diff --git a/ Convolutional-Variational-Autoencoder.ipynb b/ Convolutional-Variational-Autoencoder.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Autoencoder with Deconvolutions (without pooling operations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/autoencoder/autoencoder-arch.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
+       " '',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/Users/ZRC/.ipython',\n",
+       " '/Users/ZRC']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/Users/ZRC\")\n",
+    "sys.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import RandomSampler\n",
+    "from torch.utils.data import Subset\n",
+    "\n",
+    "\n",
+    "from torchvision import datasets\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coke.visualization.image import show_batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_EPOCHS = 10\n",
+    "LEARNING_RATE = 0.005\n",
+    "RANDOM_SEED = 7\n",
+    "\n",
+    "# Architecture\n",
+    "NUM_CLASSES = 10\n",
+    "GRAYSCALE = True\n",
+    "NUM_LATENT = 20\n",
+    "\n",
+    "# # other\n",
+    "# torch.cuda.empty_cache()\n",
+    "DEVICE = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_transforms = {\"train\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()]),\n",
+    "                    \"test\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()])\n",
+    "                   }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_indices = torch.arange(0, 59000)\n",
+    "valid_indices = torch.arange(59000, 60000)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_and_valida_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = True,\n",
+    "                                transform = data_transforms[\"train\"],\n",
+    "                                download=True)\n",
+    "\n",
+    "test_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = False,\n",
+    "                                transform = data_transforms[\"test\"],\n",
+    "                                download=False)\n",
+    "\n",
+    "train_dataset = Subset(train_and_valida_dataset, train_indices)\n",
+    "valid_dataset = Subset(train_and_valida_dataset, valid_indices)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_dataloader = DataLoader(dataset = train_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=True,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "valid_dataloader = DataLoader(dataset = valid_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "test_dataloader = DataLoader(dataset = test_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "data_loader = {\"train\": train_dataloader, \n",
+    "               \"val\": valid_dataloader,\n",
+    "               \"test\": test_dataloader}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+7/q5TdMUzbb3cI2NAqSD4Motg/5sn+E9823Vlo9/6bs+P3bJnT7PnGgbFn337it8XvrmMXHvcdUFr/v8+zJrqZlZZ/Vz+Z/sPUZPX+1zdNsHPsdYfQlhF1ggItqJxSKizlou62LxrZSP7R3t87A8a2U5sWC3zxcV25usPet5n+/aaJuGVd3+RscDAZIl0PqSU2yrjS3/l6N9/v7FT/r8ub729/0Vy672ueHXQ30eM6ft80DDwPjL2T45Xb/2igyz91v8/cH2G2o1duTPA4+vXNPl9+osvlkHAAAAQoqLdQAAACCkuFgHAAAAQipze9YDO7WNu6fW59vX2lJwtxxnxxQNtuXfjqqoafMli3Pj+9p/MuIJn+/9d9td8aWhp/g8+L53bUgs6Yg0EQssh6hLrY/wyF/bMlZfee9mnwd9cZ3P08c87PO6YdanKCJyTH6wj9B2e/ztC5/wecI9tnRjS81WO7wXdokDuiN2wP6OHzTXPt8/PMH6xu8bYfdK1UZtybef1Z7h87Oz7BwiIjL0NaubjWfZfVRfusyWa7xloC1LPKFgi8/7h7FrKVIksJO8Drc+8AvPsmWCP9vXlkAsySny+WvDX/b5v28+2+e119n9Gw3b7PjLp8yPe+szi+ycdOMxdq/G9MvO8blszKk+txRaf33deLt/5M5z7vP5O/M+a28Q6MfvDXyzDgAAAIQUF+sAAABASGVuG0xAdOVanyvr9vpc8brtRhUrsanFnSW2VF3Qjkj8tMcN/2gtAf915EM+P3+h7YbasP5YnwteDCzp2Nz2UpFA2MQtP9pgObCKlZTk2ePVubZDY6OzehMRKQzsuJgbaIPJr7J2gKbRg3zO2bCxa4MGUiHQfjnoRdvV+sMiW6pu/JkjfW6ut3oY8pLVw6j51sYiIhJbt8nnMTX2/D8fO8nnYBtMntqSdi7PpvSBXhVYjlRqbDfrp9840efBU+t9/uIAa485s9BaMY8Ya9dXjc7qZFO0v89TCuJ35+0faKn5fH9rRx5xoY1j2dmVbQ67MWaXxr9aYy2aw+6z93abt0pv4pt1AAAAIKS4WAcAAABCKivaYIJTk3GrS9S0PY2R1+aj/9f242wFmL9W2w6M350w2+cfnX+lzxOWWNtMyzqbIgXCRnPtrwZ34pE+r/lEX58Hf3yzzyf1txVcTv/gcp93vjUk7nVHfNzu0P/P0Y/6/M/HPevzv110lc/j1w33uWWtvQcQdi2BFq7Kp+3xpsWBNq/A6jE5S9b6HK231oBDxVZZDdXvObrd44CUC6zgFd21y+dx91uLyxMLz/L53glTfY4NjN8x/iO5hfb4J8Ys83ngwPjdeY/Lt+u+xU3W8vyH9R/3ed2mchtqs313nbfNzn9D5tnrFL9srcyxXt6dvsNv1lV1mKrOUdXFqrpIVb/V+niZqs5W1RWt/y3t6LWAbEDNAImjboDEUDPZozNtMC0i8k/OuaNE5BQRuUlVjxKRW0XkRefcOBF5sfXXAKgZoCuoGyAx1EyW6LANxjm3RUS2tOZ6VV0iIlUicqmITG09bIaIvCwit/TIKEOqYr5NYT54ot2V/90TrA3GlQamcnK4RSAbpGvNBFtfoqfaKkarrrKVkj592ls+F+TYihO/n3emz8Mft8/5yFcWxr1H3WKbtv+HGz7v86+PsI2UTj1jkc/LFtrxA2iDyWjpWjedEWyJaW+FI9ZsQaLStmbm2Xmh/B0771RUWttkrKyvtCXax85Hf594ks+bP9sv7rg7Rj7u8+82fcrnupm2OdMRb+32WVusAnWPrWLWstFWYkrltnwJ9ayr6kgRmSgic0WkovWDIiJSIyIV7TxnmohMExEplOK2DgEyFjUDJI66ARJDzWS2Tn/Vq6olIvKoiNzsnKsL/p5zzkk7/+hwzk13zk1yzk3Kk4K2DgEyEjUDJI66ARJDzWS+Tn2zrqp5cvCD8IBz7rHWh7eqaqVzbouqVopIbU8NMqzy31/jc8E7tmLGPf1tlZj89TZlE6u1xfiR2dKxZiKBKcjlgdaX759jS1nsidq3L9Nnn+PzkXfbnf7RRXaH/qFT+yUPWxvNnrxTfP7dN2xFgM8Nmuvz1ydbXQ343w5/BKS5dKybzsgpLPRZ8622onV1bR0OdFq614xrsXbKYLuYtLNgXnBryiHv9/H5g5HHxR23odrOVYs327ltxFJrX469vyTB0aZOZ1aDURG5W0SWOOd+FfitWSJyXWu+TkSeTP7wgPRDzQCJo26AxFAz2aMz36yfLiJfEJGFqvpe62O3icjtIvKwqt4oIutE5Kp2ng9kG2oGSBx1AySGmskSnVkN5nWJn3kIOqedx3uW2nByiop8ds02neJaAquwuJ65hze2d5/P/dfYwvmbRpXZ49ttrLF9djwyVyhrphN2n1Ll82c+Zq0oOWr184dXzvZ57KM2nRhsfems0lm26svfLxjr8w8qn/NZy5oSfl2kp3Stm6Bgu0vOUJt6bxxu54ScJjtX6Bu2ycphBc55kUq7V7CguO2NY5pd4NTe0t4fKdJdJtRMdwRbaAa9E3+dt+GSgT7nF9hxsdxAjfbg2JItncYKAAAAZBUu1gEAAICQSmid9bDIKSnxueVEmz7P3XHAZ11tG6jEDtjjyWyJyRnQ3+f9g+zfPXmBG/z71LDVBdLD1pPtM3xyn9U+/+DtT/s89s/W+qJvftCt99NI4LsCZuqRpoKtL7Hjxvm89Mv2+BFjN/u8YfYIn0cstnPI4WjgPdZcW+3z9RNmt3W4rG+2FoDijWl5mgc6FrWWsj418S2TdTFrkT6x0paWWTnoKJ/b3nYpnPhmHQAAAAgpLtYBAACAkErP+bHRNg1Y8VPbmOis0qU+z7j1Ep/7vGCrTiRzRZbtF1kLzqVffcXnv6w40efiZ/KS9n5ATxo+21aWmDPVNiM6piowhX+EfeYHLSv1ObpjZ8Lv54ZX+ty/3/6Enw+EweZ/sL/vR1xu7WMzh8/yOU+tHfKhq072+aUzxnfqPfrk2xT/z0fe4/O5RfWBo+xc89LOCT4Pn2W1SVMmstHl5e/4/G+DjvGZNhgAAAAA3cbFOgAAABBSadkGk7PDlluZ/5JN13/tcy/5/OWfPebznzac7vOO5473ufI1m0J0eRGfa08s9nn/0PZXjxlw3HafH1k10eeSp21yJe9Da82x+5aB8ClaaZ/nZ947zudbTn/W51XftGNmTp7sc9VLtpxLvxcCn/k9gaWRRCRypK2WsfxWW+Hi/mPu9nlxk20mo1vsGCCM9g2z5pLPDpnv83H5dk6JBZY7+k75mz7fWPZGp94j+K1aRSTf5wK1/Lf91gYzd6G1qx1Zs6pT7wFkqjy1TZHSdeUxvlkHAAAAQoqLdQAAACCk0rINJrrNpuLH/LmPz9/cdJPPQ65a5/P4/rU+v3u2TRuuOC5wL3BgaqS01F5/WJFtAtPQEv/HtXXRYJ8H2+ynlL61yeeW3bvb/TmAMIltrvF5/N39fL5ji62sNOFMW+3ilqnP+Lx0iq3s8soXbAq+sXlY3HtUle7x+a6RM32ujVotfveVz9o4Htnb+R8ASIGRz9hKLf+S9xmfn5q03OevDLHVws4psnaVcuuU6bR76+y888CmKT6vf9NWSRv/tK16FtttNQdkEhezNuX8zfEtl0/UnODzTcOsRfrAYHtOpMJqKbrVrhPDiG/WAQAAgJDiYh0AAAAIqbRsg3GNjT5HF9tU45CNNnVfX2OrxLxdNsKeHPjnSVm771DgU3ACMXhDsYjI6KW2kUvuSts4JrprV2Cw7a8mA4RJrMFavmTuQh9H7xzt89alo3z+9dH2eGzUAZ+vO+Ytnz/ff0Hce5RFbN7/3UZrYbv1/ct9rn7WilTfWeIzlYQwyntzsc/jd4/xecPrtvLRd6psk6Km/t17v6Jaq4SSzbbG2Jjl1r4ZXWbtai7GOmTIUIHPttu4Je63Nj5jK/+9+8WRPjeVB+qhLFCMtMEAAAAA6Aou1gEAAICQSss2mPZE6+xu4OLH51rujffuhfcAek2gfSu63DZV6bdyrc+l/a3trGXCcJ8fnHq2z3ePPiP+dXPsdfNqbVWMivm2sUy/t2wlp5aWQ3rPgJCJax97d5GPJe/awyW9MA7OQchmsX374n497Elb3ezPhXZOGrjFzkFav1/SBd+sAwAAACHFxToAAAAQUhnVBgOghwXuvg+ueqRvWq5+s3tvQeMLAKA7oitsRaRhP17d5jHpdK7p8Jt1VS1U1Xmq+r6qLlLVH7U+PkpV56rqSlX9i6rmd/RaQLagboDEUDNAYqiZ7NGZNphGETnbOXe8iJwgIheo6iki8jMR+bVzbqyI7BKRG3tumEDaoW6AxFAzQGKomSzR4cW6O2hv6y/zWv/nRORsEZnZ+vgMEbmsR0YIpCHqBkgMNQMkhprJHp26wVRVI6r6nojUishsEVklIrudcx+1/GwUkap2njtNVReo6oJmaWzrECAjdbVuqBlkK841QGKomezQqYt151zUOXeCiFSLyMkiMqGDpwSfO905N8k5NylPCro4TCD9dLVuqBlkK841QGKomeyQ0NKNzrndIjJHRE4VkQGq+tFqMtUisinJYwMyAnUDJIaaARJDzWS2zqwGM0hVB7TmIhE5T0SWyMEPxZWth10nIk/21CCBdEPdAImhZoDEUDPZozPrrFeKyAxVjcjBi/uHnXNPq+piEXlIVX8sIu+KyN09OE4g3VA3QGKoGSAx1EyWUOdc772Z6jYR2Sci23vtTcOhXMLzM49wzg1K9SDQOa01s07C9RnqDWH6eamZNMO5JhSomzTCuSYU2q2ZXr1YFxFR1QXOuUm9+qYplo0/M5Ir2z5D2fbzIvmy8TOUjT8zkivbPkPp8vMmdIMpAAAAgN7DxToAAAAQUqm4WJ+egvdMtWz8mZFc2fYZyrafF8mXjZ+hbPyZkVzZ9hlKi5+313vWAQAAAHQObTAAAABASHGxDgAAAIRUr16sq+oFqrpMVVeq6q29+d69QVWHqeocVV2sqotU9Vutj5ep6mxVXdH639JUjxXpIdNrRoS6QfJlet1QM0i2TK8ZkfSum17rWW/dYWu5HNwOd6OIzBeRa5xzi3tlAL1AVStFpNI5946q9hWRt0XkMhG5XkR2Ouduby2CUufcLSkcKtJANtSMCHWD5MqGuqFmkEzZUDMi6V03vfnN+skistI5t9o51yQiD4nIpb34/j3OObfFOfdOa64XkSUiUiUHf84ZrYfNkIMfDqAjGV8zItQNki7j64aaQZJlfM2IpHfd9ObFepWIbAj8emPrYxlJVUeKyEQRmSsiFc65La2/VSMiFSkaFtJLVtWMCHWDpMiquqFmkARZVTMi6Vc33GDaA1S1REQeFZGbnXN1wd9zB/uOWC8TOAR1AySGmgESl45105sX65tEZFjg19Wtj2UUVc2Tgx+CB5xzj7U+vLW1V+qjnqnaVI0PaSUrakaEukFSZUXdUDNIoqyoGZH0rZvevFifLyLjVHWUquaLyNUiMqsX37/HqaqKyN0issQ596vAb80Sketa83Ui8mRvjw1pKeNrRoS6QdJlfN1QM0iyjK8ZkfSum17dwVRVLxSRO0QkIiL3OOd+0mtv3gtU9WMi8pqILBSRWOvDt8nBnqiHRWS4iKwTkauccztTMkiklUyvGRHqBsmX6XVDzSDZMr1mRNK7bnr1Yh0AAABA53GDKQAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIcXFOgAAABBSuakeQLpQ1bUiUi8iURFpcc5NSu2IgHBT1W+LyJfl4G5wC0XkBudcQ2pHBYQb5xogcaoaEZEFIrLJOXdxqseTbFysJ+Ys59z2VA8CCDtVrRKRfxSRo5xzB1T1YTm40ca9KR0YkB441wCJ+ZaILBGRfqkeSE+gDQZAT8kVkSJVzRWRYhHZnOLxAAAyjKpWi8hFInJXqsfSU7hY7zwnIn9T1bdVdVqqBwOEmXNuk4j8QkTWi8gWEdnjnPtbakcFpAXONUBi7hCR74vtSppxuFjvvI85504UkU+KyE2q+vFUDwgIK1UtFZFLRWSUiAwVkT6qem1qRwWkBc41QCep6sUiUuucezvVY+lJXKx3Uus3heKcq785oUoAACAASURBVBWRx0Xk5NSOCAi1c0VkjXNum3OuWUQeE5HTUjwmIPQ41wAJOV1ELmm9MfshETlbVe9P7ZCSj4v1TlDVPqra96MsIp8QkQ9TOyog1NaLyCmqWqyqKiLnyMGbfwC0g3MNkBjn3A+cc9XOuZFycBGDl5xzGTeLy2ownVMhIo8fvOaQXBH5s3PuudQOCQgv59xcVZ0pIu+ISIuIvCsi01M7KiD0ONcA+D/UOZfqMQAAAABoA20wAAAAQEhxsQ4AAACEFBfrAAAAQEhxsQ4AAACEFBfrAAAAQEhxsQ4AAACEFBfrAAAAQEhxsQ4AAACEFBfrAAAAQEhxsQ4AAACEFBfrAAAAQEh162JdVS9Q1WWqulJVb03WoIBMRt0AiaFmgMRRN5lDnXNde6JqRESWi8h5IrJRROaLyDXOucXJGx6QWagbIDHUDJA46iaz5HbjuSeLyErn3GoREVV9SEQuFZF2Pwj5WuAKpU833hLdVS+7tjvnBqV6HFksobqhZlKPmkk5zjVpiLpJOc41aeZwNdOdi/UqEdkQ+PVGEZlyuCcUSh+Zoud04y3RXS+4metSPYYsl1DdUDOpR82kHOeaNETdpBznmjRzuJrpzsV6p6jqNBGZJiJSKMU9/XZA2qNmgMRRN0BiqJn00Z0bTDeJyLDAr6tbH4vjnJvunJvknJuUJwXdeDsgI3RYN9QMEIdzDZA4zjUZpDsX6/NFZJyqjlLVfBG5WkRmJWdYQMaiboDEUDNA4qibDNLlNhjnXIuqfkNEnheRiIjc45xblLSRARmIugESQ80AiaNuMku3etadc8+KyLNJGguQFagbIDHUDJA46iZzsIMpAAAAEFJcrAMAAAAh1eNLNwIAAAC9JTKgv89150zwecfREZ/7rXU+D3zC2vmjdXU9PLrE8c06AAAAEFJcrAMAAAAhRRsMgM7LsSnE3GFDfd4z2XJ9tR3T1O/Q57f9svl7LA9Y0eJzyfubfY5uqfHZtdgxAIDso7mBS9gTJsT93qaP2cmn7wV27rhl5Os+/+iNS3wunxM4WdEGAwAAAKCzuFgHAAAAQirr2mBy+va1XF7mc6xfseUC+2PZN8webyqJ/7eNC/xS7aZiyWm2XxRvbfa5aEWtzy3rNiQ4ciD1cisrfK65oNrnUdeu8PlHQ+f4/PHCprjn52lE2vJqg+V/XXmZz2tfHu5zxTx774I3l/oc27/fnuwChQikUE6xnTtktH2Om8vt8YI123xuWb/Rju/s5zjQlqY5ak+PBZ4fi3butYA0kFNY6LM7eozPy24sjjvum2c85/OU4pU+/63+WJ8LNubZE5rtWi2M+GYdAAAACCku1gEAAICQSs82GLXpvpyiIssDra0lOtgWxHd5NlVYX23H7zzSHj9QbVMgef1s6v62iY/7fGXJ+rhhlOTYdEzUxXxe3mxz+j9Y92mfVz1jUzbVz/XxOfaBTekDYdY01lpR9px5wOefD3/C51jg+HmN8VOTwQn5YZG9Pk8pKPD55WPstT4Yb7X0D2d83ueCW62tQBdZCw6rxCAsdKjVyuqrSn2uOm2TzxueHWaP/85aYmINgb6wQ183sAJGzugRPjcNtXNe7l47h+mSNfa6+/Z1auxAqATbvQKrkC27vsTnFy78ZdxTiu0yUW5aa9dhqx8Z5/OYp6z1rKVma1KG2lP4Zh0AAAAIKS7WAQAAgJBKyzaYnMCUuTtqtM8rrrSVXq6/8CWfTyxe6/OAHFs5YkSuTeOXRew12xe/kkWzs0n9WGDyf3Se3WH85zFP+bz9GzY1+Z1P2YoX+84PrEQTXNkCCJnc3TY9n7/EpvZvKL/W58ao1cm2xYPinq9Rm5scfZKtiPRPI573+bTCep+Pzsv3+dtjXvD5l8d/zufSpYG6pA0GIREtt/NRdKyda5470lor7xgy3ueXHz3K51hwZRiRuNVhckZa68yS7wz0+RdnP+TzHzee4XPTj22zmNw577T5mkCYRfpZu8vukwb7fM+F032uzo2/hvv2ZquBrf9t14mVz7znc0saXW/xzToAAAAQUlysAwAAACGVlm0wGtjYqOaUfj7f/9k7fT4iz6bDI2JT7/GbsnSm9aV7IoGVa8pzbEr/mop5Pv/oa9ZCUPU/NkVDSwzCJrZwuc8jVlj96G8sFwdWRhrQXNPua+VUWIvMD877ss9Vn7PVKx4f+6zP4/NsU7FtZ1tL2cAn7L2jjY2H/wGAXhItstNrQWH3PpfBDZaW3FLu811n3+3zqQXWalM1cpbP13/1ep9HzW2n5ZKWGIRY44ljfS7/2lqfTw3UVe4hbcoLf3q8z/2eW+hzul5X8c06AAAAEFJcrAMAAAAhlZZtMBKYZg/uwJIf+EVeYEok2IpSH7Pp882BVSv+d+epPs98a7K95s74qZX2NFfb6/7j5Bd9/toA27AlOI6+OTZl2TggMAUZOAYInVhgBaTgdGIXphbdepvCLF1mG5qtqA2sIGOzn5KjgRUx8oJbLwHhEGxX2XCabZp304TZ3Xrd4EZIVcN2+DylwDY5ylNbheyYfNvk79oJ831+5UQ7z0XetNYANhND2ORWV/m84VRrdXxu1Eyf98Tseulj//vduOePezuw4dGBA5Lu+GYdAAAACKkOL9ZV9R5VrVXVDwOPlanqbFVd0frf0sO9BpBtqBsgMdQMkDjqJjt0pg3mXhH5jYjcF3jsVhF50Tl3u6re2vrrW5I/vLbF9timKZWv2JTgFad91eeHT/+Dz/MPjPT5l++d53PRApuyLFtm04YTNtT5rPs7dxf/9tOH+PzG2DE+B9tgghY32hTPqMfs/RyrWWSKeyVkdRMGkX62elP9uUf6vOUKayP7xQm2aUxMrPXlvYZqn8uftWnRGDWTKe6VNK8ZHWmf0Za+9tltdm23Uw7L2+nztqn23LKHauOOc01WH5vW20ZIC8bZOez0QjuHFQRaYo4rWu/zIyee7fPQ+Xb6pw0mrd0raV43Xo7VSd1kq4fKs6ylpTjQKvzz7af7POqJvXEvFdu6zX6RAasddfjNunPuVRHZecjDl4rIjNY8Q0QuEwAedQMkhpoBEkfdZIeu3mBa4Zzb0pprRKSivQNVdZqITBMRKZTi9g4DskGn6oaaATzONUDiONdkmG6vBuOcc6ra7hyDc266iEwXEemnZUmZi3DNNiUYW7bK51F3Hefz9e/d7HPeXnvbEUttyjx/kbWoRLfZlEln15nYeYPdWb/nE7YaxuWD3vG5wdn04twGawH43Qdn2rjffb+T74hMcbi66Yma6Qk5hbbahYwd6eOWs8r+78Gt6sbbajJHHWvT8z8Z/lefTy2wY2bts1bLH758uT33Rds4qSXQIoDMlYpzTaJqzrQWlRGTbep+ctHqNo8fkrvH593WFSYDcw85NUetJiTXfrQ87bh9pcFZS0x+nT3XZUBrADqWTueanD72D4adR1pLzPTRT9jjgQu0v62f4HPl6s1xr5VpG+R1dTWYrapaKSLS+t/aDo4HQN0AiaJmgMRRNxmmqxfrs0TkutZ8nYg8mZzhABmNugESQ80AiaNuMkyHbTCq+qCITBWRclXdKCI/FJHbReRhVb1RRNaJyFU9OcjDCd7FHnnZ2k+GvtzxcwMTi3GbTuQM6O9zbGSlz/WjS+KeX3W9TW3eN8JWsBibZytVPLzX7mj+l7cutec+YVOTyDxhr5tk0DEjfF7xRWtX+cx5r7f7nKtL5/k8Nte+K9ge2Kzsh7Wn+fyXVyyPe6TB55aarV0YMcIsE2qmbrTlW4e96vPJBW13GNTFrJWsaEtgQ7xg24uISJ6dL8oH2+phR+QFN3sJtKUF1EeLfC7ZEmibiaW86wFJkAl18xEtyPe5scz6XU6xSypZ1GztMfX77DNfWTYg7rVyCwIrhu2w+29jDQ2Sjjq8WHfOXdPOb52T5LEAGYO6ARJDzQCJo26yAzuYAgAAACHV7dVg0k1wUxY3yjYm2jvaHt9badMsuyfaRhOTj4zf4Oinw6wNrDo3MOUSWE/mqe3H+zx4tk3xlPz1vcDxQPppGtzH5+qJdif+1we+4XNFpCjuOTmSL2157YC1mz38orW+HPEHuy8quqLtFTWAVNLgdHuR/W3eJ6ft1SjWt1jrym/XB1ojZ2/3OdrUHPec3CGDfa7sa20w/XPabn3ZG7P3XhqorfydgRYAx5kH6advYAWko6pqfF5x9ei443ICHV9FW61ls3CPfe6Lt1id5K60c1h0a/jux+WbdQAAACCkuFgHAAAAQipj22DiVncptZUq9p0yyuf1F9nxV02Z6/OpJSt9/mTxrnbfI0dsij/WTjPLkSU2TfPOUUf43HeK7YAR+ftCn4Or2wBhVrDGpu03PWWrHt1caDtbf23onLjnTCnY53ORWkvMxibbSKl4s32HQOsLwi4yqNznggrbHG9kbvDcYZ/1v+w5yedtM4f5PHj5fDs8Fr8azJ5J1soypWS+dGRdi7VyBjeOGfqhbSIYO3TFGSANVOfaddeM0Y/5vG9U/DVYvtrqSjuilhc3DfH5x0su9LngEWujGfCX3T4HN+FMJb5ZBwAAAEKKi3UAAAAgpDK2DSZSPdTnzRfaFP2JX/zA5xeGveJze20s3XVr+fs+X/m5t32+aZItjZr3dZsKja3d4DMtMQizlrXrfR7yO7uT/sCTNmX/1a9Pi3vOb6+4y+ezimxliqv6vevzH0awPDDSR3SItVkeP3STz2Pz2j69vrPH/r4f8tI2e53D/H1fe6WtIPOZ0o7bYKJi0/4xZzm4co00BFarcbTEILwiat8rRwKP9wushlQs8Z/h3MCRpTm2Adj4PGtxuWLyQz5fMuACn6NzrLWtZZOd21KJb9YBAACAkOJiHQAAAAipjG2DWXqztcHcdr7dMXxl3zWBo9reoKWnjMq1aZnfjn/Q53+afqXPkS9by050/UafaYlBmAU/ny2Bz+3Yf98Zd9x/TLQlmMYdcb/PeTZTLy7fWtJyCm2aM9YQ2NAFCInItj0+f7jVWsBWV9nGRmPzCqQ7rppgbWITC4Itm21/33ZknuUHJt7j8789+SmfD3x9nM9uma26FJbVL4CPRAMbeB1w9vn8sMk+6B80joh7TmPMfq84sEHZKUV2DTghUJfBdjGJhW/DML5ZBwAAAEKKi3UAAAAgpLhYBwAAAEIqY3vWY4XWc1SVZzvJlWh7vYP275b1LbZM1g83XezzB7WVcc9ofseW7GoYbn1UV520wOevlr3uc3DnrVGBP/lfj37E54v+9Zs+T/gJOzkiDTlbJiu2b1/cb21+5zif54+s8vnTfay3feTYrT43nX60z7kv2tKnQFhEN9vntWGNfabnHzXc5xG5tvzbz4Y/4fNTjx3j896o3Z9xqK+W2me/QPvYe7u2e2vz1O6PGpBj56ahRXU+r4qUC5BuXmkY4PMP/udLPlcsOBB3XGS/3Ue1d3ixzw993WrxuQlP9sQQewTfrAMAAAAhxcU6AAAAEFIZ2wYz7K+W7xh/rs9HjPmLz8G2lOWB5ap+uOEyn1f9ZbzPFe/HT7Pkb7Td6mL9bWry9XFTfH747JN9/ueps3y+tp/tVDoi1/5vuP20mT7fOdF2Oe2/bYfP0d22VBiQTkrWWl7ZMMTnvBL7TJcW7Pd5e1+bzs/Yv6yQ1oJLHY69v97nX264yueZn7Y2xkfHPuPzTQNWdeo98tppfYmJtZw9s7+/zz9beb7P2z8Y7PPQ122Xxz7rltnrtNgyk0CY1TRbG8zgd2w538hbi+OOc422XGO//Uf4vLG+pAdH13P4Zh0AAAAIKS7WAQAAgJDK2JnlkuW2Asyal21nq7PX3GwH2QyiRHbbH8XAD2wnq6Gv2m6MLeut7UVEpCUWlbb0XWIrzpT3PdHndyfZOIJtMME7988vrvH5Nuugkf4fVtgvaINBmsoLLA6zP9a7OwgDPc29u8jnqr2jfV5ZYvm2vpN8/nzpWz6PD2zjGzwniLTf+tLs7Bz0cO1kn5uesNaXcS9vs9dZttLy4X4QIKSandVG5EBgZ/do+5/oxiHW+jKoZFu7x4VZh9+sq+owVZ2jqotVdZGqfqv18TJVna2qK1r/W9rRawHZgJoBEkfdAImhZrJHZ9pgWkTkn5xzR4nIKSJyk6oeJSK3isiLzrlxIvJi668BUDNAV1A3QGKomSzRYRuMc26LiGxpzfWqukREqkTkUhGZ2nrYDBF5WURu6ZFRdsGOybbhQ0OV3eleuiDP5yGv2kYsut/uKo5tsU0uWhrs8c5yzTY1o4GZmRxtewOLoEK1/0scdxSkpXStmd7QMNCm+styrSem0VmN7mywlS/y9wSmOZHRMq1uXI1Ntw97wabhX9h1qs8zj7XWla+ePsfnm0oXxr1WkbbdMrY9ZivRzF0z0ucj5tT6HF3euRVnkH4yrWaSSu1cs/NIa00+t8xakGujtvLYpj22mlJlc/haZRLqWVfVkSIyUUTmikhF6wdFRKRGRCraec40EZkmIlIoxW0dAmQsagZIHHUDJIaayWyd/u5WVUtE5FERudk5Vxf8Peeck7jbNeN+b7pzbpJzblKeFLR1CJCRqBkgcdQNkBhqJvN16pt1Vc2Tgx+EB5xzj7U+vFVVK51zW1S1UkRq23+F3ld7mvWf/Oac//X5W3U3+Dzkke0+t2y3TYe6KzLGVn3ZM9YeP77PhjaOFomJtcesDLTQlC6yaRypTd740PN6s2a0wP6SjVTaFyjR8n72+M69PresWWdPdm3+Hd7NAdnnNtK/X9xv1R1r0/bHF9k46gPT+Ru32b1QR6wJ1GhSB4kwSsdzTXti9bZBkrz1gY+DbQEYqRw7yuff55/p8w3nvRf3WkUqbVraZLWSu67Q5+jydxMdLtJUJtVMeyJq3yv3jVhr8v6htrFlH43/7jl3iLVC755o55eL+r/v87P77AJt3yKrJbd3bfcG3AM6sxqMisjdIrLEOferwG/NEpHrWvN1IvJk8ocHpB9qBkgcdQMkhprJHp35Zv10EfmCiCxU1Y/+uX+biNwuIg+r6o0isk5Ermrn+UC2oWaAxFE3QGKomSzRmdVgXheRdibh5JzkDqcLglPuffta7mfTHn1zbNqk/wRrJ9k/2TaqKHh+t71mO5sdHSoyzp7fUm7vvfZcW83ikxfO8zm4EVJQg7MJ/p9u/qSNda39DG7/gU6NCanX2zUTKR/o8+aLqn2uO9U+MwXLhvo88jFrm4kusU1SOvu5b1NOYKOKUrurfu8ZY+MOu37S6z4fn2+tlYubrWZ0g03nt6xd3/UxIa2E/lzTA7TRVkGK7LCVypoO054WbJt8uf5In4u2tvdHh0yVcTUT+Nxr1H6s/YE2yTF51tGz6Wx76oSlI+NeasuZdl68cbKttDSxwFYh+8Hy030ePrvR51gXVgHsaSwOCAAAAIQUF+sAAABASCW0znoYaW5g6vAkm3L/yrGv+Twp36ZQvjd+ts//eubVPo971+4cju3eY2+QY/+e0WK781hEZNkPbbr/7lNn+Hx6YbO0zV6r2VnLwcpmayF459mjfB61dK3PLftt8X4gKDrE7mKvO8VaX5aedZfPKz9mU3xXn3Sjz9U3BVaP2WrTiy4aaIkJTskH2l00z/76CLbi7PrYcJ+H3BS/IcsNpdYWtq7FNnq5ZdkVPg96rwdWqAFCyBUFVnIabtPz+dp+S8v+mJ1fHnz3ZJ+PeM3OW1QQ0pFrss92/m6rgXmN1hoZbGO54/zASn99rol7rQfP/I3PkwvstR7dN8TnnW8P9nnAPFuxqePtK3sf36wDAAAAIcXFOgAAABBSad8G45qtxSVvwQqf//T4uT6XfsamTa7tu9bn3ZfM8vln5bYKy8C3bJWXhoE2fXLJ1baShYjIf5fZ0qWVEZvSj7V7c7aZtc/aD37xn9aOM3KW/QwtO3Z2+DpAcM4u1mAlvSVqLTFj86yF67GJf/T5/G9/1475i7Wy5Cxeba+5z+onMtpaXLadYdOJ20+36cufnvEXe/3iTXFDLcmxcZw/93qfK/9o7QD5r9iGLkznI5NFy2wVpF+d9LDP/XPy2zpcREQ2B1bJ6LPcjnPvsxES0ltsr23eN/wZu/75UtVXfF7x6f/x+fxia/2af+5/xb1WaU582/JHfrrErvWqXrXzVizkrcZ8sw4AAACEFBfrAAAAQEilfRtMUHAKZeRTll86Z4LPNwQ2Jrqy73KfTzjbNl9Ze4atDFOcY6toTC6w1TJERMoiBdKR4KovM+rG+fzrZy72eew7tjlMbNcue3J3NqlB1tBla3zuv/AEn5863TZM+Wr/dT4Pz7XpwRmX/87nB848zeclu61Na1+Tbag0vnSbz18c+LLPE/JrfB6bZ80rRWp38YuInLv40z6XP1jsc+FbS3yONjYKkKlyjrHz0bJrrRYnF9iGfTkSXzdB31n1GZ/LltqGepwvkPaCK4+ttGuyYc8f4/MFR17q83MTrBX50LaXvc7OIxNf+IbPIx60NrLC+bZaWfQwG5GFAd+sAwAAACHFxToAAAAQUhnVBhOcQslZbatQzH3fNkv6W8UbPn8iMGsysSAWyPHtLia+7SUn8G+dPbEGn7dG7fF7dpzu86zZU3we/792p7Nbv9nnWJSpTCQmeBf7kDfrff6vCmu1emTylg5fp77RVpZobLbNxir62WueVbrU5ytKttsYArWwoNE2Trru0Zvi3qN6jk3b951vK85E6+sFyAYtA+3EM3SstZUdbgWYoPU7bRO0qj0thzkSSF+xA7aaWZ83rV2lae8In08dEX9+CdLAKmnjF1mrcc4Ka4VOp/MO36wDAAAAIcXFOgAAABBSmdUGExCrs9VgxjxiU4X/2HS9z9ee9ZrPN5XN87mz05HP7O/v838st42Ndn9gq8kMsK4BGfu+LeAfXbSsU+8BJEKXrvV59INVPu9/c3CHzy1pJx/o09fn/6643OdflAYOCtxIn2elJ+Of3hr3Hm69tadFGxoEyDb5660FcseTttLS0Tum+fytE16Ke87cPaN8LnzF6rFgtU3p0xCDjBJoa45ut5WSInMsl0rnBNd5SddGY75ZBwAAAEKKi3UAAAAgpDK2DcY1N/kcmfOOz+N32kYxT6w50+f7JtiqLZIXuI34MAo224oZg9+2yZVx82yTmpYttllM514V6LpY8O72D60Hq/DDrr9mcHuWvu0e1bZ0nXIEekrLWtvspeJ+a4kZvGCkz384+6K45xTX2ET+0Fetlaxl/cYeGCGAsOGbdQAAACCkuFgHAAAAQipj22DaE3t/ic9D3rfHhyTxPbgrHwDQpsAqF3Fta/MX+lg1v/2nc34Bsk+H36yraqGqzlPV91V1kar+qPXxUao6V1VXqupfVLVz6x0CWYC6ARJDzQCJoWayR2faYBpF5Gzn3PEicoKIXKCqp4jIz0Tk1865sSKyS0Ru7LlhAmmHugESQ80AiaFmskSHF+vuoI+2Oclr/Z8TkbNFZGbr4zNE5LIeGSGQhqgbIDHUDJAYaiZ7dOoGU1WNqOp7IlIrIrNFZJWI7HbOfdQ+t1FEqtp7PpCNqBsgMdQMkBhqJjt06mLdORd1zp0gItUicrKITOjsG6jqNFVdoKoLmqWxi8ME0k9X64aaQbbiXAMkhprJDgkt3eic2y0ic0TkVBEZoKofrSZTLSKb2nnOdOfcJOfcpDwp6NZggXSUaN1QM8h2nGuAxFAzma0zq8EMUtUBrblIRM4TkSVy8ENxZeth14nIkz01SCDdUDdAYqgZIDHUTPbozDrrlSIyQ1UjcvDi/mHn3NOqulhEHlLVH4vIuyJydw+OE0g31A2QGGoGSAw1kyXUBTZo6PE3U90mIvtEZHuvvWk4lEt4fuYRzrlBqR4EOqe1ZtZJuD5DvSFMPy81k2Y414QCdZNGONeEQrs106sX6yIiqrrAOTepV980xbLxZ0ZyZdtnKNt+XiRfNn6GsvFnRnJl22coXX7ehG4wBQAAANB7uFgHAAAAQioVF+vTU/CeqZaNPzOSK9s+Q9n28yL5svEzlI0/M5Ir2z5DafHz9nrPOgAAAIDOoQ0GAAAACCku1gEAAICQ6tWLdVW9QFWXqepKVb21N9+7N6jqMFWdo6qLVXWRqn6r9fEyVZ2tqita/1ua6rEiPWR6zYhQN0i+TK8bagbJluk1I5LeddNrPeutO2wtl4Pb4W4Ukfkico1zbnGvDKAXqGqliFQ6595R1b4i8raIXCYi14vITufc7a1FUOqcuyWFQ0UayIaaEaFukFzZUDfUDJIpG2pGJL3rpje/WT9ZRFY651Y755pE5CERubQX37/HOee2OOfeac31IrJERKrk4M85o/WwGXLwwwF0JONrRoS6QdJlfN1QM0iyjK8ZkfSum968WK8SkQ2BX29sfSwjqepIEZkoInNFpMI5t6X1t2pEpCJFw0J6yaqaEaFukBRZVTfUDJIgq2pGJP3qhhtMe4CqlojIoyJys3OuLvh77mDfEetlAoegboDEUDNA4tKxbnrzYn2TiAwL/Lq69bGMoqp5cvBD8IBz7rHWh7e29kp91DNVm6rxIa1kRc2IUDdIqqyoG2oGSZQVNSOSvnXTmxfr80VknKqOUtV8EblaRGb14vv3OFVVEblbRJY4534V+K1ZInJda75ORJ7s7bEhLWV8zYhQN0i6jK8bagZJlvE1I5LeddOrO5iq6oUicoeIRETkHufcT3rtzXuBqn5MRF4TkYUiEmt9+DY52BP1sIgMF5F1InKVc25nSgaJtJLpNSNC3SD5Mr1uqBkkW6bXjEh6102vXqwDAAAA6DxuMAUAAABCiot1AAAAIKS4WAcAAABCiot1AAAAIKS4WAcAAABCiov1TlDVQlWdp6rvq+oiVf1RqscEhBk1A3SdqkZU9V1VfTrVYwHCTlUHqOpMVV2qqktU9dRUjynZclM9gDTRKCJnO+f2tu5+9bqqeywWgQAAIABJREFU/tU591aqBwaEFDUDdN23RGSJiPRL9UCANHCniDznnLuydVOn4lQPKNn4Zr0T3EF7W3+Z1/o/FqgH2kHNAF2jqtUicpGI3JXqsQBhp6r9ReTjcnBnUnHONTnndqd2VMnHxXontU5LvicitSIy2zk3N9VjAsKMmgG65A4R+b7YDosA2jdKRLaJyJ9aW8fuUtU+qR5UsnGx3knOuahz7gQRqRaRk1X1mFSPCQgzagZIjKpeLCK1zrm3Uz0WIE3kisiJIvI/zrmJIrJPRG5N7ZCSj4v1BLVOr8wRkQtSPRYgHVAzQKedLiKXqOpaEXlIRM5W1ftTOyQg1DaKyMbAzO1MOXjxnlG4WO8EVR2kqgNac5GInCciS1M7KiC8qBkgcc65Hzjnqp1zI0XkahF5yTl3bYqHBYSWc65GRDao6hGtD50jIotTOKQewWownVMpIjNUNSIH/4HzsHOOJbWA9lEzAIDe8E0ReaB1JZjVInJDiseTdOocCzQAAAAAYUQbDAAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIcXFOgAAABBSXKwDAAAAIdWti3VVvUBVl6nqSlW9NVmDAjIZdQMkhpoBEkfdZA51znXtiaoREVkuIueJyEYRmS8i1zjnFidveEBmoW6AxFAzQOKom8yS243nniwiK51zq0VEVPUhEblURNr9IORrgSuUPt14S3RXveza7pwblOpxZLGE6oaaST1qJuU416Qh6iblONekmcPVTHcu1qtEZEPg1xtFZMqhB6nqNBGZJiJSKMUyRc/pxluiu15wM9elegxZrsO6oWbChZpJOc41aYi6STnONWnmcDXT4zeYOuemO+cmOecm5UlBT78dkPaoGSBx1A2QGGomfXTnYn2TiAwL/Lq69TEA7aNugMRQM0DiqJsM0p2L9fkiMk5VR6lqvohcLSKzkjMsIGNRN0BiqBkgcdRNBulyz7pzrkVVvyEiz4tIRETucc4tStrIgAxE3QCJoWaAxFE3maU7N5iKc+5ZEXk2SWMBsgJ1AySGmgESR91kDnYwBQAAAEKKi3UAAAAgpLhYBwAAAEKKi3UAAAAgpLp1gykAANms9hun+dxQ3vYxRTXO54GLGnzO37DD55a165M/OAAZgW/WAQAAgJDiYh0AAAAIKdpgWmmu/VFEygf63DRuqM/N/eL/uIrX1vns1m70ObZvX08MEeiYqsX8fJ9zigo7fGps3wGfXUuz/YZzbRydXJF+/XxuOXa0z7vHFbV5vEYtl7++2efoxi0+u+amJI4QaNsV017y+Z/Ll7Z5zN17hvj8s/fO91lXVvncP5CLdgY+4CJSsKPR59zawHln63afY/X1iQwbyHq5lVaXB462+msYmOfzgA+sVS26bLU9ORZfoz2Nb9YBAACAkOJiHQAAAAiprG6DyenTx34xZpiPNaeW+px/Wa3PHx+yKu75M185xecRT5f4XLRsq8+x3XssM02JHpZTYp9DN3a4zzuP7tvhc0s/tOl1XbXB59jevXZQT7XEDKv0ccWN9tfSmgv+p83DN7bYmC79yfd8rnhsv8/R7dYi0ButPMhOr2wb5/Ok4jU+F+dY68q4ghqfnzzVPtNHnlnsc6Oz1rPbtx8f9x4zV5/gc9Mim7of/PZgn/su2+Wz7rTzTkuNnY+AbKd51h66c+pIn4fdtMLnLw153efv3nWjHfNfm3yO7bdzTW/gm3UAAAAgpLhYBwAAAEIq+9pgAqtltEwa7/PK6+3fLX844y6fTyzY7fP+Q6bSp33apkq+efxVPi+fO8Lnqldser9ozkKfYw22MQbQLcEVYIbb6kXLr7PWl6Wf+W2HLzP+2a/6PO5PtiJL5J1lPvfK5zZQZs2u7TvuI4G865iYzxWvl/msu6wtwLW0JG14QJD7f4N8/t7JNmXeWNZ261VkjLVwPXny730uVDv+prL5cc/55/IP7flT7Fz10GesZfNXK8/1ueGlMT5Xz7DPvjtg9Rs3jU+bGLJEzshqn2um2vnl9VGzff57o9XYgQo7v0gkeObpXXyzDgAAAIQUF+sAAABASGVdG0xksE1Zrj/TNly582P3+jwwYpsanbXgKz7rawPiXmvQhbYR0r+Metrn0WNsVY3bPn6xz0urJtp73PVmokMH2pRTbCtK1B1pn9HKCbVtHd6uuRfc4fMp+d/0eewfjvBZ33i/K0NMurJIgc9/v/SXPl/x+nd9HrDB/lyidVaTQDLlvv6Bz9XzbKWJYHtakObZaffmQV/wedvHKnzecWIs7jl/vuh3Pp8S2N/sihJb8eiTx9/n8/5jbXr/2S+P9fnnD1/u86j/tFpmIz9ki5pzrc4unzTX54jad9eLG2yDpDGPBDYLPGC5t/HNOgAAABBSXKwDAAAAIZV1bTC7z7JVLkZMXefzyLydPt+6xqYKh93aFHjyyrjXavjANlL62j983uc7Jz7k8zeGvOjzF8+xVWIq/mqrdrRs2tzp8QOHyulrGyHVnmj//n5ywv2BowqkI6U5Nr/et79N97WU2OZheV0cY7LlBL5nKI9YO5tL3c36yFLBlYYSXnWozlaGGbTTVh4bPKck7rB//fOXfN55jLV37TraVnH55Onv+vybKpvev6bvep+HfP4en79/zBU+j7jNNnCKLos/zwHpLnekbRC4a7JtPvYP5a/5/OheW7nvF3/9lM/j3rM2t1i07dXJegPfrAMAAAAh1eHFuqreo6q1qvph4LEyVZ2tqita/1t6uNcAsg11AySGmgESR91kh860wdwrIr8RkfsCj90qIi86525X1Vtbf31L8oeXfE0ldof+iBJrfXlp3wSfNz490ufKZW+0+1oFe+0O+mH77Y77r19uG2Nc/4mXfR47ZJvPrn9gmnNTh8NG+rlXeqtuAhs1tPSxafGxeR23vrSn4UNbVaZouX1A2VoIPeheyaBzTafEbFo9usPORxLMIqJrrZVl8IqBlv9u+cO/HefzURNP8nn8J1b5/OjYv/pcdaK1xFz5r7Yh2pg7j7U3nmcb+SG07pU0q5uWc+zzWbhmh8/R9bbCXjI3smuutH+rDKrY4/OIXFu96XfbbZPMqpdtNaa4zcNSqMNv1p1zr4rIzkMevlREZrTmGSJyWZLHBaQ16gZIDDUDJI66yQ5dvcG0wjm3pTXXiEhFeweq6jQRmSYiUijF7R0GZINO1Q01A3ica4DEca7JMN1eDcY551TVHeb3p4vIdBGRflrW7nGptrC+2ueKeZ1b+D64kURws5iBY071+cNTbdWXfvkNPu/J7delcSIzHK5uEq2ZHVPtTvcTJq46zJGdl3+0TRU2jLGNxHID0/HdFdzMaevpZT5PPfrDtg6Pszdmq1d8esnnfC5ZbzXmmpoEmSNTzjXdFd1ubQMSyAWL7eGRq6wtc1F/WwEtMs4m008osDa5Gafe7fP3nvq6z33ndXu4SLFknmsSpbl2iRmbfLTPm78a+Lv5Q7tGGvm4rUjmPliatHFsON9WNPvGqJd83ha188gbNaN8HvTBFp/D0vrZ1dVgtqpqpYhI638T2yoRyE7UDZAYagZIHHWTYbp6sT5LRK5rzdeJyJPJGQ6Q0agbIDHUDJA46ibDdNgGo6oPishUESlX1Y0i8kMRuV1EHlbVG0VknYhc1ZODTKbcBpvp2dFoUyOTB9gGSe8cb3fVD7Y18zutqb+tODOksM7npXusbSynMSyTK+gJvVk32yZb/tPw4N/JXV8N5pfHPuLz9475is+BPb66TYttM6OdJ1k9/KDyucBRhdKWemd36++YbdOow9dZm05LU7MgfaTruSb2sRN8bimxbcMKdlhLVs76rT5Ht/bMl5w60doM1l3Q3+cBR23v8Llv7B/nc25D7DBHImzCXDeab6utbDjPrrd+c+IffL5z8Hk+71poG0cW215EXeJOO97nI86y9tBr+y33+fn9VT7vWG4rK5XV2AZjYdHhxbpz7pp2fuucJI8FyBjUDZAYagZIHHWTHdjBFAAAAAipbq8Gk276rbGpybeX2t2/l5zxns8nXGsbQaxed7LPfd5aE/dawU0scgeX+7z7KJvSH1NoGyHNWmjTMhN2b0h47EBbYv3s8zY+z9pGYtL1m/uPzd/lc0t7K3rl2GZMOUX2vlrUduuKiEisfq8dF1gpILevtayMym3/+R/JC+S9Y+znb662qczITvsZgis3Acm08vM21V8+bLfPa7fail/FK211luKaMT0yju1TbIOlO8+51+dL+timLs3OjnmgvtLne2ae7/OoxYGWnWQPEllF8+1v6qJJtnLRsFxrDz65dK3PTw6wa7JEF5LMKYw/byz/jLVZ/nLo330uUWsPfWybbc40OLDykWu0VWLCgm/WAQAAgJDiYh0AAAAIqaxrg9E3rcVlVJ+JPv+szKYBH5803eeZP7E792f+7uy41xr8tk3pbzu6xOfjj17t8zv1tmHNwFdsurSnVgRAFmqyf3PvitmGXv1zOm4naU9EbUWjaJG100QG2QZJWmITlQfGWBtY3Sj7nB+qfIFNf/7/9u48Ts6qzvf499fVnXTSnX2jkw7ZyMIqIKssAoIgIuDGBa+IXkbGEUccHdzuzJ0ZlxkdR2W83tGLwouoyOIGCIqsKsgaAiEkIWQhIUtnTzqdvbvq3D9SnN9T3A7pSqq7nqr6vF8vXny71tN51S/PST2/55xcp59kb+hX3OpIIzN+ivOV9/wg5uldvqHL9I7x/oSXSrfBBpB08jGLY/7BoffGPCzTtztCbst5i+eKLl/R5f4dw2J+eOsRMd97t2/eN/kmXw2ta+Wq3hoiak3GWyWPGuUbDQ1K7NHU2s/bibNFLmCWbKXMHje94L5zT/ONKk9v9NauJYlDzdNzvT3t8N8v8Ncqbhh9gm/WAQAAgJRisg4AAACkVM21wSjnJzgan1kU88DWxOnB6UfH/DfDfHH8T/+Dn1aRpE+8dkHM4+r83Mr5w73V5otPfCDmw+/31WTYEgml0rzYy/jGzcfH/Onh/nntbw0qRoO8DcYO93avtg/65im7vCNGrWf66kZPzrhrn6877fd/HXPTIm+XmThiWcy7g68MU+y4F773v2I+bdanYh72UlEvA/TY/F/PiPkbH/ZT+n874vGYh9f5Z31g3b7bxHoi2e6yKedHkrs7fFOkH758esyZJ3yDpJZvPxHzofLM8Qjlctdab0ceuK64DbnqBnqr2St/XTid/bdRf4p5ZMY3ZPrEsnNjHvOYf1+d3erHueTr5nYlVobJla9Bhm/WAQAAgJRisg4AAACkVO21wSRk231lilH3vBLzr7eeF/ND1/opzrNHLSx4/lfH+ZX/wxNXPa/NJk7ldPgfcdcavyIZKJVDf7Yk5js6fYfpzVf5qbx/HTOrqNdsrvPL8h875Ycxd5zkV/E3eKeMmiz57/59X9L/9Pn/GfMHxn845uWbfMWK723yzcOuHzG/x2MGymHcD3xDvbm3DI/50vdeH/NhH/Njx+2THjmo97vgpQ/FHG7yXrQhj/qqNBO6Vvpj9nj7ZXFNBkDKJeZd0w5dU3DX8Lo9MWeDz8NywdSdumN8NZmV7xwac+sDvtFZeMnniaGrb5vH+GYdAAAASCkm6wAAAEBKMVkHAAAAUqqme9YVvP82u8n7kgY/5LsdhldaYr5/1FkFTx/1fzpi/uhg35G0PedLAIUGf4+C5YC2bz/AQQOFutZtiHnc73yJqgfMdyjc+iHfzfT743xJuZ5I7oQ65CD/eT8s8VqbHvHaGjXXl2u85SMnx3z9GfSsI91yO3b4D4k8+i7vb21/dkzMZ0z25Us3HO09tzrajydPneq7aEvSkDrfsffMMd6b/lCTv252w8YiRw6U37cn/irmp/51Qsyv/tOo7h5eoMH82owPDH6+4L6x9X7tVCZxTdW3JvzG3+9/+ft1ZP3YtHKPX3vy+y2+DOqoBYl6pWcdAAAAgMRkHQAAAEit2m6DSUrsTJXd0h5zXWIZxuVXHlXwlKn9fKmgF/f4MkGN5ksDjWz19prsMYfFbE8W7oYKHLDEZze3zE8Ljvu9/1v8Kfkucddf6S0nXxvjLTHF7hZ6sPqf7u07yw/zXRbfNeWV7h4OVJSCtpREbl7op9sHv+BtLJ1jffnSExd9tuC1zj7Xl4d8/3BfhjV7ndf4b6Z529thP/Xayi7wnbqBvhS2e1vYvFv8GPT038+O+d0Dfb41sXm9PzmRc0q0Eyd21y683duM33hfNvg87tB6byk7tNnbl5Ove/s23yX40S3+OuXEN+sAAABASjFZBwAAAFKKNphuJFdt6TxhasyffNcfCh73UIe3xfz6Vd918YOT/arkEQN91Zddw/xU/773eAQOXOj0dqzc0tdibnncT73/7pQjYv6H0X+KOdkGszt4q8xHlr4n5hdXtMZ8wTRfqeW7Y58oeqzfO/L2mDfOaI55asOGxKOoFFSX3C4/xZ57dXnMttxb2A5be2jBc/7S7i0E7Rf7afx/Suyiffj7Vsf8LyMviXnC3SfG3P++Zw902EDRcrt3x3zI3Utjvn7SlTH/43Rvg7lgwoKYzxrseVTGV0o6qX+iXTPR3pKxwu+e13X5qnyLuvz4siXr87uOnNfSC9u95n45+60xHzHL2527Ovt2BZik/X6zbmbjzexRM5tvZvPM7Lr87cPN7EEzW5T//7D9vRZQC6gZoHjUDVAcaqZ29KQNpkvS50IIR0g6RdK1ZnaEpC9KejiEMFXSw/mfAVAzwIGgboDiUDM1Yr9tMCGENklt+dxhZgskjZN0iaSz8g+bKemPkr7QK6PsC3W+2L1N9FP9Sz7iVwhfNvjFgqec85drY86u8dMp61oH9cIAUSnSUjPJlpjM8rUxD3nAVyU6cft1MVu9n1IMXf7v+DGP+l8Th67z04D3vf+YmD8/5uEejenqRVfE/MGxz3lu9o1eVme95m5snxhzZ/AavXbokm5ff3POWwws2+1DkFJpqZukzFBvXdx5yrRuHzPgKV+9KLmSWNGSK5ItfrXgrkPv8s/+os3TY/6bDwyO+QfTfh7zbef+MOb/PuCvYj7svgMfHtInjTXzhgHG2LXGj0FTb/a2lN2tQ2N+aIavaPTbQ06Jue4Ib4OZ/7afxZxc8eXlPTsL3vry56+Jedcir+OMHyJU1+nHmv6JxZsOm+uv1bV0mdKgqAtMzWyipOMkPS1pTP6DIklrJI3Zx9OAmkXNAMWjboDiUDPVrceTdTNrlvQrSZ8JIWxN3hdCCJK6XYzSzK4xs1lmNqtTu7t7CFCVqBmgeNQNUBxqpvr1aDUYM2vQ3g/CrSGEX+dvXmtmLSGENjNrkbSuu+eGEG6UdKMkDbbh6VhdvhuZ4X4qZv3JI2K+7e3fj/k3HUcWPmdhU8wDjt0c8/lD58b8l7aLYx6x2dsSUN3SVjPZDb7Cyoif+Gd15M/3/1dA6PLWl0zLIf46z46P+YIRn+jROMb82NvFvnWWr1jRebGvanFk/1Uxz9o6MebdWR/rvtpgPrXc662pjXqrNGmom/rJE2NeecnYmDNn+3ny9nZfUWL60pH+5INpg3kT2YXeJjYykdu3e6vApz52ecw3TvWWmOvf+kDMd731LH/ROQtjTNY4KksaaqZY2Vf87+/6xB54ox/xXNfk86u2/+Gr7WVP9XbNHcH/jv+fyy8teI8x3/MV0Bqe9BX6kqsxVZKerAZjkm6StCCE8J3EXfdIuiqfr5J0d+mHB1QeagYoHnUDFIeaqR09+Wb9NElXSpprZq/vefxlSd+QdKeZXS1puaTLemeIQMWhZoDiUTdAcaiZGtGT1WAel2T7uPsdpR1O37KGfjF3HukL4m9/t1953FrvVwV/5O4LC56f8bMxhRsh1flGSFuWDI959IveHpN4KqpMKmsmcVV+8pR3sae/u1b4xi0jfpzMxQ9p6vOjY77hUP9jmf/2m2I+bfwfi3rNFf/lm5gNe8Y3bWJhmPQrZ91Yf998q+38lpjnXP9fMbfn/Fhw2jO+wopsX0PufUNufSrmzQ2+ksZN170t5suG+kZI3/ybd8V8+Ge9lSe7taDNGRUilceaEgnTJsbcfpRv0pdcAWZ5l//qr941peD54+Z7u1i2QltfkopaDQYAAABA32GyDgAAAKRUj1aDqVaZVj/duewdfuXwkyf/n5g7EtdHD37DAhSTP+ZX05/atCjmr712UcwjXvDTNLnt3h4DAEiHusneBtk+tftFMdZmvXlxwue2xZxdvcYflGyJCb2/uEayfad/u4/vz2t947N/HeOb+f3u3O/F/LkBidUzaINBGiTqZ8PxvuHXx0/1ZWKSG9/duvm0mMf9wVc8k6Tcpi29McKy4Zt1AAAAIKWYrAMAAAApVdNtMLsm+WYWmSP9NGBznZ9abAh+FfKnr/9FwfPPG7gs5n9bd3bMa38yMeYRt82OObU7QgFADds1fkjMg6d0f/p8eOKrrZev8xbKMU95HvbYazF3rVpdwhF2r+PiY2Pud4234/z28DsSj2oUUAkyo0fF3DHZb3/vYF9tb0idr+J3+ACvsbkbfXUxSQqd1bUpHt+sAwAAACnFZB0AAABIqZpug9nW6qdTLpw0u9vH9LeGmC9pXlFw34I9A2L+w+9PiHnKX9bHnN29+6DHCVSjsM1XR2p60jdo+dy0U2L+dstTAnpb4zO+mlf2qCP9jhM9Dqvzv+9vvdhXDFv2Lm+nXN05LOYdOT++3LHk+Jhzzwz1993ozZGjbvbNi7pOP6ZgfOvf4q0sXV4qGnW2twHcPO3WmJvrmmNu6/KVa76+2lcqU5EbogG9brB/bvcM963sJtT7VHXW7kzMX7v3fTFP3TGvlwdXXnyzDgAAAKQUk3UAAAAgpWq6DSbrZyk1ul/3m0Lk5BtNLO+ygvs+cuenYp58lz8/LF9ZohEC1Su30ze3GPX8jpj/cPrhMdMGg76Q3dIe8/i7vLXkyP6fjPmTV/425r8asjTmUxo3J14pmV1yNYvHpvuGRZu6/LT/w5dNj3nyIN9wT5Iual4Vc6P5CmVnDFwc86SGZnXnDzt8WY1VX5kac/+tc7p9PFA2dYnvjzPeItbffKq6onNEzC1P+GPCrupuOeabdQAAACClmKwDAAAAKVXTbTCDl/mi+T+YdVbM644ZHPOwej89f9MjvvGRJE273U+dhgVLYs6xAgywfzm/2r9hkbceDHzM2wQuGnlJzJk6b0lbOOfQbl9y6oIOf3nqEAega+mymCfc7H//37zRV1L5/iGJlshe+MprtVoLfn5cb+n2cf/Zg9dq3OB59P1PxMwmfUgbS7Sy1O3wVV+eSvxV/n9fOzPmQS+uizmb9eNJNeKbdQAAACClmKwDAAAAKVXTbTCNc32To4k/Gx/zQ9NPjTnb3x8//T4/5SJJuWX+/MApd+CAZddvjLnlQW9Da187LuZQ560HM/68rPvX2eSrcYQ9e7p9DNBT2bX+d/7IG9e9ySMBHKyw2VuLWx4fG/PHdl0b85DEQkn9VyQ2swzeJlmN+GYdAAAASCkm6wAAAEBK1XQbTPIUZ0Mij35oH4/v7QEBtSqxMkx2oW/00pzISV29PiAAQF/KbvXNJZt++XTMk37Z/eNraUUjvlkHAAAAUmq/k3UzazSzZ8xsjpnNM7N/yd8+ycyeNrPFZnaHmfXr/eEClYG6AYpDzQDFoWZqR0++Wd8t6ZwQwlskHSvpAjM7RdI3JX03hHCYpM2Sru69YQIVh7oBikPNAMWhZmrEfifrYa9t+R8b8v8FSedIer2TaKakS3tlhEAFom6A4lAzQHGomdrRo551M8uY2QuS1kl6UNISSVtCCK9f57VS0rh9PPcaM5tlZrM6xVrkqB0HWjfUDGoVxxqgONRMbejRZD2EkA0hHCupVdJJkmb09A1CCDeGEE4IIZzQoP77fwJQJQ60bqgZ1CqONUBxqJnaUNRqMCGELZIelXSqpKFm9vrSj62SVpV4bEBVoG6A4lAzQHGomerWk9VgRpnZ0HweIOk8SQu090PxgfzDrpJ0d28NEqg01A1QHGoGKA41Uzt6silSi6SZZpbR3sn9nSGEe81svqTbzexrkp6XdNP+XmjaWyfrwVm/OKgB4+CYWbmHUCtKUjfUTPlRM32GY00VoW76BDVTRd6sZiyEvtsDyszWS9ouaUOfvWk6jFR6fucJIYRR5R4EeiZfM8uVrs9QX0jT70vNVBiONalA3VQQjjWpsM+a6dPJuiSZ2awQwgl9+qZlVou/M0qr1j5Dtfb7ovRq8TNUi78zSqvWPkOV8vsWdYEpAAAAgL7DZB0AAABIqXJM1m8sw3uWWy3+ziitWvsM1drvi9Krxc9QLf7OKK1a+wxVxO/b5z3rAAAAAHqGNhgAAAAgpfp0sm5mF5jZQjNbbGZf7Mv37gtmNt7MHjWz+WY2z8yuy98+3MweNLNF+f8PK/dYURmqvWYk6galV+11Q82g1Kq9ZqTKrps+a4PJL9r/ivbusLVS0rOSrgghzO+TAfQBM2uR1BJCmG1mgyQ9J+lSSR+VtCmE8I18EQwLIXyhjENFBaiFmpGoG5RWLdQNNYNSqoWakSq7bvrym/WTJC0OISwNIeyRdLukS/rw/XtdCKEthDA7nzu0d9vfcdr7e87MP2ym9n44gP2p+pqRqBuUXNXXDTWDEqv6mpEqu276crI+TtKKxM8r87dVJTObKOk4SU9LGhNCaMvftUbSmDINC5WlpmpGom5QEjVVN9QMSqCmakaqvLrhAtNeYGbNkn4l6TMhhK3J+8LeviOW4AHegLoBikPNAMWrxLrpy8n6KknjEz+35m+rKmbWoL0fgltDCL/O37w23yv1es/UunKNDxWlJmpGom5QUjVRN9QMSqgmakaq3Lrpy8n6s5KmmtkkM+tTPBjNAAAfoUlEQVQn6XJJ9/Th+/c6MzNJN0laEEL4TuKueyRdlc9XSbq7r8eGilT1NSNRNyi5qq8bagYlVvU1I1V23fTppkhmdqGkGyRlJN0cQvh6n715HzCz0yU9JmmupFz+5i9rb0/UnZIOlbRc0mUhhE1lGSQqSrXXjETdoPSqvW6oGZRatdeMVNl1ww6mAAAAQEpxgSkAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWS9h8zs78xsnpm9ZGa3mVljuccEpJ2ZZczseTO7t9xjAdLOzBrN7Bkzm5M/3vxLuccEVIJqP9YwWe8BMxsn6dOSTgghHKW965BeXt5RARXhOkkLyj0IoELslnROCOEtko6VdIGZnVLmMQGVoKqPNUzWe65e0gAzq5c0UNLqMo8HSDUza5X0bkk/LvdYgEoQ9tqW/7Eh/x+boQBvohaONUzWeyCEsErSf0h6TVKbpPYQwgPlHRWQejdI+rx8pzgA+5E/nf+CpHWSHgwhPF3uMQEpV/XHGibrPWBmwyRdImmSpLGSmszsw+UdFZBeZnaRpHUhhOfKPRagkoQQsiGEYyW1SjrJzI4q95iAtKqVYw2T9Z45V9KrIYT1IYROSb+W9LYyjwlIs9MkXWxmyyTdLukcM/tZeYcEVI4QwhZJj0q6oNxjAVKsJo41TNZ75jVJp5jZQDMzSe9QFV/IABysEMKXQgitIYSJ2nsx9iMhBM5