03_DCGAN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DCGAN MNIST\n",
    "\n",
    "This notebook is for implementing `Deep Convolution Generative Adversarial Network(DCGAN)` from the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) with [Tensorflow](https://www.tensorflow.org). <br>\n",
    "[MNIST data](http://yann.lecun.com/exdb/mnist/) will be used.\n",
    "\n",
    "Reference: [hwalsuklee's Github](https://github.com/hwalsuklee/tensorflow-generative-model-collections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import datetime\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm = 'DCGAN_MNIST'\n",
    "\n",
    "img_size   = 28\n",
    "\n",
    "batch_size = 128\n",
    "num_epoch  = 50\n",
    "\n",
    "n_latent = 100\n",
    "\n",
    "beta1 = 0.5\n",
    "\n",
    "learning_rate_g = 0.0002\n",
    "learning_rate_d = 0.0002\n",
    "\n",
    "show_result_epoch = 5\n",
    "\n",
    "date_time = datetime.datetime.now().strftime(\"%Y%m%d-%H-%M-%S\")\n",
    "\n",
    "load_model = False\n",
    "train_model = True\n",
    "\n",
    "save_path = \"./saved_models/\" + date_time + \"_\" + algorithm\n",
    "load_path = \"./saved_models/20190809-11-04-47_DCGAN_MNIST/model/model\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import MNIST Dataset\n",
    "\n",
    "Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist.load_data(path='mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28)\n",
      "y_train shape: (60000,)\n",
      "x_test shape: (10000, 28, 28)\n",
      "y_test shape: (10000,)\n"
     ]
    }
   ],
   "source": [
    "x_train = mnist[0][0]\n",
    "y_train = mnist[0][1]\n",
    "x_test  = mnist[1][0]\n",
    "y_test  = mnist[1][1]\n",
    "\n",
    "print('x_train shape: {}'.format(x_train.shape))\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('x_test shape: {}'.format(x_test.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Generator(x, is_training, reuse=False):\n",
    "    with tf.variable_scope('Generator', reuse=reuse):\n",
    "        xavier_init_conv = tf.contrib.layers.xavier_initializer_conv2d()\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "        \n",
    "        # Project and Reshape \n",
    "        w1 = tf.get_variable('w1', [x.get_shape()[1], 7*7*1024], initializer=xavier_init)\n",
    "        b1 = tf.get_variable('b1', [7*7*1024], initializer=xavier_init)\n",
    "        \n",
    "        x_project = tf.matmul(x,w1)+b1\n",
    "        x_reshape = tf.reshape(x_project, (-1, 7, 7, 1024))\n",
    "        \n",
    "        # First deconv layer\n",
    "        h1 = tf.layers.conv2d_transpose(x_reshape,filters=512, kernel_size=5, strides=1, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h1 = tf.nn.leaky_relu(h1)\n",
    "        \n",
    "        # Second deconv layer\n",
    "        h2 = tf.layers.conv2d_transpose(h1,filters=256, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h2 = tf.layers.batch_normalization(h2, training=is_training)\n",
    "        h2 = tf.nn.leaky_relu(h2)  \n",
    "        \n",
    "        # Third deconv layer \n",
    "        h3 = tf.layers.conv2d_transpose(h2, filters=128, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h3 = tf.layers.batch_normalization(h3, training=is_training)\n",
    "        h3 = tf.nn.leaky_relu(h3)\n",
    "        \n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(h3, filters=1, kernel_size=5, strides=1, padding='SAME', kernel_initializer=xavier_init_conv)     \n",
    "        output = tf.tanh(logits)\n",
    "                \n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Discriminator(x, is_training, reuse=False):\n",
    "    with tf.variable_scope('Discriminator', reuse=reuse):\n",
    "        xavier_init_conv = tf.contrib.layers.xavier_initializer_conv2d()\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "\n",
    "        # First conv layer\n",
    "        h1 = tf.layers.conv2d(x, filters=64, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h1 = tf.nn.leaky_relu(h1)\n",
    "        \n",
    "        # Second conv layer\n",
    "        h2 = tf.layers.conv2d(h1, filters=128, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h2 = tf.layers.batch_normalization(h2, training=is_training)\n",
    "        h2 = tf.nn.leaky_relu(h2)\n",
    "        \n",
    "        # Trhid conv layer\n",
    "        h3 = tf.layers.conv2d(h2, filters=256, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h3 = tf.layers.batch_normalization(h3, training=is_training)\n",
    "        h3 = tf.nn.leaky_relu(h3)\n",
    "        \n",
    "        # Output layer\n",
    "        flatten = tf.reshape(h3, (-1, h3.get_shape()[1]*h3.get_shape()[2]*h3.get_shape()[3]))\n",
    "        \n",
    "        logit  = tf.layers.dense(flatten, 1, kernel_initializer=xavier_init)\n",
    "        output = tf.sigmoid(logit)  \n",
    "\n",
    "        return logit, output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GAN(x, z, is_training):\n",
    "    # Generator\n",
    "    G = Generator(z, is_training)\n",
    "    \n",
    "    # Discriminator\n",
    "    D_logit_real, D_out_real = Discriminator(x, is_training)\n",
    "    D_logit_fake, D_out_fake = Discriminator(G, is_training, reuse=True)\n",
    "    \n",
    "    # get loss\n",
    "    d_loss = -(tf.reduce_mean(tf.log(D_out_real + 1e-8)) + tf.reduce_mean(tf.log( (1-D_out_fake) + 1e-8)))\n",
    "    g_loss = -tf.reduce_mean(tf.log(D_out_fake + 1e-8))\n",
    "\n",
    "    return d_loss, g_loss , G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 1])\n",
    "x_normalize = (tf.cast(x, tf.float32) - (255.0/2)) / (255.0/2)\n",
    "\n",
    "z = tf.placeholder(tf.float32, shape=[None, n_latent])\n",
    "\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "d_loss, g_loss, G = GAN(x_normalize, z, is_training) \n",
    "\n",
    "# optimization\n",
    "trainable_variables = tf.trainable_variables()\n",
    "\n",
    "trainable_variables_d = [var for var in trainable_variables if var.name.startswith('Discriminator')]\n",
    "trainable_variables_g = [var for var in trainable_variables if var.name.startswith('Generator')]\n",
    "\n",
    "update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(update_ops):\n",
    "    train_step_d = tf.train.AdamOptimizer(learning_rate_d).minimize(d_loss, var_list=trainable_variables_d)\n",
    "    train_step_g = tf.train.AdamOptimizer(learning_rate_g).minimize(g_loss, var_list=trainable_variables_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize variables\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "\n",
    "sess = tf.InteractiveSession(config=config)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Saver = tf.train.Saver()\n",
    "\n",
    "if load_model == True:\n",
    "    Saver.restore(sess, load_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 / G Loss: 4.00108 / D Loss: 0.36782\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2 / G Loss: 7.86072 / D Loss: 0.82258\n",
      "Epoch: 3 / G Loss: 5.03641 / D Loss: 0.26739\n",
      "Epoch: 4 / G Loss: 3.41506 / D Loss: 0.61428\n",
      "Epoch: 5 / G Loss: 4.99872 / D Loss: 0.27721\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6 / G Loss: 3.89599 / D Loss: 0.24249\n",
      "Epoch: 7 / G Loss: 4.26712 / D Loss: 0.30656\n",
      "Epoch: 8 / G Loss: 2.75122 / D Loss: 0.50679\n",
      "Epoch: 9 / G Loss: 1.84333 / D Loss: 0.59732\n",
      "Epoch: 10 / G Loss: 2.23371 / D Loss: 0.32055\n"
     ]
    },
    {
     "data": {
      "image/png": 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AiDdXGbXU28477wyEfPaqd4pwuOKKK0q+lloociElrpnR6V5Yc1EUjPzL6TzmpZCVutIU6q19++23DX4+b968sp3LFbnjOE4bJxOKvIH9Gn2fjvNtKhudvitVKkUmv7bisstJNRRF4QzUdASCyrr99tsDMH36dCDYKu3r23bbbYGQ5TCtyJWrQ8cphVqrLJV5//33B4ISVw9Hsx+1utINN9xQ9P0nnngCCOMF2l9RUhB6Qs2lloq84JgA7LXXXkDwdasn0rNnTyDEl6dXx1Ed0rhWOXLX17quNBdFPp1yyilAqBOKfKrmgtSuyB3HcSInk4q8EgwbNgwIsaEabVeulXJSDUWhpz6EiBtlpJPP/vTTTwdg3LhxQP24cv32ih9X5se0v1T+z9aorVqprHReckUrpWcuXnjhhQCMGjWq6HOhaA/5PTVWo4gFaHnvLguKvLkoIkNRKWll3r9/fwCeeeaZVp8r64pcGSLVhqR7e62J7moMV+SO4zhtnHajyKVWpSq1UowiMspJNRRFoSJ69tlnATjggAPS1wGEmGlFKLz88stAyDWt78vfmx6j0CpMUvalUKs4cqlk+bjlA9Y4wsEHHwwEmzR2P6iXMnnyZCD0YgptNXHiRCDkylcEjI6pfC4iJkUurrnmGiBkR5RdtNKW7rPWRK9kVZGrV/LAAw8AcOyxxwIhF7vGksoRS5/GFbnjOE4bJ9O5VsqJ8k1LQaXjgmNbi7Ewdl2qUjkiFIefXklesxWlTtMKPZ1rRWg2bEzod9Xsuu7duwPhdz7++OOBppW4kJrWbF7NrC3s0Q0YMACAjTbaCAjrv6bnGcSMVsHR/aSIJ40ZqA5qdmNbQEpcq03pd1adGT58OFAZJd5cXJE7juNETvSKXApbvjopJz0ttV3Z74R8zIp/1azGGJHKvPPOO4v+p9dn1Hvtr881SzRtM+0XY4Y7lVV5thWxo7w9iq5o6RjRjBkzgDBr9q677qrbpggorSQT86pCjaE6MXLkSADuvfdeINQZxZ8XzkWIzQ4qS5cuXYAwe/fUU08t2q7enPKZ1xJX5I7jOJHjDbnjOE7kRO9aUde4sQElbZ85c2bRe6FUlDG7VhojHe7W2ICuFhsYOnRo0edp10tM9OnTBwiLLiu9wOWXXw60vruvZFEDBw6s+yy2AfNCmrs8oAaRzz333KLv6f7TAi5ZG+CVq0cpBwqXj1QwgNoCpbZQeKGCAOSumzRpEhDCcsuRKKy1uCJ3HMeJnOgVeXO55ZZbgDCNtlu3bkD2lEMtkKJID3ZKgYwdOxYIoWaF38kShamNtQCEeiVDhgwBQmKjchGzCi9EtlN6W4XcaZKL0vwq7YMWOEl/X4vBPPjgg3XbstCjU7jx+eefD8DRRx9dt02LliuBmtB9oDqkwU2FHzaVHrqauCJ3HMeJnHajyKUKlIZU4YdpP3J7RCl9Z82aBYTeiqZdK8Qu62FkCn0D6NWrFxB6E1qEu5opKcpNOsS2nOiY6cVDlCRME4DSSjz9fYXzFoasZkGRaxnGwYMHA0FdQ0g7oElj6bDdadOmAWEyWJaUuHBF7jiOEzntJmlWNclq0p+mkE8wPSGoHFTSJopIUBQBhN6EElllUYm3NGlW586dEwhTwbMwviP1qtSuvXv3BmDKlCl1++i3aC61Tnksf3qnTp2AkGivlkrck2Y5juO0cVyRV4BYFXklcZvUp1RFLiVeyyRNlcTrSn1ckTuO47Rx2k3UiuPEjny08uEqukiRJYrMcLJPevGW1npGXJE7juNETlV95I7jOE75cUXuOI4TOd6QO47jRI435I7jOJHjDbnjOE7keEPuOI4TOd6QO47jRI435I7jOJHjDbnjOE7keEPuOI4TOd6QO47jRI435I7jOJHjDbnjOE7keEPuOI4TOd6QO47jRI435I7jOJHjDbnjOE7keEPuOI4TOd6QO47jRI435I7jOJHjDbnjOE7keEPuOI4TOd6QO47jRI435I7jOJHz/8+AmqA5b4RTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11 / G Loss: 4.12701 / D Loss: 0.19121\n",
      "Epoch: 12 / G Loss: 3.41717 / D Loss: 0.44428\n",
      "Epoch: 13 / G Loss: 3.43995 / D Loss: 0.24728\n",
      "Epoch: 14 / G Loss: 3.05237 / D Loss: 0.45194\n",
      "Epoch: 15 / G Loss: 3.06047 / D Loss: 0.37452\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16 / G Loss: 1.85226 / D Loss: 0.58204\n",
      "Epoch: 17 / G Loss: 2.56004 / D Loss: 0.65937\n",
      "Epoch: 18 / G Loss: 2.86730 / D Loss: 0.38894\n",
      "Epoch: 19 / G Loss: 2.26278 / D Loss: 0.53998\n",
      "Epoch: 20 / G Loss: 2.91522 / D Loss: 0.37492\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21 / G Loss: 2.20680 / D Loss: 0.63151\n",
      "Epoch: 22 / G Loss: 2.55431 / D Loss: 0.79801\n",
      "Epoch: 23 / G Loss: 1.83601 / D Loss: 0.55887\n",
      "Epoch: 24 / G Loss: 2.97195 / D Loss: 0.41435\n",
      "Epoch: 25 / G Loss: 2.60280 / D Loss: 0.57087\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26 / G Loss: 1.78911 / D Loss: 0.62173\n",
      "Epoch: 27 / G Loss: 2.36843 / D Loss: 0.60049\n",
      "Epoch: 28 / G Loss: 2.47037 / D Loss: 0.65394\n",
      "Epoch: 29 / G Loss: 2.70155 / D Loss: 0.43939\n",
      "Epoch: 30 / G Loss: 2.69900 / D Loss: 0.48312\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31 / G Loss: 2.60142 / D Loss: 0.32977\n",
      "Epoch: 32 / G Loss: 2.16996 / D Loss: 0.93049\n",
      "Epoch: 33 / G Loss: 2.01185 / D Loss: 0.46202\n",
      "Epoch: 34 / G Loss: 2.55092 / D Loss: 0.44521\n",
      "Epoch: 35 / G Loss: 1.80773 / D Loss: 0.77080\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 36 / G Loss: 2.50386 / D Loss: 0.64849\n",
      "Epoch: 37 / G Loss: 3.03653 / D Loss: 0.41920\n",
      "Epoch: 38 / G Loss: 2.80823 / D Loss: 0.38585\n",
      "Epoch: 39 / G Loss: 1.68100 / D Loss: 0.43322\n",
      "Epoch: 40 / G Loss: 2.21378 / D Loss: 0.90511\n"
     ]
    },
    {
