{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import keras\n",
    "import keras.layers as layers\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5.1 - 5.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drawing uniformly distributed n points in the unit ball of R2\n",
    "def generate_two_circles_x_y(n):\n",
    "    def in_circle(x,r):\n",
    "        return (np.linalg.norm(x)<=r)\n",
    "    def in_band(x,r1,r2):\n",
    "        return (in_circle(x,r2) and (not in_circle(x,r1)))\n",
    "    def get_rand(r):\n",
    "        return np.random.uniform(-r,r,2)\n",
    "    \n",
    "    x_vec = np.empty((2*n,2))\n",
    "    n1 = 0\n",
    "    while(n1<n):\n",
    "        x = get_rand(2)\n",
    "        if(in_band(x,1,2)):\n",
    "            x_vec[n1,:] = x\n",
    "            n1 = n1 + 1\n",
    "    n2 = n\n",
    "    while(n2<2*n):\n",
    "        x = get_rand(1)\n",
    "        if(in_circle(x,1)):\n",
    "            x_vec[n2,:] = x\n",
    "            n2 = n2 + 1        \n",
    "    y = np.ones(2*n)\n",
    "    y[:n] = -1\n",
    "    return np.array(x_vec), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#label points\n",
    "def make_label_function(function):\n",
    "    def label_function(_x):\n",
    "        return_val = np.array(np.int_(function(_x).flat))\n",
    "        for i in range(np.size(return_val)):\n",
    "            if return_val[i] < 0:\n",
    "                return_val[i] = -1\n",
    "            if return_val[i] >= 0:\n",
    "                return_val[i] = 1\n",
    "        return return_val\n",
    "    return label_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotContourLine(func, value=0,  minx = -5, maxx = 5, miny = -5, maxy = 5):\n",
    "    #This plots the contourline func(x) = value\n",
    "    \n",
    "    samplenum = 1000\n",
    "\n",
    "    xrange = np.arange(minx, maxx, (maxx-minx)/samplenum)\n",
    "    yrange = np.arange(miny, maxy, (maxy-miny)/samplenum)\n",
    "    \n",
    "    #This generates a two-dimensional mesh\n",
    "    X, Y = np.meshgrid(xrange,yrange)\n",
    "    \n",
    "    argsForf = np.array([X.flatten(),Y.flatten()]).T\n",
    "    Z = func(argsForf)\n",
    "    Z = np.reshape(Z,X.shape)\n",
    "    \n",
    "    plt.xlim(minx, maxx)\n",
    "    plt.ylim(miny, maxy)\n",
    "    plt.xlabel(r'$x_1$')\n",
    "    plt.ylabel(r'$x_2$')\n",
    "\n",
    "    Z = np.where(Z > value, 1, -1)\n",
    "    plt.contourf(X, Y, Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_2d_data(function, data, stage_names, labels, value=0, minx = -2.5, maxx = 2.5, miny = -2.5, maxy = 2.5, samplenum = 1000):\n",
    "    label_shift = - min(labels)\n",
    "    shifted_labels = np.int_(labels + label_shift)\n",
    "    fig = plt.figure()\n",
    "    \n",
    "    colors = matplotlib.cm.ScalarMappable().to_rgba(range(max(shifted_labels)+1))\n",
    "\n",
    "    xrange = np.arange(minx, maxx, (maxx-minx)/samplenum)\n",
    "    yrange = np.arange(miny, maxy, (maxy-miny)/samplenum)\n",
    "    \n",
    "    #This generates a two-dimensional mesh\n",
    "    X, Y = np.meshgrid(xrange,yrange)\n",
    "    \n",
    "    argsForf = np.array([X.flatten(),Y.flatten()]).T\n",
    "    Z = function(argsForf)\n",
    "    Z = np.reshape(Z,X.shape)\n",
    "    \n",
    "    plt.xlim(minx, maxx)\n",
    "    plt.ylim(miny, maxy)\n",
    "    plt.xlabel(r'$x_1$')\n",
    "    plt.ylabel(r'$x_2$')\n",
    "    \n",
    "    Z = np.where(Z > value, 1, -1)\n",
    "    plt.contourf(X, Y, Z)\n",
    "    \n",
    "    plt.scatter(data[:,0],data[:,1], c = colors[shifted_labels])\n",
    "\n",
    "    legend_stages = []\n",
    "    for i in range(np.size(stage_names)):\n",
    "        if(stage_names[i]==None):\n",
    "            continue\n",
    "        legend_stages = legend_stages + [mpatches.Patch(color=colors[i], label=stage_names[i])]\n",
    "\n",
    "    plt.legend(handles=legend_stages)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x): #activation function phi_2\n",
    "    return np.multiply(abs(x),(x>0))\n",
    "def ident(x): #activation function phi_3\n",
    "    return x\n",
    "def deriv_relu(x):\n",
    "    return np.multiply(1.,(x>0))\n",
    "def deriv_ident(x):\n",
    "    return np.ones(np.shape(x))\n",
    "def least_squares_error(function, data, labels):\n",
    "    prediction = function(data)\n",
    "    return np.linalg.norm(prediction-labels, ord=2)**2/np.size(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class for L layer neural network\n",
    "class LLayerNN:\n",
    "    def __init__(self, neurons_per_layer, phi_per_layer, deriv_phi_per_layer):\n",
    "        self.L = np.size(neurons_per_layer) -1\n",
    "        if (self.L != np.size(phi_per_layer)) or (self.L != np.size(deriv_phi_per_layer)):\n",
    "            print(\"invalid input!\")\n",
    "            return None\n",
    "        self.weights = self.initial_weights(neurons_per_layer)\n",
    "        self.biases = self.initial_biases(neurons_per_layer)\n",
    "        self.phi_per_layer = phi_per_layer\n",
    "        self.deriv_phi_per_layer = deriv_phi_per_layer\n",
    "    \n",
    "    # initializing weights by drawing iid uniformly distributed random numbers in (-1,1)\n",
    "    def initial_weights(self, neurons_per_layer):\n",
    "        weights = []\n",
    "        for i in range(np.size(neurons_per_layer)-1):\n",
    "            weights = weights + [np.random.uniform(-1,1,(neurons_per_layer[i+1],neurons_per_layer[i]))]\n",
    "        return weights\n",
    "    \n",
    "    # initializing biases by drawing iid uniformly distributed random numbers in (-1,1)\n",
    "    def initial_biases(self, neurons_per_layer):\n",
    "        biases = []\n",
    "        for i in range(np.size(neurons_per_layer)-1):\n",
    "            biases = biases + [np.random.uniform(-1,1,(neurons_per_layer[i+1],1))]\n",
    "        return biases\n",
    "    \n",
    "    def feed_forward(self, data, get_steps=False):\n",
    "        nets = []\n",
    "        o = np.array(data).T\n",
    "        outs = [o]\n",
    "        for i in range(self.L):\n",
    "            W = self.weights[i]\n",
    "            b = self.biases[i]\n",
    "            phi = self.phi_per_layer[i]\n",
    "            net = np.add(np.dot(W, o), b)\n",
    "            o = phi(net)\n",
    "            nets = nets + [net]\n",
    "            outs = outs + [o]\n",
    "        if(get_steps):\n",
    "            return o.T, nets, outs\n",
    "        return o.T            \n",
    "    \n",
    "    def get_deltas(self, data, labels, f_of_data = None, nets = None):\n",
    "        deltas = []\n",
    "        if np.array(f_of_data == None).any() or np.array(nets == None).any():\n",
    "            f_of_data, nets, outs = self.feed_forward(data, get_steps=True)\n",
    "        \n",
    "        delta = 2*(np.subtract(f_of_data.T, np.reshape(labels, np.shape(f_of_data.T))))\n",
    "        deltas = [delta] + deltas\n",
    "        for i in range(self.L-1,0,-1):\n",
    "            W = self.weights[i]\n",
    "            deriv_phi = self.deriv_phi_per_layer[i]\n",
    "            net = nets[i]\n",
    "            delta = np.dot(W.T, np.multiply(delta, deriv_phi(net)))\n",
    "            deltas = [delta] + deltas\n",
    "        return deltas;\n",
    "    \n",
    "    def backprop(self, data, labels):\n",
    "        delta_Ws = []\n",
    "        delta_bs = []\n",
    "        K = np.shape(data)[0]\n",
    "        f_of_data, nets, outs = self.feed_forward(data, get_steps=True)\n",
    "        deltas = self.get_deltas(data, labels, f_of_data=f_of_data, nets=nets)\n",
    "        for i in range(self.L-1,-1,-1):\n",
    "            delta = deltas[i]           \n",
    "            o = outs[i]\n",
    "            net = nets[i]            \n",
    "            deriv_phi = self.deriv_phi_per_layer[i]\n",
    "            delta_b_t = np.multiply(delta, deriv_phi(net))\n",
    "            delta_b = np.sum(delta_b_t, axis = 1)/K\n",
    "            delta_W = np.dot(o, delta_b_t.T)/K\n",
    "            delta_bs = [delta_b.reshape(np.size(delta_b),1)] + delta_bs\n",
    "            delta_Ws = [delta_W.T] + delta_Ws\n",
    "        return delta_Ws, delta_bs\n",
    "    \n",
    "    def draw_minibatch(self, X, labels, K):\n",
    "        ids = np.random.choice(np.shape(X)[0], K, replace=False).astype(np.intp)\n",
    "        return X[ids], labels[ids]\n",
    "    \n",
    "    def run_stochastic_minibatch_gradient_descent(self, D, L, nu, K, S, check_rate = None):\n",
    "        errors = []\n",
    "        for s in range(S):\n",
    "            minibatch_data, minibatch_labels = self.draw_minibatch(D, L, K)\n",
    "            delta_Ws, delta_bs = self.backprop(minibatch_data, minibatch_labels)\n",
    "            for l in range(self.L):\n",
    "                self.weights[l] -= nu*delta_Ws[l]\n",
    "                self.biases[l] -= nu*delta_bs[l]\n",
    "            if(check_rate!=None and s%check_rate==0):\n",
    "                errors = errors + [least_squares_error(self.outputf(), D,L)]\n",
    "        if(check_rate!=None):\n",
    "            return errors\n",
    "\n",
    "    def train(self, D, L, nu, K, S, check_rate):\n",
    "        return self.run_stochastic_minibatch_gradient_descent(D, L, nu, K, S, check_rate = check_rate)\n",
    "    \n",
    "    def outputf(self):\n",
    "        def f(x):\n",
    "            result = self.feed_forward(x)\n",
    "            return result.reshape(np.size(result))\n",
    "        return f\n",
    "\n",
    "# class for two layer neural networks\n",
    "class TwoLayerNN(LLayerNN):\n",
    "    def __init__(self,input_neurons, hidden_neurons):\n",
    "        LLayerNN.__init__(self, [input_neurons, hidden_neurons,1], [relu, ident], [deriv_relu, deriv_ident])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Task 5.3 drawing the 250 points and labeling them\n",
    "circle_data, circle_labels = generate_two_circles_x_y(250)\n",
    "# apply to our TwoLayerNN\n",
    "two_layer_nn_53 = TwoLayerNN(2,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "errors_training_53 = two_layer_nn_53.train(circle_data, circle_labels, 1e-3, 20, 50000, 5000)\n",
    "rt_training_53 = time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_function_53 = two_layer_nn_53.outputf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SUEpPEESlAs3GNW7WRMojNzdQR7pWaZhTVoNsG7K5N5XXxBCUMtcKekQg5Xz2qoNh0qub2eykBB178C5gUASGAnODbBvWaiv7eSEgV5bNDz2m5LkfqJ2dpFy21tDbO+BaDcKZpqmeBhARZK0KDoKpareBLM2hWSvwPNXaQ7boZK1NTcob9yZQrQa5vJRKFZ5CUIeYNjIiiHqj6Gk4ASgCQ4GFI/IX3ie062Z6KHCvB9VfnPrrcSAMaKJUhh650xMpc75tZ0gQqFVT00mifri9EGq3PU4Zxryz2ZKmz2Q4hPb8sT/DBFhr+Dc3k8KNAKAZut0GSbH/lFaBhaAIDAVODJjZpKKyjHfGB5pCa7udUAzNfYnWiTQXlcum+e44A0vGtalWS20uMw2EPSPKxwxIYbSMQlE/ISDqc7KS9gnWOlN0Uff6sMplk2IEoIhMoXkSM1hqqteDVQSGY0URGAokwEqZgmTQEU2VitECOoK0zZQV5gzwyJ1LJ5+ZJzyQjYCb8jzI1daxpKRYKeidlLSLbUNmrPh1p5ucaJUG1NDUCY4qtab1uFN7EkpBD0fgoRkjlcuAbZlUUfg+M5hLk+cpcLwoAkOBCJGJfGyy4m4XWvlRd/NhXnuRyqKJc2fJZGu9ON/pFP2ojW+4d+q49Q9+zly6108fU4bnMjOnr74BqG4PVmu2ofxCICUyI4Mg6N0xTTgsoIdj829uznUJsgq3t+NGERhOCVhrs5KS8tDSH7o/SJ2seDAEZ6Q3FndxnV1MzYJlzbVKZs9PPy+zWcEeIDDo0QjNpWvYvFxGua7wwtc8jq/9wStZWSLgjeavn3/p03Dpr6aDbaXh4398+8fwhV++m3h841IZD3z9MzDqp3wGh7jCZt83O0jPC1JVNVCjDm53UsYx3cDHwxG46pm+DCFmj5Xo0GskBWajCAwnHFFeudcfM0BKJYgJ7f+FXMvLMZH3/cMNDFIiPT8BM3F7XrKrlmhuQxuyLHBa0Aka3+bBpx64Cx9+wdsSj33mz5fwzu98CjZ3DMto2LHw0XdexKgn8ZIffzT3fBe/qIfH/rYOrZIRxHMJ63dP7wyWz7mZMXOvnhLzIlKpDS+sNfTOLqhRh2wG0uNKRYVizpA+CSU/RK2abDyMgyhhZFTgeFF8AiccRgUz+MGFDJDRCLrTWXh6h6QEI6UwyHzoWjZEZBrAJlk2RCbnLgT0cGjYSpaEqFTm3jlRuQR0uynsHwAB+8d98kVceUEZH/iun5t7zB98y13whsn74g4k/vjd5/Gi112GU87WI3rB93wef/bvzgKJBTSjeX6E1h3TAdopa7zwex/HH/7KHXAHyWsedIVt6krdSBqbqlWIStnoY6XtILs9YH0t0bGs+/2ssB6JMYpSCdyojz/jUIF3eelUsstuZRSB4YQjcxU2GIIX5C8QQlSrUIMURpC9fy0bZjZpiFCzv5zdgSxrNWgpTf5dG3VOUR+vIOU+O6iJCKK5Ar25BUZkOw27pPAFT/s0vu/ffGpf573xuWyJjc6GjdU7s2smlqMxfRcIO9dKuPzJGu582jQN9UWvvYzGqoePvPMiups2Lj6lh8uP3nUgAT5Warqu1OlAKz9b4ypQUI1rT1G5DGR0dcePk9UquFIxKrhi3PnMSoE1R3UfEIzibLF7OBYUd/2kI0NbB4D5Mce8EHg4jFzFqFqZu0s4BFmWmUB321HaJlzR7QeRN4Lvj8fa6Zj+gywLznJ5odLYG99wL37jjT+PP/vNs3jfT90DN5aj90YSn/7Py7jyD1VcfMreFWAvPLmH9oYNTEzxRMDSmYy0XIBP/kELQjLUxNzruwJ/8/urqYGBCHjeq6/hea++Fnv0EwCA1z7npXseP2BW+qm7gv7ACO1lSZj3+onPiYSAWFlJFJ8BIynOgwF0UA8iIcxnHyw0WCkjvpfSt6J6fVCjAXmMGle3K4rAcNJh2+m8cSESQUFtbwcTsHmaRyNwrZZJfcxC6I0cFoMPssXXvX6Ssx5MQGq3ndtBvQg8/P33JdJCn/uLRiIohCACLn+yvq/A8I0/8hg+9/GlRDrJqSh89fdfgV3KL6JLm1N7AIkY0s5OQWXhrQ89uK/gwG7GroAIVHKMjlIaUh4XJQe0vmbOqVTQ1dyP4k50RyzLpKGEMN/blKJ1NL5OB1wuFammI0Zxt084ZKOe2kUslsZpJB6OzIprMoXe6xk20xxg5gTlklLYT+z7ULtt+FtbUJ2O0crJO+cwnV4J35/52oPgrQ89OFUrWL9nAKs0fU0iRuvi/miydz+jix94zz/gri/uwHI0Vs6P8NKfeARf+4NXZr72i796C5zy0Uib8cxvnI/WOYm3PvQg3vrQg3t6TSahgDlfpjwjHUhEECXHCBgC6XwC34fa2TEpo7wdcTiUUf7uq8Dicew7BiL6NQDfCOAGM99/3OM5aSDLiNfpXh/seSApIWq1hGRCJv+fYHj6OT/wKbE6yzK2jhOpHu260Ns749d5PtRgmJsWyq5GHg7yJsXnvuIGPvrOi/Bjt0pIjaUzHp70TzKYMnPgvi9r41/83if2/LqldQ+v+rnP4r0/eh9IhJp4hG/6sUdx7r6MgDon9rJ7ELUqVNr3x7YhLAscpIImQTn1HlbZEtwRfGVoxLNANJmpK3AEIN4Lb/wwBkD0PABdAO+ZNzAs22f4uWvfcrgDO0VQ7fZYiCwOIojlZYhSeq0h6gieXL0TTdlx+jc30znoUkIuL6cWp1W3m24hKSWstdXc97QXzLtKfuyTNbzndU/CxqMVgIH7ntPGq9/yaSyf3b+R0BJZWBY2iIC29rGrvZnx0AFBA/DB6Ny08cmPtqAU4f4XbqF5fnGr4+9/9Q/B+a+zdy96NIJud8Z1pVLJ7EiFMHTpsCM9pEuXy4kd6yRYGSe3XBBB1OvQnZR+iAnI9bUilbQg/P61d/4lMz9r1nHHHhgAgIjuBvAfbufAEBaP9XBoUjmVilmJz+FDwJ5n+OZZEAJUr0FOeCVr1zW88pTvANVrkIExPWsNtXEzdwxUciCWlxNjZWYzLqXG1yCCbDYX5ti119QJAHS3LEibUWmMA50EoSEs2CAMWaPL/swJ/pwsoUYWRPCeNTNc1ris0lf8VZI4K0sQwRLYZY2raojZVzoY5tk9RA5zGXWlSGJbypn9LPN8XwAz4etON1cbS8xhOlRgfswbGI49lXQ7IzRO165nhNBCOWIk86pUqZjGn6y8rm2DGg1w1upLa3C7AwUkg0N8wp6EH9sdzNNdPHKh+/0omJiXEWSrCXZdsGvSYBQUEsPax35XgvsJCCHqrWQKo0QCd8gKCIAggmZGCw4u+32EZQAC4JCAYoYPhgORCAoIXutAoE4SXU7uriwQzsty4vgSBC5aFVzyD9cTe57UEhHlynVHfgtzIMsfPHG+Ws0wmZYa0LZlWFBgwHGi+haViqLzceHUBAYieg2A1wBAWZwsA5b9YLyani4aTx07GEADkDm2i6LkQHXzJSW42wNigSGXIx5b0Rt3t/JM1VMeDIFakgVFREaLKFj1se/DDymsQGZNIwsHCQhZOCfLkBMTvMVAUzjY1C6WhY014UQ9EENW6HF6flwQoZISGJbFdK8BBdepkUSPD1c4Lrxv+6W17gWcob4KALAtiFot2gUQkelPOYDLX4HF49SEY2Z+FzM/i5mf5YjTz2vWvX6CXjoLPBggL+2X1aWaPChJgyHbGN5MQYipXgKx1ABm9UXMciWL02pDBLILs1Ka+2HczAMJgpVS3RRkUktVklgTDgQRJBEEEcok0RJOak1UBzuKSVjBa9NwRh5dquQw7uEUcnaYcrlIDZ0GnJrAcKthpudA6otydgPzKJOmbMtlc8UwTAJzHSqXUg1wiAhWcwVitRVJHEwil94YjjHtLQT1lTQ8/P33pU5mzMBf/YdVvOVb7sfPft3T8ftvv4hhdz+yHTn3FMCKsKcmdEEEgWwbyrae3k34rFODHwXnqtPRbd7f+tCDcJ988dDOL2oZq3/bPlS9LWY2hfTBMJMOzZ4H1elAtdvQo9HMBcntimNPJRHRbwL4pwDWiOgKgDcy868e76iOAHul4IkZrmhzKJNSSrMbEZleicZ86TlhWaDlFWO9GRWUAQiZPSGEyKlpsD/9Q85b3b7/p+/Gn773XKQbdP3hCj7+O+v4sQ/9ba5G0dSQAIxYowSRCACaGT3tp6aAAEwFBWaGBnBVDaBiwUbABJemcDIDidmFCHSPcI5653veAeBwUktUKoGqFVM3CL+XYVPbAcC+D1ZGKmWqx8bzTJc9EH3HqFZN+H6rXh/cHct28HA01mo6ZTaxh41jDwzM/G3HPYbjAJUriS/pzOPr2cVnAOaHmFX0y2Al7Rfk2JCrLejBEFDK9FSUSmDXhQp8BshxTL+FjP2ALStdyj9Q1owjLyhsX3Xw//3b8/BHMTrtSGL7agkPvX8d/+2rrqePG8CacLAkbBCAETRuqBGuqSEuWhWj2hEcO2SFpZTdQuY9IcKucjFgDQFAwwSFJ1jV3N0FYIKQh713Oy8C++2YzoVSIMsCBf7fwrIOpHmU6jEdI2Qws3l+YtHBvT607UCUHLDS07835oAYsRhPjlsJRSrpmCCqFVC8vyBM5TTqEI2Gyf0LYYp1K8szJ3VRrY6/3OEkZFkQa6uw1tcWFhSi4UoJWTcG8KJSAff7RmPJ9wGlwIMB1NZWovOaHCdd5lqIxL2YlQd/5K8aqbIRbl/iH/4o27DmvCxHkz0FtYKL0tyXS34f19QQG3qEK/4AfVaZP4609INmhgThbquKJ1o13GvVcIesQCC7thCeiwF0UtJPR4VF1R1CbSy1uQXd6ULv7JrFygGZRTrNY3owMAsTwDyXtRMNmvPYzUi1MhvlgAIJHPuO4XYFEUGurIA9LzJBodJYeVTsVThMa1C1AqpWAM3GUP0Aqpt7AWud3simtaGwxrbzorkC7vWjmgKVS2ZnEbzveSappTUvtTQgpEbzjvQfuQ3DFpqcpAnAnVYFfa2wpV0MmWFDoE7pJkBpVqLMDAKwJMavIRg6at5OIQwKV2K02IOiRhLLwoYEocPJhjsC0BR2tGPqaB9b2oXGYnYOutcfM5LCiVop6N22sR/dBzhUW017rt8HqpXcDCqHuiPBwiv14Iya2e2MIjAcM8i2cyfwyPJSacC2oH0fGI6iDlSqVsChpEWsM5VyqK2LBvt+5o+OXdd0wrbbQCDYRiUHstVKppmQHhQcCAgydYDw7E98dhv1pocXvGQb3/66DbTOehj2BXY3Lej6EC4okecHAJtEapmZyLCS6sKwkIBxGmheZPaXzEhDhWmQipBwF7BjaAW1jDD4OSywJCxc9gdgABdkGeVYcFwWNmrCwqNBH8VbH3oQ3/6//khkP7pXZFmPsjufN/fU64I+n5wDAJjUZuqEH0h3Awh20+l9PotU871VUASGE4yooJa1Te71zKopkq8MttnDIbQlE81mhwkSIrvwLQTU1lZCLI1HLpS/Bbm6mrlTsEG4YFVggaL+gRtqhA77EAL43z74KVyoWyhVzHlrDY1awwUzQaOKx/x+gjbqcpr/QWyYsUlrkbyZWROiIEKNLOziYIFBghJBITy3HQSHEetEUAiflwzUyUI36Mv4jTf+PF77wZdGYwdmB7gI+Uv3uRolQ+jh0Mh05CB0jiMi0FJj2m7UsiKmHAXyMAlZcGZTuzuinfVpQlFjOKFgZqjd9uzehKyg0c8XYouUUjc3oXZ25xM0y0BcXz/1uTQFTW1Wg6wUfubDvzf19B1WBXaQnw/7B87IEsqBoMRdayIKConrEUEAWJXJngsfjB77B6Yn7nXVGxVHY+q1cWhmeGkyq3tEOWNHFAaeEqWHOxkwouL4mQ//Hrbv8qBubEDd2IC/tZ0tvx1DZgF3jz7lHKSfAmXBjIslvaFlpQK52jJSMoH1rWw2E5+XKDmQ62sQSw2IRh1ybRVyFpPuNkURGE4qtD6YyXuO3HaorcTDoVG5HI1MoTivY3UG5MrKuFkuLKQvNbJ/2MzwBz2I7g385HOejbe/6qno3DSvr5BIZfIQgItWBU+0arlfXCJCNWUi3NH7F8szQ95/UBmyDx/pwWF3AWmkydRZCM2PE1jSAAAgAElEQVQMn7Xpo8h4Pi77xwy87RX3Y+VvYiJ44fdlhoS7CCxYE6D5vbmjMaUJQoaQ0tBQ11aneiLIsiCXGpAry8b9LSWIE5Exg6pUDtfD/JSjCAy3Kpzs7bHqpPgfA1Aztu55ICFgtZpmFdZqQgZMKLLsjBQCQ7gK3lDCdwU++1+W8Ivf8RQwIxKZm7pGwCaah0KaNlFWMlbNedDBan+gZ3hP5AQNIgKBcMUfwIWOzqmYcV2N4C6g9DxkDZUReHa0jx4raDB03MIzYkSNA+Zn/nwJW1fKUN7E1MA89lhIAXseeORC1OtArWaIFLWqWcXvNVWTcy9FtQJZrxcaSoeMosZwUiGEES3bz66BCLKRU3zO8vJVal9FwsSlJ1dx5RLQTQYiEjxlUqN9gY1HK7j8dzXc87T+zP6/WX0B2xOembVAxmJehJPm42qAIWusC2cq5TI5nqycPDNjxBo+GI/5gyhFNlpACimOx/0BLlhl2DxOK92IBZ4r/gDnZAnloIriQuOaP0qEpY1LlVQDIQBJYcUAkcxJPBUpBESrue8VuSg5UBle57RHu9oC+0MRGE4wqFwG9zKa1ggACbNNF8JQBX0fZNsQtWr+j1KI3FTTImEUVlvG5nE0guUoWI7GsDu9ihSCsf35Eu56Wg/b2p0qpuYhnMgBkzJyoXFWlmBDoM/+ns/lssa12KRaEfJAAXNJ2NjWHhSC1M0hSDGEgadJNhrSAoHgEIHYsHt9MK6oYZQmSPsGnP+CfmpXvlNV8FJ6UEzj2UQqLGhI27d9q22DSqUpRlIkRV/g0FHc5RMI1nrsYxAD1eumvyF8XI4nq7w8LmttJqLA2yGrS5oqlUORBiApQNUKbGH8f4ddC4i4RmP4nsBdTzPdqVvaw5A1VoSNKs2elBnArvawpV1UycLFQEabiFDi9ORUuMJnjFlJobTF42qYSEd5zHCQv5uK7xomHydmrAgbm/pwbSrPyhLqMTnwpnBQj1FWARMQShBYl6WoaN3WHm5qF/c8s4M7vqiHy39Xgz8yiwshNco1hd6vNlH5nvFkzczpfuRAZN+6n12DYRAtgUduYA9LoEp54bsFVgq63wd7PsiyZi+obiMUgeEEQvd6qSkkHgxAtaqRlgiP9X3oTtd0HEsBUa9DOI5ZQfvGkD1KHQkBsbQEUa1CK2VkssPeh1IJYk69pGg8zOaH1R+YHYhtQzam6X/MjAo20BtM7hLGwcGpKDzrJRtoXhhPNH1WWMX8k8GINTSMWukkLTOrBrCtXVgk0Ah+CiPWuK5GUzWKLe2iJvffPS6I0BQ2BICb2l24Nc8SWWhJB9ZE0T6krNbJQiegpFogXLQq0T0yzXk2HJLQYPzi7z6M7Q0L//fPncMfPdjE/S/cwsv+1SWsnPs48FBMX2mfTDZmDl7LZneQUSSmcgkoH45UBQeqvhHF2/OghkMjKlnQV0+Gg9tecSs6uMXhb9zMTPXItbWoMUyNRuCdFL/iGbUJudoChBibsTvG33evUO3OdFMT0ZQP9Gt/5aN4x3c8FXfc5eEJXzjE4w+X8Om/rQAgSFtj/e4B/ul3X8VzX3k9QWoJJSbmSQFpZjzs92AH5jdyztdc8QcIs+xpMk5xPEFW4IjsFeU8vH/NjCErPK7mV9cNtZey0BQ2WjNSZW3l4bo2q/0zopTo0s4av2bGlnaxncLmeu1zXmom182tzGvKM+vT13Bdk36KIc9+9rDgb22n19osa/8psFOAwsHtVgWNexAyDddnFKzV7q4pJIY/WiJQc2VP+VvWOr3TlRm614dcXsKnHrgLH37B2/DY39bx07/+CL7wGQNoRSABXPqvZfzLVz0Rd9zfwQ//1t9nvdXs68fSQBqMq2oY/TvrdfFFUMjGiZdeZy2ROuyjySJzAg5TSXkBQhChDIkSBDzowAUO6Gk15eNQI4l1WYqa/Nraw8ZEKoqAmUEhFOmTAC7ICkqULdUxudtoCQc7KT7WYUPiDz7xK0xX/uR5UkQfIzG8icWo3tkBHbWvcxYBw/cPTMC4FVAEhhMIqlTSi87BFje+Bd4XQnZJrGNabe8YbjiRkTf2fZCQgJ2uGQSlsmUwfC/Ryfwlz3BR9wlOmRFOv/feP8AP/ewVXPXMKtaBQE1IaABd7UPBGN4YG83plW1P+9hhM2ENYzQaH4YBVJ7QKWJmbGoXdkCG7bCP/h5d03a0Z2S4GbkT8Tz1kIawEpLeawKJ1XmZBM7FrEDDdI8AYVt7cEjAZQ2eMynV1j7OyfLcQSEOC4Tp0AA89skaGs0BOjcn0j3VamrjmPHjyGjIHA6NL8hRIUs3qQCAIjCcSIhaFcp1x6uaoGFMLi8ZLvlhfKHZiJXpwXDMBgl8FtKMeyBl5ji+9EXXoDzCJz7awqW/ruOHf3wzCApjOCXG8/67HXxOd7Eek8JmGGnsa2qIHitc84e4wxr7MaugYeuaHmVOiX6K2hHDFJC3ef9KmhrAo34fK8LGEtmwKF9OOwsEo1M0GVxWhQMJwpZ2Ux3iQle5urASNqNZYDaB9boy96qcU8TPWyWn9YS4A4F3vOp+DNrTU4iMCUCy70OHJkw531tO2XUcJihQBE57/HbfLQBFYDiRICKjRun7RnlVSpATGL2kcMkXdFXzA45TBBmAUoZ62ErKWZMQqT7QdkXh+d/5eTzwDU/H1pUyRj2JH37DFtISNdJi1JVM+B6EP8lzsoyH/R5G0Ljk99AQNmwQhqym/JST7wKopSijCiI0pY3uHNIOedAwjKlteLgoK4HIHyUax/KmFc0MDSPRPTV2IqwIOxLzyzP2CVEJehImJ3bNjOtqGN0rO2dUzIwO+6jDSpxbM6Oj050iPvkHLWT1/OnBELJeg+r2sunWk+/piGsMol4zBIzRaEzAcJw9EzBuVRSB4YSCiAxjY5IhYVvAPlxBZ4IZGGVQDz0PrPXUrkEsNaCJolrD+S/o4Vt/+mF84qOr2LhUjuiOf/mfGviyr2onrBiYGUPWaFCa47IJI1WS6AUTm8sKPU73U44jbcINkX6l/YEBXFYD1EiiTNJoMWkPd1vZwoWm0U2hlLNyp0DYLgtTMiEpBWQFxg01iu4dAHjgVNVYzYzdgKraIx/rshR1nqfVNEL0ti1olXE/tTbpyDmDAjlOuvf4IcLI3i+DlTJpU2mBrINTVQ0FdmDqf7YFUalOqQifBhSB4ZRBlMtQ3V76trxWhahWwf2+oaIyA46T3AVkgOq1dE+FAKxUqg+0XGqAG3W8+c9+F9I2Y3rP654UBQUA+D/feAFPfXYPpapGuWJkGRjADTVEU+avFNeEg2VhR6mTAauo0DwJC8Z8J+05Zt5zTWEe9FglJuAd7WFFTFMwdVDjWJ2j0S6rH2LeoqjHOjGmEDfUEOdkOervCHcvYV2jywpdvw8JszNKu48NsrAkLLzsFR1s/PUu/tODTTCPx1Sq+vjvf/Ev8fl/rOID//vd6e+vXAp6awBRKRuZ+GNK35CUC+tdmFJDdl2o/mCKpXcacPpC2W0OEgLIULEkEIQQkPU6rPU1WGfWZwuY2TZEc8VIdOewQjiDxbHxDffibR//nSgoAMAko/P65RL+h694Mv7tm89ia2DMYR71+3DB6Oj0PQDBKH+GufhQYbVCEmcCps55WcZ9gVvaPVYVd1tVnJOlqS912LS2pQ63uQwANrWbqlnEAIY6S+puGlnc/nleVyaZujvqscJlf4C29tHX489hclQK6UHhgizjjCyhKiw064TX/8Jl/Og7HouedyoKdz29i6d85bZRdElx2QMAsmxYzSasVtOI2R1CUGBmI93d74Nd78CquvNAtTvTCzZm02d0ynC6wlgB8wUfpueSeNAH6slURtTpnCLDTdVKUlPJcYAMobQ0KuHD338fPvBdPzf1+D//hUt4ylN91BoKn/lEBf/mgfO444lDXLh3gKHtoRdLTvdZoa29RPEZAK6pIdYmGtUAk1+vw0LVkpBBMxcBIM6fOEesUpk1i0SFZBCYxl3UoRTFNTWEn0OljWPPPggpqAsrVU3WhcYNvfcCfIXklAOeUwKe/007+Iv/XMajnynjy162gS97+Q0IATzjRTfxwTffibQ9Gh1S01qIqHkNGE/UjgO5snxoOxNmzqSPH0S1+LhQBIZbCWm+BwBEvW7SN/FO52o1oWcPGDaJSgsMRFNa+1lB4axwcO+XD6MJ5MnPHOCB334Y7ggolQHNZbiscUWNJRo2tIu29lGdoKuezVJZBaZkuWdJVTgQWBcOGkEAGrDCDTWaWbOYFxYIF2L0UmAcGEKHNATXrWDaYjTelzFkhQrJA/HpF50KqFJ69UZIxnc/8OhUEFq7a4SX/uQlvP9n7gERwxuabSQ16ocuO6F2p3sl4LrGZvaIzKsSOIUspyIwnDIQUXZnc0YBz9QClsD1uumoFiJ1B0CWBbG0ZGQ0YpDNlcQEleXLHE28KT+EUuCeKIJJuilsbAWTiQ0CEaYaqfoBU2YqXw/M1dkch4BJS4XnqkLiCVYVl/w+7CBF5TOjy/sLFSGTKA4KgnAtVkS/poY4K8uoBWyiyCM6qCsMtI9yRlfyvEGCAXRnyITvFSromJgcAQNQGWma5736Gp72VVv4xEdaAAHvf/tzDj0osFKZzD0eDIFDCgxGwmOapQcYCuxpQxEYTggif1ulQbaVqSEDAHKpYYpccRBBzqDakRC5dQQgLAaWwK5nFjoT48gKClUytNP03PjENYjQEDZ2tY8LsgwnEHIjGB2h3SBg3FQuqpYFYo70jsIO4LQ+gCwwc1RwHY/JTNp3WhWTkoKZ5NZRwuMxmYx5YSHdJyKslYTRRgO4qoa4z6qlMoyqQY9CGnTsPiTex8R73Q3UZReJjvaxmiFbHtqCpqF5wcXzv+saAODBXzpugbrFphJN+kgZxp5tQSw1oJQy/UdxCmz9GHYpB0QRGA4BzBwZl4R8/0nKWthMZr48NvQk08i2p1bqIchxIFdb0N0eWPkgK5DaXhDzgYhAe+SVLwlr7ok6xIWUTtw14cBljQErCJggUCYJwcAIGtvKgweNhrBy6wrh5KnAEFmTNhGs2DkoeN05q5xI/8yDASssBRP3JIZ7WL2HASsNXfbBGmAw+lrhrFVOBE3ATH3hqwWAhrBhBf0faUwlCcK6dFAj893pBNRVHT1vOq7LJNHVPmqxnRGD8fkMhlga3vrQg2MBPphaAJiNN/Oi0i05Piah//MiwEpB7eyMpWWYQbUqrFYzUA5QIEueOjZSiNM56hMMZobe2Y0KTgwA3S7EyjJEkKefbPyZ1J0HAHgedK8HWU/fBZBlQa4sL3r4ucjaLQAI1tzzQTOjq/1UWmeoQmozYV2UorMygJHW0Ur4sj/AeVlGyEGiiclxU7nos5GByEo7paVnwmBhZ8hApKFGEmcCGmj8nDpITaVpqfZYoYb5PR7MTsnHIDa5P+r3cVaWUI2dhwBzX4FIciPsGPcmajsE4K5wxxS8viFslEjisjKGQnda1ajrPKQZb6gh3EB6ZD/goGkSvh9tJ6leT3RM7xcUKAQkaKNERnl4gWmkSG8MGCu09vrQlg1RLp3agBCioKvuE8zG6tDf2oK/tQ09GJidwnCUykLQu23zvFJzN/5wnvftEeNTD9yV+3xH+4nu3zhCYTlmjtRFezm5fBsC68IwkuJ2nnVhRfacPkxfQMi3N5MW4/NqgM/5PXTZx0WrCidHG2geSBBawsY5WTI1iqmxUhR8wrGG7/OGGuG6SmcA3VAj6AwrzrQ+BgLgTDjIKXBqs5wIOqgFjXdKkggOiYQ+U4OsqSK+CI6rkDSsMIw7rQURBIBl4WDEGhYI68LBRVnBunByu6tDPPx995pJO2TwMAPM4E4nkxK9V5BtG92veh1UrUAsLUG2WgvblbBSmZLjOsN57rShCAz7QLgr0O2O+YJ4HnS7Yx7L8cWF55mgcfpICvjwC96W+3w3EKXTsSAQVxoNp7lBIDk9Yp15G2xK338QgHoQGGTQxxD2N5g/AudlBQJAK/A+yEs19TOCWeiyViKBu60qmkFRfU04uNuqJrqr04IFkVFDzetaUGBc9rO/K2k7mbQc/16y9oIIS7FUkEPZSrEOiVSDJNMnIeBA4C6rimVhoyIkloWNu6xqrv0pAPzSs385U/1X5zRY7hUkBGStCtlomBX8IplBWmczjQJ11tOOIjDsB54H9qZ3Bey6swXuiDB3ZDhlhiFX1RCfV0PsaA+b2sV2YEgTrvgpYP+sCSdISYyiQBJH3o84PDKNBQSYO3tBllFLYfYAiFbz19UQV/UIw1gw08xQbCS8AeOGFl91CyJIEFaDVbcDs7LOJAlMfM4OBJbIQjUIbh5mS3xMvrfJc+61yBy/mss6c5fnsc4cGQNYk04i8Iaf8brM71HobtlQpfRwxkdkN3tg5KWJmKG2tk99cDjdibBjArteJsGBpDRFtTTY4eqyk/781IVO35drwCrKg9+TYrIjyNBGb2oXbfaxCgdWyiozdVKHSVkBQR9Dxhgqwop2K2nneczvR/WDx9UwaN4S8GFqHxpAGWZVnDaumrDQAqMZKKCm1iqCexHivCxHAYFhfCOu+ANcU0PcIStzF+6nnOWUh3NyWmI8/HtSWK+t/UBoUI79K2IF+NC7IWw8nGR/hcJ6mbTkGWvNJzy9gzJceCl7nb0SHo4LRARq1MHtjN+x70N3u6ammMMuPMkodgz7QRblM6R3TrIfiMZdl8yGNTEPYsU91hqq24V/cxP+5iZUr7/wVYmRERhB7exA7exAD0dzX6NEpjdhiazoS5XutDzeLzkQmceE41HBSj50EwtppIOMGkV8BTuJsBg8WVQesMKW9tAOgwIJ3GFlF0IZxks5XgOZvM6u9qLdQOhbHe4+ZLDzOCfLGLLG42ow8z6HlNvJdFIpI6iGqaxEag8MCeCJVg1nZDkSzHNZR8f02MeVIMW1qV2MgufCz2DIChvaRdZ+Yta3pbqs8LX//ArYmhi3EBCniO8vKxWIDGIIAHB/ALWzC7VxEzpLnPIEo9gx7ANULgHdbsqKnkwfgBDgasXsLITpGg4byiI2xjzXsc0kwMH2NJ6b5W4X2vMWxkxiZuh2GzyMmb2PXFC5PFNv6ZwsGalrxHoB1ABDVqjS9FeMYXj/FcoOCxwUb8P+hj4nHc4GbFa11ZQu4hCRo1rw/67255KDmPSMjkMzw2Udrf4nx+yxxqZ2gaAGwmBDt03ZOZUgIGGMhjYDD4bJzunJQLcibHS1HwVIOyUwAeYe7ygPrUCkkIJg1BLO1PE2BC75/am01hlZmgo820ET4q72ouAYvzftFBmOSXzdD17BnU/t4pdf8wxAB37j1crROrgtAGRbEVU1FcHjx+JQd0AUgWEfICEgV5YD79qQEicgl5ejD59SJLPZV9mWglMXIYjABYuHo9SCHY9GRjJ4EdQ4308EhegawyE4hUa4ebmEUV/iSV/gBvaUY7okYCwkH/cHuBhIKUxORk+wst26QunoJWGjxz52MzwBrqphpPaZlev3wPi8PzCr58wrjlEhmZpCCsfVD1JllRSJCAawyz6WhJ3QFcrbDRAIpWDVvqlHWA0oumk7EXM8UBMWRoEc9oA1ain9EwSgKqbvfVZaoyGshLdznSTqKZ9r6JOxpT3YEAnToD4r3EyR6e7tWHjfm+7B3/y/q2BNeOoLtvAtP/UIrGZz6thTBdvODwwxHLlD3QFRBIZ9ghwHcn1tvPq3LFN46vUBpUxgmGRD6Gw7TIRdyVqDSg5ErRYFmTwRLna9hQQGztnubj5zLLS39biDf/29X4Rrn65AWMBPvfsRXHjuNP1WwKQ5toOVcOK5YCWf1rUbTjISBEsIlFhgRdh4LJjcJ9FhHx3l4x6rCjnR8MZsUidPsKpgAD32A5poOmokjSx1Tk7YIYFt5WYnYRmoiOQOgTLerw/G+YnO77QgOokqCWwF/25rD80Ju9EwZZZmWJQGwnTar5HTWX5WluCyxpZ2cVO7cEjAY51aSNcKePM3Pw03Hy1DeeamffKjLTz6tw383B//Dn70eS+ZOb69IFQQ0P3+eDdSqx7Kaj001DI9Ezo/j5ahY3ZSUQSGAyA00wFiio5h4W84BLpdyNXW+EsZBI/Uc5XL2ZIWWTWJoHEnDcxsTNrlnDx+kX2Mez48J/D2b7sfW1dK0EoAI0D72WmcFWGjzypDJiM9/TH5nCACGGgGBevUoWNaVC88T7iuJwA1WLggBa6odJroek4KKTyfzcA5q4wt5WI1lqYJdwVrOf4SoaRFyARSzChTeuE4bwwlSAiMPRMu+300hROt3ne0h7b2cFegQDsLYdCcBwKm/wEEtISDDTVCO+e1n/rjFexcdaKgAABaCQx2JT7yyxehdttmMVQuLcSXQXe7CSVh7vehhsPk73CBIMuCXFs1PRieZxQM0o5zTkdhPcTcd4qIvpqI/jURPSP4/2sWNQgi+joi+kci+iwRvWFR5z1KqN12ctJnBrROaLGTEOnbSSKInK5PUSmnM1yJpr5wzAzV7UFtbEBtbkLd2IDqdGZPODlyAZ3ntAAAD/9FA52btgkKAf7gfc3MnbRDAsMcSuS8CJvbsjDP5Beep0TTbKMqSawLZy6HNyKCAE3Zb0by3xkpIA2zut9RLja1i8f8/pQcSPx8eTA1mvF7UDAaU5f8vvFXYI0nWjVITDfKTfaZGJaRj+FEF3Nbe6mfW/j+4vTUPCrFtc9U4bvT08yob+HDv3jRpCpdF7rTOTDNk5VKlZeH1vn9RQcEEUE4wS5/0iuFAraVfbrW4HsZ7XcD+D4AP0lELQDPWMQAiEgC+CUAXw3gCoCPE9EHmPkfFnH+o0CuFnsgdxF+4UW9BrasYKurI5GtPNVJkhJyZSVaXQEALMvUNCYZMf3BVGc19wfQJCBzxLxICIiVZejdduJxsbwMXTdfk/YNZypA/dH7V/D6X7gMK6XlQsGkNFqwYbOYyrlPjR3pXshAUsGzJWysCDO9j6CxERSp5wHDFGzd4AUXZDmqB8w7KRGQ2lGdN6kLGPZT6MFcz5Cxnvf6fkZCzALh7IT8NzBO0+1qD132I0vVLqtUZ7seK7isUcLsHWeVLHQydg3n7htAWjqxYwhGBO2L+H8B5Ztc/D7ZSRxKbKR9jq4Hrhq1Aoxcw4KqVqbqgHrkQne75vcsBKheg9zDeMTykqn9DYZgsDEiKi24we4IsJfA0GHmHQA/QkQPAHj2gsbwHACfZeaHAYCIfgvAiwGcmsCQC2aonZ0oh0+lEsRSA1Zlb4Je5DiQa6umCE2UGUg4oyWf+31wrZo/eZVKoPU1w6YCQE6Sg333l3SgJlZ/Wgu871fW8PL/6Wai78fQS815rvgDtIQTrbIZJvUjgskq/Blvaw9rKd29cVvOdeFgKZb/LkPiDlnBdpBrnxV8CIg0fupkJYrE8/549/MTJzITtlZDlEmgmaFUOqvZL6TCZtVJshr/AESssSFrDHk2hdLK2P1MIi+c3vXFHXgjGRxF+a/ggGixz8CQlypiQVCbWwkShxoOQUuNaOLXrgu9E1Mt1hrc7kAxQ85ZOCYy8ttYoGDfcWAvSbcPhv9g5jcAeM+CxnAHgMux/18JHjs1ICLAyehSpmRhl0cjqM2tfW2ZiQhkWfma9lndo/OuhokgSg5EaUxrDOUwmhdcPPeV1+BUxj8uq6Twod9uYld5UeewZsaO9iL5bA2T6ripXAgQbBIJYbst5eIRv5cQc5t6W2AIIBEUojHD7AKuqSEGWsFjjQ77UxabYVE2LJI2MhRhJ+U84o+H9yhNz2gWBBkKayvWA5F4jwEbKwyCacV5s1vIvm4oIT6JcMwrwkYlRkG1QGiQhVrKDiavxyQ6L0wx+l6rhrOyNLXj+/N/fyaw+KSJV2XgIHUAy8rpMRLpzL5ON/ocsyw4uds79Z3Me8XMHQMRvQ3Aa5n5d+OPM/M7Dm1U6eN4DYDXAEBZ5PsOHAfk0tK4+Bx+iaRIn6hZBz0Ch2BxaFnpaS05v5JnHr7lpx7BPV/awcd+/TyGHQvPeNFNvPB7Po8tobDjj2BBwMtof5os7oYNf2UhsKOACol0thKMRIMdsHcmQUFPwHVW6MUKyxYIq9JBDRY0TLCKO40x0rujNYzIXYUElgL5izS2UBbbKA95KSs3aHa7J8WrIbweYJrc2hm7hh4rLOfI2ZmJvAw/GEM5uKfhiB5Xg2hH1Z9TATb8TBuwULEkLsUkyx/7RB3+KGshE99FBOOrlKFHLtgdASRMX9CcDaERS2hCuVUsNfJ1mDzfLOwyNJyi3/QpSwcdBPOkkjoAPkBEr2DmPhF9LYD/hZm/fEFjeBzAnbH/XwweS4CZ3wXgXQCwbJ85ceGbpDTsBNc1dFXLMjaZKb0BYT4VWHxgkI36tIkPADHDxGdeEAHPfslNPPslN6ee08jW7rFyVrKVoHxZzuhFCHPhAulrTeZ0CWgfHKibJj+DKkk0hQ0r84fO0GA0hJ0ydU2Pf6/I0nCSMPdQBx4SWWCYe5VWGxgEyrU1ZPtj2CRg07iBLn7UBVnGI8HEflONULGqCc+HvLoKEUGwSdGF5j0Xn9rH3/1HFVl7hrDKGqWKRm/HCW8AqF4D9/oJerbq9SCWlyHmXESRlLBWW0YBVTNgme9UZvGZeczIkzJ9UUV0WwUFYI5UEjP/JIDfBPAxIvpTAK8HsEjm0McBPImI7iEiB8ArAXxggec/Mpg0TAmiWoV23fSgYA4E5OGwFMhxIFtNswISIjL8EZNsiSNGntLoPEJy52UZa8JBL0URlQFsq/TGQQsmfdIUNpbJwkVZwQVZRlVYcCa6l8P00TU1ipRbQ/XWwy4eEhEsEhAwtZY8JhcBmZIUAHBNjXBdDTMlvdP+PT43RQqpHhiP+n1saxeDOc2GQnlvb0QYdiWe+23XYDkM0HgslqNx51N7+Nm/fMj4lCwtQVk9mOMAACAASURBVK6vGZ2xNMn6dnvPqRySEmSP+zhEVo1AyogOnuW0RjNqc7ci5kklvRDA9wDoATgP4LuZ+R8XNQBm9onoBwF8GEZF+NeY+e8Xdf7jAHseOG/rKvId0lhr487mjkyhuVIx7IZ5i6O2feK6ShlGkqI+kdfXzKZhDOnmNeGEUA+F8QD0tI+qMITNEWts6FHqTmWJrEjtMzzjrBWvZk7k4I8SYVG+o30skwU7ZkIUHRPUISbppZPossI1NcJZWUpm92elhZCsLSgYEsEWPLSEnSiap6XXlGb8wa+fwbt/6k6wBs48cYBX/Mzn8KfvPYfPPrQEaTOe9eINvPyNj0BIQDhj2Ze8dA+73oFE9kSpBK5Vze8yHLMgyJWxS6IolcBLDXC3F0lrU62aGVTYdaF6vSBDYBt24Sk36Akxz7v4CQD/ipn/hIieBuC3iej1zPwfFzUIZv4QgA8t6nzHDZ1iCJ48IDtnyVob9kSsNsGdLrTvQy7laxaddNzQI4CAeuxrt6ndiMK5oUYoWxUITubiJ3sFasLCw34vd59hLCvzG9bSYKio+6eSxhHv5E4LdlOTKsykH3o+pL0mlPiYB1324foKd6ao3OYhq58jFBqsCglmoCUd2LFuc2ZGZ9vCe/+POyJ66rXP1PDeN9yHf/nhv0br4ig1K8PMhiadpyG2gA9E1uvgatUEGSEAe7ozXFYqQKWS+RmF0MNhgtrNagTlupDNptFQOuWYJ5X0Amb+k+DfnwTw9QB+5rAHdkuDGarXg+r1obZ3oNqdSKpbDwapBWseDE3eNP6YUlDdLtRuG3qQnjo4CSCYbtkzsgSfGZf9Ph7z+3jY7yWKwX6QuogXhbM6pNNUReOopYjczQsHtAeXhGBMMRZTqES6oz1cVUNs6BH8QKXUZY3rahgpn8aPv6FGxjs7o4sbMI5xcRe2Wcgq2GeBiHIprz6MdHcnUGHtsYre+2YHeO033YdhJ/l65RE+9u7zECIZFJQXiByO3HR72/i4FuRNQkIY4x4nXw4767sHBIEsjcHEDNWdU1L/hGPPoY2ZrwbppQIZEOUyVFoHZhz9QeIHqwYDiJUVwM0Q2SMCe37E0NCjEfTObvQ0D4dA34JsNU9UPlQAuGhVYENEchArwsZVNYSXUjw1aqz5OX3C4UnPEBEsCIxYoZzDyIkXYkM5agWGA4GRNjpCkbw3A7t6vBomAJ4/RFVINIQdrdCbwo5kM7LGBhjXOAVOiN5lIes25a2IKzSW0qgLQ4Dt6Wm5coWxqREAfOJPWti+OT2lKE/g6j+O0zH9XYnf/PH78ImPtKC8jZmF3Uiy/hjAzMaYS2ujfyZlpGqQigzLz9OGfe15mPnw+stvAZBtg6qV9Pb8HOh2G8jSVImxJ6Kt9yR8H7rfh9yn6XnYwR39CBagLbMi7CgoAGNq41lZitgvcViUzl6Kj5HBaEkbbe2nMnMAU69YnzG2LKrpLCZSCM2GvbSrvaiZbxaawkYrcLAL7244hgokmJAbHABzD5vCmSswZN0fIP99npGlhLT4qnCwrd3c93nHk3vw3ekzWiWFJz7LfF+Zgbe/6n5c/cfquBs6Z6crlpeOTWeIlZqioFOlAspREDhQH8YJwq3xLk4gZKMBudra2xdF61xang7TTjnS3WnS2fOAlYLa3ILa3oHebUNt3ITKEATbC+oZKp0CBCegsMbv0CBjIgt1fQBAkkBD2Dgvy1iPFUMtUDDxmlX4jcA6NPwTt+9UHLi1pUxKAkGdIWdy1gA+5/fwiN+fOyg0yIqa2yRNm/zEqaOz0oKT3yobhJawsSqcKd/lz6tB9J7jpkfX/WzmUi1mLBT+aQkHd8gylgN70km3ttU7R3j6124lGiBJMEoVja/4Z9cAAJf+uo4bD1dSJDJSQDStPXSEUDs7ZmcQu0ccSGpkyXacJmntPJz+KskJBlkWZKsFtbtjtpjhJECUuRUlyzaaRe1ORq1hYGoNWRPHPnbczGx6HyZrGL0e9AELacycOaYzsoRysCodQeO6P8IoxYDHTOYackKfyJjb29jVHkokcSbGQGoKIwT3qN+PBPiGWsEmAR10FxOAJwiJScnq8Nx572k0EcAsGJqmmyE/DQBNmS1lHYKI4AbjzDvSB2OJLCxLEwTj5fIV2GhrDxuBGu2QNR7xe2ayB6HPChq8rzpMNfC+AMyOw4fG4/4wes+vfsun8Ye/cgf++D3nMepJfNHzt/HiNzyKxqpJsWxcqoBojjwgAeI4U0i+Avz0RYoe9CGbTbPYGA4jfSaq1Yzg5S2AIjAcMkgKWK2w4UYDlgU9GIDTile2DZICJEugNcd0cKZ5Mbhu5N0wCVHZx4pFqcyuz72mwxJjgenmdWICesB4NRxvaCuxwEWrgkt+D1fVEMvCxpIw2feO9iFAaGYUnGvCmnI/Ixj5jA77iQJ3nObJAB7zB2gKGzVhQTHDZ52r5AqYybsck74OHezC1EyfFa6p4VR4sOfYoGtmjKDhMaOe4n4HjHdPWayr+HsP3y8DEftrJdhZcOx8kwwoDUyppsbZYeF1LBa4YJXxWMCUkhbwNT/wOL7mB6Z6VAEAF57cg1YZk321AiFltFM4VsezkDWYtgDT5n7J5SVwo25+hwtSFjgpKALDEYHijTSVCrTnB6uN4AAhExaaRGRcTlJPRhD1mtF+j+c/y+X9yWwEnO20H0H/zv2xQaokcV6a1RNhTN00fxgEmpLHIDaubaHO0m5sQl/J6ULOEoAI2VB5gnEKbHScgtV1gyzU5hDgZhhpiQqZABGfFKqQOCNKuKHHqq9pjm9Z2FYeJFHkE512bZtE7u4j672XSWA1xUI0DA7h5zTvlCyI4LDAvVbN+EErN7P7HQAuPqWPe5/dxuceWgrE9aITQcbMqY4dVo7acew3RqHB1i2GIjAcA6LVRr0G9jyQkKmcarJss6WdBBtnKlkum07RsFi83+Ya20YWf6X7zL03yhFMp3Ka9POWdsFgrInpACaIokne9CE40ao5z0hmyBqLUs/qso81OIn0UlqRWsBQYtNWiSKgfNaFFRnZLM/YhTAzPGhcV0GzHpsej1BtNs6ACq1FZyHtE10W02E0rc7hsTaOeCnHTCJ8TQUSd1oVPOb3IwaT8oG//6MWNi+XcOdTe7j3OW187//1KXzorXfhz377LHrbjpGeb9RPTlBAoGKwvJRg/gGInBb9rS2AjbbTXppPTwuKwHCMIClzBcJErQo1Gk2t5KlSHntLL6A4R0SgegPcmeBgS4ndr5zF7ZlGhWS62B1M/8FuRrHW0D7NavNOq2L0lYIfXA2WyWNzcsK7qoYYscJ6iu4UAwmfAAmCTQQv6B5OAwO47A9wVpYiDacszDNZrssSPMWpvQmT143n6oGxE9tK4CGtgh4CBlCeISzHMCm4SeQp2MZhgXDFH6AijK6U4Pz3CyASRWxJB9fVCNtXHbz5ZU9Df9eC8gjSMqmkH3rv3+PFb3gUL37Do3jtc146cyzHBVEqgVZb0P2gt8i2wa6bSANzpws1GiU6qG8FFIHhBIMsy6hFdrqA5xnjkGolW/flAJDVCti2jNGPViCnBFGtgCt7L1Bm/TzMxBH6AZg+gXjR1w+Mfeo03eQVCrRdV8MoUTHg8fR+TQ1xTiYLf9vajQJN6OMQpqN67KOvFZrSgQRhyAo3gzSID8bjahj5F4Svjb+veScBQy210dV+wvshjkk58MRzQMB68iYe56nJOkwDAcCWdvH/t3fmwbJcd33//s7p7tnv/t6T/CRLlhGObCyWgIgNBMoL2ME2EUuKEEgIEFMFVFlgICYmAZI4ocplEgJx2LMAMQaDMJQLjM1iCoxt5BUJY0W2rF1667139p4+55c/TnfP9Ex3z9x17n3v96kC692Z29N37tzzO+e3fL957tbtkvuY5rxXwwgWT0cDnPOq7vXix4qUZZP6CwD8nx+4HTvPBKnjXxQCjz3QxB/89M34ujc8Mvf1TwLkedArzvOcRyOYTk5tMBwdWLLjpCGB4YRDvg9v43h0j8j3oVcPPmGadPxMk9hIAsCTZoB15acLbsdGuGxDMICAKDfHreBy63n9+102+GzUyx3IWotfZ3IxbMBDU4/Td/U4DfJY1EMYf1+yyF60IdpxwLIAwIx1HSy0uAJuNmOXI6ywj0o805EU4BnuZHC5wM+6iMejPm7U1dSm1MC9txEsutYUdka1OcJqzn1MB5jkxBOwwg1eDQ9HXdTjzi+FgqJs/L0hWwy7Cp/565WMDSwAREOFD/722WMNDByOnKIAs6vDTXiN7O06xb8j2+1CSWAQhGIYbmd/brL4DJe/TwauGG4nnDcDEBZ0xVi4LqciDDgzYZywnjNLMWP2E6dBbvJqULEkxq4d4VIcrCZdzwjAGoLMjrnQrpQZvbiJ4HHTR5M8NJWGYXevZYXaMiIwHjP9VM58eiq5jMdN3xXZlYZlhiaFevxu09TPkDQFNMlDS3m5Gk6Zf8Od1KylwrFrG7nnT6eROGmkSK6XaCd5s/W3RTHdrhPFS15jOHQ1jf20wpbVQJLp6BNUJzkIEhiEI6HDBoN4hsD1zs8azhd/rysAT6ZKElXRbskkbxGLOJEBcVMAxq2YK8qHTwpPmqwooqtD9HCDrqZDXgO26HGE9YmOn2QyevKE0+EIHXN4sgmLSJbn0eYI7Yn78EDYnLBfnYTgHPLqOcV2mjh1RHB6TwO2qLWAm17QxSOfaAI8/h7tW3zh116aCQqm23Ne5clJhJLthPtetbaaKrEuChuTCQrp18MQ5uo2SMVtsdXqQkHClgyWgsgFsiVNaR82EhiEIyOKXdP2wxNTqZIeR3jGlKdbCMBWXA9w32Nw0QzRjwfm9rpDVORMhHzQzI58FO/Yk/1hEvIGbLEWmwD1rMFVOyr1ojgpVEgVzm/MO6lZAA/nqN1+21v+H37qG16IKFQI+xqVRoSVMyP8vn01NvmR1L/chiF4OnefpqpiocHtbdDW1p525ByGxbMII/cb5WEI9PtOFbVs0n04BMpUk5mvqbZVCQynDDYGHBmQV97RdFgkfs+HyYbysabckj+ExcV4lwmM+++3tBPMSHakNfIQUFQomQEA53UNlYn+/gY0ql4NT0YD1LyayzGTE/JL9qPz6gTJzECe4B+AmURQjw16RRaRJwCFRKjQBVsLd1q4Iae9GHCnHganacHp1JFlRsfmJ7JuuL2Pn/iLD+O+d57Bhc9WccudHXz+Ky7j9XfdCLO7WyxElwc7hWFq7KHxYtGNQDxTVCRzAbjXLo3vnnfNeDEAEhhODcwMu72T2QVRpeJExk5Rm1zS4ZMsQlVonNc1PBb1oYnwLF2d6Z1PCqHndCXjJ+zDiclVlcKIORMUku9TDFSVxiNRD+vKR5U0Qra4asN4unpcbJ/OrydfK9ot+yBUSSMClwask0KDNG7Q1XR9I1RwwQwLLU45ngT3aHZynYic/lI8IFhEbcWkOkkA8LovejXs9pV93T/vJZAgaeVeTAabB0OgJDCUCf1BKei11T3d20lHAsMpwbbb466IpKNlOITtdqGbhzXedbQoYKY7CHCL74b20aBin2LA9eBrEAxcELhJuz9kRYSgYDuniFCFwg441Q5KuBhPPBMAAuEWrw41JacNALd6dbcAmjCdizinK2hOyGAYMB6P+vvO+R81Csg9FZzVlXgSfZbktJRXVzBsccGE6PLiP/E9d90N29+nXwHRnlVWiQh6bdVJywBzF/dSKkG+PA1iTadjOL0fJ9dOUuwahpndUTbvsQNoGR03PvJNY4iocChumsTr+IyqpKqfyTVyJbSZ0VAezqgg98POcOkgA2cg1GOTKpACSP2ePVI4qytokYcV8tysBY09oTUolQA5THwQKlOCejXSWFXejIpqGUW6SwTMiBMuBqGzx6AAADD768KC74GCvbdSUxBAn9mCWl2BWlkpHLJRZacFd6XCR+aZDJ1G5MRw2pkjz3ySGMHm70yZMWKLSonMg2WniJr8tEWL4nQLqes0cieVuvLwaNQrXMxGYDxpBvBBOO/V4E+9hiLClg7S/55+LIBKTzTTJM5rSVG6PWdR1XBptSB2YCM469O1uFMqIWSLJ0wfASkY5sK21aKFv8yIaNeO0FTejHIUx8ZEi/LPfuIHcQafdv8IfGCRhTSxe1MEVa25af99pkyJxvLd5Hkw21fH9QJmULM5N+gkNalcTtHf4KJIYDgFEBHgefmeuPvYRQHxKWQwBA8HACmoeu3Q7BOLsHCzAdPpJAZw2YzwrBzhsmSad8gGz5gB6qTTQmgReTMFigiaXWF7t0B3qUoKN+iq69UvuPY8KTwFYHrJTAQFkxpGgzysI8BjUa9wiuFZuupOChM/QyorPvG1ChRu8xqwQDzPYPHklLQGAPRsBKjFUzHJ7EbPDDMpqOT3cWlOh9gkZ9716fS/Va3mfD7mLaZxPYFqTaj6vN384pDvQW9tOU8Ty87ic4FuIqoEheUKtUTPiKNCUkknFBv3WkeXL8O0O1B5rlFE0K3Wnq/t/Beuwu7uOr/dwQDmylWY7qyj2tf8yev2c/uFXLQhrtgQUazoObAGT5g++jC4YsPUUAcYC8Y9FvXwuBmk6RqvIG0EjFNKeY9rotT/YeYxEM7rWqpaOu/6eRjk79iThTX5PkUED8XezQEUgoL0Tl6+nyZSWgGcfPma8jMObCMwrtowVVKdawREhOHlCn7iNS/AD33Dc/HRv2hg+4rCbmTi6fDFUkLT8wpEBNrDQs+dTuZe2RiYdgfRlaswu7upV/peICKoIHDezwu2mJLWoBxnRKpWYxHKaws5MZxATK+XFeqKeuCBgtpYd0Y9UeQMfRp1kNbg0Qi2243bWD2oZqO0dY4Hg1xvWu50wBMCfQBwxxseBT50uD/fVTvKlbW4akfoscEKuXneyUlpADN6Rel9F0wdT2NjBdM8Vuaon86DmXHJzKZIgoLxukSBNe990ESFtZh5EBE8dlacgDtBPB71YTH2oljkOpFlvPO/3ITPfsxtPN7wT1rwqwYveMlVfNf/+NTc7wdmg0KCqlRyNyG5ELnPauCDo2hstYlYu2gwgF5b31f9Ya/oZgNcCWJ5jVjmPvBPVVfgokhgOGEw8+ywDwBYCx4MoVdWsl8ehrDb2+PvNwYmHLqBnYKdTKH9J5ETA9uPp8MhMWSLiwX+CUUnhSSNMu/P08lcZANisnNvqvKOqCJ/6EkCUsBU7t2WVBJswUPDAq2pRZm0CPVZYSv2hlhXxYvY5M9nmTHoKvzJvWuZ54wGGvf/8TquPBFg43z+7+jSoxXc+6Zb8fF3nwHoIqhWg2o0sq+rncx83uYk58ZSr3Pb7symoBgwu7vwtjbnX+sQIN+HvgZPCNNIKumkEUUoWuI4nF3Q7e5uzhPhFFmLKDs+q9nXPux00n7pWVPo0ZxHkjKxsRXnE6afKQxXSeEWr4415bsib0l6ZRHJ6bz6QwQnKjd9bctcLD8Op45adD+TqbZFUkJ5MhezrzlOMfXZ4D+97jz67dnv8wLGxYfzU0GdKx5+/B9+CT7+h1tuAbcM7vZgd8aeBhyOYC5dng0KRZ9Jfzw4xqOCuoYx49/1cOgcEk/wkOFpQE4MJw2ligtzU388ZjAonh4t0XVRtZr73mkIuaeMo0gn7YcOR9hEAJqSm04E8CZbTNPFE2MxvGnOTlljFklJL0LSNZXHU2aA814NHo/Vf3btCB2O0CIPdaURxYEiKRpftSM0ycutiTCAjhmhzwZrOoDH8ye4AXdaquXoHTEznjHDjA5V49wQyrOw0ZQ6aqhw9rnZFunvfvBbUPvWflxUztEmGoauFqA1zM5O/ud78nOcyFj4HvTaxKmFFFAwbMijKD45x9dmuNNKq1nug5H4p19j1pwHRQLDCYO0dsWsnIV92och1zc6fXLxqYACH9RsupRV8sdAWNhspH3Zw4d/7wx2nvFx+4t28fe+YvtYZGIYrj3Tz9kBW7h2zgoprE2kTDSca5kC4YIdn7gUgLzJhklRuOQ1gfKF18btm0UCfxEYj0Q9VEnBg8KADQwYN08Uuy0z1pSPp8wgDTBXbIgbKDuUlogJPh3/LO0ocqkw8tww2tS9MnPqftfmCCvsoRbrRiU/Z8dGM/f+ku96Eh98xzkMo7GQnV81eOHLr2D9xnGQveeuu1FDHCiKNiNEri7GmCuDQfU6VLUCkAJNdalRreaE9qapVNypZCrgcL8PrgS5ZlZsjBt8S4rX5BzbrsUOo/0ggeEEkk5rjkZj+YtmM/OhZebSP7J5nR+6UQfXquBwBChy1qALBIWHPrSCt/6L54MtMBoovO9/W9xyZwff+6sPwAuOvp/bgAt39SPYTCdOQpJOuWyHaStp2Z1GcIVkTQp9a1BTLk8/ueDa2HfAxj4IRS2wk7jir/udrcfzCMk1k/89pyt4OJb96LJJ23uTe2YwnozGpz13Ioqw4vlpnSV5f2wcRC5OtJY+YQYuMFyp4fKjVbz/zxq4+a4d3P6iYUZaaOvZQ3z/b/0NfuvHnoOHP7KCSsPgy7/1KbzqBx5LTwgzeF7+dDAzSHvzi0CIZbFb+ZP8qlGHNSb2So9PFUEAVa/BbuenmbjXB6YWe2Z2RezJv59YcoY2N2cC0vWIBIYTCCkFb2PdHXOtzdWjJ6Ji5UjMni6KXmfRQvM9d92Nn/rAvfjl73kewt74DyfsaTzy8Sb+8jfO4Sv/+dMlVzgcduzIGexMfI1jeesBW5wtaPNkAB4pmDgVwXALbx16ZsHftiN02ECxgQdKC9abqhLPKTAum3ChYFBEq6DYreBaTpN20Is2xFU7Qo00bIHs+BkdwM/xSTBwlqI3ezXsTnhf/PIbbsV97zyDsOdOTEHtWXjxNz+Db/zxhzPXvekFXXz/O+7PfC1zQpi+93oNJjbEyeD7IN9zmxmlyk8Nc+o8qVd6FLkWUs8rNdDJq8HwaFSYkrL9PnRBYLqekMBwgiGtXQdH0eP1eu7RmprledX98v1//1XwBhdnvh72NT70jrPHEhgGbHHJhthSQUan6KIZYksFAMe2lzl6TCN2k9cqnk5+xgxwXtcQYDxd3OUIO3aEG3QFjQkZics2xGeibuEEbAUKZ3QFVVKwALZtmGtClGDHGZoZeOoVInDGu3qaJuVvHDweF8XXVYAKaXzgox7u+90zCPsTwb2v8ZdvO4cv+5ancePn5i/6i3gzk9bOina3naZoqFKBiq0xU+2iK1eLL7LIsNm0V3pJl1DuxsckfWw5v0kpWgOQwHBsMLP7Y1Hq0AS3VKMOy3asl0RxjvYQJ0WzEPqjACpnFkB5xycLsGNHaNsRqvEuuh6rhhaFQhvLO2xOeDVYAJfMEI+ZPipQ8IkwZIsRGOfioDAZXDZVgBHb3B17AHLObxN1jXUVwIfCMza/NXjIBlVWM7v8Eeye3NjKmJ78rkPjmb9pYTScXXytITzwp+szgWEyICQOa2VDYeT78DY3CmdLyPehtjZhL13O/f79fHaJCKrVynboEQDPy9VAIt8vPJlcS77NB0ECwzFger14NiHptvCh11YPbANI8eQzN5vueK72I4a2BzwNlbOhCmoGL/7mZ47udXOwcN4HPijXfzlZmAyAHRvCA6E1IcWh4LqSjHHdRMN4nVBAKpA3iSLChgoQmkG6cCs46Yq8ziFFhKbycMmGM9pJhHhYL+d3NemqFoBg4KayFbkZD4rvOxHF67NBn01aUC6DAdxw0wjKY5gw+1ztMSqNccDPBARj3ZR8krLxPOjVlXL/AWthe323A/d9qInBSaU1eKUF3p3SmPA9N0m8D1StCngattN1tTmlCq9FngZVq65WMYnW+379aw0JDEeMHYYT3UPxAjEawWzvwNtYL/w+ZgaHI/fhZXYiYkGBibm17v+SusMRkaYCrm4DzGAPqPgGd3zVVXzpN1w4stcto1HSo385zs8rAM/xGoWLfc+Md8llNqAVUni2V4eFa+88pyszHsiTONlq51swSZGSLBFhTfsYGYuzibZS/FiS/LBx3SB5zRo0DJxHtiooyk9y4wvbUIpn9JyYgS945SUneDehbZTIp2RSLPEEst7azN3ccDiC2d4e78qHQ5heD3pzI32+rtVgPQ+22wOMcZ/tRn1PG5vJUwmzm5lIi9/GgNsdmGHoNmHTv/uVFjjwXfCyFqhWoKcH8a5jJDAcMbaX014HOGtBYwrTSrbdAffHCxYPh6BqFXp1PPnM1sLs7Lo/hrR7qQGdo+lyWJDvQ5/ZctPT1sIEPh74yI34gRc9H//1Q/ce2esWUZR04YnHyoTvpk1qopkMf3y9eNFNahSTonhFuLrG7NVsgf9Bcq/ncnwTkk9J3gyHYtfauqGCwusyM3a3Ff7bd92OzWcPcPHhGvyKm8tmQ4gqG/i3r3z1WAU1+b5wBNicYyKzK9ROfdaYGWY3Z1bBWthOJ53cZ2Y3yRxFbpNhDHg4AK2vz021sjGwu2N/EqpUQLVqvvx1GIJHoxkvByJytQmKZ4F6fVhmqGbzwCf5awEJDEdNkf48kZO5UGqm9sBRlAkKCTwYgCdUUO3u7niHlOjHdLqwWkMd4ZGYiEC12etPph8+8z2fg9/79jcf2T0kdGzkis4FjwFOQK5osZ+Wj67G8tUK4wU4rz123r7SMqPDUa4E94DjBblgh7/XPWsiyjdkiwpm3dYA4P776vjJ77kFl55wxVi/ZvDsz2/joftvBdVnU3EpxhRH37xCrbWFn3kejruH0pTP+EYBwzC7bXjraznfHT/NWpgrVzJ6IjwclncmDUNgKjC461zNBDDuD2AiU3qSv16QwHDEUBDkLvIAw4Qj4Gqsc5RMeq6uZf6AprHDIbTvg60tfJ7t9o4kMLC1LohNd4XkcNtbH8I9bx0HiqM6TZiJtM4kF8wwIz19Oe5kmpaPvjLR418lhfO6lruwTjM9CDdJ8vU6RM13bgAAG/VJREFUaWypwKnGTj3nqgmxqfMDWukpJE8rihlDtrhsQ5zTVdTj84UF44IZ4kPvXcGvfN9tGPbGf+6jvsaDf7kOvVlemyDPK2yLztXiKkvFTDzEgwKDqTBEdPESEG8+VD2bXnIn1Zz3vazNVecU23s5bbWAO8mPRkcuQX/SWWpgIKJvAvDjAO4AcBcz37fM+zkKVKPu5CemPoRUrc2K5Y0imO3tUjcpSkxaynrBowjRhYsu0DSbB/6QMzNsp+O6nyYGi/TqysLH7snTxGEHiQ5H6EVR2l7ajU3uJ9mxIxi22NABNAgDtrhsQoQTwWNTze6ciwKA5eJ00GTKaVX5qCuNR6PsQrjNI6wjwHR4TU4SRTv46W6fZIajzRE+9ocb+P0334JB28Mtz+/iy7/zcdzxFT088GdrmaCQuV44Ki8i+54bXJueai4o7pJShZP7NPm5Lmu8ij/b3OnCjkYZWQyOiluAcyHkF5SLrkPkUrwSGJbK/QC+HsDPL/k+jgzSGnpzA7bbc8ddraDqjYJTBNyOvGTyMu3L1rp0wA3MQDhyRcKNYqXVRbD9/rglNnm9MITdbe/LBP0oUk4WKO31B4AOG3SiYivUoMQq004s1pYZETj2oC5HEcFjhQbpTKsrA3gi6uNGr5rWQCwYT5sBbtBVIEf/KJENH1iDZixB3uUIF02IP/6lG3Dvm54Dtu5n2H4mwAPvX8Frf/Hv0NoagX2AptdColzRxOxTCHp9DbbTdbt8jmcTSjSI9OqKa1BINi/M7nsmhi6pUpntCsqBhyF4FIF8t1SR7+fb3BKBmg1XgJ74myjs/vN8IO/EnUxpX+fQPHXGY7kJoj8D8IOLnhhW/bP84q1vOtqbOmKiK1fyZYeJoNZWXXFvZ6IvmxlqZcW15cWYfn+25S8HCgLokrzt3Hu9dLlw8EefPXOonRzLKGAn3KRrqKnZpd6yG6Bb1U5zqWMjXLUhmuThrK4s9PNfMSEu5wj5AWPNpiThpAGsqQBN5aUpL4Y7Ge3Y2SmH3q7Gv77zS8E8ex/D81XUhw2naDoNEfSZrSPpxGHmuMHCgiYUUtPHrYW5fGWudhLgOoiSUzQzu59l+vs8Db2x4f470T/KUQzIvP6ly7lT2tdyjeEPn37rh5n5i+c9T0LjkqAgAOcFBmaQ5zm5ijNb7pTByLUg1LUaWGt3GjFRcdFvHy5XGcr+eK0tnc7eK0eZcprHFRvixinROsvO+WyXI+xOvY+7HKFiFVZLfA6Sa4wKJBgAzLihGbiaSFEgmeavfvNs4cGx8sQAdG4Fam01u9GIW4+Pqj2TiICguEuKlILe2nQNFaMRODL5InxEmWloIoLeWIdtt9MaG1Wr2RPMAqdjUgp6Yx2m3QZCp0mWXEc4hsBARO8FcEPOQ29k5nfu4TqvBfBaAKiq0//LU/U6TH9WNpuajTQATJqYF0FBAB0Ebid1YVauwr3YYnUAG4bgbg9sDSioOIc4pVwQy2sFVGrha++HySDxyZ98Nt79kp8+stcC3MDc02aAM7oCD7SQtMVF66oUq8oHY+wNMVkDYMxPc+0Va4HdiwFqrQhPP1hHYS9TMtBXqYDObLlTajwVvOyefdfdVgNqNad2evnybO2BaLbVVOusHPd+X9/z4K1fu6eDgyCppCXC1rrd/nAIKAXVqB9I9tfs7ubmX9Xa6tzrTtuJum8k6I1NgNm1CE59Vha57lFxkNPEzjM+di8GOPfcPoJa/k6+SBOpCA2gQhrMjM1YMwlw08pPT0xLHwYfedcmfuvf3YZBW4OZcP6ODh57oDnjnQDATROTcrWtWu1EF1U5HLkZiOTk63vQq6uHJiEjSCrpVEBKOSXHQzq+qlYLFjQubBOBWs25i7ezE80ZxLMuIFC9BrW+Bu67Yz9p7U4TS1xkJk8T/V+r4ec/9//O/Z5BR+N/ft/n4lPvX4PnW1hD+NrXP4KX/qunZp6712XcAKmPwuOmn54c5mfQ98an/7qFX3397RhNiOA98vGV4m8YjZt2TX8AWmlBBcFYbr1omn4JUOBDb26Op/gZ4HAIjk/OJ+U+rweW3a56N4CfAXAGwLuI6GPM/DXLvKfTDBFBr7TArabb3VOxXEOGshqEteBOF6z6rrvpBO7eat/axz1wgWI65WQi4BPv3sTfvm8ND31wBVeeqMKMFKJYSO5db7kFZ24Z4s6vvnKo93TYASHh3T9zM0b9IhvMiTZRrXMbBni3PZbDSET/1tfTrp+jgKMonvL35nodEJFzeuv2skZSaEOtrkKJyN2xsNTAwMz3AlheG8o1SurVsChldqIJ1sJ2uhlJjpPIHW94NA0SzIybz38aT32qgWFPA6m49piwr/Genzt/6IHhqLj0aAW59QQi6NZ4ZiW6cgUzgkjTJEKD29tO92jBzwwzu4LxYOi66GrV3FkIZnaGU5OSLZUAarW86M2j0XjGZ+JzaXd2QEfURSVkkVSSUGonOkluAfoIYGtdqoNwoFSH7fXx2Y9OBrL86+xeOPgu9OmHanjw/auor0W48+VXCmsXB+Geu+522ljI6eNndoNoMURq8XQYsytKB/NTg8zslFYH48+C6fVArSb0lDmUbbdnJVuGodtglKRPbcl8Q6IZJhwtEhgEAFN2okUcw07N9gdOA2rSS2CfXrw8nD9ApbTF8758e8/XTl+Dgbf/6G344DvOxtdjvP3fPBff+2sP4NYvKPHk3gOT9RTVqMMMh7OT9FPKoFSvleoHTcMlk9yZ54VhJiikX293wNWxtDYz5w+iAa4GVlZXKzu9noBmmesBkREUACC1E9Vbm04GIe85JVIdh4FTzYx77WNTmMSLlxcYhJphgUBmrcYH3/OC3McuP1bB237kNrzpq78Av/jdz8MjH59dzD7xRxv40O+cxWigMRpoDLse+m0PP/+dd+SKku6Fe+66e8Y5jTzP1Xoqgfv5tHaDj43sbl1VKqDka0lqsfD9YNACpwUAuUEhfaxE4yv7xPLFnSoFJwLmmdbVuS9lDMz2NqILFxBduAjT7hRqXAlj5MQgZEh6xMca/BO54cZ8H+n9wMywvT641yt+zmAI2qO7l6rVYMOCE5DWoMCHajRAWs8M1j3z6Rre/Jo7EQ4UbKTw1IN1/O371vEdP/spvPBlY2vK97/tXMYDOyHsK3z2oy3c9sXzJ9OnmfZEmIY8b6E+ft1sgut1p4ekCOx5sNvbMxP31Gotnq4rFojKPEZELrWV19gwp5uNAt9JZkylLqnZ3FPzQzpdnQYCBvd6sFF0ICWA6wEJDMIMbip0w7U6WuMmscuE1g5AagSTNwWefeKer53q9CcpDXL/b14Xzj133Q2zvZ3dATNh1Nd4+48+F5/30vvSzXcU5h+6iQATLbbYfs2fvA53vOHR9N/Tngjz4HAE04m9DTwPqtmAinfWpFSqr0UAaH3dyVQPXBuoqlXT5y6CqtXcYGYO08OYeqWVmjqNn+Q658ogIqjVFZe2GsYF7mp1z+3RRQqqHIbgKDqyz/S1gLwzQi5O0sAH4WhnFXgYAtH8nMt+vHhd++6K2zUndo9TxWxmdgXRfi8+GVWh6jVX/M5h53KAzmUfrS33+Jd8/QU8/JEWwv7sTvbWL8yeFt6x+0X4i5fdMvO8O/DozNcWxYYh7NWJGslo5P5dMHyY7OSt6QKRgR0MYBex6ky+3/edWN3k3Au5OlCev7Pe3IDt9eKF2Ieq1xba9adT/wcZoCyqlxEkMMxB3hmhEOf54I7zVKkcibMVh7OF1AzkJMoBwOzsuj9o33cDdgumFcpOPDNOeaOOk0kvUK7lkPCjr/zadBF8y/vvxX2/ewYPf3gFw56G9i0i6yH0NvFDX/GPF7q/g2Cnp9Unvp4XGPIMalKrzgVbQXWjAa5WXVAnAlWCws8GaQ3dKj8hHBmeN+6KmoRxIudxThISGIRc0u4gwC2Su+3clsQDk6NmmuJ7UI0mGOxyxTEcRTCDgSvCHmDXx8bky59HEVCtADmFVqpWM4vn6198tzt1BCGIQ1hF8GqL7YoPhaLhRGNyHeJ4UBSI2dVxcpz58iCt91zzOW5UvQbTz0kn+d6JlgY5CUhXkjADWzsOCsC4B73dObhS6xSqaCGiuBYQ+LMaTvE9Fe2WF4VjVc38BzFe+OLnUCWAysmPExFUpQK90nLGSIcYFNhacGSKO2mKTnEFU+9c1CrFyPd2nrgP2+87b479dIgtAdLaFZkn5zsqlUMR4LvWkRODMENZS6IdDKCb5dpObK0bUorTPtO77ElI66wkNDOgFPTamnNPs7bQC4LnDOTNpcSkhrSCbrXAjYZ7/QlP7uOAYz+OtDOHCNRsQk/t0qlRzw2cVNBBRr6fP/hGVNgtlPH9IMSnx9bMvZxEyPfhbW7MON8J5UhgEHIoGzCa853GZMzauT8AOt1SnaVUEjqKABDgTfgQl3oIH+yPvLQnnlSs76MPJC3OxsD2+4CxLhe/oBhcJigAADO43YbVKlM7ULUarHVtmE4fC6BaPeOWlvmxgiC/jdTTuekVNiZrBhX//rndBleCU5Orl4CwNyQwCDNQUAGQn6ZR1fIuEbvbnjVrtxa23S49wtPEjpWthZkwYikShDtwjjsZost7qNuF6XZBjfrcE1IRdjiE3d4ZX3MwAJIBtTJjn36/UH7EdnuZwEBE0M0GuFF3qqRKlV47NbrpdtM2XqpV3TxHXuqpRAbFdrvQKydbO0vYHxIYhBnsKH+Clebo+TNzoQzDolOxzJxv+ajIBZxEjK1WLdwVL4otSZml99PtwfrBnlU9k1TQDFEE2+tDF6R6TLebL4GePiE/rZaoki6CCyZNYIGAVzYlvPCks3DqkMAgZGBrC32kac5p4VBefzDragcAsAyVWFHG1qc8GrnJZkUz7bRpkIoiQGlQdZzCcYv2zuLBqt8D9jpHUVKk58EAyAkMhb4YE+xVEuKgkFdQkwCcJPukr/MpSSsJ85HAIGSwZamDnV2g2SgsJhMRqFrJLV4vqohZNFjmbsCCajV3qtjZdQts+gId52Gc2JxeuerMGBKl7Tal7a2TfsEL3dO+tHXy5yDcQwWpnigqnJ9Ivu+oZEmKmKehZK5eRSqbUq9BNZuSz78GkHZVIcsc0x6724a5fKWwZVG1Wi6lMVk89vTiJutFu05COvPgJB2mZBli7X9mhu103c8xlshxj+/slqp+FjHPdzsXTwM6/89LFdVGynwxiA48t7EfiKiwwwlA+t4CAPeKayPC6UICg5BlkXSAMbDdfME7Ugp6c8O5bbWaUGur0BsbC09Nq1otf0etVLp7LV3YR6PZoJEQO4ntCc9z97RHiMgV26fF5WrVwkBDWgMFqSK1tro0CQfVaICazXF7b8lnxE4IIbK1MN0ezG4btj8QVdNThKSShAwqqMAUdCRNwsNBoaY+xTIJwOwilwxK8TAEKTXjHU1aQa+vOUOaZBH3faflk9QISnpm5y09NgyLu5yCAKrZdLpJll3dorp/r2HyPOgzWy5tZa1TDZ2zuOvVFdeqmriewamK7kXo7rAhIlcsj08OPIpcCilvoU/alGOZjfQ0MRgA3bht+QikVYTDRQKDkIE87fL4eVIR2Wfu+dqpDHKchmIAZjicGZYi34e3tZmmq6YXElUtkNNmdgt/pQIU3X+vD9VqOfnpzI9DUK2mW8z9w2vBTOouCz9fucCY6FTZ/sC1zoYhVLNxMoTfSnybk5/V7OxmAwezO2l2unPVVYXlcwI+ZcJJQ7Wa4MB3ssUF08X7mSGwvV5uxxG324i63VjdtOIWQK2LhdmqFWAQ5AukdXulE80wBhT4cS9/zw2x+V7qyzAPZnbfF6erqFp1p55DLrja4TDTHcbDIUwYpnUGNga203HFekVQ9caBTjd7gYigVlqz7bhaQdXrLqAX1Kp4MAAkMJx4JDAIM7hdbhWqWo0nma8CHC/o7Iqx+8m7l3YCJaeIwcAtgJsldQnLQFTWvVSm1krpMJ1eW3WvyQyMRjC9HoA55vZXtzPBMtnN6/W1Q1uUXdtqvj6U2W1Dr65kDWgsYHd3QWb/w3h7RVXde2R7PcBYIAjc+6ZUuZaSdCydCiQwCKWQ1tBbm25nao3TPtpvOqNsJz9JXIfQjUb+w+3d8sUfcB0+OQsUTV3Tmdu3MwVr0+s5XaIkp84MDkdOQDDvBBVFziVtH54RuTAX/3yjkfPmzpME7/bA9fqx5fDJ83Inn0kpN8We814tqt4qLBcJDMJcxsXkg6HqddhwZ/4TASAcATlxITHWmYvnOWXWbqIh5NouZ1pFC7qYuNMBVyuwYQhut1E6lxCfOPY8BFfEvF11UUsxUSr4t1dSe9XYi0LVagdKTenVlXHxOdZwguesVIWTjwQG4dhQlQq40QB3u+WDXEBGKnlfr1WvQ1UCcL0+Dgw5i1yhLAbFmkVpW+6cE0rBzMJ+IKKsJemixMq0eyXPXtVGI1BYhV7dXyF+fNIMXV3H8wDfl+G3U4IEBuFY0c0GuF5zktlEzlMhZwdcdEJxlqMFhefkOc1Gqm1ERHMUWgu+zuXy41M3tb8huBJUqwVjGdjDwBgF+1M7zbVXZVfv4UZ936lDOoL3RTgepKFYOHZIOeloFQSuaJvTo2+vbsPstnOHovRKa3ZnTAS1sgJ9ZquwNpGHKpPqWGQYTmtnKHTIeX0icrv1oqA2/XVPg/apdFpmr1okiihc20hgEJZK0reflzrifj83/5+Y+yDwAe3mLtTWZtoVs6fXj83t94paX4Pe2oS3tQnyj+bgTUROYiSP6YU8Mi5Ftx+K7FWJDuRFIZxeJJUkHCvM7HL3g6FLNdRrLlVRUFC1vf5Ma2zGjxpxABmNQHN8DopIzO2zNYViqF47tklkZ33qOqfmwf0+uNkAjHXzGZ5eKA2kalWYgqAiqaDrEwkMwrGRqp7GQYARpyrK0jnx/AQzxxLPJl8WfI7PwTxIa6hKFaaXYx4/SbUKXbSLPyr2oDHkVGXN2LeiEkCtrpab92gNtbYGu7Mzfr0Je1Xh+kMCg3Bs8GCYfzIoEr2Dc5NjY1zXjC12XANi/aaDyFKXSD2kDAbg5mJT0vvBDdtF4CgCedrNA+xlcU7qIolG0TB0TmtzBt9UJSi2VxWuOyQwCMdGmSQz1Wvg3pS+kVJQzYYb6DIl07TpRQ7oAU0Etbo6q6M0BQ+HoAXd43g0AkeuXXNeLWJmsjrO8at4Qnu/cK+/kFvbpL2qcH0jgUE4PooKmURO2bRWg+3F+kXxv8Eo1GuauXzt4CY2qhKAtjZhdnYyff1TNzz3OmwtzPa2u0Yys+H7pdIZtt3J/qyx8Bx3uqCVVqGzHoDCSeP4QnPvVxAmkZYD4dgoNKihuAc/lljw1tehG424w2jOopZIU9dqhyZJQVqXmtwvUpB1i3wcWJL012jkvl5AkY8ED4eurbZoN08E3WwUDgUetx2ocPqRwCAcG+R5oJVWbF5DaapEr5d0EylVfNKoBFCtFvTWJvRK61Bz4uR5uW2stNICzZlyZubiRb6knjL/pspeM57vmH4PiKCaomYq7I2lppKI6M0AXg0gBPBpAP+SmcsTvMKpRtdq4GrVpT2InKZRWcdMPOhlrk59LLSCXlk5UsG4pI01qY1QpXLwonNJ8ZyCIL8OE0tJqGo134cC7EyAiKA3N5xcehQBvgd1jKJ6wrXDsj8x7wHwecx8J4AHAfzIku9HOAYorinQgto5FATQW5ugRt0tzq0m9ObmsSx4pLXTXarXFw4KZUXcsrSOajVzJ7oTYxtK0klT7xm1xqcl0hq61YReX4NuNiUoCPtiqScGZv6jiX9+AMA3LutehJMNaX1sXgOHgV5pZawtAcQuccVpHdLa7fgHA1ef8LRTOY0XdyJy7m7D0J0slCr0jhCEg3CSPlHfAeDty74JQTgMyPOgNzedf3RkAM+Dqtfm7uBJKeiSVtjUKnQPdqGCsFeOPDAQ0XsB3JDz0BuZ+Z3xc94IIALw6yXXeS2A1wJAVZ2enaNw/UJanapTjiAkHHlgYOaXlT1ORN8O4FUAXsp5Uprj6/wCgF8AgFX/rDRmC4IgHBHL7kp6BYAfBvCVzDxfvUwQBEE4cpbdsvCzAFoA3kNEHyOin1vy/QiCIFz3LLsr6XOW+fqCIAjCLMs+MQiCIAgnDAkMgiAIQgYJDIIgCEIGCQyCIAhCBgkMgiAIQgYJDIIgCEIGCQyCIAhCBgkMgiAIQgYJDIIgCEIGCQyCIAhCBgkMgiAIQgYJDIIgCEIGCQyCIAhCBgkMgiAIQgYJDIIgCEIGCQyCIAhCBgkMgiAIQgZi5mXfw54hoosAHlnybWwBuLTkezgpyHsxRt6LMfJejDkp78UtzHxm3pNOZWA4CRDRfcz8xcu+j5OAvBdj5L0YI+/FmNP2XkgqSRAEQcgggUEQBEHIIIFh//zCsm/gBCHvxRh5L8bIezHmVL0XUmMQBEEQMsiJQRAEQcgggeEAENGbiejviOgTRHQvEa0t+56WBRF9ExE9QESWiE5N98VhQkSvIKJPEdFDRPSGZd/PsiCiXyGiC0R0/7LvZdkQ0c1E9KdE9Lfx38frln1PiyCB4WC8B8DnMfOdAB4E8CNLvp9lcj+Arwfw58u+kWVARBrAfwfwSgDPB/BPiej5y72rpfG/ALxi2TdxQogAvJ6Znw/gHwD43tPwuZDAcACY+Y+YOYr/+QEANy3zfpYJM3+SmT+17PtYIncBeIiZP8PMIYDfAPB1S76npcDMfw7gyrLv4yTAzE8x80fi/24D+CSA88u9q/lIYDg8vgPAHyz7JoSlcR7AYxP/fhynYAEQjg8iuhXAFwL44HLvZD7esm/gpENE7wVwQ85Db2Tmd8bPeSPckfHXj/PejptF3gtBEGYhoiaA3wZwDzPvLvt+5iGBYQ7M/LKyx4no2wG8CsBL+Rrv/Z33XlznPAHg5ol/3xR/TbjOISIfLij8OjP/zrLvZxEklXQAiOgVAH4YwGuYubfs+xGWyl8DuJ2InkNEAYBvBvB7S74nYckQEQH4ZQCfZOafWvb9LIoEhoPxswBaAN5DRB8jop9b9g0tCyK6m4geB/AiAO8ioncv+56Ok7gJ4fsAvBuuwPibzPzAcu9qORDR2wD8FYDnEdHjRPSdy76nJfJlAL4NwEviNeJjRPSPln1T85DJZ0EQBCGDnBgEQRCEDBIYBEEQhAwSGARBEIQMEhgEQRCEDBIYBEEQhAwSGARBEIQMEhgEQRCEDBIYBOEAxFr7L4//+z8S0c8s+54E4aCIVpIgHIwfA/DviegsnHLma5Z8P4JwYGTyWRAOCBG9D0ATwFcxc5uIbgPwRgCrzPyNy707Qdg7kkoShANARC8EcCOAMDZiQWzWcz3rAwmnHAkMgrBPiOhGOA+OrwPQidV2BeHUI4FBEPYBEdUB/A6cn+8nAfwHuHqDIJx6pMYgCIcMEW0CeBOAlwP4JWb+z0u+JUHYExIYBEEQhAySShIEQRAySGAQBEEQMkhgEARBEDJIYBAEQRAySGAQBEEQMkhgEARBEDJIYBAEQRAySGAQBEEQMkhgEARBEDL8f1hG1XJ0SzBFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 11.920520782470703s\n",
      "errors = \n",
      "[4.0595912587097995, 0.2206979270428516, 0.21103963943765558, 0.20716254898512654, 0.20489478730516134, 0.20350871770842902, 0.20278939655484163, 0.20207124915140268, 0.20177405128940232, 0.2010296074469828]\n"
     ]
    }
   ],
   "source": [
    "plot_2d_data(classifier_function_53, circle_data, [\"-1\",None,\"1\"], circle_labels)\n",
    "print(\"runtime = \" + str(rt_training_53) + \"s\")\n",
    "print(\"errors = \\n\" +str(errors_training_53))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_two_layer_net(nu=1e-3, S=50000, data = circle_data, labels = circle_labels):\n",
    "    two_layer_nn = TwoLayerNN(2,20)\n",
    "    start = time.time()\n",
    "    errors = two_layer_nn.train(data, labels, nu, 20, S, 5000)\n",
    "    rt = time.time() - start\n",
    "    print(\"nu = \" + str(nu) + \"\\n\"\n",
    "         + \"errors = \\n\" + str(errors) +\"\\n\"\n",
    "         + \"runtime = \" + str(rt) + \"s \\n\")\n",
    "    classifier_function = two_layer_nn.outputf()\n",
    "    plot_2d_data(classifier_function, data, [\"-1\",None,\"1\"], labels)\n",
    "    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nu = 0.1\n",
      "errors = \n",
      "[2.0601777035108295, 0.17545470126931834, 0.13763345066421856, 0.12105044346999784, 0.11982983467145317, 0.13523038119097458, 0.16245814629131236, 0.12372051835967146, 0.1301236662488902, 0.13260095173775602]\n",
      "runtime = 11.628299713134766s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 0.01\n",
      "errors = \n",
      "[3.9343879573041405, 0.1952951725412935, 0.19410923709934705, 0.18433944070193803, 0.17708965408273045, 0.1673663973424434, 0.15698328845042156, 0.14977852035968972, 0.14142000396108165, 0.13523786118754505]\n",
      "runtime = 11.579858779907227s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 0.001\n",
      "errors = \n",
      "[3.098778527202344, 0.2553696622420633, 0.22337170099213796, 0.21407969782861705, 0.2076830487779923, 0.2025724429892168, 0.19787651710656104, 0.1946121572581375, 0.19240624072980383, 0.1903482366388415]\n",
      "runtime = 11.567792415618896s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 0.0001\n",
      "errors = \n",
      "[6.005400784867709, 1.0202366510730618, 0.6985169499854228, 0.5251997966360475, 0.41743005452976173, 0.3529178113864018, 0.31193854796480064, 0.28529682206814916, 0.26812372882948976, 0.25648197264048]\n",
      "runtime = 11.78718113899231s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 1e-05\n",
      "errors = \n",
      "[5.364966839297966, 2.1364643482980727, 1.472064076478019, 1.2690529239549506, 1.1681838429548845, 1.0994525870204015, 1.0460299376399442, 1.0028920231305287, 0.9661515194428845, 0.9341485147908128]\n",
      "runtime = 11.678821086883545s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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haYh8/YPATqHME6SW0diQzoeWsTiyrS3ncREIoPn9Wa6XnU3eFuWAVlXZ77RZpZStgC1pZyOl22KIQrMmwI776MVvItyahp2LazHs4cw6xtldZHgkc8/qqjN49s7xpOLZH5dUXOe/j44g1qbTuMnHoue6lAJgp77GNF77y8ids3hHVH4//RCnG8oO57kHKpHcpUrBXkz+HXp/3W3KtOyK8LZ2ZEcHVktLpvJZCFFwEJEoEM9w2fW4imEPxxuQfOXeFfhC6TqDkInhk5z+rQ2M3adrV7l9jXMcQvMoWrZ72bIyhOHNFbxmQmfdO31Pv+zpRiqaAgHlgaZeDgSVbhHhiGUhEwlkItmnXHmlFFY0hlnfgLmjDrOhEVmsuyzf+6Rp/VYMVmur/RozE/SAVMpuJJhKoYXzuMkMo1+1Dq47aefhupJcmDa3jZ++9z9WvFJOKqExY14L4crsdMpxsyM0bfajVLbQkKagcmwCpQRWKleg6B7J6Ol9b3DXlzTV7ggh0MpKkS3dul8KQDdygtS7EpHO2nGqNAbsZnxp9IryoiwI2TnKtBPL7qJKeVmvRXt6SYnd0qIH3Sfi9YWCKbixOFY680gEg6hYrEt5eDzo5X2rJenEdSftPFzF4ALYFcr7fbwp7/MnXbWJFS9XZLmKvAGLj31hG76QZMzeUSbM6WDD4hKsZJchqnsUH7toW7/XpTp3oH3YyWo+n12zEIvZvm6f1y6mGuKWBiIUck7ZhCw3l9Xcgl5TXXC9SinnOgJAdkR6VQzCa8+clh0RO51X19HDoV5jAQUWVNTzKhpFq6xIDw7SELrrtBiOuH8Vl6IYu0+UKx9ZyuQD2zC8krKRCU65vpZTb+gqu//KfSvY7+ON6F6JZkhGTYtwxYPLqJ7Q92wgJaXto65vsGc1NzT22vOoO0LX0cNh9LJSO7A7DPrcaIEAIhwuqlZBJXp5rZ0dTJ0osq2G8HjQK8rtmdOVFX1WCsqyUMkkSko7g6nIVt0qFk93irXFj1IKZfVvkFFvczpc+odrMbgUzaQDOvj64x/kfT5QYvHFX68mlRCYSY1ASf/6/vyj7UCslhZIdXNNSIlsbkGFQmhez6AOgdlVCCHQQ0FU2qVltbVBvuKu3oRkoVGdO3lEp620W+1Z0d1mZ+tlpbl1Jk6kYy25g4wEWrjErWkYBrgWg8ug4/GpfisFgFePHp2tFLqhIhGslla74dwwHUPZG0IIOxbi8ztbD0oVHtuZFryOTfogf5B3kLBa22ylYC/G/icSQVmWXWdSEs7bSgNsxWI2NGLVN3QpBQCpkG1t+edJ5MENQg8+rmJwGTYk4xp/vnZa7/MOlLIDrQ41AXlPSe9yzR11mDvqsFrbhl6x5GvxEQo5FpcppbA6Omz3Wl29HcT1d1MumoZWWtq3brF9REkJeQS3jETt5oHBIEZZaf56k1TKdnflsSysPg4yqnn6wz4d79I7rivJJYuW7V7+fesElr1cQSBs8rEvbuOoC7YXPZt5IDx03VTeW1Bd9PEqnd7Zm0tJKdVjBrLdwM1KpdCrKofEJaUsC9ni4HbxeNDz7Phle4etDDqxJFhx9IryXedakzJdxezwnGUh4wlU3F6j8PvBY9hWQefrzJO51PM6LkOLazG4ZOhoNvjZyfux8J81tNd7qVsf5MmfTOKvN07ZJfdevKAKlRh84Za3TbaUfXZb5L2HUn0KnspI1HlN6ZnLjtfPMyTI6ojsOuWm6zjVswCgCWRrq62wE0k7BTeZxKiswKiqLLrAUBh9L/Zz3UmDi6sYdhPiHTqbl4XoaNp5Rt5rD44i1q4jra6PRTKms/DxETRv62caY5G01XnRjT66dgyjKIGoUqazUFKquB1sAWQigZl27Vj1DVgdkaIUhDLztAgRwnlYj2Xllcc7c4etTNN2wdU3YDY2oZLJ/IOPHPodqXiiq7V3MWanEP2KkbjupMHFVQzDHKXgX7+YwI0HHcIdZ8/mprmHcP9V00jthJ312rdLMRO5vm3DK9m8fOcGNKsnxnOK5zL4fLlCRYiiB9oIw3AO8qYL3/qLSibtgrJOF5VSqEgkb+uLnDU5XlQ5N6/TdWf3TaFrDZDOLrUqkZ6xbZr261UKvaIC4fPZyjkYtCfB5btOOvVWyxMsByDdT0mvrOj363GthsHDVQzDnDceGclL94whFdeJdxiYCY3Fz1Xxzx9OHvR7jZgcR9Nzd32d1c07E69fMu5Sh52vEOjhkB0LKAkj/H5EOIReXVW0ABF+X57sH6BA9k9vWHkKzFQ01qvVoOUTpPmmrQmx07KQlGVhtaatgoZGrPT6ZUfE0dJSHRHwGOjlZRhVlegl4cKFaumZD5rPZ1sb3UaOCq8XvaYaY0SNXfE9ACXnWg2Dh6sYhjkv/H5sVrUxdDWvM5ODazXM/8K2nH5HukcyZkaUsTP63tYCbLmy+s0ynv/dWN55sppkPP9HbuPD49DKSu0+PpqG8HXtIIWmoQeD6GWl6KFQn7p/CiHQKsodn1OtxWc25VDIhdNbxlM+F5iUeaeqaaGQLVg7X7th2Dv3ATTgU5Zlp/7G01aBZaHa7SZ4eae7dXZQ7UahPlTdn9ODQfSaanugT3W1rQw0zQ7Gp0xkIonV3oHV0ZEZsNQXLr/wa30+xyUXNytpmNPR5Pyll1KQjGkYXls4mUnB/56o4b1/VxMoNZl3/namH943oTdySoxL71nBQzdMpa3ei5Kwz9EtfO62Nf1aeyou+PUFs9i8LEQqqeHxSf7x/b245h8fMHKKcyBV8/t3TmvspLOQU8kkKpXqn3A1dEg6KABBr/70QrMhZDyB7rCefKNCB4KMOgfBVTRmK+h8Lcwj0ay/k9A0tPJyZGtr1nEiEEDFYsh0PEhomm0VpJvmdVorTnUrViSKKClB70OPK+/KzUUf65IfVzEMc/Y6qI3lr1RAD/97aU2SQKmtFKyU4M5zZrNlZYhkVAcUS1+o5MQrNvOJK/r2Rdn7yFZufv1d2uo8eINyQIVqL9w9lo0fhLtmOJgayajivq9N55vPLO73dftDwdnGptkvxaCHw+lJcdmIYKiIoHgvQ212ESqPwkQIhM+bf9fu8Ljm8yJqqu1rWla6qjma1WwVsC2d8jLQNLuRX4EhPaq9HeX39clCvPzCr/HbP/+q6ONdcnFdScOc079Viy8gu3z/QuHxW5z9o3UZb8R7/67uphQABMmYznN3jae9oTiBZyZFxs0jBJSNTOUohe1rAvzl+qn88ow5PPbDSb1mKv337yMySqETpQTb1wRpq8te104PHOZrEyFEv1tIdPYayrSw1jRESbhwkLXz3AJFaLtymFDeKW1KFW5Tnkfx2RXdXruBITgHzE0Tq6XFThXO0202aym99Y3qgWs1DJwhVwxCiHuFEHVCiKVDvZbhyJi9o9zw9CIOPq2ekVOizD62iSsfWcqc47p2qkuer+imFLrQPZI1bxfO3GlvNLj7yzO4dp+5XDdzLr84bV+2rsoVbKvfKuXnp+zHwsdqWP9eKa8+MJofn3AAdesLtT4o1B00+/edHTjUAgFnYaZpA/LRC68Xo6oSY+QIjJpq9GCwqBRaoWsIhxbXIhzeaVlGTuRVYh4PmmHkrV4umIVk5W/BncG07DTi3hCiXxaUG2sYGMPBlXQ/8Gvgz0O8jmHLyL3iXHj72rzPhypMhCZRsoeeVxAozf/lUwruPGcO9ev9SNM+t3ZxmNvPnMP3XnuXcEXXuX/91pSsILiV0rBMwT2X782Ft63JGurTySFn1PHi3WN7pMAqaibHKBvZ91GfA0Ho9hxjq7WtK2js8aCXlfa7OEzTFMeftoVPnrkFj0fy0rOj+PdfJ5CI57dANE0xdlKEREynblsA5fPZgV+w24Pv5AZ4PREeD1p5GbKtPRNPED5fZi6DVhJGSmnHRDob5vn9vVhFRRSypeMNvdZ8KNWvVuCu1TAwhlwxKKVeFUJMGup1DDVWyg4ev/NkNb6QxRHn7GDUtBgl1Sm8gcIZLkeeu4O3/zGCVLz7o4pkTOe3F86icmyCU26o5eBTG7LOW/PfUpq3+rDMbgpFCcyU4L+PjuD4S7cCEGvXadjoYBkowZblIW49fV/2ObqFi3+3Mqss4MTLt7D8pUrq1vtJRAy8QQvDI7nortVZl+n3tLY+IjwejOqqTI+k7n7rsookx5+6lVFjoyxfVMFrz4/ETBU2qK/78QccdnQ9/qB9vTET1jHv+Dq+/vlDsooEOzlgbiNf/9FS/AELTVNs3hDix1/fjx1bh26AEHTOr/Cm212IrPdFCIFeXmYX3VkW6HrvyqsYRasUwu9DpFKoeDzvYVp5Wb/nT7uxhv4z5IphT8YyYcmCKtb8t5Sl/1dBe4OXVHpXvvi5KjRDoRuKw8/ZwZnfXZ+3Fmv87AifuXkdj353L7u/XEqAEhlXTuMmPw/dMBUgSzk01Podd2ypuM62NV07Qo9PomngrJ4EqbjOytfKefm+0Rz35a6hPL6g5PqnFrPs5QpqF4WpHJPkwFMa8Ictoq06SglC5Wa/p7X1l56CZtrMVn7yh3fRDYXPLznmpO2c86V1fP3CQ4l02G4mn99i4pQOmpu81G8LMHFqO4d9rB5/N6Xt80vGTYowd349b76YPed61NgoN922KOv4SdPa+ekf3+HiT83LX9y3ixC9xFr6Mm9B5qnvyLpeOuVYKy1Begw7CwoFXttqEppmD1caQJMu12roP7uNYhBCXAJcAjBm7O4/ODwR1bj9rDnUbwiQiNiZRNnOVIE0BdKEtx4dieFRfPo7G/Jeb+b8Fvxhi1ibASr3y5SK6Tz1s4lZimHszGhOthOAN2gxaf/2zO+GV3HQqfW8+69qx8posFtnvPHwqCzFAKDpMOe45kxMpL7Wz68/N5NNH9htFcbuE+HGR57jp+d+Iu9r27korvvxUoLhrkB7IGQxckyMc760nnvvmM6p59by+a+txTIFhkexelkpC1+pcfR9B0MW+x3SlKMYPv7pLeh6jxoRHSprEhx6dD1vvzJip7y6oaBg/ymPgRYKZTrA7owUXJeBM+TB52JRSt2tlDpYKXVwReVus+y8vPSnMez4sFMpQKEIWyqm89pfRjnOVO7kXz+fQLTFwCrg/mjekp0JM3HfDibu147h6xKKmi4JlJgcekZ91rFn/3Ad0w9vxfBJ8vmQzUThv0sqLrjt03OoXRS2YxQpjU0fhLntzDn87OUnC567s6iqSVAzOteV4fUpjjpxBwce3sDnv7YWf0ASKrHw+SUz5rRw3qUf4vPl2lCJhEb99ly324jRMTze3PfNMBRX3LQCo699ooYzBVxJelnv86hdhp7dX8LupvzviZqcVM5CSEsQ68h//LKXKh392t0pH5NbVHXZAyuY/4VthCuT+EtMDjqlgRv+tRhfKFtQ+YKSyx9YwbdfeI/SEbk7QsMrOfBTDTmPd2fxgiqSMS0rSK6UwEwI3vt3NXcsfLzg+d1RSiHjccymZszGRrt5XT/mK6RMDSHyKLqU4NMX1ma5fwA8XvAHpaP8k5bgxX+PyXl8x5aAYx8/ISAQNDn82Lo+r324UijTaWcG15VSyEQCGYs7NyIENi8P8tgPJvHIjXux/JXyYhu+7nEMuWIQQjwCvAXsLYTYLIS4eKjXtCvQPX37RIbKTYJl+TOMvMHChWiegMUp19fmPO71S06/sZZb3v8fty59m8/fuaZgxlDNhARf/sNKfCErY2l4gxaV4+Kc+NXCPt2mzT5SDi0xElGdps32LvKOhY8T+0vvwVjZ3mG3dU6l7NTHSMRu+FbEN13TVGaH3tbs5cOVpVg93tp4TGPha1XMOiC3gA1yN8VKQaRd5/tX7k9zQ9eOOFya4txL1/Lpz2/Iu5EOBCXTZg6gNccwQ/h8iM5q5c4X3VnUNgCUabfMcNoAqFQKq74B2dqGbGvDamgk6c/+rL10z2h+eca+vHzfGN54eBR/+soM7vvqdFc5ODDkMQal1LlDvYah4MjztvPkTyf16IOkegxBsb9UnoDFGd9ZX7DLwtEXbuOZ2yfkXg8oH53klBtqOfSMwjv6Ypl8YAffe/ld3vr7CBpq/Uw9rI0DTmpg2f9V8uKfxhBp9jDrmCZOvHwLpTVdSmb6RuE1AAAgAElEQVT87AgenyLRI7PVF7IYP6crYPmH6Q/zjxcOzBuUVpblPJvAspCxmO2zdsDnt/jydas49lPbMAzJ+tUl/ObH+/Czb87hF/f+j1DYREvHAVYsLuPE07fh9RU5Q0DAM/8Yy9J3KwiXpIjHdIJhk1/99b+UVSQoVCoRi+ps2zy0mUmDimXZ/a3Ky1BK2fUQA6jNcJwxHQiglYQRQtiDmNJdX7sjNtWx8vUyZsxrpa3ew5M/m5Tl7kxGdZa+VMmKV8uZ+bFepgbuYQy5YthTmXf+dla9Xs6K18pBgWYoPD7Jqd+oRRPw/rNVbFoaonpCgk9etYl9ji78wT3m4q1sXhZi0XNVGB6FlIJRU6Jc/uDyrHqEwaJ0RIqPf3VL5vdnbh/PC3/oavj32ubRvPevGr614H3Clfb9957XwsipUbauCmaC2IbXomp8gtnHNmVd/6zS9zhr4XtcfegZOfdWqVT+KWLJZN5A5rdvXczsg5ozsYGp+7Tzk7vf5Yqz5/LFT83jwMMbqR6ZYM2yUg49qp45BztbC0rlWgzRiEZZZYoHF7xKSVkSKTU2rg9RWpHEUyANXynbZfXys6PzH7SboJRCtrTawee0ALcj7BUDuq50mjGd7r+kBwP2cw7bfiEtXn9oFDPmtbLytfKM0u9OMqqx6NkqVzH0wFUMQ4RuwCV/XMmmD0Kse6+EshFJZh/XnOluOvfsvvmcW3d4OeLcHcy/aButdV4qxyUYP7tvs3P7S7RV5/nfjSWVyC6Ai7YavHL/aE6+dlPm8a89spQFvxnPwsdqQMHBp9fzyas2o+VxPd+x8PEc5WCnMObRDHkuNGZCxFYK/h6xE7/FL/+8kHder+Yf909i5ZJyRo6JcdSJ23Ha5DopBaXAH5CccOq2bs9JpuzdXtDKUwosU/CtSw8iFhmcr+KhR9dzymc3Ei41ef2FETz9t/HEY/a1PV6Ls76wgRNP24quS15bMJKH/zAlk5I7UGQk2pWR1CmoLQvZ2ma3DukHqsCUPRWNQtA5dmMjiLfbnwePTzrGkoQG3oA7SrQnrmIYYsbPiWS5UXoS79B5/+kqWnd4mbhfOw2b/bz58CjMlOCQ0+o54rwdPHT9VFa8Wo7Hq0glNA45o45zf7rretNvXh5C9ypSPWLbZlJj5WvlHH52HY/cOIWVr5cjNMXs45r5xtOLs9xM+fCi8fv/PcmV8z5FslPxeDwg4BPnNnLBtTuoHJkiHtVoaTB4+9URPPHQZBrrszODRo+PkUppOYpB06C8MsUxJ21j/knbUFKQTGoEQ8ULi27jBXKu3dt5KRNm7NvKulXFDR0qxAWXreX0C2oJpAvuJk7p4IRTtnLV+XNJxDVu/vX7zJjTmnkPTjp7Mwcd2cgVZx+OaQ483Jhv9KhKFjebO+c8h/bePQ4AQHg9jhaDN2BxUDo9e+YxzThl/hleydyz6nMe39NxFcMwZvPyIHeeMwfLtFtsa5pCSZEphnr2Lj8v/nEMiaiOmdAx09+hd56qoWZSnBMv31Lg6oNH2YiUYyqtEIqyUUl+cfq+RJoMe+1SsPTFCm5bOYfvvvReXkvBg2CMEcBAoIDH3nqR226azUvPjEEIwelfjXDRVVszQjBUIgmVJDl1/GZOOH07V547l7ptXX77jetCeL35s5a6igcVHu/g7SCdLIzuBAKKQ46q55m/jx/QfcorE5z5+Vq8vuyCu5rRcY47ZSvrVpUwfVZblmL0ehVVIxIccVwdr/5nlMPa04K3WIFeKIrb2xvRAxmP2206CtDZKkMIgSgtQfU4ftzMCIecbgt9X1Byyd0ruPuSfQAFCixL45Traxk3a9dY1rsTQ56V5OKMUnDP5TOItel2gzwlkJaWVSGbiutEmj05RWepmM7L9+WmTHanbr1dDf3TT+zH/VdOY8vK/hcYjZwSY+w+0ZyZzYZPMmpqlEQke460NDXaGzwse6mCps0+Oppz9ydjjQAeBJoQ6ML+99ofLaXmh348XovzL1ufUQpZ9zQgGDK54PJsi6l+W4C3Xqoe8HjkvrZV6nS1x2Kao9y0TGjYMfBuqjP2bSXlMLjJH5AcclQ902a2oWm5CwiGLPbZN9u/3hnsterqserqMZuaixqak7djbLqSuVhU2v2EUvmVTY/Z0HogYE/5CwTsXk9lpVz1t6VZg6f2ntfKT95ZyPm/WMvZP1rHD15/h2O/tM3p6ns8rsUwTGnc5KNlm5feW0s6Px9rz58vvnl5kNvP3JdUwlY2W1cFWbKgisvuX860uf1Lm/zKvcu556t7s/7dUnRDohuKs3+0jk1LQ46dX1NxjQeuno6Vsq2IvQ5p4wt3raakOkVAaGiInJ2qAO47+SnUyYU7t+oGHHBYY87jz/x9PEccV59TgVwsfdz0ZpAWLH23golTIlSNiGd1lkilNJ5+dGDWAkBrsxfhIHstE5rq/OzYGsAycxcfj2ls3dRlWSml7BkT3TVoKoXV1GyPUy0g4LVwCCuZzB7u04fZ3J3IWP7eSeg6wu9DCwZz1iIMAz3d/C85Y5xjSrgvKDnw5NzPhks2rmLY3REqt62FUEw9NL+Af+wHk0lEOwO4oKRGMgZ/u2kvbnphUb+WEa40ueqRZbTu8BBtNRgxOY7uUShL4AtZ3Sq8baQliLd3ffzWvl3Kry+YyTefXYxWoNc/6VUrrWcLkWzaWnMDqrMOaMlbzJaPeEwjldTYsjHI3rPzv6eFlIZugNcn+cbFB/O9O99n1LgYlmUffNcPZrJhTW777b6yYnEZrU1efP5YjuL596Pjqf0wRKTDwOe3Mm4zKcE0NV56usu6VKmU89Q2pexU4JDzfGmVSqFSJlo4jLQshGmCoaMFAn0vaivgktKCgfzzsl0GDdeVNEypGp+gYnSS3Myb7N8Nr8TjtzKDfHSPxB+y+PRN6/Nee8N7JTgJ1R0fBkklBtbMrWxkitHTY5nd2v6fbCBYnkLr5mYSDi4NaWrU1wbYtDREXMne7aQCW/dYVOPxP2fXQBx6dD2f/fK6omfySGkrhZsuO4jPzj+GdasKC+9Ol5GTTFMKateGqNsW4KtnH8GV587l25cexHnHzOf153N9+/1D8O2vHMTmDSHiMY1Ih060Q+eum2eyfnUJ0tK4/guHsGJJOamUIJUU1K4N842LD6ajvZsSNa38gtnM9cMppTCbmrCampHt7ci2NojF0ErC6OFwvyqdNV/+/N7+tOB26Tu7pcWwaUX/Ut92J6yU4PBztvPvX04EJJap4fFLO26m7KzMQInJF361mnBVihfvHsPWVSEm7tfO8ZdspXJc/myOQJlFqi73C+vxyT5XZPeGx6+4/sklPPaDySxZUImStjKLO6RIapqieauPCXMiNMskFZo3r/XQE6UgmbDdT//66wRqPwxx3Y8+oHpUnGXvl3PGBRuLLlaTEjatC/GzG+dQu9ZWCDP3b+mXG6mTE0/bwj/us7OlttQ677oHyo6tAa44ey5nX7yeY06yfefjJkcIBE1iUYP67QG+cfEhhEtSaLqircWbjjt0WV/CMPLXiHhyxYVdeNYj/pCOURhVlf17IR4PwufLyUgSgUDRhXJuu+2BsVsqBiArt70vPXZ2B6KtOr88Y19atvmwUhq6R2J4FGfetJ4jz99Bw0Y/VlIwYkoskxJ5/s/zp6e2N3hIRDWqxicQAo754laeuXN8psU3gMdvccS5O3pNsewPpTUpjjxvOx88XwkaxDsMcrvJgpnSmDCnA4AmmSKuJOWah6DQe82MSSXhP4+P46HfTeGgIxq45U/v4vVKNN0OzDrFFZSy/f9SkilCkxLiUZ2bLj+Qpm4pr1tqQ0zYK1Lw/em0GpweN7yKk87exIO/mVbwdQyUa3+wjLnH1GUC82d9vpZ5x9dx5XmHkUraf++Odg9TZrTx/bveZ9qsNlJJjeefHMM9t08noQw7gt9T2GtazshRpZRdUOiEaaIsq18WgxACrawUlUii4jFAIAL+QbcWmrd6efm+0dQuKWHcPhGOuXgrVeMLpMfuQey2iqE7HzUl8exd42nc5MdM2lLI7piqeOWB0cy7YAc1E7uCc83bPPzzR5PZ8F4JNZPifPyKzex9ZCvJuEbLNi8P3TCV2kUlCE0RKjf53C/XcNylW2ja6uWtv43C8ErMhMa+JzZy+o0b+rROKeGF34/l5XvHEG01mHRAO2d+d31OYZ1lwj2XzejRrgO6KwdvwOLg0+upGNMlaKLKoorihIHhVaxdUUospnP5t1dmNb7zeJSjwJYS/n7fJEaMjnPUiTvQdPsav/7hzCylAPD3eycxd37/G90ZBpx1US3BkMn9d00vOOWt7yg+fsYWzrt0HVUjElmWjdcvqRkVZ94JOzKxhBGjY9zyp3cytRo+v+SE07YyaVo7sYiHvee00Fzn4ZG7anjpnxV2lk9JODfwXMxoTqfVKpU+V9nWgYPSF0Ig/D7w75xOrNtWB/jlGfuSSthdfte/W8Jbj47k6r99ULCuaE9BFNN0bLhR5hmhjqj+TK/H7a5K4tuHHUzr9twvhOGV/ODNdzKFYStfL+U3F8xOCz37y6XpktHTo2xbHURanV+4ri+eN2Bx438WUVKVYvkr5cRadWYd20z5qL6P2nz0O5N56+8jsywPX9DihqcXMXKvLuW14f0wv7pgFmMnpJi4d5wt63ysXhwABLpHUjMpxvwvbuOIz2ZbLDqCSUawKHeSVIp1ZoRbak/mrlGvZ81XyEc8pvGNiw9h7YpSNE2h6arg1LY/Pvk6YyY4F3FBl7VQaLnJhMaqpaV880uH9Lq+9FUJl5rEo3reIrTPfmkdZ33BOX23k+efGs0d35sNwDU3L2X+J7dh9PDmdcZIOv8G8ZjGw3/Yi8cemOy8MtPEamxyfA5AH1GTI/RVMmm7n7qhlZUVjCv0h96++3edO4vVb5bR02qdtH871z25ZFDXMpyYMWHbu0qpg3s77iNhMeRjd7UkdMNZWStld2XdvibAX26Ymg4iQ/cPt7Q0tqwIkS9jx0wJHrhqKltWhO2sIQnP3jmBrz64jNHT8wu9nkRaDN7826icGQyphOD5347jglu7ZlRrGvzw/vXsvX8MaQmEBhtW+vnWeXsxdnY7V/11meM9CqmDzg2NAiSKbVYcBXx74jP4jaDj2ZbVJbQTcZ1XnhvF2hV2KqWUAlkgBRbgledGcebn1+PNs4kVwraOOov2nBSE12d3Up0yo40dWwLMnV+Px2fxzhvV1G/LbqR32MfquOybKymvTCKl4Pknx/DHW/fOUhCdbS4KKYVkQlC3NUBpRZKb73qfKfu05Q3Cd1fM/oDk3EvW89QjEzJuqKzXaxiga2Dl3luEw7lKobMZXo/NqGxpQdRUD2haW1/58H+lOH1GNiwOI2XvVesfdT7SiqE7nUoiOWPcsA9MHX7ODhb8ZlzWvAZNl0zcz67s/OWZc4i1GuQXnfkFnDQ1Nrxvfyk6Z0QnIjq/On8WP/rvO2i6Xfy2aWmYyrFxJh3Q4Sjg6tf7M26orOtbGhuXhLMeO2D/JGFT4PV3BjphyuwYX/vpZral+2h40QhpOhLokCYWCjP94+3xepRSRKRJi0qhgLjqEkwmioSS+NGyBFMqCX/9416MGhdDCHjpmdEsertvwdEnH57ACadtpbQ8mTeQrem91zpIKZh/0jZOOmtzJs31kutW88gfJ/PoPXsBMHP/Zm746QfdXGKK40/dij9g8cRDExk/OcLGdSGSCS3f3KQMlqWx4ImxfOOWJUzeuz2vUnBat1IwYnTcMWBup7Y63DwYRHeYyaASibwZTyoeRwxSGmpyxrhej/EFLaKtudLf63ees7Gnsccohk68KzdnlMSKWybwn2PvHOIV5XL8pVtY81YZG94vQUrbggiUWlx05xr++/eatDAeyKe357mCRFRn9ZtlvPXoSJb8pxLNUCglqBof58qHl1FSne1qqhqfyMRAsq6kSUbvHcVKCZY8X8mG98NcdWNjWil04fUpjj6lhQ9lBzWal1LNk0mGqda8bLfiRJTFdjPOWCOAADQhsJTCVJLtMpFXHppInDKxN9eG+OufphT7JuXQ3urlinPmctp5Gznx9C1UVCVzWnoUUwSnaYqTP7MJX4/35ILLPqSyKslffj+Fc760Pqu9Bdg7+GM/tY15J2xHWhqarlizvBTD4+w2syxobvBx67dnY6YEM/drwZMn68yynEc664aiuTHXzaOUwmpucRT0erBbwZxpIuPxzpMc7w2gHKyO/tI6pfe4xJHn7+Dle0dnbb48Pou5Z+9wFQN7oGLozj7f3MjVDD93k8en+NrDy6hdFKZ2cZjKcQlmzm9GN2D7mmAvk98KF37lQwD/e6KaJQsq7S6p6eSM7WsDPHD1NK74y/Ks40uqUxxwUgOLnq3KWo/hU3zs81u55eT9aNrsJxHRueqbTThta3VDEbZ0SjVPJo7QufJRup91ZoQEkg1mhBLNgwdBXFl0qPzxAwGEhJHjxvB44TNf2DDguoH2Vi9/+d1U/n7fZG750zuMmxQhGLKIx7R0Mz1VMC02mRBE2g3Kq3KzeXQdPvmZzRz18e1ISzi6M4QAf0AB9nsw64CWTGyg+0tOxDXuvHkmrzw3ChCMmRDBsrTMed2REl79zygOP2ZH+to28bjGK8+NIuqQWlyouZ2MxdHDIXuqXqS4QO5gxxh64+RrNlK/wc/SFysxvBIrKdh7Xiunfyt3mNWeyB6tGHrSaUmsu3wqT130iyFdixAw6YAOJh3QkfX4hH07ePcpyyHDB8AOoApdYnX6hIVMV0YXVhZmSmPNW2U5SkdZGqveKCfaqhMsyxYq5/98LeHKFG88PIpUXGPUtChn/3AdS56von6DP9PD6d2XSzjs+LZujersHWdcSUqE4bgyBQSFTiStBJLKIqJs11Ih9AKvs6J68FIRE3Gdr194KAfPa2Dm/s3Ubw/w7huV/OHxt/KeY1nw4cqSgj5+w1CUlKYc21tArjXSU3koBc2NXn71w31Y+OqIzOPbNwdJxjUCPSb9pZKCp/8+jj/eOoN5J9Rw6Q2rCJeYKAUvPDWGP/5ib+eFSJnfApASZZpFKwXh9VJwklEfeeh7t/Z6jOFVfOl3q2jc5GPHhwFG7BWjesLAPx/N27y8+sBotqwIMWHfdo6+cHtRXYSHGx/prKTBIPaXAH+Y/vAuuVcxJCIaN88/kLa67n2UFJoh+dR1Gznqgu3835/G8ubDI0klNGbOb+Ldf40o0FtI4Q1IPnHlJp7//VhiDq0kQPH1JxYz+QDnL7pSIE2RKY77zuEH0by1K91z5PgEv3pmDb6gxB9QSKVQwGYzSoXutZVDD4lnKcUOK05A6JRpnowdFFNWJtDcEwOBX+iM0H3oPQOfSrFgyTTuusg5w2awuOT6FZx67uYcAR6PCf78m6l87vJ1OcLZiXxzH3pzcygFq5eGufbCw3Oemzu/jut/8gEer0TXbauio93gys/OpaXJdr8IoSgpTxHtMHIytDRdcuxJ2zn2lK1IE565v5TX/l2W1dgRQCsvA9NEdjh/XoTfZ4/nVKAF/Ai/v88tuQsxVJb/5mUhbv/MbMyUhpXUMHwWHp/iuicXZ2XpDSVuVtIgEbgglnE31Z88pajdyM7EF5LMPq6Jt/42iq6Yq7B341IQKJGcfM0mTr7GHo4jJSxZUJ3Xwph6WCsnXbOJ6Ye38fZjNc6KQcDWlaG8ikGI7BnWPf3uOzb5uPioGXzi/EY+fe1mpNeiLR1gbpcmYT3XahCALgRl3dxMAAFswd9oJanRfYSEnslMymctKKWQwN4zF3PHwkWOU+EGiwd+NY3jTtlGMGRl7ealpbF2eVm3v1lhnORkMbJTCJg2q4Oa0bGcLKf/vjyC6y46lNPO28iosTHef7uSpx8dT0db199cKUFbs5NbR/G9Oxcx+4Bm/OkMqBn7tnLocW3cevWErsM8HoTXW7AbqzA8jsHpwSSVECx9oZKWHV4m7d+eN4liMHnkW1PSPcHsG5kJHSupeOwHk7n8/hU79+aDjKsY+kDN0x9y9dO2UBkqS0IpeOeJXAsgFdd56d4xnPjV7BkMmgZHfW47r/55VJabSGiS4y7Zwuk3bsw8NuOoFnaszU319PgsQuXFFzNd+csNzJxlEiqxWLMkwH23jGbsXnHGTIkR96SIyK4dc1RZtMlUVvAZYLsVp1r35dQwaEIQxiBo6OjpDqwCu5dgoV1nQlmk0ld3mgo3GOx3aCM3/OQDjHS6sZS2q6ap3s/PvjmHhjo/RhEtR8wUIHCcIFcMUsJRJ+zgn3+elPPc+tUl3PH9WX2+5v6HNTFr/5aMUgAIhCRHndLKY/emWL8iiOb32xXKQoDPDwUshp3JjnV+bj9rDqm4hpUSaAZMObiNS+9ZkdWGezCxTNi4OEzP745SgtVv7n4tfFzF0E+6WxK70nSVlt2y2olYm/Of89Qbaom16yz85wgMj8QyBcdcvJVTrt+Yddz8i7bzxsOjcuY7GF6YdYzz/OOejNS8TDkynhHoMw6Mccvf1pFM2LJCKj9JJdlsxTJKoF4maZMmwR7pqiPzWAACctpyF1IKQgi8aNRoXkrSCujRd5/lik99jLptg7NzrRoR5zu3L85yE0lpxyK+8unDMU37PV3yTgX7HtyUE6DuDCCnkoJVy8qYnQ4q92eXKwCvb3DHVe5/WCM+hxGYmqY48DiTTTuy5zoLQ0eUhFHt2TEyUdK/xnp94d7LZxBp8nS5uJKwdmEpr9w/muMu2bpT7ik022o2HWZi7I6jQ/fwMo7B4epDz+DqQ8/g8gu/ttPvpRswanrU8blJBzhPvNI9ivNu+ZCf/G8h1/7zA255byGn3rAxR+jUTIpz4W1r8AYt/GETf9ikpDrJFQ8tw+PvfafVKXidKpV96ZCDJgReoVGhdbkvPAiEgBaZolWmsNIqI6pMnGJgMn2dvqBhu6U6h/4Ehc49T79ORVWcWQc085kvrOf4U7biD/SvzcPxp27N6cekaXYw+eB5Xf3/f/bNOSxaWIXs0cRUpM2lpe9WMGV6B5qWrRT6Ego0TY23XxnR+4F9oLXZi+kwpc80NccW5wB6MIheXYVWEra7rVZXoe/kltkN88exY50/J+6Riuu8+deRO+2+mgaHnFGH0UMhe/wWR563Y6fdd2fhWgyDSGeNxLwXajmr9L0+nWsm7ZGXTVv8TNi3gymHtOXdLZ7zo3X89sKZpBICJTU0XeLxKc78bv5W2wDBMotgmbNS6eTATzUy+/hm1r1TgscnmXxge97xm1nXFnbaqXPfm+zfNSEo0Ty0SpMxuh+v0DLB5QaZpFXaWRwNVpKgYSCUQhPCngEMtMlUTuyhEEop293U08JQivuef4Wk6cWHSTKp8+XrVnHjJQf1eQZzVU08p+4AQNNVVmpqtMPDD6/ZjyfefhGt5/uiw4FHNJFyEMBSgmUJvF6VmaEjRG5WkpTw9N/HsX71wGc8dOflZ0dzwWUf0jPtWCl46//yKyGh64NWuFYMooCV5TSoaCAoBVtXBmlr8DBhToSzvr+exk1+1r9fgq4rLFMwY14LJ129sfeLDTNcxbATeO24CbyaGs0J539AqMLk4NPqc1LWPvyf3bQrGdOYNreVZ+8cTyKiY6Y0dEMxfnYHVzzovFOfemgb1z+1mAW/HcfWVUEmzOnghMu2MGLy4GQ+eP2SGfNaez+wG6Wa0edd/Bjdj09kVyhXa16SShJTFhq2EvALHU1BAkmzlSKFpEQzCsYVOi0NC4WWHhHaEyEEhgKPx7YSDI+FlPCtW5fwpVOOpC/1IEveqeS4U7fh92crByFg2fsOPmbl3NtaCByL0ISAN18cQSqpEY/pvP/fKq65eRk+v4XX19Uk0DIFqaRmZxeVpfjYJ7dTPSLOsvcreOf16py2H5U1cS65bhWHHt2AtASvPDeKe+6YlqldKK1I8olPb2b6rDbeeHEEhx1dj9DsdyYR1/nhNfsTj/VPjCjTtKWrkZuV1l/+/PNfcfPCA2nYkB14N3wWh55RPyj3AGjd4eG3F82kfn0AzVCYSY0TL9vMlY8sY9vqAHXrA4yeHh207+Suxk1XHWSUUsiWVlS6HbHSBV6vySV3r2Sfo+3Zus/eNY7nfzuOVNye4Sw0lc5W6fpyePwWx1+6hZOv3TQEr6LvjNb9hLXiBIRUihaZojzPrj8iTTqUSY3m65aQa8cedkg719xAMDqtWMAW8t37JzVaSaLKZFTaInESPEopx8fjMY0rz51b9NyEufPruObmpQTSmUidl4xFNd76v5H88juzc8757h3vc/C8Bsd6BqfYQjIh+P6V+7N4YXXmsYqqBNf9+APmHNycdZ14TGPB42M5/rStaJrCH5BEIzob14W48csHk0zHkHx+i7ufeIPyqkQm0J1MCDatD3PluYcxenyM2x98G69P4vNLEgkNMyX43U9nsHVjiDXLS3vtL+WEsiy7Z5JpZl6oCIezKqb7yx0LH6d2SZhfnTvLVpJxHV/IpGZinGse+wBfgZ5SfeHW0+ewcUk4a5a5N2jx+TtWs9/H8zcWHGqKTVd1Ywz9RKVHHZpNTZhNzchYzHZ1xBMZpQAgLEUqpnPvV/fGSgmat3n5z6/Hk4zpGT+onWGU6xP97z8G10+8M2mXJjJfHxylMj9SKeLKIqLyl6p50KjR7Iwkkf7RhCCsGQSELdRMFI0yiSTdSE8pFIqtVowPzQgdymScEcyrFArSTTBX1cQ5/ytr+cYtSzjlsxsJBLNjEGMmRLj+Jx8QLrXQ9a6ZDKmk4He3zOC27zpnAP3qRzOJdBh55zf0fFw3FJOmZgdymxt9TJ7ekaNc/AHJyedsIhiyMr2WgiGLydM6OOXcLrfGUSduJxQ2s7KfvD7F6PFR9j24mUuvX0UwbOJLW0E+nyQQtPjEmVtYtbSMqpFxLrl+Jbfev5ArblrOmAm9F7RlWlKdFroAACAASURBVGl0prOmo+6qvd3uvTQITNy3g++//i6nfbOWY7+8mQtvW8P1/1o8aEqhabOPLctDWUoBIBnVeemeMXnO2r1wFUM/6LQKZFu73Vc+lUK2tduPxZw7lMbaPVx7yEmser0cXS/uA9rVNnv406FMosqyBXSPH+hymsSUxRYrTqLA+E5POgW1JwIIpxWDnrYYOoPJ9o/GaD2ABlRqHjQKu5qieZSZHjDZvCHI9Fmt/OHxNznrog0c/fEdXPS1Nfz+n29SUdVVIfuJM7dgGLnuo1RSo7EuNwjaSXODj+s+n7/1ds9l6zqcd+n6zAjXTkrKnIWpUzsNn19y3Ke2ZX6fMqOdQCg3Y0bXFROndrDfYY05SkfT7Gl2k6a289tH3+Kkszazz36tnHDaFn71yH+ZsW9L3tcE2ArBcs7SkZHC8a++EK4wmf+FbXz6plr2+0RTVtX9QIm2Gmh50o7rN/gHPB53OOAqhv6QSqFSub1uVDLZa/rIwz88iJjllMfd8zzFtLl98/MPNdusOFutOC0yRaNM0iyTdlA5LbiFEASETrXmRQH1ViKjSLpTaIffeWRJHreVwI5dhDRnv3Wn1bLDirNNJoh3U2ZSKSxlt/C+Y+ETXHPzMgIhK5Na6g9KyiuT6SAsTJzazoGHN+TMNeikvDL7M7LX9HY+8enNHHRkA5qm2FIbpt5h7kY+PF5JaQ9FsHGds7sr38ewewC2dm2IWDRXBFiWYPOGEMk8PbnMlMaXvr4af9DCk64LMAz7/bn8xpUFX4OSMm90WMnBa6S3Mxk1LZo3wN3R6OGOz8zZ7ZWDqxj6gUqm8rY6LpijnZ5lqyWdvgAOqYCJ3e/PE1MWDTJJc7pozalArSydqtqmTCTOfn5HoY7tsoJ0HUOeNQQ0Ax2Ro3A62WhGaVd2UuyWtDJrkknqZYINZiTTtnv8Xh055xoexdxj6jj3kg+57cGFTNgr4iiEDY9i2SI76Kwbku/e8T6/uH8hX75uFd+4ZQn3/Pt1akbF+PmN+5J0yH13Qipob8vWQo/eOynn/vbIUkFPORuPafzn8bH4AyZHHLeDREIjmdCzNvCppKBxh49Fb1ey4IkxJHrUzCQTgpeeGcXMA1ocrZLJ09tzLKjuCI8nr9YSu7iRXn8xvIpzfvwhht+ipyCwTI1tq4M8fdsE1r5d6ljXsDvgZiX1h3xTPASZUYUq3i0bQQj08jL7cSltv0BWywCnjqiClu1dX5R4h86Ld4/hnadq8Pgk887fzrzztxeVSlosUsIHCyp5+7ERCAGHnVXHnBOaiiqy8gmNoNCxlKJDdTa+zl+gBvYMhnzHQFcri84jmmSSBLbQiSkThSf3XesMZopcxSCVIqLMTAV0JzFlEevWsdUvNMbq+QOhlqnxmS9syPjeexKL2vMPOltSnHreRvY7tClr3Kjfb/GNWz7guosO5abLDuSnd7+bt7Ee2LLUYygu/Opa7rtzeubxydM6sEyRVVEtBJimwIxpGB6F1yfTQ4QEFVUJ/vLCqxmlYXgkG9fZ86yVFLz10gh+d8sMlBL8+TdTmLl/K1P3aUNKe47E2uWl3P2LvTns6Hp8Dum5qaSGVcAFKjQNEQrlNtjTNLTAwILP9Sf3v6V6XznktAY6Gj08/uNJyB5psMmYzot/HMPrfxmF0ODi363sc5bfUOMqhn4g/D7o6HDY+Qi7KZimoYIB27LQBMLny0ynymRj9DivJ4bXYvoR9ofJTAp+ecYc6mu7OpY+8ZNJrHmrjIt/t2pQXpNS8OA101i8oIpk1L7HitfKOeCTDXzutrUFzx2l++xW19gqrgYfW6wYcWURFLkfMYWdVRQQ+dWCUoo6K5FRmVFlZXVWjSlJVFkE0fOmyXYqh86zOqRJney9g+YIh1YcncRjGuvXhNj/0NxKcClh++YAD/x6Kpqm+M4d75OM68zcvzlLKYBdqDhlRhulFUmWvVfJw7+fwme+uD7ruO7ZSULYVsinztnEK8+NytRZjB4fc2yzIS3BPx+cyGe/vD5zfqjE4pwvbchR9GPGx7jktCPZsTWQiYkIobj6+8uZNK0doSlQAiEUf/3TXsRjBk/9dQJn91hvIq6x4IkxeeMqmdceDiE9BjIaBalsKzoY2KUT3AaDCXM68PgkCYeRq8rSiP8/e+cdJ1lVpv/vOfdWrs49OTAzDDkjDIgoKCoiJljlZwB1Dbgi6phW14CYd42rKIq6KkbMEQRFEUFBJOcwzAwzw4SezpWr7j3n98e5VV3h3qrqnp6hG+v5fNxlKty6VX3vec8bnudJm8e//oZD+MjfbqVrYGbkyScC8+svMUcgpMTq7TF3W3mgSEqs3t7KxS1CIWQijoxNXfDacaGNyQtpK6JdLs98nWkU3nHVACPbojVSFcWcxb1/7mP7Q7NDHnrsriR3XTMVFMBMWdxx1SBb72msY49sjbD9oTgJbZMQdqWHUG4EL7ViDLtF3x4CwH52nEHLv76utcZF0y1D2EKQ1o6v3PYON8+QWyCr/BnSACU0W5wsm5wMu5qY+5QRExbhgNtCa81tf+/n7lv7fY3LSkXBb69YwWkv3MHbLrqfE08Z5hmn76J/QWM/yhxPEAq5rDlokk2PJLniG6so5GVT7+hwRPG004Yq/773tj7yucbztWzNMSeOYtu6JsH1O6aUmmecvrNmQX/qs4Y44ZRhojGjxBoOayJRzX99+m5sW/HTb6/ixj8uolgwCq2FguS2vw/wf184sOH4WinciQmcXUM4u4Zwx8cRto3d14c90I+VTMxKUNjXApern5Ii1tVYTqqH1nD7bwebvmauoZMxzBAiHMZaMDi1+7dt0Bo3kwXXRYRCiGiktlauXP9ZRDDlKSmNln0oyvt+f0PFNe2Rm3pqFuzKOQjYdHsXSw/a82mOB2/opeTT03CKggdu6GXFESb1H308zDfedAg7H44hbfjo5ZtYelLjmKLElJfGVJF+WVs7LrOYGzyBvd29wEwd2VIS0ZJeGWKLk6tIZVQjpR1SrsNqO45VR3jTWmNhgpAGMtphyC0XoxqREBaLreYS0Mc/a4jf/mg/b8S4sWluhzRHrxup0UKS0p+bMLo7zEX/exfL9suiXLPoW7ZuWroTAo4+YZjvXboWMJ4JZ7/6MSyrUGkE53OSm/68kGNPGmnLu9iyNNE6KfDTXrDdVx5cWor1H7mXLRuT/Ogba7j8y2tZvirLjq0xdu9sLAVprXFHx2omkXShiFsawxocmFW5bfN5cMeVA1z3f0vJTtocdfoIz37T4w1eIrMBKeEt37uPS151GIWMhVsSnqth3eh5QZINkA2Zq+gEhj2AMDRVwLA43dGxyqKv83lIp7EG+qd2Q17w8D1WNIrVNeWVfPELzwQMYad/RR477OLUGbILqelZ6L8bLeUFE0NhuheUCMdaT3vEuhzssGow6rHD2tsVmVP/0isOZ3RbxMxwF2ior1bOTQh6ZYisdqfVXK5/TgoBGvpkiGHl/10ljaJ65eOUpbgFkMBmqSXZ5vqPFC9oUkIqHy+k4WPf+Cc/uWwNr3zTZu9xr+GrNa996yO+fR+toVgURCKaQt7U4SfHw6w5OFXDdG7FNxUC1hyUJpEskUmHyGVt3v6qE3jFGzby1NOGyGctfvfjFfzpt0u55Iqb6elrnaEWC5Jb/rqg/pN8XxuNKU553i5cd4iXv2ETX/2fg/jjr4I9lnWxSEMXHEAr49dgmhdmEzULvgy/+uR+3PC9JRWZ+T9viXLbbxbwX1ffSTQ5+8FhyYE5Pn7zrWy4pZstdye58vMrG+6hcFRx0NNajPHOMbSdvwkhniOE+IYQ4mjv3+fP1kkIIZ4nhHhICLFBCPG+2TruvoQ7MVl7V2sNSqGq1CWFlP66MUIgA1if69edxYkv2039dKaQimjS5ZBTamvdWsNV/7uc9x59Ap88/Rjee/Q6fvmJ/XzvzWoc+4KRQAWIY14wDMDGW7tIDYdqiD3X/qwvcDELC0leq0DiW7sok9uC0My1rf44ESEbSkVxYbFAhvH3kquFEAKJ4KXnP4qumrwUAiIRjWX7l2vKbOTf/2wZP/jaGt5x7jrWHJRqkL9oZ10slSQLlkwNN0yOhbnsMwfz2uc9g/84+2mMj4T5/rXX0zdYaPjblEqmMa1csybnshY3/HExD97dU/O6a3+z1HeUtazPFAppIlHFBe97iO4+/4ANgOP6RzsNOptF5/PoYhGVSuGOjgWWBNvB5FCI6y9fWuM94hQlk7tD/P3He48sKi048KmTPPtN2znq9BHCVZlWOO5y6KljrH6Kv8DlXMV0MobXAW8GPiiE6AeOno0TEEJYwFeA5wDbgH8KIX6jtb6/+TvnDrTWPg1l7znPG7d8wctkAm2XG28KEQ4jk4mmY64Xv/BMdKRIX+9OMmMhlILFa3O84WsPNhB3rr98Mdd+bXnNzXHD95YQ7XI5423bAj+ja7DEG7/2IN96S5WVozATFck+890mh8INweO6X/Tyzs9t9Z3ldzETSv2ECGlZ2Y2Xf4v63WEzsx23asHolyF6pVneCyh2u617B2VoDIGu6L1hqRUlJqxKeasdCEzQq7ffbLaoR2OKh+7p4bqrDDP2lDN2IK2ZLYK2rRnaEfV9buGSHO/82H0NE1NlNvZVP13O369byKnP24lla/56zWLu/Ec/9X/Ym65bwM5tcVYd0NzgxnEETzlpmOuuDGD82m2OzWnAddD5PGKG00mP3Z3EDquGMe9S3uKBv/Rx8it38Y+fLeSeP/XRs7DIM169kxWH15ZBH7yxh199YhU7N8TpWVzgzHdsZd3Z7Wssvfp/H+HO349y808WorXghJcOcewLhve6SdBsYzqBIaW1HgfeLYT4byCYtjk9rAM2aK03AgghrgBeDMybwNAUWuOOj6MLZlclIhFkdxd2zP/GDoIIh5l0VkDc5eLfXUPfUv9d2h8vXd7g1lbMWfzp68t43lsbLSerceip43zq9lvYcEs3QsD+x08SqqqVrzomhVusvemUkvzsskFe+h/DNdIKSmtGPZXUbU6OfhmukNI0pvQjvZ5C+RPGVIlB2TjLrrUm642TLpDhGn5EFItlVowxVaKv6vGg4COAgmejlhR2JSj4vTYIM7nHpYQLP/gA6ckQaw+d5KWv2exb/3ccavSW/DSTrv75sorIXT2e+fwdZorI77wluK7kvtv7ue/2/hZnLOgbLLRc0CxLN2foT8dtSIPOF2CGgaF7Qcn3XKSl6FlU4NMvOIrRxyMUcxZCKm799QJe/qlHOcFb+B/+ew+Xvf6QSiloZEuMKzxXtqeft7Otc5ASjj1zhGPPHGn94jmM6YwCXFn+D631+4DvztI5LAOqleK2eY/NGwghIBzQXBJUggKYDMIdGZ1RyiyEQNg2H3nJmYGvSY/6n0c+baHaKLGGIppDnj7BwSdP1AQFgL6lRU56+c4a4xE74nLVj/uYcEsV5nBZJK8sn60wctrDbhGJICRkjSfzqFtkk5OpOLL5QaGR4EuaE5gsYKebJ6dcSlqR0sbsp/p3Vh7Hojzh1BWgCFsv51H9OFDRb5ru3zAaU7z/s3fx/96wiWhcNS76RcHEWJj77ugJ9nyWMDwUzJaOJ52Kg1w1yqqtz3/ZVg5/ypTI24LFOU574XZOPHWIULj2Aom2YTATjije9qF7+fnf/8R7Pnk3vf2148A1fJ52MMPppN1n7s/KI9P0Lys0yIZYYU2s22FkW6SyadJKUspb/OSDayjlzQ/96//Zr6E/UMxZ/PazK1uWYp9saBnOhRBfBNZrrX9d/bjW+pK9dlb+53E+cD5AVCZbvHrfw+runmo+lxcMSwY23nShuEcWh2Vrynr3uKUHZ9h6T6MW/4L98rOiF/Oyj25i9VNSXP+dJeRTNkc/f5jT3ridUeky7hSwkZRQvqWd+uZu2RMhKiXjLsSE9J9WAkpaEfJ8G+ohhCCCZJd2yVQ1lm0EA1aYBDYKE6zGVanquNr38xQw5BaICUm3x9IW+GQfAdNVzVAtkV2P7VvivPd1x3P5NX/1XR/Li/u5b97IH365nHSqcRNw642DnHnONt+JIjB6Se/+2L0M744ghWbNwWmckkAp878PvvlYNtxv+g133dLP8ScPtyRRRuMAipOfPcQhR05w/kuehlOe7S9Nb3ZfxKKoQhFdLICQhhfUhuNbeVT1wu/fxzfedDDbH4gjbe2ZVG3g2q8tb1j0wfymW+9Nsua4FLs2+GcqhYxFPmXtlcmmuYp2wnMK+I0QIg4ghDhdCPG3WTyHx4EVVf9e7j1WA63117XWx2mtjwvLPZfnnW0IyzJuVT3dxq2qr9dMLPktAl49dTZQ7138bxdtJhSrna0ORV1eevHGWfk8IeD4lwzz7l/dwwf/dAcveNdWYt3mhlFAMSAo2AESFmX9JICosPwnmIC0NpmA3zG01pXyUDUcNLvcAhudDJudbCUoxIXFsirJbp8jotB0yVDL3sVMpmj83qK12aGnUyEyqeYRvFSSHHyU/5TLPbf1cevfBigEyKkIAQuWFDjkyEkOPNw0v2NxRSLp0tXt8JFL7kB6pahvfO4gMmm7cqwgJdgy7JCmq7fEU581VPVgk+8ixNT/AJFMoDNZ1Pg4OptDZzK4wyOofGtSYhm9i4u859d386E/38E7f3EPn7rtFo4+Y5RYj//9plxB1Ju6G1jpn92EImqvTDTNZbQMDFrrDwI/Aq73AsI7gdmcHPoncIAQYrUQIgy8HPjNLB5/n0EIgYxEkPE4qliEoAtaCGZT7rE6OKxdN8n6H9/LoaeO07OowIEnjXPh9+/jsGc+seNyfhyEMoIFuKewxIoyKMNkfBRRNTDm+o9l2pix2T4ZokfYLLdiLLWixKVNWNTuIMvlo51uoaLcWlZvne15+3oIAQMLCySSJX79w5W+pLUypNSkAufiBf/z3iP53w8f6itiWv01/L5SKKw45GhzrezYGuc/zj6Jn317FQ/d293WXiaecFm1Nm1+S6XMtJ3vB9lYCwaRvT3I7m6sBYMIy6qRrC9DTU42Ldv5SWH0Ly+w7OBs5TY75TU7GryXhVT0LS2wxLPKfcG7txCK1r4mHHN57gXbZlV6Zj6gnVLSacAbgQywBHid1np2dBgArbUjhLgQuAawgG9pre+breM/EdClErqZhLAUTQXDtDIz3iadFohYzDComyxO69edRe77MS478Ifsd1SaCy6fW717jZGkSNbV9ZXWjLlmMcholwS1WUN5QUhKu0KAyyiHuLTNVJJW7FYFij60tW5hs8BjV5eP6FcOqv5vpTWxwExi70K5gljC5fqrF/O8sx9nyYqcd17Vr4GJsRAP3dMTcBTDqP7rNUsAWH/x/Vi2qugwtYpv0ahLvMpzYnw0wg8v258fXrY/516wgbPPe6zS/yhrMFUjm7F47D6BO+RN8lgWsqvLyNF7rH8RjSK7kqZXEw5756ybym7rYinwnmmH8Xz4aWM8+02P84evLscOKbSGroESb/7O/ZXvcMRpY7zqM4/wq0+uZmJXmFiXw3Mu2Maz37Td95gbbunm6kuWM7w5yn5HpTnj7VtZfIA/R2a+oaWDmxDiz8BFWusbhRBHAN8D3qm1/vO+OEE/zGUHNwA3lUJnm1wgQhjWp08hWSuFOzLa0JsQsShWd3s+xPV9h7kCgdEhSlbpJ42oYqXEYyNYYccqVpyBY61as9HJNM0zLASr7Pi07Ua11iZABZS1pgOlzDinbemaHafjeOzuum3Z8K4I/3H2U/n6r/5OT3+xRlTPo8Wwe0eUD17wFHZsbU8KZeWaFJdccXOgNHg9tIZLP3UwV/10he/zC5fkOPakEQo5ybkXPMrgwnzl2K4DY8MhXvvUg2tZ9EJgDfRXGst+jHc1MVkZ7W6AECazCPsHhulc76kRm023d5PsK7H6KanAQOkUBVYomIV+1zX9fOdtB1LKG6azkIpwVPPOX9zNskNmz1ditjFrDm5a62dprW/0/vse4Azg43t+iv/C0Bo3k8HNZHHHxnEnU8b/FszOyqdhrXN5dF1tQLsubjqNOzGJyuUrC2l93+GJhgC6hM1CK4KjNVudLFucLBudTE0z2EHzmJOtaQoHMaSD+wMGCTHz3D+MaJsXUTknrSt/NscRFAuSX/9gBR9529Fc9tkDGRsJozVs3xLjyx8/hNRkiIKn2e84gnxO8qWPHsozz9xJLOE0KK2WiWWDiwqcec5WhGjvDJeuzFEotP9bCAGnnrEj8PmhHTGu/vlyrrtqKe969Tr+cf1CHEfgOnDrjQO8/cy1jdIqXjZQ//esTH8VisFBoXxeIf/INl1F1a4BhyOfM8qa44KDAhjGf9DzWsNPL1rjNbO98WglKWQlv/rUqmmdz1zFtAvdWusdXnmpgwDIaBS3WcYAkM3VLD5uLofs7YVigISBEOiSU5nQUIUCanxKylfn85C1sfr7EEKwft1ZcyJzkMByO0YIQ3BTWtMrQ+xw85R0YxHcqLE2r+kL8BWxmw0IIbCRFLRLlOCsoXoaSWnN3bf0kZoIs3L/DI/c181PvrW64hl95z8G+d0V+1GWV49EXXZui3Pcybt52mlD9C8oIi3Nv71mM7msRSzAglII0+A94+xtTI6H+Mn/rWn5fUpF/wDaTKjvwMMmDWtawUmnDREKKW65YUFDljI+GuGT7zkKMIuomyuiJifxm7jQVQ0KrRRqMjUVDFpkZmXJej/sbeE8peDRW7pJDYdYdUyK/mVFshM2qRG/QCXYfHvjROB8xIw6oFrrJ0chbS9BhEKIeKx5OckHanISAtJltAY5Rd5SE5ONr3EcVDaLlTAL0vp1Z7H7zP3bvnm0hi13J0mP2qw6Jk2id88np3plqBIUgMr/X2RF2OQ0pty2CDbgMedovJ37rRCTyqkQ3+qR0S716j9+xwrKSNopIiltppd+8vX9+fE310yNaDbB/3vDRs553SZcVxiegJ4qKR1x3BhuSVAsCsLh4MgXjSvOOvextgLDXbf0e4J/tXAcU6rzu9yE1Lz9ovs48vgxc9lJeM1bN/DTb6/iR1/326EL03cINdECK2uK6TrPZ/Ng4PnLnu5KH2JfY2RrhC+9/HDSYzYIcEuSk16+k7M+sBlpafzmHZKDs+Nb/USjI6K3l2B1daFjMXMTtMuOUQoZjaACSEFqbBwdizXlP+h8ARJTMtkLrnyU9Ve2zh5Gtkb48rmHMTkURlgapyg4421bOf3ChsnhaSHpQ0gDw3wOIyihTQbgPZ4LWOhrlFeFpEtIEsJmUpXY7Ynr2Qi6pFE8yiiXIbfAwippbwE1TOusckj4kNwkEG7RY1DARifDqz7ybhZc+WjrHwI49YydvOzfNzd4M1Q+V4LyvA/8CG7VSHY7VIewpSsznHrGDsJhzU1/WcBD9xj3OMeRXPy2Y/jIl283XAxp2Mo//fYqtm6K897/vrfhc2wbnvK0RmXWV7xxE0ceN8rfrl3Ezu0xxoYjPPpgV+UchGUhIpHGspAQyLJGWMlpf1RbGC+TIOztjPjr5x/M6PYwWk39EDf/dBFrnpLiqefs4qafLKrhRoRjLs99c7DszHxCJzDsRQjbxurvx50YNzdEtc5BQLAQdgjZ24OaTAX0GnKm1xC0ywpYTJqVlrSGr772UIa3RGpugmu+vIKVR2Y45BkzH3XVWgee00IrQtTrBRRQ7HIKFHwMeAyjWmEJ2aC82i1DTKgSEWFVgoAA+iRMqhKPOdmKAF9euYSERGEkNgSwn7RAU/NZ5WM3+04FL4D94MOfZf2VZ7FgSY7l+2XZ9li84txWj5f9+6ZA4lkZdgi2bIyxYHE+sKQEplH9vLO3ceY52+gbzNPVY7grUsALX7GFP/9uCV/55CGA4IG7ejnvOadw3MnDxBMOd9w8QLEgOeGU3TiOaBDyA9DGgq8G0lIcefw4Rxw3jlJGlXV4V5QPveXYyneWPd2oTBady4HWRgusKznlSeI6TIXoJhAgm5SQ9jaGt0QY2hiruR/AeJRcf/kS3n7FvRSyFrf9ZgFWSKGV4Dlv3saJ5wwFHHF+oRMY9jKEJbH7+81irhTYNiqXQ6ca/YQJhRCWRFgRxGDYuL35zHVTLFa8G+ohY8HTKtUjrdXY8XCc0ccjjTdBzuIv314y48AggaJWhKsE9GBqBLWa0BbRkuV2jM1Ohh1unh4Zotvb/aeUg0TQF9BwTkibfhmuZVVj5DNS2qlpcOeriHAa2OLk6JMhEtLG1RpHq6ZKrmD6EFEsJCZzuOK2qwiXIpS8EtA/bxjk0+8/AqdUe76LlrUuLboObLi/hx1b45xwyrDva7SGfN7ije9+2Df7iMYUzzxzB3/5/RLuu6MPgELe4m/XLgLg7Fdv5twLHsUpCYRsLKlprc3geF1En7JNNe60sbhi6YosF3/pDt7yspMqr7GSCUg2mjuB2SwFbmriMaRlVTKFZuY9e9vGs5CxAkUO82kLO6w573Mb+LeLNjGxK8zAikJb8vbzBZ3AsI8gLIvyqImMxVAloyRZufekhdUzNY4qhCBQ3EgIZDJh9OyrJDhENNpSZiN2bo711GYPucngmyAzNrNLJC4sllhGKFAwVQoy/9MIbyR16isJhDaubWWdpYmqBb3XYyH77R8bnZ+pfG6XsMnrYFloF210nLxyVJewSbQhwK2BRWVlVkBEFOWqx3En7+ZtF93HJR87lJLnoXHkcaOEw60XjlJJ8tNvr6J/QYFjThwl7OOr7JRg8dK873NlRKKKpz93ZyUwgCm9lP2spRCV8zWXkK5IfBiRw/Zg2bByTYaf//1PPHBXL9/6woFsfDi4AStCIaMIUO9kKAVWon0nt73ddF58QBbLJ5OyIy7HnjkVsOM9LvGeJ1/LtWPt+QRACIHV020kNLq7sXr7jKFP3YyiCBo+18YntyLB0d2FNdCP1dPddupdPdK64vAMyqdBGYq6HPW86atErcndzAAAIABJREFUCgxTuWzzWT2mOKqKjKqi78Irhags8haCxVaEtXaCtXaiKeksr/1lOGaCtHZQ6Bp2tR/XR2JGYi2fkdpIVPOsM3fyo+uu5/kvM/qQZ7x0W4NMdzW0hp3bolz0lmPZsjHJnf8Y4Ltf2Z/6qmE+J7nrn/24zRRNvePVZywAPbIxjFb/fcr2rI73G/iJCdZDSpOlHLVulE9/+5+sWD2VDWutUfkCKptFF4vGVa+v1/iSeHIYIhLB6u+fU57Plg3nff4RwjEXaZsAHI679C8tkOhz+PzZR/DZlxzJDd9fhFt6YspdexOdjOEJhLCspgJhMhHHLRQaUm8Ri055SzdpzrVCue8Qjile9pFH+clF++MUBFpJQlFzEzz93F3TPm5MWP5idxj+QXUmUA2ldaXUs8KOGX0lb8FKYBvpDF1bnd7h5ilolwU0/g4aY/1ZhoUgJAQlrQMlOjSw1cmxyIoQo/n8f9ORWgGxuMvr3/Ew27fESXQ5TYVDS0XJf77+eEaGpuTYf/m9VVz766W86FVbOOyYMSZGw/zhV8vRWnPoUXc3PbdSUXLdVUsaHm+mYFsNG8E2J0dMWkbOXLfWhZISwhGXl79xI595/5GGZ1MvLBmysfr6jFth19wTw6zGEaeN8d4r7+LGHyxi7PEIB58yzn1/7uNXn1xVUWnd/mCcO38/wIXfn2JQPxnQCQxzGMK2sfp6cVNpk3pLiYjHpiY8ZgHl4PDUc3az9KAc139nCRNDIY549ihP/X9DRJo0QAPPO+hxIUCbHX7e4wlUN30dz9gnKewGq04hBFLDLjdfM8FUXt53unkWW7UeF2OqWAk0ZR+Hcjkqox2yyqXPCmMhyGuXYbdIEYWD5nE3X2mRlt9b/b3azcyiMcXZr97MDX9YzKFHj/k2lAt5yU3XLagJCmWkJsP84Ktrax6TUpOaDBGJuQ3s6GJBgoAff3M1jz7YyJRPKafGg6IZltkxSih2OnkW2VETHLzngpRlLcvwIMBzNazvg5UcVDpTY2M7EwSpC882Fu2f498u2gzA5juT/OKjq2v8Too5i43/7Obhv/Vw0MkTAUeZf+gEhjkOEQph9/e1fuEeYP26s9h4wVp+89rP8OovPLLHxytP/NRDaU1KmR38djdPnwxVFty0chhRRTQQFsK3ximBkJCM+WQcGe2y2cmSlBYCQUY5lLyw0et9TvVimMAmadmVhS2OxQo7xlYnS9F7Xzno7FZFUl7AUgBa02eF25bbGFhY4Lorl/DclzzO6gPTxOJuZQNdyEt+/cOV/OBr7TdTlRK89w3H8f7P3M2qtWmUgkzK5rorlzC0I8YtNw4GTkaltEOPDhHxuCV+siMV1jkQ1pLFdoxNToa4N/klEYENZK013ctTFA9YgrzRf0JH5/Ozli20Q+TceFsXN/14IcWsxbEvGOaI547OyPZhw809OD5lo1JB8oevLntSBYaWWklzEXNdK2k+Y7Z2YElhsai6+Yyp3+9yW0soJz35DKtu4XW1ZpebJxPAdQjCajuO3YYwnvYIa9KTxJhUJYZVsaHoJIDVdsI0ncUU6RAaM4lSUfDbK1bwf184CMtWnPzsXZz4zN1MjoW4+hfL2dSkUdsOBhbmiURdtm+J04qWV/237RI2CWmhtMYSkrg3Nix8voPSmiG3QJe0iYtGkcPqfyutedzNMTYpeN/R63D9SH9CYC9spB/qcsmpfLwyCc62W2ZoD/z3Sq551hcbHr/mK8u4+pIVOHmJ1oJw3OXAk8Y5/xsPTjs43PyzBfzwvWtRPt/JshX/c9ctc16eu12tpE7G0EENZktKI61d8h6HQCLIaqdmVLT5ex0GCdfUtbXXF5huUABDpmsHQoiK53R53DUkJNvdWsKh6UNkWWxFiXi5TV4rstohUYxVRkhLRUEmbfOL764CwHUk11+9hOuvbqz9zxR+5ad2kNIOqSqimY1goMp+tRoC45BXHxRgqqQERutqyC2Q14pYFyw/LMNjdydB173HZ3LOzWTRmUydVsfUHFozET2AQ963pWHabnxXmN9/cWWNB3Qxa/HQjb185bxDCccUxzx/hGNfMIzdhGlexuP3J1CO/7UUiiq23Zdg7Qk+igTzEHNnDKCDOYP1687i9D+/fY+P43iuaaNVtf528biTo6hVZSomqx22Oc3HAgWmH7C/nWB/O8ESK4qNML2IGWTGUhgTIb9x2BKarW6OjU6GjU6GbW6OUVXi4+88mjtu7uexRxP87icruPCcpzI2MvMBgX2FiJCB/A2F4aMEQQGPemZI1RIl27btR80S4xEgZDJp/q6OYxrUuRw6na4tT2lvysDLItT4OLoNBYHqabuHbuzBshrfU8qb4HDPHwe44gP788WXH45TbJGR/LWXv/1wMUFZmXIFyf4nhxwGdDKGeQftumjHRdjNJ5r2FH47sNlCvwzRK8PGTwHFbm+XCVPcg0ErjGDKWzkmbMLCCZTMAFhmxYiIKTJdAouoHWO7kydmx8yYryfkV96PtuoTaExfw0/wD2hwgbjj5gHuuHmgrd/hiYCkLFQIWe2gMNnCYm+8uB7K06YqlwX9SkdpZbo5P5s8lhufvV/lOWGDNTiAzhtlYGGHENEIulgyumDTMVLWRmFYJFoPXpSz3kjcbbL1Nd+hmLV4/IEEt/9ukHVn7w485t9+tKim6VxzJKlZuCb3pPFigE5gmDfQWqPGJ4zDlRAVLoOcBndhJphtldbyhE95EYpiscyKsdXJYQnBUivaYKpTboQusiJsrhLeCyHok2GiUlLSuiYolN8nNUSlxWNOlj4ZIiosiloxpooeu3qKK+JXXxcE75ZDCKLCwkGT0y4v+s57WMOGPf6N9hYSwmKxFa30TAQRhtwCdhMFWUcrbNHIXBdCkM1YjI+Geder1zE57l/mEVIazkL5vY6DGp8Zk76djKGM9evO4tN//XVbRcRi1uKOqwaaBoZioKOeZnBlnjd/e24ZY+0pOqWkeQKVSk3ZHnopty4UUJnMXv/s2fJ3kNAwHQRm8e23Qiz1dq1BPgwWUz2AiJCstON0SZuIsIyxjt9nCkEUiYNmtyqy1c2xSxUoev/e6GTY5JVBFLVktjLJbZUdZ7Udp6vKYGiRFWGlHWeBFWGpFWWVHeeEX96zpz/RXkNXT7GSFVRbli60IvT5kN7Aq/Brn7+XEKQmbC752KG8+d9OCgwKflC5Ge6qhZi2yur689/Cm79zP7Fuh2jSafBCnzq0Jh7gCV3Gkc8Z9X0vwOsvfZCeRU+eMhJ0AsO8gNYanfNXXJ2utPdMsX7dWbzoO+/Zo2OEkP7EN6+W304XoMxxXiAjFWZ1+Ri+Etpak5A2C7zSVcPzmHKQizEQymrXyGlXiekJIbCFZKEVoUvYdAvbcC2qFlgLwX99ujnpbCZYvDzLAYdOEAqXS1maI54yyhkv3cphx4zRUozOw1Of5T86KhAN4oRllAoSEWB8IST89ZrFvuzqpnBnqCcUshHhNm3oPIQf3MbnLnktn7z1Fl77pYc577OPeH2A2u9kRxUnv2pn02NZIeXLXBcS7rx67pYOZ4pOKWm+Yx+OG6+5dAPrL515aamE8t+Zak1JKyJNXNeUNoqo5W8bDRg/ra6Bl//bwmQqcWmzxckGLqUlNNvdPCEEy+wYobrPkEIwaIUr/13/3OoD0gwsyDOyu3FSaOnKDM9/2TYWLs5z+039XHfVUgr54O/bN1jgQ1+4k/3WpnEdgRBw+SVred7Zj7NoWQ4pNVoJtj0W56ILj2HpihyZlM3WTf78gEhEUSpIItE6Fn1ArUUp+ONvlnLiqcMMLiw0PNfMc7opwiFo4dYGGBq1ECAFMhozbP8ZlEzDD24jFNEcftoYYAhrXz73MIp5M6vmlgQves9jrD7WR9SyCm5JYodVjcw2mNuvkN17vb4nCh0ewzyBMzJaa25SRjiE3Td9ApzWGp0voAt5EBIZjwXaJ/phpsGhvscAZtHf7uRZajc2QMuibgXtst3NE/X4ERb+7OPq67n+eVdrht0Ck9q/bBAVksVW1MhGtDi+33P5nOStLz+R7VtqlUWPP3k37/v03di2wg5BLisZGYryjvPWkU37/+Zf+tHN7Ld/qsar2XEALbCrxN0M4UpTKFhYlmbnthgXv/2YBoLb4uVZLv3pTUSi7e3YC3nJlz9xMLmMzbs/cW9lBNd1oViweM+/Hz8jDobWGnf3cNsbGpFMYrXRcG6F6utVubDhHz3kUhZrT5hsy5BqdFuEjz7r2JrRVzAeDBd8937WrpsfY6qz5vncwRMDVSzijo3jjIzgptJIPxljIbC6Znhzjo2hJieN324+jzs6hptp38R8pn2H3Z6InuONoeaVy+Nujhwuo6pYEW4DEzBy2mWrk2Wbm8dCmBHUgLIRTJWUfHsUQlT8HxqeQ7DMMlmCbOP4fkhPhjyi2RSk1LzjY/cRjanKIh+LKxYszvPiV27xPc7KNWmW7ZehXkPRsqgJCmD+bYcgkXSJxhQr1mT47Lf/yVnnbeIpJw0jpXn9zm1xfn75fhVRvlbrciSqWLVmkht+FuIDr1zNbdcn2bElwo1/XMQ7zls3Y2KeEAIR92dl+0Gn0zXBXrsubiqNMzqGOzlZ8UpvherrVVpw4EkTHHX6aNsuhf3LC5x+4VZCURchFaAJx12Oef4w+x8/P4LCdNApJc1BuNlsjV+DdrLovET29xmjHscxhj6JOMKy0KUSKpPxxlhtZDJhdO8DoPN5YxxU/3g6ja4S6GuFIH+HVhhTJV9ZizFVIqtduoWNQJDWtdad9XpFlfNusouvhtKaUsOAafnYe3YraA3f/PwB1M+5r1iTJhRq/MxIVHHyc3b5WmX29hc9xnDt+9qppFiWkeB47Vs3UCxaDO2I8t7XHU86FWLjg12VCk0rZDMWm+8R4Djc+48k73+FKVGJSASrd8/kLGQk0v4mRAhzrYZDaMeZEuUDdKmEm89j9fa11X/Y0wm7M962jUOeMc4tP1+IUxIc+4JhDnraxJNKPK+MTsYwx6C1NmSfeiiFzhewurux+/uxursQloUqFHFHx9CFIrguulDAHR1F1+vdV39GPqDGKwS6OL3piti5uVmbWgIoaMVuVWRIFRr8nIMyBbN/aw0jc1EbEMuMX79pqZr3tlH6WLG6cbHLZ22sAK+LXNY/GG14sAvbJ5i0CyGME1w84bJsZZbXv/NhAF70qi2Bi1j113MdyGcl1/+6sY+gCwVjOhUA7bq44+M4Q7txdu/GrdvxAyZ6hdoMxFpXvM5VKt2Y6mhwJ9vfse/ptbrq6DTnfGwjr/zvRzn45CdnUIBOYJh7cMrWh43QxcYFXfndFBqjyBqEZhmBnNmVPhtTS62QVW6NT0IZgRPmXllKeVacj7u5GrntqJDsZ8fplSHCQjZd/FtlI0JAT1+jIdCu7TG2bkpQv5bmsha//dEK32Nl0yG+/7X9A32ayqdZKoqWJaFQWPP05xrp9HJZyQ/plIVTMj2EO24e4O0vPoiCH6FLCLQT4MutFO7IqNmkaA1KozNZ1MSUuJwulnCHRxoz1qBrMmRXsl9dCjBcct2pv3WhYBwSmwSv2dzIPFnRCQxzDVIGF4Drbh43nw9mjzbJGGQsoMYrmFYDuh5rLt3A+nVn7bUbL60dXBqNY8oEuOox00qfAphQJbY4OQp1RLWFVuPI60yHMXJZyW1/H/R97uPvOoqhHTGyGYtM2qJYkFz7myX87U8LedYLtvOuj9/Dqy98hMXLpjKOX1y+ik2P+JdsSiXBjX9cyP994QC2b41RyDe/jcsB4fc/W+57uWgNX7z4MF687tm8+Phn8+G3HsvuXQHlIq0Rtn+fRmVzvteuLhSN/IXWuBMT/td39YmVg3DIxurtrXo8+HvqkoO7exg1MYFKpXCHR3AnU4F/z/I1OrI1wo6HY4FB+F8VnR7DHIOwLH/rQ2jwYfD1ja68OPgmEuEQIpk0JavyTSjA6u1tayRQK+XJHChzrHC44X3r151F8eDlXPrdS1oer11oDAs55NMPUMBut0BESHplqHI+Fsa1TCIYUlMZlwT8mA31waH8X83KTLms5KF7e7j1Rv/AsHtHjDe+6GkcevQ4fYMFHrqnh9REiC/+4B8sWpYnFncpFQUvfuUWPvnuoyoB5opvrOGdH7uPWHxq1VIKRndH+NR/HgkIrv3tMs48ZytPO20Xy1ZmicVdZNW67ZTg5r8YJdO//mExp5/9OIcfO16z/7jhDwu56TrjB11+TCbiZuNRH4QjkWAplqDNiBCmL6ZpKYMh4nFkNAJCNgQgEYsZob16RCImK6k7V53LoSNhXzMr7bpcuP8phKwCwoJI3OXVX3hkxv7mTzZ0xlXnILRSuOMT5kYry1/Uje1prXGHgin8IpnASvgbsld/ji6WQApEKNReUCgWccfHqdJVADtk7BoD3h8kiTwTLPJIZn6jqNvdHD0y5Pu80prNTobyEiuA/e2EP7FLK4bdApaQ5JRLTEoGPUJd9fE2PdRFaiLEdVct4bqrlvhLTAfgnNdv5OVv2NQwPjo+GuK855ziWa1q/uM/H+S5Z21HuaZsVCxK3n/+cTz26NSOvqevyGe+/U/6F+QrY6VCQDYjSU+Geed56ypCfkJojj5hmOe+eBtOQfGLby7h0Y2Dvn9/XSpNmUQJgYjFzGBDwN/ZTaXRWf+mstXfDwLckdHmP4xlYQ/6E8a01qjJlOeV7nlChMPIRBw17p+JiHAYq6+34Tju8EhDkArHXN7/hzsYXNkGz2Keot1x1U5gmMPQrmsu3gA9emdod2DZyVq4YNY1lIJuKGhv3nw2NJeqzeyrz8tBs9nJstKO+RLlXM8noLqctMSKEq9zM1NaM6KKjKsSEtOcLqHpljYD0ohsu2gu/fhhXPOL5TP+Hl/+8U2sPrAx48tmDEdg8yNT46CLl2U5/NhxJsZD3H7TQEMA+q/P3MUJp+wmVDXK6romyIRCmmLB4upfLOOn31qN40gz5lnHpBexGFb3nnlDaNc1C3/9NemZTTW7fiqQEnuBf+ZV/TnacYw1rm17m5WAEpWP0ZUqFo1eU31PPKR41hu28+L3Pdb08+czOn4MTwIIy4ImCqoiHvdNrUUyuXeE9RwnMBDpfB5aBIbZsGPMa8WwKjIowxW1fhfNbrfAoAyDNrIZfnpMJW2Y1xKBizH9WWbFCHtSHQJj+TmhSiy2IiSqtJFGPF2l9z7jRV5Nv/b4aw+Z5E3/+SAHHT5BNmtz5Y9X8MOvrwnMIvIBrGcpdQMjeufjcXY+7v/bCqE58dTd2Hbt38WyoH+w5FUKHV722s0cePgkF7/5cF95FZ3LoeOxpmPOrSAsy1jRTqYqZEwRiSC9gCOEwOrtMSOnQWhjVLrBK71JX8zP+8HIcpT1daseLkmu/db+T+rA0C46gWEfQWttbhYpZ00uWybiKK2m9JKEV6OdBoFoehDBjfFpxKFygJgJBwJMMzmlSkSFhUIT91RDg05Bac2kKlXGUsH0JIbdAlvdHBEkISEoaEUJzSIvKFQHl65CjK9/6HhfGYvlq9P89zdvrfQCurodXnLuYyxYnOfzFx3ue06bHk5w8BG1445aw85tUXZsbc30FUJXqilCBOgZVR07ElMcedwoK/cbZdNowNRbodic/+Ix45rxXEQohD3QH8gtEaEQcnAANTzi+/6ZXLtCCGRXV+2EngBs23fQQoRCgdexiIRnXVF4PqITGPYB3GzW4yZ4d3IohNXb0zaRLAjCYz7rZNKk59JfDG3WYFtmR+dTCgicdGqC2Lm5GXs+KIy3dAjh679cXphcYEIVsRF0VXEVJGYqyXWNBlOh3HSFikBeNaJxxSvOf5TNG5Ls2BpHa0EiWeTDX7yTQ4+eaAiM0Zji6c/dxbe/eECDUU805vCsM3f6zsCX3d2kpVi2X5ZMyqanr0Q86bDh/m4iUZcL3v8ATz11N0Jq7v5nP/ff2cuhR41jtbibtRLsf2iOTXcEBJ4g+W1XGZZ8Wd3XtrF6uptnF0qZKSXXhVAIWUWclJaF7u5CT6Zq3xOyEdGZOdLJWBRsC5XOmJ6IlIHHEraFiEZNllsNy6q8Zzay2/mMTmDYy1CFYtX0UHkAvYQ7PtFQ+6yG1hpdLJmLV2sjIuYz/WM+RJn/CdEerXWGqJQCxsZrdlwiEp7xDQ17dhMmmjCWR1SRMa9XsNpO+Arf9cswWXdKobaZDejqAzN86Uf/IJex+PxFh/GeT95Dd68T+JMXi5Kl+2UbAsPBR07g+lhECgFnnfcY2x6L89YPPkA4oohEFEobHSYpIZux6e4pVaQxjjx+lEzaJpe1SXQFnwuYq29odxfgP5vpV3Ypy6fUEDE8BrI1OOC7udHFkjeg4F0jhQJuNos10F95vRWLoWwblcmC65prOxGf1samOivR2nAmKAcv10Wn0riFotmE1f/tu7vQ4ZAJXkpBNIKVaGys/6tmD53AsJehsgF+CaWScbUKKCupVBpdpV2vCwVENIrV0z31mFK4E5PmZqhML7WeRtoTiFAIa8GgYU8rb1x1D7gP1ZhJgAgandBVz/k7NRjUm9Q4aNITIbrrNHS051Efi7vE4i4XffFOQiHddCEOh1WDdhJAIW9h2f5nnkg6vOcT9xKOTD1vAYmkydKisWLNZ5rpZsWvfrCCl75mC6EA7+JSSTD0eJi7r2Mq6ysfSGtkQAariyV8h/y1RuVyDdea1hp30qcRrBQqncbq7q68TqXSlb6Vdl10IY/o62tZatWua6aTvCAgIhFELIr2U20tFtGlUoOXgxDC9CaExwXK5lBaI5PJht/hXzF76ASGvY0g/XkhjMyFlA29B+04NUGhDJ3PmwahtxCrycmpHVKZ2JXOoCwLuQc7+FYQQiBie+/41QS54sHLefS1MnDcNa0c03QOeA6MnLbfcqm1Jl8lu/Gqj7ybU3fcyjs+ci9dPU71utkQAOqbvfXI5yQ3XbeQseHGXfiDd/fgeuOn9ceVlrGKnA5icUWyy+WR+7tZe+gk4argoDW4juD2v3bzubcvh1LVIm/byHjcZHxBEc51g6OvH7tYqcBrXhemmMuVkk/NiWrcyRR23XhpzTGUwh0dhSqfCF0oTJW5gj63LjCY44zVBDCdy+M6bmAm/6+UPXQCw16GCId9F3nQuMUSjHmEGq0N07Ont+YGqocqFLBCIcNBCHidymT3SmDQSpkgVj8VshcRfnAbh7wP1hPMpn7G6TtYf/H9hCJTC9WQW8CpWtFGvEmmcjmpLOc96prfcP26s3j6kbfx0a/dXuECgFnntG4cDmtGUC83hI9aN8Ib3vUQP7xsTY28ttaCn1++ivMueLThvUo1HUTzzVByGYsND3Tz/UvX8q6P38vRJ46glSA9aXPZZw7ilj91k9uVaTzhUsnU25ukPcK2pzgD9c/5ZYrNUqjqRns+wGCqWMTZPezxJqImcFWPJucLNUFh6okmciZWYyYUxNKmVDIZRkAW/K+SPTyhPAYhxMuAi4FDgHVa61vbed984jEEzXaLWMw/YHiTFCqVanwOkMkkMhE3SpPNyEJCmECTTO5xqUdrjUqnzfRTFbHI6une4wb6bCHZXeL4p+9GCLj1xkFfu8kj/xP+/dTbWNCd5eFbk1z+5bU8tmFqdv8Tl93K0esaRyn9dvb5nCQSVS1bOsWCYPvWOG99+Ykod+q3isUdvnP1X0l21e66S0WB0oJIxH/XrVxQWlQyFseB8dEI57/4aeTGHVQ6TTJZIN4rGJnsR4QjvryFMmRXV9NJINNjGG9kNUtpegx+/JrRMV8WdHWZsxkHp+Y9kXCNLEaz7+J/ALAGBxuuU3d83H9jJQSyu6utjdV8DA7zguAmhDgEM2ByGfDuJ2NgAK8mmsmadNeSyHjCzI0HOFnJvl7UmD813xocMFLb0zA8sfr79ig41MuAl2EkmGfo5DUH8cM//4WevsYFzXHAKclKJlEsCEZ2R+juKZLoaq2Cms1YfP5Dh3PTdQtrHl97yCQf+NyddPU4oI2w3hc+fBhv//D99PQVG/oF+ZxkaEeU++/s5enP3YVlaW756wK+8bkDGdrsQrqxnyV7e9ClkmnM1qPNRdBsDDJml689bkJXYy2+8nrXNcGkPL2mtXlPT3clkLgTk41TQQGw+vsRnhqryuVQ9dNM3ncRyYT5nlX3hNXb4+sV7aYz/vIadZ/XDuZTgJgXBDet9QPQWrlyvkNYVgOr1MkE6ByVxyl7e1ATVXPZWiO7uyslHCEEoivZOPLnA5XONMgCTAdBvtK6UKix0pzv2L415hsYSkXJNz57EGees41Y3OHGaxfx88tX8bRn7+LCDzzQkpMVT7gccOhEQ2DY8EA3//78p7PqgDRSwKZHkmgteOsrTuSlr9nMiafuxnUE+bzEKQluvHYxV/98OYW8xSUfO6xyHOW6kPaXnlaTKaz+vkD/Az8doYbXCIHVlYSu9nwYhGVhDfR7AxYKUaWQWobsSuIWiy21kwC0U6os1CIaNQGw/n2WNCPTsdiU02GAYgAYvoSbzTZurEKhaQUFMOWl2ZR9mQuYE5IYQoi/8CTOGPzgptP+uzjAWmBSXzOyWjS7tHAoYGqkaLIR1wludLchM9AMTaU3vAzmyYBjThzhg5+/s6bHkM9JfvWDlXzvKwf4vufN/3U/z3/p402DQy4r+fpnD+IPv5y5hEYzuJkM2idbKMNetNDIUVdvNLzR49maKJsJjL1s3mQ0jusvwicEsqcbWRXAtOuiUqlKKUhEo00zmMDPdxzcVAqKnhZU+Th7sNGZ69nDnMkYhBDXAot9nvqA1vrX0zjO+cD5AFG5Zw5ScwEyHsfNNcpmi2SicoELIVru6EQ4jBUONxfVa/OGUcUiOpNFKxcRjhiHOClNA92v7CVl28eeD7jj5gE+98HDeeO7H2ZgYZ58zuIX392PH39zTeB7vvqpQyjkbV4ByGhNAAAWsUlEQVT4/7ZSLAoSSTPFU1aIVi6UihY3XON3C8wcWusKqbGpvWU5A41EEAsGjQ+Cxwp+ojM94QnzEYt5vbiRxgkoIRpHTS2rVo57pp9v2zPyS2+G9evOYveZ+/ODD392Vo+7r9HJGJ5AaKXMbr9QACmRiXjNzmi6CGrMyd6elsf17SNIgdU/AFqbEcG6a6Wd485FtBInBE04oigVJVq3t3h29xVZc2AKtOaVb9rEgUcYc5pND3XxuQ8dzrbNs8ctUbk8Kp2ams6x7anyST1CNkLISqnlicwQWkEXS4YDUc58QzZWT8+8zUjnYvYwL5rPlZP4Fw0Ms40yaagy7eT1IawWchVNG9lSIuIxb+zWpP3Cskw2MYcXGT9opVATnrSDMCJqIrF3CIHxZAkpIJ2a3d9IF4umsTtDiO4uZDg8JbcexKZ/glDJhIQAjXEt9DLnuXSe08FcChDzIjAIIc4CLgEWAOPAnVrr01u9rxMYmqMsdkaAR3LD60ulBpmLBkhpppvm2e5Na22a5IWiCQh+Ok89PcYcZh7AGRszNXE/VBs8WZY/Aa0aZTOjvr5pN1ynA+04HsvfDnR/q4ebydYaSeH9nSL+ZMa5jrkSHOZMj6EZtNa/BObGL/YkgpiuZlIztlYZSpnppipJjrmOis6P4wSzdzGyJfMlMDRj0ltdU5wVZ3Q0SBJpCmWhwfHxQE6C/9u0aRjnzW5exqK+gnpaa+OTUC3ZEgkjexq1i2reVyp5opPUXJdqYgKxYHBeZg7zjRj35OkcdjBjVOxEWyCIdzHb0Eqh8gWUNw47U6hszjRbWx0iaLGdBrTjoLJZVD6/R+fcCoHlO61Nr6H8uib+yL7vLTVpYNe8VKMmJ1Fj44aLk83ijoya0c86qFSqUbKlUDRyGE2gmvAb9tU1uLewt/zQZxsdSYwOAEMEqtiJBmEf7NRULm80oGpKCN0zanLrQnsEKj8CVNufobUZnSw3/YUAUsbqdC/0YGQijlsoNDLp65RBRTzWVD+oHlrrtiw1dLFoMoX6x1NpdHRKWltrHchQ1rlcc05Es8A6B3qie4r5kD10MoYOABBSYvf3YQ0OQEC9WczAc2E6MKqZ3qx9uU+iNWp8wug0TRftBDIhkEn/5rN2XdzJSZyRESOh4BM0daFYy+D1ztkdG98rmYOwbdPriYTN97MsZHc3ss49T0YiiPJj5dJi4O+hEeH2gphfUKg810Tjq/aFzX8XEQlgYms97SCuXRd3fBxnaAhnaLfxpZ4jwWUuZw+djKGDGpRnxKc0+Ktqwy2sO2cKrTUqmws0kgezIIlpunvJWAwV1Ki1LEQ4hEwkfBvq2vMcqJRAHBe3UGwY0dW5XHCpquRAmwvudCBsu605fiuZRMfj6GIJIQXato3XcV3ZSHR1tV+3D3qZEDXPCSGCx2hbZFIiHEJEIg1lI5FMTmv4QStVp1Om0dksynH2SAlgNjFXs4dOYOigAUJKrP5+KDmG7GY3ShrMFioN4lY17hns8io6/ZUyj/k/7UzhqHTa9zPVZAoxODXi6S/oXTnpaZ/zTKCLJdy0521g28hkAuntrIWUFQMeAYi+PjOllS+gvcaxnMYuXMZihpjpg3oyptXd1TjtJkSDPEzDcTy2sy4WTXAQAhmNTrs0F6SgqotFtOPstWt6Jphrkt5z55fpYE5BCAHhEIK9y1XQhSI4rcZnjMrmdCGEwOruNrvmst1j3dy+GWctonJZLzOKIuMxM+fvh7IOd5lRHI0GZyX7gOehisVawcVSyfw7gHxY3skrNwOOi8rnUe1YdZbfHwoZsbrqBrKgRiCv+rXWQD8qm/UW4hAyHmtr119h/e8JgTKoXyaYc4EB5lb2MLd+mQ7mFIzng0nnRSSyVyS2dbGxkVoDASJqSkjuxKS5oUMhQ7Brs6zQLONpcMorpXHz+UAPAnPAqiZvNAr5vFmEql7ut1DuDSgf1dvy436Bwc+gpmLV2eYoqJVIoKNRE9SFMEY/AdeGsCysruYZwl6DbU9NRVVDM6f5ONW9h40XrOU3r/3MPj+HTmDowBeV6SAwC+FkyrCo47PcZ5BNbtCQjUwk0ega7wntOLj5vGnC7sGuT7uuvyeG40A0Aj6NVhGN1k7/CIHV2+uVPYoghZGe2FcLT5AUhuv6Kt/qfFAg1qaP06Yzn7Csafd89jVkPIab8yknhex5w9pfc+kG1l9qAkXu+zEuO/CH++RzO4GhgwZopaaCAkw1YFNpdDg8qym4jEVx/XTxhekFACg/QyJP/mOP5MQ9VU3fhVJ7I59V5kQiEkb61MdnpewRdI5KGU0kS/rv5sv+zY0n5ft67effDCbbCXqOfZM9zjaEZWH19eJOpioBVEQivn/D+YDYubkaJ8O9WXLqBIYOGtBsJFHl81jJ5uq2hqCWB6/sU7/LroawrFrvCa2N/EZvL0IIszAGSDv4jY9OC7KJpaUlsbq60ImE+fwqT+59Aa210XUqT+YIgUgmsep26SIR9zdRCpggE6GQf0tciMCeiJvLTfl+CLzssavhXOYiRCiEPdBfGVGdj6zpIFSXnGbbD6ITGDrwQTOCUYt3um6NWbvO5SGdaaqzVJGEdhxAQLUPcVMP4T27yZvOxAvp6ftYeyQtrl0XlcuBq0wtvk0xuJqgAKA1OpVCWbKmdyBjMZQyY5imKQ4iFkcGlPxEOOw/RmpbvuUV7bq1ZlDe31+nUuhIeE7X6qvxZAoIfjjkfVtmNZvoBIYOGiDCEcC/qdlKU0hNVslBVx5UqFSq6ey9qNqxaqVwq4xYggTh9rjGXSbR+T2VyeBmMohEvGWGFARVKKDGJ6aOmc9DmaDWZKFSTWxfVSZbExiEEFjJBDoRr/gzNDu2EAKrvw+VyVTGeEUsavgcfqWnJhIUKpPB6p4/2ln/SqjOJmYSJDqBoYMGqJI/g1W00POvOM75PdcmK1Zrr9FcXzeXwgSccr0/Fg3cFbcL1aRkVjmfTBYVCk9b1bNcCmqA46CyOayAUk8rN7agsprwWNDtwASTJLQR8JqxhNtmOnfwhKKWYX1pW+/pBIYOaqCVCvSRFvtAgVTnG13tAFAa2dtTmcMXUqJLJcMhkKKhIVoJUo4D0kJEp0o4ZtGeaD9Y5bIwXR5FE1c1nc+DT2DQWjcPCuyZrtNMIOyAngSAUkYJtezrPE/KSh20RicwdFAD1ax0MDEJyURgM1kIgYhGfJvXItreGGQgsQxAKUQsZrKKiclajSKRNh7GZZvT0TFwPWVVAaREZby12i+4rXOakbZOezyIGjhOS/7E3pIlCUIrDSV3bIyKbEo8hkzumWdyB3MDc3/mrIN9i2b+wUqhJlO4I6OBonayq8uUNKqbx7aFbKamWY2gXaegwnkwkg51sgye9r/W2sg6V3swaO/5icmmqp9BaOW77QvbAsv/9pJBvZFmvhhC7DFvYyYQQgROOAGV3xZAZ4N7Ix3ML3QCQwe1aKcc4LqojL/gnZASa6DfuG11JZG9PVj9/W3PvctYzH9HLWVl99p0YS+VGoNGGZ6T2LRg2+acpoky8a1BXC4WDQw0wrIgoFQke3ueMAkHmUggksmp8d4m14iqEkLUSuFmsriTKVRu7/pUdDC76JSSOqiBDEdwAyaSqqEL+UBNfeHJJEDjIqeV8qZuiggpG7yjhSUNKWlicqrRGgoZLZ82hOtaLT2qWAyecgqHkcmk0U1S2vQtojP3Gha2jbVg0JStlDKqoS0Wd6unu86X2qiKTkfobrYhhDDNci9z0CXHlJD8FvrymHK9Om0+DxlvbHkekOP+1dEJDB3UQNiWqeP7SUXUvnLax67IIHtlKA24hUIDWUqEQtiDA5VyVf1CIqMBctpam4U/EoGg88/mkF1dRn665usIZFfSLOah2RvBLPdd2n69NIGxzDRWubwZnS0WkcnE3BB+a+LbXP6u7sRkbeDQ2mSa6UxLddUOnnjMgausg7kG2ZVEh0OeNaZ/M3gmHAKVzfpOHOlUCieT8WQnImYBtKxgYbZoBPJhf4G0TLYpoxnXRYRD3ix/1pDYQnagL0PDuWpt3ueVq0Q0arKeWW64qkKhZjpMFwq4xWKlz6BdF5VOm2a9FMh4Yo+ym+lACIHs7mocx7UkMh43AT2gV6XzeegEhjmPTmDooAFmlxtFRqMek3kMtLega09vZgZ196aTQOUsIp83C+BAk76E0uA0m15qptYqKmQ6q7fHfKbWUCp5vsUtzO3HxmuCZXk3b/X1ztqibMZWfcp5WuNOprB6umsNaBSoyUmEO3My3nQho+Y3Utms8cwOh83vJmVzt73OxNK8QCcwdNAUwrKwBgfMzlS5RvtopuWMZjv5anh9CCvhb7mpUpPNF38IFJcTdcc05vapmoa1m80aXaJyTV1rdLGEdhz/DMpxjEvaDDwjfKF18PcrlYw3t58BTSaLjsf3WQ1f2LYv81lIaVjsPr9Vu+qtHTyx6ASGDlpiqpm8Z5DxOKo40fqFAMUS+MSFsrFOS9g2IhxCZ8oaQmbssmFUNGCKSafT6GgEVSyiUyma8hK8jGPaJLggtNpVB40UC1ER/JsuKvaqnheFjMX2qDRl9XRPNZ89DSdsY6XawdxHJzB0sM8gIxF0IoHOZJoTucAIve3JZ8XjyEgYHY9PBQafRS5QFkN4mkWVsdwWGUoAZ2EmEELUWpK2C0+Zdrrws1dVTglRjGL1zKwRP5VpFk1fx7YhFOqQ3+YJOoGhg30KK5lAx2NGMlsI40DmswMOylCM5WhA47n8mmSiom0khGih0BrwuG4uP153UjMjwTWB7OrCVRqmQRgT4Zmpnfraq2rT79GJ+IxLh2Iv/C4d7Bt0Boo72OcQ0khHy3DYNG19ZvTV2DjuZMqXFGV1dzXujIVAdndjLRgM7E34QTaT6miHDGdZWH2zP5svhDC79aCgVv+4bSFmqHTazF41SBSxgyc3OoGhgycU5bl9v9KRzuV86/9lcx/CIbAM70IODlSmYqb1+Z65/XQh+3qxBgewBwcQob2TeAshjMSIH+oXcsc1JbqZIMheVYg98qLoYP6iU0rqYJ9Ca21q9/mCKTXEY6ZUEdBQVdlcw2hsjR81XgAplRAtfA6CUDa3r+0pBEPEY/uMiSxjUcBMTrWCzuXQyQS4yvAzbKutMlCgvSoz1InqYN6jExg62GeoqJ56QUDjlSqalXM8/oTW2pN4dv1lwVv4HLSCsCxkJIqb9TGPr0Y0ihW0i99bmIbGkFGVdWt9qnt6mpv3WBaytxc1MTH1eVX2qv+/vXsJkeyu4jj++91b/a7qydhjSEiCDxRhiAFBRHGhmARGiQlKBF0ISYRsFBSEYOiF4AMXATdGCIOKm8EHGIkgkkxAzMaIIoMkThKiIBkRVDRxxrG7p7qPi3trqv/dXY/u6ql/Vfr7WXV1FdVn/tTcU/f/OAdHD4kBYxNr63vfGfQqeqeqm1xsbla7ZrZ6d1yT6vpNo5Sl7lPq4aq1NUVzuFPSB1Edtmsr2m25UVbnAfZzce6si3RqFK1vVJ3WBhx8K+Zme7dXxZFDYsDY9CvJ7MUFxeUd9Y2KQkVzqTrQtdnnNO3VNxmxB7St4tix3XWUdoj1dXnI7nFx5YqiXW3XHLQWsetkdT3HX9QntA8qLv9vqG5t29ur4mgjMWB8ei1k2lVl04UFbV2u6xfVjxXqWa9p19svjN7EppiblU+saPO115J9/TsCHvg+sbWlzVdfrd6jc2ZjZqZv6Yyti5fSf2tdeC4u/VdebvXsrCep50nj+o0Gxgtsx5YDjE3PBjWu9+DXJRYax4+rXFqqdxgNuKh1SlMvLBxaSQqXZd8m98MsyFYX+TqxdKa/rlypft9Drz4Ssb5ebavt9W3eVtlc6nkocNztQDH9SAwYGzca8nKrbl7jq1Ml5fE+u4mKovedxtysilZL5YkVlcutQ50Td6Ox5zZWL7fkAaecI6L3Rb7PesrgoPr9zfp8x84xsFU0qWaK/ck6lWT7EUkflbQh6U+S7o+I/hO8mGrlwoJifr6a9rCrmkb9dszUB702/73jY1EWKpeXr2nBuM421s7aiOfmRl907rN47tnZvddh6lISxfz83n0oFFUTIFvlyhuqcunttjTTUDHGonp4/cj9iTkr6daIuE3SS5IezhwPxsD1moKHrJ3j2VmVJ1bkpcXq4txqqlxZGcsFz2VZ1V1aXBw6KfRbxO03rVO0mnue6O40tnFnOmnHmLnVvVtyWapsNVUev05ls0lSwIFkvWOIiKe2PXxW0r25YsFkc1mOrdfAYSiXW0lrS0l1l7je0zouy+ob/9patT7RKKsqp/XF3XbV3W19o7qzKIqevSOAUUzSJ+oBST/KHQRwGNxoqFxZqfpHtzelRkPF4sLAb/AuCpV9tsJebRW6j3ahwH5d88Rg+2lJN+zx1GpEPFG/ZlVSW9KZPu/zoKQHJWm+mJ5vjji6XBZTdZcDdFzzxBARd/R73vZ9ku6SdHvsVUqz+z6nJZ2WpGMz17MxGwCukdy7kk5JekjSByJicPUyAMA1l3vLwqOSWpLO2j5n+7HM8QDAkZd7V9Lbcv59AMBuue8YAAAThsQAAEiQGAAACRIDACBBYgAAJEgMAIAEiQEAkCAxAAASJAYAQILEAABIkBgAAAkSAwAgQWIAACRIDACABIkBAJAgMQAAEiQGAEDCEZE7hn2z/Q9Jf8kcxglJ/8wcw6RgLLoYiy7GomtSxuJNEfHGQS+aysQwCWz/LiLenTuOScBYdDEWXYxF17SNBVNJAIAEiQEAkCAxHNzp3AFMEMaii7HoYiy6pmosWGMAACS4YwAAJEgMI7D9iO0XbP/B9k9tX5c7plxsf8L287a3bE/N7ovDZPuU7Rdtv2z7S7njycX292z/3fZzuWPJzfYttn9p+4/1/4/P545pGCSG0ZyVdGtE3CbpJUkPZ44np+ckfVzSM7kDycF2Kenbkj4s6aSkT9k+mTeqbL4v6VTuICZEW9IXI+KkpPdK+uw0fC5IDCOIiKciol0/fFbSzTnjySkizkfEi7njyOg9kl6OiD9HxIakH0q6J3NMWUTEM5L+lTuOSRARf4uI39c/X5R0XtJNeaMajMRweB6Q9IvcQSCbmyS9su3xBU3BBQDjY/vNkt4l6Td5IxmskTuASWf7aUk37PHUakQ8Ub9mVdUt45lxxjZuw4wFgN1sNyX9RNIXIuI/ueMZhMQwQETc0e952/dJukvS7fE63/s7aCyOuL9KumXb45vr3+GIsz2jKimciYjHc8czDKaSRmD7lKSHJN0dEZdzx4Osfivp7bbfYntW0icl/SxzTMjMtiV9V9L5iPhm7niGRWIYzaOSWpLO2j5n+7HcAeVi+2O2L0h6n6Sf234yd0zjVG9C+JykJ1UtMP44Ip7PG1Uetn8g6deS3mH7gu3P5I4po/dL+rSkD9XXiHO2P5I7qEE4+QwASHDHAABIkBgAAAkSAwAgQWIAACRIDACABIkBAJAgMQAAEiQGYAR1rf0765+/ZvtbuWMCRkWtJGA0X5b0FdvXq6qceXfmeICRcfIZGJHtX0lqSvpgRFy0/VZJq5KORcS9eaMD9o+pJGAEtt8p6UZJG3UjFtXNeo5yfSBMORIDcEC2b1TVg+MeSZfqarvA1CMxAAdge1HS46r6+Z6X9FVV6w3A1GONAThktlckfV3SnZK+ExHfyBwSsC8kBgBAgqkkAECCxAAASJAYAAAJEgMAIEFiAAAkSAwAgASJAQCQIDEAABIkBgBA4v/6+9tsGUygmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# testing for different learning rates nu\n",
    "for nu in [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]:\n",
    "    test_two_layer_net(nu=nu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can observe, that for decreasing nu the speed for the convergence decreases. On the other hand a too big nu does not converge against an as optimal result. For smaller nu (.001) it also seems, that we might got cought in a local minimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nu = 0.001\n",
      "errors = \n",
      "[4.804354107081105, 0.2715155773786812, 0.24186504376341833, 0.23038211548700027, 0.2198710274130424, 0.21075126559111956, 0.20465971521462978, 0.2006199588439357, 0.1963284295516966, 0.19255382580470534]\n",
      "runtime = 11.736968040466309s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 0.001\n",
      "errors = \n",
      "[0.8345123080488744, 0.21360078585219075, 0.20551854779722892, 0.19991118575081526, 0.1958509453259831, 0.19268760603676277, 0.1905133823420486, 0.1889113999501826, 0.18757880048673933, 0.18617696729757138, 0.1851871962457977, 0.18422015268974967, 0.18251935064149005, 0.18085848436311114, 0.17966319428254302, 0.17844141596738392, 0.1771456320364131, 0.17619765124935743, 0.17563880665407094, 0.17440149900707186]\n",
      "runtime = 23.342732667922974s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "nu = 0.001\n",
      "errors = \n",
      "[1.4796213790410409, 0.20904409488728778, 0.1978731327651603, 0.19221127282861586, 0.18873789054770196, 0.18678137330523514, 0.18434257976581628, 0.18257558325839834, 0.18008482850118415, 0.1778065869209826, 0.176161313169211, 0.17453583556876456, 0.17321445236307378, 0.17167287952352175, 0.17026842127991157, 0.16826092849417978, 0.16670789304173592, 0.16559959467014682, 0.1635789067731162, 0.16218048350836792, 0.16023853124048457, 0.15887559725857345, 0.1567536834225225, 0.15543674667576543, 0.1536842170901788, 0.15196797037097978, 0.15077206412319197, 0.149666816373745, 0.14831292701110088, 0.1472395004443205, 0.14607650116873291, 0.14524241636507146, 0.1435320781098006, 0.14208052453109446, 0.13783309018680143, 0.1352586135823846, 0.13396782326474485, 0.1331837362311137, 0.13180631330696094, 0.1311283140822732]\n",
      "runtime = 46.64025020599365s \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# test for different amount of iterations S\n",
    "for S in [50000, 100000, 200000]:\n",
    "    test_two_layer_net(S=S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can observe, that for increasing iteration numbers, the accuracy also increases. But one can also see, that for several iteration there are almost no changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_MNIST_data():\n",
    "    # Load MNIST data\n",
    "    (X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
    "    img_rows, img_cols = 28, 28\n",
    "\n",
    "    # Normalize it\n",
    "    X_train = X_train.astype('float32') / 255\n",
    "    X_test = X_test.astype('float32') / 255\n",
    "    # Store it in the correct format for Keras\n",
    "    # The image data has a single channel (grayscale values)\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "        X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "        input_shape = (1, img_rows, img_cols)\n",
    "    else:\n",
    "        X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "        X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "        input_shape = (img_rows, img_cols, 1)\n",
    "    # Store the labels in the correct format for Keras\n",
    "    Y_train = keras.utils.np_utils.to_categorical(y_train, 10)\n",
    "    Y_test = keras.utils.np_utils.to_categorical(y_test, 10)\n",
    "\n",
    "    return X_train, Y_train, X_test, Y_test\n",
    "    #To use `X_train` for fully-connected inputs, reshape it. Use the `input_shape` \n",
    "    #variable in the first layer of your networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    \"\"\"Create a plot showing the training history of `model.fit`.\n",
    "\n",
    "    Example:\n",
    "        history = model.fit(...)\n",
    "        plot_history(history)\n",
    "    \"\"\"\n",
    "    x = range(history.params['epochs'])\n",
    "    acc, val_acc = history.history['acc'], history.history.get('val_acc')\n",
    "    f, axarr = plt.subplots(2, sharex=True)\n",
    "    axarr[0].set_title('accuracy')\n",
    "    axarr[0].plot(x, acc, label='train')\n",
    "    if val_acc:\n",
    "        axarr[0].plot(x, val_acc, label='validation')\n",
    "    axarr[0].legend()\n",
    "\n",
    "    loss, val_loss = history.history['loss'], history.history.get('val_loss')\n",
    "    axarr[1].set_title('loss')\n",
    "    axarr[1].plot(x, loss, label='train')\n",
    "    if val_loss:\n",
    "        axarr[1].plot(x, val_loss, label='validation')\n",
    "    axarr[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading and preparing of MNIST dataset\n",
    "mnist_data_train, mnist_labels_train, mnist_data_test, mnist_labels_test = load_MNIST_data()\n",
    "mnist_pixel_num = np.shape(mnist_data_train)[1]*np.shape(mnist_data_train)[2]\n",
    "mnist_shape = np.shape(mnist_data_train[0])\n",
    "mnist_data_train_flat = np.reshape(mnist_data_train, (np.shape(mnist_data_train)[0], mnist_pixel_num))\n",
    "mnist_data_test_flat = np.reshape(mnist_data_test, (np.shape(mnist_data_test)[0], mnist_pixel_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.4 a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model with linear stack of layers\n",
    "model_54a = keras.models.Sequential()\n",
    "# add layers\n",
    "model_54a.add(keras.layers.Dense(units=128, activation='relu', input_dim=mnist_pixel_num))\n",
    "model_54a.add(keras.layers.Dense(units=128, activation='relu'))\n",
    "model_54a.add(keras.layers.Dense(units=10, activation='softmax'))\n",
    "# configurate  the model for training\n",
    "model_54a.compile(optimizer ='sgd', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 1.2459 - acc: 0.6852 - val_loss: 0.5964 - val_acc: 0.8559\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.4940 - acc: 0.8702 - val_loss: 0.4043 - val_acc: 0.8917\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3854 - acc: 0.8933 - val_loss: 0.3441 - val_acc: 0.9040\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3404 - acc: 0.9038 - val_loss: 0.3124 - val_acc: 0.9116\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.3132 - acc: 0.9106 - val_loss: 0.2918 - val_acc: 0.9167\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2931 - acc: 0.9157 - val_loss: 0.2741 - val_acc: 0.9227\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2770 - acc: 0.9208 - val_loss: 0.2609 - val_acc: 0.9258\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.2628 - acc: 0.9248 - val_loss: 0.2501 - val_acc: 0.9297\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 3s 48us/step - loss: 0.2505 - acc: 0.9285 - val_loss: 0.2379 - val_acc: 0.9322\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.2393 - acc: 0.9315 - val_loss: 0.2310 - val_acc: 0.9338\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2293 - acc: 0.9341 - val_loss: 0.2207 - val_acc: 0.9382\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2203 - acc: 0.9372 - val_loss: 0.2131 - val_acc: 0.9391\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2118 - acc: 0.9396 - val_loss: 0.2045 - val_acc: 0.9429\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.2041 - acc: 0.9417 - val_loss: 0.1978 - val_acc: 0.9435\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.1973 - acc: 0.9440 - val_loss: 0.1932 - val_acc: 0.9446\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.1905 - acc: 0.9458 - val_loss: 0.1859 - val_acc: 0.9468\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.1843 - acc: 0.9473 - val_loss: 0.1820 - val_acc: 0.9472\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.1785 - acc: 0.9488 - val_loss: 0.1756 - val_acc: 0.9490\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s 46us/step - loss: 0.1732 - acc: 0.9504 - val_loss: 0.1713 - val_acc: 0.9513\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 3s 45us/step - loss: 0.1677 - acc: 0.9519 - val_loss: 0.1669 - val_acc: 0.9512\n"
     ]
    }
   ],
   "source": [
    "history_model_54a = model_54a.fit(mnist_data_train_flat, mnist_labels_train, batch_size = 128, epochs = 20, \n",
    "                        validation_data = (mnist_data_test_flat, mnist_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fms9c5Di88HnY/Us4/6Nw0+N9np1niiVTrNvSwBOv1dJwPMIFU0p44o4lfGTBZL3lUCk15okxuWn6rq6uNjU1NaO/o0Nb4OerrXvSP/ggLL+3966WvkTiKZ7Z/D7/8fp+jnREuXhGGfddew4rzp+obedKqZwTkS3GmOrB1ivc2zaMgT88Ca/8vdXscuf/wIxL+129K5bk6U0H+e6b+2nuirN0dgUP33YRV5wT0lBXSuWdwgz3aLvVDLPrRThvJdz0BAQq+ly1PZLghxvqeOr3B2gLJ7jq3Eo+v+IcLpsTynLRSik1cgov3Bu3ws8/a/Xj8qGvwfK/Asfpv0HZ2h3nqd8d4Icb6uiMJfng/Incu+IcFs8cuC1eKaXyQeGEuzGw+Xvwyt9BcALc+TLMPPW7Vlab+r++uocfbzpIJJHiuoWTuXfFOSyYWpqDopVSanQURrhHO+AX98GO5+CcD8HN/wHBvptVvvfmfp58Yz83XTyVe1ecw7mTirNcrFJKjb78D/fD26xmmOMH4dqvwhX399kM0+N3+5pZOK2ER29fnMUilVIqu/pPwbHOGKh5Cr73QetHMFb/Eq764oDBHomneOf9Ni6fW5nFQpVSKvvy88w91gm/uB+2r4O518ItT0Jw8MDecvA48VSa5XP1ThilVGHLv3A/st1qhmndD3/yD3DllwY8W8+0obYZl0O4tKrv2yKVUqpQ5F+4H9wAsS747C+sTr7OwMb9LVw0o4wi7XJXKVXg8i/lln4OFt02aN8wp+qMJtjW0M7/uWbuKBWmlFJjR/5dUBU542AH2FzXSiptWK7fPFVKjQP5F+7DtGFfCx6XgyWz9BuoSqnCN37CvbaFS2aW649pKKXGhXER7se74+w83MHlegukUmqcGBfh/tYB63e6Lz9Hw10pNT6Mi3DfUNtCwONk0fSyXJeilFJZMW7C/dKqCtzOcfHnKqVU4Yf7sY4o+451aXu7UmpcKfhw37jfbm/XzsKUUuNI4Yd7bQslPhcXTC3JdSlKKZU1BR/uG2pbuGxOCKdDf+RaKTV+FHS417eGeb81rO3tSqlxp6DDXdvblVLjVWGHe20LoaCH8yYV5boUpZTKqiGFu4isFJH3RGSfiKzpY/lMEVkvIu+IyDYRuX7kSz0zxhg21rawfG4IEW1vV0qNL4OGu4g4gceB64ALgE+JyAWnrPYPwFpjzGLgduDbI13omTrQ3M2Rjqj+pJ5Salwaypn7UmCfMWa/MSYOPAPceMo6Bui517AUaBy5EodnQ622tyulxq+hhPs0oD5jusGel+kB4DMi0gC8BPxVX08kIneLSI2I1DQ1NQ2j3KHbWNvClFIfVaHAqO5HKaXGopG6oPop4AfGmOnA9cB/ichpz22MedIYU22MqZ4wYcII7fp06bRh435tb1dKjV9DCfdDwIyM6en2vEx/AawFMMZsBHxAztpD3jvaSWt3XJtklFLj1lDCfTNwrojMFhEP1gXTF09Z533gWgARmY8V7qPb7jKAjXZ7u15MVUqNV4OGuzEmCXweeAXYhXVXzA4R+ZqI3GCv9iXgcyLyLvBTYLUxxoxW0YPZUNvCrFCAaWX+XJWglFI55RrKSsaYl7AulGbO+0rG453AFSNb2vAkU2ne2t/Cxy6akutSlBpXEokEDQ0NRKPRXJdSEHw+H9OnT8ftdg9r+yGFez7Z0dhBZyzJcm1vVyqrGhoaKC4upqqqSm9kOEvGGFpaWmhoaGD27NnDeo6C636g5/725XO0vV2pbIpGo4RCeofaSBARQqHQWX0KKrhw37i/hfMmFTGh2JvrUpQadzTYR87ZvpYFFe7xZJrNB1r1rF0pNe4VVLi/29BGJJHS9nalxqG2tja+/e0z79bq+uuvp62tbRQqyq2CCvcN+1oQgWVzKnJdilIqy/oL92QyOeB2L730EmVlZaNVVs4U1N0yG2qbWTC1hLKAJ9elKKWybM2aNdTW1nLxxRfjdrvx+XyUl5eze/du9uzZw0033UR9fT3RaJQvfOEL3H333QBUVVVRU1NDV1cX1113HVdeeSUbNmxg2rRpvPDCC/j9+fl9mYIJ92gixTvvt7H6iqpcl6LUuPfgL3aws7FjRJ/zgqklfPXjC/pd/tBDD7F9+3a2bt3Ka6+9xkc/+lG2b9/eeyvhU089RUVFBZFIhEsvvZRPfOIThEInX5/bu3cvP/3pT/nud7/LJz/5SZ599lk+85nPjOjfkS0FE+5bDh4nnkrrxVSlFABLly496R7xxx57jOeeew6A+vp69u7de1q4z549m4svvhiASy65hLq6uqzVO9IKJtw31DbjdAiXztb2dqVybaAz7GwJBoO9j1977TVeffVVNm7cSCAQ4JprrunzHnKv98Qt1E6nk0gkkpVaR0PBXFDdUNvCRdNLKfIWzPFKKXUGiouL6ezs7HNZe3s75eXlBAIBdu/ezaZNm7JcXfYVRBJ2RhNsa2jnng/MzXUpSqkcCYVCXHHFFSxcuBC/38+kSZN6l61cuZLvfOc7zJ8/n/PPP59ly5blsNLsKIhw31zXSiptuFy7+FVqXPvJT37S53yv18vLL7/c57KedvXKykq2b9/eO//LX/7yiNeXTQXRLLOxtgWP08GSWeW5LkUppcaEggj3DbUtLJlVhs/tzHUpSik1JuR9uB/vjrPzcIf+pJ5SSmXI+3B/60ALxqDt7UoplSHvw31DbQsBj5NF0wuvbwillBquvA/3jbUtXFpVgceV93+KUkqNmLxOxGOdUfYe62K5Nskopc5QUVERAI2Njdx66619rnPNNddQU1Mz4PM8+uijhMPh3umx0oVwXof7Rvsn9bS9XSk1XFOnTmXdunXD3v7UcB8rXQjnfbgX+1wsmFqa61KUUjm2Zs0aHn/88d7pBx54gK9//etce+21LFmyhAsvvJAXXnjhtO3q6upYuHAhAJFIhNtvv5358+dz8803n9S3zD333EN1dTULFizgq1/9KmB1RtbY2MiKFStYsWIFYHUh3NzcDMAjjzzCwoULWbhwIY8++mjv/ubPn8/nPvc5FixYwIc//OFR6cMmr7+huqG2hWVzQjgd+ruNSo0pL6+BI38c2eecfCFc91C/i1etWsX999/PvffeC8DatWt55ZVXuO+++ygpKaG5uZlly5Zxww039Pv7pE888QSBQIBdu3axbds2lixZ0rvsG9/4BhUVFaRSKa699lq2bdvGfffdxyOPPML69euprDz5duwtW7bw/e9/n7feegtjDJdddhkf+MAHKC8vz0rXwnl75t5wPMz7rWFtklFKAbB48WKOHTtGY2Mj7777LuXl5UyePJm/+7u/Y9GiRXzwgx/k0KFDHD16tN/neOONN3pDdtGiRSxatKh32dq1a1myZAmLFy9mx44d7Ny5c8B6fve733HzzTcTDAYpKirilltu4c033wSy07Vw3p6597S368VUpcagAc6wR9Ntt93GunXrOHLkCKtWreLpp5+mqamJLVu24Ha7qaqq6rOr38EcOHCAhx9+mM2bN1NeXs7q1auH9Tw9stG18JDO3EVkpYi8JyL7RGRNH8v/VUS22sMeERn1S8Uba1sIBT2cN7F4tHellMoTq1at4plnnmHdunXcdttttLe3M3HiRNxuN+vXr+fgwYMDbn/11Vf3dj62fft2tm3bBkBHRwfBYJDS0lKOHj16Uidk/XU1fNVVV/H8888TDofp7u7mueee46qrrhrBv3Zgg565i4gTeBz4ENAAbBaRF40xvZ9JjDF/nbH+XwGLR6HWXsYYq719bgiHtrcrpWwLFiygs7OTadOmMWXKFO644w4+/vGPc+GFF1JdXc28efMG3P6ee+7hzjvvZP78+cyfP59LLrkEgIsuuojFixczb948ZsyYwRVXXNG7zd13383KlSuZOnUq69ev752/ZMkSVq9ezdKlSwG46667WLx4cdZ+3UmMMQOvILIceMAY8xF7+m8BjDHf7Gf9DcBXjTG/Geh5q6urzWD3j/Znf1MXf/Ivr/ONmxdyx2WzhvUcSqmRtWvXLubPn5/rMgpKX6+piGwxxlQPtu1QmmWmAfUZ0w32vNOIyCxgNvDbfpbfLSI1IlLT1NQ0hF33beP+nvvbtbMwpZTqy0jfLXM7sM4Yk+proTHmSWNMtTGmesKECcPeyYbaFiaX+KgKBYb9HEopVciGEu6HgBkZ09PteX25Hfjp2RY1kHTasKm2hcvnhvq9V1UplRuDNfOqoTvb13Io4b4ZOFdEZouIByvAXzx1JRGZB5QDG8+qokHsOdZJS3dcb4FUaozx+Xy0tLRowI8AYwwtLS34fL5hP8egd8sYY5Ii8nngFcAJPGWM2SEiXwNqjDE9QX878IwZ5X/ZDfv0/nalxqLp06fT0NDA2VxPUyf4fD6mT58+7O0HvVtmtAz3bpnth9p5fU8T9644ZxSqUkqpsW2od8vk3TdUF04rZeE07ShMKaUGkrd9yyillOqfhrtSShWgnLW5i0gTMHBHD/2rBJpHsJyRpvWdHa3v7I31GrW+4ZtljBn0i0I5C/ezISI1Q7mgkCta39nR+s7eWK9R6xt92iyjlFIFSMNdKaUKUL6G+5O5LmAQWt/Z0frO3livUesbZXnZ5q7UcIhIHXCXMebVXNei1GjL1zN3pZRSA9BwV0qpAqThrsYdEfGKyKMi0mgPj4qI115WKSK/FJE2EWkVkTdFxGEv+39E5JCIdNq/KXxtbv8SpfqXd33LKDUC/h5YBlwMGOAF4B+AfwS+hPVrYz1fElkGGBE5H/g8cKkxplFEqrB6SVVqTNIzdzUe3QF8zRhzzBjTBDwI/Km9LAFMwfoWYMIY86bdjXUK8AIXiIjbGFNnjKnNSfVKDYGGuxqPpnJy1xcH7XkA3wL2Ab8Wkf0isgbAGLMPuB94ADgmIs+IyFSUGqM03NV41AjMypieac/DGNNpjPmSMWYOcAPwxZ62dWPMT4wxV9rbGuCfslu2UkOn4a7Go58C/yAiE0SkEvgK8GMAEfmYiJwj1g/0tmM1x6RF5HwR+RP7wmsUiADpHNWv1KA03NV49HWgBtgG/BF4254HcC7wKtCF9XvA3zbGrMdqb38Iq6fAI8BE4G+zW7ZSQ6ffUFVKqQKkZ+5KKVWANNyVUqoAabgrpVQB0nBXSqkClLPuByorK01VVVWudq+UUnlpy5YtzUP5DdWchXtVVRU1NTW52r1SSuUlETk4+FraLKOUUgUp78L9SHuU/9l+ONdlKKXUmJZ34f7s2w385Y/fpqUrlutSlFJqzMq7/tyXzw0BsGl/Kx9dNCXH1SileiQSCRoaGohGo7kupSD4fD6mT5+O2+0e1vZ5F+6LppVS5HWxcX+zhrtSY0hDQwPFxcVUVVVh9bumhssYQ0tLCw0NDcyePXtYz5F3zTIup4NLq8rZUNuS61KUUhmi0SihUEiDfQSICKFQ6Kw+BeVduANcPreS/U3dHO3Qj39KjSUa7CPnbF/LvAz3nnb3jXr2rpRSfcrLcJ8/pYRSv1vDXSnVq62tjW9/+9tnvN31119PW1vbKFSUW3kZ7k6HcNnsCjbsb851KUqpMaK/cE8mkwNu99JLL1FWVjZaZeVM3t0t0+PyuSF+vfMo9a1hZlQEcl2OUirDg7/Ywc7GjhF9zgumlvDVjy/od/maNWuora3l4osvxu124/P5KC8vZ/fu3ezZs4ebbrqJ+vp6otEoX/jCF7j77ruBE12hdHV1cd1113HllVeyYcMGpk2bxgsvvIDf7x/RvyNb8vLMHWD53EoANu7XphmlFDz00EPMnTuXrVu38q1vfYu3336bf/u3f2PPnj0APPXUU2zZsoWamhoee+wxWlpOz469e/dy7733smPHDsrKynj22Wez/WeMmLw9cz9vUhGhoIeNtS18snpGrstRSmUY6Aw7W5YuXXrSPeKPPfYYzz33HAD19fXs3buXUCh00jazZ8/m4osvBuCSSy6hrq4ua/WOtLwNdxFh2dwQG2tbMMboLVhKqZMEg8Hex6+99hqvvvoqGzduJBAIcM011/R5D7nX6+197HQ6iUQiWal1NORtswxY7e5HOqIcaO7OdSlKqRwrLi6ms7Ozz2Xt7e2Ul5cTCATYvXs3mzbdEbswAAAZDUlEQVRtynJ12Ze3Z+4Ay+fY97vvb2HOhKIcV6OUyqVQKMQVV1zBwoUL8fv9TJo0qXfZypUr+c53vsP8+fM5//zzWbZsWQ4rzQ4xxuRkx9XV1eZsf6zDGMPyb/6WS6rKefzTS0aoMqXUcOzatYv58+fnuoyC0tdrKiJbjDHVg22b180yIsLyuSE22e3uSimlLHkd7mB1RdDSHWfP0a5cl6KUUmNG/od7T7t7rX5bVSmlegwa7iLylIgcE5Ht/SwXEXlMRPaJyDYRyWrj94yKADMq/NoFsFJKZRjKmfsPgJUDLL8OONce7gaeOPuyzszyOSHeOtBKKq3t7kopBUMId2PMG0DrAKvcCPzIWDYBZSKS1Z9IunxuJe2RBLsOj2xfFkopla9Gos19GlCfMd1gzzuNiNwtIjUiUtPU1DQCu7Zo/+5KqTNVVGR9N6axsZFbb721z3WuueYaBrtl+9FHHyUcDvdOj5UuhLN6QdUY86QxptoYUz1hwoQRe95JJT7mTAiyQS+qKqXO0NSpU1m3bt2wtz813MdKF8Ij8Q3VQ0Bmz13T7XlZtXxOiOffOUQilcbtzPubgJTKby+vgSN/HNnnnHwhXPdQv4vXrFnDjBkzuPfeewF44IEHcLlcrF+/nuPHj5NIJPj617/OjTfeeNJ2dXV1fOxjH2P79u1EIhHuvPNO3n33XebNm3dS3zL33HMPmzdvJhKJcOutt/Lggw/y2GOP0djYyIoVK6isrGT9+vW9XQhXVlbyyCOP8NRTTwFw1113cf/991NXV5eVroVHIgVfBP7MvmtmGdBujDk8As97Ri6fW0l3PMUfD7Vne9dKqTFg1apVrF27tnd67dq1fPazn+W5557j7bffZv369XzpS18a8AuPTzzxBIFAgF27dvHggw+yZcuW3mXf+MY3qKmpYdu2bbz++uts27aN++67j6lTp7J+/XrWr19/0nNt2bKF73//+7z11lts2rSJ7373u7zzzjtAdroWHvTMXUR+ClwDVIpIA/BVwA1gjPkO8BJwPbAPCAN3jniVQ7BsTgVgtbsvmVmeixKUUj0GOMMeLYsXL+bYsWM0NjbS1NREeXk5kydP5q//+q954403cDgcHDp0iKNHjzJ58uQ+n+ONN97gvvvuA2DRokUsWrSod9natWt58sknSSaTHD58mJ07d560/FS/+93vuPnmm3t7p7zlllt48803ueGGG7LStfCg4W6M+dQgyw1w74hVNEyhIi/zJhezsbaFe1eck+tylFI5cNttt7Fu3TqOHDnCqlWrePrpp2lqamLLli243W6qqqr67Op3MAcOHODhhx9m8+bNlJeXs3r16mE9T49sdC1cUI3Ty+aEqDnYSiyZynUpSqkcWLVqFc888wzr1q3jtttuo729nYkTJ+J2u1m/fj0HDx4ccPurr76an/zkJwBs376dbdu2AdDR0UEwGKS0tJSjR4/y8ssv927TX1fDV111Fc8//zzhcJju7m6ee+45rrrqqhH8awdWUOF++dwQ0USare/n/jYkpVT2LViwgM7OTqZNm8aUKVO44447qKmp4cILL+RHP/oR8+bNG3D7e+65h66uLubPn89XvvIVLrnkEgAuuugiFi9ezLx58/j0pz/NFVdc0bvN3XffzcqVK1mxYsVJz7VkyRJWr17N0qVLueyyy7jrrrtYvHjxyP/R/cjrLn9P1R5OcPH/+2u+cO253P/B80b0uZVSA9Muf0feuO3y91SlATcLp5ZqPzNKqXGvoMIdrG+rbn2/jUhc292VUuNXQYZ7PJVmy8HjuS5FqXFHfzRn5Jzta1lw4X5pVQVOh7Bxv3ZFoFQ2+Xw+Wlr0V9FGgjGGlpYWfD7fsJ8jr38guy9FXhcXTdd2d6Wybfr06TQ0NDCSnQKOZz6fj+nTpw97+4ILd7CaZr7z+n66YkmKvAX5Jyo15rjdbmbPnp3rMpSt4JplwOpnJpU2bD4wUDf0SilVuAoy3C+ZVY7H6WDjfm2aUUqNTwUZ7j63k8Uzy7R/d6XUuFWQ4Q5Wu/uOxg7aw4lcl6KUUllXsOF++dxKjIFNB7RpRik1/hRsuF80oxSf26G/q6qUGpcKNty9LieXVlVouCulxqWCDXew+nd/72gnzV2xXJeilFJZVdDhfvncEACb9JZIpdQ4U9DhfuG0Uoq8Lm2aUUqNOwUd7i6ng6Wztd1dKTX+FHS4AyyfE2J/czdH2of/Y7ZKKZVvCj/c7XZ37QJYKTWeFHy4XzClhFK/W5tmlFLjSsGHu8MhLJtTof27K6XGlYIPd7Da3RuOR6hvDee6FKWUyopxEe6Xn1MJoE0zSqlxI//CPRmDut+f0SbnTiyissij/bsrpcaN/Av31/8Zfvhx2PH8kDcREZbNCbGhtll/vFcpNS7kX7hfeT9MvxTW/fkZBfzyuSGOdsQ40Nw9isUppdTYkH/h7i2Gz6zLCPjnhrTZ5XOtdne9a0YpNR7kX7jDiYCfsRTW/cWQAr4qFGByiU/b3ZVS40J+hjtYAX/Hz08E/Pb/HnB1EeHyuSE21bZou7tSquANKdxFZKWIvCci+0RkTR/LV4tIk4hstYe7Rr7UPmQG/LN3DRrwy+aGaOmOs+doV1bKU0qpXBk03EXECTwOXAdcAHxKRC7oY9WfGWMutofvjXCd/TuDgO/p331DrfYzo5QqbEM5c18K7DPG7DfGxIFngBtHt6wz5C2GO9ZlBPyzfa42vTzAjAq/fplJKVXwhhLu04D6jOkGe96pPiEi20RknYjMGJHqzoS3yA74y+DZz/Ub8JfPqWTj/ha9JVIpVdBG6oLqL4AqY8wi4DfAD/taSUTuFpEaEalpamoaoV1n8BbZTTT9B/wnL51OOm340COv87Vf7KQ9nBj5OpRSKseGEu6HgMwz8en2vF7GmBZjTM+vUH8PuKSvJzLGPGmMqTbGVE+YMGE49Q5ukIC/ZFYFr/3NCm6rns4PNhzgAw+v5/u/P0AilR6depRSKgeGEu6bgXNFZLaIeIDbgRczVxCRKRmTNwC7Rq7EYegJ+JnL+myDn1Ds5Zu3LOJX913FgqklPPiLnXzkX9/g1Z1H9TZJpVRBGDTcjTFJ4PPAK1ihvdYYs0NEviYiN9ir3SciO0TkXeA+YPVoFTxk3iL49FqYubzfi6zzp5Tw47+4jP/8bDUI3PWjGj7zn2+xs7EjBwUrpdTIkVydqVZXV5uamprR31GsC37ySXh/I9zyXbjw1j5XS6TSPL3pII/+717aIwlWVc/gix8+j4nFvtGvUSmlhkhEthhjqgdbL3+/oTpUmWfw//05+OO6PldzOx2svmI2r395BX9+xWzWbWlgxbde4/H1+4gmUlkuWimlzk7hhztktMHbAf/uM9DPJ5bSgJt//NgF/OaLH+Dycyr51ivvce2/vM6L7zZqe7xSKm8UfrNMpng3PH0bHPw9lM6EeR+1hpnLwenqc5MNtc18/Ze72Hm4g8Uzy/jHj13Akpnl2a1bKaVsQ22WGV/hDpCIWE0zu38Ftb+FVAz8FXD+dVbQz1kBnsBJm6TShmffbuBbr7xHU2eMjyyYxLXzJlFdVc7syiAikv2/Qyk1Lmm4D0Wsywr43b+EPf8D0XZw+eGca2Hex+C8j0Cgonf17liS77xey483HeS4/eWnUNBDdVU5l1ZVUF1VwYKpJbid46O1SymVfRruZyqVsJprdv/KGjoOgThh1uVW0M/7KJRZ3+UyxlDb1E1NXSub645Tc7CVgy1hAHxuB4tnlHNpVTnVVRUsnllGsc+dy79MKVVANNzPhjHQ+M6JoG+yv5M15SIr6M/9MEycDy5v7ybHOqLUHDzO5rpWauqOs6OxnbQBh1j301fPssL+0qoKJpfq7ZVKqeHRcB9JLbVW083uX0H9HwBjndVXzIYJ82DC+SfGoXPBE6ArlmTr+21W2B9s5e2DbUTsWyqnlvqYM6GIWaEAVaEgs0IBZlcGmVERwOd25vZvVUqNaRruo6XzKNS9CU3vQdNua9xaC+mkvYJA+azTQj9RcS67WtJsrjvOtoY26lrCHGzppi2j4zIRmFLiY1YoSFVlwBqHrPGsUICAp+87epRS48dQw13T4kwVTzr9W67JOLTuPxH2TbutYd//QtoKbzewqHQGiyacD5XnwcxpUDKVTs8E6pPl7IsGOdCa5GBLN3Ut3fx6x1FauuMn7WZSiZdZoSAzKwJMKfUxqcQaJpf4mFTqpTLoxeHQO3eUUhruI8PlgYnzrCFTKgnHD5wI+57gr/s9JCMAFGP9vNUFAMGJUDIVyqfCrKlE/ZNolhD1qXL2x0rY2Rlkbxu8ubeJps4Y6VM+dLkcwoRib2/gTy71MbHEaz0u8THJPiAUefWfXalCp80yuWAMRNugoxE6Dlt35nTa447D9vxD1jqn8pZCyRTSwYnEfJV0OctpdZTTZEo5nCrhYKyYA5EAe7u8HO5M0BlNnvYUQY+TymIvFUEPoaCXUNBDqMhDRdBDZZE9v8haVhH04HHprZ1KjRXaLDOWiYC/3BomLeh/vXjYDv1Ga+hs7H3s6G7Cf/Rt/F3HmJAIc/5p+3BAMERq4kRi3hDd7hBtjjKaTBlHUsU0JQMcjvloaPGxtd7DwbCbaLrvi7nFPpd9AOg5IHgoC3goD7gpD3goC7gpD3oo87sps6f1Xn+lckvDfSzzBCA01xoGEuuC7mPQdQy6jtrjY9B9DGfXMQJdxwi01DGh6yjnpmJ9P4cHjDtI0ltGwl1CxFVCt6OITimmLR2kJR3gWMzP0U4/h6Je9sTctKX9dBo/nQSI4gFOtPcXe12UBd2U+e3wtw8GPeFf6ndT4nNTGrDHfjclfhd+t1O/8avUCNBwLwTeImuomDPwesZArAO6mqwmn8hxiNjjaBsSOY470oY72kYgcpxQpNFe57jVTUOmU76XZcRJwlVEzBUkKkHCjgBdxk9HJEBbt4/jKS/NCWt43wToIECHCdJOkA4ToIMgYby4nQ5KfG5K/Pbgc9nBf+KAUOJ3UexzU+xzUeKzHhd5XRT7XAQ9Lr2orBQa7uOLCPhKreFMJSInHQiIdUK0wzpYxDqRWAeeWCeeaAfFsU57fgfEjlnrJTqB2GkHhUwpcRFzFhF2FNGdDNLZGaS9PcDxdIDWlJ+mpI9D6QC7TIAYHiJ4iBoPUU4MMTw4vQE83iA+n49iv/uk8O85KBR5rSFoj4t8Loq8ToL2vKDHhVMPEiqPabiroXH7raFkyuDr9icZsw8K7acMbRBtxxltJxBpIxBtp7J32bET65koDPU7XjFIx4R4h4cYXqJ4iBgPEeMmbDx0Gx+d+Okwfhrx04WPLuOniwBdxkcXfpKuIClPEeIpBm8x4ivG6w1Q5HMR8LoIepwEPC6CXmtc5HUR8FgHiJPGHhcBrxOP06FNTiprNNxV9ri81hCsHN72ieiJoE9GrOmecSIMyaj1CcMeO5JRfIkIvkSEUnueSUZIx8KkY12Y6HEkXo/EO3EmuhH6uHMsaQ/hnkknYfyE8RI2XsLG+gQRMfYBBC8tGY8jxh7jJSZejMuPcflJuwMYTxBxBxFvEIc3iNNbhNfrI+B1EnBbBwa/x0nQ68RvT/fMC3is6xN+jxO/24nbKXrgUCfRcFf5w+2zhuJJw34KwTr5P+0DQDptHSDiXdani1iHdaE61pkxrxNXrJOSeBcliTDEw5hEmFQ8jImFScfDkGhBEhEkGcGZjOAwidOL6DlgRE5flMBJ2PjoxkvEeOnGR8Q+kHTg46jxEu45WPQ2S7mJi5e000fa5QOXF3H5MW4fDrcfcftxev04PQGcHj8uXwCvx4vP7cDncuJzO63HPWOXE6/bOmicmH9imV7TyA8a7koBOBwnLkwXTx7yZsIg/4lSCevTRCJiHTzsg4I17j5xQImHIdGNO95NaTxMcbybVLSTdKzbOmjEuyDegiS6cSQjOFJRXKloH/uzh35uiupdzQgJXMRxkegZTOa0kyguOnERNxnr4CIlLhIOLwmHj6TDR8rpJeX0k3b5SLv8GKf/RDOe24/DE0A8fpyeIE5vEJfXj9vrw+t24XVZBw2vy4m398DiwOuyDipel7VMP5mcOQ13pUaT020NvpIz2szBEH4D0xjrOsapTVS9TVWRfpc5UnFcyTjEYziTcTyJGOlk3B5imGQcUglMKo6k4pCKQ6obRzqBIx3HmYrhTkdxJWO4kqd/UW4oUkZI2geSJM7eg0rSOInhotuel7SXp8RFWlykxE3S4bEHL2mHl5R9kEk7vRiXD1zWpxhxecHtR1w+xO3H4fHhdPtwuL24PF6cLi9u+7HL48Xt8eBxufG6nXhcDmtwOqxppyOvDjIa7krlK5ETTVX+M9wU6z//iARA5qeTZOanlBPXQ0wiTCoWJhkLk4x1k0rESdkHk1Qy0XtgMakEkkzgTiVwpRL4Ugmrf6Z0AkknkVQCSUdxpuO4TAx3Mo4rHcdjYniID17rEKTtTzWJ3gOOi3acJOxPMElxkbI/wSTFTcrhIi1u0g43aXGRdrgxDjfG6bHHbsTpBqcHnB7E6WHykuuYd9HyEam3PxruSqmzM4RPJyN6MOlP5ieZZMw+2FjT6XiUZDxMMh4hEY2QSsZIJeIkEzHSvZ9aEqTsTy3pZAJScYx9wLE+uVgHGWcqjiudRNJxJJ3EkY7jSHfjNAkc6SSuZAKHSeLqGezPHy7SvaW+VVQCGu5KKTUEmZ9kTuEAPPaQM+lUb1PXUufoV6LhrpRS2eBwgsOJ9HHwGZXdZWUvSimlskrDXSmlClDO+nMXkSbg4DA3rwSaR7Cckab1nR2t7+yN9Rq1vuGbZYyZMNhKOQv3syEiNUPprD5XtL6zo/WdvbFeo9Y3+rRZRimlCpCGu1JKFaB8Dfcnc13AILS+s6P1nb2xXqPWN8ryss1dKaXUwPL1zF0ppdQANNyVUqoAjelwF5GVIvKeiOwTkTV9LPeKyM/s5W+JSFUWa5shIutFZKeI7BCRL/SxzjUi0i4iW+3hK9mqz95/nYj80d53TR/LRUQes1+/bSKyJIu1nZ/xumwVkQ4Ruf+UdbL++onIUyJyTES2Z8yrEJHfiMhee1zez7aftdfZKyKfzVJt3xKR3fa/33MiUtbPtgO+F0a5xgdE5FDGv+P1/Ww74P/3UazvZxm11YnI1n62zcprOGKMMWNywPqxnFpgDlZ/P+8CF5yyzv8BvmM/vh34WRbrmwIssR8XA3v6qO8a4Jc5fA3rgMoBll8PvIzVad8y4K0c/lsfwfpyRk5fP+BqYAmwPWPePwNr7MdrgH/qY7sKYL89Lrcfl2ehtg8DLvvxP/VV21DeC6Nc4wPAl4fwHhjw//to1XfK8n8BvpLL13CkhrF85r4U2GeM2W+MiQPPADeess6NwA/tx+uAayVLPekbYw4bY962H3cCu4Bp2dj3CLoR+JGxbALKROQsfgF72K4Fao0xw/3G8ogxxrwBtJ4yO/N99kPgpj42/QjwG2NMqzHmOPAbYOVo12aM+bUxpufXMjYB00dyn2eqn9dvKIby//2sDVSfnR2fBH460vvNhbEc7tOA+ozpBk4Pz9517Dd4OxDKSnUZ7OagxcBbfSxeLiLvisjLIrIgq4WBAX4tIltE5O4+lg/lNc6G2+n/P1QuX78ek4wxh+3HR4C+fsR1LLyWf471Sawvg70XRtvn7aajp/pp1hoLr99VwFFjzN5+luf6NTwjYznc84KIFAHPAvcbYzpOWfw2VlPDRcD/Bzyf5fKuNMYsAa4D7hWRq7O8/0GJiAe4Afh5H4tz/fqdxlifz8fc/cMi8vdYP7v9dD+r5PK98AQwF7gYOIzV9DEWfYqBz9rH/P+nTGM53A8BMzKmp9vz+lxHRFxAKdCSleqsfbqxgv1pY8x/n7rcGNNhjOmyH78EuEWkMlv1GWMO2eNjwHNYH30zDeU1Hm3XAW8bY46euiDXr1+Goz3NVfb4WB/r5Oy1FJHVwMeAO+yDz2mG8F4YNcaYo8aYlDEmDXy3n33n9L1o58ctwM/6WyeXr+FwjOVw3wycKyKz7bO724EXT1nnRaDnroRbgd/29+YeaXb73H8Cu4wxj/SzzuSeawAishTr9c7KwUdEgiJS3PMY68Lb9lNWexH4M/uumWVAe0bzQ7b0e7aUy9fvFJnvs88CL/SxzivAh0Wk3G52+LA9b1SJyErg/wZuMMaE+1lnKO+F0awx8zrOzf3seyj/30fTB4HdxpiGvhbm+jUcllxf0R1owLqbYw/WVfS/t+d9DeuNDODD+ji/D/gDMCeLtV2J9fF8G7DVHq4H/hL4S3udzwM7sK78bwIuz2J9c+z9vmvX0PP6ZdYnwOP26/tHoDrL/75BrLAuzZiX09cP60BzGEhgtfv+BdZ1nP8F9gKvAhX2utXA9zK2/XP7vbgPuDNLte3DaqvueQ/23D02FXhpoPdCFl+//7LfX9uwAnvKqTXa06f9f89Gffb8H/S87zLWzclrOFKDdj+glFIFaCw3yyillBomDXellCpAGu5KKVWANNyVUqoAabgrpVQB0nBXSqkCpOGulFIF6P8H8e+2/KaWpxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_model_54a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.4 b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding dropout to the model from a) to hopefully prevent overfitting\n",
