05_Ensemble_Learning.ipynb

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   "source": [
    "# TensorFlow Tutorial #05\n",
    "# Ensemble Learning\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This tutorial shows how to use a so-called ensemble of convolutional neural networks. Instead of using a single neural network, we use several neural networks and average their outputs.\n",
    "\n",
    "This is used on the MNIST data-set for recognizing hand-written digits. The ensemble improves the classification accuracy slightly on the test-set, but the difference is so small that it is possibly random. Furthermore, the ensemble mis-classifies some images that are correctly classified by some of the individual networks.\n",
    "\n",
    "This tutorial builds on the previous tutorials, so you should have a basic understanding of TensorFlow and the add-on package Pretty Tensor. A lot of the source-code and text here is similar to the previous tutorials and may be read quickly if you have recently read the previous tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flowchart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following chart shows roughly how the data flows in a single Convolutional Neural Network that is implemented below. The network has two convolutional layers and two fully-connected layers, with the last layer being used for the final classification of the input images. See Tutorial #02 for a more detailed description of this network and convolution in general.\n",
    "\n",
    "This tutorial implements an ensemble of 5 such neural networks, where the network structure is the same but the weights and other variables are different for each network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
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fRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3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\n+y1Eps78iraSJI02M4D9iWV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q8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr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/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr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\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4gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+\neeyQhLS0hP9MgdWfyb1Ayc4HB2TtzzLZ9IorkxCRkt3+tl6rDfr596a9naOe3RE2qke1p86piRpv\n6u36Y3WH2V2HczI8mmkKkUfa1W53fwT0O3q6v7y9b4pmmQIhQ+fl4cuoB8/Ik2qN0Mv6QnvfkPq/\nVYdt6tYL3oLq5xq6muEaGRoa4tFHH2V83E/iUuM95bC9nmeQ53lanebzn/98r0zWJJNJ3vGOdzA5\nOdnhRC8UClQqFb2t4zg6sC2oEiSl1ONbtf/g4CDxeBzXdfn2t7/dc7tjsRhHjhzhscce08GI8/Pz\ngF+6cnJyUp+H4zhUq1W9ryqdpGyPx+OMjo5q1SgVwKky3nuJEIJ9+/bpbOXZ2Vlee+21yLFuo9Hg\n0qVL+rpIKUOO6EQiwd13360d5/v27WNsbAwhBBcvXuy5hLkQgsOHD/Oe97wH27ZZWVlhdnYW8Mes\nKohYoYKGle3xeDx0LyjnM0AulyOVSmFZFufOnbvqudUrsWfPHh577DFs29Z9VxEMTJBSUqvVujrR\npZR6vhb8a5BIJHRfevbZZ3tqt8Fg2FbMAbfiO9M34wTHfwb+xUYbYTD0AuNE3wSYSTjDjUKxeIFq\n9cXIdfV6AylrkesAstkk733vBE3fUAgJ5D2XbHUeKsuR+4tGA2v3boioYapqYW4ko6Pw2GOR5mF5\nDfZcep5EoxLpMBdUSJZeZ3ZuPnKCuVSrsUh0MpQEhu+4g/y73oVoC7IRQHxpCXHp0jWdU08QAh54\nAG6JCqyUzMwk+P7ZTNdJu9QA7DkcPe8ea9SwLp+CRq1zAyHAdXGOHcNbWYk8ttucVNkoZDwOo2PI\n8U6FJFmvkX3kEVIDA9HPGM8jcfIkLC11d0qkUpBMdonckNQO3Ul9dJL2niUElI+/CrXFazgrw2qs\ndeIqOLmqfl/Pun0bRbfgQ5Xd0S6zuN2z0YNEnWt7H9mOcv9RXIu8bPs9Zehkrj7E94q3kIj5AzUp\nITdkMZa3W08JAReLOc6cIVQTPRFzSR4/xuBi1Y/PEmCVitjPfhlKxea+As6cgZtu6r3x4+PwUz/l\nF3mW0h+M7dsXrisuYM4ZY/aOn2oFGVo26V0PkTzV2kwC8Zk4U1M3kfCkb3ej0R9HejwO+/fD1FTr\nWT04CIcPQ7Bc18wMfP3roee9JeGhiZNM7RKhYcCBfIpsYIpgiWXStBxevSAWg7vu8od2wVuvWoVA\ngir1eIqXMkdI0fq7LpEM8W3GCbucc0A+sMwF+qEVIYAUkCUwU+m6LJ4+jbRt3Tec1AI/bH0UJ5lv\n9X8pcW/aj/uG21GF6KWAv1lO83XGaDnP60DEC8/1smsX3HGH7ovCheH9w+w+ER4Knj0L7UPceBwu\nX+4cMv7SL4Ud6889F763DddPcAynnLBX+xyqVCracR109pXL5Z47FpWdjuPoMoRBx6ZSTwqeV9DZ\n2G5PeyBotwSUXtldr9epVCpIKalWq9pRLoQIOc0dx9FtGjW+TCQSujyO53mUSiUajQZSyg7HcC9o\nNBo6a9t1XWKxmHaQZ7NZ3Y6O41AsFrVDV22jSCaTDA4OhhQQVNBFv9q9VquxsrKiFZNUu7quS6lU\n6mjfYF8IOquV/epcbdvWAQL9KOGk7qWgykDQzqCzv72sVvBeVucRTHi6Ed7bDAaDwWDYShgnusFg\nWDdWVi7jeceJzrAQgEU3V28mM8Cjj3pkMnbEeogVXDJfXoJiF8ed52FPTUGEbJqQEtFF8my9GBqC\nd77Td6YHkYBwXMb+6jvYhYVIh6eUDtPlcyw0MzCDCKAIzNNFUVQIkrfeSuIXfqFTqcKyiJ85g/jT\nP72mc+oJQsCdd8KDD3ZMQEsk85f38OLpg3iyc8ZOSn9e/Mjj0RN69vwM1n//PViImBUEpOvSOHmS\nxoULHW0nAC8q4mE9icWQw8PI0bHOdU6NgfvuQwwPRzvJHQfKZX/GOmq9lJBI+LOmUUhJ/eZDlO98\nIx1OdAtq6UHkt566+nMyRKIkI9UkUKPR0Jk7rutiWRaO4+hJMNu2SaVSN4TctOd5eqI2WPt9165d\npFIpJicn9bbtcprbHdVXVP1zJTWpgg3UOjU5GovFdDMhR80AACAASURBVB/azoEXaqJZTdYWi0Vq\ntRr1el1PKtu2zcDAAJ7nkclkSKVSejLWEM2yO8DJ6k5iti8BK4EdEpKZsJNtoer7dIPP5WTMI3Hh\ndQZqRfQzZX4evvgFmJtrbVivw86dvTd+cBAef7zleLYsSKfbno+SZXk7l24OBJtIf7PkdGAzAckl\nm/GRHSSo+wscJ3wevSIWg8lJ2LOn5Y0eHoZbb22dixB+MEBTkjhgJodvucThvW19Op1HkNBbJSk0\nz6N32LYfC3Hnna2hnevC8eN+vIHCSSY4K/eGnOESj/2MsBcRcqJngAFaI5IG/ZnoEEAC35Gu/kJ6\nUjI7M0PQ9RdPLXJP9vPEkonWQilh70OwP6dvACkkLx/dDewO7F3DDwHoocNHCP8l46abWk70BmQn\nM4yOaZ8+ALOz/r8gUsJLL7U+ex7cey/84i+iA5yFWD0203B1qOdyMiqC/Co5d+6cVqSp1+taotxx\nHEql0nUfvx0pJZVKhXK5jOd5DA0NaZntfD6vHbRXwnVdXaYHWtm9/QqCVCWUpqenkVKytLTE4uIi\nQgiSyWToO6vVasg2x3FYWVnR462BgQHtTHcch+9///ssLS0hpYws2Xi9lMtlnTXfaDQYGRkB/Gzs\nBx98UMuzVyoV/v7v/z6ynCT4GfcPPPCAluIvFotUq9W+BhAuLCxw6pQfjVav13WfdByHy5cv68AK\nz/OoVCqhAIBarabHq0qBSmWnHzlyhF27dmmZ+F7cS0E8z6Ner+uxYTBYJJ1Oh8aMAwMDV3XcXihO\nGAwGg8Fg6B3GiW4wGNaZ1V4Euq0Ta5iQidAYbGeTOwS6KmdLXzLUUlqnbXiB5VFnLgL/Ig7tL++S\nbbxpWFVS3EYQnfaizzty9yt0KpV1GPHdXdtsXeluv7jeidfgvdTlPFs9rlPBIHgIw/WhMlbURF2x\nWGRlZQXXdZmZmdEZPblcTk+IjY6Oks/ndRbEdp6EqVQqLC0tUSqVdNaPbdv82I/9GLt372bfvn2h\nrI9gHc7t2ibQmsAulUpUq1XdN5QD2XVdKpWKlqBUmUo3Qp8pl8vMzs5iWRYXL17k5MmTVKtVFhYW\ndH3TdDrN/v378TyPPXv2sHPnTizL6vkE7HZCCN8Rp7uN7Bx/6M8i/IwQoYWB548QYW+7ZfXv4bIG\nXWoBCGmFnLfK9NB27Rv0e7wQJTvfviyi3fyxTLvx7QGt/fs7EGVie7+IGr9e6fOVlveDdjsFgBAI\nERwjyUD7CrVkfYkc7xMyvtstFhWQauTc+8d2eQZ3U8FZ676boR2CSgCr2RMVfNiefbzeqHGdCpZX\nv3fLeg5uv1kducHrEAyM3YhA2SiVoutVdzLKRwaDwWAwbD6ME30d6TaYu5ZBXlRt8271zrdrJpHB\nYDAYDDcCKoNYZd+obGK1TGUTq8wMVZdR/b6dCbZH0AGcSqXIZDK6lmZwe9j+7QLdJU/bM/KDbXIj\ntUswK19l6wcnLpWEaVAO9EZoH8PaiVb4WW8rroMr9WfT3yNZqxP/2o7WZ1YJjjRsLpS0uQqCC2bc\nqkzV9u0VsVgspEYUDMZU6kaA/tkPlBqOymZWz0+lghNlu23boXFbMNNYCEEikcC27b46Si3L0lLo\niURCB88lEolQm6pSQcH9gqWDXNdleXmZeDxOo9HQajdq/NFr1Bg4+P3gZ3YvLi5qifRKpcLy8rK+\n9kIIcrlcyCldq9W0pH2tVqNWq2FZFo1GQysK9MN2oMPhH8zu9jyvoya6ulZq+1QqpeuJx+PxvgaG\nqntU9cdaoDxgvV4PfWej0Qhd93YZ+oGBgVDbKol3dXyDwWAwGDaQAeA9wLuBfcAgsARcBJ4F/ha4\nsFHGrRfGib4OSClZWVlhpa2urhCC2dlZPvrRjzIXIfH3UlAzLUA+n+d973tfh0yrEIK3vvWtPPDA\nAx0DLSVxajAYDAaDYWuhss3Bl4+cn58nFotRKpWwLIt4PE4+n9dZ1vl8Xk/UdAuw2y5kMhmdef/z\nP//zWtb98ccfZ2pqilQq1ZEdciNg2zY7d+5keHgYIYT+2Wg0qFarxONxbr31VnK5HFJKDhw40NFW\n25V4PE4mk0EIwdTUFA8//LCWD1X1To8cOcKDDz6I53kMDw8bJ/oa8B0DDYRoOWU8z//XLm7SkSgt\nXVxP4qgVQUdfYEOvq2TPddoONDwPS01wCxE2vLmV54GUSkY2fI5BPOniSs8/H6DRJ7uREtdrfQ9S\ntgxqP5eO82ka325X2wk1PK8P2dKy6UxyQpdZma8uvzIlnOks8aSkXYjYa/4LigD0w+0gm9/jBo4f\nXCaan+3m56bRrZ9t10Iim30q2InU0Xt3Bspx13BdbU/DozlWcNbUPdWtGfzc3tWMs6d31Go1pqen\ncRwHz/N45plnWFhYQAjB0tKSriENvjNc1XyWUrJ//37e9KY3Af6458knn+RLX/oS4KsV3XbbbcRi\nMe3c7TWu63L+/Hmq1Sqe5/GFL3yB6elphBC88sornDt3Ttter9e1Q3fHjh088sgjep4rnU5z+PBh\n7RB94IEHGBkZ0cGjvcayLHbv3s2RI0eAzmCF4Hza8vIyly5d0p+r1ap28AshmJ6e5kMf+hDFYpF4\nPM7999/P2NiYVpjqNZlMhpGREaSUzMzMcO7cOaSUlEolPvaxj2kHb71e56WXXtJO9kwmw2/8xm9o\n+XcpJc8++6w+bjCr+syZM+zfv7/ntudyOXbu3ImUUsvig9/+4+PjoeCRlZWVkJx7sGZ6IpHgnnvu\nYXBwEAg7qgcGBrpK2F8r1WqVubk5bNtmfn6er3/969RqNRqNBq+99lroPI4fP87y8jLgt+ng4CDZ\nbFYf64Mf/CC33347nucxMDBAJpPBsiwuXbrUl75uMBgMBsMa+RHgj4A9Xdb/HP7Ly7b3MW/7E9ws\nOI6joz8V6gXoO9/5Dhcj6jEX2mrXKRKJBEeOHOmQlFQvTDt27OgqJWUwGAwGg2FrEZzAcl2XWq2G\n4zi4rqszcyqViq532J7hs52JxWK6/vuBAwfwPA/LstizZw87duzYaPM2DCGElrYfGxvTk+Sq/8Ri\nMfbs2UM+n0dKST6fv2EcxSqzCfzJ44mJCRzHIZfL6Yy3yclJJiYm8DyPXC637YNRrhfLsjh37rt8\n+tP/DstqORnSaQiWvhUCvvtdv+51sJt5rsdHnjxLPhXIiqxW/Tregcnjc5bFzrYg4utFCMHs3By/\n+pu/ia2usxCtmuIBFhYFpZXw/RGLtclcC7BrFYZPfpdYtQRCUHVdLjQDN3pJqV7nD774RYYDE/Ek\nkzA2Fo5cWF6GZj3kEK+8Au11WhOJ1gkJQa1e59zsbI9td/nqV/87P/jBl0P+5eXlcECCbUMu196+\nkvzpo4xkMgSdzCvAMi0nugecaToheombSPCJbJa/D7xry+b3B90ctm2TWVrCCgaxSwkvvOAXHA+0\n57HTGbLZoeC3MDDwIrb9eM/stiyLz3zpSxwN9APXk3z3xTiLS/aa8uHb/TgvvQQf+1iou3D8+Hd5\n5JH/rWd23+gotRRVC7pcLiOEoFwuhxI0lBMdWmWAgpnGtVpNO24HBgb0+LGfWa5B1aSVlRWKxSKW\nZbG4uMj8/Lz+m1Kr1fQYJR6PUyqVtBPd8zxqtVpIianfBANXVyMWi4Wc6iozXTnRlYO3UCgQj8d1\nMES/2jsqE105pQuFgg4SdByHYrEYqjOusp7VviprPnheatt+EMw+d103pKYVzDT3PI94PK5tb89E\nV+8EqVQqdG7qO/qB6peqTJJ6RysUCrq2u+d5zM/Ps7i4qM8rqAihSjCpPqLUGtarzxsMBoPB0IVf\nAD5Gq4bnMvACsACMAzcBN2+MaeuPcaKvE1eqlXO1g7rVJDoNBoPBYDBsbtb6/G+vXdg+dgjWLlTb\nqAmvaxlfbBYn6lptD55nsG3UBGbU9tfKZnCkqvO60jUK9otgmyjZ+2CbQXgCcy3Hj/q+jabbNW+n\nvU3UvsHjtLdRcP3VjrU3Q9v0m7vvvptPfvK/4brhyd6oU+/WfGuqEN3M3lIT5L1gz549/Lc//uPe\nZ2O2nWg8kSCXy/Xs8IODg/z+f/7PfpB28Lu69be19tuI/WOxWM9styyLX//1f0ehULy2AwgQazwX\nq6nI0Sssy+L//vVfp/DLv3x9ygJtbRx1KNu2emr729/+dg4dOtSx/HqnDqK62+TkpFHA6wNRdZbb\nn2GbtZZy+/N2teev+tleYmWrslHj6uCY6FpsaN9+s1yDrTDfudo9Gly+WdrUYDAYDIYI3kXLgV4A\nfhn4BHQIgt0G/PT6mrYxGCe6wWBYR0TgXxC5yjq13sKywLKiX5wsq/myKET0jE635QHLNppu5ycs\n4ArnZuE/2aLOQ6yyTtJ8qbMsZLuDSE1yXOV59BwhfNs6Xpq9puPHl/bssmuzb3Sus5SjbZW+YRE9\npd+tPdcT32wlHNq+EhDWNd8Pa9lGCKuZfdRePgQsa6NbZ3MjhODo0aN85CMfuaJj1vM8Ll++rDOO\nFhYWmJ+fD5VpsW2b2dlZ7VgaHh7WEoHXMmn2gx/8gAcffPBqT6snCCGo1+t89KMfJZPJXHGyrFgs\nUigUdM1NNel68uRJBtoyLKWU1+woE0LgOA6lUmnDJr2EEDz33HP89m//9hVtUJlfrutSKpV0LVQp\nJY1GA8uyeO6553Tt0XQ6rWtIXu3EnpSSo0ePcuedd177yV0ntVqNT3ziEzz11FNX3HZlZUVfx+Xl\nZc6ePYvneVrdQUrJ3NwcZ86cAXwFqLGxsWtqG/V9wTqZ25FMJsPBgwc32oxrIh6Pc/PNWy+A3rZt\n9u7du9FmXBM7d+6kh/7hdWVqaoqpqamNNuOqyWaz3HrrrRtthuEqEELorGgpJdlsFsdx9HMonU5r\nR6nrujq7XCmoqGxdNc5U2dWpVIqRkRGdHX369Om+2B8MzkulUrqMSi6XI5/P62dpvV7XY7NcLkc6\nnSYejyOl1EpDKiMZWskk6+1M9TwvNIasVquhOvWu62pFKCEEyWRSZxur0kvJZBIpZd+CTIIBgCoI\nUPWhYPulUimd4ZxOp8lms2SzWT1GLJVK+lzT6XTfg0iD17PRaGjVBNd19RhffZ6bm9MZ3GpfRTKZ\npFQq6XMNBkL2Q6VL9UvbtkkmkwwODmo595GREa0c6nkeo6OjoYDoiYkJhoaG9DkE27k9CNZgMBgM\nhnUmgy/hbuE7zR8H/rHLti8Bv7ZOdm0oxoluMBjWCYllnQKeJ9otuboTvVxO8dRTiyQS0S+ddqXI\n0Is/wK6Uoh1/ngfT01Cvd6yvS8nr1SpvuboT6ilLSws89dQXyeWGOlc2HPI/+D5WpUhU+0jXZalU\notrlBbcKRO/pM3zhAseeegoR4US/NDtLsQ9129aKJyX/dOwYNceJcKJLXl8e49jMMaSMaJf/n703\nD5fjqu+8P6eqeu/bd1+kq8WWJVm2kbxgFuMFG0gMZpjXIcz7ZiYBnEC2dxgIkxlg7DhPYN6sEzKZ\nyYR5M5MnCSEvS3jCkLAEHAOGsbGNAS/I2NqsXbpX9+qufXutqvP+UX1On67uvpKs7rtI9Xkeqbur\na/nVqdO3Tp3vb5HQ27uMiJ6fI3fwAHZ+rvUKnke1VMJv0a4COCJEV+ptni9nz87wla98Rdd9a8Bz\nET98Gs5Mtt7Y84J0osVie6H80CHI59uGKZUXKpSPHSfcs4SAl146iG1H6efaMTY2xn333dcwCbQc\n27dvb/jc7ZIt27ZtY8+ePasycZNIJHj3u9/NiRMnLnpfrey/2EnX2267jZyZn3oFufLKK/mX//Jf\nntc5XMx5vpzrvnXr1lUT0VOplO4z52N7uG0u5Pf0ctpGSsmb3vSmjkZPR0RERERc2sRiMYaHhxkZ\nGUFKyR133KEdshKJhE55rlJaK9FN1cQ+cOCAFuHUvgCuu+463vnOd5JMJimXyxw6dKjjtgsRlJTJ\nZDL4vs+1117L+Pg4QgiGh4eZnp7W61arVS3Y9vf3s2vXLu04kEgkdEYGJVYqh7eVTnNdKBS0c50Q\nQVnGU6dO6THE4OAgr3zlKxvquRcKBWZnZ0mn04yOjrJjxw6klPzwhz/suH2O42jRNp1O09vbqwX7\nkZER3Xdc19X9RDlnvPa1r2VsbAwpJQsLC3zyk5/UNeyvvvpqRkdHtSDfjWcDM2357Owszz33nE5x\nfuDAAd0/isUijz76qK4tblkW119/vR5fpdNpYrGYLm85MDBAJpNBCEE+n+/4OCydTrNhwwYsy6K/\nv59cLqfTsd900026zX3fZ+/evbqMgRCCPXv2NDjCJZNJ3e/Vb8eyLJLJZCSknz+DgKp5eha4tD1Y\nIyIiIrrHuwHlZf7faC+gX1ZEIvoK0u3Bz3ITpi8nNWdERCe54YYb+PM/v7gMH0KUaCsFZyxKb7yz\nfXTwcr8P4O2xGNffcMNF2fdyGR0d5Zd+6RdqkYItBOuYpHjn7ctGPsff+lbibb7LASPnsKHY5u9D\nrreX97z2tXpCYCURQnD77bfjOE6rVgFgFHgzBV5WXHgyRulf/NSyWy4nQ/2M43DDKvaZ9773PSwu\nLlIsNrZO0NUl4rpr4bpr2+/kjW8894GWuW9IwKbY8rvt2zesmgi7Hujp6eFnfuZnVtuMNUksFuOu\nu+5abTOWZbX69dDQED/7sz+7Ksdey6yHPgNrJxVqRERERMTaR0W5qqjsTCajs8ek02ktmEJwHzSf\n1YrFop4bUvNAZiR6f38/qVSKUqnUtahoMxI9kUjoyPlMJkOpVNL3RFNEz2azpFIpbNtGSkk8HteR\n6GYJmtVARaKHI+jNet0qqw+go85VlgAlcvu+35U2V20E6GxVSkR3HEdHYqsoeSWiJxIJLdoC2knB\nrEX/cjPxvBx836dUKmkRfWFhQdtSKBSYmJhgZmYGCBwHrrjiCqrVqm7rYrGot69UKlqE7kbmAsuy\ntHMHBM93Kppf1UdX59TX16cdBSzLYmhoSDsuQJBRSdVwt21bOyyshVJS64i/B26tvX8r8NVVtCUi\nIiJiPfPLtVcf+K+rachaIhLRV4i5uTmOHj3aVB/n1KlT5PN5nbLIxHXDZQYCLMtiYGCgyZNSeS22\nIpq4i1htNm/ezLve9c7VNmNZVut3ksvluPfee1fl2OfLarXNjh07mqJw1xJRn4l4OUT35OWJ2qc9\nUdu0JmqX1aVcLnPq1KkViQzM5XI6vX4nqFarTExMdL4meohYLMamTZs6NiHu+z4TExNBTfQu02nb\nJycnyefzHdnXcti2zejoqE6x3AnOnDmjy2N0E8uyGB0dbftsf6EsLS0xOTnZ9RTYKtK4p6enq8eJ\nqHOu1ObtsvKsVlr0c9HKnrVg43L16M8VsLKSY5ROtlXY7m5dh3O13fm0X7j2+GqnQle/LVPAb/U5\nvE1424iIiIgOkCQQRV9HEH10APhT4NQF7mcr8K+BKwjE1UeAP6e5RnbE+mYYuL72/kfAYeO7BEGq\n93mCPnBZEYnoK4CUkkOHDvHII480DbzPnj3L6dOnmZ2dbbldK+LxOFdffXXLh2qVpisacEWsRaJ+\n2Z6obdoTtU1ronaJiIiIiFgL7N27l1/91fvJ53OYmWFs28JxzEw2ko3OFP32HMJYTyKYzW3Bs+N6\nqe8HFXhMKpUF3vjGG7n//o90LC3rxMQEv/6BDxArlVASsXRi+JmepmwsVqmAqJTry6WEmRlYWGhc\n17Kgvx9qkZ9V36eSTvPJz3ymY+Li4uIiv3n//cwdOULKMR7pXTco1WI+R6bTsGlTw/ZSBquVysaz\nKZKsyBMz5sJcz6NgWXzqM5/pSCkL3/f5o//0n3jxoYfImaK8ENDXF7SdwvNalpWRAwMwOorZ18pl\naPRJ9ymVZvnYxx7g1ltvpRNIKfmT//pf+dHjj9NjCvO+jzx5EswSLbEYYnAQwhGntq37hd48FsdL\nZHQX8n2f+flZHnjgP3D77Z0pNvXEE0/wnz/+cfr7+hrbc34+6AgGlf4RpNOc3yrsQ9Gqq+XzZ/jA\nB34pcvTsAmZU6rmeAarVKouLi1pIdF1XR6o7jrMiAqNZ1zkej5NIJHSa93AkurItmUxi27Y+T/Xe\nTCW+0lHRyjmsWq1SKpX0d2abqijoyclJHZU8PT2to7+llMRiMeLxOL7vdyW6OBx9rt47jkM8Hm8Q\naPv7+/X7dDpNoVDQKdLz+XyD3SqC3sxm0A3U9bRtm2QyqVO8m7Z7nqfriKt1e3t7taNUKpXSkeGr\n4XgRFvDNz7FYTGePaBVh3iqTwGo7AqxDPgc8VXt/eLkVIyIuIzLAN4FXA/sIxM93AO8F7qgtOx9u\nAr5BkOj0SYJkp/9XbV/3AN31Ro5YSW423n+P4IHrF4D3A3tqy33geeB/Af8FmFlJA1eLSERfIaSU\nVKvVpsGS67oNg/PzQT1EtEoDFaX7iYiIiIiIiIiIiIhYKYrFIvv2bWVh4f2Yj5c9PT2MjAzVxUEp\n+eDQJ/g/e76CkPWJYdeO88U3/S4LPeNaFq1U4Pjxui4pBBw58hAzM9/u6OR4pVIhXizy39/xDuK2\nDVLijWyk9KpbwXQAEJB44Uc4J17CUDvhM5+B555rFNFzObjrLhgcBGC6WOT+J57QwkQn8DyP6uws\nv7F7N9eMGEV7zp6FZ54JFE4hAht374bf+70GG6WEH/3IYv8hoRcLfG4XjzHMmeCEhWA2n+dDf/d3\nHbU9PzHBr54+zV2mI0QsBnfeCbWUwkDgnPD444FCbhp+513ID36wQdU9eBCefbZ+ip5X5nOf+w8t\ns729XKSU5Gdn+eXXvIY3XH990LZCQKmE/PCH4cyZ+srDw4g3vzlwpjBt7+nR/aK2kNKGq8jvvEHX\nEKpWi/zu736UpaWljtmez+f56Xvu4V/9zM8EdkPw+tBDjQ0nBBP3/irl0XGErJstBBjZwxECpqZg\n797A10Ft/uUv/x4LC93PMnC5YAprPT09DemgzXkf872Ukv379/OXf/mXWJaFlJLx8XH27NmDlJJt\n27aRyWR0avFuCbojIyO6NnUmk9F/Q8rlckPGxXK5rEX1arWqs1SodO7Dw8O6Hfr6+kilUriuq9Om\nd5OlpSXm5+cRQjAxMcF3v/tdbdvGjRt5xSteoUX+F198kfe85z06hXehUGB6elrXIN+1axevfvWr\n8X2ff/qnf+qonUII+vr62LJlixbGVYrzcrmM4zi6zZPJJLfffrsuE+C6Lg8//HDDNVhYWMBxHCzL\n4uqrr9Z958UXX2ybLfNicByHVCql+2pfXx9CCBYXF0kkEjrjipSSG264oeF3cMMNN2inOuWkYZYR\n6CaWZRGLxbSzh3KSc12Xubm5hsjzXbt2NdxHTac0dc2U+N/b20tvby9CCHK5XCSknz9/stoGRESs\nQf4jgYD+EeD3a8teD3wd+BTwGpavYgnBw92ngRjwWgJnFQH8P8D9wIdrx4m4NNhmvD8FfJnAUcLE\nAnbX/v088Gbgxyti3SoSiegrSHjwE3kWRkRERERERERERESsfwTBo2X98VIIByHi9WgsJI6wSQiB\nEI0CkG3HsayEFtEtq/4v2BdYVndq9wohiNs2CcsCJK5t4zpxCEXjxhwnENoNwRHLCl7N6HS1vGZ8\nvFu1fIUgZlk1u2soexobLhCpDaQEx1F1V9VSnxg2CWGjRPS4bWN12HYBOEKQMPer7Kw5MjSci7me\nlPiWBfE4GP3BcYJN66vKhj7WOeMFtmWRMA9mWXiYcfEga6KfMM9HysDIBkd4iec4OHZd7JHSw7JE\naI8Xa3bghB93HIQpoqt+WjsXKQSOHcOzE01TqmYQqhDBaYSTCQSCYsfMvqwJp6d2HEeLh+Y8Uqu0\nz8VikVOnTmFZFr7vMzg4SDabBdBinRnl3Q3bVfR5OIW1qvuslpkieqFQ0OKvEtHj8bg+31gspqOh\nux08oqLLVR30UqnE3Nyc/m5oaIhMJqPb0fM8nn/+ee38orZXZDIZ+vr68DyvK+Ku4zhaTE6lUjqa\n27Isent7tfidTqfZuXOntqFYLPKlL31JOwv4vo/rutpRI5PJ0N/fr2u+d6MURziKXkW+x2Ixcrmc\njvhX/cqM2L7iiiv0uah66qrdu1F73kQ5dyh7zEh9MxMDoH9/iljonqxq15tZC1T0fzRnHBER8TJJ\nE6RxPwL8gbH828BngXcDtwKPnmM/9wBXEziqqGwPEvhN4BeBfwP8DtA5j9uI1aTXeP/LwBiwBPxn\nghT+BeA64IPAtcBm4B+BVwLTK2noShOJ6BERERERERERK0ixWOTpp5/uaGRfp9m6dStbtmxZ8eN6\nnse+ffs4e/bsih/7fMjlclxzzTUrEgEVZnFxkWeffXZN1AYNI4Rg8+bNbN26dcWPvdb7DMDg4CA7\nd+7sairUtYFseC9lY6pnX6pyVaEvpK83ldRfpfE5vElXECI4jmmIYU/zQknTSZrLl1unW4SPp99L\nfX4ACNkcdiIJ4goMIbqDOm57GvqCrDsiNPcCELLhNMK7WTGtIXQ9w4cVLHPdTUPVZRLUI7+72eim\nU0LYQeEcm5mv4fcR3edC6p6b6aRNIW+t1EA3bTBtamfbatSHblf/vNX3ym4zc4Dv+w3OAybdvgbL\n1dpu9X2rNPmr1U/CdcHD34X7y1rp08th9g/zc0RERESXuY1ASP8SzUPnfyAQ0e/m3CL6TxjbmHjA\nV2v7eSVB6u+I9Y9ZO1oJ6HcC3zeWPw58BvgaQT/bAnwU+NcrY+LqcKnP5ERERKwRZmZmeP7551fb\njLbYts2uXbsYGBhY8WMXi0Wef/55napsrZHL5di9e/eqlIuYmJjgwIEDK37c8yHqM8uzZcuWVRHU\n1gMnT57kN37jN7jiiivWXHSBEIJjx47xlre8hV/7tV9bcfuKxSJ/9Ed/RLlcXhWhejk8zyOfz/OJ\nT3yCETN98gpx4MABHnzwQbZv377ix14OIQTHGDEcLwAAIABJREFUjx/nzjvv5MMf/vCKH79QKPDx\nj3+cfD6v63yuJUqlErZt88d//Mcdq4e9FkkmHWKxFEGmP0DCli1x9lwvsVT2cyRptnJKvqqhJrpn\nOQwN+vRklvRSt+ySnpzBlYGzkQBc+zRBCbYOo3LH16KGBTZOpYDE0/ZIQMTj0NeLlkx9HzZsgK1b\n6yG5UiJzvZTHtyEHh0BCubCIn3im83YLAclkUPNcTcqPjMDNNzeI6IXx7Zw+3DiGkwhkscRY2jWW\nSfbuz/GjvBWcoRAsFheYXerw32JRq3+u0tlKGUSWj49DNqtF5sWpAX5Q8iguGrXGpYR9w8ivnwKh\n6kNLSqUsUvbpTOWe10W/hXQ6SMuuImsTSeZvuhN/bgEl/du9WXpGxrB7Mg2GeLk+vMFRzI4lJcRP\nHNK/CVktYS0tcu7smuePlJLiCy+w+PDDDX0jeeoUcUNMlwhOnYbFYuvDm0OCQiEot6B2p6oHRPpQ\n9wmL0OEU6cViUUd8q2hkNaYKR8CulL2m8BkeW4YF57BIuhpjZXVs1Y7q1fy+VX12UzA1I8NX6jl6\nOYG5XTuq8pLqGqjo+m5lKlgO1dbKLhPlpKAizFW5gnbC+0qK1e2cQcz+cK62XGvPhBEREeuea2qv\nreqe7wutsxzXLrOfF439RCL6pUF4kvn3aBTQFUvAfQT9wiZI6/5vgXKLdS8JIhG9g5gDPhOVEqld\nTfSIiMuBp556it9997vZ0OI3AsFf3GTLbwJELIa9YUPbsAfPdZk/cwa/Wm35PUCmdpwwPnC0p4eP\nfOIT3H333ctY0R1OnTrF+973EV56KYaefG7AB04D7f5eCGCYRoexOqlUmlyut+V3AKO9Ja4aXWwZ\n95IvlSgnEnzq05/WEwErhZSShx/+Br//+58nlRprOSk35MyxOT6hJ+ibcF1oV1PS92F+vl4bMnx8\ny6a06waqg6Mt2+bo0X08+OBHVq3PPPDAg4yMjJJItL4utiWDSKhWSBnU7iwU2ocSlcvBLHQbFlIj\n5FPDtApTy+dnufvum/jQhz50rlO5LPE8j+uuu46PfOQjqzKZuRxCCP7mb/5m1cYnarLp/vvvZ7Ch\nbuzqUywWeeCBB1qO9VYC13W59dZb+fVf//U1N9H26U9/etUiwZVA8L73vY+bbrppVWxYjqmpKX77\nt3/7ko88GhhIMTY2jBApILi13v2Tkl/9VYltqzhuyfPPvIVHX/qJhj5sCZ+7XjFNT/KUXibzS8iJ\n/41U93ABX0/u51Fh1MzuFIuL8M1v1iK1Jc7uPdiveRWkUsZKEgZ6gn96kYRbbw0EVSOdu9vTx+yd\nP4XbPwzA/MIU1Ucf67zdtg3Dw4GQr+wZGgpEdOPecuaExRf+wWlwXADJHdsned3mKb284sPPf2Ib\nT+7N1h0f/BmSmc931m7HgV27YNOmuuIaj8Mb3hC0JYCAY/tt/s2f9XL8VGj0/tnD8L++g1pRCJ87\n7tjFO95xs96d67Yd3l0clgWjo3DllfoAvi84/KFPUHEdHSWfEiWujh3GptKwebVvjMLoVu0AAJL4\n/r30fevvEbVGL7geiYmjnY1Hl5Lpv/xLjn7yk/WRoeMwftttDF53nV7Nl4JHHxMcDyUh8LxgyGz+\nGevvh23b6tnphQhE9Yju0C41tZSShx56iH/8x3/Uf1f379/P6dOn9TobN27kp3/6p3XN6JUce0op\nqVQqOvtSJpNpeKZcWlrS44dqtUqlUtHb2bZNLBZrKVavBIuLixw/fhzLspientb1zpVtg4ODui2z\n2SylUkmnOx8bG+O+++4jl8uRTCZXNLuTqi2vxHGVVh+CmuhmO/q+z/T0NDMzMzpd+o033khPTw9C\niBV3Fi8Wi0xOTgJQqVRIJBI61bvjOGzdurWh//i+T6FQ0A4AnudpB4dkMkkqlUII0fX66MrR1vd9\nbUM4nbvpHFwsFnWpACkl2WyWTCaDKmNwruwMEREREeeBmkxp9ZCu0m4PXeR+1LLz2U/E+iBct+VT\ny6x7iCAq/TYgBdxU+3xJEonoHaRcLnP06FGq1WqTN+rjjz/O1772tSYRvVwu67pJERGXMtVKhVun\nprjX95tkPUkgcI8SZJFshcjliF99NaJNKtTS4iLPv/QSpYWFlt9bwFVAq1gaF/iDcplqpdLi2+7j\n+z6zs0mmpu4Fci3WqBJkzlmkWbCUBK4BbyDIoNLcups2bWXjxutohS/h7t2T/NKbDmCL0LZCcOjU\nKf7wq1+90FPqGJVKldHRd7Jp05ubRHRfSu7oeY5/MfgQsVaRaUIEIvFLL7XeeakUzAiWy81CspT4\nyTQT9/4C+VveQHPTSD7+8QeortJsoe/79PcPcv/9v8XQ0HDzChIScR/bavPgXa3CI4/AsWPtRfSp\nKWgT6S6BA+NvYt/47U01R4WAAwd+SLn87fM/ocsQ27ZJJpNrLr2zZVk4jrOqTn5qsqvbE14XiorS\nWU3MmpdrBdVnVrNt1MRvJtMFgfUiWVxcXPV+sxJYlsBxLH1P8H1IJCTZrI9j18/fTiaoxpINzm+W\n5RNzpok7Pnqc4/hgVUHUxmYC4qJL91wpg/uiZQXvPbfmhBZKMx4uAu37gVgdjxv3UomIx5HxBH48\ngZAgY4nu5L4WjbXXgUCgTqfrIroAGYdKRTSbICVx2w9yiROcbbnqkC/F6oH1fox4ugu2O05goxrc\nxWL1tpSBguvFHPIyw7wXEg89B0r1e5QQknI5HLHYpXTjqhh4Q010gZ/K4fkxHYnuoRxjDbukBMdB\nOjH9O5FIBAKrWtHXUXTJA0CWSviu2yCiy2pVO48oXBcqHg2/UdcNhssqE736yYS5DP7UrRrtIpl9\n3+fkyZM8+eST+l4zNTVFoVAAgvtjNptl27Ztq5JVTNmoRHQzKl4JhEqcVuKj+s4UIlfjPlqtVrVA\nWywW8TxPi56WZZFMJhsi/FXwjPp83XXXMTQ0hOM4TTWxu4mUsqG2vCnmtnLGKJVKFItFvU5fX592\nZF3psbjruiwtLWlB3LZtbVcsFqO3t1eP9aSUnD17tuG6mBHhtm031BbvpiCt2lw5LkD9N6vGqGr8\nrhxLzL4ei8W0s8Nq/U4jIiIuOdT0d6nFd8XQOssRo/aYcJH7iVgfHDfeLwFHz7H+8wQiOgT10SMR\nPeLceJ7H3NycfggwOX36NIcPH24aEPm+v2oiTETESuMACVon4owTRKK3ezwWQNqyEG288IVlEW+z\nb7V9vHb8MDatI9RXFgG0i0SHwMKGQpWh7xyCM2wW0YVIYFlthAUJjp0kHY/hNBV0FCRjsRUph7kc\nlpXAtjNNIrqQkpidIGM7xESbK68mOtt9pyagwwiBLywSsTjVeKapWYWQbSNCVgrLskgkkiQSra9t\nOl7FaSei23YwUR2LtZ/xdJzGSWIDiSQRixOPZwi7vggBsVh8uSD2iIiIiIjLhuZ7SNMS2fab0FZd\nrhV9Md8rDO1dO+CtgUCyC9GfhGjINt49WgkaZurhbh67Cwjj9Vw9uel9uNj4CgmG7YTJ8OJWNdEj\nVo92acWhnjbanHdaS9GsrbI3Lpe+PVxPeqVZLp18qxTeYdtNUbXbLBe9HO4z4ZTp7erUrwSm+N3u\nX9jGsJ3LrRNet9O2t1u+3PHalS6IiIiI6CAqYrNVvbOB0DrLUSAYsvbRHI2u9hOOXo5Yv+w13p9P\npKEpgq6taI8OE4noHWS5NFPtvlPLogFTxOXCcj1dsv4mzC4Z1m3jhyLFIuqcq1nOdd85531Jtu03\n0S0totOYk0sq0gNoqFFpRtqY6T8v9YiO86kFGZ5cDy+/FAlP1KuoLDMizuwfsVhMO0ddDv2m00gZ\n+tsvVR+U5qJaPw0rdDQLhyrcVe80fIAOo/bddCLLYCqLTSIogY+ZrL125bdmtu/yNi9/SmHxxNhr\nt5pd9Q2z3YXKIR681s+uVds1djZls9kaqz4UCfX/ps/1LxobuWuN3tguwnhf759BDgYjJwTUPofN\nUvXPw6ZHdIZwTe7Z2Vmd8XB6elpnOZFScuLEiYb7+fbt27n99tv1fq6//vqG8ZJat5vCnRKPfd9n\nZmZGR3Q/++yzzM7Oahvm5uaYmZkBIJfLceWVV+osTZZlkclkGubSzFrv3WZgYIBdu3YhhKBUKrFp\n0yYgaDfXdXnooYf0ebz44ouMjIzQ09ODlJKtW7dy1VVXMTIygmVZpBrKg3QHZUs6nWbjxo1IKSkU\nCrzwwgs6Tb5lWXz+85/XYxw1NlLtnMlk2Lp1K6Ojo3pfKrq+W84A5vWNxWKk02kdkT06OtowHqtU\nKvpcFOb3iURCt0M8HteZkizL6pr9Kmq+WCzqbASTk5MN0f0TExMNdvb19ZFO10vxqTr0Ct/3dc33\niIiIiJeJiihukbqSkdA659rPzbX9hEX0C9lPxPrgEDBFcL1zBGnaW6coDRgz3k+3XesSIBLRIyIi\nIiIiIiIi1jRqwlJKqdOUqvSapVIJIYSu3wgwOjpKPB7X6TYvZcyJdjM9pGov27Z1GkszjepqpUdd\nKXzfp1wu67aZmZmhUqmwtLTE0tKSTu+p6mUODg6Sy+VQdTcv9X7TaRKJoEZyPQ04pNMC15PU04VL\nUikYGGjUlC3p4T/9fapuvSSPlIJKZhiZDibyEYLiVB4pulB6x3FgbKxuVF9fPb27SanUmMNaSjh+\nHA4dMk5cQiqDzH0Tme0Nli3Nw9xMx82uejb7Z4bwErU2kpB1cmytePUMSwJsLHI5q6HNpQTXTnC2\nmtNKqithcMRhy5Z6Km/XpeOZZXzLJj+whZnRq1EOeb4dY+JIFi+R1A56p0+63DL6Etc4jcKH37uE\nP7TRCP2W3LilwsbpZ5G1ha5XIV2e7azhECjH8/MwPa1TrkvfYnpmM0Uvpn0/snEbd2MOP2b2FyhZ\naebmG//u9pAiY/Y/14UuCG5J26bHsurCuWXhnz1L/qWXtPrtI9jif5c0Q40iejxJfscNup9LCT29\nNuPjMSyjJnpPTxSt3knCIroa7xw8eJCJiQl9D5+cnGwQxrdu3co999yjxwM7d+7U35mR691Ob63+\nzc3NadH/qaee4oUXXtD2uq6rhdzNmzezdetWvQ8lPisxUb2aDgHdQghBX18ffX19DcsUP/zhD/nb\nv/1bXNdFCMHc3BxDQ0M6Jf34+HiDGN1tlFAspSSVSunjzszMMDs7q2u1e57Hk08+2dB+vb29WtDN\nZrNs3LiR8fFxvY5Kl96tdrcsSzvCqnEZBM6NGzdu1HXnq9Uqhw4dahCnVf9QbaBqqKvtVTr4bojo\nZh9XY07P86hWq0xPT+s2B/R36re3a9cuenp6mtrA3O9K9POIiIhLmh/WXu8E/iD03Z211x+c535+\nqrbNi23280MiLhU8gnqy7yFIe3sX0K7Gawx4fe295BLvB5GIHhERcUmxbgOqI9YW0QNrRMSaxIyo\nVq/LZfo5X6F4PU9StWoTM/1ou7a6lAV0k3Dkfato/Mu1bTrJwABcd129goqUMDomKJdsKoYWPTIS\naNQNTVwq4334d8gfPagXVTdewdk/+DTepu1AsP6U24M89J3OG9/TA7feWhfCe3thcRGKIaf7I0fg\nzJn6Zynh61+HRx9tOCEJyM99Fl8IBOBLCWOdF1IWykn+v+dvoP/ktfq418y7/MotJTKJ2t80AWnh\ncNX2cHkeQcEZ4kf5wQa7X/Eqi/HttTVE0ATf+15n7facBMd23s0LN92lh1uVCvzd38cpFISOQh+z\nJvid13yBfqvuXIH08V71Oio/8WYw6oqnHv0WmS//pb6GZc/jy7P7Oms4BB4FBw821HN3fYdnJ69l\n1ksG0d0ShofiXP/Kq4j3GWH9wMxJi/37zXTPgi29Ywy/7hb0E0y1Ct/9bsdNH0om2VqLygyOLZna\nu5cTzzzT0Dfe0vu/iceMAlhSwqZNyAf/OvCWgcDxIZbAy/brvm9Z8NRTkYjeLZa7l7W6Z4VTYYe3\nXUlapZpvNTY5X7tW8hyWS+duZq1Za+OGVtddsZ4jnNe6A2irMabpuHK+9l/obyIiIiKiBc8B+whE\n0HHgZG25DfwroAp8MbTNGwEX+Lax7O+AjwI/B/wZ9ZHtduC1wBPAsc6bH7GK/L/ALxA8nDwAfJ1A\nXA/zfwPqIfcxYGJFrFslIhE9IiJixRDxOJYQLRMZWr4fTBotVzvKjP4J47oIKdvWIVwfjx8q92cY\nC8tKEGRRaVUuwiaZVGloQ+0nJOm4S8qd09E5JlKCU8njFQvNk15C4BeLqy4oS6mDfZqWS2EFE5m0\nWEEIpG3jtek3wnWDSZxlaqYLJMJ363VN1baWSna5ivgeMr8IiRbRSgK8TBzirfqTAA+kdJDEaJcm\n1fEkVrXaZja0dlFE89eixbKI82clJkvW48SZGWVdLBbJ5/M6El1FpJgTnalUSkeil0qlZfetJnNV\nGkm1j3BqxbWKSk+uUkmqSXPXdfF9H8dxdBSPGYljTv62Sv1uYkbIADrKZy2j0muqaB6VtSCfz+v+\nY9u2To9r27aO3nccR0fsq32FUW1iWRaJmrCkoqjWQ7/pNEIEAropoltWs3OjZQXrNHQzGygWkIuL\n9WWFIr7l4DtJvX9pO91J0S1EEI2urpt5Eia+H0QJK+PV2DV8r5QS3ELj3XV4qONmS6DqO5S9mPG5\neSV1bZp+2cJqGMlIGTRDLNYYFN357izw7Ti+k9JN7HlQcaFUqV1rwHMEWadKn21kH5CSSlJSysQb\nRPRETBL3yiCDZb7vY3VrnOb7gcE6Fb2F50k8Dy2ie74Ay0bYoXTnonFMG6Sht8CJNS7scKMLwBIC\n27Ia6917HrJcblg37pVIWcY8mZQgSxCXwb8argMVYxip+llEdwiLcOcS2MLfmWOklRgHhgXz5QT/\nVkKjuY+XI7S/XMxSMMvVOzc/L2dTOPq5mynR1WsrIbfV+3bL2p3XSjyjmFkS1Kt5Pdo5Qprvw5+7\n1d+Xc8hsZ0sru1t9VsuiWukREREXyUeALwBfAv49QQ30XwOuBz5OXVhXfA2YozEF/AvAXxGIqn9O\nIKQPAH9U+/7+7pgesYp8H/g08LPA64DPA+8FVGo1Afwy8Ie1zz7w4ArbuOJEInoH8TyPubk5nWJU\nIaVkaWkJtybYmCyXoicWizVM4Kl9JZNJHMdpOYF5OU7aRawThGDgjjvYct11LSU7+/hxEo880jZn\npBSC8qOPtt2967r0FYtk2x0eyNL6j161zfKVJQFsAfqbvhEChobeh+O0+1shuO++IV7ximSz3m1J\n0t95hL7PPNhWDE+9VObod5cIuzcIIThZKlEdHGy53UpRLMLCQvNy3xeUto0j73oD2C1yEAhBad8+\nDn/yky37VSyRYMuOHSTapOwVsRhD9hx9c3ubS1gKyHYjTegFYB06QPz9v0IiFm/6zk+kOPXOf0fx\nFa9tWX7T9yxmvVdSSoV+j7UPUvrc8MLvMny4fUhR8u5r6d8jWzU7PT0QmpONOAdSyob7vilwdppq\ntaprQqvafWs90mFpaYmnn36aarXK97//fZ26dG5ujoWFBYQQpNNpPeGUTCb1xK05XlITcWYUVE9P\nD8PDw6RSKfbs2aNTQ15xxRX09vauyvleCDMzM7o+6nPPPafF87m5OarVKr29vfo8YrGYTnuvnASW\nmyRWNTJzuRwjIyNIKYnH4wwNDa35PlOtVpmYmMDzPEqlEl/96leZnJxkenqaM0Y0sTpvx3Ea0t6b\nDgatJr37+/vp7e1lcHCQO++8k0QiQSwWa0rRGRERERERcSEIIZiamuLBBx/UpUWWlpb02C2fzzc4\nCM7OzjIzM6PHQM8++yxTU1P6/pbL5cjlcg37h+D5eXFxsaPjTSEEnufxsY99jGQyiZSS+fl57bA2\nNTWlx21Qr50OQd3o48ePa3scx6Gvr69J1JVScvDgQe67776O2Q3B2PBTn/oUjzzyiD5O+NwUMzMz\nHD9+XNterVaZn5/X2/z4xz/m/vvvb6qFLqXkxz/+Me9617s6Zrdt23z961/npZdeAoJ5SVU3vFKp\ncOrUKV3XXDmjmjhGhopYLMbExEST3UIIDh8+zN13390xu9XxvvzlL3PwYJCJxnVdyrWHSDOdPwR9\nJZ/P69+BOnfzupiflVOjEIJDhw5xzz33dGzsalkWP/jBD/jgBz+IEALXdSkUCnrMuLCwoNtc2W72\np+985zu69jsEtedjsVhTnzt9+jRXXXVVNM8bERHxcvki8CvA7wMP15ZVgT8lENjPl/cRRHy9m0BM\nh6D+9TuBb3XE0oi1xq8A1wI3EqTzfytBdoNCbbnyEJfAvwMeWXkTV5bV140uIcrlMocOHWIhpPZI\nKZmYmNAD2fB37chkMgwMDDStPzQ0RDKZJJVKNW3vONEljVi7pMbHye3Zgwj3eyGCsJcnnghCXlrg\nuy6lU6dAtoqnDtyekrTOLwKBzhej9R89Sev475XFAXqAXNM3gTg0riMJwyQScPPNcPvtzTq5tHyc\n43M4019vK6LPTUtOHW5eLoA84N9yywWdSSeRMugSrYLJfR+8ZBY2b4ZWkS+WhTs1xdzkJLJFv0rk\ncvi9vUHa1hYHFo5DyqpAeYawEi2FIOYtH93adRbmsb//FHarX0QqQ+GO+5jbbLUW0X2LCX+UJae1\nRi5x2TVThJMnW0dDSYlTXCSebP49CgHxZl0/4jxRoua5JnnONxXg+ayz1sVQqNdEr1arlEolPXlc\nLBa186JZL9PzPD3hpER0UwxVIqnaJpvN6kkwFb28HiI/lJ2e5+mJx2q1iu/7lEolKpUKiURCT8L7\nvk88Hm8Q0dV+FGZ/UOt6nqcnALsVRdVplK0qUr9UKun+UigUGtaBxswD4Sh9M3pfYds2tm2TSqWo\nVqsN/Szi5RC1W8TLJ+o9EZcSqVSK3/zN32Rpaanrx+rt7aW/v9mJ++WSSqX4wAc+wOTkZMf22QrH\ncdi8eXPH9mdZFvfddx/Hjx/v+n38He94R0dtv+eee9i+fXvX7bYsi23btnV0n/fccw87duzo+thS\nCMFVV13Vsf2Nj4/z0Y9+tEEo7wZCCDZu3LguMkBFRESsWf4H8NcEYmgMeB4422bd1pPOUAR+nkB4\n3wWUgKeBZqEr4lIhT1AK4E8IUvnHgZtD6xwBPkBQQ/2SJ1JcO4zpSatoV4/qXLSKQlOTvauZYiki\n4mUjZf1fq+XnYLnefbn3fN8P/jU1LQJfBqku200xtqsj3y41/lpCgMqF2YzqV0Isn+K/Xd9rWN4k\nFa+hnOUt7BeBfe1MFKJ+fdu2TZD7s/V5rpVTv4RQ0TmnTp1CpSk/fPgwUkoqlYqOukin02QyGeLx\nOGNjY3pMkMvldBStisJIp9OMjIw0jSXMdNNKfF3reJ5HoVBoEIkBEomEjrI2I6rz+bwWfc3lqh3N\nMZmK7spmsywuLuqx1npoF4BSqcTCwgIzMzOcOHFCp71XqcnD9TpnZ4MsGuG2Uf+qNa8lIQRjY2M4\njkO1WmVgYKBhf2sdy7JIJpNBWmfLYsuWLWSzWXp7e3U0frFYZG5urkFwh0bnErOdzL43OzvL0tIS\n1WqV2dlZUqkUsVhs3fSbThOUXpEIIY3PQc3nhtup7+pyIHpbv4q0YmDUYZZ2rOn21i19QEqJ66vf\niAQpkGGvTAH4AiGNO6cUWMLGshvnm2Qth3rD+KpLYwZz7BcMeyS+L/F0W0l8KRGySmOjByVxpLQa\nTFPjSbXsPIfoHUEgsUTgnOcDAr9FTR+J9D2kV9Hp3BGBkVIYY5ZujtHCzzNSIvAR0qsfVgpc12ry\nDfa8+jUTovb3xZfge+jr02pA3wmzEXhY9e6LbCj1VO8yoYte+yw9rzGzk+UhvbJua2GBXCdOVmsd\n27a5ZRWdmC8G27bZvXs3u3fvXm1TLgghBDt37mTnzp2rbcoFs3nz5o6K8ivJerU9nU5z5513rrYZ\nEREREedLCXi8A/uZrP2LuDyYB95FkKr9bQT1zx1gCvgBQZ+6bBwpOiqie57H2bNnqVarpFKphtQ7\nlwuRiL2+8TyPpaUlKpXKinh+R0REREREKM6ePcvTTz+NEIKZmRm+9a1vIaVkcXGRarWKlJLh4WGG\nhobIZrPs3r2bWCyGZVls2LBBp82MxWLYts3g4CBDQ0NNYzEVQQvBuKW8xnPvCyEaalqrdOUQREon\nEgktnEO9jE6lUsH3fS0KmyK6qiEOUCgU8H2f3t5e8vm8jlL32pQXWUuo67ewsMDc3BynT5/W1zOR\nSDSlufQ8j3K5rJ0zlDhsRpmrdrQsi0qlotvYrBe+HjBFdNu2GR8fp7e3l0wmo1P/z8/P675QLpcb\nBHAz1azKWuC6rl6nWCzq9/Pz8zrqfz30m25w/HiRr33tLELUU5EsLibZuTOHZdUcEoRk4HvfoPfg\n0w0ipy999r/+vZRvFdQ0eOz+PvrGRkhmgs9CQCrVHW30TCHLn/7wFhwrEMOLbpzpah8+dSc8CaTc\nPpJewbBBcv2mTWy/7583iJG2rDLCEXpEOdhDqYRz4EDH7a5UJIcOVUgkVGYcSakAT7+QJl2rVCMF\nxGfPsPNHX27wf5PA6Q2v5OzwLq2eSglPPulx5Eg9G4rr+pRKnRV0pYR8HubmDD9I3+fe15xCeF5Q\nV1xAdvYY2a8+DktnGzZ2jp4g+eyzYFwdf2ycwk//rF5Wcau4i3/VUbup2cnERFA8vvb3MyYc3rTx\n+5ScbE0YhyU/xV//+S5KfmPJoKkplxMnVHqlwNY3D73AK7Y+VO9tnge1tMydQgrBc9t/iqFNrzHO\npcoVz36W4aP1clkScCyrqbi5NzfHzG//tl4ugVgsTiaTQwjlqAX2k4/Dts6lqY6IiIiIiIiIiIiI\nWFWOAv9ttY1YbTo6C1YoFHjsscdIpVLs2LGDXbt2kUgkzr1hRMQaoVAosHfvXk6ePKnrMkVERERE\nRKwkKgpWiRhmfWb1XqWeDv8z1znfVO35IMsVAAAgAElEQVTnkz5+LWBGS/u+r1O4q9fw963WNd+H\na4G/3MxBaw3zei53LqodWi1Xr+G+eK59rjXC/SLcJ1T0ubkcaDrf9ZLCfjU5c6bMwYPzmI+X/f29\nnDnTg20rER36n/wemUf/VgtvAK6TYOIXP8fiwNZAQJWQTAqGBgSqLKsqFdKNP1WzpTR/t2+3dgCY\nm4OXDgrC/hC9/aPketDarRDw9rffgPV6qaN4JZCmxGbrKXpEHhCUFxexZ2Y6bne1KjlypEoQABBY\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97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VBHUqlaKvr49cLqdT6S5X\nd/ZSJ5l0GBxMYVlKuIFYDGZm6uMyX0LpxnHY3Y9587CAgd4E0jHGHksesRdnEUt1UdUpLnbFeS0t\niuxxfkzCCkK63cEkpVt8qGvoCAEPP1wXz9U/y2pOlGQ7Nm6mj0ptftz1PKTd+cfueByuvtrmiiuC\nfUugp7rA0EuP4+Tn9DI7cxOMjTUMkIWUZPMTZF94tH4pYg7DV87A67yayAszsx6f/4fOtrkloDfr\nMtJXCTqFAEplmJ0OBHJlp+cFA0Dfbxzc2zYUi/XP0scTNpVyPTmO63ZJz5USb2EBT8oGEZ35+cDe\nYCUSsTRX75C4fTSI0f3TZQYH5hDmCLu3D4aNlNTVKrQa210EAhhimq0crf/yBDA6ANlRYz1B8ZpX\nUsgO1zeu/ZaHtxjl6S2Jk88baRAiuoG6p0gp6evr00J5b2+vdiyEILV0Ph+MvdX9XUUXq/uVEkJd\n16VcLuv33bi3+77PxMSEdsr7zne+w9mzZxvOSdk2Pj7Oli1bkFIyMjLC2NhYwzi11T21W6V2VJS2\ncqY8deoU+/bt0221tLSk7U4mk/T29ja08fDwsLbXcRyGhoZ0hqNkMqmdNbsloqu2NUv7VKtV5ufn\n9bW2bVtnJVDnfPLkSd23PM9jZmZGb79p0yb6+vq0iJ5u4bzdCdS4U5UbUG1kCs0X2lfNqO9ujM1U\nVjDf95menuahhx6iUChQrVbZv39/Q3+JxWIN49CBgQHtPCKlZHh4WJfcuvvuu9mxY4ceg0ZERERE\nRKwwMeAVwI7av3Dd7jngALAfeAHovJf7GiS6I0dERKwYgmDirEXyxiCyQc1ItkJK8GXbYHEhg0lY\nQQtNj1qsbLv9L3fcFaWV9QAWsnG67QJ3e34P6q3abS0jg44T/Gv3YHwe17XdeQq177adbg200HJ9\nGtE2c0PAMuem08Au83uEZdpgDbTNOsecKDIFY9PBzkwdab6a/8yIW5NwWs31RtjucOrSsBNCOEV7\nOE252Zbh1JzrtY0U4X4S7h/m+Yf7iTnZfSnTymHV7Dft1r3QWqiXOq1uSeZtNBjlyJq62ZArWmf3\n1pvL8Mbd7oNqtCiDpNc+YIiwYTOWGx6sJFI2isWy3sp1WgmdepyAMYYSgYeiT739ZYuBe0cMxxhr\nGHaGr7kRbb5WaLrsYbtlsJaUNJSakgQ+A+p7jaTx/Lr2XNLmOSM8PpCAH1oz9HsIlq2da3K5YDp8\nmaKgeZ8OR7y2eg0v6xatxhvmd4p2ti9Ht2w/137NsWLY1nbvz2e/nabVNW5nQ7sxTLuxYbc41/Vf\ni2NR0952z2aK8LNKq2e4Vvtdi+cdEREREXHJcSNwL/CTtffNEX6tKQA/BP4R+CLw465YtwaIRPSI\niIiIiIiIiDWMOVHqOI4W9lR6akBHGKn1FxYWKBQKTE1NoVJ3lkolqtUqAwMDOm18vEWpg/VIq0lC\nIQSzs7M88cQTCCFYWFjQdc/Hx8fZuHEjvu8zPj6uayiqevOt9rteUSn9zZSo1WqViYkJHSVz4sQJ\nTp06pSOkVJrSO++8k6GhIcbGxi5JodjMcGBSLBY5ffo0QggmJiaYnZ3VbZNKpZBSsmXLFm6++WZ6\ne3sZGBjQv8F2aUgjIiIiIiLOFzV2q1arSCmZnJxsKDNijk8WFxdZXFzU9/nBwUG2bt2q9+N5HolE\nQkdUq9TqKm15pxFCkEwmdWSt4zh6LDI7O6uj5gHy+bxOmT44OIjrujoSPZFIsHnzZn2uiURCj9U6\nXd9aocq5qLI3ql2LxSITExN6PbO2PASp6GdmZvSYPZFIUCgUdASyGm97nteVjEeVSkWP6SqVCsVa\ntg7P8xrSs9u2Tblc1nZ6ntck7rquq1P+x2IxMpkM0L1nhnK5rDNnqSxaEGRSmp2dbXAINrMnCCHI\n5XL6XGzbZnR0lEQiofuPyqTUjf5SqVRYWFjAsiyKxaIuoVCpVPQ1UL9JM6uEEIJEIqGjzNV5x2Ix\nnU5fbVupVC7J8XeIVwEfX20jWtDZlDAREd2hVkuI97A2EomGuXa1DYhYlj7gPuAXabxWJ4DHgYPA\nEWA2tN0QcAWwE7gFuK3277cJBPX/CXwKWOqW4atBJKJ3GN/326YYbTURqx5qwtuspMdnRERERERE\nxNrFHD+Yaf3Ck3cqBXU+n6dYLLK0tMTs7Kwea1SrVT2RqkT0S0nwazXOWlpaYv/+/QghKJVKejJz\nYGBAi+gDAwO6Lc3JqktBQId6NLmJqpE5OzuLEIKzZ8/qvmKmNL/22msZHx9vSK16vlFi64F251Kt\nVvVk+NzcHEtLSzqKTjmvDA0NsW3bNtLpdEOt2ss1nfsFIULRzbWAaKlea8uaomZXqN+Z6a7V+ws9\n9IoF0GMEbJv2YgR4q5XUiq0i01vu0PjccZtD9lzkMQSi4aS73e6izfuWKzbYJBoXttt3t07gnNe+\nvhqhLtAUbN9+84gO4Hke+Xwex3HwfZ9HH32UM2fOIITg4MGD2tELAscvJYwD7NixgzvuuEPPKd10\n0036Hq7EViXcVSqVjttuWRYjIyOMjo7i+z6ZTEaLjXv37uWZZ55puPeq+2Ymk+HKK6/Uda83bNjA\ne9/7Xp0SfXR0lGQyqVOrdxopJbOzs0xMTCBlUMv9yJEjWJbF5OQkjz76qF63WCySz+d1u2azWbZt\n26bH4j09Pdx00026VNOb3vQmxsfH8X2/4Vp1yu65uTmOHz8OwPz8vK5zbqaVB3Sd7XD0tLkv1Z8s\nyyKXy2m7e3t7u1IXfWZmhpdeegkhBMeOHeOJJ57QNekfe+wxfUzf9/V4TJ3Lnj17dCmjdDrN29/+\ndkZHR5FSMjY2plPRLy0ttSx5dDEsLCxw6NAhLMtiaWmJ0dFRPM/TzhcTExO6nVXadwjuf0ooV2Qy\nGYQQup765OQkQgjm5uYuh3nh62r/IiIiLpyB2uv/WFUrItYbPcC/B94P9BIM7R8BvgB8GTh8gfu7\nGngb8NPAa4H/DnyMwEHqvwCdHfisEpGI3kHi8ThXXnllywF9oVAgm802LXcch9HRUWKxWNN3V111\nVcsalJfKxGVERERERETEy8cUNcNpuhXtUgG2SzV4KWGmbFefw6lFVWr88HaXA2Y6+7CYHG6nSyGt\n/YWi2sZso/Dv7FypOy8nXDdPsXgUIerOPbYNCwv1dSQwcSbP4RPFQPQ08BaXwLbr8mKhgHVmElEo\naOXuzNwcfhfauOz7HC0WiVkWSIk/P0/p5BFIprVaaFlB2WvXrQuJatnkZKgmes03ST3eLSzMUC53\nXjRxXZezZ0+SSqW1iL40O0Fc+pjuUXalQmJiolGYlRKUGGKmTJ+aguPHdR3y+YWFhon+TuB5HhNT\nUxw+caJ+7EolaEzzOdrzAhvD17xQCNZX+D5zCzNMnjqMkKK2aYViMd/x36QvJVPAMerZzYWUpAuF\neo+WkmpsnjMTx3GX8g3b52dPkp+aChLuK0G7VAo6Vo2S65IvFDoaTiSl5GyxyOG5uWahu1yurwec\nmjxOcTFfr+Uug77c4OtgSZzpM8Q8D2EEA8xKiVHdPeIiMe8xZnRwqVTSkaoQzDWZ4maxWNTieDho\nw/d9HQndjSh0henAZ44zPM+jUqlo280gFCGCuttKiC6VSg22muO2bt5vw+NEQDujKkxnBKhHeCvb\nE4kE1WpVOwB0OzV6eNymIrbNLETmeubnVvtSryrDTjedBJXN0Bg5XyqVWFpaahDRVX13COZSVdYA\nCPqc67o6un4l+ol5LOWooF7NdjR/d6pu/flcl8tkXPkNApFlrTEC/PlqGxERcQ7mgM3AR1ibkej/\nB/C61TYiooF/BnyCoN8sAX9U+3zoIva5r/bvDwmcov4N8PPA7wG/QBDp/p2L2P+aIBLRO0hPTw8/\n8RM/0XKg89a3vrVt2qZ2g9F4PN5SXIfLZ4I3IiIiIiIiojXnqn+pJl3VRI1KbxiPx/X7SzFqVkrJ\n9PQ0ruty8uRJTp48CQSTbyrV4s6dO3nlK1+J/P/Ze/M4Oc76zv/9VF/TPT33odE9Oi1LliXZBvkQ\nGNuY2ygBE3MEG4PZBMLml7CJd8lmsyFAwmYhCSFkE1gwECBA4EdsbMAOYDBgkA9syZata3RrNKPR\n3H13Vz37x9NVXVXTPZqje2Y0et6vV09P1/mtp57