     "data": {
      "image/png": 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9lv2uXis7dW1agfhH27DF6psA0exXsQ1tFKENTsRaa60FRFlfQquw41vlVWuzRvcVXfuMGTOA8vbThh3KsovXtKk1vrLTcRyng5NJH/n7778PRIo8TpozCKFVfsk2qHJiVpGaUj2Ngw8+uNn/Dz74IBCpTWUgKAagLazkR41fr3ypeUEKUqtXd9xxRwBWXnlloFRZyjeujamzllHxR6h/ajvErbbaqum5ZLwkOTvr1q1bs/9XX331Zr+T50gSq9ZXbbMzwzbbbANEM9gkmsXovtB41UhckTuO4+ScTCpyVWKTcpRCBNhzzz0BePzxx4F0feUDBw4EIlVz4403pnbutiDVpOyVm2++GYjaLZ+3anXLN3jyyScDkSLX5s3vvvtu03trZxnFM9LcDaUatCJT/k5VMUySVKqyxUknnQREeeTKUFK/a8QMsVK0ClqfH8Dw4cOBKCNJq36VraNMly+++AIorcmi47TKWVkqYocddmj2ujyhPqDYV3ImLs4//3wgmvFk4VpdkTuO4+ScTGatqGqb6qTIjwlRpPiWW24BooyRSlekVUp8ReLUqVOBaN9MqTGpvWSebKOi7lIUPXv2BCJ1Jf+m1LJWv5arUKccY/mHlT8fRz5k5cy+9tprQDRjSs6U0raJlKNWGCrzSMjP2VoGj+4P9S/t8qIYQXzF5/XXXw9EWUKtreyrdz1yobpDEPl/VatIs7VKxwHZVbt2KWNDKMMsGXOohkbdP8pg0mwlWTVSs5R+/foBpavC64lnrTiO43RwMqnIpSy1U7yq9kGkHPStqX3zVLlPe1omj28NKTNlbshPHH9Mx0jJJlegxs7ZEEUhH74qPMrHJ2WYrMl+xBFHAHD33Xc3O07XucsuuwBRHebkqsc4+jzWXXfdFp9P2yZqx6xZs4DSXWhi52rvqZqQQpOPVb7Ucso8LUUeJ2mHtt7/UuTJWiuq1x7Pcqm25kjafUUzWNVKkZ9fKPtr5513BprvuJQWrsgdx3E6OJnMWpFKUL3veNaKvhUViZcPdO7cuUC004vqJevbVPmx8n1rJ3nVFteu6uuvv35Je5QTqx3E42o9SyiDROpYGQXyeUuN6f/Jkyc3O37cuHFAtCJQ1R7/yI+sz0orILOCVgAnFXc5Ba7rUA0R+f67d+/e7Lden6x8B9HqR80k06x+Vym1apNWMSqzTPeVZm3aGxeifplVtIJ52223bfa47vuRI0cCUWwpi7gidxzHyTmZ9JEniftdL7zwQiCq0Kd6IuXqXievr5xvUKpT2QnyM0OUjys/fGsKo9G1IlRXfNiwYUBUvVFR+dZsJRtIXbWkyKVcpepHjRoF1MYfXAubSEFr1V25Cnbag/KYY44BoiwOXbMyppRHrbiB1LfWM8RfWymN8JHXilNPPRWI7kfFX1Ql8pxzzmk6VusRKiWNvhLv01ofkYx5Pf3000AUB1BsqREzLfeRO47jdHB8IHccx8k5mQx2Jom7MrQ4RUXfVQhKmy5rM9n+/fsD0TRIAT4tS1eAT4t55CqYNm0aEAVDId0yALVAC3+0hFjuhdGjRwMwZMgQIEoR0zRTATzZKkl8SqlgspawZ2GZchxN8bVgRYt05IrTloGnnXYaUDpdVqBL7hL9bkTqWRbRVohJt5vcekrpyyrxdifbqr48ceJEAHr16gVEqc1Z3CjDFbnjOE7OyUWwsx4k09BqaYdGBzvLoeClAoEK3J1xxhlAFFTW8mqpFqlVqVqI0jUrXabcaJtIKSqQm4XUwDwHO7fYYgsApk+fDkT2VekGpexBVKSt0nss7WCngppKbRbq25MmTQLgjjvuAKKifmn2IQ92Oo7jdHA6rSKvJ41Wn9Wi2YlUijZhkJ9YPsH2bECcN5ukQZ4VudBmI4MGDQIitVquIFslpN1XBgwYAETKWwuDtChMZZ5VFO2ll15q7ymrxhW54zhOB8cVeR1w9VmK26SUjqDI64H3lVJckTuO43RwcpFH3hmR3zpr+dmO46RHpWWWXZE7juPkHFfkGcWVuOM4FW+MU+d2OI7jOHUm1awVx3Ecp/a4Inccx8k5PpA7juPkHB/IHcdxco4P5I7jODnHB3LHcZyc4wO54zhOzvGB3HEcJ+f4QO44jpNzfCB3HMfJOT6QO47j5BwfyB3HcXKOD+SO4zg5xwdyx3GcnOMDueM4Ts7xgdxxHCfn+EDuOI6Tc3wgdxzHyTk+kDuO4+QcH8gdx3Fyjg/kjuM4OccHcsdxnJzjA7njOE7O8YHccRwn5/w/TtNAYs4dWWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41 / G Loss: 1.26145 / D Loss: 0.55897\n",
      "Epoch: 42 / G Loss: 1.74328 / D Loss: 0.62891\n",
      "Epoch: 43 / G Loss: 3.26989 / D Loss: 0.35692\n",
      "Epoch: 44 / G Loss: 2.11031 / D Loss: 0.40998\n",
      "Epoch: 45 / G Loss: 3.39902 / D Loss: 0.39096\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46 / G Loss: 2.13323 / D Loss: 0.49962\n",
      "Epoch: 47 / G Loss: 2.54932 / D Loss: 1.08607\n",
      "Epoch: 48 / G Loss: 1.94757 / D Loss: 0.41966\n",
      "Epoch: 49 / G Loss: 2.51574 / D Loss: 0.30599\n",
      "Epoch: 50 / G Loss: 3.09060 / D Loss: 0.39425\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train_model:\n",
    "    # Training\n",
    "    data_x = x_train\n",
    "    len_data = x_train.shape[0]\n",
    "\n",
    "    for i in range(num_epoch):\n",
    "        # Shuffle the data \n",
    "        np.random.shuffle(data_x)\n",
    "\n",
    "        # Making mini-batch\n",
    "        for j in range(0, len_data, batch_size):\n",
    "            if j + batch_size < len_data:\n",
    "                data_x_in = data_x[j : j + batch_size, :]\n",
    "            else:\n",
    "                data_x_in = data_x[j : len_data, :]\n",
    "\n",
    "            data_x_in = data_x_in.reshape((-1, img_size, img_size, 1))\n",
    "\n",
    "            sampled_z = np.random.uniform(-1, 1, size=(data_x_in.shape[0] , n_latent))\n",
    "\n",
    "            # Run Optimizer!\n",
    "            G_out = sess.run(G, feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "\n",
    "            _, loss_d = sess.run([train_step_d, d_loss], feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "            _, loss_g = sess.run([train_step_g, g_loss], feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "\n",
    "            print(\"Batch: {} / {}\".format(j, len_data), end=\"\\r\")\n",
    "\n",
    "        # Print Progess\n",
    "        print(\"Epoch: {} / G Loss: {:.5f} / D Loss: {:.5f}\".format((i+1), loss_g, loss_d))\n",
    "\n",
    "        # Show test images \n",
    "        z_test = np.random.uniform(-1, 1, size=(5, n_latent))\n",
    "        G_out = sess.run(G, feed_dict = {z: z_test, is_training: False})\n",
    "\n",
    "        if i == 0 or (i+1) % show_result_epoch == 0:\n",
    "            f, ax = plt.subplots(1,5)\n",
    "            for j in range(5):\n",
    "                ax[j].imshow(G_out[j,:,:,0], cmap = 'gray')\n",
    "                ax[j].axis('off')\n",
    "                ax[j].set_title('Image '+str(j))\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3bp1A9I8NLo387IWvDrfffddkwMurZx/zjnnjPH/+POuB2az1UNm0EYbbQSQjUOPdLGvf/3114p4EMeGPqC99torm/fe83LVjPL6HbOFVdFd/1uqn9fS727veP/rr7+aZN86B83aVdX0iCiVJdXRcmbvthWz0PQ0emSM980jovRhVmKnI5SjIAiCIAiCAupSObIGhk/GDQ0NWWRqrQcr8cqhhx4KpINYfXVv+7333gNShGskbJSlW94KuIU1QSBFmqVQjvzdqlGiyuXeeGuqWVtjqDAjBYo/+K8UWANmzz33BJLqYtu8l9dccw2QMsF83+w2qzKrrOXvgwqSUd7iiy+eqS9GWe1FhdHIxgrJ+qSMzlQi1ltvPWB0ZWVrI3lQqHVKPABSz8BLL70EJMVIdbSSmTP+TftIj55eG2tsOd/0U3nd+YjZA2dLMU9Kxcsvv5z52Vw/VC+tlSWqCd4XfY8q1voAa0ERE/visssuA9Jhpl5DHlX3auJ6vfvuu2drsllYzrVSKyDeJ3+/YxeS96klL2tLv9vx5SG/pfSqqSg7xzxA19pivr/XXnu16fe3VTUbE65p+rs881OPkXifPHS4kmefhnIUBEEQBEFQQF0qR0btejL+/vvvJudzGflZV8U9Y6MmX0UvjvvweR9Mnvw+uMpTKVCdyp9vdMoppwBJUWgN1vowa0jeffddoDK1c6xPYrSkinX66acDaa9fxUhUjPr27QskP0I+Oje6s+KtfTd8+PCSX59jz73wZZZZBmh62rqRplHoiBEjuPvuu4HkOdKH41hS3fJ91ZpqZKu5t29GoNGoGU7ORc+vsi/15JlZYzanlbVrjRdffBFIGT9WVbbfnO+OMT05KsbO1Wpn3o0Jr0UfWHO13Gz70KFDq1qFHdKcv/XWW7O1WvXFSupWNXeMlkrZePTRR4FUib+Qtp496Xpg7bVy+gb1P+bXR9XetlKKse18MtvMjEA9jOLuiFXPq1GxPZSjIAiCIAiCAupSOTKqGTZsGDD6adS9XJ/QjfCMwn1izbvh9RDo58l7JYyyrMVjROM+uHVcSrH/beSTV7WMVoxSW/MEbzaN++i229+helEJjJbMInN/2WyZ/Pl41vHYcMMNgXRKe/76vW/6FPQmeN8OOOCAkkf0VtnVa2NU7jV5BtP7778PQJ8+fbJrUDlrTglyzPp/9btZ6VafTyXwvunzch7o0TFjUmXIeaNvzPli35QzG7IU5KPtvFLg/NFb5c+bzVdLypHePusCuZbpbbOWkRm8jqsPP/ww60ev3znm9btuuhZ73d6P/NdtZb/99ssU1J122gmAJZdcEkhj0srYX331FZBq1bVWaXVs3nvvvUA6Z2+++eYrmTczrxiV0scjhZW9IV1XMTXYyoXjxZ0Zq3ebQec4s26alfdLobC1OcOw3X85CIIgCIKgA9FQC5FOQ0NDqxqhuuPT/EQTTcQaa6wBwFNPPQW0/JRoLRxr5agk5SNgn8L1x+g1MCIuJT5dv/3220DyB/n0vMgiiwBJURgbZheZeWJlY3njjTeA5LWqpJ9FJcSKt0ajRmX6P9yHNjo1AlAVNOtLL5ZjwJ97/PHHgdHqTrk9FPm6WCoLKi1tUUzM0Ojfvz+QFDbPb6slvP6DDz4YSH1ifSnVv1pYb1qD16WnyPnkXPQMMudqLWSpme2osmLW6/fffw8kBTqvinmtCy64YNZfeuoci9b3cjw73lVK8wq7Xrz2ZInq71MRPuKIIxp9bbv1vz300ENA08ynYjFT2XpyW221VXavzJRua50ddzjsm169egEwaNAgIN2n1swTr9+stOuvvx5I6q73xc+EUmXsFoNr9S233AKkcyL1UebPRPXEgfZ8Hjle9Du6a3L//feP8edHjRo1RmNxKEdBEARBEAQF1KXnyKdN95bnmGOOLOuq2Cdun/x9svXsrfwZUZXEp2WVIaNRlRW/bk45Uu3q27dvtj9vhGuk5xO8ylk1MqD0itl/iy22GJDab0Vwo1IjATOkzL6Za665gKQkip6eHj16AJXJxHPcOX5KMY7yEZ7+sVtvvRWoDZVC9EOpwKrq/utf/wLqTzFyznm22vHHHw+kKsSquaoYtdQXqjb6OcS5bh0154+Zts6vc845J4u2VSX0K+WzeO1Xvy48nw1S/+v1bAuu1Z4RZuX85ZdfHkjqvn1mJpRquRnMxeLp9Sps0DjbtD1sttlmQKoIrYJ01VVXAcln2a9fP2DMirM7DJdccgmQMmTN/MpnTprxVUnFyD5xrbf+m32Uxza7lue9a/n1ozCL1L9lf+uj1Q/6zTffAEmdK3auhnIUBEEQBEFQQF16jsSnzIaGhlYrINZB0nNk1FTN++HTsIrKAgssAKQn3RNPPBFIlU7F+7DbbrsBoytH+54VRo0e/LoW8EnfNqni2Qdm0YheACMno1Xvj+qhVYu//vrrsrW9EuTPSrLWh++Xw/fWVs444wwgZeVZvVwPUi2sM0atjhvXjPHGGy8bU/rcPBVcj0h+bFrPyfpPtYTXZ2aQarHon/FaXHcKK0I3h/fMzDDHpp4R1ap99tkHSEpIvnZZe1DF8rxM/VF576IKu3V/rAzdnHKgUu066pieaKKJMsXIn2nreLbt+r/0snpfVTm8f/oqzd7aZJNNsmw9z5zcdNNNgaY1+fSLqcyb9VcJHA/Op2LGFqT7qnqXz1pT/bJu4Y8//pipcVazdy7nMy71i+VV/fAcBUEQBEEQFEFdK0ftwSd469WYpWZti2qiH+jyyy9v9L51fqygbeaIZybpLxo5ciQ33XQTkGqcVHK/ubWYZWEkmD/PShyrRnH6xPSDWL+lrVVsaw3VCqtPG317pmAtnK5tJO080nOiMmtGYiloa70Sx5PVva3v88knnwCjFUzPvtPvlo90/ZvOq549e7apLZXE++U6MWDAACBllrkGWrXZ7M7vv/8+i77NzvM6XUf8fnO1c8zWVBEpR0Vo+1WV0qw8+y6vculd8lr82lfvk/PM+zNy5EgOOeQQAM4+++yStF0VXNU8f3KByot+IjPPFlhggaz9ea+l7/tZpvrpulgJXA9U/V0P7AvXeNu0xx57AOkzwL5r7mSKseG67+dDvh7iCiusMMb/F8pREARBEARBEYyzypFPqO7Lmp2h870aWVxiVOHTtwqC+7CerbbUUksBKUrzafuWW25h3333BdKefy1ju43ajXi23XZbIEUfeoiOPvpoIGVtdRSlKI+RodVk9Y3pPTOjpZpz2BpM+h7eeecdIKmYpZxHbVWO9MXYJin03OQjVX0pKmJmPlqDq5ay04rFrE/VZP0gVqw3a+2pp55qU72dSqOyo2J04YUXArDooosCTdUHM7/y6kRzfhj7+K677sp2FEq11ji3Xeuc037+SHOnARSil2jgwIEA9O7dG6jOZ5j30hMDrDX02muvAWnNzveFdY/M/lQpU+XUT+b/0zf03XffZZ8b/ow7LJ5hed111wHNezRDOQqCIAiCICiCcVY58gn8nHPOAZJK4VNoNc+A8mnaKHyeeeYp6v+Z6dCzZ8+sSmxQ/+gxMovRqMlsP30JlWTuuecG4PXXXweS10AF6Z577ql4m5rjrbfeAlL2Z967NsEEE2S+BD0QVr52TlVTSQ7Gjmu5CvoVV1wBwAYbbACkjLBi0R/lGtqrV6+yKfCqX1YS94SHseH4VSlyzOqt6ki4rliTy3npGjjxxBPTtWtXALbffnsgnevorlBLhHIUBEEQBEFQBOOsclQP6BE45phjgHTyt8qSHiQjB7NwqlnlOyg9+hP0GFk76M477wSSx6CSaqfVeM866ywgjUUr/9aSJ8czto499lggeRJOOOEEYHTWWkf1rY2LOF/07+h/sUq3/haVw48//hhI66rzy0y9So4N57heJGvzeGbdG2+8kSlibT3fLWhMKEdBEARBEARFEMpRENQZRsb5qLKSao2niVtj6+qrrwZgzz33rFgbgqA15LPT8ud3BeMmoRwFQRAEQRAUQShHQRC0mdbWHhpT9F4La1AQBOMmzSlH8XAUBEEQBEHNkS96WY7nldhWC4IgCIIgKIIJqt2AIAiCIAjKg8dPebyGR8PUQ/mKau5shXIUBEEQBEFQQHiOgiAIgiAYJwnPURAEQRAEQRGE5yjo8Ew++eRZwbcouR8EQRC0RChHQRAEQRAEBYRy1IHwoMVBgwax3HLLAfDDDz8AMPfccwPtP5TWoytqOdNhsskmA+DFF18EYIEFFsgOG11ppZWAdOhk0DrMeLH/a8Gz2FFpbYHNIKgEnTt35o8//gA69joaylEQBEEQBEEBoRy1wBxzzAGk4w4+/PDDKrZmzNjGe+65B4Bu3bplkf3w4cMB6NSpE9B+5aiWFaNpppkGgBtuuAGAueaaC4BffvmFddddF6jtSEdVztdVVlkFgGOOOQZI937gwIGNXt955x2gvAqDKsYOO+wAwC677ALAKaecAsCzzz4LwG+//daorfWiekw66aQAbLrppkBq/7333gtUx6tWrntnX0433XQArLrqqgBsv/32ALz//vvceuutAHz00UdAOtz477//Lkubysl8880HwGmnnQaksfvTTz9VrU31zC+//FLtJlSEUI6CIAiCIAgKCOWoGWaeeWYA7rvvPgB22203oLaUI/0fL730EgBTTz01AMceeyxXX301APPPPz9QnxFfsey5554AHHXUUQDMOOOMQIoMTz755JqJEscff3ymnHJKAOadd14gqRT/+c9/APj++++BpBxNNdVUwOi9fki+Kff933//faAyfew8WGGFFQDo378/AF9++SUAl156KZBUrc8++wxIipJZg9VEZe7YY48FYJtttmGWWWYBYKKJJgJSn9x+++1Auu72Kq/VQKVozjnnBKBnz54A7Ljjjo3e9+f+/vtvDjroIAAef/xxAC644AIAnnnmGQC+/vrrSjS9Xbg+Oq/WX399IPkv9SQGwZgI5SgIgiAIgqCAqJDdDMcffzwAe++9N0CW/fXBBx9UrU1ihHfkkUcCcMIJJwApqr300ku58MILAVhwwQUBGDx4MJDUhnpmpplmAuCII44AUlTvGUIqKCoEDQ0NmXdC/85NN91UuQYDs88+OwA9evSgd+/ejd6beOKJx/p/VVu8Lq9TNUZ1UPWmHDjmXnnlFQAWXXTRsf683q4vvvgCgLvuuguA8847D4BPP/0UqI6SpNJ49tlnA6PVItuht9BXOeCAAwC4+OKLgfpQYu0zlVTVIK/FcVQMri2XX345kPqxFtbD5lANfPXVV4HkSVTd3GeffYDaUDPFjGPVrdtuuw1IbR82bFjmMdRretxxxwEw22yzAXDHHXcA8Ndff1Wm0XVOVMgOgiAIgiAogg6vHBkdFZulZJRlhOzrBhtsANRGttbCCy8MwPPPPw8k5UHPzTnnnJOdvFzpqMi2lEOhWnrppQGyTBq9EmIWxWuvvQYkz8GSSy6ZKQHDhg0DWlY+2ot/7+STTwZgoYUWAmDdddfNMgede0b4efy+WYj2rxlUjlVVsF69epX0GsaEEaxR98477wykqNVry1+T1/K///0PSKrf5Zdfno3VcmObDjzwQAD2228/YPQ1qYB07doVSJ5D+9GsT8ecX9cy3bp1A+D8888HYLXVVgOaH2+ukb///nvmb8v/rOuf92uttdYCkreslth8880BuOWWW4A0Nq+88koAdt99d6C6GZV63OybG2+8EUg+w7yCOTZc6/3MOvjggwF4+umngfZn6tZ63S3b56vrv/fQr/V8Ood/++23UI6CIAiCIAhaosNmq2299dZAyszo0aMH0LKisd566wEw7bTTAnDZZZcBtaEY+QSsd8A2ucd86qmnAtXdQy+HYqTv69xzzwVooryoPOy///5AUlK8D+uss06mtl111VUlb9+YsK6SkfViiy2Wtenbb78F4K233gKSn02/gRgBXXLJJY1+3gzKXXfdFYArrriiPBcxBn788Ucg1TfSt2Pb9VPtu+++QPJ9OHYnn3xyAE4//XQAhg4dylNPPVWJpmfjRb/Mddddl7VJD5Q/c9JJJwHwr3/9C6BqSmxbcH5ss802QBpfLSlGjtG///47q3105plnAina1sc366wJ9IaZAAAgAElEQVSzArDJJpsAZB7HWmKNNdYAkmJg3958882Nvm4P7VVTunTpAqS1W2U2j+Pv559/bvK3/KwqVMohrQv2pf6/tuK4KmetuBlmmAFI3rbPP/8cSOvHxx9/DKTdk3vuuSfz/6mUeg+32GILII1d8TNqq622GmtbQjkKgiAIgiAooMMpRz49qvj4xOkTqU+eeXzq1o+gN+LBBx8sW1tbi9lp1pgxS+Gcc84B6iOqLYa8YmLlXiMXszCsyrzRRhsBzVduvf/++7n//vvL1+ACVEwcR2adfPPNN8BoZeXRRx8FYIoppgDgq6++Guvv/PXXX4EUneqpUjnUR1VJjNZ8db6cddZZQKpSvsceewDQt29fIF2z3rQVVlihYsqReN88d9DXQp544gkADjvsMCB5QBZZZBGAire5Nfz