GAW/CzEaZ2dB8HiDpPEkvl3dUQHrVyrGmvtwDqAQhhKfN7JeSZkvqkvS8pBvLOyoAQJVpkTTTzDLa+2XanSGEqlyKDkDPWQhcaA4AAACkEW0wAAAAQEoxWQcAAABSisk6AAAAkFJM1gEAAICUYrIOAAAApBSTdQAAACClmKwDAAAAKcVkHQAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWAQAAgJQ6qMm6mV1gZgvNbLGZfbFUgwKqGXUDFIeaAYpH3VQPCyEc2BPNMpJekXSepJWSnpV0RQhhfumGB1QX6gYoDjUDFI+6qS71B/HckyQtDiEslSQzu13SJZL2+UHoZ/1Do5oO4i1xsDq0eUMIYVS5x1HDiqobaqb8qJmy41hTgaibsuNYU2HerGYOZrI+TtKKxM8rJZ38xgeZ2TWSrpGkRg3UyfaOg3hLHKyHwi+Xl3sMNW6/dUPNpAs1U3YcayoQdVN2HGsqzJvVTK9fYBpCuDGEcEII4YQG9e/ttwMqHjUDFI+6AYpDzVSOg5msr5I0PvFza/42APtG3QDFoWaA4lE3VeRgJuvPSppqZpPMrJ+kyyXdU5phAVWLugGKQ80AxaNuqsgB96yHELrM7FOS/iApI+nmEMK8ko0MqELUDVAcagYoHnVTXQ7mAlOFEH4n6XclGgtQE6gboDjUDFA86qZ6sIMpAAAAkFJM1gEAAICUYrIOAAAApBSTdQAAACClmKwDAAAAKXVQq8EAQDHqJ02Iec15Y2MesCkX85C/+I7LXW1r+mZgAACkFN+sAwAAACnFZB0AAABIKdpgeoHV+x+rHTk15p3jmmNumrMq5q5Vq/tmYECZdRwzJubTrpkV84ubxsXcLm+Vaf7t5pjD7t29PDoAANKHb9YBAACAlGKyDgAAAKRUTbTB1A0cGHPo7ErkPb3zfsOGxbz48qExn3H23Jjn/e+jYh5yK20wqFJ1mYIf2yf5XznjGzfFfMmU2TF//My/ivnwp0bG3LXSW8eAWlI3aJD/MLk1xmxz/5jr123125f4ikrKZXt1bAB6H9+sAwAAACnFZB0AAABIqaptg0meNsweMyXm+rXtfvviV3vlvbOTDok5TNgZ83tGvBDzn45PtsH0yjCA8jCLMTN8aMFdgy7wTY4uava2sFGZEHP/lh0x54YnTv+vLOUggRR7Q/vYzjNmxNz6D4ti/uq4e2M+56HPxHz45zbGnN3ixzwg1RLHDst4DVi/fp77e+uXgm+ml926rfC1qqz9i2/WAQAAgJRisg4AAACkVNW2wSz/9NExH3vhgpjn33F4zGO+1zttMJ2D/ZRNc9P2mJfuHh3zoFf5dxKqlCU+26NHFNx14dg5MU9IbB72Sqe3wXS+1hRz7qVne2GAQAolWl8yUyYU3DX9n1+K+estD8W8w8tGtsufn233lWGASpEZMjjmzqMnx7zyHQNinvT2ZTFv2OHHilF/66vwSVL2Ne+bDF1dqnTMGAEAAICUYrIOAAAApFTlt8EkV54YMTzmo961MOZrWx6O+X+MmtHt47MbfYOWAxpG4grlNad6/vz038Y8b8e4mJvaqutKZeB1df0aYl57RmEbzGGNvhpMJlG7/776/JiHz/Xbq+2KfqBA8vg1bEjML/9j4SpK/zzqjpiH1DXG/PgOf06/DYkVZEKiPwZIsdzpx8b8ylV+7PjkqY/EfESjb4i3JeutL/806+KYR3WuL3hdS7RZJlszLeM5ZH01md7aJLNU9vvNupndbGbrzOylxG3DzexBM1uU//+wN3sNoNZQN0BxqBmgeNRNbehJG8wtki54w21flPRwCGGqpIfzPwNwt4i6AYpxi6gZoFi3iLqpevttgwkh/NnMJr7h5ksknZXPMyX9UdIXSjiunkuc3shN8M2Izhj2eMxN1hlz/Q4/7Zjb5iu1HPQwEov275zgp1MuavIVZ17YfmjMmT2cpqxmqa+b3pTYzGLrlMK7Rmc6un3KS+u9dkeuTPfpSPSOWqyZ+kPGxLzsY776xQ2n3lLwuCMavB2sTl5fazq9DaZ+p1CDUl03iTavugG+osuGy98Sc/ZSb0H+WqJteFNXc8x/N+uymJse89snv5DYQG/9hoK33nTZcTFvme637xnhtTRgpU+Bx8zyeWL/DV5MdcvafKzJduk+bjU70AtMx4QQXv8N1kga82YPBiCJugGKRc0AxaNuqsxBrwYTQgiS9vlPDDO7xsxmmdmsTu0+2LcDqsKb1Q01A/z/ONYAxeNYUx0OdDWYtWbWEkJoM7MWSev29cAQwo2SbpSkwTa85OcNkitPrD/eF9Qf329jzHsS/ybJJM6wh92l+3AmrzDODPDTLA2JNp0FW/xUf9OSLTGz3kXN6FHd9HbN9Lo6/8x3DS78dDcmWtKStq1K1O4SXzGm8reywEFKzbGmVOpbfVWw1Zf65kcfueLBmM8ZULg62YouX7VibL0ft6b291rpHJTaXxl9LxXHmrqBA2Ne/I/HxHz6Wb7J198d4p/7J3Z43+R3H/U2/Mm/9ONGvzm+yWV28+aYvULy793lv86eUX4cuuLEp2MefYZvHnb/OUfG3LbVj0db23xM9e3TYm5a5S0+TWv89et37v+PsV+H/z51f3p+v4+XDvyb9XskXZXPV0m6+wBfB6gl1A1QHGoGKB51U2V6snTjbZKelDTdzFaa2dWSviHpPDNbJOnc/M8A8qgboDjUDFA86qY29GQ1mCv2cdc7SjyWA5NYeWJbq5+WGFrnVwmv7vIlRut6aaGJ0NoS86Bmv5J4zh6/AvqVxf6Y6Yvm9M5AkAqpr5tSq/M6rBvipxCPnLGi4GGjMsklK3zzsIbNiVWd2taWfnxIvWqumfoWb4Fce76vCnbkh+bH/Jnhnq9vO6Pg+Wt2eU19duwDMSc3iKnbY0LtSXPdWKIN5qvvvT3mi5v87/ifdfgqSP/+6EUxT7nd273qHn8h5p62DQ9/KrEB326/vvYXm0+L+chTlsb85Yn3xTy5flvMHTk/Nm3K+YZkv9/qK9rM3jw+5vbd/ph9ee013yxw2p/2+3BJJbjAFAAAAEDvYLIOAAAApNSBrgZTUR5pPzzmxs1vvGa4NDpm+OYUU0csjvnxbb4af/MSX7kmdLLxC6pHZvjQmNdc6Kf575z0rYLHtdZ768uCPV6L9dv9FH7o480mgN6Q3PBo/fmTYh50+eqYv9V6b8zf3vjWmP8888SC19o20Wtl3oXzYp67ozXmAeuoG6RMnf+9fnzjypgH1nn71gMbjoh55LP+/XHDS76h5IGsmNe1dFnMTYl82O+9NWfTO4+K+aqL/sofM8nbdN420ltlTm3yud25g7wOzx7kLWwjEi3YIzO+6os3ikr/PPy8mJepZ/hmHQAAAEgpJusAAABAStVEG8yz6/20fOOm3tmCaPsYP8kxYaBvaLEj1y9mS7x13aBBMee2+2kT5dgiCZUnN95XuzjnE0/FPDKT6e7hkqRbNvlV+U2r/RR+6GQrJFSmzDBfeWzDO32Vi2FX+qpIt0z1VTHu6PDT8Lf9xBfvGP/zlwted9EXvJ2yqa7IzfwSKzVlmr39QGNGxhhW+2n/3I7E8YiWNByMLv+7/EcbT4/54yMej/lTYx/x29/pbV39ts2IeciTXj9dq9v89Q/g85n8fA+465mYZzyYqI3DfM54/zFnxvzTk/13CPXdv3fjCF/xbMqoDTH3q/M/i+fneVvcNPkY3gzfrAMAAAApxWQdAAAASKmaaIM5buSqmJ89ZHTMA4f6Ci5hl59aTK5GYcnT+G84pW8Z/7fOlmP8qt93D/ENjzZmm2P++TF+hf+Ot/sKNU1PLYk5u9FbaDgFiVRLnF7PNvlKR6cNWhRzgwprpjN4m9e8Lb5JWGN7ov2LVjBUEKv3w+iW871dZfzHvQ5+Mul3Mf9m+4SY/+u374p5yvdnx5zdtavgPUYevS7m5KoaT22bEnMwX3nDGrz9MjPCW3PaT5sY85r3+zFv4o98NaeGZxbGnNu+XcCBym5uj/mlj3ptXPVNbxH7v4ffGvPzZ/4w5huOOjbmW391TsyTb/SVkbIbfL50sCvsFXzW5yyIcWhi/8qhP93/69Q1+qZIuQG+KeauxMo4M3a+5I/p4fj4Zh0AAABIKSbrAAAAQErVRBvMf7T8OeYHvuSnH/7hoktizvzJW2IaN3n7ybbxfupix0RvdZGkgSP8quLvHnNbzKc1Jh+3OaZzzvl+zHefPDHmb838QMyH/ucLMRdclQ+kTGaKn85/9UI/3ffOAX5qcmBdv4Ln3LPdN6TY+Au/8v+QP/nqFzTBoJJkTz065tZPeevLzRPvi3ll1j/VX51zYcyHfds3WXlj60tSY72vJNFofnxqzngryx4/hGnP2cfEvPxCP8x/4fx7Yn5fs4/1pM6/jXlah69UoVl+vASKlmhpzL3kn7eh10+N+UNfuTrmO47/ccyfHf5izGde5ceHvz/pgzEP+KGv2jLgfp87SeXbeDKXrOPd+1i56QBanPlmHQAAAEgpJusAAABASjFZBwAAAFKq8nvWk8ssJtqAsvJe82TfbLKfdvrxP4p56THDY96V2HV0aMaX8xla5ztTSdJA8z7C8fX+754G86V7ssEX5mmu6x/zqPqtMecK23qB1Mokljtdd+aYmP/+vXf7YxJLyCWXapSkm1efEfOQV/3ajmz7VgGplliqtO7oaTG3f6kj5hta7415gPnf960ZP1Z89uiHY/63r1wU86BF3seb9adKkj457rcxD6/zw/a1w5+M+fSrX4l51Ud8ucYZ/VfHPLXBj2HfXO+7MY58xN+wboUvDcn1IyiZRP96WLg05pbvHBHze674TMz/6x2/ifmyZv9MzjxyZsy3fOVtMd8/2bMkjbs9cT3I2nUqixIuv8036wAAAEBKMVkHAAAAUqri22ByiZ1HJ/7Gl0n8266/jnnyua/GfM5I351tSMaXRkyeKnxL45aYZ+32peae3ZlY0krSiQNeTfzk7S7tOT/VeO1rvkPd7Pv8dM/Qxf74iS/7uJO/D5A2ltiRbedob3dJLgMn7buv68UXvIZmrPTPfZZdS5Fydf18l97VZ3ubyeen3BHzii7fCfRHG31Jx4mNG2K+pNmXQxx33k9iXppoK2tItFhK0kXNftzqb16DzfWNied4i2dy5+wvLXp/zG1zDom55S9ec6NmLY+5YBdtoBckl1Wsn+3tW9O3+lKMNyz0Ja1vvWRFzA8e7i1hXxz5RMyDri5c+vQPK97u9z3k92W3VmbLJd+sAwAAACnFZB0AAABIqYpvgynYIWvOgpgnbfXdFdvn+6mVnw+f6I/3s5raPcxP6e8a7S0qjWv93zPJnU0l6d+P8cd99dxfxnxIvbfRPPHsjJgPv9mvgO5qW+PjEJBiidVd9kxr8Xy0t5ENSqy4tCv4KfwbNr614KXGPpaooVVrBFSKkFjZoXGj5x8t9xWOli8ZHfPw5331mD/29xr6z6POi3nCpPUxv3/c8zGf3+RtL5I0KuOrtby4x495n5h3Rcxbnx8R84D1/n5Dlno9Tn3FV8XILl4WcxdtaCiTgp3aX/SdSset87awDZsnx3zWlZfG/JMZP4s5ueOpJN10gdfljCXj/I45VdoGY2bjzexRM5tvZvPM7Lr87cPN7EEzW5T//7D9vRZQC6gZoHjUDVAcaqZ29KQNpkvS50IIR0g6RdK1ZnaEpC9KejiEMFXSw/mfAVAzwIGgboDiUDM1Yr9tMCGENklt+dxhZgskjZN0iaSz8g+bKemPkr7QK6M8AF2v+tXtA5M5+aDEJheZ5qaYbfCgmHOJzVpy27YVvEfdR0+JefHpfspmez8/ZTlgjf97KNn6gupVqTWzL3XNvrJE21t9JYrPHXt3dw/XjsQp9VuePL3gvsNfSJyGZyMkJKS9bkKnt5OM/EtbzBtzvsLKtEW+iV7dgmX+5Jw3O449dGzM26Z728wPjny3P/5Dhe/94cHe4vnNVe+JOXObb+Y3+R5fZSbs8dU2wm5fYYxml+qS9po5GF1r1sY84vdeexvNNyR78EuHxfzhwb5ijCSdcbS3kq04xJ/Tb05Jh9lniupZN7OJko6T9LSkMfkPiiStkTRmH8+5RtI1ktRYOFUGqh41AxSPugGKQ81Utx6vBmNmzZJ+JekzIYSCr8TC3itvut1XNYRwYwjhhBDCCQ3q391DgKpEzQDFo26A4lAz1a9H36ybWYP2fhBuDSH8On/zWjNrCSG0mVmLpHX7foWUSpyuL1gov4eL5m+b4FfcT+rvv/6c7b76TP8t3dYIqlw11UzuSN/IaOcJfuX+x4Ys6/bxHcHr4pA/veH7gA2JDVcCtYFCqa6bxPGia+mymIckcsHD9/U6C3wDsQELvRVz0pzWmDdc1qx9ee7ZqTFPn70x5mxHxz6fg+qV6popkewG/5wPf7E95ue2TYz5vw1aVvCc1sTmlq/VmypdT1aDMUk3SVoQQvhO4q57JF2Vz1dJ6r6BFagx1AxQPOoGKA41Uzt68s36aZKulDTXzF7I3/ZlSd+QdKeZXS1puaTLemeIQMWhZoDiUTdAcaiZGtGT1WAel7SvcwjvKO1wUs4K/xh2jeuMeVzD5pi/9do7Yx7z8m6htlRbzbS9zU/Jf+yoh7t9zI6c18JjO6bEPOyhJQWPy25pF9CdaqubnkiuQrb1OF9V5iNDby143PZEx9jQl/2PKCxf1XuDQ+rVTM0k5l6hwVvHmjK1M7/q8QWmAAAAAPoWk3UAAAAgpYpaZ73WZQYNKvj51CMWxzy9wU/vb9vgpzbHr/Xb2ZAClWjbBF/X4gNDZifu6RfT3E5fo/frD1wa8/Rd83p1bEBFGz0ixtXv842MhtQVdjZ8YdX5MQ9a4RvE5Hbu7MXBASWUbCO2xPfEIbFuUnKFsMTjM0MGx9w+0Y81JzYvjbnBvD1Gknbk/PjU/cKVlYVv1gEAAICUYrIOAAAApBRtMPtR19gY85oPHVlw3zWjfxpz8gTMgGUNMWdfLlwNA6hGs3dOjHnaT3xzltz2Hd08GoAkhUbfNfKwlvUxN1jh92hP/O6YmCcv8BVguthYDGmWaGWpG+jtK3WjvP0rt943PMpt397t49dccUTM933pWzGPzAzw577hre9+8S0xT2/z41ClVgzfrAMAAAApxWQdAAAASCnaYPanzv8909VUeIV+o3W+8dGSJEuej8mxBgwqjx3nLV8Dx3tby/DEP+/bc756xbxt42LObNoWc1d448lJAK/LDfSWyWvG/znmgdav4HFNbX7yPnRsE1AJLJNoEJ4yPsbVX/Wbd87xY0dTYo+vzUf6sePKt3tttNT7Jn3tOV8N6X0v/7eC957yE68ZW/hqzLTBAAAAACgpJusAAABAStEGU4Rg+77vP9afGXPzyko90QLslVm/JeYdHWNi3pVYfeLpXWNjfvjpo2KevnauvxCrVQAF6se3xvza232jvbcPaIv5xT0NBc9pXuUbIYVt2wVUgpD1NuC6Nl/1JRtGxnzJe56MeUjG21qOGrAi5qP7rYv51o4JMf/Tbz8Y87g/FrZcNj33sr/fjspflYxv1gEAAICUYrIOAAAApBRtMPuTWNR/94jCU/oD63bH/MJmP7XZfysrwKCyZdf6Bi3jf+FX65+/8PMxNyQWpZgy208z5qrglCPQW7pahsW8+3hvaWk0Xznj31ZeWPCcxsSmLrnduwVUhEQbZHbjppiHzJwc8y/OPjnmfof457yu7sSYd272zY+GPu8tYtMe9eNUWJ5YSkZSdudOVRO+WQcAAABSisk6AAAAkFK0wexH6PKr8IfNL7zvo/f9dczNy/wU5rhX/XQPW8KgEoVO3/Co8d5nYm69txyjAapHNrER0tgRfqzYlPNjzbyHpxU8Z/L65TF3scISKlFig8iBv3k65hkvT425c0STPz7Rgly/2Tfmy72UWOWl1GNMMb5ZBwAAAFKKyToAAACQUrTB7EdIXHk/9KdPFtw39KfdP4fWFwBAdxo2+YoXrz3XEvNFOz4e86G/6yh4Tm7DRgHVKLtgUcz7+vaYOVUPvlk3s0Yze8bM5pjZPDP7l/ztk8zsaTNbbGZ3mFm/3h8uUBmoG6A41AxQHGqmdvSkDWa3pHNCCG+RdKykC8zsFEnflPTdEMJhkjZLurr3hglUHOoGKA41AxSHmqkR+22DCSEESa9vf9KQ/y9IOkfSh/K3z5T0z5J+UPohApWHugGKUys1k3vRV7OY9GIPn9NLY0Flq5WaQQ8vMDWzjJm9IGmdpAclLZG0JYTw+lpTKyWN28dzrzGzWWY2q1PsvIbacaB1Q82gVnGsAYpDzdSGHk3WQwjZEMKxklolnSRpRk/fIIRwYwjhhBDCCQ3qf4DDBCrPgdYNNYNaxbEGKA41UxuKWroxhLBF0qOSTpU01Mxeb6NplbSqxGMDqgJ1AxSHmgGKQ81Ut56sBjPKzIbm8wBJ50laoL0fig/kH3aVpLt7a5BApaFugOJQM0BxqJna0ZN11lskzTSzjPZO7u8MIdxrZvMl3W5mX5P0vKSbenGcQKWhboDiUDNAcaiZGmF7LybuozczWy9pu6QNffam6TBS6fmdJ4QQRpV7EOiZfM0sV7o+Q30hTb8vNVNhONakAnVTQTjWpMI+a6ZPJ+uSZGazQggn9Ombllkt/s4orVr7DNXa74vSq8XPUC3+ziitWvsMVcrvW9QFpgAAAAD6DpN1AAAAIKXKMVm/sQzvWW61+DujtGrtM1Rrvy9KrxY/Q7X4O6O0au0zVBG/b5/3rAMAAADoGdpgAAAAgJRisg4AAACkVJ9O1s3sAjNbaGaLzeyLffnefcHMxpvZo2Y238zmmdl1+duHm9mDZrYo//9h5R4rKkO114xE3aD0qr1uqBmUWrXXjFTZddNnPev5HbZe0d7tcFdKelbSFSGE+X0ygD5gZi2SWkIIs81skKTnJF0q6aOSNoUQvpEvgmEhhC+UcaioALVQMxJ1g9KqhbqhZlBKtVAzUmXXTV9+s36SpMUhhKUhhD2Sbpd0SR++f68LIbSFEGbnc4ekBZLGae/vOTP/sJna++EA9qfqa0aiblByVV831AxKrOprRqrsuunLyfo4SSsSP6/M31aVzGyipOMkPS1pTAihLX/XGkljyjQsVJaaqhmJukFJ1FTdUDMogZqqGany6oYLTHuBmTVL+pWkz4QQtibvC3v7jlgvE3gD6gYoDjUDFK8S66YvJ+urJI1P/Nyav62qmFmD9n4Ibg0h/Dp/89p8r9TrPVPryjU+VJSaqBmJukFJ1UTdUDMooZqoGaly66YvJ+vPSppqZpPMrJ+kyyXd04fv3+vMzCTdJGlBCOE7ibvukXRVPl8l6e6+HhsqUtXXjETdoOSqvm6oGZRY1deMVNl106c7mJrZhZJukJSRdHMI4et99uZ9wMxOl/SYpLmScvmbv6y9PVF3SjpU0nJJl4UQNpVlkKgo1V4zEnWD0qv2uqFmUGrVXjNSZddNn07WAQAAAPQcF5gCAAAAKcVkHQAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWi2BmGTN73szuLfdYgLQzs5vNbJ2ZvVTusQCVhGMN0HNmtszM5prZC2Y2q9zj6Q1M1otznaQF5R4EUCFukXRBuQcBVCCONUBxzg4hHBtCOKHcA+kNTNZ7yMxaJb1b0o/LPRagEoQQ/iwpVRtLAGnHsQbAGzFZ77kbJH1evusVAAClxrEGKE6Q9ICZPWdm15R7ML2ByXoPmNlFktaFEJ4r91gAANWJYw1wQE4PIRwv6V2SrjWzM8s9oFJjst4zp0m62MyWSbpd0jlm9rPyDgkAUGU41gBFCiGsyv9/naTfSDqpvCMqPQshlHsMFcXMzpL09yGEi8o9FiDtzGyipHtDCEeVeShAReFYA+yfmTVJqgshdOTzg5K+EkK4v8xDKym+WQfQK8zsNklPSppuZivN7OpyjwkAUFXGSHrczOZIekbSfdU2UZf4Zh0AAABILb5ZBwAAAFKKyToAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWQdAAAASKn/B34+cVdEnUvhAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(train_dataloader))\n",
+    "print(batch_samples.size(),labels.size())\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uSoWbdAenhgnaWlDwFd3x6IGKR20+c4HW2Y9HfsnmSev0Ohdq1QOZeE2hfpGuc56ZFTtsHurTeU5DSD8zTj/gvO/rddeXvvJlac3s/pweXHb+vA9tHpOmtfvLuuk2j31Gx2rieDAmP1knIiIiIvIoTtaJiIiIiDwqJdpgXEHnv092NA+xOaeur98xVr48XdIPfkaXXGpn6hJk4yRdWrnoFG0HeGHTcTYPfUkPvBiy7GObA7t0BwCigZQ2utTm5iklNh+cpK1c3//mEzZfllsV8fgcn94vDTnoytrmUTbnb9V6DW7eHv2AiQbQoCWrbfat0wNUbhjzfZvTWvXv/hFr9D09PKgHeqGzM+J5TUAfM2S503721kibz/jmj2x+5vO/s/m/L7rf5h8d+LrN5b/V606oWdtDifoiot23UNsjp+Rpa1aW6N/rX1qj78nSN/R9H6zT3ZT6ate3TrZ53pffs/mXJe/avKhRD216dfFMm0e9sdxmtw4HGj9ZJyIiIiLyKE7WiYiIiIg8ipN1IiIiIiKPSrmeddeMAj0x7oHxE23O6+rOPWj/rJ4Y9/E1ugXQN056y+YpWdqTOMyvp1+NSdMttG4oftPmKzq+bXPBxkJ9MfasUz/zTdPvTlRfVGBz2Tn63YnrS5+xeWianrh4ZlaDzXm+rvvSj+ZzhStsfvbME2wOfXSiju/t1SDyGuNstxuq1OtL1sfO39nONonBlpboX8Tpmw1V6daoObv1tN9dzomn5Wm6BV7nRL3WuFuvEsWKCTob69bpdrsPP3emzYvyzrB55Bt69+yV+h2OYB+3EzWnT7V56IW6xemPhuoc69XW4Tb/9i09qXTK07p9dzCOfeoufrJORERERORRnKwTEREREXlUSrTB5O3RbRm3HS62ecHod2xefPpJNstJx9tsVm+wOa1ct+ICgOqrdBu70vO1PeDe8udtrurU17t51ZU2d+zV9oAHL/6zzTMzdeknc5Bzgmk6lyypf/mHDrV5y1d1Gf3r85bafM1gPfFtbHp3DWNZ3dwO/KmhzOaPDusWjecXrLf5nOy9Nt9+ymKbf/jdL9g8rlZPSQ1u03YDhHi2KXmD6ezoMseSf5Ru3dg8Sq9zx2Vo60t1wGlF26PbAUe0KxDFitvmdVi3BJ3wYL3ex+d8TlyrtwcPaAtlrzinxaeNKYv40car9Tp0+5jXbC7yaw3cvl1PEB77N62f0Hadz3kFP1knIiIiIvIoTtaJiIiIiDwqJdpgBq/RZZYP1+ry+dYReoLp/5myxOabfnSNzcUvzLL54CRdcgGAiy/Uk7Bucr5hnOUszfxkyxU2Z74zyOb2qbos2mn0/4YnD+uJX7JO7++v32MzFy+pXxRq60vRZP0W/7UF2voyKk1bXzqNvhP/0a5tWte/rSfS+fZlRLzEoB36+UDWAV12fOXUz9j83XNfsvnGgq02f/t4rbEnJ56vz1Ol3/Q3bIOhZOfTWms8WXeAKTtB28dK/Nk2v9+muzkVbtBrUzxPY6QU4fx9HNy49Sh37DtzOPIU3tOn64mpZ2Y5u7s4862du4tsnrJcxxfsp7a1vujxk3URKRORZSKyQUTWi8hN4duLROQVEdka/mdhT89FlApYM0TRY90QRYc1kzp60wYTAPBDY8xxAE4F8G0ROQ7ArQCWGmMqACwN/5mIWDNEx4J1QxQd1kyK6LENxhizB8CecG4SkY0ASgFcBuCs8N0WAXgdwC39Mso+Cm2rsrl8if4H5ncHabvLn097yOZnZt9h88/LLrX5qqJtEc/7lcFrbR7iHP5yMKQHTxxXWGvz26fn2vyTz7xq86tNuvvMI2/MtnnCUj0ww+zRZRzytoStmTRdXt+3S9uxfll8gc1NAf0m/fo6PVCibbPef9Jjh2yWXXpoCwCEGvXwJNPhtIJln6rPO0t3WfIVaM35RNtmjC+yJY0SXzzrxl+o1wXJ11Yvc1Dfy0HnvRtPaWW6A8ye2VoHvy7XHS9qg3oNerxOW8aGrHF+B+4Gk/AS9lrTF04bWFqJ7mBWe9HYiLt9c+gim9NFP5f+1b7pNuev0euZV+q7O1H1rItIOYCTALwPoCT8RgGAvQBKunnMQgALASAL0Z9mSJTIWDNE0WPdEEWHNZPcer0bjIjkAXgSwPeMMRH/CWKMMQC6PBvWGHOnMWa6MWZ6OjK7ugtRUmLNEEWPdUMUHdZM8uvVJ+siko4jb4SHjTFPhW+uFZERxpg9IjICgGf7NNxvvWe8+IHNZb4ZNv+s6HKbX5v6oM1/G687U/xv+o17v7PMUujT2+8oW2Zz5fBXbP7zvrNsfuXFk22e/NgBm0OVVZrb9YAk8r5ErBk5dNjmUS9qW8DKD3WnFncHl7INeoBFaLPWVegou0y47QZmynibD0zV551fqO1l7nL+sv2TdBy1ejuMPpYSW7zqJjReW6/qT9JduLL3a8vJoPW6QxL266FDoUNNNptAp97HdDk/ijjIRTJ0tyTfIG2/kczIiVP7RG05qzlVD3u5+KzlNl+eq/X4ZLP+PqvemWjzhI1rdNxsg0kKiXit6Qu/06bWMGeMzV+46dWI+52ZrfX654YTbH76qTk2lz+bOLvs9WY3GAFwD4CNxpjbnR8tAXBdOF8H4JnYD48o8bBmiKLHuiGKDmsmdfTmk/XZAL4KYK2IrA7f9lMAtwFYLCILAFQDuKp/hkiUcFgzRNFj3RBFhzWTInqzG8zbALrbeuHc2A5nYOVUasvJ7rXDbD5woi7jl/j1m8eHQpEb5Tc4q+9Bo/+Kmky6zavbdJnm7u2600twSbHN4x7Upclgq7u8380yKnmaJ2vGWXr3uUvs6fpeDR3UZfScp3UXo5xuDhrqdtnQ+ba+LzfyS0stp06wufoyHdOP5j5v83k5+tpPNOn91/yjwuaKBueQixDrJBnEs26axjk7wFyqy+dZOfr38ZaVo2weXKm7UBRs1etC+qE2myXY9fvSpOmCdkeRtrQ0jtZa7MiP/Ncw4qKPbX50/BM2fyZDH7+jU3cPu2vnZTaXvqnXs1CL3ocSnyevNf3BuaagVFvC9lysbWcLC9a4j0CWaD395eV5Nk96Ug/JDFbuiOUo+1Wvv2BKREREREQDi5N1IiIiIiKPimqf9WRzaKq2opxweqXNJX5tE3BbX36zT79FDABv7dXdLFo7dMmlsU6XVIe+o/+Ki1/UA16CtVtt5l4W1N8iWl8mlNvYOkZ3vkhr0caWzE27bQ7U7tPHui0xbmtNtu6A5CvSHV+ap+puGgBQ8yVdkr/vVD204owsvX1th36G8Ju1unxZcbfT+rIl8oAyor7ozNH38gWjNtn84+L3bM6brDXkczoP7jpUZvPWVt3OujWkO724cv26s9eJOXpo2JcH6fvb3V0MAILOjkch6LXmYFDbWm7Y+iWbA3/QVoGs53THGKJE5C/UQ/cOTNPry7/MfNrmHF96xGPeb9c/F67XejXVu/pjiP2On6wTEREREXkUJ+tERERERB6V0m0w/g79tv6hDl3Grw3qMuXNOy+1ueGmyCX9oirdUB/OQTAjg7pkaTq0jSbYEbmbDNFAEWdXlvpTdBnxd7/4k81NIa2B7z+4wOaxD+vtwW1VNqeNcg6SOVtbAern6Y4YT875Y8Q4xqRpG02eT9sKtgX0MVe+/R2bJ/5Gd+MIOoeEEcVS8VPrbX6n/lSbn/naiTY/eso9Nk9xdlG6Nt/ZUcLJoV40OKaLs8sF/N3e77DRa9K6Dq2bhav+yebCR7T9ctBSPViMbZaU6MwI3a2v7jSdt30lX9vIMiWyDeb763W3yqIten1J1B2R+Mk6EREREZFHcbJORERERORRKd0GM2itfvt+2zt64EWVc0BGTVOBzYNr9dAYAAgccP7czcExRF4gebk275+my4gV6dpmMtin7+Gffelxmx8/Z4bNexom2VxWoO//m0c8rM+ZoXV1fHrkjhh+0SX89R362hcvvUkff6+2lJnNTosBa4z6SbCpyeacNzbaPGab7qryvTJtz9pzur6vh8/W3SWmFmnO8UXX9tgY0Hazl147OeJn2bW6m0XRJj0IZsy2g3qn2hobIw7XI0pA4uxg1nCizsP+5VzdAcZtfTkUinzP+54aYnP6hs02BxP0sEl+sk5ERERE5FGcrBMREREReVRKt8EEd+luLuMe12XNWzd+0+asel1yDO3THQMAAIbfs6fEYJr1G/D5W/W/0TucJUF3SfGKXK2NM8Y+YXNzSB87yKfv/1Fp2joGZHU7jt8fLLf5v5ZeYPP4v2ud+VdtsTnUrrtgEPUbpw5CTksMNmrOqNRrxLhK3Rms/XVdbl+do7tWOOcm9Y5zOanYVhfxI2nWJf7Q/gM2B9vaQJQsJE2npO1n6U5MjZ/XOvz8IG2NPBzSIrtw3Zcjnqt41SGbgwcPIdHxk3UiIiIiIo/iZJ2IiIiIyKNSug3GOEvswQ269D5oQ9f3Z9MLJSrTosvoQ1c127xg69U2zxhSbfO1he/ZPDHdbXFRnUZ3Z3F3dvnFzkts/nBrecRjClZqK8HEd3RpUrZU2Zyoh1ZQcjOdurtLYHuVzX43x+i1uO8RpSL/KG0v23uaXit+cvzTXd0djzSNs7ljcUnEz2Sn7gCTDDuJ8ZN1IiIiIiKP4mSdiIiIiMijUroNhihVhNq05SutcrfNB+8eb/OzxaNtfnTcbH1svh5SFME5W8J/SP8qGfqh3j75o8iDxLD9Y31e5+AWk6AHVRARUWy0lxfbHJyk7Zrn5FTZfMjpR15SO9XmoW/ujXiu0KHG2A8wjvjJOhERERGRR3GyTkRERETkUWyDIUoFzrfhg/v22Zz/qJOdu0d+r74PLxuj5yEiouTWXqgH85UW77e5yKc7wzzbMtTmjSvH2Dxxz9qI5zKBbto3E1SPn6yLSJaILBeRNSKyXkR+Gb59rIi8LyKVIvK4iGT09FxEqYJ1QxQd1gxRdFgzqaM3bTDtAM4xxkwFMA3AfBE5FcCvAfzOGDMBwEEAC/pvmEQJh3VDFB3WDFF0WDMposc2GHNkm4bD4T+mh/9nAJwD4Evh2xcB+FcAf479EIkSD+uGKDqsGaLoJFvNZDRq68r2am13ubngDJufX3+CzZPvqLU52NrWz6OLr159wVRE/CKyGkAdgFcAbAPQYIz55N9sDYDSbh67UERWiMiKTrR3dReipHSsdcOaoVTFaw1RdFgzqaFXk3VjTNAYMw3AKAAzAUzu7QsYY+40xkw3xkxPR+YxDpMo8Rxr3bBmKFXxWkMUHdZMaohqNxhjTIOILANwGoACEUkL/9fbKAC7+mOARImOdUMUHdYMUXSSoWbSluqJehOX6u1bnftUQO8TROrozW4wQ0WkIJyzAcwDsBHAMgCfD9/tOgDP9NcgiRIN64YoOqwZouiwZlJHbz5ZHwFgkYj4cWRyv9gY86yIbADwmIj8CsAqAPf04ziJEg3rhig6rBmi6LBmUoQc+TLxAL2YyD4AzQDqB+xFvaEY3vmdxxhjhvZ8N/KCcM1Uw1vvoYHgpd+XNZNgeK3xBNZNAuG1xhO6rZkBnawDgIisMMZMH9AXjbNU/J0ptlLtPZRqvy/FXiq+h1Lxd6bYSrX3UKL8vr3aDYaIiIiIiAYeJ+tERERERB4Vj8n6nXF4zXhLxd+ZYivV3kOp9vtS7KXieygVf2eKrVR7DyXE7zvgPetERERERNQ7bIMhIiIiIvIoTtaJiIiIiDxqQCfrIjJfRDaLSKWI3DqQrz0QRKRMRJaJyAYRWS8iN4VvLxKRV0Rka/ifhfEeKyWGZK8ZgHVDsZfsdcOaoVhL9poBErtuBqxnPXzC1hYcOQ63BsAHAK4xxmwYkAEMABEZAWCEMWaliAwC8CGAywFcD+CAMea2cBEUGmNuieNQKQGkQs0ArBuKrVSoG9YMxVIq1AyQ2HUzkJ+szwRQaYzZbozpAPAYgMsG8PX7nTFmjzFmZTg3AdgIoBRHfs9F4bstwpE3B1FPkr5mANYNxVzS1w1rhmIs6WsGSOy6GcjJeimAnc6fa8K3JSURKQdwEoD3AZQYY/aEf7QXQEmchkWJJaVqBmDdUEykVN2wZigGUqpmgMSrG37BtB+ISB6AJwF8zxjT6P7MHOk74mD5+DAAACAASURBVH6ZRJ/CuiGKDmuGKHqJWDcDOVnfBaDM+fOo8G1JRUTSceRN8LAx5qnwzbXhXqlPeqbq4jU+SigpUTMA64ZiKiXqhjVDMZQSNQMkbt0M5GT9AwAVIjJWRDIAXA1gyQC+fr8TEQFwD4CNxpjbnR8tAXBdOF8H4JmBHhslpKSvGYB1QzGX9HXDmqEYS/qaARK7bgb0BFMRuRDA7wH4AdxrjPm3AXvxASAicwC8BWAtgFD45p/iSE/UYgCjAVQDuMoYcyAug6SEkuw1A7BuKPaSvW5YMxRryV4zQGLXzYBO1omIiIiIqPf4BVMiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JkvZdEpEBEnhCRTSKyUUROi/eYiLxKRLJEZLmIrBGR9SLyy3iPiSgRiEiViKwVkdUisiLe4yHyMhGZFK6VT/7XKCLfi/e4Yo1bN/aSiCwC8JYx5u7woQE5xpiGeI+LyIvCh0/kGmMOh0+MexvATcaY9+I8NCJPE5EqANONMfXxHgtRIhERP46cvDrLGFMd7/HEUlq8B5AIRGQwgLkArgcAY0wHgI54jonIy8yRTwEOh/+YHv4fPxkgIqL+ci6Abck2UQfYBtNbYwHsA3CfiKwSkbtFJDfegyLyMhHxi8hqAHUAXjHGvB/vMRElAAPgZRH5UEQWxnswRAnkagCPxnsQ/YGT9d5JA3AygD8bY04C0Azg1vgOicjbjDFBY8w0AKMAzBSRE+I9JqIEMMcYczKAzwL4tojMjfeAiLwu3J58KYC/xXss/YGT9d6pAVDjfDL4BI5M3omoB+HvdiwDMD/eYyHyOmPMrvA/6wD8HcDM+I6IKCF8FsBKY0xtvAfSHzhZ7wVjzF4AO0VkUvimcwFsiOOQiDxNRIaKSEE4ZwOYB2BTfEdF5G0ikisigz7JAM4HsC6+oyJKCNcgSVtgAH7BNBr/DODh8FLLdgBfi/N4iLxsBIBF4W/n+wAsNsY8G+cxEXldCYC/H9lMCWkAHjHGvBjfIRF5W/g/bOcB+Ea8x9JfuHUjEREREZFHsQ2GiIiIiMijOFknIiIiIvIoTtaJiIiIiDyKk3UiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JknYiIiIjIozhZJyIiIiLyKE7WiYiIiIg8qk+TdRGZLyKbRaRSRG6N1aCIkhnrhig6rBmi6LFukocYY47tgSJ+AFsAzANQA+ADANcYYzbEbnhEyYV1QxQd1gxR9Fg3ySWtD4+dCaDSGLMdAETkMQCXAej2jZAhmSYLuX14SeqrJhysN8YMjfc4UlhUdcOaiT/WTNzxWpOAWDdxx2tNgjlazfRlsl4KYKfz5xoAs472gCzkYpac24eXpL561TxRHe8xpLio6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL1XRGQhgIUAkIWc/n45ooTHmiGKHuuGKDqsmcTRly+Y7gJQ5vx5VPi2CMaYO40x040x09OR2YeXI0oKPdYNa4YoAq81RNHjtSaJ9GWy/gGAChEZKyIZAK4GsCQ2wyJKWqwbouiwZoiix7pJIsfcBmOMCYjIdwC8BMAP4F5jzPqYjYwoCbFuiKLDmiGKHusmufSpZ90Y8zyA52M0FqKUwLohig5rhih6rJvkwRNMiYiIiIg8ipN1IiIiIiKP6vetGxOSiMaMDJt9BYMj7tY8s9zm3bP9NgeGdeqdnANipU3vk1utuXRZk95p1UZ9aCAQ1bCJiIiIUp2/eIjzB2d+Nm6EzQ0TdbvK5lKd97UVh2yWoN5esEmfcthbdTaHqmtsNu3txz7oo+An60REREREHsXJOhERERGRR7ENJsyXlWWzjB9j8965RTY3jot8zPATa21+cOLfbJ6RKehKXbDF5sebTrD5D5POs7ni7uNt9q/ZanOoRR9LRERElNKclmV/QUHEj2qvnGhz01i9PTBS21Ry8hr1B07LckGGtjIX5bTaXDJbW5aXV0yxecKDOpUOrt/cy8FHh5+sExERERF5FCfrREREREQeldJtMJKZabM5scLmys/n2XzjRS/ZvLBgQ8TjMyXd5hD028P/01Jo865OzbOzK/V5C7TF5ZLz1tl8XvD7Nk++o1zHul4fazo7uvp1iDzPP0XrTBp0STFYf8Bmvr+JiKgr7rzNN260zfUziyPuN+U63VnvhpI3bV60b7bNb7yvbceF67WlJrcuaPP+4bqrzIxvvGPznEuX2HzP5kttLq7KtTnU3Hy0XyUq/GSdiIiIiMijOFknIiIiIvKolGuDkXQ95EimjLd561Xa+vLMF263eUK6/iuq7HS+LgxgW+cgmze360b7f16mu7tk79bN+B85c6/ND015wObRadk2vzX/dzZf8tHNNo+s1V1pAnv0eYg8x6fv+bSRwyN+tPVaXaocvEXzsGVal4Gqj/txcERx4NSEv1AP1zNlWh+BwZmIlq9VD87zN7XpD+obbAzW1+vtJvIaRpQI3NYXmazb8m39su4A84V570Q85urC5Tb/quYim2v+qK2YFY+91+Nr5+ZqW8uTZdpCc9c1f7b58BhtoRlWqGNiGwwRERERUQrgZJ2IiIiIyKNSrg3GXzbS5s1fybf5iSt/b/OYNF3SWNehy4bXrboh4rl87+hy5oh3Dts8eYN+CznUqkuTreum2fzzn1xs86Ixr9lc4teWmIYTdIlz+JvOhv9sgyGvcQ+nGKrfnq+8cXTE3X50+TM2P75rus2Nh7SNLKc3bTBOW4GkR/nXWFC/6W8CgaPckejYSZq+L/0lw2yuna+H7s351gc2/37ECptbQt3viJQu+t5f0qy7jf1q44X62i9OsHn4Y/oeDx482KuxE3mJu+vLlq/qvKvyS9qK0mmCEY/5Wd2pNtf9h7bODHqu59YXl3t9yTjukM3D/driklet88RAza6onr+3+Mk6EREREZFHcbJORERERORRKdcGs2+uLrfPPG2TzSX+Tptv3qO7uWz58XE2j15bFfFcpnWH5g5dtgw6y+zut++zd+uyyTubdJnSX/66c389XOn4yTttbh2q7Tsp938aeV7a6FE2b7lR89NX3x5xP3d3pf/37GU2T1y/z+bIxUzly8rSP0waa2PjRG1nM35BT/K3Od/Q/2Btj/cnOiZTJ9m4YWGOzX8/X1sux6br3/c7OvWd/4f6syKeqiOkdTMhu87m8/L0oL7XT7nP5j+N1ZbLx3LOtXnEf77b6+ETecWhE3Q3vN9e9pDNbuvLO23pEY954cHTbS77SFsro258dNrZvjJB29ZK/AP7WTc/WSciIiIi8ihO1omIiIiIPCrlOiqCevYKalv1UKOLVi+wedBd+m3jnOXr9LEtLX16bQnokicCulwfdFpfQtC2mQyfLti09Ly6TzSg/EN0afLA7FKb/+XyJ22e6BxCBkTuZBEa1m7zvtlDbT58je6cUXL6bpuPK9RdkEZmrrJ5bKa20KSL1sy7TXr4xcfOrhlbntPbS3VVk6jP/Mdr68vGb2rry6Jz77J5U4cehHTFi1+wecTr+tlZfmVTxPNKUK8LlbmTbb5v9nybT7pCr1W/Ln3e5g1Xauvnwad0J5pAdY2+QKi75jOi+Mvep23KP1l9hc23hHRiVPR0TsRjyt7W1pdglDvoude2fZdOtPniQc/a/PO9c23Ore3/XcX4yToRERERkUf1OFkXkXtFpE5E1jm3FYnIKyKyNfzPwqM9B1GqYd0QRYc1QxQ91k1q6E0bzP0A/gjgAee2WwEsNcbcJiK3hv98S+yHF3sly2ptbqvS5fYhLbqMkf6R7hLT19YXVzBHWwJyi2P3vORJ9yOJ6uYT/mI98Gj/Z3V5sOJGPQjs83m6/Jguzg4un/Kb056w+aNpZTaPz9QaPTW72ua9wVybf18zz+ZlHTqOHTXaTpP/YabNeXt0mb+sssFmpzGN4u9+JGDN+HJ0+X3HF7Q+FszSw+5ebjzR5r89N8fmyYv1vSg79DCVYFNkG4xxdhUT50Cw0TW6S9jbpbpz2eArX7V5+uAqHUfOrKP8JpSg7kcC1k00MtbpdaDsd7rbmIS0LvybNkY8JtCoB1VG3eY1TOtYrqy3eZQzY37hNT3Ur2Jbz7uZ9VWPn6wbY94EcOBTN18GYFE4LwJweYzHRZTQWDdE0WHNEEWPdZMajvULpiXGmD3hvBdASXd3FJGFABYCQBZyursbUSroVd2wZogsXmuIosdrTZLp824wxhgjIuYoP78TwJ0AkC9F3d5voAS3brc53ckR94nh66WN0eX9nafpMv51E1/q8bGrNpbbPHmfLulw6T7xHa1uvFYz/kJtd2w4T3dSkWt06e/3Zc/ZnOfT9/nRZPn0ILFMZxeXJ2p1efFfd+jBSek7tY2s+COtAn+H/iuqqG3T2yurbA7u1w+eQibu/0rpGHjqWuO0orSfPsXmsWdW2XwokG3zMy+eavOER/S9GNywRZ+zt+9LZ0nfNGs7pb+96y3D0sW5v3BbsVSTSNea7gTr99ssTo64Tx9fwz9UWyjrZhfb/NvJf7X51Rb9b54R7zh1VbMH/e1Yd4OpFZERABD+Z10P9yci1g1RtFgzRNFj3SSZY52sLwFwXThfB+CZ2AyHKKmxboiiw5ohih7rJsn02AYjIo8COAtAsYjUAPgFgNsALBaRBQCqAVzVn4NMZO3jdGmlfZa2snxt8EfOvXS5tNPo0kr+pnSbffWHbGYbjPclet2Ic5hRx9SxNu/9rLauPDXlYZuznMOOXmzRXVh+vUMPbQGAzqDeb3el1kbOTr09v1rf4ZM26vte9u60OVjb8wdFPOYlsXi+ZpwWkrQSfe9u+ZLefkvJGpt//e5nbZ70lO7uEly/uU/DcA9sqb9Yd0KaNLPK5najbWUbm3XHGN9hbZvhdSQ5eL5uPMyXpbuVtZ6sB4aZy7TVZmqGzttOffcrNo/fqPcJNjf31xCtHifrxphruvnRuTEeC1HSYN0QRYc1QxQ91k1q4AmmREREREQe1efdYKgLzk4Bh0u1nWBuuS5/Fvq09SUE/RL2srZ8m3N360KlGYBlFqJP+AoG27z7DF0q/M4pL9o8LVPbXeqD+v5c26aHVlRvHB75vB3aMpBTr58V5OzVGsjbqTu6SPVum4MN2hJDNNAkTdsSm2aOtvlXs5+0+bWDujPM8KV6eTUr7OGSx8RfMc7mfXN0R4ohX9UDyO4fr4eMbejU68vzG4+3edLeDfqkho0wlNpkvLa+7DxP63vR8Y/ZfGfDVJuHPOVsb1mnBzUNBH6yTkRERETkUZysExERERF5FNtgYsXdKaBcD0I6cLzefnXxezYHnL0qaoPtNn/3vRtsnrRSd7xgCwANJMnT5b62Cm1LuSpfdzE6GNT/1q/s1FaZk7OrbH760j90+xp7A4Nsbgjp6/1qw4U2F9w3yebcV9fbHGJbGA0wSdfLZf2JmoenNdj8+nsn2DxpjXP4UXfPmabP43N2ecGQgoj7bb96iM2fv/Rtm783RK8pbmvlC43O0v3r2q4WatNaJkpF7iF/u8/Vuvry+W/YvLNTb7//yXk2lz+ruz0NxA4wLn6yTkRERETkUZysExERERF5FNtgYsQ/SJf0q67WQyi+eYnunjE3Sw+UORzqtPmZJufb+v+uSyuhqpqYj5OoNwI79Jvug5eX2vzvU86zuSSj0eYHXptrc6azy0vbxMhld1+a7vqSkak1cOOUt2x+6eS7bP5a/hdtDu6bYLO8q8uRRAPBdOpBQ8XrNO/q1GV1FGtLY8ewPJsz9jj3cVpfUKS7Lu367DCbM+fti3jtv0z+q83TM/Vgo2zR1pddQb196R49LGnIGq1TrT6iFOLs0NdwgbZWjryiyuZL81fZfO3Kr9k87gHdkSzQGr82Mn6yTkRERETkUZysExERERF5FNtgYqR9eoXNBXP32rywwDmEArrp/poOXSL9r+d094uKGt3xwnRq2wxRvAy/e6XNVY/qri1VkmvzxNa1+gCji+3ubhf/i093SvrvW7QGxnxeWwAuH77a5v86+zKby97txcCJYsgEdU+XvA37bd7SNsLmJXPusPnHpVfa3NKp7S7fKl9mc75Pl9XHpB20+U/7zo547X/+6zdtrrh4q96v/O82/3L3fJuDj2tLDdZ80NWvQ5QyZOpkm5u+qG1hL1c8ZfNt9TNszntaD6cMVG3SJwp1t69T/+Mn60REREREHsXJOhERERGRR3GyTkRERETkUcnbs+5s1ZM2rFhvz87q4s5H4fTfhurqI34UnKp96k0/1D6oh6Y8qC/nbK21qVO39bp509U2T/yTbtEYOMyTGclbIk497KcTENObtH/9QFC/z3FGTqXNd8zQnl5fltYxT2WkAeH0q4aq9e/sl/4wx+ZHps22OXe0XhPOLtM+85aQnii6uFb7ZFet0K1Jxz2l1woAwKkapxd8bLPfOTn7je36+Emv77I5ENBtJolSRdoo3XJ4w7f0u1Z/PfF+mxc16hzub89pHY9/frPNwTj2qbv4yToRERERkUdxsk5ERERE5FEJ2QbjLoFj8jgbd84vsLm9QNtX8qbo8nlZQUPPz++c89YW1H9FmzedEHG/jCG6/P6bic/ZPDbNWaJ3nuuV5in6vK8NtTlQzX3oKLVlHtQ6Wds8yuYzs7fbPCRXT2hEum6DinanZcDwjEbqf8Z5zw19fpvm5XpSaWexLr2vHHqyzcszT7E5Z5+2qEyqOaDP/6nTq5tv0K3n5uTpEv1Pdl1gc8Fr2nIZ3KXbBxMNJF+ubukbnKZtJpVX6bzIZIRsTmvSlmWfHmqNQTs0D3ujVp9zq14T4LSBpY3ULVQBYMPPtA3mV3OftHl5y3ibH3hWt0id8JC2OQf3ay16BT9ZJyIiIiLyKE7WiYiIiIg8ytttMO6OLqN1SWPfWZoPX9xk81cmLrV5TKYuaZyRXW3zCL8uFXb7stCllXajy5TLRudH3G+Qc/rccenuLi5d7zgzOXOPzR2zdNy7bz7d5mErdHk1Y78u+8sO/XZ/sFF3GSBKVG47W9sQrblJObqEvz2gJz9WVWvr2KQ2rSW2vlA8BWvr9A9Odj8Jy3NP8vXrdc1tpwk59zHTj4t4jYuP1xOC3WuNuwPMhDWH9fE8/ZoGkH+o/t3ccK62mZjr9DTq75S9afPyQ+U2F2XoPGdCttZPZauewvvSWdpCnLVe50vZ+/Tv/v2nRO7a8ttzHrO5KaTXmvtfPsvmise1LTq4UXds8qIeP1kXkTIRWSYiG0RkvYjcFL69SEReEZGt4X8W9vRcRKmANUMUPdYNUXRYM6mjN20wAQA/NMYchyO7vX5bRI4DcCuApcaYCgBLw38mItYM0bFg3RBFhzWTInpsgzHG7AGwJ5ybRGQjgFIAlwE4K3y3RQBeB3BLTAdXoksrdedo68vYr2+x+Y4xS2we5MuweUW7LjU+0fgZmw8E9JvKJenaTnJ6ji6BTMvQfy3pos9zfnb3Bxb5oO017g4wrtOydMnl4Rn32PzW8RNtvuc0XeI5fEDHWvzO8TYPW7bb5sAObfEhb4hnzbh8ObobhW9Ikc0h55vuoZYWxEvrOSfanHeaLpfOz9X6fvTQSTYXfqg7wHCZP/l4pW76g3EPJurmkCLJ1mtI5RdyIn72y6L3bG5x2r581foY38e6K403jnGh/hbPmvEXaIti45m6K1/mAm1R/N2ExTZfs+IGm2X1IJsDufp+fqFEt4M5cYLuiPR/Zug8r+0UvQ6816gtN98e9lrE+KZk6GfRdx/S8RnnI+qDJ+jv0HnqaTa3DtO2zHTtWMbw97TVzPeRHtg3ENfRqHrWRaQcwEkA3gdQEn6jAMBeACXdPGYhgIUAkIWcru5ClLRYM0TRY90QRYc1k9x6vRuMiOQBeBLA94wxEd9wNMYYoOuPk40xdxpjphtjpqcjs6u7ECUl1gxR9Fg3RNFhzSS/Xn2yLiLpOPJGeNgY81T45loRGWGM2SMiIwDUdf8Mx6azXP9jcP+Z+q35d8e+bHO70TaV/69uhs2LV+vBE9mV+ib0O6vnhyt0yaXlNG2hmVakh04cTbvRx6/s0G8bP3NQD8BoDOgy5RmD9XlnZ1fZfGHeeptDFfrfT5tb9PdfVjfN5iFr8nQQzsEB5B3xqpkIFWNs/PgC/X5RyXL9ln3G+p02hxoO2WwCzukUvdltxTmcQpzdLiQt8q+Y9rl6sNje63U3pT9NfsrmNqPP9US1tsEMW67XIO7/kpw8UTcDydnxzFekh/pdP+/1iLtNydAL12/qdbm+wLlURexKQykjbjXjtCnvPVX/zn5r0sM2/3DnJTaX3qHtK+nL19gcatb2Yre1ZveVuiPSSzdom8kDY3RXmQWD3cO/uv+Pjfm5G50/aNx4xkibT8jVtpuvDa6yeVmrzre+X7DA5nE7tV4Hog2mN7vBCIB7AGw0xtzu/GgJgOvC+ToAz8R+eESJhzVDFD3WDVF0WDOpozefrM8G8FUAa0Vkdfi2nwK4DcBiEVkAoBrAVf0zRKKEw5ohih7rhig6rJkU0ZvdYN4GnFOCIp0b2+FEahumyxpjS3W5wz206EBIv1n/8l26k8qUl51DUxwdo7QdoL1Inz/Tp8v+ftEFh5DR79VXdmorDgC81KzLNP+98mybRzytLTWZDTq+N8/RXWlKpuvv0xHUpdCWV7VFIf9jfe2K9z62OVCjBySR98SzZlyNk3RJ8dprX7L5LyfOtbngLT1UpaBSl9rTD2qLigT1fSiBkOa2rndkaR+jO8905Ef+FTPqZt116S+jntXXdtb4btml65TB54bYbFYt7/L1KDl4pW4Gkj9fl9gPnqY7np2dF/lBaJZoHT384SybJ613DkLqjwGSp8WzZkJ5On8KDtJrxM6g3v7eSt3pbtKKDfrYblpfMKzYxkCO/lp+0Xf3oVCrzS+1DLe5sk0zAIScfy05Pr1WFadpO+WcfN15rCGoX7D9zX7dqawpqC3OwSynyny9/spnTAzsqxERERERUa9xsk5ERERE5FFR7bM+0ERX3BEMOa0p3Sz4BZxtQuvO0iWRA3N0CeTG6a/bfE2+fiO5xK+7trQbfeGagLa+fPkj/SYwAOQs0m8DT3pDN8gP1u/vcnzlS7u82TlOCRiMyi7v0/UxGkTdy9mtrSyPbp9u8z/O+qPNg8/Rlq1Fjbp7zD8O6WETh5ydjmpb9DCL3bt0yRIdWp+/OudJm6/Ki9yEwG1hawxp/pOzg9L7z+oSZPlTTl2FeNQLJRdTrq0vx39/rc0nZUT+jd8U0jbNrGqtWX9drc28RtCAcuZncHbw8jvzM5Opd/IN0RZkydT3cMN5FTbvv0J3Vfn1yQ/YfF52vc13H5pi86O3fdbmwsUrI4Zn2nXullaqhyI1f0ZrrjNPr1uDKp2WslW6Q59rLP5h80DXGz9ZJyIiIiLyKE7WiYiIiIg8ytNtMBkNuvRXVV/Q5X1K/PrN4+e++/+6vE+Wc2BLjujOK+mij+10dn15uy3X5m+8fqPNU26POBgMZru20QTbI3eKIYo3//v67fvhPxlr8+zrfmTzrZf83ebP5elOLV8ZVGVzyFnvDDpLnKHjTZe3D/Zp20y6U28AsLRV/3zjB9fbPGSJ9rCNfUNfO1C3D0TJRNK1BaC9WJsgv1ysS+yZEnlp/nq17pA0fLleFwMf14AoHnxN2rKS3pBv87QMfe++O/93Nj81R9tXOp3DLE/PedHmijR9bx8I6XXnis3X6GN/qy3Oha9p64s5yhwssFt3B8yq05aaLJ/ODU2ntxvJ+Mk6EREREZFHcbJORERERORRnm6DSd+oBwEVvqyb639nwhyb/1j6ts0jnB1d3F0nuts95oFG/Vbwr968xOYxznkUx23SZfjgzt0RjzedXR8KQ+QF7vsztGWHzRP/pMuXj7x0kc13l6TbXDdDn8dX4hyQ5HO3AOjmdd2dm+qyIn424m2txQkbDuoP9uy0MXDIaTczPOqFkouvotzm6ou05mZmap21f+ptv/bZyTaXb3RqhfVBcRJy5kMj39SdwS6ccanNL03Rg++uy9c2S9d2p/vkmq1fsHn3s7o72aiXdIe9tI836Rh6237s1Emiztv4yToRERERkUdxsk5ERERE5FGeboMJHmiweeir2hKzufYEm2eXTDvm589s1CX9yducpffN2jIQaGsDUaJzl/4C1bqMnr5XDy0qzNTdkQpX6zfuQ7l6u3P2RbfE2VnJ13wgchw79Vv5wcOHnR9wOZ9Sw+EK3dns2/Nettnv7Fr2m/0nRjxm+Pu63B/cG3nQGFE8uLuv5CzfbnP7v5XbPGPMjehJmtPzlbNXd4Mp21Jtc6Bm17EOM2nwk3UiIiIiIo/iZJ2IiIiIyKM83QaDkC6nu8sgmW6O1UvF6HmIEom7lBlxsNeGxi7u3cvndHKw23sRpQ5/8RCbGyboZffawWttPuRchO5/+ayIx0/apte8AA/gI48J1ju7tbymuagPz+ntI4oGHj9ZJyIiIiLyKE7WiYiIiIg8ytttMERERImuSHeAaR2mjWJZ4rf5/fZcm8teiWwCCNVH7qpERKmFn6wTEREREXkUJ+tERERERB7FNhgiIqJ+JO16KFletR5+dMG6L9m8e0+hzcd9VBPx+EBLSz+Ojoi8rsdP1kUkS0SWi8gaEVkvIr8M3z5WRN4XkUoReVxEMvp/uESJgXVDFB3WDFF0WDOpozdtMO0AzjHGTAUwDcB8ETkVwK8B/M4YMwHAQQAL+m+YRAmHdUMUHdYMUXRYMymixzYYY4wBcDj8x/Tw/wyAcwB8soa3CMC/Avhz7IdIlHhYN0TRSeaaCVTvtHnYHZpxh8aJ7v37f0iUBJK5ZihSr75gKiJ+EVkNoA7AKwC2AWgwxnzyd0oNgNL+GSJRYmLdEEWHNUMUHdZMaujVZN0Y8jOkIQAAIABJREFUEzTGTAMwCsBMAJN7+wIislBEVojIik7wmGRKHcdaN6wZSlW81hBFhzWTGqLautEY0wBgGYDTABSIyCdtNKMA7OrmMXcaY6YbY6anI7NPgyVKRNHWDWuGUh2vNUTRYc0kt97sBjNURArCORvAPAAbceRN8fnw3a4D8Ex/DZIo0bBuiKLDmiGKDmsmdfRmn/URABaJiB9HJveLjTHPisgGAI+JyK8ArAJwTz+OkyjRsG6IosOaIYoOayZFyJEvEw/Qi4nsA9AMoH7AXtQbiuGd33mMMWZovAdBvROumWp46z00ELz0+7JmEgyvNZ7AukkgvNZ4Qrc1M6CTdQAQkRXGmOkD+qJxloq/M8VWqr2HUu33pdhLxfdQKv7OFFup9h5KlN83qi+YEhERERHRwOFknYiIiIjIo+IxWb8zDq8Zb6n4O1Nspdp7KNV+X4q9VHwPpeLvTLGVau+hhPh9B7xnnYiIiIiIeodtMEREREREHsXJOhERERGRRw3oZF1E5ovIZhGpFJFbB/K1B4KIlInIMhHZICLrReSm8O1FIvKKiGwN/7Mw3mOlxJDsNQOwbij2kr1uWDMUa8leM0Bi182A9ayHT9jagiPH4dYA+ADANcaYDQMygAEgIiMAjDDGrBSRQQA+BHA5gOsBHDDG3BYugkJjzC1xHColgFSoGYB1Q7GVCnXDmqFYSoWaARK7bgbyk/WZACqNMduNMR0AHgNw2QC+fr8zxuwxxqwM5yYAGwGU4sjvuSh8t0U48uYg6knS1wzAuqGYS/q6Yc1QjCV9zQCJXTcDOVkvBbDT+XNN+LakJCLlAE4C8D6AEmPMnvCP9gIoidOwKLGkVM0ArBuKiZSqG9YMxUBK1QyQeHXDL5j2AxHJA/AkgO8ZYxrdn5kjfUfcL5PoU1g3RNFhzRBFLxHrZiAn67sAlDl/HhW+LamISDqOvAkeNsY8Fb65Ntwr9UnPVF28xkcJJSVqBmDdUEylRN2wZiiGUqJmgMStm4GcrH8AoEJExopIBoCrASwZwNfvdyIiAO4BsNEYc7vzoyUArgvn6wA8M9Bjo4SU9DUDsG4o5pK+blgzFGNJXzNAYtfNgJ5gKiIXAvg9AD+Ae40x/zZgLz4ARGQOgLcArAUQCt/8UxzpiVoMYDSAagBXGWMOxGWQlFCSvWYA1g3FXrLXDWuGYi3ZawZI7LoZ0Mk6ERERERH1Hr9gSkRERETkUZysExERERF5FCfrREREREQexck6EREREZFHcbJORERERORRnKz3goiUicgyEdkgIutF5KZ4j4nI60SkSkTWishqEVkR7/EQJQIRuUlE1oWvNd+L93iIvCxV5mfcurEXwidajTDGrBSRQQA+BHC5MWZDnIdG5FkiUgVgujGmPt5jIUoEInICgMcAzATQAeBFAN80xlTGdWBEHpUq8zN+st4Lxpg9xpiV4dwEYCOA0viOioiIkswUAO8bY1qMMQEAbwD4XJzHRORZqTI/42Q9SiJSDuAkHDnxioi6ZwC8LCIfisjCeA+GKAGsA3CGiAwRkRwAFwIoi/OYiBJCMs/P0uI9gEQiInkAngTwPWNMY7zHQ+Rxc4wxu0RkGIBXRGSTMebNeA+KyKuMMRtF5NcAXgbQDGA1gGB8R0Xkfck+P+Mn670kIuk48kZ42BjzVLzHQ+R1xphd4X/WAfg7jvThEtFRGGPuMcacYoyZC+AggC3xHhORl6XC/IyT9V4QEQFwD4CNxpjb4z0eIq8Tkdzwl30gIrkAzseRJX4iOorwShREZDSO9Ks/Et8REXlXqszPuBtML4jIHABvAVgLIBS++afGmOfjNyoi7xKRcTjyaTpwpN3uEWPMv8VxSEQJQUTeAjAEQCeAHxhjlsZ5SESelSrzM07WiYiIiIg8im0wREREREQexck6EREREZFHcbJORERERORRnKwTEREREXkUJ+tERERERB7FyToRERERkUdxsk5ERERE5FGcrBMREREReRQn60REREREHsXJOhERERGRR3GyTkRERETkUZysExERERF5VJ8m6yIyX0Q2i0iliNwaq0ERJTPWDVF0WDNE0WPdJA8xxhzbA0X8ALYAmAegBsAHAK4xxmyI3fCIkgvrhig6rBmi6LFukktaHx47E0ClMWY7AIjIYwAuA9DtGyFDMk0WcvvwktRXTThYb4wZGu9xpLCo6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL0UwE7nzzUAZn36TiKyEMBCAMhCDmbJuX14SeqrV80T1fEeQ4rrsW5YM97Cmok7XmsSEOsm7nitSTBHq5l+/4KpMeZOY8x0Y8z0dGT298sRJTzWDFH0WDdE0WHNJI6+TNZ3AShz/jwqfBsRdY91QxQd1gxR9Fg3SaQvk/UPAFSIyFgRyQBwNYAlsRkWUdJi3RBFhzVDFD3WTRI55p51Y0xARL4D4CUAfgD3GmPWx2xkceTL1S9ZdJw62eb6f26JuF9mesDmQbfn25y29MN+HB0lsmStG0nTv0r8pSNs3jt/VMT9Dp4Ysnlw2SGb2zrS9blWD7J59EuNNpsV62IzWEooyVozRP2JdZNc+vIFUxhjngfwfIzGQpQSWDdE0WHNEEWPdZM8eIIpEREREZFH9emT9WTiLyy0+fDcCpuDN9bb/MrxD0Y85m9N2iLzWP6FNvNfKiUrt0UMFWNs3Ht6gc2HZrXZfOFxKyMef16BrsJOSq/r8jUWT5xu8yNZZ9pcviL68RIRUWpLG6vXquYpw2xuLe56tpbe4rRrfrjX5kB1jd4pFIzhCHvGT9aJiIiIiDyKk3UiIiIiIo9K6Y4Nf/EQmxvPnGBz01d1B4o3T3jY5hzJinj83Vtn2zy0rr0/hkgUd2mlI20+OGe0zXvO1qXCS6Z/YPP3hy6z+Y3WcRHP9X83abtYc6sewnHt5OU2LyjU/Pqp2pLmnzDW5mDljt7/AkRJyl8w2ObWWVorh8bp7kpFm/Ta5F8W2ZZGlKz8FXrtqblwuM2dZ+j87jMjdnf52NoW3ZFs6wq9/k24T6fMoR16OKzp7OjbYHuBn6wTEREREXkUJ+tERERERB6Vcm0w7uEtbdPKba7/oh549OYpd9ucJRk2v9OmS4sAEHinyOb0Xfot4QCIEpt/iL63az+r36QffLWeVv3QuKdt3hfUQ8H+s+5cm19aenLE8459RutsaIdWyt03aUvZZXNX21yWd9DmXaN1mT+NbTCUoiRdr0ktsyfavP+GZpu/OF7bXR56+mybx2iHGlHiELHRl52tN4/SA/jaywoiHlI9V9ssTz5vo83/Uqrbzk90aqnT6O4uh02nzavH6vP+uO6fbC59pMnmYG3XO5vFEj9ZJyIiIiLyKE7WiYiIiIg8KjXaYHx+jeVlNu+cp0sgD06/0+YhPl1maQzpAS/fWLEg4mnHP11rc6Dq49iMlcgD2k7SnVciWl8mPmLzfQ2n2Hz3G2fZPPINfZ4Jz66KeN5Qm9aTzzmIDId0OX9fUA9e2tWsS5CZNQ02D+xxFETe4R9WbHP154zNr56s17C33F2YjLYQECUKt93LP1wPMmo+UVtfdp+hU9jJp0e2Rt5f9pzNE9L1ulMb1M+o/96s7Z4NznVnfIbO7U7P0naXSVdutrnxTd1hBvv2a+6nw5L4yToRERERkUdxsk5ERERE5FEp0QbjL9Kl9KprdAnluxfrMsmMTF0qDEGXFrcGdAeYkffpt4sBwOzhjhSUnA5M0ff6rPx9Nv9Hne4s8erfZ9g8+fE9NrsHFumxSf9b52fKbR41Ub9NPyFdD63Y06C7zJRtWdfzwImSXPtEXX4fU1Zv8+g0bd8MGv0cTo5WhEQe5SsfZfOOq/Q9/+0v/4/N3xxc3e3jA87VZ1mrtrv8YPUXbM5aqocfGZ/OAcd+cavNc8e/aPMtpS/YfFP5P9s8aJMemBlq1l2ZYomfrBMREREReRQn60REREREHpUSbTCH54y3efTZumyyYPBW5176r+LDdr31mhe/bfOUFdsinjfY3AKiZDTyUf3W+/ZVk2yWkLaIla/TtpTg4eiX/pqHa6tNeV7DUe5JRJ+oulh3yfh1+Wtd3mdbm+6ekb/ddHkfIq9xD63cebm2vvxlwR02T8/ocB7R