qrno+30NKVq5c6QyMLvbBKjuNazqdpre3l/Pn\nzyOEYHx8HNM0CYVCrFu3zom4jsVihMPhRZWxoBx25JXdD86cOeNEyp0/f94ZwO3s7HTSuS9dupTG\nxkbq6uqcMgtwad6jd3Z28vKXB8nlPuOZLoTSQd38xy8lP36izEb87SYBs+ARUAuWxUuuvbaq/bGh\noYENt9zCp/v7SxPzGeQX/mnCsqYJr3qVd1omAz/60cTtGoYraldK1q1bXdVUuHV1dVxzzRUcPnw/\nPT2l73FhFjDe9EZPu4lAAB59dOJGOjrgbW8rfRYC9uyBp592JkkpWbVmTdVsF0Jw5fbt/HzvXn61\nf39phpReYRyUN0IZJ3TicVi+3DNJZvux/v1vPdOWLWtg2bJlVbEbirbfcANPWxZ7/c5XtspsY+Ww\nvv+FCf1aSAth+S6KMpH4kY6Oqtq+YuVKfrBiBX/rjyJNpyfs2zrxBWSZ7zHP7YIAkc/D617rWaYQ\nCLBm/fpF/5sx15Rz2LIsyyPOllseKOss6F5uLpjM8cwvHNpZX9xOfOXE4Lm01S1O25TLJOmu++62\nfS4EXduGcsfgnm+3o/tZwf884Z9ea8qJyeUcGSoJz36nz7kSoMv1XbfN7v5Qbll3H5rK9bGIOQV8\nd76NKMPq+TZAo5kCtrvy/6bk37mQWIkW0RcKIeCvgT9APWn/X+BPgIEq72c/8Luo1O6fBN4CPAp8\nGPgoC7OfTgktolcRe8CxHO6UqxrNpUzF5wBZfKCo4DwnixE2M9p2aRdlI5jkgnTY8yEE0jAmNIEE\nmOUD1sJ/OJNIWa5nWKVzV+kYpCylCp2wVSq2K8V5k5s1z+0mL3DsFM9theOY9bCI3ba+LUlh27TQ\n+9XFizviwl0H3V1rz04JmMvlME0TwzCoq6vDMAyi0WjVUxouNE6dOkUikeDIkSMcPnwYIQTBYJAV\nK1YQDAa55ppreN3rXgcoYTlbjMSby0HD+SCXyzE0NEQqlaKnp4ezZ89iGIZT8zsajbJ9+3YCgQCh\nUIiGhoZF31dsEokEY2NjCCE4dOgQjz76KHYd1LGxMYLBIDt27GDFihVYlsXatWtpb28nGAw69V4v\nVdavX8+nPvWJOdlXIBDwlLWYLR0dHfzPD3+45vdCkz0nzoT6+nr+6I/+qKaRpDbVtF0IwT333DMn\ndgMVHdJnghCCu971Lt55551V2+ZkVNP2HTt28Hef+cyFF6wC1bw+L2UMw3BSgUspWbJkieOwlU6n\nPSm5/encV69ezcqVKx0RrqGhwXESsyzL+b2q1bmyo5ftCOb29nbHcXPjxo0UCoWy6cRjsRjLli1z\nornb29uJx+PO/a59zLW8T6urq3NqgLe1tbF8+XLsGtZbtmxxji+dTjt1vEF9J69cudKxvb6+nmXL\nlhEOhx2nRLvmdS0cWO007bazbCwWQ0rp3KPY8+x+ZdtQKBTo6+tzMhfYEdP2uSsnTlebUCjk3Gs2\nNjY65ZaamprYtGmT07ftFO/uiO7169c7Y611dXU0NjY6zo72uahVfwmHw05N9nA4TC6Xw7IsGhoa\n2Lx5M+3t7WXTuRuGQUdHB01NTV90GKsAACAASURBVM62urq6qK+vR0pJU1MToVAIuya9RqPRaDSz\npBn4DvAKoA94J/DDGu/zFPBbqBTvn0OJ6DuAtwPVTXM2R9TsCSeXy3miMuYaO2XVfN10SCnJ5/PO\nzc98YKcWmq8H2YXQB0zTrOqAlWbmSAknT3qzNLrnnT4c5PSRJoKGNaFOJhJkIES2NQOifH9KpQRP\nPRUhlTLKaoahgMF3Wq6gtT47YV7eynFkdA8vn9mhVQfLUuFMZQYVBdDy2tdiVRqgLxQIP/44sre3\nvDCazcLwcMVdx02Tla2tZeflTJPgPEaqCmmx+ddf4foTv5owzxKSrlgS4+Uvq6wIJxLwwgtlZ4Wi\nUZZfcw2F+vqyQrQQglgg4Au9cWbCL34xZ/VZy2IYEIlAMbLWjZXPM3jkCOme8hl5BFBfV0eo0u+D\nlIh9+9S2yw2YCIPsda8kd+3NCHztYwgGA+eQnJ7mAWmmils4d3+2B7dsUdhO5WgvEwqFMAxj0Yp9\n7pSKIyMjjIyMMDY25gxaBQIBotEowWCQUCjk3B/NlZgzX7gHPe06ou6XEMIZXDcMg5aWFkcUXqx9\nxcbdNplMhkQigWEYpFIppx5oPp93nFSi0agzyBmJRDwiwaWMPSh/MVJtcXsuuVgH1y9Wu+Hitf1i\nvkYvVSKRCKtXr6atrQ1Qwrj9m3Xq1CkGBgac3x47hbUtenZ3d7Njxw5nW3Ztb1DCX1NTE4ZhkMlk\navI7L4QgHo/T2NiIlJI3velNzrz3vOc9FSN0/QK5ZVkeJ8fx8XEKhULN7k8Mw6C7u5tt27YBcOWV\nV14w+ryS7fb23P/b6eyjZZ7dZoMt8tvtHY/HHQHXH0EfCAQcsR9gfHyc++67z6nBXV9fz+7du+no\n6HC2bZcRsO+zq01bWxvr1q0DVEnLl79cjcq4zz+o9s5msx4bbBHbXj6dTjvXQq3stVm2bBk33HCD\n8zzh3te73/1uz7J+O/xR/2NjY86xxmIxotEoQghaWlou+XtMjUaj0cyKNlQk+FbgF8DtKCF9rvg2\n8DRwP/AbwCPAq4HUZCstRGqirlqWxcGDB1mzZk3ZOuBzwblz50gkEqxbt25eRFzTNDl06BAbNmyY\ntwfWoaEh0uk0q1atmvN9Syk5ePAg3d3dNDQ0zPn+Ac6fP8/IyAgbN27UN54LhNOn1as8If5DNqAU\n9HLx1gaQKTPPxkDKyoOggUCAB5dtoqFhYnystNIcG2ub38BiKZXY7TdCSkQoRPOtt4LLW9lDNot5\n9Ciyt7f8/FwORkYq7ro+FCLW0lJWEE7kcgTnUUQR0mLj89/mWsqcNwTGrusxXvfWUgFSz8pC1fY8\nf77stkPt7Sz97d+Gzs7yQrFlIc6dU6kuy2376NFpH09VCQSgrk69fEghGDx+nOFnnil7xQigQwhi\nFTZtX3GVvjulMEg0bSDxsi6E71ZCCBgOnqIKse6aSXBHloO3nuDAwAB9fX3O4JEdvbN06VLC4TB1\ndXWLLuLarpMopapP+MADD3Ds2DHGXPV8Q6EQW7ZsIRwO0+pyHHKnCl1MbWJjOxUahsHAwACPP/44\n4+Pj7Nmzxxk0tZ0KYrEYu3fvdu5d7WisxYx9/nt6enjhhRcwDIPnn3+eU6dOOX0iGo0SiUS47LLL\n2LJlC1JKVq1a5QxuLsayCBqNRqOZP/yletzZhvwOXP703HY2Gfe2/NsuN73a9tvv7sxJ08Fdh9wW\n3mt9n+b/Ta+2WF8r+/1lnfxtNVkJKHfqcXd9+rnEFvzdzr7284tNuTTods15e3n/q5Yiut1XyvWR\n6QYy2de0+3mk3HnUaDQajWYa1AH/jhLQv4dKrT4f4vVx4EbgIWAX8DVUhHr5utcLlJqJ6EeOHGHJ\nkiXzJqKfP3+egYEB1q5dOy/7LxQK9PT00N3dPW8iuh2FtXLlyjm/8bIHIzs7O+dNRB8aGuLUqVOs\n13XZFgyTdUPLAtOstIBgqoLcZPtwHhZ90+VCEfvKGV/B5omLicmTZ0/eMBW3vxAe2oS0ELJM0v2p\n2iZExZTnAmaeln0BtM2FEJMd2yTzLtjfjGJ0c5lrUwgtn88F9sCNO/rCFgPd6cntZe2BKLu+9UK4\ntquNPbBmmiZjY2OOM6F7AK2uru6SqPFdDrtvpFIpkskk6XTacTCIx+POQGRTU5Nz73opicPZbJZU\nKoUQgkwm4wzc21mV7EhOO0VqJBLRaYuLDA8Ps/fZZykUCt4Z0/meKZv1ZOL6y5cv57LLLqta30yn\n0zz77LMky6VKqiLRaJSdO3dWrc/k83n27t3LSDknSX9bzvL7vq6ujp07d1YtCvv555+nr69MAESV\nxY5gKMTWrVudSN5qsH//fs6ePVu17VUiGAxW1faBgQH27d1b80I7Qgguv/zyqtZzv1SZTPwrN286\nzpHzVe96IW5vMVKpjabTdvPVzm7H1krzLzUuxWPWaDQaTVX4DEq0/gEqCjw/hXUCwL/OYF8DwO9N\nMn8EuBWVRn438BFUTfaLhqqO/FiWRTKZREpJOp0mlUoxPj5ezV1MmVQq5dQpmo+B0lwuRzqddtpj\nPkilUs45mOvBcjuVUiKRIBarFOtYW5LJJKlUyknNOV3cAoRGo9FoNAsVO/ra/n+yuoW1rGk4n9gC\nsS2i204F4E1/b6dyr3V0ykLCjtyxI4zsyHT38RuG4USj29Mvhfaxrx27XQqFAoZheKKcbAeU+SxR\ntNB58cUX+ei73sWmZBLnqUtKlemlu9u78NgYpFJeYVdKOHsWbBFeSojHYdcu9V7kTF8f3Rs28JGP\nfKRq6XD7+vq4996PMD6+FiECSAmt0TSXd54naLivAcmgbGVcep2D02mV8Md9OIEALF0K4bA6lHw+\nw9mzx/n2t7/pqYE6GxKJBP/7Yx+j7swZmt0ZaTIZGBrylAcyW9vJbL8Wv4vb0JA6HW7bR0chny9N\nM80s4XAP//Ef/0Zzc/Os7bYsi3/8zGc4f/gwS1tbS8J5oQBPP63sL+7clJJEoYDl+y6KGAbRQGBS\nhz1TSl5oaOB/fP7z3HTTTbO2G9T3xef++Z/pffZZlrrbQkrVpy2r5LgZDEJLi3p3LxeNQizmbfRU\nSp2IIgUpOXDmDB/6yEd45StfWRXbn3ziCf7pD/+QNa7rxkJwrmkDI7FlrraUrI4OUGfkPOunC0H2\nnl2CJe1oZ2huVpe3e6jlwIHnee977+Ztb3tbVey+lLEjzu3f5Ww266mpHA6HnfubWCzmcdCJRqOk\nUqVAI8uynPmRSIT6+nrHOazWv2v++y23SGq/T2aDfcxSSkKhEIFAwKnZXWvc98zue233PHu+Hf0/\nX06q9v7dddDtNna3fz6f55e//KUzLZVKMTo66tQdt7M1dXV1ATjZq6B294V2H/BnhfL3DX+/ASb0\nYff67vu2uXR6tMfF3Y6FmUzG03+ampqcbE/+CHsdha7RaDSaKrAbeDdwElWHfCoCOqgHxrfMYH+H\nprBMClUnfS9wLyo6/ucz2Ne8UNU7iVOnTnH77bdjGAZjY2PU19fPW6SPfZMyX2ko7ZpNn/70p+ft\nxieXy9Wk5tJUGRsbm/BAN5dks1ny+Tyf+MQnZrR+2QgJjUaj0WjmCfcAojs1ZqFQ4NixY86A6blz\n5xgbGyMcDtPQ0OAMttqvxSQE2gNO2WyWRx55hEwmg2VZ9PT00NfXRygUorOzE4Curi7e/OY3E41G\naWlpcdrLXZtzMTI0NER/fz+GYXDgwAH27NlDOp0ml8s5Eefbt29n/fr1tLS0EAqFPKkxFzPj4+Mc\nP36cQqHAE088wVNPPYUQgpGREWegtqmpide85jWEQiE2bNhAZ2cnUspLItX9VLEsiyvHx/nTVIqI\n3WcsC664Al7/+pJgKAQcOgS9vV4R0TTh5ElIJtV0y4LWVnjf+0oivBD88Mc/5rFnnqm67abZwtq1\nH0WIEBLY1jXI7133JNGQO7Jess/cRo/lLZN17hwMD5cOR0qor4ebbipV4RkdHeSTn/zTqgoQUkoi\npsnvb93Kpvb2khg9OAjPPKPaFEBa5DdtZeC/fgyEAbKk8+7bB0eOeG0/dAjGx0vTcrkRTp/+UFVt\nF6bJu26+mRu3by/ZnUrB3r2qtFFx5znT5GQqRd5Xh7gtFKLzApnecsB/F2JidoRZIKUE0+SunTt5\nxdatJdstC86cKbU5KLF82zZvyR3bsWTFCm//7+2Fw4edaZl8nr/46leranu+UGB3YyNvXbHCscUU\nAX656V0cWPYKR0QXSHYveYK2yCglpwvJuUQ9H/vRdeQtu4QMbNoEd9yhnEVAmf+pT/01hcJFlZlx\nwWIYBrFYzMl+MjQ05Dj5B4NBWlpanGU7Ozvp6OhwfrcymQznzp1z5udyOeLxOEIIGhoaWLFiBYZh\nkEgkajpW404xb3+H2I58tq2TOai5hWshBE1NTQQCAfL5/JyMcbmFTdM0PY4JhUKBXC7nHEckEqGt\nrW3e7pvcNdH94qv7maGvr48PfehDJBIJQB3j4OCgc5yBQICtW7dy+eWXAziOhrV0PrWdRWwb3WJy\nnes71LIsx2HWxp1dyrIsMpmM0wb19fXOfW4sFnMyDNWaQqHAiRMnSCQSjgPrmTNnSCaTzrm56qqr\nnDrwbodO+7jDxS/WxZo9TKPRaDQ1JQb8PapS5zuB4WmsK1E1zKfCWsC+Ib1/iuucBn4X+Cbwj8BV\nQPUeempIVe+Yz50757lZ12g0Go1Go9FUB3d9c/eAimVZjI2NMVaMZEsmk2SzWSfy2l0/czGmMbcH\nn06ePEkqlXLqoyeTSeLxONFoFCklDQ0NrF+/nng8TiqVIpcrRdot5gGqTCbD6OgohmEwPDxMX18f\n2WzWE8nV0dFBd3c38Xh8xrVLL0ZyuRxDQ0MUCgX6+/vp7e1FCOEZaI1EInR3dxMMBmlubnYEjWql\ntl4sBICoEDiyhhBgGCoS1y2QBALq5e9j7s9CqFc47FHoahXlJ4SBYUQxjAgSCAbGiYVCxELufUnC\nRpigFcMugmMHHAeDXiE6GPSaHg6nauK8JISgLhCg3v29Hgio9nYEXsgEAoTD9QjhjuiDUGii7f7T\nEwhkPetVy+5wMEh9KFSyMxQqnffizgNCEMYbPy+BMGpkaLK+EJCSWvzaCSAUDFIfDHpFdHdDgjoe\n++UYL1WniES8y4bDpeOnGIFsGFUtjSOAkGEQc223IAKEA2GCwXqXiG4RCYXVuXFZEA2FCASiWCLo\nHEooNOESJRAIXgzVji4KykWilquXDKUoZJtcLjfBQdC9jl3Hea4Eupnuw1/X2l1ffS7vU8rVnrc/\nu9/nE3+fmKyuezKZdER0OwrafQx2+SdQfalQKNS8zd3n2t3e9jz3Mv51Ktnlj3CfS9zOIrb4n8/n\nHXv9GY/s93JOEBqNRqPRTJPfB1YBXwUem+a6JnDNFJYLACdQIroEPjeNffwbKq37K4E7gS9M08Z5\noVoi+h7gg1Xalkaz0PjlfBug0Wg0mksT92CKe+DQn+7QLZCHQiHC4TDRaNSJ2rGj1xcbdtmWRCLB\nwYMHHUcCW0xvaWlh165dSClpb29HCOGkMl/MA1Tu1K/nzp3j8OHDCCE4efIk4+PjTqaiQCCAlJIV\nK1awceNGIpHIom4XUKlM7QHh4eFhDh8+TC6X48yZMwwODgJKOI/H41iWRUNDA21tbQSDQeLxuBMV\npeuh+5DSk0IcKb0v/3LufmZZ6lVuHXcUe436pme34PrjTefuPxRnKTnxs/+Q5xS7Pe3/pazYfH47\nJSBF6cgtqG0dbekKjbfttgWUorAz//JUGWyb7f8r9c1yy/mX90+r1XewlCDd16hApSbAc5IFvg4s\n1Lruvu6+XuwlHWeM2livKTJVwbaS2LtQBF/3+3RYCPa7WWj22PgFaJtywrNbRHf/7+8vc3Ws/v34\n7ZmKHfN9XmZS091/nIv9flyj0WguYR4CngL+BThS5W2HgA+gxPA/q/K23bwGWF78/2fA4Wmu/yco\nEf0PuMRE9P3Fl0aj0dQQgb+eo3deZcqN47rnLXgMwxtZ5Js340OwG8U/oG3jS6E5LwhR+fgms88e\n3TMrDAHb61ZqV3doVrnutRA6TqWBW3sAmgtcGZUe8AEx2UN+8U+l62khNM1iw1/7zz+w4o5CCofD\nmKZJQ0MDS5YsccT1xSj6jYyM0Nvby+joKI8//jjDwypTlX3MXV1d3HHHHUgpqaurwzCMCfXAFyNS\nSpLJJLlcjp6eHvbs2YNhGPT39zM0NIQQgs7OTurr65FSsnnzZq677jonWmYxk8vlnJSavb297Nmz\nh0wmw4EDBzh9+jSg+s3KlSsd54tVq1Y56XPjrhrdGhctLbBqVSnqXEpYtw6WL/dEopsFiWxsdQmG\nQKFA4OxZRCJREh1bW+HgQTh/vrhcMRW8Wf1U0aEQLFlSMjNQH2b/QAfhgFlKaC0lydYojZ6S6BIj\nk6LFynh+T+uigoZghKhQ92d1RhaDGtxPWRYkEqVQYEAGgsirr8H59ZcWia7LOXjQ+5Mvparl3t7u\nut2xLGTiGbL9Q85vTDo/zlBuOhkAp0g4rFKduwXma6/15JIPpFLUHzpEwZeCN5TNUiimtbbJAWlK\n9zxZIFuL73nLUjn8T5zwRKKbJ06ouu420ShGLIZwHyOQPX2a1GHvGFP+/Hkyp087x501TZKDg1UV\noyWQW7KS9Matqo0kFDAYopW+s2AUHScMYKizmXAoiPsOMhGKEI0KQsVuLCVEzXHCJ84SDhbrdAsw\nRgYRcmUVLdf4KVfn2p12HFQUrB31Cure0U5rPR9ZVNz13PP5vKc2dF1dnef+1H8clu9Zby6FRfue\nyM5Qk81mJ7R/LdOczwa7nUzTJJFIOO1o1z/PZDKOg+769eudftHa2ko4HK4YKT0f+Guku9vbnd7d\nH1E/X04jUkrS6TTJZNKZFggEnBIEthOD+37bMAznHCxGp2eNRqPReAigBO4/A34N/DMqvflIFbZ9\nK0rc/g5wtArbq8R7XP/PRAR/EngcuB4V+f5UNYyqJYtvNFWj0SxYLCuNlONl50mZRTlKVSKLctAq\n/yBkGFFise0EAvVlBbxQCDZsgObmifNME4aGahf4MSVyOVVUs0yqZWkYDD/wAKZrkNSNsCzi6TSh\nrq4JgqlE1Z5015j0LiAxN28l/5rXe9OtohbPnx9A7t83w4OqAoaB2L0bsW7dBFFXAqKtDRoaKovB\n0Rjmu+4uLyRLC+PAQcSBA2XXtYwAQ12bycRXTJwpIBlsmt6xVJuREXj0UYjFJs4rFIgODFCggoge\nChG/6SZiq1eXv6KkhEcegZMnK4rwgXqoa5/Y9EKUv840M6dcFIn/c6VlLqWUgO5BZHfqynJtcam0\nyYXwp468lPqL/zj9g62V2uZSaZ9ps3Ur7N5duo+REtavh507XfcXklwGcnnfb4dZoH77NgJJVzHu\n4WH4zGfUDRqo6cmkqrFeZRoa4BWvUBm5Ac6ebeFvf3GtRxOVEt74RsHODcJ1nynpaD9NU+I0nl/b\nYBAjtgwC6r4tGBglLGpQizWfV2Ku7WggJXLbDvJ/8ieImHL2EEJy4jmD//OXEwflX/c6uP56l8Zr\nFVj1uT8h+uRPnfMwLCWnli+trt2GoZwkurq8Ivo//VPpJADh06dZ9clPqr5gIwS5Q4dI7tvnEafP\nAicpnYUCMOSuR14t8nn4xS/gueecSdI0yb74IjKXc4K6RV0ddRs3Iny128/39dHT2+uxfRA4LaVj\nuwmcbGioskeiYPQVu+l/8zucYPSCCc982eCnj7qD4A02b76C4Ubp2X0qIOhaVur7FrAkfZyWf/sS\nYZmzVyb6/NOwc1sV7dbYuDMS2Sm4bfyieTabZWxszPnc0tJCW1sbwKQ1yGuBlJKzZ8+SyWQAVWLG\nXU5n6dKlrFy50jmOrMtBxrIsp7yKbfNclpzJ5XJO2nO7zrzd/rFYjObmZkfUXUjCp7uvJJNJnnrq\nKUc0HxwcpLe31zmuhoYGPv/5z7N06VKklAQCAZYsWeI5R3OZQt/OrmXfj7lLDliWNaH/jo97x5bs\nczGfqfYLhQLHjx+nv7/fORdr1qxh2bJlzn1lKBRysmcBxONxXQddo9FoLh3eBxxERY3vAD4F/APw\nAEqQfoSZ1wnfXXz/xixtnIwu4Lbi/yMoB4CZ8HWUiL4bLaJrNBpNCSkzWNY4lI3GsSpMBzUklgH2\nUUloN4xmGhouJxisLzs/GFRjue3tE+cVCvDiixc0v7bkctDXN0HIBvXwN3ToEHnXAJszDxCGQWTV\nKuq6uspve3RURc1UiDQvrL+M7H96PxjenwTDgHzPYeQn/nJmx1QNDAPxm7+Jceut5aPOBwbUAHI5\npEQ2tWBed0P5Yz/XT+hvPoE41192vgxGOP/qqxjp3DBhnhBy/kX0sTH46U89A842wrKIDgyo/8us\nKsJh4q96FbEbb6wYzW8dOICcRESPxMBqA3+pVMNQIrp+9q8e7oHSbDbrRC7YAyz5fN4T2SOEqpu+\nGOuf+7EHjvO+SEW7FnwoFCIUCiGlJBgMTisV5MWMXSfeNE0KhQK5XA7DMJx+YkelxWIxLMtyBiwv\nhRSS9jHaThd2O7n7hGEYTn+xB2wvpVrxM6JSmmrD8EwTovjZ3ZSi2OcMV8Yhf1vX8JqdmNSlcuaj\nco5jAXsV9+qVP9YWASIQcO7phDHxd9qzuM9uQ5oEZcFZKYBkknxA1cWf0ty+5so1+gJDWJaTJUHY\nnyv12UJh0kxKEmqTCUoIhAggHWeyystNuddWSNakqT7+WtD2e7nU1+77HPs33Rbm5uPe0J3lxr5n\n8zsDlKs7Plkq8bn4Pbbts2tY2zWu3dMXKu42zOVyThR9Lpdz7g0Bp/RRW1ubp6/Y685HDXq3/X4H\nWPfnye5bJ7tG5oJCoeCpJW/fV7opd40uNIcMjUazKIijSjDvQt22HQH+huml3xbA24FbgKWoJFDH\ngC+jIqk10+MYcB9wFxABbM/f30CJ03ngX4EvAb9getWSbigu/3C1jC3DOyhpyt9EJQSbCd8vvt8w\na4vmAC2iazSaOUT43ivNrzRvGoM6/rXLjMG55y0IJjFEQNn02vb0SQ+h3MC2a54QIKRAJXF0IUEs\nlKGxWTz8qmMo9zAqSoOzk7VNmTZYIK0yqe2Iyc/ebI9BsICunUWMlJKxsTEnMqenp4eenh4nLaA9\nOBQKhZyBmrVr19LV1UUwGCQcDi/q6Ou+vj727t1LMpn0RCtt2rSJeDzO1Vdfzfbt2512siOhFjuW\nZdHX10cymeTFF1/k6aefRghBIpFgaGiIxsZGbr31Vi677DIsy2Lt2rVO+y3GfuLGjnQTQjA+Pu5E\nyKVSKUAdf0tLC1u2bMGyLFauXMmqVaswDMOph67RaDQaTS0pV4/a/uwWQUFFTOdyOedeZ7KyLPOZ\n7todUWz/XygUnN9kf1p6tzjqFxZrbbfb4Q5wHO5sytUO96dAn4/7KXcfAdW+6XSadFqNb6fTaU8J\nKPfzg213ue3V+ljcbWk7Ntr/u5fJ5XIT+ojbPn+7z1VEumVZzrNaJpNx+ostoNu22X3anWXBtm8+\no+c1Gs2ipRH4ObAFeBQYA34bJYLeBDw9hW0I4KvA24Be4AmU6Ps+4PeAd6IiijXT4/8C9/imBYqv\nMKpd34OKWP8s8DWg/wLbjACXodK4VyM1fDkE8F7X59nUM+9B9cmts7JojtAiukaj0Wg0Gs0Cxj2o\nmMvlSKfTngEZ+2VHjtiR6PMVPTJXuAeh7Kgm+xUMBp0o9FAohGEYWJZ1yYjogBM1VSgUnIG6QqHg\nDNRFIhGi0agTiX6p4B+odEeiu6+rYDDotI2ORJ8FUno8rqSQIKwJkeiyuChCAgKkLOs8WCsTp7qn\ncibJCWtfwLmxikgpsZz9F/u2dM8vvcqsrdrd93mukK6/5eeql5T+cGepMhzIMosXUdHztToLqs1F\ncX9SSqQQSMNw0rl7vAzdKetlsfL4hEwL3g+17EEXSvIgsSY2nUBlJ5Clj06jS1+H01QFd0puUJmI\n0uk0Qgh6e3sZdpU5OHXqlMexMhqN0lysreROH21v1xYm50qIDoVCjlPn8PAwo6Ojjq2JRIJjx44B\nEI1GaW9vd9Jxh8Nhli5d6hFI7RrYtbLdfY+QSCQ4c+YMQghSqZTzv5SSjo4O4vG4Y6tlWY4gDUr0\nj8VicxZVbN/zSSnJZrMkEgmEEJw5c4ZvfOMbTn8xTZM1a9Y4zxXxeJzW1laam5udY7ejqO3j8Nch\nr5XtAMPDwxw9qkq4JpNJnnvuOefe3TAMmpubnYhuwzDYtm0bkWLZjEAgQGdnp5MWXUrpPB+4xfha\nMDw8zOOPP04ulyOfz3P06FHnHAQCATZv3kx9fb3TjkNDQ47zJsC2bduIx+0yLPpeU6PRVI3/hRIo\nfxdVdxuUoP4rlDC+FRX1PBk3owT0p4EbgWRx+tWoKOlPA//G5PVZNRN5CiUgVyqGadeE2gR8DPgk\nKur/s6go9XK1cpegtN5a1kK/FiXUg0oXvGcW25KoqPxtKMeMBT1Yp0V0jUaj0Wg0mgWKlJK+vj6G\nh4cRQnDkyBEOHTrkEcdDoRAbNmwgHo97ohwW+yCMPcAaj8eJRCLccsstTtryW265hba2NlasWFE2\nLeilgH3c9fX1dHR0ONO7u7tpbGxk06ZNrFu3DiklTU3zXJ5iDgkGg47YsHz5cm6++Wby+Txbtmxh\nZEQ5bHd3d3PVVVchpaSlpcVJg7vYr6lZMTwMBw6UaqJbFkSjqlyNLSIICPzwJ4T27fekCJdGgFOr\nrqEQWVGsUyMIBnpZksgSKg78C8DK52si0tWFCqzvGCMUVCJTakBytAfSrsd4KeHkyShr1tS5TBCM\njrcSTrlFUUlYFFiT6aUuUByTSiQgPdMsd5VJixiP1V3L8egyx8hIYTltB8MYrlLcQ0OwbUKZasnq\nSD9tfUMlwdbKk8smGJXSmPP67QAAIABJREFUSU0+zswL8lWiYAoOnKon3tpsm4JhwPJAmGC4KNQK\nyIy1cDT4KvJ1JaFBSujcfp7lO3s9Ou/po/CL54oVAQCTAgPiySpbDgUjzPOdtxBdcpkjKBuYbN28\nnzpRqiGMZSHGx339VTIUXs+LTZ1IWbK+bxyODZW6kCkLnA8/RbWdAPbuhUjEa1IsBjff7O6+Jmv3\nPkDH3nOe/Q8Wmjg59CYKqGvEktAYj2GtWAVGsZ8LAUeOVNVmjTd62xY+M5kM4+Pjniw7tiBn/27Z\noqiNW8yd6/shwzAcZz1/BG4mk3EE23g87hGmbfHf3QZzFa1ri8m2gOuO5rbtdpeCsVPW27bOh3Oi\nO3rbjopOJBKcPn2a8+fPA+pctLW1Of2hvr6ecDjsOFnYgrY72nuu7LYjzUdGRpBSMj4+ztGjR0mn\n07jrtbtF8w0bSmXX3CUL3OdlLlK653I5zp075/SLZDLpnAO7xJS7rFQul3OuYcAptaTRaDRVpAG4\nGzhOSUAH2A/8CyqS/FXAQxfYjv0U8XlKAjooUf1nwCuBFcCJWVt8aSFRzgyvmcKy0eL7DlTt9L9B\nCen3AY+7lrMHdcaqZGM53u36/4tV2N5o8b0Z6KvC9mqGFtE1Go1Go9FoFihSSvr7+53ol6NHj3Lk\nyBEsyyKXyyGlpK6ujiVLljiRF5ZlOZEji51wOEx9fT0AN998M6AGnG+//Xa6urrm07R5xxZ93SJ6\nNBqls7OTxsZGLrvsMrq7uwE1+Oce/F3MhEIh6urqEEKwbNkybrrpJkzTZHR01BEh2traWLVqlROx\n769jqSnD4KBS6ezvHcuC1lbIZkvTDIPg/d8i8KUvIazSgLZZV8+RT+4hufwyRDGQOhpsoGE0Q8we\n+AdMw6iJiB4NmWxeNkwkGAQBpw9bHDwgGR2HkpAoueaaDtavL6X0l1KQSXeQzbU7IqQE4tYoXaef\npE4OK2ExlYJkkmqTMBp4KHo7TfWbnZ235AXb9grsLiulEk6vu86/tmRj4jSdZw6UHI0si77MGBnL\nco56lAuHp0yXvGnwzNEmxkSH0qElBIOwq1n5XdgMDXfwUPhNJF3TpISdO6HjNdJpcyHg6Hfh4VEI\n2N1PZhkaurfKlkMhEOGpFa/h/JpXglSuIJGAyeWvOkA4lisZNDwMDz6oHCic71WLc10382vejEXQ\naeNTp5X/idOHZJp84c+qWjNHStizx6txGwbccQe85jV2FgjANNn0ofuIHXZdy0iMQDeHut5ATpRE\n9Pb19VgvWw8hs3Tczz6ra/3UiMlqQ/udvPypuSvVi641k+2n3LGUe022Xi2ZShtf6JxMtu1a2ey3\n1c5oYD8TXMiZolzfmau2dv9vv9xZtfxZgS7U5v5t1VJId9tnC/eT9YXp9BeNRqOZIdejopm/W2be\ngygR/RYuLKKfLb7HysyrR/nbnrvANq5Aa5DlGEdF8E/V884ovkIoMftulGD+VeBzlB7bauXJ1wC8\ntfh/FvhyFbZp94tq+21XHd2BNRqNRqPRaOaYyQYI/cu4B7/KDS6WG3S0B5mmK6QvlGhb2/bJBrzc\nx2njrp9ZbvmZHttCaRdgSmk1LzQI7W+nmfQV/77mm6na4B+U9a9brr6m+/qrlV0XPZWOU4jSPPvd\nNL1iuGUBAlGUFYXrkzfre20HwEXpQzFazLN3e5YnO7eaXEag8rdHDfpBqbUM94QJuy1nDlJAWTNd\n3wuuV7VxuoTzp1ITTfxe8h+T57OzXYPaWK62Lgi4Esa7O4TPUN96aloAMenYllF1y6dyeboXnLC4\nKPYFu30liJq1rwaUQ2QymSQWiyGl9ERCZzIZcrlS5gN/mZJMJuOJmk6lUiRdjjz2dZ5MJmsScWzb\nnkgkkFKSSqU89aLt/8FbazwQCHhKFlmWRSKRcO577GO0y/hUG7vtxsdVhtRUKuWk0PfbbafXt22z\nI+btz3Y0tP8ewHaCrTa5XI5kMunJTCCEIJ1Oe9rYX3O8UCiQTCYZGysFrWWzWadf2CVt7H3Ugmw2\n69ieSqXIZDLOcbjPtb/tAoEAmUzG0+apVMrJWOC+D87lclXPDpDP50kkEgQCAZLJpNNH7DJKtt3u\na9Ju93Q67elP/nNgY6+j0Wg0M2Bj8b2nzLzDvmUm499R6cH/CHgMeBJ1g/6fgetQ6dwvlHLri1PY\nj2Z62A9JLcAHUPXp/3NxWqUU8bPldiBe/P+7wGAVttlSfK9l9HxV0CK6RqOZQ+yBvnIP65MNdgnf\ny4+cZJ5vS2UHGBf+INBkRzfbAU6BQJRpfsNYIG0jhDKm3CCPPa/cw6UQCKMoklQ675VGl4v1N4Uo\nto30r1uT8fBpM1mfuOAVVXFkvbiMmHx4VA1KTNyJEAuk3yxwTp48ycMPP3zBAZ1CocCLL77I4OAg\nQghOnDjB4OCgU+cPVGTtwYMHOXtWOQmPj49z+PBhDMNwUghOFSEE+/fvZ/Xq1TM/uFmSzWb50Y9+\nRGNj4wWXPXHiBCdPnvS0hxCCpqYmpx6ojT2INRu7kjWIJp0Ohw8f5qGHLuQsrvrN6dOnSafTHDhw\ngHPnlHN4JBIhnU4TjUb54Q9/SGdnJ8CsUncKIXjuuedob2+f0frVIJPJ8POf/5z+/v4LLmsPbgqh\napwODAw4A/72gGZDQwNHiiGboVCI1tZWYGYOAyMjI86AsEaj0Wg0F0IIVTv8Yx/7mJNlKJFIOEJo\nKpXyiIl+Rzg744pNXV2d57ONaZpkMpmqiou2LR/96Eepq6tz7r3cqegrOQAEAgGn3Aoop0E725Ab\ny7I4ceIE99xzT9XsBpXd6Bvf+AaPPfaYY2symUQI4UntDup+yl1Gyb7ndrdDuSw2UkqOHDlSVdvD\n4TA/+clPOHHihFOfPZfLOfc5w8PDTt+xLIuRkRGnjcfHx/mLv/gLJ0U6MKF+uL3s6dOnef3rX181\nu0H1zQceeIBjx4459dxtJ4Z8Ps/Q0JBjjxCCnp4ej2Pj888/7/RfIQTRaHRCf7afn3bv3l21Z9RA\nIMC+ffu49957EUKQzWbp7+93HBTS6bTT54UQnDp1ilisFMRpOwjY/ef73/++5xzY9Pf3s2nTpksi\nu5hGo6k6tjg5VGaeLX62TmE7aeAG4K+BJ4AEEEZFPf8h8PdT2MbXuHC0+qXIy1Ap2mf6JV9ARZ0f\nREWifw11njZVxbqJ3OX6/0tV2F4IWAucAmrjqVdFtIiu0WjmDCFeRIhHmKBIQnHaZNk7MqjSLZUG\n+WNkszEKhXjZufk87N8PDQ0T51lWjvPnT9YiQ+iUkFIyDDximrRUiBg8BxQmETtb02nqKj0UplIq\n6qvcfMvCPHmc3A8eAuF/4IS+vt55FY0sy+KJZ55RgyvlTtDoKAwMVEzvKuvjWIPD5Y99ZITA0R4Y\nGSk73wqE6Hv6MZKnT0+YJ4Skr+/k/IkiUjJsmjySStFc5qFaSsm4aZKr1Gcsi8YXXiBUyQFBSuTQ\nUEWBHSGQhw4iH3qwbL85dOjwnNbSu9jo6urK7t692xMlNBnd3d10d3cDsGPHjrLL+KOKbUHZHeUw\nVV7ykpdU3E+tqaur4x3veAdnzpwhkUhccPm2tjZH4HSTz+cZGBioun133XXXlMT9WtDd3c0rX/lK\np273hWhubqapqYmuri5uvPHGCfOFEFVro+3bt7NtYtHlOcHdZ2ZyPHb/aWtrc6b5I9MHB2fnZP2O\nd7yDWCw2/YvxYkI7CWgWELo7ai5m6uvr+fKXv+zUCq8loVCIlpaWCy84Rerr6/n4xz8+K6fFqSCE\n8Pxuz5ZAIMC99947J8+91bb97W9/O294wxuqtr1KCCGIx8uPtcyUt7/97dx2221z8kzdUG4gaIZ0\nd3fz9a9/fU7sjsViuqyQRqOZCfYXR7kfZPu5NDSF7QjgPago5L2oWuhh4LXAB4FfoyLUJ+Nviutp\nvHwVuHqa6+RR52QUJWR/Cdjnmv8csBNYBZysgo02lwP2oNJJ4HtV2OY2VMmBZ6uwrZqjf4k1Gs2c\nsH37Nj772fPFB41KHsCT/X6HUJliJiNHeSe7SdJZogbadux4A9u3z48A0NXVxV0f+Qjjo6MVrFdf\n1pN9YaeEIDXZTiZ7wDMMyIyWndXQEOHd734XodBU7q2qixCCXbt2YRgGQ5UE2YaG8p4RbsaGK8+7\n7bbKbSMEESGIVDgrd975G/MmGi3p6uJdf/7nJMbGKvYZpJzUnXFcpRqovMAHP3jhfjNWvt90dLRy\n5ZWv0BHpZRBCZKWU7Xfeeed8m7IgCYVCvPrVr55vMxYknZ2d3H333fNtxoIjHA5fNH1GCLF4vYtC\nIaiv99ZEj0TU74j9WyIlRKPIllbv70skRjBsEAxKVRNdQDAokcLrdlmroWrTgnTWwAwYICCbs/fk\n/Q0rFASZjNt0CekU4UzWs2iAJAUjQFZEAEFOFJA1Sn1dV1eqIy6lOg2Fgv/nWzI+Lr0/+dIiUzDI\ni3BpkrCQvkTotfoVNwxwBwsGAhCw8gSskuEBqSI5g0G3Uwvk8xajo96a6LmcIBIxPN2vypl7XfsH\n2z9NSpBBiUylccYjhYBMRh2k5/5ZEpQWUSuFdN3V14eDNDSEneORUpVSr7Ll1AXyxEMZ7LNqGBC2\nJIGc69qy8gjT9GV/khjSIh41yRnFaFYpCUckeRkkK+27TYEpdbRkFegXQtw9n9ldZoMQYkImoIuF\nhoaGqgqtc0UsFvNEOV9MXKy2B4NBlixZMpe7PDGXO9NoNIsC2yusXLS5PW0qd5xvAz4KfAF4L6XI\ntnZgD/AAsAGofgTD4ueGKS5noaIO7Trk91HZKeFhlIh+G/CZ2Rro4l2u/79M5QjH6XBb8f2RKmyr\n5mgRXaPRzAkrV67k7rvvuvCC88h8CX6NjY286c1vnpd9T5X5apuNGzeyYcOGedn3VNB9pjJaQK+M\nEGLBpyrSaDSaabFjB/zO73hFw3hcKYK2IGeaWPf8J6zdb/aIs5ZhcOW6TqxIMepSCDhuISLSKbAn\nUK6StRDSh8ZCfOXhdoJGGAS8cAAK5kQR9rnngh5xU8g8t/V9nl1D3/HYVWhu58W3/hGF9uUAjIWG\nGA0+WXW7GxqUL2AxUQkA/f3ws58pId3+GR4ayvP882OeGu+GIbnj9St49a5lpelWnpXBZprs40MN\nFlTbjTIQgK4uWLu25GMRFAXWJPYRTWeLe5a05mJs33w5adOb4nb//iT/8i/Dntr0GzY08KY3laJp\nTRO+//0qG45q13374Nix0rSoyPKBQ/dBcADH7aC+Hq6+uuThUOTK4SRd5/5VeYoUSV+5mcS2Xc7n\nXA4+//nq2377+md43ZYG7KtIAJ2ZAk1Pu8bApEnk/Gml4rtU/fYlo/z5e3uRYZUSXCLJmUGeyl8F\nBacQPScKP6Vb10qfFUKIcXTtUI1Go9FoNLPDjkIu5/HTVXw/PoXt3FF8/194hdPzwGeBj6Oi0r88\nfRMvaZagosUnI4OK1N6DStf+LWD8Aut8F/gz4K1UT0QPUUrlbqEcKmaLgepbFsoRY8GjRXSNRjMX\n/B3wbS2qVUa3TWV025TnImmXs/NtgEaj0WjmgNZWuOIKFX0OpZBdd9kKKWHDRuQWf91PSYvMI7Cw\nBVRzRDIaKI3UCKrj7l6ObN7gWG8dwogggLODSmZ0V0uRUlV/OXWqNE1ICScP0znwE8/2Uh3d9MgG\nEpEVCAnJfIS8mFjrdLaEQrB6NWzc6I08HxtTQizYpXkk+/blJojo11/fxEikHif428qx2gjjrpwc\nQRXaqyZCQCxW8rEACCJpKAwTLWQcHdqycrQ2m6SlN3HO6GiBX/0q5fhmCCFpbY2wenVpe6apdOxq\nIyUMD6tKSTYxLMxkDxi9pYnt7fDyl4MvTXZLoY+W4ZM47iBSYi3rxLy2FPWfycADD0yeLGi6CKC7\ncZirOk8jXPsml4dBl8eFZUEm5fXCsCzqjBzbN6YgUqqVfHq0gV8ca6NQjD4XQEpefBGlGo1Go9Fo\nNIsQ24P3ZuAvfPNuLr4/NYXtTBa1nvAto5k6v4vyEfc/JGZR6fKfAf4ZJZxXTEBahqeKr12oiPQ9\ns7YUXkfJGeOnwLFJlp0qrwcuAx6iumnna4YW0TUaTc0RQvx0vm3QaDQajUaj0dQIO6TYnQZayolK\noJQT1HDhTx4uhGfqXCAEGO7dV1jGPc9wLSx9C6ojKL1qRaUmd6c6L388AiFUSnSxUOqHC19rCeGp\nBGAvoo7Hf1BiQuWAWpWq9bepANV5jKL9/n7vNkTaawhngpQTba8F0t6vs/3i5wt1GPu8SOGKoJdq\nO67+c3H4dmo0Go1Go9FcEhxBpfy+AZVu/XBxehD4bZRY+x3fOu8BTLwZcQ6gBNndwP/xLb+7+P5i\ntYy+RKgHPkBJQLdQ7Z5G1Un/IvDELLb/CeDrwF9TqmM+G97j+r8a+bKCwF8V//9kFbY3J+iiVRqN\nRqPRaDQajUaj0UyDBaU9V6BWgmztkJ736eiy8yXiXnRNrNFoNBqNRqO5FLgXdTv9EPBbwGtQwvkV\nKPGy17f8P6Ayybr5O5S4+wmUKHsbKlX4A8CtKLH3h7Uxf9Hy56ia8jlUrfMHgbcAHcD7mZ2ADvBN\nVAT6y/EK4DNhGSpdP8Aw8O1Zbg/gj4EtqNTzj1Zhe3OCjkTXaDQajUaj0Wg0Gs3MkVLl0DZN57NV\nsCgUcKWFhnxO1Rv36p2SdAGkE8ErsRKCcasRs5gdUAApCsVo2tqYbwvOgQA0NamM1u75dXUQdD09\nG4ARDUNjo9eqWMybC75G2Bnz7dTtoJo/GMSV6hzCYYjHhS+dOxiGoFBwCe0WmNE4ZlNbaXvSwgpU\nechASgwzR9BMldqcAgVhkBdB7JT+eRkgkxVkfCp1Pu+P8ZcUCpJksuBsr1AoUCjUpgCAEKr9RDHo\nPCAEuVCMjBF3bCdUD4TAClKS2QVBESIYDJWmSQkBQwWv18TaMsa7KRSQmQxCFJO8S4mIRFSufVdN\ndGKxCVHq0hPVfjE6bGg0Go1Go9Esan6MErz/BvhGcVoCFQX8Z2WWH0FFqLvZD7wa+DRK/Pzj4vQC\nKtr591FR1Jqp827gWVRN+W8wvXTtU0GixPhfAn+PSu2/b4bbupOSfvyvqDrts+FGVHmBMeAPZrmt\nOUWL6BqNRqPRaDQajUajmTmjo9DTowp1A0jJQK6ZY+kuZFF4ExJOngkwMOjV8ixL8PxzQbKuIZv8\n+HJ6z32KPKr4tACGeZJd9FfddMuCZFIJo1LCpk1w992lQ7F54QU4edJte5A1Da+jPrbMEfcFIIIN\nRJd2YIXVUqaphPlqk8nAk09CryuGxLLgJS/xLpdOh9ixo3WCfhqPBzlwwDVBBmm468O0GMOAOpbR\n1DiZh+6rqt0BM8eGU49yVaSvuF8wA0F6Nt2EGY6q9OAC+gcCfOWbYUbHveufOhVDymUlsyU8+eQY\nZ84cdBwFpMyRSo1V1W5Q53HlSmhrK4nGIaOOn13+P2mMZFFp5SUiHCJidWEkS8MtUkJ36zkuX7nM\nKR2AlIiVq1X/KG7PFuirTmMjLFlS8rCwLAqPP47161+X3BECAYLvfz/GqlVeVTwSgdZWV0eWkI1i\nWmD6ygloNBqNRqPRaBYM3wL+f2AjEAJ6oPiANZGlFab/DNgONAKrUULqcSBfTUMvIZaiotBrya+B\n/4ZyoHgQuJaJmQemwm2oCHSAL83SpstQfTEIvA84OsvtzSlaRNdoNBqNRqPRaDQazczJ5yGRKIVq\nS0k2F2MoFfIogmfOwpkz3kBt04RnnjVIuYZzcrkYxzMvJeeK4hVkuJbBqpsupYo6t0X0pibYtQui\nUW8tbtNUwrVTPhpB/fLlBDuKqm+RoIwQzNURKEYXB2v0xG2aMDjo8VsgFoOlS73tWygYtLREJpTp\nHhpSvg+l6QbjL91BuA2n3nVybAjzMX+5xNlhSJOGZB9tY1HHmEIwwiHRQNZoxi55ft6EYydgeNgb\nFD02FkRKr1fC4OAog4NjlM5DnpaW6o/rCaHauKmp1DcMI8hA13bG64o2osqjRwHDlc3AktAalsj2\nlCuIXkJD3CmnXlNCIZVOoRSuD8PDyKOl8SsZCsH69bB9uzc9g53SoNT5IRhw6rk762sRXaPRaDQa\njWahYaFqm8+WMeC5KmznUqfWArrN36KcJ34X+BEqo8DJaW7jhirZcjnwMNAKfBj4WpW2O2fomuga\njUaj0Wg0Go1Go6keAkcMnTCrwrSJr/lV5C4GQdDdljOJXvavI8BTlrxmuu4Ew0uR/Pa7YEIG8Wkc\n41yo0pX3XHn6AutUvgb1fJqKOu47N/NVl16j0Wg0Go1Go9FM4AOoCPJNwC+AnfNgwyuBx4CVwCdQ\nNeEvOrSIrtFoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNBc/JnA3Kip9BUrM/hAqtX+tiQEfR0WgtwH/\nHfjjOdhvTdAiukaj0Wg0Go1Go9FoZowQQuUQL76kYSAMA0NI92Sn3rP/VWGrZV61s99vj9+ucrYb\nwndwhkAIA0MIDOFepxa2C4QQE+zyt7W//S90LsrNr77luHaq/jeEQExrv+5+YZR51QZ1LmWF7AmT\nv4wyJ0EYdtoGe/u16S9lr1Emtpp/OadTBALedYWBMeE61qHoGo1Go9FoNBrNAkICHwTeiapj/5fA\ns8Abqc3NewB4O/A88F+B0eK+/rIG+5ozdE10jUaj0Wg0Go1Go9HMmN6BAX769NNEAqpWtQQGCi0c\nzxz3qKGnTsH5816B1LJgbAyy2dK0fF4ipeVL5b0P9dxfXXK5Mc6e/QlChJBSlX7+6U8hEvGWhH7m\nGejpUf9LCUJY1I/0MnxmAFeRa7IyzMH8CGlZhxCQSo2QSIxU1WYpJblcihMn9jA+3l+cpkpel2vf\nbNY7TUoYH4dk0js9HIbGxtLnZHKM0dHhqtqetyye7evDsBsSKATCHIg8Rj4cd1ry/HllYybjOXIK\nBVlM9e8e8xkG+l3TTGAQWeWc/KaZZ2RkL0IEnH0JofpFOFy0UKppkYi3Nr2UkG4cJnHmHEYxrbsE\naG5BHulxtpfLZTl7treqtltS8uLp0zy6b1+pp5om1vAwlhCeljT27cNIJr0p3IVQNdXtdQWcHwqx\nvyeGaZXaYWDgGJa1vGp2azQajUaj0Wg0mqrwFVRK979Didr3A3uLn78OZCqvOiXiKKH+D4ENqEed\nrwD3Amdnue15R4voGo1Go9FoNBqNRqOZEZ2dnTR3d/PggQMeMc7CwJTeiGDThHh84jZe+tKJJZgt\ny7uMEAV27ryGQFGorwYNDQ3ceONmzp//rjMtl4OHHpoYCZ3PK/vdPNNnsu+cV+yUgCkPIO0q2FKy\nY8d6wrbKWgUikQjXX7+NY8eeJJf7tTO9UIBUauLy5fRYW+x1c/iwX/iVbN1aPduFEGx7yUt47umn\nOe7eEYLCsYc9BlkWXHfdRNulLHc8VvFVIhbbwNKlS6tit237S196Jab5DEKc8MwzTb/Yrz5PcFwY\nszhwxtex7YhvZzlJW1tbVW1fsWIFP2hp4Ttnzngb74orYNMm78InTsDp0xM34usslgUF0ztt3bog\na9euqeo1qtFoNBqNRqPRaKrCMWA3cDMqMnwncB+qVvmDwEPAE8CJShtwIYC1wLXAG4DXAbY79o9Q\n6dv3VNH2eUXn29JoNDPhJPDXwD/MtyEajWZR8xUgDPzWfBui0Wg0i4y7gL8Hmma7ISnl+y3L+szs\nTbowKu16dR9hpZRVj1guh2FUN8X4XNkN1bX9YrUbLl7b59Lu4jX6DiHE12q4m3Hg/cC/1HAfGo1G\no6kdNwA/B76Iqte70FgNHAd+CNw6v6ZoNBX5OepaCuD3Jl0YfBr4AHAN8PQ826Ipz8uB9wK/CdS7\npg8BPajvwUFUSnaAZqAD6AbW4R1LGAW+CXwOeLKGNs8LOhJdo9FoNBqNRqPRaDQzptpi5VxSC2F+\nLtB2zz0Xq+0Xq90ajUaj0Wg0Go2mZjxWfEVRDkOvAl4KbANeUnxVIgM8jhLMvw88CuRqaex8okV0\njUaj0Wg0Go1Go9FoNBqNRjNXvBT4V1R2u7/1zTOALcAVwBIggoqE+ilweIrbbwReC6wA8qgUpr9A\nRVfVkseBTcAu4IUa7+tiYSmwH1UTdcsstvNe4L8V339cBbs088+bUNfKQsPWS0LzaoVGMznR4vv9\nqIpSC40r5tsAzZRJAw8UX6Duw7pQWTmagIbi9FFgBDjFIqhzPh20iK7RaDQajUajmS11wDJguPha\nDAhgDeqB9Ng827KQiKEeqFJA3yy2sxLVxierYZRm/jh16hT3338/+Xz+gstWyig91SDZLVu2cPPN\nNxMMVucxdmRkhO98598ZHvZ+bZWzU4iJdpavz+2tKw6q9vo73/lOIpHILC1WZDIZ7r//fk6f7i1r\np59y9c8r4T+ehoY4d975Turq6mZgqX/bku9973scOnCgjEFixqN/goknIhyJ8IbbbmP16tUz3KoX\nKSUPP/ww+/e/ODWbyrS3cP5M2LjnYygc5vWvfz1r1qyZtp3lOHLkCA888GCFlO7+aVOPWPf3cyEE\nt9xyC1u3bp22jZpLDoESzjtQ5Zvc3IgaxG30r4TqsN8B3gecq7DtEPA/gP+CumdxYwG3F7dRKxqB\nFlR6XY3CQLVJapbb+QbwV8AngatZmOmLNdOjkfLXukajuTD2A9Eb5tUKzWLEAnqLLw1aRNdoNBqN\nRqPRzJ6/Av4/YCfl6x+FgCtR9bCagQHgC1PcdgS4DbgFVXspiYpCerT4urByNzPqUHWgssX/NYpb\ngX9Hebz/xiy280FUn7kO2FMFuzTzxIkTJ/je9x7mFa+4hUCgFLCTy0Em4xUTjx6F/n7vNMOAq6+G\naNQ1TUhikQIBQ2KLei8ePMjx48d52cteVjURfWhoiC984ZssXfpqhFDbzGRgYAAs19C8BHZuHGbj\n8qQjMZoSfr6viX3fTl1oAAAgAElEQVRHG5zjkRIa6k3eeOMIrY0mIEhkMnz7oYe4/fbbqyaip1Ip\nPvvZb/LrX29GiC5nejwOq1d7xU0poVDwtrmU0NkJLS1e/fb4cUi5JA7TTFIofIu3vOX2qonoD3zr\nW9Tt38/G5mbHGDMQ4viam8iHYk77ptNw+DC4fTOkVMfY1OTZKitGnmPduV8iimvngQdTKS7btKmq\nIvq3vvUg3/mOQTC4EbtfSgnJpOovQpRs3LnT26cBlnfmWbc6j+HWqfv74cQJ52POsvjB8eNs2LCh\naiL6/v0v8Kd/+iSmuZ2SaC5Rgb3jznJCCFatWkM06tUd83k4c6bUV6SEyy+X3HGHpC7srMwPf/hD\nmpub2bx5c1Xs1ixqbgeuR90/DvjmNaM66P3AXqAfJcJeAdyFilxdi4pk998DCuA+4B2o+8V/LG4j\ngrqHfCPemp+ai4sxlPPFR4E7UfW0NRc3X2Rh10Sv1XOmRlMNksX3LSxMp6I/Rf0eazQXPVpE12g0\nGo1Go9HMho3A76GihvwC+haUWL4NNYBps4+piehXAl8HLi8z778Vpx+Ypr2ahcHHUek4Pwm8jIWZ\ngk4zRTZuvJx77nkvoVBJNUylYGzMu9zPfw4vvOAVdINBeNvblKBrEzAkLfV5AoFitxCCH/zgB/z4\nZz+rqt1SSmKxNq6//ncwjDBCwPCwEm9Ns7ScJeG2V5zm5h3nS0KtBVZgOb3j7Y4oKiV0tRX47d/o\nZfXSHCA4PzrKi8eqn8xCyjhC3EEwuNnZdzwOGzaoNnVstyCbnRgZvXGjEtzdwuivfgXnz5eWzeWG\nOHOmutmIQ0Jw2+rV3LxqlbPzfLCOPbveQyba7IjoIyMQCHhFfSmhowNWrHBNExZXn/guLwueQAjl\nPZCVkp5TpypEXs8cKUMEAq8lErkFW0S3LNXPTbMkoodCsGMHtLZ62/eqzTl2vTRNwB3B/eKL8Mtf\nOo2eMk1OJRKV0zbMyG7I53dRKLyV0lethfIT6y8ei8QwBO3tu2jyeimQTisR3cayYE235J57JA3x\nUscaGhrStdc1U+WDqM74+TLzHgHaALPMvC+iUrpvR6Vqf8A3/3dQA/ZnUBHtPb75/4VS+lvNxcl9\nwIdRfeiL82uKRqPRzCu2cH6AhSmiL5YMhRqNFtE1Go1Go9FoNLPiY6hI84+VmddBKVLo1yhv6ZdN\ncbsbgB8B7cD3gU+hItDjwGXAW9DRARcz/ajB899HRYbdP7/maGaHREoDKUvqoD/VuS0wll3bt6yU\nFkJKDP8KNRLopBSAMaluKSUYSISt6zvL+nJaI5TtxZVElYVcz56EcPZfjaaZaKr/2KqH+9wasijt\nSmNK3jSe9PRSCcDuvMn/j70zj7OjKvP+91TdpW9vSXdnTyCELaxBwAgqKgKigqigooOjgsvoqzMu\n44zvOL4zo+iM4464jsso47iMy4yi4wwgKooCQcAAARK2kJA96X25fW9VnfePp+pW1e3qdKdzb2fh\n+fIp7r2nTp166tS5nbrnd57naW6fQ1aU5un0v7Vy3c5ezGtej9vElvU5KovH1FTtOU6AE124iufK\n9DkdOBvJHV4vcoPk5ZyM25HnybORvONJEb0AXB2+/7NJ2p6q/b2RB85Ank+LyHPMXUydE/R4JFJT\nCcmR/jv2vnDwaUg6ofnIQoK14XmyjskhC1W9sJ5BIvycEL6/FVg/yXnOCOvcg4gvp4dt5YE/hOVT\nsRKJMtWGiCU3IvlS95V5SB8tRP7A9iMLbrNs34r8PrgQyaV96wzOpyiKoiiKMm1URFcUZaacDlx+\noI1QFOWw5kj2L+ey0nyWISG9HyA7jPs6ZFJsLRIW/QqmJ6Ib4DpkUu3LSO7LJPcCP5iZycpBxHWI\niP7nqIh+SFM/s2/D/2erBPVim8UYkwqJPvmJZidgwYTT2LrXUGy0WCxBandAQuHNSqTeQKIQ4jWr\nrM1YkAD1fT79xQyz0OU2FHJtPGJEFifs30RVkyX7hnWsTV9mkwwPLFh8auHcI4vrxoi1JtV/0+7L\nJtltzMQc5mJT8nyGwNoJHvyBletJhqy39TdCUabPn4SvP53h8dE85o668hcjwvNjyOLLRvIaJHLO\nkrryAPGMfnPGMS3AvwF/Svqv063I4sF6D73LgU8gv3/qWYcsHn2wrrwbEbx3A6uB74evERYJaf9O\nJnop/h5ZDHAk8DVElE7yw9D28Qx7zgjbPauufBjJR39NxjGT8X7g78lOnXQD8KKM8p+G9l6BiuiK\noiiKojQZFdEVRZkJZSRMmuY2URSl2fzwQBug7JU3I8+T/z7J/l1MzHU5HZ6HeNLsQkJvNpqzgIuR\nnJogYT9vQnKsZ4UPBVgMvBE4CfGSuQ2ZdBzJqGsQD/wXIpOTncgk5O+BbyGTjPUcgUwAb0Ymf5+G\n/Dt7JPLv7s+AHzFxErQbyS3ahywsOA7JGXpMWPcXyP3Zm9f+SuAViOeSi3j8/xj4416OyaKELKp4\nWng9FcRD607g10BvXf27kQUY5yOeWhv28XzKQYI7PkZuYDf5XJy1wQyP4/SOJaQDy/b1Je66K586\n1nEgCBxKpZprMXPmOFx2STtz58rPVWNgrOJiJwjw+4/vSzhuN3QuHhyE4eE4PDeIWF31Hci5oecz\nuMZyur0H1+uNK1roGPLovHk3dHiAkVjYe/Y03O6OtoDLLxpmfnfs9NfujHJkcTuuif9MDNLBIxyX\nCrNtrYStT4ZutxaOPRZOPTUuGx2FG25ouOlQKEAtx7rFcfIsHdmAV41TFc/fPYy/YS2VoXJsN9A5\nv0TXcGeiyy3Lgk2wdCm1wRYEcnENJm/HOWfsv1heXRePRTeHu3o1ptgi1ljo6C5w1slLaZ0Tj3Vr\nYencYUxvf9ygMZITffPm9GAbyfpnZeYYA+edl+eEE+Lvp+9b7r13ERs3ttXGhmMsLzytnyXzhoi/\nuJayn+PUkxdBGC4/CGDV8WXye3rjlOqOI1+cWVroohzSPC98XTODY1+KCLh7mBjK/Zzw9W7k2fTV\nwAVAK/Kc990ZnvPPkAWdFvg2IlRXEG/xC5DnnSyuAY5F8r6vQ54j3xfa+S9MdEg4FXmm/BDyPLQb\nWaz6FsTz/kbEU7z+WQrEC/+nyDPs3wCbkGexv0RSLq0DvjSJnd9DPMD/DvHePy6085XI8+Df1tVf\njTzTtQL/jTyb7gjL/wrJWV5BRPapuBz4p/CaPop4v1dDe56NPJ9mcUf4+rxJ9iuKoiiKojQMFdEV\nRZkJxx9oAxRFUZSDgpeFrzc3uN1Xha8/AUb3VnEf6UEWZpybse99SJ7u92fsewYiYs9PlL0ayb15\nDhMnNH+AiNL1/CnwAWQS+O66facik6r/i0zIfoH0s/rrgf9APLiSKsXS8Lh1yKTld5CJzYgrEFH9\nBUwU0h3gk8C7mBi/9+/Dfe/LuI4sjgB+iUwYZ/E1ZCK4nl8gCxNeGp5POQRxyyMUdm+jFCXjtpbS\n4CCdu3bW6lgsm+/p4de/bp+gs918c554CFqWLy9y+ukdrMgXABEBR8puU/Q5zxNBOfLS7e+XrV5E\nH/dcSXYdiei+z7n215xbvYlaUnSAgQC+PyYHRQcXizSauZ0B77yyn5OP2xP/Rdi5E+64HbxwLZAN\n2JI7iv8uHZc61lr43e9g7dr4uo2Bj34UTjst1kEHBuCe6QT03ReMEQG9LRbMXeDYgbvjlQwYgh1b\nOGnt56E3/vNqgdz8+eR2JRw1I/V/VcJw34cm5KEv2jFeOnId51hbW81kSi2suPD95Ht6xEJroaMD\nzn4etLel/1r39cK20Hk2GlybNsEjj6QH28BMoiFPjjFwxRVFXve6ViJxvFKBL36xnRtuiE/tmIDX\nPvcujl88UKuHBS9fYtvyBTURHaA0Nkph2yawQXySBtutHJa0IiI4iAf1VHwZGYx5RNw9B/E0v5yJ\nntyR4NqLhH0/o27/uxER/Cqmnw7oKODzoQ2vC49P8iUmz7F+AnAK6XDvNyELFC9DnimTC00/gwjZ\n9XwDeXa9DFm8+vGMOp3IwoILkWdBkEUDTwLXAm9jchG9CxHckws87wX+C1lA8AHiv2ROaE9raOtH\nEsf8HHluXwN8LDz/VPlwXxm+vpWJC6evIyt3hrAWuYcnIs/2jV+ppiiKoigKyDPY8cQRY4aAR5jo\n3HFY07yUW4qiKIqiKMrhTBewCvGwXtvgts8OX+9HPFF+hoT234qEdnwd+/4cW0A8Zs5FBOdLkAnM\nhchE6z+S7d2TQ7ydrkfE9AXA85EfDicCH8w4ZgTxrFmNiNw9SF99J/z8Y7LDVoJMZF6DiPknI55L\nV4Ztvhrx9M5iCTK5+5mwjYXApYgn0/OYGBIfZCL2Pcgk62uA5eH5XolM+v41MrE5HT6FCOj/iUwa\nF5D89U8L23lgkuMib6LnTvM8ysFIfcjyWhjzxFb/ecJWO3hW0ytnmZ5ZnnWgcUJhsW6LYmc7TnNz\nRVsj0xe10NpRPuuozyfPMR6ZGN2qpEf6PocgbwgTx4o1ZmImbxNfW9z/ZlYMtZmbEa90SxhvPpFT\nfIJJdZ2d7Pz6rdG224lbVF5/jdKf0bUYCU0fxMdF4d2bbbNyWLIYea4aAwanUf/Pwu0qREDfiTxf\nZT13zglfr0IW5/018lxyTPg+iqb3oX2w943I5PFPmSigR0yWY/1TTMyXfi+yiNJFnguTZD2Dgnwt\nPx++P28vtv4tsYAecV34ejLyXJbFB5kYIekniADegzwbRpwftrUeuQ/1/BHxbG9HnrOnIgrXMdmi\nhsmiQ1WQ/jJMHglAURRFUZSZcyYytzOIzMv9IdzWAwPAN5FnrKcE6omuKIqiKIqizIQzEaVgPdn5\nEveHZeHrauDTyATiBkS4vzDcXoJ4ZU93BexbkDDujyITsYmYuuxEQkhm4SL5FpP5Ln8NvAm4JbTh\nXaTlkjdktNOLeKIvQMJ/XkJ2XvdFSI7wLyTKrkMmMT+EeJb/V8ZxXYjnz/9LlP0YCff+9fC4axP7\nViFhPgcRkX1jYt+Pws93IJOr32DixGw9Ua77NxN7HlWRie69LbK4N3ytz6upKIqiKMrhRRTRZ7qe\nw6uRZ83FSCjzdyORbS5DFgpmPZvkkRzgX06UfRIRir8UtvExZAJ4KqIQ8TdN094kd05Svgl5hu7J\n2NeOLJY8FpiH9JcJy0EWYmbhA3dllA8iz2RdyCKDrBRLWXZaJL1QF/IcuTEsjxY8PgCcPokt0WKA\nUyfZn+RW5Hq/hixM/V/gPiYXz5PsQRaMLphGXUVRFEVRps8bkWiHk2nH7cic16uQ6JS/mCW7Dhjq\nia4oiqIoiqLMhEXha+MTz0pYShCPod8iXiYnIZOoL0cmBS9HJkKny+vC138mLaBPh3/MKPst4n00\nj+lP4FliAfxZk9TZQ3riNyLKTHzSXtr+aEb5/05y3BuQidl/JS2gR9yFhENdBDx9knMmiTzKTplG\n3STR+JmHLvA9tJmWV2rot2vS2wT/3oR7bNILuRnEnrkWay2BtZleuzbjP3HLDSZWPlBkG55pe4Al\nqPtvFg3FmoQ1JrvPLGAdJ7XVrsYG8YYlMNE1NXvETJPpeJdn3q8mjiFrJfx6uNnEWA+CMANB/akj\nWwy1UYMJxEl9Krd2RZlInLdjetyFiLzXAx9GhNmtwEXIgsMkkTf1OLJ4sJ6vI97oJeKIR1MRPes+\nNs36SbZPUh55rtc/8zwTWTD6LeAfkAhC5yOCe/Rs1Uo2fUy+oHWy883EziXh66XEHmn127vCOnMn\naTfJtUjY93nEOdH7kMhN509xbDLEvKIoiqIojeFM4CvE//5/D0kNuAxYgTiD/DLc14qkY1k8yzbO\nOjpRpSiKoiiKosyEeeHrVPkOZ0IVCXc+inhQJ8Nh/gSZaPsoEor809Noz7BvOTiTWOChScq3AUcD\nHcCOuv0XICHSj0F+cEQeR1E4zXlks4FsD5yoDzoz9gFsIdurajvird+O9EM06RgJ43ORUKlZRLko\njwF+P0mdiO8jXvA3Ix7wNyMC/hNTHBd5LDlIH9X3o3IIMHDnnTy2bRuFhFDY2dnJgkWLMLU44QEr\nWi/irLOPTR1rLfT1Ofi+hHG3FpYttszLD9NT00QMHQxhmiCOdrv9vGbuz8k78tO4urCd4bOOIDDp\nVKy7R1r58o+X1a7HEPCsZS9m1Z8fRy3YuzGSw/urX5X85MaIMjl/Pg3HWhgbg9HRdC7wrq44HzvQ\n3dfLeXd8bMKhpz7eT3/fUJz62hhKT76TxztX1kLXDw1BudxYs0e9PF958OncsFWCT1igxanw3qN/\nTFdhhCg0uymVyH3845Av1OyxwIOPtbDm/mSuccvm3aM8+v0hoovxrcf64VZe2ljT8RH3S4d4cUcO\nWBIEFBJ9zo4d8IlPSOLxZJz8Y46BVavSgnqxCMuXp3Oi904W1XlmWGvZ89WvsvnGG0X8BgI3zzln\nvY5V7zsvNsfCov4+2NGbstEdGaPnW9+Lx1VgMSeupPLKl2MKRQCMY/Dn9mhYd2UqIk/o7hkevxX4\nHPIM+FLSz4BPJF6zwoNXkUWDJzC5R3c90V+ayXJzN4oS8ST0PyILHJPC/UlIKqIDTfQFvx5JkbQ3\n1k+jvSrynP9JZGHEcxBv9z8Jty8C75jk2Oi5Osu7XlEURVGUmfE3xM89n0Oi+yTZCPwPMi93MRLp\n5s+BD8ySfQcEFdEVRVEURVGUmRBJK8UmtD2ICNO3IxOm9fwImUBdhnipb56ivQ7ivItTibr1VJg8\n36UXvtZ7wfwrkpMTJCzlBsT7fRgR3S9g8tyUk4UXjc41mUIx2XGRb2GUKDmaEI4mH68Mt73RPsV+\ngKuRH1vvQsJ6vSosvx/xrP8y2YsDkuOnwXKdMluUN2+md9262o9LCzhHHMH8009Pieg9BcvyFfNw\nEsPYWigUoFqNj+3p9mh3d9CW+IoVGW+KiN7mlDmj9CAFxxVj5vXAqlbIJX4qG7juF+38bt1c0Qgt\nOMay4kUnsuqsRdSUSWNg61YR0YeHY0HRn05k2n3EWum0SiVdViolvIcNrX19HLfp5gnHHrdnG5R3\n12y0xnDHwJ+wp28lxkrx8DB4Hg2l4ue4bfsK1g6KY6UFOpxR/k/7AF2FMMWttbBsGc4ll2B6EuuN\nLOz4NdyxJf5DZgzcu+VR7rz3XuIVAT5zuyb7EztzAmSVTzvx+fPW4tV7jw8Pw69+BQMDaRH93HPh\nhBMkGX1ELgfd3emxUmzwP6vWMnrH7fT//ne1eAOmpYWjn/1Mus87L1XP/GwM+hNj1xic3btpu+kn\n8WAIfDzvhYy/8UpMa4