{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Published on June 20, 2023. By Marília Prata, MPwolke.","metadata":{"_kg_hide-output":true}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport plotly.express as px\nimport plotly.graph_objs as go\n\nimport plotly\nplotly.offline.init_notebook_mode(connected=True)\n\nfrom pathlib import Path\n\n#Ignore warnings\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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lhrz6f/NHaHjeLK/20caV0ovtIea9C8dLbMNeijyfJKX60WlsbTxX9/QSFTV1hrZXTiwtCzUu6RhsWM/T30z3alwVmzyfoGoqrXiFPflAcqSwSdmEw85/uUdNUlVPa0HmKuzZvqHyZ5U2xFIqfil081WfRNWr6a6FwWmH3fJaz3AqWs9wKlrPcCpaz3AqWs9wKlrPcCpaz3AqWs9yK911eCElrRAovKw5CTgpWValV6H5SoJISm65VV6CSgo6UqqR8JFMHRKH7yX+6t5Twkf8//fST9FOV1PqVo+SnuhKWfqQ8plWptOqVoPvI/AKtTqVrSTlPK8ApLgQpc8nkiPZ++pFylpS2ZIOaVo6TLWNavUorvyn+hZTQ2vCbKQJLib8ZJCc709evX5d+P/zwg7SyXLUxbnW/Pq5qfNW4qlgpMM38115fTvzIn3LuJ7RyUs5SZaTVzypaOZUBejlAK5ZV7JS3dpiFj8Kg+qvKya1tKNS8ofywyhs77NJHpVA87e5nOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWC5tbwQ5WnVyoCUpaoZj0KQLWZSYpPSmK5VzWKoq0DJFi+tNC0E3UPP2S9PnjyRlNO0iprsFxOquQlSXJONZvqR8pmU2+rqbnourUil87dv35Y2zaMMorDSClw6JuWhej/9yBwDKbBpNTIpRimOZGaDNi5UFdt2bzBIsUqrfdWwErQCnTZfpL+HTTFxpWtI6alVBmtXLZtRiXhSmPRKaILcFGazFeDFQGWVyoy68l8L+U0vLsgMB+UvXaePO0FuKmfa1ez7Qb/6Wn0Oxb2YvKkUBx1PhmEYhmEYhmEYhmEY5vfJW1Nu04pRWtFcCFIo049WTpNJDtUkhvqje0nBSQpwUoaaQdfQc/YLKThpJSs9R111TfaNyaTDiRMnJOUcKRAjkQgGBgakleSk2NaGkcJGSlhS7pEicW1tTfpLinkKO0H2w+l6Ml9Cq8RJka3eR9C1pNS3U25TXClstFEihY3up7QjRSspMg+bYuJKq4VJwUnmMSiclJ6kpNavltdSTDzJT0ovkpm9AKF8pRcflK6kcCZoI0W6ll6c0Orl/UCKWnopQvlH8SA/qRzQM2j1PpUl8puuo7+kZKa4UxrRPeqqboojmZwpB1Kiqxs20vOpPtFzKe7F5A1B6UjhIrM6WhvgWuyuOeh4MgzDMAzDMAzDMAzDML9PJOW2Xml6GG5SeJFikZSUejkptEkRZrV6l6D7SKGmKidV6Dz5S/7Tc8yer8XKTStZSSFICkpSDtKKbVIMktL0/Pnzkk1sUoiSkpZWbpOijq4hBd7g4KBkz5iUh2ReRN0AcXFxUVp9TAryixcvSpszkkKb7qNnkVkTUrxSvEhOCke6nq4hCoWX7qfwktKQwnbu3DlJ+Uv+kVK+EKWkB1Gsm+JK9sq1cSWFrzautEKYNpCke0j5S+lJaUvpqaL3n9LRKp50vZpvqhkQ7VcCJFfzgV5gqGGjlxPkpjBr0T9fj15OzyY/KJ7kJ5UDUjJTmSAb5RRfQn0WXUd5TGWFFMyk8FVNvhSD/vnkJiU/pSH5R3Gj59MKavV52ryhlzPavKH0pbQjqKyT8pqU9JROZuiv0YeH/LKKp1n4tbCb3VrYzW4t7Ga3FnazWwu72a2F3ezWwm52a2E3u7Ww+2i7C1H117/+9fCX9CqQ0lFVMmqhVbikFCxko1cPKQnv3buXVcgRqjKRTHscFKTUpjCSKQqKhx6Sk6KeVgJTGPWQQpYUtBRus9WrpJClFbSF7i8ExZuU8UShsB02dnEl7NJTTzHxJLvldmZG9pvOxVBMvAla8UwvZEjZT0rocqCXOqSwphXkpGinlwW0cpqUyWYUE0byg8JlFbZirqlkPBmGYRiGYRiGYRiGYZj3n7ODH6AnLO+9qOetKrfJBAWtvL1z506eUo0U07TauVjlNinlyDa16gcpKz/44ANMTEywAo353aFVbtNKcYZhGIZhGIZhGIZhGIZ5Vzmyym2CzEbQjzZeJLMF5UIKcTJTQSYq6McwvzdYuc0wDMMwDMMwDMMwDMO8L1gpt01tbus5SDkpoMlcAtlbLrRSu1j/6X7yh/zTKrYPMvwEy1luxWHLY7EYxsbGsortoxY+PSxnuRUsZ7kVLGe5FSxnuRUsZ7kVLGe5FSxnuRUsZ7kVLC9PXojqv/zlL/+tHL81yF4y2fwNh8OS3WPa5K5UyBQJKbbJNAltXscwDMMwDMMwDMMwDMMwDMO82wTa2+FrblZc+RRn1PoQIIU0rbYmW9lkc5s2nSsGuo6up/voflZsMwzDMAzDMAzDMAzDMAzDvP+8dZvbemiTye7ubhw7dkwyr0Crumk1Nm0Mubu7i+rqaukaWqnt8/kk+8JkW/jx48e8eSTDMAzDMAzDMAzDMAzDMMx7hJXNbcksid6mydt0kwKbFNrRaFRSZJMCOxgMSiZLaIU2Hbe0tKCmpgaJRAKzs7N4/vw53rx5o/hQ2fAQ7Ga3FnazWwu72a2F3ezWwm52a2E3u7Wwm91a2M1uLexmtxZ2s1vL79ltZZbkyK3cZhiGYRiGYRiGYRiGYRiGYRjCauX2kbG5zTAMwzAMwzAMwzAMwzAMwzDFwspthmEYhmEYhmEYhmEYhmEY5p1DUm7rbZroYTnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWD5wcoLwTa3GYZhGIZhGIZhGIZhGIZhmCMJ29xmGIZhGIZhGIZhGIZhGIZh3itYuc0wDMMwDMMwDMMwDMMwDMO8c5ja3GY3u7Wwm91a2M1uLexmtxZ2s1sLu9mthd3s1sJudmthN7u1sJvdWtjN7mJgm9sMwzAMwzAMwzAMwzAMwzDMkYRtbjMMwzAMwzAMwzAMwzAMwzDvFazcZhiGYRiGYRiGYRiGYRiGYd45TG1u62E5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61g+cHKC8E2txmGYRiGYRiGYRiGYRiGYZgjCdvcZhiGYRiGYRiGYRiGYRiGYd4rWLnNMAzDMAzDMAzDMAzDMAzDvHOY2txmN7u1sJvdWtjNbi3sZrcWdrNbC7vZrYXd7NbCbnZrYTe7tbCb3VrYze5iYJvbDMMwDMMwDMMwDMMwDMMwzJGEbW4zDMMwDMMwDMMwDMMwDMMw7xWs3GYYhmEYhmEYhmEYhmEYhmHeOUxtbuthOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYPnBygvBNrcZhmEYhmEYhmEYhmEYhmGYIwnb3GYYhmEYhmEYhmEYhmEYhmHeK1i5zTAMwzAMwzAMwzAMwzAMw7xzmNrcZje7tbCb3VrYzW4t7Ga3FnazWwu72a2F3ezWwm52a2E3u7Wwm91a2M3uYmCb2wzDMAzDMAzDMAzDMAzDMMyRhG1uMwzDMAzDMAzDMAzDMAzDMO8VrNxmGIZhGIZhGIZhGIZhGIZh3jlMbW7rYTnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWD5wcoLwTa3GYZhGIZhGIZhGIZhGIZhmCMJ29xmGIZhGIZhGIZhGIZhGIZh3itYuc0wDMMwDMMwDMMwDMMwDMO8c5ja3GY3u7Wwm91a2M1uLexmtxZ2s1sLu9mthd3s1sJudmthN7u1sJvdWtjN7mJgm9sMwzAMwzAMwzAMwzAMwzDMkYRtbjMMwzAMwzAMwzAMwzAMwzDvFazcZhiGYRiGYRiGYRiGYRiGYd45TG1u62E5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61g+cHKC8E2txmGYRiGYRiGYRiGYRiGYZgjCdvcZhiGYRiGYRiGYRiGYRiGYd4rWLnNMAzDMAzDMAzDMAzDMAzDvHOY2txmN7u1sJvdWtjNbi3sZrcWdrNbC7vZrYXd7NbCbnZrYTe7tbCb3VrYze5iYJvbDMMwDMMwDMMwDMMwDMMwzJGEbW4zDMMwDMMwDMMwDMMwDMMw7xWs3GYYhmEYhmEYhmEYhmEYhmHeOUxtbuthOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61g+fsnD/UPY3gwAr84Porh0/J7lxdCUm7v7Vmb3WY5y61gOcutYDnLrWA5y61gOcutYDnLrWA5y61gOcutYDnLrWD5+yf3dQYRDAbgFcdHMXxafu/yQvCGkgzDMAzDMAzDMAzDMAzD/H5o8KPzWCciPWH4qpOIRVextraKpbWEcoENdL/PJa8aTicRLfY+Hd72XoSONcJJjt0U4k+WsRxPSbJicPtD4n4/3NXkSmNT3D9fQljKvf+wsNpQkpXbDMMwDMMwDMMwDMMwDMO8/zSEMTzch85mRTGt53US0blJTC4WVvB6e87j/EAn3KoHyWV8c21KcRSHOziI82fD8NYqJzRkkqu4d2sCSy+UE2Y092Lkoz4EsoHQsLOBhXuTuB+1UJKXe/8hY6XclmKgt2nCbnZrYTe7tbCb3VrYzW4t7Ga3FnazWwu72a2F3ezWwm52a2E3u7VU3N0xiIufDSJYSLFN1HoQPHsBl84FjPd7ujD42Ze4eFaj2NZQdHjahzByTqfY1iw9dng6MTgyioinwP0NEYyO9OcrprVLl10+RM6NYKjjgO5XOGx3IXjlNsMwB8OJUXx5xi9/WqOQjt/HlbEFxXXUGcKlr0PwKC6JfbyNZRjmd4YYqF46L9oO3WA3I9qPm6L9iCnu3xU0eL4wAL9+VcrrOO7fGMPCluJmGMaCwx+XREa/xIA/bySH+L0rGHuoOBkDQ5e+Rig/k7D8zTXw6JFhGOYo4Bft9Gi2nc5sx7E8Ow9nZBRBlxiXTkTh7u1HyK8ovvdEGz4u2vBn0uXwnxrF0El/VqmdSafhcCr9ZEl9shgb/2dubJzeWMD0rfuIijGx2x/BwAf96GyQH5JJLODa9fvIXz8t4vH5CEKNyjVbq7g3qazybg5h8OwAwj4lXDsxTH03jmXZpVDu/W8H25XbDFMsNGD7+uv835ejEUVaeQ77eQzDMAyzb0iJe86o2MZW9Per2Ca2FjB2exnJjOJWqfWj/5MhBBQnwzCHBC1A0I2vv/76EoYUMfO+I/qqL/X5/zUunVPEDMMw7yttYfjVF5A7MUz/cwzTy3HsKqcQX8b02HeYXtuR3VUetPcE5WOBt8UrK7b30kiuTOPGXAJpWVQS7rPBrGKbFsBM3pAV20QqvoCJ8RnEFY8d3i4MdsvHWbojaFcU07RYZGZcY77khYjDjcmsf3AFED7rVhwK5d5/BHn7K7fPXcLX3Xmvt03JvBEzosw2Uskk1p+vYmlmGUfPvPlhY1zBYf5WpxhokDMA7eIMs1W2xtUIB7sa97Cfd6iYlX3bt33GfDqyq2h45TbzNiiyTyEyb9JIb4s+ZXUZ87Z9ikl5KGk1lhv+SASRDj+8jW44axxwaL6wUsOSeLaKhcUFxPe5ktWszTSwl0FGjOB2X6eQTK4j/mQJM4/326P24sKf++GrUZxZUoj+fBWTyioHM4wrAksn+fgbXLutOAzsP2z7wy3idNEYp4woJxOinKwpbtOyVCTFrJxoDqFfDMIDYvDdWFsNhyhrWSjv09vYfLmB2MqCRb6b9TUFyPoZR3R+HgsWm9+4I6O4OJDfLxCplXFcvbVP1b9JX1OQTEbUs01Rz6KYX9h/PXs3OZg8ZQ6TCo5LTOuNsU/jldulY+yHj+LKbfP2wLpPZRiGeQ/Q9H9a3YTUdtPK7StjkM50DeHCyRb5uvU53LgTpbNSv9jvTmBxZkq2Ra3tT4vuk93ieZeVvmIHsdvfYfyxJMjDLea1l5V5rV6Pou2fE4tXcf2uyVhNF7arImzqVeXe/7Yo2ea2noOWFwNNzhy1bnhaAgj1DuHif32JSx+G4RWygw9/F4Y+vYRLn2t+nw5BfX9z8M+3lmtxeEMYOqE4FIrxP/BRuOCEx/b5duIKxs+Mcv1/23I7Dvr+ox6/SsqDH1zIr8efX8BQ97sTfjNYbi23wlHjhMvjQyf1KV9dxkgf9Sj52PpvG74GBM+M4vKfL2N0IIxAiwcuZ75im1DDEugZwOilLzHaJ58/kPhXieeLPtXp9sAXCKH33EV8/Z+XMNxTevyr+tvhNSiPCTf8J0L7C19JWPh/yjpsRLnpmyc/MYReQ0eaQWJpXKPYLg9ayaFVbOc9vzmM4c+/xNefiXB0+eF1O/MV2wTlvRhLef1BKd+/ujScHcvsm6yfIQyMXsaXZMOwQRHp0i+1MIbJJ8ZhsTt4BkNtikNDufljwEHl3gv/cbmeXfygS5SGwpT7/PLkQcuxJ1Hu8wtikadaDjb+LLeT23HQ97PcWm7HuxG+wu3Q2w4/y1luBctZXgzOejEmVI61SPevTOHG99dwTfxUxTaReDiJ7//1s+Umi/bh64FXHVelNxHXKbbV+1Pziawy2en2ZcNK+kl/ozrnSCKxmB+W7PMfxpBQl5XXNUKe/VTifrv4Hay8ENKsZ2/PevH2Qcv3RZUTnq5BXPiPEZxwH3T4XXA3euDJ+7lRl5Uf9POt5fk4xaRtKK+S2vrvPo3+jsLTO9vn24krGj8j5fr/tuV2HPT9Rz1+lZTXNejrsQdu57sTfjNYbi0vGocbgVOjGI3kt4W2/lvKAzj9x08xfMIPt6mStQCif/O2yuaXDi3+yqYpX3wSyVP22fl/OuAraN/M5e1Ed6XCV5DC/g+0W4eNBmjlpm9O7sdwWL8KUpBcwf17hQe/pZHCxuO4ciyjPt8dPo/Lnw4imB2oFofDYz6oLwdncxjnFFMjZukX+3UeMeVLzyxVHnT19SqOHOXmjyVUz8R4aeRcYaMo5T6/PHmd5diTKPf5xaLNUy0HG3+W28ntOOj7WW4tt+PdCF/hduhth5/lLLeC5SwvyMMEUqqZvIYghsX4OdSsuBWs7o+vxbBVbviCHtSp+tvtlLxSXEP2/i0RVlW5XFuXHbPv7TXBpe5j83obCd2XiLnnLyCljrlrXPAoC0nKv7/M+JcpL0Shed87g6MuwPYa9Xi60H+qeJs4kQ9C8BZZEsgsTHJT99vaVqSV57CfxzDM7xl6OdhfIWUfmagYRqTFpbhLw+nal9GKMnHA1d5vqezLo2EQ/ibl2AyXHyFlBfqhc9hh647Ab1jZuoPYgynkq6PLIBHDjJkpFdpp/Yz5bu32uODuUQ4riEOMQ/oKpu8yph9tQG9+2+HrxKDJ6uCDxQFPZx+ManVGj3WeMu8cZJZKP77e3Hzrnxszh8U2Uob8T2KTCwDDMO899zETTWbHoa6WMIY++xohGoNWOeFuN37JWnFqHVlFbHonqRyZoVUuO8WoXcWJalU5LvrzJeXQjOS2qh13wtmoHJZ9/9HkaCq3t1YxeWtS+U1j/lEUq/Ekdt4och0OTwjnPlEXyTOUrb7jg9nPBixpG0a4kD0SE+Zvyp9maH/azzQqzWE/j2GY9xCyf/bNN7nfv8YwMbOMjW29ek3Q4JMHN2XiPnMe/VZt614G6ZRmUikGDhntS+rauvLNRSiknqj9qfjdncfyyirimzv5z8vigKf7HEb0m5aY4O7xwWv51ZjoizoGlGMju7sizm8yxp9ZuDIm14nfbnb3l3zKDVup9Ib8mgGnQmIV0yb282gF9qZGmWD2S71WLs2SwcbatIniKYSRoZBxA0sNmfSOxu8U0iLdcoiBatHvwsnWrqYeid/1X8QY7WnSZCMdUY6Urw/MSM0uI25Yve1FoL9C68jTcdzXhPObb65jXJT91U1jSFHjgf93u0915fKUecfQfG6d+01gXhEz7ztRTP2oz/9rmJhTxAzDMO8xsdvXcGM2hpRWv0jzhhovwp9cxNdkAnl0CGHdiu6K4akTI/Ay6HEb5x2lUO79RxRpOqS3aXLYbgN7aURXospvCbPTtzAxdg3f/f0qJpfMdyN1tYayK28qHT6d00Dln1ea2xRXAJFz8iSx8P1u9J/qtLQ3SZQaHnaX5tZT6vV6Sn3+Qbv1VNr/Ut0GSry+3Oez+4Ddyt9CSNdvxbE6Lyb2Py1gw/DS1AlXu3Io0PtvwDQ8IQwGvXIHqyedxOrcJK7+7VtcuXoN3//v9/Kk8p9X8O3fvsEPN2exHE8iXeWQPvu1ja9d+ASZXbU/jeLJ0iymbk1g7Pvv8O21SSy9NO1R4Q/JPWrh57nR26Zb2WCmlG7y563G1fq39MtV/M+V/8G3f/82+yP3iskmf8loTq69/oe7ZuErIWyeQvGTKc7di/YmY24n1hfwyvT6eUwoioRs/ue5p5HQl8s3CazNGp/fcDaMgOnoNIOd9WXc/eFv+PbKdxr//4UrIt3oBc/U/CoSOxlU1+ZGAXr/DejkL2OPMHPzmqkdbWedx+Bfzr1sukmhu7kn78uJwvfL2IU3J08gtjiDX//3FlYNj3WSGUGJUp930G47DsK/xNqSZZ5qKfX57C7XrRwUoPLPy3frqbT/75tbT6X9P2y3nkr7z252a2E3u7UchDsxN46rf78uxsNR46IfMoHsF/O5C1/gwkDA1j89ts/Xrc4p2X9a5KMcE4d+v831B+0uRNVf//pX07Vbh8a5S/ha2QE0i80uo4EPL2Oky6iSTTz8BtfvKQ4tzSH09wTR6ffCXVMtb7BEO8GLQpXeTmHzeeFd8427hJtRYOfwMp5bHCa7pmt5s4H56zcwU8j/7hF8cS5Q8K2NfkdWCZP8MuysrduBPfVENBy/xuAO9mMgHIS/qQ5OSotMBunXKWysLmDm7rKYeppw2M/T4O3uR29Ivr+62iFvAKf4kVhbFnmXQPDjEYQaSamxj53Q91H2RYk02d3cYuf6ipVBN4KnBtBDm5TVOWlPLhny600aqUQcq8szmNFurKDLF8JYpgIYuvQxQrplh+n4DMbG5vPzqMGPSE8Ene0+eGurAcoT0SxnRN+wK/Ik+XIVy/MzWH6hXK/FJCxm5IfPpH7RLsHTSfT2htDudUvlyiGlwTYSsSXMzBWTll6Ez/ajS4lHdsM3Klvbm0g8s86T/DYphdVfrmJizZg/lC/pVxtYXZjB9GO70m4H+d+PUKfwv94p1ydCqQ/JFxZpTxxQHTWlxHol7Yydd/kOYre+w/iK4sxi1t4WqPd9F/DVKRN7z6/juH9jDAvFtPnNXnhfJEpOA2N8RCj1bWYeAQz/acS4WdxeAgt/u477itNAg0iPP+nrxypWHZ3o1HXPqegYrk4Wb5yj9DjoOMCwmdIjyvdZXftil35WmPTNO7EpfDeu3UqS8Iu8GzXZ6C8j0uumSK+Y4rbCDW+zGD+90CsyS+xrzNrYxAK+uW6RAm3ncfkP+hfs++hLzZ5NK7fV3e51mI3rCo4fJfLbP3U8QG3s9ss4okv0eatREZyj0P1iWpHZxubLDcRWRJunaafLGnsW5IDytJhxmsAYJ82zGwZx8VJY97WFRdjOXMTXJ/JfYBXc6d+EvDZGHSsjiP6BHgRbvKijTX9pfCGNb9aw+NsUlpT+zdszhIHj7fA2uCAuk/Jx93UC8ZVF3J+N2pr1oH6vP9QJvzqGoDirY6nNdTGWms8rC/mY9ENbq5jfcKIz0AK3ulkx+ZcW45L1KBbvzyBq2ueYlAeTemOZbyZ4ewbRfywAn2bsLI9HElh7dB9Ti6X2am6RX5d1fUIGG3Pf4sas4lTRtp0iX2f+fsO4Ev2U6J/78vvn5OOrorzqc27/9d7Yh4mx2twGnCJdWmijX3UIVXQbYkfpbQxhl7dlt0MNok71hYxzENuyyTAMc/hIbbcrgehzMcIW7ZYru1+SmBveFnND068xBdrxkq0uR0Fzj6nOLYtbjPUvK2N97RhZMx6weWauLde21+Xe//Y4O/gBesLHFVc+hrn3u0Ds1rLJajuR9U0mn0v2iYLz2RB6u/zwkNJHVcrQTvBkt4Y2U6Jd8z+/jPNho8J837yt52qp8SF0JqQ49PgxdLKwYruSuDvOYHT0C1wa7kVnizyYlxCjO1oFFBAThQufV85uetnPawhj+PMvcfFc7n5pskAofkh5d2lUUWwfUSpVBhsiGLl8CcN9nfBpBuUS5JfTBY8/iN4Pz6O0j/xlm8R6xXZGNLCTesV22zAui/Qe6BETJgqDmidSXESeuD3wdfRiSHm7emDUdeLiHwYQ8nvgUieRIkEctW74ukRafjqKiIVJC2/PeVz+6iIGNfHIQmXLreTJpWLj4Ub7wChG/8OYP1IeNwYQPncBl4q1oWyCOziAC19cFv4H4W8Uk3l9mEV9kNP+K1z6KCxCZM9htwmFicCtbwTfpLBuUGyXRqTVY9K5isnt7SIV28Q+FNv7I4bJFaP9Y1S54T2hHJvgPyXaFeVYJblxH0sbxgm62xeukB3z4jjssPmpDCvHWbYSlvbrrDCaOBFl8qFesS1oCOZ2WteQ2VjAeFGKbSJlotguHTe9oFKOVVJJmzA8i5uYX3Gj8ZRyeCC4pf4wnxQKBrVdtMdS/5dr/9TxALWx7pZO9A5fwuVCbZ8YT5z/U6H75b7DS/3nmQHRGh0t9pWn+2VrGqsb+lbICW+7eaoMtOq/zEggXqRi2wCNlT+6gMufD6O3wwc32cFUxxfS+CaEwU8vYag9gIFPv8DFsyE5L7N9LfXdPnT2DePCH/I35M1D1Fe6/7Lo94LaMQShPovK07mL+OrSeYQtxhJ5NIh7aJynhpsg/2hcIvrmYTHGG9FtlHwgqGNn+ppEN3aWxyN+hM5exBefDpTYt6cQfanPWwc8ou3Wk9d2inztPGuMd6RF3z+nsKlXLJdb7w24RfkQfnnyx9B5fuk2cy6aI9rGBMTYVKpTZnMQTdk8sDkwwzDMvkgjcfM6vvv7dczE1a9bXWJuWjlThhKvM9m5F819C9OZ2/jxTRo5i35p7KpLlF1uy/Y9N+5NI72pHJZ9/9HEOP9+J5hHfNMwFTctGO4Gk0mnGQ4x8DgzIgavirtM3tZz9bjawhgwGSC7T/Wjy6oeVRKHB36/y7KwORoraDe9rOdFMPLpIIKNReSeOok4olSmDEYwekFMRIrZqayqurjnKQTOjRhsEpNi++a1KRimz01iwF5Mele54IsMY/SgJnJiImIZDpcf/R/2m05Q3JFRjJ4tctM3KR4fF6WUdnj88NdZlvaibSgboI3qzkXgK+YtmJiweI4N4uJoERO0w24TDLjhP0YTq17dKsYMktGZMm2O+qWXDHoyG1FMrCmOo8Zc3GgGQ9TmuoIbMvoRatbncgqJaArxxxviSIe7BZH9lL99cfhh89Ya83tnK258VjE0DKLTp6sZWxtYMttIsqvRpK6lsDY7s79n74fmAMJnR3EhqBtQvI5jyXZF/FJuk5wsDjHGtm1B9oW3PYzB0QuGsU/62RImzdK3QfR/ov3z2zbaDriPnTHdiDUy2I/OhiIafWed4YXMW6OsPN0/86vrYtqUj9MbMJlwDUC/WWxmY3V/X0kouLw+675Z9FmhT0ZsNwh2tfXj/Bmz8hvA0Cfnit5g2OHpxOAF65flRSPGeIGBC/sbAxQNxe9MUWNnV0sEw8WMEzTE15KaCb2Ms0GMfZRjmX6DeShvQL9BtEn/vJPEqrb+V6Del47wq30AF/Yx5jmKbQyNd4cjfvvx7gHPgRmGYfZPAvNjq8hu9VjvhfGVahmsJHL2vusaRQ9WgDYxPlG7rdfbmk3qY0i9Ug6dbvjalGMD/WgkO5fEmx0ks/1dufcfTaRux86GyUHL7TC7f3lrWznSUOs2FLpOwwodC8Tgtetk/p7521vKBkx64y179Plg4c2fyn2ulpLSL6NT+ju8CA3ppwYhDB7XfTKvs7ujxf755eWviqslaPnWqBD7LV7q87TxC30SMbdfmlHyO7mDdKGEKkBJ+ecJ4euvv7b46T8pLkwlyiClh199W2iLC+4e5dAGaeDbrVs9IybOM78YFduUfu6mIhX1Ek74j+cmNFL6k+kMk3pKpKU6rvzM2pUScfg60auZkErPbxjA+X7d590qos7qq60MKaUH8XHHPgt4Hi74usxrV+HyabFRHYWZPnc1wenvx/Cp3LS1pPKvQ6qjNvcX5b+hXl3G6Ef9+RNx0aYnlm5j/E6+8sbWf4PcC6dJRqdezihH+dj5X67cDvn+ZRMlo0j/urC5/21h+PRKl60NLNOA59kM4oZNv3M2vPVUJvwaKhg2wi58JDeuBBbtyra88XEx92vxnwoYNsJMxES4leM86p35bSgh2rq4ZuBZ6vOtEW3rGW09Er/PRjDY44dLE5BMKoaZcdm0gZ3/L7M7sOdw1nUqR2WE3+nHgDac4nfxk0GE816qZZCKzWLsZ7NPQd0YEG2EWf+Xof6ExgJ540LRXpOZpbyyJ8YTzWZlQ+5vdsRgImff0QGnZmJR9NgzXW7+lp6nWvadPypa+cMoNgwaTC8C+i9ITrWgMc/bNNZXzV9J2se/0jjgDfRmFbfq80OfnDN8pSYhmSPRlgMNtYVflpeOC4HeYZ2i155i87dg/KTNbEU51g0XnP4IBruL9x8rcRj2gnU3Iqi53302AF/2E3IFMmenVeo3BNGoS9D0Zhy5beorUe/3j6u9F8OadsA+ffbfxkhYe48dadPrEufAhca7oqxLm2jr23yaf5waMC3nRZePArCc5Vaw/PcrJ/Mj8pjnC4x0KSd1HHz4HiOhfs1b44W/TzlWUO/3d3uz7ePO5mp2PlBV9Rwx0SfJuOHtzu/hs8/v88Or9o1b61Bno+XfX278y5MXQhqJ7O2ZjapyHLTcDrP7U6KTNCDSoFo5lBEZJSZ/REZ09PEny5i/O4nJu/NYjiWNnbWAFFPajw6id27g2vdLpps7LWU3gbqBqbzP2Mt/rpZS0i+9vmYw2eJsDeO85q24/5xegZtGfHUd+Wbtc9g/v8jwkS3dWBTRJ3Ekd0zyz9kIf4EGxoqCwSvyebn4DSBs0GxnkFyZxNVvr+Aq5fW173Dl2+sl2Ygrt/zvj0qUwV6EWk00/Zkd2b70LeEf/SQ/E9ghZWcx7RCtBtYPfC1sEVP6ZRX1Sp4uzdCzZ7D0ZAMpk6yVJjRKWZLSf2UKN0T+GTfPI4WmWo+v4cad3BSnIPTWMr5auFyJtG/UlGN6fuhsF7z6eV86geXb1/HNt9/i22+/wdXJBWyYfKIfiJxUjq1Jp8imYhSrcTGhMS3ufgSVYy2FyqfbbKM6kfexuTFRH+TN/L75YRqrW/qHOeATkz21i7Qs/8XU0aB1/bH0v0gyqTiW7k1gYtpoL9XWf4PcA6OuMw1RDU3J+t/dj+EPh/N+gxG/7fPLjb98fyr3aZqWqmpT/4M9LaJk5pNKLCsDrhTmN4yRdTS1ixbFSGXCn6OSYSPswre315o1T5Ajl9/292vlJqvO34h6XcDcQqTepH0WdUprDsX4fNE2DubK2IfnPhR/+1GRbyT20kg+mcfNX8cxr9gntov/4rbxrUp1da4ClZZ+JfA6idX5m5gYnzM3/9PWj05jo4347HV8+4+r8ljg2n3EtW22w4v2Xm3+GduC1JMxXPmn3N98d0W0o3+7irFfZ7Ac30FGs6yy6LHn4wNKHxWTPNVSdv7kyc02GTWaJulv023Wu7OBaAH7jyXFnxR26zFEV8TkkTaWUk4bUPutmBh/GN/NiPFHbnWX9PyGQdOx5U5sHmPXlM1x/3Yd009Thmc6fEERX8VRCNEvbzyawcTYVXzzzTe4/ssMogZNsECv6C2C4vLXbKyojJ2lzWyvinHzOJaT2tjJL9yL858Q46O8+wkx1upT5X70B3SmaiR0Ly8NX7uIcK5rXtlUpN6bIW/wO/OrGD+JPPrmh3HMrIixuCLN4Yb/RK41tk+f/bcxEtbei6Fz6XNg/6lO43iXFq/ckDfRps2zr96P58Xd4RV9sMkLguLLhzksZ7kVLP/9yuNb6thT9BEnBk1NZe3t1SMy2pn74uVVvrnB8sO3hYXnCaXfF3Pn8EjeWFy6v3kQA9nNglKIL+b0FCRffpLIftXk7hzIe9Eq3d8QxkhYXdCawcbT3DdulbjfioOWF0Lf/bxndIoim0T0rhiUiI5+7NcpzIhCEV2cwdT4NVy5GUVKn25kZ7TI1aeFeVvPJTYws6qfvLvRrr4Vb+hHf2f+6CYjJv5TLxXHAZFen8FVMTC8MT6JSTG4u/bdTawa5utOOCu0+mFfzzOsBhKItBm/pVd4JcwVQUeKCpTBiB8e/SqYTBLLE9/h+vg05leEf/ST/CTbVOO4v65cVxAvRs/pVgOTnza2iD01GcQfiomSkqfT8/TseUz/egNXvxcTDcMMwUXWBirPVhTX/y4ma2MT2XJlfNHhgMujffsZQdBg1yOF1VvXMaXZ3CcVvY8bt1d1ZU341igmpMqxOWlszIrJ49UboqxOigmumNBMGP1BtRONyqE9bvS26SeKolN7IJ4xqzG38GIJE+Ni0qmf+IjJvXGTu3wOu02wwuH2S5/hX/7yEobflv3H1k4Eu4L5P9PJ+tsmhK5mY3lOLOfWFqfm40jo25cC9k8ry9sIm/lK/X3RHYFfV94zL9eyqyQqgxeBoK6cdXXCp0jLgnaWP9aLkc++wuXRUu3q5iDbwwdOrQedvSO4+NVljJrsb+Du8umUYCIv1hcwNqdRhW8J9/1YnrkET5P2+0GNPUMFZ21uBY6MmKw8mcfU2A1MLiqnjhIVytNiic/GDPUz3zRJL/y6vU5SzxdgYpG+RFKIXruCqz+OY/LWBMa+/w43nxg6JGBzKddvjYvxxw/zxv5PpJlbo5B29/oNX2OQXfwb4zOajaPFpPmmGAcZ7I6LcVnQpm1KreHGnXmsKi8GEmvzmPx+0mRs4oInUOra7SIwGyturWI6b+wcw9Qvy3l5W+iFeyFmNvSGPh05u9tdxrZThRZuDCoyg73tvU2sazalrEy9NyOFtR+nMP9EGT+9iGH+1jVMrhjLmMsTKGGF/VFrY9wmZsHE2HFxLO/lWGphDPdjeSkIr10SMgzDVIjo4hrU96UO0fh8fPkChvvDiunRarh6hzB66aJmQ9004iv7N37mjwwqi0ryF5Ok7s1gTe0GXAEM/scoBkN+6vmlr60u/SGcfVmYji9hRm8S5PE0ltVxg8OLyGdiHtsbEHe74Q8NYvSPZ3IL1US/PDenHKuUe/8R5J1VbgdNbFwindatwFmVFHmThXbmXhODvxf6gaSTzN6Uydt6rkz89gLyxgwChzeEoRNi6n8mpPtsj+xz3tcMQA+ITEb3jBiWDJtYOeCsVw7LZR/P83uMNoCTL6YPPm0OhAqUwUaTzaSeTmOqoN3gBBImK7u0OP1B4+eeb7aRtLRF7Mb85HcYu6d/yaAgJhrzz4wSl/sARsp7u7o2JoZJo50DaXKRpUF0EfqE3NrAfbM4r93Hhn5C6hRdnI2Cd/eNLv5rS0jok4Q2dFIO7ek0brSYXsfqrEkObM1g9YX+7YIL7g7lsBCH2SbQLtC0Wir7u47xWzNYXtetznN6EDw7iuGK23+spncLR5igyWpzkeWvTdqP7k549WVDb7NU1MkNkxem3rbc5/oHwlEOWxEYN5LcQXy5sAV405esDmcJipFSoV3StfXoG1wdm8D0/CqSeU2AA25/BMOfVsqkwj5Ix3FfE85vvqHVi9OYf6pbLekQA/jIMC5oTCkRRrNeGWxupnQvBcRvL5WzmUi4xeRAOSSb4vqy5vQP4PJXX+LypyMYlCYQb5sjlqdmG0tqTZOc0CtSU9jQvLzaPxns6vre2HLCOOaocuSfE/2fcQ8gJ310lKXT8IUFmVExs4ufwoyJ3XFXfc5MT/GIsUnUuFFwXX3lWwczs3E7Wwm49HXFJ9JTte9JuETdUw6LYimRs32qoNrdDgW9hcc3VaL8nKKrTOxt6zb+rUy9L57Yrajx5UhtXQnpctTaGM3GZyqZTWymdOknfrtb+V8quJv2k4IMwzD74NkUxmfi2FHG0Q63D8HeQXTSfNvpQ6Q/JG0ALJNB8vEkxgp8IVYM/s6Q3PYFA7q9D2KYuLucU7TX+REeGsXlry9ipD8INQiZ7Rjm7yyYjxvEfDau6v1oHts/gotkfnMonNuTKy36unuT4ml6yr3/6CGFWG/T5LDddpjd760zGcakU9Knx7nrxeBDDFYld3MAvWfPY/SzS7j0H1/iqz9/ha/+62v06jduIrLbjcvYBdcYvlfScyXEc/vOflzkc+U/ZvHVonfrIRs+04/0g1ox4Q1fRK/u00h6C/RrzM4/a7m4QvkrY3e56l98Vz/slnlbzzNuCiZ/Wq4Pj1347K63vH9rFbdu35LNfSi/fPcCNvQzHx2y/3LZl9zN7SWU/Vz4jJ+8p5HaiJcXPzNqRUP+UW49mNE/TX0SE5nQ6SGMfCricjkXl9FjJtPtKtlIkW14Sgy/IXxb24bJqJaqDpdh4pd+lZs05/uXQsJgg9YJl1bZahsduiBe0Da8IfymbhPTGjspUxu65F54pXubJsKsvijRX69HKzevo2bhy2FwK38LIV+fQGxlHlM/3sDMc116V7kRPDWYVeDYhV9coBzIVFUlYTQjnFvNbxd+PXbX24ZPh/n9XuOEVJDeNtb30DGjEmHnxUrWZql8vSgr6yaKcZEG/QGz5+fQu/VYXW8etqjGnipd/6pw2Nrsw2N0v6T36gUp2j+zjSR3EoiuFL5/ycSkh14xYnyeclAA/fUGdPJXz59iaWYC12iVpn4FYUsIgyH983Vu5a+WTDoXL2P4rd16cnJavbiE2YnvTVZLOuE7PiitplGvN9pRd8B7PGfOJfcL5y8YyHuJmMLdudykJYvDCXdLAGGaQPzXV/jis2GEm2WRXXz02KXHfvxLxVet81Rj2sLOf9vnm4TXuLFkzjRJpMOb35++jOWtZNI/z86tJ0/+LG1QDuuh69O7hYz6yXJjWdpBSrGEYQjfw1TeimBCu1m+TfDz/ZvdzI4zVEr9KkIfPj0kN9vfxRUYMKkrQ8h+XS0hxgnduvjr00Pr3ooiqY8Q2d1GL0LajTr3EojrXpDQi5mgib3t5MaDvDQqt94Xk15aqqpmsanf6kXThlimh4D6s+nZ4tsYQ/isg2u8XocxPCZjR9r7yZB+4teTv/eTU5RN+/iyWwu72a2F3aW5Xz38GTduLSCW/yY/j8y2GIffvYFrt2O2/unRyl2q/cLXO9lFcln52hSu/Thtbk5sLyPGZAu4/dM4HqYKPJ++JvpxEg+fp0z370hvRjH983Xc1en7Kna/wmG7CyGltN6myWG77TDc7z5r2CmdSG3J7xO017uDA/jDn/4faYOc/p5O+Js90uDFUePQ67A16MOrHBTALH703NHLX0nPPdXTUeRzZcz806J36yF5anYGK/olDrSCVPvsTALLdxaK8s8affiUgwK8a8/Ty8u93vL+vTRWHq/I5j6UX757p6BtdBXVf7UM/tdnn5RQ9m3CJygrfgVwd57BkPIZr6l/zWEMf/4lvv7TKIZOhhBoEXFx768+GSgx/MbwKQcFsJfrLrC53k5eevit3XrKvV6PnVxcofyVsXu+rW9516ewsJYwvpwQbSUpugjb8BnCs4CUyb6kbm9IUjrahV+P3fW24dNhdr/7rPGzeUqblOhS8683M7EjBmpP8m3AEam7JuY/4Ia/J9/oq1l4rCh8faGw5W2IIV1fMGwnQrbhMbqfmbxIcqJOGaMU659bTPT1eUAD2hWr+xMmL9Z0m9IYn6ccFEB/vYFC4VmbwYZe8QQXPG2tyrGM3v/jJgsVdndzsTKG39qtx+z62NyGQekHlwcB0QfZ+WdLVXWe8nXvqZi0fD+JBdrrwkxTWuWAqzmIwT9cxGB76c83i5+WsvwrlKca0xZ2/ts+3yy8JhtLyqZJtCupZBLP81cy6Z9n59ZjG14d5fpnDJ9yUAB7uc0FJVJu/KwR4zdn/v3G9NC641g3GDl3wzvamadozmysYuxRPP8lgcuLrn6vbsVcCpulbKBjhVLvS02vcq+X3KQYKbKNORvQPc/68fsIn3KwH6qd5vHTwG52a2E3u7Xsx00mQcevXYH8Re8kVqlLSG9ggcyT/esbfPvP69mv4C39eziGK+oXcNempFM5eRhuZQFRJpXILrjJu//FEia/F+H4gUyjqQsaRRiufYurY/clM2OWz9+K4t5PV/HttTFMZO+fxPgP3+CK6B+WXhzw/YLDdhdCt0zoXcCNyAdBk4n4DpJr+Z8muiOjuDgcgd+tiSZtjiOtShGZ9st1fPPYaFKgXN7Wc/OJY+pBvk04PamnM7hfoXHd+8CqyYpZt6/4jwOPEpUog9uGVbSVSo8MWaTIp8qDrtPmu6XTBpSXPh1EsFEzqd3LYOdFDMvz05K95qv38jenOVKs7RjC5qzX20RUERM1ZSPQHGnsWJptOQhMVh+7RNurHOoxW+W/rTeP+S5RkgkXI9GXBo0Q0NCJfp3pgyNBQwRDQZMPl/XmPIi+TrToi6dIqcCH6q7j2l/EpJ8mHUNnnr25ivEWw5Y0LtWHq64Us0gmm6HtJRCbtTG3sBhHUv9JuxjW+brPH7ht5GLJM9FkgtvExFx6e1U5OkzyTUnsGLREGWzM5ZvvMP9dgzyt0SAmDfdpr4tv5c3+lsw2InR6ETpZaFvTo4VdnhKO6nLGCiYbS5JpkjM6s0NvNrBaYLPVo4SxfXCJMZpyqCfiNvQ96UK7EdtxooJ+WZB4bWz/aFND8/qh/V0p+TPvhfWkbjW9A16/tjwqppweL2jsmROiL+jUlVuTPq6i9b4oIiYm4LYN5ldsOTJtjBjv6vskUU/nTdNM91MUQgzDMIcPfdEbRVrSn+5ihzaWrpiOzIs6Rbm9uWGzi84L2tRaXdC4jzBsxbGavT+KmI25WAPl3n8EeMeU216EPx5Ff9a4uwaRGQt5C7SCGDjhz1tBg+0Ypq9dkTaDkzakWzP5NLlYdKtzchzwc0tBayReTzqOpV/fBcs5h0cqkTIoId1tvQUVekeXypTB6JZx+ak7MCCtLts/GTHpuYcbD4y2ICW78GJip2egryt/A0rapf3mt/juh3FMzSwhqm7Qsy8OwRbyVsI4yWjwYcAsHdsH4NPb106nkDj0l1CrSBm+jW5Bp5lytqEfnc36RNxB6qlyeMSJBHSfuVeA+OM4koYXzA74Tl7ASJ+9YujQaA7j/Ei/0Q6+gFYNa815ELTJaNmDBpcfIc3K4krxNsO2ajDLI6qLu6XACywTTDaSxMs4pm3r/byJvXuBuxPDFwdtN3WtKKKv9JpFeM/qW6Mgbe2gI4Od1MEqLP29BV4uaups/JW+/3PA21a+vWna7G+aNiK8Mo5V3SMcHj9sX4kUHHseAMXm6Suj+Q63aFvyX7C44Sihgho3lnSiRWeTnjZbLWyR/uhgbB9EXDrNypIb/Z0thvzdebWflz3Cr2OV8suaeFK3d4XAmP8VYnYdm1aLt2gu+JgO4piJ6ca5uhea6c24oY87qHpfCPcpk5eyOykxAts/ZbcxxVCwHYpj+7VyqEJfEx3FF/sMwzA6lu5PYpI2aFbcFUF90byXwIZ2kwfmQJCGmnY2TA5abqDKqdl0Ioz+oWGMfHoZX/7XRQx2iAGyclmONOJLk6JL1eI3TJrSyTiW9qEkqqp6ih1DZ+1D+LNh9PcE0XP6DCLZndFNnisGW/t5rkrJ6ZclhZnZFRMlSwaJx1OS/Vyi/Pzbb/gqg13wio7fQ5NVcE4/+mnn2m5FIUW22z+4iPYSlAb7zz+Z0u+vUNlf2jB+ui8GqeHzX+Dix/0IK3W0q7sPgx+N4tJ/XMaozZuAzItFjP26hISZ2RwxVPafHNK9TAiLybWuxu8ksaBZyVx0/gqMq/Md8IUvSjskB3v6MRApfbWZXfZUVT002XDKjc4PL2JIlCs1fGRG5sK5TsMkKv0ylq2rB4F5+qUQNexIqShnT9EuzgpiAvvxSES3Sa0guS59QkWUkj/mlHu/Od72MAZHL5u/LBWTS7U/sfXfTP5sCstxE6Wjw4XAqYv46j8uYuTDQfSq9efD8xhtNZ/8lZ9+4rHVan8qfqKcD304gguXv8TXn9HmKSYap9eivZiUUyDnfz/am0rQThVElKOOAeW4uPBbId9/MGEjikn/VNxoJxduDyKinyjmfuNGkhlsPJV3Zbe7/+HCqtHeqsDpDWP40pe49MePMUTtm8j7cP8QhkeC8OrraxlUedrQSTu6fxSCxySoqc1HypEJbZ3w6FctkqkAzUKFYtLPCq3c7e9E74eXcL4r3ziBxF4KiUXlWBBfSxry1OGLiHobytugTfXf296L4QsjoiTmcJ+7hC8/L2dTt1XLsWe4fwARnQ17PXbpY0qD3yZPc7Mzyf+ttGFhAL1g+fiLSxg9PyzK3AVc/k/R1ho/rSAPlAMdJhtLkhmyHPIK3VLy/yAo5vmpqHFjSipLF0b60ZZNYHnxTkRvd38vifWozcselzc3RiVE/kXM/BKhSNj5paOo9DVTONMLtlFt/HK4/SEMjoxiKFSk/3mI8aPFWDb1Yjnbd6furho3a8ySEcOUBYP/xdZ7leaOPkO9N8cF71mtH7SRrej3T+bbnSZSidzm6Xbp02DTxljfLbC5QH6+VTvUJbdD2TlwHKubhhQUY8cRDGvLqEJVVTMCoq258Il5CpZePvJhOcutYDnL9STWotlFc5Xy3+9TNl1+uYEFTf9VKf8L8b7LC1H9l7/85b+V47dDRw9OeXXLxmob0dnZqfza0eptQkO9E9UF4piOz+KX6Q3d4LoDPae80PpcXdeEQL0Du856NLWHcerUGXzQ4TH4+/rlHJbyVh2m4Q32orU+/0LyrzXQKdn/rXrxACvS0v1KPtcO47MyqWd4sLKhuASpNVS3nkCbW95cTyL1FFPjj5A3PvR142TADc1VRr8Ik/x6/XI2P9xl+aVLg2KuKfN5i1m/1pFyBxFsrs0b71XVuMUA9rjIt1M4dbwL/uY61BjK4msk5pZQkvUIs7L/OoG5JStfWtB9sg3a7KRBeuqZKH/Z6FaoDKaTqA+EDeVeJAjqPK1oV+tohx/NjW7U1oiiFdOEwyRf3iSf4jfpghTWXjWhO9iYv/qj2o2GxhdYlIxeEc0ivu358a2tR4C0Mw4XGlqD6OkbwNDxFtTqgmlWBtLeIHpb6/PH89V1aGptR2egFY14Ie5Rv8ExpqNp/hRR/tZfe03iWgevKFd9J0+it68fkWMtqM/LV0Emiej0JNY089GW7pP59dmQ/zIdPaeQX7xKK6Oply4Eulshik0OkfcNrd040duL3pN9YmLTAY8+4ell4/yPmNOGp5Ltix1m9arWK9dfze94sB3Nbqfp3G7n+QPcjuq/bjApD5Sms4umabqx8hL1wWMiD4xPqKqpQ0NTM/xq/WlqhNtpvE5Ko8friqs4jPkOOBvV/lT8RDn3NjWg3qkvbCoi/2Z/wR39zrVnRD1r1b0120shHo1h4+UmNgv8Xtc0ibgp16u4quGKPkLMRP9PmMXBsn88xLCZ8rIOgRO6drmqTpStFTyy9WgAZ4daUafN/p1nmPslmt10xpKtNcQyrehuy69fElXVqHV74KX2TeR9e6sXTQ11JmMps7bBrK+phjuQX4/6erpwzN+EWrPilEkgevsR7ctnirtP+KHP6NRz3J99YlAGWmLSvlBf0kbh6+vLhvVE9zH4m2pNx5KZl1HcfvQsN458uQPPsSC8Lu3FVaht6sBx0f6dCHWh+3gPwr2ncOb0aYS7/GiqfoUnCyuiF5FJi7bodJsPzf4uHBfP742cQE93N0LhCPrP9MCrL3vJVfwiwpDDeuzZ3tqoGXsWQ3F5eqqnu7Q8NSv/gqqaWrgbm0SZq4d5cyP6Lu2YQcd6fTt6/br+WmVnHbO3onipOEuluP6xuDGAbZ+cSsBlGEtVoaahFV1h0f9H+tDXH0ZHo7E/Sscf4Me8ztQkTGIsIY1R+0S/LPzqj4TQ5jH6ldlYwsSM1oSbSXnIpPDswQq0T7QfczxDTYuYZzTkZ3K1W47fCTFu7g71oCfSL5rq04iEOtDckEHi5hJK6dpl0tgVY5BurZk6lT1RNid+07Tdou9sFenaYPI2b+8lnoyJcqw4sxRZ73t6RVwK1HvCWL6qUecTfkjtQK/I7whCATEm1GfSmw0sTc7A7N24GeW2MS1dxYwnS2uHNl97EDwmymheEtaiSZRRCl+oqxvhcI9IgzPoF+W+u60JNa+eaMbeDMMw7w++piZkXiXxbG0eT8y+tmRKJtDeDl+zshO7Dv0L43eMDHbW7uP6WP6GMjLzWNevDHW44DveL+/SLDrUTr/HOLAoAH3eZrI4SmEHqeyKn8o+txIs3FlGIhv4NOKLE2CDJObE74xjpthR5ZGlUmUwhZlbYpCtX7FRKdYmsGSi9dBuLkmrdGIv9dc44enoxSDF5WyvmCB44Sq2JZtdQ6LgSh5Rk7cP6HshEdfpx3pbkQoOR4HPtdPYWLyHKcPs65DYmsHkXAFb5hTmvFV0KmJi9GQGUyXa0TxSvI5j4d6y4iiHGKZ+uYfVrcI9x5Ejs4PY/esYWzD2qAOtxlVXmRdRjGk2HjH7jT02miBClReBU5Xbz+Dth20BsYSxpngD/dImolaYbea5k1hFKSUwtTCGyYUN7Fh9rn/oZJCMWu3r4Ud/i3EFderFku4rvEMgk8TK7H3dODKOqd9WTFfFU/vndHvgaVQ2aVabwlcJ0WNpeJm/4aeDbPl76D431I3zc+RW62spfux5GBTK0wVE1w3fLpTP3DLiBbw1M5t0dLEYS1XJfanZxtiZrVXMTJfw3ZbilymiX6MwlPTSqAQWpufN80qEyVmn1BXaBFwNnubrqFIxW11N0EaSelNOyw8LmK3b0tXVLBWo9zYU3ghdzM/mJjFTsM00oQJtTDGU1A49m8K9qPl4l8LnpvQTP5cIoJoO2q9BGIZh3ieis1PSnOP+0kH1wIyWAqOgd4DXSUTv3sB3v5gptokUpu4vmw9QNKQTUcSKGUjQgKOQ0nNvVzO4qPBzK8HWfcw8lVMpk1h+txVPB04KC2PXMUkD4kJK0L20ccOUI0UFy+DWAsZuL2CjqHlr6RsfLkwvGT8b1W0uuXBHTJpsFOyZ7ZjhE2Zz5gsrbAW7hQQVIHb7Gm4uiHJVTDAzKcRmx3Dj/tt9DUVKs+vTUSSLSRdRLxJLtyWzM+9q903KhOkbY3mfjZXF1hIm/nUD0ytJZZOSEhB93Mb6Ie3PIEhvRjH943cYN1Fso2EQ/iblOEsGiWc2G6MQc+YvlNy+sK3ityiOSNgWVjaMCpeGAHpPKMemmGwkKWpP/GHpL1di92/gxq0FxItqYDRIm/Ou563ULJu9HWws3MS12xbt14leBAzmvVLYWD5k1fbOBhYmrmHKrO9am8K1ieLTdOeVrr4WXeczSK7cFn2T4tRS9NjzgLHJ0+VfbmPZYH5Liyhn8bjtuCQfk40lJZKI2222etSgsdSNaUQ3i8ux9Isl3B6fsDUnl3ljn6AV79fMoPj9WHz8Mq829/9yYiUO42OUjST1iH5Ab3qbSL3Mmf4wUG6915PJmCp68xBjvtVp8xfLllSijSmGEtuhksa7pBw/vKEOwzAM8x4jKbf1Nk0O210MNICjXb4T8Sjmp37AN/+4hslFuTcs6D8NUMRga/l5EmntAFAMNNKpDUTvj+H69Un8Iial+f2vmX+y0nN6SQzOt9PkRY436bxJbVXsjvzceIHn/vaz9Nxxw3Nl7NJL79Zjdn3s1yXEX6ewNnsfr8r0z4jeP+WgAEf9eVVVrxC9N4arf/8GV8cmcPfBsrxr7KN5TP86hn99excb+j2yNLubG/2zdusp9Xo90vVq2S9UBpWyb1YGDc+P/YYb313H1PwqNlK6si/IUPnfjGP57gQmxeSppPhtzWD2iXGFh8Pbg2FlA5qq1EMxKZzE/NNE/k72e3KbEFucwo1/jmNidtV0sqIPz6uHov5Ni/qwSbu6a5+cRvpVieEX2Fyed31MpPu//vdn3H8UR0KXllIbJ/ImtngfY99fxficeftm+NZYR+nht3a/enQL164o+Z/UpRmVJ5EH8ZV5TN24IinCD759sXErf4tClKFMegfJ9VXM376Ob/81gUcpa/8N2IWn6iWWbl3Dlb9dx/jMEmLrSakc6/Oe6lFqM