/RT27TZtTblx6bU2T3hED+NLX7PB5oYLptg8dbDugOaDtsd8JkN3Fgxk6+2S3v9TaX6yTkRERETkUZysExERERF5VNK2wXSeP93m1ht0if2/xj5lc6boTi8fduhG9gtWXW/zlN/rZvfBA59aqu+nze+J4s19r6etbNUfhPQb9sF2p1/MRL/UfuA4/azg2sItNnc6TxUK8UAXooavnmbz/Lkrbb4gR3dR+rBdr2ePr9cDyya/pUv62gBA5G0BPaMoovUlU3Ta2mr09m/tPD/i8R89eoLNk984qD+odA6wzNCWsubhej1aWLjceQ19vZ/VzrE5f7teF0PH0AYaLX6yTkRERETkUT1O1kXkXhGpE5F1zm1FIvKKiGwN/7PwaM9BlGpYN0TRYc0QRY91kxp60wZzP4A/AnjAue1WAEuNMbeJyK3hP98S++Edu11n6JLgD8e/bfOUDP3vkz1B3c3ltp1X2Fz4SJ7NwcpN+qRse6Heux8JWDeW814PtcRu16O08tE2Byv0eWdk60FK9zfMsjnjfT20gpLe/Ujkmukr0ZYvf0FBxI/q5moDyxVFK2z+/9u78/Aoy3N/4N97JjshgUCAkBDCvgiIigjigrgUbC22WqtHK7a21lO7WOupnvac9vT8enrscll72tpWq5W21p2KOyoFxQ1BEJAdWUwgIYGwBAIhM/P8/mB87mdoQmaSycw7M9/PdfXqN8ksT7zeO+/LPPf7PPmi0/i/2KltAP2f1ZWWgjW74jpM8pyHkCZ1Y5zVxoo+1Dx91bU2twZ0RZb9jdorUzlPvw8A5St15b/gbt3YzwRabQ5NGmnzkamHbO7nL7B5b0jbXea/P9HmMfVOi3Sg+xvMOvxk3RjzOoDGE749G8DccJ4L4PI4j4sopbFuiGLDmiGKHesmM3T2BtP+xpiP9xevA9C/vQeKyE0AbgKAPBS09zCiTBBV3bBmiCyea4hix3NNmunyajDGGCMi7S4FYYy5D8B9AFAkJXHfncFdRD84Re/+HXHudpsvLtCVJrKcA/KXDefZ/OG8ETaXv6YbwgTj2Pri79vH5pYJVTYfGKZTmfvG6n+i3h/otGi/1+p0TFudu5nZmpOSTlY33V0zyVTzmQqbPzdG29MKRKcRH1mnKzkNW6AfGOk6NJSJkn2u6W6+/Hyba740JuJn109+zebxOQdt/tCZfX9vU5XNY99xVoBJwBQ9eVdKnWuM/pXv87Ze8xxp6GuzuzFfv4O6Goy8tybipQKtx9AWXw9tnamdpDX349Mf1vdzVplZcqTM5gELnUvmPSdOZnSvzq4Gs1tEygAg/P/1HTyeiFg3RLFizRDFjnWTZjp7sf4MgDnhPAfA/PgMhyitsW6IYsOaIYod6ybNdNgGIyKPAJgOoK+I1AD4IYC7ADwuIjcC2AHgqu4c5D/x6V2//v79bN78VW0bebjyOZsrsnSq4+nDepf9/MWTbR4135k23KN3+UZLsrWVRU4ZbvOe04tt3q83HiNv1AGbz61Ya/MtpYtsvufsi2x+9YxTbB76VInN2a+tstlwutMzPFk3HtDzEzq1eWPJWza/c3Swzf4NOk1pNmhtUHrLyJpxzmW+fjrVX/XprREPu6HXUpsLnE1hbts50+Y+7+gKaME6fpCaKdKqbpzN9YJbdIWwHCe3+9ST/MyXl2dzwFkBpmWyrgBzRQ/dOGlnUK+lHq7T1clK3tHzV+CgPjcROrxYN8Zc086PLozzWIjSBuuGKDasGaLYsW4yA3cwJSIiIiLyqC6vBpMMvh66osvhCeU2P3fur2wema3THtsDuvnKHctvtLnqOb3jN7DdWWElSu5KNK3njrd52+X6/S9N17aWb5SstDlP2v5PnwXdzOL3FUtsPlK+0OaJR261edRaXWEmULc76rETdRe3LjB+VMTPrh+s9VDptKfdVD3N5j4f6ApHbO2idOYvLrJ5/+SBNn+r7JGIx5X4tV3mraO6Udi7i3XVmBGvOK2cLS1xHSdRKjPjtDV562f0Guu28c/b3GL0XPPWkUE2r31Nnzt092p90QSvxMdP1omIiIiIPIoX60REREREHpWabTDO1OHuyXoHfB+/3g/sF/13yJ/2TbW5ZIFOvfsXvd21cYwcanPt13Ta8elJv7X5FGeVmBajU5kbW3UK5ZmDE22+tni5ze4qNoU+bevJrWqy+dgonTr1sQ2GkqWdFZrW/WvkrnhT8nWVi/datHZr3tZ2tuFv6mPYBEPpxl05LDRMj/t9V+nqEjPyd0U8p1D0XPC/2y61uextPY90ppWTKF1lDdBNW7dcrq1jt12iKwXeXLzD5g3ONdnPN11s87DffGhzsFlbqhONn6wTEREREXkUL9aJiIiIiDwqddpgnGn2UIlOafSc3GBzNnRTpKAJ2Tx/m67U0q9GV4CJleTmRny96Uu6OdFPJujd+27ry8HQUZuXHNVNL+54/wqbq/5Hp1923a+bNv1n/1dt7ufXdoJhfXXTpj1lVTbrfxWi6LjHtC9fW63grD7RrqAet+JsOrF3eqXNj13864injMrW1x33mrMy04IjNnNVI0o7oucm/wBtE6s+T1s6V011a8WpRQCHjLZZNryqrTOV726xObFrUxB5jC/ynFVz9TCbvzT7FZu/XNx2m+WTB86w2bygq+wFd2+K4yA7j5+sExERERF5FC/WiYiIiIg8KmXaYNyNkA6O1laRBRPutrnIWTHFbT85urnY5twabZuJZtrQ50zvN1x7WsTP/uNTT9k8u8cem1uMrkrz/boZNr8993Sbh/z5A30hpxWhNdRx+8EH23UFmFHrD9gcauvBRCdwW18OXKHHtP/6epsn9tENVnyiR1bAWdFoSY2uhhRYpTU5ZeYam0dlR67n0ui0zmRt1JrOqanR14ridyBKJf5+pTY3zNANV0Z/dmNUz79x22U291uprZzB+oa2Hk6UGZzWl6yqQRE/GnGFtq9cW6wbUmZBzzsvHdH80Nvn2DxmnrMCTHxG2mX8ZJ2IiIiIyKN4sU5ERERE5FG8WCciIiIi8qiU6VnHMO1H2nWxdhG5fequBc26vFWf1dpDHtpe3eFbSZb+Z5Eh+r4zv/FGxONm99huc2NQ+3ovXfllmwse1l7eskWbbQ4eOmxzVp/eNvfK1t73bGe5L5e/TnuOZceHbT6GKGK5uD66zGjjzBE2X/3vL9l8doEen80hPcaOOX3qffx63N5SusjmugmFNo/K1vsoCpydFwFg0qNft3n40/tsDu6sO9lvQpTSAkPLbG44v9XmJyvnO4+KrBXXxvkjba5cp7suBpz7o4i8wN9L7xFsumC0zYcG6nnEtPMxcWGtc223yrm/cLMut+jr0cPmlmljbO7/o8glFv9j4Iv6M7/Wltun/o2X5tg85reN+n4Nujy2V/CTdSIiIiIij+LFOhERERGRR6VMG4xv/yGb83f0OskjT06c1gB3AtFdotGM1zaBTd/WqZsHSt6OeK3qoP7n++7Wz9mc9bS2tRQv1KW5QgcO2uwfM9zm9f+qv89/937C5p4+3Qn1P+sn6ms6sz0hp52GyOUr0Om+5jN1mcWLbn/T5i8Xb7B52vIv6pMX6TFcUK8tXg2na/18ddbLNt/WW1to4CyNdaISXdURsmOXzaHWzu8sTORF4uxk3TBRa+K2s16wuTJL28f2BZttnrb0pojXqlys547gbi7XSN7im6DtLpuv03PHJy9cZnN57j50pK5FW2g2H9Kdfj+sm2BzoFZrqXSktg3/tFzrCgD6Oq0vC93Wl+dvsHnkQ002Bzdqqw1CXlmwUfGTdSIiIiIij+LFOhERERGRR6VMG0zIuTu3zzq9s35Da4vNI51pxzPzdNWXveN06r7kPWeXq/U6de8r7WtzzTk9bf7rlF/b7E6rAMCnV1+jX8zrY2O/f+hujCakzTZm0libq8/V6c+7LnnY5nE5OtYnDw2w+e/zdXetIW/r1E8w6L3pGvIGKdNpxNqztdR/WPq+zWuP6fGW/ay2Y/Wb7+yseExXr2juP04fL7Efe3tnHbG5cKe2guW8s97mUHMziFLdsfPH29xykbaxfL5onc2tRtsvdwS05bLHC3oOAgDfNq3HIFvGyAPkjFNs3vwverz+4JPayvv5nrU2L2jWFpff10y3uSRX/95fVfquzf/RT1ff21+lrZgrWnQH99E5u20+8frMBz231Qd0fNmH2lllr0TPf8ZpLzbuikvO9ZyvUFelCQ3VMfnrdTW0QLVeC3ZVh5+si8ggEVkkIutEZK2IfCv8/RIReUVENof/v3dHr0WUCVgzRLFj3RDFhjWTOaJpgwkA+I4xZiyAKQBuEZGxAO4EsNAYMwLAwvDXRMSaIeoM1g1RbFgzGaLDNhhjTC2A2nBuEpH1AMoBzAYwPfywuQAWA7ijW0YJIHTkqM2FW/UO3m9u+bzN80Y/ZnNVlt79O/2C1Ta/3qp3FQ9+SacxWp3pjabT9L3O0L1hsOmE6Ucz32l9eXaLft+5k/jQ+bqyTM3FOv1y/hm6LManCvTu/nsadWrpD4tn2DzyWZ1aCW5wNkLiphie45WaaS3Xab2+Z+p0YYvRtpbfNVxgc581uuISeuuU5Z5p/W32XaAbR3yih07n73PqZ12r1tXU3MhWmb9Nvd/ma2u+afPwPZX6oA+clWVivSu/nY3EWCfe55W6iZeGiXryuGHUYpuLnVW+Vh/T4/uLq26wufzdyJUzuOoXtSWZNVN/ZpHN/37p322+slBX+VreopeY337+epv7v6Ovs7tI/2b/eHapzX885S82n5Kt7WKVWfudUTgXaCcxIXenzWdeoC2Xb1XoKmnZ1brxWEGdjkkCTuuLczo6VKGPaRmo59Tyl7TFuUci22BcIlIF4DQASwH0Dx8oAFAHoH87TyPKWKwZotixbohiw5pJb1FfrItIIYCnANxqjDno/swc78Bv86MrEblJRJaLyPJWtLT1EKK0xJohih3rhig2rJn0F9VqMCKSjeMHwsPGmHnhb+8WkTJjTK2IlAGob+u5xpj7ANwHAEVS0vm5aGc6XHZq28i+J3VFifm36kovswt1NZhflL+qj/mc3lX/X5Wftjl3m06zXDhqlb4t9C7kza06RQMAeY36s8DIcpv3jtO7kpsv0taC35/2qM1n52krz4vNumrHg09fZPPIeVpzsl5bX4wHF+ynSF6omUCeri4xspfWTND5u729SVu56ifpHfPN5TqVd8Z03TjpP8uft7nF6OvfWv0Jm1ft1jvjF03SthcAmJyrLTKzZiy3ecGxSTYPqDzD5oJqrROp0xWhUKTjax2gLTvBfB1TyK/TlP5jWqu5H2mLQXCzsxEGJZ0X6qYr/L30WGwu12PurAL9+33UBGx+5qAe96W/1tZNs0HPQQBgAgEQtSVZNXNkgP59vbG4zub6oLaEPL3/LJtH3ets5uW0JTaN1+uqxqPaItZq9LPkkHPO2hfSNuUlR3RlwCVN2sYCAGf31HbK8iz9m/8D5xzWv1Lfo9FZWW/+IV31rPpoic0Nx/S8M7KH/iddvm+wzXWvDUF3iGY1GAHwAID1xpi7nR89A2BOOM8BMD/+wyNKPawZotixbohiw5rJHNF8sj4NwBcArBGRjxdo/h6AuwA8LiI3AtgB4KruGSJRymHNEMWOdUMUG9ZMhohmNZg3ALSzxAIujO9wohPcqytSlD33kc3/dcoVNh+Y8ZLN1zibUFzbU6cuZlzwfzbXnKutKyV+nWbxQdtjxufoVA8A1F6uq8MUF+tmL7eM0PeeUaCrxOwO6nvctedMmx9+d4rNYx7V3y20ZbvmFvaTpQqv1Exuox4zS7YOszlvkJb9LZX/sLnxmzrFN6NA20N6ik7ArWvVevjupitt9v1BpzJ7OyvD/HWkrm4EAF8s1jvxf1m21Oa11y2x+Y8zz7X52RUTbe61WpcKPlyu79FnotZ0eaGumlScrXW8bp/eX7X/ac192QbjGV6pm5j5tPWqeapOxVeO0w1hJuXouaLWmW5fWDvK5h4L37OZaxdRNJJaM1EcpK1Oq+SxgdoitmuankcmfeoDmx8Y+KLNA/363LXH9M0eajzf5ucWaRvZwCXadgYAz5+t11ihgXouGFCq54gppdttHlugq9iEnBacHc3aBrNtv+a33tBzW8U/tE2tZJm238SzYTmm1WCIiIiIiChxeLFORERERORRUa0G4znOaiiBGl3sfuRterfx/d/4pM01X9Dp82/0ecPmns70pbv5kQ/aruJyN1oCgA8v/JPN+4LNNu8P6XTM4wdPs/kPr+smR8P/pi0Ko9/TDZKCR3W6hqgr/DVaDz2W6uYPjefosXd2nnOHPjSHnJnVPx4Yb/PvFutqRcMe06l93xJtafEVaJ38qerSiDHlfVVXCvhUoa7MNMBZueUH/RdrnqUZs9ChJqcFp9X5HW45fLXNhw6yyYDiJ6u/toB99CX9+z1vxCM2+yXb5oXN2irTsExbsnqALVmUOsRZoOhASNuA3U2/vtF3sc2P/Z+u7DW76H2bh2TpdVir8/nxAmeVvB9+cJm+/iParjnsCWd3pRMMbeeWWn9fXQFtXcVom1f11pZLn7N6WHatbsLUv1E3F+x70Nmc0rkm7a61+vjJOhERERGRR/FinYiIiIjIo1KzDaYdplWn5cv/oJtKrFqi0/gzZ5xtc+XM7TbfM/QJm4dltd0Gc6LmkL7fJatusDn7r3rHcO83a2wefWiTzaEmnRIKccML6gbBhj02l7+sGx6dN/Df9DFl2hIjPm0PCR3UafvyhdpOMnqx3ukeOqCbdrmNJaFmbQkb+IcVEWN6+s3Ty+V7AAAgAElEQVQLbL73vNk2N43UGvAVtiIWoVb9zKHiWf2Tlteg9Zm9W8faa6e2nUWuH0AUu/pZugnKFSO0zXJottbQ/MN9bf7pAt2Mb+Rdq23msUipZOBb2vI19bSv2Lxm6p9trnSupW4t0b+72aKtMj6nXfGSNdquGPiLtsEMfl2vo4L1usJeZxoa3dUE0agtLlm+thfVCTitlTBOlZrEtlPyk3UiIiIiIo/ixToRERERkUelVRuMK3T4sM2yWttPBlf30sc8r6vEfL34FptNO9MhJ1O655B+UasbvwQOOt9P4hQKZR7jtFeFturmYSN/o3fum1ydqofocS8Bvafd7NOpwqDT+hLNMRw6YXUjWaNtNBXVukmGFOh0qXFWB4iKO476vfrtY9oG47aamWB33a9PmehwudZNZa4efz7ns7BNR8tsLtil33fPU0SpJHu5/i0v+52ucDSy+ms251R0fHy31OrqYRUL9W95yRt63RbYpxsZuSuvdIp7vjDOec7jfWj8ZJ2IiIiIyKN4sU5ERERE5FFp2wbjMi264kWgThe1h5PdxpfYm2C6byF8oniIqIHqmpM8MnHjCO6uT9o4iOIlr0Gn1fe06qpLjzTphkcPvHWezSOWsPWFUp+7ol3Ou9qyMqpWW76CRXkdvo6/SdsssUvPCcF9+7o4wvTCT9aJiIiIiDyKF+tERERERB6VEW0wRERE3WHAEt1k5UmZYbNxVlcavlo3CvOv1k1dPL4ABVFU3JYYrGtq/4FtYAtxdPjJOhERERGRR/FinYiIiIjIo9gGQ0RE1EnBtRttLl3b8ePZ+kJEseIn60REREREHsWLdSIiIiIij+LFOhERERGRR/FinYiIiIjIozq8WBeRPBF5V0RWichaEflR+PtDRGSpiGwRkcdEJKf7h0uUGlg3RLFhzRDFhjWTOaL5ZL0FwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5oiqZ11E/CLyPoB6AK8A+BDAfmNMIPyQGgDl7Tz3JhFZLiLLW9ESjzETpYTO1g1rhjIVzzVEsWHNZIaoLtaNMUFjzEQAFQAmAxgd7RsYY+4zxkwyxkzKRm4nh0mUejpbN6wZylQ81xDFhjWTGWJaDcYYsx/AIgBTAfQSkY93QK0AsDPOYyNKC6wbotiwZohiw5pJb9GsBlMqIr3COR/AxQDW4/hBcWX4YXMAzO+uQRKlGtYNUWxYM0SxYc1kjqyOH4IyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggY5eaOQZQ/HK8ie6NGDqGhFJ9hAyRVzqhjWTfKyZhOG5Jo2wbhKCNZNGTlYzYoxJ5EAaABwGsCdhb+oNfeGd33mwMaY02YOg6IRrZge8dQwlgpd+X9ZMiuG5xhNYNymE5xpPaLdmEnqxDgAistwYMymhb5pkmfg7U3xl2jGUab8vxV8mHkOZ+DtTfGXaMZQqv29MN5gSEREREVHi8GKdiIiIiMijknGxfl8S3jPZMvF3pvjKtGMo035fir9MPIYy8Xem+Mq0Yyglft+E96wTEREREVF02AZDRERERORRCb1YF5GZIrJRRLaIyJ2JfO9EEJFBIrJIRNaJyFoR+Vb4+yUi8oqIbA7/f+9kj5VSQ7rXDMC6ofhL97phzVC8pXvNAKldNwlrgwkv2r8Jx3fYqgGwDMA1xph1CRlAAohIGYAyY8wKEekJ4D0AlwO4AUCjMeaucBH0NsbckcShUgrIhJoBWDcUX5lQN6wZiqdMqBkgtesmkZ+sTwawxRiz1RhzDMCjAGYn8P27nTGm1hizIpybcHzb33Ic/z3nhh82F8cPDqKOpH3NAKwbiru0rxvWDMVZ2tcMkNp1k8iL9XIA1c7XNeHvpSURqQJwGoClAPobY2rDP6oD0D9Jw6LUklE1A7BuKC4yqm5YMxQHGVUzQOrVDW8w7QYiUgjgKQC3GmMOuj8zx/uOuAQP0QlYN0SxYc0QxS4V6yaRF+s7AQxyvq4Ify+tiEg2jh8EDxtj5oW/vTvcK/Vxz1R9ssZHKSUjagZg3VBcZUTdsGYojjKiZoDUrZtEXqwvAzBCRIaISA6AqwE8k8D373YiIgAeALDeGHO386NnAMwJ5zkA5id6bJSS0r5mANYNxV3a1w1rhuIs7WsGSO26SeimSCJyKYB7APgBPGiM+Z+EvXkCiMg5AJYAWAMgFP7293C8J+pxAJUAdgC4yhjTmJRBUkpJ95oBWDcUf+leN6wZird0rxkgteuGO5gSEREREXkUbzAlIiIiIvIoXqwTEREREXkUL9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixXqURKSXiDwpIhtEZL2ITE32mIi8SkQGicgiEVknImtF5FvJHhOR14nIgyJSLyIfJHssRKkgU841XLoxSiIyF8ASY8wfw5sGFBhj9id7XEReFN4FrswYs0JEegJ4D8Dlxph1SR4akWeJyHkADgH4szFmXLLHQ+R1mXKu4SfrURCRYgDn4fjOVzDGHOOFOlH7jDG1xpgV4dwEYD2A8uSOisjbjDGvA/DUZixEXpYp5xperEdnCIAGAH8SkZUi8kcR6ZHsQRGlAhGpAnAaju8SR0REFHfpfK7hxXp0sgCcDuB3xpjTABwGcGdyh0TkfSJSCOApALcaYw4mezxERJR+0v1cw4v16NQAqDHGfPyvtSdx/OKdiNohItk4/sfzYWPMvGSPh4iI0k8mnGt4sR4FY0wdgGoRGRX+1oUA0urmBaJ4EhHB8Xs81htj7k72eIiIKP1kyrmGq8FESUQmAvgjgBwAWwF80RizL7mjIvImETkHwBIAawCEwt/+njHmheSNisjbROQRANMB9AWwG8APjTEPJHVQRB6WKecaXqwTEREREXkU22CIiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixToRERERkUfxYp2IiIiIyKN4sU5ERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWLdSIiIiIij+LFOhERERGRR3XpYl1EZorIRhHZIiJ3xmtQROmMdUMUG9YMUexYN+lDjDGde6KIH8AmABcDqAGwDMA1xph18RseUXph3RDFhjVDFDvWTXrJ6sJzJwPYYozZCgAi8iiA2QDaPRByJNfkoUcX3pK6qgn79hhjSpM9jgwWU92wZpKPNZN0PNekINZN0vFck2JOVjNduVgvB1DtfF0D4KwTHyQiNwG4CQDyUICz5MIuvCV11avmyR3JHkOG67BuWDPewppJOp5rUhDrJul4rkkxJ6uZbr/B1BhznzFmkjFmUjZyu/vtiFIea4YodqwbotiwZlJHVy7WdwIY5HxdEf4eEbWPdUMUG9YMUexYN2mkKxfrywCMEJEhIpID4GoAz8RnWERpi3VDFBvWDFHsWDdppNM968aYgIh8HcACAH4ADxpj1sZtZERpiHVDFBvWDFHsWDfppSs3mMIY8wKAF+I0FqKMwLohig1rhih2rJv0wR1MiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH8WKdiIiIiMijurQaDBEREf0zydLTq39Af5uPVZXa3NI3R3OR3+amwRLxWqadj9Vy9msuqA/ZXLirRR+zqdbmwO4GfUIoeJLRE3mIT2sjq19fmw9Mq7K5cazffQaM+6XR6NZMUbXWQM+N+oPguk3Oc50nJxE/WSciIiIi8iherBMREREReRTbYMJ8eXk2S0WZzUeG9bH58IDsyOcEdHqk9ypnbmXrRzaGmpvjOUwiT5LcXJt9wwbb3DS6d8TjjhXq5wPu1H72YWcKv/qIzf7NNTYH9zbGZaxE3SWrotzmI2P1PFJ/utPuMkHPCVX9tUVlakm1zf/db1nE6+ZK5LnnYy836/cf23OWzUu2D9XnrtA88PV+Nst7G2w2rcfafH0iL/AXFdrcNEXPL32/ud3ml4c9G/GcAl8O2vJSs56r7t15gc0fLtA6GbRJ68ortcFP1omIiIiIPIoX60REREREHpXZbTCid9xLpU5ffvRZvXP/9Nkf2HzvoJcjnr4rqHcSz5x/m82VC06xObfhqM2+loDm+n02Bxs1mxa9i5/Ic5y78n09Cmw2o3RqcsMXdMry+5c8HfH0zxZutbnIp61nLx3R17rtvatsLn1ihD7+5fU2Bw8ejHnoRN1t98xKm4uv3mnz3GFP2jwhR2uoxbTavD+k54eGf1qoRc8LjSE9bVdlH7b5xwNfsjmvXD+H23GWvt8VE262eeTPdNofG7fZyHMQeU6ptiPXTdbjedHwF50HRV7ObmrV2vA7y8FMzdM2y4uHv2Dz532X2Nz8dz2fBddv7tyY44yfrBMREREReRQv1omIiIiIPCrj2mDcjSp8xUU27z9dN6qY9tmVNt9b/qbNIUTekT8kS79+afbdNm+9tERfN6jT+8/tPdXmFc+Ptbny5SYd3/sbbfbKXchEH/MX9rD56GRtUdl2tbaUbZl1r80hRG4o4dbQEaPH9yX5+rg3z/6dzTeVXW5zY/Mom3NfXK4v6pFNK4iCl2lL45OjHrU5V/S8UxPQafhlLdp++XTD6VG9x7IdOkXfs1Bf64z+unLSlKIPbb6haJfNb5z3a5vP3X27zaP+T1eJCezQVWmIPMFpWW5vg7AT3bTxWufpeo64snyFzZ8t1NbK6wa8Y/NtN19n88jvOC1iAW1VSzR+sk5ERERE5FG8WCciIiIi8qiMa4PBqTqVvuUKbYP5zKy3bf5e6dvOE3IRjcFZOU4+5PxE86weeufyti8vsPlnl860ef9XqmwObd5uM1tiyAvMEJ223z5b/3ws/cTdzqPy233+0hZtg1l3tMrm0/O323xGjj7/uxW6wsX113/R5mGvaXtZxMZjbImhJGrapJuAPTtKV4ZpCukx/YvXZ9k85Eld9iVvpU63n8yIVl1RCT5tD9hZXmHzT6+bYPMN1/9W30P087msQbpahsmL7jxHlAyt/Xra3Gvc3qie0/jSQJsrnttt87PF023+xc2fsHnJxffYfPV5b9m8/MzTbPYtW2tzolti+Mk6EREREZFH8WKdiIiIiMijMqINxnfqGJs3fFWnz38x/WGbz87TO+YLpP1p/Hbfw/l3jw/S5mMKnCnIMTkhm/9fxbM23/zra2yWH+iKMb7letcyN62gZDE+PYZNtrac9HY2ODpk9Pi8fP3VEc8//LBOTfas1tauP3xTp+SXT/qbzeNydNOYO07VTcnu+dfP2lxx7/s2R7TEECXYqHv1PPLA63qMSkhrZew6nZIP1e+xOXhYa6AzfFXaotba+592Vfrnx/vYMkapwX9EzwN79xae5JHOc5zOYTmo7ci+xv0259ZoW/SuoLaCjc7XOl5cebbNRSt0QyZ4rQ1GRB4UkXoR+cD5XomIvCIim8P/3/tkr0GUaVg3RLFhzRDFjnWTGaJpg3kIwMwTvncngIXGmBEAFoa/JiL1EFg3RLF4CKwZolg9BNZN2uuwDcYY87qIVJ3w7dkApofzXACLAdwRx3F1nbOI/sabdNWXX13wF5svytfpkOxOtL7sDuqGFD+rv8DmkLNq/5Uly2yelqdTOW7bTEWWTr/8fvgjNn/6vO/aXLWzv82B7R/FPFZKrJStmw4cHKV35U+fqHfGu61fLUZbvGrf1rYXABj2eq3NwZ2aD3xhnM1+p10sH7rK0uxC3ejlwA26SswDU6faPPh72oIT3LjlZL8KeUw61Exg2w6bC5wWl4jHdLHdJeK1Zpxh89ardIr+9vNebOvhaHJqM3exnhfRuLuNR1MqSIe66YivWXtafA09T/LIjplmvW4b9A/Nn6/4V5sfm6Eb8x24Sjet7P3uAJuD1Tv1NRPQEtPZG0z7G2M+PtPWAeh/sgcTEQDWDVGsWDNEsWPdpJkurwZjjDEA2r1TRURuEpHlIrK8Fbwxkgg4ed2wZoj+Gc81RLHjuSY9dHY1mN0iUmaMqRWRMgD17T3QGHMfgPsAoEhK4n/7udPu4svVdpL9n5lo8+fOfsfmWQU6pQE4d/a244jR6Zfv1Z0b8bNn39XF8vsu09cS57d8edTpNo+cut3me4c+YXOZX1twqrJ0tZqcKY02t75eoq/PNphUFVXddHvNdEFLsdbbWUW6OUvIORc0OStfFFZHPt8c1PrzVQ3Sx/XSVVyCzlS9+7rFzoozN/faYHPBGD3JPF00o+NfglKJd841MQrFsd3FPc8Fp+t5Z+t1+v3/nabnlE/30LaWZS16brrm5dtsHrOwQce6/0D8xkpekPLnmggBXd3Id6zt1fZOxvTsYXPLBD3v7DxfN+k7a/RGm4dm6XXfD8c/Z/P95Z+xOatWa8zLbTDPAJgTznMAzI/PcIjSGuuGKDasGaLYsW7STDRLNz4C4G0Ao0SkRkRuBHAXgItFZDOAi8JfE1EY64YoNqwZotixbjJDNKvBXNPOjy6M81g6RXJ0tYjDMyfYnH2DTlFc02up84y2f+UWoyu1rDim0+23O5u6HFtQGvGcMa8404hb225NKR2odw9X7x5i8z9uHmrztT1r0ZY5w7V9528jZtncd43exR88eLDN51Jyeb1uYuHvVWzz0RKdghyaozOrbv1saO1rc6/NkX2Q7p34h88Zpq/Vewc64r7H0had1vzZq5+yecyeOpsTu2UFdVU61Uyn+LRdxT9scMSPGif3s3nvJ4/a/NNJT9k8u4euPrPKaRX46urrbB7za213CW3eZnMipvGpe2R83bTjwAhtp2zprddhZoK2Yn5//NM2X9tTz2f79KlY3Vxps79F23GO3wqQOF2+wZSIiIiIiLoHL9aJiIiIiDyqs6vBJJVk6bD9Zbp8aP2/6BT746N0c6Ex2XrHb3vWteoU5LfXXmVzyc90uj171QcRzwk2OSvLtDMl4m6SUfp+H5sf3DHN5mvHPdnmcy8r1Pf7/VCnDaafvg7YBkPdrVSPt+ZBOl1+Zq5Oqbc6h//mFp1yzPnghPYWp3b3jtU8s/f2Nt+62VmN6bUjOo5b39b2tNH/p9OX7kYVRMkk2dqi6cvX1kq4uajQxqNDdMWv7edGnrPOu2S1zXcOWGBzZZauJPZei3729k2nfbPoz07b5Fq3JZQofX3uPG0jPq1Az0Pn5+sSZcU+rdHFR7WWfrD5Spv3vanns6ptumJM8JiemxKBn6wTEREREXkUL9aJiIiIiDwqJdtgfIXamrLvrIE23zlhns2D/CG0xV1RYldQ7+y9q/pym3P+ptORviU6laKP7pzcj3STo11LdNwrR+pYT8vRfz+5U5zHqnRVjUCpTmvKli4OiqgjOc6UfK4eq4U+3YTsUKid3e8k8vOAY6cPt3nghTodeX2v95xH6cZgG1v1T9T3P9AaHfN9bX0J7HRWUwp1tUqJusDZvEjG6mpH+0frikpH+mhNHK7Q/rHR03R1lnmD/x7xssOztQ6ynPo4ZLTu/mu7Tt3nPKjnsIJ5bH2hFOfUlel4L0sAwE/6rbDZ3dxyY6u2vjxycLTNv35bF88Z+6NdNhft0dcJHtWVmBKNn6wTEREREXkUL9aJiIiIiDwqddpgnGkQ9NNNV+o+qdMbn+qh04hFPm0hcbmrvnx3s94xf/jRMptLHnm7S0NtT2DrdpurHtN/J1079is2Lz/vdzYXirYZ3D9trs23rfiqzQO6Z6hEMfE59Zkn2mqGfiURj9v6OW2puadyoc0VWboqxr5gs833119sc8E8bSUIVK/r2oCJuoEvX887G2/UdsV5l/3K5vE5Ha9OBuR2/BAAuwLaRrNpzSCbR85fbnNit24hig/J1RoI9NHzQ6Bf7KuwuBtd3vjODTZX/FlrceRLy/Q9Yn6H7sdP1omIiIiIPIoX60REREREHpUybTC+Qp0GOThe22DumfoXmwtEpzRC7Uz+/W73DJsPPaGtL30f1jt+Ez1t6L5fq9HVNkKiPwkaX9tPIOpmckRXnJDmXjbvC+md8e7mEucXbLb5md+cGvFaj1b91eZx2XogHwrpcX/LR5+0ecv9erd+yePJq1GiaBy+ZJzNYyd8ZPMpOd1zqh2UpeeF3kN1tbHQ5FNslrdWdct7E8WdT9uUmz490ea8r+qKX/OHP+48IQfRuH3952we+Ig+J3fh+zZ7/ZzCT9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo9KmZ51qRhg8+6z9N8Y5+fvtTlb2u5f+k7tFJtXPDre5ooXd9gcaGlnB8Z4cvqxTL6OtUeBvrcfgrZUt/bRx7R4vbuK0knQ2SG05xbdefcfzRU2X1G4x+ah2XrvyG+Huv2FQH+/HvfZovVwxZZP21z70BCbS5/dqONIRI0SdUHhBw02r92s9fHmIK2J+mBPm3/54UU219X2trnkncjlHX3OWnJTv6bLMv687C2bS3sctvloL13mNLpFIImSQ7L1nLD3C2fY3P/67Tb/asgTNldkxX5Etwb0XFPYovdHmdbYl4FMFn6yTkRERETkUbxYJyIiIiLyqJRpgzHZOo0RKAraXNBO64vrlefOtHno0zX6Orvq4jS66Ph6FNh8aJhOU/5knO5OWuBre3e7n62+xObK9UfbfAxRdzBO+0mP3TqFuGCfLlN3ReFim7OgtVqR1fZOwgDwmc2fsrnur1U2939JdyIO7G0EUaoIVe+yecRD+jf+397WXad9zga/Rdv1b/moQ0f0MdVaAwAQHKrtZ/Ut2kbjcz5vu2zAapt/c+ZgmytfiHr4RAnXep62Jh+97IDNP696ymb3U+Uf1Ov13N836NLAb51zb8Tr9m5nF/tUxU/WiYiIiIg8ihfrREREREQelTJtMO3xtbN6iqvndl09JbD9o5M8snv5+ujd/o1jtFVgZn6zzX5nF9aHm3QFmB6Le9icvXaTzdoQRNQ9fHl5Nhun3Pa1aFuXW4d+0c8AjoQiW7b+5cPLbY5sfdG6DNQmtj2NKF7cljHf0g9s7rvSadd0dusNHdX6MM5qYRhaGfG6m+dord3d790239sPZ5ULfgxHHubrodcz1Rdqbdw++kWb3d15f75XW1+ee+Jsm/tv0CugA2dHrpLX26mB8yu22PxOv0k2FyN1dFjSIjJIRBaJyDoRWSsi3wp/v0REXhGRzeH/793RaxFlAtYMUexYN0SxYc1kjmj+/R0A8B1jzFgAUwDcIiJjAdwJYKExZgSAheGviYg1Q9QZrBui2LBmMkSHbTDGmFoAteHcJCLrAZQDmA1gevhhcwEsBnBHt4wyhfl76z9o952ld/QPumhHWw+P8PqBkTYXb9NdMYJ79rb1cPKItKgZZ0q+5ZxTbK6brtOOc0o/QFtajT7mjaORE401j+qGR2XPbbU5ULe782OltJAWdeMwgUCbOYI47WOF2hqw81NlEQ/7rwt1c7FZBftsrg1q2839H06zueyt1NnshTovVWvG16+vzb1O1Q31Lu2hLb5LW/Tc8ZeVurHl6Kfq9YWclrKXDo+JeI/rivS1vtJnic0vjNOWmj6DdOOyQLWuFOhFMfWsi0gVgNMALAXQP3ygAEAdgP7tPOcmADcBQB4K2noIUdpizRDFjnVDFBvWTHqL+jYUESkE8BSAW40xB92fGWMMANPW84wx9xljJhljJmVz42PKIKwZotixbohiw5pJf1F9si4i2Th+IDxsjJkX/vZuESkzxtSKSBmA+vZfofuE2j4GIx/j/JaSpV+0OzXZRe57tI6rsrlulu6G8fyIJ3R8TpEcCunGGMvqdEWAvk3OThrkeV6umWiEpk2wefsXtMb+MO0hmy/I15UsdDISaDF6rC48ODbidfu9o+cRtr7QiZJVN+6KR1KhLSjHKrSNMXuf/m02azdrjvU84ra+9Cmx+eD04TaPunJjxFOmF2y3Ocv5BPSv+8+wOfAPbS3IWfBWbGOilJWK55pQsbZ8De9Va3OZX4/tX+3Rc1DxMr1GCm760GZ/Xz3mf/HWJyLeY8qF+riJOXpNljW8yeZjVaU2+zzeBhPNajAC4AEA640xdzs/egbAnHCeA2B+/IdHlHpYM0SxY90QxYY1kzmi+WR9GoAvAFgjIu+Hv/c9AHcBeFxEbgSwA8BV3TNEopTDmiGKHeuGKDasmQwRzWowbwDt7jx0YXyH0z5p1RUm/Id1QqDZ6F3vBZKDtjSXOdOO/fvZHNi5K55DtHxDtH1lx4X5Nn/7zOdtLvTptI7bNvDbxok2y3M6RZq9dZvN3dO8Q/HilZqJhq9Apx1loN6DtOMbepTNO/OPNg/J1oaXA86d+MU+bSNwFfgjV6Uwuc7GL+4mMCFu75Xpklk3MlhXhfjoM3qOKLlAp+i3rx5g84j/1r/rpkmn1V0RtVWgj0evIhv3TdaaG/Q1ba15sErPFQCQL/paS47qafv+N863edQSbTHruDmU0kEqnWtite2wsynkbuf8YPToDu0/YPPABc75BMCGc7WdbWJOg80iTnWk0OZhKTRUIiIiIqLMwot1IiIiIiKPimmd9aRqaLSxZI3eAbzycr2reGqebhDhc/4dMvwi3Xxl78Yqm3vO0xukO7UyjDON7y/Wqc0P5+jU5o8+94jNVxTq4v9u68vyFp3ifO5/LrC5/4J1Ngec6R6iLnGOWzN2qM0bv66tWb8//c82u60vi47o3fM7W3WljJuKt9ucLfr6E/KrI956cV/duKUgX1tnQocPRz18ong7OF6n3Ed/UjdT+Xnl0zZ/yXetzb4+euxLTra+kLPSy7Hxg23eN0Jra98pOg3/nYu13eXmYnejvMiWzoMhXXnpK099x+YxD+j0fnDjFhClmpDTxeOu7pfn12uy1gK9nvMX6bWW9NBrp10zI1spR+fUOl+lzqVue/jJOhERERGRR/FinYiIiIjIo1JmbiC4V9tg+tZIG44AAAt1SURBVC7T/J11V9r86sSHbC5yVqf4y7C/2zzt6i/bnLf3VJuzFr4X85j8w6tsbv6NTsEsGfMLm3s743BXrnFXfXn+x9r6Uvz8GpuDzc0xj4moI5KtZd94Sk+bfzDlKZvdDY9uqr7E5jcXj7O5tZ+2ct38ifv0+87d+u8drop47/w6PaZDPL7J40r9Wis/Hf6kzb97dIbNLUF9jE+0ZWxWH13a+tx8bXHJc1plCsVpoTnJ6Xjq21+1ueoZ3ZwpuHlbWw8n8jQ5pi0uNU29bN4T1GP7t5Uv2vzInbpS0t2zLrI5cExbLhdM/1XEewzJctosnfaaYy1ac76W1FmFjJ+sExERERF5FC/WiYiIiIg8KmXaYNyF8M02XWGi5GejbL795zpd/8OBL9lc5tcNKf4w8S823/z162zOrZqquUnfa+84nbLsO3l3xJAG9NAVWn5QvsDmPj59vzdb9N9Dd2zU9zv2d914o//LuupLROuL4dYW1L2M88/1Ap+7mpIe9w1HC21u7a3Tl2eO1Cl4d5qx2Vnp6JGVkyPeb8z+vTYHeXyTR+Q26nG9vl5X88odqlPm47J1yvwnA3WKvr2J9F4+Pb26mxq5dgb17/13q2favHTtsIjHDX9Yx5e1UlerCXEzMUpBZutHNuf/eLTNn7j9Kza/eoZuxndNka50dPbUD20OGT1PuW0vQOQ57Dt1eh4qXqTXZ741q/W1oh9+UvCTdSIiIiIij+LFOhERERGRR6VOG4wjdETvGM5epVMiS5+eYPMrc3Ta5PM9t9t8Wo7+++T3p/7V5leH6ioXh4K6gcX4Am25mZavrwMA2TrLghJnyvOBg7rRzF3/uMzmIfN0KrNknbYQcMMjSqiQtp8U7NFp9N2teld+ALqB1+2DtMXrowElNk/I3akv6fy7f1dQ79Dv/a672gWAA4c6OWii7pO7Rqflix/XFpQz/Nq6+KdT59o8IceZSnem211HnNW/fn9AN0j61Wpd/St7lbaYlb6v7WOjd51QJ1t0fNxAjFJd6KiuNuZftt7mkvv1Gu6sPd+y+bapr9j8tV7RrYD0v3vH2vzKU9oGM/h13QwzmEK1xE/WiYiIiIg8ihfrREREREQelZJtMO4qKcFDOo1ROb/B5p8WfcbmF8/VBfVvd1ZtmZxrnKybEbncKc4Q8iN+9pM9421+a4+2vux4o9Lmkc816RNW6nRPIKAtMUSJZILa+lK4ps7mX704y+bSyx6z+YpCbYmZlqcrIq0+prVxxeZP27zl9Sqbh76uq78AQOjgwU6Omqj7BBv03NFroa4Lkbe3yuYvnnqrza09nCe33QUDcRZqya/Xc83gjdoCkLNlu82BWq0trvJCmcK06Cpk+a9vsHlkQ5XNc5deavO9A9spuBMUVjs1946ew0Lbq9t6uOfxk3UiIiIiIo/ixToRERERkUelZhuMy5kuDK7XdpfhD+lUZvWGETZ/4RTNgb56931nFK3Jsblwp45j6MpaHdOW6O5cJkoYp2YC23WViRF/1o1bftR0jc3/XqmrWrj8jbrSS7/3dMpx6CsbbQ427ot8EjdCIo8L7tHWrexXNZe9Gv/3YjMkkQo1OW3D72prcp939dt9OvG66dBUxk/WiYiIiIg8ihfrREREREQelfptMO0IbtLNknq5ORHvnYD3IIq30Gq9E79ydedfh8c/ERFR/HT4ybqI5InIuyKySkTWisiPwt8fIiJLRWSLiDwmIjkdvRZRpmDdEMWGNUMUG9ZM5oimDaYFwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5ojqBlMR8YvI+wDqAbwC4EMA+40xH688VQOgvJ3n3iQiy0VkeSta2noIUVrqbN2wZihT8VxDFBvWTGaI6mLdGBM0xkwEUAFgMoDR0b6BMeY+Y8wkY8ykbOR2cphEqaezdcOaoUzFcw1RbFgzmSGmpRuNMfsBLAIwFUAvEfl4NZkKADvjPDaitMC6IYoNa4YoNqyZ9BbNajClItIrnPMBXAxgPY4fFFeGHzYHwPzuGiRRqmHdEMWGNUMUG9ZM5ohmnfUyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggW4cJ1GqYd0QxYY1QxQb1kyGEGNM4t5MpAHAYQB7Evam3tAX3vmdBxtjSpM9CIpOuGZ2wFvHUCJ46fdlzaQYnms8gXWTQniu8YR2ayahF+sAICLLjTGTEvqmSZaJvzPFV6YdQ5n2+1L8ZeIxlIm/M8VXph1DqfL7xnSDKRERERERJQ4v1omIiIiIPCoZF+v3JeE9ky0Tf2eKr0w7hjLt96X4y8RjKBN/Z4qvTDuGUuL3TXjPOhERERERRYdtMEREREREHsWLdSIiIiIij0roxbqIzBSRjSKyRUTuTOR7J4KIDBKRRSKyTkTWisi3wt8vEZFXRGRz+P97J3uslBrSvWYA1g3FX7rXDWuG4i3dawZI7bpJWM96eIetTTi+HW4NgGUArjHGrEvIABJARMoAlBljVohITwDvAbgcwA0AGo0xd4WLoLcx5o4kDpVSQCbUDMC6ofjKhLphzVA8ZULNAKldN4n8ZH0ygC3GmK3GmGMAHgUwO4Hv3+2MMbXGmBXh3ARgPYByHP8954YfNhfHDw6ijqR9zQCsG4q7tK8b1gzFWdrXDJDadZPIi/VyANXO1zXh76UlEakCcBqApQD6G2Nqwz+qA9A/ScOi1JJRNQOwbiguMqpuWDMUBxlVM0Dq1Q1vMO0GIlII4CkAtxpjDro/M8f7jrheJtEJWDdEsWHNEMUuFesmkRfrOwEMcr6uCH8vrYhINo4fBA8bY+aFv7073Cv1cc9UfbLGRyklI2oGYN1QXGVE3bBmKI4yomaA1K2bRF6sLwMwQkSGiEgOgKsBPJPA9+92IiIAHgCw3hhzt/OjZwDMCec5AOYnemyUktK+ZgDWDcVd2tcNa4biLO1rBkjtuknoDqYicimAewD4ATxojPmfhL15AojIOQCWAFgDIBT+9vdwvCfqcQCVAHYAuMoY05iUQVJKSfeaAVg3FH/pXjesGYq3dK8ZILXrJqEX60REREREFD3eYEpERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWL9SiJyEwR2SgiW0TkzmSPh8jrROTbIrJWRD4QkUdEJC/ZYyLyMhHJE5F3RWRVuHZ+lOwxEXldJpxreLEeBRHxA/gtgFkAxgK4RkTGJndURN4lIuUAvglgkjFmHI6v3Xt1ckdF5HktAGYYY04FMBHATBGZkuQxEXlWppxreLEenckAthhjthpjjgF4FMDsJI+JyOuyAOSLSBaAAgC7kjweIk8zxx0Kf5kd/h83QyE6ubQ/1/BiPTrlAKqdr2vC3yOiNhhjdgL4BYCPANQCOGCMeTm5oyLyPhHxi8j7AOoBvGKMWZrsMRF5Vaaca3ixTkRxJyK9cXz2aQiAgQB6iMh1yR0VkfcZY4LGmIkAKgBMFpFxyR4TkVdlyrmGF+vR2QlgkPN1Rfh7RNS2iwBsM8Y0GGNaAcwDcHaSx0SUMowx+wEsAjAz2WMh8rCMONfwYj06ywCMEJEhIpKD4zcvPJPkMRF52UcApohIgYgIgAsBrE/ymIg8TURKRaRXOOcDuBjAhuSOisjTMuJck5XsAaQCY0xARL4OYAGO32n8oDFmbZKHReRZxpilIvIkgBUAAgBWArgvuaMi8rwyAHPDK5D5ADxujHkuyWMi8qxMOdeIMbzRnIiIiIjIi9gGQ0RERETkUbxYJyIiIiLyKF6sExERERF5FC/WiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH/X/jBWYG27+ZQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(valid_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(test_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Reshape(torch.nn.Module):\n",
+    "    def __init__(self, shape):\n",
+    "        super(Reshape, self).__init__()\n",
+    "        self.shape = shape\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return x.view(*self.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class  VariationalAutoencoderZrc(torch.nn.Module):\n",
+    "    def __init__(self, num_latent, grayscale = False):\n",
+    "        super(VariationalAutoencoderZrc, self).__init__()\n",
+    "        # calculate same padding:\n",
+    "        # (w - k + 2*p)/s + 1 = o\n",
+    "        if grayscale:\n",
+    "            in_channels = 1\n",
+    "        else:\n",
+    "            in_channels = 3\n",
+    "        \n",
+    "        \n",
+    "        # (w-k+2p) // 2 + 1\n",
+    "        \n",
+    "        # 28x28x1 => 14x14x4\n",
+    "        self.encoder = torch.nn.Sequential(\n",
+    "            torch.nn.Conv2d(in_channels,\n",
+    "                            out_channels=4,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            # 14x14x4 => 7x7x8\n",
+    "            torch.nn.Conv2d(in_channels=4,\n",
+    "                              out_channels=8,\n",
+    "                              kernel_size=(3, 3),\n",
+    "                              stride=(2, 2),\n",
+    "                              padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            torch.nn.Flatten(),\n",
+    "            \n",
+    "        )\n",
+    "        \n",
+    "        self.z_mean = torch.nn.Linear(8*8*8, num_latent)\n",
+    "        self.z_log_var = torch.nn.Linear(8*8*8, num_latent)\n",
+    "        # in the original paper (Kingma & Welling 2015, we use\n",
+    "        # have a z_mean and z_var, but the problem is that\n",
+    "        # the z_var can be negative, which would cause issues\n",
+    "        # in the log later. Hence we assume that latent vector\n",
+    "        # has a z_mean and z_log_var component, and when we need\n",
+    "        # the regular variance or std_dev, we simply use \n",
+    "        # an exponential function\n",
+    "        \n",
+    "        # Hout=(H−1)×stride[0]−2×padding[0]+dilation[0]×(kernel_size[0]−1)+output_padding[0]+1\n",
+    "        \n",
+    "        self.decoder = torch.nn.Sequential(\n",
+    "            torch.nn.Linear(num_latent, 8*8*8),\n",
+    "            Reshape((-1,8,8,8)),\n",
+    "            \n",
+    "            torch.nn.ConvTranspose2d(in_channels=8,\n",
+    "                                     out_channels=4,\n",
+    "                                     kernel_size=(3, 3),\n",
+    "                                     stride=(2, 2),\n",
+    "                                     padding = 1,\n",
+    "                                     output_padding = 1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            torch.nn.ConvTranspose2d(in_channels = 4,\n",
+    "                            out_channels= in_channels,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding = 1,\n",
+    "                            output_padding = 1),\n",
+    "                            \n",
+    "            torch.nn.LeakyReLU(inplace = True)\n",
+    "        )\n",
+    "        \n",
+    "    def reparameterize(self, z_mu, z_log_var):\n",
+    "        # Sample epsilon from standard normal distribution\n",
+    "        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(DEVICE)\n",
+    "        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\n",
+    "        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\n",
+    "        z = z_mu + eps * torch.exp(z_log_var/2.) \n",
+    "        return z\n",
+    "        \n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.encoder(x)\n",
+    "        mean = self.z_mean(x)\n",
+    "        log_var = self.z_log_var(x)\n",
+    "        \n",
+    "        encoded = self.reparameterize(mean, log_var)\n",
+    "        decoded = torch.sigmoid(self.decoder(encoded))\n",
+    "        \n",
+    "        \n",
+    "        return mean,log_var,decoded\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1            [-1, 4, 16, 16]             112\n",
+      "         LeakyReLU-2            [-1, 4, 16, 16]               0\n",
+      "            Conv2d-3              [-1, 8, 8, 8]             296\n",
+      "         LeakyReLU-4              [-1, 8, 8, 8]               0\n",
+      "           Flatten-5                  [-1, 512]               0\n",
+      "            Linear-6                   [-1, 10]           5,130\n",
+      "            Linear-7                   [-1, 10]           5,130\n",
+      "            Linear-8                  [-1, 512]           5,632\n",
+      "           Reshape-9              [-1, 8, 8, 8]               0\n",
+      "  ConvTranspose2d-10            [-1, 4, 16, 16]             292\n",
+      "        LeakyReLU-11            [-1, 4, 16, 16]               0\n",
+      "  ConvTranspose2d-12            [-1, 3, 32, 32]             111\n",
+      "        LeakyReLU-13            [-1, 3, 32, 32]               0\n",
+      "================================================================\n",
+      "Total params: 16,703\n",
+      "Trainable params: 16,703\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.01\n",
+      "Forward/backward pass size (MB): 0.10\n",
+      "Params size (MB): 0.06\n",
+      "Estimated Total Size (MB): 0.17\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_nin():\n",
+    "    model = VariationalAutoencoderZrc(num_latent = 10).to(DEVICE)\n",
+    "    summary(model, (3,32,32))\n",
+    "    \n",
+    "test_nin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = VariationalAutoencoderZrc(num_latent = NUM_LATENT, grayscale=GRAYSCALE)\n",
+    "model.to(DEVICE)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_func(z_mean,z_log_var,decoded,features):\n",
+    "    kl_divergence = (0.5 * (z_mean**2 + \n",
+    "                                torch.exp(z_log_var) - z_log_var - 1)).sum()\n",
+    "    pixelwise_bce = F.binary_cross_entropy(decoded, features, reduction='sum')\n",
+    "    loss = kl_divergence + pixelwise_bce\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, data_loader, optimizer, num_epochs,batch_size, device,metric_func = None, random_seed = 7):\n",
+    "    # Manual seed for deterministic data loader\n",
+    "    torch.manual_seed(random_seed)\n",
+    "    \n",
+    "    loss_list = []\n",
+    "    \n",
+    "    for epoch in range(num_epochs):\n",
+    "        # set training mode\n",
+    "        model.train() \n",
+    "        for batch_idx, (features, targets) in enumerate(data_loader[\"train\"]):\n",
+    "            features = features.to(device)\n",
+    "\n",
+    "            ## forward pass\n",
+    "            z_mean,z_log_var,decoded = model(features)\n",
+    "            loss = loss_func(z_mean,z_log_var,decoded,features)\n",
+    "\n",
+    "            # backward pass\n",
+    "            # clear the gradients of all tensors being optimized\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            ### Login\n",
+    "            loss_list.append(loss.item())\n",
+    "            if not batch_idx % 50:\n",
+    "                print ('Epoch: {0:03d}/{1:03d} | Batch {2:03d}/{3:03d} | Loss: {4:.2f}'.format(\n",
+    "                    epoch+1, num_epochs, batch_idx, \n",
+    "                         len(train_dataset)//batch_size, loss))\n",
+    "    return loss_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 001/010 | Batch 000/921 | Loss: 45389.08\n",
+      "Epoch: 001/010 | Batch 050/921 | Loss: 44131.24\n",
+      "Epoch: 001/010 | Batch 100/921 | Loss: 21885.48\n",
+      "Epoch: 001/010 | Batch 150/921 | Loss: 18890.12\n",
+      "Epoch: 001/010 | Batch 200/921 | Loss: 15557.18\n",
+      "Epoch: 001/010 | Batch 250/921 | Loss: 15639.79\n",
+      "Epoch: 001/010 | Batch 300/921 | Loss: 15269.14\n",
+      "Epoch: 001/010 | Batch 350/921 | Loss: 14441.80\n",
+      "Epoch: 001/010 | Batch 400/921 | Loss: 13856.73\n",
+      "Epoch: 001/010 | Batch 450/921 | Loss: 13564.78\n",
+      "Epoch: 001/010 | Batch 500/921 | Loss: 13332.94\n",
+      "Epoch: 001/010 | Batch 550/921 | Loss: 13040.77\n",
+      "Epoch: 001/010 | Batch 600/921 | Loss: 13785.67\n",
+      "Epoch: 001/010 | Batch 650/921 | Loss: 12373.28\n",
+      "Epoch: 001/010 | Batch 700/921 | Loss: 12537.45\n",
+      "Epoch: 001/010 | Batch 750/921 | Loss: 11847.59\n",
+      "Epoch: 001/010 | Batch 800/921 | Loss: 11792.15\n",
+      "Epoch: 001/010 | Batch 850/921 | Loss: 12379.78\n",
+      "Epoch: 001/010 | Batch 900/921 | Loss: 11866.46\n",
+      "Epoch: 002/010 | Batch 000/921 | Loss: 11773.14\n",
+      "Epoch: 002/010 | Batch 050/921 | Loss: 11846.30\n",
+      "Epoch: 002/010 | Batch 100/921 | Loss: 11435.83\n",
+      "Epoch: 002/010 | Batch 150/921 | Loss: 11258.23\n",
+      "Epoch: 002/010 | Batch 200/921 | Loss: 12070.00\n",
+      "Epoch: 002/010 | Batch 250/921 | Loss: 11034.30\n",
+      "Epoch: 002/010 | Batch 300/921 | Loss: 11381.75\n",
+      "Epoch: 002/010 | Batch 350/921 | Loss: 11948.15\n",
+      "Epoch: 002/010 | Batch 400/921 | Loss: 12120.62\n",
+      "Epoch: 002/010 | Batch 450/921 | Loss: 11244.60\n",
+      "Epoch: 002/010 | Batch 500/921 | Loss: 11394.31\n",
+      "Epoch: 002/010 | Batch 550/921 | Loss: 11750.06\n",
+      "Epoch: 002/010 | Batch 600/921 | Loss: 11752.36\n",
+      "Epoch: 002/010 | Batch 650/921 | Loss: 12104.79\n",
+      "Epoch: 002/010 | Batch 700/921 | Loss: 10770.80\n",
+      "Epoch: 002/010 | Batch 750/921 | Loss: 11131.94\n",
+      "Epoch: 002/010 | Batch 800/921 | Loss: 11654.01\n",
