Created
August 2, 2014 21:57
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| { | |
| "metadata": { | |
| "name": "" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "heading", | |
| "level": 1, | |
| "metadata": {}, | |
| "source": [ | |
| "Random Forest Classifier" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import pandas as pd\n", | |
| "import numpy as np\n", | |
| "from sklearn.cross_validation import cross_val_score\n", | |
| "from sklearn.ensemble import RandomForestClassifier\n", | |
| "import random\n", | |
| "from sklearn.tree import DecisionTreeClassifier" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 28 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Import data with pandas:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "data = pd.read_excel(io=\"dat_random_forest.xlsx\",sheetname = \"Sheet1\",header = 0)\n", | |
| "len(data)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 13, | |
| "text": [ | |
| "1516" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 13 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Sample into train and test:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "sample = random.sample(data.index,1000)\n", | |
| "train = data.ix[sample]\n", | |
| "test = data.drop(train.index,axis=0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 25 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Instantiate some classifiers:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "tree = DecisionTreeClassifier(max_depth=None, min_samples_split=1, random_state=0)\n", | |
| "forest = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=1, random_state=0)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 27 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "5-Fold cross validation:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "tree_scores = cross_val_score(tree, train[[\"A\",\"B\"]], train[\"T\"], cv=5, scoring='f1')\n", | |
| "forest_scores = cross_val_score(forest, train[[\"A\",\"B\"]], train[\"T\"], cv=5, scoring='f1')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 42 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print(\"Classification Tree Accuracy: %f (+/- %f)\" % (tree_scores.mean(), tree_scores.std() * 2))\n", | |
| "print(\"Random Forest Accuracy: %f (+/- %f)\" % (forest_scores.mean(), forest_scores.std() * 2))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Classification Tree Accuracy: 0.994158 (+/- 0.013029)\n", | |
| "Random Forest Accuracy: 0.996694 (+/- 0.010924)\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 39 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "10-Fold cross-validation:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "tree_scores = cross_val_score(tree, train[[\"A\",\"B\"]], train[\"T\"], cv=10, scoring='accuracy')\n", | |
| "forest_scores = cross_val_score(forest, train[[\"A\",\"B\"]], train[\"T\"], cv=10, scoring='accuracy')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 50 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print(\"Classification Tree Accuracy: %f (+/- %f)\" % (tree_scores.mean(), tree_scores.std() * 2))\n", | |
| "print(\"Random Forest Accuracy: %f (+/- %f)\" % (forest_scores.mean(), forest_scores.std() * 2))" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Classification Tree Accuracy: 0.993000 (+/- 0.015620)\n", | |
| "Random Forest Accuracy: 0.996000 (+/- 0.013266)\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 51 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Here's a plot:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "fig = plt.figure(figsize = (10,7))\n", | |
| "plt.scatter(train[\"A\"],train[\"B\"], s = 50, alpha = 0.5, c = train[\"T\"])" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 80, | |
| "text": [ | |
| "<matplotlib.collections.PathCollection at 0x10b7bf290>" | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
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YnE7MxcVozWZQFEJjY5icTsoeeICGZ56ZUykHSFahH754kf4zZ/D09KDSaGaU\neFgog9NJxOOZVs/LUlZG9b59y6pYbCZGpxP/0BCRKfXMnGvWULFrF5o0k3ohhFhpBgYGqKurm/PX\ny7LgInHW17PxpZfwdHWRiMWwlpdjmbIdPTgygq2yEmN+Pkoigd5qJTA6SiIanfUHUCKR4Novf8n1\n3/wGALfbjeP4cSba2rjvD/8QrV5P0caNWEpL8U7mUNkqK1NlDpREgtDYGI66OiwlJaBSoTObCYyO\noijKrJO7eDRK1wcfMNrSkjo2dvUq5aOjVO/dSzwSoeO99xi/du3L81euULl7N5W7dxP2ePji//wf\n+k6eTJ3vOnqUwPe+x7rnnycWCtH+u9/hmpL/NNLSQs2+fZQ98ABao5HNP/whxZs3M3HtGlqzmdIH\nHqBg8olULBTC291NcHSUeDiMEo/j6etLfubqavwjI3S++y5DFy+SiEZRgKGLF7FVVVGwfv1sf7WE\n3G5uHD48rZr8yOXL1H/96xRu2DDr+2ejM5mo//rXKWpqIjQ+js5iwV5TsywnVul6kxmdTtZ++9u4\nu7qIer0Y8vKwV1fLxCoN6e2WHYlfdiR+uSOTq0VkzMtLWzAzMDJC/9mzJKa0MglNTBCamKBw/Xqc\nGZaFbhlvbaXtrbdmHO94/30qHnyQ0vvvB8BcWIi5sHDG13l6exn6/PNpSd/xcJih8+cpXLdu1qVJ\nT0/PtInVLYOffUbh+vWE3e5pE6tb+s+eJX/dOsauXp02sQJAUbh++DDlO3YQ9funTaxune87fRrn\n2rUYHQ60Wi3Ve/ZQvWfPjPu42trw9vait9vRTylK2nfqFPkNDfSdOEH/2bOp4+FwGBIJWl59lYqH\nHsJaUnLHzz/R2jqjTY8Sj9N/5gx59fWLUm5ArdWSV1cH8/jNaDnRGo0UrF271MMQQohlQXKuciA0\nPj5tYjVVIENrl6m8fX3EJ+s3AThu5XfF48miobPdf2ws7W46JZFI1eWa9f1pJGIxgmNjBDKdj0YJ\njY/PKB56S8Tjwdvfn7HFTywYJDyH8WVqjxPxegm5XHh7e6cdN0xOhlxtbaknfXfiGxrKeN/l0Jsw\nl+S33uxI/LIj8cuOxC935MlVDugsFlCp0k5w9BbLrO83OBwZ32/I0Fpmxv0z0M7h/hnfr1KhtVjQ\nZdjNh0qVbDGTocWNWqfD4HCkLRMBybw17RyWxjItn6l1OnQmU8bcKGN+/pzyptIVcoVkIVit0Tjr\n+4UQQtwTYCc1AAAgAElEQVRb5MlVDljLy3FOadNyi6mgAEdtbeq1kkjgGxzE199PfMqTrsKNG6cV\nY3S73QAUrFtHYVNT6ngiHk++f2BgWvkCe3U11vLyGfe3V1Vhn0MVcEdNDaY0bWry6uqwVVSQt2oV\npvx8lHiciNdL1O8HRSG/oQFrWRnFW7YkdyUqCvFIJPUUr2rvXvLXriWvri7tbsrCDRuSOWKzyKuv\nR2s2k4jFkvefrA1VfN99GPPzKdu+HXNhIWqtFrVOR5zkTs61zz2Hc0qF+mgggLevb8bTxPyGBtR6\nPYlodNr1S7ZundduvsDYGCPNzbgnexveLuLz4e3rS1XxX46kTk52JH7ZkfhlR+KXO7JbMAdUKlVq\nchGamECl0eBsaGDV/v2Y8vMB8A8N0fb739Nz7BjDX3yBp6sLo9OJweFAo9PhWL0aFcn8rYRKRf2B\nA2w4dAjb5KTJNzBA21tv0XP8ePL93d2YCwvR22yotVqsFRWpIpVqnY6ixkaqH3lkTpMDjV6PvbKS\neDhM2ONBo9NRtGkTVXv3ojOZkuUPDAbGW1sZvXKFiMdDXn09Nfv2YczLw2CzYSoqIjg2hruzE7VW\nS83+/TQ88wym/Hy0RiO2igqiwSBRnw+t0Ujp/ffPebeZzmJBpVYzduUK462tRP1+ChsbqXj4YfQW\nC5bSUiylpYQnJvD29WFwONjwwgusee65VH/H0ZYWbr71FgOffsrwpUuEXS6sZWXJnZ42G4l4nNHm\nZiZu3iQeClG8ZQuVu3bNqbFyIpGg4+23Of93f8eNN9+k++OPCbvdOFatQmcyoSgKQxcucPPIEQbP\nnWN4smiqtaxs2fUW7O7uprq6eqmHsWJJ/LIj8cuOxG/h5rtbUNrf5FjE50NJJDBMWSqLhcNc/eUv\n8Q8MTPtavc1G44svTvta//AwiqJMS8KOBgJc+cUvZlSBNzqdNL744rRls1s5QgtteRP2eFCp1dMm\nZaGJCVpefZVEPJ6abER8Pqylpaz/gz8g7HbT8uqroNOlJjOB4WFs5eWsff551BoNkCxIGfZ40Gi1\nd1zKvJ13YICrv/gFKq02uUynKES8XgrWr2f100/j7evjymuvobNa0RkMoFIRHB2lYMMG6p98End3\nN9f++Z9nFL0s2bKF2ieewN3ZybVf/QqNwYDGYEBRFCIeD2Xbts2pPU3fqVOc+R//Y8b1N7zwAhsO\nHWKstZUbb745Y9m36uGHqXjooTnHQQghxFdD2t8sc3qrddpkCcDX1zdjYgXJiZB7slXLLZbi4hm7\n27w9PWnb64QmJpKtYabe32bLqpegwW6f8bTL3dlJNBBIPtlyuQi7XCixGN7eXrx9fbg7O4mFQsS8\nXny9vfh6e0lEIri7uqZ9bpVKhdHhmNfECsDd3k4iFiMeCiXv73ajJBKMXbtGYGQEV1sbSixGxOXC\nPzSEf3CQRCzG2JUryadpbW1pq4mPtLQQcrlw3byJkkgkE+xdLiJuNyhK8gnXHBLaB86fT3v9rqNH\nCU5MMN7amjafbviLL4hNtsIRQgixcsjkahmIh8MZzyXSnEvX3y3jte9wbrHE7jD++GQT4rQU5Y7v\nnatM8VPiceKRyIzz3ZM5T4lYjHg0mnECk4hGiUciRDMk3Cei0Tv+3d0SmcyRm3Hc7yfm9xP1+dKe\nj4XDGXeZLhXJ2ciOxC87Er/sSPxyRyZXy4C5pCRtbpFKrcY8h4Ruc3ExqjS5OSqNZk7vz5a1tDS5\nm/E2WpMJc0kJ5gz5dTqzGUtxcfb3z3B9U34+psJCLBnOm4uLMTqdGet8WUpKMDqd09rrTLtvefmc\ndhtmKjRatHEjlvLyaZsapnLU1KBbZu1vhBBCzG7BCe3BYJCf/vSn1NfXY5tcZhocHOS3v/0tV65c\noaCgIHX8dvdaQvtsdCYTap0OT2fntOWhsh07KG5qQqVSoSgKnu5uRi5fJk9RUOJxDA4HKpUKvdWK\nSqXCM3UXmkpF5a5dc64griQSuDo6GL18OXWdW9efjd5mIxYK4evrIzA8TDwUQm+3U7N/P3k1NRhs\nNmKRCMGhIYITEySiUQxOJ9X79mGvqgIgEggwcPYs3R9/zNjVq8mJZZodipnuH/Z4CE6pl6XWaql9\n/PFk30e7nbDPRywQIB4K4SwsxFRQQNWePViKi5PjD4dJRCKE3W40BgN5tbVU7NyZ3BRgt+MbHMR1\n8yaBkRGifj96q5W6J59MbUi4E6PTiaezE/+UelnG/Hw2vvQStrKy5PX7+6fVzNJbrdTs35+2Z+VS\nkmTY7Ej8siPxy47Eb+Fy1v7m2LFjPPHEE9OOffrppxw6dAiAN998k2eeeWahl7/nlN5/P+aiIjxd\nXSiJBLbKShy1tanee4PnztH90Uep3oG3Jk8Vu3ejUqko37Hjy/Y3ioK1qoq8OfYNVBSF3pMnp1VR\n7z99mup9+yjbvn3W98ciEcZbW+n88EMSkQiqyfpXeXV1lNx3H4lYDE9XF31nziSXCNVq9HZ7cuLX\n1EQkEKDllVdoO3IkdU2N0cjWP/5javbtm/X+WqOR2scfJ3/NGnwDA8nJUV3dtCdaiXAYd0cHUZ8P\ntVZLLBD4sjXRxASd777LjTffREkkUKlUmEtK2P0Xf0HeZKkGvcWCY9UqIh4PaoMBc0HBnCaeAJbS\nUu7/0z9l6Px5fP39GOx2irdswTn5P6rR4aDhuedwtbURHBtDb7WSV1+PqaBgTtcXQgixvCxocjUx\nMYHZbMZ4WwFF85RdaXrpKzYvKpUKR00NjpqaGecCo6P0fPJJamJ1aztt35kzOOrrsZWXo1Kryaut\nJS/DEtOd+Pv76T99etoxJZGg5/hx8urqZv0hP/jZZ1z6h39AmVpM1OXi4v/+35Rs3UpwePjL3XCT\nxVAjbjctv/gFRZs24WprmzaxAoiHQlz9p3+iaNMmzHOYZGiNRgrWr0/bK3Di5k0mbtxIPgkrKqK7\nuxubXk/PJ59gr66m59gxrr/xxpefXVHwDw7S/MorlO3ciaejg7GrV4HkUqsSi+EfGqLv5MlkU+nJ\n3Y53YikspO62X0am0lutFN9336zXWWrSmyw7Er/sSPyyI/HLnQXlXGX6C5pa1UGn0y18VGKawPBw\n2sRmJR7HPziY9fV9Q0NfPhGbIhGJTFvKymTi+vXpE6tJ/sFBxq9dw9XRkXY3XHBkBHdHx8y+grfG\nNTCAp6NjDp/gztLtxIRkXIPj40y0tqY9P3D2LK6bN/FmaN/j7e9f1gU/hRBCLI0FTa6Ghob4f//v\n/3HixAmam5tTx+NTtpvPtmQyddfCiRMn5PUdXnd0d6d2uN1y67XWYMj6+t19fdOu3z3lfhq9ftb3\nx1SqZDPkSeFwOPlarUZnMuENhVJV5SFZYd7tdoNGg8ZoxBcOpz1/q/1Ntp+vf3g4bfxUWi0anQ6V\n0TjtezcejxOPx9HZ7WiMRgZHR9PGR63Vzik+d9Pr3bt3L6vxrLTXEj+Jn8RvZb+eq6yKiF65coW8\nvDzKJ6uEHz58mGeffRZFUThy5AhPP/102vfdy0VEo4EASjw+r1pTsVAoWSR0YiLZi1ClIur3ozOZ\naHzxxazqVgGEvV6uvPoq0WAwWWNKUYj4/Zjy89lw6BDayUbHkCwOqlKrpxUmHWtt5YM/+RMCIyOp\nHDElHqd85072/vSnBEdGOPlf/gvRUAitwYACRP1+SjZvZud//s94u7v55Mc/Jh4KodZqUUjmSJXt\n2MHO//gfU1XKFUUhMpkzpUtTGT0ej+Pt7U3uQpySDO/p6eHqL39JIhZLVZjXmc0UNTVR//Wv0/nh\nh7z/J3+CzmTCUlZGLBTC3dnJxhdfZPdPfoKro4PW119HYzAkP99k/Eu3bmXVgQOp+yiJBBGfD43B\nMC1mc5WIx4n6/Qt+f7bi0SixYBCtyYQmzZPnW+d1ZvOyqxwvhBBfpfkWEV3wbsHx8XHeeecdIpEI\n9ZNJvw6HgyNHjnD58mX27NmDJUMxyHtxt2DE66X35Ena33mHwfPnCY6OYnQ651QwU63Vos/LY+L6\ndfrOnKH38mXyq6upPXgw2VYnS1qDAb3VyvjVq/SfPYunpwd7ZSW1X/salsJCAPwjI/R8/DGdH3zA\n0IULRHw+TIWFaA0GzIWFWMvL8XZ3Jwt2JhJUP/oom/71v072HXQ60eflMX71KoPnzxNyuSjfuZP1\nL7yArawsVTJh4vr1ZPuaQIDK3btZ98ILqUmSr7+frg8/pPvoUUa++IJ4OIypqCg1Ceg9fZrP//7v\nufizn9H57ruEvV6sVVXoLRYMDgdht5uOd9+l5+OPmejspGDdOlYdOIDR4SCvrg5LSQm+/n5GvvgC\nlVrNhkOHWP3ss1gnyzVE/X56jh2j++OP8fX2UnTffVTv25dqvO1qb6fj3XfpPX6csZYWFEXBXFw8\np3wsgPHr15PvP3GC8Vu7JYuLU5PVr5KiKIy2tND+zjv0nzrFxM2bqLVazEVFqZ2qI5cv0/HOO3z6\nq18RHx5Go9fPeTen+NKJEydkx1YWJH7Zkfgt3Hx3Cy54cmUymbj//vtTEysAm81GU1MTTU1NGSdW\ncO9NrpREgva332bk0iUS0ShKPE5gZARPTw/5DQ2z9s9LxGJ0ffABgZER9GYzMZ0Oy2T5gYJ167J+\nihANBul