dcgan-on-braille-images.ipynb

{"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"accelerator":"GPU","kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":853989,"sourceType":"datasetVersion","datasetId":452244}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"#  Deep Convolutional Generative Adversarial Network\n\n* The architecture is comprised of two networks: a generator and a discriminator.\n\n  -  **Generator**: This network aims to create realistic data by generating samples from random noise. It takes random noise as input and generates data that ideally resembles the training data.\n\n  -  **Discriminator**: The discriminator, on the other hand, tries to distinguish between real data (from the training set) and fake data created by the generator. It's trained to correctly classify the input as either real or fake.\n\nThe two networks are **trained simultaneously** in a competitive process. The generator aims to create increasingly realistic data to fool the discriminator, while the discriminator aims to become more accurate in distinguishing real from fake data. Through this adversarial process, both networks improve until the generator creates data that is indistinguishable from real data.\n\nDCGANs have been successful in generating realistic images, such as human faces or objects, and have applications in image generation, data augmentation, and other domains requiring the generation of synthetic data.\n\n![image.png](attachment:aadfd40b-1eba-46b9-a8aa-a62929b87147.png)","metadata":{},"attachments":{"aadfd40b-1eba-46b9-a8aa-a62929b87147.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABXgAAAJqCAIAAAAe0L9+AAAgAElEQVR4AeydB3wVZfb3Jz0QktCLICgouoJ9LdgVu6iADayIbcWCFfva29pWXXtn7b2hKIp0ERBEei/pye3Tnn7ez5m5uQQi7/7XRUzImc81mfvMzFO+M8F7fvcUC2gjAkSACBCB/y8BE2wNT2nc0vAo7TdBAgbAM2ALcBhIAA7gyyiIKlCJSMSRAKCZ9CNcOHYqJp0YGE9IW4JQAC5XtpsCEGAkd5KgBGgJBkzQDwfs0ACAwpcx+FuCVCAMHpFgNIAGkAZfGs/E/4CDlEEjHnU17hrmxsrBs6XrRgGiACnlaHAAHM6jAK4B34DYGC/2HLw2bsYRaSMCRIAIEIGtRyD8bND4E4IONmOCf/q33nRoJCLwJxOw/uTxaXgiQASIQJMn0PhDQ+OWJr+Ilj5BE+gAEkAp4AJ85WmTAF0LOmlCPUB4jlOrQRkAzT0jPW5YgnuuMBK1Ax1Y+IFqgCpDWmhQwbUK6WqUD4IfgeCgg0sC7CatLASfMUPRAX/KQGjQKBxo8CQwCeDVVSx7+r67R1186f3PvbQ0keCgXbcOlA3gkdDQ0h9iWj8RIAJNmwAJDU37/tDstjYBEhq2NnEajwgQgWZHoLGs0Lil2S2qBU7Y910pGPobaA3oJhCtWzZ18mdvfvnlxHc+mvDVt998/vVn733x5XtfjPv48y9+nv+rB5AwEHFNzNVch5JCgA2FAx28AgUB9QPUCIIXCzwXdKA1oHcDOhaEb3TmZBGM7igQEmQoNGifA/OMiK1bNue4A/fLtbKPGDJsXlWdBHCduGQ2ACOhoQU+tLRkIkAEmhEBEhqa0c2iqW4FAiQ0bAXINAQRIALNm0BjWaFxS/NeYYuYvQajQAqQ2ngOiNj65T/+/fLTu+RZhYXtLautlVtgZVtWTp6VU9C2U9dTzhz2yvufzltVaWtIcUgxpiCQD8KIBNQaAr+G0EEBvRIwwCHQGjC0IXRiMKZea2goN6RVCRQawgkBaCM4cE/x6pWLpp90yIBcK/uEsy9eUJvCoIzAgUJwJ4iboNCJFvGw0iKJABFojgRIaGiOd43m/McRIKHhj2NLPRMBIrCNEGgsKzRu2UaWuk0vA41/YzC6gTEAG5z1j900smuuZVlFucU9O3fv2a1X9+479+3Yo2dOXraVaxV37XHlLfcsWRvnmJ4BbO6Keu+EgNMmQgP6MqQFhuBwkI5BGPR0YKhKhFqDDHwcgiCKIJpDC6NxTlqAcUFVr1k6/fRjjiopKDnnilsX1Pg4ZCBaMD9M0EBCwzb9jNLiiAARaM4ESGhozneP5r7lCZDQsOWZUo9EgAhsYwQaywqNW7axJW97ywlzNDCMmwA7njAiblctuW/U8PaWdfChJ77w5lffTpk2fuK302bN+vKbcffcc/Ohh+5nZWW37bLjmNsfXVvtcgBXcl+jU0MgDBgwHNWB8L0RBnjoxYC6QHoL4ykcFDWMj9khw6SNCqMusJPgJw/ECzChT0R15eqfhhx+cL5VcPaoW5dEtCcw9WSgNahgCBIa6unSbyJABIhAEyNAQkMTuyE0nT+ZAAkNf/INoOGJABFo+gQaywqNW5r+Klr4DE2QRyGMahCMA3gitva+Uee0s6yhZ1xa62MKhLiTwAyOxgMTWzpv0jmnn5JjZffead9PvprhAfiBxwJmizTKYEUJjrEYQa4GjR4LwgeOcoIJ84sHOkKwi6cpVV+GwoBRGpQEJQUqDiKYmJIcVAKgMrJu9uDDDsq18ob/7bZlMZQmBNPcx4SRJDS08GeYlk8EiEATJ0BCQxO/QTS9rUyAhIatDJyGIwJEoPkRaCwrNG5pfqtqYTMO3Q1sx5MCUokkJlNwax64emR7yzpk4JnlLsQF1CXjBnQiWgkQSVX8+tYzD3UsLOzQaac33vuujoGtg7gLLHLpBy+OikO90CBAeSBdkDytNaC8EKZxCJJBmiA9BAftA/gCC18oydCZgQU1MhljGgtYVkTLZp162AEFVv6wy25dVCWxBoYA5vFAaPCpvGULe2xpuUSACDQnAiQ0NKe7RXP94wmQ0PDHM6YRiAARIAJE4M8nEOZBkGjwKw48ziJr7xl1fqllHXLcOT+usG2AlFA+EwBC2OtAVy+f/uX+u/TOL+xy7xNvlXtYgcLzk2AcEHGQKFUw38ZQCgOJRMzhrg8QlcpWUgG4HnNtDzSwJFc2w/ST2vOTFalEmQHH1o7NfemC8MCVqDUwLoNckuVVKyafdsRBrazCoRfesDyCGoX0GWC+B07JIP/8h4hmQASIABEgAkSACPzfCJDQ8H/jRGcRASJABIhA8yagQXNMhbCR0DCi1LL2OmToojqsGBF3fMfx8DRWA7p69pdj+/Xo1LZz3+fe+6FGQVQaxhIY4CBioBOTvv/ymtFXnHry0KOPOm7okJOvu+HaV9/5eHWd4wN4Jigs4XnC5cAwSIInaubP+vbRh8ecd/6ggccNOOzoI/525eiP3vq0tswO8lJCdTTJeRygvHzZ96cdMSAtNERNIDR4WJCThIbm/fjR7IkAESACRIAItCwCJDS0rPtNqyUCRIAItFQCaY8GpZTWUoukF1l/7xUXtrWsAweetSKOxSF04Dkgk3ERWwvJ1e89/1Ax1p7Y7Y2v51ZIqPGZEUlwKllk7YdvvjBg/92zLCvXsgqysopyrSzLKmjd/Z6HXl20qjbqSV9roXwMezDOioXTbr1+xJ792pcWW9k5Fp5q5WZZrXt16HX7mPtmL6yIKaygqZQDel3VsomB0FAwZOR1S2MqEBocwAQPjDwaWuqjS+smAkSACBABItD8CJDQ0PzuGc2YCBABIkAEfg8BhbUluQ4yJyjHi5Xdd+WIDlnWSadfss6GJFeceeD5WOZBJ9ctmn76iYdlZVlHDB4xe52XwKQOCsD261bdffPo9sUFWZZ1wP77XHnppU/948G7bxt90rFHWFZRu/Y7XXnD3aurEhwg6XpGczdZec9tV3QstkqLrTPPOPLGMZc+/vgD99115zGHHFloZZcWtL/wqjuW1WC9TSU9YCtjq6edjh4NmwgNWpFHw++55XQNESACRIAIEAEi8OcQIKHhz+FOoxIBIkAEiMBWJqDRYwHjGhiANK4fX//gVSM6Z1lDh128PqGxKqUR4HggdHT9yrtvvcbKsnbs1//1zyclAWwlDbg8Xvbxmy+2Ksy1cvNuuu3W2bN/qitbB5o5ifJY7erHH7w7PzsrK7/N2PfH+QBJHxxbJmpj77326p03X/Pxx286Xk0qValFHeho2YIZD990Y2lhaY9d9/9i2kIfwHHjwFYl1s5oKDRgsQzfAwBlJHk0bOUHhoYjAkSACBABIkAEfjcBEhp+Nzq6kAgQASJABJoTAaNQTQiEBqOMzRNr/nHVOdtlW+079Rg28sqrrrriobvuuOXqMZeff+nAQw/v0Klj3332ufvp5ypcLGxp2zUAkXlTvzjyoL2srLyRV964tibGlAYhQPs1lasAMKpizJUXZWXnHz1o+KxFNQwzKwAIU7l6Zby2MkiyIAGYFysDZy3Yq5dOm7DXLrsWddvpmXe/qrGV0gygvG7F5N8UGoSh8pbN6WGjuRIBIkAEiAARaOEESGho4Q8ALZ8IEAEi0EIIaCw2YaTUQgMHHTPx5f+86qxe2VZeXkFucUfLstoVt2md296ySqys0mGXjv5s+pz1vnIBfM5A14Iue/vZB9q1yT/kxDMmLihPAiQFlpwALZh0tE6CWzPtiw/77NCvbZfdxn40FetYSiWxPkUCwPaYHU+kIlV1C6ZPm/3Jv6e/+/z9o0f26FRaun3fx9/4JOoDYAbJ8ujKKY2EBqw6IQwYCAtPiI1vmA4ulBs3AmacoI0IEAEiQASIABEgAn8SARIa/iTwNCwRIAJEgAhsVQIaFAMjpMasiqCjEF/x+NXDt8+xitu0233/Q/faZ++999pj91337NyxV/de/V5894sqAzGAmAQuXNDVtYu+veni0zqVFO576AmfTFnwxbS5P8z8eeaMH78Z9+WqdWtn/zxz0Y8Tn7z3LssqLerc/9MfFrqoLkQBosYvrylb9NGH795y8x1HH35Mz46ddu1U0q9TYfdCK9+yuuzU/8k3P0+hLCDAXeGU/Tj0yAEFVsHQkdetiIRVJ5jB0InQo4EbaCw06N+SFUho2KqPFw1GBIgAESACRIAINCRAQkNDGrRPBIgAEfgNAibYGh5o3NLwKO03SQIYxgAgfOUpYKDiEF1zz6gRJZZ13HFD1tU4rtC1dRVTJnw2+NhDcy1r7332f+X9z5MAVQwq4zEwSa9i/lXDjiu1rPZtO1lWq/yi0uzcnFwLxQLLKigq6VRYkGfl5lm5HVvvuO/rX01OAXh+BbD1kz9546QD9y6yrO3adSxpU1zYutUef93n9DNOPfuUo9rkWMVd+jz37vgENx6LAl8RXzt18LGH5OW2Gj7y+rV1WgFwLgOhQQZuDcKg+EAiQpN8xGhSRIAItGwC4WeDxp8QdLAZg7mAaCMCLYcACQ0t517TSokAEfidBBp/aGjc8ju7psu2HgHtC5sbP8VTQnsgk5CovG/0qHbZeUNPH7Guzk8wKbQnkmsnfPzKqUcNyLGsk4ae/cvqGswEaUDyaPXSmVedeXSPVlaH4uK+fffos+seHbt277db37/utWeHDt1L2nXdYcc+O/Xbe6+BQ0+84JqPJk2L8STo6hXzvjn/5GPaWlbvLr1uG3P7W++8/d30Scuqy1ZXrPr4zef37Ltj+x67Pv7aJ3GhABxQa2pXTBx09IDsnIJhF1y7rgbrZDCODgtplQFIaNh6TwyNRASIABH4rwiQ0PBf4aKTt3kCJDRs87eYFkgEiMD/SqCxrNC45X8dg67/wwlgLgMDkmGBSwY8LmvX3n7ZyNaWdfiRJ6+sTCV8rUGAiYGqnTL+41379LKsgpvveXRlFKs+uHZV3erZ155zdDvLGnTiSRNnzJ+zsuaT72dOn/Xz7J9nzfxp2orlS2f/NGvixBnfTPpl8uwllZEIFsQUVX+/8eJcy9pj9/3eeHOcK9GnwuEpAw5AfMmP4/fsu2OHXv2efntcTGk8314aWzX1pCMPzLLyzrzgmlW1kgF4IqiWARqMQLmBPBr+8EeFBiACRIAI/B4CJDT8Hmp0zbZLgISGbffe0sqIABHYQgQaywqNW7bQUNTNH0hAKQGgXeEp7YNKQbzintGXF2flHHbEoDobOIDUIl6zAniNcmruvPm6HCu7/54HvvnZt5ioEZjxyu699pxiyxo6dOjyylQMYE0KPIzHkMakQDugtJHg+uD7wBxbu7WpqkXHHrJXdlb+zXc9FmGBuuDzWLzKia8EXjn549c7tspr3bXPv97/OiKNAU/WLXTWzBo88NDcrMJhF16zMiJ9ADcUGpQioeEPfDioayJABIjA/0yAhIb/GSF1sE0RIKFhm7qdtBgiQAT+CAKNZYXGLX/EuNTnliVg0GsAuBRgBOoCiep7rh1VmpN3/Ilnxj1wJCgA6UeMXwUy/t0X7x++9175VvapZ1y4ZF0tUxJM6pXHb9u+rdW1a+cX/v3R6gSminQBOFaLSDKvynNt0KAlYEfc92tWx1bPPe2Ew7OyC6665cE1cVgTh1+WrhB+IkhFufrRMZcXWlZx976Pv/15JROYOcJfn1j24+nHHNUqt+jsi65bFZduKDRgfglJQsOWfR6oNyJABIjAliVAQsOW5Um9NXcCJDQ09ztI8ycCROAPJ9BYVmjc8odPggb43wkYrEaJ6biMBGWbaMXtoy4tsKxjjhlSXiejNuoDGL8go6ASfrRs7CP3dsrJsayivz/w9KLlawH0glkThg8+MsuyThp85uc/zEsBJCUkfdeA7fo1ALB40ap/j/10yg8zQeEQ8TW/nDfkGMvKPvy4oXNXRFCVUABGOJUr3nnm4YP6bl9oWX32PPi5j74tc30FAlSkZsH00wYOLMxpPfyi0auiooHQoMEoCp34358C6oEIEAEi8AcRIKHhDwJL3TZTAiQ0NNMbR9MmAkRg6xFoLCs0btl6s6GRfi8BwaRR4LvMCA7ShWT13dddVZqXf/jhJ5XXCmaASQDNpB9hTjXouFwz/44Lz8uxCvrtNfCpFz7gOK74YfzHfXp2zbGs3fv/9eqrb372uZd/mDxp4vTvX3/ntTvuvfegw47rvev+Y269P14XAe2lKhbdN+byru2KLMs68eTTn39p7BefffXB2LcuPvuc3Xp0H7Dbzl1Ki3v03euFD76uE9qTLuh45cKZgw47LMfKO/P8y1fVuH6YoyGsX0lCw++99XQdESACRGArECChYStApiGaEQESGprRzaKpEgEi8OcQaCwrNG75c2ZGo/5XBAJznfsCOAc/ySPlN19xSb5l7bHHgUtW1TEDsSTjzAPwPKfKuOXgVSye8Hmfbr0tq/0Bh5/++Tc/YZlJ7nzy7usH77N7SW5OvmXlWtZ2221X0qWzlZ9r5eRa2QWHnXjmZ99MwyJmmoNOrl0w9YIzTujarlWOZbXKy+/eqVu+lV+Q3eqyEZc9/fCDe+++m5VddP+TrzAAnzsgEmWL5gwcgMkgzxh+8arKeFB1QkLgi0EeDf/V3aaTiQARIAJbmQAJDVsZOA3XxAmQ0NDEbxBNjwgQASJABLYMAckNWuwKtO+DZqmqdU8+cM9OvXqMvPDyRcvKZRDXoJSS0sbkjioCsja1duFt195SUrz9Xvud9PZHP8RtpTApI1sx/8f7b7jy4P479+nauWP7DkUdu3feuf+hx5/494cfm7l4na1BaOCuA14EdDS6bv6Dd1y7e9+eXUqLu7TrtFe/fe6//8lIxJv78+wTTzx+1/77vfDaBwLTR2jfrvbiFScee0xpScfRo2+JxRjOV2G0h5RSKaE0U0poLbXWwSdaoLrsW+bhoF6IABEgAkSACBCBLUqAhIYtipM6IwJEgAgQgaZKAIUGDYZr5TFQAjRLVK2fOXXSyhVrMXujQo+GYO7CGMdNlbHkOhDJ6rKqWTMXLV1Wl7DB9iHlcS1cEPFk2eLVP09dPHv67B9nTv5p4dyVVcvWV9U6ng/AAIUGzQUIG3QSTDJZt2bl0nnLFs5ftmjxquXrYgkVpIzUvy6cP2feYsfB5BGSC1AuAKuoqJg6Zeb6NdWQjpgApUFqFQoNgcpAQkNTfchoXkSACBABIkAEiEBAgIQGehCIABEgAkSgxRAwkLbehQTugxaYGDIoE5FylS8CDkZgSkhwuLIVVpRAFQA0CAa2hzUlEql4cAIqCGA8wXiSgQ94KgoWBlymBdMQ+D6AshlPGhDoSMEZYwIMnuZhsU3BNA+CLEB6SjEOIJT2sSUc0Rba55g3QmASSamFNjwUGuoddMmjocU8urRQIkAEiAARIALNigAJDc3qdtFkiQARIAJE4PcSEEL4vi+5CrUG6brMTkjf8X3XdjypgrgKZZKJmGRJABYXjh+Uw2SOC1yCMOijAOBKLoEzFnHtKubGbNuOpaQjIZF0PZ9LZbQGdEJQChRX0vc190Cjp4MGEXgmcCM87XnAEl7Kc4XioHwJSislknaiti7quUJ6gcpgsKql47kKQBkZCg3GYDQFBlTQRgSIABEgAkSACBCBJkmAhIYmeVtoUkSACBABIrClCRjQQnHBuJYYQ4G6gtGA/gvS910pMU0CY0Kgp4OnDYt4rouuDKgXoOODwRN8aZgxzChPOkI7WG8SdDpXY+icAForoTzPMAZKGmMYQEpDghtXGOYr3/UYtzm4EoQMrg3iJhROTGuN08K5GRGOKIzGdnRx0Cg0BCpDkLZhS/Oh/ogAESACRIAIEAEisKUIkNCwpUhSP0SACBABItB0CRjQCmQ6FKI+9wEa74oJ7vheEve1FiJwbNDcaCx2yYzh3JfClcz27QTn0tRrDeinoD1HOjZzXNf3PMzvYIzymG07cebYRnAltM8UD7I2SADXk9zB0AlAeYLFnSgH7NDzuW1jnsk0viBuQvqe4C7nSQC8RLBAYgARCg1NFzTNjAgQASJABIgAESACACQ00FNABIgAESAC2z4BA9pXLsMIhtCXAaQAIbCOg+tGtXaDHAtaY20HacdqhZtMuxVg7gWBJSF8n/mBDBH4HEhQnmIp5jjMD0pRQKhlSJChfACAQ9gpP5JIMh6UqNTpRA6C+/FkRAeJG7hWtu8xhYoDTg1zVQr0o5C+wYQPmFkShQbPp/KW2/5jSiskAkSACBABIrCtECChYVu5k7QOIkAEiEAzJhD4GKQjEP6QZYQqgAAusOADag1Kog9DYPozrV0lfTuVCOIpQHg2xkpIA1Ib4TPuMBHkaATggWQguWCCMyU5OkrgpiTnkoXREMwIpmR9QAV2KUSQcAEzOgL4QksVBFxoLpnLGeZfwDyQUjAJGrSPqoTvpYK4DMakYzS2GKxlITZKzRASCyYQdhiqFeEq0j/TR/EdbhtD3uhd5k1waiYkJLwuuFSHjel+MgdohwgQASJABIgAESACGxMgoWFjHvSOCBABItCIQOPEe41bGl1EDRkCYaBCxu5O27oZqzaoAyHRBMeMCcErXRkCcyI0NI0zlzTYCTuvH6vBgfomAMyDkLaQ08Z25hi+D7UGGexkDgQ7md42zGej4TYcr78uY5xvOtAmy6g//zd+b+hUm0DqMChO4CSD0hXo+BB0HswknA5qEFIByhyoeoRnYDbK4IVHUchIB2YEcRnYX3BiKH2osE/MYRlIIZjAQkkQHL0z0ncBgtiTYAid5vEbi/yNBVETESACRKCFEAg/GzT+hICBeVpvJBO3ECK0zJZNgISGln3/afVEgAj8Hwg0/tDQuOX/0E2LPSW0htHUT5u7gSW8waAOrOg/XmhIG+Ab34b03DZRGdLG/EaG9Ob1iI17/C/eZRBkdhpcXC+OhBIJOleENTLTokAILVQN0kKDJwGdI4J1atASJFayCHSWdOlN7D5UEwL1IFQVwqqcuNZQkAjf4/WCgRIbCmhKhdIDJrBMd7IRnwZTp10iQASIQIskQEJDi7zttOjNEiChYbNo6AARIAJEICTQWFZo3EKsNk/gt4UGNGvTW0Nrf5N9PCNjhm+4IrgwLQfU9/L/OXVzPQRmd2bEDR39Rs9oW2deG878I/cyw+FOve6QphGkjWCZNwElEYRa1DsnGIlagUFXkY0knkyvaU8HrVAASnt8YIcN/CACPxPMPRGsHcUOGYaK1Ascf+TyqW8iQASIQDMjQEJDM7thNN0/mAAJDX8wYOqeCBCB5k+gsazQuKX5r/KPW0HGtG0QPbFhtA1HG1jymUY8b/MyAR7aaNvMqZtpDr/f32issLcmKDSEjgkNJsYAvIBYqCxg6sngtcnbsBG1BmSViZuoD52oj87AMA2JtTAE1vvU6LWAaSyCHoPYDSwCGkRPBMwbzGMj/vSGCBABItCCCZDQ0IJvPi39NwiQ0PAbUKiJCBABItCQQGNZoXFLw/Npf2MCGUt+E6EhaDf4s14IwO/VN5YbNu6poerQ6EjQ0PDy3z6j6bTWr7qRXJKeYsO1oDCQtu7Tv7xNhYZ0hgs8L5OUIeCZ1hrC6zODBoNsOGRAM2AcxQsHDEONQaf7CYQGtkHFSE8gk7uh6RClmRABIkAE/kwCJDT8mfRp7KZHgISGpndPaEZEgAg0MQKNZYXGLU1syk1qOhmDGWeVtlJxT+KX5+jbj0JDmL6hPkBgo0s2WkzGUN7UmWET94RmYAb//5aCa85ACHfSCgISxEPo0bDBTwFdFXTwwislAA9eQYENHcRZhKjTnIPEkGHZzqA90BQ8EB64ADZoN+0eEWgWgddDIDRkHCJwDr+VPnOjW0VviAARIAItiwAJDS3rftNq/xMBEhr+EyE6TgSIQIsn0FhWaNzS4iH9FoANX33Xm8rBWcpobThoD5QHkoGSwoAPIIwWIMOXMlIZGWbqTosTDe3yhvsbRm6YN3FjMzhzPproG7ZMcyPVoqGRv+H8LbvXcPRwf6P+NzkciDQ4z7TQIBBYoNGka3SKoLam0kojTBc9E3AHNQSk7YPhxqCuwwE5a+MZ44BiGDMRZI1MAaSwgmYKdArvgwguRq1BY0gFBPEUgadDoHRsTHijqdMbIkAEiEBLJEBCQ0u867TmzRMgoWHzbOgIESACRCAg0FhWaNxCqH6DQNp6Duo1ZMxmAFQQDAus2RRID6TkBl32OX7zTkJDPcgMsTTGjdsDf4e0Awimb5RYiVKgYKA0wrQBkoHWILUC5YN0QflKCWYUB82Ba+OBdgKth4MApSBp8BJpXNCJ4L5gzAQGZKC4EcgKoecJajVhDY6NVJv6+dFvIkAEiEALJUBCQwu98bTszRAgoWEzYKiZCBABIkAE/kcCaQt5g9BgFHr3Sy2kcpRXC17trz9O/vT997/6fnpSQp1jS9DciLiTwHMkZ56P38cLicauAsO1UWACv38AUKLeCJYYIiC14JJhhYXAG0IZLFqOdct1OgAg7QtgFGPM930hM7khQlsatNZSSq2Cr/LTtrRE4xzP55xpTCgBYIwJKqIrIZjr2pz7GU6e52l0MEifBgBCCBlsKtjCUurpidV3hYZ7MPQmVdY3eRuY9xq0MUorZRR6hkhtuBZuMlINWtaUlb379jtTZvxUazMXoIbJuJToAKG4n4oZgfN0pUz6ftK3AUQyVsVTESdSM3H8t3PnLLElpCRqPYxFAGUIfBP6MwRuEUFtiqD4JRLAIAzaiAARIAJEgAgQASLw2wRIaPhtLtRKBIgAESAC/yuBzQgNrp8KEhnGa1fOu+TMU3fq3mPE5detjzMfwOauJ/0NKSG1yWgNmiktDfeFlFpKnUo5KAU0+LbfADDBXd8J29JHGrzhzDM6dPhHLSA8IlGQSO83CEwIv7HHKgwaL0mfrRX4XiBqSOl5TtryB62U2kQU4JzLQP7IMJRSsmDLKBFKbWqra41doQgiRCQJa+kAACAASURBVOY0Y4wQgjEvSIuQDlgIMz8GSS587taCcRN11dddPbptuw4jLvnb3CUr18VTNb5f6yZd39GKBRkxcNW+NL6CpOsx3wbpg2FffPj24Qftv/++B33yxZwUw3gM14vhDRIeEs6UvgwDU4JcGoHQgLRoIwJEgAgQASJABIjAbxIgoeE3sVAjESACRIAI/M8E0rb+Jh4NOuXGGK8DiNStmH1o/53yLeuoE89YFxcsEBpCw7a2trq2tnqDAKDSzgue43MeWP6B8a8Y9xzXdV3P8zIZJevzSuLVG3pIF2WQUjDfdxkTQmoRhAT40oh6rSG4RgcpKqUBLqTreY7nMcEbdBWAQXcGL6m1RBGAMQBIJBKu625CbXOuCkJk/CYwH0S9ArLh6gYKSTApNPoxL0OgbgQRDbhOF1RCulVgnHf+PbZjh675rYsfefpZHuSKZEE0hSddzv1Ao1GOzV1Xq2ApyXjCtxNg2JoVv/zt4mE5ljX8rNFlZUG2SMzkwGy7DgcOnBo2whgkhiCZYcOtoj0iQASIABEgAkSgEQESGhohoQYiQASIABHYIgRCj4MwBiEsWCCDGAHjxhNrwVsvapYMPWL/0rz84ReNrmaYWSDhJsPwhyAeIRAXpBKevyH8AUBIzbgMTH+NBjf2H3Sr0D52uQjlg6BUZqAfYD5E1A4Uc6XvKMnTtjoAU+By5UvD0zoGmuBo82MeAxbkUtxg2DMfPFf7nnLdMFYCBRSDgRw4gVBrCLGFXgmhR0OYz1IpJeq3Bu4PweSNxhARz5GCaYmnYcpGrbhQuEwumdKhzwVmcwRfgwpzMmDkCI8CrwReFa9efeopp7Uu7nz7vQ/X2C4H8BRLiLgETwFGg4RKgdZgDNTWJBEbKhnasyNg7F/nTTxw393bFmz36vOfx5OYj0EBuF4y8IOoV1hC1QYXmw452SLPCHVCBIgAESACRIAIbJMESGjYJm8rLYoIEAEi0AQIBNZsEAcRJFkIPRuUVsC4qAZeJqoXHrvPX9pk55w1YnR5UFkxdGfwPMdOJZjvYnYGHlxrwE468XgydFtgCm1dtPMx7wDa+aEVrILogqBAQlqaQHMahYawlGa6mmZ4iVQmjCMI7eq0B4HSmKNBMtBMazeohoH9o1IRegIYENykUimBHhhSSC/szfO8TKxEmI5BKRVmZwin1/Anhh5IpTnDftH6r1dMglUFiSVw4qFrBlahNCBUuhqlAnRJQA8PwVFlYKuAV7z5ytMdOu148FGnz19R4QPE3WRNvCKaLBPgSFAOk0xib4zrZNIGg+ktuC/AgGPHwzCWN156otDKHnjg8ZOnLETXEoW5ORWuDk9Lv3ANgbwSNDRcEe0TASJABIgAESACRKAhARIaGtKgfSJABIgAEdhyBOqFBgxQ0BpNVAFGaQGe0nXA1vKq+QN3711kZZ101mUrIspD8xW/MBeCSYFlL4N34NsOKDSMPY8ZAF/rpO9zNLclGM6cZDIZZ0w4TMaSXiTpykBuCK3j0DAOchkIUEwyW3iulJhCIeX5HkcniI3O18ZoHoROMABPSNtxUqmU4zqKeeB7xnNR+KgPfMBcjFxsGi6RISilNMbE4/FkMuk4DmMMk01qzFKJQggy0SAEcC4cx47HkvGE4zi2bduuk/J8zDMZpIgIpZBQRZGYpDHUTxiIdcCWqsTSk489LK+w298feqM6rdegVgDgGOBM6ZSvUxyXGeAV6KxhZKS2LhlPSS44s12vctmCqUftvVv3Nu0eefT1iA8pg0ID50mUIVAHyagNJDRkbi/tEAEiQASIABEgApslQELDZtHQASJABIgAEfifCKSFBrmJ0ODKpFBVJrUUoouGDNizS1HxSWddttYGV0M0HtmQZBErXWrtczTsw/ITgFUZZVAI0+a+RAsfbWaFdSKCLIYMzWmmgGmQKvjaX0sUDrQfvDgWeqyPtrB95jI02h2uPZlOCRnY06HjA9PaDnIdoEeDkiBY2sQP80EK6SWSdaFMkUnryDmPx+Oe54XRHIEesQGhlHJDlkeMJdHAPJxlqDigd0M69UHozhAqIAzAUcbmPPCpkFiYAxertYyDvwzcX6Z//UqXtoV9+x05buraKoYlKhU4ifgqkK6dTKkAV0pCzPc0uFJUO8m1gQwhhcBslMYo34+Cib366M07lBYOOvnCX1bwFICLE3RBx3BAjXwkChVKA1b7qJ/phtXRHhEgAkSACBABIkAEMgRIaMigoB0iQASIABHYogQCp4LQSQEN+HqPBgMMIA72KlW7+Lg9dmltWQNPvSAe2MM+w4IUtWXL7r/9plEjLvjmy/FgIBlJPPXPp0eOvPj8EReMuvqqL76bYCuojCXwm3nl/Dhl4l133nHxxZeec+6FY266/dNxExIcwpfDsEKm8pOKJzSPAUuuXPTz22+9cevtt5xzwchh515w05jbnnv+5eposi7FkgKCMIzAo0H5RqYA7ESifOqUiQ/cd/9ZZwwbMviM664d88yzzzPGPNd++eUXL770ks/GjceIClQMuOAuZ7ZWPha15M6Eb8ffeuvtF4287LjjTrj8ilFjx45dvHQJCzwxfKniyRha654NLCW9xLKF819+/rkrR1117rnnjxgx8rbb//7OBx/PX7YiwTHPJDoXaCMYx0yWmjsg0SvDi0LqF4jPvvr8U/It69yRN69PgBMIBEln3colUy88c+g1o67+btJsF6COmXWRanQkgWqAyBcfvXHV6KtvvuPuCZOmxpMJ30uBjM8e90rvttltO/T+YMKiOF4CgTtDAgzmyAjkItBBGAcJDVv074Q6IwJEgAgQASKwDRIgoWEbvKm0JCJABLYsAYyZN+mvmsOeG7ds2RG32d4wMEIbdN1Pgax11i8cfMA+xZZ10hkXropLrEsJjrCXVS/9Yb/tO3Rv1eofDz8+b8HKW8bcmWXlZeXlW9lWXuv8Aw4/8h/Pvlmd0CD5+PfG7td/l1zLysvJt6zctiXt+u/513uffLGCQx2g1Q1GSrsawKmtWvr1Oy+ecsS+BQWWlW1Zua2z89rkW9l9t+990613fjNjbgKgzMNECL4fF34MwK4qn//kI7cdtO/u+ZZVmGNlZVml7Yvy22Rfcsl5k74ff9IJJ1pZra++5ZEl6z2jQbiSuxEUUFTN0gWTbr9lVK8enSwrLz+/bavWpVZWdn5hwUmnDv7gk28rYr6NeSY1Ez5oLxVf/dH7zw0adHT79u2zs/ItqyDLKrCsgq7dd3rsmedjAoUG9NHwFWZ/RNFBpoywsdBmCmDl0mn/Hrjn3l3b7PDg46+vixmUJISK1C3+btxLvUsLCy3rroeeWe9CAgUI7cSXgSmLrv3l6EMGWFmtDjp+yPw1a1LMi1XXgpeqnDPuhAN3Kijp+Ogbn8cAEjhacKcCFwbME4HOIFj5AhNEhHk2t9knlRZGBIgAEfivCYSfDRp/QgizAm/yQeK/7p0uIALNjQAJDc3tjtF8iQAR2OoEGn9oaNyy1SfVDAYM8hAE6QTqI/zRqcFITLWobBAxp3zJ0AH7tbVyThl28TpbO8qgS7+/LL5y4jF/6dm9oPCUoefccu8TPXvuduB+h9x0263Dzz+rW7fSLMvq85f9HvzHC4/e/+hJRw3s2bXrkMGnXDnqqiMPP6JPz+2yLGv73fZ68s3PV9vo/O+n4uBHJa/99su3d2iT0znH2r3/LkPPGTb6ltvG3HTbkfvu09qyLCv7hGHnzY+guuBLoVgcwI5ULr3nzmu6dWmda1kdS9ucN3zw9ddfdt5FZ/TauUvvHTrs0me7vjv0Li7tcd4V96+JYkyDsIX0sWbnj9M/PX3IYTlZVpZlHXrwsYMGDRt9zQ2HH3lYdo5lZVlHHHXKxBnzXYCqVIwzB4zz9bhXd+/fxcq2+vzlL6cNO/9vV1x37jkX7dJ3j5479H/gsWeqU7atMZtjfcAHMJApYI4G33PArJw17tndOvfu1/vQD8dNSwEog1kwACLJ2l/+9ffru7Vq3bVn/5c/+iEVuEWALvNr5rz02F0lrYv2OPDox177IIL5HkWkqg5vSvWvo84emNOm6NI77l8W9aLSYKIG7QBIzKVpICjrQUJDM/jToykSASLwpxAgoeFPwU6DNlkCJDQ02VtDEyMCRKCpEGgsKzRuaSpzbUrz2JzQYEQQmBAIDacdtH/77Lwh51xW7gHHr9BtkOvKF3xzSJ8erS2ruNvOh5563pQZv8ZjjgFpJ8unjn/7+AP7FVpWm/y2rdpsf8Rxw5976c2Uh0EPdqJq0lfvn3TUAMvKPWHI+T8vjWCERl0tgG1UdPxn71w37MK3H395zdI1LheVkepEsia+/te3X3i4fYei0u7dX/liYhIglUqASOro2qceut3KsrILC2+7854FCxc7bgKACXBWrVvw0D3X7bx9+46ti3pst8t1tz3vaEyKkKiNKF5btn7uOecNzsqxdtipzxNPPef6mOoRDDA3MnHCh8ccNSDHKhgx4srV6+sAIFa2yqlcNmb0uVnZ1n6HHfr5D9MjQvsAkURy4YKlX305ee7c5SmBGRmwYAQTRgVZHUAmje8bkMIDvuirf9/bsaDdcUdfMHdFHaoJHJjjomMF1JbPn3L+oOMtq83ZF1w/b1FZUMGzcsGcT/bZo49l5T7w1Nj1KdRiGMhILAqGgax88p6rsgvzDzn1jJkr1icM+NwLIkEk1tQ0OiM0hLrDRk4+TenBo7kQASJABP4UAiQ0/CnYadAmS4CEhiZ7a2hiRIAINBUCjWWFxi1NZa5NaR7/F6Fh6ID92mXlnjr8kjIbPKFB2mAqyxZ8d+iuOxfntNntkBP/+c64qA+uJ10vhvazv+bFO0ft2AqjC9rveOAz70/HL9wBbC8OkDT2mrtvuLhD66Ltu+/64Wc/ciyKmQJjc1FbtvLX+JIKqA18/wGSXtz1KsFUxst+Hjz42OySkstufWhVHcPck9JeNnXcKUfsZ2UVHDFo2Lxl6zmAz5nHbKY9ZZLGr/jnfWOKLKt1bvuHnvgwFqR24MkYQOT9t57Msqzitp2efP71lWVRZSAWdaXvgIiBqXt37LNdO3Tp2rnPa69+jM4d0lk1a8KgI/bKz7duvOueWol5HFdGIo7A4hqgwXHASVeLAM6l8jVjwgWRUAyzX2oG7vzX/zm6KKdk2Pk3L4/KOIDvKZZKcR6VrMopX/TOs493Lem84/b9nnr0eTAgvbKHH7o+pzBr4KAhC9cmUJDgjINwBFPKAV7xyeuPtClptdM++03+dZkLkGJMKA6gjVFay6ACBhYPJaGhKf2d0VyIABFoKgRIaGgqd4Lm0TQIkNDQNO4DzYIIEIEmTKCxrNC4pQlP/0+b2uaEBlCBQ76I2WWLBx+wb4mVNejMkWviSioA4YCuLVs4+ZBd/1Ja0P7ki65dq/Bb91jK1eCAqQN72ZSxDw/YrrBn27YX3fb0vAQa5ylf10QqJKsCd/VHL/+jZ0lJvlXw1L/eYwAuFx6L2sn1GKyB2Q7Ai8ZcL7GuYsmUyZ/M/GbsZ28+uVOfnlmtSy+7+R8rKhnmH4hXvf3IrXv17LjrXoc+9OKnaxPaNmD7wgC4vuN5MTCRxTO++uvOvfOs1v948uPKFCRdBpCMr5l+6ZmH51vWbbfd72vM4BjmiQQjtIfpG1y76ozTz84t6HrZFfdVV3ggnOXTvzzlyL1yLGvI2RfMWLo+ARBFBwysBsE5xkFgEQ2ZrtEJWAdTeqCTimF4hLRVdMaDNw+3rFZnX37XSgdqMQUGgJZSuFhIQidrV/966fAhpbnZZx43qGZ1+btvvd6uc+lOe+3xycTJqCMIiKVsT7oMlCuT4K6b+tW/e/TouP3Ou3wz82cHIOYrHwtTABhlNMf6HTijUGiguhN/2l8WDUwEiEDTJEBCQ9O8LzSrP4sACQ1/FnkalwgQgWZDoLGs0Lil2SxmK070/yg0FFvWiaePWBUNai1KDm5F+cLpA/rummcVnXb5mLKgjEI0iRUetVcGbHXsl69O3qNHcU7Webc/OSsOFS5E4o42aOqDrp719bt9SktKs9s89cy7SQEJzjT4oBKgbbeiYsnsn599+vFLLj3/8KMO6LNj+1265vXpnI8+CJ23//sTb6yLanC5u2bhXReeul2Bddgxw+au10lM3AgOk67rx+NJ7iRARcrnTxo5eFBRTsmdD7+ZAqh1IgDVc79+fp+uWdsVlbz03Fsz5y6fPGv+ynUVy5YtW71s8fxZU8rWLPhx5qRjTxpiWe1PO29MeRWDVNxZ9+tjd40ubmVZeYUjr7ll2uLlUa0Xrllhc99mJu6ZBJOOr1A+UPgygX5hKym5Aj/qrP/umosGWlmtzhx1zwof6gxgVU/BsSyGn8JoCJOcM+W9U4/o17ttm1uuvP6QAUdbBe1ueOChRJApM+lhgknfdz0QCjxQNb9M/2y3XXt036HHh19PiEosPOFILBgCWqADhWYQ1LiUoAQVuNyKf0o0FBEgAs2CAAkNzeI20SS3GgESGrYaahqICBCB5kqgsazQuKW5ru2PnPdmhYb6ZJB22eIhB/611Mo++ayL1iSDHA1ag1MZWfnzwD337tymyw0PPFULUJ0UQmqubFB1wNevmP7xUXvumJ+XM/qJl8sCFSAZd5xE1Phx0PElM77bp2evnu26P/PSBzEFdcJXwIDHl82ecsm5Z/TbtU9uUHWiVVH+3nvsMnC/XY/cd9duXTsWdex27b1Pro8JYJ5fvvjW807qkmudPWJMjYAqBtUpJ5F0fZehwS84yNqKud8NOezAPCv/pntfiqAUYnO5+OdPH/9rW6vEsvbd/eDtdvxLUeeubTu3z82zenXr1qW0uF3bovadO2UVdcxuv+O5V929ZG0cZQOdXDpv4kXnnVpUVGxlFXTutdOBRw18+a2x6+uq/WBpmM9ShcEh+FNL9ChwhJC+B36ds3bCZeceZuW0GnrFnUs9FBq075tUynG8lMcrKqpAJ8As/OjNW3q3z+nWplPbNrsMH3H79GVrEwARz0e3CK4NEzZLGfAAonOmfbZ3v1477Nj93x9+VuWaWgkxVi80KA+0B6AVqg5KoOJAGxEgAkSACGwgQELDBha0RwQASGigp4AIEAEi8B8INJYVGrf8hy5a5OHNCQ2Nk0EOPvvSMhc8zJuIQkPNktkD+vRtn9/+rsdfKGcQ99CkrY2WBz4LtdO+eOPA/jsUFre67B//nG9r3wTxAkZ7yTpQqSUzfvjrDr27tO74yNOvJwBiwBV4NUvn3nftKCz7UFBw9ogRz73yyoeffvbLnJ+Wz5m8dNYPpw46JrdNyQXX3RLFCbDk2l+uG35MqWWdee51tRzKkpilIEiJoDDpY0UZeGUQWXpk/106tGp/92NvzVmfcMCOJGaPe37MMb1LOue0Ki7q1qrjdm136FXYrk3nrh3aFRX36tytXduSVsUlHXru1PeAI6+9+58rqmypFYAHora2fOn7b791yMFHWFZ2flFBflHeKWed/up7H62P+y5A1BF+UuKJtvBdgQkpfGY8B0RUVE69YsRR2cXthlxx+8KUioauB4JjFQwNSoPPqgF+qVz78fEH71Zo5bbK3eneh95NAlQrXevYBiBZFwepHTfBRBxMbMqE9/batfvOO/V64/1PKjyIGah2g8quqC1khAYQWISCQida5F81LZoIEIHNEyChYfNs6EhLJEBCQ0u867RmIkAEiMBWILA5ocG3E4GBHfOrlp+49+492pQOPvvSGgke5h1gICKVC2cesUu/dlkl9zz6bDWHlKvAgNI+FxFglXMnf7rPbr3yS1tf/OAjSxhwA2hSayG9FGhnyczJB/beuSSr6F8vv5cESABzvJqxj945YMfti4u7jbn76eXrkhyCPqUAFbfL55983ICcwqw7n3hifSIBOqkTq267/LTWlnXS4EvXxTFDRMR1OOdOIo4eDb4N3rr44ikn7btnkVV05Y1PVQhw0B2gbMI7Dxzcu027wuIrr7vr/e+nvjPh27c///jzLz+bOP6777/8etxnn06c+N3nX3879oNPZ85fagtwBLO9uORRMB5wMe+n2c/967Fzzz5lxx07WVlWz757v/zu+CoXJ8AFcIcpT4CC0L5HoYFHvPWTbx19mlXQatjoO1YLTFdhuAe+BwYcV2D9S4hL9evHH97bscjqVtI5x+pwwkkXfzVtfgogyhQmgPBd4dlGs3i8wvhV0yd+vF37gj69t/9u+qyIhqiGmAxGRKGBg+HGGBnk05QQCBBb4TGiIYgAESACRIAIEIFmSICEhmZ402jKRIAIEIHmQGBzQkMqVgfaAb9ORdYcv2e/Iss6/IQzKhmmQkAbVkfXzp2y33Y7trYK7n3shTgAZlrURmkfTBJM7eJZX+71l25WjnXDs68tk2BLA8IH5XM3AspeNWfa/tvv0LWw/TMvvFXLwQetZeyFO6/ZPtfad//jZi+z/SDNgDGQqK2FxLrEmp8HDtg9Lz/rurvuXV1b6/t1ZStmjL7ohOI865Ajz1i8RvkANalkmI9Rcw9kCmTliikfH9ynV5FVfNvd/45qqHYSKW/VvCmv77tz0fbb93r384kJgGoNSY1OC8LlQVpIDoAZJRk6RuBLgubGj0YrUtEa4TCUSyAZrV74zedjTznhGMvqeMyJl81cVI1qAfM1iEBPwXyQxhiWiIFMQGz+C49cm11YOPji0YuTolaCn4obO6mZklxojoUwvvzyxYMP2rlnl3b33373sYedYFlFI0fduGh9XYRDSkI0FcEMmSBQ+pF1n737QttW1j777jFn0fKEwbCROK8XGjQHLRoIDUFIRXN4DmmORIAIEAEiQASIwNYnQELD1mdOIxIBIkAEWgSBzQkNgF+Ke350nVO+ZPgRh/QsaTf47EurODANaO6yyvVzp5649wF92ve+//FXltcGdQ8McGZzuwLc1Yunf3DoHl3alOZf8ehLsxMQZQaMAGCGRUDWrZwx4eBePbsXljzzr7EpBRxk+epfbjnv+PaWdfygYasi4BuoqWO1NXHwHZDRD194pGdpm5JWbW+/9+nqBH5bH6tZ9NgDl3fvlFdU0uv51yakcyUIx2XxeKJa+nW85pdn7hy1XbbVIa/znfe+65jAFwNs7v4yZFB/K9s697KrfllXnQJISHCZjkUwHYNnRxwnog0oBZJDymZ1iVpmguqc6JQBmjOAiPKXg6oc+9zTJXm9O7Qb8Mn4JSmAmHEYpM/knCshvWQM62j4a7586/G27docPWT4wjo7CaAkB8G4bWOkA1s378fPh58xNMcquPLSa0TS+eazt9sXZXXo2O3ZVz6qdrFHF3wJtsvizI0Aq33+0b/nZVsHHHzITwuXxxQkgtyT6EMRZmYIhAYd1MKQQEJDi/grpkUSASJABIgAEfh9BEho+H3c6CoiQASIABH4DwQ2JzRI30FBQSWS6xYO2nfPYss6fuj5a1PoyY/tum71nEkDeu3a2iq+/aFn6zTUxZnvMi1cEFHw1i6Z8s7Bf2lXUGBd/tDLPyfBRosXlQtQUWDVy6eOO7hXj+75Rc89928W2O8iVfHCnX/rmmXtstueb30xKeEETv8GQPgrZ08+/8SBRZZVktPxrnteqoqAlBwgOn/uJwOP7G9ZbY49/oK5i9ZwgLpUncRCk4zxundeuv/ovXcotawd2+98/Y0v16bA56CU49m/vvjizW1Kc628gkefe8mBdIVLKTB3AgBLubGf5sz//ofZFeUphX4NIunXVVSuY0zgGrgAiACUgap87uEHSnL69N1h0PjJ61MA69yaFCZMEOjaYRSgKOGAccBdN/3LsT27ddh+512++nF+RIPjuegnwV3AiIzyay45raig0+79Bs76cTloiJfPvfLik3Ms64RB5303Y00gNDAGyZhTC+C51Utvv/rCgvzs4wefNnPJ6mTg6oCCgjFgVOZlDOaBlIaEhv/w/NNhIkAEiAARIAItmQAJDS357tPaiQARIAJ/IIHNCQ3cTRmZApVwK5aec9RhHXLyB599aSVDsxxtWxkp+3Xm0bsf0Dan412Pv5AISi4EsxQYOsHWL5/y/mF/6ZifY13/6NjlmB8BPMUcP6Z5FFjtqunfDNihe/eiNv96dmydaxKODWB/9crDHSzLyrZOHn7Gu++/98vceT/PnnP37bfsu1Ovg3fZde9ee7bP63XTmOejSfA8LJOp1ep/Pn5TuzZts6yis88e+ea7H3wz6Zs5i39+58v3b7t3zMBDdz9kjz5dC4t6tOt9zQ3P1doQT0IyVgNQZsd/HfW34VmWVVLSZsQFF//z8RfGfTFx/q9LJk764e0P3rrh1pv773PY8SeP+GHKAgXg+ImPPhl74YXDR11+2auv/Hvy5Km/Lpj68+yv3n7l0f69d8yxOo0ccf/KMlxgnUwycHzlqnSBSekrhqy8yuWzvh04YM+cnKwnX32nmkPcxwANACdZu/SHL15pV2BlWe3vvPd1pYMIDqhcNOvjvr26WVariy6/c9G6uA8iwqvWV68G48ZW/XzuoMPbtutw/V0PrU1yF8EKqZkBYYwKX4A7JDT8gX811DURIAJEgAgQgW2DAAkN28Z9pFUQASJABJocgc0JDWCkZHGZrKhc/NPQAfsVBR4NlQwcLC4hjVezbPaUQ3bZq8Bqde2dD5VzwwA8z7GdGHdrILlu8eSPD+nXPceyRt3+z3nVxgVwlesKB6QLqbqlk7/Zp2eX0rycJ55/zUWb24DxyudOevjmS0s75Fm5VoeOpdt17lRcVNK2TXH/nt3vue76oUedmmu1P+fcW1M+pkGw3WqAyJpVM2+88pLeXbvlWNmWZXXZvluv3XpbbfOt1ta55w596+Xnjj34yIKc9jfe+WIKwGGg0fJPAkSXzZ929SXntm+VnW9Z7Vp1/MvO+/71r4d07t4zq3WBlZOf16bnZVfdW1bDMUhD+Z9+/tbBXv657gAAIABJREFUA/rnWFZJm9I9+u95wP579+7VOdey8rPyhp15xZQZa+p8SKFnhJbAXGEz6Qjt+cpLCl8ABxFzKxZfe+Hp+ZZ15Zg7Kz3McyGA+07ZN5+9ekC/XvmWdcY5V/yyNMoBsNqliXi1v7705IM5VnZh0Xb3/OOZOi/BwUNfCeNFl/x01D47d9+h70sffZMEHNfxkoBHeVDCwmgwQQwFCQ1N7m+NJkQEiAARIAJEoKkRIKGhqd0Rmg8RIAJEYBshsDmhwU3GtEiCTlYsmnnWYQd1yMk/7bzLl9QyO/TGN1g54pBd9ujUqtNdjz1dG8RFBAkcsUiCjFXMGP/J3rvumJWVc88/X1lYnnS0YkGNS3TlT6XWzJl5wG69O3Us/fvjj1dwEXGdVLwW4tWQrBlzy9U9+vRo37ZdQXZhdlari0deNvfHyW5t+VUXXdyj8w6jb7y/NqU9JZJOjVBxVA2cis9ee/qYg/YvwMKYltU6t9c+ewz72yXzFvyqhDxj8FlWdskN9z9XqSChBDd+MhFzYjFQnl+z9vUn7j/hoAO2K+maYxVlZbWy8lqVdtnuuMFnPfnsh0tXJRlA1LMVyPLKZffdc9VuO7fPt6xcK7u4sDQ/p/X2PXa67a4H1tTYDuZskDHmC25SCZtp3zeeNK6rnJhgjmYgEuBUvPboHaV51v6HH//d3OU2QHWqtqJ83ivP3leUndd/t/1eevv9Cp9HfO1jFkoGKrlq/vTjjjgwy8o78OCjPv1yXE1dNXqN8NTnLz3Sf7uSI44bMmlxeQIgIjTjSQDbYB4HDGxJJ2tQoDVIVB1oIwJEgAgQASJABIjAbxMgoeG3uVArESACRIAI/I8EDCYmSG9po9QAGCxiCcYHmQQ/tnT65DmTpvyyZF2lo1386ty4qTpQ/uzvJk/7dkpZNF7j+0xJIT2pBeMOcBskn/TdhLnzFsyavyyolyA9ZiuNOQRAareqav6saV9/8+XS6qoogB0YyCCFseOJZN2M2T9+8tGnX33x7bRpc8rKq7EMBLdXL1v8/vvvL1tbyQAc7hoQjhtXLA4qzmtWrlk8/5d5sz//+ot3Pv1g1pJfKhK1yvDK8orddtkjt7DjDQ8+uywuUqBjLIEpHX0NzMdEjG6kctmCmRNnjP/8u3FfTfhu8vQJk6YtXVMlNPgC/Q4kaI+5WF/SWbdm+Y+zpn7/9WdfTBj3w4RvZqxeXe0bPCcqpY++DNrwINsEWvtCYaoInlLaZpLZcXCql/44bo+dunbuvuOzb34Wl5DC6hXxX+dNnPzt5ClT5qyKVKVARxSC4Akb7JRJVq9dNm/SDxO+m/i949mY7kG6qYpVpx91YPuCnKtuvKtKoDtDUhkhbS3jBvhGQoM2KDRQjob6Z5t+EwEiQASIABEgAo0JkNDQmAm1EAEiQAQ2ImCCrWFT45aGR2n/NwlgTkGNLyGY0TKQGyRWoDAYsOBJrEUhtcJDRmM9S6G5RvUADXgllMIkiPiSQkuFURYKy0VqTFaowi0oABn0gPY5Jn1ggjPGjOCBwBEIHyh2gEQrWkvJpRZcMlWfuNF1XTsew8oQXICWRvhM+L6WPo6GwRFGVgNEx3/ydsd2XS2r/VNvjI8BlHueC9LlQnJlBDfCN9zT3FNCa4kaC9ZtwEFByPClRZj7ElckglfgIRDOTeA5TGmmtECjPrDpgx8KuwmSJgSFIAzXwJOGrT1v2FGWZV1y6c3zf8UaFxIY15GgGIf20e1BJkDb2ggmwWEiEQdcWEoB82UUTBxE9M1nH2uT37p37z3GT/rZAai1MRQkkYrjbQpcGRpIRXh7yZ3hNx9yaiQCRKAlEwg/GzT+hKCDDf8nSBsRaEkESGhoSXeb1koEiMDvItD4Q0Pjlt/Vccu6KPMJDGswaC2E8INNCKE1SgBSSiGElFJrrZTinIeHlFIi2EI1IbPPGAvPEUKwYAvfhsKDEIIHW3g+diWVUdoo7BxH58zlzBM8yIAACcdmqEdg7gTgiidsw3U8nqxJJV1QHDQzjuGVICvB1L723KM9uvfpseN+z479rtrH3Aw+aE/ghGWwhYOGawmnkZlM2GiMCc9s/FMpFTYqpbTWmc+mgQqBQgM6YmjQAgwHLWw7uXT61A/79OpRWtTju/HLpIZaO4ZOCdoxmnnAksATIGytGNfKR68II7jHU1wlHb8SIBpfM++8U48tKup0/a2Prq12OXp2oP6B+ghJCi3rz5RWSwSIwO8nkPnfXObf7bAvEhp+P1O6sjkTIKGhOd89mjsRIAJbhUBjWaFxy1aZSPMeJPMJDL8PN4Yx5gYbehwE3/OE9v8mQoMxJlQlQtGh4X7GYg+FCSllaJmHmoXv+4wxpVRo0vu+nzlBSsk5zwgNrsTUjL4UTHAUI5gAaZTtobdBkCEiJb2Im4im6rhfCxBP1i4ffsYgyyo87JizPho/JyYxzMExHhPpITZRCjYRGjLyQUYB2WQn7Z4R/GooNNTffmFAaBWKH0rxVFAUM/LoQw8W5nYYeMQ5P/60OiWZBE9pV2mfG+aB8DCcQ6GHiELynPvacK1dAJul1v/roZuLsq0DDjpy7qIKT4EtgBtIMWYAgviO+pHpNxEgAkSACGyeQOZ/cyQ0bB4SHWlBBEhoaEE3m5ZKBIjA7yPQWFZo3PL7em5RV2U+gYXf7YQmdKgjhC2hp0Pm81lGNQjbQ6+HhvuZllC5aGiTh+JCqDKERzPmfeatUFIoybVyMKIAwhgN5vmKcTcSn/rt95+8/d7KpUu4ERI0x3ILKEc4yeo7b78+Py/Lyiq4+a7HlpY7QcIF8KTDJQu1j4zw0XBK4cw3EREyKP4/OxkgodctFpsMhAYpQDEumY3aR3xtTUX5xRdemW11uu3vT6Qki3hRbnypmTJcGUzdiCksgpfNfYxg0RzTYRhvxsQvjjiwX9vi/GdfeJ0BxFyoTfoSS2lwA1pq0aKeUlosESACROB3E8j8by7z73bYVfgv/CaNv3sUupAINBcCJDQ0lztF8yQCROBPI9BYVmjc8qdNrvkMnPkEJkSYcGHD1ENfhlACyLT+R6GBcx46KYQxF2HcROhNkPk8F9r84duM2Z+OrVBSaiWMVgBJ1+FSYDIIgTkjypavGn3p5f1697nswgtffe3FbyaOnzJz2sdffHLf/XeNvOh8K8vKyss97dwLf1qw0gfwDQjpAQgpeShthINmZI7MisLPmqHWkNFZMlgyOxndZJPPpmmhwShjFKZjlFoxXzKb+3XJWAUYPXP6nDtu/8eXX02Ju26c2Z5ivmJKckBNAkMwGBgPTMoIAUoKz4vVgWZL589+4N5bx771alUkwdHDwcQdTFqBLyMDl44NST0za6EdIkAEiAAR2IRA5p/xzP+DwhM2+cd8k6voLRHYVgmQ0LCt3llaFxEgAluMQPjRoWF3jVsaHqX93ySQ+QSWsbcbZiLINIZ+CmGIhJQytLrDM8MeMvsZJ4WMB0Fo24dREuEcwiiJsJ9Q4MicjAELgUeDAkh5big0uLYDBlKR2OP3P9Q6Kzc3rGtZnL9dr+6tS4tQYsjO7d5rh0uvvmbiT7Oj0jAAZoziWLhBY+5HtOczbhrhZ82GkRGZcI+Ma0a48IY/w5mH/YQ/N+EZ9o8cBFPMBeUHCRvA97mdYljYQoFrdEoyV3jS98DzwReSC9fIJMiIdGpSUUy6KRgWsjDS9uIySJ+ZZJiS0+GKa6VAc+nVCw2kNWxyE/4fe+8dHldx7//LjWKMcULyzc0tudzkyR/h5nnuzZPcJMRVVnGFUBIbQgDbqttXWlV3AzbuBTfAmGAwuGlXkhvBgCnGWLTEvSNZ2r7albaffub3O2ekw0FHa6try2ee8xzNzs6ZM/Oa2dWZ937mM/ASCAABINCRQKzvbfwN30F96HgxvAYCSUcAhIak61JoEBAAAn1NQDnZU6b09T2TsDzpCQy3Te7iEadgCaBToQFbAeASpHgsRiRJYocOOL80h6dFEwY8S29bwiDu6cCIywqw48NQIMgJ+00gZ6N164ZNf/nTrH/50b133DX8e/eOuWPknf/vRz/OyJz6xp4Dp69e9zGMj2H8NEUjBrEkR4YF84h2p+LyEYINLrCfSLmZhgSkQ0Rql5TeliJ6g8RxcaMNmmUEzxI8zbAkFQ6GGIYhSdIfjhAsCkSpsOD5kRZcTgg7agqmDRGeCiCqhYu2EEFBaKBYOhDmaYbmOW8kSPGoJRRlBOmEFbf5ZBiWEI01xM0+pGqJTiLBebqMB0SBABAAAgIB6Utb+l+AuYDQAOMjNQmA0JCa/Q6tBgJAoBsE8KOD/AJlivxdiHdKQHoC6/RdZaK00kF6esN5JPiSc0eO4+TGEXhlBMMw8kc9YYaP2mWA9pvhmTvF0CxCBCX4PkSCWwIWscK2mYhBzibHqc8+/ezUJ2fO/PPEyU+/+PwfLa3hIMFEOMEBJIFQFNF0m0EBw3Pf3lGqZPuthL+SpQN+6JS/1Wm8IzFc3bYpPsfxVJvQQAmbQ4SDIdEDA+0N+GjRDoHGe0YI+2rywoadPEuKLiEJ0eWEsPCEIBGLhAUf4u6eDEIRhomytLiJJsPyVLtXh47mDDLFo9OKQyIQAAJAIBUJSF/a8v8+kv1ah8RUBARtTjECIDSkWIdDc4EAEOg+AeWkUZnS/VLhioEm0On0WJq8t70rGDbwgtCAnUO2z+qFOTve6lHwxChsyhBFXBRRFCJZJC5AEPwt9nOLpLoK92GQsAGluLREWP2ABL8NPMnxURqRNC/UsK06bQ0T6s8K6yM4AgluG1ie43nEi7XGfiLb/DII3hmwOIHLZ9oaLmtcW5GyFIgCASAABIAACA0wBoCAnAAIDXIaEAcCQAAIdEJAKSsoUzq5DJLijMBNpsffTuGx0CBoDYimeIrkSJIW9oMkoxQZpUlKcGTJCRs/UMJ0naKRsK0Dz3LClH9AhQbR4kIpNKAwKwoNtLg3p7xKwk4T4kEjYa0EtnrgRD0CN79daBD0CNEKAgsNohTx3abdhGSc9TlUBwgAASAwcARAaBg41nCnRCAAQkMi9BLUEQgAgUEloJQVlCmDWkG4eZcICJtKyNWA706X215JQgOHaEY8KJ6iGJoiGDrKMpTgPpJDgtYg7OVAsaLQwAnrErpUh15lwlVsm/O3qQbCq2/TRQcLSFgJ8m063j9Cajgv2j5wgtCAhQSh4mIJ7cUIJcsWTcQSGjqup+hV0+BiIAAEgEDiEwChIfH7EFrQlwRAaOhLmlAWEAACSUlAKSsoU5Ky4cnUKKwyfLseon1WLf3m/+1v9u1T+LYs2LWD8NN/2y/87Y+SLC9YNtA8zwoLEPp73i06qxQUgPaKSi1oSxHSBYFANEbgJKFB3nAsKAhbXbKcqJUIFhGS0NAuOAh+KtqWirSvFpFuiocEtoxIpuEBbQECQAAI9J5A+38H4a+8NHAGKacB8dQhAEJD6vQ1tBQIAIEeElDKCsqUHhYNlw0UAfl8W5qEt028v1sH4QFR1Bo4jhE2wmAE0wWG4ViWZnlKWCiBnyEF6YEV1AdRaBBn9t8tqG9f3URoaLtR294QYu0ZQSwQpQip4UIunIUR7BU4cbUHXjrRCQdJxpBH2kwfBCVCFCP6toVQGhAAAkAgsQngZwPlEwIIDYndr1D7nhIAoaGn5OA6IAAEUoaA8qFBmZIyMBK1oXi+jR0pdEloEEQEiucolkHCwfIMx9KIYgQrBjG0OUEQ5+6S58X+w9NBaJD5VsCGDOK57fbY1AK/kIQC4aVMaBDaJwYhHWdqu1r8I10mj7RlxCYfgh9KCEAACAABICARwF+q3361tr8BQkM7CfibWgRAaEit/obWAgEg0AMCyocGZUoPioVLBpJA94UGGiFSOMSZNr6cQgyDmLa1BnjSzrWtmviOmWx/NOxboUFcpPEdoQHvQNGWLuoGJI9I0e6gvSqSXiC5X2A5bLjRLj98d+2HlF8eAaGhHSf8BQJAAAgoCYDQoGQCKalMAISGVO59aDsQAAJdIqCUFZQpXSoIMg0eAWkFwbeKgDSF/m6t2pZOCN4dScRTiBO2hcTbStCCcUP7IgksNAi+GYQp+rfFfre0PnslCQ3i7dAthAY6ptCAF3wIO0xgoYHtvtAASyf6rFehICAABJKJAAgNydSb0JbeEwChofcMoQQgAASAABBIAAJYWOhORdtEBHxJR12i4+vuFNyzvDEb8J16imUrUxS3xNJIvwskivtCAhAAAkAACAABIJACBEBoSIFOhiYCASAABIAAEAACQAAIAAEgAASAABAYKAIgNAwUabgPEAACQAAIAAEgAASAABAAAkAACACBFCAAQkMKdDI0EQgAASAABIAAEAACQAAIAAEgAASAwEARAKFhoEjDfYAAEAACQAAIAAEgAASAABAAAkAACKQAARAaUqCToYlAAAgAASAQfwTk/snl8firKdQICAABIAAEgAAQAALdIwBCQ/d4QW4gAASAABCIcwLySbs8zsVZkNdNHo9zvFA9IAAEgAAQAAJAAAjckgAIDbdEBBmAABAAAkAgkQiwMUKc6QycXFyQxxOJNdQVCAABIAAEgAAQAAKdEQChoTMqkAYEgAAQkBHAk0BZAlKmyN+F+MAQ6NALPM9zHMeyLEIoGAziOH6JzwNTq67fJZbwgRBiWZaiKFwUIwaapsPhMEIIv+Q4TsrQ9TtCTiAABIAAEOg/ArEkY/xtz/N8/90aSgYCcUgAhIY47BSoEhAAAvFFoMOEFiEQGuKigzr0i1xowM9zLMuSJMnzPJ6TR2MEYpACHSMQBIFrSpIkwzD4CZWiqGg0ihCiaZphGCxGxEU3QCWAABAAAkBAJABCAwwEICAnAEKDnAbEgQAQAAKdEOgwoQWhoRNGg5HUoV8koSEYDBIEgRDCRg08z0ciEZwyGNWMeU/5I6k8Ll3AsizHcfglFk0QQlh9kPJABAgAASAABOKEQKff5PifEccJy+XipJ5QDSAwMARAaBgYznAXIAAEEphAhwktCA1x0pcd+kUSGnD1eJ4PBALRaJSm6ZAYAjGCf5BCMEbA1YlEIn6/PxQKEQQRDAYZhsFaCW4RbiN+fpXiEAECQAAIAIFBJABCwyDCh1vHIQEQGuKwU6BKQAAIxBeBDhNaEBripHs69IskNDAM09ra+s4776xevXrdunXbtm3bvn37K6+8sjVG2DxIIUZ1tr788subNm3avn371q1bt23btmHDhuXLl7/22mvhcJjn+XA4LPfOgJdRxEmPQDWAABAAAqlMAP9Xwmc5B/DRIKcB8dQhAEJD6vQ1tBQIAIEeElA+NChTelg0XNYLAh16QRIasDPI119/fe7cuXPmzMnPz8/Nzc3JydHECNpBCoUxQlFRUV5ensFg0Ov1BoMhNzf38ccfX7x4sd/v5zguEolgoUHyQ9ELhHApEAACQAAI9BkB/F+pw/8mWDrRZ3yhoEQjAEJDovUY1BcIAIEBJ6B8aFCmDHil4IYdXXLKhQaWZffv36/RaHQ6ncFg0Gq1arU6N0bIG6RQGCPMnTv3qaee0mq1KpXKKIa8vLz169e3tLRwHCf5aMDuG2DRL3wSgAAQAAJxQgA/GyifEMCiIU46CKoxwARAaBhg4HA7IAAEEo+A8qFBmZJ4rUr8GnfoBbnQgBCqrq7W6/Umk2n+/PnLli1bsWLF8zHCc4MUno0RlixZsnTp0rVr11aKYf78+SqVau3atT6fj+d5mqbBliHxBy+0AAgAgSQkgP8rdfjfBBYNSdjT0KSuEQChoWucIBcQAAIpTED50KBMSWE8g9x06Sd9nudZlpXm4bW1tTk5OTqdbsmSJSdPnnQ4HJcvX7527dq5c+dOnz59/fr1+vr6ixcvXrhw4fwghTNiOHv27Pnz5y9cuHDx4sVLly5dFsOFCxcaGhrq6upeeOGFyspKlUq1Zs2a1tZWhBDDMCA0DPKYg9sDASAABDojgJ8NlE8IYNHQGS1IS34CIDQkfx9DC4EAEOglAeVDgzKll7eAy3tMQCk00DRNEERVVVVubq5Op1u4cOGXX37JsmwoFKJpOhKJBINBUgyhUCgajeL4wJ9pMTBiwDtZ4nEVFQPP8/X19evWrSspKcnJyVm5ciUIDT0eJHAhEAACQGAACODvcOUTAggNAwAfbhGHBEBoiMNOgSoBASAQXwSUDw3KlPiqcSrVRik04G1BampqCgsL1Wp1UVHRp59+ihBqbGx0uVxOp9Nms9ntdqvV2tTU5HK57IMUboihURHq6+sdDkc0Gr148eJzzz2n0WjmzZu3YcMGEBpSaVxDW4EAEEg8AvjZQPmEAEJD4vUl1LgvCIDQ0BcUoQwgAASSmoDyoUGZktQA4rpxnQoNNE1XVVXl5OSo1ery8vIvvvgCIeR2uz3tobm52SUGr9drHaTQ3NyMq+MWA66Py+Wqr693Op0kSV6+fPn555/HQsOmTZtAaIjrgQiVAwJAIOUJgNCQ8kMAAHyHAAgN38EBL4AAEAACSgJKWUGZorwKUgaGgFJowF4hzWbzM888o1KpFi1a9MUXXwSDQZfLdePGjfr6epvN5nA4JKMG3yAFSV9wOp0OMWDTCpfL5fP5aJq+evXq/79iwmAw5OTkrFu3DoSGgRlRcBcgAASAQM8IgNDQM25wVbISAKEhWXsW2gUEgECfEVDKCsqUPrsZFNRNAkqhARdw8ODBOXPmYKHh008/tdlsoVAIiwter7elpcXn82G7hkEyaLBiocHtduMFHZLc0NDQ0NTU1Nraevbs2eXLl2OhYe3atSA0dHNoQHYgAASAwIASAKFhQHHDzeKeAAgNcd9FUEEgAAQGm4BSVlCmDHYdU/f+SqGBoiiSJPHSCZ1Ot3jx4i+//JIgCK/XW19ff+PGDY/H43Q6rVYrtiBoX04x0H8lZQGbM2AjC7vd7nK5vF4vXjrxwgsvGAyGefPmwa4TqTvEoeVAAAgkCAEQGhKko6CaA0QAhIYBAg23AQJAIHEJKGUFZUriti7Ra64UGvAekNXV1SqVSq/Xl5aWnjx5kuM4r9fb2NjodDr9fr/T6cS+ISVnDZKLhEGPNDQ0OJ1OuTPIuXPnrl+/HiwaEn2sQv2BABBIbgIgNCR3/0LruksAhIbuEoP8QAAIpBwBpaygTEk5KHHT4FhCg8ViycvL02q1FRUVdXV1DMM4HA5ne5DUBLxyQXopj0hLGzpE2svor79NTU12uz0UCl26dGnFihU6nQ7vOhEMBhFCNE1zHId31oibToCKAAEgAASAAAKhAQYBEJATAKFBTgPiQAAIAIFOCChlBWVKJ5dB0oAQ6D+hIdaul/29xAKvnohGo1evXl21apXBYMjNzd20aRMIDQMyoOAmQAAIAIEeEgChoYfg4LIkJQBCQ5J2LDQLCACBviOglBWUKX13NyipewT6T2iIJSjIrR76I46dR4TDYWzRoNVq8dKJDkIDtmvoHizIDQSAABAAAv1GAISGfkMLBSckARAaErLboNJAAAgMJAGlrKBMGcj6wL3kBPpPaOivpRG3KtdqtTocDrnQMGfOnPXr1wcCAfnSCRAa5MMA4kAACACBQScAQsOgdwFUIK4IgNAQV90BlQECQCAeCShlBWVKPNY7NerUf0JDB9cM0stYlg59le5yuTweD0mS165dw0sncnJyNm7ciIUGiqJYlkUI4bPU/NTobWglEAACQCB+CYDQEL99AzUbDAIgNAwGdbgnEAACCUVAKSsoUxKqQUlVWWmmzfM8y7I0TeNdJ/rPGaS0G2U/RWw2m9PpjEQily5dWr58uUajeeaZZ9auXRtLaJA/2nYlnlTdD40BAkAACMQNAfk3sLxSnBik/1bytyAOBJKYAAgNSdy50DQgAAT6hoBSVlCm9M2doJTuE5Ae3fpcaGiKEW619KG379tsNrx04uLFi88//7xKpXrqqadWr17t9/sRQiRJMgwjt2iQP9p2Jd59xnAFEAACQAAI3JqA/BtYnhuEBjkNiKcOARAaUqevoaVAAAj0kIBSVlCm9LBouKzXBPpPaIjECMF+Dj6fr7W1lWXZhoaGdevWFRcX5+fnb968uVOhgeM4+aNtV+K9Rg4FAAEgAASAQCcE5N/A8rdBaJDTgHjqEAChIXX6GloKBIBADwkoZQVlSg+Lhst6TaD3QoPT6fT5fHa7vbGx0W63t7a2hkKhxsZGl8uFz06ns7GxES+UqK+v763FQvv1eHcJm82GI2632+Vy2e32hoaGb775BiF05cqVVatWFRYWarXaLVu2eL3eQCBAUZTUZISEbds5jsP+GrC9AysGr9eL1QqGYSKRiN1uRwjhnPiRV1Ioet0DUAAQAAJAAAgIBGIJDUAHCKQmARAaUrPfodVAAAh0g4BSVlCmdKM4yNqnBKRZd4+XTjgcDrfbjc9ut7u1tdVqtR4+fHi5GNavX79x48bVq1dv2LBh06ZNq1atWtN3Ya0YVq9evXbt2o0bN27YsGHt2rXr169fuHDhiy++uGLFiuLi4vnz55eUlCxYsGCbGLZs2bJp06YNGzZs3Lhxy5Yt27dvf/HFF3Ecv/XKK6+89tprOL5jx44XX3xx7dq1O3fubGlpwQsuQGjo0wEIhQEBIAAE2giA0ABDAQjICYDQIKcBcSAABIBAJwSUsoIypZPLIGlACPReaHC5XA6Hw263+3w+j8cTDAZv3LixcePGvLw8tVqtF0NBQYFKpTIajRqNRt1HQaPRaLVaXKBWqzUYDHq9XqPRmEymvLy8srIyvV6vVquXLFlSUVFhMpnwu/n5+XPmzHnqqaeefvrpefPm5efn5+TkzBXDM88889RTT+Xk5KhUqtzc3Ly8PKPRmJ+f//jjj1dUVLjdbml3TKw1wDAekBEKNwECQCBVCIDQkCo9De3sGgEQGrrGCXIBASCQwgSU8zFlSgrjGeSm915o8Pl8DofDarV6PB6r1RrvRNopAAAgAElEQVSNRltbWzdt2lRSUlJZWVlSUmIymTQaTV5enk6nU6vVxj4Ker0eiwtarVav1xcVFRmNRq1Wq1KpcnJySkpKtFptTk6OVgxFRUUGg8FoNBaLAb80iAFfaDQasRKh0+lwBYuKiubPn19RUaFWq5977rlgMIjXVoBFwyAPWbg9EAACSUoAhIYk7VhoVg8JgNDQQ3BwGRAAAqlDQCkrKFNSh0a8tbT3QkNzc7PL5cJCQ319fTgcpmn61Vdf1ev1ZWVlpaWlL7zwwpo1a5YsWfL8888vW7asso9CWVlZeXl5WVlZSUlJaWlpRUVFeXm5yWTCSyeWLFnyXHtYtGjRsmXLioqKSktL58+fv2jRooULF2IRoVwM8+fPX7BgwcKFCxctWrRgwQKcf/Xq1eXl5UVFRTqdbvXq1aFQCCEUjUZBaIi3MQz1AQJAIDkI4GcDeEJIjt6EVvSeAAgNvWcIJQABIJDkBJQPDcqUJEcQx83rvdDgcDi8Xi9eOtHQ0OD1eiORyI4dO3JycoqKihYvXvzOO+9cuXLl66+/Pnv27OnTpy/1Ubh8+fKVK1cuX7584cKFixcv4vj58+cbGxvxjS5evHjhwoWvvvqqrq7u9OnTONulS5cuXLhw7ty5s2fPnjt37vz582fPnr3YHi5dunTmzJnTp09fuXLl008/Xb58eUFBQW5u7qpVq4LBIMuyFEWB0BDHwxmqBgSAQAITAKEhgTsPqt4PBEBo6AeoUCQQAALJRUApKyhTkqvFidSa3gsNdrvd7/dbrdaWlha73e5yuXw+38svv5ybm6vVahcuXPiPf/wD2wJQFEXTdKiPQjQaJQgiEomEQqFwOByNRiORSCAQwHGSJBmGoSgKW1jIN5igKIogiGg0SlEUzsNxHEVRJElSFBUQA0LI5/Nt375dq9Xm5+evXr06HA5zHIf3ngAfDYk0xKGuQAAIJAgBEBoSpKOgmgNEAISGAQINtwECQCBxCShlBWVK4rYu0Wvee6HB6XSGQiFsy+ASg9frfe2110wmk8FgKCoqOnHiBEVRVjHY7XZHHwWXy+V2u51Op10M2CdlU1OT3W53Op14k0ubzYbT6+vrm5qa8EaYdrsdV8ZqtdrE4HQ6cdztdtvtdo/Hw3Hc1atX8e6YeXl5K1as8Pv9IDQk+miH+gMBIBDPBEBoiOfegboNPAEQGgaeOdwRCAABIAAE+oxALKHBbDbn5OSo1ery8vK6ujqGYRwOh7M9YEFBmurj+bnX67VarX6/v7W1defOnQaDobCwsLS0tK6ujiAIp9Pp8Xj6SGToSTFSnTtEHA5HU1OTpDXYbLbGxsZoNPrNN9+sWrWqoKCgsLBw1apVgUBA/hAsceuznoCCgAAQAAJAAAgAASDQTgCEhnYS8BcIAAEgAAQSkIA0YeZ5nmVZmqYRQgzD9J/Q0C5WDPTfDvqC/KXNZnO73TabDQsYVquVJEkQGhJwOEOVgQAQAAJAAAgkCQEQGpKkI6EZQAAIAIHUJABCA7azaG5udjgcWH2w2+0URYHQkJqfCGg1EAACQAAIAIF4IABCQzz0AtQBCAABIAAEekgAhAYQGno4dOAyIAAEgAAQAAJAoN8IgNDQb2ihYCAABIAAEOh/AgMvNMjXLMRDHDuYwGfshwKWTvT/uIM7AAEgAASAABAAAjcjAELDzejAe0AACAABIBDnBEBocLlceHMKm82GN8VoamoiCAKWTsT50IXqAQEgAASAABBIYgIgNCRx50LTgAAQAALJTwCEBhAakn+UQwuBABAAAkAACCQaARAaEq3HoL5AAAgAASAgIzDwQoN7kEKsZRodlk44HA5YOiEbIBAFAkAACAABIAAEBoEACA2DAB1uCQSAQGIR4MUgr7MyRf4uxAeSQCyhwWKx5OXlabXaioqKuro6hmGw/wK8KaU0aXe73U6nE8/VvV6v1Wr1+/2tra07d+40GAyFhYWlpaV1dXUEQTidTux2UbpWGWlpaWlubrbb7S6Xy+fz4Z0g8GYQ+L5Yo3C5XB6PR3n5zVM8sYPT6fT5fPgWWGigKKq+vn7t2rV6vb6wsHDFihXhcBghRFHUzXuH53lODBJYnJ9lWXmE47iblwPvAgEgAARSjQB+NlA+IXT6pZpqcKC9KUgAhIYU7HRoMhAAAt0joHxoUKZ0r0TI3XcEpPkwz/Msy9I0jRBiGGbghQZJRMATfpvN1iQGu93udDodYpC0ACkFp3flbI0R7HZ7U1OTy+XC7zc1NTU0NITD4WvXrq1evVqtVufn5y9fvjwYDCKEaJqWPwp3iCu7hed5giAQQiRJYq0BJAYlJUgBAkAACCCE5N+ociAgNMhpQDx1CIDQkDp9DS0FAkCghwSUsoIypYdFw2W9JhA/QgO2X8DGBc3NzVhTwEYQko5glwUpsYuRWCs2vF6v2+1uaWnBFg/YOoOm6W+++WbdunUqlaqwsHDNmjXYooFlWfmjsDyO35J3CNZuJH0Bo8aKg4Rdnh/iQAAIAIFUJiD/RpVzAKFBTgPiqUMAhIbU6WtoKRAAAj0koJQVlCk9LBou6zUBacY76BYNLS0t2E4Be2e0iQHbGmCjBpyO4zabrYv6gpRNplF0jDY2NuIVE3a73Waz1dfXBwKBa9eurVmz5umnn547d+7KlStbWlpYlo1Go/JH4Q5xZW/wPE/TNMdxShlCmRlSgAAQAAKpTED+jSrnAEKDnAbEU4cACA2p09fQUiAABHpIQCkrKFN6WDRc1msC8SM0uFwuu93u8Xi8Xq/H4wmHwwzDRCIRiqJCoRBBEDgSjUYpigqHw9FuhkiMEI1GA4FANBoNhULRaDQSibS2tiKErFbrtm3bcnJyCgoKNmzYEAgEMGz8yKs8Y7tfrCmwLMtxnMRW6iVlivQWRIAAEAACKU4AhIYUHwDQ/A4EQGjoAAReAgEgAAQ6ElDKCsqUjtfA64EiIE19B92iAXtkaG1tDQQCjY2NDQ0NeC1DU1PTtWvXGhsbbTbbN998c+PGDafT2dDQ0NjNgE0klGeXy9XU1OR0Ou12O15ecePGDb/ff+7cue3bt2s0GqPRuGbNmuvXrweDQYqimBhBAkiKgaZpbMUQCAT8fj/uT4qisCMMyTfkQPUz3AcIAAEgEO8EQGiI9x6C+g0sARAaBpY33A0IAIEEJKCUFZQpCdisJKly/AgNdru9sbExEAg0NzcfPnx45cqVq1ev3r59+7Zt29atW7d169ZXXnll06ZN27Zte+2117Zu3bqtm+HFGGHr1q0bN27csmXL5s2bX3rppe3bt69fv37nzp1btmyZP39+YWGh0WgsKirauXPnm2++uXfv3ldjhB07dryiCDt27Ni8ebPZbHY6ndjkIUnGDTQDCAABINDXBEBo6GuiUF5iEwChIbH7D2oPBIDAABBQygrKlAGoBtyiUwLxIzQ4HI6GhoZoNBoMBt98883HH3989uzZJpOpsLDwmWeeKSws1Ov1eXl5Go2mpKRErVaruhk0MYJWq83Pz8cFGgwGvV6fm5trMpm0Wq1ery8vL3/uued0Ol1ZWVlBQUFeXl5BjJCTk5Obm5ufn4/fz8/Pz83NzcnJyc7OrqysvHTpksQfb+0hvYQIEAACQAAIwK4TMAaAQAcCIDR0AAIvgQAQAAIdCShlBWVKx2vg9UARiCU0mM3m3NxcjUZTUVFx6tQpmqYdDoezPbjag9vtdjqdeKcGr9drtVr9fn9ra+vOnTsNBkNhYWFpaWldXR1BEE6nE28hgfPjC10ul8fjwVtL4l0nWlpa3G73nj17DAZDSUlJRUVFeXm5SqXKy8vLycnJy8vT6XR6vV6n06n7LqjEUFhYWFBQkJ+fnyeGgoKCwsJCnU5XVFSE2yKl47cKCwtVKpVarZYUDLVajRUKtVptNBpLS0vXrVtXWFi4fPlyt9uNN7aUgA9UD8N9gAAQAAKJQQA/G8ATQmL0FtSy/wmA0ND/jOEOQAAIJDgB5UODMiXBm5jA1ZfmvZKLAYQQwzADLzTgfR98Pp/T6dy9e7darS4qKiorK1u8ePHzzz+/bNmy+fPnL168eNmyZYsWLVq4cOHSGGFZN8OSJUtwScuWLXv22Wefe+6558Xw7LPPLlmyZOHChQsWLJg/f35lZeWiRYueE8Ozzz67bNmypUuXLl68GFdmwYIFlZWVJSUl5eXlxcXFlZWVixcvNplM5eXlIDQk8McDqg4EgMAAEgChYQBhw60SgAAIDQnQSVBFIAAEBpeAUlZQpgxuDVP57vEjNGAjiVAo5PP53n77ba1WW1JSsmDBgpqamrNi+Pzzz8+J4auvvjpz5szpGOFMN8Pp06fxFWfPnj137tz58+cviOHMmTNnz569cOHC5cuXL126dO7cOfzyH7LwT1m4ePHi+fPnL168ePbs2StXrnz++ecbN27EphBg0ZDKHzFoOxAAAl0kgJ8N8LmLl0A2IJDEBEBoSOLOhaYBASDQNwSUDw3KlL65E5TSfQLxIzTgJRXRaNTv9+/Zs0ej0ZhMpgULFnz22Wc8z3Mc19raynEcTdPhcJjjOLy5g/JMdTPQYqAoiiRJgiCiYohEIi0tLX6/PxKJMAxD03QkEgkGgyExhMPhSCQSjUYJgiBJkqIomqb9fn84HMb1QQj5fL6dO3cWigGEhu4PTLgCCACBlCOAnw3gCSHlOh4aHIMACA0xwEAyEAACQKCdgPKhQZnSnhf+DjSB+BEaGhsbrVZrIBDweDy7du3Ky8vDRg3vv/8+wzDBYLC+vt7tdjc2NtbX17c7i+jkb7v7iG78xaU4HA673W6z2axicDqdVqv1hhgaGhpu3LjhcDh8Pp/b7Xa5XE6nU54f781ptVobGxuvX7/e0tJy/fr1zZs35+fnw9KJgR7TcD8gAAQSkwB+NoAnhMTsPah13xMAoaHvmUKJQAAIJBkB5UODMiXJmpxAzYkfocFqtdpsNr/f39zcvHv37oKCAq1WW1xc/P777xME4fF4rl+/7nA46uvrm5qaHA6HLUawdzM0NjZKMoHNZrPb7Q4xYC+VTqdTkhXcbndLS4t0W6xH4HNTU5PH4/H5fB6Px263h8Phq1evbtiwQa1Wg9CQQB8HqCoQAAKDSACEhkGED7eOQwIgNMRhp0CVgAAQiC8CSllBmRJfNU6l2sSP0IAtBYLBYEtLy759+3Q6ndFoNJlMH3/8cTAYdDgcTU1NXq/Xbre3tra2tLTEslhwdzNIu2m4XC632+3xeJrFgEUHt9vt8/nwjhhYCpHECCm/VwwNDQ1Wq9Vut7vdboZhrl69umbNGhAaUunDBG0FAkCgVwRAaOgVPrg46QiA0JB0XQoNAgJAoK8JKGUFZUpf3xPK6yqB+BEanE6nzWZraWnxeDzYRwPeyfLUqVN+vx/P4ZubmxsbG30+n91u72TVhJgUS4CIlY51CclsAdtDYNsKr9frcrkk6wmXy9Xa2uoRQ4erHA5Ha2trc3MzliFIkrx06dLatWtNJhNYNHR1LEI+IAAEUpsACA2p3f/Q+o4EQGjoSAReAwEgAASAQAIRuInQkJOTo1ary8vL6+rqGIaRfvnHqwnwvN3tdmMnjna73ev1Wq1Wv9/f2tq6c+dOvOFCaWlpXV0dQRBOp9Pj8TgcjlgTfpfLhS/3+XzS9pZGo/HEiRPBYNDpdOLlCTabzev1yutwkwL74y3JRwO2aJBe4iphnYIgiOvXr69fv16lUmm12qVLl/p8Pp7nGYbBY0PC3sWhItfmaJrmOA5fKEW6WA5kAwJAAAgAASAABBKCAAgNCdFNUEkgAASAABDonIA04+V5nmVZmqYRQgzDmM1mEBo61SkkZaH/hAZ5p0jdxrKsFO80g/QuRIAAEAACQAAIAIFEJwBCQ6L3INQfCAABIJDSBORTVhAaOlUWOiQOgNCA5R6EEN4sE2tAHMdJnYXFILn0kNKDGBoPBIAAEAACQCDpCIDQkHRdCg0CAkAACKQSAWnuChYNHQSFWC/7W2jodIUFXnPBiQEPT5ZlcaJ8VUUqjVxoKxAAAkAACACBZCYAQkMy9y60DQgAASCQ9ARAaIglKMRK72+hQRpyrBjwy2g0ihDiOI6iKNxlkr6A1QfppXQ5RIAAEAACQAAIAIHEJQBCQ+L2HdQcCAABIAAEEAgNsQSFWOkDIzRIKgPDMFhlwMslKIrqsGIC5+ywsAJGNhAAAkAACAABIJDQBEBoSOjug8oDASAABFKdAAgNsQSFWOn9LTRgBw3SdhJnzpw5duxYU1OT3+8nSZLneY7jaDFgiYFhGNAaUv1jDO0HAkAACACBpCMAQkPSdSk0CAgAASCQSgRAaIglKMRK72+hAY8+LCVcvnxZpVL97//+b05Ozo0bNyKRCH6XIIhoNEpRFE3TjBhYlgWjhlT64EJbgQAQAAJAIMkJgNCQ5B0MzQMCQAAIJDcBEBpiCQqx0vtbaMAuHimKQgi9+uqrQ4cOTUtLGz58+G9+85vVq1dfvnwZD0iO40iSxNmi0aikQZAkSRBEcg/auGpdX3nHwOXAOVkJxBq03W2vshxcgjIdUoAAEEh0AiA0JHoPQv2BABAAAilNAISGWIJCrPT+FhoQQhRFkST57rvvTpgwYcSIEaNHjx4yZEhaWtqPfvSjWbNm7d+/3+PxYHMGhBB22cAwTCAQkLw5pPSYHtjGS5+gLt62uxNLyJ8cBLo4PKRssVotZZBHujsI5ddCHAgAgbglAEJD3HYNVAwIAAEgAARuTUB6QoXtLWMpCx3S+1to4Hk+GAwihFavXn377benpaUNHTp0zJgxw4cPHzZsWFpa2pgxY3Q63aVLl7AkIfUg7mxs6SAZOHR4t8PLW48PyNHXBLo1gezrm0N5g0YA+n3Q0MONgUDCEgChIWG7DioOBIAAEAACCHad6CAj3PplfwsNHMfhtQ9Hjhx58sknf/7zn6elpd1222133HHHyJEjx4wZkyaGf//3f6+srLx+/TrejSIQCEQiEawjYIeRSOzcDo4b8EuQG3rw0b/lRLFDhh7cAi5JQQIdho30MhYKKQOOxMoG6UAACCQBARAakqAToQlAAAgAgdQlIM05waLh1hqDmKO/hQYsECCECIK4du3a1q1bx44de9ddd40aNQpLDCNHjrztttvS0tLuuuuu//u//9u+ffvVq1fxCI5Go3jTCryeguM4uZNILEBwYoBZSnc/8x0meNJLqRycgvF22IJUygMRINBLAvgTDYphLzHC5UAgIQiA0JAQ3QSVBAJAAAgAgc4JgNDQRX1ByjYAQgNCCG9vybJsa2vrP//5z/Ly8p/85Cd33XXX9773Pewe8q677sK6wz333DNv3ryPP/5YWi6BRQosK8h3vsS6gzyl8zEBqZ0RwAqC8izllQNnGEaZE6dI+TtEJOUCIilFoMMwkF7GggDb2UqIIAIEkp4ACA1J38XQQCAABIBAMhMAoUFSELoY6W+hAW8kwXEcwzB+vx8PPp/Pd/z48ccee+xHP/rR8OHDpTUUt9122/Dhw9PS0n7wgx/k5eV99NFHkj9ILCvgaQme4rIsK+2FKbd0SObx3Xdtu6VwIAkNcshY1pGfY5UD6clNQD4G5PHutloaXR1WRfXdSIeSgAAQiBcCIDTES09APYAAEAACQKAHBEBo6KK+IGXrb6EBdyJJktj8PhqNBgIBnHj9+nWz2Txt2rQRI0bcdtttQ4cOvfvuu9PS0oYNGzZ8+PDRo0ePHTt21apVZ86ciUQiktAg/bouCQ2S+iD1fg9GTopcwovt5AUbE6b9+M7cUOLA84hleXEeyNE0TbNUtw6GoxmOhHOSEqAphuz0IKhopwfFsRTHMsJBY2GCY1iOYRmKZmlGOFi2/fPLIYSPtsHII4THbfvg5ITX301qfwv+AgEgEL8EQGiI376BmgEBIBAnBLAJqLwyyhT5uxAfSALtj6qCV0iWZaUV/haLJS8vT6vVVlZW1tXVMQzjaA/O9oDn3k6n0+122+12r9drtVr9fn9ra+vOnTsNBkNhYWFpaWldXR1BEE6n0+PxOBwOacaujODLfT7f7t271Wp1UVGR0Wg8ceJEMBh0Op1NTU0ej8dms3m9XqfTqbx8YFIGRmiQXDniOQbDMLinWJa9du3aunXrfv3rX48YMWLIkCEjRowYPnz4iBEj7rjjDiw6TJ8+fd++fU6nEyEUjUbD4TBCiKZpgiDwz6E0TePS8IwZjzfJVHsgh9+g3ov79u54GibOxNqjHI+EgxXOSDwzNEsQVJikCTzH41nEUCziUSRE0iSHOBQNUywtTOo4xJMcQaIIjQgGkdJBoagU7xBhEcmiaNfP4uW4tH4504igxfp3OMe6b6Ln7267utlekkH0LQ8aUTSiGERTiCERJxw8zSIGIS4aCYX9rWQwzBAkYrGwwAuiA0NxPMUjGonZhO8NMUZxPMvjEc4hnkE8JxyCHhHXmoP0LST9Y/r2QwoxIJB6BEBoSL0+hxYDASDQTQJKWUGZ0s0iIXufEZCe5+RCA8MwktBQUVEBQoNcwhh4oQGrA7jLJYHgH//4h16v/9GPfpSWljZy5Mjvfe97eC9M/PI///M/n3zyyY8++ghfRVEUlpDwfhZYxSBJEi/TkHw6pNgHUyE0iLBEoQGrDAwWGlhxbkazlDiDE2Z9og8NhqZJkoyGI36SjrCIYRDt9rtaos0BquWq8+K15jPnnZ9/fePTU1ffP3Hx2KeX3v3sygd11z44dfX4qavvd3K+dOzU5b+f6s755OW/n7x8rMNZLPxY78+fXXnvsyvvKs+xSlbmxCn9nV9JAKd0tz7drWc3y8c9flzs9++cP7vywWdX8Kg4Xnftw68aTpyx1V10/eO09esG/zdexuVnPATXyqMwQlGESCwoUGSUJMl2QwaO58RhKWhigo7Aioeom3FCfkFoELUGEBr67D8nFAQEBoIACA0DQRnuAQSAQEITUM5elCkJ3cCErjwIDXIRoSvxgREapB0ipPUOkkYgrapobm7+/PPPn3766X/913/FjiG/973vDRs2LC0tbciQIbfffvsvfvGLsrKy06dPRyIRhmHkA5XnBSN/mqaldPypTJ3PZme25IKCwPOs7BB/COYRL6ydQIjlGIKMhIORsJ9kwgyKMChEokALb7NGr35l+8RS9+br72/ZUvPCgh1a46a/qNY8NOfZ7NmV4x4rfeCx0t/Pqhj3+Pzxj8+f0Pm5fOzjFQ883rXzrMoHZlWM7fQsFL5gbO/PsyvHzZ7/B+U5VsnKnDilv/PH4tDd+nS3nt0rX+h33PUTxAHw7Xl25fjZleNnVYz7c/nY2ZXj/7p4cu6K6arVjyx9TfuiZfHf3l379gebj55688yND53RKwHOTqEAg8IMIgk2GiHC0WhUMKvhEM8KBydY2OBDEMvapLE2iwbxjbZ35V8GcRRPtW+hOEIPVYlLAiA0xGW3QKWAABCIJwLKqYsyJZ7qm1p1ubnQoNFosEUDTdPtKycc7Ssn2hYvwNIJl8uFmbhcLrsYCIK4fv36+vXrVSqVVqtdunSpz+fDc3s8vCTsytEmPWrjdQ3S0gm8/CEshmg0KhkjNDQ0vPrqq+np6XjPy9tvv/2uu+668847seIwatSoiRMnrlmz5vLlyzzP4wtDoZAkW+DlFR32zJNXL1k/rTKhoX0+JtifSybobfYOvKgytAsNPGJoJFiqRykUaKZuNATPHvx69wt7yoybnyp6+Snd9sdzNz00d+P0ZzZkFW6fUrA9I3/b5Lyt6blbJs17ccK8FyfM3TQ+d8ukzo4JeVsn5m2dWLClq2ecX3nO3TIhpQ4lAZySKBDytk7M3TIhZ/P4uZvGztn4h3kvjsvbOlG9PVO9NTt/bUbe6izthgeNG/5UvPEvz76m31L9/Fe2j662nvYhJynKDeIyHVE8ENdGCEt92kauMKp5TjTDEeSydoEhvj01SN9+8q8g5ZckpACBFCEAQkOKdDQ0EwgAgZ4TUE5UlCk9Lx2u7B0B6XlOvnSCpmmz2Zybm6vRaMrLy0+dOgVCg2Ts0N8WDdKj9ndcDoqzB4qiJIGAIAi/34/3mOB5/sKFC88///yvfvUrLDdgA4dhw4YNGTIkLS3tX//1X5944om9e/d6vV48XqJiwHFs1CDdTqoAfhenJ99nFluYi44YGFFfIEW79DbTdGli1i408JGAn2cE23UehVroplNXj+08unbJ65qK1+f++bmxWRW/+NOa3819dXL+m9nq/dNNtQ9p9mToD2QWV08tOzSz4shD5YcfLD04w1QzrezQzE6PiiMPVRx5cP7hB7t4rjgi5FQeZYdmpNShJIBTEgVC6cHp5Ydnlh+eWXZohqlmapEl22jOKq7KKt2XqfvbBPWOifq/Zau3Z/915dhHl/5+1tJJ83cVLn2reOe7G+tuHA8iN4OiPGKwX0hhrHKIIjmGokULHCw0iK4Z5EJDHGsN0peP9I+pd//f4GogkNgEQGhI7P6D2gMBIDAABJRTFGXKAFQDbtEpAel57uZCA0VRYNGAtYYBExrkz9y4d3APsiwrlwl4npf8Lxw/flytVv/85z8fOnQolhjuuOMOvDMF3gJTpVKdOHFCkifC4XAkEpE8gCq1Bnzf5NsLU7aOnRMd6ZHiAvioqDjIfv7lhN+HOcHdP8GjMI18ruDVUxff2Xl4dcW23GeWT3106e9L980us/y5pPZRnWWqtjpbf2iqpiYjZ+9YlXmSxpyutUzWVWfoazINtVmG2izjwWx9TWZnx2R9zWRdbVfP+hohZ78ehtqMTo9YN+00s6E2I1Hyd7ee3W2vvnZSp4e2eoK+dlLR4YziI5nGQ5P1tZO01RN05rHGqj+U1UwqtWQY92aYDkyvrJ1Vap6l3vXQYyv/8Oiysc+8MG3RTs0b722uu/xec7iRRWSE5rAzEZJgySiBWNEpg3CWlgJh96btjh06/X8w2E3zkKgAACAASURBVInyL73BrgvcHwgMPgEQGga/D6AGQAAIxDkBpaygTInzJiRx9ZRCA564YosGtVqNLRpAaBgwi4abDzaGYUQncG25gsEglgkQQhRFMQwTCoX27NmTlZV1zz33YLsGvBXFvffee+eddw4dOvRXv/rVypUrL1++zIlWEizL+ny+QCDQtoVeu9ggPfHjvTCTTGvAQgMjLGlnZEKDaM4gvodXvIsOMiiaiRC8l0SuSy2f7DuxZcnOgry1D+asn6LaOaNo38NPvzpWZ5lafGSm2pJeWDVeW5Outkyc9/bv9LVTdDXZGkum2pyhNmdoLJna6iycorFkKg+1OUNlSVdb0rtyVos5Oz1rqif3yaGtyej0iFV4p5m1NRn9nb9TCGpLenfr09169lX5uKr6g1n6g1kSLl11urF2fOnRycUHJ6v2jlfvSzcdfrjy3cfLjs7SH5ipe2um5m8z1Vsf1Gx45NnXNO989aabum7z20IsxSJEMxxF0G17ULCC8wbJ54joxYGX+4q8+bfNwL8rfe1I/5gGvg5wRyAQPwRAaIifvoCaAAEgEKcElLKCMiVOq54C1ZKe5ySLBiw0VFVV5eTkYKHhs88+A6EhToQGPCSx3CBJDKQYWJbF79I0XV9f/8orr/z617++Qwy33XbbyJEjsdeGtLS0f/mXf5k5c+brr7/e3NzMMAw2cMBCg9JZA97zIgWEhrZFE4JHPRqxFKJplqQjBB2IsF4/urH3000r9qvnrcma9fwDc7dmFO17uKz2MWPtDOOhabqDWSpLuso8QVuTrrVM0lWnFx+eqq3O0lZn62qm6Gun6mqmaKuzNZYstTlTY8nq9NDVZGtru3HoarI7PZQSRs9SxPpnKc+xSlPmxCn9nb9TCAJMgX8nR6z6xErvtJCbJMYqJ1a6vnaKpEmpqiYXHkhXVQlSUUHV7wv2/05XM9F0ZJq+dmrungl5+ycVHZ5R8d6j5UcfMlU/qH0ze+768U8tn1Cy7YnN1UvOu75qplwUolnEMYyw2WqbL0hO+GrnEM0hWtDUhPEtag1xuYAChIYUeOiAJnaDAAgN3YAFWYEAEAACQCBuCXAchzc4QOLefdXV1Xl5eVqtduHChZ9//nkoFHLHCHa7vbm5WXozGAxardY9e/aYTCa9Xl9RUXHy5MlgMIizOZ1tLiSlebs8YrVa/X6/z+fbvXu3Wq0uKioyGo0nTpwIBoNOp7Opqcnj8dhsNq/Xe/Ny5GX2eby/l070ZoTgTsT9SFFUXV1deXn5/fffj5dR3HPPPT/+8Y/T0tJuu+224cOHjxkzZvbs2ceOHYtEIgghVlznLakVeM9L7BIiFApFo1FcOK6epE/1praDeK1otdBmzsAhihcOmmc5nhWnZzyiaGFCxqAoibyO0KU9xzc+s3Ly42t/l/Nyeqn50Yqjf9aap+Tvm6SunqypTRcO8fd/gyXLYMkymrMNlmydJVtXndXF4yYT11hvdbFkyBYvBPCQ6NpZW53VZqkhmMNkqs3Z4pGhtqTn7fuD2jyu+GBmxdEZCw49WvL2gwVbMp56Pt20cc4HF2pDqDmMAhEizItCA0UI4iOPaIYnOEQIK4N4WhAaONE35CB+AuHWQAAIdIEACA1dgARZgAAQAAJAIO4JyIUGlmXNZvPcuXMLCgoqKyu//PJLlmUlKaFDxOEQ9qHAuy1YrdZAIGC323ft2lVUVKTVasvLyz/77LNQKORwOJqbm28uEIDQ0ONhgrfDlPbCpGkaSwaBQODo0aOzZ88eNWpUWlranXfemZaWdscdd9xzzz3Dhw9PS0u77777Fi1adO7cOWzXwDBMIBCQXE52qA+2bsBSVIe3Eu0lJ7jQE9dN4F96hZ9Sxd0B8QaB7lZfBIVJ1HrFU/ey5bm856fOr5pdbJlptEzTV0/B80BV9URVbbrq4ERNrWCor6vOMliyjVVTjVXTjVVTxfmtkKir7sq5k9/eY0kMOD1e5s9dFlOgwgIBLD/d6qytzlJZMlTCuhtRYqiaqjZna8wZastE098z9YcmFFT9Ln/f79X7JhgOTCmteqhy/+PPrMio2Dbnzfe3NLReoBDJIhSOUDTFc4KLEYLmwhyKtG2qEscWDYn2NQL1BQL9SwCEhv7lC6UDASAABIDAwBCQhAaSJGmarq2tzcvLU6lUpaWln376aSQSwVJCp+cbN240NjZardaGhoZIJOLz+V5//XWj0YgvP3XqVCQScTgcHo/H4XDcxMQAhIYe9DU2NsauFbDQQIuB4wSbaVygz+fbtWvXxIkT09LSbr/9duy7QVpMce+99+ItME+fPo3zsyxLURRWKxiGwQUSYpAy9KCq8XSJIDQIVgz44HnB6aMoNDC8sLw9ikg3Y//a+uH6/ZWqlQ/OWzvZ8Pa04tppJYem62uytTUZ+oMZ6ppJOft+hy0ahBX74jTSYJ5qME83mAWhQVhJIVg6dOXcuUOEWI4AID1RCQijQhgqtzhXZ6mrM9XVWRrLFK0ZH1l6c6bWMkl/MF1TM05l+YPKPE5bk1F0aJrp8EOVB/9kevOP81anq9c+/ObxzdbIN1EUjbKC3EBQUZIJM2yYl4QGXjTbiadPI9QFCACBTgmA0NApFkgEAkAACACBBCMgCQ00TfM8j4UGnU43f/78L774AiHkiRH8fr/L5fJ4PD6fz263UxQVCASw0FBQUGAymU6dOkUQhNPpdLvdIDTgYSFJAL0fJdKqZklrwN4WotEowzCUGATzaZ4/derUypUrf/GLX9xxxx1paWkjRowYPXq0pDt8//vfnzt37uHDhx0OB64Vy7KBQAAhhLUG7DxSegvft/f1H6QSOCQJDTz7rcrAIlrY5ZKOoMBXto8Wvqp+YunEoh2PLDv017y/jS14e7y6apLWMllfk6mrnaypnpRfNU5aNyEaGogrJixTdRZBjOiaxBDTbWGizqVjeJGE5nSLgLpa8BiqNWdpzVl64cjEQkPh/nFqy3jtwUmGI1nGI1N1B6eoqjIK305f8s6sxdWz1NtmqNc99uo766+1nieFhT8UwUYYnuARwXIRjseOSAbpMwe3BQJAoJsEQGjoJjDIDgSAABAAAnFJQBIacO2qqqqefvppg8GwdOnSr7/+GiEUjhFYlg2Hw9FolCRJr9dLEERLS8tbb71VUlJSUFBQXFx86tQpkiQdDgcIDQzDYLx9KDQoRxOWAPBSCIRQNBrFegHOWVtbazQa77//fuwbcsSIEWPGjMELK0aMGPFf//Vfubm577//PnbcgBAiSZKiKKnC0rYXWNdIWLlB3NWSF3zjYdcMLIMYlqc4lkSULXqj7sb7G6oXPf1CVv7WrArzY8UHpi/4+yMGS5Z63yR1lXAU7BuvtUwufXcmnj1qajLEI0tTIz8yxJddOXd76cR3byS/KcSTgECGpnqSplpQtQRhy9x2aC2ThR1SDwoNLDRPLqhKL6iarKrK0lRl5u9+YPHRR0r3P/z02nTjtif3nnz5Wuu5EGolUUjcV4VhGYKiCI5jwJ5B+Z0JKUAgPgmA0BCf/QK1AgJAAAgAge4RkIQGvNp/37598+bNKy8vLysre+ONNz788MP3Y4T33nvv2LFj77333kcffXTs2DGcc+3atfPnz1epVEVFRZLQcJNFE/gtWDrRvT67VW6WZaWdKbBRA0VRCKFvvvnmb3/725w5c37yk5/gZRTDhw+/++67cfz2228fN25cZWXlxx9/LOkL0WgUO27gOA7rDtJ2mFKeW1Unzt4Xd7DEKgPHIJblGUFlICLIf7H5q22HnnvyuczcrZkLDv7ZUCXoC/PffVhvztTsTy+unWqozcrfO65w/wTTkWm66rZVD+1aQ4aqVjg60x1uNgG+uUeGTt8FrSHRCHRFcmrLo62ZqK2ZqBMPQ/VEQ3W6wZIh+PsQdieZoq2doq7OKhRdRepqphUdmqba94DRMrHEMtW4Z6Zq+0zjtif2nnjJi6xBzisKDRxD0SRJCiuq2rxBxtnnEaoDBICAggAIDQokkAAEgAAQAAIJSEASGrCx/Z49ewoLC5ctW6bT6UpKSsrKygwxglqtNhgMWq22uLjYZDKVlJRUVlYWFBQsW7ZMq9VKQoPdbne5XHjLhliKAwgNfTtw8NaVuEMjkQhJkthCAS+CqK+vf+65537729+OGTMmLS1t+PDhI8UwdOhQ7Mph+vTp27dv93g82K+kpFmwLEuSJMMwCa01CFv+sZzgj4ERtpdgWZ5mCQL561vOV9e9XvnqvL+sHK97c0b5kYfz9481vZNdcGBCwb7xGrOwdWXx4elaS6Zo3TBZ8MWAl9zXZGB/DYKHyPZ9KLpuLd8tX4md6g6QGNcEureUJl0QGmrH62qkY6KuOl1bnZW3f5LKnG04NNN4+EHjoZn66mkGS7bBklHx3lRdzTi9ZUL54Qd1b0x98oVxi14r+PBCtT3wDUGHEI9YGlEkJ25CwXGobSvcvv3CgdKAABDoWwIgNPQtTygNCAABIAAEBoeAJDTgnQ537do1b968Z599tqioyGQyGQwGfYxgMBjKyso0Go1Wq9XpdAUFBQaDwWg0Ll++XK/XFxUVffbZZwRB2Gw27MohlsrgcrlAaOhB38uXMMgXMsg3qsTmDDRNh8NhhBC2a8AbWJ4+fXrhwoU/+clPhgwZMnToUCw3YCcOaWlpo0ePfvLJJ9955x1stkCSZDgcJghCcGUvBpZl5V4ne1D/wboE2zLgTSxFoYEl+XAYNX9wzly8+a8566cUv/1I+aHHTEdn5h0YW2gZV3Qku/joNOPBbNWBiYX7Jwl7TNROkdSBtgUUeKtL8dx1iQHnlIrqYiSuJ9VYfIGzRKDNb0W66Laji2ds0YCFBsG0Qbi2OksjWjToaqdrLFMKD0wu3Jeuq8o0VmdqayZqasZpqycU104t2j8tZ+ukvHVTFr6Ud9n9eUvEznGMMMgZYVdLHiGaFSybIAABIBDnBEBoiPMOguoBASAw+ATkkx9cG2XK4NcytWuAl0tgn38IIb/fv3PnztmzZ//1r38tKCjIyckxGAyaGEGlUun1epUYSkpK5s6dW1FRkZ+f/+yzzz799NNarfbrr7+ORCI2mw17iwShAU/7B2vEYfNp6e4cxzU2Nn7yySdarfbHP/5xWlrakCFDfvCDH2CtAbuK/OlPf1pQUPDRRx/hq7DWgH034BSSJLGuEWtfTOl28ROhaeFHXTz1YhiORhSFQievvTN/R65660OqnVMrjzy26Pgs/aGpefsnqizgshH2xRhgAko9QqyAJF60R7BNjfHQtELzZI0ly1Azvcg8o7LmsdI3/jh3xaSXDy9zERdZFA5FgoJFg2DTILh3jZ9Porwm+NlA+YQgyanyzBAHAklPAISGpO9iaCAQAAK9JaB8aFCm9PYecH3vCHQQGnieP3/+vNlsPnz48HvvvffOO+98+OGHh2KEmpqaI0eOHDx40Gw2//3vf6+pqfnggw/Wrl27cOHC3Nxcg8Hw+eefR6NRu93e3NzsdDpBaBhcoQGPFJZlI5GIZNoQjUavXbu2Y8eOBx98EIsLo0ePvrs9YN8N999///r16xsbG/FDP0KotbXV4/HgJRUd/EH0bjwOxNU8QgRJ05SwAyjNMjQiXGT97g9fnLtySsFL2fr9042107Q1GQVVEzU1WcYj07troQD5gcDAEajO0h2cWliVpbVM11XPMJinl9f8sXTvzPxN6erV045f2EMgL8UTJEmzNOIY0aphID5k3b4HfjZQPiGA0NBtlHBBUhAAoSEpuhEaAQSAQH8SUD40KFP68/5QdpcISEsnpHkj3kiCpmmKojrY4XdaorSGHyF0+PDh4uJinU5nMplOnDgRjUYdDgfe4RKEhsEVGqQfMymKIggC96y0MuLatWvr1q377W9/iz01jBw5Mi0t7fvf//4Pf/jDtLS0oUOHTpw48e2336YoCnt84Hle2p+CIIhwOCyfKsjjnY6ZwU0kCMGAnEeI4gkKhT6v/2DRDtVfl0/U7ppWWvug6sDEeW8/oDJPMh6aZjwkiA5wAIE4JVCdJfiGrMrSVE/V1UzTmqeYqmeUmmca/jbtT5W/3ly98HLz1xQKs4ijKEa+T+3gfgCVd4/1jQFCg5IVpKQCARAaUqGXoY1AAAj0ioBSVlCm9OoGcHFfEMBGDSzLMgwj/dAtnxJjn4LKM8Mw4XCYpulIJCJNXF9++WWVSmUymcrLy48fP04QhNPpxEYNIDTIqfZF13WvDPnqBtzX0Wg0FApJBtUMw9TV1Wm12v/4j/9IS0vDO19Km1OMGjXqF7/4xUMPPfTOO+/4/X58VTgclvSLWLWJw0+9WGeOoENRPuDjrK8eWTN3+dSi1x8tMz9cXDutYN/YggOCa4aiQ1Py906I0xkmyB9AoCZDW50l7HNpztZUT9XUZKvNmTpLdlnNzArzI3mbMg2bZ711fLuLaGIQLWyuwonLJ2J9UAc1HYSGQcUPN487AiA0xF2XQIWAABCINwLKCYYyJd7qnJr1wVoDdhaI5414Iop3GbgJE/zjNp4/49/G33rrrdzc3NLS0oqKivfff58gCJfLZbPZmpubQWgYXKEB9yPP83jbiA7dGo1GsY2Dz+fbs2fPnDlz8J4U2DGktAXmnXfeOX78+IqKijNnzuASCIK4udYg/SYZJ9thCrv9ERGOp8J0Sxh5PjxXU7D8kbmrs5cderq85o8ac7rKPM5wML34cLbePDn/rXHSNpagOACBuCNQnZV/IF1dnaWqyVbVZqks6WpLuvHg1EVH/1zy9iOzlowt25zzZcMJEoVpREXJCM/H6a4TIDR0+EKGlylOAISGFB8A0HwgAARuTUApKyhTbl0K5OhrAp3O93AitmuQ35BhhN/BOg148wIpMxYdqqqq8vLyioqKOlg0eDweEBriQWhQqgzSCghstIIrabfbt2zZMmPGDOy4YciQIXffffddd92FDRzuvvvuhx9+eNeuXdevX8cDwO/34z0vY53jZYsKXlgyQZHRKBFgUCiAbC9WLXm45HcVe56orHlMb85SHRivq51kOJiurZqg3ZduNGeB0BB3s2uwZZAIiEKDpiZbU5NVWDNZVZteYB6vrUlfcOzRor0z/vzc7/PWPHro67eDyMsgkqQjCHHyKb08Ln2TD0okVk3wv55O/2cNSj3hpkBgYAiA0DAwnOEuQAAIJDABpaygTEng5iVs1WM9tOF0/GAnNS5WZpxBbouL4xaLpbCwUK1Wl5SUfPLJJ9Fo1CmGm6gMsL2lRLtfI8qexc4a8E2j0SiOSMtnWlpaPvnkk2XLlj3wwAPYccOwYcPuvPPOIUOGSHLDrFmzjhw50traSlEU3vay0zNWH+JCa+CR4H6f5yg2RKHWC+5PK7fnPLUyfVHtX41V09VVk9QWQWjQ107SVk0w7s8orp4CQgMIDfFLoDpLVZWhrZ2irs5UV0/WHMrINf8hv+oPZX+fqds3Jf+VKapNj24/vOJay1kKhXkkbDkhn9LL4/365XPLwmPVBISGW6KDDElJAISGpOxWaBQQAAJ9SUApKyhT+vJ+UFbXCMTSDiS/jyRJ4p7CXh7lj4DyuOQDUoowDLNnzx6NRpOfn19cXHzy5EnsDNLtdjscjptoDVar1e/3+3y+3bt3q9XqoqIio9F44sSJYDDodDqbmpo8Ho/NZvN6vTffveImt+j9W2632+Vy4Qq4xYBf4hS7GAiCuH79+vr161UqlVarXbp0qc/nw6sVcOfEgt+1ruvLXJJIhBc+0DTNsixFUeFwWNj9UQz4fh9++OFjjz2GXTaMGDECL6YYNWrU8OHD77jjjvvuu0+tVp84cQJPCZRnLD2wLBsnQgMZIhDPsSjcylnNn72q3fiY4fVHNLuzTQen62ona6onaKsnFNVmlBycUlyVrd87WdhEUPoBGSJAIK4IVGepzZn62ukaS5a6erL+cGZ+1R9y9/+++Og07f4s075H8jdNL9s+76OLB8PIx3Ck9N0u/ybH8b78cul+WfL6yK8GoUFOA+KpQwCEhtTpa2gpEAACPSSgfHxRpvSwaLgsPghI2gSemiKE9u3bpxaDyWT65JNPIpEInuFLQgOeokuTdkd7wDsmYp1Cp9MVFxeD0DAwnayUBnAKwzBYFmFZtrGxsaamZtasWWlpacOHDx8xYoTkwWHEiBGjRo267777XnjhhStXruA6430oQqEQHiE0TRMEgaUNScKQTy3k8f5rtaB0sBziEElEWBRuZuoXvFQ4e+mEiqon5rz6h6JD0wyHMrU1EzWW8bqqdP2BjOKqbJN5CggNoLPEL4HqLJ1lqnhki5VM19Sma2onamvSdZbs8ppHC7fN+Mvi9Dfe20yiVh7RZFT4GGLJL67+Hcf6BpBq239fC1AyEIhDAiA0xGGnQJWAABCILwLK5xhlSnzVGGrTTQKdCg1aMZSWlp44cYIgCLfbLTlokFQGudDgdDodDoff729ubsZCg16vB6Ghm13R8+z4UV55RghhgQD3Ms/z58+f37lz5y9/+Uts1zB8+PBRo0ZhJw6jR48eM2bMgw8+uHnz5vPnz+PahMPhlpYWyeAlFApJ/iCkxJ7Xu/tXSkIDTREMCl1qrivbPm/W82Mramar92boajO1Bydra9K1lkl6c6ahKqvogGDUAEJD/E6z48q4YFAqIxMadNXYn0i6MIZrMoTtJw4+qnl1+pPPpr90aIWPaeQRSVGU/JMeP/+RQWjo/vcZXJHMBEBoSObehbYBASDQJwSUDzHKlD65ERQyWARiCQ0ajQYLDdFoFAsN8hUHHo+ng9Bgt9ux0PD2229rNBqDwVBUVAQWDQPTrfKJhzyOEOI4Du88EgqFSJJkGCYSibz33ntGo/GnP/0p9tTwwx/+cPTo0Vh6GDZs2L/927/NnTv3vffea2lpwQYRSveTNE3jBRoD00DpLrh1iEccT/lZ17FzVcZtTzyxemz5wT+VHn1IZZ6gqp6oqZ6kq84wWLKM5uyiA1MMVVkgNIDQEL8EBKEhW2eZarBkGyxZBkuGrrrt0Jqzyg4+bNg985lV6St3my45v6BRmOMYbFKElzLhT0Q8/F8GoUH6moIIEEAIgdAAwwAIAAEgcAsCyscXZcotioC345tAp0KDWq1WqVQmk+njjz+ORCJYYsBLJzq1aJA7g3zrrbfUarXRaAShYcB6Xv6IL4/j5dy4GsFg0OfzEQSBX/r9/qNHj/7lL3+5995709LShg0bdrcYhg8fjtWH//mf/6msrDx58iTLslhWkDubvPl2mP3XcGlaxSLaFW3427EN+u1/fnLDuJKDf6x4/495B8YWmMeqLem66gxj9RRRaMjSH8gEoSF+p9mDYkQQVzetztJWZwtaQ7WgMuBD1BqytJZMU+3MkgMP5W3KLNvy9AdnLATy84iTfKZgxUHSFvvvc9eVkuXfPPL80mdWnghxIJD0BEBoSPouhgYCASDQWwJKWUGZ0tt7wPWDSqBToUGj0ch3nXC5XG63W7n3BF4xYbfb3W53U1OT3+9vaWl588031Wp1cXExOIMcsI6VP+J3iNM0LW1CIfmrx/uYIoTcbveOHTvGjRsn6QsjR47EPiPT0tJGjRqVmZm5aNGiq1ev4kIYhmltbZXUCmmG0yHSfw2XJi0MIm3hK2v2VuheeuTpLeOKqqcXH52Sd+CBDkKDYM5QJfw+DDNtIBC/BAStQVg0IVriCFqDuIZCsMQxVk8pr3lE/fLUvFUz9h7fHkZeFjE0K2wQo7Rr6L/PXVdKln/zyPNLn1l5IsSBQNITAKEh6bsYGggEgEBvCShlBWVKb+8B1w8qgU6FBp1Op9Vqy8rKPv30U5IkPR5Pc3Mz3njC4XBgxUFSGWw2m8fjuXHjRiAQaG1tfeONN9RqtclkMhgMsHRiUPu27eYURWF/CtLeGRzH0TQtWSh88cUX5eXl//3f/33XXXdJ215KriJHjRqlUql2797tcrmk5kSjUb/fL011OkSkbH0eaZu0II5GkYbg+QU7VKrtD819eYKhdoqmekKhZZzaMl5rmST+LJxlNGcZqjL15skgNMTvHDuujAsGqTKamgyNcOt0XXV6u0WDoDIIo3r/5IqDDxnfmPGXZRO2H1weQh4aUQwn7C8DSyf6/OsFCgQCfUgAhIY+hAlFAQEgkJwElLKCMiU5W54yrepUaIhl0YA3gMS7TNjtdpvNZhWDx+NpaGgIBAJ+v3/Xrl3YGkKv14PQMLjjSPLXKPmol9eH47hoNIoHQEtLy0cffZSTk/Ozn/1M0hpGjx49bNgw/PK+++5TqVTHjx8PBoMcJxhv8zzfwZBBeim/S9/GxVswPGJCfMu1wNfGTX/J25Kdu3NCydHpheYHdAcn6Gon6arT9ebJenOmeEzGi95hpg0E4pOApiZDVZuuqk3X1kyUhAaDRRAaig5Ny9szvqR2umnvQ39a9LsN5gVh5CFQmOUZ6bMWP/+RcU2U9cFVjZ9dgfv2GwlKAwKxCIDQEIsMpAMBIAAE2ggoHxqUKQAroQl0KjTki8FgMHzwwQeBQMBqtdpstqamJpsYsNyA401iaG5uxkJDIBDYtWuXSqUqLS0FoWHQBwbejZKmaWmtBE3T0rYRoVAIZ5AcLtjt9jfeeOPBBx+85557sL5w5513jhw5UpIbfvnLX65YseKbb76RVmHIZxdSvP8aLgkNLZTrcktd4ZpH5m6cmP/6uMr3Hiw0P2A8nGU8mCkoC1UZuqp0XZXw+7C+JjM+Z5hQKyCgFW0ZNG1Cg7DZBPZjahBdNpiOzJi3+w+mmmml+x96dOFv1u6vCCFPiPezvLDxRIePG99/n7qulSzVp4OmAEJD1/hBrmQjAEJDsvUotAcIAIE+J4AfHeTFKlPk70I8sQjgX7zxxgQIIbwOf+/evQUFBXl5eSaT6csvv2RZtrm5ubW11e12e73elpYWHMEeIsPhsNvt9vl8qrHciQAAIABJREFUTqcTIRQOh1977bWioqKysjK9Xn/y5MlQKIRdRTY3N9tsNq/Xi51KugYjSDtlYK8T0kvcFiygEARx/fr19evXq1QqrVa7dOlSn88nLTrAE+zE6uVYtcWGCRRFke0B57RarS+++OJvfvMbrDWMGTMG74KJF1MMGzZsxowZO3fudDqdePxgnYJhmFAohPe5kGSIDrfuMAPB73aa2OFCKaf4/cPSrLC3ZV393/+6dPIzGx4wmbOLDmeoLeMNNVP11dME7/1VmXrLJL1F2H4C1k3AfD7OCWiqJ+tqM3W1mWpLusacLnqFzFZXTTYenKo6kG46OL206pGHKn+1+FW1D9kowaKBEhZO0AzH8IgTPhwc4mkkvOAQ/oggnkVI1CKENwck4GcDeEIYENhwkwQgAEJDAnQSVBEIAIHBJaB8aFCmDG4N4e69J4B/1kYIBYNBnuf37duXl5en1WqXLFly+vRpnufxb+DYrSDeK5FhGEIMCKFoNMrzPJ5khsPhPXv2YAcNWq0WhIbe907/lYCFBtytlBiCwSC2cAmHw6dOnVq8ePH999+flpY2YsSI22677Yc//OHIkSPxFhU/+9nPnnjiiXfffdftdjMMQ1FUJBLBAykajZIkSRAESZJye5neN6R9JsNyiGVQ6KOLNbMXjJ374gPlB6cWHc7I2fN7Q810ffUMY9X0dqFhQpv3/kFaex/n81uoXrwQsGTqa7LbhQbBE6TB0iY0FIpCQ4koNCx8Vd36rdBA8xSH6O8IDawgNLA8L6oMjCA0CAcIDb3/6oESgED3CYDQ0H1mcAUQAAIpRkApKyhTUgxJsjWXpmk8P5TEgqqqqoKCAqPRWFlZuXfv3g8//PDIkSPHjx9/VwwffPDBsWPHjh8/fuzYsXffffeTTz45evTosWPHamtrP/jggw8//PCll14qKyszmUx4c0SwaIjDEYM/xSzL4n3ypN3ysD2CtM7C7Xbv2bNHq9ViJ5HDhg0bNWrUnXfeiS0d0tLS7r///uLi4hMnTmCjhnA4HI1GCYJQfkvwPN970QEXy/MsjzgS+d/95//H3ntHR3Gke/+s1+uwu96769171+f+c+95z/nt++7eu+8mg0FhNDkIBRDBYLKkid09eRRABGODDbbJGaGs0eTunqAASIAJBmObIEAkAUJCWUJZo0n1e1sNYy2CXcMlCarOnKGmpru66lM9herbTz2POSltQvLmiCWlcWoXe17+X6HQ8LysnKGy81AEoNDwHM6SsEmQwP+MABQa/mf84NmQACTwEhC474Lhh5s6vwSExnwXw6EKad9+AACTyYRhmMFgUKvVixcvzsjIwDBMr9erhpNOp6N3RtABLDMyMlAUpb/VarUZGRkGgwFFUXrfAbRoeD7vD3rXNO24nvZdf3cNH6JtE8JaQygU6urq2rx5c3x8PG3O8Oabb/7nf/7n22+/TcsNr7766oQJEzZt2nT9+nVasQoEAkNDlGn3yA0UtNBw36kjfOl/yurukfcRGjRuzvyCv0GhAQoNY5IAFBr+6Y8fHgAJjDUCUGgYayMG2wsJQAJPnQAUGp468qd9QfpZdNhNAwAgNzdXLBZrtVrNcEIQRCaTqVQqFEWlUimKogqFQqVS0eUajSY1NZU+GEVRDMMyMzOXL1++ZMmStLQ0KDQ87eH8YdcLB4wIqwz0eeGAlwAA33AK13fmzJlPPvkkIiKC1hd+9rOf/eY3vwmHw/zlL385depUs9nc0tJCu+rw+ynH+IODg/39/bTvDwBAOCAfPbGE9Y6wu7j7KhHhNoSFBn/QNwS6q845Zi6OWLRpUqY7VuvhppomQaFhTC6zH+rh/wt5MBQawj9ymIEEXhQCUGh4UUYS9gMSgASeGAEoNDwxtM9XxYODg/TK0OfzmUymhISE+fPnS4eTWCyWSqVyuTw1NTU5OVkikaSkpMhkspSUlOTkZBRFFyxYIJFI6GMQBMEwTKPRqNVqBEGg0PB8DfOI1oSX+qN/47QiQLtaCAQCYVMXr9d7/Pjx1NTUX//616+88gqtOLz++uv/+q//+uqrr44bN+7f/u3fUlJSPB4P7bCDvhrtbJKWGMJ7NMISQ7jkHsljREu/z4aFhiG/1wd6vrzonJUVtXDjxHSnUFfKk9miodAAhYYxSQAKDd//ymEOEnhBCECh4QUZSNgNSAASeHIERi9CRpc8uavDmp8aAfqZ89AQFTXtzJkzDoejvLycdsRQXl6+b9++iooKt9tdVla2d+9ej8dTUVHh8XjcbveBAwfocpfLtX///vLy8i1bthgMBo1Go9PpoNDw1Ebw0S50d+l+JzSe1+sNh7qkKwwGg319fXREEnqXTVtbm81mmz9//ptvvvnWW2/RviF/9atfvfHGG7T08Lvf/W7z5s1VVVXt7e10JaFQiA6r6ff76f0aYaEh7CciXP4PjBrutjZACw2HashZWVGLNk3KcImg0DAmF9gvpHnCI3QKCg2PNn/BsyCB55gAFBqe48GBTYMEIIHng8BoWWF0yfPRUtiKRyFAP0YOh2ykoxXSD7HpQjp2wD1V01vx6UVjOJwhvQoNBAIej0ev1ysUCr1eD4WGe9A9hx/v+4umt9LQtgxhd6G02Qt9ezQ3N+fn5//lL3+htYZx48a99tprb7/99quvvvqTn/xk3LhxXC43Pz//+vXr4RCq/f39D9o6EbZrCAapiHwPohQWGnyBIS/oqqy2z8iclLw5YrFnsq6UJzZHQIsGKDeMSQJQaHjQbx6WQwJjlgAUGsbs0MGGQwKQACQACTwBAncXctS/I5850/mRK0D6SLo8GAz6fD56/RkIBAiCkEgktOfII0eOjI460dTU1PycJbpJt4bT4ODg1atX161bR/uzXLFiRUdHRygUCj/qH8nhCQzCc1olHQuTjoJJ77IBAJw6dUqj0bzzzjs/+tGPfjqcfvzjH786nMaNG/fb3/52zpw5OI63trbSvert7fX7/X19fTTDM2fOVFZW0oYPPT09tGhFcw77KB2Jg74tAwHfkN9Lh7ecnjFx/voJme5YfRlfYomEQsOYXGY/ggnAC3bKsNCAEhyFg4U6uCpSoLTzZBamihQo7ByDO44Ob7ksB+0GjV7Q6w96AwEf8APgpyJZUr8LEPJTMV+pyK/B4IjwlpRqFxz5I4J5SAASeDoEoNDwdDjDq0ACkAAkAAmMDQIjhYa7izrKpJ1+zjxygf1DhAa1Wg2FhrEx8D+glbTQQHuIDAsNtLXCmTNn5s2bR4e9pONf/uhHP6K3UYwbN+53v/udwWCgbVvo63R1dXm93pMnT8bFxSUkJDQ1NdH+I8NBKwKBAF3zPe26e09CoYEDBYUXigAUGu75qcOPkMDYJwCFhrE/hrAHkAAkAAlAAo+PwP9QaKCfSBMEIZVKVSoVFBoe38g8+5ro+JThPQ606kR7WAgGg62trUajMTY2lt5J8dprr/3617/+zW9+Q8sNr732GofDSU9P//bbb2kFwev1fvLJJ+PGjfv5z3++bds2urawzQitONy3z8Nagx9aNLxQy+wXzDzhEboDhYb7/tphISQwlglAoWEsjx5sOyQACUACkMDjJhAWGmgHDeGdEaN3zo+2aKAXh8FgkCRJqVSqHk7QouFxD9Ezq++uNcH3Fi50UwYGBmhvkQCA2traVatWTZw48fXXX6edRL799tu/+MUvaLnhpz/96bRp0woKCjo6Oo4ePcrlcseNG/fWW2/98Y9/PH78OF2b1+sNe/2g77F7OgyFBigxvIAEoNBwz+8cfoQExj4BKDSM/TGEPYAEIAFIABJ4fARGCg0jtYaRmyboq90jNIRt6WmhQSaTqdVqjUYDhYbHNzjPvqbw7RG+HwKBAN2srq6usKnCV199JZVK//CHP9D6wltvvfUv//IvPxtOr7zyyn/8x3+8//77M2bM+NWvfjVu3LifDCexWNzY2AgA8Hq9Pp9vYGCA3qkxWuEavi2pqBPQR8MLuN5+BFuAF+MUKDQ8++kNtgASeMwEoNDwmIHC6iABSAASgATGNIHwSpLuRfhjeGEZ7t2DhIZQKESSJBQawqBe7IzX6+3r66Njo4ZCITquBACgqanJ4/HIZLL/9b/+Fy03vPrqq2+99VbYd8Nrr70WDof5yiuv/PrXvy4oKAj7ZRgYGKDrvK/QAEAQCg1QZXihCECh4cWeKGHvXkoCUGh4KYcddhoSgAQgAUjgAQTCygL9ffjjPxUaRkbEdDqdMplMM5ygRcMDSL9QxaFQaGBgYHBwkN5rMzg4SN85165d2759e2xs7G9/+9vw7omf/vSndP6NN9745S9/Gf7I5XKrqqrCN17YMeT9tAYoNEBnkC8WASg0vFAzIuwMJEARgEIDvA8gAUgAEoAEIIHvCYSVhXARXRL+GM7Q5WEnDlBoCJN5qTJ0EIqwDuX3+71e79DQUH9/f9hnx8mTJ7Oysv70pz+98sortMRAb5cYN27cq6+++pvf/ObHP/4xHbEiPT29ra2NtmsIBoO0fcT9hAYw5PcOgb6DF4jpGRHz1k9I9/xdeEuVXaiycZUOhtLBwHAOhvNeqKffL8ZmAdiLkQSg0PBSzZuwsy8HASg0vBzjDHsJCUACkAAk8GQIhIUJn88XDhngdDrFYrFSqdRqtQcPHuzt7b1169b169fb29tv3brVNpyan7/U2tra1tbW2Nh4+/btmpqaDRs2yIfTypUrW1tbAQB+v592SRBeVz8ZqGOsVjoaRVhpCrv2GBwcpLc/0KErPR4PhmE/+9nPaAeQ9B4K2pzhzTffpJ1H/uEPf8jPz6fPGhgYoL1C3jcChS8wNAT6DtWQMzIj5294L90TayjnSWwRSlKoxCer7EKl/XuhAX0EoeH7U1goMfI1/CAd542ok/r2jpwxXI4QvOHXnUfuw19xMJxD14OQLOpFcB7qNfaFkpEMf0j+xTJYGKkp3DcPhYYxNu3B5kIC/5wAFBr+OSN4BCQACUACkAAk8CAC/1RoOH78uM/na21tvXnzZkdHR0NDQ3Nzc1NTU+tzlhoaGuhWtbS09PX1Xb58ecOGDWKxWC6Xh4UGKtzCsO9DKDSMvB9or420N9CwnQsAYGhoiJafBgcH6ePb29unT59OSww/+clP3njjDVpf+NGPfvT222/Txg6xsbHV1dUAgMHBwd7eXtpkZuTlKCEDAFpoOFDjmrb4jtCgr7gjNGCEKCw0oDhl0TBCFPjBy9c7QgO9JI5BiZi7csNDCA3I8JISwzkqBxQafoi4MPKYHzxS9123j7lCKDTc8yOHHyGBsU8ACg1jfwxhDyABSAASgASeHYGw0BB+2g8AIEmStmjQ6XSHDh3q7+9vamqqq6vr7OxsbGxsa2trbW299Zyl1tbWlrtpYGCgtrZ248aNYrFYJpN9/PHHtEVDMBgMB1l4dsifryvT5gz+4RQIBGhdgM7QFi6BQOD27dsAAJ/Pd+LEidjY2DfeeOO1116jJYbXhtMrr7zy5ptv0tYNb7311ooVKzo7O+l+PjOh4c5K9dGEhu+tFWhLBNqogc4/lCFD+GBo0TD2CfxD6QQKDc/XxAZbAwk8BgJQaHgMEGEVkAAkAAlAAi8tgbDQEH7aHxYaVCqVXq8/cOBAb29vY2NjXV3d7du3m5qaOjo62p+/1NLScuvWrZs3b9bV1fX09Fy/fn3btm0IgshkslWrVrW2ttI9hULD6Fv9vhYNoVCIVh/a29tpoeHq1atSqfS1114bN27cL37xCzrz+uuv//znP6fDT/z85z//xS9+MW7cuHfeeWfbtm2Dg4Ner3cMCQ2U3cSdrRP3ERrC6+SwdvBQmfDpYzYz0lrhh+T/4bJ8zBks/NMGQ6Fh9MwCSyCBMU4ACg1jfABh8yEBSAASgASeKYGw0DByQUhbNKjVaoPBUFVV1d3dXV9fX1tb29zcXFtbe+PGjWvXrtU9Z6m5ubmxsfHWrVtNTU1DQ0MNDQ27du1SKpX/b228evXq1tbWsGvDZ8r7Ob34yNG/p4ler5cWofbs2fPmm2/S/iBfeeUV2gEkLTG8/vrrb7755htvvEELDePGjZsyZcqJEyce5KPhKWyduKMC0P4URr6P9q0w/G14/X9f+WCEBkG7b4DOKV8yHQEKDffMC/AjJPASEIBCw0swyLCLkAAkAAlAAk+MQFhooDO0I8Cw0JCWlvbVV1/5/f7Ozs6Wlpb+/v62trbOzs729vbnzEUDtZWjsbGxqampsbGxu7v7/Pnzq1atmjlzpkQi+fTTT1taWmhbhvs6JnxidMdMxeHRH9nikaxqamqysrL+/d///Z133vn973//zjvvhMNP0JlXX32VzoTf582bV19ff18J4zkUGu6rL4wovKMv3PUTCYUGKDT8PQFo0TBy7oB5SOCFIACFhhdiGGEnIAFIABKABJ4RgbDQQEsM9DtBEGKxWKPRpKenHz58eGhoqLOzs62tzev1dnV19fX19fT0DDxniXZb6Pf7ac+FTU1Ne/bs0el0YrF4zZo1LS0ttMeB+4ZafEbsn6PLhkKhkbICfRsMDQ0NDg76/f6hoaHm5ubTp08fOnSosrKytrb23LlzZrN59+7ddrvd7XYbjcbMzEytVqtQKPR6fWpq6vjx4+fNm1ddXf3MhIYHxYYYad0w8pgR5bR1wwiVgRPWFxDizvaKsAUEzEACFAEoNDxH8xlsCiTweAhAoeHxcIS1QAKQACQACbycBEYKDTSBUCg0UmjYvXv3qVOnDh8+XFVVdeLEiaqqqoMHD1ZWVh54zlLlcNq/f//evXtPnjx54MCBjz/+WCKRiMXitWvXtrS0+Hy+B1nyv5xDH+71fcNb0gYg9Ff0kTRAAEDYz4VvONF+In0+X0tLS2tr69DQUFtb25EjR44ePdrS0jJWhAY5yZHf1RrCK+ew1iAneXLy7qaJvwuN+fePtf+pgT084EUlAIWG8IQCM5DAi0IACg0vykjCfkACkAAkAAk8BwSCwaDX67VYLBKJRKfTYRiWkZGh1WpRFFUoFBiGaTQa3XBSPiChTzg94LJKunkqlYo+QKfTLVu27P95Z1AoFGvXrr1582YgEOjt7aWjNj4HpJ/3JtACwQ9/p/sT1q3CAUTpABbhj3cOA2DI7x0CfVUXnEmZEfPWT0j3xOrKuWLrJCUpfDzhLZ1shGQhBEeBs2l7BJTkYk4e5uJgLg7qZCJkjIJgyvEYmZ0pszPF9hg5ycGcPKWTpyKpd5TgSG0xCpytcHIxj0BZKkQIntTGktvYaqcwLEY8/QyCsxUOltzOlNuZCM5GCQ5Gch9XM+iOy+1MhYOFEhz6KioXH8HZGMmlL01fiz7gcV13zNcDhYbnfUqD7YMEHpoAFBoeGhk8ARKABCABSAASGE1gaGgobDzvcDiSk5P1en1WVpZWq1WpVAiCKBQKBEEwDFOpVGq1WvOApH/C6QGX1SiVSrpVSqUSQRAURbVabUZGxsyZM1esWFFXVxd+FB9+Mj8aAiwJE3iQxEA7hrzv+8htKfReDPqwkeV0/aEnLjSwhnUEhoJgUkoBzlXg3GG5gSPHYxQEQ0EwEDIGdbIpZcElVLqEiEuADYsLcmuM3BqD4RxablC6+FKcJbbHyAi20sVXufiog0sf8EzWxvTyHiO5quHG0LoALTc8lvbQmkVYv6AVDVpioOsPXyuceSzXHfOVQKEhPHfADCTwohCAQsOLMpKwH5AAJAAJQALPmkAgEKCDGlosltmzZy9atAhBEMndJBaLU4eTRCKRyWQpz1mSyWSK4US3LTk5OTU1VSqVfvDBB6tXr75x40bYCUVYT3nWvJ/r6z+s0OD1en0+X5htMBj0+Xx0eMt7zBmogXgKQgPOQKgX9cwfcXDvvChbgBiFIxrBGSgRg5FspZOnJAVKlwhxCjGnAMV5cisLsbBUDp6GEKhIAUJwZHZmqo0hxVmYk6dycVUODmKhlIiHej2uhXRYAsBILv1CCc5jXPMrnTy6NpWLT+cVDhZVcjf2J0bw6Xw4E/7q7zIv6haJB/ULCg3P9XwGGwcJPAoBKDQ8CjV4DiQACUACkAAkMJqA3++nn/afP3/eaDRahhOO4y6XyzOcnE4nQRAkSbofnJxPOD3oyjiOO51O+lun00mSJP2xoKBg3759PT09tCuBsNwAYPqHBEZughiZf5AAQTvjHCk00LfTaHMGylMGCD3hrRMslIihpASchRF8zMFHccoSQeFgqUgORjKVJAMlo1GcoXCwFDa23MaR2tgIwVcRQg0h0OLUS22nRAexOUqBs1E3H3UOixQ2htrB0hIclePZCA20CYPCwZLZYmS2GHr3xGPcOqHxCMNCA4Kz5XYmLZFgBB9xcDGCryQFiIOL4pRAQ2f+Tl+4q0c8LmFlzNQDhYZ/OJ/ALyGBsUgACg1jcdRgmyEBSAASgASeRwJ+vz+8UKRjNIz0BUi3+L7rxuehMyMbP7I9AwMDQ0NDdEk4wuXoZ+wjT4F5Wo4ZqS+E8w+CEz6AViLCH0ejpm6qUPBJCw1KSk1gakiOihRoCCHm4CvtXMTO0jq5ahdbQ8YoSYYKpwwTlHYuaudJrVwEF6qIWA0h1Duol9rOQywsqTkaJbnKUj7qZMrtUYgjWoMzNThThbOeiUXDSCsG2mPCHYuDBz1pf8hyQ0UcZQMy7JFBamVIrQxaxVCSArmNjRF8lVMot1FGIiqnUGHn3F9lwF++8J9QaHjQ1ADLIYExSwAKDWN26GDDIQFIABKABJ5XArTKQLdu9EKRLvc/IN139/5jLHzAZf2jWd4TspFeANOHheMmjD4LlvwPCdASwz+o5OkIDSo8Rk1QQoOWFGoIocouxGxcxBKjdDBVeLSKiFITDA3J0pJ8nVOkccYpyVglGYs5hKiJqyrh6qx8vUOgIQQYzqFsGQiGxBaB4Ay9m63DGfKiCc9KaKAlALVboC0VaUtFSidP4WBJrYzH9eTfUBFHW0konTyxOUpiicZIrtzOVJICiTkGxXkqp1BqYSrsHLVLJLOyoNBwhzwUGv7Bbx5+BQmMTQJQaBib4wZbDQlAApAAJPAcExgaGhrpMZFesYdtGe5ZwD9v/aAXusFgMBAI+P3+oaEhv9/vHU5he40HqSfPW19evPbQN48/GHjSFg2YPVLpiFITbDXOVzn4mJWvMLNkJQy5eZLc/J7C+h5inYTaojAbC7NxUbtAScQpiTiVPXak0KDFBaiDjTrZcjw61TJRbpukJRlqy6TUvD89K6GBcirh5IVdNobDTzwuoUHtFojNURjJ1ZXFSq0MiSUawdkpJREYwU8tiaYNGcQmBhV6wyWSWphQaIBCw4s3TcEeQQI0ASg0wDsBEoAEIAFIABJ4PAS8Xu9IW4bwspyO1zDSCmCkYfw9+cfTlAfXcs/lwh/pM/6BCALFhQdDfQzf0J5ER1Y0et8NvSMjGAw+BaFBZp+osE+i7BccPMzKR8xCeZFQns9HCtmKghh5AUuWz5blsqQ5HOluviRbiBqnqq2JenucwSFIc3AMDo7WzkKs0SklEzEnT+2ZLLVwxPnRSCFDVRSlyB2vtd9x04ASLPp1dycFD8N5Ksf3ThzopfiwnwgeQvDkJAch6VM4YXWAPjf88U6G8ncgHH7xqRiWOFVnuidJbU+QFgkW7mEm746S5keiNobKSYWiRAgOFUqDck4xvKOB2jRB+apAyBiE4MhJHjK8jeLOtWhnCnc3VtxtPNWwRSXvYS5O2r4EhZ0rM3Mws2jRnmi1Y7KkKEZp52lJodQYKbfGqF0isYWJ3G0hhvMo6w/qilS//r5HdxDdZXVnw8XwKRSu4YNZGBGDUY1n3QlHOtxCOSGktrQ4OGoHS+1g0a4x7oK6cy0U5yEEnz7rbh/vVEthIVlyCjhV8x1Z5M6Qjdj3QbDoYKjU6X9P5u61vh+s+5dAi4aRP36YhwReCAJQaHghhhF2AhKABJ4kAXolNvIKo0tGfgvzkAAkAAk8IQL05POknUEiZEyy410pOVFDcnSECLWLMNu0D92qrYdWbqnMXF+u/WLf0tWujA3li7MPrNrs+nCr56PPXOnLLAtUuVGS3b9XFPxvlfVPKmIiSkYhLo66LFFNvq80z15sE39WoV5fIf6ImLbYEad3CFAHU45HK4goBcGQ2xlUxAobV0MI01yT01yTNYRAYedIcK6cECod8ahdlIJzFhHRYkckgjMwF0fl4ckcDLk1GrVEq+xMpZurcgtUJLXwRnAmRggQy1TEOl3jSVK7RBobU2/nYwVTtMULF9s06/at2HN02VrPXLXxPcQyXlMqEuNMMT5BVcqgtjmUsOTWGEO5QIaPl7kjZB6exC2Q4By5g6UhOQa3SIkLqPoJjoJgIvYYzMpQmqNRSyTmZChcDIk7OoVgyizCNPvsT0uxLQfSP69E06zTlEVsnYVtwGN0LpbSyUuxsOV4vMY9Q+tKVDn4SgcDxSPlONU7lYODmimPlaiTrXSzUTJa7pikwCM0HqqPKCFC7CLMKlTaRGo7X4NztY4onTNa72JgRLQE50pwPmLjyix8CTkzxZKAFUWll0QYzNFqU7TKwdOXxcut0UqSqXZylaQAwydLbKIUK0/s4CBuIUryKUHExtcRQrWTm0pGz7O/K3VMULpi1KRIYedSvjbIaMpQhfIPKkKIYQcczmgpyZTiHMonqFOE3pE/KMWEVhYoHefuiy65q84MH/MAoUFJ8OU2tsEdp7dNTcj8y/JcrAc0DYE+f9AbCPiAHwA/CAUApZCCkB8E/CAQBIFgkCqkvg2GqBgtIPiEfo/3VBvWbe+RZcOOV+45Hn6EBF5sAlBoeLHHF/YOEoAEHgOB0bLC6JLHcBlYBSQACUAC/4zA0xIaWOheDuJhU34ZiniykrilZQprjbF64LtmUH0LnG4CN+rBtVZQ2wPqWv1X2kLXznV+dbjOajmZtbF05jJLpM74Z5XlXTUZTW2dsE0kcdg1AAAgAElEQVRWFE7PsMrNZ3ddBkevgb2HW7cbTCKdna9zCjQuntrFpl5OrtYtRK2c4T0aMTIzi/KVSPIRlwAlBXoiQeOMU5QJ5OUcpZOF4Sy5gyW2xyAuDuZiqQmm0sGQDb9QG0Npi9GQHIWVIytKkJckyW2TFxVFIsaJK8qS9ObZS+zq7G+yq4PftoHv9teuW4ELFEV/QxxcmZMjd01AnBNVTpHOlaR2iChPlq5oqTMqleCm4nwpwZXjMQpLlLwkQuXgIw6e3MGS4zGInaV2cHR2yi0FYmfJSU4qwUm28FHL9C8OpFXWG6+DY98NkbtPphtKhKqCSSrTe0p7hMrFxtyxySa+xByrMAkUJUylLVpNRiPOCAUZqXWytU6u0kkZFMgcUTJHhModo6/gic0TJVaGggpaEavB41RWAVbCUhRHqqwRKscklWOSzD5poZWdbBdiViFijZ9bPFNsmr3EErfSyltm5abb2Eo7W+5gaUtFKMESmyNSS6JRXKh2J2Kl8YpSkczJktii5KZopZWjdnAQe4zUzUT3cpSeGIl5oriQidpF2jKOuoyFuTiYUyiziaRWvtQeiTijlGVcZalQSVLy0N+JCMM2GmGVgTaaCFtt3LHdgELDP/vtw+8hgTFHAAoNY27IYIMhAUjgaRMYLSuMLnnabYLXgwQggZeSwFMSGgiOjOTLiVi1ZZrKOENRNGu5J7P0UnkTuDUI6nvApW7Q1AkaesH1IVDvBbf8oH0QdAyAlnb/2fP1DvuhrM+LkxbnxqQbmUvssYaSBMWOxIy8ZNeFnE5woROcPNSevaRsGmV6YIzRGlmLccES92QNzpaVRKV7YrXuWIyMlTtFEidf5uRQriJskUpTBGqJlDknKdyROpJrwIVKMlZBiiQujszDop75O5kygo0SrHSSleXmaWxMtZ2vtiZqHdO1pVMVOBexRqa5JiMliRrrgt3VW2pBdSeoPnB92ypiZrpVQHmaILkK8j2JfbzMxlPiU5X2JMQ0WWEVKGxCBI/DyDjKYsLFVjkpowbMxkIdbDnJUjjZKMnHCJHanqCxTUFNCag1SWGaJs5Nwgrmbzzw4fHbpW2g+hIozf5Wqy3iqIsjdfYopX2iAo9QEAzMKaRtE1RWgYbgadzUkl5BTpLa/4aSEaiHqXCyqd0iBBWHQm5nqlxclYtLeZoguCoHT2Vna2xslZ2ttHKUBFfj5qjKBEj5FMyTpMMTdZaZShuy/dSmvfVb7WcyPy2drrOxMGc0WsaSe0RSQiC1sRQOlpLgqp1cBGek2N5T7+Oinhilm6kppdQfhY0hd7AoGwoHpTqpLHEae7zSwZDbJsrtDKrXzqkqPIHaloJHq5wsnYujd3N1Lg4VGBX/uw0gUGi4x8zhpZzAYKdfLgJQaHi5xhv2FhKABB6BwGhZYXTJI1QLT4EEIAFI4GEJPC2hgbewJFpmE+kd7+stc6WFc9ItWut3xI3B2o5A9amrjn2ni/Fju4hjW/edyt7/bf6RC8SRq3svd54dAG0B0Hyzrcp9ZNXnlumZOYxlNlGWNV6TN0W7Z8b6cj15eQtes27dYUS/bxrmEahLWIZiVrqFk+7gqx2UTwe0JJqKl+maLC+NS/HwxU6mGo9Kw6PSiBgtyUDJCISIVFs4WosItcXJiVixky12MiREtBSPETs4UhsLM0WoSiIQY5QWF2oskxUmgcoVp92bQD3AL4lcVMxU2JJWH9Wbrm0ma9ZtLVMsNU9ZQiYqTDzKU2MpU1nGUpACqVWosMSqHfEKk0Bjn6wjEzSEUG6LFFsmDIeQEErNMTJbDLXjg2TJCK7MxpMY+alFfMwSr7QkYabpSNEMVeGclSSS+/VK943NxWeXriBnYnksrZmbRvK0TiZKRkvtkUoXX2Hnq+yUeQJmpbYJyEiGDJ+EkRFKMhrBqXU+5hRqPAkYIaCsD0gu5TQBj5E5GAoHC3VwMZynIYRqhwizceXWGImDmULESuzxGlN8mnHu2gOfHe493ApOHm3Y8annA2UJQ0FMkLgjUgi+2BmnKk/UlsUh9hiZNQJxRCNElNj6ntgxSUpEImS00smi9lYQfIWVg1n5BldSGvm+0hKLWCNVriiViy2zsmQWPuaI1ZF8Dc5ELdHykomI5T2lPXKks0+4dQJunXjYiQ4e/2IQgELDizGOsBeQACTwBAmMlhVGlzzBy8OqIQFIABK4S+DpCA0owZLj0SjB0eGJStPUhbkJWovcfdHZAxqvdpQWlRmW7pqFrBfJ1zE124TYZqFy+9SlVmRj5dp9l/feHKrtB3XXBvZbvklbZmJj+X9LI9hZpUlae6KiWICWiAyORK0nYb6dKS8TLd87ZYVnstZGBbNAyBh9uSgN5+vtfNTOk5G81FKBrIyrI9npOFODM5UkE6NeXK01TmuZilqnKByT1XtFWDkbdXNRt1DhmaJwJsotXHFRlNIm0JHUzgKFiYPgQsQpRAnKp0OKPQopF+r2zdCQU/SFgvRCrsHCVlmY0mIu6ojDPAK5myFzxiAeNkJGS8zjF5eLlpQKF7uEBoKNOSLk9iipPVZBJGBkLObkYS4W6mSiTjZGUs4OhuNZsBGcjdp5SlykJxIMuEhriVYVRaSZ41RFsRpTvNYaj1mFKM7HSK7UHq12iRCHQOtMSiudhdjjJBaewiVQurmZpQmLnfF6RyxmFqAWgRKfrHHHqz2xUpwlc7JkHp6iPBYpjVd4EmSuBDket7R0ht4mkJdEpZoZC6yCFEuC1jQ9yyzJ/abwfPBiD7j45ZWdq+0faM0cFTExxf6uxCVKIROQ8iTUE7fIHC0xx2jdsXpPvMLKUzlFCLX1I0KBs9P3JaR5YmUlMTrXNMQ2RVIcKzPz1B6mbl804olIto6XOzhKXKB3CtKd/DRSoMcpCxTMHgktGu7+ZKl/odAwkgbMvzwEoNDw8ow17CkkAAk8IoHRssLokkesGp4GCUACkMDDEHhqQgO15d4VY3BOVtkSF+YnqiwS5xWyG9S1gQPmg+rlBVN0uUJdMTfdIVKV8BXFInFxQmrB7HRjRvHxkhv+iz3g6umuop2HFqF7/mzAY5bsnapyTv4ge+KUjf/1wfY/pxqjkgmO1MU3uISZToHayZXZmQtMMQsLGIvtUzV53IXbJszLfneRhSF3cTU4V2WKVlgi5LZIymuDXaQqmYIWJkoLhCn5zNTCialF70qLJypsbNQ9RVk+Q1NGeX9ckB+dWhyDmllanHIkOT+PsTBnkooUSByMFHvUnKLIaVv+PG/9n7UFMekkB7VEqh0iFRG3sCg6ccvvp+3+I+pkqz0sifFdtDhKnhchy34PKZyodUTrSnkaTxLqTEq1CMQmlrgoUlowQVY4CTFGoTYGRsRoSmNQV6TMESV3sCgnDqboRdn/Z9bG/xTvHK8s4i8mp6eR02QmgaSEIzYy5mS/q3PGyotZKkucDp+RWiicn8tKKWGnGGOQIp62ZHImPjWTTFDZeKnGqFRTpAJna8tiUbdQYucsNDPnFcfMK2YtNHJlRl6aSaAviFGbonWeycqKD+T4+1hBkjZ73o6Dey75rgyC2qqzW9N2TE7Z9C5S9DeFdbzEwZ5nYomJyYgrMcXGEZvZMiNv4Y5oZVGc1pwgL+bM3zN+UeF7GpKT5mQpTJEYHp9iFKWWxGL4ZKlt4qzc/2923v+R2iMN5YmYVZiaE5GyewJayKAcVbj5Gjf00UC5oAwnKDSEUcDMS0UACg0v1XDDzkICkMCjEBgtK4wueZR64TmQACQACTwkgacmNEht7ynwSJ07TuOcuaBkhtwiM1fbO8CNOq8727NIn8vDClmYlXIZIMVZqQRX6o5daI6fs3Om3mhwX/a0gxtN4KvKqxtWlMRm2gQqq0hqFqLWpDTHtHRzrLKAoXVNVuBcmSlCYoqQ4FwpmSDFZ2Ml89Jz5izePt2wTaDdxcaK2Uo7j/J3aGEbSpkaN1NlF2KmBGVxkrI4idoaYI7VFzAzipmZRpbOGCMxRSVbmWJnLFKelGLhavBYjYWFFjLUxgS9ZabemKgtmayyChbkRS3KFanNszKLZ2UapxpwEUbEpJXy1TgXMQsVxjjEGIeWxOtsCYvJmVh2gjZnWnrBtLSiOK2RhRZHyIoZYiM7tVigKIk1GGOXGGOXFk1Oz+OiOZGpe/6qdEZS/iNJyh1jiileXjI1zZ60nJyyxCowmGIMDp4WF6AWnsoem0FOyXAkZNriJTsjkBx2ui1Ja0rSmJP0+DSlOV5VkqDI56uKOZlOQZqHLbH8ZX7xH1PM4xU2NmLmKox8tJivLBZoTcIMi2iZRbgsPzpt59/Ue97V2/lKz/tyfLa6+P2MfPHOquxLAxe94Oqx6t0f5byv28nNMvEXO5hK8/iUwr/IbTzUOSXFLJBZ4/S2Gco98el7pi/OmZ5ZmJhuFKqMDMw4XmP+b439L1JztIKIV3mmKYdtRqQmJmbl64kpStNUtTFJXRCvyhVheVykkC01x0itjLCDBhh1Alo0POQkBw9/cQhAoeHFGUvYE0gAEnhCBEbLCqNLntClYbWQACQACYwk8NSEBpl9osw+CaPCKE6bUzRDYVESl8q6QONtcLDkgHxZyWSdhYdYo2UkI4VgSNxceYUQKU9aUDItJXfBlkPbzvedbQ9dqGl1biYWZpXEp2ZzpMVTNn394d7WvL1X1u7cn6zMi9bZBWmuOIVVMC+XJzXN+uzYatd1m/tUwZGLJSdqc/aeW7vrS8Vy5zSdLV7riFU6GIiJieQlqfIWZNmUmw+tsFxcv69hx7GGnBP12VVXPss/rspyJYhNrEXWOIVzrunWDnfznu1HdOqdkz+yKTy1uVWNeYXffrjCOUdrmr7u0LLyRpOrZvtaMhkzxiCWiTqcoTFzNx3NqGyzkFeys4yp2j0zN+434Oez99dZDzcSxLktn3sk6myOZMckaTZ708lluWe/KLuy9fC13Ydqtju++mhrmWIFPk1eOJ7aO+COVZBTEdvcLI8y97vV+29tLzmtX+WMl+dMEO+ZiBaJPiqXFJ5ZU35j1/Z9hhVFc1fbUnKOrzBVr3Nez9nfWEJe2539zYoMyzTx1r9i+ePTnQydm6FysQ3lUxft5hosczcfXOKo2bq/Pu9A/Z59lzaWnV3lOZm5yzl3aR5Xnc9XOGYbKtCtxz/CawoPN33bBroDoOF6496jl/OPNeTsrf1izzHFckKgKWaqS6YrimbN2xWvdyTnnfrMfXH3gZriA9V5h67kVF7bnnMifYlJpM79U4Z1vN7FytyfpHZOERtjPzmEuZp32a9u+YjANDnzNu7LdF/Zsb9hd8nZFZ/um69y8KHQcI/fR2jRMHISg/mXhwAUGl6esYY9hQQggUckMFpWGF3yiFXD0yABSAASeBgCT01okNonacoECsdkuX3mIvNCxGJw1VT2grae0EH8kGpVyZS0Yp7WyjW4RUpSICd5Egdb4YqVuaekmGYsxpdU3TgwAFoHwWXiyPI1ltny7HiDQ26+VNwALtwOHjh0aqVmy8S0Yp7OPBXJn5ZpRY2ncy4MnLkNWvtAaw+41gfOe8GpK/22bfulynx2OpG4xD1TtmsqukHxhX1b6YWqGu+lVlDXDRqGQIcPtPpB3fXBypLTK/WmmeL8OeuPbr4G2hpBh+tr+25i/bHaw92gvQfUXvUfyDv+oXL7DMvp3E7q9NPEN8uVOX/OckXprTxD0XRXjbkLtNf7rxTs22I9trkBfNMGrjWDm+2guRPcOtNRud29ePHOGavN0kNtlfWgoQ80DoFGP9WAxlO3yooOr1hhTVTsmZDpik93zZ67Le6Tiqya4JEucPZcj2U9sSCjIC7L+oF6z9ytlZ9dHbjUB7q/uXyopHT73u/Mt0FdF2htBS3dYKATtNaD41UNGze4ZqdnRxmKY7QWnsI8BTMv+pBcbjtHXvTe6AJ9PaC3D3R1BRr6Q9d94HJ1g32LHVVunzZ/96wVBz/6NniiBbTXAX9TIOgH3QC0DIKbPeBqPThWdW33lnJlVuFC5WZZWk7Wun1bPNfLroPLPaDZC9oHQk1d4GYfaGwA5yuvFpYczlxjm6LK+1umi5fumibJTdp9fGMLaLjpv4IfMxft33Wm9cvbVCCS6lbw5aYDqRoLS0N+H3ICWjRAi4aHmeEe87H0hBl+D9ceLrknEz7gBcvc083wxyfdTSg0PGnCsH5IABIY8wToGXlkN0aXjPwW5iEBSAASeEIE6MknCEJDfu8Q6Ku64EzKjJi3fkK6J1ZXzhVbJylJIUaIVHah0s5VOhgoThmxozgVHPFhXiyZfZK2VIDYRXLHjEXmhZjZ4L5QOQDaenxVRCW2uigurZCrMXEMzlgMp+I1KHGR3MFNdXPmmPnSQrG92tEDWvygznl4+cdFU1N3ilQWWf4Z801wvdd3rOroh2tNSYuN8aqC9z8i0isuEZ3g5hBo7gJXa7qOnenYf6F7fxM4cm3QZf56SbpRKM9jY0VTM41Y4f6S6qZrXdRSvKMBXLrc9+3Zuu/q2q8MgoZb4CvP5c9X2OZhe1I27d9xoaupaXCgpv58beuFHtDVAupvhk7WgYMbyrXyjTPMx009oKMXnHd9tdKQ/delOAPL435oV1ec398a6GrzNl289dX5ltKr3spLg4ere07UDl7tplb1t8/c+vJAdcHJevxS8MyFoTNnWg+frjtU33V1EHR3gfrzHRU7yqRpOYwsm2gJMSt567S1pR9dHPyuG1w722b5zDLXsEeQWTwT2T53s2fT5c76vqCvs7v5esPpVm9NL6iruX3qxPXj127fag22doHL14ZK3d+sWFM8JW0PU1MkUFlm6nC54wZ+LnDpFmi7PHD9dPPZ6sbT9bcv9QQaeoM3veDGsSsFa3FUUThXT6IFZ7ftqyuvA94OAPyg63bP+cvtX17orTx8y1Tw1ceZefPQjXNW5Kwmv626HLzeRsGvbwdXrnV8d7Xl2+rmE3W+y22gpQlcqWkrNR1MM+RM1JuiDPYkZeGC7Qd2XRmob/e2X75ZfaH+RNNQ9W1woR58Wd1vWlM6O9MpWrJvMnQGOXIGgBYNI2k8z/lgMBhehAeGUzAYBADAv/oebdSg0PBo3OBZkAAk8BIRGP0fzOiSlwgH7CokAAk8OwL05POkhQYMZyG2iQYPh4o0aU9MNs9RmjVl5ysGQVOvrwyvlHxUJNIXsZQmlsYlUBBMhYOlJeLFphhZBXOhk/NBzrSdJ3a2ghuD4JL75JIlhZz5u9iITbbtW+N1cLMffOs5mLXaPEOVl4DsXrS5fP3l5lMh0Fh3s9RSmvGxde4y57wPSyUbD6UXnlixdT+WZhJJc5ipuYkbD6452/JtP+hoCJ2puJq9aR+20rYwMzv1U6vBeGqN7dLH2V9LV9inZOQt2rN/88W2i33g9m3f5Rbvqe+a923bu3Slfc7nlanp1tkpG943HXfdBt5u3/XyoxuysgVLLfELt8Quty/be/5Y00B3P7jd7j199PqO7CrZR/bZS02L1rk+OXjtSDvo6QHdXaErPeB8ee327Yf1q20LPiwWbyr99EBdVT2obQCnSs98/FEBL6uIu8w6S7Jt7hrXF+d6LneBxtMtxWssieqcSH1JrGTH1PWlay52XO4JdIdAe2vPiW/rigoPLVmWMy9jZ/JG20fWQ3safTV94NLVFjLPjS3O5mmLOGoiUV06c9fFlZ723Y66TZ9VKNOK5iw1LlxjQzfY0w9f8vSDuoZgRd4RVGcXyYycNFPixw7Udoq81H2lP3jxm/N5G6ypy0zTV5JTM22J8pw4xfa528t3Xuy/1Alqqwfc5u8+XIsvWJ7//ipT6jJz6vrK5eUN5C1wdRBcPNWQ/4VjmiY3GilIUBVLt1blnO241he47Q+1NPeePHB2d25Fxhd4ysb9KXojx+DiD8tb3xs1wPCWUGh4dlPmw135nj0v4ZPDI/iC/flHd+eHv4eB/MAMFBp+ICh4GCQACby8BEb/vzK65OWlA3sOCUACT5EAPfk8BaEBtU7KcHNUdjbqiE81z1aZVcNCw61uX5mjSrLcKFIVc+QWtsotkhFsuYOjdcSlFkRrDgiVVYlzc6ZvPrz5VvBKFzhbUZ2VVcRKzuapHdj2r631oNUHLhAHshRbGCk7hAajynzS0tp7zTt46eiRz5evi07Z9Adx8XspxRxxviijZPZy2xydebKsUICZUh3XbLfBrTb/BdfpdUuL42S7/6oyMpDcWMlukdI8WU/GLnHxM62xiwsX7anYcKXtjB+0tg99e+Dczk0ejWQbe/72/5YWT5AVxYp3zrGd2tcFAr2Bun3HN68oSMiyTJHlzTCUaB3fVXSCPi9o+7bWtpGcj2z9i3T3hIXbGMiOedsqdlS3XekHfbdDNec78M9K52H5UfI9kdLdQsnO+RsPbjkzeLYT1J5pyl1nTFicw1hmmSbbNW+V6/NTty/fBq1n2kvW2hNVBZFaa2xq9tR1e9dc6r7UC9q7fOcPntqyxpiU8tl/zf/iL9gukSF79sfF6oOXyzrA9dvBr8ljH64oitNbeUoXX+xgLzs065PDCzKIxAVb35u7cZJ4G1eyQbjo47jd5buuDZxrCu21fafOcnJQ03tYfrQye+rmii013ecC4OKR0xvSd/JSNv9ZaZyksnElximZuGb/tcpu0Fzr31d0XJNRzBRv+2Pylr8g+ayFexiLcmOz3IbSm3gHuNwaPOb6bvniAtHCnUJFkXTrl4UXeq8PgHZv6PLpa/mbLPPVm2OS17+rK2YrSxjpFZMltijoDHLkrBBepo4shPmnSeB/uJYOWzeEh3Js/R1It3/0+w/H8mj9hULD07zJ4bUgAUhgTBIYPb2OLhmTHYONhgQggbFGgJ58nrzQwFGaozNcIirig21qqomyaPBc2DcAWroC+y0HkSxzAmIWSmwipDRR7OLLcJ7BliDPZaVVTDGUz0nZPX/7l9sbApc7wLflZ5csL+LKdselWXV7vsKbwe0AuIofzFLsZifnCDOsavy8rQPc9IHaE+c2rSkQSHf+TkmMR3BmaglTY56yhJyjtU8VFyR9cnj910Pn+0DDmZuO9eQc2c7/VpT836WHhGnlcTI7JxmPWWiJlFtjVCVx+oLUreXrLnec8oKG6kbHNrdCVzRZXDgRdb9rOMhaYGTKiubbzlXcBr294FzZydVLi3k6C1fjnI6UzCv+Jq8TNLWDi7Yja5cWJuhL2FqCh9njsOL5q8gVh24c6gHNt/z77F+nZVgE0vwIlYOnJt9fmJeyfO+6I91nO0DjjX7nZuu09D0Tl9gTxDnvryhddfJ2dSdoPtthWeuYiRVy1Lak5D2zP9v/RU3v5W7Qcqm1NKdUod/znjz3TxmlohVVs1UlCZrCubtP7L4MzreDb51nPlpmi9XgTKmbOdcSkVrCVBTyZLt4WHbCRwSy7cCq7Mp1e/Zm7z3/3dXe+rbAsb3VK5bjIr2dm+lI0hYtyDtpr/U1DIJLR6u3fW6fm+VIzHDGya0iqW3O2iNrLnjPd4OGL29uX00k6MwReoKhdbLT9k6WuFjzzdwFhR98sv/T6u5T/aDufGvJF8T81NxEsTF13dE9F0JXe8GVG53kHs+CrPyIFa7YNJKnc/FSTZOUpUIpzkKJ7yNcQouG8Op0rM03Y769oVCIhk+vsf130+glN10yNDTk8/nCB/uGk9/vDx9P1xbeYTFWAIXbf08m3J0fmHnY/kKh4WGJweMhAUjgpSMwWlYYXfLSQYEdhgQggWdB4KkJDaiZleaKU1kFqH2k0NDUGdxrPoQssSTIzSJaaJA4+Qqcm0FMRguYGjxBYZoh2ZGScyy/IVTTCk7YjmuWF/EUOxLTijWFxxxtoHMQnLN9mbmUTJQUceT5s7YfXndx4HTPsDfHsupPN1TMXluVlI5zxTnvKfI5GnOC3BS/KH/GrnPWi+BWB6j2HP14WQEbyfu/cvOfUOdEiWVSqi1KUcGXuLkyG09enKguSt5Wue764PkucOXA+e2fmj9QFXAWFv5VTP6Xcl/kvGI2YpRYzpR2gs4ecNJ1fElGARMtZkjNQlnx+9bq3G5Q3wJO51UuTcuLzcBFclukws5VFE5fYtLsrSnvAw3NYF/eIZmumC0zRioJrpJMml+4MNP9aWndsVugrmGgfJvt/fS8iAxHXHLejGVlH5+8fb4LtJxpNX1mnYnlcZTmGQuz56+pWH+++2IXqD9ZW7jOMiOzJEJHTNKW8jB37NycaGn+9G1f77wAzrWD79zVq7IsQmVJlJzgS+2TF+0SLbWIdx5YSdbknLi9//zQt7X+mibQ1gJAN/ANgNOVp5cvLmKqjAy9barKmLLtK/OFobpecO7w2fVrrDPSinmqEn5qoRDFk3ec2loHrrSEqosO6zS7x8sL/ya3TUIJjpxkLcLfSyE58wqSdGbDgRuH+0DTrf7S7Aq52jxLYk5efWhzdbDmNjh3um7Pp4VcbPvvM10cBGdgLtYiy3tyFxsKDfdY4NOruHsKn8X88dJdkyYfCAT8fj+tGgwNp7uCw33+pZfifr8/fGQwGAyvz8NDObb+DnyQjhDu1w/MPOwNBIWGhyUGj4cEIIGXjsDo/05Gl7x0UGCHIQFI4FkQeDpCA0pwEAtL55mM2nmIIz7ZMlNpQT01pQOg/nag1HxQvMQkwow8uYWrdsWiDiZmj9Q5oxFrpNwqSsmfrirU2i4QN8GFOnAw+1Dq8hIBuj0xM19V8rWtDTTdBl9bv9Itw9mocYIsl7uMRE0XSk77LjSC5g5QX9f/5YU2U+mprA3O6ZnFXFlezKI8nrR4bt45xw1Q3zxYZS6TfVjE/MgtWOyOR60cjUNgcMct+XKWbv80jJgqN09DLfM2Hl59w1fTBa5Vndm51jxPXxwrNzKUzkn6clFq/nRtvs76VUUXuD0IviGPpRvyuIpCXnJuIpq3kDhn6gH1beC77FIDtp2lLBIN15EAACAASURBVI7Re1gqkqctmbEaTz94sWoQdDR5vyyoVKcXCQwOXpqbp7SJxAXzlhKrKq582QWab3Z5ttlmpxUy0onE+Xmzlno+Pd1xZRDcrmkyrjcnaXI4GtP7yTsXfFa+8WLXhR5w49iF7avzRItNUTqckWplKZyJElOC1pG6/diOGt/ZHnCqsvqzlYVx2E6mpmT2UlKTc3Tn8VtftVBxRtvaQFMjuFYfvNAQvHGtt3UQ9ALw9f6vdcsKI1RFE6UFXMS88LMju8+Bq15w6usrn602iZDt45X57DTbTKVxXvHZnbfBtcaBE7nl0rScSINDgDmEWnyKEhehZFTa3nhZyUx1AVZ2vrIbNHQOluZ6FiqNiammBR9VbToTvNwDLp+8lP25aapyx18xC3t+4URVmQAr5ahLuepSLtw6MXJ6CK9ORxbC/FMgMNKigdYawvLBfTQGv59uUjAYpIUGv99Py0OBQCC8Vqcn4bElG4Ubf0/mB+oL4cMedsig0PCwxODxkAAk8NIRGC0rjC556aDADkMCkMCzIPDUhAaZLUZbKpIRXBkRv8gyS2lRemrcA+BGp99lOZC6zMhTFcYgJpaGEKA2BmKdhJDvIWSMwpYkLVj4IbH6wK2Dt0B1baB06/4Fy61xaHaSvhC1nrG1gfpOcNx5IjOtaKLeHmGwCZRFU7CilDV7P9tbf6gRNPeDxkFwsaGn7FDNuh37xYYiIVooXELITOfdbaCjbagq3zF/ye6JHzljM51TU/PZBkeixiJSEbFiMz/VGCc2T0cc89cfW3XNe94Lbh25sGtlbqJ8F0NuZOjcTJ0zVpb/gT4/w3y0rBt0+cAp91cZ6QVCrDjWgKeo8iXWkwV9oL4pcGSHC1XujMkkhFonU26KURZM+YxYfKjmYA9oreupKjqgxXZFZjoFS8r4qImfnDMrw5zlOV/ZD9qvtpBbLXPSi9kGV9LcvPeXej4923HVCzpqm42bLUn6XI7WPDN196LPKzZc7q7pBzdP1eZ9khe31MY1uLgSnK/e+77YNHnhrilf7N90qvu7LnDqcM2G1UVx2NZoQ2HyZ6Vrvrl1qhN0doOWU02HiTPG/KNbd1Z8uhlf4/jK3ey7EQLHK7/Wfk5wFztixIVsrVOy6WTOmdD5bvDVlxc/WuuI1eZPUhXzMhwfIDmzbedyB0FjY+9X2/FFGXuis4jJGrsIKeKjJqEKj8ksT5QXzlTmKMrO7xsETf2+8l22mak7WQsLZq0+sOUiqOsH10/UZGftYKmyx2fuTZLgXO3+eAkelWJ6F3FEPlrUCQznKBws1MFVkQLUzpNZmBjBl9vYOnec3jY1bvFfl+diXaB5CAz4g95AIBAKBEMBQL1CoSAI+UEgSL0oa3m6HARDwz9TKnDAU0j0z3P0XwhQaHgK8P/BJUKhQDDoDwS9/sCgzz8w5OsPDN8//gDwB0L+oI++nQKBUDAA/D7g9Q709Xf19nb39/f7fL4QANQ3w5swhu+0QAj4Q8P3VggA+gVA8M6LvuOGy+80KXzQ3a++P5g+6/vycG1UUNQH9WhkfdSp4c93s8MnDrfnbs3+IZ/f56ViFfm9wSHq5R8KUCYeAerDYDDoDYCQzx/yUfKLLzDkDwz6/UN+f9DvA8Mlft8wpjv13enziEvcvdD3bR4+BgoN3wOBOUgAEoAE7ktg9B8No0vueyIshAQgAUjg8RKgJ58n7aMBJVippkkKJ1tRHiv1JImtyRqLgTiL94O6TlBhOyhfZeIaiqKQQoYGj6WWgiRH4opBy6YsKpyzYGvq7qq8W6Fr7aGvK6vXfmyOXeGcKje9LzOKiy4Ut4G6zsHjnkMfanYx1CUxWa745Z5pOlOCYk9CemHqOufKg1fKz3d82wua+sD1Cz2enCrtkhyuarOo4srBFtDd7jtRUqZM3zYx3chI88SjjtiM0hmYWYBZ+VKzIMUSt9CUJLHMW3fk0zrfVT9oqPr605XZnHQrHyNiENsEnVOQmpuoLVKZT3vaQGtf6ETpsSVLjbF6a7yyZJauWOaqtvWAhlvBA7sq5Oo9DJ2Nk7UvCbWI9CUfrDRr91aX9oPW672l9pMGfXFUmpOlsjMwy2RxwYJ069J9tV92gsY236Ft9nnq3BiNI/GDnGlLyz4+23XBC5rrOy3rCkTorolq69R5O2Z+Wvrptf4rPtBUU2fbaJ6jK4xBHUyxg6NwxaIWgTx32oaDu854r7SAU3vPr15pjkF2vbsMxyzV1nbQ0jx44cDF7csLkmavjZTvTkrPXyT9bM5G165mcMsPzjiPaJfnjV9snqQw8jX2lK1Hd18GVyjB4sr6D818Wfaf9c6pae5FivyF+CVrD2ht9J7e7UHTd0dmmqPS7Qytmauzi1QOvoacKsmbmVasqbpS2QdutN625zrmSHeyZcZ5nx/afil03QtuHPxuY8b2SeqCv6ncglQnN5VkYaUcmX0CRkZgRMwPD6dKRWB1cJUEH8M5iJ2FOti00CC1MDGngHI16knQ2qdNXvzu0lzsNmgZAIOBELVtnt48Tz9bDoVCgdCd2IThx7ZP+bEz/fMc/RcCFBr+wTRIw6EPCKOjLQgCgQC14A4G+/r66Mw99YQNFr5f5IdX3d9nAoMDPQB4A4Ge/v4WQBn+9AIwAEBw0Af6hkAA+Pyg3+v1Ukv7AAgMgUBwcPgAXzDo9wX8ASpALOgfolbqgZDfG+rzhnr81FnAFwQB6iQwLD14qQP9lOI15AsNF1IqGPU1rULQTaI++O+8QsNHUyoZ1TNK0aBeQUr9CA2Bu1oGVbsP+LxD1D0+fAx9fr8vSAEKBX19ffQl6KYGQn6ffwD4vWA4NudwvZQQ1+/rG/D3g6AX+P1UT4NgCPj6gb9hwNsXBGAIBLp6QiA4BPz9VFRcHwgC7wB1du/gQL8vOOAP+cHwWQPAPxAEIR8Ieamre/3U1UNgWLihuzLcZT+AQsM9dyz8CAlAApDAvQTC//OFvxhdEv4KZiABSAASeHIE6MnnKQgNKmeMppQt8/BS7HHJRfO0JTr8jK0PXO8AVaYq+cpiVroxSmvh6UoT5QQ/1c5OxWNTSpJSc8UriTXH6r8aBM0N3RWOw/rFedE6C2+hMSHVklpwsagV3OjzfXfkm81ZBYnKIjZSyECLo9Rmpt4mynTMXGYXZ5VovnCuKbtQWg9qm8CZypptG+2zluyc6rm4twl09YKLZSdXf5jL0+ZH6pyi9L3TtI4ElUmgs4sQM19ii0uxzhCXLFhVtrK27/IAuL7/xIcfZkfqbSwUZygsEzWEUFY4VVOEFH5HNIHGnsBxz5eLlxqFaY7JqDHJUCJzV5O3QX1tf9muvWJ9QUwGwTO44zBLfKY95VNiSWXN3j7QdKULzzsk0RSMT3extARLaU1YlDNHa8zYe/1gN2i+1IZvMH1gKOJmlr6/qPiDZRUfne48OwAaaluL1+Tx0exJBmKmJH/e+sr113ou9QbqTl8u/qJopraAoXKyJDhHRvIRM1dVNPOL/TvOB+o6wMV9NZ+vtLMVeyasrkg/3H6kGzRX15duts9Ubv0vvZWfQbyvyp2h3yPeVZl7vudcJzhRdnzxFzbuRzgTKeJJC+fsOJp9JXi1E1w4XLttLZ6oKXhPg8dKjFM0Zln+qbwmysDkKnny4xUF3IzCienmiEwiNsOdpCQSEPs0ccHcFUTWkfpDXeBSXZsxm5iD5giUluR1VdtOdVV3BM5XnvwifedEZeFfVB5eqpOdSrIQD5cWGtCHERpQgqO089Q4H8N5d4UGHuLgSayU0CDFKaFBQwsNOapO0PYgoSHsog8KDU9uCnrsNf+DP6iC9DoZUA/V73td+txhOenu0/Xv9QVq6Rvw+X1D/b09HYFATwh0B4IdAHR2dF47/c2hb46faGjsHwLACwJ9gW7KFsYH+m8Hh3qDAHhbWmqPHj1ce+1GAIC2rt6+oQC1xqaqDAaANwAGAsDXN9RPywhDwcBgYMAHvCEQpBf8oWGRwO8DAR8IekOBQcpYgFqCh2jJYaTQQJngUBoG1ck7usRwJ4IhEPQHA/5A6K65AHXuUDA04A919XuHhrUKWmgAQUo4GOwZGAwGh6h2UhoIJXgM/xKoRvf2AUoPGAqBATDY1nT+zPWL12/UNzf1dXQD0ARAFxiurq/P6xvsD/QB0H3zxrkLpy/fuNoaAqDPNzAQAt9fcViRoS4BhnWIEAgMBqmehmhEw2MVDAFfAAoN971vYSEkAAlAAt8TGP2/4OiS74+GOUgAEoAEnhgBevJ50kKDCo9JI2LSyGiMiJFbRYqiD5ba0spqHH3geiuoyt0nNeyJUuSOl5uYUoI3xxz5QTEzNW8mkiNe5fp4X115G2hoD12oPLXx8+Kpi4snpRFsiXUKYpcUnzM1gxtd/d98eXLT4vwpymIBWsxFLGwlFUSTqbAxpCb2vJ2cWV/wllh0xzr3t4HL37Th2z1I2rZp2w/u+K73bC+4dq7ZumuvVJfLkOVHUA4aTJNV+WxNIRMpZCA2oZKYhVmka0o/u+n9/9n7DvCorXx7uVAMgRDCZpPdTW8veWmbQsBlenOht4RAKMb29OJCLwktBQLJ0ptNMcb2jKQpNpCEXpIQltC76WDcPV1d9/9dDXjZJXnv7f8LeUme9OmzNbLm6urMjDz33HPO71oUXNt2aMaHJSmFTjhhbqpUOFx99esG2tcZyg57WkCAAae37JsxZYOiyKXJWZdZVGHYfGZLENRdiX5d/HXe+A2p43GJ2SUxVqZPQMfO9Uzdfm5HBDRcJTZv2J1nXv1KPvq2w5lmrcgcVzJy/KbJX1/cEQA3alq8S/H3J5VpJ3gHjyoZMr1q5jH/KQLUXfK7Pq8c5FirKEDfyV7x3vwt8y+FzjHg5rELGxdsHFJUmlpUpdC7Fdku+biNcvP6oQu/XnaGvtIATm4+umAm1s+wWjp365TD5A9hcO3YpcqZK1MtCx+fs7WfrUw3cn7qLKdt9+VdV8jzBDi6//jH88sVEzf0Mq7XWsrGLNtbco653ArO7z298qONmY7Vb0xwqXLXqe2u3HnbPz3SeigMrp1pQVdsGVO4urdhxRu2cq0F6z+qTDNiY5belb344N9OkkeawMkjl5d9Xjkge7nMXD562bfrzrOX/ODM1wc/m7iyt2PD645qVY5XkYPLTNWaHCzF5Ekzu2Hhif/5+t8RDVkObEDm5NenlVhaflrRIBIN9+zGcw8bvvMLVdt2TIpC0zRFUSwLRf4syxIEETug7bA7uhUbwN+x4x+bHEmFGTbMcH6CamxqvXjwwDZFyhtvv/LGvI9XNfpBgAVNZJgBDM1E4OCcjJ44fGDc6JF/efSpSVPntAS5CHtrjE2yTDgaikQDLEO0qRJYnqJZKiYluE1GwPl8jhYkCrdIgtsqBg7qFeCIXFhjlh8OQCohtlI0TxCAJAFJcVGSgFwDT1BshAc0DxgOxOwdcDzPABClKYoheSbKhPyAogAPSJ4PMmSEjrIcyVNROhyGSg3Bh8FGoxQdDAWuViyfndXr5Veef+WFl3utx6svhak6AOoYABUKVIiIhhk2euzQV2OH93n75bemTZjV1NQUIsMBgojwXIShCIZlKMhs8IDmeJJhAUUDiiBpmmQ4muJpioHbgGUAz4lEwz/ehuKWiICIgIjAjyJw97+0u/f86BPFnSICIgIiAj8vArGbz70mGqDBflNPhzPZ7paZXZnG9e9Nqxi/+YQ7AK5cjmwv3uqYsFJnXC7LW6PN25A5aq1Cv2HALO/UlbuX7L65uQ6caQSn9p4r/cI5Nn/J25aSl/Kx1JxNfXI3jF5/uLwZ1IXoIzsOfJa/QmPflP7hllHz9ppnfjXGvClzbLEkt0RuWd9/5ELd1E2Ob5u3NYOavzfiC/Fs/QLlxE127KzrJjjbCk58c7lkvifbsEJpWKWxr+hjW6KbsWnA3Op3524fNaV6TFG57W9fLblCXA2Ci5v/Pmnq2p72TVIzqrWhWUX4O+b1g23rctcfdDaClgg45d39waR1kkKX3FjW37HJ4DnmawX1NYEvl3mzC9a8PQlPM2Fp+kqNsXT45PIJW05uD4OmWvrr0t15+etfL8R62st72ysyjBtGTa6YsvXclzf4U5eDVSt9YyeWqh2VWe+t6D/NO/1w05EIqDvRuGkB/k7hxoyCymHvf/HObHTO2eZjDLh+/ErpZxsH5K99s8ibZvLIc1FlzialpezdNfvXX+Vv3mSPew5++iE2YMzS5IJNhq+ufRkBdddb96335UxeljyxNHPsIoVhWT/n0eUN4HoI1NHgyJ7DH0ERR0lPe3kf66bsJXtLztHXQqD2mzNls0v6Oxb3nOZMd5RlWDaOmFBurz7pDoDrYXBiR83iee7R+auychZlGFYNHr0iy7Dh3U92zdjWWNUALtfy35fvts8q0+UWKw0bRy7et7oGXAmCmh1H/jZpTUp+6RuFkCWR5WIKU5UuB5cZvQrTv8MymHG1QDRo/1nRoBKsE2oDJs+vbiMazK3gZhREftQ6IRINP+/d5pdp7U7rRCx0kOf5SCQSO3ub+SXmkmgLZWzbf7uTP040xKiKULiV5QgeRCimJRC6cerEd71efr4jgsjT+h46fD0MQBPDRwHJcK2AbQVkK7ph3R+7P4wg9xls0643RmgAwhQL7QNEmIuNsKMEGRZYCRCl2UDMRgHtFRwgOAbmIJA8F+UADXMdomQkTEUIKhyNhnkoPeBizglOCBShBKVAhKIZjuVZwFA8Q0PiIUYlQFUAF6HYQJQNkhzB3KYkIkSUYmiSjgjKBRJwBEuEARz+Q06EYGiWFRQN/O0sCZajIkEoQKBurp5rfe4BpGNCBySxu7TPO95vjtYBcBNeQwREm4lgK+DJUwe39HrpLx2QxBFDxza3trCAC9N0hIHeCMicCGDz0IBBESwgodwEMikx8wQHhMwLhmAZQiQabr8/xd8iAiICIgI/gcDdtMLde37iqeJuEQERARGBnxOBX4ZoMLvlORW99JjEXKXLdfYdtfKdwnXjvUc2N/J19eTpXUc3Yvu/KNs9v3TPgtL9n6/b84nr7ysPNX57njreAI5cAzt3X1y1rNoybW1W/roUQ+lLhVuUBueg7DWj1x6o8IPWIHfcs/uD8SVK+3rlR9VG9PTyLy86N323atXWT0u++qzkqy9KtizafrK6HlxoAMe2nPp89qZ007Ke9tJ3F++df/Dm7lZwvQmc33/Ju27fglXbP/2i8oM1VXN3nin+rn696/ScD7Bs24q8pVuXXSWvNIEz3r8XTCp5zVSaYnRq89FBE7DhjvJh+Ruz135fegNcD7AnvLs/mFIqKUJlBZ73HBVmF1Q6NNb4t3/hHGNf/tZUr8LmleuxzOx170yqnLL5zK4AaL5O7Vi7LWdiea+pm9PG45L8yvS8knfGl+VvPe9rBGdqAu4V3lFF6xSO8szskiFzt8461PT3ZnBp39XVc5yDHevTiypH6FeMWVA171TLDwFw7uj19R9vzLSsfsnh6mnxyCxVGUZ3H3vl+8u3r7pMXq4HR6uPfzZ388C8ktQil6nq4tYgCBF8y8nLWyu3zV9cPXfR9s88ZyrOg+M3weUWcN7PffvlgQ8+XCf5AFUVuQePWTNs4Y6Vh4M1QRC4Gv7Bd2Deuu35pQcmLdiin7Du3fyV7y/2zv+h/vtmcPkqOP7djS2+HzaucC0oq16+ccsX1d+XHLq5tRYcaQBH919YN3mVYlqFtsg92LBpxMdbFxwNH28GZ/bWrJxcIi0se2t8tdKEy/SowuxLz8aVBq/mHhAN/TKn/HVaCSQaSJFo+DlvKv/LbbWRCxzHxSpQsiwbCATaAhpi/FHsYezgGDfxz1xDTDLwrwGKPA9IkgwG/cIwmIpEWyjGH2y55l6/4tUnHn0gqcennxXXEaAVgHqqhQONgLzUcO5788jR7ZD7JPKBW3b8EIwCkoVkASdM4HMsCVjAE4DwEyxJEFRTgKiPclEaAH+Yaw2TUTZIMX4mGuIjJGBYkiRbgiGCg2EKFA2phBjLABiobBCcDDAuIkiSNE1DFQBNszTUCAQJriUYCYdaOS7E8dFAtLU+GGgMRYLhUGtrc2trM81EGTYcCt2ko82ACdGRQDgUiDCUYJ0AFENTZJRliAgRrG9pYDg6GvEDNgDC15dMGvPneKRdXDySeD8S323ivJXno6AFgChLRoP1oaZawATqz+0doHrj4a4PFdqnt4aiYS7GMoAoFSTIEEMCmgKhaGtzsNFPwqyKQCgYDLWEowGSjlA8zfKQbSHJqEg0/C9/usTTiwiICPz6EbibVrh7z6//KsQeigiICPwOEPhliAaTW5mHS/M8MuPmdD3af8yaYVNdU7dd2OUH/ghoCIHLEXAtDNfaMLgeBDURcC0AbtwEJ460VlT+feInziETSzRFpZoiTG1y9Zy0s09+1QjTRn3ZEVcLfPrx6r/PnlqRkbcyZVLpSN+ZjXXgchA0t4LaFvZqM3spDAtP1DWBs7tq1i3wjJlUmjIJlZrLdVM8ucU7/3bg8t5mUBsA9XXgIpxsp863gPMEOFMH9mIn5xYWDzZ/8d6a7YsbwPUAOPvlmckzKnpbyiWGSo2tsr+tbKB5Q7/xlWNdpyuaQS0Fzm07Mm9Gucxa1tuOvVPoslSf2+oHTU3gUMnX9vzVvQpcKWa31IBn6ctHzP36o103v2sBDTfBnvIDdmvxaxO9yRPcUkeFZuyqvoVlpl211X5w7gpTJdguVLaydP36oQv3fHSSPOIHlw4FNn3sG24u1to3DrOvHbf6m6UXuRNBcO6HuuL5WL+CDW84XD2NaKrJq8tB03PWD16xc+kVuiYETu248MWsLZl563sVoGNKDhWfbjobhDOoza3g8iX+Yi24cQ1cOBr59uuT2FXyewoc/ubCwtnlqikuuQPPyl4/aHrVLM+5bdf5a2FQFwKn6sG+i2Br1bmFH5a/W7BqcNEq/eKvvthRv+MiuNQIGppBY4BqYLlWhrkGwBUCnLoS3bGnpmQhPs62vNdEp8qC9sktHbZo/98uggt+ULPz4vLx62X55T0nVilNqFTvVFi9mdmYWl+lM7nV/3PfxE8qGpwSizemaMjMR/tlTf7r9BJjANSSIMTyUFH/L2GQoqLht3iLu5M7aKs9CQCAA28A64mEw+FIJBKNRtuiN9qIhtj9kIfD9zai4V+5BgAAQRBwtp8lWv0NDBvl6UDzhR9G9VO0R5CevVR7j930A9DMhXjQBMA1dPX8BxO6JCDdZn+yujECKI5lOaKlpVaIhySJkB8ImY88CQiCIrlogPFHARti+KZWkiBZIWmyFVABQBGAh4YI4rbzIhBmIN8gBCryLAezGGFkAlyFCAoO0ARgIjxH8bGjoJuBEKIQGIKhgww8s2DFYODlkLFUywBPtwImyFEw3jJE3SIaWA4QRIRmoizUHTAkICg6CKhWEK5dPTXvmSTk2aef6aXIROLve0s9eMOXP9RxsHGKjlDBBkA3nD6AK3s+17X9A0bDpGtNAT9NBWiKYEnYWS4KW4X5lSQLSEE4EesVTIQgKBJKPyga4gbEMMjf4idS7LOIgIjAL4vA3bTC3Xt+2R6JZxMREBH4P4rAL0M0QIO9V5bnTjFXKyyeDOPGQTO9hVVn0dORwzXk4avckRaY1HjmJqhpBVfqwPF6cGLvFdR9fOHSr8ZO2SCzr+xpX5tqL1eaUOXIjW+ZqtKN2DDzpuziQytqwA8Xua/wkzMLKtJz1ipsG95dtvfj7xt31YOaALgcAGfD4GwQnK4h9viOLZ2LZ9vXqfMrUyZskeY5k00VuonrRy/yzd52xns6dOgCf/waOBME15rByavszq/P/+3zrWPzi9MdqwYt3TbjSHDnOe7rymO2yc5eFqfMiGqsrkxDqc5Qqit0vrPu6KKz4Ntadof3+6lTyqWGDW/pK/rYnNnlJzZcASdPhH2rdpgLNqSZy9/Wu1IM7gx9xbC5O2b4LnpOkN+dCKEbvjPlrXrZ7nzLXtnb4VTnretTUD4WO7fmItj3Q8v6pV+NHV+e7qjMGre+38wvi3bV+S6Cb/c0r/mo+j3Hhoz8ssHm4ncXbvvwgP/LGrDny0vzP/L0mYJKCvGU3PJkI67OdmpHr81a8+2Sk8FvLnPbsWNTiip7jln7n9kb1Nb1o9ftXLH/7Pbr5MkmcKEWXLoITuxurFi2a/oCz6RvbrgawL4tZ2dNKkszb3zdgEps1UPMFbkff/WJ5xR6nvihFhy+Ab47B7ahZz75AO+XX67LKe5n3Jg9e/ecsisV+yLfnGZOCv6LWoI8FSS/O3x1/cZ94xduGVOwXlFULh2Pq/I2acaV9l9yYN4x/sAJevumIzMLyuT2yuSJPrXFJTdVqKzurJyfiWgwY6q8O4kGrG/W5FdjRAMlEg2/o/tfzBMR4xRgJUUhjgEA0NDQEFMxxJQLsKoIy95mFoRSk7eJB5alhcyDmE0AVjRtW2mGo2g2HCFYnqMYOkpGYF4jiILQ5YoVMx9/uGO3bl0/WVpxNQRaeYoDLc1XDozMlHZAOr8z2HT0/E0CAIJqpInrAAQACNGE39/UGA2Q4RYWSgp4QAi8AiGYJsIhEkY8gNZQyznAtTbdvFJ/s66xJXS5Nni9kYklKcJ0S1igARaT5KCAAQ7vSQADGKKRABduBKAVcC0tgdrrzQ3NgXAkEGytb2xpCpCMcC4ORIgoSUBaIdzaCECAjFxvrq2B1R8AF45GSJ73k0wUltKAIJA0EaJDUUBEQYjm/YBoAeGm4snmJxIRmUwx87MveivkiV26Zw63fXOqlYilXbJ+wN+8dnq7svfL7RK6ZRun1UX4iODIiJIhmmwkg7VUKAI4ngeRCNUYivhJiqm5cLWpMQSvRyi/EWWgi4QSiYbf0edUvBQRARGBe4XA3bTC3Xvu1bnFdkUERAREBO5A4BcjGqxeeR7a2+xJHCxRUwAAIABJREFUs3hU+g3awor3Fm6fVvztZ4u3f7hq/9x1hxb8bdcHf9vx4ZpvPl2+bcbybVNnbBpdtLavfXWyY92bE5xpE70qC64eUy7J9WrMWwaa0CH69UNmVVs3Hvm4+LvCaa7+hvVyO9p3Ij5iYuWYDzHLku3TNx74pPzQp2v3TV+zd/KnvjxHcR/TGlURmlHkUxlcvYy+NBOu0K/WWpYPmlXpWLx9zrJdc5ftnlnxw7w1uyct8OWMX5sxvjRjGjZofFn/6ejIZXumlB+fPbuqb/bq/8ze2NuEqQs8WdZKTYGnr3lT5jTvuDXffVC2f9KnzsEFG9LyUaUF7WMsHzJrc8HqA3MX7bBOKc+0l6fle6R6LFWPq8eUagvcYz/bNX3tofllR6Z+iPexV6Y63GlmZ+8Ct87m6mcpG/LptoLi72es2u+YVJpRUJE+qXpI3sZ+1op3v9g1ueT7mYv3WQs3ZUzCB3xQPTJ3VaZ9/bvLDswsPfHxkm9MhZtkE1BJISbRb0q1+tIt1f3M6KCiTWNX7ZlVcXDGJ+4hlg1/LfCmFWzul12cYVn2znx0/Prdczce+KTk7/MW7Zn8IfaeozhzStmoFXumlx+dNcs9xLT2zXw82exLc3zZJ69ioHnj+zNQ26q9H63dN7v4mw+KD3w4FRtWiCttXnleZfrosv7ZlcNtPv3sXZNXfj+/dP/n5bvmb/xqxrqvxs/aOCD3b2/YymQTfVprZe8pX2ZN/HKIqXLABDx3yXdzVv995rztenu50uJMnVCtszqV5nKNDRINSqNP++9aJ8yo2or/U0aDGVPpKyVWr9qIyQuqMwvRvn0nv/pBsTEIpSiiouGOm8LvaDOmUuE47vTp0z6f79SpUzEzBQAg5qeIXWuMm+A4LnY8w8Qohh8hGjhBU8CwPM1wESIaJQkO8C2NVwHX0HRx7/vD0uLiEV3/kdsOnGuMhBkQcJYsfDgp8S8P/sfmzT8QALTQ/mj0IgA3Wm6eRCuKx7z/rkwilacqFVKdzTZp8/b9YQBaeRABAJZ7iAYAF+aDV49//+X0ydb0DNXbvZLVugH9h46bOO3znftOhwlYbhJ2CVappGGYosBMQH0C4KFCgWoGVN2J4zs+nj9F2z/zr2/1TH6zlzJZ6rAW7dpz2E+CCA9CBMkyUSYaAjzZdPPE5KKxI4b0+W7f7kAgsGXrV5rMvqr0Plb71G07v4tESRowISoSAdEICNEgCImGaMuq8ZYeCJKe0c+7ffeipQuROKRz96c2oN81hmEtDYpp4aiLF098Ke/9UvuOfxxjnnmhmfYDSHNwfBQwjYAPAJa7cPbcp59O1+iSeye/mZWV9corPfW5jmrv1031QZaH1EmYAyFeVDT8jj6c4qWICIgIiAiICIgIiAj8vhH4pYgGZf7mdAMqy69WFVRrczZIctaqjKX93l+uGrNGO3adLrtUN6ZUO2qtZlSxclyJ0rhOVVjRp7AyY3ylerxTXoBK7Zjc5FbrPTqDr78eH2DFBxVgAy2lmuxlb+Usf8NeKi1CM+yuDGulzrRJnbdBmr0udUxJ8vslvd9d9vqoNb0MmxR2LL0Az7Q5tfpS2ej1vXJwif2r9IKqLP065bhVWtPGAYYNWe8te3v0itdyi/9q2ZicX6l2oH2sFZmmUrVhrTJ7ucywSmEpldpQhcWtMXs0Zkxmdkmsbk1OmSJ3nTq/NHPSelXB6lTrepkDTTc61SZnhqW8r2m9Nq8kWb/+TbOzt90nM7olJo/ciKr0ZWrDugzTWp2pJCVnzeuFXrXVrbDhUptH7fD2taD9DOsyslcoTcUK6zqVdZMGhmiiffUVffQb0vUlKttGrb1M46jQOCp0tvIMU1lW3rqsnLUaw0aluUxqrUhzuGR2XGv3Zdk2983fPACmYy5OGzu/p6NYPtmrnbAl3erNtLr7FmH9CzdlmItTcla8OWZlb/1aaWG5arKrj6kkI291Rt4qjXGd0u6U5vvSzL7eee7kXFQ5ZoMit1hr29A3f0OGabXcUCK3VKizXb3H4imwmqanrwXva6xM15cq8tZKc9dIc1ZLxq2R5JSk5G5IMTnTbB51QbW2yKfSl/Uet1Fqd/d3oMPGFWdkr9HYnX0EdkBpRRU2l9qBZtqxdJNbqXfL/l2iwYJp4IqrTbjCjMHVhCpNqLxwa+bYst42j7YI66/Nf3HqylwCNLIgCudNb09xt33kf6oIYtsB4savDYGY2yXWqzZDBADg6NGjzz///KBBgxYsWHD58uVYnUuWZYPBYOzgmMyhrf4lD4fwLAdYYYOHwYrClD4DU0P/deVYApA3QfjchjVTHnwAaZ/UtXDy3DDFBQKtqtTeHZCE0SMKaq5Au0Vj6DoA1y8c3WI3Du/xwH0JCQkIkhiHxCfGJyBIfIpMs2yj90IztDc0BRoB1wq45q8r1yreejU+DkHi45G4Du2SeiBI1y7dHl+5xhWhhLxESCuwMCgSVqC8VYGitbEO6izCjZvRYrnyFSQRQRKRxI5JiUh8RyQ+AYl/9dXeH3224di5ZsF4QTKRCE8EAg2ne7/+eOf2yPSpM6ZO+7Dbgw8ndOiEIB0eePCJhQtXNQfC0MhBESS0ZBAk74e6jNCNNVMLuiPIG2+pvjly9uKloxnanvFI+zHZk0/V8FCgQQcZ/tq1ml09X3kSSeie4/jkUoALAEDybChUD7hGwLfu37Z9+DvvJbZDkASk6/1JCFzaJSZ0fvzhxx3mgtOnrxEcaBCSL8SMhl/bJ07sj4iAiICIgIiAiICIgIjAjyPwCxENuNqM68Ztklg8Kke1LhdmHKjs7n55FdrcSnWOS5GDKceh8nFOeU6lTF8pMzsVdlRtR9X5Lk2+S23H5BZcbvAo9R6dHh+gr+xvQfsUefoUYjLLxp7WTakTPRlFqK4Q1dgxuRVLNaK9c129sp0pY53SsU5ptkuWg0oNqMzikttcqnyXzo6lj62QGn1aq1eZsyklp0xhwfuY3ZnZ5Slmb0+z53UT3suEyk1olhUfUOjtM6Gqj2GD1LxRZanQWrAMszvd5FabsBQjmuyo1hlcSmO5Mt+lmVQpLyyV2MrTCz0DLZjO5tE63DqLS26oeFvvfNvsSXNsURvdMotHYXUrjC6ZuUJhdaosTomhIsXuSzfjagMqMaASswCXEXIiKkuZ2lKhtaEZVneWxZNlwjOMTqWxQu5wagtd6UWYthDT5uM6M6rVV2pyKlQFm/sVVGUVeNMdmMaKqg2VipxK6bgKyYQv+9grlY4NiomV6RPc6RaXMqdSluNSFG7OKPQq7XiqxdXb7Eq1Y8qJuHaKNyvfmWWuyDCUac2VWodb66hSWn2pRk9va5UizykxVsgLUG2hS2vdJDNVSGw+VQ4uyfHKDF6NxatzYFp7pdJa3stY9qbdJ7X45AaPPM8DC0mYPHKLV2v3pRdWaUyVUmOF3O7pY8P75mxUjStTmbEMs0crJCyobC51gSvTgaab3XKjO/XfK2+Jq2NEg/lfiQalvUqrr5SMr+4zER+cWfjKB6tNMGwi2ixUPIS1DxmGiQ1WGQamzzE/scSCAO7dz5847U/uvnc9+W21fGeg451a0V27dgmjeqRHjx4jR45cvnz52bNnYW0DjuN5PhblwHFc7EWnKOpWuchbdSOha6BNy/BjRAMNyBYAGhpv7H1vWHICggwa+M6J42c97uqnH3vmycdeWLna1xAAYVigMuC//r15pCYRQR574vFhI8bOX7h45aqlE4usPd96DUESO/d49qPFGxsiFGQNQNN3X5dre77aEUFefz3VNmHmgsVrP1+2zmyfMirb7Nm8jWDhQTTMX+AhJwJZBhawLGBpKIWINpatWvLWS8/GxSNPv/Coffz4hYuWrlqybHK+/eUXn0YQJCHpjxNnLGwKwVKaLEkAJhxpPjOkb0r3Lp0eefixbg/+6cnnXho5OkeT3jd7rH3Llr1hkmGg1AK6NijAkHwQgCYQubp6esEDCPLKG+k7D54HIPDBxOxOCPLUYy+vWL29IQyVC2Hq2tlj1aqUFzt2+uNY69wbBAgDQPE04IJ86MpWfG3q2ykI0u6xZ57IteQsW7F4wYIF06d/oNNo2yNIByTOYik6d7U5CE0goqLhx/+Pi3tFBEQERAREBEQERAREBH51CPwyRIPJrdFjmWPL1Qa3zupLz6uUmXFN4Za+UNjv1Zo9KmG+Wm7zqPPd6gJUk48qbbjUgsttmMaG6myY0oLLzW65ya0xo/0M5X0sFenj3ekTvfJCLK0Q002u6l/oVBe65AVoqh1PtrlTYUFNPNOM97d5hpnx/kaXzuCUmyvTHC5JkVs13pepL9caXTozJjNhaSZMbff2t1cNMPm0jq0SS3UvPZacU6HI2dTP5Bxc4MmauDnTjiltcIa8L2zN3ceIq4x4ihHt7diiNbmhGt/hURbhUkel3Obql+9714Jl2L0ae5Xa6pUbPSkwn8KjsldnGjAVHEi7FWZcYsZkNo/S7lXBmENfeh6myqlMG1eZDMtzeFRWj9biFqblMZ3FnW7xZJi8OqNXZfJIzW5pPq4txHRFbk2hV53vVVjgMF6RiyvN1RmWqgybO8OOpVtRtQlV6l3SPDTFvhkWvBiPayZ7MgsxrV5gGUxV6WaPxuJRWTwK+NOtteLpBa5MSGF4M2xwzK8zYxl2T2a+J92OqyyoNH9zutmttODqAq+20Ku1Y3IbLs3frBXaybB64KkLXZmFlbrCCqm9sndBlcwOSQqdxS0YGVwqo1NrdGoLvLBB+Pq6dVa3xojJ9ajS4NYYPRBPKyoQDWi6A9Na3FLTz0c0mHG1vlJW5Os7AR0yaGqveWWTKBAQ3N9wwHnnMDU2Cv3VfWLFDv2XCNA0HUv0jKUztD08ffr0c88916lTJ2G2HElKSho6dGh5eXl9fX1LS0ssu+G/bBj+sa0WJjQs3LHyLAd4jo00AlC7ce2sh7u1e+HJx2dPm61W9ElMeGDwsNxzV8IBCpAcBfjWiqVzuiHIS6+8XIpXH6upo2DDZNR/9cyRvWNGDkeQbkPfd3x/9DQLSJ68Ov9DS1cEeeW5N7Z8eSQqOAiCFAyDrG2qawg0hOgQARgCcJQQyiBUoOBg/QY6DMjGPZsrU197PRFJTEtL81RXxUIWAM+R4fqDB6pGjxmAxCW+3FP61f6DLVGKJSk+0hKoPzZmuPb+pM4I0vm1N2XFG/EwwV6/0XD9qp+ExTFAhKGaQ4EAQcCyl2wEgDpAXlgz09YNQV54JWvr7ouAJ4/uxwcpe3ZAEIlk6M5vLwpxDIEj+yuyZC926PDAe7mTb0RhRkOUCADOf+3Ufv37gxGkw8OPvbioeG1tsAWmW0JCgz557IAle3gHBPnjQ39eUrzpJgWukiLR8N++ScUDRAREBEQERAREBEQERAR+HQj8UkSD2ubrq8fTLV6d1aOFU+KY1F6ltXhUNp8KTlmjqVZMku+WF7lVRS6N3Sm3uNPgiBqPCeAh0WBxSy24ssA9wFqRad0EyYhCt9SOSS1OndWVaalU2iolVjTZivay4ilmTGbBMixoP7MTyh8smA7SFniaFU+xYqlWVJHv7gOH4h6J3SeFI2osw4xlWTwZBjwlzwMrceY5NfoKgdSoVFjRlHyvyubWmfG+BryfHss04irIFOCpVp8mD5frMYnFLbXjaWanzOjsZ3UPNTq1Zkxl9SrNXpnek5rnTtPD2fWMvAq1EdWYcIUBTTViaRaPwuxRGXG1yZuhx9W5TllOZZoek5g8UmuVylqlgtIJt8aIqw2YyoDJ9R6JwZtq8qbaMKUdVRdgCgcus3okRndqricl2y0Zh8pzXAojqjGjWjuuhaoKn9pWrRyH9sp1JdtQWSGutGNyo0tm8CjtX/bJQeW5mCIPNq4yoBojqrFUqM0VcoG8kBlxlR6H4NjQDHulxlqucHgzoEwAUwl6DbUVk8ELr1LbvTqbWwdNJbjGhmblOzMKXMp8VGqs7GVwpplcKqtLa3OprE6l0anWO1VWtybfo8t3Z1owjQmXmL0yo1eVhyuNkN3QCESDyoGm2/H/L6JBoEIsOCxUcds6oTK54GrGNTllEgeeNcXz3sjZyk/WTwzxzRygGQZqGWKz9zHJfWykejsf8F9/xz449+7nv57vv3t873ry22r5p26rx44de+mll9q1a9ehQ4du3bohCBIXF/fUU08NGDDgiy++aG5ujj2RYRiCIKLRaIyhiP1sa7MtykF4Nf6JawA8uHH1AgDNNad2DM1Ivj8eefHJ59rFdXvwTy99vgqNANAaZQGgb547OlzVuwuC2PIn3IzAiX0Stk4AvglwzVvclY/8+cWX3tCUOqtpluCpG/Nm2bskxL35SvKZcyFSKCrRHIYJiQygGUASgIwCkuRZkgMMCziGBzQLKBKQAcDWrfhoUteEzs888WplZRXFAp4HTY0hmBBJ1wNw7euvSx75Uw8krsP42Z9daQkLSY+tdOjsyKHKBCSx51vqSveOWGFLQc4h2DOgegKePcZZUHSYA/WArimeA4mG517qi2+7SESigGvyrf/88W5JifH3z5i9rD7EsoA+8z2emfpsYkLnMYapN6NcgKGpaDMg6n1li1584o8J8d0+/GR1CwfDKWBpEABCkTrANR3/ZnNfee84JH6U3nG+lW4UFQ1t70VxQ0RAREBEQERAREBEQETgV45AbBTBAZ5iSAqEd5zyDpyUPHJhzwnVGQVbVTnO3lYPnE63oTorqrJiEjMuseBKqO13K/+ttdCns+OqfLfcjqeZKt82uXrZ8DQ4UPfILGiK2dnb4krOx9LgyNmpMldKzZ40E1zlwlkgy2DDYVJDkaePzam1lsMhqx1XQXdARXrupvQ8NCMP0xgwuQFPM+IpJizNgkotLrnZKbNiEqtHYvWlWqpS9d7eOXhqriu1qCrTgSvsnt75vhSHR252Ks2V6RY0C16aV2H1qm3edLunjwPTOtAUO9rTgcssuFqPZeZiGUYMyvvhhLxHKVTulOdiEqMbKhQMLkUemmnC+hkqdCaXBo7J3UqDR5mHK+Ew3pVhcKabsQwTptajCj0KR/ImTJvnVBk9WoNHDR/iChMug/INj9TohnSA3q0yokqDS2FAZQY8Lc+bYvCmmTEZDDJAFVZMZsZTIT/iTsnBU/VuhV5wXphxnQVXW3AlRNgjM3jUBkxpQaU2LNWKpxjx5Bx3Miw46tWYPVoTrjO5NUIJSTkkXzwpVohhCixKiitNaLrNmZlfkekoz7BjmSY03YBqTJjWjGotLpUZg04Qhwc+Ue9Ly/bIx+EZeiwL8gVulcGZZnTJ7C7deCxzgltd6JNbfHKjV2HE5DaP1ubOMrk0RizN7JUYvYpcTAGFD9D0obCiMFbDimvN/x+Khn8iGmRCRsMtoqGwuk9eucKGZk3EhisML/bs9/yEGfYpUyZNmjRpypQpU28vU6ZMmSwst3eIv38bCBQVFY0fP37ChAnjx48vKioqLCwsKiqaMGHC6NGjY9aJ+Pj4Bx98MCkpKS4uLqZuePjhh99///2lS5ceO3YsdrsWClVAdQvHcW1FK6B5gmc5jrm9/hP3QxF0a2MDAGFA1m5a+dFTPTp0QJBuXR4baZix5/iNMADBaASw0QPV2Ct/6PZQ+/YFE2Zu+fbU9kM1O749sm/ftr/vrTrz/Xbn+jVPPPVqlx7PL1pVDhMXog2eypX/+fxjcUj84EE560u/rmukoZQAgKaIP8CEWqlAFNAkzzIsJABuEw00IAPElQP29zLaIx2yMkafPlcP8xRhtUoo0yGJGwBcuH5lx9DBWiQu8V3ThAutUFfBRRqY8Jnhg2RxSLvRY4ou36Sg64GEZV85GkQjTCAaDDPhKAPLVAAe0FSYA82Aubp6rvV+BHnmxQzXVxdIGEQZvnnqm7xhGe2QhJQUre+rfQxHN13cq015OiGhi33ivEYahFgKYhW8unLexPsSkKeefuvrb2qCAFwLEi0BkmNpim4AoIGoOznVNKYdkpis6LPl0LlakWj4lX+fELsnIiAiICIgIiAiICIgItCGwC9DNMDhLiazYjI7Bge6FleyBUu2uNOsHhlcMYkVTbFhKXY8zYHLbJjajEGZg8kjFVa5QDfIbbjcjsnz3Wo7prS4lFAv4NHZvJlmd2YepjN4Mg3e9NhwPWbEiCkOHF6J1ZNm9iSbvMlGDxyi671yo1cFSQqX1IynWLwpVg8ci8Ipd3eWzZspuDk00LaAqx24osCdUuhJtaEyE6rMxVW50DShtuDqfFyb79HBYAWP0uCBMQQx9QFMrHRDx4EF05lxITbSoxWiB2CSAmRtvJlWX7rerTJgSmgGwXUGF+QjIMHhgRoEqxuO3g2oJA9NM/mURsFbYYPeDSVUf/hkpipoJBH2QCpB8JVAPwXUgEAfihqeV2AZYkoQEy6x+nRmj0Z4FVItXonFJ9V7pTlwvwZySZjGjKlMuMzkSbNUSWxVUgsKpR96t0zvVVrcWgeaWeTqOx7rY8Uzze50g1sDBRoYxMECXR4ygTZKyfP1zvamjcMz8vAsyES4lfC18yoceEaBO73Ircj3SEyeNKjLwKWQ08GyoKsFv0UqGVGlFdfaMDV8S2AyGAzpFhrBJf8WqxUzd0BGDCoa7iQaNFCRUameWDV4hm/Mq0MeRu6DLnBx+d0gEBcXFx8fnyAsccISexiTMNx///3x8fGxi+3cuXNSUlLXrl1jDzt16jRs2DAcx48dO+b3+2HZyNvLbWMFB8te8jAK4fbapvZgaYoAPA0jDsmGxppvxw5UdELin3j8r86vT1wm4EQ9rLDgr9+5fvUr3bt2RpCHHn7m8ZdSu/z5Pzo+8PCfHnrwqYfvf/HRHs8++mcEuQ+Je3DO5+sbWigi5Pc31Hy+cNpTTz0Uh7R7+KGnR4zSl5Z7fjh53k+zJAB+mhJME5DygPaN2MpwgA6Gzu4wDZR0RLrk6j+80UjBapchWIiTpulQ+DpPn264vsua+y6CJL6uG+L+5jhJU3TwRnPtgWEDUrp27mGyzLxYS5GwtGaQpMKxlEkWMAwgWUhXQGqapYIciLBM/ao51q4I8vTLOtfOS1AEQYS55stfV6x564UX4pB2Y3OsLS1NTZe/0aQ8iyD3WSd9VhthW8kQAKFo/ZnZRWOhyUI++PD5SCsATRSM3KTCAQBaAHUtdPnwZ5MLunfu/urbqvKdh6+LRMPt96T4W0RAREBEQERAREBEQETg147AL0M0mN3KPJfcCGfahWlzj0YYnCtNPi30ILjhrLvdLYGWBzwVDikF/Twct3vkBq/UAIUDMN7PhimtWCoc07rhbD+cja9ON1cr4Ay/YKyA8+HudLMbDobNHo3Rq7D45FCW75YZMbkJU8FG3Dq7J9Pk0kBZgVuV51EYhMGw1au2uOHcPrQw4Mo8XA4Hw5gMmhRwjU2Yutd75QYfTHO045qYsB8q872KGB1g9KQbfTqDT5HnkVi8cJB8S1Pg1tg8WqiAwOVWt8Lq05irdHoPPDtMRsS1VlRlwiUWjwyWnHDDMEUrCrkG4RoVRq/C5lHmu2GAhcOtNnpVRq8KDu/d8HJitRWsbpVdkBVYXHITKjdjqhgBYfXIHB6pzSs1YmkmHHI9sFiGR2OpyjBXZxi8MBzB4pJb4So1oVKDIGEwYCqbS2XDIMdh8ClMbshxFKKaIjcMdDBWKfOqZDleiRBsKbP6pFZPWkztYvSkmN1S6LNwZeW71Ba3NLcqNacqLderMUBKRWJBUwyoBEo53IKaA800Y1kmtxIW48AVFkwDgyQhpZJqxlMFlgHmbhpxxc9FNIzbKDVXamd8NeLjr83Jo59F7kMSOsXDQP/4+JiuvkOHDomJiQkJCYmJie3bt48NWe/+mXiPl7vP+F/vucfd+c00/1NEQ0JCQtsr26FDh65du3bu3DlGMbRr165Hjx4xgUOXLl169eq1atWq1mY/jDwQSk0IEQxwyp9lKMgm/GNlYZUHuAoUAx8myGDYfwMQV+dPMTx2/0PPPPX2nlOtjQC0wjiDAAjW7yxe8uYj3TojSHxc0gOPPIckdY9Puv8PD3S/v11Cj44d7+vQ+f5Hnn3mLe2i9V/W+wUBAh9uqD+4bv2sgX0Vj/yhO4LEI4ldekoyl63Fa1t5UjAacIAXukQBjoZhkBwP2FDdIfewtOcSkCSDdXZDCOogIiEy0BSkKIaEuZVXm67tyxs5CEESUwaO/erEFRb+p2r1N3w/qM/bXTo/ZDTPuVgLFQ00YFiOAAyEgoU2jSBBhSkyBLgAx7SygKOp8IqZ+ZBoeE2O77/gZ0E4RAIyRN+8/NGkie0R5InHnt5c7Tl1uDpd9VckodtI86wLLdEmIgT4YOjaccM7mo4Iota9d/R8NABguU1A8/4b1wDVCKI36OunF8+Y2rX9A4888cYXm3aco8WMhl/7NwqxfyICIgIiAiICIgIiAiICtxD4ZYgGk1udh6lMXp3Zl2nyZpiF+fwcTJnnVsFAAVwII3RLjWiy3pViwOSweCQO5QCQaLitFLg1osZSoULBK8nDpZAO8Cj0nhQ9/rYRhUUToA8fEwIX3Jkmr87kU49zpeZiEtimUIkARks6tRan0L4v01SVnge5BpnRC/kCPSqDaQVujdGntVRrbdVq260Jf52Qd6A2VyvM1TIrLNOgsrt00LiBwkoK8LnQs6A1erR6rzQXT7b4lCZMZXApjC6FGYUz/zZcbsYlBizZ5JHqvcoYl2HxqPLdahsK/SMWdxpsFtfaUVgwwuqGGQ2xSg12t6IAg6sDh4IFvRs6LGDJRiF3AEYPYJDFgFyDQEZADkJItTDjEiHtMs2Aplo8Mhg8iSn0LnUepstza4XYCIUNlztwRb5bafMozR6Vwa0zYlqo78AgqQG5FVxqdEmsLimUdXikxip5XrUk15NiwNMsXonVJ4E+FxSSMhavxAYTOmEeZD6qtLiledUVFJSjAAAgAElEQVRpOZsluVWaPJ/W4lPavHK7V2PzaeCJMKXRpYPVNLxqKKbAVPZYxQqBToKUk+dWNmSMjfq3uAYY0PCvGQ0ak0uTXSqxYBmTq4dNdI54aeAfkc5Ilz90uu++ru3atevYsWNnYenYsWP79u07dOiQlJTU/ieWDvd4+YnT/uTue9yd30zzSUlJnTp1ir2OScISe3jffbFakkiXLl3aKIakpKTExERYSlFY7rvvvhj1MHLkyHNnzvtbAjGi4VbBCcBxrFA/EhINbRRDG9EQjZKNAEQA6weRS3OKcp9++Im/vq7d8t3VFgCuNF0HXBOgmnYUL3r9j12f7N7lo7nzd+w7smXXgW9/OHHy+Klvd+w+cfCHndv3HDhes/PohWNXwkEGsBSIBhoZ6ioANxqvn/hu5+a5s6a++OKLCNL5T0++8ennZdcbOZqHJScAT8FT85AHgZYPNkRc3PWO5PkEJNFgn3mlMRziAEGyLA3FCESkHoC6SMMJw/vvIEi7t/uOcH17LMqyAATI1mNZqr/GIV3thV+0MKCFASy8agJGP/ActF+wfiEDkgacn2NaAGB4Krr0g6IuCPL8a9KqAxdbeBBlACCDgA2d+XZX6ssvdkIQa87ob3dhAwdrkfv+NChnSo2fCnAMLEZRf9YyIiMJQbTp7xw62dDKAD/BAYoBLAmoesA3g5ari+fM7Nbpocef670G398gKhrEby4iAiICIgL/LQKxb/Z3Hnb3njv/Km6LCIgIiAjcIwR+GaIBKthhqOGda2wP/Hl7AAntAP+oYtg2UIyFQdx+GCs/IVSggPkIcIU5DlKzG5bAhGPL26twulvH3DqFYHkQVAbwsNv9+adj7tzZ9izBhiAkC3piPg6loBeA9MHtzscaEdoUjrFWqcwejQnXmd2ZFg+MmYShj25ZHppiqpKbfEo4q48qHT5tkU9jx6TQPIJKYGWHtk66NVafzoRBEsHhUuQ7ZfkuSQEGaQ6oibgTFqEbQpdiHWuLz2iDtG1D+NM/rh32v+2JsTYF54XgicD/CZnYYYKlBYIQ87PcfsliURrQ2SG8EGobdFXAhzGqKKZJEdpvO/JWT2J0QBuMMQ3I7Vf5zrdH20X9DzbgewDGUppxIfYCU5kwtdGlM7p0I0tSsktlH+0ZN907+o13/4I8iCCJCBInrMIoMw6Jj0Nuqev/q/2xIan481eMQFxcXEJCQlsWQ2wjISHhoYceQhCkY8eOSUlJ8fHxnYQlISGhW7duGo1myZIlhw8fDoVCFPQlwIXneZZlYxVJhBsm3L5z4WBJSYZk/AA08uRVELqwYPbEeKTjMy9It31/PQhACIoDQiBy4xvXmpce6PjnpA6bN+8gAGgloRUBmgUIGvCA4WB0QZDno0IQAw/PDQDPMCF/pKEOsFFA1X23xzts6AAE6dAzpe+hEzejAEShkiHKgjDHk7H8CMAGQcvxWbahHROQZFnWwbO1QQCaSZ5g6Eiglmi5ANimo3s3S3q+kZjUZXThtL9fq2egYiMMmGuD1Mmd2z1psCxqZIEfAEhAMDRcWYrjSZKLUjzB8AQPTRh+KNMI162c80EXJP7Jp97+ci/MWQjBfkcBdZNrvLBkmqMHgjzfvcu6NYv7DhuK9Hh8eOGsmjAfhoVFQ4BrXjqn6JEucc8/+6J3674QB0LQnsGQ4UYAGgB55dyRfcOHwItVZgzfceB8WCQa7tH3ALFZEQERgd8TArH/T3de0d177vyruC0iICIgInCPEIjdfH6BMMi2keT/nQ2oaHBqDc50IwaDJKAfxKPSY6kmj9ziUZkwGE9oRVXQ7FCRbEMlFlRqQpVGFI6KYbkNrxYGK+Bw0B4jGhwuiR3KPVQmn1bIofgfDLn/zczO38mrc5toEGwXEE8TBquZGl26/Kr+hkrNjO0jHJsGvfbOI4/27CrN6D1wcL8Bg/oPHDxgyKChQwcPGzp42BBhGTRk4MDBAwYOHjB44JDYOkhY4JH3eBn6by73uDu/meYHDhzYv3//fv369e/ff9CgQUOGDBk0aNCAAQNef/31Hj16tGkZEATp2rVrUlJSBxjaCCtQpKSkLF269OrVqxzHkSQZDodpOlYAARINMASBFwb+dxIMt7c5wIc5MkQ18+wNQF8GxLV5MychSKcXXlPu+P5yBAB/OAKzD4namyf2jlAld0GQYcNHXWuGc/80B3McQ0GC5aHtIUAGKMBEeCpEEeFo5NrFq7UXrkLnAgNANAhAEwD1yxZ93L17jzd6avcdhMUjo7C8ZZSGRAPFcQzPUYALgMj5VfPyu3Vud9/9D81bUVZHwgoXBEcxZD3gmgHbunLe7ASo5Uj6cPHKZqF5JuqnGk7qkv8ah/x5TM68Wgo0AhBleFjQAnINFMdTFE/GVoInGC4IRQeRG0tmTu+MtHvh2dR9318NAdBIUQwIAb4RMI0ntrqGvvXSHxBkcL+s13pLkO6PDsufdpmCnQkEG+jAVax44WtP/bHbfZ1nzvm0tjVKAsi2sJSfj14DoGHbVvSZZ55K7NB18gcLaq6FIqxonbhHXwTEZkUERAR+RwjcTSvcved3dLnipYgIiAj8ehGI3XxEouHnHWBbcKUd1UCPRkW6uSLD5My0YBkwutKnsXqhiMDm1uX7+hR6+9mxTKtLCzMgBDeBEVUZXMLcO6rRozDYAmY9eGBcghCvAG0meo8u16O7Qwki0g13IfDTRIMFz3y/JGXi5iFTvSOGz1TOLZlwtbmGB0yLv7k10BL0h0KBcDgYCQqLP9gaJSNRMkJGqdhKCEuUjITE5VeJAEEQkUgkHA5HIhGCIEiSjJWr3Llz57PPPhtzSXTv3j0pKSkmxejYsaNCoVi0aNGJEyfaJAxwRj4aZRg4zR9TNHC3wyH/qdTE7Qcsz9GAjTABInKZCdUwocszpzjiEzr2TOvz9TdnCACiMCQhCkLXQOTGmk8+6JaAPProo39btvLy9ZtNTS0CgwHqGupP1ZxZUrJsy+4vT146HaHDNedPF9pMY4cN2btlCx8hgw11bLTuysVD+ryxcfEdpOohR07XR2FtTKho4PgoD1kGwXHBBUD43A97Kgf0USW26yBLH1ixecfVpuYIHWbpFtJ/Y+9md4ZMGofE9xs8/JuTNWEAIgxDRZr4YM2oAZouHZ40Wr9o5EATLwguINEAV45jKJ6meJIGFFRw0K2Ab+b8l5fMmnF/fOeXX0zbtf9sEIBGIhKmWyAHwTYD/3Xsi08e65SQiCDd//jnpD89kz1h5uUwEwEgFGwGXKD2zEHjyMGJCPLnvzyxaHnxtbomkqYAIFtqzx8/sPP9d4cgSHyvFNXBoxfgXl4kGn693yXEnokIiAj8WhC4m1a4e8+vpa9iP0QERAR+1wiIRMPPSzHEWoOJDBhMi3TgWTa8rwXvI0ROwAINFo/C4EwzOOWQa/Ckw3gFVJ1fnRlLXoB5mR6t3Zdlr86Evgm30lqlsviUFo8Clr3EVEZMm4dn5uHpsFzF/021wv/wqmMOGrfSKOAGrROoxohqJm8fml0mnVA9sLBycGbh67OKC5qpG4IUnIGh/EL4H/wpLDx058P17v2/67vC7/DimpubH3/88YSEhPbt28cohj/84Q8KhaK0tPTo0aNtFxwVFpZlKQqWdWzbH5MzAADYH1sYGN7AMnyEIW8CqpYJX502yR6f0OH1ZDW+9VtSMEAAnqT91wHbev7QnrHDBiQmQLdOSkpvi9k468OZs2fPzuyT9dBfHr7vD93e14/e9e1OHtANNy+/2z+9E4K88OeHHSbb5ImTpky0KRS9EAR5+OEnPv2suDkECGiyYDie5DkSVp1gOBjtwAUAcx2QV0rXLnn66SeRuMRHnnxm+Oj3P1nw0Weffjhu1LvPP/5oIhL/4guvOvGt0NTBgRARBUyQ9Z8Z0V8Rh3R/f+yMG1G+FcSIBkHwANMaOIpjKZ6mYY4CJYRKtoLgjc8/nN4Jaffif/TctudIkANBjgoTLRzVDAK1gAn4zx7NHpgJ/UntOiY9+Bfr1DmXBeUCRYahWYMN7ahyylJ6xSHxj/zpsXdHjp42bcq8j2Zbssckv/ZqIhL/hx5/+mLxGj8BohwIMyLR0PaWFDdEBEQERAR+AoG7aYW79/zEU8XdIgIiAiICPycCItFwj4brBgzWzrBWZ1o2Z+qrdOPcymw0NdvV27FFacSTc51vG7BkI5qcU9kzD+1tq1YKZTjUZg8MgLRWKcxeiR7vne3qaa6WWnxSWNhCCBowo+lmVyb8iYtEw11Chjs4iDvDIGPQwTojLk3R1gE55fKJmwdNwocNmNxzXtkkCgQonoiSIYKK0iTDUCxDsbSwkDTxU4oGSlx+lQhEo9GY6iQmZIgRB9Fo9ODBg21Zj+3atdNqtStXrjx27FjsZhqzS7R5JTiOYximzS5x5w33x3gGluHYKEOwIAqTIJnGQOOF8Q4TgsT/8bHn8K27oe+B4WBMY6QJHkC3/vDt9tkfTnz00T907BDfsUNCnBAYgcQhiZ06Dh01YiNWcbPlJg9IhmjZsxUbNyj9T13awdyQ+HbxiXEIgjz2+DOffbbiZgMsWgnLWwIK2iVYCtBCFQpINAQ58joATYHWq2tWL5NIUmNpIwntb6WOdOncLV3Xv7i4vLGFYABoaA1CNg2Ea8/vz1K+GYd0fm90YU2jv5GhSUhe3CIaeB7QPMfysH4mC6IM0wp4WEpDqC4R/8RjL1RiWyKQ+ODChJ+nA3RrPaCCgAlvcZW9+corsLZLh85F02ddrm9hAGAZigg2saEmyt+4f9f27FHvP3B/txgHFIcg7ZH49kg7WbJqzaqymw0higNNAYISFQ13vhfFbREBEQERgR9F4G5a4e49P/pEcaeIgIiAiMDPi4BINNwLosHkVo5zSWDhzM0aw2ZNtkc52iUdUy7JLpeYUbVxkyxvXUpOSUr2mt6jV/YctSZ57Fpp9lp5zgaFoUxqqEjRl/fMc76pR3sa3L0sValmr8TkkRtxWFDD6krPd2bYXbo7cyjvxSX81tu8FfnpVsaUILFEDJNLZURVY0pT7Fi6vbxfVtFfZxZb/fzNENVKswTD0bCiIQzmv2XLZ3kocxAVDT/vPed/pbW9e/d27dq1e/fuAwYMWL169aVLl2LdaG1tJUkydhtkWZYRFoqiYnvu/m522y3xL78ZmqUYNsrSLSzZHAnUuipKx2SPLZo8/cTZSywQsh4ZEnAEgKUlw4APBv3XcdeGyRPswwb3Gzig38iRIy1We8mGstYwHPmzgItE/TATkQs1nT9aXrxkbHZOv2HDhw5/b8LEyTi+paWFZnkQjLA0z3G8wDIwHGQdKCAoGsI818hzjfBcgKw5f2rxogVjRr83aHDf4e8NzdXnLVu+5sKFmyw8EYjSXDAUYVkagKi/7viKv820mYtc+I4mmqkjwhQslnk30UDxgCSIRkC1gmjrjiqfITtn4oRph46cpOCxHEEGIedBk4COAI5suXmlePnyUSPfN5ntu/Z9GyJohoWqIY6K0pEgICOAp69fqlm1bGle7rj3Rgx7793hpmzr/A8XnvihBmpFhPNTsL6GqGj4X/n0iCcVERAR+E0hcPe/rrv3/KYuSOysiICIwG8VgdjNR8xo+LnH1fI8tLfJk2aqkhp8ilxUmVuptVYOLHQOn1tlnl/tWOjNn4855qGO+XjRx/j42ahjDm6b6zHM8YyY5soqKJc4nCnjqxVTdmRY3Klmt9SIK4w4TDS0YZoil2o8prRh/9V8/s99Ob+9c91BNMhucQ1CKVBHVabFrS3y9S1CBw6a2lMgGm7QIMpw5D+IBsE6ASMAActwNMPRgvkdWuBjE9oMdysm8Lf6sf/99rtNicBxHE3TJEnSNM2y7MGDB0eOHDlnzpzDhw/HpArQCHC7rkQsiOFfULkzA7LtT7Eb5l0/hfwCOsxSQcBGACABoFsDLbFhPLTjsBxPQzqApYIE1UJzQVhzAZAsFWbhMBuaKyJRhgUgTLBwHM4Jg2uOAEwQkK1sqClC0Tea/XVNrZRQv5LnAUmCcIRieVgMAuZJMgLLAIkGRihbEQqFa5uarjJMCCZOsgRFhigaVnsgSDocZilohQDhCB0lYHGNQCAAuCjgm5hQbdAfoThoyvCzJOxJjJCA7BuAcgae4uEfo5FIvUCF0IDliFCYICjhQI6GbAcDeAYwQmAEYFiGADznb2qmItGYL+l25IVQcSMSBPAAGvA0TQQpOsIwHBmrvcHCK2tqDkZphuJYgqGRtldC3BAREBEQERAR+FEE7qYV7t7zo08Ud4oIiAiICPy8CMRuPiLR8HOPzOUWd5rVJ7FWyUweud6ltbgGTcYNcz2TDrXsron8cJ08Xkscr6VPNoCaG+DCBfJkHTh/mf7++9qKigPT52Lv2EvkppIUS1mawyWzYjKYAYmr9bja4tYU4dLxbpkN/+c6kXe4Bn7ua/ntsQxCzU5YX9OCK024QDTgCpNANMBKpW6N3ZNe4Ow/eEav6auNjdQlBkR+imj4KUXDXUPNn3nHv8yY/7cPf+bT/2aba0ttjOUpxFgGnucZhjl27FgkEoET6RxHEETsRtqW+Hh3LkPsyDsbjPERP4oNABxDRzmWpMgQHDMDhmYJGjAszwWDQTi6ZliOgkUVIkwgCHUNQqoCPJIjoxRNwhwQCta4FI5lOBYGLxJMuIUONAAmQrIM5BCEv3IMoClAUzwPOJgByUdhOgMtEA00JDU4nqQYP6wKIVAeNBXhWHhq6OFgqNgpOA7QDCBIliAIlqU5jmMZgg7XwTKcPIhSfEM4BAkDjo5peqDSBwAWsgyQaCDpZkEuQbNkhCMowELWJhQJMhwZJUOCKAhEAkGGglU1OI4BPAeJDZomg0E6CrkVmgHBEAErVEAFEc0SATrSytERoZ/wUnkS/oVl4MVBygaaUziRaPh5vwCIrYkIiAj8DhGI/Ze688Lu3nPnX8VtEQERARGBe4RA7OYjEg0/7+DcgssdHqXdLbN6lXqnwoz2M5e9ay3R/z/2zgNMiiJt/AOoJDnT5e/uu/N/3qkX9Dw9FRCWDTOziWgmw+aJG2AB9dQzZ4xnQBERSRtmZ/OSDGcWVGQjOe7Cpsmdqqrr/7zVM8PALrjwgbK7NU89vb013dXd73RX1/urN7xc/eJeaa9IfTJ1CfSwSI8EaGsnPeKhLj91++kRRA/IdGdtW8Waz558ojDV/rpx3rv6BcUGu1Of5hg3tyQm1RltLh6d7Rxj5aDh5GylOM5WEgegoSjKUhxjd+otRfrMNdE2p9FUFJtXnjyvaOKERdc9uNTipc0y9YVBg4opFPYBHYmVYCWrB21T07rO0jMZ0ax2GtoyohpU5W4jCERu08/XVRXMTzRXiHDwhXAKCYSQFobjTEmJYCVUJIwVRLQ0kAqYABCisvtIxRDgQKaSSKWA4hdlQZZFWZZZZBBVy+igsLwOigJRQpAiAbxQAkgJiFgQsCQrWJFVJFEkUTh/FFBpgOWdOMbBAdRzLGAkICwiJGtC0JxCQv8ShLQCTh/M7wPEBeYYGNwowExCVSVwJWIggPkTgd4P5EICrZ8AyMBIxApSFYQVQBgIiwxDHGUTKvg7gGUQcBJZVmWBSAEsiUhWFASRJiVMFYwwElTkV5EfI4GdDwo/cUBAVIRVsN3AqsxBw5m6Y3k7XAJcAn1WAl0HDV1r+uzF8wvjEuASOJckoHU+HDScadAQm1Oih5yUpfqMwhhz8WTTyum2pZaXql/dI+x30/b9rrrP6ko3freqpm5Fae3yyobC9bVVH3y3bnvLVwI9INKDHXT7hzvXvFiS/dCKSYvei80uiMpyRqeUxaWUxWU5o82OsWf2hPtea7aiuGyHIQI0GC1F+oy142xOY1ZxdF55cm4QNJg89KDE4kFqrhMcNJxL/dPpn4vm+BBmDUGflxBfkGVZYp/TP8Cxe2qpJVWCCEFMMUbMnAH+ZS43wBo0nVmmskjlABIFJMpIkRnxOIopgryC6e3ACKAoWJSIIBHIuIkViMkAYRmQiLBfpQFwZNAiKTBGxkI3qphIBEvsqlUwCkAUY83xR8H4mKIhNsScFBgr0UAJloka9OAAVsBMKQA6BEEDVj1Y9cH27PoIwphImIgQL1LFlECaDrC4YIEVEFUxVsB5RBGBNcgSVhBQBgJFJrCvxjjAPQKclZiwmDCJKjMcIXHQcOwdx//jEuAS4BI4gQS6YoWuNSfYlVdzCXAJcAmcSQlw0HA2dGyrIzbbYbAX661lsenFMZkl4zPX3Jm1LP359c/Xer/dI23dWPfOE+/Nnb8kPvcdvendONOKibnLU/PfND+x6t6iz5Y1eeu8tKON7vxi19olJSkPLovKe/efuaXjMsoMqWXx5tLkrEK9xaE/G2feZ9qMAA3RluI4u7M70HDPtQ8u5aDhTPYn505bYdCgsQbNfiG8DGfMOGMnrAEqULBVAnP4WgGvgQgGQbBKFIpkikQCRSIyIqD2E4jqyHgACpIGZhYRtG+QVQmpAUwCkEZTAV0eHDGwBDUAGmQNNICCz1wowIiAmVEQQiHGQsgyB/4lhJ0PJKogEN8BzASAibBtCMthSTRcwnwdWB6LMGhQQqBBgiyWqsAMa+CgrFmW/CKCMkSAhuBxKZJURVQVGagHoQoBNwwtnqVKwJdEJTKGxBYEsELQ4kGCA6kSnC1B3KLhjN2xvCEuAS6BviqBrliha01fvXZ+XVwCXALnlAQ4aDgbqrXVEZtXZMgujs0ui8ksHmtyJmSuvdW0bO7idc/sQnVttPGjnW/9+92J5teuN737j7nvXTdn5ci0VeOz3r3T8sbdeUsyXq585YvmL120RaI7N375yPPvxS98+4aFznEmpzHdmWxzTjKt5ektTxY5wurQ24r02Q6jvVhvLooEDeA6EbZoSL7n2vvfNrm4RcM51SWdoZMJ9mxMFQ7npGT/MRigsoCfZ+hY0IyqZSthq2z6XzsAm9kHSwOVlRCAUBWVQIEgDohQ+JZFQ0QUYiJAWEQV1HAwRIB/YF1klgtgLAARC4hKVU1Pl1QIzxA8JMSCYKEcgjWsXjNHgPMJXjRhW2nJMAFasFgP0KRWGMlQtAtioCEUOkKzaFARqxQYdAi3SVmbYSQRtIE4emhKQQIh+w5gE+w6tECTQbsHLeMLa5JRCJFQKMwdQ9YEyEED/LL8wyXAJcAlcBIJdMUKXWtOsjv/ikuAS4BL4ExJgIOGswEa7MWx+QWx+QVR+c4x1uKb7U69dc1E29LZL1Y9vVeqFeneLftWPLPqjkXvjF3kiLIWjc5wjs2sNORsui237M70pbda3jS9vOHVr498LtJde1rfW10964HloxatjbIWx5uKwOY/Z22StdhwNs68b7QZCRqshbGWIr29JN5SZMxYy0HDmeo5zvV2tJ4tvIQpd/Y5W+cddC4A3BBcZX/AsAE+OKi9w4Q/i37IcpoAYoDtmbvDMUutHbAwQGCUgFQW1hFwBtudIQPYUcME4YtSGWXQYkaGK4MrkUo/tBI+KLRIIEtEuDnAHCGtn/GI4CVpbhkQJZMGLStCoAE2gHiWWgn9BYQRrISTYA4V2vmz0JLaiYX4RkQVnJyCqAx+FZrRQ8hghIOG439W/j+XAJcAl8BxEuj6tutac9wu/F8uAS4BLoGzIQGt8+ExGs6sgm0vjp2/NiZ/7dh852ib4ya7M9a6drxt6eyXqp5u6tzioU1f7Hj76RW33rM8Kr9wTNrqm1KKRs0tG51VrTc74ucuS5z7xox71txbVlfSSRv8dNMH39z36CrDvBVj7SXJluLknDWG+ZDnkrtOnNCoQQMNOUUJ9mJDd6AhVovRcKxFQzC9JY/RcDb6mR+9Tc2WQcMNZ+Vkgqq4BggYBwhp2+xvmCaESAHgCG3jsCVBWAUPr0DYUcxKKNaBthPo812YQnCv7utDJ6PtGDzZIEsInoYW+JFBCgYXtPbAXEI6ZnuiwQWNcRxzpcH2gudGmKkFClayncAOI3xx0OjRcw4aW4SOxAgLYoSFRaMMmWxw0HBW7l7eKJcAl0BfkkBXrNC1pi9dL78WLgEugXNWAhw0nFnEoLVmdcTmFOhzCmOyy8dklo40V8RlFY43vz3r+aqn9oh1Abpn8+7lz62564E1+vziqIyCW8wVBntVgsWhN62Ota6aZF6Zmrcqf8WWd5tpk0C/+mr/y08W3W59Jyq7fKKtLMG86sZ85xie3vIkPxyAhkJjCDTE2Yr0OcdYNIRBw3URrhMcNJyzvdQpnBi4KnT3iXSaOCsjrpA7g+byoGnUAK2Y1s0m7TXTAJaqEdwbjurckWjgOH07pF9rRgRBewFQ3Y/xO9C+1TwaQC0PO1Cw9CisXg06NYSPFbJ6CDpzQNSHkJEDAwQKOGhgqgVlCKn/7ASC9QBBgvXanyA5gUtmm4SsMI5uDzsEWQOIi0L0CBLkGXDhWjuhq2OmHAxkhL9SKQcN3d3dvI5LgEuASyBCAl1fcl1rIjbnq1wCXAJcAmdLAhw0nERfPe2vrI5Ya2GsrXicvTwqqxQ4QkbhxKxls56rfmqP0uChOz5pevPxFbctXBGbs3pc6qox9qqk/KqJMP2+NnZ+6e32otTZb8x+yPnox4c3+OjW746881TJNPPSqPlVt2ZXxGe+d02+c6TdEXXap9fndwy6ThRrFg1HQUNkesvcoonJ91x3/1KLizZL1CerEGw/aM7A0lgyDSwUR4+wnJeaMgkK0TFm3mfr4QS39qOfyKOE1ebISr4OCin7/AiiAH056CIBcRUZRohwJghZNECqR829IQwagrYJQS2bJWsAWgI+F1oJ3wNBcwbwnwi5WxzlBeAKoTBWAPq/dpBjQQN4MYS/Cu4IR2V3egg0dGfRoGjnBlLV9me3vwYagiRA24ItQ4cIgQYACsFdIS4Di08ZNHNgYSTZVbFmtEYYaNAaOQqNQu1z0OY5XhQAACAASURBVPAj3Nv8kOeSBMImQXyFS+CEEtBC2gTfGKy7DdWcS/cyPxcuAS6BfiABbQzLXSfOtO4dbS2JsjvG5JaOsxZFWUuMWWsnsWCQz+6Qt7lp0xf733l67dQF7yYucEywORJszsSFNbfOfPumzMIoa/mEWatun7N87sPlj9fsLgvQ+t0B5+LSmba3ohZUTrA6Y6xrr59fOtJawkHDCV0n4NcsjrOVGKwOvbkI1u1Oo61In7km2lpiyCqMyStPnlc4ecKi6x542+qmLTL1IxIEDZDLLwQatLSAoDcy0ACqHChxoenls98/hFXM4ybqOWg4+7I/7SOcCEKxMWFIYQbF+qgaHbna0+OGWwrtEB5zQkVE86H60OGO3zG4adA8gW0VcapBJBA6iNZ0xH/drrImI44bOvQxGx9/HqEvQxuH/obq2V8OGo4RB/+n/0kg9FyFYF2kMnni9fBLS+ue+LLPSKD7+yGEFY5+G6rpf08Mv2IuAS6BH1UCHDScacSgab/RFucYa8mYPGeMrTDa5kgwrZ6S9U7qs+ufbRC2tNPaj3e9/tjKW3OWxuaujjetjs1aG5NbkpSxelxOld5WlTSnYErqqrmPVT3xwaENblq33V2yuGSW/c2o/LJkW0mMpeDGbMcoDhq+54djoMFSEmsqDmadANCwdpyNgYb5EaDBGwEatAB53wMaQAE6kTJ5hh9mDhrOsEB5c71cAhw09PIfkJ8+lwCXwNmXgDZ0iDxO15rIb/k6lwCXAJfAWZIABw3fo6+WnHTa/ITfRltKoi0lUbmlcdbCWM2iIeud1MXrnt1H6gJ0+zeH3lxcdOui92LvLTHklkTnOg3Zaw25hfF5lbFZjqiUtRNS3ptxX8G/qprKO2l9Y7vzeccc25KxucWJ1hKD2TEuq+gW1v7pnVv/2IuDhrPUZfBmuQR+PAlw0PDjyZ4fmUuAS6CXSKArVuha00suhZ8mlwCXQO+WAAcNZwc0xJqdwBqyS+PMRdGRoGGXvNVL677c+8rTq5Pyl42aVzjGXDDSVHSLfU1s9tpYS9FNqatvynJMzFg5a+Gqe2saK710e31r0fMlM7KX3mIriDWX6C3l8VnF/QMWnJDj9ODyOWjo3T0TP3sugW4k0P9AQ9jD5CytdCNkXsUlwCXQuyXQFSt0rendV8jPnkuAS6CXSICDhrMEGlizGmiIsjgNmQUTMt5Jf3bdCzvFWg+t/3Lvq0+vTpr3zqi8oqisoluyHGMsa0fb1t5sK7nB5BhlK7/NvHruo6VPbnV/HaA7v2le/mzJlLx3/2ktHJXpHGurSjI5jUAc/i96eJ/fl4OGXtIF8dPkEui5BDhoCMXfOFPcoeey51tyCXAJ9BIJdMUKXWt6yaXw0+QS4BLo3RLgoOEsqeuQeMIRm1saYy4eY3HGMdCQ+uy6F/YoTT664+uDSxYXT160Oia/3Ggpj7NWxZkdN+eW3rxg/ThbaXTaqkmm5WkvrH9lF97eRrduaHz2kbWxC9Zeby+5OdMx0laVZClL4KDhe344Dhp6d8/Ez55LoBsJ9D/QcGox/47GfjtxXMD/4zbd/Cq86hyWwP/x5+a7n+MS6P7W64oVQjWnejndt89ruQS4BLgEeigBDhq+R189rZl/qyPWXqy3F8fmOWOsRaMtpeMyCxLTl6c+s/6FWu+3rbT2k50vP7oyyb58jL04NrUkNrVsnKVspL1iZP56Y9oq/ew377i/5IGC78r20l0NgZp3PrHlLf/7IucN8ypHpRf9016eYHUmWhzcouGkDhQcNPSwC+CbcQn0Hglw0HCqesIZ37733Cz8TEECZ/wG4A2eUxLo/i4PYYWj34ZqTvXkj7bA17gEuAS4BE5DAhw0nD3QkB0CDVYngIaM5XOfWb+4UajtpI1f7H3jqYLb8lfG5pclWyoSLdVJprJxWUVjrcWJs5ck2d7OXLO1sEFq6qQHP25595mKu9OXXLmw/OaFNdHpBTfZnPEWRzwHDd/zw3HQcBrdAd+FS+DclkD/Aw2QA6e78qMpkN9zg4T0me/ZjH99ShLQpKqqKiEkUsLBARw5SRqkU1Us+fa9SwLd30fhG6bLClZV3BU/nbiy+/Z5LZcAlwCXQA8lEHxPUVVGkkz9m+pLpywaNWPxjQsqEvOq49IKRtqc8daSBHtRvK0ozlY81uIYa3XEci335Fqu1aHPK020F8blFkXbHVHzqo2W4mTT8jkvf/Ty9sB3It3zxe4lT6yY9K/3kvMLk9NWxpgdyVlrkrLem5i19A7Tq7OfK32mMVDvoc1ft1e/9d/8fxUmZy6/we4Yk1+dbC0xmgqM9pJk/hOc/CewnCHQgCkhFKsqDPYh+SVRoXRJbxl+m2OMVRUSYGqfyPVQ3an9JewT3gdj3LVlcrJxZnhXvsIl0OslwEFDCDqck6Ah3A/+3zu+Xn+rntELCAv2jLbKG+t3EugWKHTlDhE1/U5E/IK5BLgEzqwEOGj4Hn31tFwnLA79vLIke7EhxxFnKxxjL4tLX21Me2v6CxsW7xTrPXTXf797/d//mTz/lcTsJUkzX4ya+3p81lu3Zr8z+7maR0rr19a5Nnvovr3SxwVfPvZY6d25a2IzVo6xlhizSydkFSRkrjZai7nrxEn9JkpifyzQAKaqhIRxAKUUIRQIBE7vsdVmsLRJrHALqqrKsixJUuRRwt/yFS6BPiyBfgQaQrolzED2vERoCD2amD1Ryydu52R3V+ic4e/JtuPfnYoEjpVqj37W0M93Kofh2/ZWCZzSLXGyjQlBCMkIyYTArAorvVUo/Ly5BLgEzhEJaK8wwi0aTg8onGgvhz67LNFabMh1GmzF46yO2IxVxsxls17a9NLOQKOPttTtrVxT9djqDU+u/mTxmxsfWfLhE8s+fWnll29ubvvISw+IdNc+/4bKrx96fO2E7HdHmVaPNhcbLaWTLI4pmWuSMtcaLA4Dt2j4HkL0A1o0HDsOpBhjhFDkSFtRlFN93oMPJmMWGONIg1nEPuEGVVVFCIX/5StcAn1YAv0FNIT7FEJQt+VEgOBU67ttnBB04nbCp3bMinbPdUtG+/Dt+ANcWljKmm2b9nthrISLVnPi34uCPR4vfVYC3VPIEz3XIXxAjrthwFhTxYQg7b7SegAOGn6AB5wfgkugz0tAe4tx0PA9WuuJgMKJ680l+qzCmLxSY7YjzlYSZylMzlmb9uon/9nm3hqgHSLd2+Lb0kGb3HTPYdpwmDZ00L2HaGMH3d1O67fsX/Vuje3JVcaFy28wr7jOXDzaWp5sKZ1icUwxF483lxjMJz7uGb+Q3trgDwUajhsHUkpVVdVYg6IoCCHNryG8WQ9XtFEljvhoNQghSZLC5IIQoiiK5q/Rbct9vgfjF9ivJNAvQEP4SQbjKBVhVe55IVQ5taLKpLuiUqWbArYV3X+0ji+kDMPfSNTar+7RM3ixYVlHCBZhIiEsKkhQkICwiIlEVBkTqZsS5BERrxG+2qckwHhTdz89wmK3hTmAIkqR9nRrz752R2k3UrgeKpF8Bm9m3hSXAJdA/5SA9iLjoOGMq9MmR0xmYZQGGqyOaEtxYnbRrMUfPf55x0dt9ECAFT9t9tJmD93roTs7aMMu+vEXHY6Cr596cu20+a+Nmv/2dQsLbrAVX2+riLKUx2eVJFqKk+3O8eYyfbrjFs4avucn+0FAQ9dxoNaNgP1h6COKYiAQCG/Zw5UTjYYQQoqiSJIkCIKXfTTocKJm+2e3xq+6r0qg74OG8JPMdEt0ItCAiNRt6TmS0LbsljKcrDJC5Y1c1U5bcxs7zgSrr96LZ/W6wrdB2EiEyVbRVERNM9Qog0qhshvK0J3+yTfrJxLoljIgLEaChjCf0rgVJpJ2L4XvMQUJZ/Um541zCXAJ9AcJBIcH3HXijNsIlMZkFNwCoKE42lQ0KqswJn31hEUV5v98+lThtqU129+t2PZWwVdvrP7i5TVfPbd2y5PLvvjXqx/nPVKWkvPOeMuSqPnvRd3jGDO/dKStYrSpYmxmSUxGYYy50GAtMWY4Y+YWjuag4ccFDScYB0KkRo0FRPYekiRFjsl7so4Q0obr4Y01KwlRFLWWVVXVbBm0f8ObHbcSeRp8nUugt0ugj4OGcLcSeowRIoqsSj0vCpV7XrAqd2O2wGwZTsgaQmd23N/gSKI7X6/efs/9KOff5U6AwD8Ya6BBxkRUUEBBAUxElcqUKkSVTlAEovLShyVwot+9+/quoEFDEtrzHtkbaDU/ys3PD8olwCXQlyQQHB5w0HCmQYOtNDZjzeg8pyHHEWN23Gxyjp5bMM7snDznzfj0JUnmNxOzXjPOfSkm9T/RptdHZrxxveWdmyzv3ZK6fMysZbeY1xgWVk2cX5WU5YhKL4tKLRub4Yg2O2JsRTGW4rh0R2yKI4aDhnMENITH25oNgizLGiDQfBzCIRvDm/VwJQwaIgfwWpuakXIgEJAkSeuLRFE8UbN9qbPi18Il0H9AAwvNoMpgtqAGsOojxEOJO1Q8GLuDhbiwVliNqnp7XqjqpdRH1cjihUrVS4lWPJR42L9+VRVUIqkEAsWxQrAKRfuEJ961fjBc2d9uWZVSrXS58K5B+LRNgiH3wjsGV4KkAdzmVYIIVoJFlRUsSkpAkKBIiixhoqgU05MUwr7ly74ngRP+6OAd0V3RkmdpS5lQCUMREcRuVSnFhCKCEVFYMEgtHiS7S48N7Xr8vQqbhG/vLjc+r+AS4BLo3xLgoOF79NXTBRBmx7jUlaNyHHG5JbH20rHZVdEZznHm8gRzUULae3EZ70ZbVsaYVsblFMTlFY2xrLreVnBjXlm0rcyQ6dBbypJsFeNNDv3cNVEpjpiUknEWZ1xueXyu02Auis4ojoN2SvRn6cz7SLOn7jpBsHI0Yb2qEqoiiiPTW7L00/DEQGYJChscN7rGGAuCX3u9ezpaG7Zu/earzfv2HhIUNhSEPJkwAMcEXuhaYQNyiOMQuaJiCP4cjLzAOigW90HBCCwfVSS07t/z3ebNtQ27XT6sUiqKojbmV9mgVMVEhdZhgKoNCdgwAAJGHjteOK7vY0MFtsUxe6mIXWt4425TtoeHGRHfHj3YCb7tZoPwUbpbCW6vtdbdBryur0ugz4AGuIlZqlzIlgs9ihrxcKqYYAkjASEfpZIkt1GlhdJmqu6jeA9F+6i8h6IDlBykZD9UqvuhkANUbabo0KmUA9Aa2keVA1BgfS/FrMh7qLAT/lV2w1I+KLj3U+pHkgtjP8x2Uqyo0K8RduJa16OFZtBqQrpHX78lI64PVLVQCfWh2teEvRUUShWqylRVVCIDPiDg8kDAhAR0YEUF5VBrAZrCCsKiSmSKJCz6VcFPFZHClrAxgraoRKmg0gCmIlvxYOpG1Euon9IAhUqRbSN1WYoq+5Yve7sEQr+y9luffCkAVqRedm9E3h6iSsFrgt2I4dtPhfxZCB5uFVxzsMLW2X8BWYmkGAqFODLsfoxgExHPBV/lEuAS6M8S4KDhrOrVVkes1RFrKYm2lESbS2LNJfrssoTciuR5lcn51RPyqyfMq0ycX2HMK9NbHdGwpUPPYj0azCWQWiK0roccEw69lS21Sp514nt+uBOABrvTaCqKnV+ePK9w8oRF192/1OKhzRL1wXivC2gAMgBMAYNKQBhd0B4YCsmfFBb2EaAAwQgQAlUJ8vo64DWOXEf21U9LTrjhqr/m5T3Q7ocBoUtUBCRKiqgQGCV6ZRgowmhc1eYPCIbJKhkCvstACiAINMUAO2BIqmAkqIqHyJ1C6+77M6dd/atf3TY9Z/NOn1+mYF5NSUCW2Ekpos9PRJlihapIoVTEFEkeNg6F40oiphCBGwMogdCVVEHg7qESCcbABEaxMtZGvBLGbiq7KJaQoKgEBhkES2ENgiEXUJIAfxApNIFCkAyHkAQ2KIGRMShNlCICltpAWPxeUZsBUSQ2ORoMPMdyZ6hUxTTgl2UZKIk2zlG18QuiGEHoOhVCWYXVtB6t9OdOvi9de38ADYQCaBBVJBDU2XHk64N7qtp3O+RDJWj/GnnPCnnv6sDu1YE9Rf69RYH9awP7V4v717BSJO4vkg84e16kg07pUJF0qEg+6FAOOJQDRfLBAukQFPmgI7C3AB9yiPvXSAcdngOlR3ZVSZ3bVLlZxW6iSugY0ECOAw3AH1i/0Jduvu+9Fg00RMIC2AW6McLUsDBokMOggSEGbaadIuj6ADREQgqIx+MXoYr1vzKlB914S1PL+s+bHJvq3i3b8sqKT558ff19z5Uverps3uOOvMcL8x4vnP8klPwn1y54vKDbsvCJQl76hATWLnyim5L/2OquZd7jq63/XmH99wr7wyvnPVHw4IuVi9/+75uF375XXrfhy5ZPtrbV7vEddFM/gy8BSt1gOaPIMtLuPXj9EwTWNSoMgDTUpbCblj3vWl3EVMP3PjB8Ay4BLoF+IAEOGr5HXz1di4YTNWsqjtZKVtG4rKJx4X81lBDMW6llrzz58kyf2IlOuLfWnyJoAP0ZBS0aABkwi4ZI0ICPBQ0yTBGxxFLszRsGDQGhE2YNUNvu7z4eecXlw3UDJ06evb1Z8Cqg8KsUCSjgkUFZ96tAHxCRFNmvyKKiSDISsMre6ZqqoSINNMBrnSgq8qtKJyXuwKF62+Toi3S6v4++s2pzW2cAItP7FUnEkooEiuXgoFSRZE+nH8NRKBUUoUNQZIWCdYI29CVUFQRVZpvDJtqcBIGRroRgngwBCfFT4ic+T3ikgZEsBDyBgC8UVD443GBew4KiSBjJMFxmvECRQZJsAo9BEIpkJAXH3Ww4jiWZTeYiTBWsyqIoIplN7QYX7Ey1MbZ22ohZEANr6BFfCG/WD/ryfnGJ/Qs0UHzgyJ7l+7c92PLdAl/DvECt1b/VLNTmemtz3dvmd9bO76zLcdXneOpyvPW53vo8b/18T23+KZS6eZ66XE9drndbPpTaXG8dtOapy/XVL3Rty/XXz3Nty3bVzeuoX9S89UHfwTVU3qUiLwcN3T5tkRYNGi9gHWvYsJxpYiqoZpBJkNm2IUpllbLMH5gxVFnzT4F3hQIWdbABpS6Bbm3qXPf5/teLvn3srU/znipPvW/11PkrJlvf1s9+ddSdL/x9wlN/S3ryauOjV+ofvlL/8FWGh67U//sq/b+v1j9ytf6xruXPhsd56RMSePTPhm7KVXEPd1v+Mem5a8c//bekJ69NfuLGWxfHznhtoumdO3OWz71nteXhwntfqli86r9r3q//eEfbXom6mblEkClgWVR8ouTWYoKwqRdVRFiCYQ17T4MNDgSp4h8uAS4BLoFICXDQ8AMr0rZScHkwFUdnFkZlFkaZiqMtJbFaJZyJgzlE9GTJQcPJJXCWQYMEBgNB0KC5LYDhAVa8vjaiuiluP9i4efSVfxqmGzhzTjaYtUrU55Uk2ReQXAIlAWbD6IN5Qc3kEKyNtVlAVSZYRJDPGhLbge0iWNQy0ECRi6oe6tq7YGryxTrdKP2cT3cooKlTRaYIg3GETKQAgBJMwaJAVbwK8SCsKF4K9ICNaWUwgfR6vdrwQJtCk2Qk+T1UVSiCbzGlXoW0CwEPlmSCFbDZoLJENIKgoYGA4nMF2lSquZCzs4joWVRMsQIMhtWBDaYo+RAWZSUAUyEqlkUJrhHyZ4HNiCCJQccItgMYa1IakBW4dmgKEVmisswMSxjFYOYeYY7wvSsRp8ZXe7EE+hloQLt9za91NmW761Llptm4/m5cexdtnEWa5igNKVJDitQ4R2qcgZpmQmmcjZtS5PrZPS9iw2yxYabYMFuun6vUzVXqZsv1M7WCGlPF2tm4Ya6wbVqgdravMcNVn0Pbl1G58VjQAL0N9AjgCyCHXSf6rUVDyEIBPGKCH62jDfJV1jczazVmKkf9ohpQwEleRoqs+GXZgySPIgegF1apR6C7D9PP6gMra7YvWlx+u+3VUXc9NXr6i1GzXhs7Z8nY2W/dMuvtW+a+OzZlzYR565NyqhPs1fG2qgQ7rCTaqhOsVcnZG1jZdNxyfM7743M28mXvl8BG9jsevwz97tqvf3SZYF0fb1lnMFXGZpTFpZfEphXGpq6KTVtx053Pj5r67Mipj//zjgdGT3/gtvkv37d03bINtZ9v7zzoBS8bmVKRyAHJI8meoIEi5NbGiqwSDIaRqspBQ+iR53+5BLgEIiTAQcMPDBqyy432MoOtVG91xlmdcbZSvb3MkF1u/IFPo+8f7ocADZBpHhwfmHeDBhrYWLFDlQ4f2bVVf93fLxkwfMbsnE6BKph6OgOi6JJRp0AEN5Za/B0e7HcF2gTRI/gDkgSOujBPIGHZD0o4uEuEQANYOmDwyKByO+nYfc+MiZfpdOOSMr4+AAYLbk+7X/D4/J2Sz6UIPr+IRQlTAu65EqU+ovoDLrCboNQjKC6XSxECBOKXU7cn0Hyk0ydhMEuQZFWRkShA0DFmbeFnNEQbY2BCBUHyefyBgKhgpFAkUr9PdSMqeBWXgAIwjg7NbEAITBZOghDk9/u1uGWyLFOKJNkviV6Pu10UvKCeqBi8Raja0eESRQYdVDkget0Bj0cUfRKSCAzCRTEA0EP0U0WhElVF6Le0MBffixi0DSL6PL7aiyXQ70CD2PKGu8nuq0vBjbPVurvptjtow0zaOJfUpaD6OUrDLNw4U22coTbOIE1zSNMcuiPllMv2NNqUAWV7Gt2eQrezRrZnqPWpdEcGqZ+lNKTITSZx+zzqWUklDTTIIdcJDhqOPk6sr0E46NkVqg+DBm2FsM4LDBpYuAVmKUdUipCsyAFF9kP0R4Cs1I/pZ7XCoqfLJqS+NNn61njr0hunPjdy9utjU5frzWuM9iKjzWHMLknMq0jKX6fProizl+utFQZbZXx2dUL2+kRWErLXJ+TUdF3G29fFZ1fzZa+XQHY1/I5dCvzoXUv2+kT7+0nZH47P/WjivI8n5H04Pm9Tkr0mwVY+eUH1lPzy8TlFsZlv3zLrlTFzXjGa37pt/vIp5pdeWv3ZtkPETQA3gO+lIro6W8FwkSAAjAgjWVFkkQ0pmPdj6Mbnf7kEuAS4BEA1YB+YCUWSTP2b6kunLBo1Y/GNCyoS86rj0gpG2pzx1pIEe1G8rSjOVjzW4hirhRLo+5rqySfMT/dbUzHkj7CUxGqgwVISa3bEMLsGiONwKiWW/wQnk8BZBg0yZJFjoIGCgwNYM2AVwfy8QpBLFY4c2blNf90/LtYNv+1uy45mRUIsghcVKHG7PW2dPlen5IbYCCq8vTGhnoAE7g9IkUVMFZjDR9Ak0dwhMajVIsUeSrykY++8OxMu0un+OW7ml7thpsHr60RKIOBto1iglAiEBgglis/dcTCgxU4gSnt7a6vLK8AYFvyF25ubiayolEoEWgA/BkKFgAK7Bzxub4eAFYlSr6oeCQRaPG4Roj0ASiCYCiLz1ACKIQlUdCGPgMEhQlGwJEmS7Pd426VAO6VaXIZgdDNRwl6vl1IkCh5J9CpIULCMwT0EImQySEEU0S3LLgX5BBTwSpJLVFwCDigqiAUJYKfBTB2UADM8DoXU7Alr4L1935BAvwMNQvMSd1Oery4DNc7FtdPwd3fT+tm0PhXXpsh1KWCJ0DALsYIb5uKGVNKY0vOCm1JwUxpuTCcNmRRKOmlMY7unofpUuXYubcpA2+aI2+b467K89Xm08z0qb2cWDRpooArEpoHQhCGLBqR5nbH5/H4YtRWIQY9BA/R7WpfKOnsIBQSWaJT6Ca3+ePd/Vn6ZcV9B3IyXbrpj8eiZr8Wa3ovPLpq0oDopt0xvccRaig12Z3xOmT67HGwiGWjQKENS7sbxeZvG534wPm8TU0EBPcRnH7M02quM9gq+7KsSYJShqsuyxmBdB8VcE2eu1psr9eZKg6XCaC2PziyKSi+ISl8TaypKsJcYrcVjUlbcOPW166c8OdG8JOcJ5yurP/+svk0LxKRSCiGUIMwTxJORJUESfZqZg6piTak4btk3Xj/8KrgEuAROQwIcNJxMWT1dmnCSNiPdJSLdKE4FMWg8goOGk0rgLIMGiUowtw5RDMASAXAAVhXQzSXIB4ddrbtqo/9yzSUDfjIzZUGLn3olqoAfgR8LrRRLLFICEaSAz+/SHHsDBJqTVNXvk0U/wgqEf2RxlxgFUAklEoAG1Ue9hxZNm3SRTjfakPr1fuZ6IQcoEagKxePuPNgpitAXSCryyizCtAImC9COyy92dB6BUA6yiAJghgAeCow1YDbKhRoiCbLXJ3ncfl+n3+tHfgigQCVFkSRJQYqm7MPZipR0EsmFRBarUfV5OyXRy6JO+ij1YaUzIHgEBawSggHO4A/xdLSCYkKJV/TJBLJmyJIgdLYTwa05lBDqZWEi4Nw8UoiDUCojFpCSqS/g4MxiWPTQruE0+ka+yzkogX4GGvAef8s7HdvvdTdkox0W3JCGa+eq9RlKnUmqN4v1ZrHBJDVmyKwojSa5ySI2ZIoN6T0umWKDSaw3S3VWKPVmqd4k1UNloDYrsC1TbbIJtZnebRmubfb2bQtI+yoq71QRZJ1gFg3dgoZgmFlGNPtbZDgADaFYtezxOc6cAf4FjzvIN6FCIFwVHCYEMSBh1gu3+OhH37W/W7U97V9rxk17/h+Tnok3rbnzng2J2c5YU8H4eRXx1rK4rJJxaQVR6QUxWcVx5pI4c3FMVmFsZnFsZrE+w2HILE0wVSWaqhKzauIzKwyZJXFZjris45exmQ62C1/2TQnoTU69qaTrcnL+hgnzq5Oyyw3WEr2pOC6rSG8q1puKE+0VRkupwVyaYKuYMG/jpPwPk3Pej7dU35a/MSZ1xU23PWec+fzCp0rLP9y3v5WKhMWLxjDDgpGMFAEoAxZZRhUOGs7B9yY/JS6BH1MCHDScBAqcja80dwlbqT6yWJ1xHDScYWmfZdAgq8eABhh1DAr0VgAAIABJREFUY8RAg0hVL8Wugw1f33T5FcN1Q6bOytvRhtyEtnpaRV8LDRyh/kDnnubSVY6nH33yofsfePa55597ZckKR1n9wUMSC9aIJIpkVYFADSxAAwzZVQANxE8ll9K6a8HdEy7V6cYmZH2xiwYQ9Xk7Rfdhxd1cXbTqwQf+VbzhMx+lHm/7559suPffD9jyFz708KMbN77vExCL1cSyosniyreW2u3W+ffkP/rcs2XrPjjSjnwydQlYZuEgseojio9ZJXRgufnD98veeP3lf/3rXw8+8Mji519ZuaZkS/0OP6VtCm2XgQVgVZaEVko9lLo6PbtbDzcVrl361FNPPPbUsw8+8uxb76zdv+8IJfS/Gz987olnNqzbKBNVJEQk4D0hCx6KvFT1qHLzx/91vvDCow8/8sAjjz714MPPLF/p+PybOkjYoVKXX+r0SIKgQtQGFUBDz1nDj9nJ8mOfOQn0O9DgOrT0SNM9rnq7vMOqgJXBHKU2Q9qWGag1++vMgYasQEOGUJ/GyIJJANZgkrZn9nhpkrZbpCar3GiXG+1Ko1VusrB9LVKDTWqw0D15ynar0GB2N+R21C+iHQVU2hUBGoCwMoyosBCGEoQzDOWzASAJ1lP96xMyr2JXfxxlAGkQlRnCEVXCVCKqRFUFC4IkIKCqmH5cG1i4eP2ou565dsozcaY1E/Nr9Bbn2NQCg6UsMafSYHbGpJck2qonz39/8oJNSTnVRltZQnZFcl5lor0iyVqeZK0Yb62cYFs/ybp+gnl9clbVeHtNUnbVeHvVccsJ2dXJOdV82d8kYLSUGC0l8TZHvL0kMduZkO3UlgZrSVJu1fi8dYnZNbGZleMyKvXmDUm5Hyblvp+YXZVsLTWmvRt913O3pr303JsfflnvhrhThAYkCGYNWTApolgiMpgdHmfLoP3bv3oBfrVcAlwCERLgoOEM67ffZwQRtmIIZ50IBYM8Jb8JCCHJy8kkcHZBA5LZQJHZuSqYxQdXMJJlkZCAit0Uuw5v3xr9l2t+NfyXqeb7d7QhH6WH3QcodVHi2btl6z3pub++8FcX6M4/X3eBTneebuDg3/7lmqdff31vaxs4JARUpRvQoABoIH6148DCqRMv0eluMWZ8tgNyRVAEDKK5ccvEmFGDdLr7nnp1Z7v8wovP/u//XKobqBs4ZMjQwcOu+eu1r736poJgoPvtN1+mTpt+6dBhA3S6wUPP0w3U/eHKP7/4yorte7xHfNiNsQ+5/cIRsJIQWj+uXrbIfscffnfZBRfoBui0z8Cf/fK3ppx7P6/d76K0VaJ+GUsQxayF0vZO164N7xdPnhgzfAhsfP7QC3UDBg+76GemLHtN6bo5t8+6QHf+xAl37NzXwsJLEQh8pnghm0bn7jUrXxo1+i8D2GGGDL5Qpztfpzt/8m13V278r0uAK9VsSCCAJQcNEd14/1ntb6Bht//w0vbt+a66DHl7OmqYheumq/WpSl2K3JAFxguNqWJjitiYIjemKk3pyo4MpSlN3pHS0+X2NGVHurw9XWnKVJoyUUOa3JgqszaVpkypIR03pgt1c5QdGd5GU9u2HOpaRZVdKhJUsHGCQLUI/JeYWReRVNIFNDAPiv5zd0bEqWEXfQxowBBrFyiDKMkehUDWIUn2sD4N7LqOeGjJBwdtj1QYUpeNS1kVn11pyF4Xl12lt1cZbOUGS5mRlXhbjdG2npWakNl/udFeHs9Kgq0iyVqRZKlJttQkm9cnm9dD8D9rN8tE24YE2wa+7KsS6P53t9Uwd4lyvbk0zlKqtzjjLE69xRFndcRnVxjt5QZbeZyt0pC9wWDfpLdtjLOtS8jbGJ3liLdV3LFo4wRz0di7Xro1a8m9z1V+1uj1qNStUI/MUk3AuALCUUPcpe4+/aoT4BfLJcAlECkBDhpOpqxyZb73SuBsggaIqqDKMB1FBArvV4QoQQTLskipiJVOqnoO1G2O/8c/h+mGzkxf1CyAQ0UA9PDWLR+WxV0/coTuwit+dXXUSP2smSkJ4ycNvvhn//vXv6XNy2nYt1el1OcWjwMNLEaDDKABeUn7/oVTJ14EWSfSPt9JJZUqgo8qHrV977i//ekCne62OblZ9zz1939ce8Xlv5w9e+att9/2u1/9z/m6Qb/5xf/cc889zrLiCZOSRgy5cPSNo2bNnHrnXZN+/eufDByg+91v//z8Sys7FdqBqQt5MPVSIuze8t+Ekf/vsvN0v//lCGPcmKlTJ02dOuWvf/nT4MGDB5x/ya3TbF82uTsV8Iyg1K/49lLaXlDw9u8v/9WAAbrLLhv+5z9flZqZdffMOZf87Je/++3vx8clXPGLy38+7Jczp5t2HXBJlB5sPUwlN8Xuw/vrH304f9iFukEX6P7397+NjY21W22xUWMv+skwnU6njx//XmElAxPUJ2ERQXSJ0NzhMXkuNU8KLS9p5AaRnR5f770S6GegAe0OtCzt2D7PXQsEgdTPInXTKcsQITdmiA3pUkMqSzwxR26ai5vS0PZ03JSmbE/BTSk9XKIdqbBlIzAF3JCKGueixrlS4xzAFvWppDFNqpul7Ej3NWS1b7OfDDSowlHQwJLTwpw+Bw0h1qASmTmxg3+cQvyEiiqVRCx4BHBvO9RB3y3dNnveyphpS+LSCsfnfmCwb4rLXh+XXROXXcWiPJYZzRVGc5VGGfT29Xp7jd5epc+u0GeX63NKjfYgbkiwVSRYq5IsNUmW9UkWoAzdFoO5hpc+IAG9qbrb0u2PHm9dH7xkSxXEDbVU6K1lemup3lYSz24kAA32ckP2OkP2Or19fZytOn7e+miLM9Zclpy7fmJOTUJmgSF12QTTG/OfdZZ/uX+fjwYoFRANBESgDIhw0NB7X678zLkEzpIEOGjgoKFvSuDsggYSAg0wxc7m9ogcAg1E7qTkeNDg9rVS9Qj273rkXtOIQefd8NeRS99cU1+/t93ta25vr9z0YdnGTTWffNji7YSQDQoEVox0nQDQoLlOIAgGeRxoQIKXCm1q6x7rHZOG6Qbphl8ee3vWq6+/tuWLDyDDpce15u1l11/95wt0uot/ctHlV/7xpjFjFt3zwJefb1EUye1uLlr58nj9qAt0gxMTptXv9x6W6SF/h0R9srf9/ZI1aXcZF9mmb6ws3tmw1dWxD8mtdds+furJR376i9/rdJc9/lJRm0DdHl/Ac0j17akqfuuav/1Fpztv4oTEqkrnoUMHXH734c7WzzZ/+vTjj1xzxR9/OmjEJYMuNWXde7Bd9hJ6uOMIJS7i3nvfouxhFw4ePGJolt1as/GD9g6XHPAe2FNfuOaNCcnjBg46f/psc+Mut0+mXhFJRA4DhROtRFIGlmvvLPWgvNkfVAL9DTTsEZuXuRoXer8z48ZMtS5F3aZFgkxDEE8hU6kHHKDUp6KGNNKQSZqySEMmBHdk4KBHy+2AGEiDiTSY1PoM3JCGGqFB3GhCddCOXDsXbc8K1Js6v8uhnWuovBssGlSYxjzGooGDhsgHIcQXQrQFlDCkBIgqUCrJ2IdUEVMUUImf0sYj6PXCzXfbltxy2/Oxs1eOt61PzvnQaAuihDg70/1slXpLld5SY7QxvsAQQ1xORVxOeVxOaVxuCbCGbChh04YEa1WCtSreVsGCPh6/ZBpmGV/2dgnEmp3dlm5/9HhbldGyLt68Id6yzmitjrdUGq3lRluZ5oADrMpWZrCVa3FDjbYag63SkFOpz66INlfEmqoS7Rsn5G7QZxaOvPvlkXc9lvN0Yc03BzWbHL8kQ2BIReagIbIb4OtcAlwCPOvEOaRjO/SWUyq919bghznzswwawBkZNP8waKAyUTWLBg00HKrdbLz+n0N1Q6dnLDoSoFQNUPXwwcZNk5P+OXCALsuWu6cV/By1jA8SgcCHnaLXRwKSIguCpChKN8EgiZdiTwRoSPl8J5wBgAapw71zq+32CT89f9jwX1x777PvdXS6WYQFSMBJZenlRx75xxVXnD9AN3DYhQseeqzDo6gqFUUZ0kOIexzLnr504KCLBv/8sWeX7ffSVkUMgJsCaW85+PUX/20/sp+IIrP89VH1CFUPC76DubnzBg39xYzM+ztFKooBqrRj1+6c1OkDdBdefdWoqup1LOIjElAAgdWF331kz2tPP/qny341TDc8LTV/X5vogxlPieLDX1S+d/VVf9INHLrokae2H2pr90EsCUV0U+pSpb01Vct1A3SX/ux/n128ArG5Ur/fqwVo6LoMcwcOGvrkC64fgoZ3XI33eL+z4kZTEDTUpeHaNFxvVupMCrCGVFSfigE0mEiTmYGGzFNYNmXgxkzSYKL1JrUuC9dnoIY0pT6dgQZoB0BDkzlQb+ncmndi0CCpqqCqwvExGvqfRUPwqdMuPGjTAZQBnCaIpFIZEzEgekVZwBBNl7Zg+mrpt5MyX46a+sIEc9Edee9PsG/SZ1WBi4S9wmAri7OXAWuwVbJ8ARuCThMRuAEsGlhhRg0V8TYoGmUIgwYNN0QuE+yV8dmVfNnbJcB+0/IeLtmNAX40SZYaMHuxVoH9i608wVaeaIeVeGtZvLUs0V6ZaK9MsFYZrZVxllJjTo3evj4qsyomsyYx54Pk3PVGa/HomS8bM57Pfnr1B3XNATaOgZjYcgDStnb36ZNvI35RXAJcAj2RALdoOFdYwylRBof+XDntH4YanMZRfgjQIFOInIiYRUMINKhyBGi4Yahu8PTMhQAaRC8Vmhu+ck5MuH7QebqpqSmbd+70U+qSJY8it3sC7oDkRQKCmOUUYxwBGiDSUsiiIQQapo1nrhNB0KCIXko8vj2108bddLFu0MSp+V/tlBRCsexFyNd6aA9F/i3ryhNvvuHSCy8cmzD+s8a9IkR8pB6v4He3UPXAlppl/zt88GDd4Oz5T/tZogov9gcCPpifYAbQWKE+V+f+vdsO7Nu8q+H9rz4tu+3WiTrd0KlpC7Yf8Eqyn6qerzc5Rv/16vN1v1iw6EW3T5JVqcPV2uY+4hGP+MVmIjS79zRMHj12hG7IzNn2ve2SH3ooP27++qmcGSOGXHjj2KT6/Z0BSj3MFwOydSotlB6k9PAdd0686NJfjR13+8FDskpoR0cbIkq3hYOGnnT7vXebfgcaAi3LOpoWureZlUYTqUshtbNpXZpSG7RokJj3hGbRAGigCQwflCYwUujhEm3PUJoycSNYNOD6IGiQGtKVRqAYpCFTqp2rbDf7Gizt34VAgyIea9EAwVM4aDj6UHVnzqAlKAK7BiT7A6KEoP/1Ivri2s1T7109eurzxowVt+VWJ5oqYlOd8ZbK+OBUs9NgdwJrsFXq7euNto3x1k3x1o3x1o1Gm1ZC8RpsNfG2Gi0cA9MkNyZZNiZAdIaaeJtm2nDMMtFWnWCv5ss+IYFKdhXHLDXGdNyShfCoSrJWJFsgdGgyRA8tT7KVJtlKkyG6R3mipSzRUpZsq0y2VSZZqxItlQZLlcFabbBuiDOvi81ap7esT877YMrCD5Nznfr0JTfeem/2Eyu+2tnuh/tbFSRfd5AB6o4+HXyNS4BLoJ9JgIOGc0Vj56DhNGjCSXY5y6BBJZA5HkCDCgEaZHocaHAdqv3SeP0/huoumJaVf1igAY+Xip3+I9seWJQyYIBu4NCBWfNztuxsahcCsC9Lne5WkA/LWIXUZ4oiKVhm6S010IAgHgRLnEk6di8MgoY5n+/CYFMhuiHtpftgqjFqhE5368x7th6kAYWywa2gCG0UefZu/nDSmBuHnjdgmmVebWugU4aBLsy7qQolB+o+WvHPy399yXk/yc9/zoeoj2C/4kaSj2Iiy7Sl1ff5Z9889ODDiYnjkuJvuum6//nj/w4fpNMNHv6TFPu93zQdwEikxLPsuYd+O2LEtX+Jq1xX5xEwogRDUTD1C2IL8ezz76/Pvuuui3XDUtJyDwfUDiyLUuuhz4rS4q77469/P2/BE7ubPR0i3d3S2dbp62xrFtz7XW3fHTqwOWee9Yq/XPfna2O31rZ0uvyUEgXL3RYOGvr2G6wfg4amTFI/h9TOpPWpuDYFNWRJ9RCvUWpIURpSUGMqbsxE27N6jhi0LU8EGvBR0JB6LGjYq3YPGvzMokE6JutEf7ZoOMacgb0twK4BMgkBZVCpy0O/rHUlpb8Uk/a2IXNVsrUk0VKm5adMtFdCrkFridHmMNidBluZ3l5lZGEg460bEywAGiJwg8YatFgM8O3RwkBDgrWma4FwD7z0fgmETVd6uAKxQqEwxBCxDIGG8kRLuQYaki0AGpLs62OzymMyy+Lt65Jy3zdY18WZ1xnMNRPzNt61aMMNk58ce/u/n1m2fuvuTh+G+xuGL919+vZriV8dlwCXwEkkwEEDBw3nigROQg1O46uzDxooRhAFUgUQcBQ0UJHIHZSEQcN507LmHRIh2DglCqWuuq/Xz546YcgQnW6Q7upr/3b3nJTFr729+7DfQyCsko+o/oDo83hPCTRIkptSn7+5KSNp3GW6QXfMun9HJzslxR8Q2yHlZOBQy7cfZd6WMPS8ATPyHvyuU5Eo7ehwuToFyeuleF/Hzk2Tbrn+4kEjHnpoqaRSCciJlxJ/697djz/y7NixiQN0wwbozjtvkO4Pl//s+r/++pbrrxgxZOCg8y6YlmbftqsZ8mdj96uPLvjNiCFGw107Dkgehfqw7FekgOL3i+0EtVPURjv2P2Yxj9ANvGt6xkGf4gXZde6sXjbxTz+9UDfgz1f988bRUf8YNeYPV11z8WW//P1vfnvD367Ux1x3zbW/G3HZRboLLrxuZPKO/QEBAR/p1pwBETDj0Ap3nThJt997v+p3oAFiNDTle7dlsRgNc9TambQulWxLwfVZwQANDSmoPgU3pAZjNPQ8OoO2ZRO0HOk6gZnrBGkwoTrmOrEtFVwn6mwh1wkOGk76+HRvzqCBBpjXVRQImSfJ9NPP99z32JrY6S8bMwuS7WVJttIEW3lyXnVSTrXBCpRBb4NisDtDPhFVCfbqeFtVuLCsE1VGO2Sm0NshfINRs2uI2AbMHLorMFPNS++XgNHK4ixYyyHaQkTRPGi6LJlLztH74ei9BDYRtgrNlEZznUiyVsRbyxNt1ZCiwlySmFMZTn4ZlVpuNG2a/cDWiVllMXf+527z68+9taFuL4Q1hdTT3X1O+szwL7kEuAT6sgQ4aOibavZpaOZ9bJezDRpAnz0paKj73HjD34fqADTsk2gHpYc9AsU+itpb6r9+67knR//9mkEs/+NPf3Pl7JyH3yj8YI8HQTAHSpGsuU7IiMCwFEFu6uMsGpKY60TQokEDDYd3fj0r9uZfnTfUPO+lTpY17VDLfoV4IadmYN/Oj5xzksYO0ukmmxfW+6gXKYLXBYGiYWB80LXv48SR1w3RDcnKfNwXoBgJyLeXdu5a/fLTv/rJTwfpho68MXpB/v0vPP/Km0teX+dc/dWH1bOn3Tlk6PCk22d9u2OfIvup79Di+y0/HaKLM07Y1eoTKe2UZK+oYJUgLIA7ia+Ztu97wmYdrtPdeufcvS4hAD2rb3fFO4ZfDL5UpxusGwh5LQfqLhg89PwLhg/SDT5Pd94gnW7g+brBl102/Dd/mGa5v6FFlCg94nLJBHfLGjho6MuvK0r7FGiAXHBQ4BkkWqYG+PUIWBkhSUUCRRAM0t2ogYZ0tU4DDXO7gIY5ABoa09XGLMIiQfZwCWEgg6DBEo7RAPEg6yA8JKrLpA2Z8rZU0gPQQCBAQzBGgwr+Vv3+c9SiAZL+sQK/ckCGkDWtbrpk+fvXx1gT0lYkWcsn5a5LzqkKRuazQ6JByAXAisFWBvEdbRUJdhZSgYVg0BTIcMA/PeQmBOIQKhD6kW2jaZLdsIYE+zpeer8EwP9FuzGOW3ZBDMH7gflBVLN4H9WhdVjRGBbDFhCgAWI02AA06M2lCdkVSblV8XaIM5qYXZVgX2c0rR87p2JyzqfT87+8zVI5cvxT42c9u7am0atQCDdFNNKgRACHyA7h6OPQ7/sILgAugX4hgeNBQ0PJlEWjZiy+cUFFYl51XFrBSJsz3upIshfF24ribMVjLY6xoJnzGAF9TC3ve5dzAtBgcxpNRbHzy5PnFU6esOi6B5daPLRZpj5MJIIVFdNgUVVCVS1LIuiuKiUEiva8gFpAEIAGDMawLP46vF5lWVRpACttVA1aNAxhoGGPTNsoPSKofl+n1HmQggYhbf7w/ecee+T2yVMuHHGpbvAvrrgu7j8rK3a0+GQMrTKLBhFAg0oIxaEkF36KXaRj94Jp40fodKPj5nyxE1wnJNFLqU9q352eNO4nOt1c65MHZerFFFMCFg1qO0UtB7ZUzx1/y5BBusnme7YyeweKJCxTwe2marPQvDl59A0X6i7My30BIeZzIezf/1XNlFuuHaLT5VkXff1VLVLAWoBgiao+4jtiSZt13nkXzMjI2XGwA4KdIdeTC9MvGqSLHz95R6vHT6mHgK0HUakMLhheGmhWmnfkzbx7uG5Qqnl+s0i9lFLs3Vm+ZNLlI664cNisO+52FK8prSheuuxtZ2llZdmGle+uXbVyRXGZY1VZ2WurCtdvrt+2r12iVCSQ44ODhn7xijr2IvsAaGDj7PC8d+jyoG8JrgdBA1UEqrCsE0357lrwiQiBBnCdwI0muSEr5DoxB7wnGtJwI6ScOMWSySwaLFriCTCLgBYylfpMuS6DxWgIWjR0fJtL21dRZR9SBAXLMvSX0DexoK+KrIoIvKQAmoTU6tC19ae/0EUyZgR5ghDFCmGTvGD5RgjxS7JEqZ/SlVVbk+Y+kpz+6m35oNHFZZYaLGVJOeuScqqN1kq9ubSLonh08jls0RBeMdpP9m14M77CJRCSAETu6EEB5mXUOFcwcmSFlj81OXtT1NwyY8a6u+Z/MtHkTJjz1j2LN/33Ow+8nhGG8ZIqS2IgyNe0fiGI3liyGuZG1J86Bn6tXAJ9VgKIfbRQLCoEcIKE9wghRQEHbhjnYyzLsoKRjCSZ+jdFgoYqQ1rBaJszMQwagDJw0ND3dPI+eUVdQIOlSJ+5dpy1xJBVGBMGDf9+2+qlLQr1dwsaNJYAuAGmHLWioQZMsEIICm3AKgnCRFSol1AXlVtbm7aOvvLq4QOG3Zk674AM8cXZ1CWisuDzugXBz7LDBaivee3rL9z812sG6YYakmZtrm1xC/AOllS/QrwKRizJpahgUSRIgZAQPqV1R96MSRfqdDeNm/H1LghH6fe5KPF7DzbNToy6aNCgGZaHD0gwmhUUQlU/xW1UaT74zbqU8TcP1ekmpy/Y0qK6ZYAmcEmKlwb2dTZ8cvu4MRcPGmE1P4wJRaJEiWf54gf/Z6juqsuv+uiDLZRAv6FSRZQOU3q488A36TNuG6AbOD01Z8cBN2S28nW88cR9w3S6m28xOt/f3K6CJ4hHFAlWKBGp1EbR4YMNH99qGDN0yAVTZlr2+2iHpAKA2Ldp/J+H/XTE4MefexmiylHqk2VMqR8sHgjLHiphMOugAlL9YNoB8dtlpCCEMMaR/qBa/9Zne3N+YX3CouFY0BBBF9gq+5aARQOVjwENSlMINECMhrRjQQML03BaoEFpylSaMknDUdDAOEU3oKFtay7tWK2BBonIkorBHxtAA1HhdCURVOowaICr6W95ZSGQL4F4vpqJCkEqVlAQNGAYdSFKfZR+s0+8/z/V0TOfmmhfnWgvg2CNMC9dbbBUxGQ640xlidk91AN7oivybbgETlsCYdBQlWCtSYJEFcEsFXpzeaJ9Y7x5wwT7ponWyjF3LRmfvvSx1z885IZXOJhlYhEmJTB4OqpBy0n2BXuDszwsPEIkf6VzCfRuCWj6ULc2jISAaZN2edqKSgkDDT4GGm4OWjQcAxoSbUVxluJxHDRwV4teIQEwwCkxWB2xpuJoS3Gc3Wk8OWhARMJYARUfHBVACSdUDXGEEGjQxtQEkB3BElZlhcKrFJ4yFewQCBYl1U2om5LOI43f3nLlXy8cOOLO1PkH/FSQqejzU8jjAOEa2gPedm8nQh6K22nbrvK33/zNRb+8+JI/Fld+JVHa7hck6pOphxkhqgRLiEiCSmWCqexWWnflzr5jxMALxurnftUoQco0SaCyt2Nn7TTj2J8OG2pZ9EwLoW5CPX6ZSi6qtFLSuvfrdbMS/jF8gG6q+V8NLgAfmFCVylTppKhV2r11Wmz0hboLcmwAGmS/JLuaF//LfolO9+er/vbN1l2iAJhSQT5/4AClzbVflEXfdM0g3cCZafPrd3WARuF3lSx76ZfDBw0b9tOHnntjlwuMGtr8Pp/fRSQvlTtQ5673S5df+ZufDzt/6KzMBYcC1EcoUTy09fMHM+IH6HRJd0z9buduGbJOyJKquPydouz3+DsUIhCqiLJfkAKyCpOmiKowaA+BBvix2CfcrfXuvpuf/Ykl0OcsGnodaED7MRIjQQOL/qhooAEm7llnqGLoJvspaGD6FFjEYQXC5CKJYlklSBCRROkBN11S/M3dOW+PnfbKpJzyeGu5wVJhtFbG26r05vLojJI4U9lxsRh6MPN82pok35FL4GQS0NxztMQlDDRoGTErotKKpyz6ONG+0WiuumPBJkPKe2PufHHm/HeqPt3lJ1TCLOU1RUj0w2xB2IALujuYKuCg4cTvOP4Nl0CvkQAh8KLXTlfLliexT+QFqCqM12FLDhr65Kx+P76oHws0+JV2TDup2nmwfkvUX/8+TDd8yiz79lYZXrWy5D6wq23/DgXSWFI/kRQiUKmdIs8nFRWX/+L3F170+5KqL2VK2zwemUI5BjRQFUAD8pKOvdZpkwfrdCPHTf/4O5dHosxqQHLv235r1I1DdLqZWffu9lI3uE5Q4u+g8hGK23ZtXjfdcN0Qne6uzIV1bQAaEKaS7CdiG5WPeHdsuTMmapjuvLxsAA1wtorn7cUPXnqe7rIZCtVZAAAgAElEQVRLfv7ue05VZW4iMGgQfC0Njy3KGqzTDdCdNy11/leNh4HPSJ4D9Z/ePT5qgE43Wm/c8PW3HpYnHsGgQqLEv/2zD7Jum/xT3fkXD7zIbL2vxQPRI9q9h6X2+nUF/7n00mGDR4yYv2jhrn17JQwzoyxpKABRRSXbGuo//OTjPQf2ywT7JfFIR7tmq6VZNGhQVWMNkf0bX+97EuCgQcs6cdYtGlBDlpbeUq5Lw9stQr09aNHAQIOsKppFA1aRBhpEVRS12Lj9GDSAjSgBRYopVgrBIsZ+igMqkSglAoJu96Pv2k0PrDXOft2QsibRUjkhd4PeXD4u3aEZMiRm1xgsFXEmMHPghUvgR5eA5pWTYK1KAHMGWLKUmVUxptLJiz5MsFfHZjqmzK+ZklOqT3lnYvpr9z1TdKiTCgoNCBJVFTngPYYygErCpnDgKeEWDX3vBc2vqHdLIGyh0MMVsHMOgQawYQytKwqEaImUBWAILHOLhl4xUc9PsocS+AFAA2EWDSxwAQvpxiwaPGKbKLdR1XO46buk0bf85IJLZpnv7cRUkRBVxG/fr3j6gbznn3/i/Y8/aNi76+CRQ20Hd1atXTE+Nm6Q7nxDwl2fbtklsawKGmhg0ZSCFg2SqiKCqdhJPS3zU6dfOnj4pDts9fuxJ0A7O9qoLPhb9sweb7j0ggtsC55oJ2AvIEoYBzqocJiitgPb/ps6cexF5+vm2O7fJ9AAobKiKkhgvhUd3j1bUyeP/9mQix769wtY0xQCnRWrX/vb5T/T6QYmTri7Zv1nW7fuOHjgcPHaVdMnGq74+YiLzh/0+9/9cab53rqWgESpDEkkOla8/vivfz5CN1B3++y5r6xY9e32HQdbD3+55ePlS1/JnjP9DxddcvUlv7tId1Ha3LxD7YJIqUvopMTjPbzTZpozQKe7+JIL583Prqqp3LFn98HDR/a1tHzx9dYX33gzceKUCbfdVuB0eiUBg0sIeHtFUoYwa4js2fh635MABw0/KGhQG7PCoKH9uzzauYbiAwRLkaABsvGqsqiKAlXCFg1Q1/8sGjTQAD5ywBoUQgIq8hLiI6oAkzkUzMzedHxrmPFC9PS3J1g3xps3Tpj3vsFSEZ1RojeXsxgN6+JtVQZLxY+uYfIT4BIISwD4gi0YnUEDDQk5NQk5VfE5ZbGmgtisteOzS+OzCkff/vyt6c9/Ve+FAQGkn1BkCZJRE82bKGzXAIghXPreS4pfEZdAb5VAD/lCeLOgqQIzKlbYR4vUoCiKzD7av5o4CMU9AQ027jrRj20EeqjhnyOb/cCgAZ47FqNBxh5EXJR6dnz96di///083flJd6bt71T8nW6K/FvWF427/v8NGqAbMuKCv/7zhsSJU8aOGX3xsGGDdANvvmnM6uJ17V7V50MwdKd+hfrZLD0lqoxVWWY5qikRlPYDMyYaLtDpbhw9+au6Tr9EAwEfxVLL7oaJY28arNPNTM87wlCC2+Ojso8KrVRqa9y8aUo0WDSMvzNl636/SEErkGWR/n/2zgM+qir9+zchIaEjirrqFte1rR2xUdMbBPvu2lBpSaamEUICCR1EVte/Aiqd9DIzaYSq2NfuYkFQEZQW0qfcfs497/ucMzMEBEUFEsI9n8swmbn1zL3n3vM9z/P7EYGIjd9/+ua9YSNDuECTcXqbk3ITxXX420/m5ViuuOJPHNerR/AFt902fMTw8IsHDrrpz1eYn3r04cQxAYEh0Q9P+PIw34qIU2gnpOX7L7YvmJX+x8suBD+NgKArrrx6VEzcn677S3AfLmL0nfNzc4yPJf+h758mPJna0Az+ES7JA2OAgmfvzk+SnvrHBf3AZmLAwD433nzTkDvvufG2OwJ7DeB6hHKhfRIeeHj7fz8A/ywa49ARNDCQypq+c7V91/f71GpABw1nCTSgXQb165RfCxoUDYMaokobQxoqeWo/a/eZi4lBwvFANp3oBw1uWRQJ+a6ZTPt33R33LYx8uuS+1LcT096NM2+NNdfHWzcxXYZoY12cZeO4zNf8fTz9jV4DnVoDYFnSETR43VJT68NT7AkZ1WOzaiKNZWPSahKtG4f/69WYx58rqN7ZKsN9WgbFWIidlATxBHENXtbQfa59/Uj0GjjXa4AFBp/6qyAIkiSxNGbmMuMPamDP5aqq+lkD0pgYJGg0PJTt1WjI9Gk0WMB1AjQaLPYwi32U2aG7TkR2ke60vhsnq4FOAg2SBlJIHkLcX3301r2Rkf17DXhscto3B1oVj0BUj3Pf/+ZnT77u6ou5QI7rERjQqy/HBQ3oN3DKZMO773/iphoRRCOe9jZEPIgIJwANmuQ5vDf5sQf7BIeMivzH+/87KEEAIiZEPbjnm3/Ehg0KDZkwJe1QG5VcUhBRPERuQ1LLV5+8Nf6+yIv6BD2ZnPbpt00gu6gywUVF8zR++8k7T9w7dmBI3+zsea1OUIrVFCfR2hr2fTV//vw/XXUtx4WEhPbjuOC/X319fXm50HRk0Zw5XEBQ4vjkr454WhHxyDxR2wh/UHYeKly19N64iEsvupB6VoZyfYLviryjrGq1pnlm58wZ3PeP45+wHmjwuFXc7nFjUVVa2wn2HPjus2XPzR1667WBAVxwcHBgcG8uIIQL7H3DkGEZObPe/uhTmRCZkDael5CqYpCBPK5N6/jnud7g6/t/whrQQcNZBQ1kt0HZOQV/axa/Tm35IhMiGvABDcsKUWWCVWr9AtItWBKwIBDwyOkIGqhU5Al/x277IULgTgTdKmiTwaMUYx4RyanILkLqP9z/sGXZsH8+P9ZaG2/ZlpjxTowJ4hcYa4g110cZamPN9YkZ2zq1b7lR37peA6wG4s2b6XQ0ooGBhmhzbaTJHpvpGJNdHZ/hiE+vS8x4/b7UzTHjX03OKd3xnSQTmj1BIIFIkTuABni48DvT6NkT3bYl1A/sXKyBU0cMbE4GGvwogcEFRVF4nrrX0ypggmqqSikDuE6439h5DGiYUj7cWpXAQIO18ihoMOv2lnpoQ9eugbMDGnxqkXQ0HWEEZmayCuIDnrZDe+vKy15duuKtj74E9tDmJqKTSA2uw1/s+PS1mg3la4qKXl5VUFbuePud9z08+CxIhMD/mGCJx5rgAw1gFAXCk2AZhRVXGxHa3922Yf3qNRs2feBRgRe4PG6EFMnj3GwvXf/q8rfehT45rFCSVMmJFaemCe62Q5sdBa/835L/0v0RFIhtlGSqfokFIre/UV+3ctmrn3/+HSIQItF8ZB9YRRB3c2tTzaZNtuqqkrLSFSvXvv3mO2D36XFv37pl2asrKre+1aIRgVChSiQQyUU0ydNyeP/OT97eVFNW7Hh1dVmBo+Kbhq9l0tjs2TNu3FiO6zP+yYx9+z0gVcmsQZEKrp+akyDnl//7b1VlWbWjakP91pLy6o1b3v5kxzfNThGcODBp9Xh4FRQcfgoaWBN3Lrbt+j6feg3ooEEHDad+tnTCnF7QwALDVRCDpF5EqkjIIYG8VPbeyEfnRU9e/UjeW5HG2pjUbQlp2yJTakZPtkem1CSkbmZxDXrqhN7P7yI1cDLQEGOpS8iojUwti7AWR6fbRiRVjE7e+MjMjyKfWn934pyq7ft4jThdArhGqQKNQ/Qql8A1qYOGTmiZ9E3qNfDLNfBrQQNbI0JIFMWWlpY9e/Z88MEHW7du3bFjR2Njo397bFRQ07ypEww0jH/+zul1CZkbY/ygIbUyQQcNJxs81z/vgjXQWaBBI4qHb1HFViI7QT4RwyC8S6b3VokncgtBR1TpMNLc3i42WD8QDy+6RVCIRBpWPB6iqWAxQWSa5axBti9RQM1d04jIEwwq5lhFECShEY+InG5Xm7MdxtCwRLACZhgE7vJIlSHPQBOwJgHHUJxEcsE6VOIWMAgu0mgIxd1CsIQ8LoIJQrAxj8fFjCZVuUUjMKdGFBnxNPkYPCn4Niehn7P4jaZ2D8+L4BsvCs6mFvDrwE65/TCYYhLCgzeH24W/3fndG3fedVvv0Atzcv/T1EJEjYiI0AUV1dPoaf1RE1uIJsHhU0sMUQCFOY9Hk1Q4UgmBsKRCNKirn0Q06KDB36p34zfnKWhwfmVQv0kiOyeQr54kX0/GX01BuwzKLgM1p5yMdtNp1xS0K5maU0459deT2Vtq35jkr5LIN0a0K1nZZeC/srR+OZU0l3SMaEAaRhpN9KIaDeezGKQvZxVac2YbDO8QRqqMMIzuCoTsbiKTZqy+46G58abiaJMtLmNjhLmGie11kV6lvht6DRxXAx1Bg+8rSKaIsdTGWB0xqeVRaaVRqbbI1LpIy2sxptfCni4d8fBzLxZ95KFjHVSjQQGfS8YX/DINR1lDN75bnYZD80dpsrh0tkYR0rBOUI6T3zvBHJ30ka959NqDsd5sx31h2oEIUWUfOnfHb/X3Z60G2GnGfgi2URjeVBRJkmRZVsFmnjQ3N3/33Xd79+799NNPv/rqq7q6utLS0sWLF2dnZz/11FNDhgy5++67jUZjS0sLgUFLCG3wnZlYxYpK3G9+Xf1A1p0TXhyWs2FMWl1kim2U2R5ntiekViak2qJT7eFWx2izI1KPaOiCXWt9lzrWgNUebXZEmx2RRkeEyR5lskcZK6OMtkhzVYy1Jm5a/bi0snH35d4+d521nRxUiAdpMsYqdIupvSVYwGrgZq8QlT5LQ8Yt/QprELOvgr0lllSsyJpCI4jh0kQIqViCXj32QM6C6JEE0SMDaFAUQi3VXQS1aKgFIZeCVEUFvUZRUgSJlxTwjIN9QDJRJRWJChKRoiKwcZQVROUPFVWTBU1wSx6n4OEFgYg0KECmewP6rzKPJV6WsKxokDalyorsUWSPpPCywmPJhSWPLGmSRGQFNBrg2FQVSTwS3VgUVRmc3iVEVFVGqqCpLoQ8CqLrwx4Fe0RVEVVNlrAkQcsjSHy7hxchwgCwg+tIs+KRCAZ2IPINqtr248FGoCdEbfPsQ3jPxx85ht85JIDrZTLMgnAGQlpEgddUlaii1E6DL3hNETVFxrIii4okqIJbkXikUslNrMEvQA8Whgr9eWE+Wgr3p7PWIOsb6pQa0EGDDho65cT7hY36n6RZT4ryBjD2QaqsqrKEiROTt3a2PZK+YsTjz98/tTbSVBmdVhOTtiHWqus+6pkaXbcGQJ2BTh0pQ5xlc6wPNABroKAh3LI90rw91rhhxCNLc1/YsvugCiaWSCSaQidfRIOfNegaDb/QqMDXx6n6syVYR53ZerPHIPZeUSDr9Zwu/ob0nD6KbrDz7IdACMGDviB8/vnnJSUly5Yty8/PN5lMjzzyyNChQ2+66aYrrriiZ8+egwYN6t27d3BwcK9evTiOGzBgwIoVKyQJ8CKDYiy3QtPAeEIl7u1fOR7sABqSK0f+FDRY7Tpo0DUaunoNnAw0GO1R1pq47I33ppWNGzf9ttlrzG3kwG8ADRqWaUYDIxFAJTCG1AagEjAmL4KpmczLoiTImkB77zImCIsYuwl2Qt4uAv8XJs4qyR5ZFRAGQygADUhWKcUAfUlwV1BUDDODRqQK7hWy5BFFUZaAX8hYU+jeKEjVFBHLggK9cBhLA/6gipRZyAoIkwlEERHDJ5SgIAhhQKoiKbKIZQmgBgL6APuGJYxEjERAkODECaBBQuDMKSrUsQK8akS3pw0jSXC2iG0tECCBiSBQW01NkGRnU5urzSO2uxsIaVGEb5cszOgf2vvPl//91RUOSSMeTFol0U1Ut6YIqiirggr7rIBivaJi2GVV4WUkqkjxyjFgwg7Wy139N1wdMXSDW9upHIIOGnTQcCrnydmex/d8DC0q8FDw8EMEeCgQYgGTJokUbdmdMPmlyImr/zHjtShTdbjZnjB1sw4afD3YrtvZPp/38DjKEGuBcIY4y8ZYS22sxRFrrYxJrYxKdURa68Otr4Vbtidmbh8xftWTWWs3vrfHoxLwvoVwBhq4qXn5m/fi9DK5s32pnnPb83f5fL01GE5hav8w5kULm0dVVab83wVfmQeB1KGw9H5ZllkOP/vG15DqQ0adeZ6yX4GdXQw0ZGVlXXrppYMHD77ooouCg4ODgoJA753jAgMD2RuO43r06MFxXGho6P33379r1y4YfcSYxTKwU1dVwd5SJe7XdlQy0DCdJmAll4/QQUPHcXL9/blSAycDDSmVEZbqWAYaErNvzV9lbNX2q4T/tRENREPMPx7oAdEwTWvQqIyCxgL8CXTsoQ+vgriAgIkA6RDI34HHSIJONYI0XlURVCQQDTIdCAZugDQVaRA9AeG38J5mE3gd2pkfraIqkL3AIi9kuhhdmwxcApZWNSxDxxySIWBFQDFgAYgxwNT9Fmv0CwThARA9AQXiMigEAdYAEwARRUWCgkT4G8PhyMAjiIZVwQnJDmLrYamtgWjKRx98WF5RdbChRZFk2AFCeMUjyk0qbigvWXbNVVdwXK/Hn7Du/K4ZMkokSUCSSIhLQSKGjakYeY+asgbQuxAhZITWMTS8Gg0rUTTgDv7SmS2yvu2zWwM6aNBBw9k9405ta6wxoqlnEoakMFnDsqaIcAOgoGG/iywpeDdy/IvRk4oezN4ea66LNFaNydgUZ9EjGnTE0HVrgLlaUtSyOdayNdaylYGGOHNtnLk6zuKItThiLLUUNGwLt76WkLV91KT1CZP/s7YKFKQIIbIk6KDh1FqRn5vLH60ggwEZ6G8xf28/aGD9up9bRWd/539iO+G4kKqqoij65+nsnT1Ptw8Dj7T43ePZJ8uXL+/bty/Hcb1792ZMoU+fPn379h00aFD//v1DQ0P79u178cUXcxx3/fXXb9myhVUfS7VggTk0gUKRVUkkba/tqPzH9HsmvDgsuzY+ozaSgQar7WjqRKp9tB7RcK50ts/n/TwZaEgqDzNXxTDQMHbaLXkrDa3afkSEk4EG2sdF3qxbRAgG60cN8ii8cl8aqAdAB5j+o7wBvgIxAc3bSwdcIBKYmEUl2EpjCFvwTqqEkaRhiUomQfKGpiFKA7zBhtRtkg4JsG3SVcsYaQhIBFjGQF8c0gq8I/wIa1ilEgp0bbCHsN/e2z1bCUg+QGNAM4kJ9O+piQNdA/jRdCg0C5vFX8ADNBwbIx5EI8jjJFjAniaCXJK7ZVZ+7pAhQ5OmGEvXFH745jvffrv7jbe3VNcWzZmXde21fw4K6nn7XZGlju1gs02IiCQgMxroWQIOYSOBTIoCEAiV2oSsCXYsXoFqfRzkPL0F0sPWQYMOGrri+e8HDarGI8qUATSogqaIQJI18n0LyX1hU+QTyyImlkLeu3VLjKUu1lqng4bzOV6gyx/7hnhLXbwFLFdjIV2CgYaNcNKaaxPM1Qmm2jhzbYylPsqyKdK6Jdy6JSJtY3hKccSTzywreVOgt2xR8MBTBp2OuXkf80dXvKi7yD6x0WBfljvZt2/ft99++8MPPxw8ePDw4cMHDx7cv3//oUOHGhoaDh8+fKRLlgZfOewr+/fv37dv3/79+7/88st9+/ax9lPXaOj0U45FMbCIGMYaGGhoa2vLzMzs2bMnBwZ0Ib169WJYgYU2BAYGsnCGkJCQpKQkQRDYgRxHxwjBCpIF0vrGl47H8kY9/X/3ZDliMuuiUipGWm1xDDSkV4JGgw4azufe+zl07D8DGkyO6Gn141JLExOzb5212tRGDvwe0ABcgEEG9p+3O6zSjjOkV0Afn/bMWf+cajoyYIEIZuKGEMIA4QZeeIHpwBhEHvjuzhQTMMc0un4wpwSbdhB0ULEiYTCroPCDxVZoVFIC/NWoqCRENHijAqADTyewv4R1wVppXIOXZGiIBmsAc2a7yt5pdANUJYEJWcCCUBAiSBbbGwjxaKrrpRcWX/GHi3pyXB8u8K8XXnrPHUP++JdLQvoFgJ1nYGBM4oOvllQddCkSIW7g8hALAnEPvphKeohwz4E1e+vT9x3bnO9j31/6/+dXDeigQQcNXfGM94MGWRMRkYDGQuqERFQJaVgkZOdBZJhVFvnUyqhJtvBJG8akvhFn2RBjrtFBQ5fvbHfdcIMzX3Ub4i01cYDDNlLK8BqNaNgYb6ljlGGMsS7BVB9r3kRBw6bw1E2jU2vDzMUjHp//QtHrHqoiLQki03b239O9F7AOGk6hJfMHn0uSpGlac3NzcXHxokWLpk+fnp+fP3v27Ly8vNzc3BkzZuTTkttVS46vTKdlLi0LFy7MzMxctmzZvn37RFHU1bZO4Yw4G7OwpG5vXrckMZ2F3bt3jxw5kuM4BhdCQ0M5juvXr1+PHj3+8Ic/9OvXj+O4qKiod999l+2iqsKIJXvvIw4wLiqR9ve+2TBhfvTEF+7JqIzMrIsy2cMYaEivSDgONJgdXT1L/xzqFeu7etpr4GSgIaUywuSIzqwdYy0Ze2/OkLnrrE5y6GdSJ5A3FoD1yP3dXG9UAItBOBloANsIGP6Hmb35C6z3TnMt4EEU4IJff9IfYoBVsG2gPXm66mMiGigUEIhGQYNMMIia+0ADputiWRws5gJAA2MNABogWACcLWjaA90I7dGrECLBsihBAwHwB9Y0TEGD/0DZG9grzKAGLOplH1ji2yW+mRDPgX1fLV0y//7IsOsGX9KP4wb0CQ3q1SO0X69hYWE5+fM3vv5OswhGby5VcbpdkHFCcwy9zII2SeyJ3f9Mwur2mBlYzZyN5lbfRperAR006KChy52U0FrRgonGMr1gF4HKQlAWBiMd8sl37n9Zlkc+tTLRXB81uX6s9Y14y4ZYS60OGs58b/l8JgW/89gZaKhhoCHachQ0jDFVjzHVJhrqxxi2xJm2xJi3RFrrw9LqorM3h6eW3vGP/GdWb24V6MOCqoG4FL2HH8MWjvmjK17UXWGf/M9DHo8HY9zQ0LBo0aKHH374ySefnDx5cnJy8qRJk56iZeLEiZMmTUrqqmWKr0ym5YknnpgwYUJSUtLYsWOnT5++Z88eNr7Ejtc71tQVfoDzch/86nGyLEsUNIiiuGvXrscff7xfv34hISFMl4GxBo7j+vTpw3Hc/8+hWLRokSAIICAny37K4L8/sr6QSlwf79uasmTsxBfuSS0Ly6yLslZH6qDhtPeB9RWehRo4GWgwOaKN9qj06nhL8ZgHZt6xsCjDRQ7LxH2y1ImTggY28k9D/jHlAtCNpwpgtGWCiAYVLCoV+qlK/c2O9XiC+RgZAL0E/yg+grQCQAbenrxGszX8HACBXgGVc6BQQIPBMxB2hIkFSMCsvuSOo6CBBjX4QAOiOtBeCAI7oUCUA0RAUPtIFbbNqAR7GGCUAXZIhVgKmHxxHBjmRAjxnjasOInmIe7GH3d8cOjr/22tsZWWFZba7bWbXv/u+0Y4QA3UIRBRed6NJJlgokiyTBRqQKHQWgTPD1YZLHnE/8p2pCNx6HgH0G9PHWujG7/XQUNngwbtR6zJICgL3pYswUpFJ7C3pH1vGjTVjU9H/6GxBohp1dLmk7bfUEPQUEqEvLezLf6pxeFPrHx46uuxxs1jU1+PNdWOSa3XQYMOGrpwDRwDGmKPgoYNCabaMabaMcb6BOMmCho2RVnqw1PrxuW/GZ5aest9M2cv23C4VQNHKo0AaNDL76sBFo6uadqKFSsmTJiQlZU1c+bM/Pz8nJycqVOnZmVlZWdnT58+fVoXK9m0sJ3K6lByc3PnzJkzb968xx9/fO7cuYcOHWKZ/Kwh1UHD7ztZfvvSLHWC8QUm0kk121BVVdWjjz562WWX9ejRo2fPnn379g0ODmaqDYMGDerVq1dQUFBCQgILZ9A0jVlOdDC27OBwSTw7Dr5hfv6BiS/cYykJy6yLSauNNdsTrJWJqRWJzOHSao+kGg16OINeA126Bsz2GLM9BrAC2FuCw6XRFmmwRVpqADSkOmKtxWMezLtrcWmWhzRKXjFIkDygAIH5R4C9JY0moJftMT1dShSoHiSVhPTCADpCTwMK6HAWJgoCfUcGIgT6vEmfP9mqvI0BxBZhYAPwlUbVHUTaoe8IGpAfNNAUCJkFIIAQAygbqLCfsKteZMCkKeEhn4ID37feXjrgEFgQXgEuUFgCmR6Y4gn2OXtO7rC33n0G0EBA2lKEjVDKIAqI50WEaBoI9hDkhklxUSQBCR2aRmQRhvU0mThdLYrqRqoIh4oICA/7QQNAGRXUKzVEv4S994MGL245EWlg9ybW5/ntLay+5LlQAzpo6GzQQH7EmsRAAzRbVA8GdwQNoDRPw7XOp6dF//Mx092FuwIo8YDXsYwRj8n2T1vuSsyLnVI8Nm1TjGkDGAQaNyRY67twJ/N3Dobri3ePGtjQgYUxywn6aqqPM9fRqTbWXBdrro8xb4oxb4rP2BqfWn174gJzfkmbBJ5YIAgHozD0YQLuMd5o6nPhdtMl9pFZV/p7gK+88sqkSZNmzpyZm5s7f/78RYsW5eTksPezZ8+ec5Iyr5PKnDlz8vLyFixYMGvWrPz8/Pnz5+fn5y9cuPC5556bOnVqZmZmcnJyXl5eU1OToig8z/sb0i5R9d16JxRFYeqbHSIOwE6VEOLxeNihY4x37Njx/PPP33TTTSyKoVevXn379u3fv39gYCDLofjLX/7CcdzFF19cVVUlSRLP84wZ+SMaOv6mGsEiad/j/HhOYfLjzwy1lkQkl4zI2jTOWJFgqrjPXPFwauVDqbZxZnuc0RaeYhtlqoo0/prpLAxi65vQa8BbAxC2EGutGWO0Rxvs4ZaaaHN1VIotzFQdlWQbmVoXO7VubMrqqH/kD19WO58nbQoBlwfWU2X8jr0qiMom/8bWxp91wO6tHXUZ6RoZWfDLStLPFA0sGNyAO2hBRJUVWZMQdLrBPBIkI2n3Hl49ElFElagysAKq8kD1INiiLHaXAghv9AHtpHuFKoFtwHpgJ1mvng09eB8D/NEZsAzGmoQgzxgWQQpbA0tDprPDHijgXki/YPIAACAASURBVKQqYBsBJpkyPF3LisvlcbW7Ee+1uiAquOqKSGJP49QNlJII2BVvcAd946UKiBIYZurhOygf+zj6t/7uPKoBHTR0PmhA5BjQANkBmixhUSQyRZfnM2gALyKAtgBgZKwJiAgyRh6NbP+07c7E/NgpxWPSN1LQsBFAg2VTvLl7dEf1o+jeNdABN5jBeCLWvAn4AphcVsMrsIZNseZNY9JfG2utHTp2sTWvzAcaZMod/XduL2g4Sh7Oo/vXbznUE4KGqVOnzpgxY9myZcXFxWvWrFm3bl1JScnatWtLTlKKO6mUlJQUFBSUl5cXFRUVFhaWlZUVFhZWVFSsXLmShWMYDIa8vLzm5mZGUjp2Sn9LZenLnFoN+BFDx9n9gqNMUqGtra24uPiBBx7o06cPYwrsNSAggCVN9OzZs1evXsHBwRzHPfLII9988w24zMgy0488IWgA7XoifO/csbDIOuk/4ellY1JKw7I3359SEWuoHAOsofJ+sy0RQIMjwmAf/asog7GqS49+6/3z7lYDDDRUJ3YADRHJDDRUjjbaIq0VsYaVsU8tjFm5cYmTNImaqGKlI2Jg0MF/pXS8GH/re9alhxwHagBBb7u+lAQNEUVBMEDo63AjdjdmiQkQqgBWFOAZyRIk2IIUPqgEuzAPYhBUWwFWQQu9jx+NA/B9zDCBCl172AQTjITZWOyAN4KDej6w/A2ImNDcBJTUFUTjM+hSiiLzGCkMEahghaxICJwvJU0TMW6XKBnViCoofLNTdYng7alghMEdg/pJUOtO7A0h8RILL25gdQV75Rsg9O0+wFc6/dafQV/unK4BHTTooKErnsC+52MdNHTv/vZ5e3Q6aOi0ZueEoCEtLS0vL2/t2rV1dXUOh8Nut1dVVRUXF5eepHQSZ4D9KSwsrKysLC4uLioqqqioKCoqqqysLCkpWbhw4cyZM1NSUnJzc5uamnxNqPf/Tqvu7rXh42rV/yc7SkmS3G63IAjM7wNSoH3xCLt373722WeHDh3K/CyZsSVTfOzTp88FF1wQGBjYs2fPwYMHcxx39dVXOxwOtk6EEAw6dlCC7LhRURZUIuwXvnmxapZh6X2pxQ+YKxKmb3kguSI8qTLcYIs02qPNjmjQgLSHGR1hOmjobp3z7gSDHNEmW2xq9RiaMRFurYaIhmRbhKk6BtBDRbSpKNbwSnza0n9VvrvKRVoElVcQXBqMNZwZ7VvWeYYcB9bFZx1mlWo40LABzDxlaEATDQ+QVCRiVUYiliUi0VBcFSOIL2vztPOiALKO0G8HrQXIvAAq4Q2QYJ3xY19ZegVEN0A5HjT4++/0CwzOm7BvoKogKKhNVZ2S6FZEoBkQs6CJKhIlkWegAQFpkBQVg80Ghr12ysgjY0mmsRl0PyRBPNLUCKkRsGJKW2gohSYSxc1qwR/UoIMG9iPpr8fXgA4adNBw/DnRFf72PUvpoOG87Yp37wPXQUOnNTMnBA0ZGRl5eXmrVq2y2WyFhYXr168vLi5evXp1YRcrRUVFa9euLS0tXb9+PQu7WL9+fVlZWUlJyYIFC2bMmJGUlDR9+vQjR46wwT1fQ9phZKnTKr47bNhfn/6xU/ZGkiRBEFjkAsaY53m32y2DFRxpaWmpqal54oknLrjgApYu0bt3b4YYOI67/fbblyxZUlxc/OijjwYFBYWGhgYHBycnJzc2NvrDGZi4g3+c1r8PhBAJYrClFnK47N1VSc8/OGl5wtSqh6ZuujfJNjLJdk+KfZjBMdzkGGWyh5kdkcxy4lexBr1brtfAWasBsyPaYosGxUdbhLkSQIOlKialMtJcFW+tTkyvute0PmHK/8XOXpPy/ncbVeKWFF5RJJUWhvbOQBNzYtCAEUGgSEC7/hrSFJEKKMBAPlIgZUGRVKfIN7ta29panO3N1LFSFYgkQmQA0WQsSrA8C12AK5r24VlgwolAA02u8H9BecPRvwBYAKrAqqZIWJE1yhTcktIMYpYqjV/ARJQlpMka1XQE7Qjw2ZRVJIImHNYUJIuqgggRFCwIkizLPO9ua2sRJB4R4hT5dsHjEXho0xhYAM0HGlGhKdQTlGo0gCHW0emYbG+2u2fgF9JX2fVrQAcNOmjoimep71lKBw3du7993h6dDho6rdk5IWhIS0ubOXPmihUrKisr19NSWlpaUFBwkoCGzvz4pxENNptt3bp1c+fOzcnJSU5Ozs3NbWxsZEaevoZUBw2n53zz1yfjC2woVVVV5pbqH+H0Q4EvvvhiwYIFN954I0MMAwcO9Ec0DBgwYMyYMYWFhSzqwWaz/fnPf+Y47s4776ytrWVCD8wbRdNAmcW/Tv8+sEOiWZae9/Zun7zknw8tGJ2z4TFrTXyyfdiUqruSHXcYqu402odT0AChDSZH9FnrN+ob0mvgV9WA2RFprYzKqIqz2iLMttFHQYNjTGrVvdPrHrWuvffJBZH/Lpn2Q/sORDyS7FFVb0QDC2c4PRd5h7V4LSq9gQQ0asDXYVaRJsoSL3ramg87Gw8QVxsRPZLLJfICAW806I/LBFRaNKyKkltEQgt2txOJIgEiCSqiaks0UkLzIwbvxjs02H6gAJ/RP+hLB20m+BuE3ChoUCHZCouK1iapjZ/+d3vJ6nUba99sbhRFFUkY9CiphQ2AEhWJCIMLMqisw3Kix+MRPLwsCRgSMACDqGDJqXkUxaMoooQkESmQaEH1HqE+wOxTBw0dThn97QlqQAcNOmg4wWnR6R/5nqV00HDedsW794HroKHT2pgTgobU1FSWOlFdXV1SUsLkDwoKCopOUgo6qRQVFa1bt66srKygoGD9+vUlJSUsomHNmjVz5sz5/4iBaTS0tLSwTq+vIe3w3NppFd99Nuw/hZh6AlN8hPgCSXK5XOw4W1patm7dmpiYeOGFFzLKcMkll1x00UXs/RVXXDFt2rQ9e/awpTDGu3fvzszMvPTSS7Ozs1k4gz9E4udAg0ZkVZGItMfz9dRXk+7LHz6t+lEIMq8JS6kdZqi+K6XqbghqsEdQPf84HTT8qq6vPvPZrAEGGqZWxTDQkFYdaamKSqmMtDjGWCsTs6seMb4ybvyc6JW1z7SjA3C1ibw/aeKsggba35cUWYUsCGrogASiuF17dxMkCx7e1drS2tJ0oLFx74HDTUeaZV4gGkIEN2nSfsnNw1JeJ0wwbaf6DlTTsUMLyRps32tH1uB77wMN7G8W44CwIkE2BNYElTTL6qGppsl/uvCS0cPj33l7h6wRp+gWVBFSHzDY1iDMQIOKsaoiUZF5yd0GSRJwMK0NDYfaXc7mdud3+/a3tPOiAl4SsDUMyhRUjBJrWNZY4oUmYwzrAXyhqbpGQ4ffUn9LdNCgg4aueBn4no910NC9+9vn7dHpoKHTmh1/L1GlgzjMdSI1NTU/P3/t2rXV1dWsM19cXMySFE4YvXASjcgz/jHTaKioqGAaDUwVsrKykkU05ObmpqSkzJw5s7m5GTzZEfU2p41pp1V3N90wO4v8oEGihaVOEEK+/PLLGTNm3HrrrT169OA4bsCAAYMGDWKIITQ0NCwsrKys7Pvvv2d14+cUX3zxxbx587Zt28Y+93tV/IxGA0Hw6C8SqYnsf3nTwkcXRqSW3m8ojUyti7XURRhqwwxVYUZ7lMkWbbLFm+06aNDlLbtuDZgdkWkVUceChoiUygiLIz61IjGj7OGUlxINix6ofne9QtoJUUFz4CTldLU63l68L5QALjbapZcUUdYUlWBZk2ShTTiy7/V1rzweOeqW66//61VX/+XPl1/9t79cc8PNt95+99joxMmPTfzsg09anB4nIY2EeBhZUCCHAhMEiu/URYquHBxmqHqDAhuiIRTwSolDB5sJuijbK++31ABCQywbQiM8Jo0YHU6f/GQoF/i3v9zy5tufy4S0ih6nyCuUDagYqRqPNVCLhIAGRVAlJ0ECkds/fHurJWXytdf89bLLLrvt1juiohOeeebFqqpte39o8aZNaJpAJJUoWNNBw+k617rzenTQoIOGrnh++24fOmg4b7vi3fvAddDQac3OCUGD1WqdOXPmypUrWad9/fr1lZWVTGqx6ETljBOFk2zgZBENBQUFc+fOnT59+pQpU7KzsxsaGlRVVRTF15CyobFOq/Put2E/aGCsgcWPEAIO8/X19Q899NCAAQM4jgsJCenZs+egQYMCAwM5jrvuuutyc3N37tzJKsTtdrtcLrfbzbInNE1raWlpampieRNut5vNxvP8yVInFBE6IW7F5SSHN3xemPzCvSmrEgzFMZkbElM3xFlqY0CRwQ6ugZbKeLM9AbInupN8oH4s3agGvBENDl/qRE24pTosxRZmccROtd+XXvCA4YX7pr048Z0vNyLiURTJ1zOHq8Tf0LE3p6vB8babviiCjqABQVACFlReU11EaN649Nmb+vfqwQVxASEBAVxor6DAniEcF9yTC7qw54Cbr7phwfxn//vND/sRcYFwAuF5HqmyRhQY/D9m/QqBzASJij741B8pTegAGjokcXQEDQQjpFBLS49GjhB8aFaa6dKQAXcPjX7zv1+7VNLEe0SMGWgAUKLxiHgg9wHLqsIj0Xng68+W5Gfd8NfLgziuB8cF9QgI4AIDuOAArldk+Lg16+z7DjtBnAFwiSSCvagOGk7Xudad16ODBh00dMXz23fbYGK8LFtM0YgIUroa5gl5/ZPWe+6bEzlh/diMTXEWcLgcY90cZ6rX7S3jLN27i949ju5UQUN86tbE1Lo7xy2xzCxtBSEpqvgEiZMsipENsMAlfHTspSte0F1on1i0LRvwlyTp5ZdfnjJlSmpq6uzZs1etWsXgAhODLCoq6ix3iZNtt6SkpKioiLlalpaWlpWVrVu3zm63FxYW5uXl/f+oe4PBwDQaYGiM6rEzNYFTfP72Nbw6mKDXlK86Op6+sgymcKx6nU6nJEnsW4zxgQMHnnnmmcsuu4yZSvTq1SsgIKBXr14cxwUHB8fHx1dUVLS2tjKVR7YeJlnPBO0gnfskpeMOdHwPxnXgdq96SMt3/CfP1Uy9L/+2zIr7MqrHWmqik21hafXxyRXhTxcOn7bhAYsjXgcNOmfpsjVgdkSnFI+eVpeYURNrtI1OrQ0zVo0w2kfmbrk/s2LclKWxExcmrN7473btICI0R0kDe4UTXjEdr5Hf8/4YS0toFGngAE130IgqEdQutoueJuJp2L782Vv6hvbv+4enUrLzZuVnTUvLmZE7O3+OZcKUay++ojcXePHAy2Y888rHP7a1EuKm92uXs0WU3FQNAf5WZUVTZOALRCGaIrnbJQ+1h2AoQaPJDoTQ3AcILADhRUwUnnpeQj0gQRBEkSdE0YhLRj8QfHhuqulCLnTIzaPffH8XT0iLLEqapqjwAKEQWSEe4B6aTLCCkajJ7vK1y2/+2+U9OS4mcsRLLyzZUl/7yosvjo2MufyiK/qEXjjkttFljq0iAVbiIipPZFkVMJKYRgPVywANCP90TM3rDyjHVMf59YcOGnTQ0BXPeN/NQwcN3aNfrR/FcTWgg4ZOa3bOddDA9CMKCwtLSkpKS0vXrl37M6CBUQa/8dsvVvpx8x8jG/6LC3e7GdhtqGP6tyAI/jphwQhMo9Hlcr3xxhv33ntvjx49+vTp079/fwYXmPTj4MGD09PTd+3aRQj0FkRRPI4vsHOyY6qL7w7o/f+kVQsacBCALZL2Q/K3pe8unfBs7MSXIqbaE9NqY1NsI1LrI6y1EQZ7eEbd2JTyCB00dNlutr5jZke0oSzcao+xVsdaqyPNNaMml9+ZVH7PtLqxacUJTy0Oy3pp/LbP7R7SigCiguDiWQENvhSGo6BBIaD3CGKOPOKR3E6EhjeWLbghNOiO2+M3v7sXtAywpKqUGrhdu95594HR4T254L/ePGql4w0nIQ2CyMuShlVFFpEqioJTEV2UL0Aihafd6RtIwKCIoCpIksFWAjgH8shuWfGoigDzMO6hEVmWfUMPWJDcKm4jpIEoB+ZaUi7kQm67efQbH+xyA2iQwWBCxQpSQUQWOxWtHRCDIiqyRxFd2+qqs9OMLzz3zMEDezHmieIhWCCCp+CVV68YfAXH9Zk285m9R1yHBdlJsESwgkSMJDDH1GSEFPC90EHDSRvr8/cLHTTooKErnv2+xywdNBzXQdX/7B41oIOGTmt2TgtoKOqkUlxczLwwCgoKiouLS0tL16xZwyw5TxjRcBw4+MVK9/d4f+2Cv7jmc3GGjvkRIJ2GQYDN5XK1t7f7PSBYusRzzz3397//PTAwMDQ0lOO4nj17XnzxxezNAw88YLfbm5ubWQ2wBVmwCavt46rad+875v+T1h4mSMEIIQkLTq35kx/fnFeU+vCsu7Mqx03fmGCqustUc2faxpGW2tGWqqikstHM5PLUX/Xer14DZ68GHNFme1xSaYSxMspaG5NiG5FUfk9adXhu7bjklyOeXhC+rGbOj66vJcIDW2OOB2c4osGbusC0EiDsAGsEtBKJCpiAChYgQngiN21fNu/aoMDLLhux8d1GRIiq8KLk1lQPQW7SemBbyfq/XHQpxw0yTF/ShMgBF9/Q0g4XNcI0RcKjSE1YbVPEdt4tEERkgYi8IvBORXFpmqDIvCyCHAvCkFWhKC5FdCFJdLe6BKeoYeL2CO2CoIDPBXbzLg1iJho9rbvmm1Mu5IJvuXnU6x96QYOiEVXFKrhwCiJuU7R2oJSqpCqCJHsaG4788MMPHoGnIpWCLDYSuZHgNqXl4D/vTeS44CeeNu3+oVmk2RMuSYJMDVWk7hUSs9jUCMagdglxDce0WnpEwzHVcX79oYMGHTR0xTPe95Clg4bu0a/Wj+K4GtBBQ6c1O+c0aCgqKiooKGBmE8XFxSUlJT8PGnwN6an+37Hr23GZTvu1OnXDLL+GqV0oiuJXbUQI+QUU3nrrrUmTJvmtJfr163fppZcy3cfrr78+Ozv7448/ZgfB8+BCz2xHfwoXfuOBagRSrRFRMZKI2Kwesr2/5un5sdY18Tm1YzPqRqU4hhir70ix35VSMRK8JxyRv2o6e53MbqQ1oFfab6wBR3RazdgpJeHJFREmR3Ry5eipdfGztz2QYxv3xIK7c14Z/+ZXVSJpVyE1AAoENZzx4lNDoJvqCBoIwgjyljB0utXm7cvmXBsUeNXVYzf91yVIxNXWqFEoQLQ2glrav9vxj7i4kNDLHp08/bAHuzXS6nKDHyXPE4lXPY2a0gq6B5osCpAXqQFTIG3O1nZnE9YEokHoA1Y1nm8hxCOLTUh0AqGgPhDMukLQiFNWWwVJUGRCBI00uhq/mm9KGsQF3XTLiNc+2uUkpAmcLwlWIToCQ+qECwOSUIimIizJqtLU3I4IcYnKkdZGTHhCWpFnL8GN7Qd2Tnrinz2De/9zfPK+Bl4ixC0SXoAQBqSKiipQAgJ1BY6YWNFBwxk/Mc+pDeigQQcNXfGE9T3j6qDhuA6q/mf3qAEdNHRas6ODBl/r+gv/d9ov1GU2zKCAHzQw1iDLMtvB77///sUXX7znnns4jgsKCgoNDe3Vq1dwcDALZEhMTCwtLWV6nOBQL4os54JFNHSs+t91uBrNyKb3SRUjmQhfNXy4qDjziYUjjesis+piU2tHGxx3J1XelVQxLL0+/ldRBrOj6zoU/MaurI4zunINOKIt1XHJFVEmR5zJEWO2x+Rve3hW/T+Sl4Y9NXd08evPHXTvVqELDZ1+Fm30u66dU1r4KGigWoy+iAakEATZDDLBAALUptdfnnt9aI+/XjOm7u1moAfuFghrkD1IaCDykdbvPv1HbFTv0IufnDStRcAyIR7B7XG3Cm1HiOIW2xs+ef+tkuJ1y5cvXf7yyhWrC9754DOnoEBkBMzJs0wrCHCQmglxKp6G/32wvXj1yv9b8tzqFesLiive+vCTRl4UwYCGiBh2UsNNmrCfgobAG28dsfWjXe2ENCuKhlkYBYAGlbh9oAFjrCoqqFIiAA+wXY0IvOdHvmUXwUe++HDrPUNv7jNg0KL/rHCrhFdJayvkbmCkqIqgqALWZBb9oYOGUzqtzrOZdNCgg4aueMr7nsN00NA9+tX6URxXAzpo6LRm55wGDb82dcJfy6xF9Q+kn+wNm82/1Pn8xp834VdqZBENqqq2tbV9/PHHFotl4MCBHMf169fvoosuYh4TF1xwweDBg1NTU/ft28c0F1inCCGkKIrH42Hikb4bHBua/R3V7Benw0wrTmrDhzbtKHtqXuz4f4/MqByXvXGctTraXB1hdISlbYjRQYNOKLpuDTiiUyojzVXxqbVjU6sTM6rH5dY9nLo+/l+zb5+9auKO/a/JpE1SeHa1yLJIe7a/49r55UXZ1UU1GjTogasEI5Y6gVWiauAsi7GCBaI2vv7y3Ot6Bf3xqsj69xohJEF0SoLY3HRE4puJ5jz89Yd3XXdVD66PNW1WOy95ZLcotdMQBueBb/73yvP/vuu2OwK4YI4LDu3fn+vBXXnjdbOfXfJDQ7tKiFsgKqRNqKLYpEpHtm8qnTF18p03XxXCcUEQOhXMBYT89YZbc59Zsutgk0gxgYJEgluJeHiBYcoFXMANtwzf8jGAhlZFAUqjYg1hhEWVOFVCxRaQqiBVUFSMSGNTm4zhYNvaGwjYiDb/sPOdxx6MCQ7iEh969NuDbSIhLR5NlkFOAkwxFQhnoBklAGVAukKPaPjlU+v8mkMHDTpo6IpnvO85TAcNx3VQ9T+7Rw3ooKHTmp3TAhpO5gpxpj8vKSn5VWKQ/lo+RdDAAISv+f3d3WD/5s+1N4wysIiGjukkbrf70KFDJSUlI0eOZPkRgwYNYoqPwcHBAQEBw4cP37BhgyiKEiQwo7a2NnboPA/DkkwM0u12+2vYLy35G2tIg96PKkBHhGBFQU6VtO1zf/FcRe6k5+41rH4ww/YPU+XYtLpEU3VMcqWu0aDHaHThGnBEG+3R1pox1qqxFntiuu1ec2F88tIo0/NjNn6yuln+FhOed3uYUKIMxAFcmH7jhXNKi/kwHvV9UGn8ghc0qPA/gAZEJAANTdtennNNKHfxn+6ofXsPiCm4m3m3JAr0CRYJ65cuubxf32v+dFPh+ipRFlrbD2KtlZDGH/e8kfxUYr/A0CCu/6WD/z46fNzwqNGhF4dwIVy/Ky4xZs7f8XUbr0CuCCICIke+/Gxr5LDrQzmuF8cNuf5vT/zzkTuGDusz4FIuuE/IxZebcufsPtTMIyKB7a1Lc/1IQQN3/a3DNn+y0wsaoMWAxAwGGjBxEawQhBUVS5BzAdqTBNQlRYlvJ0Q4+O1nMzOSgjjumuuuLavd5oGwCAiEIERFskuW3EgVKWWA30IDJwyY9NSJUzq/zpuZdNDQtUADvUhVDcuiJoqQQ6VhsK0BZenf+0RyTp3Tvuewo6ABQ2MrYg3sLQWNvOazt0xMP8be8twxd9x87uxq9+jbd8Gj2ADngHlrnHlrrHlTrLku1lJNp1p4D59sOmpvOaNMt7c8LW3YuQ4afpW9pb/GOoIGpmt4wqAGVjn+GXzt8HlHHFjldKwNdv/dtm3b008/zQIZQkNDBwwYwNwre/Tocckll2RlZe3Zs4cty2oeISoWT61G/b/FcdXr//y3vNEgU1t0SbT3pfJ8CyIenjS/892WaS8bk178l3HNw5PXxafV3muujhpfONRUFe4f0P6l6IZoPXXCX1f6m99YA45o07HTz51Ujui0uoTU2oSU8hhjWZy1ZNykpZGmlxL/Y5v6o+dTkTRJssfjoo6PGkGqfJZAg+b1j2SgQaXelkSSiQSJBpKmyZpI1Katy+ZdE8z97e/DXv/kR7gYVRo7oBG32/3Om9tuu/bqEC5w0hPWH75vIwQLcgMhDYf3vzflqYQQjhsQMmj+rGUff/Ljj4dc3x/c89ZH9TnzTD36cAMu+dvakjddMmmVSJvSjEjzqpWLRt19XYZxfHXpmh0fv9/e3LTjs88/+2zH1GlZXAB3yZ//Wr1lewsveSQRPDSdB+YaJg/kuOt9EQ1tCiWSSIOYC4iT8FDZSOhngCglRrIkwP9CO+JbiCZ9/fnHT49/JIDjEhPHFJdXCBCDQVplIhOiKC5CPKLgRCoLLaHhDFhF2lHjiWMaNF0M8pjqOL/+0EFD54KGUqIdQJqsEBVpEM5EVJUgWcOSqAkikSVqqgupYBQXQvt1fhTfA64XNEDelw80IB9ouOv+OeET1yemb4o3b4wxbRhj3Rxrru96vXc2dt1hBNuyOc6yGbqX8Ib1fjvO0/E9+5bOz5Y6wfxdsP+s79Iv1YD316e/NQMNpi2x5vpYiwMmoAz1dNoUZ90y1rLhjsR/m3NL/aCBckfqxA0NgndIh97Hz+jwTjdpen4VaCgsLCzyFX+0QgktBQUFNputpKSkoKCgvLy8sLCwuLiYGUAUFxeXl5ez2crLy0tPX2Hr/OlrQUHBrFmzZsyYYTably5d2t4OkuYdnRF8LerR/9nP6TdTwBgz9YGOr0wNseN6ztGTgB32CXeeBRoQQi3iIBqaCILA5vTrKYiiuHv37meffXbkyJFBQRCwHBISMnjw4D59+nAc179//0ceeaS0tNTlcrGYhY58oWPmhf/9CffkN3zIPOs1DCHLGsIYKQhLEuFb1SPvfPv6vKLsp5YkGteOm1p7n9ExamL5UMvGKGNtpLE63FQTkVoXnbYhxlodabKHGctHmyrCTBUR5spIkz3KDPr/MZaqGKMjwlAV9tPJWB1+womCjHD9Va8BhrTMVbFGe6yhItpYGWOtikutjk91xJjsEaaqyNS6aGttlMEOUTbpdXHW6tikstHWuthkW5jJFp1uH5e0Ii7phfil9TO/aHzDSQ4KqF1WJRXC/ulN7yx1XKnBJI1oYI/hVP3RO2avgg6kJGInUdveW/HS3QMHXv6Hv81asnLr5veqy2uqK6uqHLbn9h6Y7AAAIABJREFU/r1o/BP/CuKCx8X+c1PdZxoioqQQ0qKoO/Jy/tmT4y694MpVKytlQtwIOvCEeBT+B0/LjtzMpwMDudGxD7zx2Y/t4GxBnFrLZzvf+/jTN48c+oFomOrRYqLxRGhs3P1B+G1/DQnk5i1auL+lVcQq0TzKkb2L0y19OO6G28K3fvydkxAXotYQYCmhiWBt4ZRJK8EegiSsIllBgqISosjN3xL1gLNh98MP3csFhlw9ZNjOfT+0CG4qfklEVRNliagugl1nnvX8hkZRX6TL1YAOGjofNIADrYaRRmOOOoAG4TjQQAVmu9wZdGZ2yPc47AUNqANowNgb0dARNMQaNySkdkHQQHuS1ro4C8MHPnBA+5bHgwZrXdzROY+d30sZTggmfqlP62UT+mxdpwb8mGkD/OJwMrwWy0CD1RZrtfnCGYA16KDhtDcwpwU0FBcXFxUVlZeXr1y5cvny5evXr1+9evVaWpYvX/7KK6+sWrXqVVpWrVq14vSVNScpq1evnjlzZlZW1oQJE2bMmPHRRx8dOXKkpaWl1Vfaji1NTU2tra3t7e1tbW1Op9Ptdrtcrra2tsOHDx84cIDJClBdd/W013+nrPBk8YA870357ghT2BnCDl9VVVEUP/vss8zMzJCQkJ49e3Icd8EFF/Tr14+lTlx33XXZ2dl79uwRQYuNqKrqdrslSVJVled5Ft3w09fTUgmQLUFt6AA3AGiACWTgsaIS5bCwv/StNdaXxk9ZNjbTdm96dcyUiruSq0cY68JNGyKM1aOnlA+fXDbMUDkqtSYqtSYmvTomvTouvRq6fL4p2lQVafz1E1tKf9VrwFgVbXBEG2xRRnusxRFvrUqwOGJNjpjU2nhjVWSyLSypcrTBFmmiYMtoj0qyjUyvj8+oHjt5ReSUF+IX2zLe2VvnJA1urVVQIfkIq8xF0c/ZT8uVdPKVMJxBQQPNUmI+DyBzQBDoNPCEl8Ahsv2DlcuHDRzUgwvpf/FVFwz44wV9Bg/sMyCA4wJ6cKF9+03PXfj+B3ucAmn3kIaWI4Q0txzYel/E1X0Ces/NX+bkiUSIi6ZmKFKz5v6B4ANv1qz625VX9Bl02eIVtoMSfMsTj4yZOQWMux04eARpKu88KBzZ+d17jsdibh/Yi0syp3y293sa9eEmnsOLMs2hHHfDrZHbPvreSYib1R5tNESiUtDQ3gE0aCohkuwmwl6t8bMFOYaAQO6WsITXd+xtVjWRqjtoWEaIDXgKwBpgCFQveg38Qg3ooKELgQaa3+SNaJAwRDSA7QyBpwe4nHXQQFMnugNoOCE4OAY0nEqvuCOMOJX59Xm6SA3ooOEXbktn9OvTAhpYjEJhYeH8+fOnTZuWn58/ffr07OzsWbNmZWdnT58+febMmTk5Obm5uTNnzszNzZ1xmkrGSUpeXl5mZub8+fNzcnKmTZu2ZMmSOXPmZGdn5/hKLi3+vWB7mJeXl5ubO52WadOmZWZmZmdnL1q0aPfu3R6PB4TIwSq++xePx9PU1MRYAwvoYEENmqY1NTWtXr36xhtvBNU16igRHBwcEhLCghoefPDBysrK1tZWVkeapjEDS/+fZ7TuOoIGQPMIgiJB5g0piKgyEfZ5dhe88VL6q49PeDEqeU1ERlWcsWrEFNtdkyvvNFSNSK+PztgYZ6oKn1w2wkLhgqUqCiLb7WFG22iDbYShctRxce/+P81VMT+dTNUxpuoYQ43+et7VgLEqOqU6+ievkVOqRiZVjzLURKRURxqrYgz2WFPVGGPN2All4RMrI6fUxBjqEwy1cUlV0SlVcda6hPS6mKk1Ccb10ZNejM4vMmz6suII/lEigoAkWUFIoRm04NCI6KPxmW+dTgoawLsB1FHAgcFJcNv7K5be3X9gTy6k3wV/DOrRZ/DAiy+9+A8cF3T5n6/LX/jSjj1NHpp30KK0q6SZqAdeW7fk+j4BQ/5645ZN76mE7GtsahQ8MsGy0Eawk7Tv2/nh62GjwrmAvtMXr2xEpEnhEXGqrh+I80f+0P5P3n7n//6zNDMzMy5mWEL4rTf+sXf/QC4wJCBp2rTdbpeTEF46zLd8s3CaIYTjbhgSse2j710UNNDsbEAFMlElwsvETbBEkIqQptC0EN7TTnDDD59tvOumK3sEB819cW0bjaeQge0oGEk06ouA6KTK+wMqz2hDp6/8XK8BHTR0ddAAeWCqDhqOpk6cU6BhI8QyHI1TYB3dk6RC6KDhfAm+0EFDZ943TwtoKCwsZOkS+fn56enpM2bMyMrKmjZtWlZWltlstlqtGRkZqbRkZGSknb7CeMFPX6dOnWqxWPLy8rKzsy0WS1paWlJS0sSJE9PS0lJTU61Wq9lsNhgMKSkpSUlJU6ZMSU5ONhqNJpPJYDAYjUaLxcLmeeyxx8xm865du9gvxAIBmFFCZ/5mp2/bvlg57/+SJCmK4nesdLvdjY2NLDYBIbRt27bJkyf/6U9/YsELQUFB/fr1CwgICAwMvOaaa6ZNm/bpp58yFuNyuURR7BgWoWnamcY0rBMEAxC0QHgD1WLTEFaQrBJFIu7v3V+Wvrs8/eXHJj4XY1wLAQum6pEG+0hzdVh6fWzGxgRrbYzBFmm0R8HkiDDawg2Vowy2ESmVw1MqRpqro34KFH7mE8Ya9NfzrQaMVUAZjptSqiMnVw1Lrh1hqAs31EQYHFEpcI4lmGoTJ1ZEmzaMtW6531CfMNkeObkyKtkRa62Jz6pKmPzyPROeHzWvwrR1V+VB+XuBCAqhfWAF4lq9ScRHQcMZzhY8EWiA1ggUCTREVJF4FK2NKI1vLX/utl69/3zpdYtfKKiorKqusa1bu2LIkCEBPQbcPfK+6m3/a5BIA5Ya1AaNNEvNX67OMV3DcZeEDrjrrtGR8fG3jxp2d1TEqNiI4cPuiLhnyMOxI+6NGtUrtD/HDZz9n4I2TJqFVkLa1Obdy+dNfSBsxA1X/jU4qBfHBfbguFv/ftnf/9ivb0+uR98+U3JnfeWmKo7qYSzunZ+T3JPjbhwa/trHeyEmQgFTCFB/hIoEdQlEJBjJRJC8LSEiI+JxtxHc+OnrJX+5pP9Vf7um9u3PD0rkiIB4gM7gNAG6cRDiAaoOOmg4fbem7rwmHTTooKErnt++x8FzPXWCggZv3gRV/oO+9ElAwzHpFV1k7F3fjTNRAzpo6Mw257SAhnXr1tlstuLi4lmzZqWnp+fk5GRmZubk5GRnZ6empqalpU2dOjU9PT0jIyMrKysjIyPzNBXzSUpmZmZKSorVV7KysrKzs/Py8pKTk1NSUkwmE2MfWVlZ06dPz8nJmTFjRm5u7rRp01iEBNtbs9mcl5c3d+7cw4cPM60BBhrOdIf5bJ4N7M7CchmY5aRfo4Hneb86w4EDBxYvXjx06FAWxTBo0KDBgwcz3NCzZ88xY8ZUV1c7nU5Jkniel2W5YxVhjEVR9MOLM3d0PtBAOz2QNYG8rAHCIIkg8W61XSSuA/I39TuK566zPrEgYvLK8DRb/IwtD0+tGzuhYPjja+402KJy33iYdoyjTNVRlppoa22MtTYqrTY6rTbaUhXxqyZzdYQ+nYc1cBJNitFTHMNSakaaa0ETxGiPMtiiTI4Ya038xNJRlDtETigZkVQellGfOLV+XFp5/JP/uWPKktHzSpK2f2NrJYclIkHvFvl6x+C+QMMZMHsyPMOUAS5duLhopxoU0rxRxRBKBJcYAr+Xdhk1EfHQtv9bcHVAjxtuiH7ny1YJ5vQQ0rKpviQ+Ki6A6x8XN2HzmzubNLJfbBZIo+T8unxh5g09uRCO6xHaN6B3Ly40kOsZyPXguAAwrQzluN6BXHBQ32tuHLF8bbVLBfFFd/M3lasW/7EfLHXTDTc/+K/HM6ZPX7ZsyXtvVL27qTQh8p6gvgOezJjxSUNbO+xdExG/W5ALhhE3Dg3f/vFej0ZBgyoRBO4SmGhMG44dItVaxxIinvYWgps/2FJyYd+eHBe8+NXyZkxaJI0HKTkJlmMBJYjWzJlr3fQ1d6Ma0EFD1wUNMqFOtf6IBtq0daNz7+cORQcNPp3IM9HR1dfZuTWgg4afu/bP9HenBTSsX7/ebrcXFxfn5+enpaVlZmZardaZM2cuWLAgLy8vPz9/7ty5s2bNmj179ty5c2efvjLn5CU/P3/evHnP0DJ37tycnJysrKwlS5YsXrx44cKFc+fOzc/PZ3DBH3yRlZWVk5Mzd+7cRYsWzZs3Lzc312g0pqenf/vtt4Ig+LvKDDec6d/l7Kyf+Vayc4DRAabCwEISmILm+++/P378eJYfcdFFF7E3HMf17t37mmuuycjI+OCDD9jeslrySz+yD9nNq+N73+3s6P+n5WApaGB0gQ02UtaAoANEFA2OUVNVIknE3aT+sPV/jtmrLf+aO2LCssi0kvunOR5Kt48zV8QY7dGW2rgUx+gkx2hDVZixOtxMcUNqTVxqTYzZAcqOp/hqdsCc+qTXAKsBU1V4imOkoWoUkBc4MSIttohUR1RaVfSU0hEpFaMNlWEWW1R23X059Q+lrI14cP7Nyf+OfXlD7v8Ob3WSQxLxqCA5Qv0WWSyDlzJQ80QmFHBaLqSfWwnrTlOWdxxowJpGVEltV9QmIh7Y9MKcP3OBf7s+3vbOgTaNNAkHPeI3mrJ3W3XFVZdc2YO7cOrU5/c0gqqBSARXy+6SZ6xXBnID+/YxTM19/pVXZy1ZNPvfzyz8z7MvLv3P2lUvv/Tv+c89s2DhwueXryjZ/u4XkK2APR+9br/lyotCOM6QnPLxjq95AsoOhPAaf6Bt7//GRQ0PDOlnyX9mZwvvhIasATm/WjhjSlAAd9PQqDc//pGHiAaNIJFg8IkAURcNAy6gGo+KBg4aKtKQxBPUvPfztyY9+dgdw8L+s8r+ows2pCJN5l1YFKnBDdFAF+YsgJ6f+230786VGtBBQ+eDBpBXoWKQGGMNH9VoYKABmhjWyJ5PF7XviazbRDScSs/2p5oL7JOfvp7K2vR5umwN6KChM++Ppws0VFZWFhYWzps3b8aMGQworFq1qri4eNWqVWvWrCkoKFhLS0FBwbrTVwpPUsrKyoqLi9esWbN69Wr/67p161auXLlq1aq1a9cWFBQwL4zKykqbzeZwOMrLy9m+rV27dvny5bNmzTIYDBMmTMjMzNy/f7+/86woiv99Z/5sp2nbftCg0iKKIs/zLFeCELJ///7nn39+2LBhzE6ib9++fjmGyy+/fOjQoRs3bmTWEoQQl8tFhd+JJEk/febWNE0URd+N7Pj/T8vRHA8aiApBDRQ0CE6eeVRpBPOKWyJ8k3Dwv99sW1A8Nf3V8ZOfTzS8Om5G1WNztj6eUZPw1PqhyfZhyY57psA0LMUxOsUebrCx8WeIhzc5jn+F0WkHZFt0fDXao0z6dF7WANiU/GSip00kWJbYR5vsYalVYelV4RmOUen2kflbxuZsiM+uGZNbe19mWeKUlyPHLx7+xLzw8neW7m78UCItMnELmBdlsJkAaQZ/IAMFa/BUzKbTciGdfCUaDPezBAFIYj4mokHFYPKI2ojWTJTDm5fO/0uP4IuuCKt4txH8HTSXiL4n5Af+0GeLplov7DHw71eOWLpyczuABtLu3F+5NOPKvtyNN93y+seADARC2pHSrvKC6sHYTVSXpgpNze2tTuXwYSdFh+4VC2cM6hU4ZMiQtz/8vA2RFkRaJVl0HybufXve3zhm2K0hoX3T8xbvbuJdoOt2hKjfL8qbHBTA3TI09u0PDwgYQAMG0OAhRGUKL5AEQSdFw7KmQEgXBDq0EKHxyMEfv/pm374WrVGkfpZIVQQ3Uby2nbCgXvQaOLUa0EFDp4KGplKCD5wMNChExpB9qYMG9Zx1nfCnTpxKp1cHDadSS91gHh00nNrd6czMdVpAQ3FxcWVl5dq1a5kKIwsNWLFiRXV1dVlZWXl5ud1ur6DFbrdXnr5SfpJSUlJis9nYlzabrbKysqqqqrq62kZLZWVlRUVFWVlZaWlpCS2lpaVFtFRUVNTU1JSXlz///PNTp061Wq1z585tamoCgXeMEUL+TviZ+TXO9lqZYSejDIqi+IM1eJ7/5JNP0tLS/vCHP3AcFxQUFBAQEBoaynFcjx49rr322rS0tC+++IL5SjD3UEKIHyX48y86cpkzDWhoz4dFNDD3CRU6CawnJmOFl5ECv6CsSpCCTRQXbtnZ+GnJ26+m/9/Tj86KnPBcZHphYpZ9TKo9PK0uzFo30rJhpLlulKkmzFQFRgBGO0wGB9CE46YUe+QJJ4MtUp/Owxrwq4Qe/6Y6ylAVlmIbZbSPTKsOz6gJA9BQOWp6TaylKCxlTbjh1Zjxi0c/Pj9yQZn1tW8qW/EhgbhlIgiqR5QFDKoMVAsAK6AsgBUYhtcoZKCvZ7r5oLETXiUCL9lg/wF/gMAhFbcTcIhs3Lpy8ZW9QgZfPnzzZ54mRJolj0ZaCN5HPLuP7HovasiQEK534v2mtz5taAHzXOXT19YMvTp0wIUXP7ey7IBTFkFwUXOpgkIkXmwWxRZCFAnEEyBtBLk95MgPU598aGDP4Acf+NfBFsVDwIfCgzQitxDU+EbZ8tE3XcVxQcap+T+6MUQ6qAf5xo8X5CX1DOCG3B73zgeHRAoaEBYx8Wiw9xhrVG2BxkqDeAOWJJEnmqK0HyBKGyFYRKRNJe2INLS7XR435FxoKsEQY4IwI5ln+hfQ198dauA8BQ3tX6You6doXz2tfTme7JyEvpysfp0i7UyWvp4ifT1R+XqiumsS2jUF705Bu6b8qkn9JkXZnYy/NuGvDWhnCtqZpH49Wdk5Be0yKDuT8dfJ0peT1N1G/itL8+cZpKWcqD8SJHv5rKZhrAJYBL8JSSEQ6QC4kY3rU4bbHU66UzgG3wDQORPREGsGM8KfTgmpmxNSN8dbN8VZNrJvmdVlvLWeTpvircdMseZNdGKrqgOnQ0ttrKUWFCVPLCp5bne8owy10ca6GBMIWHSsihjTBjB37PBhN8ol0UHDKTQBZ2yW3wAamJklCyYoKSkpKytbt25dRUVFUVHRrFmzmCJDbm7uq6++yoQb1q9fX1hY6F+qqKiILXvmXgtOUop+thQWFhYVFZWVlRUUFDzzzDNWq9VgMEyfPv3gwYNMxYC1w2fspzjjK2b7r2kaYwrMR4NRBhaDwLInmpqaXnzxxRtuuCE4OJjBhdDQUKbOEBAQEB0dXVFR0drayvQdEUIQe+grrKLO+JGcaANsiJWGMYDBJTWtYx0zQu0nfIaXGCOIiVYVIsnEc0Te++7u+qXVs60vPPTkMyOTXxmVWRGTWjHS4hhurhph/X/svQd4FFeW9y2PbRh7bDP2BM/ufrPvtzv77jczOzP72ONAFEqdFciYIJTVOSsiEQU2yQSTBCh2UE7d6qiERM4GTMYmiyQUO1dV1/2eWxe1ZWxmsE123acobreqblWdulVd91fn/I8xWG4MlTWESevDxfVhkDg88CRuCBca6OmnaAFxI+O7pnBVC0diDRXWj+FXjxZXj5HVjFdUh8rLwxRl7ORN42ctD05cFZlZINjW/Nnhm3sHgMNNja4x3I/B4iVwD4m7AeGCfgCkD5AYlVuF8vEl4Uv5Rx0/QV1Wd9/dfwM04AD3wHgCyhehHxBd9qJP//PVYW//7i87Dt9yAnC12+0mMIzoAcR14LlQsG7em6+/FPTCK4rcVWcu9wESXD7anjgpLCjopT/9fby5bbcPhkD43YR3wNNHAq/b00sCfN/ho8dOnevpdQCvGzhupsdO/rdfvPa3/37H1rTXC0C3C44MgLe3+8tDufxpb74IE+PIcz45fbOPytl729Vz7OP54mFBQX/6/8YfPNTlI0GXw00AjABOHxxfEHD1u2MPQAKfn3QDgHkG7gCyH2A9NptlxZr1dbZdt50kHJPgXj/mgqyB0qyAkOK7bkr0d7QFvm0BGjQ8LaCBGlLfCxrgwxANGij4ChMHP8XpLb+NGAaxAgQB3/yriS0zR4gM1NQYIfrGFJCKpFZ5/kHDUCshQzElZjTRoAFaQN4UKTO/H/WpNKeix0M5cfpxagAx+IgAbxCwUA8MtDvjt3/j7v2GBg36wfLTAQ1I2zKgOoF0GUiS7OnpOXz4cEJCwi9+8YtXXnll2LBhQUFBb7zxBoqY+MMf/pCTk3Ps2LGhK94DGlAsxr2d7LF8HgQNYFAHErEGKp7cjweyXSIUQs1xEuA4cHtA7xXHF62nqzaZ58o3T5j5yXtJm0bF57+ftO2DlOKRwrLx8hpGuiEq2xKTYY5Ms0Q++FxtiVRZ6eknZwG1Leq7Jl5y1ejUmrGSughlA1tVw5bqmKn5ofFrgmd9MjZ+OUudn1jYtv7YnYN9oLcfuG44ejE/7KA4BmDEBO4jKMpAkm6YIQFQlGEwrSVGjfIf9Vj3HtAAL+vBkTk1zsYdji5H71XguWEpXfv/vj7sv//rnb2Hr3oB6HFDhYkeR68f6wLg5p0rexNjWUE/C/o/f/zrVk09HOHj3prizSNHjg564WVezKT6BuPegwe6env6XP0nzp48ePhAYWlJCJuXuSjv5Nkv/T438PVWb1j1m6AXfjv8jbmZi/cfPNN5y+X2EPaG6sSJrPf+6+03f/7C8FffTF/06bkuRz/0BLkDsK8WZfFf+9mLf/8rZ/++m14/6Pb4vMDjAn0eyBxxHLECGERBkKSTJAdwbz/h7QXAcfqLfTETeC8MeyVy6pyOA1/4APDhbhyBBpISaKBBw2O5yT8fG6FBw5MGDb7LAPdCXRbKRYwgcXg/HfRoGAoa7vLU56Pf/bOjGHxd9Mx7NDAlVqYkABogOEAeDTylbcjUxFM28ZQtPGULR9bCkTdRuMHKkpqYkkam1MiUGr/l0RB4K/5sezQM9WJgy6xMiZkhNkWIGmmPhrsIhgYN/+xe8X3//lyCBv0PKs83aBj8EYH/B4Id3G63y0W98AOgt7e3uLj4/fffR+kkgoKCXn/99UB2iZiYGIPBgKJIcBxHfCFQQTEXaBPfFmj4vn3yhy2PQAMl5fYN1vC1a8MghQy073G5McxLAgwHTg/ovug61nSyorDtE8GaCclr2PGrQmevHBO7emzihvH8LRHCogh+UUhqcTC/OPgB56nUkvT8p2YBfsn475xSi0NSikIS80Pi1ofErgyJXRYe/zEn6eMJynXx2u2bTvYcuU129hDdDuDyUckroUQhldQAXm4E1A1AjgxUxAQBod5d0EDiVEjBowcNX4OFuxcR9SCO+yArAMDvw5yAHABkf33hml++EPTLEW8b7fu9lBCDDwCH00u4eoGvC5DX9rXoPnz/j0E/C2JET933+VnoQuDsLy8r/Zff/erFoKBfjXjt//z7/xMZGTn9o5mjRge/NPy1oBdfefmN32QvWXajfwDyQcfN03tahJMi3wgKGhYU9Oc//e+4cawPR47799/97u03XkmNnTZ2zIdBL74+R5J9sds7QOAAdBPuC5myuOFBQX/6w7jtbV/5AOjHiQHg6CG6XMDjBhgcbkBRR5LyU+gHMAzE7XPdcQ9ct1mq/vyn/xv0ws/+/M6HdeZmCjR4ccxNnREKNMAZXWgLPJAFaNDwdIIGjABegvTdBQ133SEpR6kHOq3P/EKDz4jPA2hAoRBUFAAMl0CggSE2DplMDLGJIbYwxBYIGmQtg6DBwpQ0MiQGhsTwTdBAJciUNg+6PzzbrCEQHxHwaEDBFLRHA+3R8ChuZE8ENOgfcSm7T/nHm32OQcPgLwgZiAHBcXxgYCBAHJqamuLj43/5y18iyjBixIi3334b1f/85z9nZWUdOnQIdb8AmHA4HIHOg/4EX3BSJfDxMVcCr1cp3PC1XgPlV45iy7/p5UQCAgNutxcjfDjAvMDpAN3XsQtHb+/ecbGx+uDWdcZ5mVvjkz7hTM0eFZX2Dkf9P+y0P7EfeM5R/w9H/Sd6TluAssD/TMz5+9T5I2OXMMSrp+SVyktb1rWfMZ3qOnTq5pHbvmteyoffjbu8mAc+6w72ZqjCAP8Rd1/ADeFlaBEqjyt0z6GUDB/hNTe4R+jVP7Uh6iufD4opksBPwAwOXoD1Fa//ZMSwoP/7x/81tez1AODAoQ+GnwCky+25eRl4rjquHEmXxsPsla//WpG9+MvLN0jgx/HehpqSmRO5I15+8aWgoGEvDX8h6OVhw18LCnrlwzEROUtW7j56wgMABjCvswt4ehwXT8hmTfmXEa+/+vNXXnr51Rdf/MXY0eM0pcW7d7Wr0tRBw0bMSFFe7fY4fD4A+nDn5XRp/OsvDf/Lf41ttn2BkaAP9/SR/Q4w4AQeL0mBBr8PKtD7vX5/P0n0uQa6/Fg/AM7jR3dNiOEOGzZs0rQZh46dwEi/h/BimBeqZlDZPUmSoJQyH6Hx6aafGwvQoOFJgwbvJRLzECRUucEhzCUoPVgaNKDnt2ceNLCkNrYMajRQYg1WrsLKUZjZchPyU4AOC3BCwQLQ9wFShrsEAfpBDHo0NLDllFODDAlGPj+g4dtMAWkx0B4NtEfDI/qVDYwVCYLwer35+fmpqakKhWLRokWFhYVIeUGj0QxVWAjUdTrdD9No0D/ich/OUPaPN/u8goZ7KAPyOECYAABw5cqVdevWffDBBwgrvPbaa7/85S8Duo8cDqeqqsrlcsGhDgAEQQSEHpGQJBJoGNo50ZJDv3mM9buq8dR7SWrkQ6lBUuqPcCR272CAGiQQGMB8lGwDAF4Sc5JuL/A6Qf9t37Xz3V8cubxzx0mz/fNK80F9w35NPZx0DzJv2K8x7tMZ99Hzn5wFDHu1hr2l356bDmjtn1d0nDQeONd64srBy3fO9/tu48BNAK+bGBhw9VADdRw+/BIE6fUGBvRf87vAWH/IRTWYDOKRgwa0TbQLd9MzUNRjMGqJ8PicpN8L/O7jBzuWLlmwsUj75a1+NwC9rn7gYc59AAAgAElEQVSn1wOTc/ow4OyH2opY7/mjB7Zszv9k9SZD864ulxtGMbivA9DbffVcvU5Tmr9l49oN8+Yv/nTtxooK4/GTFwZ8YIAgKXUK3OvqA343IPpPH9xevO2zhQvnL1q6bNOWko4de3GCxAi8fc+e9Nwl5pY9Pj/o64NKkQC7vW+HZcn8+ZtWazuvenEABvyuPrK339/rIn0+P+EnfaTfC1Xh/F6CdOD+AZ/X4ffDJBgDA9dt5ob1n62125udLg8EDTjmwTGYDhN6cgASOWEPOSl0lbbA/SxAg4YnDRo8F4eCBhwmp4LRE197NEDyQF3YgeQ69zuZz9H3g0+KzzxooChDE1dh5ymbuAqYhIIpNTIkBp7KCqe7ARRfh06wJE1wgpKQlB4kpQTJkhko0GBE3hB3HRmkzQEk8ewKJdJikPAkSlupk25hyWvhJDUFOgCt0fDQ72o0aNAPlucPNAz+cMD/A9oEQ7M/HDhwIDY29tVXXw0KCnr11Vd/97vfIdwwfPjwf/3Xf83Jybly5QpKV+nxeNxut8/n83g8TqcTZd8I+Ec89G75QxscTE83OP6hXqNC1wYCPkjcCxowH0mgN5FQZZ5Sj8ehDzQaSqG14LMHcPvAgBf0+4DTC+vuB5n7gBunJ9oCX1vAiQMHDhwEcBLA6wc+Ar45x/1+3OVyuN1OElBKC6Tf63T4PR7gR7kkh+CDAGW4O9Yf0tsfj+M+td27G4eXWIDrwVeCBEyDgftJH/Q5AF4S4L0+3AGAw+/yEC4/IL1ezO/xAQInuzuBpxd6E/nhw70Dgyvc7LnW3XcRAIogYFBkkcD8Aw6PG/P39bsxHD74e+CEe/0+j2OAdDuBrw+QTgC8bq/DR72WDMRP4QD0uSBNIAFwOp0AuEkCqi3gHifcVz9szQ1cXuDu9fX4YDoaYjAyxecnvTjpwkkXzKWBu53O2xjWBz014NtPeGfCoFAl4SYIDNkCJqoYHJn80DsXvd5PxwI0aHjSoMF9kfS5kUcD5dTwDdBAEsjFgQYN/mdUDDLg0UAlnjAzxMYwYV0IvyY4pQpOydXUVBucXBucXB+cZGCImhgiG5J1gPhAbuIozBxF4/MKGobqPiJcgggL7dFAezQ8op/hJwIa7udx8LC+1/+g8hyDhqGUAYGGkydPrlmzJiQk5MUXoT57QPrxNaqMHTu2rq6up6cnECiBYZjL5Rqa3bO/v//b7gyPqJc+WLNUxDpyhfwaNAz1a0DBFNRggRow4H74gAGAnyQJHPNiUBKOACTwOJw+l9vvoX5mEd6HQyrST2BwcEh4H3BO+OGS9PRTswBOeL49YYTHDzCM8Hi9bq/Xjfsw+EB7d6yOE6TP43PD3KswjQuA6qUYTr1Vw6j+SWVeRMNa2HURg7gLJmCHppIyPthl8iOW+g7QAHfSQ3hxSlQCB6QX8wwM9Lnc/fBgAeb2u32EiyRhTgfMDzzw8vL7yT4A+jG3A5IJSo+Rul4xANxerNc90Et4fT4P5sPgWkj+gSAwp7vf43MOOHsdjn4ENtx9fV6Pw4e5/ACmpHM5MafDS5Jkv7vXTQxgAPopuDGo8kgAzAf6nd7b0GfED3AfVKb0gAEfDFeBIw7KIwGjEAlO7TnMc4fhbuoUeAFw495+j7MXc0PfLowETj/hJGA8yN2zQSI5zm9GZv0IS9OrPscWoEHDEwcNF/wQNOAodAK5hJHwhgUv+29rNFBPC89xh7x7aIMvpp5djwbT0AEzS25lyaEvQ4SoOixVF5pcFJZYGJ64jZq2oEpYYnFYgoYtbGALDGyRgStp5MjMXLmFq7ByVTaW3MwaTG8JW5NBMUiWrJkls7Pk1m+KNcDwCpacShgptXIGJzSM58jMHBh/EZB1gAtzpHc/UvtppRoMrE6FadxdHtXt32zz7peoTdQCVYcboraF9gc2O+Svd7c4KAbZBCtSK0vSyBQbGSIDS2xmS1DUCfUnKj/o4G6bh7ZDHSncEE9q5knh8bJkdoZ8cK++oWQBzcKSm1hyat+ohQPHPth4wDKPrhIQ8oRBNLRHw2O+ndGgQT9YnnvQgM41QRCtra1z5sx58803YQq4l1/+1a9+9Zvf/ObFF1986aWXfv/732dkZJw/fx71Q5IkfT4fiphAhMLv93s8HpQI8ykHDUNE8QN6DXBgAB8bqEcHj8+N+1FaPmpQR1JODCiLNspvhVMDHZwagPioQQd82/GgUyDVBV15bi2AEpp8c+7HoXbjvXOcyp9IiS3c7YVQIgR3u51erxsldPDhmA+Hr8lR/6Q6HAJhDwAaHtcgd8hT911Q4oe5If0Y6SeoHScB8PjcHp+z39ntB9DBwe109ff7cAA/9GMeF94NgHNgoM/Z7wUEwF3A7/T4vS7oKwCg1wBJwNgRt8fn8nnduId6tYhBpUmAYbjb53VDVwgHhqzkdDtgPNcghfH7oRO0C+/3YI5+dx/ycejz9OHQycLtw1xuF45jgCBhFAcGPBjwQt8RSGruamtSyWgImOsD95F+n5/wwAAKvxeOTbxQPgMjAfRowCFxgJuF/2iPhsf85PIMb44GDXdBA3FGhJ0R+c7wfWeScWoizqQSZwTUPPXB59hZAXZW4D8t8Z8WURNqQeA/K/aeSAVnRfhpvu+00HVS1v1FGtlVDoirfsKLARzes0j4ngGQmB9gHtLjodLPwHS11BUNx94UBX2Gu9sD7/ozAhoGR7MyO+WDYENaDBy5hSVrZIiNEeIGmDBCYeaom8Okxg/mFIyZsyEut+Ljwj0FDSfLrWctu6/Z9lyubT1V3XJKbzu7ofy4YJ5xhrKWy9dGiasnK60MvmFcopGlaB8vNDPkVo7SypA2hkoM4VIzQ2IPEZnGCuo56S3hIitL0sSW2JgiU3R6U4iwmpNmY8pMkTIbT2zjCSxR0iaOFAICrqSRJWxgikzQw0JhiZAYGKIGrsQUrWpjSKwhEnOY0spSWZkyiDlilM1MvoUtaYpUtEQqoHgEQ2Rjie08iT1KauFKTKEphtBUE1vexpI1RwgbmJJGdlrrWIGBKTNNyW6NVjYyhdVsqYGtMI9JqQ2RmFnKZo66laNohqEBYitLbI1Ob2dIrGMT6uFHoSEkoSxKbOCKjVyRmS20MYVNoamm8akGtryJp2oNE5omZ7dHqm3hUnOYxELlpzCESQ0shTFabooRNvJSGtiCxnB5U7h6O0exnSvdzhS1hQvs4alWuEW5aby4KkRWFZVt48BNNHJFZp7EzlU0s+UwXCVCZKbwEJVVFMYvoAnGsHwTQ3x93r/5/QOyCRo0PPCN4BEs+L1AQ1lZmY4qSKZBq9Xq9fqqqqrS0tLq6urKysrMzMysrCyFQpGbm7tt27a6ujqdTqfVaoeuiNbSf1fRUgUN+PV6PVpXq9WijT66eXl5uV6vLy8v12q1tbW1lZWVeXl5crk8KSkpOzu7s7MTADAwMACzsOE4HAXcp3i9Xrfbjc6S3+8PpIEMOBSg9R7WaQzsRSAagiAIh8PhdDqREAN89en3u93uwMebN2+uWrXq3XfffeGFFxBl+AVVUMQEh8MpKirq7+9HRxpY0ePxeL1eDMMCaSbQET2sA3l47VAjhiEjoUDL3/UdNA7yXR+c+9F4GA4R706kH/96CgxmHrASOEF05Tm1AIHSTD7QnCQC94F7KjhV0K0Ywbu7ApDwEfd+/ODuIB92xa/BRKC/P+4KkkQkSBiEgOM4BguUSxycMAwjfBjpg7Kr0K0DenzA4odjfjjhBO4LXIYB+8AGqTYJAhsyocuTDJBTqgLDoAZtiBMkjEmh5lQLlAYLtVEf3CicfBjhgTtF+FDnhPkmSALKOkJXE0gdqNbubheSIwoewSCLQZ7yzbvK/c7U4z4X9PaecgvQoOHJgoYy4L9K+O+CBvhsRAntItDghhGUJIy5guK7fnhTuO8t+CnvZt979wZ/pJ9yj4avB5xUUL2NGnZSL/ApbQWG1BgubwhTGcfL68cKdbw0TcamluqdFz+/6O3HgYekgLMfoGRIXR5wpRdY9/TLFhpDP1rFSymZkWafmt4epdjBFLdEp+9gSszhgnq2tJGntHEVVp7cHp3WylbbuJnNoYJGpsTKllgYooYJ2fbILOuo1PLRKVXhfANHaOGJbVxqWB4qrIsQ10SpTVHqFobEGi41R6U3T1Q3ccRGhrBxfKphrLgxVGVlQUgBPQvYEkt02naesgW6Y8hMbAUkKWyJhSkwsvgNMUp7pKKNLW3lqDo46u0MiSFcVM/LbAuTQRrClRpYgmquuI6nMEVIDNy0Fk56G0fdylQ0wbAIsTlK1hyjbv0wsZyX0Rypbo9Rb5+gtExS1CUttAmWtqav/2Kiyhops03L2c9TtY5NrRsvNIRJjSH8mhB+Tai4kaW0R6rsHIU5QmlkqY3hqRUTZcZZ6dunpreNl1s/FDaE8o1ccRNXup0n28GTtELOkmbjZlnCVA3BospotT1aZueKzBECU7i4MUJm46lao9PbadDwva/VZ20F9GSGhP3+qRjkUF6g1+sRMni4oEH3raLVajWPuOh0Oo1Go9PpCgsLUfjGsmXLcnJyZDLZggULLl++HEgJCfn2gxUcp5KlUQsHnpvvPtE+WAv3W2rw5+Br2QUAgMfjCYQ5BFQbERZBGALDsNbWVolE8m//9m+vvPJKUFDQ8OHDkTpDUFDQ3/72t7y8vHPnzgVACYILaMzg9Xp9Pl+AMqAdCMCL++3n0/994LzcUwlY+J7K9z2ie1anPz53FqBAA3wP/wDT/UEDugmjTohM9H172lOyPNKCQawB3S7QoT3g/J7L8KF/vN9u3K9b3m8HnhJr07vxjFqABg00aHgau+7gffAZAw1380RIGlmyRphaQmkeLTe8J68eo9BN/6R+vf3IsW5fHwAuAK7fAddugENHHQeO9l24Cc5dJ7+8AQYA6PKB5fk7Y5I2TRGVxfBreXzDRFkLT2zhCY3s1Dp2cl2kwBgpNHMFBk5KHYdfy5I0MKUNYcK6SLUtSmWJENcwVLURqrowVQMj3cpSNnOVLTxVK1NpiUizhKUZguWV4craUKllLN88TmBlyJt5CitPauLKLQyxMVRqCVPaw5TW8WJjcGpdcGpNqLJxnLxujKR8rLQ8TFHNUNaxFEaWrDGMX8eSWhiS5hChLUzaHCq1hErruOrGMSm1YUITW2piCxtYKdXRclO0whqSWhsmagiVGOBcUBuWXB2RUsPhN3DExhBZVeQ8W4TCGsqvY4sqExYYNhlPW084K3bdmZZW9uGMfK7SwlJZx8sNjPTGCFUDWwHzgzIUZpbKwpYbmXJDuNrAyraEiSsjRNVskYElN4VkWMLSTRxlHU9aO0HRHCVpiRTaWQJrqKAxRNIYprQyVc3hQhNLbOXImzhKO0NlCVOaGUobS2lnygKODIEK7dHwNN4ifvA+oWevpwQ06O9THpZ2w/3aKS0t1el0KMVGdXV1eXn50qVLMzIy+Hz+vHnzLly4AABAI3AXVXz3KdCrlnrtj2GYx+OhXuthAwMDgQfWHzmKQKsHWhv63Iw6gNvt7u/v93q96COlggarFy5c2Lx586hRo5AiQyCpRFBQ0GuvvRYTE1NWVtbV1YXWwnEc7Tz66PP5Ar4MQznL0Dpakp7fY4HBH276/+fVAndfgKPX4P90fk/3CHwcap3Al89oZaju7NDjGloP3MHuqdzvkIeu+yD1+7Vzz+YCHx+kzaHL3K99+nvaAg9iARo00KDhQfrJ415m8B73zIAGtszOktqYEij3yBDDoThMY6m2fiioel9QOj2voXDX5WsA9ABwovNOQ/PhhcsbJBmVs4VFwuxa1dKG5IyirBVG897ewxfBks07pkm1k8TV7OTakPhatsAcKbGGxVdOlNs+Su+YrGiJkdgmyWwTZSaeoI4D1RxMbIklSmXnqawMWUOotCZYUhWuNkfmdLBU7Uzl9ghF8ziJMSLDzMmxcOdZgmU1Y4SmcEVHhGzHmGRTKL8hWm3lKk0suYkhtzKUTSx1G0vZTOkgmCOUprC0xvD0hvC0ulBVTZiihqE0cpTWcJE5TGILFdkZsjZO2o4IhZ0hNU7Iag0VGrgKe4y6NVIG3SgmqFpi0ts4Snu42MCQNrIVUHIiUmbhisxcPgwMiZnXHKpuGCNqYEgbOZKytHWtB64Q3QB0nOpPmV82fvYmrtQQIm6IUFujF7aGKIwcRStb2sqUWSDHEdeHi2rD5A3MDEvU3GaWwjieXx8sMoSk29k5LTHpjRPk9RNVdp7ANEHcNFXVzpY0jU0xhSvbpiw4HCqwsqXN0WmtUVmtrIymEKUpWNo4XmhgyiyDERM0aHjcV/3j2d5TBRq0g0VDlcFPMPLikRa0ofLyco1GU1VVVV5evmTJErVaPWvWrOzsbCRYEPAO+Adv8gOpH1HMQuAM+gcLupMHvv++lcEfAvg/ajLAGgJREogy+Hw+BA4wDGtraxOLxb///e9RfMQbb7yBPBqCgoL++Mc/zps378yZM2hPMAxDigyBHSNJcqgjQ+B7ukJbgLYA5VQ7GMJwN8zhH3/8qdjsR97ofipmoo/zJ2kBGjTQoOFp7PiDz5fPDGjgyJvYUKnha9DAlpt4SuPomZunKrVbDKevY6AfgO1Hz+Ws1EwVrGXFFb8/oXDUzGq2pCkspfLvkz4Lj90al2XI3rA/NtcYnqILTa3hprVz03cxZG1MSStP0c5TtHPlrVADUmrjymwwdELeMkWxc4pi1wTFbqaoLULUzFS0RMhsETLbe3OqWIr2MFEHS76HqdgxOtUQIm0Ikzew0qzwrb60JSrzYHTGQYa4JUzUwFU3hooqwiU1UPpB3sxVtvMUbTypmS0yQt0HKZwYcitTaWEozBEyW5jMHiZtYaXvZCrbwyUtPFVHVNpOjrSZJbZDKQeZNUJkZortPNV2XnoHS93GVDVz0ltY6iamookla2ZL27iS3ZHifTzp3jBF60iBkZW+c1L2rojk0ux1rV/1AicA5vYvBZklE1JLZma1hQkaRyYZIlSt46XNYcJdTOFurrRtgqplksLOE5sYYmO41BwhMXEUzVHqXRz13vHyjrFiO1NsjJQaY+Q2VkpdFN80Tdk2Sb0jSrWDKd8+LtUSIYQKEVy5hSOHBxUmg9wkQoyyigYQA6rQHg1P4y3iB+/TUwUaysvLkfqDjip6vb6srAxJJyAW8IjmtbW1er2+pKQkPz9fr9dXVlauWLFi7ty5KSkpS5cuPX/+PI7jAwMDSIIh4BiMIquHzhGDwHHcTRUMw9A3g5zB/xCfvwd/FOD/iDJgGIYAhNcLtesBALdu3Vq+fPl7772HQiRef/31t9566+WXXw4KCvr5z3/OYDDKysq6u7uRPwsiKYGORJKk1+sdqkkR+BNdoS1AW4C2AG0B2gK0BX6ABWjQQIOGH9BtHvkqg8+UzxJooFiDlSU1MSWNTCl0auBJa8NmrFm6Yfu5azBc4uCp2+l5hew5SzmpRVxZS5h4Z4TyUJhq7zixjauwT1LYIhL0Y2ILuIp6rtISJjMzszqYmTvDpM0MiT1K2frhLP2fJ25456Mto2K3vTttwztTNoyeVRLNt3KT7THSnRxpB1PewVC0jU1tCBPUvzejhCOyjppdG5Zi4chaRsbpxsRte++jDe9O3RKcWBMhMoekNEbwbVHqNpbKOlpQFqGqY6oMLKkpnG8IS64LSagInrll5MR146ZtGTl56wdTCsbM0kWk1lE5HZrDpM3jpc3MtB3BosZ3Z5aMml0SnlzJSqnjpNSPmV4y+qOiUTM0Ycl1kapWtsI2OrHivdji0UmlH8YV/H1G/nvTt438SBsyu54Z18xIag2XtIXIWrjpO4NTqv7IzpEvrbpwy9/tAJW1+6bMXvQBKydGUMESNnyYUBsqamLJd0Wrj7BFHeFJNREJWsacktCZBWNnFYyNLXlv2qaRs4pDEuqY4haWci9TsYshsoWn1HIlprCkqvA52tAZxSGx2kgJlHv86+Sto2drx83RhieXs8X1MHhE2cIWtzAEdlqj4ZFf2096A08VaKirq6utra2pqamiSk1NTW1tbV1dXfUjLvX19Whb5eXlDQ0NNTU1H3/8sUqlEovFa9euvXz5cuAs/QN3BrQMcgEILI+CDh4FaAhsDiWGQB/dbjdCDAMDA59//jmfz//Vr34VFBQ0bNiw4cOHDxs2DDk1/PWvf01PT9+9ezday+FwDAwMQLk2gkAwJSD9GDgQukJbgLYAbYHvZYHBB9d7//9ejdAL0xZ4zixAgwYaNDyNXXrwPv3MgAauopmraKaSNUIRAZjJUtzAEZYlZ+ssOzvdBLhwDXy6uWlCwipWcsHU3A5m+l7m3CNj1bvfEZrGyU0Tslo/SmudJLEEx1dwFdZJc9tZabbR0rpgRT2UYEjWcRJ1k6Vl8QtqxSuNaasblcvrBQsq4zOrZ8obwmdpman1PGUTR90aJm8cn1r+UY45fc0+9apdEXO2sBOL43ItSfMbVSsssiWNgvlN0o/3xec2M5JKx88uipQaWSrrh6lVrKwmdrqFKa5nJJdHiSvjsw2KpcaMZQ3pi+vVi4ySeeY5KsMEfhU7qYottHCVbaHyltHCeraqLnlFs+CTxhj+xpjkbYq8NtFcS9ayPfLFu2YoTLzUSlaKlissmZKmTcqrESytla0wqlfa1R+3iebaZgsaIudU8URNLJmdqzZPnVsfP1+zqbr9RjdOEODQvovLV1Skpm1TLLMIlm+fmGnjSO1MYRNXbGPyqzjCgmnKotScCsmiOmmeSbbEnLbCnphdHcPXspIrIqUtMWm7eYq2cKExVFg3Qd0oW7lbvqw9ml/ITMifPa9Rtma3cs1uweLm2WkNU6S1k6SmiZLmaEErJ8XOltjo0Imn8Y7w8PbpqQINJSUlRUVFBQUFW6lSUFBQVFRUUlJS/IjLli1bioqKysrKSkpKtFptUVFRbm6uVCoVCARLlizZvXv3jRs3bt++3dPTc/v27Zv3L5cuXbp27dqNGzd6e3v7+/uvX79+69atQJjDoJ78g8pJ/oOTPPiLcDeAwuFw+Hw+j8eD8lz09/evW7fuT3/604gRI4KCgt54443f/OY3CDG8+eabDAajsrKyp6cHtY+EJAAAAWWHe7Z7P7ZCazTcYyj6I22Bh2KBwNX9UFp7DhoJGOQBKw/rkO+3uYfVPt3OT9MCNGigQcPT2PMH73fPGGjgKZu4CisCDeGiehZfs0qz9+wN4CKA0fZlgmQLa9ZarrCGJbMHS5vDs3aNVFrGqBp581vDZPXhCVWzMnbGLzoSITIHpzSME9WNklZGzbdMzaobOWlpVNy6/MqTx26CC07Q6QJ9AFztBx3HXIs27Y7mF4fGbQsXlofJakMklZOyG1ZWnT3TA3Yc86qX1soX12w1nG866rnuAt0YOHUNtB8HG2tOChZVTeBv4/HLWHJrmKolWG4MkVRHJBdHC0tky+wa+5XjneAOBi53gWvd4PgFoDNeFeeaWDMLwmPLo2TNLMX2DxLKpBv3HekDx7pA1ip9ompDcc2Z01fAzQGw6wuQsXxn2KwNjPiNipW2beZzO7/Ejt8GnRjoBuBSD2jaPbDqsz18ZV3wtIKwpIpZi5vLDnq+8oOrPuCgckgDL3C5wE03OHQN5Nuv81fs4AkN42ZoIhIKpypLcjbaqndcPnEDXHWDy05wvgec7wJtRwbWlh5OzKxjxmnGxZYxxMaYzJbRqVrh2r07LoLzDrBGu2+S4NN5W9oPXAcXvaDpKPi44EissoYTX8ZNqJ8kaJ0q38mV0BoNT+MN4SHu01MFGhYuXLhgwYLc3NwcquTm5i5YsGDhwoXzHnHJycmZP3/+woUL1Wp1VlbWokWLsrOzFyxYoFKp1Gr1smXLVq1alZeX9/HHHy9dunTJkiUf37+sWLFi+fLlS5YsycnJyczMLCgoOHXq1MPyaBj8IfgOgYaAPMS+ffvi4uJ++9vf/uxnP0NwYfjw4ajyt7/9LTs7++TJk0jYEsOgUCUKuAAwQz0siIYQBEzFh7rZdwIFRE8eYj+km6ItQFsgkDoX3TGeFYME7kuPaIcD7T9g5WHtxv0297Dap9v5aVrgJwoa+k+K8HN8cCoRnIwDp1P8J1OJMyLsjAg7K8DOphBoOpNKnBEQcP49JqoFgf+0xH9aRE2oBQF5TuI7yQfnxMQZAXZG5Dop6zmRDu6UA/Kan/RhAMeoRymUwJogfR7S89NMb4nudIPPqTglPoQTwI/DdEoePwkTpLtJ0Hq458OJi0OTNFEqG/TnF5u5Cuj3TiWYtD6u+dfpLZEYZEx6W7iwYXxKDUdhZkgMbLFW13G50wcu3wHL1tmiZ66elKqdprZGCBsYUmNMTlOooiJEVc7IqItQ1Ecpm8OTG0OTaiektbHE9hCRKXK+fZy0MHjOUkWebvverj43uO0F/X7g9oOuHpgd0wnArpP9m+qORok2hPO3MBTlo5K3MESbDUf6+gG4MQCqLIdaDnR2ukC3H8ZudA3AVfoBuOQGZc2HktVbeXFbo2StbNUu3tz2/52xPlq0ZaV2/+HroAuAOyRcq88PegmYDqMfgOZ9A9J5BnZsUZSwMTzVxhA2LtGfQQqXWqO9oKb5Dg4G/GCABEcvA8XH5pBZKzLWte38Eo7quykhzDsAXHNDfuEiwcWrIL/48AyhPnRW/pzFFu3+vksAXPQAHwCYh3T3AsIPrjnAWQfQ7rwTJSsNnbk1KqlwTlqh+UDniVtkDwB3ALiOga/6QS+1e7e94FIXsOzqSltqD4/NHxtXHKGoDZFrZ33ccA2A2wBUNh3QmvacugOPrhuAU7fBioIDkQn5kUllM1UtE8U2VnIDRwazk35z+nZ3Gnrev/3Xf/qNnS1tZsvscCtyE6xLW1mSJhi1Ia+Fk9REZUultCHkTZEy8/tRn0pzKno8AF4PfpxKeDuY2How5WW8A64AACAASURBVC2V6ZrOa/3Pf8cfCmgoKSmpqqqqqKjIzMzMzs5WKBTz5s0rKCioq6vT6XRaLVRz1FGlrKxMq9XqdDqkiYD0F0pLS2tqanQ6XU5OjlwuVygUaWlpcrlcpVJlZWWlpaUpB4tqsKiponhIBTWvUqnS0tLS09MzMzOzBktaWppUKuXz+UlUSU5OTk1NRR9TU1MFAkFycnJKSgqqxMXF8fn8hISElJQUhUIxY8YMuVz+xRdfoPEDGsP/81Ny/yU8Hg/6I0on4ff7+/r6EBEgSfL27dvr169/5513UGqJ4cOHv/zyy2+88QYiDgwGw2w2u1wuFBaBsAISmEB9AMVc3H/j9F9oC9AWoC1AW4C2AG2BH2UBGjTQoOFHdaCHvvKzCxoiROZIVTPUJhQ2cJUWptQYraxsPNpziwTHv/JnL6yJnrlhhszwUUYbU2xgSqpZMv3I+HXvzVn9vx+t+suUNaNji9+blD9y+ubQeC0zxRqTtos71/Zh6obYnM0N7afdfkgW7Ptubdbv01Qc2LDNVlq7++hFZz8Ax2+CeRvtk1UlDIk2VFDMEeeXt1/sJ+GI/Xo3OHrO0XakT9t4eu3W1mXrDQ1tZ493uvsAOHmzZ+UG84yU4mhhU6Rq91hBJVdRurbiwOdXiU4/OHITaKyn1pe3r9EYVpfWN+z68poX3HCDSsslflYdN0HPSDaFJdbmbj70+TXQQ4Brvf1nbwycvelvOXjTtOf28tLDE6WFk9VlW+y3j3SBIzdAgenUx4Uti7aYlxfZdebPz98CAz5w9BSmmm+cmFISI9PPWWycX9pRZD/U4wUkCY4duViqb19dtGNRwX7F2o4YmW705FXJWVXmXVcdAHQ6waEL7kLz52urdq/UdGypO2zaffnIBa8DgF4M1Dd3pmZWs/i6yAzjOGnhzKUVh2/i173g4h3XqWu9hy+67Ee6Klq+KjCeV31si04uihHVTlE1ccVGpoAGDQ/9gn7qGnwioAGlkNBoNBUVFTqdTqPRIDnGJUuWZGRkoAG/UqlEDgVSqXQQL6gQX0h72AU1q1arAw2nUyUrKysjI0OtViMSoVarEYZAO4nqaG8zMzPR6rm5uZmZmRkZGfPnzxcIBAsXLrxx48b3BQ3otv/Nmz+kzcjvAPkjoJ4UUHC02WwCgeDf//3fg4KCXnrppWHDhgVSS/zlL39ZsGDBgQMHent7A7kz0