73/8G4PDDDwfSvBHHkdmO/fr1A+Ddd9/Nvq/K8OSTTwLJt6O6pFLouur6WQtZfK4fhdmqkPrdumDtoVRnTE4//fRAGlfifdTbp+I9atSoTBlyfTQbb+WVVwZS35gN6xrkOtPWz4lyZr+pfplVbQ245jyMfr3ddts1Ue9Utszqc8fBjFm9RmOa94WEchQEQRAEQVBAh8lWUyEywtlyyy2B5CswM6G5p9/u3bsD8PDDDwMpilp22WUBKpZRMyZ8ejYLwbOCVl99dSBFdx0FvTXW77FvjQj0Fpl1Ugt+MDPpHnnkESB5a4wAzRy58cYbsz3w5557DkhRUx7npiqL12/2ml4So89amMtiNOeZZfrAVPn8fu/evbNq7tVi/PHHZ7LJJgPS3BowYACQMgLFqsqbbbYZ0H4fRylxnngOmmNQHIuuI1a/Vh0b2zzSY+Tc9Herajr+XTeriW2zD10nrdVkW72WarL22msDTXco9Nqo/hSqPfbzPPPMAyQFxD7abrvtgKQ6ed16VfVcVXPddP7r6fPzWY+ibXPM+rnt6QH28Z9//pmplt4z18ltt90WSNXcm1sfo85REARBEARBEXQYz5GOfE/V1pF+5513Ai3vl6o0WRfpoosuAqqrGIlPxgsssACQTgh3T9Xvm700YsSILGK3arKem1qukK26MtdccwEpQjI6NftCP1ktYARzyCGHNPparAejZ+XJJ5/Mrs9+y2PUZMSnh8Do0cq4joNaUowkX9fIE9GtF2bfWuelmvzzzz/ZvDAyNXstrxwttdRSQPK7eT6ZPrJqosqdx7649tprgTRWVZiKGT/6VV544QUgqTF6yFRMrQNVzoymlrCmmL6VPLa5FnBO5yu0uzaYlaXPsPBn8yqdCpLv660yk9BdFD1mZmZX8rxQVXB9QKpcfu56JqM1yFw/fFV1Lxy75fK5hXIUBEEQBEFQQIdQjjp37pxlbvnErR+lpSwln2T1EBiN65qvBYxWjSpefvnlRt/Xg7LOOusAY65nYnT40EMPAU0VtmpidOTp2aoJRp+ehaQ3ohYwW2XQoEFA8qyJGRGqe2aYzD777Fk/GskZud19991Auh96q6zLozqhD8HMsFrGsWg2Tv58OOsmVRvH2ieffAKkCt5muOQrRltB26rkZq9V8zyrCy64AGiqXjoWVV5boxhJ3gOSx/crUQunJfTw6cUTs/MKVZj20t46R67lqj3uDlipXWXSzMOxZZq5bqrGuD7YJ/aRbe7Tp0+j13Li31RBto1+/lq7zXP/msusU9mtBKEcBUEQBEEQFFDXypFPo5tssgnLL788kPZd3dNsaT/SfVd9HJ5to+egmqgwWDNGbOuQIUOAtMcuX3/9daY2WANjuummA1JNJKtqb7jhhmVoeXHofbB+kz4po84zzjgDgKOPPhooPjrzvpWz7pPVh5dZZplG75sxYRaK+/4qmkcddVTmV1HVzLfTPjPz0kyqjTfeGEhepAceeKBUl1M2nKNmgb700ksALLHEEsBoRcmTx2vBO2Ub9NCo3llnJl87x4yYY489Fmi+jlqxjD/++K3OIlIhyXtsHFdWM/csxrbcZ/+P5xquueaaQJpr+uL0aJWillBb8T7YV1IL/rY8P/30E5DGj+qe6p9Kivfz5ptvzvw39q/nRLquNHedee+ZVbkrgUqxuyCuaeJ4sj6cStIzzzwDpB2dShLKURAEQRAEQQF1XefIOg9DhgzJajz4hGk0qvvdDCEjWPcur7nmGiD5PFRhPP+qmrgfa7aWXgH9VVYdVknSP9SnT5+sdkzeA7HSSisBSXUy4qtkzQsVEM/PMfLN1yF5++23gZYVIDMYrAysv8W/U8qMQxU469sYOesbUknKV+su9IG1dc7pczGK1KN18sknt+n3lYJ8NWIjXmswGfnONttsQKrWayT5+++/Z+qMmaWqvfa7f8PxUUlUMz/66CMgeUHEvjQrMa9gt5aGhoZWjw/PqbOOjX2hx8YMslL4NVTKVNjznhvboHJfDfbcc08gZRw7R10H/Cwwk7c9lOqketvouuwpCM4T1/Evv/wyqxVmxqC7BFJ4DhukMayv1s/AauDa7qkHXqfrgn4x76dnN+o3LcfzStQ5CoIgCIIgKIK69hz5VD1y5MjsyVpnfs+ePRu9GoW6t2uWjE+q7m26n1sLeC22/bvvvgPSvrQnZouR9SeffNJs1ozRpEqR+9OV3NPVQ6RiZFvM+LJyrVGZCpqvqhO2Wf+KCoRRvBFTMcpRsRGg1cgde7bVCKi5891KEfGo8qkMeG6Z3qxKZkqZMed177DDDkDKmPT+eN3e37wKOOmkk2Z1WLwO56bX43g3iqwk+t+sjL3uuusCTa9H9fa///0vkFTL1vZJa8aJc1e1Ku+xuf3224GkPLcHlY181p74tWO0mliLyXlvm/JjsRSUSslwHG2yySZA2tFwvDmfZpttNl5//fUx/o6nn34aSLXEVI4qSUvrqPPIV72Y1p5SmbWKt/WO9AC7VlTiHNG6fjgy/XGzzTbL5DoNsR5Aazny9ddfH0jHA8w000yNfpeH9mm8NEW3GlK+uCA54DRD+tCULyLogBnbYbnK/S4clUy57dKlC5AOqxS31zzWQLx+P3Cd/KZ5OgHdKvS+uKVqCnMxuPi09CCl4dQPPQ3GzT0UlQL734dHr9OHxUoe9rnWWmsB6TiQaaaZBkh9YZ9J/oPI7/tAPGrUqOx6/BDL/84tttgCqM7DUb5In/da47XbqWussQaQ1hG3MByL5cCHfw+DFR+GNGKXYlt5wQUXBFLyRL5ooVi6opoY4BoI5h/YSjlXS7WtJrbN7cu9994bSKUl8uUJCvEh2USPrbbaCmi6XpYTA1fnd0tBt/NJY7qvrukGn8cffzyQCgBXojhzbKsFQRAEQRAUUNfKUeFBiqoOPsmb5uzXRjxGfJp433vvPSAd77D44os3+t3VRGXIp2TTw/PbaWJxs7FFCMqWqiqVNGKfcMIJQDLneiCg0qkYjRoRv/HGG0AylNqnqj2WBPBaTIVvTaRU7PaHf0PJ2pTTcuL1WrhTLOhZzogwX8RRo7/qjvcjv63tq23z+6bIW57i66+/zqJF56DJEZq63YatBrZfI75bWap2JkW43ji2lf8t3OmWeClxnuTXKrcs2lNWwP4zwcAihPmDQP052+J2Yv/+/dv8t9uLc1MFTcVaTCNvz3ajOw/2a6m3tJ1XbmMWXoO7GUcddRSQkkM05jtvHnvsMQA233xzAN58802gvOuF7XVtsuhjaw+X9jNv2LBhAKy22mpAGm+hHAVBEARBEFSYulaOxkT+qTjvWzACNHI11TgfEVXzGAAxolYVM6KeeuqpgRQJ+L7HUYwJI12P4jj//POByhbes9yCmHpvlGT0qX9DBcj7oFJkn/n/3ec2PdQjH4rBv1nsfchHdKZJaygshfk1j6ZMfXOi8bQcqAztvvvuAOy8885AU79Y3jSf96DoTfGYFVNyNc2PHDkyu6f+LiNe/X96R6qB40I1Rl+H90GjqMVVPbLBSF9TtMVmS+lh1Lju0SWmeJsWrTquutHSGFe5m2aaabLjHTwM2jmm6Tvfz7L11lsDKVmgGgq8a7dzVPRX5gsQtoVSlAEYG+4aeJhw4TrlWHMNF5NF/Lyw1I3r5L777guUR8UU57Bj0LbutttuQFoHLCcwdOhQIK0fJjY49/1sc+z6GVAJQjkKgiAIgiAooK6LQBaDEZyudyNiI/5aOCakJdzHHzBgAJAyZMwE8Gm6d+/eQIokIT3JW1DR+2GJ/bYWq2sL7kNbKv6kk04C4JJLLgFg1VVXBWCXXXYBkmqx5ZZbAsmbpK/DsWvZfDOFWuOjMkIrNsJVCVCJNBq1SOBnn30GpL12o7nCA37tL4vR6XvLFz3UU7DiiisCSUFSSbGEQSmPajDLyqhUhSSfGdkSRqdGrSq11VSB2oMF9yzS59jzQGEVIQ8SNZPMfrf0QTmKy7qm5T15Kq5G6x4XYcalfiKjfD1+M8www1izosaE98O1SdWiGqiY6cF0vdCL5HEbtbz2q26Zka3H6a+//srUlebUK9VNs3sdDxaN7devX3kaTVon9Meakp/PYnO9VYnUS5QvkfHEE08AydtpuY9SpvJHEcggCIIgCIIi6PDKkfUhTjzxRCBFftZrqQd8qjbq1Bvg3rK1MFQgfvrpp+z/GCUceuihQFI0VCUqie20rL/oETCasO2+7360Y1UVzAyae++9t81tMsIvtgim2UrWr9Hnka93Yq0Vo/l99tkHGH0EiAeAWoDUfjXq8ngYFSN9ckZLenIcw6WIooxUVRvsg5bIFyi1xorzzmurRNG2cqLako/W9SLttddeQMpSU5VROTLyL0dhPsfexRdfDKTjM8QxadSuyqWC2Z4DWZ2jHv2iuluKo0raitfjboEK7TfffAMkpayWCv42h8prodqj6q9Kl8fxYEay3ivXHQu3lnNO2gbnjR6iVVZZBWh6gLPjSB+ddQbzSm05COUoCIIgCIKgCDqscmQZdqsvq7boqTDCrSeM5q1nYfaBVVXNAJpkkkmaHKDr4YTtPWqiPRVhjeiuvfZaIHmprGCbr6ZsG1Wa3H9WOSlFdGoWSGujqHxk5HjSz5Cn8H41d4xBc/dWtcG/Yb+r8pQCfRp60PJHUXh/PvnkEyCpFGabeKSB0XlHQ1VPJcj7Y19Zj0U/iGNaT5rerdZUbW8tqn8ekWSmWbHHZXgto0aNyv6tyuSrCqERvmqwam4t1IdTERs8eDCQ1ArHrt6qahyv0VpUj72WiSeeOPM9brPNNmP9vx6dZRaz66mKdSX9f86XvK9SmqvZVQlCOQqCIAiCICiCDqsc3XLLLUA6X8asIWvoVLIydKlRgbHasBVRC1UQ1SQjWb9ub3+3VWkpxKja7LTrr78eSFG2fZM/zLUcNYTydY7aen/MGNJrYkTtNbrvX1gfxr+lGuHXjl2zac4991ygtEpRHqNtlSNVCNW5gw46CEjqlX1SC0pBJXGsGpW3hH1qtN+jR49G75cDVS6zlq688kogZZDlx7zeG89kfOyxxzKvh1mY9nP+rDlfa+FzRBzLziN3C1RKXC/1JNUy9pXjbtttt83e87wxfUl5zExWMVO9MVO73LWa6oVQjoIgCIIgCIqgwylH7mWaleXZR1bobO0ZL0FjrFdRr/Vq8pT6VG0xSjPT7vHHHwdGZy316dMHSN4pzy37/PPPgXSyeTXmZr4Ctv3cUfq7vThePDssf95dHvvQ++fZjc1lGpUSPYp6bmaeeWYgKQ62aYcddgBS9mu9o7JiRX7rgKlyWcXb2jn1oH467gYPHpzV3BJVSevCmXl7yimnAHDggQcCaSx269YNSDWUxnVCOQqCIAiCICiCDqcciZ6JpZdeGkhRetA+yqW0BEE9Yp0sq76rUOvJsmK6fh69j/WgVnQU9Gi6dhVWq683OnXqlNWoMxuxudpkXbt2BZKSZnae6nA934dSEspREARBEARBEXRY5SgIgiAIOioqQwMHDgRS/aZ8TSHxfEPPrgxGE8pREARBEARBEdSFchQ+lyDomIw33njtrjEV1D+xxpcP723+zMq416MJ5SgIgiAIgqAIalo5yp/cGxkeQVCfmDFTWCEcRkev9VytPmgfoRgFheTHg5mGql3tOZmhOUI5CoIgCIIgKIKaVo7KgfWPrAqrg99Tp8UIN6LacYtKRLKVipYjKg/GJUpx7mMw7hHKURAEQRAEQRGMc8pREARB0HFQIfU1lKOgNYRyFARBEARBUAQTVLsBQRAEQdBWok5WUA5COQqCIAiCICgglKMgCIJmsKrwvPPOC8D//d//AfDjjz8CKcvVOixBEHQMQjkKgiAIgiAoIJSjDoqZGxL78bXH7LPPDsDTTz8NwJJLLgnAN998U7U2NYcKyumnnw5Ajx49AJh66qkB+O677wD4+OOPAbjkkksAuP/++wEYPnw4ULuZRM6XFVdcEYDzzjsPgAUWWACACSaYoNHP/fHHHwD07NkTgEGDBjV6PwiC+iaUoyAIgiAIggJCOepAWCH2tNNOY5999gFSJL/MMssA8L///a8qbQua0rdvXwBmmGEGAFZZZRUAbrvttqq1Kc8kk0wCQP/+/QFYc801gaSkqEjOMsssAMw444xAUpqmmGIKAJ599lkA3nrrLQBGjBhR9ra3hkUWWQSAhx56CEhnOnme42OPPQYkpWzRRRcF4M477wRg/vnnB+D999+vUIuDUuNZno7h66+/HoB3332XAQMGADBw4MDqNG4cYcIJJwRgiSWWAOCII44AYK655gKS12+LLbYA4Keffirbrkg8HHUAnNRK/H369Mk+vLp27QrAlFNOCcTDUS2hmdeH2g8++KCazRkjjhu3m/IPRfmFybG47LLLArDccssB6RieCy64AIAzzjgDgC+//LImttreffddAD766CMgzZtdd90VgJdeegmAn3/+GYA999wTgJNPPhmAs88+G4BNN90UqN3tw3GZzp07A3D33XcDKSixrx27vhoYdO/enYknnhiA++67DwibQqnwXq+++uoA7L777kBaP6aZZhogHfslrpU//vhjFpAccMABALzzzjslaVtsqwVBEARBEBTQYZUjn0h98jQCdlvASPHDDz8EUpTg16bm1kOE4LUedNBBQIruIcn8P/30U+Ub1kq8jllnnRVI0qp90KVLFyBFERqa3fIZOnQoAL/++muFWtw+CvsJYMEFFwTg5ZdfrkZzxsiff/4JpO0wt49++OEHAF599VUgtX3yyScHkhrm/HObav/99wdgpplmAmDAgAHZ4c/VRCP10ksvDSTjtUprfh249tprATjllFMAWHzxxYF0/b/88kuZW9x+VEzc4lxsscUAmHjiifniiy+AdF+mn356IK2fqp6TTjopkNZTDewqZ25TPvXUU2W8kjGz0EILAUnN23rrrYG0rhTLyJEjmXnmmUvbuArg+qJCZt9tvPHGQCpPsfbaawPw7bffVqxtCy+8MJASOw4//HAgtVml2dIZrjeOR9eV2WabjTnmmAOARx99FIDNN98cgOeeew5o+2d4KEdBEARBEAQF1OXBs0Z17gOvttpqmUHSKKh3794AzD333EAyehnRet0+oRoJvffeewDccccdQDLlffXVV41+vpbQ/OqT/1RTTZVdn/fhuuuuA2rTC6GKoPlujz32AFKfiW33VaXJr/WHGNXXOqowb7/9NjC63yD5WmoJ7/V0000HNI3sHG8qCb/99htAFtVddNFFQPISqNSef/75meJZT6iAmfJ/0003AbDjjjsCtblOyEorrQTAscceCyS1zD7u1KlTtk76Xv5w1zx5D5qvrkka9su5/ugpOuSQQwDYZZddGv1tUQ395JNPAHj++eeB9Nmg6uc1vPzyy9nngP+3WnTq1CmbO6onfnb56vsbbbQRAEceeSSQkgn83HSMrrfeekBS+crJueeeC8BWW20FJPO74yK/9qnc7rTTTkBaK217z549+fe//w2k5A9N2yqH33///VjbFAfPBkEQBEEQFEFdKkc+ERuNzjTTTNlTv5GN16WS9PjjjwPw9ddfA/D5558DMM888wBpH1ofi79n2LBhQHpyfeWVV1rT1IqgwqKvaNJJJ82u273ylp6eK4n3drPNNgNSxo9Kim23DIEqhN4iIxzLFeywww4AnHjiiQBZJFHrOLbmm28+IKkptZbm3hry809UIMx6sw9ff/11Vl55ZSCpULWMngiz2lQltt9+eyApSLWIKp6ZdyoMRu2WLWhoaMj6z0hedVrPh55M/XGui/o8VOA/++wzoLyKi2v2zTffDMDyyy8PpDHo3/b6HnnkESB5U2pR5fN+6wNTqdxggw2y68r7+3zNFyx1PTGLK7+b4uephUzLwZlnntnoOrw+++S4444DUsmPyy67DEjZrhaZHRP59H/LblgSxc/u5p51QjkKgiAIgiAogrrMVrv44ouBxnvJqgsqQ19++SUAV1xxxRjf94gGIyGj9rXWWgtI+/E6+o1OalE5MlOo0KPjXm0tZm4ZDR1zzDFAUk7MNrNek9G5feSTv9ep8iL6WZpTL2oF9/zz7WzOz1FPNHfPjc5VMFUeFlhgAbbccksg+ftqESNdx6aKrGPz9ddfr07DikDVzkhaxcgMIJVX1fU///yziefI9VF1rxbUTRUQ/ZQqRnpvLNh46aWXAvDwww8DtbkueJ/NJNM/uc466wBN/ZfQ1DfrWm9f6anq3r07MDqzC+CZZ54B0mfiCy+8UMpLaYReYOsX5RUjP28de94HVcCxKUaS32lwbVl//fWBtn8ehHIUBEEQBEFQQF0pR9NOOy2QHO4+Md9+++2Zz8S6Pq3NilB5ss6Rv9v9W6OsWmSvvfYC0lM3pAqiPkXXAkaje++9N5AUJGugWI/EbIPmsA7SgQce2Oh9s9RqMTIsxLoj+t283lqIxttLc+rXCiusAKQ+Mqtt5MiRWVaRR3FUo4q77Ta6Puecc4BUj8W5pYIsDz74IFC6qrzlwCwePX3OD1U8a1gVKrR5H1K1s7QKcd44Xjz6xTVa7+FVV10F1Gb2p/j50qtXLyBVWtdPJPbDgw8+mClljrn8oc56T+1P61mppFlzzEOky1EDz/niDoxj0LGnB0nFyPe9TjPsWoNZl9bBc3eorRmSoRwFQRAEQRAUUFfKkcqDGRCebzR48OB2qwVGju7xWunVqMP92VrCp3Md/YVR+wknnADUVl0jvTbugatqHXrooUDLipHXt9122wEpInKv+Z577iltg8uEY8v+M+On1hWvYjBCPPjgg4HkKfDgYz0HMmrUqCySVQFob2XbttCtWzegaZaMa05zvPHGG0CKeGsRFQT9lipI+inNLPN1+PDh2cGeqku1MDZV8fROOY9U/dddd12gtirMN4dz//jjjwdSheh8hpk1mMzyao3n1fszZMgQIO24uF6qvJWjb61dt+GGGzZ63yrW+k1L8bdVe61N6D288cYb2/V7QzkKgiAIgiAooK6UI53rc801F1DaJ17PDurTpw+Qnj6twaMXqZaworKVT2XEiBG8+OKL1WjSWFEpMhr3a7NLWmKppZYC0j62/W+0bwRZ6xiVyy233FKllpQOFSHnphVwzaTMZ4w4l6+44gquvvpqIPnkWprX5chG1Kt41FFHAcnHpxL2xBNPAClbzevabbfdgORzqcVaTfqFrCVj1K43w3Mn9fLNO++8mbqgJ8SMwlJdnxlVxcx9VZbzzz8fSGPMbCTr2uTP3TLDy4wpr0815s033wSqU+dIj9Fhhx0GpDHtSQxWrX7ttdda/bv1811++eVAUozMZjNzzFp/5UBvnn3gXD3ggAOA0nlhu3btmnmwVKCtym6melsJ5SgIgiAIgqCAulKOpJQRoxHvWWedBaSMOB387svWkndHNtlkEyDt78rw4cNrqiK26MtwD9ysNTODPDXbrBMVJiNbVT0jST1KVkqvF6znJLWoNrSEfaNvxWxRx2Q+28b5Y02VbbfdFhhdi6W187kcHglVB6sn+ypG9mbZWIfFKu9m4W2zzTZla2NbsS16dfQemRWoav7kk08Co2s4XXPNNUDy8aiyqMK0t36a97sYnP9mI7mOuHabtaoyVpi1C03rAdmX1tWyplAlcQ3QP2v1f7O0ilXTC7Efb7jhBiCdoady6IkClThDzZpT4vxvr3fXvtM3d+mll7LKKqsAaUxdeOGFAHz66aft+luhHAVBEARBEBRQl8pRKfHcFffUjTKMRvQi1CJG7fnqqa+//npNZs94b/UMmX3i6cnuERu1GuG4h27Gk/hznqJdD3Tq1Cm7XtFrUw8Yvef9H126dAGa1jkyWreqfd++fYHaqr9VDPmzGvfYYw8gKWXW6DKi9fTwWsI1QY9Oc/z888/Z9ehP0ltl9qFKR1tpjbI23XTTAWmd89U25MecGcZWxjaLyUrgru1W1Nd7Vcn6WmbUed5de9BrowJvH6kY6Z9TUaoEr7766hjfb4siBsmj5rw74ogjgNFqmWPJteWSSy5p09/IE8pREARBEARBAeOscmRGg859o5Gnn34aGF11u1Zxr33NNdcc4/cHDhxYkydNi54ifSdmU5gRqAfHSNdsPCvjurdsNkYt+TtaYtSoUVkU7n0o5vygamP2idGnqqXeI/vAyNBry2dK1Zti1ByqE6oNZrWpQJu9Vq9Yddn10DGr96iS6B1x7FmLTt/TAw88ACSlyEw7x55j9OijjwbSWNUnV42K7KXAzwE9Nj169ACSku7X3p9Kkq9Kbh+ochV7z1WMrANlNqmVxQEGDBgApPFRqs+DUI6CIAiCIAgKGOeUI59gzU6zEq4uek9FruUMIp+mVVJEtciaLLWOZ9/42hxmtZmFoteoFn0dLTHffPNlkY1nytXSuVV5zBS69957gdHth+TzMEL0VPodd9wRSH3lmWotVT+vN+xDr79z585AqtpezzQ0NLD99tsDSQFT1a2GP06lWPVKJdkK0M8++yzQfEaxNWWx4FcAACAASURBVKk22GADIKkW+YzEekEPptl21k3zPq288spA22oklQoVRj167sx4coP+ID+znE+eoqB3zwxLvVn+nK8jR47k/vvvB9qfQZknlKMgCIIgCIICxjnlyDpG+l18ArW+RD1FuPlzqlQgPvroo2o0p+SYpWK2hdfneUT1yHfffZf1mypDLXqOVFjNVlIxcr5YZdisNSM8I0VRQfK1FuuFtQWvJ1+dvh6vz/FoltPhhx+e1Y5RGVO1qaaX0bHnGq0fqjlUN82C9TrNXtNXVU9MMskkWUVoFSPXxQsuuACormIk7ry8++67QMpMNtssf0qAGYOqYs15GVWgnH+vv/56Vouw1IRyFARBEARBUMA4oxwZReh61zVv1O7+bT1gFle+vocRVT1GRGPCDCfPkDPror2VT8eGEUu5FIAuXbpk/eRrLWYWWgnZOmBGbPoaPPHac+Gaq6vlz+uTq8VstSmmmIL99tsPSOuC6mS+bxwfeiKMdGW11VYDUmRbjr6deeaZAVh88cWBVFPGswX11Bh1q5hYQblbt25AyqwzitdfBumEAOta1eLZki1hNprVmlU1S1UHp5K41vft2zfrL9co10nPAa0FHHu77LILAM888wyQ5o+Kq1/7Ko7lu+66C0jK02KLLQakz+211167bKdBhHIUBEEQBEFQwDijHFmHZLvttgPSk23v3r2B5iPftlCOU8MLsa1G5Z5j5T5vPdX9GRNG43ogjL5POukkoLzXV27PyBdffJGdGv3iiy+W9W+1h2HDhgFNK9qaTWK0aranfeOp4s43vSt6DzxTqlD1NGpsa/XctqK606NHjywry7HmdZoJYxZr9+7dAVhqqaWAphGv6ovvl0M56tevHwCrr756o7bq2/Bv6hfyOvXw+XN55dl15bjjjsuqLRvB1xOuH3oVxTpIjz/+eMXb1F622morAA499NCs30477TQATjnllKq1qyXee+89ICmP+sT0HjWnJDtmVYpUnvRXWd28nGeIhnIUBEEQBEFQQEMtqAwNDQ1lb8Spp54KwCGHHAKkJ1r31OspQvKp+pVXXgHSfqw+gdlnn73kNR8qiZVszZRSdbCvarkuUEvMPvvsWbaJVdoXWWSRajZprJhRZ8TnWWp5dVQ/h/XCrPtjZKgPTtVMD1NDQwNXXXUVAJdddhlQuYwvVZ5zzjmHXr16Nfpec+ui120bVWutqN+nTx+gvOf9qUrNNttsAKy//voAHHzwwUDyFqkQidfkNagwWXPm6quvBuprLSzEqsmOVdcL+8ozDV1P6gHbbBXoueeeO6tjt9FGGwG1fbakY22GGWYA0jphBf3mcH1wPdF3av2tm2++uWRtHDVqVMOY3g/lKAiCIAiCoIAO7zly/9nsEp9krQVRj+fqGPFZv+Pss88GUrS+33771VTmQrHYNxtuuCGQogfPzKlnxUhGjhyZ7Zd/8sknVW5Ny6j02GaVEbO7zDoxy8sz2PKoQFkvSX744Qc+//xzoPI1gvTkHH/88Vm1eT1FeWXMOaeqYl0nK/5+8cUXQGWuwb/h+HF+mNljW73XRu0qKUOGDAHS9VuzqhZ2EdqCY8/1cIkllgBSH6q0uFtQD9hnN910EzBaMYLRGa56qWpZMRLHVF4Bcm33+ypkevtUM1V3r732WiBlx1aCDr+t5iGFF110EZDSYE3vNA22HjE9etCgQUBa2IcNG5b9u56215wwL730EpDK/pse/eSTT1alXaWkV69enHnmmQCcfvrpQNryrYW52BKmhVtMdf/99wfSPHPLx770A9r3DUY0ow8cODArJFnKpIj2YqDhg4jbAfkil0Hl8aHHgNdtWZMD8uU43LZ+6623KtrOtuDWoMf1rLvuukDanu7Tpw8XX3xxdRpXArSEGIz079+/0deFB8rC6PUBUrmJcpQCiW21IAiCIAiCIujwylGPHj0AMhOsxjZT+mspWm0rRlIWdRs6dGhVDohsL6Z/K4Nr/nWrRsN5KSl32YUgCEpL4dERkA6gzZcmcLtx5513Bmr7aBev6b777gNSGRPXJRXaSy65pEOsVZafMcW/S5cuQLreF154AUilQMppqQjlKAiCIAiCoAg6rCHbwmimubuX2RGeuvN4TaYT1yteh1GCvhX9LaaDl6IP88X7OuK4CIKOiIZzfTkqzK4XGpUtbVDLipFql0URVUpEf5FlLjrKOmVig4katUgoR0EQBEEQBAV0WOVIV7t7mz5xm3rsHm9H8Bx1FCyoZ2qxpeMHDx4MwI477gik/Wgzh9oSTXWUCCwIxlUOO+ywRq/1hMq15RVMXXdduueeewA48MADgdo8nLreaO2h4qEcBUEQBEEQFNDhs9XyXqPmFCO/39b7EVlPlSPudRAE9Uy+3o9FRK0D1pa1zXUx/zquq04tfV5EtloQBEEQBEERdHjlKCgP+ZoitTCOguoTql71UI2QcV0xqCTjwrjvqNcYylEQBEEQBEERhHIUBEEQBO2go6oq4wKhHAVBEARBEBRBh6lz5Onfvj766KNAqsQZBEEQBOUglKOORyhHQRAEQRAEBXQI5WiVVVbhkUceAVIVzO+//x6AOeaYA0j1I4IgCIKglJT7/LYZZpgBgH79+jHddNMBMHDgQABeeeUVAH788UcAvvnmG6C8J9mPC4RyFARBEARBUECHUI4aGhqa1N3x6frGG28EoEePHgD88ccflW1cjeF9mmKKKQDYfffdgXSGz/vvvw/A+uuvD8T9qiTTTDMNAGeddRYA2267LZDOnJtxxhmBjh8RTjbZZACsueaaAAwdOhSAzz77rGptaiue7Wjffvrpp9VszhhRbd9ll10AuOCCC7ITBFZYYQUA3njjjeo0rgguuugiAHbeeWcAJp54YgAuv/xyAI455hgAfv31V6A+1rSJJpoIgJNOOgmAvfbaC4BJJpkk8zVtscUWQPI56a/t168fAFdccQUQuyZtJZSjIAiCIAiCAjpEnaMJJpiA7777DoCpppqq0ffcCz7yyCOBFJX/9ddf7fmTNc+EE04IpKjQ+zLppJMCSVFbZJFFgBRtWVV3nXXWAVLWXzmZcsopAVhwwQUBWHzxxYHRURLAdtttB8Crr74KwAMPPADAnXfeCdRvhojq3fbbbw/Av//9byApJypEDz74IABHH300AJ988klF21kOOnXqBMD000+fqbyOwdNPPx1IFZ/ffvttAPbcc0+gPuauHhH9IM6reeedF0hqYDXJK5UqdTPPPHPWXtXLu+++G2h6JmU16datGwBDhgwBoGvXrmP8OT04G264IQBvvvkmUNsK0qBBgwBYa621gLSO//rrr1lfuMbPPvvsACyzzDIAjBgxAoC9994bgBtuuAGorb7L41x3Tf/oo48A6Nu3L1BeT1fUOQqCIAiCICiCDqEcNTQ08OKLLwKwxBJLZO8VYrS58MILA/Duu++250/WJF26dAFGR+Bep6c+uw897bTTjvV3OB6MGG+99dZ2t8uoYOaZZwZgqaWWAuD3338HUsS6zz77AEndag731jfZZBOALFOxHmhoaGCWWWYB4K677gJgySWXBNIYVSl7/vnnAejVqxdANsbvv//+irW3WFSC7BMVSft4o402AtJYUFkZb7zxsn8bCYvR4i+//ALATDPNBNR2xO/YffjhhwFYfvnlgaQCnnDCCQCccsopQGWjee+9XpVTTz0VgNlmm63R9wuj9Oeeew5ISnJb68aVow6QivMzzzwDJOW5OVTDXNP2339/IGV51cJnoSrqF198AaQ58cMPPwCjPaL2iRnZKtAqrq4Xw4cPB2DLLbcE2r5OVqKGk7sFqoBm4umDK+c8CeUoCIIgCIKgCDpEttqoUaNYb731ALj++usBWHvttYG0V2tka5RhJPTyyy9nv6PeMKNBH8O+++4LwOabb56pSF5/c3jd+ev3Cb69jD/++Bx22GFA6pOVVloJGO0Vg6YqnxHet99+C8BDDz0EwAYbbAAk9cvI77HHHgPKX2ukPXityy+/PIMHDwaaRoW77rorkMak2TarrbYakBSmWuT4448HUvajHjf7srm+LsT+M0pUdVIxq2XFyPXl/PPPB2C55ZYD0jXddNNNAJx33nlAZRUjvXu9e/cGyObjrLPOCqQ+sa9GjBiR/R8VaP06t9xyS5vaUA71Qd/WuuuuC8DTTz8NJIXRv+mrypjKrH1kn1xzzTUA/PzzzyVrY2tRVXfd/uCDD4DUVteKQnzP9d+sVu+L64jKWi159szmPPbYY4GkBqpQVnNND+UoCIIgCIKggA6hHAFZtprqgnWNLrnkEgA6d+4MpAwNa2OsvvrqQG1kjxSLUYX7tGZv5TP1xoYRnJkNRr5GTWOKUFqDUVr//v2zqDOvYhkVWL/mq6++AuDmm28G4NprrwWSYqDqp3Lkvnwtq35GrTvssAMAV155Zfae9XuM8Hxff9gaa6wBpLFtDapa4vDDDwfggAMOAJKa6bWoGKkCqQaaQdS5c2c+/vhjIEW01md55513gBQ91zJ69Oxnr/+///0vALvtthuQ1JlK4Bzcb7/9gKQs6HkT77vz8ddff836zXXhvffea1Mb8uPA9aYUSpLtdv1YYIEFgLQ+rLjiikDy3Gy++eZAWofmmmsuIPWNWaB6AauBXjU/l1577TUg+e7GhnPM63WOTT/99ECam61VjsqxvubVPD+Xfd/PNsdfNWq7hXIUBEEQBEFQQIfIVhsbU089NZAin3y2lvWPTj755HI1oeRYD8Zo3f1a329oaMiiQF+N2FTI9LU89dRTQIrS77jjDqD9nggjxccffzzbRzeS8W9effXVQIrYVILyFV2tDnv22WcDKQIyUrTttYg+odtuuw0YHbWppKmIGB3pK7COiSqeEX8tZanpa7AeiX0iZgC99dZbQFKDvAbVMEjRYi17xppDtdZ6Rtac8b4suuiiQNuzvNqD50qa9agP0bl9zjnnAMnTp5Ly9ttv85///AeA7t27A6lS9gsvvAAUryao0uT7tpKn2Ouf0i/lPPN+yIUXXghAnz59gPocj4X4uXDUUUcBcPDBBwPJY1VLOI9cL+wbPW/lXOMjWy0IgiAIgqAIOoznqDl++ukngCybzRoRRjQHHXQQkLJMqhHhFYsKgz4qzz9z37YwCnOP1mjpxBNPBODLL78Eko+nXNGR0elqq63GfPPNB6SzjfQINPe3jSpVXYxiVSdUoPSv1DJmX3z99dfA6Iqv9oHeDz0PKkSqDvpYaslrZN9cdtllQFPFSGXBmlUvvfQSMHaFoBbU62KxerkeCTMmzfwSo/W2rCelUlX08KniWUPHsaha7velc+fOTbxjqg49e/YEivettDTHK9H3enE23XRTIPWZ50nOOeecQLq2iy++GEgqRr2i8uy9Nlu4FpUjazJtvfXWQKrJZJ94Pl4lCeUoCIIgCIKggA6vHPnU7LlcninmE6nRlOdbWROilqJZVS49RmbE6OuxrYU1ZPyemUD6Wyp9XSNGjGj1id5WfN1xxx2B1EdGoZdeeimQVMFaRDXP88D0njzyyCOZ/8voPJ/J8+STTwK1eX32hbWXxD7eaaedgHQeWr3jnNKPc9VVVwHJW2TVd+eoNaw8I6otlGqOqh6rjlt1WPVLT1JeOfr9998zT5nX7/oxtjpVraE5L1I58b7qLdp4442BpBxZc8d6R8suu2zF2lYO9IupnFmJ3v7PeztrAat+u/NgHx133HGN3q8EoRwFQRAEQRAU0CGUo4aGhuyp3+hA1cFTja0TYWXswv8LydlvtlZ76/yUEr1G7pWrNOTR3zDppJNmP5M/6d2922rUjWgJ+9DIToVMzG7Tg1TLWM/GTDtrrhx55JEstNBCQDpfyzOR7Odnn30WgLnnnhtI0VMtsM022wBpPDnfVExUKjsKKmVmfJlV43UXnpYOqfp0Lc0vM33M2LWivjV19LwNGDAAGD3uzBZSzTSzza/bSykUgHzdtOaq/TeH3kd/XrVXxdb5ODZ/VSW9U61Fv99mm20GpDP0zOCuReVIlUsPkmtgNegQD0edO3fOUk394FxsscWANMBbwkVQibWtD0cNDQ3ZtlAxhbuKQfOnBksLWuZlYo12U001VSal+4ChobJr165AOpywFjC13S0LF+b8sQYaDH2IKkWZ/3Ivbk5ytzQeeeSR7NgQH2bzHxQeTmoRzG7dugHw5ptvtqkNpbxGP5DyDwceG+JDkttsn3/+ecn+djWwQKcfKPl76baQZnpT+msJDf6a5E3SsKyJVgMfAOeYY46sXzUx11Kw6Py3TzyOaOWVVwbIEkC0Slg2wj5zKzRfkNO+9QPadfbHH39sMn4tm+LDYi2Obw9Bti9NBGnt55IPjZUoYOqDqOUXijl2qFzEtloQBEEQBEEBdV0E0qfJjTfeODtw1qf9lvAp2Kdqf5fqhYeAFotP1xNMMEH2O41A2kv+AEW3NIxalfALFQh/1ujJ+yOWNnjwwQdL0sa24BaFkW1zx5/kTZter8UjNQeb4l/LGM1B80fWWMLAdNYzzzwTaPt2YimVI7cEL7jgAiBF645//5ZbE24resRNqdTUcqPibLFUi9HlUSFTqa7lwoH2kW0944wzgDR/CqNzr8ux2N7DSksxBp07hxxyCAAbbbQRkLaLVHFU7l2H3T7y5/09t956K5AKmoqKrluk1113XTZXa+HzsiW8bo9B8bq1WKhM1yLzzDMPAMOGDQOSQduyJ+VQr6IIZBAEQRAEQRHUtXLk3nP//v0z43VLeL2qDh7K5xOphbI0xbaESpUR9dChQ1ttxjSS0URuWzQgt+Vp2UhNz4SeF9/360022QSoTkRklKBx0IjPKFUjqYUFTa3VU2AkbFQ3ZMgQIKkVFrurBfR6denSJYuKWlIZPGpjiSWWAJpGyNXEcWRKuwesWgxRX4c/Z/S+1VZbATBo0KDKNbYNrLrqqkBS7/KeB1XaBRdcEKitQp3Foq9Dn5TzCuDpp58GkqrUXuWoPaiELL/88gDcd999QFp7i/WjuE7o3bSUgb9flUL/nH/n77//rvj66DWpYNrG6aabLjPUuzNhYWN9UH5uWFDRY5k8NsXrr0VcNz799FMgJQ2401EOZTaUoyAIgiAIgiKo62y1fArmmL7nMRlGemYPvfbaa0CKPlRS9Eq0dGCie6D6QT788EOgeMWpENt26KGHAiljziy10047rVW/b6KJJuL2228HkhLm9eihUF2ppnJo2rflFVTQhg4dCiR/Sr6fLWZ25ZVXAqlcg9GF5QrMIKomZqa5z3/aaacVHf2oQFYzam8O+8Q+NGPIwpaqXKqCqrwbbLABkLxutaBcF2J2jAcF51UJ23vzzTcDad7XI/PPPz+Q1ptCVGk8BsXx29YU/PZ4jvy/eo3yilE+hT+f4i+uL/n0cJVYS6VY0qCSY9PPE7MDVVBU8/JrJKQsPNVYdxi22GILIK0fffv2BWpbMRIzECWfHVtJQjkKgiAIgiAooK49R0Z5gwYNYo011mj0PUviH3HEEUDaXzdK8knc9y0I9sQTTwCpGGQ+MnQP3gJ+Zo75e9sSWRlt531QKif6VZrzMvl0bVZCt27dsqKWKhdGDaussgpQnx6JPPa/Hh49TNY/snZVJUvOi/4plUQz8eacc84WPUNGxHquzObTP1YP2DcWv9S7Y9aata1qYf0pRMXLgonOH9GLY2HO/NEb9YDri14+fWKq6n/++WeTNWiPPfYAUo2k1uJ4aMtcVEFXpXRuuR7aV//617+AVJBzyy23BNKxS83hfNTT6FFTlcBMOdWqpZZaCmiqiunZ++effzIfUn5seo+tZ6QPUPWvFhXoPKp6rn0qz9YxDM9REARBEARBlahrz5F7rM8//3wT5cho4+KLLwaa1grKY9aNT6geGGoUosKkGpE/NqA96oR76VaGFiMkqww/9thjQIoMxKwE9+IL96X9Wes2dQTFSLznxx9/PJCyNLwPHi7sQZKVRI+GmSX77bcfUFymmWqkiqH1SuoJ52a+dpWRsvOnElV3W4PrRh7HWu/evYH6VIxUHK644gogKUaqE9Z4GzBgADfccAOQvDD6+swI1e9SLO1ZH117VUpsr8qRJwSoTjpv8p8JzeHvVb1Zc801gcoc1t2vXz8gKUYqI17L0UcfDSSP7GSTTZbthvw/9s4zsKoqa8NPLKgDytiwoqjYUMSGvfcy9t5GxS72ig17F/uoiGVQFMXeG2JDxd57Hzs21LE78v3I99x9c5JLbpLbEtbzJyS53Oxzzj77nvXud61lF4TsZ9qss84KwCabbAKQ85+alVgL2a6FcD3wPFjFvRqEchQEQRAEQZBHu1aOfKIfNWpUTn3JNmUtlLmQxadv/79PrM09uVqPoTX4N62pko0A/N4xGAk0p4JBqvKqYqHXo5Zoa9Vcr23fvn0bfC/2lRs6dGjFvC1m0m2zzTZAmh9XXXVV0e9x+OGHAynDyyi+PWHWib4Pr7XHVMl+TS1BT162J6MqQntUXj33Hpu+SdGLoloxevTo3Jr05ptvArDzzjsD8OyzzwJw6aWXApXxjFl/yKrdqiwq5CrHp5xyCgBdu3YFGq8H2V6Mvk5U0rz/9FmVA6/Juuuu2+DnKnP6Rz2GueeeG4DBgwfn1jt9SDYF9ns9RnowrZfWu3dvIJ1Pz5/X0DpJ1ew0oCKrcqQfN3qrBUEQBEEQVJl2rRzJgw8+yPHHHw+kPdysgpR1/ZuZ4dOyUURzXYB9ovWrNXVag2Myk8woOjv2YpSifEaPHp2rddFSb0AlMftKZcS6RRdddBGQPFheGyMa99TtDZXte+V5HTt2bNnGXgijWr0r+sSstzUhVJ3MrrnjjjuA5DdoKW1R5lR2/L96hYzkzGLS92FdFnv5WUcr66PTe1JrmTOeq7322qvB9x7//vvvD9Se0tUSnF9ZxcC5aWbu//73v1yG5CuvvAKkHmuul5XMMtQjM3LkyAZjcY56rxXaJXCu6ptSOfG+0tvpNTf7rZx4/vSTqlp5LDvttBOQPl/0fkE6Hj1Vqpp+tpnp5TpizSpVbD+7fB+vu6+vpnLktXbtzs7VShLKURAEQRAEQR7tus5RPkYN2Qq91h/Sqe/TshGsHgif2O1jZcaCke+TTz4JwMMPPwzAa6+9BqT+Nm3BiEUvgD19jNKaw55B7pEPHz68pruDiz6GwYMHA42VNNUFz7EKkspRVmETI+G9994bqPccVep8WCnaeXLAAQcAKaMm/34z8tV3YFadfjFrBNkbqaUUqu48IfTa6H2wkrU1YKxebh0cq/NmK9tm/7bX0mxQ65fUCq4bWUVZj5E91KpRM6uteC2cT8OHDwfSfWS07hq5wQYb5DIms30PVQhVXyqJ6sro0aMbfF9onnsPqsJk/aF6OK1pl60rV4msLtcLfUGe9yyqQhdffHGuY0Jz18B72Z5qZmD7OWOW3qmnngok71ItoEdzyy23BBr33SwlUecoCIIgCIKgCDqMctQRUP0yq8T+TdZWEmsr6dUxO8Gf18I1LQbVOlU4I7ZCWXuFMJq3ptPAgQOB1B/rl19+qdg5MbLWB6CKZd+3cePG5fbRzbA0gjVTQyWptV4jaY1yJFb27tWrF5AU1kLvLSp0KjD6pqytowJba94dVRQr5Xsveo3sodie8ZqpFHhs2d6U+fPE+XvOOecA6d6q5vVz3NYSU/Vz3Hapt4p5IYWlT58+QH2dPICbb74ZSJmmlcD7yr5ufrVbghXZXcteeOGFgp0SCuF1V3m3Dlx23awlXAOtPeVnhZ9xpSSUoyAIgiAIgiII5agG8UnfDAVrFRm9ulfsE3978Bc1hcdptGS/OiviZrttix4rey1ZKdZeUXoHqjG3s/4Oqw6b7TXppJPmvAB6i0aMGAGQ64dXqu7Z2YwzKWa+qJyYCdi/f38g+b/uvPNOIHV29/Vvv/02kHwh+jZqLTsti5lczimPR89ia31ftYi+Fqsvr7feekC6lp06deK7774DYKuttgKSf6cWPFc9evQAki9FdUuF/YILLgBSxfxCqKTotTFTsZjM0qC8+NmnAq/XUb9UKa9RKEdBEARBEARFEMpRUHVUOPRO2SvOehv6VFSU7DVm9+xy7EOXGj0tV111FUOGDAFSVmK5xq/60V6VxSAIJk5cu6zzNGDAACBljarwloJQjoIgCIIgCIoglKMgCIIgCGqetvbjbIpQjoIgCIIgCIqgQ/RWC4IgaA8U6pPYVGamr9FrZ4ZOR/KQmblpFlwt7GQEtUsl50coR0EQBEEQBHmEctSBMNK0AjOknjxBEFSfQrW7miJ7P3ek+jsem/0RrbjdXpUjM25rrfJ70HpCOQqCIAiCIMgjstWCIA8j2myNoGpW287S2rF4THV1dRHhBjVNObKSgqApIlstCIIgCIKgCMJzFARNUAvKSqmjZpWjWuiPFQQTIhSjoNrEtloQBEEQBBMlsa0WBEEQBEFQBPFwFARBEARBkEc8HAVBEARBEOQRhuz/R7Pq7LPPDsD8888PwHbbbQfAEkssAcBXX30FwEMPPQTAJZdc0uDntYpFyu68804AnnrqKQCOO+64ag2pII713//+NwC9evUCoHfv3gD8/PPPAGy99dYAPPHEE0AqeFkLZuqJDVOvl112WQDOPPNMABZccEEA3nzzTQBee+01AE488UQAPvvss3ZlEHddeP755wFYa621gDQHa8HDObHSuXNnAM455xwAtthiCwBefPFFAAYMGADACy+8AMDvv/9e6SEGJaZTp07MPffcAMw888xAKiz6zDPPAPD999+36r1DOQqCIAiCIMijw2WrqQD59LjYYosBMNNMMwEwzzzzNPj92muvDcBss80GwIwzzgikkv2dOnVq8P6eL6Pdb7/9FkgR8g8//FCqQykJno/jjz8egCOPPBJIKsscc8wBwLhx46owunpUHVSCHONCCy3U4PcqQn7vJ/JW+wAAIABJREFUMRi1X3jhhUBSx2qRRRZZhL59+wJw1113AfDFF19Uc0htYoEFFgBg//33B9I19P758MMPAfj73/8OQJcuXYB0n1x22WW5udkeFKSNNtoIgBtvvBGAE044AYCTTjoJmHiUo1VXXRWA+++/H4BffvkFgGmnnRaojnp72WWXAbDZZpsB0LVrVwD++OMPAL777jsAjj32WAAGDx5ctrFUsoilf2vjjTcGYO+99waSQrbSSisB6TPu8ccfB9K9Wov4+TzLLLMAaYfGNf6DDz4A6o/pn//8JwDzzjsvAF9//TWQdiB22mknoPDuTmSrBUEQBEEQFEGHUY6mmWYaAFZbbTUgRQd6BKaYYgogKSnN0VyDSH9uxLTKKqsA8Nxzz7V06GXFyHafffYBUmT33//+F4BDDjkEKG8UVQgjHtW7K664AkjRgnz88ccA3HrrrUBSii644AIApp9+egBeffVVAFZffXWgNnxgju30008HYPnll88dn8c/dOhQIKl3p512GpC8VW2llFGs76Wfw4jOeeXfMEo3er344osB2GCDDYCkwEwxxRTccsstABx++OEAfPLJJ20eZ7nYZpttALj66qsB6NevHwBXXXVV1cZUSVTeva5TTz01AE8//TQAyyyzDFBZBW266aYD0trbo0cPIN1PrgNzzjknAG+//TZQr+KWeqwqHvqfWut3KUR+SyH9bjvuuCMA3bt3B9I1cCxZVNL0dB588MEA/PjjjyUda0tQ1fJaXHPNNUBa+3v27AkkT/Css84KJHWoKVSib7/9diApillCOQqCIAiCICiCDpGtVldXx9JLLw2kfWcjdsl6hdwT9yla79DYsWMB+M9//gOk6MNsm3feeafB/7cxqRkRtYKej+WWWw5Ikb3odzF6qAbuBZ933nlAikJl5MiRQNobn2GGGYDk91ApMlJceOGFARgxYgQA6623HpDUvUqgQqnXbdiwYQC5jIqmmsnuu+++QJpTXrM999wTgHfffbdNY1ItbYsPxHHvscceAAwaNAhI80yVy99fd911QLrvvE9uvvlmACaffHIAHnzwQbbccksgKUZHHHFEg/9TSyy55JJAGls1o+1KoldMdc979ddffwXSeaikYpRVXlUVnOejRo0CYK+99gLgpptuAtJnQznGOuWUUwLJ//Lss8+W5H29h3feeeecirL++usDKYu3WLz3VD1dP1WiKonzSvVK1d+1vNidnqbI+o5bSihHQRAEQRAEeXQIz1G3bt1yvgXrrBhVqOjojVAZeuyxx4DkjSgUVWf9Gj7J1mJUm4/eDiN1n6LNElp88cUBeO+99yo+NqNv1atu3bo1+L2qw+677w6kqHTRRRcFUmbgW2+9BSTPgXvLRm8qR/fee28ZjqIhzpN11lkHgNtuuw1IUZqMHz8+N3dUtByv++eqm0cffTSQagZV4171uDbddFMgKY1GfCqsZox4XxV7f/Ts2TPnX9FfYNRYi/eY1+Kggw4CUtTt2GsZ59dUU00FpDWvGGX18ssvB5LaIF7vNdZYA6hO7aAddtgBSAqSa5qZlB6nyrPHa7ZrKfEc+7Wt58Nj2HnnnYH6zDM9RaXC7C49S6qB5SDrWXTXwM+AYpUir+