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@Tony-MK
Created August 19, 2018 18:54
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MNIST Tensorflow.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "MNIST Tensorflow.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/zana-Afrika/7c3636bcd63947a3cd2f396014e58904/mnist-tensorflow.ipynb)"
]
},
{
"metadata": {
"id": "Y1UCPGDXTIWQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 50
},
"outputId": "655aba1f-cae2-4fda-b338-56cbc7bde877"
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"!pip install mnist\n",
"import matplotlib.pyplot as plt\n",
"import mnist\n",
"import tensorflow as tf\n",
"from keras.utils import to_categorical"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: mnist in /usr/local/lib/python3.6/dist-packages (0.2.1)\r\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mnist) (1.14.5)\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "2Jl4SC5Ak4NO",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"class NeuralNetwork:\n",
" \n",
" def __init__(self,learningRate):\n",
" self.X = tf.placeholder(tf.float64,[None,nfeat])\n",
" self.Y = tf.placeholder(tf.float32,[None,nClasses])\n",
" self.Ycls = tf.placeholder(tf.int64,[None])\n",
" \n",
" w1 = tf.Variable(tf.ones([nfeat,nClasses],dtype=tf.float64))\n",
" b1 = tf.Variable(tf.zeros([1,nClasses],dtype=tf.float64))\n",
" \n",
" logits = tf.matmul(self.X,w1) + b1\n",
" self.Ypred = tf.nn.softmax(logits)\n",
" self.Ypred_cls = tf.argmax(self.Ypred,axis=1)\n",
" \n",
" self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,labels=self.Y))\n",
" self.optimize = tf.train.GradientDescentOptimizer(learningRate).minimize(self.loss)\n",
" \n",
" self.isCorrect = tf.equal(self.Ycls,self.Ypred_cls)\n",
" self.accuracy = tf.reduce_mean(tf.cast(self.isCorrect,tf.float64))\n",
" self.sess = tf.Session()\n",
" self.sess.run(tf.global_variables_initializer())\n",
" \n",
" def predict(self,x):\n",
" return self.sess.run(self.Ypred,feed_dict={self.X:x})\n",
" \n",
" def learn(self,x,y,bsize):\n",
" nBatches = int(len(x)/bsize)\n",
" for i in range(nBatches):self.sess.run(self.optimize,feed_dict={self.X:x[bsize*i:(i+1)*bsize],self.Y:y[bsize*i:(i+1)*bsize]})\n",
"\n",
" def metrics(self,x,y,yc):\n",
" return self.sess.run(self.loss,feed_dict={self.X:x,self.Y:y}).mean(),self.sess.run(self.accuracy,feed_dict={self.X:x,self.Y:y,self.Ycls:yc}).mean()\n",
" \n",
"def plot(LRs,m):\n",
" x = range(epochs)\n",
" leg = []\n",
" print(\"\\n\\n\\t\\t\\tCost Graph\\n\")\n",
" for lr in LRs:\n",
" plt.plot(x,LRs[lr][m][0])\n",
" leg.append(str(lr)+\" train\")\n",
" plt.plot(x,LRs[lr][m][1],linestyle=\"--\")\n",
" leg.append(str(lr)+\" test\")\n",
" plt.legend(leg)\n",
" plt.show()\n",
" \n",
" print(\"\\n\\n\\t\\tDiff Graph\\n\")\n",
" diff = [LRs[lr][m][0][-1]-LRs[lr][m][1][-1] for lr in LRs]\n",
" plt.bar([str(key) for key in LRs],height=diff)\n",
" plt.show()\n",
" \n",
"def plot_metrics(LRs):\n",
" plot(LRs,\"Cost\")\n",
" plot(LRs,\"Acc\")"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "OwLal-auT_Im",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"nfeat = 28*28\n",
"\n",
"xtrain,xtest = np.divide(mnist.train_images(),255.0),np.divide(mnist.test_images(),255.0)\n",
"xtrain = xtrain.reshape(len(xtrain),nfeat)\n",
"xtest = xtest.reshape(len(xtest),nfeat)\n",
"\n",
"ytrain_cls,ytest_cls = mnist.train_labels(),mnist.test_labels()\n",
"\n",
"nClasses = len(np.unique(ytrain_cls))\n",
"ytrain,ytest = to_categorical(ytrain_cls,nClasses),to_categorical(ytest_cls,nClasses)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "cDoJ6UBPITLf",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"outputId": "2043ff7b-2f14-4702-f959-cf320411a7f1"
},
"cell_type": "code",
"source": [
"epochs = 100\n",
"# logarthims learing rates \n",
"Learing_Rates = {10**-i:{\"Acc\":[[],[]],\"Cost\":[[],[]],\"Pred\":None} for i in range(5)}\n",
"for lr in Learing_Rates:\n",
" model = NeuralNetwork(lr)\n",
" for e in range(epochs):\n",
" model.learn(xtrain,ytrain,128)\n",
" l,a = model.metrics(xtrain,ytrain,ytrain_cls)\n",
" Learing_Rates[lr][\"Cost\"][0].append(l)\n",
" Learing_Rates[lr][\"Acc\"][0].append(a*100)\n",
" l,a = model.metrics(xtest,ytest,ytest_cls)\n",
"\n",
" Learing_Rates[lr][\"Cost\"][1].append(l)\n",
" Learing_Rates[lr][\"Acc\"][1].append(a*100)\n",
" Learing_Rates[lr][\"Pred\"] = model.predict(xtest)\n",
" print(\"Learning_Rate: {} Final_Cost: {} Final_Acc: {}\".format(lr,Learing_Rates[lr][\"Cost\"][0][-1],Learing_Rates[lr][\"Acc\"][0][-1]))"
],
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": [
"Learning_Rate: 1 Final_Cost: 0.2918251669558497 Final_Acc: 91.14166666666667\n",
"Learning_Rate: 0.1 Final_Cost: 0.2442148184482209 Final_Acc: 93.28\n",
"Learning_Rate: 0.01 Final_Cost: 0.28602339230415863 Final_Acc: 92.06666666666666\n",
"Learning_Rate: 0.001 Final_Cost: 0.39880665480495203 Final_Acc: 89.34166666666667\n",
"Learning_Rate: 0.0001 Final_Cost: 0.8226383987050695 Final_Acc: 83.91666666666666\n"
],
"name": "stdout"
},
{
"output_type": "error",
"ename": "TypeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-86-28f647fe3bbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# checking out graph results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLearing_Rates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: plot() missing 1 required positional argument: 'm'"
]
}
]
},
{
"metadata": {
"id": "VCBdLiS9hntl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1609
},
"outputId": "e39836d7-6665-4994-b9c2-e40b270280de"
},
"cell_type": "code",
"source": [
"# checking out graph results\n",
"plot(Learing_Rates,\"Cost\")\n",
"plot(Learing_Rates,\"Acc\")"
],
"execution_count": 89,
"outputs": [
