaGitywMA4K5DiAUAYAXC4bC6urp05swZDQ8PK5vNan5+Xu++965aL7XSRRwAgDwjxAIAsAJXr17Vq6++qrm5uWWB9czpMzp96rSy2SxBFgCAPCLEAgDwMViWpfn5ebW2tuq1115TNBpd9v3JyUlduXJFFy9e1Pz8/BpVCQDA3YcQCwDAx5DNZtXT06OLFy+qs7NTyWRy2ffj8bh6e3t19OhRzczOirFYAADygxALAMDHkE6ndeLECV26dOmW04UHBgb0s5/9TOPj47JM8w5XCADA3YkQCwDAbUokEhodHdWJEyfU2dl5y+MWFhY0ODioixcuaJBOxQAA5AUhFgCA2zQ1NaW3335bnZ2dmpmZueVxmUxGkUhEp0+f1uWODho8AQCQB4RYAABu08jIiF599VXFYjF5vV55PB7ZbMsfqS6XSwUFBXI4HDp79qw6OjroVAwAQB441roAAADWk0wmI6fTqZqaGj311FNKJpMKh8M6duyYwuGwzGtrXw8cOKD6+npZliWXy6VAIKDp6WmVlZXJ4eDxCwDAx8VTFACA22Cz2VRdXa3PfvazsixLpmkqFAopGAzq5MmTmpyc1ObNm/XMM89o586duWNqa2vl8XhkGMZa3wIAAOsaIRYAgNtgs9lUU1Ojmpqa3NcikYicTqfGx8eVTCZ16NAhPfHEE9q3b98aVgoAwN2JNbEAAOSBZVm59a42m40RVwAAVgkhFgCAPDAtU5YsGYZxQ5MnAACQPzxlAQDIA8u0pGuNhxmJBQBg9RBiAQBYIcuyFkdirfdHYgmxAACsDkIsAAB5YJnL18QCAIDVwVMWAIA8ME1GYgEAuBMIsQAArNDSXrCEWAAAVh8hFgCAPFgKsRKNnQAAWE2EWAAA8mBpn1i22AEAYHXxlAUA/P/s3XlwnPdh5vnnbTRuggBBErwp8dJBUdRh3bdsWXJk2fIReeIcTibxTLKpzU52tmpqs7O7k92tqa3KpGqyqUnGSezJ5HAcx3Zky7Il2brvg6d4k+J9gyBxEXf3+9s/nvdVN0AABEVcDX0/VShK6O633377xft7n9+Jy1TcnViiJRYAgIlEiAUAYBzEcSxJjIkFAGCCEWIBABgHtMQCADA5CLEAAFym4WYnBgAAE4NSFgCAcZBO7CTREgsAwEQixAIAMA5YJxYAgMlBiAUA4DINNzsxAACYGJSyAACMA1piAQCYHIRYAADGAbMTAwAwOQixAABcpnRSJ1piAQCYeIRYAADGAWNiAQCYHJSyAACMg+IxsVEmoiUWAIAJQogFAOAyDTc7MSEWAICJQYgFAGAcDJqdOKJ4BQBgolDKAgAwDtKJnSS5SzEtsQAATAhCLAAA4yCOY0n6cHZiAAAwMShlAQC4TMVL7EiMiQUAYCIRYgEAGAeDxsQSYgEAmDCEWAAAxgGzEwMAMDkIsQAAXKbi7sSMiQUAYGJRygIAMA6KW2IjWmIBAJgwhFgAAMbB4HViCbAAAEwUQiwAAJcphMCYWAAAJgkhFgCAccDsxAAATA5CLAAA44CWWAAAJgchFgCAy1TcnZjZiQEAmFiUsgAAjANaYgEAmByEWAAAxkFxS2yUiSQyLAAAE4IQCwDAZQohfPgjJS2xpFgAACYEIRYAgHEweJ1YuhMDADBRCLEAAIwDxsQCADA5CLEAAFymEILy+TyzEwMAMAkoZQEAGAe0xAIAMDkIsQAAXKZBEztFYnZiAAAmECEWAIBx8OHETkomdiLFAgAwIQixAACMg0GzE9OdGACACUOIBQDgMhVP7CQxJhYAgIlEiAUAYBykY2KZnRgAgIlFKQsAwDgonp04iiJaYgEAmCCEWAAALlMIQXEYvMQOAACYGJSyAACMgzjPxE4AAEwGQiwAAOOguDsxIRYAgIlDiAUA4DKFEAYtsRNlCLAAAEwUQiwAAONgUEtsREssAAAThRALAMBlKl4nliV2AACYWJSyAACMg3SdWIkxsQAATCRCLAAA46C4JTbKsE4sAAAThRALAMBlSid2iuNYUrJOLBkWAIAJQYgFAGAcfDixU5RM7ESKBQBgQhBiAQC4TIOW2FHEmFgAACYQIRYAgHFQvMQOARYAgIlDiAUAYBwMXWKHIAsAwMQgxAIAcJmKuxNLLLEDAMBEIsQCADAO0tmJ05ZYAAAwMShlAQC4TCGED7sTS7TEAgAwkQixAACMg3w+/2FLbPoDAADGHyEWAIDLNNyYWAAAMDEoZQEAGAfMTgwAwOQgxAIAcJmGtsRGmUgiwwIAMCEIsQAAjIM0xEZRpEyUUUSKBQBgQhBiAQAYB6wTCwDA5CDEAgBwmS7oTkyABQBgwhBiAQAYJ2l34ijDEjsAAEyU7FTvAAAApa6iokIrVqxQJpPRsmXLlC2jeAUAYKJQygIAcJnmzp2r3/7t31ZbW5sWLlyoyspKWmIBAJggUUgH8AAoGZ2dnfqTP/kT7d+/f6p3BYCkgYEBNTc3a2BgQFVVVWpqalImw4gdYLrIZrP6/d//fa1bt26qdwXAOKAlFihBfX19euaZZ9T69luiOAamh4ai/+6Zsr0AMNQxSZsrKvWVr3yFEAvMEIRYoIR9KZL+I409AACM6PtB+tWp3gkA44rbXwAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMkgxAIAAAAASgYhFgAAAABQMgixAAAAAICSkZ3qHQAAfPwESQOScpLK5MKobEr3aLCcvH+xpGpdWo1vSF7fn7yuepjH+yXl5c+dvcTtAwDwcUeIBQBMqiCpT9IHQTouab6kKyJp7tTu1odiSaeCtD9I7ZIeyEh1kqIxvr5X0okg7Q3SLEl3ZwohNU4e3x2k5iBdGUmLI2n2uH8KAABmLkIsAJSw/UnYOiQHpHAJry2Tw9NNkXT1WBPaZRiQdCRI24K0U9KB4NbIe5MQN3cS9mE0p4O0X9L7Qdon6VgSQm+U/x1t9wbk4LszSDsk7QvSKUmfiKQ7ktdGcnjfGaSfxNIuSauD9JWMtH6KP7sknQwO10ck9WjkcykjqTz5qZO0IDl/6pLfAQAw0QixAFDCziUtfu9JOhykNjlQVcstm7OHhKMgKR+k85K6JDVKqsxMTojNSTot6e0g/TgJ3nMkLZZ08zQIca1ywHw2lt6Q1CLp2kg6H6Q4Gr3LollIiwAAIABJREFUb05uWd0UpOeCX5+TVCEH9TI5xA5IOh6kzZLeCVKzpAcn8kNdgk45xG8ObiU/KX+GSiWVDJLKIn+WTHClSbmkpsitzWvkVuVGjb3VGgCAj4IQCwAl7IaMtFrSQ0H6o1h6KUhnJV0t6b4kXKTSsZodQdoYpNeCdFh+/mTISlqa7NOhWOoK3p/pYrak6yIpZKTdsUPsWJVJaoyk2+TKgXdG+GyVklZH0lXBofH+SFp4+bs+LlZG0qJI+pSk/xZLf50cgysk3RhJX4rcIp2XdC6StgfpFUlPBenbeekXIukLkfQLGYdbgiwAYKIQYgGghFUomRQpkpZLapBb96olLYukdUOSRCxPKrQskpYH6VuxWwcnQ5mkeZLWJj8fyF1up4s5kVQepOpIqot0SX2zs5LmR1KV3NqcGeG1aZD/SsZhcbGkJdMk7WXlkFohB9fFks4kv7tC0i2Rw2mQx/WuiaRVQXpJ0g+D9HLwuVURuxt10zT5XACAmYcQCwAlLiO38M1NWsqi4CAyWw6NF4ikBXK3zzcjB9/J2s8aOdwsiqS6ML1CbLWkqsgtiJWX+NqMfOxrkmM7UtfjMkn1ciCcjtLPPkc+d8rkY9KYfGfFlklaIp9nG5PxxG8FqUmuJGmazB0HAHysMKs/AMwQl7JMTTqW8XORtHaEQBV0YWNkGOH3F3vdUGUauQAay+tHe93lvFYqjF/9KK+PNPaCdSz7Otp3cCku9dhkJZVHFz8OTZEnpbpZnmX6nKRXw9i6Yn/U7+tyvmcAwMxASywAfMwMyEvHtAbptsjdaIvFyXMOBge6RZFbKc9LakkCygJJ85KW31RO0tlkTG6/3B21Phq9VbP4rfvl8bonkv+fl7xHxUU+T15SW7Jf7Uq69sqth7UXeW2fPBnWiWSm5Dq5O/FYxuoGeRbf9Jjkk/ft1sUDVpt8/HvlyZDqhzyeThTVJVc4LIsKrzsafEwbI7/faKG5T4Vj0y23sC6ILj7b8qXKJD/puTPa5++XP8eR4O+qPtmnKo3+WfLy8TodpI7kvZYmx+5SW84BAKWNEAsAHyPpGqgH5XBzf8bdQfNy4DmazBp8IEiHgichulsOpnuTLqNtwV2X18gTGTVFDnJbg3RA0plkUqP5yWvXyWG0WPq/adj5IHh25Z1yeIuS11wXPNnSiujCVuZ0pt/tyX51hUJAnx1JK5P9WzokbEsOdPuS9zuddL/uTmYhzgWPax1JKDoeO4O3lUnePxP5+A0XgtOwfCQ5Tq1yqHxIDvshefyYfOz3S6oIXkM3lpfu2RX8nIpkTPP6yDM7D/f5TgdpQ/JvLtm3nKRscCVEOv5VckXGIl36+Ny0EuC4XMlRI2lV8m+xfnkm7W3y93teUl/aYhtJTcHjaFfL581QZ4K0Vz7HBtKTJpL6g7vFz1WhwqIqmSH5yjFUYgAAShMhFgBmoLRFrK/od+nsxNuT4Ncrh7z65PdtQXovSD8J0tPBrYyPyC1kG4OX8dmTvK4vePmZf5l0Jd0TpH+MHbzak/ftC9LXI+lXI8+UPJx88vxXkmD9WtIi2i+pLEh3SfqVjEPWHA1uqTsj6Y0gfTf2a+vl8b3vy4HpymT/PitPQpSG4F45SP5zLL0oB6pPyl1hD8QOix2jHNtuSTuSz/t6cOhaLweyXckx6B/mdc3B+/tMLG1MtnO3pOvLpKvk72yvpJ/H0vPB+7FC0t1B6s9I3wsec9ovB+al8vI8i8o8s3D6+fJyRcVLQfrr4CWCrkuC4vty8J4rj1mdIwfOqyPpnmj4EJueN7mi/4+T92kOhbVx2yRdI3/XQ8ditwYvP/QXscdB18mTe70nVyb0yt/VVyJXXhRXcuTktYWfDNL3g4/1FcnEWz9PKjzmy8G8Rg7jayNpTkaqnaZjjwEAl4cQCwAzTCwv33IkSDtj/y7I4addnkm2U9K1Q17TK4eLejnAxpLeDQ6EN0XSF+TWvLflJWQOBukvg7u6LpRb0r4gh7h3g7RJDhn1kXTfCPt6Vg4mS+WfX4ncurpX/tkgqT5INbH0WKbQ4hgkfS+W3oodsv+wzK14sbzO6d8G78OfBqkh48+UTky0NUhPxg5F90TSZ5PWzFgOWv8YPK7zzAj7/FLy2heC9/fByCE5J+mnwcf31XBha2zagFirpOV3mMeD3H24Vg7sR+XZfw/HrjS4OXLA/lFwt+s3Jb0f/L0tTD7f2eBw/f/G3sZjkfS7ybI3m4L0jLz/J+Rj9kg60dYIga87+Hs6FnzT0CtXeDTLMxL/PHl8ltxy/8nMha2pG4P0N7H0jqQvR9IXk6Whjkr65yD9aeyQviiSbpW7B0fJe50M0t8ln2mR/FnWRz6Hr459LLbIFRkPRT6P5iUzKQMAZiZCLADMMDk5YLwnt9hJDkfdcjfZDUkXzGuLgkZWUkPk1rGjkuYFd3dtlAPDpyIHlHI5fJyNC62nV8sh95ZkRtrTyet3xtJJuZtwn/zaoWMes3Lr6b2RdEcSptqC9/GZIO2WA1CDpHvkwNord3t+K7j19DNJa/DSyK2Dc+QAu1nSETm0XpPMGtyebO/F4CB5YyTdHblLdCT/tMot1UNDbL8cIF9Jttkk6c7I4T0dV3x38vnfHCbEzo2k6yV1RO6u3Tbk8Yzc8toauVV3e7KNeZI+lfExnp98p0eTf8/J4bI9Kqw3e0gOtgcl3S5/9tXJ/uXl7+R7wZ+lPJLuihyAF2t4J+UAmU8qRLqT49gpt6J+ILf6Pxh5orAVurA78Um5K3Gr/B3fkLTWzpdbcivl492cbL9cblnuSFpwdyfHf20kXZV0L++S1B1Jb8mtzd2SrpTHeS+MaIUFgJmMEAsAM0yQQ2NrkE5Hhd91yUEhlteVLVYuB9bGZEznQjkUrI6kL0TSXUlrZiyPeXw6KgS1GyO3gF2fbHNlss1vyF11u5KfOl0YYmsl3Sjp85HH52YkKZKuDFIUezznUbn77Zng/WtPQu4+eUmbxsitiu2hsN0KOfgOyK8/FqR85LGmm5Pg9Sm5NXpB0bFYEDmI/lXSXbVYlzwmdWMSQB+RA1XxxFirIumGMPws0fPk8ZodkfSqPKa2WEYOZwPyGNOqZDvrIulrGXcBzkoaiKQb827xPiS3gha36h5NAmz6eeYWPTY/CZnVcoitkVuRF40yfrRHhRCaS/7/vAoTNKXf6WIllRDy+VR8g1ElVxSUS1oe+VwYkCsk8vL3NSCPd+0JHtMsOShvC67YqJXXQk4ncaqUu1HPS45TTg6vV0WDv1MAwMxDiAWAGaZSbh39tYz02aLUmIbbp2Npfxj+tVKyNqgcTmrlYJB2zczIgWO+HMpOJ/8WrzWbTV43R255G5ADZtUwXTwr5bGrDdHggLsykj6fkX4UO4D2y610y+RgsyO4le6w3GW4QoNf3ym/1+Lk31yyH+8k4bdKHgc6d0jYqZbD1nAzIp8L0vOxx5vWyy3ZQ1sc6+RQPVKGinTxpZAyKhynerkLba0KBXYkfyeNcotuhxwGU90qhNpuOXSmZsnf3Rw5bHbJwb5BI7dcXhNJj0fSv04OcK/8fZ4I0s/lLtQfSPpukLbE0mOSvprx61L3Rx7H2i2fm5K7Pb8RXBlyXq4gCcm/qZzc6juQfO5OOfQqOR7zIv/UJRULzUE6k7S6AwBmLkIsAMxA5XJgmTPk93m5u2VWbuUcLrdEcpBK/x26bmokh7w06KXLqxQ/XiYH1DSsFQeToe+V1YUttGmYXCjP5JtLxub2yqHsaPC/ayPpNyOH0qHBMCTbnxu5BS+fvO5s0nLXmLzP0P0ZaZ3Ybjmsdcqtr3OHCeVDj8Vw0mM7kqjoZ6TjXyUf3zT0FR/fBZG0KPixD4J0JPLjGTkU9sqhcSDZRlPk2Y5HUi6H8/nJ/+fl4LhQDuxXBk9ytUvuXvzPklaFweOQ50WeafmMpC1B2ptMxjU/2V65XFERNHgN2Bq5dXuWPMZ7i3z80+f0qxDagzz+evYoxxYAMDMQYgHgYyQjjxvsj9z6VjXC8y7WG/NiYa04DBaHkrFKW4Pr5aAZ5ACWzrqctvAulrsyz49GXis0fe92uTW3XYOD+lj1ya2PPRo97F7MWF5zsecUt+YOPbbL5VbmeUlo3Bfc8t4UuVvuB8FBcJ7cNXyxRj4PhlMmfyfVkStJGiWdSLa9Xf7ZFtxSvSh5TToL9bvBj50K7tJ+XeSJo2pVaGEtVhd5NuLlweObPwjuhr1Q7kp+JN2WvOTTcl247jEAYOa5WIUxAGAGieTWqlsjzxBbr48WMidDcdAsk1vYKuR9zaswzvdEGH5Jm1Reha6qaRfavNwNt2+U1w3VL0+41CevT9o+zORN4+2jBN4VkSebukE+XjuD9OPYs0m/Fzyj8IDcIv9Q0h13uO7TY1Emt0jfH3lirdQxeVbh1KngZYP+fewuxNdH0p+VSb+d8eRYDRq+i/Vs+bPcLGmJHISfD+4WfjhIP026xi+U9EQSius+4mcBAJQOWmIB4GMqbd18MfZEQl/NXNi9dirFctDskbvILo884U91cGDaHRzMfixpQbhwiZgeFdZmnSNPTtUkB/cOJTMEp32OR1Ac8KvkINWcvH6fLlwmZ7TXT5ZyeXKtX4z8+Tvl2Yr3xP6osyT928jhcf04tFpm5G6/xV2r6+TW1SB3XX4uSH8ZOzw/kvG+1Wj0scHptqvlpZCOyYH1eJCelrsnx8EBelXk9WmXjsPnAQBMf4RYAJgh0tbGof89kj55gqTN8hIlxd05Q9G/wwWxj/L/lxLmuuUxu+eUrD8qdxmuksPRVfJ6tSfk5XLWBgeepcmkSgPyhFDPBakluIWuTF5DdK48IdVWebztTRq99S5tya2Ww9K+4BmBNwRPYrRYF07wVPy5cxrc0hmG/PdwxyUM89yRDB1vnLYY71PSrVhJt91kP5vkGYmviC4cM138/nEY2/v3hqIKgcTiyJNP5eXjv0fSDrlldbl8HNMxuv3y95Uei3zyu0zy/z3yDNORpE9H0m3yJFQ5uevwCnkisOUR3csA4OOCEAsAM0BQoZtrOuHNgBxwoiHPS8eVngqeqfi0CqFCRY+nEwYN12U2ViH0piFv6P6kXX7TIDdcIIrlkNKfPL8s+d2Z4LVY24K7x94TFZboqZNbEOfK4yM3S/pB8OvuT7rGdsrr5P5dLD2WccDJyuFtsbz26BZJmyStDw572WQ/zyXdbdPP1ppsr1Je5/VdeWmbd+XlehbI2y9LvoOOos/aLwfxoMFje9Njkx7nYmmIS4/hcMe/+Jin20udCx57+mdB+h8if/710eAJo9Ku2j3J50p/p6L96i/6/3R257R7d3oe9coB88Xgf8vloHxVEipDcvyK1yvukrt1VyT/pmvDxsnxa5WUD660yMotrz8IPq5fj6Tbky7Q6f6mn6Ur+Szl+mhjlQEApYMQCwAzQCy3XLbJgeOUHE7bgifgiVQIp53J5DivB+l7QbojmTwnmzynO3l9vxwq2oKUS1JBGl7OJz9K/i3uVptPXteebKNfDlYLpQvSRaccuG4KblGbFzmMbJD097Hf695I+kKmsI7pnMj7fEPek/0clfRS8NjPbyWh8rQcHjOSrpOXeymXdHdG2hZLryWh6KlYaomkX02C0cngQHY8CV1tQfphLB2PvGbuIxnp1VjaE/yZvxFLhyMvZdQoB+/nikLwQUnfjqV18nqvcyPvVxrcT2vwEjjp8TyVbKNLDnXFQTckx607Odbnhhz/g8n32y/p+0HaGrvlulKFWaUrkkqA1ZJuzbhioFyFAHtWXhs2L7fSn062W59UEuTkFtb3gvRCkF6UJ5FaLunLkT9vTfJ1L0uObZx0bf5Z7LVgl0Tu6v1Oct7m5XV4vx17+1/KeLKmDbErNfZK+k8hOY5DPs+syDMd3xK50mOkNW8BADMDIRYAStjB4Blbd0l6O7hVKycHih8Hr6NaEQohNg2Yp5IuoEclfTZyd8z2IG2U9GxcmP33g+AlU9pjt9ZKDi6bQmFtz1eCWyGb5QB5IEgvyOEqJ3fr/QdJj8hdd5sih8rbIwfqw/IaozslLU0+w/HgdWW/kkw+tFSF8ZPlcjfYz2akTCz9PNnGXnmyn9nJc1Ykny2d7CcjB50HIgfXnyfv9Vpwi2JdSMZpJq3ZZclr2pPPMUvuJvv5pIXx5eBxms8F6Vjs4FQrtyIGuYDNBx9LRT4OG4P0k+CxnX3y8X82lsqTybZ2yqHw5eT49sqtvd+OfbzK5SVqni/axrYgPZds4+6Mxw03Bj/3lHyMDybHpCzZr2zw52mKPCb6iSQYHgrS+8k+nFChQuSV4HOgJiq0lvcGh/6Tchfl2yJPwvSQHFzLlCx7I1ck3CV34d4mVw4sCw636yPvz57kvD0e/Dnmy0F1QeR97U6C7Lnke8oWfZ7K4O/4jUi6N+N1bYvX1gUAzCxc3wGghJ0LnuDobUmKPKFPqlUOHyOJInePXRc5KLQm2zolL48iuZXriDwONF2G5Z0ktK5LntMjh915yeO7k/+/KiqsAfqBpGvlYLkkeW1/RqqI3Yp3RA7WC+TQuFCeqOdzGbfuDV2PVXLX4bpkGxvkcaD5IGWTcZL3SvrVjFtu067SWbm1bnYSgLfLwWmXCt2FH8y4pXW+vETMVfL40abkeDychCrJ3Yo75PGelZIelHRj5ONYGbklMR2v2Za0kDbL4S0dj3oiOWbr5AqAI0EaiHy8lHz2TaGwFM6mpOVyXlQ45qfkgHebHEYXRQ7lc+X9CCq0ouck9SUt0R8koXpZxu93QG4dPSe3aK5Itp+XKzjSftJR8nlnRV6mZ0XaOq7BkytFyTG7KXJQrkm/Jzmg3xN5ZuLNkYNzJjlHHs34PMnL339jcm6ky+eknyUkn6U7+R43BKk5lm7IFNaXBQDMPFEIYZRbHADTUUtLiz7/+c/rwXfe0n9kJpOPtbTLb59GHnc6mkhuwaqRA0N30bbSxzMqdN1U8px0zdb0OeXJc7LJPvWqME4zHW9ZU7SdOHlej9zF9HQotHgulLvdpmvEZjX8GMeQbOO83P31dHCr45zIgWduEmKGrgebBrn0fc/IgW6JHOZrJW2MHf6WR27ZrUr2O6PCWNBOeXmftNV6mRws+5IW1+WRJzeqSz53OuazT4PHsGaT7dck2+3V4CWDMslzqpPPkT4eD7ONWjlA/ySW/jJI/1tGuiEqhMb0u+lJ9ntLkP4pSP8ikj6XtOT2qDBmdzTpuZHuX/rdDjfj8IB83rRJ6kg23FT0/fQkx7IyOYZ1yXZag7to/3nS0vp7mcK5kx7L3qSb8jl57GwmODA/nnGXZeD7QfrVbKWeeuopPfzww1O9OwDGAS2xAFDC0jGB47E2ZlYObBdzsWV4qsawnUzyvMrkuY1RIfjOUiEQjyZtDaxQoWtsv9wNOQ2dI713hdxKWRu5tbU/2UZVst0bIrfopt2QoyGvr5GPw6zIQSpOXl8ht6LeLi/5U63Boa5Ko0u7JI9mpOOfjl99Jrhl+lNJV92ro0JX8vQnJ+/3XLk1uV4+3rM0Ma2X5fL3XCt/T1LhWKf/XRV5H9LvrUduIf+vwZ/5jqLPUvx50hmO++RKiVPR9Fz3GAAwfgixAIApE8kh73LCUxpmK9P/uQRVGj5YzhvDdiINHzor5e68k61X7pb9VvAkV78QFWanLq7kCJLipPt42nq7KHJFwkSKlIzHHeGxocsUnQteAuq5IN0vj/lNKxuKtxEnFSAtwWN2G5NW8MqJ+BAAgGmBjogAAMwAXcEzEZ9KJuz6afCkUKfChWvT9qswQdR7wd1u10yzrrdHg8cm98gty68Gj83u1uDPk5MnmHoull6L3bX4zoxnUgYAzEy0xAIAMAPMiqSbM9LVsWcuPiavFfuD4MmTGuRW7155DHGVPOvz/5PxrNEX6+o82a6IpBslLU8mmnohSPtjT4jVILfI5uWx1L2SZgXPZH1bxl2XhxubCwCYGQixAADMAJXy0jafyUi1sbRPDn8nkkDbIHcdrpCkyLP+3hZ5yaH6aPrdEDRGDtdfS2aLPi53gW6Rx8jOTv7NRF6j94aMZ7S+Ohp5PDQAYGaYbmUWAAD4CNKxxV+OpPsyXj92t9y1uC24wJ8fufV1RSStTWZPnq69bivlNX6vjTw2dmeyTuwZSbkgVUeeUfqKpCv0uojWVwD4uCDEAgAwg2TkVsxbI2ldkPqiwpI+5XIrZVVUWLJnOkuX8VkdSYsj6Z7g5XqCCrNMVyazUTPJBwB8fBBiAQCYYcrlZXNmwuRG6czFNen/AAA+9qi4BAAAAACUDEIsAAAAAKBkEGIBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBkvsACVsS5C+GU/1XgAAMH1tnOodADDuCLFACcpkMmpsbNSGpgUUzsA0kM/n1dXVpTiOlc1mVVtbqyhiUVNgumiqqFBlZeVU7waAcRKFEMJU7wSAS5PL5bR3716dP39+qncFgKTm5mb91V/9lVpaWrR69Wp9/etf54YZmEYymYzWrFmj+vr6qd4VAOOAlligBGWzWa1du3aqdwOApM7zncrn8+rs7NSZM2e0dOlSLVy0UEsWL1FNTc1U7x4AADMOIRYAgMtw6tQpbdy0Ubt379bJkydVWVWpDRs2qPqeakIsAAATgNmJAQC4DLt37dZPf/LTD7v3N59u1g++/wMdPnJ4ivcMAICZiRALAMBHEMexTp48qe3bt2vLli3q6emRJHV0dOjdd9/Vrp271NzcPMV7CQDAzEOIBQDgI8jn83p/2/t6//33dfLkSeVyOUlSb2+vjh49qs2bN2vP3j1TvJcAAMw8hFgAAD6C/v5+vfjCi9qyZcuwj7/11lt66823JnmvAACY+QixAABcoo6ODu3Zs0dbtmzRsWPHhn3OgQMHtHXrVu3bt0/d3d2TvIcAAMxchFgAAC7RqVOn9PLLL+vAgQMjrtfc3t6uvXv36pVXXlFra+sk7yEAADMXIRYAgEuQz+d18OBBPfXUU2ppaRn1uUePHtUPf/hDnTp1Svl8fpL2EACAmY0QCwDAJTh+4ri27/CMxCO1wqbOnTunTZs2aceOHWo+w0zFAACMh+xU7wAAAKWk9Vyr4jjW2rVrP/xdT0+Pdu/erd7eXs2ePVvXXHONysrKJEkVFRXq6elRR3uHFi1cNFW7DQDAjEGIBQDgEkRRpCuvuFJPPPHEh79rbm7W8ePH1dvbq6amJn3pS19SRUWFJCmTyaipqWmqdhcAgBknCiGEqd4JAABKRV9fnwYGBj5cF1aS9u3bp69+9avav3+/7r33Xn3nO99RbW3th49ns1lVVFR8GGwBAMBHR0ssAACXoLKyUpWVlYN+V1dXp0zG00xks1nV19dr1qxZU7F7AADMeEzsBAAAAAAoGYRYAAAAAEDJIMQCAAAAAEoGIRYAAAAAUDIIsQAAAACAkkGIBQAAAACUDEIsAAAAAKBkEGIBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMkgxAIAAAAASgYhFgAAAABQMgixAAAAAICSQYgFAAAAAJQMQiwAAAAAoGQQYgEAAAAAJYMQCwAAAAAoGYRYAAAAAEDJIMQCAAAAAEoGIRYAAAAAUDIIsQAAAACAkkGIBQAAAACUDEIsAAAAAKBkEGIBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMkgxAIAAAAASgYhFgAAAABQMgixAAAAAICSQYgFAAAAAJQMQiwAAAAAoGQQYgEAAAAAJYMQCwAAAAAoGYRYAAAAAEDJIMQCAAAAAEoGIRYAAAAAUDIIsQAAAACAkkGIBQAAAACUDEIsAAAAAKBkEGIBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMkgxAIAAAAASgYhFgAAAABQMgixAAAAAICSQYgFAAAAAJQMQiwAAAAAoGQQYgEAAAAAJYMQCwAAAAAoGYRYAAAAAEDJIMQCAAAAAEoGIRYAAAAAUDKy47GR97Yf1QdHzo7HpgAAKDknjh5SZ1efJOn02fP63nPvq6q6Zor3CgCAqXH9VQu1bvXCCdv+uITY597Yq+//bNt4bAoAgJLT33VW5wcqVVE7VydaY/3nv3tdmbKKqd4tAACmxO9+9c7pH2IBAPg4K6+u19KbnlCI88qUVSiTKZ/qXQIAYMYa9xC7cOVx1c7uGu/NAgBQQlqmegcAAJhU7S0NajnWNCnvNe4hdv6y05q3tHm8NwsAAAAAmKaO710+aSGW2YkBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMkgxAIAAAAASkZ2qncA46OjuUfNBzumejcwheZfUaf6hTVTvRuXJMRBJ/e2qbu9f6p3BVOksrZci65uULb841unevqDdnWe7Z3q3cAUyVaUadHVDaqs+fjdkg305nViT5sGenNTvSvAuCrLZrToqgZV1ZVP9a7MWB+/K+YMdXhLi178i50qK5OiaKr3BpMpBCmfl+7/rWt146PLp3p3Lkk+H/T2P+3XsW1nVfbxzTAfW/lYmr9itj7/BzcrO6dyqndnymz+yRHtfe2Eysqmek8w2eJYqmms0uf/4GbNv7Juqndn0p0/16uXv7mbkJklAAAgAElEQVRLHae6lKEMwAwRx1J5bbke/4ObtejqhqnenRmLEDuDNM6W/vBfSquXTPWeYDIdOiX94V9P9V5cns/dKf3O41O9F5hsf/8z6fmDU70X08M966U/+JWp3gtMtqfflP729anei6n3m49KX7pvqvcCGB/Pb5D+7Jmp3ouZjxA7g1SWS3deJ33iqqneE0ymbQek6hJvxLpigfSZ26Z6LzDZ3t5BiE0tauRv4ONo/wlJhFhdvZzzHzPHqXNTvQcfD3TeAAAAAACUDEIsAAAAAKBkEGIBAAAAACWDEAsAAAAAKBmEWAAAAABAySDEAgAAAABKBiEWAAAAAFAyCLEAAAAAgJJBiAUAAAAAlAxCLAAAAACgZBBiAQAAAAAlgxALAAAAACgZhFgAAAAAQMnITvUOYHqLY6m7T+rrl6oqpcpyKVs2yfsQpO5eqT8nVWSlqorJ34dLEcdSkJTJSNFU78zHVAhSLi91dvt7qKrwuRtN4heS7sP5Hu9DRVaqrpy89x+rOHhfI3k/AQBjN+3Km0iqKJ+e5Q0wngixGFEIUm+/tOewdPCktHqZtHyB1Fg3ufvQPyDtPSqdaJGaGqXVi6XG2ZO3D5eqb0DKx1J1lQsTguzky+Wl1k7p7R1SbbW0crG0rGlyKz9ysXSuU9qwW6qplJY0SWuWTO6NzcWkf1/5vPerunJ67R8ATHe5vNSWlDc1U1zebNzjEL10vrR6qe9BgJmKEIsRDeSlbQekp9+S3tkprVos/fJD0r03TMJ756R9x6T3dktb9kmHT0vl5dKDN0pz66ZfiO3qlU62SJv2Sls+kObMlv7V56TaKql8Grcaz1Snzknv7JC++5IL8dvXSl//nDS7ZmLfNx+718C7u3wzsfOQdPysdNNq6ZM3+2+obIpvKuIgHW+WNu+T3t0tNbc5yJZnpcZZ0q3XSjesllZPs8ANANPRqXO+R/ruiy5vbrvW5f/s2ol933ws9fT5vTftlXYclE6c9fX7UzdLq5aIWnTMaIRYjCjEUke3dOS0L45RkNq7Jue94+D33n9cemmTdOCUNLferbDdfZOzDxcTJA0MSAdOSh8ck3Yckt7a7sJk7Qrp1x6RqiskEWInXV+/dKZd2n3EoXHpfLc2ToZcLJ1udUh8ebPU0uGuxOtXySfNFOofkNq6pBc2Sc+9K72xXWo/L/X0O7DW10j7jjvYlmWkhXPdigwAGF7fgNTSLu056sy4ZJ4D5mQYyCflzV7ppc1SS6crJG9Y5Z42wExGiMWIysqkFYulq5dJx09L993oLjKTIZv1e91xnXTohNTRM+X3/xcIQWrvlv7xebdWv3/A3YqCpGtXTPXefbzNme2KhNWLpTl10q3XeIzSREu75K5f5e7vx5ul9p6Jf9+x6uiWth+Q/uyH0sa97iWwoMHd3tq6HbiffN0VM7mc9Pi90hULpnqvAWD6mlMnrb1SWr1Iaqhzb5aKSShvMkXlzckW6Viz1P7BxL8vMF0QYjGisoy0qFH6yoPuCjmvXmqaMznvnYmkubOlq5c7EO465prO6SQTSXXV0mfvkj5xjUPsH/+j1HZ+qvcMdTXSuhXSv/+aa6XnN0iVFRP/vplIqihz8Lv2CgfGrYcm/n3H6sBx6W+ekZoapN993JVETfVuid19RPr+S9IHJ6SDp6T//qy0bqW0sHFyKgAAoBTNTsubX3eF4PwGj0udaFHkXj5XLJCuuULaflB6fxqVN8BEI8RiRFHkMZ2rl/pnUt9bLgTmzHJ4nq5dGivKpWuWO2zXz5L+/EmpY5K6XGNkFVlXgsydgrHTmYxDdONs19CXTZMZf/tz0qlWj3P/3N3SgzdJN67xvg7kpauWSQrSd16Utu6Xdh6Rjp3xjJuV9VO99wAwPZVnfb2firk6MpGv4XNnuxV4upQ3wGQgxOKi4tjjO4J8gRzuIpnLF8ZfZMscgOPYN8fp0h1jmakvDh67mI/9ulze7zfWCWbSfU3Ho2QyUjYjRUOWuwlh+PEiH75PlDweBj82dD/SAkSSaiulqiyzAU4ng87L7IXnQD72c8qLvrf0HJSSc3mMSyXl48K5W5bx9svGcM4Hefx58XlblvFri8+lEJLTcYTzNoqGeU4yO3YUeabxXN6tBp++1ZOPVGQL77dqsfS1z0ibP/AY+BCkcx2ulJlHiAWAUY1LeTPMfcZwhitvsmMIsJNV3gza17w/Z/Fn/Cj3h8BQhFhcVEe3u/L29EmL5l54QxsnE9l0dPnitHKRL4ht5919saLCtYSL5yUXvxHeJw5e4+z0OW8vm1xY+3Nj288Q3Gp0pl06ddYXxvpaacl8L7OSzhKcD574p2/gwm1UZAsX2Z5+f7ZUZbl/RqrpjCK/lrU2p4dc3udSZ3cyvnvR4IJyIOdZJY+d8SyOc2b5Ozzb4d+HIF25SJpV5ZuOkQT5uc1tfr9zHa6RPzvG7u+5vNTVI50862WBQvAkZovnubt6ekOQy/ucTcddF6vIurdCT78/V3rDkIl83ldXepKx29ZK//fX3dV56KzZ5Vn/bS+e67/X1i6/bjK6YQNAKRta3ly5aPA1diDv+5K0vGmY5evz2Q6/Lv4I5U3zOb++sW7s5U0+WUs2LW/ikFz3x7m8SbcTgu/n0klBVyzy5+vokg6ccJncONv3aaPdHwLDIcRiWK2dXtZm31EvdXOmXVo4R3r4tkKIPXLaF6G9Rz2OrjwrrVzoGVB3HvLsrKfO+SK1ZL500xrpnus9XqTYQM4TJL26xbP89vV7W1090pk26cQ578tIcnkH5nd2SLsOu8tkRdZrpqUX6PtukK5f4dlWz3V6Jr+tH3gimzj4wlmWcRfL1UscRJ99Rzre4trKhlnSXeuk61Y47AwnrVGMVPjB5ApKzsvj0t5j0gfHXfFw7XIX0v05F/x7j0m7DnnsZ3ef9GsP+/vae8zn0NkOb29uvfT43dL1K10hMtT5Hmn/CenFTVJPrwvsTEZqaXPBfeiUW0CHk4+lo81eimdrMhlHR5f3J5OR1iz138zta921/miyLM7OQ17SKZ/cOMyvl25cJd1/k7Rtv7sC7zvuz7NqsSf9uPUa31g1NXgG4uHWg40i/92FpIa/vExqrJ/4ZYkAoFQdTsqbfcd83a1MhhgtnisNZKTmVt8jpeVNV6/0tUd8vd17VNp1xAE0yPcqYylvXtrkpdzS8uZsm+97Dp12sBxOWt5s2uNlAKXB5c3qpdLNReXNsTNeaWHXYb9vcXlzwyrpgWHKm5WL/dgtV7v8Ky6HMxnpygW+v9t33OXeqXP+/eK50o2rpXvWew4GYKwIsRhW23nfWD/3jteSbOmQ7lqbLBOyxs852iy9skX6+QZp+yFp3mzp1qt9cX1nl1/XN+BAUF/rx5bM89pp6UQxubyD6nt7pO++4G2uWiwtX+AL3Ka9vmh39fl3w2lp977+8DUXEkpag/cc9Wtb2qXjZ6SBB6QHah2OD5+S3tgmvbvHLWf9Oaky6zGM6TjKJ1+Rth10S9RNq6UrFzrgjojUOvWCv9tXtkgvbJS2HpCWN0m5Oz0B10DOBf1rW6UnX/NNRG2NJ8bI5dyVdt9x31R0drv3QWXWNdQ3rC56m+Bt7Tkq/fw96e+ela5Y6Frm+Q0u+Hcdlo61DL/UwkDOf2OvvS+9sME3OCsW++/h5Flp52FpwRzpU5/wWOurlrpHxL5jXrZn52HfJARJN62SFEt3r/eSVE+9If18o7RsvvTJm6QFje5RkC1z5U7FCFf9OPlMHd1+/qJGh95ZhFgAuEAIrjR9Zauv41sPeFWFz90pffZOL7d2+JSv80++WihvrlzgcmHzPmnvkPKmokyaVe1QV/w+aXnz/Abpb591mZWWNzvHUN60j1DenDrn5QEXzJE+WVTetHcVypsdhwaXNyGW7r3B91c/fkP62QZ/7gdvcmNHHDxT8mvveym37YcK94DdvQ7Rb+10Be+5Ts+9cvMaB9g5dUwkiLEjxGJY6ViHmsqRLygheNzHrGq3Yh5vkbp7pEMnpduvk/7HL/rC/P1XfJF9Y4fD5sLGQiDt7Jbe3SX9r9/wRe7h26Tf/aKDY2untGGP9L9/Uzp4cuR9eHen9K2n3Xr0xAPSZ+7wRXHHQQeVP/1n6Z9fk8rLfXFe2iT90qccSv7iR9JLW9xiJnk90SsWSee73Vq1YqFbw37vCQfw6mk6wRQKghzYaqsu7No9kIyxXtIklWV9k3G+2+fB+pXSjVd50qMNu7yG6vaDrqRZ1Dg4xA4klS9/96wL6cXzpN/5gmf7zWZcO//Nn0jffLpQg12stdNL3HzjR/5b+D9+w9svz7qA/6NvS7uOej3XjKTf+0Uv4bB0vm80/uR70vde8v4vmictnu9w2tToyT3m1Uv/9gnp/ht9szJa97RU/4C7tbV1Sw2zpU/f4lp36mYAYHhxcHmT3gcVG8i5DFoy3/dKudj3PH/xlFtbb7pKeiwpb97cLm07JD2/0cO2bhymvPn759xDbPFc6Xcel+5YVyhvvvUT6a9GKW827fX7Lp0/uLzZ+oH0R//gifxeTMubLw8ub/6/70n/lJY3c30vVF7m4JuWN//zE9IDSXlTkU3mUCnznCFlZQ7Lr211qL9tbeH+8MnXvLLDG9td9i1tcoMBMBaEWAxrXoN02zW+sT3aIh0/e+FzVi91TVp3r2+449g35L/ysGc9bZrji/ixM251OtPuLi/tRbP3btvvmsGjLV6H9oGbfGGMMg4hff2uvRuuS2Ycez3LjfvcevbF+3xhXjzPhcl1K93duSkZ33fghIPD/DkeQ3LVMk9k09XrWsrOHunNbQ7D2TJfYG+9RvryA/5cVRXc0E93UeSKir5+t95vG7LcQH2tu+nOb5Ceedstl0EOnw99QrrlGtcEL2p0wbv9kHT0tNd8LdZ+3jcTm/d5zPcvPuAlFuYnXe0ryt16f/0KacfhC/dz/3Hpey96P1cv9RJNc+ulssit/Z++VTrT4cqVV7dJT3zSBfucOumacunRO3x+Pveea8nf3uluXM++7Rblr37SNeVXLhr7zN6tndLP3pNOtfi9vnS/W3EBABcqLm+6ey9c3qahqLx59m13qw3BlfwPfcL3F3PqpMWNvuf4sLw5M3g7xeVNebn05Qd9fzOovFnjYLxjmCV2DpyQ/ulFqbfP+/OJa9zjrKxMWrXUFZbN7Ul58770xINu5W2s87bT8ubZ91yevb1TumGNy9CBnBsF7l0/uLxZudjv19sn7TkmDQw4zP/Sp/z+Cxv92tOt7g13tMX/3dYpiRCLMSLEYlizazz5wOnWkWcmXdjocHmk2ZPGhGpfIB+906EvnUTnuhXulnu6zReo8z1J95i8x1O8vt3dedet9EU4bT2rLPfFf90Kd6M51zn4/ePg7i57jvoCnMsXxpykWtr9WXKxC5mDJ6T+JBDX10p3rkvG0Z6TXtvm7i9Hmz3RwOJ57qJ567XeFwJsaVg0192yDp++cFKi2ir/LGyUFjRINRWuub7relegXLXMz6upcuVLJKn1vNTW5XM2igpd4J97T9p/0qHzU59w4EvHmc6qdvfia65wBUuxgbz37aXN3r+O89LOg4XH25P3qipPJvBodSVQ74A0u9zn853XuQV55yGfu69vc9ffzftcg/7F+3wTUVs1tmPW2+9ucc+843P99ms9e/F0XdoKAKaDRXM9DvXwaV+zi9VU+WdhoyvnayqkXJDuXueut4PKmxaXHxeUN7HvY372nrvvrkzKm4VDy5sFnvthpPLmxc2uiG/vurC8iYMfSyeMGq686exxj7qTZ33P1tTo8ubaK6Uv3ef7xeLyZsEcN4Icb/Hnm1XtoWKfucNlZto76Lor3QJ7JGns6OwZ3+8HMxshFiOKlEwFP8rU55lkMpgokupqHfxmVQ+eBbapwTWG6ezBPX0et9He5Vq9rQfcAtXUcOGkT9kyFxKza4cJsbFD6akWT1Dwnec9jrV4wpoQfIFunCXVVbnm78PZ4JOFwh+9w612r26T3j/on8VzpT/9N9J96ydn0XKMr0zG3Z1GW6qgPJmJWkFaOs814qmGWYXKm94B/+Rid93qHZBOnpPe3OkC/YZVbrmsGHIDU1/rc3fo0gHdve6VcKjZ/3+mzbX0qfSczUTSkkbfAPQPuEY7nWRp1WKpb70nM3tpS7Ku62HpwRsdyO9ed2nH62yHa/Bf2CR9/bNuWWZCJwC4uLKi+6CRlGfdpTjk3SJZXN7MmeWeONIw5U1/obw5cdYV/VcuvHCY1+yLlDeHi8qb50Yrbyp9n1Rc3qxc7Huhd7YXypsdhzznwt3rRi5vMpnCcZlV42EvdTWDh7fMa/C9n+T5Snr6Rj6GwFCEWIzqorPspmuajfKkinJ3gZEK08Pn8m5hOt/jiQzm1CXddS+huTMEzwLbdt4FwL/7ZdfwFV/cP1zDLPLFec7swYWH5OB869XugvnyFl/kZ1Um+0RLVEkabq26C56TnrvDjCEqzw4uaIuf0tnlczef94RPDbWXdt62n3elS0VWumOt9PCt0l3XDXmv5JxNZ81es8wTbhRbPF/6zcfcRf/kWT9/wZyRZ88eThy7AufZt92y/PjdruVfNdoEZgCAgjEsDTPabVJ2yIR7xeXN+e6kvImT8mbWRytvyrPSnWulh2/xSguD3mss5c086bcec4+64vKmYcj91FAXW2e9IluoAA6hsM4uMBaEWFzUaBegNCyM9pxM5sIJD/J5j4Po6fP1M46HzRKjCnLX4b5+XwTXLPX08A0j3MSnF+uhBUB1pV8zv74wIUHvgLTroGd4rVt6iTuGqXepfb+H3GFkopHX++3uS9bXi4sK3Ut4v95kjeIo8k3J1cukB28e/rlppU9mmO1XVbjLcm11YQH7w6f9037eNd8jrWmsZLudPdKrW92dK5uRvnivx5UPt7wDAOBCY6k0TZ83nEw0/DVecktqa6dDbFreXErx1tvve6QPy5vlH728Wb7QXYbT542lvLnYvhbfH5JfcalGucUBLs1Y1kZNnxMHB9iBnMdstJ5395XhpocfdXvJBgdyHtPX2jn88/LJmNi2rgvfo6/fgfrwabcYV1V4SaEnX/NaZn0D1A5+3BWf1wM53xjEwa2YHd3JgvBjPEfSbeVjTxjV0jby6/sHkr+NfofmVAg+nw8c9/lZXeUKmDd2uDfBvmS95dF093kij3983l37779JeuQ2zzwJAJhAo9wsFT/Un0sq+9Pypsv3TKOVN8UPpfdIcezxqWcuVt50jlLenJD6ckPKm81eC/Zi5c1FPvKYHgeGIsRiSpSVefKkmsqke3Hs4Dh03GuxEAqtX5IvzvOT7ixdPZ74YM+R4V93+pz04kbpvz3tSRKKvX9Aev19r2v2G5/xMj0DOc9k/Or7noJ+ID9+nx2lrbbKXc3LMoXuuMdbRl5kXirUoAf5fK2vdYjde9wzUh4+Nfw5tuuw9F9+4HOx9Xzh930D0u4j0h9/xxN6/Naj7pbcWCe9t9tL+5xuG7lSKBdLG3ZLf/OM9+vBm6TP33Ph+O98XGgBAABMrtoqX9cvtbyJk2t2w6xCebPvuMeyjlTe7E7Lmz2Dy5v+Ad9b/fF3vO76bz0qPXKrNLfOyyB+82kPa7nURgjgchFiMaqgQq3ecDeyxcGy+MI57Ibkx4MKa4zNrnE3xhCk9/d7yZ3hXptuv7gGMhN5FuSmOb6gb9jjGV/f211oPc3HHnf7wkavk1bc5ad/wDO7vrDRLVeP3eWp5j99i3T/Dd7XN7dLT70unWl1jejFDla6b2l3G0yR4u9ihHE2xRUiofhEL9rGh/9ZtI26ap+7leU+n860ST9/zxUlg15e9Jp87NpvyROfzav3ZGP9Az5vv/+y10Lu7i289eHT0nu7pNe2FLrMp49tType+gakT1wlffk+V76sXOia9Ld2uob8yOkL96lvQHpls/TKFulsu7uW3XGdZ7tMu1D351zZ88PX/Lye/