HlHCDuwuaeud9I7RFuAtgBtAdoCtAWeIwvQoIEGDU9Xd/7ms+Yz49HAkTeF8o0cuQ29DOcqLQyJYXpmQ8cZjxOAQ0eccqVm4uyts1S2CWltTJkplF/Gk5XFzjco13fI13XI1rbn5h9IW709c02zcKFluqSVJ2odza9mpGnybZ/3AXDbDWqbLyXMNYbM2MaZXRQ2bV1o/Kq5m81XPOAWBmrarggWNoQmFIWl6jiiwgLL+dsu4MJA5y1QWnlUnFMfnbBtFHchd/aquPSCLYb9nZQLgMFySiDVc+Pq2ALz2ISN2VubT9/GBwA4dBV8XHRgIr+UHV80UVoclrAmYX6V+bCjD4Cv7oA1RXsjE/J5fEN4XOWSbceOX4NOFi4A9p/pzf60boLws2hZIUNQ8sHsbWy5Mbv40lpj/8LiM9GSkjHTPxk5ddH7k+YGf5SzdKv17E3oXrF+24HZknJ2SmVIkp4hKpZ/2vhlF0Qkza0nPopfGp2wJSS2mCs1TEozhc1av6n8izsO4POD7Xvv5K60RMStHh+3NjRhQ1j8Z5H8TVmf2nYeczoBOHsBLFnTykkoDOfrx0uLRJ+Zj9z0dePAC8CJL3tXbG2eKtoYw988Ta6PFmgZiTqYH1RmDRcb2Ip7fBnQx297KNAeDU/XTeN77c0TAQ3lVNFqtZWVlXq9XqvV1tXVlZWV5eXlpaWlKRSKrKyshQsXfkKVRYsWLaDKwsGyaLAsfkgFtYeaX7BgwfxvFRTBsXjxYhQ3sWTJkoULF+bl5S1dunTRokWLFy9GlUWLFi1btiwvLy8rKys7OzsxMTE7O/vatWs/HjSg04QkGAAAPp+vt7cXnejr168XFBSEhoa+8MILQUFBw4cP//Wvf410H0eMGMHhcIqLi2/evIkWRlIONFlA1qDntAVoC9AWoC1AW+BxWoAGDTRoeJz97Z9v6xkFDZGq1lC+kSkxc+QWntLGlBrHp1bPyDbuOuuF+SYOO6Uy3cS4kikKe4TMEiw3juNrZ8xv3GbvOuUAlwhwwQs6veBiLzhxjaxvua6ctzdsesX7swvlW3cd7SIGANh99HqSMj9kdmFIfG3EnGpOatWYpE1s+doS694bTnCpG6wpPcZILBqfUMISFG4zfekEwOMDFvs5obw4Ytqm4GkF7ATdR6r6cbPWyFbV7em83QfAwYPd83LNUbFVYXH6Cept5TvP9JPg7NXevM220Jlr/87bGBFbG5FY9bcJa/4WtYS/uHbfWccAAC2Hrgvml4fGFnw4ZdPSgiOdLuAB4NTFvvWluyJmrnwnetnIWYVMSSNP1cqGKT+tzJSK9yeuHTf9M1bi5mhJESt5NSthcdbqst0nr7j8oNF2IV6sZSVU8+TNPFWjcHnTiU7Q7wPbd1+cmrCSHacJT2pgSqwxKssMddne07iXBKfPOFetbZ8SXzDmow1MUQVTUj1mTtGH0zaxk7Yu2bz97A3gJYDJfjE+vYaZWvpB/GfCdaZTXaAfA1dvEkX6HfGywpBpa0ZN+ywsviQisYLBr2dLm0NEljGiBlaajSX/NmugQcM/v2yfoSWeCGhASSs1Gg0CDRqNpq6uTq/XI5UEhUKxYMGCjRs3arVaFGGhGSzawaKjyuDXP/b/wVa1qKHSwVIyWNAXAVlKjUZTVFRUWlqKKkhCsri4uLCwUKPRFBQULF26NDc3NyUlJSsr6/r1698XNNzTf4ZyARzHnU5nf38/WqalpWX27Nm//vWvkQrDW2+9hTJZBgUF/eEPf1i0aNGhQ4fQkoFclT8+guOe3aM/0hagLUBbgLYAbQHaAg9iARo00KDhQfrJ41vmGQUNUeq2cKGJITaxZeZIlT1cVD8qXj8ls37fRXIAgL0H+wUyTVS8JlphHSUzvierC5aXz8qz6HYOXMGhXkAXAZ0CCABwEhw6fGvePDtvWn6UtGhLy7kbGDh/5ebqNdpQjuLDmJXjY/Xhs7ScFN27s5b/7zR11tqCM9f7nX6gMXzJit8SPGcLR7xN13HVCYDTB1asMk6fsyEmqYqZZJg9fx9HZgpO0M9aYKg/erUHgDNniJXLtk9JrGTGFYtX1e6+eKffTZgsu2Ml6z+M+iR0mj6abw+bUxEaV/xuzCeMOZ+UGHb2keDIhS75J6WMlA3BcZ8tKmq/4oZ7bmz6XDa3amJyxXTFjmjJronqQ5MzD3JE9tC48rCZ+WHTV8sWmz6rPKlrvV7ZcbX1i9snOns7e7oH3L6OnV+mSAsi4nQsqY2tMAtWtZ26A1UhOvZdEWaUMBPKJqTt4CiaxyaWqFZbLvRCr4Tq+kNSRfnk+LLJCsv0+Tsnzd0enFo9fcHuCEHZzIxC24ErPgDOfuXOWtHE5RePTd6ctLzhzC3gwEFbxxW5WjNbUjUzvSVaZo1UNEUqWqLVHZHpe0NkLWOEphC5mQYNj+9Sf0JbIqhCkiRKbZifn5+amqpQKBYtWlRYWFhdXa3X6zUaTVlZmV6vHyq1gDwR9Hp9VVXV99Vo0FOltLT0Ho2GJUuWZGZmKpXKxYsXFxUVoVSX5eXlKNslmgcyXQayYA7964+so8ZrBkstVeqoUlNTU11dXVFRUV5eXlNTU1FRUV1dXVtbW1lZWVVVhSrV1dX19fXV1dUrV66cP3++SCTKzs6+devWjwcNKMbBRxUkpnDnzp38/Px3330XIYYXXnjh7bffRuESw4cPnzhxokajcTgcqFv5/X6n0xmIuUCsAf24PKF+R2+WtgBtAdoCtAVoC/zkLECDBho0PF2d/hkFDZGqVobYwpSY4ct8pS1MWPfhHO3kjJq9l4l+APYfHlCmVUbHa2IU1mCF+T1ZbUR6PVddqViz59Pyc+uqvvisrKPa1n7+aqcHAx07z8mVmgmz1sUtKis/cAFKElzvajHtr6//Qmu+UWzrqe1w6ptuLK85tMl+uLxtT6eDdOJA13AuKnlrRNJWtnRL+Z7OW27Q5wK5OfrpsVumS+3jE03T5h34e2z5qPjqqdmNZQcu3QHg9ElsWV7zxFg9M644d1vT6W7c7wcnj12qMJ4sMV7T250Fxp782ivVHQMae+fW2iOHzlz3ALD/7PXk3E1McT5bVrioZPuX/cBJAk3FjukJRRMTjdMVB6Kl+9iSHcFJhuA43ayshk+1x2u239h3juz0QUXGTgLcpmQsvYQf+IHFuF+aVjRBWB3KbwgWVYvX7fiyH4KGlp2XlPOrR07Oj1K1M+TWD+I2r6g4dB0H1wfAkpUVsQkl0xIapiqbmRJziLjh3Tn6qQuPjE0qGx+/fHPtDhyAK53ueaubWMkFocKC2QvKT90AXhLYm6/EJm6ZlFwTJbKxpLYwiYUhsfJU2yPTd7NUHaFSW6i4kQYNT9ft4BHszRMBDfeIQZaUlNTU1Gi12vnz56vVaolEkpubu23btqqqqvLycr1eX0yVQfeCkkGHg9KAJ8KPrKCWUbMajSbQWkkJ3BZyc9Bqv/Z30Ol0yIsh4HCh1+vRknq9XqfTLVq0KDMzUyAQpKWl/YDQCXSeUWYHgiCQcKPP5wvoNe7atUsoFL755ptBQUGvvfbaiBEjhg0bhkQf//M//3Pp0qUnT55EjSAPCKfT6fV60blGqSUQuRjqK/EIOhfdJG0B2gK0BWgL0BagLfC1BWjQQIOGr3vD01B7RkEDR96EMgVEiAyRKjtTahydUDY1q652/8VbPnDlMvhsdceMBE1Mag1bYQ5VmTjpphB+BYNvYPJNDGEjS1gh+qSx6Vh/Fw5se67GS7cyY5fP+qREe/isEwASB64bAPhAHw4TW3oAGMDBDcoV4pYfpoS43Q/K6s/NlJfxRKVh/HWl7V85AHC6wcpl5tnxJTNkHQx+28TcA+Eq24dxukmZ1br9Z28B8MVx5+J55qhZOla8dlHRnvO9lE+FGzgx2GYfpeMwQG3OBcAABr0tfABsP3BdklfzQWz+h4lbsra2XnBCL4NiTceUj4onzLHw+E3R6g622jomtXDyXP221rNfukAPAF8NgCPX8N0X/I0He0tNJ207L96+6Qc+0FC1R51ROkVSxRDVsVV1ss92nOkCfRiwtVxW5daPmrKZq7CHyRuC+ds2Np68BcBVJ8jK08YmV81Mtk9VbA/nGzhyy3hhI0O+c0xSdUjimrWV230AZuVYsnEnJ7XkgzlrpuWWHr/q95Cgqe3azOQtMak1EQLjWH7NaH7VOGFVuLiBJbVx5a0cuY0hNn4z3wSt0fA03BIe8j48EdCAnCNKS0uRGGRpaWltba1Op5s7d65KpUKOAFu3bq2oqNBTBYlHojnSdygvL6+oqKh82KWCKoFN6KgydB/QnyorK3U6HdoBvV6PfC4CTh8VFRV5eXk5OTlIbOIHiEGic4xAA47jGIb5fD6v1wug49WZlStXjho1avjw4S+++OIbb7wxYsQI5NTw61//msPhFBYW9vX1oRbQKqgemPt8PpoyBKxBV2gL0BagLUBbgLbAY7MADRqeLGioQOktCfI70lt6AI4B0k9Syezg61+SyvX42PrGk9nQN0GDnzpkPwn8foABmN7SS5Bfp7cMT7yb3pIpecLpLZkSK5Ww0BouNEapm3gqa3By9Uc5htWVbZf7AYEDQ91XSSmayFklUTJDdKY9Kr0pXGwI5ltGJZqC+daxiZWz5zU2HSdvesCOQ72J6uKQWR/P/FirPXRhAAC3C1w61X/+lLPlQOeecwP7Tt9uP3b5VLf/aBex46s+2+GbhtbOjzfsnijUcwTFIUmfatqhRsOtW2DdmrapMwuik+0M4fZwqZ2b2TReUDF9Xk3ZgXO3ATh10rXsY9vk5CqeoGZF5enzPQBzgdsXnJ+fuL3j2PXd52+1n7p86Ktr+09f2X2i69A515mL+L5jA/mVJ0VLWsPFDe/FF2Zt67iKQwaxbqONN3nbxCR7jKJ1QlZrhKxqXOqGzK2WE/2QMuw9d1NrPZqUUzxFUfxRZtkUSf6KrS3nLvi6u0CL/ZREXciI2xoqLI/KNPBXNh+5DDx+YLZdVc5tZCSW8dS2EFllhLzgs/rDNwhw2wfSF2hnJlROmmOaJG5mikyTc9q5Ga1jRE3B4nqmeOPa6nYvAF19YNG6XSGzt7EUOuXmtnNdoMcNKmqOR89YEy2sm5qzJ2puGy/Lzkm3MGQNYYJ6ltQSKbOxRabHkt7S+nV6S5hKE2a7pCiViSWrhxNMbwlzW8J0rXfTW64U58L0lhgABImTJAFIdHn6AUx5CQud3hLZ4Z/OnwhouEejISAGuXjx4rlz5yqVSpQds7y8XKPRoLQUAbcC5G6AEEDA9eBHVhA+QIEhWq22tLQU+TiUl5cjfBBoH223rKxMo9EgXIL2EMWABDAEOhCUp/Py5cs/JnTC7/cTBIFww7FjxwQCwdtvv43IwogRI37729+i+r/8y79kZGScOHECnXG3G2Y+BgC43W6Xy0WS8Arx+/0+nw/HcfSz8k/7Br0AbQHaArQFaAvQFqAt8BAt8BMFDX0nhNjZVPJkAnliDjiVTJxIwU8LvacE3tOp3tNJ2Okk/EwycSbVf1ZInEn9XhN+ToidFfhPS/ynRcQpIXGKj59OwU6lEmdE2CmB/7TAeyIZPyt2nZTdOa4GPVUAu0TgHhJgfkD4/ThBYH4/7geEh/D6SIwg/eipy48TJOEfHF08xA7wBJpCj4Bow0NlutBjIo7j6CkTJU5HL6Oohf3A78UJt5fAHX7Qerjn3ch5jGQdV25mSsxshY0hhXO27NvSfY/pmwhRI0/ZFJPeRg0R4VgxIrkwbVXZgdM3vH5w9ASWNreOM3UdJ1EXKW9kyK2MtCZuTvvERTt56rqwpE2qlY17vnD194B9e2+Js/SM2aunZ5eXtN/oIsCpr3zr8y1J0nWixXWCVW0cVUVUVu3Mpba45fapORVzFtalzLdMFldOkDeEJhdFpKwusBzzAuBwgsy5+knxxbxUK1PUFqmys5WGceKyCTkVul1n7wBw/KQz7xNTDF/PElYsKD12sgsQPmCrPyCSr5kl3Zgwrz52vmlWTk3svLpZufWx8xpSFzam5ppnpVkmKpvCZPZxwor5xfsu+0A3Bj7NtzFm5EfyTVPm7mQrG0fFb5ukKq3ceaEPgFOXXZ+sM8XEbh09YdvoWVVjkyrGJ23M2mg+etHpAaCy8VBKZiFLpAuRVIfKyoWrmy/0QDXHjp3OZFkNK6WWqTS/l7Q5KrugwHb4pg/0OMHG/I6oKQXTUmzT5NvHxFUGy+rY81vGqMx/Ty3mKjZomj/3APDF+T7FAks0v+r9OQXJnzZ/1QNDJ0r0hz9KKYhIrIjJ2MmQ2xlyKwtOZoYCSjPwJPZIiZ0jfdS9xTzYRe1DKxAuyBpZMgM1NVKsAXYhjqKZp7C8H7NCMq/ijhc6j2CkH7IGSBb8JCQPGLw6vuYOT+CKfiibhHc6vx/DqMMZ0mLAeR4AQBDEUHf6IUt9j+oTAQ36wYJiKBB3QI4AaWlparV63rx527Ztq62tDchDDK7xVPwfAA2IL+h0OlTRarUVFRUajSYvLy87O5vP52dnZyMxSMQaht7th9bBN4vH40GpJQIuCZ2dnVu3bv3ggw8QVhg+fPirr776i1/8An3kcDg1NTVoLfQjgmGY0+n0+Xwo7ALH8YAuwzc3RX+iLUBbgLbAI7QAwprfhpvoN+4f3AYf4T7RTdMWeHIWoEHD0wIaIGugQANBQtDgw724H0M3prtPxs81aMAwDN1/A/nM0JDj6wEUrMFhFQGg+mDr4Z73eE8RaKAc701chT1K3UIpNZhZUguDr5ssXqO3ft7rBzcGQHHl4SRFQWT8xoj44kmZrQy5/Z34smBR9aTMOo5gk2JJ+b5jPR4H2Lf7iiRbHzprdaRYs8V84zYBvrgEcj8pZ02bN2bap6PjS99PqAlVNo+XNPw9rmhk/DYGXxfJrwmdpY8WW8MT9azUjVXbL/Q6QXcPyFlcN0NYHiNp4crboxU2pqR6NL9k8vzK6v2XIQI451n2qZWXUjRydr5wTfuO88DtAx1tZ8XK9eFT8yLii7kSU7igPkxYNza5clR86egZWz+YvDkktnKCetdYvnlksjZ7W8d5B7jlBWuKmlmJWyLljRESU6iwfnyiJm5evfXQLScAB4/dkqcV8KYWTEy1T8s4GCaoDxNsXl29/0Iv6HKDhpZT8RmFTFEZU21iqCr5q2ynb0J1idbtfSnSGnZSVaTa9kFy/tjU1XlFzee7IC/YsadLlmWOjNVHJ9dPUNqmztvBymgarzCPSi2Nn69pPd7pAmDv8eupmdWhccUjk7Xxy+wnrpMeElTUnpwt1oTM0UWIbSyZ/e4kt0YoTQyFCYIGcTNHGhj/PyLiMDQcA6XJtLJl5ntBg+wua6BAgwmChvllAdCAAZwAfgLGsngp1vD8gAY0Oh2KG1DE/nc+nA1lEA/+G0qDBv33L48aNAAAkAsDAKC3t7e1tTU9PR05MgwbNuytt976+c9/jhDDH/7wh7y8vDNnziAkgc5mwIECwzCcKgRBDKXYD9496CVpC9AWoC3wYyxAg4YfYz163efPAjRooEHDY+3VKAoXOS+QJIn8F9BTJvJ07e7uRr4MyBXWh2MOh2Ogz4G5PSTuxnAXRj6NoIGrsMP3z3IbV2FniE3QyUJmZYlqxsWuVa+x7DzvgToF3X5d4x7ZgkLOnJXhswvYKYZRM2qC4xoihfWjp3yakFmy78yAFwD77pMpORpG0uawxG1z8w+d6wF3CLB9X2dmXjVr+vqQaaWRQvskeWtEfOm4mZvGzNgyO7MlJWs3d3bDJNEOblJjVEpp3fbbLh8YGABLllln8Mu4KRaebMckVWuUzDCer5m1qK52/+UuDJw42b/808aJgqLxiVsjVdriluu9XnC5E6zbao2JyxszMW/8jE3sxBpGYt2YWH1IQtlUhXl2ZstH6vbJmbtDJNZxwrJ5xbsuemFwxDptc0T8mvDUsuj0HUxpy7iE6thcs/lg3wAAB453KjI3RH/0KS92GytJFxy7ITa7yHao00uCvgFgajqXqC6Oklbx0kys9ErphtZzPaDfC1rabqSISsdN2MDh14bz9e9/tDY+u96wo7sPgE4n2Fp5fFpKAXPaBm6sJjLBOG5q9ciJ1VHJDWtLP7/hAG4Ayq17YjOKWaKSsUKNaG3rV93QF0Bf/fmkxE1jZxVEp7UOehPAQT5DaWTJTTyp9XF5NAx1akBc4x+BhijFPaABEKSfgHoa0KOBBDiKmqAuY+g6/owWBFXRKBGlhHC5XE6nE90rPB4PSkDgdDodDkcAR/6Ag6VBg/77l8cAGlwuFzqbNTU1Y8eOHTZsGCILI0aMeO2114KCgn71q19NnDixuLh4YGAALemgylAIhU4u/eYQ2Yee0xagLfD4LUCDhsdvc3qLT7MFaNDwjIEG9CD17M7Rayvk14rjuJcq6M1k4PERiYT7fD6Px+MHMAvd3aEU6XN7+l0+7Cn0aOApmyBZkMLEE1+DBlljcLKGKyuZX7zj4HW8D4BuEtj2nVm6oXFK6qY5qvrZCutMmXmGtGYyf8viDbYT12GKCsOes4nzKmLkFWHJpVESja7lUh8ATgAaWy9J59bPElXHKy2TkjWRsSvj1YWL8w/U7vRrGr2Jqu3TxG2RSfW8hG2axgteEvT2gcXLG6ckFzPia6Pl7ZMVLZGihuCUkhnzqyt2fHXNAfNoLsirjEndGqWo+DDus7RNO49chhv64jLxmbZDmqtPUGtjEgsnpOg4SUWJueatDbcq2ryq1Z+PnlUUmWYfLyrNLmi54IEHtbrEFha7Kjy1bPLc3SzF9jEJVdMzTdW7b/cBcK2PLKvrkGRvi45bEZ36mXqNqaL93MUe4MNB101QbzgTJymJFFaHiWvHSUoEn9lO9sF9OHx8IG+ZcULcptlpDQkLmiOSi9kJpRmfbv+8098DwOHLxMaK/apFNdMSN02JK52Z2pCial9feOXMReAhweEztzM+KZ0gK4lKN45KLRasbj7bBdwk0FR9PjFhQ/Dsgmm5O5HoIxUoYWYpjGy5KVLyVIIGpT1KYfogZhnyaIAuPeTzDBoGBgYCEKGvrw9lKxw6kkQygUO/+b6/rzRo0H//8hhAA4qDwHF8/fr1w4cPDwoK+o//+I+33noL4Yb//u//zsrKOn36NDrdJEkG1CLRNwRBIIr9bY9ltAA9py1AW4C2wGOwAA0aHoOR6U08QxagQQMNGh4rtUCSE+ihEOWxR0HXly5dKi0tbW5u7uvrGzrSIKnAbD9OUlHoGOH34E9l6ARXYecq7EyJOULUiEADVPJTWMZL6v8ev4Ul3bqwtH3Pl/39FDK4dIc430kePOXcedSx86hj3xeOc9eBg4S5Hs72gtK2C7PmNvCktWxx7QfT1iUtKDcdutkHQC8ARy+C7YedrQf76lvPNW4/fPj8tS/7wQU3MO66k5xRMVGoZ8Tls+JWllq+cAHQeYdc+GntdHEhm181Ka0tWmKKSCr7YPbWaXNravbc6Afg9JdETl4VM3YdR6IbJyjkqXUrK46e6ALdAFzsAwdOe1r23jBuv2jZ22k5ePPQNei5cLYbfKo/NlmtY8vLRiety9psuewB/QB8VrqDG7cpWtrAlBrDZcZQQfWk9No11UdPd8ODuukG1r2nSxv3Gw5cOusEVzBw9rbzUqf39i1gtt6YLdCxUyBoGC0s5GUX1hy+dMMPejFw9LSjpumrspbOdXVnPppbFRy3MVpWvK7uZcLpNgAAIABJREFUyMk+0AXAdRycuOZobD9Wbz/euqfzxJfgjhN4CHCpE3y2bUd03EZmipkhbvt7XEnSJ9aj16AXjKHpcoKqLGROEVdu5sjgxJPCOVtuempBAwzG+TZooNRaKJkGnAR3P1Afn6Gfnnt3NfAWGsfvylsGwvUdDgdyfULeDYE10dA08PEBKzRo0H//8qhBw//P3ntAt3Hd+b9Ul5x4/5u3m5fs+Z//+W92/29f3m7ilI2TuKiQKIPCIlnNdqzKDkwDwCKKVLEkd8dxUbEosYJgr+ggSIqWZMfdsePeiyTbalYjUWfuO3cuAcOUSJMSKYLk75454CVmMHPnOxcXcz/zK8TNgby+9dZb69evnzt3LonI8MMf/lCpVFZVVX3++eekA1y6dClqChd1miBX/1oI1Aj7D2wGCoACoMAwCgBoGEYcWDUNFQDQMMlAQ+wQNhnrg75j0fvF119/XaVS3XTTTRzH1dTUfPzxx2RLEaFgMHjx/KX+CxcFbMqAYzRcEOMuRoOKdUVBA8kXoKQdaoMryWi/XVe7MOugljnAP9TReOjjj8/iJ/Z9CM+lfSRXpVQ5cxF5X/jikbqXC55+XkM3LVxXq2Uc8qxadfaBnF3Ne9tefe8snuqfFvDEnuzhEkLHg8j20vGiR+3qdY+nZFcrNh5MztnXePjEeYQ+OYO2P+m+i69RZDWlGLpTdE6N3vr7e8ru3mZvf6UP04Sv0L2PeeVr9iRlVacWO2/euF/DVzxY/3rv+8KXYQwILgnofGgg1eVphN78Cj1mPryS3buUK0/MKk3K2b+r+rkTUnv21b6uWVsm31CXmudILXSp+Q55TvXdRZYnml947VjfeSlZ5lmETiL0URDVH359T6P3r38/1Segzmd86QZrmt6eWuClNrXfnPFYfqm998PzxIjjPEJf9KPaI2f+tK1tcVaVItes0T/9VMffn/tMPCHtEzdSErMPodP9yN3z6cNPdK/NMSvvNi/lXkjSPfO7dTXcU3/94Bt8Fs3eE2uMDbfeVSrPbVczLjXj0jKuaJqJ+HSd0PCeVM76x9QH2S21JOsEtmiYuqCB+E8hhC5cuPDiiy++8cYbBDsSSyi/39/f3x+N4HB1s0oADZbRl/EGDf39/QQZEMz017/+ddGiRQkJCf/yL//y8MMPv/nmm8SfLhQKnT9/3u/3E3s3EvfR7/cTAHF1MTsG/STBv6AAKAAKXIsCsXfmsfuJwvTYN6EOCkx5BQA0TDLQMJV6pCiKJHaXKIpfffUVRVEJCQnz5s37wx/+UFBQ4HA4jh07FgxLjzdxZk8RhX0YO4jolC/uQAPJr0ksGtQcTn6h0NvVBhdlsqqN7RqDVZFdJ19fta7Q9kjlWzWeY82HPnU89+lf3zn7wrvfdD77RZvnowPVrxq32dKYBopuV2S3JmW0qnWuO/J6UlhHUkZNKlPzeNvHrS9esL98rvuN80fevvT8ez7XC2d2N76Xc68nNb128eqqNH2PRt+lyGq53/xJ79uo5ZmLOVs7l7MOWbpTRR9VZx+6w3R0cWbz8mL3A/UfWF8JWzynmB0uVVa1hu9YuqXndxnVN288kFrQwD956KD7k65XL3ifP3XopfMvvit4Xjpfbn9v5/7eO3L3/HH5gxqdWaavV/NNebufbX/+kvtF/5bHjqrW1snWNt1V0ptW4FIy1kXpdUvSy+4srH2g8vnWo1/3vCUe/gDZXvftqH5+qeHJlaZ9Tza9+cJH6M9l767Lc6zI71Ky9pQt3lv0lSmbzJsPHG0+fL77FdT7N+R8Ce2sfDfF0CZjHEm0/Q937b+roD3/8b8esB7reP6C69VzPX8/a33+01rv2/ubXlvHVaf86WDq+ubkTNfq4hcUnGdhTkvGg0fbnu3vfMm3c/fzy3Iti9bVag1dKtajZjzamDQTWvo7/8YEcRjbqJCjCwap5V3Dggac+ZU4Fk0NiwaEEDFk+PTTT4uLi1etWvXUU0+9+uqr0TkkGTSi/17FeAigwTL6Mt6gIWqcQojDxYsXy8rKli9f/sQTT5B3BhmzXPG6R3dC1g51u3/Fz8KboAAoAAqMiQJDjTwAGsZEXtjJpFMAQMMkAw1kqJq8ryTMGwkJHgqFfFIJBoMnT57Mzc2NZi+bOXPmz3/+c5qmvd1dn332mRjGoCHkvxgWfPGZdUKus0UtGrSGTjXnlt5xyfWtCl1rqqlzRUGvRudSprcl57al6RtTcg+s4EpXsnuX655aqdu3Sle2PKsqOb02Mb3l5nvqUo1dqzYdlmV0qBnv0vznFm60/mFNbbKxQ0tXLePLV/IH0rL3LNdVUutweEXFxvrlrFer61qa96LW8NyvVlQrs1tXGtr/lG+VrTUv5bvVzDNq5tmkjS4t3Z2ksybqmlV04zJD81KmjsqoVOkaV205TJk6F3H2RKN9EV1/y/p9qWzFhqK61bn7V2QeyNxkTcss/+Oyh5Kzy+/Mb0tj25INrpRN3TLWmpRVmayvWsnWpGSaU7Lt9xS/pMxtuW19zaKMlhRT18pNXSlca6revNpYl5ZbnUbXprJ1Cl3Fksz9FF15R37Lyvx21caK1NzmNIPnP+8oTeTtygL7rdnVf1xXuqaoc7muJXlDzd0GeyrTevvGBrmxd6Hes7LoiCqrNXFtzWqj626TIyW7chlfnsbtTaN331NQt3jVfs269tXcs5pcb0qea5GuQWlw3rK2Oi235i62bhVdv9pkTzN4U03PqBivisE5JqQ0Ezj7A7FxGDe+EKUVowYNS7n2W1If4LbUfNOP80yEcSLcSNASjN+klBOSa9FAipZJ9yskNZiMaQihc+fOIYQ++uijpUuXJiQk/PjHP162bJnD4Xj99dfPY98jXAgsGMnkk2wf+wqgwTL6Mt6gASEUtVkggTlCodAXX3yBUw0Fg6dPnyZmCyRgJMFMFy5cIGSBJD2NvcSxN/okcEPsWqiDAqAAKDB+CsSOP7FHAdAQqwbUp48CABoGQEP4XV3wXV3wvZzge5lhsrybFX43J4xfR7FIe8gR3qGFd3TSQvaQE35XF3onV3w3N/BWZvh9uu8t9tTrRnS6Xuj/EIlBhEIXL32DkOD39/uDPhEJgZAfP6tEKBrgCgmiEMLxriZ7iaUkUePnEydOrFmzZubMmbNmzbrhhhtmzZpFYoD975/9a1ZW1uHeI33nL5D0lqfPne9DyPvS6YUr7k9lWpZv6pHlWhWMIyUfz6LHf6IYnTF+f0XNuJRZbaocm1rvkOa0XqXeI9O5EnNsizJaFmU0LcpsXJxRLy2NizMaF2W0KDgvxXrUrF3L2HEEAcarYA7L2aOL9F23ZbffsrHmlnVlC9eVyzZalOkdykyHMrs1hbdreWdSTtsinU1p9FBGZ5Ku/ubVu7X6ei3TotC1qw0emd6WlN1yR2G3krXL9LYlWS0LMxoWpdclZjUqdO0U49Qau1V5hxT53TKjU860JGXVLFl/YPGfDsrW1MvWtsjS2xVZNkrvUnOdauMzVP4RmfHwYs6zUNe0OLtGlm2mchvUOpea8aYZ3MkGh5pzUwxGLUnZLUmZdUkZNUs21CZtbErMaknMbl2U27Iwp/m2rMbb0uvVdKuG6dBwTrXBozR6FEanjLcrGMeida1J69oU61tUGa1UTpuSdiTx3TJDz+LMdll6q2Jju2Jju2yjNSmjdUlmw+LsGjXfpGXbk/XdybrDWt1RNeNVGRooY53S4FTSLnVWs3qjhdpoUWU1K2mXiuuh2ChocKlYD8Xh9BPS8v2X9dr6WOxRvk1viTNcsjaK7VBxVjWPw0YoaZtc15FidN9h6PiNcotxe93x0xi4+fxhX38oEESBoBgM+rH1eAAFAygQDAdCQZKaIf5fSXaJy19FUTx58iRxoJDL5TNnzkxISJgzZ86PfvQjhUJRWlp67NgxMvmMdaAIBALRyA7RFDZD/YpPCGiolYolppB3duzYkZeXZzQaS0pKDhw40Nraajab6+rqYjaMiyppbVVVVV1dndlsrqysbGpqMpvNFoulsbHRbDbv2rWrqKgoOzu7qKjoxIkTSCojv7GO/VEYSX2oH0FyXHgFBUABUGCiFIgdnWLbMPLxMPZTUAcFJrsCABomGDQg8Ziv/wJCoUCwT5Qe3PuDvsrqinvW/mn9xnXp6ekbNmxYt27dhg0bNq7fsH4trkzqkhEp6enpGRkZmZmZWVlZ2dnZ69ev/9d//deEhIQFUiGUYdasWbPnzklISPj5f/x/WRvTrW2NoXB/GKFvQujom32/T9l26137VYxNzbkVjEPFu6Xp4nhPFEexfzXjorLt6lynRu/WsJ1qrlPOuhPpzsU6j5ztJUsS35PEdycZPEkGV5LBJec9Cs4VBQ0q1qNge5K4XmX+kSRjVxJnk+nbKdqmZrxa5mgy+6w8x64xONUG5xK6ZSHTJs+zK032JH3TbRsqU3irhnMoaZvW5FHorfLshrQ8t4Z3UaxHrncm5dqX6KxJtF3FurScV6lzK5nOJN4rM3YqOJeStil0HbLsDnVuF6XrUeh7ZHSXjO5KYrqWMD2L2V5Z3pHFfNcipmOxvlFONyqZdmwUwHZJcRbtOPsG45bTdpm+Q65vlee2KHV2Fe1Wsz0aQy9l6pEZuhJ592IWMwWKsZO0oGqDS8m75axbRnvlOV2KnE51jlujc2hom4qzKwxuhaFTSbsw79A71DqXUu9R0C4Z45CxNoqz46vP9KjowxRzmGI9lKGJMjZQBg+2XMh1qrPbMbDQWynWS/GH8CsbSS3JuSi82CnOPv6sYRSgQaFrV+nbVpo6Elc8+sBTnktBKSKq5COBXSZEJIoSeRSQKOB/JUOHyfGTFHsrFluPxmgIhUJLly5dsGBBQkJCNNPhP/3TPyUnJ5eVlX322WfBYNDn8xGPfXLO4XDY5/NFAcRQQgBosIy+AGgYqjvB+6AAKAAKxCow6BctugpAQ1QKqEwrBQA0TDxo8PsuhgW/iE1E/UiyZcjOzUqYkYCXmHLZGzHrpkp1xowZ86QySypz58794T/cmICVmDlv1uz/9//871xdRpvN/vnZYNfLZxauuH/RmjKKtqaYulQ8ntYqWee1PW0eBUQY2YE8Cp1bqZcs8zm3indSHJ4VJ9F2Jd9FFoWhEz/GN9lkeW2yvDa5wa7g8VxUzeBohSrWoeAdSUa7It8lM9kUvF3J2jWsW8t5tVy3mu1S6J0a3kMZXYlsx2KuTZ7nVJqcctqqyLWlmZ5J5nopuivFeBhbRmRak/EHPTiEBOOl2G4575XzHg3rTmW9VJZTlYtZA2Xwag1dWkOXmj+k4nqSjd3JRi82NzA4cUtYRyLjXMQ4ZCZvksEl420yrl3Ftat5bMWgYj2YUDBWBWOTczY530Hx7ZShRcW3UHS7irFr2E4N163mvUq+M5F3LuEccrYTGxewHg3n1HD4ST7FOZSMW83jzbS8K5mzafg2vAdDi8LQrua9Gt6rNVi1hjaNsUVtaqFM7ZTJig/EWynOoeA8cg6fmkLCB0q+S8V1qeluFe1WMdLO+U6sOSdZMUj5JijOgT9oaFcY2inOMbIre9VdZXSgYanJsdLU9l+L89Sriv6yx7x7z/7dTz1dWVldXmEur6iqqCirqKioKK+pKK8pr6gqryyrnCSlfIhSU1NjNpvLyspKS0tvv/32n/zkJ1HQMGPGwID44x//eMWKFeXl5a+99hr5tQ6Hw5cuXRrkoj/UDzmABsvoy3iDhqEu1lDvx97Kx9aH2h7eBwVAAVDg+igw1IgEoOH66A9HiTcFADRMMGjArhMoFAz1iyiIKUPAFxZDxVs2z5ozc96CuTfccMP8+fPnzp07f/78BfPmz5uDK5O6zJXKnEiZO3fuvHnz5s+fn5CQ8IMf/ICkT589e/YPf/hDUk+YkXDDDTf8YMEP586ctWDujIQZCf/4zz/Oydv+eOUzty+/L5VpoWirmnNrTV4l65RLz+fHeaI4ihkmxbnwg33WreI6VbxbzTnVnJNi7BRjV9KOyCLNzNl2Bdsq41oVBquCt6tY6Tk8gx+5U7xVYWhP4luT2DY5bcWP9Gk8c1bSLrnOQdFdWr5XZeyS8fZEg43Kc6mMnXiiTnenGg6r2R6K7ko19qawXirbqsHb2yjGqaa7o6BBRTs1OkeazptCd2l4L57eYxjRKWe65Uw3FpM86ucdSgNZnNgfAXMHyQSAd2gMTi3v1LBuFe1UMA4F45CzmEpgLGLsUBvb1cZ2ub5Zqe+g9A4V3UnRnQralUTbl9A2BYtbIllDOLSsVc1asRMB51LynVg0VsoKwXRQTKuSaZXTHZJriRf7F3AtKr5Jog9EMTyBpzi73GBPNDqSDC6yJTZtwCADQxMV71YaHArOJeddEoZwqDgrXlhX3IKGO4u77y60/Y9/X5WQ8O/zbvyfCTPmzJgxa/7cBTNnzJ0xc/bMmQl4mTEX/ztr5owBf6NJwB1nDFFmzpxJ7BfI15+4TsyYMYOMGwsWLCBjBclHsHjx4oMHD77++uvRn9VgMEhu5qLvXF4B0GAZfYk30HD5ZYV3QAFQABSIBwUANMTDVYA2xI8CABomGDSIvo8GgQYBhUu2Fs+YlTBn3mxCGWbPno0n5HPmzpk1e7KX+fPnE5sF8ooByoIFN9xwwxVBw4wZM2bOnoWhxKy582bNvvGGOQkzEmbPX5B8Z0bRQ42/lBeosuvkuW0KPTabx+YMPE73ED8LxTnkBjz1VfKYNWjYrmSmS0t7k2lPMu1Kph3JjD2ZdqTqXal6T6quO1nvlWbLDsmwv1vFdOOYhSx+zo8XFrsGqJkjWvZ5Dfuciu5V6nrU+qNa5qia61Vw+Bm+2uDR8FKAQ7pTw3Yq9FYlY0022pKNHSQaApnta/SdarpbzntknJPSO9S59mV0dyrTrZaAiJZxqGhnkr4zSY9dKlS5eBsV7VQzLsmYwpPKuzW0HXs0MDYN59TyLi3v0tB27B/B9Sg5aVYvxUGQXBLw/F/NO7DNAtuZwh7SsofUbBfmCFwnpgw4KCMGCmrWrmbxaVKsR0F7Ffoupb6L0h9S4aVbpe+m6G6ZvldG91B6D6V3KWmHgvYo9D0K3WG1/qia7qU4l9xgTTS1JppaJcMQ7AOiZJ0Ug1ul5N0K3i7nbJKrhdQqjDbsUjxIF0Y88WfRkMy2ri12/M9fbUhI+LeEhB8mJMxMSJg5Ay+YOMyYmTADw7c5MxLmSGsmAWIgTZw5RElIwGAxISFh1qxZM2bMWLBgwZw5c376059Gg7aQcYPsZM6cOT/5yU+USuXu3bs/+OADEmIWXCcs41AANMTPTRu0BBQABeJZAQAN8Xx1oG3XXwEADRMMGhA6EQz0CWJAEHFqcISEkBDU0bkJMxJmzBq428a+AzPInGLSTCSGaih5kBldi89LCgAZtYsmtg44OsNszFl+8i8/xbOOGbNnJSTcMG8mpUp6at/Th19932x789dU0eK15RrOkWz0yvQ2md6mNXnjhzIMPCfnMWiQwgd41XS3dmDxahmXlnFoJdCQTLtS9d603J5UXbeawSb9EmjwkjgCarwlXiRbgG4V3atinlWxRygGg4Zk+oiGeUbN9kSMJtzJTGcy00npXZTegXNeME1qQ6vG2KbhO6RpPHbKSKG7UthuyoCDL6pZB6YeOV61zqugXdg1g+nQMnZMLthuFd2JA0zQTpW0aGinhnZq9XYtbdPorcQ+QsXh8BMqBoMGyeUBgwbcJKZbyWDzCimCBrbmwK4TbBdZMBXCVh6d2HyDdRFDD2LOoOI6pQ92STEXetXMEQ17NJk7ouWOyJluJY2hg0aHuYOSPqSkn6H0R7S6Z9V6HJpBbrAuyWtJzGvBdIN1KJl2irHLmUM4IgbvkvEdEmjAThYqFttQ4KCbNA4MqeDtCgN5f1xZ1ahcJ1pVuoaNJY7/+KMuYc7P5//wpz/6v/75p//3v9ww/wfz5v5g3rwF8+bPwsu8BXPnLZi9YNbsBbPmz5/cRk9z585dsGDB7Nmz58yZQ0YGMlbMnDlz3rx50WANJGTsjTdiv6qEhITf/OY3FRUVJG0Buckb5qcULBosoy8AGobpUbAKFAAFQIGoAgAaolJABRRACAFoiAvQgFDI578YDmPviZAQrKk1Z2Zn6OhcmqZ1Ol1ubq5Op9Pn6nKzcyZ7yc7Ozoop2dn4lHJzc9PT03/2s58Rf2wynRiwl54/LyEh4f/82/+zNDmleJPpxZeeDQjieQG98F7w9uX3Ja6vTDV5tIZOHFNQ8smPK9BAGoNDLdBuPF3Xd6roTik+gjeJdUmLI4mzKRibkrap9Tg5hZbGAR0oziW5HuDYAWrGk6z3anWdap1LrrPJaauMtck4ZxLrkusxVtAScwDegQMlSNQghenU6DuxawbXoeTalFwbnnVjC4IuEuMghca2Fdj9wejQ8K4U1qvK8Sp03iTWk8RZcfoGrkNtwF4YKt5JkiBgBwrJ6QOHotRbk1mHirEpGAf+iJQPQs575NioBNtfYPMEBiehoGgMGpSsUwrcYMURHPRWvDBWGYddRTS8S82RDRwyyedCybtxBEfeiQ+NnU3cxKFDw3s0RuxIggGBvker71UxOHkEue5qbBaB60lG+5K89kRTOw5yybjUOIQEBg0y5pCMc8r4jgGPDynHhAR6XMn6HmINIa0aV8owKLcFvtZSJ5H8Pi7LOqHQta4sdG4osf2vX61LSPjXhJk3zpg1c+7sOVKsApyOIRLJBZs5EIsGbOAwGYrEB67wErVcmDt3LoEL0ZS3M2bMIKFb5s2b94Mf/GD27NkJCQm//OUv77jjjj179rz99tv9/f1+v9/n88VmoLj8Zx5Ag2X0BUDD5R0J3gEFQAFQ4HIFADRcrgm8M50VANAw0aAh9BlCIYRC5y7g9JaBgE8QQiS9pYDCIkKCIBCTYDE8UCFOyJP0lWTrjDY+mt7y5MmTa9euJXYNZI5B6rPmzP71b3/zlz8//vYbrwd9OMNlCKE+hA69du6Pafcmrq+kaOuSrBYV7166qTfe0lviEAOMR0t7NHo3zv6ow8SBYr0KziMzdCUZO2VGt8zoVhidSgPGBDjLI4MdH3DIACkCAgENWtqbxvZgOwXWpuQ6FIZ2pcmuNHqUvDuZdRIXBunjePKvoZ2YPnBeNedWG5yUyU6iM+KcjkwPZg1cJ24SjZ0IFLx9wNCAwQYIct4l5zvUXLuaa1canJTRpcSzX5z0AXtwSDN/DefQ8s5UgwOntGAx70g0eJOMPQpDl5LvVDBWnIGCsakYuxTMUrJW4FwqHrsqYIMFBsezpHAWiXalsSMKGnDyCMYlZ91K3o0ZB+9QSeiEhIfEESLxubcpuRaK7dDouyRPii6JYmBNCNqQXCdw7Ey5Qcqmwbi0PPbawAEpmG4Zh+NK4N0aME2QTEUc2FoEg4YeHM1h3CNBDqIYBDQMsIbL01sqdO0rCl13F3b82+9z//F/Lfn5Tbf+4qZf/va3v/3Ff/7XTTf9+qabbrrpV/+Jl5t+/cubfv3LX/3il7/6z19O8nLzzTf/x3/8x+9///ubbrppzhyccebGG2+cP38+sW4gjleET/zsZz8jGSg++OADktLy/Pnz5Lc8EAgM86MOoMEy+gKgYZgeBatAAVAAFIgqAKAhKgVUQIHpa9Fw/i1d6P1s9PZG9NY69E6m8FZW+F1d8F1d8L2c4HuZYbK8mxV+NyeMX0exSHvIEd6hhXd00kL2kCO+Twfeykbv68Pv5gTf1fW9xZ59Mx+drkfCF2HB7xcC/nAoGAyGgv5gwBcI9vkFXwCFQkjESezCSAwL0pxcmDK9lozFUeJw6dKlZcuWJSQk/OhHPyITjH/8x3/893//971P7/nw4w+ktH5CKNgnooBfDJ8Joq6Xz/xx6XbZhoo0gxtbyNM2DU8eFw+ay038v2r8bH9gkR5fE+cInMaSPI2XXmMt6nGbiW2/tD2mFdIy8DycZEmIPMmXwiVKz+el1IzRdBXkxLFNRCRlIw6LSOHgix4cAZFvxZka8ORfQht6B0VblUwrxbapOOxBoOTxnB+zD9aBAyJEcmdgXxXOlmZwK/UdSbo2pcGhye/Etg9cj5bvxXYHvAvbVkimDXjyLNlBRNsgPcMfoA+xE/sYKQZaTk4n8sGILYBEK6SoCt8xByBCSbph6cieSdoOaQ8eiSN8R1XCGqRLE90VOfT1fB1gDThCJ+dUcXbs68F2aAxOinHKcq1pBd5lJtvvNLvy7m364PMLJ89+c/z4Fye//vLrr858/fWpk18f//rk8a+//vqrr099dfLrr05+PYnKqVOnTp8+ffLkyVNS+fLLLz/77LO+vr4zZ8588cUXn332mVarJSEhyYBArBhmzpz5z//8z0uXLm1ra/vyyy9jb+li68OMkwAaLKMvABqG6VGwChQABUCBqAJD/RKR293o07Xo9lABBaa2AtPUoiFuQEMdEo+FBH9ADAaEcCgUCgUxcwiG+v2Cz3cZaBDRlAUNJ06cWL16NbFimDdv3pIlS6qrqz/65ONTZ06Ssw4GfMHgBRH5gki8hFDXK6cGQIMUZSCeQUN0DhwHFcIaPBTfShlaSD5IKd6kh6Lb1XSzmmlSsS04pybnUXA4CaVSb5XltCVm2WU6j5rv1ho61axDnttG0bbUPK+2oFPO2SijM7WgR5nb+bvVDdjNgcP5NaQAllaK7ZAe1Nsi546zaQws2GuAPMy/nhP7CT/WZWeN4316KGZAtEGgQWPsTDXY/lv70OYHHV+dR2Fs4hRCSJDQG8IVbA+Fiygtk/fnShRFv9+Px8AQPqNwOLxq1SoCGv7hH/6BuFTceOONubm53d3dn3/+OQn6GHtLF1sfRgcADZbRFwANw/QoWAUKgAKgQFSBoX6JADREJYLKtFIAQMPEWjRg0BAWA0EUCoqSZ0QYV0Nh33QDDW+99dbixYtnz56dnJy8Z8/KyQUAAAAgAElEQVSeZ5999ty5c9LcSRARViYcCiDkR8jvE0IAGiLz9quYNkvWBHw7Zg04b6UrChqoAcrQRjFOBdutYHsUnFdr8mpNHrXpEMUfUnAenJMyp3VRVvPtWe1y3pPIu/+Q1XxbdqvW2L0s7zk13aPGgSHd2IgAZ8q4EmiQMneSgwJowNdxWNCgNXnTjPbfJT+8+UHH1xcwaBBFTBsmO2gQhAFmGn28IwiCz+dDCBHHB5/Pt3TpUuIlMWvWrJ/97GdZWVlNTU2ffPJJX19fOBwmv9Oxt3Sx9WF+xQE0WEZfADQM06NgFSgACoACUQWG+iUC0BCVCCrTSgEADRMNGtBxAQUJaBAEQRRCKBwSxMB0Aw1fffXVo48+unPnztdffx1ThVAID9YIXey7cLHvAp6B4PmVPyz0+YRQH1g0XFMWTwe2ZeCt2FuBTPvx3tokW4YWHAaCcSuZLgwa2O5bM5oT9TaVsUdl7JHSZ7qWF/fes+vFlMIjlLE3ie9ewnoW5toXZtiUud3LTc+r8bQ54uIhRTccbNEAoGGQHcewoCGt8NBSk+O/tQ9tut92/CwKiqi//1Iw4JvsoCHKF2IrBDH09fVdunTpww8/TExMnDt37i9+8QutVlteXv7ll1+S3+aLFy+SSjQow6h+swE0WEZfADSMqo/BxqAAKDBtFQDQMG0vPZz4FRUA0DDRoCFi0RBC0sxaDCMhLKLgdAMNCKGLFy+SB5WEMiCE/MFAMBwIixg6BAO+/v6zwdBFEgwSXCeuyqhBiteAbQ2k4I7fzvldKq4dR21gO3CwRpyTskvJdMnZThKpQc52Lsm23r6x4bb1NUnptVROG5XjSMxwJOa4KL432XiE0h+Sp/fK03tU+i6KcQ7kp5ACSV4BNAw4TVzmRHBN9OQqjDsm5COXnfWwoCG1oCfNaP+N+oGiB+zEooHkppnsoCH6axQFDcRdgryKonjs2DGdTrdixQqz2UzCPQqCcPbs2f7+/uhnh88uEd1sUAVAg2X0BUDDoF4E/4ICoAAocEUFADRcURZ4c9oqAKAhXkBDUJTCPU5j0BAKhUh+DYIbBEE4dea0iLDrBI4qH/T7/ecQ8ocQOhuCGA1XN0kmHg2EMkhG+9IsFzMLziotdowJ6E6KcePsmEy7im2T6xoSs+sVOfVafb0mt1q5bt+i1Y+r1ldq0muT1tfedo9Zkd2xsuDZ5caX5endlM6rpF34szROVzFAGTDXIA0m0+zY16s7kcn7qdGBhuS8rhTe+ivqvsL7rAQ0kBgN4RAKh0UhHBTCfmnmLIbCeCGz6Ph/JV/2UCgUCASCwSDJrRMIBMLhcDRnxMsvv/z888+Tf8mWhDKEQqEvv/zy0qVLV/ezDaDBMvoCoOHqOht8ChQABaabAgAaptsVh/MdXgEADRMNGoQvSDDIoIgduLCDwHS1aPD5fH6/nzhpX7x4UTLwQP6gzx/EnttSos8+AhpO+gA0XN1MewA0qBmcyYIEZSTpGFS8U0rW4JICEzopup1imjRs9R9X36/N2Z21w7qr8rWDjo/ruj9v7PywwfleRdPfK1vee6TspYziFnV2GZVVq9HZVLlupc6p0DtjQYMUrOGKoOHqTmGyf2p0oCG1oCeFt96k3JW/s/3LczhGQyDgmwKuE9GfpViLBmzE5PcT9BDdACF04YLkPBX7llSPBnq4bM1wbwBosIy+AGgYrkvBOlAAFAAFIgoAaIgoAX9BAawAgIaJBg3hz4NhH8k6MZ1BA7FiwC4SwWD0q/nN+XMiEoLhgPSO4PN94w+cB9eJiHXAVUy5rwQapCybKt6t4jopVoqwwHSomToNdzCV3c3sqtlT//zRd4UvAugUQmcQwpk/cLQM5BfQyQvI/fxp7gHbwjWPL1pfeUdhj0KPQYNCb8cWDYydYm0R0OCQmh21ZbiKxk+Nj1wNaPgVdd+m+22nLmHQIGWaEEQB4RAmOPNtULqtkSx/RMn/KvY2J17r5DsuitgEI8oLyL9kFcGOhDxGXST6+/t9Pl+URMSOFeRTI3kF0GAZfQHQMJKuBduAAqAAKBD7qxurBgSDjFUD6tNHAQANEw0aIjEaiEXD9AkGOeg7dkVj75AQFhBe8MAthESxXxD7/WL4AkLdr56+Zdm98o2VaTHpLSkm+uR8akxKx/4scOJJ1qGi3SrarWFxhgiKcylZp8rYI9N5tMZnVhT1KnW1izf85c6ip0v2tvz9o36/gC4gdNyH3jyFXvoUvf5Z6Mvz6KvT6Ow3mDWcDqDS9nfuMFQosmvuKOyW6xxDgwbCGmJP6vJ3YtdOyfqQoIFwGSkhqJQTFIe6cKoNnhTe+vvUR9mShrM+nMpSEEJ4lBDJF2jqpLccNCAM+pfcol3+Omiz7/2XGFAIghAKhQRBCAQCe/fuzczMzMvL27Jly8GDB5ubm6uqqqqrqxsbG6uHLhaLpba2trq6eteuXTzPGwyGkpKSgwcPtra21tTUmM3m2traGqnU1taSf2tray0xhUzdd+zYkZeXZzQaS0pKDhw40Nraajab6+rqYjaMiyqABgQFFAAFQIERKACgYQQiwSbTSAEADQAa4qK7Xz6FwG7bogCg4RrsF64wUZdAgysCGtwaiTsoWeeSbOfiTKeW703hnLKM/Wu2VpkPvfnJBRQS0MkTyO49fv++I4aHHLr72nXb6/httflbLQ88au88/OUbn6K9Le+k0uWJ6WYMKWiXUu9R0q6BkJBSmIZI7MlBWEGKTDktAkDGXggADVcz4FxxfIiaQox8j0OBBr1ev2nTJjLVJ6Sgvr6+qqqqYYhSX1/f2NhYW1v74IMPmkwmo9FYXFy8f/9+AA1FRUUnTpwgV4RctUHeMSO/WLAlKAAKgAKTTgEADZPukkGDx1UBAA0AGsa1g41057FDc7QuIBFAw1iDBjfeIbFo4JwDoIFxJxuPqLneFEO3LNMi2/j49gPuN08FLiH0yQdiY82buVyDbPXeW1Y9ffu6isUbKm9ZvSdp9d7E5U9k5LXt2Pcq++Az2tyGxRuaFmfYVUw3Dv0wkMHREXlKb8NmFHiJnXJf/k7s2qlaB9Aw0jFhPLYbCjQwDLNly5aKior29naLxWI2mxsaGkjFfKVSW1tbX19vsVgeeOABAA1ms3nXrl1FRUXZ2dkAGsaj38I+QQFQYLIoEL2DHcRYAbxOlisI7RxbBQA0AGgY2x41lnsTsT86uE6M5ZRbzQ0CDXi2TzFuDf+MQteZlNGctLEsZ1fTobfOfIPQp6fQfTta0jceTLm7PDmjWZbdQRm604qOaAxdywxd6sw2bVZTSk6TKrtuqcG5NK93Ubpdwx3ScN1qtosciIAGJWO9DDQQyjAIPYzlmX4XasTPngE0jOUQMdp9DQUaTCbTzp07KyoqmpqaqqqqysvLa2trh3FhqKmpsVgslZWVO3bsIK4TW7ZsKSsrA4sGAA2j7ZOwPSgACkwlBQA0TKWrCedy7QoAaADQcO29aLz2AKBh7GfLvDNq0ZDMOjUcAQ1Oiu1W6J1JmZaVproK1wfnEDobQDVNz6+65zHtqt135LbcVXSYMngWcy5lQY8ir+v2LKssx5li9Cj1bX9cW5GU27issFfDdY8MNEQpA4AGKcko66EYd8T6o0MK02CTfE8gRsMYjy1DgQaGYbZv315WVtbU1GSxWKqqqkgUhpohSmNjY0NDQ1VV1b333sswDM/zxCACQAOAhjHusrA7UAAUmFQKAGiYVJcLGjvuCgBoANAw7p3sqg8AoGE8QAPFuSgGB4OUQINTjS0anBpDV4qpM5lr4B51H34vcAGhoy98qeP2rc6qSstqXsa5U0xdiYz9NrpDZnQr87spY+/CLIeC86Zu6pXTHUtyWtScW2voIikzJe8JbEQQmTwPcp2QQANnl2I3xI+twfVpCVg0XPV4MAYfHAo00DS9Y8eOyspKq9Vqt9vb2tqam5sbGxvrhijt7e2tra21tbW7du1iWZbjuOLiYrBoANeJMeijsAtQABSYzAoAaJjMVw/aPvYKAGgA0DD2veoq9hg7NEfrEKNhfEAD9pWQQINbw7rVDA7cmJTTIdc1afSVOyuf//g8+vICKjt4dPWaJ1bobKmGwxTbvUTnlHN2pcGh5DpUvGPNjr/J9Z0y2ptW/FxK4SFtfndawREt36PUe3EwSL2HPJAnoOGyYJAAGmKghhTSAiwarmLQuIqPDAUa8vLy7rvvvtLS0oaGhra2tqamprq6utra2uYhCnGsMJvN9913n0EqxcXFsbEkIesETsQqFaL5VVws+AgoAAqAApNOgegd7KChD8bDSXcpocFjogCAhokGDejzsBgIotBAeksckSAkiAG/4POhUAiJgojEsLTgQUsYk6sehzuJHZpj69EYDTirn5TeMiCEL4kx6S0NbjXrUNI2DY+fn4/9zPw78Qtj5oeT9H3eSXHYhAFbMbCdarZLhe32nTjPpa4xhSnfZ3v3HEIfHEf3bmtdunKPJtuVuulVpenoIp2DYjtk2Zb/Xv7YTakP/Vzz8H+lPn77WktieuOtf6pOymhSc+7EbKs816XQuRV6p5ImR7FHojMQdwkiILhOeL7tqIxXxXgjoKGDYtso1oYBzbfpLe1XTm+JM1xOl/SWYzVkDQUaioqKduzY8cgjjzzxxBN7pbJ79+59+/btGbqUlpYeOHBg586dRqPRZDJt2bIFQANYNIxVR4X9gAKgwCRVIPYONvYUADTEqgH16aMAgIY4AA3IHwENSLwcNCAxBjRMl54ZHanJVEpEgoCCIvIJYn9YFPpF1P3K2T8s2yHbWJ1ixKCB0js0PJ4wfzt/m6QgYJybrWZcKsauZNrxbJbtVrC9crZTwTg0rFuZ3XgHX+N67eI3CL3+8Td5BQ0r19Sm6Y7cnnnoFp0nkXemGNruKWzN3daau71xzebqnAc7cre1p+c1Zhva7tQ1qnSNKQVuaXpMXjHLiC5wXWIU8EQSc0jAhYAGuhNrxbZRXEuUMuD+zLuTWcfNKY8wxfVnfSiEnxKHJO6I0ABoGICPInljuowQV3+eoVAIIXTx4kVBEPr6+g4ePLhx48biIcomqRRJhdQLpFJUVMQwzP33328wGPLy8nie37p1a2lp6XSL0VBTU0OcS7Zv315QUKDX6wsKCo4fP371lwc+CQqAAqAAKAAKgAJTRQEADfEMGgLYomFag4YwAQ1hJIQioEEQvgUNiekSaGBcABpi5rHDmV2oGa+KdiqZViXXoWB7IqDBJs9tT8qoWWWo73zj0jmEXn7va9ZQm7a6OnG9TcYfuZ33/mZjzVJj20Hbl28fR++dRm+dRx/60Wfn0DsfIo/zVMmOHiq7JlHXGDtJjlIGAEDfvToxoIGzS1EtuikCGriWCGgYgDUqrlMCDX++MmgYYA3450gCDVPW4mmsfnAFQQgGg1guEWsXCARKS0vvuecedohiMBiMMcVgMPA8z3FcYWFhVlbW9u3bjUZjUVGR0WgsKSkB0ACgYaw6KuwHFAAFQAFQABSYAgoAaADQEI/dOGLRAKBhOGrw3enrSLb0qOluFd2pZKwSEehRsD1yzq5gW5NyGxeuK19hrHX/DYOGVz84nV/YuOzuCi1tu+Pev1IlPb9aV5bCNdcf6jsjorMIvdePPg6gCwj5QujV5y8VbXXIN1areAeAhhFcFAANEznmhMNhhJDP5wsGg8eOHXv00UdXrFiRO0TJlEpWpGRmZqZLhWGYu+66q6CgIDc312QyGQyG6Zl1AiwaJrIrw7FBAVAAFAAFQIH4VgBAA4CGeOyhABpGMF8dCVkYtI1HxXRTdJeSxtkTVUwPxXYrWZuCbaa4ltvWHVjG17Y8d/YcQu8d63/wIc/qdeVaXYsi35WUb79VV69lmjc9+Uq59Vh158fNL31iffW9T88GQgL6+ysX8wuak9ZWpOV3xVoxxNbH53QGnd1k+RdAw0SOOYKA7T58Ph+2jervP3r0aF1dXcMQpU0q7ZHS1tbWIhWHw2E2m+12+86dO/Pz8/Py8kpKSg4ePAiuE+A6MZGdG44NCoACoAAoAArEkwIAGgA0xFN/jLQFQMP4zMzxFJeiO5W0Q0U71XSPiumWfCiaNaaOhelVS/mGUsenZxE6fg4dPHBk9T27U3NbF+d2JBpsSpNdTbdqchs02eakjU/9fs3WFGaXxf7ChQvojRcvFhQ0qzdUrdzcCxYNI7hwABoi3/Pr/jcaBpw4UBDviWj98uaEpUKCeAmCEA6Hg1IRBOHChQs+n2/fvn0cx5FgkPv37wfQAKDh8l4E74ACoAAoAAqAAtNTAQANABrisecDaBjBfPVqHuBTnEvJOpW0TUW71XSvmu6hsEVDq8pgS8xpSDO0bj/40ofn0Gkfstv+vi79qeW6FhXtVJnclNGp1Fup3I5F6Q2/XPnkLTm70wr2tfW+Hwqjd17zFxe3pWVYlud3qSLpEmLNGSBGw3evJoCGiR9zAoGA3+/v7+/v6+tDCBF8cPlrNE44GZEEQQhJRRRF4oLx2GOPrV27luf5bdu2lZWVAWgA0DDxnRtaAAqAAqAAKAAKxIcCABoANMRHT/xuKwA0fHdqejVM4Up7cFCcXcl1KBmrinZr9b1qupdi3ArGJmMcctqqZq1/Kmrp+fvZ8yJ674ML9z/clrrhoCK9SanvoBi7Uu9JMR1NK3lFXXxYXtS24t7G1meP9QXRS8+d3bSpOW1jjUZvBdBwJdkHXT4ADd/9tl/H/4jfRNSugVCDYY4fCoWIUQN5DYVCwWAwEAhEw0k++eSTGzZsMJlMW7duBdcJCAY5TF+CVaAAKAAKgAKgwHRTAEADgIZ47PMAGkYwXx00ff3+fynOQfHtSq5NybQPgAb9YYruVNCuJNqt4DxJOW0L1+yt9r5/AaGAiGzut9Yw5ckZlcm6+qUGuyLLduv6jsX0IdXmI7/NrdBsOtj07Id9AnrpxdPFW1pT1lfffrdZRX+b0jLWqGE8TmfS7hNAw4SNOYIgiKJISAGBBSTb5eW2DOSdoVwnRFEkphB79+7Nzc0tLCwsLi4+cOAAWDSARcOEdW44MCgACoACoAAoEGcKAGgA0BBnXVJqDoCG8ZhFE9BA8a0qzq5mPFp9LzZqYLwU61EZexbl2G/f2KTIrjE9aj/61jc+hL7xoTrnW9nFlar1j2oyS2XrKqnMFqXO/qs/lf5izf1rH6y0vfTW1xfRyy9/tXV7e+oGyzK2S818P+8Yj1ObVPsE0BCPY87wbYq6TkSdKRBCoVCotLR0/fr1eXl5W7ZsGR40WCyWmphisVhqa2t37NiRl5dHsmOSj5vN5rq6OkuclVqpVFVV1dXVmc3mysrKpqYms9lssVgaGxvNZvOuXbuKioqys7OLiopOnDhBxCSiRYWNGpJE34EKKAAKgAJTTAEy7g0a/RBCsb8dU+yU4XRAgWEUANAAoGGY7jFhqyIjNaS3HMt5O8U5FAarwmBVsQ4MGuhurb5HzXgozpWodyj5To2xU5FTn6Yvvb+0+7WP/JcQOhVAbb3vlzzRunFT2bKsPSkZpdrM8kVr/6LUPVqwr/GFj074EPrbm+c3b29P3lCzwnAILBpGgDwANEzYwHLVByYjUjQqJJkzA2gA0HDVPQo+CAqAAlNSgcjtK/4be4IAGmLVgPr0UQBAA4CGeOztkZEaQMPYggaXgncoOLxPCTR4tbRHzeLADQtzWjT5nWmF3crchtv/9ERa9p4/V774xufoHEInQ+hvx4Pev31hP/qB7ciH7ue/9r569si7fW+cQJcQ8onoxdd8Bdsdmg01aZyb0juU9BWWEUy/x/JM4/twABriccwZvk0AGmpra8GiYfhOAmtBAVAAFIjcvgJogL4ACmAFADQAaIjHb0JkpAbQMJbTb4pzKTiPgvNgXwnWo2ZceGHtKs6epGtTG5wUa1PSLcl0g2Jj6XK6uugvvS2Hz7x9Ep1C6AxC5xE6J6KzIcwXziN0/CJ6+V3U0fX1o3tf2mhsSc6sV+s6IBjkCBgHgIZ4HHOGbxOABgANw/cQWAsKgAKgAAn9E7mDBYsG6BGgAICGtwA0xOPXIDJMA2gYU9DAehScV8FJQRlYl5p1kEXF2ZWMVWt0yXQtSn3byk2dKVzb4rXlsg0VK9imh83vtb1y/plPfH8/E377a//rn/re/RJ5Xuyv7Phy0yPPrdY1p6yvWZrdlJzbJstoUrOOEcy0x/KkJuHhADTE45gzfJsANABoGL6HwFpQABQABQA0QB8ABQYpABYNABoGdYm4+BdAw3jMnynWQ7ESaOBcFOfAISFxsAZs10AxzhRTp8bgVDJWDe/S8C6KtalZ65INtXfk2VYUtSzfVL1uu3ldSfkqes/ynH3rNrWuLXatMNhkG+rkG5vu4D0rDN1UthWCQY7gwgFoiItBZlSNANAAoGFUHQY2BgVAgempQOT2Ff+NVQBiNMSqAfXpowCABgAN8djbIyM1WDSM5cN/inNR2KgBUwaKwx4TUlRIr5ruTuZxVEitwa3hXUk6q4xxJRf0pG3ukdHNibrGmzMtv91Yc2tmY2J2qyyjKXGj5aZVT/0ho2wRV7fE0Kzg7SmmnuXGZ5dyh9WMdwQz7bE8qUl4OAAN8TjmDN8mAA0AGobvIbAWFAAFQAGwaIA+AAoMUgBAA4CGQV0iLv4F0DBO82fMGiTKEAENLjWDQcOyvGcXp7fJcq2ped0qrlPBeVR53iVMx2J982K6LcnYqcg7nMg+I2OOaA1HtcZDmsJOWX57UkGbosgmMzqX6JzKLG8y/Qyl90AwyO+7dgAa4mKQGVUjADQAaBhVh4GNQQFQYHoqELl9xX9jFQCLhlg1oD59FADQAKAhHnt7ZKQGi4ZxevjvUOFgCiSeAg4MKS3fHkviEa4IlcCJKqQQkl6K9aqwzYIHZ8rk7WQhhhIYWEirvm+a/e1RpuuWABriccwZvk2DQANCKBQKIYSefvrp7Oxso9G4c+fOvXv3Njc3NzY21tfX19XV1dfXNzQ01EuloaHBbDbX1dWRSm1tbXNzc11d3datW/Pz8w0GQ1FRUXl5udVqra6utsRfqZXKaLNOwI318J0K1oICoMDUUyBy+wqgYepdWzijq1EAQAOAhqvpN+P9mchIDaABpuVTTwEADeM9foz9/ocCDbt37167dq1Op7v33nuffvrp6urqysrK8vLysrKy8vLyiooKUrdYLBUVFdXV1TU1NaRSV1dnNpu3bdtWUFBgMBg2bdp08ODB9vb26urq2tpaS5wVAA1j36Vgj6AAKDAVFYjcvgJomIpXF85p9AoAaADQMPpeM/6fiIzUABqm3jQbzghAw/iPIGN9hMtBAznCnj171qxZo9fr77vvvvLy8sstGohRQ2NjY21tbUNDQ7TS0tLS2Nh47733FhYWGo3GwsLC0tLS1tbWqqoqAA1jffVgf6AAKAAKXCcFIrevABquk+BwmDhXAEADgIZ47KKRkRpAA0zLp54CABriccwZvk1DgYbdu3dv2LDBaDQ++OCDpaWlHR0dbW1tLVJpbW1ta2trbW0lTKEhpjQ2NjZL5d577920aZPJZCosLNy/f39LSwtxT7DEWQGLhuG7B6wFBUABUIAoELl9BdAAPQIUwAoAaADQEI/fhMhIDaBh6k2z4YwANMTjmDN8mwaBBkEQAoFAOBx+4oknMjMzN23atGvXrocffnj//v379u3bs2fP7t279+zZs3fv3tj6bqmQ9/fu3fv0009v27Zt06ZNeXl5RUVFpaWlBDTU19db4qwAaBi+e8BaUAAUAAWIApHbVwAN0CNAAawAgAYADfH4TYiM1AAaYFo+9RQA0BCPY87wbRoEGhBCgiAghPbs2aPX60tKSrZv375169Zt27Zt2bKlWColkVJcXFxSUrJt27aSkpLNmzdv27Zt69atW7Zsuffeezdv3lxQUJCfn19cXHzgwAECGhoaGixxVgA0DN89YC0oAAqAAkSByO0rgAboEaAAVgBAA4CGePwmREZqAA1Tb5oNZwSgIR7HnOHbdDloINvv3r07MzPTZDKR5BEmk8lgMHAcx7Isx3E8z5M6wzAGg4FhGL1eTzZgGMZoNObn55PPbtmypaysrK2trbq6GkDD8NcC1oICoAAoELcKRG5fATTE7SWChl1XBQA0AGi4rh1uhAeLjNQAGmBaPvUUANAwwmEgjjYbBBrC4bAgCKIoPvzwwytXrly/fn16evqGDRuys7MzMzPTpZKRkZGZmZmRkUH+zc3NzcjI2LBhQ05OTnZ2dnp6uk6nMxqNBoOhoKBg69atFRUVJOsEgIY4uvDQFFAAFAAFRqNA5PYVQMNoVINtp64CABoANMRj746M1AAapt40G84IQEM8jjkjaVNkXBLJxmfOnDl8+HBdXV19fX1LSwtJKtF4pUJCP5IgkS0tLc3NzU1NTY2NjQ899FBGRkZJSUlxcfH+/fubmpoaGhoqKioscVaGcZ2ok8q2bdvy8/P1en1BQcGJEydGIiZsAwqAAqAAKAAKgAJTWwEADQAa4rGHR27oATTAtHzqKQCgIR7HnJG0KTIuiYFAgGzf399/7ty56L9D7YSYP4giJhSiKAqCEA6HQ6FQeXk5y7IENJSWljY3N7e1tTU1NVnirABoGOrKwvugACgACoACoAAoMJQCABoANAzVNyby/cgNPYCGqTfNhjMC0DCRY8u1HDsyLomhUOiK+wkPUUKhUOyaUCgUDAYDgUBVVVV2drbBYMjLy9u7d29NTY3FYqmtrbXEWQHQcMXLDW+CAqAAKAAKgAKgwDAKAGgA0DBM95iwVZEbegANMC2fegoAaJiwgeUaDxwZl/BfQgqCwWAoFApIJRgMjnz/xK7BbDZnZmYajcbt27dbLJbm5uY4pAyEfdTW1lZVVdXV1ZnN5srKyqamJrPZbLFYwHVi5BcdtgQFQAFQABQABaaVAgAaADTEY4eP3HNvBRwAACAASURBVNADaJh602w4IwAN8TjmjKRNkXEJe0AQvkC8IaKfjW4wqBKUSkgqhFD4pVJWVrZ27VqWZXfs2NHQ0NDa2mqxWMgE3hJPBSwaopcYKqAAKAAKgAKgACgwQgUANABoGGFXua6bRW7TATTAtHzqKQCg4boOJmN4sMi4NBBqIRwOk3eCwaBPKkMdi2wWXSuKIvGkKC0tvfvuu3NycgoLC8vLy4m9AICGqFBQAQVAAVAAFAAFQIHJqwCABgAN8dh7Izf0ABqm3jQbzghAQzyOOSNpU2RcwtEcY+uCVEjkBWK8MOiVbEA+Et04FAqZzebs7GyTybRly5aqqqpoQgpLnBWwaBhJ94BtQAFQABQABUABUCBWAQANABpi+0O81CM38Rg0iCgkIkFAQRH5RLE/LAr9Iup+5ewflu2QbaxOMbrVjEtJOzS8h2KcKhbmsaBAnCsQAxpYh4rxqhgvRXdSjJNi2yiuhWLsuC4tKq4zmXXcnPJnprj+rA+FEBKEEBLDSEQDCxLIl1Z6Y6AeL1/jKdeOyLiELRpIIX4QgxwoIiu//Ttog2jiibKysqysrPz8/JKSkvLy8oaGBovFUlVVZYmzAqDh22sJNVAAFAAFQAFQABQYmQIAGgA0jKynXK+tYh/6hUWMGMLS9EpEQfRd0HDL0h3yjdVpBreKdSkYh9rgUbIAGuJ8jg3N86hYskSkIKCBcWO+wHZg1sDaIqzBrTZ4Unj771MfZUsaoqBBxKzhiqDhen1L4ThXpUAUNxDXiWAwWFlZuX79+vz8/M2bNx88eLCpqYkEXCQT+7h6ra6uJqEfq6ura2pqol4eJBnnjh07Nm3alJOTs3nz5mPHjhF5onYcV6UWfAgUAAVAgcmnQJRHRwd8cg4wHk6+awktHgsFADQAaBiLfjR2+xgEGsIR0IDEIBKxRUNQFPoQ6nnl7K0AGsB8Y/Ip8F3KwLqwRQProQZAg01iDR0Sa3BSTAxo2PKtRQOAhrEbb67rnqL3nVcNGmomqNTW1gJouK59BQ4GCoACk1MBAA2T87pBq8dLAQANABrGq29d3X4BNID3x5RWAEDD1Q0MU+FTUwM0mM3mmpqa+vp6s1TAomEqdE04B1AAFBgjBQA0jJGQsJspogCABgAN8dWVATRM6Wl2xF9g8lkijFXLATTE14BzPVtz7aDBMkGFOErU1taSisViqa+vr6mpMZvNABquZxeCY4ECoECcKwCgIc4vEDTvOisAoAFAw3Xuct9zOAANABqmtAIAGr5nBJjCq68dNExU1IYoaCB8AUDDFO6lcGqgAChwLQoAaLgW9eCzU08BAA0AGuKrVwNomNLT7LGyC5i8+wHQEF8DzvVszbWDhroJKsRRoq6ujoCG2tpakiADLBquZ/+BY4ECoED8KwCgIf6vEbTweioAoAFAw/Xsb99/LAANABqmtAIAGr5/EJiqW0xq0EAyTTQ0NNTU1ABomKpdFM4LFAAFrlEBAA3XKCB8fIopAKABQEN8dWkADVN6mj15LRHGquUAGuJrwLmerbl20DBRrhPEkKGurg5Aw/XsMHAsUAAUmHQKAGiYdJcMGjyuCgBoANAwrh3smnYu4tyWQhiFEBJwestwnyD0BUXBh9CRNy7etmznbXftX13UK8u1Kllnkg6/wiwdFIhvBa4MGqQMl3YlY6XYDhVnVXF2inEqaVdyXteyPPevqPsK7+8460NhhC5ePC+Eg0hEAwsSyHeMvHFN3zf48LgpQG49EUKkEg6HQ6FQMBisrq7OyMjIz8+///77Kyoqurq6amtrW1pamocorRNUYpvTIhXSkObm5oaGhqampr/85S9btmzJzc0tKSn5+uuvw+GwIAhRsDJuusKOQQFQABSILwUANMTX9YDWTLQCABoANEx0Hxz6+MOAht7Xzt26dMdtd+1ftemQXGdT8W6Z3gagIb7n2GNlFDCp9zNq0JBmdBLQcKYfg4ZLly6IQghAw9DDRjyuiYIGwhqioGHv3r133XUXx3Fbt2598MEHy8vLn3jiiQMHDuwbouydoLJ79+49kRJtwr59+3bv3v34448/9thjxcXFeXl569atM5lMx48fj73VBtwQjz0S2gQKgALjo0Ds6Bd7BEEqMB7GagL16aAAgAYADfHbz4cBDV0vnSagYWVhj0Jv1xg75bSd4ib1FBQaPx0UGB1o0Jq8qQbHTcpdBfe1n7yIQYPP1ycZ+IBFQ/wOXMO3TBRFQRCIRUNdXV1OTs6WLVvuu+++/Pz8rVu3FhYWbtu2rWiIUjJBpVgq0YNviZTCwsKioqKSkpItW7bcf//9er1+8+bNZ8+eJQqQMwXThuH7A6wFBUCBqaQAgIapdDXhXK5dAQANABquvReN1x6GAQ3eF08tXH7f7XeXrijoVtKO5LwuOW1X8W54pA8KxLcCowYNyZyNWDR8dR6DhkDAB6BhvEac67JfMv0mRg0HDx5ct25dfn7+tm3bcnJyGIZhWdZkMnFDFONEF5NU8iLFZDIVFBSYTKbCwsIdO3akp6ebTKYTJ04QIQVBCEtFEAYcfK6LwHAQUAAUAAUmTAEADRMmPRw4LhUA0ACgIS47ptSoYUBDzytnZXc9suieg3fkeZW0IyW/W6a3qQ2e+J5kTocn9nCOwyswatCgZa2/UT9Q/LCDgIZQKACgIX7HrJG1jMzAQ6HQgw8+uGLFiqysLJ7n16xZk5GRkZmZmZGRkTtEyYqDki2VHKmQZq5fv37Dhg06nW7VqlUmk+n9998nHiIAGkbWHWArUAAUmDoKAGiYOtcSzmQsFADQAKBhLPrR+OxjGNBw9O+XkjfuTlpfmWZwy3W25LyuJJ0VQANwlrhXYHSgITmvS8tab055ZPtfOr++gC0awuEggIbxGW+u315FUSSP+g8dOlReXl5fX9/c3FxdXV1fX2+xWCorK2uGKNUTVKqqqqJHNkdKTU1NWVkZ+Y+cQllZWXt7+5kzZ0JSgZCQ169LwZFAAVAgPhQA0BAf1wFaES8KAGgA0BAvffHydgwDGl7+ILxCd1CRbk7lXUk5HVqTNzG3A0BD3E+zh3/aPx3Wjho0JHO2W5b95b7dh05dioAGMQzBIC8fLibRO7HeExcuXCDT8ingX+Dz+fr6+hBC/f39gUAgHA5PoosCTQUFQAFQ4NoVANBw7RrCHqaSAgAaADTEb3/+LmgIo5BfCPvDouBH6PVPhbu4cnVmTQrrSMpu1Ro6E7NbNfx0mKnCOU56BdSMS80Q4uChWGnhXEoWp7dUMR1q1qpm7RRjVzCOlHxvCm+9feVjDz39DMk6gZ8ShyKgAee2hPSW8TuCDdWyKGjo7+8nfCEYDJJpeSgUGmZ+HnsLez3rxP6CvApSIUdHCAmC4JdKMBgk5xsMBi9evOjz+UKh0FAKwPugACgACkxJBWJH5tgTjI6csW9CHRSY8goAaADQEF+dnIzRZEQOi6EACgVRWEAink+FkRhGobB4SUCvfdJf8KhNnX5QkVGnoe3JfKcyp03DOSjaRjFOinGSZ/tqzq3m3BoeYjdM+sn5VDHWcKhZh5b2aGmvmvFSrFfOe5IMLrnBruDtatauZezJDH5VcXaKsy8t8i7Jrv7jHTv/XHbovB+JIvL7g9icYSDpBAENsa/x9XWG1kxtBWJvqWPrU/us4exAAVAAFBhKgaFGQgANQykG709tBQA0AGiIrx7+XdAgDIAGUXpwOwAaUF8YvflFf/GTdmr9Xk12gzrXqtY70gxuDW1TMfbLQYOag2wUABriRIEY0EB3U6w3yeBJNDqSjAOgIZl2JNMOLePAoIG3Li3yLMk8KFv70MGWly4GkSCgQGDgKbGIWYMgETgBoZD0DYHY/vE1mk351sTeUsfWp/yJwwmCAqAAKHBFBYYaCQE0XFEueHPKKwCgAUBDfHXyQaAhiEIhJAoiEqWntiI2Gxf7wuj4BfRk3XPq9bu1OY1avV2eaV2x6bA8t51QhkEWDQAapoo5QJzAgmtphkPNuGIsGjxy3pVkiIIGh5ZxabFjhYviXBRnTylw3rZ272rugP3Ip/3YnAcFg0FRDCMJMRDWEEEM37pRxNdXGlozdRWIvaWOrU/dM4YzAwVAAVBgOAWGGgkBNAynGqybugoAaADQEF+9e0jQICLJsTkUEoJ9AupDyP3C8ZT03bJ15csNnUlZ9qUFzyTm2GBGDQrEtwIOFYsDNKgZ4jqBQYPc4JBcJ6IMwqNmSOAGh4JuueXux9n72l5+v88vokBQFMJBQcQZLiVzBuJFgT3lI0t8fZ2hNaAAKAAKgAKgwPRRAEDD9LnWcKYjUQBAA4CGkfST67fNYNAgCsSiQbIRD4fFQED0+wTBh9BbXwh36p7+Q9ojK4ydSp2b4g/J2U6KhXAM1/K8HT47vgpQXBQ0YJqgYj0KzqXgHQreIa2SAATdrWK6FZyX4hyLNphvv/vxB0oPf3oaBQTkDwZEISCiAEKh74KGKGu4fl9VOBIoAAqAAqAAKAAKxCoAoCFWDaiDAgAaADTE17cgFjQIghDGoIF4oosCCoeRPyD6fEKgL4y+OIvydjbfmvZgiq5dw3Yq+B513mEV10kCQJLH2oM8KeL7Wff4TnHh3ONBAYpzUJxLxWLKIIEGF8VhyiCBBvy+iulWMT0UOwAabv/TAW3m01W2d077kF9A/sClUPgSQn4Rg4aBHJeRLzC4TkSUgL+gACgACoACoMBEKACgYSJUh2PGrwIAGgA0xFfvHAwaBEwZQjjYnRhCwSDyh8SLfqG/LyicuYRqWt9Ykbk/aU2lhnWrTIep/CMAGuJhOg1tGEoBKfKCBBQwa8CxGFSsg8IJJjCAoFgvxXZTEmigWK+atS9Zs39Dfp33xVN9IvKHkc9/wRc4GwUNA6SBIIeYVJfx9ZWG1oACoAAoAAqAAtNDAQAN0+M6w1mOVAEADQAaRtpXrs923wca+kPixaBwSUTIF0KvvX3RsK154cqntIxdW3A0ie9R897LLRqUNLZXhwUUmHAFBoMG1qFicYIJCTdEQAPbrWK8KtaTzLSr1z1t3Nn28jsX/QgFwyGf/9yl/lMI9RO/ie+CBuI9cX2+pnAUUAAUAAVAAVAAFBisAICGwYrA/9NbAQANABri+xsg4slUWLJoCGKLhv4wuiSgS6FQIBRCp8+hB3d3LlzxsDKzVsW7l215fmnh4Ts2PUMxzsTsdjXn1vAeJe1Qc+6oD8WgyoTPPKEB00oBJetUsk6KcVOMW0U7VYwdJ2RlbbLc9ju3vpCU61yc5ZDlOOU59qVGz2137VOveby585MLQZK+MoxQf0g4J6BLQ4CG+P4uQ+tAAVAAFAAFQAFQABQABaaNAgAaADTEe2cnhuEhJAZROIjtx/sFsb/v0jkkogt9qK3z3VX6p29Z9ZjWYE3O6/zd3WatoXNpwSEl7VDo7SrWRTFOAA3TajIfzyc7GDREWIOKdSlpV2KOIy2/N83UrdZ1pOpbNOkH+K1Nz7121o+QL4iCoX7JlqEvLPQBaIj3YQvaBwqAAqAAKAAKgAKgwPRWAEADgIZ4/wZEQAMO0xBEgTDyC6L/4oUzCCFfAH10PLj9Sedty3emcfWpBtuvVx7U8J7lRYeVtCMpp4MYNQyyYoj9N54npdC2qacA6XsR3OCMGjUkG724x+baKcZ+Z/Ehra7xj8seXp65p9b29rGT2Jyh3xfy+S8i5EeYtfkHwjKQPwP/YNsfKKAAKAAKgAKgACgACoACoEA8KACgAUBDPPTD72mDiF0n8BIWBUHEGf4C/ecREgQRXQoj++GP7jHu12TsWco3pRmdSwsOaXhPYna7XGdLzetONnrlOlssXIitT72pLJxRPCswCDRQjFNDY+8JDevGLj+c4/drKu/c7FnKWG5Wl+TtaHjv03B/AIkiCgbD4ZAPoaCIAt8mtgTQ8D0jB6wGBUABUAAUAAVAAVAAFJgYBQA0AGiYmJ43qqOKJMOliMICEgRBFEJi0BcO+URp4vX5GbSn9mjSnfdSG/et234k2ehNyumQ62xqzq014GyXCr09Fi7E1uN5Ugptm4IKMNhmIWrREAsaKMaeWuCV6xo0OnNy+p517H7noY/7pMAk4TD+BiAxFA71B4MD5gzYfiEKGnDKCbBoGNWgAhuDAqAAKAAKgAKgACgACoyjAgAaADSMY/caq11HQYMQRiJeBDHoCwYuIST4BeRD6Pk3T9+tezTpzodS+fYlWW2yXGtqXreG98h1NggGOQWn65M3hwiJ/siQkJBOihmIB5ma1/27u6uWFnnv2tb9uxUPLFm984nyZ459hcIi8vuDIV8/zu4qhoKBPr8/4jfxLWgQMIYA0DBWww3sBxQABUABUAAUAAVAAVDgmhUA0ACg4Zo70XjuIGolLohIFBAGDSHMGoQwzveHUNAfDvkR+vICqmh8fg1Xtnht+W0bGtWce1lhL8U4ZblWFeuCYJDAGuJFgRjQIKWfkMI00M4UU6dM16Yx2dIK2hbe80h6QfmRl0/19eMOHwz4xKAP93shGAr6QyEhEpOBWDQI+IsBoGE8RyHYNygACoACoAAoAAqAAqDAaBUA0ACgYbR95rpufwXQIBk1SDOrkD9w0Rf294f+f/beA06KYmsf7s1sYAmLZBTJGRFQQMlhycGEYL4m0pIxXfWaCAKCZCQHBQmSYclBvZhIIjnuLpt3dlKnqu6qPv/vVM/sLsHvvt7X916Bnl//hpme7uqqp2uGPU895zlojpfhgvGzdnR44YsOr6zrPTK51+idnYdsSkza1nP0ro5DN9ti9aKSdTuBoltSMjIR+LzrdzY8IHEEPjubg8DNCNhz46YZsuvmI7sN395dlJlIREXDtuCGuoYOgza+9tnJLsPWtRz4+cvvfrVq+ymfhtYMhBgWp2BhrpChqdxkYIFhBrMkMH1CEA3CB/JuNIMsQrIwUQc3+PNkky8o9ChQfgQ+CsAU+Ciws8g/BfkoBS+KfPj7L+8G9IuMsfBl4av/X3D+GJqBpoKcWoBKK3KBwP0tbLXgVWF/bj7rumaDjRe2WtBG4a5bCIUKplbBi+uaLXqu+H/qxsOuP8B55yDgIOAg4CDgIHDHIuAQDQ7RcFtObssS6RQWo6ahE4NQrhJwKfDW1AM9B61p//yynkPXdx26rtPQ9T3G7ug2dmf74VvaDdvaMSm5y8jdXUfs7jIsueuwHT1H7u4xbFe3pF3ieU+3JNy6Ju3rmrQvcfie4LYrcfiuziN2JY6w6Ybf4yOc/XcjAokjkjuLiYHPw3Gq2FuXpN1dknYm4lZYWqL7sG39xuzrM2pP4rCt7QZ9027oxs6jt3cZk9xxxI7eb+zvPGj9IwPmPZW0dPmmX/M1YAA6kS2LWSJZSMh4LPFAAuK6xw1vr/vszn7DgZnAkXcx7YIcgbCQY4EajhsiJggIg4MpxFCAihAuvGW5BZyj8QseU7DZhEXAfVacW/DRjUgX/cB+fXNcWvSYInejsKngiUFCBHsV3IIn3HhMYW+LNl9wVpHDiwzsur3YcvDcIpcTu4L7wUKgimzBFmxIcQgFh4qeFrxD9xxGxblgUsNugyrE1AnmuxFCKUWLX7wlwE0L2ylsAftjX9cQ8x7FaxZljJnMYjZBhMI2VLQx4FTcS2wBLXzwhqHkzWRgEmAUsRTN479MmP2Ie0rAEvlI4t6Lq6EJiiiibGLGErcsZnLcdJPpxGAGF+1wWfUS3Q+i+BHDKjCmZTHGLJMB5WjWyizcI0ZguxhjP+35FoCrcKRF8AtiGwDC+cdBwEHAQcBBwEHg9kfAIRocouH2nsWmaRqGYZompdSvwbHL1uiJOx55bFKPV5cNeGtX9xFbOwzZ1HX07ic+/GeXcbtaD9nQdsjGxBHJ3Ufu6TFiX6/h+7ol7eqWhIqGbknBODnJphgKg8bOI3Z1DsaT9jJ1ER2ErYZwnu9SBOyJUfBss1GJI5KLaGfQ/TFx+NbE4Vu7Jm3tOmxH9+E7+4zb+9g7B7uN3dVm6MaOI7f1e/dgu9dWP/rsvCeGLp755c9nUnUD4y6MnQXRYPML1z3f3l/aP633giVAogDBomITEauIGVEJwuxYOCB2sIATxqnBTMo44ZYdGNphdmHIxwT0RYkG+/RbRIJFA+vgx8GQOTjIoscE9xVE6LgjeGLBS8EXFN1deMwNVELRtsXrG8dSOKqCQ2/qQ9ETC64aPLwoy4A44yaemP0yeFyRzttHcYNRzk3A9DaTyJQjwwC6jMY6mqahp6/oicXAUKndbLDlQqKBWMxEzs1OlMNT8I6ijscEC3cy4PoNRAMvgMuO8AO9xssF+s8ZcAKWfaKgrLFvgp2yDGCGIBo4NxkSCDhlNMO0iQaDUYNrnKuMKYYpW4JBsCzGGbIchiAaxJNhExyCYrA7b2KKUxDA4EhxOIF9RT6yh+g8Owg4CDgIOAg4CNzuCDhEg0M03N5zmHNumvgXoWmaKmU6wN6jl4d/uKLjgCldnv+y99A9iUP2th+U3GnEtg4jN3QcvbbLmA1dRmzuPHRb16EHeo74TqRFbEsc8Xvb9sQR9oZyhsQRyd2Gb+82fHuPJOfZQWB71+HbO4/c3mkUPnceiVOoa2Dbkjh8c+LwzV1G4NZpJG4dcdvaZvA3vd7a1/uN/W1f29z2tc29xu3rM3Z3h9e+bDVg+uPD5ny24tDpdEpENIWLvbiseuvH7f2l/RN7b4dnhUFaYa4EF0VqTGaZJjcMZhBcmw5GdRg/Y3xor13j6nPgYbdkMwtoBROMA2+kD4LH47/X9+HGI6//tOC8wi4X7PrXL2weIWDSUbRh8foWn17Xt6InFLnW7+wOQmUfedNB9o6CZq7/nBtGwVI/s1CzgPoSDOUpBtu2nCGgaCjkBbAx0U4h0SDEJty247GCtwOPY8xWrAhNi2Af8ExuUoY8AcNvDaoTLJS0CE2DaJpbYBqcGcwyBaFgie+YLZ4oIBrwNCFSMDhqLbhhcWJQ1GWgxoFYYHBGKFGw0DKY+JabSF0ItsvE6wiSCueB3fMg0YBXEN0QqBVFrAgJUoCo88JBwEHAQcBBwEHgtkfAIRocouH2nsQ2xWA/61RTuZ8A/PPktdEfru06cH6HAV91fmlbt2H7Oidt7Thifacx67qO29Bl9OZOw7Z1GrQ3cegBTIsYuS2wjdqSODKwdR2xRWzbug7fHky2x6x71D4IrsF5dhDoOnx7MHUC2ahuw7d1G76lh9i6J23sOnyjzTV0GrG148it7UduazdqW6cxyV3H7Wo/dPsjf/smcWjyU28d7Dl4XfO+458eMX/mqgOn0mV7lVUzTF3H2ObWNMONuRO391f4f9P7QOCG/4hYFuX0yA7YUvXA6jd+inVxMe4rCO9E2CuIBtx3wyMoHAiE9Dd8euu3wVaC/waPKrji9R/YuwMxbvDY3/83oCwIdqxwHEWavxXREIzdbzyhyJWKtCD2BvsZ7GHw0ILjiuwIMjIFzQf6qRPZThZQVdXOS2Em+H3UpJj8IGgADLt1SgpvSvDi9hiLIBMgGjggQWByZhhCLxDgDwKXFscHexb4V9BJwXseOI4LtQsSBDhPbiYaiOAnBHdgoJ5IJNdQkyDRwEzTULlFdV1HUQYg7xDIbxJOKXaPgoSDDYVhqx7EdCvoCvavAM4A3jfCfcNYnLcOAg4CDgIOAg4Ctx8CDtHgEA2336zFv9KCsZadOmETDQQLXsoWaD5iHTvvmbXiaM+XFjzUZ2bfUTsHvP99jzE72w7Z0CFpc7exu3qO3Zs4fE+HwTs7j9jTeUQwVwIdH7fbi9IiaETlQo+k5B7DdvUYtqfn0H09huGRIpMCXRuczUEgcfiermjtgU4fPYcl9xy2vTdu23oN3dpj2NauSdsSk3Z0Gr6z44jd7UfsbTdyd493Dz382tq2Q7c8+9EvT725v+UT8zo8PWPwu6u+3HLkVIoLV4HBVDSfosm4CG+IldlbkQ235Zf2z+50oVMAOi0YwDQhp0e9fmEaBR5Egepg6MCwaocNrGFyFDsE3Rmwa9dFfoGAOdDlYOz9eyP4V5//3nloUCDi0kJJxfXdKDjRJhEwPBbbdUcFO/47RIPdRvAgPBODe7G6LkZtfxJIhbA/EqcUPSM4wAJYsMPCgYBToVcQK/q28wWu53MLnRM4MEVTqcEsAEJA9iOtgDkIQbCxx8LfVPQKryouiruDncQXzKLU0hlowVQFC8sPFdBGeHCAOAiOQvQBLJNZumkQauqaaVLBLVgmMMpM3TQpYagaQg2ChZsZyHNARcNNRIPGKTo+GOjOgLoYQrlGdGpqJtMtMLAFob4Q/bdbwraF5MG+SFDOEISyAN7APbbfB944/zgIOAg4CDgIOAjcCQg4RINDNNyW85hz8deqZd1ENGiqmod/3QJczoGpSw/2fW1Oxxfnt35hSdtX1iYm7eg5Zn/30bs7DdvWcejWLsN3dB6+r/PwfYm47RFVJ5BrKMiPKCAaeg7d03Ponh7D8BgnunYQsBEQvo8BoqFHUnLPYcm9h23vMxS3XkO39Ri6rfuwHYlJO7sk7e6UtLf9iH3tRu5tk7S17bCNvd/c9eRbO1sPnPtg4rvD3vv64C9Z+SqoBjeAmpZKDT81FPSTE4TarXiGYLByW353/7RO21Z7InGfgUmB6cCpoBwwp1/IGTgwammyKXsN1U91YvsC2gyFvaBtWGBiIByMffGlHeXiarcd+BY+39T3gnDx34sTi6zeB5u+VYtBfuEWVELw8IKPrkusCDZ6w+iCoa8YdQEaAoUiHxU5KTi6YPwf5ClM9GLExAhhfmifG2jBMAjDrAVBjRSQAhbY9VkNk2s6+kHiI6hNCN6EG4kGahEDKwtpgJuByQkMDRHwdOwZBvYiPwPJAmaZQQ8FnA2GxZnFmUhpEGSAiT2gxMD+/S7RgAkXDHMiAGcHJYbKqYaMFdOEwyhQBpouqCowDVNDnQSyFZhzoQc2/gAAIABJREFUYedT2Koa0VN7OEXQFIMO3rjr3wQQETudJwcBBwEHAQcBB4HbHAGHaHCIhttyChcoGtCvizEuHugKSZmuEmJQCtzP4VyOb9n2n199b3mbAdPav7i82+BtPUbs6zIsud2gDR2Hbeg6JhkjRsE1CF2DXVoCc++7jtgmRA2BZ8E4BAgIJ2XAQcBGQKTVXDdbeiRt6ym2HsO2dh+2zVY0dEnaaYsaOo7Y+fDfVnUZvr778K/bvzS756vT3p2+6eCRdC/BqNjkTNP9pqFiIrtFuEUwYsGqE7d43JZf2v+DTmN8hw7/TCTtYzwulPGMmhpYFC3/GBEcBKZUWIDxoWkJtMVSNkaoHMuF4iMQ+RWJpW8mGq6PAwtixQK3yEA7RUZacMz1p4rLiafgInzRPgiuwz7zut0FEXiRCwReFpAjgh9BrgQfhW0EXt3gGoBHFBANgpqxV+MDp9/AywRpkWDTyOYEWIZCDUKApgGN6PY1TRPLTzCinjpxfOniZQcPfK9qhmGBT9UCmBT2spAuKVA0oPGCRUwUqSi4UVmXFZWCIqo8iEFisQg0U7DNPzFvxgumlxpMZ0g04FVspkP0zTJMJCpsJxQxDeyr2iMXhgp4hoXOCwa3sBlqKGBowAx36sVVixYuWvT1+Ut5BibecDxAEA3INTCOZ+GcogC6TX8U3t/CYQoAi94dnGniDt44SwJHOv84CDgIOAg4CDgI3I4IOESDQzTcjvO2sM+49CTUDcIgDD3KbW9vEwwVdB0gh8D2Hy5+suDAi2+ue/TJOQ89Pq/L6+v7vbmz9xvbuozYKOoFBLIhcIFaOD6KZ9sDsqjDH1r92SkVTqTtINANzTu232Ajink39n5MmiisN9Fl+NYuWHhic+LQNS0Hft78iY8GjJm/ZMsvF/NMgoXygBqMGRjYYEEEU2dcN7hCmeoQDYVf9Vu84qhaB8zYL9SuYzVQikvflgZM4363Jyv92tUrly9fvng17XJadkqWJ89DfARVD3ZlxOtDwaJEQzAWtEPE3wkUi/oUYB+vjxV/59TAYXaQX3hG4OgiSgox6oJGRMRsUwk3wPF7RIMIs+1j8TKmEAUo4pkUMBG2uEMQDcFajHafxIULmIjriAYRuouPCuiPYLeLwKYo/nxXFlgk5fzxN0cPC5FCn3/hlSupmQWgMbR1LChvGSAaAiyDzQvgj7ph4e2SQXf5c9LTr17zEHAboHAcj3CF1DnyEUg3cMNHfVfcWWevXk09eynt7MUL5y+eu3Q+5fKF1EuXLl24cO7yuUtpKRmufL9CzEDZSTGwIkSDwIujqIFxQplKqIxEAyd71q6sWaFCmbI1v1y3XxSgNRTVg8Uv8CHkC1iCE7kGC3SxBfxEsUX7Lha5b0V2BJwjxMCLHOG8dBBwEHAQcBBwELidEXCIBodouJ3nb1BbbisaMFM7uJmcUc6IxVQOMoc8AvuPZf9j1s4+r818tP/UNs/N7Tb4y14jNyWO2NYhaUu7IVvaD93aecSuxFH7uozc2ylpb+Koffh69J4uo3fhNibZ3rqO3t3d2e4+BIowUIH6I/aeziOSu4ze1XXsvq5jcbZ0HrW700h87jhiZ8ek7V2Gb+0xenv30Zs6Dfm69cvLW784/+HHP35+3NJ5644cvYoBii5iXcIpfgkLokkREAZCaIy17t7HLbQc1+3CADnfnYNSBQ75bj/WoNG8YPiA5jN/9m8//3P65ImtWrSMj4+vUKlilfurJVSs+nC7bgNfGvrpjEW/nk+XKfgIBqsWgKrqRNMLQlzLslRVRZW+TnABnFtE01lA/BC4I1j70KAqJXbYrGgq0p2GaTFuGIaiKPYtNZipU4KhJGeyLNvNYp4/IRaAIYpi2C0i22QQyy7MaTFmUk08TCzdYDMYXFY8QtIvyBVxGhZxwEQbrIBADZVS3c67ESUkuV/xUYrxuK5qis8FphfAB+BX1Txb5aGohqJjboFMFGKoAIZBVV3VcOwm1zTsJGGoAShkZIRGAFNRuEWp7nV7ZJ8fuMUMrqlYm1UzMMBWKTFQWqICdYGaMfiFJ6NCI5955uXU9DydAbVAN7FakGCJmODUGENfBZMxg5sM7RsAdIrWDG53OpC8jHPHX3zy8WpVa7776cxUmfsBZErBMg2/h+gqSicsCpb36q/JI1/u26Txg/UaPVzinlKly5SqWqVGpfL3J9xT5v7q1SqUrVKzRoNpM+ZrJvgJMVBKAJxzyhkVxTJ18d8J1YmuqybTOepgCDAVqHx4y7rSoWGlytResGIXYaAT5GVQfwQWtkQIekcCJ4aqqPk68foVn52Ygy5CBkHJBSJmmiaW90SezMSqSXi/LJwm6A0R5M3v3q+9M3IHAQcBBwEHgTsFAYdocIiG23su23GHTTRgLq5YSwrQDWKZCU0cTK5Q7lKt8+najsMpU5Z++9JbX3Z65rMmfcY/NGBWu9dX9B67pe8byT1G7uiatK3XmP1P/v1wz1EHeow+0GP0vh5j9vQYs6vb2J321mPMLme7CxHoOXb3rbfRu3qN2d1r3L4e4/Z1G4N0Q/ex+3uM29chaUu3kVu7Dl//6Atf1OvxYd1u73Z46fO/vbNq2ZYze37JuJDDPKYQgnNDt3SOYm9BKASjSUE7oJPcXb7CeR2rcKs3dgFCOwbGHzKsPaGAmnvq8J5P3hnV9pEWkhQqhYQXiyseGxsthUhSSJQUES+FxIXFlh029sO0XDRzUY2CEBqjRCMYqDNmyT6/5pftJXdmmCY1OOeEEEopIcRgWASBmoZmoLOgwcwAGSHkAIwxneIxBjPtG8vB0gjSGRxDaVNTVNvR0LJAFQ/bFIBbqGqxRf24UC/iUmYCwbBWaPUNjIF1XcdldMsyDEKoggJ+YcFocyWMMUI0DobBaMA5F4UDFHUB4LF4HtFy9+/bs2jhsh9/POHx6zLF+o2E60gvmFRExYLbEJIIRVN109BNQ9aJouqUmshsGJZBMC0iwJEJUYVB8ZM8r4IhOA7PAC6Dkg7+lNGv9I+JiH72hUHXsry2lAAVCLZ0IlhtlGNtShNvK+MWw+wFnSrMkomWA9x3aMu6++4pK0kR81dtTNN4nsE9RMebTjShZGPANTBzzv1zda9HqofhrY+TIkOjosPDpIjwkJjQyKjIuBhJiipVqtKkqXMwvYGbMlH8fq/f75d1vDEKZ4oRqPYivoacIc9EMKdJ953ctaVKdEz5ik0Wff0tsUClyDIYpmqglA5AJ1q+2+/3C8cK1Cr5/ZjBIf6LFZkVzDCpQUjAK6RQyYEYIiQO0XB7/zni9N5BwEHAQcBB4HoEHKLBIRqunxG327sbiAahQLUwdRYXNxmYBhgGmERkwGI4oXNIy4cDR7PnffXjW1O2Jk3YOGDs8g7Pf/ZQvwnN+0x6uO/nTXpOb9j1sya9ZzfpNbdJr7kP9J79QJ+Zjfp+3qjv9EZ9pzfs+3n9fs521yHQ8PGZN2+NH5vZtO/MZn1mNu07u0nfWY36zG782NymT8xr3n9esydmtBrwebdX5z03buXoSZsnLzm0Ovncvl8y89WAikEEOdyPgSmxFelFtOqBwC0Qgt1uX8k/sb+34hYK93GwZEWzV85FBQmCLIM7ffeapYmtHgyXpGJxJRo9/OgrSaMXLF0x4/OpX8yZOf7TqePe/aTfwJejS1Vu3fmxo6dTsD4FLq0jBUCpbjKsR2DzOygEsBkCC9PviaZjYUXxoFSIUFA0bxKD6pRQE1fy8Xisf0gCbrUAsq4pRDcsThiG79TEtXoMr8VGNHGJQsiwgAIaTCDHhDQTpSKqF93A6Bv7JvYTzWIY4VvcDO40ie7XVK+u+SnRhPiC61QxuaHr1O/TGJIChpAzuABcx48e7NkjsUb1OgsWrPDKhk9DXxsTE3koIRrRZar68ZeTM6oq9shsQscwQddMqjNuWij3EKVDLUJMnRCNqpqhGSjRwfQVZidiyEBzwHNh5PN9YyNjnntxcHq2z85bMQIyDdtEIQCK8N1AfCzGZVURNBySI6DmTnp7bKmoEj16PHnkYpobIN80/fjbzlBKYAHRZZwALOfakbVPtq0dGRLVs++zEz/7dM78GQvmLJo3e/HUmZ9PnTNzwoRpU6fOP/zzaSo8JgrKT2KNDIPIwiGSYs0XfBBDV6ismQrjOjDt3N7N1WNjy1duvnDNYaSoqIFZTqbsV2TkVxgvcH/glubz59oTCW+Rgbhahi2ZAaIzWaF+Fb0qcMoAC9TpcBQNhd8F55WDgIOAg4CDwG2PgEM0OETD7T2JixINSC6IhS3cyU1gJv6hbIjKdtzgBPXAJkO6Adf1DMjwwplMvn7/mb9PXzvo74uGf7Bq7PjNr7+9pv+gRf0HLXsyuD0xeMkTQxY9MWTh40MXPpa0yNnuQgT6DVt48/b40IX9hy4eMHTxU0MWPzFk0WODFz85bNmAkSueHfvlmMk73pqyddqSg9u+S7mcg8Z1GgeFADGFVF7UVmQc1zFRhmMVlC3E9d2i2+395fxf976QVLjVK26BrON6sUZkSj1gukHPObRp1aMNa0VJUtWq1T6cPP3XlAxbG+D35IJlmsySDXDJ5vS5y979aNrZyzm4gi8wxygZEwcwwjdNSinlqAcwKKVFMyZM01QUxcR6BfhQVVXTNJQtoK4Aw12wQPb5/X6/HZxTzvAHhzOVEp1iIYbrbjAuxlNN0TVNUxTFJ3t1qtk8hMGoosm2bAE4IMtgmCg0QNtQg+iqQajFODMpJZppIDWAq+5IJeAQLC6cRO2sD8DIVkTCOtWyTJoO4Fq5fHaFe8oUjys9f/6XCgEfZR4d6ROvphhMUBKmjpkEHIla5FKoSSgXE1jYZ4qR6qqGNAf+0gp3HCFQwDtiQa7sz/W6Vc1rqS7QssB75c1XB0SHFxv4/OCULJ8m/BKJyHQTQKIcooBxw2QK8U64LRJkGVjOT3s2NatbO7ZYqYVL13kYCoJ83KSAjo1MR75GVbzoPWFmZR5b+0zHupXL3Ttn4ToqMkrwR98EneMA7WKWaKLAgKGVo8zRgRXNPggegGxLAetHOUOGBp1ZDWDy2eT11SIj7qnw0PzVP+oBRYNmMsyRIRo1fX7QDJMhAaEbMgCxLEY0naLgwsTNJLqmaBqmpWDehPCNtBNSmEUZJ46iwf5aOc8OAg4CDgIOAncGAg7R4BANt/dMvoFosKupiRpjuIiISlqmgakzXcGy7hjYga4xVTMNYSRGAXwmy/QoqblySq6WkmNcyTQvpNErGXDJ3jLhUsGWxa9mMWdzEChA4EqmKTZ+IcM8n8EvZkGKC6554UqOmZZn5Pm4JozscGWUKqahUiJTolBUw3MMeOzqebgYzkVVPEyXcIiGgp+kW9ELRfeJJWR0A9DAzANwnf1l35NdO0RJUvt2Xbfu/j5bNWVhhOGWPUTxmZrMGPMqOgNwy/Ryaq4JGG3iCnzAFhDl7gZVVM1nUwm4qI2hfMArgZCAVEHXdUVRkFkIPriJIaXq81tos0AVBeNJnRLdRF8HNBoUZQxs1QMzTBG9opGDoanoyyAehJoKoQplhgUKoVgAUqQ9mAZhRMfyimBSomiqTImGaQtITGGuBxqImjpnOpYsYYToKKTA9AqKkgluIRWAs41Ry3QB5GlyyuKF0yqXr1ChfNWvvtyiUsj26JlePwHwEsPEuNzg1GsquWDIlu7DNixQVMPjp36VUQO1YpRiVoghuA8kOCzkRnQi61RTTeohqiBjTKbmgy8Tci6OfKF/VHjswOeHpGT5VAvZXiIy3URUj1KO64kGWw0APm8eWDlgpM+f9lF8sWK167Y4cPgcsgymoZo6VrU0xOhQT0A4V4FkpP24svuD5UOk6EnTV2CtCK4p+aruJRrXdKCIAwdKkQ4QPo5+2ZOpyS67dIXGDLfilxXNMBjjyBDpSKKIPCbqvZi8pkZkeNkKLeZ//TMRigYOisn9eKNVAroJdgoF0tk4l8Biuqwo3nxOFKwEynTT0DDhRdBNDMCnMa9KDKyEgr8ADtEQ/D45/zoIOAg4CDgI3AkIOESDQzTc3vPY/gPd9mgQ5mf2n5FMeOlhmUCLo7mahanD3GL4VyfVDSyBqaHSVyU+aukiC1esA9qrgaIumcmRmrAjP/sZXbqKRoHO67seARPr5+FGGYasRLBX5nW2edw0VF3JN7R84Ao3ZFGGTxBeOlVVnRpMoFiUaCjUNdzeX87/de+Lkgq3fE11AxeKwQc0neSff3/ca+GSVP3+Bpu2fq8DZKtmutctc9Wj5KO7ATosmNm5OYQGrA0ZoMaEcVBVGSzKqFeTczE+FK4ZqFzg9t1BFYH9sMdkWZbP57PzI3DJGjMIxI8DB04NzS9rmmaagasYdqVNi5kcN9vmkOvURLNJjM/RzpDqWHVE/P7Y2RE2PWGi4sVAigHDVE1XsMaBOAotDAxC8VPgPq+LEsXiaCVAiSIsHmxXQp0K10jTAKIyGytO0gHy9+/e2Lheo8oVa2/d8q1pgZ9iUo9uD97iRPcTXxYY+WDJon4HWi9QChpFEsJehFc1dMFE/CxiEp/izSJarlAzYAFJr+bX0eiQoqaMaaC6hj7bPzqyxMAXh6VlK7YNqhFoSrg7BImGYOqEGKUFqtcFViZxnXzl+cdKly4z6p0p+QRUC1RDw3QGMA2KCgs0BDWFJoW73ac2921xryTFvT9hsdfUDSCgAdcsA6iPeFWZcAKqQjWiut3pzHCDZZfhMNCfQigaZAVLb4pUO+6lmsIJ2i5w5dr+DfXjoivd23rhuuM20cAsmZpu9GjgIgnHT4mwF2UWVRSXnVci5AwEDIXpPsyesEBWiM10Ew6qYRELzUWwTKtI+vtff2+cBhwEHAQcBBwEHAT+Egg4RINDNPwlJuK/3YlbEg24UCz+uBMF1rkopc4x21ZTC+TNonQa/nlKLOYnxKfrKjq2G36N5vsUzeCoE0a1MJqxoZBaPEyDONtdiICoOklveDaZrhO/bviIqRBTrOUKBsswDNPkOiqmKU4zOwQ1GSNChC8s4Lhp6ToNCOMDs9+WjtvruoHV3X/7e3FnnHhLcqFgJ3BL9/mAEzAyuXxh3/ZltaqVk0JjP5i4QLZABcj0+nRQLFAok7kFmDjFDMPUvF6vqhFFNbw+jCfF0rkBoJk0nxlubviuXDmz4Zs1n38+bcKkibPmzf16/bpjJ3+1SQdCSH5+PqZFWFiH4sC+/VMmTkreuk3zy36358KpM6tWrJw7c9ayZctWr1594Ltvc935JoCfaBpDcQTaQ9pKBFz+9zHV88PBnTOnT/7gH+999PH4GbO/SN5/ODVX9hFc8GcAiqozk1qmdvbEj8u+mPH1V0uI7memvm/vzsWLFsybO3vP7p2i/4KXsOj5c79t2fzNwgVzp0ye9N57f9+0beuPR457vYbFMI0MLO7KvLB+9ezZ095/9aWnokKLSVLc008NnTh58YcTZ81c9NXxSxl+QZYR4gXuA8vjy7r408Hkz8Z/NGP6zFmzFn6xcHXynsNZubJNN6gW14VXoq7nc9MtDCDklLTTS79c/MmUTz6ZPGHCpPGrly/PT0sBonwwenRURMkBLwxNzZJtRsPOarHEb3FBJoEdbGOihynMK7kOVvq+LXPvq1KyUtXqa3b+4hcUBGcEMztQK4Q0n8aYT5c5MLB83nM7nmhTKzKi3AeTV7qJzsBAFlm1DNBk020JxQbVDU31AWZgyEDcJ346tHjRvEmTx0+YMnHilMmbt2w7e+4SFcYJsmX4mIaGoVxJ3bu2fkxE+SqPzF97oijRYAH8euTEwqkzJr/3yTffJGfkerHWJVNUbz4SF9zw5lz74eDulUvmz/hs8pRPJ69YuXrHroOXUnJsMwuFWR6i6pw6RMOd8cPljMJBwEHAQcBBwEbAIRr+ykSDnfYqSl8x/LvWThx15m5RBH6faAgoFMSCMzqx2ZnPgUpmusHRgw9jQKFqxueClTrbDDwYgQRW2wr/mg80HFhxFA04r+90BAIrz/b6c8Ez2giibh83sQyO1fVwhRm18VjmMLDKbVJLLDwHSAeONELhLC6wGAxKxwvVDWLWFR55t70q4BRu+QL9AlUNTD+QNPCfm/jukHBJatW+3z9P5akAOX6M1U1Qvf4MkymGBV6Z+GU3+hWgpwMVxQXxF0BVZUa9XM8D5uY0/+D+Lb17dwkNkfCBhSqkkDCpc+eOGzdulhVcwycMfLIXwEi/dL5/7z5hUvjAAS/88OOR5J27mzVtEiZJYZIUFYYN1K5bZ/YXi9LzfDILEAca+jX6ONUM1e/OyZz48TuNG1QPkaSwsLBi0cUlKeL+Oo0GjXrrXJrLQ0G3QMHEBE3xpU18b0wxSeqe2O6XI4f37NnVsmXLECk8RAp9/rlnVMXDDTkj7eKKxQs6tm0TEhIihUhh4VJsTGSIJHXq1GXdhuSsPA2NLC04uHdzj04PxoRIUZIUHR5TJuF+KaKCJJWWQovfV6vR/NUb0j0+hM3wgZqTcvbHd0cPKl+iWGxIaExYjCRFSFJEnYZNPp78+a/n0mQGLo3ouBRvcMuLTgpmzt5dXw98tnd0QjEpSgqJiZJCpBKRUX9PSjr9w/fvj3sjLLTkU88Ou5zpUTh+ZxTOCDM52AI0Q5ScwMpBjAmRGfr4UuCKlnXy03cHxxYLSez9xPlcI4+DwiyDqqApXFYsgrYLaM1o6piTYHhSfvyyx0OVYmPKj5/+dZ6qoweKDhTvHZFpPlFUVCdQTG3gpuv82R+nTfno4WaNI8LFvY4Il0KkcEka8OQTyXv258ka9tPiGiNA8s9tXV4tTCpd7uFZq49oWHpTAfAD8/78408vPvdKtFSsQqnK77w34dzlVPG/i47mlMR/8bcTEz76x4MN64aLuREZHiFJoVVr1H7rvY9Pnk/VLXDJ1CW7GBBRcaPIT8Pd9oV3xusg4CDgIOAgcGch4BANfwGiwRIZr0IziUbizOQWJVwXqaGYXos1tgNEg/MnyP/0+3dLUf91JwdpmxuOvO6Ywjc28jbp4DzfdQig6wfSBoUDL7LHJlkKPyqcNf/+q4LW/v0mbs8z7YHfsu/4kYDdLiqDlSA0vw9MH1iZWubRF3t2rFgi4dWRH13MBxVjX1At0wSDM+L3eSi3fKqCoaaaq+ouylS3TN0qp6h08IGZDyxHybswZ/o/atYqK4VKsdExj7RsNWDgEx06tSxZPDxckurcX3fxwjW6qA9JuGbo2f6UUwO7dA6XYlu3e/zV0X+v37JVdMnovv269e7WuWWTRjWrVgyRpGp1mk2dt+Z8Js1RQaYqNd1UzQGQv9+z7YWBT4RFYuhZIqHM40883bNXv+LxJcOiIqXQ8IGvjT6V5lEA3KbfhAxOL016Y1C5SKlD64cHDXm1beeO4VFxccXK3Vu+1qBBrxmGW/WnTvn4rSoly0WHxFW8r3qzNq37Pd47scOj95WJj5Sk6vUbTflihW10efncydkT3h6Y+FDvNk3CpbBi8RVrteza7rEXe/d7aviIUZu+O+Q2FZDzIC/l0s/7n+3TJVySokLCqifc+0yvgU/2ebxajfukCCkiIWH0h3MvZWMRHwbgys8Cmgcs95uFE5vXKx8aJoWVi63QtF6/5wb06NGjXsVKpSSp9UMPJHbrKoWXH/DSW+n5ml11gqLVA5pWCroWM1GoYRnox2gB1UDLR/LC8lw+fvihOrUrl6sy9fMv0lWWB5DPqM4IUBMUjtoVBiZwYhmYiKTkuU6se6J1FSkscvKc1cwC2U+QC2FUph7CNeSaTUY1H4A/Pe3ooNcfj4qLkMLCa9Ss/VT/AQOee/6BxvXrVykdK0lt23RZu+WAR6hLXL48sHw/r5nbuFSJhAqtl2494+M8J/cc8NzcC0eTXnk9MryUJJUa++akS9fyVIOj4yPW2sw+f3j78wOeCg2LkiSpfr06j/Xu8+Kzz1Wrfq8UKkXGxbyWNO638x6Vg1fO9qtp1FCQZHEeDgIOArctAkVp8aKDsDN8nRXDopg4r+8GBByi4b9NNFjpaHwNpmETDaJWwq2IBky0vW4Z9G6Yns4YHQT+GgjYfzoU7cvNe4p+6rz+dxH4Y0SDqWtg+IHnXjtxoEO9OqXCi48bP9sdCFtB46rOVHTZQ54QFQzo9ch8HHwMiA4YohKAtPSrxHcVeO7i2eMrl48rFh/61HNPbd6y7ciRI7LicuVf3bpxZf++XcvFJ3Tq0Df5u/N+AJUpAPmQfWbE031LR5ULj65S46EOPV94ee32b1LSzvpy06+dO7lu+bxGdWtKUlzH3q/s+yVTAVAMTdOzAPJTz/00sE+32GJh99auNnHWjGNnzrs8Sl5e/ulTJ2fOmHpPuTKxCVU/+XxZqofomPFwBeDiwklv1i9funxCuao16/bq//TkGfM2bziwa/t3J0+eAPCnXPzxreF/69chcemcJacuX0rJy3LlZ6edP7l/3Zfd27aUoop1feZvZ7MUmYGp+Q3X1exfD3w9d1KVChUrVG/43rwvL8qQ5/G78rPTZZcJBIz89MN7nu3dOUySGjZtumT51xkXcvOvujMuXT158ufxU9+NTogtUa7evCW7M1wMfUmYApb72J61nRpVjZGkB5o0WLJ1w4+pV3JUOSM9Le23E9Pee6NVs1ooDylCNJhoO0FRblCEaCDUogSddID4uZJhyVfBcq1bvKiYFNG8Sdv93x5VAa5p/nymaoxYlIPKscQFEg2MoJgIgCu5x1b3fKhUdHzMtAVfo+8Fx+okmJgQrMGBjj0mBUs9fWLvyKRnBzw/cNHyr07+esbvV3STpV298OWsjzs9WCsmutSQUR9nulH84NFdwHPP715VIzK8TLkW81b9IKwYcl1Xj018Y3ikFHpPmWpTZ3wlM9Q4eamG3hmKy3fu+7/1elSSQqvVbzZt5pwTJ07mZuR5XfnHjh2eO39KhcoJUnjU5M9XEZzw43rYAAAgAElEQVSfxOJ5BnWIhn/3l8M5z0Hgr4GAQzT8Ne6D04u/CgIO0eAQDX+Vuej0w0HgL4vAzbTCzXv+sp2/rTr2x4gGtAFUZdA9pw7srlXinrjQ2PHzl7oB/NSvaDkM/JrmApP5s3NWLvvig4/GTZr0/sRP35346d/f+GDcu9Nm7jzym0fkCQDky5lnujzycIgU2v2Jp3Ye/gHLSALImkek8efvS17domnjsNCYV0ZOuJLHNFMHlmukHxn5TPdy8QlSeOnezw/dd+KMjicRsAgwzfKlfvqPcZIUER1//9T5W3DdHTNr3KBnzvr4jWISiiamzJt3yeXCRH00IjSAG3lpF//xzpuSFN+kadc9h36laB1xmRtnp78/pEJ0dGRohboPdJ+2cLnHpLjwjYVLOJb2BN/Zo4fO/PJPi8oUFC/JR4tLAzMgVi+eVapS5ZLVGmw6eNyPaT0ESDaYmd9tWVG+TEKJCtWnrN6eJwZrMt0AVdWwQsRn4waHSFKJKvd+tHBZLpbAwDGhesEirvSzfxvYJ0oK79Oz//HLKTKyOL6Myz+PfeXJWElqWKfx1h3/dBtI4qDvhewGLuddOTp78qhaVUuHhZUY+MKYzFzZtoFkWPSRiWwjdLjEDAgDbKKB6T7mzwR6zZ16YkCfXmFSxJtvT7iageYOPm76TY1w3TQp2mESrNwppICGomjA5bTDS3s9XDokMvTR7v2TRrz/j/cmT/5s5vhpn02ZMeOTSVM++eTzdWuSr6XkgMFAdf14YMeZU+cxhYZhXVJMoOMq8Nwv534qhcU0b9Pr2LlUGfNz3JaVeSr5q1pREdUqPDRr3hYGBqFZH7w9qnh4ROmS94wc8+61HF0HSPXmZ/uzUPiiZs36x9h4SQqLKfPZgrUegvgBenoSANk0MkaPelYKlTp2efLHny4TWVe9+VhAxFE03FY/WE5nHQRuQMAhGm4AxHl7lyPgEA0O0XCXfwWc4TsI/GsEbqYVbt7zr1txjvjXCPwhokHEx34/6PKpgweqlyhfIjph8uLlGcCy/VluXwqAj+p5YNIzv/zU7tEmIaHCPSFcKlEiTAqVEmo0+HDOkgv5ebiGr19bPPXdkuGRbR7tdvjkFb9QOvh1nwUm2iJCPvDc998ZFRIa1aLTwCPn3Bhx61mQ99uQJztES2F1GrX6Zt8R+yx3frY/N4u60oFlXzi6t1GdOlJI/LA3puf6ASUVVv6VIzv7PNooITpm/IQp+Qb4AdzEUDUCpo7xrO5OO3u6Wf02UWGVP528lGExhWtgXJj2jyEVYmOjoxq889Ga31JzZXQvMH2yW9X9GNGbPtBdWCECfKqVk+1OyU/NyP7t3Kn9W5fNnlSldh2pVKX5a3bJuHJugJ5puU5t+2pmQon4MvfWmfzl1nN+uJyVQw3hjAj5KYe3t6pVKTImeubaDdcAsgBcKuNIvVjc7wUmHzmQXL9KlfDQqMlLFqciF+P7Lnl154cbxYdHjHtzglvB1JXMXM3r9fs92VTLAiM94+zuvh0fCA8p9twLw7NyfUz442CyBBZbEHU5wLAsYdQoyAcwFEvJBp57cOvK+ytXqFGjwY79v7hU8GgW4WiOYHDN4BoxVCyyaXGLmyhSQPLF5z27Man/g5FxxaS4CpIUFyrFSiERUmi4FIKJIJJUYmD/IT/985Tw8FC53xXMiOKyx3U18+qR499/v+urzz5+My6+Ys0HOiQf+sXHTQW8FuSdTF7VKC62YaWmG9ceNE35kwlvRcfE/X/JJR9+MuXM+RTFhAyPL0vPpyCb6uXLx/Y8Wrd65VLl3v5ohltU9PCrFjcAC47oeQC5x49tafVI/ajosu9/MAe9OLDopukQDf/6R8I5wkHgL4yAQzT8hW+O07X/AgIO0eAQDf+Faedc0kHg9kLgZlrh5j2314j+qr3940SDogBVL/z4fZ0K98ZExX84d2E2gN9SFJID4NPlLGAk9+rFkcNeavPoAy2aNn6k6QP1q1UJk6RyVRsvWr/HwzkDf96lfz7X9eFSEdGDB711NkvPB7iWnaphsUbzWtoVt+uq7Ln22aTxMTFl7qvf+ettRxiA4csGOfVvPdtESuE9+r10Ptv0AniJQQ0VK95QL/BrNPtYr46tSsSXGfXerCs5pmloYOZsnv/JAxWLN63XePW65BS3nq6ydI/skf2Mek0505957sLxX/p16R8qlX/rjWmaZmnkGlgpcyeOrly8ZNUq3bfszvEBuNAFUZapR9NF3Q1OLNXlyr786+nvv9q8cPDIFxMfadvvkTbNq5S6v0y4FBperm7zDQd+dWEVTgNYDpipu9bOKRUXnVC55vQ1u9MtyJZ1LO5IckBN275gfLkQqU69unt/PXWFWed9PpWS3LQUf2a2nJ4FlJ744cf2j7YrVuKecdNm5WFsT1bNmlqvcqWmjZptSD7sFcYNbpdHVNPUPL50sHJBu/Ryv9bFpNAXnhuSne1lohoL5kwwoWhgKljUstCkyLKngKUCzQX3lSnvjYyLi+nz9AsnLuViqgsD2a8zonKuUlAVS1YsDatKMNMiBsdSD1npv6x8sVttKVQqW6PJwy27tHy4Q/NWbZo92qZNx8TmLdq2atntw/enXziTDpQDl8HyaL78H747+MWcyUOGPte2+6MPtqhXo3J0pVLFJCm+QdNe2/Yc04RZBoB88fsdDeNja8ZXW71g3ddfLSlVJrZUxepvfTjzyG+XbacWLB4BGgMXdf18cN3nDcqUbtWoxZoth1wMLqbn5bh8usJTL16VXekWzTx/9uCTT3WVwkoNHvGpyweKBoRaDtHwV/1pcvrlIPA/QsAhGv5HMDkH3TUIOESDQzTcNZPdGaiDwL+LwM20ws17/t22nfOKIvAHiQZiAiFg+lNP/9SqcUNJCh/y94/zxeqxAYYFiqJmgakAU8+d+uXE0cPU58m7enXD0kWNqteuWKnxwhXb8lVdJ7nXTu58oVuz0lGxpRKqVWrUtvpDravVKNeoTvlGDetUu/++h5s17NKxTe1qtSSpeNO2/bcfOitrJuhe8Kc/9kjz2LCSvR577XSa6rYgT1Y1olrcRHGBdk5NO/R4l4fKlL7nzY+/uJJnmUwHPXPJJ0k1ikvxUfH1mrSv8WDbuq061Gn6cN3G9Zs1q9eyac2mDSo3qH5vXMQ9knTPe+/N8fuZqqQBT5nzyegYSWpU/6mte3IUdJcwVVAUVDHoYKrc7z6wc8uI4YMbNKsbUylaKibFSCFNqlRuW6f8vaUkKTT0/qZtN397Kt0PmqlqylXQL+1eN7tMXHSxEhU++mJNJkCuzn1YksMH3kuLPhhUMUwqXbpko9YdytRtUKfFw02b1q9XtVyrBvVbNHigWcNmNWrWk8JjpLiEDxd95QcAVZv9zt8rFItv26bL0SvZWH6SG7qSZ+huWc7Jcmcy5gWWPmnMi8Uk6flnX8/K8phMEAomCDmDYZmKxQm6ENmFWiwOlh9I1tXjBzu3aBwdGzNl/pJUn6UAGAYwnQPRgKkE02Zk2dKoZeAHuiFKycqe01sGdqweERs97uPPT5+9duq3i6fPXzp/9erVjIyTZ87/9MPJ1Cs5Fl6aa660reuWPPP047Vr3Z9QKiIkFBUPlauV6dyqTqX4CEmKb9lu4P7vLxAAly4DKL/t29i4dHyCFNepRZeK5UpKodLH0xcrQv+iYYkQgwDJ0TI4ZIJ6bPPct6qES8WkiHoPtm3StnulmrWbPtyiRfNHq5Sv3LBOzU7tmj/Soi7WB4mp+PFnq3wU+QmHaCj6W+C8dhC4HRFwiIbb8a45ff6/Q8AhGhyi4f9udjktOwjcIQjcTCvcvOcOGep/eRh/jGgwVAqGBkaG7Dr52gt9QkOldj2fPXpZ8TDwmSAzXWNejqvWul12FK0VGUk5+kPXFi3vrVh/2fLthgkA8tnvV73U68FISZKiS0sJVaW40vFxUnykFB8bGhkVWiwyStSSjC5ZqnrvAUn7fjqL9pJcASW/V7OHY0PLte347I+nM32izCkF6vbmA/eBclK/tn9gjxZRkeFD3p6WaqdO0MyVE0fcV0ySpPCwElWkmHJ4xahoKTq8WKyE9RpCpRIlouLiytdr0H767K/dPkKNXLDSF056Gx0QGvTcsueSh4EKhkJlrzcbLAW4cvnEz0/27CVJxSJLlXu0T4+/T/54xdIFyWuWpR1J/mLym/dVr1a6av2F6w+m+8FEF4l83XVsy8qpVcuVrlK17pQlG13CiEJFwYVXSTv22Ygn7wuXokOk+IQKUlRJKaJYdKhUNkYqGxsVgY6OURElypep26hqyzYfzlnm8jLI9S9896OKxcq0ebTbzxfTstDrIR8ghxq5GvWqFidUYbln3nn1iShJeu6ZQRnZPiqIBhQwcAoW4Vy1kBgSRANqAwgYWaacsmL2pwnRUfUfaHr0QpqLg9cAxS/0EjoBTgjoMqiFRINGNUUFw5d1ZEOv5hUiY4qjGSRH8wXhwMAZmBwMzpFjwNqW1Hvyp2/btGwuhYRXvr/GoCGvTpk2fumqpcnJ3/z27aZPxr4eH1++ZYfHv/35rGyAzjQA389blzcuU7xcZNmKCdWlECkuIW7yrBVegn6ULkX3qsQE04V+n1ng+2neOwMbJ5SIkULjSlaQiidg3k5ESLFiMWVKlwuTQuOKRUeFSVjnsnbLibNXX85RsVKuwR1Fw3/5F8i5vIPA/w4Bh2j43+HnnH2nIeAQDQ7RcKfNaWc8DgJ/OgI30wo37/nTL3pXNvgHiAbOQVE0Sn2MXAZInfXZuFLxUfdVa7541Xc5CmT4Id3r1XCZWyOmYgH4FdkkPrB8J7/d2qzmvfEx5RcuXu/XKCOulKMbX+zVOFyS+j37/KfL1705dcbiBbNWLfp8wfypc+fNWLp42eIFK5cvXjd/wZola3dczfcapmz6MsCX2++RNsXDK3Ts+vyptHwFIMvvomD4iA+lAeQ3lnPolSfbhUnSy6M/zdKxwiLIKSsnJVWLk2rUrDvivcmzl61ftGbz4tVrZi36Yt6iOQuXzFq2YvamrWtnz1++aMWWXQeOexRimHlcTfli/DulQyM6Jz5/Js1AQ0RDRxtIzWN40w3X1dkfvxslSRUq1fty4yGbNdANTAoAI23byhk1a9eqVLvp5v2/ZsmYTMJZNvDUtYs+qhgXXbNGg88Wf5OqQ7bK3X4fsic0e+X4wdWipHLF495465Otu35asGTNrGmTv1w4c+6MKfPmzl66YvX8latnfrVm4qKluw4fwyBe1ua99V4pKbrVQ51PX3OhogE8mpGq0Wy0HbBA1/w87+y4F/tESqHPPjP4WpascTAtEB4NGF9j3gSWBQEkAQgF3c2Uy2r+mWf6di8RHTsoaUy+AZkK2kQwA4iP4F1lWBpT4UThJKBo0HTBXCi5Rzb1b1u7TLnKE2etzM1VTGoRgzKLKrrXL+dZlgKW3+e+mJ12ctSgl+OKRddq2HzTnkMKZbqJZAf6XuZf3rtmYWxcydrNWu/94WieqllgWlrOuQPr6sZH1LnvgWeeHTnw+edK3lOqQqU6S1dszXVTCqAw0IApzAOQC96jKyYOKS1JVUuVHTbm7UXrNny1af3C5Ys/mjBxztwF69Zu/GLeokXzFq9csfbLtTu/PXIuV9V1EP10zCDvyh8+Z9B3DAIO0XDH3EpnIH8KAg7R8B8iGvj5oeTUa3B+iHn2dXp2sHp6eP5vY628VeCUt/xTJrLTiIPA/yUCN9MKN+/5v7z+3dP2dUSDZYHYMPoTq9DcsphliVLAHOsq5PsUC4ipXwJ+8crpna2b1g6TYl944e/n0yCfoc9ituLP1z0UKLEshaqKmgE869zxLe1a1ipbscqCL79xE0On7txLh8a+0j1ckoa9MS4TII2KAgGWqSjZhLjx0kLVb1qQR5kCzAIPmDk09XT/Du1LRZfv+djffkvJVAFcxO0zvSro1HABXPJf2fVkpwfuKVFizEdfnM8BnRLQMw58NblmSalxkwc27PleB3DrjGAtAlCIrhqKRr3MooSDTMGjiW6AB/T08aOG3BMZ26p9vx/OZasACjdkVaGKG3h+5ul/dm/RqGR45Dt/n34tHzwgDCZVN1HTQU9dM39SZERYQqVaKzcc9KOegZssF4yUr+d/mBAu1bq/zswl67MoFvzUiA5UATl9/Yw36pSW2j/S+tAPZ/0M/AZ4ZbSCJIbKsZQEVzRZMyjhlBgUuA7etFnvjY6VQhvVbrlz/ym0lLSIAbJmuLilud0usDRwn366y4PhUsTA50am5RmKhUO2UF6hWabfZLoFoBPOiYX5ENwDkL5jw7wKCQk1qzfasGWPwsGlc4osBFbaAAMpCZOhzQLlloliBYLGnboMXM744Zu+zauFh8e/8f4Mt4sCB0II+npSH4BC9VxuZAGkf79/VfN6NcKk0Pkr1mOfGaLtc+tgcMhLmfbm4JCw0MZtOm7/4QcNu8pBdx/fuKBhqchq9zVLPnTh1PlLnTq3C5fCO7fu+s032ylAtkw9BpO54vOngHZxzZx3a5SKaVqv4aGff80xIEfBahN5is+0y3cA8jGWmGnC30FXTY/JdCRfxKMgXLHfOs8OAg4CtwUCBd9cLElf5MHF44adRT53XjoI3JkIOESDQzTcmTPbGZWDwJ+IwM20ws17/sTL3cVN/TGiQSamBSYn6QDXwH9u/FuDykbFJ8TdO+GzZZdzTRVjPzVH9udrvlzZawDR1UyA7OM/r69Tp6QUEzlz5VoFwz2T5p+d9v7rUZLU+/E+5/I8XgDdAp9ft7PvFcXv9vh0nZpojgA+Tn3+dOB5IF97sWfnuKj43o89fyY1w2VqOmgGqCau1vsArsopB57q3Lx0dNwbH3yR5sYAGdSM3JO7uzS7NzIyfMaiZR5qEoB8TTUwlAVCRP1JonGkTzDuZZYpPBpcn7/3donwmA59Xth/Kk1GDQDzK7LiybF8qUd2ff1Q9XuKS9KE8XN8FLwcshQjFw0XFDX95LiXn4yNinygWduvNx5yKeBRZU3PAjNt87Kp98VH1bu/1ryl690M8jWek+vC8N2ffXjD3NZ17imXUGbRCqzKmeHBKhJeTVMtqjNNkYXLI+eqx0MVr+q5BvTqrrWzqpVJKBVdfvrnGzwqaADZ/lyNeU3T48q4KmdePvTNnBa1ypeOq/jq4I/SfUgDeSjRmSrSTTRmoZZAJyLJgfiBuXTXiTeGDygZn9D3sZd+PZ1CsYIIMwVKYAFD88ciG2OcEc50bhHgPvn0nmfaNY6Pr/jpjFV2RQlCDIGkB7jCaC4YmRa9tD95cYXY0JqV71uxbpubgUsQSKIwiAyp5wb1bi+FSq37Pbb72NFcg3oVHZiWtm9t3WJSqVL1Nx+4RAF2bN/UsWmzWEnq1KHzl+s35mjoIqGiv6UC4Drz045WDe6PDpGmz1/qB/AYWCFUttBTxEOQqbEYCjSwoidnFmKmOETDXfzT5wz9DkHAIRrukBvpDONPQsAhGhyi4U+aSk4zDgJ3LgI30wo377lzR/+fHNkfIxpUylBvr7lBzwHmOvP9jme7ti8mSSXLln1z/KTTl9IVE1fOKbIJBgZyag5w1/cHV9eoe09E+bJzv9nqFWkIoOec2L2+VZ17oyTpb0OHnLh8zeYYdGKYTKcmYcC9fs/ug/uPX04hqK7QgLnBdenJ9g+FSVL7zr0vZrgxB8CSGfipRbjhAyMl5/Tefm2bxodFv//RwnQXwqjnp4L7/IfDnwuTpIYPNt+8e5eXal4NMzuAA/oAiPXu86cuHtz3fW6Om5m64ksBI2/SG2MTYss+0uvlzUdS3RxHRLiOUS33nP9xZ+9HGsZK0sghY9MzZdmwxwtE8f20c0PbRjXCJKlB44e+3rCXAujc1Gke0LQdq2feWzyiUqnSsxas0MVlDVQ7WGDI3is/D3+uW5gkte3Y8/tfLusCQC8lQnnBmUkNhVw8fvG3H36lXh9B7ubyuVMbBzzWIVwK79D6uW+/Tdc4YFBN84ieAdznunDs2S4tiktSiFTysadHX8g17FKgDAzT9Gkalgv1E2IyCwtVaPnAXCe/W9+4TtnS91SeNucrGatKgGaYJsbwyMigt4OJxwY2dHyg3CKoWWD5GT9807tpNUmKfXfiQqwCqjGDWobJFVlU6DC9wFxMu/LdzhX3FQ8pEx3x3kcTVDFGt9vAaUIh+YsZDe+JlkKluu07bjj8oxcgw6WAaZ7fuLSmJJVOaLJi80lRS1PbumRx0yr3hUtSrfqNlm9IvuzWcijVgbv86Zp6bcI7Q6IkqUGTZjsO/uT2mya2bfotvx8FKwYx9DNnz/949ERKRjazkDIzTeooGv6TPzfOtRwE/nQEHKLhT4fUafC2RsAhGhyi4baewE7nHQT+EwjcTCvcvOc/0Y87/xp/jGighoXVEKnB/W4gHlAzD65d/HTXR9B4Lzqk32P9ly37Onn3gR+P/HLizJEDB7Z+u2vb5lXLhrw2UAqTwstWnPHVhjyGYTkQL/gyF37ybnEJT3158OCvNu78+eTllNTM1NRre/fvW7Nh7evDXmvRvs3Crzcg0WBxkPPAnTK0f8/i0TFduj9+Ic1DANyyi5puC8s2EuC5nis/v/Z4r2rl7nv/H7OvZTMAYEoOsNxLP+/t27WzhFxDo2mzpu//7sClS1fys+Szx6/s2bhn3pQ5PTr1HDl01JlTZ5mpMZoN4Plw1KhKCVUf6PTcrt+wyiPFVAGCV7Fkd8pvI57rV1ySGteq+8W8RSeOn/L5tf2HDo9IGtmuSYPGVSvcW7FCq9Yd1nyzkwKoJjUtGXjudztW1q9YspgkvfC313/+7cKZ8ylXrqZfS89BzoHnH9y6okXThlKIVL9R05lzFnx7+J9HTp5Ic+WduHD+wMHvP3jnk9bNuo0d+r4nF5MRAK663IdXfzW1aqVyYVLpPj0Hr1yz68yVlGzXlV9P7N25fsnIF/rXSihRJT6hXOk6L77y3oVc6hFaEYb6AR04McHSOcf0BIuAkmNknVsx48Pi4VLzlp2+PYbaAZ2BZurEUC3gJmdINDBRPdPA/loMODe5RXTiBcutnNk/qHfbUgn3TZixkjFQvQrKH9AuQthAMoJVLY3c80f39WvbNFaSmjz40NJVGy5eyfJ52MG9vwx57vXGZYrXr1C8YvWq7Z/q/82337kB/BTpjdPfLG8QHVGtevvlm45noPBFA5/7x02bmtSuKYVInfs8vf27Y3bqSq7utUA5/v2WxEcQxqYt2iz4YvnBA9//eubE1Zwrxy4d33V47wcTPuret+9rI0cd/PmInU9hmKiutr/lBeHKnf+ld0boIHAHIVDwzb0hS8JJnbiDbrIzlD+AgEM0OETDH5guzqEOAncnAjfTCjfvuTuR+bNH/QeIBozIUM8gUt5NMFQfFpVUMo4d2vD6sKeLl48Jk6RIKSoirFhCQkKt2vdVqXJPpBQaKYWHhYRGxca379F/1ZaDGR6CBoAmBeJXL52d/tboulVKhYdIUrGYe2s36Ny5V5/eT5S+p6IUEi6FSs1aP7rt0E8+DG4Z97hY+oVnEtuFSeEtW3c7cTaHWGAwlDVg4QBcavekn/npqcROxaSIN8dOdLlxaKbuQvGFmrt3y6YHGzQIkbDSRKXK5erVqd+9U9+OrXrESMUjpagwKertN95TZEINFUUB4Jnw5tjSMeXK1mmXfDSdAHj9HtXQqKlhYUjVlbxmSdsmdaIkKSYitFXzBxM7dqhcpVrxuFLtmzd/7ZkBFcqXLVux0rKVa30akxnVGAr5XanHhz73WIwkSSGhtRs/2OTBh3v3eWLl2k1X0q4B93Pftd1b13Ro+1C4JEVHSLWrV2z+0ANdevet3qBJfOlKklQyuni9CZO/ysxWqUkMKwMgLTf7+AfvDC9bomyYFBseEXPf/fd26dqqTdvGkZJU//77Jr338WOJT0aFln+y//Az6T6/UBAgXIYGlok+CwAG10w9D1h+2vFDSQOfKBEROebt8dd8qPPI8fvQEgJNPcWyPwsQDQgzKyAaqIWCBF/GL9sSH0BFw9sT5mIAb1JqMJOhUyQKRygBUweuyJmXty1f+GDVqmFSRGRkyYeatunXZ2DlexvGRMU/0bHlawN7liifcH+zB7/ctjmXKshGWeTUlq+rhYeXKFH3i9WHcmS/Tt3MnW/m5Hy1YHb1qpUkKaLJI50/nLnolyvpCs5KAlrmD7vXtm7VPESSiodHly9RulnTxn2e6FW1Ya3o8glSWGjJipVGf/DhZZdHBciTLVTOOETDn/2b4rTnIPCfRMAhGv6TaDvX+usj4BANDtHw15+lTg8dBP7LCNxMK9y857/cxTvk8n+YaDAIR67BQjNFy9KA+UBJv5Lx26Iv5/RL7FIl/p5iEvIGGNKHSuFh0TVrNh40dPSmbQdSs3Rd5CAwAE31mf58MDTf5TPzJ73dufUD0fExUmi4JIVHRsRIUlS12g0HJY3ctHN3HmVuYqCigSos4+rTndtHR5ZI7PXM2cv5uOaNGn9DllVODSDuy7/99EyfPvHhcYNfH5eS5lY0n9t7zfh/7F0HfFVF9r7pEEJHimJBFLFhwa4LipW1YAHsna5g7+W/ura1gA0VsaCCgFgRQVRYUUBBVCAJSeikv7x6+/Tz/525LzESWXXXEuG+3+S9m7lt5rtlZr455zteFIQLgi3798LrRg7v0b1rli5ehtGyVV7nTKPg+L+d9vLL06tqkwLA9S3bLKdW+b3XjulcsNNB/c757NsNXICTSnKhiJAcqQ3CzdoF708fePyxuYbRMT8n2zAyclrcdOtdq7759ov5n+y9d88OnXd654M5GNIB5Sc86tcCxFd+MW/EpUNbtmqBNIqRvf8BhyxY+k2tmcJQoNIhTvSLhbOvGXZu57ZG6xw09MhqUWBktjJy2g4YeOEzL8+tToEroSYetV08GshI7ZZVU419VBQAACAASURBVCc/d87AU4MaGZlGfuvsAw7sPWvWO1LA3bc/lJ/Z5fwhY8oqcFyNgheeSVMmMKaJBumQOupVgYgtmDml/34HHdizz9yFy1IK4lzU2kkt/oialMgtaesHNIAImCb8VykldBBKZ+2id/rtv6uR0eqeJ150OSXUdqjrMbRnUADUJ4K4aDrhm2BGpjz1+MDjT8wycrKMbIw/2arThOdfjNese3/G5JxWLXsc0OfTL5eYvitAOsnIp69P3rOgTbv2vV6c/rGPXIdHLAu4lKnaSU/+a4/uuxpGy70O7f/CW3NRrIE5QGIgk98uXzRmxNW7d+yYbxj52dl4L2blGAVtD+934qMTn19TXWVps5CQaNhO3mFhNXZsBEKiYce+/mHtt0YgJBpComHreyL8P0QgRGArBJrSCk1zttol/Pe/QuDXEQ1KACOcUOkLMEGawpOuBb7LgQlgFaVrFr37/uw335z5xhuvvPTyy69NnfHhvPlLV6ytRFJAYTACiKXMqBWtc2Ied0FQYJ5Xu27tysUff/LRU89PfHXK5BdeeO61qe98vaI0lqJaFAEIOheYwEyg9r9nvfPSC2++PfvzhCtNyh1qYxhFl+ppaQLC/nj27GcnTPxuRYnAITJDqUjh+E4SiA+u60cii+bNfffNN15/5dUXX3jj+UlvTp3xYfH6aq0aCBtr61KeJVUCIFmy+PO3Xnl92rsfVSZdJFY46ib6DHwuKHOFG5dOdM1337z75tS3X5/y3FPjJ7/2qi04SLAT1ltvz5w0ZdL6zZs8IeK+R4HHElsAkiCTm9Z8+8KLE5+Z9Pxzz06aN/fTTZE6D5TgVHge0g2o87Dxs4/emDr56YkTJjwz8bWXXn9/9rwF68prLYA6H3yAuJ30LBOICxQjPgBPlqz8fOpLT82dPXPSpOdfeHnKd2UbXMAtP/188asvzJg/e7EHkOJIDPm2heqZArhQvvKJiCpRJeJrH7h+9O753YZfOHZLxIljQVVKEIp1ZkpStBaRImAWJIggEAkqZ0qwiIdcirnp83dfn/jK9G821GAgDVLnSstmKPWA+g6cKuaDICAskHGQsdXffPnM+EcxoOmsaa/P/WiTjf4gsbqNz02a8t6H//Yd8C3q2oQ5DqRS77w2bcobs4u21NlA4jxWVZdwbQLSYbEtX340Z8KjTz314qzv1sUIQNJMRavLMSoHkNrazQtmv/v+66/PmjL1iUefeen12bPmLFm5pjJF0Y8lQuyYjwEyQ9eJ/+qlEe4UItCMEAiJhmZ0McKiNAMEtnuiQUtsKR37ivnAN/nVU5Jlt5rFY/ja0arkKlUcEA3DROkYVjqGlY1iZSNE2XBMpSP+i6SPMEqUjhGlY2TJGFE6Kkg6vOUwtXYULxlJSq+x14yLF96qotNBVUpFGWBIcCmlklyLWlEifQacY2dYKSExKYEurOEnRCBE4A9HoCmt0DTnDy/UdnnCX0c04Ay1AsqEyagbuP0TDGcYjEQxLAFjwsI5c1BABMaMcPSg11fa5J5jWAcMfgmSoNyjqV0ebOlHpaIC37dMKqrQJwLDQHAAC9/MPoBj1W5Es/+UDRIHyxy1D/2kk8K5dQApwSc27q4ZAW1wQTkwAi6XOC2vXA9PyyUILnGCHUUEuA76aDIspw+QYhhnwfPqQKRQk8H30SRBAnh+MK/PFY6vuURGAJiDzQXnzEqB4ii04OFhXdtDXQOEgAuMpukR3IlImkBPE+V5xMZBOo7BgeKWSIAgsKh94YJEuUyc/1fg+4ihwOCSaBnhIl/gI0oclEsQbT8l0bohCcpW1GZM+PWYl5sJjhoVSEdwAAdFPAOyhzPHY4I71MITQXzd94vO6n9MG6PVM4++bFGoYyxAg6ASJEcLDqUVGiRSDBKYJhqQd0A1TQBKHBBJEBiQEwkD9LbwOPgOd/Bq4zXVCcNhogsJqCh164LgF75wLR0cVHuXMAytigogIBzAuJtaDYTavlbggBhNEmCaHdDioG4Mm3F9n/gKLB9vAx0gg+oongyYiwhiXBFwCQS5HC+0jsyJapcyJBq2y5daWKkdCoGQaNihLndY2Z9FYDsgGrZZx/qnXc90KCa4D2yzWzMlVXaHVTKOr7tWlAyTRVdAyXC1ZrgsSRMNdO0IpokGWTpclo6QZSP52p9Icv3obaRr5Hqd1o2FH9I1sHYULb5arRtJyoa7ZdeapTfF19wOybeAbdYhxBkGacNuENNeplwqiknydC8Ku1E8JBq2ebHDFSECIQI7MAI4U61TQD1ITdHivLfQ3vxoV6/wHaoHyWh8r8eQOJJENUkRhC9A7S7UFWSgcPiKJK8KJsz1MFZTCJr8FdiaBMNDIfXkuj4gDiHTBJT+SU+262VcJZFMCDKDgXH9N54FT649ALCQDUk7B2B5gooEGwTbNWyfLjaWXB9CixYECAQVDOoY5NTnC+0f0bimWIL6tbo4+KU3U1TDgquDM0itw6gLibvU5wbQUVBUY4iVaoS53lITQ3oArucAkKLAa4LeDS4KRLpu9J7bb8w2jMP6HPrt8lU+gKWUo/mXQC4RT5fev54ySKOF/waXDCQWQOqmNGhVA+Trr47G+oeqUYRCJ4nhJlGgsv4yaaKh4TzpyqcrFYTXxBsJKxXcQVi64FbUpUTBBf3hUqajcSqBIgz1R9ILuDPebJqZ2IGf4bDqIQIhAiECIQLbHQLbN9FQ3+lDI0uBMyF8s1X5cnzNrcmisaT0Glp0FV11uSy6ShRdzYtHkTWY/JIRpGQYK7marxnG1wwTpaMCI4Wtvv01w38+FY/0i0dSTMNZ8dX2yktI8ZVW4eXJ4pF1hWNrV93MIlOBlQvup8kFYFIn5BQkDRJ2eXESBGNf6X7ydncPhhUKEQgRCBH4TRFoGCj/TgtCf4JBZHCKoPi/0+m2m8MGNFDapiBNEATmEQQUlYJ8/93yweed06Z1q2tGj/GIT7Xtg4s2E2ki6QeiAYfyDRxAwMxoPunPAGtbN2+aZ2jys60ybus4YX6IQIhAiECIQIjAXxGBHY5oIJGp9vp73bJb1IYboWwMlIyAstGw9hpZci0rxUTL0IFClI6QJaNkySi19lpVNu6XJ7H22kZpnFiL+0LZtVA2hhRdpdaOIqUjA4uGRPEd2qIhJBr+ig9OWOYQgRCB5ovAtgZyv1U+urnVf34JCvXb7ui/9VgFBEHADqANAaF2yoyC9oz45JOP33jjteLiYhRu1DE8fLQaQaIBTQfQfiBY0t8/cA3BMevP8Mt+f6vrsa2zNWEY0hnbOu+2jhPmhwiECIQIhAiECPwVEdjBiAa22a18JVV6u1k0jpWOZUXDeeFVqmiEKhpFC0eRwlFe0SivaIRXPIwUDeOFmFiRNkkoGo2GCY2+Rcm1KMTQ9FsLPQR2EA1KDbJkjCwd4a++nJcO84qHuWvGpIrrNRrY5l9j0RBqNPwVn7KwzCECIQIhAiECjRHYimvgnm8FLiPoZYD0ARBGmZI+Z0SgV0HgjLA10ZBe0UxbxpBQaHzJw+UQgRCBEIEQgR0NgR2OaPCrXrZLbnWKxsrSsbJ4hFh9FRSNhOLRUDoOysah/cLaMWrtGFg7Csp0WjsK1o7Ef3/8jfINgYjDj75HSC0h2fCNm5WMQruJ0hGk8DJZejVdc5VfMsoqHpsqugli0wCJBoqusOhfih682qc0UIXkSmKU8GBt4Ai7o92gYX1DBEIEQgRCBLY7BBq8HjCERuAEISV3HMv3fSFEUF+hJOVEa2LqDJTZ1CoIAb/QQD80V3RCoqG5XpmwXCECIQIhAiECfwQCOxzR4FVNSZXcaRXdyMpuZMXX0MJRovhaUTyOF19P11xPSsaRkmtJybW05Bq+BhMtGU1LRzZNBKUcmiRUeRij0yi/JC36QNaMYcVjeMlop/AqWjrSLR6BFg1F18cLb1bRN4GWh0TDH3Gnh+cIEQgRCBH4HRBoGEyiyl/4+RUI/MA1OI4VcA2aVcdDMIZcg5ScMcI5xbUBrfCXIhp+BRjhpiECIQIhAiECIQLbHQI7GtFQaVa8WVd8f6ToLrP0brPo1tSqm+2i26ziO1KFtyWKbksU3RIvvilefFOi6KZUOl2fKB7XNKVKrv+pdGOq5CadbkyW3pgqCdJN1pqb7JKbEoXX2qU3xNdcHy+5OVJ0W03h3TT6NrDKkGjY7h6rsEIhAiEC2z8CAcUQuN0H8pANpMNWC9s/Fr+sho0NEZouM8EJo4RRLgXj0nVdKTlnHsb4wPgUOiZGU6JBHyh9tK1w/7l/f1mpw61CBEIEQgRCBEIEQgT+GwR2LKJBsgiQb/3oB37kbYi/I+tmiMg0qJupojMh+T6k3gXzbZ1mgTkLUm9jMmeCOf0nvpPTITkNtv6eDomZOnM6pKalE24zHVLTVd1rkHoTklNlcgaLz/Rj7wIUSRYJiYb/5s4N9wkRCBEIEfjzEAjGsI1ZBs75tga2f14xm9eZm5ILDTlM8GA5kUr6lCgAzPnVREM61NS2LsRW+c0LnbA0IQIhAiECIQIhAtsXAtsz0RBcKd2xEFJyqaiSBKQFMgkyCqoWZDUmUQuyTiedoyohSLi2Fng18Mqf+BY1IKrgJ74rQGwBuQXkZlAbQW3Wy1uAbgS2oT5tBlYpWY1gcQw5ng7VnhZoCGQaAjvSxt+hRsP29eiFtfkrIRCMTxqXuGlO47Xh8vaNQMAvNIxaG+gGzjkhRAghpSSE/OWcKRpqtNXCb3U1G2iFn1zwiO8RX2kZSMoZbiO54L7nmlKQtEXDT8o01Os2BOWUEq0hGoQefrLwlFIpZWCHElyvXxtJ5CcPG2aGCIQI7OAINH55NoaiodVonBkuhwhs9wjsWEQD0g0SRRfreQeqJFWSKyExaTJCKqJkkPQqiZMqwdrG31qSSvzUNwPlA7gArgJbgQ3gY5KeTg5IRylPKiqUFFIH6wqJhu3+OQsr+BdHIOg6NK5E05zGa8Pl7RuBhkFpUE2lVDBqBQAhBOc86FMGmZTS7RuNX167n+QXFIBPGBcYuBJpBFBcCo/4rmtzTilxHDvpuSb3Pe57ivG0WMOPj5XeV0pKqed5jLHgWlBKOR6Fuq5rWZZt247jWBZKTmoNiIYL9evilf7yKodbhgiECOxQCIREww51ucPK/iwCOxjRgMN7LhQV4AlwdPIEkPoUZFoCguQJ8ACITmyrbwVUgd/0O+AXJFgSUlwnCbZEusEDcAAXbABHgaeAKeCofVXfw9EeqIEfav0ETdB70pex0eLPXtZwgxCBEIHfEoGmtELTnN/yfOGxmjcC6SCL9YVsTDQopRhjwXR6sBwMeuu33cF/fxCA3AoI102P/AnxACTnVAgmBEsrQTaIQf6YX0gHvWx0rAbwAYBSGlwIz/MabQIBARSYnDSMChpvEC6HCIQIhAj8dwg0vFK2aiZCi4b/Ds9wr786Ajss0UA01+AxIMg7BAkZhwauwQnYBwUBxRDQDT98a5bBb/oNCgkITS5YHCyOLIMrwZdoxeAp5SjlSHCk8vRJOdpEhETDX/0xCsu/vSPQlFZomrO9YxDWb5sINBANiUTCdV1Pf4IR71/Le+KH+f0fL22z5r96xTaJhoAL8DyHMQIgLSullPCpl7KSTKCFCOdcMO45ru96PzJq+KkyCCEs/SH6o8NYsGQyGYlETNMM9rAsq2FI8FPHCPNCBEIEQgR+NQINb5WQaPjV2IU7bI8I7GBEAzpBaPlqAQq9aEFIkKI+ScxEB4p0QieLtHOEwh0aJ/SnaJqExIjgKI/NlaTaPwIdQSUeGROuDaQYeH2OPkXwYgotGrbHRyys0/aAQFNaoWnO9lDPsA7/FQLBDDnnfKu9m+ZstUFz+7dxF7nx8m9UTqmbQN1GYlsYkA7pYwdYBSyDAlZTW6FtD8EhPteqDa7fyCphK7sGPBR+CCFB534rgYbGdA8hxLKs4HQNdUwXIvwJEQgRCBH43xBoeKuERMP/BmS493aCwI5FNCjJ00SACJQa9KhfgKjnGhoRDTJNQHAhBfuliQs0gGCAPENg9imZEEogv/BTKT27k34vhUTDdvJUhdXY7hAIHtHG1Wqa03htuLzjIBCwDIwxSqllWStXrly7dm3QxfzLEQ2/81ULiAZWTzf8QDTUy1hIxggXvgKGYknAy2ur123ZErSfREhUc/B9RLWBaEizFemmNAA8oBWUUhs2bFi9enUymVRKua5bVVWVTCaDOrquG2g0NIwKfue6h4cPEQgR2CEQaHilhETDDnG9w0r+HAJ/GaKh8aPbePnnKhgEu0pHndCBJ9KGBgwoA0qBU5AUJFMS5RvS3hPoUsGAM9CCDg2+FT+/kNYA01MxRB9EH0dhDwk/+JPuEukFbTERuk787FUMNwgR+FMRCN45jYvQNKfx2nB5+0Yg6EEG94AQImAZfN9/9tlnBwwYcOmlly5btsz3fUJIgEPjNuv3WN4W2j92gPj5/7ZVtm3t+Su35zrKEgXFdNJmftp5UHIBChj1OSOOndRMBFv05cKDDu979gUXzflsYSSZdClDo0ApsLJ6r0Y2hvUH1EAQQhhjX3/99YUXXjho0KB58+YBwPLlyy+88MKTTz55zpw5AVyxWKxxvRrqsi0ww/wQgRCBEIGfRaDhTRISDT+LVbjBjoBA8yUa0iPz+ovQEIZKL6BGVJBTv77Jb/3+wTMvtZuDVJplQBIBE00npBvSnIImGhh4DDy9FsmIeqnIQM2BB3KSEhgHqhPjwGR9UipwjWASqE6Mg+Ag9F5pGiLwuQgKE/S30oWsj9+FtETjpO1Cg4wm9QwyfkxebGOjMDtEIETgv0MgeEIb79s0p/HacHn7RkA1hApC6QBOqU+o43vmnXfcYhjGLt33+OjjBQqAcVzt+359+5VutjAyhQ6tGNjKCUnw3/qm4sdtHJOCYOIiSPWH+tFvY7TrWwo0Hwg8+NDmAsNioNBBw9A6CHsUNFf1qzjlhHKW/lDFqGIMaRTtaZh2NhQKvQuDhk5yEZjsCY6sPJeCS8YlCzYI9sITaR4fmzzhS+EL6QtJRCNTQa38KH3bAcWJEwXwUtGqswedWdB+p36nnvHZ4q/8wO8QpO06Qc31qYOegDY51PgwQl0nRbw6JVLTpr2Rld2ifYcuXy/7ljERj9WNGT08PzfrsgsvLS1ZzwEopMusw1siMsFDvdXwoDG24XKIQIhAiMB/RqDhNbLVmyR4926V+Z8PFa4NEdgOEGh+RIMeMistmxBEusJolByjMCiBHpgA0iO2Ai4Exq+q980E1FQM/CJQnloHnsRuAwQpPQGiDQsUOjFIAejnoBNXDUfRl1SfnXPApFdxdIdA6YV6cwRtmJAOG4GHDj6BXWiaJUiXTSJloHt+eKj0uqCOkgrp1gezQBfToIMY+FhgpC+9r85Fd9b67mP6ZI1+GuxRA5PUtLdqow3CxRCBEIH/CYGmtELTnP/pBOHOzR6B4A0cfHNJJAYeYgJjIWMDocDktO6Bf9ycmWH03OfQz74stBkkHUKZsKxUJBKh1KfEwRl7yzVNW+ALX0riCt90/JiPbIImvBm4rquAceX7vsuZ51hV1K9DFhpbBFQRUgApCxUNgxiNAOC5LJV0XI/p1g11DVzPVMoBaQJGOMIi+r5rWwnXsRj1dRsJlssSpkewFaSemyLUNL2U5dsY9IEK3EeC72DDxzyfU+G4LOUSis0iw4iUSdtN2pxiI8k5tpEUEzP9OKMOMAYCOJe277ncF7gXxZZUUY9aHDAClO+7oDCAJSWSuEq4gibjoGIgY68982T7/Pa773nIp4sLCUCS+FErGk1FdCsvAxMS3ehLLgVjwvdUXU0qETNB2KA2uMnv77j95vyCbsPG3I4GilhjXr25aOiZJ3Ut6PzEv56PEmWBFmrmAuupmOfaUncqGi50/S0Z8vj1SIS/IQIhAj+HQEg0/BxC4fodC4FmTTQE7X9ANCiK3Szf9aTkkWi161uBSDWKLOqB/g9Eg2IgaMBUNCIa0hIJKuibgNKzR2iVoI0RBHISwfgff3Etw44Sw/CTmNC7Iq2zEDiM4shfx6dsCLul9/+BDgh6fLoOmkTgGMwSO4qgPJAe9mx+iJ1Zv1vDTo2IBqlpDiQa0Ga0ntWov0+DPlBAhTT2fa1fH/6GCIQI/M8INKUVmub8zycJD9CcEUhTvcGrmitfYnjjgGigOnpxXLCqh++/LjfL6LXvUQuWrHUUpAjH973nBBS14L4UBBR4ju9xXyiuCAHJfGm6AsM7JqLCTLgAknDHo5bWEZAANiNRxXw7YYGQjFAF4Li+53moOKCAEJZmqRW4HrN9IrAxIkFAZaUcnxKfC2zLkC5H/sBykOkQAB5PN2tSEU2XcI+4ABxNA1ykD8yUm0oktWwy2uYhYQE04cZ909Z6jtheOjZ3XeRdas1UbbwGgAjqMM/GEJXaEoEDp8yx7ag+O2fSC0h8LrERxFMHxDqqPjogazev+uzqIYPb5LZ/8JFJNSmsCcXS6h0Vwg4gTdtKpJIIA1YPPS+CxLwoQEnd5s/OHnR6+049x0+cZuH0BCQjEfBrp016okNWft8Dj53z5fKIAlMoh1CJnAqX1NOcETbtATj6dmxoYYOF5nyLhmULEQgR+PMRCImGP/8ahCVoTgg0W6IBZ3saEw2e6er5nMA7EwftQmkrAC3riISCniDCwb+kSlI9R6GRrh/DB1tgHDIc7KPFKEf3BxYsN341BBs0rEUrWZyHYViAoAMiQbtIIEmBISY0eZE2pm2gAho4A21hIIFR7lHKsZuXZh8k5Z7eq4HjwILp86LsFdYPD41TQDpIBvDAxiF9AwX9nobvBinvQM27Od1lYVlCBP7iCDSlFZrm/MWrGBb/PyPwS4iGiofuG5uTaey97+ELlpR6ACZFQzbiW7FoRAqmDQpSQUtEFYmmYpwgj+0r38dxOjYerpkCTnxiU85Mk+JZgTtunFIfAKgvbBODO2LrUD+0jtTUWXETB/UKOOeE+QKJecenCSHROALbImxa0EdQUs/3HMuyfCI8X7gEdZG5AMdzLQvL5tuW78Q939TmCcCBS+UBI0AYtVjK9JLETjLHY1g0x3OFNpcg2ErqIgXUvWSUOQmSjNGkrTxsQKUPxJMURSs87uvAzkA5UUCYdAjlSBZwCcIDWTftxUf22a3H0UecsLEqTgA2Vkc2llcxgVWj3POpE0/WecRNs+4KfJ+mUpZpmpSzeHwLiE1ffjZljz1263vkgJVl5SYB4gGzGUi7snT54JNOamnkjbn5nqgAW4Dp+5Qjc4EkCHIx2yQadDv9n2+ScG2IQIjAjo5A49FEYyxC14nGaITLOw4CzZFoaLBQ0D0YhUEohZ5ikIq6juearmt7xLVsl1Ce9oyo73TpToIemevZFryQjYkGpAoCd4uAHMDIEmkThx+ueTonyA/4i/SsTHAo3cPTa7l2wWg4Q7Aj/oufH/lZYA9QqHr6Qx8HO3+aK0nvj7tpr1p06EDbB/3RvR89gZOuaf2KdJHS1WyoVwPdEOSkjxL+hAiECPwvCDSlFZrm/C/HD/dt9gj8IqLhgXtHZRvGnr0O/XRxsSPBIkQB9f2k0GYCgQueZ5nEdwM/Cd264XS9IwlhLm7AfTcVC3zlLFdpr0GwHS/p2vhyZ+kWzXNcMx5DVgKZc910BDy4biA4p4S5DKjLPN3GoTGgk0ri/kh1az0C3UhJAZ4rfaIJDQXc4ZrQ9z0/SYET4J4yCYuTZK12JQRO0bqPaBMOmyvTownfcYDYyjNtC/elQGtTuLEWPKJoM4EaRvrUYFmOVOBSRiW284ShYYgvTYYcg8KySSdVVXjZ0IEZRs6ttz9oa0MLlzKueYygyL4gKdfUmpDg2l4gJImCFHgOoYAArx3/yO0tWuRfdPlwAuALSCaEniRwwa16a9KEPXbqeEy/gcuKKwiAy2SaaEBJizQyP7ZoCFpVbN+b/V0aFjBEIETgT0YgJBr+5AsQnr6ZIdD8iQahg4fhOF1S3UnSlp/pIT9KMOiOVfB/ehBe7wsadAuCVYh7WkMh6JQ18AhBl6L+36AzkTYTCHYNnGnTnQydpb8ClQc0u2g4Q7AQ9PQ0EaCvdrqA2q8VjUCZbTqm5eEUk973x0QD+8EjI11gHTATCRHtJJI+WeBPgUfT1Wm4rYI6hkYNDYCECyECvwECTWmFpjm/wWnCQzRfBH4p0ZBrGHv16vvZlwHR4OBIlsRKC799YeKTCxd8rAUa+fcrv5n90bsTX3zuzbc+nL9gWcpHGzyfmQAOs+Orli19edKLr7z8+sQXXl64aLnrI53sAtQR9IAABdS1QPigCDCneOWKLz777N1pM1554aUpk6Z88ekX8doEaGbeEUhhoLMDsV0zgWGcFU8mIl8v+/Kll1589dVXX3t1yocfzIlUx0FBeVXsxRenvjdrPjZLwE0n6im7zq1JuFUAFkgvvmnTojmfznhtxsTnn3v0mSdnzP5wXWWEAKQURIRnArOpa8USkCTgAcQSRV8tenfaSzOmTn5r5rSpM2bNX7qijkIk5aEKo8BSIbGAdhAeB5ugWgIF5XKz5pMPXt+7R9dWbXf5eNHKFKOOQqMMx068+9b0J8dP+GbFdwLA52h7kEwmU7EIAIvWVsz7ePYbM15b8PXnHjay5IO3Z44dd+Nb783mwFOewygQE6gVAxn/bsHMI/fdrcfuBzw64XUaiCTpkoBCUUkUt6y3XNQ34w9Nakg0NN+nMyxZiECzQSAkGprNpQgL0iwQaI5EQyN+AC0p0dwIxbOZ9H1QrK5qy6rvV5aVrWtwYkAgGw3x9cyGr7QDLVoBaEcIbQ6gPSCwh/PDODzYr55KCGQUgrXpuYugzxHQEMEVC3Zp8HFo5DqBr5c0d4DjfzRYTVMfaJ3KfJLSTrMeimbrrkzQ30IvCZySUjoCBRIN2psDj6ZzKChUHQvUJepZjcAF/TrCzwAAIABJREFU19PdJF3U9JmCXlEg1tAs7rCwECEC2wECQdehcUWa5jReGy5vdwj8IqLhX/eNyc809tn3iM+/KvMBHO4A2JJFx42+Otswhg45p7amYtHnCwf+/eSCti2MTCOvVZte+/V9/pUZMdMVwOpiWx576P7DDzgwLys7KyPbyDAOPuzIic9O3VxFTIAEQNxzfWI7qRqQZqSqbNprz/U78pCWGUZLIyvXyMw1cnp13/vW625bsbzYIWBywDIwIqillYF40cpvx44btfOunY0MIysrK8MwunbqeOv1N3+xYPF99/3LMAr6nzC0cE2NkGC5CQEOhxSACWB/s3jhledf0Dq7ZX52i7y8HCPTKGjXfvAlly/8amWM4kbJQMBBMl4bg0hy1gsvXjhwYJecjDYZRr5hGEZO670OuOqeh9bVoUeGr4NLawcQLsGlYDoiSWUSWCJeUXLfbde0yM7od+oFm1NgShlzIo5T+93yf5858PgswzjnnAvXbYgQAaZDY9EabVrhPDX+wZ136Zxb0OKpV54vq9yIDiAWSZmeyzkFm0jfc0EQsOIRoLXm5qVDTj4822h58y3/SnqB+gMwgfLSQVSO+kY/uIVDomG7e5TDCoUI/J4IBH2Dpj2E0HXi90Q9PHbzRaAZEg2NTQc4KjVqogEH2IJGKzfefv3YA/fd76ILL19ZuDblUi41ExEM3LUng1SUK1eAJ4DgvkGmBAwwqXWntIhDEOVLe67iKqktO3UwS4XSUBjpSqtAComEQTo6V31sMIEWlhjEi0pBpeBCCczCuRAqdaBv4YKwqRREKYzEKQladQoT+2Ngur5DZJrwoBJDgmF4LYFFCGZUdDBM7LhhPDNBpHQFJp9jmLDAVJZpJsXFbwy0Vk+1/GAEgTNG4SdEIETgN0Ggaaehac5vcqLwIM0VgZ8nGhSteOL+a1tnGfvte+TnX62tJxpM6ddcedHgFlnGKSef+PzzEwcPHtyyZd5OXTr0O+GY/FY5RoZxwEFH/fOhCW/Pfvee++/erfvu2Ub20X0PP/zQA9u3x7V79Tj03n9OWl3rRQDiNJiDT1ZuWnHFpWft1DE3L8vYc+cuJxx19IlH9dtn1165Rl6+0er8IVd99uUaJDkUONQGZYJMLl346dAh57Vsm2NkGd122/mIow4/5phDu3fr0L1D+36HH3XkYcdmtui812FnLfyuBu0gOAXpgDDdROWMqZP7HnRAfouWhpHZu3fvU04+ofc+e2YbRoaReUz/kyfPeLeWqTjnrm+BIhVF3//zppsKsnJbGFld8loc07v3cX0ObNO2fcZuex1xwZXfl1fbAmxPug76MjDqU2G7PM7BUWADxJMVhRecNcAwjBvvf3qTCRY6XVAAk7lbJk98sH1BbpuCbg8/8pLjIx/PuQ2QXP39Z/3/drBhZJ5y+qCizRuTFON3MF93GdB6wlTATBNdNYhvO/F1oDY9ftfIXCPn8ktvXFsRs7TCJWEUqwwYqgo5/0aTBPVeingDhJ8QgRCBEIH/jEBINPxnfMK1OxoCzZNo0O08tvQNRINgnovq3Imay4eek21kHn7YcauKN6L7JWecc4wywXRcCAZcYN+FK5crHb28Pji5DgpOuPAE9wWngnE9tMf4W6gyJSlBT1WPSI9zyjnHmN8MxR6JwA2YwI5IQ2IM5asoZzrqOB6PU8aY8JgkjEqaECzhcmpzSanPqOsRm5AEkOqKtctmznpz+nuzSzdhf84TwheEMcKZLgJlWDQKyHlg3AvGmce5zYRJhUm5l461LglSD8rGb7T2bOgZhRYNO9ojHNb3j0CgKa3QNOePKEd4jj8NgV9GNNx3TUGmsW/vYxYuXutIMIkJMsad8juuv6Zzu3Yd23fo3/+EkSNHf/TRR9W1FRVVa9+cOrHfMQdlGS3btt/1kGOP2+eQg0888czFi5ZvWbchWrPxww9eOmXAEdlGq+OOG/r+ktJa0GPxVA21KzaVLrlgyCkDTz32mScfLitauWLxF9Hyqk3FG+677f+6te3SpqDLTfc+XeWhRYNQNojIhm8XXHzeOUaGsfu+ezz9yrOfffF5Ycmayup1i/794R3jxvQ79NDunbsauR1OvPi2xRtRdlIJqcyoiFYsnf/h/j17GEbmWecOmfzaa4u//qq6anPtlnVTJz1/3sBTc1vk/H3wkG/XlmMEymgtSGfF53N27dS6oG2nq0bc8O9PFsUqqms3lMz56L2HX331ibfeXVVR5QF4FIivff4El4pQsH2ZFBADiNnVxQP7H966des35y+vZGiUoacYatFxxNw44vLzszIKBpx4wfJvNjMMb02s5PqnJ9zdpk3mgX36FpdV2qjQgEfmRAs1owqm63op9LOwKAZ+4rXgl859c8Je3Xbfe6/Dlq1eF/UYNsQMJwoCP0QM0NGEaGiU86fdguGJQwRCBJo/AiHR0PyvUVjCPxKB5kc0AFohaO1nkMCY9JhA+wL0LyUeeLGrzj8nPyN3wPGnF62r8gFs38POAUt7VaYSdnn5ZgnUpkmXmDo4JepX+T7G2fK4z7gHwD3Xdm2HEek51HV9JjhVKMEogHncicejGFdMiz4GATCxryMEJZ7nmp6LMyRCB9xmDIOSCcYlZdwngnGfC4/YisQkTcQ9t87xtFo4c11b0JRV8d29N1zWe7+9z77wsm9Wr49aLEWYjwJfOuoYJ8SyqI7UZdkEQ2Iw5nomw3kbjyvbdOLJpEkp5wJ9Q4R0bSdBcY5LR/OUgE4maIEReOaGylV/5KMUnmt7RqAprdA0Z3uuf1g3rZKTFgpGb34vCG8pMSQQQwN+FZOk/Il7R7fNNA7Yr//CxRttBRY1AeLSq7zl2pF5GVkF+W2vu/6WlauKAx1E9KQjWxbNndqj666G0SqzfaeThl6wsdrEYa6QwJMgyue+81I7o3WbFnv+84V3alAsATB0pTLrtqxc9tWnK1d+baaiqNSjuPAd3IuRh++5KzMzu++J55XEseUQLA5sy/MP3pCfnbvLHr0eff7JCDEDdR8mLQCbxqrffPbpvbp1MYy8o4eMXbDBcwHcZBLcBNlUeu7x/XKNzDMGDf108TeWwDiQeDpmg5P8fsmnvfbZycgx7nv0maSpwKcyVv7IXaOyMoy+J53xxfpoQis4UBFlooYDq7VMD6TFmR0E1UShIYzEoYAocDirBVW79ttPjj/iwF267zb5g3/XAto5SGzHbJB1ICKzpr64S+ddDaPNPXePT6UwNGbhqi923aWgXftWDz78jOODSVDyIQhYhRQFftBZ0pfSQ6lJhkaFqmLpvCl7dtu5Z8+D3nhnrgvgCGw1sZsBkmNsp5+waAiJhgDN8DtEIETgPyMQEg3/GZ9w7Y6GQHMlGjg29Qookw4TNE00UAe8umHnD2pltDix/5mF66o9TS8ASOlzalGcy9CDa59iz0ECRWsCQtEJQgKVyhPMdlO2k+Tsh8DdkitKaV0sEk3VJZ2ElqnCo1hx03dRZ8HnyrGJ7/u6y8KUJKDQgAG7MFwGQcWwY6Jls1FGG6OCo6yXrxW8AC0xLCyZIhu++ejM43rntcy+dNS1NSmM9BV3mOlr+kMS4L7wHc90rZSrKy8YztqgkQKhNuFWED8cMIh6ynGTWgkSO35cUzHaASwgGgKNBo3FjnZHh/UNEfgdEGhKKzTN+R1OGx6y+SCAFg2/hGhol2UcsN+AhUu22BJsZgHElFdx1w3X5Rg5+/TqM/PtDwlVjEN5ZYWZrABVsen7uReddVZmRsu9jzx68ocfudjigBmPeYkKkJVrln182G775Wd0vvLGB5ZXW0kAh7pAUNQQhKud8iR6CzK3umLjxrLCwhWL7735mo4d2vU9Zcii0lpLSObXln/34fkDDsoyci666rqla4odgIjlexxMPy5VCpxk9arvrjr7jKyM7OPOH7Miig0p6ia6kU9efbZH6zZ777b3gi+/twHtIzCsA/dAuOhYQWP/vH9cRp7xt5MGryqsBQ7xDSvvHTckP8fY8+CjZy4trQGI4l5m0tkQj9UAgOnbKeoRii6N2LKhyR6SHpwnQUVB1iya/ep+u3frve+Bc5aX6H3BtnxFbaAJUCa3Io8+8EB+Tqv99jl4bekGx06OGH5FRqZx40231CUY4WB5aC6BdAiGoNZa0fq6ceAezlS4hEZAVZYt/+jQ/fbZqevuz099G5tq7SDJsUwBr1HPUSBVge2vFmnSKpnN534MSxIiECLQLBEIiYZmeVnCQv1pCDR3ooFLK000SAbEArd2+PlntTJantj/7MJ1tR4gfYAeDhKkQ0nCo7avFHjEj1sxj9jod+B7wAVn4PgCTSe1LiNOXPjETZrSp8BxmVIfI1sAt4llmklKfUkxPAQSCAC+x3V4C05IyrNjgLNYgvoM3R20Y4TwCBAMDU6YS0iCWxXEjESJigsgxBO+GY/VgfRrVy84ue/uGVnG2Dv/YXGIWsJkkHC55ZhIRnAXXUs5WosyRlBUQmHkcMdxXNclzKXc85Cz0FM0vksp+oaggUOgOCG5DGQj06EogtgZf9q9FZ44RGC7QaAprdA0Z7upbFiRn0LgFxEN4//v2naZxoH7nbxwcaUtweRINIBfeef147KNnOOOO2XFSpQx9hnURePcTwDdFCn+5N7rRmUYxpGn/70okXQAohbyy8qLAq0qX/nF6Yce1cJoNXjYDRsIWjSg5oJfAyKG43MpVhYVvzH9zVtvu+Hqqy8eePJxR/fdZ49OLbIMo995I76r1NZuylwx58XD92jbtVPPl6bOj3CZQlFJbEVc4RAW12NwZ8Lt1+caxoH9z1y+OWF5PpCYv2XFPVefu3OrFldfNnxDlRVjUJFMJuwk9Uw7Wc1p3DSrJjz+z50677z7/ie/N78ISfnEpjefuWX/blkZRs75w277tKRqLeUp8HxwaiNx1+Ipx7apS4TEoEvED2JeIhXvRNHWgGx599XHd27b6pCDj1hcUhUHsAN1ZSGJZTEzBtKLbC655Ly/d2rb8ubrxz5w/8Nt23Q+6JC/ffb5NxQg6SnLVcSXkmsZJdRUQgEGhYYYxAOHgO/6dcCrasuWnnDcEW067PTIxJdjDFwFRAJlSDSgOUnDJ000BPxSSDQ04BIuhAiECGwTgZBo2CY04YodEoHmRjTg2BjtF9JBG/0fEQ00BW71yKFnFRgtT+x37up1dY4mGrjvaKeHQNMJTQw87SiBrALalFJcq8N9UYAURatNzqmViAOh2HdgUttByGQyLjBsuRSKB7pQIHHqCfuFRDImpCK+H1fKwVgPCnxXm0Uo4D5hjiMdXxCfcRvniEgUuONqS1fkQYSHRq2SOGVLzz++T0HrtuPuesBkOHHkY9xxPX8iXKAmbikZoOtIEAIDe7eBHScTPGnGuHB0ZHGJ8pPaUoPp7hEKWCqKRANqQwazRSHRsEM+02GlfwcEmtIKTXN+h9OGh2w+CPwiomHCvde2z8rcf/9TP1tSbQFYwgaoA7/82quuyDXyBg26ePWacodgy4SDWvDAW1dZOO+esVdkGcaxg876tqa2ypMWk5Q40omCiPlVZePOGdI5p+3Zl46olFDtuY6fQD+C1MZ5s98dec24ffsc2q4zRpHo2LHlXnvu1Gv3tt0LjGzDOOqMYfNXRYkE5VZ//MqDfbq07HvwyZ8s2WJp5wFPW+7Z0mHYWtngWU/dMbaVYRx3ytnfrqvVSpDxdUtnXXxyn53ysg/qc8Sp51194nlXHNav/wkn9jv79FPPOPWEs887bdDgM/fac++snE69+p43fW6hizVK1hbNv2/Uue0zsjOMgoP7Dbzi9jufmP7i+hQKEmHYJwW+5C7nAiBQL0LTRVQ1SqGNhrflvSlPdG3d6sADDltUuCUKGA4TG0AO1OHSccFJgFO+aO7rp/Xv0yYvo1vnHl26HjDtrc83VROLY0vqeqh3pA09qLZipNqtUnLwHEhRYCjJ7FdG1n512klHt+vc+Z7Hn6khGDrUF0BoWkFZO2sEN17QhiLRUB8Gu/nckGFJQgRCBJojAiHR0ByvSlimPw+B5k80pOotGiiQFLgVo4aeUWC0OrHf4NXrYgHRIIgrbXPurJkvjR+/YPZc7XMBRaVlL7/60sSnn3juqcdef+ml71asMm3siMQcdOEkzEd1SSFXf71s/AMPfjBjppMyQUHKMj+aN+fx8Y9NmPDEG2+89s4776zfXBH0MFyPWHY8CKa1bn3R5MmTX5z0ytqyTcgCCI5mCD5jnqvAZiRSvGTu689PfP29f6+p9gSn0kl+v+yblyeMH3/dpcfu3j6vRcEBx5x85z8f/9fTk+55+Kn5Xywz7RSSF9IDSaLlFfM/mvPyK5P+9dgjT0wYP+vtd79ZvrK2Jq6lsDmA8/kX81544YW3Zr5XW22iPAVVaFmhghQQDcGkTEg0/HkPVnjm7QuBprRC05ztq8ZhbbZC4BcRDePvGd0u09hv39M+W1JtqsCioVZ5m4ZddEHLjILLrhi7ocIMnPwEgOPEACrNiqW3DB/SMtv4+yWXfbl2c0wPmFOpWuLUAk84FetuGDJ455z8QRdcWcXAAsF4HETlN3PfOOG4ow0jL7Nl+yGXXnHJlRc/9Mhd7709+aO3J98x4vzuHdoffMLl7y2tQh8Cp2rG+Ft2yUE7i/lfVicBLCUsBxxH1DlRm0Zx9J6qe/r2kV1yjdMHXbyyrBZtDWS8bOmM8085IA+DU+YZWR2N/C5GVmaGYbQwkMjAvywjK6ttfkHPfmde++m3VQ6Ay+PAyqu/nf/Ggw8OPPSYXCPbyMzM6tD6tIsu+mTZ+qg2oxAAJuV6EoD6xMb2mhC05lNJoBXvTJnQKb/V/vsf+WVhRZ2A2pQNAKkUClOABGGh44Nbvez20YPzDKNNwc5HHntBVQwcgFrb90Gi/SD3FVCBFoZopoCMjlQcPFMmKU5juMCqako+P/mEIzp07XzX489Wa6LBlY2Jhgavw5Bo2OopCP8NEQgR+BkEQqLhZwAKV+9gCDRDooErVLQKZnx8LhsTDUlNNPy9TUA0rI2jaYGQirpWzZahA0/MN4wLB5298tuiV1+dfsihR+Tm5rZukdky02iZYfT/24CJz79ZE+fofQrgERfZAcEfuvPOAiNzwFFHry9cs+rb74YPH25kYNiu3BYYV6xdh7ZXj7rm8yXfBBNQSSsqlElYbOqbk9u2bdtr7/1em/ImelVIDkqiCGQyBmCnkusevmVkS8PYdd/+0+Yu5z5JVm68+6Zbd2lVsLthdDGMrOyWRlY7I7uVkZlnZLYac9Ptm7asB/CUm1j11ZLbxt2wa9fuLVq0yGvZwjAyDSPnoD5HvP7adM+nCtjGLSsvuezsgoKCg/scPWvGfJDo1uG6tlSeBEdbNGgnD7yPgwgUO9gdHVY3ROB3QKAprdA053c4bXjI5oPAzxMNipSPv2d02wxj396nfrKkKikhyZMA1dLdeN3wq3ONlhdePGpLxPMATKJcJoV0Acq9+Ioxl52Wl2UMumTY0rWocJzy0KNBiTioZN2GojFnDuySkXXOBVfVaCM4ANtav2TseSflGBmHHH7CC6+8ZeFomnukDt00WOXLj9zUqWVOn79dtHSDwDl6mfjg+bu7Zhp79Rwwb3GNrc36iIsaSEmeNFkEmAPUfOGeUd3zjWP7n164LkEdAiJZ/v0HF//94PxM44hjTr7pn8889MLUp194buLTj01+8vFJE5741/iH/+/Rh/754FMPP/ryS1Pnl9U4NgTilybY1UCcTUsW33vtqEGnDdh5925GdnaPPv3+79GXquvQGcJiEt0e0VER+X0kGogF0gRVO+vVCW1zWx3Up983a+MJBZbPFYDloMcjcQJxii2pDQsuPb1vuxyjbcHuu/U8Ye6iLXEBKQVRN44BL4WrMOIEo+BwcANdDQ7E5CmLEHRRlNW1axb2O+qA1h07PvjcKxGODikeKkIFrhONfSdCoqH5PIBhSUIE/hoIhETDX+M6haX8oxBo/kRDkgn0uQRJgSTB3TJ6SEA0DFm5Lm4HTgfMq1m7+sx+R3bOyz756GPvuvWenj0P3P+AQw477LCh55x+/NGHdGzVIsvI7rXP0c+8+HZVykkQ3/Jc9A4V/Mn772uflXXcwYc+9o8HTuw3wDAyd+m+2/DRo84eck77jm1y8nONzOxTzxr88YIvMWgFtVwapTIx8+3XjMyMLl13nfbmLO3XIEBw5hAzXsdkTLCqCXePyzeMLj2Pnf3FOjSUqNr00dvvjRw89PKjD9gv38jLa7vr/kedevb5Zwy9YNBFl7305jSPpgCcTz54+8wTBxRktso28vr3O+nsc4b263dKixbtunbdc8KTE+PJmO1FF381u98JBxs4mdPt2aemirTfBUeWASypiH7HBbdPSDT8UY9ReJ7tHYGmtELTnO0dgx28fr+IaHj8rpFtDWOf3qfMX1yZkJBgMYBy4a67bew12UaL08+6tGRL3AVIEOYyqYAQura26ouRl56UaxinDLpseVmCANQloym73CPlAElp19xx2ZDdWrY4+7yr1taxhEdBkaKP3zqiW/tO+Tu9OmVOygcL/ftYyt4MUMVTqx+64dxWhtGn35DFZU7CI8BqF7z5cM92Gd13PW7m3DKtoMwUQXdCArYPcZR1jJf/Y9RZ7TOMv500tKwK20ZgVnLdlyMG92uTk3vt9XdvcSAOYHEvVrfRra4AywRFOXBfe/oRQnzXoxxH6gTA9HDkDtL069aWFy/56J0pJ51wjGFk9urVZ/78RYSjJkJCCBeII0y0NyBEkx1xUHWffPB69w47779vv68KIyagqwXG3wCwCa+s2AikRka+e+G+q/bpYBx/6EH9/nZmbut9/37hrR8v34j2FGhVmJDS5EAJyh3ZFCwpiBQYpdoH4lCBkwGsvOL7OX0P7FHQruOz095LAJgKAr0n1Pts5DhRT9aHrhM7+LMfVj9E4FcgEBINvwKscNMdAIFmSTRIHYUKrRdRo4FIKlQ90eBUjB5yRhuj1YB+aaIBZasUsTYWXnzyMXt0aN8yK++kE0+/6NIRM996b/XqIsncyg3Fb0x68qg+BxpGm9POvGJZ4YaI7aKDqGuJeO2rjz+yc6t2u3bqtstO3S666JKpM2Z9teL7sg0bk2bqu++XP/DA/W3atGnVpuPlI64vj5i+pCmnWsj4ggUfdO22y177HDzrvY+RgPAczjw0IFDSo7WCVb388J075eT2OPDUdxasQSbCSzLHia4vK18w67y+vbLz2pw/8uZlq8uq4smI41Uloqjwlay6/5br8g3j4N5935n18bpNtRsrIhs21yxcuPTll6d9PH9B0kwo8FYWfjH0gtPz8wt69ugz5eV30ZqUg++7aNGgPKGoxI5Z6DqxAzy7YRX/QASa0gpNc/7A4oSn+oMRwJltHIVi/CJMSH/jKJg1hLeUUCdJ+WN3j25tGL17D5j/xaa4gDiNA1Rzd8ONo4flGS3OHHTJmvV1BMDmMuW4KOgjt5Rv+vzGkedlG8aAgRd+vz6pA08KBabrb1G8NlW5ZviZJ3YwjPMvuibJ0A+AW9EPJz3eI9fYtV2PuXOWU4wMCaaXNFMbgVdtXj3vyjMPbWEYx58z/Iu1FsZGgviX7z3bq1NuTla3/3vo9bKKSgE8FU2CVDaNMhUHP7FxxRcXntS3hWGcOmjYN2WOmSLIPribHr71qpYZxkmnnbOm2q6haAyIVhjUB+p7VtJ2U9ppEMWJcVwvuBDC9H20GeSOlarQoTFiIJJL539w6IH7tWrV+omnXkwSVCaK+r4NrqdsHNpTAor4yUpQsaJvPjuk94E99jh43qK1pjY/DLgG10mhaIVMzp32TNc8Y+8uBe/PmPbvhYt7H3CUkdHh1geeqvWVLRWhJuo6AScod+S4kOLCEdxPuAmqdRbcRDVYJaWLZ+63d4/WnbpPeXceSmlIHVADjTyAo1eH/mArmrZoqBdKSq8Jf0IEQgRCBLaFQEg0bAuZMH/HRKC5EA3pXjsGouQQxLPEtp1y4aFCtZJo0eCnwKq95rxBBUarE/oPXrk+Zuv5E+kl/NKlFx/bJ8/IyMxtP/bWezbXxNNDbfwhfnXRM/+4sWVmfsdOvZ55YRYBcCVI5oIfe/OxB/KMnLzcTgcdeuSHCxa6WoNBG1ACcOHE6u6/+YZ2efm77NH32Vc/IAApuw547acfTd+p087ddj9w+vv/TmizVcocYvuCUMoTwGsnP3DrTllZXXocM/erLQIJCBdlKVmKrf/iqoF9s1sWXH3zvZaPPRoPIOWaQFPrlyy4aEC/XVoV3HLLPUkKcQVxgb0f9DCV4DieRzC+JqPO0iWLHnt0wjNPv1hUuE7hwTFJCUJJhl4cjAM6WejgFA2+pjvm7R3WOkTgt0GgKa3QNOe3OVN4lOaIQGAdlg4erJT4IdIPp4y76OInaxnZ8ui941oYxr69j1n01aakAEv6APFEpPj2saPzjMxzzrx48+YYTvsL7ni2fv1HnEjhyEvPyzKMswZfsby0CqMtgnJ5XLIakDFrY+H1Fw/NNzIvvHxsxJY6uHJ87tTx3VsYrY12k56dxRUgcQ6cWrWQqP787dcO7tkxwzDOGnb3p4XRhO+BiteuWz728sHZRmbfvsd+/PmXBIkAbDeEcEA5fl3lLcOH5xtGXkbrMy+8acGyLQqApCpAVH61YOqu3doYGcbt9z2U4iiFkCIMw1X4ThD8iHFY8vX36yviGJBaSOqkkrUbAfWS0SgCFFEoaWlvWLl84EmnGbkFN9z/aA3Ddi3lYlglxj2VHsxz6SWARpIVheecdmKr/HYTp8yNMjwjUQzcGMQrgdjL5nywz249DCPniacn1sRqAZyXnns02zDatuk4c/aXQQDqRCruuD7XrhkplhTIZWCZPXTC4MIuB7H+nZfuz8lpvdu+xy4vXpcgGJvDl9h6YluqFEaV1q3CdFubAAAgAElEQVTqT3w3x5szLFOIQIhAM0IgJBqa0cUIi9IMEGi2RANGf8B5I06JwJmjtOuEWXftuee0MlqdcPy5q9ch0WBi/CoTNn975bF9OrTqeMjfTp8xbyGGcmDCcbzymjpKbOCVKz58tdcuuxtGm/sfnmwzdDMQ1AGv9u3xD7XObNe2w95PT3692nEtgC3JVDSZ8hxXeJ4wk9/Of/+o3r2N7F0vHnnfxlozZtYB1Hz9+TsdO+y8c4/Dps35qg61uzhXrmDamAAcYLUv3Htdl5ysXfbp/+FXmz2uhHB8kpJedXL1nPOO7WFkZV445uaq2rjj0uqEiWSIsEv+/dGgow7NN4wrrxpZbWNUNAfAURC3bZ+w4FbByRaFISl8j6P3rQ7s7tvIcaDNB4bZUAyYQIdTDzuCAd3SDO6zsAghAn9pBJrSCk1z/tIVDAv/HxEIiAamNIeriQaJhg0KhBBceEKZXEUYrXzw7rEdWuT2O+7v366usgTEiYOCDG75TaOGtTQyr7pkVDTi4ItaMiEYdWMgYvHylSMuHZqbnTP44qu/Kt4YVfjm5+AJWgd2lbOx8I6Rw9vlFZx/6egtdTbzU6BiX338Wr8+PVoYLc846eI5c7+MJKxIXVVk87rHbrv9qJ577rVz+4yc7INPuXRVBFKMAtjA6ma88nTPbu2yDeOyy6+eu+DL4o0VmyprEvHI0gUf3zZ6xHF9+nRv27V9wR4nDRq7uLDO5yJetw4FFqNF99wxtnWbFkZG5p33P7jku8LizZVbovGYZSXikWVLFt9y893nDbnq+SkfYAgogPXF3856+cn33ni+cOU3kUiEOLYbryORyomPPtQyr8DIbv3S+/OienrAJ0ihc0YU+kdIxtCoAWQytun764ZfnJOVffFVt1RYkACI2FEQJpC4u2bVncNGtm3V7ZCjTvtqzQYHPSri3335zqABaMFxwaU3rCiKBq0icijaGoIDOhVy4XF06KCMWeCVQ2rlS4/eulPXfY4YcNHaijpHIg3h65CYAdGA3hMh0fAfn4dwZYhAiMC2EAiJhm0hE+bvmAj8NYgGNAqQPpB4YNHQQDRgJw7dSW2oWn3JEfu2bdFu8BXjiitiLkAsZTLB47ZNqQm8Ila44NRj/5ab0/GWO8cnfOzJMeqAWz3z8Qfa5nbZeffD53/5bQqwW1OeMh1CBadoWyEdVVNyxZkDDWOXAWdcVx4jFFxQmxcvnNqu7U4773HU9HmrIjg/I3DqRSp0YwBb+ZVP3jG6U3Z29/1Oev/rcleibQR2rlTSK/lk6HF757UquPqGuyMxVymIpajpekCTVWXf3DL60jzD6Nyp2yOPPF1cVmkzNFWlAI5QKcc1UwnqY0BNLS6B00fMsyXRMlkSO1YSzRgUxdkbT6CrbL0LxY55a4e1DhH47RBoSis0zfntzhYeqbkhsBXRoITQdvZoFNBANEQ5q77/zrG5htFzz4M/+2K1D+AqLlTKtSpGXXZJnpF50eDL6iImig5IDOBMbJR7jGz49uJzT88yjBNPO+vLlaVpilmY3I+CSPoVa8dcdFGukTloyJVRRyrmgzLrNi174r6benbpkWUUdN+919CLLj3l5BP379mjo5Hd/4A+F549sGPXTvv1P/v7ahLn3GEJgGQqWjr+wVt77tw+2zC67bzb4ccc3++UgX8/68w9dt+l9x673nv9dTddhnKVh/S/ZMVm7gpw3SjwGuC1VVvWjB51devWrTMyc9t17HrokceeMfj88y++7OijjmiZk2sYeQcfdvz0Dxf5mvcuXvX1+WeekGcYXbt0OO3vA8eNvWboOWcdvHePPMPo0L7zXQ88VlRdiwQ6FUKkiQaBsSp4LBFX3AMes6qKn3v03g6t8w89/KQtcRmVUJGoBWWruk0T775tlxb5nTv1+NdTr1Tb3BZU0Fpg1Z+9+1qv7rvkteg+etwD3xetRRNIAJcKTzfhCiihNhFIPQhhAcS8LUuvveiMjp33GXb9I3FPascPIKhBrQKiAW++kGhobo9gWJ4Qgb8IAiHR8Be5UGEx/yAEminRILQRIygRWDSg64RygUbBrrpm8FlpokFHnfBRpSAJFasuOmK/bCNv8BXXb07QKFFJ20FDSQBOksA2xYsXDD3t1I7td7vt3idjBEz0NfXBq371gXtaZXfa98BTPl1cGOOQBBSvFgDMs30rBiwBsTU3XnZ+i+x9Tjr9lrgHAjzmrfr3/Elt23TstvvR0z9eU8sgBcxTppRcUEeyCPiV428b3T4rq2vvE97/upLoc6GRqkwmV354Zt/u2S1aXj72zo2VKYz0DRC3XOzVgfnNknlnndo/28jMNloc3fdv115z01vvzN5YXUvQfFRRStFFQkAy6Ts2kVxwYiL/Ijl3EQZt0SB/IBoaukp/0L0UniZEYLtFoCmt0DRnu618WLF0BB/tOqGEUorztEUD51xbNNhMxhiJ3HfHuFZZGd2795714eceEg3oTEfdyKjLLs3PyD379HNLSzYIJSknOG0uGZDoltJlwy+7IC8r+7TTz/5qZYkDkJAkkqpVIgXSTG0qvXoIej2cO/TKjVVxbcXmAq+tWL/isfsf7rXHfoaRnZWd26Z1q1067HTfjXdsWV3y7IR/ZbfM2fWgY+d8tcZC4cmkxyIAcS+54bVJjx936MFZOpiRkZlr5Ga3bpv/yIP3imRk+oQnWhq5fQdcvmwL+ntoq4uoZ5cDeKXFK58e/8S+++yXkZGlw1rm4EmNzIL81ldcOeKNt+bEKXL3PmdmourJR+7arWtBELwpLy8vC2NjGscddugrr06NO8zX5gwuOnSgm6RAmUZ0x0CpI9cEZQKpnTtj8r577NJlpz0+mL/UAjCBO2bt3BmvHtN7r3zDGHzupWvWI1uBATK9OlBJq3LdFYOHGkabdh17TZvxXlWkDiNo+r4vkXEQ3JeCuD76aBDcPl61ckH/Pr06dNlv0rQFaPyI1pK4ZaDBkQ7a1NB6brUQPgshAiECIQL/EYGQaPiP8IQrdzgEmj3RwDjORaAUgQ00AjaKQeYbLfsff+7qtUntHiClXQMbV4w44ehco9WZQ0ZVWtLWvRAFMmbGzWQ50I2p0oWDTx7QIrPgngeei/gQ4wrn/Gnt8/93S15Wp6P6XbBsdUVSQh2HKBUKwLXiVqIC/Bqgm24fdnG2sfdR/cduqqWeSDF/xdJFkzt36oJEw7yyWgFJULa0hCTMTSq/CmTd83ff0CErt3Ov49//qhq7MpwQkgIRiyx/59QDOxvZOReNua20PJHS3qpJn2otKluJZGnhtw/f98+De/XJM7JbGnk9e+49Ysw1H3/+Rdz1KRNmymUMCEnPt3BiCpIETohlBUQDA6GJBnQ3CfpN+B1+QgRCBP43BJrSCk1z/rczhHs3ZwQaaTRoooExnDeXEhhjjHtcOJTHfVK3ZOHcRx94YOILr62tiNkSUtThygblLvxo7r/u/9e0N2bE40kFkkviuj4oweyIFdv88QezHrr/vhkz36lJWijqQF2K3LIDzCTRqg+nT//HnffOevejWIoKDsQxQUQBkrHamjmz5z322GP333///f+4b94HH6UqU8ChbM2qhx9/aPzkN2q07CIFadIY4XUA/8/edYBJUaTt3rxsIAdBQDCggodiDpwoihJE0FPEfCpmvUMxZ1Ex5xwwewZUFEwIBkAQASXnuLBsnNTT3dUV+/ufr3pmdmDx9PwNKDXPPLs91T3dVW/3dFe99X3va0tS98P0qW+NfeXu0ffcceeY+x+7/633Xw9wLB+/+YKzWhQ0OfaUK6aupPUMPMGQ35YJHYsn6qs2Tpv0+ftvvXPfXffecvPohx58/NEHHhv/3oT6iI1WnYDPzZibACCJ6NpF86Y/P/aZW0bfftvo2++6c/SLzz65ZtkyEaCrJY7qA8qZB5JJ6imFh4kz/Iu6D4EDKrp2wdRhxx1Vkldy0WVXr00kkwBc+ovmznx0zOi7brp5+rRZPACHY0Il9SIBqQeSmPv1tLtvf/iG6++eNmNu3EWBBqIpjADA90iA+SPKI4lkohK82refvn+nFs17H3nyd0uiBMDl1Nd+GSGlgFfhFuRC9sdt+SI1dTMIGAS2AQQM0bANnARThW0IgW2QaEAfhVREgwpkNtEgasBFe8sSq1gTDbZ26lJBshLWfP+vAUc1yWl+9HHnVMSlh7MrkgkaTeIsE8iK+LIvTup7eJFVfMudT9QQqCaMBi7Imqdvv6Y4v1XvvqfNXVKVkLCJBDWOj+dHEoxcFTUgNo48c1heTrcj+1+ztooxtONaMnva8zu1b79Dh/1en7AkglMu4AAqXqOclYyAiDx141VtC8o77NFv/MxNHoBLfcxpUNH4wo9OOKRrUWnJuVfdsi6O+4oIqErECUsEMiH8GAQ8YGrujLn/HnFR/8P7tG7aNNfKOaxP3w8+niR03in1gTjcdVg8bkcjNRjUgJwCEicS4yMEB84BFdFTRMM2dLGZqhgE/qwINKYVGpf8Wdtm6v2zENDGE5plCHT+mpIYai8EKhoKSTj6LURB0oCLUCDARi8iGgB1nRiGROhJcy0fIARwyoRgXOsEuyApCK4UMAUEHxUgQHDhcKceFAXOuEtSugMUKPEZjwQQDzTLQSnBRD+BqXLCATviYIQFKg/LeiI9HTRHlJekESFs1EEI7SKZ9J1kAJQpTKyorlhw9pC+hZY14NSRM9b4DkBd0kXNH+Cul8BdK4GJgVzEaiKCoYCSa1PqSYcIm2FCQhLAD9AlSvKoYFFCPayRxgczGAJlu4wI8CVhPIm2mMLnxAkCNGi2haq1XRko6dWDsxFY7TP3j+7QonWPvXpNmfNDQk8b4B8UikbhISog7tK442IIIUkAp1hTTMUAwhA9XzGXk1DUmbmUExRjtu1qAGf98u+PPqBneW7x7XePjXEkLGzfoxJPWYqTz6YVGi//rEvFbGQQMAhsvwgYomH7Pfem5VtDYFskGgIlUg6NSDSodERDEof9jYgG7AzROli74KJ+RxZZTfsNOmd9TEQ5aihKNPrGbgyoiuoFk4Yd9feWxc1vvP3RmIB6IZI0BmLTC2OuL8hp9rf9BkydtdxDvgAdznWXzeNulUhWxFbNHvT33iUlPUdc/lQcFRIIwOovP3yoa7s2zZt1feHNGVGFfSw7cEXgBX4URBSSVaNHjGiV02zXvYeMn16Ju/UpJqC6lXXzJgztvUtucf4/r7pxraeqcS4JYpwQLwHEJpF6JxpHnQfUviQbVi15+enH9tljtxwrd9DQYd8tWo1q4a4PTDCiqK8lsrF7xClzMQZVcRZwDoxDaAiK7TAvg4BB4P+PQGNaoXHJ//8oZg/bOALpHmQ61D60+5FocimVx5jtOwlOmUdVxPUTOLIWEomGOHNpeDdmgia8GFU+BuqjVSbV+o4MTTNVkHS8BHGJ5FT5EigqFgfoIBlwHGBHY47UWjwSklRGXWK7xBMYDaAE4V4CE+gggFii3qG2BIgmPMenLvM4CAHSZ8TzPJokJJrUdIMHYCeiKxmtXLtq1vATj87JsYaeM2ppPXiYaIHfItRziSelVL4PTJMUejzPfWAEj0V8PLyvH52ucIVKChZJJKokMhPgU64DNzCtMOlhloQCLoSDRIPiAWdcou0E1kMHEgJNBMkqUNFFM784ccDAosLSx8a+EuHKlUFtNEIIwfAEPChDXwymqQzO3FgMCQ+PIRmiUzMSPOlwR0itKE2l8NBOivE6gMTj999WbFl9D+03ewE+ml0BHupN63xJKTBvAuWWtyYGuY1fmqZ6BgGDwLaBQPoxgf+za6T0a4vC7A3MskHgL4nAtkU06F+gyiYaAoaS0BxTJzTR4FVcNGxgcU4Y0RD3AYifRI2GiuXnHnF4k5zmJ556WYRDXGKKplDU547k9UDXVi/6/OS+hxVbhTff9qiH+wLbrQW6/sV7r8uxinbYqdc7H0yJ+QH6ezsedR3hRoBHILA/G/fazjt0KS7vcf2dr7sB+NwGvurDV+/o3KK0OLfsiec+qKdIT8S5y6SjSARoxFu97KJBQ8qs8u77nfT+1Ars43AFigR2Rf2CT046as+8ktzzrruxUkA1QCTAGRjseBEXe4NCOS6xPdenSTQ/Y84jY+5sUlSyx9/2e3fyNExwtetx+kuB7wOVOCET9xyqCFdESJ9LFuqZK6UwBxbndMzLIGAQ+P8i0JhWaFzy/z2G+f42j4A+6aFmoJ4B193I1GMLmJA+ahsI5YtASwwqoojnJ3F8HYBggRAiAEEVYQG6LTBtpyS4x6jLiccZBtOhkCEnLOA6oACpCOFT4VPQJo1BgCkDHOV+iQQ0fhaCKakTCQKsGGFB3I2xAIkMzO9A1h6FgZVSrkuQxVbg2x6z407dRqA1LLlesprxH7y8087tCpuVXXbTvZUuOPgARdNMwnE0j7ZHyIMIoGjmDByE1homnmBc0kAmpdLRhSJAzgGVj13i+DjQxxduxFEEgUot/SB94D6yLKAoZ3GKbaMKuBTci4MfBRUHEnlt7NiWLdru2qPX7CUrqNaPRHIB8wZTL0L0cgCYJKGdK3HvWoATfaCAU0oF4RgJwYjkUSXr5s2dfOh+ezYvaPKfl99lAUQ8NBFhCnUpMcnC91Gi0hANaYTNf4OAQeAXIBD2DRr3EAzR8AvANF/5CyCwrRANOIefegkpJVM4qaB9HJBoIJhj6eqIhvWXnTGkOKf4oIMHrtzg6Y6GAB6XKxecesihJbkt+g85Z12UOtin0WGbgU/cTcDW1Sz98tgDexZZuZddfqsjsTfk+XUgNz575xVlReW5xW3/3rf/tNlzPYVf1GLVhCY2Lflh5qCjB1lW00OOPu3reRuqk5T4NtBNa+Z8fNS+3Utycgcdf8oPyzf4mPyKIbJYSZZ47u47Wli5RVbT3fc5Ycq8qAe6N0PioKI18z85qW93K9c661+Xr7aTMfSogKTjYbZIrD5WuQH1sUA5zGOY/0FB8TdffKkgv6RD1z3fmfQlGnqp6PSJ/7nzhtEPPvTykjUJFyAqBAHmK0dIV0m+5WzMX+AiNU0wCPzRCDTuNDQu+aPraI7/ByKAjyv9xjqEM+QSwxmQWcAiHYQf6GQCiQ7EqXg0JBIwzU2zwpiMITF1MP1GH4RAf0nhXz3djjvHIAd0F8Ll1J4likui/aYKUARBERSp5AI1j4FSL1GxatWXn32OyQUuigpjjh2mUTig3Plzpx9x5GFWnrXvEUd88MX0jRGMhiDUs0lSaNujlLsycg3pVmrhRBQn1nkRHG2VsSGaaMA8QWyXrjjOEei31irGb+gv6Wrr5fAQPg7wAYQH3PaiG0C6sbr6K6681sopGnjCqcvWVqLsAhdMcM9z0pUIm66/iBCjjgaHwAdFgCLVgtbXCsP7GOG0trZm8cknHlmcY428eOTa1bW+gogXcJ2SEfpNpK4eky6RAsL8MwgYBH4JAmHfoHEPwRANvwRN850/PwLbJNEQqMZEAyER7m1gdcvOP2lgWVH50ccOW7E+WZ1gkWgNeBG1btm5Rx/drlnHU868fGOCRbiyPcI5ZdIjbjXQDfWrvx0+oO/O7Trdf99zFCApVSK5CeTGsWOuKiloklfUqstuexw7eNCDjz+6dPmSdcuWrF0077uvJ51/9tmWVdauc687n3itXjtG6j5eUtSuuufay4stKy8vb+TV13/61bRNsWhtrPqDD/5zxw2jjj3ssM5Nd+i2U692Xf7+1qRFLkBVLIHxsSpev3TqxcMxQvVvB+8/dty4WYuWrFq1adP6anCcOV9/PubWq5977pFv585YsnJRwq5ft2r5u2+O69d3QEFxi5PPOn/emrUS+PLvJpw3tE+hld+p835j3/waMy8A4tIhgSYasGeru5+YNNHQ8f3zX6imBQaBPxKBxp2GxiV/ZP3Msf94BFKJauFAVVMJyDLooXU4rg6H2Q2F2hcpUCCDLKIh0FxDOIDXNAReaHpInk00oKpiimgIuQakITCMQo/5cWyNoQyY00GkTAo38uXE8ccceui1l//rlZdeXrBgQXV1deX6dXOmTX/56WdOGDTYsvJbdOx877PPVsbiGMXAiU9siaw3Jh+G1pX4QEnzC5khfhBg/UP5hJS9csBQaShAoiHDMuBXtS9SSMFkzhXqYoZJFkJSShmxISABjwfCBYA5cxadesZ5N9x85/wlq2zCHR8jQEJCQf/FWqQ5HWQpMJoENS2kr/MHkf1QOmuEE0qqKzcuuGv0qJGXnv/tN3NQXEMnWejAEzS1CoMvcPeGaMicHrNgEDAI/O8IhH2Dxj0EQzT871iab/wVENj2iAYI0P1L4V9MhFUo06SDMDFuAETkguFD8qz8g3v3X7q2XtsvCFAuXbP0+P0PyLOKBp1wdkWMYMqoSzCmFG3OkwHZuHzO53327V5kWTffeG9tRBAMK40B3/D4TZe0btZqn/37nHfhRUcPPKZZmxY779r1iIMP7NGlc9uykhwrd6ddD77jkddWRz1Xd7lcN8liEaD28tlfDel3SJ6Fr6Yt2xzUu0+fow9v3qqsvGnxhedfcMwRAy2radP2e380Y0WY74oaDcqB6No3Hr9rpx1bWjlW+04dDzjw0GMOO+65B8bSWPyryR/06LljbpHVrfuOBx7Yc9gJxx96wGHFxS0tq6zPcSePnzLN5r7k0eWz3j9z0AGFVn6Tkp1uf/DNVZEggjmuggQOFw6m8molMSQZDNHwV/iFmjZsEwg07jQ0LtkmKmoq8YcikDVKTRlVZIbEehXO62diHEID5jBsQbMIYVwDXlkhxYAiivodcg24Qg+twyiABqIBm4x3/HDUnWIucKAvXR5TqFLkfDNx3M7Nikstq6wor1evvU84edgRR/XfpUuPkvyWuVZ5p532um70/RsSjo6+4IIkAoaBAxjaEOAjRSfhbU25ANUx0++Q5cDgBnQAxbqmwxnChUBbR4d5Cmmg9DMeFCpUCuE7CR1n4XluHFNLJBASVFVHwnYJTQJQStAIE4kCjArR8R1hw4UOFQm5BtSNTAVbKA4YHoiClZXrl2zasAZTMBTYhHGMedAsQxbPsBnRkHUthRXOKjCLBgGDgEFgKwjou1/qT/ZqQzRko2GWtx8EtkWigQcK5a1w+odniIZ4og7zNOvXXnrGyU3ySw88tN/U2YtsgT5hEJDaBd+f1OfwkoKygSecvmgt5jJwoSRHj3PASNK61Yu++Uf/vi2KS++4/aH1lXGURQAXaMUDV5/ftkW7gcefNn/xks+/mrz3Ab2sHKtti6ZFllVoWQMHDHnm5YlrY4ELUOvZCeJS3+O2i1mhdvXMr8Zfct6pnTq0saxcKye/pFnprnvucsW1o+KO+9CDT1pWWdfuh039flUMNapAcA/8BAgnunrx7TdctceuXYsKc/Ot3HKrxe0jR7OEXVGx5NRzjitsall5Vp5lFaNfeUGLNl1PHzHqs+8WJRRgrIKMrZ474dLTjmlT1mLf/fq9+t70WIAdKAeYLz0uCBINOhgUO3ao0JCaZNt+LmjTUoPAb4FA2GvI3nPjkuy1Znn7RGDz4WhmnJ0ZverEgfC2rDdNj7fTaGV9zkzXb75P3JWeh0/FrqWn4vFWH26ZSbhQAfOV9kRSdnTVgkduGjW49/6FlpWbZxWXN7fyyiyrabOyricOGfHe+1/XJJB/p4pJ6QLT/hQBjv899KRMEw26mln1yTQwXNAKEGHgQ9igTHPS3wkpg6xipRNMsAeO4gjcF9SR0uWC8AAicRcCcB3Us8h8UT/UQgBSREO4KpOoItFBE7M4sP8QUFBEKY9ztN7UQQyKA7jo/4yMj0JVTPwpp09AGsR0hTPljQoya8yCQcAgYBBoQCDFMeh/DaXp9PDN7jbZq82yQeAvisA2RzSEvSiGUlEyDH3UEQ0YPIqdBhL95pOJzz419sOPv4xTFLlyqIvlzJv10ccvPD32w8++tBWWCyEo8V2XCB5mojpfffLhGy+8NH/eMo+iybZSNvCap28dVZLfvNf+/epjDg/UzLmz3nrv7f+8+sqzjzz68YcTli5bXeciy0AAYm5SYA4tcI8Jn4DAAf7G1Qs///jDd94a99zzL4195dWvZkyPEocBLFyy8umnX3ny2VfXVNYRCBzmuU7cidWBYCB55eoVH41/98Xnnn7uiac+e2fSxmUbtTIWqaxf9eWMiRMm/Gfi+289fNeYx+5/7NPJM+sIimvVecy249pmvG7p7M/fe/PtSZNmbIpyX6eB+EqgDKRg2CyBsbcYfhvmyP5FL1zTLIPA74lAY1qhccnvWR9zrN8LgXAI3fho2WPshrUNw9HUUmqzlDZBFsUQfifcquFbGbZAD3jTQ2hkEDKvcOPMKp2joVkG/ZDE56QM/SxQBjIAyqXNEtXAbFa/Yf2i2W+/8cLLr7zw6JNPPfbUSy++PH7ihzMXLayUOspiUyRZG49S5qJWgqAQ4PifSiTK8fmLzAa+0rUNSzI4pFmG7I3Sm+oy3DIsyGpCimhIqzwqnyQx/wPQeSoWd9DYgmjdJD15QDnzPEcKP022pHao0QhVIIQMBEVlBgDJQLogMdYPQxiZzzzXTXpJ7ZQRoGtoUieq4E+5oWHZVdT1blQQttD8NQgYBAwCW0Eg7Bs07iGYiIatgGWKtgMEti2iQWJeJ3ZntLKUJhoC7HBQgZLUFD0gE5C0fVdQrZiQEH7MiXt2DDwH4nE37tiEeQD1JMkYU1SGjt/cTyIZ4bvYWdLRmy4nhERA1I0dc0uTgtY99zt21twlYTyqxJ6aClBnG4+LspGAClt4MaiAEuZ6vqt9uRmzQflaBgskJsWCI4ULoqKu1k4SVMDmuC8BKu7GPGI7yQQnKEuOgaJoDpZqJUior6607dpAm66rgOIqhXmyoVlXnEFV3PU8T5GE8uq4U4uTPwCuH8RdanvaYl0GEnNqMeVEBkvFW84AACAASURBVEJh6qsJaNgOfsGmib8LAo07DY1LfpeKmIP8zghs9TaaGV1nrdXj0RQjsNnYFAfkDRkTmZF61tg2ZA02+65uZWYmP0w3yG65XhWO0lE/WE/Lp+f9BXINUnKtBMkT0RrlJbVaAcXnoB7G41NIu1SEuRmUg88h4bEEowLw8SQcGzg+BHVTMmkgWIWwcZmhvo4+1A+6kEbZopapVqGGgn437CFkB8KIBop6zyiFKTg+GQX3KMVABqXAZ5RxNLBI1yRdmdDZQz/nGtYGXCnlC0C3DCQaklImNNEAnssEkYJDkitXSc+PeE51ALwhoiG9l1QL0zXPLs5unFk2CBgEDAKNETBEQ2NMTMn2jMC2SzTgFEtA0YUhJBqEUoziNIvE4TtV4AbKRxkqwCxL3w97RaHCE87FCInRlppZcJIJ7LsQB7stmMCqKHDG4uBWPzf6trKiTof3PXPyV3MEQJz6kWQCyQhfcIY9IycgcWZjCoZCvkEwPCLDzppIurakHtaQ+pyjBrjN0FQc2QHCfNsLiCuowxWRmMnBlWYuPKooE5xT7TrGtc+Z4MQBdEgHThUXaK0O6OlJXTeZdDyiPcWwZ4mdIoFZGNqjQzcPgxcEk0piDKh2PgsEdp5Q46Jxx297vtZN2w0CvxiBxrRC45JfvHPzxW0Ygex5+7CaGZYhFcCfqrwejzaQBZmt8C4sdCx/2n5CP5VwNJue4Q+JhgY2IRza4tAaJQxCLYb0sLdhoB+gewWS8AL3r8tDxQKux/6hBwT3uZ0Mn4O2y+IOkQocx8bnCHGYRwSTPhGOz7lK6T6iyA/jwvMyD9AU+Y8MRUOCRrpQtxPpijCiIUNDaFRSPIVua2oGQVtRpiurW4dECbZY+0qicZJwfT+uQxswKk+C8gUPLT81pSIZ+oBqAcpA/92sVirQz1mOUAhQjlI2V66Q4CQ5tggljcAJOKURTDrUupU/J6Kh4exswxerqZpBwCDwhyNgiIY//BSYCmxTCKSJhgxpr2u3+aeGHpNeGX7cSisaekLhynRvaSubbq1IKXQFD3tKAnsdSgcLYL4CDuLDvVECgksZ+CJwJGOgGPZDtNq0QFUGj1Gbe+FEjKKSOLjoui72kKRAxQchfIYjf1A2eLVP3n6bZTXvue+gOQvWhSqKyE4oFY/GKG4EPvbkKB49bLcOMSUgXY7zLUhJUB+ZDgBPcxBIUKDHVrg9B+4GwISiQmHPh2sZKt1lUVJREfh+mC6KahQQ+AH3MYjTF9zn6NGp6QbsmQkBPkXyJAgCKbHDh54amDai1awU0hth5ChPW6Ol6rA1qH/FsnRI8E/v8n+8HH56h2YLg8DvhkBjWqFxye9WGXOg3xGBzOMPb/L6lSnZPFlAr0s9BLOeF/pJoKP0cKSdfpCl7oapcft/Jxo2i3fI7AH3xPWzMWWWmbrhh5XCEb4+qNSjawUOQRI8rbYAASPh45X6DtfPMtR9FIGrXZrxIRQ2AZ0rQj6F68fXFkSDPhi2uaFXkH2fD5fTJWGoYii/mEIC1Z71u6HzgNJC4bGElJxh8CLSDSgeEYQWHmn8Q5YhTXBo+FOimELhnIJ+LpMAPBH4SNPrynKGUYpapAmTGn8+0ZBuRfo45r9BwCBgENgaAoZo2Boqpmz7RcDC/kTqHepb46esfk86TBF7LaGQVXoap+GLuIfwW+lhZ1qeOpy6+XnwhkRDuHetca07CjhVomsY1glVEniYVxlKEKQ7drrymaZosgClCsLo0EAnmwpkGZIOhgOAcoHHQdmP3X17QX7ZnnsdNm/JRoxT4NrnKgBKiQBIICURtjq96/T/rI6RLtqiPKxzqvuV6oRlA6YhwfJ0SK3eJrVTPKLE3FiqAqaUwDiM8OSkm5O+kWmVckzJTcOQXY1UHX4e+r9oKz3dFHrF4/c3wwQDUnUWiuZG0hZoyJNoTfK0UHnKy002XDmbgZbp3P+i+pkvGQR+JQQa0wqNS36lQ5nd/FkQ+K93p8ztHluTHhv/SMs223bzbf7LqvCJrO+c6e9stnX6oPq+nFmTvkun12Ld8BVu0DBvn/kCrsxsHG4b/g0Ls0v++3J6J+kaZA6aVZC954aKbfZwaThIeocNJakl3GFYf+y3hNRLw0apNVtb1bCRWTIIGAQMAr8IgXT/HP9n78BoNGSjYZa3HwQaEQ36AR3GaoYT7/o5nXpU6x9N+smd6YjohTQ9EU6ra3Yg7AZs9kP7b8BmRzSkv5TiPrDToPcWunZrpWgZkgg/tkeltLF4+hePO8ekVUExd1NRu5rFN4KyHxxzS3FxSZ+jjpu3ZCOmXfhAiRCc2k4sTJFI1+THjvP/L8/0ljILiLDmF/CvNg9LDWrSvImWyU43LXQXS/cFs+rz21e9MdGQOqY+X2miQYbnKwx1NURD1hkyi38aBFK/wKz6Ni7JWmkWDQIGAYOAQcAgYBDY7hBo6JsbomG7O/mmwVtBwMIfgjbcRv0obYitsy1TsZONv5GeedZrMlxDev5gK2sb7+JHSlJsX3pXeqtMfmxqfkIfMAwxSEc6/PjeMiMBpCfSRINPMe+AxKuFXQ0yefM1/7Ks3G49es2Ys8zxUb4RjwTK8RIcAj8TKvAjR/m1izNcg0qbk292y9r8rpU90ZT54q9do5/YXyoiI3MhbEFuZM6XpiQ0tA2TY6k6h9dM+srJNCSz8BM1MKsNAr8DApmbSeZYjUsyq8yCQcAgYBAwCBgEDALbIQKb9dqz2m8iGrLAMIvbEQKhRkNmULfFwlaACIeUqRWbfQi5gHSiwVa++hNFqR+hDrnXP1SpFaFR11ChUmJDQsdWj77F3rcYBmhpA4kqDgLbqEgMeAJ44tH77uzcuUvvI4/9YeFa22sgGiglDJQj2RYj5y2O8mt/3AL/zUJztxbB8d+2/7Xr9mP7wzOeTTRsjli4tmGbdORFQ80N0fBjyJrybQeBLe4nGJqtX9tODU1NDAIGAYOAQcAgYBD4YxEI+waNewiGaPhjz4s5+h+FgBUO/DJjRf0xTI5oSJfAoWOaU0j/1xXGFTqyoGGa+pc3JPwRZlIktBADFyibgHpWYZ1CuiF9jHCwmv60+f8tkqPCoAah0CWLcypIAkQSWGLh99++8cabEz79IpZUTIHrSsfxfI8w5odH3HzYvPkxfsdPmTvX5sdsGK5vPoDffKvf9hPWYQuiIX2RpKqXWasrsmWdDdHw254fs/dfA4HGnYbGJb/Gccw+DAIGAYOAQcAgYBD4syKQ6a5vMQwxRMOf9Yyaev//ELB01IDMWEXpFHoOAUPbbYU6CJkp6RSZEOYWpI665aAxDI4PV6ZHmz+3gimiQXuAoztlwGTAODAGwgdFUSM7Ld+kNRF1rkfKcOsnjxESDXgINKFIItHAbaA2cKJUynyMB+js4PtMMNSb1OLXjZIVfvJIv8EGmcpLrFT2qxH+mZOUvdVvuqxPc4ZKSEt1AJqxQ0qrEuuflrHE60ln6KRzQ1IKDuGFtjldErbuN6292blB4Gch0JhWaFzys3ZkNjIIGAQMAgYBg4BB4C+KgCEa/qIn1jTrFyJgCcWF4jQIaBAwFaCZpKB6aI9cQ0g0hKZSegwbqJQSI1pRhoKFgWKBYtpKajPdhF9INEiFbhHoZRUSDQI9LPWbB4prO/JwbIpHD5gemv6sxofDdS4F5xSkD8oD4Wn6AggLPI4+llnxC0pAQPjvnDqxlYaE1ZZSCsGEEGk9yPBW1uDdkCFhssbqW9nbr1yEvAJeEBJPjQrPizZRUxzw9IFkINE1Q4bGGamT2yB1qZBRwrchGn7lU2N296si0JhWaFzyqx7Q7MwgYBAwCBgEDAIGgT8ZAoZo+JOdMFPd3xgBi6EPAydSEhH4QjLBFaNoso0RDSmiIZ1hjxPUWyMaRIpoyFhRpjMttph//+9tSYUVKdybCqgEKgOhyQUcxIaEiB6VAhoqSmABZQH9X4kGTSUodOpWfmgkjm4UAoMamNQJIgEwhqaS/ytR8t9b94vXptQlkGVgaHiOr4zrxDZANOgME45XgJCaawiJBhb4UvnIWHEuZcC0vyWeW4HO7GgAkn6HZ9YQDb/4CjFf/B0QaEwrNC75HaphDmEQMAgYBAwCBgGDwDaLQNg3aNxDMKkT2+wpMxX7TRGwHMcJAKhUEsClAgAkF9HamoAzxWgozuD5xPPd1Jx5gCPHrFB5jG8nngNKpt4YGI+UQ2h0+d9rH46isxKZ0GaCUUcqL5qoTbqOCvSUeaAoCyuAHIZS4IvAFS4FHyfLN39J/cr+qWcvh4EYOC5WVM+3MzSkUA1JGTrIv+Eb/73+jdeGtprZ5WFIQsMeN1/K3jK1rMFVWggynSuhGPd86mjby5RCpO/7SqGOBhcE0skf6biGUF8CT80WNp9ZUG/lyBmJu83riJ/CrcO2hBYeCDNXQIKAAfJBeKIw4MVXikiapDEAj9kxENInAvNQmKZIpEJFTsGkxPwUrCFIGYhUoIwuyQrK2EwOc+s1NqUGgd8egfAXkX2cxiXZa82yQcAgYBAwCBgEDALbGwLZ/efsthuiIRsNs7z9IBC6TgAhlAtVsX5TPObg6BE9LwGj2oOAc+p5jk5S4IJ7AIoS3/M8x3NjcdtOuphtgSPUNNGAjAP+0FJxED+CZWbsmj0yl5Lbdq1SDgANQKgAGA0i9TbzOWo4+gnPTrhJz3UlEeCD9IAEmNHxP7zQ1AJDAmQoQqEXsCCkRcI6/9ht4n84TKNNs/eZvdxow1Q0CEYBMKGJBiUVpcxh3HW9BKXU9xnn0tcvPSZHWkHnlGRULBqIhp84VqPDZ2+fvbzFhuEqxJ5hdAgNOAEeMjwMrxURAAFwwXdAKp8iI0J8SSnHmBhDNGyBpvm4zSOQuuCz6tm4JGulWTQIGAQMAgYBg4BBYLtD4Md6zoZo2O4uBdNgjYDlJh036QSc6bQBHOV6LqcMk+bDWewgkIy7ACiP4NOk8F0IMPAhlP3jATg+TxIf+QIl0B1CMkx00GEC8sdJABVO2WuXuPBcKBWGLbgAyR/mz3j3vbeWLF6ZEjdUEK3ZQJLVOtkB9SFDosEHLgDZkMavHzu/WQkRaUUJ3c6sGI30V7M2DYuyj5Le6P///0clD9MIhbBwAJp0ohhyIkAgwIoQEhINOqgBuZjN3ynfkP9/Fbe6B03P6BAKCQS4GxDOuRJ4cUhQPosAOFXLFk79+NNJn38VJwEV4LrEEA1bBdMUbuMIhL/97Eo2Lslea5YNAgYBg4BBwCBgENjeEPixkYIhGra3K8G0N0TAUpIjcaB4omZTtLo6Hk2s3xhhABFH+VqzAIeSKBNJtD8FA0D/BzsRW7lqTdylTI80icAEfYyklxQEVZLqPHxU+fsxoDMRDZkNpJQ+sTmtr1j/wz//eVJ50ybnnnNxpI4KCtTxAWfIHenbFWtW19clqQIXhAs+C8KJdJxND3/GmR9z9q+9YVlTJKmDbs4uZD5ttjZTP82JZPaTVbzlYrhNdjW23GKzz1snGgihnucpJRj3FEpRUNupWbZ8fjQaZTTF0iilfN/D8xKwIAwt2IxryMQ1bHa8MD9iy6Kf+pxp+GatkzqiQWiiAXzUuuBhygz3SI3ya1968N7uHTp1777vZ1NnSY08wmIiGn4KbbN+W0MgvOyza9W4JHutWTYIGAQMAgYBg4BBYHtDILu3nN32zKAgu9AsGwT+8ghYjNjA3YWzvjp/+NCjDzvwuP4Dbrz1Ph+g3oWoB8QXmonwpXQ5xsML5kU2rV06+pYbDzm096hrb1u0epMnIeYJVPtDosFHQwfph0SD/N+JBghifnLduf/8R45lHX/cqbF6nRsRAElWz/9+yjmnDd1z166jb71n/cY6LxA6RP/Hoya2dvZ0kkQqeiMdswGhKECoRxiOh/GrjSIatra/rZeFN5Tw79a3aChFWYqwAg1lDUvK9RIqIKvWLLz62ksOOnifa6+9duWKtZn1QrAAyQXUssy8s+IatgxqyNAEmT38zIXsW2fDECvQ/A8DgqqPDPNQBCbccOlLXg8y+tTtN5VbVn5+03c/+cqXwNB7whANPxNys9k2hEDDNZ+uVOOS9Brz3yBgEDAIGAQMAgaB7RGB7N5ydvsN0ZCNhlnefhCwcKTIE3O//PDQ7p1KLCvPsrr1POSzGUuTAD5gZgJKQgY8UNQXmBcBgRupXPHPM061rPyBJ5y+aFUNBcBZ9QDQpFBRUATfkqEMwo+TAFtNnRDUAVZt1y4+94zjW5SXnDb8gngUPBeTBQCcH+Z8dkCv3fIta+jgYT/8sNzlWDsuhRCoZfAzf8NadDClH4GSlahGoSUbAswN0d4IUutB6nIVYKi/0poTaUHEn3NxZEQTM4Eb2beerGWpWYatEA2UUqUEF8RxYwB00ZJZhx+xr5Vr7bHHHrO+nQsoWcGRIEH1A3xLhS4PKqDhO006cE1hYJUzVdJKCptFf4TQZQDMqt5mi1tpeIA6DIKCB4IAQzVQrjiXjHvcrwFW+/YjD+zWvM1ue/SaMnN+nEiX6pQPE9GwFShN0TaNQPhLyK5i45LstWbZIGAQMAgYBAwCBoHtDYHsfnN22zN97OxCs2wQ+MsjYHFSR+Lrln/3Sd9eO7UutMqKC6yiVvsdPXxehR+XOA5H+QZGQFCPBx7jAI5Xv+6S88/JyS085cyLV2ywfYCapNQjWonsROChcyS6EGQGuVuBUWiZATQu4FwIEQ6DJUuCrIttmDt86FEtSktPO+Ui3wPOwKfS9TatXDnt3jHXnnLi8ffd/cjqlZu0B6bwfEII8X2fUsoYC/cW7jx7/JxZ1q6KMrTJROJEoOalkujPqAKGhpo4aNcUiURSguNeU7vN1q3M7HCLhdBHg3OOX9NflFLbcGyxXeqjkIpqjoCGFQv1FMNIkJBEoMwGIHWRdaPvvGbICcdcffXVlRtrlAJCiJZpcKWiXBAhw7crlRe+VUB0zgWSPSl4pWZlhOBoObn119arqRTSOfoVfi3cDBQEeGmAA8wBgpcKRUMJwQmoCLiVL9x2c1PLat6q4/uTv4n5yBfhFw3RsJUfhCnaphFoTCs0LtmmG2AqZxAwCBgEDAIGAYPAb4yAIRp+Y4DN7v9kCFgANrC62IqZf+/evsSy9th9t6btulilHe984r3ZK2IMwPd9N17PieNzYFyCSrLoun9fcE5ebtHJZ12+cEPSC2MfkFWQEGiiIWCo2ogWDg0JCA2JCHqJM19wyijSBJRSKXHwz5kLooZGlo44bWizwqL+x5xkJzErwGMSwAGIBjwWra2KRT1kQABqaqpw8px5glMcBSuJ8pP6TSnxGaGUU8p9RnzmUkooJUL6+BZCcOBci0lyIUUq40MqKhRnKtB+jBinQR3f9wjujDGMnkArTLyNKBS81JqXARaFWpiBVIwJ32foyuHYrhP3iO37HuVMSNDvIGt8j8uceZy5nLmMe4z5lDPKBOFCQGA7CQi4k6iVPnINTMQU+D4jAUAkGvc8HwJIJmwd0UC5tJl0mPS4QsYhQ16ELIPWcaBc+nrfhDNfCoYYCMUFkgiS67dgStIUCEG4kocMiIeMDiWEhm4XPqO6rgq0SWhSsYRwfN/nlHHJpPIUpk7E33vkgbZWQesO3SZ9t9TF6wNDMEKiQSiMH8F0FQwaEQG6oiK1E+a2pM4inkvzMgj88Qg0phUal/zxtTQ1MAgYBAwCBgGDgEHgj0PAEA1/HPbmyNsiAhYO4FV86efv9tl9x3aty48/6cRhp5xqWaW77D3gg6+XJQFq3USoAhCLJvUY3hHR9ddecFZxbv7gs0bOqaQugI3jaK10oNB0EkevyAPoEb9Cw4pAj4Y9qoIUEaAgQLtKrSagiOt7np/wqM9ckLU8vuZf55xenl905pkXV9YRm2NuQLSuApQjfQcC8DkQpvMpAkHcGkrqFPV8jzBQVAJPAk+qALjD4qCA+TLmRAR4AFQpj/K4S+ohkK5DowmG+RJILXggvMCPB8IFwGwEbI4AmcRjKx9bE4/HN1Rtsn0/STw9ZOY+sZOuzQOVcH0AiNdVx6uq49FkKO6A3IFfV1e3VgS+RHFNEfOwegEAZ77nuK5DicuASwhEINxkss4jNhE+gcANVIx5LvPceAQEh2QS3CTjnsToAT/hJ5hC9Yu6yiT4wJ0o82s8XsXA5iBd6hPqcK6JAA5Jm3EqFLgB2BQcW8YIjYIiwncFk3GXRmxPIK2hOPESkVonXuW6dRwYBeEBIcJFt0pMJtF+oBgAggKfEiDuYrtAgZPwo7brBSopRZy7LriujNrxjSDcDx9+qENek5J2e7z55XyirxDBCWqFgtRJI4EMkK/RuTlMobwDfhApwc7wWtkWfzamTtsbAo1phcYl2xsmpr0GAYOAQcAgYBAwCGQj8GNEQ/Y2ZtkgsP0gYEkaB2mvn/reEd3aNW3W4qpb73jj9Vf36t4jp6jd+SPvXLi+3gNwA5L0ExjPj0M/R0XXXn/+GU2snIFnX/ndJrS+tDnT9hIKJAWFVhQ+jhX1QFHiVH4oRRBKIACTwBl164lTB4riUFXHOMRd35cUwN60at7pgwc3K2g64oIr4z66TSSIy704SKpcHxTqQQgA3+N2pBYjMmQUJAcFLvUdlwJFjsDlMdQo1PILODoG4rp1brIKwBUqGR5U6v3YhGNVFQPlYd4HMg/ScQl1PRRtEODURUO1CaE9FhhAkng6ksINMExBrVy7RusgchACMOoAKioqlPIA4lg9oB7j2q4DiIIkQVYiwKQMhQhRAZgf4kKgYwOwHhAXDHkRAOraQCmrrQPbFj6RupYUVHUi7nP8kgYiBhBRUC/AEdqGVFM8mhzQbI+SIJVHVDTCagi4WlYhAb7rO0lfolGonYgJ4oTmIxiKgnRGYCvhBJQClRifApyqQOfCRGvramqqQubIF5o4EXgsBsjlxEEmwLZlPSExEPSdMWM6WE3KO/Z6a/pyHy8RobinJNU5K8DxYAK5BUy0QVdUHqjQohMpDNzeRDRsP/eibbqljWmFxiXbdANM5QwCBgGDgEHAIGAQ+I0RMETDbwyw2f2fDAGLUQJ+fdW3b/Xr0Tonr+Xdj72VSMQeuu/WghyrY8duz734qQcQVbLKqZWgJPVAJXl0zXUjTm9i5Qw6c9R3G3G06zDMbQhQ/pEGaaJB8wd66loKX/BwmppTFhAXpQNFEhSpr6v6csoXb7/57muvvvXKG29Nn/ENJv37ztUX/bvEanbK8EsSPo63CWc4Dk/GP3jj7bdee2f6t4tQHTIA5rkA0Uj9io/GvTv+jfcrKjZCAN6m+MwpX7z/6X/e/vjtce98vnJ5jCiwfUcnX9hOonbCh+89+fhTY194fdLkWWs3xCIJRgQQ6mG6gUwyFiUk4rp1SsRxTK6cudOnvPv6qy++MPbxZ154e8Kni1ZX2Aypk6Rr+5JKEHX1m0BRe8WS95979q033o3YOKbmPDlz+kevvvDQ+Hff+OCDCdNmLqjD+AOwqRaglJy6emyPFAkHz5k/85sPXn/9teeef+ONNz/6YsqMJQttCBheTgp5EiGmTvpk7NjnZ8xdSADiTGDYBQduJ5VXA1AnoW5t9dLPvpwy7t0P/vPGay+/8PyEDz5eOH8VIqfPhEA3UO4oN5AJCJw1i2Y/99jDEyZ+rIf0IllX/e1XU5546JHXnv/PxxOnzlmwrt7D2jIAxycAynXsaG0VngWEKrJq9eKPP/vw2eefefuN/3w0fuLcOfOrIwkPIAmQAI8BQf8RSl+95Zb2VnGrnQ96b+ZqLwBMyuAe/tXxKCHRkB3RsBnRELb9T/aDMtX9ayLQmFZoXPLXbLlplUHAIGAQMAgYBAwCPw8BQzT8PJzMVtsLAhZOG3u1NbNeOa5Xa8tqc8UtLwYAlevmnnjMgWW51nHHnv/DIqc+gBpFGIi4XQVBgkXXXTfi9BIr57gzRs3ewHF4SSnyCEEAiqqA+dr0UIWGDjhFLYjAsH8Agb4S3ENbioB+N2v6lVeO7NCho2Xl5+UWF5U2bdWq1T233bxmwaLbR91WbLUcMvi82hiOdbGSLPndlE96deveoXXnCy6+rqLaYz44yYSC2s8+fbVDs1Y777DL119OZx4ZPeqazq1b5pRYVoHVssUuV428/4up32sZS3fm1x8de3Sf0pLiHCs3xyrYccfdrrnuzvmL11OABBWe8LlwKKlVfi3OzQeRTZULH7n32r8fsEehZZUUl1q5RVZu0YF9jn78+RexVQAxN+kyB5srnWduuLJLYcEuO3f/ata8FevWXX3Nv/bcrX2RZRVaVnF+wYEHHnHX/c8uXF2TlGhEiYIRThRIFCSpXr/ywTtG/63rLm0LSkutvDwr18rN7bLf3o+/8/q6eMTmPifO+qWLhw04Kt+yTj9v5MJ18RjD/A6fuJgoAvFkYtVb7z179oWntWjTNi+3qDA3J9+yypuUHHjAYaPvfPz7+euoRNbAB3AkcxObgNZdOeLUYss67LC/z5g5e8pnn553+vCe3XYpL2qSZ5U1K+t62OEnPPbs25UxjuEkHkX8gTMSBRpbvXT2A/fc1PvQnsXFVkE+2pTkWzm99t73jjH3L1xXnQSoE06ExhAeQl686cYOVpN2uxzy7oxVDrqQCMGJIRq2l7vLX6idjWmFxiV/oeaaphgEDAIGAYOAQcAg8D8jYIiG/xky84W/NAIWxyD4WPXM5wfv09LK7XTeFU9RFFSs++zN+w/cpU2J1e7KK59ZXMujOPL24qQKIMFj668//4xSK+f4M6/6voL7AC5FsQMZqEAxGTCiiQZUTZRYAsB96QlgSjmBHwXlCLtmyqQJffr83crJtayCDu136nf0gIMPOaxZs2ZNrJyTBx7/970Pb9mk81lnXFVVD66AZDIByl30zRedWrTKs4pGXHgNkSgbiO4GUPPJhGeb5ua3LWt37TU33XTTLXt13W3Xju17FWXAmgAAIABJREFU7Ntlhy6tcqzSpoWdhp549gcTP/v0kw+P6L1fy/Im++y158BBx+y55+45lmVZBUNPPGfadyuSGL0AmCYgbVBRgMi0b8ZfeMkpeblWfo7VtElh/2MGDBw02MpFFqBdx50eefyFeBLDCqLJGMZK8Po7zx3ewbJatuxw3sjrB58yrLx5k247t+vf58C9d+1ShL6hRW3a7XrNrY8s2xhHN1CJsQIgIovnTj3j5MGFyEfk9ui8e/+/99upfSeEpXn5DY/ev9aJYRKKYmsXzTv+sP2LLev8y27dmERhTIc7rlsHyl4wZ/LFFw4vaV6YX1Jo5eTvusseA44+8m/duhTlWtjAnLJhZ1zy6VezN8VpDKUbAaRN6lZecc6JbYqtgw88ZPQd9xx71NFN8vL26tbtxOOH9OpxUGmTdpZV1m7H3e99/KWNUd8PIOE6rhMDcBfO/XrIsb2RdsmzOrdvc+TfD+190P5lhYV5llVS2uyCkTfMW1+TBEgqX0kOvv/Krbd0zGvSZucDxk1d6mDsBJ4yqf67RgMyOJraMKkTf+l7z5+qcY1phcYlf6oGmcoaBAwCBgGDgEHAIPArI2CIhl8ZULO7PzkCVoIQCOzqmWMH7Nm8SXm3y29+KeEx4NV+5Tc3nj+01Gqyc6e/P/Xm1DUUIuD5KDoQF/GKGy44s6mVe+IZV8+vEKjIQLVTg5IqYAqQeiDQ4ByJOf8K1QAoqQNaD6Tu2y8mHnJgLyvH+tu+Bz7x7Euz58zfVFm7Zs2ar7/64qJ/nrXv7j2KraIdyrpcMOKmiiomAKjvAI2smPXlLm3alOSVnn/RtQ4Hx4ekEwWo+n76O4fvs0/XdjvvunvPfww/4+7b7p7+xRf18Yr5i2ded/moDmXtC/LLdunW48CDDjnooAPuG3PHnFlTV6yaP/mLCfePua19m7a5VvO77nux0oUEgM8osCSI+kXffPiPk/paBVaHzs0eeOiOL6d8sXTxsnXrV3382fgLLznHyrVat+7y8SezBIDtUcwYUHVjb7r8kI5tS5u267pv72NOPPGam6774vOPIxvXblw6//N3xw0+eoBlNdlx531ffHdSnScEyina4G18aPSVRZbVuUP7Rx58YvoX321YXbVi2er3J0y8/cH7x03+bF08QtDLQ6xfMHfg/nu2KSg487zrV9VjhoIjYhDEE3Urb77msoJcy8rJPfP8i155fdyihcvidVXLF3z7/jsv3Xrrta13aG/lNhl62ojllYkkQMRnoBLgVVx+5uAyZCIKDjj4yBOHDLvvrnt/mDtv1YqVSxYtfu2VVw89+BDLKtizV++PvpxNte5jLFoDPDH547eHHNv7lOP7vfLcY99983XNxvU1G9d9PfnTW66/umXL1s3a7/bYq+NDOoNSCpy9dufoDnlNWnXe552pi1ydwcGFIRr+5PeM7bL6jWmFxiXbJTCm0QYBg4BBwCBgEDAIpBAwRIO5FAwC2QhYNrUB4rXfvHlst1ZNyjr965Zn4gQEjYO3auaEZ48/7KBCq7xP/wvfn7ayHqSLI1xbJjbcfMFZza28k8+8atFGiRKJKetHrgKqgFMdpY/+AUxxjjETvqQqoCRZhVaa6xfdMPKCPMvq3rPXex9NYdrCwPeZlDwQRLiRB0ff0qqwWanV/LxzrtlUg+oPaE7B66qXfLt3l05tmra57N+3IJeBQoEUgsrvJr3cuWnTfKuoS49eb3/ypeeh6IFelVw3Z9q/Tz+pZXkTKye/4249n3tlHMfakQAcBXE3uua84SflWWUHH3rC9CUbowF4LgM7pjYuvv3fp5eWW613bvPQs49TvG0AJYyQOoDo2jXfDerfJ8cq+9cldyxZFtMJITaI6hdv/3enfMuySvfte8LrEz6OUw8rrgRIAsL97J03u3XZ2bLKLrjytoXrNgkAxetr184+76Qjiy3roosuipPAUxD3IUzKYAA1dswTCAtIHlmxaEDP3cot6/Rzr14TwyQICbbi1eNee6pz29Z5VsH1N99TUe3IQCsyMEdiVeNc1D7yxJjC5sXlbXcc+87EagZVru96mwBq7rx2ROvSXMsqOv6kcyd/8R3qRmhnDQDHS6574cm7WpYXWTlFN4x5LEYxewVTIWhi9ZI50yZP3LR2KYo1oCcoRZFOIInq9aefNjy/dMdLb7x/2aaYg4qYPgj++l2j21p5rTv1HP/NEgKADqZpokHrVKZcJ7T7Zsp1Ak1RtXepvkyNGKSGwfz5oxFoTCs0Lvmj62iObxAwCBgEDAIGAYPAH4mAIRr+SPTNsbc9BCyJY/V47fR3B3Xf0copP/NfoyOuribdxOoXPHfvtS3ycnNy2t/16NvVoGogHoDDbSQaWuTkDzvjqsUbtKMEp5xTIf0gIAE6I6IMQcBUQCX1JRdAuFABI4lKoHVrf/jqyP17FBbknXv+JSsr6hlAfcxRSuFQViSlt2nutE8O7dmz0Cq68ILrHAKEBYp7QGuqFn2zZ7s2LYqaXXTpDSiI6HOMnOAVK795b7+uXTu163riPy+evbqKaqcJJxkDZQOrevKmi3ZsVdymfYdTzx+1uoYSrlwv7rF6DM1IrLzt3xc2z2uzU8eDxk2Zi14UAiBen1g0/cTeuxeWWPc+91hcKTyWjdPxnNZ6zkqAmonjX2td3mnPXfu++NIXDICwBPcqxj12y06luU1bdr7qjqdW1ESpJlACLqRdDzyxfNbkE445Ije3pP/QM6fOWYz5JDSy/PvJx/XerWmudda55yxYV2Nrj4qIh9wNlcolSFVwLykitYkVi4Ydtm9zyxpx6c0r6xAOX1b59qrR111WZOUe1+/kjz6d7QdAJVAmkAVQNkDU8dYtXzntzHOG5peUHDF42NcL16JPhYwA33j+acc0LbLa7djtqbEfROJBJMajUR9ASVENauOcKW8ceVAPK6fwnMtvXFnluug5SYRbF3CbeZHQI0NQhxGbJGvtuvWVK38Y/o8hltV8+IU3LN6AbacU+arX7ry1lWW16dT941nLKAC6bxiiYdu7C5ga/SQCjWmFxiU/uROzgUHAIGAQMAgYBAwCf2EEDNHwFz65pmm/AAFLBQRUvHrqOwP2aJ+XVzpi1JiYjzQBl3GAOq967o2XDC+y8nvu0+edr77ZJKgEopzqq88a3iqvcMjJl6+JYA6Ak4zhwB54IlrJeVKgVQHHPHuF8+RcgetjXAK3a0DG3nhyzI5NC3bpuvNnU2bYDFUDYjYRggmUlXQAary6pf8+97RCK/eM0y91CMRsGkY0bJj39T6dO5blll559RhXoZliECRBVi6dMq5DQUlJXqtHXny7HiDqAmNAfQ9EHLyVcz54sn2zvBZt2j7+2oRkqOCYiCf9BA7q7ZUfPv9Ia6tlu6Y93vn021pttQl+8q17RnVvafU6aM+vF82zAWI+Jo1IjJLA6pHkijnfTum5+yFF+bvc+9C7XgAJPw4QeeHuK9vmW23b7TlpxqpQ8SHiCIHGng6IOogtv/2Kcwqt3GMGnjx36TrUSgBeU7HgmhHHt8ixmpQ3HXXnQ0vqWRwgiskSgEIZAUjqA47Y3bpFcwbv26Pcsk4964oqF6MLACLrl3w14LB9iqyCB+95IaFNIjwJsaStTUNdLqJMbAK++u3XHyoqKui8+z7vf/2Djce1hbP6gtMHFOdYB/Ye+P2SupgLPkNXTgDlJSuAroP48usuOC0/r3TACSM2xLT5RYCOGQAu8+t9t3716sUvvfzM1VdffspJg4YOPLzvAbt2ad/Cyml6/Jkj10VpgkvKXJDOy3fc2DbHatdpzw+nL/ACYCrg0kd/0kCGL8DwBSUDlPaQgVBKBQFgeIWWadAOqb/gqjZfMQgYBAwCBgGDgEHAIGAQMAgYBAwCBoE/DAELxRqlXTX9zYHd2+Tnll9w5T0xHxUWkoGb9DdCULn0m3eP6rV7vmWdPfK61TYRwNxIxcgzTm6ZV9x/6IWLNjCuwLORZajauPqGay7v3+/wwScMPbLfMYP69fvH4BOO7DNw0ODTXnzlHdclgRcDv/bJu64us6yB/QctXr4x7mMaAyZXgJDSBQw0qKSRhSPPPaXQsv5x4og4DqoBbTV5XeUPX+/TuXNZbvmoq+51Fbg4DE2C2LB0yntdStq2Kev6/Juf4jy+G7gexxl1mQB/9fyPn+vcuqRF2/YPjn2nmoEfgO0RXxII4pBcOfXN53Yp2alLq70nTJ7tBDoIob7m8VFndSy0mjQr6HvSSYNOPe2IYwf37Xt8v74Djul7yHEDDj5xSO/jBx3dvHSnstLuDz05HtkR5QLUPX/vqB0KrB3a9/z8m3WO5kGiHuZBgHRAVANb8fjoi9qUNjn++GFzl67TuQ+gWGzq+GcHHraHZeUWt+7S9x/n3XjPMx9+Ns31EJIgCKjvMTcB0rHXLD7jyENbWNZFl9+2Pqpo4CpRNeerN4/ar9uOTXd4543JURdiHOkHCejsoLjHRMInlQAVX3z4bNvmzXfutu/bk76rtJn2oKy59OwhpXnWgb2Pm7M0EveACiBIBinh1wOthrolN597SpPCFscPu7gygZka6FUBUc+peuON54cM7d95p/ZNSgutPHSd2KF5Xs+dStqV51i55YPPGjm/ohYdLrgDMvnyHTe2y7Xad9pjwrR5XgC+UlQZouEP+7WbAxsEDAIGAYOAQcAgYBAwCBgEDAIGgd8BAUuP452qb14c2KNpYW6ri654JOajEkMM/CpnA/AaEJvefOKqshyrpFXHx14azzHBIXHzZee3LW0x6JR/r6zTHgHaWmLxgtn9jzwo37LyCnKt3Jw8yyovKs3LKcsraDnmnieJJ4AnZWLD3ddeWGJZQwYPra4lSQYxghPdlLlKeWht4a8S9QtGnntKWU7BkMHn1kRx/9rHsT5FNOQ0u/LKBxwBrgIZOCArl3z14a7lO3Vuttdr73zhAdi+T1iSSQ/TB9zVsz96qXPrlq126PzA2NcrCXcDIJjMwZWIgLPs2/de6N5s564t9vrsy7kYZcDAq1h/y7mDW6Jfg2WVtrDyUDIxxyouLSwvwAVt5WDlFha133XPPg+/8F4CTRbiglW+fN/VnQpyOnU4YMr09UmBg/MExcpju0QlePMev+2fzfOsY44a+O2ClbUkiBGKq1Tdopkf33j9Dd26H2BZzS2r6T57H3TdtTfPmPW9yzCHIuFEgMdiaxYNObBXuWVdOnLM0o1EIIMR+frD5w7YpU3HZju89/bnHkA1EU5AJXDPTfqOq4BzWg+yctbEl1s3KW/Tutu4T2bHGQgWAXf9VSNOaZqfd9Chg+YurrUpcj2EECUpSB9oHKqW3PLPYeVFrU487fKNduBg2EGUxFa88NyD3XbvauVYHbvscupZ51513fVPPfnoxHEvTh3/9LCBh1p55WdcfuuKuqSLRIONRMNdN7XLtTp03OOjafNctRnRoIRUGO+Bu+bAOKQiGjAKRiszaNUIo9HwO9wEzCEMAgYBg4BBwCBgEDAIGAQMAgYBg8CviYCFI1bpVM8cO2iv8qLcHS4a+WS9DzYENUHSA56MbQJaw2umjTjlIMvK/9t+R373/VzFndtHjSyyio/6x2VLIziL7vseBMJL1L396jO33HDFvfePuffBBx645+77xtz94P1Pjbp69LffLeUMAj/ux9bdcc35ZbnWMUf2q6nzGQoJYKYAZQ7KN4IN7kpeM//S04a2KCw75eRLKzZh3L5mBlIRDeVWiyuvfMjlaHspVBKgaun0j3ZrunPH8p6vvj7ZB/C4K8Cm4AsRh+iSr8c90755qzbtd37y9bfrIdDWmxyHtawOkou/Gfd01ybtdyzt9unkbxlmOQSytv6ef5/eLt/q3mOfMy4Y+dgzTz31zOMP3PfwvWMevP/ehx5++NHHn3jqzrsfuP6m+594fty0+Ss8TMcgwKtfu/e6dpa1U8fDJk/d6NBAAhCOHpxSeSArgc57YvRZpZbV/5hBS9ahB2SEUCEJt9eDijvxxFtvjj/j1BF77vK3krySPCu/9xEDPvpimgdQ69QDONGKJf337t4yp8nF/7p/SUUokBmd8fELf+tY1rKg7M03JvhI0kDEjwtgOFAXmAehWAzoppnjX2lX0rpl893enzQP0yNE3K1efM2I4eU5OfsfcOzshZuSDE8B8ZNceGhLyWyoWjr6n8PKCpsPHX7phoRIIuMRWfzdxIH9e+cXFhw35KRPPp8ec5GeYD4FGgO67srzT7Rymgy/+Pr1NvO0bgWI+Mt33bRDXm6nTnt+Mm0+BqHIwFfoSxIEyDL8JNEgkYYwL4OAQcAgYBAwCBgEDAIGAYOAQcAgYBD4MyFgSRzHu9UzXhq0V/PC3PbnX/lkPQUbWLVI+CCYTwNSD7B6+qSnjjhonzwr/7J/Xb5i1fLbr7s+P7f88GEjF0QgJtAVEh0oAw4kKvwYl+FIWNnRmJJQXW2jl2PCE34k8CrvveniEsvaf+/9Zn+3zOcQtZUKgHEvUNSuXweqKrFq1klHHlZqFY0494bKOnBR3kGBrN0wfwqmTljNrxj5sEPB4cClDcHGxdMnaqJhr1dfn+QDJHmcBgkfqBDxILL463HP7NCydesO3R5//d26AHxQjnJdlpQ0AnTFl28/0aGgbcdmPSZ8Mo0BIN3BgpceuLFdkXVkn4ELllWjqQTwurpIpD7hOswjIkBaJHAIMh0R9MMEhtoN8dfvuaG1ZXXqePinX210iDajECg3ICQBVQXkh4duO7PYsvr3O27p+loHIEa5UNSOVoByIQBBJI3aG5Yuf/iOO/bYuZtllZ18zuXfLl2TQHEIt2rlDwP26dEyp8klIx9ZUYOijwDx7794/dA925VZ+U88NrYy7rmaaHD9BKJFuOfGqVsDIvLBC0+2b9qxe/e+H329VDthOMCrrz5neJll9dqv76wFG5IMeQlCHa6IxwnwONQuGn3O8U3ySwYOG7E27rsgAlLx4RsPd2jTbMeOXd8bP9nTXI7DtHwmt6Fu4agRQ6yiZsMvvv77tVVI99AoyPiLd93SPq+gc6cen01d6CpwBBINYnOiQYJiqKrBOKBGQyaiAYUqDNHwZ7qZmLoaBAwCBgGDgEHAIGAQMAgYBAwCBgFEwGJcgnKrv3n9uL3aFea3HzHqsToGSaA+qIiXcD2fMxdgY0CXv/747S3yrTbtWj/69NMXnXdh05adjz77prn1EA0wKsLzHGJHgSVxxIqDSWQHfOJyKpTECfO6+iiKKQb1Lz5+c7NCa4dW7R5/7GUfMzFQ+NCnjh7RJ0DUzXj/xQO7di62Ci6+4Pa4hwkIAQhQmzYumLRP5w5lVtMrRj6c9MGlwHkCyOqFX72/e/MuXVru9erbk3CwTWNJHiXAlXLAWTJr4jOd27Rt3WGPB158d60nHFC2SCZdW9I4BBumfvDijiWduu5wwMRJMzA0Q8cnfPTGIzuUW21bdfxk0uyI6/ugbM/VkfyQiLtCD4dDtUJPiARxHTcGynn97uvbWTk77tjnky83+D5D6weGDAnjLkA1t2ffc8vppblWv6OPm7VwdR2DGGUs4AHGWHDFuBuLBU4UpJvctO7R+x8sb75T2677jR33GcousMTa+dP/cXCvUiv37IvuXR0Fgs4SdevmfXbO0MOLMEriH7OWrHExJkR4fhI4SIdq6co4JCpGjTgnzyobdMKFM5fVx1GhMway9qp/nlJuWfvu1++7hRttjjV1OaFA4zwZiAjU/TD63EGF+UX9h523Nu57IMGreObeqwota++eB65YVYNeIUkgEpyED5Ismvza/rs0s3KKzrritrlrKr000fDSmFvb5xXspIkGR0BSBl5giAZz6zEIGAQMAgYBg4BBwCBgEDAIGAQMAn9lBCyiCARO7bQPB/foml/cesQ199VS5Sg0rayurUm4DhF+3EMzh+qFE88c0NPKsfbv3Wfnrt2Km3fqfdYNUypkXQBxn6GQInPBTyiadJlHMGlACcFi0XpCiARIJOPatqD2h2/G9evdo3lZef/+Jy1eukkoEDJgaDmhQPmLv/zwmtOHdi0rLbaKzj77ehdHp//H3ntASVVl7d+nOgdoguQsCAojgmnMjqOOY3Ycc9YxDyiYMCNREBDFnDOCYkBFQJQMiiBJkBy6CR0r3njy/r59bnWD4/i+47tm5j+Mp1at7qpb9557zq+qa/V+7t7PBo72lDt2rJnet3PrRqRk4MAxmRBcqhlLgL/5hzkfHNi8fafmPV55//MkQA11HRFQLPX3wF+9/PMnOrdq0rxd99Gvfb41gKQCXwvfV2EGPRoXTJ/cvkn39q2OmDb7a1dj5wXNgvUrPr74vMNyCTnljxcsXbs1MEUd5uq6CAMHtKDp5HeL5m/64XvsrAEQug7I4MWH72gdy23V5sRpc3ZgQYF20aeBAg0dgFoQP4wZccN+ZaXnnXfF8vUVRhSAtJ9JJuOcmx4UwL3EVtAJUO6sadMaN+3WvO3hr0/6Cl0egvTuNd9cddLR++UUXXT9kE0pLDJRLMFq10x+flSP9t1ySNP7Rz69vjJB0UFSonhDOUAidDe9/fyoHp3alzRqP2LCO1tcSAJoVSfi6++9/srmscKjjj576Q9VKYkzzQiODUjAZ7IGqr8ect0fSopKz77y1nJHelqBv/P18Q81yiGH/Oaw+fOXYdEEVqngmwaZ9JuP3dGhhJBY4TV3Dt2S8FwAL0yCzLw2ami7vMIuHQ/5Yt4aR0KGQyQ0AE7S3EHtndGgpTItN0zHDZvR8N/8zWPXZglYApaAJWAJWAKWgCVgCVgC/7UESKhczGhYOOPM3/TIL2p2473Da0PlSe15npRYV0EB6jzsayhrln382pB27RsXlTUiJK+kaaczb3jkm93oHJkKOeUMG1hoCppJUFRLP/SMVYAQilMpXD8teULRXdLbPnroHY2LiwjJf/jhUQu/Xr5j187ausq169bMnvX50Ltu6dm6cduCkhaFbS688HbX9L/kygO1EzMaOrduFCseeMejGYo2DIxlgFeuX/DJAc1at2zS8dm3P4gD1HKORRBRX8nMyq+njm3fvHi/dgc8MfGrGsBCD6xq4ODEk9qvnDf9w1Zl3crKen38xeI6Jh0GXASgKz754InWTUvyYkXnX3T1tC/nb9y+ffuu8tp4VTxRvfGH75998vFbrr16wtixyXgCNHjpFEg6/r5+rQsL92tz9LTZ2zhlKHOEUoYq8DMAaeGtf+S+q0vyY+ecfeFy03XCCfw1a1e++tqLb7/z+pJvF6XT1Znkdurs+G7J7DPP+CMhZSeecsmSFdtAg3YzO1d+fdYhPZrn5F/Vb+Rmo2AEQRJETXzLypuvuCaPlBLS6J77Ryz5dnkqkQ4TfnznrrU/LHzqmUcO7t4+l5ATT/3Tkg1VSYA4ui2keGLTAzdf17Kg8fEnnLdiY11WaOAsBOEDpaJaVX7z4LVnlTVqcsm1Ayo9TKAAWjt94tNtmxbHCLnwoktmzV7gU1Vb6yyas/S2664/tFPjzi1ySGHJuVf3K08FQSQ0COeVUSPa5Jfu3/GQWXO/dwVkhAqVUMDBeDSg1qCh3gxSSC1QaFCm9wdut6UT/7XfO3ZhloAlYAlYApaAJWAJWAKWgCXwX0yAYL2D9LbOn372kb0Lixrf/uCIirjjcMmlQHUA8+9VqEOh0iBrQFY+8NBNJBc7LzRv3umSq+7ZWM5dBtUJFy+8g9R6zx0008CYCKS5au366TBMAHjAEnW7Nt1/9+0lBbkxQvr06X3yqb+74KI/nXrGH2IF5NAjet5y6/VnnXJu67L9L7/8zjXrarAKQ4YgMjVrlvRq06qA5Nx2x0MuQJ2vk6kMyMz386Z3aL5fUVHz16fMSJi+klQBowGwJMiqaROfbL1fSUFp2Yjn3soY3YRLlk7Veaka0O5bLz1fVtK2eYueU79c7gH4AHHfAXAzdZtfenxkq6LCAkJaNW161OGHXXTBhZdffuUJJ57conUHQvKLSpuOn/BcOuN7XsADX7rJV8aPLMvLLW16wKezVoIGP52QocCqD4VmlzKz/dH7bykk5PcnHL9izUZsJ5FKTP9kSt/endq1Lvrt4b3OOfuP1/3lqgsvvmC/1q1IbqznoYe/+e57fkDdRAIErV6z4rITjikl5E9X3V4eQHUANakEJk34dbvXrL7h/Ata5jUqI8VdW3U594/nXXX59cefdEqzDi0IygI5p55yxsy536B7hZYJLw6yDrzKB2+/pYjEDjv0d/O+WV/nqVrHlyCocLBbBHg6UzHirlvzSN4fTv3z5i1J4y7pVW1eNnJwv7LG2Hijd+/e115+w8XnXN0kp0Wn5p0uOPu0008/sVn7Dhdff+u6CjS2wDyOMP3co6Mak8L92x38/pTZ2DNVKqakEIxzLgTKClprpZRQpsNpw99ZvQNk/e+GF+wDS8ASsAQsAUvAErAELAFLwBKwBCyB/3QChKWSWGM/+9PeHVoQkjPgvkcSgcBEBsVCxTKm2iHUAVeOUGmp6jZtWnDqKX1ihBBSfM6Z16xbWyM1BMZNUKDZglZ4iVpjbrxmoJnHXIHPFeU+QyMGtBHwM9VbN64aPfKhAw/omO0YmUeatGjao+9BU2Z8uGL99zffMCCXND3r7OvKdzroeygoBM7qr77o3KisgOTcfPvdcQaeOQ/4mRnvv1NWVFRQUPbau595GlMwvFAq7oN0Iax797UJbdo0LWzc+OExT9aYfpOmRsMTYRy09947b7Zu2YXktpg8dWGlB9WhrA0cYzrhBVXlX0x+87RjDm/TuLiAkFxCcnMKCckvadb2kKNOfPmt99dursAOCpyDFsJLPj78gTxCGrfs+vb7s7SE0PUo5RIgxCaOwq3a/Oh9/RrlkBOOPXLuwq/R6ZCG61Z+c/utF7fZj+TGSH5BrKCwmMTySVHJmRf8+Yu5X6b9JCoUfgr81Jp5X/3p6CPuKfv2AAAgAElEQVRKCDn38ltW7HJqKdQ66A1BM1UgvC3fLBrWf8AphxzVhBQXkEJCCklOPikt3r9372effXXxou88kxiS8BISPIBMcteGW666pICQXr1+u/DrH7DdBn5QOWMpTBJRQU35+n5/uSqP5Fx4/pXlW5MggGVSIFPbNi4cdPdVrdqU4tsfa1RIGvVo2/uVCW9u2rDxpn43ktyck846Z+3mCvSqRG3If27MuMY5JZ3bHDR50gxqVB6plZRSCCGl1BqVhEhr+E//Q7HzswQsAUvAErAELAFLwBKwBCwBS8AS+McIEJbOgKS0csun77w8evSYGXMWYfkAAJeMKmzCEGU0GKHBkSoFkJg5fdKYx0aNfeyZaZ8syKSxpj7lBhzLEZjUQmqFV6qx2B6LKVzqMRBGExBKhuh9qNA+EDT1nboZ0z4e/djwMWMffXjY4JGPjZ45Z1bST0pQC75aMuLhJz6eusAxjopoFSl8Wrnj6ZEjxz06ZvqX80Ps9YCV/CB5enfF+DFjR44Y8/0P20KBk/d8EztjHYe7bs3Sp58Z/9jj4+Z/twqv6oeC480JnCoAr2LbxifGPzNsxJPrttX5RlVJi5CrgIdpkBQyya3fr5j8+ksjHnrg0WFDR44cNXjYyBdfe2fxd9+nPM7QoxKMwwIH7q38evb4MaMeGPb4ig27ADClwvWCUGimTNeMMLF68RfjRz406d23dlbHQ9NKQ/PMropV7058ety4YYMHPzTo3gfvf2josy++snHrFqwvwDsF6oCk3u7yD1567vHhI6Z8NqeWY+ZFhrOQOsyvxRoN7ou6+Neff/nMqCeHPjDsofuHDBkx6o33Ji9d870J5XGegUa7R8Yd4EmgybnTpg5/4OEXn3+rsjYINcQzfkgdrQMjsnAQ7uzpHw19+KFPP56ODhIKvLo4CKySqShf8fbEl4YNGzLi4ZGPDR735dT53MHP2hdzZj44Yvj0eQsCBbXJDA8d0HT1N0uG3Tdk/KjnN2/ENIcA1QUUGqSUSmE6Q/QpxWYT9mYJWAKWgCVgCVgCloAlYAlYApaAJfBfQYCAViyTBK8OFJoypELuofkiCBlyGfpaeVpRFXLlCekJ5SidBggE40oCC8F1MY0h4Ixje0IqNbZkUAoTGkAKrbD+gkpTfm/y77VCEwe0cpDUBNJcaSYlZ4IKPEhpEK7rCheDeC9A+0BsuwAcaBoE5ekMKPQgDBSkHMap8pJJUEIxzkKckh8IbjIrGA048znzlKZoSwk60iawzwU3ORJ+XMs0AHVdH7tvUjyRI5WnTNfJdB3LpIJ4HUgOytw1BsM6sp0ACLgS5qlSgocO91OgsGVFnSfCSAARLAgZU4AFJCjCOKBc6SfAdISsS3u+54D2NWQAbS44Y4xRTAeJbkpS10lo4aEogxOgykmCEA6LLCbBZcyIAkHg1GBbTm6ySjB5wsA3RgfmTQApIBNwj6HBZxBmuFuD2oQIIQhBo2lnIsPjmchQg0spaeiD9kF7uC7M14Aw0PjuhZ5iSSysAKaUMO+nwcEBkxRwBqj+ZNuIBBl8l4VUgfF3VMAV+IzbjIb/iu8NuwhLwBKwBCwBS8ASsAQsAUvAErAEfpYA0VJJ6kunJkhUur6HRokAbhgoSaWiVEuqpVBcqtDcfUytBxH66LgoGLge9ptQwCX2OqRYbq+MJYNEvz+8ao3Z/yJk9eEmCM1DLQLNQ+plGPUoDVw3E7KASpHKpLHHguNqD/Me4inqafBB1bjmur0M3No6UBAw7YWahnjVP15VRz1fMYyHAdfCMHiW0vd9qRWVgpkOGgK0E/hCglKR0KAAqBAZxjJh6GuAtEsDASnfDyUKE6a8QjPX99MJETgi9HwvHQQel4JxmfYwZKacCYWaDPpfMkexNKIESFDph4FUKBugPYMCx/VlmAHmMC+tlEA/Ri8MAk8I1w9rU05VyDzOUSjBqN6naDChuMBKE8xrEKEnqa9wbOVT5YSKK0i5DuW+0kFdfJfgfrK2SlIjHIQidILQDwLPTydT8Vq0q8SsEy9keAKB8/TqAIUAtJDwQu0x3AFAGYkBnHQmVbuLB3WoKMmAUo7JGwq4h9KDkiHjPsPCGso8ylysa+GcMhGEgmd86vjGcMEwEaEXsaQBppngW2TqJiKPhiipQSnMcfjZT6h9wRKwBCwBS8ASsAQsAUvAErAELAFLYJ8iQFzXxaBaU+omvACv7aOtAAu0wsIHqRXXCvMUNIvuUjAtledSRk1jAAyNhdC+ghAv3qMZpPFpMKG6NsnxDGPTwMTkuFVzJkKPBz5oJQULQ4yfFUgn8L3A91zMWQBf+ynGFV69j0snwRIYsHPfiaMxoVTAjfkk7qmBY+pFoLkAxkBK0CoMPNOzAMN0j3OmJBPcnAgTFFwH0wdMDB8I6flBJgxDtJcAQG2ChwJNKnBkhTUfUvOQUQ/vLOScanzFIONhSF2potQMqoXn+ekU4/EgpDwELBgBplBoEFJjVC985mUopVEqAo4BXAOVJhMkCJnv0tDnWGuhFQiqRWAmKZTkUuL8M5kMplRIJJBBoSHUIFKZeEAxA0QIhvG/kJKywHEV9uo0czWn8Y2OgSflHggXFAUeBgH1qZEfAMIw9F1sZIlHARUsQWmcC5ejo4IKnYC6oZJcKRFQIzRg6kMgKO7PWOj5aQVYC8EFCGGWpgMaup7juw4PvGijFRr2qa8HO1lLwBKwBCwBS8ASsAQsAUvAErAEfjkBEsXMUoSYdiAwpJYYayqT2YDRfPbyPm6J7iaa1MCZDgKKxfY6svmj5hATptbHt1GtAcbGSmLzBTSJxCGlwLwDvNLOsVACr2lrZboz1J+QmQ6Z2KNCZLRHgfo0ZXIH8CiNJQCmXML1tQZKOVpCYFjP8PK6VloJLhRT2qXC4+jIaCoSVHYCJig3Bgs4cw1cY+ZBdFMmpA85x8vyOFmFaojZAZeP+QgoQ2RpaDClH4pKFZpwHl9jeCC6NyhA0QB/YbqAMhkEWH/BsfYAhGBChlR4MpIkuA59bqQNLcIAd8Y2kJGggiNg4oSp+gClURTAegZUhSRWVmCuBtOcY0NSnJsMAuWFWBehUUFIe2gKESUUGEQU6xpQh8ERBDpWZms2Ah/THCIVBsBVOmACq0two3nvtJZcmgwHEGHo+x7Djpl4UpyqQndMhBkBMRvNgWZ4gSSxzUTk0VDPHP0gGx7bB5aAJWAJWAKWgCVgCVgCloAlYAlYAvs0ARKlMDREzlEsLdFnIKqiaMgcyNr1aQ2eF0RrNvGhMiFlFLWafaKINBs5Ki4xhI0idiFMAwrzXEslonoHM1a0D+6A18Sz4gbXCgUH4LVenfEjUJyKwGTm4/B4jInbuTSP0ZgRcASM7KOyhWgNVAoT7OPOGKobqUJqJjT6PkYj48ZIrcCYGQ9HLcCcJFpsJDEoJTTWhAilBGNYRGAOF5HioEEEkgtAJgJfMkKAqSVBjYMxUBqTN8x2cxKuQXCJbg7ZJUQg6uFrzE+QJk0ALSGk8ZhEMSbq14BWEYIpVCPMqzi2kKEZOTq35gyfRjOhzLguaoWOD9TDAhaN7oxMZQN9PBWqMMpkavhS4d28GcjATATZGMvPSITKzlopQZmn0QqjARmnwmMC1RB8O0wpjfmYRTjtT0vAErAELAFLwBKwBCwBS8ASsAQsgf9OAlmhYc81//oQ9ydCw8+tf0/uA+4RxZn1KgOmMpiel1hDgTeTDRFlA+yJSLMj79mAsgQOZeJTITAZn2M8j2aRKCOYLIuogybGr+a56WuBL+C9IQ5vmM6PBs9OE40ns5JGw37ZwoFsZB5hya7mbwGoSHFoSHaIzi0xpjbJCPgTx6mPxY3vosnpyE4GX4tY1O+zF7d6/nuI7vUeGUHHHBRpFpEFpEEtcT4Rh6yegadoWF+9EIClDeYeNQTBzT++NbytkdKEL9YPEs15z5jm2GxGw17jo/gSJWs0HGmFhh9Dts/2GQLZ76+95vvTLXu9aB9aApaAJWAJWAKWwK+OQPS/gf0P4Vf3xtsF/wwBLJ2IkucjO0CT3i/qM/ajbP8ol/7vDpCNOfcEkNkYOtoZL7T/X4SGKKg12fgaiwIEKgLZ+BmlikhowC2amzuG1pGKgdkMkZyRjeGzGRn1QXJ2FVlBIhICotM1LMXE/0bjiMwZfxqEN6BAraFeLKgXORqUmvqqh2yYHUkY0c8orMc5mUyLhsllw/1IAdkjAfz49SikxznsvR3XjbcGoaF+XoZDw57ZxeCJshkrBoXZnD07yh/R/g3r3OtcODdzbHafH8kfe06DkzTvHe6WveGjrERSv8n+tgT2DQI//afhp1v2jZXYWVoCloAlYAlYApbAv4ZA9L+B/Q/hX0PXjrrvESBRVBl1JTQB5B5fAFwNNib4kdCwVwhaH5o3hJcND/DIbLTcEO/+A2wwCt0TlxqhoT4erg/IzSj1c9jrqrs57Efnzx4eXbffc/ndDNAgCtSfLsqhiBZk4uF6oWHPgQ2D//yDqCrCjIIKQv2cowPqaeEp99zrSzrqt5jfmBNhMiOyZ8/unyW4R2j4X5BGY+JOfze8b6D3N6+ijrDn0OxbgjMxmsKPjqqfdXSKvdZrTlqv5jRMM0LQ8NQ+sAT2GQI//afhp1v2mcXYiVoCloAlYAlYApbAv4CAFRr+BVDtkPswARIlL0QWhhjVY44AZgFkr1T/rNDQEDfvHTbv0QlMkInXtP9hoWHPgEY0aHhqHvwo8N077m6Ie7Mbo2nXjxCVMPAo0q4fAw9puIyPe5q9s8dnSycioSTSI7KvNATVP/MAd1ZopFA/85/Zb89w0fB775Zl2SA0ZPfdc4gZPFvu0fCp+/EI0eaGbebp30gJDUfWT/VHSgQ27Ixkhfrz4m4/EhrwU5FVhaITZUf88Vl//KxejGg4uX1gCew7BH4qK/x0y76zGjtTS8ASsAQsAUvAEvjnE7BCwz+fqR1xXyZAUFkAlRUa0P8gEhpQIdhTTFEfjEdRef2VbRO+/k0uQBS0Y1yavZvAuyH8RlQN8Wd23yy+hqA3el7/dK+9Gx5mj4ieZzP5fzSsGblBg4jO3rCGaHt2DDxs79uep/UT2CsIj07YYItgqia08XaMxAvsQIHrzU5s73F//HgPHuNLudfCzMPoZTO1hpdwYtkp1QsNe8+w4TGe6EcHZc/8d3aon9PeL2WhZe0dsgNhVku90BCN/iOhoX6c//n3z4kd//NR9lVL4D+CwE9lhZ9u+Y+YqJ2EJWAJWAKWgCVgCfw/ImCFhv9H4O1p/0MJEOzqgM0dsCmjMVpEoQE7RZqiBRM2Y/fCKADHaFMrrrN9B7RU2KkhqtmPYlPjEYAuiSCj9g+Rk0LkHRD9+f0oys7+RRpngazhQkTKRL97Rcz13gDGhKHerwGDenOuqFAha9OAHg3RfOtD6IZxcOxouuYs0fbsW4PzMsF89rn5hS4M9Xc8dXQwZkpgi0ps9CiUMao03opaUcMEe1mauzI3IbWIGnxK7O2IHSvMmLj4PSfb6+GeadT7Ypo3IFIBGuYZzSWqDYm8KrBPJ/aPjPJJojyE+tXtpVPsUSLwRTxvw8KjJWI+S7bhZ7TovxEaouXVfyr2LOF/emSFhv+Jjn3tP5xA9EW19yR/umXvV+1jS8ASsAQsAUvAEvi1EciGNebXr23tdr2WwE8JEJABaFEvNAjQqDtoABpJDxjK/0hoEEoyJYXiQnElpBLSdISIMhjQnVEDhrvZgBXLCQT2TzTRdRR2owgRdY4wioD5Y/yx0NAQ+tYLBFHLSzNJxUFgG0UjcKDgoFD7MAE8qh7R3XRx/Ek43TBsQ9JBfWwfBebGdRLXnr1lz44x/F6LQhwcIJDcFywQIZeh0tQAo9gTE5mgyoImllIbfQFZBZwFnIWScsmkNIpAlByw18/6E++1KVIAsoJIpCxEm6JZ1J8XqNJU6YizighHP+vHbBBJskUQ2dKYbP5DNKb5aawl9hIaTDUNvseRR4OZG75/2dIJFCnqkdW/XeacDU+yr+J563esn5T9bQnsIwR+Kiv8dMs+shQ7TUvAErAELAFLwBL4lxCI/jew/yH8S+DaQfdBAiajwVgLGDlBRZUUe2U0RCkADSGiEoKZu5BcSK6EUFwKLhTW7GPvRgY6bBAaBGipqJIhCKokZyhSaCnxng2as1qDjCJ57FWJwWg0Vv01cBOhYtxuBA4zIGocwEFhPK+Z0sKMiZqG0T6klH8b5+4ZtmEte8XzOHVUE4wFY/0OOIQBYoQGk6PRkKahw9APqO/RMBChxIoJjlqDZpIryUFyEBzbclIJXAVcOT5NBdQNKQ+FkWnwxT25IHt/crLR+N/UpCCTnwoN5qQmmULoUCgqFJX4rkglspzrRzZCg3mTwEgGUWkMnmtvUvWPI1nBHBsdWC82ZXfYI1v8rNCw98hmoOjQ+vnY35bAvkTgp/80/HTLvrQeO1dLwBKwBCwBS8AS+GcTsELDP5uoHW/fJkCi5gjZIDAbjeOS9goLswG/UkorJpmDSRBMYNiL5QPaB+mhugCuK4A7AK6kIV7Mx6qM0PVrQWZ0mALgnlaYKBFyPBAgBKBGKYhC6D0BNob9KCwIEAwEighcQcBMIoQAEdLQFQLrOzyfZ/zADWlIFWcm98KMolGCkAKiRAlp9IsoySIrcNRbXdYXDeD1eaxBMM0m6i/dZ7M5sMxBasFxPmZAyYAG2nOjq/oSYPvu3ZgCIEJQHFenTCjPgFGE5ItUytsKEJc8KTgOHipWG8QdoKGBgEF81hoDF5WVE6Kmo0qaVaFBI+6D+RxUyehuxAyBsoJQnINkIDzm4pKUEo6vKWchlSjCYKaDSQPReHqTThJoCDRwZWw/I9Uhqj+Jij5MjgcWdkS5Drg8zBapvwnMntCB0oHZw3xgzJdr/V9DvUhU/9z+tgQsAUvAErAELAFLwBKwBCwBS8AS+JUQIP/4Oo3QILifAh2ApH5d7Y6tFVvLd1eFIaoLAF4IoB3QGWACMx2A+yoNgC/Gd2ysrNi6I5n0ATjFZPvQHBJKwGyIbDaBMZDEWFYoTC7gAu/YIQJlD8qAUp5KYEANIuOmPZ8xCb4QgdBMgowaQuIDtE4wHSIbTCgxXI4i96goQOBTUyWRjaWVycXgDQYHeErMzkBzR621ycXANAUNHIQP1AE/ndxZUbW7WgAEClWKVDIOoHDlRqRBOSIEiaF9RsAOgEonubO20ncyWHYRAPVBZZQIMYQHXJQMtEJdIFIfcLo4UJSzQPG8aNUpI0EEdYcGFcAIBzQSGoIMZ77RZzA3JJVKmb3QuMGoNwoQLQonzBR7GLGoXk0w0/6pwFSf8lDfsDMrQqlstQhKEWiK0XCrHy6SKP7xz5fd0xKwBCwBS8ASsAQsAUvAErAELAFL4L+BwC8UGqTxAfCdlQs/HHrPNZedd+7NN9/xxoezd1NIKMhg/4o0QAa4lBxCHXg8DpCpLl8zfNBtN1x52auT308rCHzhOX6AmoT0pA4Fl0AVhGgGgJkMGg0XstJD5LOIXQ8wR0EJoAEovmjh3CuuuOyqq6557/2PfCYDoTm6QmIErBUIITjHyFwBiySM6KfJcchGySZxAHtRYjZBVJUQtWyMovdsckUAEGA6A8oepkZE45V84B7Q1MLP37/i3NMuveSid6d8nObga1RMAJM4aAAhx5QPHbBIzEhwvu39Sc9cecmFV19828xpyzjDuD8U9fUjoLHvhwq1DqRmDNBeEo/ESgcUGkxNB5ahgJaYRGLGR2eKyAwCazRUIGk6TGecWlPBITRnkSaBSzEjoW8FWkdQLQIhPSODMG48O6O0BW3SHVDRAM+ICLwhscWgRV+J7J6RhoAzrLeMbJAZ9mRBWKHhv+ELwq7BErAELAFLwBKwBCwBS8ASsAQsgV9K4JcJDVhhgF4B9L2X7+vZlpQQUlrc+rwr7pq7Jl7JICUhFAlBa4AxLVUo/cAIDVtXLzzlsF7FMXL7/Y+kFMa9SoIHPAPMUzyUlGqPgY/lCXu6PUZdJrjWgRaeZn7oJLnviNBj1HtqwhMxQmKEDBkyLONTl0oq6usdoivymqMDpfFHlJpFd44ihopC5cinIOqsYVIb6g0TsodH6gMKDehGgFUcQgg0cZSKauEArVs07Z3meSSXkGtvvHVLteeYKN31nUC7LmRCCBkIzqXmIaiqXdsWXX35ubkkr3P7Iz796Ft0etCSYQUJ3pFp1mIy6xOBSRz1RRORyoCT1Doy0xAQFUqg5BBlfTAUa3jKqQr9GoBg28Yfpk2btmLjVgdlF1NDYiwzsXpCUVCB1h7HWhRqfCSyfhYRAo0qQ8YIDSarw1SBSNR4GAo3qJBkN5mkkMhTAzMaoqoK00cjq0OYz8ov/UDa/S0BS8ASsAQsAUvAErAELAFLwBKwBPZtAr9QaJDgpTVIPvPd+47oTJoRUpLbslmHo/sNeXVtjU5iowrq+lWKuwCciSCgCWA1yYrVF59yfKOc2G33Dk0IrHEADQ7QNPieDgLhBzIdqgwaGepsY8X68JuB9DVzFXVABTgmx9SBeXNnn3n6Geecceb7k6c4PnepDE3HiOjqPeYCyMiwIYrb0eshqpUwIb2Jf42iEZVRmGhfKQykfQEhJlNEhQZYOoGWjViXIZjmoZTcuC06wGqCqlUXnNqnWeO84048ZcHK8hqGrhMBZwz8QKdcLJfgMvSAZcAvXzr7/d8c1C0np+zmW0Zs2RamMwETVAMEoUTxIzJQ0GjEoM0dp4ilICjqmCAfEx6kxLSKBivHemkEd1VYiZJRshpETXzHuuEP3nfS708bPO7FZZurA7SKUKHAXiGoU2APUZMhktUJMK0j4mBkF2GEhiArgSh8yZxIGF9LfAuyh+M4Uc8LhbT2umHiQ1bOsU4N+/a3g529JWAJWAKWgCVgCVgCloAlYAlYAv8HAr9YaMgkGahg7pT7j+pMujYpPaB9X5LXqUWPE9/+fFlFBptiOjzFZRLAY9wPvRSE1enyVRefemzj3LwBD45KKPBDYBwc5TvghjoIlRvIZCCTQgZKKSoVM6EzRvvY9EFgWYEO6+p2Cu37QVoqKjjdtmlzxZZywTQVWHdBUSfQJu2AqzCE0AcaKO4z6Qcq8BTF1AnBOTdWBhITLpTCxH/MG8CIXoXK9QAFAk+7oeCCSRyBUQ5YgKE5AxFqqTDtQjkY0mfWvTj2jrbNi3ILSl+YNKuGQzLAsByAc+WkWJoD1W4K8zbimyY9Pzo3ltekZffnXp7pUEh7PhUetvbgkuEt5Jzj5BW6TqKNppbG+RILEgQaRSiqpWCcU8GZZlyHHALsM6FRQRABlxnKqkBVgqj69qsPjjqkZ06s8OZ7R60sd1BoYCKQ0seEEbNm7HsJkjLOsQVGKDhVLFTMhzBAqSUjlY9FGVHvDAFUS4pAvRA8IQMtAmAB1o9wDx9jpgUClOaGRh7aWGY2pI78Hz6V9hBLwBKwBCwBS8ASsAQsAUvAErAELIF9lsAvFBpM4QMob857gw9pQQ5o0uLqi/7a97cnk8Lm51054Mslm32AtKSeqtaQYIzxIASdiG9desZRvQsJGTjkibS5aq80ODLjQoqBG6qMLxKhSJioFS/aCxNlZ1sx4tVxCZqFLKkhMk0I8FI/NojAogCmI4MHTrlPmSdCLxIalO+KwAmom+F+mrM0Zx5lAfVNeO4rSbH/JOPYf5JhU8wAhY9MAtwUBK5CYQFCCiEVpooABAWGQoMJ+32p6oBtXjnv7cN/04WQvGsHjCh3oMbFdAyQQkmaoj5G814K/Hhy7eL+V5yfV1h20llXzV6yzcccAQbgen4KyzEEk1xwAaHQgdC+ECHllHJjyIBqA9fANGeCqjCUAQ08PwioH/AgFDRUjCrGBBd+xtkhw52gqpbOev/wHl0KYkWDH3s5qXB9QoIP2ByEahQDsNVHqLhHQz8ahwU09LnnCdeTmVClmHQZR68IzoAJzqQfKtfljsdc3DHwle8q3+VBSAN0m7BCwz77528nbglYApaAJWAJWAKWgCVgCVgClsA/n8AvFRqUEpKnd82eNOTIDuTILn1ee+aDOwfdT/Lziso6PPfaJ0kBcQFJVc0gwTm6IoCsq9y06NwTDm9cWNzvoXGVAi+agwZPpkJICMhwnfZFgvIU8AADa1PJkLVKEKaVAtOMhRICplKuVwnKM44BmPHPJfgcPE59mvHDOKVpyTxjS6BwH40dK7J9NE2BADavUAHVnhTmmjx1IPQgDIExpqkDNAk8CdrTWC6BMgSnUXcGFBo4BYV1BJiQoRMgtlVv/Oovl57drEnzg448bcGaqjrTzJIHoRLSFUZ0CFKQ3r121odH9tif5DYZ+cwkHyDuimR6N0DKy1SzIGMqEXDRDJmAw5VHhR/wrNGjBKGkkKFknvYc5WZA+lKF6LawV9cJ87mgIOtA1e5cO/+s448sKyoZOuZFLyrGAFRoPGyrKRhDj07szSlENq3DKAUa+aCFg889nwceUz5VIaOUuYzhuxNlmZg0BaydwcoOaTJOonaYe2U0YCXInuqJf/5H1o5oCVgCloAlYAlYApaAJWAJWAKWgCXwn0zgZ4WGqI1Aw9Qjtz8peeCnIaz++uMnercg7Yq6fPnpyo0b1190yZmNSkpPPOnsT+asjmOVvwjBr0vEJfWBVcYrlp11wmGNi0v6DZlQwSE0Hg0pd3farwBwNd6xD8La776dM+2zr6ZPX/btd1/MmrN9e1VtbZCMMzeDboUMHQE8LeIgMzs3rFs6b8n0z75et7GWAaSClOtXS5UAndHCS9bWrF62/OvZcz7/ZOrUaZ8tWrF8a128LuB+IChF8SOUPmMZbK+p47s3fLPg8w+/mvbp/IULZsxfsHZnZTWDjAKPQuCkAAJHMVcyRT3sJiFQaMDenjoNsFsk1jw96sFcQoqadXl5ypcphZUjwvgrhgC+YI1o2MwAACAASURBVCAy4Fe/OPy+/YqLOh54+KcL1zgSUmkncCqB14Fyd29a9903C199+ZVPpn2xcOmqles3OVzhOhnqFLWJAAUXLSVLJys3A3dAuQCpuQs++eKLT2Z8Nu2zKTMXz1m1dnUlZyjRrF373expbz818u5DurRslBe7+sZ+734675vlO9/9cN6iVVscBdiZwqnCs5tupDWV2zduWDt92ozpn81auGDprFmLtpbXMIk5FBQgxL4dgdJpHlb7mcqPP/xo6bcrN2/cBhpE4Cz9euHSZauWLFvrZAIASCaTjuMAoGKzl0GD9Who+AOyD/ZtAn/zfdjQ0nXfXpWdvSVgCVgCloAlYAn88wjs1YTN2M3980a2I1kC+yKBXyw0KO6DTCz+aPxBZaSUtP3w3SVShHNmv9era8uivNw7Hhq/NQk1AhyNnRFMVF5du2P5GSccWlJU3H/oUzuM3aLpiugygRGv79ctW7F43OOjju3bp4iQxnn5jYqKCck55vjfDxs2fuOGSq2wU6UEcLwa4e8GlXr9ycc7Nm/XuePhEz+Ym2LCl56GBEBc0JoVyxbc1q9/i+Yt80gsLyc3t7Co00E9b3/okVWbt+NFey7SmRrXxx6QoV/z/sQJ551+ZIdmpUWE5OfmxfKLjjzhpMeff2NHLeZBJDLplJdwgHqKauYDDUHgTHwFgfI12w1B+YJpkw7t2SOWX3bn4DGba5wQ0yCwwWMI4DAHRMLZvHzA1ZfkkpwL/zLgm41V2E0TXRdclqiY/t6bF5/1x0aFucXFhSSWFytudOgxxz3x/Is76xKhxjjfMZ6Mvp8AmQRRB271dwumDxh4dbeDWuTnk/wYaVbcqkub3hdf+Nely7dVp91BD97dqhm2AikhpICQ3JKmpKAFIa1bdDh83Avv7Eqmfe6ZzIYUdXbMm/PJrbde06F96xjJyScF+aS0Tatul116y7y5q7dsdwLAspZ4phY7UNCq1d/MPqrvUQf3POKdNyfX1dU9+fiIzp1b5xc36t33qKXLVgqhwhBtJgCAUoqdOW1Gw774ZWDn/PMErNDw82zsK5aAJWAJWAKWgCWABKzQYD8HlsDeBH6x0IDuBSrz7SdPHNI6v2l+99dfXWRi5+oJo/sVEtLxgEPe/vTb2gAjbVeGSacCAIWGPx7Xp6ioqP/wCbsUUA2aKUGr8Jp9zdbnnh532FGHFpcWFRBSSmKH9+zVuW1b7F2ZkxPLa/ynP1/zwYfzEynNNPg0o4JKUIkJjzxYRPJKSrp+8sUKByP/NEAc2I6PJj1/wjF9CckhJL9d206dO+8fyy8iOfmHnXDK53MWorSIzR0TAInyrWsH3XtHqzaNYzGSR2LdOu7fYb8W+YTEYqTrAd0eHfdKTcYsAcAxfS+MA2KoJZZUeBKLNUxSQCKs2TKo//UxknPc706fsWh5RmALTY2nkYw7kCmf/voThx/Ug5DCJ17/qDyDOgOwzI6N3w9/4J5eXTrnEWyQ2ffQg9t37hIrLCQ5pEW7NsPGjtm0q9JIFeCHDg9qAWrdyjVvPTf62MN7msWR1m32O6DL/p1bd42R0rKmHZ9+5e3yePWop4Ydemjb/dvmdGpeUJqf37RNtw49jy9r2/vYUy6c+NGMmmRGcB+U8+3CGXcNuL6saX5ewf/fIZT02P/AEw4/vlWj1rmkMEYa9T7w9wMGjF21yfcAEm5KywSw6tXzZh7U/oCinKZDh4weM/7xQ/p2jeUj5q4H9vpq9lzPCyInSHSowJ4iSoiGvIa9P2z2sSWwrxKwQsO++s7ZeVsCloAlYAlYAv8uAlZo+HeRtufZNwj8UqEBex+AcudPerRPq5x2zfq+9MpCN1AaEjs3zDz5uM6E5Jx8+tVLltdRDXHmZHgVQLx2x8o/HNO7qKDwthFP7Tb2Bypkiu4Gsfu9t17o3KkdySGHH3PMuEdHL/xilltTW7Fl44xZnw0bNbRd+86ElJ504gXrN8Q9gSYCmlaDt/PVMcM7Nm3drdtxU79cXUtZZbIC5K7ta7686Ixj8gj57TEnvv7uR9//sGnTtoqZX80dMnrssMcnLFr+PeVM85RyN4n0piEPD8rLL2zRsd2Nf71t4sRPNv+wfdvaNXM/n3L7zRcWF5LGTTo89dLHaYA6DT4A1VyKEARXQgsJrtAZ9IqkWMehEtPefbFV46LmzVs8/eqkBIcAG2JyKjxM1ti+dMClZ5bkFfzm0BNmLvnBx49EEC9fO3b4kGZNy1q1atPvtv4ffvzB5q0b1m3aOO2Lz2+4+VqSQ5q2avHkSy+nFTgCHDcBkFLOlvHD+ndr3zRGyAm/P2nk2Me+/fbbH9asXTR38fAho676y03Pvv5aebJic9XqH9bOnPjaiBP6HliaX3z1TffPXr5rxZaaZeu27KqJMxYCCzasXHrDVZfECClqVNrvjjveenvyD6vWV2/dvXjWvJeee/7yi67II41KCjreP/L1FDYrBd/dBeHu+LplJ/Q+qnVZ56OOOa3zgb1OOv2ECc+PffK5Z2bNmb9te0WU0WByGaIPvcJz2a4T+8Y3gJ3lP0TACg3/ECa7kyVgCVgCloAl8CsmYIWGX/Gbb5f+dwj8YqFBcgHS++qlwd3ySKvGB730xqIkmiAGwls/8ZVHunZuQ0ijW24csWVbmAGV0Rgnx8tXnXF035KCwttHPFWl0cFR+wFAfM2y6Scdcygh5IyLLlq0cjXnEhSokIFkoUjUJLY/8eSYtq07ENJ88ODnKqrRl1HzJPDaV8cOLSGkSeNuUz5bEpfS4QlgOz99Y0yPtsW9unae+P7UECBjrBkF+h3IyrRTk3FD5mE6A9885eVh7Vo2J6R44APD15bHEw42bwTFQdSUr59503VnEVJ42rnXL9/upo2NIlMSW08K2SA0pDkaMCQSFaBrd34/5w9HHlRAyG33DKv2wOPc9eKuWwU6tXvJ9N8d0DaX5Pe/Z8ROTzMAEHUfv/5EhzYtSSz/1rsHbdpdxQA4oGklAN9VseHGG68geeTks8+Zt/wHND8ADioxY8qz+7fF9INLrrx2+dotGR+rMyLXTCHYD5vXL1231IUaCZUAOzetmH7S4b1K8hs9MPqtGgUOds0QUoWgQlpTOWHosDZNmufmFA4dM2F3iqL7hcI2GGh1Ce6WTcv7XXt5o4Kijj2P+nj+8hAU9tcIttWuWXj0gX1ySdO84o6/P+eKz2bPQWsMB1tO4DQAfN837gzRJ0wpJazQ8Hf+2uymfZaAFRr22bfOTtwSsAQsAUvAEvg3EbBCw78JtD3NPkLgFwsNLKQgvSVvjT24iLQq6/7C2/OrOaSxrUE8tWvFI/f2LyA5bcp6fPD+PA9gp1sN4CbL15x9zGFl+QUDhz1ZKbApBPg+hLtefvKhklyyX4tWb3/6uQfAFPhu4CQynPlM12pIhH7l2FGPlha0+93vLvvki+88rH3ygNe8MX5EGXoPHPj5VytdDJMpqKqJTzzQjJCu7dpMmvqFhyUPUOeHSTfA9pCAxQ5MpAESqmbFn0/oVRLLG3jn4E1VKc90rwwZcD9D01sBKpYu/rh7996xovbDnn57dwiBAi7wLoTSEqTEJpQZIX3Qjh8HXQ2ZDUP7X1ZCyG+PP/vLJZvRypGng7AGVGLuW092KSL7Nev01Ksf+wBMBPH1CwZecVouIX++4i87POoAZISsqotzTmWYAe2uX724e48uJJY3asLLCVQaRNX27++8+YICQvoefdyiFZuYaZ/h+WEqlZCmQkGA8rQvIROG20HtWL/44xMOPqC4oPGDj7+9OYBqEQY6o1gSmFOzeuXFJ51SSHLOOeeixd9vcgHSAfbTgFD4cWyEAbpq07LppxzXl+Q2uvmB0dvicawQodu2fvPpcb36xkjTvkef98m8NbgWQLsKTOzQ2GxU4JuKN875XiqDNYPcR74G7DT/NwJWaPjfCNnXLQFLwBKwBCyBXzsBKzT82j8Bdv0/JvCLhQaKzRe8Hya9cFzT4rZN93/6rRnbpK7gnlQ+hIllcz/70++Pb0pKbvnLwO931tZiF0bfKV93/rFHNCssGvDIuErMVwAIfJnYdH//y5qV5F121bXLyyurFSRClr1QD4KqSqq2g676ctoHbZt3LSvtMebZKUkBQvtBuvy1cUPalRZ16fCbjz77uo7JVJAEb8eSj18+pnuHAkLOv+LG6UvWpTSaLAgAKoVDAyfMUB4HXv3lxOc7F+V0b7P/l18s8kEnpUQ3hMBcmeeoROwoX3vWWZeQvFb9HnikmoahQFeK0PSSUOYWKuUqHWDAT0FWgty+eMozXZsUFZV0vnv4S7sZ91Azcaq3r3jkhvObEfK7ky+Yu6wi7uvQrf3uo2cuOLJjz/07vz75Ywdgh89cjY0tQQNL14HKVG1dfdGfz8ktKO1/99CKKhe0+nb+9BOO6N6yeenAwWMrHHAYMMEFzho7XFLKQ+O66bkp6VQDq9nw1Ucn9OySEyMDRz+1XWFXS64c8OPgJdbNnP677ge0LC5++fU30mjzCK5pUYkj+Wkh4wDV/q6vB916Kclpdsy5189fuUFjvsXOHd9+duSBBxbm73dt/2FbkjimD5jNwCmmekjMi8CbMk0u93KCtELDj//a7LN9loAVGvbZt85O3BKwBCwBS8AS+DcRsELDvwm0Pc0+QuAXCw1SCxDuNy+P71sYa17SesKbU3ejEyP4oQc6AFrzxlOj2hUVNIrlj3jiWXQlkEF6x8bzTjyqWVHxwMFjKwMjNFA/s235JacdUUzIg4OHVwtIAzhShZQzrrkMFQ5ZCVC9ecXi3l37ErLfsPHvVGJ/SQYqMfHpkU0JadOi26y5eHXdoQGEtX75yscfvKNFaQHJKTzypNNuuH3g+9OmV6WcNBV1fhgqpnScpjd//uqEnk0bFZGcc8+54Opbb7zohmuu7XfbNTfccu3V1/S76fo7brvh2muuatGqG8lp0v+hByv9NGWKUR0wTSUG0gqNLLHxRJozCdRLrgNV4W1ZdOZve8dI2VkX3LJow/Y4Xu/PfD1nyql9u5cQctd943wAlwqA1KcT7uxdSrq2anfu+ddefGP/m+554NLrb73upv7XXXPDTVdfOejW6/5yyfndD+hKYoV/vXvI9t0ZkGru1Clti2LtWrR4YdKsNLb5xIheyFBiH0zleoHnYaAvKQMZAItvmv3x2Uf2blSUf/uwxys09vDEVA7hgFs3581XezZrsn+btvO//rbCDR0AX0IynoEgBE01OJzuhHDra2Mfyilpvf+R50ydt0riO1hdufyL3/U5rFnjTnc98nyFh0JDsMfxMftJF0JEjScAlJTclk7sI98Adpr/EAErNPxDmOxOloAlYAlYApbAr5iAFRp+xW++XfrfIfCzQsNP99Va49VrUKC9RW8/2adJTqsmrV9867O0wtwBxkLfrQLI7KpYcc8tl5YQ0qPHQbPnLwId1lZu6nNQp0alxYMGj3U5hD4H7qd+WPLnYw9pXdR43NinK32awdIJ6XuOBkhl4iGrAxWHTEVq89o+XQ4sLWr70NjXkphEoEBnJj81AoWGlj0mTlmYCMA1fRWBJ3es+3rUg7f96bTji2KkMIe0bdfhrIuue+WDrypck93AqyHY8vzgv/ZqXlBESPPmLUhhIcnPI6WlJDcnPz83l5A8QoryG5FYWevOvR4cO7YmDKXCfpRCZu/41GQ/OKEbioSUu0BuB3/rsDtvLCWkb6+jJ89aUoeuB7s/f3dCs6LiJo06jn/yPazdYA5A5Yfj+h1eRkpIYW6sLSloTnIKSayQ5JUQkl+cV9IotyCPkFheMSlu8vD452rSFLh6d9wTXXOLjjzgkJkL19dJUAJkwDnnVDGXOqGk0pQvoJsDTwOv2TDn49/36lqak3//iJfKHVx4EKaAZ8CtfmX0I60K8w479Kj5y36opRCngMuh1CRT+Bqon6mEmm2Tx40uaNz6wOPP+2zRBhyd1WxbNKNPu56lsQ4jxk6spBCXgoKqTz/56SfFbrEELAFLwBKwBCwBS8ASsAQsAUvAEvj1EvilQgP3eQAqM++dJ3o3j7Vs2vqF1z9JBeBQCKkbhDUSXIDE8gUfnXxk9wJCLr/0skRiN0Bw8qnHFJaW3PXAmFQAgc9BBmzbmguPOayMFIwf97yPqfhAOVOSB9RPpusow7x+COPpjWsO73rQfk06Pvn6p2mAUFLtVb81bnBzQjp17PPhtBXYR5ObNpA0DjrDk+XLZ0+9769X9erSqrgoj+Q0an3Q0XeOfG5TVQrAy+xe+e7j9x7RsXGzAnLJJZc8POqxO4eOuP2B+24bdMfNN9945+233XX7PXcNfPCOux65Z/Codz+f6YAJxY01g5CYy8AjmUFDEHiUp4SqFmInqKqP3pxwcOvmbRs3u3voBBfTOMofvP0KQopPO/uGuYu3h1jl4AHbMvP5Qb1LSDPS/OrL77n3wVED73345tvvHjz8sbsGPXz3Xfc/cPd9/W/ud8egB4aPf+rNqZ8lAwpcfPTkU11I3uEdey5YujWD5QkQZMJ0xg0Fj9QPqpgfOkqGoF0Qtau+eO/k33QrIfl3PvTsDg8ymJPAQbrg17w8ZkjTwpzfHHLkolVbHIBdaVPzoBRQlzlxz09hX04/M3HUKJLX5DcnnT9n5U60XpCJHUvnHHPAUS1Leo19+v24hJQWFA0sTdbCr/dvx67cErAELAFLwBKwBCwBS8ASsAQsAUvg7xD4ZUKDlJyrAHRm/sQnDtkvp3Xzti+9+VlKgKeAy1CodDLApAbQta88Mbhds0ZlJYWvvvJ8MlXzpz+f16hF60HDn0lQCCWAYuXfLTzv6MOKSN7ddw/Z7YmEhNqEF/gYyIfMQ59F5YBXu2beVz07dOvYvuezEz9PaBCKgky/++TwFoS0a9/n/c+XZbjpmSBc36kBTTFUlpSnq8JExZRJr/zhrLNIbnFRq/2fe+M9jYUX8U9fHXdIm5IDO7T66IMPfQV1Aa/zPQ5SCAZaKQqegy0TEoHakXbDPUKDFEqiEKKzvgNYRqHR+VHJWoBEfPuKAVeeW0TIyWdfXBWILRuXHHVoN1LUctSzH9cGkKTg8biXWL186pNHtIx1at5p5sxVvoJaX1RngmQoq9OuH1DQSoZeOp3cUb2rvG43BwZhesZrL/2mcZPuLTq8/fHcKo6KTCiByqjbAwjBwjAUgjHfwdIJHl89Y/Jph/UsKyoZOOy5HQI8DYJTmaryd65/5+nH2jQpat+5ywdfLqikWK6CqRZeADQARRX1VCoObnrcnffE8puedtktS9bXhujcuWvbklnHHnhs67LeTzz/UQogrTVDxcO4MfydD5XdZAlYApaAJWAJWAKWgCVgCVgCloAl8Osl8IuFBuzSoFPz3hn/myaxFk1avfDmp+ms7SJX4Ne51fH0DoB4zZZlA268ooCQE4/77RtvvHbsib8ra91l4NBnd3pAAaQInYqNj/S/qXlh45NOOnPagmVJY9zoOlxp8IJMgIUGFNy6ic893aVlx+OPO+ONqfPiGOMLUJmJTwxrSkjrdr0/mLEs6QtUECBQPBO66aqdu9xECntValeFNQsXfXXy6aeTnNIrbxpQnUgD8FVzph7ctqR905L3Jk0OJXbLQHPHMAAtlZBehroudlDAVgzmJ8bTEk0HhOJMc7SoqL+ZVgtc8QzoJOjaKS+ObJpHDuxz2OfzFr30wrgmTQq7HX7yZ9+UZwBqAh2qFMDO6pVT/3BwmwKS/+wLUxpO4UgdaJAA1MuAwn4OGvs5cA0BBLXLv5h6et+DGxFyy50PrdiRcMzEAgF+wLUELYEb2wbsBqIo8MTyz9499qBOhbk5t414apOPq1BCajcFwt363YJjDz2IxMjVt/Yrd1mNhGpHhNyUXviOdJMggtSa788/8SQSK73lgdHrdnpcAejqLcu+6Nv10CalPR5/9oOUggw2llBa66zaUQ/E/rYELAFLwBKwBCwBS8ASsAQsAUvAErAEfpnQoCTFuFrG5779eK+msWZlLZ97/aOEgDS2kKQuSzBgIc8wZweo2nkz3j2wc4uygtxTTz6lVdtOjVt3u/HexzckTM8CHoBXN+Pd1w7s0IWQwvOvvmnVtiqRbZSoTIGCAOALvph26tFHF8WKr7t+4NL1VdgCk7pA46+MfrhZLNa+y+Gfz1tLFTDu+G4lp3EjVoAUKA1kastBZ1RY17/fTSS/9KKrb960u45Kkd614apzTiwi5Jyzz5w3f7EwhguMscB3Qz8Ajd0UtpTXrdu6u85jacoxcUEJIzRQoajULCoYUBJ8j2kJIvT81E7QtWsWTTq2T9tmrZpf3+/2P55+Siy/4NK/Dl6+W9VpqAmkR2sBKv3yhfdd/6dcknP0cWcuWLLWE+Ax5VKRoiGVAkfWYtfO7WvXra52agKJrSFSFWsf7v+XIkIO6Nnnxfc/qREKW3JGrSUVHlFVm9mxuzadcambAp76ftZHR/foUFiQ89DTL2NzUWzxwcNEHGQQ3/bDTVf+ubg41rl7t+cnvlsVCEeiSIN1Hckk6hTMe33smLZlzRu36vzie9N3J5VEcadyxYL3e+5/cOOS7o8/+0FGgSsUmmUoKzTYLxBLwBKwBCwBS8ASsAQsAUvAErAELIG/JfCLhQbJ0qASCyc/3bdtabvWnV5+57OkgpRSHMLadKWJ25UTLwcd9+IbHxl0c9N8dFiM5RS27dZn4MgXKig4AEkvDcqF5K6hg+7OjeXlljTtf8/glau3Ve1Oo6OACDaXb5r66Yfnn31GASFHHHrsnHnLa0K8OO86CaCpiRNGdygra9/1yMmfL2EoDQTVu9d9OfO91159Ydny73ZVxR0X2ygAT3323usHdetIYgV3P/xolYeNNYElv5r6Vt8Du+YScv75F3w+fdb27RWcMq1EKpWKJ5xZsxfddvdDQ0c/uXZzBYb+ePsbocEkNWBJArpTgoIgVQW6ztm9+MEBF8UI6dipW5Oy/Ro1bTvmlU93SkgCpIVKO7tBVkFm4+LPJx59xJGEFF906XVvvT15+/YKbUoYEq6rARYvXjxo0KBHhg/bsHN7IkwC0kqtWDDtpKMOISSnU4+eL06cuHzjxpAqPK/L589b8uIbkyd/Mr067TiYkuBsWDzjD0f0yiXkghv6Tf16tQ8QcBW6HkgO1Jk1ddLpvz+GxMgBvQ6a/Om0LbvjtRnmesxz3Lqd5U+PGdmmrHEeyb3ypgHb4mE6gEwmBap85ZIPDup+cOMmPSa8+FEA4GMBidYNxph/+6Gyzy0BS8ASsAQsAUvAErAELAFLwBKwBH69BH6p0MBB+qBTsyc91XO/ghbN2rz01sdp9DmUHLyApX0mlYbAqQFWAxDftHL2xaefUERIjOS17tzrvrGvJAED75SfEZkqkKmV87649tKLcvJySSyvz8FHXvSnS26/rd+NN113zInHtunQPj83r1OHzuPGPFtZ62c0ljNQPwnSfWfC2JbFjZu27vXqe3MdFA94qmbjYyMGtmpR2Ltvz0uvuvb+h4cM6HfL5eef3bX1fqUxct7ZF8yY83VComEBOiYGtc8/OaZ961a5JKdDu46nnvT7/rfcet+gey+77Iojjzm+oPF+JL/xVTf0/2HLDpQRlKgXGrB6wmQ0RKkHJrPB5BRoFoBKAt384Zuj9mtUkEvycknpoUecMn3xJnQ0UGiFAOABrwMZD+u2PzV+bPcDeuaSvA77tTn52KNvu+XGgXfeceoZZ+/foxc2ocgvvezam7bFU2mQnswoyIBKfDr5lWP69M4lpLi4+PjjT7zxL7feOWDQxRdf3apjt/a9+vz1oSG1nPuaAUtVbvru/tuuKSQkVlDS5/hTz7v86mtuvvXtd95N1dXiuxSkJ730VJ8eHVH9IeSQQ397U7877r538EUXX969e7fSkoIYIWeee/6sxctCAC8ERj2Aim2bZnfZv3tBaefxz03xFPgU6yas0PDr/dqwK7cELAFLwBKwBCwBS8ASsAQsAUvg5wn8MqFBSo7dDVjyoxdHN8dItXDcU2/UBdQH6rM6gICidwBWPSiWAFqpnG3T3nmmZ/vm+bEckt/4pvserZZQ6XMOkmeqgdaBV7tjw4qH7r9zv+ZleYQUxnJjhOQXxEgOada69VVXXztzxmxp+kpgB00AESbBj497YFAhySlu2m3itG8TWO7AZbhr8ltj2rTMieUSkpNDYvm5ubl5hDSOxW684soNP2wRAHGuHC1dtwa0o4L0B5PeOfaI3+YRUoD3nFxCCMkhOfktO3a5vv8dK9fhIbVJtCNQSgglhTTdPdEMEu+SY05B6JtnQgBPAd8x+5OXj+jZPY/kNSpqdcmlf127PZOWEPdCDUqHqXTlFszjUL6brps29ePfH3nUAS1aNo2RQkIKCopiBSUkVtyu28E39b9vxvxlO9JoU+FAsKt2C0AKVGb6e29f++fzy3JyiklOAcnLIQW5RWUkr/Dw0878ctWaKibQ/EJnAFIbV809/4wTcwkpLGocKyhu06Xr8NHjdlfWKobgwYsvmDbl9usuKyIkLyc3N6+YxKKz5/Tqe8jYJ59eX7EzNHaYdXUudqzQW7/5elLzVq1JTotHx7+ZpuYtjuomoh4cP//xsq9YApaAJWAJWAKWgCVgCVgCloAlYAn82gj8T0IDXrU2twgKlhDgjYGmG76eNXTgLXcOuG/+4tUUJQCOQbH2tQKJzSC5EBnF64Durtu8aPILo/vddP3d9z8y5atF5Y70jJWCcOrAqwJ3F/BaGdR+s3DmuJGP3NH/liuvuLj/7bfe/8jDkz/6KOn40uxMAWpc3+MUtA/SWzXnq7tvveP+IU/N/W67g24BEmWE9PrFc9974L5+519y0WXX3XDtddePGDJ0/oyZTlVdtEcIEKdeSNMgM6Cpov6WNetef+7Fu27pf+u1111z2RUDB945fPTY6XPmV2cCFlVFYAtHDHyHggAAAqxJREFUrJ5AlUHWmxJEfR3r+1wa2UEDdcGrhMyul58cP2jAoAH9Hpg2fbFH0QFBAwjugwqAZtAHIeqSEaQ3f7t40lOP33TBeVf9+U9XXHblRZdfe9dDIz+b/e32Ks/H9UBcQ0qHHmQ0pNBv0kvo2popzz4/6q57+l9383XX3nTDbXdOeHvi6l3VmDcB4GpukjbqFN21cdX8J4cN/cvFl19zzTUjxoxZtX4rpmFo4EGonDjITNX6pdMnvz5k0N233njTzbfcdtud9z398mtfr/rewzcSHAUe2kyCZI4QGynbOOzRkfc+/PgnM5fUZZSQZlUK9RPrB/lr+8r4da4XvVr2+jKMHiuV7UHz62RiV20JWAKWgCVgCVgCf0NAa23Krn/0H0K0BW3U7c0S+DUR+MVCQzKdwqvcIgOZWj8l/r/2zmU5jhCGov//jU4WzsQzbboZoHlLQtdFp5JFVt4PezY6VFGlA1zRjIGEq8dsdTX+EQ0sShyFLNoH+Ab/Iz5N4RkDuSseuR2nv6IHn2p/Iv6COrCN5r2Fz5K9YhShk0mArtiObBMlRVfKYQOdIMp7NA6W5tiI3NsoN+gN2Fp5pFY7sLszOI9KYKUssXAQdVxr8y0fPT3nZAoZ5GLajKbcYyYSG1KS+UcjNA2pP138Kxrwv2j4d6U/LifRK/KBaMAsWYKViQJIudYWe3HIDvVk5tJybSc0gSL2D/Q0wx4ULopJOqdXKuKMvZh/TAI4za7/Eg3FwR3wJ+6fw1dhmMZGp2KwwFa6o0KIbTyAA2qxG1gX7HE3+yWD0KcGupyIBLBFevTjLqUQ427jb3vmyzJ4wA29RINK91M0yFsf8vbuQpvbPea0izEVw3rR8EqHxSvXukTDK+/+qn0RWAQWgUVgEfgmgSUavglqLXsFAl/8DPxUKp4p+wAAAABJRU5ErkJggg=="}}},{"cell_type":"markdown","source":"# setup my Environment ","metadata":{"id":"DnZ7PsaRllQ9"}},{"cell_type":"code","source":"PATH='/kaggle/input/braille-character-dataset/Braille Dataset/Braille Dataset'\nsave_path='/kaggle/working/best_generator.h5'","metadata":{"id":"SXgwpe1OmtDE","execution":{"iopub.status.busy":"2024-01-03T10:15:08.341332Z","iopub.execute_input":"2024-01-03T10:15:08.342049Z","iopub.status.idle":"2024-01-03T10:15:08.352493Z","shell.execute_reply.started":"2024-01-03T10:15:08.342015Z","shell.execute_reply":"2024-01-03T10:15:08.351422Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"## Import Libraries","metadata":{"id":"ujc9BcvEm3cO"}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n#\nimport cv2\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n#\nimport os\nfrom tqdm import tqdm\n#\nimport tensorflow as tf\nfrom keras.models import Sequential\nfrom keras.layers import Dense ,Conv2DTranspose ,Conv2D ,Flatten,Reshape ,BatchNormalization, MaxPool2D,LeakyReLU ,Dropout\nfrom keras.optimizers import Adam\nfrom keras.losses import BinaryCrossentropy","metadata":{"id":"Z2BS1eGdnEAX","execution":{"iopub.status.busy":"2024-01-03T10:15:08.354239Z","iopub.execute_input":"2024-01-03T10:15:08.354556Z","iopub.status.idle":"2024-01-03T10:15:27.035446Z","shell.execute_reply.started":"2024-01-03T10:15:08.354527Z","shell.execute_reply":"2024-01-03T10:15:27.034546Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Functions","metadata":{"id":"jAn5QzSFpKut"}},{"cell_type":"markdown","source":"### For Reading Data","metadata":{"id":"Srg58Ci0pOsY"}},{"cell_type":"code","source":"class cCreateData_Gan:\n\n    def __init__(self,path) -> None:\n       self.path = path\n    #\n    def CreateDataFrame_Gan(self):\n        '''Create DataFream contain Full Path of Images '''\n        arrData=[]\n        for imgfile in tqdm(os.listdir(self.path)):\n            imgPath = os.path.join(self.path,imgfile)\n            arrData.append(imgPath)\n        return pd.DataFrame(arrData,columns=['Path'])\n\n    def ReadImgs_Gan(self,W=28,H=28,C=1):\n        df=self.CreateDataFrame_Gan()\n        arrImg=[]\n        for i in df['Path']:\n            img=cv2.imread(i)\n            img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n            img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n            img = img.flatten()  # Flatten the image to 1D\n            arrImg.append(img)\n\n        x_train = np.array(arrImg)\n        x_train = x_train.reshape(x_train.shape[0], W, H, C).astype('float32')\n        x_train = (x_train -127.5) / 127.5  # Normalize images to the range [-1,1]\n        return x_train\n\n    def DataSet_Slices_Batch(self,buffer_size=512,batch_size=128,W=28,H=28,C=1):\n      ''' Call Only This Function To Create Your Data '''\n      x_train=self.ReadImgs_Gan(W,H,C)\n      dataset = tf.data.Dataset.from_tensor_slices(x_train)\n      dataset = dataset.shuffle(buffer_size=buffer_size).batch(batch_size)\n      return dataset\n\n\n# create Obj\nobjCreateData=cCreateData_Gan(PATH)","metadata":{"id":"mbUGrIf_pWB7","execution":{"iopub.status.busy":"2024-01-03T10:15:27.037301Z","iopub.execute_input":"2024-01-03T10:15:27.038312Z","iopub.status.idle":"2024-01-03T10:15:27.050110Z","shell.execute_reply.started":"2024-01-03T10:15:27.038276Z","shell.execute_reply":"2024-01-03T10:15:27.049261Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"### For Display Plots","metadata":{"id":"u-n7ndIxvFpg"}},{"cell_type":"code","source":"def display_20SampleImages(dataset):\n  plt.figure(figsize=(20, 10))\n  for i in range(dataset.shape[0]):\n    plt.subplot(4, 5, i+1)\n    plt.imshow(dataset[i, :, :, 0], cmap='gray')\n    plt.axis('off')\n  plt.tight_layout()\n  plt.show()","metadata":{"id":"WxYvzBVKvNGk","execution":{"iopub.status.busy":"2024-01-03T10:15:27.051237Z","iopub.execute_input":"2024-01-03T10:15:27.051503Z","iopub.status.idle":"2024-01-03T10:15:27.075147Z","shell.execute_reply.started":"2024-01-03T10:15:27.051480Z","shell.execute_reply":"2024-01-03T10:15:27.074170Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"### For Deep Convolutional GAN **(DCGAN)**","metadata":{"id":"28XH1k7vurNk"}},{"cell_type":"code","source":"class cDCGAN:\n  def __init__(self,latent_dim=100,batch_size=128,lr=0.0001) :\n    self.latent_dim=latent_dim\n    self.batch_size=batch_size\n    # build empty Netwok\n    self.generator = Sequential()\n    self.discriminator = Sequential()\n    #\n    self.cross_entropy = BinaryCrossentropy(from_logits=True)\n    #\n    self.generator_optimizer = Adam(lr)\n    self.discriminator_optimizer = Adam(lr)\n    # lists history\n    self.gen_hist=[]\n    self.disc_hist=[]\n    self.disc_hist_total=[]\n\n\n  def build_generator_model(self):\n    '''' Build Deep Convolutional Network to generate new images (28,28,1) '''\n    # input = latent_dim\n    self.generator.add(Dense(7*7*256,use_bias=False,input_shape=(self.latent_dim,)))\n    self.generator.add( BatchNormalization())\n    self.generator.add(LeakyReLU())\n    # output = 7*7*256 = 12544\n\n    # Convert 12544 (1D) to (7,7,256) (3D)\n    self.generator.add(Reshape((7, 7, 256)))\n    assert self.generator.output_shape == (None, 7, 7, 256) # (batchSize,W,H,F)\n\n    # input (7,7,256)\n    self.generator.add(Conv2DTranspose(128,(5,5),strides=(1,1) ,padding='same',use_bias=False))\n    assert self.generator.output_shape == (None, 7, 7, 128)\n    self.generator.add( BatchNormalization())\n    self.generator.add(LeakyReLU())\n\n    # input (7,7,128)\n    self.generator.add(Conv2DTranspose(64,(5,5),strides=(2,2) ,padding='same',use_bias=False))\n    assert self.generator.output_shape == (None, 14, 14, 64)\n    self.generator.add( BatchNormalization())\n    self.generator.add(LeakyReLU())\n\n    # input(14,14,64)\n    self.generator.add(Conv2DTranspose(32,(5,5),strides=(2,2) ,padding='same',use_bias=False))\n    assert self.generator.output_shape == (None, 28, 28,32)\n    self.generator.add( BatchNormalization())\n    self.generator.add(LeakyReLU())\n\n    # input(28,28,32)\n    self.generator.add(Conv2DTranspose(1,(5,5),strides=(1,1) ,padding='same', use_bias=False, activation='tanh'))\n    assert self.generator.output_shape == (None, 28, 28, 1)\n\n    # output(28,28,1)\n\n    return self.generator\n\n\n  def build_discriminator_model(self):\n    '''Build Deep Convolutional Network to classification real image or generated  '''\n    # input img (28*28*1)\n\n    # Layer Conv 1\n    self.discriminator.add(Conv2D(64,(3,3), padding='same',input_shape=[28, 28, 1]))\n    self.discriminator.add( BatchNormalization())\n    self.discriminator.add(MaxPool2D(2))\n    self.discriminator.add(LeakyReLU())\n    self.discriminator.add(Dropout(0.3))\n    # Layer Conv 2\n    self.discriminator.add(Conv2D(32,(3,3)))\n    self.discriminator.add( BatchNormalization())\n    self.discriminator.add(MaxPool2D(2))\n    self.discriminator.add(LeakyReLU())\n    self.discriminator.add(Dropout(0.3))\n    # Layer Conv 3\n    self.discriminator.add(Conv2D(16,(3,3)))\n    self.discriminator.add( BatchNormalization())\n    self.discriminator.add(MaxPool2D(2))\n    self.discriminator.add(LeakyReLU())\n    self.discriminator.add(Dropout(0.3))\n\n    self.discriminator.add(Flatten())\n\n    self.discriminator.add(Dense(128))\n    self.discriminator.add(LeakyReLU())\n    self.discriminator.add(Dropout(0.2))\n    self.discriminator.add(Dense(1,activation='sigmoid'))\n    # output (Real || Fake)\n\n    return self.discriminator\n\n\n  def generator_loss(self, fake_output):\n    return self.cross_entropy(tf.ones_like(fake_output), fake_output)\n\n  def discriminator_loss(self,real_output, fake_output):\n    real_loss = self.cross_entropy(tf.ones_like(real_output), real_output)\n    fake_loss =self.cross_entropy(tf.zeros_like(fake_output), fake_output)\n    return real_loss , fake_loss\n\n\n  def train_process(self,images):\n    ''' Calculate Gradient and Update Wights'''\n    # Generating noise from a normal distribution\n    fake_img_noise = tf.random.normal([self.batch_size, self.latent_dim])\n\n    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n      generated_images =self. generator(fake_img_noise, training=True)\n\n      real_output = self.discriminator(images, training=True)\n      fake_output = self.discriminator(generated_images, training=True)\n\n      gen_loss = self.generator_loss(fake_output)\n      real_loss_dis,fake_loss_dis = self.discriminator_loss(real_output, fake_output)\n      disc_loss =real_loss_dis+fake_loss_dis\n\n      gradients_of_generator = gen_tape.gradient(gen_loss,self.generator.trainable_variables)\n      self.generator_optimizer.apply_gradients(zip(gradients_of_generator,self.generator.trainable_variables))\n\n      gradients_of_discriminator = disc_tape.gradient(disc_loss,self.discriminator.trainable_variables)\n      self.discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator,self.discriminator.trainable_variables))\n\n      self.gen_hist.append(gen_loss)\n      self.disc_hist.append([real_loss_dis,fake_loss_dis])\n      self.gen_hist.append(disc_loss)\n\n  def Fit_GAN(self, dataset, epochs, save_path='best_generator.h5'):\n      # build models\n      self.generator = self.build_generator_model()\n      self.discriminator = self.build_discriminator_model()\n      # compile\n      self.generator.compile(optimizer=self.generator_optimizer, loss=self.generator_loss)\n      self.discriminator.compile(optimizer=self.discriminator_optimizer, loss=self.discriminator_loss)\n\n\n      best_loss = float('inf')  # Initialize with a very high loss\n      for epoch in tqdm(range(epochs)):\n          for batch in dataset:\n              self.train_process(batch)\n\n          # Calculate generator loss at the end of each epoch\n          avg_gen_loss = tf.reduce_mean(self.gen_hist)\n\n          # Save the generator model if the current loss is the best observed so far\n          if avg_gen_loss < best_loss:\n              best_loss = avg_gen_loss\n              self.generator.save(save_path)  # Save the best model\n              print(f'saved {avg_gen_loss.numpy()}')\n\n          print(f\"Epoch {epoch + 1}/{epochs}, Generator Loss: {avg_gen_loss.numpy()}\")\n          print('==='*20)\n\n      print(\"Training completed.\")","metadata":{"id":"HlBDC1_tu8sd","execution":{"iopub.status.busy":"2024-01-03T10:15:27.077362Z","iopub.execute_input":"2024-01-03T10:15:27.077739Z","iopub.status.idle":"2024-01-03T10:15:27.106452Z","shell.execute_reply.started":"2024-01-03T10:15:27.077708Z","shell.execute_reply":"2024-01-03T10:15:27.105776Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"# Start \n","metadata":{"id":"2hG24ophe3yq"}},{"cell_type":"markdown","source":"## Read Data","metadata":{"id":"PhCiWFFKe-wy"}},{"cell_type":"code","source":"buffer_size=512\nbatch_size=128","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset = objCreateData.DataSet_Slices_Batch(batch_size=batch_size,buffer_size=buffer_size)","metadata":{"id":"hwasHHmqe9A3","outputId":"8f16a495-05f4-40ae-9a53-d08fbcf266c3","execution":{"iopub.status.busy":"2024-01-03T10:15:27.125865Z","iopub.execute_input":"2024-01-03T10:15:27.126255Z","iopub.status.idle":"2024-01-03T10:15:42.987335Z","shell.execute_reply.started":"2024-01-03T10:15:27.126221Z","shell.execute_reply":"2024-01-03T10:15:42.986422Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"100%|██████████| 1560/1560 [00:00<00:00, 378499.12it/s]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Dispaly Sample from Data","metadata":{"id":"HDMFZCTGiNn9"}},{"cell_type":"code","source":"X_train = objCreateData.ReadImgs_Gan()\n\ndisplay_20SampleImages(X_train[:20])","metadata":{"id":"G2rGaybygAPk","outputId":"62b53559-2387-4567-db56-3e2f8ecd7572","execution":{"iopub.status.busy":"2024-01-03T10:15:42.988521Z","iopub.execute_input":"2024-01-03T10:15:42.988850Z","iopub.status.idle":"2024-01-03T10:15:47.361325Z","shell.execute_reply.started":"2024-01-03T10:15:42.988823Z","shell.execute_reply":"2024-01-03T10:15:47.360303Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"100%|██████████| 1560/1560 [00:00<00:00, 326144.66it/s]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 20 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Build & Train **DCGAN**","metadata":{"id":"b__kM0VaiViN"}},{"cell_type":"markdown","source":"### 1.Set variables","metadata":{}},{"cell_type":"code","source":"latent_dim=100 # noise\nepochs=700\nlr=0.000097","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 2.Create Obiect from cDCGAN\n","metadata":{}},{"cell_type":"code","source":"objDCGAN= cDCGAN(batch_size=batch_size,latent_dim=latent_dim,lr=lr)","metadata":{"id":"V1F2eTbgf5Rt","execution":{"iopub.status.busy":"2024-01-03T10:15:47.368861Z","iopub.execute_input":"2024-01-03T10:15:47.369243Z","iopub.status.idle":"2024-01-03T10:15:47.417057Z","shell.execute_reply.started":"2024-01-03T10:15:47.369208Z","shell.execute_reply":"2024-01-03T10:15:47.416257Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"### 3.Train Models  ","metadata":{}},{"cell_type":"code","source":"objDCGAN.Fit_GAN(dataset,epochs,save_path)","metadata":{"id":"E4frhPCSi1hL","outputId":"3b5807f1-2397-4b87-a028-248a7fe92a97","_kg_hide-input":false,"execution":{"iopub.status.busy":"2024-01-03T10:15:47.419607Z","iopub.execute_input":"2024-01-03T10:15:47.419906Z","iopub.status.idle":"2024-01-03T10:50:51.501085Z","shell.execute_reply.started":"2024-01-03T10:15:47.419881Z","shell.execute_reply":"2024-01-03T10:50:51.499814Z"},"_kg_hide-output":true,"trusted":true},"execution_count":11,"outputs":[{"name":"stderr","text":"  0%|          | 0/700 [00:00<?, ?it/s]/opt/conda/lib/python3.10/site-packages/keras/src/backend.py:5805: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Sigmoid activation and thus does not represent logits. Was this intended?\n  output, from_logits = _get_logits(\n/opt/conda/lib/python3.10/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n  saving_api.save_model(\n  0%|          | 1/700 [00:19<3:51:28, 19.87s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1554713249206543\nEpoch 1/700, Generator Loss: 1.1554713249206543\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  0%|          | 2/700 [00:21<1:47:41,  9.26s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 2/700, Generator Loss: 1.1561882495880127\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  0%|          | 3/700 [00:23<1:08:29,  5.90s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 3/700, Generator Loss: 1.1676801443099976\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  1%|          | 4/700 [00:25<50:17,  4.34s/it]  ","output_type":"stream"},{"name":"stdout","text":"Epoch 4/700, Generator Loss: 1.1781456470489502\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  1%|          | 5/700 [00:27<40:32,  3.50s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 5/700, Generator Loss: 1.1866711378097534\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  1%|          | 6/700 [00:29<35:37,  3.08s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 6/700, Generator Loss: 1.1953542232513428\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  1%|          | 7/700 [00:32<32:31,  2.82s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 7/700, Generator Loss: 1.2027196884155273\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  1%|   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1.1845180988311768\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 12/700 [00:43<27:26,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 12/700, Generator Loss: 1.1788403987884521\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 13/700 [00:46<27:47,  2.43s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 13/700, Generator Loss: 1.1755552291870117\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 14/700 [00:48<26:34,  2.32s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 14/700, Generator Loss: 1.1750009059906006\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 15/700 [00:50<25:21,  2.22s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 15/700, Generator Loss: 1.1756105422973633\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 16/700 [00:52<24:27,  2.14s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 16/700, Generator Loss: 1.1759064197540283\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  2%|▏         | 17/700 [00:54<24:12,  2.13s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 17/700, Generator Loss: 1.1734176874160767\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 18/700 [00:56<23:48,  2.10s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 18/700, Generator Loss: 1.1717637777328491\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 19/700 [00:58<23:23,  2.06s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 19/700, Generator Loss: 1.16951584815979\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 20/700 [01:00<23:01,  2.03s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 20/700, Generator Loss: 1.1685073375701904\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 21/700 [01:02<22:44,  2.01s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 21/700, Generator Loss: 1.1679621934890747\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 22/700 [01:04<22:37,  2.00s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 22/700, Generator Loss: 1.1641110181808472\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 23/700 [01:06<22:38,  2.01s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 23/700, Generator Loss: 1.1586371660232544\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  3%|▎         | 24/700 [01:08<23:40,  2.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1543822288513184\nEpoch 24/700, Generator Loss: 1.1543822288513184\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▎         | 25/700 [01:10<24:43,  2.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1528109312057495\nEpoch 25/700, Generator Loss: 1.1528109312057495\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▎         | 26/700 [01:13<24:59,  2.23s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 26/700, Generator Loss: 1.1531902551651\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 27/700 [01:15<25:14,  2.25s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 27/700, Generator Loss: 1.153359055519104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 28/700 [01:17<25:21,  2.26s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 28/700, Generator Loss: 1.1531659364700317\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 29/700 [01:20<25:34,  2.29s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1523438692092896\nEpoch 29/700, Generator Loss: 1.1523438692092896\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 30/700 [01:22<26:04,  2.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1488661766052246\nEpoch 30/700, Generator Loss: 1.1488661766052246\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  4%|▍         | 31/700 [01:25<26:25,  2.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1443603038787842\nEpoch 31/700, Generator Loss: 1.1443603038787842\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▍         | 32/700 [01:27<25:53,  2.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1417064666748047\nEpoch 32/700, Generator Loss: 1.1417064666748047\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▍         | 33/700 [01:29<24:53,  2.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1410669088363647\nEpoch 33/700, Generator Loss: 1.1410669088363647\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▍         | 34/700 [01:31<23:51,  2.15s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 34/700, Generator Loss: 1.1418437957763672\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▌         | 35/700 [01:33<23:08,  2.09s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 35/700, Generator Loss: 1.1425014734268188\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▌         | 36/700 [01:35<22:38,  2.05s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 36/700, Generator Loss: 1.142455816268921\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▌         | 37/700 [01:37<22:26,  2.03s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 37/700, Generator Loss: 1.141674280166626\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  5%|▌         | 38/700 [01:39<22:29,  2.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1408054828643799\nEpoch 38/700, Generator Loss: 1.1408054828643799\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 39/700 [01:41<22:28,  2.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1402921676635742\nEpoch 39/700, Generator Loss: 1.1402921676635742\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 40/700 [01:43<22:23,  2.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1400423049926758\nEpoch 40/700, Generator Loss: 1.1400423049926758\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 41/700 [01:45<22:23,  2.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.139251947402954\nEpoch 41/700, Generator Loss: 1.139251947402954\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  6%|▌         | 42/700 [01:47<23:02,  2.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1380614042282104\nEpoch 42/700, Generator Loss: 1.1380614042282104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  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Generator Loss: 1.1337931156158447\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 47/700 [01:59<26:06,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1325243711471558\nEpoch 47/700, Generator Loss: 1.1325243711471558\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 48/700 [02:02<26:20,  2.42s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1308237314224243\nEpoch 48/700, Generator Loss: 1.1308237314224243\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 49/700 [02:04<26:28,  2.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.129204511642456\nEpoch 49/700, Generator Loss: 1.129204511642456\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 50/700 [02:06<25:47,  2.38s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1279064416885376\nEpoch 50/700, Generator Loss: 1.1279064416885376\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 51/700 [02:09<25:12,  2.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1266218423843384\nEpoch 51/700, Generator Loss: 1.1266218423843384\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  7%|▋         | 52/700 [02:11<24:24,  2.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.125604510307312\nEpoch 52/700, Generator Loss: 1.125604510307312\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  8%|▊ 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Loss: 1.121911883354187\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  8%|▊         | 57/700 [02:21<22:57,  2.14s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1217153072357178\nEpoch 57/700, Generator Loss: 1.1217153072357178\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  8%|▊         | 58/700 [02:24<23:18,  2.18s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 58/700, Generator Loss: 1.121940016746521\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  8%|▊         | 59/700 [02:26<24:26,  2.29s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1213831901550293\nEpoch 59/700, Generator Loss: 1.1213831901550293\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▊         | 60/700 [02:29<25:02,  2.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1205147504806519\nEpoch 60/700, Generator Loss: 1.1205147504806519\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▊         | 61/700 [02:31<25:28,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1199164390563965\nEpoch 61/700, Generator Loss: 1.1199164390563965\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▉         | 62/700 [02:34<26:01,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1191598176956177\nEpoch 62/700, Generator Loss: 1.1191598176956177\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▉         | 63/700 [02:36<25:59,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1189743280410767\nEpoch 63/700, Generator Loss: 1.1189743280410767\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▉         | 64/700 [02:39<25:49,  2.44s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 64/700, Generator Loss: 1.1191128492355347\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▉         | 65/700 [02:41<26:11,  2.48s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1188876628875732\nEpoch 65/700, Generator Loss: 1.1188876628875732\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"  9%|▉         | 66/700 [02:43<25:14,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 66/700, Generator Loss: 1.1189297437667847\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|▉         | 67/700 [02:45<24:22,  2.31s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1187626123428345\nEpoch 67/700, Generator Loss: 1.1187626123428345\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|▉         | 68/700 [02:48<23:40,  2.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1183712482452393\nEpoch 68/700, Generator Loss: 1.1183712482452393\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|▉         | 69/700 [02:50<23:10,  2.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1178780794143677\nEpoch 69/700, Generator Loss: 1.1178780794143677\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|█         | 70/700 [02:52<22:49,  2.17s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1174465417861938\nEpoch 70/700, Generator Loss: 1.1174465417861938\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|█         | 71/700 [02:54<22:36,  2.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1171265840530396\nEpoch 71/700, Generator Loss: 1.1171265840530396\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|█         | 72/700 [02:56<23:23,  2.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.117116928100586\nEpoch 72/700, Generator Loss: 1.117116928100586\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 10%|█         | 73/700 [02:58<22:43,  2.18s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 73/700, Generator Loss: 1.1171647310256958\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█         | 74/700 [03:00<22:30,  2.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1168133020401\nEpoch 74/700, Generator Loss: 1.1168133020401\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█         | 75/700 [03:03<23:02,  2.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1162364482879639\nEpoch 75/700, Generator Loss: 1.1162364482879639\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█         | 76/700 [03:05<23:54,  2.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1162241697311401\nEpoch 76/700, Generator Loss: 1.1162241697311401\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█         | 77/700 [03:08<24:23,  2.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1160268783569336\nEpoch 77/700, Generator Loss: 1.1160268783569336\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█         | 78/700 [03:10<25:00,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1159882545471191\nEpoch 78/700, Generator Loss: 1.1159882545471191\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█▏        | 79/700 [03:13<25:33,  2.47s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.115980863571167\nEpoch 79/700, Generator Loss: 1.115980863571167\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 11%|█▏        | 80/700 [03:15<25:36,  2.48s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1159077882766724\nEpoch 80/700, Generator Loss: 1.1159077882766724\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 81/700 [03:18<25:47,  2.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1156456470489502\nEpoch 81/700, Generator Loss: 1.1156456470489502\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 82/700 [03:20<25:50,  2.51s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1152750253677368\nEpoch 82/700, Generator Loss: 1.1152750253677368\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 83/700 [03:23<25:18,  2.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.115157961845398\nEpoch 83/700, Generator Loss: 1.115157961845398\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 84/700 [03:25<24:22,  2.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1147313117980957\nEpoch 84/700, Generator Loss: 1.1147313117980957\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 85/700 [03:27<23:42,  2.31s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1142761707305908\nEpoch 85/700, Generator Loss: 1.1142761707305908\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 86/700 [03:29<23:14,  2.27s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1142367124557495\nEpoch 86/700, Generator Loss: 1.1142367124557495\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 12%|█▏        | 87/700 [03:31<22:43,  2.22s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 87/700, Generator Loss: 1.1145265102386475\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎       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1.1136186122894287\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎        | 92/700 [03:42<22:00,  2.17s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 92/700, Generator Loss: 1.1137415170669556\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎        | 93/700 [03:45<22:36,  2.23s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 93/700, Generator Loss: 1.1136910915374756\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 13%|█▎        | 94/700 [03:47<23:35,  2.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1134259700775146\nEpoch 94/700, Generator Loss: 1.1134259700775146\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▎        | 95/700 [03:50<24:20,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1129889488220215\nEpoch 95/700, Generator Loss: 1.1129889488220215\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▎        | 96/700 [03:52<24:40,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1128050088882446\nEpoch 96/700, Generator Loss: 1.1128050088882446\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▍        | 97/700 [03:55<25:05,  2.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1126335859298706\nEpoch 97/700, Generator Loss: 1.1126335859298706\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▍        | 98/700 [03:58<25:31,  2.54s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1125513315200806\nEpoch 98/700, Generator Loss: 1.1125513315200806\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▍        | 99/700 [04:00<25:25,  2.54s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1123862266540527\nEpoch 99/700, Generator Loss: 1.1123862266540527\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▍        | 100/700 [04:03<25:29,  2.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.112243413925171\nEpoch 100/700, Generator Loss: 1.112243413925171\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 14%|█▍        | 101/700 [04:05<24:29,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1121233701705933\nEpoch 101/700, Generator Loss: 1.1121233701705933\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▍        | 102/700 [04:07<23:43,  2.38s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1119585037231445\nEpoch 102/700, Generator Loss: 1.1119585037231445\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▍        | 103/700 [04:09<23:05,  2.32s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1117323637008667\nEpoch 103/700, Generator Loss: 1.1117323637008667\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▍        | 104/700 [04:12<22:40,  2.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1113337278366089\nEpoch 104/700, Generator Loss: 1.1113337278366089\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▌        | 105/700 [04:14<22:46,  2.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1111599206924438\nEpoch 105/700, Generator Loss: 1.1111599206924438\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▌        | 106/700 [04:16<22:25,  2.27s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1107075214385986\nEpoch 106/700, Generator Loss: 1.1107075214385986\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▌        | 107/700 [04:18<22:11,  2.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.110107660293579\nEpoch 107/700, Generator Loss: 1.110107660293579\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 15%|█▌        | 108/700 [04:20<21:53,  2.22s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.110021948814392\nEpoch 108/700, Generator Loss: 1.110021948814392\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▌        | 109/700 [04:23<21:46,  2.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.109789490699768\nEpoch 109/700, Generator Loss: 1.109789490699768\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▌        | 110/700 [04:25<23:01,  2.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1096091270446777\nEpoch 110/700, Generator Loss: 1.1096091270446777\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▌        | 111/700 [04:28<23:16,  2.37s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 111/700, Generator Loss: 1.1096888780593872\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▌        | 112/700 [04:30<23:28,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 112/700, Generator Loss: 1.1096199750900269\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▌        | 113/700 [04:33<23:57,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.109452247619629\nEpoch 113/700, Generator Loss: 1.109452247619629\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▋        | 114/700 [04:35<24:25,  2.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1092054843902588\nEpoch 114/700, Generator Loss: 1.1092054843902588\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 16%|█▋        | 115/700 [04:38<24:36,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1090325117111206\nEpoch 115/700, Generator Loss: 1.1090325117111206\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 116/700 [04:41<24:50,  2.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1086479425430298\nEpoch 116/700, Generator Loss: 1.1086479425430298\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 117/700 [04:43<24:36,  2.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1083369255065918\nEpoch 117/700, Generator Loss: 1.1083369255065918\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 118/700 [04:45<23:56,  2.47s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1081372499465942\nEpoch 118/700, Generator Loss: 1.1081372499465942\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 119/700 [04:47<22:59,  2.37s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 119/700, Generator Loss: 1.1081899404525757\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 120/700 [04:50<22:13,  2.30s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 120/700, Generator Loss: 1.1081608533859253\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 121/700 [04:52<22:00,  2.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1080785989761353\nEpoch 121/700, Generator Loss: 1.1080785989761353\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 17%|█▋        | 122/700 [04:54<21:54,  2.27s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1078155040740967\nEpoch 122/700, Generator Loss: 1.1078155040740967\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 123/700 [04:56<21:42,  2.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.107456088066101\nEpoch 123/700, Generator Loss: 1.107456088066101\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 124/700 [04:59<21:31,  2.