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\t\t\tCost Graph\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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+7Tl+/BhXrlyhvLycJUsWUVR0he+/X0tubg5Dhgxj/PhJnD9/DoVCYZLXf/+p\nSY+cVUoldlozsvKLTB1KnbKyNmfUpK5kZehxKG1J4pLFpH65htK8XBxG3Fn5EJkQQjRE9dEy8oMP\nPmXcuIk88cRUlEol/fsPwNzcAg8PL2bPnoW1tTUajYaXX36N7OwsPvmk4snwSZMmmyQnTbZl5F8z\ntzz/42ry9SUsnzit2RSp/EuJ/P7ZFnwTD+B42204T5iEQnlzF0lkJiHjkDwah+TROCSPxmHylpGN\nWYk2EYVFCfoiA1qL2r/n1piFxxYRZ9GaXB8HOu3+FUNONm5THkVpZmbq0IQQQtRCk77nDGCh0IKm\niIzcxj9LWG31CPGhTXtnslQ6TrYaRcbJcBLfX0Rpfr6pQxNCCFELTb44a9XWKBSQlNN8Wi2qVEpu\nv7MDHQNbkIuW463HkBGbTPw7C2Q+biGEaASafHG2NbMFICWv+RRnAKVSQb8hfgT180FfZsaJVqMo\nTE7h0vw3uRITberwhBBCXEeTL84OlhVP7aUVNL1ZwmqiUCjoEeJD6NC2hAwPwG3CBErz8oh/923y\njh01dXhCCCGq0eQfCHO3dqLsspaC0hvrStKUdOj6Z1OMjoNR6JwI/9/PlH/yESX3jEM3ZGizeYpd\nCCEaiyY/cg72DKTodD8Uua6mDqVBOJlixR9O/Yh2DyZt3VpSvviccoPB1GEJIZq5m20ZWd1269Z9\nS2hoL/R6fZXH+2vSk9pYuvQ9Ll9OvIGzuXVNvjhrLdSoVUqy84tNHUqD0LWXF3Y6S2Ks/DnnO5Ts\nvftIWPwupXnyzqMQwjT+2TJy1qzZLFmy6Jp1li5dxLx5C/n449WEhR0iJia62u1+/fVnMjMzcHJy\nrvJ4SUmX2b59a63je/rp52jRwuPmTu4mNfnL2uWUY+2eSkZZGtDD1OGYnL2DFWMmB/Lr92e4nAhF\n/mPpeGETJfPfwOPJZzD3aBqtNYUQjcfNtozMzs6ucrvQ0NuwstKybduWKo+3ePE7RESE8/nnKykr\nK+Py5USSki6zZMly3nrrTdLSUiksLOThhx8lJKQfM2Y8yrPPvsCuXTsoKMjn0qU4EhMTmD37VTp0\n6FYnOWnyxVmBgmK3k5QWWlFWVo5SKfdXLa3MuGtCF3b8fI7oSDjRbhw9zn7NpQXzcJ86DeuugaYO\nUQhhIt/tjOLIuVSj7jOonQvjBlbfzvZmW0ZmZ2dXuZ13Db3tJ06czIYN3/HQQ1NZvXoFBkMJy5ev\nIisrk549gxk2bOSfxXcWISH9rto2NTWFRYuWcejQAdauXcsbb0hxvikKhQJNuSVlmiJyCorR2Zib\nOqQGQa1RMWR0B8L2xmBpZYaiR4OHAAAgAElEQVRH/8dI/mwVlz9ciuOoMRVzct/klJ9CCHErbrZl\n5M3ORt2+fUcAbGxsiYgIZ9OmDSgUSnJzr2033LlzVwBcXFzIq8PbgU2+OANYqawpUiaRnJkvxfkf\nFAoFvfq3+vMnT1ROLpxatQ42/sCVuFjcpjwKVD/3qxCi6Rk3sM11R7l14WZbRqrV6utuV1saTcXU\nztu2bSE3N5ePPlpFbm4ujzxybdMLlUp1w/u/Gc1iaGRnbodCAbEZaaYOpUE7GV3KSesgovyGkXfy\nJJfmv4E+IcHUYQkhmribbRlZ03bVUSqVVbaEzM7Oxt29BUqlkj17dlJSYrpXcJvFyNlN60hcEVzK\nTgHa1bh+cxXQrQUJMZnEpblSEDCB9mc38MfMWbg8+DA23YNMHZ4Qoom62ZaR0LLK7f7739UcOXKY\nzMwMZs58ioCATkyf/nTl8Vq29CUy8hzLlr1XeV8bYMCAgcya9Sxnz55hxIi7cHFx4fPPV9ZfIv6h\nybeMBNgde5h10d/jVhDM7DvvNupxmpqS4lJ2/nKO6Mg0rMyhc9yvaPNT0A0ZitPd96BQN4vvc0Yn\nLfqMQ/JoHJJH46jLlpHN4rJ2sEcXODOE4pT6fU+tMdKYVTwo1rOfD/oiOOY1glL3lmT9toWE9xZi\nyG5ec5QLIYQpNIthkIXGAhdbOy6nF1BWXo5Spqu8LoVCQfcQHxxcrCnSG2jrH0Tyms/IP3qEuDdf\nw33qY1i172DqMIUQoslqFiNnAAeHcgzmmWTlFpk6lEbD18+JfoP8UFpY4vbo4yT1vQ/9lTISFr9L\n+o8bKK/igQohhBC3rtkU50SbPZi3DyM5s8DUoTRKMefTOZus5ljbceS6+JH58ya5zC2EEHWkVsX5\n/PnzDBo0iK+++uqaZQMHDmTSpElMnjyZyZMnk5KSAsCCBQsYP348EyZM4I8//jBu1DfBwcwBhbKM\n2AzjznzTXLTyd6bPwNZcKSrjqG0fLncciv58JHGvzyH/1ElThyeEEE1Kjfec9Xo9c+fOpXfv3tWu\ns3LlSrTav98tCwsLIy4ujrVr13Lx4kVefvll1q5da5yIb5KbtRNxRZHE56QA7U0aS2OkUCjo0tML\nlxa2bNsYTkSeG9nd78fv9Houf7AE+4GDcLp3HEqNmalDFUKIRq/GkbOZmRkrV67ExcWl1js9ePAg\ngwYNAqB169bk5OSQn59/81EaQUudOwApBekmjaOxc/e0496HeuDlqyMpR4liwgzM3FuQvXM7l+a9\nSVE9t1UTQjQNDbll5F9OnjxOVlbmDW93M2ocOavVatQ1vNv62muvkZiYSPfu3XnuuedIT0+nY8eO\nlcsdHBxIS0vD2tq62n3odFao1cadFu2f75B1UvjwXRTkGrKv+26ZuFZV+Xpwegjnz6bgH+BG6ciu\nRK1aQ9pv24if9wY+D07GbfgwFPJU/FXk9844JI/G0ZDyGBYWRmpqEhs2rK/2auuHHy5m9erVuLq6\nct999zFmzJ1kZmZWud2PP/5IUVE+rq6uODlZX3VlFyAhIYG9e3dy772jbyjOHTt+5eGHH74qd3WV\nx1t+leqpp56iX79+2NnZ8cQTT7B167U9Mmszz0lWVtXfbm7Wv18O1xRbAaAvzyY5JQeVNHWoleu9\nZO/gqq1cdsIqkNyeXrQ9t5HoT1eTvP8wbg9NQf2PTjLNmUz6YBySR+NoaHncsWMPvXr1JS0tD1tb\nFzIzs4iNTbqqZaSVlTVqtTUZGQX06BHMtm27yM7OrnK7wMBgQkJu58cfN5Keno9eX3bV8V59dQ4R\nEeEsXLiY8eMnsWDBG+Tl5VFaWsozzzxPmzZ+fPXVGvbs2YVSqSQkpB/t23dg27ZtREREMm/eQtzc\n3Op0EpJbLs6jR//9zaN///6cP38eFxcX0tP/vnycmpqKs3PVTa/ri7VGS6srgzgbV0xGbhEu9pYm\njacpKTWUUXzFQHJmKdneo+lcGgHh+4md8yqu9z+ATY+epg5RCHEDZh94q8rPB3mHEurZB4A14d9y\nMSfmmnV8bb15OOA/AOxPPMyWuJ3M7fPSdY9nypaRa9asolevPtx552hiYqJZunQRS5Ys59tvv+LH\nH7egUqn48cfvCQoKpk2btjz77Au4ubldd//GcEvDx7y8PKZMmUJxcTEAR44cwc/Pj5CQkMoRdHh4\nOC4uLte9pF0fFAoFfnZ+lBdbkZJp3FF6c6dSKxk6NoCQQW0oLiklrMSPSyEPYCgtI+mT5SR9+gml\nJn7mQAjReNRny8jTp//gxx+/Z8aMR3nvvbcpKKj4t2rAgNt55pnpbNr0A0OGDL3h/d6qGkfOZ86c\n4Z133iExMRG1Ws3WrVsZOHAgnp6eDB48mP79+zN+/HjMzc3p0KEDQ4cORaFQ0LFjRyZMmIBCoeC1\n116rj3OpkavOChSlJGXk0amVo6nDaVIUCgWde3ji4W3Ptk1nuZCiJ6Pz/fRK305e2CH0kRG4Tn4Q\n666Bpg5VCFGDmka6AA92nFDjOiEevQjx6FXjeqZsGanRqPm//3uegIDOV30+c+ZLxMXFsnPnNp58\nchqffvrfG9rvraqxOAcEBPDll19Wu/yBBx7ggQceuObzmTNn3lpkdSC27ASWQTuJytYwBB9Th9Mk\nObpYc88D3Tm0OxorazNa9nyZrN+2kLHxBy5/uBTbPiE4j5+ESltzWzchRPPQs2cwq1evYPTosTW2\njHR2duHAgX3MmTOXnJzs625XnX+2jOzQIYDff99NQEBnYmKiOXz4ACNHjmbdum946KGpPPTQVE6e\nPIFeX1Btq8m60Czm1v6Lu50DpEKqXl6nqktqjYq+g/0oLy9HoVBgN2QYpwpb4Bq+ldwD+ykID8f1\nvslYB3Y3dahCiAbAlC0jH3nkMebPf53p0x+hrKyMZ56ZibW1NdnZWUydej+WllYEBHTG1taOrl27\n8eqrL/LWW+/RqlXrOs1Js2gZ+ZfonFjeO7YcTaYfS+6ZatTjNVXGeKozKiKVbRvPolYr6eyiR3dg\nPRgMWPfoicvE/6C2szNStA1XQ3s6trGSPBqH5NE4pGWkkThbVtyLuKLIxVBaVsPawlhat3Nm0F3t\nUamVHL9sQUTvRylr3ZH8o2HEznmZ3AP7b+pBDiGEaKqaVXG21mhRlmtQmBeQll1o6nCaDYVCgV8H\nV8ZPCcK7tQOXk/TsMwumYPB9lJeUkPzZShIXv0vxn/OyCyFEc9esirNCocBaaY/CQk+yvE5V77Q2\n5gy/pxO3jWiHQqnAoXsgPm/OxyqgM/qIs8S9/iqZm3+m3GAwdahCCGFSzeqBMIBA+15sOxZLik0+\nYNqJUZojhUJBu05u+Po5Ym6hAUA35QmSfjuKbv9a0jesJ/fQQVzuux+rtv4mjlYIIUyjWY2cAYJb\nBFKa7kladrGpQ2nW/irMAGF7Yzh6oYSTAZOhz2CKky6TsPAtklevxJCba8IohRDCNJpdcXbVVcyx\nnWLkubzFzQse0Iq2HV1JSy1gV7onqSNmoPb2JffgfmJfnUX2rh2Ul8kDfEKI5qPZFedsQyZWnQ6S\noDhh6lDEnyytzLj9zvaMGNcJrbUZp8/lccD5DhR33Qfl5aR+/SWX5r6G/nykqUMVQtQRY7aMTElJ\nZsaMR5k+/RFmz55VOcV0bm4uzz77JK+++kKVMURFXeDSpbhaxZuRkc6cOXNu9DRrrdkVZyu1JeWW\nOVxRZVFiqJ+ZXkTteLdyZPwjPenay4srhSW49OuDz7y3sO3Tl6L4eBIWvkXSyk8oycoydahCCCM6\nceIYCQnxrFjxObNmzWbJkkXXrLN06SLmzVvIxx+vJizsEDEx0dVut3r1Cu6+exzLl6/C09OLX37Z\nBMCiRW/RuXOXauPYs2cn8fGXahWzo6MTb7755k2cbe00uwfC/nqdqsyigJSsQjydTduQQ1xNY6ai\n922tCQz2xsJSA1hRPuRect2743jsJ/IOHyL/xHEcho9EN2QoSjMzU4cshLhFx44doV+/AQD4+PiS\nl5dLQUH+VS0jbWxscXWt6AbVu3cIx46FkZ2dXeV2J04cY+bMivnBQ0L68c03XzJmzD3MmvUq585F\nEBV1/poYLl6MYuPGDezZsxOdTsebb84mODgEnU5Hnz79WLz4HdRqNUqlkrlz36agoIBp015ixYr/\nMn78aEaNupv9+/dSXFzM0qXLazWN6PU0u+KsUCiwVdmTZZ5BXHKuFOcGqqIwVwj7PYbLl3LQuQ2l\nWxc9yp0byPhxAzm/78H5nnFYB/VEoVCYMFohmo60dd+Sd/SIUfdp0yMI53urb5Rh7JaRhYWFmP35\nxV2nc6hsjnG9gtm6dRt69erNgAG306FDAAaDgeDgPgQH9+HIkUP83/89T9u27Vi16hN+++1XQkL6\nV25bWlqKt7cPkybdz2uvvcTRo0fo33/AjSXpX5rdZW0AF60TClUZ55Nl0ovG4I4xHenQ1Z2sdD07\nwuFiv6mYDxpJaW4OSZ9+TPzb8ym8GGXqMIUQRmLMlpG3Mvtghw4dAdDpHFmxYjkzZjzK9u1bycnJ\nuWbdLl0qOu45O7tWtp28Fc1u5Azgq2vB+bwIYrITAWlh2NBZWGoIHepP+y7u/L71AhfPZxCncWHI\n46+g2f8z+cePEf/WPKx7BOF0972YubiYOmQhGi3neydcd5RbF4zdMtLS0oqioiuYm1tUrnsz1OqK\nK3hLly7iP/95gODgPvzvf19SWHjt2z4qlaryv40xHXGzHDm3d/TDPM+X1LRSyspkTufGwsXdlrEP\ndGPAMH90jlpatPemxfQn8XzhJcx9W5F/9Aixs18i9dv/UZonk/oL0Vj07BnM7t07AGpsGWkwGDhw\nYB9BQcHVbtejR092794JVDzk1atXn1rFoVAoqmwJmZOTjYeHJ8XFxRw6tB9DPcxi2CxHzn66VgSo\nB7AvN4mkjAI85L5zo6FQKGjfxZ12nd0q7zNfzNNy0edOuva6Qvn2DWRv/43cfb+jGzoc3eA7UJqb\nmzhqIcT1GLtl5JQp05g3bw4bN27Azc2dYcNGUlpaytNPP05+fj7p6anMmPEoDz00le7dgyrj6NIl\nkCVL3sXKyuqq+MaOHc9LL83Ew8ODsWPH8/77Cxk4cHCd5qRZtYz8px3HEvh623mmjGhPSCd3ox67\nKWkMreV2bT7HuT+SAfDr4Ex7dSJXfvuJ0vw8VLa2ON45Crt+oSjUpvsu2hjy2BhIHo1D8mgc0jKy\nDuRbXsSsXRgXktNMHYq4RbcNb8ddE7vg5GLNhbNpbD5rQerIJ7EZPoqyoiJSv/6S2FdfImf/Pplp\nTAjRKDTb4lyu0aOyzSQ6K97UoQgj8GipY+yD3bltuD/mlmpOHr3MlYC++C5YiP2gwRiys0j5fBVx\nc14h72iYFGkhRIPWLO85A/jYeUI8pBUlU1pWhkrZbL+nNBlKpYJ2nd1p3d6FqLOp+Pg5oVAo0I68\nhwzfXujO7SV3/16SPlmOmacXjneOwjqwGwr5fy+EaGCabXH2svEEoMwih6QMvUxG0oRoNCrad/n7\nOYLjB+I4c/wyDs4BdJ/aF6uTO8kPO0TSxx9i7uWFw52jse4aKEVaCNFgNNvi7GBhj5nCgjJtLnHJ\neVKcm7AuPb0oKS7lfHgK23YU4OoRRLfpt2N+dAd5YYdIWv5BxUh6xJ1Yd+8hRVoIYXLN9l8hhUKB\nu6U7Sgs9UUnppg5H1CFbe0sGjmzPuIeD8PVzIiUxl1+3JpDQbgg+b87HpldvihMTSFqxnLg5r5B7\n8ADlVbzrKIQQ9aXZjpwBAt06EH20kLhi6XLUHDg4axk6NoDUpFyO7IulVTtnzJytcZ86DVX/oZQd\n2E7uoQMkr/6UjE0/oLtjGLYhfVFqpLmGEHVt2bL3CA8/g0Kh4Omnn6N9+45XLT9y5DCffvoRSqWK\n3r1DePDBR6rdLiUlmblz51BWVoajoxOzZ7+JmZkZubm5vP76K1hZWTJv3sJrYoiKuoCZmdmf71DX\n3q5d27nttkE3f/JVqNXI+fz58wwaNIivvvrqmmWHDh1i3LhxTJgwgZdeeomysjIOHz5McHAwkydP\nZvLkycydO9eoQRvLYJ9Q3HL7cvlyGaXy9G6z4eJuy4h7O+P4562MlMu5fP9DDEcse2D55BzsQm/D\nkJVF6ldfEDPreTK3bKa0sNDEUQvRdDXGlpF/KSkpYe3a/93QNrVR48hZr9czd+5cevfuXeXyOXPm\n8MUXX+Dm5sZTTz3F3r17sbCwoGfPnixbtszoARtbSzcbLqXmk5Sux9NF7js3RyqVEo+W9iTEZpEQ\nm4VHyy4EPnU75uEHyN2zk/T135H5y0/Yhd6GbtBg1P/ojiOEuHUNsWVkSUkJK1Z8hFqtxsXFlRdf\nfJWioiLmzJlFcXExJSUlzJ37Bl9++Q0XL0axaNHbzJw5y2g5qbE4m5mZsXLlSlauXFnl8g0bNmBt\nXZFABwcHsrKycHdvPDNuldhHoWkZS0xyOynOzZSTqzV3TexKUkIOR/fFkhCbRWJcNl6t2jH07eHk\n7N5J9o7tZG3ZTNa2rdj27oNuyFDMW3iYOnQh6sRXyw9W+XnXXt4EdK/4vd/xUwRJ8dnXrOPqYcvg\nURWXpM+evMzxA3HcN73qwd1fGmLLyIcemsTSpR9ja2vH8uVL2bVrO+bm5jg7u/DSS3NITEwgIyON\nSZMmc/bsGaMWZqjFZW21Wo2FhUW1y/8qzKmpqezfv5/Q0FAAoqKieOyxx5g4cSL79+83UrjGl6WM\nR+0az8XkjJpXFk2au6cdd07owpjJgbRs7Yijsxa1tTWOI+/C+dX5OE1+EI2TM7n79hI35xUSlrxH\nwdlwo3SgEUL8zdQtIzMzM0hIiOfll59nxoxHOX78GGlpqXTs2Jnw8NO8++4CEhMT6N+/f807u0lG\neSAsIyODxx57jNdeew2dToePjw8zZsxg2LBhxMfHc//99/Pbb79VfpOpik5nhVqtqnb5zbjevKV/\nCfBozaULscTlXsbZuXadS5qb2uSxKXF2tqFTV0/Ky8pRKBWUl5Wz/vOjlJZq6f3Q8/iUJZPy88/k\nnjmN/sxprFp60+KukTj374fyOr/jzS2PdUXyaBzXy+P/vTakxu0nPNyzxnVCB/sTOti/xvVatvSg\nuLigMqasrAz8/X0qB39t2/qQl5dduVyvz8HHx5OsLG0122mxsdFgYWFBTEw+Hh7ulevY21thbq6p\n8vwtLDTY2Vni5qbD1dWVtWu/uWadn3/+icOHD/PNN98QExPJ6NGjUauVRv+9vOXinJ+fz9SpU3nm\nmWfo27cvAK6urgwfPhwAb29vnJycSElJwcvLq9r9ZGVd2x/zVtR2QnJXM1cAkvSXSU7JkZnC/kUm\nyIeSYgNObjZEnknml/WnsdKa0bnPRFqNKEa/Zzt5R8OI+mA5MZ9/iV3oAOwGDESju/q+tOTROCSP\nxtHQ8tixYyCrV6/g9ttHEBl5Dp3OkcLCcgoLK2I0N7cjOzuXP/6IxNnZhW3bdjBnzlxycrKr3C4w\nMIj16zdyxx3D2bjxZ7p2Dao83+xsPUVFJVWef1GRgczMfIqLlZSWlhEWdgpf31asX/8tXbt2Jysr\nE4PBQO/eIUyf7sYHHywiNPSOavdXk+sV9Fsuzm+//TYPPPDAVcP7TZs2kZaWxpQpU0hLSyMjIwNX\nV9dbPVSd8LKpuH9SbpFDYloB3q7yrVxcTWOmZsAwf4L6+vDH0QTCT1zm0O5ojpmpuGviJHzvuZfs\nXTvJ+X03mb/8ROaWzdh06479wEFYtPGrbG0phKhaQ2wZOWvWHBYseAONRoOTkzN33XU3Wq2WN9+c\nzddf/xelUslzz/0fTk5OGAwlvPrqi8yb947RclJjy8gzZ87wzjvvkJiYiFqtxtXVlYEDB+Lp6Unf\nvn0JCgoiMDCwcv2RI0cyYsQIZs6cSW5uLiUlJcyYMaPyXnR16rtl5F/Kysv4v91zKNZruMdtCrd3\n9zRqHI1dQ/uG3RAUXSnh7KkkoiPTGP2fQFQqJUVXSshIysEq/gzZO7ZTnJgAgJmnF/YDb6fViMFk\n5pWYOPLGT34fjUPyaBx12TKy2fZz/qePjq/hdGwq7cvu4Ol7qn8HrjmSP+LaOXHoEod2R+PSwobO\nPTxxV2SSu3sH+SeOQ1kZKq0VNsEh2IUOkKe8b4H8PhqH5NE46rI4N+sZwv7yRLcHmXXkIJEF2RhK\ny1Cr5L6zuDHunna0bONIXFQG2zdFoLUxo2PgnfiNGk/xkX3k7fud7B3byN6xDcu2/tiF3oZ1t+4o\nNRpThy6EaIBUr7/++uumDgJAry826v60WvMb2mdShp6oxBwCWjngaFv9q2PNzY3msbmytrXAr4Mr\nfh1coBxSk/KIj84kJ6+UrmMH0Gb8GEodXSkr0FMYGUH+8aNk79mFIScHtYMjaht51qE25PfROCSP\nxnGredRqzatdJiPnPymd41F7XCQ8xgc/T3tThyMaKXsHK/oN8aNXqC+Rp5NxcK6Y9ECpVhOhd8J2\nyGS8x5dRcHAvufv3kb1tK9nbtmLp1xbbvv2w6dETpXn1f7BCiOZBivOfLpWcQe2exJm4NEb3a2Xq\ncEQjZ2auplOPvx8uvFJYwslDlygtLcfcQk27zj1o/8pw1HER5OzZgz4inMIL50n75mtsevbCNqQf\nFq1ay5PeQjRTUpz/1N6xLQkFl4nLi0N/pTtWFpIaYTwWlhomTO3J2VNJRJxK4lRYPKfC4vFoaU/I\n5MdxVRSSs38vuQf2kfP7HnJ+34OZmzu2IX2xCe5zzXvTQoimTe45/0NY8nHKSszwtW6Fu2P1c7A2\nJ3Jvyji0WnNKy8rw9NHRubsnOicrruhLSIrPITDYG0sHOyzatkMR2Bddp/aUG0q5En0RffgZsrf/\nRuHFKFAq0Di7oFA33y+O8vtoHJJH45B7zvWglZ0PKoWKMtsMwmMzCWzrbOqQRBOlUiv/fHjMlfy8\nIqxtKv5AE2Iz+eW707h72tE+5G5aTriPKyePkntgP/rwM+jDz6Awt8Cme3dse4dg6d8OhcxoJ0ST\nJMX5T2YqDa3tfDlfHsXpmCSg5vlghbhVfxVmAI1GhaePjoTYLJISctCYqWjT3oN29z+Fq6qAvMMH\nyT14gNwD+8k9sB+VvT22PYOxCe6NuZe33J8WogmR4vwPHZ38iU/LIT0/h4ycKzjayStVov64e9lz\n5wR7crMLOfdHMpFnkok4lUR8TCb3PR6M0+ixONw5misXo8g9uJ/8Y0fJ+m0LWb9twcy9BTa9grEJ\n6onZnz1vhRCNlxTnfxjkHUp5aiu+KbxAeGwm/bu0MHVIohmytbekZ39fevT1ITEui+IiQ+Wo+NjB\neJLiC/HvNhyfeyZRcj6c3EMHKTh1kowfN5Dx4wbMW/pg07MXNj16onF0NPHZCCFuhhTnf+no4wDA\nWSnOwsSUSgVevg5XfZaTqScxLpvEuGzUGiW+bZ1oe8dEfO5/mMJTJ8gNO4w+IpyiuFjS163FolVr\nbHr0xLpHDzQOUqiFaCykOP+LXpWKtV8E4YnFlJV3RCn38UQDMuiuDgT18yHyTAoXwlO4EJ7KhfBU\n2nV247bhIdj2CaE0L4+848fIPxqG/lwEV6IvkvbdN1i0boNN9x5Yd++BxtHJ1KcihLgOKc7/kqRP\noVQXR3G2lviUfFq6ybSKomGx01nRs58vQX19SLmcy4XwFHz8/i62v+9NxErrTZvJPXA1L6XgxDHy\njh6hMPIcVy5Gkfbdt5j7+GLTrTvW3Xpg5ib3qIVoaKQ4/0s7nR8AKrsMTlxIk+IsGiyFQoGbhx1u\nHnaVn5UUG4iOTKO4qJTjBy+hc7SidftWtHmoF+6aUvJPHCf/+FH05yIoio0hfcN6zFq0wDqwO9aB\n3TFv2VKe+haiAZDi/C9Olg44mOvIsM3k0NlkRvX1lX+sRKOhMVNz/4w+xEVlEBWRyqXoTI7ui+Xo\nvlgGjmyHf+gA7EMHUJqfT8Efp8g7fhR9+Bkyf/mJzF9+Qu3ggLZLINaB3bBq69+sJzwRwpTkL+9f\nFAoF7Rz8OFAURlpxCrHJefi625o6LCFqTaNR0aa9C23au1BcZCD2z0Lt2bJiCtBSQxmbfjiPp68H\nrcZ3wc1Gjf7MafJPHqfgj1Pk7NpBzq4dKC0t0QZ0Qts1EG1AZ1RamTVPiPoixbkK7R3bciApDJV9\nKofPpkhxFo2Wmbmath1dadvRtfKzjLR8UpPzSE7M5ei+WOwcLGnV1plWIybS6oGHuRJ1oeLy96kT\n5B0JI+9IGCiVWLbxQ9ulK9pOXTBzd5crSkLUIUV5eXm5qYMASEvLM+r+nJ1tbnqfRaXFvHv0Q5LP\nO6LJac1700NQKpvnP0S3kkfxt4aWx+IiA3EXM4iOTOdSdAaGkjIA7n2oB06u1pSXl1NaWkZp8mXy\nT52k4NQJrsTEwJ//XGicndF26oK2c2cs27ZDaWZWL3E3tDw2VpJH47jVPDo7V/9Mk4ycq2CuMuPV\nXs/y36xz7Em8TOSlLNr7ONS8oRCNhJm5unJ+75KSUuKjM7l8KRtHl4pL1+kp+fz07Sm8Wzvg06YX\n3oOGoSzSU3D6Dwr+OIn+bDjZO7eTvXM7CjMzrPzboe3UGauAzpi5uJj47IRo/KQ4X0dwB1f2nLzM\nobMpUpxFk6XRqGjl70wr/7+bvejzi9GYqSrfo1YqFXi0tKdlax/aPdIbtaKcwgvnKTjzBwWnT1cU\n7dN/VOzPxRVtQABWHTth5d8OpYVMgyvEjZLifB0xZSewCgjjaGRv7hvij0YtHYBE89CyjSP3tQ4m\nPSWf2KgMYi+kEx+TRXJiLh0CW6BQqcC7DSXWLfAeO47SrMyKAh1+hsKIs2Tv3EH2zh2gUlXcq+4Y\ngFWHAMy9vaWTlhC1IMX5OtILMyi3yqTILI3T0Rl0kzaSohlRKBQ4u9ng7GZDUF8f8nOvkJmuR6Wq\nKK7RkWns/e0CZuZqvMB8Iu4AACAASURBVFvp8G7VAa8H+9LCXElh9EX0Z05TcDacwvORFEaegw3r\nUWq1WLXvgFX7jmg7dETjLH9TQlRFivN19HDtysGkI6gckjh8NkWKs2jWrG0tsLb9+xK1s5sNHQJb\ncOliBlERaURFpAHg2sKW0fcFYtXWH6e776E0L4+CiHD04eHoz4aTf/QI+UePAKBxcsayffuKgu3f\nHrWdXZXHFqK5keJ8HX72rbDRWJPvlMLJU6kUFhmwNJeUCQEVRdi1hS3l5eVkpeuJu5jBpehMlEpF\n5dsNMRfSOX8mGS9fb7zu7orrg+aUpKSgjwivGFVHniN37+/k7v0dALMWHli1a4dluw5YtfVHZW1t\nylMUwmSk0lyHSqmim2tn9iQcoFSbxvHzaYR0cjd1WEI0KAqFAgdnLQ7OWgKDvfnn25lJl7L/n733\nDo/ruu+8P7fMnd6BwaA3drCIpEixqFC0aMmSZcm2FCmWW2wnG5f4SSzHeqNsVvYb2+/mXW/WXmdT\nVoniZG3HdJEsq9sWZXWJauwNBAEQfWZQpve5+8cdDAABElVAgQTP53kubjv33HPPXNzvKb/zO5w6\nHuHU8QgAHp+VhhYfja3raN6xE3SdbG8PqWNHSR09QvpkJxN7Boz+aknC3NCIdflybMtXYF26HN5k\n6IlAsJhQvv71r3/9TIFOnDjBLbfcgizLrF27dsa55557jj/7sz/jF7/4BaFQiM2bNwPw7W9/m7/7\nu7/jF7/4BcuWLaOmpmauqCukUrl3/hRzYLeb5yVOq2rh+aGXkJCInPZw+bq6C8r5wnzl44XOhZSP\n0/8/Glq9LO2oweO1IkkSY5Ekw/1RRsMJ1mxsQJIkErqZtKee4K4r8X/gWuwdqzFVGRN5ZE/3kjl5\nkvhLexl/9GFGn3+BzEA/pWwWxW4XluDvkAvpfTybvNt8tNvNb3jujDXnVCrFX//1X7N169Y5z3/z\nm9/kX/7lX6ipqeHjH/84V199NWNjY/T29rJ79266urq488472b179zt+gIWk1dXMFQ3bOJWwcuJU\nnM7+KMsaPQudLIHgvECSJDw+Gx6fjTUXN1AslhgZjJHPFSthjuwbZP/eflSTTG2Dm/oWLw2bdlJ/\n3YegmCfT3U36+DFSx4+ROdVFqve0