+      "Epoch: 002/010 | Batch 850/921 | Loss: 11474.12\n",
+      "Epoch: 002/010 | Batch 900/921 | Loss: 10943.71\n",
+      "Epoch: 003/010 | Batch 000/921 | Loss: 11686.91\n",
+      "Epoch: 003/010 | Batch 050/921 | Loss: 11768.64\n",
+      "Epoch: 003/010 | Batch 100/921 | Loss: 11677.51\n",
+      "Epoch: 003/010 | Batch 150/921 | Loss: 10823.33\n",
+      "Epoch: 003/010 | Batch 200/921 | Loss: 11277.84\n",
+      "Epoch: 003/010 | Batch 250/921 | Loss: 11520.21\n",
+      "Epoch: 003/010 | Batch 300/921 | Loss: 11373.36\n",
+      "Epoch: 003/010 | Batch 350/921 | Loss: 11605.54\n",
+      "Epoch: 003/010 | Batch 400/921 | Loss: 11344.90\n",
+      "Epoch: 003/010 | Batch 450/921 | Loss: 11211.00\n",
+      "Epoch: 003/010 | Batch 500/921 | Loss: 10853.55\n",
+      "Epoch: 003/010 | Batch 550/921 | Loss: 11364.78\n",
+      "Epoch: 003/010 | Batch 600/921 | Loss: 11096.88\n",
+      "Epoch: 003/010 | Batch 650/921 | Loss: 11526.54\n",
+      "Epoch: 003/010 | Batch 700/921 | Loss: 11076.70\n",
+      "Epoch: 003/010 | Batch 750/921 | Loss: 11697.93\n",
+      "Epoch: 003/010 | Batch 800/921 | Loss: 10987.28\n",
+      "Epoch: 003/010 | Batch 850/921 | Loss: 11169.60\n",
+      "Epoch: 003/010 | Batch 900/921 | Loss: 11118.75\n",
+      "Epoch: 004/010 | Batch 000/921 | Loss: 10632.36\n",
+      "Epoch: 004/010 | Batch 050/921 | Loss: 11193.16\n",
+      "Epoch: 004/010 | Batch 100/921 | Loss: 11230.08\n",
+      "Epoch: 004/010 | Batch 150/921 | Loss: 11071.93\n",
+      "Epoch: 004/010 | Batch 200/921 | Loss: 10609.30\n",
+      "Epoch: 004/010 | Batch 250/921 | Loss: 11265.78\n",
+      "Epoch: 004/010 | Batch 300/921 | Loss: 11246.93\n",
+      "Epoch: 004/010 | Batch 350/921 | Loss: 11190.55\n",
+      "Epoch: 004/010 | Batch 400/921 | Loss: 10691.18\n",
+      "Epoch: 004/010 | Batch 450/921 | Loss: 10801.95\n",
+      "Epoch: 004/010 | Batch 500/921 | Loss: 10135.94\n",
+      "Epoch: 004/010 | Batch 550/921 | Loss: 10677.15\n",
+      "Epoch: 004/010 | Batch 600/921 | Loss: 11265.51\n",
+      "Epoch: 004/010 | Batch 650/921 | Loss: 11526.92\n",
+      "Epoch: 004/010 | Batch 700/921 | Loss: 10990.92\n",
+      "Epoch: 004/010 | Batch 750/921 | Loss: 11758.14\n",
+      "Epoch: 004/010 | Batch 800/921 | Loss: 10900.31\n",
+      "Epoch: 004/010 | Batch 850/921 | Loss: 11274.41\n",
+      "Epoch: 004/010 | Batch 900/921 | Loss: 10719.29\n",
+      "Epoch: 005/010 | Batch 000/921 | Loss: 11320.06\n",
+      "Epoch: 005/010 | Batch 050/921 | Loss: 10852.65\n",
+      "Epoch: 005/010 | Batch 100/921 | Loss: 10842.25\n",
+      "Epoch: 005/010 | Batch 150/921 | Loss: 10985.18\n",
+      "Epoch: 005/010 | Batch 200/921 | Loss: 10757.76\n",
+      "Epoch: 005/010 | Batch 250/921 | Loss: 10980.56\n",
+      "Epoch: 005/010 | Batch 300/921 | Loss: 10679.02\n",
+      "Epoch: 005/010 | Batch 350/921 | Loss: 10916.35\n",
+      "Epoch: 005/010 | Batch 400/921 | Loss: 10735.33\n",
+      "Epoch: 005/010 | Batch 450/921 | Loss: 10850.87\n",
+      "Epoch: 005/010 | Batch 500/921 | Loss: 10320.64\n",
+      "Epoch: 005/010 | Batch 550/921 | Loss: 11523.04\n",
+      "Epoch: 005/010 | Batch 600/921 | Loss: 11205.94\n",
+      "Epoch: 005/010 | Batch 650/921 | Loss: 10674.83\n",
+      "Epoch: 005/010 | Batch 700/921 | Loss: 11139.79\n",
+      "Epoch: 005/010 | Batch 750/921 | Loss: 11043.64\n",
+      "Epoch: 005/010 | Batch 800/921 | Loss: 11379.76\n",
+      "Epoch: 005/010 | Batch 850/921 | Loss: 11137.25\n",
+      "Epoch: 005/010 | Batch 900/921 | Loss: 11314.13\n",
+      "Epoch: 006/010 | Batch 000/921 | Loss: 10395.93\n",
+      "Epoch: 006/010 | Batch 050/921 | Loss: 10482.25\n",
+      "Epoch: 006/010 | Batch 100/921 | Loss: 11515.95\n",
+      "Epoch: 006/010 | Batch 150/921 | Loss: 11441.51\n",
+      "Epoch: 006/010 | Batch 200/921 | Loss: 11061.13\n",
+      "Epoch: 006/010 | Batch 250/921 | Loss: 10842.22\n",
+      "Epoch: 006/010 | Batch 300/921 | Loss: 10894.73\n",
+      "Epoch: 006/010 | Batch 350/921 | Loss: 11720.62\n",
+      "Epoch: 006/010 | Batch 400/921 | Loss: 11245.84\n",
+      "Epoch: 006/010 | Batch 450/921 | Loss: 11273.43\n",
+      "Epoch: 006/010 | Batch 500/921 | Loss: 11343.73\n",
+      "Epoch: 006/010 | Batch 550/921 | Loss: 10898.97\n",
+      "Epoch: 006/010 | Batch 600/921 | Loss: 11132.20\n",
+      "Epoch: 006/010 | Batch 650/921 | Loss: 10567.51\n",
+      "Epoch: 006/010 | Batch 700/921 | Loss: 11181.18\n",
+      "Epoch: 006/010 | Batch 750/921 | Loss: 11037.67\n",
+      "Epoch: 006/010 | Batch 800/921 | Loss: 10786.92\n",
+      "Epoch: 006/010 | Batch 850/921 | Loss: 10722.83\n",
+      "Epoch: 006/010 | Batch 900/921 | Loss: 10484.91\n",
+      "Epoch: 007/010 | Batch 000/921 | Loss: 11194.28\n",
+      "Epoch: 007/010 | Batch 050/921 | Loss: 10692.05\n",
+      "Epoch: 007/010 | Batch 100/921 | Loss: 10654.63\n",
+      "Epoch: 007/010 | Batch 150/921 | Loss: 10316.07\n",
+      "Epoch: 007/010 | Batch 200/921 | Loss: 10243.13\n",
+      "Epoch: 007/010 | Batch 250/921 | Loss: 10259.13\n",
+      "Epoch: 007/010 | Batch 300/921 | Loss: 11124.15\n",
+      "Epoch: 007/010 | Batch 350/921 | Loss: 10570.22\n",
+      "Epoch: 007/010 | Batch 400/921 | Loss: 10378.32\n",
+      "Epoch: 007/010 | Batch 450/921 | Loss: 10753.97\n",
+      "Epoch: 007/010 | Batch 500/921 | Loss: 11049.02\n",
+      "Epoch: 007/010 | Batch 550/921 | Loss: 10426.00\n",
+      "Epoch: 007/010 | Batch 600/921 | Loss: 10704.39\n",
+      "Epoch: 007/010 | Batch 650/921 | Loss: 10887.13\n",
+      "Epoch: 007/010 | Batch 700/921 | Loss: 10803.31\n",
+      "Epoch: 007/010 | Batch 750/921 | Loss: 10829.36\n",
+      "Epoch: 007/010 | Batch 800/921 | Loss: 11095.64\n",
+      "Epoch: 007/010 | Batch 850/921 | Loss: 10611.91\n",
+      "Epoch: 007/010 | Batch 900/921 | Loss: 10572.98\n",
+      "Epoch: 008/010 | Batch 000/921 | Loss: 10471.42\n",
+      "Epoch: 008/010 | Batch 050/921 | Loss: 11047.63\n",
+      "Epoch: 008/010 | Batch 100/921 | Loss: 11249.25\n",
+      "Epoch: 008/010 | Batch 150/921 | Loss: 10560.03\n",
+      "Epoch: 008/010 | Batch 200/921 | Loss: 10668.32\n",
+      "Epoch: 008/010 | Batch 250/921 | Loss: 10499.23\n",
+      "Epoch: 008/010 | Batch 300/921 | Loss: 10400.92\n",
+      "Epoch: 008/010 | Batch 350/921 | Loss: 10859.54\n",
+      "Epoch: 008/010 | Batch 400/921 | Loss: 10822.59\n",
+      "Epoch: 008/010 | Batch 450/921 | Loss: 11075.27\n",
+      "Epoch: 008/010 | Batch 500/921 | Loss: 10644.42\n",
+      "Epoch: 008/010 | Batch 550/921 | Loss: 10794.20\n",
+      "Epoch: 008/010 | Batch 600/921 | Loss: 10733.19\n",
+      "Epoch: 008/010 | Batch 650/921 | Loss: 11162.96\n",
+      "Epoch: 008/010 | Batch 700/921 | Loss: 9849.97\n",
+      "Epoch: 008/010 | Batch 750/921 | Loss: 10785.50\n",
+      "Epoch: 008/010 | Batch 800/921 | Loss: 11271.96\n",
+      "Epoch: 008/010 | Batch 850/921 | Loss: 10715.61\n",
+      "Epoch: 008/010 | Batch 900/921 | Loss: 10217.48\n",
+      "Epoch: 009/010 | Batch 000/921 | Loss: 11179.12\n",
+      "Epoch: 009/010 | Batch 050/921 | Loss: 10986.02\n",
+      "Epoch: 009/010 | Batch 100/921 | Loss: 10615.51\n",
+      "Epoch: 009/010 | Batch 150/921 | Loss: 11271.40\n",
+      "Epoch: 009/010 | Batch 200/921 | Loss: 10481.72\n",
+      "Epoch: 009/010 | Batch 250/921 | Loss: 10838.91\n",
+      "Epoch: 009/010 | Batch 300/921 | Loss: 10799.99\n",
+      "Epoch: 009/010 | Batch 350/921 | Loss: 10713.62\n",
+      "Epoch: 009/010 | Batch 400/921 | Loss: 11029.77\n",
+      "Epoch: 009/010 | Batch 450/921 | Loss: 10744.25\n",
+      "Epoch: 009/010 | Batch 500/921 | Loss: 11118.08\n",
+      "Epoch: 009/010 | Batch 550/921 | Loss: 11066.55\n",
+      "Epoch: 009/010 | Batch 600/921 | Loss: 10683.15\n",
+      "Epoch: 009/010 | Batch 650/921 | Loss: 10663.87\n",
+      "Epoch: 009/010 | Batch 700/921 | Loss: 10861.18\n",
+      "Epoch: 009/010 | Batch 750/921 | Loss: 11218.32\n",
+      "Epoch: 009/010 | Batch 800/921 | Loss: 10726.44\n",
+      "Epoch: 009/010 | Batch 850/921 | Loss: 10402.06\n",
+      "Epoch: 009/010 | Batch 900/921 | Loss: 11070.47\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 010/010 | Batch 000/921 | Loss: 10425.69\n",
+      "Epoch: 010/010 | Batch 050/921 | Loss: 10542.37\n",
+      "Epoch: 010/010 | Batch 100/921 | Loss: 10785.21\n",
+      "Epoch: 010/010 | Batch 150/921 | Loss: 10476.09\n",
+      "Epoch: 010/010 | Batch 200/921 | Loss: 11013.08\n",
+      "Epoch: 010/010 | Batch 250/921 | Loss: 11131.85\n",
+      "Epoch: 010/010 | Batch 300/921 | Loss: 10687.36\n",
+      "Epoch: 010/010 | Batch 350/921 | Loss: 10992.34\n",
+      "Epoch: 010/010 | Batch 400/921 | Loss: 10533.26\n",
+      "Epoch: 010/010 | Batch 450/921 | Loss: 11188.06\n",
+      "Epoch: 010/010 | Batch 500/921 | Loss: 10398.12\n",
+      "Epoch: 010/010 | Batch 550/921 | Loss: 10894.27\n",
+      "Epoch: 010/010 | Batch 600/921 | Loss: 10381.60\n",
+      "Epoch: 010/010 | Batch 650/921 | Loss: 10705.90\n",
+      "Epoch: 010/010 | Batch 700/921 | Loss: 10714.91\n",
+      "Epoch: 010/010 | Batch 750/921 | Loss: 10704.41\n",
+      "Epoch: 010/010 | Batch 800/921 | Loss: 11017.69\n",
+      "Epoch: 010/010 | Batch 850/921 | Loss: 10575.25\n",
+      "Epoch: 010/010 | Batch 900/921 | Loss: 10409.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_list = train_model(model, \n",
+    "            data_loader, \n",
+    "            optimizer, \n",
+    "            NUM_EPOCHS, \n",
+    "            device = DEVICE, \n",
+    "            batch_size = BATCH_SIZE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(loss_list, label='Minibatch cost')\n",
+    "plt.plot(np.convolve(loss_list, \n",
+    "                     np.ones(200,)/200, mode='valid'), \n",
+    "         label='Running average')\n",
+    "\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([64, 1, 32, 32])"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_batch, test_tagret = next(iter(data_loader[\"test\"]))\n",
+    "z_mean,z_log_var,decoded = model(test_batch)\n",
+    "decoded.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 15)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x180 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "##########################\n",
+    "### VISUALIZATION\n",
+    "##########################\n",
+    "\n",
+    "n_images = 15\n",
+    "image_width = 32\n",
+    "\n",
+    "# axes (2,15)\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=n_images, \n",
+    "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
+    "\n",
+    "orig_images = test_batch\n",
+    "z_mean,z_log_var,decoded_images = model(test_batch)\n",
+    "\n",
+    "for i in range(n_images):\n",
+    "    for ax, img in zip(axes, [orig_images, decoded_images]):\n",
+    "        curr_img = img[i].detach().to(torch.device('cpu'))\n",
+    "        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\n",
+    "\n",
+    "axes.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tryit",
+   "language": "python",
+   "name": "tryit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/ Convolutional-Variational-Autoencoder-checkpoint.ipynb b/.ipynb_checkpoints/ Convolutional-Variational-Autoencoder-checkpoint.ipynb
new file mode 100644
index 0000000..eb808a9
--- /dev/null
+++ b/.ipynb_checkpoints/ Convolutional-Variational-Autoencoder-checkpoint.ipynb	
@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Autoencoder with Deconvolutions (without pooling operations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/autoencoder/autoencoder-arch.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
+       " '',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/Users/ZRC/.ipython',\n",
+       " '/Users/ZRC']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/Users/ZRC\")\n",
+    "sys.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import RandomSampler\n",
+    "from torch.utils.data import Subset\n",
+    "\n",
+    "\n",
+    "from torchvision import datasets\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coke.visualization.image import show_batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_EPOCHS = 10\n",
+    "LEARNING_RATE = 0.005\n",
+    "RANDOM_SEED = 7\n",
+    "\n",
+    "# Architecture\n",
+    "NUM_CLASSES = 10\n",
+    "GRAYSCALE = True\n",
+    "NUM_LATENT = 20\n",
+    "\n",
+    "# # other\n",
+    "# torch.cuda.empty_cache()\n",
+    "DEVICE = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_transforms = {\"train\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()]),\n",
+    "                    \"test\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()])\n",
+    "                   }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_indices = torch.arange(0, 59000)\n",
+    "valid_indices = torch.arange(59000, 60000)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_and_valida_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = True,\n",
+    "                                transform = data_transforms[\"train\"],\n",
+    "                                download=True)\n",
+    "\n",
+    "test_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = False,\n",
+    "                                transform = data_transforms[\"test\"],\n",
+    "                                download=False)\n",
+    "\n",
+    "train_dataset = Subset(train_and_valida_dataset, train_indices)\n",
+    "valid_dataset = Subset(train_and_valida_dataset, valid_indices)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_dataloader = DataLoader(dataset = train_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=True,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "valid_dataloader = DataLoader(dataset = valid_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "test_dataloader = DataLoader(dataset = test_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "data_loader = {\"train\": train_dataloader, \n",
+    "               \"val\": valid_dataloader,\n",
+    "               \"test\": test_dataloader}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(train_dataloader))\n",
+    "print(batch_samples.size(),labels.size())\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uSoWbdAenhgnaWlDwFd3x6IGKR20+c4HW2Y9HfsnmSev0Ohdq1QOZeE2hfpGuc56ZFTtsHurTeU5DSD8zTj/gvO/rddeXvvJlac3s/pweXHb+vA9tHpOmtfvLuuk2j31Gx2rieDAmP1knIiIiIvIoTtaJiIiIiDwqJdpgXEHnv092NA+xOaeur98xVr48XdIPfkaXXGpn6hJk4yRdWrnoFG0HeGHTcTYPfUkPvBiy7GObA7t0BwCigZQ2utTm5iklNh+cpK1c3//mEzZfllsV8fgcn94vDTnoytrmUTbnb9V6DW7eHv2AiQbQoCWrbfat0wNUbhjzfZvTWvXv/hFr9D09PKgHeqGzM+J5TUAfM2S503721kibz/jmj2x+5vO/s/m/L7rf5h8d+LrN5b/V606oWdtDifoiot23UNsjp+Rpa1aW6N/rX1qj78nSN/R9H6zT3ZT6ate3TrZ53pffs/mXJe/avKhRD216dfFMm0e9sdxmtw4HGj9ZJyIiIiLyKE7WiYiIiIg8ipN1IiIiIiKPSrmeddeMAj0x7oHxE23O6+rOPWj/rJ4Y9/E1ugXQN056y+YpWdqTOMyvp1+NSdMttG4oftPmKzq+bXPBxkJ9MfasUz/zTdPvTlRfVGBz2Tn63YnrS5+xeWianrh4ZlaDzXm+rvvSj+ZzhStsfvbME2wOfXSiju/t1SDyGuNstxuq1OtL1sfO39nONonBlpboX8Tpmw1V6daoObv1tN9dzomn5Wm6BV7nRL3WuFuvEsWKCTob69bpdrsPP3emzYvyzrB55Bt69+yV+h2OYB+3EzWnT7V56IW6xemPhuoc69XW4Tb/9i09qXTK07p9dzCOfeoufrJORERERORRnKwTEREREXlUSrTB5O3RbRm3HS62ecHod2xefPpJNstJx9tsVm+wOa1ct+ICgOqrdBu70vO1PeDe8udtrurU17t51ZU2d+zV9oAHL/6zzTMzdeknc5Bzgmk6lyypf/mHDrV5y1d1Gf3r85bafM1gPfFtbHp3DWNZ3dwO/KmhzOaPDusWjecXrLf5nOy9Nt9+ymKbf/jdL9g8rlZPSQ1u03YDhHi2KXmD6ezoMseSf5Ru3dg8Sq9zx2Vo60t1wGlF26PbAUe0KxDFitvmdVi3BJ3wYL3ex+d8TlyrtwcPaAtlrzinxaeNKYv40car9Tp0+5jXbC7yaw3cvl1PEB77N62f0Hadz3kFP1knIiIiIvIoTtaJiIiIiDwqJdpgBq/RZZYP1+ry+dYReoLp/5myxOabfnSNzcUvzLL54CRdcgGAiy/Uk7Bucr5hnOUszfxkyxU2Z74zyOb2qbos2mn0/4YnD+uJX7JO7++v32MzFy+pXxRq60vRZP0W/7UF2voyKk1bXzqNvhP/0a5tWte/rSfS+fZlRLzEoB36+UDWAV12fOXUz9j83XNfsvnGgq02f/t4rbEnJ56vz1Ol3/Q3bIOhZOfTWms8WXeAKTtB28dK/Nk2v9+muzkVbtBrUzxPY6QU4fx9HNy49Sh37DtzOPIU3tOn64mpZ2Y5u7s4862du4tsnrJcxxfsp7a1vujxk3URKRORZSKyQUTWi8hN4duLROQVEdka/mdhT89FlApYM0TRY90QRYc1kzp60wYTAPBDY8xxAE4F8G0ROQ7ArQCWGmMqACwN/5mIWDNEx4J1QxQd1kyK6LENxhizB8CecG4SkY0ASgFcBuCs8N0WAXgdwC39Mso+Cm2rsrl8if4H5ncHabvLn097yOZnZt9h88/LLrX5qqJtEc/7lcFrbR7iHP5yMKQHTxxXWGvz26fn2vyTz7xq86tNuvvMI2/MtnnCUj0ww+zRZRzytoStmTRdXt+3S9uxfll8gc1NAf0m/fo6PVCibbPef9Jjh2yWXXpoCwCEGvXwJNPhtIJln6rPO0t3WfIVaM35RNtmjC+yJY0SXzzrxl+o1wXJ11Yvc1Dfy0HnvRtPaWW6A8ye2VoHvy7XHS9qg3oNerxOW8aGrHF+B+4Gk/AS9lrTF04bWFqJ7mBWe9HYiLt9c+gim9NFP5f+1b7pNuev0euZV+q7O1H1rItIOYCTALwPoCT8RgGAvQBKunnMQgALASAL0Z9mSJTIWDNE0WPdEEWHNZPcer0bjIjkAXgSwPeMMRH/CWKMMQC6PBvWGHOnMWa6MWZ6OjK7ugtRUmLNEEWPdUMUHdZM8uvVJ+siko4jb4SHjTFPhW+uFZERxpg9IjICgGf7NNxvvWe8+IHNZb4ZNv+s6HKbX5v6oM1/G687U/xv+o17v7PMUujT2+8oW2Zz5fBXbP7zvrNsfuXFk22e/NgBm0OVVZrb9YAk8r5ErBk5dNjmUS9qW8DKD3WnFncHl7INeoBFaLPWVegou0y47QZmynibD0zV551fqO1l7nL+sv2TdBy1ejuMPpYSW7zqJjReW6/qT9JduLL3a8vJoPW6QxL266FDoUNNNptAp97HdDk/ijjIRTJ0tyTfIG2/kczIiVP7RG05qzlVD3u5+KzlNl+eq/X4ZLP+PqvemWjzhI1rdNxsg0kKiXit6Qu/06bWMGeMzV+46dWI+52ZrfX654YTbH76qTk2lz+bOLvs9WY3GAFwD4CNxpjbnR8tAXBdOF8H4JnYD48o8bBmiKLHuiGKDmsmdfTmk/XZAL4KYK2IrA7f9lMAtwFYLCILAFQDuKp/hkiUcFgzRNFj3RBFhzWTInqzG8zbALrbeuHc2A5nYOVUasvJ7rXDbD5woi7jl/j1m8eHQpEb5Tc4q+9Bo/+Kmky6zavbdJnm7u2600twSbHN4x7Upclgq7u8380yKnmaJ2vGWXr3uUvs6fpeDR3UZfScp3UXo5xuDhrqdtnQ+ba+LzfyS0stp06wufoyHdOP5j5v83k5+tpPNOn91/yjwuaKBueQixDrJBnEs26axjk7wFyqy+dZOfr38ZaVo2weXKm7UBRs1etC+qE2myXY9fvSpOmCdkeRtrQ0jtZa7MiP/Ncw4qKPbX50/BM2fyZDH7+jU3cPu2vnZTaXvqnXs1CL3ocSnyevNf3BuaagVFvC9lysbWcLC9a4j0CWaD395eV5Nk96Ug/JDFbuiOUo+1Wvv2BKREREREQDi5N1IiIiIiKPimqf9WRzaKq2opxweqXNJX5tE3BbX36zT79FDABv7dXdLFo7dMmlsU6XVIe+o/+Ki1/UA16CtVtt5l4W1N8iWl8mlNvYOkZ3vkhr0caWzE27bQ7U7tPHui0xbmtNtu6A5CvSHV+ap+puGgBQ8yVdkr/vVD204owsvX1th36G8Ju1unxZcbfT+rIl8oAyor7ozNH38gWjNtn84+L3bM6brDXkczoP7jpUZvPWVt3OujWkO724cv26s9eJOXpo2JcH6fvb3V0MAILOjkch6LXmYFDbWm7Y+iWbA3/QVoGs53THGKJE5C/UQ/cOTNPry7/MfNrmHF96xGPeb9c/F67XejXVu/pjiP2On6wTEREREXkUJ+tERERERB6V0m0w/g79tv6hDl3Grw3qMuXNOy+1ueGmyCX9oirdUB/OQTAjg7pkaTq0jSbYEbmbDNFAEWdXlvpTdBnxd7/4k81NIa2B7z+4wOaxD+vtwW1VNqeNcg6SOVtbAern6Y4YT875Y8Q4xqRpG02eT9sKtgX0MVe+/R2bJ/5Gd+MIOoeEEcVS8VPrbX6n/lSbn/naiTY/eso9Nk9xdlG6Nt/ZUcLJoV40OKaLs8sF/N3e77DRa9K6Dq2bhav+yebCR7T9ctBSPViMbZaU6MwI3a2v7jSdt30lX9vIMiWyDeb763W3yqIten1J1B2R+Mk6EREREZFHcbJORERERORRKd0GM2itfvt+2zt64EWVc0BGTVOBzYNr9dAYAAgccP7czcExRF4gebk275+my4gV6dpmMtin7+Gffelxmx8/Z4bNexom2VxWoO//m0c8rM+ZoXV1fHrkjhh+0SX89R362hcvvUkff6+2lJnNTosBa4z6SbCpyeacNzbaPGab7qryvTJtz9pzur6vh8/W3SWmFmnO8UXX9tgY0Hazl147OeJn2bW6m0XRJj0IZsy2g3qn2hobIw7XI0pA4uxg1nCizsP+5VzdAcZtfTkUinzP+54aYnP6hs02BxP0sEl+sk5ERERE5FGcrBMREREReVRKt8EEd+luLuMe12XNWzd+0+asel1yDO3THQMAAIbfs6fEYJr1G/D5W/W/0TucJUF3SfGKXK2NM8Y+YXNzSB87yKfv/1Fp2joGZHU7jt8fLLf5v5ZeYPP4v2ud+VdtsTnUrrtgEPUbpw5CTksMNmrOqNRrxLhK3Rms/XVdbl+do7tWOOcm9Y5zOanYVhfxI2nWJf7Q/gM2B9vaQJQsJE2npO1n6U5MjZ/XOvz8IG2NPBzSIrtw3Zcjnqt41SGbgwcPIdHxk3UiIiIiIo/iZJ2IiIiIyKNSug3GOEvswQ269D5oQ9f3Z9MLJSrTosvoQ1c127xg69U2zxhSbfO1he/ZPDHdbXFRnUZ3Z3F3dvnFzkts/nBrecRjClZqK8HEd3RpUrZU2Zyoh1ZQcjOdurtLYHuVzX43x+i1uO8RpSL/KG0v23uaXit+cvzTXd0djzSNs7ljcUnEz2Sn7gCTDDuJ8ZN1IiIiIiKP4mSdiIiIiMijUroNhihVhNq05SutcrfNB+8eb/OzxaNtfnTcbH1svh5SFME5W8J/SP8qGfqh3j75o8iDxLD9Y31e5+AWk6AHVRARUWy0lxfbHJyk7Zrn5FTZfMjpR15SO9XmoW/ujXiu0KHG2A8wjvjJOhERERGRR3GyTkRERETkUWyDIUoFzrfhg/v22Zz/qJOdu0d+r74PLxuj5yEiouTWXqgH85UW77e5yKc7wzzbMtTmjSvH2Dxxz9qI5zKBbto3E1SPn6yLSJaILBeRNSKyXkR+Gb59rIi8LyKVIvK4iGT09FxEqYJ1QxQd1gxRdFgzqaM3bTDtAM4xxkwFMA3AfBE5FcCvAfzOGDMBwEEAC/pvmEQJh3VDFB3WDFF0WDMposc2GHNkm4bD4T+mh/9nAJwD4Evh2xcB+FcAf479EIkSD+uGKDqsGaLoJFvNZDRq68r2am13ubngDJufX3+CzZPvqLU52NrWz6OLr159wVRE/CKyGkAdgFcAbAPQYIz55N9sDYDSbh67UERWiMiKTrR3dReipHSsdcOaoVTFaw1RdFgzqaFXk3VjTNAYMw3AKAAzAUzu7QsYY+40xkw3xkxPR+YxDpMo8Rxr3bBmKFXxWkMUHdZMaohqNxhjTIOILANwGoACEUkL/9fbKAC7+mOARImOdUMUHdYMUXSSoWbSluqJehOX6u1bnftUQO8TROrozW4wQ0WkIJyzAcwDsBHAMgCfD9/tOgDP9NcgiRIN64YoOqwZouiwZlJHbz5ZHwFgkYj4cWRyv9gY86yIbADwmIj8CsAqAPf04ziJEg3rhig6rBmi6LBmUoQc+TLxAL2YyD4AzQDqB+xFvaEY3vmdxxhjhvZ8N/KCcM1Uw1vvoYHgpd+XNZNgeK3xBNZNAuG1xhO6rZkBnawDgIisMMZMH9AXjbNU/J0ptlLtPZRqvy/FXiq+h1Lxd6bYSrX3UKL8vr3aDYaIiIiIiAYeJ+tERERERB4Vj8n6nXF4zXhLxd+ZYivV3kOp9vtS7KXieygVf2eKrVR7DyXE7zvgPetERERERNQ7bIMhIiIiIvIoTtaJiIiIiDxqQCfrIjJfRDaLSKWI3DqQrz0QRKRMRJaJyAYRWS8iN4VvLxKRV0Rka/ifhfEeKyWGZK8ZgHVDsZfsdcOaoVhL9poBErtuBqxnPXzC1hYcOQ63BsAHAK4xxmwYkAEMABEZAWCEMWaliAwC8CGAywFcD+CAMea2cBEUGmNuieNQKQGkQs0ArBuKrVSoG9YMxVIq1AyQ2HUzkJ+szwRQaYzZbozpAPAYgMsG8PX7nTFmjzFmZTg3AdgIoBRHfs9F4bstwpE3B1FPkr5mANYNxVzS1w1rhmIs6WsGSOy6GcjJeimAnc6fa8K3JSURKQdwEoD3AZQYY/aEf7QXQEmchkWJJaVqBmDdUEykVN2wZigGUqpmgMSrG37BtB+ISB6AJwF8zxjT6P7MHOk74mD5+DAAACAASURBVH6ZRJ/CuiGKDmuGKHqJWDcDOVnfBaDM+fOo8G1JRUTSceRN8LAx5qnwzbXhXqlPeqbq4jU+SigpUTMA64ZiKiXqhjVDMZQSNQMkbt0M5GT9AwAVIjJWRDIAXA1gyQC+fr8TEQFwD4CNxpjbnR8tAXBdOF8H4JmBHhslpKSvGYB1QzGX9HXDmqEYS/qaARK7bgb0BFMRuRDA7wH4AdxrjPm3AXvxASAicwC8BWAtgFD45p/iSE/UYgCjAVQDuMoYcyAug6SEkuw1A7BuKPaSvW5YMxRryV4zQGLXzYBO1omIiIiIqPf4BVMiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JkvZdEpEBEnhCRTSKyUUROi/eYiLxKRLJEZLmIrBGR9SLyy3iPiSgRiEiViKwVkdUisiLe4yHyMhGZFK6VT/7XKCLfi/e4Yo1bN/aSiCwC8JYx5u7woQE5xpiGeI+LyIvCh0/kGmMOh0+MexvATcaY9+I8NCJPE5EqANONMfXxHgtRIhERP46cvDrLGFMd7/HEUlq8B5AIRGQwgLkArgcAY0wHgI54jonIy8yRTwEOh/+YHv4fPxkgIqL+ci6Abck2UQfYBtNbYwHsA3CfiKwSkbtFJDfegyLyMhHxi8hqAHUAXjHGvB/vMRElAAPgZRH5UEQWxnswRAnkagCPxnsQ/YGT9d5JA3AygD8bY04C0Azg1vgOicjbjDFBY8w0AKMAzBSRE+I9JqIEMMcYczKAzwL4tojMjfeAiLwu3J58KYC/xXss/YGT9d6pAVDjfDL4BI5M3omoB+HvdiwDMD/eYyHyOmPMrvA/6wD8HcDM+I6IKCF8FsBKY0xtvAfSHzhZ7wVjzF4AO0VkUvimcwFsiOOQiDxNRIaKSEE4ZwOYB2BTfEdF5G0ikisigz7JAM4HsC6+oyJKCNcgSVtgAH7BNBr/DODh8FLLdgBfi/N4iLxsBIBF4W/n+wAsNsY8G+cxEXldCYC/H9lMCWkAHjHGvBjfIRF5W/g/bOcB+Ea8x9JfuHUjEREREZFHsQ2GiIiIiMijOFknIiIiIvIoTtaJiIiIiDyKk3UiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JknYiIiIjIozhZJyIiIiLyKE7WiYiIiIg8qk+TdRGZLyKbRaRSRG6N1aCIkhnrhig6rBmi6LFukocYY47tgSJ+AFsAzANQA+ADANcYYzbEbnhEyYV1QxQd1gxR9Fg3ySWtD4+dCaDSGLMdAETkMQCXAej2jZAhmSYLuX14SeqrJhysN8YMjfc4UlhUdcOaiT/WTNzxWpOAWDdxx2tNgjlazfRlsl4KYKfz5xoAs472gCzkYpac24eXpL561TxRHe8xpLio6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL1XRGQhgIUAkIWc/n45ooTHmiGKHuuGKDqsmcTRly+Y7gJQ5vx5VPi2CMaYO40x040x09OR2YeXI0oKPdYNa4YoAq81RNHjtSaJ9GWy/gGAChEZKyIZAK4GsCQ2wyJKWqwbouiwZoiix7pJIsfcBmOMCYjIdwC8BMAP4F5jzPqYjYwoCbFuiKLDmiGKHusmufSpZ90Y8zyA52M0FqKUwLohig5rhih6rJvkwRNMiYiIiIg8ipN1IiIiIiKP6vetGxOSiMaMDJt9BYMj7tY8s9zm3bP9NgeGdeqdnANipU3vk1utuXRZk95p1UZ9aCAQ1bCJiIiIUp2/eIjzB2d+Nm6EzQ0TdbvK5lKd97UVh2yWoN5esEmfcthbdTaHqmtsNu3txz7oo+An60REREREHsXJOhERERGRR7ENJsyXlWWzjB9j8965RTY3jot8zPATa21+cOLfbJ6RKehKXbDF5sebTrD5D5POs7ni7uNt9q/ZanOoRR9LRERElNKclmV/QUHEj2qvnGhz01i9PTBS21Ry8hr1B07LckGGtjIX5bTaXDJbW5aXV0yxecKDOpUOrt/cy8FHh5+sExERERF5FCfrREREREQeldJtMJKZabM5scLmys/n2XzjRS/ZvLBgQ8TjMyXd5hD028P/01Jo865OzbOzK/V5C7TF5ZLz1tl8XvD7Nk++o1zHul4fazo7uvp1iDzPP0XrTBp0STFYf8Bmvr+JiKgr7rzNN260zfUziyPuN+U63VnvhpI3bV60b7bNb7yvbceF67WlJrcuaPP+4bqrzIxvvGPznEuX2HzP5kttLq7KtTnU3Hy0XyUq/GSdiIiIiMijOFknIiIiIvKolGuDkXQ95EimjLd561Xa+vLMF263eUK6/iuq7HS+LgxgW+cgmze360b7f16mu7tk79bN+B85c6/ND015wObRadk2vzX/dzZf8tHNNo+s1V1pAnv0eYg8x6fv+bSRwyN+tPVaXaocvEXzsGVal4Gqj/txcERx4NSEv1AP1zNlWh+BwZmIlq9VD87zN7XpD+obbAzW1+vtJvIaRpQI3NYXmazb8m39su4A84V570Q85urC5Tb/quYim2v+qK2YFY+91+Nr5+ZqW8uTZdpCc9c1f7b58BhtoRlWqGNiGwwRERERUQrgZJ2IiIiIyKNSrg3GXzbS5s1fybf5iSt/b/OYNF3SWNehy4bXrboh4rl87+hy5oh3Dts8eYN+CznUqkuTreum2fzzn1xs86Ixr9lc4teWmIYTdIlz+JvOhv9sgyGvcQ+nGKrfnq+8cXTE3X50+TM2P75rus2Nh7SNLKc3bTBOW4GkR/nXWFC/6W8CgaPckejYSZq+L/0lw2yuna+H7s351gc2/37ECptbQt3viJQu+t5f0qy7jf1q44X62i9OsHn4Y/oeDx482KuxE3mJu+vLlq/qvKvyS9qK0mmCEY/5Wd2pNtf9h7bODHqu59YXl3t9yTjukM3D/driklet88RAza6onr+3+Mk6EREREZFHcbJORERERORRKdcGs2+uLrfPPG2TzSX+Tptv3qO7uWz58XE2j15bFfFcpnWH5g5dtgw6y+zut++zd+uyyTubdJnSX/66c389XOn4yTttbh2q7Tsp938aeV7a6FE2b7lR89NX3x5xP3d3pf/37GU2T1y/z+bIxUzly8rSP0waa2PjRG1nM35BT/K3Od/Q/2Btj/cnOiZTJ9m4YWGOzX8/X1sux6br3/c7OvWd/4f6syKeqiOkdTMhu87m8/L0oL7XT7nP5j+N1ZbLx3LOtXnEf77b6+ETecWhE3Q3vN9e9pDNbuvLO23pEY954cHTbS77SFsro258dNrZvjJB29ZK/AP7WTc/WSciIiIi8ihO1omIiIiIPCrlOiqCevYKalv1UKOLVi+wedBd+m3jnOXr9LEtLX16bQnokicCulwfdFpfQtC2mQyfLti09Ly6TzSg/EN0afLA7FKb/+XyJ22e6BxCBkTuZBEa1m7zvtlDbT58je6cUXL6bpuPK9RdkEZmrrJ5bKa20KSL1sy7TXr4xcfOrhlbntPbS3VVk6jP/Mdr68vGb2rry6Jz77J5U4cehHTFi1+wecTr+tlZfmVTxPNKUK8LlbmTbb5v9nybT7pCr1W/Ln3e5g1Xauvnwad0J5pAdY2+QKi75jOi+Mvep23KP1l9hc23hHRiVPR0TsRjyt7W1pdglDvoude2fZdOtPniQc/a/PO9c23Ore3/XcX4yToRERERkUf1OFkXkXtFpE5E1jm3FYnIKyKyNfzPwqM9B1GqYd0QRYc1QxQ91k1q6E0bzP0A/gjgAee2WwEsNcbcJiK3hv98S+yHF3sly2ptbqvS5fYhLbqMkf6R7hLT19YXVzBHWwJyi2P3vORJ9yOJ6uYT/mI98Gj/Z3V5sOJGPQjs83m6/Jguzg4un/Kb056w+aNpZTaPz9QaPTW72ua9wVybf18zz+ZlHTqOHTXaTpP/YabNeXt0mb+sssFmpzGN4u9+JGDN+HJ0+X3HF7Q+FszSw+5ebjzR5r89N8fmyYv1vSg79DCVYFNkG4xxdhUT50Cw0TW6S9jbpbpz2eArX7V5+uAqHUfOrKP8JpSg7kcC1k00MtbpdaDsd7rbmIS0LvybNkY8JtCoB1VG3eY1TOtYrqy3eZQzY37hNT3Ur2Jbz7uZ9VWPn6wbY94EcOBTN18GYFE4LwJweYzHRZTQWDdE0WHNEEWPdZMajvULpiXGmD3hvBdASXd3FJGFABYCQBZyursbUSroVd2wZogsXmuIosdrTZLp824wxhgjIuYoP78TwJ0AkC9F3d5voAS3brc53ckR94nh66WN0eX9nafpMv51E1/q8bGrNpbbPHmfLulw6T7xHa1uvFYz/kJtd2w4T3dSkWt06e/3Zc/ZnOfT9/nRZPn0ILFMZxeXJ2p1efFfd+jBSek7tY2s+COtAn+H/iuqqG3T2yurbA7u1w+eQibu/0rpGHjqWuO0orSfPsXmsWdW2XwokG3zMy+eavOER/S9GNywRZ+zt+9LZ0nfNGs7pb+96y3D0sW5v3BbsVSTSNea7gTr99ssTo64Tx9fwz9UWyjrZhfb/NvJf7X51Rb9b54R7zh1VbMH/e1Yd4OpFZERABD+Z10P9yci1g1RtFgzRNFj3SSZY52sLwFwXThfB+CZ2AyHKKmxboiiw5ohih7rJsn02AYjIo8COAtAsYjUAPgFgNsALBaRBQCqAVzVn4NMZO3jdGmlfZa2snxt8EfOvXS5tNPo0kr+pnSbffWHbGYbjPclet2Ic5hRx9SxNu/9rLauPDXlYZuznMOOXmzRXVh+vUMPbQGAzqDeb3el1kbOTr09v1rf4ZM26vte9u60OVjb8wdFPOYlsXi+ZpwWkrQSfe9u+ZLefkvJGpt//e5nbZ70lO7uEly/uU/DcA9sqb9Yd0KaNLPK5najbWUbm3XHGN9hbZvhdSQ5eL5uPMyXpbuVtZ6sB4aZy7TVZmqGzttOffcrNo/fqPcJNjf31xCtHifrxphruvnRuTEeC1HSYN0QRYc1QxQ91k1q4AmmREREREQe1efdYKgLzk4Bh0u1nWBuuS5/Fvq09SUE/RL2srZ8m3N360KlGYBlFqJP+AoG27z7DF0q/M4pL9o8LVPbXeqD+v5c26aHVlRvHB75vB3aMpBTr58V5OzVGsjbqTu6SPVum4MN2hJDNNAkTdsSm2aOtvlXs5+0+bWDujPM8KV6eTUr7OGSx8RfMc7mfXN0R4ohX9UDyO4fr4eMbejU68vzG4+3edLeDfqkho0wlNpkvLa+7DxP63vR8Y/ZfGfDVJuHPOVsb1mnBzUNBH6yTkRERETkUZysExERERF5FNtgYsXdKaBcD0I6cLzefnXxezYHnL0qaoPtNn/3vRtsnrRSd7xgCwANJMnT5b62Cm1LuSpfdzE6GNT/1q/s1FaZk7OrbH760j90+xp7A4Nsbgjp6/1qw4U2F9w3yebcV9fbHGJbGA0wSdfLZf2JmoenNdj8+nsn2DxpjXP4UXfPmabP43N2ecGQgoj7bb96iM2fv/Rtm783RK8pbmvlC43O0v3r2q4WatNaJkpF7iF/u8/Vuvry+W/YvLNTb7//yXk2lz+ruz0NxA4wLn6yTkRERETkUZysExERERF5FNtgYsQ/SJf0q67WQyi+eYnunjE3Sw+UORzqtPmZJufb+v+uSyuhqpqYj5OoNwI79Jvug5eX2vzvU86zuSSj0eYHXptrc6azy0vbxMhld1+a7vqSkak1cOOUt2x+6eS7bP5a/hdtDu6bYLO8q8uRRAPBdOpBQ8XrNO/q1GV1FGtLY8ewPJsz9jj3cVpfUKS7Lu367DCbM+fti3jtv0z+q83TM/Vgo2zR1pddQb196R49LGnIGq1TrT6iFOLs0NdwgbZWjryiyuZL81fZfO3Kr9k87gHdkSzQGr82Mn6yTkRERETkUZysExERERF5FNtgYqR9eoXNBXP32rywwDmEArrp/poOXSL9r+d094uKGt3xwnRq2wxRvAy/e6XNVY/qri1VkmvzxNa1+gCji+3ubhf/i093SvrvW7QGxnxeWwAuH77a5v86+zKby97txcCJYsgEdU+XvA37bd7SNsLmJXPusPnHpVfa3NKp7S7fKl9mc75Pl9XHpB20+U/7zo547X/+6zdtrrh4q96v/O82/3L3fJuDj2tLDdZ80NWvQ5QyZOpkm5u+qG1hL1c8ZfNt9TNszntaD6cMVG3SJwp1t69T/+Mn60REREREHsXJOhERERGRR3GyTkRERETkUcnbs+5s1ZM2rFhvz87q4s5H4fTfhurqI34UnKp96k0/1D6oh6Y8qC/nbK21qVO39bp509U2T/yTbtEYOMyTGclbIk497KcTENObtH/9QFC/z3FGTqXNd8zQnl5fltYxT2WkAeH0q4aq9e/sl/4wx+ZHps22OXe0XhPOLtM+85aQnii6uFb7ZFet0K1Jxz2l1woAwKkapxd8bLPfOTn7je36+Emv77I5ENBtJolSRdoo3XJ4w7f0u1Z/PfF+mxc16hzub89pHY9/frPNwTj2qbv4yToRERERkUdxsk5ERERE5FEJ2QbjLoFj8jgbd84vsLm9QNtX8qbo8nlZQUPPz++c89YW1H9FmzedEHG/jCG6/P6bic/ZPDbNWaJ3nuuV5in6vK8NtTlQzX3oKLVlHtQ6Wds8yuYzs7fbPCRXT2hEum6DinanZcDwjEbqf8Z5zw19fpvm5XpSaWexLr2vHHqyzcszT7E5Z5+2qEyqOaDP/6nTq5tv0K3n5uTpEv1Pdl1gc8Fr2nIZ3KXbBxMNJF+ubukbnKZtJpVX6bzIZIRsTmvSlmWfHmqNQTs0D3ujVp9zq14T4LSBpY3ULVQBYMPPtA3mV3OftHl5y3ibH3hWt0id8JC2OQf3ay16BT9ZJyIiIiLyKE7WiYiIiIg8ytttMO6OLqN1SWPfWZoPX9xk81cmLrV5TKYuaZyRXW3zCL8uFXb7stCllXajy5TLRudH3G+Qc/rccenuLi5d7zgzOXOPzR2zdNy7bz7d5mErdHk1Y78u+8sO/XZ/sFF3GSBKVG47W9sQrblJObqEvz2gJz9WVWvr2KQ2rSW2vlA8BWvr9A9Odj8Jy3NP8vXrdc1tpwk59zHTj4t4jYuP1xOC3WuNuwPMhDWH9fE8/ZoGkH+o/t3ccK62mZjr9DTq75S9afPyQ+U2F2XoPGdCttZPZauewvvSWdpCnLVe50vZ+/Tv/v2nRO7a8ttzHrO5KaTXmvtfPsvmise1LTq4UXds8qIeP1kXkTIRWSYiG0RkvYjcFL69SEReEZGt4X8W9vRcRKmANUMUPdYNUXRYM6mjN20wAQA/NMYchyO7vX5bRI4DcCuApcaYCgBLw38mItYM0bFg3RBFhzWTInpsgzHG7AGwJ5ybRGQjgFIAlwE4K3y3RQBeB3BLTAdXoksrdedo68vYr2+x+Y4xS2we5MuweUW7LjU+0fgZmw8E9JvKJenaTnJ6ji6BTMvQfy3pos9zfnb3Bxb5oO017g4wrtOydMnl4Rn32PzW8RNtvuc0XeI5fEDHWvzO8TYPW7bb5sAObfEhb4hnzbh8ObobhW9Ikc0h55vuoZYWxEvrOSfanHeaLpfOz9X6fvTQSTYXfqg7wHCZP/l4pW76g3EPJurmkCLJ1mtI5RdyIn72y6L3bG5x2r581foY38e6K403jnGh/hbPmvEXaIti45m6K1/mAm1R/N2ExTZfs+IGm2X1IJsDufp+fqFEt4M5cYLuiPR/Zug8r+0UvQ6816gtN98e9lrE+KZk6GfRdx/S8RnnI+qDJ+jv0HnqaTa3DtO2zHTtWMbw97TVzPeRHtg3ENfRqHrWRaQcwEkA3gdQEn6jAMBeACXdPGYhgIUAkIWcru5ClLRYM0TRY90QRYc1k9x6vRuMiOQBeBLA94wxEd9wNMYYoOuPk40xdxpjphtjpqcjs6u7ECUl1gxR9Fg3RNFhzSS/Xn2yLiLpOPJGeNgY81T45loRGWGM2SMiIwDUdf8Mx6azXP9jcP+Z+q35d8e+bHO70TaV/69uhs2LV+vBE9mV+ib0O6vnhyt0yaXlNG2hmVakh04cTbvRx6/s0G8bP3NQD8BoDOgy5RmD9XlnZ1fZfGHeeptDFfrfT5tb9PdfVjfN5iFr8nQQzsEB5B3xqpkIFWNs/PgC/X5RyXL9ln3G+p02hxoO2WwCzukUvdltxTmcQpzdLiQt8q+Y9rl6sNje63U3pT9NfsrmNqPP9US1tsEMW67XIO7/kpw8UTcDydnxzFekh/pdP+/1iLtNydAL12/qdbm+wLlURexKQykjbjXjtCnvPVX/zn5r0sM2/3DnJTaX3qHtK+nL19gcatb2Yre1ZveVuiPSSzdom8kDY3RXmQWD3cO/uv+Pjfm5G50/aNx4xkibT8jVtpuvDa6yeVmrzre+X7DA5nE7tV4Hog2mN7vBCIB7AGw0xtzu/GgJgOvC+ToAz8R+eESJhzVDFD3WDVF0WDOpozefrM8G8FUAa0Vkdfi2nwK4DcBiEVkAoBrAVf0zRKKEw5ohih7rhig6rJkU0ZvdYN4GnFOCIp0b2+FEahumyxpjS3W5wz206EBIv1n/8l26k8qUl51DUxwdo7QdoL1Inz/Tp8v+ftEFh5DR79VXdmorDgC81KzLNP+98mybRzytLTWZDTq+N8/RXWlKpuvv0xHUpdCWV7VFIf9jfe2K9z62OVCjBySR98SzZlyNk3RJ8dprX7L5LyfOtbngLT1UpaBSl9rTD2qLigT1fSiBkOa2rndkaR+jO8905Ef+FTPqZt116S+jntXXdtb4btml65TB54bYbFYt7/L1KDl4pW4Gkj9fl9gPnqY7np2dF/lBaJZoHT384SybJ613DkLqjwGSp8WzZkJ5On8KDtJrxM6g3v7eSt3pbtKKDfrYblpfMKzYxkCO/lp+0Xf3oVCrzS+1DLe5sk0zAIScfy05Pr1WFadpO+WcfN15rCGoX7D9zX7dqawpqC3OwSynyny9/spnTAzsqxERERERUa9xsk5ERERE5FFR7bM+0ERX3BEMOa0p3Sz4BZxtQuvO0iWRA3N0CeTG6a/bfE2+fiO5xK+7trQbfeGagLa+fPkj/SYwAOQs0m8DT3pDN8gP1u/vcnzlS7u82TlOCRiMyi7v0/UxGkTdy9mtrSyPbp9u8z/O+qPNg8/Rlq1Fjbp7zD8O6WETh5ydjmpb9DCL3bt0yRIdWp+/OudJm6/Ki9yEwG1hawxp/pOzg9L7z+oSZPlTTl2FeNQLJRdTrq0vx39/rc0nZUT+jd8U0jbNrGqtWX9drc28RtCAcuZncHbw8jvzM5Opd/IN0RZkydT3cMN5FTbvv0J3Vfn1yQ/YfF52vc13H5pi86O3fdbmwsUrI4Zn2nXullaqhyI1f0ZrrjNPr1uDKp2WslW6Q59rLP5h80DXGz9ZJyIiIiLyKE7WiYiIiIg8ytNtMBkNuvRXVV/Q5X1K/PrN4+e++/+6vE+Wc2BLjujOK+mij+10dn15uy3X5m+8fqPNU26POBgMZru20QTbI3eKIYo3//v67fvhPxlr8+zrfmTzrZf83ebP5elOLV8ZVGVzyFnvDDpLnKHjTZe3D/Zp20y6U28AsLRV/3zjB9fbPGSJ9rCNfUNfO1C3D0TJRNK1BaC9WJsgv1ysS+yZEnlp/nq17pA0fLleFwMf14AoHnxN2rKS3pBv87QMfe++O/93Nj81R9tXOp3DLE/PedHmijR9bx8I6XXnis3X6GN/qy3Oha9p64s5yhwssFt3B8yq05aaLJ/ODU2ntxvJ+Mk6EREREZFHcbJORERERORRnm6DSd+oBwEVvqyb639nwhyb/1j6ts0jnB1d3F0nuts95oFG/Vbwr968xOYxznkUx23SZfjgzt0RjzedXR8KQ+QF7vsztGWHzRP/pMuXj7x0kc13l6TbXDdDn8dX4hyQ5HO3AOjmdd2dm+qyIn424m2txQkbDuoP9uy0MXDIaTczPOqFkouvotzm6ou05mZmap21f+ptv/bZyTaXb3RqhfVBcRJy5kMj39SdwS6ccanNL03Rg++uy9c2S9d2p/vkmq1fsHn3s7o72aiXdIe9tI836Rh6237s1Emiztv4yToRERERkUdxsk5ERERE5FGeboMJHmiweeir2hKzufYEm2eXTDvm589s1CX9yducpffN2jIQaGsDUaJzl/4C1bqMnr5XDy0qzNTdkQpX6zfuQ7l6u3P2RbfE2VnJ13wgchw79Vv5wcOHnR9wOZ9Sw+EK3dns2/Nettnv7Fr2m/0nRjxm+Pu63B/cG3nQGFE8uLuv5CzfbnP7v5XbPGPMjehJmtPzlbNXd4Mp21Jtc6Bm17EOM2nwk3UiIiIiIo/iZJ2IiIiIyKM83QaDkC6nu8sgmW6O1UvF6HmIEom7lBlxsNeGxi7u3cvndHKw23sRpQ5/8RCbGyboZffawWttPuRchO5/+ayIx0/apte8AA/gI48J1ju7tbymuagPz+ntI4oGHj9ZJyIiIiLyKE7WiYiIiIg8ytttMERERImuSHeAaR2mjWJZ4rf5/fZcm8teiWwCCNVH7qpERKmFn6wTEREREXkUJ+tERERERB7FNhgiIqJ+JO16KFletR5+dMG6L9m8e0+hzcd9VBPx+EBLSz+Ojoi8rsdP1kUkS0SWi8gaEVkvIr8M3z5WRN4XkUoReVxEMvp/uESJgXVDFB3WDFF0WDOpozdtMO0AzjHGTAUwDcB8ETkVwK8B/M4YMwHAQQAL+m+YRAmHdUMUHdYMUXRYMymixzYYY4wBcDj8x/Tw/wyAcwB8soa3CMC/Avhz7IdIlHhYN0TRSeaaCVTvtHnYHZpxh8aJ7v37f0iUBJK5ZihSr75gKiJ+EVkNoA7AKwC2AWgwxnzyd0oNgNL+GSJRYmLdEEWHNUMUHdZMaujVZN0Y8jOkIQAAIABJREFUEzTGTAMwCsBMAJN7+wIislBEVojIik7wmGRKHcdaN6wZSlW81hBFhzWTGqLautEY0wBgGYDTABSIyCdtNKMA7OrmMXcaY6YbY6anI7NPgyVKRNHWDWuGUh2vNUTRYc0kt97sBjNURArCORvAPAAbceRN8fnw3a4D8Ex/DZIo0bBuiKLDmiGKDmsmdfRmn/URABaJiB9HJveLjTHPisgGAI+JyK8ArAJwTz+OkyjRsG6IosOaIYoOayZFyJEvEw/