Err7yS+2xfc801Adhqq60a/K1TTz0VgKOOOqrJ9wrPURAEQRAEQRG0a8+RT5lnnHFGTgnxafHhhx8Gkl/lyy+/bNXfyEbrtRjNZll99dU55ZRTgKQYeRxWxK6GYrTyyisDcMcddwCNPUaOzWyt3377rcHvVQEL+buMXh999FEgRSWLLbZYyTK/CuG8M+ry/FprRZVszJgxvPTSS0BSX1SOzCy0GrsZHF5D/XGVwHvLStaHHXZYg7GoGKnitfb8vvvuu9xzzz0ALLXUUq0fcJnx+urLcB0oh/pQaszkVYncZJNNgHQtrR1z/vnnA/XzzOu/7bbbAkkZFNdTa1RVs9q0qp3XxPsm67XTV1oJBba158P14u677waSKq7PqKkMNI/PzDj9pdldjwcffBBImYWqn65DKvXOg1LitTjwwAOB5E20qnVzuHugSqR/7Pnnn8+tiyqHfq8i7edAi8fcqv8VBEEQBEHQQWnXniOrWT/xxBO5J2ufonW9P/LII6UYYrtgvvnmA+ojBDM3xAwvn9iNoiqBUYPZZaoNYk0qVYrWzkkjpWwV43333TcXcTT33m2tCeT/96vRbDHvq9dGJcVaHkY+RnqVwPljTRh9Kn5vle9SVIQ/44wzgNTH0Eyfcqt9LcGI3Wuz/PLLA6nyfi1mralCmP04fPhwINXgcW6qdromXHzxxbz//vtAqiWWjfCtCO69W01F3Wuj2qL35NprrwVSVqwqjNlqr7/+esnHYkeFlipHHoOqumuBqp9f89Gb6Lrg/1UxsSq919DdBJUi+2t6XvTf+n0pcO1XrVS91D/aHHqL3BV48skngabXUf/WRRddBMCll14KpD6IhQjPURAEQRAEQRG0S8+RUbiVQfOjGvefrUuUzR7I1ieqBeWsENk+cYWiEaMVlRf33CHtv95www1AZRUjURFQCZFXXnkFKF02lv/fqNdj32uvvXL1hpqL8K1T1NqMjUKV1Ys5tjfeeAOAgQMHAqli+C677NLgexW4cmL07biPOeYYAP71r38Bpe0h+NBDDwEpwrc6rtFmIbxWWW9aOTCbxkxLa09VsoZWsZgh6TXbbbfdgOTx0yflGumaaNX8Z555JucPzGaSmoWpclQLHkwVFOsZmcVqNXPXHeeJNXXKQWvXMNdus629Vk1lb3322WdA8nDqNbISeLamnZ+XqsFXXnkl0FghaqoGW1txR+fkk08GUseKQjif9PbpbVPlm9D5VRF1vTz33HNbO2wglKMgCIIgCIIGtEvlSK+RtXzcQ4UU6fikqXpg9d0HHngASG53lRSzb2qpM7hP0c3tX7s/bV2f/AjAnlbWAKo0dXV1OV+CHgiPy0rWpY6+zRRzDvTp0ye3Z9+cclTNrBsjeOuOiBGRnagrgX3s9G2V01NjdKwXpNg6LpW8VqrTziOVo1paL0TFTe+dfjHXABVIqzd/8cUXQLoOPXr0yGVM+jNVCWvGVEKtaykffvghkBRIa3LZld5aTSq05aCl80HV33pGfpYVqvfz888/51S7Yivnq7boPTOzO/t760CVApUv+5mpIBXCtU+1x/pZLbnH9S6q+n/66actGHFjQjkKgiAIgiDIo10qR1bA7NOnT6Pf2evJr6oUPhXbZ8V9ZxUj9+fNRrGnWi1jfRyftvMVNDMWjPTKWfV0Qkw22WQFvUaOrdToqXDv+aGHHio6Aqmmh0JVwjpPYpRuR/SXX365qPdrS+admSvlpq6ujgEDBuT+Dckz1hyV9AuuuuqqQIrozfSpJVzjzF5yvqgYueZ5frPnT1/mwQcfzD/+8Q8g3Q9mQqnG1mKnANWHO++8E4Drr78eSLWaXNvLMeaW+nU813rYVEsn1GUe6hXcYj+b9J7Zb9Qq9342iuetLb0Xs+y6664AHHnkkUDh41Jpu//++xu8viWKkRmjVt1W9dZD11pCOQqCIAiCIMijXSlHRit28dbD0hTWSNFbpMPffVozOOxobIbMa6+9BqTK2n5fi1ht1JorMn78+FwGkH6CavHXX381ihqsXl7KSKUpjEo6d+6cu57W/KglPD/W8OjVq1eD31uryxpDxdLWmk2VYP7558/VTNIfuO+++1ZzSE1iFKrqoDJbS6g8ej7F+0DfUKH5YJd310ZoXAF8v/32A1KGlNmLtTTHvEZZhUVVQhWslH6xlh6/97wZl9k6Rr6f51+1p1u3brnML+tWiTsJZqHZU9LPuCzeb4V6jrUG1xyrbefvZuTj/HHXw0rrLT2PO+20U66ukd46szDbOidDOQqCIAiCIMijXSlHRgRmI5h1sfTSS+eUIqNs9zwL9dOx1oP7lAcccACQ+jupvFiDx6+1gJVP7XeUzWwYMWJEzsdRbeadd95clOT1s1ZKuTFrbdJJJ2X//fcHUvXUcqtWLaF///5A48rh9pAz46OlvrFa8oNksZr7ddddl4uevceaq2hbSfRtmCHrOmN/vFrC7BwVE+vdrLLKKgCcfvrpQDq/KpVmCZrNNemkk+bqWJ1zzjkAPP7440BSfSvZ56+lmFnoHBMz8MyetZdjNdYCPTWex2zPNK/loEGDgHRM/fr1y6kxXpsNN9wQgLnnnruov+26YK+1wYMHt+4gmsA6Rvm19vLxXPvZ3dquCCpta665Zk4xUgkslV84lKMgCIIgCII82nVvNSPOGWecMVcbprVZWb6X/Xh09htlmVVQC+dL1UsFTfRF9O3bN7fvWm0mmWSS3L65NT3MGCw266q1qFiNHTs2F6l1794dqI36NNansku2EZDqhOeppV6jWkafoFW+Z5999lwNJbNOaqnqtP4Nx+tXo/VqZYE2hX4Po3I9RGaxZTOqVBCymWf3339/zqPntallFTKLNXWeeOIJIPlevL+seac3qxq+TK+F/i57P8pdd90FpO4CKpjvvPNObueguQw5P6uyr3PtO+KIIwA466yzWncQTWB9o2+++QZI43Ysen5VmFr6eeq1VPV0DYX0eWLl8GIVpOitFgRBEARBUATtynOUxfovRgKleC87F1td1sqlqhDVVByMGKwMnsXeQrWkNIwfPz5XV0VPjVF3uZUj1cA///wzpwAWqjxbSVQjzDYxopWNN94YaPt1bE0tGqNM69xYk8psT7Nm9KT4ehUho7VsRDjXXHMByVdk7as///wzV7m3lhQjsR+VPPfcc0BtVoj2nO+8884Nfu61sSO6Hjezm/S9WAfp8ssvz6mX7RErZWczvlT5vP9qQQ07/PDDgcbqzjrrrAPAzTffDKSaftm+aZCOU5XPdcM+o3qtZpllFiB50Mrhm7O2Vnad9XPTzLmWKkZ6mFxH8v2ZrhuqbOE5CoIgCIIgKAPtWjkqB9Y1+uijj4D6PkOQMgGqqcqYnZatHeFT+d133w3URkQk48ePz2Uk6OeyroaKnz2QSs3SSy8N1GfjzDHHHEBSk6rVQ22qqabK7Zdns2lGjx4NwMiRI0vyt1rjjzOCNavPKMw55X2gkqrqYP83MwTNfPJ877333kDyFemp2Hbbbcvat62tmBXj8Xnv1YL3sFisKaPHRE+O6rg1m4YMGQLUlo+qNbgeWhFb1cW+mvae8z5bdtllgcY1ncqJtfrMFMyi8rLCCisAybvTFNavsoO9Sr2eKz8X9FuOGTMGaP5zQo9oS9bKtddeG2ishHm/qF6p7hVCP61j7NevH5D8RPLHH3/kegSWuiZhKEdBEARBEAR5hHKUIduF2j1OK8Guueaa1RkY5Cqj+kQvVkI1Qqw1jFSMQPQ6mCVhVs0222wDtN3XpZ/FKq11dXU5VaZaKoVKy3nnnce8887b4HeqL3pCSqVKtOZ9sv9H5dRMyA8++ABInhvnnNknqhLW1CmEEebyyy/Pfffd1+rxlhvnomPTx9EesWu5feKM7lV227tilEXflL4xFST7w6mCWsNJBakS3je7GqhIZnG++XnktcqvyeR6+sYbbwBp3Kq2Hpc/b+mOQmt2IFxnC/XtU530de7EWOfJ+nzZNTKrnDlXhw8fnlOlS71+dJiHo2wrkeZSUJ1sfjWt0w/obAn+5mTAcuINpAk2K+27LVVLhQ3zcYty++23B9L2mpKyBvPrrrsOSDeIaZ/N4bVfd911gWTM01T80UcfccIJJ7TtINqIEv/KK6/cqK3HeeedB6QCptXEMVmEThnc7TWPwwd0P3DcfsoaMX0/H6ZcBP06ZMiQXMkCy3HUEh63i7tNXNsjiy66KJDuFx9sR40aVbUxlZOBAwcCaUtGM7MfpmI7kUMPPRSgImuFDc99+PZ+8PPK8h42z11ttdUAWHzxxenWrRvQuFWHgUupCnS2Jkh13CNGjADS56n3jw/mHo8PsK4nnodC64ifwz7QDxs2rGwFSWNbLQiCIAiCII8OoRz16NEj96Tq07SRr2nSqi4+eaoMGbWqzmSfWJXvbExbDUzNzTYn1PxqGfhaxad+mwB77jUMmh6ugqSJ1wakbuloxrPsv0qDqaoqas4Bt/OOPfZYPv/885IfVzEYKa244opAwy1Rt2qNWGuJY445BkjGaVNpLRfhNfN+UbpXNj/ttNOAVGAvmxquOfiEE05gq622AtL5qCW8Xs5J52itFFktBhWUZZZZpsHPvQbtOW1/Qnz33XdAWvtVziwlcuGFFwJJsc7uJpRzm9fPp+x2kcqRqrdrnuvHtNNOmxuXc1BlvhZaumQtAhrPVY6yTcgLNaYV135bKbmeVqKkTihHQRAEQRAEebTr9iFGdSNGjMhFA82VVG8On0htE3DkkUcCyWtRSTwWo3dNrEbrNh60eFstXMuWoJ/l1ltvBVLT39ZihKjKp3rh/n4lsYy+x+YeO6Q5pufKcdcS3ltGpyYmOMf0t+lb8T5ROWpuLt5www0AbL755jz66KNA4zTdWsBUav1gRvwWoatk+ndLcZ2w2J+tFlRUvN+K9fa1Vy644AIA9tlnnwY/zxbyVEk65JBDyj4mC1E6n0xScUwq3Sq2+QqT95Yqbikbx5YKvUMq0KqXzeGxPfXUU0Bquq0CXY4yNdE+JAiCIAiCoAjatefIVMZddtmFww47DEh7s0buOvuNhC08qGrhE64pujZcNN2zFtSYAw44AEhFrszEu/TSS6s2plJgdOQ1M1pQpVCdyEbr+luGDh0KJAVN70Q1GkmK82nzzTcHYKWVVmr0Gj0CtdD8thDeWzbENIPHqNpz7/2hN6BYzGpZY401clFzJbwexeJYjPD1UqiUOeZaVo4sMuoxeF5tH2J2U0dH76LrzCKLLAIkH5nqaCWzJc2CtGBjr169GozJorVNtTtSXarlzEnXtuOPPx5IpXFstGszZBUx10Qzjd955x2gvGtBc7tMoRwFQRAEQRDk0a49RwXeq8mf18JxthUL8ql+1bLyMLFipGckaKNSlcs///wzV2+lpWpLELQElSNV8LFjxwJpbpaqQWfQenbccUcg+aLc8cji59eoUaNyjalj/SgN4TkKgiAIgiAogg6nHAVBLaCCaQ2fU045BahvUWCDyCAIJm6s+zPnnHMCyaNo5flzzz0XgM8++wyojVpGHY1QjoIgCIIgCIoglKMg6KDof/JreNSCSlNLGYhB0BShHAVBEARBEBRBu65zFARBYbLVrIOg0jSnGIWyFNQqoRwFQRAEQRDkEcpRDTGxR1ET+/GXmjiPQa0TczSoVUI5CoIgCIIgyCOUoxpiYo+iJvbjD4J8QkkNguoRylEQBEEQBEEe8XAUBEEQBEGQRzwcBUEQBEEQ5BGeoyAIghokvEZBKdHDNs888wDw1VdfAfD9999XbUzlYOqppwbgb3/7G5CO86+//mrR+4RyFARBEARBkEcoR0GHZZpppgFgiSWWYPDgwQBMO+20AFx88cUADBw4sDqD6+DYbXy66aYDYMCAAQBsueWWALz44osA9O/fn//85z9VGGHQUXCuzTbbbACcc845ACy55JJAUhLsLfjSSy8BcPnllwNw++23A/Dzzz9XaMSVQaVoo402AuDqq68G0vnq1asX0L6Vo0knnTR33W+++WYAFltsMSB1BlhxxRUBeOqpp1r03qEcBUEQBEEQ5DHRKEfdunUDYJdddgHS0/RPP/0EwDfffAPAnXfeCcCYMWMA+PTTTxu8rhxkI5+vv/4agF9++QVoufegR48e/Pvf/wZg/fXXB8o7/mox2WT103ebbbYB0nlbY401AFh88cUB6NOnT+7/TD755ADMP//8FRsnpChu6qmnZuaZZwbg/fffb/A7j+fXX38F0nWfZJJJGnw1IqolT8oUU0wBwI477gjATjvtBMC8884LwAwzzNDg9bPMMgtQH+VvtdVWQIrsOxJeM+ddz549AXLH/OSTTwJwzz33VGF0bUcl9pFHHgFgxhlnBNJa1lKfRzF4vzi3NthgAwD22msvAOaYY44Gr3deeS1WXXVVIK0XXgMVzFq6r9rCP/7xDwAGDRoEQJcuXYD02fbxxx9XZ2CtwGsnzrsllliCc889F4D55puvwWude7POOmvr/mar/lcQBEEQBEEHpcMrRz169ADghhtuAJKKYJQuRgubbbYZkJ46VY7WXnttAN56662yjXWfffYBYPnllwfgoosuAuCaa65p0fv069cv9x4nnXQSAAceeGCphllyVM584p9++ukb/N7IZ/bZZwdSJPDjjz8CSQUyOyHL//73Pz7//HMA3nzzTQD+9a9/lWz8E2L77bcHYP/99wdgzjnn5JZbbgHgs88+A5L/Rg+A3gCVQ49v3XXXBeDtt98G4KGHHgKS6unrK0GnTp0A2G677YD6OQew8MILA9C1a1cgRflZ/Plff/2VuxfLrRz5N6+66ioAll12WQDee+897r33XgD++OMPIM0tI9TRo0cDaY6+++67QFpfnF9LLLEEAL179wbS3F599dUb/E2P2b/j+1TS/+E1/P3331v8fz0PZ599NpDmru/l8aiOlpKFFloISCr/nHPO2eD3rt3ffvstADfddBOQ1MqllloKgM0337zB63fffXeg/avsei31+c0999wAfPTRRwCcccYZQFKgawnvC5XW1VZbDUg+ItWufffdF4AFF1wwp1o7J/0s97Uqgy0llKMgCIIgCII86mphf7Wurq5kgzA6XGSRRYCknKy33npA471L8TxkI11/Pnz4cCB5KkoZ5TqmN954A4C55poLSFGXUahRbXOMGTMmFx2pfC244IIA/Pe//y3RqNuO51p/Sv/+/YEUCXbu3BlIUYQRnhGv+PPffvsNSNGrEeOwYcNyfoIvvvgCKF90OOWUUwJJIbjsssuAdE3r6upy4zVyc9wep+qBSpjnQfz/qpgnnHACANdff32pD6cRRu2qW6IiW+j+Eu8nv77wwgu5697SbJKWsuiiiwLw7LPPAknVaWpc2XXA+ZJVlvx+qqmmApLnJquGZt/fr157o/svv/yyTcdYDPqBNt54YyApkK+//nrR76FCeP755wOwyiqrAHDUUUcBcOqpp5ZkrE2hb9R7y/vh0UcfBVL22Q8//ADA/fffD8B3330HJI+S6oNqn0qL2W7tDdcPPZhDhw4FGitjZunVEl6D4447DkiKkaqQ96Nrpseaj17NSy+9FICzzjoLaN5bNX78+Cbl7VCOgiAIgiAI8ugwniOfLI0azFgykhcjNp9As9kUPpFmI0e/L+ShaAuOyUjHMZjV5DE0pxw5tiWXXDL3b1WWpp60q0337t2B9KSf9YFJNmtLb41eG70FZugNGzYMSNkolVRHN9lkEwAuvPBCINX5ySebfZa9NmZWinNUZUWVRp/HbrvtBsCIESOA8hyvPjCjdZXZfPUlH5VVVTz3/c0w0je28MIL597r6aefLtv4oXGl3Hzfk8qQ64JKkHPS+8if6+sQ39NaOePGjQPglVdeafC39X0Y5X7wwQdAZRQjj9daU4cffjiQMlpV1yeEc1ZPkYrRc889ByQlqZzceOONQFJCnnjiCQAOO+wwoPA66fGr0OtBKrTutDdUp71HZezYsUBjtbcWWGeddYC0dlmTynVDX6aKbPbzHNLn5mmnnQYkP6k/by0dY1aQPiA0PjZ1EiHJ46b/3XrrrQAcccQRQJKas4u+C3Y5UlPFCSBup7jN9Oqrr07w/2eNiZBkyexDUrFbdOXA7SI/MLOLkw8/Gpd9+HFbxJtAE20tGAtXWGEFIBn6fShy3nisvXv3zl0Tt2Z86PFh2IcFP4h80HKOOgedy27hOV9KuXXqg5pbUY6x0EORH/onn3wyANdeey2QFjnn3/PPPw/UbycdfPDBAFx55ZVA+YzZjsG/7cPloEGDuOSSS4C09eLDj3NV2d/jNh3cufvOO+8A9eZuSKZ5H36y5Rmkkg/ubq0feeSRQCqv4PxxXrrV1xTO66OPPrrBz70nK2FmdtvZpJNiS1v4YOuHp+ulD7C1uN1UDN73J554IpDuMc/HHnvsAbTOeF8ufFA1UcWHIueeBTwNorRIiMf23nvv5RJVTJIoFbGtFgRBEARBkEeHUI4mnXTSXAEwowPxCVOZ2yjVLRh/b9SgxJyNjCthZDaCMaJROVAubU45srDlX3/9lRu/UYRRQzmVr+YwWtAopwohnuP8FGuobIp6S/E8W3BN5UjuuusuAK644gqgfjtC4+sDDzwANDaemx7v+VBREV+nomY07xaHKc5twbmXvVbZLUDvHxUXy1Fcd911DX4vKii77rorUG+WdXtVk+/LL78MlG+uqsypLv/vf//LbdGqWrklK9kWJ27ttAcsgaFxX8XI83vMMccAE1YWvHdXXnllIKXDu014xx13lHrYzVKswuj1ttCmaoTX+J///CfQ9m2YSuM12WGHHQDYdNNNG/xeFW/kyJGVHVgRuG5aqFecg+5sbLHFFkBad1SW3IYbMGBAThEuNaEcBUEQBEEQ5NEhlKOePXsWbAfhPqTRgf6ObERrOqv72VnP0ocfftjk/ysFvqdRugUnfVo21dkxFlKxHHO+h8dI3SfuavpzPE4bBLoXLvfddx+Q/BsT8j5UG/0ZBx10EACHHnpog98b1Zpe/MILLwD1HrfsHNJ7JBdccMEE/7ZRlZG/Ks8pp5wClEY5MrXc1P1CipFGXM2wtpFo7j6xqOKnn36aU0o1jK655ppAKrtQarL3wJxzzpmLZD2n1VRYS4Up+3pRsqqmRWYLqXz5qL4431UtVOyL9bNUouCnSqyG80MOOQRIuwqunzadthFte0NDuf6+rHdTb1ktN9TN7gp4b7q++vk1ZMgQgFwD8ddeew0or2cvlKMgCIIgCII8OoRyNPXUUzdSeoz89t57byA12yuE0Ws2Y8wnU/enfTovh/PfMarMmf+oAAAgAElEQVQ2mHmnc3+BBRYAkgqRjYDNvDvllFNykZ2ZOX5vhFxNBcm/nS2LsNxyy1VjOK3CkhGOOVvsT/+YabV6ckqJGVFmlLkP3xa8JpZXyG/am4+qjkVWH3vsMaB4xcXXvfvuuzlPnY0jzQSzqGWpVRzXCo911113ZeuttwZSFqvRuOplftp/rWOG3QEHHAAk1Vz08qmsFKMs6GvTD2hpAtvEFEspFSPXMueoWWiul/7ee9JdAe/ZlhS9rCWyfsBsuRA9ONmU/lpC1SvbFNasNRVr15dsGY5KEMpREARBEARBHh1COSpU0wiSImTkl92j9Cn84osvbvC96Pg3s6ice+VGqe7rqyAZGVgGX4XAKNdMPFWySSaZJHeco0aNAuDvf/87kJ7Aq6kc6YXIKkczzTQTkLIvzNKqhTpGYqaPak2XLl2ANK/MILMIaTkUI8+bf8u/7f58WzDazjZOFa+FLRb0N2XVwOa8AL5vvt9KX5NF60odJTom/Qr5qpjX0fYX1l9RpTUKNyq3aKMqjD6wcjfPnRAqRnqJtt12WyBdE8eop21Cnj6vhedIX5LvpQpz9913l+4AisT6VCrl88wzD1C4dY3zSGVe1b/YuVpLTDLJJLkWVqqd4j1oe5RazvK1oXP2M8BraCNrr3E1FNtQjoIgCIIgCPLoEI1nl1hiiVwWWnafWVVl0KBBQNqXnmOOOQA477zzAFhppZWAxtGHkaH1GIx0y3nefJo28lNBKZQxlP1/+Rjhmk2lF8YMjUpef8enF8JrUqjZr54aK+GqwpQjiig2irQSu54cMRrVx2BF5VKO1THqmTCat46NKo6RY2vQc2ddn6wqq9qgyqeCIFZiL3TcKjT6ix566KGc4iE2eVZBLLVyqPJgxlyXLl1y60a2cnyhBrKiX8cMSxUn769sJmI5cO2ySvUyyywDNL6vPI/WJDr99NOB1JjT+6uuri43p/QYea+eccYZALmK4tnaQOX0ZulvMmt3scUWa/J1/m2vlWqec9Nr7PW3UauKZS2z/fbb51RMM7rEXQK9qrWInl3XSdsJFcLMQueqqmd23WkL0Xg2CIIgCIKgCDqEcjTllFPmqur27NnT9wRSFGFk7xOntTD8mo2yjC6s5GvmTCVr7zgmozgzaIx0immY6PX9/PPPgVSXxGyrSuLxqDrYpNVIV3XOaMKo3ejBfWi9WFY1LuXYmrsfzNLyGHy9UaeKSDmq7Rr5GsVbjVpvgZH0W2+91eq/oQphVd2sWmmvMDMnva9UVqxRZMaZDUpffPFFIM1ZVYC99967kXKk2uk8KLX64rXWhzfHHHM0qoytyqL3yutqfz+9OP7cXmwen15F67Mce+yxJT8W72XfO9uwOKu4ZVV1PWp2DdCTM27cuEb91vyddeNcV1UWfb3Xrrlq/q3BeWJPRRVUj9OGsmZOuivgmAYMGACkmlyeD9eT2267rcH/q6XMROtMDRs2LDcnxfvdn1ezb6bn1F0P13QV1q222gpIddQ8x66fepEKNYx/5plngNSwthSezlCOgiAIgiAIiqBDKEd1dXW5PXCVnkKZC83h+bBnkP3KjIRqoTeZNSJ8+s4qCDPMMEPuyTpbkVbPgF21a+H6iyqF2Sf6eoxKPX4zGKzfUskKsKpVZhAapVndWU9SKeeJioDnw/5/XltVGaO0tlxTqwgbbVt3RIwI9QWJYzGLTTXQHob6XFRejfqHDx/eyDuhOmHV+1qK4MX1xTmrEnbEEUcA6Z70dWeffTaQ1Iu2+Ki8Dy688EIANtxwQyB5aMwgu+GGG4A0f1T7rrrqqgY/b8qrKM4lx+t893sjfF+3wgorAPD000+39vCKJquEZXcLsj93bptxOmzYsAZj1idmP69Sd3lvDZ5f1zy7J0Dyj6644opAaX04bcW16PHHHweSApm9Rqqe559/PpA+01zrVMPy+yBCWmeynQlaQyhHQRAEQRAERdAhlCNI2WfWJclWDS1E9viNbPW32Aeslvt8GZ36dD7TTDPl+ursvPPOQIrs7X1lBFLLx2Vmk2rMgQceCCTlbMEFFwRSRmEl5rLRmR4T/VCqOfoXSoFzePfddwdSlGUUpRpohelPPvmkzX/TeaLSowIp+jWsziv6oVRSVBj0JGWvjf6Rzz//PHcus3/jzDPPbMORVAeP34j3wQcfBNI8WXjhhYHSRPkqIS2tc6XionJkfTR7kH377beN6tBkK+u///