lH+toFECG7hP9bs1v2BUa6Z/TlPKNbS4Wv0SOLY519zq5/PeYjpZlAZEzy0Y6i46P7ow3GoI20v3aY8EdKCOVJlMkHSmfbhy5sPy7zkvidfXN7MLpQ37+1OypsTw5Q3uz28pG9gcHmz7YBXbugfkG5eUyhvVqTlzQ6XKUPLG6loqNgo94fpr2PGxOISMSYWIwryRSuX98Wlr79wI57K5aWeXl+oYvnGZOhFKJcvvK5/wDc2ZWW+kV86X1o4RzpxzhfQRY2eeXV2rWseW9q9Ru35Hr+upc3L/rR2uoZy5WLP1Fde5qV+XtjoG/H+nGsfczlPGf/Tt10jeec6j4HNx271fWu737e2ymvGLmx0rWdHl7e354i7uNy+1muIFk9rP+hYBS/Vkh6rELy/MTWTUyIfFyoycnmfd8XnZZyEuf6cJ54YyA2uRc7Hbq0cur2yjMegLmqUrmjyeXK6VfrRa/5ddYXHBqXr350+5/c/3+Pz8PQ5t+IubJRuWClt2i9tP+i/rVk10vpV7qGQS3oCbNjj/aif5ZuRXN7n7StbpXd3eRmcW672MgdXL/di8qdb3U34x2940qn5DYWlDzq6/dj3X5b2HvVjyxf4/XYeLIxP7+p1Bc93X/Q+feLqZMI0+nthFO3dbuXZvM/zCaxc7PUmh8rlfZ5u2CPPDj7f51l59sLxeJ09XkbtaLP/BtavcgvQ0NnAgamSlg9xUtE+dAhScXmjsZQ3+aLypkpaONc9btqTSQWfen2E8qbV20kngzo1pLzZvF/acdDLDNYm5c3covLmvd3+26yvHVzevLpVenen17W95Rov43ZNcXlzWnrqDZdTaXkTRf4cPX0uU/LxheVw+lnz+cI909B7TGA0hFiMKI69rmtXTxIG2ws1d6nuPk/73p/zc851XNil5Hy3dL7XF/LWZHuZyF0X71rnm5O//bm060hhrOwDNzls7jos/f3z0qGTvqC/s8MX2P4B6d4bpGuWSbeskV5d4gW1tx/yWrHff8kX7nwy+UBDjfRLD7nFqbbKn2PHQek//YO0YrFnZF3W5PdYtsDdK3/0ukPz4Wbpvzwp/ZtflB66ZfAsgqlcUoh1J1PjD+Q8a3L9LKl64r4ijKCnTzrX7nOyN+/WnrRCIb3RaOnwGOn6Wb456Ck6t7v7Bq833NcvtXZIFY3+/hfNk/7FJ6WB55Pa621e8/Vosys8TrdKP3xd+tGbPt8PnJBe2ihVl0sP3Owbkq/9gnTiH3yDvvOI9B/+u5f6mV3tiTLOdnqd2d/6rMepRvISVc+8LT2/0Z/tVx725E7ZMvdqeOwuv+4Hr0g/2+D9XNok3bTGn/v9/dI3fuixTB1dUn21tP9YYSbm9O+vq9fn8sqF/luoqxl54hEgtXWf5xL4zovSFfP9N/K//NKFz2s77xvgf/cN37R+9k7p//x1tzgN7dL+wTHpP3xL+uC4dOUi6Zc+6WXRFs2dnM8EXExPn3u15PKe26OlqMfABeVNrdcG7x5LeTPHlTWL5yblTV56t7i8OeMZ7k+3Sk++Lv3wDW/n4AnpxU3+W3rwZk/I9OuPSie+XShv/jApb+qrvV9nO1zefP2x4cub/gHplx922ZUtc5nw2buks+ddKfrzjZ7BeFlS3mTL/LmOt3iMbXdv4RgVO99TWBu2rdPvCYwVIRbDOnxKen2b9PSb0raDDqnHz0o/esOPf/pWdzF5cbP0k7d8cYqDtGmf9Jc/9o1vwyzpnV3Ss+94HEYu7/GnP3rDrbafvkW6boX0+D2+2L22za1ET7/t582pc1fjJXOlrm5faCsrHG6rKgpTs9+2Vvrtx931cesBX9A7u6UzHdKsKqm+Rvri/Q6q1RWuWXzuHenJV6XdR91VbeVit1LVVfsGa/8Jf6befmkg9uyt3/yxw/Qv3CEtSNZT6+13l89tB90y1tntGsX9J6T//F1/vjvXDZ7SHhMnBHdten6D9Oy7HteTT76/P3/SlRhR5G7nuw77sZ5et6R2nHerZl2tu57/7L1CN6ftB/36h2/zd9owyxUaXX0urN/c7lalU+e8jl5tlc+FNUu8fmt5mc/ZqkqfwwvmSHdfL/36I+4l8O6uZLbuXp/X/QM+rx+5zZU1DbPccvryFukHr/pvr6nB/9bXei3ZXOzPdPR0MulUr3smnO/2Gn+11f4cr21zl7RczjdLZzsLvQuCimrDI+mRW/y3QYDFWAzkfL61tHld7qGVnqkQfO083epzrf38yL1W0sfPdrjCKW3ZAaZakHtzPb9Beu5dh7Bc7HVU/+uTrtDMZDy0Iy1vuvt8r9Le5fJmdlrebCgE3+0HpT970tf/61b4vH/oFl/Ty4aUNy8PV95kfa9TXekeNAsavZbrr39G+ulbYyhv6lzevJKUN+8f8OoN2/e7vKmt9mfZfcRdiD8sbzY5lN633mXS/hNuoT3T5mvD1gPSt56WPvkJDwN7Z6eP27YD3sb2Q9JTb7o8+vSt7pVE0YPREGIxrP6cL4rVldL1K6S1V/j3s5KQl3aRjGN3dVnY6MerKgo1a5UVDoJ1SbeV9Sv9nJoqX+zzsV93+1rf1Mypc2tsugRJHEsL5rnryvIF7lp89VLppqvculRR7pvrlYv9Hmkr6oGTrg3NZn3hvWaZ9MX73P0l7eLS3evn373eQbehrtClJZ+XFKT1q33xTwuWinK3JKf7Jvnf872uNU0nyIljB5u+Ad98dY0y3gvjr6vH59aCOV4/OASfxx1d/ikr8xjn9aukq5b5nKitdEHe2ePHO7t9Y/Do7d5mdaXPmZ4+h7+qCr/2kVv933U1LsTj2K+dO9s11tevcGG9oMGL1F9zhc/z2Ul3rcfv8fvMne2umAM53/TUVLq16YEb3V1ecuDs7nWgvXmN/3bSblpp2q6u9Hk+t75wPOK4EOYry/25x6KszDcbq1liCmM0r8HX67uvl65cIK0a4dypLHdrzwM3+s1Bm4sAACAASURBVLy8fqXP+eGWtWqY5cqnufVu6blyIet3Y5pIwls+9nX+/iHlTWe3r6PNrT7H1ywtlDcDA67wyablTeXo5c2apZ68b9jypk664kq/R1rerFuRlDezi8qbu32/M1J5c39xeTNwYXmTj4sqkCIH5WuWe3upOC7cJ+XyUlOj90nyPVRPn/e5rsb3h7XV0rqV3l/JDQmtnXQrxtgQYjGsNUul1Uukf/355Bdpk1RUWBPt0Tv8E4Y8/uE/kXT7tUXzFwyzDcljKB67yy2lZ9ql42dc2ze/wf82t7n2siJb6PJbPC61POubm68/5i7DZ5NtzE620dSQrGWbvGZho/SvPid9/XOFbRTvz6IklD9y++j7LTmQf+Y2/wz6nMXHAZMmilxj/dAto38fn79n5MckF+Yjndfp9x9FruhYe6VruI+cdhBumOVzqHfAlRtf/bQDdWNd4XWSlCmTrr3CC8v/xqM+Z8/3+AZ/+QLfWGSLrtDrVrmw/5+K9rt4f2oqpd/9wjCThgxZx3nME2cMc74Do7lhlStv7l0vLZnvlpThzK71c9YsLUxe09Qw/HNXLZH+r9/030d51mNs/3/27jtIzvy+7/zn6Tw5IQ5yxmIzdrGJSy65TCIpRkmmKMv0lc+ns3R1d3WxLKnOpbpTlcvlunLZdf5DupMtW7JkWQziUmJOu+RGALvAIqfB5Dw90zk/z/3x7Z6EGWAGGHSjZ94vFrjAdM8zv+5+pvv5/L6/sNwezkA1OY70safsTzU/b7666PNmW5f9PRqXvvIxC7GV372lPm/+4afu/HnzaDlY/vfz2j2/PQ0h6be/IP2T+e2Wbv28WeZxybFK9J2uD4HbIcRiWc78N6Ml3lCcRW9ISx/k9seYf6xwyC5kWhpt+GU4aG+qm9qsB7CyKfZyb26OU34jLq96HCwPN17qgsdxbtucOz72xfddcDfefGtqJa/His7JO9xeuTngt2rq3m3WCx0M2LkcDNg57HlzKxkvxe+znvedm20UgK/8b79v4Y+vtOd27XLucHvlPsD94DhW8Tm0a27Kx3LCobkRPAH/8veb/f3YYiNvCLB4kDwonzeV6VVr9XlTeWz3/Hlzu9tXcZ0FLIUQiwdG5c21YdFQscX/vptjAPeL48wtdDGf3ze3YNJtv18297uZFcCwDgQDVh26E7/PRrLcSeX3i98PgM8bYD76NAEAAAAAdYMQCwAAAACoG4RYAAAAAEDdIMQCAAAAAOoGIRYAAAAAUDcIsQAAAACAukGIBQAAAADUDUIsAAAAAKBuEGIBAAAAAHWDEAsAAAAAqBuBWjcAWAueJ5Vc+6/jSAF/rVsELK3kSq4ryZECPjtfAQAAsHKEWKwLhaLUNyZl81JTRNqzTfIzzgAPoKEJKZaS/H5p71apMVLrFgEAANQXQuw6kshIf/J30nffrnVLqq9YkiZnpEJJCgelze0bp8I1Ni3NJGvdinvzzhXpD/+s1q2ojqmYlM5JPsfO01Cw1i2qndfer3ULHhwX+zbO7wDmvHOp1i14MPzotJTJ1boVwNp471qtW7AxEGLXiUhLSJEtLfra2Vq3pPpKJVfFbEmp6bxKRVeBkE9NHWEFQn75/BsjyQY3SQ2t9ZeGHEdq29qoG1fz+jc/rnVr7i/X9VQquEpN51TIluT4HDV1hBRqCMgf2LjDBjp3Nm2Y39PltGyOqLe/Zd3/DsxXKrjKpgvKp4oKhPxq7gxLjrNhOh/n69wZViC0Md8D/AGfOnY06fSYT6fHat2a9ctzPSVncirlXfkCjpraI/IHNuAvWxV17QooEGZu2/1EiF0n9j65SdsOtdW6GTWRnslq7HpM73yjV8mprFq3NOjEl/aqa3erGlpCtW5e1YSb6u/X2e/36flfP6hSwa11U+67fLqgmZGkTn2rTyNXYgqEfHrskzu165EuNXc11Lp5NeMPOIo0118HzFp66rN79dgndtW6GVUVn0jrxqkx9Z6eVNfuFj3/9w4qEPLL8W28C2vH56ixDjsh10JzV0Qf/W+PyS15tW7KulbIFnXylRuKDifV3NmgE587oKb2cK2bta45jtTQunGuQWuh/q56saRgxK9gZGP2+MQn0xrvj6tYKEmS8pmixm7G1LW7RS2bmHD4QHOkxraN8SY/PVLS6M2YssmCJOsZnxpKasuBVm3f1FHj1qGWIi1BRVo2TogpFV3FxtMavRZTJl5QPl1UYjqjbfvbFW7aOM8DJJ/fUVMHYep+ymeKyiZzSk8XlBzPyye/SvmiQo2NCjfy+4b6tTHHr2DdKGSLig4mNXhxWoWMhdhsqqC+96c0PZxSIVuscQsBqZgvKT6RVu+ZKaVmbOJXqehp5GpME70J5TNFeS6VCGwM6VhOUwMJjV1LKBMvKDaeUe+7E8rE8/I8fg+AtZSO5TR8eVqxkYxSU3nFx7IavjqjZDRb66YB94QQi7rluZ7iE2lF+5OaGczMDkktZl1N92cUHUjyJo0HQmo6q2h/UtHetPIp62zxXE/x0ayi/SnFRpNyS+t/SDUgSZP9cY1cnZZb9CRPio9ldP2tcSWncwwrBdZYbDyty2+NKB23DtRcuqDrp8YUHarzFSGx4RFiUbdc11PfuSmN9sSlxdc9njR8dUaDl6M1aRsw31hPTP0Xpm69QPekycGEbrw3oXx5JAGwXnmup0KuqNFrcQ1djM1WXd2Sp3y6pNGr05oZTdW4lcD6kYxah/50X1rFrHWUlvKeZgYzmupPKjGZZvQD6hYhFnXJVnnNavRaTDMj6SXvMzWQ0vj1uNKxnEpFqlyoPrfkKZPIa6I3oYmbiSUvFhLjWY1cmlFiKqNijiCL9atYcDU1kChfPGfnOh89qZhzNXAhqqn+BEPrgTUy0ZfQ2I24svHibCeq53rKJYqa6E1orCcmj8sj1ClCLOpSNpXXZF9c0wNpZWYKS94nHc0rOpDSZH9chQxzY1F9pUJJ0cGEov0pJSfyt44YkJRNFDU9kNZkX1yZBBslYv3KpQrqPTupmdG0tOjCuVR0NXhhWuM9cRWyzBEH7oXnWSfq8JVpjVyfWfI+471x9Z+PqpgvUY1FXSLEoi7FJzK6/Mbw7CI5y95vMqPLrw8reYf7AfdDNlXQtZNjig7ffohkLl3U9ZNjmhpiKCXWp1LBVTKa081TU4qNZm69gye5BU/TI2kNX51WcQNsuwXcL8VcUdGhhKb6UkpN5Je8TzpaULQvpehgQvk0Hf2oP4RY1J1sMq/poZRGLseVT93+jTcbL2j4UkwzIynlUktXbIH7IZ8pKjaW1uiVmFLR23eiFHIljd9IKDqQVCaeW7JiC9SzxFRGo9dnlBjPqJBdeti850rRwZR6z04qny5QHQLuUjqe1833xhUbTauYX7pDqJR3lZjM6uaZcSWnWQQT9YcQi7qTnskpOZVVIeMqEPEr0hpUuDkgx+dIsn3nIi1BRVqD8of8KqRKSkxkLBwAVZJN5hUfzyifLMkX8CnSWj4nA/a26zhSqDGgSGtQocaAijlPqWhOyWiWi3esK57raWowqf5zkxZgb3N6x8YyGro4rcRUdtmLbwDLKxUtnN44ZSt+304mnlfPqQnFxtKzOzwA9SJQ6wYAq5XLFBVs8Gv/ia7Zr+UzJQ2cnVYuVVSkJahdj3fIH7BQ6/gc+YKO8sv0/gP3QzHvynVd7Xy0XcX83Lk3eG5G8fGsfAGfuo+1qbFtbrP5hragcgzrwjrieZ4K2aImexMafH/6jouXuUVP2VhRQ5ejCjUE1LmjuUotBdaH9ExO0YGEor1pFbK3D6aFTEnR8paEXTub1ba1qUqtBO4dIRZ1p3VzowIhv7bsa5v9WjKa1fj1pHKpohrbQjr6YrdCDeXT25HCjUE1toVq1GJsRI1tYW0/3KHOHS0LFqmJj+YUH8/KH/Bp3/Et2rR77iI92BBQQ0vIyrTAOuC5UiaRVzDi19ZDrZInefKUTRQ11ZuU50nBiE+R9oAaW8PyB30KNQRUyBYXdP4AWJl8pijX87T1UKu80tw2VpN9KeXTRQXDfnXtaVIgNDcY0/E5dKCi7hBiUXea2sNqag8v+FpsNK1AyC9JCjUG1X2kQ+Gm4FLfDlRFpDmoSPOt52BDq3Wm+PyONu9pVffR9mo3Dai6zp1NCjaUL5o9KTqUUnw8o2LWVWNnWDseaVPHtiYFw345PkehhoCCYX9tGw3UIV/Ap9bNDTr0/FZVZqYUcyWlpgeVTxcVagxo71ObZj+LJKm5wzqQgHpCiAUAAPeFz++odUuTWjc3av5U7+GrUfVfnFRiJKeuHc06/ql9s6NsJEmO5DAiAVi19m1Nat+68PctlyroxlsTio1mFG4K6Mjz3Wrb1jh7u+OIEUCoO4RYAABw31QukOdfIvt8Tvma2ZHjOPL5yn/8XEgD92LJ3ze/My+kOnL8/K6h/jF2AAAAVJ83+38S19MAgFUgxAIAgBqYH2AdgiwAYMUIsQAAoKo8z/7M7hlLgAUArAIhFgAAVJ3nzcuwhFgAwCoQYgEAQNV5TIcFANwlQiwAAKiBeSmWUiwAYBUIsQAAoLoWzYklwwIAVoMQCwAAqqqyu453pzsCALAEQiwAAAAAoG4QYgEAQNV5s2VYR47DPrEAgJUjxAIAgOqrzImdDa+kWADAyhBiAQBAdXne/FIs+RUAsCqEWAAAUHXuvB12fCLHAgBWjhALAACqb94WOyRYAMBqEGIBAEB1lQOsJ1mAJcQCAFaBEAsAAKrKk+R5c6VYR6xODABYOUIsAACoGUeSHBIsAGDlCLEAAKDqvDvfBQCAJRFiAQBA1c0bTcxIYgDAqhBiAQBA1XmzCzs5jCYGAKwKIRYAAFSVVWHnDSh2HKqxAIAVI8QCAIDqm79PLAAAq0CIBQAA1Tc/wFKGBQCsAiEWAABUH1VYAMBdIsQCAIDq8iSvnGIdR3JY2QkAsAqEWAAAUH2u5rbYccSQYgDAihFiAQAAAAB1gxALAACqjimxAIC7RYgFAADV5XmSW95jx3HKQ4kZTwwAWBlCLAAAqDpPlmUlkV8BAKtCiAUAANXnzv2VDAsAWA1CLAAAqL4Fk2KJsQCAlSPEAgCAqirPhpVEfAUArB4hFgAAVJenBUnWccp7xQIAsAKEWAAAUHULFnUiwAIAVoEQCwAAAACoG4RYAABQY5RiAQArR4gFAADVN291YubDAgBWgxALAACqy7M5sZ5XrsESYgEAq0CIBQAAteOQYQEAq0OIBQAA1Tc7nLi8vw5JFgCwQoRYAABQfd78SbG1awYAoP4QYgEAQG14YjgxAGDVCLEAAKD65hViSbEAgNUgxAIAAAAA6gYhFgAAVF2lEOtIcsr/AwBgJQixAACgqrzFXyC/AgBWgRALAABqhwALAFglQiwAAKguTwvKsY6NKQYAYEUIsVgffI4CDY4CTT4FIlwJ4cHlD/vsPG30yfHXujXAg8App1gAAFYmUOsGYG3ERtMauxGvdTNqJh3PK58pypGUiRd0/e1xBcMkBDx4ElNZOZJKRVcD70eVGM/WuklA1U30JuSVK7Hx8YyuvjFGIRa4Twq5kjLxvCQpnymq9/SEGtvDNW7V+uYP+tT9UIcaWoK1bsq6RYhdJ/rfn9JP/viiwkHJt4GvBIKSUoMJvf7/na91U4BlBSWp6OrM16/VuilAzTSUr+2i16N69Xq0to0BNoDGsOQms3rnP12udVPWtaIr+SJBff53j6vhSHutm7NuEWLXkc4W6Q//sXRoZ61bAgAAAGw8Pzgp/Zu/rXUr1j9C7DoSDkonjkpPHa51SwAAAICNZ2C81i3YGFjYCQAAAABQNwixAAAAAIC6QYgFAAAAANQNQiwAAAAAoG4QYgEAAAAAdYMQCwAAAACoG4RYAAAAAEDdIMQCAAAAAOoGIRYAAAAAUDcIsQAAAACAukGIBQAAAADUDUIsAAAAAKBuEGIBAAAAAHUjUOsGAAAwn+tJ2ZyUzkrBoBQJSeHg6o5RLEmZnJRIS21NUiQs+R+gbttiScrlpXhaam6QGsJSwF/rVq0vrmvnQCYvBQNSQ0gKrfI88iTlC1IqY/8Oh6SmyJo3FQCwSg/QRzoAAFKhKPWPS6+dlc71SJOx1R8jkZauDkivvC7dGJGy+bVv571IZaQbQ9LfvWHtTOdq3aL1xZOUL0o3hu08Ot8jRROrP47rShMx6e1L0psXpb7RNW8qAOAuUIkFANRcsSQNTUhvXZDevSZdH5KSGekjT1qVcsemOx8jnpYGxqQ3zkvv90g9I9LEjNTWLG3rqH0FLZWRhialN85JZ3vsMU7MSP+NT9rWJbU21rZ960G+IPWO2nn03jXp5qidWx97qnwedK7sONG4dOqydPqKdLlfGo5Kh3ZIn3pOOrb3/j4GAMCdEWIBADXneTbscyQqnbxoIa9QkvZtk5LplR2jULQgOzIlvX7eQowkjUWtKldrhZIF2bFpC1lvX7bH/cvP29Dijcz1bPh4wG/Dx+/1OEOT0hsXLICGQ9LRXfb1lSoUrYPh/E3prYtS37h97dmH7r5tAIC1w3BiAEDN+f3S5nbphUekJw6trPK6WDgk7dgs/dKz0oFuyeesfTvvRSgobemUPvGMdGS3FOATWJIF+UJRGpyQpuL3dqyAX9q+SfrAo9LjB6StHXd3nKYG6ZH90jMPSYd331ubAABrj49QAEDNOY4Npz2yW3porwWR1WoIWRA+slvau1XqbJEepBwbDkpdrdKBHdK+7dbWBy1o10I0Ib13VfruW9Kl3ns7lt8ntTeXz6M90tYVDh9erCEk7e+2ocO7t9xbmwAAa4/hxACAmnNkK8i2BSx8Njes/hh+n4WPhpAF4oaQHqgU6/dJ/pANl21rkprCFt43spIr9Y5I33lTGp2Wdt5jYHQc6yxob5I6W6XGu5wH7fdLLY1SR6v9FwDwYCHEAsADzpMNufS8hV93NBeCXG/xd5XzmzNX7fO8hferfP9SQcrzbA6n69q/A34LYbcLXZ6k0rzvCQbs55Vc+1owcOs2N55nC+8UXTuAr/wzfPc4TsjnW/mWOl65jcWS/dvvsxCzmiqpWx4S63nW/kD5+5d7vnzO8u1zl3itK9+z0tBbOV8qh6mcC4vPmfk/x1l0e0Xl9ZPseXE093glexwB/+oCeeWcnopZFfZbv7AKtevaz1vq8VYeU778PPt8t3mey8/v4tew8jsw23a/Deu+3evk+GYPedtOkfnn0fzz4E6/NwCA1SPEAsADrlSScoVbFyfyOVJj2AJBaolFaxzZRXRlVd5CUUrN28rFcaxaGfQvDI1uOSj0jUrxpN22Y7MN07zdojulkjQ6ZYsr+X0WSqaTttJrMmPDM9ub53KAJylbsO8Zi1r7OluXfiyrtSCcO7cvyOYLNhdzJGohqrPVhqE2hlcWZEuuLRrUN2r7klbm5rY0SqFlPmVvaZ8zd6xcwf4sFgnZ8VYSzoslex7nh9hgwL4/ECjvoZqfC3NSeVGl4K2BdDohxVP2922dFvziKVv92e+TNrVJ3Zvs+CvNam7Jfv43X5O++XNbOGlrp201FC8v5NUYXrg/cNG1hbF6Rmwf4aaItGur1NJgP3sps09x+S+FkjSTlAbG7Xzd2mnzZpc9r+9w7lR4nr32U3FbWCyXt1W1d2658+8NAGD1CLEA8ABzXdt65p1L0snLUqFcsWxtko7ulj7wiAWfn52xi/tkWrMX3rs2S48dkD70uIWNvjHpp+/aHqz5orS5Tfr089KerXPDd89ctz01e4Yt8GTztueqK7vfYwekZ4/ZBbrPsYv3vjHp+qB0ZUC6NmjhY9dm2+rkveu2lUy+YKvwPnNU2r1ViqXs8Zy+Yhf8gYBUKNiKsCNT0pXBNXjybpM+iiWbi/nORelyX7kaXA5+Mympo1k6flh67mELOkH/rccoFKVTV2we59CkPV+pjB3DlXRkl/TEQempo0tX+xb/2/