Qi4nsA9AMoH7AXtQbiuGd33mMMWZovAdBvROumWp46z00ELz0+7JmEgyvNZ7AukkgvNZ4Qrc1M6CTdQAQkRXGmOkD+qJxloq/M8VWqr2HUu33pdhLxfdQKv7OFFup9h5KlN83qi+YEhERERHRwOFknYiIiIjIo+IxWb8zDq8Zb6n4O1Nspdp7KNV+X4q9VHwPpeLvTLGVau+hhPh9B7xnnYiIiIiIeodtMEREREREHsXJOhERERGRRw3oZF1E5ovIZhGpFJFbB/K1B4KIlInIMhHZICLrReSm8O1FIvKKiGwN/7Mw3mOlxJDsNQOwbij2kr1uWDMUa8leM0Bi182A9ayHT9jagiPH4dYA+ADANcaYDQMygAEgIiMAjDDGrBSRQQA+BHA5gOsBHDDG3BYugkJjzC1xHColgFSoGYB1Q7GVCnXDmqFYSoWaARK7bgbyk/WZACqNMduNMR0AHgNw2QC+fr8zxuwxxqwM5yYAGwGU4sjvuSh8t0U48uYg6knS1wzAuqGYS/q6Yc1QjCV9zQCJXTcDOVkvBbDT+XNN+LakJCLlAE4C8D6AEmPMnvCP9gIoidOwKLGkVM0ArBuKiZSqG9YMxUBK1QyQeHXDL5j2AxHJA/AkgO8ZYxrdn5kjfUfcL5PoU1g3RNFhzRBFLxHrZiAn67sAlDl/HhW+LamISDqOvAkeNsY8Fb65Ntwr9UnPVF28xkcJJSVqBmDdUEylRN2wZiiGUqJmgMStm4GcrH8AoEJExopIBoCrASwZwNfvdyIiAO4BsNEYc7vzoyUArgvn6wA8M9Bjo4SU9DUDsG4o5pK+blgzFGNJXzNAYtfNgJ5gKiIXAvg9AD+Ae40x/zZgLz4ARGQOgLcArAUQCt/8UxzpiVoMYDSAagBXGWMOxGWQlFCSvWYA1g3FXrLXDWuGYi3ZawZI7LoZ0Mk6ERERERH1Hr9gSkRERETkUZysExERERF5FCfrREREREQexck6EREREZFHcbJORERERORRnKz3goiUicgyEdkgIutF5KZ4j4nI60SkSkTWishqEVkR7/EQJQIRuUlE1oWvNd+L93iIvCxV5mfcurEXwidajTDGrBSRQQA+BHC5MWZDnIdG5FkiUgVgujGmPt5jIUoEInICgMcAzATQAeBFAN80xlTGdWBEHpUq8zN+st4Lxpg9xpiV4dwEYCOA0viOioiIkswUAO8bY1qMMQEAbwD4XJzHRORZqTI/42Q9SiJSDuAkHDnxioi6ZwC8LCIfisjCeA+GKAGsA3CGiAwRkRwAFwIoi/OYiBJCMs/P0uI9gEQiInkAngTwPWNMY7zHQ+Rxc4wxu0RkGIBXRGSTMebNeA+KyKuMMRtF5NcAXgbQDGA1gGB8R0Xkfck+P+Mn670kIuk48kZ42BjzVLzHQ+R1xphd4X/WAfg7jvThEtFRGGPuMcacYoyZC+AggC3xHhORl6XC/IyT9V4QEQFwD4CNxpjb4z0eIq8Tkdzwl30gIrkAzseRJX4iOorwShREZDSO9Ks/Et8REXlXqszPuBtML4jIHABvAVgLIBS++afGmOfjNyoi7xKRcTjyaTpwpN3uEWPMv8VxSEQJQUTeAjAEQCeAHxhjlsZ5SESelSrzM07WiYiIiIg8im0wREREREQexck6EREREZFHcbJORERERORRnKwTEREREXkUJ+tERERERB7FyToRERERkUdxsk5ERERE5FGcrBMREREReRQn60REREREHsXJOhERERGRR3GyTkRERETkUZysExERERF5VJ8m6yIyX0Q2i0iliNwaq0ERJTPWDVF0WDNE0WPdJA8xxhzbA0X8ALYAmAegBsAHAK4xxmyI3fCIkgvrhig6rBmi6LFukktaHx47E0ClMWY7AIjIYwAuA9DtGyFDMk0WcvvwktRXTThYb4wZGu9xpLCo6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL0UwE7nzzUAZn36TiKyEMBCAMhCDmbJuX14SeqrV80T1fEeQ4rrsW5YM97Cmok7XmsSEOsm7nitSTBHq5l+/4KpMeZOY8x0Y8z0dGT298sRJTzWDFH0WDdE0WHNJI6+TNZ3AShz/jwqfBsRdY91QxQd1gxR9Fg3SaQvk/UPAFSIyFgRyQBwNYAlsRkWUdJi3RBFhzVDFD3WTRI55p51Y0xARL4D4CUAfgD3GmPWx2xkceTL1S9ZdJw62eb6f26JuF9mesDmQbfn25y29MN+HB0lsmStG0nTv0r8pSNs3jt/VMT9Dp4Ysnlw2SGb2zrS9blWD7J59EuNNpsV62IzWEooyVozRP2JdZNc+vIFUxhjngfwfIzGQpQSWDdE0WHNEEWPdZM8eIIpEREREZFH9emT9WTiLyy0+fDcCpuDN9bb/MrxD0Y85m9N2iLzWP6FNvNfKiUrt0UMFWNs3Ht6gc2HZrXZfOFxKyMef16BrsJOSq/r8jUWT5xu8yNZZ9pcviL68RIRUWpLG6vXquYpw2xuLe56tpbe4rRrfrjX5kB1jd4pFIzhCHvGT9aJiIiIiDyKk3UiIiIiIo9K6Y4Nf/EQmxvPnGBz01d1B4o3T3jY5hzJinj83Vtn2zy0rr0/hkgUd2mlI20+OGe0zXvO1qXCS6Z/YPP3hy6z+Y3WcRHP9X83abtYc6sewnHt5OU2LyjU/Pqp2pLmnzDW5mDljt7/AkRJyl8w2ObWWVorh8bp7kpFm/Ta5F8W2ZZGlKz8FXrtqblwuM2dZ+j87jMjdnf52NoW3ZFs6wq9/k24T6fMoR16OKzp7OjbYHuBn6wTEREREXkUJ+tERERERB6Vcm0w7uEtbdPKba7/oh549OYpd9ucJRk2v9OmS4sAEHinyOb0Xfot4QCIEpt/iL63az+r36QffLWeVv3QuKdt3hfUQ8H+s+5cm19aenLE8459RutsaIdWyt03aUvZZXNX21yWd9DmXaN1mT+NbTCUoiRdr0ktsyfavP+GZpu/OF7bXR56+mybx2iHGlHiELHRl52tN4/SA/jaywoiHlI9V9ssTz5vo83/Uqrbzk90aqnT6O4uh02nzavH6vP+uO6fbC59pMnmYG3XO5vFEj9ZJyIiIiLyKE7WiYiIiIg8KjXaYHx+jeVlNu+cp0sgD06/0+YhPl1maQzpAS/fWLEg4mnHP11rc6Dq49iMlcgD2k7SnVciWl8mPmLzfQ2n2Hz3G2fZPPINfZ4Jz66KeN5Qm9aTzzmIDId0OX9fUA9e2tWsS5CZNQ02D+xxFETe4R9WbHP154zNr56s17C33F2YjLYQECUKt93LP1wPMmo+UVtfdp+hU9jJp0e2Rt5f9pzNE9L1ulMb1M+o/96s7Z4NznVnfIbO7U7P0naXSVdutrnxTd1hBvv2a+6nw5L4yToRERERkUdxsk5ERERE5FEp0QbjL9Kl9KprdAnluxfrMsmMTF0qDEGXFrcGdAeYkffpt4sBwOzhjhSUnA5M0ff6rPx9Nv9Hne4s8erfZ9g8+fE9NrsHFumxSf9b52fKbR41Ub9NPyFdD63Y06C7zJRtWdfzwImSXPtEXX4fU1Zv8+g0bd8MGv0cTo5WhEQe5SsfZfOOq/Q9/+0v/4/N3xxc3e3jA87VZ1mrtrv8YPUXbM5aqocfGZ/OAcd+cavNc8e/aPMtpS/YfFP5P9s8aJMemBlq1l2ZYomfrBMREREReRQn60REREREHpUSbTCH54y3efTZumyyYPBW5176r+LDdr31mhe/bfOUFdsinjfY3AKiZDTyUf3W+/ZVk2yWkLaIla/TtpTg4eiX/pqHa6tNeV7DUe5JRJ+oulh3yfh1+Wtd3mdbm+6ekb/ddHkfIq9xD63cebm2vvxlwR02T8/ocB7R/RT27TZtTblx6bU2T3hED+NLX7PB5oYLptg8dbDugOaDtsd8JkN3Fgxk6+2S3v9TaX6yTkRERETkUZysExERERF5VNK2wXSeP93m1ht0if2/xj5lc6boTi8fduhG9gtWXW/zlN/rZvfBA59aqu+nze+J4s19r6etbNUfhPQb9sF2p1/MRL/UfuA4/azg2sItNnc6TxUK8UAXooavnmbz/Lkrbb4gR3dR+rBdr2ePr9cDyya/pUv62gBA5G0BPaMoovUlU3Ta2mr09m/tPD/i8R89eoLNk984qD+odA6wzNCWsubhej1aWLjceQ19vZ/VzrE5f7teF0PH0AYaLX6yTkRERETkUT1O1kXkXhGpE5F1zm1FIvKKiGwN/7PwaM9BlGpYN0TRYc0QRY91kxp60wZzP4A/AnjAue1WAEuNMbeJyK3hP98S++Edu11n6JLgD8e/bfOUDP3vkz1B3c3ltp1X2Fz4SJ7NwcpN+qRse6Heux8JWDeW814PtcRu16O08tE2Byv0eWdk60FK9zfMsjnjfT20gpLe/Ujkmukr0ZYvf0FBxI/q5moDyxVFK2z+/9u78/Aoy3N/4N97JjshgUCAkBDCvgiIigjigrgUbC22WqtHK7a21lO7WOupnvac9vT8enrscll72tpWq5W21p2KOyoFxQ1BEJAdWUwgIYGwBAIhM/P8/mB87mdoQmaSycw7M9/PdfXqN8ksT7zeO+/LPPf7PPmi0/i/2KltAP2f1ZWWgjW74jpM8pyHkCZ1Y5zVxoo+1Dx91bU2twZ0RZb9jdorUzlPvw8A5St15b/gbt3YzwRabQ5NGmnzkamHbO7nL7B5b0jbXea/P9HmMfVOi3Sg+xvMOvxk3RjzOoDGE749G8DccJ4L4PI4j4sopbFuiGLDmiGKHesmM3T2BtP+xpiP9xevA9C/vQeKyE0AbgKAPBS09zCiTBBV3bBmiCyea4hix3NNmunyajDGGCMi7S4FYYy5D8B9AFAkJXHfncFdRD84Re/+HXHudpsvLtCVJrKcA/KXDefZ/OG8ETaXv6YbwgTj2Pri79vH5pYJVTYfGKZTmfvG6n+i3h/otGi/1+p0TFudu5nZmpOSTlY33V0zyVTzmQqbPzdG29MKRKcRH1mnKzkNW6AfGOk6NJSJkn2u6W6+/Hyba740JuJn109+zebxOQdt/tCZfX9vU5XNY99xVoBJwBQ9eVdKnWuM/pXv87Ze8xxp6GuzuzFfv4O6Goy8tybipQKtx9AWXw9tnamdpDX349Mf1vdzVplZcqTM5gELnUvmPSdOZnSvzq4Gs1tEygAg/P/1HTyeiFg3RLFizRDFjnWTZjp7sf4MgDnhPAfA/PgMhyitsW6IYsOaIYod6ybNdNgGIyKPAJgOoK+I1AD4IYC7ADwuIjcC2AHgqu4c5D/x6V2//v79bN78VW0bebjyOZsrsnSq4+nDepf9/MWTbR4135k23KN3+UZLsrWVRU4ZbvOe04tt3q83HiNv1AGbz61Ya/MtpYtsvufsi2x+9YxTbB76VInN2a+tstlwutMzPFk3HtDzEzq1eWPJWza/c3Swzf4NOk1pNmhtUHrLyJpxzmW+fjrVX/XprREPu6HXUpsLnE1hbts50+Y+7+gKaME6fpCaKdKqbpzN9YJbdIWwHCe3+9ST/MyXl2dzwFkBpmWyrgBzRQ/dOGlnUK+lHq7T1clK3tHzV+CgPjcROrxYN8Zc086PLozzWIjSBuuGKDasGaLYsW4yA3cwJSIiIiLyqC6vBpMMvh66osvhCeU2P3fur2wema3THtsDuvnKHctvtLnqOb3jN7DdWWElSu5KNK3njrd52+X6/S9N17aWb5SstDlP2v5PnwXdzOL3FUtsPlK+0OaJR261edRaXWEmULc76rETdRe3LjB+VMTPrh+s9VDptKfdVD3N5j4f6ApHbO2idOYvLrJ5/+SBNn+r7JGIx5X4tV3mraO6Udi7i3XVmBGvOK2cLS1xHSdRKjPjtDV562f0Guu28c/b3GL0XPPWkUE2r31Nnzt092p90QSvxMdP1omIiIiIPIoX60REREREHpWabTDO1OHuyXoHfB+/3g/sF/13yJ/2TbW5ZIFOvfsXvd21cYwcanPt13Ta8elJv7X5FGeVmBajU5kbW3UK5ZmDE22+tni5ze4qNoU+bevJrWqy+dgonTr1sQ2GkqWdFZrW/WvkrnhT8nWVi/datHZr3tZ2tuFv6mPYBEPpxl05LDRMj/t9V+nqEjPyd0U8p1D0XPC/2y61uextPY90ppWTKF1lDdBNW7dcrq1jt12iKwXeXLzD5g3ONdnPN11s87DffGhzsFlbqhONn6wTEREREXkUL9aJiIiIiDwqddpgnGn2UIlOafSc3GBzNnRTpKAJ2Tx/m67U0q9GV4CJleTmRny96Uu6OdFPJujd+27ry8HQUZuXHNVNL+54/wqbq/5Hp1923a+bNv1n/1dt7ufXdoJhfXXTpj1lVTbrfxWi6LjHtC9fW63grD7RrqAet+JsOrF3eqXNj13864injMrW1x33mrMy04IjNnNVI0o7oucm/wBtE6s+T1s6V011a8WpRQCHjLZZNryqrTOV726xObFrUxB5jC/ynFVz9TCbvzT7FZu/XNx2m+WTB86w2bygq+wFd2+K4yA7j5+sExERERF5FC/WiYiIiIg8KmXaYNyNkA6O1laRBRPutrnIWTHFbT85urnY5twabZuJZtrQ50zvN1x7WsTP/uNTT9k8u8cem1uMrkrz/boZNr8993Sbh/z5A30hpxWhNdRx+8EH23UFmFHrD9gcauvBRCdwW18OXKHHtP/6epsn9tENVnyiR1bAWdFoSY2uhhRYpTU5ZeYam0dlR67n0ui0zmRt1JrOqanR14ridyBKJf5+pTY3zNANV0Z/dmNUz79x22U291uprZzB+oa2Hk6UGZzWl6yqQRE/GnGFtq9cW6wbUmZBzzsvHdH80Nvn2DxmnrMCTHxG2mX8ZJ2IiIiIyKN4sU5ERERE5FG8WCciIiIi8qiU6VnHMO1H2nWxdhG5fequBc26vFWf1dpDHtpe3eFbSZb+Z5Eh+r4zv/FGxONm99huc2NQ+3ovXfllmwse1l7eskWbbQ4eOmxzVp/eNvfK1t73bGe5L5e/TnuOZceHbT6GKGK5uD66zGjjzBE2X/3vL9l8doEen80hPcaOOX3qffx63N5SusjmugmFNo/K1vsoCpydFwFg0qNft3n40/tsDu6sO9lvQpTSAkPLbG44v9XmJyvnO4+KrBXXxvkjba5cp7suBpz7o4i8wN9L7xFsumC0zYcG6nnEtPMxcWGtc223yrm/cLMut+jr0cPmlmljbO7/o8glFv9j4Iv6M7/Wltun/o2X5tg85reN+n4Nujy2V/CTdSIiIiIij+LFOhERERGRR6VMG4xv/yGb83f0OskjT06c1gB3AtFdotGM1zaBTd/WqZsHSt6OeK3qoP7n++7Wz9mc9bS2tRQv1KW5QgcO2uwfM9zm9f+qv89/937C5p4+3Qn1P+sn6ms6sz0hp52GyOUr0Om+5jN1mcWLbn/T5i8Xb7B52vIv6pMX6TFcUK8tXg2na/18ddbLNt/WW1to4CyNdaISXdURsmOXzaHWzu8sTORF4uxk3TBRa+K2s16wuTJL28f2BZttnrb0pojXqlys547gbi7XSN7im6DtLpuv03PHJy9cZnN57j50pK5FW2g2H9Kdfj+sm2BzoFZrqXSktg3/tFzrCgD6Oq0vC93Wl+dvsHnkQ002Bzdqqw1CXlmwUfGTdSIiIiIij+LFOhERERGRR6VMG0zIuTu3zzq9s35Da4vNI51pxzPzdNWXveN06r7kPWeXq/U6de8r7WtzzTk9bf7rlF/b7E6rAMCnV1+jX8zrY2O/f+hujCakzTZm0libq8/V6c+7LnnY5nE5OtYnDw2w+e/zdXetIW/r1E8w6L3pGvIGKdNpxNqztdR/WPq+zWuP6fGW/ay2Y/Wb7+yseExXr2juP04fL7Efe3tnHbG5cKe2guW8s97mUHMziFLdsfPH29xykbaxfL5onc2tRtsvdwS05bLHC3oOAgDfNq3HIFvGyAPkjFNs3vwverz+4JPayvv5nrU2L2jWFpff10y3uSRX/95fVfquzf/RT1ff21+lrZgrWnQH99E5u20+8frMBz231Qd0fNmH2lllr0TPf8ZpLzbuikvO9ZyvUFelCQ3VMfnrdTW0QLVeC3ZVh5+si8ggEVkkIutEZK2IfCv8/RIReUVENof/v3dHr0WUCVgzRLFj3RDFhjWTOaJpgwkA+I4xZiyAKQBuEZGxAO4EsNAYMwLAwvDXRMSaIeoM1g1RbFgzGaLDNhhjTC2A2nBuEpH1AMoBzAYwPfywuQAWA7ijW0YJIHTkqM2FW/UO3m9u+bzN80Y/ZnNVlt79O/2C1Ta/3qp3FQ9+SacxWp3pjabT9L3O0L1hsOmE6Ucz32l9eXaLft+5k/jQ+bqyTM3FOv1y/hm6LManCvTu/nsadWrpD4tn2DzyWZ1aCW5wNkLiphie45WaaS3Xab2+Z+p0YYvRtpbfNVxgc581uuISeuuU5Z5p/W32XaAbR3yih07n73PqZ12r1tXU3MhWmb9Nvd/ma2u+afPwPZX6oA+clWVivSu/nY3EWCfe55W6iZeGiXryuGHUYpuLnVW+Vh/T4/uLq26wufzdyJUzuOoXtSWZNVN/ZpHN/37p322+slBX+VreopeY337+epv7v6Ovs7tI/2b/eHapzX885S82n5Kt7WKVWfudUTgXaCcxIXenzWdeoC2Xb1XoKmnZ1brxWEGdjkkCTuuLczo6VKGPaRmo59Tyl7TFuUci22BcIlIF4DQASwH0Dx8oAFAHoH87TyPKWKwZotixbohiw5pJb1FfrItIIYCnANxqjDno/swc78Bv86MrEblJRJaLyPJWtLT1EKK0xJohih3rhig2rJn0F9VqMCKSjeMHwsPGmHnhb+8WkTJjTK2IlAGob+u5xpj7ANwHAEVS0vm5aGc6XHZq28i+J3VFifm36kovswt1NZhflL+qj/mc3lX/X5Wftjl3m06zXDhqlb4t9C7kza06RQMAeY36s8DIcpv3jtO7kpsv0taC35/2qM1n52krz4vNumrHg09fZPPIeVpzsl5bX4wHF+ynSF6omUCeri4xspfWTND5u729SVu56ifpHfPN5TqVd8Z03TjpP8uft7nF6OvfWv0Jm1ft1jvjF03SthcAmJyrLTKzZiy3ecGxSTYPqDzD5oJqrROp0xWhUKTjax2gLTvBfB1TyK/TlP5jWqu5H2mLQXCzsxEGJZ0X6qYr/L30WGwu12PurAL9+33UBGx+5qAe96W/1tZNs0HPQQBgAgEQtSVZNXNkgP59vbG4zub6oLaEPL3/LJtH3ets5uW0JTaN1+uqxqPaItZq9LPkkHPO2hfSNuUlR3RlwCVN2sYCAGf31HbK8iz9m/8D5xzWv1Lfo9FZWW/+IV31rPpoic0Nx/S8M7KH/iddvm+wzXWvDUF3iGY1GAHwAID1xpi7nR89A2BOOM8BMD/+wyNKPawZotixbohiw5rJHNF8sj4NwBcArBGRjxdo/h6AuwA8LiI3AtgB4KruGSJRymHNEMWOdUMUG9ZMhohmNZg3ALSzxAIujO9wohPcqytSlD33kc3/dcoVNh+Y8ZLN1zibUFzbU6cuZlzwfzbXnKutKyV+nWbxQdtjxufoVA8A1F6uq8MUF+tmL7eM0PeeUaCrxOwO6nvctedMmx9+d4rNYx7V3y20ZbvmFvaTpQqv1Exuox4zS7YOszlvkJb9LZX/sLnxmzrFN6NA20N6ik7ArWvVevjupitt9v1BpzJ7OyvD/HWkrm4EAF8s1jvxf1m21Oa11y2x+Y8zz7X52RUTbe61WpcKPlyu79FnotZ0eaGumlScrXW8bp/eX7X/ac192QbjGV6pm5j5tPWqeapOxVeO0w1hJuXouaLWmW5fWDvK5h4L37OZaxdRNJJaM1EcpK1Oq+SxgdoitmuankcmfeoDmx8Y+KLNA/363LXH9M0eajzf5ucWaRvZwCXadgYAz5+t11ihgXouGFCq54gppdttHlugq9iEnBacHc3aBrNtv+a33tBzW8U/tE2tZJm238SzYTmm1WCIiIiIiChxeLFORERERORRUa0G4znOaiiBGl3sfuRterfx/d/4pM01X9Dp82/0ecPmns70pbv5kQ/aruJyN1oCgA8v/JPN+4LNNu8P6XTM4wdPs/kPr+smR8P/pi0Ko9/TDZKCR3W6hqgr/DVaDz2W6uYPjefosXd2nnOHPjSHnJnVPx4Yb/PvFutqRcMe06l93xJtafEVaJ38qerSiDHlfVVXCvhUoa7MNMBZueUH/RdrnqUZs9ChJqcFp9X5HW45fLXNhw6yyYDiJ6u/toB99CX9+z1vxCM2+yXb5oXN2irTsExbsnqALVmUOsRZoOhASNuA3U2/vtF3sc2P/Z+u7DW76H2bh2TpdVir8/nxAmeVvB9+cJm+/iParjnsCWd3pRMMbeeWWn9fXQFtXcVom1f11pZLn7N6WHatbsLUv1E3F+x70Nmc0rkm7a61+vjJOhERERGRR/FinYiIiIjIo1KzDaYdplWn5cv/oJtKrFqi0/gzZ5xtc+XM7TbfM/QJm4dltd0Gc6LmkL7fJatusDn7r3rHcO83a2wefWiTzaEmnRIKccML6gbBhj02l7+sGx6dN/Df9DFl2hIjPm0PCR3UafvyhdpOMnqx3ukeOqCbdrmNJaFmbQkb+IcVEWN6+s3Ty+V7AAAgAElEQVQLbL73vNk2N43UGvAVtiIWoVb9zKHiWf2Tlteg9Zm9W8faa6e2nUWuH0AUu/pZugnKFSO0zXJottbQ/MN9bf7pAt2Mb+Rdq23msUipZOBb2vI19bSv2Lxm6p9trnSupW4t0b+72aKtMj6nXfGSNdquGPiLtsEMfl2vo4L1usJeZxoa3dUE0agtLlm+thfVCTitlTBOlZrEtlPyk3UiIiIiIo/ixToRERERkUelVRuMK3T4sM2yWttPBlf30sc8r6vEfL34FptNO9MhJ1O655B+UasbvwQOOt9P4hQKZR7jtFeFturmYSN/o3fum1ydqofocS8Bvafd7NOpwqDT+hLNMRw6YXUjWaNtNBXVukmGFOh0qXFWB4iKO476vfrtY9oG47aamWB33a9PmehwudZNZa4efz7ns7BNR8tsLtil33fPU0SpJHu5/i0v+52ucDSy+ms251R0fHy31OrqYRUL9W95yRt63RbYpxsZuSuvdIp7vjDOec7jfWj8ZJ2IiIiIyKN4sU5ERERE5FFp2wbjMi264kWgThe1h5PdxpfYm2C6byF8oniIqIHqmpM8MnHjCO6uT9o4iOIlr0Gn1fe06qpLjzTphkcPvHWezSOWsPWFUp+7ol3Ou9qyMqpWW76CRXkdvo6/SdsssUvPCcF9+7o4wvTCT9aJiIiIiDyKF+tERERERB6VEW0wRERE3WHAEt1k5UmZYbNxVlcavlo3CvOv1k1dPL4ABVFU3JYYrGtq/4FtYAtxdPjJOhERERGRR/FinYiIiIjIo9gGQ0RE1EnBtRttLl3b8ePZ+kJEseIn60REREREHsWLdSIiIiIij+LFOhERERGRR/FinYiIiIjIozq8WBeRPBF5V0RWichaEflR+PtDRGSpiGwRkcdEJKf7h0uUGlg3RLFhzRDFhjWTOaL5ZL0FwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5oiqZ11E/CLyPoB6AK8A+BDAfmNMIPyQGgDl7Tz3JhFZLiLLW9ESjzETpYTO1g1rhjIVzzVEsWHNZIaoLtaNMUFjzEQAFQAmAxgd7RsYY+4zxkwyxkzKRm4nh0mUejpbN6wZylQ81xDFhjWTGWJaDcYYsx/AIgBTAfQSkY93QK0AsDPOYyNKC6wbotiwZohiw5pJb9GsBlMqIr3COR/AxQDW4/hBcWX4YXMAzO+uQRKlGtYNUWxYM0SxYc1kjqyOH4IyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggY5eaOQZQ/HK8ie6NGDqGhFJ9hAyRVzqhjWTfKyZhOG5Jo2wbhKCNZNGTlYzYoxJ5EAaABwGsCdhb+oNfeGd33mwMaY02YOg6IRrZge8dQwlgpd+X9ZMiuG5xhNYNymE5xpPaLdmEnqxDgAistwYMymhb5pkmfg7U3xl2jGUab8vxV8mHkOZ+DtTfGXaMZQqv29MN5gSEREREVHi8GKdiIiIiMijknGxfl8S3jPZMvF3pvjKtGMo035fir9MPIYy8Xem+Mq0Yyglft+E96wTEREREVF02AZDRERERORRCb1YF5GZIrJRRLaIyJ2JfO9EEJFBIrJIRNaJyFoR+Vb4+yUi8oqIbA7/f+9kj5VSQ7rXDMC6ofhL97phzVC8pXvNAKldNwlrgwkv2r8Jx3fYqgGwDMA1xph1CRlAAohIGYAyY8wKEekJ4D0AlwO4AUCjMeaucBH0NsbckcShUgrIhJoBWDcUX5lQN6wZiqdMqBkgtesmkZ+sTwawxRiz1RhzDMCjAGYn8P27nTGm1hizIpybcHzb33Ic/z3nhh82F8cPDqKOpH3NAKwbiru0rxvWDMVZ2tcMkNp1k8iL9XIA1c7XNeHvpSURqQJwGoClAPobY2rDP6oD0D9Jw6LUklE1A7BuKC4yqm5YMxQHGVUzQOrVDW8w7QYiUgjgKQC3GmMOuj8zx/uOuAQP0QlYN0SxYc0QxS4V6yaRF+s7AQxyvq4Ify+tiEg2jh8EDxtj5oW/vTvcK/Vxz1R9ssZHKSUjagZg3VBcZUTdsGYojjKiZoDUrZtEXqwvAzBCRIaISA6AqwE8k8D373YiIgAeALDeGHO386NnAMwJ5zkA5id6bJSS0r5mANYNxV3a1w1rhuIs7WsGSO26SeimSCJyKYB7APgBPGiM+Z+EvXkCiMg5AJYAWAMgFP7293C8J+pxAJUAdgC4yhjTmJRBUkpJ95oBWDcUf+leN6wZird0rxkgteuGO5gSEREREXkUbzAlIiIiIvIoXqwTEREREXkUL9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixXqURKSXiDwpIhtEZL2ITE32mIi8SkQGicgiEVknImtF5FvJHhOR14nIgyJSLyIfJHssRKkgU841XLoxSiIyF8ASY8wfw5sGFBhj9id7XEReFN4FrswYs0JEegJ4D8Dlxph1SR4akWeJyHkADgH4szFmXLLHQ+R1mXKu4SfrURCRYgDn4fjOVzDGHOOFOlH7jDG1xpgV4dwEYD2A8uSOisjbjDGvA/DUZixEXpYp5xperEdnCIAGAH8SkZUi8kcR6ZHsQRGlAhGpAnAaju8SR0REFHfpfK7hxXp0sgCcDuB3xpjTABwGcGdyh0TkfSJSCOApALcaYw4mezxERJR+0v1cw4v16NQAqDHGfPyvtSdx/OKdiNohItk4/sfzYWPMvGSPh4iI0k8mnGt4sR4FY0wdgGoRGRX+1oUA0urmBaJ4EhHB8Xs81htj7k72eIiIKP1kyrmGq8FESUQmAvgjgBwAWwF80RizL7mjIvImETkHwBIAawCEwt/+njHmheSNisjbROQRANMB9AWwG8APjTEPJHVQRB6WKecaXqwTEREREXkU22CIiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixToRERERkUfxYp2IiIiIyKN4sU5ERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWLdSIiIiIij+LFOhERERGRR3XpYl1EZorIRhHZIiJ3xmtQROmMdUMUG9YMUexYN+lDjDGde6KIH8AmABcDqAGwDMA1xph18RseUXph3RDFhjVDFDvWTXrJ6sJzJwPYYozZCgAi8iiA2QDaPRByJNfkoUcX3pK6qgn79hhjSpM9jgwWU92wZpKPNZN0PNekINZN0vFck2JOVjNduVgvB1DtfF0D4KwTHyQiNwG4CQDyUICz5MIuvCV11avmyR3JHkOG67BuWDPewppJOp5rUhDrJul4rkkxJ6uZbr/B1BhznzFmkjFmUjZyu/vtiFIea4YodqwbotiwZlJHVy7WdwIY5HxdEf4eEbWPdUMUG9YMUexYN2mkKxfrywCMEJEhIpID4GoAz8RnWERpi3VDFBvWDFHsWDdppNM968aYgIh8HcACAH4ADxpj1sZtZERpiHVDFBvWDFHsWDfppSs3mMIY8wKAF+I0FqKMwLohig1rhih2rJv0wR1MiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH8WKdiIiIiMijurQaDBEREf0zydLTq39Af5uPVZXa3NI3R3OR3+amwRLxWqadj9Vy9msuqA/ZXLirRR+zqdbmwO4GfUIoeJLRE3mIT2sjq19fmw9Mq7K5cazffQaM+6XR6NZMUbXWQM+N+oPguk3Oc50nJxE/WSciIiIi8iherBMREREReRTbYMJ8eXk2S0WZzUeG9bH58IDsyOcEdHqk9ypnbmXrRzaGmpvjOUwiT5LcXJt9wwbb3DS6d8TjjhXq5wPu1H72YWcKv/qIzf7NNTYH9zbGZaxE3SWrotzmI2P1PFJ/utPuMkHPCVX9tUVlakm1zf/db1nE6+ZK5LnnYy836/cf23OWzUu2D9XnrtA88PV+Nst7G2w2rcfafH0iL/AXFdrcNEXPL32/ud3ml4c9G/GcAl8O2vJSs56r7t15gc0fLtA6GbRJ68ortcFP1omIiIiIPIoX60REREREHpXZbTCid9xLpU5ffvRZvXP/9Nkf2HzvoJcjnr4rqHcSz5x/m82VC06xObfhqM2+loDm+n02Bxs1mxa9i5/Ic5y78n09Cmw2o3RqcsMXdMry+5c8HfH0zxZutbnIp61nLx3R17rtvatsLn1ihD7+5fU2Bw8ejHnoRN1t98xKm4uv3mnz3GFP2jwhR2uoxbTavD+k54eGf1qoRc8LjSE9bVdlH7b5xwNfsjmvXD+H23GWvt8VE262eeTPdNofG7fZyHMQeU6ptiPXTdbjedHwF50HRV7ObmrV2vA7y8FMzdM2y4uHv2Dz532X2Nz8dz2fBddv7tyY44yfrBMREREReRQv1omIiIiIPCrj2mDcjSp8xUU27z9dN6qY9tmVNt9b/qbNIUTekT8kS79+afbdNm+9tERfN6jT+8/tPdXmFc+Ptbny5SYd3/sbbfbKXchEH/MX9rD56GRtUdl2tbaUbZl1r80hRG4o4dbQEaPH9yX5+rg3z/6dzTeVXW5zY/Mom3NfXK4v6pFNK4iCl2lL45OjHrU5V/S8UxPQafhlLdp++XTD6VG9x7IdOkXfs1Bf64z+unLSlKIPbb6haJfNb5z3a5vP3X27zaP+T1eJCezQVWmIPMFpWW5vg7AT3bTxWufpeo64snyFzZ8t1NbK6wa8Y/NtN19n88jvOC1iAW1VSzR+sk5ERERE5FG8WCciIiIi8qiMa4PBqTqVvuUKbYP5zKy3bf5e6dvOE3IRjcFZOU4+5PxE86weeufyti8vsPlnl860ef9XqmwObd5uM1tiyAvMEJ223z5b/3ws/cTdzqPy233+0hZtg1l3tMrm0/O323xGjj7/uxW6wsX113/R5mGvaXtZxMZjbImhJGrapJuAPTtKV4ZpCukx/YvXZ9k85Eld9iVvpU63n8yIVl1RCT5tD9hZXmHzT6+bYPMN1/9W30P087msQbpahsmL7jxHlAyt/Xra3Gvc3qie0/jSQJsrnttt87PF023+xc2fsHnJxffYfPV5b9m8/MzTbPYtW2tzolti+Mk6EREREZFH8WKdiIiIiMijMqINxnfqGJs3fFWnz38x/WGbz87TO+YLpP1p/Hbfw/l3jw/S5mMKnCnIMTkhm/9fxbM23/zra2yWH+iKMb7letcyN62gZDE+PYZNtrac9HY2ODpk9Pi8fP3VEc8//LBOTfas1tauP3xTp+SXT/qbzeNydNOYO07VTcnu+dfP2lxx7/s2R7TEECXYqHv1PPLA63qMSkhrZew6nZIP1e+xOXhYa6AzfFXaotba+592Vfrnx/vYMkapwX9EzwN79xae5JHOc5zOYTmo7ci+xv0259ZoW/SuoLaCjc7XOl5cebbNRSt0QyZ4rQ1GRB4UkXoR+cD5XomIvCIim8P/3/tkr0GUaVg3RLFhzRDFjnWTGaJpg3kIwMwTvncngIXGmBEAFoa/JiL1EFg3RLF4CKwZolg9BNZN2uuwDcYY87qIVJ3w7dkApofzXACLAdwRx3F1nbOI/sabdNWXX13wF5svytfpkOxOtL7sDuqGFD+rv8DmkLNq/5Uly2yelqdTOW7bTEWWTr/8fvgjNn/6vO/aXLWzv82B7R/FPFZKrJStmw4cHKV35U+fqHfGu61fLUZbvGrf1rYXABj2eq3NwZ2aD3xhnM1+p10sH7rK0uxC3ejlwA26SswDU6faPPh72oIT3LjlZL8KeUw61Exg2w6bC5wWl4jHdLHdJeK1Zpxh89ardIr+9vNebOvhaHJqM3exnhfRuLuNR1MqSIe66YivWXtafA09T/LIjplmvW4b9A/Nn6/4V5sfm6Eb8x24Sjet7P3uAJuD1Tv1NRPQEtPZG0z7G2M+PtPWAeh/sgcTEQDWDVGsWDNEsWPdpJkurwZjjDEA2r1TRURuEpHlIrK8Fbwxkgg4ed2wZoj+Gc81RLHjuSY9dHY1mN0iUmaMqRWRMgD17T3QGHMfgPsAoEhK4n/7udPu4svVdpL9n5lo8+fOfsfmWQU6pQE4d/a244jR6Zfv1Z0b8bNn39XF8vsu09cS57d8edTpNo+cut3me4c+YXOZX1twqrJ0tZqcKY02t75eoq/PNphUFVXddHvNdEFLsdbbWUW6OUvIORc0OStfFFZHPt8c1PrzVQ3Sx/XSVVyCzlS9+7rFzoozN/faYHPBGD3JPF00o+NfglKJd841MQrFsd3FPc8Fp+t5Z+t1+v3/nabnlE/30LaWZS16brrm5dtsHrOwQce6/0D8xkpekPLnmggBXd3Id6zt1fZOxvTsYXPLBD3v7DxfN+k7a/RGm4dm6XXfD8c/Z/P95Z+xOatWa8zLbTDPAJgTznMAzI/PcIjSGuuGKDasGaLYsW7STDRLNz4C4G0Ao0SkRkRuBHAXgItFZDOAi8JfE1EY64YoNqwZotixbjJDNKvBXNPOjy6M81g6RXJ0tYjDMyfYnH2DTlFc02up84y2f+UWoyu1rDim0+23O5u6HFtQGvGcMa8404hb225NKR2odw9X7x5i8z9uHmrztT1r0ZY5w7V9528jZtncd43exR88eLDN51Jyeb1uYuHvVWzz0RKdghyaozOrbv1saO1rc6/NkX2Q7p34h88Zpq/Vewc64r7H0had1vzZq5+yecyeOpsTu2UFdVU61Uyn+LRdxT9scMSPGif3s3nvJ4/a/NNJT9k8u4euPrPKaRX46urrbB7za213CW3eZnMipvGpe2R83bTjwAhtp2zprddhZoK2Yn5//NM2X9tTz2f79KlY3Vxps79F23GO3wqQOF2+wZSIiIiIiLoHL9aJiIiIiDyqs6vBJJVk6bD9Zbp8aP2/6BT746N0c6Ex2XrHb3vWteoU5LfXXmVzyc90uj171QcRzwk2OSvLtDMl4m6SUfp+H5sf3DHN5mvHPdnmcy8r1Pf7/VCnDaafvg7YBkPdrVSPt+ZBOl1+Zq5Oqbc6h//mFp1yzPnghPYWp3b3jtU8s/f2Nt+62VmN6bUjOo5b39b2tNH/p9OX7kYVRMkk2dqi6cvX1kq4uajQxqNDdMWv7edGnrPOu2S1zXcOWGBzZZauJPZei3729k2nfbPoz07b5Fq3JZQofX3uPG0jPq1Az0Pn5+sSZcU+rdHFR7WWfrD5Spv3vanns6ptumJM8JiemxKBn6wTEREREXkUL9aJiIiIiDwqJdtgfIXamrLvrIE23zlhns2D/CG0xV1RYldQ7+y9q/pym3P+ptORviU6laKP7pzcj3STo11LdNwrR+pYT8vRfz+5U5zHqnRVjUCpTmvKli4OiqgjOc6UfK4eq4U+3YTsUKid3e8k8vOAY6cPt3nghTodeX2v95xH6cZgG1v1T9T3P9AaHfN9bX0J7HRWUwp1tUqJusDZvEjG6mpH+0frikpH+mhNHK7Q/rHR03R1lnmD/x7xssOztQ6ynPo4ZLTu/mu7Tt3nPKjnsIJ5bH2hFOfUlel4L0sAwE/6rbDZ3dxyY6u2vjxycLTNv35bF88Z+6NdNhft0dcJHtWVmBKNn6wTEREREXkUL9aJiIiIiDwqddpgnGkQ9NNNV+o+qdMbn+qh04hFPm0hcbmrvnx3s94xf/jRMptLHnm7S0NtT2DrdpurHtN/J1079is2Lz/vdzYXirYZ3D9trs23rfiqzQO6Z6hEMfE59Zkn2mqGfiURj9v6OW2puadyoc0VWboqxr5gs833119sc8E8bSUIVK/r2oCJuoEvX887G2/UdsV5l/3K5vE5Ha9OBuR2/BAAuwLaRrNpzSCbR85fbnNit24hig/J1RoI9NHzQ6Bf7KuwuBtd3vjODTZX/FlrceRLy/Q9Yn6H7sdP1omIiIiIPIoX60REREREHpUybTC+Qp0GOThe22DumfoXmwtEpzRC7Uz+/W73DJsPPaGtL30f1jt+Ez1t6L5fq9HVNkKiPwkaX9tPIOpmckRXnJDmXjbvC+md8e7mEucXbLb5md+cGvFaj1b91eZx2XogHwrpcX/LR5+0ecv9erd+yePJq1GiaBy+ZJzNYyd8ZPMpOd1zqh2UpeeF3kN1tbHQ5FNslrdWdct7E8WdT9uUmz490ea8r+qKX/OHP+48IQfRuH3952we+Ig+J3fh+zZ7/ZzCT9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo9KmZ51qRhg8+6z9N8Y5+fvtTlb2u5f+k7tFJtXPDre5ooXd9gcaGlnB8Z4cvqxTL6OtUeBvrcfgrZUt/bRx7R4vbuK0knQ2SG05xbdefcfzRU2X1G4x+ah2XrvyG+Huv2FQH+/HvfZovVwxZZP21z70BCbS5/dqONIRI0SdUHhBw02r92s9fHmIK2J+mBPm3/54UU219X2trnkncjlHX3OWnJTv6bLMv687C2bS3sctvloL13mNLpFIImSQ7L1nLD3C2fY3P/67Tb/asgTNldkxX5Etwb0XFPYovdHmdbYl4FMFn6yTkRERETkUbxYJyIiIiLyqJRpgzHZOo0RKAraXNBO64vrlefOtHno0zX6Orvq4jS66Ph6FNh8aJhOU/5knO5OWuBre3e7n62+xObK9UfbfAxRdzBO+0mP3TqFuGCfLlN3ReFim7OgtVqR1fZOwgDwmc2fsrnur1U2939JdyIO7G0EUaoIVe+yecRD+jf+397WXad9zga/Rdv1b/moQ0f0MdVaAwAQHKrtZ/Ut2kbjcz5vu2zAapt/c+ZgmytfiHr4RAnXep62Jh+97IDNP696ymb3U+Uf1Ov13N836NLAb51zb8Tr9m5nF/tUxU/WiYiIiIg8ihfrREREREQelTJtMO3xtbN6iqvndl09JbD9o5M8snv5+ujd/o1jtFVgZn6zzX5nF9aHm3QFmB6Le9icvXaTzdoQRNQ9fHl5Nhun3Pa1aFuXW4d+0c8AjoQiW7b+5cPLbY5sfdG6DNQmtj2NKF7cljHf0g9s7rvSadd0dusNHdX6MM5qYRhaGfG6m+dord3d790239sPZ5ULfgxHHubrodcz1Rdqbdw++kWb3d15f75XW1+ee+Jsm/tv0CugA2dHrpLX26mB8yu22PxOv0k2FyN1dFjSIjJIRBaJyDoRWSsi3wp/v0REXhGRzeH/793RaxFlAtYMUexYN0SxYc1kjmj+/R0A8B1jzFgAUwDcIiJjAdwJYKExZgSAheGviYg1Q9QZrBui2LBmMkSHbTDGmFoAteHcJCLrAZQDmA1gevhhcwEsBnBHt4wyhfl76z9o952ld/QPumhHWw+P8PqBkTYXb9NdMYJ79rb1cPKItKgZZ0q+5ZxTbK6brtOOc0o/QFtajT7mjaORE401j+qGR2XPbbU5ULe782OltJAWdeMwgUCbOYI47WOF2hqw81NlEQ/7rwt1c7FZBftsrg1q2839H06zueyt1NnshTovVWvG16+vzb1O1Q31Lu2hLb5LW/Tc8ZeVurHl6Kfq9YWclrKXDo+JeI/rivS1vtJnic0vjNOWmj6DdOOyQLWuFOhFMfWsi0gVgNMALAXQP3ygAEAdgP7tPOcmADcBQB4K2noIUdpizRDFjnVDFBvWTHqL+jYUESkE8BSAW40xB92fGWMMANPW84wx9xljJhljJmVz42PKIKwZotixbohiw5pJf1F9si4i2Th+IDxsjJkX/vZuESkzxtSKSBmA+vZfofuE2j4GIx/j/JaSpV+0OzXZRe57tI6rsrlulu6G8fyIJ3R8TpEcCunGGMvqdEWAvk3OThrkeV6umWiEpk2wefsXtMb+MO0hmy/I15UsdDISaDF6rC48ODbidfu9o+cRtr7QiZJVN+6KR1KhLSjHKrSNMXuf/m02azdrjvU84ra+9Cmx+eD04TaPunJjxFOmF2y3Ocv5BPSv+8+wOfAPbS3IWfBWbGOilJWK55pQsbZ8De9Va3OZX4/tX+3Rc1DxMr1GCm760GZ/Xz3mf/HWJyLeY8qF+riJOXpNljW8yeZjVaU2+zzeBhPNajAC4AEA640xdzs/egbAnHCeA2B+/IdHlHpYM0SxY90QxYY1kzmi+WR9GoAvAFgjIu+Hv/c9AHcBeFxEbgSwA8BV3TNEopTDmiGKHeuGKDasmQwRzWowbwDt7jx0YXyH0z5p1RUm/Id1QqDZ6F3vBZKDtjSXOdOO/fvZHNi5K55DtHxDtH1lx4X5Nn/7zOdtLvTptI7bNvDbxok2y3M6RZq9dZvN3dO8Q/HilZqJhq9Apx1loN6DtOMbepTNO/OPNg/J1oaXA86d+MU+bSNwFfgjV6Uwuc7GL+4mMCFu75Xpklk3MlhXhfjoM3qOKLlAp+i3rx5g84j/1r/rpkmn1V0RtVWgj0evIhv3TdaaG/Q1ba15sErPFQCQL/paS47qafv+N863edQSbTHruDmU0kEqnWtite2wsynkbuf8YPToDu0/YPPABc75BMCGc7WdbWJOg80iTnWk0OZhKTRUIiIiIqLMwot1IiIiIiKPimmd9aRqaLSxZI3eAbzycr2reGqebhDhc/4dMvwi3Xxl78Yqm3vO0xukO7UyjDON7y/Wqc0P5+jU5o8+94jNVxTq4v9u68vyFp3ifO5/LrC5/4J1Ngec6R6iLnGOWzN2qM0bv66tWb8//c82u60vi47o3fM7W3WljJuKt9ucLfr6E/KrI956cV/duKUgX1tnQocPRz18ong7OF6n3Ed/UjdT+Xnl0zZ/yXetzb4+euxLTra+kLPSy7Hxg23eN0Jra98pOg3/nYu13eXmYnejvMiWzoMhXXnpK099x+YxD+j0fnDjFhClmpDTxeOu7pfn12uy1gK9nvMX6bWW9NBrp10zI1spR+fUOl+lzqVue/jJOhERERGRR/FinYiIiIjIo1JmbiC4V9tg+tZIG44AAAt1SURBVC7T/J11V9r86sSHbC5yVqf4y7C/2zzt6i/bnLf3VJuzFr4X85j8w6tsbv6NTsEsGfMLm3s743BXrnFXfXn+x9r6Uvz8GpuDzc0xj4moI5KtZd94Sk+bfzDlKZvdDY9uqr7E5jcXj7O5tZ+2ct38ifv0+87d+u8drop47/w6PaZDPL7J40r9Wis/Hf6kzb97dIbNLUF9jE+0ZWxWH13a+tx8bXHJc1plCsVpoTnJ6Xjq21+1ueoZ3ZwpuHlbWw8n8jQ5pi0uNU29bN4T1GP7t5Uv2vzInbpS0t2zLrI5cExbLhdM/1XEewzJctosnfaaYy1ac76W1FmFjJ+sExERERF5FC/WiYiIiIg8KmXaYNyF8M02XWGi5GejbL795zpd/8OBL9lc5tcNKf4w8S823/z162zOrZqquUnfa+84nbLsO3l3xJAG9NAVWn5QvsDmPj59vzdb9N9Dd2zU9zv2d914o//LuupLROuL4dYW1L2M88/1Ap+7mpIe9w1HC21u7a3Tl2eO1Cl4d5qx2Vnp6JGVkyPeb8z+vTYHeXyTR+Q26nG9vl5X88odqlPm47J1yvwnA3WKvr2J9F4+Pb26mxq5dgb17/13q2favHTtsIjHDX9Yx5e1UlerCXEzMUpBZutHNuf/eLTNn7j9Kza/eoZuxndNka50dPbUD20OGT1PuW0vQOQ57Dt1eh4qXqTXZ741q/W1oh9+UvCTdSIiIiIij+LFOhERERGRR6VOG4wjdETvGM5epVMiS5+eYPMrc3Ta5PM9t9t8Wo7+++T3p/7V5leH6ioXh4K6gcX4Am25mZavrwMA2TrLghJnyvOBg7rRzF3/uMzmIfN0KrNknbYQcMMjSqiQtp8U7NFp9N2teld+ALqB1+2DtMXrowElNk/I3akv6fy7f1dQ79Dv/a672gWAA4c6OWii7pO7Rqflix/XFpQz/Nq6+KdT59o8IceZSnem211HnNW/fn9AN0j61Wpd/St7lbaYlb6v7WOjd51QJ1t0fNxAjFJd6KiuNuZftt7mkvv1Gu6sPd+y+bapr9j8tV7RrYD0v3vH2vzKU9oGM/h13QwzmEK1xE/WiYiIiIg8ihfrREREREQelZJtMO4qKcFDOo1ROb/B5p8WfcbmF8/VBfVvd1ZtmZxrnKybEbncKc4Q8iN+9pM9421+a4+2vux4o9Lmkc816RNW6nRPIKAtMUSJZILa+lK4ps7mX704y+bSyx6z+YpCbYmZlqcrIq0+prVxxeZP27zl9Sqbh76uq78AQOjgwU6Omqj7BBv03NFroa4Lkbe3yuYvnnqrza09nCe33QUDcRZqya/Xc83gjdoCkLNlu82BWq0trvJCmcK06Cpk+a9vsHlkQ5XNc5deavO9A9spuBMUVjs1946ew0Lbq9t6uOfxk3UiIiIiIo/ixToRERERkUelZhuMy5kuDK7XdpfhD+lUZvWGETZ/4RTNgb56931nFK3Jsblwp45j6MpaHdOW6O5cJkoYp2YC23WViRF/1o1bftR0jc3/XqmrWrj8jbrSS7/3dMpx6CsbbQ427ot8EjdCIo8L7tHWrexXNZe9Gv/3YjMkkQo1OW3D72prcp939dt9OvG66dBUxk/WiYiIiIg8ihfrREREREQelfptMO0IbtLNknq5ORHvnYD3IIq30Gq9E79ydedfh8c/ERFR/HT4ybqI5InIuyKySkTWisiPwt8fIiJLRWSLiDwmIjkdvRZRpmDdEMWGNUMUG9ZM5oimDaYFwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5ojqBlMR8YvI+wDqAbwC4EMA+40xH688VQOgvJ3n3iQiy0VkeSta2noIUVrqbN2wZihT8VxDFBvWTGaI6mLdGBM0xkwEUAFgMoDR0b6BMeY+Y8wkY8ykbOR2cphEqaezdcOaoUzFcw1RbFgzmSGmpRuNMfsBLAIwFUAvEfl4NZkKADvjPDaitMC6IYoNa4YoNqyZ9BbNajClItIrnPMBXAxgPY4fFFeGHzYHwPzuGiRRqmHdEMWGNUMUG9ZM5ohmnfUyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggW4cJ1GqYd0QxYY1QxQb1kyGEGNM4t5MpAHAYQB7Evam3tAX3vmdBxtjSpM9CIpOuGZ2wFvHUCJ46fdlzaQYnms8gXWTQniu8YR2ayahF+sAICLLjTGTEvqmSZaJvzPFV6YdQ5n2+1L8ZeIxlIm/M8VXph1DqfL7xnSDKRERERERJQ4v1omIiIiIPCoZF+v3JeE9ky0Tf2eKr0w7hjLt96X4y8RjKBN/Z4qvTDuGUuL3TXjPOhERERERRYdtMEREREREHsWLdSIiIiIij0roxbqIzBSRjSKyRUTuTOR7J4KIDBKRRSKyTkTWisi3wt8vEZFXRGRz+P97J3uslBrSvWYA1g3FX7rXDWuG4i3dawZI7bpJWM96eIetTTi+HW4NgGUArjHGrEvIABJARMoAlBljVohITwDvAbgcwA0AGo0xd4WLoLcx5o4kDpVSQCbUDMC6ofjKhLphzVA8ZULNAKldN4n8ZH0ygC3GmK3GmGMAHgUwO4Hv3+2MMbXGmBXh3ARgPYByHP8954YfNhfHDw6ijqR9zQCsG4q7tK8b1gzFWdrXDJDadZPIi/VyANXO1zXh76UlEakCcBqApQD6G2Nqwz+qA9A/ScOi1JJRNQOwbiguMqpuWDMUBxlVM0Dq1Q1vMO0GIlII4CkAtxpjDro/M8f7jrheJtEJWDdEsWHNEMUuFesmkRfrOwEMcr6uCH8vrYhINo4fBA8bY+aFv7073Cv1cc9UfbLGRyklI2oGYN1QXGVE3bBmKI4yomaA1K2bRF6sLwMwQkSGiEgOgKsBPJPA9+92IiIAHgCw3hhzt/OjZwDMCec5AOYnemyUktK+ZgDWDcVd2tcNa4biLO1rBkjtuknoDqYicimAewD4ATxojPmfhL15AojIOQCWAFgDIBT+9vdwvCfqcQCVAHYAuMoY05iUQVJKSfeaAVg3FH/pXjesGYq3dK8ZILXrJqEX60REREREFD3eYEpERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWL9SiJyEwR2SgiW0TkzmSPh8jrROTbIrJWRD4QkUdEJC/ZYyLyMhHJE5F3RWRVuHZ+lOwxEXldJpxreLEeBRHxA/gtgFkAxgK4RkTGJndURN4lIuUAvglgkjFmHI6v3Xt1ckdF5HktAGYYY04FMBHATBGZkuQxEXlWppxreLEenckAthhjthpjjgF4FMDsJI+JyOuyAOSLSBaAAgC7kjweIk8zxx0Kf5kd/h83QyE6ubQ/1/BiPTrlAKqdr2vC3yOiNhhjdgL4BYCPANQCOGCMeTm5oyLyPhHxi8j7AOoBvGKMWZrsMRF5Vaaca3ixTkRxJyK9cXz2aQiAgQB6iMh1yR0VkfcZY4LGmIkAKgBMFpFxyR4TkVdlyrmGF+vR2QlgkPN1Rfh7RNS2iwBsM8Y0GGNaAcwDcHaSx0SUMowx+wEsAjAz2WMh8rCMONfwYj06ywCMEJEhIpKD4zcvPJPkMRF52UcApohIgYgIgAsBrE/ymIg8TURKRaRXOOcDuBjAhuSOisjTMuJck5XsAaQCY0xARL4OYAGO32n8oDFmbZKHReRZxpilIvIkgBUAAgBWArgvuaMi8rwyAHPDK5D5ADxujHkuyWMi8qxMOdeIMbzRnIiIiIjIi9gGQ0RERETkUbxYJyIiIiLyKF6sExERERF5FC/WiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH/X/jBWYG27+ZQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(valid_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b45N1AAAAIKSYrAMAAAAhRRtMH2iJHWG1b4adKnf+IDu19MdbL/Z55JO2BVBsr7UWAGEQqbRWro0ftPatL01+3ueqAlvWfK1tkM8711b7PGiVnWzKVqRAooJSq6G2Oqu5z463bX5faStNeMzIBXZ9ce9tT+PogNTR4mKfjzt3s+Wi4CmkRWkfR/DaJqNG+Lj5Yts69e/PfNLnIQU27rjYtsQbopYHNFh2Tc0pG2t3+GQdAAAACCkm6wAAAEBI0QbTB60X244ZJ5zyrs8Fassjb799nM/TXtnic9TZsiYQBgcumebz8efZbkVXDFzt89J2a/36l9V2UumYJ+1b8rS+AN0rGGnL8A1nWj1dMsBq7h/rL094zMCNB3zuj10ygJSI2OfBMwdv9blC+3f+03z+VJ+3fsB2nPmrM173+dpKOzH1sLNxLzlc4fONz/+Nz1PX2En18da21A22Gz1+sq6qo1X1BVVdpaorVfWmztuHqOozqrq+879VPT0XkA+oGSB51A2QHGomf/SmDSYqIl91zk0XkTki8gVVnS4it4jIc865SSLyXOefAVAzwLGgboDkUDN5osc2GOdcg4g0dOYmVV0tInUicoWIzO282z0i8qKIfD0to8ykAtvBJTJ1QsKPGq477PM/1dmOGT+utx1gRrxmSy7RBjsIALkrW2um8cp2n/991FM+10ZsZ5hvbpvrc9Hvh/hc+uJSn60JDOi9bK2bZEWHB3amOMEOEAvuQLFsx8iEx4w9fFiAo+VLzRyLlo/O9nnbFR0+f2f2Iz5fNTC4s5LVX2PMroX/s+sMnyfdbc8jazb56Drs/umSVM+6qo4TkZNF5E0Rqel8o4iI7BCRmm4eM19E5ouIlEp5V3cBchY1AySPugGSQ83ktl7vBqOqA0XkDyJys3PuYPBnzjknIl1+Y8A5d4dzbpZzblaRlHR1FyAnUTNA8qgbIDnUTO7r1SfrqlokR94I9znn/th5c6Oq1jrnGlS1VkR2pmuQ/U6tdSVSZQe/bLpmWMLdbjvtF3a/wA4wG18b6/PE1+0b0OyRkT+ysWaunr7Y51klscBPrBXsrXp7b09YsM3nYOtL8NCX9xPcNcbFYl3eRyP22loY+OuqwD5n0GI7VCM+aYzPsdKe/3or3NdqY1hju3Gwo01mZGPdJKt9kC23nznaDtA77Ow911ZfkfAYbUuYfwFettdMJDDfig60f1MUjgv8XT7U6uHAFMuxYnvs0YZdb7vv/d+6V3w+rdRaXyLa9T9Q1nfY93FfefYEn49723aMibelfweYoN7sBqMicqeIrHbO/TDwo0dE5PrOfL2IPJz64QHZh5oBkkfdAMmhZvJHbz5ZP0tEPikiy1X17c7bvikit4rIg6p6g4hsEZGr0zNEIOtQM0DyqBsgOdRMnujNbjCviEh3aw0XpnY44RAZZN/Wbzpvks9fu/qPCfc7oXifzx9fe53PNW/Zkn50S306hogQy+WaGTvU3vO7zx3tc3FTbdLPVbbTdrgo3NdiPwgcGBatsi89tdYE2msC/+u2D7QFwr3zrK1l6GDbaaM7u1dW+zzl9lH2ups29/hYpFYu142W2HJ7c521bX291nZdagu874e+nfg/gzvYnMbRIVuFvmYC7+l1zcN9brONxKQg0OAx7ATr1nn3Ovv7uHWiXSv+NPcnPh9f3P0UtkithTLmrFEzLmVd3v9A3Npa7t1pO/qN+5YdnJTJnc56/QVTAAAAAP2LyToAAAAQUknts57TAocfRaeN8/nD//Ksz5+oTGxp+X2z7Yyx/091Pte+utbnrve4AMKnJW67VMQCy5eFgUXWx6fagRLy/b693j/umOXzU1um+Rx4ablw7Cqff1T7ZlLP3+rsoIo2Z5UYXMq8ve40n//QMNfn2h9uTuq1gPdTMM5axvZNtzf45KIBPr/bYa0uBdGjdtpzHDWG7ONarbVk7Z9n+rzkRjv0qyay2+cXT3zAHnyixeD1KNj10+F6N8MK3i+461JToK7u3W/Xo8VPT/d5jLzWq9dINz5ZBwAAAEKKyToAAAAQUrTBdIpUDvR5zyTbgeJLVXZoRYkWJTzmX35vuyFNeLbR59ievekYIpBWjyw/yecrBy/x+ZzS9BwQ9M81trz4reGvdHmfIrXPE+Ji9XfYdfjc0U2LwLcbz/P50VV2sEXBLmv3Kdltz1+7pH8PuUD+ODTJtr8YdeIOn/fFbBekH+063+ehLya2XEbZDQZZKN5mu7iMetLmRU9fdbzPs0uf8bk6kp5TVN+NWhvM4812Lbj9Odv1ZfKvrcbGb1nvc1hamflkHQAAAAgpJusAAABASOV1G0xk8CCf91xu3/698Muv+twSt+X26S/OT3j8pD/ZoSvxLVvtB+6ob/IDWWDqd22Z8vPXfs7ntomB9pBujt8oLLZWmZmjtvl817jHEu5XpsVd5p/tm+LzfRvtW/nNB+0AC3fI/rqqfsN2b6rcYkutGrWWmMJm2w1marMd5qQdgbaedqvvePMhywL0TfAgpP0T7L37j2Nf9nlT1G5/9NVTfZ560HZBEhGReFgW44EkBN63bu27Pr992yk+n3PODJ+/cubTPs8fvCFlw/jMyk/5rL8d5vO0BXatijUEWpk70tP62Rd8sg4AAACEFJN1AAAAIKTyug0mOn2cz7susqX0rw17w+fgpvlVL5UmPL5g83s+xw4fFiCbxTbYMuX4+6yVK1Y1oKu7J3AR+3f/7spxPl9Q9+XEO3bTRlO6z5ZLR+yyWioI1JV22M4ZWh9Ysty/PzAQGzetLMiouL0XNdAZua5thM+3v2s7Fk26397f8RbLQC5wgb/Lhz610eeq5dU+3/nOZT7fft5Bn780/QWfLx5gh07+W8M8n194y3aYERHRuF1shi0J5Je3+xwNzOHCjk/WAQAAgJBisg4AAACEVN61wRTWjfR583m2vP/V0/7sc1XEDkXa32Eb5ZftPWphPbCTBJBLops2H/Njg0eHVfVxHK6bDISdi9r1YcRrtqT/aNu5PpfvsmuKLl4aeGz4dqMAUiXWuNP+EMh1O6xFrG1lnc93TP6wz7cNtpaWivesfqasCLRDiojGAm1o+wM792XpoZV8sg4AAACEFJN1AAAAIKTyrg3m8CRbZomfYksjn6m0bycfdrbMsr7DFvIjhxPbYByHHwEAuhK4PrhFK3wetqibu6d7PEDIRRt2+FwYyNXP9/zYXN/9i0/WAQAAgJBisg4AAACEVN61wbRW214VY4fawSrxwCLKsy1DfP7yG9f4PHmr7QwjIiId7AYDAACA9Onxk3VVLVXVt1R1maquVNV/7rx9vKq+qaobVPW3qlqc/uEC2YG6AZJDzQDJoWbyR2/aYA6LyAXOuZNEZKaIzFPVOSLyAxH5kXNuoojsE5Eb0jdMIOtQN0ByqBkgOdRMnuixDcYd2fLkL/0fRZ3/50TkAhH5eOft94jId0Tk9tQPMbWKD8Z8Xvee7Qzz1YHn+/z0shk+T/vGJp9jR2+mz24w6Eau1Q2QbtQMkBxqJn/06gumqhpR1bdFZKeIPCMiG0Vkv3PuL8esbRWRum4eO19VF6nqog45nIoxA1nhWOuGmkG+4loDJIeayQ+9mqw752LOuZkiMkpETheRqb19AefcHc65Wc65WUVScozDBLLPsdYNNYN8xbUGSA41kx+S2g3GObdfVV8QkTNEZLCqFnb+622UiGxLxwBTrfgpO5Fi0lN2++bAfSbLQp9jAvRNLtQN0J+oGSA51Exu681uMNWqOrgzl4nIxSKyWkReEJGrOu92vYg8nK5BAtmGugGSQ80AyaFm8kdvPlmvFZF7VDUiRyb3DzrnHlXVVSLygKp+V0SWisidaRwnkG2oGyA51AyQHGomT6jrxx1NVHWXiBwSkd399qLhMEzC8zuPdc5VZ3oQ6J3Omtki4XoP9Ycw/b7UTJbhWhMK1E0W4VoTCt3WTL9O1kVEVHWRc25Wv75ohuXj74zUyrf3UL79vki9fHwP5ePvjNTKt/dQtvy+vdoNBgAAAED/Y7IOAAAAhFQmJut3ZOA1My0ff2ekVr69h/Lt90Xq5eN7KB9/Z6RWvr2HsuL37feedQAAAAC9QxsMAAAAEFJM1gEAAICQ6tfJuqrOU9W1qrpBVW/pz9fuD6o6WlVfUNVVqrpSVW/qvH2Iqj6jqus7/1uV6bEiO+R6zYhQN0i9XK8bagaplus1I5LdddNvPeudJ2ytkyPH4W4VkYUicq1zblW/DKAfqGqtiNQ655aoaoWILBaRK0Xk0yKy1zl3a2cRVDnnvp7BoSIL5EPNiFA3SK18qBtqBqmUDzUjkt1105+frJ8uIhucc5ucc+0i8oCIXNGPr592zrkG59ySztwkIqtFpE6O/J73dN7tHjny5gB6kvM1I0LdIOVyvm6oGaRYzteMSHbXTX9O1utEpD7w562dt+UkVR0nIieLyJsiUuOca+j80Q4RqcnQsJBd8qpmRKgbpERe1Q01gxTIq5oRyb664QumaaCqA0XkDyJys3PuYPBn7kjfEftlAkehboDkUDNA8rKxbvpzsr5NREYH/jyq87acoqpFcuRNcJ9z7o+dNzd29kr9pWdqZ6bGh6ySFzUjQt0gpfKibqgZpFBe1IxI9tZNf07WF4rIJFUdr6rFIvIxEXmkH18/7VRVReROEVntnPth4EePiMj1nfl6EXm4v8eGrJTzNSNC3SDlcr5uqBmkWM7XjEh2102/nmCqqpeKyI9FJCIidznnvtdvL94PVPVsEXlZRJaLSLzz5m/KkZ6oB0VkjIhsEZGrnXN7MzJIZJVcrxkR6gapl+t1Q80g1XK9ZkSyu276dbIOAAAAoPf4gikAAAAQUkzWAQAAgJBisg4AAACEFJN1AAAAIKSYrAMAAAAhxWS9F1R1iqq+Hfi/g6p6c6bHBYSVqo5W1RdUdZWqrlTVmzI9JiDsVPUuVd2pqisyPRYgW6jqPFVdq6obVPWWTI8nHdi6MUmqGpEjJ3vNds5tyfR4gDDqPAWu1jm3RFUrRGSxiFzpnFuV4aEBoaWq54pIs4jc65ybkenxAGHXOSdbJyIXi8hWOXLA07W5dq3hk/XkXSgiG5moA91zzjU455Z05iYRWS0idZkdFRBuzrkFIhKqw1iAkDtdRDY45zY559pF5AERuSLDY0o5JuvJ+5iI3J/pQQDZQlXHicjJcuSUOAAAUqVOROoDf94qOfjBEJP1JKhqsYh8WER+l+mxANlAVQeKyB9E5Gbn3MFMjwcAgGzDZD05HxSRJc65xkwPBAg7VS2SIxP1+5xzf8z0eAAAOWebiIwO/HlU5205hcl6cq4VWmCAHqmqisidIrLaOffDTI8HAJCTForIJFUd39n98DEReSTDY0o5Juu9pKoD5Mi3jfmEEOjZWSLySRG5ILDl6aWZHhQQZqp6v4i8LiJTVHWrqt6Q6TEBYeaci4rIF0XkKTmykcGDzrmVmR1V6rF1IwAAABBSfLIOAAAAhBSTdQAAACCkmKwDAAAAIcVkHQAAAAgpJusAAABASDFZBwAAAEKKyToAAAAQUkzWAQAAgJBisg4AAACEFJN1AAAAIKSYrAMAAAAh1afJuqrOU9W1qrpBVW9J1aCAXEbdAMmhZoDkUTe5Q51zx/ZA1YiIrBORi0Vkq4gsFJFrnXOrUjc8ILdQN0ByqBkgedRNbinsw2NPF5ENzrlNIiKq+oCIXCEi3b4RirXElcqAPrwk+qpJ9u12zlVnehx5LKm6oWYyj5rJOK41WYi6yTiuNVnm/WqmL5P1OhGpD/x5q4jMfr8HlMoAma0X9uEl0VfPut9vyfQY8lxSdUPNZB41k3