47z3Cbjd6qxVjXh4qtZp4MEj+2rVEg0FuvPEG7o4OlFiMRCyGr7+fsMuVPB8I0Hv8OHmr\nV1O5Zw+VDz+MqbAQYjHyGxrwDw9z8Wc/Q0kkKFi/HkdNDb7BQRKRCKUPPEDY7abno48wl5RQ1NRE\nUVMTGqMRtUqFc80aQhMTXHv9dXz9/anCoJ6eHhLRKM76ekavXOGTH/2I4c8/JxYIEHK5GPj0UzRa\nLeU7dzJ8+TKn/9t/A8C5ejV6h4Oh8+fRGgyUPfAAg59/zpm/+qtkD8WqKowFBYx88QUmh4PSbdsY\nuXKFC3/7t0R9PvRWK2qdjonWVgw2G4WNjXj7+mj99a8JjY+jxOPJJ18dHWhNpjlNft1dXVw/fDhZ\n2T4eJxYM4mprQ2+zZazhtZjGr13j5pEjRH0+lHicqM/HxI0bWAoLMRUWMnblCm2//z1Rv5+JsTHM\nGk1qg4DsaJwf6e2WHYlfdiR+C5ezUgxi7nwDA4xfvz7jeHB0FHdnJ0UbN97x/d7eXtwdHcmaVjYb\nZZPLgP6BATxdXeSvXZvV+DyTbWhUavW0JUZXezve3l6igQCB4eEZ75u4eRNfXx9htztt+52Iy4V/\nYIChKbWzpl6n59gxap94glggQDhNFfORlhZKt23D3d09rQZU6vylS5Rs3crA2bP4+vtnnG994w1q\nv/a15PnJpPapd7n2+uup84Hh4RmfsfWNN6h98kmGzp0j7PHMuH77O+9QvX8/462taTccDJ07R9HG\njdOWUNMZa2lJuyFg6Px5Chsb59S8OhvDFy/O3HCgKAx98QXOtWsZ+vzz1Plb/zAriQQjly+T39Dw\nlY7tbiM7tbIj8cuOxC93pEJ7DkR9vrS75SCZwzSbiN+/oHNzFb3DNaKBQObzikLU7884BiWRIOL3\nE3G50p6PRyKEXK6MMVAmc5AytYdJxGJEAwFCGdrLhF0uQi5Xxh19ofFxQhMThEZH0573Dw8TdLkI\npJk43np/2OMhnOHzRQKBOfUGzDS+sNc7p/Y62UjE4wQz3D84Npb6O0p7PkPchBDiXieTqxww5uej\nzlCawjyZ0zTb+1VTcndSO9dUqkVZljEVFKRtX6PSaDA6nRgzVCFXabWYCgoyVilX63QYnc6M7Wf0\nVivW8vLkEmIaGoMBg9OZ8bzObE62r5ncUHE7e1UVtsrKGUtzt3Y25tXWJs9nKLaav2YN9vJyHBke\noztWrcJcWIiltDTteXNh4Zw2G1gztNexlJSgneWpV7bUGk2qSv7tHDU1aA2GafGbumvSLssL87aQ\nXUfiSxK/7Ej8ckeWBXPAXFRE2fbt9J06Ne14/tq1c/oBZS0tpWTLFgbPnZt2vGjjxml974JjY3h7\ne1EUBVtl5ZwmbgC2ykoKN2yg98SJ5NMIlQpzURE1jz2GpbSURCxGwfr1qUKat5Ru24a5uBi9w0F+\nQwN9p08TGh8HtRpLcTG1Bw9iLiqiZMsWqvbswTcwQCIWSxbijMep2rMHR3U10WAQ55o1BEZHifl8\nyRIJBgMlmzdjzMtDrdNhr60lMDhIaGICjV6P3uGgfMcODDYb5bt2UXPwIKGhIcIeD2qdDr3dTu2B\nA9grKqjYtYvqAwcw2mxEAwHUej3xUIjyHTuwlpRQsWsXRffdx/CFCyTi8WSFebOZDYcOYS4spHzH\nDnqPH8fb20s8GkWt0aDW61n9zW9isNkoWLeO0eZmguPjqdioNBrKd+5Mu+vudgUbNuC6eTPZJDoQ\nQGswoNZqKXvggTknxGejePNmXG1t056yaY3GVFHT0q1b8XR2TmuyrTWb51X0NBYO4+nsJOL3Y3Q6\nsVdVyY7DKTyeMN3dHiKROGVlVsrKpKekECuZ/OuWIxW7dmEsKGDi+nUSsRh5dXXkr1s3p3walUqV\n7INXUsL49es4V68mr76egvXrUz98R69coeOdd4hHIgCo9XpqH3981nwuSO5G1JhMKIpCNBhM7lBT\nq9EajahUKjQ6HasOHMBWVYWrrQ21VotzzZpUrpfWYEi+P5Eg6vej1ulQqdVoJiv46202Srdv58o/\n/RO+3l7UOh3OhgaKN29Ovt9oxFRYSN/JkwRGRlBrtThqalJNp7UGAygKPZ98Qmh8HLVOl+z999BD\nAGisVgrq6+ns7CQ0OorWZMJSXIxxcnJpKinBuWoVl//v/yXsdqPWailsamLd888D4KiuZtO//JcM\nbdnCWEsLpsJCirdsoXzXLiDZ4HnVgQP0nT6Np6sLQ14ehRs3pvo2GvPzafjWtxi9cgVfXx8Gu538\n9evnXC1fb7Wis1oZOHaMqMeTnNhs3jynptCLwV5VxdrvfIfxK1cIDA9jLi4mf8MGbJNPHO01Naz7\nF/+C0atXU38vBRs2JBt2z0FofJy23/1uWjHWgvXrWXXgwKz5aHebdDkvnZ0ujhxpw+dL5u1pNCp2\n765i586yObdYuldIzlB2JH65k1Wdq4W6l+tcfRVCLheXf/7zGfk5Gr2ejd///qzNhSdu3uTYj35E\nLBBAZ7GgKAqxQABTQQF7//qvsU5OcjIZvXKF4z/+MfFIBJ3VmtrxZquoYM9f/RVRv5+rr702owq8\n0elk40sv4R8a4uovfzkjL81cVMSG736XkUuXOPmXfwnxeLIO1mTj6oL163noJz+h4913efeP/ggU\nBWNeXvIJkN9PzWOPceB//S8GP/2Uj//DfwCVCrVGk3p/5cMP88hPf4q3qyvZnkejwVxYSCwQIOL1\nkldby9rnn2fs6lVuvvUWqFRoDQYSkzsmnWvW0PDcc1n/ABw8f57O99+fcbx482bqDh7M6trLQce7\n7yaT4m9T89hjlG3btgQjWj7C4RivvNLC2Fhw2nGVCl54YT3V1curcbcQ96r51rmSnKsV6PZ1c29v\nb9rE53gkgi9D65apxltbiQUCQDK5/dafg2NjjN+2FJjO2LVrqSdmUZ+PWDD5g8Lb18f49ev4+vrS\nttcJTUzgHxzE29ubNuE/MDKCf2iI8dZWmKygnojFUCb/PHbtGu62tuROzEQCFIXQxEQqAb/r6FFG\nLl78cjlTUUjEYoQmP1/f6dOMX7+e2slIPE5gaCi1M9Hd1UVwbAxPV1fq/bFQiMTkzj5XR8eitL9x\n3byZ9vh4a+sdNxsshfnmbEQDgeTfXxquGzcWY0gryu3xGxjwz5hYQfJ/h97e2Te73GskZyg7Er/c\nkWXBu8AdC03O5anKHd6vmkPOz53uP5enOnf6GhXccXyo1Rnfr9ZqQaXKeF6lVqO+0/1VquR/Ge6v\nusO15yNjjG/dfyVTqTJ/f+SgQOpyd+f/dVf4370Q9zD5120Fun3d3FZVlTZ3RWsyTUt4z6Rwwwb0\ndjuo1RgLClK7Ay2lpRSsWzfr+wvWr0/mZ6nVmAoKMOTlAeCoraVg3Tps1dVpJxDmoiIsZWXYa2pQ\nqdWpOlu3PoulrAxLWRmFGzYk88IMBgobG3FM5jIVNzXhXL06WUg1Te5a7eOPU/bAAxQ2NaHSalGp\n1WhNJsx2OwDVe/dSsHEjjsndgiqNBr3djnaytU5efT2mgoLU+dvlr1mzKHlRzjVr0h4v3LBh2eUk\nzTdnQ2cyUTCZm3a7bOuzrUS3x6+01Epp6cyCyxqNiupqe66GtWJIzlB2JH65s+AK7dm41yq0L4Zo\nMMjwxYt0ffABQ59/TiwUwuh0otHrkwnlRiO9x47Rf/YsrrY2NAYDa559NpVU7R8dpePtt7n8D/9A\n14cfEvH7MRcXozOZMOblYSouJjw+jrevj0Q0StGmTTR+97vkT/7gd3d20vrrX3P+b/6G9rffJuzx\nYC0vR2c2Yy4sxJifn3q/kkhQsnkzG154AceqVRhsNvQWC56entSSntHppPbgQUz5+RjsdjR6Pe6u\nLsavXiXi9eKorWXVgQOYnM7kjkS7najfz/AXX6BEo1Tt2cPqb34TW0UFtro6DFYrw5cupZY0K3bv\nZvMf/REFDQ3YKivR6PWMXLqEf2iIeCRC+Y4dbP7BD7CVl2N0OlEUBU9nZ3KJNBjE2dBAzaOPJivW\nO534h4fpeO89Bs6cwdPdjb26mjXPPJNqfzObgXPnaHn5ZT7/2c/oP3sWJRbDXleHWq3GmJ9PPBJJ\nFjGdXB7Nq6ujcs8etJObApaaq6ODnuPH6f3kE/yDg2iMRoyOueUDmQoKkjXFJutlqdRqSjZvpnT7\n9nt+x6BGo6aw0Exvr49gMLncrNdr2L+/hoaGO+dKrhQ+X4Tz5wf54IMuLl0aIRqNU1BgQquV3+3F\nyiEV2u9CSiJB90cfMXLpEjDZwmBoCF9/P6u/8Q0URaH7449JJBIUNTUBEI/F6D56lMLGRkgkaP75\nz+k5dix1zdGWFjxdXWz94z9GURSCQ0Oo9XqsZckdSiq1msDwMMpkHtO5v/kbuj78MPX+/jNn8PX2\nsu3P/gwlGk3u8jMYku+ffEo1tWp78ebN2Gtq8A0MoNZqsVVWpp7KRPx+xq5eJerzobNaUWs0+AcH\ncXd2Yq+qwtXRQcvLLzPS3JycfKhUDH/xBRq9nuKmJpRwGFNZGfv/5m/w9fSgtVpTE0eA8Rs36Dp6\nlIrJ39rUOh2uri6GLlygZPNmwm4341evEgsGk+1ttFo8PT14e3uxFBczfu0arb/+NVqTifx161Cp\n1fSdPUvB+vWpHYd3MvzFFxz/8Y9TVeRHmpvpOnqUhyIR1nzzm2j0elY99hiFGzcSGh9Hb7Fgq6xc\nNhMPV1sb1w8fJhGLJb/3xsYYa21l7be/nbEG2FQGu52GZ5/F29ubKsVw6/vsXnPixIkZTw8qK228\n+OIGent9RKNxSkut5Ocvj0l1tqLROO+808HNm1/mJg4M+BgZCfC1r9XN+3sgXfzE3En8cmd5/Ost\n7sjX389oc/OM4xM3buDp6SHidtN99GjqqdAtnp4eqh95BGWyjMHtuo4epWrPHvR2OxM3b6K3WtFb\nv6yvM9rcTPF99zHa3DxtYnVL629+Q/X+/eit1mSJArsdg/3LpYyhixcpamrCMrnb0Oh0pl1Gc3d0\n4OvvR2sypZbkAAY/+4yixkb6T52i/+zZGe9rfvllqh99lFgwiHtq0vTYGN6uruSSVGMj/WfO0Hfy\nZOp0PB5Ho9EQ7O+n6tFHCQ4PE5qYQGexTGukPXDmDAXr1tF38iRjV67MuP+1X/2K6n37Zq0n1vPJ\nJzPa8yQiEa4fPkzVo49itNlQqVTYyspS5Q+WC0VRGPr881QS/y2JSITRS5fmNLmCZP5bpuVVASaT\njjVrclN6I5e6uz3TJla3NDePct99xVRUzF5kV4iVSJ7LrgAht3vabrupjTfDLhf+4eEZEysA4nF8\nQ0P4BwfT7sZT4nECw8MZ27coiQRhtxtvmr59ALFQCP/AQMb2KEoslrZn4O1CU4pvTht+JELY7cY/\nNJT2vH9oCG9fX8bxR4NBIh4P/sm+grdoJp+suTo68Pf3Z2x/E/Z4iPh8uHt70553dXRkHNu0r2tv\nT3t8oq2NYJqejctJIhrFNziYej31e887h52oYrp77amBy5W+fVMioeB2z7+1070Wv8Um8csdeXK1\nAuit1uSusTQTJIPdnpx4pTuvUmEuKCBjKTONBtOdWrSoVOitVsxFRWlPq/V6TEVF055WTXu7RjOn\n9i/6DLk7ap0OndWascWPqaAAS0kJ8Qz9+zQGAzqLJWP7HFtlJaaiIjItTOisVnRmM7YMxTLtFRUZ\nrz3t6zI83bFXVuasUOhCqXU6zAUFuNP0dzTPsYiouHfZbOmLJKtUYLV+tQ3JhVhKMrlaAWyVleQ3\nNKTqBXV3d1NdXY29pgbbZP+8il27cLe3pyYzYZ8PR1UVhZs2QSJB+QMP0P/pp9OuW/ngg8mcLJUK\ne3X1l/WeJhWsXYutshKt2Uz5gw8y2tKSaucSC4epe+IJSrdvJx4KYa2oIOx2E5us8K7R63HW10/r\nuxcYGWFiMtk+f9069JNLgM66OgYLC/H19xP1+1O7Bovvuw9zURHlu3ZR1NSEb2AAFAWVWk08EqHx\nu9+luKmJkMvF4LlzM56SlW3bhjEvj4qdOynatAnnmjWpXY1Dly5Rf/AgBWvWEHA6GbpwAU9PD6Hx\ncTQGA+bSUmq2b0dvtVLx0EN0fPghRY2NydY3Wi1Dzc2seeqpaQVWPd3deHt70dvtyR2MkzlTlbt3\n0/Huu1/2mFQUPL29rP32t2ct8DpVYGSEsNuNzmLBUlqak5wllUpFydatyc0IiUTqe0+l1VK8adNX\nfv+7zUJzXiKROP39PuLxBKWlViyW2dsqLaZQKMbgoB9FUSgttWAyze3+NTV2qqvtdHd7ph1fu7aA\nysxR4g8AACAASURBVMr5LwlKzlB2JH65I7sFVwCVWo2tqgq1TkfE6yUUibB6714q9+xBb7Gg1mox\n5uUx2tLC4Llz+Pr6sFdXs+6FF3BM7pTLa2hAZzYTdrsxOBzUf/3rrP32tzE6HMkE86oq1BoNEa8X\nrclE2bZtVOzahdZgQGc2YyoqIu734+npQaXRsGr/ftY8/zy2khI0ej0hl4v23/2O3uPHGW9tRWc2\nU7V3b6pFSvexY3z2P/8n7b//Pd0ff8xYSwu26mrMhYVoDAYgmXvl7uwkEYmQ39BA+YMPYrDbMRcW\norNaSUQi+Pv70TscNDz7LOUPP4yjshKt0Zh8OpRIJBOm8/KoeOghSrZuRa3RYCktJR6N0nbkSPLe\nV69Sdv/91D39NLbyctQGA97eXobOn2ekuZmIz4ezvp6izZsxFxZiKCwk6vfT+vrrdB89ynBzM2X3\n30/NE09gLSkhEYtx/fBhzv/d39H1wQf0fPwx3r4+HHV1GGw2LKWlaIxGhi5cYPzaNWLhMGuefpqG\nZ59NJd3fSTwapffECdrffpvR5mZGmpsJu1xYKyrm1D4pW6bCwmTl+mAQj8tFRWMjNfv24ayv/8rv\nfbe5NTmdj8FBH2++eYMzZ/q5cmWMq1fHsNv1FBbmpkxHV5ebw4dv8NlnA7S0jHLjxgQFBUby8mZP\nutdq1VRV2dBo1Hi9EUwmLdu2lbFrVwUGw/x/t19I/MSXJH4LN9/dgtL+ZoVJxGIoijKtIXDE56Pl\nlVcIezwk4vHkea0WndVK00svTVuaiwSDqNXqZL++NOLRKCqVatpOtdD4OM2vvEI8GkUz2W8w6vdj\nys+n8cUX8fb3c/xHP0o1RUZRiE9OkB76yU8IDA5y7Mc/JnZbtfGijRt58M//nIjLxZVf/AIArcWS\nnCR5vdgqK1n/B39A9yef8N4f/iFqvZ6i++4j6vczeO4c9U8+yWN/+7fTakHFIpHk06UpdbXa33uP\nD/7kT1ASCbRGY6oHYsO3vsXev/5r+o8f5+if/VlyCay4mFggQGB4mIZnn2X3X/wFN48c4cN/9++A\nZEshJZEgFgqx8aWXePi//ld6jh/n7F//9Yxl2dVPP83mH/yAsatXufHb3ybHFwqh0elQaTSUbd9O\nzRzaKYxcukTb738/43jlQw9R+fDDs75/sSiKkvwe0OnuyZ1+SyESifPaa1cZGJi+LKvTqXnxxY0U\nF3+1EyyfL8Irr7Tg8UzPj7Jadbz0UlPGZb90otHJpuhSgkGsQNL+5i6n1mqnTawAvD09hD3Jx+5q\njQbN5MQo6vPNWOrTm0wZJ1YAGp1uRgkAd3c3sVAo2TPQ7yfq84GiEBwbw9vXx+jk0x4lkSAeCiVb\n8SgK462tjLe2MtLcPGNiBTDS0sLEtWt4urpS/foibneq/Yy3rw//4CAj/5+9846O8zrv9PNN7wWD\nGcyg9yKAADvFIsrqlGRVW5ZkKY43J4rjOGe963XiZJ2zOevj3eRsdjexd1PcktjR2pZtuUmUbEoW\nRYqkWMECggRIAIMODMpgei/7xwyHKDMsAgkS8vecw3OI7879yp0LzDv3vu/vd/o0sUCAiNvNyN69\nTB49CqkUzj17lmx1yhSKBYEVwHRXF8lolFQ8Tszvz1nKDLz5JtOnTmWq4eJxEqEQvsHBjN4U4Hzr\nLWZ7enB1dubuLx4KkcjmePXv3s1MTw/Tp0/nzYcb2b+fgMvF3DybF5lKlZOqmD579prsbWZ7evIe\nn+7qIpG1HVoJBEFAplCIgdUKMjERWBJYAcTjKYaHr14sslxGRvxLAiuAQCC+ZKvvasjlUjGwEvmt\nQZzpq5DF/lCpfJWC19B2raQXleEvbkvF44WvH4sVbk+nSWaDlkLtqSucPxWLkbqG4GLxa5LZMbnk\nE5jPlxEgGY2STCYLt4fDJKNR4gUS6pPxOKloNOe7uJh0KnVN70+h/slE4orvzc1A9CZbHtc7fslk\n4Y2FROLmbzokk0s9Qa+l7WYhzr/lIY7fyiEGVx8CdA5HXvsXiVyO7gbktulKS3P+cIJUmvu/XK1G\nW1qKqb4+t9o1v11ttWKqr8dUXw/Z1RqZRpO7V31pKea6OnRlZZfvWaHInUtlNqMpKcHc2HjZhG3e\nqol940YsWdHUK1HU2Jj7v9JkQpJduSvfvh3LHXdQ1NKSa9c6HJmtSaD0zjsx1dZiaW3NtQvz/P7K\nd+7E0tSEdV77fErWrUNXXp6z61mMsbq6YKXlfMz19fmfq77+trPHEbmxlJRo8269CQKUll6bO8By\ncDh0KBRLPybkcgkOhy5PDxERERAT2lclixMS5RoNMpUK7+BgbntKkEozScfZD+awz8fQnj30vvoq\nYwcOEPH5FiREJ2Mx3D09TB4/zlx/P6lkEpXZjCCRINfpSEsk+IeGCIyOEs+qbFc/8ACGigo0NhtI\nJMxduEDU4yGVTKK2Wmn/vd/D2tqKxmpFptMhV6kglUJjsWBdu5a6xx7D0tiIwmAgGYsRcLnwDgyQ\niETQlpVRcd996EtKUJeUIJFKmT59mnQqhSCRYCgvZ+MXv0jphg0AuPv7ufDTn3L+hz9k4v33iYZC\nOesetc2GqqgIlcmUUVmvr6d8xw6aPvEJipua0Nhs6CsqMNXUIJFKsbS0UPfII1Q98ADm6mrUFguJ\ncJiZ7m5SyUzeiLmhgQ1//MeY6+pQWa1E3G58g4O590RXWsqaT38arc2Gwmgk6vEsUKxXmc1U3Xff\nNUlVKE0mfKOjuM+fJzg5STwQQFdaSvVDD12z/c6NQkyGXR7XO34KhRSDQcHAgCe3iiUIsG1bGW1t\n1pu+RavRyFGpZAwOenM731KpwD33VFFfv/IyIuL8Wx7i+H1wRPub31JK1q1DY7fjHxmBdBpDRQW6\n0lIgsw129p//ma5//ufLHX7wA9Z+5jMZ+5tUiuF338XV2Zlrnjp1irLt26m46y6S8TgzXV1MHj+e\n26Lyj49jqKjA2tZGNBDANzSUMz0WJBKQSDL3Qka2wdvfj3PPnpxUg7K7O1dtFpyaou/11xnYvTun\n2SXJeiYWNzRAMolMo2Hzn/4poelpZEolCrOZZHa70D0wwJG/+iv6X389d/8ytZrof/tvtH7ykwSn\nppg6eZKZs2czwZFEgmdggOK2tsz1p6fp++UvGdm7N6cJptDr2fnVr8LmzUgkEnQVFWz+kz8hPD2N\nTKNBrtfncqfUJhPrPvtZyrdtwzcygsJoxNbRkVNbV2i11D76KMVtbYRnZpBrtblKwmvhUo5XUXMz\n8VAImVKJTKslGQ5fxwwRWa00N1uwWNQMD/uIx1OUlmopLzcgkaxM7tu6dSXY7RpGRvyk01BRYaC0\nVFy1EhG5EmJwtQoppFVSyD5l8uhRul9+ecnx7n/7NxxbtqCx2XCdPLmkfeLIESxNTXiHhzPVavPy\ngxKhED2vvkrJxo14BgY49/3vQ2phDoZ3YAD7pk1EZmcZfuedJfY6vT/+MfaNGxl97z16fvjDJdc/\n9Y1vULZ1K+4LF+j6zneWtBuqqjL6XQcPLgisABLhMF3f+Q6l27YxcegQF37ykyX9u/7lX3Bs2cLY\ne+8x9NZbC9qiHg9nv/c9SrdvZ/LoUZx5qvW8/f3Y1q9HoVaj0Gop27aNsiWvyiBTKjPbk/O2KK+V\nme5ugvNU8hPhMIlwmIljx9BXVKxogrmok7M8Puj4Wa0arNZbtwXscOhxOG69VY04/5aHOH4rh5hz\n9VuAx+nMm/gdD4XwOp2Ep6fzVrulEglCMzOZisM8ideJYBD/yEhmOyy1NLk1MjeHZ2BgScXi/Hbf\n8HDBdt/gIHN9ffiGhvK3Dw3hGRjIrZAtZqa7G29/P74C7a7jx/EOD+N1OvO2Txw/jsfpLGxf09e3\nxFrnZjB/u3E+/tFREuLqlYiIiMhthxhcrUKu95uHymQq2KbM5iEVQqZWF066ztrjFLS/kclQGo3I\ndfm3ECQyGQq9vmDekUytRmEwFGyXazSojMYFZsvzURqNmX8F+quKi1FoNCgL2O9obTaUBkPB8VMa\nDAtW4m4WigLXV2i1GcX3FUT81rs8xPFbHuL4LQ9x/FYOcVvwtwD7pk3YOjqYOn06J3sgkclwbN6M\nfcMGZEolaouFWFa/CkEgnU6jtdnQl5Uh12oxVFYSmJjIVfKl4nFsHR0Ut7aiLCrCWFuLXKuluK2N\ndCLB+OHDWJqbsa1bR2R2Fo3Nhkynw1BWRiqZxN3TQ3FrK5amJhKRCIbKSvyjo5k8pnSaVDJJ49NP\n49i6NeMhaLORFgR0djupeByP00ntI49ga28nFgigLSkBiQS1xUIqHsc3PEzzs8/i2LiRiNeL2mbL\nrNBdIp3mjk9+ktLNm0kEgyjNZtQWC7qyMhKhEDPd3TQ/+yy2tjbSqRT9u3eTTqdz95eMRql+4AG0\nNlvulLFAgODEBHKDYYEtziUS0SgxrzcTNF5jvhVAcUsLc729C8y7AWzr1y/RPBP57cXtDpNMprFY\n1Dc8HyudTjM7m1kltVjUotaZiMhVEKsFVyEHDhy4rqoPhVaL2mYj7vczd+ECANUPPEDb7/4uRQ0N\nSBUKEpEIo/v20fPqq0ydPIm2pITK++7DUF6e6V9SQmB0NCN8GQpRunUrLc89h87hQG02ozKbme3u\npueHP2Tm7FnKduyg6ZlnMNfUoDQYkGm1uI4epedHP8Ld04N9wwbqn3gCfVkZ+tJSdOXlJEIhvMPD\nKAwG7njhBVqefx5DaSlamw2tw4HP6WT88GFigQD1jz1G83PPobFYMFRUoC0tJTg2xuSxY6TTae54\n4QXqPvpR9GVlmGtr0ZWVEQ8ECExMoLHZ6HjpJeqeegpdSQnGmhq0djtzfX2MvfceqXictk99ipqH\nH0bvcKArKUGu1+O+cAH3hQsIgkDdRz9K/RNP5Kr1hvfto/Pv/57zP/whI/v2EQ8GMVZXI1NlLEKm\nz55lYPduxg4dYubsWZLxONqSkiWCrflQFRWhMBiIuN0ko9Gc36Ft7dolgqk3m+udeyILuRnj53ZH\neOutQd5+e4iTJ12MjPgxm1UYDIXFgq+HqakQv/61k717hzl1aoqJiSAWi/qWGC+L8295iOP3wRGr\nBUWWEJ6bo/fHPyaZTrPpi19EEASmzpzhwquvUtzaStTv58TXvobH6cysAKXTjOzbl/HY+x//A4lE\nguvYMfSVlbRmNZtS8ThTJ09irq9nrr+fI3/91/hGRnJbhH0/+xmJUIjiNWsIjo1x7H/+T0LT0zmv\nQeeePQBYW1uJ+f3MXbxI+b33UvPoowBEZmbwDQxga28nPDPDbHc3poYG1MXFGZV6hYK53l6KGhoY\nPXiQA//lv6A0Gql56CES0Sjnv/99UokEpZs2EZiYwDswQNtLL9H++79PUhDwDw8THh+HNWtw7tnD\n/j/7M9RWK6Vbt5KMxTj9zW+STqdxrFuHb2SEme5u7Bs2YN+4MWP/Ewoxe+4c2rvvZrKzkxNf+1qu\nkjLq8dDzyitIpFLu+OQn8TidDLzxRm7lKR4OM3bwIBKplLJt2676/gmCgK29naKmJmI+H3KtVtS3\nEgEgkUjx1ltOnM7Lau1DQ17m5iK8+OIdyw6wIpEEb7zRz+TkZSeBvr45fL4ozz/fcs0GziIiv22I\nwdUq5Hr3zafPnMHT3w9AYFFy+ExXF6Hp6ZzFynw18vH338d14gQaq5WoN/PHe35au294mMDYGBNH\nj+aSvuerqQ/u2UPTkSMEJycJZKvdFrS//TaNTz+NIJWSCIVIhELMT892X7xIcHISX9Z+RyqTobFa\nc+2zPT2UbtmC68QJQi4XIZcrtzIHmWrE+scfJ5lViZ+ZJzUBMO314ti0CVdnJxGPh4jHs8CqpueV\nV6h/7DEis7OkEwkSi9TQp0+dwr5+Pa55EhXzcb71FtUPPYT7/PklW3oArlOnsK1bd03mzZCpOJTN\ne/5bgZizsTxu9PiNj/sZHFxqg+PzRXE6vXR02PL0unZGRvwLAqtLTE2FGBry0dxsWdb5rxdx/i0P\ncfxWDjGh/beA2BX862J+f86XMB8Rrzens5SPRCRCPOsFuJhUIkHU6y3cHo8T9fsLVrylUymS0WjB\n66eTSeKRSEF/vmjWp7CQHlQqHicRixEr8PxBl4uoz1fw/IlolEQsRsTjydt+ycew0PgnLvkwioh8\nQCKRZL5C32zb8q2RrnSOaHT51loiIh9WxOBqFXK9/lDGioq8VWVSpRJDVRWmurq87XKdDnNdXUaB\nPU8Cq0ShQGO1ZuxdJEunktpqxdzQgLHAPrW2pARzfX3m/HmQazSoi4sXJI3PR6HXo7FY0BfIIShe\nswZzQwOaRcnlw1npB7XFgspsxlDg/sruvBNzfX1OjHUxmpISlAZDQXsaS1MTWrsdfXl53nZ9aek1\n2d/cTojeZMvjRo9fcbEGhSJ/3p3Ntvyt4+JiNVJpnt99iXBLdLfE+bc8xPFbOcRtwQ8RgbGxjKZT\nOo2+ogJdWRmCIGBpbaXhiSfonS+kKQg0fuxjWJqbScZirPnd32Wys5N0Vs9KkEqpuOsubO3tpJJJ\n7Bs2MNfXl1nlEQQURiMlHR2oLRbKduyg/d/9Oy7+4hcZPS1BQKbRsPYP/gBbWxvhqiruePFF3D09\nJCMRBKkUiUxGza5dmKqrSUSjFLe2MtPdTWh6GolcjtZmo3znThQ6HcbaWgxVVQzu2UPE7UaQy9GX\nl9P+yCPItVrKd+xg/R//MalkkpjXi0QuR6pSUdzWhrGykpjFgn3rVsKTk3gHB6lrbsbS1obO4UCm\nVFK2fTuNH/84s93dpOJxBKkUqUpF6+/8DnqHA6Vej7e/n+DMDDG/H6lCgUKvp2zbNqRyOaXbtuE6\nc4bQxASxUAipQoFcp6P+8ceRKRRYWlrwDg0RnpkhHgwiVSpR6vU47rwz58OYiEQymmNuN3KdDtN1\nKLhDVrNscJDI3BwKvR5Tbe2KyESI3FqKilTs2FHO3r1DC1awOjpsVFQsP3B3OHRs3uzg/ffHFxzf\nvNmOw7Gy1ksiIqsJMbhaheTbN588cYKhd965HBxJJFTecw+OTZuQSCS0PPccxtpa3OfPA1Dc2op9\n82YApAoFxR0dTHV1EZydRRAE9GVl2NauzZ1LqlDgHRgg7HYjSCRoiospu/NOACQSCfqKCmoeeojw\n9DSCTJbx1DNnvMcUSiVKk4nQ7Cwxjwchmzt1Sb8plUrhHR7GuWdPJviSSFBZLDl7mlgoRNDlIhYM\nkkomkQgCMa8X3/Awtvb2TMVdKkXPj39MKhpFkEjQ2u04Nm4EMvY2Q2++yelvfjM3XoaqKnZ85SsU\nt7Qg02iwNDcjEQQiHg8ytRqN1Yo8u6okkclISyS4e3uJBwJIFQoMlZW5SsBYKEQ6kWCur49EOIxE\noUBfXk4sFMr0l8shmWTuwoVccGWsrUWWNZCOBQIM7N6NZ56YqcpspuHJJzMFBlch6vPR/8tf4hsd\nzR1TW600PP74ghy1G4GYs7E8bsb4bdpkx2JR4XR6SSRSVFYaqKszIZPdmI2JHTvKcTh0udyuykoD\n9fXmWyLHIM6/5SGO38ohBlcfAkLT0wy/+24usIJMvtLIvn0Yq6oyGlMqFZU7d1K5c+eS/oGJCSaP\nHsVcV5fz+wMY2b8fU20t8VCI8cOHM+bHRUW59uG9ezFUVTF24AD7v/xlUrEYMrWaVCpFKhrFXFeH\npbGR8Owsp/7pn0gnEkjkclKpFOHpaU79/d9jXbMG9/nznP7WtyBrypxOp4n6fJz6xjewdnQwfugQ\nR/76r4FMoJJOJkmnUoRcLmwbNjC6fz/H/+7vMjclkUA6TXh6mtPf/jbF69Yx9t57CwIryKi7n/3u\nd7Fv2sTY/v28/9WvAiDT6UiEQpBK4RsdxXHnnQSGhvD09aFbJB8ydvAg+k98gpF33uHMt7+dubxC\nQSoex93Tk5FM2LEDd08PvpGRBduLl7TAGp58ktlz5xYEVpBRr584epT6xx676vs/febMgsAKIDw9\njevECWp27bpqf5HVjSAI1NWZqau7OUbKUqmExsYiGhuLrv5iERERQMy5WpUs3jcPulwLqvAukUok\nCFyDPUtgYmJBYHaJZDRKYGKC4MRE3mq3RDhMaHIS98WLOXudRDhMKpukPdffz0xXF+6eHtLZSrtU\nPJ6z0vEODuI+dy5T4Zc9fzqVylnxzF28iLunB/e8Cr5UPJ67l8nOTmbOnFkYmMzrP/bee8x2dxe0\nrxl+911mz5/H3dt7+ZkCgdy9DO/dy2xXF/5C9jwjI4TdbqbPnr18+Vgsd/3hfftwnztX0F7H63QS\n9flylZyLmevvzwi7XoX5FY7zcV+8SOIGJ8yLORvLQxy/5SGO3/IQx2/lEIOrDwFXEqK8FnuUK71G\nKpcjXOH8glye297K21+jQVqoXSJBolTmtteWnFsmQ6pUIlXkFyuUKBSZ9gL3L1WrkcrlBftLNRok\nCkVB+x9F9t4lha6fzR1TFNCcUmi1yFSqgs8vSKVIZTKkBZ5fKpNlFOGvQqHxlyoUuZwuEREREZGV\nQ9wWXIUs3jc3VFSgNBqJ+f05H794MIhco8FQUXHV8xkqK5FrNMSzOUKXUBcVoSsvJxmJIFOpMrIL\n4TCCRIJMqURjtaIvK8O6di1ah4Oo14uxuppUMomnv5/yHTtwbNpEeHoahdFIzLtQj6d0yxZsa9ei\n0OlQ6PWkEgk0NhvJRILQxATl27Zh7eggGQ5n7GKkUooaG0mEw8yeO0fdI4/k7GukWi0SiQStw0Ey\nFsM/NETjk09i37aNRCiEVKMhFY0iUSohK/HQ9PTT2DZuJDI7i0ylQqZSoauoIB4M4nU6aXjqKUo3\nbWKurw/XyZMo9PpMoJlOE52bw9LSgspkwrZxI5LvfS9nLXSJ+scfx9LUBOk0M93dpFIpEqFQJqCU\ny7GuWYNcq6WoqSm3epZKJBCkUgRBwNrevkADK51OZ/S+FIoFyuyW1la8eVbXrO3tN9weR8zZWB5X\nGr94PEkymUalujV/lmOxzPxVKG7N9aPRBIIgFKx+BHH+LRdx/FYO0f7mQ4BUoUBpNuPt78d14gT+\n0VF0DkfGvqWAjMB8ZCoVGpuN4MRETnNKU1JC7a5dqC2WXB7V0G9+w/A77zB38SLq4mLqHnsMfWkp\nhooKDJWVJKNR5i5cIJ1K0fjEE7R96lPY2trQlpSgczjw9PdnNKEkEsruvJO1f/iHmKqqMhY6Vivh\n6Wlmzp4lHY9Ts2sXzc89h6mqCmNNDbrKSpLBILPd3SAINH/iEzQ98wymmhqKGhvRl5URmJjA53Qi\nCAJNzzxD03PPYcm2q4uLM9V409NIFAoaP/YxWj75SYqqq9HX1GCoqCDq8eAZGECh0dD64os0PP00\neocDldlMeHaWsQMH6Hv9dfwjI9ja23Fs347aZMJYVYXKZGL2wgXifj8ytZrmZ5+l9YUX0NhsqIuK\n8I2M4HzjDUYPHCAwNkbxHXdQ8/DDGfPpoiJigQAT77+Pp6+PqNeLtb2dqnvvza16eQYGGHr7bUb3\n72eurw+JTIbaakUQBFSWjJBj0OUinUohyGTYOjoo3bJF9B5cBYTDcY4fn+RXv3Jy7NgEbncEk0mJ\nVrsy753fH+Pw4XF+/etBTpxw4ffHKCpSrViQ53ZHOHBglD17nJw6NUU4nMBiUV8xyBIRWWlE+5vf\nAg4cOLDgG0gya0UTCwbRZu1l4uEwU52dmKqrC26LzcdUU4PuU58iODkJgoDO4cj1C0xN0fWv/0pw\naipXQThz/jy9P/oRlsZGQjMz9L76KuGZGYqamxGA6e5ulEYjZTt2IJVKqX34YWzr1jFz7hwypTKz\nYpX15Zu9eJEz3/42yXicooYGkEgY2b8fhVabWTkaGOD8yy8TdrszmlWCwOBbb6EuKqJsyxYmT57k\n1De/icpkovLeewEYPXAAXWkpFVu34nU6GTtyhKbnniMdjyORy/FPTOAfGoKtWwmOjOD81a9Ik9Gm\nEqRSXKdPY2lpwbF+PcPvvsvBv/zLjAyEVEpkZoYDnZ0gCJg/+1nkGg1rP/MZHFu34h0YQGk2U7pt\nG/JsYDNy4ACd//f/orZYsLa3k06lOPvyy2gdDtpefJHA+Djuixcp2bCBVCKBRCYjPDeHx+nE1t6O\nd2iICz/9aW5lLB4K0Tc+TjqdxtrWhlQup/Luu7G2tRH1elHodAW1w5bL4rkncn3kG7/33huls9OV\n+/n06SlGR/0891wLev3N9e9LJlPs2ePk4sW53LFjxyaYnAzy8Y83olTe3I+IcDjO7t19jI1dzi08\ndGiMubkIjz1Wv8SAWpx/y0Mcv5VDDK4+BPhHR/H09yORSlFm5Q0gkzDuGxlZUAF4JWQqFcbq6iXH\nJ48eZeLwYQB8g4O54/1vvEHtww8TdLlwnTixpF/fa69R88gjlGYlEXR2e85bcD7jhw4xdfr0kuPn\nfvjDjH1Mby/uebY2l+j5yU+ofvBBxg8fzqxoLeLs975H1YMP4untJTA0lLP+8Xq9GI1GgqOjVNxz\nD5PHjxOembncMRvE9L3+OhV3383ksWN5k9q7/9//o+LeeyluagKgpL2dkvb2Ja8by65WBcbGFhzv\n/fGPqXnwQWbPnSMZDi9Rkp88fhxLSwszZ88u2XIkncbV2YmlpSW3Rai2WFBbVtaORGR5TE2FOHNm\nasnx2dkwAwOeZdvXXI3RUT99fXNLjo+M+Bge9tPQcHMqEC8xOOhbEFhdorfXzYYNAcrLr13rTUTk\ndkLMdl2FLP7mEStgL3O1tmslND2d93gqFiM8M7MwMJlHMholXKDvfCJud97jMZ+P0PR0wfaox0Nw\nepro3NIPB4CQy0V4aorwov5GozFz3bk5Im53wXsMz84S8XpzvoiL8Q4MFHz2+fgKVBt6h4eJeDwF\nzxH1eklGIgXbI3NzJK9gTXQzEL/1Lo/F4xcMxkgk8vvX+Hw33xopEIgVtM/x+1fm+vlIpdL4AOQr\nsQAAIABJREFU/UvbxPm3PMTxWznE4OpDgMpszmtPgyCgmreS9UEpZN8i12jQlZWhKyvL367TXVPO\n12L9qEuoi4vRV1SgLdCutdnQl5UV3AIzVFejLyvLu1oGoLHZ0NhsBe9fX1qK+kr2Om1t6K6hYMDc\n0JD3uKWpCY3NVvD5NFYrMo2msP2OzVaw0lFkdWA0qgrmFhUV3fz31mRSLdl6m992szGbC1TKSgXM\n5sJVyCIitzvituAqIur34x8ZYaC/n/adO1FlV2D0ZWUZ+5h5eksAluZm9Nfw4X+JiMeDf2wMQRAw\nVFRkKvQA+4YN1H70oxkRUJkMQRCIh0LoKyqwr19PaGaGmoceIjA5iUQqRZBIiAUClO/Yga2jI3f+\n0ZPdTJ3rQaZWUr5hLUVVmaDNsXUrVfffn9HTSiYRpFKSkQgNTz6Jra0NlclE1T33oCwqIpXNmYr4\nfNhaWzHX1pKMRjPJ++XlOaHSwOQkjg0bsDQ3ozAYKD5yhLDLRdTrRSqXIzeZqNu1C43Fgn3TJgbf\nfhv3+fMkolEkUilyg4GGJ55AbTZTumULjm3bMgKn2etHfT5aX3wRczbwSqVSzPb0EBgZQWE0Utza\nmrOvKb/rLsaOHsVcU0MqHkeqUDDndNL8zDOoTabceze/WlOQSnFs3IhEKsXS1sZMd/cCA2tBKsW+\nYcOKSy3crjkb0WiC0VE/oVCC4mIVdrvuliiIX43F41dUpGLTJjsHDy7cMi4vN1BTY7zp91NaqqOt\nzbpka7KpqYjKypvve1lRoae+3rxka3LtWht2+1L7ptt1/q0WxPFbOcTgapUw19fHwBtvEA+FGB4e\nRujro2bXLopbWhAkEqruuw+d3c5sTw/pdBpLczPFra0LSvavxMy5czh//WuSWdFJuVZL7SOPYK6r\nyySOb9vGib/9W3zDwwgSCZaWFpo+/nEANMXFVHzkI5z9139ltr8fiUxG8Zo1lGbtcQBOfO8VDv7d\nPxKa9QBga65jx5e+QOO92zFVVVF9//0M7tmD+8IFpCoVJevXY8nmL6lLSrCsWcPpb3yDYDaAK1m/\nnoaPfhQAfW0tpVu2cOpb3yI8NYVEKsW+eTPmxsbcsxTV1TFw4QK+oSHkGg320lIUWXsbjc1G9UMP\noTQYmD13DpXFgmPTJszZXKriNWtoePJJBvfswe9yoTQaKb/rLszz7Hl6XnmF/tdeI5kVU7W1t9Px\n0ksYa2oobm2lfOtWzr38MhG3G6lSSfX992PP5qJpS0poeuYZZrq68I2OZhLf29owZXPl9A4HTZ/4\nBDNdXfjHxlBbrZn2mpoPMpU+dMzMhPjVr5yMjma2wKVSgY0b7dx1V8UNs4C5mWzdWobRqKK7e4Zo\nNEF9vZk1a4rRaG5+taAgCNx7byUlJRrOn58lnU7T1FREa2vxioydUilj164azp7V09vrRiYTaGmx\n0NpafNOvLSJyMxHS6UI77jeP3/zmN6xfv36lL7tqifn9dH33u8QXqXVLFQraPv1p1EXLs6UIz85y\n9rvfzQUGl5DrdKz59KcJTk6y7z//54wtzDz0ZWXs/Ku/IjQ1xf6/+AuS0ShyrZZ0Vs/JUFnJXf/9\nvzN5tpefvfR5YqGF+UHlG9bw2D/9Le7TJ/n1H/wByXgcfWkpyWiUoMtF2Y4dPPAP/8DUyWx7LIZM\npSKd1amqvOceHvjHf2Tk3Xf59R/8Ael0GoVWSyqRIBEKUffoo3zk619n5De/Yc9nPoNEKkVttZII\nh4l6PDQ+/TQf+du/ZebMGQ7/9V8jUSoxlJdncr2mpijfvp3NX/oSF3/2M975wheQqlRorFbigQCR\nuTmaPvEJ7v2bv2Fo716O/a//tWRcqx94gI2f/zyDb73F8a99bUl77cMPs/5zn1vWeycCr73WR3f3\n0ry0J55ooKVFTPAXERFZPp2dndx3333X/Prb/2udCP7R0SWBFZARyxwZWfb5fSMjSwIrgHgggH90\nlNnz55cEVgD+sTFme3qY7enJJFan08QDgdxrfcPDzPX2MnGme0lgBTB6oovxk2eZ7e4mEQ6TTiTw\nDQ8TdGXK0scOHGDq5Elmzp7NrKil0yTC4dzq2vC+fbhOnGD2/PmMvU4ySczny11/4Ne/ZvrEiYxA\nZzpNKpEgODFB1JNZPevfvRtXZyez585l2iMRPH19hKYyWyQTx47hHRxk+swZAJKRCP6RESLZBPrB\nPXuY6urKtS9m/P338U9O4spTCQkw9v77uWcV+WB4vREuXsxf8DAw4FnhuxERERHJIAZXq5DhedVn\nN2Th8UrnSKeveI35XoD5u6evcv4UV3qCqz3ftTx/odekUilIpQq2p9NphHQ6r68iXH72gu2Zk+S8\nCvNc4IrPfjtyu3mTXWl6rfya/NW53cZvtSGO3/IQx2/lEIOrVYCuvBx5Hv86iUJRsJLvetBXVOT1\nF5RrNOjLyylqasr5/ykMBuRZ8U+t3Y6lqQlzU1Ne/zx9WRlFTU3Y1rQiUytAAJlChlSeyQMrXXsH\njrVtWJqbM/0lEkz19bnqudItW7B1dFB0xx0Z/0RBQK7T5e6lfPt2Stato6ipCSQSkEpRWa3Is7lU\nVfffj7Wjg6LGxkw1pSAg02pzXoG1u3ZRsnEjlpaWzHjK5RhralAXZ/I97OvXY6ipwZpNyhdkMjQl\nJTmLoeoHHqDojjuwrlmT6S+ToS4uRpZ9rxybN6N3OHL9F+O48050JSXX/kaJLMFkUlFfn78itqbm\n5idki4iIiOTjA9vfHDp0iOPHj9PT00MoFKK0tJTJyUl+8YtfcO7cOSwWC3p9fgE40f7m+pAplaiM\nRjxOJ+lkEqPRiFShoOaBB25IUrNco0Gu0+EdGiKdTGauqVZTu2sXutJSNMXFyPV6wtPTBMbGSCeT\nFDU10frii1iamtBarchUKiaOHyc8PU0iFEJjs7H2j/6Iovp6DGV21HotnnOnSfk9yEhQ0lzL5v/w\nx5R3tKKrqsJQUYGmuJhEKITObqf+scdofOYZStasQV9Whspsxj8+TjIaRaZWY9+0ibV/+IdYW1vR\nl5ejsdmIejykIpFcwvmaT38aa1sbypISNBYLQZeLVDyO0mCgbPt2Wj/9aawtLWizAVM6kcDrdCLX\naqnYuZOaxx9HV1yMxm5HbbGQjsdJhMNobTaqHniAlueew1hejqakJFNB6PUSdLlQ6nSUbt1K/ZNP\nojab0ZSUkIhE8AwM5JZTiltbaXn+edTmaxNpDLpcTBw9ytjBgwRGRzOWR/NkNgKTk0wcPszYoUME\nxseRKpUojTe+2qyygCzFraSoSM3ERIBAIA6ARCKwfn0J69fbkUpvr++P+cYvmUzR0zPL/v0jnDo1\nRTAYx2hUolReWzFKPJ7k3LlZ3ntvlNOnp4lEEphMyg+lfcztOP9WE+L4fXBWzP5m27Ztuf/v2bMH\ngKNHj/LCCy8A8POf/5wnn3zyg55eZBFFzc1o7Hb8IyOk02n0ZWU3VI3b1t6OvqwsJ8Wgr6zMST2E\nPR7GDh3COziIIAiks7IDE8eOUbZ1K4loFASB1hdeyFjEyGQo9PqcwGXM4yE40Evz44+QiMSQyuUk\n4xHCw/3AR0j4/cycPcv4+++TCIUQpFJCMzO5ar/Q3BxzFy5gX7eORCzTPx6J5BTP/ePjjB05gt7h\nQGOzIZFKifr9eAYHqQJSgQBz/f2UbdtGIhxGolSSjEaJZsVFPU4n3S+/jLunJ+PNJwiMHz2KymzG\n2tiIz+lk6J13SEWjKHQ6BJkMV2cnJR0dODZsIDw9nQlqxsYywqpzc4wfPox9/XqKamtR6vWsfekl\nyrZtwz8ygspkwtrRkZNquBpBl4ueH/2IeDCYed7RUWbOnaPxqacw1dXhn5ig98c/zuWa+UdHmenu\npvHpp38rKgptNg3PPdfC0JCPcDiOxaKmrExfUL/pduPIkQn277+cOzk87MPp9PDkkw2o1VevGDx4\ncIzDhy8L3Q4NeRke9vHRj9bddPsaERGR/CzrN29wcJBXX32Vp556CgDNvK0rxTX42YlcHyqTCZXJ\nlNEqyWOzslwK2adMHjtG3y9+seT4+R/8gPK77kKh1WaSwucR83oJuVwU33EH40eO5O0/YTJRsX07\ngbExXJ2dKI3GBastF3/+c0q3bmXyyBGc2QB+Pj6nk7IdOxg/eJDBN95Y0h6cmKB8xw4mjx9nZO/e\n3PFoNIpSqSQwMkL5zp2MHTyY176n+9/+jYq772bs/fcZ3bdvafvLL1N2992MHz6ML2utI5HJMon3\noRB9r72GfdMmFFotEpmMko4OSgpsEV6J6a6uXGB1iVQiwcSxYxhrapg5c2ZJwUEqHsd14gTG6uob\nqvd0u+rkqFQympqWVzW7Eiwev7m5CEeOLHUAGBryMTjou2q149RUiOPHJ5ccv3hxbkXsa1aa23X+\nrRbE8Vs5lrVmXl1dzec+9zmOHTsGLEwclufJ4RFZnfhHR/MejweDBEZHl9jLXCIVjxOZm1viqXeJ\nqMeDb3SUwOTSDwfI2L8EJyYK2s8EJyfxj45mzKbz4Onvxz86WrC/x+nEPzaWC4wWM3P+PP7JSbwD\nA3nbp7u6CFyhv3do6Jrsca5GofELulwkIpGC708gu40qcvvi8USIRpN522ZmllboLmZuLkIikb9g\nYnb26v1FRERuDstOSFCpVLncqmTy8h+Jq31bnl+1cODAAfHn6/j50rGVul5arSY670M6Go0SjUaR\nyOWoLBbGpqcXVDAODw9nhE4lEuRaLSmVKm9/mUqFymwmRMZM+RJerxev14s0217o+gq9HnVRETK9\nPlP5lyWVSpFKpVDbbKgsFgStdkH/S+dQFxejMptRFBUtmLvJZJJkMomhrAy1wYDKZst7fmNVFWqL\nhbhanff+NRYLCqNx2eM/G4nkHV9lNvfOXaBdVVSEVKG4ofNhx44dt3z+r+afF4+fVqtgfHw07/un\n1yuuej6dTsHo6EiB/spb/rw3e/xu9f2stp/F8bsxn7/XwgcWEXW73RRlxSt3797No48+ys9+9jOe\neuop0uk0r7/+Oo899ljevqKI6O1JIholNDWFIJGgLSnJbHEBAZeLfX/6p7hOnlzw+tpHHuGur3yF\nZCzG+VdeWWKAbGlpof7xx/END7P3T/8UiUSS2XYUBDwDA5Ru2cKdX/4yIZeLQ1/5ChGPJyNbkM3r\nqrz3XtZ/9rO4L15k75/8CRK5HKlUChIJodlZ6h58kE3/6T8xceIE73z+8whyORqLJZMTduECbb/z\nO9z5pS8x3dXFgb/8Sxo+9jEEQKJUMnb0KJbqatb90R8xfvQoe7/4RcyNjSj1etKpFK5Tp2j5xCdY\n/7nPMbx/P7/59/+emM+HIJVm9LzCYbb9xV+w7rOfZfrsWQ599atLtMjaf//3aZyXdxhwuQiMjaE0\nGjFn1dfn45+YIDgxgdJkwjwvcdLjdNL7k5/kig0AEARqH34YW3s7nv5+en/60yXt9R/9KMWtrdc7\nDUQ+AOl0msnJILFYEqtVc13q6m+8MbDEfsZsVvH88y0YDFf210ul0rz2Wh/nz88uOG61qnnuuTvQ\najP3kUikcLmCJJNpSko0Yi6WiMh1cr0ioh+4WvDtt9+mq6uLc+fOsXbtWkwmE0ajkddff52uri52\n7tyJNluyvxixWnB5HDhw4IZXfXiHhuj7xS8YP3yY6TNn8A0PoykuRqHXo9DpMDc2IpXJCM/NoTab\nafrYx2h98UXUFgtShQJDeTmpRIKY348sa19TvmNHptLRZEKqUjHV2cnQb36Db3AQx6ZN1D/5JKbK\nSpR6PYJEwtzFi7hOniQeCFC2dSs1Dz6ILlupJ1MqmevtZfLoUaI+H9X33EPl/fdjKC9HX1qK0mRi\n7sIFJk+cIBYI0PjEE9Q+8QR6ux1tSQnJeJxT//RPnPve9xh6+22KGxupvO8+jJWV6MvKkCoUjO7f\nz/DevfjHxqh54AGqHnwQfWlpxjzabicRiRCankZXVkbHSy9Rs2sX6qIitDYbxpoakqEQsUAAfVkZ\nzc8+S82DDyKRyUilUvS99hqdX/saA2+8wfC77xKemcFQU4NCoyGVSHDx5z+n8//8HwbefJORvXuJ\nzM1hrK1FrlajMptzyvCJaBS1xULFzp1Y16xBEARURUVoLBZigQDJaBSN1Url3XdT3Np6w/31bsbc\nW+3MzUV4440B3n13hK6uaXp63Oh0cqzWpfIp+cavtFSLXC7F640gCAJNTUXcd18VFsvVjZsFQaC8\nPJO87/FEkUoz9jH33luZM16enAzwy1/2ceDAKF1d0/T3eygqUq6IMfONRpx/y0Mcvw/OilUL5luV\nKi0t5fnnn/+gpxS5RUR9Pvpee23Byot/dJSBN9/kjhdeQKZSYW1txdraSuv4OFKFAm3xQu8vjc1G\n3aOPZhKvJRLk6ssfDJOdnRz6r/+VWCCArqwMUin6d+8mHgpR3NqKz+nk/b/6K0inKW5tJRWP0797\nNzG/H3NTE5PHjrHvz/6MNKCvriYZi3HmX/6F8NwctvZ2pru6OPo3f4NMpcK+aRPpZJKBN98EQcDW\n1obzzTfZ9+d/TjIYBEEgGQ5z7uWXkSoUVGzfTt/u3ez78z8HQF1cTCoe58x3vgNA6YYNuE6coPu7\n30VbUUHriy8SD4Vw7tmDVKHIrUA5NmygpKODsNuNXKtFMe+LxfihQ5z+zncgu7KUjEQYeOMNFHo9\nbb/zO4wePMiZf/mXnExDIhKh77XXUBoMtGR/n4oaGzHV1ZEIh5EqlUgX5TQWNTdjamggEQ4jU6ly\nq44iN5d0Os277w5z8eJl42GvN8ru3f0YjSpKS5eaDy9Gq1Vw113lbNpkJ5FIodNdXzGQTqfgIx+p\nZMsWB6kUudUqyBhav/HGAFNTl/OvpqdDvPZaP5/6VCtG4+oLsEREVgPiX+BVyI2u9vANDeW11wlN\nT+MfHcVcX587ZigtveK55HlWKydPnCCU3TJMhMO54yPvvovrxAl8Q0PEsjlLbp/vcvv+/cycOcPU\nyZNEs+2z83Kb+l9/neZnn2X23DniwSDxYJDw7OXtkcE9e2h+9llmurszgRUskO3uefVV6p54gqlT\np3LVdoF5VXe9P/sZjR//OFMnT5JKJPA7nfidzsv3t28f9Y8/js5uBzKVglqbLe/zk1yatDz8zjvU\nPfooE8eO5ZUTH9q7l9pHHslVUEqkUhS6wh/WV2u/EYiVRguZmgotCKwukUikcTo9S4KrK42fSrW8\nP8f5ZBvGxvwLAqtLBINxhoZ8tLevruBKnH/LQxy/leP2UtgTuSUk4/HCbXk8B6/7/JGlvoKQkROI\nh0LE8/gWQqbaMBEOkyjQPxmNZrwGC7QnIhESodCCgG4+cZ+PZCxWsD3m8ZCMRIgXaE+EwwXvfcF1\nFskozO+fjMeJ+/35+4VCGQ0xkduWRCJFKpU/bTUWK2B7tIJEo4Xv4Xa4PxGRDyticLUK+SCVC1dC\nZ7cjSJZOBalCgTa7KrMczA0NGXuaRZhqarC0tl6x/dJ2Vz5sHR1YFrfPyzGytrdT1NiIscA+edmO\nHZgaGzPXz9O/4iMfoailJWOfk6e9OKsOfzUs85PK599fRwcaqzVnn7MY29q1eVfCbiU3eu6tdoqL\n1QVzoxyOpau4Kz1+drs2r9K7RCLkvb/bHXH+LQ9x/FaOD5zQvhzEhPblMTw8fEOTEi955flGLqtE\nCxIJlffck7eq7XpR2+0I6TSuU6dyJsZKk4nNX/wiFdu3o7XbkchkCFIpKpMJXWkp5qYmWl58Efva\ntaitVgRBwOt0IlUokKvV6MrK2PD5z1N2552obTaS0SjB8XGkMhlytRqt3c7az3yG4tZWpCoVqUSC\n6dOnc/eksdvZ/MUvUr5lCwqjEdJpol4vCr0eVVER5vp6Ol56iZKODlQWC4lwGEEQkKtUqIuLMdbW\n0vT00xiywVUsGGT23LmM9pXLhUypzG2RqiwW4sEgEqk0099qxVRbS+NTT6ErKUFdVIR/ZGSBXpfO\n4aDtU59CY7Uue/xvJDd67t0uTE4GOHVqit5eN6FQDL1egVx+dfsYmUyCwaCgv3+OZPLyClZ7u40N\nG5ba76z0+KnVMlQqKadOuZiYCDI3FyWdTnPPPZW0tVlveMHDzSbf+MViSS5enOPUqSlGRzOrwKsx\nWX8l+LD+/q4EK5bQLnLruNH75oIgULZtG/rycnwjIwgSCYbKSgwVFTfk/DKZDH1FBZv+438kNDWV\nyU3KVgECRP1+wjMz+IaGSEajCFIpMreb2CVx0mQSuUZD08c/TsTjyQRYWm1OtFauUmFpbiadSBCa\nmUGmVKJzOHKrStqSEmoefZSSDRvwDQ0h1+mwtLSgyDoKyJRKlCYT5XfdRczny5xfr0eWNaOWKhQo\nDQYS4TBRvx+pQoG6qCiXCxXz++n75S8XBKeTR45Q/9RTmKqrM4GWTkciFCLq9yOLxdAWF+fsb7R2\nOxu/8AVcJ04QmJhAZTRiW78+F7jdTnwYczYuXnTzy1/2EY9f3iarqzPz6KO11ySp0NBQxIsvtuF0\neohGk9jtWqqrjXm9/W7F+CWTUFlpwGhUkU6nMRiUpNOZNL9VFlstGb9EIsU77wxx6tRlKYvDh8d5\n8MEa1q69vVZ9bwc+jL+/tyticCUCZFaqjNXVGKurb/i5XadOcf4HP1iStK22WLC0tjJ28CAn/+Ef\nlvSLeb3YNm1i9swZTnz960vaLc3N2DduJO734+7pQSKT5ZLLE5EIo/v3Y6yuZvLoUToX9R966y3M\n9fVYOzoYO3iQ89///pLzBycmsG/ZgufiRQLj40vGZuzgQQyVlcz29i4IrCCjGTb+/vsYKiqYPX+e\nyMwMxkU+f+OHD6OvqECQSFCbzVTff3/hQRS5KcRiSd57b3RBYAXQ3z9HX98c7e3X9gFts2mw2ZZK\nL9xqZmfD7N8/vCC/yuuNcuTIOHV1JioqDLfw7pbP0JB3QWAFGe2v/ftHqKsz5oRURURWGjHnahWy\n2vbNfYODeavhwrOzeJ3OgvYyc319zPX04CnQPtvTg7u3t6D9TdjtJjw7u8Se5pKa+lxfH/6xMeYu\nXszb33XqFB6ns6D9TGBigojXi39RYJVrHxsj5vXim6eePR/f2BjRedWRq4HVNveuxtxcJG81HcD4\n+NIK2uWy0uM3NRXKm7ieToPLtfrscRaP3+Rk/mcIheIF39ffZj5sv7+3M2JwJXLTkWnyf6MXpFIU\nWm1e+QYAaXY7TVagXaZWo9DrkSrzfzsVpFKkSmXh/ioV8uw58qE0GFBqtQs0u+YjkcszW4gFnk+q\nUCBRKBZoXi24vkKBVDQ4v6UoFFIUivx/BvNJG6w2VKrCeWP5Et1XG4WeTxCWL20hIrIcxNm3Crld\n983T2aRwiVS6IGCxdXSgMptJxuNobDbSySSBiQlK1q7F3NRELBxG53AQj0YxVlaSjMXwDAxQ/+ij\nlG7ejFyjQWO1khaEXPvchQvUP/YYJRs2EJmeZvzw4UxSfLbqMBGJYGlpQVNcjK29nYs//Snl99yD\nsbIS0mnO/+xnlG/ciLGqipING5B///tIlUqUej2pRILg1BQNjz+Oua4OiUzG5MmTC+1lss+l1Osx\nNzQwdfo0qUSCZDSKRCZDIpdj7ejIqdtPnz2LymJBplKRSqUITkxgW7u2YGB2M0hEoySCQWRabS6f\n7Hq5XefeB8VsVtHaauXkSdeC4zKZQG2tccGx2dkQwWCC0lIdMtn1fy8NBGK0tm4klUojkaxMslNZ\nmZ7SUt2SVTiDQUl19cItwYkJP6lUps+twOOJIJUKV9zKWzz/amqMqNUywuHEguPV1Ubs9tVXDXmz\n+bD9/t7OiNWCIjeEwOQkg3v2MLx3L66TJ4n5fGhstpz9jbqkBK/TycSxY0TcbsrvuovmZ59FZ7Nh\nKCtDX1lJZHaWiePHScXjND/zDC2f/CTGqir0ZWVo7XaCExOMHzpEPBym+eMfp/7ppzFVVKDQ60nG\nYgzv28fgnj14nE4szc1U3nsvKqMRrc2GRKFg+K23OP2tbzF5/DiVd99N9YMPorXZMFRUZOxzenuZ\nPH6cVCJB08c+Rt2TT6K1WlGZTCiNRsLT0yQiESRyObZ16yjduhWpQpEJHGMxJo4fx93TQ8zvp2Td\nOio/8pGcOXXU52PgV79i4M038Q0OYt+wgfKdOwuuit1I0uk0011dDLz5JmPvv89cT0/OP3K1VYvd\nDOx2LbFYktnZMKlUGotFzf33V1FXZwbA7Q7z6qsX+MY3TrF7dz/d3TMYDMprUl+HTFB14MAov/qV\nkxMnJhkdDWA0KjAab34+kFQqweHQ4fNF8Xozmmnl5XoefLAaqzUTfAwNeXn55XP88z938atfORke\n9lJcrLkm+50bgcsV5O23B3nnnSFOnpzC641SXKy+ppUntVqO3a5hdjZMIBBHKhVobLRwzz2VaLXi\nqrDIjeN6qwXF4GoVcrv5Q8X8fnp/8hMCY2OkUynSySTByUmiXi+WpiaiPh/Db78NEkkmmKmsRKZW\nIwgCRU1NRD0eRt97D0EmQ19ejrmuDo3VilyjwdzQwGRnJ/u+9CVifj/G2lqURiNj77+PRBCouvde\n5vr7Of6//zdRrxelyYRMrcY7MIAgkWDfsIHhfft490/+BNfJkySjUSJuN2OHDqEym6nYuZPRQ4d4\n78tfRqJQUNzejrqoiJF330Wp11O2dSsAWpsNS2srluZmHBs3Ym1ry23p+UZGGHr7bXQOB5amJoxV\nVUQ8HuQqFfqKCqZOnuT4179O1ONBIpORTqVw9/ZmEvqbm2/6++Pu6aF/9+6MCn0qRSIcxtPfj8ps\nvm4drdtt7t0IFAop9fVmmpuLaGuzsmVLKQ7H5cDp+98/x09+0ksolCAeTzExEaSz00V7u+2aApC3\n3x6is9NFIpFicHCIdFrJwICHhgbzimw9arVy7rjDQmNjER0dVjZvLsVszkgVhMMxvvbA+JS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I2N5T3HzErR5KxqxdlMut15X9tspmZVI2a0Dw4SnZzE19ubs98/MEB4HvY7+So2g6OjJKK33sJE\nJD8eTwi/P/d7MDqaxzZKRESk4BGDqwKk0H55aJ25xQYVej06hwOV2ZyzX6pQoDQa0ytDN6JxONAV\nFc19/RtWe0zTRskaux2NzZb3HDqXC7XFgjGPfY6xoiLva5uNIU+eg6G4GKXRiKmyMme/vqQE9TxM\nnfV58he1DkfOQoSlUGhzb7Wx2zXo9dffA9Os93Mt2N8UGuL8Wxri+K0c4ragyC2n+O67Kdq5M2tr\nsOHpp3Fu2ULU70fncmVtDbq2b0djs1G8Zw+u7duzcq42vvhieksQUitJE93dKHQ6bI2NaXuaou3b\n6S4rY+qGFah1X/gCOqeTorvu4trRo+hcLgRBQK5QMDU4SOW+fWhtNkr37OHq736XlXPV+NxzeQO/\n2ZTccw99R4+i1OtJkpKWCI2NUXvwIEqdjtK9e3F/9FFWzlXN44+ntwQhf86VfeNGxtrasnKuXNu3\nr7hUg0gmFRUmDh6s5fDhXnQ6BYlEEokklSi+ffvaUcj3ekO0to4hCEnq621i4CciskTEasECpND8\noVQGA9aGBlRGI/FgEGNVFRtffJG6p55CqdUiUyoxVlQglcuJ+f2ozWZK9+yheOdOpDIZWpsNjdOJ\nXKkk6vejLyuj8YUXqD14EI3FgiAIdL71Fqf+9//G/dFHXPv4Y8bb2jBVVqKx2VCZTNjq65GpVEQD\nAWR2O1v++I+peuSRlM+fXk/Y66Xtl7+k/+hRPJcuYa6qSp3fakVjs6G2WJApFMRDISx1dTQ+/zzl\n99+POs+q22zUZjPJeJyBzz5jvL2d2NQUZXv3UvP44yg0GrQOB+bp3KhEJIKtsZENL7xA2f33I5FI\nSESjuD/+mO5332WstZXR5mbCY2MYysqQKZWozWb0paUIiQSJaBRDWRkVDz6Yth9aTgpt7q0FZDJo\naxvnxIlBurq8aDQKvvjFOrZvn3vVdSU4c2aQv//7k7z9dhdHj/bz2WcD2O1aysvXntSCOP+Whjh+\ni0f0FhRZk9jq67HV17PtG98AmQzZDcKdGpuNyoceouLBB1PinbNK4CevXWP49Gk0Dgcb/s2/AUEg\nODzM4PHjmCoqGLlwgYs/+EGGRtRoSwuXf/5zdr/0UtpL0FJXhxCPc/7iRapnVasOnjpF6y9/iVKv\nT/kdSiRMdHbS8frr7PrWtxg4cYLz3/seGrudiocfJuz1cvW3vyUWCGRUK+Zj/MoVvB0duLZtw7ll\nSzqIGj59mqpHHgHAtXkzrs2bEeLxLNHRsdZWhk6dut6QTDLW1obaYqH8gQcAMFVWYqqsJDkt1SCy\nNgiH4/z85220t49TVWUkFAoSiST40Y9aqKw0s3nz3Cuft5KxsSDf+95FBgauq8kPDQX41389R0WF\ngbKytRdgiYgUAuKncAFSyPvmMqUyK7CajUQqzdIWmuztJSkICNEowcHBdIL65LVrBIeHGbt8Oaf4\n5tCZM3hnrGemkcrlWTIgnsuX04bMs8VA+48fx9fTg6elBZJJQqOj9B89yti03U3fp5/mTXafzWzD\n5dmBj+fSJaL+TAuRXGruY21tOc872tJCPBLJaLvVgVUhz73VoKPDS3NzqujB74+RSKS2BqPRBJcv\ne1b57uDy5bGMwGoGrzfMpUurf383Is6/pSGO38ohBlcia568quPJZNqkOd9xyRxBV9bz8jwnmUgg\nxOPEb5A5mH3cfM6fK/ADSCaT81JUv1FmYfb9iYrsa5t4XCCfIUEisfrvXSyW/x7i8RWXQBQRuW0Q\ng6sC5E7zh9LnqdbTOp1onc60vcyN2BobMVZXZ7UfO3Ys43E+oU3nli2YqqqwNzamGqRS1FZrOsnc\ntW1b3krA2Zjz7NOba2tRGefedsmXO2Vdv37eQq7LxZ0295ZKba05w17HNy2tIZGwJnSj6uutmEyq\nrHa1Wk59/erf342I829piOO3cogJ7QWI2+2+o5ISVUYjyWQS/8BA2nRZodNRPW2/o3U4SAoC4+3t\n6X6Nw8GWP/1TTOXlAASGhuj54AOuvv46sWvXUCqVGKZ9ETUOB/FgkJHz54lOTpKIRDBUVLDlT/8U\nfVERGocDhU6HVCYj5vejKyqi+J57qDl4MF0tGPJ6GT1/noGTJ/H39yNRKNIyCiqzmdDoKKPNzfj7\n+4lNTaErLqb2c59DoZtboVtlNhP2egnPEiLVFRdTsW8fCu3KVnUV6tzr7fVx8uQQ588PMzkZxWBQ\noFbf+pRTlUqO3a6luXmUUChOJBJBr9fy7LP17N9fiUwmRRCSXL3q5bPPBmlpGSUUimE0KlEq82+f\nLxdGowqzWUVz80h6FUutlvHVr27innsW5hu6EhTq/FsriOO3eET7G5HbkqQgMNXfT2BoKFVdWFWV\noQElxOOMNjenpBi0WhybN2MoKQEgMDrK6X/4B0abm9PPlyoUbP/zP6fq4YeJTk3R9d57xAIBQqOj\nyDUaVCYTrq1bsTc1MTU4yMn/+T8ZPnuWRCSCRCZDbbOx61vfouL++wl5vVx57TVCs8ROJXJ5yn5m\n/XrC4+N0vPUWUb+fmN+PTKVCrtFQuW8flnXr5vX6E9Eok243ofFxVAYDxsrKFQ+sCpXWVg+HDnWS\nSFz/qCsq0vHkk+sxGrNXbW4FfX2TtLSMEgzGWbfOzIYNDuTy1MbBqVODHD7cy+xP4ro6CwcP1qJS\nrUzNUWenl8uXPQgCNDZaWb9++X0pRUQKGdH+RuS2RCKVYiwvxzi9EnUjUrkc17ZtuLZty+obOnUq\nI7CCVB5T+2uv4dq5E19nJxOzks6j08rw8WAQ87p1DH72Gd6rV1EaDDBj/yMIXH39dYp27MDb3p4R\nWEEqH2vgxAlM1dV4Ll/G39+f7osHg8SDwXS/9CYJ/jPIlEos69aRbe4jcjMikTjHjg1kBFaQqoi7\ncmWcnTtXZgW9rMyYs/LO5wvz6af93PgT9+pVL93dPhoaVibIqa21UFsrzi4RkeVCzLkqQMR984Vx\no3joTN7LlNtNYGAgvz3O2BgRn4+J7u6c/b6uLgLDw6ntyhwERkaI+v35+4eHiU5lV2qtZQpt7k1O\nRvF4ctvMDA0FVvhussdvfDxMOJy74EG0x8mm0ObfWkMcv5VDDK5EbntUeSxklHo9SqMRhV6fs1+m\nUiFXq9Facyf2qsxmVAZD3qR0hUaDXKXKe32FVpuhwC6y/Gg0cjQaRc4+g2F5rYEWg0YjRyaT5OzT\n6XLft4iIyNpHDK4KkLWqVZKIxfAPDREcGeFWpPLFIxH8g4MEPbn1d0Lj4wyePs3otA7VDM6tW1GZ\nTGidTkzV1ZRv2oRco6HqwAGMZWVY6uqQqbJzb1zbt6MyGnHt2IE8R+J5zWOPoXU4sNTX59SnKtq+\nHYVWi62hAYlcnjKinpoiHg6n+nfsKLjgaq3OvXzo9Uq2bcu2mVEopCterXfp0ihqdS0+Xzjd5nLp\ncm79GQxKamrmVv+/U4jFEgwN+Vm/fvst+Wy5Uyi0v99CRqwWFFkWJnt76Tx0iL4jRxi5cIHA8DBa\np3PZkq69HR10HjpE/7FjjFy4QHh8HK3LlQ5Oun//e078/d9z4fvfp/vdd/EPDGAoL0djsaC125HI\n5Qx+9hnujz8mODpKxYMPUvu5z6Gx21EaDOhcLqKTk8SCQeQaDSV3303xrl1I5XJ0TifGsjIik5NE\nfD40djv1Tz5J9WOPIVerUZlMaByOVJ5WKIRCp6N0925cO3YglclQmkwIsRjD588z0dmJEI1StHMn\nxXv2IM8R1IksLy6XFoVCitcbRhCSlJToeeSRaiorV0Z9/OpVL//0T2f4h384za9/3c65c8MYjang\nSSKRUFqqRxCSjI+HkUigpsbMI49U4XTOXUl6J9DbO8mhQ50cOdLHhQsjDA8HcDq1aLXiyp7IyiFW\nC94BHD16dE39AglPTHDppz8lFsjMYTFVVFD/7LM5V3UWQnBkhEsvv0wiGs1otzU2su6JJxg6c4YP\nv/lNotO5VDPUP/00e7/9bQY++4wPv/nNlI+g0UgsHCYyPk7D00+z+6WX0s8XEgmik5PIVKqcQWE8\nGiU4NITCYEBjyU7+FeJxolNTWcd7Ozq48tpryDQa5EolgiAQnZykbO9eytbQ+zgf1trcWwjBYIxI\nJIHRqEQmW5lF+0Qiwbe//SlvvtkBQCQSQaVSYbVq+Kd/2s/mzc70c/3+KPG4gMmkynIpuFOZmAjz\n059eIhBICenOSAlUVJh49tn6dMWlyPwo5L/f1Wah1YLizBRZMr6enqzACsDndjOVJ5l7IUx0dWUF\nVgDj7e0ER0YYPns2K7AC6Hr7bUYvXWL43Dlifj+RiQmm3G58166RjMfpOHQow1pGKpOhtljyrrbJ\npw2mcwVWkKpYzHW898oVkoJAPBAg7PWm7jWZZOT8eWJBMWl5pdBqFVgs6hULrAAuXBjlvfeyCyLG\nx0OcOZNZSKHXKzGb1WJgNYueHl86sJqN2+3LadsjIrJWEIOrAmSt/fKYySHKReImffM+/w3+eTMk\nBYF4JJI3QIkFg2ltqdmoprfiYn7/igQ30RyBJ6RWwnIFjWuZtTb31jqBQJRI5Lr/jWrWNnAgUFjv\n/WpwYyXlbAHMcDiPr5BIXsS/35VDDK5ElozO5cowPJ5BrlajdTpzHLGI8+dAZTKhtdsx57C4AbA1\nNGCprcWUp9+xeTOmqqol399cGPMoIhtLS+dlfyNSuNTVWamuzp2YXlsrJqzPhculy/XRglotx+kU\nRXRF1i5iQnsBcvTo0TVlYaA0GomHwwQGB9NtEqmUin378gY+Czq/wUAsFCIeCpEIh5EqFKgtFsru\nuw9DSQkam43Q+DjeK1fSxyh0Onb81V/h2rIFtd1OZHycsNeLVKFAplajKy5my9e/jnPTpnndQ8Tn\nY+DECa4dOcJEdzdytRq1eX5fjkqjkajfj5BIkAiFkGu1GMrLKb3nHtR5thhvJBoIMNbezlh7O6Gx\nMeQazapUGq61ubdceDxBWlpG6ez0EgzGMRqVy5LPo9cr0enknD49RDQqkEgkUCrlfOlLdTz1VH1a\nJmJw0E9Li4fu7gliMQGjUYVUej2qGBjw09IySne3L52XNbu/pWWUjz66xoULw4RCcZxOzYpuf94q\njEYl4XCcwcHU6q/b7cZiMbNvX0XeoFUkP7fr3+9KsNCEdlGhXWTJSGUyKh58EGNFBf6+PiRyOaaq\nKkyVlctyfolUCoLARGcn0akppDIZWpcrrWyudTi465vfpHjXLiY6OlDo9bi2baPk7rsBkKtU6Kuq\ncIRCRCcnkSgU6BwONHn0q24kND7O+e99j/5PP023qa1W7vrmN3Mqwt9IPBJh+OxZ+j75hEQshlQm\nw1hZed0Qeg7CPh8db7yRofKuMpmo+/KX0RcVzescIvnp7p7gd7/ryNiCamy0cuBAzbL4DzY22vj6\n1zdz7doUgUCE8nITjY3WtM5Wa6uHt97qIh4X0sfs2FHEvn0VyOVSWlpGeffdLuLx67VHu3YV88AD\n5chkUt57r5vvfvc80Whqm0wigWeeaeArX9lQ8AnfMpmUBx+soKLCSF+fn7Iygfvua6CyMrd2nIjI\nWkEMrgqQtbhvLlMosNXXY6uvX/Zz+7q7GW1uRutwoJ02SgZwf/wxhooKFBoNWoeD9V/6Us7jh0+f\npuuNN1IPJBJIJvH39REPhbA1NaGcwzx58LPPMgIrgPD4OFdeew1bUxNy5c3FKPuPHWP4zBkUej0z\nxeNhr5eOt97CtnEjUunNvwA9LS0ZgRWkVtKGT59Gf/DgTY9dbtbi3FsKsViCI0f6snJ7WlvHqa21\nsHGjI8+R8yMYjHH4sBuvN4xSKUOp1OL3xzh1apiKCjMlJXoOH3ZnBFYAZ84MUVtrwuHQ8dFH7ozA\nClJ+hDU1ZtRqGS+/fDkdWEHKu/w3v2ln0yZHTo2vQkOhkFFfb6O+3gYszw+2O5Xb7e93LVPYP2tE\n7gj8s7YbZxP2erM8/XIx0dV1/cEs5ZGx9nYmr12b8/jxWduNs/FcupRlrZOLsciO+KsAACAASURB\nVBtETWcYvXCBwNDQnMdn3P8svJ2dYrXhEvF6wwwO+nP29fUtvRrN4wni9eYu6hgcnGJkJMjUVO7E\n9oGBACMjgZzVcslkaiuxo8PL+Hgoqz+RSNLVNbG0mxcREVk0YnBVgNxp/lCyPCtDEqkU6RyrRgBy\njSbj8Yy3oEylynvu2ShuOH72eecjkqrIszI237ypvNdXqZasIbZQbre5J5dL826dqVRLH1uFQpqR\nG+V2u2f1yVEqpTkTtgGUSikKRf6PaKVShlqdX0hzObY01xq32/xbacTxWzkWHVxdunSJ1157jV//\n+tf0Tf96Hxoa4pVXXuHVV19lMM9qg4jIQjFVV+cMIkw1NehmVSMmk0mifn+WdIN90yZkSiVSuRy5\nWo1SqwWJhPL77sMyK0ExffwN8gjObdtyXr/8gQfmlfNUtGMHTOeHzabioYcy8r6SgkDU7ycRy1yp\nsObJzXJs3jyv4PBOIRpN4PdHEYT56yJbrRoaG7PtZ6RSCTU1C8vrmZqKMDKSKbvhcumprs4+j0Ih\npbraSFGRjoqK7H6lUkpVlZmSEgNlZYasfpVKTmWlkcZGK01NduRyKaWlesrLDajVMiwWNZs22Rd0\n/yIiIsvHoqsF29raeOyxx9iwYQNHjx6lrq6Ow4cP88wzz7Bx40Y+/PBDGhoach4rVgsujTut2kOp\n16O2WgkMDZEIh5FIpZhraqh86KH0qtBUfz+9H35I70cf4bl4ESEWQ+twIJXL0RcVoTIYGGttZaKz\nE7lEQvkDD9Dw7LPpir/J3l56P/gA98cfM9bcjCAIqeNlMnTFxajNZiZ6eogHg8hUKiofeoj6p55C\nmcf0eTb6sjKUej2TPT3EQyFkajXVjz7Kui99KRXokVJx737vPa794Q+Mt7WBVIrW4UAilaKx2ZAp\nFASGhxHicaQKRSph/557Vnzlai3OvVgswfnzI7zzThcnTgxw7doUer0cs3l+1ZQulw6/P4bXGyaZ\nTPn67d9fOZ3jMzc+X4Q33ujgu989z6FDnXR0TGAyqXA6dUgkEoqKdPh8EXy+CEajCbNZxaOPVlNV\nZUYqTfVPTITx+VI/CiwWNQcOVFNRYZzu1zM+HmZyMtVvs2k4cKCasjIjSqWM0lId4+MRzp8fYWgo\nQGOjna9+deOS88XWImtx/hUS4vgtnlWxv3n77bd5/PHH+eCDD3j44Ycz2nIh2t+ILIZYMEjQ40Gm\nUKBzuVJVhKSq+Vp/9jOiN4iFFt99N5X79hEcHaX11VdBIkGIRJAoFCQFgaLt2ym//36CIyNc/tnP\nssRQy+67j7K9e9OPAyMjTPX1oTIYsNTVLfj+/cPD+Pv7UZlMWGpr0+2+3l7af/UrhHhmUnX1gQMZ\n1Yhhn4/IxAQKnQ6tXVyVmOHkyUEOH+7NaFOpZDz/fAPFxdmrPrlIJpOMjAQJh+PY7Vp0uvn71v3o\nRxf5zW8y8/KcTi3f+c7e9KqUICQZHg4QiyVwOLRpCYYZUv1+YjEBp1OXtaWXSAgMDweJxwVcLm16\ny1IQkrz5ZgctLaOMjaW8E61WNaWlep5/fsOCXoeIiEh+Vtz+5v3332fXrl0AGW7lCoX4R32ruFP3\nzRVaLaaKCvTFxenACmCioyMrsAIYOXeOsNfLRGcnsUCA2PSWW1dbG4lwmKEzZwj7fIxfuZJTZX74\nzBmiU9eTmnVOJ0Xbty8qsALQu1yp42cFVgBjly9nBVYAw2fPZii4q00mTJWVqxpYrbW5FwzGOH06\nOwUhEknQ3u6d93kkEgkul47KStOCAhK328fvf59tbzMyEuTs2ev2NlKphOJiPW53c1Zgdb3fQEWF\nKWeulEwmpaRET0WFMSMXbHAwQHv7ODKZFKdTS1GRDqVSxuhoiJ6ebEuoQmetzb9CQxy/lWNJwdUn\nn3xCXV0d9ukP+0TiejnwXP5Ys9/ko0ePio8X8Li5uXlN3c9qP+5qa8tIFHa73bjdbhLRKLFgkM7L\nlzP6R0ZGUv2RCPFgkK4b+meOjwWDxEKhW37/nS0tOa8fmZwkEYms+viu5cehUJy2tq6c4zdTpXcr\nrz8xEaW/35MukoBUwYTP58Prjdzy6/v9UXp6enO+/qmpW3998bH4+E57PF8WvS145MgRHA5HRl7V\nb3/7W7785S+TTCY5dOgQX/jCF3IeK24LiiwnI+fP0/Xuu1ntCr2eTV/9KuPt7fS8/35Wv8poZONX\nv4rn8mV6P/ggq19tsdD04ot5q/WWC/fHHzNw4kRWu7G8nIbnn0+LpYpkE40mePnlS4yMZEtSPPBA\nObt3l97S64+MBPibv/kYjydbDuEv/mI7jz02/xyNxV0/yE9+0pKlkwXw1FP11NXNzwFARETk5ix0\nW3BRCe0jIyO8MS3K2NbWxqlTp9iyZQsmk4lDhw7R3NzM/fffjy5PCbqY0C6yGMba2hg4eZKpa9dQ\n6vXpZHaFXs9UX1/GFh4SCRUPPICpqgqFXk9gZAS5SkU8FEKu0aCx2SjauRNjRQUKnY6pgYFUECMI\naYmF0j17MJaVASAIAp7mZgZPnSIwPIzSZEIxS0ZBiMcZaW5m6PRpgsPDqMzmedvTKLRavB0dGVuA\nErmcyv37b5vcqngkgq+nh8neXhLRKEqDIWNrd7HIZFJUKhlXr3pnS5hhsai5//4KNBr5kq8BKb2q\nkycHuXx5lGg0gd2uQSKRoNMpkUolnD8/nHH9jRvtPPVUfXqL0e+P0tHhpb8/NUf1+uWp8tTpFESj\niSxNrro6C3fdVTRvhfaJiTBXr3oZHg4gl0vRasW0DhGR2ayI/Y3T6eQ//+f/nNVeUlLCCy+8sJhT\niiyAo0eP3lFKu0I8zpXf/pbWV18lMS2zYCgpYdtf/AXOzZtR6vWs++IXGbt0iYmuLhR6PbaGBqzT\navEShYKI10vLj39MZHISASjasgVbUxMASp0Ond1O5zvvEB4bQ6ZSYW9qQjOtBh8NhWj92c/ofPPN\ndG6Upa6O7f/+32OpqyMaCHD55ZfpeueddL+toYEt3/gG1hvyq3KhdTppeOaZlChpfz8aqxVbU9Oy\n2QctJ4uZe2Gvl663374u2CqRYG9qonL//mVZFdywwY5KJae1dYyJiTAVFUaamuxYrcvjvXj5sod/\n/udz9Pamtv6UShlPPrmeZ5+tR6mU8/jjtZjNak6eHGBiIsLmzU7uvbcUhyNVCep2T3LoUCeTkxHc\nbjfV1ZXce28Z99xTMmf6xHzYu7cUu11DW9s4sZhAXZ2ZxkbbvHW62trGeeedTiKRRPr1PfJIFZs2\nrb1qwzvts2+5Ecdv5Vien3UiIreQkYsXaXn5ZZiV0zc1MMCln/4U83/7byg1GtQmE6V79lC6Z0/W\n8f1/+APnv/99SCaRazREwmHGWlu59JOf4NqyhYnubkYuXsRQWoqhNLWNJMTj9B85Qv0zzzD02Wdc\n/e1vM87pvXqVK7/9LXd961sMHD9Ox5tvZvSPtbXR8bvfseuv/3per1HrdFIxS7PrdmLo9OlMJfxk\nEk9LC/ri4pQG2DJQW2umtnb5jXyj0Tg//3lrOrBKtSX4xS9aqa01s3t3KXK5lPvvL+f++8tzHJ/g\n8OHetIwCpNTT//CHa5SU6JfFI0+hkLFxo2NR0gtTUxHee687HVjN3PP773dTXKzDbp9bJFdERCQb\nUaG9ALnTfnmMtbZmBFbp9rY2fFevznm8p7UVBAGSSZKJBMrpStb+Y8cYbW1lsrc353G+nh5CHg+e\nlpac/YPTW5Sjzc35+2/wBCx0Fjr3YqFQ6v3LgTePrdBaoqfHx8WLw1ntyWRqxWcuRkaCDA1dFxad\n0RlKJlMrWqtNX5+fYDDbXicaFZbF/me5udM++5YbcfxWDjG4Eln73GzrZB55O3mPlkiQcpPKVonk\n+r9815ZI8uYOSWbOcadzs/Fd46RuMfd9zra1Wdy51/brX+v3JyKylhGDqwJkMWWhhYy9sTGnErmj\nqQnLunVzHm9rasoIwiLTeVvl992HrbERY57cJnNNDRqbDfumTTn7S3btwlRRgWPz5tz9u3djKCmZ\n8/4KiYXOPYVGg33DBiDlEak0GJBMv5czOXFrmepqC9u3Z1scyWTQ0GDNcUQmLpeWkpLrKv5D00bd\nUqmEior5CZzeSsrKDOj12cnrKpWM8vLVv78budM++5YbcfxWjkXb3ywFsVpwabjd7tvOxiDs8zF4\n+jTuw4cZb22FZBKNzYZEKkXrciHXaBhra0OY9t0zVVWx+etfRz+P4MVYXo5cpcLT0oIQi5FIJCjZ\nuZOt/+7fYSwrQ2U2g0SCf2CAmZIvXVERVQ8/jFKvR+dyASmLmuT09qS9qYkNf/zHaKxWtC4XSUFg\norMz3e/csoUNL7yA2pIqhQ8MDzNw/DjuTz7B19ODVC5HY5ufvcpaYjFzT22zMeCF891JLvZKCKvs\nVOzYSOWurStu37NQZuxpurt9jI+ndLPUajkvvLCB/fsrkUolRKNxPv7YzSuvXObNN68yMhLEbFZj\nMqmQyaSYTCqOH+/nxIlBenr8gJQDB6rZutWFRCIhHhdoaRnl8GE3Z84M4ffHMJmUK2K8rFLJMBqV\nnDo1RHv7OENDAZRKKU88sY7q6uXPYVsqt+Nn30oijt/iWRX7m4Ui6lyJzCYaCHDltdfw35CfVLZ3\nL2X33Zd+7O3sZKKrC7lGk6rmsyxMw2fo3DkmurpQ6nQU7dyZJXMQGB4mODqKXKXCUFGBXKXK6Pe0\ntjLV14dSr8e+cSMqw/Vf9oIgMNbWhr+vD6XRiGPTJpTTUhHBkRHafvWrDKkIiVRK7cGD6VWd25n2\n9nF+93o7Qa8PIRpFrlZjLrLyzDMNFBfP7c24FvD5IrS0jBIMxqiqMmfoR7322hV+9KOLGVIM9fVW\n/tN/upuiIh2/+lUbH3zQg1arIJFIIpVK0OkU/Mf/uBOHQ8uRI9f49NMb5n6ZkSefrLvlkgjJZJJ3\n3+3G7fZlVAsWFek4eHDdvKUcRERudxaqc7W2fzaK3BH4uruzAiuAwVOnsG/cmF79sdTWZlnHLISi\nbdsomuXVdyM6lyu9SpULe2Mj9sbGnH1SqRTHhg04cgRLnra2TA0uICkIDJ48iaWuDtltbBUlCElO\nnx5CSErSJtkAwWCcS5c8BRNcmUwq9u4ty2ofHvbz+utXufEnanv7OGfPDrFli5PXX7+aNmWezdmz\nw9x1VxGnTmXb9/T1TdLd7aOp6dbqnA0M+GluHkUQrr+AYDA+LSkxSU3N2lu9EhEpBMSfJQXI7bZv\nHh7PXXWViEaJTEws+/VWevyCg9lfngChsTFigUDOvrXKQscuGIwxOpqtng4wOJjtB1loDA+HGB/P\nVmdP9QUZHg5kBFazbXJm+qLRbHV1IO95l5OJiXBGYHVj31rjdvvsW2nE8Vs5xOBKZNVRGo0526Vy\nOQp9Yaxs3Ix8uVUqoxH5LbbWWW3UajkWiypnn81W+BpKVqsqr9q61arGatWg0+XeILBY1Oh0SuTy\n3FV5RmPucVtO9Hpl3qJNg+HWX19E5HZFDK4KkNtNq8RcW4vGml155diyBa1j+VWi841fZHKSia4u\n/P39JIXcqwmLwdbYmJW/BVC0c2fO9nyMj4fo6ppgeHj1VrsWOvfkcinbtxdlfYErFFKamlYuoT8p\nCEwNDDDR1UV41urRfInHBZqbR/nss4GM8S8rM/L5z2cnuZaU6Nmxw0VVlSnDX9BkSomGlpcb2bHD\nhdOpZfPmbPFYm02zIltyZWUG1q3Lzl0sLzdmVDNGo3F6enz09PiIROK3/L7ycbt99q004vitHGLO\nlciqozIYqPvSlxg6exbv1avIlEocW7bg2rp1Ra6fTCYZOX+evj/8gVgohEQqxVxbS+X+/Rl5QotF\nX1pK3ZNPMnT6NP7+flQmE85t23Bs3Div4+NxgePHBzh1aoBoVEAmk9DU5ODBB8sLwgNu40Y7UqmE\ns2eH8HojFBfr2LGjaFnUyedD2Oej98MPmejoIDntHVl27724tm+fl5ZTb6+Pn/70EqdODZJIJLHb\nNbzwQiMHDqSCpi99qQ6DQcnhw278/ijbtjl55JFqyspSK7JPPVWP0aji44/dBAIxduwo4uGHKykp\nSQUv991XhsGg5OLFlG9hXZ2FHTuKMBiWx3/wZshkUh59tBqHQ0tLi4dkMklDg40dO1xp+xy3e5L3\n3+9Jb+/abBoefbRqxd4/EZFCRKwWLEBuZ3+oeDiMRCa7pUneN47fRHc37b/6VdZqlX3jRtYdPLhs\n100mk8TDYWRKZcokep60tIxy6FBnVvu995Zx773ZSda3kqXMvURCIBpNoFbLV1Sgsuvttxm5eDGz\nUSKh/umn5yyQEASB//W/TvHJJ9cy2hUKKd/+9l62bbteABGJxAmHE5hMuVcjQ6EoLS1t3HVXbl20\nWCxBIpFcEQmGXESjCSCJUnn9+n5/lJ/+9FJWQr7BoOTFFzeuSAA4m9v5s28lEMdv8Sy0WlDcFhRZ\nU8jV6hWvnvN1d+fcBhxvayOUJ9l+MUgkEhQazYICK4DW1rGc7c3No0Sjq7dFs1BkMikajWJFA6vw\nxASetrbsjmQSX1fXnMd3d/s4fnwgqz0WE7h0yZPRplLJ8wZWABqNkkgkv+WNQiFbtcAKUhIMswMr\ngL6+qZyVjlNTUa5dW337HhGRtYoYXBUg4i+PpXHj+CWi0ZzPExIJkvHVD15mm+rOJhZLEI+v7MJz\noc09IR7P+x7me99nE4sliMVyj39qpWdhFNr4xeP5cw9jseXLS5wvhTZ+aw1x/FYOMbgSueMxlJfn\nbi8tRb0GVNRra6/nfclkklntloLIuVpN1BZL/vc3T/tsKiuNNDTkngPr1t3+GlAulw6FIvtrQi6X\nFoxGmYjIaiAGVwWIqFWyNG4cP0ttLY4b/AMVOh3l99037y286NQUQ2fO0PnWW1z7wx+YyqNttRg2\nbnSwZb2aGnk/JVPNVEt6aSyXctdd1y2kwl4vA599Rudbb9H36acERkeX7fqzKbS5J5XJKNu7l4ig\npLd3kqtXvQwO+jHW1WNdv37O4zUaJc8914DZrE63SSTw+OM17Nhx3XOwt9fHRx/18vbbXVy4MEIw\nGMt5vlzjl0wm6e6e4PDhXt55p4vm5hHC4fmvmAqCwPHj/fzgBxf41389x4cf9jI1lb2VtxgcDi0P\nPliREdRLpRIefLAcp3PlpTQKbf6tNcTxWznEakGROx65Wk3VI49gXb+ewMgIco0Gc1UV6hzyELmI\nTE5y9fXXU96E0wyePs36L34R8xIU5dP3F53CMXSCxNAgoVAM5ZQMp2wAdaQY0BIcHeXKa68R9nrT\nxwyfPcv6J5/EUFq65OsXOsMRPX3OPcg1QyiiEaIGC62JMiriUuaz9rJzZzH//b/fNx00xamsNLJl\niwOtNpXM3dIyyjvvdJFIpLZoL14coaPDwuc/XzuvHKrz50d4//2etJjnhQsjbNgwyWOPVaNUzh3c\nv/FGBz/60UWmbS15661OHn+8hq99bfOy5HDt2FFEUZE+nWNVVmagtFRctRIRuRlicFWAiPvmSyPX\n+MmUSix1dVjq6hZ8vvG2tozACkCIRuk/fhxjVdWCE9hvZPTSJeKTE9jtGmBadDQeYej0aQxlZXha\nWjICK4BYIMDw2bPLHlwV2tyLxRJ8+mk/Qx6A6ZWmSWBykqr28YzVp5tRWWnKKT0QDMY4cqQvHVjN\ncPWql87OiSz7mhvHb2oqwpEjfVkq6Zcve2hosLJ+/c0DfLfbx6uvtqUDqxneeaeLu+4qzljdXAql\npfo1EVAV2vxba4jjt3KI24IiIktkKocvIoB/aIjoIgQrb2Sypyd3u9tNPBRiors7Z7+vp4d4eO1Z\nmKwkExMRRkby2e8sXYx1fDycs5oOYGRk7vN7POG8W4j57ns2bvckfn92Yn4ymeoTERFZHcTgqgAR\n982XxnKPnzKPRY9cpUK2AAX2vOc3GHK2K7RapAoFqjz2QQqdDukyy1oU2txTq2Uolbk/5nS6pY+N\nWi3La1+jVmef/8bxU6ulSKW5j9do5t5Y0OmU5FsYvR2LHQpt/q01xPFbOcTgSkRkiVjWr0eS4xvO\ntXUrCp1u3ueJRyIER0ezzJxtGzaQywDOuW0bMoUCe1NTzn7X1q1L3pIsdAwGFVu3upDJJFgsKqxW\nNXq9ArlcsizVfna7lg0b7MjlUiwWNVarGp1OgUolz6jyzEdRkZ66umz7Ga1WQXX13ArojY02duzI\n3vorLtazdevCrKPGx8OMjYVYBV1pEZHbDtl3vvOd76z0Rbu7uykuXp5cgDuRioqK1b6Fgma5x09t\nNqO2WgmNjREPBpFrNBTv2kXxrl1I5fNLaxxtaaHrrbcYOH6c0UuXSCYS6FwupDIZGpsNlcGQOn84\njEKrpXTPHlzbt6f67XaUOh0hj4dEJIJCr6dszx5c27YhkS7v76dCnHsmk5L+/ik+/vgaly6NIpFI\neOSRajZtWh7fSrNZRXe3j08+cdPa6kGtlnPgQHXOfKkbx08ikVBSoiMaTTA+HiaZTFJWZuDRR6vn\nJXUgl0upqjIRjSYYGEgF5du2ufiTP9lEbW120JYLjyfI++/3cPhwL+fODTM4GMBm0+Q1pF5NCnH+\nrSXE8Vs8g4OD1NRk+4jmQ7S/ERFZJuLRKNGJCWQaDao8W3m5mOjspP03v8lSia946CFKdu1KP46F\nQsSmppBrtTm3ImPBIDG/P2//nconn7g5fnyASCRBPC6gVstRqWQ8/3wjZWXzf5/y8fbbXVy8mJJP\nSCSSaDRyNBoFf/RHGxYkV+D1honFBGw2NTLZwoPi3l4f8XiSykojcvn8jo9GE/zyl2309U1ltNvt\nGr7ylQ235daiiMhiEO1v7gDEffOlcavGT65UonU6FxRYAYy1tua03xk5f574LBVxhUaD1unMGzgp\ntNqb9i8HhTb3pqainDs3DIBKJUOnUyCTSYjHBTo6vHMcPTceT5DLl1OaYmq1HJ1OgVQqIRKJ09U1\nkfX8m42fxaLG6dQuKrCCVEVjba153oEVQF/fZFZgBeDxhOjtXXsJ8YU2/9Ya4vitHGJwJSKyykQm\nc3+JxQIBhMjyiEHeqYTDcaLR3DYtfn/uKr2FnT+/BVEotPTz32pCofxipYVw/yIiaxUxuCpARK2S\npbHWxu9m9jsLSYhfCdba2M2FyaTKuzVXXLz0sbVa1RiNuStCc113rY2f3a7NWe0olUpwOFZegX0u\n1tr4FRri+K0cooioiMgq42hqYrK3l4jPRzQQQK5SodTrKd61a9kT0u80lEoZu3eXcu7cEB5PiGAw\nht2uxWJRzinQOR+0WgX33lvK669fZWwsRCKRRK9XsG2bi5qa69WCY2Mhenp8RKMJiov1lJcbFrT9\n19nppaXFQzQaZ906C5s2OTO2/0ZGgrjdqZyr0lI9ZWUGJDkqSG/E5dKxa1cJx45larVt2+aktHTp\n+WgiSycaTdDbO8noaBCdTk51tTlvQC+ydhCDqwLk6NGj4i+QJbDWxk+qVEIyyVhbG7Hp4Mqyfj1y\njWa1by2LtTZ28yEajfHWW110d/tIJARUKhlf+MK6ZZMcMBiUlJTokUolhMMJ7HYNDocWhSIlg9HZ\n6eXNNzsJh+O43W4qKyu4665iHnigfF4B1pEj1/jnfz6b3saUySQ891wDzz7biFwupbXVw9tvdxGL\npbY/pVIJe/eWsmdP6bwCrL17S3G5tHR3+xAEqKoyUldnyau/tZoU4vxbCqFQjPfe66a1dTzdZjKp\n+OIX6ygpWXhu5Z02fquJ+LNYRGSVGWttZaq/H0NpKdb16zFWVpKIRBg6dWq1b63giccFfvObq3i9\nYcxmVVpi4KOP3Jw8OTD3CeYgGk3w0UduPJ4QCoUUs1lJOBzn5MlBurt9RCJxDh92ZxgxJ5Nw8uQg\nPT1zq/ePjQX5f/+vJSM/LJFI8otftNHcPMrUVJT33+9NB1YAgpDk00/76e/3z+s1yGRS6uttPPZY\nDY8/XsOGDfZ0YCiyurS3ezMCKwCfL8KxY/2iHtkaRwyuChDxl8fSWGvjN9HZmbPd29FB1D+/L8iV\nYq2N3Vy43T4uXhzN2dfWtvRqwdHRUNqmJpkkI7m9v3+K4eEgY2OhdNtsnaH5BD8dHRMMDWXb6CQS\nSTo6vAwN+XPa5whCkoGB7CrAQqfQ5t9S6e3NrjhNtfuYmFh4scudNn6riRhciYisMjJlbrFGqVye\nU/ldZP4olTIUitzbW0rl0sdWLpfk3T6TyyXI5dJc4vnT/XN//Oaz7kn1yW56jsVKOoisHRSK3Jk7\nUqkkr+2SyNpA/OsrQEStkqWx1sbPUl+fs92+cSOKBeRdJZNJ4tHoLd0uWGtjNxdlZUbuvTe7GlMm\nk7Bpk31B50okBKLRTOkCh0Ob06ZGJpNQWWnC5dJSUWFKHz8wMJzur6rKPC4ajRMIZJowr19vZcMG\nW9b5dTo5TU12Skv16arEREIgFksAqcCrsjLTczIev95fqBTa/FsqtbWmnMF5U5Mdg2HhSe132vit\nJmJCu4jIKmOrrycyPs7AqVMI0SgSqRRbYyNFO3fO+xwTXV0MnTlDYGgIjc2Ga/t2rPX180povt35\n8pfX4/fHOHGin0Qiicmk4pln6rn77vlZcMViCZqbPZw/P0wwGKO62syOHS6KilJJ7A8+WEEsluTa\nNR/JZKqC8IEHyikvTwU3e/eWcPXqOB995GZqKsTmzQr+7b/dmE5I9vkifPBBD598co1gMMauXcXs\n319Jba0FnU7J1762iR/+sJnW1jGSSSgq0vHiixtZty5lb3PvvWX88IfNHDvWTyIhsGWLgxdf3Ijd\nngq6AoEo58+P0Nw8iiBAQ4OVbducWCxrr2BCJJO6OisPPFDB8eP9RCIJpFIJdXUW7rmnZLVvTWQO\nRPsbEZE1QmhsjLDXi1ynQ19UNO/AyNfTQ/uvf40Qv76qIpFKWffEE9gayYDnYgAAIABJREFUGm7V\n7RYU8bhAa+sYfn+UigrjgmQGPv20jyNH+jLajEYVzz/fiNWqTp9/YMBPNJrA5dJmrCr8+MfNfPxx\nLzabdnp1SUAqlfDSS/dQXGzgBz+4wOuvX804f22tmf/yX3bjdKa0uAKBKFeujBONCqxbZ8ZmSwVO\ngiDwL/9yjrNnhzGbU/cSCsUwmVT87d/ejcmk5q23Orl0yZNx/rIyI08/vR61Wvx9XQh4vSlTbY1G\nQXGxbk1Wct7uLNT+RvzLEhFZI2hsNjS27C2guRhtbs4IrACSgsDQ6dNY168XtbJI5Tctxqh5cjLC\n6dNDOds7Orzs2lWcPn9FhTHreb29Pt55p5NAII7HE87oO3NmmMbGOO++2511XGfnBOfODXPgQMoo\nVqdTsm1bUdbz2trG+PDDVLXgTGI9wLVrU5w7N8L69VZaW8eyjuvrm6S310d9/cLnm8jKY7GosVjU\nq30bIgtA/NQtQMR986Vxu41fcGQkZ3t4fJxENJqzb7HcbmM3F35/LK9FjM83d7XW2FiIQOD68T7f\ndfmF8fEw4+OhDJmG2Xg8oZztmc8JZ8gw3HjtqakogpB7c2Jycnnnxkpwp82/5UYcv5VDDK5ERAoc\nfXHu3CGdy4VMJSo5LwWjUYlWq8jZN7MleDOcTi0mU+73wOXS4nTq0Otzn9/lmtuex+XSolLlrnos\nKtJhNivzbiHNbCOKiIgsP7LvfOc731npi3Z3d1Oc5wtBZG5ma+WILJzVGL9kMklgaAif2008GESh\n0yFdJpkFmVqN98oVhMT1SjCJXE7lvn2L2ma8GXfa3FMqZUgkEtrbxxkcDKRXk8rKDNx3X9mcOUtG\no4pYTMDjCVJcrKeoyITVqqakRM9TT9VTUmIgHI5z9aoXuVyKVCpBpZLR2GjjqafW5w3sZrDbtUxN\nRWlryxSa3LmziC9/eT02mwa/P0pX1wQDAwG83jByuZQNG2zs2lWclnLw+6N0dk7g8YRQqWSoVGsz\nY+ROm3/LjTh+i2dwcJCampp5P39t/gWJiNxGJGIx+o4cYejMGZLTAZCpspKqxx5DY7Es+fzG8nLW\nP/00oxcv4h8YQON04ti0CcsCPghE8mM2K1CpZIyOBgkEYqhUMpxOLQZDbn2yG9m9u4Tm5lFOnx4i\nGk1QXW3kiSfqsNtT1Xp795bh8YQ4fXqIQCBGY6ONAweq00nrc/Hccw0UFek4dqyfcDjBrl3FPPhg\nedp/zuHQkkzC8LB/ulpSidOpTQdQnZ0TvPNOZ1oFXqNRcOBAFQ0NYj6WiMhiEYOrAkT0h1oaKz1+\n4+3tDJ48mdHm6+1l8LPPqHnssWW5hrG8HGN5tp7TcnOnzb14XODVV9s5eXIQo1GJ3a7h2rVJfvjD\nCxQX69MJ7Tc7/siRPgwGJfv2VTA0NERRkY0zZ4ZYv96Cy6Xjk0+uTQdFJchkEvz+KMeO9VNVZaa0\ndG7/OINBxcGD6zh4cF1W3/h4iO9//wL9/VNYLGqkUgltbWO43ZOUlxtwOnUZgRWkqg3feacLp1M3\nr63PleROm3/LjTh+K4eYcyUicovxdnTkbB9ra1tz9jYimXR1eTlzJlUtODkZxeMJEY8nSSTIWYV3\nI8PDAfr6rtvQRKcLDAQhSW/vJEND/rS9TTAYY2oqSjKZsrdxuyeXfP+XL3vo709df6acXxBSifqt\nreP09U1lBFYzRCIJ+vqWfn0RkTsVMbgqQMRfHktjxcdPyF3NRTJZcOard9rcS711ud+jfFV4N3vO\n7JyXZDKZd2rM9C+Vm50ikRBu2j+f17fS3Gnzb7kRx2/lEIMrEZFbjDlP7pO1rg6VYf5iliIrT02N\niU2bnEDKsmbGK1Aigfp665zHu1w6XK5U7pRSKU0nwEskUF5upLhYh82mmdUvS/eXlc29JTgX9fVW\nHI7U+bVaOTpdKkFerZZTX2+ltFSPRpOdNK9QSCkrE+emiMhiEasFC5CjR4+KVR9LYKXHT2WxEAsG\nM/SodEVFVOzbh0I3d7n9WuJOm3symRSnU0NHxwTDw0GCwRg6nYKnnqrn0UerkMmkJJNJrlwZ5/jx\nAc6dG8bvj2E0KlGp5MhkUoxGJZcuebhwYZQrV8bRaBTs3l3Kjh1FKBQybDY1g4MB+vv9eL0RrFY1\n995bzoYNdiQSCYmEQFvbGJ9+2seFC6OEwykF9hnj6ZT6vIdPP+3n4sVRIpE4JpMSpVKGTqfE6dQx\nORlJb/+tW2fmmWcaufvuElQqOWaziu5uH4lEaqVKoZDy8MNV1NSYV23c83Gnzb/lRhy/xSNWC4qI\nrDHkKhXVBw5gb2oiNDaGQqfDWFGxIFNmkdUjFkvS0JBa5YlEEpjNKuRyiEYFlEo4e3aYDz7oSW+x\n9fT46Ojw8uUv16HTKTl82M0vftGGXC5FEAT6+qYYGwtSX2+mpsbK4GCQgQE/kUiCREJgbCxEb+8k\nW7c6UavlHD8+wNGj1+13ursn6OmZ5AtfqEWlknPsWD/HjvVn9Lvdk3z+87UolTIikTiCABqNfHob\nUkIoFCMWS6BQyGhosOF06ujrm0QQkpSVGdK+hCIiIotDDK4KEHHffGmsxvhJZTJMlZWYKitX/NrL\nyZ0294LBGMePD+D3x5BIJKjVcsLhBG63n6tXvdTVWTh+vD8rd6mvb4rOzgmkUvi///dCOml9hv5+\nP/v3V2K1ajlxoj8t7zBDd/cE3d0+XC4tJ08OZN1XR4eXnh4fVquGU6cGs/rb28fZtMmBwaDk9Okh\nTCZVhphpa+sYmzbZqalJSYFYreo1VxmYiztt/i034vitHGJwJSIiIpKHiYkIk5O5bW48nhAOhzZn\ntR2k7GdCoXjeqr/UNmCYSCSRs9/jCaJQSIlG89nbhAFJXvsbjydILCakt/tyHS9KoYmI3BqWlNAu\nCAKJRO4PBpFbh+gPtTTE8Vs8d9rY6XTyvPYyBoMSrVaOQpH7Y1SvV2KxqDNWjIRZ5YEmkwqtVoFM\nltueRq9XotPJ89rX6PUK9HrFTfpTx0tyd6eT2wuJO23+LTfi+K0ciw6u3n33Xb773e8yPDycbhsa\nGuKVV17h1VdfZXAwe6laREREpJAwmdRs3erMatdq5axbZ8Zq1bBlS3a/TqegttbM7t2lPP98IwqF\nFJNJhcWiRqtVUFdn4e67i3G5dDQ12YnHBfr6Junp8REIRDGZVNTUmCj6/+3deXRb93Xg8S/2fSEJ\ncAFJkOIi7to3S7IkW068xHHl8TiO48QZT+1JmiaeeE7qpOlp6sk5PXNyOu2kbTJ2fRInPWPXaeLE\n8pZGtiVHsrxItqjNonZS3DdxAUkQxPYwf0CERBOUSICiSOl+/iLww3t4vHqELt7v9+7NtVJVNblS\nekaGkUWLnOTlWSkvzyAajdHRMUJb2zCBQASXy0RxsYP8fBslJU6iUYXe3lF6ekYJh6Pk5FgoLnZc\nlZhdDcFghLa2YSIR07wsESHEp6V8t2BZWRkWiwWj0Yjtwu3ku3bt4v7776e2tpadO3dSWVmZdFu5\nWzA9crdHeiR+qbsRY5eXZ0Wn0zA4OAbAokVObrutmLw864Vxy4TxkpL4+Hjj5ays+HqnlpZhQqEo\nt97q5dFHl7