Ms7J++Iv38fXve/X4bGrtzix1r3/Y7D7Uulqw9b12w12M6aYtKPbJPOnFUOrjDAuTbF6QzN2xBJckWw3rhYenQTnsMN0ekq/3StSELw4/tt3m8w1N2Pk7F7TFsapUe3iu98Kh0eOfygXK+kaj0g3ekv31Tev+GlMzaefLtX0g9QxakP/WsdPyIPSdvX7THMpOwczmasD+tTdLTR6Wnj9gWOMtV8F1PunBTutRn5/hkzL7W3mwLQJ04Kj26/9aqqaPbh1i33Olw6rId9/qQPdfprIXxjhbpxBHpmWP2/AaWOI8AAKtHiAWAB1gl2FwbkL7/jnR1yILl/m7pq5+wC3DXk24Ol28ftPu3NEgfPW5VJtezVfxiKQsMr7wpybPA8vwjthJwriBNTEs/PCm9fk6KJaWH99lF/ciUdPqazeN89iELaw/tkbraLNz0jVno+vEp6exN25P1kb3SngHp5BXpQq+UyEidzdL2Tqm9RXrnsvQ3r0knL1lo2rPNgu65Hguw0YSF4fuhWLLq8JsXLDRdH7KqcUeLBbP3rkvDExbWcwXppScsvM2vCqay1rnw7detzcWiLQKUK1hF9vQ1C/19T0iRsAWYlcytTGdteO1/+I6Uzkvbu6QjOy0s7d8+N+T5djzP2jcwLv34tHS+1zotPv8BOx8OdFsVcmzanv8fnrbA/MLDUkeTLao1FrWg+t03bbujSFiKPWeV+5sj0psX7bGmc3aOPLTb2tjWuLJ5ramMdHWgXHX3WZtjaeuIyRet0+Dpo9au929If/1T2y6nrcnOvYmYhcarg9KLN60KumervUaLOwcKRQvesZSF17M37HlO5yzMP3XIqrMdLVZRXq56vuTjyFow/sE70rtXpWTOtoWKpaQLfXaOXOy1+3W1WmgmyALAvSPEAsADzO+3lVZ//WPStk3SP/t3VmXb2iH92kfmLtz/x1+1i+Svvyadvmr7Yn7+RemLH7KLcsex8PL3PyGdvGp//6e/YWG4MWIX23/9M+kbr0ltzdL/+Y/ttqaIND4j/dHfSD96V/rmL6S+Eel//nXpo09ZG13Xgkxzg/13YNxCQjIjdZermK+ftyBUdK1q+y/+3ILkM8ek//Ur0pZ2CyL949Lv/bGF4vsllbVg8S//wp6bl49Lv/Ml+3s0YXuD/rM/kX7yntQ7ZqHjxUdtiHDFzRELVt/8hVWXf+eL0oHtkj9gld0/+pY95v/8E6ss/v4/tErfneZGOrLKus9n1cVfeUn6+AkLb+GgVazvJBCwKvCu8hDwv37V9jqdvb28aNEXP2jDg2NJC92VOaelogXUTDnklVwpGpNee886RLo3SV98URqetIB8fdgqut87aefOl1++8+M8sEP6va9ahfW//FT6sx9ITx6S/v7H7LxVeaj81QHpj78lvXHROgl+7zft3M0XLWT/T//WzpVgQPrCi/bYFofEeEr61uu2yvDhndKvvWRtv9grvXfDOi0awnb+fvkj1jmzUgPj0r/6K/sZTx6Wfutztj3P8KT087PSv/hL6Rfn7Jx7eJ9VwztaVn58AMDSCLEA8IAL+K0i98RB6dF9Nvw2nZNuDNlKvk2dFlY++Lh0ecCqZL1jFsjmL9yTSFuA3LvNAsP+bgsK0bh05pr09Vftfk8etAvulgYLRMGA9LkPSoOTVg07fd2G0e7dboHi6G6rROby0vu9Nl9xc7v0lY9bkCp5dlH/2AELTN9+XboxYsN1P/2c1N1lYbm5wQL1U4dtiOvI1P15Ps9et+B0fdiqkx85bs+jz2fzWf0+6aXH7TkenJC+9lN7frdvsiHUI1MWvr71un3fEwet06C50Y5xaKf06x+VesdtuPLJK/Z8dbRIu5apUmZyVtH+ix9aZe/DT0i//ILtmbvccOblVOZCNzfY69C2RAXY51hw62q1Nl3sm7stGLCqb+4RaTQqjcekwXGbT/qlD0knHrJAn8pIPzolvfKGDSU/dcVC8S+/YMe+3dzdgN9WkG5tmpsvGgzYc9jZOne/VNaC7HTSwuGBHeXbPRta3d5oVeNE2v40N9waYkNB6WC39NkP2PO6qc06Wc5ct/m4712zx//dt6WPPGltWsmQ6KFJ6d1r0qmrdr48e0za1mWPOxS013T3Zvv9Gpu24d2b2wmxALAWCLEAUAcawlYBe+ZIeWhkUnrjvAXSbZ22guqOzXaBXixJYzPSzVELYQd2SH5HGp+W3jgn7dtqcxgrcyvHotK5m9L7PdIHHrbKb+e8C+2GkIXeh/dJr561oZzneqyyeHCHBexMzn5eOGhVvoPdVuHs3mQBrLJK8Su/kL5/0hYL2rddeuYhe2yVlVzbmqxatmvL/QmxJdeC+I/ftWC0e5s9rkrlMByUtnRIH3zMHk/PsFVUX3pS+sBjFrYHx23e8OV+6bPPW5Bva577Ge3NNpf26C6733DUgtKhHUuH2GTaguvAhM2H3dIuffJZ6cNP2rHuRTgwF8iWKo5WwmQl+DmOVf8r59H+7fZ6NoSt7S8/Jb3wiP27skJwNGELMw1O2PDeaNzCWsMaDAdvjti+wZs7pccP2vNRcq3yOTFjowAqqyVncjZMerFISDq2x4LmR5+aW6xra6d9/+CEBeWLvfY7snPzykLs8KS9viNRm9M7MiX95N2526Px8giFiA3n7x2ZWyALAHBvCLEAUCeaItILj0nv9UivnbVFml4+bmFS5XmQ2bwFEc+zYaxnrtl8U8ex8PvT92zo8eGdc8cdidp9SyWrcnW1Lvy5Pp8FnYd2SU8ckH521sLdtQFJz9t9nPKWJo4s1D60x4JwZX6h37F5gjdGpLcv20JHHc1238VDTze1LwzRa8XzbN7tyJQtVuRIammS2loWtiHgt8WY3rho1ceRaWloyraDaQzbkOf+cfueLR23Bs2A37722H7pQo/01mVb/Kl/bOl2DYzbCs3f+Ln09z4sfak8hHi5x1Byy6sOL7ENj1TeWqZSBV20uu7tsD6XJQAAIABJREFURvkuXsTI51hF0e/YfqkvPGrzXSvh1HGsA+Djz9iw6njKqvEjU+V9etcgxO7fYUOx01k7pt9n81rfu2bzt8fj9hrNPi9LPCfBgAXwjuaFq03v3ip94UPS116zRaMKJQvGqezK5i+PRW0uesmVXj0jnb1262rWrmc/t1J9Xap9AIDVI8QCQJ2IhKVHD9hCTIWCLYDUN2pVzaaI9Pr70sCYtKPLKrGX+mzxok8/bxWmwXE7zt7tNjS2YnTSFjcqujacNrJM+Ghpsgqbz2dVpsnY3G2ze7suk5I82ffMJC2IdDRbm6u5f2bJtcA4GSsPpw1K4SX2g/U5FubnrwKcSNnzvHOzPecDY+UtisJSaJntUzpaLPx5nj3/04ml7/ezM3asfNGqiUttr1NxqU/6zz+yBb5iy1T1fucL0sefXt0CRXertdEW8mosD8POF+353bt9bY4fCdlzPhaVrvRLf/Ej6fqAnYsNYas0r3Q/4MUqVehtHTYCoORadT6Tu/P3SjYaolIN/srHpE+euHUxMk/l4d0B+1nz51UDAO4eIRYA6kTAZ0NND5aHpV4btgrSjSGrtr510cLQ516QXnndwsTlfhv2eH3QgtSxvVb9bIzMHTeetiqj51k1dqkhmZKFuo4WC3l32nrkFp6FrmTaLuyXq5rdT165Dels+bGW/3jeom1VyuG0MWxDoYslLahoTicsjMuz25Z7vlqb7I9Ufs6WecKaGuzYo1Gb03yuxzortnfdGkQTaRuye+rKwk6E+X7lQxasVsvTssXdZQUDdl60N0uj0/Y1Z4V7q65EoWidH6+dtbnMY1HrSDmy2563rncseN4Nn2Ovb1OkvKqxVtepkiuUzyXZa/X4QRvavxzXq26nDQCsZ4RYAKgTlXmjx/bYvpY3R21BmlNXbE7slX67kP6Nj9nf371mizGdvW7DihNp6RMnbL7j/Gtp1y0HMc+GhMaWCQXhoIVfx7Ftcjpbl77fcnJ5q9R5rgXnVNZC4J22HFmrsFsJz65XnkNZXiArV7B5v/MFfBZwQn77nuYGm/fqOOXnq3ycmYStwryUhpD9cRwbol0JtIs9d8zuNz5j85LbmqVdW8uvVevCvU+by4sbZXLLV3a7Wueqy87s/9kTcMen8i6ea7/P2hwOWqjtbF3ZnNI7cT3rXPn5Wen//bZ1QHz6Oem/+pQtntU3Jv35920u8T0pP2afzyqyi8+F5TiO/d5J1gExMrV0iC25Now9mbGFq1Z6fADA8gixAFBnDu60VWu//nPp/euSXxY8D+ywlXK7N0kfeUqaSdlczm+/IV28ad/3W49bAJ2vvUXa2iXdKC8ENTC+9M8tlCzweeVtVnZtXV2725ttrmElTM6kLLht7Vh6SKgnCzJrVbD1ORbgW+aFyYkZ217oQPfCKplXrrJmi/Z9Xa02/9UpH2NTm4WnmyNWHVxKvmh/fD5pb/fyVbrdW21f3cawzS1996qFnu2dtoLz/NVs93dLv/2F5RcxkmzBosoQZ8c397gqVefleJLcyl+WuHG57y25Ujxp50YkZOfGckPSVyOTsz1q/68/k6ZjNk/4v/uSjUZYruNjteeKJ+u4SWelzQ02wqFthfOx25rtnPA50qlL0sHttgDaYjMJGxHxo1O2cvNTR1bZSADALQixAFBntnVKh3fZXL6ZhFVcZ1LSZ563fShbG6UXHrb9Yi/227zYoN/C4u6tc1uaVOzeYsOM375sCxZdHbQqX3PDwora7Iqwnl3s79m2dPs8d97iQ2WVamRnixT0SUXPhkK/eV76pWdtSOfcAea+t1i6y6GxnrWj8ndXFiY3t9uc4a3t0mTcFrS6cNNWSvY7c/ePpWzF2WBA2rfNAmUkZEXNgzutw+DUVZtL3DNinQjNjQvDeDRhf3yOhc/uTQvb55YfZDhox9uxyRbf+sU5m/v6lz+yx/7Bx8vDXcvDnHeucF6l65XnjYbsOPGUbX9UUSja4+wft6q069rzvVQQLLm2aNji1yKbLw+vTkntTdLeLRa6Vzof1ykPs/ZkW/bkyj8jm7fh7/1j9vxGynvb7txiz6dbrm7m8jaXu9LGSsfBSqSyNrd5KmEV2Mf2W+dEeF7b3Xmvk7coyG9pt98Bv8/mp//0zFwH06byXrPZgo2UePOCtXnx/GsAwN25y+UQAAC10tJoIfLhvVZxG4vZhf7BndK+bgstD++z7Xf8jq0IvLnDVgyev51Kxd7tVvHb1CbF0jYv89Rlm2voli/c0zmrPA5NWtg6snvhdjGVUOF5duGeytwaeNqbpZ2bpD1bLShc7pO+/44NfR6fsUA1GbMh0NG4fX80biv3TszMHf9OPM+CTK4gybOwVihXVFsbbaubxw5YKL0yYHu+Vo4vWZC7MSRNTEtdLdKHH7eQWwkgR3dLj+y34cHjMelCrw0Dnp1r61pA6i0vurV/u3R0j1VIZU1Sobyvrsp/b220fUY/8bR1RKSzNq/5h6cs7K/0sc/nyNrY3GCPaWjShuAOTVpAvNxvQ82vDdjrlcnZkNjhSRvuPf/HZfIWKOOp8hzh8uMYm7b2xTMWxJ88ZB0SK11sKVTeAsh1pckZ+9kD4zYveHDc2iVZFTmWslWxhyaszeduWCdBoWihd6i8Vc7QouHFJdfmMCfScwG35Np59fp5O8/3dUsfPV5ewXhe2yvnjmTnU2FeQN6+yc6F9mZrx9uXbOGpn52xjqX3b1gnzQ9O2nD+Y3ulrvYVvngAgNuiEgsAdair1VagHZq0KusLD9uQ2NamuWGzh3fYn/d7bRuexw4ufawdm6Snj0jPH5Vev2Ch8v/+K+n3vyq1HrXj9Y3aHqbDU9JXPyG9+KhVNSuyeSmWsIATT1rgzRctBFfCXyhoCxZ9+WXpT78v9Y5J333HAsXLx8ur0E5Lf/VTq14VSxayWsqrBL98XOpqs+Msp7JgUyxl4cSTBbLKvFXHsUrZr74knb9pITYStrnELz5mC/SkcxY8rg/aXq9f/SXrFKjY1y2dOCI9c1g6dU1655LN2fz9r1rYzeUtbL19yR7DP/ms9NThuW2DKpXeyZg9P6nsXNX3Cx+04PvuZetQ+Ns37Rj/y6/b873aFYc7W+ycyBel8732fBdLFqZefVd656IF10zOwt+PT1lg/sQz5Up7OclOTEvfe0f6zAs2CqCjxdp+9rr0nTelTNa24PnEs6tbvKil0UJ2qWQdMa+dtefn3as2/3VLh3XEZPIWDP+fr9toghuDNle2Z1TKFaXxqPSNn9lr/+wx6R99Zu5nZLLWKfPEQQuSWzrtOT97Q/p3fydls9KnnpF+7SML9/uVpGTKwq/nWYdKYt6K0Hu2Ss8clR7ZI713w34X//pV2/pnS7vU2myPKRSQnntI+sCjt1/4CQCwcv4/+IM/+IN7Pchrp2/q4g2bRLV174gaW9nNu9rGb8Q1eXFCX/mY1N1V69YAuN8cxypeb12yUPeVj1l1sbVx7vZ42sLS9UHpV16yYanzt42p8PmkSFDascX+HkvZkOKbIxZcT16WfvaeXfg/dciOta/bhqq6nu09+zc/l77ztlXRMnmrWk3M2PGby0HFcaz6ualtbnjr4IQt3nN1UDp3U+obsWqX61n1bEu7haaje2zP0IbwwkrZfMmMDe/9yx9KPzhlw2Q9WaiMl6vKXa0WVNqbrR3Zcni7Xh5W/MY5e6zXB20I8OdelJ4+Otc5UHm+GiM2tLVS5bs2ZJXNU5dtlehXz9j9P/Co9PkXLRwnM/Yz/stPLRDeHLU25csdANm8VdGv9NuQ4kzBAvVkzCrDkzNW4dy2wvd4x7GtXZJpKRqz6ms0YR0SF27ac3FwpwXJ6YTk90u7NksP7bXnvLnB2nK2HNDk2Wt6pd/+/OiU9LPT9rXPPC995jl7nVYTtP0+e1xXB+x8zebt5xw/LJ14yF6jRHpuBe3RqAXWjmbp+BE7tzJ5q1yHg9Lzj1gHS/cmezyua7cNTlpl/Mx16yD49ht2bjeE7TX+xDNWSfaX5xBPxaSfvCd98+dWrZ2Ml6vnBQuyLU32+9fcaK+HV14UbWzaOgRi5d+9xrB1vvzKh60jpDIsHMD6dea69Hcn/Tr6we1q2RS58zesI4mpNk0N25yXE4/u0lPHdt7hO+4elVgAqEONYenQLqs6ZfNWBVtcRdrfLb30hIWMh/fOzdNbSluL3ddxrNp56ooFvMFxu3D3STp+yBaleXjfvGqoZz/f57NQV9l/NhSwcLZ4HmVHi4XtbN7mIZ68YiHD77Phr5vKofXILptneKQ8x3B/twXH2w1T9cpDh5MZ+/5K1SsStNWGMzlrS1PEqta/9mGpOWIhZaIc8lLpudufOSZ94DELvIt/7rZO6WMnJDkWYi7ctCDoc6z66fdZgD1x1F4naW4l5ETaHk9X21z7ggG73ZNVc3/pufJz55X3Xy1Y+1c637OiKWLP95dftiHl4+VtcFob7fHt3mIBvL28ovEj+6wqvbm9HNqduePs22ZfG5+xL6dzUkPEzsEvvWQrBjet8nqtpdHOp199SbrUb1/bt0368JN2vJJrbd/SYR0N+aLNBX/6IQu6x/ZaZXZ4Stq71Tpqju2z52x/t/SRJ2014DcvWhAdm5aKBavedrXa8OeXn7JjzB9mX/Ls+W5usJ9xZLd9vbPFXr9C0eZZd7TYiAh/eb71uZv2fY5sC6BDO+z2Dzw6t1I1AODeOZ5375sX/OEf/Vhf+8E5SdKjL72rTTuXWdoS9825Hwzo0tcu6pV/bsPWAGwMPcP23/3dt95WLEmJjAXRnZsXrnJ7OyXXwlbf2Nx8ze6u2w/jvRv5glUx+8bsAr+r1QLh+LS12ysvILV4Iaq1ls1b0J+M2c/as80qZiud11ks2XDg/lEL8x3NFuYfpEV8Sq6F7MkZa+++bnusmZxVWQPlbXLmd3SMRa0S+R++J0WT0ldelj71nJ1HUzM2XHZLx8Itfe6W59lrHvBbuxYfL1eeZ53MWnW+UtH0PBs2Xija15cKiZ4n5Ut2Xk3F7PXesck6ERrXYBXl+W2Mpayy78iOv6XD2gpg4/jT70m//W+D+vzvHtf2IxtrIvzQ1d26evKYJOl3vvK8futXn71vP4tKLADUsdvNsfP7rNK4d9vqgqDPsQvvXZstAAT8Nix1rQUCVuUMlQNjKDC3B21lK57VzgG9G6GAVdHamiyEhoOrC2V+X7lSud3+HQw8eBU3n2NDoiMhe00rQTESknZstkr7Sl7j5ogFwM1tdv+1Gh7rOBYoHWfp5z5YXp24ITx3nlS+r6XBKtbLtcNx7Ps3t9lr7Lo2bHutz61gwFZojpRDayhoPxcAsPYIsQBQxxpvM3zTcezCOrjKd3rHscV0lpo/u5Z8jl3oL67whoOSqli98vkszN1txbcSkoKNa9uuteQ4FtoWB7eAX2q+zZ6rXnmrJM+zxZcCfgvsqx02vBLL7f0q2Wvk8y19Lq/k/K4E9vtZ1V/ufAYArD222AEAAAt4Ku/XWrDwWirZcN5CafVb/QAAsNYIsQAAYIFSyVbbvTFk8zyTGVuReDpuQRYAgFpiODEAAJBkc0vTWelSn+0hW9nmqK1JkiO9cd6qtE8ctNV3V7r4FQAAa4kQCwAAjGerRo9MSiNTtvXM7i1zN8eSttr10T22yBIhFgBQC4RYAAAgyRZPam+WPn7C9g1eSjBgq/v6CLAAgBohxAIAgFk+n1VZG9ZwD1UAANYS/agAAAAAgLpBiAUAAAAA1A1CLAAAAACgbhBiAQAAAAB1gxALAAAAAKgbhFgAAAAAQN1gi511JJGR/ugVqbur1i0BAAAANp4zN2rdgo2BELtONLSF1Li9Va9cqnVLaqNU8pSMZlUquAqE/GrpjMhhnAEeQMnpnPKZonw+R82dEQVCnKjYeEoFV9lkQdlUQcGwXy1dDZJPcmrdMGCdyWWKSk3nJEmR5qAaW0M1btHGsHlvUMGIv9bNWNcIsevE3ic2afvh9lo3o2ZSMzm98zfXlYhm1dXdrGe+cJA3DzyQTn27R8PXphWK+PXMFw6qs7u51k0Cqi4+kdaNk6PqOTWpTbtb9MJXDisQ9MnxEWOBtTR4cUrvfq9XknToxDY99MEdNW7RxuA4jiLNxKz7iWd3nQiE/QqEN2Zoy6UKKmQKSk7mlBjPKRIOqpAtqLE9pFADpzgeDMVcSamZrNLRnBKjOYUa/cqnCvIHHEVa6BnHxlEqupoedjV0Kab0TEHZzoJmxlLadrBdkaZgrZsHrBvpmayyiYISo7nyv/NySyU1dUTk8zMKCPWNMxh1LxnNavjKtBLjOaWjecVGsxq8HFU6lqt104BZ2VRBg5emND2cVjqaV2oyr5FrM4qNp2vdNKCq0jM5TQ0kNX4joWyioPh4Rr3vjSsTz8vzvFo3D1g3pgaTGr8ZVzqaVzqaV3QgqdEbMRXzbq2bBtwzQizq3tRQUtdOjSmXLkiS0vGcrrw5opkxwgEeHMnprC79YkSJyawkmxPYe3ZSY73xGrcMqK7J/rhGrkblFj3Jk+JjGV1/c1ypaFZuiRALrJXBS1ENXZme/ffkQFI3To8pnynWsFXA2iDEom55nqfERFrR/qRig1mV8nbxU8y6mu7PKDqQVHIqU+NWAjakKzqQVLQ3rXzaLh4811N8NKtoX1KxsZTcIj3jWN8811MhV9TotbgGL8Zmq66uKxUyrkauzmhmJFXjVgL1r5AtKjqU0FRfSqnJ/OzXs7GCor1pRQcTyibztzkC8OAjxKJuea402hPTRG9CuWRRnlu+ICp5yiWKGrsR00QfVS7U3tRQUiNXZ5SNF1Qq2HnqeVI+VdLUQFJDV6ZVyJdq3Erg/irmS5rsT2iyP6nkVE6qFF09qZh3NXAhqsn+xOx7OYC7k00W1H9uUrHRtAqZuc+WYt5VciqngYtRxSfo5Ed9I8SiLnmep2K+pIHzUxpfZjjm6PWYhq5Myy26zLNCbXiSW3I11hPX4KXokhfn0aGUes9MKJcqcPGO9cuTcqmies9MKDaalhYNPCgVXQ1dnNb4zbjyGX4XgLvluZ4S0ayuvjWqxFT2ltvzmaJuvjehqaEkw/dR1wixqEv5dFHRgYSm+tJKRwtL3ic5mVe0L6XoUEKFLFUuVF+xUNLMSErRvqQSYzkt1ZeSqQzvGkgyvAvrVqnoKhnN6uapKcVGl6gAeZJb8DQznNbw1RkVCwyvB+5Gav70ldSt1z6lgqvYYEbR/qQSE2k6+VG3CLGoS8loVj3vjSsxmVVpmYudUt5VbCyjnnfHlUkQDlB9+UxRfecmFR1KqZhb+jx1i55SM3n1nZtkpWKsW/HJjEauzygxmVUht3SnoudKU4Mp9Z2dVD5d4OIauAuTAwkNXY4qny4tWWn1XKmQdTV+M6Hhq9PyqMaiThFiUXdKBVczY2n1nJ64YzhNRnPqOT2hxGRGJRbOQRW5RVfpWE59ZycVG7/93KNCpqi+96c0NZhSkbmxWGc811N0MKmBc1MqZktzc2GXEB/PaOjStOKTWRWXCbsAbuV5noq5ksZ74hq+MnPHIflTAwkNXppWLlNkWDHqEiEWdScZtZWHp/vTKmZuf5GTTxc1VR6qmZ65dW4IcL9kErYn31RvWtn47bczKBZczVSGd00yvAvrh+d5KmSLmuxNaOD96TsGU7foKTNT1NClqOKTvGcDK1UquJoZTSran1p2+sp86WjBplwNJpTPLD0tC3iQBWrdAGC1cumifH5H2x9qn/1aKVfSZG9KhVxJ4aaANu1tluN3JEk+nyPX85RLF9VSq0ZjwylkSyrkS9qyv1lt2xtmvz7Vl1QmVpDP72jT3maFmubehoMNfmWTBatUOTVoNLDGPFfKxPMKNvi1/Wir5NnK3NlEQZM3k/I8KdjgU0N7UI1tIfkDPoUaAirmiyoVqMQCK+WWPKXjeTW2h9R9bO