4H46hJmfrku9XHjs3ET/0oMnxkUb6q3SfE8yrsfvsGV7yex9MLcf3ePz7C6mDZ2jc/ah6eq6gGWzVZ1ulsQ6ba/X49e3mARNsLa/3nLsBF24cu6Hy+Y7GruhL/n47yN1fLuN91IMoYZzdcaRT9fkIrex8//+h/Rb01hfkVpY6hsqQoQUejUsVRyPYbl+Sn8/P3/4LsfprIrxfX+F4vcfol0Xxbt17Xvsi/mzMMbwPke3abHgsz6Mm7FC8ePsHPbopT91AvRZ1F7/M8fcV+j2Db4L/q/savXMHbfpO5K6an4dfc6vrsZzb9/cQw/jN3HUmwDSbO8EGOyxPNl3B+7hmu3ZGWbMX6vrMeeNulRcvpoyObpo1yeFvY/hqnvb2BqMZYXTil9qH2dvIbvxsZEWut6aZvwPn/y0vjSKBHHvFLX7OJr59ZjJ9dTkn9bS7j1v//A9dvzWFXLkloe1L7ouZjjiL7oyg/T0r4VxvArB0RqFRNXvsfknLFvzpZLMd+4Jvo1VUl+WPGLxhNIvab6oYQp2+dsYHVuEtd+MN9ToDj3goivtiIIlI0kjde/wvRT/ThXpPOa/HKk4PPWRJkX9f7n3+zq/Q9SvVfR+0dzx9WJb3Ft0iTPhT/Z+iXGfBNLqcLhUTC4l8R41q6NiefaGP34rPjxpDwHvivGrlZzYG34suNd0Sao5VMNmxROpf2b/uE7TCj7LJQcf3YrRzLsZrcWdrNby/vmLkTVX//6V7UbZErGiwBtTJSMIsamwn4/dI/gi3OB/A3ANpfxzfdTioN51/C2B+FBEtE1Xj7CMMw+OTGKL8/olbYZJB+N49qdd2yl6QESOHcJH3d78j8d3Etiefza2zPD9E7x+xl7Bj/5AsPt2tFWBhtz3+KG8vKKYZi3Bc+BGYZhmMPn7OAH6AkfV1z5vLtmSY4ECcRWuFN/r+g6j0v/cRHn+813Rkf7AC4M6BTbAvrEkXl3kXZHZsU2wzDl8HAKy7kNLhQc8HQPYqhdcf7eaR/CmaBOsS1Irb7F/QXeOX4vY88Qupp1o603CayxYpthjgA8B2YYhmGOFqzcZhgtK2mgzovO3iFc/PprfPXnL3H580u4JH5ffPkVvv4kAl/eDuiCNxuIvmv2HxmGYZgKk8J9s83IHB6EzgznmSf5XdIQwei5EDy6kWcmuYzpX3mba0ZHXwh+nW57Z30ZJpaVGYZhGIZhmN850hTDzoYJy1luxfslT2JbszOKfmdvI2nE529Z7m5+tOJnhOUst4LlLLeC5Tr5symMz5hsWtsQxPBor+GLoCMXfh0VkzdEMPLHfvj1L4dfxzHzyxQKqbaPTPgLwPKDk9Nmq/mjrh0knuRvtnqUw0+wnOVWsJzlVrCc5VawnOVWvO/yQrDNbYbRMXTpa4Q8isOKTAqx+Ynsxn8MwzAMwzBMmTQM4uKlcP5msVtRjP1rEvydHMMwDMMwzO8TtrnNMCUwPz2F+ZU4EsrO41nUHdI3N7A6P4Xr315lxTbDMAzDMEwFcff68xXbgtSLZVZsMwzDMAzDMKbwym2GYRiGYRiGYRiGYRiGYRjmSGK7cltv04Td7NbCbnZrYTe7tbCb3VrYzW4t7Ga3FnazWwu72a2F3ezWwm52a2E3u4uBV24zDMMwDMMwDMMwDMMwDMMwRxK2uc0wDMMwDMMwDMMwDMMwDMO8V7Bym2EYhmEYhmEYhmEYhmEYhnnnMLW5rYflLLeC5Sy3guUst4LlLLeC5Sy3guUst4LlLLeC5Sy3guUst4LlLLeC5QcrLwTb3GYYhmEYhmEYhmEYhmEYhmGOJGxzm2EYhmEYhmEYhmEYhmEYhnmvYOU2wzAMwzAMwzAMwzAMwzC/Y9wID57H+YGwOGLeJar/8pe//Lfepgm72a2F3ezWwm52a2E3u7Wwm91a2M1uLexmtxZ2s1sLu9mthd3s1lKsOzL6JS58eBqnTp3K//X1ItxWh+31GDbTNv61DOKjD46hpaUBNXNLWBOnin2+CrsPzh1ob4evuVlx5SMpt5VjhmEYhmEYhmEYhmEYhmGYd4aW7pNoc1chGXuCZxub2Hwp/7YdbnibW9HeVo/E0hq2lOtNeZXAXoMPtS+Xce/pC6SV08zRwEq5zWZJGIZhGIZhGIZhGIZhGIZ5h9nF9rNJTN7K/ca/v4778TQcni709SmXFSSFpVs3cOPWkjhi3iWq/vrXv+4pxwzDMAzDMAzDMAzDMAzDMO8MZJZkwA/E713B2EPlpMqJUXx5xo/M2iS++yUKDF7EV11OxOeWgXAEAbcTSC7jm2sJjFw+A//rZXz7w7R06+BnXyHkkQ6NiHvU69AcxvBgHwJNLjgdQCa9g821OUzdWkJCvoIpk7ODH6AnfFxx5SOt3NbbNNHDcpZbwXKWW8FyllvBcpZbwXKWW8FyllvBcpZbwXKWW8FyllvB8qMtFxcoB+ZI91dXw1FTB19vHwI1aWzEYthI0lptcd7pELKckYvt7RRSr3S/XUjX7KaV9d0NEYyODCLodWBn4ymiK6tY33HA2zWI0dFI3uaUbzt93nV5IaQc29uzXrzNchu5tw3BY/6Cu6ke+fCXK/+9x5/lypE5LGe5FeXJvWgLdsDfoDhNONrhf1flXgS6OqV037//sh+t7oMIX44jI28OmPaTRyZ8BWC5iVyTl0cyfBpYbiEX+Xiss7Xg2JU40uEXsPx9krvhPxZEQGNG9GiFzwjLWW4Fy/cpV8YY9Uc1fApHXS4uUA5yhNoa4UQGqURUc78D1TsrGP/HVdwYH8eNm/PK+Xzmb17Dte81v9/WIS3N3onh/s8L0jW95/rhr00j/tt1XPvpJiZvTWDsmmwOxemPYLBbukzibafPuy4vxFs3S1Jwif9eGil6QxJfxcLiAuKWVt8Pi7D8iULtNuIzVzG+KE41DODi5xF4qWyvTeG7X5blS99J1PhtYvnv16F8XGFNBeMvlwVN2pbFIC7+OYRGxZVHZhupVArrT+YxvxA/oraU3vXwvyuY1Gnm8OkZweV+P+pex3Hv6jiWlNPWuDFw8RIiUuMTw9R345Bbn320Y+8UatvwtuJXKN1LoRJ+vEMU6ifVcq/9nLHk8vu2y8PvjPdqzPeeIX1eLEZNefWpAIeUj+FPLuNMax22n9/D1V+K69neZdQ53ebKt7j+njRGlZ2bFCb0yRcYaneJMX4CC2Qb9kjMe98BTPtR5l3lMNuQw6rbJcFjjLJRzZJsLExj8aVykl4eHg+hq8UNh3bece4Svu52Y2PuW9yYlS5UiGD0ywH4d8hEyZRyTksA5y+PoLMuieWJa5hao3P9uPDnXng35/HtjRnpqixt53H5D52oVs2hMGVha5bkbUJL+U1/Thc8jT4EegYw+tlFDLYrN7xV1E8UnHCoK+XdddJLG8IpwlweQQx9egmXPj+PXuXM4aLGT/xVzthSwfjLea9J27JwoFpfptRfrRueZj9CA6MirYdE8/Q2sMvrox7+9wWTOn2o9OL856IcfDokSsTvmCoHnFS2RV5UK6fscaNO3CNR7USu9dlHO/ZOobYNbyt+hdK9FKz8eA/rRKF+Ui33alpIlFp+33Z5eIfpOy/60Eu48EEJJa2iYz7BfsLwLvA24iV9XqyvTwWodD4WoFq0bxQepwjb7wE1/d+t6Fr3OXKcDn6M6HI65QOHE3VWnxMw+Zj2o8y7ymG2IYdVt0vikPqm9x8nfJFhDH+o/voRahENayqGmV/0C2p2sZtWDovCjcjoMDrdGWw8GFcU24RLtEWiXDVH8NWfv8r/nW8XdwlZdZ18KXNgSNVHb9PksN1E8vE3+OYb+fe3v/0N3/wwjsmZZWxsZ0T59CJ8bhQR5dPzSj+/VHcezybx8615LC9O48aP8mcM+/e/Du5GDzyNjVIFUNm/fzKluvVYXl/R+CuUeX++O4llKk9K2aLfv8YmML0Yx44oWo7GEM59ElKulSnvecW6NXltef1RDX+Od92tp9L+W7vdaJTKgVuUCJnK+v/uucUJ5UCm8PVxTI5PYn55CdNjN6B+RGb0rlj/ZI6+WzlQqLz/du7ncro/ktP9wb78U/JO+HF37Mds3hFVVfl1ojj/chxJt+gnxyblfvLHnx4oEnN0txfhv3KgUFR4NPyu3e5GUc5EWWvITTZs74/fyuYljXlKep7A4NaFoWz/jopbjZcnfyJXMf8V9G49Ba/P1sm72bErUerz7NzQOyvs/1Fz66m0/wfj1vQ5FfEvRynu+R9v4O7iMuYnxzD5TD5XSf8JdrNby+/drafS/u/LbTFerIj/Gt5vdxobC7cweWsy+/vlh7/h26vjmH8hX2Hnnx5VHjg3gn6/E+nnM5iczX0/n709vW20zS1+yc0kklvbykX2z2e3tbsQknJbb9PksN16JPmLGKLzU7jx0wzi9Dal1o/wKX9OruGw3Xq2VmYwdTe3A2ql/S/Xv1Ldeuyur3j8y7zfzr+t+CqW7o7hxoMNZITb1RJERBZJlPu8Ut3ihHIgYyc/auF/1916Ku2/nVtPpf1/19zihHIgY3n9VhQzU9NYUgYqhNG7EvwTHH23cqBQef+LcFO635HTfd/+KX4svtDLlQOFffuvcFTcqajcT77QyfXoxfb+KwcKxYZH5ffu1lPM/Wpe0pinmOu1GNzKX5Wy/Ttibn0EK+2/3q3H6no5HxezY1ei1OfZuQ87/m/brafS/h+0W5xQDmQq7b+1O4HFu1OYieaUJZX1n93sZrcVlfZ/v+5C48VK+a/yvrt3t1cQXYlmf2uG+Ya1W48kbz+PwW4PyPzZ5E8LeaZh9/bS2BWXZMT8Js82t+Z3407OJInd89lt7S7EW7e5PXTpa8m2Eq3cvnZbOanD/9FljB5zi35/Ad9cvy+d6/34Eroa0lh/cANLziEMdPnh9dQhrbdr1xxCf28InQ0uOJ0ZpLe3kXgWxcLMct6ANo8GPyKRXgRb6lBNLwnebGN95T6mFv2y/R1nGvF7VzD2UL5cG5apFflcDjf8wq/eYy2oq3PCkU4jvbOO6Pw8FuJqlSATFX1ocTrg8rjhrMpgZzMF6QuJrRVcK2DYXqJjEKNka+zNOhZuTOk+s3Cj/5MRdIqk2342jbF7ceW8QvcQLkRa4EyvY+7HKURV+0LOJJa/uYb5YD8Gwp1orHPAsSvCtLWK5fkZLGsUSUT58ZeRy4KStuthDJ0OoYXuAeWb+T2FGcKlr0Pw0MpnERcza0n213gR6u9FqKNR+lwwk97GdiKG6KIxDVS83f3oDYk0q3XC6RBx3U4h/mQG04tqaSs2r8sIP30OfKwR6fW5vEZUJfjBBfS1OHPy3vO4GGxE9fYapn++D10pkTkxjIshL6p34pgZm8aqctqMvPKQVPORPrNKYztVKB/VdNnEyvf3kTo1gB5RZjwi/1M6u2tufwS9vUHJT1dNGjuiTtvaHy+xTpechnnoy40oB4a6Q5/BdoG2tXA3uuCgPQaSO9LLikLPNIPSIhLphL9e1JNq0aHu7mJ7PSrSooR9CrRxXQD6B3rQ6RFhEqHZSa5i8f4MouRXQxD9fSEEvG7UUVua2sTq0v28iVgemrbX5QJ2dnaw+XQZ82Zt74lRfHnGL9qiOO5fGUO8h9r0FrhF/kv1bn0Z9+/kXqBlMc2n/HbMUHf20ydoqWibq1JKW5Nf77PttFtuX6mdzuaZLZ0YHO2H37WLxPJ1TKrlPw8/Bv4wiPa6XWxGr2OCmiiL+pGtny7RBqrpG1vGzGw0v34a/CitTpTSDhQ9XshDTRtx34Joyx4rpxXc/SMY6aCMNms3Qxi6EEGLCJfaL5r2k2q5F4PknE0/m/JroNzyYOyjd17FsSrakOL7W237PYGYWn9rRSvyOqW0tXLt8vYMop82Y5TCt41UobotoQ2bqK9vdrBtNxbQtPVOTV1antOkgVL24HSLPsYBiDAm6VMo0RObj2PyMc3LLlG3T4q6LfXjUQSpD+tshFv09em0aPtWF3FfWwdKDIO2vGfHFk9FHhnK+/76Ums/FQ4gbUsOgyA3zhIOMYOh/lUq471kO1OedJrbyMynIvlog2z7U4wV4vdxZSyO8AcD0qfRUh9KbZambhihfiGCYJsXdaIu0Tg8kxb97vI8Zh4XqDHFtr9ZSh/nau9x0nhKtNVquvQWMaczo5T2PL/vSJaYplqK63PKm5uUkL55ZU+dCxRfnwtxGGWCnlHsWLTo/lg/VhN9k+kYMq8fnc/WWVe1yKFdi3GnHQXnDR4lT/LbDtP2JIuajwXkJYxL7dKv96OL6GqsNh+DKkSGLyLUJK55PoOxaasZXY5919Flp1Rn/E2NYtxsv69Onl5oXsw7pDmJmC9L/YPNvEOgDaddn1JO3S6qzKvzhd0Eln+YhLzloI62AYyeaUddRtTx6xPSl4zWZUlTP+XJVUH9TCXaDxHT4sZh+4yrTAnjUG07eT8ll49mD9w1qey+M6rN7bx5fSEkm9suk2tNbG43iHMXxLkaMd7O2tnW4sbgxcsIN5rsm0Dzv1Ad4svjov9WzjH7xsrmdvVf/vKX/1aO3wodPafgFQPU1y/nsPRUOakj1R7GKbroTRJzS3JJ6j41iLbGGqT3OnAy0oFGURmqyWjS9nM8WNmQrnFHRnBpOIJAYz1qRadeU1OL2jo3mlo70H3ch8xaFBt6Gzvtg7g4MohuGii5xPW18j3eQDeC7k288YgJW3UGqWcPoDwmF5b13DmZgCjkf8BgNylphD81NXAKP+vcXrR1hxFsSCH2dFMM+QM4cSaIZtFASh0oqlBDz6afaIrVOJuSbEH4dCe8DXVwpBYQzRrOJ3ox8GEHmoQ/buceVoQ/2ugG+4dwQsSz9nUcE4/oG7gWdJ9sE/F7jeRuJwY+6Ia3XoRBDG4o3PWeVnR0B1H/cglrmgpbfvxl5LIg0nazAb0f9KFNxKmWlMQiD+R7utG6+0Q8Q59pZnSg55RXpN9rJOZEeJWzeTQEEelpgnPvFeJzj6B8BSgjGrCRz4cRaW9CvQizU4S91lUHdxOlQRi+zCqieeFwI/LJJQyfDKCJ0qy2BjVO8be+Ac2i7HQ3prG2+qKEvC4j/N2nMNjmQc2bjWxd0BI4MYCgrz4n32nDif426bPomo1FPDHpxwcGz6OzqRZ7yUeYiloPEbPlIaXNRzk91HzsqH2JRzFtq6+mSxUynhBO9JBipEaq02821bbBjeDgKD4Z7EaLW/azWqnT3rZuhLuakI49wYsK1OmS01DFtNwodScURiviWHm+Iy7sxqlBkeauGvmr5apqqW7QtYWemY8bYTGA/WTgGHwN1L6JZ1F5ozLa3Iburg64Nh8hL4kLkY1rBm29p9DtFf4p9Y7C3XnMh92ndei/8AG6fZSGSlta34jWjqCok6uGOuntG8G/ncu1vWR3tNZVj0bR9h4PB1AjJiHPtGHzdeNkwI3qTAovPb0Y6m1Dg2gzsvWuuR3HT5jEyTSfcu2Yvu7sq0/QU9E2V1ByW5NrG/LaaaV9lfLseLe4b013nxlJNHb3i7LshsfpwPyyyUSoaxAfRnyiPu7g+bRIf/KyQP0InL2IP6j1My99O9EdqMHL5WfIZp/Bj2LrROntQDHjBSNJtITPoJNe5lRtYSG6qZyX6R0YRodoE2sbRLsYFeVMm9Rd/RjqoTR7jfiE3Dab9pNquX+d0LT9hcuvOeWUB/M+ur6huUA7XYhc+51292BArb9i7JBta+s2kOocwWhvAJ5s+OS63X2sHgkR/7wnif5tcPQTTdiqUZMdC4Q1faoGXVuvrUudQU27o5S92hqppEl236U+WJQ94zjGiGle+k9goKsZ9SJENd2ncSbolepzTY3a9ok60LqLJ+IGKcwlhCGvXmnHFqK8d3e48PJRTJN2xfSlpfopOIC0LTkMhnEW+av2U63YTdWgTdRJ5NWnwlQkH21o6T6JNnc1MqmXaOgdQl+gIdeHSnXjOE60i7gu69N7ABc+/RDhQJNc/pVxuNzvinauOYPVaH4YSmp/iZL7HoHuHjkP5HQJtuxi29kGKQss5nT57GNcl+070mg+fRYRvz5NaWyXEM/PLz1Giutz9j03KTV9s2VPOxcorj4X4uDLROlj0WL6Y9OxWnYM2Yq95ytYV7/uz/ajSex2DuCDbrnOSv2Pck930KSPscJy3vAKVcLP5vr8tsO0Pcmi5qNRXuq41C79tttPoF/UD7co1+uLT4yKZwxg8KNOMU7dQ3JpSjeGNaOcOirqz8kIOhrr4BST3