77QMqQu/baaxv8vj3gemlWrN5Os2mb63FYCfSZqjhOMskkOcVo3XXXBer7E9Yaen/NSst6F63RpmdPRd7PMr2KKkzW+HNXwc+x1Vdfvc3rfihHQRAEQRAERdBhlCNVBp8ozdzJ7qf7JKraoAph1ppZJkZ+eo9q4TwVy2STTZbbu9V/4BO3FbKXWmopAD788MPKD7CFGHXYb8eMBT1WRimVqFNidG7U6XxaeeWVgdSlviXVaZ2j+lbWWmstINX5MQPMv+lcVElzv76Uc9RsMyvDF6ofZpao94mKpb/3/3l/qVbowVlttdUa/W0jd7OG2iMet96J7bbbDkjXSnWsmqg8mZmoanjwwQfn5rVqknNPRV1PWkdA/5uqr14j15tqqGGuz0OHDgWSX+y3337LecxUU2oRO1NY/8s13Hnl57RrW6Fz7H2kku2Oh8rlfPPNF8pREARBEARBJegQvdUgZSzdcsstQFJ+fCI1m2jgwIFA8mtYHdbo3UjZCsHtSTGSP//8MxfJW7dF5chMjT333BNIe9mVwMhUtcD+Y/fddx+Q/BlZ9K+41+5Xa82YgbfTTjuVYdQNsc6RkZF75HourGNjXZjBgwcDMP300wOwyiqrsN566wHpWnh8em+M6PXxiFGXmStWyi3HHHX8l19+OZB8X94nelJUzBybipOv01vksWU9LPnoTzLDpT2SzShVATByLtYLWU6cs2ZnWU9KX9SYMWNy66PZic7FctTvKhbH7drufeJcbS0qRc4/a5g5Z8vRH7EQzhsrrasYeY9ffvnluUrYtYzzRKU96zlyZyPrZRNrkKkiZ72Pfr6Vk1COgiAIgiAI8ugwypFP1rrfjTLMCjnuuOOA5Lmx+3a2foLVh2uxtkpLMAqy3sjSSy8NpMjWHlqFntzLgX/Dr9aC8esJJ5wApP53RrJZFSJbAVYlymtezmu35JJLAikzTC9G9+7dgTQPzbbQa/L8888D9YqT51wVIRtVZdG/8+yzzwJwzDHHAMkfVw70TO23335A6md1xRVXAClqF8+9GT9ZJlRLR/QvWbepUkw++eQ5T5SRrnNMtdPx+zrnsHNSNdCO6dZ5cX3xfQupo5VE35DRu3PWCtOqRpDGXWwmXDnxvlbpMpvsySefBGDzzTdv8Lrm8N41K9Jr5WdEOe+vQqiGeyzivX/QQQc1uD61imuW5/aggw4CkiKk8u79oPJuBrLZv2biev+5Q9S/f3+gvDs7oRwFQRAEQRDk0WGy1XyyNMLVC+HxqUY8/PDDQKrca5ab+9b6YKoRNZQDO5A/9dRTQDpe94RnnXVWoLLH67WyUq++BqNxMwn9vdG7lWDtOq1aYbaNWV6VmNOzzTYbkOpqqcSVAhUzMwlVxvSPVeOe9Zplay8dfvjhQMq0KxaP4ZtvvslV2za6rFRkbPXvM888k0033RRICt/MM88MJK+YXjIjX+ek3ivnYla91LtoRd+LLroIqNwx5uO9b10cPTUqJape99xzT8XH1hJU71QYvVZmm/n9SSedBCQVw5+7TljFXV+P18QsUCuQVwJ79o0YMQJI6pj1f/SumaXVXnDd0DOkQuR9lF3L/D7b4cLPhj322ANIXshSENlqQRAEQRAERdDhlCOfvLN7ttnjzHZhN9PJrLZaOC+lwGjJuhJ6sjw+94StvlxJb4H+lNtvvx1I0VMx/hRIvhj9Y0bnlUDfkMdglGnV2qaysUQ/i1GhVdjtNu7XanbXbg4jOxW0bbbZBkh+KFUKMSp/8803gTTvhg8fXrXjtD7WjTfemMuAam1PRiPbzz//HEi9C80srIW+V/rgsuqD2YFmGrYHTwskhce5lL3nsr3Vst+LXQJUJfwMqITv1LEMGTIESH4wx6q3xt6h7R0rxKsgWUsqe985B2+77TYg7fTcddddQGk/n0M5CoIgCIIgKIIOoxyJnghr4eipKYReAr05pehPVUsYTamo6a0Q/S36eax9Usl5offBKOnEE08EUjSRjfRUt6yP5DFVM8PQzBcrs+tz0MulT2KxxRbLRUMqX7VwD7YVr5VeHNWgrAenFo91ttlmy82hvfbaC0jZh2YSqi7oSfI49cdZyfiJJ55o8Ppawu7zeryco3379gUaqsaVyPxsK9maW/pQzBz0995n3oOu+WZenn766UBlKuxn8Tybcad6bqV9q3d3NPxc0gvr56/+UevwWWOqJR0HWkooR0EQBEEQBEXQ4ZQj0Q1vfaPtt98eSFGG9RKM9K2S2lHxfKi2WGfCaMkMMDNXqoFRutfKMRrVq8LoLbrpppuA2u4Enu1JNn78+JpUT4J6rHVj9p1zS5VFvIa1rIhlUUlRqTVjtT2MPQjKRShHQRAEQRAERdBhlaMgCIIgCIIJEcpREARBEARBEcTDUQWoq6srunZPEARBRyHWvqC90mEaz9YytbB1GQRBx8KHDr/WYtp9rH1BOZlkkkkaJLuU9L1L+m5BEARBEATtnDBkB0Ee2S2AStwfpvuXuyRB/rHVwn0fBEFQbcKQHQRBEARBUAThOQqCJqikslKpIpahFgVBEBRHKEdBEARBEAR5xMNREOQR7T2CIKhVoixC5YiHoyAIgiAIgjwmGs/R9NNPD8BUU00FwCeffFLN4bRbJpmk/nm6FmuqFKKuro7JJquf6vp72tP4gyBoO926dQPg8ssvB2Dddddt8PWBBx6ozsCKwM8tgF9++aWKI5l4COUoCIIgCIIgjw6vHFlD5thjjwXgtddeA2DIkCFAbSoI00wzDQAXXnghAL169QLgzjvvBGCDDTZo8LorrrgCgEsvvRSoV0fGjRtXlrHV4vlqjoUWWoijjz4agDfeeAOAE044AYgMrqBlTD311ADMM888QFKkR48eDcBvv/1WnYG1gllnnRWAAQMGALDxxhsDMG7cOLbeemsAXn/99eoMroT87W9/A9I9v9566wHw888/A/Dll19WbCx6hppbdyaffHIAllhiiQY/HzNmTHkGFjQilKMgCIIgCII8OmyFbD0m7733HgDdu3cH4JFHHgFg9dVXB2pLCdHP88QTTwCw1FJLAY0zFLJj9v/Jjz/+yOKLLw7Au+++W5axtie6dOnCcsstByQ1bv755wdCOWrPFBuFl4JFF10UgPnmmw+A3XffHYCuXbsCcMMNNwDp3n3//fcBcgquKkU1UY3YZJNNADjmmGOAemU1nz///JPrr78egF122QWA33//vVLDLBmui66FV199NQBzzz03ADvttBMAw4cPr/zgCuA1uuOOOwBYddVVATjyyCMBGDRoUHUG1oGJCtlBEARBEARF0GE9RxtttBEAc8wxR4OfX3vttUBtKUZiBJzNRvjjjz+ApIINGzYMgBdffBFIWRiXXHIJAJ07d2bJJZcEJi7lSCUh26vsl19+4e9//zsA0047LZCURc9t0Da6dOkCwIorrggkr8pXX30FlEc5KaditM8++wDQr18/IM0T/X+dOnUC6lUWgB122AGAZZddtsH7dO7cGYB77rkHgGuuuQZI56USqpf3xcknnwzAHnvsAST/1A8//ADAt99+C9SvmVtuuSWQvJmPPvpo2cdZarzH3TWYccYZG/z+448/rviYmmPllVcGYLXVVgOSh+3ee++t2piqwZRTTgmka7bpppsCMNtsswFJ/Zx22mnZe++9AbjllluA0q3poRwFQRAEQRDk0eGUI1WDDTfcsMHPfZr06bIWMYp07O4vGz2YbWHUaURo1O6+dNeuXXM/64joJVANWmONNYCUhbLffvsB8NNPPwH15+Oggw4CUiRf6n5mXgu/OrYZZpgBSBmGL730Uk5defDBBwH47LPPGvyfBRdcEEh1V7744ouSjrUteHxGcH379gXg+OOPB2CBBRYA0jV65513ANh+++0BeOGFF4DaU26dFyol+nB69+4NFK5MrIK08MILN/gq3tMqSl5blSS//vrrryU4iqbxb+qxUTEyc1dfizzzzDM5xX2vvfYCUpZUe/Ieec30i+kPe/PNN4Hayvzyc8s13/tn3333Bdp/1qBKkJmDiyyyCJDWDz/zvF9UMV0n9Iyq/OvNAvjXv/4FQI8ePQA488wzG7xXawnlKAiCIAiCII8Ol6228847A3DZZZcB6Qnc6Hz22WcH2keWkmM3qlUJcewqDQceeCAA33zzDQAXXXRRh/LSGMWaYagfYplllgGSt0BfhFGH0fqcc86Ze6+BAwcCcOqpp7ZpTEY+1pZSzZOVVloJSBGTY2yqd5uqgdGQGU56yqw5U67aVcVgPZ8DDjgASJ4c52Bz6Dmybstbb70F1MZ92KlTJ+666y4All9+eaBhReJSomL53XffAbD55psDKYu2lKhGWB9tnXXWAeD7778H0lr43//+t8H/u+mmm3JKp2uK3ivVzvagIKmeq8Ca/auS9vbbb1dnYE3gmqYn7aGHHgKSotIaZTHri6umWrvFFlsAaf3t2bMnkNbHYsmuF3V1dbnjs/K5OwfFztHIVguCIAiCICiCDuM5MiNB30229o/7y7UQqRaLT/o//vgjkCKB9ddfH0jZJ/o6LrroIqDjZGBZAfzKK68E0p6y+87+3mtqZKzXwCj9nXfeyUUu+ixai+9tNmShWlSFGD9+fKP6PO7D+70+JZUyr7O+qUpUYXauWefGaMw6P9n7y8jWuejc9b7U73H//fcDyUNQzR6HKiuPPvpozlvUUsXI4/SeUyHM9iDMZlI6h8844wwAll566dYdxAQwKreat+i5ySpGjm3GGWfMHYfZQmYEvfzyywB8+umnJR9vqbG+kWql975Zv7WA1+iUU04B0hpwxBFHAK1TjKaYYgog7Z4MHToUgJEjR7ZtsK1Af5u+IBXo7PrRHNlMbu+rKaecMrfOq2KrwLdV3QzlKAiCIAiCII8Ooxz5tGy/IPGJ8+67757g/zdSdh/eSM/9+a+//rp0g20hRhfuR9tF2ijXiq8vvfQSAE8//XSudkmps7IqyXHHHQdAnz59gOTrMerIRh8eq14C1Y6RI0eWXDFUxSqkGBnhGKU//PDDQH0kZTTovrsRu96Igw8+GICZZpoJSBlDRn7lzLg0srOWlupVfnYIpPvqlVdeAWD//fcHkrLgMeoX06tiltuhhx4K1Kth1Zqja665JgBzzTVXTr0rFj1UzrGPPvoISNGr3iWPzTpI1iTzmju3s7W5SsHMM8/c4G96zQqpdfrnlltuudy81s9htpTrYS2jSmndN1UGa+XU0proOuLnjmuA60ZL8HPCrETvXXnyySeB5F0tJyqPq6yySoOxFVKMVF6zSqtfvd+sQK8iteSSS+Y++/XJqZS6NrV27Q/lKAiCIAiCII8OoRzNOuusucgsGwEaJWWVI59IVV1uvPFGINVp8fd2bNb3YXRVCVQO3K+1KmhWrTCqz88kMhtGtemDDz4AUkaXPqaxY8eWbfytRfXCzEOjcbNPzEazaqxeHKOLcmZ1ZedNFtWts846C4DBgwcDTUfcWe+R31vV3P5WXl9r8Dz++ONAaa+dPqfzzz8fSLWjjABFJcH7abPNNmvw8yyqeGeffTaQPDZmPw0ZMoRXX321VWPO+nqKRZV4scUWAxpXTm4Kr5HXZM899wQKKymuJ65H+l5UccRjKIcX0vvBqD1b92eWWWZpMMbtttsOqFex/L8ep1F4LfsZVdJVPe2faGZlLXmNxPvOOWxmYbZLQlO4Luh7dB1UEfW6q6iopLl+lIPsHNttt90ACtbd83PIel/Oxemmmw5Ix6C/VG+gCnT+Z+GHH34IpM/sqHMUBEEQBEFQQjqEctStW7ec50F8atSv4dOkT5p6Aqxv4pNpFj0T5557LgC33XZbg/crB47R/mjulRfytxh9ug8LKRvGztvieTHbxAw3I/pa2I838snW0FHFMFKqRqaTEV4hD5rVWVVKJnQ+s5GN399+++1Afb0ZSHWOjKbsu3Tddde1/AAyOHf0J6ic2ndKBdIMuXPOOafB2JpTbfx9dv/f2l19+/ZtsXJkNNnSrD3vH7M9jz766AY/nxAqRHqMivXeePxWOc+qhNnaZaVE9Ue1R2+GPhd7rJmR6O/Hjx+fq8avl8qelGa42QOsGIWjUpip7PV9+umngaS41mKmstfIdULv2YTuK+f/jjvuCKR7MluV3tepTlWy0r4V5u1aUOge8zNLBTrrScreL5L/vVlphx12GFC6z+ZQjoIgCIIgCPJo18qRT5ndu3dvpPzozDfDxydx/4/KkXuhPqEayVkTQ6+AUVU5uotncYwqBsXW0DGa3WSTTXj++eeBpLZYV8Z6Gj7Zuyd8xRVXAEkRqYaC5HGabaAy4N66Y3IP/eKLL670EHPsuuuuQIp4sj3HvIZtOY/ZPl3+jQEDBgDJm+S+vWqg/h/P34QiZn938803A8lTkyU7B1sahRfyqugvawnF3g9ZHLPRqt61qaaaquB7um7oscpWQi92rHPNNVeD763Y7/pSDlXDeaHC6Jx1buonyvqgIK2DZk6qKGb7PxaaL5XEDOVDDjkESPfcxhtvDBT2w9UC77//PpAUI2szZf10+TXuvP99rdf5xBNPBNL1dn1Uia+Eyue47V7gcTVHode15F63o0CpCOUoCIIgCIIgj3atHPk0veqqq+aeMH3S1oOU9SWopNiJ2iddPRYqKvbfyVbW9m+WE4+huWq9vk6VyArRQK7OkU/kequst6M64778G2+8AaTzog+kkgqS59iMKcds5Ofeuv4oMxes3ltJPL/2nnJff/fddwdSPzezHJ999lmgPhLK7qPrDbj11luBlOnRVEQPqa/bU089BSQfiHVtWlJV17E0l4Xk66zNpBLiGLKVa0XF1Xon2UiwNT3M2hoB27dKdWRC0al1m/TlFIvvqeKUrYBt5lQ5fSBeC5UGVU7PuQpaU6rgCy+8ACQF3bXF9U8vSTWVI8dtPTTvIxUEvVVmvaqSe4+6FlZTWXId+c9//gOkelh6vFSHVYOWWmqp3HU1O8ssZTO+vFfNdi5HDa1CtFbVzVLIa9QUeo5KfR1DOQqCIAiCIMijXStHRj7u20PawzXyyWIlUiNZ2XbbbYEUhascZft0+TdVDMqBT812GbaPl6qXXonzzjsPSHWQmhpT9mna/WkjEpWNY489Fkjdwe1af/rpp7fpWKaddtoWV+t2jEaA2X5mZqmZxWeWSiW71nuNrHfkuTcqN6POauZGrzPOOGMuS8j/+8wzzwDFR10qhdaw8v+X01Og7+u+++4DUhan18rq7Kp9vt5qxWYQifPyhhtuKNuYs3h+HZN935rCdcTrWEhZs0aSXjPvKxVYvY3icVeyz5VzU9+h57xXr14NXuecvvXWW3OKoGuOGUCeM69/a2tNlQLX4m222abBGMwoNlPXelYen5mWqoIqatWo+eaY7aU2fPhwIClHViZ3nn333Xe56tl+ZrnrkfXVumvi3G1rr7FicH6rZnnP6Xfze8fmvNIv67GopP3jH/8A0rxrao30b5Z6DrbrhyO3D5TuIaUxFvogtgGpN70XxW0PpUhT972hlJe9eJVAmdQPXI2Hfghqnm6NmdOHQB+wDjzwQCCdSx8i20q/fv1yW5xuF/lwVywenx9q3vx+MFfipi+EYzC93rRhDaymROfjHCyED5PZJAO3Miyh76JXiSQBFyAfgtxWs2SEwUY26JCsTO6929K2HW3BMTj3m2pl4Gsso1HoociHP7c7nNu+p8eVbbsyatQoICVGVBIf3CwCmDX8u824ww475K6358MHLD+kPIfVeCgSx++59gP3rbfeAuCxxx4D0pz1vvJB3Wt31VVXNfh5NZJRfKCzrY6lQNwSM0idfvrpc+Nz/c9eA7cXvXYGjZVcJ70WHo9WCbeXXcMsFeF95vG69lnOxUDYY8vHQKbUbVFiWy0IgiAIgiCPdq0cqaCMGzcu95SsHJcl2/YhK8WNHj0aSOmRvt6nc1NaqxFVONasalWK9F+ftn1vCw26ZdNWbrrpptzWnRGchmILDDaHKp/RuZGiRb8qoZw0h/NHk7jKkS0MNIH+/PPPues577zzAkm9dD6bgqtBW0zVr0bzT+fa9ttvD6RWE5tvvjmQronHpsLgdkB+gVJI91fPnj1zW+CVKtKXLbeQT3OJF0a2yv0qZYUaakq2iGQ1TMD+TY28WYzAm7qfNOKLqkw1yW6xqJBouHftzp5rVRpLpbjWmUTheagkfg7ZbkjFRLXYZIuZZ545Z+IvNIdUiEwS8TOxGq1fVNbdHjTxp9C97ueruwt33HEHkEpHZP/fa6+9lvtdqT8HQjkKgiAIgiDIo10rRz45P/bYY7lCVxogffL2ydUUfc17RoZ6bPJN3ZBMrqoeDz74YHkOogiMVvWzuN9v0bPW7CWbJv7vf/8bSAb0zz//HEgqT1sZOHBg7hyrIuihMtLLRgNGhKoNAwcOBFJ5BsdYSVNrsTgnLYVg41WLkv7666+561foujlHs2iC1UhaDYzOnItXXnkl0NgMKipKegGddyqWo0aNqnhbB31ATf1d557NS73/TQKwsKAG2uYUI+eDpl/9MNXAYytUPkGFIV+RsWyGxXK9zqUuuNcaTExQEbGgqOpDIWXFY7BxtfNA32A1lCNxTbCg46BBg4A0tt69e+fUlELtczx+56aqU3NztRIUe6/rZbzggguA1G4omyD1yiuvFGzl1Faqf7aCIAiCIAhqiHatHMltt91G//79gZTBYIqgT6z0EWQAACAASURBVOK63PWvZPH1Kk8ffPABUN1sDDFqM8vJ7208ePjhhwNpX/f999/PqSuO36KP/t+hQ4cC6Xz5Oj0RbU0LVy3q06dPo6aKlgtQEbLpqX4nfToqJY7Za2k0qypYyzhm20UUQ9++fRt877VpaYPWSpCNzrP3iyqZX7OFLYv1nZUSVeEHHngASP4hSJGpkbrp7xaD9doYyRZCNUNlzTlfC5h5K0bzpu936dIl15Baj5nYFLkWjseMR8fi7oGlTUx1z85Rr7FKk4qKnqNaQqXWYqJ33313s59JXk/9PZ4X19GWFImtFltuuSWQdoAKtS+69NJLW9yAulhCOQqCIAiCIMijrtL7/U0Ooq6uTYOoq6vLqQn6cJrDp2eLU1kYrJafqj22M888E0hPz0ZOql1Dhw7NeV3mm28+AM466ywgKUXZWhi+Xm9WqbLyOnfunFO2jjrqKKDle98qJltttRWQFLKOitlqNgb1WnjNarmRZhYjPqNYVUF9U4suumjFPUeiz/COO+5glllmKcl7qkDbyPmkk04CaiOj0muRzdLy547922+/zV0n71V9Hf68pbXKyokZyGY9qurpHVL9M2PQDEN9ZdnCrRZZbO+ovlx99dVAOk9mvdYizjezIb12omp22mmnAekzpS2MHz++yeq7oRwFQRAEQRDk0SE8R+PHj89VnLW+zFprrQWkzBQ9D9aNMMowoqsFBa05rNrtXrpl8bPViXfcccdm1RnViFtuuQVIqlSp6zj99NNPHH/88UCqsmu7DxUQr41/22uiUmQV1fakmLQFvR+2Gcmen/aEqkTW6+cxVZPnnnsOqPfy2VKjtQqS1Zf32WcfICnRtbSuOBYVFaNwvYDWxenWrVvuuunrMzuvGh6x5vB4zCQ0k1IPkUqJPkqVJdWvfffdF6huJmE58Lz4WWBV+1pWjvr16wfAMsss0+Tv/QwwU7FTp05lq/wdylEQBEEQBEEeHUI5ghTRbLDBBlUeSfnwqdm+Tz5dP/zww0BSyfJVI6NFVQezbu6//34gRVlWXS0H/m37uJkpZ+aPEas9n6RQtfOOTraCsZ6IPn36ALVRY6ZYnH/Z7EczZyaffPKq9saD+nmnP+PGG28EUo/FQnhcqppmBL333nsNfl+LqOB63p1fKkiQ7ln9b/pWavm4rIxtlXq9jn7Vs2dfL6+5XquORjabd5NNNgFSNf9awrln14Pmdj7MZJ566qkbqZmlyjAP5SgIgiAIgiCPDpGtNrGij+OAAw4AUjXiAw88MFdnxdpIKkbNVY8Nqk/Xrl2BFOEbValKmIFYCzW4isVKt3pyVC0WX3zxRnV3qonqq1msfrWivjWSrHiuWmHH+/aA88kqzFaetzbZ77//nlOKvF7V6MsVtA0726uMqbDY97CWPIwqRa+88goAvXr1avJ1jln165BDDsl1c2jtHI1stSAIgiAIgiII5SgIahTVCLNMHn30UQDWWWcdoHBvpVpk9tlnB5KPzHWnd+/eHb5uVRBUAzMO7Qu6wgorAKnWnbXtagGVI9cC62llsdPAdtttB9QrSG3dBQnlKAiCIAiCoAjaVbaaT5ftyWsRBK3F6MhqsXoGsn2G2gNfffUVAOPGjQPqs0ygvmpvscqRx10LancQ1DreJ9b2O/TQQ4GW9XmsFN7bet30w/lzPYp64MxsLqd3NpSjIAiCIAiCPNqF58hMEavXWlskCCYG7Ext3zvrHDVX3b0YpcXX+LXcqqzqrzVnJtRzzDGZReU6YDXqIAhqC6uPe++2tBK+vdRGjhwJpOxRv99oo40ASlobLTxHQRAEQRAERdAulKOsx6IWxlxJrGCbrTI8seD1V20wmlB1sPZFW+bFxOpnya+KDOn4a8HXp8qkcqS/YGKruRNey6C94Brtvdratdkafs75cs79UI6CIAiCIAiKoF0oR0EQtC8mViUuaBkxT8qH51Z1OOsrbGmFbBXMurq63Ht0hOsWylEQBEEQBEERhHIUBEHJCH9MEATtiVCOgiAIgiAIiqBdVcieWLHOjT1xnn76aaC0tR6CoBSEYhQEQUcglKMgCIIgCII8wnNUw1jPx34z66+/PgCrrroqAM8++2x1BhYEQRAEHYDwHAVBEARBEBRBh1eOVF/69u0LJE/EKqusAqReLb7ujTfeAODVV18F4LzzzgPg119/LdcQm8UO5pdccgkAu+22GzDhvlRBEAQTA2ZIuk726NEDgK+//hqALl26APDhhx8C8Ntvv1V2gEGryVbwb2ltpmII5SgIgiAIgqAIOmy22uyzzw7AxRdfDMDaa68NJIXI3i/fffcdkJ5IF1tsMQB+/PFHAG666SYA3n333UoMu0nsbPz+++8DaexB7bHIIosASaFcc801gTTP5plnnpxq+eCDDwIwfPhwIKmZd9xxBwCff/450DGq0AbVxx51V1xxBQBLLLEEAPvttx+Q5mM5ovNyYP+thRZaCIBtttkGgN69ewPw6KOPAmkXwOPq2bMnAC+99BKQ7k3X1VpcX1VQpplmmlxvQcepEtaR1gn7iV500UUAHHzwwQB8++23FRtDh9tWcwG49NJLAdh+++39G0CaSN4wJ554IgA//fQTAJttthkAK664IgCbb745AG+99VaphthiunfvDsCLL74IwIABAwAYMmRI1cZUy3it/dqpU6dG5e5L3bx0/vnnB6B///4N/nb2+wnh2FzUd9ppJyBtB0wsREuJ8nD++ecDsM8++wDpPPsQvuOOOwLw+OOPN7oGbk1pL7AJdjWaAM8000wAHHbYYUB6uPNhKUuheeSa8NFHHwEwePBgAG699VYA3n777