dUcyAoTeYkqyg1ooV9bchXqT4X93SIxOWFdeX3ha9tezk2ep6ZKkVNxBZE\nwG52zs/nI17et5NOyxNxdJPPP122hK8jiJdao7g73EDu0euAa4C3A/8O3FZXP0f8HLsFRVEUZW+c\njETBM8DDSEq6fQ1B5SIRQo4Kj/0NMsehHH4kIyZ+ZpI6PrKQ8eLw87ObatFBgIa9URRFURRFUWZC\n5PnR1YS2Hw5fJ5sYS4rmWTkl60mKszP1fpou5yMC+kbgaUju8YuR0PRvRTy2DwaisJ9vQyaH97Z9\nexrtVYG/Re7Hi4HPIl5TpwCfR/JdZhHdj3Gml59UOQgx1k7YMEYE9Lpw1lNGr44+G4PM9cyGZ6uD\nwWCMg8HBWFO3iRWOCTdHREOwENRdSOStu9ew9g1iuu0bZ8JWu9ZwAwemisbfIJMNFseYeHPAMHGs\nxPc/3Ex4PxKWGwsOdSH4m2d+vUXxIpH6i3SciduB9tJOfj+R5AppEotBkvdhwvW44Mh3Jm57dr6p\nyiHPELK4DibPqT0VZ4avm+rKf4p8/ZcAR2YcdySxB/r9GfuziFYgvXxfDJwBUbiSRyfZf0GTzz9d\nov44D/GebwZR7voo/dEZGXVOQRbHPs7k4egVRVEU+BTyb95XkBzXvwRuZHqL5CN6gFuR9HL/AnwV\neJDsRV/KoU+0KrZCdtq/iGTEmb0tYDwsUBFdURRFURRFmQkbw9fphsTcF6KQ64sm2Z/MuTQ4SZ0k\nyR8Ax83QpunywvD1W8DajP3HN/n80yVaqLAY8era2zadPo4oI2Hc341Mcr4I8aK/kuxcWcvC18f3\nyXpFURRFUQ5FfhO+rp5k/8VMLtBeTJwLvX5R4pNhmUG8o5KRN3OIN5VBPNazns+y+BoSSei5TMzB\nHrFkkvJ9IXomO4eJwsazkQWPBwO3AGuQKABfIY7yVM9zmN6i1ReSfa/zxM/LWWl+orFzyzTOoSiK\n8lTlbcBfAj9AHB8KwPuRRf9f3od2/g04C/l3MAcsQAT1DwOvbqC9ysFB9ExSYGLUnyTJBYuHfVQC\nFdEVRVEURVGUmfBHRFw9itiDplH8IHw9i+xJuChs1C6mL77+LHy9aq+19p9o0jYrRGkReEWTzz9d\nrg9fr6A5IfkjbgAeCd+vyNgfTYT+JmOf8hTCNtN9+EAwWxd0oL2bG8Hhdu/hMBzQCQ7na1Nmg+gZ\n74WT7P8g8nx3E/B14J/D17uRZ7lWZLHeNzOOfS8ipr8C+B0ywX814kF3GbLQ721M/6/OHuDPkMWA\nn0NE23eHbXwQ+C0Sbnx/+QPihb4cCen+3vC8XwZ+hXgOHgxYJFf5VuBPkYhDn0aElfcSRyH6DTBv\nkjaSfDJs6zrg75Br/gukX09HFnJm5V5/Ufj6g4x9iqIoisxJfAhZCHZV+FpF/k29CZkDOGka7Twb\nuAj4T+ALSBjvXUiUvTLwkUYbrhxwfpR4/6pJa8FrEu9vbJItBw2aE11RFEVRFEWZCT4yyXUxcDYS\nRjOLo4gXbi4IXwuk8zvuJu3tvAaZMDwP+CLwRmA03HcmceiwLzL9idDPIN7QlyMTfP9IOkf3CiS/\n+v6KuevC1zciHky94eciMhl61H623yi+D/w1EnL++8CbkPuQ5DRkkvSvp2irgPwgj0K7JVmF3Osq\n8armJGeHr7+ept3KQYjJ53Ha21MrtJ1iEes4iZzoEFiD76e/tNZCPh+niY4+e4Gh4sfhpP2gOWKx\ntRZbLmPdMCf62BiMjNTlRLfkqFIsip3WSlh310Th2xNhr0FyWre0SOUgSOfAbpzhsa1R0egodmws\nlRPdeh4USxOOdXJ5jOumQna7DuScABMe7jpBU/LQR1HBo8swjqFsiozSgvSlBYrkA4NJZGy0YS76\nlkJAmDEAgJxrkKmN5BhpwngxBretnZybq/VKrlDEdwt4biIHu1vAaW3D+H5sh7VQKE6wysfBM8Va\ntYrxCZrg6+AUCvKdjApaWjCeBwP9tTrWBpiqJ3nZk4szrIVSSXKiGwO+T5AvUqlSyywdFivKdPgN\n8qx0PuLFvbVu/0+ATrJDmG9FQtN+nvQzXMQWxAv6i4hI/4zEvrWI+H37Ptr7A+T56NOIR/pzE/tG\nkNzd+0sFeBnwH0ju2k8myr8Ybi9uwHkawWPA05HnvtcA76nbvx0RWrI8yOu5GRFyXl9XHiDj4J3A\nWN2+LuS3x+PIYgpFURRlIs9A5l6+i/xbleSHwAuAlwIPTNHOJYljkvQhf8MvRsT4qdpRDh2uRRbM\nnQZ8Iiz7F2pP/XQgEQ3+Ivz8cxqzoPCgRkV0RVEURVEUZab8G/LD6VImF9HvZmLe9BNI5318G/Jg\nnuRNwO+REGHnIl46XcjEXQFZ7fpP+2DrY8iK6e8iq7LfDNyJCPxLkZyLn2b/RfTvAx8AViKi8U2I\nIvT80P5rmDjheCDwkByf/4P8gN6ETGo/gXj/L0fE70GmFtEd5Jreg+TGehCZcD4SuXcFYs+yJCVk\nUrif2DNeOQTpftazOPmSS2iJlFFjcAYGcHbuTNQK2LluDg9vFgE6IpeDq66CrsRfCWNcHtixgPW7\nbNQcf3xiLl7QeHEx2LmTkWuvZTw6t+NQKBQmeHi/8K8+wvOufgPGig3WWubs7IctW0gJtpUKvPWt\nMD4ubYyOwi+b4ES4ezd84ANQiMXbcc9j9+goNuEp7J96FuMf+heMceNjbcD8G75L1x3/W1OzHeC0\nleP4x26D8Ir6Bgf4Ses4jcRxYOFC6OmJtX7XlPhC+99QcEV0tli63RwX7+ykvZw+/oSlA/z9lXti\nbRr46nfbue328xP5yccxpvEOEfm2Dk770Bd51upzZO0Ekld8o51DMshf3g1Y8vI3kHfjFQAWKA7u\nobT7STHdGKwNeLR9FWsWXVrLL171xni8tKuxSxeMYdGrXsXxZ50Vj9QgwP3Nb3C++pX0WH/GM2DO\nnLS3eXs7fP3riYUlls29c7nhRyW8oHYK1q6FY45ppOHKYcxnkXDgr0fE2CQfCbdFSMqXuchXaBMi\nnHpTtL0R8ZpbAhyLPINsYv9Cjf4K8Yw+Dnk+Anl2eYB4kWfEKVO09dpwq2ddeOyRyPPXOJLHdijc\nn7UyaOck5UkmS7vUMsVxZ+9l3zbgDcA7kMWteaQfdiLP20Fd/bp/KGu8Gwk1fAKSc7eILD7dzMRn\nxojXhvU+l3EeRVEURTg1fF2TsS8qm+rfq2Sdydq5OKyjIvrhwxiSXuYaZKHbtYhDyjbARZ7PDDAA\nfAmJJHPYL6VVEV1RFEVRFEWZKf+FeJy8AplIq/cWAQl9WZ/fsZ4nM8o2IsL2R4BXIj/QLPID7RvI\nw3x1H+29Pmzz/cBLkNCeICupf0g6dFUA/GKKc/wemZhNTqAOIYL5tYiA/GrkR8Uvgb8F2pAftffV\ntbU7PN89k5xrPNxf7y0+HJY/sRc7b0YUnnpd5gkknPo7kNXGZyKLFMaRCcyvIp5ASXaF50vmE60i\nk6AvQELwrwzL+4HbkL74zwy7Xop4m13LxBXyyiGE29JCYd48im4o1EaiXF9folaAj0O1Cm7CCxmg\nrQ06O+Oa1hrGx12qiZ/jFc9tThRpz8P29WGRL0j0RUnO9lugjVFau0lN2ef7AvAyvHY7O2Nv3kJB\nXOsbje9L/4Qy158AACAASURBVCa83K3n4deL6KNjeF0LJ4joQVuH2JZY+FDMAwW/1gFjeT+14KFR\nuG6do78xDDrdqXHhGqj6ae9ma6EtZ+np9Gv3JzCWtpKDMW2J2+BgTOOnOozj0NK9gNYlRxH4sU19\nfeAHsQ+9LYC/YC5ugdpfXWvA4sOeLckLp2qKDOe6ahK8RwHPKdBIT3oD5Do6KMybh4nGhu9DeQy2\nbk1FI2BsDFpb44OtlW3BAhnH1oIBr1JieMTgJb+jFRRlunwdeCvwV4iX9WBGne3hNlO2MtHLfX95\nmOyoOo1kU7gdCgyz/3nJA6YvvrQiz9IbkGgEiqIoSjaLwtc9Gfui+YTF02gnqrO/7SiHFsNIFMVO\nJKS7iyxsjAiA7yGpdaZa3HhYoCK6oiiKoiiKMlOqiPf2x5FVql/PqHPpfrS/HfEYfzMwB8m7tb9u\nkeuRsO4goag8ssX/cUQU3huT5Vd/AgnLCeJBNUjaWyZrwnHNFOfbM8n+x6c4DibPOwoiXn883HJI\nn/Ttpf7vM87nI6uTPxN+bkG8kobYO29D+vmzU9RTDjUi4a0OQ1pvnm4679lK+z3ZaQxgjUktQ5lU\n0K8X1JtFUvhsbMPEywhmL996/dnqx0qtMKtLZztFd8b5TOK1di2pvAXZxyWPnXWMibf68qyxWyuz\nE2zOakZR9kKARO35LvJMdu0BtUY5VHg18oz5LvZ9Ia2iKMpTiSiXU9YitYHwtTVjX1Y7luzf9fvS\njnLo4CDRXt4efh5Gohc+hvxsOQm4EFkM+Sbkee7js2/m7KIiuqIoiqIoirI/XIOI3FcD3yFbkG4E\nA1NX2WemEnkbQf/UVQ4aPPYuoE+XcrjtjRcgnvofRX6QKYc6WcJ58rOxWAuBjUVDy961utquJouk\nkehZ74GeOv2+CuK1i8heUHDQMYs2TjVUanWm2159/QPQ5VGO9uS1ZS4CyFgJkLS1WXabmoofC+Hy\nhQwkGkEyyXzyIupfgUm/uIqyb9yApI9RlOnyjXBTFEVR9k70W7wjY18U/2s68zZl5OG1nYmC/L60\noxw6fIRYQP8NEr2xPhLB0cB/I+lYPoZEJfjX2TLwQKAiuqIoiqIoirI/VJGchlcBq4A7Dqw5yiHC\nKiQE2GG/avkpQT4veZPdOGR4UKkSdC+ofbYE9PRYjh0awEmoi24OWls6KBQSYcmtpeB4tdDTxkAp\n7zHUBE9Xv1BiaOlJ5IzIjAWvTL7ci7HJ4BGGqlvAGycOz23BLVfJj41N9D6PwmEbIwJlbnZ+dpt8\nnlxXV0qidVpbsCO9YJx4lYANcL1xsS0Rzp2hIejtjQXVwUHwGhuhz3GguxsWL07rsPl8WsN1nYB8\ntUy+GleyQOD7DPtxZYulrVjlpMW9cZkdZ8Q2Npd7RBDEW2RrLpeKqo8xsGdP6uuABbp6q7SOjJAU\nsgtUmDMnXrzheY2P/m+BXSMlnujrrHW6CXy6OxbRcdRR6fHb3S050WsHW+jokIuspWuwBEZSMyTD\nufuHfTZERVEURVGUQ4Jd4WvWYrWe8HXnNNqJ6vQwUUTfl3aUQ4OFwHvD97uAl5PtZPEYkprvfqCA\nCO/f4jCOEqMiuqIoiqIoirK//E+4Kcp0+dSBNkBpIPPnwxlnSI5tAGsZL1uGh2K3cwu84tgNvHzz\nzRiTEMwdh9HjziNojR0lXDzm253kwhRrxhiGtvTR258UthvDyMJjufOqj+A6eSywsHc9z1h3HXk/\nIcJay+62ZezZZGJPdWtZ/NhOWp7YkFZ/29vhwgth7lwp6++HG25ouN1AWtgEij09LFy9GpsU7efM\nhXuuJ+Vjby3O4JMijiZZswbWrYs/j42JGtxAikV46Uth9epYRK9WpYtGRxNmF8ss6HuIueOJRNsW\ntnoLuH98WUIwt5x51Aauf8evwMj9Gfc9/vkXjU6FLPZ6nuT+jkR0Y6CnJy2Y9/fD9dfLmoTk0Djb\n7ObF3EfOhEK2tSw5pcS5555ZuzuVCtx6a8NN50f3reQPfecQjQPXBFx17nLOu3IH8ZfUioKfXBEA\nctPmzk0suIBxN8fOXaaWB90YGB5uvN2KoiiKoijKPnNf+Hpmxr6o7P5ptHM/khruDCSN3EzbUQ4N\nLkBEcYAfs/cohQ8jnuoXAIuBpwF3NtW6A4iK6IqiKIqiKIqiKMrMcV1oaYlFdEQcDxI/Ny3Q2QGt\nnaPiFR0SOA5PFixewvs2B7Tikyd0bTWGYq7xAjqAzRUo9xyJ6xSxQNUOQls7xk/8VLYBgZPH88HU\nnIgttuqL6plUSj1P+iLyRq9UJoqSjaIuEbXJ5chFXsORPcUCjI+kj7MWAk/sSnohl+uyMJTLDXcv\nNkbWGXR3x0J0pRJ7c0ep513Hkgsq5P1YRLeA9QLKfp5I+LVYWgoBR3UN18ZV2fdpKzTWg35v1K1l\nqAnKg4PpoVHO+ZBPhzPI49HWFhflco33RAfDcKXA7pE2aiK6E1AudcGCAGxiDGRFHsjn5QITIro1\nDr4fD4/J0qgriqIoiqIos85tiAB6MVAEkiGaLgtf/7vumC7kiTSZju7niGfyZcCPEuUdSHq2TcSC\nvXLoszTx/olp1N+YeL+Mw1hEb9KveUVRFEVRFEVRFEWpZ3ox2WdVj4tyWM/mOZvF/iiZE5J4N48Z\nmzmpiYfF3VMURVEURVGU/aWKRH7rAb4IlJCH5bcCLwH+F7in7pidiHdxkl8BtwOvAa4Iy9qBrwFt\nSD5sXUZ5+JBceT1n0loxcyc59rBDPdEVRVEURVEURVEURVEURVEURVEU5dDnY8BpwBuBPwE8xIP8\nPuCqabZhEfH8BuDbwJeQcN8twFfDz8rhw4bE+xcgCy8mWyRRAp6b+Ly+WUYdDKiIriiKoiiKoiiK\noswYYwwWCJKhxQ0YE0yoF0Q7QywGYyyYoPYr3YT/DyLv4rqw5Y2332JMUJshsEB98HiDwWDF1lot\nJlxPzdYw9HUzXTOstbGdkWt3vS1ZTKcvw3vaDLLGRlQOsU+5NYbAJEqsjAuDjePqWzmuOcH+szAY\nE2Te8vrPqW42YnmAIUj2rDGpvojHVyNJjtnEe4N8x5Ix56O47PVlydD/jnyX0+0piqIoiqIoBxEe\ncDlwDvB8IA/8EfgZUMmo/6LwmHoeB1YBLwNOAcrAjRzGobufwvwW2A3MQ3Kc/yUS0aAeE5YvCD/f\nxfTCvx+yqIiuKIqiKIqiKIqizJh169bxpS9/mXwiMXTVM4xXDanc1Ts2k+/fNUFEH7h/E36+pSai\nu/h0MISbkEYfevRRcnOTEeMaw9DQLm666YsYIz+NO0e28ccnH8ANqikhsd/+mOGH7q/ljzYEzNnx\nMO29m9KCY0sL9PfXcqIPj42xbfv2xtsNfKdaZVEyZ3l/P9x/f5yg21rJU//ooxMb6O+HoaG07Vu2\npBJyj1arbBkebqjd1eo4N974YzZseKBW5nmwdm2cXt5aaC9WKG/ZRmveozaGrKUvmMMOr4fkuOop\nb2dB+XFMWOYFARt27myo3WJ7hV/96ids3PgANpFHvFRKp70fHYV77oGxsXT3jjqPs9ldh5sQnv09\n/XibNtWux/crrF//IBdffGFDbe/ru5lqta92HsdYfvrLQR55dKRWBkii+vpY+7mcJLEPL8Ya2LrV\ncN99Lr4fH7t7951Yu6yhdiuKoiiKoij7xa3hNhU372VfGfiPcFMOX8rA3yMpAAA+CTwHiULwGPKj\n4STgLcjiDAAf+L+za+bsoyK6oiiKoiiKoiiKMiOWL1/ORS95Cb7vp7JS513IF+tccduOwGSIbHON\nU+e26wJzU+2t7OrixBNPJJ8QefeXnp4e3vjG19DfP0gkJBoWYYLLJmTYnuu4zDEJpRQHs+R4jD02\nXTHhhQ7Qns9z+atfTalUapjdra2tvPqtb2XHRRel7aw7d6q8HmsniqV19VqBy9vaGma7MYZLL305\nDz/8CI6TWEhh4bzz6upSwHGOpG4E0Y2hC2oitgjni3BYWKuXA1723Ody/PHHN8TuyPaXvewSHnpo\nPcY4dfvSdTs64MIMDdxwFA7pa3KNwU3dswIve9lLWLlyZcNsP/nkk3nPe55ICf9gcJ05GNM5vUaS\nUSaAJUfAy5ea1BBynAtYvfrpuInFNIqiKIqiKIqiHDJ8CVgMfABwkAgEL5ukbhl4O3tfgHFY0LyY\neIqiKIqiKIqiKMrByBuAa4E5+9uQtfbtvu9/Yf9NmhpjDE6WSLwfBEGArReTG8yhajc03vbZtDva\nGsVs2Q7gOE7DbLfWEgSzE/A+tPu1xpjvNPE0Q8iE3beaeA5FURSleTwb8Yz9JtPPzTybLAc2Ar9A\n8gIrysHIrch3yWU2MxtNn88Bfw48HQn3rRxaPAN4N/BCoLtu31bgeuAzpPOoH7aoJ7qiKIqiKIqi\nKIoyYw5lz9NGi9uzhdo9+xyqthtjDunvqKIoiqIoiqIos8oa4ArECfsIYh25jIjoTylURFcURVEU\nRVEURVEURVEURVEURVEURVEALLDpQBtxoFERXVEURVEURVEURZkRQRDg+/6snMtxnIZ61Fpr8X1/\nVkJ0NzKXO4DnebMWWryRtvu+P2uhxXO5XEPDuR+qts/md9R13UPWY19RFEVRFEVRFKUeFdEVRVEU\nRVEURVGUGfHggw/yzx/8IIVqlaTkZz0P63mpuqajA9Pamm7AWuyePVAn8tXLw+PAM174Qt729rdT\nLBYbYvv27dv58D/8A+M7dsS2Ow7k81AvYFYqUHc9ZIiF1nHx2jrBlZ/aQeDT0pLnmms+Q1tbW0Ps\nHhoa4iMf/jDbN24kn1hU4FmX0aAIpG2vvxRroVy2VCrp8kIBHMck6gXMnevwhS9cQ3t7+37bba3l\nE5/4JGvW3IfrtqTsay/5OCZx14MAxsbE2CT5PNTf/2qV5MVYoOK6vOd97+OMM87Yb7sj2z97zTXc\n89vfUkzed2vl/Ek7HSfqzHQjQSBbAi/fgldIjgtLEFR4z3veyerVqxti++23387nP/8FWlpKqXIT\n+BhbtyhgZGTidzEIsOPj6bKM84y6Llf9xV/wghdo+lhFURRFURRFUQ4PVERXFEVRFEVRFEVRZkRf\nXx/5++7jzQsWUEiotf727Yw//DAmFBetMRTf8hbcF70oLThWKvjvfS9sjVOrWaBCLNQZ4HZjePyI\nIxrqCTw6Osr2u+/m/+Zy5CLb58yBZcug3uP9oYdg8+a0Il0qpQVda/E6e9j24supdi8ECyMjA/zg\nB1+kWq02zO5KpcKm9et5xfLlrFiwIDo528rzuanvDKo2tt0YyGX86r/lFsvatTZVb9Uqhzlz4jqe\nN8TGjZ9rmO3WWh566HEqlXNYunR1bRgU8pbLL+ijrdWXm24M9PfDL38Jo6PJBuCEE+DpT4/LjIGH\nH4a1a2v3phoEfG7tWnbv3t0QuyPbH3/oIZ61YwfPmDcv3uH7sGGDiPjGiI1tbXDGGTI+kmN9ZAQG\nBpKtsnvx03ji9JfUlj143jg/+9lX2LNnT8Ns37VrFytWnMiLXvTi2BwLxdF+cpXheEwHAfzwh7Bj\nR2qc29FRRm+/PSWuB0BSajfAv+fzbLvssobZrSiKoiiKoiiKcqBREV1RFEVRFEVRFEWZMfMKBZ42\nZw6lhPBWGRhgLBIVQ0qLF1M48cS0sDg+znihkGrPAmViEd0BdgDbm2B7Wy7H0zo6KEa2d3XB4sXi\n8Zxkxw7Ysyctore1iVCasLwyt5uuY09lfMERYGFwcBdtbR0NtdkYQyGf56QlSzhp6dLauR8bW8y6\nlqdRsdKf1sZO0UmshfZ2H5FCTdimpa0tx9y5pnZ7qtVeyuXG2u44Obq7j2PRojNrtrQUAk47cTtz\n2hMi+p498MADMDQU97m1sHw5nHxysjNE/N2xo+b5Pe77dG/Y0NBQ7gA5x+HYjg7O7O4WW4wRYbml\nJfY6txZaW2H+fBkfSQYH67zTLVuWHEn+uDOJnPA9b4zOzh7qownsD8YYli1bzqpVZxAEcV+2Du+k\nWB5Ii+jz58P4eFxmDH4ux2BdXwZAMi6DAywwBg3kriiKoiiKoijK4YSK6IqiKIqiKIqiKMqMsRCL\niokyW1fHWiv1EiK6tXZCaOig7vgAMsNHN4zI9qRt9WHEk58ny0Vu5XqCwBJYMLXLbY71NtWX8r7+\nEuq6u3ac2JS+Q1KeDOfeFLPD89TbGL0JryUIQgNsbKYN4q0mMicuMixvZq54Wz8OssbLpB2fHMkm\nvF+W5K0Iavem4ZYTZIyN2ofJFhyEFestsiSXYEz8ziqKoiiKoiiKohwOqIiuKIqiKIqiKIqizBxj\nxHM76WUbhUNPeLTi+xPzR9d/Tra5t8+NxHXj9jPynAPizt3amvaKrr/m0PXbMQFuGOza4Gc01gAm\n9JnBGEvB9TBWbLJhubXuhEPjMO9hLSOR6ZPR6Y1pfLdH540c/a2Vz1XPMF6NTmbAdzGmAE6cOx1r\nCYI8QdlQk28NOJ6L4xRr96JqfQLTJJ9o15Ut6n9jZFzk8mKSBb/UxphfJKgWSYrmttqCrbSEphuw\nAeNVB9cfr3miB35lYp7y/cVa8DxMpRKbbS2MjcLocLpetSrf08S4thYqxU5sLoguEWN9jB0ncccm\npkBQFEVRlL1zPvDTA21EBlGYIf2HTTmYiZ7aP8bBuY7xWQfaAEVpFCqiK4qiKIqiKIqiKDNn8WJ4\nznNSIlq+VMJZv148igGsxd20CW67baKIXi6nmjOOQ1trKybRXovnNTw8NyDi+FFHxWpxqTRRSLcW\nLroIFi5Mu/KuXSv5uBPCer69k6W5XQShELqHflpIX19DcBwRbxMhwxe2elza8yBBIqj2tuEOfvH4\nMaTDgxvmz3dYudJJrXG49FLD0UfHtYaH4dvfbqzZuZykNT/zzLgrg8Bw+/pubJDI0e51Ulz0Gozv\npY7fubuTJ7/bVftsLSxqP4Mjjzi6do2eX2Fn6dHGGg4yvleskAsIx7V1HLwrXldbfWCAbbtyfOpf\nu9jd78a9bmB0YJzhvlGwcQj987wR3nT0/9TKyn6V7tEnafRcaMvD99F26w21Trd+gHvLzbDu/vRK\niUplwgKNkXw3N1z6TQKnII7/BpaVH+VZAz8nnwjqXnriieZ8RxVFUZTDlSPC7WBF/1FTDmYiEf2v\nDqgVivIUQEV0RVEURVEURVEUZea0tYmQnvA+d7q6cHK5WEQPAlFld+xIH1utgpcWSo0x5PN5TC7+\nueo2S5xzXejoiIXzfD7b/Xr5cjjllPhzEEBvL2zblqrvtJZoc8qAePiWGa15pTeUepduoNW1rGjp\nT3j/ixjq++lLshZKJYfu7nRzRx8NK1fGZYODotM3EseBnh5YskS60BioVg2PPtbCeJmaN7fjlCiV\nOkk6lBvgiX7Y8Ej6WoZXlCgsnV/TnT2/zFius7GGgxjb0SEXEI3rXA571lnYto6ag/nIo7BmELZs\nS6/HGByo0tdXqYXMNwZW7HmQ7tH7MWHZqOdT9EYaPmvvDvSS27YZE3cS3Hcv3HFHenAcdZQsJImw\nFq99Hk8efT5eTgaDBUoj95DfdR9FO17rG7e3t7kRIxRFUZTDjf8C3n+gjchgCfBLaFY4IUVpCKPh\n62UcnJ7obwEuOtBGKEojUBFdURRFURRFURRFUaZisnzYWfsPCNkCZlZpZH69uB69Rinim0V9KnFD\nwpaETfW2p+ol69i6z80k0TEmTARukn1lxQbH1Pn/1xtaK0uEp28WUWx+W/d5qpj9cdT81DUazYCu\nKIqi7D8DwPoDbUQGUQgh/YdOOZiJViH/BGhwLqCGcP6BNkBRGkWTEoUpiqIoiqIoiqIoTxmyFNcD\nLiork3IQOgzvz3CZVQfoxMn2xeRD8utga/+bTkVFURRFURRFUZTDCvVEVxRFURRFURRFUWaO60pO\n6Cj8uoFKZw/lxcdBELsct8yZR6GlZcKx5ogjsHPm1OrhuHidc8HN19rzB/on5ipvAGU/x4aBeeQd\nCUXf0paju6cN46RV2eHBFspPJs4fQE+1hc62trSCWyrJNfhhBFDfb4p66geGXSMltgy1hyWWXMGl\nrVjEMVHQbstoUKQ8XnewtXRWdnOk15/y/m7f5ZDviD3R80MDmPJYQ+22FsbHYWws7hbfh45Wj1I+\nUdEx5ItOKse2MTIExsbS7Q0Nwa5dcZnvyzkaTnSy3t7Y+FwORkfBiVIZQMGzHNnt01JND43hdstw\nV4AlDue+cG7UGSY23m9C9NiWFujsjHOiez47ikcw4PSljFy6cAnFzmJCE7dU8kvY02vwwuEfAD1e\nC7uchRRMtXbsqPN44+1WFEVRFEVRFEU5gKiIriiKoiiKoiiKosyczk445phYRAd2tJ3AuuVvIHJ5\nDqzllI4nObK0i6QbtLGWwgc/mGrOcwv0zzsGP1eUOgaGf38L9rE/Ntz0xwbn8bqbrsQxBayFlSfA\nG85yaGlJhBo3cMuvW7h/fSFON24CrnzmsVz0zHwsgEaVrZX87wAjI00RRQfLRa675zTmPXEiABbL\nggWGZz3bkI9T0/P4uMOjj9a5aduAV+74Hs/r+wHJezH3Gy3kW1wRUA2UqhWK/Xsaarfvw6ZNKT2X\nnGt53hmDlIpxJErfugzSiTVurcwY2LoVNmxIt/noo3DrrbEWbC309zfUbKFahbvvhkceqRlvCgXy\nx6+Erq5w9QEcUfb51KsG8CpB3L3WYjs68bt7EjnrDZ0PP4m552FqFYMABgYab/tJJ8EFF9RyuVc8\n+Ood5/PjP3pE60UcBz71jhzHrzS1tS8G2Lwlxw//T0ttMYa1cOyRxzB83l+Qz8UV7221HK/BDhVF\nURRFURTlcGA+8FJgNdCNpODYAPwn8OgBtGvWURFdURRFURRFURRFmTmOA4VCSkT32jsZnbeQmogO\neLlhcAegLpa46e4Wb/boc65IMO9Igpx4rRsHgu552I2NF+iqQY6tI10YU8RamFuB0RzYpFe0gb5R\n2L4z1j8dA6NeAVpb0yJ6RKQQB81JUehbQ3+5BcZaw/NBYVySeAYJ7/KKhUolITAjua3bvAGW+Fuk\nc6Md/S2p+5D3PEwTvOir1dhT3FqwOUtbKaC9xRejsVStwbOJa0GuwXXlepL53INAxPmk13c1dpBu\nLGNj4o0eGV8oYMbLUIld3/NBlSVzRsPFEzUVHbqKsDBRZoAdVRivxFEWfL/xY8YYiRTR3l5r23rQ\nVyjxpJOvndpxYKwb/IVgIxMMeGMwMAjlsjQVBDAwVqTfLZLPxacoO62NtVtRFEVRFEVRlNnGAa4G\n3gNkPeB/HPhquH9kFu06YKiIriiKoiiKoiiKojSUelnZ7HPy6MQBCa/YZmASbWedo7bPpJyIp9l4\nc6yObKm1nmFX0u76svhTQtBNXmB0YBNE9Og0SSG8zuJptZHVJsxi7vHaTZgw2sMxmyi3iS110zL6\nvFnUdYwJA8sne91EtibKat+/OjMzj1UURVEURVEU5VDFANcBfxp+tsAaYBNQAs4F2oG3AMcALwYq\ns27lLKOxthRFURRFURRFURRFURRFURRFURRFUZ6avINYQN+NiOZnA5cDlwBHAzeF+88DPjzL9h0Q\nVERXFEVRFEVRFEVRZs4Et+DUS+2DwWKNFbf01Ebaq3cSb9xmeRgHNr1ZE24kNithrJNb7KJbdz3U\nb43HRrZGdgeT94+1Ezf5ny9xu20A1p94gU0KRW8xE3rIQGIs2FT4+fi49PXWtrr7F9CsXg/tyBqf\nWeM33eGTNJa4edH7ZmCMxGt3HMlFYAzWmszbXX8ZWZcb3Yua2Xby8acoiqIoiqIoykFPAfjbxOdX\nAr+pq7MLeDnwcPj5ncARzTftwKLh3BVFURRFURRFUZSZMzQEmzcncqJbyoNL2LNnQRgwWvj1zh7c\nQYNJlLnG4yUL7qQzN1YrM+2d5J9/JG5rST4baboZka57SiNceOpduE4ea2HpUsMxAznyY+l6Tz9q\nKXPnzo9txDCan8PND6brOQ50dkSpxQ39w30MVwsNt7tUtDx91RjLFksaOmvBLToMD7dgwo4yRlJs\nH3103cHWsLNlNb9q9VL34rRTPbrmJOKNl8uwZk1D7XbxWeZvZKX3ADZUXa3vcOc9C8EthBnRoaUQ\ncMyy3bTmY3MATunxee1zvPTl7O7FbtteGyCerfDb3q0NtRvABgFDfX30jY3VJHFTKDDn3ntx58yp\nqch+ocTgshPxC6U4I7q1tNgybTt2pBeXlErwzGfGg9vzYPfuBhtu4e67obW1ZqNrDc9cehbulcfh\nJAzq74f77osFcWNgy5YAzxvG8+Lm5hQ9zjyiQqkQV3zi/uFweYSiKIqiKIqiKIcYZwOLw/e3ArdM\nUm8U+DTwJaAFeAPwkaZbdwBREV1RFEVRFEVRFEWZOf39sH59pBxjsYyWHbaNrIzFRgO//90S1q9f\nkhLDi2aMs4+8hs78FiK11CxcROm55xO0doEVYbpYbI7pS9sHuPqcGyk4LmAxrovT25JW7K1l3inn\n84yj5tVE5yAw/PLm+fz7bfNTVfN5OGo5FFvk89joLgbKpYbb3dYacPHzhjnp+IHISLbtKfLLe4p4\nfnq1wapV9Uc7bJxzIWu2X1ArMQYWvbqXrhWV0DXcyH3dsqWhdrt4rPQeYHU19hcf9Qr8082voLfa\nXhPRl8wd4+0XPk5HWzV1/HOWjPPsV9atcLj/frjttlqi7vHA0j/2aEPtBgiCgN6dO9luLZG/uJPL\n0X7LLbitrWGJpdqzjO0rL6I6d2HNAd0CPTsfoHXzg7GIbi0ceSScdVZ8kkpFVOxGc/PNcM89tfPm\nczle/pftXHLJcTUbgwCuuw7Wrk17oe/Z41Ot9uJ5tlY2v7XMC1b20RkNbcdw95r+pqZ0VxRFURRF\nUfaJucC7gecDeeCPwDXAhn1s51TE4/gUoAzcCFwLjDTMUuVg4PTE+8kE9IhfJd6/gsNcRNdw7oqi\nKIqigs9RrwAAIABJREFUKIqiKErDMNTk8FS5xRBgwnDe8ZaO2W1rjURR3mvtNkGgMwZcAzljyYXv\nI/tTmwHj1IW6Npk1Q+/7cAuF3WbgGHAdsdk1ZtJTZUUaN8bBkgMTbW4c6rsW9rvx0wVRtzlGJiMc\nwDE2HAJhn1mDtVk9K8flMje5fznAxTZPzJ0QF590HHNb+9+Ejo+/F3Uk6zWhzyfYHr6PxvTebndm\niPawzDGJjeZ8PxVFURRFUZQZsQC4G/g7YBzYAbweEdKftQ/tvAi4C8mJHYV6+kfgdmBOo4xVDgq6\nEu93TVF3Z+L9qUCTlrwfHKiIriiKoiiKoiiKosycfVDPskXEhlkyA/bh5NOMVD2rYmKjo2cfkGjc\njeiwQ0XBPVTsVBRFURRFUQ5hPg6sQITzC4FLgacDAfANphehugR8HRgCViEex88H/hzxSv9go41W\nDijJcF+tk9aauN8FVjbenIMHFdEVRVEURVEURVGUmVPnnWtTW9oBNpPAgg3iikH2AXttY3+pd3lP\nbWGZY8EE4SbXF4TmBlbCYQfBRGflptmddC2XglrXTbVl2lnfD7OIBSyBbCYIteZJbMlyrY86P3lh\nTbMzvWUYKPtsRv3kOMfKfwassVgzeYv7Z3T2IIj8/5NntXXVo8Mh9jo3psnfRUVRFEVRFGV/mAtc\nATwMfDtR/hDwHeB44PxptPMyYAkiuj+RKP8ysA14E4e5B/JTjE2J96dMUbc+YdiSBttyUKE50RVF\nURRFURRFUZSZ09oKS5bUcqIbLJ3eXI6tmlRO9Lvu8hkcDFI6bSkXEJxxJsxdgYTyttg5XZRNK8FY\nfGyl0hzT/eFhhtesoRIa5ToOpVwOU5cTvfroLsZ6bqnlRLeOyzHdz2TueaeRFD4DC+VxB2ulnuPU\nuqWxVKuwcWPNPoC28RZObJmPb6MTWknS3tlJUpC2FgoUKZUKiXthKe3ZAv4AtTD0Q0MwVpd/fH/x\nPHjsMbE/tNvxHZY+2EtnNU+UFH3eohK5c46GQt283OOPT8wZ3toKZ58dC+q+L/ncG4xxHDrnzaO7\nWIzHNeBs3ZqKhe6WPbq93fgmX7tGC7gtBXbNPyl5J9je383jv2irlVS8Mpu25horpRsDy5fDscfG\n6rfr4rSWoL8XE+VEt9DidlIq5VLf0YVzxnnrafdjvaDW3ClHuRQqRWrjyhi5p4qiKIqiKMqB5jlI\nDvSfZez7GfAWRES/YYp2zksckyQA/ht4M7AauHXGlioHE79B7q0DXAJ0A72T1L2y7nNH88w68KiI\nriiKoiiKoiiKosyczk44+mjIRT8vLT2mm9OcWER3HLj++ir9/V7Ki7WtZPGffz4sryAuuZbALTLq\ntOOPSB1joFxujverPzTEwF13kUOEzhLQwkQf6HHzc0bCvNYWMIUCq66+mgWXn0xSRB8dhVt/n2dw\nSHKU53KJbmkklQo88AD09tY6Zk5rK2cuWZpObt3WBitWJDzMJTpAe3sXnfMSIrq1tG1/DDZtoSai\nj47C8HBj7fY8uP9+eCJ2ZnF8n2PXP8qY59X6t3P5keRfeTWUFsQ33hg59tsJhxpr4RWvgMsvj+tF\n52gwjuPQtWQJC7u7MdG5fB9z110yQEPyIyMsrDxJvY/3rvYeniytrt0LY+D2OwzXX29wIod6m2fz\n5gIND/t+4onwnOek+tJtb8Pdtb1WxbfQmm+hrT2XOvuc4hh/+bzbyFsvvBSL09NNvrwSqm6tvaat\ndFEURVEURVH2hePD14cz9kVl0wm/HdV5JGPfI4k6KqIfHmwBrgdejoji3wReCdQ/5L8euLyubKrw\n74c0KqIriqIoiqIoiqIoM6c+tDZgjME4sRQou00YJjop0dkwTrRb0xuN49Q8vpuOtSK6snfZ0tS/\nN+Jxn3dNKgK364TXPrFLmkNiZYEBXJMw1trQVTp5gCxsSNoICVtteHwUz7sZF1AXbt1YiwkqOH61\ndh3Gemnj6o+vf+846fdN6nhjDE596P8oPn7CpsyzGwPGTVxXrTrR0clmGoox6T6KyjLqmcQuG74v\nOJZCMr57VmLAA5QKQFEURVEURUnRFb7uydi3u67OdNrZnbFvX9pRDh3eAzwX8UK/BFgLfA0J9V4C\nLkXC/ANsBI4K3zd45fXBhYroiqIoiqIoiqIoinLYouKmoiiKoiiKojxFiHI7ZeXaqdTVmaodC3h7\naUf1xcOLjcALgJ8Ay4ATgE/W1QmADwAnEYvojc+ldRCRtX5YURRFURRFURRFURRFURRFURRFUZRD\nh8grOMtLvCd8HZpmO2aSdrr3oR3l0OJuRDx/D/A74vG0FfgecC7wUeDoxDFZIf8PG3SliKIoiqIo\niqIoijJjqp7H4NgYlSj5t7UMO6OMOQOp/M+e5yGODLFntLWW4fIYA6NVKbcW3/EYNUN4brl27Ph4\nmVTc9AYRAGPErhgBMMhE3+1RoByWR1aMjo8zMJSeNxobg3LZoVw2od1DBIHfcLuttYxUKgxEeait\nBdeV3NxRTnRrJSH76GjdsTBazlMuB4nI4gHD42XylXGinOiDlQp+g+OLW2DUWgaCoBZavOr7lJH+\njcgHAUPlMtVyOd1AtTohdDrVqnR82F7F86h6WQ4z+2/7WBDQ7/upnOipEOkg9pXLKZvAMhKMMOYP\npMKej49LNoFa+HQ7RhBUaeRYt0C5WmWgXJZxHdkU2RjWCgLD+PgQlUolFc59vDrEUKVKjjgnOpWK\nHO/GOdErnjexLxRFURRFUZTZZmP4ujBj38K6OnvjceAZwAJg1yTtPL6PtimHBiPANeGWRQE4PXy/\nE3hiNow6UKiIriiKoiiKoiiKosyIfD7Puiee4P2f/SxuQhysmjzjtBBlhzYGNm70WbIkLbK5Dnzq\nW2XaSkFNNwyMQ8UtYU0cZbC3dxerVp2AaWDeZdd1GZs/n38aHKyJ5g4yI5Aloo8nCxyH0i9+QcvD\nD6fqeT709hk8z4QLByr4fi+O07ggcMYY/GKRT//hD3QUi8kLgmIxnZvadaG1NXW8BYbLeUYruVTp\nzyo7KATRUgERt3f5fkNtz3V28g3X5aeR+A8E1rJn4UL8hACby+f50fe/j5u8PoBdu6CtLV12333w\niU/UBFzfWjZt306hUGiY3cYY8h0dfLNa5ae9vfH4sBbmz08L+7kcfPObci8SjFOgbIskR1d/P+xO\nZJm01qdY3EyhcFnDbC8Wi/zk/vtZ8+ST6XFdLEI+H58bw7aBEhUvHd0zZyvcMf44TlLYz+fhzjtT\neesf27GDk1paGma3oiiKoiiKMiPuCF/PQzyGk5wXvq6ZRjtrgFeHx6yr23c+sv74DzO0UTm0eTGS\nIx3gxgNpyGygIrqiKIqiKIqiKIoyI04++WQ+9ulP4/uN97ZOYoxh3rx5DRVGFy9ezD9/4QuM1bxx\nm0NLSwtt9cLvftDR0cHfX301Q0ND2CZ7/haLRdrb2xvSluM4vOu972XPlVc2pL29kcvlOOaYYxrW\nnjGGd7zzney+4oqm93kul+Poo4+euuI0Ofvss5l3zTVNt9txHJYvX47rTifFpqIoiqIoitIkHgHu\nBJ4HrCD2Fi8Ar0XCc/+k7pg3I3nO/y1R9kNEhH898AVENAc4BViNiKc7G2++cpBjgPcmPn/5QBky\nW6iIriiKoiiKoiiK8v/bu/M4S6r67uOfU3frvr3PPgMD4wy7CMoqyqYQUTES9+jzKD7GxEiIKIlL\n0BAjidFsKuISfRkhJC5xX1ERRZRAUBRZFFl7YPa19+671Xn+OFW3qm7f29PdU909M/19+7re7qpT\nVb+qW7dfwLfOOTIrnZ2dnHLKKQtdxqwUCgVOPPHEhS5jxrLZLMcee+xClzEr69evTzUgnk/r1q1j\n3bp1C13GjPX29nLaaactdBkiIiIiMn+uBH4E3IILwkeBPwGOB94O7G5o/wlggGSI/gTwz8BVwHeB\n63FzoV+Fm4npXXNWvRzI3gacE/x8E27e9EOaQnQRERERERERERERERGRg9/PgBcD/wJ8Kli2HdeD\n+ENN2j8ODDVZfjUuML8CuChY9ivgNcA9KdYrB4bVuJD8P4F7G9YtA94HvDn4fRC4bP5KWzgK0UVE\nRERERGRWfN9nZGRkzoeKBsjn87S1taU2L7rv+4yOjuLH57OeA57n0dnZmVrd1lrGxsaoVqup7G8q\nadc+NjZGpVJJZV9TMcZQLBbJZtP7Tx7j4+OUY3O5z5W0a69UKoyNjaWyr31pb29PdcoFEREREZm1\n7wWvlUAO2Aq0moPrmBbLa8A1uN7sq3GB+s50y5QDSDtupIK34z7njbgRClYBJwBe0G4IeD7QP/8l\nzj+F6CIiIiIiIjIrd999N++56iqWL1mCFwta7a5d+Bs3Qixc99auxSxfntyB78NDD0GpFC3zPCgU\n3HtgoFrl5Be9iHdffTVtbW2p1L5p0ybe+uY3UxwYSNTeVLOHBAoFaAgMrfGodvZiM+5ftWu1ChMT\nY/zXf91Id3d3KnUPDg7y1iuuYGR4mGJ7e7SiUoHR0UStNpuj2tlD4uwseOVxvEqJhIkJ93kEqr7P\nYCbD5778ZXp6eva7bt/3efe7r+bWWx8mm4325xmf9R07yHmx/6ZXqcCuXVCLLbOW0e5VDC85EoIz\nskCHHaXbDkLwGfrWsqNc5ur3v5+zzz57v+t2h7Zc89738sBNN9ETuy/DuhK/+j6ViYlJy71ly8ge\ndli9TsB9XkNRpx/fWnZay1Uf+ADnnXdeKrXfeuutfOhd72JZLhfVCIx3raTcnvxcN2+GxucECpkq\nxy7bRcZE51OiwKDXiyW6FkNDW7jyyjfx0pe+NJW6RURERCQV21PYRxV4MoX9yIHND14esDx4NboZ\n+DPg4Xmsa0EpRBcREREREZFZKZVKHHPkkbzj8stpC0M6Y6h++cuUr7kGwt7SxpB/+cvJvuxliaCW\nchne9CbYujUKF3M5OPLIKKA2hlsGBrhrz55Ue7xXKhXyQ0N88OijyTcGo41qtclB+tq1sHJl9Lu1\n1No6GHjG+VQ7XHA9NLSbD33omlR7u9dqNaqVCm9985s59uijoxV79sC990bBs7VU+5YzcvJZmHiM\nbi1tmx+lsHNTdM2thY0bYWysvmxgfJy/uf32VGvfvXuMXO51LFlybv2wxUyJvzn58yzNj7hGxrgA\n/StfgeHhWNmWBw9/Hnde8B4wGbcMy4n+/Zzu34Ex7jMs1Wr87S23pNr72lrL6O7dvLZU4tzu7uhq\nWpt8AASolUrs3biRWqVSb2eB4skn03n55ZhMJmr8m9/A7bfXr/m47/PB3/6W8RRrHxsd5eJsllcc\ndli9Ht94PHz6a9i64exomQ8f/zhs2hQ9v+JbWNY9yD+c91Xas9F9tck7kp/mz6eK+44aAz/84b8y\nMjKaWt0iIiIiIjKv+nEjDlwEnBX83AnsAh4BvsTkYd4PeQrRRUREREREZNba29pYtmQJ7fEQvaOD\nCWMSIWKhWCTX15cMoycmIJt1qV0Y6HqeWxbrOduTyey7t/gs5DyPZW1tFPYVovt+MvwH6OiArq7E\n+dTaO/H6llHt7MUAmQxks+kOb22MIZPN0tfby/KlS6MV1kJ3d/TggoVKTw+FpcsnhejtY3toKw8n\nQ/Surig9NYaM55FPcTh0t1uPbLaXfD7q1FDITLC02MnyNutuFGNcD23PS4xGYK3PtnyRzq7lLkS3\nLkTv9ftY4XcS9k6f8H3astnUhqAPecbQk8mwovGa1JKjYlarVYwxxO8WC3QWCnT19SVD9O5uaGtL\nhOiFlGs3xtCVzbK8rQ0T3Ku+ybCrs4exnuX1O6NWc8+tZLOxr6KFfC7Lss5OitmqOxEsY14Pnfnl\niRC9UOgA0v+OioiIiIjIvNkB3Bi8BIXoIiIiIiIish9c73AbhcnGhZsQZG7196BNokd3s2UklxlD\nw9p0GeNeU/Vyn2pdPIgGlyMGu7Ph72mzU1y36JcmP4WmKGoOHlZIalZX7Fxs+H8N52ctFov1qZdv\nCe4/ayd/DnNZ+TSO0fhJ2HC7hnNqtU3q6t9TsMYG1zN5UIvrfe4lrm+0adSuWaVzWr2IiIiIiMi8\nU4guIiIiIiIis1apGobHMlRyQQ9b42FqebxCwXXFxuWbFZOn5ueSwaHvUyh2YLq6oiC7UIAlS1wP\n3XDjanVOwl1bq2EHBrCxoeRNsTj5WLXapPm5GRpyXXfjc5AXxvAffxi/2ImxYIcHYDy9obkTtRuD\nNV7iSQXj+4k6/ZERKo882LAhVLdtxN+1JTpvazHbt0fDuRuDPz7u5iZPWa2W3G3ZNwzQS9bLgjVg\nIJOt0tm3DC+Xjz4L3yff10F3N4kQvVDOQDkPwXDu1GqJHuypKhZd7/EwtPd9KqOjUe9/oFou49vJ\nMbNvMtSy+URPdL/q4w8O1nueV3wfv3FS8v1kgYlCD8Odq+r3im88JrwilQqEU51b37LK24GXKyee\nR1iTHcarVcHU6nu01sf33ccVtpvDZxdEREREREQWhEJ0ERERERERmbWte9r4nwd6yWZc6G0NrB1Z\nw4kbjsKzwaDWvs+WjiPZO76mHtoBmEqJo047i8KG7dHCjg44+2zo7AwaGfjlLxPzY6fFDg7i33cf\nlSA1NKtXkz3vPEzjkN0jIzA4mOzt/PDDbtjx+skYbK1GZfRaKsHQ7yXfx+9oT71ujEct104lX4wt\nypIdHcVUKvU6y/fdz44vXgk2ORT9kmoFW6smlnmVCp7vR9v6Pn5fX6plW+suY3xk/Hwuz835i+lq\n98OO0vSu2s1zX1mg048+c4Nl7VPPpPsskwjRO3d1w5Z1UYherUb3TppyOTj1VDj22Hpi7JfL7L72\nWmp79sTO0WKr1Umbl9q7YdlaTKbg2hlLac+PGfv+9+vjApStZbRQCEZ3SM/vjnohPz3r5dgg9bbA\nqNdNaWeska3xrp5P0lV9PPEQSbY9S2FsLWSC62t9/Ow4ZSzVoFn4nIuIiIiIiMihRCG6iIiIiIiI\nzFqlZhidyJANgmcfS9nP4RUKiRC95uUp2xzGRgGdwce2d0C5IwruOjqgt9fN0Q1ueVeXC7LTVqu5\nHuUE03H39ETHjPP9ZG94a12Avndvsm21it2xA1upRAOmr1+fft0mqNHz6sFz2DOa2PDmdnyMav+j\nrod6TI3Jw42bhmVA9BmkxNpkp35rwfMMI6bHDYEfXLSsqeH39gG5RJX53qIrqR6iGwpjGcgXYhN5\ne3PTE90Y1xO9qyvqdl0qUatWqZbLievnRSUGdYL1PPxsIRai+9SqlsrQUL1tFfBTr91QKnQz0rGy\nfoEtUC5BtRK7fX3LyuxuluW2Rg8kAGTawF8TnZD1o6Hgw0XqhS4iIiIiIocghegiIiIiIiIya+GU\n4vXfW7Xbn4PMQ0pnGt6nt1HjyZtwSvR5EQa34TFNQ/hvgnqabTeT39PS+GzCpN9Jnk/CARzUxu+d\nVmU2npMJ/m8+rn14bBv7fVIb4x5KaHH1kw1J3ldzMNOCiIiIiIjIglOILiIiIiIiIrNmMUFP6PD3\nIKyLT5RsbescPDGh8sJMrhwFjPagmeB5n8GlMfXPorFp49nt6/e0xG6HScvix7U0r6HV+SSWzmWg\n23hf2PpdM2WAHtsg+twM4Tdndg9wzICl4ZraJp+FpeX5RQ3CZXbS/kRERERERA41CtFFRERERERk\n1rKjAxS3PkS2Pkw15MtDsHIl9XTN9/E6i2QyJOdEx8CypdCGC32thbY2TLUKExNBIwPl8pzUXsu1\nMbjiGLJBsplZtopi70pMPpfotpvZvhMzNhaNaA1USyVqlUoiza5Wq9RiQ13PVbbo+240+ZGRKOf0\nBsu07diJKZeCug21iQnyRx+DaQhHs+NjeKVSomeyyecxmUy9jfF9KBRSrdvzLCuW1Fi+rFIfhj6X\ns/QVqhRzUSDdVR3FGx4BfyQ2hL6PKZfwYh2lDZbxSpbBkSImWFGqVpioZpocPQWFArS3Rxc9kyF7\nxBHQ3V0P0a01lCrJ/9RigbHcCipbPUxQmjWG0cFeBs2xseHcLSNmgnTjdEubHafLDiXmRM+3tVE1\n+XorYyHTXoDxtmg4d2vx24pMtC/Bepn6/vxcBz3FCr6JHlxoy9VSrFlERERERGThKUQXERERERGR\nWVtx11c549bPkTNRQNd+8Qsxf/teCENZa+nsPBzT7toYwmm7C3hvuRzf8wGDNRYGB/G+8Q3M4KDb\n1hh48kloa0u99oFVx/O1119Hxsthgb4+wwknZsnG/k3ZetD792+l866vJHoeb7aWnQ3hdAbos7Y+\nk3fj3ONpGR+HO++ETZuCBQaK9z/G4df+C97YYL3G4tnnsOGO/03OEW4t2TvvJHP/r5Pd2Y87Drq7\ng/0ZCkNDeJ/6VKp1dxZ93n3Zbs4/a7N7EgDA98lueRJTrdXPxezaReaW29xTArG6c7195C56bmzU\nA8vPdqzkmz9ZWT/Fmj/Bb7YvS7VuwN3Lxx4LZ5xRr92zlhVnnZX4jEfLOX6xaQXj1UwUhRu4594c\nt749H2tp2Lb5lfTnL4ktGyfv/R2Xptwn/cTavfxetcf1fLdgPUPlpNOpHrkhOpLvUdxzFGwrxh5c\nsEy0L+Hnp/wRvhfUbmFJ+zgvW7KNbPhEjGd4/N5BPDNXj42IiMghaDnwzIUuoolVC12AiIgcOBSi\ni4iIiIiIyKx5tQqZ0ij1mNYYjK1CPh+Ft9ZiMp6bQjxq5rK6XL7+b6YGsLnx+jZ1czW8uvHwc0Xw\nCljAz4HNulf90Ma6uaJrtUQdFvBJ9hn256bKpnw/yqEB/JrFL1cwpVK9PqzFK3ZQ7/4Mrkd3IY/J\n5ZIheqEQ9Tw3xv0cD99TYIB81lLM2+hi+YDnu1d4bOM3OUHf9aiP30S4nt9V36vff76fCeb2Tpkx\n7no0XBOTySTmojcmhy0UsV4u1ghqBiqV5C4rfo4SuSizxiNLur3oDeAZS9aE188t9T2LzZAI+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kfRi+pvPPHx8N2taA24BbgIlg2TeZeZDehftnnyrwZ8BJuIcg+ma4nzR8GHceb1uAY8+XNbj7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\nRERERERERERERERERERERERERBar/w89lh7LUZYtOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('images/02_network_flowchart.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import time\n",
    "from datetime import timedelta\n",
    "import math\n",
    "import os\n",
    "\n",
    "# Use PrettyTensor to simplify Neural Network construction.\n",
    "import prettytensor as pt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.0rc0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets, but we will make random training-sets further below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of:\n",
      "- Training-set:\t\t55000\n",
      "- Test-set:\t\t10000\n",
      "- Validation-set:\t5000\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of:\")\n",
    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class numbers\n",
    "\n",
    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test- and validation-sets, so we calculate them now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.test.cls = np.argmax(data.test.labels, axis=1)\n",
    "data.validation.cls = np.argmax(data.validation.labels, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for creating random training-sets\n",
    "\n",
    "We will train 5 neural networks on different training-sets that are selected at random. First we combine the original training- and validation-sets into one big set. This is done for both the images and the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined_images = np.concatenate([data.train.images, data.validation.images], axis=0)\n",
    "combined_labels = np.concatenate([data.train.labels, data.validation.labels], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the shape of the combined arrays is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(combined_images.shape)\n",
    "print(combined_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Size of the combined data-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_size = len(combined_images)\n",
    "combined_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the size of the training-set used for each neural network. You can try and change this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_size = int(0.8 * combined_size)\n",
    "train_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not use a validation-set during training, but this would be the size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_size = combined_size - train_size\n",
    "validation_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Helper-function for splitting the combined data-set into a random training- and validation-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_training_set():\n",
    "    # Create a randomized index into the full / combined training-set.\n",
    "    idx = np.random.permutation(combined_size)\n",
    "\n",
    "    # Split the random index into training- and validation-sets.\n",
    "    idx_train = idx[0:train_size]\n",
    "    idx_validation = idx[train_size:]\n",
    "\n",
    "    # Select the images and labels for the new training-set.\n",
    "    x_train = combined_images[idx_train, :]\n",
    "    y_train = combined_labels[idx_train, :]\n",
    "\n",
    "    # Select the images and labels for the new validation-set.\n",
    "    x_validation = combined_images[idx_validation, :]\n",
    "    y_validation = combined_labels[idx_validation, :]\n",
    "\n",
    "    # Return the new training- and validation-sets.\n",
    "    return x_train, y_train, x_validation, y_validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We know that MNIST images are 28 pixels in each dimension.\n",
    "img_size = 28\n",
    "\n",
    "# Images are stored in one-dimensional arrays of this length.\n",
    "img_size_flat = img_size * img_size\n",
    "\n",
    "# Tuple with height and width of images used to reshape arrays.\n",
    "img_shape = (img_size, img_size)\n",
    "\n",
    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
    "num_channels = 1\n",
    "\n",
    "# Number of classes, one class for each of 10 digits.\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function for plotting images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_images(images,                  # Images to plot, 2-d array.\n",
    "                cls_true,                # True class-no for images.\n",
    "                ensemble_cls_pred=None,  # Ensemble predicted class-no.\n",
    "                best_cls_pred=None):     # Best-net predicted class-no.\n",
    "\n",
    "    assert len(images) == len(cls_true)\n",
    "    \n",
    "    # Create figure with 3x3 sub-plots.\n",
    "    fig, axes = plt.subplots(3, 3)\n",
    "\n",
    "    # Adjust vertical spacing if we need to print ensemble and best-net.\n",
    "    if ensemble_cls_pred is None:\n",
    "        hspace = 0.3\n",
    "    else:\n",
    "        hspace = 1.0\n",
    "    fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
    "\n",
    "    # For each of the sub-plots.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "\n",
    "        # There may not be enough images for all sub-plots.\n",
    "        if i < len(images):\n",
    "            # Plot image.\n",
    "            ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
    "\n",
    "            # Show true and predicted classes.\n",
    "            if ensemble_cls_pred is None:\n",
    "                xlabel = \"True: {0}\".format(cls_true[i])\n",
    "            else:\n",
    "                msg = \"True: {0}\\nEnsemble: {1}\\nBest Net: {2}\"\n",
    "                xlabel = msg.format(cls_true[i],\n",
    "                                    ensemble_cls_pred[i],\n",
    "                                    best_cls_pred[i])\n",
    "\n",
    "            # Show the classes as the label on the x-axis.\n",
    "            ax.set_xlabel(xlabel)\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a few images to see if data is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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/6B9RqVTo9/v4/X6sVuvZFr5ut0u32yUej/PgwQNu375NPB4/zUOSHAP9fp9S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\n/6/Ghib4QzPAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffacb9a2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first images from the test-set.\n",
    "images = data.test.images[0:9]\n",
    "\n",
    "# Get the true classes for those images.\n",
    "cls_true = data.test.cls[0:9]\n",
    "\n",
    "# Plot the images and labels using our helper-function above.\n",
    "plot_images(images=images, cls_true=cls_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Graph\n",
    "\n",
    "The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n",
    "\n",
    "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n",
    "\n",
    "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n",
    "\n",
    "A TensorFlow graph consists of the following parts which will be detailed below:\n",
    "\n",
    "* Placeholder variables used for inputting data to the graph.\n",
    "* Variables that are going to be optimized so as to make the convolutional network perform better.\n",
    "* The mathematical formulas for the neural network.\n",