iq8wvMHKzAkj4asXmhzF52nxbxDmpOo/YNo/zpF/y/avzVd+0eU2qfsr26XUOaTjejuF/neINKiah7Lz2UftATPfogIzQt2nmNKhI+eVLAsNfdi5DNxvVo/q+U2S51jBmo2sZw3uSqv/ShpHLbPuFIYSxqHZtvJDDzdJxAW8xcKV5XjDV4qY2a530X+vL4QHT045a0xuVYZi++q4wk/hj49h053FXY2nmG7rhOdnZqfeGA6FsNSxodwZyvauoJoqhHdSl0DWo+dwod9XfC697D1WISx6IaIKUSgvR2+5mbFlY+k3LazYXKQ8mKU221dEXQ1OoGd3IBVvq8abo8o3smneDg/h0fRVTyNPUMyJapL+3lcPNeBeloZ+/wR7k39ipu3F0Vn6EB9c5OYeHnQ2iZ3dik1fKJTH/2kFy0iPJntOB7dncAPN+8gur6L2mYf/P4W1IkGoUr4qa0E2cYxr2K40X/hU0SaRcnOpBB7cB+//nQTd2IvsOdqQYunFq4mP5rePED0xQs8fjCLubldtJ6gSS2twPoHrs/NSfG1Tt84GoOn4KPB2d46Flc13dipUzjrr5cHbSL59vIUuH6c6g+DkjXx5CfIehZ1Ul0DT2s9dpV0m5hawvM3taIQNYkGpBbeZo/oMFezHWb58Zfjp+ZpXbMXjq0o5qduY2zyDmLbLjSIZ7tFg+f2+bD3YBnr0h0y5umjUQ7PPzJREHjR+0k/OuvFpGPzCW6LBMh1XQGcv/ghOqgzoHJw/w5+/eUWFsVIylHvQ1ODS6RPIKvkl54/MIo/djfAkdlBfOkeJm6IuM6tYMvZhBZvA+qbWqS4rmxsYOWByFdtXv/tO1yfFfmvlG2ZMsIvNdS1ItmfSQMdffrkJluK/E0SnvYeNIsBUq3TqMSpqjqD04M+1FXtYH1u0jAQ0vuv5qOzsRH1e1uIzt/G7bFJkfeUfk1iMFCHel97dsAg3+9Tyl4tGhqr8XrjMR7MLGD56SrWYkmQiSr3mVGM9nghunskV+Zxa2oMt+6sIbFXA6+3Ca66RrSJ8r5BdVoOSsE6vSLqtMuiTmvTcCFqXKqiTcOcPFduqK1anruFiZ9vY0mUm6oGH5rdLrhbW1G/IcpNag1LVL/n6hGkfKYVy/9zFT+Lc+rgXsWsfLtPjeCTcJO8uprq6a8/4OadOSle9S2tYrBbjyZPDaKiXLwxuV9LlYhrn4hrtWgT3bsvsHT3pkijB1jfrUdra6MYtImO+XgrGjIku41fb97GyiulTorJg1vES9seoHsE/3Y2ABe1vXFqQyZF2/sw2/a6xYC4pa0VuwvyAFeKn69Lnpw46tDsdYkB3RLu3Zbb7I0dB+pE/rrFJKVZc5+ErqzL5CsHY2r8tX2CFK58/7V9gnbcYUz/ODxioNJSJ7e5S09fKecFJm1uPHu/ts39UWlzA/hYlJl2ta25NyWlb6G2RkZtG6idFm1O6ilmb5ONvjtYS+yhpsmLpro6NNB9L0T8X+nDn88LRyuOdzTAKeJTo3/JJ+jqP4MuUZbwMoofHihSs/rRdR6fnvaLcFH9nMWvEz/j9t05xNIiXX1iIC4Gqs11G1haU0qK6scrkXePKe/M68T4/LyQ51p8fTtwe+pnTN55iheiHWg2aQco/9rDfZrxwqoYL8xnxwtbr8TsqwDxxk6c8tWB9qnKnyT249SAqMuSYTfRuOyJ8Ycm4fy9/QhTRos0+0l5YWDaTxah3M6W3wJUVXUg3KctD6uiPJBdaPPyoCZ/Xh+dTop2+pZop2/JfXR1M7xN9aK/Fe10ZlW0K7neUY9cP9T2u0a0305sr/2GiV9E/q9si36+VZrQ1zYFcaxZyJ7OYvKWaLsX1rHrFO1ikwi5qxEN0E6G3KJLHcUJMeEwD5sLdY1tCIjy9Cim1r8gzv/xNPwucctmFLOTN/Hz7XuYj6XR0CLqkltMcEWcpHLxdEmUM1HW3EGpDNKq2v+5+rM4px3DyBjrf35equOXbF7WiOe4dvFcjAOmbt3E1EoCGZdPjHmcqHG3ipRS4qmEYV6EgdrfQmFo6L+ATyPNhn5PLe/17ma05ylb1fJj7EufxjblvvSU8POk6ucDSz/l/sM8bedir+FpEWMbq7R9bh6v/DAUjteqaF/U0hc4dwFDx9yi38u1MVP3Yngl+qimFj86GkSbTEoeTX0yyz8Vyzqpy8fbK+rYNT8frfwnWkLyWKG6TpTb2rRIo3u4Tf4trmNbTM99XjExrxdxbdnV9GFiLPGHQYjuSC7/s6KvEmOJe3Nr2K5tEu2puMfTBM+rBTzZVJ5favurH+cW6ns2xfgzqdwi7hm6MIRjbtHwKeGaGJ/C3bVXcDWIsW5bBxppPEUL9pU5nV36NIj2/A8m47pC7bmE0ndUC1mjOKu2Dw8o7B6lvfG2wLOxiKeW/d8aHom+YHa2ruA4jMKv9h/6ucmaOg4ynZvsI31N+oOqqhZ0WdTn1I5F/AqUiTVtmagX4zqLMjE5MYWHz18VHI809GvGonHrsahaj9U5gv38XQylXyxj7taEaG/k+bvH14x6lxt+MUddF2n0isqXOn6sEfGq19RZUceyfUytV9Rd3RxZYFo+S5k3xBeybYdZe5LzX22XdfKOj0Vc2w3jUjNdhToEtEu/9GYjOsLNqKsV8+xXD43K6zOn8UFLnUjcdczdfgK9WAuFv9Dcq6g6Wi/mgZkkVh/OYn45itWnMTxTv1YWmKW/HD+gRtTlBscrrM5O4pa+/xey/P7fvE+5PR3DRqZQX5lLy1Lqdmnzrxeoaj0u+iYn6oVfc9qBoqCqqgunz3RBdF1iXvADckNspSxpyhd9EThy8ayId+H66W4RYzZJ1yDfUVb7IcZhZ/44ip4ixmFyG7IBR2s4L675+RvEwJnuvLhWVTUUNw7VvmBQ20nq92teY2P5AX5bXBZl6yliL7dAy8eyyu289DMiha+DFtAWo9zuxIn+gEhLUTbrm9DUpPs1VGGTXtyIcf/qaw8Cfr+ou+2S4rtdzKVrRT2I/jaO21G19JmXfy0sLyy3Um5LUzM7GyYHLbcmgONi8EekxKDaQCqKye9vYmZRtqezGqcK5sbgqU7xrxA/uYnvfprGsnQ+hfjyNMYUO94OTxf6+nLh858Kwy8aVOzEMP3PMUwrn/1ticox+f11LIgegMbNRdE9iJBPJK8ozMsTYoKuflr0Iob5m1cxvUbVzwl/10DZ6XcvLofTLSaPckrJRFo8cuNLm3JWNaLllCIgGoJopIv3EqJD0vvvwO76DG6Myem2t7eF+MIkrolJBIUaDV4Elc09C1JG/B2vKE8nMb8mx+uFlGfifrpMDF78J6TTWazTx4nGcx9qdssVv49Gcek/L6K/xYkM7Zo7cU+jtBDpeLZfMiuA1CpuUjkQDbRUeuLL0gZqM3LhER1Gr3Q9PT8iyihVptTTSYzdVT8jE4Pue6JzXU0gtb6BREabOxoqHH499uVrC9PP5RC7mrtE95NP/ZlWeKncb8WxYPg8qrD/zupNLPxwDZPza0reL2Nm/CpuSkvDRd4f7wdZ0dffTxs03PhxCvN5NrIiGOoWgw9RnpOPRVm8NQ/R9wkSWJsX7h8WRPqKbGnsQr+o0yqF6jR1eMXWafv0k+XZcvNaTJB+vI4p5RO4LSo3P97AAgWwyoP2HrP9+Atj9nzPXgrrybQob9Ny+6boFyleE9PyANPR5EdY/LUNv/IXEPklwimnkWgrFyYw9khOL4cjjdh9IVuW45R4LOrkbWUgS+0B/ZXwY+hkALS/dzqutiE0JFfa3n9OI0ZVn/ZQGDbZQ4FmxRvzIv9zbfazR7k2m+4Lni1Qjwog+6/rE5S2Te+/2idoMUu/+0p9oTa3XiM3a3Oz9+e1ufIpKjNk2SLb1ijpW6ityccBx7a47183s21lYm1eajslEwHivvYTIfv8X15BgvKkygv/GflcjhCCzZSbGWw8lU2C6cn672+U8j0jInftVu6zzBeLkxhfjCO5uY6EmLAZsA6eLvzGdkBuHxKIFWgH8u6XxgsTeeMFy/S5F5f7HXdjfp93wgfS92e25U/YG3398nkJN4JNchlNrC/Zpr8ddvfnxGp5mLAsD1nUPvrNBualdjqW66NvCfcKlUTRTodOSe10IfLD50D62TSu3lTyX7T50zfuy3XeIcKXWMbYTaXtpv5EPGdpg1LQAU8LtVYKJ4YQ8irjBwqLLmzXHyZEuot7gv04mX2+X0wqxR8RnyURZ/VT1r0Xi5i8uYR4Mon1RErcVRr26a+TO3axPnNDGgdQGuy9WBNjnjEsKPH0BrRlRciVv+aEcDbkE3eJ+jd3Pa/fk8r7rShSwgOnP4z+NumGPPR9qXxvCIPHtX7OWfopx888bfFiyT5tTSOoD0PheJ3yqx70oq9Tbl8TD+keuYzt7b3A8t0xXL0TQ5reQumwy7+C6PJRHrsa89HWf1UsgrYxfwM3VP9E+adw35iJS4oWpz+IQbWNaXAjlUxg5/WGPIbKfk6fEPfcUOqMC97OYO75Jba/hnFuob4nclK6XkJ0jl0e4UcmkQ3XFj2f6vkYjevTcIh2UYt1+kSkVbba9jyvHBQY12Wp2kZU0z5IYb8xCVl/6YYv5LfNn1Lk+rmJNA4qMDfZV/qaoA2eWX22DH+BMpHQlgmN8t8szJS/VuORhoxmLErjKin/KJ7GsagBm/k7vfC78cOU0t7IY8gbP8plAp52RLp08dfXWaWPuS91QMJnb/4cmTBLv5LmDVbpL7AuX26c7euQwqQfl5rpKgyYpp9gaxpxSWNNbcQx6VQOkb6tXulIUvzZhv9EwblXUXVUxIXq6MTMkhTG6BO1LZOxSh+HYxurE1cxYTo2Ef1/RwTd2fvN+xTqH4rpK0up26XOv5ajoi2nC8S5AfqrYa8rCC9VUtG3rt6Tz+WhSR8/fQ1L14o55sxP+vr5XU6vEh7Ojtm0yVty+yHGYd1N1N7bj8PUFkEf1zz/u7sMcd3rOlvcOFTRF+RRlUZc1Pcbd+blsrWi3CtYGLuCb765grEFi/gJpPDdviZfazBfsoCxK99oTJwp7m8K/K6I8YFyZWpJlDlx7dWxCWkjywnRP35zRcRnSVv6bdJfwHJreSFyI50jCNkzGv58WO5sxWBqVQwC8xGNWHQSMcWVpaEXfmq7RWVZ/tUgFQ3/AuafKY1jq7oVn2iIfHK3l3giGivpSEsK98VgQO4i7Yl0+aRBxY5o7Iw2eUQDcE8Udtq99UHM0NmWzFIC0ov/Bq9mABEU4xpaOriJlWWlAWrRbDsY9sJDf7fEIFUZ8OTYwcbjfCP5Eo/j2KRRuIiZu106U5By4p9YM8lT0VknpHA6UdcknSkSF3yi8whqf8f88NAb+Z0E1lZWsJoXfzd626jwiLL1eMIYDpEqCw/kztnR6BdD83zq6v2G+MQmr+Pqj+O4r2vUiqPU8O+Te6vYoAWMLi+6uuVTMm5EWpSBkJjAmFtvMye9Hs23N6UQmxEdELVXDT6EDYMMMah7YFL2+jrRIoqz8BTLt83q9H0sxal2OuDrUIcPla3T1qjlRjwrKgbjhniLZ925KXVy9x5ll0Htm9jcJMauXcHVm8ZY4dkGaPGL9ImtfKYoMomYIb9StAqEDtKbiOtthKnP0dIWgl9qWJJYvWOSjyIXpp/Iww9q341q/hTWZmeM94k2e+qpnG7etl5DHbNlX32CBRVpc8tra6T7Vszui4kBlKwwcfnyN5s1J5pVVnlbB/PTtrtTGYyKtmZOPmWHw+VBQDlWSc2O4dr3Y5icM0n7UthXO6BSYLxgyZLS73jg1czOg36y0gpsPhGTH9FuOjw+TTqH4ZWUVEkkFo014OAorTyofXRqbQYzZu20mAxKfUIxL7OziEnfor5NWsa20k4kX04b6vZMUjkj2iuV3s4WKX3T68um44fUvSVI2VzjQ6f+hUyNi/Qe+WzNiPbyGsZowqucOjB2RBuzoI9lCjMv5XMOJ6V6kZwIwkeXb61iRrMjfxYxVoq+IE2PG96gvlUs0JeW42el0nY/YTjVDlpAJgaVWLpncs/jaayqM9tKUMl8JFJrpnFNLUxhlToTernYq8R1awn3x6/ju3/cMB1DLdPye4Gjmt445FNc+7u/vqc/QEouygIRPrNwiXGkNLYrlrLac8HLVUwa2ocY4ltyg+OslcdklaL4uUm5fbsZBepzERxkmdj/WLRAf6yO1fYSiJqNIUWZmJoQc8db97CoH0qb1llRLp8p5i9dbnRKZ6w4xHlDWePSAumncP/phrhCRLm5C5pX2uKZEfikcpoS+VzEjK7MOpp5ETWpo8WR2YhiwuTe2C3R/8sDGgRVpXNZfWVpeoeSy/yK8mKJ2njd4pzQMa80Dsu8XNPYnzbDj1CLNINAcs1sjqlpf8lWfJd8Lkfp7ce+xmElxrWscWgqjnmT+n6USMVX8188MYfCkVFue7q/xtdf5/8ujw4gKCkLdhCfmTAZTO1iVx7D5NPVKJovwc42UnrFoPJzpdNSw++skxsLWiHupk8ARdVPFpqQPk4gVfgLZg1++NxSk4DNuEnjR2zFsWryFnNfbM1jXRoca1Znt3XCQy3Gyw0sza5jUzR4TjEgUZVJ/b5G6W8qYbahSBrbJit0i6ec+KeR1nzlXz47YsIvv6XM/VYR3xQtssuLzr5hXByNyOVFIiSvUMI2tlPmZSfoEmGUC4+srBIsrCWkwZPD14tLX1zEyNledPqNHWjplBr+/TKPtZfSUAjeY5qhkDoQEgPN2Gwpqu0Mkuum20mIvF+ArM9ww60fcYv70vlfbUn4abMf8TeT3DDfpEKQHcDWqwrHStZpO9RyY/GsFzE5/5SVARVDDGQ6u8Lo/0Be1X/xshgwZ0dVxbNr2piWCNknpL+pzYIb2KXuKoph+rRUOqMhLQbcJvlPpMTgQJpQFDVB0bGvPsGCirS5+2trcqSwOasc6nkoyjUNck3vMxJ/LAbkdNDkQ0QzgFQHozticGs98BZE15GkZ7o7MfLlJYx+2I9we2WVC/trB1QKjBcsSWF+Q55B51Zni/omwiGvwJ/BOpVzZyP86mTiVAsapS9dxOS8QB04GEopD7k+emfbZV72ukTZk9pG+5fZOczb79Lww0sbEVr1IVgWfaCcme5GNZejcp0UNb3z4y9xaXQY/T0BVLYEFkF6W4TEhOS2XC5LwO9zy5PL19twmeZRUNpkjXDV61vFAn3pvvysbNruJwxB5eu49GbcROFEpLC6VaDv3Q8VzEcindoosDgghfiW1LOZ5KGMt12kSe+g9OXeyKeXcIE22NdTUvu7n74nCC+ZIxGxLzyuX0WqhPF7ee25CElaTjc9S9vm58ujlLlJuX27GftoWw+8TOgoaSxqM39PJU0VeERiTZ4LySuINRSqsyVxiPOGssalNuOZuTXpxbs0T9QsWHL3+OQvcRMxzBRRnsqto+XMK1LJGeVIzwISUtbklM7l9ZWl1G0dRZV50We9kMuStyVf39AprR7ZQXzZboTth5supXFeoc00s8p4Mk8indFQavux33FYKXEtcxyaSZe04I75/UAlF3qbJoftlshkkHmj+4mBS1IMou7e+CfGNG9nbP2vd8oRa+jMN+eg/R2XVyAQ8v3qBn8Z7KYK+Z/GLg0UbKiq8sKpNm7ietvw6tx67O9/hai0mkReKUhyd1DuNJMvF/Cq6pHc4Lk86Gyj6yPw0aeFopFMPI4b/NNjJ9dTdvzt5LJF2yzW/omB+O1b0opZ9Xfr9q8Y+/47XJsWA3ERPqe/H8PZz6lccEqfVYpJnFm5kX4h0BfTKtLzHo5hcjGBtPDPIQYTgZ5+nB+9jK+/+hKXPx1GWGMWqLT4lhp+cb/yV8X4POWvgiqfX10XTxMp4O0U3ZCMu1d0cCR+KQY2ykBT75/Bf4ncwMt4/atsPaoW5cQYPqP/3lq5QNFAqeDzHyrKT+GU1wBq6rRmkJx/v7FOF/Rfwdytlhur9kNG79ZT3PPcCJ4ZwaX//Apf/2kU5z8cRO9xMRA4JvJLGizksPVP+auil+uxlKttr2bQYXx+MruSkzD4Z7heca8oSgWbyaDRO3GixD5BizH85E7l2lxlRU2uzX0opMqKX9HmHgvQ9bk29+WKali49LaGMI2fBtm9kxsQ9pjJc0juZzOI0TsX0Xb7elTtdgRBaSnMDhJPcsoM8+cJnk1h/LeYPOlzeuDv6sXgJxfx9X99iS8+H8GAslpGf7+xPVIOFLTXa9sBFUN4xIRU2w4UDK+CnfvVk6TINZHTyursKo9iXkZZgf/oJWn9aEWr/Kom0kqmE0ROJ+QvXfT+iRPKgTnGy63DV9z1+vKg9tFO+CJm5Y5+AwhIyo587MKjD5Dd9bq7hVwNm9yHFLp/4ZWiwHKoK77jmPrlPmKpjLjICY8/iN6zI7j49df48j8uYeSM/Em6Ibw6bMNr49ZjK1f+qmivV8u70xcxyR/5NyB9p5xD/zj9872u0vyU7y8ubQlDfE3CU1K8lPvrqpWG0MQ/lfguqYHy0YfHED4dhuAb7lcOFGz9L+L66Gu5PXOK/iIrbw5j+LMv8OV/fY2Ln4g06Q9Lk/1AiwcujRfZ60tqf/fT99SRZSEZzZgpPz5xWfmpIV+e734b7bmdW5xQDmT259/++nYzdN6XFh5RJn6ZsS4Tuev3Ox5pKHosStiGXx2r7e1K/a7t9SW6xQnlQMZ4ff68wdw/zbxB778Ow/O17GNcah6eHDn3PFalHR61C5Y0X5iuy6t47fxT+wu1jpper5176eTFtH9ainXvqIrquh7p7376SjP0z9Mjy0uff8VnlS+WtQtI1NXmOwmsKl/GGp6fdefmmFoltf765I7SliqFxuhd/onC7vxxmIr++ofqOEz58o7keXH1KNebxDX3jOLHocWHX4bd77e7EHIforNpcthuIhn9Ft/+Xfe78h2ujU1hUbKpmcPWf+UvfaKUv+LV5Le6YQxPEeG1wuidtX92/hdzf3w5IXVUtFLwmHCHm0j9I7/hI5vKsiLGjUYxmNkL+kEL4rGTxKpoJEt9vh36y4sJfx52cu3IWrBf/1JLE5h/Rg2z0Q6mSByTFdP636q0sZ3qX+zudVz523WMzyxhNS7y47UY5TuccLcEMfjpJQwpbx2LDV+WAvJC4dfdbeKf8lchK38YxQZ5l/3USwyEfFSOMthYy31OrvfP4L8Ou+sN95d4fdbdpgwUbTA8T0fRz1Mw+Ffq9TqKeV7o4wsYPhGAp9aBzHYS8SeiPC7OYPLWOK5/c1/+ZE/B1j/lr4persdOrsf4fHXyIGPwz3C9Tp7elld+F8DoneZEkX2CFmP4ZXe2zfXIq7NzbS5ptVXltxueY/VAV67NfRLTBbCEtoawjJ8g353Gju555tensLAuf1HgbTspQi0Q5csrhTmBFY1JGqvnpRbGcfXvVzE2NY/l2AaS27TqyAlXYwCR4YsYjYj+R3e/sT1SDhQM1+swhMef3w5YhZewdccW5VVCyursPTHZlHL6pbwCfyu6Kf2V97wIwu+REg3JNfn1jt4/cUI5MMd4uU34SrpeXx4ySMbMylv+L6bZY9guPPoA2V2vu1t/e8H7/aqyUyveWsD41W9xVYwb5x/FsJHckZRttOotcEL+yklrI98M2/DauPXYypW/KmbXZ5LKVz9WP+ntFN0v/cli8E9xFutn9v4i0pbajULPU9HKiwqD/ksnC//M0Mvtr1cOFIz3KwcKtv6XcH1a9A+Su2EAFz8dlPY7cIo6m1qndFrG/F3FdudKrgfU3l96+1ta32OGPj569PKSr690e27jFieUA5ny/KtE+ioHCqWGZ+uBdZn4wwlpmbKG0sLcff7ToseihG34lb8qpcbXzi1OKAcyxuuVA4VS/dNjuN6MEsalduHRuhdWNkRu0pROMUfW0AvJssWbDTFfpVGLvX9FtV/audd+7tdQqju9k28vo5S+0gy9/3pIvq/5l+g/N8gOOpnrUMxPRdq9oiaK7H+xkv0ayfD8IsKjxZl92S9j9C7/hJ1bj17eqo7DFCS5Nq4n5fbFLK45ih+Hlhp+dr/f7kLkl8r3hZfKCr/dbSxrVrya/mbUaqauJnTBrduwMEuDFyYvok1YQEoyiUefzKjrWQ6YZ6sQcw0RfFqd3Y8WejOoKK+J+FpS6uQ8TWH42z0ilnIjU/4nXGa8hfjvk2XlLWfOfqJaDnaxvWJSXvJ+MyaNNG2yMS0mINdx9R/f4psfpuXNbXSbsFQKY/jLYRkrL6iUOOFtF0MhUY4k+8lkb7fQ5+4FccKtbARrJAw3bdYiaum2RmlixYLy2S5NpAuWqIBblqXTyqYSlazTdiSw/Zr+WjyrYvQj5Kf8ziCxcB3f/vMaxn4V5fEubaqR21DjrZBQ2l6XO2uT0UCXSHNagfAmLbVJedSISZdyaOCU8glnJgPZ4mgJ7KtPsKHsNrfctka0rwa7egptPrlc74k6pvlqwYrU3Zhs287Tgl4RF9XeHm2SU1o/kUJ8eQZT4zdw7Z9X8O21SSxIK4gKbApTAvtrB8oljlUyAyVykFZny+ZlcsrrbDkg++uqaRp6IVCWaa/9UEp5EH20nJRIvzArb9rfNBZK+py1XIobPwQaZFn6tTGXaYOlmTvjuHHtO1z59iomH8pmwwptJnVUya5OTydM8iX/N71Q3Ae65fpZibTdTxjUe6zqfqS+EuOgg8FpMUbr98gxyoj+ifD3d8orZFOrmCTbrj+Oi7SYkjaOI9udbtp4uSDFtL/76XvUNsOqXkaUT+eL4+2054dBuX17pTmoMnEAY9FixpAHyiHOGw5iXKrlsbJZuNOLgIiL/5RfejFPNo8LGfzQ8zbrqLPWuCuPjGrOQjzytaykP4i+0pz9lvkUptdkO+geH+0b1IvOZmmEjbho1+0pZo6pmo7KVMC8aznjsGLjepTHocy7zPup3H64gSRN0t0tiORtjpfDHe5Hf0i7+V/OhpOk2DPBf0pMEJVjO+RVe6QnKDCZp5Uun1/CpZEB+JRT5RFVFJNuNJ70wVMjGovNeE4psaJsBtkgJudSQ0V280pTWZTC4cd/f4SUT67IBI7MAjaS1CS70XJC/ZRLR4OYxPWH4Fc/LRK4/SH0n+01bNqCF0uYeC6vsnHUFlt6iscY/hxOt88k7SMISMsxzYkuyjbQnd4A+rvl8p5+sWpvb9cEt69A3ne3o5GU23spMTiQT9myKq+SRUMLek0n0LkdwHc2VyVzAJWo08Wn4RLiUrlxoqXT/CWG/8woLokyf/FDvVW6UlE/TxPpd99kKNXuk1cJvw1oB37pE1h5MG1GKCjbccbWunGATW/6dZuQyLjR3yZ/npkR95Xccu2rT7Cj3DZ3f21NDhe8QfP7AiEx+aKDVLLghkNGZhS7+x609PajnXZJF2WsqE2HiOYAes8OIKwP61YU96OyAgy1IszSyX2yr3agfKJP5B3g3U29snkZ2mA1q7yOynYHqxpFeyK/gMkrB4dGaeVBNavja9dtIppF5Ge/6NM0JrUOi+z4QUyMTPuQhkH4JVubmpcMomQFegcxENbHJoXovSgSUgGkFYvSyXcD5asAh6hbg6ZtgMilvn70lmLbfl9+Vjht9xMGdc+FgnVfnUAfUZr85nFtEOOkRlkhkXohtxrqJ/Zkp9tot9+NsMfEXlBJ7e/++h7VNnjBeqluPlcsb6k9P3jK7dsrxIGXiQMYi2bHkC3o1JhbzOHHwB/E3PHzixiWLVJUmHLnDU4x9zGpHeqXcFoOZFyqRd0snOLSj1Az+ZLG+moJM7q3WEcNm2GqtIchr51KIZkd0BxAX2lKGWV+VrGDTgtI1A2StzawXJTSNjfHLFQu0d0lbwC/t4n1khekGdnfOEyhyLge5XEo8+4iKbftbJgctNyO0v2fwfJzGoS5EBgYRV+zTt7ci2FRWXqHRjE67M/ef38lLnX2Tn+v9PmeiiRvH8JgZ/HdS3x2FQlqhxo6MfhhV36lbQhiOByEt9EjmqkkXhjC74Z3QPd8C1R5NC5v6uDx+4W/uU0AZPkCYjQDqfGKyblwphOIPZTERftfCuXFP5+yw2cqJ5tZFxQbXBkkYjk128wjeSdsV2AAo31enf9e9J4THWTvEEZHhqUGn2yYD50fQm9PH86c06u3vTgjGWgVT1HeMOdDeW0zui0x/NlJoLtdpH0QDer9It0HLxo3u8iL37NlbNBkSgwsI1J538FGVCkoBSiY/lLea55PNEQwOhAQNVMUwedigC3+FpW/z2bEGEsUqCoPus4O5+2aTPLAuRGEaMyyl8Sa5s1woTotYVWn89JQU35N0lAN//yyXG4cLSKOotyoSHLR5vR3++ERZR5pE3Wjs1F07sqxDmP6aFaWiDjlyaluDbRL6ativD8fa2mpzCttrxP+3lH0igGJ9vnuyKhSZtOIr1DuG8PnPT6SNeFDkNzbN4Swj7qrYjZeyUf2P79PoHCpSHJdn6BFHz4thdpcmcJtroq+rcnH2Nboke47md9+uIPDONNBJVa0C2szeG4RfkIbP9XuvicYkQejiRhm48Xc78bQhyPo74mg/4P8DW4lm5yt8mY/eL1jXNlSyHulTuSlv0k7oJWbtQNW+UcUJVdfVHj8Ut3Xbq5EcnlDYQe8ckaLdM+VAzv/7bAPn3IgkMqDrp1r6MovD+oUJD6j9NHeEEZEO51/l+hfPhxEf28/RkYvwOqbo6LSzwIzaXb84Okyjh/EZGfok5C8F0RyDQ+jsg/uc8MY6Q8jcmoob1NUer47qKxcF3mzY/K1kNPjM76YVig7fkXG3zQMz+7jKb1wqvIi9El+v0dQXR/sE33LJxdwwVQRZPJ84adch2Q/u1SbmAp6P6X0K5C2RENXi03atpjGSxsG63gp4VtZwBqtE5Dq/lDWTzl+bkRGI/BJSod87NLfDtv8s/NfFUtxzYVbxou+obAc7p04lufks6vqKvV6L9p1/lNfGGqWpm4SavxLbX/30/dEF9fkDQqpXipj3Wz8aWzX45MnlRos00e053L5No7riELjOi3l5k+e3GQctl//y+3bVWwebxG+wmWCZMWWiZz/ZmHOH4vmYTIWNcMYfnUM6YCvxzh/9/b1I9TmgUcZSu83f1TM5HnzBt34yjBv0NyvvvxxdwxiWGPj3h0cxMU+GiPqKTwulbAYl6rYxe/5irxZuFPMSzopKmQCRTMOtU2f+GzBuRfRbldHrb23fr4rgIE/6squUq7oXCaxmhufFuhTVP+L6SuLo5z5l2oHXaTlSXmxTiI2a/1CQHO/OseUyqW+fprMrYm8x5tglf754zD9GDF/HLZg+FLRLK658afK89mnluPQLptxqFX4CZa/3/JCVP/lL3/5b+X4rdDRcwreWtG/vpzD0lPlZBHI92WQevYAj00MliVEG9t0vBWNrgb4Qz3oPtaBgK8TPf39GDjZiQYyS7QTw/yNeazLtwAb69hr7UabGKm7207gRHcArc0BdJ86i6GeVrhexZGocqOuWn6u6DMktGFRz5HF/63aDnT46lHf1IlwOIh2fws6w/04039cNqz/Oo65iXtYlz41ITbg8p9AW0MN6lpOoDfSi77OOsw9yrcpVZANF9pOtMFNcdt7iSdjj6B9SbbR0IHe1nqpr8kkljH+OBtzhRZ0n6T7XyMxtwTjU1X5wcTf1B8NpZWVDvSc8qJW/Nd44iT6evvyf/0RHGuplzaoSa8/wJ3b8opliZei8HjDaPXUosF/HD1dx9ARkMPef6YXnR5x194OYvM3MC+FcwPb7qBkH9Hl7VLKjR/txyMYONuPQINI8UwSj+/+irXsKhxtXvdk83p+WVV8lhP+HXiOBeF1OVAr0r7n5En0nhT3nDgGX+0rxBNisFVXjUzqGR4YEjqFhCgnJ0S+SW3KzjpmJ1dQpPUQCTmf0kjEt+HpOI5w6Bj8vhYETwxg8PQxNNJETuT97M072JAGCYRd2UvjWTyD1mAbGuqb0Hk8jGDAj5ZjPeg/PYgTrS5RrjNIrtzGzw81S50K1OlQv3Wd1qfhiV4xwCyUhmo90pWbE90daGtpQ1fkNM72yW1OJrmMO7+sIBfCXXil5zjReEw8I9KHUEsai1H9NvBaNuBoCaNDPMfdFsbxzlb4fKKdiih1q/oVUrtOOJW0tK8qPTglKpZpefB142TAjepMCs8erGTtLMqY51kiuon64DF466ntPYGu9ja0tgl/Bs7i9LFGqczuPJvFzekNaQIhoT4nGUMMPhwT93Wr950+g1Od8n3p+Bxu3tPcZxr2/HCp8c/vE4T/Sp8QPnXK0Cfoa0VBym1zDWUmaNPWEErbkI4jlmxAW1cY4WC7qGOirou06j/ukzYcyyQf68paEWw40BruQIOzSgpzYvVXPIhlUzuHNt2lOKWx5mjFCZGHTneb1N77m1sQOBYW8TiN4830qUYa8Qe/Yk6t9AXLXX6dOCnaxlydMLYDx9qUdmCgcDtg17fYI9pr0X60SRktsm31JzzKy2gPOiKtqKcR/JuXWL65nBtXKJiGQS33rxOYW1JrkHn5LYyuPHT3FFceTProTtGmtnWfxKmBUzjeQnkm0vLxFH5dM3sxq8W6/bbsu83KgQjbs0wrutsajOOHsyfQWidFCMtTP+OhYn44/bQarT0iDLVutFH/4Pehpe2YqN9nMXC8GbXilnR8Hr/OatqPXS+Cx0TaORvRJbXxIbTsLsKy+RVo8zI7/jTNSw2F5JZhEOU95URHewvq1X6vIwA/tYv9Azgl8pbildl8jKlf15QxgF350ft53NbPgmkr+tHTIbu0FZPbsuL1VIlXCmuv6hHs9MLl8op+VdzT5oPv2AkxzhrAscYM4vEduEnTXigPdBRfJzXYyXW0dJ2U2o1kTIztfMdw/EQ3OtpapXp2+uwpuY0X6RGfu4l7StuY3nSj43gL6lxNCPZ0IdDqg78jjF7Rh1JfuJPagdNZU177u5++JyXKgxjrHvPKY13aRK21mfZoUcZ2e3HEt92S2bnixukW4zqL9jyvzTDMYwR2cgPW4zC7/sO0fdtP+hbRH9iXOBWLMiHqS9FlQtQxq7mPdiwaPua3HIuqYTdtPzUY5u+aMeSpzgYxFqQycUeMp5QyUWydNR3HmpA3b+gR6WAxb4jlysTma4/c5jlq0dQpz+F7T/YiEhTtmbjnpclco9C41ExXoYbbLv3y2EqIedgJNNfLY7qd57OYFGP04rGuoz3F1FGzSmOBHD9RNteSaBD53qPt/yX9AXUOYk49fROPs9sPmPcprf4ui74yPy2Lq9vlzb82qloR7miAkybYewk8vfUA5kNsJVya8kX1c1Nqf+tzc0xfm2gfxByzX5lb74i59UQpc2sLShiHLWqyXqWouL7exzhUU99nF807mcjol7jw4Wn0BRuwvriazW8jfgz/6TLOi774ROtuyWWVeXsE2tvhazZf0q9/yf4esYCJn25Ln+TQ5hnuRh8CXQH4GulttWgw4guY/HE8u/pKJoWFsTFMrySR3oNkkiDQ1Ql/owt4FcPMZEw0n8UTv3cdY3ejSIqbHC4PfAFlp3OR6unNKKbHx7CgaxAWfp7EkvS2S9xT44CjwVOC3TH1szKB6NCW5KMcs+vYlGyxZ5AQE5GDZj/xPwikdNT/RBqkt5OIPZzE9R+NbxMXbt7A7YdxaZdxpzsXdpo3ZVJxLNy6gXHN2+/4nXHcXtrAjkh+udwExQDAJw3ypetvj2NK16sUm9eGsNPPMvxxTN28h6i01FDgUO5JizIhwhGjT4UsSC1uyDsd03HJ9nZzpNfGMUOdUZ0HfjFxCLSIuif6uH3n/dYCxsan5Xg5XPC0BKQ09tRRgUpidfYGrt3Wr4reb53eXxpmy41UDrwi3uJZzRRvuc25/cuUzkwEPWcGMbqhSn6G29Oie3ttZPkXkbYxatsccDX60SnKm1S3dkX47ixZdOSHQQxTv2ja3mYRPnUX8cwONpYmcePnhQJh3Mb4lGjHXuvu20uLwfM0ro8Vuq8Y9tMn2FF+m1tqW6Ml/us9rIp65KJVnxQXjwuOPTkuN6/py1oxqHb3BZpNh4ri4RjGZlez7T3Vedo9Xqqfr5OI3hXyhWL8s6kT+2oHymdhPSlykUgiYcjoGawr7VlmM74vM06VoNTykO2jX8t55vV3otPvhYc6aSnPRFresVxXdGCkFkR50YQtO34QE7h0chUzP17T9alULmawKhdAeERcgkpb7xCtPLUfY/r249kUxmdiUt2T23g3PNKn24eIXRie3cf1nzXlXW0XpbyV43Xj+yndGMCGkv08gLTdT7zWhJ+3FxCntqFGCQeNs5xpUSenMfVKaYuPIjtijLggxogiHdR6Rm28VM+mRT3Uto1b9zFxe0k29+J0S2Wf2lKvuxqp2AwmnpNAxz7a3/30PTEx/lHHOA7t2O6N6P+mp5BSxo5F85ba83z2Nw6zo5y+vSIcQpk4mLGoZqxWpR9DphB/eFu0AwdZJvY5b6A2757cZxHSnEH80i+WxNi/0FzjIMalWnKbhdNxcfaddbytOhqfwL0nogRp+3/xSKkcTuj7f8FB9JUmlFXmVTvogsyLGKZLnAdn21+pfubPMXfWl0RZqaxepfRxmIYi43qg49AGf0GTPxLdkYM1DcW8Far++te/ljoUeQfxis6CTGAQ1MBZGf1X0dwjGm/Dju0l4m0PwiMHQHgXRcxmZRBVyE6fC7vFXPsOUHL8jxClhd0tOhuf1AETbz2vFb+xvSFtRFQc/bjw5174alKI/nwVk0XZA8sxdOlrhDxp0WFdwRgNgpvFQEhJwIrlvcZP4WmR9XOfdXpfaagtB2LQsbGKuN2AQ4pTdXHXqihhkx+zg40nYtAjCY4IZYQvV++KbbNLYT99wsGz33bSLQaDPppoFFvWDhhtPDIl1RsddnViX+3A+89+ykPunjLz7AAouV5oy0WR7Y70jOq324bahuEg2vtS/TyItN1HvI5am1c8pY1DSi77Au09xdbl0p+jGeNUqiwehfbcrs/ZJ/vJx0py4GXioMaiB+Vv0ZjV1whGvxyA36mZ5+iQ2yeUWI4OaFx66gK+6vPBsRXF2L8my1Puvo06qikDRY9NDqPcvNWyuY85ZpkcRhtWqXEordweUOyHZtZn8O2P5ktOej/9Cv0t8vPS8fu4Mrb/10jM4XJ28AP0hI8rrnwk5TbZNNnby+m42c1udv9+3Q1nL+JPPV4guYyr16bwqsT79crtUp/Pbnazm90q7GY3u9mtwm52s5vdKm/HfSKn3L7/D4wtVNr/SrobcPazPyFMU7rHV/H91KsK+89udh89t6zcTiPx0g1vYwJL165LK8fzrm8YxMVLYXhfJpDyeuGM38c/fn6YlbuDAxjsow10nXCQBZZ0CuuPZzF1byX7EmPws68Qqo1jZiaF4NkQvLUOYC8DaVX7zUk8SmnD50XPh0PobW+Eyymuy6Sx8zKGuWn6ip/k/bjwnxF4d1Yx/r38Eiob3oYhXLzYBffLBVz5MbfHWrHpofI+ua2U29LrCu2NBLvZrYXdvwM3vYFu98MfGsYnx2mjigw2nsxLDXip/ukp9X52s1sLu9mthd3s1sJudmthN7u1sJvdWirtFieUA5lK+79fN62ADfj96D73ibzh45sNrM6nKua/CrvZreVouTNIrG4gU+VF4JS8IatW7j8VgLcqg42nCXGlTFbePoSRcxEE3BlsPosiuhLDZqYO/hMf4uJobpNTyfSQ04vIuRDqUnFx3SriqV04G4M489EA6rPPo02vR3G2ywvHzjpWV6JYXd+BwxvE4MioslH3jLT63tHoQ6hNuikbHr8Iv1c8K/Uip9gmrOP//rsLIa/FZxjm9014AOc/GcXoUBAe0SqkXyxiZvbwPrBiGIZhGIZhGIZh9k944DxGRkcx1OUB7YuQWJ7BzAGbrWCYI8fcMuI79LIngpBySiaEiN8tbcC5PKecyuLH8JkQPFUpRCe+w/XxSUzeGsf1725gKZGBU/g1qLXj7XRh98lNfPfDuLhuQjL9EyUltbcdvao9775h9PudkumT69fGMHFrEhNj13D9tzjStX5Ezsqhm4mRot0NX7esjJfxI0T7ldALqnvKKcYSVm4zDINgdQbJVAqpzQTij6Yx9sP9fdtly7zJiF8ameJesDEMwzAMwzAMwxxxdpFJH+V5ThDO3SRSYk6XfBHH8t0xXL9X7jaKDPMusixt1gqXH6E+5RRB5kZckDZsXVZOZWkLw9cApJ8vYTJvs8wEph/GsQMXvJ1B5ZxgL4HVW9oNVWOY2aDFgS642+Uz/QEvHG82sKTbeDu1MA8peN5OUWsFc6tYTwNuXxhZ9XZbSApP5uXaW9us/l1DUm6TDRMrWM5yK1j+7sujd27g2tWruPr9dYzdWcrbxKRU/6d/+Bbf/v0qxhdldyXCZwXLWW4Fy1luBctZbgXLWW4Fy1luBcvfR/kSxq/K85xflo5i+KKY+vEaroo53fc3fsbUYuFNH99O+HKwnOVWlCsXFyA+G0NizwFvoF85CfS2eeHYSyA291w5o6HRKW3quvNqwej/ShybacDl1qysfpPGjnKoktpTDJ0o90s2tmt8iPz5K3yV9xtBe724wOFAnXTlAqIbtNTci3CbHD9/yAe3eEJ82ajaPuj0O+ryQkjKbTsbJixnuRUsZ7kVLGe5FSxnuRUsZ7kVLGe5FSxnuRUsZ7kVLGe5FSw/2nJxAbA1jdWNDBzNAQySmZCGQXT6HMhsrGI6uU//i9W5au/fS2P7VQop/S+ZRHIzhW3lsuVoAjtwo6UnKG5vRZhMkuwksPpYuUDD207fty0vBJslYRiGYRiGYRiGYRiGYRjmvWB+OY4dZWNJeSNJ85XQEq8z0gaTzlqN6RGVBi/cTiC9nVROFEd6V/yzm0T0+2u4Zvb7cQpR+VJgZUHaWNLV3IVgVwQtbiD13MR8ClMQVm6/z7QNYPTzS7g40g+fcup9IXTuAi59fhHDJ5QTDMMwDMMwDMMwDMMwDPNYVhi7/QMYoI0kt+JYMFkJLbGyisQO4GoJIaJuCKkQ6CcTIRkkny8oZ4pjaT0B1HgROCOenUcIQxcuYKRfu91lHMsvJEPc6Al74UIKG8tsM78UTG1us/t9cLsxcLoH/kYndlZnsSGLJCrjf4634V6eT2DX40Xw5AhCFfBPC7vZrYXd7NbCbnZrYTe7tbCb3VrYzW4t7Ga3FnazWwu7K+sWJ5SDOGbWk5LC2EsbSb5YFmcK3b+M6WgCGacf/X8cxWBPEMGuMPpHLuPjY25kkiuYm5Ov11Po+am7S4jtOOA9cRGXPu5HuEv42dOPjz8fQMjnhbtONkqi3i/bCXfB5xOBTcQw80w6XSC8OX5v7kJU/fWvf92fQZMK4+0ZxtDJABpdTjgo7Jk0kuurWPxtCksv5GuYEugewRfnAnAllnD1+rSyO+sgLv45hEbp2IbkMr79YVpxFIMXof5ehLv9uTzcyyC9k0JibRH3dZsUqoQ/uYwzrbIZ/Xx2kd7eQSq5iuX5GSyblIHQyBcYCriQWLyK63e1+88yDMMwDMMwDMMwDMMwvwcio19iwL+D5W+uYUo5R7a2L14Kw4sElq5dx/SWch5DuPR1CK74fVwZy63IDgyQYtsPt8bGRfrFEqYnpxFV7h269DVCrjjuXxlD3lruc5fwdbcL8XtXMPZQOdcQxvDH/Qg20naVCntpJKMzGL+1pOjpVNwYvHgZYS9Yx1WAs4MfoCd8XHHlU/V//+//3SNNuJXR7oOVu3Fy9DMMtLlkZyZD/8NRo5SmzCaWJ77H7advK3zvolxUin/7d/R4M9iY+xtuzKryc7j0/yleuf3Xf92WDm2f33EGo4M98NdbWLlJJ7B45yamo/kVNPLHP+OMX1PRzdjbQfy3H/DTA13l7hrBfw63w7UTw+1//FzQHtHhp38+LGc5y1leCJaznOUsLwTLWc5ylheC5SxnOcsLwfJy5Q1o7fShzrGL7Y1VybSJln353+BHp68O1ZltbKw+x1aB+/s/+y/0NSYw9z8/YEY5p+fg43905YMfDFkrt5Xjt4L7zL/hTxEvHJkdrD34FdMzcfntRXMI54bOIOR1AjtruP2PcTamXiwNg/i3P/VIb6cWvvlf3FNO2+PGmYt/QqTZgXT8Hv7+UxE2hRoi+ONnZ+CvFceZFDYeL+K3B7JtI6rAoVAPIuFOSC+qpBcV13D7Kd0ooyq3jc/zor2rHcHeCLro5oyIyzURl7yGpQsj/zmMdpcoO7f/gXEuIAzDMAzDMAzDMAzDMMy7RMcw/v3jLtRtzOBvNwrYQPmdY6Xcdqga8bfz143edq9k+Dv1ZALjM/HsG4y9jUe4/b/3sLYjHK4WdJ2w8of/av+6e/3wklmQZAJ3i7g++zc0iK5mkRt7m3gy9yB3vuBfN/qH+2XF9us4Zm78/3D99gNJsS3Jt+J4dP8XXLt+D/HX4hpHI0IDQ/Bn7zeS8/8F1lbm8Ou/fsUqve1weBHodWvk9HcF8c20+OtCc6dsjD9fzn/5L//lv/yX//Jf/st/+S//5b/8l//yX/7Lf/nvEfzb9TH+/f/5Cv/1cRfcmU2sPJgr7r7f4V8rqv/f//f//W/l+C1wHJHTAbirU4jfvZu1YZMjgcZjvWitr8Hu9iwWV5XTpnTig08/xpmTx9HiWMKqdgfFLH6c+eMfcFZc0+ZcQvS5clrg9p/EmQ8/wEBfP05FQggda0dj7TY211Mg9akRN/wnT+ODgTPo6z2JkyfCON59DF7nK7zU39N9Dp99NIBImxOLL+px+twQPjhzFmf6g6ibX8KachmtVj4+MITBs6dwsk/8QiEEOxrhSK7hhWxrvihO9p0RaeZA6vldPHxSrJ0et4j/GQTqHEg/f4AfZtaV8xa0fYAP+5rhxA7W7lzDVC4i+bxex+PXPvR0elBTW4/arQdYeSmLWkK9CLirkUk9w/yy2TO3UN8Zka5xpF9h/nH+NesNHej118Pp2EN8IaqzWcQwDMMwDMMwDMMwDMMwR4+W7m601wLp7Rd4/NuvuPPEXAPJAO0dHfA1NyuufN7yyu0HuPHt/xf/9//+E+MxM7kb7tpqyZ1Jm8m1f58ghTo0NnnRGTxtfl33SXS1NaKxEXgVzZ1vH/wcn396Bsf9XjR6XHC5xTWt7eg58yk+/3wQ7er96t+GHnz873/Cp2d60N4qrvWIcHrE32Y/jkv3fICA9vraBhEuem4LPvrjOfR1tqCx3onqGgdlgHydO4I/fPEZzvV2ooWurXfBLf62tPfggwv/gY/D9fJ1Wn9N/wbhdVOa7WJ7M17E9crf7g/Q7RX3Kau2i7nPH24ROSRIPsXUI5vrHz3A42eb2Hz5GhlxU/a89C9R4D7lL5F5s22UP98GLe5HvduYT/yX//Jf/st/+S//5b/8l//yX/7Lf/kv/+W//Jf/HsG/6/d+xtV/XcW//vdn3H0sr/gt5r7f418r3vLKbRs6PsTZE41w7iXw5NclrJFpCwvW4Ue404PauhrUzC4hppxX6Tp9FiFPDfByBf87J0vdpy/iQq8PNUhj8/EcJm/9iF9vr2Ij40RzcxPq3T60+98guryurMZ24/QfRtBDiuDdbTxbmsYv/zuOqZllPN+th7+1EXX1TWhyrmBxTXnj4utGX3sDqp0eNFW/xvryPO4tPMKT1adYe7kFWpR99tNPEWqsxu6rZ1ia/gX/Oz6FmcdJ1Hhb4PXUw9vShDfzyyKOdgRxot+PesdrvHyUWyFtjRtnh88qq7bncf23IlZtC070DaKlDkit/YKZVbu3SynEHi9icXER0bhyStAS6lNWbscwZ7Jy2911HkMRH5xV23g+N4mVhCJQ2fIhdKoFdVUZbK8vmKz+ZxiGYRiGYRiGYRiGYRjmXaWjo7Pwym3l7xGkHecHj0krg9PPHuNOMUrL5cdIkKa4you2QflUjuPobq4Tf3ex/uSufEqcGzregmo6N3sNV3+dxdoLOp/A2tyEcK8gtQc4/ScwEJBuEDQis7WOzdcpPJm6gh/vPBJXEyk8m/8Ft56SYYxqeMU9BqrSeDZzHddvz2Ll8Yr4rSn3noS3gVZbp7B260fcWVY0uFsruHvjFlZfpLC+kUCmQT5tTR2cNfQ3je3H0gl7QkPo0qzaLo42uGmTSPGcrZflGwNxuNpw/qPzeb/Rz7/E5Y+6RBlII/HwFsZMN4zcxus39NcJZ6N0gmEYhmEYhmEYhmEYhmGY3wHV/+f//J8juHLbjd4Ln+JkczXw+hl++/k2ntus2pZ5iW1fCKGmWtRV1+LR4lNltbXg+Gmc7fag5s0LLP60BGnx8MmzGAo2oHoril9/fgSD/jz5BK72PvjrXajKPMJDaXVyEvEny3g4v4CofhWxYMvdiZMB4Wd6E789VIyEt4TkldupNdz6ZdH4HLQi1BdAQ7UD6a3HWHqmXQWdxJOlBTx6HMNGUWnQiROnm+HCayR+ewhLM+USbpz96Cza6xzYjt3DDzOSdr8IjqHnNIU5g9TaHIyLrtvQe+40eoLHcKwz/9fWkMbT53IqtNLK7YZqOFweNHmb8n6euhqIFMFW/AkePpjHxivpFh1q2olwxMzCwTAMwz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tarfile\n\ntar = tarfile.open(\"../input/acm-sf-chapter-hackathon-big/product_data.tar.gz\", \"r:gz\")\nfor member in tar.getmembers():\n     f = tar.extractfile(member)\n     if f is not None:\n         content = f.read()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:30:44.342835Z","iopub.execute_input":"2023-06-20T23:30:44.343311Z","iopub.status.idle":"2023-06-20T23:31:48.132170Z","shell.execute_reply.started":"2023-06-20T23:30:44.343272Z","shell.execute_reply":"2023-06-20T23:31:48.130961Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#We have xml files","metadata":{}},{"cell_type":"code","source":"tar.getnames()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:31:53.315485Z","iopub.execute_input":"2023-06-20T23:31:53.315881Z","iopub.status.idle":"2023-06-20T23:31:53.330256Z","shell.execute_reply.started":"2023-06-20T23:31:53.315851Z","shell.execute_reply":"2023-06-20T23:31:53.329217Z"},"_kg_hide-output":true,"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tar.getmembers()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:33:01.054861Z","iopub.execute_input":"2023-06-20T23:33:01.055316Z","iopub.status.idle":"2023-06-20T23:33:01.070438Z","shell.execute_reply.started":"2023-06-20T23:33:01.055281Z","shell.execute_reply":"2023-06-20T23:33:01.069331Z"},"_kg_hide-output":true,"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tar.list(verbose=True, members=None)","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:33:43.511471Z","iopub.execute_input":"2023-06-20T23:33:43.511912Z","iopub.status.idle":"2023-06-20T23:33:43.535291Z","shell.execute_reply.started":"2023-06-20T23:33:43.511865Z","shell.execute_reply":"2023-06-20T23:33:43.534176Z"},"_kg_hide-output":true,"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#Extractall\n\ntar.extractall(path=\".\", members=None, *, numeric_owner=False)\n\nhttps://docs.python.org/3.7/library/tarfile.html","metadata":{}},{"cell_type":"code","source":"tar.extractall(path=\".