Fca7IQdZNxXGuyzPvVTF8m672iqvNFZL6ISKmUp/vlgKxHzQDJo26A5FAz2aMvXzDdJiKjA38e1XlbAufcHc65Wc65WUVS0oeXA3JCj3VDzQAJuNYAyeNak0P6MllfKCKTVHW8qhaLyMdE5JHUDAvIWdQNkBxqBkgedZNDjrkNxjkXVdUvishTIhIRkbuccytTNjIgB1E3QHKoGSB51E1u6VPPunPucRF5PEVjAfICdQMkh5oBkkfd5A5OMAUAAABCisk6AAAAEFJM1gEAAICQYrIOAAAAhGgI0FIAACAASURBVBSTdQAAACCkmKwDAAAAIcVkHQAAAAgpJusAAABASPXpUKRsVFBR4bObMtbn2IAiu89LS9Pz2uXllqsG+xzff8ByS4s9wLm0jAMIg8IRNT63nDTaZ1eoPg9Y2ehzdEu9PZjaQAhpSYn9YfpEHzuGlPpcsqPZZ7d5q8/xQ4fSO7ijaFGxz5GaahvHgYOWm22s1ByQOXyyDgAAAIQUk3UAAAAgpPKuDUYm2HL72r+xtpTioW0+j3spPS+tdSN83nWWtQBU1Lf7XLRguc+uw24HcoEW2l85ey8Y7/OEL67xeUzZXp8fu/tsn2t/tt1nF42ma4hAUoLv6fipU31e/zfWWjlxvLVzbXnOrkHjfm2tL2lrg1FrKysItOl0nDHd5+2nWJtO7avW+qJL1/rsDh9Oz/iArgTet5FBlXZ7oGXLlVjtuVUbEh6ea9cIPlkHAAAAQorJOgAAABBSedcGc2CqLaeMnmBLk3uay7u6e0pFh9tr773Y2m5ay215cfSSAT7H9tEGg9wSqR7mc9s1+32+ddSjPtdEynx+5dIJPusvbPeKXFviRJYJLtGPGulz8/9p8vn5aff6/ODBk32+u9DaYKL1thtMumihtePIFGs9u/SnL/o8u9xaCP5OvujzqI12zYrt2pWeAQKdgi1lBVVVPh887zjLn7DdiiYO2e3z4Wvs2iIiEt1h87tc2MmIT9YBAACAkGKyDgAAAIRUXrTBBJdW9k+yf598pu4dn+9eOyft4yjcZwceFdQP9fn8i1b4vLHSdoyRwOEUEo+ldWxAf2iaPcbn80Yt8bk2Ym1ov26yGtix2PL41s3pHRzwfgKtL8EDvdb8q/1d/qvJv/L5R7vO93nBPaf5PO7Ot32Op3yQ/1tktLXprPrCQJ/vGWQ7j81+7Ms+T312n8+x3dZmAKRb7KwTfF57nc3bfjD3AZ8vKLNdwTZFrTXy20M/mfBcutt2FcuFnfX4ZB0AAAAIKSbrAAAAQEjlRRuMzLSDKiKn2A4UB6KBHWAWD0r7MFxRxOeOwdbW8vEhb/j8zxXX+awRu7+jDQZZqKC0NOHPWz9qu7h8v+otnyNqnxs81Gg7Z4x4M/C+z4Fv9CN7Bf8+PjTTdnSZP/NFn2cV23L79YvsfTz9MVu6j7ZYO2S6RAbb9WzvbGsl+81F/+XzN7Zd4vPYPwdqa1NghxpqDmkWqbZDjjZcYYd2/eR8203pgsBBeSVq15Rxhbar3pqv2k56IiJTbpvks66w3Y6y9XAvPlkHAAAAQqrHybqq3qWqO1V1ReC2Iar6jKqu7/xv1fs9B5BvqBsgOdQMkDzqJj/0pg3mbhG5TUTuDdx2i4g855y7VVVv6fzz11M/vGNXeNw4n1fdaO0un5/0nM8/X3auz5MetWWWdH1D/3C1jePk6e/6PCRiSzkSUUFOuFuysG5SoiDQvjVtQsKP/mrGUp9PCrQMxJx9q3/ltlqfJ72x2e6TyjEijO6WkNVMcCexSJ29L7edZ7efMWC9z1etv9Lnumft7/JYvbXBpEtk6BCf986b7PPEL6zxeXqRXWtefG2Gz1NX7/A52tycriEiPe6WkNVNMvZ+wK4RJ59m7SrB1pcytetDXKw1a6DagV+3n/ObhOf98diLfH7vuVN9HvP4AZ/dMquNsO+41+Mn6865BSKy96ibrxCRezrzPSJypQDwqBsgOdQMkDzqJj8c6xdMa5xzDZ15h4jUdHdHVZ0vIvNFREqlvLu7AfmgV3VDzQAe1xogeVxrckyfd4NxzjlV7fYr4865O0TkDhGRSh3Sb18t33q5HQRx3ZyXfX66cbrP1Y/aN4/d6pVpGYeW2Gs019lSzq2jH/c5EljWCR68gdz1fnWTqZpJlYJiW5psOCdxl6W/r1zt88AC+1b/gkAnmG4p8znWuDMNI0Q2ysS1JlIz3Od3P2U7wHzigy/6vD9mk5wtT4/zedyb7/kc7YdDWVydjXXnbPv17xz5hM9fCewAM/7PNqb4jkCdsQNMTgnjtSYyzA4Sazzfdgj73sgXfN4Vs9uXtQ/zud1Zm+VHBtiCwoVlibsszZ70O5//bfBZPj+31w7ArNlgtRtvaur9L5ABx7obTKOq1oqIdP6XKyrQM+oGSA41AySPuskxxzpZf0REru/M14vIw6kZDpDTqBsgOdQMkDzqJsf02AajqveLyFwRGaaqW0Xk2yJyq4g8qKo3iMgWEbk6nYN8X4GdJwpOsG/AD/ygfbt91gDbeeXBJ872edILm3xO1zJlwcRxPu8+xVaZZhRZ/tXBaT5ri23Y71y69qVBuoW+btJIi63dq3lO4tLkuMJ9PncEdoD5t80f9rl6Kcvw+SiMNeMGDfR50JmNPn916BKf//89p/hctc52lIjtSM+HmcEdanT6RJ/fu8x2gzn/9Hd83thhLQfLf2k7wFS/bjszxdsCfWjIKmGsm57EJtb5fOqUzT4PLbDrxec2fMznLa9aC1rMOovlobNtN5evjnwq4TWmFNln0V8ZZq3QT11ih2S2bLW5V9nTy3wO48FJPU7WnXPXdvOjC1M8FiBnUDdAcqgZIHnUTX7gBFMAAAAgpPq8G0ymFZTamsh7l9ohXd8+7j6fX22a5PPgwB740R22rJnaMdkuF7tOt6XJS8+0pdPVHXb//3jdvqE/bY+15rhYuDfpB/4iuDQvdbZL2NXTliTcryZirV0tzlrPNiy1Zc4prwZ20UjlIIFkBXZGicXts61YYAevIYWHfN471doyC1tP8rl0l7WZRHbuT3oYsWGVPreNsB0stl5gr3f1Ba/4/MmqN3z+/+qtxWzoL1/3mSZLZMqhOtvxa0a57ejy671n+LzzD2N8Hvvfb/pcONx2htm4wlpafn1z4m4uX69+0efaQmtn+/b0x3z+xpnX+TzxZZu3xULYBsMn6wAAAEBIMVkHAAAAQio722ACBwdpuS2nDDh7l88zS7b7/PUX7IvQU9456HO69pzQsaN83nOqLTZ+afjzPj/cdKLPE35j94kfsPFxOAWyRcEgW6bfPduWKT8/9FcJ9xsWseXIF1vts4IB9Zaj27YLEAod1oi1Z+9gnw/ErUXx84Ntt7Ehn/qtz29+5Difn1h3vM/Fy63lq7dap1obzflT7GCxH9c85/O0Yquh3xy0137nZWsDHS+7k35tINXahth7tbLQ3tt/fNdax4auC7SiBOot2L489FFrpVzzmcRDWtvsMiSxwM562zusXbqoyeaSrj3QmxxCfLIOAAAAhBSTdQAAACCksrINRiP2DXgZaksaNx73qs8Vai0kw1+1X9MtXZnewYnIntOrfZ5z8pou7/PH+pk+V75gO2bQ+IJspAMH+Lxvut1eHGhZE0lcjvzKymt8HrI6PYeSAX3hGq1tZPT9tq7+qxNP9/mqQYt9vrjcdjK6eqAdivSfIxf6fPg8W25vifdu6f1QoG6GFNhhYuUFthvayvZWn3+4yrbYnniXHRDI/mIIgz2n2jtx9oCNPh8cbTuyPD9ljs81LwYOAgseClY1yOdP172U8BqDC7r+LHrhwbE+l++wGVe8LXw7wATxyToAAAAQUkzWAQAAgJDKyjYYUfs3hiuzJcGzymw55ab3rvB5wPb+XWJvG2pL/1MG2jeXN3fYbgI73rPDkirFxg1kI1duy5eV0/b4XKKJnwdsi7X43LFgqM9lS9b7zFI9wiLe3Oxz6XPv+Pzm1dbr9fSJ5/jcONv+7v+bD7zg8/wqa3X8732n+nzn4rN6NY4RzxT5POOm5T7/YrS1fr7ROt7n6CrbnSm2qetWTCBTal6x68KC2VN8vmDQKp//NPU0n4eca7vE7D7BWr9i5x3w+eyy+oTX2Bs49atArcXlWyOf8HneubZTUvXL1h4TW2+HU4YFn6wDAAAAIcVkHQAAAAgpJusAAABASGVlz7qLWVdr5MAhn+/bN9vnaRW2XdXTw60vqaS83Od4i/XP9lWkyraQPDjDeuT/boht2fU/B+0Uu4q11oMIZKNItW1RuvPswLZ2J/zI54FakvCYFXYgpAzYYU2FsT170zBCoI8Cp0i7w9b3Gt+42edBjXZy9uC3rFd8wf2zfH6h7AyfI622XeO0A71737tyq6OmqOW3Dttzffe1D9nzPmDPG4vzLRCEy9DXbH722qfstN3PDX3N57sv+7nP6y8aYY8ttO+RjCzc5/OPdp2b8BrP3GdbP1ZfutXn/5xopwx/aIZ9D+XlS6xHfjg96wAAAAB6i8k6AAAAEFJZ2QYjgdPcXJMtibyww9pdvjv5IZ9f+rTdvr3GTg6tWtvz6XEuYltxNY2KJPysbZj9rK3Glho/fdrLPg8tKPO5JWbLl4UtnFWK7KYDAu/t4VYLxxfZdqqRo7Zu/OVO2+aubFegJ4alemQRF7X3bmy/bR8nwbzFYrAKgn/z9/Zdv/0fzvT5Q1W2XePXN1zlc93jdn2Kr3u3l88M9L/Ytgafm35/is/f/LS1cv1sjG2xeG6ptc2s67DW5+tXfcpnvcfaMkVERr9qpwlvHDTG5+drp/r80So7ffjRWSf6PLJupM/Rbdvf71fpN3yyDgAAAIQUk3UAAAAgpLK0DSbwDf2WVp9bH7eTsH5eMdfnz419yefFnxzn88oDtT2+VIHaa9UWHU742egy+ybyqQM2+3xmwklatvvMmkM1Ple/fUiAbBarHuRz+/TWLu+zO5b4Pn/5FdsRaXJ9YMeKFI8NyDZaaJfj+OwZCT+bcLmdcj23fJ3P/7HlEp+nrrUWnHhH/57aDSQjuLPSiCdsvrS8zE4Gvuji4T6fMcLaut5oHOdz8V12EnzFk3ayr4hINLDb35in7LTsH429yOf/e+bvfA7uDPPqh2wnp2E/z5I2GFUdraovqOoqVV2pqjd13j5EVZ9R1fWd/63q6bmAfEDNAMmjboDkUDP5ozdtMFER+apzbrqIzBGRL6jqdBG5RUSec85NEpHnOv8MgJoBjgV1AySHmskTPbbBOOcaRKShMzep6moRqRORK0Rkbufd7hGRF0Xk62kZ5fuIt9lySt1D9u3fTS2Tff7GHNt0f+ToPT4PKmnr8fkPx+x/olUrxyT87K3A2n39qfYP1+l19k3nusAGMtsODfa5aPEan9kXJreEvWb6QktsR6MDEwf4fNNM++Z+PPCOXtZuh8SIiIx4I/Bu37pDgL/I5brpDS22XZTqLypP+Nnto57xeX2H7XpRttkeo7s4WCzf5ELNROvtwKJRf7Tbmzfajiyv1llbStlu2w2w4rFlPscPJ7YpB9ulC95a6fOwyXb40e8m2fPeXGs19tQF03we/uv0HKSZrKR61lV1nIicLCJvikhN5xtFRGSHiNR085j5IjJfRKRUyru6C5CzqBkgedQNkBxqJrf1ejcYVR0oIn8QkZudcweDP3POOenmA2Ln3B3OuVnOuVlFUtLVXYCcRM0AyaNugORQM7mvV5+sq2qRHHkj3Oec+8tCRaOq1jrnGlS1VkR2pmuQ7ytwmEpwOWXonZZrnrP2lfbR9q3glgrL3SmI2nt88vOLE34WPBhj+Tfs0IonPm7fbj5+yFqfC9WWbwoGWgtBbB/f3M81oa6ZPohUD/N5/2T7t/7nB9u39VudHTZ2z87zEx5fsd6uI7GDCdcUIGfrpje0uMjnIWcktoiNLGzy+WvL7SCkmkVWa7HGnPyfBT3IpZoJzuFKg7mb+8e7uf1owbna0KX7fX5rqR2YeWiE7Rr4oYkrfF52uh2kWfiK7RgTfM7+0JvdYFRE7hSR1c65HwZ+9IiIXN+ZrxeRh1M/PCD7UDNA8qgbIDnUTP7ozSfrZ4nIJ0Vkuaq+3XnbN0XkVhF5UFVvkCMHK1+dniECWYeaAZJH3QDJoWbyRG92g3lFRLSbH1+Y2uGkR3Sz7RJTEMjJdmi9364tJfvtpw3tg7q8z6ASOzimeYS1E8i+fV3cG9kqF2qmO7FaO4SibXTX7Vu7YrY8+MaLxyf8bNJeaxHr7RIm8kMu1013tMh2c3Gj7JC+r014NOF+pYHD+VpetWvH8GWbfO7fRXmEQT7WTF/F37Gd+OqeO93nf5t6mc//ctxDPv/xulN9nrbarn+xnbvsSV369/Tr9RdMAQAAAPQvJusAAABASCW1zzq659RWoiKBBf6I2r+HdrUO9Ll49fr+GRiQQrFy+yujuMLaYA47W4R/rW2szxN/k3hQS2xHVmxKAPSLyIjhPq//hB2sd3ZpY8L9NkWtabNys11fog2BXWO0m26IfliiB7JGoB4qF9qOM5ufs+tW9SRrWX72Az/y+YaHvuxz2QK7T7zJdmtKFz5ZBwAAAEKKyToAAAAQUrTBpIgGllZigX8DxZwtWTrX3Ze2gezQNNqW4y+ZYIeENQcOQlpwYLLPuvdAwuNdtEMAHBEbbjuHferSF3yuKChOuN93Nl/hc3mj1VBBuR0RX1A12B4QaImJbt2WkrECuSa6bbvPo56znV7mTft7nxeff5vPLZ+3A5VKd9hhm7rMDr9M12FJfLIOAAAAhBSTdQAAACCkaINJkXiRLTuWFNgySFRiPh+ORew+/TMsoO8K7H3bVmX/vj+zYoPP7YE2sH3ttjTv2o86OImdKQATaFcZFGnt9m4tHdYWs+dUu3q0XH6iz5HaFp/L3rCdx0b8mDYYoEuB61Fkve0MM/ypKT6/fIYdQvar4+/1+W+O/4rPQzdV+BxL0yGXfLIOAAAAhBSTdQAAACCkaINJkQMz7Bv651as8flfd53i88GXa3yulI39MzAghYqbbNlwYfN4n08qsaX21bvsfT46xhI80Ff/MvEhnxeNPM7n1/ZZXrpwos/Dl3bfUgPgf4sdOOjz0FfsuvVPP/+0z0986d993jPPaqxq9Sh7ooW0wQAAAAB5hck6AAAAEFK0waTBd9Z82Oe2BfZN4jGP7/U5LkCWCBzsNWSZLRX++ZnZPm87xw5kaVtnB724ji1pHhyQvSK7rZ5+9Pw8nwsufDzhfosOjvP5xZW2U0XlctslZsJC2w0m8o61WXKtAXohbjv3xbY1+Dz6IZsm/9d1Z/r8+RMX+Pw/J17i8/C1lfY8B62++4pP1gEAAICQYrIOAAAAhBRtMCky8lk7OKYgWuXz8IXv+Ryt3ypA1gkcHKGrbXn9uN9N8HnDBluaH7XVDgVz7bZLEoBE8R07fZ58jy2f377t8oT7le+wGpy20Nop3bv19lxthy0HlvQBJMdFA9ew7Y0+P/zg2T5/9hPWqtY8xg43qxk2xJ6INhgAAAAg9zFZBwAAAEKKNpgUGfjgG13eHu3yViA7xdva7A+LV/o4dHHX93dd3wxAjqqnRSt8rFvU/WNocAH6T7zVDj8ac9tyn39abbs3Dd4WuNLF07P/Uo+frKtqqaq+parLVHWlqv5z5+3jVfVNVd2gqr9V1eKengvIF9QNkBxqBkgONZM/etMGc1hELnDOnSQiM0VknqrOEZEfiMiPnHMTRWSfiNyQvmECWYe6AZJDzQDJoWbyRI9tMM45JyLNnX8s6vw/JyIXiMjHO2+/R0S+IyK3p36IQPahboDkUDNAcqiZfhDYDS3e1OTzxK/0b+tzr75gqqoRVX1bRHaKyDMislFE9jvn/jKurSJSl54hAtmJugGSQ80AyaFm8kOvJuvOuZhzbqaIjBKR00Vkam9fQFXnq+oiVV3UIYd7fgCQI461bqgZ5CuuNUByqJn8kNTWjc65/SLygoicISKDVfUvbTSjRGRbN4+5wzk3yzk3q0hK+jRYIBslWzfUDPId1xogOdRMbuvNbjDVqjq4M5eJyMUislqOvCmu6rzb9SLycLoGCWQb6gZIDjUDJIeayR+92We9VkTuUdWIHJncP+ice1RVV4nIA6r6XRFZKiJ3pnGcQLahboDkUDNAcqiZPKHO9d+xJaq6S0QOicjufnvRcBgm4fmdxzrnqjM9CPROZ81skXC9h/pDmH5faibLcK0JBeomi3CtCYVua6ZfJ+siIqq6yDk3q19fNMPy8XdGauXbeyjffl+kXj6+h/Lxd0Zq5dt7KFt+36S+YAoAAACg/zBZBwAAAEIqE5P1OzLwmpmWj78zUivf3kP59vsi9fLxPZSPvzNSK9/eQ1nx+/Z7zzoAAACA3qENBgAAAAgpJusAAABASPXrZF1V56nqWlXdoKq39Odr9wdVHa2qL6jqKlVdqao3dd4+RFWfUdX1nf+tyvRYkR1yvWZEqBukXq7XDTWDVMv1mhHJ7rrpt571zhO21smR43C3ishCEbnWObeqXwbQD1S1VkRqnXNLVLVCRBaLyJUi8mkR2eucu7WzCKqcc1/P4FCRBfKhZkSoG6RWPtQNNYNUyoeaEcnuuunPT9ZPF5ENzrlNzrl2EXlARK7ox9dPO+dcg3NuSWduEpHVIlInR37Pezrvdo8ceXMAPcn5mhGhbpByOV831AxSLOdrRiS766Y/J+t1IlIf+PPWzttykqqOE5GTReRNEalxzjV0/miHiNRkaFjILnlVMyLUDVIir+qGmkEK5FXNiGRf3fAF0zRQ1YEi8gcRudk5dzD4M3ek74j9MoGjUDdAcqgZIHnZWDf9OVnfJiKjA38e1XlbTlHVIjnyJrjPOffHzpsbO3ul/tIztTNT40NWyYuaEaFukFJ5UTfUDFIoL2pGJHvrpj8n6wtFZJKqjlfVYhH5mIg80o+vn3aqqiJyp4isds79MPCjR0Tk+s58vYg83N9jQ1bK+ZoRoW6QcjlfN9QMUizna0Yku+umX08wVdVLReTHIhIRkbucc9/rtxfvB6p6toi8LCLLRSTeefM35UhP1IMiMkZEtojI1c65vRkZJLJKrteMCHWD1Mv1uqFmkGq5XjMi2V03/TpZBwAAANB7fMEUAAAACCkm6wAAAEBIMVkHAAAAQorJOgAAABBSTNYBAACAkGKyngRVjajqUlV9NNNjAcJOVe9S1Z2quiLTYwGyCdcaoPdU9SZVXaGqK1X15kyPJx2YrCfnJhFZnelBAFnibhGZl+lBAFmIaw3QC6o6Q0Q+KyKni8hJIvIhVZ2Y2VGlHpP1XlLVUSJymYj8MtNjAbKBc26BiITqYAkg7LjWAEmZJiJvOudanHNREXlJRD6a4TGlHJP13vuxiPyj2KlXAACkGtcaoPdWiMg5qjpUVctF5FIRGZ3hMaUck/VeUNUPichO59ziTI8FAJCbuNYAyXHOrRaRH4jI0yLypIi8LSKxjA4qDZis985ZIvJhVd0sIg+IyAWq+pvMDgkAkGO41gBJcs7d6Zw71Tl3rojsE5F1mR5TqqlzLtNjyCqqOldE/sE596FMjwUIO1UdJyKPOudmZHgoQFbhWgP0jqoOd87tVNUxcuQT9jnOuf2ZHlcq8ck6gLRQ1ftF5HURmaKqW1X1hkyPCQCQc/6gqqtE5M8i8oVcm6iL8Mk6AAAAEFp8sg4AAACEFJN1AAAAIKSYrAMAAAAhxWQdAAAACCkm6wAAAEBIMVkHAAAAQorJOgAAABBSTNYBAACAkGKyDgAAAIQUk3UAAAAgpJisAwAAACHFZB0AAAAIqT5N1lV1nqquVdUNqnpLqgYF5DLqBkgONQMkj7rJHeqcO7YHqkZEZJ2IXCwiW0VkoYhc65xblbrhAbmFugGSQ80AyaNuckthHx57uohscM5tEhFR1QdE5AoR6faNUKwlrlQG9OEl0VdNsm+3c6460+PIY0nVDTWTedRMxnGtyULUTcZxrcky71czfZms14lIfeDPW0Vk9tF3UtX5IjJfRKRUymW2XtiHl0RfPet+vyXTY8hzPdYNNRMu1EzGca3JQtRNxnGtyTLvVzNp/4Kpc+4O59ws59ysIilJ98sBWY+aAZJH3QDJoWayR18m69tEZHTgz6M6bwPQPeoGSA41AySPuskhfZmsLxSRSao6XlWLReRjIvJIaoYF5CzqBkgONQMkj7rJIcfcs+6ci6rqF0XkKRGJiMhdzrmVKRsZkIOoGyA51AyQPOomt/TlC6binHtcRB5P0ViAvEDdAMmhZoDkUTe5gxNMAQAAgJDq0yfrAAAAQJ8VRHyMnj/T5y3zin2ODYj5XLnWprAjf74k4anibW3pGGHG8Mk6AAAAEFJM1gEAAICQog0mDbTIlmwKKgfa7eXlXT/AOR/j+/b7HD1lsj02Gve5cE+zPXbnbh9j+w8c03gBAOkTqaryWcvLfI4F/v6OjBju8+EJlkVEDo61A2tK91sbQFGz5cKmdsv1u3yONuw41mED6RdofdFTp/u86Wr7LPn3H/iJzxGx+dJHyj7vc12xzbtERIQ2GAAAAAD9gck6AAAAEFK0wfRBYd1In2Mjh/rcPMbaXZpG2RJPu62EJrIOFxlYP8bnvRfaMo6L2/MUNNoS6chX7HXLHn6rdwMHAKScFtolVadO9HnnGfaXf+sw9bm8cazPh0ba7e6kpoTn/cjERT6/uWecPW+TtVk277XrTvmG43westZeo/LtRp+jm+vtBeLWTgP0p8jEcT6v/yt7P396zkt2e3uNz7e9e77PwxZY64trtzawXMQn6wAAAEBIMVkHAAAAQoo2mL8IfCM5MnCA3T6i2sdodUXCQ+rn2LJj66ktPv/1tNd9/mTVGz5PK+56N5gOZ0uQT7faa59UbDsF1ERsB4EitbGOH3Kjz5Mf7vLpAQD9oOA4azlZ95nBPn/rsj/4fEn5Jp+bnLW+dDj77KyiILEtpcM2wJAbhrzW5WuX2FNJ5GLLfz5ku4r9+DdX+jzujkM+x/bstQfQEoN+tPNca+u95EI72Gh8yU6fv/ns1T5P/qXthjdgqc21At3EOYlP1gEAAICQYrIOAAAAhBRtMJ0igyp9bjnDvsXfcP1hnx+fc1vCY8YUdt2akqibg5AC4oEFnPNK9wd+Yv/vOew6uswSC6x9AmEQPOSiIPH9qSV2uIsWBf76iQTqpyPqY/yw1Z8L3M5SPUIj8H7ffLXtc9uv/QAAIABJREFUWvHFeY/7/MkKO5io1dn7fnuHfV72wL7ZPq9tsucREdnVYu2R0VigvtT6Yy4audbnb1bbzmCfqbRdX6be8F92nzV/63Plc7aTBofroT8dHmzXiKHF1uLy0v6pPg97y+rELV3ZPwMLGT5ZBwAAAEKKyToAAAAQUrTBdNJya2nZO7XI5zfO/InPlQWJLS0RPfZ/6+yL2e4xv22a5PNDO2b63BHvrrXGDFxf1ON9gHQrKA/UxiTbEaN9WGLNbDvPDrGoOs2+7T++0najeH2d1cOIp+39XbV4l8+xdRv7NmAgRQqm2/u16mxrd7mmYoXPDYGurfkbbWeLA7fbIXgVm6wFILIn8VCkqoP77A+xwJMFrkELLjzD8o3WyvnSCb/3+ZTiwEF7n7V6ijaMs99n8Rq7T6ANDUiHuF0SpESt1XHRjtE+175z0OfAxkh5hU/WAQAAgJBisg4AAACEVM62wUSGDvF59+VTfA6cQSHDFgaWFvfYLiwaWGXcH7edWjZFA7tRiMjgAvsG/a/22RLkY1uO9/nAZjsYo3KD/dto0GZ7ruKDtrtL4Z5Wn0tczws+Y/baARvR97kfkAqRGjvAovmMcT7Xz7P7XHGaHWxxQvnWhMdPKLbWl9GFtrRZHqjLxpHW+rLmzBE+r2gd5fPT22yngL1rhvo8+b/t+WMbt9iTsnsM0iRWaTscja+0NphhgYPsbtlxms/b/zTO55FP284W8VZrUYl2HPW3eS/ev5XPWvtKU9zq48LPfdTnx6c/6PO90+71+SNz/tHn0e8Ns3Fs3dbj6wLJKhxhux0NOMvascaW2EGQLWts7qSrl/pMGwwAAACAUOlxsq6qd6nqTlVdEbhtiKo+o6rrO/9bld5hAtmFugGSQ80AyaNu8kNv2mDuFpHbROTewG23iMhzzrlbVfWWzj9/PfXD6wW19fOCMlt2XP+P1vpy8QW2hPLE4hN9rlpjy5eywdpgRj5nO1NcWWDLg+WN1hIjItI2xP6tM2CHLVNWN9g36Gv32XPpXjtsIh44eMIF2mviUZpZcsTdEua66UHhWPsm/t6z6nzecZ69zy84aZXP3xr2ps+nldh7e/tRh3bdG2gXe3uvtbVcPXKRz3PK3vW5OtAq89Wh9honl1uLy9I6233mke3n+Dz6D1ZL0c3vCULvbsnCmokX265dZRFrabyvqdbnPz9jBx5NesRaw6IpPIAoeJhRxUvrfd4z0K6FS79jl/w5JZbbTredaKKvWAup0AaTDe6WLKub7VdN8PmTY5/0uVQDLcGtdu2It1mLWCoF23E6Jli9tg+yVkwNTPvKNtl8Lr7F6rg/dk3q8ZN159wCEdl71M1XiMg9nfkeEbkyxeMCshp1AySHmgGSR93kh2P9gmmNc66hM+8QkZru7qiq80VkvohIqZR3dzcgH/SqbqgZwONaAySPa02O6fNuMM45p6rdfkHXOXeHiNwhIlKpQ1LzRd5A60ukosLn+vkzfJ5/2VM+7+6w+5RvsV+5qMG+uR8NLLPomg0+1zVU+hzbG9g9RkSqBtnPgt/kDy6JsAcFuvJ+dZOWmumFpmvm+LzjHHvZmSfaAUTfHrnA57GFVg/PHprm8y93nOvziqdsCV5EZGC9PW/pfquO7330Up9rh1tLWsMOa7X86dn3+Ty92Gq3KHCQxt6PDfD5nS0n2es2NPrMQS/ZKSPXml7Ydl6pz58YZO0nDzWe7PPQZTac/mjJiu2xD1qHvG11+pPtF/t88rgnfP7IlHd8frPadq4JNIoiS4XxWtN6rh36dcGA1T7/pPEinyu2pG44hy+z93Tjqdbi0jbWdvQbVmMtl9UDrC0sFrcGlPWNtlNS5Uun+DziyUBr25b6FIz4fzvW3WAaVbVWRKTzvzt7uD8A6gZIFjUDJI+6yTHHOll/RESu78zXi8jDqRkOkNOoGyA51AyQPOomx/TYBqOq94vIXBEZpqpbReTbInKriDyoqjeIyBYRuTqdgxQRkQL7xn2k2g5B2X/+cT5fft0rPl9RYct68/78FZ8nP2NLHbGt27t8qeDuLMHlxKPFUvhNfuSW0NSNDcjH4K5J7WdY+4r7tB1OcdfkP/k8pchqZkm7LQPeuOYTPu9/yQ4vqqi3r8+PfXh5wjDizba8KIFDv0pmnulzw/5qn2tet3F/Y/BHfH5t1q/sOWWPz/9Ra206Z9TZMmVFcbG9LG0woRS6mvnfA/QxeOje4DnWYnVCiS2Hf3+HnRQ2Zmvm3nMFB1t8XrRkos8tY23njVMH2A5Mr5bZzjW0wYRf6OumkxbZ38EnjrS5V0ngFMoX37br0bQ37YCk7tqJtdCmsJG62oSfNXzQdhvTy+wa8c9TrEX61BLb7Wh/3MZXH7X6PqXEWi7LJ9vfAZ8bfYXPOxttHjqg0a6jqdzFpsfJunPu2m5+dGHKRgHkGOoGSA41AySPuskPnGAKAAAAhFSfd4PpL5HAzivNc8b5XHajLafcMOQ1n7+2xZbMRz8Z+Fbxcvu2vuMAIuSJgpLAgvbkcT7Wf9aWwn8/zXZbGVtoNfOzvaf6/IvXbaeX0U/YkuCYJ5f4HFz6SzxGTBLbccptq7CC9sB9Kq0uDxxn4z7cZLtuDCywfLytXsqrbfaKRc2Buo+xLxP6RgttF4mW2bbsfebwxT5v7rA2MbfRdiYqrrfl9v6+6rjATmUVm6ydtM312+YfyFPBNhU5cZKP4wes9PmlFrt98HK7f2y1zdWCz1NQZTuEdUy1VpctF1p7p4jIl695yOfLB6zzeX10oM8/3T3X58fXT7fXbrBrU9lY27nmhyc+6PP/Gf2ozx+fYq3WFW8M8jm+I3VtMHyyDgAAAIQUk3UAAAAgpLKmDUZGDvdx60W2lL562h98vr/Jlib3/mCcz+UvrPA5zk4QyEM60Jbkd86yZbp7T7/N52lFtsy/st0W63/x6nk+T/wfa5sparRdYtzEcT4HPwHQo5ba4+XWs3KozpYtna3Oy6TRtrvGD86z+h5VGGwgsN/nsLMxzV/6aZ9Hrm21122j7tE3WmotWVsus2vQ96oW+fyDejvca/hia8nqj4OQgLDRQPvlux+2687XKm2XsH/f/EGfK7Z13SQWbH05MNfmefuuOeTza7N/lvCYQ87q794DdkDZz5ec43PNk3Y9mvisHf4X22U7ukQG27i/cpdtqvO7U37hc/ugwHWuPLEdJ1X4ZB0AAAAIKSbrAAAAQEhlTRtMe7Ute4+ZYsvkHc52eXhk50k+l+60b+G64E4Qgd0ohG/DI18E3usa2KJlf9y+9d7h7FvvMbE6qaqzw782/vVgn0tq7bFThttp1vHAY5vaE49VmTvclj+vHbzQ5yKx8UUCJTqm0L65H3PWprMvZge9PNNqh2EMerDCnnPFantsnN1g0DdabO+/q894y+dI4L277kVbop+wJHM7wABhoBHrb4xOsr+zqyPWvrJlj7W4jNofqJTAXK19xmifiz5r879VM2zHl31H/RV/y9YP+bzmrsBhS4/ZAWDRBjvwqLsrhIvZBbNlu12Pds20658GH5ymeSWfrAMAAAAhxWQdAAAACKmsaYNpGWHf2v2vSb/1OXg4yn+O+5PP53zuZp9HPjnT58Gvb/U5utWWKYFcFj9gO7cMf97e99+56nKffzzV6mpGse2w8sIpd9vznGxLfAXBlrKAjsC38Es1kvCzosCfC6Xnb83HAs+1rsNa265feb3Pg75vLXKDltlhG7Hm5h6fH+irJ5pO9Ll6ma2HR7ds7eruQP4ItMGMHGrtlAO058awyPBqnxtOsXne/ZPv9/lAoKVz7uIbEh5f8wObM1YvW+ZztLVVUuGBPXN8HrrCrouxrQ0pef6j8ck6AAAAEFJM1gEAAICQYrIOAAAAhFTW9KxHS60/9oTAFlpBtRHbSudPF9hpVivPHunzrasusQe8PtbH0t3Wc9ReYa9Vsc16EAduTOyB1cCWkK2jbcu44r3tPheuq/c5tmdvl+MG0s1FrUcwts22qxryD+N8/uaov/V5ywftr4aTZ23wefyAPT6vb7JThbc12SlvA4rt/f/BWushFxG5YfDbPg+LDJCePNdqPY+ffeVGnyf+t9VeZNk6n+PBfkS2ZkVfFdj7L3gKcHmB1VBH4PhdjQXec2wXCnjDy21r4JLA150SvvoUyK0zx/hceJ5ddwoCW6VetuITPtf+a+Jnz7pyrc/xtjY5VhoYoCuzmm6OWk984WFrnndR+75XKvHJOgDg/7V352FyV3W+xz/f7vSSpLOvnZCQfWGRJYEAAsoqqCPKqICKDKKMDjowjndkeOZexqvXi8/M6MwdlzsMIHEukInsKltAVsGQsGYjJGQPWchK1k5317l/dHG+v2bSpCtdXfWrqvfreXj4dHUtp3jqy+90/b6/cwAAKcVkHQAAAEipkmmD6b3BTy18de1HYv7l6Gdjrjb/2+P4urpE9lMoRx//y5j/MHlCzNtb/BRnQ7WfMtlwwHdsXL/PsyRlgp8emVj3Vsxr9gyM+fWVY2Me9PTkmAf+8gUBxRCavU2ldbG3kNQu89N6k9eNiXnb77xdbEutf56rE6f++jcllqxr8JaY2dec0O61P/OhZBuM37474zX3w3dOjvmehz4c86T7E21orydaX5qaBHSH6gH+Wd507hExf6yP75z4+91HFXRMOevjx7Zdx3ut1CdO7+/PJFpL6R5DN6iyg3+w2nUrJvL+gT49nTbMl0GtTWzB3ZqYg1W91X6p1NautL70SEyNjxge47dm/D7m+Tv9uFi9PzFwdjAFAAAAKguTdQAAACClSqYNptfCt2Ne/PNjYh53zsSYbzv99pgHVu+N+cgeflri2Npke4yv1JJPyVP6i0f7uf6/Gn5JzE0bT4q57uF53TIOIBcdtcfULD70Y6sHDIh51ye83evCUUva3W9gB18P3LjptJgfvtd3hht/7zs+pjd8VRpWekEhWK23hu1t9FPuR/bwVYdqqg69G2OhVfXx1cl2H+07Qf75tCdj7lPl7+2Hr18Y85Fr/dgJFEu/N3z1mMdfOjrmb398TsxfGD0/5v+46OPtHj/k94mV+Db46k3JldE6Uj1qZMxL/sKPbT/r83rMP336vJgnr/PdWRObqubVIb9ZN7NRZvakmS02s0Vmdm329oFmNsfMlmX/PeBQzwVUAmoGyB11A+SGmqkcnWmDaZH01yGEoySdIukaMztK0vWSngghTJT0RPZnANQMcDioGyA31EyFOGQbTAhhg6QN2bzLzJZIGinpIkkfzd5tpqSnJH23W0YpqeXtDTEPvHuH59f8ityr1/umLplaP03eY/SemIf28xUlThq8OuavDnou5qm1vrnS4Wioqo/5ZO+60bfG+inI//GRS2Me+3CXXg4pk5aa6W7VgwfFvPMsX1mpz5XrY/7rQXPbPaZvVc+YVzZ7Ld73R28Lm5JsfVmyLD+DRepVSt10l+q+fWNuPm58zOvP8u/kvtzvlZhf2O/37/tQgz/PW76ZDNs6pVsqaya5kVg/b8caXu9zuNp2OyF1YLG3PY6721ufLx7i87yfnDA75hFfWdHu4UvHeg2Mudc/38nnrervqz0dONbnkqvP8onbTeffGfM/bD435lGP+BzT1nibdnfJ6QJTMxsj6QRJcyUNy35QJGmjpGF5HRlQBqgZIHfUDZAbaqa8dXqybmYNku6RdF0I4d3k70IIQR2szmpmV5vZfDOb3yzWQ0bloGaA3FE3QG6omfLXqdVgzKxGbR+EO0II92Zv3mRmjSGEDWbWKGnzwR4bQrhZ0s2S1NcGHv4SDonVHzJ7E1erv+arTYx97eAPrT7aV6doHuwbGz3y4caYe3zOr+H90TDfuKUp+GZMj+71UyaS9PhOv0L5mN5+6v+C3r6SxugefvplRM12H8cQf16Un1TUTDeo6u0brOybPi7m3V/048MDk2bFnGx7kaSWxIn1G9/2q/eHPu+nRTPLVuZnsCg5aaubkPHjQmIBGO1NPHsmFHEF5EQ7QWbS6JhXf8JbMS/7qLd47kpsIvONl78Y89gXfePA1m1+nEL6pa1mrMo/Y6Gnt5PU2MGbqgb19Tbl5obBMVc3+6ottc8vivnIXb4C4De+cXnM5x/j95GkxVO8zXLfSG/H6Wn++M0n+3xwz/l+/+uOfjTm1/d6Xf3hjhNjHvmit9O0vtvu76Nu0ZnVYEzSrZKWhBB+nPjVg5KuyOYrJD2Q/+EBpYeaAXJH3QC5oWYqR2e+Wf+wpMslLTCz975yvkHSTZJmm9lVklZL+nz3DBEoOdQMkDvqBsgNNVMhOrMazHOSOrp095z8Dqd7tC7yq9uTpxJGZE6I+b5jj4v5h0NfjnlXxjeK+dc17d/u5t+Mivl30/xq5UEz/HTK6AY/PdIc/CpptXTiamiUpHKomY7Y6BExrzvb//fxh2m3xDy42ltlmkP7U5937fLNJl5NXOE/6pk1Mbd0YtMKlJ9U1s0+3+Cu30r/LL/RPPhg91aoTgw/sSqGMl1bV6Wql69QVjXEV2HK9Pc2y7Xn+uouX7zwqZivGvBizN9Z+6mYG//dWxTC6sRKGmw4VjLSWDPtNh1avynG+xb4fOtPzvBW4z89wvO/T7sg5rErfYUxbdoSoy3zY8XUv/XP8MufOL7dOAbv8c9xs3fBaO2V3s785bOejvkz/Xze97PNZ8X8x7t83CNu9rG27i3s5mFFbLYDAAAA8EGYrAMAAAAp1anVYMpV7SrffKX6Db/it+kMP42TPKU/Z+pv2j3+xsG+Gsyn+vpmE9PqamNOtgG8dcBbCOo2VfR/epSoPRN81+qRx/smF0MTddIafAWNNS2JJTQk/eQXn4151N2rYm5Z3/2bSgC5Sq7y0Odp36DrmV1TYh5f7wtt7B7urS99RwyPOez21sjDceB43+Bl5UV+fDlhuq9I8Y+N98Z8Vk9/vZt3+HFq0/f9eeqe9OXTMs3e7gnkS+tuX+ll6ve8leXb//K5mG879lcxj/uS19LfTPnTmPs8NSnm+u3e3tJa46+1ZXr7VrMzTngj5u82+uouE2p87vXcfl816UuvXhlz/18mNnN64PmY/chWeHyzDgAAAKQUk3UAAAAgpSq6F6N1s7fBDH3ZW1S+uvpjMd859skOH3/DYL8yuEfiP2Wy9eWe3b5qwE1z/iTmKT97y8eRy6CBAkuuRLF5mn/On516R+Je3gazO/hOeBe/8rV2zzXiiW0xt7y9QUCp+3SDt8fsveahmB/73FExb9s3pEuvcfmYOTFf0Ns3AhxW7fW4K+Ptm7fsnBrzj5/x49nUuT7W1hY25kM3S6yC1LJ6XczDv+sb6l3+Q28/ueOE22J+/vSfx7z3w4nWl8RiRcnFl+qt/aI4vcxb0urMW8du3Oyruzzw69NjHnOnj691vc/P0rI2Et+sAwAAACnFZB0AAABIqYpugwkH/Ar4hnmrY37r3/xK/7/8S7+q/opBf2j3+GNq/bTLosTV9P9z7SdjXvqgX8U89dd+mqXlna2HO2yg2yVbX1Z9xzeb+M4lvuLEgKqeMW9v9Q0ifrb9xJiH/pNfbS9JWuEblLH5CkpJSGyQ9NAdp8W84/NeB98Y8lTMF49fGHNzFz/qyXaXOvPX29Lqqy19f9O5MT91v9fgUXeuj7llp69uQ/2hoBItMZnlPt8a+YOJMX/po9+Oedcx3k45Y9LKmL823Dcyuu71S2IOoeONJsN83whp+Fx/3jGLfTOwlk3eFt3VTcy6A9+sAwAAACnFZB0AAABIqYpug0meBmzZ7Av2D3rUb39tu185/Lnz/dSiJI2evCnmDS82xjz8j34KZfTCROvLqjVdHDBQGOFo3zylfpqv4HJZH/8MVyeusJ/X5KcZ77rr7JhHv7qg3fNm9rXfJAkoFZn9fvp89D3eWrJg3XEx3/DnvmnYz8bNjnl8TUOXXvu6DdNjfmKNt1a2zu8f89BXfHWXMW/6SkstK73lAEiDkGgbtte8NXLUpmExt/7eP9ubB42N+X8M9M9/47rE8eQDurpq3l4bcybR7tKyf//B7p5KfLMOAAAApBSTdQAAACClKrsNJilx9W/rps0x93zYWwAmvT253UP2jRga8/g3vCUmsyrR+pI43QOUirXn9on5qvGPxNyryltfXmzy0+7XvfqlmMfcvTHm1j2+SowkVqBA6Upu8JJoLem3xY8R77b4Rkh/MuFvYm6t69pL93srE/PQDV539Ut9HC3rvDUnfWtZAAcXWnwzr+RnWIlck7h/MndWy6Hvknp8sw4AAACkFJN1AAAAIKVogzmE5CkavbSo3e/qX/LMaUeUk/1H+1X2Fzb45i57M/6/jF9tOSPm/nf7ahety/z+QLnL7NoVc++753ouwGuXw+l9AIfGN+sAAABASjFZBwAAAFKKNhgA/0XY4au+rG3xDY/ebvVVKX73yodinvr4sphpCQMAIH/4Zh0AAABIqUNO1s2s3sxeNLPXzGyRmX0ve/tYM5trZsvN7D/NEnuPAxWOugFyQ80AuaFmKkdn2mCaJJ0dQthtZjWSnjOzhyV9W9JPQgizzOz/SrpK0i+6caxAKSnpujnicd+86Nphl8acyVjMox/03Lpla2EGhnJW0jUDFAE1UyEO+c16aLM7+2NN9p8g6WxJd2dvnynp090yQqAEUTdAbqgZIDfUTOXoVM+6mVWb2auSNkuaI+ktSTtCCO8t87pO0sgOHnu1mc03s/nNasrHmIGScLh1Q82gUnGsAXJDzVSGTq0GE0JolXS8mfWXdJ+kKZ19gRDCzZJulqS+NjAc4u5A2TjcuklDzfS8/8WYR99fjBGgEnGsAXJDzVSGnFaDCSHskPSkpFMl9Tez9yb7R0han+exAWWBugFyQ80AuaFmyltnVoMZkv2LTWbWU9J5kpao7UPx2ezdrpD0QHcNEig11A2QG2oGyA01Uzk60wbTKGmmmVWrbXI/O4TwWzNbLGmWmf1A0iuSbj3UE02aNk5z5v+6SwNG15jZoe+EfMhL3VAzxUfNFAzHmjJC3RQENVNGPqhmLITCtSmZ2TuS9kjaUrAXTYfBSs97PjKEMKTYg0DnZGtmtdL1GSqENL1faqbEcKxJBeqmhHCsSYUOa6agk3VJMrP5IYTpBX3RIqvE94z8qrTPUKW9X+RfJX6GKvE9I78q7TNUKu83pwtMAQAAABQOk3UAAAAgpYoxWb+5CK9ZbJX4npFflfYZqrT3i/yrxM9QJb5n5FelfYZK4v0WvGcdAAAAQOfQBgMAAACkVEEn62Z2gZktNbPlZnZ9IV+7EMxslJk9aWaLzWyRmV2bvX2gmc0xs2XZfw8o9lhRGsq9ZiTqBvlX7nVDzSDfyr1mpNKum4K1wWQX7X9TbTtsrZM0T9JlIYTFBRlAAZhZo6TGEMLLZtZH0kuSPi3pzyRtCyHclC2CASGE7xZxqCgBlVAzEnWD/KqEuqFmkE+VUDNSaddNIb9ZP1nS8hDCihDCAUmzJF1UwNfvdiGEDSGEl7N5l9q2/R2ptvc5M3u3mWr7cACHUvY1I1E3yLuyrxtqBnlW9jUjlXbdFHKyPlLS2sTP67K3lSUzGyPpBElzJQ0LIWzI/mqjpGFFGhZKS0XVjETdIC8qqm6oGeRBRdWMVHp1wwWm3cDMGiTdI+m6EMK7yd+Ftr4jluAB3oe6AXJDzQC5K8W6KeRkfb2kUYmfj8jeVlbMrEZtH4I7Qgj3Zm/elO2Veq9nanOxxoeSUhE1I1E3yKuKqBtqBnlUETUjlW7dFHKyPk/SRDMba2a1ki6V9GABX7/bmZlJulXSkhDCjxO/elDSFdl8haQHCj02lKSyrxmJukHelX3dUDPIs7KvGam066agmyKZ2ccl/bOkakm3hRD+V8FevADM7HRJz0paICmTvfkGtfVEzZY0WtJqSZ8PIWwryiBRUsq9ZiTqBvlX7nVDzSDfyr1mpNKuG3YwBQAAAFKKC0wBAACAlGKyDgAAAKQUk3UAAAAgpZisAwAAACnFZB0AAABIKSbrnWRmq8xsgZm9ambziz0eIO3M7FozW2hmi8zsumKPBygFZnaBmS01s+Vmdn2xxwOknZn1N7O7zewNM1tiZqcWe0z5xtKNnWRmqyRNDyFsKfZYgLQzs2MkzZJ0sqQDkh6R9PUQwvKiDgxIMTOrlvSmpPMkrVPbZjWXhRAWF3VgQIqZ2UxJz4YQbslu6tQrhLCj2OPKJ75ZB9AdpkqaG0LYG0JokfS0pIuLPCYg7U6WtDyEsCKEcEBtf/BeVOQxAallZv0knam2nUkVQjhQbhN1icl6LoKkx8zsJTO7utiDAVJuoaQzzGyQmfWS9HFJo4o8JiDtRkpam/h5XfY2AAc3VtI7kn5pZq+Y2S1m1rvYg8o3Juudd3oI4URJF0q6xszOLPaAgLQKISyR9CNJj6mtBeZVSa1FHRQAoNz0kHSipF+EEE6QtEdS2V3rwWS9k0II67P/3izpPrWdrgTQgRDCrSGEaSGEMyVtV1svLoCOrVf7M1BHZG8DcHDrJK0LIczN/ny32ibvZYXJeieYWW8z6/NelnS+2k7zA+iAmQ3N/nu02vrV7yzuiIDUmydpopmNzV4od6mkB4s8JiC1QggbJa01s8nZm86RVHYXZPco9gBKxDBJ95mZ1Pbf7M4QwiPFHRKQeveY2SBJzZKuKceLfoB8CiG0mNk3JT0qqVrSbSGERUUeFpB235J0R/YP3BWSrizyePKOpRsBAACAlKINBgAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWQdAAAASCkm6wAAAEBKMVkHAAAAUorJOgAAAJBSXZqsm9kFZrbUzJab2fX5GhRQzqgbIDfUDJA76qZ8WAjh8B5oVi3pTUnnSVonaZ6ky0IIi/M3PKC8UDdAbqgZIHfUTXnYujQOAAAeLklEQVTp0YXHnixpeQhhhSSZ2SxJF0nq8INQa3WhXr278JLoql3aviWEMKTY46hgOdUNNVN81EzRcawpQdRN0XGsKTEfVDNdmayPlLQ28fM6STPefyczu1rS1ZJUr16aYed04SXRVY+Hu1cXewwV7pB1Q82kCzVTdBxrShB1U3Qca0rMB9VMt19gGkK4OYQwPYQwvUZ13f1yQMmjZoDcUTdAbqiZ0tGVyfp6SaMSPx+RvQ1Ax6gbIDfUDJA76qaMdGWyPk/SRDMba2a1ki6V9GB+hgWULeoGyA01A+SOuikjh92zHkJoMbNvSnpUUrWk20IIi/I2MqAMUTdAbqgZIHfUTXnpygWmCiE8JOmhPI0FqAjUDZAbagbIHXVTPtjBFAAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWAQAAgJTq0mowAADg8FT37xfzntMnx/zO8e0PzXXbPDc+tiHm1uUru29wQEpU9eoVs40aEfOeSQNj3jmmfc00DfAcqg/+vD32ea7fGmLutbk15j5zV8fcsmlz4kn9/oXAN+sAAABASjFZBwAAAFKKNhgAh6fKzy1W9az3PNhPTbY0Dmj3kHfH+enM1lqLuW5nJub6bQdirtmyN+bwlp+OzOzff7ijBtJj6OAYN3yxKea5p/9zu7v9Zs/omP913+diHrxpS8yZXbu6Y4RA3lVPGBtz68CGmA8M9ONIS0//Lnl/f8/bj/H2k1Ef8pawm8Y+0u41zuq5O+YlB/z48vs9U2O+f/1xMa9bNtTHscKnxg2L/JilzYnvt4O3yhQC36wDAAAAKcVkHQAAAEgp2mAAfDDzdpXqPn1iDqP9qvzdk3xVi22TvT2m5Xg/FSlJv57hp/ePrqmN+d93jor5/62ZEfPKhX5qcsKsGn+i+Qs7PXwgrTJ9/LT/F4+aF3Ovqpp29/tyX293+fsT/ZT+4Hkj/U4L3+iGEQL5t+rSxphbj/VjxOlHLon56Ia3Y26o9rbHafWrYt7a2jvm/aF9zfxix8SYf/rqWTH3e8ZrbthzvszSxEVzDzrWwja7dIxv1gEAAICUYrIOAAAApBRtMO9JnOq3Wj89n1yMX7XtT7N0qKUlxrDPT99k9vvV/gp+KjP5euHAgcR9CrvoPnAwVT17xrz7rCkxb/3ynph/eeJPYz7OP87/RXPiI90UvE6u7LfK87GeHx3v7TXX1V8e88T5hxw2kHqtDV4sNw5ZnPhNx8eahlHvxtw03NsAaugMQ4m45HNPxZz83O/N+Pzn4b2+UtJPV3sby48WfSrmhtX+fXPd9vbzpcG/fTPm8VteOeg40tLi0hl8sw4AAACkFJN1AAAAIKVog8mqHuQbuew8268irvnqxpj/aeLsdo/pX+WnbFrlbTT/tOncmJ+774SYj/y1L+Bvzd4CsONkX1Wj3+N+6qZ1p5/uVKaUTtignOz7yNEx77zSN155ctotMTdY8rS9qSP/sHVazJ/o+2rMHbXOnN3Tr9b/zKm+WgZn/FGpah7uH3Pt0y/FTNMkSsXMV06N+WNnLoh5f/CVWq6//4sxT/zfvtLRpAO+MpJafV4U3tc23JpsKS4DfLMOAAAApBSTdQAAACClKq8Npso3bKkef2TMaz89POaLv/R0zF/u7wvlj+7hq2JIUo01xNyaWN3l74fP8fwZv/9jE72dYMiwnTFfPuaRmG8f+PGYh92TaInZsvVg7wboFj3Gem0s+4T/Tf9/j7k75n5VB+9d2Zm4ov/Up77Z7nejZvn/cu4648yYv/7JR2O+ZsDSmGvM67WhR2I1pQ9YLQMoZ1XJBcOay+tUPyrDpJ/55/ZvR14c8w/G3xdzywBvFbY6P9a0bt/ezaNLp0N+s25mt5nZZjNbmLhtoJnNMbNl2X8P6N5hAqWFugFyQ80AuaNuKkNn2mBul3TB+267XtITIYSJkp7I/gzA3S7qBsjF7aJmgFzdLuqm7B2yDSaE8IyZjXnfzRdJ+mg2z5T0lKTv5nFc3abHKF95ZeUl3vpy9WUPxXxJH19rYlmLt7qc++jX2j1X1V4/Ra9MIvb2K5St2f8esj7NMf9g8v0xj6nZEfMvzdtgULpKsW6SrS+rLxkZ8ydP8VVYZtT7CkXbElfi/3KHr/Jyx13nxDz+Kd84SZKqX/cNMCas9Ne4Y+XHYp590Ykx33X07TGf38dXDfjVz78e85TvLok5s8tXq0FpKcWa6aqaLXtjPv11bwd45Jg7292vocpXydhymrcHDFpwTMxhPmskVaKSrJvXvcV347N+7Hi+0Vfi6z3IayMM6OuP3bipe8eWUod7gemwEMJ76xBulDQsT+MByhl1A+SGmgFyR92UmS6vBhPaFrfscIlXM7vazOab2fxmNXV0N6CifFDdUDPAf8WxBsgdx5rycLirwWwys8YQwgYza5S0uaM7hhBulnSzJPW1gUXZt6HHcP+jcs3nRsV8/mdejPmiBj+F+I/v+CoVDz4xI+bJv97d7nmr9iQ+3IkF+ZsH9Y5542m9Yh7/iVUxD6325/rGssv89vl+Gj/sbt9CgJLXqbopVs3smTo05oFn+QZe3xjsqyPVJzY/enS/t5H9+3Mfifmo21fE3LLpnXavkUlu7rVkWYzDNvtqR2/XTo7558PPiPnvhj4f80/P/1XMNyz7Sswj71wec+umDv+3hNJRUseaXGXq/RB85jBv50qugvR+gxt9JbGmwf1i7mBfMVSmVB9rQpPPnXpt8pfd2OSf58Z+3nK5Z3xjzL0zEzr3Ihv8Lbcm51IlusHk4X6z/qCkK7L5CkkP5Gc4QFmjboDcUDNA7qibMtOZpRvvkvSCpMlmts7MrpJ0k6TzzGyZpHOzPwPIom6A3FAzQO6om8rQmdVgLuvgV+d0cHvq7DhjTMyjEq0oXxj4Qsw3vu2rsLz0oF9hP/HX3g7QumJNu+dt7eB0Ss2EsTHvG+pX8f/30b+JeX2rn+555+EjYh658OWYM/v3H/T5kX6lWDdbjvUWlz8/wj+Hk2r8M/xms38m/2WFv5Vx93gttGzY2LkXTLSOJTf9anx6W8z3HeltaJd/1uv19HrfGGPkp1b5U84Z6M9PG0xJKcWa6arWBm9e+eGw1xO/YdMvdE4p1k1VL28P3uXTJZ3YsDrmY3uvi/lHV/hqYft3dG7J+H4LvK1z8AI/btUu9uctpVbJLl9gCgAAAKB7MFkHAAAAUupwV4NJPZvurSwbLzoQ842j5sR8z46TYn75fr//mDu93aVlrZ8y+eAXtBj3TB0S8/Bj/DTLuB6+mcV/bPPXa3w2sQJMi98H6G5V9d7isu/YfTEfW7825kxi1a+Hd/vndvuzvhrMqCd8pZauyrz+hj/v416j/3qGn9X9ycgnYv7IYF9V5sl6b5sBAKRD8ljz7ieOjfnEM5Z6rvPjTlPwFZG+d9yDMa8+MDjm6sSx6ZOJTfMk6d9O9JXEfrPMj1s1r/tqMsNe9NUB61/yVcySm+ulZU7GN+sAAABASjFZBwAAAFKqbNtgln7Frzb+xSkzYz6pzjeU+Iu5J8Q8efb6mA+n9aXHyBExrzvb/wb6PxMeinleYsH/B148MeZJL/rmTCWxkwdKW5WfXrRR/rmdMXZVzBNqfEOK7Rn/nM9e45/b4XO7f8e7+o2+mcXjC6b6LxJtMHVVzTFnahLvLfE+S3UjDAAoWR0ca0Zd562LN4z0OdItW0+P+YGlH/KnWdUz5r4r/elba/zYdPv57Vsgxw3wVcVuOP6RmGecsirmvzznkpi3zpwS84Alvmll1aK3Ys7s81bR5GpmhcA36wAAAEBKMVkHAAAAUqps22CGHOmbpoyr8dMhOzJ+6iKzpS7mlpW+GH+HkqfVJVX3bYh51eVHxvzls5+K+fyefhr/62s/EvP42em4whiVx6r9c7zzBN844tJBz8Q8pNpr45ad4/z+f/T7D5y/KOZCN5lUJb5nmFLnG5fd0+grDjT09la45NX9QClravH67dOSKeJIgA+WPNbsH+sb1p3WZ3HMP1zvG1Ku+ddJMY+f9cfcXuzn7X88MMRX5fv5J/405u+f4xskfevEJ2M++3/+Oua/X/MnMb97o7fH1Mz1lcoK3RLDN+sAAABASjFZBwAAAFKqbNtg8ibR+lI9YUy7X638wrCYv/LZR2O+pv+SmDe1+oZM8zf6AvwjF/ri/6xTgUKy2pqYt031v9eH9jh4q8hPXjk35gkP+31ad757sLsXRI15XZ5e7ys87Rjvt/ed56sv0QaDclHzcP+Ya59+KWZWEkPahGaf/9Q++XrMz5890u/U6q1cffe+4o/t4mu3btkS88C7/Fg1+F5v8bz/w+fF/C+f9ePct2b8PuZeP/OWnVn/zVt2ej6TaIkpwPGFb9YBAACAlGKyDgAAAKQUk3UAAAAgpcq2Z/2dt72vb+2UvjHPqPOlFP/6vN/FPPN3pxz0eaqrvJ9qyoDN7X53zWB//Iy6rTHXme+29fu9vmvX3jd8TK1b3/zgNwAUQEsv7wysN98JNLk0YtVqXw7RFr0WcyjwDm5JyZ71pEzy/2hVdtD7AKWsytuA2/UEA2mW/Ky2btn6AffM1wv68Sk0+W7brYnc8xnvR5+6yudqd5z5sZi//e3ZMc/4wbyYH73ttJhH3r8m5pa167oy6g7xzToAAACQUkzWAQAAgJQq2zaYCb/yHUK/2f8LMd8ybWbMX+izNObzjvFleDrS/31/2nz42WtiHj3Ud0m9Yay3x8zeeFLMI/6QWKQxw4KNKA6r8bLvMW53zP2rfEe2TOLv+Or93k6S2bu3m0fXOa3B29My8mysXwcA6ITMHm+L1tIVMQ5r9vnj9478fMxf+9RjMZ/0JW8JfXbwcTGPvcd3ts+8fuh5ZWfxzToAAACQUkzWAQAAgJQq2zaY6hcWxDy81/ExX7nMW1dqp/iuVo39PO9r9h0eN2zxXRBrVviqGJJUlfhTZ8iR3k7QHPw/6+LVjTFPXeSryfhJFqDAzD+4g/r4acA6S7Zmpfvv+JbEvr87M77KQI9kl04LrWYAgE5ItCZn1r4d84Q7amP+jy2+SsyFlz8f8yWffjrm3607M+ZBvmlrlx3yiGxmo8zsSTNbbGaLzOza7O0DzWyOmS3L/ntA/oYFlC5qBsgddQPkhpqpHJ35+qxF0l+HEI6SdIqka8zsKEnXS3oihDBR0hPZnwFQM8DhoG6A3FAzFeKQbTAhhA2SNmTzLjNbImmkpIskfTR7t5mSnpL03W4Z5WEILd5oUvvo/JgnLhoZc9OEYTHvH+gL4lc3+5ISEzckzqu/6q01krRm1pSYbxj5UMwLmvw1alfXxZzZ9E6nx4/SVao1k0bN/b317MjRW2LeH7y+r1vzyZj7L/eNnTLv7urm0SGfKrFu7ICvZPTEPt/o68z69psdJTcB2znJbx86bkzMLStW5X18SLdKrJlCaLeJ0mLfwPKI9b7B5gOnHBvzrJNuiXn2yI/EPCiPY8qpZ93Mxkg6QdJcScOyHxRJ2ihpWAePuVrS1ZJUr16HO06gJFEzQO6oGyA31Ex56/RVZGbWIOkeSdeFEN5N/i607Tt+0BWOQwg3hxCmhxCm16juYHcByhI1A+SOugFyQ82Uv059s25mNWr7INwRQrg3e/MmM2sMIWwws0ZJmzt+hvRoWbc+5upE7uhvymC+IUxVQ0O731055YWYJ9f4acobVp0c88DFXiPtFuBHWUtzzSRbxN5ePiTmFRMGxjyqx46Y94/w+1dPGh9z65tvddcQo31DfGWma8c8GfPKZv+eYcmdU2NunLcs5tZdtMGUmjTXTXeo3udtW7O3+nHj1BFPt7tfsg2m37FbY947yeu3ljaYilRpNVNUiZXUmpt8+rymxa/ftWZ1i86sBmOSbpW0JITw48SvHpR0RTZfIemB/A8PKD3UDJA76gbIDTVTOTrzzfqHJV0uaYGZvZq97QZJN0mabWZXSVot6fMdPB6oNNQMkDvqBsgNNVMhOrMazHOSrINfn5Pf4aSP9fDT8AdOntTud0N6LI15SeKq/qUvjol50h+91YaNkCpD2msms8dXOJp0q2/m9Q+TLoh5zKS7Yv7aaX5K/s6VPvxR/+Kf7cz+/XkbX/Ugb8fZ0+in/4f32Bnzb3cdF/OIB1bH3LJ1W97GgcJKe93ki9X4Jist/bxPeESdf76rraP/DIAr1ZrpMTxxvWu1/z8+s8NroKhtw4n6q+7TJ+bdH5kY85XHPRPz0+/6yoC93z7o5QFdlu5tCgEAAIAKxmQdAAAASKmc1lmvGMkVYPr6CjBrrmptd7eT6v30+482fCzmIS/5aZCWVWu6Y4TA4cv45zi8sijmpttPifnfvn1mzH837KmY75wx3R871VeGUeJ5DkuVnwrd/jFvNzvqkiUx1ycus799wakxT9q9wp8n075GgbSpHjk85rUf7hnzjUMWJ+5VI6CcWJ23fK35sh87MolZ6BG/97ZM/fH1PL64z+mSrc1Wnfi+OtGOU9XXW1/2He2bXB642tssp/daGfN/+7erYh79sK+Sls/WZ75ZBwAAAFKKyToAAACQUrTBHERVL98iqen4sTH/00l3tbvfhBr/z/fcUr9KeOLqvQJKzYBX/RTf756dFvMxF/iqL8cP97z8aL8Cvt8rXXvtqmO99WXjGb6y0o8afSOkO7d568vYXyQ3G9vXtRcHCmjPUb4SxlmfeamIIwG6V7L1ZeefnhDzdVfeG/M9G0+Medcbo2LuaKPKw1E90Dct2nWmz9V2jvXWl31D/Zgy9VRvcfnLEf8Z8/pmf55rZ30l5vG/fSfmlk3ds/8U36wDAAAAKcVkHQAAAEgp2mAOwhJtMFuO9dM4x9a2P73RI3GiJjT53z3WnFhtozsGCHSDzFu+utGEu/xz/w/Dzo/5pul++nLm1+pjXtv3tJiH/vz5Dl+jx7gxMe8+aqg//rNeM/942qyYVx0YHPNvnvaVaCa97CsFZFp8lRggjXqMGR3zmpN9NYq7hj2RuFeDOjJ+9tdjHndfU8w1r70ZM+sgIc1aa3xFlpE122O+dfzsmO/+/tEx3/JlP6bseatfzJk6n1XVD/eNk/bt9OORJF34oYUxT+3lK7QMq/HWs0HVvvrMroyvzPTC7gkx/81LF/tj/9PvM+EVbwlt3bDJXziwKRIAAABQUZisAwAAAClFG8xBWF1tzMkrhOut/f2e3e//+fq/5qc2q1a8HTOnJlEqQpOfXq9a6KcNR902Nebv7Lg05otnzI+58c92xvzYh06Oeegf/Gp7Sdp2oa/ccvo43/Dobwe/GHOrvND+7rWLYj7yEd9iIrOXFZdQOkKtHx+ae/sxZUfGvy87f+4XYq592E/7S9Lk57b6c63wjfZa9+/P6ziBfArN/v/sIc/4vOiv/p9vItTnJF9JZWhvb0uZPnxtzDMmPx1zsnVleZOvrLRgl29eJElbmnrHfP/O42JescIfU7vJ53A1e/y402+Fr0g2do0fa6oW+cZlLbsTGzh1U+tLEt+sAwAAACnFZB0AAABIKdpgDiL09dMnw6dtjLne2v9t8/2Vn4x50GI/Hdm6fbuAUpZsM6l7blHME/dOjvmxVafEvOcob6E547g3Yt5/jJ/+l6RBzYlNMg741fvfef2zMbcs7Bvz8Ll+GrV+3vKYaS9DSXnHNxwbPcc3Vvn8O9+JecjLXkO1T7ffLKm1+UA3Dg7oJhn/P3XLSl9tbNyvvOVk7x+HxLyjYWDMG/v5fOuF4R/y50xMw2q9+1LV+9u3otS96z/X7PG2lsnrfAWZ6m3v+gOavMZat/ocLiRqz5+l8PhmHQAAAEgpJusAAABAStEGcxCh1v+zTB3gVyTXvK8NZsVq39Rlyg5vGyjmqRIg3zKJFSeq5vkKLqOW+YoVLRNGxPzSR4+Jee+Y9hsWJVdNqt3tpymHrfNTjfVLVvrzbvKVAlozNL+gNCVbI2se81WURjx28PuzmR7KWcuKVTHXJnPiPr0TeVAeXztZWy0d3it9+GYdAAAASCkm6wAAAEBK0QZzELbHT/s/vnRKzK8N/kO7+1Xv8P98dqCUTqgAhyd5ZXzrO96iYol8xAtdew0qCQAAd8hv1s2s3sxeNLPXzGyRmX0ve/tYM5trZsvN7D/NrPZQzwVUCuoGyA01A+SGmqkcnWmDaZJ0dgjhOEnHS7rAzE6R9CNJPwkhTJC0XdJVH/AcQKWhboDcUDNAbqiZCnHINpgQQpC0O/tjTfafIOlsSV/I3j5T0t9L+kX+h1gE232l/YFPDov5q/VXtLvbkPmJH7bu6O5RoYRUZN0AXUDNALmhZipHpy4wNbNqM3tV0mZJcyS9JWlHCOG99tJ1kkZ28NirzWy+mc1vVtPB7gKUpcOtG2oGlYpjDZAbaqYydGqyHkJoDSEcL+kISSdLmnKIhyQfe3MIYXoIYXqN6g79AKBMHG7dUDOoVBxrgNxQM5Uhp9VgQgg7zOxJSadK6m9mPbJ/vR0haX13DLAYWrdsjXngbS8k8gc8pjsHhJJWKXUD5As1A+SGmilvnVkNZoiZ9c/mnpLOk7RE0pOSPpu92xWSHuiuQQKlhroBckPNALmhZipHZ75Zb5Q008yq1Ta5nx1C+K2ZLZY0y8x+IOkVSbd24ziBUkPdALmhZoDcUDMVwtouJi7Qi5m9I2mPpC0Fe9F0GKz0vOcjQwhDij0IdE62ZlYrXZ+hQkjT+6VmSgzHmlSgbkoIx5pU6LBmCjpZlyQzmx9CmF7QFy2ySnzPyK9K+wxV2vtF/lXiZ6gS3zPyq9I+Q6Xyfju1GgwAAACAwmOyDgAAAKRUMSbrNxfhNYutEt8z8qvSPkOV9n6Rf5X4GarE94z8qrTPUEm834L3rAMAAADoHNpgAAAAgJRisg4AAACkVEEn62Z2gZktNbPlZnZ9IV+7EMxslJk9aWaLzWyRmV2bvX2gmc0xs2XZfw8o9lhRGsq9ZiTqBvlX7nVDzSDfyr1mpNKum4L1rGd32HpTbdvhrpM0T9JlIYTFBRlAAZhZo6TGEMLLZtZH0kuSPi3pzyRtCyHclC2CASGE7xZxqCgBlVAzEnWD/KqEuqFmkE+VUDNSaddNIb9ZP1nS8hDCihDCAUmzJF1UwNfvdiGEDSGEl7N5l6Qlkkaq7X3OzN5tpto+HMChlH3NSNQN8q7s64aaQZ6Vfc1IpV03hZysj5S0NvHzuuxtZcnMxkg6QdJcScNCCBuyv9ooaViRhoXSUlE1I1E3yIuKqhtqBnlQUTUjlV7dcIFpNzCzBkn3SLouhPBu8nehre+I9TKB96FugNxQM0DuSrFuCjlZXy9pVOLnI7K3lRUzq1Hbh+COEMK92Zs3ZXul3uuZ2lys8aGkVETNSNQN8qoi6oaaQR5VRM1IpVs3hZysz5M00czGmlmtpEslPVjA1+92ZmaSbpW0JITw48SvHpR0RTZfIemBQo8NJansa0aibpB3ZV831AzyrOxrRirtuinoDqZm9nFJ/yypWtJtIYT/VbAXLwAzO13Ss5IWSMpkb75BbT1RsyWNlrRa0udDCNuKMkiUlHKvGYm6Qf6Ve91QM8i3cq8ZqbTrpqCTdQAAAACdxwWmAAAAQEoxWQcAAABSisk6AAAAkFJM1gEAAICUYrIOAAAApBST9U4ys2vNbKGZLTKz64o9HiDtzOwCM1tqZsvN7PpijwdIOzO7zcw2m9nCYo8FKBVmtsrMFpjZq2Y2v9jj6Q5M1jvBzI6R9DVJJ0s6TtInzWxCcUcFpJeZVUv6maQLJR0l6TIzO6q4owJS73ZJFxR7EEAJOiuEcHwIYXqxB9IdmKx3zlRJc0MIe0MILZKelnRxkccEpNnJkpaHEFaEEA5ImiXpoiKPCUi1EMIzklK1GQuA4mOy3jkLJZ1hZoPMrJekj0saVeQxAWk2UtLaxM/rsrcBAJBPQdJjZvaSmV1d7MF0hx7FHkApCCEsMbMfSXpM0h5Jr0pqLe6oAAAAKt7pIYT1ZjZU0hwzeyN7lqps8M16J4UQbg0hTAshnClpu6Q3iz0mIMXWq/3ZpyOytwEAkDchhPXZf2+WdJ/a2jDLCpP1Tsr+xSYzG622fvU7izsiINXmSZpoZmPNrFbSpZIeLPKYAABlxMx6m1mf97Kk89XWulxWaIPpvHvMbJCkZknXhBB2FHtAQFqFEFrM7JuSHpVULem2EMKiIg8LSDUzu0vSRyUNNrN1km4MIdxa3FEBqTZM0n1mJrXNae8MITxS3CHln4UQij0GAAAAAAdBGwwAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWQdAAAASCkm6wAAAEBK/X+i3BBDhqQO5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(test_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Reshape(torch.nn.Module):\n",