    "* A loss measure that can be used to guide the optimization of the variables.\n",
    "* An optimization method which updates the variables.\n",
    "\n",
    "In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Placeholder variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
    "\n",
    "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_true_cls = tf.argmax(y_true, dimension=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section implements the Convolutional Neural Network using Pretty Tensor, which is much simpler than a direct implementation in TensorFlow, see Tutorial #03.\n",
    "\n",
    "The basic idea is to wrap the input tensor `x_image` in a Pretty Tensor object which has helper-functions for adding new computational layers so as to create an entire neural network. Pretty Tensor takes care of the variable allocation, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_pretty = pt.wrap(x_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have wrapped the input image in a Pretty Tensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code.\n",
    "\n",
    "Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with pt.defaults_scope(activation_fn=tf.nn.relu):\n",
    "    y_pred, loss = x_pretty.\\\n",
    "        conv2d(kernel=5, depth=16, name='layer_conv1').\\\n",
    "        max_pool(kernel=2, stride=2).\\\n",
    "        conv2d(kernel=5, depth=36, name='layer_conv2').\\\n",
    "        max_pool(kernel=2, stride=2).\\\n",
    "        flatten().\\\n",
    "        fully_connected(size=128, name='layer_fc1').\\\n",
    "        softmax_classifier(class_count=10, labels=y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty Tensor gave us the predicted class-label (`y_pred`) as well as a loss-measure that must be minimized, so as to improve the ability of the neural network to classify the input images.\n",
    "\n",
    "It is unclear from the documentation for Pretty Tensor whether the loss-measure is cross-entropy or something else. But we now use the `AdamOptimizer` to minimize the loss.\n",
    "\n",
    "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Measures\n",
    "\n",
    "We need a few more performance measures to display the progress to the user.\n",
    "\n",
    "First we calculate the predicted class number from the output of the neural network `y_pred`, which is a vector with 10 elements. The class number is the index of the largest element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create a vector of booleans telling us whether the predicted class equals the true class of each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saver\n",
    "\n",
    "In order to save the variables of the neural network, we now create a Saver-object which is used for storing and retrieving all the variables of the TensorFlow graph. Nothing is actually saved at this point, which will be done further below.\n",
    "\n",
    "Note that if you have more than 100 neural networks in the ensemble then you must increase `max_to_keep` accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saver = tf.train.Saver(max_to_keep=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the directory used for saving and retrieving the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "save_dir = 'checkpoints/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the directory if it does not exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(save_dir):\n",
    "    os.makedirs(save_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns the save-path for the data-file with the given network number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_save_path(net_number):\n",
    "    return save_dir + 'network' + str(net_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create TensorFlow session\n",
    "\n",
    "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "session = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize variables\n",
    "\n",
    "The variables for `weights` and `biases` must be initialized before we start optimizing them. We make a simple wrapper-function for this, because we will call it several times below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def init_variables():\n",
    "    session.run(tf.initialize_all_variables())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function to create a random training batch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are thousands of images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
    "\n",
    "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_batch_size = 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for selecting a random training-batch of the given size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def random_batch(x_train, y_train):\n",
    "    # Total number of images in the training-set.\n",
    "    num_images = len(x_train)\n",
    "\n",
    "    # Create a random index into the training-set.\n",
    "    idx = np.random.choice(num_images,\n",
    "                           size=train_batch_size,\n",
    "                           replace=False)\n",
    "\n",
    "    # Use the random index to select random images and labels.\n",
    "    x_batch = x_train[idx, :]  # Images.\n",
    "    y_batch = y_train[idx, :]  # Labels.\n",
    "\n",
    "    # Return the batch.\n",
    "    return x_batch, y_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-function to perform optimization iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def optimize(num_iterations, x_train, y_train):\n",
    "    # Start-time used for printing time-usage below.\n",
    "    start_time = time.time()\n",
    "\n",
    "    for i in range(num_iterations):\n",
    "\n",
    "        # Get a batch of training examples.\n",
    "        # x_batch now holds a batch of images and\n",
    "        # y_true_batch are the true labels for those images.\n",
    "        x_batch, y_true_batch = random_batch(x_train, y_train)\n",
    "\n",
    "        # Put the batch into a dict with the proper names\n",
    "        # for placeholder variables in the TensorFlow graph.\n",
    "        feed_dict_train = {x: x_batch,\n",
    "                           y_true: y_true_batch}\n",
    "\n",
    "        # Run the optimizer using this batch of training data.\n",
    "        # TensorFlow assigns the variables in feed_dict_train\n",
    "        # to the placeholder variables and then runs the optimizer.\n",
    "        session.run(optimizer, feed_dict=feed_dict_train)\n",
    "\n",
    "        # Print status every 100 iterations and after last iteration.\n",
    "        if i % 100 == 0:\n",
    "\n",
    "            # Calculate the accuracy on the training-batch.\n",
    "            acc = session.run(accuracy, feed_dict=feed_dict_train)\n",
    "            \n",
    "            # Status-message for printing.\n",
    "            msg = \"Optimization Iteration: {0:>6}, Training Batch Accuracy: {1:>6.1%}\"\n",
    "\n",
    "            # Print it.\n",
    "            print(msg.format(i + 1, acc))\n",
    "\n",
    "    # Ending time.\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Difference between start and end-times.\n",
    "    time_dif = end_time - start_time\n",
    "\n",
    "    # Print the time-usage.\n",
    "    print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create ensemble of neural networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of neural networks in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_networks = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of optimization iterations for each neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_iterations = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the ensemble of neural networks. All networks use the same TensorFlow graph that was defined above. For each neural network the TensorFlow weights and variables are initialized to random values and then optimized. The variables are then saved to disk so they can be reloaded later.\n",
    "\n",
    "You may want to skip this computation if you just want to re-run the Notebook with different analysis of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural network: 0\n",
      "Optimization Iteration:      1, Training Batch Accuracy:   9.4%\n",
      "Optimization Iteration:    101, Training Batch Accuracy:  84.4%\n",
      "Optimization Iteration:    201, Training Batch Accuracy:  90.6%\n",
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      "Time usage: 0:12:00\n",
      "\n",
      "Neural network: 1\n",
      "Optimization Iteration:      1, Training Batch Accuracy:  23.4%\n",
      "Optimization Iteration:    101, Training Batch Accuracy:  92.2%\n",
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      "Optimization Iteration:   9901, Training Batch Accuracy: 100.0%\n",
      "Time usage: 0:13:42\n",
      "\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    # For each of the neural networks.\n",
    "    for i in range(num_networks):\n",
    "        print(\"Neural network: {0}\".format(i))\n",
    "\n",
    "        # Create a random training-set. Ignore the validation-set.\n",
    "        x_train, y_train, _, _ = random_training_set()\n",
    "\n",
    "        # Initialize the variables of the TensorFlow graph.\n",
    "        session.run(tf.initialize_all_variables())\n",
    "\n",
    "        # Optimize the variables using this training-set.\n",
    "        optimize(num_iterations=num_iterations,\n",
    "                 x_train=x_train,\n",
    "                 y_train=y_train)\n",
    "\n",
    "        # Save the optimized variables to disk.\n",
    "        saver.save(sess=session, save_path=get_save_path(i))\n",
    "\n",
    "        # Print newline.\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-functions for calculating and predicting classifications\n",
    "\n",
    "This function calculates the predicted labels of images, that is, for each image it calculates a vector of length 10 indicating which of the 10 classes the image is.\n",
    "\n",
    "The calculation is done in batches because it might use too much RAM otherwise. If your computer crashes then you can try and lower the batch-size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split the data-set in batches of this size to limit RAM usage.\n",
    "batch_size = 256\n",
    "\n",
    "def predict_labels(images):\n",
    "    # Number of images.\n",
    "    num_images = len(images)\n",
    "\n",
    "    # Allocate an array for the predicted labels which\n",
    "    # will be calculated in batches and filled into this array.\n",
    "    pred_labels = np.zeros(shape=(num_images, num_classes),\n",
    "                           dtype=np.float)\n",
    "\n",
    "    # Now calculate the predicted labels for the batches.\n",
    "    # We will just iterate through all the batches.\n",
    "    # There might be a more clever and Pythonic way of doing this.\n",
    "\n",
    "    # The starting index for the next batch is denoted i.\n",
    "    i = 0\n",
    "\n",
    "    while i < num_images:\n",
    "        # The ending index for the next batch is denoted j.\n",
    "        j = min(i + batch_size, num_images)\n",
    "\n",
    "        # Create a feed-dict with the images between index i and j.\n",
    "        feed_dict = {x: images[i:j, :]}\n",
    "\n",
    "        # Calculate the predicted labels using TensorFlow.\n",
    "        pred_labels[i:j] = session.run(y_pred, feed_dict=feed_dict)\n",
    "\n",
    "        # Set the start-index for the next batch to the\n",
    "        # end-index of the current batch.\n",
    "        i = j\n",
    "\n",
    "    return pred_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate a boolean array whether the predicted classes for the images are correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def correct_prediction(images, labels, cls_true):\n",
    "    # Calculate the predicted labels.\n",
    "    pred_labels = predict_labels(images=images)\n",
    "\n",
    "    # Calculate the predicted class-number for each image.\n",
    "    cls_pred = np.argmax(pred_labels, axis=1)\n",
    "\n",
    "    # Create a boolean array whether each image is correctly classified.\n",
    "    correct = (cls_true == cls_pred)\n",
    "\n",
    "    return correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate a boolean array whether the images in the test-set are classified correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_correct():\n",
    "    return correct_prediction(images = data.test.images,\n",
    "                              labels = data.test.labels,\n",
    "                              cls_true = data.test.cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate a boolean array whether the images in the validation-set are classified correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validation_correct():\n",
    "    return correct_prediction(images = data.validation.images,\n",
    "                              labels = data.validation.labels,\n",
    "                              cls_true = data.validation.cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-functions for calculating the classification accuracy\n",
    "\n",
    "This function calculates the classification accuracy given a boolean array whether each image was correctly classified. E.g. `classification_accuracy([True, True, False, False, False]) = 2/5 = 0.4`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def classification_accuracy(correct):\n",
    "    # When averaging a boolean array, False means 0 and True means 1.\n",
    "    # So we are calculating: number of True / len(correct) which is\n",
    "    # the same as the classification accuracy.\n",
    "    return correct.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the classification accuracy on the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_accuracy():\n",
    "    # Get the array of booleans whether the classifications are correct\n",
    "    # for the test-set.\n",
    "    correct = test_correct()\n",
    "    \n",
    "    # Calculate the classification accuracy and return it.\n",
    "    return classification_accuracy(correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the classification accuracy on the original validation-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validation_accuracy():\n",
    "    # Get the array of booleans whether the classifications are correct\n",
    "    # for the validation-set.\n",
    "    correct = validation_correct()\n",
    "    \n",
    "    # Calculate the classification accuracy and return it.\n",
    "    return classification_accuracy(correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for calculating the predicted labels for all the neural networks in the ensemble. The labels are combined further below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ensemble_predictions():\n",
    "    # Empty list of predicted labels for each of the neural networks.\n",
    "    pred_labels = []\n",
    "\n",
    "    # Classification accuracy on the test-set for each network.\n",
    "    test_accuracies = []\n",
    "\n",
    "    # Classification accuracy on the validation-set for each network.\n",
    "    val_accuracies = []\n",
    "\n",
    "    # For each neural network in the ensemble.\n",
    "    for i in range(num_networks):\n",
    "        # Reload the variables into the TensorFlow graph.\n",
    "        saver.restore(sess=session, save_path=get_save_path(i))\n",
    "\n",
    "        # Calculate the classification accuracy on the test-set.\n",
    "        test_acc = test_accuracy()\n",
    "\n",
    "        # Append the classification accuracy to the list.\n",