JpUzyWQ0Mhjh7tZdeuZk6fHsBmM7BxYwG1tW5UKhX5+VbUahUDA2Oo1SoqKjK57bZi\nMjNNqFQqxsbCHDrUw+7dLTQ2+sjMNHLzzQVUVGShVquIxRSOHOll374OWluHcLstbNlSwKJF8+9u\nwGSamgZ55ZUzfPhhB+3tCh0dI2RnWxbklbdr7Ub8+50t1/RuQbP54oJMvX56fbeEEGI+Mxq1bNiQ\nz8qVOYTDUaxWPapL5tpMJh0bNxawcmUOkYgyYTwSUXjjjSYOHuzmjjsWodGoOHdukH/916OUlTnJ\nz7fws58d5q23zlFcbCc3V0N9fRfHjp3H7TaxalUeVqueLVu8rFmTRzQam9DTcHh4jGeeOcSBA90U\nFtrRaNQcPtxDY+Mg+flW8vKs/Ou/HuPMmQEqK7NQqVQ0N/t45plDFBba8Xjmdy2r/v4xXn31DIFA\nBIgXRW1q8hEON/HAA5XodLPT/FyI2TarRUQvrSis08m3iqtF5s3TI/FL3Y0cO6NRi81mmJBYXcpk\n0k0aP326n337OggEonz8cRdvvnmC7u4Afn+Ew4d7+fjjHt5++xzhsMLp04M0NPQxNBSit3eU+vru\nCfs3m3WTmkUfONDDvn0dhEJRzp4d5NSpfvz+MF1dfurru2lo6OP48T7CYYXW1mFaWoYIBCL09gY4\nerR39oM0y86d8yUSK4CWlhYgfjdme7u0jpqpG/nvd67N6pWrSxe3T/UBNO7SBpLj/+DyeHqPjx49\nOq+OZ6E9lvjJ47l6PDYWoa9vAACHI77GyefzATA2FmF0NEwgEL/bUKOJX4UZ/xwdHQ1fcf+BQJhA\nIH43o8EQXzgfDAYvbB8hEIgk3u/T7x8IRK95fK70OBSKJBKq8Smt8cfh8OJrfnzy+MZ5fOnM3HSo\nYmk0sGpoaMDpdOLxeAB4+eWXuffee4nFYrz++ut8/vOfT7rdzp07WbFiRapvK4QQC8LAQIDvfW9P\nonnypb73vXXk51v40z/dwdmzAxPG1Gr46U8/w113lV12/42NA3zta2/S3T2xjpZOp+bHP95KUZGd\nJ5/czchIaMK4Vqvmhz+8mSVL3Cn+ZnOjudnHr351fFIdMZNJxyOP1GK3G5JvKMQsq6+vZ+vWrdN+\nvTbVN3r77bdpbGzEaDRSXFzMpk2bWLt2LS+++CKxWIzbbrst1V0LIcSC0tk5wtmzg4RCUfLyrJSU\nODAYtGRkmHjooWp+9KMP6e0NoCgxTCYt999fwcqV2ZhMer71reVs336asbEo0WgMg0HDypU5bNxY\nkNj/4cPdHDt2nlAoRlmZkxUrsjGb9ZSUZPDf//sKmpuH6OkJEI3GcLtNuFwm1q71oNdr+PKXq/np\nT+sZGQkTi4HJpOGBB2qprZ18F2IyihKjudlHS8swsVgMr9dGcbFzyhIQnxYKRThwoJtTpwbQaKCy\nMosVK3JQq+OrUiIRhXPnfLS1DaNWqygqsuP12lGpVBQU2Fi9Oo/9+y/efa7RqNiypVASKzGvpZxc\nJUuePB4PDz74YFoHJK7s0ilVMXMSv9RJ7CY7caKP118/SyRysYZVdbWL228vxmDQAgpbtxYxMDBG\nIBDG7bbgcBgYL3lltxtQq1X4/WGi0RgajQqLRYdWG08+duxo5JlnDk0oFnrPPWU8/HAtRqMWvz/C\ns88eZng4RCwWv2r19a8vuzC9qKGgwMaDD1bR2elHUeJ3Ny5a5ACmlxzt29fBnj2tiatH+/bBhg0F\nE5K/qSiKwq9/fYJ///cTie01GhWPPFLHtm2LUZQY777byr59F5OnDz/sYOvWIlauzEWjUbNpUyFe\nr5329hE6OtrYuLGawkL7tI5dTCR/v3Mn5eRKCCFudGNjEXbvbp2QWAE0NJynvDyDnBwzzz9/go6O\n+LSgz+dLrH0qLc2goiKTF15ooLc3AMQTD78/zGuvnWXJkmxKShz8278dn1SF/bXXzrBsWQ5ZWUae\nffYwsVi8aOi4558/xqpVedTWuti1q4VQKL6+SqWCri4/PT2jFBU5rpikdHf7ee+9tgnTcrEYfPBB\nO2VlTnJzrZfd/tixPl566dSE7aPRGC++2MCSJW60WvWEq1IQv1K2Z08rJSVOMjKMaLVqysoyKCvL\nYO/eZkmsxIIgydUCJN880iPxS53EbqLz5wMMDIwlHevqGmFsLJxIrODionKI128yGDT09Iwm3b6p\nKb7wvK8vMGksXpJgkL4+Pb29k7ePRGKcOTOA221OJFaXUpQY3d2jV0xUenpGiUQmL8uNRuPbXym5\namoanJR4Avj9EZqafNjthknrqQCCwSjd3X4yMib2O5TzLz0Sv7kzq6UYhBDiRqLXa6ZsX2MwaDGZ\ndFO2xzEatZhM2im3N5m0mM1aNFOUcjKbtZjNetRTfIqbzVO37okf35U//vX6qbe/3NjFY5i63qHF\norvC8UkNK7FwSXK1AI3fGipSI/FL3UKOXSAQZnBwjGg0eaPj0dH4+Ezaq7jdJhYvzpz0vE6nZtEi\nB4sXZ7BunSfx/HgZBKtVx7JlOVRWZrF6dR5ms5ZVq3JZty4Pt9uI3W5g6VI3VVVZLFuWi9GooaIi\nk8rKTDIzjWRkGFmyJJsVK9ysW1eAyaShtNRJaakTh8OAx2Nl5crcC4VELVitehYvzqC83ElWlhGb\nTU9R0cT2N42NA5w+3TfhucJCG1lZpkm/X0aGkaKiK0/P1dW5EpXsL1VRkUlNjYuiIjtW6+SaiB6P\nlfz8yQVOF/L5Nx9I/OaOTAsKIa5rwWCE+voeDh7sYmwsQl6elbVrPZSUOBPjH3/czaFD3QSDETwe\nG+vWeabVe0+lit+5plLByZP9RKMxsrJMbNpUmEgqHnqoGo1GzfvvtwNQWurki1+sorw8A4D77itH\npYLf/76R0dEw69fn8/DDlZSUxMfvvruEcDjK66+fJRCIsHlzIfffX5FIjr7ylSqMRjVvvNFIMBhh\n82YvX/pSNeXl8aRv2bJstm8/zQsvHCMSUdiyxct99y1O3G139GgPv/3tKXbsaCIajbF1axH33VfB\nqlW5mM067r67lHfeaaG1dQiA/Hwbt95ahNl85ULROTkWvvWtFbz44nE++aQXtVrFsmU5PPRQFTZb\n/P3vuaecd95pprPTj0oFxcUObrmlaFpXxoSYr9Kqc5UqqXMlhJgre/e2sXdv24Tn9Ho1Dz5YRV6e\njT17WhOJzziDQcuXvlSV6A84Hb29o4RCCi6X8cJdghcpikJjo4+xsQhFRfZEYqEoCj/5ST1vvnmO\nQCCCosQwGrUUFNj4m7/ZQE6Oke9+dw/bt58mKyveS3BoKEhBgY1/+qdbKSpy8t3v7uadd5qprIz3\nEjx3zkdGhokf//gWiooc/OAHe3nrrXMoSoxYLL5ofskSN3/3d7dgMml48sk97NjROOF4163z8A//\ncAv5+fGrU+ONnwHcbjMazcwmPcbGIjQ2DqJWqygry0jcCTkuHI7S2xtAo1HhdpunXeZBiLkyZ3Wu\nhBBivhsZCU1qIwMQCsXbzVgseg4c6Jo0HgxGOH26f0bJlds9dQVntTp+x9unnT49wB//2ArE11iN\n6+wc4dChbiwWHb/97SmCwSijoxcXxg8OBtm3r4vW1hF+97uThEIK3d0XF7Y3NfnYt6+Tvr4x3nmn\n+cIxXExYjhzpZf/+TgwGDW+9NTGxgng5hP37O7n33nhypdGor7h4/XKMRi3V1a4px3U6DR5P6vsX\nYr6RNVcLkMybp0fil7qFFrtAIJJoL/NpQ0NBAoEIweDku+kAhoeTb5eOT8dvaCiY9G4+AJ8vyMDA\n2JTHNzgYpL9/jFAo+Rqy8fFkd/sBDAyM0d8/lqi39Wn9/cnvgryWFtr5N99I/OaOJFdCiOuW3a7H\n5Up+RSknJ17M89O3+4/Lzp5ZL7FUeDw2HI7klcYLCmwUFzvIzp68oBzii829XjsuV/Lj93rteL02\nLJbJa6PU6vh4cbEds3nyBIZWq8brvfKaMyFEcjItuABJrZL0SPxSt9BiZzBouekmD2+8cZZo9OIV\nnOxsM4sXZ2A0atmwIZ/33mtnbCxCOKxgteqw2w2JBecAw8Mhzp3zEQxGyckxk59vm9G6oE8+6eHj\nj7sZHTUTDJ5j06Z8dDod+fk27ruvgjffbCQUUohG4+1xiovtLFuWg8Nh4LHHlvHcc4fRaDSML5Hd\nsCGfDRvyycuz8c1vrmR4OEQ0Gm/6bDbrGB0Ns25dLvn5Dv70T5eiVsfw+YIXFtwbGRkJs3ZtHgaD\nmkcfXcq777ah18e/a0ejMZYuzWb9+txZ+leYPQvt/JtvJH5zR5IrIcR1rbrahcmk5eTJfoaHQxQU\n2KiqysLhiF/xycw0MTAQ4PTpQcLhKE6nkdtvL06sgWprG+LVV88yNBQE4muXVq/O5eabCyctzE5m\nx45GnnrqfZqaBoH42qrHH1/J1762DLNZh8WixeeLJ2/hsEJ2tplVq3Iwm+P73rq1EJ8vyIkTfQSD\nUUpLnXzmM0WJNVD5+TZ+9KN9tLXFe/85HEaeeGIVWVnxK2Iul4Gnnnqfzs54c2enU89f/uU6LJZ4\nDaotWwo5caKPs2cHUZTxu/W8WCzJr4gJIa5M89RTTz0112/a1NREXl7eXL/tdWPv3r14vd5rfRgL\nlsQvdQs1dhkZRsrKMqipcVFYaMdojCdOihLjjTca8fmCZGWZcLvN2Gx6enpGycmx4HQaef31sxOq\noMdi0N4+Qm6uJWkNqEt1dg7zN3/zPp980nvh/eJXp/bt66SmxoXZrOWv/updPvigg2AwSjis0Nw8\nxN69bdTWuvF4bLzyylnCYYX8/Pg0oE6noatrlOJiB62tQ3z/+3s4cWIgcVXL5wvxwQdtLF0av/L0\nve/toavLj1arRqtVE4nEOHCgmxUrcrDZ9Lz66llsNgOlpfEWMy6Xia4uP+XlGUmnFK+lhXr+zRcS\nv9R1dnZSUlIy7dfLlSshxA3r/PlAon7Tp7W0DOF0GmhvH0463tY2nLSA6KWOHj3Pvn0dk56PRBRO\nnuxHrVZx8GD8bsZAIJIYD4UUTpzop6IiC58vfsXs0v6CihKjvX2YpiYfDQ39F/YZA+IJlt8f5cSJ\nfkZGwjQ3+ya9//BwiE8+OY/DYZzwvuPCYYX29uHL3gEphJiaLGhfgGTePD0Sv9Rdb7FTq+PNjJPR\naNSo1WpUU7xgOmuuNBrVhKlDtfrSn1VoNKop29dc7r0hftw63dTjWq0KrfZy4+opf/fx45tvrrfz\nb65J/OaOJFdCiBtWVpaJ0lLnpOdVKigqsuNymZJWah8fv5KVK3PYsqUQgM2bC7jnnlKcTj0mk5aa\nGherVuWyYUMBEK/cXl2dhV6vwWrVU1fnJi/Pkrh6VFxsp7w8A71eg1arwuu1s2xZNmvWxKf/Kioy\nqKnJQq9XkZFhoKbGxcqVOVRVZU06LpfLxLJl2RQU2LDZ4muvcnMteDwW1Op4X78rNXUWQkxN1lwt\nQDJvnh6JX+qut9ipVCpcLjO9vaMMDYWAeF/AjRsLqKtzo1aryMoy0tBwnkOHemhtjS8av+22RSxb\nln3ZK0sARqOO/HwLXq+dxkYfXV2j3HKLl69/fRl33VWK2awjL8+C3W6gs3OEsbEIGzbkJ8a1WjVG\no4q2tmF+//tG9u3rICfHwoYN+dTUuMnMNJGXZyESiXLu3BB+f5iVK/P4i79YzR13lGK3G8nJMXHy\n5ACdnX6i0Rher43vfW9dosWM2aylsdHHzp3NHDt2/kJ7G2+i/c58cr2df3NN4pc6WXMlhBAz4Hab\n+cIXKmlvH2ZsLIrLZZqw1qi5eRi9XsOGDflEIgoGg4bGxgGWL8/G6bzyHXXvvdfJ//k/H2MwaIjF\noKlpkOPH+ygqsrFkSS6HD/eyd28bWq0ag0HDmTODRKONiVIKv/vdGX7yk/rENN2hQ700NJzH47FQ\nVpbFnj2tNDX5qKlxEYvByEiQl18+zeLFTqqqsgmH4fHHVzAwECQSUcjONjE2FiUYjKBSqfjtb0/x\nyiunGW+E1tp6kr6+AIsXZ2CzyR2DQqRCkqsFSObN0yPxS931Gju9XsOiRZOnB4eHQ+zb18Ho6MVq\n7aOj8QXgZ84MsGrV5a/A79/fznPPHcHvj+D3X1w4/t57Hezf341Go+Hf/70Bny80Ybve3lH27+/C\n5fLxy19+QiQysYz62283c/fdpTQ1DfHLX35yoZp874TXfPazi3A4zHzySS+KcrHG13ibnObmIc6f\nH+XVV08n2f857rhjEbfdtuiyv99cu17Pv7ki8Zs7klwJIcQUhodDExKrSw0MBK+4fWfnKB0d/qRj\nPT2jdHf7JyVWF7f1E4koDA4mb0MzniRN1aanqys+1XlpYnUpny9IZ6d/wl2I4xSFCb0KhRAzIwva\nFyDpD5UeiV/qbrTY2Ww6TKbktZ6czuRtay6VnW2esn1NVpYRt9uSWFA+edv4YnaHI/m4223C5TJh\nsWimGDdit+unvOvP4TCQnW1Cp0v+38BctP+ZqRvt/JttEr+5I8mVEEJMwWYzsHr15DYwDoeBsrKL\nC77D4Sjnzvk4daofn+/ilaabbsrnkUfq8HptLF3qpq7ORXV1FjfdlMvatXnU1bm5//4K1GoVsVi8\nfpVarWLpUjfr1uWxZUsRX/5yzaT3v/nmQtaty+Pmm3P5yldqJ41v3epl5cps8vKsVFdnUVmZQWGh\njbw8KzU1WZSU2CkqsrNqVS533jl5ke4ttxSxZo3cdCREqmRacAGSefP0SPxSdyPGbs2aPEwmLYcP\n9+D3hyktzWD58uxEw+fe3lF27GiirS1ebNRk0rJpUyHLl+cAsH59PqOjYXbtasHvD7NiRQ533VXC\nsmW5F8Y9BIPRxPjatXncequXgoJ4KYQHHqjA5TLz5ptNBAIRNm3ysmVLAYsXuwD4kz8pJSfHzI4d\n5wiFFDZvLmDjRg81NfH3Nxg0PPPMIfbv7wSgsjKL//E/VmIwxD/+/+zPllJU5GDXrmai0RibNxdy\n552LEu2B5pMb8fybTRK/uaOKjfdMmEM7d+5kxYoVc/22QgiRslgsRjQam1AUVFFi/OY3J2hqmlgF\nXa1W8eCDVVgsap54YjevvXaW6upMjEYNJ08OADGeffYObrrJw+OP72Tv3lbWrMnFZNJz4EAPiqLw\n7LO3s2pVHv/2bw10dflZsyYXnU7FsWN9+P0RvvKVWrRaePzxXbS2+njiiRUYDHqeeeYQAwNj/N//\n+1nsdj3/7b/t4NChnkTB0FgM8vKsPPfcHSxffvGqXCgURVGiGI3JpyGFuJHV19ezdevWab9erlwt\nQHv37pVvIGmQ+KXuRo6dSjW54nlPj59z5ya3l1GUGOfO+fD7Q/y8cPc1AAAeDklEQVT+940AiTY1\n4xoa+jAatbz7biuKAh980DVh/MiRXjweG11d8QXx+/dPHG9u9jE8HOTIkR4AfvCDDyaMHz7cg8mk\n5dCh+PilX6M7O0c4cKB7QnKl12uA5Ou35osb+fybDRK/uSPJlRBCpCganZi0TByLEQ7Hr3YlEw7H\nLlwtSr59fGzqiYX4/qfY+ML2Gs3Uy2ovt60QIj2yoH0Bkm8e6ZH4pU5iN5HbbSInJ35XnVqtmnDn\nXUGBjbq6TNatm7wwXKWC8nIndXVuamria6eMRk3izkGtVk1lZSa5ufHq7QBOp56cHFNi+/x8K9XV\nLgoLbQAUF9uoqIgvsjcYNFRVZVFbm4XXO7mNjdWqp6Zmcluc+U7Ov/RI/OaOtL8RQlz3Wlp8fPRR\nN0eP9jA8HMJm0ycWdKdDo1GTmWmgu3uUgYExhoZCOBzxOwyXLMkmM9OM1arj0KFuRkZCqFTxJOqJ\nJ1bzn/5TCTk5dtxuU6LWlU6noaTEwX/9r3Xcc085JpMOu93A2FiIU6d8tLePUFRkZ+XKXGpqXDgc\nRoqK7FRVZRGLqTCbddx1Vyn33VfOxo0F5ObasNv17NvXwdhYFIgvuH/yybXcd1/FtH7HcDjKqVMD\n7N/fyenTA4TDChkZhsRVMb8/xLvvtvHaa2f46KMOAoEIubmWCWvThFjopP3NDUDmzdMj8UvdQozd\niRN9vPbamcT03PHj/Rw/3se2beWJq0LpGBwM0d4+TH//GNFofKqtq2uUUCiKVqvGZNLy3e+upavL\nTzAYxeOxoigKOl38vTs7RwgGI7jdZhQlvmC+sXGQoaEgRqOWXbua+Z//873ENJ5KBV/+cjXFxTby\n8x0cONDN3/7t+0TjuRMqFfyX/1JHbW02Ho+NBx6oorDQxtGjvUQiClVVWdx8c/60frdYLMa777Yl\n7jQEOHq0l1Wrctm6tYhoNMaLLx5n+/bTifEdO87xn/9zBQ8/XINaPbsJ1kI8/+YTid/ckeRKCHHd\nCoWivP9++6R1Tx0dI5w61X/F9jVX4veH2bu3Db1eQ26uJfH8uXM+GhsHcbvNvPjicXp7AwD4fD4c\nDgcAXq+D/Hwr//Ivh2lvH5mw3/ffb2f1ag+FhVaefvogkYjCpT2in3++gY0bC/F4hi+MXxyLxeCX\nvzzKTTd5Elen1q8vYP36ghn/fp2dfg4c6Jr0fH19N5WVWfT1jfL662cnjb/66hnWrMmluto94/cU\n4nogydUCJN880iPxS91Ci53PF6S3N3kbl66u9Nu7DAyMMTycvH1Nb2/86tV4YgUkEiuAtrZhAoHw\npMRqXEuLj2hUoaMj+Xhzs49gMEJPT2DSmKJAS8vQTH6VpM6fH026IF9RYvT1BWhtHZ7UlxDiSW1r\n68isJ1cL7fybbyR+c0cmxYUQ1y2jUYvRmLx9jdWa/PmZ7V8z5doik0mHzWbAYEhe3sBm0+NwGDCZ\nkn/HtdsNOJ0GjMapxuPbf7o8xLip2ubMxFStf+JjWhyOqadV7XaplyVuXJJcLUDSHyo9Er/ULbTY\n2Wx6li/PnvS8TqemvDwjyRYz43KZqa2N3+3X3e1PXI0yGrWUljopL89g82YvHo+FNWtyL0yVZZKZ\naWLZshzWrs3jnnvK0GjUFxJBLTqdmuJiB6tX57J+fT7btpWTk2PhjjsWcffdpZSXO6mszGLNmlw2\nbMhl27ZyvF4bDz9czSOP1LJ0qYslS1wTpjwVReHs2UFOn+4jFIpM+j1isRi9vaP09IxOKP/g9drw\neKyTXp+ba8HrtVNX56KycvJdh3V17kRcZtNCO//mG4nf3JFpQSHEdW3NmjxUKhWHDnUzNha/k+2m\nm/LJz7fNyv69Xhv79nWwa1czPl+Q9esLeOihPNzueImGlSvddHQM8//+XwM+X5BNmwp5+OFqiovj\nU4T3378Yg0HD9u2nGR4OcsstxXzhC5XU1cWTwi9+sQqjUcv27acYHY1w661eHnywmqVL4wVAH3qo\nGrNZx/btJxkbU7jttiIeeqiaJUvi2589O8BLL51k375OolGFJUuyeeCBSmpr41N2PT2j7NnTSmPj\nILFYDK/XwebNhXg8VgwGLXfdVcL773dw5kw/sRiUlWWwfn1+Ihn82teW8corp/noow5UKhVr1njY\ntq0Mmy39mwWEWKik/Y0Q4oYwMhIiGIxemEqbvYv23//+Hl566QSlpU70eg3t7cMoCjz99GeorXXy\nV3/1AS+8cJysLBNarYrBwSBOp5F//ufb2LAhn7/+63f5+ONOtmzxotNpOHy4m/Pnx/jHf9xKUZGV\nv/iL3ezZ05a4gtTfH8Bk0vH005+hoMDGE0+8w549rWRlmVCpYGgoiMtl5umnP8OiRU7+9m8/TFRx\nH5efb+OHP9yI02ngV786Tmenf8J4ZqaJhx6qxmK5OC04OBhvSO10