JM2_Regression_Project_v0.10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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45hH3TSPunUzCW0LCU4LinYjqkSpGdU8wfz/zd3RNRHWWmNKck9Bck7rO5evFaI5CdEceuj0LzToWrXMUqmWkOWrtI9E7hpuOUGsbiNoyBKV1CEqbXA2kE2ooxls2g7YDk7n2zkiOlzzJrtH3sXHEHWwu+gMn3utO3dGh2Kuz8XcU4LXl4TJVhNM6EYe1BJtlIpbOiXSYoJ9Ec9MEE/SNEvRXb4K+N2e2Pc7+j+9iy+u/5vP5t/HR9J/zyax/ZdPyX7Prwzs49vW9XDrSnepLL9NUOZTOpgxcHYX4bZMJO2YQdc0m7plP3LuQuHcRSc9iFM8iNN9CUybkvQswvAsxvIswvIsxvEswTNAvRfhWIvyvYQRuwv6NFOiDaxDBFOz/BnoJ20/QI18gYhsw4hsxTMBLyG9CJL5CJP8GekPZxt9r69+Bni7QG/+foF//N9AnPkTE3zUBJCSMXFPQbbkoHaNQWl4lUd+beM3zxG88R6z6GRLVzxCvfobIjZ6Ea1KK1Ejwp+AfrXuaWH1P4g3PEG96jmTLiyitfdDa+6J39EfvHNIF+rFggj4H4c5Dl5CXLt4tx2I07wQ0XwlaYBp6cC56aDFaaNnfYB9cjRZcieJfmjIz3tlEPNMIuycQduYTsucSsObgs2ThtqTj7BiBo20I9paB2Jr7Ya3vQ2fls7SWPknzhUdpOpOCe82Bh6n+9iFuHHiI+hOP0XC2G/Vnu9Nwsgf1J3rQcPIZmk73pvVsP9rOD6Tj4hDspcPwVAzDf2MIoYZBRNuGkOgcjWLLQ3OUoHsk6BdihOQqbjVGZHUK+OHlXaCf9z3YL8QIL4XwSozQSvTgKkTgNTTPUuL2WUQ6JuBvzsJeM4ymsr5Unn2EC4d+w/GdPzLBvu/LNPZuuIW9G2/hwFdpHPk6jZPf3MqFvf/EtSO3UXHyDqrP/YX6K0/RdL03LdX9ab0xlLbaMbTWZtFck01DdQ71VTnUXM+lsjSH65ezuHpuNBdPDOXM4f4c3f0Ce7Y8yfZPHmHD2vv5YNndvLvoTt6c+2t2fD6QUKAzBXrVtYlo3VhCVUMJlPfFe/lJ3OfuwXbil3Qc+REdh26lY38aHfvSaN+dRvuuNFp33kLzzltp2fOPtB++jc4Tf8Z2oReu8n746gYTbhxFsjkXtW0SSscM1M6Z6NZZaJZZ6FLW2WhWeTzDlOh6XVhnImzTEdapCNsUdLvUZHR7Cbp9Arq9GM1WhGadgGKdQNJaSMKaR8I6noQtk6Q9C8WejerIMyGuuiagueSHK2fyKei+yWjeEjTvRIR3Arp0K+4CVFceiisXxZVN0jm+S9kknDnEnXnEnYUm6OOuScRdk4m6pxDxTiPmmUnUOy8V1fhWkfS9QTLwJokuyWMlsAbFv5qkbwWKbymKbxGKbw6qd3aXZqF5Z6J7ZqJ5pnVpOpoZRUxDl5OU63vyTko5LI/8/YsQ0n3JG9KdbS6zhSsH4crHcBViuArQHbkIR7YJDN2ai27JRWktJlI7A+f5OVRvLODE3D7sGns/m4bcwVfZf+L4miep3j8Ie3UOQUsJIVcJPkcBbns+LlsRLttEnNYS7JZibJ1FdLZNoK1lIi1NxbTUFtJYkU3ttXFUmI7+Rc5+/TiHv7iPb9b9gW1v/pbta3/Lrvfv4OD6ezi962GunHiKmqt9ab0xAltzJu7OQny2SYRMyM8j4ZHXTMZgy1GlvMtQvUvQfTKOWWQ6eeM70H8P9p4lYMJ+OcK/CsOE/U1X/wYiuAZDgj74JiL0NiL8PiL8kenm9cgGRGwzIv4VRmJLSjcB/3dw/xpD2Y6hyFFKgl+6+C4p0uFLN78ZQ9n0N0dvuvn1iOQXkPjUdPSGCfr3EJHX0QOLEe7p6HIlZxlLsnUAiYY+xGpeIFr1HNGqZ4hW9jIVrupFqLonIRP4vYjWSPX8O0efaHyOZHNvlJaX0dr6oXcMQO8cirCOwLCPxXBkYjizEO7x5nfJMMcchKcwda/4J6IHpqIH5qIHF6GFlqCFlqKFVpiQVwPLSPoXpVas3ulEPJMIu2XUlkPYnkPwJug70nG1jcDRMgRb00BsDf2x1rxMR/lztF54iqYzj1F3/H5qDt5H1bcPULHjL1zf+Rcq9z/EjaOPUn30CaoP9qD6QE9qD79A/dFXaDoxiJZTw2g7O5LOS6NxlI3CWzWMUP0gos0DiXUMJ2nNQnPIe2YGemBBF+hfxzDjutcwboI+PA/xneYjwotSbj+0EhF83ZQuI1nnXOLWSYTa8vA0jKWjYjC1F3tw5eh/cnL3/+bgllvZuyGNveu7QL+lC/Q7f8CFfT/h2pFfUXHqTm6c/ysNV5+m8XpvmqpepeXGENpqR9Fam05z7Xia67JpqB1PXXUON67nUFGaybWLY7h8Zjhnjw3g2L4+7P+6F7vWd2PLuodZv+ZBPll1H+uW/J5vNwwlfBP0un8LibY8oo05RGrHE6kaQajsJbyXHsN++k+0H/lXWvb/L1r33krLLgn4NBq/SaNxp4xufk7H8T9iP/sYrqsv4asaSqhhNNGWDJS2ArTOqajWOSmo22ai22YgzHEWml3Cfg66dZ4pYVuAsM9HOOai22ah22ciHDMQzqlozhJ008EWoduL0GzFqNZCE/SKPR/FnoviyEF15KA589Cc+WiufDR3QQrmHgn2EnSf1ERTwluM8BSheQrQ3Pmo7lxUVw6qKxvFNR7FKaGfQ9KVT9JVRMJVQtw9mbh7KnHPdGKeWcS8c4l7F5DwLSfpew3FvwbVL+EutdaU5l+D5nsdtWvZp0k4eReaUYN0oinNM/NkmS/r3hno3ummhGcqhncKhm8Shq8EwzcBwz8BI1CE4S/E8Odj+PIwfPngLwB/IZg/Mxn8MzCkAjPANxXDMw3DNQPDNh2jeRrR0im07M7lxLKX+WrcA3zW//d8MfL3HFz8BLV7RmKvKCTYOZWoezoh5yT89iK8jjy8jgI89mLc1iKcnfk4OgqwtRZgaS6io6mQ9rp8miuzqC0dQ8W5QVw90pvzu57kxNZHObTpQQ5vvI9jW+7nzK6HuHz4KSrO9qa+bDBtNWOwN2fjsRTjt08h5JpFxDWfhHtxCuz+VWj+1aZ03yo07wozmjGjGp+E+3xSsP+vzl46+uUI3wqEP+XshRnjrMEISNC/iRF8CxF8ByO0DhH+BBFejx7ZhIhtQcS/RsS3YyS2YyR3fCeh7EBXbp5/0/X6TeinXL0Efsr9S8j/PejN+Ca5Hj35GUbyk1Run/gIPbEOPbYWXebfnllmVq/Y0km2DyLZmAJ9uPIZwhW9CJc/Tfj604QrehKo7EmoqgfB6p6Eq3sRvdGTWO2TxOqeIt7wNMnG51Cae6N2gV7rGIhmGYawjkLYxyFs6Qh7BsKZgeHMRLgzkLA3PPkY3iKEfyIiMBURnIu4Cfrg0lRNIbgCJdDl5v2zifmmEfVOJOzOJ+rMJuIYT8g6nkBnFt62sThbRuBoGoa1YRDW2gF0VvY1I5jGU92oP/oQ1QfupWL33ZRtu4erm+7h6lf3UrrjAa7teojSXY9y7ZselO18hspvX+bG/oHUHR5G47GRtJ4eS/uFcVhLx+CuGEGwdgjRxoHE24agWMehOwpQpXHyy/hJrkgk4F/HkNFNZAlGJJXXy/hGD89BD0nNR4QWI0IrUhFOWH4Hl6K656PYpxPtKCbQlIX9xgiarr5A+Yn7Obvv3zi09Yfs/VJm87ewd1Ma+7emcXR7Gqd2/QMX9v+M0qO/puLUXdScf8QEfUP5izRV96e5ZjBtdaNorRtHc10mLQ3jaazPoqE2m5rqbCrK0ym7MpqrF0Zw/uQgTh3qy+Hdz7Nvy9N881k3tq57nE1vP8T6N+7k0NYRREKWlKMXwV0olukk2qcQby0h2pRPtDaDUMVQvFdexnH2cTqO/o7W/f+bpl3/SMM3t1L/za00fvsz2g79AeuZx3Bdfg7/9YGEa8YSa8wh0VaEKvN122xU2wI02zyEbQ7CNhthn4tun4/uWIDmWITmWIYu5bw5LkFzLkJzLkB3zkZ3zkB3TUJ3TkBzdIHeOgHVVohqz0d1fF8S8rnorhx0dy66uQwtNGEvvBMxZNbom4DuK8bwTkDCXncXo7kLu5SP6pK5uAS+dPj5KK5Ckq4JJF2TSLqnkvRMI+GZRdw7n4RvIUnf4pTL9K1E9b2O7l+LFliD7n/DlPCvQfhWp0DjXZrKj30Lu/JkmSnPx/DNQfhmIHxTEL5JCF8JQgLdXwSBfAjmQDALQhkQGocRGoMRGo0RGoURHI0IjsUIpSNCmRDOhVARhCdhhKchIlMxwlMhPB3DPx3DOgVuTMKzP4sTS3vx+ag/8lbv21g36N/ZM+8RanYMxV1eRMQyg6RnNgnPDGKuyUScRQScefgd+XitBXg68/F05OJuz8XZmou9OR9rYz6WulzaKjOpuzaKyouDKDv9MlcPPcOFfT04f6AHlw89TenxZ7l+tg83Lg+koWIkbXXjcbYU4++cStAxi7B7HlHvIuLepSieZWjeVWi+N9B8ctJ8A927Gs278v8A+i7YexZieG5GODKvv5nVS9ivwgS9X8Y3axGBNzECb0HoXYzQBynQR9YjIl+hx7YiEt9gJHZhJHZjJHeBsgtD2YXokjxG2QnKN6bMycB09inY/7egV9ajK58ikh8jEqnirJb4AD3+FiIsf9e56J4SVEcmSudgki2vEK99AengwxU9CJU9RbDsKQLXe+CveAp/5VMEqnoQqnqaSLXM6LsRr+tOvL4HicZnUJpeQG1+Cb21L1p7f9SOoeido9CtYxE2CftxCMc4DGc6hnucCXvhlq6+AOG7Cfo5iOBi9O+im1XooZWogaUk/QuIfw/0UU8eUdd4IvbxhK1ZBDsy8LWNwdUyEnvjUKy1g+is7k/btT40nu1JzeHHqdrzF8q/uZurW/7ExS/u5OzH/8n5z+7iwpf3c37zQ5zf3I2Lm5/l8le9uba9PxW7h1G1dyQ1h8bQdDKd1nPpWK6OxVU+kuCNIUTrBxJvGYzSORrNmY3qKUEPzEaElqCHViFkdBNZgRFZhGFm87MwIjMQ8t4xNRMjNBfCCzEiy9DDspC/BM2zEM0xm4SlhHBrNq7aEbSWvUL1uce5eOh2jmz/ETK6kUXYvwP9Tgn6f+ba0d9Qcepublx4hLrSnjSU96a5egAtNUNprR9FS306zfWZNJugz6ShLpPamkyqKtOpKBtD2ZWRXDo7mHMnXuXkwT4c3fkc+zf3ZPf6nuz8tDtb3/szx3eOIXoT9ASPYthXoduXoNrnkOicTKK1iGh9NqHKcfivDsB1vhvWE3+kdd9t1O/6MXW7f0zTgV/Tefxh7OdfxFvan3D1SOINOSRlJ03HDDTbHHTnQnTnEnT7IqRjN2zzMeSxYwm6YznCtRLhWo3hegPD/QbC9TrC9RrCvRLhXopwypxSdiBMRXdOTIHeVoxmnYhqK0pFNI58NGcBqkMWW/LQTMiPR3dLyY4BmTnK5WcRhgS7vxjdV2S64xToi9DdRWhSrsKuyEcCvwDNVYTqKkZxTURxT0Zxyy6P6SieuSS9EvBLv4O8dJkm0CXgAxL4q02gGP7VGL5VZlEQ32IMCfnAPIzAXAz/HAz/rBSAA5MRgWJEoAARyEMEczCCWYjAGERwOCI4GBEaiAj1QYReRA89jxZ6Hj3cGy3cBz3SDz08CBEeiwjnoEeLENGJ6JFi9LDslpiA6i0k2ZxF4Ngozi95inX9b+f1Xr/gzZd+xrbJf6T66/4ErheT7JyB6p2D5p+D5pMrjCkkPUVE3HkEnfn4rSnIezuy8bSPx9UyHkdzDvamHKx12bRXpdNQPpobpUOputSfqrMvU3H6RSrOvUjNxVepuzKU5uvj6KjNxdJUhLNjMgHbHKKuRcQ8S4j7lpPwr0TxvYYq3bvvdTTf2i6tQfe+/n8Fvfge7G8WZqWz17vyemFOxGsxAmvhJujDH2CEPzEjfmQAACAASURBVEWENyCi0s1vR5iQ34OR3AvJPaYM5VtEl+SxhH8K9jtTzv57EY4JeuWrLke/+bvoxnT0/wX0WvwjTNAn3kZEZU1hvrkSTYFe1lj6Eq973gR58Ho3gmVS3Qlc747venf8Fd3xV3YjWNmdcHU3YrWPEat7gnj9kyQaepJsfNaEvdbSB7Wtv1nk1TpGoFtGd8F+LLp0984xCNfYv4Hem28aj5Sjvwn65V0ZvQT9KtTgMpKBhST8c4j7U44+6snvAn0mYUsGgfaxeFtH4moejr1hGJaaQXRUvErz5RepPf4UlXv/Stn2P3Nl892c/+JPnFr3R469dQdH376DEx/cy8mPH+Hkp09y6rNnOftFHy5uGkjp1yMp3zmGqv3p1B3LoulMJu2Xx+IsG0mgagiRuoHEmwejyvqVIxNVRp4m6OVktQohC61mbLMAIzoHQxqjyGREpAQRmYiISMM0HSM8GxFZiBZZgh5cgvDJaG0ein0q0fZcvA2j6KzsT/2VnpQev5PjO3/K3o1pfLshjT2mo7+Foztu5dTuf+Ti/p9z7ehv/w+gH0hrzbDvQN9igj6LxvoMGuozqK/LpKZmHNWVY6m4NorSi0O5eGYgZ4+9wqn9L3Fs5/Mc2vYcBzb34tvPH+Lc/kxiEWvK0RM6jOFegeZZgepZiuZaiG6dSbKthERTPpGadPzlg3Ffehbrmb/QcvR2Gg79mpaj92I70wvX5f74r48gUp9FrLWIZMdUNOtchGMhwrkoJcdiDPtShH1xl3t/Dd21BuF6E8P9DoZnHYb3Q4Sp9xHet9DdqxHuFebF1J1TEc5ic+ml2wrQbfmoMq5x5qJKB28CXo456M4sdFcmZnuYaxzClYnhkrmjLC7lonty0Ty56N48c5THmluuAHLQZSuZMwvVMd6cOFRHEZqzGJn1yyKu5p6O5p6N6lmI6pXQeA1hOszXuyC/AiOwDN2/As2/At0vjxdjyOKPfy5GYBZGYCaaX+aEk9EDxeiBAjR/ASJYiB7MQw9IuKeDPx0CY8A/BCPQFxGUYO+FFuqOFnoUNfQIWugJ81wP98AI9YbgENPxi0gOeqwII1YMYRnrZJsrnGRbDqELY6ha14uvRv6Wd57+F95+7p/5Ku93lH7RA+fV0SjS8csVRmC+ucSVrX7COx3DIye6CSRcRUQdBWaBzW8Zj7czE2dbBo6WDGxNGVjq0+moyaC1Mp3G8tE0lA6j4eoQ6kuH0Fg2jNbKsXTUZGNtLMLdPpWAbS5h12JinlXEPW8Q975BwrcWNfAWqv8tVHlsOnkJ+jXovtcRXjmpLusqvM7H8EjN/Z7mYXikFmB4FiK8XZ04sr3yJuzlKku6ev9aDP+bIKOb8IepbpvIJvToNvT4TvTEt4jEPkRyP0I5gFD2I9R9CHUvhroHoexGJKVSLt9QdmIo32DIWKfL2evqFnR1K8KU7ND5EpGU+fznGGZ08zF6ch168n10WYyNvYkRWw2yhdE7Gd2RjdY5DKWlD/H6ZwhXP4nv+mMEyh8jUPa4KX/5EwSuSz1GoPIxQtWPEKl9iFjtoyTquqE0PIXS2INk87Mkm18yi7tK2yDU9sFoHUPROoejWUahWUej28eiOcehuTLQPdno3nw0/wS0wFS0oIwzFqKFl6KGl6NGXkOJvE4yvIpkaCnJ4HySgVnEZWeZNAeuHMKODMK2sQQ7RuFrGY6raRi2umF0Vg6m5eor1J3uRcWBRyjdfi+XNt7J2U/v4OS633N47e/Zv+p29q38PQdW382hNX/l0FvdOPzOMxxf9xJnPhvIxc0jKd0xlsr9GdQdG0/DmfG0Xs7AVj4Sb/UgQg2vEm0dQNwygoQzk6RnAqpvJnpgIUZwJUZwFUZoGUZoAUZkdmoVHJ7SBXoJ+9TK2AjPMtswtcgisxgt4yu54tJcsgswn2DjWLNdu7XsBSpOP8iZvb/k4NYf8u3GNPZ8eQsHtvyQIzv+kZO7f8qFA7/iyrHfU37mHqovPkx96dM0lr9EU9UAM7ppqRtBS/1omuvH0FwvI5wMGusyaKjLoL42gxtV6VSWjeO6jHDOD+Pi6YGcO9aPU9LZ73mBwzuf4cDWJ7h4PI9Y1JYCvRHah+pYgOJebLYOCc9SkIC2zUXrnEGyZTKx+gJClWPwXXsV58VnsJzpjvVsD1yXeuO9NphQ9RhiTbkk2iehWGea0YzhkJDvgr1DQn45unM5umsVunstmvs9DM8H4P0Yw7se4fsS3b8B/J8hfOvQPW8hPCvR3fPQnZMQjnyEPQdhH49my0S1Z6A4M1CdGejODDTZ/yudiHMUwjkSw5GS7A2Wy1HdkYEmZ3RnJqpLKsOU5kpHcY5DdUjJNqxx5t+tmXl/PrqryPz3dZeMkOaiuxagu5ege5YhZHzgWYXhXYnhWw6BxRCUXyDZYzwfXd4UwRmpbNN07JMQgVQsI527IV27fzzCnw3BXPNcD2QgAmMxfGPBNxLD9yqG71mE/0k0/+Novr+i+/+MHpB6EBF4FCPYHQK9wT+8C/S5aNLJRwohlA2ysNZRRPRaAc0b+7O/5F4+7ftzPnjxJ2zJ/DVX3++O+/xQkm0y858OQdlDPNe8qaWrN3yzwDsDWTPQXZNRHSUkHcXEbPmELDn4OzLxtI3D2TwWW+NYLHXj6KzJoP2GBP5YU21V8rUs7I35uFsn4eucRcguHfxrJtgV/7so/nWo/nVo/nXo5vgumu9NNO9aNK908m8gvKsR3uVmVw2eBeCei+Geg3DP+p7kudRc0ygIcyKQcdlic4LQvSvQvatSk4ZvDcL/FkbwPYyQLMR+gYhuRo/uQI/vRk/sRSQPIJIH0ZVDCFXqIELdj6HuTTn7pHT4u019P8oxYa9uQ6hbMJJbIPkVJDdBcgN68vNUPp/4DFmENSGffBcRfxsRW4MRew3CsntoGroj34xY1JZXSDQ8T7jmaXwVT+AvS8lX+gS+a0+YwA+WP07w+qMEKx8mfOOvxGoeJV7bnUTdkyTqnzIjnETTSySa+5FseZVka38k8JX2ISgdI9E7R6JZx6Dax6I6x6J6MtF8uai+IhT/FNTAbLTgAtTIUpTIClQJ+chakpHXUWSEE1qCEpxLwj+VmLeYiDuHqDOdsH00gY4ReFuH4Woagk3GNtcH0HSxD9XHelC2669c3Hg3pz/6A0feuoP9r/2O3Yt/x455t7N97h1sX3AXOxc/yK5lT7Bn1dMcWPsCxz8YwPkNI7jy9ZgU6I9n03A2i9bL6SboPTcGEmzsR6StPzFrCvQJCXpZA/MvwAguxwhI2C/BCMme+pmI8FSECfpJXbD/PujnoZt99wsRoUXmylyaP9nqHGlOx1s3lM7rfaiR8c3B33Fkx/9i3+Y09phdN//AkR0/4uS3/8z5g//B5eN3UHb2HqouPkydBP31l2iq7E/zjUG01A2npX4UzSbsx5qwb6pPp7E+k4baTGoqM6guz6CydCxll0dy9dwQLp0ayLnj/Th1uA8n9vfm6K6nuXqmiHjU3pXRB/aZObrqWoTqWoxwLQbXUlPCsQzNsphk2yxiDUVEajIJVY7GXzYM37WBBMoHEqweRqR+HAm5sUm6eZvs/134naM3XEsxnCswHKswnKsxPGsR3vcQ3k8wfOsx/Jsx/NsQge2IwDYIbcYIfG7CXnhWo7vmI1yTEM7cVLHIPgbdPgrdPhLNPhLdMRLhHI7hHIKwD0Q4BpjS7YPQrEPRrMNRraNQraNRrWNRbGNRHfKLPArVPgLFPpykTWokim0cii0L1T6+a3VQgHBOwnDOBOcCDOcSDNcycEstAfdiDNcCDPc8DNn37JMxzExEUG7ImIwIymViAbo/F92fh+6XS+B88EnAZ2H4M0zQ48+DgCym5iEC2WjBTHT/WIR3CLrneYTncYT3L+jeezE8d4H3DxjePyK89yK8D2H4uoH/JfCNwAiPQYSy0QNF6IE8NG8WemcByetTsG7P5Nycp9g07De83/vHfJXxC668+xfsJ/uSbMoA9yQIzsAIyYwyJV06meBsM2bCPxu8sxHumeiO6Sj2EhLWQuKW8UQ6MvC3j8PTOg5n0zicjVk4GrOxNWRja8zF2VSAu2UC/s7phO0LiDpWkHCvRfGtQ/F+iOb/FM3/Obr87P2fofs+QvjfQ/jeQpiAl05+tTn5m5m7bKN0z/8e5Gcg3DMQrukIlxxnfifDPavL4S9EeGR//TJ070ozAhI+6ejfgeAHGKFPEJENGDK2iUk3vweRkJA/hJE8jFCOIpQjCEUeH8Qw3b109SnQG+puDPVmjCMz+x0YqizQbk1BPrERErJv/gtE8jN0uYkqLjdQfYiuvIeefOfvQR9ZAv6ZGK4ihHUMWturJJteIFLXk0DVk/jKuuMr7Y7vajdM2Jc+jv/aY/jLHyVY8RChqgeJVD9M/MYTJGpu5vWy3fIF4rKw2/gKieZXSbQMINk6mGT7MNTOEagW2Y44EsU5AtU9Bs2bierLQ/GXoPpnoAXno4aXoYZXoYbXkIy8jRJ92zyWrymhRSSD04n7JxL15hJ1pxN2jiJoGY6vfQju5gHYal6l/Vpf6s+9QMXBJ7m09QFOfnQnh964g91Lf8eO+bezZfrtfDnpt6wv+T0bpvyJjdPvY9PsR9m28Cl2rXyWw++9ypkvhnJ52ygq94+l/kQWTeczaL06GlvFMLy1Awg2vUqkYyBx+yiS7vEkvBNI+qajyhWrjGBCy8zOGhGW/fNdoP9/RTfTMCKzMMJzzB57EZZdO0sQ/kXonlmo9iJiben4G0diq+pP/aWnuXbsbk7s/BkHtqSZsD+47Ycc/ebHnPz251w4+BuuHP8jZWfvTYH+ak8ay/v8X0HfJEEvs/rqdG5UZFBdNo6K0tGUXx7JtQvDuHp2MBdP9uf80f6cPfAilZemkIw7UqBXnPtRWpciHCvQZcTiXADSibuka5XRyUo0+yKScpNSWz7x5iziDRnEazOJ1WYQqR9NvGk8qtzx2jkDwz4fpJs3oxsJwuUYztfA+QbIqMbzHobvUwz/RkTga0RgJyLwLSK4DxH6FsI7MEKbzZtduNciXHJlMAVDtgnKYpGEu30IwjYYQ8o+EBz9Mex9EbaXMbokbP3RLRL2Q1Atw9Asw1EtI1CsI1BNDUW1yWr8YBTbMBTbaFTZjy43VtiL0Z0lCOc0hHMOhnMROJaDczm4pJaAawE45oD8GYf82YmmDNdEs5tHRkO6hKwvHeEfh/BlIHzjEd7xCF8mwp+B7s8ydxzKjhoCslNGOv5itEB+6v/z9Ed3dUO470K4foNw/xLD/Svw/BLD8x8Iz53onvsRvscRvhcx/EMR4VEQGA9eufklB8VaQLxyCs5vCyld2oedI/+Tj/r8bz4f+TMuvXUP9lO9iDUOMmMt83cITYPoJPTIRDQ5xqajR+ROyAUQmg/BBakoyiMn4Flmb3LSlkvcmknUntpFHGhPJ9A2Hn9bLp62QjwdJfg7pxGyziHulAXWtaje91F9Eu5fovo2ono3ofk2o/s2YfjXY/g+At+7GBLE3q64xrMcw7PULLSaLt107NLJT8dwTTUle88NU6lz+TruaRjumV3RjoxDZFH8+6B/D4IfQugzjMgmjNjXiNhuRFxGNhLyRzGSxxDKcYQiRwn8wxjKQYSyzwS9oX6LBD1SZnF2Rwr0yg6E2XL5FYYqIxu5c/ZzRFIC/lNEXHbcfNAF+v/i6COLMeSK0FOIbh+N1t6PZPPz5iaoQHU3vGVP4L3aDe/VJ/CWPo639BF81x7CV/Yg/vIHCFbcS6TyAWIm7B8mXvswcbmD2txE9QKxhpeJN/Ul3tzPhH2ibTDJDgl7aXqGojiGoLqHo3rHoPqyUH2FqP6paLLFMnQT9GtRIu+gRt9HDUvYr0UNLScZnEk8MImor4CIJ4OwaxRB21C8HQNxNb+KraYvrdd6U3vmWcr3PsG5jQ9w5J0/sXvp79ky699ZP+lXfFL4K9bl/DvvZ/877+fdzgdFd/JRyQN8MbMbWxc/w743+3Li04Fc2jaMyv0jaTg5jpYLY2i/Nhxb5WA89QMItrxKpHMwUfsYEu4c4p6JJLzTUfzz0IOym2YJEtxyJ6wwi7BTEf8V9JEpGJGZmPFNREY4KdAbgSXospblnEiiM4Ngy2hzp33z1ReoOPUgZ/b8G4e3/ZD9X6VxaNsPOPrNTzj57W1cOPg7rhy/k7Kz91F18RHqvgP9qzTfGNzl6Ef+vaNvSKepQcY3EvSZ1FZkUnM9Bfuq0jFcvzySsgvDKT07jNLTQ7l87BVqrs0gmXCmQO+s2Ib9zHTiDUvQbUswHPMxnHMRMqaQuafccehejO5chGqbSbKzEJn1yscUJJqKSLZmo7YXoHWUpHribXMRNtlVI7UQ3bHUdPO41ph5vPB8iO5bj/BvxQjuRoT2I0IHMcKHU8/eCO/CCG5B+D7H8LyNcC1DOKamYhvrGIR1KLq1P8LSDyz9MCx9MawvoVteRLO8YI66RZ73Q7MMMGGvW4agm7AfhtI5lGTHEJTOQaiW1CSg2kajmZAvRHPI1cNsDJecpJaZExWuFeCUSsVahmMehl3uDZAbugrRrXlo1hw0SzaaJQdNntuyzbjIcI/F8IzG8Iz5rpNB82ag+8aj+wvQfXJL9qxUh4VvFrp/KsJXjHCnI1wvoTvuR7P9G5rtR+iOH6A5fozu/CmG+98Q7j+iuf+M6v0rqq8HmrcvmncQhnsUWNPR23KIV03Gvq+E8tcHsj/3Hj7v9xO+GP5PnF51J9ZjTxJr7I3uGpbq7glNwAgXYETzEbE8tFghWmwSIjYbI7YQPbYAPbYIIVvRTEezwNwDoLhLSLgKzHbUhCOHmHU8UUs2UWsREbldXG4ucS4k4VqJ4n4T1fMhmm89mn8zun8remAbul9qK8L/FfjWg/dD8L0N3tUgoxrPYoRnASnAzyHl0mek2kY9UzDcJQi5Z0JOuK6JGO7UaB47S8xJAPdMM+oR5t8l4xtZW5HRzbsYgZug34wR3ZECfWIfhgn6YxjKcQz1RJeOYahHQD2MISOcLkcv45vvRzemo098jUh+jaZuQRWbUbT1aKqEvXwcwicY8U/QEx+gJd5Fu+no42sw4qswonIH51R0Xx66cxR65ysoLc8Sq+9BoPoJvGWP4yl9HPfVR/BceRjP1b/iLb0f37V78F/7E4Hy/yRUcRfRyj8Tq76PWM195iMRIrWPEql7imj9C0QbehOV7r65L/GW/iTkIxc6pQEahGIfiOIcjOoahuIZi+LLQfNNRAtK57sYPbwKLbwGNfwOSvh91Mh7aJG3U3FOaAHx4AzT1cd8OUQ8Ywk4huHpHIC7tR/2egn6F6g93ZNrex7lzJf3c+itu9i+8Hd8PulXvJ/zC95M/1feGP0rVo/+Ja+N/Q1rsv7A2/n38fGUJ9i86Fn2rH2ZE5/159LXg6k4MIz6U6NouTCCjrIhOGoG42kcQKB1IGHLMGL2dBKufOIeuVFxJorM1wML0WUE89+CvqQrzpEF2Rkm8M1HJ4QWYwTkHo45CM8kVHs2kbZxeOqH01HelxvnHufSod9z4pufcnDrrRzYcitHtv+UU9/+kouH7uDK8T9RfvZ+qi8+Sl1pLxrKX6K5sj+tNRL0Q2mqG06TGd+MoaVhHM1mdJNOQ20G9TVZ1FVlfQf7G+XjqCodS8XlMZRfGEnZueGUnR5IY8U8lIQrBfobB97mwrqBdJ4qIdEqIxcJ+lnmElhu1hHmhh0J7VUIxyo0s11S7mKdZkY1avtEtM5J6JYpCOs0hOyBl/m+CfoFCOcShHMVhgn6VGSj+zaaD2ESwT2I0AH00CFE5BAivDfl6AObEb5PMGROL+Mk6ZhtmRhyu7YJ7z4Iy4vQ+SKG5TkMyzPonb3QO59B63we1fIiakdf1E7ZQjYQrUMWnIagtg9FbR+Cah5L2A9H7RyHKsHsmIjmnI4uu3zcixBuWUuQ3T8pF4lnMUgX65GT4Ew0xxQ02wQ0Sz5q+3iU1kyUlnTUpnS05mzUliy0tnGIztFgGwWO4WBGTMPQ3SMQnrHm7kPDU2I+n8WQzzeR2aFHQj4T4ZCrlacQ1jvQO36C3nELojMN3fJDdNuPEPbbMOx/QLffjea8B831AJqzG5rjefSOQWgNY4heyaJzZzZXXxvGvsKH2TjyX/hq/M84s+o/sRyW2+NfQHf2hcBQCKVDKAsjnI4RyUDEMtFjsqg7ARGbhhGfgxafhxafjx5biIguTim80KxFqH7ZjTQRxVWEYi8wVxJJWwkJx0zizgUknK+RdL2D6v4I3bsezfcVum8bmv9r9MDXKeD75UpuA4b3E4T3HdPJSwePuyuLd83uimPkM22mmDeYkNfPPRHDVYzhLEY4i7o2i90c88FRDM4ScElnL5+DswDds5RUp9QbCLlyCHwAoU+/5+h3mUXY/x70BzFkYVbpKsqarZey1VJK9tjL3nu5maqrMCvzetmBo34J6mfftVSKxDoT8lrybUT8TUT8DRP0IrYILTodTT5nxjka3fIqSqt04j3NZxV5yrvjvtYd95UncF95BPeVB/FeuQ9v6d14S+/EX/ZHgmV3Ei6/m0jF3UQq7yZU9WdC1Q8SvPE4odqehOt6Ea5/jmjji8SaXibR1o9kxwCSloEkrQNQHANIOgeS9AxH8aaj+vLRApPQQnPMgqwm+8pldBN5DyXyLmrkLdTwGyihFSSDC4kHphHzFRLxZPH/sPXe4VVWWRv3vNMcnWobu2MZOxZAEZXelRZ66C0QIKEXqYrYQBC7ozPWsZfR0RnH3gtIh/SenOQkJ6f39uy9f9+19hPe+a73+/7Y1wnghSHJuZ+117rv34r6phH2TiLQVEBHzUiajgyj6rv+HP6oN9+/dg0fP3YZb9x5Hk+VnsnOWadxz6RT2TrudLZOOIM7J5/DtukXsmPeFTy+vCcvbunLO7uG8unfbuOH18dw+N8FVH4xkYbvJ9B2aBydIvR14wk3TSDWNtV6+dP+JaSDq8kEN5ALb8aJ3GlZNk5sC//bukkcH8SK40bO/1foTXwjJiazuC1IO1Ns0RLSTLbMJFQ7hdajo6n+oQ8HP7ucL989hf+8+gs+ePUXfPzm7/j83TP4/oOL+PGzyzj4zdUc++EGqvb1p0YspkdHU19RQF3lBOqqJlFXPYW6rqFsXfUMaqtF6GdRVTGH6rI5VB2dS8WRWZQfnkn5gVmU7Z9N2f5ZlO2bSdmeQpor7iKf9XcJ/bs7+PCumzn02kRCx1aTF897h4SVVmF8x6/DboVrq+tOsT1Ka8b1xkviVbdJEGcVxrvSBnJUu1zpZYgqQn+ntVEav1zBH0eHnkFFXsaRlk1MhP7fmPgH9kjrxsTewEReRIWeRAXEnbPBDqO0d6qFL5nWEei2gejWvujWPujWm1GtN5L39CbvuYWcZwC5lsHkW0aQbx5lbWT5pvGWBSMYANU0ASUugxZxGYh3eBG6Q1o0G1F+CTKJdWqbHUyLY0aH70BLwMJ63cWBIkKxxnXhSNKubTFOcxFOwzyc2tnkq2eSq5hFtnwGuaqp5GsnoxrHo1tGY9puw7Tfim4fge4Yje6YbIfLdCyBzlKUbxHKJ17mAnTrYLTnWlTzWaiGX+HU/xRV/1NM408xzb+A5lOg+UKM51JovQjTeiHacwmOpwfZupEk9k+n9Z3J7N02nPfm3ciLE87m5bmn8PXOK2n5dz9S5UPR3qEQGg6RURApwEQnYuJTMIlCTGImJjkPnV6MzizHZNagMxvQmY2Y9CZIbYHUVkhK0GSbhVw5YUn3rrKVtX04d6y2MxbHfw+O/2Hygb/iBF9Ah15F2wreFXkdex0dldnMC6jQ31CBx9F+sdze09WHvx3T1Z7RnSvszEZ1uhgLscWKPdb2sX0yUyl2T2cxWtgtvgWYjkW4X+PlYHv3m+1AXQbpUtWr0MOYyOOYmDBuXkQnX7M9ei09+ux/MNmP3dZNXqp6OZ9g8h9BXvr0butGqnllnTdisZQAlSvy2nkTlXyNcNNf6Ch7kHDdw6jQXyHzNDr3FEZeJSSVewSVewidOS7096FT23CSG3EklSoOmNZx5JtGkK4dRLS8L8GD/fELuuTHPvj33kjnnh4E9l5D8McrCO+7jMiBS4geuIzYgcuJH7yM+OHLiB25ktjR64geu4FIeW9ilTcRq+pDrLo/idrBpBqGkWm6jUzzaNKe0WS8o8h2jCbbOY5sYCq54DzyoRJykTXkbK/+PpzEbvLJR8hZwZdWzoO2hZOJ3UMmst7t1YcWEPfPJtYxnXDbZPyN42irGEX9gSFUfN2Xg1LVP3817z1wMc+tOY+Hiv7IvVNP544Jp7F54h/ZPOVM7px5HvcWXcqjy3vw4pY+/POhoXz27K3seXMURz4YS+UXE2jcM4G2I+PorJpAsGEi4ZZJRL0zSEjCPbCMdHAd6dAmawV1k72CcxAX0SaUtGX+V+hL0cmS/yP0t0NcnDkbMfFNENtkjQsmtBJH0vNiIa2fQnt5AfX7BnPsq2v57t9n8fEbJ/KfV3/Jx2/8ns//caYV+r2fXMaBr7px9Lvrqdjbj6oDw6k9MpL68rHUVYyntnKiK/RVrvvm/y301RWzqSmbS/UxEfvZVB6ZReWhOVQfmkfN0SLqjy6k7tAcvHX34+QDrtBXitBvuZFvnxpB6w8lZFo243RsQPvWYnyr4X97nNILFcuaVLt3uZW29M87JM16uyuWHSvQgizokOj2BvfPjgt9YDcm+IRtySi5skffxIn9ExV/H52Q8x4q+jYm+gpGhnHBR9D+O1ECIhLynHc8uu1WW8GLwBtPb4znBrSnJ05rD/KeHuRabiTf3Jd800DyTUPJNY0g3ziKfONYF/jVOB6neQqOhCe8s9HtggpY5YqJXOdF5GXAEt2CikoPTzzD69FyVY2Ie2YdJiyDyXXo0GqUVJXSVCbhUAAAIABJREFUy/cuQbcsRDUW4dTOI18+k+zRGWSOTidTNpls5VjytRJUGYxuHoBquQXVIjeQ21Ctk22bBe8sVFthV1tqMLrlJlRTN/L155GvPgWn6jfoqhNRNSeg606ChtOg4Vxo+BO66QxU06moxnPI111N6vCtdHwwkYO7hvDe/Ot5buwlPD/lDD7Zdjn17w8mcUTmFyMxgaGYyKD/PTo6HB0bg4kXYOKTMcmZ6PRCdHoZOrMKk1uLya6D9O2QXA/JzZC8E5Pchu4Sex0RYuRq8K/ASG9cnDGB+9GBx1EBcVe9BOHXMeE3bPtOR15BR2Ve8wwm/JQ7qA/sQvvvdofc/rVuz91W7aXozpKuswRlsw5S7RZjRNBlYO+bb4+11Eqk3zcX4yvC+AQJIS0cGdZucGc/Yt8NPIAK7rbtGx39Czr+HDrxMlraN2mxTf67S+g/Qec/7TrysbRtpEcvzpt3ULl/4EhyNvcWdAm9yr9FPv0q0aanaPj2fsr/uZ6yN5bQun8jKvkkOitC/5Qr9JnH0dnHMOlHMNZeuROTEp/3NlREHETCeyoi1zyddP0k4lVjCR0eS/DgGIIHRxE8MJzQwSFEDg0gdqQfiWN9SZXJGUD62GDSZYNIlg8gUdGfeMUAYpWDiVYOIVo9hJicmqHEa4eRqBtBquE20k1jSDcXkGkdQ6ZtNNmOceQ6p+AEZqJCReQipWSjq8nHt+Ak7ief2EkusZts/CFy8d12MCtVfS66hUxkNelwKangQpL++cQ6ZhBqnYyvvgBP+W00/DiEik/78uNbN/DZ37rx9v1/5tnbz+XR0rPYteAMthedwfbic9hZeiEPr7qSZzb34o0d/fngL8P56pVR7Ht3LMc+nkDN11No/rGQtqNT6KwqJNgwlUjLdGLeuSR9i8kEVpIJbiQbuoN8ZCtOVEJT4qe/ExXfbGdRKr4KZV034qVfYUNTtmXTJfBY4uUWjO3Ty8xqA4SloFlKxjuPeNNU/FUTaDk4nIqvr2fvhxfx2dun8J/Xfs2Hr5/Mp/84i28+uJg9n17Bvi+v4bAI/Y99qD4gxNbbbDq2zlb146mrnERd5RQaKqdSVz2dmqoZ9lRXzqS6fDbVx2ZboRexrz06l/qy+TRVFOOpKMFTJvOxXah80BX6w2/dx9tre/DFI8No+baUdNMW8lKNd67H+NZC50p7jr9BjG1r3I2WQW3gHrcKtrZDaXus7LJCrsJ0rMV0yCDzDkznvV1um+NC/yJaqvroW6j4O6jE26iYVHevoMPPum92/3aMbw26Q/rdhSjvKHTbEFvJG8+N0NIDmq9Ft1yD03INueZryTX1xGnojdPQh1xDP7INA20SMF8/knyDBEQmuq0a7wI3fOVbaW144rW2YSYBSdkwxTp0bI17uhJyJroKYmsgdjsmJk4UEXvXcih9YBF75VlMvnExqnou+bI5pA/PJXFwGqkjBWTKhpOtGEy+uh9O3fXkG24k3zgQp+lWVNNYTPNodNMwVGN/dNPNqMYbyNddS7bqKnJll5A/eh766Jnky07DqTwTU3smpu50dN3JOA2/Il9/AvmaM8kc6UHwk2GUPTGIfy25lr+NvITHR57D28sv5tjLA/Dvk6pwMgTGokNDcML9UJFb0NGbUNE+qNgAdGyQDWaZxHh0ahY6tQidXoHOrUZnVmNSMrBd44p9Sir8Lej0neik9Ds3YaLyQFxhPeAmtBGC92KCj6ICz2BCL4L04cOvggzk5fttBf5hdHCXHZIa/9auOYn87JVC52LoXGjpiscF3RX1InSHnPldrBbhtRw/EuWfhZEbUqcIvlT48pBYju5cbcXe+O/C+Le7ts3wbnT0cXRMAlNS1b+OTksq9j3I/Aed+QiV/Rgn9zFO/iNU/kNUXrz14rh52+IQVO4NyMp507ZusslX8VbspOObbXR8cT+Vr63mq3tHs+fvU4m3iIVSXDeCQX4anX4KkxIs8uOY5GOYxGOQeBgTe8hFNQS323mVar+DvGcDmYbVJKpXkaxeRaJ6OanapaRlZlYvP4OLcJoXo1qWoJqXo5rXWlJs3rOKnGcZmZYSUi2lpJuXkm4qJdNUQrpxEanG+aQbZ5Numk66aSqZ5ilkWiaSkVSu9Ow7pF8/ESdQSC48j2xkCfno2i7uzQ5y8Qet0GfjD5KL7XDtlvG7beWfi6wjG5b+eClJfxGR9ukEWybhqxtnUb8NPwyj/JN+7HvnBr54/iree/TPvPHAhbx87wW8uO0CXrz3Ul7efjWv7erFu48P5MNnR/DVq2P58Z8TOfLRFCptf34mnv2zaTs6i86qWYQaZxH1iIdfmFWlNtWeC24hF9qKE9naNYyVn1k5d6Dim3Bi63Bit7sIBHHiyM+zFXVh3my1qAShd9ohbvROG2ojtB4jYcr2hSSaZxCqnkjbIXEU9WH/x1fz5bvn8eGbp/OfN87i47fP54t/XcK3n3bjxy97cOi73lTs7Uv1gUFUH5H2zUjqyqSqL7CVfX3FJBoqJlNXNYXqqqlUV06jpnI61eUzqCmbSW3ZHOrK59JYMZeWyiK8VYtor16Kr2ox0ZaH0PlQl9C/9wD/2NKL754fi++A4H9F6DejOjfZoSzCR5Hepq2E1tkqSwfEVtQl9uIpl5CV9Lalx90hVa4I/Rq0Tyxu0tPeat9Q0ndV4adQkWfQkeeRag6p4CMvoUPPWcsl/kfcwWeH4BLEaTMVp3Ukjmcg2nMzuuV6dNM1qOYrUU2X4TRdRq7pMjKNV5Bt6GbF0antTrauJ5m6myzLWxCvtj/fOgPHuxDVIZWduDNkmLfBdWOENqKlWpfKPboKHVuGjpWi46X21cSWYWIrILYKE5M/X4UWpICEiaR69S3FeEvRrUsxws+vXkSqrIj4oVnEDxYSPzCW+IFbSR0eQLbsBvKVN5Kvvol8dV9U7SBM/QB07c04NT2Rzz9f051M5XWkj3UnfaAHqb3Xkt57FZl9l5I/fAm67Dx0xSk4lSehqn+OU3UC2SPnEvu6N9Uv9OH9lVfyxMhzuP+Wk3lqyml8s7snrZ+NJ1U1E+OV4XABKjgEJ9QPFboFHeqNE74eJ9oDFe2JivVGxwehEwWo1Gx0ZglOZjkqvRKTWoVJrcEk10Lqdkx6PSq1wT1J4XyLRXMlJipHeDubLfVPhR/BhGXI+qw9Jvg0KvgoTmCX/XlSgTtQ4qSRnzUZoPoW2WrcrcqFwyLCPQvtk5i+ZCNmWIa6m5WQvIT82o3x645ZuEeSnrNQvnkW9qbFqigPZjt/2oTxb0MF7rPBNxN5BNNV1avkK5ZzI/gD0u9jMv9GZf6DkxM//QcosVbmJC37T4tGEPYNmTcg/Roq66KK021/ofnjtXhfX0Ddc9P54t4R/HPVLXx01yhqPlyHE5JW0V8xiafRySdQ8Ucx8UfQcQlvPWzFXifl4fOo9fkTfRzCT2ACj6J9D+O0P4hq34Xu2Olal333g5zO+0AcSuG7bSqb0G5rIdWRh1CR3Tj29WH7eybk5lmM/0m0/2GUbzt5313kvWvIe0vJty+2oMBM+wyy7ZPJdIwn2zmBbGAW+eAinNBqnPAWHOHRS0LWVvfbuyr6neRj28nH7iUf3UYusplMeD3p0FKS/gXE2sWSW4i/ZjzeI6No2juU6i/7cezDXux7tzvfvn4Nn790NZ/9/Ro+f6UnX7x6C1+9PoRv3y7gx/cmceSjqVR8MYOab2ZRv2cOnv1FeI/Mp6N8LoHqOYQb5xDzzCcpuHHfCnKBdeRC/1foxWIpaVe5ycu8SW7zkoM57si520UaC+0yJhRLF1ks2GLZZ2Ai2yAsVt8N5L1LSTXPJ1I7E9+RCdTvGc7hz3vz9fuX8dFb5/Of1y/k4zcv5fP3uvHNxz3Y+8WNHPymL+V7BlG1fxiVh0dQfbSrqi8roK58HPUVE6gvn0RtxRSqK6dQXVFITUUhteXTqa+YRWPlPJqq5uOpKaK9phhfbQmBuhUEa0tItD2Mdroq+vrvn2LfK5No+LKYSPVaMq3uINV0SiUugSd3+Gi9yf611jeqAwLhustNrlqMwTZ05yZrtxNLohE7pE+wBZIkvR3lF7eEuBweRNk+/VO2PWPf8GF50/8V3fk4xrcbIy4dAW95izFtU9CtI902R/ON6ObumKZumIZLUI0XkG8+j1zT+WQbLyBTfyG5uj+Tq7uUfO1lZOu6kWm4gXzjIJxGaVVI4k/Ss8ttsIbAegisg+A6GxLS4dVocbxEVmAiIuqLMbFie3RsETpWgoktxcSWo+WIiEXWQXiD/TsIrHGHffKAa12H07SKbH0JmcpFxA/OI/T9VMLfFxDbO4LkvgGkD/Qjc+QWssf6kq/oj1PRh3x5L3Ll15CruJJs+VWkjlxDbP/1RL7rRejLXoQ+60n0q2tIfd+N9L6LSB88g9yR01DHTsM5fBbp766i9c0b+OKOq3h83BncdcvvuWfA73ht2UWUv34r4UNFOC1LwCckv3E4nUNRgQHoQF9M4CZUsDsq1A0dvgoduRoVvREVG4FKTkeninESpajkckxqJaRXo5NdJ7UalZGzCp1e4z4EUqvQ1qa5Ansdjm10PcuCCg6LN/4htP8BK/ACiHI61+AIUtq31J3J+IpcwJYNvE1D+6bZEJzukOzEFDvfUB0TUR0T3NM+EdU+CdU+Bd0+Dd0+Hd0+wwV22cDcHLT9O4+H4MSVc7vNQdifZYGeRXZiosK8EbDZi9ZPT/otSL8L6fcwNjwlASpBIvwTsu+C+O1Tb+GkXoOEOHZeJJt5Hif1HL4fNlL+5Dj23H4dn5R244PSPryzqD/vlY5k75PzSbUIJvdhVGwXjiw+SdzttsDiwka/17pa8gkZdt6PSogLR/jp96Oj9+BEtuHY5PXdVtARwYmIE0SS2HdihOoZFXumBPl2YATJHNuJTuzCJB/CJB92X+XWkHjcpoLtv1tuNNFnMaEnIfAwMlsTLIm47hzfWnIdpeQ6FpLtmEu+cx4quNiy6vORdeRFKOP3ohI7UMmd5BO7yAmrPr4LFX/ACn4uupVcdD2Z0HJSflncM49YiywIGo+vYhRth4fT8uNg6r8bRPWXgzj2SX+OfjqQY58NpfzzkVR8MY7yr6ZS9e1M6vfOpnH/XFoOFtF6ZCHeY4vxlRXjr1xAqHYB0YYiYi3FpLxLyfhkrraRfBe6xH7tIvfahSKyVMSRZTThezGWhXT/f5HWMfm6yef/IDomADw5D7pHoHiRHSBYjc5tFsaY9ywn2bCYUOVsPIcmUfndcH74sBefvduND1+7go9fv5LP372Orz/sxQ+f3cL+rwdw7PuhVOwbQeXB26g+PJKao2OoKSugtmwcdeXjrdjXVkyiplzOZGrKJlNfMZWmqpm0Vs+jvXY+vroiAnXFhOqXEmlYQax+GemORzFOV0UfqHmF1h+WECxbQbxhFRnvOvLtLsJAAkI23WpbMyLYIvSC1N1gxVsJj8YKvdgQ5cEgrRqpxqTCXWEZNU7nOpQEWwJ3YwI70WKZFC994HHXUx98FO3fjbI/THeh24VwucBS9ZDtN80D0U29MY09MA3dMPWXoesuwKk/i2zjH8k2nEG2/ixydeeQqz2fbM2F5Gv+TL6+m92ko2TJgmcKRq730ucNrLEQMRNY735s6ZDLXCpkuMQlQ0pqNSJJ1QVoe4rR0SXo6FJMdMX/VqpENoEgDoSDbiP34i3fYnMHAnJzPGvI168kXbaM2L6FRL6fRujbsYS+HUHo+8FE9vYnvq8fiQP9SB66heTh3qSPdCd75ErSh7uROHAtoe970vnl9bR/3APvB9fS8VE3/J9dRfCby4nuuYTkvkvIHryY9A9/JvCvKzn68GW8NPsstvX/DRt6n8DuCefz2a4RNH++mGTNBlS7DNbF8y8iOAbdMRzjG4jx9cX4rkd3XoUOXI4TuNSmcJ3IQJzYBFR8nk3ayoDKpJZhbGW/Ep1eiUovR2WWojKldnCr06swGfn9pda1o5KlqMRyVGwNTmQDjqV33ukSO/0rLVNIcMoS9Re2vlAUJYYvoTjTLosxJnW9TsR0yBFH0nh0+zg3JNc+FtU+BuUdi/KOQ3snor2TMd5C9/Yi/BbB8HYUYToWou3QWxw6clOV/rcwZSQ5KzhjwRY/jhLBS7wAyVcg+RYm9Y49KvkPVPJtdPJNTPJ1m/lw4i/gJJ6D2F+tYGbELpl4jOaPinhvyaW8NuFMnh99Pi+Pu56/T+nFq/OG8M662/DuW2mpiDmx+UkYJ7kOlVyLSq5HCUExsQWVvAOVkEGh+LzF1rjaFhpObCkqKuhg4TctQgcX2kyGDgnVtAQdFpTFZrclGboLIneCZCHid0Bimx2g2zi/RPql5Za8C528F53YaR8+OvIYKvQ4WvAkoScg+AgEd7opcOH9BySEtgoVXE5e9imEVpELb3B73zFBI9xnA1XWlRN/APdV7Jj3oOLSH99ILryabLCEdMc84q1TiTZNIFQ3ls6qAjrKxtN2ZAKeg+PxHJxA68HJtB6aRuvhWXiOzqH12DzaKxbQUVGMr3KRbVV0Vi8hULWYUNUiInWyjGgJSc9S0t5VFiusAkKOlQH8DlRIjthrxXUlZxcmtAtsgE5uQAK7k4ewexx5FUxGRAb3D1kYnhIoXngnhHaCfwfGtxXdvt5iYGKC+KiYT9PBqZR9N4YfPx3MN+/15ot3rufzd3vy1Qc38v1nfdj3VX8Ofz+YY3uHUb7/NqoOjaL6yBhqjrlCX2sFv4Ca8nHUlI+ntnyirfCbKwtpq55JR+1s/HVzCdUXEWkoJiYI8sYVpJtkd8Vj/xX6cPPLdBwpJlyzhGTTErLepTjtErvehOkUod+G6txqxVpJrFzA/VboN6Gkl+oXOJkkRre4Qt+5pquil16ovIldy6Lx39HlorgfI1+Uzh1updB5t8UKOL711sOuxH/eNtlW8jT3xzT2Rjd2Rzdcha6/HF13MbrmPJzaP5KrP5lcrZxTyNWcTrb6bDLVF5CruZx8ww04zUPc5QpeYd8IlnhFl7jLujmx5wm+uMQNLYWLMJH5XWcuROZiLHdmHjpS5MLGoqU28WpknVp0HUQ2Q0TSi9sgJITEeywSwfHdh9O5zVI7nZZ15GpXkjq6hNi+Ofi/nUjHV7fS9sUgvF/2wfdVH/zf9SX4wy2EfuxNbH8vkvuuJ77vBmJ7byTwzY20f9ILz7+70/xeN1rev4qWD67B+8k1+L+6hsi3VxP77hICn5xL3Qvn8NGq03lw2K/ZeNOv2DriD7y6pj/lb6/Bf2gHmebd9mouty/TUYL2Tse0FSBOJtoGYtquR3uvQHX8Gcd3EU7gClS4Nyp6K8q6ceZZj70wdOyANr3cCrtKi8AvQWfEobMUnVmJzqywwq/SJahUiYWsOfESnKigol2mvmXpdwqaYqpbpdtqfHKXUEsobhzGW4DxjnEfSu1jMBKOswG50V2/J69yRqJljuMdjfYWdAXqJmC88pCQCn8Wpn0upl2GtceZ/WLLXG0TtSa43gLnTFgi8UKPfAKdkADVC5B4BZN41YLOVOJllDhz4s9ZAJpgE1T8CZzEoyAVX2wnudR2CyWLHC7ljcWXsnvw6Twx8nJeGHMDL467lr9M7sHTc3tz9N1ZqPBd5CNbrPBJME0lZVC72Qq8Tm7GSGsstRadlNDOckysFOS2GZXbpnCRpqGD41CdI9H+UZjAeExwKtomr0u7SKhyExUHSTEmuQiTWopJrcAkVqLiq1HxtUgCWsUFz7vZwruUsObDUulKdbvT/tuI7nYBcBF53W1FUYtrSeiisiMgfCf58GabOhUUQl42UcXvRiXuQckNpeto+bUsL4luIB9eSS64hGxgPumOmaS804h7RPSnEa6fQahOznRCdbMI180lXL+QcMMiIo2LiDYuIdJYQqShlIitZJcSqVtKtHYpicblJJtXkGldQ867gbyIsMwVg0I/lXT+g9hUtLS17HkIIygMEXgr8oKufggVc48jr9GH7L9bltWo6E6U8Onlhhp6AIIPQOBem5Z32jeRallNrGEZvopimg7PpWpPIce+GsOBT4ex5+OB7Pl0IHs+H8g+CYx9P4Rje4ZTvn8klQdHU314LNVHC6zY15SNpbZsrFvhd7Vymiom0VpViK9mOv7amYTq5hCtn0+8YSHJxiWkG5eTa16G43sEjlf04dYXaC+TL+Ickk2zyXrmkxN/uDhpROA770WJmIvTRoTdhlZE5KWq39zlwJFqfvP/EXoJsIgtTEIsK9EdazAdt7tp0o5N0LER0y59+NUuY75dBH4aylOAah6Gkp2WTT1RjVfj1F+GU3cx+dqLcGrEhXIG+epTyVX/gVzV78lV/Y5M5cmkK88iU30J+YbuOM2DukReXBfi1hBhX+Ge4DJ0cJEFm6ngAlRoPjo8BxOehQnPgLDAxKZj5ERm4IRnoSKCFRDw2HGxX4MRN0REEqNbIXY3RIRS6TJZVFCuu3Kd24hqWUm2ZhHJo/MI7Z1G+1ejaflkMI0f9KHp37fg+bAvbZ/0pf3LW/B/04fw9/0JfTeA4DcD8X0xiNYP+9P07k3Uv9WDhje70/BWTxrf7UHbBz3p/KgHnR9cTtOrZ7Lv/tN4YfLv2HzjT1nf51c8Nu8KfvjbItq/e4x47QvkO15Cdz6N6thhwXXiFDLNhZim0ejGIejGG9DN3dCtl6G9l6A6r0QFe6EiQskcg0nMgMQ8TKoYnRZhF1Ffaj826WJMeiFGft+6dOQhUIpOL0WnlrlCn1iIii1Eh4tQwTkov3CJJthqXHsl2ewe1XYr7hEr7QhU23B0+xB7TMcwjNxCxKIqVlVv12k7/rH8HaPsw8F4x9oHhW6fiO5q6chNwd5mLOxuoVsA+Je7wavgRmutFWGTvrgsITHxZ/73KNk8JeCz+GPo+ENW1E1sByq+3YoYso1IKmRxZiTlvbGBLx4eyta+p3Jn37N4eNjlvDD6av4y7hqennkDPzw/zrYSBEksVj8lrRupqlNirdyOydyNyWyyMxCZiUhimfgSy0TSthCZihFbbGgoBPqA/2YIDMIEx2DCk9GRKTjhKTjRKej4ZFRiEio5FZWcg0rMQyfmoOMLMInFro0wIalQacdJMEgSoOu7mOxbIHk3xLdDTET/IUz8SXT0aUz0r5jI06jwo6jwA25LSfzp0lqyqVMJ2MmKvm2WEinr++SouPy59MNvx4muIB8uIRcsJucvItM5h3THHFJe6a/LmUvKW0TGW0zGW0LGuwyb0fAut8s/Up4VJFtWkGheQaJJqtlVZFvWkG11uxNKRF4MIQJKPC70IRF6QYjL2Ymx2OpdqOMtLvva1baR1o1t4ewEu3xkh0Uzq5g8sO5BR9xdB8LNQdo/QUGtbybTuo5o/Ur8VUtpO7qIlgPzqP9xBlU/TOboD+M5+O1IDn53G4d+uI0je0ZSvm80lQfGUnWooEvox1JzbDQ1ZXLGUFcxjqbKibRWT6G9ppBA7TTCdTOI1s8mUT+PdONCsk1LyDUvJ98iuaCH4XiPPtz2DJ0VhUTrppJqKiTTPJ2s7CK1HmgR9u22ShXAmK3erQ1RrrobbHJWcLDaL7wXgUtJW0B61Su6rGxL7DVZy8o77xI7rDTeZeAtBW8JtC3BtM5HtxWipbL0jLCtGlvF13cn33A5+fqLydX+iVzNueRqziZXdQbZypPJVv6ebMUfyFX8jkz5r91fV51Lrk4GtX3RngJ02xzoKAXxYAfWuC4QCSQFil08gYiNxRTMxoRE5EXYCzGRSZjQREx4Ejo8GSc8FSc8E0c4NIIniJTa9o2OijPHZWDIZF56py6eeBcmLElg4ftswbSvwmkuJlM1n9ghqeoLaf14DI3/HErtWwOoe2MQje8OpuXfg2j7eDC+L2+l86vb8H0xivZPxtDyr1HUvzmEmlf6UfviTdT+vTe1r/ai6c1etL7Vi+aXrubog+fzYcmZ7Br0W1Zd9xO2DPstb20ZSt0H9xAtf4FM65so/zto30vkPA+Tb9qM01CCqpuJU1dAvnYYTm0fTEMvaLkeWnugO3qg/DdZvIKOjsbECm2gSifno1Mi6CLyXUKfKobUQkgtRqdKbSvHZKRyXIZJilVN2jcLUPH5mKh8radgguNBqlDfrSjvQFR7P5S3H7q1vz2qrT/u6Ydu74Pu6INul48HoNsHob2D0d4hNg+gvfJQkEXXkpR2X3XbSEybVPijbVtHe+WhMgnlm4TunGIJp+5GrhI7ULeLPoR0GdmOlsFsTLDFT1rRVzIkTTyEiu+yO0bF9iiLpWVZhV1YkdiEtpZT8VqvhcRaTGYDtV/O44Ex57Ci+4lsG3Qhz47ozrNjrufR8Vfy2RNDyYe2QlZ65zswqZ0gqdiMe3R2NyrzICp9HyZ9JyYlMfwSdHQ+Tmg6KjIKHe2HDl+HDl6MDsjphgrdhAqJbXYwOjIEHRmGiYl19ja0RVlPQ8dnoRLTUYlp6GQhKiVnOio1F5Va0FX1C73RncuI+OukrNmTPar3kk/uwpGvR0IeiJJBeNwub5H5i7KUUVkYvg0jzhT5OoljRXDAMsS0Yi87W+/CyA1CbhSxFTiRZZaSmQ8vthC149vgZCOc7K3VoRWY0GprgJAsi7LdgrU26JjvELjYalvBO7LBzrsBRxL6PhmU3ovp3A5+cXUdJ83KrEi+z/dhpECL3oeOyQNIHtyyXvC/26csytguKbkP7ApCaT/dhbJfC0mJb7WL0mXhvInITGSbbefKzTkv+O2m24nVryVUvRpf+TI8R4qpOzSXqgPTqThQSMX+SZTvH0/5vrFUHiig6tA4qo+I2I+lWoReFpCXjaZBQHDVE2ivEwJoIaG6qUTrZxBvmGUdU9nmYvItJXbftjiutMw8jwt9vO2vBMtl/V8Bqcbx1lKVbZ2N4xVipFTxD6D94oqQSnU7TkhWuMlmJBnICh5BvpDUB2yxAAAgAElEQVTSlpFhrPiTb7cgMNXp7jN1pFKXdox3HqZtPrTOA89MTEshpnkSuqkA3TQCLbbCxhvRDdeh669C2Sr+Ipza88lVi8D/kXzlaeTKTyF77A9kjv2W9LGTSB07kVTFr0lXnUa29gqcxgGo5gn2/6nFmheQvrwMYJehOmXRSDFOQLDF0zE2nSpX30J0WM5kVGQiKjwOExqDDo3FhOTjSejQNFv16/ACtADDosswx09kOdLO0ZZWucWiiq3jQWybnRtAbjOepeQbFpGqXEhk/1w6vppK478KqHhtGEdfHkz5a0Oo+scw6t8fgefDUXg/GUf7p5PwfjSZlvfHUfvGrVS9NJjKF/pS/nwfKp7vR+1Lfah/8XrKH72Sr9adx4vjT2bzDSewpufP2DXpfD5/dBot3zxgq3nV/iba+xr55mfI1u8mX7uVXNVSMhWzyFRMJl81DlU7HN3QHy0PSgmjSTLX1x/tH4IO3WoDVTo+HRJFkCy1Am5F3LYCBIu8CLeyFzumtAgWW1SytAq0HKFpJhZg4jPRsUmoyGhUcCi6cwCO7xac9l5ob09MW0902/U4bb1Qbb0wbTdgvHJuxHhvQntvQXv7Y9oGYbyDMd5BmDZp041CeUbbG4DjHYTjHWxvBk7bcBzvcHTrWEyrtIYmYGxgbSK6Q9bnyTLs1aig7KG9F2WHbY+DVO1SrcsDXOBXUqHG7gCx20UlLCP2u9vdCji5Dt3lQDJiOU1txuTuIlR/O68s686ya09kY/8LeGL4lTwz+lruH3EBnz8xCif2AOQeRYulUjz06SfQ6UfQmQcwmd0YSctmtlsLKwlZIFOMic7ECU8mHx1KPio007Mwod+hO89AdZ6HE7oEFeqJiQ6A6AiMtcsOREVHQrwAxDabKETHC7E7DuLD0fGB6MQQVHwsOj4Nk1iIE1uIkyiGdIl9sCv5HtoB/IauGcJmdFJS0pKjuN9dth55FB15nHz4EbsNTMtwOLrBHuElibhLO0csikaO3H4SwlNaRj6+glxM9tLKJiu3PSoVv7yvdEyCSmJtlpapIENuR9nwokD2RHc2uqFHn6CDxTUouzCktSxsJEm3u8dmZexOiLsQVo2J3u1SLMVBIw8ie+7vurk8iInvcvfKJu+xw3JZPiJcelkgrhNCvJT9s8K+2YyOSrEnG6nEvSefp2RK5PO5nWz7WlLN64jVieCvxFe+lPayJXjKimk6Np+GY7OoPTaV6qNTqT48heojE6k+Op5q6dWXj6OxsoCWqjG0V4+ls6aAUO14onWTSNQXkmqYQVa6MS3zyHkW4nhKLMhQd+76b0WfaPsrISv040k1TCbTPI1s6xxX6Nul977TCr0FjIW2o0Tog4ICEHKgCPydCC7WCr21xq2zw1gJs1g2vHU+TEV5pe8+GdMyCdM8FtN0G6pxKE79QFTdzai6Hqj6K1F1l6BqL0DV/gmn9k/kq84lW3EW2fLTyZadSuboH8gc+Q2pwyeSOvJLkkd/Rar8ZNLVF5Gr741qHov2yDBPUALLIViK8Ze4Iu8T5PBCVKdsvJ8O/unowFR0cAo6OBEVGo8KjUOFxWM+Ch0a7Yq9MOHDggwWOJmQJYtQMRnUzu86RS5PPrYMJfZLG7BaY6PRwnGXLIL2LsfxLCPXsIxUeSnhHxfi/XwGdf8cz7HXbuXQS8M4/PJwyt4cRfW742n4VyGN/55B079mUv/uVKpeG8exF2/j8HODOfD0IA49NZSjTw3k0CO9+OqOK3hj3nnsHPBr1kk13/ckniu+hr3Pzcfz/XbiNc+QbXqBVN1TJCsfIlm+ncSRTcQPlZA4PJPUsYnkKsfi1N2GbhiGbpG8wmBbNSvfEJR/BCY4Eh2ZhI7OdLdYJZaCwJ6kWrdCL0NaEXpp3yzApIswtjJ0+8IquRidPC70s63QqNgYVGgo2t8Px3cTqr0HxtsN03oFTuuVqNar0W1XY1q72Vfddh26TR4EN2Labka39XNT0m0D0K0D0B4Re+EdDUC19UW1DkS13IrjkfbPUHTrCIxnDKZtDE77aBzfFJtoVJ0CPLsbwrsg9iA6/iAm8aA7NIyvc/vYkpiMS4BmhcssjwnTXHrawv/Zik7djUlvx2R3QfZhyDwK+cfJB3bw5SOj2NDnFNbceBa7B13MU6Ou5IHbLuS7v021w1vyD2PS2yB7J2Sler8Pnb4LMtvQGcEgbLQ9euJCPZ1EPjiSfPg2TGwMOtobFTobHTwJ3flrdOAP6LBU9n3QgQIISQtnKDraF+JjIDYeExuJEvFP9EHHb8FYK63YafvZP9PxKejEfPLRhTjCPrK3tCJUush9gHe143RKHFiSq5CHnVTm0p6R1swunMgj1sop27yUdbRIO0fQ1+u75gGy5WkjxDZYPLCKrcaJryZv8yvyHhKhX4uS95LkVmLy38vrOoxkXcKylU1mhpJo3+QO1C3pVtrKbgEqrWYLwbOL4mXdpAzcN1mulLRc7W3juNCLdTJ+PyTu62pRPdjVonoIk9iJTrquKFkWLgKvBfQnx+IQxFF2/POTW74sERIc+Uq0YKYDK128d7u0k9aQbl5Non4VkZrlBMTzXrMEb81CWmrn0FQ1i8aKGdSXFVJ7dAK1RwuoLxeRH4u3ejSdNaMJ1owhWldAol40exKZpqlkm6eTb5mF45mP8hTbV1nqhNOVjI23PUOobDKJ2qmk6meSaZpD1lOE0y599W3u9idZEmIRBtvd9Kjle2+Gzs0uh8Q6TlwuuCB9xcKoOgXENBNjSZOj7ZtMNw9FNw5FNwxE1/fFqbuRvPjGq68mX30pTs0FqOpzUFVn4FT+kVzl2WTKziJ99AzSR04ndfgUUod/S/LwCSQO/IzkwV+QPPw7UuXnk6ntSb7pVnSrDN1KMB3SPpJ05lJ0hywtKUG3L0HbZdmyjGRGl9hPxQSEPTMBJUOtoPjLRYBE6P8r9vIQkIrfhKfZAZgTLURL71O2P0Xk45kouVLHiru2RMnrAoygh4OL3HmFwNlkSXrDBtKVq4keXELHV3Ope7+QstfGcejvYzn08jgOvzGJ8n9Mo/KdWVT+YzYVb87k2CuFHHy+gL1PDeeHR4bw40PD2bNjIF/e0Yt3ll7BYwXnsuGG37Cm+8/ZcdtZvL2mP/ufX0Tj5/cSOPwEocOP0fnjDjr3bsW/ZzP+71cR3FNMdP8MUsfGk60ZRb5+NE7jWFSLVMWjUO2jcHyjUf5x9kHohKehInPcQWB8KYjoJYTbLTwcqd4Xdon7fExqLlqOYBSS81HJIgtKcyv6+ejETFRiIioyAh3oj9PZywLcjPfP6NbzUZ7zcFovQLdegPFcgGq7GN12KbrtCkzrtZjWnujWGzGtN6Nbb3EzFp7eKE9vmyrWzX1sCtn+vMnPXcsgtGcAqnUYjgi9DIA7F1lzgQncB0HXZWFsP1ZWzG3Dia8nH19mxU7JMpfkfJzkApxkCU5yNSq5CZW+Hy1Vd1aSrU9D7lnICbTsOcjL0u/dVH0wl/sLzmV1j1O566azeXTIn9k54kI+fXQ0KnI/ZNeiUvPRmXkYGWwLSC65BJNejEoVk48vRAmDKDIGE+yH478FJzAYE5SC5AZUQCr5k1D+n6KCJ2HC12L8t6F8o1DyEA1dhYpcDdGbMGKZjVyLE7kUHbsAFb8UJ94NJ94dJ34jOjEIE5dtZZPJx2a4vXz53OR7mZ6LSc9DZ0TwJTEtbbplbpVvxV7aVzKfkEp9pw17iYPJCT/WtTzmbvKhDTgRqdpX2COZFRuws4K+1q69NPGVYDMrK21mxa7EFFa8VMpRWeCzGRPe7Iq25U9J4SniLgwjaSvLSsnjS+IFTS2/txEdXI8KywNaUOIbLatG4HwmJi0lEXrZAbDD/dzjD8HxXIM89JMyhxGksVTzsolK2mjigpJVgyLukq0R67W482RDnLz3ZQ4oiI7F7s+axZwsw/EuI+dZQap5BfHGZUQalxJqLMHfsNDaJL0182irmoWncirNFZNorpxIW3UBPhH52tGWsZ+sG02qfiyZxvHkmiaRby7EaZmO9sxBeYpQLWInvu+/Qp9ofZ7QsekkamaRqptPpnEh+VbBogr/RSr6B6DzQYxfEMOy9WmbRQbQuckVeivyG0CStAKd8gkLXCa+xZiO6eAdi24dhmqR4WpvtFTdtb1QNT3JV1+NU3UlucpLyFX8iXzF2aiK08mXn4xT9geyZaeROnI6yUOnkzx4GokDfyC+/0Ri+39GbN//EN9/AskjZ5CuuJps3TBU83SM9P7tUnGJJQtLfplb3XtLMN4l4F0I4r6QJchdYi+0R+OfhAlMwFhRG4MKjrJHh0aiwyPRIRlwFWDC4zGRcajoWHRkNDoyBh0pQEcmdj0ABD+8ABUp6toWNRclG56Ci13Xj7DRZc1i8zoyNSuJHV6K75sFNHwwi/I3pnDo5fHsf3k8B1+dxJHXCzn62gyOvTaDwy9NYf/fCvj+0WF8s3MwP9w3hK839+VfS7vzwoxLuGfwmazq+VvuHHAqf515NR9tHc3BF0qo/c9WWr66n5Yv7qbxk000fbYGz+fLaf+6mOCeucQOTXNbN3WTyDcWkm+ehmP3iBbidBTidE7D8U9HBWdaVK0jrauoZApkIYNUuEsxCVlbWIxOLrCibkUhNRuVnIlKzkIlBZImwj8fEgsx8YXoxFxUotC2E3R4IDrQE91xGbrtHLTnFLTnZPtqWk4Bz6kYzxkYz9kYz/kYzyXQeiV4rgVPD4znelSL4DB6YJp7QlMvaLwJ03Qzpkle+2Ca+6JaBHo3ylJFhZkjiWhZ82jtcbav7C5+dsQFE9+Ak1yLE5db2kJE6B0R+vQinMwqnMwWVOY+VO5xdP5ZdP45dO45tMDKsrJI5GFUbgdk78B3eCFPF1/EimtP4o6bz2F73/O5q8+ZvLKpG0lvIaTH4MRHohKj0fEJblsrJqsjxdY6GRWfhI4NRwV7oHyXojquQXX0R/sG4nReiOM/CdV5Asr/c7T/9xC8AeQW5u9N3n8OTuh0dOhiVPACVOR0nPCpqOBpmMip6Oi55GOXkEtcRS5+LSrZCxJ93TZOYjQqOQmdlN69JKRnY9JzMJl59nsphFOZx4gjSKfWoFPr0GlBZGywDCREHOOPoKJPoiR1LNu8QrLw5Q53y5pguWXpeHgFRFbbwoFEKchtUX6u4qVdr/JztsbFkViRl41twqCSnctSpYuYS1ZnCya02WVTyZ91PQyUAANlq1RojW0Lic3XvR0Is0ZmB/fa2YGJSV9+Jya+2w2sxWQuI+dBmw2QnIO7oETmckKzXNMl8mKTFWdTMVqcUNGZdnmQFkptYA4qMBcjoEKL5JA2dhGqrZi8Z7FNKSdbSkk0LyPeLKJfSrB+ifXEd9bMp6N6Fh01M+isnUSwZiyR2jEk6kaRllM/hlzjWHdLWPNknJapaM8sdKssGpKg4D3gdEHNkm0vEy2fT6p2Aem6ReSaluC0laIEYeATn7x4RB/CBB5BSa9eptedd4Jvs7uMo3OjFXgjaF/f+i4g2gpkAGvaZ0JbAdozGKf5RpTYJOu6o6quw6m4hnzFFeTKLyVbdhG5Y38id+xs8sdOI3f0d+SP/IbM4d+TPHgyyf0nk9h3MvG9vyXywy8I7/kfInt/SvzA70mX/ZlsdV/yjZNQrYvQ7YI0lsUYq9AdpV1nKbpdMAVLoX0JSFUvwCtZHSjxeP9MjF/SooVW8LV/PCo4FhUcjQrdhg6NwIRvxch1WY5d7SevIyByK0RGYqTdE5mEI3a3kFvJm4hYM8Wx47Z7nOBiHLF0SsbAuwLHs5Jc/UriZcsI7l1M6+dzqPvXdMremsLhVydz6JUpHH65kMN/n8KBZyew54nRfL1rKF/c3Z8vN/fnk1U38fa8a/hLwcXc2e+PbLjlVB4cdxGvL+vDVzumcPjFpVT8cxPl722g8t01VL+3nLoPSmj+dCG+72YROTiNZMVUsrXTUU1zUK0L7ZpGYQDZjV5+uY0stKvklFALw4vdQbSkhGX5uH1dgomLyBdZCJpJzsEkZ2NSM9HJGe5JyMez0Mm5tu0jQm8r5MR0tG0nDEUHb3BFzPtHlOckdOsJKM8vMc0nQNOv0C2/Qbf8Ht1yKqblLPBcAC0XQ8tlmJarLArDNF0Ljd2h4QZo7IUW11azDOd74TQPxJG2jXceyMPWL/wd12ZngtsxIaFZbsIRMRA6o1hBk6U4iSU49t8nLqOVrgsmcz868yhGmPL5l8B5FfJ/x9gl37txUneST64iL62sxCySjVN5/97LWXP9z9nQ+0y23Xwud958Lk8vO4dg7Q0Quw4dvhEdvgEdvh7CfSA8GBMehQ6PxUSHY8I9UJ1n47T9HtV6HnivR7d3w/H9FqfzJ2jfz1G+E1Gdp6E7L0P7u6M7r8AJnIYOyv6CC1CBU1Dhn6FCP0cFf4UKnogOn4YTOd+KfT5+FTp+HcR72TaOTgzDJAswyUJ0aho6PR2dmY7JzOxCY8jDW8TeHcDbwbx1WskAXgJz63HEM28Hm7Lc5TEQeFxoNyp0d5f4ypBVvP9LMJFijLSnbFBRAotypEJeZnv2wlEScbeBMAmFhQUTLEK/BSWibrdGSQpbUu4bUeENKKFLSk8/Ig+KrkR7TBZ/i6toEyaxFRL3QmI7xn6euzA2HPWQDUhp28qT4ex9Fs1MfIOdE0gVL0gUExX33UrXjRcucndP2HmfmA0K0aIrndPc4b+vEN0x1Q30yW4NaZG3FpFrLSbXKpyxFSSbltsqP1q/lHDdEkJ1xQRrFxCum0m0biLxugkk6wq6lsWMJddUYIm8jhV6cS3ORHnmopr/j9CnvW8Sryhx2fINS8g3y/JtV+jliSB7XZGAU/AJlESkZZm37267XIR2QRoL7VIAZiLy6xFypW5faVslxjsb2oTcOAhH3nQN3VC13VCV3XDKupE7djnZY5eSPXoxuSN/Inv4LLKHTiV78DdkDp5I6sCvSe7/NfEff010z0lEvj+RyPe/JPLDT4nsOZHEoXPJVvYgV38rjlxV2pZhOla7+IX2ZegOmTyXuGLfvhzVvsKFkImn3l/q9u07i+0Sce2fiw7Mstx47Z+CCorYSwvnNkxoBIRlVd9Qe0x4qHUzyJuR8BBMaBjGVv7S359sF4rIikBj1wJOR4flzHIdPoH5OL4iZJm5jZi3rbQLW0Twk+UlhPctov3rBTR9NJea92ZT/uYMjrxcyIFnJ/L946P44oFhfLjlFv695gbeWdydv8+4kkdG/YmtA07j7uFn8fTc7ry/eSQ/PDaPwy+v5PBbt3Pg9ZUcea2EqncW0/DxArzfzCV0YDqJiqlk6meQa5qH07YEJVA6C3kTZ8NqdHAlKrSi6yy3e25tcljwBraiL7HVuUnMxxX4LpFPzsSkZrhQNBH4hJzZ//9CnxgH4gYJ34zyX4Fq/yOOiHzr/6A8P8E0/Q80/hTd/HN0swj/SZjm32FaTsc0n4FpPhvd/CdU0yWYhiug/mpouA4jFtvG7mQbBXY3EFk+o7zCzNlo8QBawGnWSy37Z+9AhYT7vhgtrTapyhJyE5mHkyxGAl9SrZr03ZB5BDLPQEZWAgrq4BXIvQi5pyCzAyTUFJVb3Fg7fzDBW1BtAznw9yu5a/AvWXbtb9jc+1y29r2IJxdegGdPTwhcgwlchfKfjw6eAaGLINgTgoMgOAQduB7l/xOq/dcoz8/QLb8FaW+1nYHT9iuc9p/heE/AaT8Fp/1ssh1nk/Wdj+q8DBM4D+U/Ex04HR08AR36iT1O8CfkAj8lH/w1OvJHTPx8TPxSTPxaiPXGxAdi4reCfH+Sk9GJSTipCTjpiaj0ZHRahF+gd7PQ6Tmo9FzbetIZaUG5vXzJTzgJSUZL//puiO5yxT76hF3KbimxwbU4gRLE5uyE5+BIYRSd57Y+ohJULLFpdQsVtGstJQTmirwV+rCI/CZU5PjZiLKrMNfbJeDysZaBrmxNi61BZgFKPrY9dhmm3mWF3ojQJ1ynjVT1OvaAPeISktaOy7fZ3NVmWtsl8DIvuN06gSQbokMLMcFZ1txhgpMxnZPRnZMw4vLqGI/qkJCfZDsmYNom2pml8kzH8cwiL+1yMWy0lJJtXmYDT8nGZSQaSok1lBBvLCLZMNsOXjMNU8k1TiZvWzaTyTdPJt8yFadlBsq2bubjtAgq5N7/VvQZ7zskK1eSa1iK01yK41mCI4AuifLL1in/I+7SbuFyBJ6wKVb5C+yi77bN6PaNqHY3cam8G9D2iDdeKui5mLZxtjfqtFxLruFS8jWXki+/jPzRy8kcuZTU4T+TPnwh6UPnkTr4R5L7fkdy3wkkfvw5yR9PILH3l8S+/wXh735O+NtfEP7mF0S++QWxvaeSPnolTl0/dIuEaxagO5ZjOmQb1XK07dMLsnax7dFr4cf7VuMIXz+wHOWXoEzXx12WSx0owgRE8KejbM9e+vXSthmGsRXWIAgPxIT6o8N9MOFbMKE+7q+DQ+1/60iPXzY9xSZhpH8fntRlJZwOsnzEOn3kFiEebnECrcP4xJmzHtO6GqdxGZnqEuLHluD/sZjWr4po+HAO1e9M58jL49n71Cg+3zGAD+64nn+suoa/F13Ok1MvYPekM3mq6GL+selmvn6kgCMvz6Pqn8spf38lZe+XUvOvRbR8thDfD0VEDs0nXTUPp3EeqlX28ArmVzIGEiSTlYgb3fSwXRIurgc5wq9xr6rCsJGrqkVDxMWPLcPVmV2CLqIuHwvmeJZ9AOiEpGrFuz0XE5tvvdsqMR8nIT1gYeGPxkQGuOsSO87Eafslec9PyLeI0P8E0/gTVONP0E1yfoZqPAHV9Gt042/Rjb9HNZ6G03gupv5CTN2fUfUX4zSIA6u3nds4XtklLCz6+9BSvUfF+ywpWMFQiyAsxomIXXGincMQm2xxzTo+Fy2tG+uiuR8yT0Lmeci+CNnnMbmncDIPo9Lb0ektmGQpRCZjAv3svEF5BR19NqblMlo+7sZDk39HydUnsrbXOWwbeCXbx13I18+Iw6gf+K+FThHkX6H9v0H7zsT4pQq/kJzvTBzfyei2X2Fafg7NcsP5LY7sKWg+Hd1yFspzFrpNWl/dcTquwensjvaL7fJMnMBvcAInoYI/QwV+hvL/AifwS3Lhk3BCv0GH//D/UHXecVJW1xvfQrF3o4migiU2bICCgqgUFem9LEtbyi69iaKCPVFjjSWJGnsvscReoiiKinS21+l9Zme2zbz33u/vc+47JP7+uJ9dYNWVnTnvuec8z/eB5HGQ+gMmeTomdQE6eQUqNdyO1oxV4kxAtU3CaZ+Cst39XHTHHExnKaZzDqpjDo4t+DLeKsPpLMPpkFHeEowdw8j8/RbbFf9XoZOQh63AETfg2PSq2XQlZpJLzrZjT4nflNGOEjpk/Ea7gJXRjBR6t9jL+OZWdymekgJ/M45A1qzs+SaMXfT+ZlEqM3V5EIvLWIBlopYRmWzmXquskY5evAKmVQLD5Yh57k4bMIIsb61vRm58gksRqOFNrqomdiNORACJS9wRjR0FT0VJvGlQPBzjXed2YBxavB0i+fWOQXvGomxWhmDTJW9A3o8LLRzR8Swj61lOV4ukVy2no2Upnc3lZJsX4TTPRzXPwWmZbefyuZaZZFtmkfPIMtb9dzjS1Yf+jDmwjM0G36Ojei25xhVozzI7/nB8S1Ey/ggIR1ys0H+3DG0Ve8oWfid0H07gLnLeTSjfzSj/Tf892ncz2rse5V+J8ZeBbxISCJJrPp9cQx+c6j7k9p5BdudZdOw8i7YdZ9L+y+m0/dKLzE/HkPnxEDLbikn/WEh6axGt3xeT+q6IxJZC4t8WEv2mG4kth5D55SSy+8UxK+EjgjgoswETAhfTweWYYAVGxkd2EbsKJUvi8EZykZtxoje523px+drQaylyrr5eir2S2Vps6n+7epnTm8S1GNEmS5GPX46ODcTEByEdm7yxTVTmzCNR0VE48evc2bPM8OXqHZ9o81yJzYL4HEiWQaIcE1+DEdZOdD1EBQm92gZkmKAEmiyjo7GCdHU5yd3lxLaXE/y+jJbPSql7fxqVb0xgx3Oj+PHJEXz7yNVseeRKtv1jGLtfG0Xt+5No+aKUwJYyAlsXEfppAfHtc0nvnkNb5Xy66haimpdg/PL35KqCsB28i7eQpZW8iC3J0y7JpCNzl09GwpMt8K0c3VqGTovpZjY6XZL/6BZ5W+gzbqE3UuAzUjTnYlrno+3DQQr/rPz8eYKdQZv4AJzwyeT8B5HzFOK0FKClwNcXoBsKMA35XzcUo+t7ouoOxqmXcxiq/mh0/fGo2pNw6k4j19QXxyemqsWuYMAmVT2IEi5J6lZLIBUiqbFoixKMqFFSo11tup2Nz0SlF7uMcgnq7ngMuv4Jkvva/jS6/UFM55047TLHX45qLUUnr0fHB6GD56N8v8dpOQTV0BPTcDzpny7k5eV/YNmFB7F6wPHcMeJ8Nl59Gi+uOpPWn4ZA8DJM+CSc6EE44WJUsDsqdBC5SA+yoSJUQG4zB6FrD0VVH0Wu5hhU4wmYhrPtuEo3XYJqvtIqigiNh8gYTGQQTkQK/SGo6KGoyMGoyJGY6EmoeG906+mYZC9M7ChM7GBXopn4PSrZBydxAbnk5eRSI8mlx+FkpqLbS9Cd81AdC/LKqoVo+/l8VLscWdouQnUsxOmah+p0C73M2xEjVnodjhxZYrfei7aFXnZ/wsFyi33WRhbmE9ji5fY2Ka9FGaspmb/HZQ4vLB+ZwefHM6kNqOR6u2R1Uu6i1/W4yJhFuvb8EQmk4IjF82KPeAJEzy9jGxnPyEeZ199tDZA2NNzuAvLAQ0mAE2JtbD3WSS3Mr4iQfgXrLvVFDKIyEhYnvgD2hK8l6PFJKJ/gOcbZTA3tHQuST9EsZyy6RQQQk3CapSOX3AHxFi1GS9aFb6DzrSEAACAASURBVBmObwU5r6vaU95lGG852rPIXbp65uO0uCfrKSPnkR2r5HcvwJHpy4FCnwu/TVfdSlTjcrSnHO2tQPmEfX2TzZC1HX30KSQC0ESftLbaXOB+ct67cDy3oT0bMJIV65VzI8ZzE9qzHu1dhfEuwninWBOU0yTa+N6oqt44e06n69czaPvldDI/nUHmx960bf0DbVuPIvN9D9LfF9D6fQGpLQWkvi0k+W0BiW8KiH1dQOybHqR+OIaOnafj1AzANI20/w1kTBSQYl+ODi2zaUMEZCa/Fh0Qna2Yv+62pEIlBorYvZiYEDiFUyMyLTFVid5+mTVUuRmvMm+fhhbzlCgcYiPRsaHo6CBMdCAmcjkmMhiiQyF6letKjIrmfITVnYtqx4gWXxa4YhBKzsEkZTSwxLJzTFIWUWJTX4KJlVspKLEV9qOKLLGpNY5/CY5vOYKY7axfQ1vlUtJ7F5HcsZD4zwuJbVtA7Mf5xH+cR2JbKakdM8nsnkHb3pm0759OZ/V0snVTcWSH0SLGtNmYoMypKzCRPMEzsh5ia+wV1KKXE+vQ0rUk1+CkVufPKisbs2C3zBKkS7fhJGkx38iZkT+zMOkS66J1/7zUTa1Kz8FIoT9wpDC2zkK1TncXjiK1TEkcYh+c4BEoTzd0UyG6vhBVV4RukM8LMPUFUFeIqelh2fyqthuqthhV2wOn5lBUzSmo+gE43jGWU2/FAzFZAt6HiUsw+C02xB15yMYrIDHf7cJbJ7hyxdZxqPR0O7Jxl4t3W307nU9guh7HdDyMarvXKnJcR+kMVEpGfINQ0bNQwgry9EM3ys3iCHLyfVUfRdevF/DN/eexsn83yi/oydrLe7F64Mn8bdZJeN66EPwDyYV7kY0cjQodigkUowPdUIGD0H4ZVx2KqTmC3O7f0fnraXTtFZXahZjaIVB7Ldmq0XTVTbMjAOEFmchkdPhqVOgidOgP6PBh6PDR6PAZ6MgATGIQJC+xRisVPRwdK0THD0InjkelTkYnz8UItrptDLmO2eTaRW20BGuCs0wjUd3IEdmlFPgF0OEeY8c4pSASW+udWIF4AKTQC8tHoiltwRWYWOx+TEwYM392Z+0iR4xVoONyVrkocCHLSmEXUFtcFrH57loUNIl1KJExJuW4UEKdOuBrkZGNPNRFy5+nVFod/2ZURvhCgieW2bssjcUHIEVeTF7y9WKck/+mvF4kcOhG91hGluRySN2QUKXNNlNa9pbKAhxXo4SpFZHEswqMEHh9pTjeaTgyPvRMRrdMxDSNQzeOzh9BlE/EaZqFapmN8Uma3gJ3Z+ZbipZ6LCNen8i0VyGmU+0TUu5StGcpukUmMcvI+ZaStWeZnfs7kb+AytMrc9HX6WpY4nZ4ssyUbl6yUAUuJq5OEd2HH8WE/wrhx9HBh9G+P6E8m1EtN6Ob16Ob1+bPOnSTnNXo5uXo5jJ081RU03BU40U4dWfiVPYmt7s3nb+eStvPvUj/eDLprb8n/d0xpLccTOrbYpLfFJD4j5xC4l8VEfuqgOhXBUS+kkJ/EG0/nURu7/noukGYlmvtzIvADAjMcaFVIqUMLnMllrIgFjhb+H4XqCZY1sSBI2MpSb66DyO5pAJrkoxc+WHGhKdegY4twMRm5yWYY9HREVajbAu9FHwp9tErwBIgpbO/Em1t6KI9H4uOiSxzGjpRgk4uQCcFA1CGSon2Xj7Ow0nMsVJMxHFrC/8idHQ+TngeKizmLmGxy//Pcrsstahlmal7l6M8y1Et8gOvsE95I6lZnuk4nonkPGNwhP0SGIcJjofwVIjMhvhcSCxGJ1agbcFbjYmvxMivk6IBXm3pnPKGEXiW07oUp7UcLfZ7WUxmXHmkJFHpzGR0RtQhk93uPD3VGnFMZqZb8NOz/1foW6Wjd4/+baFvnYa2hfZqtEgBQ8ejvD3RzQcKfSFain1dAbr2wClEVReiawrscWqLydUej5ZFrEdgaEJRFXTHX/I/W5HgiblmvYvkEPOcLOOjYoSThedoTGosplVStmQcJRROKfKiiZcUqKdwOu4i276KXKYcJz0PlR5PLnUl2cSF5GIn44RPwIj8s3EApvp8TOVpqMrjyO09ieyvl7Dv+UtZN6gbC8/tzopLT2TFgBP507VHsvPxM8EjMZCn4QR6o/2/x/h64HiLUS0nohv/iKnrjdp7Mh3bzib9/aW0/zqIXOUwTM0kcntn0b5rLtmaFeSa1pDziChBKJ83YILD0KHz0MHjIHS6NZzl/INxwpego2eiYsegYt1QcSn0B6PjR9sFLSLHbB0O7ZNA5u6Zctqji+hKyr5ilYvA6BAUhhxx0op34kChl2QyUefI7mahVS1lE4vJybivTWIpBXMtuIWNaKtjv98alIzFCwhFVJao61GJDWibp3yLW9ylm7dFXiSWoqWXLn4NKrEK9d9CnxcJ2NuaGKxkrn6g0IvOX7JupchLZ78pr/2/K2+IE/rnJnuszj5+63/HmEICsMdKy0WfLwasP2GiUlfEa/SA20QKOE3UP6JGFPKvhTSusCobGafIuEU1T0M1jkc1jMZpGI1qGItqmIjTOMNVDopE3DsPLWMcUUDKztS/GuWXpnWtddprv0S3rsZ416A9a3B8Em26ipw9a8jJTSDy8P8KvRN7ia5Gaf0XYnzldqGp/aJYEZa8LFv/DKGHMaHHQGho/ocw3nvQnlvRTRvQDWvRjWvQjasxjavRDasx9SswDUswDWXohunohuvQDZehas4lt68P2V2n0LH9D2R+Op7WrcfSuuVIUt8eTOqbYhJfFxD/soDoF8VEv+hG9LNiIp8WEvqsgNDnUugPp3P76ZjKfpiGweAdiQmMhcAkpNibwHyr+FGBFVY5JE9cebPr6JM23UqnXoD0i5jU85iUhJQ/g5b9Q/wJjOSUxh9CR++DqNiYJRx9NSYqs7fZaOmSIqPQEenqB/6vsz/Q4UuXb49gf4djoqOtPl/Hp6Pt3FEMVmK2mmcXTk5qvvt569w8OG0p2gaGz7eyLCcqC9w5LqAqVoaOlOEItiE+z94CtJjBonIqMDFZBC3AWIaLBJKLP2AqOiauXlEAzLSGL0E96OQc90EjvP1W6YRk0boMnRR7/fI8uE2KvRAnpcgvxpHZekY6NCmCMoefhZbrfGYCKiNd8FiUtddPRKcnW7aKbhXn5Sx0q4xGZmNaS//fcf9Mvkb8CJOgVZbe/dER0dAfgpJCL+OaWungi9E1Raiagv8eXV2ArpJTiFN7FKq+nx3jEZLAEkmoegAnLNm/orFeZ7NltSzfwwI2K0GFJ6Ei41DRcZjYeEhOhNR0TGuZNUUhevCOhzCdT1q3qtN+E1nLiRFn7yw7nsvGL6Ir1otc+GhU4Fh0s9woTsGpOh61+0T0ntPQu/uid12B78Oh3DP6SBae15Pyi49ixYDjuHXgIXx922nkKvuh/afZBwWeMzFNh6GaeuLU9qFzz2Vkd11Exy/nkfz6UsJfjCT5w2iye2eQ27eQ9C8LaN+5FNV4M8Z7K453JdovPP4J6OBIa0TTobMhdDEmOMBmDOdCJ+MEj8WJdseRQi+Gq9gR6NjRmOhxEDsPEmKyGo1pk9taKZ3RWXQlxBshzmdBYJT/96PriF6IkUVs5wKUFHk5glRoLSWbmktOTIZtK6BD6Kdy5GEq7loBn4lO3cUBq5Tgg293OfeyL0oIJfbAkSJ/s3Weyq1TJVflC700KKvsa9aqwaTQ25Cg33b0+cCQ9GaUcHbEqWvdztLtC9ZZHiQ32xQ5m00hM/i4626Vm6Ar4RSnrRR4aSAew8SfsvVDRf+OkomHBOyIJ0NSy8IPogN/RvnvxPFtxPGsIte0mFzjbCTWNFcvJkU543HqJuHUT0M3zsC0lGA8c9FifvIuRsmUxe8m90nsqRFFoQ1lWuc+SHw3ovzrcQLrcALy8UYceTBE/woqjyl2Es+TbS61UXj45Eog/7CEMAtydzM6JHCuh2yh18G/ovwPob1/QrdsQjXdhK5fi2lYDQ2rMPWrMLUroW4pprYMXTsPXTsDXTMKXTsYVXUx2T1n0bmrF+3bjyf94xEktxxM/OsetnNPSMf+RQHRTwsJf9yD0Mc9CX3UncBHhfg/KiD4WSHJ744mt/MsqOkPzUPANxwTGIUJjoPAZEzAjQgUw5cjT1SJsYtJkZe4ujfR6XfRmXfR6bcwafm15JW+hpbwk8SzOAnZR0i4w8MujS4qHgGRRM7HhKdhwmPQkatRUVGJDEJHL4PopRDth4lcjApfgA5fhAlLpz8cYmK6Gu+iFVLTUKmZOEmxsIvOXgqr6NFXolJLcRIVqPgSdFxkWmWohMgz5Qh0TX5vodXkW/lmfGm+Cxd+/kpMcqk1MumEEDjLILUEWivsEZkarYswAhRrXYKyhVu6UmHQrMvz4uX7kCIvaAcJDBGkg6hrJHxFdO8yW8/P2WUMkylBZ6ZhRPedHo9uHYNuHZ3/OM5qwElPBeHjpGZgUjMxUvBTvz3ye1JYp0HKXcoSF0fnmTj+I1AtxSiZz9cUQWUxprIYXVVsO/lcVQG5ykLU/p7WXKekk2+ZBSKvjd6Kit2JE70LR67WwduQxDObyRuWAj8FE5low1e0PIzDY+3nUuzFIS1wOy3IalFsCHxLXJHifm1bZR/MJAV8J9I5MSRdho72QocPxvgORTX+Dqf2aHKVPXB2HY3edQ5m12DYM5KubeN456a+LLm4B/PO68HKS4/i7qFH8OHyk0huPRvjOR48vaD5VEz9kai6I3CqzqLt54Ekv+tP5ItL8H94BZ5/X0fkmxm071pG689LiX6/iI59q6HlNvBvRvvXoiTw3i9eiKHkQn0xoYsg2BcVPJVc8FhywaPQoaNxQkfihE5AR09Cx47BRA+HyPEQ7YOO98VJDMRpFajdGEx6os0Tlm4dIZVah6yQSaXLl8K/xM7nxUGbsy5aMYCVQnsJyOJWzFa28y/HWKyCoDHWotpvwWmTfcf9OO0P4bQ9ZJlCKnUvjkR7Jm6FxEY3+0G6fBvr6b4+VXKFlcOKJFaOVYOJ7Pf/FXrBFOSDRPLpUKp1k40QdNU0d7lpU4IvSKzFJFZjJJsiLkhzGdnc4mKso3JDfMDWBremPItJvISSoPvEq0jT7MReQMX+iY4+iw4+iwr8Hcf3GDnvA+RaNpFrXEu2fhHZ2lnkaiaTq5lIrmYSTs0UdO1UTINABmdaeaTTNNc24E6LzOqX4wQFELnSRcyIRDsk6sY1bnRrYIOreLQKSFFBrkMnnvxfoc8lnqXLU2K3vcYnc/UNmMBmTPgudPhuK6dUYdHPP4yyqTZ/wfHdi+O5HdV8C6ZxPeS7eF27HFOzDFOzGFM9D1Ndiqmehqkag6m8Gr1fOpNz6fi1l9XFp77rSfzrYmKfFxP/vIjYpwVEPi4g9O9iAh/0wP++e3zvF+H7UDr6ItLbjkHtOR3qLrDXXQJXIURDHZTgbeGUixt3EWL4sm9yMXrFn0XH30SnPkClPybX+pHNqpW8WpP5AJN+D936Frr1ZXKp51CJpyEmxV44PzK+Er6+ZJLOwoQmYEIjMKHB6NDl6NClmFA/TKgvKnA2Of+Z5PxnoALno8OXYqJD0LGrbGyfSlyLSsjCTzr8hTZrkuRGSG3IQ9Kkq64AOakKqyGWZaFKVuDEhTu+HJPMm0ssS+MmN7MyJR832PQrnRHtthhNROXghhgbybsUXbMgC8R4kha7+WpymXV5BYI4/Na4Bijp8PMJWpKoJaYVI3jb9AJXdtg6z87ZSZdCeiakp2Ckk5es2Vax5csZjWkd5y44ZRSSEgXSFExS9hRyBBwnZ6obSC6h5Ekxoo3FxK9CR85FBY9BeSUQvQBTU4jZX4TaU4TaW4SuLCK7r5COvYeT3XcaqmYIpmUuBNZZj4ckRjlCKTxg8AtsQvvXoQKLcIKzyAWnosIzrUJChUpxQlPRkUnoyHiUnNgUHFFexeVhvBzHFpAlkJqHiU+H6FiIDkeFZPE6EEJnYoI9Mf5uqKYjceqPRlUfi9p7OmpXf3I7r0LtGQ37Z1P39iRuGn44s/9YyOqBh/DwqMN4a94RBD4+HdN0JLqpJ1qWy9WHkKs8ErX/LDp+6Uf0qwF43htM8zvD8H5yA/HvF5HefiPhLasIb1lDdt/tmOY/o733YgURvnJy/hnkglfjBAdggsIIklvDmSj/SSj/KZjQOZjghZjQJRA5CyX7gZiofo5BRU8kFzuVbOI8cqnLXallejII8VL8AZ1L8125OGNXWGKp6lyK6hRD2WJynYtwZHbfORvTMRPTMQMt0DTJIbah8wtx2hfiIq5vRHXcjmq/H9X+CKr9MUz7o+i2B11KZPJ2q40Xfoyxu6P/jRbdG6g0KG4okH3N2gZFFGIiItjojm9EYWMBdGKQktm8dPSbrGxSpaTQ34GWJLSEjIJW2rm/zP8takHUWYIgl6jL6F9dFaIU8/jLmPjr6MRb6MTbqPgbqNir6OjL6PBLmODLaP8LaO/TqJbHyTX+iWzdrXRVr6RLRBH7Z5LdN9We3P4p6OrJbrGvn4FuKEE1zsFpzqtwZFRrnf0VaEG6yAktQ4XEnLoSJRMYkbhb4+rNNj1PS3jMgY4+l/gHnZ6pON45bsi1pDsFBcYlhf5ey40X8psK/QkVuhcVuAvlvR3VIqObjdB8ox3bqLplqNqlmJqlmGop9HMxVbMwVVPRlePQ+0eg9l5Odkdf2n/uReqHw0l8U2Q7+MjHRUT/XUzog0IC7xXg+1cRnne60fJ2EZ63i/G8U4Tv/UKiX3en/dejUVW90A1nYTwXW/CWE7wGFbweE5iACcyyCxAnIAlX4uJ9BBN7HhN/F536GJX+DCf9qf2o058jx2Q+xUjRb5XA8pdQiecgJnLSh90krbD8JQpWYb6bciQ3CP9QtH+wBW1p7wUo79koTx8czyk4vlNR8oYKyLz5Qte8EpXObzA6NsxC03S8FOJLIb4KEtKBSAcpXbh038IcP/DCFTPGGhyZRSZF4rgRCSi2J3MLCFxJZGv2GirsFXHsCQDqDrALpvsslU/UBe7vyQteoFQ34rRJ6r0EWshDQXjnLstFpVdYx6u1c6fFyTofLeOlVhlZyMx9NgiyOD0D0jLTlo5eSIrX588NduYtxEvXOSxmsnHoxAR0QnYWk/JHPh9vjxKHcULUTdegoxegQseivUXuAramCLOvELUrf/YUkN3Tg679p6LqroaW+ZiA3LyEu+Req2VuKnJKE74dRL4qqOzQYpzQXBy59UkQjcV0iK9hDipSghYURmQCKjKGXHgsTlS8FNNRsVl2BGYi16JDgzHyAA9fiAqfjgr2wviPxQS6YwJF5Lw9ybYcjNN4DLrmXNT+K8jtH47aPxVqy0n/tIBH5/Wi5OwClvfvwaOjDuXZcYX88vhJUNUbXXM4quow1N5D6Np5FO2/9Cb53Xn4P7qEulevpu7VMTR9OJ7Qt0sJfb8ez1driG69hVzln3AaH8Bpuc++P+0MNyjMJwH8jQf/BPBeD57LMS190d6LyPmG4niuxAT6Y8KnkQsfSS5yNE70ZHdJmziHbKIfTmqofXhjMwnKsOEzHYJokGK/EmzQzBpM1yp01wqcrqXkOpe4qptOeTAIIXOaPYJK1m0ix5QivwiTXYbpWovuuAXdcRe6/T5U+4OodknBklwAIXv+yY34E9OTcGRSkvAmJiop8HIrXvr/4j4PcHLsjUwkllLsJdglsxGTkQxYuanJolYkmBIbKGObTXmjlcg4Zfa/werzdeJOi1/WiUeRwqmjz6Cj/0RHXkRHX8FE34DYmxB7CxN7HSNFPvQCKvA8xvcixvs8qvlpnMa/kq39M12Vm+jYu5aOXYtp3zmbzp3T6dw5lc5dk8ntnYCqnIyumY6uL0E1zEU1LkI1L3UXr8LvkjokcZghwSoIu8st+BLfqsLr0RGJe5XkNFlM/2Z0k0v8jU7PeLLeGSifaKoFG7ARIjLjvNfNnwzdhQluxgRuRfs2YlpuQjdJgZezFtWwCl2/HFO3HF1TjqleiK6eg6magZZvfN9Y9N5rUbuvJLv9Atq2nUziu8NI/KeI2GcFBD8oxP9uIf53CvG+VUDLm4U0vVFE4+sFNL1eQPObBbbQx7/pSdfuI9G1v0M3noL2nIf2X0ouOBTHfz3GBlUIBqEMJVd4YfVEHsVEX8TE/2ULvUl/jpP+DJX5Gp3+BmPP15D+DNP6L1Tra+jES5iY/EAftUodHbrZ/XsJLEZMYMY/BuO9CuMbgvFeal2ZqvlsdEsftOdUtPBaAn9EB89xT/gcdLgvJtwPEx6CEx1ldfpaNPVWyikBFO7s3KTmuywZkf0lJdFqDUq0wcmbUEJLbM0Xdllktd2M7pCsVrn63oZj04iEBigpQneBjZ6TuDpJDXrQcjzI3AECoGq/CWULvZhHpJtfgcnksbRWVbPIyiBFJeMuTkvzqhpZsgrBciZkpoFc59Nj0a1S3IVwmT/iHpYj+IjEDZYdpOKiThHHcf7YX4v7eAxO4jq0nPg1rp4+dDzaV2TllaamCL2vALWrALWjgNzOAnJ7j8Q0XAa+OTbjwI4Zw3eDcJnCEqQtPHjBeOSx2SF5c0hHPx8nOBclErhYCU5sBrlICSoqe42p7kgnPB4VGp+f4U9EhSWJ63pUoB8qcDo6KD/fs3ACvXACh+P4utnv1fgPR/tOxfH9ESVM/6b+mPph6PrrMXWl0LQGVbmE9++8hPL+xay4pCePjDyKv15TzBtLjybx9YVQeQ5672lkdxxD5uejSH3Xh8hXfWl+rz97nr6KnX8fy/7XJ1H/0SLqPllJ/aeriGzdQLZaxgK3k2u5G+W9E+3fiBR75V+E9s0DX6mFCTr1Q1D1l2I8Q+jyDqereSCO73S7X8iFjkVFzsAkxZ070DWxpa5Gp69zgWiZSVZiqTrmu0W6owI6V0LnOkznekzXOnR2DaprJU5nBapzkcUlmA7p6mdgxGjVPtn6KbKtFdZ17M7qV7kPi44brfNYtQuT/y/QfiDu8H505g43uPu/BV5yIaTAu0dGjPIalgeB5D7b7l9Gb7aYS+PjauiNNDVW6ilNjbzuXYaOJc/KgldctUnR5m+yMDadfBCdfBKTkDHNCxgZAUdfQkVeRkdexURfh+hrEHkNE37Jjmtyvr+RbXkKp/kfqMZ/4NQ/SbbmYTor76Vjzybad66jbXsFbT/PpePnGXT8MoX2XybS9et4cnsmoSuno2tno+rnoRoXu8IWr8jVxRC6AiSf2r6ey39T6MUbtMbuooz4YSKys3j0Nx197Ak6mkaTbZmC45vncmFkvhOUGf0dtrNHirxPFj3rMS1rMM2r0E0rUXIaVqHqV6DqK9C1S9B1ZXZso2VsUzUNUzkBvXc0zq6RqF1DyP5yAW1be5H85kjiX3Yj8ol08cV43irE80YBza8V0vxKEU0vF9H8chGNLxfR9Hoh/n+Lnr4HXXsOxtQdhm48znbQWgBXvsFoCafwT0D58oVedg1BiTh8DB2VH9DbmOQH6OQnqORnqNav0OmvMemvofUrSH2KSX6ISbyLTryGjj2NjjyMloecbwPasxK8i0FuPt4xOC0yLhgMkmUraIfGC1EtfXG8fS1m16J2/fIgOgMdOg0T6oMJnY0J9EOHhttZvwlPRIclu1W6xzHomNAyJ2LiM1xWhk24EnfgMkyrsEDkhXujdfdZ9ofEylnE63pURo7boRt5CAhCtW0Tps1NrreFX4p8Row9GzGC1U2vtkAykcAJllbs50rUJJk5OJnZOBnRuZfYZaosk+3SVObpdmQj3bxQDifZZaxJj4GUnBvyaAhxC1+LiY3CRG9AZuEuP0g+H4WOybkeHRWo2XWWn67jQ1GxQehIX5SYh2QUIrLKqmLMnkKcnQVkdxba/AGn7jwIzMQyl0L3WoUNQXFs3wmBW6w72wkIzG45KlRBzr+ILrGbi2xN0MQhWa7PdOmlUenYZ2DdjNGpqOh0nIh8nIqOTkEFx6EDI1HBi8gGjkUFjsgboU6x7lztOQjlL8QJnIAJDMQEr0cLUMw3ASNSusbJ6GbBdK+G5qVUvnwNfxl/GLcNOojHrz2Ox649ioev787uv/2R3K/yHulPetspxH88hviWc4h9MYSGNwez9fHBfPvYDex4cRZV7yyh9oPVNH26jujWm2jfexNdNRtwmm8F72aMdxM578043lV2/6YbZpOtGkN2/xBMbX9o6o/TdAm5pj/ieE7G8f8BFToDYpdC8hp0YhAmPhBS10BG8MaTMBkZv8zGaZMu3XW/SrgM2ZWQXY3JrkVn16G7JEpyhU0cM11lmE5R4JRgOmahOmZYvb2Tlh2REE0ls0AwyDLzX2YjKGm/2cYd0vYgpv0xVNuDNoxFohVzopO3xb3cEmNJlkMqj0oQVr/chP8b9yljSBlXrgZBdmRcCJ/cWK2JS3Kg7YjUDROS5S4JyYF2i7xK3o9K/Q2detne9k3rG5jUK3Zko8MvooMvYALPQeCfEHgWfH9Dex4j1/QXuur/TLb2AbqqHqJr/0N07n2Atl130vrLjaS2LSW1dT6tW2aR2TKZ9HdjSX83mswPE+j6dQq53TPI7S9FVZeh65faBkGk6zqwwe6abCZ3WMKUJOi+PH/E7S+yTjFurUNFVv7/Qu/EniDbOBqnZTJKpHnSAciTwy/b3Q22uzfyH/CvwXhWYuQaYeH2FdZJqxvcIq/qFqHqFqBr56CrZ6KqpqGr5Ok0FrVnFM7O4eR2XEHXT+eT+e4kUl8fQfSzboT+XYTv3W40v1FI06sFNL5UQMMLhTQ8X0Tj891oeL6YpteKCH1cTPqHbuT29kDX9nQdkc0no1sEbjUQvIKhHW8LvfZKFyMB43dggo9gwrIceQ0dfxsdfx8d/whHCn7qU7Qt8B9jEh/Yrt/E3kLH5Kn9OCp0H0qWW83r9nrcFQAAIABJREFUcBoqUOIkbRJK3A04LUPRTVdghKnS0A/dcAlK3jwtl+VJiv1tUpPyn4wOnIgOnoQO9LKdoApeCuGhEB6ODg1DhUXFI+cql88elw5YyITi1BR8wlxLxBPuhyxZLRNEuCASJycvcntEm1+Oso7VlXmkrqtdlmAH1xEoxd2d0WuhTqYrXJeq5MHKgtUqY6S4z3CBY/LGFtVMssT9PpKijpnmLljtEnWqlSOq9ERU63iMOFxbpaMfgUleY93E4ipW0WtRUtBj16GsqWwEKjLMLrVN+CrkqOgQVPQylLg5Q3Ib+h3GV+wapaq6Y/Z0J7urgM69h5BrOM8GxxO+0d48dfBeHO89KO8mV0xg5WhL3JBxIVRG5PNysr4l5MSIYqMES9HhWW4uQVwQ1FPR8UmouOCqZVwzA6uWEsVUaCImPAITvoys/w/kfAehhL3TdCym4Q+YpiPsPsHxH4MOXAyhURCWfc4ilLcUp2G6dTKqfOBO7IuJvLrkRP589SH8dcQJPDWuFw9c05NPNpxM5JNBtG3pT/zbMwltOYXwlxcT+PBqKl+4nP88OICvHr6ebc+UsOulxdS+uwbPp+uJfb+B1l/XWiKqatiAab4F1Xwzuea83LluBZ37JtOxZxC5/eehq89ASVpbXR9Uw5loj+CgB+AEB6Ijl6Fi/XDi56MSF4DM51tHQmaydTo7mXlkU0KyXAJdS+2s3kZIdi1FZ+Uss4fOCkzn4jwWYY5FJaj2A3A7MVfNQ7UJK0duB2LCErrpMos9pn0dpu0mTLvk2N7nBppn/mIRyI5dosqMfjk6WW59KZLt7AoNyjHymhYpsHhVLGRMQGOLraBApMHKkkAlhlEaKMmFFiXcQpQU/MRKW+hNQub1gkB4FN36HDr1Nib1HqTfxSRet2MbFXga5X0S1fIoTstDqJYHUU33kau/i87qTbTtv4XM7lvJ7NhMevtmUj/fSvzH9US2VBD+upTol1OJfj6e+OdjiH9xHfEvryX1n9G0bZ1Ix8/T6do5h9y+xTjVomBcjxGVo+82tF9S+Q5wvMT1L/DIhZjwYghXQHgFiOM/LJjkx/7X0YvrNdc0GdU83QJxRNKjvYsxvqWI4UgFRcMp+aIVVn5pPIvRLYtxWkR7X45uWIaqdzt5XVuKqpECPwFVNQZdeQN6//Wo3SNxdl5D7pfL6PrxbDJbTiD+5SFEPpZuvhDPm0U0vVJI/YsF1D1XQM2zBdQ+XUjtM4XU/rOQptcKCX9STNtP3XH298DUSaE/HKfpd+jmMzEtAzCe4WjPBIy3BONdmDdsSbrTA5jQ39HhF+xVS4ffRIffwYl/gBP7wBZ+Yu/aWZuRmVvkeXT4KXTwfrTvTnTzRpy6VWRrFuHIprxuArnG63CarkE1DMbUX4ap64+ul2IvIC2hJQ7KR/LJ0usolICofMeg/ceg/L/DCfbOa5svRgUlkFskhQPQVsVzBU7sSneWH7sed9whnHwpQFMwsUkQm4QRdYj8WqST+aPjM1DJUnRKHgwia1toX+QWDpVeirIn/wZIi75ZvqYE1ToT1TrDKmBEXiiduxLJY3q6/X2dnGnTtwTHLItkUcmYtBT5Kfbo1qlWHimcc526FpUahk5dhU4MRcXl4SUqpatRolaKyLL1SnR4MMousweiQwNxQgOtuUeFzkQHTsb4joKWIhBHbHU3nL0H07XvYJs4hm8qhISeeqc1qzj+zXR5NpPzbLCaYx0ow4QWYKISCF8GsYUQk9mmqBZEjroEE5+HktFNdCZKblASvWd3B0IhlaQxyQxeYL/OjnRiQoO8Csd3LjnvUaiW7qgmuXEcjmqQot8d03wYeHqDX5yu0yAokLgynEaZtwpwaoE1EbZtm81nt57BQyN6cvegw3l89Ek8OeoYXpp9PPv/cRGJz84n/Pkf8X12Po3vXkTNSwPY9dQAtjxwGd88eD3fPDaN756cy/bnFlL3wXKC364k+fMy2vctJ1ezhs6qNXRUrSZXv4Zs5Voy25fQsX0Czp7B6L0X40gA/Y4BZPdegaobCp4REBiOExxENngWuYgsYnvjxPuiUpeiW2V8I/JZmbMLkVS4NiKlFA29OGHL0F2L3JNdiO5aaCWW1kXbOQfdKaiEudY962TK6ErOtT4E0yV6+1KcDnHcitt2JQjuuGMtqnOli2vOyNhFGPGP2PhGlfozyo5WBEUgRd3dH4nj2hZ7C0U7sFOa444dhSZpfRulFvcsTY1OiXlxDsifyXsltSSv3hFmjjD1H8Nk/oFOv2ZFHCb1ISTfw0TeQAWew2n5K9mm+8g23ElX3W101t5CZ/UGOvavJb17JakdK4hvW0Hs+xWEvy0n9PVCAl8uwP/ZTHyfjCPw8XUEPhpJ6OORhD8ZSeTTESQ+H0Hq69Gkv59Kx8/z6Ny1lK59a3BqNqAabkM1b0K33AK+9Rjf8rwhVV5Tc11YX3AhBCsgtNJFtYty8IBhyom/SLZ5LrnmBeRaBKwjIn3piGUevRjlX2hfrNovxX+xm1wiPJyWCndJkO/ote3mZ6Fkc1wpRf46u4DV+4bh7LoK59fBZH+6hI6tp9P6zTHEPutO+MMCAm8X0vxqse3cpbBX/6OA6r8VUfVUEZVPFlL9dAFNrxUQ+bSIzl+6oaS7q+uBbjwYp+lodNNpmOZLMC3XQMsE8JSApwzdshLtkb+U+zCBJzDBZzGhFyD0CiYkT+W3MeG3MZE3IfwaJvQiBJ/BBJ9CW6+AdPI3uT6BWtk9LETVTkfVjcWpvx5VPxKnbii6zi30pq4fpl6cugMxjf3QzX1QXinw3VG+HijRhUsn6HPhU1LMdEBmo39Eh0ShI5JMKfj9UNF+qJjo9KXLH46KjkDZj8PQUfcYMW5JOlPsWnRURiDXo2KjUHa5OckNCbHKlhL3RS2KEYF12YWqW9wlS9RJTcZpnYQjCIDkZOsSFca+ktMqHb3L35eYRSVHir4U+gNF3vL4ZdQkXz8GlRqFSgn07WpU/EpUbIh7IoNtcTfhKzChQRhRKwX7QeASCF6M9sti/Sx08ETwHw2eQ6G52BZ60dFnqw8jV9/H5soi11PJMw6KPvl2GxnnCB5bzCTB5dYdTXgJRBcjUYGOeAtii6zr2ZHZpiCj4wtQ8VJysRK0FP2E4KXl/3G27RIRmalotOPibZjjupsjw3ECF6J8J6K9B6ObC3AaC8jVi5mrB9QdBo1/wDRfaPMXnMB0HCGBNi/AqZ1Grm4mTvNcuvaUsePxATw5vju3DCjgrit78uSoI3h0xOG8t6IXNc/3oeW9U2n68Fxq3zyPfc+cz7YHLuCzzf35+PYRfHzvRL56uISfn55H1VuLafpEaKRlpHeUk9lZTmp7OZkdK2jdvpTkDwvo+LkUtX062W3jyXw7yhaT9m0Tye4ZT7bmBpR3FDo8HCd8OU6oLzpyBip6Hk6sP47A5lJX48gDXHYxAjZrn0qubRq5thnk2ktQsnAVdHHnQjuXl9m8fO7q6QV4Jh39HFT7XJtX2xGbQ1diEXQsg07R5Iv2XgxXyzAdq3G6VpLrlAhDuXHK4lUWquJglVDuB1GJe+yi1EkJekKKuisQMGlRhM2B1hJonQm2eXEzI+xrNjXDvqalsbEy36T8rGU3VoaTXEousc5m3arUI6i2ZzDtL6Hb3sGkPoLkRxB/B0RJ4/07uYYH6Kq5na6qm+nYdyPte9bRtms56R2LSP68gPjW+UT+M4/gF6X4P56K99/j8HwwmuZ/DaP53YG0/GsALf+6FM97l+P7YCiBD4cS+feVxD65juTXk0l/P4/2X5bRuUsUVRtQtbehG25HN92CaVqPaVoGzQuheS40l6A9szDeOeAtw/gqwFNu0fL/K/TJd2zWYpdvDVn/KrKB5eT85eR8i8j5ynC8wk9eiPaXY/zLXEKkzP08K9HNqzCNMqNfihLdfE0JunoSpvIG1F5R2VztLmB3DCL7Sz+6fjyHju9OJvnlYUQ/KSQkCps3Cml8vpjap7tT+WQR+x8vZP9fi9j7aCG7Hymg6m8FtLxeRPyLQnK/io66yDXO1EtHdTiq6WS04Gmbr7Ih1zbsunkupnk5tNyM8Yi562GM7ymM/2nwPwd+KeovYwIvYQIvYPzPoj1PYTyPYjwPolvuRjeuQzeKN2AZyO6hWsZSM1F141G1o3FqRqFqrkHXDkLXXoIRuWfdBZiGC1B1Z1k9tWrpYQmMVj0iln5vIdpXgPL3QPkPR/mPQ/tPxARPxoROxciSL/RHS3HUkQts4Rc9vrZnEDo8EBUR/f5ge5zoYJyoLHevxIkOJSejn+gITOx6TF6/r2MyjpiGslGIIm2Uh8BknOREHOHqJyfaI5+bxGSrDxfdu07NzBudSm10ndv5uItZ0zrL1b9L9y9ySQljSQhbXv5dY1EJYb7IA2ooKnIFKjzIdu1GeC4BiQTsh/FfhPH3Bb+MYc5Fe85Ge/+A9h2O8RwMLT0xzcWYhmJU3UH254zvKhCzU3w9KnYbjg1fv+u/hd4EbsT414HlLAnnSGbxJeSCM3DCc2xkoKAlVFTiJBfgCMAuIdyhJVbu6iTEubzQKp4QZYfVaC9FS3yfdIQJeeBehA6ejvafgPIeSq6lG9mGIpyabpianlB9pE1IyzUOItcyzr5/dNMictXC/Z9OW+V0MjvmUfPi1bxQeih3XFnAhv6FPDriaB6+5gQeG/17Pt94CruePpnK13tT9Vofdv/9LL6+8zzeWX0Jb60dxoebJ/HF/TPY9rdSdr88j5p35+L7aj6RrQtI/LiQ1LYKIt+WEfiyhPTWOXRtKyH55TjCH40i9Mn1pL+fgLN3OrpuKqp5Mo5/Ek5oHDpyfR7lcSkmfik6MRCVHIxuvQaVlkI/BiczASc9mWxmGk5G5u2zLMVSwGb6NwXfNVQtsSRLd0YvXX2JpV4qQVi3zwcZ/4jxyso1ZXG7GNNVTk5UOxIw3+bO1LtiFXRExGuyGQQlnJIQcjE9iUxYzHylKGvMmwmpGZCalpf0Tsq/3uWjyHvdG6l4WYwt8nMhudB6JoRhYxewEgqefgbd/iqq7Q1U6zuYxHsQewdCL2BkXNP0EF3Vd9K++ybaf11H5ud1pH9cS/LHxSR/KCX27UxCX0wl8PEkvO+NpuntYTS+dQWNr/ej/pVzqXulD/Wv9qHx9XNofPMimt65lJZ3BxF8bwiRf19L4vNJpL4pJfNDOe3bV9G1ex1O1U3o2o3o2g2omtVo8SrVL8DUz0bXz3RdtU2zUU3z0MKxahLU+G+iBJ3WT+gK3kFXcDPZ4M1kA2vI+Zfa5ZUUeS10Q590+DK+WYn2rUF719qlrG5eg2laiW5Y4pqjamZgKsdj9l2H3nsNavdQnJ1X0LW9H10/nUfH1lPJfHMsyc8PIvpRIcF3CvC8WkjdM92ofKI7ex8pZM9DRex5sJBdDxSy4y8FVD5ViPfNYpJfFZLdUYCpcnXVuq4Y1XAoWmakDX0xDUPQDdejZfHVWIJuXIRpkuXXbejme9FSwD2PgPcx8D6B8T2B9j5ulyem5SF00/2Y5ruh6Q5040aMqIjqyzG1izDVZVA9zz7IVO0UVO0EdM0YVPVIdM0V6JqLMbVnY2p7Q93J6Lrj0A2HoJuKLHGRlgJoLkA+Gk8B2luA8RZhfD3RgYMxgUMwwcMwwaNQwRNwQqeixK4ePBsTPB8dvAAdvBAdvAgV6o8KXeqe8KU4kQE4kf75MwAdHmSVPSYiMkWJ6ROFj7g/RSo4Ph+P6ObhutLGifnZ9CQXrxqfbRVAgmVQMgKytwC5As/Jd0+ip5ffl25oFiRngNwc7Ohjso1elJuFmMVkPCNjGS3jqcDFGP8FGJ8bFWi8fwTvGRhPb3TLqS5u2PLme9oxiG7qRq6xCKf+UFTTKXbJKQgHE12OE7+JnOS7igQ4cCc6sBklx3cLyncj2rsM5Skl2zIZJzAeLZjY0CwXNhVZgBKHcVTwB4sx8eUW/aASgsN1PQvC8xFpqdO6EEdGAW0VYB3Bsn+43A32EKZN8BRb8IWT7zT0RNX0QFcfjq7phVPbj67qa90uvn6R1U237y4huX0yqV/m4f1gPO+v7cX9I7ux7sJu/HnIcdw7+CRuv+JE/llyMh/d2otvHjqNn5/qxfcPnsr768/mxYp+vLxyOG/fNJZP75nMj0+UsOcF6ern0/DRfFo+n0fw6zKCn8+n+YNpBD6bQvTzqXjeHUPDG8PxfTiS+PdjyVbORDdKOEUZyiuBGLKYnm6jB01MluhDMMlB6NQgyx8y6WHotASLj3cdz2lxRc/NH8E5H8AVywhGjszwXQOVIIvdQi+US+HZz8B0TUZ3TrJ8e2O/dj5KbgRd4qYtQ3WJ8coNNFHtq8gmy+mILrHJVLRuBunuJcM3cwsqs8r+nJTcVO3t8oBkVxqO0fZI9rPsX8QhrhJyxCVeionPx8TLLY5bC+4gJVJkCYR/GZ15G5V+G5V8GxV9HR36J9r3GKrxfnK1d9GxZyOZX1aR/mEZqS1Lif9nMdGv5hD5cgqBT8bgeW84LW8PpfG1gdS9dCG1L/yRmn/2ovLp37H/6WOpfPYEqp/vQ+3L51P/Rn8a3x6E792rCH1wLbFPx5P8eibp7+fT8VM52R0rye5ehbNvFapyFarKVTfKTlTVzcapm4VTPwvVUIpqXIBulEK/BBN88H+ZsTr9hbWJO0HpjOSNIq460W2WYTzzMHZmv8j+nsDOBI9gZNHpkWK/CtNUgRbUQd1sdI2MbW5AxjV6z5U4uwaT+3UQXT9fSOe2M2j77gSSXx5K4pNuRD8owP9mAc0vFdpRzd5Hitj5QAE77yvg13vz574CO77xvVFM6utCclLoqwswdcJAETPNQej632Pqz8fUDbbjFFUvDIlpqIbZttgLnkE13YpqugPVdKct5qb5T6jmu3Ca78RpugOncTOq4Va0LLLq16HrV6LrFqNrF6CtH2AupkYKfSlOzUyUuNiqx6Gqr0NXD7GFXtf0QQtrpfYwdH03+/3J92ipi00FkD+C3ZUrv24pRNsuvzjf5RehRGXiP8xa06Xg6+Af0AEpJn3QvjMshhbfBRj/xZjARRCQ4t8XHTwPlT/2oRC42Bq5dOgKF2wVHomKXI8Kj0IJwiE62kLahKops37BJAitU0dnoyLzcWILyMXLyKXKcFrL7DzVsZRKoVXK4qsMKwNNlkJSuijX2WqD1GPiMpV59tX5Ii9jGVn2nY32nIH29EG3nIKWRXrziaim41CiObfI4Z6ohmI7Csk1FtLVWIzTdAL4+kN4DETnI9IxJ3IXufA9qPAd6NDtmPDdFqmdtTN6F4+tLERqIo4oX4KTITQdwrMx4VJck9R8JDzeRMUUI2HOG3AEqJVc6yYHZWR0INGB83DaBNw1G9Uuzt8bIClBIAPRMXGcnoPxCRNfZL/HYap7oSp7k913Hu27BtOxezRt++eS2r2I1l/mEvluAtHv5+D9eDqfbTqHR8ccxNoLi9k04CjuuvL33Hz5kfxlzLH8c/6pvL7yDD6//VQ+3tSLlyrO4O8LL+H5lcN4c8M4PrlrMlsfncHuf85j/2tl7HtzLlXvzKbqrenUvTWNxrenUffGOPa/MIL9zw/F+8Ew0j+OJlc9HdWygJyQEUWVFJKR1jwXny3E1rgY+vI7luRQVEqiBUdiMqOtM9bmyeYLvWmbh3skaMZduLp8etHJy5J1QZ5TL6obOfJAkFHPNHTnFLSYqOTXHSU4nQdGQDLvX4jOLsYasDpWotrWoEQ9I/GDYmhKbcJk7kRlxPi0AZVelqeHSpGXsBYxJo5EyQ0sLslco9ExicOcYvdcsmzXwq+KLbA8KxVbhyxgaZX4wOcg/TYm8z4q/S4q+QYq/ByO9xFyTXfTVXMHnftuIbN9Da1by0l9O5/4lyWEP5tK4N8T8X4wisZ3hlD76kXUvHg2Vc/2Zu/fTmTPE0ex67HD2PnIwex85BB2/fUY9vztNPY/ez7VLw+g/o3BtLw9DP/71xL+ZDTxryaR2jKTth/m0fXLErp2LCa7ezG6sgJTvRRTV4GuX4Sqn4tTX4pqmGMf3tLJm6alttDr3xZ6k/ncmqGcwJ0o/0ZLSBMMJp75GI/AdUROKE9+l2pp1Ti+tW6ht+ObMreDrpvidrmVI1D7hqJ3X44jI5vtA+j8sS8dW08j8+3RJD7vQfzjYiLvFeF7XcY2hVQ+UcSuvxTyyz2F/HxXAT/dUcBPtxfw090F7Hu8EP9b3Uh/U5wv9IUYgVvVCdVQULUnoOukmx6EqhuBqrvBHa/UT0HVz0EJsK1xJU7jGpzG9ajGjXbOpRpvRDWsJ1e3DqduDbperkPLMbUiE12MlsJeXYKumomuLMlLRuehqkrR1u17oNBfgao+F1X9e5SlJ+bhW2Ldr3ehXDTmuep5trpuLEQ6VtPcHdNSZMc7ylvgOkEFZuU/1JXwBY5G+4/D+E7E+H6P8Z5su2A854PnPPCfgfGfBv7TwXcGyAPBfybafw4qeKHt/mXsY1U94WHYoO/Q8PwVfbxd7Gop8KIwiYmWfAFOZAlOtIJcrMLOLQVm5na2wpF31Qu0LoRUmbvMkpm9db5OwEjcYuwGiFwLITHiSNj32RjfqXYsozy/Q7ccj24+1jpIVeNhqMaD7QNbRjT24V0jcLJicg1FZJsPRvnEdXq15QyZ6CIbQq+C9+EE70WHN2FCwmPajAqKnHAljiymLOBOcBgTMf7JbqEPz7BKGBWcZQu9jixGR8XqfotljuvUHXRFNpKLb4TMRmhfbVG8ohSROXSuXYiWY1Di+E2ORidlyXwZJnQBBCXx6feY5rMwdRfhVPalY/d5tG0fQOtPVxLdNoHQD6VEvp9F82ejafpoKo3vTWfLA/15ZsZhbBBN/QWHsPnKY9kw8CD+NOwwnpjaiydLevNyRR9eWtGbx+f15pHS83hm2VW8ceN4PrljIlsemMq2x2ew45+z2fniLJtEtuelaVS9Mp09z41n5zMjqX7lGoIfX0PbNhkzjrTKNCc0n1x4FY6YyESlEatwcxjEHCZdsGjnJTw8NQKVuhYtSqrMDZjMBBd7IbJbmxo214bJmHbJBc6jiqXAi6JGGPWdpXY+L127e+RhMM/983aXZy8BJk7HNFvs3a8vRXfNQ2cXoLuk2K+AzhvtklaiKx1xKgsqOLXJjnJcONlatLCYZAwZl+IuQoAh6PgQlEh2ZYxo5bxyk5UjDY7cYObkwYU3YhL3QOvjkH4F0v/CZD5Etb6LSryME3ySbNM9dNZutDLW9K/rSPywjOg3cwl/PpngR6MIvDcCz1vX0PDa5VS92Jc9T5/Kr08cx08PHcqPD3Tnh/sK+eFuOUVsu6c7P913JDseOYW9f+9L1fOXUffqUJrfHIH/vesIf3wDsS/GkfpmCpmts+n8aQFdvy4gt6cMU1UBUqcalqObylHCp29ZgBZWmWcJtCwFzwq0CGWCD/2vo1etH5ML3o4T3GQZN8KRt6Mar4TMlqC9czCSQWjJloLIXG8lbMqzFuVZgfAYdMMUTO0N6OoROFXX4Oy5ErVzEM72/nT9dBEd351D+396kfnyCFKfdiP6YTHBd7vjeaWY+mdkHl/A9j8X8eMdRWy9rYjv8+eH2wvY/3gB/reLSP+niNyOIoxoqmuFZliIU9cdVXcMuvZ0qw3WNVdjaq5F196Aqh2HrplhR0qqbi6qfj6qrgJVvxolyIa6FZja5ajqpWj5y6sux1QtgsoFUDkPKmfBvmmYfVNx9syia888cnsXoPbORe+bjt4/FlU1DF19Ebn9J+NUHo6uLkJAWwLeEqqiqZXbhzyU8kz133DVTWMhSHpSU6FNUTIthcjRngKXWij0Ru+haO/haM+R7vEegfEehxEWihzv0RifpA2dCJ6TwCfhE73zS03p9EWzPxATEhXIVZjQUFRoCDo0Eh0ej45MQUVnoOISdbgIFV+Gjq1Ex4QXsxYjrI+kMNvFuetyc2hdDCkp9AtAgsITM+wC2EYu2sXwSExoGAQG5XNdZTkpu5SeOE0H4TT2RDf0QNV3wxEiZa2c/MNbPq/qjqouxpHOXrJiZUEtBrPIFJzgQpRledyDCdyNDtxmYU4uwnUVjncxjrcE7Z9iAx6U7wa09wYcv4xvhNw5Ex0qtXA4FV+BSt1o2eQqcztOZjOOJRqKrG+NZbc4EpjSPpNch8yjJcxccMwT0VLoE8PJRi4lF5AHay90sDfKf74NJ8/WDSaz6wpSPw0k8UN/oluGEfl2It6vxlH5/giq3hlP8wez2PH0NfwfV+cBHlWV/v87MwlFXburrn3tioAKIiC9twAhEHrvvXcEVOzr2nVdd9d1XXvDgkgPhBACBEIPkJ7MpPdp955zP//nPRN39/f3ec4zoTyYwfF73/N9v+XL2TeysXNzJj3QgjVPXsuGJ67h5S7X8V7iH3hh+E1sSbyDZ0b9kU0j7uClUQ/xzpSu/HvZMLY9M5LdL45gz8vDSHtnFIfeH03q20kc/SCZjPcHcvSDruR+35mG9G7oM13h4lPows7YpQOwK2egKldjB5YTDSxoWk5PwhF5aY0s03shvgizlJe4DlFgNSaiGoc3uVtj/bFKAt5MC9f0pnLzKeiILGYl12ZijKYJjUNii/8D9JJ1I6FoDVInKfLKsTjSSxscB6K+CY3CCU1sctXOMXp8k3YZXoYTXIIjy9kKSWmUXHiJMHgaJW1RUm5Sk4xT1ROnUnoi2kHFY6jKdkbYIPJdV1JlzekL5Qm4ZaOR4UFXSkrmi7g170HdJ1D3DdR9j1vzFariQ+ziVwhfWkfj6UXUHZtFVeo0SnePpeiXIeR915Xcz9uR9882XPzrA5x+706Ovn4jh16+iv3PtWDvJi97N1nsftpi9xqLnassdq/2su/py0h74SaOv/kgZz/swMVPupP/eW9KvhtI2U8JVOxIpGbfGBrSJhInPXBQAAAgAElEQVQ6Mp1I1kzsM3PQ2UIpi49J9qSiiJRoBJFYSnXrfFwxvJYsRss+teL1/6pudO1WnJJV5ijh34sXoIpnG5esNJ5ow9PPisnWJBazeBm6SKIxF6FFeSMLgJwR6AsD0Nm9cc70wBbK5mgHoumisnmYxpQ7qd91LbXbW1L9Uzxl3/ko+cpL/sceLvzFQ9ZrFoe3eDnwtIeUNVbsrLU4uNnD2be9BL72mYneOe6LhVs1JRg62bKouxKdfTs6uzX6fCfU+Z6GO1fnB6LPJeKeH2sAX0kvavZU1IXZqIvi3p2DPjcbdXYW6vQM9JnpuKenok9NQp8ejz6VjD45EnUimcixcTQcmUTo+GScrAmok/Jrg3FOdcQ5fTf26atxTsejT4uD04s6KyYfy4C+AP5vwK8uWMj5X+B3xRAkCY2mPclC5VmofMsEekl1npayiXxReLREFzRHzDm64EpzpFNVF/nQRZfhFsoS8xp00e3o4vvQJa2Na1j5O6H9XXBLuuD6u6H9PU0ukC4djqlMNAmZ0lYv2d9CY8Sac7S0S0kOt/zYRMBKhLHw2LNxq2fiVk/HrZqErhppzE9OpSxeO+KUtUcZFc2DuOISzrsOlSPlIPE4l5rhXJT8+DhUthfnvEQN/zeBUp+NN+Yo54KFnStqpbugopcpbVel43H8smBdC9JbXLIZRwxBBUtjUc1F8pmdiioZjytZNsVDsYsGoUoS0KWybExClUonsExxs2JJh9Iy1LgRFZSzFhVcihOcixOaGcthaZxlCsHtRjGQzTC5PxIU59aKhHUwdmUXooFW2MX3ES28h0j+Xaj8VqiLTxE+2dtM82Up7Qns7kjxju5c+K4rJz55iqzP+nH2y+Ec/qAv3y+9h1f7CNB7mPNQc1a1u5KXe97EP8bcwyuJd7Gs1x0s6n0nqwfdzcvJbXlrylP8dUF/vl6XyPYtI9n5chI7Xh7FD88M5fuNA9n5cm+O/aMrBT8/RX1GR6JS25l7L7pIVE29UWVSkymqIym9mW8W0k6FTPKJqGrhtYeY49aMJ1o6nbCASM1cXMPDD8cJyc1GqBnh4af8D0UjqpvJ6MgkdGQCbmQsbnQkbkToGdHPi8lKCuMlpnsWofJphKWRqX4Oqn4cTsMw45x1gxObIo/n4kYXQnQpWhy34eUx2aUUkdcsIeJfgl0u+U4C8lLWLWmV03BqhLZ5FFXeCspbm5Ytp6o1urKtKWLRImaQKJLyXqhyoXTGoCrmoyo3oqtfx635ELfmE9yaT9FVH+GUvUm04BmCF5ZTmzWdivSRlO4ZQtGPPbj4+WOc/tsfyXrnJrL+dC1HX7qK9Oev4MDmluxZ34y961pwYONVHHzmOvZvupY9q69g26Jm/Dgvjl+WXc6+p28m/ZX7OPV+ey7+qxv5X/TGvzWB8l9GUrN7HA0HpxI+MgM7aw7q7HxU9gJ0zgLc/IW4RQtiEkrJvDemqXmmJtUtXWpMVZSK+esdUNXIP5Yry4aiReY4RQtNs1Gs1SSmnTf59EWitpGnhAC9ZCALyM83jjsxEOmcYbgX+uOe72VoG1nARg+3I5zammDKPdTvuYHaX5tT83MclVvjDXAXCW3zDy/n3/WS+YqHQ894SVnrYc9Ky5y9qyzSNnk5844P/9degik+3OPxIPLKJoBQMkFnSyHDTejz96LPP4o+1xl1tjvqbF9j1nLPjsQ9l4R7fhTuufFwbgqcn4p7Zhr69FTUqanorMnok5PQWRPQJ8ais5JxToxAHU/EOTKSSMaEmK716CSc46PRx4fjHu+Nc7w10eM3YWc1R2V5cOWc8KFOetCnPegzsQAuLcB/1oNz7rcMdYnd9TTdTGLA717yIEfKNVRTk5IrXam5PrR59SI/VvlxOPnxOHlyJK9cfk5em6HyBexvwS0SPl948SdQRZ3McYufRIuDuKQ32j/IWPy1cX9OMYXlqkIywKVsQT7wEiIlNXsbUJINLu09krdTNT/Wq1o5A105FV051rh5lVTnVcii+D5U4I8o2SsU3YTOvwYlDVDZcTgmYlhuPB7Uudjfhz5jYc5pC33Shz7VDCWBZRflQXctbkDKXSSKINm4WaU9THJrdPF67IKVTSC/yDTuUDINt3gCTnGyqW9zSoYTLU7AkVaf8iTs8pHYZaNj8c9Vs3Ekw1xAQnjexmfRjZtMJIQKLkIFF5qpnqBM92vQAi6NMYu9pCPSMNssZxGtfZXcjLqiSh7HKbwXlSv0zZM4p/oSPt6fipTuFGxrS84PD3D284dJe/dx9r3Rk/3vJLDrzwPZurI17w9rwey2FlPvj2dhm8tZ99R1vDfsTt5KupsFPW5j8pO3MrPr7awYeC/Pj3uMd+f14vO1Q/n5udH8smU0328cypdr+vDV2qdIfbsHhdv6U5/RH/tsd+zcx3CKH8Eplf9GyaiK6bGim6rZUCUKJinXkQwioaMGmoYtAXzJKLIrRhCtkIfadFSj6N2TsUX3HpRmqd8klE0yytDUJh5e1DVNJzImtoAVHj44BiU3o+B4nLpJRCrlBjEX6kVZMxWnIQHJwXHrZuOK87VhPjq0GBVdjhNdgbJXokOxuA7dsAxXYkGEeqsR5+tKXHGF18stLQnbNJU9gCuduWUPoyoeRFc8bPwqSsQLFU/E1GDlPVEVEmQ3HfP5r3wZp+otdNX7sejy8jdR/i1E8lbReG4utZlCvyVQ8ksPLn3Shqy3byf9has5uLEFqWvjSFkTz761Ldm79gr2rr2aAxv/wNGXHiTz1TYceaEVqevvYNuCq/huxmX8vOAaUtbfztFXHuLMX9qR80kXCr/tT/m2JGr2TKQ+dQahjDlET8xHn5VEYFE4LsQtnI9bIrTMzNh+pUwkxLPRFQtw5e+jfAVumYSarcKt+ct/gR6J7i1agBaQFypGAuyLl+KULMORgDOJEihejkz7ckWWhEtpoRJOSOVNQuWNwL00BDe7L+65nk1A34lo+qOE9z9E4+7bqP31Cmp+8VL9g4eyr32UfO6j4F8Wlz7wcOYND0dfsDj4tIc9KzzsWuph5xKLXcssUjd6OPNOHIFvfARTvOjjcXAmDn3WMkeJ1PJcc/S5q9HnbkVLTsjZ9ugzXYyG3z09FPdUEu7pRNwzI3BPj4HTE02KoD41ASXT+fHxqOPjUJljUZmjUUdH4RxLJJo5BPtoAnbGCKIZ44kemYp9dDzOseGoo73QR9ujjoll/QacYy1QmR7c4xbucY8BfS1gL0mLp30meVFAX0BMkhclP10AT2c3FWcY6kLoC6Gk/jvxS3WeHGMaavpa5XjNwlLlxKNzpYVJlD3C+csDoSVu/g24hQK0D6KLHkcVdTTHLe6ALu6MLumJ9ks41xCc8iSc8smoiiUmvM2teQltDCnPo6qfQ1VvMnplE/Ak2d8G6GcbkJfuW7dieGzpWt4WVXYXjv8GnJJrUMVy42iJzo1HX/DhnLdwzlio0x6cUxbqf44+KSBvoU9Y6Cyf8UnYYkCS5W2gP3ZpUgygS6cZ15/rX060YBl24WIzybuFc6FoJph+YunNHEm0UJawI1D+JFRJIqo0MRZnUDnOFLQ7VbOxDXUjIVnroH6LObpuk8kod2rXG65eom0lG0giJWQZKCUXTpU0b0nonKRZzsOtmYorxSXlQo+1xS1ojbrwBKET3Wg42peqg73I3/Y457+4m5P/vJu9rz7E12va8/3mXvz00gB+3NCRTyZcxdIOFhPviWP2Iy1Z1O5yXup5A+8l3s6ibteS/Nj1JHe4lSnd7mTF8Ed4fWZ3Pl85iO/XD+X7dUP5cmV/vl7TnV2vPEXOd72pTx+Ac2Ywbq50kw4yMcyGoqucaaZ5cxurllrLUSA3MnlYyXuoGYaukc7joehayS8a0dQWFosXVkFJpJyFIzn0kl5pjtAr0jQlhqcpuFI4Iq9BoWvGmiP0jJKMm9BwdEgctuMgOAkaZ0KDpKPKwn8ETn0yTvVMgv5phMpnIg9dJ7oM216BikoOzgrcoEz2y00JO7WLERksJsdmHbpRJnupHxyIU9YKVXoXrhjwyh/GLW8LZR1QZR1wykWmLLJfMfAlmHIfVS6lNC+iJKFSsuTLX0UFpC51FZGc+QTPTqPm6DhK9wwi96sOnHjzLvY/fQ07lrTk1/nN2D0vjt0LWrBnyZXsXXYtB1b/gcOb7+PUq+05/2ZXTv+pI2kb7ufnedfxzbTL+Wn+1aSsv40Trz1M9oftyP93FwJbB1O9cwwNB6YRFpA/Ph/nzAIQmjnvN5CfjSvO7tJYKZFUF8peTVUsRFcI5boKXS7xNevQNR+CqolN9NR8bVpKVNEybNP1Kh2w67H9G7AlrrjkadyS9ajiVeYhYCqsxFSVF7N2O5eGoi8OxM3ujT7TDXWqC87xJwgfepjQvj/SsON6an9uSdUPHiq+tgh85qXoEx95//Bw4V0Pp171cPQ5DwfWeg3Ib1/o5ZcFHn5dYhkq5zegb0zxoI55cQ14Wv8zMcehz1yGOnsD+szd6NNtcU93wj3VBzdrKPpEEvrEcHTWMNyskXBiLGSNMZSMc3wMTuYYnGOjcY4mm+ndOSLAnkDkyECiGQOx04dipycTzRhHJGMEzpF+OEfa4WTch5NxJyrjRtSR36GOxOEes3AzLfRxT+xkec2k6p7y4TbROjF6p2mibXpguQL8TRO+TPq/PQCE3+f/O+bXLohprDnupTjMojfXMq+Y6f8KdL4sPe9GF7RCF7ZHF3XAlVMsUbVdzRVelw7GKZNJV0pOVuJWvgC1r+PWvoZT/RJO1RZUVRPQm8q2JbGyk6rJuBWjDcdJWc+mMos7UeL6LWqJUyg3C3GMCvcuKim52Vg4Jy3U8ZhySjJrnOOSXSNplE1hZfJzkmdzXgpH7sT1P4VdMpRIYCSqfFys3lGyuKVcQXI/jPprPhin9nScAtkpTcAuHEu0cBS6RFQ2coSbT4JycRUL0E/GMYA3F1W5AF0lC1kpl1iPqliPU74Bu3wd0bIVph/UrRUH5iLsygU4FctQFatQlUua8urn4tbMiTWIifu39AkTBawK2hK50JaGUx2oPtyVwu29OPVxJw6+1YpftzzIvxY/zEeLH+XrzT3Zuqkbn0z5PSs7WUy428fMVs1Z8FhLNjz5O94ZfDPrel9P8qPXMKTNjYzudBfzB7XhhYmd+ce8bny6uDv/XtKNz5d15tcXO3Ph297UZfTBPtcXN2cAbvEwtH8MumwqyrznWaiqGSZv35U00RqJqhhstOaya4mUjSZSLhrzyaiaqaYcx2QfSYJp4yK0qF+CC1CSJR9e8N88GzFHGXCPqW9i9IuEmI3ADSWhgxJmJnLKRNxQMgQlo34SiJY+KH+2lMePNr20IpOMVk4jWjU79u+KLsaOLkFFl+FGBOxX4giN07ACJMNGtPaS29SwAR1chytprNUCft1wzA1TvCkSxdwJypoc2ca8J6qwfmiTNyUKrFXosi3o8hdiJfKBzQbz7Pz5RC5Op/HURKrSkyja1oOTf3+Afc9exw8LWvDFRB9fjY/j56nN2T3zCvbPvZb9868nbcnNHFnzR45vfJCsZx7hyIYH2LXkNr6fcTVfT2nBj3OvIHX9Hzj55wfJ/Xt7Sj7vTsVPCdTtGUMobQr2sVk4p2ahz8+EnFm4BSJxn4H2Sy/GJHSZ7Jomx24jTUCvzOdzDbpcGvU2o6s/+i/Q65ofjP7YKX4a5d+EI4XggS3Y/hewAy+i/VvQJfKm1+EUL8ORXtn8KTg5Y7AvJmFfGIK60B8lS9hTT2FnPUn0WFtCB++lcc9N1G+7guofWlDxtY+yz7yU/Mui4CMPOX/1ce4tH1kvWmRs9JKy0suORT62zffy8zwPvyz0sn+9l9PvxOH/xkvDXgt1xDL0iDppmZCr2MTsNVd+feYq9OnbcU+3wj3ZEfdkL9SJBFRmIk5mAur4YNTx4ejMkejjSTjHR6KOJ+NkCsCPIpoxEufwCKKHhxM5nEDo8EAi6f2w0/oTTUsgnD6U8KF+RNOfwjn8ME76Hdjpt+CkX499+ArU4XjUES/qmPWfo2XCP+EBoXR+m1zl9VQTtSP0jsTvCoVhQF9oDZn4vYbecGWh27TYjbUpeXHONUOduxL3/NVwoYWhe36jfVxR91wS17Dkmv8BXSDcbBsD9m5BR1yJqC3uEuNqAzLJjEWVzzUAR9VrUPMWbu3r6KpXcKqeQ1U9bXh6VbkMp3JxLFJArv9lg9Cy4BWJp/DoRdegC2SfIDcL2Tl4UPLAOufBFdrqlAC7RfSYReSIRfSoB/uol+gxH9HM2JFft2XSv3g1bnFb3NIBRP3DcAKinZ+KrpiLKluGK/0ApZshsNZUqamCOdh5M4jkz8ARR7TslUx/8HhcfzJaVDeSalo8DOUfhe0X1c0UdPkMlJmMZphFlmR928VziYq6zMQkzEVXiPxuEnbFRKIiO5Vu32rp+ZVSimWxh4RI8yrnoSrl7zIBVdqXaGFnIrntiFx6gsZTXalIHcS5L/uz/40OfP/0Q/xjXivenvII78xsz4fzOvDB+DtZ3+UyptzbkhkPXMaCNlew/NGreLH7jTzb7zYmPHYtgx64nFHtb2JOv4fYPLYdH8x9gn8v78jWZztz6MNuFO0aQOj0MFTecBPu55aOwC2TvcREnDKRzE5EV02IHSlNMTRN95iipk5iHyYSDEwiFJCYbKngW45Tt9AEjzn1c1GGvlpmTEw6uAAnNA8nNBvHdMYKZz8WHRyDKyeUbEBdh4egQ8NMYqWkVmoB/qAEo8UybwjOML2ymChjoXdGG0MVQfEuzEY1zsK1F6CiC3EE7CMy2a8wVI4OroLGpcZJq0OrUcENKIkhDq7BlS4GURCVdsAJPIAj/pOAeDq6osq6oEqlw6IPrqlaFFXWNAisgNJNqNJnY9Wjkv5ZKGq9mUTOTaL++BgqUoeS+/2THHrrVr5f2YJ/TPTw7lAPHyb4+DK5GdsmXM7OyVeyY+pV7Jx2Nb9Ou4ZtU69m68Qr+HrcZXw+7jI+G9ecrybF8/O8yzi08RbOvtmKwo86U/ZlP2q2JRJMGW0Mbur4RLQEm10YH8uml86QkvEo+fwGxuFKrHb5JNxyifmY1RRoJn0M60yPiA5sMUmb/5noVc3POMXPovwvoEtfRpe+iir9E07pa+bokpfRJVtQJRtQxctNH6WTNxH70iiiF8RCPRh1ri/6TA+crM5EZJrPaEVw/5007Lqamh/jKf86ntLPmhH4t5eijyzyJMPmPR9nXvNy7DmLtPUWe5ZZ/CIgP9fHD3O9/DTfQ8o6D6ffii1u6/dYOEdiV3yVJdd84cR96JMC9D7U6cvRp25Bn3wYN6sDKqs7zomB2McSsDMHYGf2wz42GCdzWOwcT8I+NoroUQH5UUQOJxEVW3j6MMLpCYQO9SeS1tuc8MG+BA/2IZTWlcjBdkTT7id66HbsQzcSTb+S6OEW2IfjcDJ8OBke80BSRy30MZnuLcj673GzLORBJTz+f4/QGk3njAfnbIzmES7bHOH3z3hxzsSjTl+NOnk7+tQduOeugewWkH0ZZMuEL0Ap2v2WsSWoKHMKZapvh1sgOeSivOiCKuqLU5wYi+uVgvCKLbHFTe17UCO526+gpTjdNOtIw45QOwugfCJu2XBUoDuqpA1u0V24haICEpOTZSKFZcdglDTSBnU6ztzAZGqPHrUIZ1iE0i1Ch+VrL6EjPsJH4wkfaUbkmLw/D+RfD4EO6LLBJh7YKCPKZqPKF6NE/uh/Brv4eZD/IYtWE82ZTzS3qedY8j9K5oJfyhrGYYthKn8Yds5A7NxBqMJEdMkYdGBirGqveKJRljn5E5tuBJPRxZNwSyfilo5Fi0FLtOWV4wzFpSvFSTvV7CnsioVEy+filM2JlUBUxGIVdMVkdMkUnLyxRC8NR11MwjmfRG3GQPK2deXQB4/z/aaH+du8e3hl7C28POFW3h7/B14ceDkLHmvG3NY+lrePY9WTzXmu95X8acTNbEi8jnVJV/Pa7Hv4eNNT/PqXBLK+G0H+7rFUZowhfD4JiqUzWaa92ajqKShxflZMj2Xui3egImaM0xLeVpNolslCz6jaYSYGQ9VOQNXMQVWvNAXZND6La1q11qIal6OD0vO6uOksxA7NJBqagh0ejx0ahRIDVDgxBvAC6qKkCQ1FBwXoZZofjg4nxCZ8o6Of2MTzT8c1Sh1ROEkiZqxoXJyzxj0bnYUbndcE9ktR9ip0dA2E14BUEcp0H16DY4B+FY75PiWBcgHIZ6i0DXbgEZxAJ1RpZxyTsRQrK5IKUh0YhZaeCVlilq5HBTYbA54uXmW6mO2LUwidHkfNkVEEdg/m/Bft2PPyjXw028srCRZbenh5rUczPhwQx8fD4vgkqRn/TmrBPxOb89fBXt7t7+Xtvl7eHRDHX4Z4+XuixZeT4tix+Coynr2dC28/hv/j3lR9k0DDjpFEDyTjZCSjs0bjnh2FvpBkxC5SKu6UjESVjEIFRuOWTYCyyVA+3QC9W7GwiZ+XQegF3MALuFWfgKqNUTe6dhdOyZ9wAq+jpPy77F106Xuo0rfQUiHofwnlfy7mOixcgsqfjsoZgyPhXhcG4WT3xznbF/ukqG1kmn+E0KG7qNt3o+Hmq7f6KPvCi/9fPko+8lL4V4vc932cf9PHyZfiydjkY/8aLzsWe/hxnoetczx8N9vD1nke9q61OPmml8BXPhp3eVCHPOhMr6EAZFJ2T8jELMeLzroMsm6GE/fD8cfQmZ1xjvXAOdYH51hfnGP9cI4NJHp0CNEjww33bmckERWAP5xIJH04kUMJhA8NJpTWn/DBnrGT2otQag+CB54ilNqe0MHWhA/eRyT1NsJp1xNNu5xIWjzRNB/qkBedLjcPAXsPWs5RD2T6INNrjpsp70EeWPL9x96DAOFvXLXw145M+WaZaxl+3z0tD7Q4dFYzVOYN2Efvxcm8E3XqStyzzXHPN4MLPhCq5IIX91I82riGY8XSKv9R3MInTQa5LuiBLhyAKk7CCUxDV6zArX7JLG7c6vdxTVn6FiNdUwboV0HFItwyCUwS89FTpklLFd2COELFC0BObJEsOwbJjnfPSVGIRAv70Md9hnJzMuKIpPsIHbIIHfQQTvUSTYsnciiO8GEv0UwPSr73kluQsnW3YgiuyD8lOz4gUawS0fos4ZznCGU/i1vwLCovltKoilebRh0CS6BEllWzsAsnEc1LRuUPR+UMwr40ADsvAVU4AlUw0lj/Vc5QnEsJRHOTcAqlmUz2AJOhQtQ5I3BLhfIRvbU4R6egyyQoTeR4s3EqZ+FIA5UAf1PNo3DfcguQvyv5s6TW0nhRCsahc5IJnx1GzZEBlKX0oXB7T85/04mTn7XnxEdtOfLeg+x9/T52/PleUv7SiiOfdOD0V7259NNw8ncPpyQ1kcqMUdRlTSCSPRPy50LxfPDPhtLpIKBuvAHiD5D/rgICU0CWqRVC0Q3GLk/AMR3CY42j2a2XassJJvxONc4zxd2xcg5ZUD+HDm1BNz6DDj4NkbUQXgahRaYr1mkcixNMMkcFE3GDw3FDQ3GDQ8wx0caN8rWA+1DccOwYjl7cscZBKzp7kVKKJFNcsdJcJamX8hqLRJBkTDeyAC2lJqLAMTHIq9CRVU1Uzirc0Crc4Gp044qYuUoy5+ukEnASTllPoiVt0H7JVeqI9ndHBfqgAv1QpSJKSIw9+AMiTZR2PUmIlIW/9LvOJJI9kdDJsdQeHknJrwM5+c/H+enpG3hnnJene1isah/Hug4teK5LPH/uHc9b/eN5c0Az/tQvni3dfGzsZPFMZ6/5+s/9LP6WaPHtlDhSVlxP1ov3k/tBJ8o+60ftD8No2JVENHU06kgyzslE1HnJ1RqCkzcMp3AEdvFIA/ZO6Rh06WR02Qx0+UzzmVQii65YjVuxGbfiRdzyLbg1H/8v0O/D8b+FU/Y2qvw9VNlfUWUfoErfRpdKue3zMeqmaI3RzUtAk8oZjXNhGHb2AJzzvYme7kXkRFeime2IZNxPY+rN1Oy5mqptLaj4xkfgUw/+j7wUfegl7z0Pl972mmk+c0schzb42LvSyy+LPGyda/HdbItvZll8N9djdKcn3vDi/8JH4w4PTpqAp9csPmNAGQN6fcKLe+Iy3OM3Qua9cKwN6ugT2BmdsI90w87oiXOkt+HXhXePpicQSR/SdGJfhw8NInSoL8G0XoQOdiWc+hThA10J7+9OcH9XgikdCB1oYzwB4QN3E97/B8Kp1xBJbUn4QDyRVC92moVKt3AOe3Eyfjux71m+bwH+2ENApn0B/CbQN8Avt5TYtO+c8qAMvWMZbt9w/AL0J+Jwjl2Fc+RWnMzfo041xz0rwCoAG6N5TEmHTPWX4lDSO5pzGyr3Idz8R9EFT6IkXrlQstInoP3z0eXrYkBf/Ra6UrL7XzIfFulddUSJU7kMV6ICZIoo7okuaYVT9AeUxBXkNTeOX7lJyAPGLJiFqpEHlewnMn2oo3LL8eIIoKfFETxo0XjAIpziJbo/zvy9hdM9RLPiULmX45beZ5I7jZOxfJzRCKuATCtPg1CKuVuIXnoenfcCulC09OKKFYfsWvCvRBcuRBXOwymciZ0v2R+jIE8KmAcQvdAfJ2cQTs5gVM5g9MX+5iHgFAxFifa+NBldNqJJ6TMYHZAzFNsvC92x6HIB0Dkx+qZmgen8VbXT0RJ1Wy1xCpIJLmUQ01DGgSuhZkkoKa2XZbE4zaXXtjAJ8gdDXm/0hU44Zx7HPt2W6Ln2RC52I5I3BLd4PG7BNMibCcUzoWgKFEzEzR2Pmzsx1qpVMAlXilf8U0G+t/LJ5jqvxQFcOt5c76kSu/9I7IpEbKlIrJLY62nEon0l3XRBrG2pcR2uAHrjRpMFrxs3oyUmWKb64IYYJSJNUkaRNBM3OAqnfhhKYhFEX98gX0tJ/G+LYqoAACAASURBVBB0w0Bo6AON/XCDA9DBIWayV8GROMHRxpugjb5+gtHXS0yCNsAuAC/LXTlzkax7HVmAG1mIji5pAvplRomjoyvQhrNfhRtejRtajdA5spBVjStNubvEeDvlQ4n626H8j4C/PdrfFRXoFQP7UukNGIqW/7aBmbh+GRRWmEh2p3Audu50IucnEswaS01aEkU/9ePYe635dsXV/DnJw+pOFvMe8bKwlY9Vj3vZ1DGOzZ3i2dA5nlUd41nSLp6Fj/pY1s7H+s5xvNrPy9+TPGydHs/BVb/n7KutKPx7dyq+HEzdT4kE947EOTQGfTwZdXooKnsgTs5AnPyhOCIwKEnE8Y/ACQjQTzM9C7pMMugXocThXbEeXbkFt/Jl3IrncWv/BbpponfrD5gJ3il9F7vsfZzSv6JK/4IqfRNV+gra/6zJQVaFK81VRuWJ8Wg0TvZwoucHED3bnfDJ7kQka/7oowQP/ZH6fddTteMyKn6Mo+xL4eU9FP3NS/5fBOQtsiXT5iUPRzZ7ObDWw+7lFtsWWHw3JwbyX82y+Ga2xY6VFsdf81D0qZe67R6cVC9KQOOoJ6ZykUiE40KPiCKnJerYDbhH/4h7tBVORlvs9HZE0zthp3fBTu+Knd6DyKE+hNP6G1APHepnpvfQwf6EDvYheLArwYNPEkp9gvCB9oRTniS0rzOhvR0J7XucUMrDhPbfTWj/LYRTriOcciWhlOaEUuIJ7fcSPmgRPWRhp3v+7zlsYf/PcdI95n0YTl/A/5i8H3kPsao8J8sTW16eEj4/RlPJw0wd9+JkNsM5djUq6/LY1C/cvuT/SDSE4ffj0Nk+TBbQpeY4OVfjSBtX/v2ogjY4QuEUDsU1i53lpgBePhS66iVUxYvoimdNT64uW4sqk7iB+eZ/BFXcH13YFlV4G3b+Vcb4JHEFQtWYPYIUdYuq5qQH+4QHJ9OLfcRD9LCHyCEPkVQf4QNeA/L1KRaN+zyE9nkJ7hcqx0PkrHgCfg8VYpAagq4YjS6TIpz5qMAqKJdMm2exC6XK8hXckjdxA2+jyl7HKX0Jp2SzkV3qQpFdLsEpnIcSzj5vPOQMx70wEOdsHxwx9Ynf42Jv3Nx+uAWDcIsHofwDUYHBuGVDjcHKlnKZooGxUzwEJdr8iinYMs1Xz0fVzDdl7o4Ut9fIJC8gvxS3bAmUC78/EbtsBNHyJCQOWQmXKtHJcuWuEF64H6qkCyq/PbHimsdwizpDWf/YArlsCq60QhWMhqLR6PxkVO5I7Jwkc+QB5haMxEQ2l4yBgGT5yBkfy/UpHYeqkDyiSWizXBU6ZzqqSjwEUmQjDUuS+y4FNc/gBoWq2WzKanS9FGevRtVJd4F0GsjvFcnjTNyGaSaXnsZEVO1gnNqEWJWk6Q2W7uDhqLoEdEM/VGNvVLAPqjEB1Tga3TAR1TARR6KORVNvjFSSWjkLFZqNjjQdAfiwTPELY/WEZpKXaV7OMpS91IC98PWxyX4NOrwWHRKaSV5X4TaKBFN2J2Owy54iKsF5xW3R/o7GNKikfrS0P0p2VdIZHBBH9SyQOPaSOTgF07EvTSZydiLBzHFU7h9Bwfc9OfrW/Xy99He8NtxieQeLWQ9ZzHnQw9LWFsse9bD4MS9zHvUxtXU8kx5pxvTWzVjYLp51XZvx2sB4PkqO4+eZzchYcwPZrz5M8d+7UfFVAvU/jyC0byQqPRn3hPR4iOmzDyqnHzo/AVUkn8sEA/TC0evADHRgIbpU5JSrURUbUFJeLhWalX9Gl7+Mrv33f4Ge+oPosg9wSt/HLvsLSk7pO6jAa0ihg6hudOFqVP4SVN5stPQYXkzGyR6Kfa4f0TNdCWc9RehYB0KHH6HhwG3U7rqKym1xlH/voVRUNv8QkPeS+7aHC69bnHnFy4ktXg5t8LJ3pYedSz38NM/Dt7Msvpxh8cUMi69mW2xfYXHsVS8Fn/io3eYhut/TNC1bqKMeXDnCgwtQHm2OOnINKuN2VMYDOOkPYx9qTfRQO+xDT2KndSKa1oVoag/Cqb1pPNiLRnnd34+QnAO9CB7oRPDAo4QOtCac0obw3seI7GlHePfjhPe2Jrz3XsL7biO87wYie68ivPcyQvuaEdobZ0ArdMAiImCf5sFOkwk/NuXbAv7/OR6iB33YaT7sdB/2YR/qiEy+XrSkc8oU3AT4MuELpy/7iN+AXnpT9fHm6Kx4XHkInBZ6R1Q8XtwzLdGnLkefuRx9vnlM2nixOc4lqV68C533EE5BR9OQRfF08C8FUxv5NG7FJnT5xqbGplVmOeUGpBRdKIghqMIOuPnywLgSJ1eMap6Yo1WWxbJMluLu474YwB/1YmfEAD4sNI1M8Ps9Zopv2GdRv9eifo+Xxj1eBPRDRz1ELjVHSYKnsawnGaB0y2egTdP9BlyZ5os3Ey16BhV4HV36IVT8E131D1Tlu6jAKzhFm9FF63Al2Kx4MapgFpGLkk00HH1uIPbpPmiRAJ/rhnOhG25+X3RhP9NOpooHmsWtloVX0QScgrGo/FG4hSOgeATIFFU5BadmJnbNLBwB95qpaOHDxVdQPo9Q4RzCEg9rIpIlUmIEdmUyunoCqnKCaa5S5Ym44sosHYJd0o9oYVd0fi903mBcydkvF259PE7paJPT4xRIKdAQtOwXhHbKH4GdNxwnfzg6PwnXKIzGoKS4vkIUUVNxy6eiy6diV0q5ueSsS4OSOJxFSbTMdKKa4myJeghtxg09ZyZ3cZg69bKEXYaqnoNTJbRUjO/XkmhaPxIaR0DjMNz6/ui6vmaZq6QMvmGYqZWMlcVL7HUCjoB9wwB04yhcI6VcEJNTNjVKiVxTeHnTHytxxQbgpZlKFq9ylsXMUpGVyATvCm1jL0fZS1C2TPYr0MLZ2+vQ0Q04kQ2o0Hrc0Bojw3SlVapafCK9iBQ9hFMsAYGPowMdccU46B+ADgxDB0biyoIzMBH8E009pcqXPeQEomcnEDo2hsqU4RR8153Mt+/hu2VX8sYIL6s6W8xt42FBm2Ysa9uMxW0t5jxqMbmNh+RWPpIe9jGxTRwLOnjZ2MvLG0Pi+GR0HDtmxJO56houvXQvJR90ouqLAdT9NJzGvSOwD4l/Zwj6TG/ci91xc3tB/iDcwgR08XB0cTJuyQTcwHS0dIX4V6ADG1DlW1AVf8KtfAsq38WVvuvaz0HXxTh6t+4gKvAXbJnoS99Bl72DLv0zTsnziBLHLVyBKliMypuLkzMVfVEcpiNwzg/BPvsb0HcidKQdDQfvp27vjVRvb0n5Vg+Bryyjsin80EPuu14uve4h+1Ufp573cWyTj9S1XnavEIWNxQ9zLL6dafHVDIvPZ1h8Octi+3IPR17ykfeRl+qfPIT3enAOetGHvegML67hwS30EVnUxuMcvgpHlDCH7saWhWnaQ0TT2mCntSNy8AmiBzsRPdiV0P6e1O/rRe3eXtTv6UtwXz9CKT0JpjxJMKUNwX0PEdrTitDuRwjvbk1k9yNE9txPaM8dhPbcRHD3dYR2XUloT0tCe+IJ740ntC+OUIrXgFrkgIeocNDmCLDLsWIn1UNwn5eGPRbBFHkoeHHSfagMH/qoD31MAD9G6/x3BxHj8wXszRL6RJxZRIuSx1AkJ324p5rDyetws36PzroZdfoa1Pl4VLZM+NfiXrgNN+cBlFQfFvSFwmQonmaWl25AigqkenF5zEJdPMdM/G7xZNziRHRBZ1TO/biXrsG95DNLX2P4Ek+ALJGzrBjAZ3iJHvYSSRMO3iK032PeY3CvRWiPh+BuD3W7Lep2eqjb6aNGXlMsIifjcAqvRJU9YHJJTAlI5RRTFoL0X4oawv88jjkvEQ1ISfOnGGmwhE4J2MtOyf8iWj6zJatxS5bg5M8icmEM9vkkVPYw1LmBJrbCze6Om9PdAKyd3wNV0Nf4QSIXJtFwdkpT1OsYdF4SFIyAQlGyyMNnHE71RHNsAW7JshepW+lUVGAawfzxBAvG4Phji1wpGhcKyq0cjqpIMEmiuqyv8Qe4/mG4JcNwigdASRJO3nhCks1UOsMkK6ryYURL+hMtlDMQu3Aoqkha4JJxCpNxCuR1Ao4sfksl0kCWsPNwRAFUIbEGC1DVC3GqRBoqURYbQPJhKtfgVK5CVa1C1Ur15LqmFEiZgpei6hegaueb+F5JeBRTnFszCrc2tsB16wehJbJYsnCkkKSur2kV0/US+Cbplkmxwpr6sU2RxokmRljXibN6JUpiNRrWYMxooYUYsBfqJiK0jVQQrkaHhYNfiY6swY2ubTqr0VEBfJno/wfoo7KcXYuOrkdFNqAjG3DNZL8iVpUpt5iKQdj+djhFrVAlrVF+6ULoCYHBEBhmspB0QBRao9HFSbhFo9AFY1CXxmOfG0/4xFhqUxMp+bEHJz+4j1/WXceHE5vzTB8PS5/0sPiJ5ixvfzmLHo9jZlsPYx+xGPaAh2H3W0xobbG4o8VzfSzeTfDwxRgPu6dbZC5pSfYzt5L/ZmvK/tmFmu/6U7dzKOHUodjH+qNOd0dnP4W+1BU3tw/kD8EtHIlbKMVKcvuYZmjNaPEiE0bpiJCm4l2o+hAqpVHvXXTtN/8FemoPoP3vxnj6wBtmkndFgVO0HlWwFC0AnyfBYFPNG5eYXue8TPMDiZzpS+hkN4KZHQkebkvd/jup2XUN1T83p+xbD8WfWhT93SL/fQ+X3vSS/aqXsy/5OPlMHEfWx5Gy0sOOpRY/z49x81/P9PzPRO9h21IPh1/wkfN3L9VbvYR3e3D2e9DpPnT6b4BvoUXpIu1T6VfiHLoZJ+0uomn3EDkkYN+a6MHHYmqZg08STe1CMKUHNbt6Ubm9J3W7ehPe15fwvq6E9j1BaO8jBPc8RHB37IR2PUh4932Edt9FcPctNO66kcadN9C441qCuy4juDuO0B6fmerDe32E93mIpHiJ7BcO2kNUQP8/xzI/F9ztpW6HRcNuy3DUziEv6rDXyDO1TPZHvea2EpNnxiSasrjVJ3xm8ewKVWUMRvIaHzMaZTVDH78BdexW9LF7UCduRZ1piToXZwxlZEvK512ovNbovE5Q2Be3cBhuYTJu0UQomoErwF8oHLJ0nQ7ELeiNzn8SN+deyP49nLsMjGTSivkZhFcXbfwxD3aGD/tgHFGhaPZ7CKV4CMrDzAC8ReNOi4YdFrU7LGq2e6jZ7qN6h4f6VC/O2Rbo4ptRJtdGCsJHoitmQMVyXNkTSGiZfwsq8CpO6Ts45R+jK79GV34HUvpe8anZK2n/n9HFQjWuR0sWSuFck+pnXxhj9kr60gici4NRuf3Rwo8X9MTJ646bL/HWowieHU/96dHYOSNQedIiJr9HKKvB2IUDcQJDmpyUSeiy0ejAeLR/nFHyKFmUFSdiFydglwwyU7sWGapY7ct6x5Z/flmC98UxtZcD0EUDsOXPLxpONHcU9RdHYQckokEeIIko/2CUfxDaP8RIJiXDxy0ZgyuVn4WTiRbPJFoq9v1FJpNI1yxFVS1GVUhe0aLY15VLUbJwl7C26rWxtE6jYFqAFqVGtdTyLURLlV6tTP+itZ9rJKOYRd9UqBpnugrELevWDkbXDkDV9ULX9TaTvQSeGX5eysMbklEN49EN00zwmVM3HqdWogkWEPQvobZgIXal7AKewRU+PbgQJQvYyCKjkXdDG9DhdSgD3htwIxtxBbzNjwXopYD8t25amfJXNf3aOlRkHW5kHYTXo4KrTYSxqpU46kRUoCuquDWq5CG0lN34u0KgH/gHIeob1y8P8xHoYqE1ZbgZhc4Zi509lvCpZBoyhlOxpzd5Xz9Gxut3sXXptbyXHM/mPhYrnvKxrGNLFj5xBVPbtmTUQ3EMvsdLwj1eJrTysKSjxZY+Fh8M9/DNOA97Zno5tvwKzj1zKzl/fgj/356g6uvu1O0cTCQ1AedIf9TJ7jhnO6EudMLN6Yab0x83PxE3fxxu4SSz85GeELtkEarkaVTp67gVH0LVv6DqE3TFh0i8zX8mempT0MVvoUpeRxe/gi4WkN+Iyl+Gyp1D9NIUopcmYUve8cVk7Ozh2GcHET3bj9CpXjRkdqMhoyP1aa2o3fcHqn+9nIof4in90kvRPy3yP7DIedtD9p88nHvR4vRzXjI3xpG+ymMkldsXW4a2+X6Ol69n+fhyhjdG3czy8tMiD4ee83Lxrx4qv7UI7bSI7LPM0tMRBU66FyV892HLTMXOoStw0m4kevAOIml/JHzoPiIHHyGc9hih1McJH+xAJLULDSk9qN7V2wB97c7uhPZ1J7T3SUJ72xLa8wDB3Q/SuLMVjbsepHH3PQR330Hjrluo33kzdTtuom77zdRtv5GGHZfTsNNHaLdFeI+X6O44c+TmEdprEZFpNUUoJwH42JEHgLkB7PES3ufFPuA1k74jap10C31YlrdC51hokWgaA1bMcWsA3pixflMbCafvM6BvDEhHriaSdouRfzpH7sLJutxIFvWZK+DcdeiLv8fOuROV2xo3rwvk9cHNG4ibnwBCDRSIDrsvOq+DqUJ0cu/BuXQrOvsqONsMN0tOzOTkHPNhH4k3FE1Y5JJp8j59RPZ5Ccn732PRuCsG8I07LBq3WzT8YlG73aLqFw+VP1tU/uolmN4cN/sKKLo3FrxWPhip7qNiHpRtQJVtIFq2EV36PFqm9rJ/osq/pD7/XzTkfYJbthXKv0WXfYL2v28+w07RJpODQ6H0EczBzplkFrNOgdAyow2w6sJB6MJeaCmsEaDPH4wuGIIqkNcBMYDP64/OF9pqGLpEQLc3yt/LOIsNOMgCt1iu1ANxCgdg5/fDLuqDKukD5X1wy3vhlg/ALRXjUhKqOBm7YDjRfOH+B2AX9sHO74WTN8DUYCrR/ZePMYXksisQnbcuHYRbOhg3kIAbaJKHFk0ytYQSM+yULUCXL8KtWgjVsrSeaxaLujSmrcb4HxZB+SIQiazUKIqBKiCZQGOhcgLaBNONwqmWmOLx6MpphPKmEi6QWGjZP0jdYmyiVzX9cepiiZYyxbv1Q9H1I5D4YgF41Si5QJNRBuinGcCXaGvJ9xdnsV2+0jiuaXguVlwfWocKrjTKGW2m+bUomc7tp3Gjz+BGn8WNbMaNrMeNrILwcgiJ+mcFbmgFhFdB09SvImvQkdVNQC9/7sJYZn7VGFPY7kp8t/9elP/hWAFOoAtuaQ+jqTc0jjygi4fgiptabk35Y7AvjSF8dhShrGHUHe5H+a6u5H32OEde/iPfLbiKt5PjeHZgc1b3/B3zOl7FhLaXMfwBHwP+aDH0Pi+TH/WxvJuXF4fE8dGYFvw443L2L7mK4xtu4fzLD5LzzuOUfNSJym8E6AcSPjAE+/AA1Ime2Kc6Yp97EnWhM+pSN1SODCixqV6LNLh4OkpqBUteQJW9i1v1EdR8BlVfoCv/ia77GXR9E3VTsxtV/CpO8cuooudxCjehClahcufhXJxMVK6+UoN2YST2hUTsbCku6E/kVG/CJ7rTeKQL9WntqNt/H9W7bqDi55YmmdL/qZeCv1vkvG9x4Q2Lcy8JyHs4sdFLxlofqSu87F7qYfsSDz8v8LF1no9vZ8fx5UwPn5vj5YcFEmzmJfs9L+VfeWj81UNknwc71YN90DIqHCdN+G8PzqF4nLQrsVNvInrgTiIH7yacdh/hgw8TSW1DKPUxwqlPEDrQicaUrtTv7Und7u7U7+pCcM+TBPc8Zqb4xt1307jzXhp3PkjDzvto3HUnjbtupmHHDTT8+nsafr2J+u03UvfLtdT/2oKGnR6CuyxCuywiu+KI7IwntNtrqAoBvIicFDkx4I8KXSORyylx2Pt9sWn/oDy8PBiwP+zFNdRUTJopcsyY27ZJkmliFn4D+pgLVx0X16mFfaQldvoNxsyljt6CloXtKS/qjMREXIbK/h32hd+jL94Nlx6DS52bpoVuuLndUXlP4eQ8isqR0ugbcC5eiZPdwgSNuZJHkxlnFsf2UauJoolDOPhgqtBQ8gDzEtrtIbjTMiqphl891G/3UL/NQ91PFrU/eqj6yUPFjxblWwXoPYSOtMDNuQGKHoOSQVCWhFsxFrd8MW75JlT5JuzARlTpc7jlb4OUvJd9R0PhZwQLP8ct+wG34ntU6ae4/r/hFr+BKnoRVbQJitYgYC9Rrkqa0oqmYheNRwkXLpNbYW+cS09hX+yCkyseg9hRBfIAGITOHWY+9/VnEonmiBKmN6qoM25JV/D3idUalvTBLemJKuqJU9gTJcqkQA/jwqSiK+JAVsVjieTJw2aSoVzsvCE4hUOwBfDz+6HyB6HyE3CKh6IkbK5sKDowBB0Q+Z8sieUMQgXk58X1KwmdUzFgLoAuuSdlwstPQgcmoIrGmH+nGMPcyllQObtJbz0RJLpCHib+JPAPxS1LMKF0ktvuVPVHVQnVNJFQ/gRCBfJ7JxrzkVM51CRb2jV9Ta2grhOqJtEAvK4fj2qYjCNJlsEZqOAsk/6pg3NwjPFJ0irnxJa6NSvRdevR9ZvMElhLoJyZ4jcY6kVF1xmQV9HNaAP0zzRN9QL0qyGyAjfym35eNPQrzc+70dXIQV6FwgmvR0vvbGhqrANYHpSivim5B6fkbtPhQKC94et1QExUwtn3R5ckgH8EumgUKi8ZJ1f8QolEzg4meHIA9Yf7U7mjK3kftyH9+Tv4btENvD/hBp4ZdC3zOl/FmLaXkfCgh8H3WYxuHce8TvFs7OfjrVHN+GzG1fy67GbSN97NyVdac+HtjuT9rRvFn/ai4rs+1O4YQMgA/UDszJ7YEvV+5gn0+SdwLnbAyemKypNBZHSsdL54Fo4YuwJ/MhO8W/0JVH8B1V/iVn6MW7cNdMNvQL/NLLGihZuJFD6NXbAKJ28++uIU1PkxqOxEo7Cxzw8jej6B6LlBRE73JZzVg1DmUwTTn6Bh/0PU7r6Nyl+uJvBDc4q/jKPwYy+5f/Vw8W0P5/5kcWqLh6xNcWSsiyN1pZe9yz3sXO7hlyVeflroY+t8H9/Os/hqjsXnM718Nt3L1gUW+5/2cvbteAKfx9GwzUdkr4Cj7z9gbx8U0PfhpLXEOXgN9oGbie6/k+iBu4kcvIdI6n1EDzxM5EAbwgdEOdOB0L4nCe7rSHBvRxr3PEHj7kdp3C0T/D3U7byd+h2307DzDup33EbDjpto2HE1jduvILj9KoLbr6dx+1U0bG9B468i+7QM0AvYB3d5Ce6MI7RLAC9GNYX3CNhb5kT3Cchb2ClyPNi/UTvy4JLFrXD16T7cdC9uRhOFI5y9yDAFzEV3f/z/HjPxy9SfacWUO0evQB/9HTrzcnRWC/RJMVn5TKKm9O3qc7/DPX8DbvYdcOF+uPAw7sWH0Rcfwr10H/ri7TgXrsM53wIlkdASzCYPiywvzjGPMYRF0z1EZMnatGAN7fEidFTjLq+hZ+p/sajb5qH2Zw+1P3qp+cFD9XceKr/xUfatj9JvLEq/tajeZRHOugydfyfaLwA61Cw9Rf+tJLejcpPJ4DEcfeAZ3LI3oeJTdPlWdOVP6MqfcSp/xCn7GlX6L9zAh+CXysg30CUvoYs3QuFKk8/tSj5ToXQei5plLBQloPN6Yl/siH2xPU5OO1Ree1T+E+aBp3P7onMTiJxNoipjOMEzg8BQLp1MPAOlPdDFPVGF3VBFXbELu2KXdEfLz5d1i7VrSQdroDfRvFE0nJ1IRFyO+Ymx20PuEFTuIOy8fgbs7fzYTcAt7YdbOhDXgPoQnNL+OP4B2P4BOAHpdpU0zqSYsiYgk+p4I6VUAWnTGmoyfuz8ETgFidhCR5SPjoF72aimaGrJ5RmBpJe6Em7nHxiLApBGMNNBLMUxot6Z8H/kmSb8y9RTDkJXD0LVDsWtk4L4ybj1M9ENs3CCc0wzlAotxgmuwAkuQ4UkNmGJ0bfrhuUoyaWpX42uX2+C5HRIAH49TngzTuRZlL0JR44AfWQTOtpE3UTWN03uq3Ek6Cwiy1jh8eUIpy9AH+P0dbgJ6CPLcCV8rVbeizwkO2L778cpucP0NUs2kfJ3wA50xCntbsL+ZJrHPxKEp88fgcodhro02Mhz7ezBRE4m0Jjeg8rt7cn5+BEOvfIA3y69hzdH38TKXtczqf3vGNkmjuRHvczqFM/TfZvzZlJzPpl2BT+t+AP7n3uYzDc6cu6vPbn4z37kfzqA4q8GUbZ1ELXb+xPcO5iIuPGP9MDO7IST1R59+jFUdjszlAjtKD4Q6V2wSxbilD6NLv8zuuJj3KovcKs+h+rPcSs+wq39X6Cv/sbQNHb+MiL5i4nmz8XJnYK6+BvIi15+GNFzAvJDiJ4ZSPhUL0LHuxA62pGGg49St/duqnfcQPlPLSn5zkvhZ17y/+7l0nseLrzh4ezLFlnPWGSu95K+ykvKcq+hbXaIG3apj58Wx7F1kY9v51t8Ndfi81kePpsRA/qUpz2cfjOOkk/jqPspNikbOuSA0B4C+F6iB+KwUy/DOSBAfyPRlFuJHLiNSOrtRA78kciBBwjvf4RwymME9z1OcG87gnvl9TEa9rShcddDNOy6h4Zdt1G38ybqfr2Bul+vp+7X66jb/jvqtregfnscDdub0fhLSxp/aUHDL/ExoP/VQ3DH/x4vwV0y5XsIydnjIbwnBvT/j673DJOqytq/KzSYxjTGMYzjGMYwOkYEJeekgBjHMGNOCGJABEWCJAUDiAFBzEpGMk0GBUFFSZ0rdXdVdc4Vz977915rn0Z9nv/zflhXNY2BYeQ+69zrDrLdW6CXt5I/Ar1w+N/7yO52gf63+4No73/yuqqiVrD/I7C74P575IKVaf58FPrno9G/tEX92hZ1IAclzuHDXjdULV96d9ti8v+EKTwZXXjq71N0Gqrwzzj5x+HkyQNCsnrkNuDKP0UuKeohURYld3hIbHUPrPJW07LBh2zwjULPrPZQv8pD3bce6lZ4tp6+LgAAIABJREFUqbUg76Fqsc/KbWOLXKBvlDebvD+hyy5AV8pr9GCw+u9HbP6OdedWTUJLX0JMysCFh/waXb0aU5uLbthAtn4tTs0KtC14/wQTm4uOChU5g2zpRLKhl9Dh511BQelTEH0EoveghaIJdkOVdLQ3CxW8gWzwOrLB61GhG1GhHqhQf1TJrTgFd1jtvXD6Srb+qMQ/HAH6zjiRjmTLOqNi3ZAKR1PVFSd+E9noDfZ7KjLIOsmz0jcc7I8O9McE+qPtH9reOJE+KFH/RHtDZR9MZX9Mhcj++uNUSGFMbztGCl2q+4LQW1WDUPGbORJ1IA8BJ9bX0g7abqIS6DbYPhioHoiu7Iuu6OWCutWPuz9W8mYS742p6oup7m/bx+RwLIohcQWLBt/U3uby3NLSZJMuh6ClbMaWcovkUqSXclgdiUq+gBI6JjnObuom+TI6ORadEGB/BSNpoc0SV/CKK+tMjkelxqFSk1DpqajMJFRmIu5GPwGTEdpGwH6cu6mnX8FJjbGjRD9vj7Eu0AvY6/SY3zZ6m40jheMNcmeQSIjOOPHLycpGX34RRF25pfx/Jdp6uYWY6O0QuxMjKaiR2y3Y68AQVMkgVNFgnPybSf/Sg5ZdN1G9rj0lX9zArhnXsviZC5l559mM6nUyT3Vsy/Aufl7p52fWnUfx1aMnsW7UX/l+2hX8+l4nCj4ZQOCbWwktGkLp0luILR9IxYp+1K7pTXNuX1Jbe5He2ZXs7ptwfmyP/rUdKq89TnFX1/8R/jeq9DGysVG2NMpUv4ep+QLdCvSm7uv/A+hrv7A9g2IOyAQeJBO4j2zwLhyxbxfdjFN4M9nCm91NPq8/6UN9SO3vSvLnG0nsvZ6mHZdTv+lcqtedSMW3OZQt9hD63ENwrpfiWV4KZvo4NMXDL+M8/Djay/fPedkq6ZQjPax7zsPa53ysfrYNK0fmsHy4lyXDPCwUsH/Cy7fDPWx9RUrC/ZR9LpuhbMwu/y38dma73+WFd+SQ2XkU2Z1/IrvjZDLbTyHz3amkvzuD9M6/ktpxEcltl5PceqWdli1ycL2Cli2X0bz5Ypo3nk/TRuHgT6Mx90Tq1x9H/bpjaFh7NA1rcqgX6mGdB9lUm9d6W8dPyzofLev9JNb5Saz3kRDQt8DvccFfaJ2NHlJydP0N7GWrF8B3wd492IpCR7b6HJxWsFdyf9jrHmhFSupq7eXzj8Dufu387MHOT/JjkWiKucpv+XtH9PcifRR37W/hal7XfWsTNXNQAup5bXFkDrfBkTcAeTiI6UluAiJf3etKRNNWTeOCvHDwzXJQXe+jaa2PxjVeF+BXeKhb5qFmqccG2VUv8lC1yEPlN15iX/sp/9pDxXIvie+PwhSfAPGL3DagapEeSon3k2QrX0ZVSSzHZNsLmy17lWz0TahaCLUb0PWbMU2bUI3rkRgPalZAxZeo6Fyc2LtkY++QKXuDbHgSqnQcSgx/0qNQ/hi69N92qybYDVPSCRXqjAp3RIXa23FCN+KEurTeKwZiwoNd3r60G6qsK7qsG7qsu2sgK+2EE5KE0C6YeA+Qw2tFV7LRm8iUtUeVdUSHu6NDvcgGu5GVP6xFvTAlch/pgRFOv7wvpnwgKiq67t448d5kJbkz3tNumnLMNVL6Xt0DquVhIgFq3VBxmd7u31PRFRWTX9MgMkW3k8wbghJJXqwnuqIDKi49ATei4l1QMaGU3CIaHZevu0NVfxD/gu0Y7oGq7ouuGYTUQ7p9w7fZknkpgbdTL45aaR17Et0sdX+yvY9GJcejk1PQ6emY1FT36+QUTHIqJjEN0zIdk5wMydcgMRWdmIRKTkSnpqNTM9Dpaej0JLRw80cmPRGTEsCfgE5OQIlbNzXB0jOuKkcOsi/ipGS7F/AXmudlTEpkllI/KLHad6KqupONX4UjfQ2lF0OZFNR3wFI38V5o6ReO3YWRN6WoiBTugNK7IHQ3puRuTNFdtuMie6g/mV/7ktjdl9r1/Qh80Ytdr1/PkpEXMefe05k2+Ghm3upn/n/9LB9xHJtf/is/vn49B+f2oOjLwQSX3kt45YNEVt1P+co7bGplfHlvqlZ0o3FtT5Lre5DZ2JXs1s4433dG/yhhkV3cno2ioajAA+jISHR0EqZitpUZm5qv0TXf2I3e1P5fQF/9CbrELZeVg2tWXK8lt+EUD8IpHIhTOIBMQX/SeX1JCcgf6EHql04kfmxH8+5/0bDtQmpzz6By9bFEl3mIfO0hsMBLyXs+Ct70kzfFz/7xHn4e4+GH5zzsGOlh80gPuSO9rHvey5pRbVjzwtGsfP4oVoz0s3SEh0UC9k94WDHcw+axXn55M4fIJ35ql/vs5piQ4+dWD5nWrTi704sjR82dOWR2HEVmx9Fkdh5LaueJpHacTmr7X10NvBxat/yDlq0XkdxyEYnN59sY5eaNp9OUexJNG46z23vD2jbUr86hfpWfupV+6ld5aVzjsdO0phXs1wng+2lem0PLOhkBfvfg2LLea4+PiQ0ekhs8pDd6SQmHv9lDxlI5PtJbfRbs5ebgHmzl1y/KFVd/LxSOgL3e47dSUuustYoc4e5bD7UC7P9r7MZ/JD3Tmslcbl907mZ/DuxvC/tz3E39iCFLnLjyMLCfbpqkUEWi/lF75f4h9xCPvSckxewkh9aNXpqEg18nAO93KZpvfa0UjZeaxV6qFnqp/MZHxVfip/AQ/9JL+ec5lH7hpfJbPynh54MnQcU/ULWd0NJfK7RN5dM4VaLrn4GpkGC916yGPht9y93ca9dDwyZ040ak3N6pX4OuWo6u+BJdMd96QlTlBzbOg/gsl8axhqpRdhNS4bvQQXGm9sYEu6GDXdGhLphIZ3SkE0om3AUtqiORXkb6YMr7gPDx5T0tOOsy4eu7Ig+FTOAGu9XraDe0dV0KT9/Vbv4SJKeDHXACN5AVeqhYlBTd0CVd0KGO7oOjvDfGHnR7ky3vSTbWEyfei2xFL1RlL0xlH6iSVqSuGEletMAtxp/2Nr9F2Y20HTp2I5T1dc2MedKd3I9MqD3ZyOU45ZfazHwTFYqsq3tXKO+CE5Xy9hst5eRu9X3R8iARtVB1X1TNLaiaoTjVrh/A1N+PkuL4+gdQUi3ZLAfP59ASQZAaj05Nw0m+gZOaiU7PxKTfsqNTb2KSb6ETb0L6DUzqDXTLTHTidXTLVFTLTEzyHUxqJiY1BZOajElPRqdfQ6eOzCR08jVStRNorHwJkxqPyYoi5yV05kVUZjTK0jiy1buxDbrlObLVj5EVaWxNf7KV7a1L1pRf2lrHeY3be1DR00otxR2tZGJ3oFoVTib8AIQeg8CjmMJ7Ufm3ow4PJbvvNlp23k716qEUzuvOzsn/ZMWzZ/H14yew5Klj2TjmOPZM/wuH5lxD0cf9CS78N5FvH6Z07dOU5T5Dee5TxNbdT+WqIVSv7Ev1t91pXNmdxKqupFZ3Ib2uM5nNXXG+72ljXNTBgZiCuzHFUhn4EsTegKp5UPUNpsalbYS6+T+BXlctwBFTiUyJaEfvwBEZWtEgsgVSJtyPTH4fUod7kRDK5tfOpH7uQOKHq2nceSl1W2SbP4XKFUdTtshL8DMPxXO9FL7j5/D0HA5M9LPvZS97RnnZ+YyHrc9I5ryf3OfbsHZ0W9aOOZZ1Y45nzYvH8u3zbVg20sOip92tXoB+4xgvP8/IIfixn6olPprX+dzDpwB9K9+tdnpwdnrI/g91Sw6p7UeT2nYCqa2nkdp6Fskt55Kwcw7JzWeT2HwmLZtOoTn3BHtYbVrfxt1OV3tp+NZP/fK21C1rS/3yHBpX+mhc6aVplY/mNbLBukdGu+Gvc39d8rV8v1keAutcsBe1STLXQ2qjl7QFe6Fy/jjutm+3fIkFsCocAXs/Vnb5m4y0lbcX78De/5+Rn5ON3/L17qfdyK1zWLZzKW/JcSWaNhSu1ZAlUcGtB1379/7kwdkrSibXt5ARI9gOj1UJye3Bgvx6AXgfjav89veqbrmfuiV+ahf5qF7oo+prL1Vf+qj8wk/F537in3uIfuql9BMX6GvX+sj+ehQmfBK64lIbK6vERRqXTBmR/gk/PwNTOR0jCapxkVe+g6r4FFW9DF23CiXJq/WrkARWp3IxOv4ZpuJjdOVH6Kp5mOq5UDEHE3sDJQmsZULhSMT2nThyXA0NgHBfTKin3a7t5i1ZQJGuVnqpS3uhS3tbWsXE+kKsD6asdeQwa7f59jjhDmTFa1AuPG93TFxAowc62hVKb8LIxh+UO4Acum9AlXRCBzq4NwGbKNoVU9aDbKQr2VJpAesBFUKtiOpmoOXs5U3BVHbEVLZDx69Dy4Yeu6Z1U7+ebPQqlKR+RjpBcW8o7ocplhvEtThB2WAvwpReCaUdIdILJO+otA+OpJnGZasVsJcHSU+0vJVUy/RBVw+yxfFO9b3o2gesC1jVP2R7hJV1yz6PbhmDSgq9Mg2deguVmo2Tno3KzEZn5qDT76HS79lPk5qDTr2DSc5GJ+agE2+jmmegm94mUzuTdN0UC/z2DUDeDCRvJy1vCELtTLDbf6ZhPM2Vo+12T+ZVq6HX2THo7EvorNA38hB4GVKjrUJHfAHya1YN8sDqjiPqm7ILbSgf0rEcvwIqOoIcvSuEEhuCikmAmFSpiuT4SUxkJCY0AhN8GF18H07+XWT330l6z500b76N6OLe5L1/HXunXcz2CWfz3Wtn8Otb51Ly8VWULupH2Yr7ia0dRkXus8Q3jyK+5QXiG4dRue5+atbcSt2aftSt6krTqm4kVncjuborydVdSG7oSnZbL5wf+qJ+HQR590KRtEy9DNGZUDUfU/3Nb7SNbPW65qv/l7pxqhaQKZaQMpk7cSRtr1h4eaFrZJPvTfpwT5IHu9Oyvwst+25yt/ldV9Gw/SJqNp5O5eoTiC89itIvfZR87KHwPaFs/BycnMMvr/r58SUvu57zsH2El83P+Nn4XBs2vHg068Yex7pXTmL9yyezbswJrHzxaJY/52fxCC8Lh3lZPsLDxpe8/Ph6DiUf+alc5KdxtfDBLh2S2eohu82Ds8ODbPUW6EXVYhUuPlLb25DadjSprX8iufVEklv+THLLKSQ2n0py8ykkNp1Ey8bjaN7Qlmbh4NcJgHtoXOWhYbmP+qVtqVtyNLVL29KwIoeGFT4avvXRtMpP02oBfNnu/zCrfTSt9rtg/xvge5DN3gV62ey9pESVs9nbOiLNdA+2mW0+V6HznWuykgPtb2AvB1qRXh4B+T0eKysVaamWaf2+3fD/QO9Y85UYsFqPukceAnZjF3Df9zvtI+mgAvA2rmG3a/AS85ds8SIXtQdXS9XI2408DH3Ur/BTt9RH7WI/NQv9VH/jswBf8bmPis98xD/1E1sg46F0vo/QfBfoGzeK2aotpuwkVIWkC/bAKRtqI4ZN5VPo6nHo6tfRldNt3pKkq4pWWFXOQ1k+8huUlZGJnn4JquorVHwBOj4fU/kxuupjTNWH6NgsG8yno6+iykbiRB60+TdOYBA6PAgTEcAXeaWYpnqiQt3RkR6oIyBfJtr3gfZwqaOieZeRDbyHpWWc8A2ocHt0WSfsRh/tgYn1xIgMs7wrRDpCqBMmJLeAG3CCN6ACN6JD16HCV6HC8tkOFemAE2nvRlREu0Gsn6vvrhiMqZBYhC62P9dUXIGJ/xMduxIdvxpTcT0m3g4lP5aymeBVmOIboKg9puhadNE/0SUXYcIXYMKXoeXNorA72aL+mFJREvXFyL3BvhXchKoQKqcnqkpAfgC6eqg1TamaB1A2xE2Om48hIK+ankM3j0O1TEEl3sCkZ6NTH6JSH+Gk56Kz8zHZT9CZT9HpTzDpBejUAkz6I0xyHiTno1vmoJrfhuZ3aYlNo7H8VVST0DxC8Qj1c2Szn4hKC58vgWvj3GYwiWxIT0SnX0VnRZL5EsYR1c1LbgxCawgbLcOhRSime9C1/VGxa3Ei56IleTV6Gip+Hjoum313m4GjBfBFeROVlNMn0WVSnyqxxaMxkeGowKM4Bf/B2X8v2b13k9h2G1Wr+lL+RUdK5l7FwTmXcPD9Syn55DrKl/alYv39VGweRtXWF6jZNpqa7aOo3vIMVRsfoWbdXdStG0zjur40retGy/ruJNb3ILm+O8n13UjmdiezpSfOrr6ofYMxh+77Deh1dAa66iPrI3EBXkBeKJz/E+g/Jl18F2kxa0i+fPFgnKKbyRT2I53fm1ReD5IHu1qQb/6lI80/tadlz7U077yC+i3nU7X+ROIrjiG6sC3hT/0Ufugl7x0fh6f7+HWCn5/G+tn9gpcdIzxsedrLppF+ckcdzYYxx7HhlZPIHX8aueNPZ/0rp7D6peP49oW2LB0pYO+znP2G0V72TPdR+KGP2NeugkP02KKpz2zxkN3qw9mW48oV5bi3zY8FzG2iUxcDk4zEFBxFYuuxJLb8icTmE0luOp7ExmNo3tCG5vWykbuA3SQywJUe6pd6qV+SQ93io6hd3Ib6ZTnUC/iv8Njt3m72qz0u2Avgy6ySh0AOTbLxr/HYkQdBYr3HPpzkaCuAb+WXosyxB1uP1eGnt3hdWmebqFk8SC6MHD1dsG9N7twtnoHWEf/AH6f1+86PXpsFZPOALMXjyjTlqCv8fta6iOWv8eH86LdGp8weP5kfcsjuzrEpnKKoES5efh2JbR5aNntoEsOTpWpaj61yaF3moXaJj5qFPqq/9lL9lWzwXgTk4594iX3sIzrfR/k8N7U09KGPwId+Il9K1o3fjVaO/hknfjVOtJe19uuye6HyCUz1GHS1cPRT0HE3QtsCfdV79j9uVbMAXfUppkpUOF+iqj5Fxeajox9hKhdAlfz8B6iYJLBKx+xY20jlBKUV7Q6yoqaI3GrHyGFWDFLB3jhBMYkJXSNGKTE1uWYlExuEjvZDlUlkggB9d5yymyxIE2kP0c7WVq/Ku6Gjss33QJcJ0ItfobsNMCMsvL9s8u3RkWvd7KHgv8gGrsIJXePGSZe1x5R3wZT2JR3qTzrSHy3Z6ZXt0RX/REtjUlw6gS9Hx662kc421jn6T7Lhi3ACl7gqqsJLUFKvWXQhJnA+OnAuTsnfyRZdRSavG5n8vuiIKIlutrHVKio0TgeyUeHzu6Gr+qGrB6Or70DbLPtHcGofxal7DN0oID8S3SSu2mno5ndQifcxqfmY1Geo1Oc4mS/Qma/BWYTJLsakF2HSC9GprzHpz9CJT0jXf0SiZhZO0zuYpjlka98kUzMV3Twdk3i9Fehfwwhnn56Ak3nVNUVJ5nzyFUiJxt79OZ0Zh1KtQC9Ujrhrk8PRySehZRgkhqFbHsTUDwFpmCo/B116LKr8aJzYqTixS3FiN6Jibl+DlqTW2H8R17iOj0bFXkXHxmPKxqICI1EFT6J+eZjM7v+S2HYPteuHUL20F7EvOxD87FqKP29HeGEvYmvuonLz41TseIbqHc9Ts+N5arcNp2bzI9RuvIf63CE05Q4gsbEPqc29SG3tQ2abTG/ScpTd0ovs1t443/dD/TwEc+B+TOFT6PAYVOx1lPw3Xv0ZStRoFuS/RtV8aTd6/qi6ycQXkCq6g3TJbWQE6AtvQRUMsNt8Mq8nycMSWtaJxC8dafn5Jpr33kDj7n9Rv+0CajeeTtXqo20HbPmXPoLzfeTP8XFoZg4HpvjZNy6HvaNz+P5ZL9uGe9j0tI/ckW3Z+OLxbHzlZDZOOJNNr53NpolnsWH86awdeyKrXjyWZc+1YclIP8ue8bPmRQ87p3o49J7HNlPVLPdbAE0IHbJZNmE/2a1tyG5pS3pzG9JiWtoi3/OREZWLHT8pca9ubkNiU1taNrUlsSmHlo1+mte7x9WmNa1a75Ve6ltBrG6Z126rdUu91C3zUbvMa6d+hWyzXhplVh4ZoXZyaPy2DQ0r/b9/f5XXBX3Z/C2N4yO10U9qo3y6wC/8fWazh6yA/VavddeKdDEtm73NzfFYnlwJV75b1Dnupi9f/368bVXsiFJHjrjWLew6bm2a5h4/juTqWJOZK+UUcM/sbs3d+d6NbEjt8NkoB9f0JA8jkZD6aFonvz/uQdwqaZb5LA/v0jR+KoWm+dxvt3hJKrUg/5Gfsg/8ROb4CM/xEJqTQ/C9HKILPaT25KCDx6FiZ6ArrrEHQTEQ6YjI2x7DVI/GqRmHUyk5NyKtnI6pfMN2JajKdzCStGq3mU+stExVfUY2Ng8n+iEq9hGm4kNMfDaq3JVZ6rJRmPBT6KA0o0nG9xCMZMVEbrObvXD2OtAPXdIHE+yHCg2wBipTNhjKbrVyOyOSzPJeLkdf2gMn3Bkt8c9lHTBRUeN0RgtIl3XDlHbDRGQkU6cbJtwTE+mBiYhBqyOEO6CD1+EUXUm2+Ap0sB2EO0Npe3SpPAz603BgAM2He0J5N4hfjYpdiBM732rBTewyTPxKTOwadLlURl6BE74QFTofXfx3F+TzL0EXnY8JnI0JnkG25C+owMXoQHt0sCemtDeUSd6P+AA625uClv8dFV1QlX1RlYNQVXeiq6UEXiKZJZpZjE8jcBpHoZonQOItTPJDdPJTVPprdHYxOrsURy1HOd+indWQWQPZVZjMCsgstmCvk1/QUj2HuugbZBpnoVrmoFvegpbXMS2vu0fc1ASryBEQF5ml/RT3q2TZJF0eXiIPdNp10iqhbazEUlyyo1Ap6f99AtP8GDQ/ZisLTYOkefZCVVyEU3ospjQHU3YCqvxvOFE5zrazPgkTk+TSf9ulQ1dKDIe0Nr2Gjo5Hh8Zi8kdhfn0WtWcYye0PUp97F7Urb6FqSW+ii7oSWdSVsqUDqFh7NzVbHqJ2x6PUbX+Uui2PULvpIeo2/JuG3Ftp2jSAxKb+pLYMIL1jIJnvBpLdNZDs7v5kdvUjvUs+B5DdMxi17070wQcx+UIhvWLD/VRMYuWFrvwEY5ebT9FVn6CF0mlY/bthKl0+j3T+7WSKbiNTNNTy8o6oaw73J3m4O4lDHUnu70Di5xto3tuOhh+upfa7S6nefBbV60+gcvlRxL7xU/aJlxK7zUsypY9fJ/r5aYyfH57PYcczXjYP95D7jHDzx5I79hQ2TTiLzVP+xuap57N58rnkTjyTdeNOYc2YE/h2lFA4bVg+sg2rRvnYJkar2T5Cn7ahamkbZOsW7lu06wLg6a0C8G1I5boAmt7kI7vVaycjjlVxoQpdsslPQoBro6h3Wk09oqaRQ6vw8qtcAG9YIbpvz/+YIyAvn7LJytQv97aOj4YV/lZ6J4f6FbL5tz4MVrobsBxz5a1BVDnJXF/reO2biT3UbpK3k9YHk6hyxFFrM9slt91VvEgev0Q1W3PV/wD7IxSPAHmrU9iayI789fLPcAFdfe/D+c5LepfHHXmYiLxzu3DwbmSBbPCS/5/Y4LNHZnlDkQeaAHzNUi9Vi0VF46X6Gy+VX3mJyxZvaRofsQU+yuf7iH7ksyAfftdP8B0vwXc8BN/OITArh8qlHtK/tEFFjkfFzsbEr0HHu5Mt74YqHYCJPgiVz6KqnkNXSQ79q+i4yCyFq5+GEoNIxSy0Bfu5qMr5qMqP3WA+idyOygH2bUxUgs4m4kTGoESlIKF8oftRgdvQJUMwwaGY4G3o4BB0cBBaOlYDA9yAsfAg1yks9EaZ5IxIiuTtmDJxsfaxB1olOTmlnXFEQy+qHSl2iXTChDtjrOO2O1qOuvJAkM9QFxfMLZVzIzrQDlV4NU7xlejATRDqB2F5o+hMNtCT5oO9SOXLw0LUO5fglJ9HtuyvFpR09AJM/FK0ZLeUXY0uFaC/ACd4Dk7R2Tj556PzL7GxF4T+grZAfzpO6BxM+FKISAxGd4z4AOwI0Hd3aSfJa68cgCNZPVItKA1ftY/aoDMlXbt1z6MaXkE3TcEkZmFSH6OT36DSyyy4K7UKpdainA3o7EbI5kJ2PSazCjLL0LLZJ79EN8/HaXofJ/E+KvkuOvmWC/JynLVqHJFdCpCPtxu9BXtR0wjQS1qldcKKG/YlVMYdAX1R4jiZUTYYTSeexJHwOZFYSrBa43/RdTejqq7EKT8JE24L4RPQZWfjRC9Bl1+Njkq3ch9MTPwKD6ErJZV0PFRORQuFGJmKKZoCB19D//wyqe9G0rDlUeo33EPd2iFUrRpAxcq+xL8VoL+Vms13Urf1Tuq33EHdxjup3XAHdesH05A7gOZN/Uht6U96+82kvxdX7M04P96C+ulm1L5bcPYNcueXoTj7/43OewRd8CwmMAEdmYkufxcd+xAd/8CmEGuhK6vnoqs/aI1AaHXGpsNzyR6UFpyhZAoGIyDvHO5L5lBvS9k077+Rln0daPrRBfm676+kevuFVOSeRsXq44guaWO5+eDHHgre83DgTQ/7pnr46VUPu1/0snOkjy3DPax/WjTzbVg/5iRyJ5zD5mkXsXXGJWyb8Q+2Tr+AjZPPZYOA/aunsGrM8Xw76hiWP3cUq0YdxdaJx7DvnaMJLGhLxWI5/vmsxFHyU1xjUg5JMSqJpd7y4a0HTuG+Zeu35iUBdz+JjXJMdKWQoo4RasV1bXrsli4gX/9HkLeg7m7yFuyFj7YjYC+g7277ltYRaseOPAQ8Ls3zrcv5HwF6+4A6IsNslWLaaAeryPEgdwf3MPs70Ftgl6z7Xa76RXLvJQJCRnTtR74+8ik/L+OGqblvBvJ2YMPWdrQeVne6oWMSPPY7B++hWSiaXI/raLWGJ6/932FpGquH91qpZNU3Hiq+9FDxmYfYJx6iC7zEFniJzvdSNtdL2fteIu/6CL3tpWSmh2KZGTkUv+On5lsPzv426MgJqOi57lYqx0jZkoUHt+7P/6AlYVOaf+JjUbFXULEJLo0Tm4mWopyYRGnPtuPE38GR/gRJsYxOR0mUR/kEnNIxOOEXUKHhqOCjbi1byVAL9AL2uuRWdMlgC/YqdCsyjtTxSWEfjcShAAAgAElEQVSJ5NiH+6PK+qPLbrf5J8pKLSUqYYANQ3MkcCwkxSY9UCWdUYGb0AHh48Vp3N2ar1RQtvcb7JhQO0ywHdoqcK6zRphs4bWook6YolugRB4+/a2WPxsQA5e8MfwLXX4xqvw8nLJzcMr/glN+Bqr876iyy9BlV6BL/4kKX4QKnYdTci5O4XmogvMhcA4C9CZ4Jip4Gip8Ko4Af/gSjPQJR67DibRDiTpIVEPR3mgxUUmTWJUUsEgRy+MYWww/wlYoqtqXUI2T0M1vtB5UP8VJLsZJf4vKrkY561HORpSzGZPdDNlNkGkF+vRyS+Po1OeQXACpj9DJD1FJ9zBLQtQ4wvlPQiUm2E8jR9mUHGSFjx8HKcmzGWszcYzo5n8LPhuDyb78P4BetTxJovJ+msrvQjc9CkLfNAxC17TDif4FJ3QUJnQsqvQMnPK/o8suR5dfjxYKTjJwKu5DV0hU9xhMhVCAM9Fls9Ch9zFF76MPvU1m32skfxhNy44nadp8L3UbhlKz7haq191M1fpbqMq9hZpNN1O3aQD1GwdSl3sz9RsG0LixH81b+pLa3p/0dwNI/9CPzE8Dbcc1B4bCoaGYw7eCSGXzBKPvRRc+gS4ZhQ5OQgnQy68lJt0h8mfhHXTlbHTVHHT1LHTDkt+zblIl75P5dShO/q1WKy+JlNlDPUkd6E7Lgc60/NyxNcumHfXfXUvt9iup2nQB8dWnEl1xLKFFOZR85qHwI6n987LvDS97Jvr5Yayf757zs224j00C8k97Wf+88PJ/IXfyP9g042q2vXMN29/5F1tnXsamaReSO/k81k88i9WvnMrKMSewbNTRrHzxOLZMOJGf3voThR+3IbbYNeHIMVAASbb01OYcErl+y4NbIM2VOIJWkN/kQzb8lHDiG91NWrbqFiuPdGkVe3z91oML8l7qLbj7LIjXLvXiTivAi7JEeOnFXvtZu0Q+heJxp36ZHHHln+Fu+w2W0/fYtxB5qMi/t2W96O//IMEUGsrSNy5Pb+mmIxu9yC1lC9/lRX0vG/qRrB8X4I+Auvy8u+23pmR+L4FpLrgLHSNFHxK4Jjk8srmLPPUIPSOpkrLBy+9n4zqvjYRuEJrmW5+VtNYs8SJa+GrZ5EUyKTr4L7zEP/XYI2t0vofyj7zuFv+hl9I/gvwML8XTPRRO9VAwrS3Fs/zUrfGgDrdBl55ogd5uUeJSjXSzBiFRm9g2KCkNiT6Bij6PU/4iOvYKJvYaRrjJ6BsuRykNaHFpQZuOjsvPScWgxBWPx5S+jAq/iA49gwo+iSqRYD5Rl93uAr0YmIqGoIsHowNDEaORitxJNnS75fCdwC2WwnFsLs4QVPhWHMkGl/yb8C02J1yH73aDpgI3o0U2WdjBBlGpQBeyoa44gRtRJe3QJdeiS67GBK5Gl1yFLr4aJcdS+esLOpPN74EpHAglt0OJBK9djw5fBqUC4pdgyi7ClP0dXXouqvR0nNKTUGVnokrPtz8vG72J/AsTvhITEKfzPzBF56NLzsSETrOjgiegwsfhhE5Ah/+CKbsAVXoh2dJLceStINYRHe/pxvdWDEXbyGQJl3saU/OMLaHRNaNtl7BqnIppegfd8iE68RkquRSVXo3Krkc7G9GZXHRGtnmZ9ZBdjckux2SWoDMLMenPMcmPMQk53n6ASr2PTs6CxAxUYjJOy0RU8yRM81T7PZN8HW3llq/aLBsBeiOTOTIC+BKB4H5KhLFKP4tuGUa6+kEayu/GaXgYko9gmm5H13XFKb+IbOAYdPAodPjPqNKzXPNeuYD9DdaDYOJD0XHpEpCqwVcx8ddRMTHkfYwu+xwdWoAqnI1zcCqpn8bSsmsEjdsfomHLv2mQTX7jUGpyb6Fu00AaNvWj0c4AmjbfTPOWQbRsu4XEzgEkd/Uj9WNfsr8Mwhy6DfLvgII7ofB2jGjnRSxT8gAq8DQqOBYdnIYOv4kpm2W7GVTsLVT8LStY0JXytvs2pmHR70DflDeLxN5bcfJvI10wkLQ1RfUlebAnzfu707yvM017O9DwfTtqd1xL9darqMi9kPKVpxJZcgyBL9tQ9LGX/Pe8HJjp46fXctjzylHsfuFodozIYcvTHnKHeVg/4ig2jj6dzRMvZsv0a9nyVke2z+7I9nfbs/Xt69gy80pyp/2D9a+dz9oJ57Dq5dNYIUqcl05k08RT2PvWieTPO4oyOfrJtrxKtk6hY3Jagd7d5o9IGV0O3AV4AfkjP7aGJgFZoVJWe2ha5aFRQF7+mQLOAthLPNQJeMssPjKiLPFZ+aBICGsWCvh5qV7ooWaRh9rFHuqXeGhY5qNhmd+d5ULpeO1xt3Gl/Pu8tKyVw6/IL1vppz8ocjKbvWTtgdmVjqYlIkHoGwF7SXnc6dIuAvaOZP3YvB8P2e/cyXwn4O6xm7sL8qLicQFekiRtyJjk8myWB6T7htOS66d5g9/+Xkrmf/0qNym0boUPF+B9VH3jpUoOrV95qZQt/nMBeS/xBT5iQtPM9VL+gWzxwsd7Cc/yEnjTS/HrHoqmeiic7CX/NS+HJ7WxKaZNuV5M/lGtQH8OqvQKVEAokE44UuBc0dMGhJnwEIxk1JQ/i46NgthYTHQ8Jipc6Wvo8omocjmSTcDE5OuxmLKX0JGX0OKIDY3GhF6AwHBMyWOo4v/iFN2NLrkDSbJUAvCFQzDFQzCSWBm8nUzgNjKBW8mUDLISTG3rBwe55iPZ4sOSailHW3G2DoXgA5iS/2CKhkBBT0x+R0zRjajizmSKO5MtbI9TeL0L6kUC8P9CF12NLrwGU3Q9FHWBwn7ogj7ooh5WGmmKuqGKr8EJSqz0xejIhZjSC6HsQig9DxM+HR0+Hl16MjpyJip8Pjp8JUQ6uMff4I32QaKLLkCVnIYKnIQOnYgKHWs3WCfwJ1TwVFTkLJzSv+KUXYQul7q9TrYQRccG2mYsqauj6gkbiGZsJMUojK2XnIBumA7NszHNc1HNn2JSiy01Y5wNqMwGVHqVHZ1Zic6usLy9zi5EZb/CyX6BTn+KTs5DJz9AyUafFp7/XZfzT4mufhKmaTJO3WRS1ZMxyTfcrT4z3jpfXUOUgL3QOKMxKWmaGgUymVE2u14ycUg+g258gozcGJoexyQew7Tcg6kfiBO9hmzweFTAbTfT4ZMwZWdgpPA++k+wHoM+aGkIk9L0+GiomOKWMkm4XnwROroIE/4UXTyH9KEZpPZNIrH3RZq/H0bz9kdo3vpfmrb+m8Ytt9O8ZSjN2yR3fgiJbXeQ3HEPyZ13kfx+KMndA0n91A9n/y1o2eILboeiOzHFd0DgLlTwPpyQSIMlMv5lt2EtPBMdeQtd9hY6OtPtEKl4C1M1C1P9JqZ+EajWPPraX9+hadedqALJ4riNVMEdpPPvJJV/G8n8obQcGkjz/t40/tyL+h97UPtDV6p33kDF5kuIrz2HyJKTCX5+HIVzj+Lg2234cYqfH8b52fWCj20jPGyQbX64n/XPnsTGl89ny5Rr2P52V3a+15cdH/Rh+/vd2Tq7E1vevoFNb1xF7tRLWf/ahawZfx4rXzmT1a+cxsZJp/PDzFM5/OHxhL9oQ6UAsAC9mJRycywlI8AlxiTZ3u0G37q9H+HDU7k+Uht8JNd7SQrIWpWMl+ZvvTQJGLeCfP1v4C7A7qV20ZHxUWMB3mclhNXfiJRQeGrPb2AvD4b6pbLR++00LBXQlzcFUeqIBl8eMEeAXjZ610krDyF5A/kj0ItsVFyzlm6xZjAfWYk+Fr+AuGh/GzEyuSNadyk/saod2eKPJEludm8SzRtEOSOuXR9JyelZJ4YvPw1rfNSv9lG30ketAPwyL9VLWg1PXwu4u4fWys+Fi/dYRY0F+Xk+YnN9lL/vpWyOl9J3/YRn+Qm+6aNEQH6yl8LXfBRM9JI3wcuh8VI+I4Ujfkzh0SjZ6Mv/gi77h+WhZYtS0XaoqETKip1/KKpcCrufgZhkzIvEbSy69BU3RltAvexFO0Y+S59FhYWieQYdGIkukRkBJU9Asat/ls3oCNCbosFuL2fBLWj5ungQ2aJbyBbfbCsHdbA/Rnj70GC0Tbjs1RqN0A0VEHfrbTj599H082AyhwZCYS9MQSdM4U3ooo5kCzrh5N+IKuiALrwBU9gOU3A9puA699N+T4C+L6agJ7rwRkxxO3Tx9ejiq1CBSy0do6008m+2+IXw2ZjQqejQ8RA5CR0+FRX6KzokQN8ZIt0h2AlTcg1aYqmPAH3wZPv36OAxqICAmzwszkWFz0OXXoouu76VoxeVkdTW3Ymu+M//AvoXXaCvHQ/106DpTUyjgP18TOIrTHoZOrMGnV6Flq9lskvQ2UVo5xu08wVO5jM7ykotP2qVY8pW/yE69R6IaSo5HZ2YYm8AzdFXqA5Ibv40VGoyTmqcpWwkylhAXicl+fIFWxJurJzyOfitsOR5TPJZaJa6xCeRXlyVeByd+A+mQVqluuKE/4yWpjS71cvycTym7DRM+d8w5Vdgj9M2xlgqG0dg4uPR8TdRFQtQ8cWY+HKILsaEPydbNI903mzSB6aS/PllWnY/R+K7p2nZ8SjN2x4gseO/JHfeR+q7e0nufIDkzkdIfvcAqd13kdp7M+l9fXAO9EPnDUQX3uK+aQrNGLzDNqWp0EPosGjoX0SHx6MjU9CR6ajS6ajo6+j4DFv/airfssIFI0mWR8rBo3tmUb9TCkWGkyx5kGTxo2SKnsQpkcyb4SSDj5EIPGy/nyx6jJaCR2g5dB8tvw6l+ee+1O3qRNWW64mvvZLIkoso+Pwcfn3/VH584zh2vJpD7kt+1r94HBvGns3GSVewZUZHtr3Xl+3zBrFz/iB2zB3A9vd6s+3drmx9uwObZ1zDpulXsOG1S1g74WLWvvo3Nk06l11vnM3B988k9NmJxBflUCvctzhTNwhtI5u7C/JWvpj7x4OnS9cIyFugX+chJUfR1T5aVnlpFtVMK9A3CFAv9lHfCu41i7x2cxf5oN3gLbCLlNBvx5qCvvZYsK9Z6KFOtnp5I1jsc8cCvzxEhP933x6ahXL6zVDlHjxFhSMPp8xmORzLUdb1B7jRxpJr77dJl46kXcqWfyQMTY6o8jAQ3l2OqeJalRHeXar6tvqwefA2i8aNcJAoB6GOJLahebWPhpXuBl+zwkv1Mh9VS7xUtsYVVHwpUklR0vitHt5u8Z94iH3sJTZPNnkf0fd9FuQjsz2E3/YRmuml5HUvRVM8FEz0UDDBS/6rXg6P83DwVQ/h9+UB1AaKjkGLhj56BtpyzVeho1dB+bVWXujEBpKpuI1M9L9kIsNIB6QA5yVUZDSOZNeUivnpWXRkBCoyHF06zDZKOaGHcAKPoIofRxU9gS58HFP0CBQ9gCm+z81wCrgbvQC9lgLm/IGowoGYwv52szZFfTAlfWwWjQv2sr1LC5QAfHf79qFKuoI8EA5JVvnNpOUPaH4vVH4ndOFNFuxV/k3ImPxOdijojM7riMnrgMm7AZ3fDpXfAV3QFVPY2fL1quhfaFHhFF+KCVyKDv0DFf4rKnQmWgA++GdM8ARM6DgInYQW3j14Njp4OYTFhXuTNWbp4D/QgbNQgVNsEqkobwieYZ3IJnAiWoA+dC4q9Dd06WWYclGbiNlLYhOG4sRut8FmVD2GqRID20hUzQvompfQtePQtZNIV06kuXwiumEOpuUTSH2DEQ4+JTz8EkxatvyF6Ow36OzXqOyXqMyXONnPUJmP0WkX6HV6Lio1txXoZ0FqJiRfxzRNIVs9kUR8HEYOtOnJZG0uzktIzo2AvE4+j06OxCSfwSSH2/JykxqJFrA/Avwtz2JahqMTw9w0zcSDmKb7oWoAqvQ0TNCHCeSgQ15UJAcVORZddhqq/AJU+bXoqCSV3omOPYqJS6OT3Ipmo+OfYuILIb4EE12IjkiczDyyhe+QOTSF9M/jSO0dTWL3SFq+G0biuydJ7nqM5K6H3M/vnyK1+1FSe/5N+ueBZPf3QB3sji7ohi7ujg70RgUH2ruR0IomfB8m/DBEnsZEnrdvrqpU1DfjcaSHQWTE8WnWeyL3BF3/Oah6N70ysv1NKjc+hFM0ilTx0ySKniNVItWBo5F/SKp8Amk5apVPwimfSrZ8Gqr0NUx4LCb0PJngsyQLn6Ll0IM0/no3NXsGE93ai8jamyhedBWHPrmMnz+8gj3vXc93czrz3Qd9+W7+EL7/5Ha+X3Ab388bzM65A9n+fh+2W7C/ka0zrmfTtKtYP+VK1k68mE2T/s73r/+d/XPOJ/DJmUQXHUP1Cr+13jevd+V/Rzb33/h3AfPWjVmOtHaTX+8ltdZLcq2XxGq/Bfom2bRXeGlc6qVhiYC8nzq7uXv+APKyxXup/locnz5LY4jrs+oroTQ81LSCfa0F+9+pHgF8l85xt/omOSKLs1Z4esvV/2+g91st/REjmAC90Dc2IkHiHoS3/18jP2eLToR/3+Z1gX67ZNFIAJwkSorBzKWKGtd6aJDjszh4V8tRW47M7gZvAX6xl8qFbmSBxBXEP/MS/0TMTq6aJv6xFxkB+diHfso/8FM2x09kto/wO14L8oE3vBRNbd3kJ/jIH+cl7xUPh172cGCch8hcP5ldbaFE/jCdhI6KaeUs+4fKlF8G5SIX7GTLOzIVd5CNPUBT4YPUHXgcFZbe4udQoREuwIefRoWfQoXl5x5Che7HkVfc4vtRhf9FFUoCq+iO78MU3YcpvgdTchc60MrRC9AXDkIV3IwpGgBFfaCwOxT3wBT3aP2DJimW3W2Msynpji7pYVt/dLFQND2h4BbM4VtwDvcmc6gr2byO6IKOUNARnXcTOr8LJq8HOq8XJq8X+lAPTJ4AfgdUXjuyAvYF7dEy+deiCi5HF12MKRb9+z/QQTmw/gUVOhkVOr4V5I+H4J8wgZPRJWegS/5qHwomeA3Z4ivJBi7ECZyFDpyCDvzZ5elLzgMrszwdEzwFHTwdEz4LLfWQcgMovxZiktczAOGlVfx2pJvUVIrq5Am0LTN5zgK9qX0VUz2RdGwCLeXjUXVvQ4uYoD6z0kmVEs18q3Y+44K8aOpV5gtU5nNU9jN0VoxT8zBirLJA/wE69T4k38Wk3obkGy4/3zQFWiQbR/Ty411jVOoFC/Aq8Tyq+TlU80h0ywh04mlM8mlL16ikBKyNtK1VJJ6FxEhbbJ5tfgIn8bDtvkXC2srPQwfaQMCHDnpQAvZhP6r0OHugdY/dXdBlt9oKP2kAkyY2AVUVnY2OfgyxzzGxLzBlX6AjC1Al7+Lkv0H2wBQy+8aR3DuKlt0jaN41nJbdT5D84RGSux8ntXsY6R8eJbP3HrL7bsY50BWVdxNK7jYSlRGQ+Gw56Pe3b5UmfDtGoqPFrVv2JKpsBKr8eZzoSzhiNItNRIsUuWISumIcpm4+qDoX6EvWTCC06FZafn2W5IGRNOe9TEtgIpnQWDLhl0lFJpMpm0Y2Np1MbAbZ6Du2pMTEp0N8snUdqqj0eb5KtuwVsuExpIqeJXH4SZr3P0jd3vuJf3cv4S33ULT+HvJX3s/BZfexf+E9/PzFHez59Fa+//hWdswdxPYPBrLt3d5sebsLm2feyIYZ17F+6uVsmnwJO16/nH1zLqfg0/MoXXwSVcuOtrpua0gSyeKGo0jktrHbqkQUWD58jQv2Iru0ipy1PpJrfCQF5Ffm0CwuV3kzWC7cumzzfuq+aUPtN35qvxEA9/5hfNR85aP6S687n3up+sxD1Rcear/yUfe1l3qZhS6PX7fIfTtosNy9mK88NC3zkvjWT8saL4k/SC0t1STKoP8dadzq8pWohyPjRhz/HnVs8+0lOkEyc1rBXhIzxYwl6iLRwMsGLxJSGxu82kvdSjloe2yZixxY3bcVH5Vf+4l/6Sf2uZ/opwLuoqLxEZvnJT7PR3ye31I10Q9kk/dTPieHyOwcQm8LXeMl8IaPkml+Cl/zUzDBT/4rOeSP8XN4tJdDL3r4dayH8IIcMj+3heCxmMiJmHKhb05Al58MZRdbwFExyV/pTTZ6K7r8PrKhh0kVP4oTegIVfBzCwyA0HIJPYYKPYYIPogP/wQnIxn4fqvBedMG9mIJ/YwpvQxfdii6+DV18B6boNptCqIoGoopvtqML+qML+6ILe7k8eYkAfXd3qyrujAV1yaYp7oIp6YgpvhFT1AFVeCO6wN3ahaLJHpb8cNngO2LyO6MOd0Ll90Tl9SdzoD/JX/riHOyLzuuBOtwFJ0+onQ5k865BH74O8q5G51+ALv4LWgpfSsTodA46cForYJ/sZvcHT7Mgr0pORBedCYUXg7wJFFyBLrwQXXwOquRUTPB4ZHs3rVu98PI65AK8iZyNjpyNKb0AXXYZJnodJtYZFe0PcTfYSwK+dPx+VPxhVKUUnj/jAn31BKiegql5HVP7Jrr+XUzzh6jEPFTqY5yUaOq/QqW/RKeEj//MOmN1+mNUaj46vQCTWYDOzENnhLJ5H52eg0m9C6nZGMnFkZAzoW+Sr9l4BZ0QpY3w8c+7hSIC7I3PkqgYYccWnSdkox+GdcLar0dY0JdgM5N0HwaZpsdtKQotDyOaeid2FU6JPDT9mJAPAjkQ9KIjHlT4aHTkr5hIO4i4nb4qeg+q7Am3syPyGiYiB9E5UPYRlH6CCc9HlcxBFb6Fc2g62V8nkv7xRZI/jCC5exjJ3U+S3v0E6d2Pk9n9GJkfHiC7526yPw0iu78LzmH570qAXuo7JVyvgxuwZ018A1ESn1wmjt2HUbEncGLDycYE7MeiY+NcwYKo1KLPYqpngap1gf7wopc4/FEf6ncNp/Hn52jOm0RLyVQSxWNJFLxIomQc6fBrZMqnki5/nYzEv4riISrX59fJxmeSrZiBUzUDVTkDHZsJ0RmY0imYyKuo0FjSxWNpKXiZxsNjqNn3ArHdw4hse4TiDf/l4Ip7+fGbf/PDZ//m+/l3sv3DIWyZ04+Ns3qx8e3O5L5+A5umXsv2N67lx3evJm/BRUQWnk7l0mNtmqUrWxTgbEtivYSLeWhZ63LhCfls3epFjdMiqhfhyWWDX+6jcbmXhuVeGpfJNi/A7KVOaJqvZY6A/BHA91EtG7wA/Rdeqj9zgb7mS+9vQG/B/hsfAvJHgL5egL51Gpd67MNFfl3ytmHfNIR2ak24dIH+SBxCa2a9bPA27fJIts+RH7uftthElDRbxFPQWt0nzU65XtfJul4UP+IR8LgA/62H2mUeqiVZUlIlv/HYNxM5ssa/8BH73Ef0Ey/lCzyUz/NQ9pGH8rkeyj/0Ev1Qjq4eyt4TPt7rbvKtdE3xDA+Fr3somOolb5KHvPGyxXs5MNrH/lEefn3Bw69jvJR91pbsvqMgcAwmchKm/GSUGFZKT7TKEqLX2XAuFeuDkqYfkcWV/bd1Y3/QJq2a0BMQEpB/HBN8yIK8LrkfJS1oRVIGfg+6QOYudOFQlJgAhfMsHOxy8YUDUUX9UYUDLGWjCnujC2T6ogvkMNobVdAdp0B49vb2oKoKb8Kxipp26CL3uKoKriObdz1O3vWovBtslKw63N5u6ybvRvShm9CHu6EO9Sb9Sy8SP/cie7AfzkGXhzXygCnoiMq7Dn34WsxhAevz0MV/RhWfhFN8EgLmTvGJqKKTURIpXXwGRsre5XvFx6EKT0fnXYzOuxp1+Ep0wfnoolNQJcdhSo5Bl/wJXXICKnACKnQiOnSKq7gJn4OJnIcpvQhdKiqTa1DRDjjlkuJ4K6bidkyFC/RaiqjtVi9ALzz9q+jaKZja1zH1UkL9LqbpA0zLR+iEHFgXoJJfoJKfo1KfolOfuFr71Dx0aq4bgWA3+Q8swGsL7u8gwWdawtBSM1yeXnJukhNt5LF1wiZb+2Btxv0zthO2peJJmuNPYlrk8CpFI8PsVm9aRuA0DCNT9xS68WlMyzOoluFkmx9DNT8IzQ9jGu9FVVyHCpwIJTnogBcT8IJ8hjw4oTY4cgMJX4oRH0SptI0NRdni+AdR4WH2LdOEJ2FCb6LDs1HBWTjFM1AF03EOTSb7y3hSe0eR+OFpknseJ7nnEZJ7Hib9g3Dz95HZfReZH4aQ+bEP2YPyBniDu0gEOqHlqC4einAndKQXqlSAXlrN7kNHH0LFH8eJP42KP4uOv+D2PseeQceGocofRFdPAVXtAv2+T59j7/QOVG16nPofn6fx0CSa8ifTeGg0DfufpTn/JZKBiaTD00iXTScVnUo6Oo1M+Qwy0VmkK94nUzmHbNVsnMpZOLF30OVvY8pnYMqnYsono8um45TOxCmdQSY0mUTRWBoPP0vNvuFEdz1FaNOjFK5+mP2L72fvF3ezY/6tbPpwIJve7UfuzB5sfL0LW2d2ZNfsDhyY/y8CX/2N6JKTqFmRQ70oZyRXZo0cFls35XUuB52QGGFLk4is0Y0paFzhoWHp76oa2eQbRSljgd5jN/narz3Ufu1FPmuEmvlKvj6yzXuobt3mq7/wUPeVzwX6r7zUSUTDNy7Iu0AvDw+PpYUsyC/3kVjl5/8Fesm/OZJ5cwToharx2XGBvhXgj5iqWj8lOsH6BCRDR0xOlo/3uo5fmywpqh8xO7kAb6ODhYNf6PlNJumqaOTI6rN6+PL5LsCXfuAh8r6H0vdaZ46H0nc9hGd5XKrmLR/BmXJ4FbrGQ+EUD3mTcjgw3sevr3j4ZbSfX55rwy8jvfw8UjZ6H7GvjsL5VVqljoGIbPGnWqDPlv4JXXoBRK+3xR7K6rkHo6W/s/QunJB0uUq1270W3G2SYPAhTOB+dOAetA3muwtVdCeqUAD+bnTBHVZVowr7oezGLpu7AHwfBNxVQT+UBdte6Pze6Pz+rdMXld8Np+BGnIIb7GipcyuQbet6q5ixRqf8qy1ICwWjBPAPt8M51A6d1x6T1x59qD3mYCc41AN1oBfO/t722JY9cDPJfYPJ7B+CkS9FM/UAACAASURBVH/v4RtRh67BHL4cXfBXVNFJOEXH4BS3wSnJwSlqi1N4HCr/z+j8MzCFZ6AL/owqOhGn4EycQxeTPfBP1KHL0PlnoYuORZf40CVt0SVHo2QCR6FCx1j1jQmdgQmdiwn/DRO5GFN6OaZM4nuvJ1vWBSVNXxVDLdAb2egrjgD9cEyN9M++jK57DV0/HdPwJqZpNqbpPWh5H9PyASYxF5UQsP8YlZyHSX6EEYBPSUzCHEz6PUxKZg5aws1Sb7kALxLKlASjSc7NNLvN6+Sr6OQ4TPJVSE6EpEQVS1Lmc+iWZ2wXbbZ+OKaVmlGpYei0APsIkpWP0Sx1ew2y6Y+0jVPSeKWbHoCmhy1Pr6pusG9LFOegSzx2TInXHmizQR/ZoPy+yX3kShfsQ33Qko0kaqzQbaig/Pf3NKZkLDowAVUyAVU0Dl0wDnVwHNlfxpLa+yyJH54gsechEnv/Q3LvfZaXT+66nfTuQWR29yWztyuZgy6FR0lnCHRHB8VsdxM60hkVkRA9Afrb0OX32M5YHX/Egr2KPw3x4RB70n0ARO9DRYdiqseCqnSBfs9Hw9ky+jLKlt/7//H13lFWVVm09w1VJFExYs7abZYsSTFgAsWcUQwoShQlZzGjgjmLuVtbO2gHbdtAhsq36lbOOd1Kt24+e+/fN9Y+F7vfN8Z7f6xRVVDqaLqYZ5655pqTjt3z6MxdTnf+KrryHieU+xi9xSuIVT1NvO4lYvUvEm18jmjj8ySaXiXZ/A6p1ndJtr5OsnmzBf9E3fMkap4mWbuBVP16UnVPkqp7Dqf+ZZz6Laj6l1F1z+LUrCdZuYJY2WLCBXPp3jeblm2zqPrX3QS+vo3dn93Ir+9ex0+vTuPHl6/gp5cvZftrl1DwwWSqPj+Xhq+G0y7pkt+68cGRf7gAaln7v0SH9xMTwBdWL4AvH//upV+A/mvXDikaeu83fvq+8af1eQ89orP/wZ2uLzx0fe7BsvbPfYSEycukgV6+7v6D34J9twD9H9NA/6VIOF56vnLZvOj/4b94ifzNb3cD+4FeLk9lt+AGncmRl8QX/7+BXuyXEnXsjuvWEddOTDJ05JI1nQ1v83a+c0PY5IK36xsPoT+5DqH2/cdO4qL5WHJp3MiClvf9dsEq7L3hbS/1b3qof11G2LuX+lc91IsWv9lL7cs+al/0U/2C32ryFU95qNjgo2z9IApXZ1KwwkveE5nkLRxE7jw/2fM85K3y0fblQHRgMNQI0B8GDeK6ORgBemWBXiQEOd6RvJhrbB+rxBCo+htwam7EEX29RsB+Frp6Jlo0d5Fkym9FV9yEFqmmPP11+U3osmtRZbIkvdwydVM+FVV2mWXsSjTz0qsxpbJIFUnlSldLFwdM2cWWbctiVUZskKZcpJgLUaKll4zAKR6JKh6HEskmeCFO0ThU0XiMyDfBSajCCeiCCejAZFTBFFTBZTiBK0kWXEvP7ulE9s3AFEoR9ERU0Xmo4lPRZceiyg7FKRuMU56BU+61o8oyUVIWU3I4pvhodPBonJLhpIqPxyk6DRU8DVV8vC2C1+UD0JUC9JnoSvk8E1XtR9XIwlHA/jBM7TFQdzLUS6ql+PTPRTWOwmmcbAvMJdvHSAFH60xM2/2o9odQHXNRHY+hQytRPU+he5/D9L2ECW/GhF9Nj7hwJM7gXXT0bXT0TUzsDYi9holusVHFxqZXvm4TLCXFUuKJdUzY/P8CvQSaPWlBXhqjXKDfiLEMfyU6Jtr8fHT/XKu9G+mHjc7Hic3FSc5zP+96lGTHoxBe5Or2cSlHeRgTvg/TJ4x+FqpDtHDpU85EV3kxlR6M/NnJn1mVl5To9tVD0LLfqD4HUz0GUz0eIyBcPRVdNQNTKdHB4uqai6qYjyqbjypdhFP4BKm8xSSy5hPd+yDRffcSzb6LePZtJLJuJiFMfu80knsuJ5k12XbEmrLxUHUJVEuEtlRaCpuX62WX0WsJ/mu6A1pmYqRIvvV+TNtD0PowtDxgayaVlMi3XAuhZaDaXKDf8cZc/jr7eCq2TqNp20yads2hdfd8WvbOpy3nCXqKnyRes4V43ZtE698g0vgqsabXSbZ8gNP6Acmml0k0PEu85klileuJlqwjVrKWWOkqYuXLiZavIFqxnkTVC6i6VzH1r0P9q5i6FzH1GzC1S3Eq5+KU3U8ieC/h3Hvp2HEv1d/fRf4fb2T3ezP4+dUr+XHzJVa/z3n/Sso+vZDaP5xE69dDkewbWSr2f+suVyM2aExA3WedNcLqY1IOkgb6qHyvSDZfCwj76Ps6w07vV356v/LR+6UAttcCfNfnXgvyoc88Lsh/6oK8AH3np+5DQBi9gH2PBXkX6Hv+6KH3Sw+9X7kPFWHz4uzp/9ZH5O9SVOJKN24cQtoSavV5yedx9XZh8baF6n+km4RczQqTF6D/yUfiJzfywe4f7JWvaPF++vYDvFz5yuWuHHilIwtkgWy98Pttkh/6aLWRBX6aZMH6lt+VZt7wpcHdS62NL/BQt9lL3cteal/0UrPJR/Xzfiqf9VHxlI9SsU+u81G8xk/xMi/Bxz0ULPSSNTeDXXP87HrET94qLx3fDMQEh1iNnrojQTTQxkNxmg62rT+yFFRNY3Ga5CRf3A5XYCRcrF4OlGagambYCAMl3uJKmZvco6eyGzFl12PKbsCUCcDfhCmbgSq9BiM6efGl6OJL3WWrdb+Ihn4JOngFpngauvhqVMmlKFmUlk6yY62SslQtm4QpmwLlF7uAHhyDKh6DFplG5JngZHTRZHShgPskTOFF6MBFtgoulTuWVO6FpHInkMqbhA5cYsE+njOdZO40VP5knMBIF6hLjkGXHWWZuy2BKc3AKfWjyj1omdJMTPEwTOExmMBJOAUn4QROQRedhCk5GlVyEKp0IPJQMBWZUDkAI0zVslUvqkZmAKp6KLrmMKg9BlN7IrruNCvfmMbRNnFTN0pypoR6idPkLttDq9tnWbDXHSLfCNBvQPU+je4Td8xL6L7/nc3oyGvoyCtoycKJvgQ2d/5pdP/T9nPJyCEmS1c5khKg34wWr7ww+fiz6Tx6SacUO6XEHqyF6HqMFJuIVi/yTWSu7aZlv9smthCVmI+TnItcxIqlkr55EBaGPx8dm4+OSu7NfdA3G9P3AKpzMk714fbPTFd4MRUedIXf/vnpSm+a5ftRlQe6S+3qk6DqLKgcjZFr5nI5dLsaXTEDXSFvkHeiSu/GKb6PVOBhkrlziGc/QHzfTGJZtxMTgM++gWTOdTjZ00llX+nWBuZOwglOwJRNgMopIPZdu4iV+AyJ2piKlgvtJimqvwXdKvZXkdbuRN66aLsfWmehW+4EaclqlkrI5f8F+p82z+fjG48gZ9MEan+4gfqf76Tx5wdo2DGf1tx19FW8TqzufaINHxJr/Jh482ekWj/HafucZPO7xGqfIlq5lnDJSvoKVxLOW00kfy3RwBoiwZVEilcSK99AsnYTTsOrqMY3MU1voZvegKYXoXEjpl58ofPsibqpmo0qnU00917afr2N8j/fTNbWa/j1rUv45c0r2fvOdRR9cinVf/w9zV8dQvvXA+gSVv7nDMLiiReGL0Fi3wnQp2USAXvJjBc75V88REWf/7NIKnLk5KP3qwx6/ijj/43NC4t3Ad5jWXzoU5/L5PcvYT91wV8eBv9/oO/70kPfVx76rGzjcf9bf/X8BvT27eK3g6l0sJlINz+5OfVWjvnFLSdxGb4satPSjnyfZPwIg7dRDm4eTb80PKXtkpKzI5e6+692bUa85NJYu6TPddNIZIEsWt/z02wB3kfTm34aX/fRYNm715Vntnip3uyh+mUfNRbgPVQ/76XqOR+Vz3gp3+ilbIOH4nU+Amt85K/wkP+4l9z5A9n3aAa7HvGw7WE/vz40gJxVPrr+NgiCB2Cqh2BqjkRXy7LrUHTzoZgmyRoZidM0AkcuE6UgQ9qc6q7AyCVqrXjap6GqZYkqF60SZXCjPXii/Aa0BfZr0aU3oEuvR5VMxxGmLrZHkU+Cl2JKpthlqThiZCGqisTSNhVVdDkqeAm6eLLrlikZjy4VJ4zYIMdhykQ/nYgTFNYumvo4sFZJkWYmQdFkK9PowERUwQScvAmkcseTyhmLk3eh/drJn0Aqfxyp/PGo/Ck4+RNJ5Z+LEzgTJ3gauvgUTMnpGGHmJQegS33oEj+m1IMp9ULxQCg6BF1wNDr3eFTOyejcUzCB46BYZJ2BqHKffRPQFQMw5Rno8kxUeSZOhc8yVFXtQ1UL2Mufv2j2R2HqRcI5E90wBpqmQpM4TG6Hljtt41Ky+Rac1ttRHaL7PgYhkW7Wo3o2Yiyr34TpexEV3pSeFzD9L2H6N2Ei0ir1tFsE3r8G3b8KI4AdfQoTlThiSal80RaQqNizqPhTdnRcWqTW4fa/SpCZgP3+a1ipDlyEij2Kjs51LZXWXrkIFZ+Pk3gUJ/YQqn82WrT48EM4vQ+T6pGilHQMQng2OvwAOnQxTvWRbnVmmQdd5kWX+dFlGRgL/MLyfRb4TeUgdNWB6MqjrCtKV5yLrhjt3j+UT0Gn5UCn5DqcoptJ5t9FMncmyZw7SGTfTDL7OpI515DKvYJU3uWovMtw8i/BKbgIR35uhDiUj8ZUXoiRWwiJtq4XnV4Y/WX2TUs1T0c3X49pvcH2/mq5YG67Fdru+m1sQ1bL9TYB9jfp5l+bFvHm5Yfyn6VnU/rNVGq+v4G67++l7tfFdOS/RH/tp8QavyDS+Dmx5q9JtX6L0/Y3kk1fEK55i77y5+ku2kAodyWhrBX0ZK2iL3cN/QWriQTXES3dSLziOZJ1L5NofJVUyxuo1rdRbW9jWl7DNL+Mtrkka1ANj2PqHoWq2ejSmaQK76J3173U/+M28v8wnV0fXMfe926m4KMrqfzDuTR+eTgtXw2kQ5j4n/zucvWvHnrSyZKR79zDKAus1lLpLmL7/yxSigvAsoDt+dJPj0gwaRlGNHkBedHghbkLgw99lgb69BJWHDfy+/83oA9/5SH8Jy99X/8X6MPfihtIZCR3Z2AXxOliklj6YnU/2NvFrEg5EsgmRSXyEJCR75OMnx/dgDSxaopMIyXc1hOf7me1kQVytful15aAuJ54kWpci6Rk0kjwWPPb/v8BeB8Nr/qo3+KlbovIMx5qXvJS/aKXqhe9VL8gIO+h8lkvFU95KX/SS9l68ch7KV7loWiZh+wnvOxaMpztT5zHzwtOZNujQ9h2v49fZklktY/efxwAxQdA9RDkmMepOAFdf4jNBTfNZ6KbLiDVeB5O4yh000RMw0Xo2isxtddjLNBfbZuTHOuamYGpuhEq5Sp1BqZkOqp4GqpEwP46y+ad0qnoYgH6qThFl6KCYm2Uv1TjUcHJqKIpOIGLUIGL0UWXYATsgxdZjd2UjkSVnocqFTfMWFSx6O+jMXbGQvF4THA8pvBCdOGFqMA4VME4UrmjSWSPJpkzhmTuWJJ5F5LKG4+TN45k7gUkc88llTeCZN45pPJPI5V/JqrwAlIFo0nkjMbJOwdTfASmxI8u9mKKBeQzIXggJnAYOu9wVM7h6KzjMTknYvKPwQQPQZcJ0HstqDuVfrQAfalIPgNwyvyoCi+qSqQIL061H6dK9OdDMPXHoWrPwKkdja67EurdNNuEaNDNt+K03Eiy9WZUx72YkAD9GnTXenT3BkzP0+jeZy2zV+EXcMLPo8LPocPPosNPY2yMwXpUeBVO3zJUeAkmshQdXYGOrsFEn8REZen6NCr+JCoumfPC2gXkZaR7dhXaBpktxSSWYFKPo5MLLNAbu3xdaH30Wq5g4/NR8UdIRWeTit6Pis7CRB/E6Z1NKvQQJjwbIvehBej7HkSFLsGpHk6qJINUibw1SaeyLw34Luibcj9UeNEVHpxKH44swWXhXXEcqvJ0dPn5qLJxqNJJOCUXkyqaSqpA3thuJJl7K8ncm0jmXouTOxWVJxLeJFTBRJyA+wboLu0vRIn7Sh4e1edhakdh6kejG0ZZsFcNU1DSgdB0FQL2pvVaW0qj26Qs/npbGG/ab4P2O6D9LkyLdP6u/q9G/7dnF/LsxEP4evZZ5G29lLLvrqfi7/dS98sKOgreIlrzBbHaPxCt+4pE499INX1Hov5P9Fd9SF/5a3QFnqVt32pady2hY+djdO15nN7cZUSK1pAsf4pU9SZStZtJNbxGsvENUk1v4DS/Zpe2qnkzqkkSBp/GaViLbpAo2fmY6jnoivtR5TNRxfcS2Xc3LT/cRtmXtxD4+CYCH0+j7LOx1P3heJq/PMDW1XV9mUHP1356xQ//TYZ11cgxlEg47mGUOF48ltGHhc1/7aXvKy+9f0yD/Od+umU+9RMSYBfGLiAvgP6Jj9Cnfjo/9tLxscedNNAL87f6/B+E2XsR2WY/ow9bS6XHPlTCf/UgQG8lpe88RMX6Kbn6UjcowL0/muAnt+BDSj5iIs38LBEPkufjJ/4fv6vF/1tA3o1QkEgFeXuRxEwpR5ESkP1avFzthv7ouoUkJ771E7FM+ml+30+TTZeUi1Zh8C7Ay8FT/RafK8+87LUgbyWaF9wjKMmsqXjWR/lTGZSt91O61mdBvnCVh8ByL4WLvWQvHMJ/nhjB+7Mv4tPZo9n22Fnsmnscvz40jNx1fvr+fSC6WFjRAIzYA6sOQ9UPhaajoOk0dNPZqKbzUSIjNI63SY5GGL1cp9bIXIeqmoaqnIapug4jKZQVItlci5HDp9LpaDvT0KVXokovQwcvR1vmPgWncLKVW4xILuJyKJqECkywsotIL1aCCU6wy1Vdej6q9FxUyQUW4LUAffE4jDAvK9sIux+LEm2+cBypgnEk88ZZFp/IHkPCAv0okrnupHJHkBKQzz0LJ+9Mkrmnkso7nlT+qTiBC4hljSX08wWEd56FCpyICh7gZgKVDEIVD0YXD0UXHYgOHITOOwSVcwQ67whMYBim+ACMgHqZz7J6VZ5hv7YPi1IfqjTDShL75Qhh9hbobf7NUTi1J5Oqlrjki9FV0+nOuZq+ImmhugHdLLrwDEzzrbaKUbcswXSutkBiutdgetZhetej+55EhZ9ChzeiwxvQYbcIXIdXoiXWuG8xTt8idP8iTPRxTHQZJioMXxatGyAuoC9sfy1GHgLC4uNSLi6a/P6Yg8cxsmyVEc98fAHEBeAXoO0i9mFMbLaVaFT0IXTsAUz0PpQweykxj8xBhe9Ch29H9c1EGqdSVcPTPckedLEPI1MiMR1eTJkPZMrTQC9vRuUZqPLBqPJhmPKjMaWnYUrPQZeMsjsbp3ASTsHlpPKmk8qbQTJvOsn8qTh5E1H5LiHQReOwE5Sfp3GWPOjyc1CV56Dk/4faEZgG+TswFtU0AS1SZtOl6ObLbbKmLUdpnY6yJfLX2VYsKYqn9RZoEw3/FvtA/o3Rf/3MQlaNGcb7t5/Bji0XUfT1DMq+vY+6X1bRnv8WvSUf0Vu6lb6Kj4lUfU6k8jN6it+jq/A1ugqep3nfCuq2L6Rx+yO075lLd+4iwsFlRMvWEq/eSLLuOVKNL+E0v4LT8hqpFnHmbMFpecmWQjgC9A0bceolgGpJuq5rLtqC/QPo8ntQxXcQzb6Fjv/cQN1frqf0ixmUf3YZtZ+fQ+MXh1nvtxwyiQ4tbpferzLp/dpl+P1/9dH/Fx8C7uFvPAj4Csvu/dJvQb73Cz89AvCfuSDf/bGf0CdeOj/22On4yEvHVh8dH/mxn3+UBvqPXZ1+P6vvsstYjwV6WcL2Wjbvo+8bH+G/+On/q5/+v2US/S6T2Ld+4n/3k/inn/j3fvdi10ox4n8XWcbNqpdIh5RczP7otZOQ4hKJMLB6vDB5vy066flrBqE/Z9D1TabN43F98XLM5dpBJbrADSCTEhA/je/4aXjbR8Mb6SXrK17L4mvT4C4MXg6fxE0jEk3Vs2mZRq5dRY9fn0HJmkyKV/koWumlYLmHvCVech7LYNeCI/j6kYk8ecsUXrxzIh/fP5YfllzM3nWjyX7qQPr+cxSm+EhUxSB09QB07UBU3VBM49GYphMxTb9DN0lb0gicujH29ZW6qa5sIyBffS1O5VWoqsuh+ipM5XR0xbXosmm/jSmbll6wTsUR3T14GVrYfNEUlEgsxaKrywJ0gl2YCtA7gQmkAheSKhTwHoMTHIkqOd+OWBfF566D49DB8eigWCfljWAcKjiWVOEYkgVjSOZfSDLXnUTOuDTQj3BZfM55JLPPJZVzFsmc03FyTyWZczyp3KNJ5Z2Ik3828X0j6fn1PCK7f4+TdzKq8BCcIgH5A3GKh+IUD0EFh6ALh2Lyh6HyDkUXHISRr0sGuW8AwkYtK/VDsR9KRPbxWMAyFRmYSj/iKlFVAvSZOLVDrYXQqTsOR46zpO6wYiKxwqmk5IjMxjRfh2mUC81rCZfcRKz6QXTb45jOJZiQ5N/ItexKdM8qdO8aVN8atJ1V6PAKdHgpuu8JVN8inN4F6LA0Pj1mLZFGumYF7EWDF2dNZC1EVkJ0OUSXgY04WIqJS8zB45i4eOLn4jJ5Afn5kJiPTsxFJR5Bxx/ExGQecnNtYg+iI7NQkfvRAvyRh9DhOzC9N6N7b7MafarycJyAD1XgQRd50UU+TNDrvk1Z2cwDaVnHPkjLvO6Ds3QIpuQwKBbp7BRrc3WKz8QpOh8ncCFO/qUk86+wIJ/IvwgnfzSqYAS6cDRGXFbC4OVtsUzmAnS53EGcj5b4Ckk7rZuIEtkmXUavG2VnJcU3VyN5RKr1egvw8sYlHbe6+Q508112Iasbb0dLtLLqcJexf3puMYtHDuP5q4/n7+tGk/fxdZR+cx81Py6jcc9LNO17neacLbQVvER7wcu0571Ce84rtO/dROOONVRte4z63YvpyF1KuHg1MVm81j5LomETycYXcGRaXiLZutm1X7a/hpIozTY5IZbLsi3opudRjetwapfZ0B5dK6x+LlTMQZfPQlvL3PUkAtPp23UdTX+fQe1XV1P3+VjqPz3GFk5LZK54wkN/FMeM3/rh91sbZfEq9kkBX7sgFSD+g4/e9JFT12deuj7x0vWxl5AA+0de2rd67LR96KXtA3fat8rveX4beRiEPvmvVi8uHbFX9v7RR9+XPsJf+Qn/yU/kGz/Rv/iJ/yWD+LcZRL+Twy0/8X9JtHIGsX/L+N0Mmn/7SP4g4yUpxeKSxCnplhJ+Jhn8Yhe1B2FuX2vP3/yEvvHR8ScZP50StvYHD+1fSMKkGyMs0QUtH4ov3kvDuz7qJV1Swsde89hFq+uiEYnGQ9UmD5UvuImTYpmUKAM3s8ZD6VMem1lTvMZH8Uo/weUeCsUjv9hD3mN+9j52AD8vPoOt90/lxTuv5+V7r2bzzKm8++hVfDZ3DL+s/R1d/xiFDpyMqhia9in7MPVDME2HY5qORzee4bL6xnNJ1Z1v25iMlGTUTcPUXoOSs/Dqi3GqJqGrpqArLkdZ3/tV1i5pyq5Kg7w4aC7DKZlinTS6+HKc4ovswktkGyc4nlThRFKBSekRoB9HKjCWVGAUqcKROEWyJB1pQd8pGo1TNBancBxO4YXuR/v1WFdyyRtFIldYvAD8WCvdJLJHEc8+n3j2ecSzziW+72wS+35HIutEkllHk8g6nET2YSRzjiOV+3v7IEhkn4OTezoq73hUwTB04SBM8EB00VBU0WBU4RBMwVDIG4YpOBRTOAyKDkAXZ2JKfb/p+VrYaBqk5KMWoCr3uay+QlwlHgv2qmYIuvZQVK1k3xyHqjkFVfU7qJW8+qtAMofEW994BU7tNKIV1xKvuwO5EFUdj6I6FqA6FuN0LkV1LcXpXoLTsxS1f3oF4B9H90kjlXjfF2DCkj2zEBVejOpfioqIjLMKYqstyIsnXkcWo6OL7aGTiS/GjTVYhImLTCNsfp5tjzLxOewfHZtjXTUW0KMPoGP32zFRAflZOJG70ZGZ0HsHuvMWdNf16PYLSJUfjJPvw5FazYCMF1XoxSmSlFV3tMg6drzoEnkI+OyfuQ4OxRQdhikSm+twksGjSQZPIVV4HqmC8STzJ5HMH0+iYCSpwO9wCs9AB89FF4scOBqnbAypcplxKDm+q7gIVSGR11fZ+AOn5npSdTei6m5FyZ973Z2o+pmohnutV141PYDT+CBO00OkGh8m2fAwyfo5JOsfwGl7HuOE0kC/aSkLxh7B2suO4/P557Fjy1UUfHI3Zd8upPI/6yj/6Wmqfn2S2h3rqduxgfpfn6Hx1+eo/89a6n5aQsveNYSDz5KsfAlVJ/bJV0g1vk6q5W2c1rdwWt9Atb+J6nwbFZLaq3dsFZyR8ua2d8Dq9OK532gDq1Td4zjVC3Gq5mIqHrQSjlMhF4+3oCtnoILXEtk1nbZ/XE7DlyOp+/QoGj7OoOVTF9jEUdIpVkgbTSAHUK6LRlwxwuLletUFeR89wsLl4OnT/SDvoXOrh84PPLR/4KX9fS9t7/nS46X9Ax+dW73ufCiA7zL/LuvAEaeOyDceev/otfJN+E/yBuEl8mcP0b96iP3NS8wmWO5Prkz73v/tfuxPHzrF7KWv2CW9RP4tx09uGJkNJBPJRySp9EVvj9gmbTaNRAh73KMuAfhPPTZhsk2Wrh/6bIxw4zs+Gt70WbukjSwQHX6zSDQ+ql9M++Gf81EuGrwsWp/yUrbRS6kNJfMQ3OAhuNZDcKWP4DIfRU94CDzmJX+Bl9x5meyYdxjfL53E1kduYtMtV/P8TVN49obJbLnrEp6adjYbLjuOvyw+ie5f5GT/AFJ1A0jVZWDqBtqWH9N4DKbxNLf7tPEcN9WyZjTUSBXf1RjxMNdI3swEnMpxaAH78ilpu+Tldunq+uHFNnk5uuQSVPFFKLlEtaA/GadkPKniC0kVXUiycCLJwEUkCiaRCIwnERhLMjCapAB9wWhUYCyOjAX5MS7QC7jvn8KxJAtGrPkgKQAAIABJREFUk8wfTTJ3NPGcUWltfhTJrAtIZp1vAT627xzie84hvvtMEntOJbH3GBJ7h5HYdwDxrKEkso4gmX0iqZyTcfJPQBcci84fjsofiglkukBeeACqcBC6cABG7hDyhmIKD8EED8YERdbZv7RNM/g00AsoqVLRmtPOHQH5NNC7FkxZyh6CqjkUXXckRjzj1UfZjlltu2wn4TSMwxF2KamdUiouTLL9dlT7TJx2uZoV2+VCnNBC3GKSBejuRejuhaieBaje+e7RUng+ROZj+ueh+ubhhBfi9D+OE5VIg6UQWwaRJRgbabAIHXXH2EOohdYHry3Qi5VyLgLsJj4bLWPlGmHsElr2MDo2Cx2biY7di4ndj4nejdN/Iyp8A6ZLQtuux2m7FNN2Ck7pEFI5flI5HpL5XjupAh9OwX7A96KE4Qc9dl8iOxMT9Fnmrwoz0YWD0YEDSQUOJBk4hGTBMTgFv7fs3ckfgVNwFqnAySSKjiURPJlk8BySxeNJllxKvPRKYqVXEy2dTqzkZuKltxIvv4t4+X3EK2YTr3yEeNUC4tVPEK9eZidRs4KEHKLWrfnvNCwnXr+UaM3jRKoXE62ZT7zlFYyTvoz9ZstKFk06nlWXncx795/DD09ezt537qTwT49Q8t0TFP19OSXfr6TsX6sp/nY1JX9bTcU/VlP9/VKatj9OV+5y+oPrSFRsJFkt/vnnSDa8iBRBWIBvfwen4x2c0Ns4oXdRne+hOt5Ht0uJcxroW15yez2b1qIaluHULiZVvQBd+TC64hGcComYvcf6pJELx8LpRHZeTMe/zqLxy8Oo+8hHw1YPzR9n2PCtdpFdRF8XW6QAuQDw5z56ZESqEUuk/LowecvmPYQ+EpD30vmhl44PfLS/56P1HRk/be9l0P5+Bh3v++j80J3Qh8L+fYQ+8dP1qeuxl/+OXNW6EQpuZo6kYUq+vYSaSVSxbceSHlnR52URa6UY93ObpS+sXUZ+fb8OL8de6U7b6Lc++3YQ+bOPsHUM+ax/3x54feF3i7nFOrnV4+bSpJeuv4H8a26McO1mWbQKwP9Xpqm0IO+3scK/AbyA/DoPxWs8FMms8lC4zEfhE14KFnnSIO8n65GBbJtzHP944mref/RGll9+AcumXMDiiaezavJprJhwOgsuOJSPHjyErl/lYEdA3m/BXhw4um4IWgKmbETsmfaAxzSOQNVegJEij5rLoNa1nGnJeBegr5BAMPG7X4IqnYIuk0Coy9Cll6JLL0GXXGxdNE7wIlLByTaiIFU8jkRwnAV2p+hiZJKBiSQLx5MUnT0whlRAQF5evcUSOQ6nYAxOwShS+e4DwCkYbR8EqfwxJPJcgI9ljySWNZJ49kgSWeeTEGDfcxbxvWcR3XMm0V2/I7rjVKI7jye2+wgSew8ivm8IsX2DiGcdSDLrcJzsQ0nlHEQq90CcvANR+QMxgQx0YCAqMAhVOABdlAmFA6FwMKZIQP4gTPFgTIlo8vtBXpi9F1XiwQkORJcMQpd7rU1TlrUu0HuhMhOqJD9HwtKOQNuPh9nIBPlcFrSq/gKchvNQdVKCchGyFJR+X9V6LapNGpjuRkseTsfDqNDDqC6Zh9AhKSuZg5aO2Z45mL45mPAjdhDve2Q+qn8Bqn8hjmXvj6Mij6EF5EXDt9KOxBbsDywTNi/ZNTLC6udh4gLoD6IlpEy0eJFsonMwMdHp70XH78LE7oboTHT0NnT0OnT4Kkzoakzr1ZjOcej2I6wlNZnlJ7HPQzLHSyrXRzLPZwFfJB2n0IdT5EPZ8aAK3RH2nyrwksr3k8rPROcPRBUMxMkfSirvcPvQtsv2gtNIFpxGpPAs+gMj6Q9Mpj9wDX2Bm+ktvIuewpn0BGfRXTKb3tI59JbPo7diEX2VS+mrWkm4eg2Rug1E6p9Mz0b6G576PybWsIFY3VrCVavorVhh/9l481sYJ5118/c317Fs6mksn3oar955Fn9beTnbX7ud7M8eIP/Lh8n5ei55Xz9G9uePs++Txyj4ajEV/1pC886VdOWtprdoBfHK9aRqnyJZ9xTxWsnGeZ5UyyZU+xZU51uWzTuhtyzYu6z+TUzHG5i21zGtWzCtmyzQ07oB07wa3bgcXbcUU/M4umYBqkqSCO9Bld4EZddD8XWogkuJ7T6Xzn8cTcNnmdTJuf4HA2jeOsgCXJtILyLDfOyj82MXkF1QzqBbwPkTr9XiBaxdcPfS8b6Xzvd8dLzrp/UtP81v+mh+00/r2xm0v5NB+7s+Ot7z2ul830voQ78L9vLfEG+9xCN84aPn0wz6Ps+k9w8Z9nDKykaSp5M+7rKhZrZSMG2tTGvydin7b2HxctmaLimR/HqRaqR4/Fu/1fv7/ux3c3kkgE2yeb7IJPRZJp2fZNBug8jEOun9jcW7OfFu+Jh11MjBk3XR+Kh83kvFc17L4suf9lG20U/pkz5KxE0jvvi1XjfKYKWHQpllXgJP+Nxr1/kesud5XRvlnAP5+ZEz+cPsS3njgatYPW0sCyadx6NjTuCxUceycuxxbL7+AEo/OxxTIsvYDFTlUNfmVz0QXT8A1XAouuk4TPPvoFGkgwk4Neeiqkalq/YuwlRfhK6agKoQoBc72kR02WSU+N1LxQN/sQvw4ocvnmSPmVLBCSSD49MjbF5AfQKposlWtkkGLiQpi9QC+eiOOCJS8tqdN55U/lhSwtrzRpHKG+kCfprFJyyTH0ksewSxrBHE9p1PfM/ZxHb9jtjO04nv/B3xnWcQ23EqsR0nEt1xTBroDyG292Bie4eS2DeUVNZQnH1DSO4bRCJrMInswTi5Ah6ZqAKRbITRi0Y/CFM0CIqGoIMHo/8X6Eu8IEtEKyv4SRWLvi8RCUegyuT4ymMXtfuB3lRmYqoF6A+zR1RK4hFqJSNnOKpOluTHoBrOwGk8G1U/Al0/Di2F6M2XIstA3TYDY8H+TnTHvejOWaiQOzp0HyZ0H7prFqbnfrAHSmJ1fBD6Z2P652Aij6KE3UcXWkAXgNf9C+2Y/gXYiSzERF1XjZtQKcxeQP5RtOjwFtzvg6jMLLCgL5LNPZj4nZjYHZjIrejIjejItZj+y6H7Ykz7BHTnmZi2g3BKM0js8xPb6yW+z0s8y0ci20cix5sGfB8uwxeWL+DuIZXvTjLPQzJPvs9LKs9HSj7me0kUDCQWOJJowe+I5I+iP38yPYGr6AnMoCdwC92FM+kufJBQ4CE6Ag/THphDW3AenaWL6K5aQk/VMrqrVtBdvYremjX01W+gr+Ep+hqfIvzbbCTcuJFwkzwANhCpXUe4Zi3hapmVxFve/S/Q//u9J1lxxaksmnIiT884jU/mTuS7Z67j57duZdf7d7Lzo3vZufVhdr4/j5zPF1Lxz0W07F5KT+F6YpWbSNa9iNO4Gadps72OTcmiVWqtOl7DdL6JEakm9D469CG683105zsW5Gl/Fdq2oCUfp/VZtxO0ZT20rLVgb5okd3wFpm4RRirgKmaiS26FUqnWmg7FV6DzJhH7+Szavz6Q+q0eat7OoOGdwTS/56flfQ+toq1/KHq7j/YPfXSk2bhl5bJglV+zEk1annnXT7sweAntesNL46seGl/z0vyGC/ytb3toe8dD+zseQu96CFmwl2Wtl/aPPMjSs+Mz98jKHk39WXR0CRbzE93mJ7Yzk0TWABJ5g0gWDMIJyKu4zECcwkxUoR8n4CeZ7/6QJfb5iO30Ed3mI/KTj/4f/fT94KPnHx66vt0fSuZ2t+4vAxEtXv63N73joeEt97JVikCk0k+WrbUveajZ5LFWycrnpPnJS9kzXkqf9lC6UXLj/RSv8xJc66VIEifFUbPCYxeuNrPmcdHjPeQu9JAz38PuRz3snJPBjgXH8s8FE9l63yW8c/9VPHv9JB6feCZLJxzHslHDeGbiYH5YPoieXw5Clw5AlQ/ElA1DV0g2uh/TmIlpPATdOBxVfzrJiotwKuSY5Ryc6rNwas5DS+tS1ThUxdh0O9NojEQSiLWtTHLfBewno4rl+MQ9QJGL1VRwDKmisS5jLxxvL1EFyJP5E+wCVWSaZL4sU0VTlV+bTKrgIpL5FxEXoBdbZJ4A/QgSeReQkI+5I0nkii4/iniOsPkRxPdeQHzPucR2nUVs1+nEdpxCbNtpxH49hfi2E4ntOJbYzqOJ7RxObPeRxHYPJ77nCBJ7Dia5dxCpPYNJ7R5KYs9BFvyT2YNwcgej8g9GFx6GKjoUJ3gQuuggdHAYOngISr4WRi+NXSLfBP3oIj8qOAAl3vriUzElJ2PKh6DsMZDP9YXLub9NbByEqjsIp+5gUrWSWX8k2BycI+yD12mQvck5bntV/QhM4wR0y6VgUy7F7SEjbVS3oTruQnXMdJupOu5Cd96NDt2N6Zb8d7lGvQ8Tvh8tva3h+9CR2WgB++g8m0Oj+2VRK0xf3DGPpkeOokSmEU1+Hjo+19onxUIp2fIm+hDEHrAgb/rvQkfvRsXvQVsWfxcmegs6OgMTuRYdvhYVvgz6xmJC55DqOBbVMhin2GdBPrLXQ3SPh+herwv6WR7iOR7iuV4SaTAXQE/k+UjkuhPP8eKOfG8msdzBxPIPJhI4lv7AefQGLqYrMI1Q4EZChXcRCs4iFHyAUPHDdJfMp7N4AR3BhbQXPkZr/mKa8x6jPbiE7oqV9FSuoqd6NX01q+mt3UBf/UbCwuSbnyba/BTRpifTs4FY4zqi9Wvor11DWL6/aimxlnf+C/Q/ffgky684jnkXHcn6GSfzwbzx/Pnp6fz89q3s++hesv44m7w/P0bZdyto/HklHfuW0RNYSX/5syTrJfpAdPh3UPslGpFphMWH3kWH3sN0fogOfYLp/Bzd8Sm640NMxzvQ/jq6YzO6/QW0ZCe3brC9oEZS2KQpSIC+aSk0LITaOVB1H0jcbOktqBI5Wb8SCq9E7x1P5PvjaP3CT/VbHmreyLSl1M3vemh510OrSDDv+Wl910+bHd9vzLz9Pa/7++/4aHk7DeaWyfutn7xui4f6zR6aX/HR+oafljc8NL3lQf7dbe95kH9edPz2D92lp1TsSVhY6AcPfTsHEM8Vp8QwNy5WZImWo6DtKEz7cOgYDp1HQudhmPaDMe1D0O2ZmPYMaPNhmj2oRolNlUMXP065j1Sph1RRBqm8TJI5GcT2+olszyD8o4/Q3z22dLv1c5GwPDS8J9nvXmpe99iDJ+uD3+Sl5vm0Fz69ZC17Shi8l5InPZSu91CyxmvlmcBKL4EVXgLLfOQv8ZL/hI/cxT7yFnnIXeAhS5j8PC875njYNmcQO5eex3dLruXDB6fy7qypPDttLKsnnca6Cw/hhQmZfHajn4IXMun/9UCc4IE4ZQMx5YPRkukixQ8NAzEN0vAjcbwn0hcYRVjkkkpxgpyGI/nqNefaOj6p4FMV4jkegSmXpZbkzIglUqKBhcXLYZMsXN1YAic4mlTRGOuoSVmHjVgqJ5Oy/nY5YBpjR/zuibyJJPMvJllwCYn8i92vrf9dZJqRxHMvIJ5zAYmcESRyBOTHEMsebWWb2N7zie06m+iOM4huP5nothOJ/nIy0V9OILbtGGLbjyS64whiO4YT33Usid0nEN91HIk9h5LcM5jk7iGkdg8juecwkvsOJZl1MMmcYaj8I9GFx6CKhuMUHYEOuqCvi4a5oF88FFM8CB3MRAcz0IUZ6KIhqOJj0UVnY4K/x5QflF7G7nfeiJ/eg671oesG4UjFoJRk1x0O9RKPcDi6fiiO/Nw2ngkNFyBSmr1taLk8DfTTUS3X4rTIUvNmdPtt6I7b3WkX4Jev70B33Y3pEbCfZcFe996D7rsHHb4fE3k4DegC8C7IKxs85jpkTOQh93us7i52yYdcTT4uv//w/wD9THT/rajIzejY7eiYgP4dmOhN6Og0dP/V6PA1qL4p0Hs+JnQiybZDUU2DSAW9CMiHd3vp3+mlf5eX/t1eIgL6+zzEsrwksr0kcgTc5XM/iewM4jk+YtkeYtk+otmZ9OceSjjvJML559IrDL5wBl3B2+ksvpuOkpl0lD5AR+lsOkvn0Fn2KJ2lC+gse5xQ+XJCpavoLFxFW/4KWnKX0VawnK6S1fRWrHEZes1a+uvWEW3cQKzpSWJNG4k1rSfetI5401rizeuINa4lUr+GvupVhEoX09/4Jnq/dBP412Y+XT6OD58Yx5+emsqOrTMp+cd8K810ZD9Je+Fz9JW9SLz8eWJF64gUriZS/CT9FS8Qq3uDVMsH6PaP0O1bUR0f4oQ+INX5PqnQh6jQB2iZzo9QnZ+g22W2ojukpfxNVIfo+CLxPINuk+aWlZiW5WjpB21agmqUYonHoF7Afi5UP4CpuINUyQySpVfhlFyBKbgEZ+fZdH87lIaPvFS/4aHuNS+Nr3tofsNDy5t+Wt7MoPkNd1rf9NP+loyXtre9tL4l8oyUZ8hIvnqmjd+V69DaLR7qXvbQvMVH6yt+ml51q/Jq3vJS+67XgmnLxx57mdv7wwDCuw8gGjiYpOibzUdjOo6HnpMhfAb0n4OJjMBERkFkJPSPhv5R0H8+pu90TN8x6L5DUX0HovsGo3szUd0+VE8munsAuisDFfJi2nzQPADT4sM0yV/SgajKwaSK/STyfZaN9P7io+Mfftq+9tP0qYfatz1UbPFSscm1SdpSkI0iz2RQvGYAwdU+gms8lK72ULrcS0CAfamXgmV+Cpb4yVucQd7CTPYtyPwfJi/RBj62z/bwy9wj2L7icr6ZfwPv3jOFl24ZwzPTRrJ+0om8ePFBfHHDIP4xy0/gOT/RX+WMfwi6fBCmym8zWSyjrxvkum8aMjF1h+CUn0Wy+HRSFXI9e5JtWzI1Z0H12ZjK82zVniofaW1psixNyWWhTYsU6+OF1vZobZLyewLyReKWmYAjTpsCmQkk80SSkWMml9kn8scRtw6Ji0nmX0oi91KSeReRyNtvmRxLPGcEsezzSGSPICle+exx1v8e2zeK2N7ziO06k9h2YfInEN9+AtFtJxDZdgyRXw+n/9eDiWw7mPj2I0jtPJ7U7hNJ7D7GAn18zxASuwcT330QiT2Hkdg7nETW0SRzjsbJPwodcMcEhqOLBOgPRBcegA643nrx0WvR44XZFw2wXvtU/smkci9AB862GTiq3D2iklgEU+XBVLuja3zW5mrqDsDUuxWFUmyi6wYgjUs0nQTNI1BNo1HNEzCtl2HarrTyjQC9krgEkXDab0R3XI/uuA7dPgPVdgO642Z06HZ0913onntQvfeie2aie2e6wB9+AAFzHRGG/ogtBrGfy0GTTZl8ACMXrPJ9VpaZhYnfh4k+ABH5Zx8Au3C9HSdyPSpyHSZ2IyZ+kwvykesx/dPR/VPR4UvRveMhdAam/TCc5qH2Z040+P5dHvp2+Ahv99uPvTu89Miv7fIS3e0lucdHUt6ws0XekTfzTOLZXqI5HvpzBxHOOZKe/LPoCkyiK3ANnYHbCAXvI1T6AJ3lD9JZMZvOikfoLH+UzvK5dJbPo718Ie2li2krWUJ7yTK6ylbRU7aBjqL1NOUupzlvCaHgMnrLV9FXuZJI3WoXzBvXEhVNvtEF+1jTGiKNa4jUC9Cvpb96HaHgYsJ1r/8X6KONf6EnPx1kFlhDf8V6kvXP4DS/gNP8Esnm12wZrlO/iXiFZNmsIVK6lkjFBpLSQt72nl2sqo4PUKGtOJ1b0V0fWcB3Wf071mmjOt9FdbyD6ngL1fE6qvMVdMfLqA4X6FX7elT7anTrCtsPqpuXoBvdMfWPQ91CTPXD6Mp7UGW3kiqdgVN8DQSnQu44kttOof3rg6h5x0fVZo91k4ge3fCKj8ZXfTS8IlKMl+bXvLS87rPsvOV1L82ve2h6zUej/L587yt+mmVe9dIgkscrktQozN5L/WY/tVsyqX09g+aPh9D93TAie44kWXI8TsNp6PZzMaERmO6zoedsTN956PAodGQCKnIJTvQKnOg0VPQ6TPR6jPwQim4YmYrpn4juH4UOn4HuOwodHobqG2JH9w5F9x6IkekZiO4e6D4Eur2YrgHQORjaM6FlIKYp0+ZqW/tcsYd4toe+nz20/c1D0xd+Gt7LpPJVL8Uv+Cnc4Cd/lY+85TJy9JRB4dJB5D2eSd4TfvKekGAyj02ezF0ksQZesuZ72TPPx+5HfOx8wMOOhwawe9kIfnxiOh/cdjEvXXU+z1x6Ki9edSLv33gMX98xjO9nDuL7WR7ynvbS/5+BGAGp8gEWaCRIStIVdV0GusGHafRg6gdC7bHo6uEkKw7AqTwWVX06pvoMqDoVp+JkkuVn4ZSOJBW8gFRAZpwNFXMdMq410ikagVM4gkRgJIlC8buLbDPJXiQK0Isk4854BOQT+aOJFwjYTyKRN4VErsxkEuKNzxlPIudC4tYyOQKxTiayxhLPupBY1hiie0cQ3XMu8V2/T0s2AvTHE992PLFtRxH59TAi24ZZsI9uO5zY9mOJ7zyB+O6jiO85hPieQcR3DyS+eyjx3YeS2HMkiaxjSOUei5N3FE7+8DTYD0cVHGLtli7QH2A/tw9PAfqgLGxleXswsX0n0fOLWDrPQ5ceaa9m5brTnvQL0Fuw96Jr/OjaAZY0yFJcDtiMzWKXN63B0HQsUgqjJPis+VxM+2hM+8XotstRrVegWq5GCt11+zV2lP14NartanTndHTXDLQ4XbpuQfXcgU6P6b0LYfdWzpG4gshDv9kjJUpY999rc2mE+btALwvXNMhH77e6vInci47cgeq/CSdyLSp6LTo2A2I3YKI3YAT4w1ej+i9Dhy9C9Z6H6T4aOoagWgfYn7VUoY++7R66f/YjJKn3Fy89v3rp3uahZ4fHsvzYLh/x3X5ie3xE9/iJ7vPSv89Lb/ZgenKOoSf3bLpEpgleR6joDkJF99NVPIdQmQvuoYp5hCr/ZyoW0Fn+GB1lT9BWsoy24mWEylbSW76R7rKNdATX0Jy/lKacxbQFlhMqWUVf9RqidQLoq4g0rCXauJ6oSDaNq4k0LKe/bhnhmuX0VS4nFJxPb81mdCptr1Td/8Rpeopk4zPEGjYSb95IvOUp4m0vkGp39Xbd9gqppk2k6p8hVbeRWOUaG18cLV9j9Xnd9iaOsHgB+K6PMV1bMaEPMGKnDL2JCgmwv4rqlOXs5t/GdL6E7nwe3fE0AvS6fQ26TVj9MnTrUnTz0rR8swTqHkNXP4qpvA9deReOBFeVTMMUT4XCizE5own/eBzNnwyheoufyhd8VG1yz/dFl6570UPdSx7qX/bQsFkeAB4at6TnZS+NL3rS46PhJR9NW/wW9Os2izNFsl48VL3hp+mLg+n+8VhSBWdB3UUgP9Q9M3D6RP+7BhO+Et0/BdM/BR25HBW9ilR0BsnYLaTid5KKz8KJPYgTnY2KzbYn2vKK6URn4ESuRPVPQofPR/efiu4/BhU+EtV3FPSdCH0nY8InofpOwuk7Cqf3MFJdR6FCR6G7D0eHhqM6DsHpOADdMQDTNgDTJLnvQ+xxTDLoJ57lp+cXD23femj41EP5q14Kn/aRu8ZD9gofWUv9ZC32kbPIT47o8Is85Cz0krvQT/5cD3se9bH9ET+7H/ay434fexacxL511/G3udP45I6LeP3Kc3jlsuP57Oaj+GH24fwwK5Mf7vLw7/s85G300Pu9WNEOwvwG9H6UtE0J0Nf70A0eTIMPVTvUlllLv6lTdRyq+mRM1anoiuNJlh1Osuw0VKkcN12IExiFUzgGVTSKVOB818NceD6pIvn8AhKFo6x1Ug6i5GpR2H9S8mhEk7f6u4D7GOL5o4jljyGWN5547kSSuRNJ5IwnnjOBRPZE4tkTrE8+mTvGsvn4vnHE9o0lunckkT3nE9l9NrHdvyO68xRiO04gseMEkjuOI7H9KGLbDyey/VCiOw8jsvMoIruOJ7pbHDjDie8+mNiegcR2ZxLbNYTY7mHE9xxOMvtonFzx2B9FMne4Zfam8ChXry88yLX1ibVPsoPkmKpoAKooEyUunYKDiWedRHj72aSyz4XS4SjJcRGdXhIaJYq3Sir0/Gip0bPJlpmo2kxU3QBM7SC0TP1BIAvyplNxmk/CaTkF3SZgP9EFewv4F6PbL0W1X45qvwLddgW6VR4Cl6DbL0N3TkV3XoXqvBbVdSMqdBOq6yZ0z63ovjvQfXeiw/dgIi6Ia3vJOtNesNIvEo9o+6LDi6tGmPyDNsbARGaiorfi9M9A9U/HRKah7EzHRGeg+0WXvwYdnooKX4zqG48jb889h0DnAEy7D12fQaLAa8E99GMG3T966f6Px52fPPbvSt82D/3bvUR3+Inu9Fv237PHR9eeoXTuO4HOnNF0FUwlVHwDnSV3ECoVJv8QneUP0VXxMF2Vc+munE9X1YL/mUWEKh6ns2IZneWr6CxbRVfZarrLN9BVup5Q8Vo6gqtoyHqC2t1P0JK3kp7yNURqV9Nft5z++hVEGlbT37DWgn5/3Qr6qpfSW7GU7tIltOXNJVS6CZVI59Gbvh9xmjeRsvnym2yJSKL1JRKtr5Fqk0yaN9Btr+LIwVPLizhNz9no4USlMPsVJGo24rRsxul428o1qdBWVOf7IEvXzrfRnW+hQ6+hQwLyL6MF3K02v8X9vPN5TMez6I6N9pJLt61Gt61AtQnYL8GeWzc9AQ2LkMIJU3MfuvpOdMXN9hLSlF6GKZqChEkldp9B11+GUf9WJmXPe20Jhhz9VD7rofo5D7XPe6jd5DpOpNu07iUvDS96adjkpf759GzyU/eCn/qXMql92Y8UalTLm8BnB9L100nEJZpWGEyPsJH7bViSsrYxcRPcBeGbMf03YSK3YGK3Y2J3ouMzceKzUALy8QdQctgRe8T6gMUe5kTuwYncSkpePcPXug+L8Hh0/7mo8O/QfWdB3/nQOxL6RmF6R2PC56H6fo/qOtvPCPxZAAAgAElEQVSd7jNQ3b9H248n4HQdjQodje44ETpOhvZjoVViBg6HugNsDkoqL4PItoE2aKzp00zKX/NR8IwAvoesx3yWvWfNk6Wrl+z5PrIekcWrn10PZbDrAT875hxK1upL+WXZzXwz+0q+vHciW284g2/uPJWfHz6ef96dwT/v9PCvuz18P8tL7gYvPf+U5fMB6LIB6AoJ3RqIkbz02gxb4WbBXno76wRwJFr3IFTV8SibGngKpuJoUuXD0JVn4BSNJ5Yt0ovYH8XyeAFJGxJ2ns2OUZIKGRhjj5uShReSCoy3ko1IN7J0FY3eZtHkjiWeN5pY3kii8jF3HPEcYfFj7XVrzLL5SSSyJ5MQ0JdfzxpDIuv/BPronnOsjTKy41Qi208iuv3ENKs/xury0R1HEts1nOiuY4nsPuG/QL9nGDFh87syie0+gPjuYSRkSSuM3h5THW3B3sk7Gh04Gl10OKrwIBfQBdQLh6CLDkDJFa2AfeEAVMGBpOThIFk4hSdgSg9KxyN4cWMQBOj97thybGH1MiLjyAxASzdtzXFQ/3t0/ck4TUegmsUVdQ66TVrAJqDaxqPsx0mY9kugYyqmfSq0XQZtU6DjEkzHpej2qeiOazAd12FCwvBnoLtnoHtuQPXdhA7fhu6Xg6Z77WGT038Hqu92TPguTN+96N5Z6H5Z4N5n5RoduQcduQ0dmYGKXI2OXAn9V+CEr7BfC+gLyJvwFZj+S9DhCejwGJye01GhwzHtg9Ftfgv0orV3/eih43s/Xd/76PrBS9cPHrr/7YJ+78/yIPAR3uajd5uX7h0ZhHYP4/+j672j5CrPbO+qDgpEk7NyDignJAQiCAQmZyEESChHJBQROScbzxh7bGMPtnEGmyQkglLnnNU5h8pVXbm66rzn/L71vNXMzJ3v3j/edbq61GItVL3Pc/azg694LN6yRXirb8d/+gG8jSvxND2uqRpf84Y0XdOyHl/rJvytW/G17sDXujN9WmSa343z9B4cNftw1O7Hc/pZfKdfwFP3PJ7aQ3iqD9BbspeWEzvpyN+DV0Ijm/cTat1NqH03kfYDhDue09x9RJQ5zQcINOzFV/MMjqJNuKvfwkgMAj2hY6i+97RqRqIJlPOnKOfPkClduX6N6folyvlzRBef0rEF72BK43jnGwy0vkSi7aW0A9b976QE7GUZ6/kPTPcvMD2/1Fy86RWg/xmmAL1HjqhthLqRif5tLE8a7JVbug5fQMBeabDfg3IJ2O/G6t2B1b0Rs+txzA7p/ZR6OCmSEKOMtPgsJlU+jdj3l+P8w3Aa37VTp5UkNppfttH6SgZtr2XQ9kYGbW9KxG4G7W9l0PlmBl1v2Ol43U7Hmxl0vJ2p3xf7f9PPbLT9fgjuby4lWTMHem4D38OkQhsZCD+DEd6lLd1WSDKvJRXvCWTK0DGokbW6l9KSR1Bx5sWf1OYNMXCkz5OY4t6LPqUbb8zwEyi9oFqFFX5QPx2YoaWo0ELM4EKQ0y9nPgQWQL9QRFeDby6Wfx5WYCZWYB6W/xpM/2xM/2RM/wStLrC8szHdc8AzHzyzsNyjMJ3naK6fjqEg2eeVQ4meysL3WSbtv7ZT+7qdsgM2inbYKNpqp3BTBrmbbOSszyBvzRBynjyb3B3TyXv+fj5dexMf3jWFjx+ZyFfrJ3Jiy0i+feIsvnokk8OPZvH1Y1kceSKL0hcy8B/OxhS1UcMQzIZhmI1nYbXKJCnVbTbMTnHKZumrft0xTMv9rLaroGUkVrPEJ4haZwKh4pl4jk/XwCta92TFDJJVkgYpFvRZGNXzUFWLMLRGfhGyiE2DfBro03EFCxgon0+8XIB+Tvo6CPADpWldfLxU3K5LSJZdS6JkEYni+SSK5+lrvGgesUKZ6GcSK5hONH8SkdzxRHLGEBGwF7XNqauI5V5BLO/yNMjnX0W0YCSxwhEkCi/TwK6BvmDIf1M3RRczUHwFA6VXap5eohIE6FXlZajqNNBLgUv6iPRSnLP/DfT6+5VnoKrP0ItaqyELJcFmktOikywlr16meZnqM9IF2R0ZWB3SrmTHlH8PiTNuHocuWWmbgOq+ELP3Uqy+SZjOWZjOufoo53yUcxGWeyl4bkgfDfrLwCuvb9Lgb7pvxXTfDj6hc25D+WVgug0reAdm6B4sDfYrSYUfJqV/Bx7AFLAPrkIFV2OE5cjSVeialZhRGahkkhcwvwFLT+03YEZuwYrciikgH74JIkuxwvMxw7MxAhMxXJdjOs7FdEoERzaxokw8R224j9jxHrHjO5KBT65H7fi/teH/zo7/WAY+2X2dzMaTex7eonF4ywXkb8NTdz/exoc1yHua1uFt2oSveQve5s3427bibxOAfxpP29N4Wp/G1bwTd+MunPW76ancRWfp03SW7KKr5Bl6y/bTV7ofR+lenCW76S3YSfvJbbQd30pvwXZ8lTvw123Fd3or/obdBJufJdz2AuG2Q/Q37sNbvQNn6Sa6cx7DWf4iRnwwAoHQd7oW0OiVPJqfYrrkvI/l/jcst0zzPwD9T0g53kY538ZyvovleI9U99uaz09JzIHzfZT75yj3BxhuccPK1z/XMktZupre9wdBXoD+PUy3XN/F9LyD5ZbzNqb7DUz3KyjXCygBe7dM9vs1Z2/1PY3Vswmz+wlU10Oojnsw2yTnRDJOlmvVhVE5g2TuOHyfnk/Lv2dS+4qN2hdsNL6QSdMLWTTL9aUMHa3b/FoGra9m0P6qnfZX7LS9ak/H7r6RwWnRlX8wBMcXlxERKsCxDAL3YQXWgoQyhXdjhJ/WDj5C2yG8FTGBmLGNGJKnEVmPFdmQ1gqLXjg2qPcVh542cgw69mS5JN2VkXVI4w2ycJLH1Ig8yt6LCq7ADN6I2X8dVv9CrMBsCMwEnwD3VH0EvC3PNZi+BZg++UW7VU9Rpm8eyj8N0z8d0zcf07sM07cc5b8BwzcLwzcS03uRVvzgPAezR9IkxWgji6bh+I8Mo+v32Zx+N5PigxnkbLdzYouN42vtHH/iLHI2jSd/7018tflW/vjALH53xwi+WDue49tGcfjxoXy10sbhldkcfmwIh5/I5siaLEqey8Tz2RBSYiypz0obeRrO0QUZAvRmpw1LO2WHYQmF0GHDas+Ejh9htV2iQd5sOleHSanG0UTKphI4NVW7UVNVsxmonEZSuxCnkaq+WmvftelJdPL6XENST/MC9ELNDC5ZyxcQL583eOajgV0WrcVX6wVrTAeULdYTfax4IbGiucSLZPk6m1jhXH2iBbOI5s8gmj+NaF4a7MM5Y4nljCMu19zRRPNGEssfQTRfpvlRxAtHkSgUXf2PiOfLIja9jE0UXMhA4aUMFF9JouRKTeGkgf5yDfZGlWSznINRNXzwnI2qOTsdkSDKm9osVE0WVo3ILUVXL1k3gxEITZJiKUouAfpBkNdAb4eOH07638JsH6YTGgdOTyDVMgaz+wKs3gvAMRLTJWA/DdM5A+WcjXLOw3SKAWkRlmcRpmcxlmcp1g9A75GJfrme7C3vLenPou8mzP7lmMFbMEO3Y4Xvxgo/gIo8hBF+ADNyP1boQd3tagnAR1dhhB/RnLwZlffvxIzcihW5ESu8FCu0GBVcigrLzkvA/yas8PVY8nQsT8Khq1GBcaRcl2M5zsN0ZmO0ZRDOy8D9lQ3HlzacX9lwfWXHLeewTQO/+1s7ru/sOI9n48w5D0/hOHxlC/BW34y77k48DffjaXwYb9Pq9OK1aQu+pu14W7bT37GLYMce/G178LY/g7ttF86mnbgad+FukGl+D11lu2nL30HrqW20ndxJhz7baD++no7ja+g+uY4u+frYWnpy1+AuWYezdCOusm34qvcSrH+WYMNBvHW76CvZQGfuKlq/+zE9RbtJxV3pCAQz/G26+FumeodM7T8l6foJKfdPMISjF+B2SSDZW0gJuOp7FeV8CyXvO3+q+2MF6JX0xDrlJvEzlOvfSekJ/2cY3p/ok57k3x1cvsrPv4lyvYNyyUI3fX4Ae+V+WXP2ad7+BSznISzHXqzerVjda9KdjV33oiTRsFkKJm7HrF+KqpqPWTSD6NGr6P3tcD3RV0h8rhRUH8im4UAm9c+mnZ6Sod70oo2WF200v2ij8UUbtS/aqHnFRttvf0R/7lQdj4vvEYz+J1HBTZihHZjhXSD9lLHdII6+kLj4JI1P7NoSnbo1zb0L/64pGtEASwiTnE36e0Zsg74hqPhTKM1HyiPrwyj58EYfxIo9hBW+Fyt0h/4lsATo/XNQ/klY/vGY3gmkXGNICl/qnonpXYjhk7MUy3cDeK9H+ZZg+OaR8s/B8C/BDNyO8t+DCtyFCtyCCixF+eZjeGZo44jyCP96AZZMOtLh2noGqerhhE8Mo+dP2dS8Z6Nwv40T6zI5se5KCvYs49jOFfzlgdl88shMvn5qOsc2jebomnM1yB9ZaePoKjtHV2fw9Ro736yzUXLIjusTMQOdgXE6czAYSqbG4SjNE0tBs1jyz8ds/RFG6xCU1OG1n4PZdh6q+Qytv1eSTdJ4JamaSQyUz8QQE1PVdAYqJzNQNZ5k9URSVVNJVc3SZqeBqoUMVC9koFKOKGz+N9AvIq6nepnsF2ig10BeMI1Y4VSi2vG6SPP0saL5RApn6QleT/GFc4gWzCOSP1sDfSx/uubqZbKP5gpfP5lY3iRi+ROI5o8lmjeKaP5I4vmjiBeMIlFwJQMFFxIvOEvz88LXJ8Q5W3hZGuiLr/wv9U2yTJazl5KqvIBU5dlpkK88C6PqHE3lmBJ4plU3GVi1GVCbCXVZWKcletemSzWUZNy0pCW7pqhu2u36SCH2fwF9uw06pI1qCEbbcJIt52C0S7/vOVh954PzCkz3SJR7LMo5GeWcgemaRcoxA8N5NcozE8M7ByWA770Oy3cT+G/Wn03lWaqHDstzI8p7PWbgesx+GWZuQQXvQIUkpuBBlEglIw/op1srvFLz+JZM8ZGHUFFZvN6teXkzfAtW6Aas4BLM/gWo4GIM0cpHbgZR2YSuxQovxAzN1BSoEbiKlEtuVmdhOTNIttjoP2XH+bmdns9s9Hxuo+9zG47Pbbi+tOH62obr2wzcx7Nw55yDu3g8/sqF9NfejK/+VjyNt+FtulvTNr7mR/C3rMLftBafTPOt2wi0b8ffsQNf+9N4O/fg7diDp+0ZfK3Cze/HVbePztLdtORup/nYVpq+2UrLt9tp/XYzTUdWU//lfTR/fR8d362i4+gq2r55mM7jq+jJXUtv/mYcRTtxlz2Dp3I/jrLddOauo+XYwzQcvoXO/B2kYs400KvQ9yT73ibpkOLvn5AUoHe+Q9L1FikNxD9BCTfveAOj7zVSjtdIOt4g6XqHlPMdTMe7GuTlz5jOn6BcP9VL3KTnfVLudzEE0N1vab280lP7W5iut1DONzGdAvZvoVxyfRPT/TrK/SpC4RgeAXuhcl5COQ+hHPtQfTtRPesxu1Zhdj2AKVN96x1YzbeANADVLNZL2UTORNz/OI+md9PJipWiINmbSfU+O7V7bdTts1O/fwj1B+3UH7LR+KyduoM2ql+10f3Xi0lUL8B03onpfRQzsAnVvxszuA8rLGcXVmQPZmyvDl7SdWZRqTPbDFFpoN+CxKOq+HqMxDqMxHpUYiMqsRkjsRUldWZyM4htwoyJ0uBJzU1a0YdQ4bu1qcOM/Jik8I6hO8B/C/iXYgbmYgSmYArQ+0aR8p3PgPNslPNKLM90/cs14J6hgd/yLcL0L8bwzyflnYPpW4blfxDL/xSmfz1WQNyKKzH9D2pVhOW7HstzLaZ7Aco7BcMzgpTrYlTvj6BdArXOJXLyHLr/fD6174yh4oVrKDxwM/96cjG/+/HVfPbENL7fPJIjq8/k60eHcHhlBl8/auPIauHmbRxdm8E36zLI32+n709DSRXLAnEIRn0WRqM0IKWr7qyOTKyW80nVjcBouBSzRSSYos45E7NF9gpD0oUQDUOx6iXw6wpU5YTByN/xJCvHkKoaQ7JyHAMVk0hWTNd0TkLUNFpVs4ik5NrIqVyszVCJigUkNLgvJFGyhFT5Mn3iRQuJ5M8gkjeNaMF0tEa+cDaR3FmE86YRyZ9KNG8WkfxFhAuuIVwwn3DBbGSyj8mRn82dTixvGrG8KUQF6PMkAmEMsdxRxPJGEMsTLf0lJAvPJ1F4NvGC4cTlWngBicJLSRRL8NllDJReTrL0CpLlV5KsuIJk1SUkqy7CkFz6ivO1CkcmerNuWBroBdjFODVoojLrJdFSJnpZxg4eKdOQ6rz/TdsIdSanQ2SuAvZ2zK5MVHcWZs9QzN6zNNgr12WYrhFYrrGYjsl6ujecU1HOKZiuaZh6gJiJ5VmI5V2C5RPAl6fKxZjeazG9i1G+RSj/9aQCN5EKLMcM3a0pHCVDTvgBrNC9EL4HwjL4SFaNiBbSLlcVuh0zlBY/KFHTCJgHr8EMXaMFDVZ4GVZkKaZM88H5WEJ1an7+IpTnTCxPNpbDRqohE//3WfT+0073pzZ6PrHT/YmN7n/Z6PvCjuOIHdfxLDwF5+AvH0GgegbB2iUE61cQaLgDb+OP8TTehbfpAXzND+FrXomv5XF8ws23bMHbuh1v29N42/fg6zyIv+Mgvo4D+NsP4G09iLNhPx0Vu2ku2ErLsQ00HtlE09dbaP56E81frOL0p7dS+/frqP3bjTR8uoLmz++k8Yt7aT26mq5TW+nO301XwV66iw/RWXSI1vw9NBzbQM0X99B8ahfJqGMQ6Pu/Idkr4P0qKZFUOt4l5XidlPNlUs5XMGSC73sZo+9lUn0vkXK8RLLvJQYc8vplzN7XUb1vovrewuiT6IO3SYpix/UuKecbGPJ3OF9DuV7HcL6O6RZAf1MDvXK+jnLJe6K6SQO8cr+ETPSmBnoB++cxnAcxnHtRjl2YvRsxex7D7HoQU2eUr8Bquwkar4PaxZiSIFg4Fd+Ry2n95VCqD9oolcXi7gzKnrFR+YyNqt12Tu/KpFbq7vbYqNgvcsPheL8aidm2EHy3YwZWYvavwQptHYxZPYAV3g/h/VgRAXnJ5tiBVJaJwUM310Q2gbzWMjGZ6J9CxdeiEusG3XxpoE+7/CR8STh6AfrVWLJ8Ct6DGRDu8jqS8oENLccMrNBALxO96Z+C5Z+gl6xJz1BSrixSPedi9YkSYiop11SUYwrKJa6/qSQ9E1HuaSB8qfcRLN9mTP9uTP/TENiC5d+M6XsCUxQQkuTnuxvDv4Kkbykpz1yU82ronQp900l1TNGcdN/Ht1P92h0c2TiPvz0yg38+PpcjG0Zy5MlhfP1IBl+vzOTrRzM4vMrO14/bObrGxrdP2fl2o52cfZl0fpRNMl9s/UN11rquydNlzDJZSoXbecQrLyVZdyGWtCRJrO5g76ksb63GTKz6DDg9RCc3GpUy5Y4kWTaKVOUoUlVjSQrIl0/VBR+J8qkMVFyddrJWpN2vYooaqLhmEOgX6mk+UbqQRPH1JEtuZqDkRuJFS4gWzB0E++k6I16omUjubMJ5U4nkTyGSJ9z8dUSLbyBSuIRIwXwiBXOIFc4hVjCbSN5MornTiOamJ/pY/ljiBeNI5I8hln+VXsyKlFJP8YVnkSg8k3jhuSSKLiIhwWfFlzNQeqkG+YGyq0iWX0Wy4kqSVVeQrL4co1JudJehqi5C1UgcgsQiiJY+C3U6E6Nenpok1MymFTdaR69DzWQhm1bepIFenqRkAWtHdQqwD4L9D6DfbcfsFqllNmbvMKy+szAdl2A5R4BzFMoxEuWQvc9YTOdETOdUrcwxPdPAMy8N9p4l4JUp/jpN65jeeSjvApTvZsz+2zGEpgzdjQrdgykUTkieaAXo78QK3aez45Ve1j6MFZGI4Zsxg0LLLEOFF6PC89KAHlyAGV6CGVqmp3lZwJrBGZiBiZgBESicjfIMxfSKD8VGojoT5+EsOv5mo+OvNrr+kkHn3210fWaj73AGnmOZ+AqGESi/iEDNeIK1swjWLSZYv5z++jvw1d+Np/FePE3342kS+uZRfM0C9OvwtW7B3bIDT8tuvO378Xc8h7/jkAZ6ee1rfxZ3y7P0Neynt3oXvUVbafl+M41H1tF4eD31/1rF6U9WUP+P66n601KqPr6eur+voP6f99D05Srav99GZ+4Bugpeorv4LbrLfkJX2Xu05DxP9RdPUvftPhLhwYk+1f8lA72HSPW9gOF4U1M0KcfLDDgOkeg9SLLnAEbPfgy59h4g1bOPZPdekj17SHXvQ3U/jyERwz0vkep+kVT3yyR7XyXV+1r6xuB8npTzRQxn+iYhoJ7m4t/4XwD/igZ4AXrTM3jcL2C6D2G4DmigN51PY/VtxOpdjdnzIGbX3elG9rYbsJqWosskZClXOp3+E+Po/Pg8TcUUPm2nYEcGxTttlOyUBWM2Fdvs2uFZ/IyN2p+eh//EdFTXtVj+W7GCD2EFBeQlhGkHKrIHFdqvs7Wt8IH0RB+VfA7pphRnX1oDLC4/yfBQUQla+uGIm0/ClzZjJSReVZL3pLxYfm4tKipqglVYEZGa3YkZWI7lFCXDFFK+eST7l2uaBf808I/D8l+J4T2bpCcTw5WJ0X0GZpfI32Sqmo7pmELKMYoBx8UkHReiHKMxHYswXWJm2Yjl24/le27wug/TtwczsBXVvx4VWIPyryTlvQfRQlvO5ViSa+JeRsp9M4ETt1H66nI+W72QP903kS83zCDvwCS+2zSMo6sy+fqRbI48mskRAXmZ5tfY+OYpG99usPHNZjsn92bS+mE2sVOS4SIWfSlwsOlSZm3Hl3z6ljMxGs/FqBcuPl1ybTWLsSo9iVqN9nQphARM1QzDqDyXZPnFg9PuKIzKiRgVMzAqZ5OqmEGifBKJMjlTGCi/WufViKRStPHpM59YmThbFzJQej2R/OvwfDefSO4iojKl58/WJ5Q3k3D+PMJ589BfF8zQr2NFNxAvWU6s8HoiBdcQyZtLNH8OcZnu82cSzZtONG8y0bzxRER2KUBfMEZTNrH8i4nln0u84BwShZJ7cx4JWcIWXUGyaCTJ4pEMlFzFQOlVDJTJNC9AfxXJKjkjSFWOwKi8EqP6csyaNNhLlLEEn5l1Gah6KRwZTK6UDHUxTP1fgV4m+DTQp8H+v4GeLjtooLdj9mRiik+jbzjKKctMAfurUI7LMRwXY8jTpXMilvNqPXiY7klYnpmYnjmYngVYnutQ7msxPPMwPTP1Alf13Yrluh0rcCtW6Meo0F2Yobuw9HR/F1ZYFrX36YUs/asgtDL9uxJchhGYjQotwAgvRIVnYwWnQ3AWZkim+CWo4EItVbZCoki7FMN/BsqXjfJkotx2VJeNaGkmPf/KpPVPGbT+yUbbn+10/MNGz2GbBvlgfjbh8jMIVV9MUAP9bIJ119B/+kYCp1fgqxeO/j48jQ8O8vQrNVfva3kKX9tmXM3bcDfvxNe+F3/Hs/g6DmrQ10Dfth9f60HczQe0YcpdtY/uoqdpzdlI07frqf3iSao/uYdamer/divVf15O9V9WUPv3u2j44lGaj2yi9ft9dOW+TnfxB3QW/4q2wl/QkvMetV8+Q93RV0mEBzn6VOAzEgLcfc+i+l7X8smk2Gp79hPv2sdA525SnbswunejundhdO4k1bENo3MbqY7tpDr3YHTuJdW5F6NrP6muA6S6niXV9TzJnkMMOJ4l6XiOpPN5ko4XMFwvY3le15LK/57iZXKX8z9A3vMiiIHKfQDDtRfDtRvTtRPLsRGzLw30qutOVOdy3YpjNS/BPL0Is1bUFzMJ507F8cmlnH4vk9w9dk5tzaJgi42iLXbyNp9BwYah5O20U/v++QRz5mN134rhW0HS/yBG8CnM0EZUaAdGaC9G6BBG+DmUNOXoaV54ecniEEefLFRF8vXDYnVDesEqS1ZddCBT/lbQGdrbkchVFRukd6JrUKIgEAlZ5AEM+aD7bwTnAlTXOJI9Y0j5Fmugt/xTwTcC03sBhj8b5bdhuWwoCQNruwirawqqdyY456D6xpLqOR/VczZWjyglJmM4lqM8T4J/L3ifTzfE+/dh+l9CBV5GhV7C7D+IFdiM5XsMPFJJdg+4bgHnHUQq7qXsp9fxt1WT+c3yK/nPey6k+K3xOP4xmpq3szm1OZOvH5NJPpOvV2VwRCibp2x8s97GNxuz+GbLEI7vyabxl8MJfz8EVTFo129MG3dkMShUjSkKnOZMjIZhGNKBKlSD0AwygUqHp0z0pwXk7ToXKCW5QRXnkKy4SLc1GeWTUeWzSRbPIVFyNYmyiQyUjmagZAzJ0okky6YiTU8SK5zUapr5xErnaKBPll1PMGcx3V9NJ3BiPuHc+YRy5xLKW0BYKJr8awjmLSSUP0fTNOGChUQKbiBacDPR/GVECq4lnLuAcN5cYgXC3ctUL08BMv1PQGSXsbyxRPOFp7+CSN6FhHPPISpgL3RN0aUkiq5ioGgUyeKxDBSPJV4ymkTpSAbKR2iQTzdSjSRVNZpk9UhS1Vdh1FyBWXMpqvp8zOoz01HGspCVG+n/Dehb0lp6uXnqiV5n3ojSJn1kqtf0TZcNs9uuTWzibzB7bJi9Es+RjXIOxXRIBeQVqN5LSPWeh3JcAs7J+jOImAc9E1AueaqciPJMxfTOxXDPwfBMxXBPYaBzHqG6pcSbRZlzIwRvQVMywR+n91Ph29JyydCdWP6HSfbeh3I/oKkcFbyBlP9qVHAWRkgAfxqIUdE/Q9M0pgb8GZihSRAcien/ESlPFqbPjum1oRw2vYjtz82k/S+ZNH6USeMfbLT+xU6n8PPH7IQKsomWZROpHEq45gJCteMI1c2gv24egbol+E/fiK9+haZuPA0ir3xYc/XepsfwtazB07wBT/NWPC3b8bXuxtchU/z+NNC37sPTvBdP8z48jXv1Ura3Yi/dFbvoLpNeYRkAACAASURBVN2i+fXmb7dT89ljVH96N9V/W0Hln2+m8s/LqfrrbdT/62EavlxPw1e7aP3+Fdpy/p3GU7+g+rv3qTz8FiV/fYbyz14jFvwB6P3/JNH5DMnu/RgyjXe+ykDnIQa6DpDsehajS0D8aVIdO9Lg3r4Zo3UTqlWuW0m17SDZvgOjY0f6RtC1G6NzH0rAvvtZBnqfJdn7nAZ5TQdpmuZNTI8Ypd5AeV5HeV7F9LyK5X0FPHIjSJunLNd+LJFXup/BdD2N6diO2bces/cxrJ77Mbuk9eYmjFYJ7E8XORs1c0lVzCSZdzXBz6+k6YNscg5m8N3WLHI22SnYmMHJdUM5vnUYtT+/hHDRAui9HTz3YnkfQPmfwOjfiBGUzGyZ5F9AhaS9/mXMyH7M6HZMTdWsTet6IyL3krNavxYFjS4dDj+FGdmEFd3+X5GrKjq4sI1KiNPGQWmlBDw9hhmSG8wdqP6bML1LMHqnkmq9FNUxEdzCZV6N4b4M5T4L5ctA+WxYTvlFzCbVfCFmxxzM7qXQdzNW7zxU12iszvMx24ejOi/C6F2I6X4Yy7MFy70d0/0kyrMWy78fq/8NrOD7qNB7mMEXILALy7dRZ5TgeZho8QPkv7iEPz44jg9vu4yP7rmAUy9eTCj3Ct2CFPz2TJo/yCTnGRtfrbHxxapsvhaj1Ho7RzfaObJxKEe3ncGxfWdS+7NzCBwZhiqToushuqZNwrZkQWi2CiefnvINCT5rGJru7myScuZ0f6dZPwTztBiDMlHVWRgVQ0mVn8FA+TkMlF2EqhhPvGAy3V9chuf7kSRKJxAvupJE0RUkikcSLx7HQOkUBkpnarVOomw+8bK5JAT0S5YQK1pKOPcawrkLCeXMI5Qzn2DuYoI51xLKuZZgzkKCeqqfTyR/IZG86wnl3EA49zoieXLkZ+W9uUQF7PVUP42IKHHyJhDJG0c0bwyR/BFE8i7TYB/Ju4ho/qXECq7QSpyB4nGkSuQGNYl42QQSZbJzGM1AxUiSuo1qNKpyLMmakRi1spS+HFUj0QgXoqRmsOZMLF1DeAaW/n+Y7kCVG6jZLFSYPe2K1aYp+2C42SDIa04+Q0tdtbxVg718ztJHnMtmTwZm3zDMnh9h9lyA6jubVO9wjN6LMbumpcti+uaCazKmawKmZwyWdzyWZ5oGeOUdh+EcR6xlOoHK+cSargXXtViBG7QKxwrdihm6FSN4A8ngEoz+mxnoux1/3TISnfLEfQ9W/y1aTmz2zyIVmIYRGI/lm5J27fpEbiycvEz4k7ACV6C8QtkMwfLawWPT8SFGYzauo3Ya/tNO3W/s1P/BRtundvq+zSCQn0W4JItoRQbhykyCVefQXzOCYO0kArVX46+bj+/0tXjrb8DbsAJv4z14mx7E1/QIvqZVeJueGNTRb8bTuhVP6w48bbvxtO3Bo0FeAD69kHWf3kNv5S66SvdqqWVHyWY6C5+mM2cfHce30XhkNZV/u42yP95A+cc3UP6n5dT8/V7qP1tLzafbqPnXPqoPv0LZ4TfI/9fLnPjzfr76t9Uc+/1eIoFBjj7l+5REx9MMdOwi2fEsyXapADxAon0/qc7nUJ0HSMp77Vsx2jeSan2KVNM6jIYNpOo3YbRs1SUhRvsWVOd2VNfTqO69GN0HSfXIVP8Cqd6XSTleTVNDzne1YkdMU8otX7+NGtTRW55XsGSqdz+fdsg6d2MJwLt3YLp2YDq2Yvatw+xLA73VeRuqYxlGq8TVztO9i0b1bF3dpvKmkThyFZ2/O5P8l7L4bnsmxzfaOb7Bpr+u+dVFxKsXYThuwnDfDZ5V4H0Sy78WFdyMCu4m5d+PEZT+y7fSHZiR3RrklWjegwLOj2pTh1STabOHTOcRmdLXDvL2ErX6DFZ0H7oQObpbx7Gq6HaMyKbBJwIpKV6Dqe3g96P6bycVuFErFqyOEaTqLtHpjaZ7FqZ7FJbrXJQnA+W26bwbAfqBxktJNS/F6roXeldi9tyGlEZYbaMxm89E3KVW59XQdxdW3yrM3odIdt/EQLe4GB8H327wv0oy9BapyDtYobfTRc7BbXgrn+DYS0v47fIxfHD9GH5391jyX0uDGJ1nYbRkk6o5i/DJTDo+spH3nI2v1mVxeM1wjqzP4uimTI5uHcK3u87k+IELqHj3Qjyfn0WqaChm9RAQmaXQN3pqz4aWoahGWdQORUklniwQGzNBtydlo+qz09y+BvpsVHk64XGgTNQ8Z5MqE257DL7vr6T/xCiiBWOJ5MukfKmmQeLFI0mUTCBROl2nTiZK5w0CvcQZXEOiZCnxomuJyBSfN0eDtueb+bR/OpO+w3MJnJhH6NR8wjkLieQuJJy7hODJawmfWqo7EqJ58rOLNNBH8ucQkYVt7nTCuZMJ5U4knDuRSM4EIrljiOaNHpzuRXJ5VdotWziKePF4Bkom6xtSmnKSxfIEkhXjSFWMRVWOQ1WNx6gZhVGTBnqj+lJS1ReRqrxIm6qou0xXNpr156CkZLzRln46av4hvVLAXkxT/y+gF9eoHOHs/0+gt3qE3xYKZyhmXybKYUf1ZmF0XoG/fCp9eTN1DSSuaVgC9OLb0GA/DeWajnJPQDlHkeqejNG5CKv3Ws3jm/4lGKLCCd6I1b8M1X8NqcBsUt6FDPRcT7j5Ov1kqpewQnEG5mP6Z2oZsfKP1k8Qiba5JDquwfIu1HEk+Cdgei9GuYelneIeyYuyY3UPIVk7HMdXmZz+UIDeRrNw9F/bceVk0l+aRag0k3C5jXClnf6qM+mvuZRg7Rj6a6fir52Lr24xvvpleBtvxdt0J95G0dM/hK9xJd7G1bgbn8TdtB5380bczVtwte7E3bobV+Nuemt20Vu1G0fVHh1z0F6wneZTO2g8uZXGUxtoPrmNlmNP0/ztBpqPPk7jlw9R+4/bKf/4Zgp/dyOFv11B4UcPkvvb1Zz89RpO/n4LJ/60i6P/uZ1//nwtv3/hNv71840Efb3pZWzK9wnx9i3E2zcRa9tBom03sdanibfuZqD9WQ3+8badJNrWk2x/kmTLYyQbVpOofpJYxVoStetINW7EaNuMEkqne5fm7lO9hzB6X8LoSdNBhuTTOwbNWG6JL5ac+n/DdP9U6+rFNGW6XsUUXl7csc4D4Hwa07kd07UtfZz/A+i778fqWKFpG9UyH1MCrk7PwahKA32iYBLR70fQ8+fzKHljCMd2ZvDtBjtHtmdQ8ZsRxBuXgvcWku7bSYq6RhcmbMUIPoMVeR4z8AJx50GSvuexwi9ihoWjlyn/KazQ6rS2N/QQptb7ihRM8jZkspfKsqdQUYla3YUVPaiLjs3YIUzdj7kHFdupc7hN0d6HN2MGN0JwreYhxRZuBMQqfh24ppJsuYRYzWUkmyaieiZgOS7R+l/lzERJuFnvcJKto4k33obVsw565annEczuGzHbZqMar0A1XYTVMhXVLD2gd2G03kK0fhaJ5gVYvbdiuqQxfh0qIAawlyH6U4i8hbd8O4dfXMav7p7Mz5ddws9XXM6RQ3MIFt0LffOxeoalFTP1Z2DUiLNzOH3/GErJG3a+2Wbj8PpMjmwayrc7hvHdnrM5duBSyt+5Euen55DIG4qqSBdp6IWhBns7qvFMYhXnkhSnp6YdRDGSrskzdcl1Boa0JtXZ9URvVkhu+zAGyiVkKluDvUQHDBSPZqBoAvFCmfBlUr6ERPEVJEpGkiibQLxsms6Qj5XN0dRNojQN9PGSxUQLRXEzh0iBmKDm4f92ETW/m0jd78fg+3Ym4RNzCZ9cSCRnPjEB9dwlRHKWEM1dSjT/OiL5iwnnLyCUN5dQ7kxCOdMJ5UwmlDOJUM4UIvpMIJojoD+FiMgw88cQKRxJtHAEsaIxJEomkSidQrx8KumF8hSSFZMxKiZqkDerx6GqxyIl4kb1laSqLteKnFTFaBIl44kWjsGovArr9EWDxSMC9DatutGhZoPdsf8voJdoAFHb/BfY/zDRayrHjtlrx+yz6aN00upQUu2X4ykZhUP+290TwH0VpvtSlJR7uC/Hck9GuWalVToucWyPB48Y+RagPLNRvgUo/2LtGzEDS1B++d4kDPd0TNdiLHHb+mUJeyNWQCTDIk6YhumboZVouEcQbphCf+PVOnfK9I3D9F2K8g7H9NjBlaXpTlMCATuHE68YRtcnGdT9R3qq7/rcjvOEHW+RnUC5nUCpnVC5jWCljf6qYQSqL6K/ZhSBmmn4ahbirbkeT+1NuE6vwF1/J96G+/E2PIin4WE8jY/ibHgMZ+MaXI3rcDVuoK9xC70N2+io3EJz4WbaCrfTUbCLjvxdtOfsoOX4NpqO7aLp2HYajm6l5ounKPnbPRT84Raq/n4PdZ8+QNkf7yDnl8v57me38NVbK/j8zTv5/K37+Ozdh/j03Uf54+sP8+Hzd/PTbdfwh9ceo9/T8wPQ/5V42wZibeuItWwk3rqFeOtm4i1bGWiV+qo9xFq3EW9bS7JtFcmWh4mffoRIxaMEix+lv/Qx4nVPkWrerCf6VPcuBnr2MdD7HEbva5i97+leWCX9sK4PUGLAcv8a5ZamqV9hen+O5X0fy/sOyiOL2nSKpWTd4NiB6dyC6dyE6diE5diM5ViXpm667sVqX47Zdi2qea5OMVR1MxHTVKpsOvHCCQRPXEb3386i8u2hHN+ZzTc7hlH5m9GE629F+e5E+e9E+R5BBTaSkob66LOo+HtYkQ8w+99B9b+JEXwVFXkurbSRrsvQU+npW3I6gvcP6n4f0IXDafpmjc7JlqldRfdiRaXN/iWs+Iuo+CFUTKb7XZhSqBARRY88PWzE6n8KJLM7uBoVuBczsAzTPx/VM45kwyUkKi8nXjcKo+NyzN7hmH3ZGN1DMDrOINkykdjpuzA7t2N1PYPRsYpU+y2o1msxG2Zg1I0iVTeVeNV1RKtvZuD0tcRqp6NaZmF1ziHVPQfDcTO4n4D+A+B/E3fBfr59+S5+c99cfnHzRD5YcTFfP3c1nrzbMR03knKOQ3WfAR02aMuGpjN0pV2qdDi+L4dR+34Gx/cI4A/n2NPncvzgBRw/NJLSt8fS/edziZ7IJlWWhVEjMsAMlBh6hMJpOJNE1fmkNNBL16ldl12nC67Ti0VpTlJ1NszqDKyKoaiyYQyUDSFekkm8ZAiJ4nNJllxBsmQcA4WTGCgaQ7TgCqKFAvSjiZeMJ1Y6hVjJTGIlc/SJl8wlnV2zkGjhPMIFs4gUzCCWP49E7jL8R6/F+cUs+o/NJnR8LuETC4iemkskd67e8YRyFhHNW0KsQNQ6wucL5TOHUM4MQjnTCJ2aTOjUFMI50wjnTCd8agr+78fjOTqW4PHxxPPHESscTbRwFLHicSRKppCQm1H5NBLl0xkoF6noNC0lVVUTNcgbleMwqsZiVI1GkiqTQudUTMZ3chy9R64gUToCGi7Vzmf91PQD0OuScEmw/P9P9Gk5ZQZKSyozUaLAkalegL5LcohkGs7Qi1mZ7DWN05OF6hqG6pJc+7R7VrkE3M9EuYdjuIej3OdjusdoFZhW5jhGYrnHYHkmozzTSbmv1sY+AovT7m5tAJyJ6ZuE6ZmK5Z6P6V6M6b8WJYvWgEgmxVcyFct3NZZXnnYvJtk9klTfWCzv5Zi+c1HeoSivHSV0jUzyzgzMXnEEDydcOIzmP9io/Q87bX/OxPlNFu48O95iO/6SDPwlmQTLMuivyCRQORx/1cUEqsfhr7oaX+U1uCuX4ai8CUfNClx1d+Kuuw9PvQD9StwNj+GoX42j4Qn6GtbS17CO3voN9NRvpqduB701e3DWHMJZ+TzO8kO4Kg7iLD1Ib8Fz9OQdovPkflqObqPms0co/Pg28j68mbzfLCfv17dy7N+W89lr1/OX55fy1xdu4W8v3c3Hz9/Nr/au4P2dN/HOtht4+fF5/Oq51fhdPwC992MSLetJtGwc7CfcQKJ1PYlWuW4m0bKZRPN6BlrXMNCyioGmh4iffoho5cMES1cSrFhFTAP9Voyup0l1P0Oiaz+Jnhe1mcpwSpTCh5geiSqWdMvfoTwSafwHlPf3g5n1EpHwHsr3Job3ZUy3BJs9g+ncinJuRPWtxexdA3J6nsDqegSz405U642olmswm2dj1E8hVTORVPlEkiWTGMgfQ/DEj+j5ZwY17w3j1LPnU/HriYTrb8YK3IURuBsVeABTL16fQUWeJxV5jVTk30mF/wMV/gAj8itU7Beo2Duo4AuYwd2Y4XUoCWL6L5C/Txs7VPhRdIxBWJyxW9J1aJFBoI+9hIq9hBF/ASP+XLoAObwHM7QdJcoeuXn0P4kZeBwzsGpQ6ii5IdeAYxqq/UIG6s4iXnE5qYarMFp/hBIzS7vY2c/DaJhBpOoOUs07MKVgvWkNqvEerJa7ME/fmOZ5K6aTqr5Bg31YSjMkp7x5BGbrxajOSzB6ZpJovRd/w3ZqP1/LVwdu46PHr+O9Wybw85tG8fGTE2j9+gHoewQ816KcYzF6z9KTHd2Z0J4FwqPX2zHKzyDwzZmc/nUGOS8M5fje8zhx6DJOvDKFonen0v77iwgdHUqiOJNktQ2rxoYpJcyi9a7PJs3DZ2FKN2e9DTTY2zWfLxSO7u6stWNWZWKWZ6HKhmCUDmOgOJt4cZau6BsovlCrVlLFk0gUT9STsgB9rGg0MZn0iyYSK5KBYC7x4tna7RorWkC8MO18DRfOIpwvmvm5xHKv0Seau4iw8PYn5xAWkM+ZTTh3FsGc2Wk6J3cRkTzh52cT0T87k0jONCKnphI5NTjJn5pCVCb8k9Po+3IkHZ9ehP9baZ4alwb7gnHEiiYRK5lGrHw60XIB++kkKsQQJmA/hWTlRO0VSFWOJaWn+vEoMYlVpmWlkcJJhArkvZFY9een4yZkid2UNk1ZsvMYVOCIfFUvwttE3ioGKhuIxFKUNj1ybP+9jO0SkJfvZWD02VF9mVi9mdCThdWVndbZO87AdJ2J4RKQH4rpHoLpHqod2Kbncm2yMt0iyxyB6RyNcl+J4b4KJW1P/plY4uj2islvFso/CzMwU9MzprjBvbNQvjkY/fMwAgux+uemF7C+CeAdgXKdg3KfDZ6LwXMhliRUejIw3Rkolx3llL2WyETtGA2Z+L4fzukPM6j/XQZdn2XgOZ6JNz8DX0EW/sIhBIqGECgZQqDsDPorzqe/6gr6qybir5iJu3whzvKlOKtuwlV3O47au3DU3Ye74VHcjY/jangSZ+MT9DU+Tm/TGvqa1uNo3oqzWWIQDuBtexl/6xv4ml/H2/AS3obncNc8i6PsWXqLD9GTf4CuE7to/WYDp79cReVf7yb/1zfwzfuL+Pz1a/jLc9fw0f6F/OeBZXx08E5+/cxt/GzHMt7cuJiDq+aw+cdTeOeZ1XidP1A3no+JN68n3rwxfVqkkPapNNi3bCAur5vXkmh5goHmlcQbHyBR/wCJ2pXEa1aTOL1mkLrZRqpzJ8nuPQx0PUuyR3T5P8NwfojhFlD/C2bgr1iBv0Dgr+D/G6bvLyjf7weDz36G4XuHlFciEA5iOnahHFtQjnWovicwtaRS9PMPo9rvRbWtQBrTjab5GA0zMWrlwz+GgRKxlcui62L6jw/D9WkmNb84g5o/jqO/djmW/x7oFy5c4lLXY4UEcF/ACL1BKvxvGLEPUdHfQvT3mNGPseK/IxV6j7jjICnvblRwA0rz6SKHfDAdxiQcvSxUw7JYlSWsyCy36IgEK7ofM/YcZuxFDfYq/nL6GjmICu9ChTanOfr+VZiBRzADYmy6A8t3I7ivBec06PkRqmUIA1XnkagcTaL2SgYaziKlW5oux6pbQrj0fuJ1W0k17cY4vQ7V8CiW0GzVdxLImUykcCYD5UsIF04lWDAJo3YSNIrbVBqGhuoWoYYjt/LP9+/jN7tv4xcrl/LWLZN5d9lVfPTAeE59cBORto2Yvke0ActwTyPlOg9DfnGEs+2UKUns9TKBD9XF1sETQ2j+aAiFr13EyedHc+qNmRS9P5umj0bi+ewcIiezSZXbMSsHezilcFnMPqcF5KUxKRurXha20nuaiTotBc1S2GzDFKCvtqMqMlDlGRhSClGSroKLF2URLzyTgeKLSJVcpRex0YILiRZeRLz4KmJFY/WJFk0iWjSTmAZ6SaFcRLxwEfEiiTUQeaTw63M0RRPJWUA0dwGRnLmET80hnDNHvxfOE7CXyV14+wX662DOdII5UwnnXk3klAD95PTRVI04ZgX4p+L7Zgyuw1fQ//1IIqfERCVu2THpG1GJ0DZTiJVP1tdExRQGKn8A+bEkq0ZhCMjXjMesnYhZOwmjepIGe6NqKmbNZMy6qxCOXucKNWani0eEIpO9x/8F6M1BsJenNOt/8vKDtI3w9T8AvaZr+ux6qYkocUR62ZOt6UTlyEa5snRomOXO1BHalkskmWdrGsd0X4jpvAjluoCU+0wMKd/xpnOZhIYxvFejfNNRQsn4Z2H5Zmu+3fTOSIsS+uUmME+/Z/rEKT5ST/AiVDA9w9M3Ffe5+gZjiozSmYHqkyOUkwS32RmoFkNUNjW/zqD5T1n0HM7EcTwTd04G3jzRzmfhL8rCXzIEf+mZBMouJFAxgkClAP0MPBXzcVcswV17I9762+mt/jE91Xfhqn8YZ/1KnPWrcDSuoq9pNY7mp3C2bMbTvgd/5wv0d79BsPtd+rvfob/zTQJtrxJoeRF/w0GclXt0dEF3/tN0HN9M6zdP0XzkCer/9SgVH9/JiQ+W8ulL8/nomYX8ctt1/Nu2G/j3HTfy3qYlvLZmHocencPGWyexYsqPeHrlCpw9XYPUjftjEk3rSDRtSJ/mp0i0rNFnoOUp4i1riDc/SbzpMRLNj5BovJ+BxgdINjxKquHJ9GK2ZTOqXaSWOxnoEqB/nlTvWxiOX5By/h7D/VeU55+Y/s+w+v8FgX+B/zNM36eYvj9h6YrBn5PyvkvK8wrKKfk2O1GO9DSveh9D9a5EdT9Iqv0uUm0rMFqWYTQKyM/CqJuGUTNeKxLETRjNv5D+3DPo/y4Dzz+G0PbpZfhPLyXVf6d23hFYjdkvpcX7sEJvYEZ+gop+gIr9HiP+J1T8Y4j+DSv6F8zYbzFCbzHg2Y/he1rrzdOlCRKv+ihmaBVmSBazj6ePBnvJrJFezDRPb0YPYEVfxIy9jhl7EyP2CqnoIVKRXajwepT8PUGJbb1THxW4E+W9DcslKYAzMUW61pVFsulsYmXjiJZMIVp5JanTF2GdHodZdRPRopWEStYQrlhLtOwxEtWrSDWsIVJyF65vp+I/MZVgzjS9pAznTdB6c3X63HQBSMdZxE/P59gHt/LKqqUcuvtaXr19Pm/cOJEP757Ikf3zcNasw+zfigqswvDdheG7lqRnRHpyk8WcPM7L4357BkZTJoY4X6szieRk0/Hniyn/6UQK3ptP0QeLqfvPyXT//QL6Dw8lmZ+FKhe+XcqXM/XRrk5pShKnZ+2ZDFQMIVn9P96rtWPV2FFVNpTcJMptGOU2BkptJKT+rThDl0PEi4Yjnazx/LOI5g8jVng2iaLLdHJkpGAk0eJxRIunpekbHTd8DXEB+6J5xAtnE5NYAw30Mr0LTTNPA3w4Z7ae5uU9Afr00nU+oZw5BE9NJ3hyMsGTk9JUzanJhE9NInxqAuFT4/URXX08dwrRUxOJnBpHTBazOVcRy7uSeMFI4kVjSZROJFE2CPLlUxiomEyycjKpSnEAjyZZJdLKMRg141C1EzBrJ2jQN6rHYVQL+I/DrLsEs2EwKVQC5BoE7AXoB3NvtKZelrM/UDgit5QoBMkcSlM16WWsLGTTR1M3PTLNp/l5S8C+b7AEpzdTL2UFVE1XWv6LvtoxBWydWRp8lfMMlOMcDOcwDFcWynMxlpZjzsDwiDN7Iso7CdM7DcsnU76AvfDw0zH9UzH7p6P8M9L8vfcKTO8lWN4LUdLS5snWi1fDORQl6ZQuG0ZvelmsZCDR7ms70eK0lLLmNxm0fZJBz9EM+o5l4TyVgSc3Iz3ZF9rxFQvYn4m/9GL85SPxV0zEVzEdb+VcPFWL8NZej/f0rTiqb6O3+nacp+/BUX8ffXIaH8DRtApXy3o8bTvwdz5LuO9dIo5fEun7gGDvTwgK6He+TrDtNQL1h+gu3UlnwVY6c7fQemw9zd+so+nwU9T+6zFKP76L3A9v4Mg7i/ho9zzefeIaXn5sES88MYuXVl/NoYens/OuyTyxbBw3jT+Hjfcso6+r4weg/yOJprXEm9YTb1qnQT3R/Lie4BMtTxJvfpx4y+PEmh4j3vQwieYHSDY9SKpxJanGJ0g2PUWqZRNG+06Mzt0MdO4n2fUSRq84bH9FyvkxhusTlPdLTN9XmP4v9LHk6vsnlvfP6bpB7wcYXolMeAlDaJveTZquMXpXYfQ8gOq6G9V5B6nWW0i1XI/RtABVP51U3WRS0rJeMYJEySVE8s4leGI4gWPZBA5n4v78LMLVc0j67sCQhLz+R0Bya8IinXwZM/QzzMhvUNE/kIr8gYHox6j43yH2T1T076SiH6Iib2IFD2EFdqEC61D9q3VDjoC7Jf2XOkZVgH4Vlj5PposSwjLZi/JmH1bkRYi8jRX9CSr+Lir2Kip6ADO8BdX/GFbg3rQj1/djlPfHKM+tKNcSlGscynEmZp/kjpxLomoMEdFnF83TKY1WzSxSpTcSyrkP76kH8eTeR7DgfhLljxGvegJPzgoch6fj+nocfV9ehfvo5YTzxzMgfG/dFaiGi4hUjOfUL6fx9soZ7L1tDluWjODg0iv52R2T+OiJiTR+IvuMXZiBDVj+NWk3rew4PNMxnRdj9gzH+oHDbbfr0CyJN5DuUrNKcrzPoPeTEdT+ZgFlv15O9X/Oo/3PF+P751Di3w8jVTwEszILSzj3GjtmbXpit6REo+ZMYkLJlNoxa7KhLhtLhW/V6QAAIABJREFUA71NA71RacOQgubydMmKLncWoC+SkohMYgUZOkM8livFEWeQLJKe1vTSM1Ik0/MgV188Lw3yhQuJF84hXjiLeP5sYuKEzZmlj7hiIzmzNMjLpB+V93JnarAXhY7w8WmAF1CfQOjEBMInJxI5OZ7IqbFETo0mcmoM0VNSFD6eeN5EYvoqoWdC30g+/QjihWNIFE8gXjKFgbJp+ojTN1UxVU/sAvY66qEqHfkg9I1RI6A/WtM1clW1IzClM1YnhEpS6Bnpr5sy0kDfmDFYRJIxuKBNq3Ak+0ZybywJmRsEfM3R/x9AL3p6m667lBiBNNgPLmi11t4GDhs47eDMwHLYMeXPCUcu3g+XKHWyUY4MlCMLy3ElSCiaSyTEo1EeCdwbi+kRfl74dwH5qWm+XhJZxeXqnaT/nOG5EOX9EZbnHE0TaT7enYkhf7fIj51SySl7BonCztRRzVK3GDhh5/TvbNT8NpO2f9roPmqnT0LLTmbiPGnHk2fHV2DDVyR8vQD9pfjLRuErl9TKyXgrZ+KtmoO7eiGumutxVi2nr/pW+mpvx3H6Thz1d+JsvAdXy6N4Wjbga99Nf/dL/x9d7xklZZl1f1fqpgHFQURBQMU05hzQUQQViRIEiQqKiopjwIBiVtRxdHR8dMYxzZhGUQyIpM65SQ3dQOccqkOlrpzrvu/fu85VjY/Petf/w1nVXV1NFwt6X+faZ5+9Cfa9T6j33/i6PsTb/Tb+7tfxd72Bt+V1XEdeoOfgk/RWPo5dBrUlf6aj6FE6CtfRuOteDvywiD1fzaD0o+v55bWreP/By3lm0fk8Mv90nph/No/PP5s10ydwx+RxzDj/WB64bTK9Xe1Hgf5Lok2riDSuJtJ4N5GmVUSbVxJtvouYdPIC9KpWEhPqpvl2Ys1LFI0Tb5bnVpNofYhkxxMkO2Rh6gWS3a+jiRtm30ek+r8i5dyM5v4VzbMNzbMV3bMFPFswPN+ju75Ed36M7nw/ra2XJam+hzG670brXqIAPtkxk1TbNFKtN6rA6ETTVSTrLyB5RHxNJhA7MIbY/lFqw9BflMVArhXfriF4tg8luG9iujP23Y4xcAcM3I8REK79FfTQm+gyeA19jR76jkTwSxLhb9Gj2zAieeiRbJLR70iF/4HuewNj4HkM/zqM4AMYgdVovrvSPtm+lRjKn164e7FVFVWOhCHfC2JhHH4KI/gCRuA19MBfIfIuhP8CgecwfH9G96zEcC7C6LsVo3c6RvdUtM7r0douR2s7HdqPh44Ryh5AOyKqjKsJlM4iWH4z8f3Xk9x/E/6SOfTnzqMvbz6egtvwlazAWbCE9i030PHthXR/fzYdP4zHkT0er/itVMlc4xKStRex/99/5O2l49hw43m8OPsKXpx+KhunjOHt2afy6+tXEWpeDAMrVFwcruUYLtHg347muhbDcRZGz0iwWzFk0UZ43lYTNJmVoRaHLegHJbhhKPatZ9L49c3UfjmFtv+ejnPzCILbs4iWWYjvM6MdMKFXmdAPpXl744iYcw0hdWgIiSobWrUN/bAV/bAMYtNALx19SgKbVcSbmfheK7E9NmK7bUgqUKTCQqjUSqhEDhxJcBpFfPd4IrtPJrJnguLqo3vPH7QZFusCWXa6RHnVRMsF6C8lUHIRgZKLCZamK1x2qQJ58b1R4F569OvnEyw+i0DRRMLFpxMuPpNw8VmEi88gWDyRYPEpBIsnECweT1jyZGV5qvyswY7+FKJlE4hVSOrURKJ7zlRgH6tM8+5JmbEojv58UtXnkjp0trJ7EMBPg/xpgyB/ClrNqei149HrRyoaTRfapn5o+mNF26SH3DIAV2Ek0uEPyi5ptaqIx/Qilfx7Dm7JDgL9/9XTC+ALwKeBXzKOVUKY3YzQOQiH32eDfpvixmUQioCv06Q6bVlakk1brXM8epcIAs4l5RKVjuyLnILhlqHquQrwNfcf0TziJX86hud0dJfIKcegC8C7hqI7hbKxkXLJQWIePEQGbx1dNoz2oSSbLaRqLMQrs7D/aqPqIzO1n9to3WKic6cJe14GvYVC4Zhwlppx7zEpBY5n3zA8+0/Cc+BUXAcm4jpwNq6DF+CsughH1WX0VV9NX9X19B++mb6aGfTXzcHRMBdn80IczctwNt/LQPvjBOyv4u/5O+62D7DXv4mz7Q389jfwdb7OQMtGPPUv4Kp9Bnft07hrn8R16HHch9czcHgD/QeeoqP0URp33suhzQsp/eRmvnn5Wl654yLWTj+D+246jbtvHMuy60dy26QR3HC2ldXzJ2HvbDsK9P8h2riCaOOqwVqpaJpo013pTn8Q5NNgv4Jo8xIF9AL6CuhbVhNv/TOJtqdJtj9PsvNlEt2vkeoR3xzh6D8l5fwvmvtHBfC650d09/fo7k1o7i/QRXnTn3bHFJsEvf9JjN410L0Mo3M2esfNaG03kGq5jlTTNaTqryBRdxGJI6IwGEOschSRfccS2pNFsCQDb54N906xHc3EWzaGZNc14LsVvMvAcw+691FSwecUJ68H3sUIfYIR+hYttJlU+Hu06C8QK8CI7kaPV5CKZ5OKfI3h/wf43oLgSxBah+FfQ8p7F5rKwhSAX4IhShw5UHzLFPCnAf9ejODDGMEn0QMbMALPQ+AFDN9TJB1riNlvJ9Y5m0TnzSTbp5BqvY5k47XEDl9DTPxYROe99xxi+yekgySqhV+ejK9oEc7cuXiLbyKy+2ZcubfQtWMu3TsW0PPLrdh/mkvrd9Oo+fRiGj8+j7YvzqVj0yn07xiPu/hsvBWiH59C97ZJbHr0TN669Sz+MusC/jLrfN6aMZH3553Cx3efxoHvryXlmIzRPwn6pqL1zyHVv4ykcwUp93wM1y1px0K1LCNpVnI9TitoqBebXOnqhVIx4y0aQd/PF9L27fW0/vc87N+MxPOThJubiJSaSO62ktpvVlSM8PYC5uLCqB+2YVRnkKqyIB28gLvcFLRDZlLVZsXzxyuFrjET22shtseqYt9iFVai5TZCJRkEitMpQeHyocoWOFwxgkiF+MxIF30mkYpzUc6TFaK0uXjQ42awYx9UyohMMlh6EaGyixGwD5Zegr/kIkXXBEouSFM0xacTKDqFcNGphAsnEi4SwD+NcPEphIvHEyo+mWDRWMIl44mUn6qcLMPl4wmXjSFaPobY7pOVPYLcOmL7TiNWeRrxA2eQlA3fqnQlqs9WLp0C8snDE0lJKU39aQrktdpT0euEnz/a0WekQb7BpvT0irpRID+oahLw/43GkYUqAfvfWxgL2AuVM6i+USlgsjErIC8qFql0mL0APQrorRi9VnSZ4UhX77BiqA5/kMbpzUhr8e1ZxJpPJFz/R1I9skE7XkkydZFjuoR7l2WrM9A9Z6B55PF0EIWN6xR05+hBkM9Q3bzMA2S/ROsXoLehC63ULU6cmaSahpCotyiVV7Aki8avM9n3gYmaz820/myia4eZntwh9BRY6S2w4Ci2Kq7evceKe18W7n0jcVWOwXXgFFwHzsR54HycBy7EcfBi+qsvx3loEo6aKfTXTKe/dg6O+vk4G2/H0bQcZ9NqPG2P4Ot8QVE1no63cDS/ibfzbUK9fyOgqJuNBDo24mt9EW/rs/jansbb/CTexnX4mtbha34O9+EX6a5YT3v+I9T/ehfF/57LZ09fxxO3ns2ySaNZcOVxzLtyBHMnjeaGc45l1a3X0d3xG9B/RrRxKdHGO4g2riTaeKf6ONZ4F7FGoXQGO/qmu4g03UG0WeibZcSaVxCTzr/1buKtD5No20Ci7WUSHRtJdL1Gyi5ul0LfiM2xBIJvwvBsVgNY3f1fNOcX6QxZsUCW1Cr7yyTsT5Pqfgi9ayV03IbefhNa23WkWq4m1XgFybpLSdZcQPzw2cSqxhPdP5LwnmEEK6z4y0z4Ck14ckzKW9pVMJxI0/kkB8TvWgawyzG8a0gGniQVeoWU7210/78g9CUIFx/cjB7eBtE8iJdgJPaip6QK0eObMUKfYvjfRfOJ+uYxNchVQK86eQF44dgXonsXoftuw/DJ41JF8YjNccp3H0nfWpLeh0i5HiTZfy9x+zKiHXOItk4j0TqFZOtkki1TSTZNI9EwlVjtFGJVUwjsnoS35AI8xRPxl19AsGIanoLF9OcsxJ0/i4GiafRsn0bblnm0/jST9k1T6dx0My1fX0v1B6dR895E6j48nbavJ+DYNhFnzsU4cq+nb9s0CjdexecrLuZ9mdLPOIO/3XIGH8yeQN7GC2n4eRLuA5eTtJ+F1n0xWs81JHumkehfTkoM0rwPw8DDaK7FpPouR7OfkOZDZdGmdYhKsdIl8KLGSqwqg8juDII5I+n76XxavzmTtq9G0PeNiYGtZkJ5VuIlmYqzT+6T4aoZrdoM0tUfloGtFa3KrGia34C+Wj63kDwoXZplkJ83Ed1jIrbbRKzcQqTMpuYEwVIzoTIT4XIrkYpMwuU2IuVZxCrkJjiBiMgb1faqGJZdSLDiIvwVl+IXjxsZrJaeT6BEHgXsxdZAnr84zcmXXIC/5DwCJX8kUHwqwaJxhAsnEC48jbAsbRWfSrh4AqHisQSLxhAsOkl9HC4bR6hiLKGK0YTKRhIpl4PnBGJ7TlSzBOVeWTlG+dzED4gNwkRiB2VD9nRi1aeTEFnl74BeqxGgPw299jSMOqFuRqM3DEV19I0ZaKJYUr70AvDSzacflYT1/wf24m75v6EkkhUg3jDS0WuDQJ+WVqaBXhMeXDp6AVb5utqeTStdNEe6gxfaRugcvU88cyTf2AZ2C4nWYcSaT1a+TJo7DfSGYyyGazyGe4KSTuqeiYNAfwZIappzPIZINl2i6pHBb0Y6MUqoIaFtem3pQbHIQdszSdRlEq+1EK8y49iZyYF/2tjznpmaz020/mima5uN3pxMevItqvqLMnCVZ+Dem4Fr7xCce4fj3D8K14HxuCrPwFl5Po7KC+mvvIj+6ktxHb4SZ81k+mtuwaGA/jacjYtxNK7A2XQ37pa1DHQ8jb/7JQL21wnY/0ak938Id7+Lr20j4e43ifS+S7D7rwS7XiPY9TK+9g14mtfhbVuHr20D3qYX6K96Bnv5U4rSObJ1ObkfzuQfD1/OPTeNY9bFI5h12QmsnHE5i6dewX2330p3xyB1E3d8RqhhKaGGZUQalxNpWkKkadkg6AvQ30W0Sbp8KQF6Afg7iDXLcPbOQU7/IeLNT5FofZ5Ex0tpjt4uYP9Xkj3vofV+hNH/BbrjawznF+hOUeJ8lLY17n2DZJfcBJ4k2Snbt3ehtS9Ea5+B1nI9ieZJJBsvJVF3HvEj8h98HLGDo4ntP47I7qEEyq34Six48814ctP+0f3ZQwgeOgPNMRnNN0cNOgXodb8A/QZSAdHHf4Ae+LcauBLZghHdiRErwkiUoycqSCX3o6X2o6dK0BNb0cNfYwT/ieZ/laT3YZJeAe9Brt63ROXGGr55GJ5F6Yg0iUnzCvDfjj6wlOTAChKeO4m77iDlXIXuWoPhWoMuH/ffidF3B/QvxuhbjN63mFT/XLSeeWid80g13azCq/1ll+AtuJpA8RwG8lfQu2sJvbtupT/nFuzbptH6w0wav5lM/ReX0fjpFdR+cAEH/jqO6rdO4dDfx9L42Si6vz+F3q1XYP9pGsUbJ/HFqsv4ZOkV/M+ss/nL5LG8N3Miu569BHfxteitl6K1TyTVfjJGx4UY9ikYfYtI9a0m1v8AKfcG8LyBPvAYKfcMkn2nE7dnkZLBbId48NgwJC6wQfTymSQqrcRKMvDtOIHuTSfQ8lkWnZ9ZcH+TiW/LMPzbs/DlWBFQju01kaq0oB/KJCVD1yrp9KUs6FUW9GoB//TnyUoBejPRfSb1fQL00QoT0XKha8xESq2EyyyEBejl88HsTzkEYuXDiFWcQLTsVKJl5xAtP0eZkIlXUrDifAJlMkz9Y3qYWvpHgqXnpIG/7CL8CugvIlB8ntLCBwsnEswbRyBvDOGC8Qrog0UTCBSNJ1QyjlDJSQSLTyBUPJJw8R8Il44kXD6SiNwuyocN+tGLi+WxyuQstm8E0cpjiR8YNG07eApx5WB5Conq09RANu13cyqpI0LZnIKuaBt5HIdeNwq9/hilupGEKZG+6vUiYRXZqpXf7IvFKK4xvZgmFI/YJvyfrr5d7BHSCWAC9gL0apFqUEcvEku9W7T0YoAmQG9B67Yq4NcH+XnhytWAts+Cek4N8DPSr++yQfdo6BuD7hyF5jwOnKPBNQ7cYzDcYsUtdI1w92PANQqcIzFcw9OdvFqEylC3hzRtIy6bGerGgRq+DkUT24wjNtVstH03hPK3rOx+x0Td51ZaNmfQ+atNDWTtOWbsuVb6Cmw4SkRuKRbFQ3DvGY5r3yhcleNwVp6Gs/KcNNAfuBRH1ZU4qyfhPDwZx5FpOI7MwlkzD0ftIhy1y3DW34GrcTUeyY1tf5yBjvX4ul4g0LURX4eA+utE+/5OpO9Dwr3vK8AP971JoPtlAp3PEux8Bm/7erzigNn4DI5DT9NZ/ih1O1dRvfl2ij6exb+eupIH50xg4aQJLLv5KuZcexF3LZxF11Ggj/V/SrBuEaH6xYQbFhNuWkS48XYiTSuINArI30GsaQWxxjuINd5JTNQ3TSuJN60kpr4mH68h1vQw8ZbHSbStJ9HxLIkuAXzp7t8kZX8XveefaD0fovV9iNb/PkmxQuh5NW2VILLM9vtJta8i1XY7ydaZJKXDbbqGeP1lxGvPIVZzKtGqE4gcPJbwviFEKzIIl1rxlprwiJXoLiuuHSZ6d5rwlI8m2SkRe9NQ4KviyFYqb3nd9xwp/9togU/T3HzkJ4xoDgjAJ/ehJStJJQ+QShwilaxCS5SjxXZgRH6A0GfoIsP0rSPhE0+cO9C8KzEG5LawAAbmgnthOtxjYK7KxUTknNLlDyxFG1hByiPfcz8EngL/BvA9Bb5nMLzPoPseRfOuQfPcSWpAOPBlGP1LwX6zsniIHxD53hS8hXMZyF9F9/ZltG+dTe+uGXRuuYnGr6fQ9PmfqPvoQo78/RwqN55K+fOj2PfqyRz868nUfXACzf8+hYb/XEnpa5fz7yVn8v78C3ln7rn8Zeo43pjyB7Y9dTnu/NtINlxLqvWPpFpPR2s9F6N9MvSIjv4+NPu9xLpWkOx7AMP1HLpnLSnPLGWTnOg5Dk0GddLViW+KeJ5Lt1hnJnXITGJ3BpHcobh/HEr7p1aaPzBj/8iK+4ssHN9mYt9swpNrJlpuQdsrnb2NWJWZ5AGTklBqsmR1QPTzZnSRVVZaSAhlsy/dyUd3m4gIyA8CfVTAvdT8fypSYiVamkG01EqsPINY+XFES08mWnIasbLTFXceKJE4QKmzlTomUHQ6/uJT8JecSqD0TIJl5+ErugC3KJryziFQcCbB/PEEck8kkHsCofwTCRWchF+qUAD+RMKlowiXHEe45BjCJUMJlw4lUjaUaHmW+v8c3W0bnC9kENudichEI/uziFUer+IEEwfEk/5kEmLNXHUK8UMTiB+eQEJsEA6PI3l4LNqR8QrkxftGq/0Det0xGHXi3y+yVfl3EAmrBUNUTUrKalK7CoYcAjXDiO4/jvjBYdA0uFDVJhYJJrR2Uzru8SjgK0dLi1JbGd02Ja8ViS0C2vZMZWusOv8e0dpnIFYJskkr2vuUSB3tGSoyErkltGWRbDoJvWsUumMIupiPOY/FcI4C93EY7pHojjFKjpnecpVN1yxlN6w5MtM0Td8gVaQGvBlp/3xZ8GqzQlMWRq1Iea24d2VQ9U8bBa+YFdDX/ttK06Y00HfvMtOdbaInx0JvviU9lC0TsM/Es3s47r2jcFaejHP/qTj2nY2j8iKcB67EceAaHAeuw1E9GcehqfQfmkb/odk4jtyGs3YxzrqluBruRGIGXc0P4Gpei7vlYdxtT+DtfJ5gz2uE+94l6vqImPsToq6PiTj/SajvXcK9fyGkAH8D/o6n8bU8w0Dt0/TsfYyWnHuo37qc+i2LOPjf6Wx58ypevfssllx/EheeZGPRtGv+l7qJ9/+DUN0cwvXziTQsVBVtXES0YSnRBuHulxNtkloxWAL8RwFfOvzlxJrvIt4q+vv7ibY+SLT9UWLtTyv7hGTniyQ7XyHV/RrJrtdJ2V8n1bORhP1FUmKk1vUo8Y57SbQtI9m2gETrLOItU0g0X0Oy/lISNecQOzxBgXy4cjjhPUMIlGUobxV/oViJmnDkWHBvt+HcasKZn0ms4UwM5yR0MQjzy5bpneje+zAGHsXwPofuF++a/6KHf0aP7IJYKSQOYCQOoSeOoCVr0ZO1GMka9MQe9Fg2hhwIgX+rYaru24CmAknuTHP0A3cqfbnhkYR78XRfoB51j+jhxXlvAcbAEvQBef1qkoF1aMEX0EPio7MRI/iaUgBpanbwKCnvauWHj2c5uBZDn1BYVxKruYrI4amED85ioGIRXTsX0rplPp1b5tP67U3UfTKJ2g8u49A7Z7H/jQmUPHsCRc8cT9lzJ7Hv1bEcensMh989i8LnL+eT20/jf2adzjuzzmHjTRP4yy1j+fmJC+jZdhvhg7cTPjKdeP1NpJono7VPxegSkF8BPXdj2FejdS8n2bWUePedxOyLSPRNQ3NIrNwJSm4nPK3apJThrKQaCVVQY0bbbyNeMoRQdqaibZreM9H0ponudyz0fpZJ76YMBn7NJJY3hFSplfhesZI1Ed9nIrHfTKLSTLJSQN+EdsBKcn8m8b021ckLXfN7kFfdfJmZkNA2pcLRm9RQVkKe1YC21EK4NINQyXBCJSOIlB5PpGwMoZIxBIpPTitlis8lVHwugcLT8BeeiL9wFIGiMURKzsSfczat/z2Rrs1j8Wafhi9vLP6C4wnmH0sofxjBvKH48o/BVzSCQLHsDRxDqHgYweIsgsVDCJUIhWQlXG4hLLcPOZzkNqLKTHSvmYj8/faPIFF5AokDo4kfOIFk1Ykkq8aQODQ27Ut/eCzidaPCSI6chFEzBr1mNFrtiLRHfZ0YwQnAS6yggLwlnTo1CPrS5afqTCQPDcNTdCyBiqEYRy2ixWzud2Avnf1vNI7ywhGrBEv6EGiX5KostNbhaB1DFM2jBrLdWejdsqthQeuxkOoRoJdDQmScFkKHj8G1ZyTRFgnSMaV5dqXFz8JwZSng11wZpJxSNjRnZnoZyyEummIFInYMg6ofmRXIny/zBHmv4tRZbyV12Ex4t5W2TTaKXjOT/ZyJPe+aqPm3hYb/WmjbYqVjxyDQ55rpKxCgN+EsM+OssOHePRTn3pE49o3Dse80HHvPxrH3Qhz7rsR54FpcB6/HUfUn+quvo6/6Rvqqp9N/aC6OIwtw1N6Os34pzsa7cDXei6v5PtytD+Juf0TFC3q7nsPX+xoh9/tEBz4j4vmasPsbou4viDj/QaD3LwQ6n8ffsh6PDGmr1uHY9wi9ZQ/QU7Qae96ddG6dT92mKeR9cDEfP3kWd103ggfmXYO9ozU9jI33v0e4bhrhutlE6xeoijXcRqxhEfGGJUQblyhZZVToHFUC+INdvjw2LledfaL1LiXJjIhSp/UBYq3rSLQ/Rar96XR1rCclpeyM1yuVjlgepzrvJ9G2nETrPBItM0g2TSXReC3xhstJ1pyjfLejB0YR3jeU0B4bwTIr/iIr/jwrA7lm+nNM9Gy34PjZhvMXE8G9I0l2y7LFn9D9t6D5byclunlR2ww8jjHwErrvXfTwZvTIDoxoAcT3YCSqMRTA16Gn6iBVC6kj6AkZyu7CiPyIHpCO/i3wPQ8Dj6F77iPlWYXmvhPNsxzNczspz3w0dxrwdc8cDM9sDAH8gUUYsvXqu4dk8M8kg0+RCj2PFhL1zytqMzcV2qACx8VUTfffjej98SwG5zRSPX8i2XEDqa4ZaF2zSLTNJXBkLn3Fs2nefBM1n17LoQ+uoPpvF7P3jVMpfXkU+c/+gfxnTqBww1jKXxnD3lfHkvf4RD5ffAZv3ziev049jReuGcuz1x/HN2svpG3zQlxFi3CUzsWzZz7evfMJV88n3nSbms1EW5YQa1pKvGEp8XqR2S4g3jaXePcckr2z0fuvg/7TMXqGg/xid2agt4v9sAz75BdOKJdMkqKKKbTg32qhS3xGXrVS/7yV1r/a6PlEqBwLwa1DCBeKesakwD6+16yUOQrwK02qwxcLhdjeIUQqMojtMavXRstNRMpMRBVFIxy9gLqZYKmJYIkZAflAcfoxWCJqHGkaBHTTQ9tQ8XCCAsilxxMsOpWBXWfgyz2TkKJg/oA/P4tgwbFEi07Gt3M8LV8Mpfu7YxnYNRpfvnzPCEJFQwnm2gjmWPDn2/AVZBAoGkKwOPO3ChRnoH5+mZlwucwOBg+p3UeB3kREAH+flfj+YSQqjyNxQGoEyYN/IFV1PKlDo5WRmXZ4NKlDo0hU/0FZFRsS2lIzcjCIJEuleakuXoBeQF6lT5kRKwlJ+jJqjyFVI+Z0mUQrjyF2UKSYNtQGrQxpj+rs203oUhJQIp24CibJTC/LCYffZiVaNwTn7iF4D4xQfjLK7fI324T0wFZufGrjVhaw2jOJ1R1DWCwvurIw+jIVv673ZaL3DUHrF+7djPD8qX4zqb6jXLxo5I8aqgm4p5U/EiYim6/KeVNuIU1il2EmWWXBW5BJ1YeZ7HjaQvZzZirfNyvqpnGTlaYfTLT8IjJLEz15JvoKzThKTDjKTDjLLbgqsnDtOR7H3nE49wrQn0VXydl0FF1A396r6d93Df2V19J38Dr6Dk6l7+At9B6cRU/1rfQeWkD/kYU4a5fhEhqnYSWu5rtxt96Ps/Uh3B2PMdCzAZ/jTUKuDwi5PiPk/IKo+0ui7o/w97yFv/NVvM3P46x5mv6qJ3AceBzvoSfwVj+Ge+9aegrW0JlzBy07bqVh6wIK3p/O1vdW43PNTxKWAAAgAElEQVR2DwJ9398J1d5IpG42sfr5RFXNI1a/kFjDYrUJq7ZhmxYTa1pCrHEZ8aYVJBTIr1Afx1WHv4qYDG7V8PZe4s0Pk2h+jGRLulKtj5FsfYRk659JtT1EqnUtydb7SbWuItmyiETTTDWATNT9iUTNlcSPyLr3BKWqCe9ND1wDJSaki/fmWBjYZcO5w0zvNhNdW8zYf7Dgzs4iUSdLPJKvOlXlpKZ8tyuKxfDeC94nMAZex/B9hBb5AS3yK1okFz1WDsmDkDqMnjqMoR3CSB7ESFSix0vQYtvQQt+h+T8lOfAGKc/TGJ4n0D3CTd+vqBZ9YAnagAD9InRVktgkQD8TXRKrPAswvCuU7FILimPlWozAOnT/M2h+0dM/hR56Ei34JFpgXTpwXDZ3B1agueeScs5Ac4q+fr6aA+C5Dd0xk2THFLyVV9P646Xsf/98dr92NqUvnkrBMyeR9+Qocp4YRcH6kyl9biK56yby1ZIxvHfTaN68YTQvTDqeDVcdx6crz6T6k1l0/7qI7p1z6S+ah6diPp7yRTgKl9NbeA/dRXfh2L2KYPXdRGvuIF53O6lmyQOYRco+l5R9AUn7VFLdZ6IrH3zp4jIVGAjYSyC13iDWwpkk9luIlZkI7bLg+jqLpjey2PuYjf1PWmh83UTfv0x4NmUykJ1JpNhEqtRMvMJCrCIN5tLlJ/abiIsXSYlZAXesQoavaV7+9yAfKbUMdvEC7keBXsBeykqwSMpGqNhMqMREqNhGQD4vGY5n10ha/jucvp+PJ5h3AsGCoQQKLATzMwjnjyC4axS+7cfi3TkMX85QfHlZ+AoyCeRnEsrLJJxrIyC+5oUmgkWWwZ919GfKIWNV9KNQkGF1u5D3L3p/KXN6qCwqor2ZxPcNVaHticoskgeHkqw6lmTVCFVa9Qi0QxIWLrGPI9BrJFZwOFpNJlptBppaPkt38mqjWJRMsnB2RADwGBL7x6NVnYheI6BvxajJwpDtZDW8lS1ac9ra+KhvvXD2osLpEIVVJsKDKw6/1Uq8diiePcMJHDw+nf87OLiVhbr0Vu1Rjt+ctk3osJGSvOAukWNaQHycejLRe2yK5jm60ZoS2keWrkRN4xCN/FH1zv8FeSXvFAsHOZCaTaRqTaQOW4juzaDrpywKXrHx0yNmcp+3KAqn8atMmr/LoPF7E80SJ5hjojdfzM0svwG9o9xMf1kGjooR9FecSH/FOPorTsde+ke6Si6gp+JyeiqupGfPNfTum0xf5U04Ds6kv+pWeqvmYq+aT2/VAvoPLaT/iPD2i3E2LMfRdCeOltU429fg6XgEX9cGfPZXCPa+SbD3HSL97xHte49w99uEe94m2PWmkmEONL6Mt/FlAs0vEmh8Fl/depyHnsJZ9RiuA2tx7lmLvehBune/TSLsPgr07xOquYVw7RwidXOJ1M8lquoo0C8k2ngbis5pXKy8bmINy4g1pEFedfQNdxATR8uGtEQz3riaeOP9xBsfJNH0IPGmtSSaHyLZ/CDJ5jWkmu8h1byKZNMdpJoWk2q6lUT9zcRrrid++Cpi1RcRrTqT6P5RhPccQ7A8Q/1CewtMDOSYcUvE17ZM+rdK1qOJrh9MdP5kxrf7ePROWbKYhD5wC5p7Hklxg/SlqRt86zEG3kL3/Ud19KnwFlLhnejRYqWyIbkfPVkJqf2Q3IMRqyAVLcCI/YoR2Yzm/4yE5y8k3E+juZ7C8GzAGHhYUTKSv6oNLEITjt6zAN0jg9k5GO5Z6G4B+vno3qUY0qV7V4PvHvA9CN7HMHyPg/8xpe/X/OvRJJs29DR68AmM4INogZXKssGQeEMVcbgCzbcEzXsrhvsmjN7riDdcTXf2mRx4fxRFz40m57GxZK8dS+7DYyl4fDyF6/7IdyvP4L0ZJ/D3m4/nrZv/wLPXDueDRadR9tcp1H0zm6Yf5tG543Z6CpbQW7SM/sK76Ml+iK4dT9CVtxZXxX2ED91Pov4etJZVGO13YNhXYPTcgda7hFTPFFL2iWjKdC19pRYlhrK5FQOtRuGFbSSrzcT2mAgVWPD9NJSuDzJVCMwvK03kPWii/lUT9o8z6P0xA3+2iWSuhViRhZhQObL4VG4irpQ1MmC1qmFrvMxMrNREtHRwAFtiQbj4ULGVcImArDld0s0rgLcQKEyXgH1IgLhQQNmMP99EoCADz84sun/IwLV1OIHcYwjk2fDnpd93OD+TYM5QQjkZBHIs+HaZ8eWY8edaCeRmEModQjg3k2C+mUCBiaBUUfrPT4O+hbB6bzbCJUfLqgbF8d024gL2cniJemivjfieDEVRyUA7WSmGcFmqUgeHoklVZ5E6lIV+ZCja4SGqUxeA16R7l8W1WuHgLarUFvIhG6nqTILlw/DkiqxzHMaRYRh1ImkdDIVRWbNpL3tjUGevZi5C5Qglp/JmM0Buba0mUk3iITOElFgjNw9XtzkEwDuHYKhK2x4bXRJdmAWdWcpqQW4HR/X3sgGuyzBfKBjF6UunLpSPBJ3IwFVUO6LmEQnloIbfnrZmkMNEfHqQ9yWUTYOZ5GEb8QNWfIWZHPpXFj+vM/PDQyYKXrJS/aGFxi8zadpkofVnMx3bLMgwNg30sjhlor80Xb0lNvpKh9NXNoresjH0lp1Kb9nZ9JZfQN/uy+ituIKeiqux7/kTPfum0Fd5C30HZ2M/MJuu/bPprpxDz0Hp7ufRd3gB/XUL6atfTH/TCpwtq3C3rGGg5SG8bY/g73oSf/cGgt0vEO58hUjn60TsbxOy/42Bttfxd7yhVDqhztcJdbxKsPNFgm0bCLY8g79hA54jUs8y0PwxWnwgDfSJ/n8NAvw8oo3zidTPIdxwK5H6hUTqlxBpuI1Io3xtgQL7WONiog2Dnb3QNuJ9I8BfL2B/J9Ldx2Vo27CKWININFcTa7yXuFgsNNxLvGEViYZlJMRKoWE+icY5JBqmEa+9gfihSUSrLiFceSahfWMJ7h6m+HhvkQVPngl3jhnXDgv9Wy30/JxJzw8Z9G02073JhH3HEMINElZ8MUg+pWs6wpFrA8tIicf8wEMqQUnziqzy3+gC3GHZfpVBayFGtBw9thtDaJx4herytWgZyUg+enQ7hLeg+79C875HyvMSmvtZjIHnMLwPpTt6yWR1S9i2OGIKNy8lQD8bwy2ALB2+dPuS3SqbsDLAXYkh26biXinmZiLb9K9HDz6LEX4FPfISqfB6UsFHSPrvR/ffh+GXYJO7SQVXkPIvVaoeBuaov7PWdS6BytNo+X4CZW/8gezHj2Xb2tFsf3gCm+6eyAfzT+WdWeP4+y0j+ev0Y/nb/JPZ8exVHPxwMpWf3kjVFwtp/GkFtb8s5/Avd9Oy6zE6s9fTk/807j2PEj20ltiRNWitazC6H4Ceh5XDKI4HMRyyUHUTWt/paL1Z6RX5vvQvq7qmy8ZlswXkKl1nIl4ldIWNYPYQ3N9YOficiX/NMvE/t1jIfzCTljeH0P2FBc8WC5EdGUTyzEQLLcSLMkiUWEnuthCvMKulqHi5NQ3yJSaiJWbCJWZCxRZVAuqhIjPhQhMhVWZChUfLQqhQykww3/YbkAfyTATyMgjkDSGYm0UgO5NAjo1gvpVAvrzWTFAOqXwBdhPBHBP+XSaC2SbCOWZCuRaCAvh5VoJ5ZrUnkP4eM8Gjh0uhHELp20ZIZgbqPcuQ2Ep8t5Q5DfQV5sHPrcT3mInvN5HcbyZVaUuXDKcPZqhlMlkoS4kMVewijlhJyUzkiPgIpbt3BfiywCZ2E9UZJKszCe/JwFc8gvj+Ueg1YjkxeCAo3yGLotsMyQKQjVoF9oMbtOKLI4Aqw842CTRJ0yRpPb4YpQ0eBrJl2y70jg1dOu1OC7rQNfXDiYv0s9OcVvF0WzHs0tmnDdXUDWBwAUvrHE6q/QS0rmMUH58+AMTWIB2CYsj3iqWyxCG2pY3bZFdAcnOT1UOJ7Mmke4uZ/JdtfH2viR8fsVC80UL1vyw0fJVB83dmOreZsWdn0pNro1dUNyKzLDLRU2xSj73FNuzFw7CXHkdP6Qn0lI7DXnI6PWXn0ltxET3ll9JdcRndFVfSvecauvdeT/feqXTuuZH2PTfRuW8aXQdmY6+aQ0+V+OLMpbdGAH8x/Y3LcTbeibNpFe7We/C0P8BA56P4Op/E3/EM4a4XCXVtJNT1JoHO1wh3v06s5w1iPa8R691ItOdV4t0vEet8iVDLi3gbnsff9Bz+zn+hJQeBXhdHydblJFqWkWgRTv62NLgLP1+/nIh83jgvXQ3ziTYsJCqUjhrWLibeOJ94/SLi9cuI168g0SgKneUK/CNKsrmCqHT6DXcRrV9FtH7Z4BzgVmL104nV3Ui89npih68mVnUxkcqzCewdi2/3CHylNgbEHzrPhEPS2Leb6fvFQs9PFrp+sNG1yUbPNya6vjXjLvmD0nuLw53hliCDmxVlIhmoKeU1/zi67yWln9eCn6BHvkcLbcEICdDnY4RL0COl6FFR2ZSiRUvRIiWkwnnpwyD4K7rvWwh8jCHD3IEXMQbWow/ci+ZZge5aju5ciOEWqmaWAnnp5A3XHFBAP1d19ZpQLp7F6N4FSm+vK9nnSrTAagz/Qxi+JzACz0LkdYzoX9KOl+FnSAXXoQcexgg8BJJwFbmLVGgZWmAxSIiybxZ4xGP/KvTuSwlVnUHrzydQ9OZINv15NG/MGcmr08bxl2ljee36obwz+wS+f/gKCl6bRNk7l1P07mRy/7aAfV8sZ/93K9i/+QFqtz1BR/56nOUbcO1eh7t0DZ6K1UTq7iPVeR8p+/0qDAbnn8F5NzhnYzguRO8diS6hFNKBKeldOnyaNrE0TnutJGtNylY4IlTJNjOt71n5cN4Q1l1k4/O5wziyPouu90z0fmPFuyWL4E4r4RwL0fwMBfbxUgtxGWCWmohJN19iIibbr8UmwkLDSOdeZCEg3XqhmXCBlEk9RgqshPNtg88JaItVho3en8x4s6VrtxKUrjzPirw2lG/FLwCeb1WvFfpG6BifdOkC+rlmBfCRHDNSIQH/PFP6ZpBnUl9XgJ8vtI9F0T8BAfoiAXgT4d9VpNyiQD4uXP0gFRWrkOcs6hajZhT7zCT3WxTgJytlo9iitoNlQzhZJRvDZlIC8DWDZnEK6KWbl61ikawOLpsdljhGE9rhDEXbHO34VfcvNtBHB7jS2YuDqNBvEucoITFt1kGgN5Pepk1z+YYArXT8LTaOmqSpoaji9NN0T6o1A+8BK8HDQ9DaM9TgVIJA9K4MUjJAlY6/S/yTRHaZQazxWAYO/IFEy0hlj5wG+LS7ppipoW4IcoBY0KSTl3xcySw4YiNRORRfkY1Dn5rYtNbEf+4y8fPjForfsFD9kZXGrzPSOnrZjM21Yc+z0ZNrpSfXgj3fjL3AjL1QwN5Gd+EQuouGYS8+DnvxidiLJ9Bdcib2svPoLr2ArvIL6Sq/hM7yy+kov4L2skm0lV1L2+7r6dx3I10HZtJ1cBZdB2fSVSXeOPPoq7mN/vol9Dcsob9pKc7Wlbja7sXT8ZCicwba1+FpfRRv61OEu14man9FOQNHu18kan+RmP0lYj3y3Eai3a8Sapeh7TMEW54i1PkBetKT7ujxbQb7QxhS3Q8rT3PFobc9QKpdwkZWEW+7k1jLCuLNchgIRy+KnCWE6xcpTj9eN49E7W0kapYSr11BtFY6fLEzXky49nYidber20Gsbpl6TrzTw3UziRy5kcihPxE5dKly6YtUnkZoz4n4y4fjK7HiyjfhzBnUxv9qovdnMz0/Wuj53kL3txY6/2ui5SsTXb8MJ3bkZIze09Hdl5N0T0Zzp4E+zZ0/qOSLmu8Vkv6/oQU+wQh+gx4SyaTQMtnokTy0aB7JaB6paB5aLA8tkoMW2oke3IkR3ArBTRiBT5BbgeroPQ+ju+5QAK875mI4ZqE7p2O4ZmG4BORno0sp+mYmhjoAZivgly5cuVQOzEfeozFwN3j+jO5+BG3gCQi8otQ44mevRyWs5PH0oFaFlaxBC4qfjgSfSJjyHAz/PHTffAzvdPBOBc/V0HcJsYaLqN18Bt8+fhzvLTqO16Yew8Ypw/ho6Wn8vO5Kvl93ET9tuIrs16ez881bKflwKXs+X03lVw+w/6u7afjlbjp23U/7r2vpzrkXR+FavOV/Jlx1X5q+6V6FZr8frWc1et9CdIekVsk6+zj0/mNUvqjRLVI68RsZlOm1pAeByWqT4t0DeWbsm7LY9dgwnrrMwotXZ1C4MpPW5020/dNKz1fDcf40BO8OGwEFuNKhW4hIt14s4G4mVmwhWmQhIuApXXqBdO1WggKoeVIC3BZC+QK80qkPUyAtr5OP7d8Pp/HLDFw7jsGbk4VfdeXyWhmoSicvfHsGgQIr/gJLuuTPypNlL8v/1uD3yc9RPyvPTCDXom4CPun8c8zI3zeQbyKsbhYmwkVmIkI1lVmU9l+AXUpZOJRLZz8I9Luluzerzj4pQ9rBAbWSm1bZ0KusasfAkB2DQ/+PEj8h1dUPgrw4gYoj6O9KAD4lJWDZICVALx45ab4+/SjALktVv0uqahWp5GDYe2sGutgpKA3+YEyh+CC1W0m1W0m0WtSj8p/ptKJ1SnyhDFHNpOkeya21obcPId6YRVycWjuy0jJOoXoGi45Bbx65YbRKuIoZo0E2sm1oVbIcZ6FnawbZL1v4x3ITn9xt5udnLBT91cLBjzJp+DqDduHmd5npyjHTkWeiM1fKTHeujZ78DPrUtqxpEPhtdOVn0pV/LF0Fo+kumkBX8Rl0Ff+RzpJz6Sg+j7ai82ktupj20itpK51Ea8m1tJVeR+feG2mvvJGOqpvoqp5Ob/U8xdkLyPdK1S3B2Xwn7uZVKnPW03ovnpb7cDXdi69lLaGOxwl3PkW480nCXU8S6X6aaPcLxO2vEuvZSMT+ChH7y0TsLxHoWE+o5x/oRzt6fFvQ7U+h9zyFbn8ael/A6HkR3f4sRu8z0Lceo0+eXw+9T2L0PIbWvZZkxxribWJfLDr6JSilTt184nULidbeRrTuNmK184kdmUfkyFyiR+YRPXIb0SPziRyZRfjITUSqryd88ApClecQ2ncKwd2SQ3oc3uIhDBSYceZY6N9poXerWXXx9s1WujZZ6PjaSsfnNtr+Y6PlawvuomHo7Seh90mQwdXoA5K7KkArlMkSdNcjGJ4X0LyvkPL+Dd0r6pkvMYLfYoR/RA//ihbeTiq8g0RkO8nwDnTZkBV9fUSAfiu6/weM4BekvP9Dwv0KKbcMY9eorVDdORe9fyaGArlp6M6Z6E4B/VlojllorpnoUu4ZaO4Z6K4Z6O7pGO4ZSpWjeySv9k7w3IfueRDd+wgEn8EIPYuu4gcfRpOc2eAjaKEH0UJrlGmaFrwDPbgIPThflRFYqNw5DQX802Dgehi4FvonEWm4iOYdEyl4ZwxbnjuRLU+fzKZHxvLZvRP4/IFL+PnZW/jl5enkvXsbJf9cQfE/llP84WIO/ncFDT+vomXrSrpz7qYv7x48Jffgq1hBpHoF8drlxFsWEW+bQ7x9FomuuRj9c5S8NeUcjyYOheqKndbWy/Vaa5UkKQEaM4l9VsJFVjy/ZtL092H8a14GD19g4uOZVg48ZKb5NRvtH2bS+40NzxYrvu0m/DtNg4BvTQP6YKceLbYRLc4kXJiRplkGQTmQayaYLSqYNNXiy7biy8nEl5OBL3sY/pzR+HMmMLDzBDy7MvDusuLLNhPYZSKQbRkEZ+sgvWNJA7WAtQJxs3ov8jMCuSbUzxIe/zegNxHKs+HdkUHvD2bl2BnKzVBdvnpNoSUN9HJQlVqUWkgWvdJgL/sEchhKWYmVZRApycSbZyVQIJp7q5KcikWzdlA2h/93mcwY7Nqluz9avwd/bbCbl85f6Jr/J9ArsJdBeprG0SUzQOq3Dl8APy1j1ARoVcn+xO997oW+EZCXspBqSz8e/VwcJVXXLwPeTtm/GHTPlEOiLUNJNsWrRqwMRKdvtNsGg8zTN0QBeKXqarIqh061HHXYqiS37uwMDn00gq/ut/H3hWY+X21lxzM2dr9l4chHNpq/yqDzBwv2Xy3Yt2fQscNKx87B2mWlK9tMd54Fe56ZrjwTHbkm2rMttOUMpS13JB35J9FeMIH2gtNpLzyTxuzTaMieSGvhebQVX0hr0SU0F15CU+FltJVdTdvea2mvvIHOyluw7xfefgG9h2+jt3YRvSLDbFiOS2XOrmSg+S4GmlczIF45LfcRaHuQcOcjRLoeI9z9KOHux4jYn0539j1pgBeQlwp2rSfc/0/01FHqxv8Lqd6n09UjPvAvo/W+gtb7Inr/8+jO5zGcL4FzIzhfB8fr0L8Ro/8l9L7n0HueJNn9IIn2O0m0LiHZtJB43XxidbcSrZlF9PAMoodvIVx1C5GqmUSqpW4mXHU9kQNXEt5/Lv494/FXHI+/9Bi8RVm4c624dpno32Gld6sFu1A131vp/NZK25dWWj6z0PKJjZaPh9DxfSbhqqHKWEvvl9T5SenIMad01bMw3IswXH8G9wY0z/PooroZkASpz9D9AvZif/AjenALWnALqdBPaOFfMcLZGKHtGIGfMfzfg/8rDN8/SHg2EnesJ+X8M7rrLnTHArT+mej909D7b0LrvwmjfzpG/wx0KYfU9HQ5p6PL7MB1E7r7RlWa5xY0zxx0eZ+eO1Rnrw8IFy8Om2tJBdeQCt6r7Iz1oOTM3o8eXqMyabWg5NPegR5KRxpqgdtJBuejhW5FC80kJQHKvhngvwUGpqA7LyPRdRaBmon0FI7jyKbRlHwwlp1/OZsdb0zih+cvY+vG68j+260U/3MpB75YQ93mtbRtvw973jIcRUtxly7Et2c+oX3zCe+frzT9sdrrSDRfgdZ5Fameq9Fkh8FzJSkxonKOVpI4cTgUoyu1USm/wOJbL11jlY14RQbBbAvuryzkPZbB01eaefFaK7nLrdSss1H/ppnOTy04N9lw/2TGs8WEb7uFQLbw6gK8psEhqYCxqHWsDGRb8OaaGcg14csWDt2Kf5cFb7aJgWwzA7ssDOw8Bve2U/FsuwTv9kvxbh+Hd6e4ntrw7ZABq0kNWb3ZFnwilZTOXEA2L/2xP9eMP8eihrFy00iXHEJp6kbRNznyuY2BbRl0fWth4NcsgjlZiscP55vVQFpuIOmuXqio34F9ucwgjm74WoiUZhIqyqL/V6sSIUQrrGp5LFlpJVVpTi+Uqe1h8ek3KRpHqJyjJZYSqg5ZBsE/zd//RtkMDmtFeaPVWhT4y7+RIdSNWrZKL76J7l6FxAjYSw12+tJNp+MKBXiFvpHuXsJM0kCfDjYRkJflq/RjGuAHVTy/B3pRzajbgQx6M6FtSJouUjy80EJCHQ2qgWR+IPnC9TZSNTbi1TbCey34i4TiHULuS0P5n0UW/r7QxH/vN5P9vJk975g5/ImNpm8yaPvRRMevJrq2W+jcbqNDHneYad9poiPHTFe+DXvBELryh9KReyztucfRkTeajvzxdOSfTkfhOXQWXkBH4QU0555DU845tBVeSFvRxbQVX0ZL4aW0FF9BW8nVtJX/ifY9U+nYPYOmgunUZN9Ea/lsuqoXYD+ygP76xTgbl6koQnfTSjwC9i2r8bXdQ7B9DcEO6ezXEup6iHD3w0TsTxC1byDa8zyRnheJ9LygKiwHgPOfGL8Bve9HUj3r0HqfQOtdr8Bb630ere9ZNMczJF1Pk3Q9j+56DcP1NprjHXTnOxiud1X8n+54JR3k3feEGs7pHfeitawgKRrr+lnEjkwjUj2V0MEbiBy8kcjBqYQPXEdo/xWE955HaPcp+MpGqi7eW2DDIyC/w4zjVxM9W810/2yic7OZzm+lk7fQ+h8LjR+baPjQTNNHGfRtG0qiaQipvuPQJJOy7wqMXnFVnKpAFfft4F4DnsfRZYDqfhVDdfUfoXv/A/5vIPA9hl908j+iBTajB2U5Srzzf0D3fQP+z8H7TzT36yTFl975GLrjAXTnHWh90s3PUOCu992C1jcNY7D0PgH/dIm3vO6UuhnDdQO6pDS5JqO5pqC7p6G756B7FqJ7lqKLJn9gOUnfClIBCSAXv/u7MYL3qPBxPSyP0tXfpygcSbfSAktV8pV42xvhRejh2WjBmaT8s9H9t2IEZmEEJpPyno/mmojedwpG5ynEGyfgOTiR7pILaNp2ObU/Xk/Dljl07LqT3vxHcRY+zUD54/j33kW4cimxQ7eSqJ1OqmYGySM3kai5nGT9OWhNZ6O3S1boSSQdx5N0nUTCMRatf5QKZRbXQkO2FmVpRoZy8kssXitHrCT32YgUWvD+ZKHurQw+nmfhxavMbJ5rZe89Fqo2mGh824z9Yxv9X1lwbDLh+cmK99cM/LtEmSNALLSIDe+uDPp/MSu/IwF08T4SoPdutyrbaq9sTu804ZYQ6F9GYP/ufNq/mErnl9fRt3kinl+y8P5qw7vNysAOE96dZry7pCz4s4Wrl5Iuf7DTz7Yovbxo5tNlVsNZ4e2lAnIzkAMpOxPfzoz0YDfbqp4Xyic9OxDKyUy0RLp6c7pkF0C6+TLZC0gfAJFSG5HSIQQKM/Hly+5ABsn9sjRmQWwgpLNXHkEK7I/uGvzvo3D4UspO4vf0jlA5Euk4WIr2qJF8AAl6EaBPxz3+HuyVBl+2aY92978DfBmEGrI3IdWS5uxFmaOAeRDAU/LvL8od2Z4WXl5KvibPKUXPUaCXA8OaLqGJFPjLnyuB55nEDluISdKYLIXVWtVsIioxgIU2nNuGUPNxBt+tNfPWHBPv3W7iu7UW8l6ysfc9K4f/nUHjdzZafjbRtt1Ep6JvxMXSRE+hlZ6SIfRVjMCxdzSufeNUHq5r/1lqWcq593wc+y5RC1OOfX/CsX8y/Xsn07tnCj0VN2CvmEzP7in07JlCd8VkOo5h5O0AACAASURBVMsn01VxAx0VU+jYPY3OiltpyJnB4W0301Iyh+6qBdhr5tNbK5z9IpwNS3E3rcDTfCfe1pX4W1cTbFtDsP1Bgu1rCXWuJSJA3/17oBewT1fYvp6oZHMfBXpj4Ae0rkcw7Oswep/C6N2A0StRfs+gO9aTdArQP4fmehXd9SYpx5uknH9Fd72F7n4Twy05r69hODZC3yvQ+xzYH8HoXIXWejvJxlsJH7mZwMHrCFX+idC+SYT2XkZgz3kEyk8lUDIKb9FQPAVm3LlmnDtFVWOmb4sJ+0+D0snvzHR8Y6H9SzMtn5lp+NBE3Qcmmv9twlc0VOl1U/3Hozn+iNZ/IUbfpRjO69Cct6ZzUN0PgGcdeF4Gec8qsvADNM+n4PsSfN+o5CvDtwk98C2G/1uMga8xBv6D4f0Iw/MeuusNUo4NaM516oZgOO5F71uC1jsH+maqMvpmofXORO+9Cb136m9l9E9VB48cPuoAck5GV+/vOlJOAfwb0dzT0USG6Z6H7l6ANjCPpMQd+iWbVoJJ7sQQwFeRhSsH/e5Fj79KBZcooPevwAjcDaEVGMLdh2aiBWagB2ZiBG9BC95Eyj8ZzXslxsB5MHAqeE7CcJ2ILiZRveeRaL2UZNu1JFtvUUtz0SP3Ea1eS6jybsKVK4gdnkuybjp6483QejVG2ynoLWPQm8ahN49Cbx+O1pOhunitPxO934YKn5CNR5HFifmVGGPJL3azSW1qalViR2DDsz0L+2eZFP/Z+v+R9d5hUlXZ9/etqu4mqeM4Y9YxiznnNCYURURQkKSAiJJzFhCzE53R8TujM2NWwJwxZyWnBppO1dWVY4fqXHXPOZ/fs88tHN/n/WM/t7uq5elGep191l57Lf5xpcPzAxy+HOuwbrrD9vt81P3BT/TpAPHnAmRe60VqdRnJN/zkBJg/9Ft6RCiS3IdlZD/0k/vYR04OgY+9jjr3QRm5DwLkPvCT/cAhurofVf84jo2PnMu2P55F8L9HkH6zgub3pPP20fShr/TnCuXi+/lWIDcDAfBWS+8ILeS31f6pH68c2qWr/0QqQNunAa+D/1JoHMcOa0WZY4H+CwF7vz3ouoS+EbD/VkBfHD098O+UIXNJNtolCqOfhJYos5bMPesDFDb8EugF7L3NYdkg/mXJIbD3PfENEkvovQZxerunxrHDWgmAqZTh7d4FK9mkla3a/5Wyw9pShy8h7pbHL3X4coAL2NvyYWoDdmBLvR9kULuXy7eDW+8gsANceb1UQsfsLU/tI46aIp30Xtc1ftTuPrSuC9C2vgxdKdJSP4VtDu0/ie9VBeE1ffnmoTKeHenjDzf6eGqUw9uzA3z/aIAd//ZR+7pIKn2WlknL3sSPB9C0+Ve0bD+Att2H0VFzPF11Z9AdvICehisohK6hELqOQuh6CsFB9DQMpjs4jK76EXQHR9JVP5KuulF01o2ko2YkbdW3k98znJaqW2naNZRs5WDSOwYR3yH6+mHE1t9K+MehRDbcTGzbEGKVQ4ntGGojCVN7bidTPZJs9RhyNWNorptAvv5e2oLTaG+YQUejgPxsuiIL6IospSt2nwfy8WV0Ru+jPbyA7tQvgT73FqphLqZxHiayABNbgrEUziKMWAYnllgKR6VWoFMrbXi3St+PlM7cj8k8gsk8isn8AZP+Iyb5GCaxHBWdiY7cjQmPpVA/lI5d19C66WJafjqb1p9OpeWHY2n+9kCav+5H5osA6U8cUh/5SLwn11I/EdHGr3EIvebQ8LJD8AUf9f/xUS0e0n932PGEQ/BVh+4tvSDSBy0dfVI6yOMwydPR6StQqVsxqXshMwOyC73vNf0XdPYPuLm/onL/wDQ9A7n/YnLPoS2wy/NZ3MyTuBm5vTyGSkkYylJUci4mNRVSkzDJO9CxoajIdejoAEzsOlTsBnTsRkz095jopZjoZejY5ejE5ej4ZZjEZejk5Zjk5ej03vo9buYqlKiEstfbwa0MbVXTQNxmMWQT2+Mx0DIWJKtW4gvzozD5OzE28GQMSjzw86NQEkfYOhHaJmLyo9H5YWjbyQ+0QE/7NZiOqzH5AZjWKzDtZ2LyJ6JbjkLnD4GWQ3FTB+OmD7XWsCpxGm74UtzaG+jZeQuFHaPQVSNwq26ip+pyivWnoMMHY0KyHLMfJtwL1dgHt6GPTcQySceCPGJVK1uMScf6nLhRzwlRujNk2LdDho0Bmj/vTXpVBXse8PPWLQ4vXO3w8W0+vp9QxsaZPirv81HzmJ/Gp8pJvtCX6AvlhF7wkVwTIPt2gKZ3AzS9H/DAWTp4AfX3y4i/ESD1VjmZd/2k3/aTebuMprf9RF/qy+aHD+bzWYfzw5KjqXnqYBKrKyw11PSeQ/N7AZrfE0sGH80f+Gj50Efrhz7yH/tp/dh7tsi8YK2PNqlPfLR/4qfdAvxeoJfPfXR85tDxeank489EnSNSTT9C4UhnL4NkGSx7QO+jy37sAb+oi2zJBq0octY73jawSC5LQ1lR4Hi1F+j3fu6zXL68JxSPfQqvL97/2/aWN7wVPt8LgAkgeQB2scrm+pasE3YJ+O/l9IXS8WgdS+8I2NvhrQC/Z5RGjQTHS5VZtY7dsi1x+qJ1t+lWdXIYeDSMDHg9dU/pRmBvBkIPeUlY9r+p8basxSiva3MFhS19MDv64m7zW2O71m8DpD8qo+rfZbw/18f/Dffx9CiHVXMcfviLR9ckP6ugdUNfOip/TXf1wbh1x6ND56DiF6KTl2DSV0N2EORuxVqRNI2zNueWHchMhsw0D1cyczDpBZBZWGomZ1hRBdkZmJRkXktNQ8WnUYzeS1fkLtrC48gH76R1z5007RpJaucwUruHkto9nOTO20juGk5KBrO7bydTNYrsntE01YwnX3cPbfVTaW+YboG+o3EOnY3z6WxcQnt4KZ3x5XQnVtARXUZ7eBHdqX9h3GZPdaNyb1JomGODvVVU6JsFuFLJObipmZjkvNJAdoE3lE3dh0muQCfuRyVWohMPWiBUEgEo3X32cXT6UdzEClR8LjpxLyo6Fh0eZvXyHVvOJ7/uJHLfHEr2y33JflFOUpYUPnQ8Pv7tAI1v+GhY7dDwio/gS37qXvBT+58ANf8qo+pJP5V/9rH9CR+xd8q8TNGIrE33xk32xU0dhkqehpv6PcX0LejMXZjsLEx2ETqzEpV5FJV9FJV7DJUT9Yx060+hMv9AZf+Bykmn/wSu/AzpB1CpZajkIrQMohMzUPFJ6MQd6MRwC+xu5Erc8BXIU0WuRceuw8Quw8QuwsQuxsQuQccvRscvsqXi8rn4u/8ek5a6CpW+ugT012HskHYAJjcA1XQjymbIit5+LLSOwFgL5BHQOhpaBfBvx+RHYFpvR1mqx0u3cvNjcduGodputKVbb8Hkb4G2m6BtMKb9JlTHtaj2C3HzZ6Dzp0D+FHTLieim4zBN/TG54zCZI9HxIynWnUih6hJU9UB0cCBu6CK66k+kEDoaHTkInZC//zJUrAIVKbemVpIsJGET1qjKhkT7cZMSLO2twktknejq7TBwg2c+lnuvjNDfe/P1XQHWXOfw0U0Bvrm9Dz/dHWDzHB87l/uo+YOP4D/KCP+ngsiL5cReCZBa7SfzpvD3PgSkxfq46R0/ubfLCL4oCi0fyTccUm/4yL5RQe71ALHnerH5/t/w6d2H8v2sE6n+87HEX96PzFsBMu84VsPf9E4ZuXcdcu95JWDvlcwJHFql05cSwLeg75RAfy/Qy1Pkl346PhWJqJQH/KK/txSPKHdEhfMLsBeZaNc3Ihl16BT5pd3cFc8eP+0/Buj4UWyfJVjFQYzcxO3TegFt9LaGi6K33+R1+nufYv72S5pHAmEE7PeWDX0p+f0L4Mvw1gJ/pRfdKOlfXu3l9kVz79E7ewHfc8j06B4lM5idot4RO4UKjIB9rWxJ+zxrArnRCbBbcBctvqReSQqUDFXldrDXRllUPxJuXjLI2+PND4qVMtCXm0gFamsvejYEyH/vI/d5GY1rAmz8i8MnSxzW3lfGuqd6E3xnH3I/HkzX7iNxG49Hx8/CZC7DNF2Jab4Gmku5FXapcSKmeTK6aTq6aSaqeSa6eQ66eb5XTYvQTUvRzSvQTQ+gm4QSXoYRg8KmxWhZpszeZ0Ug5FZgSxrj7DLc7CLczDxUYg6F6AzaGyfRGhpHc63YI4y0AG+N0H4G+TG01IyntfZuWuukq59CR2gqHeHpdITn0BleRJuAfXQ5XfH7bXWEl9CVeuZ/QF/MrKFb/OQbZ9ETnUtPbD6F+HzchAD9bFRyJjoxCxOfA7H5kBB6R4BPBrfL7FBWpe7HTa/EzTxkaRHpmEn/AVIr7a1AJ6eiE+Mx0RGo+gF07TiL5h8OJ/N5H9JrHRIfOUTf9RF+K0DoDR+hVY79xQy+VEbd836q/xOg6pkKqp4OsOevASofD1D5tJ/sl70w9RWYaB9vSSfRG5U6BJ06C5UZgJsdjsnejc5Ox83MR2XvR+ceR+WEdvqT7ex19lFLQ5nME5j03zCZP6Mzj6EyK0s3lhWY5BJ0fB46Pg03chcqOgYduw0dvREduRIdvhQdvgQdvgwducL6tqvYRUjp6IWo6Pno6Hm2TPR8lAB/QsD+aowd3l6NTl1j+XudGoBJXwt2YCvqHBnSjrEOmWJ7rER/3yxGbaUqed8L0JvW0aj8WISzd9tuR8lgtu0mdH4IpnUU5EdD+23o9ltR7cPRHUNx24XOuRjdehEmL/5AF6JbzsG0nIppPhrd9BtUtq89RIuh31Ks60+x7nx06Gzc8DH0NBxBMXQ4OrE/bjqATkt0XC9I9MIIdSPhz+l+qHSZjZLzNhpLa+uisBAflSpxtpQtUAHNcmL/rWDzQj/vDXJ47zofnwwt46uxDj/d62PrXA/sqx8PEHyqjPC/A0Re8BF72SG52iH9xv8q86ZD5s0AyTXlJFeVk1zjI7HaIbU6QHpNGdHne7Nlxa/4ZOKRfDP9DPb89QwSrx1B5q0+ZN92yL3pJ/tmgMzbDtl3HO81AXyhdT7wkftQuH8BfFEDBWiVAfFHDu0fO3Ssdej4RFQ7nqTSo29kY7a8BPYC/B7gi+5eun2RenZ8LYNZv9XYy7Prm4AFegF7GdR2fFtOxze96fi6D7KDIEod8d0Xiwjb2a/3URDQXy8eQXv9gf7X0f/c8W9yUJbK8dm8XcndtTbQe+2fSweAdPxmu5cJgHT2ku61XUJhvHB2b5Arm7fS8UuqmA/s0pUYsjnkZM9hazmmugIjSVcC2ALwMkCt9mOqHUy1bPDuS7GynzUgM2IpvKscV4JCZPAry08y8BXZpNgtC9Dv9lGsFHsDCZ8RmwjP0qLlizJSH/lpeNOherWP+nf6kf7mYLp3HQ2NJ0LyLKz0uPkKdNMNNj/Cbpm3jkS1irPs3ejme9DNUy3Au9m5VmEnedFu01yKzfNRzQLyIuy4H9O00gP6ZnkuRzXdh7LvrcDk7sfkHoDsQ5B9GLIPQvYBSyHLf0d2JcY2n8tw04vpic+lIzyVfMPdtNSPo7lmLE3Vo2mpGUNr3R201k2gtX4S+fopXlcfmklnaA5djfNot7WIzshyq7XvbLyP7uQvgL6QXk1X7TS6GmbQFZ5Dd2SOBXzXdvfzKMZmUojNwo3OxUjFhMufaztcnViETi5FpeUbvR838zA695iNBCTzR0g9Cqnl6NQCVGoaOjke4rdhGq+lUHU2+fUH224+9q5DTBag1pTTsNpHw2uO7eTrny+3nHzVM352Pl3Oricdqv4UYPujZdQ8X07b+j5omcZH90El9sFN7ItOHYFJnWMljSot7o+TUZnpFNNzcQW8s39EZf+Myf4BnX0ElX7QzhhIP46x9RBCU7miNsqI4mgJOjEHZTNs70ZFx6MF6CO3osM3YsJXQuPFmMYLMOEL0Y0XoiLno6LnoKJno2JnoSJnoCOnYaSiZ6DiF5SonKswiasxiSvRSSn5+FoL/jp1Ja4tkWQOQTfdihGgF2fMpptRzTehmgchQeKin6d1OMimrAC9DS2/vUTdDMHIQlXrGGi7E9MxCtU2EtU+CtM5At1xE7rtGkz+ckzbJej8hej8OZj8yZjW36Gaf42bq0A1+zBpWUXfl2LoMNzQ79DRA1GRX+OGDsSN7G+zQE3GsQBPshwjPuHp/VCp/W1Xb7t7u8K+F+g9G2P5JS7sFFuDMtrXVpB6LcDuh/18cpvDW9c6vH+zj09HOHw7wceGqX62zfezSzr7x30E/+7Q8C+H0HMO8Zf9JF/1kVzlkFrlI7XGR+p1P6k15aRWyZ/rI/6aQ2yVn8SaciLP9Wbz8n34+K5D+Xz6Gex84nyiq04h+eYBZN7yk31DwuX9pN/wkX5TyiHzts9SQNn3/GTfD5D70Gc5f6GImt4vt3SRdPhtH/vpWFtmu3uhdiylY7t6AXg/nbZ8dFoa5xdAbzd1xbJB9gFEelpmN3o7vvb09h1fl9MuyqIPPcVR17dl9Hwv1hABu5MgOvuCGMGJIVrJIE26fYlqlI7f6/T3cvd7aRzh7r1kL4+791Q7FvhLKh5jMwBEpy96/TIL/pbPlwGuLGLtlNhHCYrxwmLkve6Nftp/6kNxW18L4Eq8jiyX74B0+VXl1iLZrRS78V60retrQ2qMnRFIwLyDu8exHb6yfvqBko++qILE3sChsM1P50Y/Ld85NMsS23c+Wn4sI79pHzqrDqUY6o+Jybb8+ZC92FsqbLoa0yTSZvm9EldZz25cNY1HyQJk0yRU02R0biY6O79U83BzsylmZ6OyC9A5EXaswGQF7O+3C5QW5HNLUbn7LOibnIC9B+gW4LN7P15pDwARhujcQ+jsw5jcw9D8IDp7H8XUfHri0+kMC6iPp6VmLC21d9BSN57WurvJ102mLTiD9uAsOhpm0xmaTXtoJh2heXSGltHZ+IC1ROiM/9//5JU9yVW0VU2mrXYqHQ3T6QzNoKtxDj2NCyiEF9IdXkRPZBGFyCJUZL5V6Ljxubi261+MSi5GpZfgZu7Dza5E50SdI5z945B6DFIPYtJLvb8s4bVSEyFxO0RupKf+XJo3HUTy417EVpcTebUXoVcC1L/sUPOCj9p/l1H7Lx+7n3bY/qSfHX91qHzMz5bHAoRW96Jna190sIxCQx/c6L7ohPhZH4NJn48SE7DMSExmEloSndIzce33IRTTQ5jsckxmBXIb0SmRj67ApBfjJuZ6N5rkEowcUMlpFGPjcYV+iomvy2hM5DZ0WGIOr0E3XIJuPB/deA6m8Vx043mo8Fm44dNRkVNQkf7oyAm2TORETPQUVKzEBcYv8/j7xKUoW1egEx74q8TvKSYuoZC8BFeUOU03YpqGoHKDcIW/bxpoeXwL+C03YyRFq/U2lHT1LXu5/KG4+cHWqtly/OKI2X4nqm0Uqn00un0MtN8O7YOh7WpM+0XotvMwFuhPh/zx6JYDUc0V6BYHnfOhc35Uqgw32hc3sh8m3hcT6U2xsZfl3y3Qp32Q8cqkeqPjXri5BXobRFGylbXuhZ4MTzo3d1M5XV9U2E66/m9lfDuhjDev8/H2jX4+Gubw+RiH7yc6bJjusG2Bw66VDtWPO9Q84VD3fz4i/y0n9kIZsZcCxF8OEH+ljPir5VaDH38lQOJlh/grDpFX/ERfLSf8775sW7kfaycdwseTz2DjY5cRfPkioq8fSeKNClJvOGTWOGRXO2SkXnfIvuFV7k0fTW8JPSTg79iDIfdOGU3vldH8fqnD/6icto98tsNvX+uzHX6HpXFKH38qXH2prPZe5JayqeuV6PS9jVqxXyijXbZ0xY/n8wC5j0TiKXy+2D+Ixt5btvqf7r4E9ELrrPulA6hDQSyfN3hWCmKn8HOXb4e4/wt4UZvLrc2CfX9DGR3f9abju164myTM3ZNo7h3eWqC3IC/0jlgsOLhbA5gd+1qTNL1bLAl8uCWZptlZgd7Zyx4QhW0Bmr+toGvDfp4Rm2j7K8twd/TBla/Z1QezSzzl5dYgtFEAd7ski/no3lRG27pymn8K0L6lnELNfqjwoRA/ETLnQvZSS8+o9KVooWmyV1t61KrcMkMxGdmzGYvO3mnl0ipzFypzNyozBZOZhUkvLNV8ZB/HTU/BzcgBICq+haXFyWXo7HKU7Orklv1cXkcvoL4SbGf/MMaC+gP2NXlf5wQzhfp5CNPyIDTJ14vKcTFuah49sel0NAjgT6S1bpLt6OWZrxewL/H1DTNoD0nNpTN0H52N99MWXMz/B+i7Eq/SsvNu8nvuIV87mY7gdDqCc+lsWEJ3wwq6ww9TiD5CIfogxdgyCvGFFOLz7LMYX4xKLUSlpRajpAPOrcRkH0LLkDb9GKQf9a4oOXlvMTozF5W8FxUfSzF+E27jeXRtOYz0R31ofLWc+hcD1L7gUPNfh5r/C1D9lN8OX7c+4bDtLw7bHvGx+U9+4h/0QVf1Rdf76a7vRTG8H8QPgVR/uzQlw02TvRWyd3ph1umJqPRMj6vPLrIDFJOSbl1qgQ0kV8kpuPGpqPhsdELmFTMpxu5CxUajY8Mx0WGYiKQ+3YAKXY0KXoqqPw/dcBa64Ux06AxU6Ezc0Fm4odPRjf0xjcdhIsdgwsehwwL4J6Fip1uwN7EL0KI7j19gh0BK+PzY5Zj47zGJq9CSkJW6CJW5CJO9EpW+DjcrZm0ixxSVzg3W2ExJmlXLUBtfKNdQ3ToC1ToM1XoTbssg771mGehOgLbxqLbRFuhdMUuzQ93bUPlrUPnLMO2XYvIXQf4cyJ8MLUeiW/bHtPRBteyLbumHagpQTAZwo/3QEiBhJZN+ZMhqnQUzfnQ2gMn4QbJCJWhCKiEDWb81pjKJkpGV0DeyZCN87NYyCt+V0SKOpM8G2Di7grducFh9nY93B/v4+DZPhfPD3R7Yb13oZ+cKH7sfdaj+q5/g0+WEny0n8t8yos8JNVNmB7bRF/1Ehd550SH2ko/IS2WEXywn9K8+bF+5P5/cfRhv3XEKXyy6iF3/uoTI6hOIv96P5Gof6VUO6dccMq85ZFf5yK32k1vto2mNj+Y1AZrfKCf1aoD6Zx0Sr/htBq7MBrwBrp/Wj0rc/d4BrejqP3M8j5y9IP+ZQ2dpULt30UqebWKuJv5OcnP4oMxaH9vOXjZqv+xll8JEf79XltkhA1srxyxZNVv+XgzkvLJcfunj7nVC9Xh0j3D3tkoyTVHqWFuFTRUUNpTbLACZB8REKPGeWDGU29AXWdBS28Xr3fPWkSEu2/3YQHjL8fsxlRXYDl3sGKQEqK17ZoW1XpDFreL2gHXj1NvFZln8euQ28GtavzmI7g37Yyp7Q2WFFytZKb75Pquu6d7kp2tTGV3b+9BTfQA6/DtInYLJnovJXYTOXobO/B6VEcHDJejcJWgRPcgiY3owOj0UnR6BTo/GpO/ApO5Ep+7EpMZhVXXJaej4LLTFgxmYxBR08h7c1BSLJSo9D5URTJN9o/tRGSmZA0o9gLZKxQctHkrHbjKPYaninHTywu3LEudy3Kb7bSkB/qYHoEkOAhG7LMNkBfDn0hWZSXtouievDN5jgT8fnERb0AP8tobptDfMoaNhCZ2h5eSD8+mIiwVCaWGqM/oiTVvGkt1yB82VE8lXz6CleiH5upV0hv5Md+yfFJPPUog/RXf0MXriK+mJL6UnuoRC9D6rtXeTC3ETC3ATCz1NfWaF90OmHsUI0Mu1RH64rHD599lvvJiaikqMxSSux4TPpqvyaOKf70vNaofq5xzbyVf/I0DV3/zs+IvDlj/52PK4w8YHpLsPWLUO1b2sn0axoS868itM7FBISmr8heisAP1QyIzBZMei0neg0vfaU9me1CkZFM9DCS0Vu4di7E7c+HhMfCrEZ2ES8j95Mjo6EWJ3YKJeF++GrqEYlDCOS9HBC9DBszDB0zENp6IbTkWFTkWHBPRPxYSOwQQPRYcORkvSfeMxqPCJqOjJ6Ohp6OiZtlTsTHTsbI/Dj10IsgcQuxIjEs3EpejUhZj0xejU5aj0Vbhp4fMHYrLinzPM87pvGf5zXq1QOaplMKrlBlTLjShrjzASG1beJtLM0ZbHL9qUrAk22NzND8RtvRrVeiVKOPv8WZh8f0z+aEz+CEzrYei2IzG2yz8MN9sPleyDG5PuXqxnA9ZhUCV64aYrcCUlKBuArANpB51wKArYi9VsShwIS0AvW5CijRbVxY4AhR9lAckh+WoZlcvLeW+oj9cG+HnjBh/v3eLw6e0OX9/p44e7/ayf7mPzfB/blzvsetRH9Z/9BJ8M0PhPv+Xuo/8NEH3OT+S5AJH/SknX7xB9voLG/5ZT9YSf9Yv6sHbcobwy5DheH3cyPz58OsHnjiLxaj9SL5db3X78lXISr5aTfK3CVuo1oYLKSa/pTWZNP1Kv7EPw3xWEX+hF+vU+ZN8pJ/ueKH6ksxd1jrdla5U5MoAtlUgw93b0AvT/P7AXn5xP/STe9JF4UySd5db6wQP7cmusJh1+Z0mWuTdJy1opfC9btT66pUqWCtZKQbh8WyXL5xLNY7v+dT6KG3y4GwMo0eVvLKNgw9Y9LX/T2t40f9aH4voyVGlBS0BZSgBfJJqykSv18xaucPnS4e8o+ewIf1/pYITyEXM1iYmUr99Rgd7ey75W2FJB2/cH0vLl4RQ37m//XG3/fC9ApLvSoWu7Q0G0/rX9MI0HQvwEdOo8VOZSdPYSVPpiVEp+Zy6D9FWQvg6S16OS1+MmB+ImB9mge5WUhcfh6OQodGK0J5m2cZ6jMYkJqJiXqKZiE1HRCeioyKono5OzSpT0UnRyBaQewEilhcF4CJN62Da5Fv8EA6XpzfyxBPYPorIrLMUjdI+bW4bbJDcBSWtbXur2pdMXjn85pkno8SVWJNMTnU1n41TaGibR1nC3XaKSAW2r7fBn0R5cQEdoCe3B2bTH/vY/r5t8RYJH4gAAIABJREFU8J+kfhxM/PuhpNbfQXrLNDI7l5Gv/xtd0efpSb5OMfMWhfQquuPP0hP/K4XYIxQjKyk2Lqco1E7jPHoaZtMTmkUxPBc3uhAVX4pKCC0iJ5r8YJ4cU2eWo6X7Fz16UmSKt2MSg1DxK+isPZHU9/tR/5qPqn/42fWkj8q/+tnyB4eNj/nY+JCfDfcH2P2vAB0/9bHh0yroww0JT98XYgdD8pQS0F/nmYylx2BsSWc/CTJTITMFFb0HE5e6y9IyKjYKHbsTE70XYvL6OExsHCZ6ByY8EhpvxoSu9br44EUW5E3wHEzwDEzwVEzwZHTwJNyGk9ENJ6AajkAHf40O9kGH+qAafoXbeCgqfGyJxjkJLdRO9GRU9KT/AX/sHHTsXDvE1VFR7VyEkSWw5Lm2VOISdOpqTOYGTHqI9zPmbkc3ix++ZNQORrcIby9dvpTQOsOs/FKAXksJ2LdPROWnoPOT7edu6y24rQOt1l61XYpqPwfdcQqqrT+6rT+q9STc/Km4LefjNp2J23Q4RQH7rKhtRC8vOZ+/s8NwlemDygrN48MI0GcCmGQZ7l57WQF6yRCVyEHZhpRFGQF60UGv92x9hReXgeva0Q6vDfSxamCAtwb7+fBWH5+O8vHVOD/f3+Nj/Qwfmxc6bF/hsPthHzV/EoM0H8GnfTQ+46PxWR+hZwI0PlNO+JkAoWd8BJ8JUPdPH9VP+Ni0uDcf3XEQz11/JM/dfCSfzT2aPU8eTOz5ChIv9CL+fBnxFxxbciOIviC3Ao8CSq72k17tIyvyzjW9yKzpRfaNcrvMlXtLuns/Te/6aHnfR/5DPx3C23/so2utQ9daH10W6GUgW6rP/XTKEtXeko7/0wD5j3vR+nEvO8j1PHxkcCv2DyVPHzvAFddOGdZ65YG9BLB4ISxd1t5ZKJ5SydatLGT9fBh4PjrF9T6KEuEoCh3p+Nf5PK7/R4ee7/tS/KkfrjVUE57fZ4PZJdRDbZNQGT/C5RtZyhLwtweAR+G4W3pbx8y9ah47zLWqHhnsegNeeU+quDVAz6Y+FDb3Q22TA8HB3V5GYVsZ3Tscuqv8FOvE8GwfSBxqu3hSF1sFm07LTfgyTPJiTOIi2zQRvwZk3yU6BB27ARUbgIoNRElYTnwIOn6btdlW8ZGY2Ah0dCgmdguI4CJyOzoyyqvwGEzjXRiZ1yXmoJOLMSkRpCyHvZVYDokVkFgJiQcx4iKQeBiSAvbS+D5c6vRlXrgcnZPh7RJU02JUbiE6t8gqduR1r5b+rOAhdR86uZBifC49UeHkp1htfWvdPUhZjb0AfMNc2oNT6Yj/Ce2W/Ohbqv9C+IsrCH1+PY1f3U7khxmkdzxOe+OL9KTepSf7KW7zV+jmL1DZdykmn6MQ/QuF4AN07llM687Z5HdOp23XFNp330tn9RSr4ukJzaUYWUIxIV2/fINLMcn7MKmlmJRQJ0LhzKAYn4hKjEQlh6FjAyjWnk3LtwdT/3IZlU85bP+Tj02POax70M+65WVsWFZO/QsV9Gzug64NoK1pklicBiD+G4wF+gvQwmunJPVIAjqGYTLDbXdPWq5no+lpvAUlVEx8ODp6u/VUJzoOE7kDHR1tX9PCxUduQYdvsHy8abgMbUH+fHTwHKg7E+pPg/pTLNCr4EmohpNQwaNRwQPRdX0806egHxXshxs6BLfxaFT4GPTPVM7x6MjxqGh/VORUVES6/DPQ0VPR0VPQsVNRsdPQcalTUYlzbJKTqHN0ajAqNQwt9siSMSscvjVLk5CT2zHNozwP/JbRIBRNq9QEjIB7fqb1u6d9NrTdi2qV7dphKNmobb8e1XEVquNydPslqLaLbbn5S+hpuoSCXIFbzsJtPhzdfBC66VB09mhM7gxM9iRU9gB0pgKTk3LQWb+Ng9NpxyYFSVqQAL1NA7IhER5PLxyshJJIClT2PR8NT5bx1SQfqwb5eOX6AGtu9PHuLQ4fDHdYO9rHF+Mdvr/X4aeZDpsWOGy7z2Hngz72PO6j5q8OdX93qH3SofYpP/X/KKfhHwGCT/upe9pP7dMOwSfL2La8L+9P2J9nBx7Ks4OO4OPpR7Hzz78lJGqe58uIvFBO9MUAsZfLLK+feL0X6Xf6kflwP5rW/pqWzw4g/9XBtH91OB1fHUbHVwfR8dUBtH31K9q+2I/WL/rR8nkfmsVbRzZiJfzc6u4leMWxmnurvrEKHA/oBeyluqQ+C9jq+EyWrsTiwf8zh29B/xecvtgx2wCVbwTsJSdXJKve4pUsXNnXfnkYlLJ0he4RwLedvvXB9yySvc5fzNU8K4a95moF0e4L779Bwl8ceoTb3xqwfjtelq9j+XMxW3O3yNdJglcvChuE2987A5CFrdLhsBfot4r+30dBXDitXYODu02oGj8FcaLcFaAoyp3QryB+MDp1LG7qDNz0ZZ5STX4nxIYkcTU6fiUmdgUmcjlEroLodWjZeWm8Dh2+DhUeiBsZhI4NQ8VuR8XGoqN3YhpHokO32EAdJb/3jYPRoaHo0G2Y0AgIjcWEJ6Fjs9AJmVEu8RwFYnPR0VnoiNQcO8/UkUUY6xnmAb9JrSzNLIXGechay8jwVVlwn4fJzsHk5qCl7AxgXmkQvBCTXQqZZZC5D1JLMMnFFGNz6WycZumbfL1IL0VnP418cDqtdRNpiz7yP6DPVj5C/QfnUPPu5dR8NJTQtzPJ7XqSzujbdGc+p6f5e3R+A7StwzR9Sk/0Jdpq/0TL9qVkN84mvWkqTVunkK+cTHvVL4C+ca43xI0voSgD28QiVGyR1enrhNAms1GpeRQTU3GTJW4sIZmkN0P9BbStO5z618otL7/hQYcf7y/nu6UV/LSsnNiqPqjtfT0NrnSDjZJOUwaJ32JSJ6My56Ky4rnye1TqepR1lByISQvw34xK3EghejXF6LW4UYnmGwqR0ZhwSU0TGYIbvhk3fANu49WoxstwG6SLl03QizD152HqzrJAb2pPw9SejK7rj6o/0ZapPwpTdxC6vp9n1yoS0Ib90cEjcRuORYWOQjV6Jbyijgj4H48Kn4oKi0LnVHTsWFTsd6jYUajYcahYf3RMLB7Owk1ciEpciUoORKVuQmdEPXAzbm6Q9cIXSSa5O6BpAqZpPKblLmidBPl7oW0aOj8PJbm3rYuhbQG0z7IBJ1oGtWJ9LNu37VIjcK1EcxAqL3r8G3DzA9CtAyE/ANV6FqrleEzLKZimM3CzZ+KmT8VNHwmZAzHZfdFNPlSTDHL9mIwPlSmBve3oPX9y01DaepRh3WY/7T+I/YCfyL/8/DDLx6ohDi8O9PPajX7euNnHO0Md3hewH+vwxUSHb6c4rJvhsHG+j633OVQ+6LDrcYc9f/JR9Wcfe/7io/YJP/V/91H/lI8aewBI51/G9pV9eP+efXj2poP4lwX6E9n95NFEX/s16Xd/Q+ajI8l+fhLNX59O/sez6dx8Hj07JM/4clTtlaigDOSvR9Vfh66/BlV/OW7dBbh151CsOQt3zykUq06kp/IYurYeQfv639L24760ftuL1i/FDlm2aT3bBKFyxHe/47OSMufzMjo/8/+szGmzpml73TjFPlncOD2vH+uh/5WEp0iClah2HNq+8cpLzpLgE8ezRhZFzzfeQWA1+rKJ+12Arh/KbMKVjWIU2eb3ZaXyFD2W45fhrnT3lvIRjl+iDv0URapp6Zy9S1lecLsAd/e6XrR/05vC+gpP0mntGspQW8oxW8thewCzvbTktdFPzxYBeLkRCHfvpyCUXlU5KtgbE9kPkodZitZNX0QhfRXFtNzeb8AkBqLj16JiV6GjV6Mj12BCXunw1ajQVaiGAegGmbHdgBsahIrcihsZjRsZj9t4F7phDG79LRTrB+IGr8cN3ogbHGxfU/VDMQ0jMKHx6Mh03NhcitG5FMPTKDbejRuagNtwF27DJAoN9+KGpqLCs9HRhRATtwHp/oXm2esbJlSPqPsWWWA3MkPMzPA277PTMDL0zcxCp+diZCErs8QD+rSA/TJLkwtl3hOdVeruhbufbLX2LbV3ko88iC6mvYWp5OaVVL16MpUvnkvlG4MIfj+XXPU/aYu9S1v6S7oyP2Ka1qFzX1GMvUVL1dPE168g+v1cEj/MJLN5Oq2VM2ivnkln/Wy6GuZRiCykEFtkufxCYgVuajlucglFWcSKzUXFZqNjM9HJmaj0NMudq/Q96PREjEgixfI3fgWtW35H/Wu92fiYn++WVfD5ol58v9JH5p3emO39bDydOODZxJrYvhhrjXsS2p7yZ+Naju4qe6WTxSTRrOv4AM+aIHo5Ono5KnoVOiJzgiGY8C1oOeXD1+KGLqMYuphi6ELc0PmohvNxgxehgpeg6y9E152Nqj0TXXsaWoC+9kRM7QmY2uMwNUdgaqWj3x9Vty86KCB/MCZ4FCZ4nGcbINy9dP3B32BCh6EbT0CHz8SEz8JETsEI8EePwo0eiWvB/iR0TED+fNyELF/Jxu2VkBYVwTVoWbDKXefFF2aHeyqC3ER0892YlsmYVvGyl6jCpai2lei2h9H5B9Fty1FtS3Dzkmw1E9Mmnf4idPsiTMdsVPvduG1j7fC22D4clb8V0+p54Cvxzmk73Xb3OncObuo0iqmTcVP9IXscOrsfusnBSOXK0JkAyoK9D5X2eaHONlg6YF0PET39ljI6f/DRstZP9D8+1i12WD3Cz0s3lfPyoHJW3xTg9cEO79zq8MEoh4/HO3w5yeG7yV5nv2GBw5ZlDjsecNj1kM/KNHc/IjSQQ/VfHPY84bDrCT97nuxD8J/7UvW3A/h66cG8Mvow/n3zkXy+4AKib15Hx4YrcHdfjaoZggqOQjWMR4fHQ2wsxO+AxHhrr2Ey45DSqTEoscRI3opKDfFuk6kbIXETxAdBfCDErkGFLkbVn0lxz/H07DiSno2H0fnjb8h/05vWkn2xgH/e+uHsPQRK/jmW25dNWwlsqaBVPHTEVM0Od8WZs2S6VoovlNhNL8bQS7iyS1l2E9ejfCREReifzq/L6fy6l3XG7PhWUq7E399PzzclRc8P3mKWXc4qUT2FHx2KP5VTXCecfQVFq+IR5ZT45Iv/jqh6HPt6YV2A4vpeuBsqUBKaslkWnPZFb9sHtVUoHbEUloPBmw/0bPXRs8PBLkSJXr66Aje4Hzp6KCZ5NCZ9Eiopjdy1uHJrT91mKWDit2Ji12MiV2MaB2BCcvjuPYQHoOsHoIM3oIKDcOsHUawfjNswDBUahdtwJ4X6cfTUj6anZqgNRCrW3kyhTrKsh1CsHYxbNxg3eCuq4Q5U6B7c0BQKwUkU68XQcQTFWonXHIlbO4Zi3Vjc4Hj7dapxJjo831La2gL+SkgKnSNc/gq0SLhTCyAxG5LTMenJnpdWaipaZOmpyRb0TXqeFZEI9a3T95VqKTq1iGJiPl2NM2irv5d83d0014whH16JLmY8oG/8YTEb/nE46/7Wn80v30Dw+wWkdz1FS/AVWiLv0BFZS2fjR7RUv0piy5MEv1lO3eeziXw9k/RPs8ltnk1r5Vw6aubTHVpCIbIcN7ESN7WSQup+irJdKlNo0dpLV5+Yb4egJjEbk5SufiYqNQOVnoqAvcmMx2RGQmYoJK+gY/sJ1L/aj3X3O3wxx8cPD4splfB2FXbDTqgb63Ue2weTOASVPBad6u91lqkL0KnfY1JXepuoomSJX4mKX+px4JGL0JHLMeFrIXwDpvF6dONVKNHCN5zuleXgz8bUn4+uvQhVezGq9nxU7Vm4NWegak5F155UAvrj0TXHYGqOxNQciq49EFX3W1T9wej6wzygrz8aXX84uv4QdP1v0fUHYYTPD52AbjwD03gmNJ6CCZ2IajwaN3wkbkS2T0/BRM6zW7YmcSkkLoPEpZjUpdY3x3PEFAuFm2w4uc6OQufusnpgJZt9LXNR+SWo9pXo9scw7X9Ct/0J3f4HdPvj9knHY0jptj+j2v6M6Xgc3bEC3TYf3S5WyePtIJf8SGgbjmobiGq/CJW/ANN8Pjp3Njp7JjSdjskdhc72wzT5S0AvShyfLSWqHFmuigdsbJwS62JZh68Su91y68Mu5mGJF/1svt/hrbHlvDqkgpcH+Xn1Roc1gxzeEQpnhMPaMQ6fjvfx+SQfX00J8N3MAOvnBti0wMfWRQ6VSx22P+Cj8o+9qPpnP2pf+jUNa44i8cFptHx9DvlvLiG46jI+Wnwm/x51PJ/dfyXpL8fh1t8J8fGQnAKJ6ajEPHRqDqRFIiw1E5OegUrcg4pNwAjwJ+/EJMeiEyMxieGYpCg6RAQwDpUeg07f7lGJMeGLr4fwAEzjNXaw3119Gu1bj6Bj3W/o+rqf1eC3f1zS36/10742YMNX8h8FaPk4QNPHfmu4Jg6bErcotsrN8poYr4mr5ucOLZ/LU8DfK0m9khIKKPteL0uBJt/uTceXfbxFrb3bt9+U0/V1wHr8C+ALz/9zgLnl/IWvdyjY9CsvKMXT7gvIewNcoXcKQu/85KfwUwUF0dP/1Ae1oQ/u1j64orDZeiDull9bsJdsAunkC1vK6dlebmkaJR45Mn+L7IeOHYZJ9IfkmZjk+ejEtajkLSj5O07I/6sJEB2Fsbst12Iar8PYm9YNuLU3ompvQtUORtXegqodZp9u7S24dcNQdaNw6+6gUHMHXTWj6aq6je5dw+jZLXkbw+iuHkKh5ibc2kEUa2+hWDvCAnmhVhL1RtNTfSs9e26muGcwbtUtuHvE42skrkSl1o5H1d6Lqp9BMTSPYngxKnI/JvYwJvEIOvEAKrkclVhsLeBNfDo6KcPee9GpKSXzxLvRAv7pmfbfoE7NRyUXokQ1mBE9/9KSNHw+XeHp5OvvsctW+fAD/wP6mk/n8MXK/fjigd+x7r/XsvvTqYR+XEli619IVz5NZsdzJDc/Q/CbP7L74/vY9f5satZOo/Hze0l8N5X0hjk07ZhPvnoxXaEVFGIP46Yew80+RjHzIK7IjGQYm1mOSt+HySzBpBdBcgEmudBaLNgfIFPq7DN3Wzmk+LOb3HBMZgBdW04m9O9efDff4adHJFawzDvta3woUWyIRC8qiUYHoJOH2qUplfb4Oy1DGTucuQQjAB+/GBWXZaazvKWmiCQyXQZ2w/Uyio3nURT1TN1x6PoT0LWnYmrOgprzUFXn4e4Wt0YP5N2a01A1p6Bq+qNqjkfVHoOuOdqr2t+ha45A1RyOW3s4qk7A/UhU/RG4dQd74C9mYHVHYIJHQ+hEaDwN03gyhI6F0O9QoUNQjQeipOOX90NnY+Rwil4C0QtABrfxs1HJC6waR3zv3cxg3OwQ3JwEld+Fbp6Lbl6Car4P1XY/quNRVMcTmPb/Q7f/C9P+L+h4Fjqfg47/oNufQXVI/RvT+R9Mh3zdH6FjJaZjJrpdXDRHQ9tozwu/XUzTrsa0XAYtF2KaL4Lm09FNB6FyvWwnb6z+XuibEl+fLUeny9FxyQf1LGrFx9wC/VZxZfRi+DKryqh8NMD7d/Xm1RF9eGFIgJeH+Fh1s483hzi8K139CIcPx5Tx0fgKPr+rF19NLOO7ex1+nOOw5cFy9jy1Lw2rDiP96el0bLqMQpVYOIyBxokQHoepGUvqs9v45IGL+dfYk3h/6RVEPhlHT+3daBnKp2ag03NwM4tQ2fmQmwnpmZCUYdws3PC9uMGJ6MZJELkHE56IaZyAaRyPCY+zIOQK+CdHo5IjUbHh6PBt6MbbMOHhmPgIT5Agyo/oYHTd5ajKkyn+eCCtn1SQe89Hq0grP6gg/0E5Le/5yX3gI/OhQ+4DhybZzi25bDZ9KJSXHAIOTWsdz3VTvPcF/D8pp1X89z8uswdFbHUFu/6vjNjrvWn/ojdtErNoqR0JbKmg86syur6WaEZPqtn+M9/vvSZ+POKs2WMHuuK0GaD7h3IK68q8xa2Suqf7R6GDetP6RTm5T8ro+aEvPZv60r1lP4pbDkRt/g3FTf3o3uKnc5tDl1gmVJdjgn0x4f3RMZFMH2spWZ08y4K8eEWZxGB0Um5R42zojYlNgMhwTOONaCt/vgHdMAglXXjNUNzq4bh7RuLuGYvaMxa3ehTFagHk21A1o3AlAnXPHXTsGkP71lHk14+idf1IWjcPp23HELp230Ch6noKVYPorrqJbsnV3i2v30TnzhvprBxIz87rKewcRHHnMIo7R1KoHE1h5x0Ud0+gWHUPhdqZFIILcBuWoRsfwkQfRcceQcUeQkWXoyPCdIjaT5Lb7rGlkpNQyQloMVFMT/Ya4pQH+CYt2v7FFlPJLEJK/Li6wjNorZ1AW+SR/wF95btzeGdOXz5YdBDf/t9FbH5nBLs/mUz9Fwuo/3IZwW8eof7Lh9n9wVIq353Lng9mEfx0Jo1fTCH67RTi62eQ3T6X1urFtDdIvNXD9KQeoyf7KIXMw7gC8jlZUtorF5JtMllWWuYNFVLz0OnpKNlgTUuQxz3o7CR000Rc2VTLjbbX3p6Nx1P5VDmb/lBO/ot+noeGONgJ0EvaTKwcnehn1R869Wt0SoY1x6HFyTJxLsTPscBo4meg4ydhoqfa0uGTbbesI2egGs+k0HAaRRmq1vdH1Z1iO3ddey6m5jzc6rMo7jmV4p7TUNWno6pPRVWfjCtAX308quZYVO2xaAH82qO9qjkKVXMkbu1hqNpDLMC79dLpH4KpPQJTdxTUHYupPwHT0B8TOhbTcCimQQD+V+jQvuiG/THBwyF4Aip4JqrxXExYtmxFqnkiSn4ma5QmQSe34GaH4uZux81Ngual0Pwo5B/HtP8R1fEUqv0ZTPtL0PEKdL6M6XoFuldBx2vozpeh+yXoeQV6XoWuF9GdT2O6/4jpXIZpm4puG4dpuwvTcXdp8UpSrgag81dB61WopjMpNv/WbtQaAfWs3w5lhcIRrl4GtTrdCxMX+wq/PaiLkjkqAdZby+jeUEarWFa/Xk7VXypYO31/Vo8/iOeH78vzwyp4+ZZyVg0N8PowP2/dFuCtEX7euN3hw3EOP8yuYMej+xN88SjSX55Lx65rUaGhViJLarxHD6ZmgXg4xaaia+8h8/kYvnjoCp65/QTemHYu9a/fStf2caiGiTZkR+hGVzbA5ZdKunqxA4nMR4fnoYIzKdZMo1A9jcKeaRT3TKFYVao9U1A1k3Fr78GtmYhbPYHC7nEUKydS2DGJQuUkitV3oYJ3oUMT0OHRdldDlgl18Eo6t55G09cHk/1sP7IflpMVzx2xZrA+PGLL4CP3ro+m9x1r5pZ9r4zMu+LQ6bMWy7JUlftYfPfFwTNAy0eeVbO4cloXzw8lEMVPi4SrCP9vs3CFzgnQKbJN8d75puS1Y/12HO9zSe36ooKOL3vR8VUvur7thbVQtsoeWdwSZY/PhqhY24Zvymn6NEBurdg59KLrpz4UNvaluLEP7rpeSBi66OG7d/kpBssw4T4Q/Q1aKMvE6ajUhajkRbZ04mJIihX4cHuLUvGJ6NjdmOid6MgQTKMIJwajgjeh6ofg1t1KUUB9zwTcqkkUqibTXTXJxpp2V42he89oevaMpWv3nXRU3knzxrGkvx5D7MORRD+8nfRXt9K8fjBt26+3luvdldfTWTmAjh3X0ia1/Rrat15L+5YBdG4fSOf2wXRtv42ubWPo3DqGrm130L19PD07J9GzZxqFmrm4tUtQwQcwjY+iGx9FyTO8EtU4FxWegYpORsXuQccnYX8+sY9Jy7/du+y/X4/5EFO12QjYm5QYNi6E7AJMRlwIFtAdmUaXqG6KJdXNhlVzeHVyP96a/1u+euoM1q25ls1vDmPbG+PY/tYUKj+Yy+6PF1D10TxqP51H6Ov5RL6fR2LdXNKb5pLeMZemPfPtym17oxjfP0x3+lF6co9QyD1MMStg/9DPgG+3wGQTTBarMkvRmXnojIR4TEZn7kHl7kU13VNaRZ5ouWYjodqJK8h+fywNr+1D+9e9KFaWocQrIyjRYw5EyzCSFB/vbQ22VHIfdPJwSPbHJE7CxE/ExE5Ex45Bx4/ARETtcgwqcjgqLHUUbugE3Ib+uMGTUcHTrUZe15+Lrju3RNWcTKHmONzqE1F7TsWtPgVXgH7PSRSr+1OsPtG+58rX1BxrS0Bf1fwOVSNAf5Att04+PgJtu365AXgHhKo/BlX/O3T9oah6Geb+xuP36/dH1x+IrpUD4wR0w4kQOQaih6Gjh6HiJ6KTF4IEnIjKKH2bzbBVmXsxuSXopkeh9S+Ytr+h2qSLfx46VkHXakzXa5juV9HdAvJvYLreRHevwfS8gel5E9MtX/McpusfGEvrzEPn70W3TbMcvgShKBnc5m9D529Gt96EarmYYvNhFHN9ULkAJuvJLH9W4NiOvgKTkFxQn7UstkAvSUfbyuiS0IgvxWemjD1/78VXCw/lwxmn8erYI3hh6D68MqSCV27288oQh1XDfbx7VxlfLtyH7X8/nPh7Z9K26SpcGZzFxoG4lyYnW/rFiJOgdENp2XoW5cIcVOMMmn8az09/v5r/3nE8L43vT+V/bqRtw0QPwOtm4AYlfU0M/FZgYguhcSEEl4D8wu5ZSPeueXRsm0X71ll0Sm2Tmk3n1jl0bZtP17a5dG2ZRefmGbRvmEbrj9No+WEGLT/NIr9pFp2V0+mpmohbOxodEjnfCCv3M9GhqLqr6Ko8g5YfDiXzSW/S74p5W4C09evpRfqNCs+EzbpylpN6q4zMO55qKfu+g1Su9JSPrfXyRz6abZVuAx8JDSSAL7SPcP4y8PUGvaLTFzVPmyh65GObkSv0US9S7/QiskZuCn1trKNV+UgkovD8cgP49hcDYEn+kvq2jC6xbPjRT8EqfcSPx6EotgfBfhgB+MQRmMQJ6OSZ6NTFuMkrcBNXl9Q0AzDxWyytJnsuYk2iolPQjRPQlm8fhBu8hWLdUNw64cyle7+L4p6pFKpm0rOr9MoNAAAgAElEQVR7Nh27ptG28y7adt1J+66xtFWOpXnrHaTXjSH62ShqVo9gxz+HsvPZITS8OZjElzfQvP46C+btW64hv/kqWjdfSfOm39O88SryGwfQtmEg+c2Dad08jNZNI2ndOMZWftNYOrZMoHPbJLp3TKawayaF3fMo7llKsXYlbv1DqOAjqIYHUMGF6NAsdHgqOjrZ3ih1bCI6MQ5dEqzo9DiMlJg1pu+19CEiVbcqnbmY7Hy7FCpeZT3pv2LcUmbsd6/M4vnJ+/LO4kP55ukz+Om1y1n32vVseG0YW9+cwO6106n7ag6NP8wjvnEB6e2LyO1aSmvNCtobVtIWXk5HZDkdEmEVe5Cu5GP0ZP5AoelRirmHcXOP4Nrnw6jcL0vAfwUquxiVnVMC+6no7JQS2E9CCfWQvdN29W7TENzY72nffAQd68ut3MqVHEvxqG50IBKwW5o6Vma5XzfhUTkmcQQ6fhg6fggmeig6dhAqdgAqLKny8vw1OnwgRhaaGo7zNPCinqk7yXb0uk6Graeiak5CALxYfRTunuNQVSdR3HMyxT2neMqKPadS2HOaV1X9Ke450Q7cVPVxqOpjUNVHoauPtADv1h1VonmE6hFO37sBqPqjLNB7YC8+70L5SPd/ALr+AHTdgZj6QzHBQ0CsgcMyoJLr7ZH25kLyakjeDMlb0elRdpVbZ2dbAybT8ig6/2fc/JPotn9Dx8uYzlfRna+gu15Fd5WAvvMddNe76G6pt9EC9J0vYjr+g2l/At22DJ2fY3l71b4It2M2un0yknalJdowPxzVei3F5v4Uc/uhLdA7Vk+vs3ullmJ+VoZJlGEifgv0bp3X0btby+lY77PpQNm3ZDO6nB9XHst3K6/lvaln8dJtB/Di4DJeGOKwelyAr5YfQvULZ5L76ip6dg/FjY2hKGvsEsmYnAnReXYBjsQcSMiWo+xvLMekHkSlhWpcQvuOyVS+eAOv3HMKz91xApufvsHm4nbuWkhn1VzcusUQFU51JSq8EFW3AFW1mOLOJXT9v8bOO8yq8uri0zBqki+ALYrBHgvYjUk0FpIACoKCgGJBBFFp0mEYQBBFQUDsMTZERQERxUTUQfoMTZQ2vd7e65TbznnP73v2ewY035c8T/5Yz7l3Zph7mXvvOvusvfdah4ppOTCL+L7pxPdOo2XfdFr2zzyO5P5ZJPbPIrlvJi17ZxDfPZXo7slE90wlun8q8R+m0npkGu0VE8lUj8GolwaejPrdixK7Dc+9WK6BGPW3kDp0BYkd3Ql90YXm9wupeb0Ax8oTCKzphG9tPv51+fjWFeBfV0hwfRHBT/MJij3DhjyCYtXweR6hjXmEvsi3F7o+L9JH8e2JfFFA5J9S/Yv0U6Sb4ZJvK3q/1vqPpWlJUteXtv2zb30hrjWSxHWiPdN/fKqniNbteRpygmjZJpNARbQKZOxTB7jbS1ipfXnaFkHyYGUPxvJegikDB4EbMAM3oSRAyP9XlO827RZrugbov43lfhTlmYAh0y/OcfbVV8NQzIaBmuQztXeTrb2PXN0ocjVjyVRNJF0xidSRybQeGk/i4CiShx4kefAB4t89QKjsfhxf38PRDwZRtuR2vi3pQ/nTval4qw+uL3oT3dGbxJ7eJPb2Ira3F5E9vQjvFvQmUt6PePkAYrsHEd09hGjZPYR3DSNSNoTo7qEk9t1Hy/6HaDvwMO0/PEr7wXGkj0whUzmLbM1cjPoFGI0LUA1zUE3TdW63cgnZP6avVpRXJMAHUYEH9Gi4fLat0H2ooFT6j4Gk3YUnYoYn2WOZoZko2dqNvoxldhD93g0z+XTO2Wxf2pPv3ruJg5/extGNg6je9ABNO8bh+34m4aOziVbPIt4wk6RjFq3uubT7F9qVu1TvwUWkA8+SDiwmG1yOERE8jxF5toPohez/P3KRpzEi8zHDs7FC07UWKn4SKjIOJZV9WMhe3CcfwYo+iBW9C8N3HamqX5E5XEhOvK+liSfbla4CbY2r3IUoTz6GR1buf4HynobydsH0/A+muzOm+1eY4tPiKMJwnIDp+CVW8xnQfA5W04VYQvZ6PPI3x2UVai9AVZ+HWXUuZmV3jMrzMCp/S67yYnJVMj7Xk1z1FWSrr9ZmbZmKy8lW9iBXeSm5qosxq+UK4CKs6otQNRdi1InMI41bmdARoheZRyp50fC7Yzaeg9JN2+4Ymuj/B7NB7B5OxpLpg4Zf6Hg11Swa90mY7jP0nL3lvQl8t4H/LqzAvajgCMzQaMzIJFRsHirxNEbyWVTLi1iiwbe9h2p7H7NtNWbbGlSbVO/rUamNHUS/FpX5ANX+HlbbSqy2V7BaFqKSs1HJEozWOeTapqPapqBan8BqfQyrVdKwBmLEJKT9FD1eSUSIXpqxBcenbsTlUsmm7PGpm0KsShmnK6J1bx7iKR7aUEDta0XsW3IJe18Zyjfz/8xHo7qxZuQv2Tr3LGo+vJbE3gGYMu4mTVI5qUXEpVTG1WQyYabtOhqcgQoUY3qno9zTwT0PS+Iy/fMx3E+SOjKVmtXDWPf4tay89xL2LutLaPN4Et/PoPXwVDKVJeQaniXrfJpUQzGtlTNpOTibxHezSeyfTXzPbKK7Z2nE9xST2DOLxG4b8b0zie+ZSXJvMS0aM0h+N4WWQ1Npr5hKpmYK2frpZBpmkGqYSqZxoh7PU80jsJxioCeb2aPsSR/XEIyav9K693KaPu3M/iX5VL/SCc8HJ+D5MA/vx3m4tWlbIT4JZVlboB07/UL+n+Tj/zQf/4Z8/J8VEPhMfPnlmK8R/Cyf4Odit5BPUE4EX+YT3SSRi/kd0YtyFVCgfXzEyyf6D7kakPCVIpKlnUhuFj8ekXSK7CB1GfuUcU8h+a2dSG4tokUM27bLJFABSVnyEqfNykKU80Qs32kQvAQr8Htyob+Qk2zlwO1Y/gHgG4jlGYDp6qdn3w3XPZjuRzA9j5NzjSHreIRc40Pk6geTq7uDTO2dpKqHkK5+kEzNGNJV42mTXZ8j42n5fgLJfY8S2zuC+L7htOy/j/juB/FvHk7NukHsWt6HjVNu4rNHb6Z0+i0cfKkXjk/+Qvjb3kR3/oXwrlsJ7roV/84/49vRG/+O2wluH0h4+2Ci2wcT2X43oa2DCXw7gMCWfoS39ye+8y4Su4aRKL+P5N4RJPeNovX7cbQfmkyqYgaZ2tkY9fNQDU+iGouxmidjOSegXLKZ/whC9JYQvf9+lP5cD8UKDkXpBr9U9o+jwuMww6KMTEaFRMKZhhV/DcwOC4Smfcv54eO/ULNxIE2b78FTPpLggbFED0+hpXYm4mvc7plFm3cG7b4ZtAeKSYXmkgotJB0WieZ5G+HnyYSXkou8oIk+F1lKNrKYbHTRcQjZ5zoIX465qBD9U5ji+xCeoxsL4hYn/vEqMh5TwrLDMokj/5HRmNH7UbHbMTyXkq3pTFYsTMXyVNKKJEXe1QnDbXuuyFq+4emE6TlJN2ott3ikn4ASr3RnEbmmPAwdVnwSVlMXrYlbjTIBcwqqoQuq/hQsraOfCTVnY1X9BquqG6qiG0bFeeQqLiJ39FJyFT3JVVxJtuoqslXXkq28nnTl1aQrryRT2ZNspVT8Iu9ciqq6DFOuAGo6ovc02V+IqjnfbuDWSxV/tiZ81SDHszDqT8Wo+x+MhpMwGk7QJm5KFrDEH0ZnbwrZd0a5zwNx6fMJ2ffVlb0VHIwS0yb5G0amoWT7LiGE/xxW8hVUy98xW97GbHkfo/UjzPa1WGkhelu+UekPUKmVqPZ3sNrewWp5BTOxGDOxALNlDkbrTF3RS3C5TOWo1nFYbaLfD0FFe2FELkRFZZbe9r5RoSI9VmkFxOHSXpiynBIjV0CuoYhc1c/IHZRmbB5tWwu1Bl3ztyK+e6EnB1eOZO8bQ9nz6o00rLuZtu+GgmskaLnvCYyY+IA/advABp8CCcaRZZTwLHKROWTDC8iJK6mE6sgSi8BTjOkoJnVoOjWr72Hd2Ct5a9AFbJvXC9eGR4iUTSe6bxbx7+aQPLyAZMV8EkdKiH0/l8i++QTK5hMsf5JI+TzC5SVEymcTLp9NdFcx8bLZxMuLSZTPpGXPNNoPTCMtMs7hKaSrJpJrfALLOR08s8E3V6/R22PIJRh+yYSYjOmZgOkZjylTP+K74huD5RmO5ehPy+HrcW/sTtMHv9LWC56VhXhWFeF6vxDHh4U4Vxfg/rAAz+oCPB8X4llTgHdtAd51YqlQiH99PoH1+fooKV2+T/I15GQQEBnoc/Hk/xmhjXaDV6r98BdyBSAN4gLdBLYnfuykrZgkbskWb6k00u3g9MS3suVcQGJzJxKlJxMvPYnYlgLiMsO/p8CerHH8HMtnZ0hYerv1Vp22pkJ3YAUGg38YeIeBR3ym7sB0yez7EEzngxjO0WQdI8k0DtfhRtn6vmRre5Op6k+qcjCpiuG0H32YtiOPkzw4ltj3Y4nvHk1s5/1Edt5NdNcgYmU2OTu/GMTBN29nU8mf+Hjktay5/3d8M+FGDi67maaPbsL/1U0Et/yJwLYb8W27BffW3ri23oFn6134tt6N/9u7CX47hNDmQQS/6Y//6z4ES/sQ2tKfyLY79QkgumMYsZ3DiZU9YJP9gbG0HXqCdOV0cjUlmHXzMRtno5qnYzkmo5wTMIXsPaOxZKRXPLd8YtcgV+x3Y8kUV0jsXaRZ+xhmcCwqJMMDMns/EeIv/Uj0ba4PifzwCPHDj5OsnEhL7STaGqaRdswi6ykhFZDswRmkQjNIBWeRCs0hHVpAJvQMmfBiUtHnSUeXkoktJRtbRi62jGx0aQcWk40tOo5cVMjdRjb6DEb0KQzt5yxezuLkVoKKzkRFp2FJFRoRghqDiozBiD6CERuBFb8LK3yzXjDKSVasEH2jpM8XknOcQE6kAImqc+Vj6Co/H+W0gbglOvJ1TqVYJ8i/UY0SPHwiiFVB44n6vlgfm/W/wqw7A1V3BlbtWVjVQvZnoCq7YVSeb1fzR3uSPXoV2YqrO3Ad2crfk666hkzVVWSqLidT1UOTvVT+RnUPzCpp5NpSkOSsmjWXYFQfa+aei9kxqaPquqG0ln+6lm7k+Rj1P0fJc27oyFzVZF+I6fg5yiXS1AVY3quxJNVK4gz9skhyFwQf1ok4ppztIxKGIJaoy7ESQvZvoFpXoqSqT32MqWWc91HtKzHb3taNW9X6N5RM6cRfIhd9nlxMZvBlimcqqnUCquVRzJbHMSS8vPUhdApWtD9W5FpU5CzMsFgiFGGFOmnTM+W1SR5Xx0m6KY9cQz656hP0hmW2rIC20k6aZKrfLuLwmz1wbBpLuGwqqSOP6ekWGXu0Kxl5n8j6uPy/xBtEPL/F/3shVsg2hjJC8zDC9qhvzjsXw12Mck3HbJ5Ktm4qyQNPUPXx3Xw6pSdvDO7OPyf/kboPRuDfMp1A2RyCu2cT2VdCdP9covvmEd27gHD5Qnw7n8ZX9jTB3QsIls8lWFZCaFcJkZ2zSZTNJrl7Fi37ZtB+cAq5qsm25NNcjOmWibNZ+mSEZDGHn7VzHKKLdXi9Ge0ofsTnJDxPmwVKfrOSwQXfWEzPQyj3cMz620iU98Sz4RRt0tb0VgFN7xTQvDIfx3t5ON7Lx7kqH9cH+bg+FEjFX4BnTaEmfZ8Q/5p8vB/n49EQ++YCAuuK8H18os5kblxZQGB9J4KfFxL4vIjg5yfoPOfQcb1fSD9f5/DqbN2v8+0wdhn3lJB2acJu6oRzTSea1v4M/zdFxMsloaoIq+nn2p/K8vfQnk6Sz0DgrxCQq9IBWL67sXz3oDz3oNxDQHZdHAP1gqPhuJds0wgyDQ+Qrr+bdN3tpGv/TKb6z6Qr+9J25A7aDg22J2i+e4jo/ocJ73mY8M77CG8dSGhLH4Jb++Df2gfPN31oWNubvS/cwoYJ1/D+sMtYM+wKSsddzw/P3UDDqj/g+eJ6fF9fh7f0D7g398L5bX+c3w7Bve0+vNsewPPtcLyl9+IrHYL/6wH4vupH8JsBBDcPIrB1MP5tgwhuGaSr/fD2YcTKHyCxfzSt348nfXga2coSsjXzyNUXYzbNwGqeiuWULdvxKM8Y3WxGZDyZ2hKrd/8QVEDGd+XKXSSd0Si9iyR+XuNQIunEl8Ex6UaCR1L1E0g3PEFKextPIeWcRkZcKv3FZIIlpINC7nNJh560ST78DJnIc7qST0eXkYnZyMZtos8J6UdFp3/uOMkL4ediz2LEniUn+n10EUZ0oSZ6U5O9uLnNweogehWdhBUdD6K16gatkP1IrNhQiEhG6/Xkmn9Nrl62KvOgXqrcIozmfAxHng6gNqXKdxSgnAX2rL0kyOsw4mNEKcciLJ0wLynzJ6AaOmE2CNF31lMyqvbXHUT/G8zqMzGrzsaovIBc5SVkRaI5RvJHryJTcS3ZiuvJVF5DpvL/E71IPIagWvBjpZ+ruhSjSuSd8zFFx6/tpqF0w/YsfbIxZSa/rost4dTbz1M1SgOrEKP5ZEznGSjX+dozx3Rfj+n5E4aYoskHJvhgR7dezvhyxSQ+2c9BfAW0vIbV+ha0vt8h5byFan0TTe4tr6CSK7CSL9iIvoAZXoIZWYgVn4MVn4qKj8dKPI4p0E3aB7ESUtH3w4r8DhXujgr9ChU8ESso7pViUZyHcuehq3nZbJacUEmaEv/xAxKIXUh884kENp2E6/PTCG3pRXvNE5ju2VgSgiMyjUS4RabajqhiDytXhGHbTM+Ot5QcY0kIk4WU+aighNg/hemZS84xnVzTJNLV42k9PJZI+ViqP7qbjdOu5u1hF7Bxwu85+tZw3F9Nwre9GP/OEkLlTxIqX0ho93NE9z5PeM9SguVLCJQ9R7BsEcHy+YTK5xEtm0e8fA7J3SLVzKD98GQydRMwpMnmLgHf03oz0rbyfh7CSyG8HEv+tjHBclRsKVb8eRDElkF0KUQWI+EVltiJeCUXYiL4HkE1D6bth+vwf3U6dR/kU/tWHs1/F/fPfBrfyqPxnXya3s2neWUBzavycbxfgOvDQlyrC3CttsnfKdbNq/Nxy1XAR/kazauKqHvzJJyrTsYnBL2+AP+ngkL8G/IISBjLxnxt3CYavxB/6B95OqtXj3j+s4jQP07A/8UJeCTl68N8mtZ1IrztZNKHfwFNp4CYEAbEjOxalO8WLPGk0fYFstTYH9NzF6ZnCIZrKKZrMMoxENV8B0bzQHJNg8k2DtMkn6q7k1RNP1JVfUlV9qHt6G0kD/Yj8d1A4vsGEdszhEjZUEI7hhDcMgD/13/G9+UNeDbdgGvTjTR/cQNVq25g16LrWDfmUlYNvoC1gy/h6zFXsv+pq6l56yocn1yJa+NVOP7xBxyb/kLz13fi2Hw/rq1j8Gx7HO/Wx/FsGYX72xF4Ng/H880wfKXD8G8eim/zXfi+7Y9vcz8N/5aBhHYOI7Z3JC0HxpI6NJXM0WKy1SUYdbMwG6ehhOilqndNQHkewxJ7FrfdszG9QzE10d+DCgy3l/UCsrRnT+Vowg+MxoovAbNj6iYXW0fGOUkHjqTd00h5ZpD2ziLjn61JPhN+kkx4AZnwU2QiT5MJLyITXkJGpBkh+ehysrHlZOMCIfqlGtnY82Rj/57ohew1RLqJLcTUEIvOuahYMSo6HRWdipA9kfEQHasncYzYwxhhsRQdDMHe5ETCafi5TqyhrsCOKWvIx2ySBp+M7cmMtr1mL9KOSARC9FryOBZE3FiI1Shzu500yauGog6i74pZdyZm7ZlYtd2wqrtjVp+FWfUbjMoLyVZeSrbiCnIV15A9eg2Zo1dros/8B6K3NfvLMaqusKHJvge5KtHye2KIxFN1EUb1OZg1Mn8vhN/d1u7rfoOu8OX51Hext23rf4ElJ6SGThgSyO08XVspKJd45lypU60MsT32344VGI7pl0u8h/RZ3wpNBlmyiC2E+GJIroDka5B8A6vldazka1rasRIvYcWX2W+Y2GLQoTISEj8XojOwYhOxolI5SCrPaFRipB1tKEZqkd4QuRZLiD7Y2Sb6QKF2rdRmZh3VvA6FrsuHSkkxKiB9II9keQGxXb8kue9cMkduxWoYieGdiBGU8IcpmNGpuq+jdG9HwmGE4OdiBiXT+BkdZan8z2J6F5JzPYkhM8qe+Sj3kxjNs8k1TCZTO47Ww2OI7RuFf9toqlbfzcap1/LGoPP5eNTV7F1xJw0bHsddOgPPljl4tz+Fd8cS/DtfJLz7VcJ7XiG4+0WCZcsJlS0hVPYMobIFRMvnE989l+Te2bQcmEZ71UQyjbIKPwnLIyT9NMq/CCUJbKHlEHkBoisg9hIq/rK+apIrLZKClyCxAivxAlZ8OSq6ROc9EJqvR5MNv8TRPYbpHEKq8k/4tpxD/UcnU//3Ahpfz9fGbXK7/s0CGt8qoFGq/XcLcbxXhGNVIc73pPK34Xq/APcHhchRrgIcH+Th/KAT3o9Owr/2BHy60Zunm73edSL55BH6TOSdIq33i/Yf/KyA4MZCbassWr/v8wI8G/Jxr8/DIw3h0iJSB2Vc+HTwngO+i7D0ZM2NduiOEL3nz7Z9gViTiCW4lmmGYjhkg3UAZlN/jKb+5BoHkGkYSKpuAO21/Wiv7kdbRX9aj/Qjeeg2Ygf6Et3Tl8iu24nu6Ed4a1+Cm3vj33Srrs5dn12OY8PlNG24gvo1V3D4b1eydd5lrH3oPFbdeTYfDziXL0dcxJ6SS6l89VIaP7yEpk960rj+DzRt7E3TpmE0l47BuXUy7u2z8Gwvxrl1Cs4t43B++xjO0tG4S0fiKR2Op/ROPKV98Zb2xru5D74t/QjuGEx0zwiSBx6n/dAU0keKyUiDv3YGZsNUlM7xnoLlnIglEzgucc6UTIzhKO8wlHbcHKY/25bsE+hG7YiOz7hIORI/uuhHojfin9ok75lG1mcTfNo/h0xwHj+S/EKb5COLflLJL0VX89HlHRW9yDfPk42KLv9sB0Sff+Y4jsk2hlTzUuHHbaI3Yk+h4gtQsQ6ij0nHeIrdRIyMg6jM1Y/CiD6kiV4FhmIG+5ELXEu2uav2MqdKFm5sX3MdPSYBxMfJvMMhUTRtkXkExwKKj/1Mx32lj0Wo2s5INW+jmz0xU/1rjKqzyVVeQKbyEjJHLydz9EobR64gc+RKskeuJnv0CrIVPchWXKZPCBk5KVTKbdHse5KrFDlHrgguJlshOn8PjIpLMCouxKjqOKHUnK0fUyZ1ZNNW1Z6Fqj0Ts+6UDlnpF5j1P8NsLMIUqam5K0pbKYhvzm9t3xz3NVg+mTkeYq/m++/S+p6l3xRjMCS8WMZbxSkvKifcxai4VJNLQRB7TifhaJe9kKxpF6P801Eyhx4ZixUZDZERELkfFRmu/fAtsUaWDM7IH7XBmRU6DRU4yfah78iKNZ1yxSVymx0TJ6EjSvxMfsij7UA+yQOdaa/8Lcp5q548Mb1jMXwyFinOfsUYkTkYQu7BEvAXg2QKSKWrA+ttorcCz6G8z5Btnkuqbgbpuhlk5VgzifaKR2k5PJLEdw8R3PUArq/vo2LVnZroX+zbjb/ddS6lc2+hYtWD1H82kcYvp+P4ZgGuLcvwbH8V/y5xfH0Nf9kKAjuWEdyxmMCOpwnseJLQzjlERZvfP53WHyaTrnyCbN14zMZJKJFtHPMwXEL487B8T+vweclZlnhLI7YUM7ECJSTf8irIhFTyRRstKzATS1FyYhbpTAIrxDnRNx1LFmzcsrp/O6HtPWhY/Utq/pZH9cs/ovZVyXfIo/6NPO2dL9V+00/xdh7Nb+fRdAzv5tEoeEeI327yulfn4RF8lGdHMq7Lw78uD8/aPNxr8vB9kof/U7va93+Wh+dTSY7LwyuTP5vydJPdrJcZ+dOwvOejfD0wvNdh+P6EKQZkzptRjptQjj91bKj3w3QMwmgWkr8To/F2cg29ta9Qrr4vmfrbSNX2pa2mD+1Vt9FypB+JH24j9t1fCO3pRXDHLQS33Eyg9Ea8/7wO98arcK6/HMe6i2haI/Yq51D34TlUvnse+5edx9dTu7Nm+K9Z1e9UVvc9nX/c041d07pzePm51L57LvUfXkzt6t9R90lvGv4xnMZvxtO8pRjn9vm4ti/EsW0Ojm0zcGyZQvPmcTR/PQrHpntxfNUf51e9cH9zM77SW/GLbLR9INHy+4jvf4SWHybQfmgaqYppZKomk6t9AqP+CYzGiZhN41GOx1DOUSjXA9pFUyJZlXcwSiwf9D6BEP8wTL9YcIhBpFguS3LWAjA7LBCM+GdkvPPJ+BaSDcqS0xIyIWmsLiMbecGu2KMvkI29SDb2EpnYK2Rir/0Er5OJC14lE3+ZbEyw4t8iF1uBwIi9iBF/kVxiOUZ8mYaZWIZKLEYlnsGSZl/czl8UKYfYVKzYJMzYBIyoRHw9ghl5EFN0YP8VmPWnY9V0xaruilkrkktXrEZBZ91oVU1dOQbT0RWBNF2tpq7QfIo9qqjvn4Y0ZGnqZi8wNfS0veYbr9QmZmZ9T4y6q8jW/p5M7Y1kqm8hXXkrmepeZKpu0chW9iJbdTPZqpswagQ3kq0V3ES25haNXI3cvoFs9R/JVt2I3Deqb8So+QNG7TWY9Vei6q/CqhfrhWO4AqtBbBkuxWy6GLPxAsym7ihHN0xHd5TzYizn5Vjuq7Hc1yOh5KZXxi2HgX+UDlMwRdPTlcADmP5HyfnGkwtIdSzxZ0/pk68Zf06TiZVYghWTIHWJiCwhF5JwmRKdPyAnB+RqKzoOS6LXQg+TC43AjA3X0YZmtA9WRMheKvrzUaEzMf2n6lxZ0yuTT50xHZ0xG+W16oxR0xWj4lRSh7uSqjofo1ksK+6ByBhy4UnkZHom8JTt5y1RkKHnUYGlEJKoyqdsL/DgIizJAY6swAq/BOGXIfgihmsJ7XVP0lI9m2TlLFqOTiV+aDyRA48S3ifb3WNwlj5C1Xo3Js8AAASISURBVMf3Uzq/D28MvYLX7r6UL0t6c/DtUVSvn0ztP2fTtPk5XDtex1u+Et+e9/DvfRvfHiH8VwmVvUSwfBnB8sWE9iwiuv8pEgfn0nq0hEzlXHIyVdFQgtm4ENW8CMM5H9P9pD4RmUEJqF+OEV2BEX8ZU66oWmWU9V2slpWolvf03oNqfRelR1z/jpV8HSv+CkSXQ1h8ziUhrRgCk/Q6f3Tn72leexZ1b55C7etdqPt7F+re7ELtm52pf7srDSu70vheV5pWdfmPaFzVlZp3TuXQy6dQ9+5puNd1xb22Mx7Bui46btG/4RR8G7riWd8F1yed8X7WBf/GUwh8cQrBf3bB/4XgVEJfnUpr+Wmomm7gky3Xy/Xeh+nvZXvDB+7A9PVHucVnqj+Wq58O+LG0PfBITOfDmM6HEF1eJBuzcQhG41BbumkYSqp+GO2199JacR+Jg/cQ+34woX13Eiy/g8D22/Bu7oXryxtwbLwex6fXaRmm6ZPLaFjTg5rVl3H03R7sW345m2f14LMxl7D2notYP+Rivh59Gbvn9OTIy1dS9/5V1H/8B+o+6Uv9xqE0fzMOx9Y5uHYtxlP+Ap7yl/DsXo5n9/N4yp/BtWsujm1TcG59FNeW4bi33Ilv6wAC2wYR2jWMUPkDxPY9SuL7SbQemkXbkbm0V84hXV1MtnYWufpZGE3FmM5ZKPcM0BGu4/VJ3fI9guV/GEuiWQOjdGqfJbKNxmjMwBis4Cis2DNgBmyvG1Qrlhn/CRJYZgdUAus4kljqv8VP/92/v83x35vg2G05ouIdiIESRDsQARXBUmHQCIEKgOkFwwk5x7/C6Lgvx/8G+t87O36X/D435DxgdEBu5zxYGl6snO+/hPzsv/v5f/06OR/kvPZjdjyWfvz/e9tww3G47OdryFG+fuz5yt/EC6YfzKB9+SZndnnRNeRrISwzbM/ZygiWGcPSf+9jf3f7aJlRbETsDr7+Wfu10K+D/j3yu4JYKoilH9Nnvy7KA6YLTCeYjh9x7PXoeM2snAMr2wH5/8pzVsdeb/vxMeNgJv4P5GuxDnR8X95DZvI4LCOBlYuhslHMbAQzIwjbSIcwUjayrSFSMT8tQQ8Jv4u2qI9MMkS2NUK2PUouFcdIJzHSLZiCTIu+b6aTmOnEj8jEMTNxVNZ+TCsb048vzwGNOJYR08CQ5/6T562SoNECStD6r7CO3T/2ffn5js+M/jvI6xjByvox213kWpzkkg5yLf8BrQ5y/xFOskkn6ZiDbNKB0dqBNgdGmwPzJ5D7x2C2O/hXODFTTqyMfLbkveAGU17jY+9PP5Z+T/rBEAR+cgyBIQjro2UEseT74siobwexcj9CZUOobBCVCWAK0n4NI+XDaPditHk64MZoc5NrdZMVJN2k425SUTftYTdtQTdtATepsJt0zE024SHX4iHX6iXX5ifXHsRIRTDSMYx04sf3QSaJmUlo2N+LYqTDGCn5eXk+xxDETIdQmTAqE9HvTfv9Iu+V6I8wovBTSGNVNHcN+Twfw0+/duy28GQMMDTR/y+F6pYryJq+FwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Notebook is created for the Explore EDSA Apple Prices Challenge hosted on the Kaggle website. This competition is a project to predict the average price per kilogram of Golden Delicious Apples by training a regression model from explanatory features from a given dataset. The dataset is supplied by the kaggle competition.\n",
    " ![image.png](attachment:image.png)\n",
    "In this project, multiple regression models are constructed, used and compared to evaluate the regression model with the highest level of accuracy in predicting how much a kilogram of Golden Delicious Apples will cost.\n",
    "Regression models are created to predict a continuous value. In the case of this project this is an extremely useful method to finically forecast either the buying or selling of Golden Delicious Apples and conduct a Cost–benefit analysis to investigate the potential strengths and weakness of the product. The model itself is an artifact that is generated by the process of training data. This training data is used by machine learning algorithms, which learn from the data and produce a trained model.\n",
    " ![image-2.png](attachment:image-2.png)\n",
    "\n",
    "We are supplied with a labelled data set which means this is a supervised type of machine learning, where our regression model can learn from these labelled data and aid in classification and prediction.\n",
    "After comparing and analysing each regression model and machine learning technique employed throughout this notebook, we will have a reasonably accurate prediction of the price per kg of the Godlen Delicious Apples along with a detailed explanation and understanding of why out chosen regression model and techniques used are more suitable and accurate than the others we tested.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data size and structure"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The train dataset given consists of object, integer and float variables. Most of the object variables are nominal factors with date being the only ordinal one, most of them require cleaning and/or feature engineering to be usable. As for the integer and float variables, little to no cleaning needs to be done as there are little to no issues with them in this dataset . In total, there are 13 columns/variables but only 12 of them are used as “Commodities\" has no relevant information and is dropped. The last column/variable is the response variable (avg_price_per_kg). \n",
    "\n",
    "Below, is a display of the variables. All of them are discussed in more detail throughout this notebook\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 0: Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading required python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.correlation import plot_corr\n",
    "from scipy.stats import pearsonr\n",
    "%matplotlib inline\n",
    "\n",
    "#models\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor \n",
    "\n",
    "#metrics, procssing and model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 1: Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As kaggle already provided us with data in the form of csv files, all we have to do is load them into dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading csv's\n",
    "train=pd.read_csv('df - train_set.csv')\n",
    "test=pd.read_csv('df - test_set.csv')\n",
    "test_id=test['Index']\n",
    "test=test.drop('Index',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 2: Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaggle tries to provide us with clean data but it's always a good idea to check for ourselves. We check for null and empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Province            0\n",
       "Container           0\n",
       "Size_Grade          0\n",
       "Weight_Kg           0\n",
       "Commodities         0\n",
       "Date                0\n",
       "Low_Price           0\n",
       "High_Price          0\n",
       "Sales_Total         0\n",
       "Total_Qty_Sold      0\n",
       "Total_Kg_Sold       0\n",
       "Stock_On_Hand       0\n",
       "avg_price_per_kg    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data contains 0 empty values over all columns\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for x in train.columns:\n",
    "    if '' in sorted(train[x].unique()):\n",
    "        i+=1\n",
    "print('The data contains '+str(i)+' empty values over all columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "none of our columns contain any null or empty values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>EC120</td>\n",
       "      <td>1L</td>\n",
       "      <td>12.0</td>\n",
       "      <td>APPLE GRANNY SMITH</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>108.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3236.0</td>\n",
       "      <td>29</td>\n",
       "      <td>348.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GAUTENG</td>\n",
       "      <td>AT200</td>\n",
       "      <td>1L</td>\n",
       "      <td>20.0</td>\n",
       "      <td>AVOCADO PINKERTON</td>\n",
       "      <td>2020-05-05</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>66</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>50</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRANSVAAL</td>\n",
       "      <td>BJ090</td>\n",
       "      <td>1L</td>\n",
       "      <td>9.0</td>\n",
       "      <td>TOMATOES-LONG LIFE</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>10</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WESTERN FREESTATE</td>\n",
       "      <td>PP100</td>\n",
       "      <td>1R</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POTATO SIFRA (WASHED)</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41530.0</td>\n",
       "      <td>927</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>393</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Province Container Size_Grade  Weight_Kg             Commodities  \\\n",
       "0               CAPE     EC120         1L       12.0      APPLE GRANNY SMITH   \n",
       "1               CAPE     M4183         1L       18.3  APPLE GOLDEN DELICIOUS   \n",
       "2            GAUTENG     AT200         1L       20.0       AVOCADO PINKERTON   \n",
       "3          TRANSVAAL     BJ090         1L        9.0      TOMATOES-LONG LIFE   \n",
       "4  WESTERN FREESTATE     PP100         1R       10.0   POTATO SIFRA (WASHED)   \n",
       "\n",
       "         Date  Low_Price  High_Price  Sales_Total  Total_Qty_Sold  \\\n",
       "0  2020-03-10      108.0       112.0       3236.0              29   \n",
       "1  2020-09-09      150.0       170.0      51710.0             332   \n",
       "2  2020-05-05       70.0        80.0       4860.0              66   \n",
       "3  2020-01-20       60.0        60.0        600.0              10   \n",
       "4  2020-07-14       40.0        45.0      41530.0             927   \n",
       "\n",
       "   Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "0          348.0              0              9.30  \n",
       "1         6075.6            822              8.51  \n",
       "2         1320.0             50              3.68  \n",
       "3           90.0              0              6.67  \n",
       "4         9270.0            393              4.48  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table contains information about 'Commodoties that we are not interested in, we filter only for 'APPLE GOLDEN DELICIOUS'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>Container</th>\n",
       "      <th>Size_Grade</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Commodities</th>\n",
       "      <th>Date</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1L</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-09</td>\n",
       "      <td>150.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>51710.0</td>\n",
       "      <td>332</td>\n",
       "      <td>6075.6</td>\n",
       "      <td>822</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>JG110</td>\n",
       "      <td>2M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>320</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>W.CAPE-BERGRIVER ETC</td>\n",
       "      <td>JE090</td>\n",
       "      <td>2S</td>\n",
       "      <td>9.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-04-16</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>18</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1506</td>\n",
       "      <td>6.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CAPE</td>\n",
       "      <td>M4183</td>\n",
       "      <td>1S</td>\n",
       "      <td>18.3</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>32020.0</td>\n",
       "      <td>388</td>\n",
       "      <td>7100.4</td>\n",
       "      <td>443</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>EASTERN CAPE</td>\n",
       "      <td>IA400</td>\n",
       "      <td>1S</td>\n",
       "      <td>400.0</td>\n",
       "      <td>APPLE GOLDEN DELICIOUS</td>\n",
       "      <td>2020-09-28</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Province Container Size_Grade  Weight_Kg  \\\n",
       "1                   CAPE     M4183         1L       18.3   \n",
       "7                   CAPE     JG110         2M       11.0   \n",
       "24  W.CAPE-BERGRIVER ETC     JE090         2S        9.0   \n",
       "40                  CAPE     M4183         1S       18.3   \n",
       "69          EASTERN CAPE     IA400         1S      400.0   \n",
       "\n",
       "               Commodities        Date  Low_Price  High_Price  Sales_Total  \\\n",
       "1   APPLE GOLDEN DELICIOUS  2020-09-09      150.0       170.0      51710.0   \n",
       "7   APPLE GOLDEN DELICIOUS  2020-04-14       50.0        50.0      16000.0   \n",
       "24  APPLE GOLDEN DELICIOUS  2020-04-16       55.0        55.0        990.0   \n",
       "40  APPLE GOLDEN DELICIOUS  2020-05-04       80.0       120.0      32020.0   \n",
       "69  APPLE GOLDEN DELICIOUS  2020-09-28     1800.0      1800.0       1800.0   \n",
       "\n",
       "    Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "1              332         6075.6            822              8.51  \n",
       "7              320         3520.0              0              4.55  \n",
       "24              18          162.0           1506              6.11  \n",
       "40             388         7100.4            443              4.51  \n",
       "69               1          400.0              2              4.50  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter for Golden Delicious Apples\n",
    "train=train[train['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "test=test[test['Commodities']=='APPLE GOLDEN DELICIOUS']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may as well remove the \"Commodities\" column as it has no more useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Date              1952 non-null   object \n",
      " 5   Low_Price         1952 non-null   float64\n",
      " 6   High_Price        1952 non-null   float64\n",
      " 7   Sales_Total       1952 non-null   float64\n",
      " 8   Total_Qty_Sold    1952 non-null   int64  \n",
      " 9   Total_Kg_Sold     1952 non-null   float64\n",
      " 10  Stock_On_Hand     1952 non-null   int64  \n",
      " 11  avg_price_per_kg  1952 non-null   float64\n",
      "dtypes: float64(6), int64(2), object(4)\n",
      "memory usage: 198.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Remove Commodities\n",
    "train=train.drop([\"Commodities\"],axis=1)\n",
    "test=test.drop([\"Commodities\"],axis=1)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the DataFrame consists of 12 columns.(11 predictors and 1 response)\n",
    "\n",
    "The predictor variables are broken down into the following types:\n",
    "\n",
    "    -object: Province, Container, Size_Grade and Date\n",
    "\n",
    "    -float: Weight_Kg, Low_Price, High_Price, Sales_Total and Total_Kg_Sold\n",
    "\n",
    "    -integer: Total_Qty_Sold and Stock_On_Hand\n",
    "    \n",
    "with the response variable(avg_price_per_kg) being a float."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 3: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1: Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1: Numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "      <td>1952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.460912</td>\n",
       "      <td>174.307377</td>\n",
       "      <td>215.648053</td>\n",
       "      <td>20053.533811</td>\n",
       "      <td>174.510758</td>\n",
       "      <td>2960.176332</td>\n",
       "      <td>408.393955</td>\n",
       "      <td>6.778893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>99.655169</td>\n",
       "      <td>373.553578</td>\n",
       "      <td>433.546159</td>\n",
       "      <td>39005.069445</td>\n",
       "      <td>308.810797</td>\n",
       "      <td>6097.416527</td>\n",
       "      <td>724.450582</td>\n",
       "      <td>2.248744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1325.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>219.600000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>5495.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>853.500000</td>\n",
       "      <td>126.500000</td>\n",
       "      <td>6.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.300000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>21082.500000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3093.525000</td>\n",
       "      <td>468.000000</td>\n",
       "      <td>8.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>369464.000000</td>\n",
       "      <td>4237.000000</td>\n",
       "      <td>74000.000000</td>\n",
       "      <td>6400.000000</td>\n",
       "      <td>21.240000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Weight_Kg    Low_Price   High_Price    Sales_Total  Total_Qty_Sold  \\\n",
       "count  1952.000000  1952.000000  1952.000000    1952.000000     1952.000000   \n",
       "mean     40.460912   174.307377   215.648053   20053.533811      174.510758   \n",
       "std      99.655169   373.553578   433.546159   39005.069445      308.810797   \n",
       "min       3.000000     2.000000     5.000000       5.000000        1.000000   \n",
       "25%       9.000000    50.000000    60.000000    1325.000000       12.000000   \n",
       "50%      12.000000    80.000000   108.000000    5495.000000       64.000000   \n",
       "75%      18.300000   127.250000   160.000000   21082.500000      200.000000   \n",
       "max     400.000000  2300.000000  3300.000000  369464.000000     4237.000000   \n",
       "\n",
       "       Total_Kg_Sold  Stock_On_Hand  avg_price_per_kg  \n",
       "count    1952.000000    1952.000000       1952.000000  \n",
       "mean     2960.176332     408.393955          6.778893  \n",
       "std      6097.416527     724.450582          2.248744  \n",
       "min         3.000000       0.000000          0.250000  \n",
       "25%       219.600000       9.000000          5.460000  \n",
       "50%       853.500000     126.500000          6.670000  \n",
       "75%      3093.525000     468.000000          8.280000  \n",
       "max     74000.000000    6400.000000         21.240000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the maximium values of our numeric data are many standard deviations(>3) away from the mean. This leads us to expect that they are outliers and will cause the distribution of the data to appear skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg           3.328254\n",
       "Low_Price           4.222672\n",
       "High_Price          3.828819\n",
       "Sales_Total         4.250317\n",
       "Total_Qty_Sold      4.445169\n",
       "Total_Kg_Sold       5.529586\n",
       "Stock_On_Hand       3.391815\n",
       "avg_price_per_kg    0.082452\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that all of our numeric predictors are highly skewed(skew values greater than 1). While our response variable appears symmetrical(skew value close to 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight_Kg            9.109698\n",
       "Low_Price           17.073369\n",
       "High_Price          13.630768\n",
       "Sales_Total         24.200524\n",
       "Total_Qty_Sold      32.338123\n",
       "Total_Kg_Sold       45.153801\n",
       "Stock_On_Hand       14.671224\n",
       "avg_price_per_kg     1.042921\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The kurtosis of our predictor values are much greater than 3. This indicates that there are strong outliers in the predictor variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These outliers are to be expected in data of this nature: For example:\n",
    "When looking at a column like Sales_Total, many places can have sales up to a certain point but after a certain amount only the biggest places can make that much in sales. This would skew our data toward the more 'acheivable' Sales_total values and cause the larger transactions to appear as outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now convert the date column from object to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Date (object) --> Date (datetime)\n",
    "train['Date'] = pd.to_datetime(train['Date'])\n",
    "test['Date'] = pd.to_datetime(test['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.3: Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot countplots of categorical data\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "sns.countplot(x=train['Size_Grade'], ax=ax[0])\n",
    "sns.countplot(x=train['Container'], ax=ax[1])\n",
    "sns.countplot(x=\"Province\", data=train, ax=ax[2])\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CAPE', 53.53],\n",
       " ['EASTERN CAPE', 10.55],\n",
       " ['NATAL', 5.69],\n",
       " ['ORANGE FREE STATE', 0.46],\n",
       " ['TRANSVAAL', 0.77],\n",
       " ['W.CAPE-BERGRIVER ETC', 22.54],\n",
       " ['WEST COAST', 6.45]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Province as a percentage of all provinces\n",
    "dict_Prov=[]\n",
    "for x in sorted(train['Province'].unique()):\n",
    "    dict_Prov.append([x,round(len(train[train['Province']==x])*100/len(train['Province']),2)])\n",
    "dict_Prov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['AC030', 1.59],\n",
       " ['DT063', 0.31],\n",
       " ['EC120', 22.95],\n",
       " ['EF120', 3.79],\n",
       " ['EG140', 0.15],\n",
       " ['IA400', 7.12],\n",
       " ['JE090', 28.94],\n",
       " ['JG110', 4.35],\n",
       " ['M4183', 30.12],\n",
       " ['M6125', 0.51],\n",
       " ['M9125', 0.15]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Container as a percentage of all containers\n",
    "dict_Cont=[]\n",
    "for x in sorted(train['Container'].unique()):\n",
    "    dict_Cont.append([x,round(len(train[train['Container']==x])*100/len(train['Container']),2)])\n",
    "dict_Cont"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['1L', 11.53],\n",
       " ['1M', 16.34],\n",
       " ['1S', 27.25],\n",
       " ['1U', 0.1],\n",
       " ['1X', 8.76],\n",
       " ['2L', 6.15],\n",
       " ['2M', 11.89],\n",
       " ['2S', 15.78],\n",
       " ['2U', 0.87],\n",
       " ['2X', 1.33]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count of Size_Grade as a percentage of all Size_Grades\n",
    "dict_Sgr=[]\n",
    "for x in sorted(train['Size_Grade'].unique()):\n",
    "    dict_Sgr.append([x,round(len(train[train['Size_Grade']==x])*100/len(train['Size_Grade']),2)])\n",
    "dict_Sgr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2: Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #a9c6fd;\n",
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       "            background-color:  #799cf8;\n",
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       "            color:  #000000;\n",
       "        }</style><table id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Weight_Kg</th>        <th class=\"col_heading level0 col1\" >Low_Price</th>        <th class=\"col_heading level0 col2\" >High_Price</th>        <th class=\"col_heading level0 col3\" >Sales_Total</th>        <th class=\"col_heading level0 col4\" >Total_Qty_Sold</th>        <th class=\"col_heading level0 col5\" >Total_Kg_Sold</th>        <th class=\"col_heading level0 col6\" >Stock_On_Hand</th>        <th class=\"col_heading level0 col7\" >avg_price_per_kg</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row0\" class=\"row_heading level0 row0\" >Weight_Kg</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col1\" class=\"data row0 col1\" >0.863182</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col2\" class=\"data row0 col2\" >0.905852</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col3\" class=\"data row0 col3\" >0.180518</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col4\" class=\"data row0 col4\" >-0.133317</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col5\" class=\"data row0 col5\" >0.294117</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col6\" class=\"data row0 col6\" >-0.144105</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row0_col7\" class=\"data row0 col7\" >-0.337886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row1\" class=\"row_heading level0 row1\" >Low_Price</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col0\" class=\"data row1 col0\" >0.863182</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col2\" class=\"data row1 col2\" >0.938140</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col3\" class=\"data row1 col3\" >0.183230</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col4\" class=\"data row1 col4\" >-0.122823</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col5\" class=\"data row1 col5\" >0.269744</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col6\" class=\"data row1 col6\" >-0.127677</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row1_col7\" class=\"data row1 col7\" >-0.141740</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row2\" class=\"row_heading level0 row2\" >High_Price</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col0\" class=\"data row2 col0\" >0.905852</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col1\" class=\"data row2 col1\" >0.938140</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col3\" class=\"data row2 col3\" >0.265672</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col4\" class=\"data row2 col4\" >-0.096456</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col5\" class=\"data row2 col5\" >0.372282</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col6\" class=\"data row2 col6\" >-0.094174</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row2_col7\" class=\"data row2 col7\" >-0.164496</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row3\" class=\"row_heading level0 row3\" >Sales_Total</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col0\" class=\"data row3 col0\" >0.180518</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col1\" class=\"data row3 col1\" >0.183230</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col2\" class=\"data row3 col2\" >0.265672</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col4\" class=\"data row3 col4\" >0.771865</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col5\" class=\"data row3 col5\" >0.962338</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col6\" class=\"data row3 col6\" >0.284693</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row3_col7\" class=\"data row3 col7\" >0.108473</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row4\" class=\"row_heading level0 row4\" >Total_Qty_Sold</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col0\" class=\"data row4 col0\" >-0.133317</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col1\" class=\"data row4 col1\" >-0.122823</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col2\" class=\"data row4 col2\" >-0.096456</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col3\" class=\"data row4 col3\" >0.771865</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col5\" class=\"data row4 col5\" >0.651440</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col6\" class=\"data row4 col6\" >0.390168</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row4_col7\" class=\"data row4 col7\" >0.087536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row5\" class=\"row_heading level0 row5\" >Total_Kg_Sold</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col0\" class=\"data row5 col0\" >0.294117</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col1\" class=\"data row5 col1\" >0.269744</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col2\" class=\"data row5 col2\" >0.372282</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col3\" class=\"data row5 col3\" >0.962338</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col4\" class=\"data row5 col4\" >0.651440</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col6\" class=\"data row5 col6\" >0.229062</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row5_col7\" class=\"data row5 col7\" >-0.001007</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row6\" class=\"row_heading level0 row6\" >Stock_On_Hand</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col0\" class=\"data row6 col0\" >-0.144105</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col1\" class=\"data row6 col1\" >-0.127677</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col2\" class=\"data row6 col2\" >-0.094174</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col3\" class=\"data row6 col3\" >0.284693</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col4\" class=\"data row6 col4\" >0.390168</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col5\" class=\"data row6 col5\" >0.229062</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row6_col7\" class=\"data row6 col7\" >0.105028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887level0_row7\" class=\"row_heading level0 row7\" >avg_price_per_kg</th>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col0\" class=\"data row7 col0\" >-0.337886</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col1\" class=\"data row7 col1\" >-0.141740</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col2\" class=\"data row7 col2\" >-0.164496</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col3\" class=\"data row7 col3\" >0.108473</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col4\" class=\"data row7 col4\" >0.087536</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col5\" class=\"data row7 col5\" >-0.001007</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col6\" class=\"data row7 col6\" >0.105028</td>\n",
       "                        <td id=\"T_aad8be95_bcd1_11eb_9642_d8cb8af20887row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x221fbe569d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.1: Date and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2020-9', '2020-4', '2020-5', '2020-3', '2019-12', '2020-8',\n",
       "       '2020-7', '2020-2', '2020-6', '2020-1', '2020-10'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating date,month and year\n",
    "training_day=pd.DataFrame([])\n",
    "training_day['avg_price_per_kg']=train['avg_price_per_kg']\n",
    "training_day['Date']=train['Date']\n",
    "training_day['Day']=[x.weekday() for x in train['Date']]\n",
    "training_day['Month']=[x.month for x in train['Date']]\n",
    "training_day['Year-Month']=[str(x.year)+'-'+str(x.month) for x in train['Date']]\n",
    "training_day['Year-Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatterplot of data_components and avg_price_per_kg\n",
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    sns.scatterplot(x=training_day[a],y=training_day['avg_price_per_kg'],ax=ax[i])\n",
    "    i+=1\n",
    "ax[2].tick_params(labelrotation=45)\n",
    "fig.tight_layout(pad=4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(1,3, figsize=(20, 9))\n",
    "i=0\n",
    "for a in training_day.drop(['Date','avg_price_per_kg'],axis=1).columns:\n",
    "    avg_date_comp=training_day.groupby(a)['avg_price_per_kg'].mean()\n",