    "        test_accuracies.append(test_acc)\n",
    "\n",
    "        # Calculate the classification accuracy on the validation-set.\n",
    "        val_acc = validation_accuracy()\n",
    "\n",
    "        # Append the classification accuracy to the list.\n",
    "        val_accuracies.append(val_acc)\n",
    "\n",
    "        # Print status message.\n",
    "        msg = \"Network: {0}, Accuracy on Validation-Set: {1:.4f}, Test-Set: {2:.4f}\"\n",
    "        print(msg.format(i, val_acc, test_acc))\n",
    "\n",
    "        # Calculate the predicted labels for the images in the test-set.\n",
    "        # This is already calculated in test_accuracy() above but\n",
    "        # it is re-calculated here to keep the code a bit simpler.\n",
    "        pred = predict_labels(images=data.test.images)\n",
    "\n",
    "        # Append the predicted labels to the list.\n",
    "        pred_labels.append(pred)\n",
    "    \n",
    "    return np.array(pred_labels), \\\n",
    "           np.array(test_accuracies), \\\n",
    "           np.array(val_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network: 0, Accuracy on Validation-Set: 0.9936, Test-Set: 0.9872\n",
      "Network: 1, Accuracy on Validation-Set: 0.9934, Test-Set: 0.9873\n",
      "Network: 2, Accuracy on Validation-Set: 0.9940, Test-Set: 0.9883\n",
      "Network: 3, Accuracy on Validation-Set: 0.9942, Test-Set: 0.9894\n",
      "Network: 4, Accuracy on Validation-Set: 0.9946, Test-Set: 0.9888\n"
     ]
    }
   ],
   "source": [
    "pred_labels, test_accuracies, val_accuracies = ensemble_predictions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the classification accuracies on the test-set for the neural networks in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean test-set accuracy: 0.9882\n",
      "Min test-set accuracy:  0.9872\n",
      "Max test-set accuracy:  0.9894\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean test-set accuracy: {0:.4f}\".format(np.mean(test_accuracies)))\n",
    "print(\"Min test-set accuracy:  {0:.4f}\".format(np.min(test_accuracies)))\n",
    "print(\"Max test-set accuracy:  {0:.4f}\".format(np.max(test_accuracies)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted labels of the ensemble is a 3-dim array, the first dim is the network-number, the second dim is the image-number, the third dim is the classification vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 10000, 10)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are different ways to calculate the predicted labels for the ensemble. One way is to calculate the predicted class-number for each neural network, and then select the class-number with most votes. But this requires a large number of neural networks relative to the number of classes.\n",
    "\n",
    "The method used here is instead to take the average of the predicted labels for all the networks in the ensemble. This is simple to calculate and does not require a large number of networks in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_pred_labels = np.mean(pred_labels, axis=0)\n",
    "ensemble_pred_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble's predicted class number is then the index of the highest number in the label, which is calculated using argmax as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_cls_pred = np.argmax(ensemble_pred_labels, axis=1)\n",
    "ensemble_cls_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean array whether each of the images in the test-set was correctly classified by the ensemble of neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ensemble_correct = (ensemble_cls_pred == data.test.cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Negate the boolean array so we can use it to lookup incorrectly classified images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ensemble_incorrect = np.logical_not(ensemble_correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best neural network\n",
    "\n",
    "Now we find the single neural network that performed best on the test-set.\n",
    "\n",
    "First list the classification accuracies on the test-set for all the neural networks in the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.9872,  0.9873,  0.9883,  0.9894,  0.9888])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_accuracies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index of the neural network with the highest classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_net = np.argmax(test_accuracies)\n",
    "best_net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best neural network's classification accuracy on the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98939999999999995"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_accuracies[best_net]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicted labels of the best neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_net_pred_labels = pred_labels[best_net, :, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted class-number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_net_cls_pred = np.argmax(best_net_pred_labels, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean array whether the best neural network classified each image in the test-set correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_net_correct = (best_net_cls_pred == data.test.cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean array whether each image is incorrectly classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_net_incorrect = np.logical_not(best_net_correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison of ensemble vs. the best single network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of images in the test-set that were correctly classified by the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9911"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(ensemble_correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of images in the test-set that were correctly classified by the best neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9894"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(best_net_correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean array whether each image in the test-set was correctly classified by the ensemble and incorrectly classified by the best neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ensemble_better = np.logical_and(best_net_incorrect,\n",
    "                                 ensemble_correct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of images in the test-set where the ensemble was better than the best single network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble_better.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean array whether each image in the test-set was correctly classified by the best single network and incorrectly classified by the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_net_better = np.logical_and(best_net_correct,\n",
    "                                 ensemble_incorrect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of images in the test-set where the best single network was better than the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_net_better.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper-functions for plotting and printing comparisons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for plotting images from the test-set and their true and predicted class-numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_images_comparison(idx):\n",
    "    plot_images(images=data.test.images[idx, :],\n",
    "                cls_true=data.test.cls[idx],\n",
    "                ensemble_cls_pred=ensemble_cls_pred[idx],\n",
    "                best_cls_pred=best_net_cls_pred[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for printing the predicted labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_labels(labels, idx, num=1):\n",
    "    # Select the relevant labels based on idx.\n",
    "    labels = labels[idx, :]\n",
    "\n",
    "    # Select the first num labels.\n",
    "    labels = labels[0:num, :]\n",
    "    \n",
    "    # Round numbers to 2 decimal points so they are easier to read.\n",
    "    labels_rounded = np.round(labels, 2)\n",
    "\n",
    "    # Print the rounded labels.\n",
    "    print(labels_rounded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for printing the predicted labels for the ensemble of neural networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_labels_ensemble(idx, **kwargs):\n",
    "    print_labels(labels=ensemble_pred_labels, idx=idx, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for printing the predicted labels for the best single network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_labels_best_net(idx, **kwargs):\n",
    "    print_labels(labels=best_net_pred_labels, idx=idx, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for printing the predicted labels of all the neural networks in the ensemble. This only prints the labels for the first image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_labels_all_nets(idx):\n",
    "    for i in range(num_networks):\n",
    "        print_labels(labels=pred_labels[i, :, :], idx=idx, num=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples: Ensemble is better than the best network\n",
    "\n",
    "Plot examples of images that were correctly classified by the ensemble and incorrectly classified by the best single network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Zv38/CwsLeL1e3G434+Pj9Pf3c+zYMXbt2kVycrIyeusAl8vF9evXOXHiBGfP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffac932ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images_comparison(idx=ensemble_better)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble's predicted labels for the first of these images (top left image):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.    0.46  0.    0.54  0.    0.    0.    0.  ]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_ensemble(idx=ensemble_better, num=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best network's predicted labels for the first of these images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.    0.57  0.    0.43  0.    0.    0.    0.  ]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_best_net(idx=ensemble_better, num=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted labels of all the networks in the ensemble, for the first of these images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.    0.17  0.    0.83  0.    0.    0.    0.  ]]\n",
      "[[ 0.    0.    0.    0.37  0.    0.63  0.    0.    0.    0.  ]]\n",
      "[[ 0.    0.    0.    0.77  0.    0.23  0.    0.    0.    0.  ]]\n",
      "[[ 0.    0.    0.    0.57  0.    0.43  0.    0.    0.    0.  ]]\n",
      "[[ 0.    0.    0.    0.43  0.    0.57  0.    0.    0.    0.  ]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_all_nets(idx=ensemble_better)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples: Best network is better than ensemble\n",
    "\n",
    "Now plot examples of images that were incorrectly classified by the ensemble but correctly classified by the best single network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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39+P1emOj9NHoi+Hh4UVRWmpRGD2Px8PExATt7e1cunSJH//4xzQ3N+NyuVizZk3stXPn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\nNJqw4v8BL2dpuqUUwqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffac9173080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images_comparison(idx=best_net_better)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble's predicted labels for the first of these images (top left image):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.48  0.    0.52]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_ensemble(idx=best_net_better, num=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best single network's predicted labels for the first of these images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.68  0.    0.31]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_best_net(idx=best_net_better, num=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted labels of all the networks in the ensemble, for the first of these images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.    0.    0.01  0.    0.    0.    0.    0.37  0.    0.62]]\n",
      "[[ 0.01  0.    0.    0.    0.    0.    0.    0.33  0.    0.66]]\n",
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.91  0.    0.09]]\n",
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.68  0.    0.31]]\n",
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.09  0.    0.91]]\n"
     ]
    }
   ],
   "source": [
    "print_labels_all_nets(idx=best_net_better)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close TensorFlow Session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now done using TensorFlow, so we close the session to release its resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This has been commented out in case you want to modify and experiment\n",
    "# with the Notebook without having to restart it.\n",
    "# session.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This tutorial created an ensemble of 5 convolutional neural networks for classifying hand-written digits in the MNIST data-set. The ensemble worked by averaging the predicted class-labels of the 5 individual neural networks. This resulted in slightly improved classification accuracy on the test-set, with the ensemble having an accuracy of 99.1% compared to 98.9% for the best individual network.\n",
    "\n",
    "However, the ensemble did not always perform better than the individual neural networks, which sometimes classified images correctly while the ensemble misclassified those images. This suggests that the effect of using an ensemble of neural networks is somewhat random and may not provide a reliable way of improving the performance over a single neural network.\n",
    "\n",
    "The form of ensemble learning used here is called [bagging](https://en.wikipedia.org/wiki/Bootstrap_aggregating) (or Bootstrap Aggregating), which is mainly useful for avoiding overfitting and may not be necessary for this particular neural network and data-set. So it is still possible that ensemble learning may work in other settings.\n",
    "\n",
    "### Technical Note\n",
    "\n",
    "This implementation of ensemble learning used the TensorFlow `Saver()`-object to save and reload the variables of the neural network. But this functionality was really designed for another purpose and becomes very awkward to use for ensemble learning with different types of neural networks, or if you want to load multiple neural networks at the same time. There's an add-on package for TensorFlow called [sk-flow](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn) which makes this much easier, but it is still in the early stages of development as of August 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
    "\n",
    "You may want to backup this Notebook before making any changes.\n",
    "\n",
    "* Change different aspects of this program to see how it affects the performance:\n",
    "    * Use more neural networks in the ensemble.\n",
    "    * Change the size of the training-sets.\n",
    "    * Change the number of optimization iterations, try both more and less.\n",
    "* Explain to a friend how the program works.\n",
    "* Do you think Ensemble Learning is worth more research effort, or should you rather focus on improving a single neural network?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
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