+    "    def __init__(self, shape):\n",
+    "        super(Reshape, self).__init__()\n",
+    "        self.shape = shape\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        return x.view(*self.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class  VariationalAutoencoderZrc(torch.nn.Module):\n",
+    "    def __init__(self, num_latent, grayscale = False):\n",
+    "        super(VariationalAutoencoderZrc, self).__init__()\n",
+    "        # calculate same padding:\n",
+    "        # (w - k + 2*p)/s + 1 = o\n",
+    "        if grayscale:\n",
+    "            in_channels = 1\n",
+    "        else:\n",
+    "            in_channels = 3\n",
+    "        \n",
+    "        \n",
+    "        # (w-k+2p) // 2 + 1\n",
+    "        \n",
+    "        # 28x28x1 => 14x14x4\n",
+    "        self.encoder = torch.nn.Sequential(\n",
+    "            torch.nn.Conv2d(in_channels,\n",
+    "                            out_channels=4,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            # 14x14x4 => 7x7x8\n",
+    "            torch.nn.Conv2d(in_channels=4,\n",
+    "                              out_channels=8,\n",
+    "                              kernel_size=(3, 3),\n",
+    "                              stride=(2, 2),\n",
+    "                              padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            torch.nn.Flatten(),\n",
+    "            \n",
+    "        )\n",
+    "        \n",
+    "        self.z_mean = torch.nn.Linear(8*8*8, num_latent)\n",
+    "        self.z_log_var = torch.nn.Linear(8*8*8, num_latent)\n",
+    "        # in the original paper (Kingma & Welling 2015, we use\n",
+    "        # have a z_mean and z_var, but the problem is that\n",
+    "        # the z_var can be negative, which would cause issues\n",
+    "        # in the log later. Hence we assume that latent vector\n",
+    "        # has a z_mean and z_log_var component, and when we need\n",
+    "        # the regular variance or std_dev, we simply use \n",
+    "        # an exponential function\n",
+    "        \n",
+    "        # Hout=(H−1)×stride[0]−2×padding[0]+dilation[0]×(kernel_size[0]−1)+output_padding[0]+1\n",
+    "        \n",
+    "        self.decoder = torch.nn.Sequential(\n",
+    "            torch.nn.Linear(num_latent, 8*8*8),\n",
+    "            Reshape((-1,8,8,8)),\n",
+    "            \n",
+    "            torch.nn.ConvTranspose2d(in_channels=8,\n",
+    "                                     out_channels=4,\n",
+    "                                     kernel_size=(3, 3),\n",
+    "                                     stride=(2, 2),\n",
+    "                                     padding = 1,\n",
+    "                                     output_padding = 1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            torch.nn.ConvTranspose2d(in_channels = 4,\n",
+    "                            out_channels= in_channels,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding = 1,\n",
+    "                            output_padding = 1),\n",
+    "                            \n",
+    "            torch.nn.LeakyReLU(inplace = True)\n",
+    "        )\n",
+    "        \n",
+    "    def reparameterize(self, z_mu, z_log_var):\n",
+    "        # Sample epsilon from standard normal distribution\n",
+    "        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(DEVICE)\n",
+    "        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\n",
+    "        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\n",
+    "        z = z_mu + eps * torch.exp(z_log_var/2.) \n",
+    "        return z\n",
+    "        \n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.encoder(x)\n",
+    "        mean = self.z_mean(x)\n",
+    "        log_var = self.z_log_var(x)\n",
+    "        \n",
+    "        encoded = self.reparameterize(mean, log_var)\n",
+    "        decoded = self.decoder(encoded)\n",
+    "        \n",
+    "        \n",
+    "        return mean,log_var,decoded\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'inplace'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-77-569a497f20d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtest_nin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-77-569a497f20d2>\u001b[0m in \u001b[0;36mtest_nin\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest_nin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariationalAutoencoderZrc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_latent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtest_nin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-76-09c6232b320a>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, num_latent, grayscale)\u001b[0m\n\u001b[1;32m     61\u001b[0m                             output_padding = 1),\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'inplace'"
+     ]
+    }
+   ],
+   "source": [
+    "def test_nin():\n",
+    "    model = VariationalAutoencoderZrc(num_latent = 10).to(DEVICE)\n",
+    "    summary(model, (3,32,32))\n",
+    "    \n",
+    "test_nin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() got an unexpected keyword argument 'inplace'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-78-7b7884d494cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariationalAutoencoderZrc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_latent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNUM_LATENT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrayscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGRAYSCALE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLEARNING_RATE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-76-09c6232b320a>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, num_latent, grayscale)\u001b[0m\n\u001b[1;32m     61\u001b[0m                             output_padding = 1),\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m         )\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'inplace'"
+     ]
+    }
+   ],
+   "source": [
+    "model = VariationalAutoencoderZrc(num_latent = NUM_LATENT, grayscale=GRAYSCALE)\n",
+    "model.to(DEVICE)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_func(z_mean,z_log_var,decoded,features):\n",
+    "    kl_divergence = (0.5 * (z_mean**2 + \n",
+    "                                torch.exp(z_log_var) - z_log_var - 1)).sum()\n",
+    "    pixelwise_bce = F.binary_cross_entropy(decoded, features, reduction='sum')\n",
+    "    loss = kl_divergence + pixelwise_bce\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, data_loader, optimizer, num_epochs,batch_size, device,metric_func = None, random_seed = 7):\n",
+    "    # Manual seed for deterministic data loader\n",
+    "    torch.manual_seed(random_seed)\n",
+    "    \n",
+    "    loss_list = []\n",
+    "    \n",
+    "    for epoch in range(num_epochs):\n",
+    "        # set training mode\n",
+    "        model.train() \n",
+    "        for batch_idx, (features, targets) in enumerate(data_loader[\"train\"]):\n",
+    "            features = features.to(device)\n",
+    "\n",
+    "            ## forward pass\n",
+    "            z_mean,z_log_var,decoded = model(features)\n",
+    "            loss = loss_func(z_mean,z_log_var,decoded,features)\n",
+    "\n",
+    "            # backward pass\n",
+    "            # clear the gradients of all tensors being optimized\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            ### Login\n",
+    "            loss_list.append(loss.item())\n",
+    "            if not batch_idx % 50:\n",
+    "                print ('Epoch: {0:03d}/{1:03d} | Batch {2:03d}/{3:03d} | Loss: {4:.2f}'.format(\n",
+    "                    epoch+1, num_epochs, batch_idx, \n",
+    "                         len(train_dataset)//batch_size, loss))\n",
+    "    return loss_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[[0.2966, 0.1607, 0.2338,  ..., 0.2119, 0.2178, 0.2173],\n",
+      "          [0.1734, 0.1347, 0.1745,  ..., 0.1644, 0.1367, 0.1849],\n",
+      "          [0.3262, 0.1219, 0.2268,  ..., 0.2850, 0.2858, 0.1883],\n",
+      "          ...,\n",
+      "          [0.2039, 0.2208, 0.2164,  ..., 0.3146, 0.2714, 0.1589],\n",
+      "          [0.2506, 0.2253, 0.2583,  ..., 0.2270, 0.2587, 0.1442],\n",
+      "          [0.2632, 0.2829, 0.2739,  ..., 0.2968, 0.2881, 0.2217]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.2026, 0.1924, 0.2101,  ..., 0.2934, 0.2520, 0.2021],\n",
+      "          [0.2401, 0.2396, 0.2931,  ..., 0.2785, 0.2953, 0.2788],\n",
+      "          [0.1957, 0.1931, 0.2253,  ..., 0.1451, 0.2177, 0.2174],\n",
+      "          ...,\n",
+      "          [0.1957, 0.2618, 0.3111,  ..., 0.2487, 0.2195, 0.2248],\n",
+      "          [0.2563, 0.0559, 0.1457,  ..., 0.2723, 0.2282, 0.2283],\n",
+      "          [0.2710, 0.3109, 0.2865,  ..., 0.2547, 0.2223, 0.2335]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.2321, 0.2355, 0.2399,  ..., 0.3501, 0.2989, 0.1027],\n",
+      "          [0.3741, 0.2701, 0.1664,  ..., 0.2638, 0.3079, 0.2015],\n",
+      "          [0.2221, 0.2850, 0.2897,  ..., 0.1651, 0.2315, 0.1856],\n",
+      "          ...,\n",
+      "          [0.1936, 0.3659, 0.3760,  ..., 0.2720, 0.2137, 0.1670],\n",
+      "          [0.2360, 0.2654, 0.2506,  ..., 0.2432, 0.2208, 0.1461],\n",
+      "          [0.2562, 0.2886, 0.2672,  ..., 0.3799, 0.2936, 0.2193]]],\n",
+      "\n",
+      "\n",
+      "        ...,\n",
+      "\n",
+      "\n",
+      "        [[[0.2318, 0.2865, 0.2618,  ..., 0.1019, 0.1556, 0.2500],\n",
+      "          [0.2481, 0.1380, 0.1840,  ..., 0.2773, 0.2735, 0.2906],\n",
+      "          [0.1961, 0.3315, 0.3171,  ..., 0.2147, 0.2192, 0.2310],\n",
+      "          ...,\n",
+      "          [0.1668, 0.2500, 0.2991,  ..., 0.1763, 0.1813, 0.1708],\n",
+      "          [0.2647, 0.1301, 0.2057,  ..., 0.2377, 0.2341, 0.1752],\n",
+      "          [0.2829, 0.2339, 0.2268,  ..., 0.2888, 0.2740, 0.2318]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.2214, 0.2820, 0.2438,  ..., 0.2937, 0.2823, 0.1320],\n",
+      "          [0.2234, 0.1621, 0.2645,  ..., 0.2814, 0.3120, 0.2231],\n",
+      "          [0.2174, 0.2925, 0.2576,  ..., 0.1842, 0.2065, 0.2083],\n",
+      "          ...,\n",
+      "          [0.2701, 0.3491, 0.3265,  ..., 0.1876, 0.1768, 0.1500],\n",
+      "          [0.2244, 0.2548, 0.2225,  ..., 0.2429, 0.2338, 0.1509],\n",
+      "          [0.2075, 0.2340, 0.2090,  ..., 0.3096, 0.2860, 0.2201]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.2530, 0.2904, 0.2570,  ..., 0.3947, 0.2929, 0.1598],\n",
+      "          [0.2825, 0.2680, 0.2170,  ..., 0.3267, 0.3206, 0.2744],\n",
+      "          [0.1848, 0.2698, 0.2678,  ..., 0.1593, 0.2175, 0.2177],\n",
+      "          ...,\n",
+      "          [0.2380, 0.3246, 0.3060,  ..., 0.1648, 0.2138, 0.2835],\n",
+      "          [0.3310, 0.1195, 0.2227,  ..., 0.2352, 0.2325, 0.3075],\n",
+      "          [0.3811, 0.2921, 0.2090,  ..., 0.2025, 0.1974, 0.2849]]]],\n",
+      "       grad_fn=<LeakyReluBackward1>)\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "Assertion `x >= 0. && x <= 1.' failed. input value should be between 0~1, but got -0.000500 at ../aten/src/THNN/generic/BCECriterion.c:60",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-74-f7252e139326>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0mNUM_EPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDEVICE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m             batch_size = BATCH_SIZE)\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-bd10e26ade5d>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, data_loader, optimizer, num_epochs, batch_size, device, metric_func, random_seed)\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0mz_mean\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz_log_var\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdecoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_mean\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz_log_var\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;31m# backward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-70-43da92c8fff4>\u001b[0m in \u001b[0;36mloss_func\u001b[0;34m(z_mean, z_log_var, decoded, features)\u001b[0m\n\u001b[1;32m      2\u001b[0m     kl_divergence = (0.5 * (z_mean**2 + \n\u001b[1;32m      3\u001b[0m                                 torch.exp(z_log_var) - z_log_var - 1)).sum()\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mpixelwise_bce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinary_cross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkl_divergence\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mpixelwise_bce\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/miniconda3/envs/tryit/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mbinary_cross_entropy\u001b[0;34m(input, target, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m   2075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2076\u001b[0m     return torch._C._nn.binary_cross_entropy(\n\u001b[0;32m-> 2077\u001b[0;31m         input, target, weight, reduction_enum)\n\u001b[0m\u001b[1;32m   2078\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2079\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Assertion `x >= 0. && x <= 1.' failed. input value should be between 0~1, but got -0.000500 at ../aten/src/THNN/generic/BCECriterion.c:60"
+     ]
+    }
+   ],
+   "source": [
+    "loss_list = train_model(model, \n",
+    "            data_loader, \n",
+    "            optimizer, \n",
+    "            NUM_EPOCHS, \n",
+    "            device = DEVICE, \n",
+    "            batch_size = BATCH_SIZE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(loss_list, label='Minibatch cost')\n",
+    "plt.plot(np.convolve(loss_list, \n",
+    "                     np.ones(200,)/200, mode='valid'), \n",
+    "         label='Running average')\n",
+    "\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mkpSUZMmjDqgqubm5xMQE5+J0S7P28NKcdUxfsQ0BRvVuy+3ndaZne9tb3ZjaarSJo0OHDmRnZ5OTk3P8g02NxMTE0KFDB7/DOKy0VPnvyu28PGc932zYRUJ0BLcP6sTN56TSLtGWSzHmRDXaxBEZGUmnTp38DiMkFJWUMn3FNv4zfyMlpUrHFnGkVLi1TIgOmppbfmEJ7y7K5tW561m/8yDtE2N59NI0rjujIwkxNrTVmJPVaBOHOb49eYX855tN/OurjWzbV0BqUhytmsbw9fe5TF68mfJdGjGRYYeTSMXE0rFFXL10OOfsP8S/v97Av+dtZHdeEX06NOPP1/dlRM82tmy3MXXIEoc5xtodBxj/5XomLcqmoKiUQacm88SVR3cgFxSVsHlPPpt25ZG1K49NuXls2uVuX32fS15hyVHXbJUQfSSZJNVtbWXN9v28PGc9k5dspqiklAvTWnPHeZ05I7V50NSCjGlILHEYwHVuz1mzk1fmrmfW6hyiIsK4Ir09tw5KpXubpsccHxMZTpeW8ZVOjlNVcg8WVppU5q3LZfKSY2srHZtXUltJiqNj87hKl89QVb76PpeX5qzji+9yiI4I4xpvwl5nm7BnTEBZ4mjk8gtLmLx4M+O/XM+aHQdomRDN/Redxg1nppzwBDgRITk+muT4aPqlND/m+UPFJWzenX84mVRMLAerqa10bBFH09hIJi3MJnPrPpLjo7j/otO48axTaNEk6oTiNcbUjiWORmrb3gL+PW8D/5m/id15RZzerinPXtuHS3u3DfieCtER4XRuGV9pzUBV2eXVVg7XWCqprXRtFc9TV/ViTLpN2DOmvlniaGSWZu3h1S/X89GyrZSoMrxHa247txMD63kBv6qICEnx0STFR9O3itrKzgOFtG0aYxP2jPGJJY5GoLiklE8yt/PK3PUs3Lib+OgIbjo7lVvOSSUlKc7v8GolOiKc9jYHwxhfWeJowPbmF/H2gk28/tVGNu/JJ6VFHI+N6sE1AzrYfAZjzAmzxNEArd95kNe+XM87C7PJKyzhzE4t+OVlPRiW1ppwa94xxpwkSxwNRNnw1Ffnrufz73YQESaM7tOeW89NtfWYjDF1KqCJQ0TuBe4ABHhJVZ8TkT7AP4B4YANwg6ruq+TcDcB+oAQoLttMRERaAG8Dqd7516rq7kC+j2BWUFTC1CVbePXL9azatp+kJlHcfUFXbjwrhVYJwbngoDEmtAUscYhIT1zSGAgUAjNE5CPgZeABVZ0lIrcBDwK/qOIyQ1V1Z4WyccBnqvqkiIzzHj8UkDcRxHbsL+CNeZt4c95Gcg8W0r1NAk9f3ZvRfdrZ8FRjTEAFssaRBsxT1TwAEZkFXAF0A2Z7x3wKfEzViaMyY4Ah3v3XgS9oRIljxea9vPrlej5YuoXiUmVY91bcdm4nzu5iy8MbY+pHIBPHCuB3IpIE5AMjgQyvfDQwBbgG6FjF+Qp8IiIK/FNVX/TKW6vqVgBV3SoirSo7WUTGAmMBUlJS6uYd+aTEWx78lbnr+Wb9LuKiwvnhwBRuObcTnZKb+B2eMaaRCVjiUNWVIvIUrlZxAFgKFAO3AS+IyGPAVFwzVmXOVdUtXmL4VERWqersKo6t7PVfBF4EGDBgQPBvTVeNuycsYtrybbRPjOX/RqZx7RkdaRZrw2mNMf4IaOe4qr4CvAIgIk8A2aq6ChjulZ0GXFrFuVu8nztEZDKur2Q2sF1E2nq1jbbAjkC+B79l7cpj+opt3HJOKo9emmbLgxtjfBfQb6GyZiQRSQGuBCaUKwsDHsWNsKp4XhMRSSi7j0s0K7ynpwI3e/dvxjV5NViTFmUDcPt5nSxpGGOCQqC/iSaJSCbwAXCXN2z2ehFZDawCtgDjAUSknYhM885rDcwVkaXAN8BHqjrDe+5J4CIRWQNc5D1ukEpLlXcyshl0ajIdmofW0iDGmIYr0E1V51VS9jzwfCXlW3Ad6KjqOqBPFdfMBYbVbaTB6avvc9m8J5+HRnT3OxRjjDnM2j6C2MSMLJrGRDC8R2u/QzHGmMMscQSpvXlFzPh2G5f3tf0mjDHBxRJHkJq6dDOFxaVcO6CqaS7GGOMPSxxBamJGNmltm3J6u2P3+zbGGD9Z4ghCmVv2sXzzXq4d0MGWETHGBB1LHEHonYVZRIWHcXl6e79DMcaYY1jiCDKHikt4f/FmLjq9Nc2bRPkdjjHGHMMSR5D5bOUOducVWae4MSZoWeIIMm8vyKJtsxgGnZrsdyjGGFMpSxxBZMuefGavyeHq/h1sb3BjTNCyxBFE3luUjSpc3b+D36EYY0yVLHEEidJSZWJGNmd1bsEpSbY5kzEmeFniCBLfbNjFpl151ilujAl6ljiCxMSMLBKiIxjRs63foRhjTLUscQSB/QVFTFu+lcvS2xEbZQsaGmOCmyWOIPDhsq0UFNmChsaY0GCJIwi8vSCL01rH06dDM79DMcaY47LE4bPV2/ezJGsP1w7oaAsaGmNCQkATh4jcKyIrRORbEbnPK+sjIl+LyHIR+UBEjlk3XEQ6ishMEVnpnXtvued+JSKbRWSJdxsZyPcQaO9kZBERJlze1xY0NMaEhoAlDhHpCdwBDMTtHz5KRLoCLwPjVLUXMBl4sJLTi4Gfq2oacBZwl4j0KPf8n1Q13btNC9R7CLSiklLeW7SZYWmtSI6P9jscY4ypkUDWONKAeaqap6rFwCzgCqAbMNs75lPgqoonqupWVV3k3d8PrAQa3J/kn6/aQe7BQusUN8aElEAmjhXAYBFJEpE4YCTQ0Ssf7R1zjVdWJRFJBfoC88sV/1RElonIqyLSvK4Dry/vZGTRMiGa809r6XcoxhhTYwFLHKq6EngKV6uYASzFNUHdhmt6WggkAIVVXUNE4oFJwH2qus8r/jvQBUgHtgJ/rOLcsSKSISIZOTk5dfOm6tCOfQXM/C6Hq/p1ICLcxigYY0JHQL+xVPUVVe2nqoOBXcAaVV2lqsNVtT8wAfi+snNFJBKXNN5U1ffKXXO7qpaoainwEq4PpbLXflFVB6jqgJYtg+8v+vcWb6akVLl2gC1oaIwJLYEeVdXK+5kCXAlMKFcWBjwK/KOS8wR4BVipqs9WeK78mhxX4Jq+QoqqMnFBFmekNqdzy3i/wzHGmFoJdBvJJBHJBD4A7lLV3cD1IrIaWAVsAcYDiEg7ESkbIXUu8CPggkqG3T7tDeVdBgwFfhbg91DnFm7czbqdB7nGOsWNMSEoIpAXV9XzKil7Hni+kvItuA50VHUuUOlsOFX9UR2HWe8mZmQRFxXOpb1sQUNjTOixXtl6dvBQMR8u28qo3m1pEh3QvG2MMQFhiaOefbR8K3mFJTZ3wxgTsixx1LN3MrLonNyE/qeE7PQTY0wjd9zEISI/DeVJdsFkXc4BFmzYzTW2oKExJoTVpMbRBlggIhNF5BKxb7wT9s7CbMLDhKv6NbjVU4wxjchxE4eqPgp0xc2ruAVYIyJPiEiXAMfWoBSXlDJpYTZDu7WkVdMYv8MxxpgTVqM+DlVVYJt3KwaaA++KyNMBjK1BmbU6hx37D9ncDWNMyDvueFARuQe4GdiJWxL9QVUt8mZ+rwH+N7AhNgwTM7JIjo/igu6t/A7FGGNOSk0mEiQDV6rqxvKFqloqIqMCE1bDsvPAIT5buYNbz00l0hY0NMaEuJp8i03DLVAIgIgkiMiZcHgFXHMc7y/eTHGpWjOVMaZBqEni+DtwoNzjg16ZqQFV5e0FWaR3TOS01gl+h2OMMSetJolDvM5xwDVREeA1rhqSpdl7WbPjgM0UN8Y0GDVJHOtE5B4RifRu9wLrAh1YQzExI4uYyDAu62MLGhpjGoaaJI6fAOcAm4Fs4ExgbCCDaijyC0v4YMkWRvZqS0JMpN/hGGNMnThuk5Oq7gB+UA+xNDjTV2xl/6Fia6YyxjQoNZnHEQP8GDgdODzlWVVvC2BcDcLEjCxOSYrjzE4t/A7FGGPqTE2aqv6NW6/qYmAW0AHYH8igGoKNuQeZt24X1/TvYAsaGmMalJokjlNV9RfAQVV9HbgU6BXYsELfuwuzEYGr+nfwOxRjjKlTNUkcRd7PPSLSE2gGpAYsogagpFR5d2E2g7u2pG2zWL/DMcaYOlWTxPGitx/Ho8BUIBN4qiYXF5F7RWSFiHwrIvd5ZX1E5GsRWS4iH4hI0yrOvUREvhORtSIyrlx5JxGZLyJrRORtEYmqSSz1ae7anWzdW2Cd4saYBqnaxOEtZLhPVXer6mxV7ayqrVT1n8e7sFc7uQMYCPQBRolIV9xCieNUtRcwGXiwknPDgb8CI4AewPUi0sN7+ingT6raFdiN67gPKhMzsmgeF8mFPWxBQ2NMw1Nt4vBmif/0BK+dBsxT1TxVLcZ1rF8BdANme8d8ClxVybkDgbWquk5VC4G3gDHeJlIXAO96x70OXH6C8QXE7oOFfPrtdi7v257oiHC/wzHGmDpXk6aqT0XkARHpKCItym41OG8FMFhEkkQkDhgJdPTKR3vHXOOVVdQeyCr3ONsrSwL2eImofPkxRGSsiGSISEZOTk4Nwq0b7y/ZTGFJKdf0t2YqY0zDVJM1p8rma9xVrkyBztWdpKorReQpXK3iALAUtwnUbcALIvIYrs+ksJLTKxu/qtWUV/b6LwIvAgwYMKDSY+pa2YKGvdo3o0e7SrtujDEm5NVk5ninE724qr6C23IWEXkCyFbVVcBwr+w03PDeirI5uibSAdiC20wqUUQivFpHWXlQ+HbLPlZt289vxpzudyjGGBMwNZk5flNl5ar6rxqc20pVd4hICnAlcHa5sjDcSK1/VHLqAqCriHTCrZH1A+CHqqoiMhO4GtfvcTMw5Xhx1JeJGVlERYQxuk+lrWfGGNMg1KSP44xyt/OAX3Gkj+J4JolIJvABcJeq7saNkFoNrMLVFsYDiEg7EZkG4NUmfgp8DKwEJqrqt941HwLuF5G1uD6PV2oYS0AVFJXw/uLNXHJ6G5rF2YKGxpiGqyZNVXeXfywizXDLkByXqp5XSdnzwPOVlG/BdaCXPZ6G232w4nHrcKOugsonmdvZV2ALGhpjGr4T2QA7D+ha14GEuncysmifGMs5XZL8DsUYYwKqJn0cH3Bk5FIYbkLexEAGFWqyd+cxd+1O7h3WlbAwW9DQGNOw1WQ47jPl7hcDG1U1O0DxhKR3F7qP42pb0NAY0wjUJHFsAraqagGAiMSKSKqqbghoZCGitFR5JyObc7sk06F5nN/hGGNMwNWkj+MdoLTc4xKvzABfr8tl8558rhlgtQ1jTBApyocvnoK9m+v80jVJHBHeelEAePeDbkVav0zMyKJpTAQXn97G71CMMeaIVR/BF09A7po6v3RNEkeOiByetyEiY3AzuBu9vXlFTF+xjTHp7YmJtAUNjTFBZPEb0CwFUgfX+aVr0sfxE+BNEfmL9zgbqHQ2eWMzddkWCotLbe6GMSa47NkE676A8x+CsBOZdVG9mkwA/B44S0TiAVFV22/c805GFt3bJNCzvS1oaIwJIksmAArpPwzI5Y+bikTkCRFJVNUDqrpfRJqLyG8DEk0IWbl1H8uy93LdGR1x24QYY0wQKC2FJW9Cp/Oh+SkBeYma1GFGqOqesgfeelMjqzm+UZiYkUVUeBiXp9uChsaYILJxLuzZCH1/FLCXqEniCBeR6LIHIhILRFdzfIN3qNgtaHhRj9Y0b2IDzIwxQWTxGxDdDNJGBewlatI5/gbwmYiM9x7fituytdH6bOUOducV2dwNY0xwKdgLmVNc30ZkbMBepiad40+LyDLgQtwOfDOAwDSchYiJGVm0aRrDeV1b+h2KMcYcseI9KC6AvjcG9GVqOk5rG272+FXAMNweGY3S1r35zF6dw9X9OxBuCxoaY4LJ4jegZRq06xfQl6myxuFt6/oD4HogF3gbNxx3aEAjCnLvLdpMqdqChiZI5HwHuzdC6rkQ1cTvaIyfdqyCzRkw/HcQ4JGe1TVVrQLmAJep6loAEflZQKMJcqrKxIwszuzUgtRk+09qfKAKOzJdO3bmFMhZ5cojYqHrRdBjDJx2MUQn+BunqX9L3oCwCOh9XcBfqrrEcRWuxjFTRGbg9vhu1G0z36zfxYVEogQAABplSURBVMbcPO65wPaxMvVIFbYtO5IsctcCAqecCyP+AEmd4bvpsPIDWDkVwqPh1Auhx2g47RKITfT7HZhAKymCpW+5f+/4wPe9Vpk4VHUyMFlEmgCXAz8DWovI34HJqvpJwKMLMm9nZBEfHcHIXm39DsU0dKqweRFkvu+SxZ6NIOHQ6Tw4+y7oPgriWx05/tQLYcTTkDUfMqe6c777CMIioctQVxPpNhLiWvj3nkzgrPkEDuYEvFO8TE1GVR0E3sStV9UCuAYYBxw3cYjIvcAduJrKS6r6nIikA/8AYnAbQ/0/Vf2mwnlDgT+VK+oO/EBV3xeR14Dzgb3ec7eo6pLjxXKy9hcUMW35Vq7o24HYKFvQ0ARAaSlkL3Bf+iunwt4s1/TQeQgMfgC6XQpNqtmaOCwcTjnH3S5+AjYv9BLPVPfFEhYBnQZD2mgv8diowAZj8ZsQ3xpOvaheXq4m8zgOU9VdwD+9W7VEpCcuaQwECoEZIvIR8DTwuKpOF5GR3uMhFV5nJpDuXacFsJajE9WDqvpubWI/WR8u20pBUSnX2twNU5dKS2DT116y+AD2b4XwKOhyAQx9BLqNgNjmtb9uWBh0PMPdhv8Wtix2yejb9+HD++Cj+11TV48xkHYZJNi2ACFr/3ZYPQPO+SmE1+or/YQF8lXSgHmqmgcgIrOAK3D7l5etCtgM2HKc61wNTC+7jl8mZmTRtVU86R2tvdicpJJityxEWbI4mAMRMV6/xOWuczumDhfOFIH2/dxt2C9h+wr32t++D9MegGkPQsrZR5JIM1tGJ6Qsexu0BNLrp5kK3PDawFxYJA2YApwN5AOfARnA34CPcc1XYcA5qrqxmut8Djyrqh96j1/zrnnIu+Y4VT1UyXljgbEAKSkp/TdurPIljmvN9v1c9KfZ/N/INO4Y3PmEr2MaseJCWD8bVk6BlR9C/i6IjIOuw90XdtfhEB1fvzGpulFZmVNcc9aOb115hzO8JDI6YIvkmTqiCn89E2Kawe2f1vnlRWShqg44pjxQicN70R8DdwEHgExcAgkHZqnqJBG5FhirqhdWcX5bYBnQTlWLypVtw+1C+CLwvar+uro4BgwYoBkZGSf8Pp6YtpJX567n64eH0TKhUS/TZWqj+BB8P/NIR3XBXohKgG6XuC/mLsMgKoj2qd+55sjIrW3LXFm7vkeSSFIXf+Mzx8rOgJeHwWUvQP+b6/zyviSOCgE8gdsE6vdAoqqquPXI96pqpfVyr3P9dFUdW8XzQ4AHVLXa1bxOJnEUlZRy9u8/o19Kc1686ZjPz5ijFeXD2s/cl+/qGXBon/trsNtI9wXceShExvgd5fHtWudqISunuk52gDa9IG2Mex8tT/M3PuN8cC8smwg//65umzc9VSWOgPakiEgrVd0hIinAlbgmprtxo6K+AC4AqtsQ93rg4QrXbKuqW72kczmwIhCxl5m5agc7DxTaLn+maoUH3ailzCmw+hMoOug6tHuMcX0WnQZDRIitotyiMwy6z932bHJ9MZlTYOZv3a1lmvf+xkCrtIDPVDaVKMyD5ZPcv0EAkkZ1At0FP0lEkoAi4C5V3S0idwDPi0gEUIDXDyEiA4CfqOrt3uNUoCMwq8I13xSRlrg+kiW4rW0DZmJGFi0TohnSzYYumnJKSyFzMnw7Gdb8F4rzIS4Zel/r/iOnDoLwSL+jrBuJKW7uyNl3wb4tXhKZCrOegllPQlJXN9mw740u4Zj6sfIDKNxfb3M3yqu3pio/nWhT1Y59BZz95OfccV5nxo3oHoDITMia+yf4768gvo370uwxxo1MCmtEc3z2b4dVH7qayIY5ENsCfrrAJhnWl9dGwd5suGdxwGp8VTVV1f0u5g3Ie4s3U1Kqtu+GOdqeTTDraddvcf9KGPkHV8NoTEkDIKE1nPFjuHkq3DHTjRT7/Dd+R9U47FrvknX6Db40E1riqEaT6Agu7d2WLi3reZikCW4zvG63EU+5iXYG2qXDmT+BjPGQvdDvaBq+pRMAgfTrfXl5+62vxo/OOoW//jCw69qbEPPdDNc8c/7/urZ/c8SQh92yFx/9zM2IN4FRWuKWGOlyATTzpzXEEocxNVWYB9MfhJbd4ay7/I4m+MQ0hUuegK1LYcErfkfTcK2fBfuyfekUL2OJw5iamvNH179x6R9Db3htfTn9SjdX5fPfuM5zU/cWvwExia6PzSeWOIypiZzV8OXz0PsHriPcVE4ERj7j9r3+5P/8jqbhyd/tlqzpfa2vE0ktcRhzPKow7edueZDhNmrouJJPhXPvg+XvwLqK07DMSVn+LpQc8rWZCixxGHN8Kya5BQqHPXb05kmmaufdD81T4aOfuwUeTd1Y/Aa07gVt+/gahiUOY6pTsBc+fsQt9tf/Vr+jCR2Rsa7JKncNfPWC39E0DNtWwNYlvtc2wBKHMdX7/HdwYAeM+lPjm+B3srpe5Pb3mP0H2L3B72hC35I33SZfva/1OxJLHMZUacsSWPASnHG7q3GY2rvkSbdX+vRxfkcS2ooL3YZN3UYExZIuljiMqUxpidteNS4ZLnjU72hCV7MOMGQcrJ4Oqz7yO5rQtXoG5OVC3x/5HQlgicOYyi163e1DcfHvINa2Cz4pZ90JrXrA9IfcEvSm9ha/AQnt3GzxIGCJw5iKDuTAfx+H1POg1zV+RxP6wiPh0mdhb5br7zC1s28rrP3UrUsVJP1sljiMqejTx9xfxpf+0TYoqiunnO1Wcv3qz7Bjld/RhJalE0BL3ecXJCxxGFPehi9h6X/gnLuhZTe/o2lYLvo1RMW7uR2NYB+gOqHqRlOlnBNUe75b4jCmTEmR+1JrlgKDH/Q7moanSTJc+CvYONftk22OL2s+5K4Nirkb5VniMKbMvL9Bzkq3z0ZUnN/RNEz9bob2/d06Vvm7/Y4m+C3+N0Q2cTtMBpGAJg4RuVdEVojItyJyn1eWLiLzRGSJiGSIyMAqzi3xjlkiIlPLlXcSkfkiskZE3hYRW6bUnLy92fDFk27F0e7+rTra4IWFucmUebnw+W/9jia4HToA374PPa+A6ODaTC5giUNEegJ3AAOBPsAoEekKPA08rqrpwGPe48rkq2q6dxtdrvwp4E+q2hXYDfw4UO/BNCIzxrn25Eue9DuShq9tHxg41u3ZsXmR39EEr8wpUHggaOZulBfIGkcaME9V81S1GJgFXAEo0NQ7phmwpaYXFBEBLgDe9YpeBy6vs4hN47T6E1j5AZz/IDQ/xe9oGoehj7gFIz+633YLrMriNyDpVOh4pt+RHCOQiWMFMFhEkkQkDhgJdATuA/4gIlnAM8DDVZwf4zVlzRORsuSQBOzxEhFANtA+cG/BNHhF+TDtAUg+Dc6+2+9oGo+YZnDxE7BlMWS86nc0wSf3e9j0lRuCG4RDwgOWOFR1Ja5Z6VNgBrAUKAbuBH6mqh2BnwFV7TGZoqoDgB8Cz4lIF6CyT7DScX0iMtZLPBk5OTkn92ZMwzXnWdiz0Xb180PPq6DT+fDZb9xCkuaIJW+ChEGf6/2OpFIB7RxX1VdUtZ+qDgZ2AWuAm4H3vEPewfWBVHbuFu/nOuALoC+wE0gUkQjvsA5U0dSlqi+q6gBVHdCyZcs6ekemQdm5Fr58DnpdC50G+x1N4yPiEnZRHnzyC7+jCR6lJbDkP3DqRdC0rd/RVCrQo6paeT9TgCuBCbgv+vO9Qy7AJZOK5zUXkWjvfjJwLpCpqgrMBK72Dr0ZmBLI92AaqLJd/SJiYbiN7vFNclc4915Y9hasn+N3NMHh+89h/1boGzwzxSsK9DyOSSKSCXwA3KWqu3Ejrf4oIkuBJ4CxACIyQERe9s5LAzK8Y2YCT6pqpvfcQ8D9IrIW1+dRVVOXMVX79j1Y9wUM+wUktPY7msZt8AOQeIrtFlhm8RsQlwSnjfA7kipFHP+QE6eq51VSNhfoX0l5BnC7d/8roFcV11xHFc1bxtRIwT6Y8Qi0TYcBt/kdjYmMhRFPw4TrYN5fYdDP/I7IPwdz3fLzA+8I6j43mzluGp+ZT8CB7TDq2aBZbbTR63YJdB8Fs56GPZv8jsY/y9+B0qKgW2KkIkscpnHZugy++aerabQ/puJr/FQ2+bIx7xa45A1XE259ut+RVMsSh2k8Sku9Xf2SXN+GCS6JHeH8h+C7j+C76X5HU/+2LoVty4O+tgGWOExjsvhfkL3AjaKKbe53NKYyZ/0/aNkdpv8vFOb5HU39WvwGhEdDr6uPf6zPLHGYxuHgTvj0l3DKIOh9nd/RmKpERLndAvdsgjnP+B1N/SkqcEvNp40KiT9qLHGYxuHTX7oF42xXv+CXeq6bMf3lC5Cz2u9o6sd306BgT0g0U4ElDtMYbPzadTqe/VNo1d3vaExNXPQbtyfKR/c3jt0CF78BzTq6JVhCgCUO07CVFLkvn2Yd4fz/9TsaU1PxLWHYL2HDHFj+7vGPD2V7s91s8fQfhszwcEscpmGb/w/Ykent6tfE72hMbfS/Bdr1g48fgYK9fkcTOEsnAOoSR4iwxGEarr2bYebv4bRL3M5+JrSEhbtJmnk7G+5ugaWlrpkq9Txonup3NDVmicM0XB8/DFrqahvWIR6a2vWFM26HBS+7vTsamk1fwe4NQbnLX3UscZiGac1/3dabgx8Iqb/kTCWG/h/EJcOHDXC3wMVvQnRTSLvM70hqxRKHaXiK8t2S6Uld4Rzb1S/kxSbCxb+DLYtg0et+R1N3CvZB5vvQ80o3giyEWOIwDc/c51z1/9I/QkS039GYutDrGtcP8N9fwYEGsqPnt5PdJlYh1kwFljhMQ5P7Pcx91n3RdA6NMfGmBsp2CyzMg08f8zuaurH4Dbe8SggutmmJozqHDjS8NtWGTBWmPQARMbarX0PUsptrelz6H9jwpd/RnJyc1ZD9DaTfEJIDNyxxVGf20/DH7vDhz9xucSXFfkdkqpP5vptIdcGjkNDG72hMIAx+EJqluN0CS4r8jubELXkDJBz6/MDvSE6IJY7qpA6GU86BpW/Bv8bAM11h6t2w9r+h/UvbEBXsgxkPQ5vebvimaZii4mDk05CzEub9ze9oTkxJsftOOe0SiG/ldzQnJKBbx4a8rhe6W2EefP+ZG965YjIs+hfENINul0KPMdBlqHXC+u2LJ2H/NrjujZBZtsGcoG4j3ITOL56EnldBsw5+R1Q7a//rdqDse4PfkZywgNY4ROReEVkhIt+KyH1eWbqIzBORJSKSISLH7B/uHfO1d94yEbmu3HOvich67/wlIpIeyPcAuL9y0i6Dq16GB9fC9W+5X9xVH7l9kv9wKky6A1Z+6IaCmvq1bblbWqT/LdBhgN/RmPow4inXpzX9Ib8jqb3F/4YmLaHrcL8jOWEBq3GISE/gDmAgUAjMEJGPgKeBx1V1uoiM9B4PqXB6HnCTqq4RkXbAQhH5WFX3eM8/qKr+rHwWGeP9xTMCigth/SxXE1n1ISyfCJFN4LThribSdbitjxRopaWuvTu2OVz4S7+jMfUlMcUtWvnZ47D6YzjtYr8jqpkDObB6Bpx1J4RH+h3NCQtkU1UaME9V8wBEZBZwBaBAU++YZsCWiieq6upy97eIyA6gJbCn4rG+ioiCrhe526g/wYa5Loms/MCN0Y6IdU1dPS53SSSm6fGvaWpnyRuQNR8u/3tIbIBj6tDZP3V9BdMehE6DITLW74iOb/lEKC2G9NDYd6MqogFa615E0oApwNlAPvAZkAH8DfgYEFxT2TmqurGa6wwEXgdOV9VSEXnNu+Yh75rjVPVQJeeNBcYCpKSk9N+4scqXqHulJbDpa5dEMqfCgW0QHgVdhrmaSLcRbjasOTkHc+Ev/aFlGtw6LSSHNZqTtH4OvD7Kjba64FG/o6meKvztbNcKccdnfkdTIyKyUFWPaf8NWOLwXvTHwF3AASATl0DCgVmqOklErgXGquqFVZzfFvgCuFlV55Ur2wZEAS8C36vqr6uLY8CAAZqRkVE3b6q2SkvdeO3MKe62bzOERULnIS6JdL8U4lr4E1uom3o3LPkP/M8caN3D72iMX94b62r4d34FyV39jqZqmxfCSxfAqOdgwK1+R1MjviSOCgE8AWQDvwcSVVVFRIC9qnpMG46INMUljd+r6jtVXHMI8ICqjqrutX1NHOWVlrr1dsqSyJ6Nbix3p/O8JDIqZIfn1btN8+HV4XDOPTD8N35HY/y0fzv85Qxo3xd+9H7w1jw//BksmQAPfOdGZYaAqhJHoEdVtfJ+pgBXAhNwfRpla0FcAKyp5LwoYDLwr4pJw6tx4CWdy4EVgYq/zoWFuVE/w38D9y6FsbPg3Hthzyb3S/XHbvDaKPjmJdi31e9og1dJsdvVr2kHOD8ER9WYupXQGob9wk3SXTHJ72gqV5QPyydBj9EhkzSqE+h5HJNEJAkoAu5S1d0icgfwvIhEAAV4/RAiMgD4iareDlwLDAaSROQW71q3qOoS4E0RaYnrI1kC/CTA7yEwRKBdursNewy2f+t1rE91y2ZMexA6nulqIj1Gh95Y9UD65kXYvsLN2YiO9zsaEwwG3ObWfvr4ETdYJdi+nFd+CIf2Qt/Q7hQvU29NVX4KmqaqmtqxyiWQzCnuCxLcfgTBWgWvb/m7ocsF8MOJ9pmYIzYvhJeGQds+bg2otMugaVu/o3JeH+1WbL5niWt5CBG+93H4KeQSR3m537sEsjfL70iCR2ScW+zO1qMyFS36F3z9N7ckCRyptaeNhsSO/sS0eyM83weGPAxDQqtp1RJHqCYOY0zt5XznhsJnToHty11Z+/5HkkiLTvUXyxdPutt9y9zExRBiicMShzGNU+73R5p+y/Ytb9Pb6z+8HJJPDdxrl5a62kZSF7jp/cC9ToBY4rDEYYzZvfFIEsle4Mpane4GoPQY4zZWqst+s3Wz4F+j4apXoNfVdXfdemKJwxKHMaa8vZvd8kCZU9xKDygkn+bVRMZA654nn0Qm3QFrPoafr3br3IWYqhKHLatujGmcmrWHs37ibvu3uSSycirM+SPM/gM073QkibTrW/skkr/HXa/vjSGZNKpjicMYYxLawMA73O3gTrfadeYU+OrP8OVzbtfBHqNdn0j7/jUbUrtiEhQXNJi5G+VZU5UxxlQlbxd8N82N0Pr+cygtgqbt3RyRHmPccN+qNg576QIoKoA7vwzZ+UbWVGWMMbUV18LVGPre6JqeVn/saiIZ493mYfGtjySRlHMg3PtK3Z7pJiRe/PuQTRrVscRhjDE1EZsIfa5zt0P7XRJZORUWvwkLXoa4JLdQaY8xsOYTtwp272v9jjogLHEYY0xtRSe44bW9robCg24f8cwprl9j0evumLTR0CTZ3zgDxBKHMcacjKgmR0ZfFRW4vpDvP3MLLzZQljiMMaauRMZA95Hu1oCFzjKNxhhjgoIlDmOMMbViicMYY0ytWOIwxhhTK5Y4jDHG1IolDmOMMbViicMYY0ytWOIwxhhTK41idVwRyQE2+h3HSUoGdvodRBCxz+MI+yyOZp/H0U7m8zhFVVtWLGwUiaMhEJGMypY3bqzs8zjCPouj2edxtEB8HtZUZYwxplYscRhjjKkVSxyh40W/Awgy9nkcYZ/F0ezzOFqdfx7Wx2GMMaZWrMZhjDGmVixxGGOMqRVLHEFORDqKyEwRWSki34rIvX7H5DcRCReRxSLyod+x+E1EEkXkXRFZ5f2OnO13TH4RkZ95/0dWiMgEEYnxO6b6JCKvisgOEVlRrqyFiHwqImu8n83r4rUscQS/YuDnqpoGnAXcJSI9fI7Jb/cCK/0OIkg8D8xQ1e5AHxrp5yIi7YF7gAGq2hMIB37gb1T17jXgkgpl44DPVLUr8Jn3+KRZ4ghyqrpVVRd59/fjvhja+xuVf0SkA3Ap8LLfsfhNRJoCg4FXAFS1UFX3+BuVryKAWBGJAOKALT7HU69UdTawq0LxGOB17/7rwOV18VqWOEKIiKQCfYH5/kbiq+eA/wVK/Q4kCHQGcoDxXtPdyyLSxO+g/KCqm4FngE3AVmCvqn7ib1RBobWqbgX3RyjQqi4uaokjRIhIPDAJuE9V9/kdjx9EZBSwQ1UX+h1LkIgA+gF/V9W+wEHqqCki1Hht92OATkA7oImI3OhvVA2XJY4QICKRuKTxpqq+53c8PjoXGC0iG4C3gAtE5A1/Q/JVNpCtqmU10HdxiaQxuhBYr6o5qloEvAec43NMwWC7iLQF8H7uqIuLWuIIciIiuDbslar6rN/x+ElVH1bVDqqaiuv4/FxVG+1flaq6DcgSkW5e0TAg08eQ/LQJOEtE4rz/M8NopAMFKpgK3OzdvxmYUhcXjaiLi5iAOhf4EbBcRJZ4ZY+o6jQfYzLB427gTRGJAtYBt/ocjy9Udb6IvAsswo1EXEwjW3pERCYAQ4BkEckGfgk8CUwUkR/jkus1dfJatuSIMcaY2rCmKmOMMbViicMYY0ytWOIwxhhTK5Y4jDHG1IolDmOMMbViicOYOiAiJSKypNytzmZwi0hq+RVPjfGbzeMwpm7kq2q630EYUx+sxmFMAInIBhF5SkS+8W6neuWniMhnIrLM+5nilbcWkckistS7lS2bES4iL3n7TXwiIrG+vSnT6FniMKZuxFZoqrqu3HP7VHUg8Bfc6r549/+lqr2BN4EXvPIXgFmq2ge37tS3XnlX4K+qejqwB7gqwO/HmCrZzHFj6oCIHFDV+ErKNwAXqOo6b7HKbaqaJCI7gbaqWuSVb1XVZBHJATqo6qFy10gFPvU240FEHgIiVfW3gX9nxhzLahzGBJ5Wcb+qYypzqNz9Eqx/0vjIEocxgXdduZ9fe/e/4sjWpjcAc737nwF3wuG91ZvWV5DG1JT91WJM3Ygtt3oxuH3Ay4bkRovIfNwfatd7ZfcAr4rIg7hd/MpWtb0XeNFbzbQEl0S2Bjx6Y2rB+jiMCSCvj2OAqu70OxZj6oo1VRljjKkVq3EYY4ypFatxGGOMqRVLHMYYY2rFEocxxphascRhjDGmVixxGGOMqZX/D9bxrKQp3X7iAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(1, NUM_EPOCHS+1), train_acc_list, label='Training')\n",
+    "plt.plot(np.arange(1, NUM_EPOCHS+1), valid_acc_list, label='Validation')\n",
+    "\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation ACC: 98.90%\n",
+      "Test ACC: 99.10%\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.set_grad_enabled(False):\n",
+    "    test_acc = compute_accuracy(model=model,\n",
+    "                           data_loader=data_loader[\"test\"],\n",
+    "                           device=DEVICE)\n",
+    "    \n",
+    "    valid_acc = compute_accuracy(model=model,\n",
+    "                            data_loader=data_loader[\"val\"],\n",
+    "                            device=DEVICE)\n",
+    "    \n",
+    "\n",
+    "print(f'Validation ACC: {valid_acc:.2f}%')\n",
+    "print(f'Test ACC: {test_acc:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reference\n",
+    "\n",
+    "- https://towardsdatascience.com/understanding-and-visualizing-densenets-7f688092391a\n",
+    "- https://github.com/rasbt/deeplearning-models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tryit",
+   "language": "python",