Ju852NIyhFpNoieiENcTaX8j\nhBBJWK16rJNnuNLy8cedbN9+mmAwSkND36fGuhgaCvOb35wkGo3R03NxAf3o6AhHjvRgsWjZuTPe\ncPk3vzk5Yfv6+m56ekbZseMckYjCyZOXts8JsH9/F21tw+zY0YiixO9cHDcwEGT//i4CgcikxAqg\nvX2YI0d6KClxTkqsIJ7ANTf7qK6+OLU3VVI1LlmxUiFuVLLmagGSefP0SPxSJ7GbaGQkjN+f/G5B\nvz+M3x8kEIgmHR8djeD3hwmFkrehGR4OMTwcTHo33vj+h4dDU7bPGRkJ4fdPXl91cfsIY2NTj19u\n7FqR8y89Er+5I8mVEEKkqKzMQXV18jYyixY5KSlxUl2dmXS8qMhGaamT/Pzkl9PGF8QnG1eroaTE\nQXl5RqL9zqW0WjWlpU6Kix1YLJMnKDQaFcXFdtzu5JXU1WoV2dmWSc8LIaZH1lwJIUQafve7E+za\n1UIwGCUSiWE2a8nOtvDYY0vweGy88MIxXnzxOIFABEVRMBi0LF+ewyOP1LB4sYs33zxDMBjj9OkB\nQiGFRYscWK1aNmwowOk08frrZ4AYp07Fx0tKHOh0KjZv9pKZaeYXvzjCzp3NRKMKihK/E7Kuzs03\nvrEEm83Myy+f4le/aiAQiBKLxfsO3nnnIr761Tq0WjUffdRJU9MgLS1DKEp8gX5hoYX1672oVMnL\nPAhxo5E1V0IIMYccDiNtbcP09gaIRmMYjRqKihyYzfGPV5tNR2GhjcHBIOGwgs2mIyfHgtUarwM1\nMBDm+9/fw8BAEIhflXr88ZUsX56J02nC5wvygx/sTYyrVPCd76xmw4Z4CxuLRcfYWJiBgSDRaAy7\nXY/Npkelir9/aamDtWs9dHX5iUZj5OSYqajIQqOJJ05nzw7yox/tu7BmK4bRqOXb317Fhg2SWAmR\nKkmuFiDpD5UeiV/qJHYT9feP8vOfH6Gx0YdKBSqVitHRMM8/f4y6OhelpU7+6Z/qqa/vQaWKT8dF\nIjFeeeUsHo+FiopRfvrTg4nECeLV1f/xHw9QU+OiuDjEP//zxPFYDP7hHz6mqspFaWmA//2/99PY\n6EOtBpVKTTSq8PHHXZSUOFi3Lp+dO1sIhRQyM+N3EypKvL3OokVOhoeDPP30QaJRBaPxYrHTZ589\nzJIl7rTbA802Of/SI/GbO5JcCSFEik6fHuCTT84D8aTn0lUWp04NEgpFqa/vSYxHIhfHz571odWq\nE9tfKhaL16eKRBROneqfNB6NxjhzZoCxsQiNjT6ACwvb46vbQ6H43YXFxU6CweiE/cZfG6Ozc4TW\nVh8DA2OT9j86GubkyfR7Lwpxo5IF7QuQfPNIj8QvdRK7iQwGzZTtbYxGzYWK68mn1wwGNQaDJjE9\nN3lci8GgYaplTyaTBpMp+XuPb6/TTf0Rr9Opr9jeZr6R8y89Er+5I8mVEEKkqK7OzdatRajVKgoL\nbZSUOLFadZjNOpYtc3PTTXncc085er2aLVsKufPOReTlWcjIMFBb62LVqizuuqsEiCdLFosWtRqy\nsozU1rpYsiSHz3ymeNL75uSYqKlxs2ZNLlu2FKJWq7BaddhserRaNQUFNlasyKGgwJaoFH8pk0lH\ncbGdlStzqKrKQq1WYbPpsdv1aDTx32XlypyrHT4hrluap5566qm5ftOmpiby8uRyc6r27t2L1+u9\n1oexYEn8Uiexm0itVl+onK7i2LE+entHqa118fWvL+PWW4sxGHRkZurJzbVSX99NW9sIGzfm881v\nruBznyvDajXhdBqJRKKcOROfRly+PIfvfnctd99dRkaGkYwMA35/+EJ7Gli+PJsnn1zLXXeVYrMZ\ncLvN+P1hzpyJ3024enUu3/nOGm69tRitVk1uroXe3lGGh+P1uLKyTNx55yJyc61YLHoKCmz09Pg5\nfXqA0dEIy5fn8s1vLmfZstxrG9wk5PxLj8QvdZ2dnZSUlEz79fPvuq8QQiwg3d1+1GoVy5dnEw4r\nZGUZaWkZYmBgjIwMI3/8Yxsvv3wKvT4+jXfkSA9nzvRTXGxn6dIcdu06x8hIkG9/eyWxWIzz50d5\n7rmjlJQ4WLEij61bi6mudvClL1UyNhalsjKT6up4X0BFUTh8uAeXy8i3v72KWCyGzxdkz55WNmzI\nJyvLjMdj5cEHq+jq8qMoMXJzLRgM8Y/+SEShvX2EmhoXpaUZqFTxqc62thGCwUjidUKImZG/nAVI\n5s3TI/FLncRuooGBMQ4e7EajUU1oBD00FOLMmQFisRgvvHBswt1+4/bt66SvL8Dzzx/D5wvz5pst\nE8br63tYsSJ+hT8vL4O8vIxJ+2ho6OOPf2y90B5nYMLY4cO93HprEQA6nYbCwsntadrahjl7diCx\n0D0Wg7GxKG1twzQ3D7F4cfICqNeKnH/pkfjNHUmuhBAiRZdrXzMyEiIQiCRNrCCemPX3j+HzhZOO\nnz8fuOL79/ePTdkep6/vytv7/SGmKiM9MpK8rY8Q4spkQfsCJP2h0iPxS53EbiKHw4DZnPyOu4wM\nE16vDY/nYhsZ5ZJGgHl5FvLzreTkmJJuH1/LdXl5eVaMxuTfkfPyrry902lCrU5+O2JGxuUbNV8L\ncv6lR+I3d2b9ylVXVxc7d+5Eo9GwefNmWbguhLhu2Wx61q3zcOBAF5GIQiSiYDbrsFp1lJU5sVr1\nPPbYUp577iiKEiMajaHXa6iszOSmmzyUl2fxta8t5/jx81gsOsJhBYNBg98fZNWqi3frHTnSzcGD\nPQSDClVVGdx8c3xRcnl5BnffXcrLL58iEIgQi8UwmXSsXZvH0qXuKx6/x2Nh6VI3Bw/2THi+qior\n6TSiEGJ6Zj252r9/Pw899BAA27dvZ9u2bbP9Fjc8mTdPj8QvdRK7yTweKzt3jnHiRD/hcBS328yf\n/ElZ4opWZWUm995bzuHDPQwNhamuzmLNmjy8XuuF8Qzee6+Nd99tIxJRyMuz8tWv1lBcHL/i9dpr\np/lf/2sfJ07Ei4lmZRl58sm1PPbYUgAKC61UVGTxySe9hEJRCgttVFW5sNkMVzx2lUrFli1e3G4z\nJ08OoCgKFRVZVFdnJW3ofK3J+Zceid/cmfXkymy+WFNFr9fP9u6FEGLeiEQU/vjHFlQqFVVVWYnn\nP/qoi0WLnJjNWn7yk4O8804rXq8Nu13P88838MILx7BY7qSmJpOf/OQQ77/fjsmkQaNRc/Toef76\nr/eSk2OhoiKTn/70YCKxAujrG+OHP9xLUZGd6upM/u7vPuL48T5ycy1otWp+//uz7N7disdjYd26\n/Cv+DgaDlhUrclmxYv6VXhBioZr1ryaXtn/Q6aau/itSJ/Pm6ZH4pU5iN1Fv7yjt7cOTno/FoLV1\nmPr6bnbvbgWgpWWYTz7pIxxWCIViNDT0cfToed5/vx2AQCDKyEg48fOJE/0cOXKe/fu7Ju3f74/S\n0NDHwYM9HD/eB0BXl5+2tmEUBYaHQxw+3DNpu4VOzr/0SPzmzqwnV9HoxT5Wqqn6NjDxH3nv3r3y\neAaPjx49Oq+OZ6E9lvjJ49l83NLSSktLyyWPWyY8vtqi0eiEz91PP77W8ZHH8vh6ejxdqlhsqhtx\nU/Pyyy9z7733EovFeP311/n85z8/6TU7d+5kxYoVs/m2Qggx5yIRhV/96jhtbROvXqlU8IUvVGI2\na/nGN97inXdaKSqyYbcbOHGiH5Uqxs9+Fp8W/Na33klcvRpnMml49tnbqajI5JvffHvS1SuLRcPP\nf34X1dWZPProDo4f78PpNKDVqunrC2C16vnZz+6Y1rSgEOLK6uvr2bp167RfP+vtbxwOB6+//jpH\njx5l06ZNWCyWSa+R9jdCiJk4e3aQvXtb+fDDTvr7A1gseiyWa7/sQK1W4XKZaW0dJhCIAKDVqti4\nsZDaWjc2m4HsbBNFRXaCwfjVpK1bi3n00SV87nPFuFw2HA4Dp08P0NMzCkBGhoG//Mub2LatmJwc\nOw6HnhMn+hN1r7KyjHz/++v5whcqsduN5OaaicUgEIig1apZvjyHxx5byu23T79VhxDi8q55+xuP\nx8ODDz4427sVl9i7d6/c9ZEGiV/qrkXsjh8/z2uvnUVR4hfZOztHOHGin/vvr0jalHiueTxWvvzl\nalpahgiFouTkWMjJufil8tixfn7zmxOACkWJ0dMzSlfXCDU1WdTWZvO5z5VSWGjl8OFeRkcjLF6c\nyS23XOz/9vnPl1NUZOfgwR5CIYWKigw2bbo4rtGoMZt15OVZiUYVnM54farrsX2N/O2mR+I3d66v\nvzwhxHUlHI6yb19nIrEaNzQU5MSJvnmRXAGYzToqK7MmPf/xx508++xB2tr8E55vbPSxdWsRtbXZ\nACxZksOSJTmTth831XhPzygHDnSTmWkkM/Ni0c+zZwfnZfsaIW4U86+Qibgi+eaRHolf6uY6dn5/\neMo2Ll1d/qTPzyft7SOTEqtxs3H8AwNTt7/p779y+5uFRv520yPxmzuSXAkh5i2TSYvVmrxeXmZm\n8rYx84nLZSQzM3kxz4yMKxf5vBKrVT9l+xq7Pf39CyFSI8nVApTKbaHiIolf6uY6dgaDltWrJxe3\n1OvVVFbO/ymvDRsKefjhWtxuI3V1LurqXJSWOliyxM3q1enf1OPxWKiqykJRYgwNhRgcDBKJKOTk\nWCgquv7a18jfbnokfnNH1lwJIea1pUuzUatV1Nd3MzwcwuOxsnp1Lvn5tmt9aNNy++3FKEqM//iP\nRkZGQqxZ42HbtjJWrUo/uVKpVCxd6qatbYhTpwaIRKLU1blZvToXi0U6ZAhxrcx6navpkDpXQoiZ\nikQUQqEoJpP2sgWK55OxsQjf+c4u/vCHJqqrs9DpNDQ2DhKJKPzLv9zO+vUFae0/FIry4osNdHeP\nYrPpgRh+f7wkw8MP1yyIqVMhFoKZ1rmSK1dCiAVBq1XPy2bCl3PgQCc7djQRDiscPtw7Yezgwe60\nk6uOjhE6O+ML432+YOL5SETh3LkhSa6EuEYW1ieVAGTePF0Sv9RJ7GYmEIgQCl28m+/StjRjY8nv\n8puJcDg65VgkMvXYQiXnX3okfnNHkishhLhKqqtd1Na6k45VVGSkvf/cXAtm8+RK9SoV5OVZ096/\nECI1klwtQFKrJD0Sv9RJ7GbG47HxZ3+2jKys+PScRqNBrYbHHlvKpk3pTQkC2GwGbrnFi1Z7cQ2a\nSgU33ZS/YBb8z4Scf+mR+M0dWXMlhBBX0d13l5Gdbaa+vodAIMzixRncfHMBdrvxyhtPQ12dG5fL\nRHPzENGoQn6+Da/XPmX9KyHE1SdXrhYgmTdPj8QvdRK71KxZ4+HrX1/G6tVBPve5sllLrMbl5VlZ\nt87Dhg0FFBc7rtvESs6/9Ej85o4kV0IIIYQQs0jqXAkhhBBCXMZM61zJlSshhFgAhoaC9PePcQ2+\nDwshZkiSqwVI5s3TI/FLncQuPanEb2goyB/+0MjPf36E5547wksvnaS9ffgqHN38J+dfeiR+c0eS\nKyGEmKcUJcbbb5/j0KEegsEokYjC2bODvPLKGXy+sWt9eEKIKciaKyGEmKfa20d44YVjKMrkj+k7\n7ljEsmU51+CohLjxyJorIYS4TgQC4aSJFcDoaGSOj0YIMV2SXC1AMm+eHolf6iR26Zlp/FwuEwaD\nJumY222ejUNaUOT8S4/Eb+5IciWEEFdZf/8YBw9209GhobnZN+XVqE9zOo2sX5+P6lM1Qauqsigq\nsl+FIxVCzAZZcyWEEFdRY+Mgr756hrGx+DSeSgVr1+Zx882FaDRX/n6rKDHOnBmgsXGQcFjB67Wz\neHEGJtPkhs1CiKtjpmuupLegEEJcJcFghF27mhOJFUAsBh9+2InXa6ekJOOK+1CrVSxenMnixZlX\n81CFELNIpgUXIJk3T4/EL3USu5np7h7l/PlA4nFLS0vi57a2kWtxSAuanH/pkfjNHUmuhBDiKtFo\n1JPWS43TauXjV4jrlfx1L0AbN2681oewoEn8Uiexm5ncXDOFhY7EY6/XC4BGo5IF6SmQ8y89Er+5\nI8mVEEJcJRqNmttu8+LxWBPPmUw6br99Efn5tmt4ZEKIq0mSqwVI5s3TI/FLncRu5rKzLTz4YBVf\n/GIVS5dqeOSRWpYsyb7Wh7UgyfmXHonf3JHkSgghrjKdTkNxsQObLYjdbrjWhyOEuMqkzpUQQggh\nxGVIb0EhhBBCiGtIkqsFSObN0yPxS53ELj0Sv/RI/NIj8Zs7klwJIYQQQswiWXMlhBBCCHEZsuZK\nCCGEEOIakuRqAZJ58/RI/FInsUuPxC89Er/0SPzmjiRXQgghhBCzSNZcCSGEEEJchqy5EkIIIYS4\nhiS5WoBk3jw9Er/USezSI/FLj8QvPRK/uSPJlRBCCCHELJI1V0IIIYQQlyFrroQQQgghriFJrhYg\nmTdPj8QvdRK79Ej80iPxS4/Eb+5IciWEEEIIMYtkzZUQQgghxGXImishhBBCiGso5eRKURSi0ehs\nHouYJpk3T4/EL3USu/RI/NIj8UuPxG/uaFPZ6A9/+AONjY1s27YNj8eTeL6rq4udO3ei0WjYvHkz\neXl5s3agQgghhBALQcprrhoaGnA6nROSq1dffZV77rkHgO3bt7Nt27ak28qaKyGEEEIsFNd0zZXZ\nbE78rNfrZ3PXQgghhBALwqwmV5deBNPpdLO5a3EJmTdPj8QvdRK79Ej80iPxS4/Eb+5cds3V+fPn\n+fWvfz3hubvvvhuv15v09ZcucFepVFPu1+l0Ul9fP5PjFJcwm80SvzRI/FInsUuPxC89Er/0SPxS\n53Q6Z/T6yyZXLpeLb3zjG9PeWSAQAOJXsMZ/TmblypXT3qcQQgghxEKS0oL2t99+m8bGRoxGI8XF\nxWzatAmAjo4Odu/eTSwW47bbbiM7O3vWD1gIIYQQYj67JhXahRBCCCGuV1KhXQghhBBiFklyJYSY\nFunKIOaanHNioUqpQns6AoEAP/7xj/nqV7+aKEAqld2n5/3336e7uxsAr9ebuDFA4jc9x44d4+TJ\nkyiKwrp16ygoKAAkftMhXRnSI3GauWTnnMRxZpJ95kkMpyfZ/7czil1sjv3Hf/xH7MCBA7H29vbE\nc6+88kri55dffnmuD2lB2rFjR+Jnid/07N69O/HzG2+8kfhZ4jc9x44dm/B3G4tJ7KZL4pSaT59z\nEseZSfaZJzGcufH/b2cSuzmdFhwYGMBsNmM0Gic8L5Xdp+/cuXP8/d//PWVlZYnnJH7TM35X66dJ\n/FInsZseidPskDjOTLLPPInh9H36/9uZxG5Ok6u9e/eycePGSc/HpLL7tBUXF/Pnf/7nfPTRR4nn\nJH4z89Zbb7FmzZrEY4lf6iR20yNxmh0Sx9Rc+pknMZy+T/9/O5PYXZU1V1NVdu/u7ubVV1+lp6eH\n4uLixDz6dCu73yiuVBnfaDRis9kSYxK/iS4Xv927d1NeXo7L5UqMSfwuulpdGW50EqfZIXGcuU9/\n5kkMZ+bS/29nErurklxNVdn90UcfBaChoWFCKfnpVna/UUwVv/7+fjIzM4GJGbTEb6Kp4vfuu++S\nk5NDcXHxhOclfhddra4MNzqJ0+yQOM5Mss88ieH0JPv/diaxm/Miov39/bz00ksUFxfz2c9+FpDK\n7tP12muvEQwGAVi1alXiD0bid2Xd3d384he/oLKyEoDh4WG+8pWvABK/6ZCuDOmROM1csnNO4jh9\nPT09PPfcc5M+8ySG05Ps/9uZxE4qtAshhBBCzCIpIiqEEEIIMYskuRJCCCGEmEWSXAkhhBBCzCJJ\nroQQQgghZpEkV0IIIYQQs0iSKyGEEEKIWSTJlRBCCCHELJLkSgghhBBiFv1/JHqM5MPOo2MAAAAA\nSUVORK5CYII=\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10b516110>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 80 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Finally, validate on test data:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "tree.fit(train[[\"A\",\"B\"]],train[\"T\"])" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 66, | |
| "text": [ | |
| "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n", | |
| " max_depth=None, max_features=None, min_density=None,\n", | |
| " min_samples_leaf=1, min_samples_split=1, random_state=0,\n", | |
| " splitter='best')" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 66 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "forest.fit(train[[\"A\",\"B\"]],train[\"T\"])" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 55, | |
| "text": [ | |
| "RandomForestClassifier(bootstrap=True, compute_importances=None,\n", | |
| " criterion='gini', max_depth=None, max_features='auto',\n", | |
| " min_density=None, min_samples_leaf=1, min_samples_split=1,\n", | |
| " n_estimators=10, n_jobs=1, oob_score=False, random_state=0,\n", | |
| " verbose=0)" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 55 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print tree.score(test[[\"A\",\"B\"]],test[\"T\"])\n", | |
| "print forest.score(test[[\"A\",\"B\"]],test[\"T\"])" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "0.990310077519\n", | |
| "0.994186046512\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 72 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "print tree.feature_importances_\n", | |
| "print forest.feature_importances_" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "[ 0.99097739 0.00902261]\n", | |
| "[ 0.60904142 0.39095858]\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 73 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": {} | |
| } | |
| ] | |
| } |
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