76KD2dV3TAtq5q2RxZ8HnU0hVRJp5XMc9INdQfQizqTiDkV8eOJoUa507fTDyv+HhOhVxJDa0hHXh2i4Jhv93oSM2dEQVC/hq1GBuRP+hTc2dE+09sWTBU61xyUJlYQf6AT7se61T7tsbZ2xpaQwqEeVvGOuLYn67dzQo1+WeHEkeHUoqNZ1TMuGrqCGvnY+1q39akYNgvx+co1BBQMMLvArBSjs9RMBLQtsNt6tjRNPv1kaux2RDbubNJB5/bMntbIOxXuCkon59eU9QfPiFQdzp3NKuzu3nBtKrYWFqXXx1Tajqnxrawjr20U+GmoKR5BS3eo1FFLZsa1NJlPd7zz9X+M1GN9yTkD/p04Omt2n6kY/Y2Z/b/gPXB53PUtrVJbVuaFnx96EpU/ecnFB/Jq2tns5781D61bm6c7WzkdwFYnWDYr+2HOrT9YMeCz5xAYFgXfzwsSdq8p1WPfHT37G38nqGeEWJRn5yF77vO4tvKf4Cachb8Z8k7cJ5iQ1h0njuOFvQwVv7J7wNwj5zb51J+x7BesLATAACoPm/2/6gGAQBWhRALAABqYH6AvUP5CACAeQixAACgqrzyKsWzk/cIsACAVSDEAgCAqvO8eRmWEAsAWAVCLAAAqDqP6bAAgLtEiAUAADUwL8VSigUArAIhFgAAVNeiObFkWADAahBiAQBAVVV21/HudEcAAJZAiAUAAAAA1A1CLAAAqDrPrfzNkeOwTywAYOUIsQAAoPoqc2JnwyspFgCwMoRYAABQXfNXdZLIrwCAVSHEAgCAqnPn7bDjYzQxAGAVCLEAAKD6FhVjAQBYKUIsAACornKA9SQrwVKGBQCsAiEWAABUlSfJmzcv1hHjiQEAK0eIBQAANeNIkkOCBQCsHCEWAABUHdNhAQB3ixALAACqbv4uO9RhAQCrQYgFAABV580u7OQwmhgAsCqEWAAAUFVWhZ03oNhxqMYCAFaMEAsAAKqPfWIBAHeJEAsAAKpvfoClDAsAWAVCLAAAqD6qsACAu0SIBQAA1eVJXjnFOo7kOA7VWADAihFiAQBA9bma22KHAAsAWAVCLAAAAACgbhBiAQBA1d06JZZyLABgZQixAACgujxPcst77DAfFgCwSoRYAABQdZ4sy0oixAIAVoUQCwAAqs+d+ysZFgCwGoRYAABQfQsmxRJjAQArR4gFAABVVZ4NK4n4CgBYPUIsAACoLk8LkqzjsFcsAGDlCLEAAKDqFizqRIAFAKwCIRYAAAAAUDcIsQAAoMYoxQIAVo4QCwAAqm/e6sTMhwUArAYhFgAAVJdnc2I9rxxgCbEAgFUgxAIAgJoiwwIAVoMQCwAAqm92OHF5fx2SLABghQixAACg+rz5k2Jr1wwAQP0hxAIAgNrwZIXYWrcDAFBXCLEAAKD65hViSbEAgNUgxAIAAAAA6gYhFgAAVF2lEGs77Nj/AABYCUIsAACoKm/xF8ivAIBVIMQCAIDaIcACAFaJEAsAAKrL04JyrGNjigEAWBFCLAAAqCGnnGIBAFiZQK0bgLUxM5LW6LWZWjejZtKxvPLpgiQpE8vr6uujCkb8NW5V9TV1hLXz4U45vvq4IPRcT4MXokpN52rdlKpJTGQlSaWiq74zk4qPp2vcotqKNIe085FOBUIbu081PpHR8KXpWjejaib7kvLKldjYWFqXXxvZ8Dl2095WbdrdXOtmVFU+U9Tg+ajymWKtm7JuDV+ZuzacGkzq8mvDNWzNxuEP+LTzkU41tIZq3ZR1ixC7Tgycm9JP//iiGiOSf4NeC/oltTVJbiylU39+sdbNqbpMXtp+rEvdRzvkr5MQWyp5OvnNmxq9OKWGDfQ+39YkSSVd/s6NWjelprIFqX1Xqz73u8cVCIVr3ZyaGr0a0/f/9Tk1RqTABul/a22w/yb7pvXWn2ycAL+Y60nprPTMlw9uuBCbms7p5//xqjKTKYWDtW7N+mWfOVL00oTevDRR28ZsAPmi5IWC+vzvHifE3keE2HWks1X6578lHd5Z65agFv7116XTsVq34u589gXpf/hSrVuBavv335W+e63WrXhwhEPSH/7X0vHDtW4Jqml8RvrdP651K2rrH31a+vJHat0KYG187x3pX71S61asf4TYdSQUkJ48JD3FBdCG9LVX6zfE7uiSPvR4rVuBavvJu5IIsbN8jvToAX4XNpqBcam5odatqK393Zz3WD96Rmrdgo1hgw48BQAAAADUI0IsAAAAAKBuEGIBAAAAAHWDEAsAAAAAqBuEWAAAAABA3SDEAgAAAADqBiEWAAAAAFA3CLEAAAAAgLpBiAUAAAAA1A1CLAAAAACgbhBiAQAAAAB1gxALAAAAAKgbhFgAAAAAQN0I1LoBqC+eZ/91nGVul7T4Js+TcgUpmbF/N4Slpsj9auHa8jwpX5TiKcnvlyIhqTFcvq18u+fZ8+Fb9MA9Sa5rf/fTXfRA8Dx7XRa/VpJ9Xd7y5/ZSXE/KF6RYSgoF7PxoCK9RY++DBeesJJ/v1ts5ZwGsB5X3O0dLv6975QuW+TeVXKkw7zO/rcneC1fzuVALnjfXbp+vfK2yhtdZnmefd4m0VCxJoaBdCwX8a/cz7gfXlUqeFFjiNfQ8qeja67vUNQEefFymYFVKrv3SL8WTvclVgm6F60njM9I7l6S3Lkr9Y/e9mWumWJImY9IvzknvXZWGJuZu88ofdpmc/Xe+ynORzlmAx4Oh6NpruhTPsw87b+mblz5eSRqNSq+ekc5cl0aia9LM+8bzrM3Z25yzmbyUzdekeQCwZiqd0O4Sb+qeyu/3i27LFebe009fsc73pb7/QVMsSdGE9OYF6d2rUv/42rbbLRcjzvdIb12Qrg7Y9c2DLl+04F1a4rq1WLLbCsVbzwPUByqxWJF0VrrUJ52+ar1ZX35ZagpbT2XJlXpH7A1/cFL67AvSjs1SOmPB9eRl6XK/BdnDO+32h/bU+hEtr+Rab+Yb563tF3stnDx3TPr409KhnVZ5+/nl08MtAAAgAElEQVRZ6RuvWSXu+GHpw09Kj+yzXtBzPdKffkeKJqWdm6UPPS49/7DU3lzrR7fxlFxpcsZeT0nauUU6cbR8W0mKp6XXzkozSWlTm/SRJ2/fg10qSZNx6c3z0qnL0qV+u+j5yJN2fuzffv8f091IpO338S9+aBcjD++TXn5KenSf9arfGJL+/XfsXN/ULr30uPTcw9KW9lq3HABWptJRd67H3tMiIenJw/Y5LNnn9eV+C3o+n3T8kHR4lzQRk96+KJ26Il3otc+MDz1utzWEH8yRKSXXrs3eOC+dvCSdu2mfRccPSx9/yq5VbhkatwqeJxVK0tnr0tsX7PpveEra3C599LjU3SW1Nq7Zw1lTnif9/H0rQLx/Q9qzRfro09IHH7Nz4kq/9MYF6TtvS9va7fruk8/aNVqQZFQ3eKlwR6ms1Dcq/eiUNJWQ9m7TbLmqWJJiSeln70nXh+ziv9L7Vyj3DF7pt4vngUn7vulErR7JyniefThMJ6XLvdIv3pfGY1JXq/T0EXvo0bh92P3VT+xx3hyxx31gh30wXh+U/vLHUjxjH57RuLRvu9Tc8OAPv1lveobtQ+zqgLRj08IhRRMzdrFz5poUCVuIvdOHvifruY3GpfM3rVd6PCbt3iI9c3Rt254vWG/3Wpw30wnp7I25c/ZSvz2O3Vukliapb0z6+qvSYPkiZSxqnVGdLZyzAOpDJm8B9XKfdUYf2jn3lu560pUB63C/1Ccd3DH39WzeOjKv9Evfft3e53dtsa8/yFW6kmvtvtw/d63S3CA9dXht2u15Vo3uH5feOCcNRa2j9pG9VuVcK55n15o+39yUrXt1+or0t69LJ69K3Z0WTvdtt2vYmyN23fqNV+3abjphHdxPHpbaSEZ14wHsW8KDZnxaOn1Z+t7b9qb/lY/ZRa/fbxfYlYtf15O++kv2BtcYtjfSR/dLzxwr9wjWCZ9PamyQnjgoHT8iHehedBHvWYCZSUolSU658vr9t+3NPpWVYmkpkbW7941Jr7whDU4wtLjaKr2xf/Ejm4f9zEPWEVFxZUD665/ahcqJo9KnnrvzB6jPJ7U127nx5CFpzzbdU2/3cioXJz3DNmT9Xs0k7YO6JEk+C/Xfft06mtJZaSYlxbP2nI1Gpa+/Zh/0DC0GUA+8cif5n/ytdOGmXa984hnrjJNsFM0P37FOy22d0udelB7eb1XWlkb7fHj2WH3MgZVsHmdDWHr0gPTUEenQDlubYc04du2zv1s68ZCNNGtvWsPjl1Wq54MT1rG8JseUTf+ajNnrOTRllerzPdZ5OxW320JB++y7WO4ESKTW5uejOuhvwB31j1ov34mj0pFdUkvD3DX7+LT1du3bbm9wWzvmAl9j2ALg2LRVrOqFz5HCAeutO7rHAuq71+dudxyruP7yCzYsZTJmPb/J8jzDjhYbmvJ/fHXutkt9Fm7zhfpZ1KrelVzrbLgxZEPZX3zMelorCkXrYDh5Wfqnvykd3b2yxR18Tvnc3mHD4i/0SqeurX37+0atgnyuR/oHn7SLrHuxZ5v0yWfmhle/32PBNl+0Dqfjh6T//SvSyKT9zFfPlud05+12AHiQpXMWXC70Sp9+3jrQg+XrEde1DrnT12wI7MdP2KJNjuyapb3Z3tcP7pC2dUiTD/iIMcmuRYIBac9W+yw63yOd6VnD48s6bbd22OfjsX3SpcG1O37FTNJGr71xwa4Z92y992M6jvTFl6S93dKFHhtuHQnatBrXs6kykZBd017qs++ZTi6/5gseTIRY3NHYtF1M+/2S85Y0MmW9lVs7pJmE9Xj2jZUXfSrZXLv93TY0s7nRPhzq7SLY77M2dzTbB93iVVxbm6TH9lsgvXDTPhxzeevViwTtTfjzL0o9QxZcJ2bsgzMUrM3j2YhKJTt3x6bt/Pz2G9YxcXin9PwjUiJj4bZ3zKq1EzP2un3wcQuMt5sD5fdZZ05Hi913LTvtKysmnrxkw/CTWTu37lVLo13ohIN2zpZcaWBcagjZ17q7pM88J90ctqB+sdfO/XDo3n82ANxvibQ0NGlVtlOXpa+32XXKiaPSlg77+kjUOgi/+5YNI33qiF2zhAL2p7VRao5Yda4e+By7DmlvthFCaz1315GFvbYmqbPVPitKyyyOeDcqn0OvvG5Fkc6WtTmuI+nhvTY1Zu82qW/EXu9tXfYc7dxsn33dXbZORDxtw8e5RqsvhFjcUSZnbzLXhqTvvi09eVD6337DgkCuHNBOX5V+dFr64SnpNz8uffFD5fmFsgDozH9jvcMVv1teKr6y1UcwsPLhPZX5rPnysF2/33piHd+dg4YnC+HFoh0j4Le2+JeYD+hzLMAc6LaAdGiHDetpjpSHI0dsWHU0Zr26j+2z+ZhUYaunsh1AtmDDlP7wP9pr8sUPWg99KmudD6ms9Cd/J7U12sXO4V324eZfFN4qQ56KJTs3/X772kouGirBtDD/vAzYOe0sul86Zx/mP3nXKgrH9tr5WHKX2Ran3K580Y4X8EmBwK1VZZ9jFyKHdtrzse//b++9gyRL7ju/bz5Tr3xVV7W3Mz3ez+zOYh2AXQC7S8IQAo84EDjyghLJ0PGOcQwpdApKIROS4iIUitCd4kTxwAtekAcdecejQMIRwO7Cr/dudryfdtXd5X3VM6k/fu+V6+qZntmeme2d32djY7q76uXLly8zfyZ/+ctRYGqIwsNU90iGbaPkmBqKA0d30OdbzQHFMMy9SdMkY8SRwNOvUcKjvVPA//hbQDzSzlL7xllyzCcjwB98mZzumurOx6L980aw7XbWe02huV1VN6BveLqKBcCVIy2ZsIF7WzblNrBtutZxNteA9RYlLIv0J8vemB7mHWvUNKlO3mpx77Xe99IFShz1t78Adk7Su/MyCSvrtIVpuXJY0rvS1P7PHnVlW60B+HzA+CA5cr36JGP0c8AgZ+0De8k5zWwd2Ihlbsj0CHkrr6wANRPIV4Hz87TfdSgOHN8LvHYGWM5T1t6zc+Tx9BDomNA3YMA2TPKaeee0TQyTou3fwIpQ06I9MRcXaPIbiJB3LeDrb4x6eMeLLOeA5QyVkYxROOp6pLKUzfB7L9DE+MRxSg4EkLC8ME/Zi4UAfvOXqK2YO4dPo1Ch8QSgCjpO4ZrrjKk3yCs7MUiKTK4MZMqU1ChdJG9t5wqkBGA69M5TGdr7nIxSf98ITQvIFICLi6ToJKK0T6s362XTAs7PAd/4IfD8CRLWOyfofsVKO0Kg05BtmGT0XkmRQB6KA2NJKrvXkE0X2skuRpMULRB19zhVG9Rnv/siPdfvfL6d0ZNhGObDzmAc2D9DzmLpOjFPXCa5rquky4wO0NxYrAINC1jKUw6LUIDkhBAdTvcNGJS5MpWfLdK8Phynf29kUJoWhdFeXKS6xkJUv8ANzl71jL+VPMmiTIF0lcx1dJWbxZEkA1IZknlBg2TTRhJFOQ4ZjVdS5BA1dGBqhHQxQ1/7ve+/RDlVrqwAIwn6W6FCqmLAWKv3SUnyLpWj740n6bp4aO27qjXIkfHNn9MK6+w47YVWXaP8/DzwzZ9RXe/bAxyc3bykUsydgY1Y5obsmgK++AlA1WjyGEtSCvd4mCaGTx6l9PRnrpHS/Pixm1d+paS9oycuUSIbQ6cQymKVjI/to5Ro6aGDNKl1KueNJt3/hfdohalSIyGwmKFMhfEw8NhRur7fXotilfZE/PgNoNkkAaYKys6ackNRm30SMtUa9P8Tx2n1brYjAVTDJCFz324yKo7sJCHJ3DmEQgbaZ+6nFddz8yRIHz1Ixp6mUmKmf/wl4J2LJLj3TVP/7hS2hQpl9X3uHeoHqtIhSLPA5VT/8/jqTfr8xfeoL1brpEjNp6l/JGLA40eAwzvI0QKQgfn9lymiYX6V+vlb54A//R71o9EE8BtPkaCdW6E+v5qn8hRBf7MlGaiPHaUMkmPJdp0aTXLQfPww7WPfNdk21k2Lyjqwndrtvt2kWDEMw2wF/DoZgr/9OZo382Wa/3ZNkK7i04G/90maS88vkDy4bxeFEG8kH4KHl8X4F2/TKqxtA033THlDo/wDTz5Adek8gqZhkrH74gkKaS67ukoqSxFB4RAdbXbfLpqfeynVyMn5zGskX7z9vMuuLJpb+WBJAOtNeoZnXwfmlum5DB8Zoyn3HovptceuOQ5te3nnHOVayBTI0VqpUXh3JEQ5Ux49RNt5AgbJmqdfpW0+71wgXe3CIiUbvJqiRYcnj5McAygR4cnLlF/FpwO1OhnuDYsc0fu30T7oWKithxXK9O/uKYoyOrKzfXxO3SRDNhICPv8IXR8J9EQNMh962Ihlbsj4IBlg8TAlhAkHKItfOEATzcFZEgBXUzRJP7gPGE5svPx6kwzGZ16jPYDVOk0oikKT5htnaa/H+XlA18nTmoy1r1/OAa+cAv79MyQIRhNkjKQLFDJ0YZEmUinJuFbc0GLHDXl57yLd+29/TskLto/T/eZWyIs7t9p/s7/hA8YGyTgeiHR7XlU3pPjITgqrNnifxR3HE/APHaBVz7fO0f6oHePtfS97poDf+iyw/wz14elhWp01dBLMTYuu+8ErwLOvAnumSbkwdDob+eRVMlD7eag9A/YbT1PY11iCVuxXcsB7l6l/LabJezwxSP0yW6TPNZ1+b5rk6b+4SJ/VGvS3bJGOB/jG0+TUmRwiJ0oqS302X6Gf9Sfpmb2VW12n/nhkFzAU6/b4KwoQ8JOBOzKwscgHhmGYDwuKQhEyX3sCOLKD5tLZcYpm0VRaxXzqATIyz14jXWHvzMaddY6kLSHvXyan9/deIANpNEmRPxfmKQrMp1Fky5PHSZfxtmSkC5Tr4C9/RHO4lzApUyRH6elrwPwKyZ7pkW5dxTTJiPvR68Bf/5Tk2Ow4rT4vrNICwJXldijuzWJarmP0BJVvWmRwTo1QBvuTl6l+TWutEVt36/adF+n89FiI2sV2SHadvkb1LVbJgeD3kUN1bpn0P1UD4B6xs5gmQ1NXyZHaeRbuD18FTlygrWzxMGUSfuUMUK2RXrjNTTA6EGm/r7Ek1Xf3VPdCgpTkYDi+l65Jxm7LIQPMbYaNWOaGKIJCNR45QAO/d8+IT6PQ4gPbSEho6s15NVNZOrvyb5+nsJD/7Xdocg74KYnUn3wL+NGbwLeep6Qzf/gbtNrr3f+9C8Cf/R3w2lngHzwB/O4XaMJMF+iYkP/268B3XiCB9dQDNIEKQUJhJQf82fdpRWvnOPBPv0xhJZBkvP7R3wB//kNA9BEMU0MUyqL2ed5ElFb81N79wMwdJxokp8auCXeVveN9+H30Hsdcp4uitMPOGyY5SP71tyjr48HttBd87zR5cOdWgf/jLylUqR9vngX+3Q+pX/7uF4Df+iXKaLycpSN//uc/ozCqqWHgU8fIKfLUx8gp9Mop4F99k8K5PnUM+P1fJSGsKOTFfuaHpAi9dR74738T+PJjFGWwmAH+zXeBf/n/0ee7JikJm1+nPj+aAAbv7z9Go0FyQHGfZRhmq6KpZBwORt2cFh1zvgAZP8f3UG4PodD2jo1iWhT19Vc/pjNT984A//TXgMM7ae/oxUXgX38b+PfPAv/7X1I4c8Ag/QgATl8G/u33gFfP0PE+v/8lkgn5EvB3LwN/8EfA992EU59/yNVVFCp7NQ/8xTO0erl9FPi9LwGPHCSZMLdC8/7Xv0OROLdCvgz89E3gf/1zMga/+hk6MtGnUVTZj98E/vDr5JDtpVgG/upHwLNvkvP+v/pKO1nWlRQ915tnyUh98jg5UKeGgf/ma8DjZ4Fv/gL4f75FyTK/9gTdW4CcxbUGcGEB+N7LdNTjfbuA3/kCLRBki8CffJva7PQctU002DZix5Mk84C14d2RAIWdT49sbA8z8+GEVRVmQ3ib8316O/lA6zOQ4PDpNOnczBlrq3la6frmL2jCu38XCYZElLyX40ngS5+kFbBcGXj7AvD6WRIWHrkynWdZa7T3wIYC5IGbSAJhg4yOSp3Cd7yEUekiGbcnLwMDUeDXn6DEApEAZVXeNkKrrIdn2yEonShuIoZ+BrvitpfnSWXuHkKQkDJ81Mc6hZkQ9I68UDOtQ5gtZWi/zNk58rT/2uNkKIYDFG47O0bhyPtm+vf3XJGiE+pNUkomhykMeHyQ+nXI65c1CsWSDtUv5KfvqW5CMl2je0aC9JkiaNzMrZBCMRSnMoMBWpEdTQBBnVZsKzXyYnsogu7Rr88K7rMMw3wE8OSvoa9N0uQ54T150Jso73p4huRrZ2i+/fufositkJ/CUndOAg8fAD62l+b2X7xDK6e2Qw7+QoVWaqsNMqanXXkymqC5O2oAjqurlGrtVdV8mbaZvHsRCAWBrzxBOpEnE6ZHaOX56M5bj/p65STw3Lt0vv1jxyiCKRoio3Q0QSudDx9oJ+zspGmRsZopULTPtjGSeYkoyclkmLZo1RpAuepuf1FIzgX9VGdPjwz4yBCNuKc5mBY5fgsVeqez46TneRmZd03RarSXZLQznFpR2omfemW0EO1kWjez6MJ8uGAjlrmrLGUoNOf9y+6ENEn/esZE0ACO7qKQnESEDM/3L9G+EI+hOK2efuoYGRQhP4WqrOQoTMdLLGXZ9HfHzea6nCXP3dUVCiV5/ChNvEK4aesDZLR4oUjMvYNpk5H4vZdodXM0Sft5YuG28AsHKLR4dry/ETs8QP3y0/dR2HLQaIfOZ0ttQ7HVL2+ifuODlEnxM/dT//T7yFhNZWkfkFcf0yJj9had8wzDMAxIb8gUgKdfJyd6JERhrd6qnyLI+Dq8g2SF30chy2+eozBay6I8CMd2k65xwA0z9o7gS+fb97J7dJV0gfIkXFqi+z1+lFabvUzGniG7Z+rmjVhHklx67QzVVSikc+2cbBt3fh/Js0OzdOxgL4YO7JkBHjkEPLyfVlohyPieX6Vn8cKivb2oG0VVSe4ecvfUfmw//V6pkywtVsmIdiTJwJspm9n6cDgxc1dZylAiJ8chAzIZ7f5cUdrhoIdngZ+9S57MCx0Hbj+4n/a4VOs0sXsJal45Cbx8Eqg0KQxaOu4qrKQkAgtp4OXTlPU1EiAh0GuMREPdewqZe4NqHbi2Crzwfls5mRxa2z8GwuSZ7ufIffQwKQLVBkUFNExSRl48QXu/W/1StvvlRnnyAVI0qg3qt7UGhT6/8B6FGFddBairbPY2MwzD3BKmRVFfZ66RcWb4aAVQ7ZlXt48BD+wD/uyHpIcsZGif5/QIRe78T/85zdtDMTK4VvMkD54/AZQb7qqtJENWugbmYpZCkJdztNo6PbLWWI0E3cy7N+lwNy3KoXDispvsKkzba3qNVUOn3A1BP13TyUCUEiQW3dVSv0Eh0meuAc+/S7K04RqXjr2xLMet5wrQ6RghP7XFzgk6Qm5+FXjrLOWdmFtuH1nUL8ki89GFjVjmrrKUBi4t0MTjN9pH1PQSC5GxoAjyhnamk/fCjpeylJTg7XOUBGp2nJID+DXKYNdJsULCw3GAaIDOCN1oCDTz0Sdfpn4GUN+L3kL/8DJeprKUhOmd8xRBsHua+qXRp19ulLh7qP1SFvjBy7Qv/GoK2LeNQuQ+SNkMwzBMN9kiRedYNs2vAb19JE8n4QCF0XqO74abpX5kgObtiUH6/bXTtE/0whwlT9JVyl1Q6zkJoVQFVnNkoEUC/Y+S+SA0TTKyaw26/2CMZMjN3ENTyYDWVFpk+O6LwLlr5DednaT20m9xIcAL/54ZpYWHn75FztpC2d164yMDvly/cVnMRw82Ypm7SrFKwkFK8u71evg8QgE6rNzbw9i5h6HWAK4tu2E+8+St2z0JHNpB54JGgoDVc55npU6GipSUCOFWM/oxH028c1klSGm5lf5RbVAG46dfB64sUsjTvhnKDBzyk0JiV2+tfpU