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1071929931640625\nEpoch 124/700, Generator Loss: 1.1071929931640625\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 125/700 [05:01<21:41,  2.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.10707426071167\nEpoch 125/700, Generator Loss: 1.10707426071167\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 126/700 [05:03<21:33,  2.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1069495677947998\nEpoch 126/700, Generator Loss: 1.1069495677947998\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 127/700 [05:05<21:25,  2.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.106685757637024\nEpoch 127/700, Generator Loss: 1.106685757637024\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 128/700 [05:08<21:22,  2.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.10624361038208\nEpoch 128/700, Generator Loss: 1.10624361038208\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 18%|█▊        | 129/700 [05:10<21:13,  2.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.10584557056427\nEpoch 129/700, Generator Loss: 1.10584557056427\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▊        | 130/700 [05:12<21:50,  2.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1055148839950562\nEpoch 130/700, Generator Loss: 1.1055148839950562\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▊        | 131/700 [05:14<21:44,  2.29s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1051597595214844\nEpoch 131/700, Generator Loss: 1.1051597595214844\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▉        | 132/700 [05:17<21:46,  2.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1050530672073364\nEpoch 132/700, Generator Loss: 1.1050530672073364\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▉        | 133/700 [05:19<21:32,  2.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1050413846969604\nEpoch 133/700, Generator Loss: 1.1050413846969604\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▉        | 134/700 [05:21<21:27,  2.27s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1049293279647827\nEpoch 134/700, Generator Loss: 1.1049293279647827\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▉        | 135/700 [05:24<21:30,  2.28s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 135/700, Generator Loss: 1.1049528121948242\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 19%|█▉        | 136/700 [05:26<22:10,  2.36s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 136/700, Generator Loss: 1.1050639152526855\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|█▉        | 137/700 [05:29<22:27,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 137/700, Generator Loss: 1.1051108837127686\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|█▉        | 138/700 [05:31<23:28,  2.51s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.104641079902649\nEpoch 138/700, Generator Loss: 1.104641079902649\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|█▉        | 139/700 [05:34<23:51,  2.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1042073965072632\nEpoch 139/700, Generator Loss: 1.1042073965072632\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|██        | 140/700 [05:37<24:06,  2.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.103696584701538\nEpoch 140/700, Generator Loss: 1.103696584701538\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|██        | 141/700 [05:39<24:29,  2.63s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1034586429595947\nEpoch 141/700, Generator Loss: 1.1034586429595947\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|██        | 142/700 [05:42<24:06,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 142/700, Generator Loss: 1.1041086912155151\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 20%|██        | 143/700 [05:44<23:22,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 143/700, Generator Loss: 1.103899359703064\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██        | 144/700 [05:47<22:33,  2.43s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 144/700, Generator Loss: 1.1036427021026611\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██        | 145/700 [05:49<22:10,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1034114360809326\nEpoch 145/700, Generator Loss: 1.1034114360809326\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██        | 146/700 [05:51<21:45,  2.36s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1031134128570557\nEpoch 146/700, Generator Loss: 1.1031134128570557\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██        | 147/700 [05:53<21:44,  2.36s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1027172803878784\nEpoch 147/700, Generator Loss: 1.1027172803878784\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██        | 148/700 [05:56<21:38,  2.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.102218747138977\nEpoch 148/700, Generator Loss: 1.102218747138977\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██▏       | 149/700 [05:58<21:27,  2.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1021808385849\nEpoch 149/700, Generator Loss: 1.1021808385849\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 21%|██▏       | 150/700 [06:01<22:34,  2.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1020630598068237\nEpoch 150/700, Generator Loss: 1.1020630598068237\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 151/700 [06:04<23:09,  2.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1019716262817383\nEpoch 151/700, Generator Loss: 1.1019716262817383\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 152/700 [06:06<23:45,  2.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1017893552780151\nEpoch 152/700, Generator Loss: 1.1017893552780151\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 153/700 [06:09<23:52,  2.62s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1017645597457886\nEpoch 153/700, Generator Loss: 1.1017645597457886\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 154/700 [06:12<23:44,  2.61s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 154/700, Generator Loss: 1.1017951965332031\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 155/700 [06:14<24:05,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1014951467514038\nEpoch 155/700, Generator Loss: 1.1014951467514038\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 156/700 [06:17<24:08,  2.66s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1013039350509644\nEpoch 156/700, Generator Loss: 1.1013039350509644\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 22%|██▏       | 157/700 [06:19<23:40,  2.62s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1011483669281006\nEpoch 157/700, Generator Loss: 1.1011483669281006\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 158/700 [06:22<22:49,  2.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1010757684707642\nEpoch 158/700, Generator Loss: 1.1010757684707642\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 159/700 [06:24<22:57,  2.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.101042628288269\nEpoch 159/700, Generator Loss: 1.101042628288269\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 160/700 [06:27<23:24,  2.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1009899377822876\nEpoch 160/700, Generator Loss: 1.1009899377822876\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 161/700 [06:30<23:49,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1007806062698364\nEpoch 161/700, Generator Loss: 1.1007806062698364\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 162/700 [06:33<24:00,  2.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1004935503005981\nEpoch 162/700, Generator Loss: 1.1004935503005981\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 163/700 [06:35<24:18,  2.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.1001569032669067\nEpoch 163/700, Generator Loss: 1.1001569032669067\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 23%|██▎       | 164/700 [06:38<24:11,  2.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0999979972839355\nEpoch 164/700, Generator Loss: 1.0999979972839355\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▎       | 165/700 [06:41<23:56,  2.69s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 165/700, Generator Loss: 1.1000185012817383\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▎       | 166/700 [06:43<23:31,  2.64s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 166/700, Generator Loss: 1.1000438928604126\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▍       | 167/700 [06:46<22:25,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 167/700, Generator Loss: 1.100078821182251\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▍       | 168/700 [06:48<21:31,  2.43s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 168/700, Generator Loss: 1.100106954574585\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▍       | 169/700 [06:50<21:04,  2.38s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 169/700, Generator Loss: 1.1001238822937012\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▍       | 170/700 [06:52<20:42,  2.35s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 170/700, Generator Loss: 1.100142240524292\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 24%|██▍       | 171/700 [06:55<20:26,  2.32s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 171/700, Generator Loss: 1.1000369787216187\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▍       | 172/700 [06:57<20:20,  2.31s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0999062061309814\nEpoch 172/700, Generator Loss: 1.0999062061309814\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▍       | 173/700 [06:59<20:22,  2.32s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0997796058654785\nEpoch 173/700, Generator Loss: 1.0997796058654785\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▍       | 174/700 [07:02<20:34,  2.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.099616527557373\nEpoch 174/700, Generator Loss: 1.099616527557373\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▌       | 175/700 [07:04<20:38,  2.36s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.099415898323059\nEpoch 175/700, Generator Loss: 1.099415898323059\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▌       | 176/700 [07:07<21:36,  2.48s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0991191864013672\nEpoch 176/700, Generator Loss: 1.0991191864013672\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▌       | 177/700 [07:09<22:20,  2.56s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0990095138549805\nEpoch 177/700, Generator Loss: 1.0990095138549805\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 25%|██▌       | 178/700 [07:12<22:42,  2.61s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0988694429397583\nEpoch 178/700, Generator Loss: 1.0988694429397583\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▌       | 179/700 [07:15<23:10,  2.67s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0986671447753906\nEpoch 179/700, Generator Loss: 1.0986671447753906\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▌       | 180/700 [07:18<23:15,  2.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0984982252120972\nEpoch 180/700, Generator Loss: 1.0984982252120972\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▌       | 181/700 [07:21<23:42,  2.74s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0982551574707031\nEpoch 181/700, Generator Loss: 1.0982551574707031\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▌       | 182/700 [07:23<23:46,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.09796142578125\nEpoch 182/700, Generator Loss: 1.09796142578125\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▌       | 183/700 [07:26<23:02,  2.67s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.097830057144165\nEpoch 183/700, Generator Loss: 1.097830057144165\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▋       | 184/700 [07:28<22:15,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0975621938705444\nEpoch 184/700, Generator Loss: 1.0975621938705444\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 26%|██▋       | 185/700 [07:31<21:37,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0973817110061646\nEpoch 185/700, Generator Loss: 1.0973817110061646\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 186/700 [07:33<21:18,  2.49s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0969659090042114\nEpoch 186/700, Generator Loss: 1.0969659090042114\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 187/700 [07:35<20:57,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0967973470687866\nEpoch 187/700, Generator Loss: 1.0967973470687866\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 188/700 [07:38<20:28,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 188/700, Generator Loss: 1.0969805717468262\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 189/700 [07:40<20:33,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 189/700, Generator Loss: 1.0970878601074219\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 190/700 [07:42<20:11,  2.38s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 190/700, Generator Loss: 1.0971081256866455\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 191/700 [07:45<20:33,  2.42s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 191/700, Generator Loss: 1.0970367193222046\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 27%|██▋       | 192/700 [07:48<21:11,  2.50s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 192/700, Generator Loss: 1.0970717668533325\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 193/700 [07:50<21:42,  2.57s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 193/700, Generator Loss: 1.0969500541687012\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 194/700 [07:53<22:47,  2.70s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0967411994934082\nEpoch 194/700, Generator Loss: 1.0967411994934082\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 195/700 [07:56<22:54,  2.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0966598987579346\nEpoch 195/700, Generator Loss: 1.0966598987579346\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 196/700 [07:59<23:03,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0965505838394165\nEpoch 196/700, Generator Loss: 1.0965505838394165\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 197/700 [08:02<23:09,  2.76s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0964081287384033\nEpoch 197/700, Generator Loss: 1.0964081287384033\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 198/700 [08:04<23:02,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0960596799850464\nEpoch 198/700, Generator Loss: 1.0960596799850464\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 28%|██▊       | 199/700 [08:07<21:56,  2.63s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 199/700, Generator Loss: 1.0962167978286743\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▊       | 200/700 [08:09<21:02,  2.53s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 200/700, Generator Loss: 1.0962233543395996\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▊       | 201/700 [08:11<20:24,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 201/700, Generator Loss: 1.0961025953292847\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▉       | 202/700 [08:14<20:14,  2.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0957367420196533\nEpoch 202/700, Generator Loss: 1.0957367420196533\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▉       | 203/700 [08:16<20:07,  2.43s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0954604148864746\nEpoch 203/700, Generator Loss: 1.0954604148864746\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▉       | 204/700 [08:18<19:43,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 204/700, Generator Loss: 1.0957223176956177\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▉       | 205/700 [08:21<19:50,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 205/700, Generator Loss: 1.0957049131393433\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 29%|██▉       | 206/700 [08:24<20:43,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 206/700, Generator Loss: 1.0957505702972412\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|██▉       | 207/700 [08:26<21:04,  2.56s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 207/700, Generator Loss: 1.0956729650497437\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|██▉       | 208/700 [08:29<21:26,  2.61s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 208/700, Generator Loss: 1.0958110094070435\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|██▉       | 209/700 [08:32<21:39,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 209/700, Generator Loss: 1.0958179235458374\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|███       | 210/700 [08:35<21:59,  2.69s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 210/700, Generator Loss: 1.0956861972808838\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|███       | 211/700 [08:37<21:58,  2.70s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 211/700, Generator Loss: 1.0955480337142944\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|███       | 212/700 [08:40<22:14,  2.73s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0954110622406006\nEpoch 212/700, Generator Loss: 1.0954110622406006\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 30%|███       | 213/700 [08:43<21:28,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0952956676483154\nEpoch 213/700, Generator Loss: 1.0952956676483154\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 31%|███       | 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1.094900369644165\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 31%|███       | 218/700 [08:55<19:39,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.094233512878418\nEpoch 218/700, Generator Loss: 1.094233512878418\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 31%|███▏      | 219/700 [08:57<19:15,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 219/700, Generator Loss: 1.0943794250488281\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 31%|███▏      | 220/700 [08:59<19:06,  2.39s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 220/700, Generator Loss: 1.094313621520996\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 221/700 [09:02<20:17,  2.54s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0939136743545532\nEpoch 221/700, Generator Loss: 1.0939136743545532\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 222/700 [09:05<20:38,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 222/700, Generator Loss: 1.0940866470336914\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 223/700 [09:07<20:53,  2.63s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 223/700, Generator Loss: 1.094213843345642\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 224/700 [09:10<21:20,  2.69s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 224/700, Generator Loss: 1.0942065715789795\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 225/700 [09:13<21:19,  2.69s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 225/700, Generator Loss: 1.094076156616211\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 226/700 [09:16<21:52,  2.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.093734860420227\nEpoch 226/700, Generator Loss: 1.093734860420227\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 32%|███▏      | 227/700 [09:19<21:57,  2.79s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0936219692230225\nEpoch 227/700, Generator Loss: 1.0936219692230225\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 228/700 [09:21<21:01,  2.67s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 228/700, Generator Loss: 1.093823790550232\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 229/700 [09:24<20:14,  2.58s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 229/700, Generator Loss: 1.0938189029693604\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 230/700 [09:26<19:41,  2.51s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 230/700, Generator Loss: 1.0938360691070557\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 231/700 [09:28<19:12,  2.46s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 231/700, Generator Loss: 1.0937414169311523\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 232/700 [09:31<18:49,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 232/700, Generator Loss: 1.0938174724578857\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 233/700 [09:33<18:45,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 233/700, Generator Loss: 1.0937960147857666\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 33%|███▎      | 234/700 [09:35<18:39,  2.40s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 234/700, Generator Loss: 1.0936944484710693\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▎      | 235/700 [09:38<18:42,  2.41s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0933083295822144\nEpoch 235/700, Generator Loss: 1.0933083295822144\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▎      | 236/700 [09:41<19:35,  2.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0927863121032715\nEpoch 236/700, Generator Loss: 1.0927863121032715\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▍      | 237/700 [09:43<20:15,  2.63s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 237/700, Generator Loss: 1.0931028127670288\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▍      | 238/700 [09:46<20:28,  2.66s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 238/700, Generator Loss: 1.0929896831512451\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▍      | 239/700 [09:49<20:51,  2.72s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 239/700, Generator Loss: 1.0928090810775757\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▍      | 240/700 [09:52<21:10,  2.76s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0925588607788086\nEpoch 240/700, Generator Loss: 1.0925588607788086\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 34%|███▍      | 241/700 [09:55<21:27,  2.80s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 241/700, Generator Loss: 1.0926599502563477\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▍      | 242/700 [09:58<21:29,  2.82s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 242/700, Generator Loss: 1.0926896333694458\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▍      | 243/700 [10:00<20:35,  2.70s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 243/700, Generator Loss: 1.0926597118377686\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▍      | 244/700 [10:02<19:52,  2.62s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 244/700, Generator Loss: 1.092645525932312\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▌      | 245/700 [10:05<19:38,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0924750566482544\nEpoch 245/700, Generator Loss: 1.0924750566482544\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▌      | 246/700 [10:08<19:21,  2.56s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0924063920974731\nEpoch 246/700, Generator Loss: 1.0924063920974731\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▌      | 247/700 [10:10<19:01,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 247/700, Generator Loss: 1.0924519300460815\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 35%|███▌      | 248/700 [10:13<19:26,  2.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0923631191253662\nEpoch 248/700, Generator Loss: 1.0923631191253662\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 36%|███▌      | 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1.0921083688735962\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 36%|███▌      | 253/700 [10:26<20:48,  2.79s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0920993089675903\nEpoch 253/700, Generator Loss: 1.0920993089675903\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 36%|███▋      | 254/700 [10:29<21:18,  2.87s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.09208345413208\nEpoch 254/700, Generator Loss: 1.09208345413208\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 36%|███▋      | 255/700 [10:32<21:28,  2.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0919235944747925\nEpoch 255/700, Generator Loss: 1.0919235944747925\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 256/700 [10:36<21:49,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0917601585388184\nEpoch 256/700, Generator Loss: 1.0917601585388184\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 257/700 [10:38<21:17,  2.88s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.091542363166809\nEpoch 257/700, Generator Loss: 1.091542363166809\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 258/700 [10:41<20:08,  2.73s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 258/700, Generator Loss: 1.0915547609329224\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 259/700 [10:43<19:22,  2.64s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 259/700, Generator Loss: 1.0915817022323608\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 260/700 [10:45<18:45,  2.56s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 260/700, Generator Loss: 1.0915521383285522\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 261/700 [10:48<18:25,  2.52s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 261/700, Generator Loss: 1.091678261756897\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 37%|███▋      | 262/700 [10:50<18:09,  2.49s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 262/700, Generator Loss: 1.0916844606399536\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 263/700 [10:53<18:09,  2.49s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 263/700, Generator Loss: 1.0917173624038696\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 264/700 [10:55<18:11,  2.50s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 264/700, Generator Loss: 1.0916717052459717\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 265/700 [10:58<17:59,  2.48s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 265/700, Generator Loss: 1.091628909111023\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 266/700 [11:00<17:59,  2.49s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 266/700, Generator Loss: 1.0916060209274292\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 267/700 [11:03<17:57,  2.49s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 267/700, Generator Loss: 1.0916162729263306\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 268/700 [11:05<17:50,  2.48s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 268/700, Generator Loss: 1.0916447639465332\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 38%|███▊      | 269/700 [11:08<17:37,  2.45s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 269/700, Generator Loss: 1.091609239578247\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▊      | 270/700 [11:10<17:57,  2.51s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 270/700, Generator Loss: 1.0915504693984985\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▊      | 271/700 [11:13<18:56,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0915039777755737\nEpoch 271/700, Generator Loss: 1.0915039777755737\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▉      | 272/700 [11:16<19:44,  2.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.091368556022644\nEpoch 272/700, Generator Loss: 1.091368556022644\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▉      | 273/700 [11:19<20:06,  2.83s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0912208557128906\nEpoch 273/700, Generator Loss: 1.0912208557128906\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▉      | 274/700 [11:22<20:30,  2.89s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0909950733184814\nEpoch 274/700, Generator Loss: 1.0909950733184814\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▉      | 275/700 [11:25<20:44,  2.93s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0907670259475708\nEpoch 275/700, Generator Loss: 1.0907670259475708\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 39%|███▉      | 276/700 [11:28<21:01,  2.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0907062292099\nEpoch 276/700, Generator Loss: 1.0907062292099\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|███▉      | 277/700 [11:31<20:26,  2.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.090593934059143\nEpoch 277/700, Generator Loss: 1.090593934059143\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|███▉      | 278/700 [11:34<19:32,  2.78s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 278/700, Generator Loss: 1.0906283855438232\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|███▉      | 279/700 [11:36<18:54,  2.69s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 279/700, Generator Loss: 1.0906881093978882\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|████      | 280/700 [11:39<18:21,  2.62s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 280/700, Generator Loss: 1.0906304121017456\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|████      | 281/700 [11:41<18:07,  2.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0905283689498901\nEpoch 281/700, Generator Loss: 1.0905283689498901\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|████      | 282/700 [11:44<18:04,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0905035734176636\nEpoch 282/700, Generator Loss: 1.0905035734176636\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 40%|████      | 283/700 [11:46<17:58,  2.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.090480089187622\nEpoch 283/700, Generator Loss: 1.090480089187622\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████      | 284/700 [11:49<17:51,  2.57s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0904176235198975\nEpoch 284/700, Generator Loss: 1.0904176235198975\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████      | 285/700 [11:52<18:30,  2.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0903486013412476\nEpoch 285/700, Generator Loss: 1.0903486013412476\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████      | 286/700 [11:55<19:20,  2.80s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0903462171554565\nEpoch 286/700, Generator Loss: 1.0903462171554565\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████      | 287/700 [11:58<19:43,  2.87s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.090258240699768\nEpoch 287/700, Generator Loss: 1.090258240699768\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████      | 288/700 [12:01<19:59,  2.91s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0902444124221802\nEpoch 288/700, Generator Loss: 1.0902444124221802\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████▏     | 289/700 [12:04<20:46,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.090235710144043\nEpoch 289/700, Generator Loss: 1.090235710144043\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 41%|████▏     | 290/700 [12:07<20:41,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0902060270309448\nEpoch 290/700, Generator Loss: 1.0902060270309448\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 291/700 [12:10<20:10,  2.96s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 291/700, Generator Loss: 1.0902382135391235\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 292/700 [12:13<19:22,  2.85s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0900739431381226\nEpoch 292/700, Generator Loss: 1.0900739431381226\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 293/700 [12:15<18:46,  2.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0899239778518677\nEpoch 293/700, Generator Loss: 1.0899239778518677\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 294/700 [12:18<18:21,  2.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.089870810508728\nEpoch 294/700, Generator Loss: 1.089870810508728\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 295/700 [12:20<18:12,  2.70s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0897917747497559\nEpoch 295/700, Generator Loss: 1.0897917747497559\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 296/700 [12:23<18:00,  2.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0894798040390015\nEpoch 296/700, Generator Loss: 1.0894798040390015\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 42%|████▏     | 297/700 [12:26<17:48,  2.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0892888307571411\nEpoch 297/700, Generator Loss: 1.0892888307571411\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 298/700 [12:28<17:43,  2.64s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0890991687774658\nEpoch 298/700, Generator Loss: 1.0890991687774658\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 299/700 [12:31<17:32,  2.62s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0890332460403442\nEpoch 299/700, Generator Loss: 1.0890332460403442\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 300/700 [12:34<18:13,  2.73s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0889883041381836\nEpoch 300/700, Generator Loss: 1.0889883041381836\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 301/700 [12:37<18:56,  2.85s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0887846946716309\nEpoch 301/700, Generator Loss: 1.0887846946716309\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 302/700 [12:40<19:19,  2.91s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.088706135749817\nEpoch 302/700, Generator Loss: 1.088706135749817\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 303/700 [12:43<19:32,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.088594913482666\nEpoch 303/700, Generator Loss: 1.088594913482666\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 43%|████▎     | 304/700 [12:46<20:00,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.088526964187622\nEpoch 304/700, Generator Loss: 1.088526964187622\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▎     | 305/700 [12:49<20:00,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0885127782821655\nEpoch 305/700, Generator Loss: 1.0885127782821655\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▎     | 306/700 [12:52<19:33,  2.98s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 306/700, Generator Loss: 1.0885659456253052\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▍     | 307/700 [12:55<18:50,  2.88s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 307/700, Generator Loss: 1.088586688041687\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▍     | 308/700 [12:57<18:29,  2.83s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 308/700, Generator Loss: 1.088531732559204\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▍     | 309/700 [13:00<17:49,  2.74s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 309/700, Generator Loss: 1.088533878326416\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▍     | 310/700 [13:02<17:19,  2.66s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 310/700, Generator Loss: 1.0885145664215088\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 44%|████▍     | 311/700 [13:05<17:35,  2.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0884649753570557\nEpoch 311/700, Generator Loss: 1.0884649753570557\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▍     | 312/700 [13:08<17:43,  2.74s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 312/700, Generator Loss: 1.0884658098220825\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▍     | 313/700 [13:11<18:16,  2.83s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0884164571762085\nEpoch 313/700, Generator Loss: 1.0884164571762085\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▍     | 314/700 [13:14<18:55,  2.94s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0883846282958984\nEpoch 314/700, Generator Loss: 1.0883846282958984\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▌     | 315/700 [13:17<19:11,  2.99s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.088297963142395\nEpoch 315/700, Generator Loss: 1.088297963142395\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▌     | 316/700 [13:21<19:22,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0882515907287598\nEpoch 316/700, Generator Loss: 1.0882515907287598\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▌     | 317/700 [13:24<19:32,  3.06s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0882244110107422\nEpoch 317/700, Generator Loss: 1.0882244110107422\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 45%|████▌     | 318/700 [13:27<19:34,  3.07s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.088107705116272\nEpoch 318/700, Generator Loss: 1.088107705116272\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▌     | 319/700 [13:29<18:42,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.087994933128357\nEpoch 319/700, Generator Loss: 1.087994933128357\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▌     | 320/700 [13:32<18:01,  2.85s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0878912210464478\nEpoch 320/700, Generator Loss: 1.0878912210464478\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▌     | 321/700 [13:35<17:47,  2.82s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0878461599349976\nEpoch 321/700, Generator Loss: 1.0878461599349976\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▌     | 322/700 [13:37<17:26,  2.77s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 322/700, Generator Loss: 1.0878474712371826\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▌     | 323/700 [13:40<17:12,  2.74s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0877619981765747\nEpoch 323/700, Generator Loss: 1.0877619981765747\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▋     | 324/700 [13:43<17:00,  2.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.08768892288208\nEpoch 324/700, Generator Loss: 1.08768892288208\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 46%|████▋     | 325/700 [13:45<16:37,  2.66s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 325/700, Generator Loss: 1.0876946449279785\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 326/700 [13:48<16:37,  2.67s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0876834392547607\nEpoch 326/700, Generator Loss: 1.0876834392547607\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 327/700 [13:51<17:32,  2.82s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.08760666847229\nEpoch 327/700, Generator Loss: 1.08760666847229\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 328/700 [13:54<18:18,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0875622034072876\nEpoch 328/700, Generator Loss: 1.0875622034072876\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 329/700 [13:58<18:37,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0874825716018677\nEpoch 329/700, Generator Loss: 1.0874825716018677\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 330/700 [14:01<19:00,  3.08s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0874203443527222\nEpoch 330/700, Generator Loss: 1.0874203443527222\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 331/700 [14:04<19:15,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0873947143554688\nEpoch 331/700, Generator Loss: 1.0873947143554688\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 47%|████▋     | 332/700 [14:07<19:25,  3.17s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0873498916625977\nEpoch 332/700, Generator Loss: 1.0873498916625977\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 48%|████▊     | 333/700 [14:10<18:36,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0873374938964844\nEpoch 333/700, Generator Loss: 1.0873374938964844\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 48%|████▊     | 334/700 [14:13<17:59,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.087280035018921\nEpoch 334/700, Generator Loss: 1.087280035018921\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 48%|████▊     | 335/700 [14:16<17:30,  2.88s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0871996879577637\nEpoch 335/700, Generator Loss: 1.0871996879577637\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 48%|████▊     | 336/700 [14:18<17:09,  2.83s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.087106466293335\nEpoch 336/700, Generator Loss: 1.087106466293335\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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340/700, Generator Loss: 1.0869102478027344\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▊     | 341/700 [14:32<17:25,  2.91s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0868473052978516\nEpoch 341/700, Generator Loss: 1.0868473052978516\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▉     | 342/700 [14:35<17:35,  2.95s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 342/700, Generator Loss: 1.0868518352508545\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▉     | 343/700 [14:39<18:19,  3.08s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0868024826049805\nEpoch 343/700, Generator Loss: 1.0868024826049805\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▉     | 344/700 [14:42<18:20,  3.09s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0867297649383545\nEpoch 344/700, Generator Loss: 1.0867297649383545\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▉     | 345/700 [14:45<18:39,  3.15s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.086623191833496\nEpoch 345/700, Generator Loss: 1.086623191833496\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 49%|████▉     | 346/700 [14:48<18:29,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0865662097930908\nEpoch 346/700, Generator Loss: 1.0865662097930908\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|████▉     | 347/700 [14:51<17:41,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0865306854248047\nEpoch 347/700, Generator Loss: 1.0865306854248047\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|████▉     | 348/700 [14:54<16:56,  2.89s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 348/700, Generator Loss: 1.08653724193573\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|████▉     | 349/700 [14:56<16:26,  2.81s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 349/700, Generator Loss: 1.0865755081176758\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|█████     | 350/700 [14:59<16:12,  2.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0864812135696411\nEpoch 350/700, Generator Loss: 1.0864812135696411\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|█████     | 351/700 [15:02<16:01,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.086370825767517\nEpoch 351/700, Generator Loss: 1.086370825767517\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|█████     | 352/700 [15:04<15:58,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0862623453140259\nEpoch 352/700, Generator Loss: 1.0862623453140259\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 50%|█████     | 353/700 [15:07<15:52,  2.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0862141847610474\nEpoch 353/700, Generator Loss: 1.0862141847610474\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████     | 354/700 [15:10<16:43,  2.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0861202478408813\nEpoch 354/700, Generator Loss: 1.0861202478408813\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████     | 355/700 [15:14<17:08,  2.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0860509872436523\nEpoch 355/700, Generator Loss: 1.0860509872436523\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████     | 356/700 [15:17<17:34,  3.07s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0860097408294678\nEpoch 356/700, Generator Loss: 1.0860097408294678\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████     | 357/700 [15:20<17:57,  3.14s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.08599054813385\nEpoch 357/700, Generator Loss: 1.08599054813385\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████     | 358/700 [15:23<18:08,  3.18s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0858795642852783\nEpoch 358/700, Generator Loss: 1.0858795642852783\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 51%|█████▏    | 359/700 [15:27<18:13,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0858052968978882\nEpoch 359/700, Generator Loss: 1.0858052968978882\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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363/700, Generator Loss: 1.0854922533035278\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 52%|█████▏    | 364/700 [15:41<16:10,  2.89s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.085412859916687\nEpoch 364/700, Generator Loss: 1.085412859916687\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 52%|█████▏    | 365/700 [15:44<15:58,  2.86s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.085312008857727\nEpoch 365/700, Generator Loss: 1.085312008857727\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 52%|█████▏    | 366/700 [15:46<15:40,  2.81s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0852556228637695\nEpoch 366/700, Generator Loss: 1.0852556228637695\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 52%|█████▏    | 367/700 [15:49<15:31,  2.80s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0851948261260986\nEpoch 367/700, Generator Loss: 1.0851948261260986\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 53%|█████▎    | 368/700 [15:53<16:45,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.085137963294983\nEpoch 368/700, Generator Loss: 1.085137963294983\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 53%|█████▎    | 369/700 [15:56<17:18,  3.14s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.085094928741455\nEpoch 369/700, Generator Loss: 1.085094928741455\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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373/700, Generator Loss: 1.0848922729492188\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 53%|█████▎    | 374/700 [16:12<17:01,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.084753155708313\nEpoch 374/700, Generator Loss: 1.084753155708313\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▎    | 375/700 [16:15<16:23,  3.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0847163200378418\nEpoch 375/700, Generator Loss: 1.0847163200378418\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▎    | 376/700 [16:18<15:52,  2.94s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0846428871154785\nEpoch 376/700, Generator Loss: 1.0846428871154785\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▍    | 377/700 [16:20<15:30,  2.88s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0845363140106201\nEpoch 377/700, Generator Loss: 1.0845363140106201\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▍    | 378/700 [16:23<15:21,  2.86s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0845146179199219\nEpoch 378/700, Generator Loss: 1.0845146179199219\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▍    | 379/700 [16:26<15:11,  2.84s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0844957828521729\nEpoch 379/700, Generator Loss: 1.0844957828521729\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▍    | 380/700 [16:29<15:03,  2.82s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0844473838806152\nEpoch 380/700, Generator Loss: 1.0844473838806152\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 54%|█████▍    | 381/700 [16:32<15:22,  2.89s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.084355354309082\nEpoch 381/700, Generator Loss: 1.084355354309082\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▍    | 382/700 [16:35<16:08,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0843106508255005\nEpoch 382/700, Generator Loss: 1.0843106508255005\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▍    | 383/700 [16:38<16:26,  3.11s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0842218399047852\nEpoch 383/700, Generator Loss: 1.0842218399047852\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▍    | 384/700 [16:42<16:49,  3.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0841470956802368\nEpoch 384/700, Generator Loss: 1.0841470956802368\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▌    | 385/700 [16:45<17:16,  3.29s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.084092378616333\nEpoch 385/700, Generator Loss: 1.084092378616333\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▌    | 386/700 [16:49<17:21,  3.32s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.083993911743164\nEpoch 386/700, Generator Loss: 1.083993911743164\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▌    | 387/700 [16:52<16:49,  3.22s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0839260816574097\nEpoch 387/700, Generator Loss: 1.0839260816574097\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 55%|█████▌    | 388/700 [16:55<16:09,  3.11s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0838886499404907\nEpoch 388/700, Generator Loss: 1.0838886499404907\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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392/700, Generator Loss: 1.0836536884307861\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 56%|█████▌    | 393/700 [17:09<15:33,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0836116075515747\nEpoch 393/700, Generator Loss: 1.0836116075515747\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 56%|█████▋    | 394/700 [17:13<16:15,  3.19s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0835682153701782\nEpoch 394/700, Generator Loss: 1.0835682153701782\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 56%|█████▋    | 395/700 [17:16<16:32,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.083516001701355\nEpoch 395/700, Generator Loss: 1.083516001701355\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 57%|█████▋    | 396/700 [17:19<16:29,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0834823846817017\nEpoch 396/700, Generator Loss: 1.0834823846817017\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 57%|█████▋    | 397/700 [17:23<16:47,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.083463430404663\nEpoch 397/700, Generator Loss: 1.083463430404663\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 57%|█████▋    | 398/700 [17:26<16:29,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.083415150642395\nEpoch 398/700, Generator Loss: 1.083415150642395\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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1.0830093622207642\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 58%|█████▊    | 406/700 [17:50<15:19,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0829542875289917\nEpoch 406/700, Generator Loss: 1.0829542875289917\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 58%|█████▊    | 407/700 [17:53<15:39,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0828677415847778\nEpoch 407/700, Generator Loss: 1.0828677415847778\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 58%|█████▊    | 408/700 [17:56<15:47,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.082804799079895\nEpoch 408/700, Generator Loss: 1.082804799079895\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 58%|█████▊    | 409/700 [18:00<15:59,  3.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0827378034591675\nEpoch 409/700, Generator Loss: 1.0827378034591675\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 59%|█████▊    | 410/700 [18:03<16:14,  3.36s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0826903581619263\nEpoch 410/700, Generator Loss: 1.0826903581619263\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 59%|█████▊    | 411/700 [18:06<15:41,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.082646369934082\nEpoch 411/700, Generator Loss: 1.082646369934082\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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415/700, Generator Loss: 1.0824543237686157\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 59%|█████▉    | 416/700 [18:21<13:55,  2.94s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0824124813079834\nEpoch 416/700, Generator Loss: 1.0824124813079834\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|█████▉    | 417/700 [18:24<13:44,  2.91s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0823240280151367\nEpoch 417/700, Generator Loss: 1.0823240280151367\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|█████▉    | 418/700 [18:27<14:01,  2.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0822833776474\nEpoch 418/700, Generator Loss: 1.0822833776474\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|█████▉    | 419/700 [18:30<14:37,  3.12s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0822200775146484\nEpoch 419/700, Generator Loss: 1.0822200775146484\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|██████    | 420/700 [18:34<14:54,  3.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0821954011917114\nEpoch 420/700, Generator Loss: 1.0821954011917114\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|██████    | 421/700 [18:37<15:07,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0821611881256104\nEpoch 421/700, Generator Loss: 1.0821611881256104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|██████    | 422/700 [18:40<15:30,  3.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0821106433868408\nEpoch 422/700, Generator Loss: 1.0821106433868408\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 60%|██████    | 423/700 [18:44<15:43,  3.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0820574760437012\nEpoch 423/700, Generator Loss: 1.0820574760437012\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████    | 424/700 [18:47<15:05,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0819876194000244\nEpoch 424/700, Generator Loss: 1.0819876194000244\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████    | 425/700 [18:50<14:30,  3.17s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.081931471824646\nEpoch 425/700, Generator Loss: 1.081931471824646\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████    | 426/700 [18:53<14:04,  3.08s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0818734169006348\nEpoch 426/700, Generator Loss: 1.0818734169006348\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████    | 427/700 [18:56<13:42,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.08180832862854\nEpoch 427/700, Generator Loss: 1.08180832862854\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████    | 428/700 [18:59<13:26,  2.97s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.081768274307251\nEpoch 428/700, Generator Loss: 1.081768274307251\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████▏   | 429/700 [19:02<13:35,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0816919803619385\nEpoch 429/700, Generator Loss: 1.0816919803619385\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 61%|██████▏   | 430/700 [19:05<13:23,  2.97s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0816640853881836\nEpoch 430/700, Generator Loss: 1.0816640853881836\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 431/700 [19:08<13:54,  3.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0815768241882324\nEpoch 431/700, Generator Loss: 1.0815768241882324\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 432/700 [19:11<14:22,  3.22s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0815060138702393\nEpoch 432/700, Generator Loss: 1.0815060138702393\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 433/700 [19:15<14:35,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0814316272735596\nEpoch 433/700, Generator Loss: 1.0814316272735596\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 434/700 [19:18<14:49,  3.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0813769102096558\nEpoch 434/700, Generator Loss: 1.0813769102096558\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 435/700 [19:22<15:03,  3.41s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.081305980682373\nEpoch 435/700, Generator Loss: 1.081305980682373\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 436/700 [19:25<14:50,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0812631845474243\nEpoch 436/700, Generator Loss: 1.0812631845474243\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 62%|██████▏   | 437/700 [19:28<14:08,  3.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0812371969223022\nEpoch 437/700, Generator Loss: 1.0812371969223022\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 438/700 [19:31<13:40,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.081211805343628\nEpoch 438/700, Generator Loss: 1.081211805343628\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 439/700 [19:34<13:24,  3.08s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.081154704093933\nEpoch 439/700, Generator Loss: 1.081154704093933\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 440/700 [19:37<13:09,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0810986757278442\nEpoch 440/700, Generator Loss: 1.0810986757278442\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 441/700 [19:40<12:58,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0810047388076782\nEpoch 441/700, Generator Loss: 1.0810047388076782\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 442/700 [19:43<12:55,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0809589624404907\nEpoch 442/700, Generator Loss: 1.0809589624404907\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 443/700 [19:46<13:08,  3.07s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.080895185470581\nEpoch 443/700, Generator Loss: 1.080895185470581\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 63%|██████▎   | 444/700 [19:50<13:43,  3.22s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0808302164077759\nEpoch 444/700, Generator Loss: 1.0808302164077759\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▎   | 445/700 [19:53<14:09,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0807873010635376\nEpoch 445/700, Generator Loss: 1.0807873010635376\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▎   | 446/700 [19:56<14:05,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 446/700, Generator Loss: 1.0807933807373047\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▍   | 447/700 [20:00<14:18,  3.39s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0807502269744873\nEpoch 447/700, Generator Loss: 1.0807502269744873\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▍   | 448/700 [20:04<14:31,  3.