    "model_54b = keras.models.Sequential()\n",
    "model_54b.add(keras.layers.Dense(units=128, activation='relu', input_dim=mnist_pixel_num))\n",
    "model_54b.add(keras.layers.Dropout(.3))\n",
    "model_54b.add(keras.layers.Dense(units=128, activation='relu'))\n",
    "model_54b.add(keras.layers.Dropout(.3))\n",
    "model_54b.add(keras.layers.Dense(units=10, activation='softmax'))\n",
    "\n",
    "model_54b.compile(optimizer ='sgd', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
    "#sgd = stochastic gradient descent optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 1.5958 - acc: 0.4923 - val_loss: 0.7834 - val_acc: 0.8199\n",
      "Epoch 2/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.8281 - acc: 0.7442 - val_loss: 0.4921 - val_acc: 0.8725\n",
      "Epoch 3/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.6352 - acc: 0.8064 - val_loss: 0.4018 - val_acc: 0.8927\n",
      "Epoch 4/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.5462 - acc: 0.8351 - val_loss: 0.3557 - val_acc: 0.9012\n",
      "Epoch 5/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.4961 - acc: 0.8520 - val_loss: 0.3259 - val_acc: 0.9095\n",
      "Epoch 6/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.4557 - acc: 0.8642 - val_loss: 0.3020 - val_acc: 0.9130\n",
      "Epoch 7/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.4287 - acc: 0.8733 - val_loss: 0.2853 - val_acc: 0.9183\n",
      "Epoch 8/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.4016 - acc: 0.8817 - val_loss: 0.2703 - val_acc: 0.9219\n",
      "Epoch 9/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.3865 - acc: 0.8860 - val_loss: 0.2577 - val_acc: 0.9259\n",
      "Epoch 10/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.3648 - acc: 0.8939 - val_loss: 0.2463 - val_acc: 0.9278\n",
      "Epoch 11/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.3513 - acc: 0.8973 - val_loss: 0.2368 - val_acc: 0.9313\n",
      "Epoch 12/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.3366 - acc: 0.9006 - val_loss: 0.2274 - val_acc: 0.9330\n",
      "Epoch 13/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.3274 - acc: 0.9034 - val_loss: 0.2190 - val_acc: 0.9357\n",
      "Epoch 14/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.3150 - acc: 0.9072 - val_loss: 0.2126 - val_acc: 0.9381\n",
      "Epoch 15/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.3068 - acc: 0.9102 - val_loss: 0.2051 - val_acc: 0.9393\n",
      "Epoch 16/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.2955 - acc: 0.9136 - val_loss: 0.1995 - val_acc: 0.9413\n",
      "Epoch 17/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.2915 - acc: 0.9145 - val_loss: 0.1952 - val_acc: 0.9422\n",
      "Epoch 18/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.2802 - acc: 0.9177 - val_loss: 0.1896 - val_acc: 0.9445\n",
      "Epoch 19/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2732 - acc: 0.9191 - val_loss: 0.1842 - val_acc: 0.9450\n",
      "Epoch 20/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2698 - acc: 0.9200 - val_loss: 0.1809 - val_acc: 0.9457\n",
      "Epoch 21/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2630 - acc: 0.9230 - val_loss: 0.1760 - val_acc: 0.9471\n",
      "Epoch 22/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2557 - acc: 0.9242 - val_loss: 0.1709 - val_acc: 0.9494\n",
      "Epoch 23/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.2501 - acc: 0.9268 - val_loss: 0.1681 - val_acc: 0.9503\n",
      "Epoch 24/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2474 - acc: 0.9267 - val_loss: 0.1643 - val_acc: 0.9517\n",
      "Epoch 25/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.2406 - acc: 0.9289 - val_loss: 0.1602 - val_acc: 0.9527\n",
      "Epoch 26/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.2349 - acc: 0.9309 - val_loss: 0.1576 - val_acc: 0.9527\n",
      "Epoch 27/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.2293 - acc: 0.9328 - val_loss: 0.1553 - val_acc: 0.9544\n",
      "Epoch 28/250\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.2263 - acc: 0.9342 - val_loss: 0.1513 - val_acc: 0.9550\n",
      "Epoch 29/250\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.2235 - acc: 0.9348 - val_loss: 0.1498 - val_acc: 0.9558\n",
      "Epoch 30/250\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.2207 - acc: 0.9349 - val_loss: 0.1471 - val_acc: 0.9568\n",
      "Epoch 31/250\n",
      "60000/60000 [==============================] - 4s 59us/step - loss: 0.2151 - acc: 0.9371 - val_loss: 0.1452 - val_acc: 0.9563\n",
      "Epoch 32/250\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.2143 - acc: 0.9370 - val_loss: 0.1426 - val_acc: 0.9579\n",
      "Epoch 33/250\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.2106 - acc: 0.9384 - val_loss: 0.1407 - val_acc: 0.9578\n",
      "Epoch 34/250\n",
      "60000/60000 [==============================] - 3s 55us/step - loss: 0.2037 - acc: 0.9393 - val_loss: 0.1379 - val_acc: 0.9597\n",
      "Epoch 35/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.2031 - acc: 0.9403 - val_loss: 0.1357 - val_acc: 0.9593\n",
      "Epoch 36/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1994 - acc: 0.9404 - val_loss: 0.1344 - val_acc: 0.9598\n",
      "Epoch 37/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1965 - acc: 0.9420 - val_loss: 0.1322 - val_acc: 0.9603\n",
      "Epoch 38/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1957 - acc: 0.9424 - val_loss: 0.1302 - val_acc: 0.9610\n",
      "Epoch 39/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1936 - acc: 0.9431 - val_loss: 0.1295 - val_acc: 0.9613\n",
      "Epoch 40/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1889 - acc: 0.9436 - val_loss: 0.1278 - val_acc: 0.9619\n",
      "Epoch 41/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1851 - acc: 0.9451 - val_loss: 0.1262 - val_acc: 0.9619\n",
      "Epoch 42/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1871 - acc: 0.9446 - val_loss: 0.1249 - val_acc: 0.9621\n",
      "Epoch 43/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1817 - acc: 0.9464 - val_loss: 0.1237 - val_acc: 0.9620\n",
      "Epoch 44/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1802 - acc: 0.9466 - val_loss: 0.1221 - val_acc: 0.9633\n",
      "Epoch 45/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.1780 - acc: 0.9476 - val_loss: 0.1208 - val_acc: 0.9636\n",
      "Epoch 46/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1776 - acc: 0.9488 - val_loss: 0.1191 - val_acc: 0.9648\n",
      "Epoch 47/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1717 - acc: 0.9482 - val_loss: 0.1179 - val_acc: 0.9651\n",
      "Epoch 48/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1729 - acc: 0.9486 - val_loss: 0.1173 - val_acc: 0.9644\n",
      "Epoch 49/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1680 - acc: 0.9503 - val_loss: 0.1166 - val_acc: 0.9652\n",
      "Epoch 50/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1696 - acc: 0.9500 - val_loss: 0.1149 - val_acc: 0.9660\n",
      "Epoch 51/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1664 - acc: 0.9512 - val_loss: 0.1150 - val_acc: 0.9661\n",
      "Epoch 52/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1660 - acc: 0.9504 - val_loss: 0.1135 - val_acc: 0.9661\n",
      "Epoch 53/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1611 - acc: 0.9513 - val_loss: 0.1125 - val_acc: 0.9665\n",
      "Epoch 54/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1606 - acc: 0.9524 - val_loss: 0.1109 - val_acc: 0.9677\n",
      "Epoch 55/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1582 - acc: 0.9527 - val_loss: 0.1095 - val_acc: 0.9676\n",
      "Epoch 56/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1567 - acc: 0.9535 - val_loss: 0.1093 - val_acc: 0.9677\n",
      "Epoch 57/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1563 - acc: 0.9538 - val_loss: 0.1083 - val_acc: 0.9676\n",
      "Epoch 58/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1548 - acc: 0.9542 - val_loss: 0.1075 - val_acc: 0.9688\n",
      "Epoch 59/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1556 - acc: 0.9541 - val_loss: 0.1069 - val_acc: 0.9688\n",
      "Epoch 60/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1539 - acc: 0.9549 - val_loss: 0.1059 - val_acc: 0.9692\n",
      "Epoch 61/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1518 - acc: 0.9545 - val_loss: 0.1054 - val_acc: 0.9694\n",
      "Epoch 62/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1492 - acc: 0.9548 - val_loss: 0.1043 - val_acc: 0.9690\n",
      "Epoch 63/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1487 - acc: 0.9558 - val_loss: 0.1035 - val_acc: 0.9697\n",
      "Epoch 64/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1455 - acc: 0.9568 - val_loss: 0.1026 - val_acc: 0.9705\n",
      "Epoch 65/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1444 - acc: 0.9571 - val_loss: 0.1022 - val_acc: 0.9699\n",
      "Epoch 66/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.1427 - acc: 0.9578 - val_loss: 0.1016 - val_acc: 0.9708\n",
      "Epoch 67/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1412 - acc: 0.9570 - val_loss: 0.1004 - val_acc: 0.9706\n",
      "Epoch 68/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1423 - acc: 0.9571 - val_loss: 0.1000 - val_acc: 0.9711\n",
      "Epoch 69/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1411 - acc: 0.9583 - val_loss: 0.0999 - val_acc: 0.9708\n",
      "Epoch 70/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1383 - acc: 0.9585 - val_loss: 0.0985 - val_acc: 0.9718\n",
      "Epoch 71/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1365 - acc: 0.9596 - val_loss: 0.0981 - val_acc: 0.9711\n",
      "Epoch 72/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1365 - acc: 0.9595 - val_loss: 0.0982 - val_acc: 0.9717\n",
      "Epoch 73/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1353 - acc: 0.9601 - val_loss: 0.0976 - val_acc: 0.9714\n",
      "Epoch 74/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1353 - acc: 0.9590 - val_loss: 0.0966 - val_acc: 0.9712\n",
      "Epoch 75/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1339 - acc: 0.9591 - val_loss: 0.0968 - val_acc: 0.9713\n",
      "Epoch 76/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1325 - acc: 0.9606 - val_loss: 0.0955 - val_acc: 0.9719\n",
      "Epoch 77/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1320 - acc: 0.9605 - val_loss: 0.0950 - val_acc: 0.9725\n",
      "Epoch 78/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1313 - acc: 0.9608 - val_loss: 0.0948 - val_acc: 0.9724\n",
      "Epoch 79/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1289 - acc: 0.9612 - val_loss: 0.0937 - val_acc: 0.9723\n",
      "Epoch 80/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1303 - acc: 0.9609 - val_loss: 0.0938 - val_acc: 0.9726\n",
      "Epoch 81/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1280 - acc: 0.9610 - val_loss: 0.0931 - val_acc: 0.9724\n",
      "Epoch 82/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1269 - acc: 0.9619 - val_loss: 0.0923 - val_acc: 0.9724\n",
      "Epoch 83/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1276 - acc: 0.9622 - val_loss: 0.0927 - val_acc: 0.9729\n",
      "Epoch 84/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1263 - acc: 0.9621 - val_loss: 0.0918 - val_acc: 0.9738\n",
      "Epoch 85/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1275 - acc: 0.9625 - val_loss: 0.0915 - val_acc: 0.9733\n",
      "Epoch 86/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1218 - acc: 0.9636 - val_loss: 0.0905 - val_acc: 0.9731\n",
      "Epoch 87/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1223 - acc: 0.9634 - val_loss: 0.0913 - val_acc: 0.9732\n",
      "Epoch 88/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1228 - acc: 0.9620 - val_loss: 0.0904 - val_acc: 0.9733\n",
      "Epoch 89/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1212 - acc: 0.9637 - val_loss: 0.0906 - val_acc: 0.9734\n",
      "Epoch 90/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1223 - acc: 0.9639 - val_loss: 0.0897 - val_acc: 0.9739\n",
      "Epoch 91/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1205 - acc: 0.9640 - val_loss: 0.0891 - val_acc: 0.9740\n",
      "Epoch 92/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1175 - acc: 0.9655 - val_loss: 0.0890 - val_acc: 0.9739\n",
      "Epoch 93/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1210 - acc: 0.9632 - val_loss: 0.0889 - val_acc: 0.9747\n",
      "Epoch 94/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1180 - acc: 0.9635 - val_loss: 0.0883 - val_acc: 0.9739\n",
      "Epoch 95/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1164 - acc: 0.9653 - val_loss: 0.0877 - val_acc: 0.9745\n",
      "Epoch 96/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1165 - acc: 0.9654 - val_loss: 0.0878 - val_acc: 0.9741\n",
      "Epoch 97/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1135 - acc: 0.9663 - val_loss: 0.0870 - val_acc: 0.9741\n",
      "Epoch 98/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1165 - acc: 0.9651 - val_loss: 0.0871 - val_acc: 0.9743\n",
      "Epoch 99/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1122 - acc: 0.9666 - val_loss: 0.0869 - val_acc: 0.9749\n",
      "Epoch 100/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1122 - acc: 0.9664 - val_loss: 0.0865 - val_acc: 0.9742\n",
      "Epoch 101/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1119 - acc: 0.9671 - val_loss: 0.0862 - val_acc: 0.9751\n",
      "Epoch 102/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1117 - acc: 0.9664 - val_loss: 0.0863 - val_acc: 0.9752\n",
      "Epoch 103/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1106 - acc: 0.9667 - val_loss: 0.0857 - val_acc: 0.9746\n",
      "Epoch 104/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1111 - acc: 0.9660 - val_loss: 0.0851 - val_acc: 0.9749\n",
      "Epoch 105/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.1092 - acc: 0.9675 - val_loss: 0.0856 - val_acc: 0.9746\n",
      "Epoch 106/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.1100 - acc: 0.9671 - val_loss: 0.0841 - val_acc: 0.9752\n",
      "Epoch 107/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1083 - acc: 0.9672 - val_loss: 0.0847 - val_acc: 0.9749\n",
      "Epoch 108/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1069 - acc: 0.9681 - val_loss: 0.0842 - val_acc: 0.9746\n",
      "Epoch 109/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1078 - acc: 0.9670 - val_loss: 0.0840 - val_acc: 0.9754\n",
      "Epoch 110/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1072 - acc: 0.9678 - val_loss: 0.0843 - val_acc: 0.9750\n",
      "Epoch 111/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1064 - acc: 0.9672 - val_loss: 0.0832 - val_acc: 0.9748\n",
      "Epoch 112/250\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.1053 - acc: 0.9673 - val_loss: 0.0834 - val_acc: 0.9750\n",
      "Epoch 113/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1046 - acc: 0.9685 - val_loss: 0.0832 - val_acc: 0.9748\n",
      "Epoch 114/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1030 - acc: 0.9686 - val_loss: 0.0831 - val_acc: 0.9751\n",
      "Epoch 115/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1067 - acc: 0.9671 - val_loss: 0.0827 - val_acc: 0.9752\n",
      "Epoch 116/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1022 - acc: 0.9692 - val_loss: 0.0824 - val_acc: 0.9754\n",
      "Epoch 117/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1006 - acc: 0.9697 - val_loss: 0.0819 - val_acc: 0.9759\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 118/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1020 - acc: 0.9684 - val_loss: 0.0821 - val_acc: 0.9749\n",
      "Epoch 119/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1009 - acc: 0.9693 - val_loss: 0.0812 - val_acc: 0.9752\n",
      "Epoch 120/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1016 - acc: 0.9695 - val_loss: 0.0815 - val_acc: 0.9753\n",
      "Epoch 121/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.1013 - acc: 0.9690 - val_loss: 0.0810 - val_acc: 0.9754\n",
      "Epoch 122/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.1017 - acc: 0.9688 - val_loss: 0.0810 - val_acc: 0.9750\n",
      "Epoch 123/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.1011 - acc: 0.9690 - val_loss: 0.0809 - val_acc: 0.9760\n",
      "Epoch 124/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0998 - acc: 0.9697 - val_loss: 0.0803 - val_acc: 0.9766\n",
      "Epoch 125/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0989 - acc: 0.9698 - val_loss: 0.0800 - val_acc: 0.9768\n",
      "Epoch 126/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0984 - acc: 0.9699 - val_loss: 0.0801 - val_acc: 0.9762\n",
      "Epoch 127/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0987 - acc: 0.9700 - val_loss: 0.0791 - val_acc: 0.9763\n",
      "Epoch 128/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0974 - acc: 0.9699 - val_loss: 0.0800 - val_acc: 0.9764\n",
      "Epoch 129/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0975 - acc: 0.9700 - val_loss: 0.0796 - val_acc: 0.9766\n",
      "Epoch 130/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0972 - acc: 0.9697 - val_loss: 0.0801 - val_acc: 0.9760\n",
      "Epoch 131/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0956 - acc: 0.9709 - val_loss: 0.0790 - val_acc: 0.9768\n",
      "Epoch 132/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0951 - acc: 0.9717 - val_loss: 0.0789 - val_acc: 0.9765\n",
      "Epoch 133/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0962 - acc: 0.9707 - val_loss: 0.0796 - val_acc: 0.9763\n",
      "Epoch 134/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0938 - acc: 0.9711 - val_loss: 0.0791 - val_acc: 0.9763\n",
      "Epoch 135/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0948 - acc: 0.9703 - val_loss: 0.0785 - val_acc: 0.9769\n",
      "Epoch 136/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0914 - acc: 0.9722 - val_loss: 0.0783 - val_acc: 0.9764\n",
      "Epoch 137/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0939 - acc: 0.9715 - val_loss: 0.0778 - val_acc: 0.9769\n",
      "Epoch 138/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0922 - acc: 0.9720 - val_loss: 0.0787 - val_acc: 0.9763\n",
      "Epoch 139/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0921 - acc: 0.9718 - val_loss: 0.0777 - val_acc: 0.9770\n",
      "Epoch 140/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0914 - acc: 0.9717 - val_loss: 0.0776 - val_acc: 0.9767\n",
      "Epoch 141/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0950 - acc: 0.9709 - val_loss: 0.0776 - val_acc: 0.9769\n",
      "Epoch 142/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0907 - acc: 0.9721 - val_loss: 0.0781 - val_acc: 0.9764\n",
      "Epoch 143/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0914 - acc: 0.9725 - val_loss: 0.0780 - val_acc: 0.9767\n",
      "Epoch 144/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0884 - acc: 0.9735 - val_loss: 0.0774 - val_acc: 0.9770\n",
      "Epoch 145/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0915 - acc: 0.9716 - val_loss: 0.0758 - val_acc: 0.9779\n",
      "Epoch 146/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0883 - acc: 0.9728 - val_loss: 0.0770 - val_acc: 0.9776\n",
      "Epoch 147/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0887 - acc: 0.9722 - val_loss: 0.0763 - val_acc: 0.9772\n",
      "Epoch 148/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0877 - acc: 0.9733 - val_loss: 0.0768 - val_acc: 0.9772\n",
      "Epoch 149/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0894 - acc: 0.9717 - val_loss: 0.0771 - val_acc: 0.9769\n",
      "Epoch 150/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0883 - acc: 0.9730 - val_loss: 0.0762 - val_acc: 0.9778\n",
      "Epoch 151/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0873 - acc: 0.9732 - val_loss: 0.0763 - val_acc: 0.9769\n",
      "Epoch 152/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0879 - acc: 0.9721 - val_loss: 0.0767 - val_acc: 0.9769\n",
      "Epoch 153/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0853 - acc: 0.9744 - val_loss: 0.0764 - val_acc: 0.9771\n",
      "Epoch 154/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0860 - acc: 0.9734 - val_loss: 0.0764 - val_acc: 0.9766\n",
      "Epoch 155/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0838 - acc: 0.9747 - val_loss: 0.0757 - val_acc: 0.9776\n",
      "Epoch 156/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0878 - acc: 0.9730 - val_loss: 0.0751 - val_acc: 0.9776\n",
      "Epoch 157/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0856 - acc: 0.9736 - val_loss: 0.0756 - val_acc: 0.9775\n",
      "Epoch 158/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0853 - acc: 0.9734 - val_loss: 0.0751 - val_acc: 0.9781\n",
      "Epoch 159/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0840 - acc: 0.9744 - val_loss: 0.0747 - val_acc: 0.9779\n",
      "Epoch 160/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0838 - acc: 0.9747 - val_loss: 0.0753 - val_acc: 0.9776\n",
      "Epoch 161/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0836 - acc: 0.9740 - val_loss: 0.0747 - val_acc: 0.9785\n",
      "Epoch 162/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0831 - acc: 0.9744 - val_loss: 0.0741 - val_acc: 0.9785\n",
      "Epoch 163/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0842 - acc: 0.9749 - val_loss: 0.0742 - val_acc: 0.9780\n",
      "Epoch 164/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0823 - acc: 0.9743 - val_loss: 0.0744 - val_acc: 0.9777\n",
      "Epoch 165/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0836 - acc: 0.9744 - val_loss: 0.0746 - val_acc: 0.9779\n",
      "Epoch 166/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0826 - acc: 0.9739 - val_loss: 0.0738 - val_acc: 0.9782\n",
      "Epoch 167/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0815 - acc: 0.9745 - val_loss: 0.0746 - val_acc: 0.9779\n",
      "Epoch 168/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0817 - acc: 0.9750 - val_loss: 0.0743 - val_acc: 0.9786\n",
      "Epoch 169/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0790 - acc: 0.9757 - val_loss: 0.0734 - val_acc: 0.9778\n",
      "Epoch 170/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0788 - acc: 0.9754 - val_loss: 0.0728 - val_acc: 0.9785\n",
      "Epoch 171/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0813 - acc: 0.9746 - val_loss: 0.0736 - val_acc: 0.9778\n",
      "Epoch 172/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0780 - acc: 0.9757 - val_loss: 0.0730 - val_acc: 0.9784\n",
      "Epoch 173/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0789 - acc: 0.9752 - val_loss: 0.0728 - val_acc: 0.9788\n",
      "Epoch 174/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0786 - acc: 0.9754 - val_loss: 0.0730 - val_acc: 0.9786\n",
      "Epoch 175/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0800 - acc: 0.9756 - val_loss: 0.0732 - val_acc: 0.9787\n",
      "Epoch 176/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0792 - acc: 0.9743 - val_loss: 0.0731 - val_acc: 0.9782\n",
      "Epoch 177/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0769 - acc: 0.9757 - val_loss: 0.0733 - val_acc: 0.9783\n",
      "Epoch 178/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0777 - acc: 0.9757 - val_loss: 0.0712 - val_acc: 0.9785\n",
      "Epoch 179/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.0765 - acc: 0.9764 - val_loss: 0.0729 - val_acc: 0.9778\n",
      "Epoch 180/250\n",
      "60000/60000 [==============================] - 4s 63us/step - loss: 0.0774 - acc: 0.9761 - val_loss: 0.0721 - val_acc: 0.9789\n",
      "Epoch 181/250\n",
      "60000/60000 [==============================] - 4s 64us/step - loss: 0.0758 - acc: 0.9770 - val_loss: 0.0731 - val_acc: 0.9783\n",
      "Epoch 182/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0776 - acc: 0.9762 - val_loss: 0.0720 - val_acc: 0.9791\n",
      "Epoch 183/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0745 - acc: 0.9767 - val_loss: 0.0728 - val_acc: 0.9786\n",
      "Epoch 184/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0780 - acc: 0.9769 - val_loss: 0.0723 - val_acc: 0.9786\n",
      "Epoch 185/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0763 - acc: 0.9764 - val_loss: 0.0719 - val_acc: 0.9787\n",
      "Epoch 186/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0746 - acc: 0.9769 - val_loss: 0.0731 - val_acc: 0.9787\n",
      "Epoch 187/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0761 - acc: 0.9769 - val_loss: 0.0715 - val_acc: 0.9792\n",
      "Epoch 188/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0758 - acc: 0.9754 - val_loss: 0.0717 - val_acc: 0.9791\n",
      "Epoch 189/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0752 - acc: 0.9767 - val_loss: 0.0719 - val_acc: 0.9789\n",
      "Epoch 190/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0723 - acc: 0.9775 - val_loss: 0.0723 - val_acc: 0.9788\n",
      "Epoch 191/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0746 - acc: 0.9766 - val_loss: 0.0718 - val_acc: 0.9791\n",
      "Epoch 192/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0766 - acc: 0.9764 - val_loss: 0.0716 - val_acc: 0.9786\n",
      "Epoch 193/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0723 - acc: 0.9776 - val_loss: 0.0720 - val_acc: 0.9790\n",
      "Epoch 194/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0723 - acc: 0.9772 - val_loss: 0.0722 - val_acc: 0.9786\n",
      "Epoch 195/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0746 - acc: 0.9765 - val_loss: 0.0713 - val_acc: 0.9790\n",
      "Epoch 196/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0731 - acc: 0.9770 - val_loss: 0.0712 - val_acc: 0.9788\n",
      "Epoch 197/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0719 - acc: 0.9772 - val_loss: 0.0713 - val_acc: 0.9790\n",
      "Epoch 198/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0713 - acc: 0.9776 - val_loss: 0.0714 - val_acc: 0.9795\n",
      "Epoch 199/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0711 - acc: 0.9779 - val_loss: 0.0707 - val_acc: 0.9792\n",
      "Epoch 200/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0727 - acc: 0.9778 - val_loss: 0.0707 - val_acc: 0.9789\n",
      "Epoch 201/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0711 - acc: 0.9778 - val_loss: 0.0714 - val_acc: 0.9793\n",
      "Epoch 202/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0698 - acc: 0.9785 - val_loss: 0.0706 - val_acc: 0.9794\n",
      "Epoch 203/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0709 - acc: 0.9784 - val_loss: 0.0709 - val_acc: 0.9792\n",
      "Epoch 204/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0701 - acc: 0.9784 - val_loss: 0.0703 - val_acc: 0.9791\n",
      "Epoch 205/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0704 - acc: 0.9785 - val_loss: 0.0720 - val_acc: 0.9789\n",
      "Epoch 206/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0698 - acc: 0.9784 - val_loss: 0.0714 - val_acc: 0.9787\n",
      "Epoch 207/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0694 - acc: 0.9789 - val_loss: 0.0707 - val_acc: 0.9795\n",
      "Epoch 208/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0702 - acc: 0.9776 - val_loss: 0.0705 - val_acc: 0.9791\n",
      "Epoch 209/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0697 - acc: 0.9774 - val_loss: 0.0708 - val_acc: 0.9793\n",
      "Epoch 210/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0683 - acc: 0.9785 - val_loss: 0.0706 - val_acc: 0.9791\n",
      "Epoch 211/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0696 - acc: 0.9785 - val_loss: 0.0706 - val_acc: 0.9795\n",
      "Epoch 212/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0695 - acc: 0.9784 - val_loss: 0.0710 - val_acc: 0.9787\n",
      "Epoch 213/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0684 - acc: 0.9788 - val_loss: 0.0709 - val_acc: 0.9788\n",
      "Epoch 214/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0689 - acc: 0.9786 - val_loss: 0.0704 - val_acc: 0.9793\n",
      "Epoch 215/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0667 - acc: 0.9789 - val_loss: 0.0707 - val_acc: 0.9791\n",
      "Epoch 216/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0686 - acc: 0.9787 - val_loss: 0.0702 - val_acc: 0.9794\n",
      "Epoch 217/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0673 - acc: 0.9788 - val_loss: 0.0712 - val_acc: 0.9799\n",
      "Epoch 218/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0672 - acc: 0.9784 - val_loss: 0.0710 - val_acc: 0.9790\n",
      "Epoch 219/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0687 - acc: 0.9788 - val_loss: 0.0713 - val_acc: 0.9792\n",
      "Epoch 220/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0662 - acc: 0.9791 - val_loss: 0.0704 - val_acc: 0.9791\n",
      "Epoch 221/250\n",
      "60000/60000 [==============================] - 3s 54us/step - loss: 0.0671 - acc: 0.9790 - val_loss: 0.0705 - val_acc: 0.9795\n",
      "Epoch 222/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0663 - acc: 0.9793 - val_loss: 0.0707 - val_acc: 0.9795\n",
      "Epoch 223/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0661 - acc: 0.9794 - val_loss: 0.0701 - val_acc: 0.9791\n",
      "Epoch 224/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0655 - acc: 0.9797 - val_loss: 0.0710 - val_acc: 0.9791\n",
      "Epoch 225/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0661 - acc: 0.9786 - val_loss: 0.0698 - val_acc: 0.9798\n",
      "Epoch 226/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0656 - acc: 0.9795 - val_loss: 0.0703 - val_acc: 0.9793\n",
      "Epoch 227/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0648 - acc: 0.9794 - val_loss: 0.0702 - val_acc: 0.9794\n",
      "Epoch 228/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0638 - acc: 0.9799 - val_loss: 0.0698 - val_acc: 0.9795\n",
      "Epoch 229/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0646 - acc: 0.9794 - val_loss: 0.0699 - val_acc: 0.9791\n",
      "Epoch 230/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0638 - acc: 0.9799 - val_loss: 0.0710 - val_acc: 0.9791\n",
      "Epoch 231/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0646 - acc: 0.9793 - val_loss: 0.0697 - val_acc: 0.9795\n",
      "Epoch 232/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0656 - acc: 0.9793 - val_loss: 0.0694 - val_acc: 0.9793\n",
      "Epoch 233/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0653 - acc: 0.9793 - val_loss: 0.0700 - val_acc: 0.9794\n",
      "Epoch 234/250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0621 - acc: 0.9803 - val_loss: 0.0693 - val_acc: 0.9798\n",
      "Epoch 235/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0610 - acc: 0.9805 - val_loss: 0.0693 - val_acc: 0.9795\n",
      "Epoch 236/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0638 - acc: 0.9797 - val_loss: 0.0703 - val_acc: 0.9798\n",
      "Epoch 237/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0627 - acc: 0.9805 - val_loss: 0.0705 - val_acc: 0.9793\n",
      "Epoch 238/250\n",
      "60000/60000 [==============================] - 3s 53us/step - loss: 0.0639 - acc: 0.9797 - val_loss: 0.0701 - val_acc: 0.9793\n",
      "Epoch 239/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0639 - acc: 0.9803 - val_loss: 0.0697 - val_acc: 0.9795\n",
      "Epoch 240/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0635 - acc: 0.9799 - val_loss: 0.0698 - val_acc: 0.9789\n",
      "Epoch 241/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0638 - acc: 0.9796 - val_loss: 0.0695 - val_acc: 0.9798\n",
      "Epoch 242/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0615 - acc: 0.9801 - val_loss: 0.0694 - val_acc: 0.9802\n",
      "Epoch 243/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0623 - acc: 0.9801 - val_loss: 0.0693 - val_acc: 0.9799\n",
      "Epoch 244/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0603 - acc: 0.9816 - val_loss: 0.0688 - val_acc: 0.9802\n",
      "Epoch 245/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0601 - acc: 0.9810 - val_loss: 0.0699 - val_acc: 0.9798\n",
      "Epoch 246/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0631 - acc: 0.9798 - val_loss: 0.0690 - val_acc: 0.9797\n",
      "Epoch 247/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0608 - acc: 0.9806 - val_loss: 0.0699 - val_acc: 0.9803\n",
      "Epoch 248/250\n",
      "60000/60000 [==============================] - 3s 52us/step - loss: 0.0594 - acc: 0.9810 - val_loss: 0.0691 - val_acc: 0.9798\n",
      "Epoch 249/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0607 - acc: 0.9804 - val_loss: 0.0698 - val_acc: 0.9797\n",
      "Epoch 250/250\n",
      "60000/60000 [==============================] - 3s 51us/step - loss: 0.0634 - acc: 0.9801 - val_loss: 0.0691 - val_acc: 0.9795\n"
     ]
    }
   ],
   "source": [
    "history_model_54b = model_54b.fit(mnist_data_train_flat, mnist_labels_train, batch_size = 128, epochs = 250, \n",
    "                        validation_data = (mnist_data_test_flat, mnist_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_model_54b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.4 c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# network model from b) but with optimizer adam\n",
    "model_54c = keras.models.Sequential()\n",
    "model_54c.add(keras.layers.Dense(units=128, activation='relu', input_dim=mnist_pixel_num))\n",
    "model_54c.add(keras.layers.Dropout(.3))\n",
    "model_54c.add(keras.layers.Dense(units=128, activation='relu'))\n",
    "model_54c.add(keras.layers.Dropout(.3))\n",
    "model_54c.add(keras.layers.Dense(units=10, activation='softmax'))\n",
    "\n",
    "model_54c.compile(optimizer ='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimizer 'adam': \n",
    "- algorithm for first order gradient-based optimization of stochastic objective functions\n",
    "- the SGD maintians a single learining rate for all weight updates (-> learning rate doesn't change during training)\n",