6HTnw9GuUmRIADszS6mzDpPLNDZ5hyzAMw1yfap32ZTodOoMjaU9ep8Hn0ynfgeHutRTCNXZBuslCmk5DODdHOQu2j1Myv3SBHJ9muee+DToWzXba/2+mv907w7wzfPlmVkoBWgzIlSgz8SunyEgfjFLSqj3TwM/fIl3sVvCiiS7MU2TdSyeojScGadU7VwKurpJDl7n3YCOWuatIp20gFCv0fz8MvX0I9UCkI4W64xqwrwF//C3y1v3qxymrXixE+0iiQUpW0Ilpt/cKVhtAsUYGdL9Mw8y9h2mRcAfaCcFMq3+CCA+JdkSw7VCysR+8Avzxtykpxq9+gvpl0A989wVyrvT2yxshQX3+3Bx5u//428AnDwO/9hhldDR0yhQZCVCfZhiGYT44Em6oqiTZUG2QYRsweo4qE7Sq6lMpWsanU9ixptHxOT96Dfj6t2nF8z9zdZXBGG0ziYXWztuWTY5J6e759PaAXk9XuRkb1HHIuLbdI+XyZdpuZTtrM/r2vZck+fjqKeAvnqVzYj/3IPDVJygLtCOB//QT+vutYFpk4P/dy8DTr1BI9T/7KvDLD9I2nZfepxMAqDK3dg9m68I7/Zi7ykCUjtURgkJ15tfx1pnuRA7QmZ+TwzR5lmrAT98G/u+/pax3n7kP+NqTtJdV6bM3xJvjwn4yhBXRPqh7Pk3eyPXw9i8yH31aYVsgYV6skae3eZ0Q3db+VrdfPvsG8CffBZoNOtrp73+aDNje/dWdxu+NcBxSYr7zEh3joAvgi48Cv/Io7dH6IGUzDMMw/YkE3S1N7hxbrtG2pUofR6QtgbpF8iDsJ+e67QDPvw/8n38NZAvAJ4+QAZuMrjUWO+ftoEFJLVWFZEulQbpKZxbeXjw5tBF8mnuWvY+uadrASoFWndctH+2cC3UTuJwC/tXfUIj0znHg93+NElz1O9XhZkkX6Bi7H79BdfvdzwOPHSU9cN3KMfcMbMQytx2nw/jrNQRnRijzsKoAV5eBc/MUXtwbVlwo01lljqSkBjOjVI531MhShhT8WNj1eqrtDH+NJn0mQX+rNcmYGI5TyI8QdB7nM69Seb141bVsznx3rxB3Ezbp7srr3DLwkzfIS91Jp7z0wo4dh/Ywza1QWJUEhcInY9TPLYscMg2zPTa8fun1L++YG9OiPVGWTQZ0oUzhw9eWaTwoCu2/SkTJIWNapFR4nnvphofVmnQPhrndWDb10+Xc2vHSieNuIUllKZP8jcIYsyX6XrHK8zBz+2glw+v4WYKM2IlBYHKQtmtkirQ3M1fsvr5SpxBX06ZcHTvGyRDNl0iOLKQByyFH+9AAGXq2Q3N2vVNXcVdIAwbtp/XO+l7NA8+8QjpLd8Xd8ePKk42OEcNH2e4jfpJPpgW8ehI4fbV/23jRQE3XSC9XafHh2gotBgQMWiEN+elz77k8nc6RFAXXcCPhvAzL0m27uhvWXG3Qd4pVyicxv0rljSao7QzdNepr7gKHpHa1ZXv12uFtYh95OJyYue209rrK7skMALaNU8a+wZfI43b6GvD6GeC+3aT0A+6eV9cg2DYC7J1ue+GaZrs82yEl5+ICCY3VHE3E5Tp9VqpSiOdyjoTRWJK8hk2LEht85wX6m6KQwDItmjgX0+5qXIWM5flVWsUN9Fn5Yj4aeArLrgk6gunqMoUzTQwBB7eTgG5a3YZqvkx9ZSFNCkuz6e6pdegogEtuv1zO0tELlXq7X3n9cvsYecb9PlIoqnUyWhfSVK90sW3cSpCitOhm+DZ0Umy8cwgdSfW4tEh13DnR/4w/htksHNnOtlqq0Xx6/25KdtN7TFmlRmdLnr5KoZH7Z8g5GexJ7mdatOr1+hmSEQNhOl5qMMbJ+JjNxcuBUG209582TNr2FPTTufMP7qO+nc4DP34TOLAdSMYpekdKmoMvLNA+2CM7KaRWUbpzftgOsFpo6yqZAnDqMpVrOe2xsZqnMTTmGsPFKuk4332RjDmfTiHITYtWZ+dXqf7FKiXNnF8hp37AWD80WNfomMJto8DoALCcJ+N8dIDukYjQcy1laMW1XCMjOZUh2ZSMUn09g79Uo+caipMMvLZCsq1hAX5X1zobJP1uzxTJO59G16cL9AzXlqn84QS1Wa3Rfi9Xl+m5vH24719uO8HyZeDyIhnlsRDpaQbraB9p2IhlbjulChmQjqQwmlJHMpvJIZrkH94LvHSaFJr/668pDf39QVLYr6SAN87SRPXbnwUePUgTpHCvH4q7ocUNCi2uN2lCfv4d4PWzQN699/k54D/+iFaqvvJp4Phe4B88Cfy7Z4C3zgE/f5cO6r66THVKZYG/fR74u1dIkF2Yp9U4nwZ85jgJlcA62ZSZrY2uU1KK/+JzdFTC6WvAT96i1c4njtMZf6kc8DfPAT96kwTqmavAj11v9sf2k7dYStr3+uwbZKzOjAI/fYOOwclX6fPTV4G/fJb62G8+BXz8MCkGPp1WtN69SOcZC5DQ/r0v0T5vxwGKdfcs2zQpHM++SnuPCq5S8e4FWvmtm8A/+RKVzTC3C9OiufT3/gX1ucePAn/wa+SUjIW6v3ttBfiT7wA/f4cU6SeP0zEds2Pd3ytWgDfPA//yr4HXT9PY+Ld/CDx8gOZihtksHIcMJc/oqjcoAsByV/SGB4DfeIqOzXnmNZr7D+8gY+m+3bQK+M4FmuPjQdob+rmHaO4eG6StUwAdpfP8u2Qcbxul/aSvngKyZdJVLi0Cf/VjkglffBR4/Bjw65+mMfXySTqOx6eTwffwQXKMfvsF4Nsv0jWXF4GfvUXO0M/cD+yeIiO8H6pCDvnP3E/RPf/hJyRzJOhIoSfuJ2fpa6eBbzxL4zHgI0M3EqSw6JEERS01LDqm5+vfJvmZKwI/eh24skzRQKpKEW8vvEsJrf7Z18jBFQmSvLqSor3BYT+dWfu5h0iWHtwBnJ6j8O2/eJYM6USUEic++yZt9REAzlyhzy8sULsZ+s2fm8tsLVgEMLeNlRzw0klKYvPySZqcl3KU7KcIj2oAACAASURBVEZVgCceICN02xjtoRh/njbpv3MR+Of/Lykz8TB5PC0L+NIngM8/TPthvYQGQT9wZAfwKw8DL75PHsBSlUJ+DmwDpkdJqX/rPHknc2XgC49SCHMiAjz1MVoR8/vo+jfO0qrXs6+TZ7XeoHJOXKKwm2iQQpYDxs2fx8ZsHQRIYfn8w+RZ/sErJFRfPkUrsyMD1D8ggb2TwHuXSbB7/SMZpdWizz5I2RTPzZFBOjlInvuZsXa/LFRIKH/hUTp+x/ABU8PAgRlyvCykSbDfv5uUmalhcuRcdQX++5dpjEwNUXbi2UngvYtUtqeA/eonaJwxzO3GsinEsGa6EQFO/zBh6VAkTaVOxmijSX9b8z24xoWb1Mavc5ggs7k0LXIQ/vQtishqWKSvnLwM/Ol3gCsPAI8eIsPswDbgt9xkTH/3EvD9V+g81O0jrqO+SKur/9h1Gsbc81aDBl37pY+TTLiSovExOUhZ67/8OMmRt86TPFjJA7/yCJ2yEAsBn76Posp0DXjOPQ88lSXnaiRIusrhWZIrhk5/i4aAgJ+SSl0PIcgIrzepLZ57F7i4SGPzjbOkhykCOLoDOHkFME0qPxairTJjSarrj94gw/Jn75DM2zlBCQdPXCbj/kqK5NmTD9BCgE8nWblzAjgySyu9FxZIth7fS5mNh+LAxw+RAZstkA75/Vcpim7XJPCPvgj87G36P1em9t45QUcshtYx3JmPDmzEMrcNKcn4TMbosOqju+jvyQh57D3lJh4GPnGElJWhOPD2BVJY0gUyJgydJvL7dwN7Z7rD0jSVDNJf/zStRC3nabIdGQA+eZTK27+dDF/bppC1x47Q74ZOk91TD5AxPBij+zqSjOzhAfrbsZ0UnjI5SKGk+2aozr3hccxHi3CAPNife4h+HkuS0gGQIjGaIKXk8A7ysu8cJyVizxR5iY/uJCV9dID2T6kq/fz4MRL++7e1HTIHt1OyijHXoz0Qod9VlZSJhOvp91ZpP7aP+vPIABnZ3kH0Txyn0DUv5D7kB+7bBTx2bO3h9Qyz2agKjZPPP0wK99Gd1Ef7rZhGQyQXGhb1+aO76Ci1XgydxsWD+2ncDMXaZ4YzzGbhHS8zMgD8vU/S3zSVzpD3PtdU0mceP0p/D/goLNh2aFtHyE/X75gAnvwY/ezpCZpKsuGrn6b+vJghnWd0gOb1yWHSVaaGKXx29yTwiaPA9lFybM6OA0/eT8ZwMkrzvuPQfD8Up3EhBOkqw3Hg4DaST4nIxnSV8UHgoQPt8q6maFVX12jcjSdJLk2PkFzbMwUc200LEckYGbHxEBnRtWZbd3rqATq/fHacDOBto+QQuG83jXstQDrVVz9Nq62WRWU+dpTaMRokx+8vPUA/X1yi63aMkxw8tovuFQnQ8z9+FLhvD+lvzEcfNmKZ28ZIgryLX378xt9VBfCpYxSaUjeBaylS0iPu5Hm9LHez47SX8EufdMOWHZqEvT0gD+0HvvAwleEdzeMhBClPB2cpU+C1ZbpvPExGSrVB3r2vmfR7jA2BewohaEX16E7gtz9H/UMI6kejCQqtKlSAf2iRERkJtq+NhYCdk3T8TanqOmk6sls+fBD44sdJSY/39CtVUOTB5x6ibJQhfzvJFEAG8J5pysRdrLjKVbT9+aOH6L6hAAl+hrkTaK4x+kf/NUUeBA1SjvsxNQz8zhdohUlVKclfv317kSDtLUzGSKmOhcmAYCcis1n4NDIs/+FT9P+NGIpTFNdTH6N9q5kCrZ5uG6N5fz19ZXqE+v2vPOruf7WoLK8vP7iP5nzVTdjXiRCkp+ybAX7jSZJFTZNkx0iCxkauRFulhuNrdZ2NMDlEcuyXHwRWsmSgx0JuEkwfPeuvPEryqHdcP34MeOSQe2RhhYznkJ/qvXcG+PT91EZDse72EaBn2jdDbaIIinTrdFINxoCvPUFHEq3kaQ4IGbSSCwBTI8BXPkXt5vdtTlZkZmvAr5r5UKEIChebHqFVWk3dWNiuEJRpWA3TdZ0ToFBoor9eEhBVIYVr22j7vroGhN1J0ZG8t+JeRlNpNXZ2nH7XNfqbF64l1+kfQpCg9YzUzkRgiqDV0eslB9PcPqgqa/uvIuieA5F2hsfO+ib6HN3AMLcbTSXFN+S/fv8TAtAUihgQon8f72Qw1l4NYwOW+bAQC5PuYDn07436phBkNMfcnAmdY0QIKu96QQaKu4e1U0cydJJJXsbeD7JXXLhyZSRBRyBqKpXnRac5zvo6mabSfla/3u10FYLaxtCv3z4BH313vSgLw+eubmvd7Wa48liAZd69BhuxzIcKIWgV6lb2MigK4OszgQm0PXbXu69nqPSWyV49RgjqB719od/fuq4DCdV+gtUzcK+HIvqfd+xdr4r1y2anC3M3UEQ74+iNEDcx1/t9H6xeDHM72Ghf72Q9XcUr73oIQUbkGl0Fm+fcURVaDe1NXHmjMejJq3712Ijz6Uafe/Vac1+Fzwu9V+H3zjAMwzAMwzAMw2wZ2IhlGIZhGIZhGIZhtgxsxDIMwzAMwzAMwzBbBjZiGYZhGIZhGIZhmC0DG7EMwzAMwzAMwzDMloGNWIZhGIZhGIZhGGbLwEYswzAMwzAMwzAMs2VgI5ZhGIZhGIZhGIbZMrARyzAMwzAMwzAMw2wZ2IhlGIZhGIZhGIZhtgxsxDIMwzAMwzAMwzBbBjZiGYZhGIZhGIZhmC2DdrcrwGwexSrwx98CxhJ3uybM3eDlkwD8d7sWt8ZLJ4H/4U/vdi2YO81z793tGny4MG3gz34A/PTNu10T5k5SrAJLGWD2blfkLvLDV4Bs4W7XgmE2h/cu3e0a3BuwEfsRIRg3EJmO4+lLAHjw3LOMToYhxN2uxcYRAkhMhrFwwcafv3G3a8PcDQZnQlA0DgryR3QM7ojjZ3MA5u52bZg7jTIMhBNb1Av5AdB8Koa2RXAqo+MUywDmI8TIDg16QL3b1fhIw0bsR4SZY4MY3xu/29Vg7jKqpmwpg0DVFDzy1Z2wLeduV4W5SyiqAl+QBf3EvgF88b87drerwdxFdOPeGwfhhIHP/KP9cBx5t6vCMJuKEAK+IJtZtxNu3Y8Imq5A0313uxoMc9PwJM8wgKorCPAcztxjCEXACOt3uxoMw2xBts6SDcMwDMMwDMMwDHPPw0YswzAMwzAMwzAMs2VgI5ZhGIZhGIZhGIbZMrARyzAMwzAMwzAMw2wZ2IhlGIZhGIZhGIZhtgxsxDIMwzAMwzAMwzBbBjZiGYZhGIZhGIZhmC0DG7EMwzAMwzAMwzDMloGNWIZhGIZhGIZhGGbLwEYswzAMwzAMwzAMs2VgI5ZhGIZhGIZhGIbZMrARyzAMwzAMwzAMw2wZ2IhlGIZhGIZhGIZhtgxsxDIMwzAMwzAMwzBbBu1uV4BhGIZhtgQSkFLCthwAgKIpUIQAxF2uF8MwDMPcY7ARyzAMwzAbwLYdNKsWMnMlSCkxMB5GIOKDqnNQE8MwDMPcSdiIZRiGuceQjkQpU0M+Vd3cggUghEBsOIjoUGBzy75L2JaDar6BfKqC3FIV+cUaypkawoN+KKoC3a/etBHr2BKF5QpKmfrmVtZt/8REGKG4sbllMwzDMMyHCDZiGYZh7jFsy0H6WgkXXk1tcskCiiqw44GRj4QRazZsVPINpM7nsHA6i6WzeWSu1iAdifG9MYzsjGJgPHRTZXrhyKmLBcydSG9uhYWAogjsf3ySjViGYRjmIw0bsQzDMPcSErCaNjJXKzj/4uptuUV8JIztx7Dl94qW0jVcO5nB+VeXkJ+vol6wIB35wQqVgNWwsXK+dNvaf2JvEuO7B7Z8+zMMwzDMerARyzAMcw8hAdimA7Nhw2zYUA2B8d1xjO6KIxDRofk0KKqAdCSuvreKlUslFJcp7HV4NoKxfXEkJyJQVAHbctComqjkGli5VETqbAHSNZId24Gibe29osG4gcm9CUQSfqTO57F4Oo/F04UPZMhKKWGZNsymDatpQzEEZg4lMTQThT+sQ/WpUBSBZs3C1fdWkb5SRiXbBACM74tjdHcMifEwAGrneoXaf+lcHukrZUDS+3UcB4r64W3/Zs1CcbUKX0CHEdJgBPW7XSWGYRhmC8FGLMMwzD0FhbM6toRuqBjZE8XOB0cwfSiJYMyAZqhQNQWO7aBea6CYrreM2OiIHzNHkpg5PARVV2A1HTSqTZTSdYQSftiWRG6uAmlLMmJVZUuvBvpDOvwhHYPTEfjDOpo1C6mzBdjOrZcpPSPTljDCOkb3RLHrkVGM744jEPW1nAjVQgOVUg2ldL1lxA5MBLHtvkFMHxwEJGA2bdTL1P5GWAccIDtfgWNT+Yq6SQ2x2UigWmjg4psrGJqJIDkZYSOWYRiGuSnYiGUYhrnHsC0Jx5bwR3Qc/+J2jO2KwwjdvBGh+RRoPj9CcT9CAwYigwZe+Y8X4ThkKKs+CbGVrdgOjICGQNgHCAFaz75FXCNWOhKx4SAe+fXdiI8G4QvcpDgWgG6o0I0AIskAAjEfgjEfXvmri3BsCcdyAN+H04qVUqKwUsW7z1zFwU9NIhw3gOHg3a4WwzAMs4X48MYaMQzDMJuPBGzLhuoDQoM+hAYM6P4P7s8MRHwYnIrAiKiAcM9S/YDbRz9MCEVAbEJ4LiV2sqH5BUKDOsIDBrRNMDbDA34kJ8PQgwokHNgfZLn4NpOZL2HpXB7NkgPHutu1YRiGYbYivBLLMAxzL+Gu4CWnw1B0gWDMgKJ+8NVSzacikvRj5sggBqcjUHV1S4cSr0EIWoT9oMUoArqhYXg2CrNhwxfUNqX9dUNFbCSA2eNDiI+GoGofvlVYx5aol5uYP5nD3HtZ2A0H+KCJshiGYZh7EjZiGYZh7iGEEAhEDEzuS2JsZxz+8ObtRfQFNBz41CR8QQ1GQO8yYqWUrb2gXmIkoQgIhY6FUVT6+XpIp72fV0oy3BRFUPiy6UBKKlfTFQhVQPSxOqWkUGrHlpC2bF0jFAFFU6CoVJ9ehMC6RqyU0q0blQtIKIoCVRNr6qEoAsG4gW1HhiABqJuY/Co84Mexz25DKG7A17G67rh7lB3He2ZAKICiKFA0saYOUlJb3yiBleI+m4SEY/X5rqDnFYpoGbDLlwq4/NYqFk7lAQk39JzeH+C2s3LjvsAwDMPc27ARyzAMc4+hGypUXQEkNjWDrVAVhAcDZAR22CCOTdmQVy4XkE9VUS02AIcSG4UH/IgkA4gOBeAP+9Y1XhxbolE1sXq5gPxyBWbTwY7jo4gM+FErNbB4LotqoQnNp2JszwAiicCafaZSSjSqFkrpGgqpCkrpOho1E1CAYMRAcjKC2GgQ4QH/TT232bBRydWRmS+jsFKFbdqIDYUwOB1BZLC7HkIR8Pm1VgjxZhprqk9BdDjQlVDLcRxUcg3kFisoZauo5BowGw78YR3hpB8Do0EMzkShCLqGEk/ZKGfrqBQa699LVRAbCcLnV2E1HeSWKmtCmA2/hmDcgD+ko5SpYf5UFu//dAG5uWor1LySbyAzX4LjOhMCIR8Z4UGNDVmGYRhmXdiIZRiGuccQioB6GwwEIWgVtJPiahXpqyUsns6jmK7BMmk/rm05cExACAWBkI7YeBDDs1FM7k/SSqq7wlrJNVBYriC/VEF+qY7CcgX1chPQgOREGMXlKi6/vorccgXNqgXNUJBbqmLXg6MY3Rlv1aNeNpFbqmD+/SwKK1U0ayaECtRKTTSrNlRVRXggj4GJEEZ3RTG6c+CGq9TSkcgtVVBcrmHxbB6VbB3VUhOOLRGMFBAZDmBi3wB2PTgKRWuvyN6+9hct49i2HDSrJuZOZbB6qYTiSh1Cc1AtNFErWVBVBf6gjsiQH4PbQpjcn0RsOARVU9CsWVi9VsTimRxqfc7G1f0qIoM+7HpoHIAP5UwNF95IoZo1Wyuqml/B4FQYk/uTaFQszJ/M4vzLKaQvl2HW7VZZq5dKqJdM+CM5AMDIbBRTh5JITIRvSxsxDMMwHw3YiGUYhmE2HceWaFRMLJ7N4eJrK7jw8gqiQwHEJwIID/phmw6Kyw3kF8qo5BoIJnRM7B+AZqhIToThj/jgWA4ycyXMn85g8UwOy+fKsJsOVF3AF1GwdD6PRtnC+z+Zh21KSAdQdIFsqozB6UjLiK1XTKxeLeLKO2lcem0Fji0RHfYjPhGAbQO1YhWrFwtwrDzCSQO5pTiMgI7BmSh0f/+9pWbTQSlTR36pguWLJSydK0DVFNhNOn93pSmhGnRUzshsFOFkALpxZ/apOo5ENd9A6kIep55bQCFVg8+nIz7hh+7X0Kg4KK00kC6XIVQgdFqHY0vMHBGIj4bhOBJm3UIpU0P2Sg3lTAPNWjsDU2TYwOTBGCzTpjDhioWVywWsXiijUaZ9vvGJAEJxHx3VVLZRzjZQK5oIxQ1UC000q1RevWwBaKBWpN9DcQNm3WqFeTMMwzBMP9iIZRiGYTadRsXE1fdWcepni1g6W4CiKTjy+WlsOzqIYMwHSCCXquLau2m8/b2rqGZNXHkrg+WrRTz6ld2YPpgEBJC6kENhudKV6NhxJMyag9PPLUIzFISGfKismrAaDqQDmHUHdscezcUztAp4+Y0MQoM69j42jr2PjsMX1CAlsHg2h59/4xRqWROVbBPX3s4iPhKC6lMwvD3W9/mK6RrOvrSIasZCIKZj6kgckcEA8ktlrF4pobxswm5K5BerOPvyEvZ9YgKxO3SMjFm3sXA6h+e+cQ5SdbD9vkEcfWoaobgfQlFQTNdw5vkFXH0rg/xiFcXFJk79fBESAsfGIgjGDOz62BjGdyeweDaHkz9eoD2sLpHBAKYPDSE84Ecg4oNtBTG2K45q2oSimBg/GMPBT01hZDZGma8lMDAexu5HR7ByuYiTP1nA/Alaed3x4DBmjw9hcDoCANAMFbqhUbg7wzAMw6wDG7EMwzDMpmI2bOQWKzjz3BLSV8oIRHzYdl8S43viiA4HoOm0IplQFUhbIrdYwcKpPMqZOqymjavvrMIIqpjYl8T2Y8MY2z2A/FIVxdQlVHNNSAewmxJCERjdGcfM4UFUsg0UV2qoV0zExoIYmonAthzUKyauvZvBwqk8bNPB9vuGMX0oiUjS39o7OjAaxLYjg7j2dhbF5TqaNRuZ+SKGd0TWNWLNqo1yuonpg0mM741jcCYCX0BDtdDAwukc3vnBHKy6jUqugbn3M9h+bBix4TvT/qmLOcydzKBWbGL66AAm9g5gYDwMTVchFAHNp2Dvx8dQyTVQytZh1RwUlupIXyohM1dCfCQI3a8hklQweUBBNd+AbTtInS0CAErpOq6eyGB4Ng5VU1BarWHxVBGOIzG6J4pDn5nG0HQE/rCvVSdVUyAEXeu9fwDwBVQEYz6Ek94+5M3JAs0wDMN8tGEjlmEYhtlUKrk6Vi4XkTpbQL1sYXxvHLseHkF8JNhlwBhBDQMTIcw+MIRypoHSah12Q2LxTB7RoQBGdw1geHsMQhEIxUrwR+dQL5mwGxQ6PDgVxrajQ9j1sVE0qiYKK1VUi03ER4IIxf1oVEykzueQulBENddEeMiPyX0JJCcjUDoy8gYiPswcGkQxVUe9bNIe0BsYUrpfRXw4hB0PDGNifwLhBBlhtulAURWcfyWF8iolksrOV2E17OsXuAlIN0tz6nwBS2cp+29yMozYUBC2KWGbXkiwRHwkiOCAD5ohYNWAZsVGcaWOzHyRzq51k39FBwOYOpREs2Yht1CBWadEUQunc5g5XEBxVcPypSLSV0sY2h7GtmODmNyXgKL0rKQK2rPbmzFawMv8zJYrwzAMs3HYiGUYhmE2ldxiBYtnsrDcJD++oIbB6Qj0wFqRYwR1TB8awpU3MxAiBymB7LUqVi6VUUrXEBsOQvOpECrgC5FhZTdtCEVgx/FRzBwehFAE/GFf18ofAORTFZx+bh6l1Rr8UQ1DsyFEh9dmLTZCOsZ3D2DlSgG1Sh3VnImR2ThiI6F1n3FgPIjDn53A5MEEQvF2NmNVVxCI+pCYCsKs2qhkHVhVB9ICZeS9jbaabUtUCg2sXiwje60KRREwQj5IRyK7UFrzfathQdHaFfIcAZbVnWV4eHsMVsPGtROryM3X0SjbqGVNXHwthWbTQm6pAgc2tt8/jD2PTGxqxmuGYRiG6QcbsQzDMMymkk9VsXS+ANuUECqg+RT4gjoUtc/5q4qAz68iMRVEYjqIzNUqANpTm09VEIr7KeOuEFDoHyiagB5UYIQ16Mb6Ysys20jPVdCsWQgn/Igk/X3PZVU0AX/Eh32fnMS2I8OwTAex4QCCMWPdsoUQUBSl7wqiogjofq3LmLMdB46UUG7jiqNtOihn6zDdVV8pJc69tIS599N961nK1GA1HKiG+5mQsJrOmmzEQgjER0M4/sVZvP6tq1i+UIRtSiydLsKREkZQw7FfnsTE3oFWdmSGYRiGuZ2wEcswDMNsDpKOdqkWmihnGpAOoAcUaAGlr/EIuKGkmoJQwkA4abSM2GbNRDFdxagVB6BDAFCEcENPAdUnoOlKX8MYoNBas26jmjVhNR0oKh0/0+/sUSEEVF0gMR4Gxjf2qEKsf8art++083PpSDIOb+OxMY7loJxrwGrYEKqA6hOIjwY79pt2k5gMd/0ejPuQnAr3dQz4Iz5M7k9i6WwRzaqF3EIVlVyTshsPGJg6kERsOLju+2AYhmGYzYSNWIZhGGZTkJKOZrHqbvgsANUQ0Iwbh5f6ghqMjnNZzYaNSr7RlWX4ZhYxbdOBWbdh1RxIm+rm2BK4E0e3CLSSRnlIB8BtvrVtO6gWGjCbNoQK6GEVRz47Q5mePyCqpkCNGNh2fBD1ioncAjkbFFVA8yswwjpnFGYYhmHuGCxxGIZhmE1BSqBZt2B37Kk0aw7MqnOdqwhfQIMRbBux0gFsU96y4Wc2bZiN9tmmti3RbFhwnDtz/qi3YnwnobN5m7BNG9KUMIs2HNPZ1DNX40MhRBKB9j1NiUq6iXOvpJBbqmzafRiGYRjmerARyzAMw2wKQgCarnaFlDom7bN07OsbU5qudu2nVFQBn1+DuEUppWoKVK1dXrNqIZ+qwmraN1yNlY5EMV1DtdBYsz/0w4wQlFhKUQSkBBxLopJroF5q3vBax5Zo1i3USk1ase5FUrssX8ojs1CCogNCJR9DvWTiyptpLF8ooFpobP6DMQzDMEwPbMQyDMMwm4MQ8AU06D61ZXxKB7AbDuqlJhxrfYNQuP956IaKUNyAeouZbjWfCl9AhVAFIIBGxUJ+sYpqodnKmtwPby/t0rkcsgtlMry3iB2rqAr8YQOq6wyQEsjMlZBPVW94bb3cRGG5iuxCmQz9HizTRildw8U3VpBZKCExFUJ8PAh/RIdZt7FyoYSFkzmsXim6Ydub/ngMwzAM04KNWIZhGGZToIRLKiVy8ovWntBGxcTK5QKaNXPday3Thtlsh//6Qzrio0GovlsTU4oqoPpU6CFK/uRYEo2CjfTVEkqZ2rrXmU0bhdUqTv9iEQtncu6D3VIV7jiqpiA8YEBvGbESl99ZwcLZ7PUXnyWwfKmAS2+t4Mo7q2hU176nUqaGk7+Yw/L5ImLJEB7+yk488KvbMb433vrOlXfTOPPCEsz6nQvbZhiGYe5N2IhlGIZh+iIlukJvaVHy+saJEEB0OIDh2WgrI3G11MS19zOoldY3YusVsxX2quhAIO5DYiK87pEt0rlRTQBfQEVyKgRfkOJeraaDi2+sYOF0Ds2qtSZU2HEksvNlnH5uHooGBGO+dTMQ3whquzV/vfkyun7vW2gLVVcQHew4B1cC5dUmFk/lceH1FGql5pqQbqtpo5yt4crbaeQWKogOBdZkki5lalg8m8fFV1YRGfJj6nACo7vimNyfwMS+OJIzQaiaQKNsIX2lhNMvLqzvKOhpTseRcG68ZZphGIZhuuDsxAzDMMxaJODYTteKmnQkHEveMFFQYiKEib0DSF+qwDYd1IsmFk7lMXlgEOGEv21kefdxJEqrdRRW6gCA2EgAiYkQQnF/lxHp1UU6gN2UkDbVpd8ZqADgD+uYOphALWeiXqSEU8sXigjGfAiEfUhMhmAEdQhVwDYdFFdruHYijStvpzF9ZBADY6FW2XRETke1JdZvB+kZ/D1taUuoev9L+pXhWD3tb8v++1VdVE1BaMCPyLAfwQEfqrkmzJqD1cslKPoiHMvGwFiYMgkrCpp1C+VsHbmFCpbOFRAbDWBoWwS6obae2TIdpC4UMHcig3K2gR0PDmPyQAKhOB3bM7o7jmK6inK2iUbZQmm1jvMvpRBJBOAP6TBCHQ8sqI6de6YbNQvNGjkUqsUGmjUbQgGiyQCUdY5lYhiGYRg2YhmGYZg1SNAxN3bH/lHbdPrul+wlORFB44CFkz9ZRLNO+1FXLpSROp9HJGlgaCbWvo+UsJs20lfKWL1YBgBsOzaEyQOJ7nNWJWDbgONQwqJmiRJGXc+IDQ34sffjE1g6XUR2vkqrsTUHl99MI7NQxp5HRjE4FYHuV1HNN3D6hUWkzhchLYHhmSgGxkPt+zsSjt1uCzqyZ/1kVdJxuhZNzYYNy3Sg9z+yde317nFFTkf7W6YDq7n+sqWiCvjDOsb2xlBcreLSq2kAQGm1gUoujdWrBYztGcDIbBxGQEPmWhGpcwUsnS3CH9cwfXgAQ9PRlvFo2xKVfB3nX1nG3PsZDM4GMXNkCMPb2u9vZDYG6UhceTsDs2ahXrKwcKKA4W1ZBKM+jO0e6KqfEdK6juIppWsoZ+uwLQeLZ3KUNEoTOPyZGfjDvo01FsMwDHPPwUYswzAM00I6ErVSA5m5MlYvllDOtDPbFlaqWDybw9BMDNHhAHz+/iJE1RXEx0I4/qVtOPtCCssXi5COxPkXl2FWbex6xEZ8NAQhgMJqDRdeSyF1IQ9/RENyJoipQwkMjIdb5dXLJgrLses8rwAADnhJREFUVVTTTVj1thGXvlrCwHgQyalo3+NsNF1BKO7HzLEkmnULS6cLACjRVGm5gbO/SOFyIA2hKLBNG6VsHeG4H9NHkhjeHoU/5Gu1SWG1iuxiuRWCXC/TPt+xnQMIxtqGtG3SWa0rl8uolyl8WkqJwkoVlXwDgcj1DTPHcVDJNZA6n8fqpTKqhXYIdnahjNSFPIZnYgjGfeuGWo/tiKOab2DpfB7Nkg3bpFXratrCfD2P1QsVqKpAs2qhXjEBCex6cAzbjg5D0RQIIdCsW8jMl/Hej68hda6AQMiHmcNDCMZ8XSHBqqYgGDcwvjeGeUuitELZiS+/vgppS+oLo0H4/BoURSAY7a736qUy6oVruPDSCqqlJib2x7H7kVHoBqsnDMMwzPqwlGAYhmHgWA7qFRMrl4pIXysht1hBdr4Ks9Zeea3kmlg6W4DhX0RkiEJ+x/fEoahK16qpUASCMR9m7x+GbUlohorVKwUUlmu4+k4GlukgOhSAEEA5V8fcyTQUVcXUoQSmjyYwNBOFP6zDNh0sXy4gc62M5fMF1PJWV4bjhVM5WE0HgzMVTO4fQCju6wpBFYqAbqiYPJCAtCUUIZC+WkajasGs2shW3ay9AlAUIDEZxtThBHY+NILIUICMz+Uqls7mMXcyg/TVcmufaq1oYulMEf5ACiM7Y4gO+2E2bBSXaf9oMVWH2SCD27ElFk/nYZsO8rNVTB1Mwgh2i1/LtFEvmUhdKCAzV0Jmrozich1Wo220l9MNLJzMQVM1BAd8GJqJYHh7zDU822WFE35M7Elg7ydqSJ0rIr9YRa1gwqw7MOsNlNNkaKo+gVDCwNTBJLbfN4TB6QgAMmBT5/K49MYqrr6dQb1gIhQ3UFiqo7hSgxHSW8Z4pdBAbqGMRsXuejf5VA1zJ7JQNAVD28IY2RnDwHgYRkhHciqE5HQI2bkKagUTjYqFYqaG5GQY8dEQBsbDULQtkk2LYRiGuSuwEcswDMPAMh2U0jWceX4Rc+/nIN29l8FY98phNWvi1M8WofoVzBxJYGhbBD6/WJMASfOpiI+FsP/xCUSHDbz9tIlK2kK10MS5F1Kt7ymagB5QsO2+BHYcH8HM4aHWSp/VtHHpzRTm3suhtNKAZijQjHZ9svMVZBeqCETSCMX3wh/W+u6jHN4WQyDiQzhp4N2n55FfrNLeXkcCQkDRBDRDYOfDw5g9Pozh7RQuWy00kJkr4c3vXkElR4ZfINre41laaeC9Z+YwuVDG9JEEKvk6Fk4WkL5ShqooCITbdVm9TIbp0rkchmYia43Yuo18qoL3fzyH1StlMrpVZU37F5bqeG9pDlpAwZ6PjyIxEYJP1dFpxZLhGEV8LIRTz83h8ptprJynVWQv/FkoAkZMw8T+OO7//HZEBmll3XEkGhUTcyeyOPcivSd/iBwKl15fRXTIT3uKXSO2sFzB3KkMMlcrkA4QjLfrWyuZOPdiCsuXDEAAiYkINJ+KyYMJNGom6mVySuhBBdExA/s/MYGJvYl1V/gZhmEYxkPIG2Xo2AD//N/8BN989gQA4NBjb2FwcuUDV4xhGIa5c0hHUqbaXAPNinXDPLpCAEZIpxVVRfQN5wVohbdRs1DO1dGsWKjkGqjk6rCaDnRDRSBqIDxoIBQ3EIj4uhIBObZEOVtHo2p17c3tRVEFYsNBOhd2nWzCtuWgWaVERuVsA9VcA9ViA7pfQzDqQzjpRzjpRyDig+5X3fs7aNZslNK0Z3M9jKAGI6S597BhNvrvGxYC0HwKYqMhaHq3se3YElaDQpqtun3j9leAQNSH8IAfiiLWHgPkJp6qFhqoFpqo5pooZ2toVOkYo2DcQChhIJwwEElSRmKhCEACtk0hzdVCc819QwMG/GG9lfypWbNQKzXRKFlw+qgTAoDqUxCM+xCMGnRN3UKt2EQ5Te9W9dHRQIGoAV9QW9M2DMMwzNZg4dw0zr2+HwDwT772MP7LLz942+7F7k6GYRiGQm/9GgbGNlcsKJqCQIRW7jxDsl4x4VgOVF2Bz6/BCOtQVLEmQZOiCkSHAptSD1VTEIj64I/oiI0E0aiYaNYsqLoCI6DDCGkQPXVQVAX+sAJ/eKMphQEkb61+iirgC2pIBsM3/vJGEIAQAqEBP4JRA9aog3o51ErM5QvqMDyDsbPd3QzC0aHAhtreF9Ao2/Twxqvm82vQDRXhhB9W04bi9j2GYRiG2SgsNRiGYZg7gmdIBqJ3L+usEKJteN0jCFVAV1Xo/s1xCGwGQgiomlhzJi3DMAzDbASWHgzDMAzDMAzDMMyWgY1YhmEYhmEYhmEYZsvARizDMAzDMAzDMAyzZWAjlmEYhmEYhmEYhtkysBHLMAzDMAzDMAzDbBnYiGUYhmEYhmEYhmG2DGzEMgzDMAzDMAzDMFsGNmIZhmEYhmEYhmGYLQMbsQzDMAzDMAzDMMyWgY1YhmEYhmEYhmEYZsvARizDMAzDMAzDMAyzZWAjlmEYhmEYhmEYhtkysBHLMAzDMAzDMAzDbBnYiGUYhmEYhmEYhmG2DNpmF3j15CxSlyY2u1iGYRiGYRiGYRjmQ0qtHLhj99p0I7aYjm92kQzDMAzDMAzDMAwDYJOM2IBfRzx65yxvhmEYhmEYhmEY5sOJ36ff1vKFlFJ+0ELmlwvI5qubUR+GYRiGYRiGYRhmCzMyGMFIMnzbyt8UI5ZhGIZhGIZhGIZh7gScnZhhGIZhGIZhGIbZMoij28eklBISEooQAASEEJBSQgj6GQAcx4GiCNi2DVVV4TiO+5mAUASk40BV1dZ1UkqoqgLLsiCE0i5PERAAhBBwHIe+p6iAQEeZgCIEJLw60PVeHeBeL6WEoigAJIRQYNu2+zvcz0TrZ69cKUH1lXJNnVvPrQgoQsCyLCiK2qpr531bzwmqQ1c7ud/x8OrpPZ93D++52s/htbPS+ptt262yvX8hBGzbal3TeR9IdF3nleV0tYFsP4vj1lN0/92DnnttO8J9N44ju55dVVX3Z+le47Q7mxBUD4hWW7Tet6LAtixI93u97QzZbhvvPo5ju+8A2Lv/EISg99vVwd32ar2X1tN0t5PoeWeddW61i9cO6P+9rpu3XpX7LkX3td5zOY6EpiqAUFpt1fnsqqJCovMdOBBCASCgqvS99phs90mvvl61hADOnn6/Yxwp7Tp57ez2p37jXzoORN/x3x7LNJba40tRvPG/tjxFCNit8d/u+6JnfHe+G7vzHXaMf+97veO/s7/3/uy1g6qqkE73feD+TONfcZ/P7n43Trsc0TP+e/uSdDrHP7qey+kd//aNx78QArZl9xn/1H/6jf/eNljTt3t+9sZI53umMtr37HxH3ne98d96/o526Px+a/wrHePfbrfxmvHvld8a/1qfuq0dwzTPtvtGZ3md/dN7P53t3Dlv9/69t1/daP5Y71qvzdaTMb1jrXPe9J7Hsixomta37/W7vzf+O//W71l65b/Xjtcf/+33oCgKLHPt+JdoP3uv7GuVie6x6NWbPpct2aUoSku2tca/ROtaoFuGeH9vjX9FpXEjevQeCJit8U/j0n3YdntB0HzcOf7t9jtw33TP+Bet+nrP0N0GNkTn+LfWGf92v/EvW/36uuMfa8e84sopb8x4tPqbex2V0aGjubqM912vL3f2c7fZOtpXaelyXfLfsV2Z1SmPu8e/oqhwpAOtpw96+itd066/N8fYtg11Q+O/XSfgg49/oHf8OV3X9o5/6p79xj8ACdjrjX9Va33WpXNBrNH9HK/vdI7/Dj0ObhneOOjsB5Zld9fJfXbHdqCsM/493bJzPvGucXr7fs/4F9RxesZ/W3f12gAC68v/ll60jvzv0jPa7WuZbflvr6f/y7X6/xr5L/vo/z0yjt7Hevp/Wy/zbKfWs7a7etf4d7zx7z7fGh3Ie5Z15gIPR66Vsd4t+8p/rVP+ry+L+ur/tgXI9eW/vUb/pzLUkXj4f5FSQlFJGHgvtdW5eyrR+9J7FZFOweS4jdU9MXRPxq1/Bbo6IJXXOXl5DbtWWeltzHYZcs19Oyd+dR0hIBTRmjB7X/Caeyrd95SSlPLOOnW2oxACqqtwAKL1bL1KDIA1gqotGNH1/dbnTvek2vUu+7R9Vxv2KMy93+38uVsZ7G3z9s+OQ/VvTTQgwdfZSduTt9OaaDonByllq2P39o/OwT04NEzXdNbVq4t739Zk3vFe+vWlfrQMWHfC9PDq1CrPu58iWoal973eSbNzsoJrcHoGarvvSld58Orntb3X37x7e/+2lS3F/buqUh1XV5bXjOHWJKqSEb3e+Pcmvb7jvzURkWNBus+7Xrt679kzolvXq73vd+34d9Z5Z/3Gf+/fesd/62fZcQ3aY6v33XbSKWS9TzoF0vrjH60JuPPZPKHe+dz9xn/ns6wZ/3L98d95Xb9n6jWYZc932z8rXeN/7Zzb/tkzOjv7Qmef8eZPT2b01q33OnXN+O/+vndNq34dTsx+SkSvwrF2blurfPUqUp19pLM8RVG63vONx3/7PazXd7zPe8dn7zN1trOnCHc+S9/x75Z7S/K/Nf675X//8e8Zl2v1gLXz+9rx35r7PCcp+ox/6dXd6bgvjVlFUVrjtVOn8BTc1ru0O8d/h+HS8TwbG/9KVzsKQYpl3/G/Afnf2Z63c/x3vRfvHYt1xr/X7C1ZQcpxy1nqLjRQmco68l+ucWB2Xyda+lin4t36PmTLIOo3hhVFIUfsXR3/9g3Hv9c+fce/RMvB4dWt//jvNpK03vGvuP1infHveOU7/ce/03L6koNHCNFaPLnx+O94b+gd/90O0NY4WDP+3Xcm2+U5PWO9S/63B21/+d+6pj3+aNHjOvL/OjrH9eW/WF/+y/XGf7fx2VnHXiO2c8yq2vrjv+XwEe226Dv+xfX1/zXy3/Uhkfy3oSjq9eW/oAWCzne1nvxfTz78/9iQrtxdbxYpAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "_6DOr5bgsl-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "\n",
        "class TextClassificationModel(nn.Module):\n",
        "\n",
        "    def __init__(self, vocab_size, embed_dim, num_class):\n",
        "        super(TextClassificationModel, self).__init__()\n",
        "        self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n",
        "        self.fc = nn.Linear(embed_dim, num_class)\n",
        "        self.init_weights()\n",
        "\n",
        "    def init_weights(self):\n",
        "        initrange = 0.5\n",
        "        self.embedding.weight.data.uniform_(-initrange, initrange)\n",
        "        self.fc.weight.data.uniform_(-initrange, initrange)\n",
        "        self.fc.bias.data.zero_()\n",
        "\n",
        "    def forward(self, text, offsets):\n",
        "        embedded = self.embedding(text, offsets)\n",
        "        return self.fc(embedded)"
      ],
      "metadata": {
        "id": "I5Fl_qyosWtL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Initiate an instance**\n",
        "\n",
        "The AG_NEWS dataset has four labels and therefore the number of classes is four.\n",
        "\n",
        "1 : World\n",
        "\n",
        "2 : Sports\n",
        "\n",
        "3 : Business\n",
        "\n",
        "4 : Sci/Tec\n",
        "\n",
        "We build a model with the embedding dimension of 64. The vocab size is equal to the length of the vocabulary instance. The number of classes is equal to the number of labels,"
      ],
      "metadata": {
        "id": "Vq_r1Nn9tSBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [1,1,3,4,7,7,2,3]\n",
        "set(classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4GKNahs4xO3x",
        "outputId": "148df931-d821-4031-f9bc-e3f0d5539575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2, 3, 4, 7}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_iter = AG_NEWS(split='train')\n",
        "num_class = len(set([label for (label, text) in train_iter]))\n",
        "vocab_size = len(vocab)\n",
        "emsize = 64\n",
        "model = TextClassificationModel(vocab_size, emsize, num_class).to(device)"
      ],
      "metadata": {
        "id": "_MBckUx9tMjO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define functions to train the model and evaluate results.\n",
        "\n",
        "https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html"
      ],
      "metadata": {
        "id": "Jm0O2AGvtq2S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "def train(dataloader):\n",
        "    model.train()\n",
        "    total_acc, total_count = 0, 0\n",
        "    log_interval = 500\n",
        "    start_time = time.time()\n",
        "\n",
        "    for idx, (label, text, offsets) in enumerate(dataloader):\n",
        "        optimizer.zero_grad()\n",