RKNuHgWWGABALbaaisANtlkEwCmn356oD6479SpE5C2E10fxowZA8BBBx0EtC/bhfOtc+fOAFx55ZUAbLjhhgDMN998QLpWpSS21YIgCIIgCIqgw2yr+eS5//77A7DtttsCKTpwW8zo6L333gPghx9+ANKT+d577w2kyKhr165lH3shPCaPxbEsuuiiQNoirEb0Vk2UmI1mLZK52mqrATDbbLMBKZKcdNJJ6dWrFwBzzz03APfeey8AxxxzDNB2KX3JJZcEkll+yimnLPhaI1nnZn5DR4Dll18eSFvB1157LZC2ejsCzt3u3bvnlNAtttgCgE8++QSAlVZaCUjbHqXG+bHbbrvltkPnnXdeICnFI0eOBOq3MyBt4XjtVKIXX3xxAEaNGgWkrdEvvviiwd/09ZW8Z71fll12WaCxijnzzDMDMGLECKD+Xsg2LTVif+211wDYa6+9gOoch6rCWmut1eDnhSik2vr/XBPcRejTpw8A//znP4HKbDNqIlf9KfS5M378+Nwuh7iN6Ljd/VBZc/2oJSO6a97KK68MpM/deeaZB0j3ofeoa3o5lKOCY6zYXwqCIAiCIGgHdBjlSM+HpjwxutDHUSgKWHDBBRu83id4I6VqMOusswJw1FFHAUnd0GQ+sShGc845J5BUv5dffhmAc889F0jm+0IR5Pjx43PX3XPovv0tt9zS4D1bW7JBhUGFUqVKhUEj4bnnnsunn34KpNRj1SYjwKx3opYivmLxWhgharB0TquG9u/fP6f8eW2++uorICUilJoZZpgBgCOPPBKAfv36NYrULbS65557Ao19bIVYd911ATjjjDOApECrygwcOBCAzz77rG0H0QJUKseOHdvk7z2mv/3tb7nvvX62KPL6XXfddQCN1ItK4Dgtfus18vica+L3/r65a+eaoHKrWlgu5TKfb775BkjnWVzjv/zyS6B+Pj3//PMNxuV5UAF0F2S77bYD4P777wfIrTvVwHOrOnzSSScBaW2Xe+65B4CTTz4ZgGHDhgGNr20lCOUoCIIgCIIgjw6jHA0aNAhI+656ih577DGgsGJklK6CYPRk5kI1IiQxU84xPfXUU0A6tgnhk7qZHT556/VYY401gBTJG1298847ADz88MNA8n9UEo/3tNNOA5Iq6Jg9FpUGvTiqPqo1l112GQD33Xcf48aNA9K5MyIuVUaUkd0yyywDJL+MimX+PMqqEFdddRWQzrn+lemmmw6ozdRi8f5xnql+mV1ipGjGpVGtWSnjx4/n7rvvBmDLLbcEktpSajzfllfYd999c8fgPFC1ck45l1SbjPBvvPFGIEXlvl7/ynLLLQckf6DZR0sttRSQ1IlK+Mg8BpVWlYasSuF99f333+fWAddFs3+9j6qB98Gpp54KwM033wykkgRmZ5mqP+OMMwJp7vnV9UUFyt0G57JK9BxzzAFURjlyHiy99NIA7LLLLkAqQuy6lb9eZZVyVRhLg3geqrl+mPm3zjrrAOnzRzy3KrTON1U7VfNyrQkTIpSjIAiCIAiCPDqEclRXV5dzsRsdGgX4pKpylG0nYlaJngMLg5nNVA08hg022ABI0YKFsIp5ivY99DpY3DKrWnhe/BtGGY8//jiQoutKFMFUfTjiiCMA2HnnnYEU6RkZ6p04++yzgRQZuz9dzawuFSL3zpsi68MxU0qfkhilW7urGtFTFueNSorqppk9KiWOWZxnHoMR4dixY3PXsdzeKtUaVRDXiN9//z13n+h1yNY1c93wWnk/ZJUU1YgbbrgBSBmVeiysMeS9fNxxx7X5uIrlmWeeAVL9HlV28Xx07dqV77//HkiRfzUVoyzOIeu++TVbm6mQB0l8vZ8drjsqMqqDCy64YMXUF4/FdXdCeE95XAsvvDCQ7k3Vl6xCWEmyyo+KpPfBCy+8AKSscXEN96vHUklCOQqCIAiCIMijw1TIdn/4gw8+8D2B5B0yInQ/efTo0UCKEvQa6BkoV6ZMMVjr4fXXXwdSppM/b0nlU7Oo9MAYEfpzI2Yj/V133RVIrSyMMrOqRinwGqni6YmwHotz06h1hx12AJIqo2LUnph88slzHofDDz8cSNfVa+BxmfF09NFHA5Vt62C0qf9Jz4DKkDWlrOeUVSS9ds7dQw45BEgZemeeeSZQr1Y88sgjAKy33npA6RUk72kryjt2M4GuvvpqTjnlFKB5P19Lq3f7ev/mfffdB6Sq1PpgKkFWkVbd0p+Yj8ensqXyVap7rhaqoDsG26a4RvqZ4Ni+//57pp122iqMsGkc31xzzQXAHnvsAaSK+vr6VP+9d6vpn81mDjZ33VVqrRum//Suu+5q9Rg8b9l1NCpkB0EQBEEQFEGH8BxBqhtiDQgrFvtUrfs/65Y3ElJh8qmy0FNmJTBa1wPw0EMPAa3znKiYWePCitAff/wx0PgJ3j1g6/40V322Lei7sBqqmV5myJmdp5pVS76HlqISc8QRR+TqVhWq3eE1Uzmqxhz0vrBy7dZbbw00HrNj+/rrr4EUnV5zzTVAqsllpWhrsXhsdXV1ZfccmSlmlpb+Eb8fM2ZM0ee4pUqHWXl6s7L9sCqJf9t7WnXcOlP5Fdt9rd4Qr1GpFPUa2bEA0vqTndueD7suVINsF4CZZ565UYNdM9z0Zl599dVA+syrhWzXliqOql9iVltbFMeW/p9QjoIgCIIgCPLoMMqRT8d28dUr45N3VjGS66+/HkhRq2qF+9B6JSoZvdtnR+yN05InX5+wVYj69esHFD4Oz4+1ZqwDtP766xf9N4sdk1lDZstsvPHGQKo/o1LWHj1FWfTYqJTsuuuuBRUjMzMuvPBCoLq+N++H/v37A0m1y2adef+8+uqrQLpm2WjVY+7WrRuQVNGxY8fmPEflIltJ2bpSqswtubc9DsfvvWpkq3dRnNvef94DxdQqKzWO2Zpc1gXzvKj6ff311zk/i4qFvi0V5faM10D/qfdotoK2Na2c25VEj6ceGz1PnTt3zl3HrH/He9YecbWgGLUWazYtsMACQJqbbVEcs5XSm3uvUI6CIAiCIAjy6DDKkRjh6oFQOSrECiusAKQMDvdt7bVmnY9yR7eQnmhn+b/2zjxeq6rc498DKOHQNb2II4GSoUg4lygqzoglAg6p5ZBDopIamppoDmWKOCAGqUjOKGnpFUK5jjkQaFlmUoqFQ2qDFJpiV/L+wee713s2vJxzOO+wz+H5/nPgjGu/a+317ue3fs/zrL8+kJ5sW1OlWm9DuejYTBUzG6yHpNekkvWNvB5VKr0geiAmTZoEpMyE2bNnAylrz0ioLUREzuXQoUOB5JvK900rRV/SpZdeCiS1YUWjpUpkBHk/qSCtqBfPbFIVWTPFhg0bVnWFzNpEvh72nHJMkyZNyirpq5iqqvg9VlvWe6XKmVd59T7+4Ac/AFLtHO8z67kcf/zxlbm4FuC1WcvNMVqjSpW8V69emdLpejWTa0WVIxU3lcV6ZKu577iXq1y754tj9FpVGGuB99d9990HJAVlWX3h8p8zc9QaXWa5qkTXs/5bS1GptP6gWZ2u3RUhPEdBEARBEAStoN3UORIzvaxjlK8OasSrt2jcuHFAqsBpxORZuxGldX+q6YMxatBzY/aWfapakuFixGGWWl5tsZqq17/rrrsCMHPmTCDVxqgGRjzWNdIn5hx4vu6YPW+2vtONN94IpIhIn0cRPUpmnp155pnAkkg879sxcjW6Vq0xU8h+Qy2pb1UUvCZr6hxwwAGN/m/mTTVxTe+xxx7A0t6D//znP5l65dyYZaZy4tqybpP3qtWIxe/LV+T391vDqx4+lnLkVZwuXbpkCpj7oCqKmVEqayuKNb0q3eNwWag6WEfrmmuuAZb2oXp/3X333UCqG1TLfcW5GDlyJJDUPNfbG2+8kb0nqfx5jx155JFAylKz44AdBXwfsdZdkbGfphXnzT7PV66vBFHnKAiCIAiCoBm0O+XIaFtFpDQ6hNRF23o/ZrXpgbj//vuBVNHWCp1GndVElUuPkT4Fo9Pm1oFZd911syjJCN1IT7/FtddeCyTVYu7cuUDyZ9QyO8/rNEo128IO5o7dStJG9fqhrJdk1e8iYcTnOtp+++0zP4FVmb1+v8fsGdUGe8zZ66kt4H2ncuYcef8NGzYMSMpBNdlggw2A1HtPhXLDDTcElvga8gqRaqRevGeffRaAKVOmAMm/4Zo1Wreumh4lsYej2WtFVDmlQ4cOWTVx154KtOpDa/v8+XrnezuuCK417yNr5FhRXxVc9T+P7wHOoacObY1838NXX30VSCqdnQhcy/VANcvTAcfqqYi1m3x/9vt9LzCDsJKEchQEQRAEQdAM2o1y5PmxkalRgtf3wgsvAEk58ezSyFUFxSw3nf86/o2Aq4l/09pEKib6h5qKNlWexo4dm0WuZlocccQRQMrC08ejf+fUU08FilG5VoyEzFgwylTduvDCC4FUdVmPVpGyMrwGxzx58uQsg+n5558HUvVoFQ07VxtV+X36PZobtdcjI8i/acadngnXtOuvHv4pvRmOsbQGmuqS2WR6bvIeoqZeS7PYZsyY0ejzeigqmf1ZTdzvXKt6NPv37w+0fk2tyNrMZ/MOGDAASPfJ3nvvDSRfl+pEPkPUuXRvv/LKK4HqVWivF5dffjmQVNvHH38cWPq9sRboh9InpcqXvyd9//W9TOXd0wQzmq2XBqlelacf3rvNVaVDOQqCIAiCIGgG7abOkT6UfPaIUbZ1ZswS8anZJ9jTTz8dSOqNke3EiROrOexGmJ1lZKOSkK8RIj5t+3S97bbbAtCnT59MZdDboafKjvZ2hK921kynTp047rjjgFSv6JVXXgGSj6Fcp2b/n++p9vTTTwNkPcruuusuIKl/RVCOjHxU7Oxq3tDQkEVRfnReHbe9kU477TQgZRZaj8Y+gE1RDxVQf4f3k2vZ/lT1zLjL3z+lWTtGrK3FNZ6vWtzW+gLms1Vbm52Wp6m16d628cYbZ2M566yzgLTHe1rgHu7PLKsmUOnfNPPOzOQi+79awwMPPACkezH/OtVyfzBj0DnzfrvjjjsAuOmmm4B0n0yfPh1Iiqs18VR4rS82d+7c7BTIrNRK+WVDOQqCIAiCICih3ShHqi75aqcqR1bW9KnZ82gzWDyvlnvuuQdIdU1qgdfg0/Qpp5wCpLN1FRPVLatZGxmYYde5c+fsus2IuuGGGwB48803gdpFDR07dsyqj1v52jnQH6Yy4pO/Hd09Qy7tSA1JQbFGjtG56lg9MTrTx2AGjOreggULMp+X3+u4VQjtJyR531wR0WtjTSavxeu30nx7x2wu17j7RxHWZnPp3bt31qnetWe1+lrtG/499wZIqoP3UktRIXIP1ItUi4zJemCfTK/b/bWcslZNfB+aM2cOkGr5lXvtrX2nKqQapDe2FtnUoRwFQRAEQRCU0G6UI7OvfDK1N4uqg94hfR1mD6nK+DRtbQjrJdXDt6GnRE9Kvn5P/mxdxckaLDfffHNWq6O19Uhay4cffsj+++8PpNf0iiuuAJIfSjxf9vuaig6cy+HDhwO1VfnK4Zj9uKxeavbzM6tC74t9u6xWLk899RRQ3/ok5VD9stq5NVWsXq6XYGXBWlwqZ2antYVMKBWZG2+8Mcvkc206n9XGzFTr3bieKoHXZw24t99+G0inCr5HOGdWZ26ruJ/mvam1rGEnqlf6oJrCukcLFiwA4LnnngNqO/ZQjoIgCIIgCEpoN8qRCs+jjz4KpEq0Rh76U3x69qM/Z2VsfT717D9jxVavQf9Kr169gBTZHHTQQUBSu4rard6owarleqqsVzJ//nwgqXhmaZnRoBpo7ZiePXsCKQopkmfAa9U3NWLEiEZfX3PNNbOofOzYsQCZv8N6VvlqzaqARfSt6CXYYostAHjxxReBlFnUXjOBymHNKvcV1c16ROuiV8caU94v1p7y//vssw+wJOtVtUFviBXDq42vl33NJkyYkGXtirWovH+sA+Ypgfukmbkq0SrMvh5WojezN3/K0FaVIyu/e0+6Fq0R1BZw3/Dkw9pM7o21eK8L5SgIgiAIgqCEdlMhW1SEVFtUgvIYCdmh2Yq+RVIhRP+CrGzReC2jhUqhKmZPLWtQwdJd4fP49cceewxIHol6+8dKMfpWYVWhHTp0KAD33ntvfQZWZ0466SQg7Sv6xKzeXg8FaZtttgFSPTD3k3nz5gFJ7Ro8eDCwJJPXe031WhWm1ntPjx49sp5gv/rVr4CkHNl7sFztLO8vFSJPD44//nggZYVak8xrdu3am63SNZ6Wh2NV5XNsqucNDQ2ZguzXfB122WUXYInaBklBM0tt0KBBVR9/pRkzZgyQ3sdVw6yVVwnKVchudw9HokTqDeX/TaP2SMaFU4TXIWh/uEG5Dj1SWxa+8VhI0GaLlXooqmTxN82eFtTzCNSjwbb0IFtJPBK2dYPHRDZDrcfDkevIh+1yJmfXx+LFi7NUeh+YNMa2BywYbCkUH/y8Nw2QLTRo6n8t8IFNA7wWAlsKvfbaa9nDke9pttLQlmDg4rGpD8fVaNpabbbaaisgPRwec8wxQCqUWwmifUgQBEEQBEEzaLfKURAUCcvnz5o1KzOYGpF69HLfffcBcOeddwLFPj41GrVYqkVUTSZYWenevTuQCph65NOtWzegaeWooaGh4iq2x2geTWghUEFSKbEcw5w5c5g2bRpQrKPcamHZF4usqryoMNWyDINz5fG05nCLFv/nP//JlCOPtFW8VPdmzZoFwMiRI4F0bNoWsfCvx2hXX301kBoiV4JQjoIgCIIgCJpBKEdBUEI9mjK2RTScW/TwySefBOJ1s7WPbQ802Po61dO749q20KJqlgX3aqmQFPk+K8LYVJB23HFHIJnDd9ppp2y+bMdkaRc9Re3R76dyptdRdawShHIUBEEQBEHQDEI5CoJloO/AKKwI90ktKELUHARB/VG9KrL3sRKEchQEQRAEQdAMCtU+ZGV5Ug2Ki8qJypHZRW1JSWmN+uM9WO16PPVQqEIVC4Lms7K/D4dyFARBEARBUEJ4joIgCIIgWCkJz1EQBEEQBEEziIejoM3Q0NBQtllrEARBEFSKeDgKgiAIgiAooVDZakFQihlj999/P5Cqxdq/6h//+Ed9BhYEQRC0a0I5CoIgCIIgKCGUo6BwWGvny1/+MgADBw5s9PnevXsDle2vEzVwakunTku2nvbYB6o9sOqqqwLwta99DYCdd94ZgNNOOw2Av/zlL/UZWLAU/fr1A+D2228HYOONNwZgk002AeBvf/tbq36/Cv7//d//ter3tDVCOQqCIAiCICghlKN2yHrrrZf5dOwKrj/npz/9KQBXXHEFUNtO3M3FyOeSSy4BUuTy+uuvA/DCCy9U/G+GYlQbVP0uuugiAA4++OB47QuCe0Xfvn2zju877bQTkKolv/LKKwCMHj26DiNsGarBq6++OgBbbbUVAAMGDACW7JOQFJb+/fs3+jkVl7PPPpu77767RqNuPnovJ0yYAMDmm28OpPHfeuutAAwfPhyA9957b4X+zsqmGEkoR0EQBEEQBCWEctSGWW211QDYbrvtgNQPa+ONN2adddYBoEuXLgCsscYaABx11FEA2dfHjh0LwJtvvlmbQS8HfShGQhtssEGjr3/ve98D4N13363twKqM8/jQQw8B8LnPfQ5IytnFF19cn4FVAH1iQ4cOBeCWW24B4IEHHgBCsasHn/jEJ4Dk5dOzctxxxwFL9g/Vh3/9619AUl922203oJg+FJWvI444AoARI0YAsPbaawPpGv7rv/6rWb/P7+vVq1dFx9lavA7VrL59+wIsVQPO18N7MGgZ8aoFQRAEQRCUsNIqRx07dgRg0003BdI59DPPPAPAr3/96/oMrBmo+pxyyilAyiTJZ3WVYlZQ586dgRQlet4+bNgwoL6R/L777gvA7rvv3ujzb7/9NgC33XZbzcdUCw466CAAtt9+eyBFgL///e8b/b8tqSxbbrklAGeeeSYAhx12GJDWpn6OhoaGVl9XW3h9HKPqqB4eP1Zz7Cog7hPf/OY3Adhmm20A+OQnP9loDPPnz+fCCy8EkifxuuuuA5JKoRrjvVkPPvWpCdTf+gAAIABJREFUTwFw3nnnAWkPUxlTLVc9L4fX7R75z3/+s9Hv9dqLQOfOnbn66quB5N9zTxfn7Fvf+hZQLKXd+8D33cmTJwNpX3C/ePrpp4F0f9SDUI6CIAiCIAhKWGmUI59U77jjDiBFQOIZuopKEZUja49cc801QMpCMBo1YujcuXP2hP7+++8D8PjjjwMwZ84cIEWPqjRGWfWIMhz/gQceCKRIyKjh7LPPBmDhwoU1H1s10Wt08sknA0nNdB5ffPFFoNiKiORrU1111VVAUjmdy3//+99AmuvLL7+cuXPnVuRv67mrBq7RtdZaC0jX5f/921tssUWjr5slqm/Fa1XF0fP3hz/8oeJjVllwz1PNc52plDz//PMAXHvttQBMnTp1qerz++23HwCHHHIIAF//+tcBuOCCCyo+7qbwOlQd9Oi5P+bx/sl7clyL+t98nfT+FamWk3N2zTXXsPfeewNpzxbn00zDJ598EijW/vH5z38egOnTpwPp/pGf//znAPzmN78B4Bvf+AYAv/zlL4Havj+FchQEQRAEQVBCu1WOjCZPP/10IGU6GT34JOrnr7zySiCdVxcRI4Pf/e53wNJVhu+8804Arr/++iwSNQI855xzgKQYqVo8++yzQHWj7qYYNGgQkFQHeemllwCYMmVKzcdUC6y7ovdD5s+f3+hjkdH3MX78eCDNodHqW2+9BcDxxx8PpEwb/WPTp0/PfG8rSjXWrj4V6/z06dMHSJmhQ4YMAdL15FWJPL4e+Y+qHUcffXTFxu5YNttsMwB69uwJwDvvvAPAU089BcAjjzwCwE9+8hMA3njjDWDZPo8///nPQFIw6tHXUMXoRz/6EQBbb701sLTHUoVZ9UF1okePHgD8/e9/B1Lm3WuvvQbUdw8sh3OpqrXttttmdeDEteQasr5RkfA6rG/mvuHYfe2dSzMoH374YQB+/OMfA+kaPRGpJqEcBUEQBEEQlNDulKMNN9wQSEqJZ+PW67DOysiRI4EUuW200UZAiiKKiBGdVa7NKPFp+6abbgLSWTPAuuuuC6RMMGtffPDBB0DKIqrFk3g5VO+M1o0mvB7H2l4w+jabJB/5ukaLlGWSx8xIazHpJXCurMFiRqVKg9d+ww03AEuynrz+emamiBHuF7/4RSD5WvTB5TPj9K2ounTt2hWARYsWNfo5lZfZs2cDyd+jB7CSOLaf/exnQPJBmVnWEoXE/VGvkbz88sutHmdzcV9wLrbddttlfp/Kufui6rnX67VYm6kI660p9thjDyCp63mfESQF0N5qlaYSfRBdk/m9zvdl94lPf/rTQPJh6t0zo9e90Q4Q1SSUoyAIgiAIghLajXLk2b9qg+fJut/NqjBry6hBL4FPx0WoFN0URkRGoUbtnjX369ePvfbaC4BRo0YByddiRGvkWssIMI8RYd7nZQ+gqVOn1nxMtSDf60mMrpyjImIEaxVzI70nnngCSJ4CvQL5aNO51v/SpUuXTM20vkw9cT/Q1+J49anY10+VwqxWM5uMhJ1DMw9VK2pZUdq/pWrVElTIjNid57yfpxa4V+XvF/nhD38IpNMA1bw8+dpSRUbFxPtpWYqR++QJJ5wAVO+6WqMY5XEtutd5f8yaNQuARx99FIBXX30VgEsvvRRIiuy4ceOAlGFYzfsplKMgCIIgCIIS2oVy1KFDBw499FAgeSHsr2OEV+6p2kwOn2SN/IqMkZE1IPQYeZ47YcKEzGNkxKEXxMyUs846C6hvFGW2ST77QgXMDuDtDbtn53vHGQX99re/rfmYmsIsEbM6VXsuu+wyIPk8yt0/fr9zbtXm1VdfPVN7rXBcj6whvVA333wzkBQjI9qDDz4YSN6iItWOqQbdunUDYOLEiUC6Xqsz18qbucoqq7D++usDaY7E+0XPYl4x0mOk4vSZz3wGSJ7NIu/11m7yfinFudCXM2/evOX+riJVkNdrqIfN9yznyPvNE5+8arXeeusBSUlaEVW0uYRyFARBEARBUEK7UI66d++enTsb6TSlGBmF2M/Kp+p61O9YUaxRtGDBAiBlpg0bNiyLFl5//XUgKWk+kddTMXJsRj5m9BglWAOnqTE6h0a5qmNGhEaWRYiYSlGtzEfCVsR+7LHHGn3e6u1ep34W+5NVEz0n5557LpDqY1kB2+rl5eZK74QeHr1wpd+v2qsHTS9Fc6lEZKyqoOdID5HZQu0tY7IUXz/vw27dumU9rpxv7yWVo1qxePFi+vfv32icklcdvB+soeP+aJ0jv1+lydpc9957L5D8L9bdshbee++9VzM109f7jDPOAJbumwZpbd5zzz1Aui575OlB/cpXvgLAgw8+2OhjPX21vv+ceOKJQPJfWjneOVC5zb/uqoO1eJ9u0w9HLvoxY8ZkE276alNvrHmpVjOoG7WN79wU//rXvzbr99YSH4aUj0vTJDWsHXDAAUBadEVAydgCe/LHP/4RSM1/y+HDgmUIRo8eDaQ3VRu2fvvb3wbqazrP09DQkF1/frP/n//5HyBtAF6nBRQt/vncc89VfZwWzPMhSBnbsZhSW+5+8Pst3ma7DMsTeBS88847Z28A9VyjbtIaji+//HKg2Ob4luL+0L17dyAdlfpg6EPBn/70pyxdPp+C7YO9BvVq8/HHHy+zkTak+2fMmDFAshAYROSDD/H6PRK23Yb3nfuID8bed7XAhr4eHy0L73/nwsDXAqz//d//DaT9Y/DgwUC6Tj/WA/eLfMNZP++YDXAtP2GpHddda0zizS0dEsdqQRAEQRAEJbRp5cjoZr/99suO1aZNm7bcn8lH66a4+xSpRGvrCqMJn2DrgU+6X/jCF4BkprYB4bKkV02HtUwdbgpf+6OOOgpIBTslr5yIEaCmXo/jLKKYT3NVmVGt6NmzZ9n03lqzzjrrZMkDYnRuWwelZD+aylxL/JtGsM6N5ulyR1hK+x5VKPFbZNTGzpajUOld3u9sikocm6o2Ku9blM7r95ijaEe0kO4P9wePBrUMGI1bDFJV0LnKN2zdYYcdllLxLHViU1ZT+6t9vPHxxx/zq1/9CoABAwY0+pr7iWvJr5dTjMrh/upRjh8tlTJo0KDsqLvaJwcql55sLItdd90VSOZlj6PLYfKDraNMj6/nWi5XViFf+qKcdaA189Dc6w7lKAiCIAiCoIQ2rRzpM2poaMiKZflUaFRhVKA/x3YimvxsJKn6YlqkZrx6eoyM6FSBVEIcq19X1TI679mzZ3auPGPGDGCJj6DeGH06J0Z4nvGff/75jb5fc6LGQtM/jZya8iLoPVhzzTVr5pFoir322mupqNAGsxYorSd6b7w/NH+q9pWLuoy2Xas77LADkIq5HXjggUAqU6DfbPXVV88i/3pGsq5Bx6nPRI+VRWSL1NJll112AVJTUr0m3ld5ldy9zH3CdHzn2NIZs2fPzlRMf5fF+VSbVFJU3qtpWJ40aRKQ1qRj0g9mUoBm8d13373Rz7uu9JWqzvj6qCp7DXqw3Gdvu+22rPipRT+rhXOjIrcs79GKNkdXjXHfLGKjXXGtWmx0n332AdLr05qWJqEcBUEQBEEQrABtWjky4l68eHGmjJieefjhhwPp7NYnTdPejbbMDtD5r4u+noqRT/ZmzOy5555A8g6IUbhNMk2bfuaZZ7Jz5h133BEohnJkxNevX79Gnzf7wkjO6zDl1qwa51CcIzMbVJr8O0YIRfBdOacDBgxYyiNmSfwiFKXTt6GCZNHHct4SI1ubX2622WZAUiVUYoy4jXrNMOrSpQtjx44FihHJqjBaYNVsvenTpwMwZMiQRt9XD1SMVYV9TV0/zoUql34qFSL3TX2VKs8qMR999NFSWWruiyNGjACSfymf5VcNTMlXJfD+9npVfvJKmXj/W4BQP91bb73V6Pv0bOnN2X///YElipzFP6uN1+jJRe/evZv8GefKn/X/eS9Zke6zpsir/2LWeC08pKEcBUEQBEEQlNCmlSMjh65du2YFsbbccksA7r77bgDuv/9+IJ2ne+6sB+nII48Ekleikk32VhQ9NRbKyismOvjN0DNKt+7TwoULs8yuWpX5bwmOU8yesUWD/jGjBqNXI0AjXOsiqfrlI0dVwiIU8HMO999//2yc+ZYuRcBClGaN5L17RqVmiupN8fqsrzV8+HAg3aOqnioPZoW++OKLhbp+r09luW/fvkDKXtOzqKpZD5+Uc+Jrqqr32c9+FkjRdSXw+twvy42hmqh4qXBZp0jV3wxKvUJ5b5X7ikpbuTnT8+e12oKpU6dONVef77vvPiDVclsW+YyvvNfM61Qhc023JZxjcU+vxX0XylEQBEEQBEEJbVo5kkWLFmXRgU/4nquWU4LM7ND/oRpThDomqln5eh1GUEZKN954I5Cu1Rot48aNy6pDG6E/8cQTVR510zg3eoz0Hln9VE+AeF3WVjE70ZYWRvV5xUjVQw9BETxHqlulNZn0s1SzeWJLUc0yGrWmlk1yralirSYVIz0Z+t+877y/nFuby6poHnLIIYWq3p7HhrpmQI0cORJIXizViVri66UXR39hS9uuLA/n2Wy9oUOHNvq695SeGCueVwP3Zv+GypFqprW0xLWnZyjfjqcc7iNWxi5VS/Vuqj5VG/2kzvWyatk5vnJZu/6srXvyHqsi43tfvjl3/tShmoRyFARBEARBUEK7UI6g5VHTpptuCqTI97rrrqv4mFYUe4upIIlRi01A8459Gy6uuuqqWRSk96gIqASpeOkRsH5JnnfeeQdItaeOOeYYoHzWmsrZhAkTgORJKgLWCTLKhxTRFqV6NyS/ivfDaaedBiQFQXzNnSOVJD+vV0AvoHOsYqQS4ZzVkub2VoIUfZttZ120WnhtmmLq1KkAXHzxxUCqReRcmaFqJllTqrjRes+ePbOaQdb3cT/x9VAJtEpzNXGerHek+mg2Vl45dm5Upl944QUgebN8Hfxo5p0qhWvTdfLBBx/UXCG09lfeZ9US5syZA6ReakXqC9oUzmm+ppNruhLNppsilKMgCIIgCIIS2o1y1FKMOjyfth9XPSLZPPYScmwqJWZv5X1UXouVYkeMGJGpNA8//HD1B9xCzAqx8vXo0aOBlOGk30GVJa9aiNdoxpTZfc5hEfxjRjhGsR06dMgyLqz+WiR8zaxWrgKkp0/vgwqCPjB9DXoCrKQsqoX+3nr2KtT/ZQbr8taJKoSZS2bfmfVaT66//nog1V7Se6PyrH/MukfuK/7fufXnvMbevXtne4pqg+qLVeqtnVTLe0zvof4vlfR8b0U555xzgFSLSzVUv6lr0lMEVYq8h2fWrFnZHlMr9HRZc0mVsDlY30q/YDVrUFUL9838PqJftRYqWChHQRAEQRAEJTQUJLqu+SDMsrDzsv4Ps23qiWf/1pzRm6PicNJJJwFJaTFyspJ2hw4dsi7qX/rSl2o06tbjdVvVfODAgUCqIK1/yuwuu0sbQRfJuyMqLapZPXr0YMqUKUBSzopQW6sp8vWOjLLtVm92pHOoaqHXyrotRchMy/f269y5c6aUqAipXnqvXXLJJUDyreTrr9QTlWWj7BNOOAGAUaNGNfp8fg4l/x7w7rvvZvuHfiaraRfJt+IerhLdtWvXRl8vVyE/Xzk6j4q0VcFHjx5d90wvvW6zZ89eymPleK1jpE+wXEf7llILf08e5+jll18GUkbzt771LSBV2K8EH3/88TJLq4dyFARBEARBUMJKqxyZmeAZpk+odgsvwuti5KdCks+yKVff4tRTT80ytoqoprQUfR9G62ZIFaF+UVNY/duaNGuvvXbmfTDCb8sYVeoL0ydmtlMRlKJyuK7eeeedTEXSn+F1GMG6H1gBfPDgwTUda0twTqyYffrppwNJiVWBdW8wq+kXv/gFsKRbgJmebeEe69OnDwC33347kPyjTeE+qnKrP1MlauLEiUBS7OuJczpmzBhOPfVUICmfZtLZWUHPWVvG9zbfl/UJ2vHigAMOACqjZIZyFARBEARB0AxWWuXIbtJ6QczG8Im0CBFvvh/VBRdcADSulQPpzNkO6DNmzGgTEd/KgMqR3rauXbsyc+ZMAPbbbz+gbXiO2iNG3h9++GH277xPxcwffRz6OmLOioe1gFQXVBv0ZKlGuF/qI7MOUL6rfVA/nKuf/exnAOy9995AqsNm9q/Zsq0hlKMgCIIgCIJmsNIqRz6ZnnfeeQBsueWWQKr029LIsJaOfv+WEVGoRMXFdWbmy6GHHpr5VazbUoR7cGXm7LPP5thjjwWSv+/WW28F0j7QHrx7QdBW8D1u/PjxQKph532okqQ/rDWEchQEQRAEQdAMVlrlSKyWagVUKwE///zzQDqfLkdp5+bSj0EQBEEQrDi77bYbkDIJPSUxE9Nea60hlKMgCIIgCIJmsNL2VhOriPokqo+nufUTilQxNgiCIAjaC9YhtA+imdqe+FRCOSpHKEdBEARBEAQlrPSeoyAIgqDtsckmmwBJVQiCFSE8R0EQBEEQBM0glKMq0p4y2erRmTkIgiAIqkkoR0EQBEEQBM1gpc9WqybtKZMtFKMgCIJgZSGUoyAIgiAIghLi4SgIgiAIgqCEOFYLgiAIVhpMlPnkJz8JwD//+U+g/VsHvO7u3bsD8OqrrwLFtn+YCGTxRxvO9uzZE4BbbrmFP//5z1X526EcBUEQBEEQlBDKURvGp2pp75FPENSLVVddFUiNL43CjbrL3XsdO3YEYPXVVwfgww8/bPQxqB3rrbceAHfffTcAX/jCFwB4/PHHARg2bBgA77zzTh1GVz023nhjAObNmwekNbn55psD8Ic//KE+AyvB+6l3794ADB48GEhzpGLkfST9+vXjsMMOq86YqvJbgyAIgiAI2iihHBUQFaHhw4cDcNFFFwGw5pprAil6XWWVVRr9f+LEiQCMHz+e9957r3YDrjK+HjvttBMAd9xxBwDrrLMOkCLBI488Eij2GXp7x8huxowZAHTt2hWAz3/+80DydxQV19pmm20GwBe/+EUAjj32WABmzpwJwOuvvw7AQw89BMCiRYsA+OMf/wjAJz7xCQB23nlnAAYNGgTAm2++CcB3v/tdAD766KNqXUpV8fpsAOrrUURck9/5zncA6N+/f6Ov77LLLkBSJ6ZMmVK7wVUR1c5vfvObQHq/+OCDD4BiKGRdunQB4PLLLwfg4IMPBtK6Eu+Tf/3rXwB07twZgGnTplVtbKEcBUEQBEEQlBDtQ1rIBhtsAFA1hzzADjvsAMCTTz4JpPPYd999F0hZBkapPmV7hnzVVVfx/e9/H4B///vfVRtnrTjppJMAsmtaY401Gn39rbfeAuCggw4C4KmnngJCQaolrtFDDjkEgFtvvRVIc3DDDTcAcPLJJwOwePHiVv/N5ra0yfuDlvV7Nt10UwD69u0LwLXXXgsk5cuffeONNxr9bTOe3n//fSCpmaq5rtX8WN9++20gqRnXXXfdcq+h3qy22mpAUgAnTZrU6PMqbQsXLqzD6JaNr/k222wDpP1U1UFU2QcOHAjAM888U6shVhXvtSuvvBKATp2WHBS9+OKLQHqfqccpg4rRZZddBiRlVkXS++33v/89AM8++yyQlFx9U1tvvTUvv/xyq8YS7UOCIAiCIAiaQbtRjvr06QPA1772NQDOPvtsoPVZIWuvvTYAY8aMAeDoo48GUkRoxFhJjD4vvPBCAK6++mpg6XN9n649jz3jjDMAOOecc9h2222BYmQirCiHH344kKILVTtVBxUBI0SvdauttgLS2Xo1UVlQrVJJUHnwLL0166QtZCX26tULgJ///OcAfOpTnwKSguKYVWbmz59f9TG5PvyY9/f4+TvvvDPLilGF1a/h91Qa792XXnoJSFk6RaBjx47ZvbbddtsBcPHFFwPw2c9+Fkivi8r0aaedBsDkyZOB5MGqB94veZVriy22aPR9rs3p06cDcOihhwL1HXsl8P3jtddeA5JK4/XuueeeQMrSqyWumx/96EcAWaaZSpD3hfvnBRdcAMBZZ53V6Pt9H545c2ar98NQjoIgCIIgCJpBu8lWu+2224CkII0bNw6AP/3pTyv0+8xwMKI76qijgOQ5qIZiJH//+9+B5LVpLvo8zj///Cxyb4sY+Zx66qlAUoyM/P/2t78BqWqqkdFGG21U03ECfPrTnwZSLRH9DOeddx6QIp0bb7wRWBKl6hUz4vG6jJ70cXz1q18FUhaNkW2RMr6M0ocMGQLAWmutBcDzzz8PJA/KgAEDANhwww2B2ihHRqF5r5HeC9W+z33uc5lSlFfpqoV/p0hZXtYB+sEPfpCpC+U8U6qy99xzD5AyRotQv8n7yHszrxi5Ho444ggAfvzjHzf6fFvFvefmm28G0r4ovj+o7tYDX+PHHnsMSB5FM+fM0NZjdOKJJwLpFMGsT/eXaqrooRwFQRAEQRCU0C6Uoy5dumRRglHDl7/8ZSA9Leezy/StGDFaA8KMr3POOQdIEbFqjj1diohqV6dOnZbK6GoL5FUIfVNGB2bpObe77rorkLLYnMtu3boBK64atgQzPVw/4ph79OjRaIyXXHJJVqvDirX/+Mc/gDTuddddF0jKmK+LPjo/FsF7lFdaHnzwQSCN0ftFT4/3qapNPTA67devH5DWTTXQ97H++usDSbUSI+B6Yv0098rdd999KaVIBVClyNo5RaiVk0fFZP/991/m16dOnQrAXXfdVbMxVRP3etUVVT/RQ6UKU4R9Q8+RKpdj0pN06aWXAun0xIy7X//61zUbYyhHQRAEQRAEJbSLbLW111478wLl6yQYpevrePTRR4EUPXiGafXlvAKlwjRnzhwAdtttN6AYZ+t5rNUyd+7czKfyyCOP1HNILUKvkWfiqniqM6eccgqwpBMzpMwZ65IY7RohGZ1UE7MYR40a1ejz1l567rnngCW+FliiDpXLgHL8+X5dfv9f/vIXIHkoihC1q4SoejlG7zezPefOnQukzCGzT2qJisLDDz8MpEysvJqzLPJeG/Fn8+rTggULgFS/yPXhvpL/Pl+nWuLYVYysE9ahQ4fsOq3krYfTWmtFRgXFzDlVXefQe/G3v/1tHUZXOcyUNaNLpd117b6hSmu19iK85+dRvbSyvkqz+6hz5glOJYlstSAIgiAIgmbQLjxH77//fhYFWG/FKNxIzeytAw88sNHHPPkKthMmTACS4mQmURGVI6/x/fffr0sk2locv5lfKij6WMxIbKricS0jwv/93/8FllaOXH/f+MY3gOQr2nfffbNaXNbUsVaMa1WVxQwvs4h8fcygrKdvx+szelVpsJaKX897bfRDNDWH1cCMQas5N0cxUtlRadZr4/1vdp41dV555RUgVa83O0/V0/1DVLprietINch+VjJv3rxsnmrh26sUvtaqXXkfoEqrVZfbKmalWQtIb5XvXXm/mGu2SIqR/ljH7r2puqtXzyzxaihGTRHKURAEQRAEQQntQjlatGgRBxxwAADdu3cH0lmlkZ3RkhGRUaNRvJWN9beoGKlW6CkwkiwSRgpm5L366qtZRN8WyKt8RnyqCueffz6Q5swstW9/+9tAir6NDFVpaoF9ivSm5ZVKs9X0udxyyy2ZxyMf2eax/ko+68Y6T/Vkk002AZZk3wGMHz8egKeffhpI98vWW28NJMVEz0qt6gmV8sADDwBLKyXLQs/UfvvtB8Avf/lLYOlehX/9618BuP/++xt9Pj+35aqcq4pWE1U619EVV1wBpDl07Y4dOxaAc889d6lq4kXGfeH2228Hkm9U3EfMglXdbIusssoq2XvS0KFDgaUVo3wNLf2y9cT7XyXdiuqq53m/pffFL37xi5qOs5RQjoIgCIIgCEpoF8oRJNUkX88oj5U5jz/+eCApRtbx8InWGhhF8hZ51mxWghl29hTTq7PJJptk0fHEiRNrPcwWY+Sjamck6OfNsrB2zr777gskdUa/i9F7LX0Sb775JpB6qOktMRLy//Yx+uijj7LrKre2PI93nkU1VD9cPVCF+OEPfwjA9ttvD6RaKo7ZjDrnzqzRe++9F1gx5ch1ke/r1VxUQ5bXL819wCxWI9eW+jX8GzfddBOwdDab+5DKdTXwNT7uuOMAuOiii4CUFarq5V7o/aOS1FawXpX7YR7Xptetr6UWvRcrhXM5aNCgzC+bv4f8v9e74447AvWdTz3AM2fOBJKq19T9r8pnJwo9SVHnKAiCIAiCoE60izpHLcGqw57HehZq7Rzd8UVE5cQI0Kdvz9CNhNZYY41MZfDJvZxKofqib8efq8e60ANhtpa+DSOffIaTHoMbbrgBgCeffLLR99cSvUbW+7EGkz4ovUnLUztUBq3OPnr0aCDNs5H9l770JaA+c+R1zpo1C0ieorzfQVybqjb6JU4++WSgZeqPv9vIv6X9DX/zm98A0Ldv36W+5mtp/zp7ha3oWrIvoD6evFql4mg9F6u/VxLXk1W4P/OZzzT6uq+9SoOvz9SpU7nzzjuBpC4Vse+Ya/GOO+4AUp2mPPn9wzXpGh42bBhQDC9fOcxQnTlzZpM9JPX9qX7Woxej96iZgZ5qlMP1VU4Nc626f5x55pnAkiy21u6DUecoCIIgCIKgGbQbz1FT+ARqVK5i9MILLwAwYsSI+gysBejzsO6N0bj9aVSJbrnllqyTvX3IylWLVm2qZxaeEW6fPn2ApaPsfFVhFbRrrrkGSJFvPTE6Led9c/2V1tbxZ/QYnXDCCUBSHfwZI9pjjz0WqG+9Esd89NFHA6m6spWxrW7u9ZtpaLRr/a0VyRjyuluqGIl1cJaFfjcr9LZUMXI/sabMMcccAyy9lo2Qfb2qoRiJ0bZ7gNXc7XeXz6xUxdpxxx0zD9306dOBpMoUKdPLe0nfWx5fa+fA+0n/l344fabf+c53gGJdo10PVDKXpxp5f1iVXZ9fPfC1zt+rzolZq/fddx+QstOeeOIJIK3mpoPUAAAJPElEQVTdc889F0h7oyc7vgeOGjWK3/3ud1W5hlCOgiAIgiAISmj3ypFRg9VEzczwLNRoaUWj0Vqif8VaEXlUwd5+++3sjNfoMF81Veqpuvj0b60c/R7lsolUyq6//nqgGIpRc/F1L60f49ysu+66QMpsM4NS9FTVM0stj1XI9RzlMarXe2V0Pm3aNKA+6pdZjMuqHj9//nwgKUhN4RpViVYxskN6uerbZt3onagmvsbPPvsskLI88/0nVY6cq4EDB3LGGWc0+hl9WtZ7KgL6KN3bVcZE9c+PKkb5/UVltkiKkXXqVFLcKxcvXpztISruYiaoJwn19In5fuo60id7yCGHAOle9FrySq3vV+eddx4AP/nJT4Dku9xnn30AeOihhzI/Z6X3lFCOgiAIgiAISmi3ypHRgd189WtYPVkFqS0oRs2ltJu7T+JmZBQJvUWqCFY195zZSsbWxNh8882B1G+nudF90XGOnDf7kIn9ub7//e/XdmAtoFy0ZkToHDq3P/3pT2szsGVgtOqeUKruuKbK9Xzze61b5Nf14uSjePH1sd6YHiyzwGqJc5DPEPTavb8efvjhLMNJH049esA1F5Ux/ZOqDvqm7PhuhXCzYl2jL730Uu0G20y+973vAUkxcs5mzJiRqSbiiYLvcUXKLLS+kQpYc72t3jeqeo888giQlMyHHnoIWPI+7kmCNcoqRShHQRAEQRAEJbRb5choQRVCf4e+FmtBtCf0O3Tr1i3LFjIqKkI9q3I9kFTv9FIZnRsJXX311UD5WjptFa9DFVNPhHN1xBFHAPWpU9JarFG1zTbbAKl+llkq9cDaPfZLK1WOtttuOwBmz54NpGwzezbutttuQPLlGNGXW4v6V8xGsw9gEXszLgt9SO6b9sAqIu5xZmddddVVAFx66aVA2hdVDFWO5JlnnqnJOJuDWY+HHXYYkPYC38c22mijTKX0a35vPTrXN4X3zQ477ADAhRdeuEK/R5XPPnH6TXv27Jl5NkM5CoIgCIIgqCLtTjnSM2BdBJ8qPbs0Smhr/YOag0/pq666aubtaGn/qWpitL3WWmsBKbo2k8GsLLEfmV4CeyMZ1Vorpq3i2vzKV77S6PNmHdayj1Cl+epXvwqkOdcXUE8/hLVizDCzJxckFWmrrbYCko9FltePrRSru9vPbN68eUDyOhYZFYmdd96ZkSNHAuneK6J3UfQwqhxZ6ds5de2ZmazaZ5XyeqqZefSwOXb374EDBwJJBYN0Xfpxioj97kaNGgUkX6V1BZt7ouGc2eFCOnXqlM3ryy+/3PoBl/7uiv62AmD7Bhs6+uKPGzcOaDuy9oqw1157AUs2co+mikSpYRxSarrydx6LI/rGpGzaFo+ZlsXuu+8OpOKEvi6XXXYZUN8ibiuKxy+m4PqG64NvPR+ODIiuuOIKACZNmgQ0Pl5r6dGt16Px1IbPlZb4K4HX5FGZx9o+yA4dOhRY8rBh4GLpgSIHIj7A+ZBj2QjLK/gG7TxbAuCCCy4AinGfeQxtAVj/755QavjXMuEDR5EC4Dzjx48H0vvxkUceCaRCpAaG+T3dNeqc2UR3ypQpQGpPsnDhwqpZZOJYLQiCIAiCoIR2pxztueeeQCq7brQ4YcKEuo2p2qis9OjRA1hiXqu0xFgJjA40qb7xxhtAeWnVCNA0YiOEIkdKzcFoaO+99wZSRG8zZFs2tCWM5JT4VcNUHjxuKgImBDhm0+ybg8ZQI9izzjoLSGu5CHhcqAqh6qDh3yjcY2pVCtfhggULMnXNlPIio9nfPX+LLbYAlj4KNeX99NNPB1KxxCKgVcCyJeWUy4ULF9K/f3+gWMeB5bBkhY3dTbpRNfcI1D3d9waLYHoMZ4KV7xXulYMGDcqOritNKEdBEARBEAQlFEI5yjcGlJaYpo2OTNX3dxkBtWevkT4rn8YbGhoy416RCiZ6tm8JeEvLDxkyBEiqg74NVUB9HSeeeCJQjLIErcG1anqr6FMpsnk371vRY2TrAqN2m0HaEqVIhelUf/Qc7bnnngwfPrxZP2PZBdWKehRzbIqvf/3rQPJ3SL6FkNG60ftdd90FwOTJk7Prawt4Hb5f5BUji6mq1Ko0FGkfURlRxRPnzH18yJAhWaubtoD3/ejRo4GkIF955ZVAUojcTywI7NzofbNcw3e/+10gefw01VeDUI6CIAiCIAhKKIRy5FOzykJpc87mon9gjz32AFJ0UIQsmWphVGFTSF+DRYsW0atXL4BCRoBmDnpmrq/B8W+wwQZAiqKMlIpY5GxFcN6MkkQVoohr1TFbSE+fjv937ixDMGjQIKDYzYHdZ4455hgeffRRIPn2Dj/8cCB5JWx8WeSUdnHPM0vLcgpei02DbcFgJlqR52p52BZGxdkyLmarVcuTUknyCollPlS79JAWWVVeHqp7kydPbvSxd+/eQGoL4p6vH8x9x8zmWqp9oRwFQRAEQRCU0FCEc9eOHTt+DK2LmH3SNkqwiJmZKda2aI8YMd16663AkqduvRFFVCFEb4DeIouaqRyZXeL1WRSyCGu2NegHs16J9ZxUWx588MH6DGw5GMEZjdsGwEjP+8y2G+2xyGoQBO2Pjz/+eJmpgaEcBUEQBEEQlFAI5aihoaH+gwiCGmG9GbNo9ITYaNfq5kVW/YKgNeQz54KgXoRyFARBEARB0AxCOaoh+jPMkinCax+0jHzvrdaoO9OmTQNgn332AZKSNHjwYCBlrsQ6CYIgqA6hHAVBEARBEDSDUI5qyGqrrQYk5ait9whbGTHDzo8rUpMrT74DtTVPwnPUPPL+lUqqe9UiPDdLaCuvQ1sZZ9ByQjkKgiAIgiBoBoVQjoIgCIIgCIpCKEdBEARBEAQlxMNREARBEARBCfFwFARBEARBUEI8HAVBEARBEJQQD0dBEARBEAQlxMNREARBEARBCfFwFARBEARBUEI8HAVBEARBEJQQD0dBEARBEAQlxMNREARBEARBCfFwFARBEARBUEI8HAVBEARBEJQQD0dBEARBEAQlxMNREARBEARBCfFwFARBEARBUEI8HAVBEARBEJQQD0dBEARBEAQlxMNREARBEARBCfFwFARBEARBUEI8HAVBEARBEJQQD0dBEARBEAQlxMNREARBEARBCfFwFARBEARBUML/A5aCVS748RYdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test = 10\n",
    "\n",
    "img = np.zeros([img_size * num_test, img_size * num_test])\n",
    "\n",
    "z_result = np.random.uniform(-1, 1, size=(num_test**2, n_latent))\n",
    "G_result = sess.run(G, feed_dict = {z: z_result, is_training: False})\n",
    "\n",
    "for i in range(num_test**2):\n",
    "    row_num = int(i/num_test)\n",
    "    col_num = int(i%num_test)\n",
    "    \n",
    "    img[row_num * img_size : (row_num + 1) * img_size, (col_num) * img_size : (col_num + 1) * img_size] = G_result[i,:,:,0]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir(save_path)\n",
    "\n",
    "Saver.save(sess, save_path + \"/model/model\")\n",
    "print(\"Model is saved in {}\".format(save_path + \"/model/model\"))"
   ]
  }
 ],
 "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
}