UbMGTDVBrEuXYV26FOvS5Ziqqy+owrNg8XJGcdY0jbvvvpu7\n77571rm+vj7cbje1tUZT7xVXXMHzzz/P2NgYV111FQDt7e1Eo1ESiQSO87D/SJIkfm/ZjZywTvBf\nj7zKY3tPC3EWCN4hiiJT97r/n5YlVZSKOv094/R1GwuAr9rOLZ/dhG3ZcrTWJfg++CGqvFb6Xz5I\n+sRxUieOk+k6SeyZp4g9Y/RZK2431iVLK4u5sUlM3iE4LznjW6uqKuobvNzhcBifb8o5h8/no6+v\nj/HxcTo6OmYcD4fD56U4T7K0wU1Tg8K+zggj4ylqvLaFTpJAsCioa/JQ12QIdjKRZaB3goHecay2\nKZeg+/f2sf+lflqWVOGvsVO34QrqrrkOCZ1sfx/pzhOkOztJd54g8crLJF55GQBJ07C0tmFtX4Jl\nyRKsbUuEkZngvOA9KVK+FffdXq8NVVXm9b5v5rf07fLTQw8QrnsYRrfz7KER/tNH1p75okXCfObj\nhYzIxzNTXe2kpbVq1nG3x4bVZuL4oWE4ZBwzW1SWdwS58WPrYeNqwPjWZEMhYkeOEjt6nPjRo6Qm\nx1mXsdbX4Vy+HOfyZThXLMPW2Gg4VbnAEO/j/HC28vFdiXMgECASiVT2R0ZGCAQCmEymGcdDoRDV\nZ3A2MD6eejdJmcV8O3avUgx/wfbGPn69183VmxqwW0zzFv+5inCQPz+IfHx3LOkIsKQjgElROLR/\ngMHTEwz1RYlG05V8PbJvkM4jIWob3NQ2tlLzoXW4b1YpppJkTp0i3XWSzMmTZLq7SO95gtCeJwCQ\nzBYsLS1Y2tqxtrVhaW1H9SzurivxPs4P5+zEFw0NDSQSCfr7+wkGgzzxxBN85zvfYXx8nO9///vc\neuutHD58mEAgcF43aQN0+FcQsFYRoZ+c3s6T+wa5dkvzQidLILig8PhsLF8dZPnqIACFwpRh2cRo\nisHTEwyengBAksBf7aC+xcu2nWuwr14DgF4qkRsaNMS6q8sQ6+PHSB8/xng5LtXrw9LWhqWlDUtr\nK5aWFmSL9T19VsGFzRnF+dChQ/zN3/wNAwMDqKrKY489xs6dO2loaGDXrl18/etf5/bbbwfg2muv\npbW1ldbWVjo6Orj11luRJIm77rrrrD/I2UaWZHY0XspPT/wSc20/v33Zyfs3NaIqwk+wQLBQTO8K\n2/a+JWzc3szwQIyh/ijDfVFCQzFM5qkwp46HOXk0RE2di+CSDVRvvxxFkY3adXc3me5TxnLq1Iy+\nayQJLVhbFupWzC2tmBsa37OpMgUXHmI+57dBppDlPz/3bQp5ndjLl/GH161ha7kEv1gRzV/zg8jH\n+eHt5mOxUCKdylXcjj7725MceLm/cl5RJKqCToL1brZe2VYZhqXrOoWxMTLdXYZo93ST7e2hlMkw\n7WLMdfWYm1uwNLdgbm4+bwRbvI/zwznbrH2hYVHNXFp3CXv6nkZ1xrj/2W42rQyI2rNAcI6iqPIM\nf+Db3tfOmovrGR6IMTwQZWQgRmgwRiFfRJLaATh9apQjrw0RqHNSU7eU6jUbqDarRnP48DDZ3m4y\nPT2GYJ/uJdt3ujKUC1lGq6vH0tSMubnZWDc2CWcpgreNEOe3yVVNV7Cz6TJ+lR9kz6sDPLlvkPdt\nbFjoZAkEgreAJEm4PFZcHivLOgyvhflckVQyWwkzMhCjuzNCd+eUUau3ykYg6OSKDyzHXFeHa+t2\nAPRikdzQIJneHrK9PWR6DbHO9ffBc89M3hRToAZLUxPmpmZjaWxCdbneuwcXnHcIcX6bODQ7AB+6\ntJXnDg1z/zPdbO0IYrOIrBQIzkdMmoJbm/JbsOmyVlauq2VkME5oKEZoKE54OE4hX6pMl9nfM84L\nv+uiutZFIOikesl6/Fu2oyhyuYY9ZLge7e01atene4m/tJf4S3sr91HcHsyNTZgbG42loQktGBTz\nXQsAIc7vmN7USZo2dnPi+UYeebGXj17RvtBJEggE88Tk9JjtK4whoKWSTio55UM5Hs0wGk4SHk5w\npHxMUST8AQcfvGUt5rp6TME67Ju2GIKt6xQiETKne8n29ZLt6yPbd5rUoQOkDh2oxCuZTGj1DZgb\nGjA3NFYW4TjlwkOI8zvkmYEX6CsewxV08euX+rhyfT0+l+hXEggWI7Is4XBOTVKwcl0ty1bXMBZO\nVmrW4eE4iVgWrTytbGgoxv0/3oe/2k5VjZOqGgdVwaX416zHpBkW5MVEgmy/IdTZvj6y/X3k+vvI\n9nTPuL/i9hiCXVeP1tCAub4RrbYW2fzGEycIzm+EOL9Dbmi/lsOjx7G1dhIbvpj7njrFZz+4aqGT\nJRAI3iMURaY66KQ6OGVxWyrpFYvvYqGEv9rBWDhBeDhRCSNJcMtnN+GtsiPZ7Iyaa/Bva8drN6y8\n9UKBXGikLNT9hnj395M6fIjU4UNMj8gUCGCua0Crryuv69FqgsKf+CJA/ILvkDpHkC21F/P80EtU\ntUZ47pDErk2NNNUIl3gCwYWKLE/NiFXf7OWmT2+kWCwxMZoiPJIgMhJnNJTE5TUcmkTHUzy422jW\nttpNVAUc+MtL4+oNuDZvqcRXTCXJDQyQ7e8nO9hf2U689gq89spUIhQFraYGrbYOra7eqG3X1WEK\n1CCbFr9Xw8WCEOd3wXWtu3h5ZB8Ej6P3+vjxb07wtds2IIsp6wQCQRlFkSuCy5qZfhFMJoWN25qJ\nhBKMhhIzZuW64WMXYW3S0HWdp37didtjwVddhf/iZtwODUmS0HWdYnSC7MAAucEBsoMD5AYGyA0N\nkhschEknKgCybNS0a+tJtTdTcFeh1daiBWvFUK9zECHO7wKvxcPOxst4rHcPLR3jnDiosOeVfq66\nuHGhkyYQCM4DHC4Lmy9vrexnM3kiIwnGwklDzIF0MseR1wZnXGe2qPir7azf2kRTmx/V48W8fCXe\nssc0XdcpjI8bIj0wQG54kOygIdiJ4VeM2vY0VJ8PLVhr1LaDtWXRDqK4PWJ+7AVCiPO7ZFfzFTg1\nB2s9G/jGyVf4+ZNdrG33ExBTSgoEgreJ2WKivtlLfbO3csxi0/j9P9rMaCjJWDjBaDjJWDjJYF+U\ntZumHDz+9F9eplAo4au246uy4fXb8VU34V2yAm/ZSE3XdYqxKNbUBOFjXYZ4Dw2RHRokdeQwqSOH\nZ6RHtlgwBWvRaoJowSBaTRBTeS2M0c4uwn3nPPLikRH+6VeHWdbo4WsfW78omreFm7/5QeTj/CDy\ncYp8vogsSSiqMVTr4Z8fZDSUJBnPzgi3bnMD23YuAaDzyAiFfInW9iok1SgMTFJMp8mPDJMbGjJE\ne2SY3PAw+ZFh9EJh1v1Vrw9TTY0h3DXByrapquqCMUgT7jvPA4qlIqPWA6xcKXP06IRo3hYIBGcV\nk2lqQg9JkrjuZmOO+Wwmz3gkxVgkyVgkSUOLrxLuwEv9hIbi/I7jgGGE5vXbaVniZ93mRpSWVtSG\nJpyKPOVnvFQiPxohPzxcEezc8BD50AjpY0dJHzs6M2GyjMlfZYh1oAZToAatpgZTIIDJf+EI97tF\n5NI8MZAY4uHu3+Kr9mHv3SyatwUCwYJgtpgINrgJNrhnnbvs/UsZDSXJpPIM9k8wHjGm2XS5pwzC\n9j7ZzZH9Q3h8Vjx+G16fDY/fhifYhn/N2hnxlbJZ8qEQuZFh8qERQ7hDI+RHRkgdOkiKgzMTMCnc\ngQCmQACtOoCp2tg2VVWLpvJpCHGeJ5pcDVzZeCl7+p5m1eYQr/yumrsfPMIdH9sgJsYQCATnBIFa\nF4Fa14zm2Hy+SCE/NS+2xWbC6bYwVvaANonbZ+Vjf3QJAEN9E3QdC+P2WfH47HiWrsG78eIZxmPF\nVMoQ7tAw+ZER8uEQuVCIfGjEGK89s3sbMJytaIEApupqQ7Qr6wCK03lBGacJcZ5Hrm+7mkORoxxN\nvcrq1ddw6FCM/3i8k0+8f/lCJ00gEAjmxGRSZjSRb9jazIatzZRKOolYhvHRFBOjqYpfcYCh/igH\nXxmYEY+iSLi8Vj76yY2YNIWiojEquXGvrMW3SZshrKVMmnw4bNSyQ2Hy4RFDuCNh0ic7SXeemJVO\nyWzGVFVtCHbV5FJV2V9stW4hzvOIpmjctvJmvvvqPxL3v0x9YDtPvDpAa9DFpWtrFzp5AoFA8JaR\n5akZvJrb/TPOrdlYT2Orj4mxFBNjaSbGUkTH0mTS+Ypr0shIgl/9x34AVJOM22PF7bPi9lpZvaEe\nR2MT5sYmdF2fIdx6oWD0cYfDxhIJGQIeCRmCPtDPXChOJ6q/akq0Jxd/Faq/6ryYZ3s6QpznmSWe\nVnY0bOeJ/mf46A6Ve+9X+ffHjlNfbae1VkwRJxAIzn9MmjrLdenrsTk01m9tIjqWJjqeIjqeZjSc\nBGD5GqOyUiyUuOd7z+B0WYyCgNeC22PF5bVS3bocz+o1M+LUdZ1SMkk+HCIfiZCPhMuLsT2XX/JJ\nFJerItQmvx+T31/ZVv1VKFbrPOXO/CDE+Sxwffs1rPQvp8O/nOobRvnuT/fzv+47yH/59CZctvOr\n9CYQCATvBI/PxpYr2ir7uq6TTuaIjqdxeQwDtEwmj9dvJzqeYnw0NeP6K69dzopyi+OTjx6nVNLL\nNXkLLk8VzlUNOGymmbXuUoliLEo+XBbu0Qj50QiFyCj50QjZvtNkuk/NmV7ZZkP1lUW7vDb5/Kjl\nfdXtfk+n81yU4pwaP8JQPI7qvGRB7m9WNDr8Rj/z6lYf115Wy0NPD/H39x3i9lvWYVKVM8QgEAgE\niwtJkrA5zNgcU33DdoeZmz69EV3XyWYKxCbSRMfTxCYy1NRPtTR2HQuTzcwea92xvo7Lr15WDhMi\nEc/icltwumtxNbXiMs+UuIp4j46Sj0QojEaM7dFRCmMRw2itv2/uB1AUvLuupvqm35uH3Dgzi1Kc\nE2P7ycQ6aVh7EbKycEYCJb3Evxz6EUPKMOuXv4/Xjk/wj/cf5gsfXo0iJlQXCAQCwBBui9WExWoi\nMEf33ye/tJV4NENsIlNeGwIeqJ1qVj92cJjTXWMzrjNbVBpavLz/xg4AJsbTTIwWcbqDOOtbcFle\nJ96TzeZjoxRGR8mPj1EYHaUwNkp+bAzV9d51TS5KcVbNhuu7fCaC2V6/YOmQJRmfxcO+8EFq2g+y\nIrOG1zoj/OvDx/jMdSsXhQcxgUAgONuoqoLXb8frt79hmK072lmxppZ4NE0smiE+kSEeyzD9M3vq\nWJi9T/dU9jWzgtNlweG2cM1HOpBlmaLJwoTiwbk0iMeuLdjwrUUpziazYVlYyI4uqDgD3Nh+LX3x\nAQ5EDnPt5npyeQ/PHRrGZlb5/auWXlDj9gQCgeBs4au246t+Y/EGaGzzIasyiahRA4/HssSiGZKJ\nLHK5NTM0FOeBnxhW5rIs4XCZcbgsOF1mrrxuxXv2zV6U4txT8HG0uI73pSO8+U919lFkhc+u/jj/\n9aXv8XDvr7llx01kf23nt6/0Y7Oo3HhZ25kjEQgEAsG7ZtIJy3R0XSefm3LCYnNorNvcQDyaJRHL\nkIhlGTw9gfV1xmdnm0UpzkeTGvv0VVyUPor/zMHPOk7NwRfXfZb/8eo/8LOue/mTD32Jf/7FaX71\nbA+5Qombd7SLGrRAIBAsAJIkoU0zHPNV2SsThUxSLJRIp3LvaboWpVVSwGbUl8Pp9zYz34w6R5Av\nrPsMH1nyQZYFGrjjYxsI+mw8+uJpfvDIMUqlc2JyMIFAIBC8DkWVcbgsZw44j7ylmvO3v/1t9u/f\njyRJ3Hnnnaxdazg/HxkZ4atf/WolXF9fH7fffjv5fJ7vfe97NDU1AbBt2zY+//nPn4Xkz0211RhL\nPJoDXS8hSedGGaTV3UyruxkAn8vM529u555f9vD0gSHS2QJ/eH0HJvXcSKtAIBAIFo4zivPevXvp\n7e1l9+7ddHV1ceedd7J7924Aampq+D//5/8AUCgU+MQnPsHOnTt57LHHuPbaa7njjjvOburfgGqL\nIc4TuoNiLlqx3j6X+OmJX/JKaD+fu/7T3PeYwsvHw6Sy+/nCjauxTZtjVSAQCAQXHmespj3//PNc\nddVVALS3txONRkkkErPC3XfffVx99dXY7QttggU+swkZnQndRT4TWejkzEmTq5FUPs0/Hv5nPvh+\nBxctqeJIzzh//W8vMxhJLnTyBAKBQLCAnFGcI5EIXu9UzdPn8xEOh2eF+9nPfsZNN91U2d+7dy+f\n/exn+dSnPsWRI0fmKblvDVWW8Jslisjks6Pv6b3fKltrL+azqz9OsVTkfx/+AZdfLvOBLU2MjKf5\n5r+/zL7Oc7NQIRAIBIKzz9u21tb12YZLr732Gm1tbTgcDgDWrVuHz+djx44dvPbaa9xxxx088MAD\nbxqv12tDnUe3ln+xqYYTL/wHqrSF6uo3ds6+kLy/ehtBv5f/9uw/8S+Hf8jnN32CjvaN/M+f7uP7\n9x7gtqtXcPP7liHLC2vJfa7m3/mGyMf5QeTj/CDycX44W/l4RnEOBAJEIlO1uFAoRHV19Ywwv/vd\n79i6dWtlv729nfb2dgDWr1/P2NgYxWIRRXlj8R0fT73huXeC3xcAID4xVJlU/FykVmngT9Z9jr/f\nfw8nhnu5cUkHf3HbBv7u3gP88NFjvHZshM9ctwqvc2HckE6flF3wzhH5OD+IfJwfRD7OD+82H99M\n2M/YrL19+3Yee+wxAA4fPkwgEKjUkCc5ePAgK1asqOzffffdPPjggwCcOHECn8/3psJ8NkiXJE7L\nyxh+j8emvRNa3c3csenLfKj9GgAaa+x87RNrWdfu53DPOHfds5fXTszuShAIBALB4uSMNecNGzbQ\n0dHBrbfeiiRJ3HXXXdx77704nU527doFQDgcxu+fcvdx/fXX8+d//uf85Cc/oVAo8K1vfevsPcEb\nMBjP8HBuI+uko6wpZpCV93aM2tulyjqVf4/17OGlkX380bWfZM1xP7v3nOT79x5kx0V1/N7OJVi0\nRek7RiAQCARlJH2uTuQFYL6bWCwuC195/CAtUj+fWrlswX1sv1V0Xee+rod4/PRTWFULv7/8IwTl\nJfzTr47QH07gd5n5xNXLWdte9Z6kRzR/zQ8iH+cHkY/zg8jH+WFBm7XPVxyaikUuMa67KJyjFttz\nIUkSH1nyQT616laKeol7Dv+Yx8MP8ee3reaD21qYSOT47s8O8I/3HyKayC50cgUCgUBwFli07aOS\nJFFllhlMO8ikBxZ8Aoy3y+bgBppdjfzg8I95cfgVuqI93LH1y2xeGeDfHj3G3qMhDp0a48OXt3HF\nRXWoyqItZwkEAsEFx6L+oldbrZSQiaRmO005H6ixVXP7xi+yq2kHyzzt2ExWGqod/MXHN3LbrmXo\n6PzoNye46569HOg6f1oHBAKBQPDmLNqaM0DAagPSRDI5li90Yt4hqqxy45JrK+PLdV3n5533s7a1\ng/9vxVZ++fQpntw/yHd/tp/VbT5u3rGExoDjDLEKBAKB4FxmUYvzpoCbhugDqJn+c2oCjHfC5JSS\ng8lhnup/nif7n+OS4EY+vPM6dm5o4Cd7Ojl0aoxDp/ayaUWAGy5tpa7qfGvMFwgEAgEscnG2qQpu\nq4tUpnjOToDxdql31PK1TX/Cj4/9gheHX2F/+DDXtOzkyzdv53hvjPueOsVLx0K8fDzEllU1fHBb\nC7V+IdICgUBwPrGoxRmgYKpiVHdTnYksCnEGaHI28Ocbv8Qzgy/yUPev+WXXw7wyso+vbfoyq1sv\nZl9nhPue7ub5wyO8cHiEDcuq+cCWZtrqXAuddIFAIBC8BRa9OP8wXEOq+D7as6NYWbrQyZk3FFnh\nioZtbKq5iEd6Hsdr8SCXm+3bmi3cteRiXjsR4ZEXe3nlRJhXToRZ0eTh/ZubWNvuR5YW1l+3QCAQ\nCN6YRS/OVRYTJ/ISsVQfi7HeaDPZ+OjS6yv7+VKB//by9/FZvHyw7f38509ezLHecR5+8TSHu8c4\ndnqCao+FnRsauHRtLXYxd7RAIBCccyx6cQ7YbJyIxwin0zQsdGLeA9KFNA3OWg5GjvK91/6JdncL\nu5p38Ge/t5b+UJLHX+nnhSMj7N5zkvuePsUlK2u4fF0dbXWuitGZQCAQCBaWxS/OVgsQI5wpLnRS\n3hNcmpM/XvsH9MRO83D3bzk8eoyuAz+g1l7DF9d9lj+4diU3X7mEpw8M8sSrAzx9YIinDwxRX2Xn\nsrW1bFkdxGXTFvoxBAKB4IJm0YtztcUQmvGimWI+iWK6MCyXW1xNfGHdZxhIDPGb3icZSAziNhsN\n+7qSZdtFXq7e3MTRnnGe2j/IqyfC/GTPSX72uy5Wt/rY0hHkoqXvjf9ugUAgEMxk0YtzVVmcJ3CS\nGj+EM3DJAqfovaXeUcunO26lUCpUDMYeP/0Uj/c9xfrqNVzesI0/vqGDRDrP84dHeP7QMPu7Rtnf\nNYpZU9i2ppY1LT46Wn2Y1PN3nLhAIBCcTyx6cbabFH6/xYve9zCJMesFJ86TqPLUT13nCFJjq+aV\n0H5eCe0nYKtia+0mLll7Me/f1MhgJMkLR4Z54fAIT7zSzxOv9GM1K6xbUsXFywN0tPowm97b+bkF\nAoHgQmLRThn5+qm8wqd2k44eJ7j8j9BswXm91/mIrut0TpziucG97AsfJF8qsKlmA5/uuLVyHmA8\nXeA3L/Tw8rEwo7EMACZVpqPFx0VLq1jX7sftMC/Yc5wviCn65geRj/ODyMf54WxOGbnoa86TqJ6L\niE/04RzbL8QZwx3oMm87y7ztpPI38vLIPppdU/bsf3/gHhwmO1ct28ZNO9r4vSuX0DMc59UTYV7r\njLDvpLEANAedrGnzs7bNT1udC1kWVt8CgUDwbrggxHkim+e73QqNbOKa8Zfw1F2FJItm2UlsJiuX\nN2yt7GeLOULJMEcyx9k7/Co21craqg7WB9bwocuW8tEr2hkZT7GvM8L+kxE6+6P0Dsd58Lke7BaV\nFc1eVrX4WNXiJeCxiiFaAoFA8Da5IJq1dV3n+4dPE0pn+YRyH41tH8LmWTmv91ts6LpOd6yXo7Gj\nPHf6VSayUQBuW3Ez2+o2AZAr5tAUjXS2wNHecQ6eGuXQqbFK8zeA32VhRbOHFU1eljd6qPJYF+R5\nFhrRjDg/iHycH0Q+zg+iWftdIkkSG6tcPNQXoVNvwTu6T4jzGZAkiTZ3C5csWcMHGq6mN9bHq6ED\nrK5aARjC/BfP/DUNzjo6/CtYVbucTy41JuYMTaQ50jPOkZ4xjvWO8+zBYZ49OAwYYr2s0c3SBg9L\nG9zUVtmFK1GBQCB4HReEOANc5HfxaH+E4yxjTfRBivkEiknMe/xWkCWZVnczre7myrFoNk6tvYau\niR5OTnRzf9cjuDUnK33L2dW8gyvX13Pl+npKuk5/KMHxvgmOn57g+OlxY8jW4REA7BaVtjo37XUu\n2updtNW6sVkumNdSIBAI5uSC+QraTQorPA4Oj0NY8eIdO4CrZttCJ+u8pdrm56sXf4l4LsGxsU6O\njB3nyOhxXhh+mfc1XQ5ASS9xf9cjtLmb2bqulV0XN1LSdYZHU3T2T9DZH6Wzf4KDp0Y5eGoUAAkI\n+m20BF201jpprXXRGHCgiaFbAoHgAuKCEWeAi6tcHB5PMKDXUje2D2dgqzBWepc4NQebguvZFFxP\nSS8xkBii1l4DQF98gN+efrISts4eZImnjSWeFjZ1LOOKi+oBiCVznBqM0TUYpWsgSs9wnKHRYZ4/\nbDSFy5JEXZWN5honTUEnzTVOGqodooYtEAgWLReEQdgkRV0nksmhDD9AauIIVa23YPMsn9f7Ljbe\njcFDvlSgJ9pLurf8hAAAIABJREFU58QpOie66Y72ki/lAfjqxi/S6m5G13WeGXyBJmcD9Y5aVFml\npOuMjKXoHorRPRSndzjO6VCcXL40I/4qt4XGgIPGgIOGagcNAQcBj/WcHMolDHDmB5GP84PIx/lB\nGITNE4okUWM1kwteTip6nLG+hzA7mlDUC9OC+GxjklWWettZ6m0HoFAq0Bvr51S0hwanUWuOpMf4\nyfH7AMOLWaOjnhZXI02uBtYsW8a21bUAlEo6w2OpilD3hRL0hRK81hnhtc7I1D1VmTq/nboqO3VV\nNuqrHNRV2ahyn5uiLRAIBHNxQYnzJMNFJ8dtH2B58kHG+x+lquXDC52kCwJVVmn3tNDuaakcs5ts\nfHLlLXTHTtMT7aU33kd3rBeAL637HCv9ywB4pOc3VFn9NLfUs3lVG4qsoOs60WSO/lCC/nCS/nCC\n/lCCgUiS3pGZpVlVkQn6rNT67dT6bQR9NoJ+GzVeG1bzBflvIBAIzmHe0lfp29/+Nvv370eSJO68\n807Wrl1bObdz506CwSCKYhjsfOc736GmpuZNr1loHumLcDrhpM6+BMYPkvKsxOZZsdDJuiCxmaxc\nUruRS2o3AsYQrf7EIL2xfppdjQAkckke7vlt5RpVVqm111DvqGV73SWsbmtmdZu/cr5U0glPpBmI\nJBmMJBkcTTI0mmJ4NEV/ODkrDR6HRo3XRo3PSsBro8ZrrAMeK2ZNGKIJBIL3njOK8969e+nt7WX3\n7t10dXVx5513snv37hlh7r77bux2+9u6ZiG5otbLv3cO8URpC9fRM61527bQSbvg0RSNNncLbe6W\nyjGLauYrG75AX2KAvvgAA4khhpIj9MUH6PBPFaq+9+o/IUkSdfYgQXuAYKCG5a012E3G71rSdSbi\nWQZHk4yMpRkeTTE8lmR4LM2JvgmO903MSo/LrhHwWKn2WKn2WMprK1VuCx6HWTSVCwSCs8IZxfn5\n55/nqquuAqC9vZ1oNEoikcDheOMxwu/kmveSFR4H6/1OXhuNc9R9LauTvyo3b39koZMmmIO5msOL\npSIjqTAesxswhm1NZKOE0hGOj5+ccf37m6/khvYPIEsSoUIfkqvIumA1V1rqKtNo5gtFQhMZQuMp\nRsbShCfShMZThCbSnBqMcXIgOitdiizhd1mo8ljwuyz43eV1edvrNKMqYppNgUDw9jmjOEciETo6\nOir7Pp+PcDg8Q2jvuusuBgYG2LhxI7fffvtbumah+WBTNadiaZ6L2WmwLYfxQ8RstbgCW898sWDB\nUWSFOsfUBCayJHPX1q+RKWQYSoYYToUYTo4wnByh3j4V7uHu39AV7QFAlRSqrH4CtmqW+5awo2E7\n9VV2csUcqqxWhLtQLDEWzxKeSBOZSBOeyBCJphmNZghHMxzpGZ8zjRLgcmj4XRZqqxzYzQpepxmf\nyxBun9OMy64JARcIBLN425Ywrx959eUvf5nLLrsMt9vNF7/4RR577LEzXjMXXq8NVZ3f/r03M1MH\n+ANV5rsvnWS/to2rGGJi4DfYbSaCrVfOazrOd86Uj+cWThqpBjrmPHvT2mvpHu9jKD7CUDzEYHyE\n4UgIl91eec4f7r+Ph048TsDmp8ZRRY2jmoC9itq6anZsWjcrzmy+SHg8RWg8PWMdnkgTHk9zeiTO\nqcHYnOmRJPA4zEat223FV651+1wWfJNrlwWnTRNN6GXOr/fx3EXk4/xwtvLxjOIcCASIRKaGqoRC\nIaqrqyv7N954Y2X78ssv58SJE2e8Zi7Gx1NvK+Fn4q2MPwsgcXNrDSs8dtTiJwl1/jsDnQ+TSKRx\nBy+b1/Scryy28ZBNphaaAi0QMPZ1XSeRT1IoFSrPaSqaaXDUMZoeYygRqlxbZfHRrLUBcChylAdO\nPYbP4sVv8eKzePBavPirPaxtNcZrT1LSdTSLRmfPKGOxDGPxLOOxLOOJLOPxLOPxDD1DcU72z246\nn0SRJVx2DY9Dw203atxuu4bbYaxdk4tNw6Ipi9a5zmJ7HxcKkY/zw4KOc96+fTvf//73ufXWWzl8\n+DCBQKDSPB2Px/nTP/1T/uEf/gFN03jppZe4+uqrqampecNrzjXWV7mMDdWHo/UTJHp+SHToCdBL\nuGuvWNjECc46kiTh1Ga+mzsbL2Nno1E4SxfSRNJjjGbGKelTTlDi+SShdIT+xOCsOP/rpf8Fp+Yg\nlotzz6Ef4bV4qPcGMJesePxuVtR5CNiaMCta5Rpd10lmCkwkskzEDdGeSOaIJrJMJHJMJLJEEzn6\nQkm6i2/+MdBUeYZYO20mXHYNp03DZTPhLB9z2jQcVhMmVTSrCwTnGmcU5w0bNtDR0cGtt96KJEnc\ndddd3HvvvTidTnbt2sXll1/OLbfcgtlsZtWqVVxzzTVIkjTrmnOdk7EUP+wc54aGWwhEfkp0+EkK\nuQm8DdcgK+aFTp5ggbCqVhqd9TSWnaZMsrX2YrYEN5IspBhLjzOWGWcsO8F4ZgKHyRi5MJGJ0jlx\nyrhgeGa8f7z206ypWgXA/z7wb8iygkdz4Ta7cNtcVHucrLEHKgZvk+i6TjpbYCKRI5rMEUsa62gi\nSyyVI5bME0vmiKVy9A7HKZbO3KVkNSs4rFNi7bSacNhMOKyTi4bDqlb27VaT6CcXCM4yF5T7zjfj\ndCLNv54YJFcscUOjk6bog+RSgyiah6rmD2N2NM5r+s4XRPPXuyNfKhDNRtEteXpGhpjIRhnPRtnR\nsJ2ArYqSXuIrT/5n8qXCrGuvbt7Jh9qvAeCnJ37J6Vg/Ls2J0+zEpRlLwFrFct8SwLBYnzRigykh\nj6UMwY6ncsRTeWKpHPFknnja2I+n8iTK229FzAEsmlIRaodFxV7etltM2C2qsbaqlX1bef1uJzAR\n7+P8IPJxfhDuO98DmhxWPre8nnuOD/DLvjjXN32Elc59xEaeYaTzB7iCl+EOXoYkCacUgreOSVap\nsvqprnZSLQVnnZclmb+94pvEcwmiuRjR7NSy1NtWCTeRidIb75/RtA6wzNNeEec9fU/z0Klf