\", members=None, numeric_owner=False)","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:34:12.920372Z","iopub.execute_input":"2023-06-20T23:34:12.920793Z","iopub.status.idle":"2023-06-20T23:35:09.473079Z","shell.execute_reply.started":"2023-06-20T23:34:12.920761Z","shell.execute_reply":"2023-06-20T23:35:09.468368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#Columns\n\ncategory: The category the sku belongs to\n\nquery: The search terms that the user entered\n\nclick_time: Time the sku was clicked on\n\nquery_time: Time the query was run","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/acm-sf-chapter-hackathon-big/train.csv\", delimiter=',', encoding='ISO-8859-2')\npd.set_option('display.max_columns', None)\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:37:49.430859Z","iopub.execute_input":"2023-06-20T23:37:49.431307Z","iopub.status.idle":"2023-06-20T23:37:59.112394Z","shell.execute_reply.started":"2023-06-20T23:37:49.431275Z","shell.execute_reply":"2023-06-20T23:37:59.111253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Code by Puru Behl https://www.kaggle.com/accountstatus/mt-cars-data-analysis\n\nsns.distplot(train['sku'])\nplt.axvline(train['sku'].values.mean(), color='red', linestyle='dashed', linewidth=1)\nplt.title('Stock-keeping Unit Distribution');","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:44:02.832751Z","iopub.execute_input":"2023-06-20T23:44:02.833252Z","iopub.status.idle":"2023-06-20T23:44:11.353528Z","shell.execute_reply.started":"2023-06-20T23:44:02.833218Z","shell.execute_reply":"2023-06-20T23:44:11.352422Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##Code by Taha07  https://www.kaggle.com/taha07/data-scientists-jobs-analysis-visualization/notebook\n\nfrom wordcloud import WordCloud\nfrom wordcloud import STOPWORDS\nstopwords = set(STOPWORDS)\nwordcloud = WordCloud(background_color = 'black',\n                      colormap='Set3',\n                      height =2000,\n                      width = 2000\n                     ).generate(str(train[\"query\"]))\nplt.rcParams['figure.figsize'] = (12,12)\nplt.axis(\"off\")\nplt.imshow(wordcloud)\nplt.title(\"Best Buy Mobile\")\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-06-20T23:52:19.239564Z","iopub.execute_input":"2023-06-20T23:52:19.240015Z","iopub.status.idle":"2023-06-20T23:52:21.732978Z","shell.execute_reply.started":"2023-06-20T23:52:19.239978Z","shell.execute_reply":"2023-06-20T23:52:21.731829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/acm-sf-chapter-hackathon-big/test.csv\", delimiter=',', encoding='ISO-8859-2')\npd.set_option('display.max_columns', None)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:38:33.145547Z","iopub.execute_input":"2023-06-20T23:38:33.146064Z","iopub.status.idle":"2023-06-20T23:38:38.578164Z","shell.execute_reply.started":"2023-06-20T23:38:33.146024Z","shell.execute_reply":"2023-06-20T23:38:38.577123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#Sku:  The stock-keeping unit (item) that the user clicked on","metadata":{}},{"cell_type":"code","source":"skus = pd.read_csv(\"/kaggle/input/acm-sf-chapter-hackathon-big/popular_skus.csv\", delimiter=',', encoding='ISO-8859-2')\npd.set_option('display.max_columns', None)\nskus.head()","metadata":{"execution":{"iopub.status.busy":"2023-06-20T23:39:37.047217Z","iopub.execute_input":"2023-06-20T23:39:37.048476Z","iopub.status.idle":"2023-06-20T23:39:38.099508Z","shell.execute_reply.started":"2023-06-20T23:39:37.048433Z","shell.execute_reply":"2023-06-20T23:39:38.097147Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Python engineer https://www.python-engineer.com/posts/tarfile-python/\n\nimport tarfile\n\nwith tarfile.open(\"../input/acm-sf-chapter-hackathon-big/product_data.tar.gz\", \"r\") as tf:\n    print(\"Opened tarfile\")\n    print(tf.getmembers())\n    print(\"Members listed\")","metadata":{"execution":{"iopub.status.busy":"2023-06-21T00:15:51.485645Z","iopub.execute_input":"2023-06-21T00:15:51.486176Z","iopub.status.idle":"2023-06-21T00:16:28.402031Z","shell.execute_reply.started":"2023-06-21T00:15:51.486137Z","shell.execute_reply":"2023-06-21T00:16:28.400797Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Python engineer https://www.python-engineer.com/posts/tarfile-python/\n\nimport tarfile\n\nfile_name = \"product_data/reviews/reviews_0010_1906234_to_2063675.xml\"\nwith tarfile.open(\"../input/acm-sf-chapter-hackathon-big/product_data.tar.gz\", \"r\") as tf:\n    print(\"Opened tarfile\")\n    tf.extract(member=file_name, path=\"./extraction_dir\")\n    print(f\"{file_name} extracted\")","metadata":{"execution":{"iopub.status.busy":"2023-06-21T00:18:30.130812Z","iopub.execute_input":"2023-06-21T00:18:30.131271Z","iopub.status.idle":"2023-06-21T00:19:00.939214Z","shell.execute_reply.started":"2023-06-21T00:18:30.131241Z","shell.execute_reply":"2023-06-21T00:19:00.937958Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#Now, how do I open the xml file above? Since below I got Errno 2 No such file or directory:","metadata":{}},{"cell_type":"code","source":"import xml.etree.ElementTree as ET\n\ntree = ET.parse(\"../input/product_data/reviews/reviews_0010_1906234_to_2063675.xml\")\nroot = tree.getroot()","metadata":{"execution":{"iopub.status.busy":"2023-06-21T00:20:46.353680Z","iopub.execute_input":"2023-06-21T00:20:46.354226Z","iopub.status.idle":"2023-06-21T00:20:46.418300Z","shell.execute_reply.started":"2023-06-21T00:20:46.354186Z","shell.execute_reply":"2023-06-21T00:20:46.416534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#Solution to this competition\n\nThat Solution was written by Kingsfield - 2nd place\n\n\"They use naive bayes as our algorithm in this competition. The features they used is:\n\nquery, time, user\n\nA. \"They wanted to know the probability 𝑝(𝑖│𝑐) that user click sku 𝑖 in context 𝑐. They used naive bayes to predict this probability. Then select 5 item with highest predicted probability as prediction in context 𝑐. Here context 𝑐 is query  𝑞.\n\nThe first context they used is query. By naive bayes, they have:\n\n𝑝(𝑖│𝑐)∝𝑝(𝑖)×∏𝑝(𝑤_𝑘 |𝑖) \nhere 𝑝(𝑖) is prior and they used the frequency that item 𝑖 appears in its category as prior.\n\n∏𝑝(𝑤_𝑘 |𝑖) is the likelihood and 𝑝(𝑤_𝑘 |𝑖) is the probability that word 𝑤_𝑘 apprears when we see item 𝑖.\nB. They used time information in our model. They divided data into 12-day time periods based on click_time. Then used a smoothed frequency of items 𝑖 appears in its category in its time period as prior in A.\nC. They also used a bigram model to improve naive bayes. The ∏𝑝(𝑤_𝑘 |𝑖)  likelihood in A use words conditional probability. They generated a bigram model and use naive bayes model to fit it too. They generated bigram data for each query 𝑞 as follows:\n\nsuppose use query “xbox call of duty”\n\nrerank to “call duty xbox\" by alphabetic order ( with elimination of \"of\" as stopwords)\nbigram: [”call duty”, ”call xbox\", “duty xbox“]\n\nUse naive bayes to fit it as the same as above.\nThen they used a linear combination to ensemble the unigram prediction and bigram prediction.\nD. They did some data processing. The first is query cleanning. In big version of this competition, we did 1,lemmatization;2,split words and numbers such as \"xbox360\" to \"xbox 360\". we also did some words correction in small version of this competition.\n\nE. They ranked the items that user have clicked by query  𝑞 lower. Suppose we have prediction a,b,c,d,e when he query  𝑞 and user  i clicked items a,c. They reranked prediction b,d,e,a,c for user i.\n\nF. They used python to implement all algorithms. The only 3rd lib need to install is nltk. They used it for lemmatization in data processing. The rest use puer python. The version of python is 2.7, as there is one function not supported in 2.6- version.  For more information, Check readme.txt in the soucecode. Please download the second attached file. Because Kingsfield found nothing can delete the first attached file in the system.\"\n\nhttps://www.kaggle.com/competitions/acm-sf-chapter-hackathon-big/discussion/2861","metadata":{}},{"cell_type":"markdown","source":"#Make public stuff. Leave our mark.\n\n![](https://www.som.polimi.it/wp-content/uploads/2020/07/KOL_NicetoMIPyou_v2_600x600.png)https://www.som.polimi.it/en/tag/keeponlearning/","metadata":{}}]}