    "    sns.scatterplot(x=avg_date_comp.index,y=avg_date_comp,ax=ax[i])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The relationship between the day of the week and the mean of the avg_price_per_kg is the most clear. Also,considering the data we have is not even for 1 complete year, the relationship between month and mean of avg_price_per_kg cannot be determined to any meaningful degree. Since each month only appears once and some of them are in different years.\n",
    "\n",
    "We will use the day of the week in our modelling since it's average is taken over many months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   int64  \n",
      "dtypes: float64(6), int64(3), object(3)\n",
      "memory usage: 278.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train['Day']=training_day['Day']\n",
    "train=train.drop('Date',axis=1)\n",
    "\n",
    "test['Day']=[x.weekday() for x in test['Date']]\n",
    "test=test.drop('Date',axis=1)\n",
    "\n",
    "train_dummies=train.copy()\n",
    "test_dummies=test.copy()\n",
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2 Categorical data  and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the mean avg_price_per_kg against the Date\n",
    "avg_container=train_dummies.groupby('Container')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_container.index,y=avg_container)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_container.index:\n",
    "    if x in ['AC030','DT063','IA400']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_mean_cont')\n",
    "    if x in ['JE090','JG110']:\n",
    "        train_dummies=train_dummies.replace(x,'medium_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'medium_mean_cont')\n",
    "    if x in ['M6125','M9125']:\n",
    "        train_dummies=train_dummies.replace(x,'high_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'high_mean_cont')\n",
    "    if x in ['EC120','EF120','EG140','M4183']:\n",
    "        train_dummies=train_dummies.replace(x,'Highest_mean_cont')\n",
    "        test_dummies=test_dummies.replace(x,'Highest_mean_cont')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_prov=train_dummies.groupby('Province')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_prov.index,y=avg_prov)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in avg_prov.index:\n",
    "    if x in ['Natal']:\n",
    "        train_dummies=train_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Lowest_Mean_Prov')\n",
    "    if x in ['Eastern Cape','Orange free State','West Coast']:\n",
    "        train_dummies=train_dummies.replace(x,'Medium_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'Medium_Mean_Prov')\n",
    "    if x in ['Cape','W.CAPE-BERGRIVER ETC']:\n",
    "        train_dummies=train_dummies.replace(x,'High_Mean_Prov')\n",
    "        test_dummies=test_dummies.replace(x,'High_Mean_Prov')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_sgr=train_dummies.groupby('Size_Grade')['avg_price_per_kg'].mean()\n",
    "sns.barplot(x=avg_sgr.index,y=avg_sgr)\n",
    "plt.tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping by letter grade\n",
    "for x in avg_prov.index:\n",
    "    if 'L' in x:\n",
    "        train_dummies=train_dummies.replace(x,'L')\n",
    "        test_dummies=test_dummies.replace(x,'L')\n",
    "    if 'M' in x:\n",
    "        train_dummies=train_dummies.replace(x,'M')\n",
    "        test_dummies=test_dummies.replace(x,'M')\n",
    "    if 'S' in x:\n",
    "        train_dummies=train_dummies.replace(x,'S')\n",
    "        test_dummies=test_dummies.replace(x,'S')\n",
    "    if 'U' in x:\n",
    "        train_dummies=train_dummies.replace(x,'U')\n",
    "        test_dummies=test_dummies.replace(x,'U')\n",
    "    if 'X' in x:\n",
    "        train_dummies=train_dummies.replace(x,'X')\n",
    "        test_dummies=test_dummies.replace(x,'X')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the mean of the avg_price_per_kg has some variance depending on the goup it belongs to for each of the categoical columns, the affect is not extremely drastic in most cases. Here we have group each value in the categorical columns with values that have similar affect on the mean. Now we will convert those column to numerical values using dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dummies = pd.get_dummies(train_dummies,drop_first=True)\n",
    "train_dummies.columns = [col.replace(\" \",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\".\",\"_\") for col in train_dummies.columns]\n",
    "train_dummies.columns = [col.replace(\"-\",\"_\") for col in train_dummies.columns]\n",
    "test_dummies = pd.get_dummies(test_dummies,drop_first=True)\n",
    "test_dummies.columns = [col.replace(\" \",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\".\",\"_\") for col in test_dummies.columns]\n",
    "test_dummies.columns = [col.replace(\"-\",\"_\") for col in test_dummies.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 24 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Weight_Kg                   1952 non-null   float64\n",
      " 1   Low_Price                   1952 non-null   float64\n",
      " 2   High_Price                  1952 non-null   float64\n",
      " 3   Sales_Total                 1952 non-null   float64\n",
      " 4   Total_Qty_Sold              1952 non-null   int64  \n",
      " 5   Total_Kg_Sold               1952 non-null   float64\n",
      " 6   Stock_On_Hand               1952 non-null   int64  \n",
      " 7   avg_price_per_kg            1952 non-null   float64\n",
      " 8   Day                         1952 non-null   int64  \n",
      " 9   Province_High_Mean_Prov     1952 non-null   uint8  \n",
      " 10  Province_L                  1952 non-null   uint8  \n",
      " 11  Province_S                  1952 non-null   uint8  \n",
      " 12  Container_Lowest_mean_cont  1952 non-null   uint8  \n",
      " 13  Container_high_mean_cont    1952 non-null   uint8  \n",
      " 14  Container_medium_mean_cont  1952 non-null   uint8  \n",
      " 15  Size_Grade_1M               1952 non-null   uint8  \n",
      " 16  Size_Grade_1S               1952 non-null   uint8  \n",
      " 17  Size_Grade_1U               1952 non-null   uint8  \n",
      " 18  Size_Grade_1X               1952 non-null   uint8  \n",
      " 19  Size_Grade_2L               1952 non-null   uint8  \n",
      " 20  Size_Grade_2M               1952 non-null   uint8  \n",
      " 21  Size_Grade_2S               1952 non-null   uint8  \n",
      " 22  Size_Grade_2U               1952 non-null   uint8  \n",
      " 23  Size_Grade_2X               1952 non-null   uint8  \n",
      "dtypes: float64(6), int64(3), uint8(15)\n",
      "memory usage: 261.1 KB\n"
     ]
    }
   ],
   "source": [
    "train_dummies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of our data are now numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.3: Numerical values and avg_price_per_kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Sales_Total</th>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.180518</td>\n",
       "      <td>-0.133317</td>\n",
       "      <td>0.294117</td>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.337886</td>\n",
       "      <td>-0.050023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.141740</td>\n",
       "      <td>-0.035710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>-0.164496</td>\n",
       "      <td>-0.040340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.180518</td>\n",
       "      <td>0.183230</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.108473</td>\n",
       "      <td>-0.026877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>-0.133317</td>\n",
       "      <td>-0.122823</td>\n",
       "      <td>-0.096456</td>\n",
       "      <td>0.771865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.087536</td>\n",
       "      <td>-0.016428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Kg_Sold</th>\n",
       "      <td>0.294117</td>\n",
       "      <td>0.269744</td>\n",
       "      <td>0.372282</td>\n",
       "      <td>0.962338</td>\n",
       "      <td>0.651440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>-0.001007</td>\n",
       "      <td>-0.041020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>-0.144105</td>\n",
       "      <td>-0.127677</td>\n",
       "      <td>-0.094174</td>\n",
       "      <td>0.284693</td>\n",
       "      <td>0.390168</td>\n",
       "      <td>0.229062</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105028</td>\n",
       "      <td>-0.040417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_price_per_kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>-0.141740</td>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.087536</td>\n",
       "      <td>-0.001007</td>\n",
       "      <td>0.105028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.050023</td>\n",
       "      <td>-0.035710</td>\n",
       "      <td>-0.040340</td>\n",
       "      <td>-0.026877</td>\n",
       "      <td>-0.016428</td>\n",
       "      <td>-0.041020</td>\n",
       "      <td>-0.040417</td>\n",
       "      <td>0.065115</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Weight_Kg  Low_Price  High_Price  Sales_Total  \\\n",
       "Weight_Kg          1.000000   0.863182    0.905852     0.180518   \n",
       "Low_Price          0.863182   1.000000    0.938140     0.183230   \n",
       "High_Price         0.905852   0.938140    1.000000     0.265672   \n",
       "Sales_Total        0.180518   0.183230    0.265672     1.000000   \n",
       "Total_Qty_Sold    -0.133317  -0.122823   -0.096456     0.771865   \n",
       "Total_Kg_Sold      0.294117   0.269744    0.372282     0.962338   \n",
       "Stock_On_Hand     -0.144105  -0.127677   -0.094174     0.284693   \n",
       "avg_price_per_kg  -0.337886  -0.141740   -0.164496     0.108473   \n",
       "Day               -0.050023  -0.035710   -0.040340    -0.026877   \n",
       "\n",
       "                  Total_Qty_Sold  Total_Kg_Sold  Stock_On_Hand  \\\n",
       "Weight_Kg              -0.133317       0.294117      -0.144105   \n",
       "Low_Price              -0.122823       0.269744      -0.127677   \n",
       "High_Price             -0.096456       0.372282      -0.094174   \n",
       "Sales_Total             0.771865       0.962338       0.284693   \n",
       "Total_Qty_Sold          1.000000       0.651440       0.390168   \n",
       "Total_Kg_Sold           0.651440       1.000000       0.229062   \n",
       "Stock_On_Hand           0.390168       0.229062       1.000000   \n",
       "avg_price_per_kg        0.087536      -0.001007       0.105028   \n",
       "Day                    -0.016428      -0.041020      -0.040417   \n",
       "\n",
       "                  avg_price_per_kg       Day  \n",
       "Weight_Kg                -0.337886 -0.050023  \n",
       "Low_Price                -0.141740 -0.035710  \n",
       "High_Price               -0.164496 -0.040340  \n",
       "Sales_Total               0.108473 -0.026877  \n",
       "Total_Qty_Sold            0.087536 -0.016428  \n",
       "Total_Kg_Sold            -0.001007 -0.041020  \n",
       "Stock_On_Hand             0.105028 -0.040417  \n",
       "avg_price_per_kg          1.000000  0.065115  \n",
       "Day                       0.065115  1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To perfrom linear regression we make the assumption that there is an underlying linear formula to the data we are modelling. So to optimize our model we select features so that they have a correlation with our data.\n",
    "\n",
    "In the following section we will take alot look at the importance of each variable one the response by looking at it's correlation with the response variable as well as it's p-value.\n",
    "\n",
    "We will also make sure we try and avoid multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Correlation_Coefficient</th>\n",
       "      <th>P_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Province_High_Mean_Prov</th>\n",
       "      <td>0.262051</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_L</th>\n",
       "      <td>-0.365193</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2S</th>\n",
       "      <td>-0.352996</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>-0.337886</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province_S</th>\n",
       "      <td>-0.165182</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>-0.164496</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2M</th>\n",
       "      <td>-0.153372</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>-0.141740</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_medium_mean_cont</th>\n",
       "      <td>-0.370769</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Container_Lowest_mean_cont</th>\n",
       "      <td>-0.393817</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1M</th>\n",
       "      <td>0.175779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_1X</th>\n",
       "      <td>0.251451</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_Total</th>\n",
       "      <td>0.108473</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock_On_Hand</th>\n",
       "      <td>0.105028</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2L</th>\n",
       "      <td>-0.089733</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Qty_Sold</th>\n",
       "      <td>0.087536</td>\n",
       "      <td>0.000108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2U</th>\n",
       "      <td>-0.069457</td>\n",
       "      <td>0.002137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>0.065115</td>\n",
       "      <td>0.004001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size_Grade_2X</th>\n",
       "      <td>-0.051624</td>\n",
       "      <td>0.022556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Correlation_Coefficient   P_Value\n",
       "Province_High_Mean_Prov                    0.262051  0.000000\n",
       "Province_L                                -0.365193  0.000000\n",
       "Size_Grade_2S                             -0.352996  0.000000\n",
       "Weight_Kg                                 -0.337886  0.000000\n",
       "Province_S                                -0.165182  0.000000\n",
       "High_Price                                -0.164496  0.000000\n",
       "Size_Grade_2M                             -0.153372  0.000000\n",
       "Low_Price                                 -0.141740  0.000000\n",
       "Container_medium_mean_cont                -0.370769  0.000000\n",
       "Container_Lowest_mean_cont                -0.393817  0.000000\n",
       "Size_Grade_1M                              0.175779  0.000000\n",
       "Size_Grade_1X                              0.251451  0.000000\n",
       "Sales_Total                                0.108473  0.000002\n",
       "Stock_On_Hand                              0.105028  0.000003\n",
       "Size_Grade_2L                             -0.089733  0.000072\n",
       "Total_Qty_Sold                             0.087536  0.000108\n",
       "Size_Grade_2U                             -0.069457  0.002137\n",
       "Day                                        0.065115  0.004001\n",
       "Size_Grade_2X                             -0.051624  0.022556"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Corellation and P-Values against avg_price_per_kg\n",
    "corrs = train_dummies.corr()['avg_price_per_kg'].sort_values(ascending=False)\n",
    "# Build a dictionary of correlation coefficients and p-values\n",
    "dict_cp = {}\n",
    "\n",
    "column_titles = [col for col in corrs.index if col!= 'avg_price_per_kg']\n",
    "for col in column_titles:\n",
    "    p_val = round(pearsonr(train_dummies[col], train_dummies['avg_price_per_kg'])[1],6)\n",
    "    dict_cp[col] = {'Correlation_Coefficient':corrs[col],\n",
    "                    'P_Value':p_val}\n",
    "#choosing columns with p values<0.1    \n",
    "train_cp = pd.DataFrame(dict_cp).T\n",
    "train_cp_sorted = train_cp.sort_values('P_Value')\n",
    "train_cp_sorted[train_cp_sorted['P_Value']<0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = train_dummies['avg_price_per_kg']  \n",
    " \n",
    "#Choosing features based on P-Values\n",
    "X_names = list(train_cp[train_cp['P_Value'] < 0.1].index)\n",
    "X_data = train_dummies[X_names]\n",
    "\n",
    "X_data_test=test_dummies[X_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To deal with collinearity we plot a matrix of the columns that are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High_Price</th>\n",
       "      <th>Low_Price</th>\n",
       "      <th>Weight_Kg</th>\n",
       "      <th>High_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Low_Price</th>\n",
       "      <td>0.938140</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>0.938140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High_Price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938140</td>\n",
       "      <td>0.905852</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight_Kg</th>\n",
       "      <td>0.905852</td>\n",
       "      <td>0.863182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High_Price  Low_Price  Weight_Kg  High_Price\n",
       "Low_Price     0.938140   1.000000   0.863182    0.938140\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "High_Price    1.000000   0.938140   0.905852    1.000000\n",
       "Weight_Kg     0.905852   0.863182   1.000000    0.905852"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation matrix and find rows and columns where correlation coefficients > 0.9 or <-0.9\n",
    "corr = X_data.corr()\n",
    "r, c = np.where(np.abs(corr) >0.9)\n",
    "\n",
    "# We are only interested in the off diagonal entries:\n",
    "off_diagonal = np.where(r != c)\n",
    "\n",
    "# Show the correlation matrix rows and columns where we have highly correlated off diagonal entries:\n",
    "corr.iloc[r[off_diagonal], c[off_diagonal]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we drop High_Price since it has a high correlation with the Weight_Kg and the Low_Price columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrics\n",
    "fig = plt.figure(figsize=(15,15));\n",
    "ax = fig.add_subplot(111);\n",
    "X_data=train_dummies.drop('High_Price',axis=1)\n",
    "X_data_test=test_dummies.drop('High_Price',axis=1)\n",
    "plot_corr(X_data.corr(), xnames = X_data.corr().columns, ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like most of the colinearity has been taken care of."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 4: Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1: Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We seperate our model building (test and training) sets for linear and non-linear modelling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for linear models\n",
    "\n",
    "X_lin=train_dummies[X_data.drop('avg_price_per_kg',axis=1).columns]\n",
    "y_lin=train_dummies['avg_price_per_kg']\n",
    "\n",
    "test_lin=test_dummies[X_data_test.columns]\n",
    "X_lin.head()\n",
    "# standardize features\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X_lin)\n",
    "test_scaled = scaler.fit_transform(test_lin)\n",
    "# Train test split\n",
    "x_train_ln, x_test_ln, y_train_ln,y_test_ln = train_test_split(X_scaled,y_lin,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand we achieve our best model scores on the non-linear models when we use only the numeric columns while including the day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1952 entries, 1 to 64310\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Province          1952 non-null   object \n",
      " 1   Container         1952 non-null   object \n",
      " 2   Size_Grade        1952 non-null   object \n",
      " 3   Weight_Kg         1952 non-null   float64\n",
      " 4   Low_Price         1952 non-null   float64\n",
      " 5   High_Price        1952 non-null   float64\n",
      " 6   Sales_Total       1952 non-null   float64\n",
      " 7   Total_Qty_Sold    1952 non-null   int64  \n",
      " 8   Total_Kg_Sold     1952 non-null   float64\n",
      " 9   Stock_On_Hand     1952 non-null   int64  \n",
      " 10  avg_price_per_kg  1952 non-null   float64\n",
      " 11  Day               1952 non-null   int64  \n",
      "dtypes: float64(6), int64(3), object(3)\n",
      "memory usage: 278.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting the training and testing data for non-linear models\n",
    "for x in test:\n",
    "    if np.issubdtype(test[x].dtype, np.number)==False:\n",
    "        test=test.drop(x,axis=1)\n",
    "for y in train:\n",
    "    if np.issubdtype(train[y].dtype, np.number)==False:\n",
    "        train=train.drop(y,axis=1)\n",
    "X=train.drop('avg_price_per_kg',axis=1)\n",
    "y=train['avg_price_per_kg']\n",
    "\n",
    "x_train, x_test, y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2: Modelling Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.1: Random Forest Model"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Random Forest Model utilise multiple randomly created decision trees by combining their output to generate a prediction\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The  combination of learning models will increase the accuracy of the result. The random forest algorithm allows easy  measurement of the relative importance of the features used on the prediction. \n",
    "\n",
    "Random Forest utilises an ensemble approach. It makes use of the predictions from several decision trees.\n",
    "1.\tSelect K random points\n",
    "2.\tIdentify n where n is the number of created regressors forthe decision trees. Steps 1 and 2 need to be repeated multiple times as many regression trees need to be created.\n",
    "3.\tEach branch’s average is assigned to the leaf node in every decision tree.\n",
    "4.\tTo predict an output for a variable, the average of every single predictions from all the decision trees are taken into consideration.\n",
    "\n",
    "Decision trees commonly suffer from overfitting. Random Forest prevents this by creating random subsets of the features and builds smaller trees using these subsets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilising the Random Forest Model:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Training "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the model to our data and applying hyperparamers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=200)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our forest consists of 100 trees with a max depth of 5\n",
    "RF = RandomForestRegressor(n_estimators=200, max_depth=5)\n",
    "RF.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Testing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand the performance of this model, we will get the mean square error and R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7354549990423779\n",
      "R^2 0.884540188182071\n"
     ]
    }
   ],
   "source": [
    "# Get predictions\n",
    "y_pred = RF.predict(x_test)\n",
    "\n",
    "# Compute RMSE\n",
    "print(\"RMSE:\",metrics.mean_squared_error(y_test,y_pred,squared=False))\n",
    "print(\"R^2\",metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will evaluate it against the expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "# Plot on axes\n",
    "ax.set_title('Actual vs Predicted')\n",
    "ax.set_xlabel('Actual')\n",
    "ax.set_ylabel('Predicted')\n",
    "ax.scatter(y_test,y_pred)\n",
    "ax.plot(y_test,y_test,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Adjusting Hyperparameters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standard hyperpameter values will not give the best model performance. For that reason, adjustments to the model hyperparameters must take place to get the best performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating different hyperparameters of the \"n_estimators\" to compare the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 trees in forest\n",
    "forest_1 = RandomForestRegressor(n_estimators=2, max_depth=5, random_state=23)\n",
    "\n",
    "# 20 trees in forest\n",
    "forest_2 = RandomForestRegressor(n_estimators=20, max_depth=5, random_state=23)\n",
    "\n",
    "# 100 trees in forest\n",
    "forest_3 = RandomForestRegressor(n_estimators=100, max_depth=5, random_state=23)\n",
    "\n",
    "# 1000 trees in forest\n",
    "forest_4 = RandomForestRegressor(n_estimators=1000, max_depth=5, random_state=23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=2, random_state=23)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=20, random_state=23)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, random_state=23)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_3.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, n_estimators=1000, random_state=23)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_4.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=4,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [forest_1.predict(x_test), forest_2.predict(x_test), forest_3.predict(x_test), forest_4.predict(x_test)]\n",
    "title = ['trees = 2', 'trees = 20', 'trees = 100', 'trees = 1000']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(4):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(pred[i],y_test)))\n",
    "    ax[i].set_title(title[i]+\"  (RMSE: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2.2: Boosting models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boosting is an ensemble method in which many predictors are trained sequentially and each succesive predictor learns from the errors of it's predecessor. \n",
    "In simple terms 'Many weak learners are combined to form a strong learner.\n",
    "\n",
    "The following Boosting methods will make use of CARTS( Classification and Regression Trees) similar to the Random forrest.\n",
    "\n",
    "We will use the following models:\n",
    "        \n",
    "        -AdaBoost (Adaptive Boosting):\n",
    "            After training, each predictor is assigned a coefficient that depends on the training error.\n",
    "            This coefficient adds more weight to the instances wrongly predicted, so that the next predictor \n",
    "            pays more attention to those values.\n",
    "        \n",
    "        -GradientBoost(GB):\n",
    "            Gradient boosting also does sequential correction of predecessor errors. But, unlike AdaBoost, the weight of\n",
    "            training instances are not tweaked. \n",
    "            Instead each predictor is trained using the residual errors of the previous \n",
    "            labels. \n",
    "            GB's exhastive search procedure means that each cart is trained to find the best split points \n",
    "            and features.\n",
    "            \n",
    "        \n",
    "        -Stochastic GradientBoost(SGB):\n",
    "            This method imporves the generalizability GB method by adding further variance to the ensemble diversity. Each \n",
    "            predictor is trained on a random subset of the training data that's been sampled without replacement. \n",
    "            Features are sampled without replacement when choosing split points.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(max_depth=5, max_features=0.75, n_estimators=200,\n",
       "                          random_state=1, subsample=0.9)"
      ]
     },
     "execution_count": 430,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision tree/ Base estimator\n",
    "dt = DecisionTreeRegressor(max_depth=5, random_state=1)\n",
    "\n",
    "# Instantiate Ada\n",
    "ada = AdaBoostRegressor(base_estimator=dt, n_estimators=200, random_state=1)\n",
    "ada.fit(x_train,y_train)\n",
    "# Instantiate GB\n",
    "gb = GradientBoostingRegressor(max_depth=5, \n",
    "            n_estimators=200,\n",
    "            random_state=1)\n",
    "gb.fit(x_train,y_train)\n",
    "\n",
    "# Instantiate SGBR\n",
    "sgb = GradientBoostingRegressor(max_depth=5, \n",
    "            subsample=0.9,\n",
    "            max_features=0.75,\n",
    "            n_estimators=200,                                \n",
    "            random_state=1)\n",
    "sgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "      <th>Train R^2</th>\n",
       "      <th>Test R^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.567357</td>\n",
       "      <td>0.757402</td>\n",
       "      <td>0.937433</td>\n",
       "      <td>0.877546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>0.120621</td>\n",
       "      <td>0.414874</td>\n",
       "      <td>0.997172</td>\n",
       "      <td>0.963259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGB</th>\n",
       "      <td>0.123121</td>\n",
       "      <td>0.406970</td>\n",
       "      <td>0.997054</td>\n",
       "      <td>0.964646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Training MSE  Test MSE  Train R^2  Test R^2\n",
       "Ada      0.567357  0.757402   0.937433  0.877546\n",
       "GB       0.120621  0.414874   0.997172  0.963259\n",
       "SGB      0.123121  0.406970   0.997054  0.964646"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dictionary of results\n",
    "train_ada = ada.predict(x_train)\n",
    "train_gb = gb.predict(x_train)\n",
    "train_sgb = sgb.predict(x_train)\n",
    "\n",
    "test_ada = ada.predict(x_test)\n",
    "test_gb = gb.predict(x_test)\n",
    "test_sgb = sgb.predict(x_test)\n",
    "\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_train, train_ada,squared=False),\n",
    "                        \"GB\": metrics.mean_squared_error(y_train, train_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_train, train_sgb,squared=False),\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Ada\": metrics.mean_squared_error(y_test, test_ada,squared=False),\n",
    "                        \"GB\": metrics.mean_squared_error(y_test, test_gb,squared=False),\n",
    "                        \"SGB\": metrics.mean_squared_error(y_test, test_sgb,squared=False)\n",
    "                },\n",
    "                'Train R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_train, train_ada),\n",
    "                        \"GB\":metrics.r2_score(y_train, train_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_train, train_sgb)\n",
    "                    \n",
    "                },\n",
    "                'Test R^2':{\n",
    "                        \"Ada\":metrics.r2_score(y_test, test_ada),\n",
    "                        \"GB\":metrics.r2_score(y_test, test_gb),\n",
    "                        \"SGB\":metrics.r2_score(y_test, test_sgb)\n",
    "                }\n",
    "                }\n",
    "pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bcHrGr/U9Zoxp5Xq7tr/rc1OcN3rhjlf3thfOz2A9TiXlXsr26BxjjOnpepMwAKcCGuhZbwPdjLP8QBpOw3gn8LUrmL/q+p6uALq7GnnheAc4whhzgXHm+90LLHH9h+OXMeYEnB4u3wxZXwCrgaGuv/dOOMMxvN8cnIJTiRWJKmttPk52ueeNMb2MMXsaY1KMMW3x/9YM8MyBO5HQHRtuQeORy0ignzFmPz/H7gJOcb1t9y3LPcaYY40xNV2/n/1xemojaYTshfN2bYcx5jhKhst7u8c1kqI1ztxkv73K1to1OJn6Rhhj9jDOMjPXUDKv5Smc4eXXAjNxGpAhueLzNOABY0wdVww5D/8NXvcw77auL3fMOwbXXGlXHNoDSAVSXWX1zoZ3KjC7HG8SRSISRv3iCZyRRv8zTrIh4+o0aRvknrVwhjz/ReBkQB6uqRufELz+9DPO/9n+Eg+tdF17t+8x4yzXcJ0xZh/394uTOC3S+tNOnO+pNk5nlq9zXM+siTO3br4rLvnzJnCVMaat62f2sOv8VcaYQ3Ey5PbF+VkOdv0fEY4JOPWtk1wNxQeAaT4jGNzuwen8a+v6mo4zmuEq9wmuWOVO2lTLlE12lzz1J2utvmL0hfOq/3E/+y/CCS41cHpQc3FeQ09yfT3kOq8JzhyTrTg9Gtfj9KrUCPP5R+BUuIzr85U4QwG9z9kfJygcidPL4/f+OD1Bq1zb7rHg7nHqeTjpaut6PafIVW7vr2P93DcVp6G1GefN5WCcIQSnu44PxxkHP8n1M8rFGUblvt5zrte+83ESK2zCyV7V2rV/GvCi13ln4/Q47e36vAzoE+TneTpOJqYC199Lc69jdwEf+Jz/EvC/APdqjdOo3+Yq6/lexxrjDDGtGe9/w/pKni+c+RTf4qSbXo9T4e/n/neI09O9y/W7vA2nY+JhIKUcz/gOZ+SB+7MFDvY55wNccdP1e3ZtgHutBU51bQ/DGQ61GWd40Wc482i9n7PNJx6NDnDfXjhDkLbgpFZ/FhjvOtbcda9+rtjxFzDY69rh7nO99u3vus8GnNETN7j2n4cTOxu4Pu+JkySmj+tzmZjic98GOIm23H8Xl3oda+b6Hpv5uc79PdTwKbf1+RrudXwm0CPe/0b1Vf2/CFG/cJ3TBKfe8Kfr3/kKnBExh7mOu/+Nu3/X83HqAmXqIEHK0c379y/A73YHVxn3wen4WBvgXtcCn7m2jwBm4CSY2opTh3kESPN6TiFl60/7+LnvnjgJrra4YtbleMVUnJj9Ik7mz604HcotvK73F39vcP08N7ji1v44ddVvgSFe590ILAVquT5vBU4K8vO81BWntrnK3MDr2It41c18rhuHq07sU+5SX17HjgVy4/3vOFZf7sq9JAljzJs447Nz4l2WSBgnnfjB1tq+8S5LrBhjHgdWWCdtr0i1YYw5E2fphKx4lyUSxllzdCVOBawi89OqDGNMG2CMtfb4kCeLVCPGmK9wEoLkhjw5ARljxuE0NIfFuyyxYoyZCrxqrX0/3mWJBTXqpEpJxkadiCSmZGzUiUjVlIyNumSjOXUiIiIiIiJVmN7UiYiIiIiIVGF6UyciIiIiIlKFqVEnIiIiIiJShdUIfUr8NWzY0DZv3jzexRCRSrRw4cJ/rbWN4l2OilBsEqmeFJ9EJBEFi01VolHXvHlzFixYEO9iiEglMsb8Ee8yVJRik0j1pPgkIokoWGzS8EsREREREZEqTI06ERERERGRKkyNOhERERERkSpMjToREREREZEqTI06ERERERGRKkyNOhERERERkSpMjToREREREZEqLGqNOmPMWGPMP8aYH/wcu8MYY40xDaP1fBERERERkWQQzTd144CzfHcaY5oCZwCro/hsERERERGRpBC1Rp219gtgg59DTwKDARutZ4tIHCxaBKvVVyMiCWbDBpg7N96lEBEp67PPYMuWSrlVTOfUGWN6AHnW2sVhnNvPGLPAGLNg/fr1MSidiETsoYegXTu49tp4lyTqFJtEqpANG2DvveHEE6GoKN6liTrFJ5EqpGNH6NwZXnyxUm4Xs0adMaY2cDdwbzjnW2vHWGvbW2vbN2rUKLqFE5HITZgA99zjbD/7bHzLEgOKTSJVxI4dToMO4KKLIDU1vuWJAcUnkSri6qth/nxn++abK+WWsXxTdxDQAlhsjFkF7A98b4zZL4ZlEJHK9Nln0Levs/3VV3DooXEtjogIAMXFkJ7ubLduDZMmxbc8IiJuI0bAa68BcO5dk2nxwBw6jZxNTm5ehW5bozLKFg5r7VJgH/dnV8OuvbX231iVQUQq0U8/OcMGwKkwdeoU3/KIiLh5vZXr1Pdp1g2ZSZOMdLK7tiSrXWYcCyYiSW3iRLjrLgDOuWEMPxY5nU95+QUMnbYUIOIYFc0lDSYC3wAtjTFrjTHXROtZIhJjf/0Fhx/ubD/yiDO0SUQkEbRs6dk87O73ycsvwFJSaapob7iISES++AIuvRSAm/o9yY/1mpQ6XFBYxKhZyyO+fdTe1Flre4c43jxazxaRKNq2DRo3dravuw4GD45veURE3C64AH75BYBTH/yAgq2lk6O4K016WyciMbV8OZxyirP95pt8sLiu39PW5RdE/IiYZr8UkSquqAj23NPZ7tQJxoyJb3lERNyGDoVp05ztDRv4Y6v/bJcVqTSJiJTbP/9Aq1bO9v/9H/TuTZOMdL+nBtofDjXqRCQ81kIN18v9vfZyEqOIiCSCV16BkSOd7ZUroX79qFSaRETKZft22HdfZ/vKKz3z6bK7tiQ9rXRG3vS0VLK7tiRSatSJSHjcQQlg06b4lUNExNusWc5QcIDvvoPmzYHoVJpERMJWVAR16jjbxx3nyXgJTjKUET3bkJmRjgEyM9IZ0bNNhYaGxyz7pYhUYaedBu6FbAsLwZj4lkdEBGDxYjjrLGd7+nRo395zyF05GjVrOevyC5T9UkRiyz26qVatkjXpvGS1y6zUeKRGnYgEd/PNMHu2s715c0mQEhGJp7VroW1bZ/uZZ6B79zKnVHalSUQkLO6EcgAFsZnHq+GXIhLY6NHw/PPOdl6eM5dORCTeNm+Gpk2d7QED4JZb4locERGPM890ln4C2LUrZqOb1KgTEf+mTYOBA53tpUuhSZPg54uIxEJhIdSr52yfdRY8+WR8yyMi4ta/P3z8sbO9aROkpcXs0WrUiUhZ8+Y56z0BfPIJHHFEfMsjIgJOFt6aNZ3tzEz44IP4lkdExO3pp50vgDVroK7/teiiRY06ESltxQo4/nhn+7XXnCQpIiKJIN1rOYK1a+NXDhERb+++67ylA1i0CPbfP+ZFUKNOREr89x8cfLCzfe+9zpoqIiKJ4LjjYOdOZ3v37viWRUTE7dtvISvL2f7wQzjqqLgUQ406EXHs2AENGzrbl1wC998f3/KIiLhddZWzBh04i/mmpgY/X0QkFlauhA4dnO2XX4auXeNWFDXqRASKi0uGNbVpAxMnxrc8IiJuDz8M48Y523//XXoIpohIvGzcCAce6GwPHQrXXhvX4qhRJyKle72XLIlfOUREvE2cCHff7WwvXw777BPf8oiIgDMUvEEDZ/uCC5zOpzhTo04k2R16aMl2cXH8yiEi4u2LL+DSS53tL78sHatEROLFWthjD2f70ENhypT4lsdFjTqRZNazJ/z6q7O9c2fMFsgUEQnq55/hlFOc7bfeghNPjG95RETcUryaT8uXx68cPtSoE0lWQ4fCO+842xs3lqz9JCIST//8A4cd5myPHAkXXxzf8oiIuLVuXbJdVBS/cvihRp1IMnrlFaeyBLBqFWRkxLM0IiKO7dth332d7WuugTvvjG95RETcLroIfvzR2d6xo/QbuwRQI94FEJEYmzULrrvO2f7uOzjggPiWR0SqrZzcPEbNWs66/AKaZKST3bUlWe0y/Z9cVAR16jjbHTs6nU8iIongnntg8mRn+7//oFat+JbHDzXqRJLJ4sVw1lnO9vTp0L59fMsjItVWTm4eQ6ctpaDQGaKUl1/A0GlLAfw37Gq4qiTp6fDNN7EqpohIcGPHwkMPOdsrVpRkvUwwatSJJIu1a6FtW2f72Wehe/e4FkdEqrdRs5Z7GnRuBYVFjJq1vGyjrnHjku1t22JQOhFJZmGPIvj4Y2coOMC8eSXr0iUgNepEksHmzdC0qbM9cCDcfHN8yyMi1d66/ILw9p95Jvz1l7O9a5ey8IpIVIU9imDJEic+AUybBh06xLqo5ZJYM/xEpPIVFkK9es722WfDE0/EtzwikhSaZKSH3t+/v9MTDk7nU1paDEomIsks2CgCj7w8OOooZ3v0aDj//NgVMEJq1IlUZ9aWLFWw//7w/vtReUxObh6dRs6mxZCZdBo5m5zcvKg8R0SqjuyuLUlPSy21Lz0tleyuLZ0PTz/tfIEzPHyvvWJcQhFJRiFHEWzZ4tSZAG65xel8qgI0/FKkOkv36hFfsyYqjyh3MgQRSQru33+/81befbekorR4MWQqVohIbDTJSCfPT8OuSUY67N4Ndes6O047DZ55Jsali5wadSLV1XHHwc6dzvbu3VF7TLmSIYhIUslql1k2Dnz7LWRlOduzZsGRR8a8XCKSvLK7tizVGQ2uUQRnHloyBLxRI/jkkziVMDJq1IlUR1dd5axBB85ivqmpwc+vgLCTIYiIrFxZkmzg1VdLkhCIiMRIwFEEJ7UsOenvv+NUusipUSdSxfmm5X15zSwOHzfOOfj336WHYEZB0GEMIiJuGzaUpAO/+264+ur4lkdEklaZUQTHH1+ynMru3VUyC2/UEqUYY8YaY/4xxvzgtW+UMeZnY8wSY8w7xpiMaD1fJBm457Pl5RdggWO+/oDDn3/UObh8OeyzT9TLEDIZgogkLXcSpZZ35MDeezs7e/UqWchXRCQOvBO8zTj2HGcNOoCtW6M6uimaopn9chxwls++j4EjrLVHAr8AQ6P4fJFqz3s+W4fVS3l6xmMA3Hj9aDj00JiUIatdJiN6tiEzIx0DZGakM6JnG82nE0lynk6njdtZ/riTDnzF3k3JuWt0fAsmIknNu0P8hnmT6b7gAwA++DgX6tSJc+kiF7Xhl9baL4wxzX32feT1cR7QK1rPF0kG7nlrB/23hkkTnT6SW3oM5sOMg2NaDr/JEEQkqbk7nVY92t2z77RrXyBTSZREJI7csan7j59z5+evA3DaNS+wY8EGzj49zoWrgHiuU3c18EEcny9S5TXJSKfhto18+sqNAIw85UreO+xkzWcTkbhbl1/Apy/f4PncfPAMz34RkXhZl1/AsWt+4JkZowC4uPcIVjRsWuVjU1wadcaYu4HdwIQg5/QzxiwwxixYv3597AonUoUMObkZC569DIC3jjyTFzv20ny2KFNsEgnP2JmPcNCGtQAcOugdT+IBdTpFj+KTSGgdC/9l8ptDAOh/7iDmN2sDVP3YFPNGnTHmCuBcoI+11gY6z1o7xlrb3lrbvlGjRrEroEhVUVRE906HALC02eEMPfs2zWeLAcUmkTDcfTedf/gSgCP7v8WuGs7aT+p0ii7FJ5EQ/vmHiU9cCcCoky7j3dadgeoRm2K6pIEx5izgTuAUa+32WD5bpNqp4fr1rVOHNn8sY2V8SyMi4nj1VXj4YQA+mvE1e/1QwBbvtaDU6SQi8bB9O+y7LwCru19IzglXYqpRbIpao84YMxE4FWhojFkL3IeT7bIW8LFxhmHMs9beEPAmIuLffvuVbG/ZEr9yiIh4++gjuPZaZ/vbbznz2GM589z4FklEqj/fNXvLNNKKikoyW7ZvT7PpbzM3PkWNmmhmv+ztZ/er0XqeSNI44wxnUXGAwsIquUCmiFQ9IStNS5ZA166uk3Pg2GPjUk4RSS7uJQrcSzzl5RcwdNpSgJIY5R7dlJYG330Xj2JGXUyHX4pIBd16K3zyibO9eXNJkBIRqUS+DbjOrRoxdWFe4ErT2rVw1FHOxU8/DeedF6+ii0iS8V6z162gsIhR7uVTMr06n3bujHHpYieeSxqISHk89RQ8+6yzvXYt7LVXfMsjItWS98K8FqcBN2He6oCVJjZvhqZNnZ39+zudTyIiMRJoKYJ1+QVw1lmwbp2zY9euaj26SY06kargnXdgwABne8mS0r1OIiKVaPj0ZWUacIFSVf/93xaoV8/5cOaZMHp0VMsmIuIr0FIEI796DWbNcj5s2uQMvazGNHZLJMH4Dnt6uMk2Trmip3Pwo4+gTZv4FlBEqq2c3DzyCwrDO9lafnssy9lu3Lik8iQiEkPZXVuWmlMHcO2i97h47lTnw+rVULdunEoXO2rUiSQQ38m+KatWcspQVya5sWOdJCkiIlEyatbysM/96YleJR/cw5tERKIoWMIm9/6L1uUybNaLzgW5uSXDwyvhGYlMjTqRBOI92bdewRa+fMlp0I3r3Jcrr7oqnkUTkSQQaG6Kr3feGET6bifhwEHZ77IimoUSESF4lku3I//8hUf+d4/z4YMPoG3bSntGojfs1KgTSSDuClWt3btY/LSzKsh7rU7i/uMu4coA11TVHiURSTxNMtLJ89Owq187jdo1a5CXX8Cj74+m3Z/OG71Wt09hvwZ7RvQsxS4RKY9AWS6HT1/Gzt3F7P3vOt5943YA7j3nNo7etw1ZlfQMTybNBKZGnQiJU7lokpHOuo3bWP64M4duecNm3HLenWQGmARclXuURCTx+Jubkp6Wyn3dWwPw+6BhXLTUWVblmFvGY2rXIbtry3I/R7FLRMor0EiC/IJC6u7YylcvXgPA8x178UabM/k0goZY0EyaCU7ZLyXp+UvfPXTaUnJy82JeluyuLVn5aA/P567XPE96WmrASlOwHiURkfLKapfJiJ5tyMxIxwCZGemM6NmGrHaZZP38BbfPGQfAade+yB6ZjT3HykuxS0TKq166/+yVNXcXsuSpSwD48NDjefSUK4HIGmKBMmkG2p9I9KZOkl4ivWrPuqSLZ7vF4BlkhnhrWJV7lEQkMfkmHRg1azl7L5zHSddd6pzwxRd8etJJFXqGYpeIlJffJeas5ZfHzwfg9/pNuOH8uz2HImmIBRqtEMmIhFhTo06SXsJULi64AH75xdnesYOVtWqFvCTQ/Jeq0KMkIonJd2hkrRW/ctLQG5yDEydCBRt0oNglIuW3cXvZ5VZWPdrds92l3xjPdqQNMd9Orao031eNOkl6CVG5uOsumDbN2d6wAcJo0EHV7lESkdgo75xh79ELe2/LZ/YrToPuxa7XcMMll1RKmRS7RKS8Uo2hyFrP51mv3uTZzlmwmsyPf62UhlhWu8wq0YjzpUadJL24Vy5eeQVGjHC2V66E+vXDvrQq9yiJSPRFkpDE3cm1R+EOFj7bF4BJbc5gZNvzuaGSyqXYJSLl7XDybtA9++4jtPx3NQAtB01j+TFNyTqmfOvRVTdq1EnSi2vlYtYsuO46Z/u776B583LfIlSPUqJk9hSR2AuWAjxQXDCAKS7iZ9fi4rmNW3LnOf3xN52lIqpqb7iIVFygDqcFf2xgzs/r/camFAPFFgZ+OZ5zf/4SgLa3vUlhWs24fR+JRI06EeJUuVi8GM46y9mePh3atwcqtxGmtOEiyS1YCvD8Amd+im9csMDKUecBsKNGTc6//HEArN87iYiUX6AOpwnzVntijW9sKrZw4ZKP6f/1WwCc3O9l8tPrKji5aEkDkXhYuxbatnW2n3kGujsTfSt7eQWlDRdJbuHODfaOC/Ofu9yzv9XtU6NSLhFJboE6nHzbZwWFRQx6ezEthszkxJW5jPrgKQCyLnuc1fUbR7mUVYsadSKxtnkzNHWN+x4wAG65xXOoshthCZPZU0TiIrtrS9LTUsM6Ny+/gC9aHM2+WzcAcPAdOaVyiNev7X+NKBGR8ipPMroiazl0/SrGv30PANeffxeLmpTkPcgIsH5dslGjTiSWCguhXj1n+6yz4MknSx2u7EZYVV5EU0Qqzt9i4oEaZ/d+MoaTV+UCcMSAt9mdWjJDIy3VcF/31rEosogkAX8dToHm7e675V9mjXU6wB/och2zDj3BcywtxTC8h2ITaE6dSOxYCzVdk3kzM+GDD8qcUtnLK8Q9s6eIxJ3vnGHfubYAVyycwdULpwPQ8cZxbK1Vm1RjKLZWCZZEpNL5S1LXuVUjpi7MKxWb6uzczvznrwTg9aO7MfZYZ76vAcUmH2rUicRK7dol22vX+k2IUtmNMKUNFxFfvnHh9F/ncf8nLwFw1lXP8FfdhgAUW8vKkd3iVk4Rqd78Jalrf0ADT2xKs8UsG30RAF8dcBT3nXEj4Iw4mDukS8zLm+jUqBOJhQ4dYMcOZ3v37oBZKUf0bMOInm0qtRGmtOEi4ssTF777Do57CIDLLnqAn/dp4TlHw7RFJNY8sclaSHFmif2XXpe+l/wfoNFGwahRJxJtV10F337rbG/bBqmpQROizB3SRY0wEamwkMujrFwJxx0HwD3d+vNli6M9h1RxEpFoChmf6tb1bPa4Zxpm0w6NNgpBjTqRaHr4YRg3ztn++2/PEExlpRSRaPI3GmDApEUMn76M4T1ak9W8Nhx4oHPy0KEcc+HNzNYwbRGJgZDx6eYLYetW5+Tdu5mbGl4G32SnRp1IlHw34jmOvftuAHrfPo6L8wrJ2sc5VtkJUUREvPkbDQDOouP3vr2QrJFOsgEuuAAefpgsUCNORGIiWHzaffU1sOgbZ8fWraAGXdi0pIFIFHz5ylSOvctJv3vhpSP5Jq1hqUXEO7dq5Pe6QPtFRMrDX6cRANayxN2gO/RQmDIldoUSESFwfLp+/hR6LZrlfPjzT6hTJ4alqvr0pk6ksi1fzknX9QLg1u7ZfNf0CMCZMzfo7cUMnLSIFON/NZY5P6+PWTFFpGoLNicl1RiKrC1zzapHu5d8WL48VkUVEQGcuGUA3+jU7acvGfrZOABOv+YFPtlvv1gXrcpTo04kiECVpoCVqX/+gVatAHjklCuYcfgppe7nrmT5q2xBkN51EREvgTLogjOM0l+M+eiVmzzbJ/7fx3wVm6KKiHiMmrW8TIOu/dplPDf9EQAu6f0wBQcfGvuCVQNRG35pjBlrjPnHGPOD174GxpiPjTG/uv6sH63ni1SUu9KUl1+ApaTS1Oflbxg4aVGZ/TO+/g323ReAGe3P5oWOF5b7mSn+X+CJiJQSLIMuOOs4eXs2ZySH/rcagCOHvMsdZx8Wm4KKiLjk5OaV6bxusSGPKRPuBGDAuYNYfFA7Zd6NUDTn1I0DzvLZNwT41Fp7CPCp67NIQgpUaZq7YkOZXqadO3fRvdMhzoeOHSka8zLpaeWf3Fvs/wWeiEgpoTLoNt+7pFF3+xf/49zlznu5Ywe+xQMXHaOkKCISNTm5eXQaOZsWQ2bSaeRscnLzPB3l3hps38Scl68H4LGT+vJdp3MY0bON4lOEojb80lr7hTGmuc/u84BTXduvA58Bd0arDCIVUZ7lBX4f5SQeKEirxeGnDKPJrOVccEwmc35ez7r8AlICzG8REYlEsAy6Obl5zF2xAYALl3zEbd9MAuCk619hfc09VWESkagJNDS8Vo2UUh3ltQp38v0zfQCYekQXnj3hElYN6RKXMlcXsc5+ua+19k8A15/7xPj5ImELd3mBb5+9zLN92MApniGZUxfmkd21JStHduPxi46K6M2diIg/2V1blokp6WmpdG7VyNMbfuLKXEZ98DQA5132OGsylHhARKIr0Cin/IJCz2dji1n+xAUALNnvYAZ1uz2mZayuEnZJA2NMP2PMAmPMgvXrlRFQYs9fpcl3ytsbk+5hn20bATgo+13wymrpPb8lq10mI3q2ITMjHUOp00qpXzutsoovUaLYJImiVo2S/8Lr105jRM82zFzyJwWFRbT6ZyXj374HgH7n383iJpqjkgwUnyQevIdbhpPwbeWjPQAoTEmlxxWjo1y65BHr7Jd/G2MaW2v/NMY0Bv4JdKK1dgwwBqB9+/YatyYxc8YTn/HrP9s8n1NwUu82yUinc6tGTJi3Ggvc98lLnLwqF4DWA96mKKXsmzjv4JbVLtMz7CknN4/sKYspLCr5p52Warive+uofE9SeRSbJN58hzcB7CgsZsEfG9i4vZB9t/zLh6/dCsDw0/rx0aHHx6uoEmOKTxJr/uJRMF8/f6Vn+5A7cqJTqCQV60bddOAKYKTrz3dj/HyRoHwbdADFwCH71OHj20/17Kvx7LNctXAGAB1uGse2WrX93i81wCs5d+Mu0BpTIiKBBBreNH7eaurs3M58V6XptWO6M659jziUUESShb94FMhrk++jyZZ/ATj4jpzAw5YkIlFr1BljJuIkRWlojFkL3IfTmHvbGHMNsBoof853kSjybdD52/+Q+R0+HQPAWVc9w997NQx4v2DJUbzf3ImIhCtQEqfU4iKWjb4IgC8PaMv9p19f5pw6NTW3V0QqT7hJ5YZ9+jKdf18IQJsBk9idWroJouZdxUUz+2XvAIdOi9YzRSoiJzcv6PHmQ2Zy+pY/eOX5mwG47KIH+HmfFkGv8V0rSkSkovxmvrSWFa4svOvrZHDZJQ/5vfb/zm8T7eKJSBIJlInX22Xfv8e1C5zBecff+BpbatUpc06fjs2iUr5kkrCJUkRizZ3UJJD98//yNOgGn30bX7Y4Ouj56WmpWkBTRCpVTm4e23ftLrN/2ZMlA1+OvWW832v7dmym0QEiUqn8JZXzdtpv83nw4xcB6HblU/xZt1GZc2qnpfBQljqcKirWc+pEElawIQT1Crbw1UvXAvDM8Rfz9pFnBr1XpubIiUgly8nNI3vyYgqLSw/rnva/QdQp3AHAgdn+p6rXr52mSpOIVEhObl7AXAADJy3Cd8JJmz9/5dWpDwJw9UX3s2zfg8rcMz0tlYd7KjZVBjXqRFwCDSGoubuQxU87o4lntuzE4ydfVuYcX3O1gKaIVLLh05eVadCN/OBpjl7njDJodfsUiv1k4U1PS1VmXRGpkECLirv5Nuj23/Q3M94Y6Hx46SV6HNuN5bOWk5dfQKoxFFmrDvBKpkadJB13T5NvYOncqhHj560ufbK1/PL4+QD81mB/bs4aGocSi0gyCdQb7r14L8CN8yZzyZKPAGh/y//YkbYH6WmpXHBMJnN+Xq/MuiJSaQJl3R01azn523eV2l93x1a+evEaAMaffDF9+/UjCxSHokyNOkkqw3KWetaZg5LslHn5BUxdWDZRyqpHu3u2T7/uxbCekaoUTiISIX+94QMnLWLApEWlzuv+4+fc+fnrAJx27Qv8V6e+er1FJGoCTVHxHeGUVlTIkqcuAeCjQzqy5+jHo142cahRJ0kjJzev7Js4L749UJ++fINnu/ngGWE/5/GL2pa7bCIi4L833HdY07FrfuCZGaMAuOjSkazYuymrRnaLUQlFJBmFk+USa/n1MWd006qMxvTrOYzR0S+auCj7pSSNu6YtCfvc5995mIM2rAXg0EHvlFkgM9UYDE7ygYz0NAxOcpTRF7dVL7mIRCzUmk8H/reWyW8OAeC27tl82/SIWBRLRJJcqCyXUHp006nXvwyEziwulUdv6iRpbC8sDuu8O754g3N++RqAI/u/xa4aaWXOKbaWleoZF5FKFqw3fO9t+cx+xRlB8OjJlzP98FMAp5OpPIJlsBMRCaRWjZQyIwncPnz1Zs92i8HTPdvhLk4uFadGnVQZoSoilVFRuWjxR9zyzdsAnHj9K2zeY0+/5zXRouIiUk7hxKjsri3LzJ8D2KNwBwuf7QvA5CNO5/njL/Ic692habnKECiDnRp2IuKPb9zw9cy7j9Dq3z8AaDloGtaUDAQsb31JnU6R0/BLqRLcASUvvwBLSUUkJzcvrONQZgRlKZkZ6Zy9bgmPfvg0AD0uf4K1Gfv5PVeLiotIeYUTo9x8Q1VKcRE/P9ELgEWNDyW724BSx8uz/lywDHYiIv74ixtuA78cT/efvwSg7W1vsrNGTc8xA+WqL5UnTkpZatRJlRCqIhLouPc8uj4dmvm9d9+OzZh7TkNe+N9dAFzXcxhLGh9a6hz3HLrMjHRG9GyjXiMRCVtObh6D3l4csjHlrtD4Jkb5fdR5AOxMTSPr8if83j9cgYZCaYiUiAQSKD5cuORj+n/9FgAn93uZ/PS6pY5byjcCQJ1OFaPhl1IlhKqIBJqDsr2wmGE5S3koq42nN3vi/DUUWUuqMfTu0JSH2teHps7wpftOv56PD+lY5j6aQycikXA31NzLp/jyjm3+KjTznrvcs91y0DS/9xg1a3nYFadAc/Y0pFwkOYUz3NFf3Oi0ahGjPngKgPP7Psbq+o3L3DuznHFFnU4Vozd1UiUEqnC49wdLFDBx/hrACVxzfl5PsWux8ccvOoqHuhzgadDRvz/vntjT7z3qpZdNliIiEkqwYUtQOrb5Vlxef/te9tu6AYCD78gJOIa8PBUefxnsNKRcJDmFO9zRN260XL+KCZOGAXB91l3kZrYqc++0VFPuuBKqrifBqVEnVUKgVLrbdu4mJzcvYC84OAuM+wtc90zOhXr1APi74yl02q8H+QWFfu9RzuRyIiJA6AbX9l1ODMvJzSPFK9Dc8+nLnLLyewCOGPA2u1MDD6wpT4Unq10mI3q2ITMjXUPKRZJcOMMdh+Us5fa3F3nO22fLf8waewsAD3a5llktTyhz3/q10xjV66hyxxV1OlWMhl9KleAODPfPWMbG7SUNr/yCQoZOW0qKgeIA7bpUYxg+fVnpwGUtS0c466ms36sBJ3YeTGGQylf+dv+NPRGRYEIt2LtxeyHZUxaDxdM5dfnCGVyz4F0AOt44jq21age8PpIKT1a7TDXiRCTkcMdhOUsZP2+1Z3/tXQV8+/wVAEw4uhuvHptV5traaSnk3ntmROVxxyVlv4yMGnVSJQzLWeqZC+cr2NAmhy3zBu7HJ3t5to+96Y3ALUIXvfoXkUhkd21J9uTFFAaJMYVFJcdO+20+D3zyEgBnX/U0f9VtGPC6TFV4RKQCAnU6uaecTPBq0KUWF/HjkxcC8HWzIxnV4zZSdhSWqj6lGHi455EVKpM6nSKn4ZeS8Nw9RcGGWAZT5HPZtP8NonbhTgAOzH435PV69S8ikcpql8mee4TXf9rmz195deqDAFxx4f38tM+BAc/NzEhn7pAuqvyISMSyu7YkLaXs/JLNOwoZluOViddaVriy8G5Ir8ulvR9mU0EhT1zUttRQ7icuaquYFEdq1EnC8+4pCiQ9Lbx/yo+8/xRHr3PGire6fQrFKWXn6XmrXzst4vkmObl5dBo5mxZDZtJp5GytsyKSpMIZvr3/pr+Z8cZAAO4861Y+P/CYgOeWd+0nERF/stplkuKnUVdsKTXscsnoiz3bR986AXDe5mmYZGLR8EtJeOG8nysoLA55zo3zJnPx0o8BOOaW8exI2yPkNbVr1oi4QTd02lLP0FB3Riko35otIlL1hZpXV3fHVr568RoAnu/Yi0lHdQ16v/Ku/SQiEsjO3cHrT5PHD6buru2Aa3STK6HTtl27PVNbVMdJDHpTJwmtst5udf/xc+78/HUAulz7Iv/VyQjrukjXRtECmiLiFuytWs3dhSx56hIAPjj0BB495cqQ9yvv2k8iIpF4+MNnODbvRwAOG1h6dFOhz9wW1XHiT406SVjut10VdeyaH3hmxigALrx0JL/vvX/Y10aaIEULaIqIW8Cea2v55fHzAfi9fhNuPP+ukPfSHF8RiYV+86dy6eJZABx78/8oqBl6dJPqOPGl4ZeSsEIt2huOA/9by+Q3hwBwa/dsvmt6RNjXVmTeSqDhVsqiKZIccnLzSs03MQZ8cz2terS7Z7tLvzEB75VqDMXWat6KiFS6OjVT2bardF2r209fctdnrwFw+jXPs37P+mHdS3Wc+FKjThJWRXt89t6Wz+xXbnA+PPww35sOEOCeaamm1FACA/Tp2CziylN215al5tSBethFkoW/ObUppvT84Fmv3uTZbjF4esB7paelanFwEak0vh1Ovg26Y9b+yHPTHwGg9yUP81vDZmHdV3Wc+FOjThJWqOQCwexRuIOFz/YF4I8eF3PA0KFk5+YxcNIiv4lX6tSsQZ1aNSoti5MW0BRJLt4VpRRjyizB4r2W07PvPkLLf53Mci0HTcMa/zMhtA6diFQmfx1O3lpsyGPqhMEADOx2O98cEN6ac4pViUGNOklY/t52hSO1uIifn3AWF1+S2Yoj330LcBpaAyYt8nvNpoJCFt13ZoXK60sLaIokB9+KUrA1NYd89hrn/vwlAG1ve5OdNWoGPFeVJBGpTMGmtey3+V/mvHw9AI+f2Id3jugS1j1HX6y16RJF0EadMaZBsOPW2g2VWxyREu4gEaghFoh7gUyA32d8gnc/U2aAt38pxtBiyEy9UUtSinUSqZzcPAa9vThoQ87tvk9e4qqFMwA4ud/L5KfXDXr+8OnLwopFvsOpFMOqF8UniZRvbAg0+qnOzu3Me+FKAKa17swznXqHdf/0tBTFmgQS6k3dQpxpAAZoBmx0bWcAq4EW0SycSFa7TO6fsYyNYSzeC7DqkXM92zkL15QJNoHe/rkrZFprJWkp1km5ud/QhdOgu3TRB54G3Q1ZQ1ldv3HIa9xrQIVTBq2JWa0pPknY3A25vPwCDCVzeQM16FKKi1g2+iLP59vPHQRQ6tpA9khLDXGGxFLQRp21tgWAMeZFYLq19n3X57OB0yN9qDFmIHAtzr+XpcBV1todkd5Pqrf7urcme8riMmuipJjS81S8G3Ts2AE//kunkbNL9V4DmBBhyr3WiipEySNasU6qJ+9KUzhOWLWIh2c9B8CjJ1/Ohy07hf2sPi9/w6r/CgK+hQu2JqZiWPWg+CTh8u3kCd3dBL97jW5qfud7nu1wrg23w11iI9w5dcdaa29wf7DWfmCMeTCSBxpjMoHbgMOttQXGmLeBS4BxkdxPqr5hOUuZOH8NRdaSagy9OzTloaw2pYYN1EtPY9fuIrYXFnuu827Qffqy558n73+6hF0//lum9zp78mIwZRfM9EdrrSStSot1Uj35VppCOfC/tbw5aRgA0w87meePvyjEFaXNXVEyss7fWzitiZlUFJ8kqPIuBeXdGd588IxyPy/VmHJfI9ETbqPuX2PMMGA8TuO9L/BfBZ+bbowpBGoD6ypwL6nChuUsZfy81Z7PRdYyft5qVq7fyverN3mCU35BIYFCx5PvPc5BG9YC8MmUOZzTpQ2dRs4uE9gKi8Ppd3JorZWkVdmxTqqZ8lSaMgo2e5ZVWV1vX27rMbjCz/d9C6c1MZOK4pMEVZ7OHO8GXctB0yCCBlqRtcpHkEDCbdT1Bu4D3sEJJF+49pWbtTbPGPMYzjjwAuAja+1HkdxLqr4356/2u9+7d9rNX5Os3/ypnL9sjvPho4/Y2vAQOo2cHfFSCKC1VpJcpcU6qZ7CrTSlFRWy6OlLPZ9PvuHVgOemp6WWq3fduwxaEzOpKD5JUOEuBfXhqzd7to+5ZXzQLLyhWALP5fWXxAm03FO0hNWoc2VW6m+M2dNau7UiDzTG1AfOw5nYmw9MNsb0tdaO9zmvH9APoFmz8BY+lKqnHC/Pyjjzl2+467PXABh25k3M/Maydcficr2R86W1VpJbOLFOsSm5hVVpspZfHzvf89F7noo/I3q2KVeWX++3cFoTM3koPkko2V1bBlyP123UzNG0+vcPAM68+ln+q5NRKc/2HUXgL4lT9pTFYEtGTimxU+Xyv+KpD2PMCcaYH4EfXZ+PMsY8H+EzTwdWWmvXW2sLgWnACb4nWWvHWGvbW2vbN2rUKMJHSXXV+u8VjHnn/wB4/ehujG93Dhu3F0bcoEsxzlorc4d0UWBJYuHEOsWm5JbdtSXpPhnffP8jXfVod892qHkqmRnpZLXLJCM9Lazn+3sLl9Uuk7lDurByZDfFsGpM8UlCyWqXGbRBd82373DhD58AcGWv4fzSqHmlPt97FIG/oeqFRbZMPc3dGJSKC6tRBzwJdMU1dttauxg4OcJnrgY6GmNqG2MMcBrwU4T3kiouPS3cf4Il9tnyHzPH9Qfg+yYtue+MGytcjnrpaaoICVRurJNqKKtdJiN6tiEzI93JKZ+eRrHXce95Kgdlvxtynoq7gTa8R2vSUsqee8g+dTzPysxIZ0TPNopVyUvxSULKDDCf9rTf5nPPHGcY+PDT+vHZQe3DvmftMOtq3qMIyjO/T4mdKke4c+qw1q4xpf9zCn8CQOn7zDfGTAG+B3YDucCYSO7lTYuvJqZQfy8jeh7J7ZMWlaoUBbNH4Q6+ff4Kz+eelz1eKeXMV1pecamsWCfVV1a7TLLaZZKTm8dAr2GT3z3T17N9ZP+3KEoJvoZT347NPPFQwyglHIpPEkrnVo1KJaADOOyf33l1qpMo9c2jujKufY+w75eelsrDPduw4I8NnkzlBkhJMRR5vXXzHUUQ7vw+97lSceE26tYYY04ArDGmJs6SBBG/XbPW3ocz2bdSaPHVxBTO34v7z+HTl4VcaNfYYn5+opfnc6h5KuWRUTutzJp2+reTlCo11kn15W7Quas0L099kEbb8wE4ud/LbN5jT7/XGQgYY9yNRZEAFJ8kqJzcPKYuzCu1r9HWjXzw2m0A/LDvQdx11q1B75GWaqhTswabCgpLxaqsdpk8lNWm1LOCdUL5S+KUlmpKzakDJXaqTOE26m4AngIygbXAR8BN0SpUeWnx1cQU6O9l0NuLgdINO3evd7BkASsfLelZqswGHcCmgkLPIprl6RTQG+JqJ6FjnSQGd4eVu1oy8MsJnPHbfAAuvHQkq+s3Dnhtk4x01uUXeOaQKF5IOSg+iYe/+odvvatW4U6+e+4yz+dzr3wq6D1TjaGwyFKnVg2G92gdND6F6oQKNPrA3z7FwcoRbqOupbW2j/cOY0wnYG7lF6n8tPhqYgr08y+y1m+jKatdJqNmLff7ut57nkqLwdMjLlNaqvG7+LhvfpVwOgX0hrhaSuhYJ7HnW3Hq3KqRZwgSQI8fP6P/1xMBGHTOQL5rekTAexnwxDfFC4mA4pMAgesfpTrSrWX5Exd4PgbrDE9LMRRZ64lrefkFZE8u3QEfiUANP8W86Ag3S8UzYe6Li0BjcTVGN77qBcnmFijbUXbXlmUWGfdu0B02cArWlD+5CjiTh2v4SUQQSKhOgWBviKXKSuhYJ7Hlrjjl5Rd41mIaP2+1p+JzdN5PPD3jMQBe7HABU9ucFvR+vt1JihdSTopPAgSuf6R6zbcslYU3SIMuMyOdtFRTpnO7sNgyfPqyyimwxETQN3XGmONxlhtoZIy53etQXSD4DPAY0uKriSlE0jfy8gvIyc0rNZ+ufu20UhWfyeMHe7aPu+l1CmruEXF55g7pQvMhM8M+P1SngN4QVx9VJdZJ5Qo2fDonN49Bby/2NOB87b/pb6aNzwbgi+btGHnqVRGVIdxEAuWloeHVh+KT+Ao2EgpKd4YfmP2u33PT01I92XQD1Y1C5TqQxBJq+GVNYE/XeXt57d8M9PJ7RRwoa1hiCiejpO8cuo1e19z3yUscm/cjAN2uGM0/e+0dcVnKu3RCOJ0CgTI76Q1xlVQlYp1UnmDDpwGGTlsasEG3587tfPXiNQBsrlmbyy9+MOJypIbq/YqAhoZXO4pPUkqwzJK/jMrybB8+cDLFAbLw1qoR2agnSVxBG3XW2s+Bz40x46y1f8SoTBFR1rDEU550tr765L7PVQudRXuvP/8ulu13cIXKsodrseD6tdNKNRy91a+dRv72wrA7BfSGuPqoSrFOKkeg4Uv3z1hG7Zo1yhxzSyku4ofRF3k+Hznw7ZDPSk9LDXi/QA3HilDysOpF8Sn5hBpFsGHbTr/XvfXmEGoW7wbg+BtfY3vNwJ3M+QWFns6eQHWj+rUDT6ORxBNuM/0VY0yG+4Mxpr4xZlZ0iiTVRXbXlqSnlX9kSKdVi/i/j54HYOQpVzLr0BMqXBb3W8P7urd2Uur66NuxGbn3nsnKkd2YO6RLWBUf30WItTBwtaBYlyQCDV/auL0waGfU76PO82yHk4XXABcckxlwQWB/+3Ny8+g0cjYthsyk08jZ5OTm+bkyMA0Nr7YUn5KAv7m8AyYtosWQmTQfMpOBkxZRUFh2dd97Pn2Zjmt+AKDH5U/wZ91GIZ/l7uzxVzdKSzXc1711pXxPEhvhZr9saK3Nd3+w1m40xuwTnSJJdeE9LDbcN3YH/beGCZOGAZBz+Cm82LFyRpa4h0RGOlQ3UK+Z3hBXO4p1SaJ2zVS27Srfus3e81SaD54R1jUWmPPz+rDf7FfG0EkNDa+2FJ+SgL837VCSaMnfu/2LF8/imgXO3LmbzhvCksaHhv28vPyCsOtGmqub2MJt1BUbY5pZa1cDGGMOwP+/K5FS3I2eYTlLGT9vddBzMwo28+krNwLwe/0mDOieXe7nhbOwZXkbYpqfklQU65JATm5ehRp0LQdNC50Jykt5Kk2VMXRSQ8OrLcWnJFDeN+rH/7GYRz50kqA+fmIf3m91Yrmud8/rDVU3Ul0o8YXbqLsb+MoY87nr88lAv+gUSaqbnNw85vy8Pug5aUWFLHr6Us/nLv3GlPs5mVFa2FLzU5KKYl0SKO8yAh+9UrK+8zG3jGdnjZrluj7cShNUztBJJQ+rthSfkkBGkLn/vlpsyGPiW3cDMLNlJ57p1Lvczwt3Xq/qQokvrEadtfZDY8zRQEecKQIDrbX/RrVkkvDCeQ2fk5vH7W8vKrP+SSnW8utj53s+hjNPxVemz/MrM8BofkryUKxLDuX53X185hMc+p8zyuCMq5/jvzoZ5X5eeZKhVNbQSQ0Nr34Un5JDuOGiXsEW5rx8PQB5ezXi5qyhET0v0HxfX6oLJb6giVKMMa1cfx4NNAPWAXlAM9c+SVL+JvIOnba0zIT+u6YtCd6gI/wFMoMJ9PzKoMXtqz/FuuQS7u/utd9O44IfZgNwxYX382ujAyJ6XriVJvCfYEpDJ5Ob4lP15psYKZy14dKKCln8dMlbuU43vRbyGn+ZLA1O/SmchEyqCyW+UNkvB7n+fNzP12NRLJckuGCv4b1t95OhyZv3PJWDAiyQGS5/z68MqmQlBcW6JBJOZt4j//yFYXPGAnDf6dfz+YHHRPy87bt2h93hpKy64ofiUzXlr4M85GzdCEc35d57JqMvbuvpZDKUTMgMp2NcdaHEF2qduutcf3aOTXGkqgj2Gt57WGYwIz942rPdZsAkigIskFkZ5aoIzU+p/hTrkov7d3f49GV+e8X33/Q309+4HYAJbc/i9WO6lzmnPDZuLyxXQgENnRRvik/Vl78O8lCjL5c8dYlnO9wsvG7u2NJp5Owyw7xDzY9TXSjxBW3UGWN6BjturZ1WucWRqiAnN48UY/zOE6mXnlYm65o/N86bzCVLPgKg/S3/Y0utOpVStmgNA1Alq3pTrEs+We0yGTVreZlGXd0dW/nqxWsAeKFDLx459cpKeZ4SCkikFJ+qr/J2RE+acCd1d24DXKObypGFNyc3j6x2meTk5gVcZipUeVQXSmyhEqW4uyf3AU4AZrs+dwY+AxRIkoT77Zt7aIC/nqT0tFSMIWSD7tyfvuDOz18H4LRrX+DfOvUrpYwaBiAVoFiXhHwrNmlFhZ5e8FmHdKy0Bp2bEgpIhBSfqqlAiZH8efjDZ+mwdhkAhw+cXO7RTe7pKe5RA4HKI1VX0Dl11tqrrLVX4dThD7fWXmCtvQDQEvNJJCc3j+wpiz2BJ9DQgD3SUkKm4W2/dhnPTn8UgIsuHcmKvZtWShkz0tM070Qiplgn3vNUVtZvzPU9h1X6I1RhkkgoPlVfgeap+bpu/jQuXfwhAMfe/Abba5Y/lqzLLwi4sLn7ueoYr9rCXaeuubX2T6/PfwPhL1cvMRXOUgPluYcxhMxgCc68kUBv8cBZT2XKhDsBuK17Nt82PaJcZQpm5+7gCVlEwqRYVw2Eu9yKN+8svJ37vVzpZTJA51aNKv2+klQUn6qZQPPUBkxa5DnnnJ+/4u7PnKRNZ1z9HOv3bBDRs+qlpwV9K6iO8aov3EbdZ8aYWcBEnDr7JcCcqJVKIubOpOTuiXFnNILw127zvUc5llgK2KDbe1u+Zz2VR0++nOmHnxL+TcNQUFjEgEmLGDVruSbuSkUo1lURgRpu/mJg9uTF3D9jGfnbCz3nemfK/WDsLZ7tFoOnV6hcGelp1KlVo0zlyQJTF+bR/oAGik8SKcWnasBf7Jo7pItn/0CvBt3Ra3/i+XdHAtD7kocjXlYFYNuu3QGPZWakKy5VA+EuPn6LMeZ84GTXrjHW2neiVyyJVLClBsL9hQ32ej4StQp3svDZvgBMPuJ0nj/+okq7t69IGrEibop1VUOwzit/8auw2HqGhuflFzBw0iJPB9TT0x/lsPWrAGg5aBrW+J+VkJGexvAerclql0m7Bz7yO9TcgOecSLLLiQSj+FT1BYpdC/7YwNSFeaViV/MNeUybkA3A7d0G8s0BR1bo2YVF/rvdNeyy+gj3TR3A98AWa+0nxpjaxpi9rLVbolUwiUywpQYqeo9IpBQXsfyJCwBY1PhQsrsNqLR7B6KKk1SQYl2CC9Z5FU78cldtBn45gR4/fQFA29veZGeNmmXOzfQzfDM/wNxhS0lnUmXEYhE/FJ+qsECxa+L8NaUyitffvonPXKObnjixD9OOOC1qZdKwy+ojrEadMeY6oB/QADgIyAReBKL3r0wiEiiTUqgJ+t7DAQItVxCJ30edB8DO1DSyLn+iUu4ZDlWcJBKKdVVDsAZTuNnkei39hP5fTwTg5H4vk59et8w59Wun0blVIwa9vZgBkxaRagy9OzQN+IxMrzgbaSwWCUTxqeoLFLu861y1CneS+0wfAN45/FSe7tS71LlpKU4dLZxcBwBpqYY6NWv4XZNTwy6rl6DZL73cDHQCNgNYa3/FSa0rCSZQJqVgr9bdwwHy8guwUGkNum+eu8Kz3XJQ6IzLKQY6HdSAVNe6K8Y4+7ylpRjSUkOvy6KKk0RIsa4KCPT77Z6f4i97nLcTVi3isfdHA3B+38dYXb9xmXPSUg2HN96L8fNWe2JikbWMn7ea5nunh4yzkcRikRAUn6ow9xq/wRhb7BndtGyfAxnY/Y5Sx+vUTGXUhUcFbdBlpKd5tuvXTmNUr6MY3qO14lESCHf45U5r7S7jqWybGoRe9F7iIFAmpWA9MYHm0BlTviQp3sa9fR+Nt/4HwMF35IS1QGaxhVX/FbBixDmAEwDvn7HMM3fFPacFYPj0ZX57nUCBSipEsS7B+EsqkN21Zal5KeD83ndu1cgTz1IDjDg4dP0q3pzkLFdwfdZd5Ga2KnOOAUb1OopBby/2W6Z5v2/k8YuOChpnI4nFIiEoPiWQ8mQad3eeh+o0X/loD892t6ueLnXMAMseOAvAs26wr8yMdOYO6RLw/opH1Vu4jbrPjTF3AenGmDOAm4AZ0SuWVERWu8xy/aIGGg4QaYPunk9f5tSVCwE4YsDb7E4Nf+pmXn4BnUbOpnOrRmUmDbuXLXB/f94LorsrcP7mv4iUg2JdAgmUVGBEzzaM6NmmVAXFN2b4qzzts+U/PnJlunywy7XManlCmXMM8OTFbclql1kqrbi3ImvDirPljcUiISg+JYjyZhoPJwHdVy9c7dluPrjsX6t3RAvUsRWsQ1vxqPoLt7Z9J3AtsBS4HngfeCVahZLYCncOSjgu+/49rlnwLgAdbxzH1lq1y32PvPwCJsxbXab70TcBigKURIFiXQIJlhBl7pAupX7/2z3wUdBKU+1dBXz7vDMk/I123Xj12Cy/59VIMSz4Y0OpJQ98pYYx8kAkChSfEkR5Mo3n5OaFrGO9MuV+9t/8DwCH3PFOwNFNObl5peo+evMm3kI26owxKcASa+0RQOWvyCpx4T1sYI+0cKdWBnfab/N58OMXATj7qqf5q27DiO8V6CWhEqBItCjWJZ5wM0jm5Ob5XWLALbW4iB+fvBCAr5sdyb1n3hjw3MJi67dTyVvvDk2DHBWpfIpPiaU8scn9Bi+QoXPGcvqK7wA4sv9bFKamBTxXHdsSTMhGnbW22Biz2BjTzFq7OhaFkujyHTZQUFhc4Xu2+fNXXp36IABXXHg/P+1zYFjXBZr3EogSoEi0KNZFX3nmoED4GSSDvVXDWla4svBuSK/Lpb0fDlnOQBHJnf3yoaw2Ie8hUpkUnxJLqNjkPT0kmL6573P9t04iuRNuHMvmPfYMer46tiWYcIdfNgaWGWO+Bba5d1prewS+JDBjTAbOkIEjcP7/vNpa+00k95Lyq+zFxfff9Dcz3hgIwJ1n3crnBx4T1nXpaameeTH+Ap+hdOVKCVAkBio11kmJ8s5BgfDmjYQa2rTkqUs820ffOiHi8hvwJHESiRPFpygpb4dTsNiUk5tH9uTFFIZYc6Dziu946KPnAeh2xWjW1Q2dyLRJRnq5yyrJI9xG3f2V/NyngA+ttb2MMTWB8k+8kohVZk9P3R1b+erFawB4vmMvJh3VNazrDKUXvPQXHC84JpM5P69X4JJYquxYJy7lmYPiFmjeCEDb+z8KmAHXbdKEO6m706n7Hpj9blhZeH07k9w0SkASgOJTFETS4RRsTlvb+z8K2aBr/ddvvDbF+eu8qtd9LNvv4JDlTEs1dG7VqNxlleQRtFFnjNkDuAE4GGdi7qvW2t0VeaAxpi5wMnAlgLV2F7CrIveU8qmsxChpRYWeXvAPDj2BR0+5MqzrUlMMj194VKlx4aAJvxI/0Yh1Ulq4c1B8+c4bCbcX/OEPn6XD2mUAHDZwCsUpwdeuA2dNp25HNi6TeVejBCSeFJ+iK5IOJwg8py1UZ1OTzf8w8/UBAAw78ybmHHRsyDLWr53Gfd1bR1xWSQ6h3tS9DhQCXwJnA4cD/Sv4zAOB9cBrxpijgIVAf2vtNu+TjDH9gH4AzZo1q+AjxZu/YQMpAIagC1qWYi2/PnY+AL/Xb8KN59/lOVQ7LYWCwmLqpadhDGzcXhhyyQFN+JU4CzvWKTZFJtz5caGMmrU8ZIPuuvnTuHTxhwAce/P/KKi5R1j3zr33TADaH9BAnUySSBSfoijSDqdI1N2xla9dSxe8dFxPxrcLPqTbPU3FHX8GBlhmRXPtBEI36g631rYBMMa8CnxbSc88GrjVWjvfGPMUMAS4x/ska+0YYAxA+/bttbhmJcpql8mCPzYwcf4aiqzFACbFUBR2iw5WPdrds92l3xjPdlqKk3hFFSGpYsKOdYpNkYlkXSV/QlVezvn5K+7+bCwAp1/zPOv3rB/WfTO9GpfqZJIEo/gURZXV4QTOSIJAvEc3fXLQsYzofHXAcwG/neCVWVapfkLlsve8Q67EV/1rgbXW2vmuz1NwGnlSyXJy8+g0cjYthsyk08jZnmCTk5vH1IV5nqyTFsrVoPvw1Zs92y0GTy91rLDYuZ97nHewACeSQKIR68RLVrtMRvRsQ2ZGOganwuLdAx2uYJWXo9f+xPPvjgTgkt4P81vD8N5UaHilJDjFpyjK7tqS9LTSw7MjiQk5uXlkT1ns/6DX6Ka1dffh2l73Bb3X6IvbllmLszLLKtVTqDd1RxljNru2DZDu+mwAa62tW94HWmv/MsasMca0tNYuB04DfizvfSS4YBN/K5L98pl3H6HVv38A0HLQNKwJ3C+gcd5ShVR6rJOyKuMNWOdWjRg/r2xG9+Yb8pg2IRuAAecOYl6zI8O6X6Ah4SIJRPEpiiprXv+oWcspLPLfQf77oyUJSk+8cWzQ+/Tt2CyiBC0iQRt11trQM8sjcyswwZX58nfgqig9J2kFm0wbaZKUgV+Op/vPXwLQ9rY32VmjZshrNM5bqoIoxjqpBKHWfGqwfROfvXw9AI+f2Iec1p2D3s8AfTo203pzUiUoPkVfZXQ4BarvvP/araS4cur6jm7y5k6GEqocGh4ugYS7pEGlstYuAtrH49nJItjEX2OgHOt9A3Dhko/p//VbAJzc72Xy08PrGNQ4bxGpCN9RB75qFe7k+2f6ADCtdWee6dQbcCpI+dsLaZKRTudWjbQ8iohUulAdTqNnjOLwf1YC0PL2qX5HN2WkpzG8R+jGnEgocWnUSfQFmkyb4spCWR6dVi1i1AdPAZB12eOsrt84rOs0zltEKirYcHFji1n+xAUALN33IG4/d5DnWDg93iIikQrV4dT/qzfJ+vFzANrdOoGdabVKHdeIAalsoRKlSBXlbzItELJBl+bzL6Ll+lVMmDQMgOvPv4tFTYI30iqaBEFExFuw4eIrXfNUdpsUul/5VKljAyYtUqImEYmaYB1OPX/4lIFz3wTg1OteYmPtemXOqZeexoR5q0slshOpCL2pq6Z8J9OG+4auRmoqx7Wox9crNtBoy3/MGnsLAA90uY5Zh54Q9NrMjHTmDulS8cKLSNJyD2dyD5UMNFz8qxdKpmIfnP2u33sNmLSI4dOXsamgUMMuRaRSBZrmcvwfi3li5pMA9OwzilUN/Mcc9yLl3onsFJ+kIvSmrpryrRiFO+SyoLCIeb9vpPbO7Xz7/BUAvH50N8Yee17Q6zTUUkQqyj2cKS+/wLM0ir/QNXbycPbfvB6Ag+/IAWMC3jO/oFDLrIhIpfOXM+CQ9X8w8a27AbjpvCF8v/9hYd2roLCI+2csq9TySfJRo66aycnNo90DHzFg0qJSFaPAVR4/inazbPRFAHx1wFHcd8aNnkPuoZV9OzbzLNabaowns6YqTCISqXCWW7lr9qt0+X0BAG0GTGJ3avgDTtxxSkSkorK7tixVt2q0dQMfj3XW8f2/U6/m/VYnlut+G7cXqg4lFaJGXTXi7uXeuL2wzDEL4TXsrGXFKOet3L+169H3kv/zHMrMSGflyG7MHdKFh7LaeObtud8CqidcRCoi1BIofb+fSb/v3gHg+BtfY0utOpX+DBGRcGS1y8Q9kKD2rgK+e+5yAN486ixe7tDT7zWpxpDum7zAizqdpCI0p64aCdXLbXHSfPtr9Lktdb2hA2h/y/hSx/7bsiPk89xDCDQuXERC8R0mXi89zTPPxFeX377loY9fAKDblU/xZ91GET1Ty6yISCi+sck9H3dYzlImzl9TakpLanERPz55IQDzmx7BXWfdUuZ+oy9u66kX5eTmMWDSIr/PVaeTVITe1FUj4QSDHYXFjL64LX07Nivz5m7K+Gz22uXc4/Ah08vMU9lRZDnyvg9DPk9DCEQkFH/z57bt2u33P6Uj/vqNsVMfAODKXsNZtu9BET3TgOb+ikhQ/mLT0GlL6fPyN4yft7p0jgKv0U2ba9Xh4ktHlrpXqjGlGnTgvOHLSE/z+2x1OklFqFFXjYQTDNxzSh7KasOTF7f1LEHw9OznaZ/3k3PS1q1st/7/aWzeWfJmLtjzNIRARILx96a/sMhS7HNe5qZ/eO/1AQAM7XoLnx3UPuA909NSGX1x24BDzS3KLiciwQUahTR3xYYy5y56urdn+8j+b5U5Xmyt35gzvEfrMstOKeGcVJQaddVIoLXpfLnfsGW1y2TukC6sbLCMHt+97xz86y+oE948lWDBR0MIRCSYcGJE3R1bmfvi1QC82OECJrY9K+C53mtjBupwql/bf++4iAg4b+mCrY3pbdKbQ8jYsRWAg7Lf9ZuFN1AsymqXyYiebbS2r1QqzamrZmrVSPH0MAVa36leehrtHviIjdsL6fbTlzw3/REALh04lm+eXBD26/+sdpkMn77M7xwYDSEQEX/cc1VCLbKSVlTIkqcuAeDjgzsw8tSrAp7ru0ZmdteWZE9ZTGFR6ads3bGbnNw8VZxEpAz3sMtw/N+sZ+mw5gcADh84maKUsh3qod68ZbXLVCySSqVGXRXlO4m3c6tGTF2YV2rIQKAFx92NsPZrl3kadBf3HsH8mvsAhN1LBc4QgqHTlpZ6roYQiIivnNw87p+xLGiiJg9r+fWx8wH4I2M/rrvgnoCn+os3gTqcCosto2YtV0VKRMoIZ0kVgGu/nUafRU5+gWNvfoPtNUs6sQ3OMO9Mr+Qq/vjGw4z0NIb3aK3YJBWiRl0V5O5NcgefvPwCJsxbXabnu6g4cF94iw15TJlwJwD9zx3E/GZtwnp23Vqle6Oy2mWy4I8NnmxQqcZwwTHqfRJJZuF0OgWz6tHunu1Trn+lzPFUV4dVsIrTpgBZNDU0XCR5BcpqCeF1aJ/981cMmzMWgDOufo71ezYAIC3FMOrCo8Kq++Tk5pUZSZBfUEj25MWA5v1K5NSoSxDBAo0vf71JoYYyeWuwfRNzXr7euddJl/Fu685hX7vk/tJzWnJy85i6MM/zRrDIWqYuzKP9AQ0UmESSULidToF8MLYkHXiLwdNLHUtPSw173kmTjHS/lbQMzasTSUr+YpN7uGU4MeXotT/xwrtOdsvel/wfvzY6wHOssNgy6O3FDJy0KKw6nO/QcPc9NJJAKkKJUqIsJzePTiNn02LITDqNnO031X+g9LmBlgWoSE9zrcKdfP9MHwCmHHEaz51wcdjXZvqZJxcoS5SyX4okp4p0Oj09/VEOW78KgJaDpmFN6f+iLjgmk1GzlgeNp27ZXVuSllo2cYF7Xp2IJJeK1FcO2LiOaROyARh0zkC+OeCoMucUWVvhOpxGEkhFqFEXReE21sobaAIlIQmUxttz3Baz/IkLAFi83yHc0W1gWN8HBJ4nFygAKTCJJKdIf/cHfDWBHj99AUDb295kZ42apY7Xr53G1IV5YXd+ZbXLpE7NsoNR3L3hIpJcgtVXhuUETpBSf/smPh/TD4AnO13K1DanhXxWJHW4UMdEQlGjLorCbayF2zByv/XLyy8o04BLT0ulT8dmQcuz8tEeAOxKqcF5VzxZ6liKcSpN7tS6fTs2CyvVbj0toCkiXiL53b9g6acMmDsRgFP6jSE/vW6p42mpBmspdy+75tWJiFug2JRRO40J81b7PVZr9y5yXaOb3j3sFJ468dKwnxcozgQaRZCWYpRkTipEc+oqINQ8uHAba4HmfjTJSPc8w92Qcw9jspTNsgQwPkBgmvt8STrwQ+94ByhZ8sA72YD7eRPmraZJRjpPXtw2aPambbt2l9mvwCSSvLK7tiyTEdc7dvk6/o/FPP6+08nUs+8o/qjfpMw5Fx/bNGClK1gDLVhsFZHk4i82paelYq3/+GRsMcsf7wnAj/u0oH+P7HI9L9gadYCyX0qlU6MuQuFMuA1UoaiXnkankbODZoZLT0ulc6tGpZ7hG3TcDbq5Q7oEXV9l7OThZG5ZD8DBd+SAMfTt2IyHskpnvCzvJOJAk3333KOGApNIkvHu5KqXnsYeaSnkby8MGAcBDln/BxPfuhuAG7KG8n3mYX7Pm7owj4zaaX6XQwjWQAtUiVOnk0jycddLfDvjB0xa5Pd89+gmgHOueibgfTPS09i5u7hccUZr1Ek0aPhlhMIZWpndtSXpaaWXAEhLMWzeUVhqXsjEb9dwwTGZZYY7zvl5fcgU4O5e6kDrq9w9+xW6/L4AgCMGvM3u1BrUqZlapkEX7vfk79m+8sNZh0pEqg3f+cP5BYXsKCzmyYvbMndIF79Jlhpt3cDHY28G4MHO1/Bhy04B719QWIS1lImn4VScRvRsE9ZQchGpnrwT1o2atZzsri1ZObIbc4d0IatdJqmm7FDIL168xrPdfPCMoPffVFCoOCMJQW/qIhTO0Ep/vUL/bt1J4e7Sb7eKii3vfJ/HsgdKLxcQqPfIm7uX2l95Lvv+Pa77LgeA4298ja21agOwbZf/hmJ5k55oaJOIQPAOoax2mWV6w2vvKuC75y4H4H/tzuHV484P+YxNBYU8eXHbsJd+cVOPuEjyCmcEkntJJreXpz5Is01/A3DIHe84c1WCaJKRrjgjCUGNugiF26Dx/UVvPmSm3/v5a2i5F9gNxOAEqE4jZ5cZmtTlt2958OMXATjnyqf5s26joN+Pu+zlaaRpaJOIQPk6hFKLi/jxyQsB+KZZG+4586awnqGKk4iUV6gOJ3CGT+a7kioNmTOWM36bD8CR/d+iMDX4upaq80giUaMuQtFo0AzLWcrE+WsosjZkgw5K5tjl5ReQllLSk3TEX78xduoDAFxx4f38uO+Bpa7LCJCxsrzfU6Dx6ap0iSSXYB1Cw3KWliRwspYVo84DYOMee9G794iw7q+Kk4hEIlSHU05uHlt2OgnfLl30ATd8Ow2ATjeMZfMeewa9d/3aadzXXclNJHGoURehSBs0wbLAeWeuDNWg81VY7Jyfuekf3nt9AABDut7C5wceU+bc4T1a+71HJN+Tes5FJFCHUOdWjUrFtUVP9/Zst7vtzYD3y0hPwxg8iVbUWSQikQg1Aun+GcsoKracuuI7Hp71HADnXjGavHr7hLx37ZpKCieJRY26CoikQdOnYzO/yw4Ea+yFq+6Orcx98WoAXujQi7fanlXmnBTjP5OlmxppIskr1DItwc694JhM5vy8vtS13kmWJr05hIwdWwE4KPtdv/NU0tNSlWBARCpNsA6ndg98xMbthbT+ewXjptwPwNUX3MsP+x0c1r213qUkGjXqYsydddJ7mGXvDk0Dri8XrrSiQpY8dQkAsw7pyCOnXun3vEs7BF+gXESSU3mWNPF37tSFeYzo6cS3UbOWM3DSIk9H1f/NepYOa34A4PCBkylKKZ3FErROk4iUKE8HUzDeI5Dy8gtINYaCwiJPnavJ5n+YOa4/AMPOuJHZBx8X9r2VFE4SjRp1MZaTm8ecn9dTbG2pRb/djbyIWMuvjznZ41bWb8z1PYf5Pc3f2nQikpx8K03bd+0OmVDALVDygftnLGNHYen1mq79dhp9Fn0IwLE3v8H2mmUrQqMvbqvGnIgAgTuYFvyxocxogHBHEvhbD3ivndv4+gVndNOYY89n/NHdApbJdzSV5vlKIopbo84YkwosAPKstefGqxyxFKwnPNDbuhQDxSHaeqse7e7Z7tzv5TLHvYc0VVbvl4hUXf5iUSD+jgUaduS7OPjZP3/FsDljATjj6udYv2cDv9cpBomIW6BOI+86Ul5+AdmTFwPhjSSYMG91qUZZWlEhS0dfDMDsA9vzcJdrCCQ9LdXv8HLFLUk08XxT1x/4CagbxzLEVLDUunOHdAHKDstsf0ADTyMsxU9GzA/G3uLZbjF4eplnphpTqkEX7vAqEam+/MWi8giUfMDb0Wt/4oV3RwLQ+5KH+bXRAX7P23evmhGXQ0Sqn3DnqhUWW+6atqRMR7W/+Faq5uQ1umlt3UZcfeHwoM/RPF+pKuLSqDPG7A90A/4PuD0eZYiHUKl1H8pq43d4pDuY+DbKnp7+KIetXwVAy0HTsCalzLXF1pYaUx7u8CoRqb4qOsE/UPIB9+cDNq5j2oRsAG7vNpBvDjgy4L2GnnN4hcoiItVLOJ1GbtsLi9nuOtfdUR2qw+o317IqACfe+FrIZ6h+JFVF2VZAbIwGBgPFcXp+XASaVBvuZNusdpmM6NmGzIx0Bnw1gR4/fQFA29veZGcN/73d3vcuzwLBIlJ9lWeCf6qfLJXescgAmRnpHN2sHgD1t2/i8zH9AHjixD5MO+K0oPf3zpApIpLdtSXpaWWTKYWjoLDIb8xye29cf2pYp+rpPbopUGW400H+h4yLJKKYN+qMMecC/1hrF4Y4r58xZoExZsH69etjVLro8heoyjvZNqtdJnMbr2HA3IkAnNzvZfLT/Y9g9b13RRuVIlI9YpO/WJQSoB7Uu0NTv/uz2mUyd0gXVo7sRnbXlny9YgO1CneS+0wfAN45/FSe7tTb77Xe1KkkUnmqQ3zy12lUHkXW+m0UPvHe4xzx9woAWt4+FWtSyEhPIzMjHQvUTC0dBDsd1IAJ1x0f6bchEnPxGH7ZCehhjDkH2AOoa4wZb63t632StXYMMAagffv2FV3CLaYCJSOJdMHyUj79FK68EoB+Nz7D6rqN/Z7mLz14oCFTyuAkEr6qHJvcAsWiBX9sKDOvN1DGXO84l2IM2GKWP3EBAMv2OZCB3e8IqyzqVBKpPNUhPkHZNXPb3v8R+QWFQa4okZmRTvO905m7YoNn361zJ9Jz2RwAjr51AjvTagGwbdduz313FVlPlsvMjHQubK8loKRqiXmjzlo7FBgKYIw5FbjDt0FXlYVKRlKhxb1/+AFOP93ZnjqVc1p04EufRprBWeA82Nw8Zb8UEX+xKKtdZljLnvjGuSJrWfVoDwCKMXS76umwyqBOJREJx/AercmevJhCr3TgKUBqqqGwqGRfelpqmQbd+T/MZtBXEwDofN1LbKhdz3PM+1ooSaiiRHJSFWmdukoWtWQkf/4JbVyVrSeegJ49yfJ6ZriNtAo1KkVEKBvnvnyxJB34gV7zVAyQUTutzFIHUDozr4gkh0iXVQrUKe1v36C3F3uuO/6PJTw58wkALujzKCsbhB9vlEhOqpq4NuqstZ8Bn8WzDJUtKslItm6FJk2c7ZtugoEDPYfUSBMRX9Fej9I7M93LUx+g6aa/ATjkjnfAlaTAPWqg/QEN/A77VoNOJLlUdFmlQPUd330DJi0C4OB/VzPxrbsAuLnHnSzcv3Sm3VQ/y0T50pxfqUrilf2y2qr0ZCS7d8NeeznbnTvDc89FWDIRSQbuilNefgGWkopTTm5epT3DnV1u6JyxnPHbtwC0GTCJwtQ0T2KDJy9uy0NZbfwmPVCDTiT5BBvJVJlSjaHR1o188upNAIw49UpmHnZSqXPS01Lp3aFpyCybmvMrVYmGX1aySk1GYi2kpTnbe+8Ns2dXUilFpLqKxXqURdbSN/d9rv92GgAn3DiWLbXqALByZLcy52tEgYhEYySTv1EJlx+1N/f1duLQm0d15aUOvQCoUzOV7buKSo1eaH9AA0bNWk5efoEnSYqb5vxKVaNGXQX5CygjerapnKFP9euXbFfR1MQiEluxWI+y11+Leeij5wHodsVo1tXdByh/6nERSR6BFhWP9G2Yv+Gcd09ZxLKHzwVgwf6Hc9dZtwbN5Ovd4RTtYesi0aZGXQUEGh8+omcb5g7pUrGbn3wybNrkbO/e7ZmnIiISTDQqTvfPWOZJdtJx4yreev1uAK7sdR/L9jsYUK+2iARX2csqlRmVYC3LHu7ubO+1F+3XLGNVOe6nEQVS1alRFyZ/PThRG+Z0/fXw5ZfO9tatkBp8zLeIiFtlVpxycvPInrLYk/a7yeZ/eGvMLQC8e/0wfm1+Mka92iIShkiXVQr0Bs238+r7Z/qUfHB3ioskETXqwhDojZxvg87NXy952B57DMaMcbb//BPq1In8XiKSdCpzPcpRs5Z7GnR1d2zl6xeuBuDF43ryYuOTWFTREQkiklTK+zYsWMZM7+yVEycOpUHBZgAOHTydXzS6SZKQGnVhCPRGLpCIQ8nkyZCd7WwvWwb77RfpnUQkiVXWMCL3PLy0okKWPHUJAB8ffBwjO18NBWXXnhMRqUzBRkS5G3QPfvQ8x692GnqtB7zNLqPE7pKc9C8/DOVNMBB81ZMAvv4aLrrI2Z4zBw4/PPj5IiJR1iQjHazl18fOB2B1vX257oJ741wqEUkWwRI/ZWakc82373BZ7vsAHHfT62yrVVsJmyRp6U1dGAIlHqg0v/4KnTo52//7H5x6avSeJSLih795K9ldW9Lj6Kaec06+4VXPdv3aafEopogkkWCJn56s8RvHzXFi0plXP8s/e+2thE2S1PSmLgzZXVuWWaAy2IKV5Rp+uX49HHqos/3AA9C3b/kLKCJSAYEWLO98yZmkuMYetBg83XN+Wqrhvu6t41RaEUkWgepfD++3leOyrwfgtmtG8Wuj5mRmpDOiZxslbJKkpTd1YQiUeGDBHxsYP291mfP7dGwW3o0LCmAfZ30n+vaFe+6prCKLiITN37yVEdNGUu+XHwGY/s1vNPnsD63fJCIx5a/+NfywmpySdZJzwmuv8fSVV/J0HMsokijUqAuTv8QD7s8T56+hyNqgC1yWUVwMtWs720cf7Qy7FBGJA995K/2/epOsHz93PqxfT4+GDenR8aA4lExEkl2p+td//0HDhs72vffClVfGrVwiiUaNugp6KKtNeI04X+6151JTYeHCyi2UiEg5eM9b6fnDpwyc+yYAFw16g7fdFSgRkXjasaOkQXfJJXD//fEtj0iC0Zy6eDjggJLtQqUFF5H4cs9bOf6PxTwx80kAel/5BJf20Tp0IhIfObl5dBo5mxZDZnLiw59AuiurZZs2MHFifAsnkoD0pi7WunWD1a55eLt2gRbIFJE4cme93H/d70x8624A7r70Xi6+7SLNmxORuPBddPyru88oObhkSZxKJZLY1KiLpTvugPed9VTIz4c0pQQXkdjxXbagc6tGTF2Yx54b1zN37M0AjDz9Oo69o58adCISN97Jmz5/6VrP/k4Pf8LceBVKJMGpURcrzz8Pjz/ubK9eDfXqxbc8IpJUfHu+8/ILmDBvNem7CvjuucsBGN/2bF485jwyZy1Xo05EYsq708m69o2Z9hAH5P8FwCF3vMPuTTviV0CRBKdGXSzMnAk3O73gfP89NG0a/HwRkTD5WzTcX4PM37IFKcVF/PjkhQDMa3oEw7o6cco3G6aISDT5djoBDPnsNc78dR4AR/Z/i8LUNDIz0uNVRJGEp0ZdtC1cCOee62y//z60axff8ohIteHv7dvQaUsByjTsyjTUrGXFqPMA2FSrDpdcOtJzqIkqTiISQ76dTr0XfcgN86cC0OmGsWzeY0/S01LJ7toyXkUUSXjKfhlNf/wB7dsDkHv3SDotrkWLITPpNHI2Obl5cS6ciFR1/t6+FRQWMWrW8jLn+jbUcp++1LN9VP+3PNuqOIlIrHl3Op26YgEjZj0LQPfLn2RdvX3IzEhnRM82GhYuEoTe1EVLfj40bw7AL1fcyKXmKApcQStYb7qISLgCDZN07/cemplRO420FENhseWtN4dQf8cWAO6d8j2Zv24MOXxTRKQ8wh0aDiVrZbb+ewXjpgwH4OoL7mXDYUeycoiWVhEJhxp1QZQnIJWyaxfUr+9sn3ceV7Xq5WnQubl701V5EpFIeS8a7rvfd2jmxu2FpKUaHv30BTqu+QGAGV8t54FOh/JATEstItVdqKHh/jLxzp29iJnj+gNwzxk38M1hxzNCowZEwqZGXQDhzFUZlrOUifPXUGQtqcbQu0NTHjrvCKhVy7nJQQdBTg7rhsz0+wwlIxCRisju2rJMcgH38El/QzMv/2YaFy1wxaN16+jeuHEsiysiSSLU0PDb315EsSvFZV5+ATM+/4nFzzhZeF85NovZXS5khEYNiJSL5tQFECogDctZyvh5qymyTlQqspbx81ZTnJpacsFvvwGBkw4oGYGIVERWu0xG9GxDZkY6BkrNO/F9g3fW8rncM+dV58MPP4AadCISJcGGht85dYmnQQdQo2g3i0df7Hw45xyu/fYd5g7pogadSDnpTV0AoeaqTJy/psyx91+7lRRXI4+ikgZhsN50EZGKyGqXGbLyc3TeT7yYMwKA3pf8HxNbt45F0UQkSQUbGl5qv7X89lgWAHl7NSJzpv+RTSISmt7UBRDq7Zr7DZ3b6BmjOPyflc6HggJIKfnRButNFxGJpgM2rmPa+GwAbu82kG8OOCrOJRKR6i67a0vS01JL7fPXmf3LY+d7tjvd9FpMyiZSXelNXQCh3q6lGuNp2PX/6k2yfvwcgGP6T2ThHnuUuV84vekiIpXBvWRKRsFmPh/TD4AnO13KtCNOi2exRCRJuOs7/pLNDZi0CIDprw+gZvFuAFoMnh6voopUGzFv1BljmgJvAPsBxcAYa+1TsS5HKMECEkDvDk0ZP281PX/4lIFz3wTglH5jOPtUDWsSkfjJyc0je8piau3exSLXWnTvHnYKT53obHc6qEE8iyciSSJQZ/Yh+9Thhtce5Mi/nLwDLW+fijUpik0iFRSPN3W7gUHW2u+NMXsBC40xH1trf4xDWYIK9nbtoaw2HLhkPlfPfBKAXpc9xklnd+ShrDaxLKKIiEdObh4D314ExcWsfLwnAD/u04L+PZzhl50OasCE646PYwlFJFkNy1nKhPmruXnuW1zww2wAjr51AjvTanHIPnUUm0QqKOaNOmvtn8Cfru0txpifgEwgLo26iNeiW7aMq++7ztmePJkpvXpFt6AiIkHk5OaRPXkx1sKqR3t49p9z1TOebVWaRCQe3BnDz1s2hzu+HA9A5+teYkPtegBs31Ucz+KJVAtxnVNnjGkOtAPmx+P54axF59eff8IRRzjbjz8OatCJSJwNn76MwmLL5y9d69nXfPCMOJZIRMQxcf4aOq5ewlPvPQ5Arz6PsLJBST1L6/aKVFzcsl8aY/YEpgIDrLWb/RzvZ4xZYIxZsH79+qiUIdRadH5t3QpNmjjbN9wAt98elbKJSGKKRWyKRH5BIWOmPcQB+X8BcMgd74AxnuMZ6WnxKpqIxEiixqfm61fz1sS7ALilx2AW7F86/4DW7RWpuLg06owxaTgNugnW2mn+zrHWjrHWtrfWtm/UqFFUyhFqLboydu+GvfZytk85BV54ISrlEpHEFYvYFIkhn73Gmb/OA+DI/m9RmFrSiEtLMQzvoSROItVdQsanv/7i01dvBGDkKVfy3mEnlzmlc6sEKatIFRaP7JcGeBX4yVr7RKyf7y3Y4phlWAtpTiVp+551aXP8YIqGzCTVGHp3aKoEKSISU97zgW9c/gmD508FoNMNY9m8x56e81IMjLrwKC2pIiKxt20bNG4MwFtHnsmLHf1PV5nzc+K8VRSpquLxpq4TcBnQxRizyPV1ThzKEfbimADsvbdn8/CbJnjWqCuylvHzVjMsZ2lUyyoi4uaeD5yXX8ApK75jcM5oAM67cjR59fbxnJeWanjiorZq0IlIzOTk5tFp5GwOGjwd9nR1MB1/PD/c/1jAazSnTqTiYt6os9Z+Za011tojrbVtXV/vx7oc4CRDGdGzDZkZ6RggMyOdET3blK0AnXIKbNwIwKGDp5eap+I2cf6aGJRYRKRkPnDrv1cwbsr9AFzV6z7+aH5YqXg2qpfe0IlI7Hg6nDZuZ8Wo8wDYlrYHOc9N5qEsp77lj+bUiVRcXLNfJoJga9EBTjKUL75wtrdsYddDn/s9zf3mTkQk2tblF9Bk8z/MHNcfgGFn3sScg47FFBSy6L4z41w6EakqIl7WKQB3h9OCZ/t69rUeOJnMWcvJapdJdteWpbKOQ5ARUiJSLknfqAvq8cfhpZec7XXrYM89STXGbwMu1c/bOxGRaDhkjyI+euRqAMYcez7j2zkj2NXbLSLhinhZpyDW5Rcw4a27aLh9EwAHZb8LxniGV7rvW5kNSRFxqFEXyJQpcMcdAPQd8Cpzn/qeJhnpdDywPnNXbChzeu8OTWNdQhFJRrt28dH9zrCmTw86loe7XAM4GS7V2y0i4Qq2rFOkjayRs1+i0x9LAGg94G2KUpy8BfW8llQJOUJKRCISt3XqEtrXX8OFFwJwRd+RfFVrXyxOL9b3qzfR6aAGnjdzqcbQt2MzZb8UkeizFmrVAmBtvX24ptd9Jcc0WEBEyqHcyzqFMno0F383A4AON41jW63ankMazCQSfXpT5+u336BTJwAevPBOPs88otThgsIiVv1XwIoRcUnYKSLJLK2kt/vEG8aWOlRYZCvUwy4iyaVcyzqFMm0aDBwIQNern+XvvRqWOpy/vTCiMopI+NSo8/bvv3DIIc72/fczdvsxfk9T6l0RiQXvJAYf/m8ALYucoVIHDp7u93zFJhEJV0WSlnjHpjM2r2TMC7cCMODqR1jeqHmZ8zXfVyT6kn745bCcpRw09H1aDpoGjRo5O/v0gXvvDRiEFJxEJNpycvMYNHkxefkFPP7e47Rc9xsA07/+lcb16/i9RrFJRMIV9rJOPnJy88ie4sSmphv/9DTovr/vcU69pU/46/+KSKVK6jd1w3KWMn7eaowtZvkTFwCwbJ8DmdjrTh6iYr1YIiIVcfc7Sykqttw6dyI9l80BoN2tE9j1wW/83/ltFJtEpMIiSVpy/4xlFBZZMgo288WY6wB4+viLeS3lCHKV3VIkbpK6UedeMHzloz0AKMbQ7aqnSZ2/hoey2ij1rojEzbZdRZz/w2wGfTUBgFOve4mNtevBriLFJhGJm43bC6m1exeLnr4UgPdancQTJ18Grnlzym4pEh9J3agrspYvX7zG89k9T8V7HToFJxGJh+P/WMKTM58AoGefUaxqUDoOKTaJSDwYW8zyx3sCsLxhM2457844l0hEIMnn1L0y9QGabvobgEPueMeTc1cLiYtIXP34IxPfuguAm84bwvf7H+Y5pOgkIvHkHt0E0PWa5z3bqjqJxFfyNuqyszn9t28BaDNgEoWpJanCtZC4iMTNX39B69YA/N+pV/N+qxNLHe7TsVk8SiUiUpIhHGg+eEapQ16DnEQkDpKzUffCC/DYYwCMGjOL7XvsCWghcRGJj5zcPDqNnM3ht0+Fxo2dnf36UdB/gGfkgOKTiMTV+ec7a/kChw56p8yruUxl3xWJq6SZU+deU6Xlwi8YO+V+Z+fChWQffTTZ8S2aiCSxnNw8hk5bys6du/j9yV4AfNfsCPJuuJeH2mWqESciceOuO/XNeYEb5+cAMPOzH0j9ZA0o+65IQkmKN3XuSlPGz0s9DbrrL7mfHLNvnEsmIslu1KzlFOzaze+jzgNgc606XNh7JKNmLY9zyUQkmbnrTid/9g43zp8CwGm3jKOwbkZE69uJSHQlxZu6UbOWU/+/P5n5+gAA7j7zJmYdcAw/zFquICQicbUuv4Dvn+nj+Xxk/7c8+0VE4mXUrOV0+Hk+I2Y9C0D3y59kRZ2GjJq1nLlDuqj+JJJgkqJRt+Wv9Sx54WoAXjyuJxPanQOo0iQi8Td18t00KNgMwEHZ73rmqTTR/BQRiaN6y5cxbspwAK654B6WNnaSpKjuJJKYqv/wy127WPLUJQB8fPBxjOx8teeQKk0iElc33cTRvy8G4PCBkylKSQU0P0VE4mzNGt4fdxsA955+PZ8e3MFzSHUnkcRUvRt11kKtWgCsydiP6y6413NIlSYRiasnnnAy8QIfzFpI/X0aaH6KiMTfpk3QzFk65fXjsnjjmO6eQ6o7iSSu6j38skbJt7dw9gIyZy1nXX4BTTLSye7aUpUmEYmPadNg0CBn+4cfOLt1a84+M75FEhGhsBAyMpzts86i3sPPqu4kUkVU30bd0UdDcbGzXVREVkqKApGIxN+8eXDBBc72p596FhoXEYkra6FmTWc7MxM++IAsUN1JpIqono26yy6D3Fxnu6AAUqr3KFMRSWzutZ5qrPydz8dc5+wcNw66dIlruUREPNK95sqtXRu/cohIRKpfo+7BB2H8eGd7/XrYY4/4lkdEkpp7radamzeyyNWge+bkvjQ98nSy4ls0ERHHccfBzp3O9u7d8S2LiESker3CmjoV7nUlQ/n1V2jYML7lEZGkN2rWcnbsKmTR05cC8O5hp/D48ZdocXERSQw33wzffedsb98OqanxLY+IRKR6Nep+/935c+5cOPjg+JZFRARnTae6O7YB8MO+B9G/R7Znv4hI3OXlOX/+/XfpIZgiUqVUr+GXd9zhZJTTHDoRSRBNMtLJAw7MfpfilNRS+0VE4m7qVL2dE6kGqlfrxxg16EQkoWR3bUl6WmqpBp3WehKRhKEGnUi1EJcWkDHmLGPMcmPMb8aYIfEog4hILGS1y2REzzZkZqRrcXERERGJipgPvzTGpALPAWcAa4HvjDHTrbU/xrosIiKxkNUuU404ERERiZp4vKk7DvjNWvu7tXYX8BZwXhzKISIiIiIiUuXFo1GXCazx+rzWtU9ERERERETKKR6NOuNnny1zkjH9jDELjDEL1q9fH4NiiYiEptgkIolK8UkkecWjUbcWaOr1eX9gne9J1tox1tr21tr2jRo1ilnhRESCUWwSkUSl+CSSvOLRqPsOOMQY08IYUxO4BJgeh3KIiIiIiIhUeTHPfmmt3W2MuQWYBaQCY621y2JdDhERERERkeog5o06AGvt+8D78Xi2iIiIiIhIdRKXxcdFRERERESkchhryySeTDjGmPXAHz67GwL/xqE4KkNiPV9lqLplOMBaW6Vn8is2qQwqQ5V4fiRlUHyKnniXId7PVxlUhoqUIWBsqhKNOn+MMQuste1VhviWId7PVxlUhkSTCD8DlUFlSKQyxPv5iVKGRJAIP4d4lyHez1cZVIZolUHDL0VERERERKowNepERERERESqsKrcqBsT7wKgMiTC80FlcFMZEkMi/AxUBofK4Ih3GeL9fEiMMiSCRPg5xLsM8X4+qAxuKoOjUspQZefUiYiIiIiISNV+UyciIiIiIpL0qlyjzhhzljFmuTHmN2PMkDg8v6kxZo4x5idjzDJjTP9Yl8GrLKnGmFxjzHtxen6GMWaKMeZn18/j+DiUYaDr7+EHY8xEY8weMXjmWGPMP8aYH7z2NTDGfGyM+dX1Z/04lGGU6+9iiTHmHWNMRqzL4HXsDmOMNcY0jGYZEo3ik6ccik2KTYpNCUSxqVRZFJ8Un6plfKpSjTpjTCrwHHA2cDjQ2xhzeIyLsRsYZK09DOgI3ByHMrj1B36K07MBngI+tNa2Ao6KdVmMMZnAbUB7a+0RQCpwSQwePQ44y2ffEOBTa+0hwKeuz7Euw8fAEdbaI4FfgKFxKAPGmKbAGcDqKD8/oSg+laLYpNjkTbEpjhSbylB8UnzyVm3iU5Vq1AHHAb9Za3+31u4C3gLOi2UBrLV/Wmu/d21vwfllzIxlGQCMMfsD3YBXYv1s1/PrAicDrwJYa3dZa/PjUJQaQLoxpgZQG1gX7Qdaa78ANvjsPg943bX9OpAV6zJYaz+y1u52fZwH7B/rMrg8CQwGkm3CruITik1eFJtK9ik2xZdik4vik4fiU8m+ahOfqlqjLhNY4/V5LXEICm7GmOZAO2B+HB4/GucvvzgOzwY4EFgPvOYaxvCKMaZOLAtgrc0DHsPp1fgT2GSt/SiWZfCyr7X2T1e5/gT2iVM53K4GPoj1Q40xPYA8a+3iWD87ASg+OUaj2KTYFJhiU+wpNpUYjeKT4lNgVTo+VbVGnfGzLy49bsaYPYGpwABr7eYYP/tc4B9r7cJYPtdHDeBo4AVrbTtgG9F/bV6Ka+z1eUALoAlQxxjTN5ZlSETGmLtxhrpMiPFzawN3A/fG8rkJJOnjk2KTQ7HJP8WmuEn62OR6tuITik+BVIf4VNUadWuBpl6f9ycGr4x9GWPScILSBGvttFg/H+gE9DDGrMIZRtHFGDM+xmVYC6y11rp72qbgBKpYOh1Yaa1db60tBKYBJ8S4DG5/G2MaA7j+/CcehTDGXAGcC/SxsV+v5CCc/yQWu/5t7g98b4zZL8bliBfFJ8UmN8UmH4pNcaXY5FB8cig++agu8amqNeq+Aw4xxrQwxtTEmdg5PZYFMMYYnLHQP1lrn4jls92stUOttftba5vj/AxmW2tj2stirf0LWGOMaenadRrwYyzLgDN0oKMxprbr7+U04jf5eTpwhWv7CuDdWBfAGHMWcCfQw1q7PdbPt9YutdbuY61t7vq3uRY42vVvJRkkfXxSbPJQbPKi2BR3SR+bQPHJi+KTl2oVn6y1VeoLOAcnO80K4O44PP9EnGELS4BFrq9z4vjzOBV4L07PbgsscP0scoD6cSjD/cDPwA/A/4BaMXjmRJxx6IWuX75rgL1xMjf96vqzQRzK8BvOvAn3v8sXY10Gn+OrgIax/jcRzy/Fp1JlUWxSbFJsSpAvxaYy5VF8UnyqdvHJuG4gIiIiIiIiVVBVG34pIiIiIiIiXtSoExERERERqcLUqBMREREREanC1KgTERERERGpwtSoExERERERqcLUqJOoMMacb4yxxphWIc4bYIypXYHnXGmMeTbS60Uk+Sg+iUgiUmySilCjTqKlN/AVzgKfwQwAIg5MIiIRUHwSkUSk2CQRU6NOKp0xZk+gE86ijpe49qUaYx4zxiw1xiwxxtxqjLkNaALMMcbMcZ231es+vYwx41zb3Y0x840xucaYT4wx+8b6+xKRqk/xSUQSkWKTVFSNeBdAqqUs4ENr7S/GmA3GmKOBDkALoJ21drcxpoG1doMx5nags7X23xD3/AroaK21xphrgcHAoGh+EyJSLWWh+CQiiScLxSapADXqJBp6A6Nd22+5Ph8IvGit3Q1grd1QznvuD0wyxjQGagIrK6eoIpJkFJ9EJBEpNkmFqFEnlcoYszfQBTjCGGOBVMACC11/huJ9zh5e288AT1hrpxtjTgWGV0Z5RSR5KD6JSCJSbJLKoDl1Utl6AW9Yaw+w1ja31jbF6Rn6HrjBGFMDwBjTwHX+FmAvr+v/NsYcZoxJAc732l8PyHNtXxHV70BEqivFJxFJRIpNUmFq1Ell6w2847NvKs6k3tXAEmPMYuBS17ExwAfuyb7AEOA9YDbwp9c9hgOTjTFfAqHGkIuI+KP4JCKJSLFJKsxYG85bXREREREREUlEelMnIiIiIiJShalRJyIiIiIiUoWpUSciIiIiIlKFqVEnIiIiIiJShalRJyIiIiIiUoWpUSciIiIiIlKFqVEnIiIiIiJShalRJyIiIiIiUoX9P2G+R5BF6pGMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure and axes\n",
    "f, ax = plt.subplots(figsize=(15,5), nrows=1, ncols=3,sharey=True)\n",
    "\n",
    "# Create list of titles and predictions to use in for loop\n",
    "pred = [test_ada, test_gb, test_sgb]\n",
    "title = ['Ada', 'GB', 'SGB']\n",
    "\n",
    "# Loop through all axes to plot each model's results \n",
    "for i in range(3):\n",
    "    rmse = round(np.sqrt(metrics.mean_squared_error(y_test,pred[i])),2)\n",
    "    ax[i].set_title(title[i]+\"  (RMSE aprox: \"+str(rmse)+ \")\")\n",
    "    ax[i].set_xlabel('Actual')\n",
    "    ax[i].set_ylabel('Predicted')\n",
    "    ax[i].plot(y_test,y_test,'r')\n",
    "    ax[i].scatter(y_test,pred[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3: Simple modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear Regression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Ridge Regression\n",
    "ridge = Ridge()\n",
    "ridge.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lasso Reg\n",
    "lasso = Lasso(alpha=0.01)\n",
    "lasso.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support Vector model\n",
    "svr = SVR()\n",
    "svr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor()"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting model\n",
    "params = {}\n",
    "gbr=GradientBoostingRegressor()\n",
    "gbr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=3)"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN model\n",
    "knn= KNeighborsRegressor(n_neighbors = 3)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "decision_tree = DecisionTreeRegressor()\n",
    "decision_tree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestRegressor(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compare models\n",
    "\n",
    "# Make training set predictions for each model\n",
    "train_lin = linreg.predict(x_train)\n",
    "train_ridge = ridge.predict(x_train)\n",
    "train_lasso = lasso.predict(x_train)\n",
    "train_Knn = knn.predict(x_train)\n",
    "train_svr = svr.predict(x_train)\n",
    "train_gbr = gbr.predict(x_train)\n",
    "train_DcT = decision_tree.predict(x_train)\n",
    "train_RnF = random_forest.predict(x_train)\n",
    "\n",
    "# Make test set predictions for each model\n",
    "test_lin = linreg.predict(x_test)\n",
    "test_ridge = ridge.predict(x_test)\n",
    "test_lasso = lasso.predict(x_test)\n",
    "test_Knn = knn.predict(x_test)\n",
    "test_svr = svr.predict(x_test)\n",
    "test_gbr = gbr.predict(x_test)\n",
    "test_DcT = decision_tree.predict(x_test)\n",
    "test_RnF = random_forest.predict(x_test)\n",
    "\n",
    "# Dictionary of results\n",
    "results_dict = {'Training MSE':{\n",
    "                        \"Least Squares\":metrics.mean_squared_error(y_train, train_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_train, train_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_train, train_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_train, train_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_train, train_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_train, train_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_train, train_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_train, train_RnF,squared=False)\n",
    "                },\n",
    "                'Test MSE':{\n",
    "                        \"Least Squares\": metrics.mean_squared_error(y_test, test_lin,squared=False),\n",
    "                        \"Ridge\": metrics.mean_squared_error(y_test, test_ridge,squared=False),\n",
    "                        \"LASSO\": metrics.mean_squared_error(y_test, test_lasso,squared=False),\n",
    "                        \"SVR\": metrics.mean_squared_error(y_test, test_svr,squared=False),\n",
    "                        \"GBR\": metrics.mean_squared_error(y_test, test_gbr,squared=False),\n",
    "                        \"KNN\": metrics.mean_squared_error(y_test, test_Knn,squared=False),\n",
    "                        \"Decision Tree\": metrics.mean_squared_error(y_test, test_DcT,squared=False),\n",
    "                        \"Random Forest\": metrics.mean_squared_error(y_test, test_RnF,squared=False)\n",
    "                }\n",
    "                }\n",
    "results=pd.DataFrame(results_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Training MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Least Squares</th>\n",
       "      <td>1.814342e+00</td>\n",
       "      <td>1.665826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge</th>\n",
       "      <td>1.814342e+00</td>\n",
       "      <td>1.665822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASSO</th>\n",
       "      <td>1.814354e+00</td>\n",
       "      <td>1.664986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR</th>\n",
       "      <td>2.052712e+00</td>\n",
       "      <td>1.931900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBR</th>\n",
       "      <td>4.634587e-01</td>\n",
       "      <td>0.522869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>6.782407e-01</td>\n",
       "      <td>0.891125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>9.205880e-16</td>\n",
       "      <td>0.879725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>2.467146e-01</td>\n",
       "      <td>0.466119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Training MSE  Test MSE\n",
       "Least Squares  1.814342e+00  1.665826\n",
       "Ridge          1.814342e+00  1.665822\n",
       "LASSO          1.814354e+00  1.664986\n",
       "SVR            2.052712e+00  1.931900\n",
       "GBR            4.634587e-01  0.522869\n",
       "KNN            6.782407e-01  0.891125\n",
       "Decision Tree  9.205880e-16  0.879725\n",
       "Random Forest  2.467146e-01  0.466119"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create submission dataframe\n",
    "submission = pd.DataFrame(\n",
    "    {'Index': test_id,\n",
    "     'avg_price_per_kg': gb.predict(test)\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save DataFrame to csv file for submission\n",
    "submission.to_csv(\"Answergb.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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