49Qc\nODQHTpPD2DbZub7tahRZIVPIEkqFcWgeHCYHmmKqxKXrOplckUQ6TyKdJ57KlbcLJNI5Eql85Vwi\nXSCZyTM0mpzl8/zNUBW5LNblxWyIttWiYjMbYm4rb08es5lVrGZVTHQiuKAQb/s06u0W/nBFA/cc\nH+CB06OEA6u5sr2N2On7iQ0/RWr8MJ66nVjdKxatwY3gvUeWZKM52+yCNyhI/9HaT1HSS6QKaWLZ\nOLGcsdim+YW3qVZq7AHiuQQD8UEKehEwCgg3tH8AgMHkEP/9lb+vXKPJJuwmOw7Nzm0rbqLRWY/V\nrPJa9AXMZjN2h41Gkw27asNu8uLUHGjT+soBcvkiyUyBZDpPMpOftm0IeKq8TmYKpMrreCrPyFia\n0ttsuNNUGcukWJsVrOVtq1nFqqlYX3/MrGDVVCyacdxS3haW74JzHSHOryNoM/OHKxr44ckheuJp\nrI1N2Ff+JyYG95CIvEqk+2dotno89VdhcTQvdHIFFxCyJOMw2XGY7NQxuxa+rW4z2+o2A+VacDFD\nPJcgXchUCpMOk50rGy8lkUuSyCdJ5pMk8ilGUuFKPCW9xK+6HkVntnBe17qLa1t3AbD7+H30J4aw\nm6zYVBs2kxWbaiXoqeGyZYa73lguTiqfxmayYlWtmKZZsU/W1NPZQkXA09kiqawh6KlMgVS2YJwv\nr/NFnVgyRzqTZzSaoVB867X26Zg1xRDsGcKtlJc5ts0qZpMy7bhxzqwpaKosCuuCeUeI8xxUWzW+\n1NFIIl9ElWXAQtxzJQH/ZhIjT5CeOEqo898wO5pxVl+C1b0MSRIGMoJzB0mSsKqGIE4nYKvmpqUf\nmvOa6eYnX7rocyTzqamlkCSZT80wjIukx+iO9s4S8ZW+ZWwIGOL83OBeHjg15fvAJJuwqRasJht/\nufnPsJpVUqU4zw79zkivZsFis+BVLdSqVlpdjdjK7ldzxRx1NT4ikUQlvnyhRDpnCHcmWySVLZAp\ni3kmV97PGefSZYHP5Iqkc+XzmTxjsQy5wjsTeTDGqls0BbNJwaypWEwK5sq+Mmt/UuQ1k2wcm35+\n2mIyychC9C9YhDi/ASZZxms2BHckneWfjw/gMim8r/5qVlZvIT78JJn4KbKJXhTNjbNqE3b/RcI/\nt+C8ZbL2J0syK3xLzxj+ixd9lpJeIlvMksqnSeZTpArpGUPEGhx1bK+7hFQhTTqfNtaFNLlirmK8\nNpYZ46mB5+e8x5+u/+NK3/sdT3+DvF7AoliwqsZiUcxsrdvM1tqLAdg7/CqjpXEsVjM21YJfMWNW\nzbg0F/UOY/rRYqmIJEkzjOeKpRKZXJFMtmiIea5YXmZvZyf388ax7OSxfJFMtsBEPEs2X3wHv8Bs\nNFVGmybgmirP2p48r5mMsNOPTz+mqVMFAs2qkc0XMamiAHCuIsT5LeA2qVwScPNiKMrPu0eotmjs\nqr+BZXVpEqMvkRw7wMTgb5kY3IPF1Y7duwarexny6/rmBILFhizJlRq63+qbdX511UpWV6180zia\nXY385eavkCqkyRQy09YZqm3GbGMlvcRSbzsFcsQySTKFLKPpcbLF7IzJT/YOv8rRsROz7tHmbuH2\njV8A4KmB5/l556/QZBOWssCbFQ2zauZL6z6HX7EQzcb5zeknsZjMmC1mzIoZRzlMm7sZl2YYB4xn\nJlBlFbNixiSrlQJOSdfJ50szBDyXL5HJF8jmSuTyhphnc0Wy+aklV9kvTR0rL7Fkjmy+SP5d1PLn\nQlNlTOqUiJvKIj5ZMDCpr98uhzMZ26bJ6yevLZ8zKdPjlCvXiv7+t4YQ57eARVX4YFM1l9Z42DM4\nxquRGD/uGiZg1fiTVdfiqd1JYmw/ybGDZGKdZGKdSLIJq2spVvcyLK4lokYtELwBmqLN8JM+F7Ik\n84V1n5k1dKWkl2Y0x39kyQeJ5mJkClkyhQzpYoZsIWsY25VxaU6WetrIFrNkClmyxSzRXIxcMY8i\nG6MxorkoT/Q9M2davrDuM5UCwd+89D+J541mdgnJEHlF47L6rXyg9SrMmsJLo89zKtqLJmuYFROa\nomG2aXjNHq6svwSAscw4A4khzIqGpmhosrE2Kxp2k21GLb9U0skVDLHPTQp4YXJ7rmOGoBtCXyJX\nKCLJMvFklnx5f/J4Jl8klsqTyxff8rC6t4siS9PEXEZVlbKQG4Jumibmk/vq6/ZNk4UCRUZVJUyK\ngkmVyueVqWsUCZOqoCrGPVVFRpGl88JGYFGK8+iex+k8dYiaj/8BLovrzBe8RTxmEx9preHyWi+/\nGxxDx3jRkK3E7RdRsK4jqCZITxwiNXaI1MQRUhNHANDsDVhdSzA7WjDb6pFkMSRLIHi3yJJszDBS\nps4RnNNYbjoba9axsWa2j/Tp48SDtgB3XPxlMsUsuWLOEPJilmwhS9BWU7lmbXUHqXyKbDnM5FqZ\nNuSyO9rL/vChWferd9SyvSzOR8dO8ONjv5gzvd/e/le4zU4mslG+9eLfGuKtmMoCbqx3Ne9gRa3R\nFfFozx7SahpNNmFSTLhkDZOiErTVVGZ2K1kz9I2EMckmY1HUcnitYrRXKQSURT5fKJHLlwyhLxj7\n+ennCiXyhWnb+RL5YnHadjl8oThjP5MtECvH9U4N/N4OElSEWq0IvCHkM45NFgwUqRLm4uUBOlpn\ntxCdDRalOO97fj+N3Qf4ab6Lql3XcFXTFTg0+5kvfItUWTRuagvOKLH/un+UzlgKt0lltW81a5q2\nEFTiZOKdpKOdZJN95JL9AEiyCbO9EbOjGbO9Ac1WJ7yQCQQLzPTaqaZoNLkaznjNx1Z89IxhPt3x\nMUO4CzlypVxZ7HOo0wroLa4mbmy/1jhXypEv5skWc+RKeSyq8W3QdR2P2U22mCNTyBIrJsiVcpT0\nUsVKH+D5wb1EMmOz0rGl9uKKOD9w7Lf8uuupWWHcmpNvX/pXABwbP8GPjv0ck6yWBdyEJptQZZXf\nX/5R/NYqiqUiP+/8FapFrQi9QzG2l3haK/383dFessVcOYyKKqtoigmLYql8m3XdMC0sVkS8RKEi\n+oaYFwozRb5Q0ClM3y/qFZGfvL5QnLqm8Lp1vqhTKBjGgrHy8UKhNMc4BYNYMifE+d1Q9aEbSP+v\nI2zZF+eHDY/z1MBzXFq3hdVVK2hzt8yYlODdML1p5NKgB6emcGQ8ybMjEzw7MoHLpLC9ZhWXLdtO\nsZAmm+ghE+8pr0+RiZ+ajAmTpRrN3oBmC6JZg5isNciyae4bCwSC8wZD3NSKW9e5qHfUVoTsjfBa\nPPzlJV+ZdbxQKiBNaz74/Lo/KNf48+TKAp8v5mfYBKwNrqSYg3wpXzmfK+VnjJvXMaZVzRVzJPIp\n8qU8hbInu6JurHOl/Bsa89287IbKM/2i8wG6Y6dnhVntX8Hn130GMKZzfbR3T6UwoJZF3Kxo3Ln5\nzwAYSo5w78kHMUnGOVVVUTUjf68oe90D+F3fswCosoJazn9VVqmx1RK0G7PeRNKj5UKSiioplfup\nkooiqYZQTxP7QrFEtcc66xnOFotSnNeubuOlqz6J/dc/5IqndB66Ch7ve4rH+57im9vuxGvxkC5k\nODlxijp7EJ/F+677IJa67Sx12yk0lzgZS3NwLM6JaIrJMpiiWnkuGSCnV9ESvIxGi46SHSCb7CeX\n6ieXHCSfCZEcnYxRwmSpwmQJYLJWl9cBVM0jhm0JBIIKr69sBO01bxByis0NF9Fqbn/TMB3+5Xxj\n2/8z41hJL1EoFSu1frOi8Zebv0K+lCdfKlAoFSrbDdMKG9vrt7DKv7wSJlcW+ukFEo/ZTbOzkUIp\nT14vki8ahYZiacryPZ5LcGT0+Jzp3RBYVxHn+7oeqhQkpnNNy/u4vu1qAH7e+QAHI0dmhWl2NvK1\nTX+Cqsg8O/Ai93U9hCqp2Ew2/suWr75pns0ni1Kcjx8c5pWTJera38/KzgdpenoVJxuc1Dbm6R8s\n4m7V6Y318Y8HfgCARbFQa6+hyurDb/VxWf2WyoQD6UIGi2J+y+KtyjIrPHZWeOyUdJ3itKbv1yIx\nxnMFnhmZAKDaYqXBvpqOqi2sXGIlnw6TSw+TSw+TTw2RS4+Qz4RhYtoNJAWT2Ydq9mOy+FHNvsqi\nqI7zwtBBIBCcn8iSjDZt0hNZks9ozAdUhrq9GdvrL6n0wb8RSzyt/O0V36wUAgqlYnm7QLXVXwn3\nudUfrxQEpi8t7qZKmDX+lXjNHuOcbpwvlopU2abiMSsaPouXQqkww9Xte8GinPiiVCrxwE8OMHh6\ngjXhp6guhPn56pvpGjVKUlVuCxvX2NA9gyQZYzA5QigVrvgtvmvL1wjYqsgUstz+1F9hkk24NAcO\nkwO7yYbdZGNb3WaWeY2S58HIEfKlQnlIRnlYhmLGZrJiN01ZaeeKJfqSGXriaXoSafoTWbKlElcE\nvVzdaJT4ftM/ymg2R63NTMCi4Vez2IujFLNhcukQhewo+cwoeik767klSUU1e1E0N6rmRdXcqJoH\nRXOham7kdyDewkH+/CDycX4Q+Tg/iHx8Y4y+b53XS6MyhxGvmPjibSLLMjf+/nr+939/kuPBy3Cf\n/CmfNXWS/tTN/O61AV48OsJjz2QAC25HC6tbN/C+FjfV1RIlNYXf4gWM/pjV/hXEcnGi2TgDyaFK\nU8lK37LK/e7veoSh5MisdGwMrOMzq2+rhHmq/7ly/4dhHanKJpxqNZsDtwBwOt7Pc8MjZHUXB8am\nvCBJ6HR4G/nYku3ous7PO/dACbxSAqeewqxn0UppHBQp5GNGbXsOdCRQ7VjMXhSTk5ykkQRQrEiK\nDV2xgmpDkjXaPa1IkkSmkKU31mf0aUkgISNh9Lf7LV4sqgWAkVQYXdeNni9pqgfMptoqBh/xXIJs\nMTetd8zYUmWlMtQlW8yRyqdmpHuyQOHSnMiSTEkvEcvN/Q9hVa0VJxixXHxGk9gkJtlUSVMqnyJd\nmCzoTP0zSpKEr/we5Io5Yrk4uk7ZG9ZUOK/Zg6lcoh5OhmbEo2P8ozs1B9Vlp9mR9BjZYrZi/DIZ\nVlM0amzVlXSPZyYqnreMb4Sx3exqRJZkcsUcp+MD5fNGmiZTVWcPVp7v5EQ3hVKh8qHRy+F8Fg+1\n5ebP/vgg49mJSlyTITVFq7zn45mJSp/hjPvpOqurVmFRzeRLBV4d2T/jPuXE0+purvT17Q8fIp5L\nVO4z+Xx+q68yROlUtJfeWN/UU5XT5Rq3ssm7CTCGH708sg/0afcrx7eldiNeiweAR7ofp6gXZ6RZ\nB5Z52yvOVl4YepnhZIgSpUp8AD6LlysbL63k5auhAzOefzLcR5dcj6aYiOXiPHjqscozVcLocHnD\nVppdxux2v+h8oJwHk7+xsV7mbefS+i0APDPwAkfHTsxIs46O02TntpU3l/Oph0d6Hp+R5sm4blt5\nEz6Ll1wxzz/sv4epFIFqUsjlCuxo3F7x5vYfx37BQGJoRpp1dNo9LRWvck8PvMAzAy/MupcsyfzF\n5j+tvE/3HP7RjDgm381Pr7qVVrfh9vhbL/4tmcr/wtT/1GX1W7mmZScAPzr6Mw5Gjs5Iu67r1DmC\n/OmGP678dj878StmvgVGnv31tjtxaHbGMuN84/n/v3KX6XF9atWtbAquB+D/feG/zXBlO8nm4AY+\nterWWcfPJotSnAGqAg4u2dHGs789yfGmnWjPPIQrl+OTt32SW3YuZf/JCIe6RznUPcazB4d59qAx\n4a7LZqKlNklrrYuGajs3NtxCwGtFkWV0XSdXypPMJ2e4Rbyu9f1Es7EZQykyxSwtrqlpJu0mG1VW\nf7mpJU+mkCVfSpJVs3jNxsc9mo0Riu1Gkpwoig9F9iHLHhTZg4zRN1PSS7wQjmHWVgOg63lKpTgl\nPU67y8cfr91AqZDhB/v/lfFUGI+cwy2DS5ZwyTJeUkiFJJMf+7lsxAu6zqDmQTHZGTKZORA6Qaqk\nk9KNJV3evmnF77Gieg2ybOJvX/l7EvnkrLiubLy08o/9885fGR/T1xG01/BXl9wOwL7QQf796O45\nf9NJe4FEPslfPvutOcPctuJmttUZH++/2/fPDCSGZoWZXmh6tHcPj5+ebbVqVa185/JvANAV7eHv\n9v3znPf72sV/UvngfvPF/z6nP+rr266mrf5GAH5y/N45nWS0u1v5ysbPA/DS8Gvce/LBOe/33Su+\nhazIRNJj/I9X/2HOMH+05lOsq+4A4J5DPyKai80Kc3n9Vm5Z/mEAnuh7hheGX54Vpsrq5xtb7wCM\nPPjXwz+e837f2HoHFtVMrph7w9/u1uUfrojzoz17OB3vnxVmXVVHRZwPRY7yWO+eWWHMisamK4zf\ndzQ9xv1dj8x5v6Xetoo4P9r7+Jz9j7IkVcR5X/hgRQSm0+Jqqohzf2KQJ/ufnfN+N7Z/ADCRKWR4\ndnDvnGE6qlZU3pX94UOMZsbneD4zl5Y9pPbFB9g3xxAsr9lT2Y5l42/YB5sr5gHjm3FiomvOMOsD\nayrbw6kQp+MDrytgS5V8BEgX0kTShiW4UWY2wk0fOlbSS6Ty6XJhfioeSZJmTHRSKtdODQ9lcqXQ\nrrzOat5msjJZkJfK6ZpuXGdRzPit3ql7SZN3nCrYK5JKg7OeqTNT6Z8eV5OzEZfmLFdGpioZtW+h\nH3++WZTN2mA0F4RCMR74yX4GeifYrB/C2fUyqtdH8DOfw7bSmOi+pOucHolztGec7qEY3UMxRmMz\nm4xVRaLGZyPgsVLtsRLwGmufy4LPacZqnp8yTrFUJFvMUdSNfpSiXqRYKlLUS1Rb/ZgUEyW9xJMD\n3QykSsTyOokCJPJQ0CV8msxX1xlN7Q/1HufZkIyEjlkuYVZKmOUiTk3l1vYlmMnQM3qc4+ND2PQU\nNj2JWc+i6HlMpTxORaFYSIB+5nGHkqSSRSKHQkGSyaNQkBQKKHjtNTS525BVK4fHT9GXjFCQZAoo\nFJDRJXBrLm5cci1g1E6eGXihEvd0sbt52Q04THbShTQ/OX7fnGnZXndJpbvh/q5HGM9MdthP1ddb\n3I3saNgOwMsj+zg8emyGtSsYH4Vby+I1mBjm8b6nyrFIM/7Br27ZSVXZCvY/po9TrXwgJNZUrWLH\niosJh+M81f8cw+WSuVQ+jwRVFj87Go00dY6fMgxVJKbdzVhPTv0YzyX43aSTDGlmqI01F1WEcE/f\n02QLucqHaPJvo7OeVf7lABwePcZgYrjyIZt8RqtqZWu5oDOSCnN09MQcH1y4uGY9VtVCvlRg7/Ar\n5bNTH0oJaHU1UVNO0+HRYyTzqRkfUwnwmD2VoT6DiWFCqfCMDyRI+Dx2Gk1GmGQ+RXe0t5zuaXeV\noNnZUPHJ3TnehT7t6Sef02v24LcarSMjyRCpQgbpdXmuKVolLxO5JBPZaFkgZopFwFaNLMkUSgVG\n02Ov+02MfHKYHJVhURPZaKXWOT3fNcVUKfhnChkKpeKcImctt1gVS0UKenHmWyJNCaYkSbNqphIS\ngYBLNGvPA2ezWXtRi3M4HCcezTDUH2XJcj/jjz7M6AP3Q7GI53278F17HarbM+vaaCJLz3CcwdEk\ng5Ekg5EUg6NJsrm5/eVazQpepwWPQ8Nt13DbzbjsGi67CadNw2kz4bRqOGwmzO9ysvm50HWddLFE\nulDEbzGadLtiKV4MRYnlC8RyBeL5AsXyL/1X69uwqgpj2TzfOdAz9RyKjN2kYFcVdtX7aXVa8XlV\nfn20D72UxUIWM2nMpRSankItxqGYolRIUyymKRVS6KXc20q7JJuRFTOyYkFWzEjltayYkWUzUnl7\nKpxW3p6+1s55C3bRxzc/iHycH0Q+zg+iz/ld4HRb+L/tnXmQHMW54H9ZR18zPZc0I2l0IY2EJMs6\nMVqExGWwbCyW5/WGCK9Xge0IHxgI5HU4AGsx4HAYA8aEMf4Dh8ERNsFbg4UXax1+gMFP72E8AiQ9\n5oGQEDrXtvXdAAAd4UlEQVRHmkFzn31VV1XuH1ld3aNuQAjBjET+IiqqKvOrzKycmv6+vL5M1ior\ns+7Kq9gxPJWmPc/D839lcNvfSH7qfOo+fQWxuS2hBVtbHWXZvCjL5k0O05FSMpLJ0zOYCY4sA8NZ\n+kdy9A/nGBjJ0tlb3q17IralNpuvittqY/lo6cbzxX1oE1GLWMletLFgr9poxMQ0xioiIQQJyyRh\nFRV/S02ClpriZDRfStKux0jeIxbMtjSFYHVTHSnXZTTvqcP16MvmyfsSIQSWneC5riwjeQ8wgerg\ngFWNtXxhXtBC6+zn0EiauGGolrrhExUuSTPPJxNZfC9LynEYyrvYfgZLZrD8FIaXwfezuPlhZDYH\nFbqFTwYhLKXIjQjCjCAMW10HhzEmzA7CbQzDVveFeGEjTBshgnDD1jPgNRrNR85Zr5xLOXZ4gIPt\naQ5WrWbu6guY2r4d+dJ2Rl7aTnTmLKpXnkfVkmVEZ81CVFCANYkINYkILc21FdPPux5DKUcdow7D\naYfRdJ6RdJ6RjMNoJq82oc+4DAzn6Oh5b2VeCcs0wj1li7vSlOxeY5fsPlPYkabEgX1n4NjeNg0+\nYUewYzEVH7irM02BZRlht9sXZjcx6nqkXY9U3gtb6dOriiPW3ZkcB4YzZWWdGo9xwUzVfbqvZ4g/\nvt09Jt4Ugqgp2PTJ2VRbJqNOhj8c6iEiJBHhYwtPHbjMi2WYbKbxfYf2jInn57FkDlPm1NnPYvsp\nDHcwaMGfpk4hYRaVuFFQ3FZwH5xF8d4QFhiWciIjLAzDwsgnSY+6YJhjZNV1cBZmeI3QewRrNB9n\nzvpu7RPpPDrIvz+zj4FeNSO4ttpkWr6T5j3PIoKZvWZtLVWLlxCfP5/Y3BYi05rLlPXpwPclWccl\ndcLG8uFR2KYu2MYuk1NO7LNO4RxsV5f/cHzSFnzQmgVn8yW+Zy2zxOdscJimwLANsATCMhCGcjJf\nhzpnLOg3Jb4AT4AvwAU84JJEFTHTJI3PvwyXT2ACWNdYz6KaBIYh+M3htxmqsC3fiklJNgSuVZ8+\n2kNb/wi2EFiGxBZgCUnS8rm6yUH6eboyHm0jFpbwMKWHKVxM6WKSZ57VS5QsnpfnWD6JKR0M6WDI\nHIZ0MGWeKA62OD3bA5bVf6DklQIPlHp4bcKYcKXYEWZwXQwnVPpmyX0h3qgcL4ySayUfXo/jEILu\njj096Ho8Pegx51Pg3SrN83zaD/Tx1hvdHN7fR31Dgi9+aRHpN3ZzdOebdB3po2awnSpnEAMfEY0R\nmzOH6IwZRJtnEJk+nUjzdMz4R+fK7b3wfL+4S43rh1vPFZzRO8EWdKUO6p0S5/P5Eqf1pb5npRBk\nsm7ovq7g39b1lE/bD2XnGkMgTIFhBkreVPfuaB7fUUZIfHoVZsQM4gyEIcAUOANZsh0pDENQPbeG\nSFNCyRhCpWsIZM5D7h5Qm5bUR5GzqysWo7Y9he0CpqB3duVdxRpH8zTlHCzTZ391nLRpYiAxAAMf\nE8kkP8t5kUHcfI5joppOUY2Jj4mvZISPjccq8yAGHilpcUA2BTIuJl5wuMwUnVSJNOBxzFfOH0zh\nBXmqNBNkiAs1qTEnbXxEGK8OyQdulIeK+gSlbZgISq6FAZQq+7Hn4rVxglFgVIyrqaliZNRBUAwr\nypTIYpTkoa4RhloKKIwx6ZdOEvu4oJXz6UEr51PgZCvNybmMDudoaFTT6V98bj//uSPYoAJJlZmn\nKjdAYqiTuQNqDaePAUjsmiR20xQijU3YTU3YkyZjNTRg1TdgNdRj2Gf+fs7vVY++lHgFH7SB4vZ8\nGfqlLShw78T7QMbzJJ5fkJG4fiEsCA+vZZiO7yvPa54n1bUvw3J4Jfd+yXO+X3KPxPck0pd4Aoia\nSBGsAzdUi14YglxPBulJMARVs5KIQLkLU8kJQ5DpTOEMKEVYs7Aeq9ouygUGgdOXZWiPWn5S3VJL\n9TnlO6X5rk/3v6l1y3ZdlEnnNVWs774dXeSHVJf9lEtnqrKcgOzoxezsxjJ83JZZ+HXl+UUyI0w7\nthvTkKRqJtPbMBshlQIXSAypzksGdxAReVwzwt7qJUq5i6KiN/GZ7x+gSfRhCslr5iLy2JhCxZlC\nGQONop9zDPV+x/wpDFCLCAwFA4lAEiHPHEP976VkjF7ZgAjiDHyEULKTGcASHlISpiNK0hHIMT0a\neanmYhTTKjdOfCmQMshJimJKMkhVluZgIKUIUlRhnBBPyfMgkKJ4jQieF0U5wmcCmUK6ouSa4vOF\nuf7qQyycS8IL1xXiBIJEdZx0Kj9GRgiVj5qBHqQbGjSoWeAls+fDpVQimAlfmOke3L9nfJBIUbaQ\nbsn8/BOeCWeuixPix5SpOIs+fK5CXqXvUky/uKKhUn51yWiw9EuhlfMpcKqVNjyYobN9kK7OYfp7\nU/T3pHFyLjW1Ub64rhGns4ODb/WzvbuOqJ8jkk8RcdNE3Qy2l2X24OvYvoOPIFU/i2hVlHhNgnht\nFXZtLVZNDWayBrOmBrO6Wh1V1YjoybsI/Sj5uFrYBeVeUOhSSnzJ2PvASPB9FScLRoGU+D7BMyq+\npjZO/0Aa3/NxpSTvq94OV0pcqdKsFga+lOQ8yaDv4RMYFqCMESlpkAaWVMtj2nHxUetFfYkqg5Ak\nXUHSBSQctyVpUznm8EXgGAWIuJLGUbUX8nBUMFhlqjhRHKmXApo6sggf8hb0Ta/cUxTvzGCN5NWk\nw5Yk0i7v9jb6MkQ6hxFCkm9O4jaUbwJhOHnqDxxECB8nWc3w9JllMgAz2l8j4mZBwME551eUmT74\nFlPTxzAMyZ6G8xiNnDBPREoa3W5WpHdiCMnB6FwORVooqmUQ+Jh4XOX+FUNIBkUN/zBWKRUqZGhc\nCCQXmTuZLNS65ae9i/AwA6OhaBTMFUeZbxwB4DX/XHpk/RiDAiApUqww1HrrLjmJQ/6MsCzq10Hl\nvVTsxRYeOWmzV7ZASTqF8s8Qx6kVypnRIX8GeaxArlimpEjRJJTh2C9rGJVVYVkKMpbwmCL68CVk\n/QgDsiasQ2WAKGcj1TKFISWehBGqCTWLVN8oUmD6Lqb0kRJy2PjBB6eMHyUjpY+QqqReGCeUEyAZ\nnBGBQVW4LsQJfMbKSUnR4ArTY8zzUgZpVJQpyk1qnMPXrip+c3q29kdITV2cmro4C5cqpx9SSjIp\nh3TKIT4lSbxlHg3NfUxrbSc1miM9mmDELY73Lls1A3O4j+G+UV72lNcZHKAHjC4X08+zsOdpmlLK\n29LexgvIG1EsPCxTYNkCyzapj7pMS7qYiQTDIknGjGPHotixCFYsip2IYSdi1DYmMWIxiESRwsSy\nzQmp5M80DKG61TlNK9/OBiOnYHh4ktBvvC8hfp6BHczJ6M065EMjJXgGqLZMGuOqJ6kzlaU/5wZx\nRTnbMFi2Rvkf6Mk47BlMIUvifQmxuM15K+aSsEwcz+dfjvUWDScCI0TCivmXMK82gZQS+2gvPdl8\nmIYM8p2ROJdVU/+L+hHuHWagfyT4gQ7SQWIgmLFwk3r/dJZsR1+YjwzqBMBr/B8YUQukz7GjfbgV\nmjy1idnMTV6M73scHZS0V1h1ONnwWFTdAlLSmbN4NVthOEXC3MRkIsJjxDNpzTZX/HuttTowzSHA\n50V3PqMVXA61iB4utA4BPq9657BPlvvJTpBlg/kPQHJUTOZfZbAXdtiEVqf/ZmyjXozgSsET/rqK\nZbpAvMpS400Ekj95n+Y45T1EM8XbrDe3AbDTX8wr/tLwxYuGk+Qb1hMA9Mh6tnqXlxkVAOvMv9Ms\nlF+Bx90ryWNTaqAALDIOhAZRq7eco3JamE7hXC+GucJsxTHTQGWD8HSjW84fECkleccjNeqQTTtM\nmV6LYQjSKYe2l4+Sy7rk0jmyozmcrIPjeKyY5TPFHMYbHeEv7U1k/HKH6s1D+1jU8w8A9jSuprN2\nQZlMNJ9i7ZE/ANBdNYvXpimXd0IWuvl8DAFr7L3UxMGLxPj3wdkIQ2CUHqZgwXSLWc1RhB2h7YDD\naEYiTIN4IorrSQzLpKGpisXLmxG2TUf7EJ3tgyqtoBtYGALTMFh6vtoHN51yOPxWr+o+KsgE19Nn\n15OoUj/WRw70gQyGBUVRJlkboybYom2wP00u66p0SmQs26S2Xsnksi7ZjFN06hDIIARV1RGEEPi+\nJJfNF+MK3W4CTMvACJSMF0ywK8rxgYyes0E5TwTOhHp0fTlGwRcUuSVEuGnEaN7FKemRKbjMtIQI\nfRWM5F0GAyOm0OPhS5XenGQc0xA4ns+B4XRZvARmVcdC74Ov9Y+Q8/xABqqqo4yMZGmMRZhXGzhr\nGUrRmc4F5QYCwyhqGFw0LXDWksnxH70jY8pUcGt7WXMDSdvC9SVbj3SH76Raokp2+aQkC+pUr8mz\nx3rpzjihAVZwvdqcsLliWi0Sn1f7UuzqTwcGFaEbU4CvtyRBSo5n8/zfY5kS97rFcv3XaSYz4yrk\noUMejl90URtkycpan7UNHkifP3db7EuZJe+lmBzx+Mq0EWLVc7DjjeHfety7te+66y7a2toQQrB5\n82aWLl0axm3fvp37778fwzCYM2cOP/7xj3nllVfYtGkT8+cr13jnnnsuP/jBD941jzNVOX9Q8o5L\n3vHI5z2cnIfn+jiOS9yGuoTET2c4emSA/p40+Vwez3Fx8y6uo1rhi+Nd+NksPRmbt5wpQVcrQWtD\ndYat6PwrMTeFa9i8OPu/I0Uw0ifUAbCgu5UZw8oN4Csz1jMcaywr6+TRdpYdVy4VD0xayeH6pWUy\nhvS40n8RYVn0G/X8w1tU8b0vmdpHY9JHWBZ/3FuHJ8sV3+LZJivmxxCmyb+9mqG9K18mM2lSlC9c\nPQdhmux5c5AXX+iomN/XNq0hFrcZHszw2EMvVZS54