        "        predicted_label = model(text, offsets)\n",
        "        loss = criterion(predicted_label, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)\n",
        "        optimizer.step()\n",
        "        total_acc += (predicted_label.argmax(1) == label).sum().item()\n",
        "        total_count += label.size(0)\n",
        "        if idx % log_interval == 0 and idx > 0:\n",
        "            elapsed = time.time() - start_time\n",
        "            print('| epoch {:3d} | {:5d}/{:5d} batches '\n",
        "                  '| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),\n",
        "                                              total_acc/total_count))\n",
        "            total_acc, total_count = 0, 0\n",
        "            start_time = time.time()\n",
        "\n",
        "def evaluate(dataloader):\n",
        "    model.eval()\n",
        "    total_acc, total_count = 0, 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for idx, (label, text, offsets) in enumerate(dataloader):\n",
        "            predicted_label = model(text, offsets)\n",
        "            loss = criterion(predicted_label, label)\n",
        "            total_acc += (predicted_label.argmax(1) == label).sum().item()\n",
        "            total_count += label.size(0)\n",
        "    return total_acc/total_count"
      ],
      "metadata": {
        "id": "bzqDwZpqtl5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split the dataset and run the model:**\n",
        "\n",
        "Since the original AG_NEWS has no valid dataset, we split the training dataset into train/valid sets with a split ratio of 0.95 (train) and 0.05 (valid). Here we use torch.utils.data.dataset.random_split function in PyTorch core library.\n",
        "\n",
        "CrossEntropyLoss criterion combines nn.LogSoftmax() and nn.NLLLoss() in a single class. It is useful when training a classification problem with C classes. SGD implements stochastic gradient descent method as the optimizer. The initial learning rate is set to 5.0. StepLR is used here to adjust the learning rate through epochs.\n",
        "\n",
        "StepLR: https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.StepLR.html\n",
        "\n",
        "https://pytorch.org/text/0.10.0/_modules/torchtext/data/functional.html"
      ],
      "metadata": {
        "id": "INmIserRtyfr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data.dataset import random_split\n",
        "from torchtext.data.functional import to_map_style_dataset\n",
        "# Hyperparameters\n",
        "EPOCHS = 10 # epoch\n",
        "LR = 5  # learning rate\n",
        "BATCH_SIZE = 64 # batch size for training\n",
        "\n",
        "criterion = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=LR)\n",
        "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)\n",
        "\n",
        "total_accu = None\n",
        "train_iter, test_iter = AG_NEWS()\n",
        "train_dataset = to_map_style_dataset(train_iter)\n",
        "test_dataset = to_map_style_dataset(test_iter)\n",
        "num_train = int(len(train_dataset) * 0.95)\n",
        "\n",
        "split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])\n",
        "\n",
        "train_dataloader = DataLoader(split_train_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
        "valid_dataloader = DataLoader(split_valid_, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFeFjcS-t7Ju",
        "outputId": "93ad84bb-0ccd-4758-9549-1f29d8c8ed81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1.86MB [00:00, 37.4MB/s]                  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(1, EPOCHS + 1):\n",
        "    epoch_start_time = time.time()\n",
        "    train(train_dataloader)\n",
        "    accu_val = evaluate(valid_dataloader)\n",
        "    if total_accu is not None and total_accu > accu_val:\n",
        "      scheduler.step()\n",
        "    else:\n",
        "       total_accu = accu_val\n",
        "    print('-' * 59)\n",
        "    print('| end of epoch {:3d} | time: {:5.2f}s | '\n",
        "          'valid accuracy {:8.3f} '.format(epoch,\n",
        "                                           time.time() - epoch_start_time,\n",
        "                                           accu_val))\n",
        "    print('-' * 59)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TS-U3Ki5t23z",
        "outputId": "e3d861d0-70dd-4518-ca87-fb91ad25a301"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "| epoch   1 |   500/ 1782 batches | accuracy    0.686\n",
            "| epoch   1 |  1000/ 1782 batches | accuracy    0.856\n",
            "| epoch   1 |  1500/ 1782 batches | accuracy    0.877\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   1 | time: 17.51s | valid accuracy    0.880 \n",
            "-----------------------------------------------------------\n",
            "| epoch   2 |   500/ 1782 batches | accuracy    0.899\n",
            "| epoch   2 |  1000/ 1782 batches | accuracy    0.901\n",
            "| epoch   2 |  1500/ 1782 batches | accuracy    0.903\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   2 | time: 15.91s | valid accuracy    0.898 \n",
            "-----------------------------------------------------------\n",
            "| epoch   3 |   500/ 1782 batches | accuracy    0.916\n",
            "| epoch   3 |  1000/ 1782 batches | accuracy    0.916\n",
            "| epoch   3 |  1500/ 1782 batches | accuracy    0.912\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   3 | time: 16.05s | valid accuracy    0.897 \n",
            "-----------------------------------------------------------\n",
            "| epoch   4 |   500/ 1782 batches | accuracy    0.931\n",
            "| epoch   4 |  1000/ 1782 batches | accuracy    0.929\n",
            "| epoch   4 |  1500/ 1782 batches | accuracy    0.931\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   4 | time: 15.49s | valid accuracy    0.904 \n",
            "-----------------------------------------------------------\n",
            "| epoch   5 |   500/ 1782 batches | accuracy    0.933\n",
            "| epoch   5 |  1000/ 1782 batches | accuracy    0.929\n",
            "| epoch   5 |  1500/ 1782 batches | accuracy    0.933\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   5 | time: 15.60s | valid accuracy    0.905 \n",
            "-----------------------------------------------------------\n",
            "| epoch   6 |   500/ 1782 batches | accuracy    0.934\n",
            "| epoch   6 |  1000/ 1782 batches | accuracy    0.933\n",
            "| epoch   6 |  1500/ 1782 batches | accuracy    0.932\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   6 | time: 16.84s | valid accuracy    0.905 \n",
            "-----------------------------------------------------------\n",
            "| epoch   7 |   500/ 1782 batches | accuracy    0.933\n",
            "| epoch   7 |  1000/ 1782 batches | accuracy    0.935\n",
            "| epoch   7 |  1500/ 1782 batches | accuracy    0.931\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   7 | time: 18.54s | valid accuracy    0.906 \n",
            "-----------------------------------------------------------\n",
            "| epoch   8 |   500/ 1782 batches | accuracy    0.934\n",
            "| epoch   8 |  1000/ 1782 batches | accuracy    0.933\n",
            "| epoch   8 |  1500/ 1782 batches | accuracy    0.936\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   8 | time: 18.78s | valid accuracy    0.905 \n",
            "-----------------------------------------------------------\n",
            "| epoch   9 |   500/ 1782 batches | accuracy    0.938\n",
            "| epoch   9 |  1000/ 1782 batches | accuracy    0.935\n",
            "| epoch   9 |  1500/ 1782 batches | accuracy    0.935\n",
            "-----------------------------------------------------------\n",
            "| end of epoch   9 | time: 18.18s | valid accuracy    0.906 \n",
            "-----------------------------------------------------------\n",
            "| epoch  10 |   500/ 1782 batches | accuracy    0.939\n",
            "| epoch  10 |  1000/ 1782 batches | accuracy    0.936\n",
            "| epoch  10 |  1500/ 1782 batches | accuracy    0.935\n",
            "-----------------------------------------------------------\n",
            "| end of epoch  10 | time: 15.57s | valid accuracy    0.906 \n",
            "-----------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the model with test dataset"
      ],
      "metadata": {
        "id": "IkIKu0GjudEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Checking the results of test dataset.')\n",
        "accu_test = evaluate(test_dataloader)\n",
        "print('test accuracy {:8.3f}'.format(accu_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RohhOuE3udQS",
        "outputId": "910a5c80-cf5e-46e9-a4aa-2f742d1c337b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checking the results of test dataset.\n",
            "test accuracy    0.905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ag_news_label = {1: \"World\",\n",
        "                 2: \"Sports\",\n",
        "                 3: \"Business\",\n",
        "                 4: \"Sci/Tec\"}\n",
        "\n",
        "def predict(text, text_pipeline):\n",
        "    with torch.no_grad():\n",
        "        text = torch.tensor(text_pipeline(text))\n",
        "        output = model(text, torch.tensor([0]))\n",
        "        return output.argmax(1).item() + 1\n",
        "\n",
        "ex_text_str = \"MEMPHIS, Tenn. – Four days ago, Jon Rahm was \\\n",
        "    enduring the season’s worst weather conditions on Sunday at The \\\n",
        "    Open on his way to a closing 75 at Royal Portrush, which \\\n",
        "    considering the wind and the rain was a respectable showing. \\\n",
        "    Thursday’s first round at the WGC-FedEx St. Jude Invitational \\\n",
        "    was another story. With temperatures in the mid-80s and hardly any \\\n",
        "    wind, the Spaniard was 13 strokes better in a flawless round. \\\n",
        "    Thanks to his best putting performance on the PGA Tour, Rahm \\\n",
        "    finished with an 8-under 62 for a three-stroke lead, which \\\n",
        "    was even more impressive considering he’d never played the \\\n",
        "    front nine at TPC Southwind.\"\n",
        "\n",
        "model = model.to(\"cpu\")\n",
        "\n",
        "print(\"This is a %s news\" %ag_news_label[predict(ex_text_str, text_pipeline)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5uhjCyCOujiG",
        "outputId": "17490e9b-e346-467e-90b1-3dbb7c769080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This is a Sports news\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Wrap up"
      ],
      "metadata": {
        "id": "b6dsMl-VqsKt"
      }
    }
  ]
}