    "- adam computes individual adaptive learing rates for different weigths from estimates of first and second moments for the gradient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 4s 60us/step - loss: 0.4614 - acc: 0.8589 - val_loss: 0.1620 - val_acc: 0.9486\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.2084 - acc: 0.9376 - val_loss: 0.1226 - val_acc: 0.9609\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 3s 56us/step - loss: 0.1576 - acc: 0.9526 - val_loss: 0.0991 - val_acc: 0.9701\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.1374 - acc: 0.9589 - val_loss: 0.0869 - val_acc: 0.9722\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.1193 - acc: 0.9643 - val_loss: 0.0873 - val_acc: 0.9727\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.1053 - acc: 0.9678 - val_loss: 0.0807 - val_acc: 0.9741\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0986 - acc: 0.9698 - val_loss: 0.0779 - val_acc: 0.9754\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 4s 59us/step - loss: 0.0920 - acc: 0.9712 - val_loss: 0.0773 - val_acc: 0.9764\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 3s 58us/step - loss: 0.0849 - acc: 0.9734 - val_loss: 0.0687 - val_acc: 0.9791\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0789 - acc: 0.9756 - val_loss: 0.0713 - val_acc: 0.9771\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0754 - acc: 0.9762 - val_loss: 0.0741 - val_acc: 0.9767\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0720 - acc: 0.9778 - val_loss: 0.0684 - val_acc: 0.9789\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0691 - acc: 0.9777 - val_loss: 0.0676 - val_acc: 0.9808\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0664 - acc: 0.9791 - val_loss: 0.0690 - val_acc: 0.9791\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0619 - acc: 0.9801 - val_loss: 0.0762 - val_acc: 0.9774\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0611 - acc: 0.9805 - val_loss: 0.0695 - val_acc: 0.9801\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0600 - acc: 0.9814 - val_loss: 0.0678 - val_acc: 0.9816\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0560 - acc: 0.9825 - val_loss: 0.0745 - val_acc: 0.9783\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0552 - acc: 0.9817 - val_loss: 0.0737 - val_acc: 0.9788\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.0514 - acc: 0.9832 - val_loss: 0.0696 - val_acc: 0.9804\n"
     ]
    }
   ],
   "source": [
    "history_model_54c = model_54c.fit(mnist_data_train_flat, mnist_labels_train, batch_size = 128, epochs = 20, \n",
    "                        validation_data = (mnist_data_test_flat, mnist_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_model_54c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building convolutional neural network\n",
    "model_55 = keras.models.Sequential()\n",
    "model_55.add(keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', input_shape=mnist_shape))\n",
    "model_55.add(keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'))\n",
    "model_55.add(keras.layers.MaxPooling2D(pool_size=(3, 3)))\n",
    "model_55.add(keras.layers.Dropout(.25))\n",
    "model_55.add(keras.layers.Flatten())\n",
    "model_55.add(keras.layers.Dense(units=128, activation='relu'))\n",
    "model_55.add(keras.layers.Dropout(.5))\n",
    "model_55.add(keras.layers.Dense(units=10, activation='softmax'))\n",
    "\n",
    "model_55.compile(optimizer ='sgd', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 1.3123 - acc: 0.5648 - val_loss: 0.4377 - val_acc: 0.8796\n",
      "Epoch 2/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.6461 - acc: 0.7928 - val_loss: 0.3312 - val_acc: 0.9068\n",
      "Epoch 3/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.5312 - acc: 0.8324 - val_loss: 0.2763 - val_acc: 0.9212\n",
      "Epoch 4/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.4608 - acc: 0.8557 - val_loss: 0.2409 - val_acc: 0.9313\n",
      "Epoch 5/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.4154 - acc: 0.8703 - val_loss: 0.2079 - val_acc: 0.9404\n",
      "Epoch 6/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.3669 - acc: 0.8876 - val_loss: 0.1839 - val_acc: 0.9466\n",
      "Epoch 7/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.3293 - acc: 0.8986 - val_loss: 0.1637 - val_acc: 0.9528\n",
      "Epoch 8/15\n",
      "60000/60000 [==============================] - 80s 1ms/step - loss: 0.2935 - acc: 0.9110 - val_loss: 0.1443 - val_acc: 0.9578\n",
      "Epoch 9/15\n",
      "60000/60000 [==============================] - 80s 1ms/step - loss: 0.2651 - acc: 0.9193 - val_loss: 0.1294 - val_acc: 0.9625\n",
      "Epoch 10/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.2484 - acc: 0.9259 - val_loss: 0.1185 - val_acc: 0.9642\n",
      "Epoch 11/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.2288 - acc: 0.9311 - val_loss: 0.1064 - val_acc: 0.9678\n",
      "Epoch 12/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.2129 - acc: 0.9358 - val_loss: 0.0980 - val_acc: 0.9706\n",
      "Epoch 13/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.1975 - acc: 0.9404 - val_loss: 0.0927 - val_acc: 0.9715\n",
      "Epoch 14/15\n",
      "60000/60000 [==============================] - 80s 1ms/step - loss: 0.1846 - acc: 0.9437 - val_loss: 0.0862 - val_acc: 0.9726\n",
      "Epoch 15/15\n",
      "60000/60000 [==============================] - 79s 1ms/step - loss: 0.1751 - acc: 0.9469 - val_loss: 0.0806 - val_acc: 0.9742\n"
     ]
    }
   ],
   "source": [
    "history_model_55 = model_55.fit(mnist_data_train, mnist_labels_train, batch_size = 128, epochs = 15, \n",
    "                        validation_data = (mnist_data_test, mnist_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_model_55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 5.6 (Pedestrian Bonus Task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "import scipy.io\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pedestrain data like in section 3.4\n",
    "def load_ped_data():\n",
    "    ped_data = scipy.io.loadmat(\"data/pca_ped_25x50.mat\")\n",
    "    ped_images = ped_data['ped_train_int_25x50'][:,1:]\n",
    "    garb_images = ped_data['garb_train_int_25x50'][:,1:]\n",
    "    ped_test_images = ped_data['ped_test_int_25x50'][:,1:]\n",
    "    garb_test_images = ped_data['garb_test_int_25x50'][:,1:]\n",
    "    del ped_data\n",
    "\n",
    "\n",
    "    min_max = MinMaxScaler()\n",
    "    min_max.fit(np.vstack((ped_images, garb_images)))\n",
    "\n",
    "    ped_images = min_max.transform(ped_images)\n",
    "    garb_images = min_max.transform(garb_images)\n",
    "    y_train = np.concatenate((np.ones(len(ped_images)), np.zeros(len(garb_images))))\n",
    "\n",
    "    ped_test_images = min_max.transform(ped_test_images)\n",
    "    garb_test_images = min_max.transform(garb_test_images)\n",
    "    y_test = np.concatenate((np.ones(len(ped_test_images)), np.zeros(len(garb_test_images))))\n",
    "\n",
    "    img_rows, img_cols = 25, 50\n",
    "    X_train = np.vstack((ped_images, garb_images)).astype('float32')\n",
    "    X_test = np.vstack((ped_test_images, garb_test_images)).astype('float32')\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "        X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "        input_shape = (1, img_rows, img_cols)\n",
    "    else:\n",
    "        X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n",
    "        X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n",
    "        input_shape = (img_rows, img_cols, 1)\n",
    "    Y_train = keras.utils.np_utils.to_categorical(y_train,2)\n",
    "    Y_test = keras.utils.np_utils.to_categorical(y_test, 2)\n",
    "    return X_train, Y_train, X_test, Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "def train_pca_svc(train_data, y_train, q):\n",
    "    pca = PCA(n_components=q)\n",
    "    transformed_training_data = pca.fit_transform(train_data)\n",
    "\n",
    "    svc = LinearSVC()\n",
    "    svc.fit(transformed_training_data, y_train)\n",
    "    return pca, svc\n",
    "\n",
    "def get_accuracys(training_data, test_data, y_training, y_test, q_min=10, q_max=1250, num_q=50):\n",
    "    result = list();\n",
    "    for q in np.linspace(q_min, q_max, num_q, dtype=int):\n",
    "        print(\"Processing: q = \" + str(q), end=\"\\r\")\n",
    "        pca, svc = train_pca_svc(training_data, y_training, q)\n",
    "        \n",
    "        transformed_training_data = pca.transform(training_data)\n",
    "        transformed_test_data = pca.transform(test_data)\n",
    "\n",
    "        acc_train = svc.score(transformed_training_data,y_training)\n",
    "        acc_test = svc.score(transformed_test_data,y_test)\n",
    "        result = result + [[q,acc_train,acc_test]]\n",
    "    print(\"Done!                                \")\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot q vs scores\n",
    "def plot_svc_accuracys(list_accs):\n",
    "    accs = np.array(list_accs)\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    ax1.plot(accs[:,0], accs[:,1], c='b', marker=\"s\", label='training')\n",
    "    ax1.plot(accs[:,0],accs[:,2], c='r', marker=\"o\", label='test')\n",
    "    plt.legend(loc='upper left');\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading and preparing of pedestrian data \n",
    "ped_data_train, ped_labels_train, ped_data_test, ped_labels_test = load_ped_data()\n",
    "ped_pixel_num = np.shape(ped_data_train)[1]*np.shape(ped_data_train)[2]\n",
    "ped_shape = np.shape(ped_data_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model network like in 5.5\n",
    "model_56 = keras.models.Sequential()\n",
    "model_56.add(keras.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', input_shape=ped_shape))\n",
    "model_56.add(keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'))\n",
    "model_56.add(keras.layers.MaxPooling2D(pool_size=(3, 3)))\n",
    "model_56.add(keras.layers.Dropout(.25))\n",
    "model_56.add(keras.layers.Flatten())\n",
    "flat_layer_index = np.size(model_56.layers) - 1\n",
    "model_56.add(keras.layers.Dense(units=128, activation='relu'))\n",
    "model_56.add(keras.layers.Dropout(.5))\n",
    "model_56.add(keras.layers.Dense(units=2, activation='softmax'))\n",
    "\n",
    "model_56.compile(optimizer ='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3000 samples, validate on 1000 samples\n",
      "Epoch 1/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.6656 - acc: 0.6130 - val_loss: 0.6076 - val_acc: 0.6680\n",
      "Epoch 2/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.5107 - acc: 0.7730 - val_loss: 0.5046 - val_acc: 0.7560\n",
      "Epoch 3/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.4107 - acc: 0.8290 - val_loss: 0.4572 - val_acc: 0.7890\n",
      "Epoch 4/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.3203 - acc: 0.8773 - val_loss: 0.4240 - val_acc: 0.8030\n",
      "Epoch 5/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.2775 - acc: 0.8983 - val_loss: 0.4044 - val_acc: 0.8210\n",
      "Epoch 6/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.2334 - acc: 0.9150 - val_loss: 0.4026 - val_acc: 0.8190\n",
      "Epoch 7/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.2192 - acc: 0.9110 - val_loss: 0.3879 - val_acc: 0.8210\n",
      "Epoch 8/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1906 - acc: 0.9307 - val_loss: 0.3972 - val_acc: 0.8270\n",
      "Epoch 9/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1731 - acc: 0.9380 - val_loss: 0.3984 - val_acc: 0.8200\n",
      "Epoch 10/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1453 - acc: 0.9460 - val_loss: 0.3734 - val_acc: 0.8410\n",
      "Epoch 11/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1456 - acc: 0.9470 - val_loss: 0.4145 - val_acc: 0.8150\n",
      "Epoch 12/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1356 - acc: 0.9490 - val_loss: 0.3753 - val_acc: 0.8380\n",
      "Epoch 13/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1356 - acc: 0.9470 - val_loss: 0.3526 - val_acc: 0.8550\n",
      "Epoch 14/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1235 - acc: 0.9560 - val_loss: 0.3956 - val_acc: 0.8390\n",
      "Epoch 15/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1066 - acc: 0.9647 - val_loss: 0.3852 - val_acc: 0.8530\n",
      "Epoch 16/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1109 - acc: 0.9633 - val_loss: 0.3664 - val_acc: 0.8530\n",
      "Epoch 17/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1061 - acc: 0.9607 - val_loss: 0.3624 - val_acc: 0.8590\n",
      "Epoch 18/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0935 - acc: 0.9660 - val_loss: 0.4258 - val_acc: 0.8500\n",
      "Epoch 19/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.1001 - acc: 0.9630 - val_loss: 0.3829 - val_acc: 0.8600\n",
      "Epoch 20/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0860 - acc: 0.9707 - val_loss: 0.3878 - val_acc: 0.8590\n",
      "Epoch 21/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0764 - acc: 0.9700 - val_loss: 0.3610 - val_acc: 0.8760\n",
      "Epoch 22/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0772 - acc: 0.9680 - val_loss: 0.3900 - val_acc: 0.8580\n",
      "Epoch 23/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0681 - acc: 0.9743 - val_loss: 0.3885 - val_acc: 0.8620\n",
      "Epoch 24/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0797 - acc: 0.9680 - val_loss: 0.3891 - val_acc: 0.8660\n",
      "Epoch 25/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0683 - acc: 0.9753 - val_loss: 0.3831 - val_acc: 0.8700\n",
      "Epoch 26/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0592 - acc: 0.9780 - val_loss: 0.3658 - val_acc: 0.8800\n",
      "Epoch 27/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0545 - acc: 0.9787 - val_loss: 0.3792 - val_acc: 0.8730\n",
      "Epoch 28/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0528 - acc: 0.9833 - val_loss: 0.4589 - val_acc: 0.8630\n",
      "Epoch 29/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0655 - acc: 0.9750 - val_loss: 0.3704 - val_acc: 0.8760\n",
      "Epoch 30/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0480 - acc: 0.9803 - val_loss: 0.3755 - val_acc: 0.8790\n",
      "Epoch 31/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0432 - acc: 0.9860 - val_loss: 0.4149 - val_acc: 0.8680\n",
      "Epoch 32/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0381 - acc: 0.9877 - val_loss: 0.3900 - val_acc: 0.8750\n",
      "Epoch 33/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0333 - acc: 0.9907 - val_loss: 0.4020 - val_acc: 0.8810\n",
      "Epoch 34/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0405 - acc: 0.9870 - val_loss: 0.3742 - val_acc: 0.8880\n",
      "Epoch 35/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0398 - acc: 0.9880 - val_loss: 0.3913 - val_acc: 0.8830\n",
      "Epoch 36/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0306 - acc: 0.9920 - val_loss: 0.4032 - val_acc: 0.8780\n",
      "Epoch 37/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0334 - acc: 0.9893 - val_loss: 0.3858 - val_acc: 0.8880\n",
      "Epoch 38/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0313 - acc: 0.9897 - val_loss: 0.3954 - val_acc: 0.8830\n",
      "Epoch 39/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0284 - acc: 0.9917 - val_loss: 0.4058 - val_acc: 0.8910\n",
      "Epoch 40/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0331 - acc: 0.9890 - val_loss: 0.3860 - val_acc: 0.8850\n",
      "Epoch 41/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0303 - acc: 0.9890 - val_loss: 0.4206 - val_acc: 0.8870\n",
      "Epoch 42/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0241 - acc: 0.9940 - val_loss: 0.4232 - val_acc: 0.8870\n",
      "Epoch 43/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0188 - acc: 0.9943 - val_loss: 0.4261 - val_acc: 0.8850\n",
      "Epoch 44/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0235 - acc: 0.9920 - val_loss: 0.4245 - val_acc: 0.8900\n",
      "Epoch 45/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0235 - acc: 0.9943 - val_loss: 0.4284 - val_acc: 0.8890\n",
      "Epoch 46/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0264 - acc: 0.9910 - val_loss: 0.4079 - val_acc: 0.8840\n",
      "Epoch 47/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0233 - acc: 0.9913 - val_loss: 0.4803 - val_acc: 0.8780\n",
      "Epoch 48/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0276 - acc: 0.9907 - val_loss: 0.3589 - val_acc: 0.9010\n",
      "Epoch 49/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0255 - acc: 0.9907 - val_loss: 0.3933 - val_acc: 0.8910\n",
      "Epoch 50/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0188 - acc: 0.9937 - val_loss: 0.3984 - val_acc: 0.8980\n",
      "Epoch 51/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0190 - acc: 0.9950 - val_loss: 0.3765 - val_acc: 0.8920\n",
      "Epoch 52/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0200 - acc: 0.9930 - val_loss: 0.4415 - val_acc: 0.8840\n",
      "Epoch 53/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0156 - acc: 0.9953 - val_loss: 0.4461 - val_acc: 0.8860\n",
      "Epoch 54/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0221 - acc: 0.9930 - val_loss: 0.4028 - val_acc: 0.8870\n",
      "Epoch 55/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0206 - acc: 0.9933 - val_loss: 0.4360 - val_acc: 0.8910\n",
      "Epoch 56/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0126 - acc: 0.9963 - val_loss: 0.4078 - val_acc: 0.8920\n",
      "Epoch 57/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0143 - acc: 0.9947 - val_loss: 0.4145 - val_acc: 0.8950\n",
      "Epoch 58/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0153 - acc: 0.9967 - val_loss: 0.4038 - val_acc: 0.8960\n",
      "Epoch 59/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0139 - acc: 0.9957 - val_loss: 0.3958 - val_acc: 0.8960\n",
      "Epoch 60/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0171 - acc: 0.9930 - val_loss: 0.3939 - val_acc: 0.8950\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0110 - acc: 0.9983 - val_loss: 0.4452 - val_acc: 0.8920\n",
      "Epoch 62/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0106 - acc: 0.9980 - val_loss: 0.5196 - val_acc: 0.8900\n",
      "Epoch 63/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0190 - acc: 0.9930 - val_loss: 0.4733 - val_acc: 0.8830\n",
      "Epoch 64/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0175 - acc: 0.9947 - val_loss: 0.3930 - val_acc: 0.9020\n",
      "Epoch 65/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0106 - acc: 0.9973 - val_loss: 0.4242 - val_acc: 0.8970\n",
      "Epoch 66/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0109 - acc: 0.9963 - val_loss: 0.4612 - val_acc: 0.8900\n",
      "Epoch 67/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0147 - acc: 0.9953 - val_loss: 0.4106 - val_acc: 0.8960\n",
      "Epoch 68/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0100 - acc: 0.9977 - val_loss: 0.4775 - val_acc: 0.9010\n",
      "Epoch 69/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0098 - acc: 0.9977 - val_loss: 0.4897 - val_acc: 0.8940\n",
      "Epoch 70/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0083 - acc: 0.9987 - val_loss: 0.4918 - val_acc: 0.8880\n",
      "Epoch 71/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0089 - acc: 0.9973 - val_loss: 0.5084 - val_acc: 0.8930\n",
      "Epoch 72/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0121 - acc: 0.9957 - val_loss: 0.4904 - val_acc: 0.8820\n",
      "Epoch 73/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0105 - acc: 0.9973 - val_loss: 0.4514 - val_acc: 0.8890\n",
      "Epoch 74/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0095 - acc: 0.9983 - val_loss: 0.4406 - val_acc: 0.8910\n",
      "Epoch 75/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0093 - acc: 0.9983 - val_loss: 0.4555 - val_acc: 0.9000\n",
      "Epoch 76/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0080 - acc: 0.9983 - val_loss: 0.4252 - val_acc: 0.9010\n",
      "Epoch 77/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0071 - acc: 0.9987 - val_loss: 0.4429 - val_acc: 0.8970\n",
      "Epoch 78/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0080 - acc: 0.9977 - val_loss: 0.4644 - val_acc: 0.8990\n",
      "Epoch 79/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0084 - acc: 0.9980 - val_loss: 0.4618 - val_acc: 0.8960\n",
      "Epoch 80/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0066 - acc: 0.9987 - val_loss: 0.4626 - val_acc: 0.8950\n",
      "Epoch 81/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0043 - acc: 0.9997 - val_loss: 0.5221 - val_acc: 0.9010\n",
      "Epoch 82/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0102 - acc: 0.9973 - val_loss: 0.4544 - val_acc: 0.8950\n",
      "Epoch 83/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0083 - acc: 0.9967 - val_loss: 0.4999 - val_acc: 0.8930\n",
      "Epoch 84/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0089 - acc: 0.9973 - val_loss: 0.4522 - val_acc: 0.8940\n",
      "Epoch 85/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0062 - acc: 0.9990 - val_loss: 0.4671 - val_acc: 0.9040\n",
      "Epoch 86/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0044 - acc: 0.9993 - val_loss: 0.4830 - val_acc: 0.9030\n",
      "Epoch 87/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0048 - acc: 0.9987 - val_loss: 0.5032 - val_acc: 0.8900\n",
      "Epoch 88/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0067 - acc: 0.9983 - val_loss: 0.5190 - val_acc: 0.8950\n",
      "Epoch 89/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0089 - acc: 0.9973 - val_loss: 0.4784 - val_acc: 0.9000\n",
      "Epoch 90/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0208 - acc: 0.9933 - val_loss: 0.4326 - val_acc: 0.8920\n",
      "Epoch 91/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0096 - acc: 0.9977 - val_loss: 0.4660 - val_acc: 0.9040\n",
      "Epoch 92/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0047 - acc: 0.9997 - val_loss: 0.4459 - val_acc: 0.9060\n",
      "Epoch 93/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0036 - acc: 0.9997 - val_loss: 0.4683 - val_acc: 0.9000\n",
      "Epoch 94/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0065 - acc: 0.9980 - val_loss: 0.4656 - val_acc: 0.8990\n",
      "Epoch 95/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0042 - acc: 0.9997 - val_loss: 0.4652 - val_acc: 0.9070\n",
      "Epoch 96/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0071 - acc: 0.9970 - val_loss: 0.5214 - val_acc: 0.8950\n",
      "Epoch 97/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0054 - acc: 0.9987 - val_loss: 0.4668 - val_acc: 0.9030\n",
      "Epoch 98/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0063 - acc: 0.9980 - val_loss: 0.4527 - val_acc: 0.9030\n",
      "Epoch 99/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0051 - acc: 0.9983 - val_loss: 0.4399 - val_acc: 0.9050\n",
      "Epoch 100/100\n",
      "3000/3000 [==============================] - 7s 2ms/step - loss: 0.0042 - acc: 0.9993 - val_loss: 0.4279 - val_acc: 0.9000\n"
     ]
    }
   ],
   "source": [
    "history_model_56 = model_56.fit(ped_data_train, ped_labels_train, batch_size = 128, epochs = 100, \n",
    "                        validation_data = (ped_data_test, ped_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_model_56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "flattened_output_function_56 = keras.backend.function([model_56.layers[0].input],\n",
    "                                  [model_56.layers[flat_layer_index].output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_flattened_ped_train_56 = np.array(flattened_output_function_56([ped_data_train])[0])\n",
    "cnn_flattened_ped_test_56 = np.array(flattened_output_function_56([ped_data_test])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.feature import hog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_hog_train = np.array([hog(image.reshape((25,50)), block_norm='L2-Hys') for image in ped_data_train])\n",
    "ped_hog_test = np.array([hog(image.reshape((25,50)), block_norm='L2-Hys') for image in ped_data_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_combined_train = np.append(cnn_flattened_ped_train_56, ped_hog_train, axis=1)\n",
    "ped_combined_test = np.append(cnn_flattened_ped_test_56, ped_hog_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_bare_labels_train = ped_labels_train[:,0]\n",
    "ped_bare_labels_test = ped_labels_test[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!                                \n"
     ]
    }
   ],
   "source": [
    "hog_accuracys_56 = get_accuracys(ped_combined_train, ped_combined_test, ped_bare_labels_train, ped_bare_labels_test, q_min=10, q_max=1000, num_q=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_svc_accuracys(hog_accuracys_56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_full_calssifier_ped(cnn, flat_id, pca, svm):\n",
    "    cnn_fkt = keras.backend.function([cnn.layers[0].input],[cnn.layers[flat_id].output])\n",
    "    def fkt(data):\n",
    "        cnn_data = np.array(cnn_fkt([data])[0])\n",
    "        hog_features = np.array([hog(image.reshape((25,50)), block_norm='L2-Hys') for image in data])\n",
    "        joined_data = np.append(cnn_data, hog_features, axis=1)\n",
    "        pca_data = pca.transform(joined_data)\n",
    "        svm_pred = svm.predict(pca_data)\n",
    "        return svm_pred\n",
    "    return fkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_wrongly_classified_ids_ped(data, labels, classifier, n = None):\n",
    "    s = np.size(labels)\n",
    "    labels = np.int_(labels)\n",
    "    wrongly_classified = [[],[]]\n",
    "    predictions = classifier(data)\n",
    "    for i in range(s):\n",
    "        label = labels[i]\n",
    "        prediction = predictions[i]\n",
    "        if(prediction != label):\n",
    "            wrongly_classified[label] = wrongly_classified[label] + [i]\n",
    "    return wrongly_classified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_pca, ped_svm = train_pca_svc(ped_combined_train, ped_bare_labels_train, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ped_classifier = get_full_calssifier_ped(model_56, flat_layer_index, ped_pca, ped_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_classified_ped_ids = get_wrongly_classified_ids_ped(ped_data_test, ped_bare_labels_test, ped_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_ped = images with pedestrian \n",
    "# data_garb = images with no pedestrian\n",
    "# n = number of randomly chosen images that are shown of each\n",
    "\n",
    "def plot_im(data_ped, data_garb, n):\n",
    "    \n",
    "    # Choose random images for data_ped\n",
    "    i_ped = np.arange(len(data_ped))\n",
    "    np.random.shuffle(i_ped)\n",
    "    i_ped = i_ped[:n]\n",
    "    \n",
    "    # Choose random images for data_garb\n",
    "    i_garb = np.arange(len(data_garb))\n",
    "    np.random.shuffle(i_garb)\n",
    "    i_garb = i_garb[:n]\n",
    "    \n",
    "    # The chosen 10 images\n",
    "    ped = data_ped[i_ped]\n",
    "    ped = np.resize(ped,(n, 25, 50))\n",
    "    garb = data_garb[i_garb]\n",
    "    garb = np.resize(garb,(n, 25, 50))\n",
    "    \n",
    "    fig = plt.figure(figsize=(12.5,25))\n",
    "    \n",
    "    for i in range(n):\n",
    "        axis0 = fig.add_subplot(n,2,2*i+1)\n",
    "        axis0.imshow(ped[i],cmap='gray', vmin = 0, vmax = 1)\n",
    "        \n",
    "        axis1 = fig.add_subplot(n,2,2*i+2)\n",
    "        axis1.imshow(garb[i],cmap='gray', vmin = 0, vmax = 1)       \n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GYvHzzPj/rkGvQPdfEB8BcF/vz/cB+FJnC4wxxow69vnGzDH6LhAj4nMA/gHALRFxICI+CeAzAD4SES8C+NnesTHGmFmOfb4xBkh8Yi6lfGKSv/qZAdtijDFmhrHPN8YA3knFGGOMMcYQQw0rLqU0osouAkolFlXiXxanKjEuBywAuYAMFhErm1SZElczSsTLolZlE5+n7jeDEjZzmWo7I1rOBGgoumaDz/TTxYsXmzoquITF5FPJUM90DQLiYBZ1XmbnAmMGzZUrV/oGAap3iN9HVUf5SB7n6h1W8Duk0vPw+5ndEYUjudU8pXbRYH+rzuN3ffPmzX3bmayMOX/+fN866rlwoEx2nuAglUxgqJo31DPPzIPKJm6LA66AdsypnWRUX65fv746zuwcMxP4F0RjjDHGGFPhBaIxxhhjjKnwAtEYY4wxxlQMfWsT1gOwriCjUVN6gYxmS+kalD6LtQdKW8LaGqWHUOex3iSrXWQti9JVsI5BtaPO437JJHwdJJmEoIpM4mplN18vm4CabcpoELPJenn8dn0G6no8LgapnTRmMi5duoQTJ05UZaxRy2jdskmauUz59sx7ndlQQflRNQctX768r01Kl87vsXpnWe+WbZuTk69cubKpc/z48eo4q4djO5XdGX1hJjZA2aTGCmta1XlKA8jjSdXhsowOFWh1rspvqzGWmQOy+vUM/gXRGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpiKoSfK7iKQ5yShKvmqEoJy0IISpypBJ5cpwSzXUQJW1TYLZrOi1kwyV25LiZaV8JXLLly40NTpGtjAQl8lLs8kc1Xn9UvCC+gktKtWraqOlUhbBbzwOFDPnPtSiY8zImJVRwmUuV4mKWyX5PTGXC9XrlxpfAm/Q2fPnm3OY/+nxr1KHM0+SvmHzPuhrsf+IBP8psgmcmbfsmbNmqbOmTNnqmPVl8pHZTYv4KTQ2aAcnoPU/KZsWrFiRXWs/D33eSbgEmjnwa7BqmwjAGzatKkpY1Ty7OmEn8tU/L1nCmOMMcYYU+EFojHGGGOMqfAC0RhjjDHGVPRdIEbEgxFxNCKenlD2OxFxMCKe6v3389NrpjHGmGFgn2+MAXJBKg8B+J8A/pTK/6CU8j+u52Lz5s1rBMgsoMxkR89knp/s+owSOzOZLPKZYBdlQ0boC7TBFhnxb3aHEhbjZgTCmazyqi11v0o0zHaq+2WbVDunT59uyjJCaiVIZiGz2gUisyNKZqxmxs5k7fejyzlmTvEQBuDz582b1+ycwoEFmd1OlL9XcFuqbfVe8fugzuPgmkzAGND6xGwAIPtNFaTCbWfnIA5mUcEeXEfZqIKAMsGFap5glP9lP63aVvD9qTmfd7zJnsd2qr5UQafcd2o8dd29bJC7nvWdqUopjwM4ObArGmOMGVns840xwNQ0iJ+KiO/2PkesnqxSRNwfEXsiYo/3gDXGmFlLX59vf2/Mu4euC8Q/BnATgDsAHALwe5NVLKXsLqXsKqXsyv4kbIwxZqRI+Xz7e2PePXR6g0spR975c0T8CYAvZ86LiFQSVCajWVD/WuVrZZOUZuDzMtrJycqYTAJm5Xwz95LRxGX0fkoXlNX8ZGzq8uxUO0onc+7cuepYJR1XbWWSfvM4VLpBVcZtZZObsp2Z/jbmeuni8+fPn99Xg6iSO3Md9e6rBPj8Hivtl5pv+B3K6NIzya1VW119pGqb/bS6N2VnRhfOdTIJt4E2KbSqk/GJSqvJddT9Kju5XkYrCrT9m9EpZuflTOxFVlPar05m/TQZnX5BjIjxCYe/CODpyeoaY4yZ3djnGzP36PsLYkR8DsDdANZFxAEAvw3g7oi4A0ABsB/Ar02jjcYYY4aEfb4xBkgsEEspnxDFn50GW4wxxsww9vnGGMA7qRhjjDHGGGLGw8xY1KkEnSy6VKLlrIiW6ZpwmoWvWdEyXy8rIM0kmGVRtrr/jPBV9Qn3QSYpNpBLyqxE4RkxLgegqICUTBLurAg+E0iSCbrKJEXNvAdA+xzUeV0DsYyZCleuXGmCFnicKz/G75l6Py9evNj3+kuWLGnKVHBdJuG0ClDo1466HgftAPr+GBVIx8m7s8n1M0FA/FwyCcaB1t92Tfif8XVqDKi2+dmpMaeeXWbdwX2Z7acMmWBVxSA3QvAviMYYY4wxpsILRGOMMcYYU+EFojHGGGOMqfAC0RhjjDHGVAw1SKWU0ohYM4JKFpV2zVauBKzZYIt+bWd23siS2UVDBV+cP3++7/WViJb7TgVRcNBG9n65XnaHEL4/FYDCdqrrq3thcbfqS3U9Fpyr8zIiYvUMMsEt2baMGQWUv8+8s/wOZXdt6hcQA7TvPtD6pExghbJbzSVsZ9b/Zeqxj1LndA1sy+wA03XuVPA8wbtdqbZV4FDX/b/V/WXWHZngmq6Bi+peMs+Ox8FUghT9C6IxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyqGniibv7Xzd/aM9kJ9r++afFNpDzLaRbZB2aTaZj2A0jVk7kXV4bJsP2U0Ehm9i4LrqT7pmlyU+1JpNpSdXC+r0WD9Uua8bD9xH2QTzHbRIGZtMmYqlFL6JlzuqpFT+rd+c8tkZRmyGxr0I6uDZ1SibEb5TOXblb6aYT1ndu7keplNAdR5mTkwu2EGXy8zJwBtH6gNHbquVzJ0XXd0vZ60YWAtGWOMMcaYdwVeIBpjjDHGmAovEI0xxhhjTEXfBWJEbI2Ir0XEsxHxTER8ule+JiIejYgXe/9fPf3mGmOMmU7s840xQC5I5RKA3yylPBkRywE8ERGPAvh3AB4rpXwmIh4A8ACA/zh9pv4zWQE/i1ozSSXVedOJEpQq8W1GDMt2q7YXLlx4HdZN3nY2OIJFw1khdaZOpk+6JpJWz4BtyLStbMwmMB8UmaTuxkxgYD6/37hWQQVdg78yQSpdkx13TTacCUBRQSNsp/J/GZtUknEOeFHBJmxTJvhD1VN1lP/JBGFynQsXLjR1brzxxqaM+yD7fNUc0M8mdW/TucboOp9m6TvDllIOlVKe7P35HIDnAGwGcC+Ah3vVHgbwsYFZZYwxZkawzzfGANeZ5iYidgC4E8A3AYyVUg71/uowgLFJzrkfwP1A91+vjDHGDJ/r9fn298a8e0gHqUTEMgB/CeA3SilnJ/5dufqbpvxds5Syu5Syq5Sya1C5pIwxxkwvXXz+RH/fdV9eY8xokHqDI2IhrjqKPyul/FWv+EhEjJdSDkXEOICj/dpRiVMz+ih2NEpDkNGWZPUBrF3J6AWyi1+2PZs4mssyCV+V3ep63Jb6l38mkSknklZtZXQrQKsJUTqdjE0ZbV8mcauql9HCdv1HUfa8zBjPaHGNmcggfL7y9+wPlB/L6JYVGS1hxm9m9I0ZH63KlP9VsG5O+eSMbk/p6M6dO1cdK3+Q6XNOJA3k/G3XuTqzDsj45Owz4PO66sS7auwzYzWTRH4qmsRMFHMA+CyA50opvz/hrx4BcF/vz/cB+FJnK4wxxowE9vnGGCD3C+KPAfi3AL4XEU/1yn4LwGcAfCEiPgngZQAfnx4TjTHGDBH7fGNM/wViKeXvAEz22+rPDNYcY4wxM4l9vjEG8E4qxhhjjDGGGHqYWT8xajYhJ5NJapmxp+t52UAHFr5mAwa6JI/tmrQzGziTISO0VXX4ftX9c59n75fJBKQoutZRY7yrIDpzf4NMnGpMlnnz5mHp0qVVWcYfZMhsJpD1B12Cz1Qd9V7z/ap3MZMEu2vybhUQwsmkVdvnz5+vjtVzy6Qx6hqklwnaUHXU/Xb1f/zMVR9kAgAzycIHZeNU2pLtD6wlY4wxxhjzrsALRGOMMcYYU+EFojHGGGOMqfAC0RhjjDHGVAw9SKWfILirYFfBglUlmFUBGZnAma4Z+TNC6kwWeQXblAlgUHQN3MnsZJIN5mEy52V2xQFymeYHFfDStS+zgTNc1iVwxkEsZjpYuHAhxsfHq7JMMCHv0ME7fwC5AAXlM5W/5/lF+XveJSq7mxeXZQIdgHYnFQXfi2pH9RP3Z2bHrcy8rMgG6WXmrswc2HU3LUUmCLJrkEomMErB95eZF6d1JxVjjDHGGDO38ALRGGOMMcZUeIFojDHGGGMqhq5B7JI4k7/zZ5Ot8nlKw5DRDGRsVm0rPYTSwDBKf5LREXRNStrlPNVv6rlwH2SvxfVU2100IpOVMRktS0Y3kkmKnbUpo5XMtNPlPoy5XubPn4/ly5dXZawvVL6OEzCfPXu2qaP87cqVK6tjTggNtFpCoPXJyre98cYbTVnGpozeVyWcZr9x8eLFvtfr6msySbDVvJXx5V3jBTK6PfWcuvrWjOYxM5dk5+6uusCu80RX/AuiMcYYY4yp8ALRGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVMcwkuRFxDMDLANYBOD60Cw8O2z1cZqvdwOyyfXspZf1MG2HeXUzw98Dseh8mYruHi+0eDimfP9QF4j9dNGJPKWXX0C88RWz3cJmtdgOz23ZjBs1sfR9s93Cx3aOFPzEbY4wxxpgKLxCNMcYYY0zFTC0Qd8/QdaeK7R4us9VuYHbbbsygma3vg+0eLrZ7hJgRDaIxxhhjjBld/InZGGOMMcZUeIFojDHGGGMqhr5AjIh7IuL7EbE3Ih4Y9vWzRMSDEXE0Ip6eULYmIh6NiBd7/189kzYqImJrRHwtIp6NiGci4tO98pG2PSJujIh/jIjv9Oz+z73y90TEN3vj5fMR0e6GPgJExPyI+HZEfLl3PCvsNmY6mS3+HpidPt/+fmaYK/5+qAvEiJgP4H8B+BcAbgPwiYi4bZg2XAcPAbiHyh4A8FgpZSeAx3rHo8YlAL9ZSrkNwI8C+PVeH4+67W8C+OlSygcB3AHgnoj4UQD/HcAflFLeB+AUgE/OoI3X4tMAnptwPFvsNmZamGX+HpidPt/+fmaYE/5+2L8g3gVgbyllXynlLQB/AeDeIduQopTyOICTVHwvgId7f34YwMeGalSCUsqhUsqTvT+fw9VBvBkjbnu5yvne4cLefwXATwP4P73ykbMbACJiC4B/CeB/944Ds8BuY6aZWePvgdnp8+3vh89c8vfDXiBuBvDqhOMDvbLZwlgp5VDvz4cBjM2kMf2IiB0A7gTwTcwC23s/2z8F4CiARwH8AMDpUsqlXpVRHS9/COA/ALjSO16L2WG3MdPJbPf3wCzwm+9gfz805oy/d5BKR8rV/EAjmyMoIpYB+EsAv1FKOTvx70bV9lLK5VLKHQC24OqvD7fOsEl9iYiPAjhaSnlipm0xxkwfo+o3Afv7YTHX/P2CIV/vIICtE4639MpmC0ciYryUcigixnH1Xz4jR0QsxFVn8WellL/qFc8K2wGglHI6Ir4G4EMAVkXEgt6/zkZxvPwYgF+IiJ8HcCOAFQD+CKNvtzHTzWz398As8Jv290NlTvn7Yf+C+C0AO3sRP4sA/DKAR4Zsw1R4BMB9vT/fB+BLM2iLpKeH+CyA50opvz/hr0ba9ohYHxGren9eDOAjuKqn+RqAf9WrNnJ2l1L+UyllSyllB66O5/9bSvnXGHG7jRkCs93fA6PvN+3vh8hc8/dD30mlt/L+QwDzATxYSvlvQzUgSUR8DsDdANYBOALgtwH8NYAvANgG4GUAHy+lsKh5RomIHwfwdQDfwz9rJH4LV3UpI2t7RHwAV8W983H1Hy5fKKX8l4h4L66K29cA+DaAf1NKeXPmLJ2ciLgbwL8vpXx0NtltzHQxW/w9MDt9vv39zDEX/L232jPGGGOMMRUOUjHGGGOMMRVeIBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpsILRGOMMcYYUzGlBWJE3BMR34+IvRExapuBG2OMGSD2+cbMHTqnuYmI+QBewNUElwdwNSnqJ0opz052zoIFC8rChQursmGm2bmaU7R/Gds0SBv5elmbMnW4bMGCdqMcdd6VK1eaMibTJ6qM287cm2ore17GJm4rUwcA5s2r/z2VOW/+/PmptjNk+jczVvn6r7/+Ot56661uRpk5w/X6/MWLF5eVK1dWZW+99VZ13HVM87sIADfccEN1vHTpUnUPTdnly5eveazOU9cTbyC5AAAgAElEQVRXfvTixYvV8aVLl5o6qi2+Ht+bup6yW/Xd22+/3ZQx7Lcy/abIzm/cVuY8XktMdp7q8wyZOejGG2+sjtWzVGTGU4bM3K36ad++fcdLKev7nTuVrfbuArC3lLIPACLiLwDcC2DSBeLChQuxY8eOqiwzoWUGooIHuVowqc7jF0i9UF1fDn7R1QJC2cllmXtZvXp1U2fRokVN2Ztv1vk81QvFA5EdPaD75Pz589Vx9n7ZJmV35vqqjG3ITkCLFy+ujtW4YIexfPnypo663wzqeq+//np1nBmXPE7+/u//vpM9Zs5xXT5/5cqV+JVf+ZWqbP/+/dVxZlGlxr1a/G3btq06/tCHPtTUUe/12bPV1sW4cOFCU4d9+bJly5o6/C4CwBNP1Fv2njp1qqmj5iAuu/nmm/te78yZM00d5acPHz7clDHst5SN7NuB9nlm5jLVVua8TZs2NXXU8+U+z64nuJ7qA34ualwom/heeN6YDJ6r3njjjaYOj9UNGzY0dX7pl37p5cz1pvKJeTOAVyccH+iVVUTE/RGxJyL2dF3JG2OMmXH6+vyJ/l4tmIwxs4dpD1Ippewupewqpezq+suJMcaY0Weiv+df3I0xs4uprNgOAtg64XhLr2xSSil99QDqE6Rqh8nourr+tKzI6Ni6tq1+ks78+sqfr7vqDVUdvr9MO4qMvidrU6ZPuv5qnfnHjPoskBkXaoxn+jOjl830pbfXNB25Lp//+uuv4+mnn67K1CdWJjOmz50715SxLEV9hmZNJNBKR7Zu3drUYZ/Mn8oB4PTp033bzmibgdb/qH7LvMfK12zcuLFvHb6+8tvK/7EfU/emyvjTrPK/fL/8vLN2qjlBnac+3zIsf1DtZPSF2bUJX0/dC/fdVPz9VH5B/BaAnRHxnohYBOCXATwyhfaMMcaMLvb5xswhOv+CWEq5FBGfAvBVAPMBPFhKeWZglhljjBkZ7PONmVtMSRRYSvkKgK8MyBZjjDEjjH2+MXOHoUaNlFL6ahS6po/JXp9R12MbM/o3peNQ2oNMfqmuOkEOw8+mGOB+yaSwyeoa+H6VZkKlseD21XkZfWE2XyPTdRzyM1d1MrkRs5qULu9C1/fHmOvh8uXLjXYuoy/k9yObI441ad/+9rebOuvXt6nfuGznzp1NHU6h0zV9jErXpdKnsJ/O9FMmFRjQXT/OZHJBZq/F4yITi6DIaACVTWoO4vPUfJPR5nfVCWbKuuZPzOKt9owxxhhjTIUXiMYYY4wxpsILRGOMMcYYU+EFojHGGGOMqRhqkEpENOJMFuhmkliqBJ3qPBa6qsSamSSl6rzMJt2ZDceVEFUJoPtdX52XTRKd2fSd62QDftiGbHBLxiYWFmcFwtx3ShCdSR6r2ubxrGxSz473IlWiaYWTXptRhv0iJ/PPnKMSXqv3k9+9JUuWNHVUEuz3v//91fHY2FhTh99ZFeyi7OR9ntX9K3/AfaCCCzOBbF0D8ga161lXf6+eL7el7M4Ei2aCIoE26KdrQKsKQuq6YUZm/cB1ugb8AP4F0RhjjDHGEF4gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmYuhBKv12TlGBHSyYzQRxKDK7iACt0FSJTPk+lKBUiY8zO4uoMhZAv/HGG02d119/vTpWmfUzglV1vxyok838ztfLZtbnPsgIhBXquWR2O1HnZWzKtHPx4sVO52Xu17ukmFFBBSWyT8oEaNx6661NnVtuuaUp411bskF6R44cqY5VAAoHvKj3MzOXqAAJ5ZP5esrf8zyo/HbGZ6jAGXU9Rvm/rkFzbGfGj6k66pnzs1LrB9VPFy5cqI45kBAAxsfHr3mtrE0KZWdmV5hB7rbiXxCNMcYYY0yFF4jGGGOMMabCC0RjjDHGGFMxJQ1iROwHcA7AZQCXSim7+p3DegvWEXT9Xp/RPiitRTbZJsN2Ky2N0jyyTkQl4VZaki7XU21nkncruJ+y+h62U9mknkHmmbPdWd0en5dNCst2Kru5TI0ldR7bpJ5JZlx27UtjMlyvz+exxxpElcyafcamTZuaOps3b27K2LdkteqvvPJKdbxmzZqmzsaNG6vjAwcONHX27t3blPG9qLZVH/C7nkkWru5XaR7ZRygfmUkunUkKrc7LbE6R0Y4PMqZAzUuZ8cTzYHbDDCarsef7U3Uym1pkGUSQyk+VUo4PoB1jjDGjj32+MXMA/7RgjDHGGGMqprpALAD+NiKeiIj7VYWIuD8i9kTEnkxaEGOMMSPLNX3+RH+f3U/cGDOaTPUT84+XUg5GxAYAj0bE86WUxydWKKXsBrAbABYvXtz9Y7gxxpiZ5po+f6K/X758uf29MbOYKS0QSykHe/8/GhFfBHAXgMcnq68Sp7LQNiOozAYjcFtZ4T+LWFVABp+3fPnypo4KimEblPhYCWb5XjLBNSrZaSbQIdMnSuirRMN8L10TOWdEy9nz+o3Byc7LJMrOiK3VWM0EymQCUDIBMHwtJ9c2Wa7H58+fPx8rVqxoyiaifBT/8shBJIBOWnzo0KHqWCW83rp1a1PGQSIqAPDEiRPV8de//vWmDifqBoD3vOc9fW3KJAvP+DHVl2fOnGnKeEMFNb/xM1ABGspv8iYAaj7PBHKoOhzglA2UzCQrVz6Z71mdlwkaUf6Vz8sEwADtM8+sA9RcnaXzJ+aIWBoRy9/5M4CfA/B0Z0uMMcaMLPb5xswtpvIL4hiAL/ZWxwsA/Hkp5W8GYpUxxphRwz7fmDlE5wViKWUfgA8O0BZjjDEjin2+MXOLQeRBvC74ezx/s1e6Bj4nmww4o+PIbGauEpmydnDZsmVNHdZMKBvU/SotSUazwGQ3b+f+VHo41kNkdBWA1mEyqg8y2lS+l6z+I0NGy6fs5me+ePHipo56Ljwu1P12tSnTtjGD5oYbbsD27durssOHD1fHZ8+eledNZGxsrKmzY8eOpuzmm2+ujteuXdvUUfru06dPV8fKZzz55JPV8blz55o627Zta8rYBvV+ZjTJmfdazW+sNwRaraSaS9hHqXbUeWzDhQsXmjoK9klqDuK2s76d+y6jLwdazaGKM8jMU5nrZeZl1VamTlarqXAeRGOMMcYYU+EFojHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKoYapBIRjYCSxZlKeJpJIpwRrKrggMz2f+o8Tm6qbMwkqFQCVhXYwYJZdT3uS3V9JdJmG1RwDYtxVb9lko1mt1tk21WSUrYp0yeqLJPwGmj7JSMQziYp5ba6Btc46bUZFa5cudIEN2QSyXNy7dWrVzd1VMAABxOqwMHXXnutKeNAEuUzOOG0Sm69cePGpow5efJkU5YJnlRBKjxPqAAJ5TfZJ6n5hu9P+RUVgMJl6vrqfjmYT90vt6VsUtfjgCL1fNWcx88zE0Cb2VRD2amur4JV+10faN+f48eP921nMvwLojHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFTO+k0rm7zMCfiV8ZTGuEjZnRP1qNwwWRKt2VKZ5rqfEzqotFroq4Stf7+LFi00dRb/AIVUne78sxs3ugsP1sjuLMNmdCxh1vUxG/kxAVSaYR9mtYDsz7wbb6MAWMx2UUhrRPh/zjlQAMD4+Xh0rkf358+ebMvZ3yh9lAtLUPLFhw4a+ddRuIydOnKiOOdgFyO2kourwu6+CZJQf4+Ad1b8c2KHmEhWkwv2bDVLJ7EjCbasgpJUrVzZlPMbUuFD9y7v8cPAH0N+3TtY2+9ysD+a2lE3cB9/5zndSbSv8C6IxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyr6CqYi4kEAHwVwtJRye69sDYDPA9gBYD+Aj5dSTmUuqLQFgyCjB1P6gIxGLVNH6RqUJoV1FErLopIrZ7RmGZ2i6n9uS53H/ZtNysptKbszGhxFP30TkEvorcZORhOS0ZZkNZeM6t+u5/VLDD5d76SZnQzK58+bN6/Rb3NSZjWm2Zeq5Nbq/eT3X41r5Ve4nvLlq1atqo6VlpB1ewBw+vTp6jiT8BpoNZZqLuG5Y82aNX3bAYAf/OAH1bHSF3JfZnwP0CZ8Vj5S+UROCp1JOK0SSavr8XlK96+0i6dO1cNbJTlnHaSyOzsOmcwcv379+qYOvy/K7iyZp/4QgHuo7AEAj5VSdgJ4rHdsjDFm9vMQ7PONmfP0XSCWUh4HwEvQewE83PvzwwA+NmC7jDHGzAD2+cYYoHuam7FSyqHenw8DGJusYkTcD+B+QO83aIwxZuRJ+fyJ/l6lITHGzB6mHKRSrn5gn1TEVErZXUrZVUrZlfnubowxZnS5ls+f6O9V/lhjzOyh64rtSESMl1IORcQ4gKODNGqYKFEpC3IzgQYqaagSFmcCJJRomcWpKpCFyQSkKBsyARqqTia5tBIRZxJXZ66n+jKT9DuTuFWVZfuXmc6gEAecmGniun1+RPRNyq7GK5cp/5v1P4xqi99rNSdwsMebb77Z1FFlvEhWv6qqezl06FB1rJJZ83kvvfRSU0fNE+zvVHAL+00V2KHKeO7au3dvU0cF8/BcmQlczAQcAbkASxWYxEFAap7gMhWoqZ4vPxc136jzOOm3Gs/79u2rjtV4ztL1F8RHANzX+/N9AL7U2QJjjDGjjn2+MXOMvgvEiPgcgH8AcEtEHIiITwL4DICPRMSLAH62d2yMMWaWY59vjAESn5hLKZ+Y5K9+ZsC2GGOMmWHs840xgHdSMcYYY4wxxNDDill4ySLPTFCBEm9mBauZOlyW2Z1CCUEz4lCVxV4JTzlFUCbLejYgRAlk+52n2sn0txL6ZtIfqeux0FfVUeLjzPUyu8lk6nSN3M+K8LleZqcaB7KYYdEvaCsTJJh594HWb2Z3/+D3QQUacPDF2rVrmzrqPLZBBbIoO/n+MnPeihUrmjoqKIbt3LJlS1Nn27Zt1bGak9Sz4+Ca1atXp2zieVD5bb6e6hN1HveTGjsZf6ui8jlQJ7MLGtDei5onVBAQ953aZYj7QI2LLP4F0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFUPVIF65cqXRYLC2I5OgOJOMGGh1EypxdUZfmNEVqOSfSpPC+rdsgsyMLoe1B1ldJLeVSQyurt81wbZC2dCPbNLxTNuqjtK39Dsvo6kF2jGW0cZOVtbvetlnYMxUuHLlSuNzu2y3mpkT3rneRNS7wcmPgfbdU/6eNWJKR8dJjIE2wfYbb7zR1FEasbGxeidDNXexJm7lypVNHaV5ZG2bSpR9+vTp6lgl6uZ7U9fL+Dqg7Tul9+O5S81lyt9z2+q8zKYW6jllknBndP/qvCVLljRlPL+odQefp55vFv+CaIwxxhhjKrxANMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMxdATZbNANJOkmUX1SmSqEk1mElUr+DwlYGWblBhYwYEr6v6VYDWTgJltyN5/RvzLwuLMc1PnZZKGArmkt5lk5dlkuf2uD7TPXPVBpi+VUJ/7RdndNfF7lzrGTJVSSuOTNm7cWB0rP6aC+xiVqJrF+SqoTCUf3rBhQ3W8atWqps4rr7xSHR8+fLipc8sttzRlHNyhgj34+gBw9uzZ6lj5Ea6jNl1QfoSTVyubuG31TJSPzMwLmSBI5SPPnDlTHas5VwVk8FhRQSNqXJw4ceKax0AbdKT6OxN0qlB9wDaoe+FALCfKNsYYY4wxA8MLRGOMMcYYU+EFojHGGGOMqei7QIyIByPiaEQ8PaHsdyLiYEQ81fvv56fXTGOMMcPAPt8YA+SCVB4C8D8B/CmV/0Ep5X9c9wVJlKxEytfbxmRkgjSUWJTFzV1F/ZkdK5RgVwlmWWiqxMCc/T4L398gg1S6wv3S9XqZXVNU25kxpp5vZveezG4Squ3MeFJjlW3iOg5aMcRDGIDPv3LlSuNLWdSvdh/J7Pyjzrv55purYxVIonb/4LZUkAr7A7XLxalTp5oy3ulC7aSi/C3v1KJ2FuHAGeVrNm3a1JRduHDhmscKtUOJgoMmVHBLJlBRBRhxUIry0RzIoupt3769qaN22OFnrOYJDvBRvl2NFR7jas5XQUc8ntSuNBwINhX/3vcXxFLK4wBOdr6CMcaYWYN9vjEGmJoG8VMR8d3e54jVk1WKiPsjYk9E7Omyv64xxpiRoK/Pn+jvs6m/jDGjSdcF4h8DuAnAHQAOAfi9ySqWUnaXUnaVUnZN92dJY4wx00LK50/099nPksaY0aRTouxSypF3/hwRfwLgy5nzIqKvtiujB1P6BPWdnb/rq6SS6lfNzDf7frouQGskMjoypUfgtpRuJaP/UHDbStfA95e5D4XqJ2UnazRUnUxS6owmT01k6v5YX5JJ3q3aVuMiM+YyusTMc1HvgTHXoovPj4jGl7CuSumzmIyvA9qEyEePHm3qKL3doUOHqmOVbJnnJaVZUz6K33+lgXzttdeaMtacKw0i9y0ntwa0VpN1kKovuW31nNQ8wX2gNJdKW8e6T6UlZH+rfKZ6duPj49Wx6svMr92sCwVa3aeqo+C5RPlt1hsC7dyl9LL93rnrodMviBExscd/EcDTk9U1xhgzu7HPN2bu0fcXxIj4HIC7AayLiAMAfhvA3RFxB4ACYD+AX5tGG40xxgwJ+3xjDJBYIJZSPiGKPzsNthhjjJlh7PONMYB3UjHGGGOMMUSnIJWuRETfJKiZJM1ZkT0LOtV5mbaUgDQTNJJBBeWo4Aema8qgTCR51+AaZTf3SzaYJ9OfXEcJojPXU+Lyrsmz+TzVjgpcYZF01+ApRb9gJSfKNtPBggULsG7duqqMBfNqnLPQX41P9a7v2bOnOubgk8nOe+GFF6rjgwcPNnU42IODSAAd/DA2NlYdqyCZY8eONWUcBKMCQjgoR/lfdb1MoFBmYwQVSMIBpOo8dS88v6hgE/b3amOIzPymgjYy87Dy25s3b+5bR/nflStXVsdqDGSSfqvE3Hx/mfXEZPgXRGOMMcYYU+EFojHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKoYapAK0IksOElGCShZ5ZnawUPXUeUokzYJVdR4LpzMBDAoV/JG5P5X5nbO6Z23KtM0i7WygUNdnwGJndb0ugSxAbvcRVdYlwEftZKCy7fMYV7sFZcgEYnGfeGcVMx0sXLiwCdJ47rnnqmP2WUAb2KHeIQXvUqKCETKBBspvKjuZw4cPN2W8K9aWLVuaOipIhG1XwTUcKKN8qwpi4Pf95MmTTR32PypQSF2PA3XUrmCqLd41RO0QsmHDhupYBXGoMn6+KphI9RMH06h74X5S84Yav+yD1TNQ8yLvCqPGKtut7i2Lf0E0xhhjjDEVXiAaY4wxxpgKLxCNMcYYY0zF0DWIrH/IJOnlb/EZXVnm2pOV8fWU1oLLsomyM/VUAlDWFSidYD+tGZDrb9WXrLVQuoZM2yppqNJacFnm+XYdF+r5ZnSJ6n75OamksJkk3EpbosYqt5UZX12TrBtzPZRSGr+xadOm6liN19dee6065qTRQO69yiTuB4CtW7f2rcP6xoxmDWj9nUqIrNpiDZ56Z9kfKP+g9HaspeN7A1r/o3yWaps5ceJEU6ba4nGi+mnbtm3VcVabyuMgq+/m56mux883M98Abb+o81iLC7S6U6VN5bGSeU6T4V8QjTHGGGNMhReIxhhjjDGmwgtEY4wxxhhT0XeBGBFbI+JrEfFsRDwTEZ/ula+JiEcj4sXe/1dPv7nGGGOmE/t8YwyQC1K5BOA3SylPRsRyAE9ExKMA/h2Ax0opn4mIBwA8AOA/9muMBaOZRNldRfUcRNA1IXAmCXeWTNJKThqqrqcCJFhcrWzMBj8wKiimC5mgoMnK+pFJZK3IPkvucyUs5jrqOSmR9KDGeCa4puvYNXOGgfj8y5cvN77sR37kR6rjCxcuNOd9+9vfro6VEF/5MU5ArxJQq0CSfnMS0CZgVoEsKgE+owIQMwEgyibuA5Vcev369U0Z94Gqw/en/FMmSXMmCTjQzovqXlauXFkdq/5WATdcT/lINcb4matE2dyWGl8qUOfo0aN921ZBMTx3ZAIsVXL4LH1nilLKoVLKk70/nwPwHIDNAO4F8HCv2sMAPtbZCmOMMSOBfb4xBrhODWJE7ABwJ4BvAhgrpRzq/dVhAG1M9tVz7o+IPRGxJ5sKxhhjzMxzvT5/or/PbE9njBld0gvEiFgG4C8B/EYp5ezEvytXv3PJJHillN2llF2llF1d9ys2xhgzXLr4/In+fir514wxM09qxRYRC3HVUfxZKeWvesVHImK8lHIoIsYBHJ28hauoxKmZRL/8nV1pCJSOg3UTqo7SiGU0eV1/Dc0k2Fa6HF5cZ+xW/ZRJVK3azizuVdvc51kdKLeV0ehlk5RmtIqqTsZ27jtlt9KYZpK3qufC7avrcb/ws8z2m5k7DMLnL1iwAKtX13EsnABZvWfj4+PVsXrv1LvAWqs77rijqbNmzZqmjP3tvn37mjoHDhyojtXiV2m9WP+mrq+0dHx/yj/w3KGur/qX9W/Hjx9v6vD1lI9Qert169ZVx0pbl+knTooNtDrBD3/4w00ddT3WPH73u99t6mzYsKEp46TuiiNHjlzzGGgTvwOtn1bjQtFlrs7EPUxGJoo5AHwWwHOllN+f8FePALiv9+f7AHypsxXGGGNGAvt8YwyQ+wXxxwD8WwDfi4inemW/BeAzAL4QEZ8E8DKAj0+PicYYY4aIfb4xpv8CsZTydwAm+wb1M4M1xxhjzExin2+MAbyTijHGGGOMIYYaVhwRfYM0lOiSBbLZ4ItMUIGqk7leVzLBLSrQIJMQmc/LBoRkAkC4TtfE2Sqxp7KT+ymT4Dt7vyzc7ppgW/Ub26Cet7IzkzBevRuZIC+ukwn6MmaqXL58GWfPVsHPzbGCgx/Uu58Z5+q9Vud1SQqt3plTp041ZRygoIIvVGAF2758+fKmDtug/IMKiOO2VbDJxo0br3k8mU0c8MNBSQCg0h+xnSrYg/3WD/3QDzV1VP/y81VBOWo+42ApNQ75Gaj7VWsTDopZsWJF6jx+Vpn5ZSr+3b8gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmwgtEY4wxxhhTMfQgFc7qzRnblUCYy5SAP7ODRHZ3jMwOLJmM5kocykLXTMCCOk+RCdLIBFZk2lbPQJVlrp8R0WZ22MnCz0o9u67BUoyyO3OeujclWu7a1vW2YUwX+D3K+L9MQJ7yydz2/v37+7YNtIEzatcSDiJQgRabN29uyritJUuWNHXU/bHfULth9OtbIOen1fWXLl1aHasgDt41BQBWrVpVHSu7T5w40bfs9OnTTR2+3xdeeKGpo8YT9znv7qPqqOupuevMmTPVMe/aArR9AgBr166tjrNzGddTz7zr7mXyep3PNMYYY4wx70q8QDTGGGOMMRVeIBpjjDHGmIqhaxA5KSh/5++qo8ske1b6k8z1MhqtjN5QXU9dX+k2BqUBVHRpO6ud7JqUOZMAmssy11fnKc2RegZdNIjZJNyZ5KaD0nhm9KzGTBXl73ksqveaxzAnXwb0+8l03TxBtc3v3sqVK5s6W7du7du2ej/feOONvvXUu899mZ0TuA/UvMjPTT0nlcyaEzmr/lbaRe5zlTyc9X1KF/nqq682ZawfZf0foPuJk16re2HtpHq+6n65n9T9ZuYX9ewyieaz+BdEY4wxxhhT4QWiMcYYY4yp8ALRGGOMMcZUeIFojDHGGGMqQokcp+1iEccAvAxgHYDjQ7vw4LDdw2W22g3MLtu3l1LWz7QR5t3FBH8PzK73YSK2e7jY7uGQ8vlDXSD+00Uj9pRSdg39wlPEdg+X2Wo3MLttN2bQzNb3wXYPF9s9WvgTszHGGGOMqfAC0RhjjDHGVMzUAnH3DF13qtju4TJb7QZmt+3GDJrZ+j7Y7uFiu0eIGdEgGmOMMcaY0cWfmI0xxhhjTIUXiMYYY4wxpmLoC8SIuCcivh8ReyPigWFfP0tEPBgRRyPi6QllayLi0Yh4sff/1TNpoyIitkbE1yLi2Yh4JiI+3Ssfadsj4saI+MeI+E7P7v/cK39PRHyzN14+HxGL+rU1E0TE/Ij4dkR8uXc8K+w2ZjqZLf4emJ0+3/5+Zpgr/n6oC8SImA/gfwH4FwBuA/CJiLhtmDZcBw8BuIfKHgDwWCllJ4DHesejxiUAv1lKuQ3AjwL49V4fj7rtbwL46VLKBwHcAeCeiPhRAP8dwB+UUt4H4BSAT86gjdfi0wCem3A8W+w2ZlqYZf4emJ0+3/5+ZpgT/n7YvyDeBWBvKWVfKeUtAH8B4N4h25CilPI4gJNUfC+Ah3t/fhjAx4ZqVIJSyqFSypO9P5/D1UG8GSNue7nK+d7hwt5/BcBPA/g/vfKRsxsAImILgH8J4H/3jgOzwG5jpplZ4++B2enz7e+Hz1zy98NeIG4G8OqE4wO9stnCWCnlUO/PhwGMzaQx/YiIHQDuBPBNzALbez/bPwXgKIBHAfwAwOlSyqVelVEdL38I4D8AuNI7XovZYbcx08ls9/fALPCb72B/PzTmjL93kEpHytX8QCObIygilgH4SwC/UUo5O/HvRtX2UsrlUsodALbg6q8Pt86wSX2JiI8COFpKeWKmbTHGTB+j6jcB+/thMdf8/YIhX+8ggK0Tjrf0ymYLRyJivJRyKCLGcfVfPiNHRCzEVWfxZ6WUv+oVzwrbAaCUcjoivgbgQwBWRcSC3r/ORnG8/BiAX4iInwdwI4AVAP4Io2+3MdPNbPf3wCzwm/b3Q2VO+fth/4L4LQA7exE/iwD8MoBHhmzDVHgEwH29P98H4EszaIukp4f4LIDnSim/P+GvRtr2iFgfEat6f14M4CO4qqf5GoB/1as2cnaXUv5TKWVLKWUHro7n/1tK+dcYcbuNGQKz3d8Do+837e+HyFzz90PfSaW38v5DAJroE0IAACAASURBVPMBPFhK+W9DNSBJRHwOwN0A1gE4AuC3Afw1gC8A2AbgZQAfL6WwqHlGiYgfB/B1AN/DP2skfgtXdSkja3tEfABXxb3zcfUfLl8opfyXiHgvrorb1wD4NoB/U0p5c+YsnZyIuBvAvy+lfHQ22W3MdDFb/D0wO32+/f3MMRf8vbfaM8YYY4wxFQ5SMcYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmwgtEY4wxxhhTMaUFYkTcExHfj4i9ETFqm4EbY4wZIPb5xswdOqe5iYj5AF7A1QSXB3A1KeonSinPXuOccjWv5zXb7XvtrM3clmq76/UGdd6VK1f61snC52X7ieup8+bNG8yPzaoddT3ul/nz5zd1VFmm7UydzLNTz4nPyz7L6ezffrz55pt4++23uw06M2e4Xp+/ZMmSsnLlyqps4cKF3Ka6TnWc8Q+TlTGZtjLtTDfs21Q/Xb58+ZrHgL6XS5cu9a3DfuTGG2+c3Nhr2KDs7urLuS0eSwDw9ttvN2UZn6zKuA8yc1d2Ps+sA7rOXdyWqnPkyJHjpZT1/dqfylZ7dwHYW0rZBwAR8RcA7gVwrQViM9B4QKmBmBnQqhMWLKhvb9GiRU0dNcgyD50HtLq+Gqw33HBDdXz+/PmmDtutyjKLOO43QA9WrqfOW7JkSVPWrx1lk3q+6l4uXrxYHa9YsaKps2zZsupYORllE/P66683ZZlnp54TjxXV3+o87peu/0hYunTpdZ/z1FNPdbqWmXNcl89fuXIlfvVXf7UqGx8fr475nQLa90O9w+fOnWvK1HvMqPf6woUL1fGbb7b5jTOTdWZeyixEgNa3LV68uKlz+vTp6vjUqVNNHXUvJ06cqI75/oH2udx2221NHbUgPXPmTHWs/P3y5cubslWrVlXHmYXlxo0bmzpHjhxpyt56663qOLsOYF+qxio/X563AO3v+Zm/8cYbTZ3M/K3GM9vA9w8Av/u7v/tyUyiYys8WmwG8OuH4QK/MGGPMuw/7fGPmEFP5BTFFRNwP4P7en6f7csYYY2aIif5e/epvjJk9TGWBeBDA1gnHW3plFaWU3QB2A8C8efNKP71H5jOl+qm3q75QkdG/8c/Uym71EzyXqZ+2FXwv6rME263qKDu76FCz/Z1pW/Uv2579hM9kfqZXdqvnwjapTxX8uSarwVFjpQuqnX5aGv/DzSTp6/Mn+vuxsbHCn7teeeWV6lh9fuN3T72L6h1if6DaVufx51sl0+Ay9e6rz4QnT9bbH6tP4+o8vh5/ulVtHz58uKmjPh+zj1D9tG7duup4zZo1TZ3jx483ZfxJWX1iVn3HPkn5MZY5bd++vanzzDPPNGXcd2NjY00d/qQPtP3Lelog5zszeln1DNTc9fLL9ZfhAwcONHXOnj1bHWfmycmYyifmbwHYGRHviYhFAH4ZwCNTaM8YY8zoYp9vzByi8y+IpZRLEfEpAF8FMB/Ag6WUdvlujDFm1mOfb8zcYkoaxFLKVwB8ZUC2GGOMGWHs842ZO0x7kMpEIkJ+a5+I+l7PIfDvf//7mzqZVAgqBF5djzUvGb1fVu+S0X90zbuYyduU0SVmNIHKHqUt4XqqbdUW60yz5zHqvEwqGgWPC6Vb4bQSXXOT9XtP3oHHinoPuK1B5Vw05nrhd0a9Qzw+Vfoa9X7wu9dVb618BuvPlE5RvVccqPPSSy81dZQukVPRHDt2rKnDWjPF2rVrmzL2rcruDRs2VMeZOUG1reIFlJaPUenftm3b1temQ4cONWXsb1WancxcyZpEVSebUzcTi6DmDtarqneDdb+ZVG+T4ZnCGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpiKoQepcCADJ79UAn5OtslBK5PBCVBVslGVRJJtUiJTFshmAlmAVjCaPa9LMuPMBujqepnrqz0+1b1kglTUefwMMvefTTaauV9FlwTb2QTYmYTxmX3KFf2CkLokSjemH4sWLcKWLVuqMn7X1W4rmQAt9V7x+6j2oFW+ht8r9T6wnZs3tzsMqsAKDi5RiatVgmsOQFEBgBxIkg2+4DlPJerm+1W+RwUYsZ3KR6q5IxO8yf2k+k3BQTFqzud9rYH2njOBkmouUc+A+yAb9MkJy3meBNp7UcFMTzzxRFOm8C+IxhhjjDGmwgtEY4wxxhhT4QWiMcYYY4yp8ALRGGOMMcZUDDVIZdGiRdi+ffs16yghqMrGznBAyjvXm8h73/vepo7KRM7X67pjhxKnchCDEmkrATYLp5Wolu8lc311PVWHBdjq+pks8qov1Xks9lX9y2WqbRXEkdlxJhO4kQlAyexcA3QPeuqyM0Qm2MaYqaKCEjM7T3DQhNppRAUqckDMvn37mjoc2AG0wQhqvuE6Bw4caOp8//vfb8p+8IMfVMe8QwqggxFuvfXWvnW4L9UOJcpP804bKriFgyFUQIoKJMnsgqOCYtgnKh/JbalgFzUu+Nlldk8DWr+Z8ffq3tT1VHAJo/qO+yUz56qxk8W/IBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpmJKGsSI2A/gHIDLAC6VUnZdq/78+fMbLUOX7/wqIalKQMrf4pVmQmkPGJVwNWOnuh5rMHfu3NnUUTqGZ599tjo+fvx4U4fvV9mY0Y0ozUQ/LRGgNSGs51F9mdEJZuzOJnzm62UTZfP1lN1sg+qnjL4wo8sE2mesksAymTFvjOJ6fT6PYx7DymdwHfXuZ7SLKrmzOo99ktJ+vfzyy9Wx0iCqxM1s+/j4eFNn/fr1TRlr1JQGm99jVUfpElmDqFi7dm3fOsqXM9mNAvqNE6D1t+r5Kv0ol6kxkEnerWB/m/G/QPvssvp57hd1vcwclGUQQSo/VUppVyvGGGPejdjnGzMH8CdmY4wxxhhTMdUFYgHwtxHxRETcPwiDjDHGjCz2+cbMEab6ifnHSykHI2IDgEcj4vlSyuMTK/ScyP1ALp+hMcaYkeWaPn+iv1+9evVM2WiMGQBTWiCWUg72/n80Ir4I4C4Aj1Od3QB2A8DixYvLyZMnqzY4+EEJX1lkqZJ/KrEmC0GV8FaJUxmVqJVFpUuXLm3qbN68uSnjoBSVuPW1115ryjgw5+jRo00dFkRnAyQYJY7lhN6ZxOSqrUzCV1WmBMlcR40dJXDPBKWoOtx+pi9VnYz4WdVRQuauNhjThX4+f6K/37p1a+FxnEnKnwlQU347E3yhElVnNiE4duxYdax8jfLlmYTTKmkyzyeZwAoVqJkJSFM/3HDCaTUHZjZdUPem+o7vT80vPAdlklsD7dyp5jfVFvvbs2fPNnV4nlB9qa7HzyobKMn3lwkoVXNnls4zR0QsjYjl7/wZwM8BeLqzJcYYY0YW+3xj5hZT+QVxDMAXe6vsBQD+vJTyNwOxyhhjzKhhn2/MHKLzArGUsg/ABwdoizHGmBHFPt+YucUg8iCmKaU0GgWVFJrh7+yZJJrvXG8ip06dauoorVdms2vWF+7YsaOpw5vHA6224uDBg02d/fv3N2Vsu9KbsN5Dac+yyaQZ1ngqLY3SjbC2I6MVVW0pwTvfnxpL2TKGtbJAq01S7bAGRd2b0rKcOXOmOl6+fHlTJ6OnzCTY5jpdx4Qx1+LSpUvNe8Q6MjWm2d+qxNWKjP6NNYEA8Pzzz1fHap5Yt25ddZzVEvJ7rN5r5SP4nc34GkUmmbWC70/Nk6qMNXJKz6nmb9b7KS0hjwtVR/WT0k8yahzymkLVYbuVL1XzMM+D2YTiXC97va5YvW6MMcYYYyq8QDTGGGOMMRVeIBpjjDHGmAovEI0xxhhjTMVQg1QiohF6ZsSZfI4KdFDCTBbDquAAdX0OkPjhH/7hps7tt99eHatE2UqMu2/fvmseA1rYe/z48epYiXFZJK2E1CrZMtfL3Es2SWkm6a2yk21QSWiVKJxRwTws3FZBSJxcFWgDilRC80yyXtU2j031DBQZQTILmXkMqDFhzFS5cuVKM9bZd6sAAvYjmWAIoPUtH/7wh5s6yte89NJL1fH73ve+pg4npT58+HBT584772zK2G+pd03NZxwgoZIdc7+ovlRl/EzGx8ebOryphEpMznMS0PpWdX31PNmPqetxHRWko+ZcrqcCWtVz4TlOzV18v9mAP7ZTXV/N8dwH6jx+N9S7ksW/IBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpsILRGOMMcYYUzHUIBWgf1CKEt0PSnSZ2WUCaAW6H/jAB5o6nFmfd8IA9G4cR44cqY5VEIUS0XK/KGHxTTfdVB0rEa8KLsnsdsKiYfUcld2ZZ6eeAe+eoII/Mm2rPuCgFBWkogTJ3C/q+XKZCjhSY4XpKnbO7FJgzDAopTR+I7OLEY9X3n0FAFatWtWUsf/btm1bU+eZZ55pynh3FQ5aAdrdrZRfUTtJcVkmiELVUwGWx44dq47VDjDqeuwnV65c2dThnatUn2QCUDI7lQGtb1V1eE5QbStfx2WqT9S4zPhNnvMywVNArp9UYFIm4Caz20oW/4JojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpiKvhrEiHgQwEcBHC2l3N4rWwPg8wB2ANgP4OOllFYAQdx44414//vfX5Xx93Glx+CyTEJmoP0Wr5JvZrQkKkkzf/vPavK47bGxsabOq6++2vc8TtQNtJobpbXgxJ5Aq79Qej/uO6VTVDoOtkHpdJTWgpNgq2fO/csaFUA/c06Wm9Xo8f0pDQ5rhZQGUY05tkE9p+XLlzdlmUTZahwaMxmD8vlXrlzpqzlU+qhMMmD1Lqxfv746fvbZZ5s6//iP/9iUfec736mO1TvLvkVpwJUfY3238hnqeidOnOh7Hvs2dX2lf+OyjRs3NnXYRymtvPKtjPJP6pnzOFHzC8+xan5TcL3MPAW02nSl3+cNDdT9Zvog66N5blb9NEgyvyA+BOAeKnsAwGOllJ0AHusdG2OMmf08BPt8Y+Y8fReIpZTHAXC45r0AHu79+WEAHxuwXcYYY2YA+3xjDNBdgzhWSjnU+/NhAO13UmOMMe8W7PONmWNMOUilXP2YPmminYi4PyL2RMSejGbBGGPM6HItnz/R32dyHhpjRpeuibKPRMR4KeVQRIwDODpZxVLKbgC7AWDLli3lQx/6EP99bZAQ1WaE+ErEe+DAger4/PnzTZ0dO3Y0ZZs2beprEzs/ZaMSV7P4VyU3VYLgW2+9tTreuXNnU4cDQFTgjArkYFQ/sRhW1VETAgf4qIAfJThnwbXq34xAVwmiOQAkIwAHgEOHDlXHnPQcaMXGStisApPYhkwCVKAdYyqYh8sy75MxRMrnT/T369atK/0CwDI+UgVfqDJOgv29732vqbNv376mTPmyftfjjRIA/a5zAAr7EEAHBXK/cNAe0AZIKJSP5DI1v7FNKlG3CqTLBPwp38pzh5qnuEzZreB6ykdy4CLQPnM15rgtdW/Kl3OZGgPq2fF5qr8zQV5Zus4UjwC4r/fn+wB8qbMFxhhjRh37fGPmGH0XiBHxOQD/AOCWiDgQEZ8E8BkAH4mIFwH8bO/YGGPMLMc+3xgDJD4xl1I+Mclf/cyAbTHGGDPD2OcbYwDvpGKMMcYYY4iuQSqdiIhGMMnHmYziSsCqxMc/+MEP+ratxKmckV+JmFm0qwSlSrDKQSmHDx9u6mzfvr0p4x1olECXs8FzJnhVB8gF3HCAhOoTdR4Lt5WwWQWJsGg3Y5Oqo+6Xx5waTyp4iO9ZBcDwc1m7dm1TR+3CwOJ1dX0VpMKCfiVI5jJuR92HMVNl3rx5jbCfx5oaeytWrKiOOWgQ0P7vG9/4RnWsfKt617kt9X5ykOB73/vepo6aA9i3qZ2k1M5K3Jbym9x3ar7JBJKo4Av2P7yzC6ADK3jOUT5L2cR+Wp2X2UlFPQMO5lmzZk1TRwUB8bhQ6weeO1WdzLjI7BwD5HePmYgaX1n8C6IxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyqGqkE8d+4c/u7v/q4qY91YRoOovsNnko2qRKbHjx9vyl588cXqWCUkZb2Hur7SwLC2Y/Xq1U0dpW9ZuXJldaw0Iaz/UBpEpfnJaNBYN6LOUf3L/aTqqDJ+dkpzlNHfKV0it5XRNwLtuMsk4VZjRyUGZ30jJ9gFdILXLnWMGQYR0bzb/F6p95o1gB/84AebOvv372/K+B1S84Tyt6wvvPPOO5s6mzdvro6Vz1JJsNlHqU0QlJ6b58GMRi2TgBpofYTSIJ45c6Y6VvObgu1U11f3kpmD2E+r56vmvMy8qNYd3L66F/bTym9nNj1Q842al/jZKX/Pdmc2lJgM/4JojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpgKLxCNMcYYY0zFUINUzp49i69+9atVGYsslfCUhcwq2ei2bduaMhYIK2GxEg3v3bv3mu0Ard0q+efJkyebMrZhbGysqaNgO5UgmdtWIld1HpcpUSsLdFXwhbqXVatWVcdKEK36l21Xz06JjRkl/uX7U0J5dT22ne8NaPtJiYiV4DtzXiYJtnrmXObE2GYYlFKad439BifFBtogvZ07dzZ1vv/97zdl/B7ffvvtTZ0PfOADfa/3nve8p6nDCe+PHj3a1Dl27FjfMvXuK5/M9VSARCaIQs2n7LdUEAVvMqHsVr6VUW0rP8b+TvlWPk/5MTW/cFCgmjfUvXA9taEDB7mqOqoPuCzr7xnl75kuybX/qf3OZxpjjDHGmHclXiAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaaib5BKRDwI4KMAjpZSbu+V/Q6A/x/AOwrc3yqlfKVfWwsXLsTGjRtrA0SAAJMJRsgISJXwdcmSJU3Z66+/Xh2rQBZG3UdGZJrNft+FzPWB1nYlmGXxrxLHLlu2rNN5KigmI6zl+1PtqF0KWHCtAoyUKDuzuwD3XWZHFqAVLWfGPNAKtZVwOzsOjAEG5/Mjonn/2f+rcb5+/frqWAWyKH7iJ36iOt61a1dTh68PtLurqDnh4MGD1fHzzz/f1FFBiTyXqIAFNQew38oEDirUvXDZCy+80NThQE3lI5VvY/+TmUuA9hmrIEi2QflRFVzI81t2FzAuU3Yz2d28Mucp+J5VH2R2/MqS+QXxIQD3iPI/KKXc0fuv7+LQGGPMrOAh2OcbM+fpu0AspTwOoP2nkTHGmHcd9vnGGGBqGsRPRcR3I+LBiGh3QO8REfdHxJ6I2JPJm2SMMWYk6evzJ/p7/rxqjJlddE2U/ccA/iuA0vv/7wH4/1TFUspuALsBYOnSpWXdunXV32e+z3Md9d2d9WFAq5FQmg2VpJlR12M9hLoPpf9gXYPSlpw+fbopYy0HJ/8EWt1GJiEzkNP78f0p7ZDSOrA2VE0aJ06c6Hu9TDJtpRVV95vR7qg6bLvSnXISd6VbUf2d0Rx21bLw2LEm0XQg5fMn+vvt27cXTnL92muvVccbNmxoLsSaQKXbU0mw2SdycmtVB2g1gCoJ90svvVQdq0TZyrexH1E+OXNe5scV5Y+UBpD1jazVB9r5LautYz+mdOmqjJ+V8pEqzqDf9YG277I6eL5eZk5Qc2BmflVjXD07JvNcVNtZOv2CWEo5Ukq5XEq5AuBPANzV2QJjjDEjjX2+MXOPTgvEiBifcPiLAJ4ejDnGGGNGDft8Y+YemTQ3nwNwN4B1EXEAwG8DuDsi7sDVzw37AfzaNNpojDFmSNjnG2OAxAKxlPIJUfzZabDFGGPMDGOfb4wBugepdOLSpUs4duxYVZZJYsnCfyXYVSJTdX1GJSllcShfH2iDD5RYVCV4zSSlVkEqfH8qATQHcqi+VIJobluJeLkPlN1KRMwBKEoQre6XyQTFZBKaA22gUNckpSoAhftFCcczyawzCeSB9lmp58J2c9sOWjHTQSml8dXsg8fHx8Fs27atOlbvwnvf+96mjH2E8hlqDnjuueeq41deeaWpw0mwlR9Vc8nZs2er42zQXCawgn2y8iuZBNvKZ2SCEtesWdOU3XrrrdXx9u3bmzqqrVOnTlXHL7/8clOH/Xt2EwL2b2oMqETZmU0P+DyVUDyzMYKiy2YRQGvndCfKNsYYY4wxcwgvEI0xxhhjTIUXiMYYY4wxpsILRGOMMcYYUzHUIBUFiyzVjhksBFXZ8JXQl8XN6jx1PRan8g4lALB+/fqmjFGiVrZTiWOViDezIwkHgGzatKmpo3Z3yXDTTTdVx2r3k/379zdlGbuV0JbF1UrUy2VKjKvE3fxc1PUzuxIoEXFGXK7aztiUERurMcfBNJkM/cZMlUuXLjU7jnBgw44dO5rzeLyqMa1242DU+3ngwIGm7NChQ9WxCrZjv6X8WGZHlEwwJdC+o8qPcECEmksyKL/Cu9mMjY01ddSz46AUNQetWrWqKfvWt75VHauAQ/b3KghUBc6wLz148GBTRz1znvdV2xxQpdrhAJwsmSCV6ca/IBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpmKoGsRVq1bhF37hF6qyjAaRNSlKw6U0GpmkxUqPxWVK28FaPqUtUUmhOZmqsomTiU9mA3PzzTdXx0ojkrnfjCbvxRdfbOqw3ghon516TirhNCeBVeexJkVpgDKJU1Ui9IxOUJFJeK00pjx+1DNQ96L6pR9OjG1mCtZ1rVu3rqnD71lWi8XvwpEjR5o6zzzzTFPGempObg0AFy9erI7VRgVKB88oP658RMYfcFtZfSOj5gS2ie8fAJ599tmmjJOM33XXXU0dpUHcu3dvdaw2T1i7dm11fPfddzd1Nm/e3JTxuFA61G984xtN2UsvvVQdq1gE1iCeOXOmqaN0iVNJXj0R9W5w21Px9/4F0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFV4gGmOMMcaYir5BKhGxFcCfAhgDUADsLqX8UUSsAfB5ADsA7Afw8VLKNTNCLlu2DD/5kz9ZG0BiWCWYZZGpEvUqISYHMag6qoyvlwmAUQEMSjDL11PCYhWkwklRVR9s3LixOlbJZDNJklXbLIblRKqAFh+zDUrEq5JgcyCHEndznyu7VeAKP89sP/EzUHA/qXNUUA7fX1bEzNfLJNjmOg5aMRMZpM/nsTU+Pl4ds89SqPda+YzDhw9Xx08++WRTRwWuZJJZ8/VUMKUKPuP3XwWyqHvhflPzC5cpn5GZ3zIJxdX11XkcvKmSWasyTlau7N61a1d1/L73va+po+B+4WBOQI8xDjJVgUl8vzy+AeDVV19tytRGE0wmAFE9cy7LBFdOakOiziUAv1lKuQ3AjwL49Yi4DcADAB4rpewE8Fjv2BhjzOzGPt8Y03+BWEo5VEp5svfncwCeA7AZwL0AHu5VexjAx6bLSGOMMcPBPt8YA1ynBjEidgC4E8A3AYyVUt75Xfgwrn6OUOfcHxF7ImKPyi9ljDFmNLlenz/R36vcecaY2UM6UXZELAPwlwB+o5RydqJGoJRSIkKKpkopuwHsBoCbb765LF++vPp7/s6eSRCsvs1ntBbqW3xGf5X5hq/qZLR86l7Wr1/flLGuQOlkuEwlG1WaBbY9kxhc1VEJp7meut+M1iLzDNS9Kd0I26nGQCZ5dkYXpHQ6auLk/s3qAtmmjHaxS3JtM/fo4vMn+vtNmzYVHo/bt2+vjpUel3XD6l1QPzY8/fTT1bHyfwrWDipNMmsHlYbs1KlWjsn+QL176nr8Xis/xpsuKJTmkXWRmblEofTV7MeU5jOzUYDSeG7YsKE6VuNC+WTWkyvN59at/4+9c4+167rK/Td87Lz8ftuxndhO3KZu2qbUfaQtEMoFAlSkSKg0ApQrinL/AFEEiJv2Hy5ISAXdW4q4CCnQqLlSKVR90AjKI6RBgQJN3TRpHs7DSf1+v2I7cR625/3j7MCZ3/yO9/A+5+yzd873k6J4Ts+11lhzzTXm9NrfGHNdU8dJsB9++OGmDfeB0leq56vGCpNNEM9k1lTpc2UaRcQcjDqKz5VSvtypPhgRqzt/vxpAu42GMcaYocM+3xjTdYEYo8vPzwDYVkr51Ji/ugfAbZ0/3wbgq5NvnjHGmH5in2+MAXI/Mb8PwC8AeDQiXvvG+gkAnwTwhYj4KICdAD48NSYaY4zpI/b5xpjuC8RSyr8CGO9H7B+eXHOMMcZMJ/b5xhjgIoJUJoOXX34Z27dvrw3oQWSvxJu9BjFk6lSbjPBV1WWSm6o6Ppdqw/2ihMYqcIbbzZ07t2mzfv36rtdXx7EIXQmUM4liM8lyVXJrDopS584yWQnUFb0mM80kReXny/efTcptzMUwMjLSJM9fuHBhVe41qEC9e2vXrq3KKtBMBVawDbt27WracBJuFfzB9wa0PjGbzJ+DYFRgG9ugzqPg/sz4DIXyG3ycskkFOPayMYKyW42LTLBHJngoMy+roCs1DjOBghm7M/NGZnOM8XA4ozHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFX0NUjl9+jT+4z/+o6pjcaoSVGay0SsyYvxMIIkKauAs9kpQqo7jOiUG7nXHF74XZVMm2GPJkiVNGxZgL126tGsboA0SUUEqSvDNqH7iPlHnVgLhjLA3s0uBuh6jRPHqGWQE2Op5Zp45j3u+t14DZIy5EKWUrkF5ypfz+FS+j3fsAIBNmzZV5Q0bNjRt1O4qf/u3f1uV9+3b17RZtWpVVV69enXTRvkMDm7ZsWNH00btNsJBKdl5glHzKfufzI5fGf+rbFL+TwWg8JyjntORI0eqMgcljQfbqex+4YUXmjp+dhmyu59wv6hnoOqYTADXRPy7vyAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaairxrEM2fO4JFHHqnqMjouJqtBzOgBlC6Rf7O/9NJLmzasm1PnURoC1h5kk8ByndKWsE1Ke5DRIC5btqxpc91111VlpTdU52a9iXre6rhMwmnutKHxjAAAIABJREFUc6Vt4WSnQHu/SgOpnjnbpHRQPObUc8rogtT1M3pZNXa4X/j6E0mkasx4nDt3DsePH7/o4/hdyGjdgHYc79mzp2nzjW98o6ljn3TzzTd3vd62bduaNqpu//79VVlp6zLa+IwGPetr2Ecpv8I+Q/W3Oo7vJZu8mzWdqp8eeuihqqw0iCtWrGjq2HalFf32t7/d1LEGMdO/6jmpfuJnkI1h4Ospf8/HZddLCn9BNMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmIpRAdsouFnEYwE4AywAc6dJ8ELHd/WVY7QaGy/arSynLp9sI8/pijL8Hhut9GIvt7i+2uz+kfH5fF4j/edGIraWULX2/8ASx3f1lWO0Ghtt2YyabYX0fbHd/sd2DhX9iNsYYY4wxFV4gGmOMMcaYiulaIN45TdedKLa7vwyr3cBw227MZDOs74Pt7i+2e4CYFg2iMcYYY4wZXPwTszHGGGOMqfAC0RhjjDHGVPR9gRgRN0fEUxGxPSLu6Pf1s0TEXRFxKCIeG1O3JCLujYhnOv9fPJ02KiJiXUTcHxFPRMTjEfGxTv1A2x4Rl0XEgxHxSMfu3+nUb4iIb3bGy19FxCXTbasiIkYi4jsR8Ted8lDYbcxUMiz+HhhOn29/Pz3MFH/f1wViRIwA+BMAPw5gM4BbI2JzP224CD4L4GaquwPAfaWUTQDu65QHjbMAfqOUshnAewD8cqePB932lwF8oJTyNgA3ALg5It4D4PcB/GEp5VoAxwF8dBptvBAfA7BtTHlY7DZmShgyfw8Mp8+3v58eZoS/7/cXxHcB2F5Kea6U8gqAvwRwS59tSFFKeQDAMaq+BcDdnT/fDeBDfTUqQSllfynloc6fT2F0EK/BgNteRjndKc7p/FcAfADAFzv1A2c3AETEWgA/CeDPO+XAENhtzBQzNP4eGE6fb3/ff2aSv+/3AnENgN1jyns6dcPCylLK/s6fDwBYOZ3GdCMi1gN4O4BvYghs73y2fxjAIQD3AngWwIlSytlOk0EdL58G8FsAznfKSzEcdhszlQy7vweGwG++hv1935gx/t5BKj1SRvMDDWyOoIiYB+BLAH6tlHJy7N8Nqu2llHOllBsArMXo14frptmkrkTEBwEcKqV8e7ptMcZMHYPqNwH7+34x0/z97D5fby+AdWPKazt1w8LBiFhdStkfEasx+i+fgSMi5mDUWXyulPLlTvVQ2A4ApZQTEXE/gBsBLIqI2Z1/nQ3ieHkfgJ+KiJ8AcBmABQD+CINvtzFTzbD7e2AI/Kb9fV+ZUf6+318QvwVgUyfi5xIAHwFwT59tmAj3ALit8+fbAHx1Gm2RdPQQnwGwrZTyqTF/NdC2R8TyiFjU+fPlAH4Eo3qa+wH8TKfZwNldSvl4KWVtKWU9Rsfz10spP4cBt9uYPjDs/h4YfL9pf99HZpq/7/tOKp2V96cBjAC4q5Tye301IElEfB7ATQCWATgI4LcB/DWALwC4CsBOAB8upbCoeVqJiPcD+BcAj+K/NBKfwKguZWBtj4i3YlTcO4LRf7h8oZTyuxGxEaPi9iUAvgPg50spL0+fpeMTETcB+M1SygeHyW5jpoph8ffAcPp8+/vpYyb4e2+1Z4wxxhhjKhykYowxxhhjKrxANMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmwgtEY4wxxhhTMaGdVCLiZoxmER8B8OellE9eqP3cuXPLkiVLqjpOszNrVrtmHc0FOn55vLpeYZtUKqBXX321Ks+e3XblsmXLup77lVdeadqouvPnz3dtc9lll3W1STFnzpyqzPem2qjnxDYC7f2qNufOnWvq+HleeumlPZ07c72XXnqpaaP6NzMu2G7uN6B9TgAwMjJSlS+//PKmjepzRt3v6dOnqzLf/+HDh3Hy5MnJe4HM65aL8fkLFiwoy5cv5+OrcsaPvPjii00b5TMyKdtUG76emkv4/eTyeMdl5jdFZs7L+L/MXJnxY5nrKxuyc3Vm/s4838wccPbs2aaNOnd2/uyFXp9dL3OQGnOHDh06UkpZ3vwF0XMPRMQIgD/BaAb0PQC+FRH3lFKeGO+YJUuW4Nd//derOp6I1eSZWfioF5brlFNRZBZje/fWO+msWLGiafNLv/RLTd3LL9e5M/fs2dO02b17d1PHTnLXrl1Nmze+8Y1VWS1QFVdeeWVV3r9/f9Nm9erVVfmSSy7paiPQvoyqDS9ggHZhtXHjxq7nVuc5c+ZMU3fixImq/OSTTzZt+PkC7UJSORoec2vWtHu283MCgEWLFlXlt7zlLU0b9W6wM1B98G//9m9Vme//4x//eHOMMczF+vzly5fjD/7gD/gcVfmKK65ojuP37OGHH27a8BgGWt+qUHMAvzPqPeP3c/78+U0bNQfxBK78ploIcDs15/E8derUqaaNOo7PrRYnfFz2gwD3pbpfVdfLP37Vs1T+nuuOHDnStFFzPP/jRpFZ2Kp+4o8wym71UYTbqTmI+1eN5z/+4z/eqa2tmchPzO8CsL2U8lwp5RWMZhG/ZQLnM8YYM7jY5xszg5jIAnENgLGfuvZ06ioi4vaI2BoRW1944YUJXM4YY8w00tXnj/X3J0+e7KtxxpjJZcqDVEopd5ZStpRStsydO3eqL2eMMWaaGOvvFyxYMN3mGGMmwERUmHsBrBtTXtupG5eRkRHMmzevquPf1DOBDkqvoHQNGZFpJihGCUFZA6N0Dffee29Td+xYvV+60iCqf3lz0ILS8nHfqjZXXXVVU8daDqXlYU1gVqTNz1OdWx3H96La8P2pZ6nGAE9cK1eubNqoQB3+Aq5s4uf0fd/3fU0bpVflcym71TjkOjUOuZ9Yb+P92E2Si/b57FvYj6hxzj5S+bGMZu35559v6tSvWFzHgZRAq+NSwWcZPZoKiFM+kT+mKD0aozSICtZ9Kru5f7NBoGyD0r9lAgfV/MJt1HmUJo/Jrh8YNVbZpkzQiKrrdT7NkOmT8ZjIF8RvAdgUERsi4hIAHwFwzwTOZ4wxZnCxzzdmBtHzF8RSytmI+BUA/4DRlAd3lVIenzTLjDHGDAz2+cbMLCaU6KeU8jUAX5skW4wxxgww9vnGzBymLhOkutjs2U1uPs43p/QmrJnK6BOA3hKCqnYZjdzVV1/dtHnqqaeaOs4xqO5F6U24bunSpU0b1s3t3NmmOlL5njinl7o+a+syWlGg1btwPjFAPzvWhCgdBetXOVcjABw9erSp4zGmzq00iIx6duvXr6/KSt+ongFrmrKJaXk8PfPMM00b1jhlkq0aM1HOnz/fjD32LSr/28GDB6tyVkPF74zSGypdYiZxPtupNIjKH7BuTflN1Qd8roxOMJPPEGjvV50700bB96L0fqouo0HkcZCdgzLXz5xLjUPuFzWXqXPzHJBJzg60tmdzO/eKt9ozxhhjjDEVXiAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaair0Eq586da0TCLMTk4A9FRrz52vXGokSmStSqBMjMG97whqr8zne+s2mzbdu2po43nl+8eHHTZuHChV2vr+6FN0pX4ufMRumqDYvLlQBcBXawSJqDVgD9DPj+VPASi8kzAnQgt1l9JpmrCubhJNjZZKd8bjWe1f3xGFNJ1vl6mYAYYyZKRHRNCKz8CCfKzgZRsfBfvS8Zn6gSV3Od8kfKH2Tea/X+9RKQkU0cnQl0YJsyicmBtp/UXKoCZ9j2jN/sJWn0eKh+4vGkxg4Hb6p+UuOX50rVJ2qdw3Xq2fG9ZDYMGQ9/QTTGGGOMMRVeIBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpqKvQSqXXnoprrnmmqruoYceqspK0Mkiy8suu6xpM3fuXHm9sShRqxL1sxBUiTyXLFlSlRcsWNC04YAFAFizZk1Vvvbaa5s2vNsM0PaL2o2DgzaOHDnStDl16lTXc6tdWrhPMkFB6txKAK76lwXBHIADtPerzq3GU0bcnBGTK7s5CIfHCQDs2rWrqeO+UwL7xx57rKnjXSeUUD6z44IxU0G34AY1Fvm9zu6kwsEtKgBGnYvrMoGLKiBPBWRwAIzqDxWMwMcp/8d9p+aEjJ9WdrP/VXNuJrgmG5TDNqlxwefqdQ5Szzezu4rqX/a36jxqrHAfZAMl+bjMDjsT2W3FXxCNMcYYY0yFF4jGGGOMMabCC0RjjDHGGFMxIQ1iROwAcArAOQBnSylbLtR+ZGSkSQLNmjilEVm1alVVXr58edNm3bp1TR3r/dTv9Y8//nhT1y2ZN6A1Gcz8+fObuiuvvLIq870BOnl2Jik0k9FxAK1GQmlSWLeRSdCpjlPJRjOJo5WOI6OlUxpT1pJkEtwCueSxPFbUGFD9+8wzz1Tl7du3N21U/2aSx2Z0K8ZkuFif3003pnRdGT2aOu7EiRMXMmVc+Pzsa9W5ld5Zwe96dmOGjHaRNdDKbjV3ZWziZ6A0/moOZJsyGlNFrz4qs+lC9tzcd5wUG8jpItXGE2xnr/ebSaA+EX8/GUEqP1RKaaMhjDHGvB6xzzdmBuCfmI0xxhhjTMVEF4gFwD9GxLcj4nbVICJuj4itEbH1+PHjE7ycMcaYaeSCPn+sv1cptYwxw8NEf2J+fyllb0SsAHBvRDxZSnlgbINSyp0A7gSAN7/5zbld140xxgwiF/T5Y/39xo0b7e+NGWImtEAspezt/P9QRHwFwLsAPDBe+1mzZjWifQ4QePHFF5vjlCCZUWJcFsyqxMbz5s1r6liAnAlSUQEESti7fv36rm3UuVhoqhJzZ9pkRMOTmdhTnYvJBLeo8/Bz4STVgE4wq0ThjBIk87mUTTzGlGiaE/oCwBNPPNH1+ip4KRM40y1IxUErJsvF+PxZs2Y1/p3fWfXuZwLU1DzBdZmgLqAd/yqIgm1S587MQer9VIF7ym90u54KrFO+LpM4n+uUH1VzJ6P6SQVtZHwy3292E4TMs+t1YwRukwmSUcdl7l/ZmRnP05IoOyLmRsT81/4M4EcBtFs9GGOMGXrs842ZWUzkC+JKAF/prFZnA/iLUsrfT4pVxhhjBg37fGNmED0vEEspzwF42yTaYowxZkCxzzdmZjEZeRAvCv49nDdUV5HOq1evrsoqsfHBgwebukOHDlVlpX1Qv8+z/kvpA1jvsmfPnqaN0heyjkFtKK/uj21QepeMbkX1AetCjxxpU5wdOHCgKivtQ0bjqfQ9Kuks6zYyWouMThFon7nqk2XLljV13L9Llixp2vC5lE2cLF6RSQIO5LSh3dpkxo0xF0sppXnX+F1X45XfWaVBz/gRpVPM6O0yGjk1b6g6Prfy7cr/KRsYfm+zGvCsbxmLujf1XNhudW9qPuW6jC46Owfy/ap5Sh3H55/MDSSYjFZU1WV1mL3iPIjGGGOMMabCC0RjjDHGGFPhBaIxxhhjjKnwAtEYY4wxxlT0NUillNIIWzlJqBJ5srBXJejMiEMzCbeBVjCqRJ+HDx+uyg8++GDTZu/evV2vpZJZ33jjjU3d9ddfX5WV0JYFwSoB66JFi5o6FjKrJM2ZxNXKJu4nFZSjhMz8zNW9sA3PP/9800aJeFlIrdoo0fDy5cursupLDozi4B5Aj6duCYXHO47HuHoGfK7JFDEbMx6llOYd7UWwr9595TM4cCUT/KbqVHAAkwkGA3IbBWTOr/wBX08FwGQC99T98vUzAThAO8eqOVfdS8ZHZRJXZ5JCq/vN9K+6XoZMQGdmzKlzZRJzq3vL4i+IxhhjjDGmwgtEY4wxxhhT4QWiMcYYY4yp8ALRGGOMMcZU9H0nFRZ+soBSBUhwnRKLqsz6LHZWbTLBFpdddlnThoNL1M4bjz76aFO3ePHiqrxhw4amzdq1a5s67jcVkMF9qXbsUPd79OjRqrx///6mDe+ucvLkyabNqVOnmjoW0WaDLzI7CbAIXR3D/a3aKXG32oWB+0ndLx+3b9++po0K1GFhsdqlRQmZeawqoTwfx+XMrgXGTAbsg9W7x3VKiK8CUBg1rlXwGc8vmaANhQqAycwlkxUgkQl+A9r7U4Ed3Oeq3zJ2Z30L910m+EK1yQZ7MKoPMmM1cz11HAc4qn7K+HL1zLlfeu0TwF8QjTHGGGMM4QWiMcYYY4yp8ALRGGOMMcZUdBU2RMRdAD4I4FAp5fpO3RIAfwVgPYAdAD5cSjmeuSD/Hp7RUCn9Q6ZN5txKR8baA06QDLT6C6UZ+7Ef+7GmjvWFGzdubNqoezl27FhVVho51kEqTcyOHTu6nltpO1jrwJpEQCeF5megdIJKX8N1Su/C/aT0q1dccUXXcys9pUrwyuNCjR2uU3rDjHZHkUl4mnl21hyaCzGZPp+1XTz2lK9jfbfyNYpMEuG5c+c2dfyuKz0a1/Wa2FidO/M+ZjRqmXd/MskkPc/6Gh4Hqn+571RMQa++VcG+W825GdT6IWOT6oPMWqiX5PTj2pBo81kAN1PdHQDuK6VsAnBfp2yMMWb4+Szs842Z8XRdIJZSHgBwjKpvAXB35893A/jQJNtljDFmGrDPN8YAvWsQV5ZSXsuFcgDAyvEaRsTtEbE1IrYeP576FdoYY8xgkfL5Y/29km4YY4aHCQeplFHhQyt++K+/v7OUsqWUskXlpDPGGDM8XMjnj/X3rCU0xgwXvSbKPhgRq0sp+yNiNYBDmYMiohGRZsSpLOhUQl8Fi0rPnDnT9dxAK/JUwtc3v/nNVVklblX/gj5x4kRVfuqpp5o2KsE1C5DVYpuDL7773e82bbZt29bUcSCHSvqdeQYq2IRF0qdPn27aZES0Smw9f/78qqzEwNddd11Tx0nGd+3a1bRR45DtVEnHMwEhvSYuVcdx0JMaqyp4aCwOWjEJevL5DI9P5TOWLl1alVWyeXUcB5uoca2Cz/g9Vr6G56lswv9u1xrvuG7J7dVx2SAVtj2TlFrdb8Zvq3lC3UtmzuWgFDXnqnHBiarnzZuXsonn78zcpRKhZ9Yr6hko+P6U3dxPmcCW8ej1C+I9AG7r/Pk2AF/t2QJjjDGDjn2+MTOMrgvEiPg8gH8H8MaI2BMRHwXwSQA/EhHPAPhvnbIxxpghxz7fGAMkfmIupdw6zl/98CTbYowxZpqxzzfGAN5JxRhjjDHGEL0GqfREKaURzbJgVEW+cZ3KoK520WBxpsqirwJXOLhFCUhZVKsCSxYtWtTUrVmzpiorEbES2jLKJhZz79+/v2mzcmWbnWLVqlVVWfXvc889V5XVDiFqlxS2U92vEm6zDerZsd3XXntt00Y9A97tJCsQZtG7ElJnRMRKtMz9ooTFfL8AcP3111dltevEwYMHq3L2fo2ZKPw+ZHYW4V2pVNBcZtcm9X5m3r3Me63eT/Wu87myQWv8jqqAkEzgYK+7q/S6S0omcCYTFKfmBD4uE+wCtHMAB/YBes7lgE4OMM2i1hj8rNQzUc+A7zkTzKMCs7L4C6IxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyr6qkGcNWtWk5T5hhtuqMqcJBVoEyIrrQUnwwRyWgelm8skv2RNntK6bdiwoalj7YpK9qk0gNzue9/7XtOG9WdvfOMbmzbKTtYofOc732nacHJp1d8KfgaqL1Uday02b97ctNm4cWNVZn0noPUXx47V28wqjYiC9TQZLV82CTX3p9LJqD7g53noUE/5i42ZErrp3ZTOivXGak5QfoznFvXuZ95ZNScw6r1WOjY+V/Y4JpNsWdmd0RsqMgnNe02w32vS7cx5FBznoDaZyOgpMwnU1VzCmnd1PXVu9cy5n1QsAMdQqPVEFn9BNMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmoq9BKrNnz27Exe94xzuqshJmqqSk6twMCz9VGxUkwkJmFZDBYtTHHnusaaMCSTjgRgmLOSBE1Skx7PHjx7u2UcmkOcH2rl27mjaciDzzTNRxKrkpi2qB9n5VX2YSmq9evbqpY6GvGnNK/Mu295rQXAmiWWCvApxUMvbJSkxrzFTA45PfIRVIwm3UJggqaTz71qNHjzZt1LvO11MBE5nAlcwclE3unEmIzKh3PxNcoq6fCVJR82KvATdcl+lL9ZyUTRyUoo7LBMmoMdBrYBT3rwqcUfP37t27q7LaGKGXgJ/x8BdEY4wxxhhT4QWiMcYYY4yp8ALRGGOMMcZUdF0gRsRdEXEoIh4bU/e/ImJvRDzc+e8nptZMY4wx/cA+3xgD5IJUPgvg/wL4f1T/h6WU/30xF4uIZteMTEZxFuwqoa8SvrI4VAl9M1ndVZvrr7++Kp84caJp80//9E9NHbdTolqV+ZwDMpQQlYMYVq5c2bRRwR4HDhzoeu4VK1Z0Pbe6F94VQe2aovo3kw2eg2k4SAdod+pR7dTYUXYyaqxmdlvhPgGA5cuXV+Vstn8+v2rD/dvr7gpmxvBZTJLPZ/hdU2OR33UOBACA9evXN3Xs25RPVnMH25AJ6lJ2K7+ZefeUTYzyI5kAmIzPUMex3ZldPRS9+hoVEMI2qHljwYIFXW3YuXNn04YDnIA2UEaNQ7ZTBW+qufKqq66qyps2bWraHD58uKl78sknq7KaF3kezgQOjUfXkVlKeQDAsW7tjDHGDD/2+cYYYGIaxF+JiO92fo5oP3d0iIjbI2JrRGxVIdnGGGOGgq4+f6y/P3nyZL/tM8ZMIr0uEP8UwDUAbgCwH8D/Ga9hKeXOUsqWUsqWZcuW9Xg5Y4wx00jK54/19+rnPmPM8NDTj9OllIOv/Tki/gzA32SPZd2CSizJZDSIGf1FJmFl9nqs93vjG9/YtGG9AACcOnWqKisdh0oMyzqC06dPN21Ya6ESoKp/1XNSaqUlZB2F0tGp6y1ZsqQqX3311U0bpZHYv39/VX700UebNpkvFI8//nhTx89AjYFMgtmMFlb1k0pWzol/lQZH2cnPPKOztQbRXCy9+PxSStfkxllfziiN7tve9raqrDTJSn/GNql3NqPJy2oAGfU+ZvSFmfMoO9m39foM1NzNG0+oOVfNEzznKT/G58r6MZ5Ljh1r1RNqPGXWJjxXXnfddU0bpYNnzSFvIALkNJZqDuRnp/o7S09fECNi7PYUPw2g3UbEGGPM6wL7fGNmHl2/IEbE5wHcBGBZROwB8NsAboqIGwAUADsA/I8ptNEYY0yfsM83xgCJBWIp5VZR/ZkpsMUYY8w0Y59vjAG8k4oxxhhjjCF6z6DYAydPnsT9999f1XHwAYtcgVZAmhWnshhXiT5VMAALi6+88sqmzY033liV161b17UN0AZpKDHwCy+80NRx4uhDhw41bTiNkAo24aARoBVpv/vd727afP/3f39VVsEfTz/9dFPHiTxVQtKlS5c2dRwEpIS2LDZWgSX79u1r6ridSjaqzsU2qXHICbY3bNjQtFFJUTPBJhnhuIL7TgnHjZlsIqJ5jzKJ3VWyYUa9CxxIt3nz5qbNmTNnmjoO+FM+ioM9sj4jk6RYvY+ZZNZ87kyibnWuXhNlZ+zOBvPwvahATX5Oak5QqfR4IwieSwHdd1ynEmWzf1dzvgpg5ftTc766Pw5SUZkCeIxPaaJsY4wxxhgzs/AC0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFX0NUnn55ZfxzDPPVHVPPPFEVVbCfxaHKuGrCm7J7HyRyXSvdv94xzveUZWVsJrbAG0AiLJJiVNZeLpr166mDfclB0wAwPLly5u6efPmVeWNGzc2bVhUq0TEStzNz+Wpp55q2qhgGq5jGxWrV69u6pQonTPkq2eggnl4q0gV4LRmzZqqrPry8OHDXW1S4mMlpGYBshrP3knFTAcqSIXHuXr3MztgqfeafRLvrAJowb7aWYPJBKmod4/fNTV3ZVDHZc6VCUhTfoV3k1E7d2UCUNQ8oQJn+HrKR2X8tgre5N1G1Ll5dy1lpwou3LJlS1VWu2SptcGJEyeqspoTdu/e3dUmtesP35/apSWLvyAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaairxrEkZGR5vfwH/iBH6jKSnvF2jb1m77SNbAmJKM3BFpNhtJRqN/+GaWHYD1lNmlxRjfGGjyl0bjqqquauoxGgfuE9SCA7l9OLv388883bViPAQDXX399VVaaHz6X0skoO3n8qH5S12NNiNIpXnPNNVVZaaWUtoS1i2qMZ8aAapNJemvMZFNKafwGv6PK/7EuUWnkMn5TabBV0uJuNgKtdlG9Z8pO1pP3qv9V7yz7W6Vdz7zrGZtYxwcAe/bsaeqOHj1alZXmXc3xnChaaQIZNZeoOu4DtVZQ+nnWvSt/z5s8qFgIpXHlflIbOnCCb6Ad00rDy23UM8jiL4jGGGOMMabCC0RjjDHGGFPhBaIxxhhjjKnoukCMiHURcX9EPBERj0fExzr1SyLi3oh4pvP/xVNvrjHGmKnEPt8YA+SCVM4C+I1SykMRMR/AtyPiXgD/HcB9pZRPRsQdAO4A8D8vdKKIaJILcwCIEouyEFOJj1XSYhbfqjYqcSq3U0maObBBCZRVoAELdFUQhQpsYFF2JpGoskkFZLCdSgCeEVsrwSxfTwXEsNBXnT/TJ6ovMwlms+OCg2nU9Vh8rETLaoxngp4yqOupcWDMBZgUn6+CVBjlI9lnKJ+lYJ+h3gUVcMjvv3r3M4n6VZBIBuVHMkEx3Eb1tQoc5P5UbThITwWpqDoO8Fm5cmXTRvk6PpcKJOE+UMGNmQ0zVq1a1bTh4EKgfS4cSAMAO3bsqMoqwEn1Ewfh7N27t2mjkmdzf6rrcSCqeseydH3zSin7SykPdf58CsA2AGsA3ALg7k6zuwF8qGcrjDHGDAT2+cYY4CI1iBGxHsDbAXwTwMpSyv7OXx0A0P5TYfSY2yNia0RszYSuG2OMGQwu1ufb3xvz+iG9QIyIeQC+BODXSinVN9My+lufTEydccVtAAAgAElEQVRVSrmzlLKllLJF/bRmjDFm8OjF59vfG/P6IZUoOyLmYNRRfK6U8uVO9cGIWF1K2R8RqwG0WaGJ+fPn46abbqrqXnzxxaqc0b9lE14zmWSjQE6zxToOpZlQejtup/Rv6vp8nNL7sb6Gk3IDWv+R0TeyBlDpbVRyadY/qPtVuhHWbSidDveJsjuj3clqjviLiDqO69T4UufmflLHqT7gZLUqeS2fK3MtM7OZLJ/PvoXHnnqH2LeqNhl9tWqj9Otsk9Jssd9S74zybdxOzW+Z62U07moOyiQZV8fx/KLaqLlk48aNVVklxVa6OZ4D1PzCawV1HnUcJ4p+5zvf2bTZsGFDU/fggw9e8PpAm+BabY6hkndz/yqdotpQ4ciRI1U5M+dOqQYxRkf4ZwBsK6V8asxf3QPgts6fbwPw1Z6tMMYYMxDY5xtjgNwXxPcB+AUAj0bEw526TwD4JIAvRMRHAewE8OGpMdEYY0wfsc83xnRfIJZS/hXAeL9B/fDkmmOMMWY6sc83xgDeScUYY4wxxhCpIJXJYs6cOY1gtBdU4IES/7KwVrVRSVi5nToucx4l7GXRshJgK2Ev26AEwmvWrKnKqp/UcSxkVsEQLNBV4lgFi3GVYFadiwOKVD+xsFkFIanj1P0xmTGmnrkKHmKUwJ3vRSUGf+6555o6FkmrscqC90yiXGMmg26+U71DXKfe62xSaEYFMbCN6tzsMzIJvtW5lY3KRyn/w/C5shsjZIIS+Tjl19QGB4sX15vrqFRHBw8ebOp4DlBJqfk4FTSi5twrr7yyKnOgLKDH2De+8Y2qrMYy2638tgpSYZ+s7kUlAuc69eweffTRqjyRbAL+gmiMMcYYYyq8QDTGGGOMMRVeIBpjjDHGmAovEI0xxhhjTEVfg1TOnj3biCxZaJvJBs87hqjzZFHCUxZ+ZoTUSiCshKccJKLuN4PaEYCFr0roqwI0MoEVmX1Vldia7VS7DSg7+VxKXM7PILOTAaB3bmGUaDlz7sx5Dhw40LUuuysCn1/ZxOOZz5MJwjJmMsiMNR7Tyv+qcc7tVPCbevd5PlFt+NzKj/Y6B6lAAz5Xxo+pflLwuTLBfdkAGK5T84YKvuBdUdQzyPhkNQcsW7asKqtA2WPHjjV1HIik2ixcuLAqK7+txiEHrqidVFQf8PpBjQvu88x8Nx7+gmiMMcYYYyq8QDTGGGOMMRVeIBpjjDHGmIq+ahAjoqt2T+kxWO+R0Q2qOqWRUwlP+fxK75dJeK20ZocPH67KSjOxevXqpo51ghktj9I1HD9+vKnjRKKLFi1q2mSSaWeS1yq9o+oDvj+lP8loaTKJYpXdyk4+v3oGrJU8cuRI0+bJJ59s6lgnsm7duqaNGqs8xjOaVmsOTb/opotTfoT9QTa5PmuvlGZMvdcZvV1GF5mdlzLHMeq9Zl+T3ayB7yWjd1YacKWx5/tV+vLMxggquTPrFNW5VR+wb+U5GNBzJdupjuO5Um1EoeYltp3vDdDjgvWyat3B78FE/L2/IBpjjDHGmAovEI0xxhhjTIUXiMYYY4wxpsILRGOMMcYYUxH9FKxHxGEAOwEsA9Cq9wcf291fhtVuYLhsv7qU0maPNWYCjPH3wHC9D2Ox3f3FdveHlM/v6wLxPy8asbWUsqXvF54gtru/DKvdwHDbbsxkM6zvg+3uL7Z7sPBPzMYYY4wxpsILRGOMMcYYUzFdC8Q7p+m6E8V295dhtRsYbtuNmWyG9X2w3f3Fdg8Q06JBNMYYY4wxg4t/YjbGGGOMMRV9XyBGxM0R8VREbI+IO/p9/SwRcVdEHIqIx8bULYmIeyPimc7/F0+njYqIWBcR90fEExHxeER8rFM/0LZHxGUR8WBEPNKx+3c69Rsi4pud8fJXEdFu3DwARMRIRHwnIv6mUx4Ku42ZSobF3wPD6fPt76eHmeLv+7pAjIgRAH8C4McBbAZwa0Rs7qcNF8FnAdxMdXcAuK+UsgnAfZ3yoHEWwG+UUjYDeA+AX+708aDb/jKAD5RS3gbgBgA3R8R7APw+gD8spVwL4DiAj06jjRfiYwC2jSkPi93GTAlD5u+B4fT59vfTw4zw9/3+gvguANtLKc+VUl4B8JcAbumzDSlKKQ8AOEbVtwC4u/PnuwF8qK9GJSil7C+lPNT58ymMDuI1GHDbyyinO8U5nf8KgA8A+GKnfuDsBoCIWAvgJwH8eaccGAK7jZlihsbfA8Pp8+3v+89M8vf9XiCuAbB7THlPp25YWFlK2d/58wEAK6fTmG5ExHoAbwfwTQyB7Z3P9g8DOATgXgDPAjhRSjnbaTKo4+XTAH4LwPlOeSmGw25jppJh9/fAEPjN17C/7xszxt87SKVHymj498CGgEfEPABfAvBrpZSTY/9uUG0vpZwrpdwAYC1Gvz5cN80mdSUiPgjgUCnl29NtizFm6hhUvwnY3/eLmebvZ/f5ensBrBtTXtupGxYORsTqUsr+iFiN0X/5DBwRMQejzuJzpZQvd6qHwnYAKKWciIj7AdwIYFFEzO7862wQx8v7APxURPwEgMsALADwRxh8u42Zaobd3wND4Dft7/vKjPL3/f6C+C0AmzoRP5cA+AiAe/psw0S4B8BtnT/fBuCr02iLpKOH+AyAbaWUT435q4G2PSKWR8Sizp8vB/AjGNXT3A/gZzrNBs7uUsrHSylrSynrMTqev15K+TkMuN3G9IFh9/fA4PtN+/s+MtP8fd8TZXdW3p8GMALgrlLK7/XVgCQR8XkANwFYBuAggN8G8NcAvgDgKgA7AXy4lMKi5mklIt4P4F8APIr/0kh8AqO6lIG1PSLeilFx7whG/+HyhVLK70bERoyK25cA+A6Any+lvDx9lo5PRNwE4DdLKR8cJruNmSqGxd8Dw+nz7e+nj5ng772TijHGGGOMqXCQijHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMxYQWiBFxc0Q8FRHbI+KOyTLKGGPM4GGfb8zMoec8iBExAuBpjGZA34PRrPm3llKeGO+YuXPnlsWLF1d1s2bVa9SMPXzMeHXMSy+91NSNjIx0PdfLL7f5Lvlc58+fb9qoOj73FVdc0bSZPbv7Dohnz55t6rjvRpPs15w7d66rncpu7qc5c+Y0bVQdn0s9X2Un39+ll17a9Xrq3tS4mDt3blW+5JJLmjaqDzL9mxm/qs2ZM2eq8iuvvNK0YbuVnadPn27acF/yMSdPnsSZM2famzFmDBfr86+44oqycOFCPkfX62Tes3Hsu+hzA/pd76WN8jWZ45TdXKfOzW3UtZQfUX6y27mz6wRup+ZXNU9wO+Xv2e7M/Aq098K+FtDzabfzjFfHZOa8jN1A2y9qrZB5Bs8+++yRUspybfGY83drcAHeBWB7KeU5AIiIvwRwC4BxF4iLFy/Gr/7qr1Z1l19+eVVWD4o76rLLLmvaqDrmqaeeaurYgQHAvHnzqvKzzz7b9VwvvPBC0+bFF1/saufb3/72ps3y5V2fG44cOdLUsTNQC58TJ040dbwAVouMRYsWVeVVq1Y1bVQdL6Szizi+v2uuuaZps2bNmqp8/Pjxpo1agN94441VefXq1U0b5Vh5bKoXOLPYVmP8scceq8o7duxo2rDdQDvu/v3f/71pc/DgwarMz/tzn/tcc4wxgovy+QsXLsRHP/rRqo4nK/V+sI9QiwX1Dil/lzmO3yHlj9gnqklXzUFqMcIou9kGnpOAdnGgPoDs3LmzqXv++eersvJjfH+ZDwuqnZpfV6xY0dTxh6ONGzc2bXjuUvMrryeAdkH6xBPtcGUfCbR9oMYFn1u1UR+Y+JmreUotpDdt2lSVlyxZ0rTJPINbbrmlHRiCifzEvAbA7jHlPZ26ioi4PSK2RsRWtYgyxhgzFHT1+WP9vZrAjTHDw5QHqZRS7iylbCmlbFE/kRljjHl9MNbfq68ixpjhYSI/Me8FsG5MeW2n7oJ0002oT9l8zKuvvtq0Ub/FZ/SN6icHrlu7dm3Thn86UOdWC+IDBw5UZfW5Xf2kwp+71fX4C636CUDZtH79+qq8e/fupg33r/rJQ/1UwZ/SM/pKoP35QP10wKg+yehtsnB/ZjRHGW2sapf52Qlo70/1gfdbN5PERfn8Ukrz81pGCsTjVY179RNvr7CcpFd9t5qX2G+p49SXVvYjym/zAlydR0llGOUf2E9nfQgfl9ESKlRf8nNRP6mrn/TZJtVPyia+nhq7/JzUuMxo3LP9lNFK8jOfiP+fyBfEbwHYFBEbIuISAB8BcM8EzmeMMWZwsc83ZgbR8xfEUsrZiPgVAP8AYATAXaWUxyfNMmOMMQODfb4xM4uJ/MSMUsrXAHxtkmwxxhgzwNjnGzNzmNAC8WKJiEYPkMllx7/hq9/01W//rGPIaEuA9jd8FW7OegSlk1HpU1gjodLVKJ0ea/7UvWSCgJSOYv78+VVZ9S/3gepvFaV+8uTJqnzq1Kmu1wdajYY6d+Z+e81dpeBnnEnzkM2VyM88ozcE2nGQ0ffws+u1P4y5EBHRVSuo/p7Ho2qj3j3WO6o2yrdynWrDGrFMzlegvZejR482bZQvZ+2i0uSxb1X9tGDBgqaO/Y+6Pt9Lpk+AVhep2qh72bdvX1VWOkE+l7pfpUvk+Vwdp9LF8HHqXng+zfrSTF7hXnN2cv9mdKjj4a32jDHGGGNMhReIxhhjjDGmwgtEY4wxxhhT4QWiMcYYY4yp6GuQioJFl5mkjkpkunTp0qaOk1Jn9tcFWqFtZlPwrKCUxagqAXQmmaw6Nwt0M0JuoA1sUIJZtlOJrVVgBQflqP2SlbCXg3LUHtLcRgVo9JpAXZ2Lx09GkKwE2aqfMvs8Z5KMZ0TMDkox/aCU0rxbmSAqfj8yQRRALvhKvY8q6T/DgXvqPpRvzbyPaj7jOUjNExzIofpJ9QEHWyi/kkkMroIZ+dzqOOX/2LfyftFA2yfZwA62QQWbqHOpgJdu11PPIJPUPRtA222jEcVEtrz0F0RjjDHGGFPhBaIxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyr6HqTSTaScFWsyixYtauoOHTrU9TglWs60YTuVjaqOxcbZXWFYoJvZVUPtNKLEuMuXL6/KKkiGg0QygTtAK6rN7izCKNEyi40XL17c9TyKrLCY69Qz4D7IPCcgFzii+pf7TvVlxiZjJpuIaPxNRlTPqODCjP9R77AKUODj1LvIOzmpdzhDJgAQaHdAUYEGbKfyDyrQgt9/tZMV910msARoA3XUXMJzGQAsW7asKmf8ofJjqp/YThVMpOp41y91vcyckPH32XHB96L8PdvkIBVjjDHGGDNpeIFojDHGGGMqvEA0xhhjjDEVE9IgRsQOAKcAnANwtpSypdsxrGXI6EY4kWlWD8H6PqVRU3qI9evXV+U9e/Y0bVhDoH7n379/f1N37NixqqwSTqs+WLhw4QWvD7Q6TKX/OHnyZFN38ODBqpzR5Cntg9JT8rNS2iFOpg20WiH1zFkjorQ0GU2rShSrbFI2MHx/Sluini9rV7Iaz0wi4ozG05gMF+PzR0ZGGh00+y01NvmdVeM+kyhbvXuZd11pzvnc6jyZd09pIJVWnG3IaIvVHKh0bOw31ZyQ8RmZOUj5XzUPcx8o/5vZ0EGNFZ6bVT9lNjRQzy6jQcwkvFZjQM3fbGcmOftENOeTEaTyQ6WUI5NwHmOMMYOPfb4xMwD/xGyMMcYYYyomukAsAP4xIr4dEberBhFxe0RsjYitnCrAGGPMUHFBn29/b8zrh4n+xPz+UsreiFgB4N6IeLKU8sDYBqWUOwHcCQDr1q2zGMoYY4aXC/r8sf5+7dq19vfGDDETWiCWUvZ2/n8oIr4C4F0AHhiv/dmzZ3HkSC1dYZGnCnTgpKHHjx9v2qiAED4XnwdoA2AA4Oqrr67K6l/CLCrdsWNH0+bRRx9t6jjhsxLHKlEpC3I3bdrUtOGkqKdPn27aXHPNNU0dB86o4zKJYZVwWyVqZZRomOuUQJjJCNdVnXoGGTG7ujd+TtkAEX7magyo58L91EsiYmOyXIzPL6U047gXUb8KhlDBhfzOKp+lgh8yvi3jMxT8Piq7M4E66r3mNuo+VCAHB5KoPmHfpp6BCqJgO1U/qcAKPlcmKEfdr/LJ3E5dP4PqAx4Xqo2aS/i5qOeUQb0//AwywZXj0fORETE3Iua/9mcAPwrgsZ4tMcYYM7DY5xszs5jIF8SVAL7SWTHPBvAXpZS/nxSrjDHGDBr2+cbMIHpeIJZSngPwtkm0xRhjzIBin2/MzGIy8iCmmTVrVpO4k7UGStfAv/MrzRpr+wBg6dKlVVlpEJW2jTWHqg1rBpSOg7V9QE4zpu6PdQwqwfauXbuqMm+ADgArVqxo6jhxqkqu2uum5Hx/SqOhjuNnkEnMndlMHWifndKIqGfANqhz8/2p+83UZXUjrKdRmp9uiVOdSNv0C35n1LvP77V6P5VP5ndB+WT1XrNNGd2yQr1HPJ8pjX1GE6f8AfeLmkuUto39fWZjBKXVV3N1xm+pNpnE4JzwWukN1YYVmSTn6hmwn1TrB/bbqi/VBg48DrLzYqZ/J9OfOw+iMcYYY4yp8ALRGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVfQ1SiYhG2MpCYiV85aCJhQsXNm2UIJkTCy9fvrxrG3UuTooNtEJXFv4CWmTKwlclIl6zZk1Tx8m7n3jiiaYNB8WovlTJu/le+FpAK6JV/a2EtiycVgJaJRBmmzLJtFVCc2UT97l6TpmkqJnnq0TFmeTASoSvjssEPTkIxQwqyv/xeFWBB8rfcqCDel9UEBe/18omDirIJHJW58rYrepUcIvy70wm4FEFUXCfqDaqf9mXq4BHVce+VPUJ96+6f3W/7BPVfM7Jw5UNalxkNoLIPCcVOJMJBFX+fiASZRtjjDHGmNcnXiAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaair0Eqc+bMwapVq6q6Q4cOVWUl6FQZzBkVNPHWt761a5uTJ082dSw8VQJdJTZmlLCYBaPveMc7mjZsN9CKf59++umubdTuMkqgu3jx4qqc2ZFE7SygAitY6KsE50roy/2khLYnTpyoyko4/oY3vKGpY6FvVsSrBMFMZscFBe9UoMaqgoNpMgEpfL8qIMeYiTJr1ixcccUVVV23XX2A1o8pn6EC2zLifOWTM+8MXy8TxAa081smqAHQgRwMz13sDwHtRzLPgIMmVACg6kueS/j5q+urOhW0waj+VusHnqvVDmOqv7l/VZAKowIXFdzn6l4yAY7q2U0m/oJojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpiKrj/0R8RdAD4I4FAp5fpO3RIAfwVgPYAdAD5cSjne7VwjIyONBo71fUoPwnVK13Dttdc2dazv++53v9u0UbqNjPaAbbjxxhubNpy4Wh33wQ9+sGmjNJdf/OIXq7JK8M39dPx4+0iUJoV1I0qjwYlElS5T6S+4f5VmQulyMppHRulCt23b1tSxVlLpMlUydu4XpUNi7YxK+DqZWkZ+5hl9Dz8DJ9I2Y5ksn19K6apBVu8Ha9uUFku9HzzOszpB1hcqP8Z2qzlB+RE+t/J/GX218pFcp+xW587olvlc6twZX5P1Y+w3M+sA1Ubp4DOaa3UvbJPSvfL9qfMo/SaPezV2etWzsw2Z+WY8Ml8QPwvgZqq7A8B9pZRNAO7rlI0xxgw/n4V9vjEznq4LxFLKAwD4U9gtAO7u/PluAB+aZLuMMcZMA/b5xhigdw3iylLK/s6fDwBYOV7DiLg9IrZGxFaVdsUYY8zAk/L5Y/29+mnNGDM8TDhIpYwKAcYVMZVS7iylbCmlbFG6LmOMMcPDhXz+WH+vdFXGmOGh10TZByNidSllf0SsBnCo6xEYFV1ysAOLdlXQCP9LVIlMM2Lc9evXN23279/f1LH4d+/evU2bJUuWVOUPfOADXc8DAG95y1uqMgeIAMC+ffuaOhbkrlzZ/gOe+1ZdXwl7WcSqgms2b95clZXzV18M+PkePny4afPKK690tTMjLFZi3CeeeKKpe+SRR6qySq6q/jHDz+5Nb3pT04YDWVQ/qcS/u3fvrsqcOHs8lMi/GxMRLZsZy0X7/LNnzza+hMerCn7gpMVqjKs69ncqYEH5v0wwAs8vKrGyeq854EYlgD5y5EhTx0GImcTRykeqoJhMkAofp56TquN7Uc9A+VueAzLPXN2vuhfuS3Wc8vdskwoa4TrV35nATPV81RjLBBj2uhGEotcj7wFwW+fPtwH4as8WGGOMGXTs842ZYXRdIEbE5wH8O4A3RsSeiPgogE8C+JGIeAbAf+uUjTHGDDn2+cYYIPETcynl1nH+6ocn2RZjjDHTjH2+MQbwTirGGGOMMYboNUilZ1gwySJWDrQAWtGwEl2qQBIOSlE7lPzgD/7guLa+xsGDB5u6f/7nf67KSlDKgSxAG7SgBLMsbAaAVatWVeX3vve9Xc+tAkKUAJvvT4mtn3rqqaq8fPnypo0SHy9durQqHzrUatvVcTwulNiZ+1y1UUJqFvqq+1Ui6eeee64qqwAUtmndunVNG3W/vHuP2qmm1x1P+H45SMU7qZipoJTSiPH5XVMBalynRP7Kj3EwgnrPVB2/68omDiJQPkMdx/evdntSx3Hwg5rzuF+Uz1L3mwlmZJuUjWruYhtUajv17NhPq6AN7gNlk9phjG1S188EGKm+ZLJBKny/mZ3RgHZ+UQGHfO5ed2QB/AXRGGOMMcYQXiAaY4wxxpgKLxCNMcYYY0xFXzWIpZRGN8C6MaVrUDoyRiVpfvrpp6uySkqt9Bdz5szpem7WDHz9619v2ihdIusY+FqA1lawnlDpGvjcSiOn9I18f2fOnGnasEZOaTbUvXDC5yuuuKJpc/XVVzd1rK1QmhS+nho7SivJSVFVXyot7DXXXFOVld1XXXVVVVZaQnUv1113XVVWWiWlMWKdSiZhfLeyMZPB+fPnG//K75oar+zv1fupjuO5RGnG1HHsy9Rx7GuU9kvpyNhvqrlM+eRuNgI57XBWh9mtjZoDM/pRpYNX/oafndoogLV0yqaMvlHN+aqO5wDlk3k8qTlQPSf25Wo8KU099686jm2ayMYI/oJojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpgKLxCNMcYYY0xFX4NUzp8/3wRAsBhVCX0ZJfpUSbBZZKqSNO/bt6+p40AKJSxmMeqxY8eaNirYgwWk6twqMfeRI0eqshL6cl8qkasKkOA+V6JwDrbgBNjjnZvPpcTHnAQcaEW8KkAjE1zD/Qa0wnElXD969GhXm1QQEIu7VaJYvj7QComV2Fo9c34XlCCZn8tERMvGZDl37lwz1jnQQPkaHufK3ytfw8epNuq9YptUIF1mswZ1bg4qUEEMag5gX6P6gG1QbVQgyfHjx6uyCtDoZg+g7WabMkmigdZ2FUjDzyUbXJdJHK1s4n5Rz459qTq3quP7VcGMaq7k56nG3GT6e39BNMYYY4wxFV4gGmOMMcaYCi8QjTHGGGNMRdcFYkTcFRGHIuKxMXX/KyL2RsTDnf9+YmrNNMYY0w/s840xQC5I5bMA/i+A/0f1f1hK+d8Xc7GRkZFGjMkiViU8ZXGo2hGFd7AA2iCNnTt3dm0DtMEHmQzqym4WAwPt7ipKHKsCGzgIRh3HdvKOIQCwZMmSpo77V+0Ac+2111Zl1d8KFtWqflKCZB4nSui7ffv2qqyCkFTwEO9KoATnSpDMwTyqn1SwFPPwww83dc8991xVXrt2bdNm9erVTV1mlyEWKav7NWYMn8Uk+PxZs2bJd2QsSkDPPkK9+woOpFC+RgWW8XyibGIftXv37qaN2tlDBcow6h3OBGDw3KnmMhVww/2pgiE4QEMF+6l+ygREZAI5lE08J6jAGRUUkwn4UUEijLo3vp4KQFQ75fC4UH2ixg4/FxWkkgleytJ1piilPACgnWWNMca87rDPN8YAE9Mg/kpEfLfzc0T7Sa9DRNweEVsjYuuJEycmcDljjDHTSFefP9bfZ9KnGGMGl14XiH8K4BoANwDYD+D/jNewlHJnKWVLKWUL5yU0xhgzFKR8/lh/r6QjxpjhoadE2aWU/8zkHBF/BuBvMscpTQrr5pRmhX+fX7FiRdNm3bp1TR3r35R2Uek2+Hf95cuXN21YN6J0XStXrmzq+F6Ubk5pBzmht9I3ct3hw4ebNqwlBIA3vOENVfnqq69u2rz3ve+tykojonQqmX5SsCZEaXK475TWYsOGDU0djzGlS1L6D9Zvbt68uWnDmh91HtV3/HX9TW96U9NG9QFrYFT/cp0TZZuLpRefX0pp3v/MV0XWbKnxqt519q1Ko6Y0YryQzWjNlP9V8LunEldn769bG+UflP6N50E1B+7du7fruZWNGd+idIJsUyYRudJuKn+bGU/qHzOsQ1fPjjdnUNr1zJyn+jJTp/qA729KNYiKiBirlv9pAI+N19YYY8xwY59vzMyj6xfEiPg8gJsALIuIPQB+G8BNEXEDgAJgB4D/MYU2GmOM6RP2+cYYILFALKXcKqo/MwW2GGOMmWbs840xgHdSMcYYY4wxRE9BKhOBhcQs6lQCVhbRKvGxgsWhmeTSQCvyXLNmTddrqSTcKkkp26ASOato7+uuu64qK7E1i3hVAMwzzzzT9TgVlMOibBWQosTOmeS1Cm7HYmCgTXitkoaq4KW3v/3tVVklt84EhKjr8fNVInGVlHX9+vVVWSXKVmJnHqu9Jq81ZrI5f/58k1yeyQRxKT+qfA2j3mH1LvB7rHwNv3sqUFIF4GSCWbIBCgwHVqg+ySSlVj6K5+VscGFm4wtlJ7dTczwHiai1QiapuhoDyiYeh8rfs93quWXGk7JJ3QtfT/UB26DaZPEXRGOMMVF/bWEAACAASURBVMYYU+EFojHGGGOMqfAC0RhjjDHGVHiBaIwxxhhjKvoapBIRjfiVxZlKMMtiUd7RAgB2797d1HEghxJrqmAP3mnj4MGDTRsWgqrdOFSABItTn3322a5t1PmVqJbvT/Xld7/73aaOA1e++tWvNm14J5fVq1c3bTjQAmgDbpSIV2WfZ0GwEjuzeD2b7Z/7Vx2nApoyOzXwceo8aocd3j1HZfZXIni2aSJZ842ZavhdVwFbHDSmglTUO5t5rzmwDWgDZTIBMMpnqXePfbCySQXq9LJLipo31JzHwR7qXnjO7TW4MBM0ArSBHJnjlI9Ux2UC+dT9cQBrZu564YUXmjaZuUT59kxwiTr3ZOIviMYYY4wxpsILRGOMMcYYU+EFojHGGGOMqeirBnHOnDm48sorqzrWeyh9ACduXrx4cdPmwQcfbOoOHz7c1SaVqHrz5s1VWWn5MolDlYaA9TVKM6E0IYw6jrWT11xzTdNGaQe5P59//vmmDWuF1PVVUljWSCithepL1vexRg9otXzq+mo8ZbQd6v5Yg6I0iPwMFEpTxcdltYSZhKv8DKxTNP1g9uzZWLp06QXbqPeMdYJKN6jgsZ95z5QNGf+rtF8ZLZ/ydZmk/JkE+Nkk+Vyn/CHfi+o3pbfrNVE1zwtKX8h9uWrVqq7XAlo7ld9WCd25neonfna9blSgxrgaY5nNPyaSGLu53qSdyRhjjDHGvC7wAtEYY4wxxlR4gWiMMcYYYyq6LhAjYl1E3B8RT0TE4xHxsU79koi4NyKe6fy/FQYaY4wZKuzzjTFALkjlLIDfKKU8FBHzAXw7Iu4F8N8B3FdK+WRE3AHgDgD/84IXmz27SXLNwQ9KsMt1Sgx81VVXNXUsMn366ae7nhsANm7cWJU5SAZoxaEHDhxo2uzdu7erTZwgFNDBNXzPKpkrC3v379/ftFEsW7asKqtEzhzwogJZlPiXE16rNqoPtm/fXpWV0Pf9739/VVbBRJkk2Eo4zslkgVb8qwTZmXOrxLjcB8ruTMCNasM2TKaI2bwumRSff8kll2DDhg1VXSbhMr9D2QT47FsygWaKbFJoRr177KdVMAL7SGVD5t3PbkLA7ZQ/4DbZpNSM8n/KT/O6QG2GwZtFqMBFFTijrpexk5+B6qdMAKAKlsoErmTWQurcPL+ouSxL1y+IpZT9pZSHOn8+BWAbgDUAbgFwd6fZ3QA+1LMVxhhjBgL7fGMMcJEaxIhYD+DtAL4JYGUp5bVPVAcAtJ+dRo+5PSK2RsTWI0eOTMBUY4wx/eRiff5Yf6++5hhjhof0AjEi5gH4EoBfK6VUGxiX0e+q8tt9KeXOUsqWUsoW/inTGGPMYNKLzx/r79X+9MaY4SGVKDsi5mDUUXyulPLlTvXBiFhdStkfEasBHMqci3+jZ+2V2rydf69XmjWl42AHtWjRoqbNdddd19StW7euKme0LKy1AYAdO3Y0dUqXyCj9BWsNlE1cp7QXSgPDugZ1HH/9VVpClRSXtSuZ5J9Aq5tQCc3ZJk5CDuhnzveX0YNk6fVcGf2UGuP8lUbpTTJJ3Y0Zy2T4/JGRkcafs99UY5o1VBlfp+rUudXcwfSalFrBvlXpwpXmPKO75w8uyicrO9lPKq0bPwOlQVSaPPatSivPGn+gnXMz+js1BpRNnORbJcXOJCtX52bfquZO9VwYdZzSM/I9q/7tVUOryEQxB4DPANhWSvnUmL+6B8BtnT/fBuCrk2aVMcaYacE+3xgD5L4gvg/ALwB4NCIe7tR9AsAnAXwhIj4KYCeAD0+NicYYY/qIfb4xpvsCsZTyrwDG+2b5w5NrjjHGmOnEPt8YA3gnFWOMMcYYQ6SCVCaLc+fO4cSJE1UdJ5hWAlYWyKqk1Dt37mzqWIyqourWr1/f1LEQVAl9uY1K1M3iWKAVrGbEsUArWFWBB3y9xYvbjQ7U9fiZqEAhPpdKWcTJtAHg6NGjVVnZnUnurI7jgBcVANNrstxMMml1brYzKxzPCJlfeumlpo6DjtS98TPnceIgFjMVvPLKK9izZ09Vx/5AjXsej+o9U36Mj1PvQq++hoMEVbL748ePN3XsR1SwXSaRsfJHp06dqsoqKEcdx32usotwIIvqN7WBBAcFqrlEzYs8V6vny/enghIzkfMqUDMb8MJwH6hgnn379nU9dzZIhccmv0+Afp694pnBGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVXiAaY4wxxpiKvgapvPrqqzh0qE6+f/Dgwap87bXXNsexoFNllWcxNNBmZ3/zm9/ctGHxMdAKcjNZ3ZXIVMH3ogIrOGgEAE6erHa6kmJcFgSrflJC6ueff74qq90G+PqqTzL3khGJKzLBHkrUm8lGr+4lE+yh4OOU3aqfMscpEXpG4L5w4cKqzGNVCbSNmShnzpzBI488UtXxzj/q3c+MR/V+sL9TPlm9L/zuqQAUtlPtGqUCMlRgGaOCNjjYQdnNAQrKbjUHZHYIYR+lzqOCVFTgSLfrA+3zzASIqEBJFaCRCTrN+HvVBzzG1JjLBMCo+830gdrvnP39RIIQ/QXRGGOMMcZUeIFojDHGGGMqvEA0xhhjjDEVfdUgllIaHRX/zq5+r2dthdIQ7Nixo6ljnV42cXQmkTJrQpTWRGkJWWuhrjVv3rymjhOQZhJlK30lJyQFWm2Fuj5rYlTCV5XAnPWMvSa9zSQPzzw3dS6l7+k1ES+jdCsZHaY6txpj/D4pDQyPAz4m22/GXCw8rtmXZ5JZZ7S/QC7ZvCKjm+Ok1Mr/ZTSIyo8pH5HRLma0xEqrznVqLuG5Sz0npVXn49SzU9pFnpuV3pp1keoZqCTYXJfRmKp26jnxfJqZX9X1lN9WfcfPOKPnvPTSS5s2WfwF0RhjjDHGVHiBaIwxxhhjKrxANMYYY4wxFV4gGmOMMcaYiuinQD0iDgPYCWAZgDbL5eBju/vLsNoNDJftV5dSWuW4MRNgjL8Hhut9GIvt7i+2uz+kfH5fF4j/edGIraWULX2/8ASx3f1lWO0Ghtt2YyabYX0fbHd/sd2DhX9iNsYYY4wxFV4gGmOMMcaYiulaIN45TdedKLa7vwyr3cBw227MZDOs74Pt7i+2e4CYFg2iMcYYY4wZXPwTszHGGGOMqfAC0RhjjDHGVPR9gRgRN0fEUxGxPSLu6Pf1s0TEXRFxKCIeG1O3JCLujYhnOv9ffKFzTAcRsS4i7o+IJyLi8Yj4WKd+oG2PiMsi4sGIeKRj9+906jdExDc74+WvIqLd1XwAiIiRiPhORPxNpzwUdhszlQyLvweG0+fb308PM8Xf93WBGBEjAP4EwI8D2Azg1ojY3E8bLoLPAriZ6u4AcF8pZROA+zrlQeMsgN8opWwG8B4Av9zp40G3/WUAHyilvA3ADQBujoj3APh9AH9YSrkWwHEAH51GGy/ExwBsG1MeFruNmRKGzN8Dw+nz7e+nhxnh7/v9BfFdALaXUp4rpbwC4C8B3NJnG1KUUh4AcIyqbwFwd+fPdwP4UF+NSlBK2V9Keajz51MYHcRrMOC2l1FOd4pzOv8VAB8A8MVO/cDZDQARsRbATwL48045MAR2GzPFDI2/B4bT59vf95+Z5O/7vUBcA2D3mPKeTt2wsLKUsr/z5wMAVk6nMd2IiPUA3g7gmxgC2zuf7R8GcAjAvQCeBXCilHK202RQx8unAfwWgPOd8lIMh93GTCXD7u+BIfCbr2F/3zdmjL93kEqPlNH8QAObIygi5gH4EoBfK6WcHPt3g2p7KeVcKeUGAGsx+vXhumk2qSsR8UEAh0op355uW4wxU8eg+k3A/r5fzDR/P7vP19sLYN2Y8tpO3bBwMCJWl1L2R8RqjP7LZ+CIiDkYdRafK6V8uVM9FLYDQCnlRETcD+BGAIsiYnbnX2eDOF7eB+CnIuInAFwGYAGAP8Lg223MVDPs/h4YAr9pf99XZpS/7/cXxG8B2NSJ+LkEwEcA3NNnGybCPQBu6/z5NgBfnUZbJB09xGcAbCulfGrMXw207RGxPCIWdf58OYAfwaie5n4AP9NpNnB2l1I+XkpZW0pZj9Hx/PVSys9hwO02pg8Mu78HBt9v2t/3kZnm7/u+k0pn5f1pACMA7iql/F5fDUgSEZ8HcBOAZQAOAvhtAH8N4AsArgKwE8CHSyksap5WIuL9AP4FwKP4L43EJzCqSxlY2yPirRgV945g9B8uXyil/G5EbMSouH0JgO8A+PlSysvTZ+n4RMRNAH6zlPLBYbLbmKliWPw9MJw+3/5++pgJ/t5b7RljjDHGmAoHqRhjjDHGmAovEI0xxhhjTIUXiMYYY4wxpsILRGOMMcYYU+EFojHGGGOMqZjQAjEibo6IpyJie0QM2mbgxhhjJhH7fGNmDj2nuYmIEQBPYzTB5R6MJkW9tZTyxHjHXH755WXBggVV3blz53q6fi+M5hTtXnf+/PmmjplAv3W9vjp35nrcRp17ZGSkqbv00kur8tmzZ5s2XJe1ketUm1mz2n+nXHLJJV3bTNYzUGTOrcZJL89J2ZQdq73cCx9z4sQJvPjii91PZGY0F+vzlb9nevW1ykcJe5s65Ucy7xCj7J49u92YbNGiRVV57ty5KZuYzDx19OjRps2ZM2eauldeeeWC58lef86cOal2jLpfXgdkzqPmssxzUc/p1Vdfbep43GXaTCaZc2fGhTrPyZMnj5RSlnc7/0S22nsXgO2llOcAICL+EsAtAMZdIC5YsAA/+7M/y4ZW5cwCIjPBqjo1oNVgffnlOr+lWsSqwZI5N9ugbFLnZhsyTlP1CTssANi4cWNVPnLkSNOGnU/GRtWO+xYA5s2b19StXbu2Kl9xxRVdz61Qz4DrVD+pc3O7l156qSeb2EED7TjgBTKgHSIfp+6l27j4zGc+M76xxvwXF+XzFyxYgFtvvbWq43fv9OnTzXE8wSm/cvz48aaOx7l6h5Qf4fcqszhRfmzx4sVN3U//9E9X5Xe/+90pm9i/q3f/hRdeqMqf//znmzYPP/xwU7dv376qzHOw4rLLLmvqli9v1xeXX355T+d6/vnnq7J6dtwnqr+5TwBg2bJlVXnJkiVNmwMHDjR1PO52797dtMn8Q0XNQZlFXObjmVrs8thUc9Lf/d3f7ex6ckzsJ+Y1AMb22J5OXUVE3B4RWyNiq/rXjDHGmKGgq8+3vzfm9cOUB6mUUu4spWwppWzJ/OvCGGPMcGJ/b8zrh4n8xLwXwLox5bWdunEppTSfVvnza+YnQYX6BM+fX9Un28xn3Kz+InNchoxjVZ+2+VNyRlsCtH2gzt1rP2W0dZmfkNTXCD6OtZSAHhf884X6TJ/5iUP9NM6f99UzUDb1osFR51fn5vcn8/OGMYKL9vndxnFGW5x9F3r1t5nrcZ2S6ii+9KUvVeXHHnusacM/QwPA1VdfXZWVT965s/6VUGkQleaRfxpWP/myDObUqVNNG/WTK/tN9TO0gu8vqy9k1FzC/p1/zlZtgHY+VX3J11M/56pn180njwe3y0ih1LyYZSJfEL8FYFNEbIiISwB8BMA9EzifMcaYwcU+35gZRM9fEEspZyPiVwD8A4ARAHeVUh6fNMuMMcYMDPb5xswsJvITM0opXwPwtUmyxRhjzABjn2/MzGFCC8TJgPUPmRQ2Si+QSSmjtAAqXQH/rp/JVag0Ewq2M5O6RLVTWgvWLqo26nqsL1F9wnYrPYZK35LRyCkdxYkTJ6qy0lHwcUo3qMYFj7msBjGTUob7PKstyehse9UK8nGZND/GTAbdxmz2/cicN5NztVedNNup2qh0Ncw3v/nNpk7NS7/4i7/Y9Vxbt26tykonqHTS7MfUPMH+NqO/A9qUOXv3thJVNb+xT1Z9OX/+/K7XV3pKvt7hw4ebNup6mSh8NU8wan7h8aTeg17zRXbz9xeDt9ozxhhjjDEVXiAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaair0EqEdEIJlnAmREtK9GnqsuIj5WQmUWtKoiCRbzq3Cogg6+XsVudSx3HYufsxvQcXKJE0yziXbFiRdNG7en54osvdrVJPXO2IbNXpRItq+N4D2XVRomdWaidSYKdGV+qXSbhNdA+z0wgSzagypjJhsdrxh9k34XM2Fc+gt9HdR62IevH2NeoBNvf+973mro//dM/rcorV65s2vD+wer6mfvtNYhh48aNTR3vhXzo0KGmDQcgquNUgAiPHbWhxIIFC5o6fp7ZgBB+duq4TICfmksyNqnj+HmqsdrrhhkKf0E0xhhjjDEVXiAaY4wxxpgKLxCNMcYYY0yFF4jGGGOMMaai7zupdAtKUcEXjBItq+N63Xkic71MtnIVjMCi1qxglgMkMruWZK6vjlNCaj5u6dKlTRtlU2bnGIUKDJosOABGBeWofmJRdEYgrNpkgoCyAVWZ3SMclGKmi14CIHjsK1+g/CbXqV0u1PuREfFn/LaCr5fdtWjXrl1V+Vvf+lbTZsmSJVVZBQ5mAh4XL17ctOFAEtWXCxcubOr4WSmbVCAJBzOqZ859qQJwMjape1FzF9ukdpPJBC+p62V8srq/zLyY2UEui78gGmOMMcaYCi8QjTHGGGNMhReIxhhjjDGmYkIaxIjYAeAUgHMAzpZStnRp3/xG360MtPpClUBS6e0yv72r3/m5LqOHyCalzuhr1HEvv/xyU8dwv6h7U7CeRulruH9Pnz7dtFEJUDPanV40QEBOf5chqxHJ6GM5wWtmPKvrZe8lM8Z7TYRrDHOxPr/Ha1ywPF4do94z9a7z+5HRKap3KuNv1fvKSaKB1peq+e3IkSNVWfnkNWvWNHWsMc/MXfPnz+/aBmj7PKsl535ZtWpV04ZtUNdXawPuF5Vge//+/U0dP/PMXK2ur+aSjL9Xx2XmUx7jasxnmYwglR8qpRzp3swYY8zrAPt8Y2YA/rRgjDHGGGMqJrpALAD+MSK+HRG3qwYRcXtEbI2IrRw2bowxZqi4oM8f6+/VfrrGmOFhoj8xv7+UsjciVgC4NyKeLKU8MLZBKeVOAHcCwKpVqyYnMaExxpjp4II+f6y/X7lypf29MUPMhBaIpZS9nf8fioivAHgXgAcudAyLLFnUqsSaLLJUIlNVl0larII/WLTba5CKqmPhqRIfq6Sdqq7buRVKRMvnVsJX7gP1NfjYsWNNnRJgZ+D+zQSyZJPX8phTCVAzSVGV4JzbZAJSFFlxN59fjbnJShhvTC8+fyyZsT9Z4zUbnMXtMn5EBaRkgtiUTep67F/VcRxsouayHTt2NHWcYFsFoHDifjVvqOfEASGZ+QZo/e2ll17atOE5Xt2vmpf4euo5qeMywVLspzMbUSgbsmOVj1Pvk1pT9ErPPzFHxNyImP/anwH8KIDHJsswY4wxg4N9vjEzi4l8QVwJ4CudFfNsAH9RSvn7SbHKGGPMoGGfb8wMoucFYinlOQBvm0RbjDHGDCj2+cbMLCYjD+JF0U1bpn7D59/n1W/smWSQ2eNYs5BJWJlNkMnXU8cdPny4qXvppZeqciYBc1ZrwSjNBGtLlLYwozfMJgRljU9G25FNws3jQD2DjCZEjSeuU1oadb8ZjamiF72WNYlmuuD3Wo3FyUrsrnS8maTQGe24eocz71VWI5zpAz6OdYOA9ve7d++uyspHrVixoiovWLCgaaP8fUZjmpmr1b1kEpErP8r91Gt0fWazCmWjGoc8N6s+ydRl+jtj93g4D6IxxhhjjKnwAtEYY4wxxlR4gWiMMcYYYyq8QDTGGGOMMRV9DVIppXRNQpoR/3LABqBFvZlgExXIwcfNmzevacPCTxWwoM7N4lslUFYBGRwkkjm3Oo8S0fIzUf3ESaHZHkA/F5VMmlF9l0ley23U/SqBLp9L3YsSbjPKbrYhE8gC5MaqEjvzvWRE8KqfjJkKeOxlxmKv45PfBeXrMv5Pvft8XDZRdibhv5oDMu91ZiOIo0ePNnV8LnXuAwcOVOVTp041bVSCbU7erfpSBbxk/GYm2CITKJQJGgHaZ6Bs4uupNup67O/Vxgyq7zLBS5lgniz+gmiMMcYYYyq8QDTGGGOMMRVeIBpjjDHGmAovEI0xxhhjTEVfg1QiohFxsoBTZQ9nYabKlq6EoCz0VwJPtYsGB3soASsLZlVQQWbnFpXVPSOkVmJnDhJRWfQzIld1bu5zJRhWz6VbUNJkktkBQdWp55sRyqt742ennq8aq1yX2TlGtVN9wHXeScX0Cx6fPPZ62TFkvLrMjlvK3zPKZ2QC+TLvVTZQMrPbCNv0/9l791i9quvc+xm+4Nv2bdvGbGxjgzFJ3KYxqoWIchGHNKc0RSWVKgTqhaMv+ugfjZRIaXtoVCltP1VKpdMkVU9VyWkQVMohiZq00CjKCSWopG1uxjgJlxhsgsH29t3bVwx4e3x/7Jd2zzGf7Xd47Xe/+33Zz0+KsudkrrXGmmuuMaff9Ywxmb9nc0n03SyQMPYTC0BkO37FgM6rr766asPqVqxYUZSZ/4s7t2TsZnUs+IMF85w6daoor127tmqzdOnSosz6ic0B8fmyQNimwVrxPViwYEGj8wD6BVEIIYQQQgS0QBRCCCGEEAVaIAohhBBCiIK2GkQzux/A7QAOu/vPt+oGAXwZwAYALwG4091PJM5VfVePmgym0YjHZDR6rI6dm9FEN5fVpEQ9AtMnZK6fSUDNrs/6LpPMNWrkMv2dtSmTuJTpMeK52L0xLU/UmLLErawuajuY3iTeS9TNAFwnE/suc79APe4yzy5zXjFz6aTPj2Q0h02J587q/TK+tN21gJxmrOnmBRl/kNVqtrsWUM9LTM+ZSea/d+/eqs3w8HBVt2rVqqK8evXqqk07PetEdTGhN2uzfv36qu7o0aNFmfntdevWFeXdu3enbMpoTBmZObaT71jmTA8AuC3U3QfgMXffBOCxVlkIIUT/8wDk84WY8bRdILr7EwCOh+o7ADzY+vtBAB/usF1CCCGmAfl8IQTQXIO42t3f/J34IID69+AWZnavmW03s+0sDF8IIUTPk/L54/09k88IIfqHSX+s9rEP7BMKHdx9m7tvdfetUfslhBCiv7iUzx/v7yeTf00IMf00TZR9yMyG3H3YzIYAHG5qQBRUZoS+WXFqFP+yNiy5c2zHAg3iv44zAQRALZJuF0DwJlHUmhE7ZwJSGJm+zCbxjOdi95sJrMgIb5kAnR0X+5JNZEyQHO+F/UKSEbw3TcydGfcZMXs2WEuIcXTM518ubNxnxPqZJNFA/T6wdzi+V6wNe68jzG5mUwyAyyTOZ/fLYAEnkdjn7H5ZkF60kyWlZv7n+PFS0RADRABgzZo1RXn58uVVm5i4Gqh9Obs++/HqyiuvLMqZeYrZxM4dxwqbpzL+PhMcy55Tlqa/ID4C4J7W3/cAeLixBUIIIXod+XwhZhhtF4hm9hCA7wJ4m5ntM7OPAPg0gA+a2QsAfqlVFkII0efI5wshgMQnZne/e4L/9IEO2yKEEGKakc8XQgDaSUUIIYQQQgSaBql0jEyQSib4golhm+5IEoMP2PViGxZ8wbKlx6zuzEYmdo7CYmZTE7E1UD+DjNiZnYfZndmBJRNckjmOCX0zfZARbTMbMv2U3WEn2slsYvfXZMeZJjsFCdGEOK7jGM4EzbExzYT/mcA25g8yu5bEOnbuzPvJ3r3MriyZwLLMvWVpugvZmTNnLlkG6jkQqHdOYW3iHL9y5cqqzdDQUFWXiaZndsbgIRZMFOeAZcuWVW1YkGt8VpkAJ6AOuGHvz6lTp9q2yaJfEIUQQgghRIEWiEIIIYQQokALRCGEEEIIUdBVDaK7V9/Do/4ikxCZ6Soy2jqmE2S6jdgukzianYfpNuJxzCambYv3l7Epo21hdrL+jZo41iaTmDur3Yl1GY1GRjsE1M8qqxOMdcymeK6svjGS0ZgCufel3bkno1ERYiLcva22lr0fcTwyDdmiRYuquozemb0v8Tj2Xsf7YOdmc0BGY8/6IKM5j3ay85w8eTJlZzuym1PEfmFtmE0xKfXNN99ctYk6wcHBwaoNS5Qdxw/TBDLiJhoZPSfTKbI+iHpKtjEDIz47pp2M43kyGyPoF0QhhBBCCFGgBaIQQgghhCjQAlEIIYQQQhRogSiEEEIIIQq6nig7Cj1ZkEaEJZzOEMWhTCDMyAj/o6g0kyQVqEWlUQgLcBFxJkAiYzcTrGaSwMbrM0E0u5f47NizzIjJmwrHGfFcWRFvvGcmiM6MMSaSPn/+fFFmid9ZH2SSYGcCnIToNO7eNnCO+bpMQFomsC37XjcJPssGAGYS4LPkynEjBtZPsY61yQTJZeabTFAkUPdBpk8AYHh4uCi/8MILVZvNmzcXZTaXsDko+sToawHg3LlzVV1mI4aYlDpzfaB+LmyssnPFuYMFqWQCwbLoF0QhhBBCCFGgBaIQQgghhCjQAlEIIYQQQhS0XSCa2f1mdtjMnh5X9ydmtt/Mdrb+96GpNVMIIUQ3kM8XQgC5IJUHAPxvAH8f6j/r7v/rci/YbneTTLBHVvga2zEhKBO6ZgIkooCUiUUz2faZYJYJgqPQlNkURcPZnUUyZJ5BRhTOgi8yQROZoBjW36yfYl12F5xYx9pEO9m9sSCVjJidvRuZZx7bZHY7EDOaB9ABn29mbX1CJtgjE5wF1OM8G8gX69i5m/rNTOBMZg5asWJF1ebYsWNF+cSJE1WbTB9k7pcFpGSCArM7Z8UdSL73ve9Vbfbu3VuUN2zYULVZv359Vbdw4cKinPV3cbeeGJAC1PN3ZjcdoB4H7Di2pog2sF2G4tzRZOecN2n7C6K7PwHgeOMrCCGE6Bvk84UQwOQ0iB81sx+3Pkcsn6iRmd1rZtvNbHsM3RdCCNE3tPX58vdCvHVoukD8WwAbAWwBMAzgLydq6O7b3H2ru29lP4cKIYToeVI+X/5eiLcOjRJlu/uhxN+BFAAAIABJREFUN/82s88D+HryuCoBaNQxsOSQUUeRTS4dNQRMC8C0XlETwhxdTKzJtIRMo5FJuJo5LqMtYUnImQYmJv1m1491rN9YXUYHmkkkyjRA8TnF+wBySW/ZeMqMQ6YdyiQGz+hOm2qemN3triVEO5r4fDOr3pk49jLJ/bMaqkyCbeZrMu9ebMN8a9OExEzfd/LkyaK8ePHitudh73XGzoxumfVtZp7I6PCB2k8y/d2ePXuK8sGDB6s2u3fvruquueaaohzXBQBw/HitqLj22muLctRJAvVzymrzM+sepnmMmzOwOS+uTVhfZmn0C6KZDY0r/jqApydqK4QQor+Rzxdi5tH2n1xm9hCAWwCsNLN9AD4F4BYz2wLAAbwE4Hen0EYhhBBdQj5fCAEkFojufjep/sIU2CKEEGKakc8XQgDaSUUIIYQQQgQaBak0ZXR0tBJeZpI6xjom6GRC3yjQZcEmLIggI1qO4mp2HmZnFKwyYTPrg5hgOpNwmgnAWR8MDAwUZSbGjcLXTJJoRjZxaoQ930wy10yy8JGRkaoNE1fHPmcC4dgvTBDNglTa2QjwfopjhQmS47mi3QpaEVOBu7cdW5mAhWzi6sw4zgR/ZdpkgwszxzG/Ff00S5Qd/XQMmJjo3JmNIOK9MF/H/FgmoCgzB7D5NPpWFoDz8ssvV3VxzbFq1aqqDZu7Yn+yvoznZjax42IdWyuwPohjkz3zOFezjRmy6BdEIYQQQghRoAWiEEIIIYQo0AJRCCGEEEIUaIEohBBCCCEKuhqkYmaVGDSKU9n+nSyjeIYo1ly2bFnVJhPYwMTHUYya2f0EqAXBTBCd2d2FCYujaJhdnxHF3UzsHW3K7tISgzRYcA17BjHYgtkUBdFZ4Xg8jp2biYYz/RSfU1MxOzs366fYjgnHow3sGQgxFcQxG8diJvAqu/tS9KXs3MzfZnxyvF4nA2dYMELcOYXNgadPny7K2QCYTMBjZnetzFaKmd1tgLqfMs88swMNUAdhskCazC5gmSBMdm62pom2s+OYL8+shVi/NEW/IAohhBBCiAItEIUQQgghRIEWiEIIIYQQomDaNYhRy3bixInquJgQlOkhMsk3o2YD4PqPTNLOjE6REXWRTMuXSRydSZSd7aeo92BamqbPIJ4720/xXEzLE8/FdIPsXmKfM7vZ9VgC8XYwbQmzM8K0O2wcxj5gupWsFlWITnLx4sUqSW8mmX+nklmzcZ9JnM/I+GR2nvj+s4TTjIzfzLzXzKZoAztP9HXZ59SpzQsyGwVk56B4v5nnDdS2L1y4sGoTE5gfO3asasN8cuZ+M/e3fv36qs3y5cuLMltj/PM//3NVx9AviEIIIYQQokALRCGEEEIIUaAFohBCCCGEKGi7QDSzdWb2uJk9a2bPmNnHWvWDZvaomb3Q+v/l7c4lhBCit5HPF0IAuSCVCwA+4e47zGwxgCfN7FEA/wPAY+7+aTO7D8B9AP7npU7ERMtRCMoEnTHRZFYwG8WZMUAE4ALdWJdJ9snaZBIwsyAGRgxKyYh4GSxZeEwkyvopPjdGJultNiAkk4g8jh0miGbHxTo2BpomUM+Iu9kzj3UsIIaJjSPs/YnBLdlAITFj6ZjPj+9WJtl800S/GV/DiO81ez9jm0xwI2uXCaYE6kTZzEcxGyJsXmr3TIB6nmLXYgE38dzsWWaC9DKJ0Jv6sey4iPcSgz+Aej5l8ytLlJ0JKGXPJSYn37JlS9Umzh0//vGPqzZZ2vaUuw+7+47W36cBPAdgDYA7ADzYavYggA83tkIIIURPIJ8vhAAuU4NoZhsA3Ajg+wBWu/tw6z8dBLB6gmPuNbPtZrad/bohhBCiN7lcny9/L8Rbh/QC0cwGAHwVwMfdvdgQ0Md+C6WbTrr7Nnff6u5bszmghBBCTC9NfL78vRBvHVKJss1sLsYcxRfd/Wut6kNmNuTuw2Y2BOBwu/NcuHABx48fL+rid/7MRtpNk6symEYifvvPaNuy2rpYx7RmGU1eZhP4bLLlqDkcGRmp2kQ72bmZ1iI+l2zy2ow2M9rAjslofrIJVyPs2WWOy2gJ2XnOnDlT1cX+ZDYxXYwQl6ITPt/Mqnc0oyOLbZivY+9HPFdTLWOcb4Ccbo/ZFLXjzEcxfxA3dWAbI0SbmN3s3Jl+iRrIjRs3Vm1uuOGGqi7OL4cP10Nk586dVV28X2b3VPp71nfxeqxNPDd7TkuWLKnq4v1lx8XSpUuLMruXgwcPFmU2b2TJRDEbgC8AeM7dPzPuPz0C4J7W3/cAeLixFUIIIXoC+XwhBJD7BfE9AH4bwE/M7M2l/ycBfBrAV8zsIwD2ArhzakwUQgjRReTzhRDtF4ju/m8AJvp++4HOmiOEEGI6kc8XQgDaSUUIIYQQQgRSQSqdwt2rJJlRaJsJYmBCUCbyjCLTbFLWTKBBFIeyczMBKbu/CBOnZhNqt7sWu7f4TDIJSFmwC7MxBsBkg4nic2F2s6CYCAumiTZkg54yNmUE9pmkqOzZsQSz0QY2nuNzyYxdITpBuwCBjK9lbTKJ7DO+NntcJpl2JnE181kZv8kCDTJJuBmxHbN7cHCwKN92221Vm/e9731VXey7/fv3V22OHDlS1cV2LEVS9H9sDMRE0kD97JoGtLL+jffL5u7MHJDd+CKei/VvHCssuCaLfkEUQgghhBAFWiAKIYQQQogCLRCFEEIIIUSBFohCCCGEEKKgq0EqjCi6zOw+wgSdrC4KSFkbdr0YBMMCHaKIOJP5HagFo0yMy+qiYDUTtMIEu0yQHAWzixYtqtrEfrr66qurNixrfjyO2c2eSybAKBNMwwTn8Rlk+gSoBeYZUXh2N4fYBxnBO4P1b7yXJgFPQjQhjr34PrLt+DL+nr2fmZ1UMrtiZcgELAC54JaMj2J2t9ulZiIygaFxDrjmmmuqNixYND47NpfEXVqAehywuTPOi3H3FSC3Cw573iwAMF4vE0jSdJ7KBFMC9fhha4U478e1yuWgXxCFEEIIIUSBFohCCCGEEKJAC0QhhBBCCFHQVQ2imVXaqvjtnyUSjd/5M9/mGUyfsHDhwqpuxYoVRZnpZE6cOFGUmQaRaS0iLAEq05vE87P7jXXs3lhd1CgwTUp8bgMDA1WbTDLXbELQjG4kk3SXafmiLobdSybhKdN4RphGhBE1KEuWLGl7faB+5sePH6/axH7JJtQVYjKYGdVWjYe9n/G9ymrH49zBtFfsuPg+sPc6+s1scv2TJ08WZTYHZc7F5qDYd8xvs/6P7VifRL/12muvVW0yejv2DNg8Ef1t5pkvX768arNq1aqqLmoVsxtYZHSREdYmo0NvuokHGxex77I6eIZmCiGEEEIIUaAFohBCCCGEKNACUQghhBBCFGiBKIQQQgghCoyJI6fsYmZHAOwFsBLA0a5duHPI7u7Sr3YD/WX7enev1d1CTIJx/h7or/dhPLK7u8ju7pDy+V1dIP7nRc22u/vWrl94ksju7tKvdgP9bbsQnaZf3wfZ3V1kd2+hT8xCCCGEEKJAC0QhhBBCCFEwXQvEbdN03ckiu7tLv9oN9LftQnSafn0fZHd3kd09xLRoEIUQQgghRO+iT8xCCCGEEKJAC0QhhBBCCFHQ9QWimd1mZrvMbLeZ3dft62cxs/vN7LCZPT2ubtDMHjWzF1r/X+8WPs2Y2Toze9zMnjWzZ8zsY636nrbdzOab2Q/M7Ectu/+0VX+tmX2/NV6+bGZXtDvXdGBms83sKTP7eqvcF3YLMZX0i78H+tPny99PDzPF33d1gWhmswH8DYBfAbAZwN1mtrmbNlwGDwC4LdTdB+Axd98E4LFWude4AOAT7r4ZwM0Afq/Vx71u+2sAbnX3dwHYAuA2M7sZwF8A+Ky7Xw/gBICPTKONl+JjAJ4bV+4Xu4WYEvrM3wP96fPl76eHGeHvu/0L4k0Adrv7i+7+OoAvAbijyzakcPcnABwP1XcAeLD194MAPtxVoxK4+7C772j9fRpjg3gNetx2H+NMqzi39T8HcCuAf2jV95zdAGBmawH8KoC/a5UNfWC3EFNM3/h7oD99vvx995lJ/r7bC8Q1AF4ZV97XqusXVrv7cOvvgwBWT6cx7TCzDQBuBPB99IHtrZ/tdwI4DOBRAHsAjLj7hVaTXh0vnwPwhwAutsor0B92CzGV9Lu/B/rAb76J/H3XmDH+XkEqDfGx/EA9myPIzAYAfBXAx9391Pj/1qu2u/uou28BsBZjvz68fZpNaouZ3Q7gsLs/Od22CCGmjl71m4D8fbeYaf5+Tpevtx/AunHlta26fuGQmQ25+7CZDWHsXz49h5nNxZiz+KK7f61V3Re2A4C7j5jZ4wDeDWCZmc1p/eusF8fLewD8mpl9CMB8AEsA/BV6324hppp+9/dAH/hN+fuuMqP8fbd/QfwhgE2tiJ8rANwF4JEu2zAZHgFwT+vvewA8PI22UFp6iC8AeM7dPzPuP/W07Wa2ysyWtf5eAOCDGNPTPA7gN1rNes5ud/8jd1/r7hswNp6/7e6/iR63W4gu0O/+Huh9vyl/30Vmmr/v+k4qrZX35wDMBnC/u/95Vw1IYmYPAbgFwEoAhwB8CsA/AfgKgGsA7AVwp7tHUfO0YmbvBfAdAD/Bf2kkPokxXUrP2m5mv4Axce9sjP3D5Svu/mdmdh3GxO2DAJ4C8Fvu/tr0WToxZnYLgN9399v7yW4hpop+8fdAf/p8+fvpYyb4e221J4QQQgghChSkIoQQQgghCrRAFEIIIYQQBVogCiGEEEKIAi0QhRBCCCFEgRaIQgghhBCiYFILRDO7zcx2mdluM+u1zcCFEEJ0EPl8IWYOjdPcmNlsAM9jLMHlPowlRb3b3Z+d6JiBgQEfHBxsd96qbvbs2UV51qx6XfvGG2+0rbtw4ULVhl0v1rE2Tc4z1cexfolknnfmGTQdN6OjoymbLl68WJSZTXPnzr1kOWtDvFYWZlO8l+xziv3LjmN2xjHO2ixcuLAoz5s3ryi//PLLOHbsWPtBJ2Y0l+vzFyxY4IsXL+7EdVN1kayPuuKKK4rynDn1BmOd8nfZ88R27L1ualPmuOijsj4y+jEGmwPi3MzaZOaE+fPntz2O3X/GJ2fujZ0ns15hbTJzR8YmxosvvnjU3Ve1azeZrfZuArDb3V8EADP7EoA7AEy4QBwcHMQf/MEfXPKkbJJfunRpUV60aFHV5sCBA1Xd8PBwUT5y5EjVhjmDOIGyNvHhMbvZw4vHsXNHhwXUA4NdL9rNYC96fBnZSxYdPVtsZ168kZGRqs1rr9X5RM+fP1+U2f0ODQ0V5SuvvLJqw16yM2fOFOVTp05VbTIvOmsT+5c9XzZ+Yx17BmfPnq3qDh48WJRfffXVqs2NN95YlK+77rqifMstt1THCEG4LJ+/ePFi3HnnnUVdZqGRec+Yj4zvOltkMNasWVOUmR+J/i7zj1oAOH36dFFmP2QwO2O7119/vWoTfWT2H6Px3Oxe4lzCrs/mgBUrVlR1kRMnTlR1x44dK8qx34DaTzMf+ba3va2qi+didsd/RAP1uiOWgXqOZzYNDAxUdfv27SvKbE5gc15sx/4BFp8ne7533XXX3qqSMJlPzGsAvDKuvK9VV2Bm95rZdjPbHidmIYQQfUNbnz/e37N/rAgh+ocpD1Jx923uvtXdt7KVtBBCiLcG4/39ggULptscIcQkmMwn5v0A1o0rr23VTci5c+ewY8eOoi7+LM5+ao0/o65aVX8637+/vnT8nMk+ZbKfcWM79uk2Or+sRiN+cmSfBdgnh/gT//Hj9baa0e7sL7bxZ3L2s3X8pMPuN6OjYD/vM+JnEPapNmM3+0dJvBf2fDPPk0kIMp+12L00+XzNrsc+J0Q7M5/LhCBcls83s2rsxbGfHecR5keiL2fv9blz56q6+Lnv5MmTVZumOsX4frJPzIzMcdGmJUuWVG3Y/BLnkqNHj1Zt4ufcjF8BgF27dl3yPOz6rI71b6zbsGFD1YZ94o3PnM35rC6OQzbm4v29853vrNqwcfjoo48WZfbsrr766qou3gtb08TrZWITJmIyvyD+EMAmM7vWzK4AcBeARyZxPiGEEL2LfL4QM4jGvyC6+wUz+yiA/wtgNoD73f2ZjlkmhBCiZ5DPF2JmMZlPzHD3bwD4RodsEUII0cPI5wsxc5jUAvFyMbPqW3/8zs9SecQwdZauhuntopaE6TiY1iHCtBZRg8g0DEwPEdsx3RwTd0ddwaFDh6o2MWqQ2cS0JFHHwFIMMN1IhPVlRnOY0SGxtBbx3OvXr6/aMP1H1PcwG5mWMKPDbKrni3ayc2fsZHbH/pXmUHSD0dFR6kvGk8njxvwYy6e7bNmyosz07GyeiGlXmG6ZpUHJnDu+1+wdZu96pl+ibu2qq66q2jANYtTms2cU61gbpn+L8wTrt0wqnIyWm2n02Bwf+ymjZwdyeXWjL2Ua1927d1d1MUVRHLsAH0/xOTC9bLyXrO6Voa32hBBCCCFEgRaIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo6GqQyuzZs7F8+fKiLgromYg3Cl+zm7dHAWcUhrLrA7WwmIlxo/Aza1MU37LrZ/ZnZsdFUTYTcrO9kGOyT/YMok1MwJu532w/ReE227Yr2sSE1HG/ZqAW8bJ7yWwWn0nomxWgZ5JXs7rYd5lN5zPJ2oWYLGbWNgE8ez/i+GRBDStXrqzq4jhn7zB71+Pey8z/xsDB7N7EMfiAXT/uQwzUAScsmXXsO2YT20BieHj4kmWgnt9YsAl7BjHokgVqZgIlMwGlq1evbnseoH4u7NwsODY+q8xxP/jBD6o2zL9mAnWyScYjca2Q3ZyCoV8QhRBCCCFEgRaIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo6GqQypw5c9qKM5mgMtYxYTPbfSS2Y6JlJiDNZEePguhssEm8F9aGCXvjLiFMNByDNJhNrC7eX8ZutrNJZkeUrE1RbMyEzfEZsCAkRsysnwlwAup+ygSpMLvZGI99wATR2R1f2p1bQSmiG2R2zmJBG3GOYLtNsfcz+kTmx9h7ldlpKNqdCeQDap/EgigYMRiBvecxuITt2HH8+PGqLvrNNWvWVG1iwGP0mQCfc2NfMp/M+iD2HevLGOAag4sAfr/RBjbfsHEYdzJh64D4nNi9sfEUr8eeb6aOBTzGgM7MPDUR+gVRCCGEEEIUaIEohBBCCCEKtEAUQgghhBAFk9IgmtlLAE4DGAVwwd23Xqr9woULsWXLlqIufp/PfC9n+g+mD4jf4pmOImotgFqncuDAgapN1DXE5NoTEW1iiS+ZvjBqEGMCVgA4ceJEUT5y5EjKpqi1YDYxzWGEaduYnjLCNBoZov4ik9AcqPuXaWmYliWOO6Zliddj2hZWF8loC4GcNqpJgm8hGJfr86NPiO8se/ejz2DvJ9NeRd1aJiE9UPtkdr34zjJ/yPxmnJeirwWAVatWVXXxXlgy7Yy27uqrr67qYoLppUuXVm3ifJbRpQP182bHsXki9i8bF3H+ZvEMbLOE6O/Ys2P+PvrgzDzFxkDG32Y2i2A2sHmCzXlN6USQyn9z9zrNuxBCiLci8vlCzAD0iVkIIYQQQhRMdoHoAL5lZk+a2b2sgZnda2bbzWw7S00ghBCib7ikzx/v79n+6UKI/mGyn5jf6+77zexKAI+a2U/d/YnxDdx9G4BtAHDDDTc0E5sJIYToBS7p88f7+yuvvFL+Xog+ZlILRHff3/r/w2b2jwBuAvDERO3nzJmDwcHBoi4KXZnwlYmNI5mE1yz4gxHFoVHUC9RCUGYjExbv3bu3KDPRcia56MaNG6s2MeEpE8wyIXNMQJpJFMuEsExUy55nhNl59uzZoswCUOIzz9jNYM+OBTTF58ICo6IAmp2b2RnvhfUb66d25wHaJwJuGiQkZh6X4/PNrAp2yIzzo0dLeWM2aC6ei41r9qtm9BHR9wB1sAULDmDBZ/H9v+6666o2t956a1X30ksvFWUWcBh98MGDB6s2zB/EgEcWqBmPY8+AzQHRT7M2LJAknp9d75prrinKLFH2yy+/3PZ67NysLhNAG9swf88CWGMgFJtL2NogM+dFGzKBohPR+BOzmS0ys8Vv/g3gvwN4urElQgghehb5fCFmFpP5BXE1gH9srWjnAPg/7v7NjlglhBCi15DPF2IG0XiB6O4vAnhXB20RQgjRo8jnCzGz6EQexDSjo6PVJuvxGzrTAsRv+ExHl9EpMj0EOy6enyVOjVpKppF75ZVXqrqMju3UqVNVXdRPMl1D1GREbSHA9R8x6Ta734xGhNWxRKkRpu2I/cS0Q1EHxPqERc5HHUc2SWlmjGX0fezccfwwbRYb99H2zEb0sQ27fyEmy8WLF6l/Gw/T8sXxyM7BxnlGo8uul3mv4/XY9a+66qqqbuXKlUX5537u56o2UZcOAD/84Q/bthkeHi7KTLO2adOmqi7qq9n9R7/JdPEjIyNVXZxf2HyTmb9Z8u4NGzYUZaZDZf4+9kvmeQO5OS+OVWY3u99Mwms2T8RzZTZimEzibOVBFEIIIYQQBVogCiGEEEKIAi0QhRBCCCFEgRaIQgghhBCioKtBKmfPnsWOHTuKOiayjMRABxb4wAIrYpAGO44JezMC0hUrVhRlJo5l4uoY3MIErIzYjgXFHD9+vCgPDAxUbVgy1Xi/V199ddUmCl3ZvbGEnPG4TMJrVseCPeLYYaLe/fv3V3X79u0rytlEvHH8ZJJZs4CjEydOVHWLFy9ue30mNo42ZAJZYj8pUbaYCty9et/jWGPvfiYpNQs0iOM6m7g/nj8TWMfaxHcYqH3Uv/zLv1RtnnvuuaouBjiyIL0YlMc2dFi7dm1VlwkAjD6KBalkgihiACTA/U1mQ4U4n7EgGbY5RSZ4iT3PeByb82KbzNgBeH9GWP/Gscren9iX7P3Jol8QhRBCCCFEgRaIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo6GqQyrlz5/Dkk08WdVHU2iSrPVBnWQeAW2+9tSiz4Au228nRo0eLMgsqiAEhTMDKxL8xSIUFkpw9e7aqi2JYtmtIhLVhwQ+HDh0qyk131sgIwJk4NxMkkRExsyz2LHAmBo5kA2fiuGP9G59vLAO8D6KdLLglG9AkRC8wa9asageoKLzPBKgxMjtgMb/C3qHMcTHYZM2aNVWbLVu2VHW7du0qyv/xH/9RtWHBFtFP33DDDVWbzZs3F+Xrrruu7XmAencT5lujP2LPJBM0xwIt2FwZfWIcNwy2uwwLFs2sKdiOL3FeYPcS5+VM0CuDBfOw+SXawIJ8o93s3rJoxhFCCCGEEAVaIAohhBBCiAItEIUQQgghREFbDaKZ3Q/gdgCH3f3nW3WDAL4MYAOAlwDc6e61UI8Qv6HHb/gsqWM8hulIMto2pg9gx0VtBdNoRA0i0xksWbKkqouwe2Fah1jHdBVRz7ho0aKqDdONHD58uK2d8XpMH8GeXexflrSU6Vui7VdeeWXVJupk2PNlNsWEstmk1FEbmtGtsGfA7iVqSVhCc2ZnZlxIuyguh075fHdvqx3OJHZnsHEe34+Mtg6ofQTThUd984033li1eec731nVxSTYTCf99re/vaqL+sJ3vOMdVZuog2Q+ec+ePVVd9MlMgx3186wN87fRb2bmBKAeByzpd7SJ3Ruzs52NE9mZ0ZxH/87Ow5Jnxzq2Vsj4e7ZByPDwcFFmGtcsmZnjAQC3hbr7ADzm7psAPNYqCyGE6H8egHy+EDOetgtEd38CwPFQfQeAB1t/Pwjgwx22SwghxDQgny+EAJprEFe7+5u/Yx4EUP8e3MLM7jWz7Wa2PfPzrxBCiJ4j5fPH+/vMfrNCiN5l0uIkHxPfTZjox923uftWd9+ayd0nhBCid7mUzx/v75neTwjRPzRNlH3IzIbcfdjMhgC0j3LAWNLgu+66q6iLQuJMsAkLCFm8eHFVF8+1c+fOqg0TO69cufKSZXZcTK4N5IIDWFJsJliN52LnjmJUljT0xRdfrOpiomzWl1FUywJpmLicJQuPsASvUbi9atWqqk0UG3/3u9+t2rBgj02bNhXltWvXVm1Y/8aglAMHDlRtMonBWVLUjPiY9Xl8FzKJh+PYbZoYXcwoGvn8OD7j+8F8eRyPLBkw823xC1UmOAAA1q9ff0kbgTq4hG3MwBbEQ0NDRfl3fud3qjYsCXZMsM8SR8c55/nnn6/avPzyy1Vd7Dvmo2NfsnkqE6TCAkLYM4iBQSyY8Vvf+lZRZhtYsOvFc7E5nz27+MwzfpsFLrLx2zSpexybmaDeyfyS3/QXxEcA3NP6+x4ADze2QAghRK8jny/EDKPtAtHMHgLwXQBvM7N9ZvYRAJ8G8EEzewHAL7XKQggh+hz5fCEEkPjE7O53T/CfPtBhW4QQQkwz8vlCCEA7qQghhBBCiEDTIJVGzJs3D9dff31RFwXJWVFrps2pU6eKMhOCsqz5MYv78uXLqzZReMqEvmyHkigsZql/WLb9TAR4DMjYsWNH1YYJma+66qqizHb6OHbsWFFmImJmYxQEM8Euu98YKMOyysfnycYAE+hGkTYbc8ymzI4+UTTM7peNw2gDu34mI74CTkSvMDo6Wo3ZOD7ZuxDr2LvPfM2KFSuKMgsuvPbaa6u6aGPciQKod0m55pprqjaM973vfUWZ7azE6s6dO1eUf/rTn1ZtfvaznxVlFijJ5qXMjlDxOLYrTSZQkT0n5hPjfLJv376qTWZcsOC+uOZgz47NefH8cfc0AHjllVeKMuunjE9mwSbsuGh7XOMwJjMn6BdEIYQQQghRoAWiEEIIIYQo0AJRCCGEEEIUdFWDODo6Wn0zz+gL4/d5luiS6cGitoMlG2Wahag9YN/wo9aA6SGuvvrqqi4m5GQaEXZ/UQfJNGpRW7Jnz56qDUv2efPNNxcspvcwAAAgAElEQVTlmKQaAH784x8XZaZBZDZlEmWzhKtRK8meXWanhkwCdZYEll0vjsOMLpLB2kRdDhsDmSTYTPPD7BRiqhkdHa3erYy/j+8H03UxH5XRUjN/ELXaTKMWk1mzd4rZGfXsTGvG/E/UHO7evbtqE7WTzI9GLSNrx3x5tJPpDRnRHzGfxa4X+44dF31y1JwCfEOFNWvWFGU2Lt7xjndUdfGeH3/88apNTNbNxgDz99G/s+NYfMKNN97Y9tyxD9i5H3300aqOoV8QhRBCCCFEgRaIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo6GqQysjICB555JGiLgaSsGCTGEjCghNYwusoiGaBB0wMG21gwRdMIJs5d0yIzJKUvvzyy1VdTPq6YcOGqk3sy5tuuqlqs379+qru/e9/f1EeHBys2sQ+YQmoWYBEFESzoBWWODWKpJmwOdYx4TgTJEeBMBNgR/ExO46Nw3guNgYyibKZTSxwJZOIPNqQFZwL0WniWGTjNY5P1oYFFy5ZsqQob9mypWrzzW9+s6qL73FMrAzUgQ7sHWZzV/RRzN/v3Lmzqtu7d29RZoEs0QezgBQWoMD6M8ICJCIZH8XasL6L44L5ughLXM2Shf/kJz8pyiyQhfVd7N9du3ZVbeLzZOsCNlfGfmFzCZs74rz07ne/u2oTA1rZvWXRL4hCCCGEEKJAC0QhhBBCCFGgBaIQQgghhChou0A0s/vN7LCZPT2u7k/MbL+Z7Wz970NTa6YQQohuIJ8vhAByQSoPAPjfAP4+1H/W3f/X5Vzs1VdfxdNPP13UxUASJmqNolom3mSi1hjowISg7Lgo7GW7j2zevLkoRxEzwANnomg5BicAPNgitmOBLFH8y86zcePGqi72J8v2zwJXIlEkDtT3y8TPTEgd75cJwONYYQLluHMPUAfTMJtYUEwcPywoJ/YlC8ph9xKfHRM2s+cSbWfnzuxuIMQ4HkAHfP4VV1xR+cU4rlnARKxjInsWoPCBD3ygKLNgCPY+xh1Y3va2t1Vtoi/NBFoAwJEjR4ryM888U7U5ePBgVRfPz+aSaBOby06ePFnVxWfA5tzo25ivY8fFoA3Whs150d+yHXeiL2fPkp07Phc2J7Ddc+I9swCjaCfbPY3tcBPrmG9nAaVxbLK5MwZmsvNkafsLors/AaB+G4UQQrzlkM8XQgCT0yB+1Mx+3PocsXyiRmZ2r5ltN7Pt7FcZIYQQfUFbnz/e37NfwYUQ/UPTBeLfAtgIYAuAYQB/OVFDd9/m7lvdfSv75CmEEKLnSfn88f6eSXqEEP1Do0TZ7n7ozb/N7PMAvp45bvbs2Vi8eHFRFzUK7Ft8Jmkm04TExNFM18C+4UcbmE0skXLkF3/xF6u6qOVjGg1mU9RRMG1J1DVkk6TG41h/x75jGiCWiDzqNzNJooFaS5fR/LDrHzp0qKprNwYBrguKdjJdUOzfqEHKnptNrkwHFH+lySTTlgZRXC5NfL67U985Hvbux6TQTIPIkh0PDQ0V5T179lRtWILt6Guif2AwuxlRl830jUy/HvuAJWmOvo35SKYdj9dj93Ls2LGiPDIyUrVhc1B8VuyrIXue8cejjJaaacczibnZL9tMlxg3o2Aa+zh37tu3r+31mZ1sXmZ9EG1gCdTjGM8kPZ+IRr8gmtn4N/HXATw9UVshhBD9jXy+EDOPtr8gmtlDAG4BsNLM9gH4FIBbzGwLAAfwEoDfnUIbhRBCdAn5fCEEkFgguvvdpPoLU2CLEEKIaUY+XwgBaCcVIYQQQggRaBSk0pR58+ZViZqjQJYlv4wCViZ8ZnUxSIMJWBlRHMqCH+K52LmZyDRz7nbCbqAOwAFqQTCziUWSZxJsRzszImKgfp6sDbMzBlZkgpeY2JqJhqMInYnEY/JcADh8+HBRZgLsGLgSk5YCPOHqz372s0ueBwBWr15d1WVSiWSS0ArRaS5evEiTBI+HBdLFAC3ma6677rqqbunSpUX5+eefr9rs2LGjqovBLZl5gvmxjE9kfmX37t1V3YEDB4oye2e3bNlSlDPBJkAdvBOvBdSbDrDnxHxPpu8yGwWwYLtMFhQ2T8RnwIJkmE+OwR5szMWNP9gGFszuOMZZkAobF9/5zneK8k033VS1iXMuC2TJol8QhRBCCCFEgRaIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo6KpaffHixXj/+99f1EVhLRP/skzkESaijXVMwJrJiM8EpNFOJqrNHJfNyB9hfRLvN7u1Ieu7SLw/dm9MSB2PY9difRfbsXER+449X7aTSgwIYUJ6tlNDDPbI7AjAglTYTipRuM12UsnsEsAE4LF/s8FaQkyGixcvVrs4xPHJ3us4XllA3vr166u6eG7mW5cvr7eQzuycEu3M7OwE1AECTz31VNWGBXtE/8N2MvnRj35UlKNfA/hOTvF6zCdHX57d4Sw+A9aG9VOsY/4v1rEAT/Zcou2sv1n/xmcXg5mA2pez/ma7rsV5Ihv0FHfTYsfFgBtmUxb9giiEEEIIIQq0QBRCCCGEEAVaIAohhBBCiIKuahBnz55daUCiPoDpo6LeLZNIGqi/4TONHNPSRd1EJrEw0z4w/UW8v6yWJUPUJ7DrZ45j/ZtJZNpUP5rRxEUtE7seuz57dlFz+Morr1RtmH4p1p06dapqc/z48aLM7N6/f39VNzg4WJTXrl1btWHamXjP2QTmQnSDdto9Njaj1jf6J6BOYgzUfoQlpWZ+K75X7N2PdrJ5gx33zDPPFGWmP2Y8++yzRfmFF16o2pw8ebIoZ7TkQN2fTO8X/QjzK4zobzOaaKDuz4x+nmnHM3pKNnaYnjz6abaJRxxjrA2bX+L1MnpOoNZF7ty5s2qzZMmSojw8PFy1yaJfEIUQQgghRIEWiEIIIYQQokALRCGEEEIIUdB2gWhm68zscTN71syeMbOPteoHzexRM3uh9f91gikhhBB9hXy+EALIBalcAPAJd99hZosBPGlmjwL4HwAec/dPm9l9AO4D8D8vdaLR0dFKWJtJnBqF/kzAygId4nFMjJsJLskIdLOJLmM7FhDCAisyiVqj0JddPxM0wsgkZM5cLxswEY9j/RTrWBsmSI7nZsEmUSjPYAEocXyvXLmyanP99ddXdZnAKDbGMwE+TZ+5mLF0xOfPmjWrbYAhG+cZX9M0wfaBAwequhikwoLBMu8ZC0D513/916LMgigyQROZBN/ZTQjivbC+jG2yQYmRTMAjOxfr7xgExMYFCx7K+D8WXBKDYNicENuwOSGzgQRrw9Yr0QY2duI4fPLJJ6s2WdqufNx92N13tP4+DeA5AGsA3AHgwVazBwF8uLEVQgghegL5fCEEcJkaRDPbAOBGAN8HsNrd34yfPghg9QTH3Gtm281se/x1RQghRO9yuT5/vL9nv6YIIfqH9ALRzAYAfBXAx929+CbnY7/h0t9x3X2bu291961Lly6dlLFCCCG6QxOfP97fs/yFQoj+IZUo28zmYsxRfNHdv9aqPmRmQ+4+bGZDAA63O8/Fixerb/1RN8F0FFGPwL67M61DPFdTfUJGj8bOw7SS0U6mfWCONZMsPOo4WD+xe4nHNd2Enf1iEG3IJtOO7VgS2jgu2PWZliVqd5jmiNVFG5hNUffEnuWqVauqujhW2fONm7Cz45roe6RRFJFO+Py5c+diaGioqIs+kY3pOB7ZO5TxmyzZ/IkTJ6q6+H4wHxn9HUvS/Nhjj1V1u3fvLspr1qyp2mTeWeZv43EZ7Tojo8Nn52E+MptQu50NzP9ldKCM2C/sXhYuXFjVLVu2rCivWLGiahPngCNHjlRtYnJrABgYGCjKWe1iHPfsq+yxY8eKMhvzWTJRzAbgCwCec/fPjPtPjwC4p/X3PQAebmyFEEKInkA+XwgB5H5BfA+A3wbwEzN7c1+XTwL4NICvmNlHAOwFcOfUmCiEEKKLyOcLIdovEN393wBM9Dv1BzprjhBCiOlEPl8IAWgnFSGEEEIIEUgFqXQKM6vEr1EQnBHMZxMGRwEpE31mgj1YMEKEiXOZ2DkTjMDEv1EUnUluygTg7LhMEu5MQnN2HBPoZo6L/cKeb0wmy/qSXZ+1i7BgqYxNkYwAncHaZMZqJnI08yyFmCyzZs2qxP9Llixpe1wmmfZLL71U1Y2MjBRlFqTCfPL+/fsveX1W9/jjj1dtvva1r1V18V1jgQ4seLLdedhxzB+xvovzWcb/sjZNj2NkkqNH2P02TfqdCQpkidc3bNhwyTJQB42wOrbGYL489kucAwHgueeeK8rsXcmiXxCFEEIIIUSBFohCCCGEEKJAC0QhhBBCCFGgBaIQQgghhCjoapAKUAtEo/CTZRSPwtP58+dXbVhQQRTxMgFrZreTTEBGJvCBXY8JlDPBCBkxLhP6srrMTgJR7MwEwpls/wx2rszuNfFesoLoeBy7fmZnHjbmot2ZQCUGe77s3cjsphBpKiQX4nK4ePFiNf6PHj1atYnEY9iYPnDgQFX31FNPFeXNmzdXbZj/i+/j3r17qzaRr371q1Xd8PBwVbd8+fKizHaAYTbFe84EVjC/kvE1zNdFn5ANSsy0aRoU16RPWLvsPBXrmP+NY4U9y8wuKXHXFoDPHXHtw96NuKaaTBCifkEUQgghhBAFWiAKIYQQQogCLRCFEEIIIURBVzWIs2bNwoIFC4q6qBlg+oBMYl92XKxjegz2DT/qAjMJQRnxXoFag8LOzXQ5UceQ0S4yfSXTNcT+ZH0Zk3ay82QSmGc1KfG5ZI5j52F61Wg70wWxPoiwMRDvN5MEl5HRSgE5DWImEboQnYZtjJDxm3EMZzRyAPDtb3+7KB8+fLhqwzYhiNpB5jf37NlTlH/2s59VbTJJqZkejSUPz/jy6Gua6sIzmydkN6doqnfL6MnjvWR1kbEuc26gfnZsPo96P5YU+8SJE1VdHAfs3Gw8xUTrmXkq02Yi9AuiEEIIIYQo0AJRCCGEEEIUaIEohBBCCCEKtEAUQgghhBAF1k3BupkdAbAXwEoAR9s070Vkd3fpV7uB/rJ9vbuvmm4jxFuLcf4e6K/3YTyyu7vI7u6Q8vldXSD+50XNtrv71q5feJLI7u7Sr3YD/W27EJ2mX98H2d1dZHdvoU/MQgghhBCiQAtEIYQQQghRMF0LxG3TdN3JIru7S7/aDfS37UJ0mn59H2R3d5HdPcS0aBCFEEIIIUTvok/MQgghhBCiQAtEIYQQQghR0PUFopndZma7zGy3md3X7etnMbP7zeywmT09rm7QzB41sxda/798Om1kmNk6M3vczJ41s2fM7GOt+p623czmm9kPzOxHLbv/tFV/rZl9vzVevmxm9a71PYCZzTazp8zs661yX9gtxFTSL/4e6E+fL38/PcwUf9/VBaKZzQbwNwB+BcBmAHeb2eZu2nAZPADgtlB3H4DH3H0TgMda5V7jAoBPuPtmADcD+L1WH/e67a8BuNXd3wVgC4DbzOxmAH8B4LPufj2AEwA+Mo02XoqPAXhuXLlf7BZiSugzfw/0p8+Xv58eZoS/7/YviDcB2O3uL7r76wC+BOCOLtuQwt2fAHA8VN8B4MHW3w8C+HBXjUrg7sPuvqP192mMDeI16HHbfYwzreLc1v8cwK0A/qFV33N2A4CZrQXwqwD+rlU29IHdQkwxfePvgf70+fL33Wcm+ftuLxDXAHhlXHlfq65fWO3uw62/DwJYPZ3GtMPMNgC4EcD30Qe2t3623wngMIBHAewBMOLuF1pNenW8fA7AHwK42CqvQH/YLcRU0u/+HugDv/km8vddY8b4ewWpNMTH8gP1bI4gMxsA8FUAH3f3U+P/W6/a7u6j7r4FwFqM/frw9mk2qS1mdjuAw+7+5HTbIoSYOnrVbwLy991ipvn7OV2+3n4A68aV17bq+oVDZjbk7sNmNoSxf/n0HGY2F2PO4ovu/rVWdV/YDgDuPmJmjwN4N4BlZjan9a+zXhwv7wHwa2b2IQDzASwB8FfofbuFmGr63d8DfeA35e+7yozy993+BfGHADa1In6uAHAXgEe6bMNkeATAPa2/7wHw8DTaQmnpIb4A4Dl3/8y4/9TTtpvZKjNb1vp7AYAPYkxP8ziA32g16zm73f2P3H2tu2/A2Hj+trv/JnrcbiG6QL/7e6D3/ab8fReZaf6+6zuptFbenwMwG8D97v7nXTUgiZk9BOAWACsBHALwKQD/BOArAK4BsBfAne4eRc3Tipm9F8B3APwE/6WR+CTGdCk9a7uZ/QLGxL2zMfYPl6+4+5+Z2XUYE7cPAngKwG+5+2vTZ+nEmNktAH7f3W/vJ7uFmCr6xd8D/enz5e+nj5ng77XVnhBCCCGEKFCQihBCCCGEKNACUQghhBBCFGiBKIQQQgghCrRAFEIIIYQQBVogCiGEEEKIgkktEM3sNjPbZWa7zazXNgMXQgjRQeTzhZg5NE5zY2azATyPsQSX+zCWFPVud392omPmzJnj8+bNK+ri9cfyfra9dlXH7iO2y95rbDeVqYAydgPArFmz2rbJ2MmOa9rnTdpkj7t48SJpWZJ5TqOjo41sYsRnEMtAfS+ZZzlRuyawfmvXT+fPn8frr7/eGQPEW5bL9flLly711asvvQXwJOaftnWdPPcVV1xRlM+fP1+1OXr0aFXH2kVmz57d1oYm7/VExHMxHxmvP3fu3KrNnDn1RmyvvVam/mO+jhFtyPh/BuuDpv0Un0scA0BtN+tL9nxj32XGAKtrug44efLkUXdf1a7dZLbauwnAbnd/EQDM7EsA7gAw4QJx3rx5eMc73lHUxQ5lAyrWsc7MLLQuXLhQtWHHxXaZl7Ppg2I2sZcxLqxZm4zdbJDHuswChrVh587AzvX6668XZdZP8f6icwKA06dPt70+e07Mpvnz5xflgYGBqk188ZkTjedh7bJjPNbFfgPqSSr22/e+973qGCEIl+XzV69ejb/+678u6uJ4Ze81G/sR9n5Gn5jx2wz2zq5bt64oP//881Wbz3/+81XdCy+8UJTZAmJwcLCqi770zJkzVZv4rmcWeuxczEfG/l2zZk3VZvny5VXdiy++WJQXLFjQ9twAcOLEiaLM/FhmocfmgMy8yFi6dGlRXrt2bdUm2s36Mp4HAFauXFmUly1bVrVhc3wcF2yssncq8vDDD+9t2wiT+8S8BsAr48r7WnVCCCHeesjnCzGDmPIgFTO718y2m9n2zMpWCCFEfzLe3588eXK6zRFCTILJfGLeD2D87+5rW3UF7r4NwDYAWLRokcfPB7HMfn7O/CScaZPVo2XOlfkkyBbEsS77aTrazo6L52bneeONN6q6aDu7/8xnH0YTjWmWJlIAoB5jbFywn+4j7BNABnbuphrT2AeZMa7tNUVD2vr88f7++uuv97NnzxYnyOh443vM2mQkRexdOHbsWFV36NChonzNNddUbaKc5NSpU1UbRnw/M9IgoP6UGCVG7Lhz585VbZiviXNAxh+weYNdL7aLzx/IzV1svonnzspw4vjJyhri52o2njIay6wOM3NcRpKX0a+mbWh85JhAeZOZXWtmVwC4C8AjkzifEEKI3kU+X4gZRONfEN39gpl9FMD/BTAbwP3u/kzHLBNCCNEzyOcLMbOYzCdmuPs3AHyjQ7YIIYToYeTzhZg5TGqB2IR2+hL2vTyjv2PHdVLv1sQmRkaDw8ikh8j0JbMz3gvTrXRK25bR0U1U14SmaZMYUReU0QlmdZGZvmP3Ep8Ve07x3J3MDSnERLzxxhuVvi+m/MjkiGO6PTaG47t2/Pjxqs23v/3tqm5kZKQo//Iv/3LVJqYlYSm9rrrqqqrulVdeKcoscIf5upjmZdGiRVWbhQsXFmXmazJzAPM98fpMg8ieQbyXTB5IoNm82FRbl9H2AbXtcSwzsnmNM2RzI7ZrM5l1kLbaE0IIIYQQBVogCiGEEEKIAi0QhRBCCCFEgRaIQgghhBCioOtBKpHMfslRQMr2ss0ENWQCYIBaFN10v9BOkgliyIh2M/2UuV/WhgmZM2LjTGJwRtNE2REm4m2agDQjAGdk7oWJ9Zsk2G6auFWIy+HMmTP47ne/W9QNDQ0VZebL4zhnASEskC6O83379lVtXn755aou7oM7PDxctYn7DjP/x/YrjkEpu3btqtqwYJqYhJolpV6yZElRZv3EgkSib80EtjGYH8nML019a6zr1OYN7PqMuO8yUN8LG88ZO5vaxM7dySBEzRRCCCGEEKJAC0QhhBBCCFGgBaIQQgghhCjQAlEIIYQQQhRMe5BKE0ElE+wyMsJXdv0mglkmvO2kODVmzWd9EINEmgYjMKFtFDs3PXcmiALIBdxkA0DakdmhhNV1cueYTLBJ5vxNg3KE6DTnz5+vgjIOHDhQlFlgRcZvsjGc2UFi7dq1VV18H6ONQO0T2c4m7F7WrVtXlNl8s3///qrutddeK8pxtxcAOHr0aFGOcwSQ86OLFy+u6s6cOVOUWYAcmycyfqxTvr2pH8uMHVaXWQdk7zczxtlYycy7sc1kdiXTL4hCCCGEEKJAC0QhhBBCCFGgBaIQQgghhCiYlAbRzF4CcBrAKIAL7r71Uu1nzZpVaTfi9/KovQBymsOMriGbWDPznT9ej2km2L1EmD4gk/CU2Rh1ItkEqFG7yGyKx2X1EVEXw5JpZ8hoNdnzzegbmd3suNgu079Mk/L6669XdeyZR5guMtqQ6d9OaTfFzONyfP7cuXNx5ZVXFnXRH2Tes0xCenauBQsWVG0ySbfZ9aIuMSbXnqgu+iRm08aNG6u6mGCb6f0yybQzmweweSr2b3YThDgHvfrqq22vD9TPgGke471k/CGQ0+Z3yt8zmJ0ZfSObz+JxTXWKWToRpPLf3P1o+2ZCCCHeAsjnCzED0CdmIYQQQghRMNkFogP4lpk9aWb3dsIgIYQQPYt8vhAzhMl+Yn6vu+83sysBPGpmP3X3J8Y3aDmRewFg3rx5k7ycEEKIaeSSPn+8v2d6OyFE/zCpBaK772/9/2Ez+0cANwF4IrTZBmAbAAwODnoULUchZhTnArUQMxP8AeQCSTLi0EyCzCzRBiZgZWSSXUa7mVi1SaJNoL5fJiLO/AMgc25Wx9pkRMSZ55tNAhufFQs2ic+JjTl23MDAQFHOiOmBZoE6ClIRTWnn88f7+xUrVnhMwhx9BPPlGZ+YSZLM3iHmI2K7TMBhDBoEmvs/5mtiMGf0DwBw6tSponzkyJGqDQtciX2QuZdsQvNY1/QZZGjqxzLBH+z8maAYdh+ZjRiy82LmuHgv2TUGo/EnZjNbZGaL3/wbwH8H8HRjS4QQQvQs8vlCzCwm8wviagD/2FqtzgHwf9z9mx2xSgghRK8hny/EDKLxAtHdXwTwrg7aIoQQokeRzxdiZtGJPIhpWKLs+L08k7STwb7XZ/RgTA8R69g3/KhjyCbRzGjG2LmiTi5zbtZvGT1CRqfIdHusLmoyWMJVdi/x2WWeb3YT9mgT2+S+qQ4q83xZPzVJzs7qsn0gxFRjZtW7lnk/4rvA3o2M3i/jI9n52bsf27AAnMx7ndUWR10i0yCuWLGiKC9ZsqRqc+bMmaou+k2mU4x9x67PtNQrV64syuw5ZXSRmeTobOywvoz3ktX7ZTY9iM8po10Hcjr4zJzQdA7KojyIQgghhBCiQAtEIYQQQghRoAWiEEIIIYQo0AJRCCGEEEIUdDVIBagFlFHEmgkYYKLLTCLprFg/BspkjmOCaCY8zSQtziTkzAhYmSCaiXFjO2Z3hNmYCezIJu2M7VhwS0bMzohjLpNsFKjHRSZZOnsGTOAexy97Bk3HYUZsLcRU0O6dzIjzWfBFDIYA6veT+YzMO8tsiu9sTAAO5OYu1h+ZJNTMb0abYgAowJNgR3/A2rz66quXvBYAnDhxoqo7fvx4UWZ2s/vNBBxGv83ulxHPld28IPbLyMhI1Sbe39KlS6s2bFzEccCCeTIbXbB+mkxQSnW9jp1JCCGEEEK8JdACUQghhBBCFGiBKIQQQgghCrRAFEIIIYQQBV0NUjGzSgAcBZUZgWVTYSYTfWYyr7NzR7FzNmgjIz7OwPqAiW8jTIAdbWA7ubQ7ZqK6DJkdQhjxeWZ3LYl2st17okgbqPslMw6z/dRkhwmgWXBWvL6CVsRUYGapHafawYIRWOBKDDRg7zULBsgEDsaglMHBwaoNe2czO22woI1oZ2Z+Yz6T+Yx4LyyYJ+52ws7DbIqBHSdPnmzbBmgWBMT6MtO/Z8+erdocO3asqjtw4EBRZuMp1mWDEuO8lA2wjGQDLJuiXxCFEEIIIUSBFohCCCGEEKJAC0QhhBBCCFHQVjRmZvcDuB3AYXf/+VbdIIAvA9gA4CUAd7p7nTWzPldbLQXTqERNSEZ3BeSSSjJNStQVZHRkTGfAzh3vn2nkmCYkktEsZHR8Tc+V1chF2DNgdsZ2meOy9xv7l2lLWOLUCBsXsQ8yulCg7k92v6wuox+NNmT7ScxMOuXzR0dHq+TCcXyy8RrfheXLl9Nzt6vL+pqoiWO+LeOTmU2nTp265LWAnHZ7YGCgqotzTtbu2C6TqJv124oVK9rayfwosylqB1ly6cw6IKOVzGgnmQ3MpqhVZ/fL1gGZBOpNdYmdJGPBAwBuC3X3AXjM3TcBeKxVFkII0f88APl8IWY8bReI7v4EgOOh+g4AD7b+fhDAhztslxBCiGlAPl8IATTXIK529+HW3wcBrO6QPUIIIXoP+XwhZhiT/sjtY8KACYVWZnavmW03s+0st5wQQoj+4VI+f7y/Z9peIUT/0DRR9iEzG3L3YTMbAnB4oobuvg3ANgBYvXq1txPkNk3ymBH1M7Fo5lzM5piolSX2ZOLfeK5swE2GKCTOBFoAuUSx0U7W30yk3amACCbYzYjS2b3EiYtNZOy5xGeXScLN6KT4ON5f5j1QYmzRgJTPH6MBOxcAACAASURBVO/vly5d6jFII/okFrCQ8YksAXN8F1jS5CaJuoHa32cCLYBcIB273zhXsSDITGAbm4OinWxejD4q++NOPHc2wXZs13TeyAQ8MpuYL4/3wgKFYtJtNt9kNnDIBql025c3nakeAXBP6+97ADzcGXOEEEL0IPL5Qsww2i4QzewhAN8F8DYz22dmHwHwaQAfNLMXAPxSqyyEEKLPkc8XQgCJT8zufvcE/+kDHbZFCCHENCOfL4QAtJOKEEIIIYQINA1SaUw7wWhGZM+Et6wuim9XrVqVsjEKT5mIOIqWWZZ1JnyN98ICSTK7qzCxdSa4JrMDAbvfeG7Whgl0M0EUGTszO4SwNiwAJbZjz4CJnTPZ7zNZ+zMBPtmdauK5mN2ZYB4hukEmiCsSd2MB8u9sJLMjCvO/MUCB+bo1a9ZUdfGdzQSNAHU/sTbRB7P7Z3ZGf5AJrGDPjfmoJjs7ATmflAkwygbFZNrEPmeBqHGtwMiMy17YNYXRm1YJIYQQQohpQwtEIYQQQghRoAWiEEIIIYQo6LoGkekPxsO+xWeSbzJ9QNQc3nDDDVUbpiE4dOhQUWZJQqOugGlbmGaCaVAi7fpoojYZDWLTRMoZnSK730wy10yC7Yy+kT0DplWKtjdNVs60JbEuo39hZMYAkNPlxD7PnluIyeDu1fuQ8TVR371p06aqDZsDzp8/X5Sz+rd4HEtKvWzZsqI8NDRUtWHHxUTh2QTQ0fbTp09XbZhWMsJ8YrzfzDyRSUDNyGy6ANR+MpM4mj3fzIYKrE8ymnP2fBctWlSUs/NiJlF20/kl1k1mMw79giiEEEIIIQq0QBRCCCGEEAVaIAohhBBCiAItEIUQQgghREFXg1TcvRKIRtFlJqiAiT5ZItEVK1YU5Y0bN1Ztnn/++aouCkhZkEq8DxbswgJS4nExmTfA+yCTcDojLM4kEmXXzyROzSTvZmTEuJnjssm7472w55Q5LpOElomIM8nKGaxPMgE30c5MAJAQnSCOrcx4jQEha9eurdow/xODL5ivY74tBpLEIBkAWLdu3SVtBPi9xHct2jhRXZzPMj45G0gSnwmb36JPZP6J3W+TjS+YTZk22YTbsY75ZHauuA5gQSrxObF1QCapeybgaCI72zGZjRH0C6IQQgghhCjQAlEIIYQQQhRogSiEEEIIIQq0QBRCCCGEEAVtg1TM7H4AtwM47O4/36r7EwD/L4AjrWafdPdvZC7YbmcNJsKMIk8mxj137lxVNzIycskys4edn507ilFZGyY8HRgYqOoiLHAlin8zYtWsODWei9kdReFMeMuI4mYmHM/YlBEWs/tlYvZ25wGaZ/KPdUyAzmi34wRrA9TPIRuo0+68YubSKZ9vZpUvie8jG+fxmIyPZu0yu1ax68UduABg5cqVRXnhwoVVmxjsAuT8HfNRmeOir2F9kplP2fXjuTIBcoxskEq8X/bsMjZlgltY37IAlNgvmd17MgGtWZsYGV+dCebJkrHqAQC3kfrPuvuW1v9Si0MhhBA9zwOQzxdixtN2gejuTwA43gVbhBBCTDPy+UIIYHIaxI+a2Y/N7H4zWz5RIzO718y2m9l29vOrEEKIvqCtzx/v77MyFCFEb9I0UfbfAvj/AHjr//8SwP/DGrr7NgDbAGD16tUev7VHHUNGZ8W+6Wf0EDt37qzqMloSpl08ffp0UWZaM6btiItktmhmSb/judi5Y11WWxc1OBn9G7s+0y5mtJPs2WUStUY72fVZXUa7mCGOAaAeq0xLw+4lo69hE24nJmFpEEWClM8f7++XL1/uTNs1HvZ+Ri3h8PBw1SajN2Y+ivma+D6yNlFzzuYE9i7GczP/x+aAWMfmvOhbWBumjY/HNdV3s37KzCXMzjjnsTbxXBmdOFD7t6zeL/YLG6sxXoCtJzL+nj0DRjxXRuPZ9UTZ7n7I3Ufd/SKAzwO4qbEFQgghehr5fCFmHo0WiGY2NK746wCe7ow5Qggheg35fCFmHpk0Nw8BuAXASjPbB+BTAG4xsy0Y+9zwEoDfnUIbhRBCdAn5fCEEkFgguvvdpPoLU2CLEEKIaUY+XwgBNA9SaUw7wSQTp2YCHViQSBQ3s6AClrj6zJkzRZkJfZsGzsS6bCLlTCLRRYsWXfIYIBcAw8S48f6aCn0ZmWTW7DyZZ8COi33H+oTVxWeXGRdMoJxJ/JsZOxPVtTt3fAcVpCKmgosXL1bBHdFvZJIIswTUjEwQQ+a4/fv3V22iT2RzEHuPTpw40fa4o0ePVnXRb2R8VKYvgdr/ZM4d5xaAb+iQ2fgisxEEI/Yv862ZwMxMkvWsTZnAKDbHx2eVTfqdGeOZwKgs2mpPCCGEEEIUaIEohBBCCCEKtEAUQgghhBAFWiAKIYQQQoiCrgepRFFlLLMAiSjMZAEaGbE+CyrIbP+XEfEzuzMwASsTzMY+YALdeC9MMMt2NojZ39n1M23YuWM7JqrNBB2x5xSfeVaMGwOTli1bVrVhGfGPHy+3p2X3Esdm9n6bBqlE2PXiM+hkpn0hJuL8+fPYtWtXURf9ZGY3jMzuGIzs7hRTOf6bBghkdjKJc0Bm3mCw4xYvXlyUly+vd1bcuHFjVRcDUDKBFkB9L5ngi0xwI5DbpYrZlNkVJt4vC/hhc/xrr71WlLPBjJHMe9D1nVSEEEIIIcRbFy0QhRBCCCFEgRaIQgghhBCiYNoTZTdJ0ptJ/jmZc0WtF9MHxLrsfcTjmuoDmN1NdZAZnWDsk6zeJfO82b1kEq5mdDpLly6t6q6//vqizHQjMVk6AIyMjBRldi9MHxthmpSYYJvpDTN9ztpEnUy0MavlEeJyyCTKzmrUIhltHxvX7LjMRgHRb2eTUsf3OKO5nOhc7Y7LJoCOsP4+efJkUWZ9ktFEs/tg82lmM4xYx87Dnnnsl+wzyJw7zh3ZOThqENmza+qXM+Mifa7GRwohhBBCiLckWiAKIYQQQogCLRCFEEIIIUSBFohCCCGEEKKgrQrSzNYB+HsAqwE4gG3u/ldmNgjgywA2AHgJwJ3ufmKyBjFBZRSjZpNKsnaZ4zop8mx3vUxACFALVpmweCpFvLFNJ5PLZgTJmWfAgk2uuuqqqm79+vVFOSbABoD9+/dXdUeOHCnK7DllxOXsuMwzYGM1iqKZSDomBo9JcBWkIsbTKZ8/e/bsKgl9xo9kAttiUNdE18/Uxeux9yG+V9kAmMwclAncY+eOPoJtVBDffXY9dv1YF30GkOvfzKYAQC45eiYgj9E0mCbCAhBjAOCiRYuqNjHgB8gFJbL5LI5DFizVyfVK5hfECwA+4e6bAdwM4PfMbDOA+wA85u6bADzWKgshhOhv5POFEO0XiO4+7O47Wn+fBvAcgDUA7gDwYKvZgwA+PFVGCiGE6A7y+UII4DLzIJrZBgA3Avg+gNXuPtz6Twcx9jmCHXMvgHsB/jO1EEKI3uRyff54fy/pghD9TfoNNrMBAF8F8HF3PzVeD+Dubmb0I767bwOwDQCuuuoqb5eQsmkSTXZc/Baf0YOw4xhNNHJA7TSzx2W0HfH+mIaBaX4y/RTtzugUgZzdjKitYJqjeG52v0wTsmfPnqL80ksvVW2Gh4erumhTVl8TYfcSnwvTEjZN1Bp1SLFPMklixcyjic8f7+/nz59f/fc4Xpn2Kmq9Mm0mapchvo8ZX8PexYwmLiZInuhcsS6TOJ9tCpD5USbjs7I/7mS0kxn9fGYuYefJzF1so4IMzE9GDWLWb8dxwLSEmfVKJvF7dt3DSM0MZjYXY47ii+7+tVb1ITMbav33IQCHG1shhBCiZ5DPF0K0XSDa2DL9CwCec/fPjPtPjwC4p/X3PQAe7rx5Qgghuol8vhACyH1ifg+A3wbwEzPb2ar7JIBPA/iKmX0EwF4Ad06NiUIIIbqIfL4Qov0C0d3/DcBESe8+0FlzhBBCTCfy+UII4DKjmKeCKEZlossoGmaiy6bJMNm5MkLbjN1NA0LYcfH+mBg2k2yUCakzgTMZETETjmfIJI7OJFA/e/Zs1ebw4VomFRNenz59umrDRMPtbGRkE5nGZ8UCbljS20yQSgxKiccoSEVMFXFsRd+W8XVM5J8J2sgkl2bnajqXZHxUljjnsXNnosQzQQyZoBFGJnAlu6FC5nqxL7OJ0OPzzG5OEWH3Euc8dm7mkzNBKk2feeynyfh3zQxCCCGEEKJAC0QhhBBCCFGgBaIQQgghhCjQAlEIIYQQQhR0NUjF3SsxaCYYIQo4s0EqUdDJBKwsa36GKDJlQlAmKM2ItDMBL+x6sU22n2I2+EzwBbt+J4Mdou2ZYCL2LPfv31/VZXZEYc8l8wwywUtNny+rW7BgQVGOzxIAFi5ceMnzZIXkQlwO8+fPx9vf/vaiLrPbSHzXmT86c+ZMqi7C/F8Tv5XZ6QOogxjYTiqZ3T8YmaCcpr4mMy+z4It47mygUCZ4M7OTylT6MnYv0aZskEpmrmZ18VzsfqMN2d3aGPoFUQghhBBCFGiBKIQQQgghCrRAFEIIIYQQBV3VIJoZ/R4/nkxi0aYJUFmbTILOdjZPBEscnbk/liQ5alKY1iyjNcjoNpj+JdMHmQTQWc1lE50g6++VK1dWdTFRNnsmTcdKPI71JdNKZvSU586dq+riWGmarFyITjM0NIQ//uM/LuriOGd+JY59lkT4xRdfrOr+/d//vSiz94WdK77/GR1bRiMIAKdOnSrK7L3OJHdmxHthPovNJRHmMzL6/Yw/yuoi4/Uy+n0232TqWJuMli8zLlh/R504AJw/f77tuTNjIKPpnQz6BVEIIYQQQhRogSiEEEIIIQq0QBRCCCGEEAVaIAohhBBCiALLCO87djGzIwD2AlgJ4GjXLtw5ZHd36Ve7gf6yfb27r5puI8Rbi3H+Huiv92E8sru7yO7ukPL5XV0g/udFzba7+9auX3iSyO7u0q92A/1tuxCdpl/fB9ndXWR3b6FPzEIIIYQQokALRCGEEEIIUTBdC8Rt03TdySK7u0u/2g30t+1CdJp+fR9kd3eR3T3EtGgQhRBCCCFE76JPzEIIIYQQokALRCGEEEIIUdD1BaKZ3WZmu8xst5nd1+3rZzGz+83ssJk9Pa5u0MweNbMXWv+/fDptZJjZOjN73MyeNbNnzOxjrfqett3M5pvZD8zsRy27/7RVf62Zfb81Xr5sZvXO8j2Amc02s6fM7Outcl/YLcRU0i/+HuhPny9/Pz3MFH/f1QWimc0G8DcAfgXAZgB3m9nmbtpwGTwA4LZQdx+Ax9x9E4DHWuVe4wKAT7j7ZgA3A/i9Vh/3uu2vAbjV3d8FYAuA28zsZgB/AeCz7n49gBMAPjKNNl6KjwF4bly5X+wWYkroM38P9KfPl7+fHmaEv+/2L4g3Adjt7i+6++sAvgTgji7bkMLdnwBwPFTfAeDB1t8PAvhwV41K4O7D7r6j9fdpjA3iNehx232MM63i3Nb/HMCtAP6hVd9zdgOAma0F8KsA/q5VNvSB3UJMMX3j74H+9Pny991nJvn7bi8Q1wB4ZVx5X6uuX1jt7sOtvw8CWD2dxrTDzDYAuBHA99EHtrd+tt8J4DCARwHsATDi7hdaTXp1vHwOwB8CuNgqr0B/2C3EVNLv/h7oA7/5JvL3XWPG+HsFqTTEx/ID9WyOIDMbAPBVAB9391Pj/1uv2u7uo+6+BcBajP368PZpNqktZnY7gMPu/uR02yKEmDp61W8C8vfdYqb5+zldvt5+AOvGlde26vqFQ2Y25O7DZjaEsX/59BxmNhdjzuKL7v61VnVf2A4A7j5iZo8DeDeAZWY2p/Wvs14cL+8B8Gtm9iEA8wEsAfBX6H27hZhq+t3fA33gN+Xvu8qM8vfd/gXxhwA2tSJ+rgBwF4BHumzDZHgEwD2tv+8B8PA02kJp6SG+AOA5d//MuP/U07ab2SozW9b6ewGAD2JMT/M4gN9oNes5u939j9x9rbtvwNh4/ra7/yZ63G4hukC/+3ug9/2m/H0XmWn+vus7qbRW3p8DMBvA/e7+5101IImZPQTgFgArARwC8CkA/wTgKwCuAbAXwJ3uHkXN04qZvRfAdwD8BP+lkfgkxnQpPWu7mf0CxsS9szH2D5evuPufmdl1GBO3DwJ4CsBvuftr02fpxJjZLQB+391v7ye7hZgq+sXfA/3p8+Xvp4+Z4O+11Z4QQgghhChQkIoQQgghhCjQAlEIIYQQQhRogSiEEEIIIQq0QBRCCCGEEAVaIAohhBBCiAItEIUQQgghRMGkdlIxs9swlkV8NoC/c/dPX6r9vHnzfGBgoKibyjQ78dzsWqOjo1XdG2+88f+3d66xdl3XdR6T1IPvN0VRomwqtuQHGscGCMdN8sNNmkZOncgFCiNGW6iAAfVHAzhIilbNH7cFCrhAnaRAgwJqLUgFUidGk9RCELgVZAdqgcI2Ldm1Hk4kW6JEmuTl+y3KFGd/3KP27rnG1R7c99xzz/EdH2D47qW19557rbXnWjx7zLk6x9evX2/qzOcnfftrszLlPEY9j6E876wwLtvXrGn/DbR27dpB91fat/YT67fl7Bd2v74xd+3aNbz55pv9A8ysem7E52/evDl37tzZKavvozJemf9lsHddoV5fuR97h5X7q+/+kGdR5yAFZZ4a2k7Xrl1ryur9hvYlm88rN998c+/9gXYdUI+B1k61D2oZu7/SBsr8wuocP378ZGbu7rv+4AViRKwF8PuYz4B+GMA3I+LxzHx+sXM2bdqEX/qlX+qUDVnUsIGpLP5YB1+4cKEpO3Kku0vOlStXmjp1kcEGPSur5w11fqzT33jjjd5rs/NqO7G2rHarCx/lhVVezqEvy/r165uyzZs3916LjZXan+zZqvNR2hvQXmqlP2+55ZamTu27ev8f/vCHzTnGVG7U5+/cuROf/exnO2X1/WCTdR3DV6+2+YaZb924cWPnWF0cvf76653jS5cuNXXqe8b8w4YNG3rvpfr72i7sH7X1PWZtovgMVuemm7pLA3ZtNi8qc8nx48ebsltvvbVzrLQl86Os76rtd9xxR+/9AeDo0aNvewwA69at6xyztqzzMtCOaea367WBdhywcVFtYHPn5z//+UNNIWEpn5g/DOClzPxBZr6B+Szi9y/hesYYY6YX+3xjVhFLWSDeCeC1BceHR2UdIuLBiDgYEQfrv9SMMcbMDL0+f6G/v3jx4kSNM8aMl2UPUsnMhzPzQGYeYD+ZGmOM+fFgob+venNjzGyxlCCVIwDuWnC8