upFzP+EctP32EPbGR7MlsksXDKVy9YrF5N/\n/+tb7H61c8z4FkBVMsqXv6l21zlyoI/n/98eDCPo/isZP/un/7mC+kkJXNfjn39VKFOJMQCct+Yc\nFi1Tlvxft75Bz9sjYWRhjGzajFouvVIZb2+82slrOzvC50NZIdjwNbUzUH9vir/9eU8oUWpzrP3M\nfKY0q27LPz/eRr7geKcksfmfaOKTK5WPyVf+fpjOIwNjEwFq6+NhmdoP9tP28tGyvAA+80+fIBqz\nyaQd/vbnvWE+JaOILD1/BjPOUcphxwuH6ek64f9aQPPMOpatUt3ge//zbQ7v7ytGB4lFIlb4t+vt\nGmFXa/n+wgAXXDqXmro4Ukqe21ri0rOkbC2LmpgzX/lB2NV6hP4TfBwIBPWTE6xcPRtQ38H+PWOX\nERa47PMLMQzB6HCWl184XFFm6admMHmKmrj44nP7cZxyd6QzzqkPv989bW/T1Vm+6iFRFWHVxXNo\nbEzyelsHe9rK3dsW6iAWt3FyLq3/Wtn157mLpzBtpnLitPMfRxgdKd+Mp3FKNZ9Yrlr2h/b1cPRQ\nuctS0zJYc/k8AAb60ry2s9y1K8DyVTNDY/2FZ9+i1OtZgdktk5jdonaVen1XBwMVfE/U1MXDb6Xj\nyAAH36y8F8GFn56HaRmkUw47XzyCZRusvmzsNpvj2q398ssvc+TIER5//HEOHDjA5s2befzxov/c\n22+/nd/97ndMnTqVm266iRdeeIFYLMaqVav4xS9+ccqF/rhgRyzsyLv8GSbBvJmVx95KaQaWVQiX\nUiKdK5GOg+/kaMk5SMdB5h38XA7fyeHm8uBeCPnzkY7DZRkXz+nHzeexDciMZvFdF6PKIT7pk0jX\nZY6bY2p+F77r4/sevucjPR/peeRG25Gui2nGWBzvUZNhEOEZBN6hdgY8tS56bu0ipDBDGVAdT5GO\nbt5+Xv14VCdbmBWpgxPSifWmOPy/fwVAJj6V5uq5QRpBp1Qge+h//R8sQ+LYVTTVnwcIMIxAVk1+\nGfnDDo6IETAMkuZCIiIaphUq1TffprP9OYRp4mcnUStqQBQ7yhAQzWTo2fIEwjBIZSLEZBVCqlZ7\n4RdeCsHoy62IhJqAJErsDhmOLkK2vZ1R0Y0wDJz+EZyMG2wmUCQzANn2IwjDIN0zxOjQ2PXmhVzd\nkWGEMHBG0+FSwkJ88LEUlTHQc3wUJ1fcNCMQCZU3wEBvis6jQ2XfXS5TfKHUSI5jh8t/lAG8wG2d\n5/q0H+yvKNOysGgoHt7fS0/XaJmMHSmOP/R1pzi0r7dMJhYv9lClUw4H9lb+UV5xQXFbwXdSqA2N\nVaFyPnZ4gI4jg2UyzbPqQuXc35ti3+vlm+MAXPb5BYAgm3F5M/DxfyItCxpD5fzWni4yqXJD1bbN\nUDl3tg+yb3d5fnUNcVZdPAeAoYHMOyrn8y6cTSxu43k+b7xaWaZxajJUzgf39tDbXf53mbugMVTO\nXZ3D7P6PzjKZSNQMlXNqJMfuXeUyoIyBgnJ+49XO4P9pLIlEJFTORw700X6g/JuaOr0mVM593Sle\nf4f8Lri0BRPIZfO8vquDSNQqU84fJu/Zcn7ggQdobm5mwwa1C8rnPvc5tmzZQnW1+lBGR0fD6zvv\nvJPly5czbdo0HnvssfelnD+uLeeJzqnWo5QSPA/p5pF5F+m56uzmkZ6HdL0gLLj3XBXm5tVzhcN1\ng3TcMXJjZLzSeyWH74+NC+4J4qXvg1ciU4j3vZL4D2f98oRGKKNFBGdEsExNqHXrYXwQpsY0CrIi\nkDGCIYyivOqlGRsmhBFkERhshhXMCFbpyiA9E4kRzHy3olGyjh/0BgTGlSEwhcAyVVk8aeCJkjIT\nlFEIYhFl+HhS4BYmFARxQqg8o5aBYakyZV2Vjgx7KlT5I7bAttW7ZvNSTWwqTEsWBgI1XBSPmSAE\neU/i5CnWUVjXguqEWm/uSUhnfUCoydWoeIBE3MaKmIBgJOUqK0kYYX4IiERM4gkbhCCddnFdGb5b\nYdaxYRoka2NMbkzS2TlEJuMWZSBcw56sj2OaBr4k6EGqoAirIkRjyuAZHszgueW+FuyISXWN8tCY\nTjlkM+VGhRBQP0l1decdl5Gh8hY4QLIuhh24P+6v4MBJIoknIuFw2fBghnwFXwi2bYZKPpNW84kq\n0TC5CiEErusx1J9BCBGu6ikwri3n3t5eFi9eXCxwQwM9PT2hQi6cu7u7efHFF9m0aRP79u1j//79\nXHfddQwNDXHjjTeyZs2ad82nvj6BZZ1ev9Pv9uKak+fjXI8FpS5LlL30/KLy9wND4wQ5fF9d+17Y\noxCGFZS/H/Q2FGQKYX7JdSHNSnFhesG9PBkZGYYj/WK+UpY8V3ItS+K84JkxacigDtwx6fuyIC/H\npl0YgBwnTsYnX3n779Sp3F8wlvI296mlc7J0A5U7qouU9SmUGDChIi8YGcFZGWaBgg/XIQl6Sg2E\nEoOiMB8EBCNGseepECZKwoQQpMMyGKFcsTxKzhEGQ6VxpeULZF0gUxovSvMKrTD6SsKEACkMxEVr\nmHzh6jFV82H9Pr7v2dqVGtp9fX1cd9113HHHHdTX13POOedw4403cuWVV3L06FGuvfZann32WSKR\nd173OzCQfse4U0G3nE8Puh4rYQSHpU4nsZz93eqx0HH+cUEWFHRBmZcqe4IJE4FCR8qijJQ01Mfp\n6x0dYzSo2Ttj70PDwg+mJQV5nJheWZ4lcWXxjM2vmCYlecigLChjqTStE9Muefcx9RI+/w7ysiRP\nv0SmtAyFsQ9ZqFfGpBGxTZycasXKQpkDmUplHVu+YvrvXMbSMgThwYSvYt2VyhWeJbwvzMiSVCiL\nL0+Qq1RGTrsh6HgSOf+T4f24tpybmpro7S2O33R3d9PYWBwDGh0d5Rvf+Abf+c53WLtW7Xs6ZcoU\nPv95tQXgrFmzmDx5Ml1dXcw8ibFTjUZzdhO2dgzjfRslscYkEaOytzbNyfNxMrrlicq6VJGXGBCM\nMSDKlbxEYlZ/dL2I7+kkd82aNTzzzDMA7N69m6amprArG+Duu+/mK1/5ChdffHEYtnXrVh555BEA\nenp66OvrY8qUj36zao1Go9F8vBHBOL8wDIRpIiwLYVkYto1hRzAiEYxoFCMWw4jFMeNxzEQCs6qq\n6CgqmcRK1nykPiTes+W8cuVKFi9ezJe+9CWEENxxxx388Y9/JJlMsnbtWp566imOHDnCli1bALjq\nqqtYv3493/ve93j++efJ5/Pceeed79qlrdFoNBqNpoh2QqJ5V3Q9nh50PZ4edD2eHnQ9nh4+zDHn\n8dv7TaPRaDQaTUW0ctZoNBqNZoKhlbNGo9FoNBMMrZw1Go1Go5lgaOWs0Wg0Gs0EQytnjUaj0Wgm\nGFo5azQajUYzwdDKWaPRaDSaCYZWzhqNRqPRTDAmjIcwjUaj0Wg0Ct1y1mg0Go1mgqGVs0aj0Wg0\nEwytnDUajUajmWBo5azRaDQazQRDK2eNRqPRaCYYWjlrNBqNRjPBsMa7AB8Gd911F21tbQgh2Lx5\nM0uXLh3vIp0x3HvvvezcuRPXdfnWt77FkiVLuPnmm/E8j8bGRn76058SiUTGu5hnBNlslquuuorr\nr7+e1atX63o8BbZu3crDDz+MZVncdNNNLFiwQNfj+ySVSnHLLbcwNDREPp/nhhtuoLGxkTvvvBOA\nBQsW8MMf/nB8CzmB2bdvH9dffz1f/epX2bhxI2+//XbFb3Dr1q389re/xTAMrrnmGjZs2PDBMpZn\nGS+99JL85je/KaWUcv/+/fKaa64Z5xKdObS2tsqvf/3rUkop+/v75SWXXCJvvfVW+Ze//EVKKeXP\nfvYz+dhjj41nEc8o7r//fvnFL35RPvnkk7oeT4H+/n65bt06OTIyIru6uuRtt92m6/EUePTRR+V9\n990npZTy+PHj8rOf/azcuHGjbGtrk1JK+d3vfldu27ZtPIs4YUmlUnLjxo3ytttuk48++qiUUlb8\nBlOplFy3bp0cHh6WmUxGrl+/Xg4MDHygvM+6bu3W1lauuOIKAFpaWhgaGmJ0dHScS3VmcP755/PA\nAw8AUFNTQyaT4aWXXuLyyy8H4LLLLqO1tXU8i3jGcODAAfbv38+ll14KoOvxFGhtbWX16tVUV1fT\n1NTEj370I12Pp0B9fT2Dg4MADA8PU1dXR0dHR9ijqOvxnYlEIvz617+mqakpDKv0Dba1tbFkyRKS\nySSxWIyVK1eya9euD5T3Waece3t7qa+vD+8bGhro6ekZxxKdOZimSSKRAGDLli1cfPHFZDKZsNtw\n0qRJui5PknvuuYdbb701vNf1+P45duwY2WyW6667ji9/+cu0trbqejwF1q9fT2dnJ5/5zGfYuHEj\nN998MzU1NWG8rsd3xrIsYrHYmLBK32Bvby8NDQ2hzOnQO2flmHMpUnsnfd8899xzbNmyhd/85jes\nW7cuDNd1eXI89dRTLF++nJkzZ1aM1/V48gwODvLLX/6Szs5Orr322jF1p+vx5PjTn/5Ec3Mzjzzy\nCHv37uWGG24gmUyG8boeT513qrvTUadnnXJuamqit7c3vO/u7qaxsXEcS3Rm8cILL/DQQw/x8MMP\nk0wmSSQSZLNZYrEYXV1dY7p3NJXZtm0bR48eZdu2bRw/fpxIJKLr8RSYNGkSK1aswLIsZs2aRVVV\nFaZp6np8n+zatYu1a9cCsHDhQnK5HK7rhvG6Ht8flf6XK+md5cuXf6B8zrpu7TVr1vDMM88AsHv3\nbpqamqiurh7nUp0ZjIyMcO+99/KrX/2Kuro6AC688MKwPp999lkuuuii8SziGcHPf/5znnzySZ54\n4gk2bNjA9ddfr+vxFFi7di3bt2/H930GBgZIp9O6Hk+B2bNn09bWBkBHRwdVVVW0tLSwY8cOQNfj\n+6XSN7hs2TJee+01hoeHSaVS7Nq1i0996lMfKJ+zcleq++67jx07diCE4I477mDhwoXjXaQzgscf\nf5wHH3yQOXPmhGF33303t912G7lcjubmZn7yk59g2/Y4lvLM4sEHH2T69OmsXbuWW265Rdfj++T3\nv/89W7ZsAeDb3/42S5Ys0fX4PkmlUmzevJm+vj5c12XTpk00NjZy++234/s+y5Yt4/vf//54F3NC\n8vrrr3PPPffQ0dGBZVlMmTKF++67j1tvvbXsG3z66ad55JFHEEKwceNGrr766g+U91mpnDUajUaj\nOZM567q1NRqNRqM509HKWaPRaDSaCYZWzhqNRqPRTDC0ctZoNBqNZoKhlbNGo9FoNBMMrZw1Go1G\no5lgaOWs0Wg0Gs0EQytnjUaj0WgmGP8fk8efU0aS6IsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc4bb309390>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\t\tDiff Graph\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc4bb685d68>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\t\t\tCost Graph\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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xnTxSY2i0jKZpbL90JQD+5CQH/vR/EM3P46xbz9B7fgln7drntqLPEUop/GTK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/+RmnoDX9/iJ0vXr8eza9cvZY5vypTXCQgIrNjBVdB9d2FTq9XQrX9jAAxnosj497+oUbce3g89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\n7+8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc4bdfdaef0>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\t\tDiff Graph\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc4bb0ab748>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "6YQKb8PR-H5U",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"def plot_wrong_pictures(LRs):\n",
" for lr in LRs:\n",
" print(len(xtest))"
],
"execution_count": 0,
"outputs": []
}
]
}
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