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0806726217269897\nEpoch 448/700, Generator Loss: 1.0806726217269897\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▍   | 449/700 [20:07<13:54,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.080628752708435\nEpoch 449/700, Generator Loss: 1.080628752708435\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▍   | 450/700 [20:10<13:22,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0805505514144897\nEpoch 450/700, Generator Loss: 1.0805505514144897\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 64%|██████▍   | 451/700 [20:13<13:08,  3.17s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0804996490478516\nEpoch 451/700, Generator Loss: 1.0804996490478516\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▍   | 452/700 [20:16<12:49,  3.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.080447793006897\nEpoch 452/700, Generator Loss: 1.080447793006897\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▍   | 453/700 [20:19<12:33,  3.05s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0804059505462646\nEpoch 453/700, Generator Loss: 1.0804059505462646\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▍   | 454/700 [20:21<12:20,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0803732872009277\nEpoch 454/700, Generator Loss: 1.0803732872009277\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▌   | 455/700 [20:24<12:18,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.080319881439209\nEpoch 455/700, Generator Loss: 1.080319881439209\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▌   | 456/700 [20:27<12:10,  2.99s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0802735090255737\nEpoch 456/700, Generator Loss: 1.0802735090255737\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▌   | 457/700 [20:30<12:02,  2.97s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0802091360092163\nEpoch 457/700, Generator Loss: 1.0802091360092163\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 65%|██████▌   | 458/700 [20:33<12:00,  2.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.080160140991211\nEpoch 458/700, Generator Loss: 1.080160140991211\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▌   | 459/700 [20:37<12:17,  3.06s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0800918340682983\nEpoch 459/700, Generator Loss: 1.0800918340682983\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▌   | 460/700 [20:40<12:51,  3.22s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0800420045852661\nEpoch 460/700, Generator Loss: 1.0800420045852661\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▌   | 461/700 [20:44<13:25,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0799778699874878\nEpoch 461/700, Generator Loss: 1.0799778699874878\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▌   | 462/700 [20:48<13:39,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0799537897109985\nEpoch 462/700, Generator Loss: 1.0799537897109985\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▌   | 463/700 [20:51<13:42,  3.47s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0799401998519897\nEpoch 463/700, Generator Loss: 1.0799401998519897\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▋   | 464/700 [20:55<13:44,  3.49s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.079917550086975\nEpoch 464/700, Generator Loss: 1.079917550086975\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 66%|██████▋   | 465/700 [20:58<13:05,  3.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.079872965812683\nEpoch 465/700, Generator Loss: 1.079872965812683\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 466/700 [21:01<12:37,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0797839164733887\nEpoch 466/700, Generator Loss: 1.0797839164733887\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 467/700 [21:04<12:16,  3.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0797306299209595\nEpoch 467/700, Generator Loss: 1.0797306299209595\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 468/700 [21:07<11:58,  3.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0796550512313843\nEpoch 468/700, Generator Loss: 1.0796550512313843\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 469/700 [21:09<11:45,  3.05s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0796020030975342\nEpoch 469/700, Generator Loss: 1.0796020030975342\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 470/700 [21:13<11:43,  3.06s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0795615911483765\nEpoch 470/700, Generator Loss: 1.0795615911483765\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 471/700 [21:16<11:37,  3.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.079525113105774\nEpoch 471/700, Generator Loss: 1.079525113105774\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 67%|██████▋   | 472/700 [21:19<11:28,  3.02s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0794873237609863\nEpoch 472/700, Generator Loss: 1.0794873237609863\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 473/700 [21:21<11:22,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0794261693954468\nEpoch 473/700, Generator Loss: 1.0794261693954468\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 474/700 [21:25<12:02,  3.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0793614387512207\nEpoch 474/700, Generator Loss: 1.0793614387512207\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 475/700 [21:29<12:27,  3.32s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0793054103851318\nEpoch 475/700, Generator Loss: 1.0793054103851318\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 476/700 [21:32<12:35,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0792560577392578\nEpoch 476/700, Generator Loss: 1.0792560577392578\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 477/700 [21:36<12:51,  3.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0791972875595093\nEpoch 477/700, Generator Loss: 1.0791972875595093\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 478/700 [21:40<13:00,  3.51s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.079174518585205\nEpoch 478/700, Generator Loss: 1.079174518585205\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 68%|██████▊   | 479/700 [21:43<12:52,  3.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0791325569152832\nEpoch 479/700, Generator Loss: 1.0791325569152832\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▊   | 480/700 [21:46<12:14,  3.34s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0790666341781616\nEpoch 480/700, Generator Loss: 1.0790666341781616\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▊   | 481/700 [21:49<11:46,  3.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0790454149246216\nEpoch 481/700, Generator Loss: 1.0790454149246216\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▉   | 482/700 [21:52<11:28,  3.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0789862871170044\nEpoch 482/700, Generator Loss: 1.0789862871170044\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▉   | 483/700 [21:55<11:18,  3.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0789319276809692\nEpoch 483/700, Generator Loss: 1.0789319276809692\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▉   | 484/700 [21:58<11:08,  3.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0788815021514893\nEpoch 484/700, Generator Loss: 1.0788815021514893\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▉   | 485/700 [22:01<10:59,  3.07s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.078826904296875\nEpoch 485/700, Generator Loss: 1.078826904296875\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 69%|██████▉   | 486/700 [22:05<11:32,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0788129568099976\nEpoch 486/700, Generator Loss: 1.0788129568099976\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|██████▉   | 487/700 [22:08<11:57,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0787639617919922\nEpoch 487/700, Generator Loss: 1.0787639617919922\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|██████▉   | 488/700 [22:12<12:08,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.078711748123169\nEpoch 488/700, Generator Loss: 1.078711748123169\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|██████▉   | 489/700 [22:16<12:17,  3.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0786705017089844\nEpoch 489/700, Generator Loss: 1.0786705017089844\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|███████   | 490/700 [22:19<12:36,  3.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0786019563674927\nEpoch 490/700, Generator Loss: 1.0786019563674927\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|███████   | 491/700 [22:23<12:11,  3.50s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0785422325134277\nEpoch 491/700, Generator Loss: 1.0785422325134277\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|███████   | 492/700 [22:26<11:41,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.07851243019104\nEpoch 492/700, Generator Loss: 1.07851243019104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 70%|███████   | 493/700 [22:29<11:22,  3.30s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0784502029418945\nEpoch 493/700, Generator Loss: 1.0784502029418945\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████   | 494/700 [22:32<11:00,  3.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0784097909927368\nEpoch 494/700, Generator Loss: 1.0784097909927368\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████   | 495/700 [22:35<10:45,  3.15s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0783686637878418\nEpoch 495/700, Generator Loss: 1.0783686637878418\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████   | 496/700 [22:38<10:53,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0783289670944214\nEpoch 496/700, Generator Loss: 1.0783289670944214\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████   | 497/700 [22:42<11:19,  3.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0782687664031982\nEpoch 497/700, Generator Loss: 1.0782687664031982\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████   | 498/700 [22:46<11:39,  3.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0782115459442139\nEpoch 498/700, Generator Loss: 1.0782115459442139\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████▏  | 499/700 [22:49<11:49,  3.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0781610012054443\nEpoch 499/700, Generator Loss: 1.0781610012054443\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 71%|███████▏  | 500/700 [22:53<11:53,  3.57s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0781077146530151\nEpoch 500/700, Generator Loss: 1.0781077146530151\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 501/700 [22:57<11:50,  3.57s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0780514478683472\nEpoch 501/700, Generator Loss: 1.0780514478683472\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 502/700 [23:00<11:21,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0780173540115356\nEpoch 502/700, Generator Loss: 1.0780173540115356\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 503/700 [23:03<10:56,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0779948234558105\nEpoch 503/700, Generator Loss: 1.0779948234558105\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 504/700 [23:06<10:38,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0779224634170532\nEpoch 504/700, Generator Loss: 1.0779224634170532\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 505/700 [23:09<10:25,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0778950452804565\nEpoch 505/700, Generator Loss: 1.0778950452804565\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 506/700 [23:12<10:14,  3.17s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0778580904006958\nEpoch 506/700, Generator Loss: 1.0778580904006958\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 72%|███████▏  | 507/700 [23:15<10:15,  3.19s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0777844190597534\nEpoch 507/700, Generator Loss: 1.0777844190597534\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 508/700 [23:18<10:04,  3.15s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0777438879013062\nEpoch 508/700, Generator Loss: 1.0777438879013062\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 509/700 [23:22<10:31,  3.31s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0777103900909424\nEpoch 509/700, Generator Loss: 1.0777103900909424\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 510/700 [23:26<10:53,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0776848793029785\nEpoch 510/700, Generator Loss: 1.0776848793029785\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 511/700 [23:30<11:12,  3.56s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0776324272155762\nEpoch 511/700, Generator Loss: 1.0776324272155762\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 512/700 [23:33<11:24,  3.64s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.077588438987732\nEpoch 512/700, Generator Loss: 1.077588438987732\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 513/700 [23:37<11:30,  3.69s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0775244235992432\nEpoch 513/700, Generator Loss: 1.0775244235992432\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 73%|███████▎  | 514/700 [23:40<10:59,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0774891376495361\nEpoch 514/700, Generator Loss: 1.0774891376495361\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▎  | 515/700 [23:44<10:34,  3.43s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0774365663528442\nEpoch 515/700, Generator Loss: 1.0774365663528442\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▎  | 516/700 [23:47<10:11,  3.32s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0773749351501465\nEpoch 516/700, Generator Loss: 1.0773749351501465\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▍  | 517/700 [23:50<09:56,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.077324390411377\nEpoch 517/700, Generator Loss: 1.077324390411377\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▍  | 518/700 [23:53<09:44,  3.21s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0772877931594849\nEpoch 518/700, Generator Loss: 1.0772877931594849\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▍  | 519/700 [23:56<09:31,  3.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0772638320922852\nEpoch 519/700, Generator Loss: 1.0772638320922852\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▍  | 520/700 [23:59<09:45,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0772243738174438\nEpoch 520/700, Generator Loss: 1.0772243738174438\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 74%|███████▍  | 521/700 [24:03<10:15,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0771653652191162\nEpoch 521/700, Generator Loss: 1.0771653652191162\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▍  | 522/700 [24:07<10:31,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0771358013153076\nEpoch 522/700, Generator Loss: 1.0771358013153076\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▍  | 523/700 [24:11<10:40,  3.62s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.077071189880371\nEpoch 523/700, Generator Loss: 1.077071189880371\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▍  | 524/700 [24:15<10:43,  3.66s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0770291090011597\nEpoch 524/700, Generator Loss: 1.0770291090011597\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▌  | 525/700 [24:18<10:37,  3.64s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.076981782913208\nEpoch 525/700, Generator Loss: 1.076981782913208\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▌  | 526/700 [24:21<10:05,  3.48s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0769222974777222\nEpoch 526/700, Generator Loss: 1.0769222974777222\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▌  | 527/700 [24:24<09:42,  3.37s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0768765211105347\nEpoch 527/700, Generator Loss: 1.0768765211105347\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 75%|███████▌  | 528/700 [24:28<09:26,  3.29s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0768474340438843\nEpoch 528/700, Generator Loss: 1.0768474340438843\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 76%|███████▌  | 529/700 [24:31<09:13,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0767992734909058\nEpoch 529/700, Generator Loss: 1.0767992734909058\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 76%|███████▌  | 530/700 [24:34<09:08,  3.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0767611265182495\nEpoch 530/700, Generator Loss: 1.0767611265182495\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 76%|███████▌  | 531/700 [24:37<08:57,  3.18s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.076694369316101\nEpoch 531/700, Generator Loss: 1.076694369316101\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 76%|███████▌  | 532/700 [24:40<09:05,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0766681432724\nEpoch 532/700, Generator Loss: 1.0766681432724\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 76%|███████▌  | 533/700 [24:44<09:28,  3.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0765985250473022\nEpoch 533/700, Generator Loss: 1.0765985250473022\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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537/700, Generator Loss: 1.0764400959014893\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 77%|███████▋  | 538/700 [25:02<09:33,  3.54s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0763742923736572\nEpoch 538/700, Generator Loss: 1.0763742923736572\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 77%|███████▋  | 539/700 [25:06<09:13,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.076332926750183\nEpoch 539/700, Generator Loss: 1.076332926750183\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 77%|███████▋  | 540/700 [25:09<08:55,  3.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0762754678726196\nEpoch 540/700, Generator Loss: 1.0762754678726196\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 77%|███████▋  | 541/700 [25:12<08:41,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0762468576431274\nEpoch 541/700, Generator Loss: 1.0762468576431274\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 77%|███████▋  | 542/700 [25:15<08:32,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0762028694152832\nEpoch 542/700, Generator Loss: 1.0762028694152832\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 543/700 [25:18<08:28,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0761340856552124\nEpoch 543/700, Generator Loss: 1.0761340856552124\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 544/700 [25:21<08:25,  3.24s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0761218070983887\nEpoch 544/700, Generator Loss: 1.0761218070983887\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 545/700 [25:25<08:47,  3.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0760669708251953\nEpoch 545/700, Generator Loss: 1.0760669708251953\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 546/700 [25:29<09:00,  3.51s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0760310888290405\nEpoch 546/700, Generator Loss: 1.0760310888290405\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 547/700 [25:33<09:10,  3.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0759910345077515\nEpoch 547/700, Generator Loss: 1.0759910345077515\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 548/700 [25:37<09:19,  3.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0759475231170654\nEpoch 548/700, Generator Loss: 1.0759475231170654\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 78%|███████▊  | 549/700 [25:41<09:25,  3.74s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0758931636810303\nEpoch 549/700, Generator Loss: 1.0758931636810303\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▊  | 550/700 [25:44<08:57,  3.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0758333206176758\nEpoch 550/700, Generator Loss: 1.0758333206176758\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▊  | 551/700 [25:47<08:37,  3.47s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0757843255996704\nEpoch 551/700, Generator Loss: 1.0757843255996704\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▉  | 552/700 [25:50<08:30,  3.45s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0757482051849365\nEpoch 552/700, Generator Loss: 1.0757482051849365\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▉  | 553/700 [25:54<08:18,  3.39s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0757005214691162\nEpoch 553/700, Generator Loss: 1.0757005214691162\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▉  | 554/700 [25:57<08:05,  3.33s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.075649380683899\nEpoch 554/700, Generator Loss: 1.075649380683899\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▉  | 555/700 [26:00<07:55,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0756045579910278\nEpoch 555/700, Generator Loss: 1.0756045579910278\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 79%|███████▉  | 556/700 [26:03<07:50,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0755400657653809\nEpoch 556/700, Generator Loss: 1.0755400657653809\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|███████▉  | 557/700 [26:06<07:46,  3.26s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0755068063735962\nEpoch 557/700, Generator Loss: 1.0755068063735962\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|███████▉  | 558/700 [26:10<07:40,  3.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0754839181900024\nEpoch 558/700, Generator Loss: 1.0754839181900024\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|███████▉  | 559/700 [26:14<08:05,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.075453758239746\nEpoch 559/700, Generator Loss: 1.075453758239746\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|████████  | 560/700 [26:17<08:20,  3.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0754204988479614\nEpoch 560/700, Generator Loss: 1.0754204988479614\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|████████  | 561/700 [26:21<08:29,  3.67s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0753705501556396\nEpoch 561/700, Generator Loss: 1.0753705501556396\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|████████  | 562/700 [26:25<08:37,  3.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0753177404403687\nEpoch 562/700, Generator Loss: 1.0753177404403687\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 80%|████████  | 563/700 [26:29<08:38,  3.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0752862691879272\nEpoch 563/700, Generator Loss: 1.0752862691879272\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████  | 564/700 [26:32<08:13,  3.63s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0752124786376953\nEpoch 564/700, Generator Loss: 1.0752124786376953\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████  | 565/700 [26:36<07:51,  3.49s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0751713514328003\nEpoch 565/700, Generator Loss: 1.0751713514328003\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████  | 566/700 [26:39<07:38,  3.42s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.075114369392395\nEpoch 566/700, Generator Loss: 1.075114369392395\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████  | 567/700 [26:42<07:25,  3.35s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0750988721847534\nEpoch 567/700, Generator Loss: 1.0750988721847534\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████  | 568/700 [26:45<07:17,  3.31s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.075063943862915\nEpoch 568/700, Generator Loss: 1.075063943862915\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████▏ | 569/700 [26:48<07:10,  3.28s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0749932527542114\nEpoch 569/700, Generator Loss: 1.0749932527542114\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 81%|████████▏ | 570/700 [26:52<07:22,  3.40s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0749609470367432\nEpoch 570/700, Generator Loss: 1.0749609470367432\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 571/700 [26:56<07:38,  3.56s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0748814344406128\nEpoch 571/700, Generator Loss: 1.0748814344406128\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 572/700 [27:00<07:47,  3.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0748265981674194\nEpoch 572/700, Generator Loss: 1.0748265981674194\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 573/700 [27:04<07:52,  3.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0747671127319336\nEpoch 573/700, Generator Loss: 1.0747671127319336\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 574/700 [27:08<07:53,  3.76s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074747920036316\nEpoch 574/700, Generator Loss: 1.074747920036316\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 575/700 [27:11<07:44,  3.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0747249126434326\nEpoch 575/700, Generator Loss: 1.0747249126434326\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 576/700 [27:14<07:22,  3.57s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0746909379959106\nEpoch 576/700, Generator Loss: 1.0746909379959106\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 82%|████████▏ | 577/700 [27:18<07:05,  3.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0746465921401978\nEpoch 577/700, Generator Loss: 1.0746465921401978\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 83%|████████▎ | 578/700 [27:21<06:58,  3.43s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0745928287506104\nEpoch 578/700, Generator Loss: 1.0745928287506104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 83%|████████▎ | 579/700 [27:24<06:49,  3.38s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074535846710205\nEpoch 579/700, Generator Loss: 1.074535846710205\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 83%|████████▎ | 580/700 [27:28<07:06,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074499487876892\nEpoch 580/700, Generator Loss: 1.074499487876892\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 83%|████████▎ | 581/700 [27:32<07:18,  3.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0744481086730957\nEpoch 581/700, Generator Loss: 1.0744481086730957\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 83%|████████▎ | 582/700 [27:36<07:26,  3.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0744296312332153\nEpoch 582/700, Generator Loss: 1.0744296312332153\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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586/700, Generator Loss: 1.0742840766906738\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 84%|████████▍ | 587/700 [27:55<06:58,  3.71s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0742597579956055\nEpoch 587/700, Generator Loss: 1.0742597579956055\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 84%|████████▍ | 588/700 [27:59<07:02,  3.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074212908744812\nEpoch 588/700, Generator Loss: 1.074212908744812\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 84%|████████▍ | 589/700 [28:03<07:06,  3.84s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0741679668426514\nEpoch 589/700, Generator Loss: 1.0741679668426514\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 84%|████████▍ | 590/700 [28:07<07:06,  3.88s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074161410331726\nEpoch 590/700, Generator Loss: 1.074161410331726\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 84%|████████▍ | 591/700 [28:11<07:03,  3.89s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0741559267044067\nEpoch 591/700, Generator Loss: 1.0741559267044067\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 85%|████████▍ | 592/700 [28:14<06:41,  3.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.074106216430664\nEpoch 592/700, Generator Loss: 1.074106216430664\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 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596/700, Generator Loss: 1.0739750862121582\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 85%|████████▌ | 597/700 [28:31<05:48,  3.39s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0739696025848389\nEpoch 597/700, Generator Loss: 1.0739696025848389\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 85%|████████▌ | 598/700 [28:34<05:53,  3.47s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0739673376083374\nEpoch 598/700, Generator Loss: 1.0739673376083374\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▌ | 599/700 [28:38<06:06,  3.63s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073915719985962\nEpoch 599/700, Generator Loss: 1.073915719985962\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▌ | 600/700 [28:42<06:16,  3.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0738877058029175\nEpoch 600/700, Generator Loss: 1.0738877058029175\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▌ | 601/700 [28:46<06:20,  3.84s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0738251209259033\nEpoch 601/700, Generator Loss: 1.0738251209259033\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▌ | 602/700 [28:50<06:22,  3.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0737802982330322\nEpoch 602/700, Generator Loss: 1.0737802982330322\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▌ | 603/700 [28:54<06:11,  3.83s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0737735033035278\nEpoch 603/700, Generator Loss: 1.0737735033035278\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▋ | 604/700 [28:57<05:54,  3.69s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0737407207489014\nEpoch 604/700, Generator Loss: 1.0737407207489014\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 86%|████████▋ | 605/700 [29:01<05:40,  3.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0737361907958984\nEpoch 605/700, Generator Loss: 1.0737361907958984\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 606/700 [29:04<05:28,  3.50s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 606/700, Generator Loss: 1.0737420320510864\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 607/700 [29:07<05:19,  3.44s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0737038850784302\nEpoch 607/700, Generator Loss: 1.0737038850784302\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 608/700 [29:11<05:13,  3.41s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0736626386642456\nEpoch 608/700, Generator Loss: 1.0736626386642456\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 609/700 [29:15<05:26,  3.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0736302137374878\nEpoch 609/700, Generator Loss: 1.0736302137374878\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 610/700 [29:19<05:35,  3.73s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073580026626587\nEpoch 610/700, Generator Loss: 1.073580026626587\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 611/700 [29:23<05:42,  3.85s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0735783576965332\nEpoch 611/700, Generator Loss: 1.0735783576965332\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 87%|████████▋ | 612/700 [29:27<05:44,  3.92s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0735418796539307\nEpoch 612/700, Generator Loss: 1.0735418796539307\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 613/700 [29:31<05:41,  3.93s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 613/700, Generator Loss: 1.0735507011413574\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 614/700 [29:35<05:29,  3.84s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073530912399292\nEpoch 614/700, Generator Loss: 1.073530912399292\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 615/700 [29:38<05:13,  3.68s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0734878778457642\nEpoch 615/700, Generator Loss: 1.0734878778457642\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 616/700 [29:41<05:02,  3.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0734196901321411\nEpoch 616/700, Generator Loss: 1.0734196901321411\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 617/700 [29:45<04:54,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0733842849731445\nEpoch 617/700, Generator Loss: 1.0733842849731445\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 618/700 [29:48<04:48,  3.52s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0733221769332886\nEpoch 618/700, Generator Loss: 1.0733221769332886\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 88%|████████▊ | 619/700 [29:52<04:58,  3.69s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0733041763305664\nEpoch 619/700, Generator Loss: 1.0733041763305664\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▊ | 620/700 [29:56<05:01,  3.77s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 620/700, Generator Loss: 1.0733217000961304\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▊ | 621/700 [30:00<05:00,  3.81s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 621/700, Generator Loss: 1.0733267068862915\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▉ | 622/700 [30:04<05:02,  3.87s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073289394378662\nEpoch 622/700, Generator Loss: 1.073289394378662\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▉ | 623/700 [30:08<05:00,  3.91s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0732454061508179\nEpoch 623/700, Generator Loss: 1.0732454061508179\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▉ | 624/700 [30:11<04:45,  3.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0731862783432007\nEpoch 624/700, Generator Loss: 1.0731862783432007\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▉ | 625/700 [30:15<04:33,  3.64s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0731486082077026\nEpoch 625/700, Generator Loss: 1.0731486082077026\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 89%|████████▉ | 626/700 [30:18<04:25,  3.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0730862617492676\nEpoch 626/700, Generator Loss: 1.0730862617492676\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|████████▉ | 627/700 [30:22<04:17,  3.53s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073068380355835\nEpoch 627/700, Generator Loss: 1.073068380355835\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|████████▉ | 628/700 [30:25<04:10,  3.48s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 628/700, Generator Loss: 1.0730758905410767\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|████████▉ | 629/700 [30:29<04:05,  3.46s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0730456113815308\nEpoch 629/700, Generator Loss: 1.0730456113815308\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|█████████ | 630/700 [30:33<04:16,  3.66s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.073006272315979\nEpoch 630/700, Generator Loss: 1.073006272315979\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|█████████ | 631/700 [30:37<04:22,  3.80s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0729632377624512\nEpoch 631/700, Generator Loss: 1.0729632377624512\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|█████████ | 632/700 [30:41<04:23,  3.88s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0729050636291504\nEpoch 632/700, Generator Loss: 1.0729050636291504\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 90%|█████████ | 633/700 [30:45<04:23,  3.94s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072882890701294\nEpoch 633/700, Generator Loss: 1.072882890701294\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████ | 634/700 [30:49<04:20,  3.94s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0728394985198975\nEpoch 634/700, Generator Loss: 1.0728394985198975\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████ | 635/700 [30:52<04:07,  3.81s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0728291273117065\nEpoch 635/700, Generator Loss: 1.0728291273117065\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████ | 636/700 [30:56<03:58,  3.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072813868522644\nEpoch 636/700, Generator Loss: 1.072813868522644\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████ | 637/700 [30:59<03:46,  3.59s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 637/700, Generator Loss: 1.0728280544281006\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████ | 638/700 [31:03<03:39,  3.54s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072779655456543\nEpoch 638/700, Generator Loss: 1.072779655456543\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████▏| 639/700 [31:06<03:33,  3.51s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0727487802505493\nEpoch 639/700, Generator Loss: 1.0727487802505493\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 91%|█████████▏| 640/700 [31:09<03:28,  3.48s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0726969242095947\nEpoch 640/700, Generator Loss: 1.0726969242095947\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 641/700 [31:13<03:34,  3.64s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0726903676986694\nEpoch 641/700, Generator Loss: 1.0726903676986694\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 642/700 [31:18<03:39,  3.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0726503133773804\nEpoch 642/700, Generator Loss: 1.0726503133773804\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 643/700 [31:22<03:43,  3.92s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0726377964019775\nEpoch 643/700, Generator Loss: 1.0726377964019775\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 644/700 [31:26<03:44,  4.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0726336240768433\nEpoch 644/700, Generator Loss: 1.0726336240768433\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 645/700 [31:30<03:41,  4.03s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0725898742675781\nEpoch 645/700, Generator Loss: 1.0725898742675781\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 646/700 [31:34<03:28,  3.85s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0725505352020264\nEpoch 646/700, Generator Loss: 1.0725505352020264\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 92%|█████████▏| 647/700 [31:37<03:17,  3.73s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0725184679031372\nEpoch 647/700, Generator Loss: 1.0725184679031372\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 648/700 [31:40<03:09,  3.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0724844932556152\nEpoch 648/700, Generator Loss: 1.0724844932556152\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 649/700 [31:44<03:03,  3.59s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0724589824676514\nEpoch 649/700, Generator Loss: 1.0724589824676514\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 650/700 [31:47<02:57,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0724263191223145\nEpoch 650/700, Generator Loss: 1.0724263191223145\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 651/700 [31:51<02:53,  3.55s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072394847869873\nEpoch 651/700, Generator Loss: 1.072394847869873\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 652/700 [31:55<02:51,  3.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0723627805709839\nEpoch 652/700, Generator Loss: 1.0723627805709839\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 653/700 [31:59<02:56,  3.76s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072307825088501\nEpoch 653/700, Generator Loss: 1.072307825088501\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 93%|█████████▎| 654/700 [32:03<02:59,  3.89s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0722897052764893\nEpoch 654/700, Generator Loss: 1.0722897052764893\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▎| 655/700 [32:07<03:00,  4.00s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0722439289093018\nEpoch 655/700, Generator Loss: 1.0722439289093018\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▎| 656/700 [32:11<02:58,  4.05s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 656/700, Generator Loss: 1.072246789932251\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▍| 657/700 [32:15<02:51,  3.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0722222328186035\nEpoch 657/700, Generator Loss: 1.0722222328186035\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▍| 658/700 [32:19<02:40,  3.83s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0721931457519531\nEpoch 658/700, Generator Loss: 1.0721931457519531\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▍| 659/700 [32:22<02:35,  3.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0721790790557861\nEpoch 659/700, Generator Loss: 1.0721790790557861\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▍| 660/700 [32:26<02:28,  3.72s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0721397399902344\nEpoch 660/700, Generator Loss: 1.0721397399902344\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 94%|█████████▍| 661/700 [32:29<02:22,  3.65s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0721380710601807\nEpoch 661/700, Generator Loss: 1.0721380710601807\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▍| 662/700 [32:33<02:17,  3.61s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0721144676208496\nEpoch 662/700, Generator Loss: 1.0721144676208496\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▍| 663/700 [32:36<02:12,  3.58s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.072068691253662\nEpoch 663/700, Generator Loss: 1.072068691253662\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▍| 664/700 [32:40<02:13,  3.70s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0720431804656982\nEpoch 664/700, Generator Loss: 1.0720431804656982\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▌| 665/700 [32:45<02:15,  3.87s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0720112323760986\nEpoch 665/700, Generator Loss: 1.0720112323760986\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▌| 666/700 [32:49<02:15,  3.98s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0719828605651855\nEpoch 666/700, Generator Loss: 1.0719828605651855\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▌| 667/700 [32:53<02:15,  4.12s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071954607963562\nEpoch 667/700, Generator Loss: 1.071954607963562\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 95%|█████████▌| 668/700 [32:58<02:12,  4.15s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0719130039215088\nEpoch 668/700, Generator Loss: 1.0719130039215088\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▌| 669/700 [33:01<02:03,  3.99s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0718910694122314\nEpoch 669/700, Generator Loss: 1.0718910694122314\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▌| 670/700 [33:05<01:55,  3.86s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.07185959815979\nEpoch 670/700, Generator Loss: 1.07185959815979\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▌| 671/700 [33:08<01:48,  3.73s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 671/700, Generator Loss: 1.0718625783920288\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▌| 672/700 [33:12<01:42,  3.67s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0718235969543457\nEpoch 672/700, Generator Loss: 1.0718235969543457\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▌| 673/700 [33:15<01:38,  3.63s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0718128681182861\nEpoch 673/700, Generator Loss: 1.0718128681182861\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▋| 674/700 [33:19<01:33,  3.60s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0717852115631104\nEpoch 674/700, Generator Loss: 1.0717852115631104\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 96%|█████████▋| 675/700 [33:23<01:34,  3.76s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0717360973358154\nEpoch 675/700, Generator Loss: 1.0717360973358154\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 676/700 [33:28<01:36,  4.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0716965198516846\nEpoch 676/700, Generator Loss: 1.0716965198516846\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 677/700 [33:32<01:34,  4.12s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071685552597046\nEpoch 677/700, Generator Loss: 1.071685552597046\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 678/700 [33:36<01:32,  4.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071635365486145\nEpoch 678/700, Generator Loss: 1.071635365486145\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 679/700 [33:41<01:28,  4.23s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0716114044189453\nEpoch 679/700, Generator Loss: 1.0716114044189453\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 680/700 [33:44<01:19,  4.00s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 680/700, Generator Loss: 1.0716184377670288\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 681/700 [33:48<01:13,  3.86s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0715672969818115\nEpoch 681/700, Generator Loss: 1.0715672969818115\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 97%|█████████▋| 682/700 [33:51<01:07,  3.77s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0715229511260986\nEpoch 682/700, Generator Loss: 1.0715229511260986\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 683/700 [33:55<01:03,  3.74s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071494460105896\nEpoch 683/700, Generator Loss: 1.071494460105896\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 684/700 [33:59<00:59,  3.74s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0714702606201172\nEpoch 684/700, Generator Loss: 1.0714702606201172\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 685/700 [34:03<00:58,  3.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0714349746704102\nEpoch 685/700, Generator Loss: 1.0714349746704102\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 686/700 [34:07<00:56,  4.04s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0714092254638672\nEpoch 686/700, Generator Loss: 1.0714092254638672\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 687/700 [34:11<00:53,  4.13s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0714019536972046\nEpoch 687/700, Generator Loss: 1.0714019536972046\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 688/700 [34:16<00:50,  4.20s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0713980197906494\nEpoch 688/700, Generator Loss: 1.0713980197906494\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 98%|█████████▊| 689/700 [34:20<00:45,  4.10s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0713742971420288\nEpoch 689/700, Generator Loss: 1.0713742971420288\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▊| 690/700 [34:23<00:39,  3.96s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0713245868682861\nEpoch 690/700, Generator Loss: 1.0713245868682861\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▊| 691/700 [34:27<00:35,  3.90s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071279764175415\nEpoch 691/700, Generator Loss: 1.071279764175415\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▉| 692/700 [34:31<00:30,  3.82s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0712764263153076\nEpoch 692/700, Generator Loss: 1.0712764263153076\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▉| 693/700 [34:34<00:26,  3.75s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0712449550628662\nEpoch 693/700, Generator Loss: 1.0712449550628662\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▉| 694/700 [34:38<00:21,  3.66s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 694/700, Generator Loss: 1.0712494850158691\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▉| 695/700 [34:42<00:18,  3.78s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0712252855300903\nEpoch 695/700, Generator Loss: 1.0712252855300903\n============================================================\n","output_type":"stream"},{"name":"stderr","text":" 99%|█████████▉| 696/700 [34:46<00:15,  3.94s/it]","output_type":"stream"},{"name":"stdout","text":"Epoch 696/700, Generator Loss: 1.071230411529541\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"100%|█████████▉| 697/700 [34:51<00:12,  4.07s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0711843967437744\nEpoch 697/700, Generator Loss: 1.0711843967437744\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"100%|█████████▉| 698/700 [34:55<00:08,  4.16s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0711379051208496\nEpoch 698/700, Generator Loss: 1.0711379051208496\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"100%|█████████▉| 699/700 [34:59<00:04,  4.25s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.071099877357483\nEpoch 699/700, Generator Loss: 1.071099877357483\n============================================================\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 700/700 [35:03<00:00,  3.01s/it]","output_type":"stream"},{"name":"stdout","text":"saved 1.0710598230361938\nEpoch 700/700, Generator Loss: 1.0710598230361938\n============================================================\nTraining completed.\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"objDCGAN.generator.summary()","metadata":{"id":"33lbSo3JnSzf","execution":{"iopub.status.busy":"2024-01-03T10:50:51.502378Z","iopub.execute_input":"2024-01-03T10:50:51.502642Z","iopub.status.idle":"2024-01-03T10:50:51.547356Z","shell.execute_reply.started":"2024-01-03T10:50:51.502620Z","shell.execute_reply":"2024-01-03T10:50:51.546486Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n dense (Dense)               (None, 12544)             1254400   \n                                                                 \n batch_normalization (Batch  (None, 12544)             50176     \n Normalization)                                                  \n                                                                 \n leaky_re_lu (LeakyReLU)     (None, 12544)             0         \n                                                                 \n reshape (Reshape)           (None, 7, 7, 256)         0         \n                                                                 \n conv2d_transpose (Conv2DTr  (None, 7, 7, 128)         819200    \n anspose)                                                        \n                                                                 \n batch_normalization_1 (Bat  (None, 7, 7, 128)         512       \n chNormalization)                                                \n                                                                 \n leaky_re_lu_1 (LeakyReLU)   (None, 7, 7, 128)         0         \n                                                                 \n conv2d_transpose_1 (Conv2D  (None, 14, 14, 64)        204800    \n Transpose)                                                      \n                                                                 \n batch_normalization_2 (Bat  (None, 14, 14, 64)        256       \n chNormalization)                                                \n                                                                 \n leaky_re_lu_2 (LeakyReLU)   (None, 14, 14, 64)        0         \n                                                                 \n conv2d_transpose_2 (Conv2D  (None, 28, 28, 32)        51200     \n Transpose)                                                      \n                                                                 \n batch_normalization_3 (Bat  (None, 28, 28, 32)        128       \n chNormalization)                                                \n                                                                 \n leaky_re_lu_3 (LeakyReLU)   (None, 28, 28, 32)        0         \n                                                                 \n conv2d_transpose_3 (Conv2D  (None, 28, 28, 1)         800       \n Transpose)                                                      \n                                                                 \n=================================================================\nTotal params: 2381472 (9.08 MB)\nTrainable params: 2355936 (8.99 MB)\nNon-trainable params: 25536 (99.75 KB)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"objDCGAN.discriminator.summary()","metadata":{"id":"F9xJL5manMen","execution":{"iopub.status.busy":"2024-01-03T10:50:51.548625Z","iopub.execute_input":"2024-01-03T10:50:51.548991Z","iopub.status.idle":"2024-01-03T10:50:51.600057Z","shell.execute_reply.started":"2024-01-03T10:50:51.548942Z","shell.execute_reply":"2024-01-03T10:50:51.599205Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv2d (Conv2D)             (None, 28, 28, 64)        640       \n                                                                 \n batch_normalization_4 (Bat  (None, 28, 28, 64)        256       \n chNormalization)                                                \n                                                                 \n max_pooling2d (MaxPooling2  (None, 14, 14, 64)        0         \n D)                                                              \n                                                                 \n leaky_re_lu_4 (LeakyReLU)   (None, 14, 14, 64)        0         \n                                                                 \n dropout (Dropout)           (None, 14, 14, 64)        0         \n                                                                 \n conv2d_1 (Conv2D)           (None, 12, 12, 32)        18464     \n                                                                 \n batch_normalization_5 (Bat  (None, 12, 12, 32)        128       \n chNormalization)                                                \n                                                                 \n max_pooling2d_1 (MaxPoolin  (None, 6, 6, 32)          0         \n g2D)                                                            \n                                                                 \n leaky_re_lu_5 (LeakyReLU)   (None, 6, 6, 32)          0         \n                                                                 \n dropout_1 (Dropout)         (None, 6, 6, 32)          0         \n                                                                 \n conv2d_2 (Conv2D)           (None, 4, 4, 16)          4624      \n                                                                 \n batch_normalization_6 (Bat  (None, 4, 4, 16)          64        \n chNormalization)                                                \n                                                                 \n max_pooling2d_2 (MaxPoolin  (None, 2, 2, 16)          0         \n g2D)                                                            \n                                                                 \n leaky_re_lu_6 (LeakyReLU)   (None, 2, 2, 16)          0         \n                                                                 \n dropout_2 (Dropout)         (None, 2, 2, 16)          0         \n                                                                 \n flatten (Flatten)           (None, 64)                0         \n                                                                 \n dense_1 (Dense)             (None, 128)               8320      \n                                                                 \n leaky_re_lu_7 (LeakyReLU)   (None, 128)               0         \n                                                                 \n dropout_3 (Dropout)         (None, 128)               0         \n                                                                 \n dense_2 (Dense)             (None, 1)                 129       \n                                                                 \n=================================================================\nTotal params: 32625 (127.44 KB)\nTrainable params: 32401 (126.57 KB)\nNon-trainable params: 224 (896.00 Byte)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Test Generator Prediction","metadata":{}},{"cell_type":"markdown","source":"# Important note: \n\nI had previous results better than this, but I did not keep most of them because I was looking to get better results, but I was limited in using my Keggle resources (**memory**), so I was not able to trian it more than 700 epochs.\n\n**You** can become much better than this if you have access to all the resources that help you train them, over 700 epochs.\n\nGood luck, if you are going to do this, don't forget to let me know your results.\n","metadata":{}},{"cell_type":"code","source":"from keras.models import load_model\ngenerator = load_model(save_path, compile=False)","metadata":{"id":"DIEfDAjAmnnp","outputId":"6c4fbb0c-23c4-470c-f1c4-9cd7522ba224","execution":{"iopub.status.busy":"2024-01-03T10:50:51.601216Z","iopub.execute_input":"2024-01-03T10:50:51.601474Z","iopub.status.idle":"2024-01-03T10:50:51.818832Z","shell.execute_reply.started":"2024-01-03T10:50:51.601451Z","shell.execute_reply":"2024-01-03T10:50:51.817880Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"\nnoise = tf.random.normal([20, latent_dim])\ngenerated_images = generator.predict(noise)\n\nplt.figure(figsize=(20, 10))\nfor i in range(generated_images.shape[0]):\n    plt.subplot(4, 5, i+1)\n    plt.imshow(generated_images[i, :, :, 0], cmap='gray')\n    plt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"id":"DZJ2iHpbmQWa","execution":{"iopub.status.busy":"2024-01-03T10:50:51.820091Z","iopub.execute_input":"2024-01-03T10:50:51.820441Z","iopub.status.idle":"2024-01-03T10:50:53.034836Z","shell.execute_reply.started":"2024-01-03T10:50:51.820409Z","shell.execute_reply":"2024-01-03T10:50:53.033933Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 380ms/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 20 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encourage me\nDon't forget to **upvote** if you like this, and don't forget to give me **your opinion** about this work and the other works I published previously.","metadata":{}}]}