b1S2KJnZq8Fg38urhoDpIdh5im6PnVf1AExDUGHPxeysmgymPWC6nD4dGdA+C7s/O0/RpAwNrlFEvEq/sDapz1J1M2qZIppmMJuUdlH0m6oIfpYDkczMcUM+PzMlXXQfTO/HrlO/ULH3k/nyY8eO9dap+kZFWw1ofkQJ1FH8NrObfbVT9Ny17Vh7s36pNjAN+NatW5uyubm5zjHza1WXqOitAe15a9wB0I6LOgYATZuq2KSsFYC2H5TxNeSde4ul/IL4TQD3RMTdEXELgF8D8PgSrmeMMWZ6sc83ZhUx+BfEzLwWEb8O4L9jPuXBI5n53NgsM8YYMzXY5xuzulhSHsTM/HMAfz4mW4wxxkwx9vnGrB6WtEC8Ua5fv47Lly93ypTEwvUbupJfENC+8zN9QtVfMC1A1VqoOaiq/o3lX1JyQCl5F1VdpKLxVHJuMc2PogNl+pran0xLqOgUFT2n2ncVRf8xzvOGXkvVSxkzbvp0woq2TX1fFJ986tSppuzs2bO911bywDIfVc9TcsyyeqydlIT/QzZYALiWTjmv2qloCYH5HMkLOXfuXFOn9pOSJxBon4Xlb2S5NofktGVjjo2LOu/XdRHA+2DSgb7eas8YY4wxxnTwAtEYY4wxxnTwAtEYY4wxxnTwAtEYY4wxxnSYaJDKtWvXcOLEiU5ZFWsyQX0VuioBC0ArWGWiT1amJCmtNqlC6notJYkng4lxq0CWXVtJyqrA2oQJqWuiVJY4VRFJKwnUGeza1XZmNzuvlrGxU8emmhi8oorSh4jQFSG5MeOgjs+hmxdUWJLkGqTy6quvNnXq/ANwn1RRkvIP9TVK8ANrk1qH+WRWVm1Xkjsryf2ZnUoAItAmz2bte/Lkyc4xCzhiz1KDUtgOPyxYtD6LslUwm5PYtWvbqX03zgBHBf+CaIwxxhhjOniBaIwxxhhjOniBaIwxxhhjOniBaIwxxhhjOkx8J5Uq9FSEnxUmjlVEporQF2iFxEqAhCL0ZbDz2P2qsPb8+fO911J2pQHa9ty8eXNTpwp9ld1W2LXZs7EyRcRbn4XVYeOrnqfsmsJsUoI71D6obaAGt/QFAQCamN2YcRMRTTCJ4hOV4DO2o0T1UadPn27qXLhwoSlTAg5rAAwLkmF+sz4ve/fYziL1PObHFB9Z7QZaH8Hast5P2e2FlSm+jp3HbNq9e3fn+NKlS00dtitObRfW3mzHrYoyT7D5XOkDNaC02qC8T0vBvyAaY4wxxpgOXiAaY4wxxpgOXiAaY4wxxpgOS9IgRsQrAC4AeBPAtcw80FO/Nwkq05/Ub/HsuzvTB9QypmtQNBJKgmJFV8HOY9oSpj24ePFi77VrclGmfRia3LS2Hbu2ktz56tWrvXWYnUpCXTXZ6OXLl5uyvmsDWhJsRSMy9HmVZLXs/XEibDMubsTnR0SvRoq9n/XdY/ow9i4cPXq0tw67VtWyMW1b9dM7duxo6jCfWLWK7P1k96u2M419fRbm16ouEwA2btzYOWZ9VPuFtaWiFWUJrxXfpvhWNp+zpOe1X9R1QG1fVqe2k6JlXOxaFUVPrsy5QzfjAMYTpPI3MvNkfzVjjDE/BtjnG7MK8CdmY4wxxhjTYakLxATwPyLiWxHxIKsQEQ9GxMGIOKj8JG2MMWZqeVufv9Dfs5QyxpjZYamfmH8uM49ExG0AnoiI72XmUwsrZObDAB4GgI0bN1oMZYwxs8vb+vyF/v7uu++2vzdmhlnSAjEzj4z+fy4i/hTAhwE8tVj9NWvW0CSVC2GJJpWAkKEi2nEJ+NVghFrGRLxMeFqDO5i4u7YBq8PuV9uT1alia9YHTBBdyxRRLdA+i5I0lAXAsPFUy9j9VWF8RQk2UZOjV8aZYNuYIdyIz8/M3iAq9s7Wd4+9i2yjAJYYu8Le9Zpc+eTJfnkls4mVKcEezB/UoBiWhLsGZLAgFWXzgKHJlllQTi2rGzyoKMGqKkowI/ObtR7rp+qT2XXYuKjtxHw7a4M6NyuBi0tZ4wz+xBwRGyNi81t/A/hbAJ4dbIkxxpipxT7fmNXFUn5B3APgT0cr2JsA/JfM/MpYrDLGGDNt2Ocbs4oYvEDMzB8A+Kkx2mKMMWZKsc83ZnUxjjyI+s1uugm7du3qlNVv7+w7f0VJYgy03/kVzQSg6eaqJk9J+Aq0egQlSSnQag3GleAbaLUOyibsqh6k9gvTKQ5NGlrPY+2mJFBXEqCyMkU3qGhbAC1RrKJXVZJwO3G2mQTKxgjsXai+lY1ppjesmwmw94Xp9Oq12JxQbWAbHLCo7fp+soTXyrvO9GiKP1J8IqtT78fsVjZiUHy7Wk/RcyoJttXNE4YkFFfmZWanmsxa8fdLSYzdXGtsVzLGGGOMMT8WeIFojDHGGGM6eIFojDHGGGM6eIFojDHGGGM6TDRIZc2aNb2JM5mAtKJu2acISJWk1EMDHRRBMjuPCW2rIFgJilm3bl3vdRa7X6UGD6kJX2tQCru/EnSkJE5lCUkV0bIqLK5tzvqgPh8bA8p4Ys/CqPWUvhyniNmYxcjMXl9dA1KAdnyqSflr4AgLUmFJsOfm5m7YJubrmD9QAmdYWfVtLBih+nfmk1ki8no/JQCQzdvKPKwEnQJakF61c2igJPO/rA8qSsChGjij3F8JnlTnvKF4pjDGGGOMMR28QDTGGGOMMR28QDTGGGOMMR28QDTGGGOMMR0mGqQSEY3YVtmhQ9npQ8kGz2BBBDWD+qVLl3qvo+xgAWgCbEVkytqpljGBsBJ8wQJJalsq/cSurQqLlWClei12bSbiVYJN2LMoIunad6wPlJ1q1B12KorAvgqi1T4x5kapY08JxuqbIwDtvWKBLGzHjD4bgdYnqv6+Ph9719i1FKrd7P7K/ZQAwDonAjywYv369Z1j1pZKYIXiW9XgQmWeGBpQWmFtosxBbDyrQbUVZVyo+BdEY4wxxhjTwQtEY4wxxhjTwQtEY4wxxhjToVeDGBGPAPg4gLnM/Gujsh0A/gjAfgCvAPhkZp7pu9b169cbPd+QZNZMM8HKqgZF1WhUzQDTdSkoegEVRaNR7VS0botdqw+mmVB1FIpNtQ8UzQ/T6A3V1ym6IEXboepklPMUPZGSGLwyzsSqZvYZp89X/FYfqo63vgtMg8gSRyva3pqEm21CMEQ3DWibJQzdTEC1oY/z5883ZcymoYnBaxnzv9Vu1UfWekxPyfqzwvq39pOqQazjiV1bmTuU+XUp6xDlF8RHAdxXyh4C8GRm3gPgydGxMcaY2edR2Ocbs+rpXSBm5lMATpfi+wE8Nvr7MQCfGLNdxhhjVgD7fGMMMFyDuCczj47+PgZgz2IVI+LBiDgYEQfZz/vGGGOmHsnnL/T39XOjMWa2WHKQSs5/hF9UxJSZD2fmgcw8wHINGWOMmR3ezucv9PebNm2asGXGmHEyNFH28YjYm5lHI2IvgDnlpB/96Ec4duxYp6yKPIcK/1lZFYIyUa0S/MCuPVQgXK/Frs0Eq7WMiWGVpNRDk28O6SdgWFJWoE1My4S+1W5VfK0EdqhlFSVJqVKmJoNXnkVJFGtMD4N8fkUZe0oie+a3qz9gCf/rnAC0QTAsKEZ5z5RNCNh5zJfXazH/p2y6oGyooPgaJbCOXYt9NRz6JVGxiZXVccGCl1iQiuLLa2Jw9iPY0OBCNu6HbHKwFH8/9BfExhcfcQAAIABJREFUxwE8MPr7AQBfHmyBMcaYacc+35hVRu8CMSK+COB/A3hPRByOiE8D+ByAX4yIFwH8zdGxMcaYGcc+3xgDCJ+YM/NTi/ynXxizLcYYY1YY+3xjDOCdVIwxxhhjTGFokMogrl27hjNn3j75vpKNngmUmYi3imEVsSizgQVWKJnfGdUGVYBdbWBCakWMqgSpKH3ArqME/LA6iuBc2W2F2c2E6vU8ZScDVqZk+x8qEGbnKaJsZScVdawasxQioje4gfkxdp3K1q1bmzJlNwzmy+v7wXbOUnyk4lvV3T+UayvBJozaJ2owY0XxNcqON+w85reVXXmU4KFz5841dS5cuNCU1euzNUYNUmF1GEqfs3dDGePjxL8gGmOMMcaYDl4gGmOMMcaYDl4gGmOMMcaYDhPVIL755pvN9/+q5VM0Kew7P/s2ryTkZBqZqn9guoaqZRmqh1A1avU8lvC16jhYAlZFb6cks1Y0Kuw8NZmrovcbcn+gfT41ebjSBkp/KtpF9byKktBW1SoZsxQys1f/OjTR75YtW3rL5ubaXN6K31LmBIa6WUJFeR+Zvx/SlkA7LwzdnEKZF9XNKer12TpA0VKzsnottZ8UX1rXGKyd2P0UraKil13uTQ/8C6IxxhhjjOngBaIxxhhjjOngBaIxxhhjjOngBaIxxhhjjOkw0SCV69ev4/Lly52yKgRlYmBFiLlp06amrF6LCUOZqLUKgllS1Go3CwhhZUMDJOqzKAESrC2Z8FYRCFdBsmp3bQPWJqx967MowmYm6mVC6mqDcn92HqPej405dh2lfRmKkHq5hczGLEafqF7xGcwf3XrrrU3Zzp07O8fHjh1r6ihBEywgpPqWDRs2NHUU/8DurwRNqMF9Q+qM8/7V3ymbPrBrMb9Zz2O+Xd1gQKmjzPF1bDKbWLBsfT5lXmYoycqVYN3F8C+IxhhjjDGmgxeIxhhjjDGmgxeIxhhjjDGmQ+8CMSIeiYi5iHh2Qdm/iIgjEfHt0f9+eXnNNMYYMwns840xgBak8iiAfw/gP5fy383Mf3ujN+zbWYOJUxUBPwvIqGJjVWhbgxaG7qrBgh+U3TAU8e3Q3V2U52V2K+JjZRccBrOptqciLlcF4PVZ1OClev2huzIowm217+o4UHZF8E4qpodHMQafHxGNsF8JPquw95P5mhqoeNtttzV1tm/f3pSdOHGic6wEzqg7iyg7Zii+ZlyBFkBrJwu+UAI02LPV/lV8u2pTPY/NUwxlF7KLFy82ZUqQa0WZlwFt7mJjvAb5svatz3L+/HlurEDvqMvMpwCcHnwHY4wxM4N9vjEGWJoG8dcj4v+MPke0/ywbEREPRsTBiDjoXy6MMWZm6fX5C/39hQsXJm2fMWaMDF0g/gcA7wLwQQBHAXx+sYqZ+XBmHsjMA87HZowxM4nk8xf6+82bN0/SPmPMmBmUKDszj7/1d0T8RwB/ppzHNCkVRZPCvumz7/Xr1q3rHA9NRqno9tivo+xZFD0l01bU5xuaRHNoUura5hs3bmzqKMmzWd8x/egtt9zSOWbJPqudzG6mG6nXZijtpGhSlDpAazu7Pyuz5tBMgiE+PzObMav8SFDHKxv3zGfUd+iOO+5o6uzfv78pO3PmTOdYSRzN3qmh76Lyriv6eVW3rOjfFIZuasGetyY+V/qA+fstW7Y0ZTWB+smTJ5s6R44cacrqnMOet64xFD2nChsrx48f7xyzhPHVzqH3Bwb+ghgRexcc/h0Azy5W1xhjzGxjn2/M6qP3F8SI+CKAjwLYFRGHAXwWwEcj4oMAEsArAP7RMtpojDFmQtjnG2MAYYGYmZ8ixV9YBluMMcasMPb5xhjAO6kYY4wxxpjCoCCVcaIEbaxfv763jiL0VRKSApr4t9qtCkGVJNzsWZQkoVWwqiT4BrRnqXVYWzKBbhXMMvExs6nWYzYpgUJMxFsDR5REpswGJQhJSXrOylgd1gZ9iYgBTfBuzLhhQSr1nWHBhXV8suAAZQxv27atKXvve9/blB07dqxzzAIW6ntV56TFUBL+K8EtzJdXPzbU3yu+Rg0Uqr5UnXOV/qxjhV17x44dTVntKzWbyoYNGzrHbJ6oqZxYH7A5qAbAsETdrOzKlSudY9YGNXtAfY4bwTOFMcYYY4zp4AWiMcYYY4zp4AWiMcYYY4zp4AWiMcYYY4zpMHVBKn07rQBt9nKAi2ir8JVdmwlWq6iUiYiVIAqGsrMIo16f2aRkUFdE0swm1uYVZdcQxW5WT9mBYKggWhlzQNt3irhc2RkIaNtcFZwrderzKbvwGLNU1qxZ0/jSOj7ZrkYscKWivrOV2267rSn7mZ/5mc7x008/3dR5/fXXO8fM17HdnpR3TfHJys4tapso167+vj4/wPuulqnBdvV+bE6oARosCInZefjw4c4xC/5g81ttT7bGqAEwLEhm9+7dTdmhQ4c6x2x3FzZ2qp2bNm1q6uzatatzPPRdAfwLojHGGGOMKXiBaIwxxhhjOniBaIwxxhhjOkxUg5iZjUagftdn38urPotpJphuRUmkrCQSZSgJr9m1mbZCOa+2AdNDVF0MqzM0Ubaib1TajfUT0zzW9mSan3Fp6VSdoJJgtu8cFSWZtmpDreNE2WYSXL9+vVe7pySbZ9o+dl71B8zXsPnlHe94R+eYaesuX77cOa56OGC4v2cMmbvUTRcqytzFdHvnzp1ryqpvYRo5Ru0r1r5Vc8j68tVXX23K6rMwvaHiW/fv39/UOXDgQOeYJWJnY/XSpUu9NrGyqulldW6//fbO8fnz55s6Kp4pjDHGGGNMBy8QjTHGGGNMBy8QjTHGGGNMh94FYkTcFRFfi4jnI+K5iPjMqHxHRDwRES+O/n/78ptrjDFmObHPN8YAWpDKNQC/lZlPR8RmAN+KiCcA/EMAT2bm5yLiIQAPAfhnb3eh69evN6LdKpBlgtkqYGWCUiZIrvVYMAQLflBE/UMSmQKtYJU9L7NTERsrwmYmwK42KEEyTHytBMCw51WDh/rqqInBlTrMJuVayrhQhPlD7z80mbYxCxiLz8/M3iANxf8xf6j4KDVArNbbs2dP7zk1yADgARl1XmI2DQ0uqf5A9WO17ZTgmhMnTjR1vvOd7/Te7957723qzM3NNWU//OEPO8csCXb1m0eOHGnqsITT9flYcAubF2vASU2oDgDvete7OsesvU+dOtWU1T5nybRZcFZN9L5x48amTg1cOXbsWFNHpXfGycyjmfn06O8LAF4AcCeA+wE8Nqr2GIBPDLbCGGPMVGCfb4wBblCDGBH7AXwIwNcB7MnMo6P/dAwA/WdXRDwYEQcj4uAS7DTGGDNhbtTnL/T3LDWKMWZ2kBeIEbEJwB8D+I3M7CTWyfnfVenv+Zn5cGYeyMwD7L8bY4yZPob4/IX+Xs2BZ4yZTqRE2RFxM+YdxR9k5p+Mio9HxN7MPBoRewG0woJCZtIEmL1GFs0A02wwXVfVpDCthZJwWknKqupdlOSmTEtSbVITcysoSb8rTBekaIVYHUU7w/qJ6UYU6v0UTSCzQelzZVyq5yn3U96Nocm7zephHD4/Ihq/pfg/JUk+K6vjnOkf2div7yNLPlxhvpbZVOcudfOC6qOUzQQ2bNjQ1GH3q2XM/9aEzOw6rJ3qPwpY/9ak40Dry3fu3NnUOXz4cOeYaRlZO9U+YBrEO+64oyn7wAc+0DnetWtXU6f+Sn769OmmztGjR5uyqulkWsL169c3ZVu2bOkcM53iUjSHFSWKOQB8AcALmfk7C/7T4wAeGP39AIAvj80qY4wxK4J9vjEG0H5B/FkA/wDAdyPi26Oy3wbwOQBfiohPAzgE4JPLY6IxxpgJYp9vjOlfIGbm/wKw2LfLXxivOcYYY1YS+3xjDOCdVIwxxhhjTEEKUllpqkB2qPBfESgDwxISqwEi1XYmbFbur8BsUhJVs6TjtZ2UxOTs2uzZFJuYILqKjVXheEUNXlKSftf+Va6zmA3KeUridQelmGmhjkVl3A/1Geq7UH0bS89TfQ3zf0pQzDjfxXotZjfzf/U8Zndtc9ZPt99+e1NWE1zXBNiL2VmDW9i8XBNjK8EuAHDXXXd1jvfu3dvUYcGzNZCEBbe8/vrrnWOWQP3QoUO912ZjnLXvhQsXOscsUKcGtwwN5gT8C6IxxhhjjCl4gWiMMcYYYzp4gWiMMcYYYzp4gWiMMcYYYzrMRJCKsqsGE/9WUenQ4JaaVZ7dj4lM2f36dhZg12bXV3YgYNdm4mol0KG2E3te9VkqTEhd+5gJhGu/sPuzPmBlQ2D3q2VsrDJqmyu79wCamLzioBUzKZRxXanvjOpXFDE+C5A4e/Zs55j5+xokxwI7WKBDtV0NlKwwH8l8eYW1tzKXDN0Bq7bLmTNnJJtqsMcrr7zS1KnXYs+/f//+puxjH/tY57gGiADAd7/73aas7kjCxs7Jkyc7x2wMnjp1qil7+eWXe89ju7LUABTWL3fffXfneOgOa4B/QTTGGGOMMQUvEI0xxhhjTAcvEI0xxhhjTIeZ0CBWVA2VUk/RqG3YsKG3DtMQKNo6BtN2VK2BkgRW1b8p167Px7QPrKzqH1RNYC1jGsRah2ktlCS7QxOTK9oOZjfrFyUZPOuXipKEeymaFGNUIqLRTFXYeFUSOV+9erUpU5JZMz1YTW68efNmbuwCVL1zfa+VZPfqeYqWkKFo1RXtJEtUXbWErA82btzYlFUd6GuvvdbUqRpANue+733va8o+9KEPdY6/8pWvNHXY3FWfhWlMjx8/3jlm7XT+/Pnea+/YsaOpU9sEaBNlsz6v45cl3FbxL4jGGGOMMaaDF4jGGGOMMaaDF4jGGGOMMaaDF4jGGGOMMaZDTDJpbkScAHAIwC4AJ3uqTyO2e7LMqt3AbNn+zszcvdJGmB8vFvh7YLbeh4XY7sliuyeD5PMnukD8fzeNOJiZByZ+4yViuyfLrNoNzLbtxoybWX0fbPdksd3ThT8xG2OMMcaYDl4gGmOMMcaYDiu1QHx4he67VGz3ZJlVu4HZtt2YcTOr74Ptniy2e4pYEQ2iMcYYY4yZXvyJ2RhjjDHGdJj4AjEi7ouIv4yIlyLioUnfXyUiHomIuYh4dkHZjoh4IiJeHP3/9pW0kRERd0XE1yLi+Yh4LiI+MyqfatsjYl1EfCMivjOy+1+Oyu+OiK+PxssfRUS7AecUEBFrI+KZiPiz0fFM2G3McjIr/h6YTZ9vf78yrBZ/P9EFYkSsBfD7AD4G4P0APhUR75+kDTfAowDuK2UPAXgyM+8B8OToeNq4BuC3MvP9AD4C4B+P2njabb8K4Ocz86cAfBDAfRHxEQD/BsDvZua7AZwB8OkVtPHt+AyAFxYcz4rdxiwLM+bvgdn0+fb3K8Oq8PeT/gXxwwBeyswfZOYbAP4QwP0TtkEiM58CcLoU3w/gsdHfjwH4xESNEsjMo5n59OjvC5gfxHdiym3PeS6ODm8e/S8B/DyA/zoqnzq7ASAi9gH42wD+0+g4MAN2G7PMzIy/B2bT59vfT57V5O8nvUC8E8BrC44Pj8pmhT2ZeXT09zEAe1bSmD4iYj+ADwH4OmbA9tHP9t8GMAfgCQDfB3A2M6+NqkzrePk9AP8UwPXR8U7Mht3GLCez7u+BGfCbb2F/PzFWjb93kMpAcj78e2pDwCNiE4A/BvAbmXl+4X+bVtsz883M/CCAfZj/9eG9K2xSLxHxcQBzmfmtlbbFGLN8TKvfBOzvJ8Vq8/c3Tfh+RwDcteB436hsVjgeEXsz82hE7MX8v3ymjoi4GfPO4g8y809GxTNhOwBk5tmI+BqAvw5gW0TcNPrX2TSOl58F8KsR8csA1gHYAuDfYfrtNma5mXV/D8yA37S/nyiryt9P+hfEbwK4ZxTxcwuAXwPw+IRtWAqPA3hg9PcDAL68grZQRnqILwB4ITN/Z8F/mmrbI2J3RGwb/b0ewC9iXk/zNQB/d1Rt6uzOzH+emfsycz/mx/NXM/PvYcrtNmYCzLq/B6bfb9rfT5DV5u8nnih7tPL+PQBrATySmf96ogaIRMQXAXwUwC4AxwF8FsB/A/AlAO8AcAjAJzOzippXlIj4OQD/E8B38f81Er+NeV3K1NoeER/AvLh3Leb/4fKlzPxXEfETmBe37wDwDIC/n5lXV87SxYmIjwL4J5n58Vmy25jlYlb8PTCbPt/+fuVYDf7eO6kYY4wxxpgODlIxxhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdlrRAjIj7IuIvI+KliHhoXEYZY4yZPuzzjVk9DM6DGBFrAfwV5jOgH8Z81vxPZebzi52zbt263LRpU6dszZruGnU+MXyXauMbb7zR1HnzzTebsuvXrzdlFXa/lWZonyjnKc/L6ij9pJYNqcP6sj7vOPvyppv6d6G8du1aU1btZH2ilg2pM6QNrl69imvXrk3fi2Cmihv1+bfcckuuW7duyH06x+vXr2fXbsrq+8F8hlLG7le5cOFCU3b1apsXWXkf2Xu9du3azjF73lqH+SM2L1ablDn3Rz/6UVOH3a/vXkBrNytT2oT1E7tfbQN2bWXuUuZFdcxVWJsoDJ3zDh8+fDIzd/fVW8pezB8G8FJm/gAAIuIPAdwPYNEF4qZNm/Arv/IrnbLaycyh1MH56quvNnVOn26TxNeFJOso1jG10esgYHVUFCemLDyUF4HBnrfaxJzRxo0bO8dsAXXzzTc3ZbUes5vdrz4Lc1C1f5VJA9AWcbt3t+9OPe/cuXNNnUuXLr2tjYuV1WuzvlT6V1nYVp5/ftFX1piF3JDPX7duHX76p3+6U6b4vzoH/ORP/mRT584772zKqt+s7yIAXLlypbfsAx/4QFOnvnt/8Rd/0dT5/ve/35Tdeuutb3sdgLfB1q1bO8f79u1r6mzZsqVzzObA8+fPN2XVTzOfUf3tkSPt1sJnz55tyips7ty2bVtTtn379s4x85GbN2/uHLNxweagaiebX9m8WK9V+xIANmzY0Dl+/fXXmzoXL15syup7UPsb0NYYypzHrvObv/mbh3ovjqV9Yr4TwGsLjg+PyjpExIMRcTAiDrLGM8YYMxP0+vyF/p79o84YMzsse5BKZj6cmQcy88CQzw3GGGNmg4X+nv2aY4yZHZbyifkIgLsWHO8blS1KZvZ+JmM/d9efhJVv+sD49G/KeaquTNFDKCifKhTdHtC2OfssUMvUT8yKtmKobqR+hrh8+XJTh+mCFL0L+zxVn5l9cqiwNlE+qbO+G6qz7fukPo06XDOV3JDPv379eqPVq2ON+RHFH7HzFBmOoptjnzernESR4QCtr2H+kH1Zq2UvvfRSU6d+YmbPxq69a9euzjHzY9X/sc+k7FmqDayf2Hn1xyMm8dmxY0fvdZiEQHkW9mt3rcfuV/uAfT5Xxu9Q7aIy5y7ll/yl/IL4TQD3RMTdEXELgF8D8PgSrmeMMWZ6sc83ZhUx+BfEzLwWEb8O4L8DWAvgkcx8bmyWGWOMmRrs841ZXSzlEzMy888B/PmYbDHGGDPF2Ocbs3pY0gJxCH2aOyXFC9M1sO/8Sjg/K1PSiSi6yKGpaBTY/WobqNqdqv9guoZ6LabHYLocJU8UY0h+KTZ2FB0oO4+V1ZRMTLtT21fR27DzGOxZqr5EeRZFT2rMUomIZqwzn1Sp74yaS6++H6q+u15f1W5X2HtUzxvq/5lN1d9WjR4AzM3NNWW1D5h2u/qMmr8Y4PMEyw9ZYSl73ve+9/Vep+oLmd6wpsIBgJ07d3aOmcaUtcHJkyc7x6+99lpT55VXXukcs3Zi/VLT+ijzOStTUvAp79xieKs9Y4wxxhjTwQtEY4wxxhjTwQtEY4wxxhjTwQtEY4wxxhjTYerU6UqwBxP5K3sSqpuZ1+ADlmyZ3U+5toJyniI8VYNyxhWkoGxmzlCSQjNhsXJ/ZSN69VoVloC0iueVoCB2nrKhPdC2C2unOn5rnzhRtlkOIqI3uI35OuX9ZH5F8fdD39maXJolrlZ8FHs2lsy62lQD5IB2nlKTLde9iZndyqYASkAG23CA7aNdg1KeffbZpk5tkxrYwuoAbRuwOuxZ6hzP2qCOA9aXx48fb8pqgA9LsK0kGVc2onCQijHGGGOMGRteIBpjjDHGmA5eIBpjjDHGmA5eIBpjjDHGmA4TDVJhouUq2mWCyio2VjK/L1ZWYSLeKupXxb8VZXcXJlpmwtMNGzZ0jlkbVLGxImAFWuE2E+xWO9m1lSAR1m5MJF3FvixQSN2VpaII5RWGBuWw560CaHX3CMX2ep6DVMwkyMzGt1Q/ogR2MOH/xYsXe+spvh1ofTILZDl8+HDnmL13+/fvb8pOnTrVOWbBCOxa9fnuuOOOps758+c7x+x53/ve9zZlL7/8cueYBVzWAAl197Lbbrutc3z69OmmztNPP92U1fmM7dJSeeGFF5oyFgC4ZcuWznHdWQXg/VLbk51X+4nttsICdWoZW6uwNqi+mo0dZSciFf+CaIwxxhhjOniBaIwxxhhjOniBaIwxxhhjOixJgxgRrwC4AOBNANcy80DfOUO+jys6NqblqxoJ9k1f+YbPdCtVM6DqFOu1mfaA6T2qvobVqVoSpqVh7a3oPardrL2VRORMT8TsVLR1ii5SSTbKtDSs72o9Vqc+C3sORV/IbFKSwCq6W2OGciM+f82aNY12uo595g+uXLnSOT5x4kRTZ/PmzU3Zq6++urjhI5gvr/ozpjWrGkRW5/3vf39T9tWvfrVzfM899zR1WALmZ555pnP8kY98pKnz4osvdo6rJhEA7r333qbs6NGjneOqkwSAu+++u3PMtH2sX6qm9OTJk00dpktUklJXvR+7NpuXqu01KTfA9aN1jDH/u3Hjxs4xm0tZ29V5gdnN5ol6nuLv1VgExjiCVP5GZrY9ZYwx5scR+3xjVgH+xGyMMcYYYzosdYGYAP5HRHwrIh5kFSLiwYg4GBEH2ecEY4wxM8Pb+vyF/l5JYWOMmV6W+on55zLzSETcBuCJiPheZj61sEJmPgzgYQDYtWtX/y7sxhhjppW39fkL/f3WrVvt742ZYZa0QMzMI6P/n4uIPwXwYQBPLVY/IhoRpxIgoQSyMFFrFX6yOiyIoIpKWaLLmthTCWpgNiiJlQEtuKe2pRqQUsvUoI0KO09pJybQZWUVRXyrBK6oAUZKkt9ah/UBE8pX1L6rz6e2rzFDuBGfv2bNmkbEX78isSTNVXjP3oX3vOc9TVkN2ti1a1dThwW3VJ/MNiGocwKr89xzzzVl9XkPHTrU1GFzUH2Pa2AJ0LYdm6dYUuoaSLd+/fqmTn1eVqf2LdA+bw04AniAT991gHZ+UfwvAJw7d67XJhbM+O53v7tzzPqplg2dO1mgJmuDIeulFUmUHREbI2LzW38D+FsAnh1siTHGmKnFPt+Y1cVSfkHcA+BPR6vTmwD8l8z8ylisMsYYM23Y5xuzihi8QMzMHwD4qTHaYowxZkqxzzdmdTGOPIhLYog+imkBlGTLTPvF9AH12z/77r9p06bOsZKoG9CSbzKbqg3sPFXPWKn6C6ZZqO2rai3qtZn+g2lJal+xPqhaJVXHUW1nz6LoIlk71Wup+o/6vEwvqyROVcahNYlmpajaNpboV0n+fuTIkaasJrzesWNHU4dpB+v9mI+qGjzms773ve81ZRWWpJn5n5q4Wdn0gNU5e/ZsU6Zo4mqdoXOLqu+u8wTzUYr+TomcZ3WYxnPfvn2957Fk3RWmb6zPx55XmeMVX74Uf+88iMYYY4wxpoMXiMYYY4wxpoMXiMYYY4wxpoMXiMYYY4wxpsNEg1TWrFnTiO+rEJMJOissQScLXFFErUy0XMWoTKBby5jdLClrFWkPDTZRknAzmAC7ioaZTUOTUleYYJaJq2sZ61/l2oogm11bCUBRUJJbA+34UQNnhtQZGkhjzI2QmfTdXgjzm1u3bu0cs2AT9g7VoAL2XrOAFyXZ8d69ezvHNSk3wIMYlGCPOicA7RxX24Td784772zqHD9+vPc8Nk9t2LChc6wkiQbaoEDWT0oASr0/owYlAbx/q03s/iwR+OnTpzvHFy9ebOrU5OTMl7IxXudhNZinPh+bX8YVYAT4F0RjjDHGGFPwAtEYY4wxxnTwAtEYY4wxxnTwAtEYY4wxxnSY+E4qfWJ8JmqtwkwlYAHQBPuKGJYFf9xyyy2916m7rbDzmKiViVPrs7B2qvdTsu8zFMGsKqpVUITFrM7QYJ4KG09s7NRxoASgsDpMlF7fizpOAK3v2PPW85RAMGOWSmY27231EWxM13eP+VbFb7NdlNi7x3xLpe60oQTkAa2de/bsaeqwYIt3vvOdvXVeeeWVzjF797dv396U1edl5506daop67sO4/bbb++ts5gNlTp2mB9TgvuU+RUAzp8/32tTnRPYddgObjVQSJ2Daj3lWbyTijHGGGOMGRteIBpjjDHGmA5eIBpjjDHGmA69YqSIeATAxwHMZeZfG5XtAPBHAPYDeAXAJzPzjHCtXu2G8r2cfa9X9G9Ms8B0gvX6W7ZsaepUbRlL3s10Mop+UkkczdpR0cgxqk1K+7L2Zpof5XmZlkXRtwzVPCooibJZH1TtoJrQvF5L0RKy89j7o2hwjHmLcfn8zKT6q4WwcV79FvPbSkJiJSE90PpWZhPTBPddB2i1gyyZNZtfar277rqrqXPs2LHO8YkTJ5o67HlrGZsnavuqfqyWqX6slinaVNWP1WupGsQKm5PqGFM37Kj1hsYLKJtD9CWrfzuUXxAfBXBfKXsIwJOZeQ+AJ0fHxhhjZp9HYZ9vzKqnd4GYmU8BOF2K7wfw2OjvxwB8Ysx2GWOMWQHs840xwPA0N3sy8624/2MA2tj9ERHxIIAHAb7nozHGmKlH8vkL/b2yN7wxZnoBhD5jAAATiklEQVRZcpBKzn8EX/QDfmY+nJkHMvOAkrvKGGPM9PJ2Pn+hv1d0e8aY6WXoL4jHI2JvZh6NiL0A5pSTWJDKkCSOTGTKxKH1XixJKvtVsy5kmci02sDuf+XKlaasCk8VkSnQPgsLrlHalomWlYCQihLEwVD7WxEgD02CXcuYmF0RCCtiazZJsvvVZ1HF3UoQUD2v3l95VrPqGeTzq7+p7z8b533nLIYyjpmvq+cpwQgnT55s6jA765xTEyQvRp1zWCBLTaZdE2cDvE1qQKWSlF9JQM1QA0L6NtAANL+t3E/1d/V+yvOy/lUCo9SAmyG+W91YhDH0F8THATww+vsBAF8ebIExxphpxz7fmFVG7wIxIr4I4H8DeE9EHI6ITwP4HIBfjIgXAfzN0bExxpgZxz7fGAMIn5gz81OL/KdfGLMtxhhjVhj7fGMM4J1UjDHGGGNMYWiQymD6AgsUISgLGmHBJlX4yUStTJBchaZMeHr58uXOcd+OAW9Rg0uYgJWlh1B26KjC10uXLjV12G4ntZ2ULPasjpKRn2V1Z9HttT1ZHyhBKqpoWKG2kzJWFQE4u5YSyMLKhgStGLMcDN05q5apwXb1XVd2EQHaHUgOHz7c1Kl+k+1awt698+fP9563ffv2pqy+1+ydXb9+feeY+RplVxg23yhzwtDgh74xAfB5WRkXSrAqu/bQ3dmqDWx+U9YPdccdQPPTzMZ6nhoYxfAviMYYY4wxpoMXiMYYY4wxpoMXiMYYY4wxpsPENYj1m7mitah11C37qq6Afa9n3+fPnTvXOWa6gqrvUxKwAm2SUqa/Y0lRq05ESebK7GZaSUVLpyROZdqSqnepzw/wPqiaHyVxNHsORafIYO1bx4+ii2TaFqXtVH2PohUa2gbGLIXM7E1urOisFO0tuxbzB1UTCAAvvvhi55jptJX3kd3v1KlTneMLFy40dZiv2bt3b+f47rvvbuoo/ohdu27gwOaJoT6j2qQmgB5ynnrtytDE6+y8WofZpKwf2LzMxpxik6LVVPHMYYwxxhhjOniBaIwxxhhjOniBaIwxxhhjOniBaIwxxhhjOkw0SIWJlqvokgkzlSSlTIhZhZ8sSSoTlVYBKROZKjABdr0WE02fPHmyKatJqFly03Xr1nWOWdAIS2ZdE66yNqmiZVU4Xu1kNg1N9lnbko0dNi4UcTejPh+zmwncK7WfgLadap8AWoJZxpDAAGPGQZ9gnr2fSmJjBRYQ8ld/9Ve99dj7obwzzLfW52PvPvNbNVCS8eqrr3aODx061NTZs2dPU7Zr167OsRKQwuooQRs1IAbQkkkrgRVqAKAydzFfXgMsmU31WdTk3UPXFPVayrM4SMUYY4wxxowNLxCNMcYYY0wHLxCNMcYYY0yH3gViRDwSEXMR8eyCsn8REUci4tuj//3y8pppjDFmEtjnG2MALUjlUQD/HsB/LuW/m5n/9kZuFhGNkLcKkhUhKBMfM9FnFchW0Sm7P9AGGihiWDWrew2UUYS+7DzWBtUmFpTD2qDWUwJgmNiaXbu2i3J/oA3SYPerwmYlwAloxwXrOyY4H7K7ALs/C4qpz6u203LuOGBWLY9iDD5/7dq1za5X9b1SAtTYuGd+swYXPv/8800dFvxR32s2J1QbmI9mu2LV573tttuaOsy31bLLly83db7xjW90juvzL2ZTX58Abb+wNlH6gMH8dPWJSlCMutuUslubEmDExmotYzuisLWJEiiprA2UIEy1nRi9M15mPgXg9OA7GGOMmRns840xwNI0iL8eEf9n9Dli+2KVIuLBiDgYEQfZv4KMMcbMBL0+f6G/Z7+mGGNmh6ELxP8A4F0APgjgKIDPL1YxMx/OzAOZeYD93G2MMWbqkXz+Qn/PpCrGmNlhUKLszDz+1t8R8R8B/JlyHtMgKkkcFQ0VS6bKdFwVNblypWoGVB3bUOr1lTZhz8bKqp1KolhVD1f1F0xbwjQaVfOiJMpWkqUDrQaRncfGU53w2ARYr8XGALt2rcfOYzqgIUmvh2gpzepmiM9fs2ZNo6Ubot1WEisD7QYKx44da+qw+yka7Go386O7d+9uyqrmkOkNz54922vnM88809SZm5vrHLMfYNi7Xn2GsqECuw5Lgn3x4sXO8ZkzZ5o6TKdY52r2tbG2uaIJBFqfzOYu5keVDQZqGavDdOiKv1fWJsqcsBQN+qCZIiL2Ljj8OwCeXayuMcaY2cY+35jVR+8viBHxRQAfBbArIg4D+CyAj0bEBwEkgFcA/KNltNEYY8yEsM83xgDCAjEzP0WKv7AMthhjjFlh7PONMYB3UjHGGGOMMYVBQSrLCRPwK0J8NRhgCEwIqtxLsZsJZhlK4Ey9liLYXaxepQpm2fMyIXMVk7M2UWxi96viXyZ+Vu0cAhtz9flYHVZWhfFszA0NUqntq4wlY5aD6kdYAEr1YywYggUx1KAU1f/X+23atKmpU989NQCw714AsG3btqbs1KlTnePTp9u0lDXhNbObBdzUdlF8CPM9rH2V/mWBOtV2JXDx/PnzTR0WELJly5bOMQteZW1Q5y4WGMXK+u4PALfffnvnWFljAFqwVH2WZU2UbYwxxhhjVhdeIBpjjDHGmA5eIBpjjDHGmA5eIBpjjDHGmA4TDVKJiF4xJhOQVlG9IrwFWkEwu/dQAaeys4gSDKHeX7lWFfay52UBKdUGtvtIvRYTiStZ+9UgldqfzG5FIMxQgjRY2yl9UM9TdzeogStq3yniee+cYlaCiKABCQtZv359U1Z9S92dAwBefvnl3vuz4ADm2+p8snHjxqZOfQ7mQy5cuNBrJ9tt5d57723KduzY0Tlm82INymHBbyyQo5ax+bTOZ8zXMl9efc1dd93V1Dl37lxTVv19DcAB2r5jQTmHDx9uyvbt29c5ZuOCtW+9H5vzjh8/3jlmwUTMb9dxz/qOjTFlJyI18FXBM4cxxhhjjOngBaIxxhhjjOngBaIxxhhjjOkwUQ3i9evXaSLLhTCtg5LYk2lZFJgmpWr5mK5LSWzJ9AH1WdTkx/VaTJ9Qz2P3V3SC7Lzab0py1aWgJO+u/aRqEmsbsDZhZUofVC2Jqk1V9LIMpR9qHWsSzSS4+eabcdttt3XK6jvDdHsKZ86cacrq+7l169amDtMzVv/O/IiiEWbvYvWbZ8+ebeocOnSo9zymE7znnns6x0wjd+TIkd5rKz6Ktcn+/fubstpOJ06caOqwvqu+fOj8wux87bXXOsfM/zENYq2n6NLreAe4DrS2wZ49e5o6rF/qWGVrk/osS5mrPVMYY4wxxpgOXiAaY4wxxpgOXiAaY4wxxpgOvQvEiLgrIr4WEc9HxHMR8ZlR+Y6IeCIiXhz9//blN9cYY8xyYp9vjAG0IJVrAH4rM5+OiM0AvhURTwD4hwCezMzPRcRDAB4C8M/e7kJvvPGGJBitVPEtE2+yRJNVtMySaTMBZxWjKiJPJfAA0ASkLPhBCdqodZTEyipV/Ds00EF5DkBLsK1eq+/aSkCKinIeGxfKGGOCZCXpd62jnGNWNWPx+devX8eVK1c6ZXXsMX+kJKVm/r7C3muWJJnNCxXFb7NAEuV5WaDkqVOnOscsiKH6AxYMwe5Xg3eYX6lBFOw6LFF1DTapiaSBNkgGaP0m85HKpgDsvJqYm7U3C1Kp/cnq1Ptt397+m2nnzp1NWW1fFmBU2xLQxtPQOZ7RO8tn5tHMfHr09wUALwC4E8D9AB4bVXsMwCfGZpUxxpgVwT7fGAPcoAYxIvYD+BCArwPYk5lHR//pGID2nzjz5zwYEQcj4iBbuRtjjJlObtTnL/T39ddDY8xsIS8QI2ITgD8G8BuZ2fktO+d/96TfrTLz4cw8kJkH2E/wxhhjpo8hPn+hvx+am9YYMx1IibIj4mbMO4o/yMw/GRUfj4i9mXk0IvYCmOu7ztWrV/HSSy91yhRdgfJNnWlCFG0JQ0mkrOi4mB6t6hiYjYp2kLVTtZO1iaJjYzZVrYWSTJudx1A0gGoya6WOkhicjbnaTqwtWZlCvR9rt6FjThkXxixkHD4/M5v3oY7FoWOaoYxzpiNjZeNiiN8G2ve/6uiAVsfGfrFlWs2qHWTaxZMnT3aOt23b1tR58cUXe6/N7r9hw4amrMLOq0mo5+ba4Xf06NGmTJnfmL9XknfXscm+klZNLdC2AfP3bNwrenJlLlFRopgDwBcAvJCZv7PgPz0O4IHR3w8A+PJgK4wxxkwF9vnGGED7BfFnAfwDAN+NiG+Pyn4bwOcAfCkiPg3gEIBPLo+JxhhjJoh9vjGmf4GYmf8LwGLfpH5hvOYYY4xZSezzjTGAd1IxxhhjjDGF4erFAWRmIxBVAkmUgAV2HSWIQUmUPTRxNUNJZs1QxNz1edXgCyWworYvs2eoGHZIEnD1PCV4SW2T+nzjFNhXljOQxEErZhJERG9w29CE+0ODwYYydC6pZWoS4+pbWNDGrl27Osc1uTYAKJHkSoAEC75gScdZUujK7t27m7I77rijc8z6t9pQg3QAYMeOHU1ZDZ5kbVI3gmCwxNXKZhxKkKsa8T9kDlpKEJZ/QTTGGGOMMR28QDTGGGOMMR28QDTGGGOMMR28QDTGGGOMMR0mGqTCGLL7h7qrhrIjiiIqZSLeKlhlwQjsvCpSVoMvhu6SUhlXUA4TWw8NdhjnLjgVpU3U3Q1qPdZOym4r40QZFw5CMStBZjaBBcpYrO8M8zVKsIfq7+s7y2ys/p69++x+ip3M31c72VyydevWzvGJEyeaOpcuXZLuV6k7fbAgme3btzdlNSCCba+7b9++XpvYjih1xxcWOMPsVPqXldVrsb6sz8eCXequNEDbvuzayo4vyly9cePGpo6Kf0E0xhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdJqpBjIheTYaikVOTD9dv+Ewjwq5VtQcsiWWto1yH2aRoH4BhyayZTSxppqIvVLR1ipZvaGJcprWo9xuaTJuh6EeVZ2F9qSROVZOzD0kWbk2imQTXr19vdGJKYl9FM6bAzlPeIeYjFQ0i85uKBnNImwBtomymNWP6t8qmTZuasqovvHLlSlOH6e0U7eSxY8easrm5uc4x0xfW9l23bl1Th81Btc1Z/7KyCxcudI5rMm8A2LZtW+e4JuUGgIsXLzZlNak5S8LN9JtD3o2hG1gA/gXRGGOMMcYUvEA0xhhjjDEdvEA0xhhjjDEdvEA0xhhjjDEdQhHIju1mEScAHAKwC0C/enb6sN2TZVbtBmbL9ndm5u6VNsL8eLHA3wOz9T4sxHZPFts9GSSfP9EF4v+7acTBzDww8RsvEds9WWbVbmC2bTdm3Mzq+2C7J4vtni78idkYY4wxxnTwAtEYY4wxxnRYqQXiwyt036ViuyfLrNoNzLbtxoybWX0fbPdksd1TxIpoEI0xxhhjzPTiT8zGGGOMMaaDF4jGGGOMMabDxBeIEXFfRPxlRLwUEQ9N+v4qEfFIRMxFxLMLynZExBMR8eLo/7e/3TVWgoi4KyK+FhHPR8RzEfGZUflU2x4R6yLiGxHxnZHd/3JUfndEfH00Xv4oItpd1aeAiFgbEc9ExJ+NjmfCbmOWk1nx98Bs+nz7+5Vhtfj7iS4QI2ItgN8H8DEA7wfwqYh4/yRtuAEeBXBfKXsIwJOZeQ+AJ0fH08Y1AL+Vme8H8BEA/3jUxtNu+1UAP5+ZPwXggwDui4iPAPg3AH43M98N4AyAT6+gjW/HZwC8sOB4Vuw2ZlmYMX8PzKbPt79fGVaFv5/0L4gfBvBSZv4gM98A8IcA7p+wDRKZ+RSA06X4fgCPjf5+DMAnJmqUQGYezcynR39fwPwgvhNTbnvOc3F0ePPofwng5wH811H51NkNABGxD8DfBvCfRseBGbDbmGVmZvw9MJs+3/5+8qwmfz/pBeKdAF5bcHx4VDYr7MnMo6O/jwHYs5LG9BER+wF8CMDXMQO2j362/zaAOQBPAPg+gLOZeW1UZVrHy+8B+KcAro+Od2I27DZmOZl1fw/MgN98C/v7ibFq/L2DVAaS8/mBpjZHUERsAvDHAH4jM88v/G/TantmvpmZHwSwD/O/Prx3hU3qJSI+DmAuM7+10rYYY5aPafWbgP39pFht/v6mCd/vCIC7FhzvG5XNCscjYm9mHo2IvZj/l8/UERE3Y95Z/EFm/smoeCZsB4DMPBsRXwPw1wFsi4ibRv86m8bx8rMAfjUifhnAOgBbAPw7TL/dxiw3s+7vgRnwm/b3E2VV+ftJ/4L4TQD3jCJ+bgHwawAen7ANS+FxAA+M/n4AwJdX0BbKSA/xBQAvZObvLPhPU217ROyOiG2jv9cD+EXM62m+BuDvjqpNnd2Z+c8zc19m7sf8eP5qZv49TLndxkyAWff3wPT7Tfv7CbLa/P3Ed1IZrbx/D8BaAI9k5r+eqAEiEfFFAB8FsAvAcQCfBfDfAHwJwDsAHALwycysouYVJSJ+DsD/BPBd/H+NxG9jXpcytbZHxAcwL+5di/l/uHwpM/9VRPwE5sXtOwA8A+DvZ+bVlbN0cSLiowD+SWZ+fJbsNma5mBV/D8ymz7e/XzlWg7/3VnvGGGOMMaaDg1SMMcYYY0wHLxCNMcYYY0wHLxCNMcYYY0wHLxCNMcYYY0wHLxCNMcYYY0wHLxCNMcYYY0wHLxCNMcYYY0yH/wvR0PhMQP9qrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x1800 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_im(ped_data_test[wrong_classified_ped_ids[0]], ped_data_test[wrong_classified_ped_ids[1]], 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try the thing with the augmentor!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Augmentor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_56_2 = keras.models.clone_model(model_56)\n",
    "model_56_2.compile(optimizer ='adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_56 = Augmentor.Pipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pipeline_56.skew(probability=.75, magnitude=1)\n",
    "#pipeline_56.random_distortion(grid_width=1, grid_height=1, magnitude=1, probability=.25)\n",
    "#pipeline_56.rotate(probability=.1, max_left_rotation=5, max_right_rotation=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size_56_2 = 128\n",
    "keras_generator_56 = pipeline_56.keras_generator_from_array(ped_data_train, ped_labels_train, batch_size=batch_size_56_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NXQEeu1N9VAU/hVVHLN3UmcWhVKRNVYNk0bC+vr5Jj6FdP3ZfauzqnJRcFyoKx8axYsUK2ofF+wB9jlkUTlVoPHXqFG1jEUQVMS2pCKiiia+88gptU1g/tmk3AOzdu5e2HT3aHLxbs2YN7aPmnV1P6tyr21PPEUbN+0T5L0XNzCrhBd3MrBJe0M3MKuEF3cysEl7Qzcwq0dM9RUdHR2kag6UZVHEh9Qk5K6qjUgkqLcAKI7Xrx6gCUiqxwpR8Ql6SLml3X2zeVcpFtY2MjDQeVykhhZ0rlQZR12AJNe/q3LN5UmNXBanYNT08PEz7qOePelwnTpxoPH7gwAHa59ixY7SNPebSfXfZ4yotsKfMmzev8Xhpgmg8v0I3M6uEF3Qzs0p4QTczq4QXdDOzSnhBNzOrhBd0M7NK9DS2mJk0lsUiT6XFuVSRI0ZFng4ePEjb5s6d23i8pHATwAtjqUJBrFgRwAsFqeJCqlCZinKxNlWsSJ1HNk8qqqeifyxyWTq36hyzWKCKXKqIpIrxMSq2WLLnqaKiriWRy5LorHpeqf1BS66z0mJ+bC5K5308v0I3M6uEF3Qzs0p4QTczq4QXdDOzSnhBNzOrhBd0M7NK9DS2CPBYFovyqBiSinix+Jzqo+JzKuLH7ktVd1NxKBZrU7EmdV8LFiygbYwan4p/sap6Kvapzgl7XKXnkd2eqvq3a9cu2qaqcP7iF79oPL5y5UraR+1typ4LKka6cOFC2saimup6KY2LsvOl4qLqmi55fne7cmLpc4T1U30myq/Qzcwq4QXdzKwSXtDNzCrhBd3MrBJe0M3MKtH2o92IGALwDQADABLAtsz8UkQsAfAdAKsB7AZwZ2bK6kGjo6O0oE3Jfnrqk2mWPlAJiNJPyEv6qPQOSwuU7sv5y1/+ctJjUG2qaBKb3+eff572UXvDrlu3rvH49ddfT/uoeX/hhRcaj7NECgA888wztE1dT2zeVZpBpT7Y41IFuEqKS6niYSXFvgB+Pan5U/PErsHSgnisn5oLdXsl60zpHqWvue0J/Mx5AJ/KzLUAbgbwsYhYC+AeAI9k5rUAHml9b2ZmU6Ttgp6Zw5n5VOvrkwCeBbAKwB0A7m/92P0APni5BmlmZu1N6j30iFgN4EYATwIYyMzhVtMBjL0lY2ZmU2TCC3pEzAPwEIBPZuZr/hwwx96oa3yzLiK2RsT2iNheUqDfzMwmZkILekRcgbHF/JuZ+d3W4YMRsbLVvhLAoaa+mbktMzdk5obFixd3Y8xmZtag7YIeYx+tfw3As5l577imhwHc1fr6LgDf7/7wzMxsoiaSk3kngD8B8LOIeLp17DMAPg/gwYi4G8AeAHdO5A5ZnIfFhlQMScWa2P2ofQBVfE5Fitg4VHSJ7RsK8P1QVQRN7aHKCmOxQlqAjmSpt86GhoYaj6u5ePnll2nbDTfc0Hj8qquuon3UdbFjx47G4ydPnqR9lixZQtsOHWr8hykAYPny5Y3HVaE3FZNj14yK26prhrWp58GiRYtoW8k4SovvsX6lsUUWCVURWHWdqXNcsr/qRLVd0DPzcQDsUb234xGYmVlX+C9Fzcwq4QXdzKwSXtDNzCrhBd3MrBJe0M3MKtHTPUVHR0dpZIdFgFTMUFUYZDE+FV1S0T8VUWKRN3V7g4ODtI3FDFW0TsUgWQzt8ccfp302b95M286cOUPbDh8+3Hj8Pe95D+1z880307Z58+Y1Hlf7V/b19dE2FgtU86cqgZZcF+oP7EoqdKrHWxLtVdS8qzlkY1Rx4Pnz59M2NhdqvVC3x5REJwFdkZTFE0vOx6X8Ct3MrBJe0M3MKuEF3cysEl7Qzcwq4QXdzKwSXtDNzCrR09hiZtLYE4vsqPiPijyxqJmK3LGNpQEdkWRRvSNHjtA+ChuHimQNDPANo1hFwF27dtE+d999N21TFfeee+65xuOsCiMALFiwgLaxc6wqAqp4J7v+VFxQRdfUpskHDhxoPK7Gp6JwLBZYWqWPza26PRWtU2NnzztVXfLKK6+kbUuXLm08XhoxZetMSYVGoGye1Pgmyq/Qzcwq4QXdzKwSXtDNzCrhBd3MrBJe0M3MKtHTlMu5c+do4Sm2F6BKpahPklmb+gR/4cKFRffFUhpq30v16Tnbj/Do0aO0j0oEsD1AVYGwvXv30ja2zycAjIyMNB5XCR1VrIrNhSoS9cQTT9C2nTt3Nh5Xe1GqNINKabAUiZoLdV2wNnVtluyJWZq2UGNnzzt1X+q52t/f33hcpVzUXKg2pnSeSvZXnSi/Qjczq4QXdDOzSnhBNzOrhBd0M7NKeEE3M6uEF3Qzs0pEaWGfojuLOAxgT+vbZQDKqlfVx3NxkefiIs/FRdN9Lt6Ymcvb/VBPF/TX3HHE9szcMCV3/jrjubjIc3GR5+Iiz8XE+C0XM7NKeEE3M6vEVC7o26bwvl9vPBcXeS4u8lxc5LmYgCl7D93MzLrLb7mYmVViShb0iLg1Ip6PiF0Rcc9UjGGqRMR9EXEoIp4Zd2xJRPwwIl5o/Z+XH6xIRAxFxKMRsTMidkTEJ1rHp918RERfRPxXRPx3ay7+qnV8TUQ82XqufCcimjcWrUxEzIyIn0bEv7S+n5bzMFk9X9AjYiaALwO4DcBaAB+OiLW9HscU+jqAWy85dg+ARzLzWgCPtL6fDs4D+FRmrgVwM4CPta6F6TgfZwBsycy3AVgP4NaIuBnA3wD4Qmb+LoBjAPju3XX5BIBnx30/XedhUqbiFfpGALsy88XMPAvg2wDumIJxTInMfAzApYXN7wBwf+vr+wF8sKeDmiKZOZyZT7W+PomxJ/AqTMP5yDGnWt9e0fovAWwB8E+t49NiLiJiEMDtAL7a+j4wDeehxFQs6KsAjN89YV/r2HQ2kJnDra8PABiYysFMhYhYDeBGAE9ims5H622GpwEcAvBDAP8LYCQzf737xnR5rnwRwJ8D+PWOD0sxPedh0vyh6OtMjsWOplX0KCLmAXgIwCcz88T4tuk0H5l5ITPXAxjE2L9kr5viIfVcRHwAwKHM/MlUj+W3UU+3oGvZD2Bo3PeDrWPT2cGIWJmZwxGxEmOv0KaFiLgCY4v5NzPzu63D03Y+ACAzRyLiUQCbACyKiFmtV6fT4bnyTgC/HxHvB9AHYAGAL2H6zUORqXiF/mMA17Y+tZ4N4EMAHp6CcbyePAzgrtbXdwH4/hSOpWda741+DcCzmXnvuKZpNx8RsTwiFrW+7gfwPox9pvAogD9s/Vj1c5GZf5GZg5m5GmNrw39k5h9hms1DqSn5w6LWb98vApgJ4L7M/OueD2KKRMS3ANyCsepxBwF8FsA/A3gQwNUYq0Z5Z2byHaErERHvAvCfAH6Gi++XfgZj76NPq/mIiHUY+7BvJsZeaD2YmZ+LiGswFhxYAuCnAP44M/nO6RWJiFsA/FlmfmA6z8Nk+C9Fzcwq4Q9Fzcwq4QXdzKwSXtDNzCrhBd3MrBJe0M3MKuEF3cysEl7Qzcwq4QXdzKwS/wdpR0sX5hvRYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MzLpTVNAzcyozz2XmLIC/B3Cz+N19mTmWmWPr168vPU4zM1tAUUGPiPnNN34PwHPsd83MrDcWTLlExMMAbgWwKSImAHwJwK0RcROABHAQwB+/i8doZmYdWLCgZ+adLZvvL3my2dlZGntiUS4VaVPdETdv3ty6XcXTfvSjH9ExtYA06zKnImMquqZeFzMxMUHH2GvesmUL3UfFFlW8k50v9XrVOWGdBFXXTBW7Y1HSw4cP031UNFF1zWTXhbqWpqen6VhJ9E/FbUsioSqOpxYqZ1E9FeFTXSRZ10f1eKqjJjvHah9FRSTZHKrz2Cn/paiZWSVc0M3MKuGCbmZWCRd0M7NKuKCbmVWip2uKRgT9pp4lJ6699lr6eCodwdapVI2bbrzxRjp24MABOsaSCSododICGza0d1JQiR/VNIl9e66SLOqPwNR+rJmRanKk0jYsZaDmQjV1UmOMarKmEivsmlYpHDVPJXOrXi+7BlWyQyVZ1HOxOVSJKbVGacmawapesLqkzv3MzAwdU6muknVyO+V36GZmlXBBNzOrhAu6mVklXNDNzCrhgm5mVgkXdDOzSvQ8tsiifCyqp2zcuJGOsWhYaaMlFa1jEbrnn3+e7qPWI3zf+97Xul1FvFRMisWhVHxONRdSUS7W2GnHjh10HxWTY82WVOOzXbt20TEWNbv++uvpPqpZlYotsutCNWFS8VO2QIx6PBW3ZY+nrvUbbriBjm3bto2OsWtGxU8VFj9W86fG2LrAKqKr5l01CWPxydK5mM/v0M3MKuGCbmZWCRd0M7NKuKCbmVXCBd3MrBIu6GZmlbhoui2q9QMZ9lgAjyeqaJCKQaqo3qlTp1q3Hz9+nO6juj6ySB5bJxXgsSuAxwJZbA3QES8V72TnRMUqS+JaKn6qYoajo6Ot29XrVdeZ6krJug+qeT906BAdY+uDsusP0GvNjo+Pt25X0UQV01T3CLu3VIdBdU7Y/aPiwCWdLFV8V8UWVdyRRRpVN8hO+R26mVklXNDNzCrhgm5mVgkXdDOzSrigm5lVYsGUS0RcAeCbALYCSAD7MvO+iBgG8AiAXQAOArgjM3msY+6x6Df/7Fth9c2v+paZ7aea5qiGT2vWrFn0c6lUivoGn82ROgb1rTpLBKhkjEoLlHyDrxqLqXSESgox6tjZOVZJFpXsUNcMm1913aqEDku5qHU01bqXx44da91+8OBBug9LCQE68cPOv7oP1DXDzolKpaiUy+uvv966vWStXkCvGcxSZyr51qlO3qGfBfCFzNwL4BYAn46IvQDuAfB4Zu4B8Hjzs5mZ9cmCBT0zJzPzmebfpwC8AGAHgNsBPNT82kMAPvFuHaSZmS1sUZ+hR8QuAB8A8BSArZk52QwdwdxHMmZm1icdF/SIWAPgOwA+l5nv+MAp5z60bP3gMiLujojxiBgv+TzUzMw601FBj4gVmCvm38rM7zabpyJipBkfATDdtm9m7svMscwcK1mVyMzMOrNgQY+52ML9AF7IzK/MG3oMwF3Nv+8C8P2lPzwzM+tUJ825PgjgjwD8NCJ+0mz7IoB7ATwaEZ8CcAjAHQs90MqVK7Fz587WMdZgiEX4AB3XYvE5FV1ScTzVQIrtpyJoKpLFsGjVQo/Hjn3Tpk10HxWhmpycpGNDQ0Ot20ter9pPxQVVNJVdMyoeq6J66nWxBmKqUZmKpq5bt651u1q7VsUW2RyqyJ0yNTVFx9R9x6j/omfXmWqwV3L/lNYE1TyOxR3VNd2pBQt6Zv4nAHaHfKTrIzAzsyXhvxQ1M6uEC7qZWSVc0M3MKuGCbmZWCRd0M7NK9HRNUYBHolg8Ua2XqOJp7HlUFErFtVQMiXXVU1G4knU0VdxNYcen5k/F8VTckUWy1LGruS3pmqkej11nak1bFeFUnSJZd0RFdX3cv3//orYDet5ZJE/FNBU1T+ycqE6W6thZdLak+yXAj11dZ6rborq/2bWmOkV2yu/Qzcwq4YJuZlYJF3Qzs0q4oJuZVcIF3cysEi7oZmaV6GlsMTNpnIfF01SXNhUpYp0O1T6qG5uKULEOb+q5VKRxeHi4dbuKtKmYFJvD0m5xqoskG1OPp46DjanFe1U8jUVT1TGwxZQBvuAvwM+xOnblxIkTrdufe+45ug/r0Ajw+KmaP6UkBqtinwo7jyo6qRYPZ/eIikGqa0Y9F4s7qn065XfoZmaVcEE3M6uEC7qZWSVc0M3MKuGCbmZWiZ6nXFgqoKSpk8JSFeqbafWNu2qcc/Lkyc4PrKG+jWcNi1TKRaUtGNU8Sq0bqta9ZCkXdR5VaoYdo0pilDyeahKlmpGpc8/G1FyopBWjElPqHI+MjLRuV3Or7hE1xtYALUlMAfx1qQSRajjHzolK2alUSkmipuQevpDfoZuZVcIF3cysEi7oZmaVcEE3M6uEC7qZWSVc0M3MKhFL0RCm4yeLOArgUPPjJgAzPXvyi5vn4jzPxXmei/MGfS52ZubmhX6ppwX9HU8cMZ6ZY3158ouM5+I8z8V5novzPBed8UcuZmaVcEE3M6tEPwv6vj4+98XGc3Ge5+I8z8V5nosO9O0zdDMzW1r+yMXMrBJ9KegRcVtE7I+IAxFxTz+OoV8i4oGImI6I5+ZtG46IH0bE/zb/u6Gfx9grEXFFRDwREc9HxM8i4rPN9oGbj4hYFRH/FRH/3czFXzTbr4qIp5p75ZGI4C03KxIRyyLi2Yj45+bngZyHxep5QY+IZQC+DuC3AewFcGdE7O31cfTRgwBuu2DbPQAez8w9AB5vfh4EZwF8ITP3ArgFwKeba2EQ5+NNAB/OzPcDuAnAbRFxC4C/AvDVzLwGwHEAn+rjMfbSZwG8MO/nQZ2HRenHO/SbARzIzJcy8y0A3wZwex+Ooy8y80kAr12w+XYADzX/fgjAJ3p6UH2SmZOZ+Uzz71OYu4F3YADnI+e83eR7RfN/CeDDAP6x2T4QcxERowB+B8A3mp8DAzgPJfpR0HcAeGXezxPNtkG2NTPfXlHiCICt/TyYfoiIXQA+AOApDOh8NB8z/ATANIAfAvg/ACcy82zzK4Nyr3wNwJ8CeHs1mo0YzHlYNH8pepHJudjRQEWPImINgO8A+FxmvmPJnkGaj8w8l5k3ARjF3H/JXtfnQ+q5iPg4gOnM/HG/j+W9qKdL0DUOA7hi3s+jzbZBNhURI5k5GREjmHuHNhAiYgXmivm3MvO7zeaBnQ8AyMwTEfEEgN8AsD4iljfvTgfhXvkggN+NiI8BWAXgcgD3YfDmoUg/3qE/DWBP8631SgCfBPBYH47jYvIYgLuaf98F4Pt9PJaeaT4bvR/AC5n5lXlDAzcfEbE5ItY3/74MwEcx953CEwB+v/m16uciM/8sM0czcxfmasO/Z+YfYMDmoVRf/rCo+f++XwOwDMADmfmXPT+IPomIhwHcirnucVMAvgTgnwA8CuBKzHWjvCMzL/zitDoR8SEA/wHgpzj/eekXMfc5+kDNR0TciLkv+5Zh7o3Wo5n55Yi4GnPBgWEAzwL4w8zsfjXh94CIuBXAn2Tmxwd5HhbDfylqZlYJfylqZlYJF3Qzs0q4oJuZVcIF3cysEi7oZmaVcEE3M6uEC7qZWSVc0M3MKvH/Rj8OFICJqL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xxhu0j52/CPAYkopkqXgamyOLdgJ6nVjcTcXCFBW7Y7dZpyohwKNmKn6o5jfascq6EUSmzmNSdw51zmtVRiN2N5T6vdj1ripPqsgyq46o1kLFINWZouz3UnvTSA179WTmCwB4XUwzM3tHaOU99E9FxPbGWzLVf0FhZmZjpu6G/q8AbgZwF4BeAP/MfjAiNkbE1ojYqt4yMDOz1tTa0DPzaGa+nZlXAPwbgHvEz27KzO7M7GZ/jm9mZq2rtaFHRNeQb/8UwE72s2ZmNjaGTblExLcB3A9gTkT0APgCgPsj4i4ACWA/gL/6Pc7RzMxGYNgNPTM/VtH8jdGeCIu1zZ49m45RB7GyCJU67FndlzrAlb2VpA7NZYdlA8Add9xR2c4OvgZ03I1Fr+pWuqtzX0qd6oh1opPA6B86Xedw6TrVKgH+e6m4oHo8WJ9aP3Vfqvopuy91e+r5zWLE6m3dM2fO0D72O9epIDncOPY8VpUiR8p/KWpmVghv6GZmhfCGbmZWCG/oZmaF8IZuZlaIMT1TFOCfdg8MDFS2d3Z20ttSiRX2V6msCBigC/uocaxQkPqke7TPQ1XYp/GquJA6E1FhqYU6CQiAf/Jft7AYS6yoOaj7qlMI7PTp03SMSlPVmUOdhI66LtQ6qeuTndm5cyf/E5azZ8/Svr6+vsp2lXJRfTfffHNle92UXZ3CXefOnaNjRsqv0M3MCuEN3cysEN7QzcwK4Q3dzKwQ3tDNzArhDd3MrBBjGlu8cuUKjeaw6J8qcKWidXXOKFXxLxWRvOmm6gObVNRRFSU6ePBgZbs6c1BFslhMShUXmj59Ou0b7WJV6uCTOjX0VbSOxSfV76QiaKdOnWp6nHocOzo6aB9T9+xatk5qjCogpWKLmzdvrmw/fPgwHaOwSKOKfariXOz5rdZCXevqmmHXuyraNlJ+hW5mVghv6GZmhfCGbmZWCG/oZmaF8IZuZlYIb+hmZoUY09jilClTaPVEFodSkSwVW2R9r7/+Oh2zZMkS2qciWdu3b69sVxGvZcuW0b46516qeCerCnfo0CE6RsXnVLyqq6ursl3FIOuce6nijCpqxtQ923L//v20j0V0Fy9eTMeoqCuLydU945XdnorjqXVi53wCQH9/f2W7mh+L2wL8MVZVPdVz+NixY03PQZ0LrPYmFmlUlR1Hyq/QzcwK4Q3dzKwQ3tDNzArhDd3MrBDe0M3MCjFsyiUiFgH4FoBOAAlgU2Z+LSLaAXwXwFIA+wFsyExeqQiDn8azczvZp8l1z3Nk5zaq4j0qzaA+7WafaNc9f5ElCVTBH1Xwqaenp7L9yJEjdAw7YxEAFi1aRPuOHz9e2a4SP6y4GcDTDOq6UOvE+tR5juq62LNnD+3bu3dvZfvq1avpmDlz5tC+0U7vsESIStqo81BZAS6Ar69KdKmibazQljoXWCWj2Dqp570681Rdn+y5ev78eTpmpEbyCv0ygM9l5koA9wL4ZESsBPA4gOcz8xYAzze+NzOzcTLshp6ZvZn5y8bXZwHsBrAAwHoATzV+7CkAj/6+JmlmZsNr6j30iFgK4D0ANgPozMzeRlcfBt+SMTOzcTLiDT0iZgL4HoDPZubA0L4cfDO78g3tiNgYEVsjYqt639jMzFozog09Iq7D4Gb+75n5/Ubz0YjoavR3Aaj829nM3JSZ3ZnZrT6wMDOz1gy7ocdgVOMbAHZn5peHdD0D4LHG148B+OHoT8/MzEZqJMW53g/gLwHsiIhtjbbPA/gSgKcj4uMADgDYMNwNTZ48GW1tbZV9o12EiRWQWr58OR1z4MAB2qfiX6oQTx0srqXieKooEVsnFU2cP38+7Zs3bx7tY2czqsJNdaJ1qhhZndjdiy++SMfceOONtE/F2piXX36Z9qkoKYvJqficOrNzxYoVle0bNvCnMitEN9x9MSqqV6c4l7o9FVlm41Q0WsUq1TVz9OjRyvbROFN02A09M18EwALVD7Y8AzMzGxX+S1Ezs0J4QzczK4Q3dDOzQnhDNzMrhDd0M7NCjOmZotOmTaPndqpIHqMqtbHbU1FHdt4pUC9SpO5LRZ5YhFNVLFTmzp3b1P0A+vFQUS5WLVDFT9UfnLFKhyqCptadzf3OO++kY9Q6qUqMbI4qWqeqN7LHREU4Dx48SPsefbS6/BI7XxPgFSQBHd9l66TWVj1HWDVDFXVUlSLZeZ4qUqvmzqqOArzyJDuPtxl+hW5mVghv6GZmhfCGbmZWCG/oZmaF8IZuZlYIb+hmZoUY09hiRNAol4rCMarKHLsfFU3cuXMn7RsYGKB9Dz30UNNjVJxswYIFle3qMGUVn/vpT39a2a6ihO973/ton4qnsUNM1CHMvb29tI9FUxcvXkzHqKgZqxSprr++vj7ap8axaN2pU/wsdXUIDLuv/v5+Oubhhx+mfSxC/Oyzz9IxdQ5LB3jkUkWPVSxw5syZle1qT1BzZ33q8VV9KmI8ffr0yvbROADIr9DNzArhDd3MrBDe0M3MCuEN3cysEN7QzcwKMaYplytXrtC0A/tEWxVaUukDliK599576ZgdO3bQPnWOJiuopD6lV5+4s0JgKuWiCj6xNW9vb6djVIEmlTBha6gKcL366qu0j6UWuru76ZhFixbRPpaAUQmDOsXDAL7uKuGkiqKxMzvvvvtuOoYlsAB+juqZM2fomLprwajfV6VmWOGuCxcu0DHqOcfOwmVpGkA/v+sk0tRzeKT8Ct3MrBDe0M3MCuEN3cysEN7QzcwK4Q3dzKwQ3tDNzAoRdYpi1b6ziH4ABxrfzgHAD96bWLwWV3ktrvJaXDXR12JJZlYfDjzEmG7ov3PHEVszk4eJJxCvxVVei6u8Fld5LUbGb7mYmRXCG7qZWSHGc0PfNI73/U7jtbjKa3GV1+Iqr8UIjNt76GZmNrr8louZWSHGZUOPiHURsTciXouIx8djDuMlIp6MiGMRsXNIW3tE/DgiXm38m5dqK0hELIqIn0TEroh4JSI+02ifcOsREddHxJaIeLmxFn/faF8WEZsbz5XvRgQvQViQiJgcEb+KiGcb30/IdWjWmG/oETEZwBMAHgawEsDHImLlWM9jHH0TwLpr2h4H8Hxm3gLg+cb3E8FlAJ/LzJUA7gXwyca1MBHX4yKAD2bmKgB3AVgXEfcC+EcAX8nMPwJwCsDHx3GOY+kzAHYP+X6irkNTxuMV+j0AXsvMfZl5CcB3AKwfh3mMi8x8AcDJa5rXA3iq8fVTAB4d00mNk8zszcxfNr4+i8En8AJMwPXIQb8toH5d458E8EEA/9lonxBrERELATwC4OuN7wMTcB3qGI8NfQGAQ0O+72m0TWSdmdnb+LoPQOd4TmY8RMRSAO8BsBkTdD0abzNsA3AMwI8BvA7gdGb+9hSIifJc+SqAvwHw29NtOjAx16Fp/lD0HSYHY0cTKnoUETMBfA/AZzNzYGjfRFqPzHw7M+8CsBCD/ye7YpynNOYi4sMAjmXmL8Z7Ln+IxvQIuobDAIaeEbaw0TaRHY2IrszsjYguDL5CmxAi4joMbub/npnfbzRP2PUAgMw8HRE/AfDHANoiYkrj1elEeK68H8CfRMSHAFwPYDaAr2HirUMt4/EK/SUAtzQ+tZ4K4KMAnhmHebyTPAPgscbXjwH44TjOZcw03hv9BoDdmfnlIV0Tbj0iYm5EtDW+vgHAQxj8TOEnAP6s8WPFr0Vm/m1mLszMpRjcG/43M/8cE2wd6hqXPyxq/Nf3qwAmA3gyM/9hzCcxTiLi2wDux2D1uKMAvgDgvwA8DWAxBqtRbsjMaz84LU5E3Afg/wDswNX3Sz+PwffRJ9R6RMSdGPywbzIGX2g9nZlfjIjlGAwOtAP4FYC/yMzqE5ILExH3A/jrzPzwRF6HZvgvRc3MCuEPRc3MCuEN3cysEN7QzcwK4Q3dzKwQ3tDNzArhDd3MrBDe0M3MCuEN3cysEP8PaivPoPJr4PsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keras.preprocessing.image\n",
    "\n",
    "for i in range(10):  \n",
    "    #X = mnist_data_train\n",
    "    X, y = next(keras_generator_56)\n",
    "    #X = keras.preprocessing.image.random_brightness(ped_data_train[i], brightness_range=[1,3])\n",
    "    plt.imshow(X[0].reshape(25,50), cmap=\"Greys\")\n",
    "    plt.show()\n",
    "    plt.imshow(ped_data_train[i].reshape(25,50), cmap=\"Greys\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "24/23 [==============================] - 7s 294ms/step - loss: 0.6934 - acc: 0.5016 - val_loss: 0.8846 - val_acc: 0.5000\n",
      "Epoch 2/30\n",
      "24/23 [==============================] - 7s 283ms/step - loss: 0.6934 - acc: 0.4870 - val_loss: 0.6729 - val_acc: 0.6080\n",
      "Epoch 3/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6923 - acc: 0.5293 - val_loss: 0.8376 - val_acc: 0.5000\n",
      "Epoch 4/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6933 - acc: 0.5059 - val_loss: 0.6580 - val_acc: 0.5940\n",
      "Epoch 5/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6932 - acc: 0.5052 - val_loss: 1.1356 - val_acc: 0.5000\n",
      "Epoch 6/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.6929 - acc: 0.5046 - val_loss: 1.1576 - val_acc: 0.5040\n",
      "Epoch 7/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6924 - acc: 0.5212 - val_loss: 0.6881 - val_acc: 0.5980\n",
      "Epoch 8/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6916 - acc: 0.5459 - val_loss: 4.2922 - val_acc: 0.5100\n",
      "Epoch 9/30\n",
      "24/23 [==============================] - 7s 275ms/step - loss: 0.6875 - acc: 0.5856 - val_loss: 5.2129 - val_acc: 0.5520\n",
      "Epoch 10/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.6783 - acc: 0.5993 - val_loss: 4.6110 - val_acc: 0.6070\n",
      "Epoch 11/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.6540 - acc: 0.6540 - val_loss: 5.4666 - val_acc: 0.6190\n",
      "Epoch 12/30\n",
      "24/23 [==============================] - 7s 287ms/step - loss: 0.6134 - acc: 0.6810 - val_loss: 5.9090 - val_acc: 0.6120\n",
      "Epoch 13/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5819 - acc: 0.7057 - val_loss: 5.9662 - val_acc: 0.6180\n",
      "Epoch 14/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5800 - acc: 0.6969 - val_loss: 6.0166 - val_acc: 0.6160\n",
      "Epoch 15/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5653 - acc: 0.7057 - val_loss: 6.8170 - val_acc: 0.5730\n",
      "Epoch 16/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.5674 - acc: 0.7148 - val_loss: 5.9568 - val_acc: 0.6190\n",
      "Epoch 17/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5669 - acc: 0.7161 - val_loss: 6.8457 - val_acc: 0.5730\n",
      "Epoch 18/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5528 - acc: 0.7119 - val_loss: 6.8920 - val_acc: 0.5650\n",
      "Epoch 19/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5520 - acc: 0.7220 - val_loss: 6.2513 - val_acc: 0.6010\n",
      "Epoch 20/30\n",
      "24/23 [==============================] - 7s 283ms/step - loss: 0.5338 - acc: 0.7321 - val_loss: 5.5757 - val_acc: 0.6370\n",
      "Epoch 21/30\n",
      "24/23 [==============================] - 7s 278ms/step - loss: 0.5311 - acc: 0.7344 - val_loss: 5.3840 - val_acc: 0.6390\n",
      "Epoch 22/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.5367 - acc: 0.7184 - val_loss: 6.2642 - val_acc: 0.5980\n",
      "Epoch 23/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5181 - acc: 0.7370 - val_loss: 5.8973 - val_acc: 0.6170\n",
      "Epoch 24/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5333 - acc: 0.7301 - val_loss: 5.6028 - val_acc: 0.6280\n",
      "Epoch 25/30\n",
      "24/23 [==============================] - 7s 278ms/step - loss: 0.5250 - acc: 0.7350 - val_loss: 6.8480 - val_acc: 0.5650\n",
      "Epoch 26/30\n",
      "24/23 [==============================] - 7s 277ms/step - loss: 0.5196 - acc: 0.7425 - val_loss: 6.2805 - val_acc: 0.5860\n",
      "Epoch 27/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.5161 - acc: 0.7386 - val_loss: 5.6345 - val_acc: 0.6210\n",
      "Epoch 28/30\n",
      "24/23 [==============================] - 7s 276ms/step - loss: 0.5035 - acc: 0.7415 - val_loss: 4.4669 - val_acc: 0.6740\n",
      "Epoch 29/30\n",
      "24/23 [==============================] - 7s 278ms/step - loss: 0.5046 - acc: 0.7516 - val_loss: 6.0882 - val_acc: 0.6060\n",
      "Epoch 30/30\n",
      "24/23 [==============================] - 7s 286ms/step - loss: 0.5090 - acc: 0.7464 - val_loss: 5.6227 - val_acc: 0.6240\n"
     ]
    }
   ],
   "source": [
    "history_56_2 = model_56_2.fit_generator(keras_generator_56, steps_per_epoch=len(ped_data_train)/batch_size_56_2, epochs = 30, \n",
    "                        validation_data = (ped_data_test, ped_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_56_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We tried to achieve better results by augmenting the input. Sadly, we were not able, to really test it, because the runtime grew so dramatically, that training the net is not really possible anymore. Also it might be, that the 25x50 pixel images are a little too small, which results in quite huge relative changes, when maniputlationg them with the augmentor. But maybe, we just didn't try to use the augmentor correctly. We tried different vaiations of the uncommented pipeline, but the net never seemd to be able to learn from the nets at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}