+   "name": "tryit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/Autoencoder-checkpoint.ipynb b/.ipynb_checkpoints/Autoencoder-checkpoint.ipynb
new file mode 100644
index 0000000..5b1f6df
--- /dev/null
+++ b/.ipynb_checkpoints/Autoencoder-checkpoint.ipynb
@@ -0,0 +1,658 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Network Architecture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**References**\n",
+    "\n",
+    "The CNN architecture is based on \n",
+    "\n",
+    "- Lin, Min, Qiang Chen, and Shuicheng Yan. \"[Network in network](https://arxiv.org/abs/1312.4400).\" arXiv preprint arXiv:1312.4400 (2013).\n",
+    "\n",
+    "This paper compares using BatchNorm before the activation function as suggested in\n",
+    "\n",
+    "- Ioffe, Sergey, and Christian Szegedy. \"[Batch normalization: Accelerating deep network training by reducing internal covariate shift.](https://arxiv.org/abs/1502.03167)\" arXiv preprint arXiv:1502.03167 (2015)\n",
+    "\n",
+    "and after the activation function as it is nowadays common practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![nin-arch](images/nin/nin-arch2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
+       " '',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/Users/ZRC/.ipython',\n",
+       " '/Users/ZRC']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/Users/ZRC\")\n",
+    "sys.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import RandomSampler\n",
+    "from torch.utils.data import Subset\n",
+    "\n",
+    "\n",
+    "from torchvision import datasets\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coke.visualization.image import show_batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_EPOCHS = 10\n",
+    "LEARNING_RATE = 0.0001\n",
+    "RANDOM_SEED = 7\n",
+    "\n",
+    "# Architecture\n",
+    "NUM_CLASSES = 10\n",
+    "GRAYSCALE = True\n",
+    "\n",
+    "# # other\n",
+    "# torch.cuda.empty_cache()\n",
+    "DEVICE = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_transforms = {\"train\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()]),\n",
+    "                    \"test\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()])\n",
+    "                   }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_indices = torch.arange(0, 59000)\n",
+    "valid_indices = torch.arange(59000, 60000)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_and_valida_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = True,\n",
+    "                                transform = data_transforms[\"train\"],\n",
+    "                                download=True)\n",
+    "\n",
+    "test_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = False,\n",
+    "                                transform = data_transforms[\"test\"],\n",
+    "                                download=False)\n",
+    "\n",
+    "train_dataset = Subset(train_and_valida_dataset, train_indices)\n",
+    "valid_dataset = Subset(train_and_valida_dataset, valid_indices)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_dataloader = DataLoader(dataset = train_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=True,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "valid_dataloader = DataLoader(dataset = valid_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "test_dataloader = DataLoader(dataset = test_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "data_loader = {\"train\": train_dataloader, \n",
+    "               \"val\": valid_dataloader,\n",
+    "               \"test\": test_dataloader}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(train_dataloader))\n",
+    "print(batch_samples.size(),labels.size())\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(valid_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(test_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/nin/nin-arch.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NiNZrc(torch.nn.Module):\n",
+    "    def __init__(self, num_classes, grayscale = False):\n",
+    "        super(NiNZrc, self).__init__()\n",
+    "        \n",
+    "        if grayscale:\n",
+    "            in_channels = 1\n",
+    "        else:\n",
+    "            in_channels = 3\n",
+    "            \n",
+    "        self.classifier = torch.nn.Sequential(\n",
+    "            torch.nn.Conv2d(in_channels, 192, kernel_size=5, stride=1, padding=2),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(160,  96, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n",
+    "            torch.nn.Dropout(0.5),\n",
+    "\n",
+    "            torch.nn.Conv2d(96, 192, kernel_size=5, stride=1, padding=2),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
+    "            torch.nn.Dropout(0.5),\n",
+    "\n",
+    "            torch.nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.Conv2d(192,  num_classes, kernel_size=1, stride=1, padding=0),\n",
+    "            torch.nn.ReLU(inplace=True),\n",
+    "            torch.nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
+    "        )\n",
+    "        \n",
+    "        self.global_avg_pooling = torch.nn.AdaptiveAvgPool2d(1)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.classifier(x)\n",
+    "        logits = torch.squeeze(self.global_avg_pooling(x))\n",
+    "        probas = torch.softmax(logits, dim=1)\n",
+    "        return logits, probas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1          [-1, 192, 32, 32]          14,592\n",
+      "              ReLU-2          [-1, 192, 32, 32]               0\n",
+      "            Conv2d-3          [-1, 160, 32, 32]          30,880\n",
+      "              ReLU-4          [-1, 160, 32, 32]               0\n",
+      "            Conv2d-5           [-1, 96, 32, 32]          15,456\n",
+      "              ReLU-6           [-1, 96, 32, 32]               0\n",
+      "         MaxPool2d-7           [-1, 96, 16, 16]               0\n",
+      "           Dropout-8           [-1, 96, 16, 16]               0\n",
+      "            Conv2d-9          [-1, 192, 16, 16]         460,992\n",
+      "             ReLU-10          [-1, 192, 16, 16]               0\n",
+      "           Conv2d-11          [-1, 192, 16, 16]          37,056\n",
+      "             ReLU-12          [-1, 192, 16, 16]               0\n",
+      "           Conv2d-13          [-1, 192, 16, 16]          37,056\n",
+      "             ReLU-14          [-1, 192, 16, 16]               0\n",
+      "        AvgPool2d-15            [-1, 192, 8, 8]               0\n",
+      "          Dropout-16            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-17            [-1, 192, 8, 8]         331,968\n",
+      "             ReLU-18            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-19            [-1, 192, 8, 8]          37,056\n",
+      "             ReLU-20            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-21             [-1, 10, 8, 8]           1,930\n",
+      "             ReLU-22             [-1, 10, 8, 8]               0\n",
+      "        AvgPool2d-23             [-1, 10, 4, 4]               0\n",
+      "AdaptiveAvgPool2d-24             [-1, 10, 1, 1]               0\n",
+      "================================================================\n",
+      "Total params: 966,986\n",
+      "Trainable params: 966,986\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.01\n",
+      "Forward/backward pass size (MB): 10.20\n",
+      "Params size (MB): 3.69\n",
+      "Estimated Total Size (MB): 13.90\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_nin():\n",
+    "    model = NiNZrc(10).to(DEVICE)\n",
+    "    summary(model, (3,32,32))\n",
+    "    \n",
+    "test_nin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = NiNZrc(num_classes=NUM_CLASSES, grayscale=GRAYSCALE)\n",
+    "model.to(DEVICE)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_accuracy(model, data_loader, device):\n",
+    "    model.eval()\n",
+    "    correct_pred, num_examples = 0, 0\n",
+    "    for i, (features, targets) in enumerate(data_loader):\n",
+    "            \n",
+    "        features = features.to(device)\n",
+    "        targets = targets.to(device)\n",
+    "\n",
+    "        logits, probas = model(features)\n",
+    "        _, predicted_labels = torch.max(probas, 1)\n",
+    "        num_examples += targets.size(0)\n",
+    "        correct_pred += (predicted_labels == targets).sum()\n",
+    "    return correct_pred.float()/num_examples * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, data_loader, optimizer, num_epochs,batch_size, device,metric_func, random_seed = 7):\n",
+    "    # Manual seed for deterministic data loader\n",
+    "    torch.manual_seed(random_seed)\n",
+    "    \n",
+    "    loss_list = []\n",
+    "    train_acc_list, valid_acc_list = [], []\n",
+    "    \n",
+    "    for epoch in range(num_epochs):\n",
+    "        # set training mode\n",
+    "        model.train() \n",
+    "        for batch_idx, (features, targets) in enumerate(data_loader[\"train\"]):\n",
+    "            features = features.to(device)\n",
+    "            targets = targets.to(device)\n",
+    "\n",
+    "\n",
+    "            ## forward pass\n",
+    "            logits, probas = model(features)\n",
+    "            loss = F.cross_entropy(logits,targets)\n",
+    "\n",
+    "            # backward pass\n",
+    "            # clear the gradients of all tensors being optimized\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            ### Login\n",
+    "            loss_list.append(loss.item())\n",
+    "            if not batch_idx % 50:\n",
+    "                print ('Epoch: {0:03d}/{1:03d} | Batch {2:03d}/{3:03d} | Loss: {4:.2f}'.format(\n",
+    "                    epoch+1, num_epochs, batch_idx, \n",
+    "                         len(train_dataset)//batch_size, loss))\n",
+    "\n",
+    "        with torch.set_grad_enabled(False):\n",
+    "            train_acc = metric_func(model, data_loader[\"train\"], device)\n",
+    "            valid_acc = metric_func(model, data_loader[\"val\"], device)\n",
+    "            \n",
+    "            print('Epoch: {0:03d}/{1:03d} training accuracy: {2:.2f}'.format(\n",
+    "                  epoch+1, num_epochs, train_acc))\n",
+    "            \n",
+    "            print('Epoch: {0:03d}/{1:03d} validation accuracy: {2:.2f}'.format(\n",
+    "                  epoch+1, num_epochs, valid_acc))\n",
+    "            \n",
+    "            train_acc_list.append(train_acc)\n",
+    "            valid_acc_list.append(valid_acc)\n",
+    "            \n",
+    "    return loss_list, train_acc_list, valid_acc_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_list, train_acc_list, valid_acc_list = train_model(model, \n",
+    "            data_loader, \n",
+    "            optimizer, \n",
+    "            NUM_EPOCHS, \n",
+    "            device = DEVICE, \n",
+    "            batch_size = BATCH_SIZE,\n",
+    "            metric_func = compute_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(loss_list, label='Minibatch cost')\n",
+    "plt.plot(np.convolve(loss_list, \n",
+    "                     np.ones(200,)/200, mode='valid'), \n",
+    "         label='Running average')\n",
+    "\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(np.arange(1, NUM_EPOCHS+1), train_acc_list, label='Training')\n",
+    "plt.plot(np.arange(1, NUM_EPOCHS+1), valid_acc_list, label='Validation')\n",
+    "\n",
+    "plt.xlabel('Epoch')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation ACC: 98.90%\n",
+      "Test ACC: 99.10%\n"
+     ]
+    }
+   ],
+   "source": [
+    "with torch.set_grad_enabled(False):\n",
+    "    test_acc = compute_accuracy(model=model,\n",
+    "                           data_loader=data_loader[\"test\"],\n",
+    "                           device=DEVICE)\n",
+    "    \n",
+    "    valid_acc = compute_accuracy(model=model,\n",
+    "                            data_loader=data_loader[\"val\"],\n",
+    "                            device=DEVICE)\n",
+    "    \n",
+    "\n",
+    "print(f'Validation ACC: {valid_acc:.2f}%')\n",
+    "print(f'Test ACC: {test_acc:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reference\n",
+    "\n",
+    "- https://towardsdatascience.com/understanding-and-visualizing-densenets-7f688092391a\n",
+    "- https://github.com/rasbt/deeplearning-models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tryit",
+   "language": "python",
+   "name": "tryit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/Network-in-Network-checkpoint.ipynb b/.ipynb_checkpoints/Network-in-Network-checkpoint.ipynb
index aeba662..4728a8e 100644
--- a/.ipynb_checkpoints/Network-in-Network-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Network-in-Network-checkpoint.ipynb
@@ -46,13 +46,13 @@
     {
      "data": {
       "text/plain": [
-       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
-       " '',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
-       " '/Users/ZRC/.ipython',\n",
+       "['',\n",
+       " '/anaconda/envs/py36/lib/python36.zip',\n",
+       " '/anaconda/envs/py36/lib/python3.6',\n",
+       " '/anaconda/envs/py36/lib/python3.6/lib-dynload',\n",
+       " '/anaconda/envs/py36/lib/python3.6/site-packages',\n",
+       " '/anaconda/envs/py36/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/data/home/zhangruochi/.ipython',\n",
        " '/Users/ZRC']"
       ]
      },
@@ -106,7 +106,19 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'coke'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-db7f413ee7cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcoke\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'coke'"
+     ]
+    }
+   ],
    "source": [
     "from coke.visualization.image import show_batch"
    ]
@@ -215,16 +227,15 @@
      ]
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'show_batch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-2ff921d4f204>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mbatch_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_samples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_samples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'show_batch' is not defined"
+     ]
     }
    ],
    "source": [
@@ -235,29 +246,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "batch_samples,labels = next(iter(valid_dataloader))\n",
     "print(batch_samples.shape,labels.shape)\n",
@@ -266,29 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "batch_samples,labels = next(iter(test_dataloader))\n",
     "print(batch_samples.shape,labels.shape)\n",
@@ -311,21 +282,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class MiNZrc(torch.nn.Module):\n",
-    "    def __init__(self, num_classes, grascale = False):\n",
-    "        super().num_classes = num_classes\n",
+    "class NiNZrc(torch.nn.Module):\n",
+    "    def __init__(self, num_classes, grayscale = False):\n",
+    "        super(NiNZrc, self).__init__()\n",
     "        \n",
-    "        if grascale:\n",
+    "        if grayscale:\n",
     "            in_channels = 1\n",
     "        else:\n",
     "            in_channels = 3\n",
     "            \n",
     "        self.classifier = torch.nn.Sequential(\n",
-    "            torch.nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),\n",
+    "            torch.nn.Conv2d(in_channels, 192, kernel_size=5, stride=1, padding=2),\n",
     "            torch.nn.ReLU(inplace=True),\n",
     "            torch.nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),\n",
     "            torch.nn.ReLU(inplace=True),\n",
@@ -352,7 +323,16 @@
     "            torch.nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
     "        )\n",
     "        \n",
-    "        self.global_avg_pooling = torch.nn.AdaptiveAvgPool1d(1)\n",
+    "        for layer in self.modules():\n",
+    "            if isinstance(layer, torch.nn.Conv2d):\n",
+    "                n = layer.kernel_size[0] * layer.kernel_size[1] * layer.out_channels\n",
+    "                layer.weight.data.normal_(0, (2. / n)**.5)\n",
+    "            elif isinstance(layer, torch.nn.BatchNorm2d):\n",
+    "                layer.weight.data.fill_(1)\n",
+    "                layer.bias.data.zero_()\n",
+    "            \n",
+    "        \n",
+    "        self.global_avg_pooling = torch.nn.AdaptiveAvgPool2d(1)\n",
     "        \n",
     "    def forward(self, x):\n",
     "        x = self.classifier(x)\n",
@@ -363,28 +343,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1          [-1, 192, 32, 32]          14,592\n",
+      "              ReLU-2          [-1, 192, 32, 32]               0\n",
+      "            Conv2d-3          [-1, 160, 32, 32]          30,880\n",
+      "              ReLU-4          [-1, 160, 32, 32]               0\n",
+      "            Conv2d-5           [-1, 96, 32, 32]          15,456\n",
+      "              ReLU-6           [-1, 96, 32, 32]               0\n",
+      "         MaxPool2d-7           [-1, 96, 16, 16]               0\n",
+      "           Dropout-8           [-1, 96, 16, 16]               0\n",
+      "            Conv2d-9          [-1, 192, 16, 16]         460,992\n",
+      "             ReLU-10          [-1, 192, 16, 16]               0\n",
+      "           Conv2d-11          [-1, 192, 16, 16]          37,056\n",
+      "             ReLU-12          [-1, 192, 16, 16]               0\n",
+      "           Conv2d-13          [-1, 192, 16, 16]          37,056\n",
+      "             ReLU-14          [-1, 192, 16, 16]               0\n",
+      "        AvgPool2d-15            [-1, 192, 8, 8]               0\n",
+      "          Dropout-16            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-17            [-1, 192, 8, 8]         331,968\n",
+      "             ReLU-18            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-19            [-1, 192, 8, 8]          37,056\n",
+      "             ReLU-20            [-1, 192, 8, 8]               0\n",
+      "           Conv2d-21             [-1, 10, 8, 8]           1,930\n",
+      "             ReLU-22             [-1, 10, 8, 8]               0\n",
+      "        AvgPool2d-23             [-1, 10, 4, 4]               0\n",
+      "AdaptiveAvgPool2d-24             [-1, 10, 1, 1]               0\n",
+      "================================================================\n",
+      "Total params: 966,986\n",
+      "Trainable params: 966,986\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.01\n",
+      "Forward/backward pass size (MB): 10.20\n",
+      "Params size (MB): 3.69\n",
+      "Estimated Total Size (MB): 13.90\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "def test_nin():\n",
-    "    model = MiNZrc(10,)"
+    "    model = NiNZrc(10).to(DEVICE)\n",
+    "    summary(model, (3,32,32))\n",
+    "    \n",
+    "test_nin()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = DenseNetZrc(num_classes=NUM_CLASSES, grayscale=GRAYSCALE)\n",
+    "model = NiNZrc(num_classes=NUM_CLASSES, grayscale=GRAYSCALE)\n",
     "model.to(DEVICE)\n",
     "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,229 +486,229 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 001/010 | Batch 000/921 | Loss: 2.33\n",
-      "Epoch: 001/010 | Batch 050/921 | Loss: 1.00\n",
-      "Epoch: 001/010 | Batch 100/921 | Loss: 0.35\n",
-      "Epoch: 001/010 | Batch 150/921 | Loss: 0.25\n",
-      "Epoch: 001/010 | Batch 200/921 | Loss: 0.11\n",
-      "Epoch: 001/010 | Batch 250/921 | Loss: 0.16\n",
-      "Epoch: 001/010 | Batch 300/921 | Loss: 0.09\n",
-      "Epoch: 001/010 | Batch 350/921 | Loss: 0.10\n",
-      "Epoch: 001/010 | Batch 400/921 | Loss: 0.13\n",
-      "Epoch: 001/010 | Batch 450/921 | Loss: 0.10\n",
-      "Epoch: 001/010 | Batch 500/921 | Loss: 0.17\n",
-      "Epoch: 001/010 | Batch 550/921 | Loss: 0.08\n",
-      "Epoch: 001/010 | Batch 600/921 | Loss: 0.10\n",
-      "Epoch: 001/010 | Batch 650/921 | Loss: 0.11\n",
-      "Epoch: 001/010 | Batch 700/921 | Loss: 0.08\n",
-      "Epoch: 001/010 | Batch 750/921 | Loss: 0.22\n",
-      "Epoch: 001/010 | Batch 800/921 | Loss: 0.04\n",
-      "Epoch: 001/010 | Batch 850/921 | Loss: 0.13\n",
-      "Epoch: 001/010 | Batch 900/921 | Loss: 0.08\n",
-      "Epoch: 001/010 training accuracy: 98.69\n",
-      "Epoch: 001/010 validation accuracy: 98.30\n",
-      "Epoch: 002/010 | Batch 000/921 | Loss: 0.03\n",
-      "Epoch: 002/010 | Batch 050/921 | Loss: 0.09\n",
-      "Epoch: 002/010 | Batch 100/921 | Loss: 0.03\n",
-      "Epoch: 002/010 | Batch 150/921 | Loss: 0.03\n",
-      "Epoch: 002/010 | Batch 200/921 | Loss: 0.02\n",
-      "Epoch: 002/010 | Batch 250/921 | Loss: 0.14\n",
-      "Epoch: 002/010 | Batch 300/921 | Loss: 0.05\n",
-      "Epoch: 002/010 | Batch 350/921 | Loss: 0.05\n",
-      "Epoch: 002/010 | Batch 400/921 | Loss: 0.06\n",
-      "Epoch: 002/010 | Batch 450/921 | Loss: 0.05\n",
-      "Epoch: 002/010 | Batch 500/921 | Loss: 0.02\n",
-      "Epoch: 002/010 | Batch 550/921 | Loss: 0.05\n",
-      "Epoch: 002/010 | Batch 600/921 | Loss: 0.04\n",
-      "Epoch: 002/010 | Batch 650/921 | Loss: 0.11\n",
-      "Epoch: 002/010 | Batch 700/921 | Loss: 0.01\n",
-      "Epoch: 002/010 | Batch 750/921 | Loss: 0.11\n",
-      "Epoch: 002/010 | Batch 800/921 | Loss: 0.07\n",
-      "Epoch: 002/010 | Batch 850/921 | Loss: 0.03\n",
-      "Epoch: 002/010 | Batch 900/921 | Loss: 0.03\n",
-      "Epoch: 002/010 training accuracy: 99.48\n",
-      "Epoch: 002/010 validation accuracy: 98.20\n",
-      "Epoch: 003/010 | Batch 000/921 | Loss: 0.01\n",
-      "Epoch: 003/010 | Batch 050/921 | Loss: 0.08\n",
-      "Epoch: 003/010 | Batch 100/921 | Loss: 0.05\n",
-      "Epoch: 003/010 | Batch 150/921 | Loss: 0.04\n",
-      "Epoch: 003/010 | Batch 200/921 | Loss: 0.01\n",
-      "Epoch: 003/010 | Batch 250/921 | Loss: 0.02\n",
-      "Epoch: 003/010 | Batch 300/921 | Loss: 0.01\n",
-      "Epoch: 003/010 | Batch 350/921 | Loss: 0.00\n",
-      "Epoch: 003/010 | Batch 400/921 | Loss: 0.01\n",
-      "Epoch: 003/010 | Batch 450/921 | Loss: 0.03\n",
-      "Epoch: 003/010 | Batch 500/921 | Loss: 0.08\n",
-      "Epoch: 003/010 | Batch 550/921 | Loss: 0.02\n",
-      "Epoch: 003/010 | Batch 600/921 | Loss: 0.04\n",
-      "Epoch: 003/010 | Batch 650/921 | Loss: 0.05\n",
-      "Epoch: 003/010 | Batch 700/921 | Loss: 0.00\n",
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-      "Epoch: 003/010 | Batch 900/921 | Loss: 0.01\n",
-      "Epoch: 003/010 training accuracy: 99.62\n",
-      "Epoch: 003/010 validation accuracy: 98.20\n",
-      "Epoch: 004/010 | Batch 000/921 | Loss: 0.02\n",
-      "Epoch: 004/010 | Batch 050/921 | Loss: 0.01\n",
-      "Epoch: 004/010 | Batch 100/921 | Loss: 0.02\n",
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-      "Epoch: 004/010 training accuracy: 99.58\n",
-      "Epoch: 004/010 validation accuracy: 98.20\n",
-      "Epoch: 005/010 | Batch 000/921 | Loss: 0.00\n",
-      "Epoch: 005/010 | Batch 050/921 | Loss: 0.04\n",
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-      "Epoch: 005/010 | Batch 400/921 | Loss: 0.05\n",
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-      "Epoch: 005/010 | Batch 500/921 | Loss: 0.06\n",
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-      "Epoch: 005/010 | Batch 900/921 | Loss: 0.03\n",
-      "Epoch: 005/010 training accuracy: 99.71\n",
-      "Epoch: 005/010 validation accuracy: 98.70\n",
-      "Epoch: 006/010 | Batch 000/921 | Loss: 0.01\n",
-      "Epoch: 006/010 | Batch 050/921 | Loss: 0.07\n",
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-      "Epoch: 006/010 training accuracy: 99.70\n",
-      "Epoch: 006/010 validation accuracy: 98.80\n",
-      "Epoch: 007/010 | Batch 000/921 | Loss: 0.00\n",
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-      "Epoch: 007/010 training accuracy: 99.73\n",
-      "Epoch: 007/010 validation accuracy: 98.70\n",
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-      "Epoch: 008/010 training accuracy: 99.80\n",
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-      "Epoch: 009/010 training accuracy: 99.81\n",
-      "Epoch: 009/010 validation accuracy: 98.10\n"
+      "Epoch: 001/010 | Batch 000/921 | Loss: 2.30\n",
+      "Epoch: 001/010 | Batch 050/921 | Loss: 2.30\n",
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+      "Epoch: 001/010 | Batch 550/921 | Loss: 1.17\n",
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+      "Epoch: 001/010 | Batch 900/921 | Loss: 0.69\n",
+      "Epoch: 001/010 training accuracy: 76.46\n",
+      "Epoch: 001/010 validation accuracy: 78.70\n",
+      "Epoch: 002/010 | Batch 000/921 | Loss: 0.74\n",
+      "Epoch: 002/010 | Batch 050/921 | Loss: 0.92\n",
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+      "Epoch: 002/010 | Batch 150/921 | Loss: 0.56\n",
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+      "Epoch: 002/010 | Batch 450/921 | Loss: 0.58\n",
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+      "Epoch: 002/010 | Batch 900/921 | Loss: 0.49\n",
+      "Epoch: 002/010 training accuracy: 86.04\n",
+      "Epoch: 002/010 validation accuracy: 87.50\n",
+      "Epoch: 003/010 | Batch 000/921 | Loss: 0.34\n",
+      "Epoch: 003/010 | Batch 050/921 | Loss: 0.40\n",
+      "Epoch: 003/010 | Batch 100/921 | Loss: 0.27\n",
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+      "Epoch: 003/010 | Batch 400/921 | Loss: 0.36\n",
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+      "Epoch: 003/010 | Batch 850/921 | Loss: 0.36\n",
+      "Epoch: 003/010 | Batch 900/921 | Loss: 0.23\n",
+      "Epoch: 003/010 training accuracy: 86.02\n",
+      "Epoch: 003/010 validation accuracy: 86.90\n",
+      "Epoch: 004/010 | Batch 000/921 | Loss: 0.45\n",
+      "Epoch: 004/010 | Batch 050/921 | Loss: 0.38\n",
+      "Epoch: 004/010 | Batch 100/921 | Loss: 0.28\n",
+      "Epoch: 004/010 | Batch 150/921 | Loss: 0.43\n",
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+      "Epoch: 004/010 | Batch 300/921 | Loss: 0.43\n",
+      "Epoch: 004/010 | Batch 350/921 | Loss: 0.44\n",
+      "Epoch: 004/010 | Batch 400/921 | Loss: 0.41\n",
+      "Epoch: 004/010 | Batch 450/921 | Loss: 0.29\n",
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+      "Epoch: 004/010 | Batch 700/921 | Loss: 0.38\n",
+      "Epoch: 004/010 | Batch 750/921 | Loss: 0.42\n",
+      "Epoch: 004/010 | Batch 800/921 | Loss: 0.27\n",
+      "Epoch: 004/010 | Batch 850/921 | Loss: 0.21\n",
+      "Epoch: 004/010 | Batch 900/921 | Loss: 0.43\n",
+      "Epoch: 004/010 training accuracy: 87.13\n",
+      "Epoch: 004/010 validation accuracy: 88.50\n",
+      "Epoch: 005/010 | Batch 000/921 | Loss: 0.24\n",
+      "Epoch: 005/010 | Batch 050/921 | Loss: 0.16\n",
+      "Epoch: 005/010 | Batch 100/921 | Loss: 0.30\n",
+      "Epoch: 005/010 | Batch 150/921 | Loss: 0.50\n",
+      "Epoch: 005/010 | Batch 200/921 | Loss: 0.33\n",
+      "Epoch: 005/010 | Batch 250/921 | Loss: 0.25\n",
+      "Epoch: 005/010 | Batch 300/921 | Loss: 0.41\n",
+      "Epoch: 005/010 | Batch 350/921 | Loss: 0.45\n",
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+      "Epoch: 005/010 | Batch 700/921 | Loss: 0.48\n",
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+      "Epoch: 005/010 | Batch 900/921 | Loss: 0.31\n",
+      "Epoch: 005/010 training accuracy: 87.16\n",
+      "Epoch: 005/010 validation accuracy: 88.00\n",
+      "Epoch: 006/010 | Batch 000/921 | Loss: 0.38\n",
+      "Epoch: 006/010 | Batch 050/921 | Loss: 0.55\n",
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+      "Epoch: 006/010 | Batch 350/921 | Loss: 0.46\n",
+      "Epoch: 006/010 | Batch 400/921 | Loss: 0.24\n",
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+      "Epoch: 006/010 | Batch 500/921 | Loss: 0.19\n",
+      "Epoch: 006/010 | Batch 550/921 | Loss: 0.27\n",
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+      "Epoch: 006/010 | Batch 850/921 | Loss: 0.23\n",
+      "Epoch: 006/010 | Batch 900/921 | Loss: 0.25\n",
+      "Epoch: 006/010 training accuracy: 87.49\n",
+      "Epoch: 006/010 validation accuracy: 88.70\n",
+      "Epoch: 007/010 | Batch 000/921 | Loss: 0.59\n",
+      "Epoch: 007/010 | Batch 050/921 | Loss: 0.33\n",
+      "Epoch: 007/010 | Batch 100/921 | Loss: 0.11\n",
+      "Epoch: 007/010 | Batch 150/921 | Loss: 0.49\n",
+      "Epoch: 007/010 | Batch 200/921 | Loss: 0.34\n",
+      "Epoch: 007/010 | Batch 250/921 | Loss: 0.39\n",
+      "Epoch: 007/010 | Batch 300/921 | Loss: 0.25\n",
+      "Epoch: 007/010 | Batch 350/921 | Loss: 0.41\n",
+      "Epoch: 007/010 | Batch 400/921 | Loss: 0.24\n",
+      "Epoch: 007/010 | Batch 450/921 | Loss: 0.24\n",
+      "Epoch: 007/010 | Batch 500/921 | Loss: 0.32\n",
+      "Epoch: 007/010 | Batch 550/921 | Loss: 0.25\n",
+      "Epoch: 007/010 | Batch 600/921 | Loss: 0.19\n",
+      "Epoch: 007/010 | Batch 650/921 | Loss: 0.30\n",
+      "Epoch: 007/010 | Batch 700/921 | Loss: 0.49\n",
+      "Epoch: 007/010 | Batch 750/921 | Loss: 0.30\n",
+      "Epoch: 007/010 | Batch 800/921 | Loss: 0.36\n",
+      "Epoch: 007/010 | Batch 850/921 | Loss: 0.50\n",
+      "Epoch: 007/010 | Batch 900/921 | Loss: 0.28\n",
+      "Epoch: 007/010 training accuracy: 87.74\n",
+      "Epoch: 007/010 validation accuracy: 88.90\n",
+      "Epoch: 008/010 | Batch 000/921 | Loss: 0.37\n",
+      "Epoch: 008/010 | Batch 050/921 | Loss: 0.39\n",
+      "Epoch: 008/010 | Batch 100/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 150/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 200/921 | Loss: 0.33\n",
+      "Epoch: 008/010 | Batch 250/921 | Loss: 0.34\n",
+      "Epoch: 008/010 | Batch 300/921 | Loss: 0.25\n",
+      "Epoch: 008/010 | Batch 350/921 | Loss: 0.27\n",
+      "Epoch: 008/010 | Batch 400/921 | Loss: 0.34\n",
+      "Epoch: 008/010 | Batch 450/921 | Loss: 0.47\n",
+      "Epoch: 008/010 | Batch 500/921 | Loss: 0.28\n",
+      "Epoch: 008/010 | Batch 550/921 | Loss: 0.38\n",
+      "Epoch: 008/010 | Batch 600/921 | Loss: 0.29\n",
+      "Epoch: 008/010 | Batch 650/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 700/921 | Loss: 0.26\n",
+      "Epoch: 008/010 | Batch 750/921 | Loss: 0.32\n",
+      "Epoch: 008/010 | Batch 800/921 | Loss: 0.23\n",
+      "Epoch: 008/010 | Batch 850/921 | Loss: 0.47\n",
+      "Epoch: 008/010 | Batch 900/921 | Loss: 0.35\n",
+      "Epoch: 008/010 training accuracy: 87.80\n",
+      "Epoch: 008/010 validation accuracy: 89.20\n",
+      "Epoch: 009/010 | Batch 000/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 050/921 | Loss: 0.41\n",
+      "Epoch: 009/010 | Batch 100/921 | Loss: 0.20\n",
+      "Epoch: 009/010 | Batch 150/921 | Loss: 0.37\n",
+      "Epoch: 009/010 | Batch 200/921 | Loss: 0.42\n",
+      "Epoch: 009/010 | Batch 250/921 | Loss: 0.65\n",
+      "Epoch: 009/010 | Batch 300/921 | Loss: 0.40\n",
+      "Epoch: 009/010 | Batch 350/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 400/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 450/921 | Loss: 0.23\n",
+      "Epoch: 009/010 | Batch 500/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 550/921 | Loss: 0.20\n",
+      "Epoch: 009/010 | Batch 600/921 | Loss: 0.48\n",
+      "Epoch: 009/010 | Batch 650/921 | Loss: 0.38\n",
+      "Epoch: 009/010 | Batch 700/921 | Loss: 0.40\n",
+      "Epoch: 009/010 | Batch 750/921 | Loss: 0.54\n",
+      "Epoch: 009/010 | Batch 800/921 | Loss: 0.42\n",
+      "Epoch: 009/010 | Batch 850/921 | Loss: 0.23\n",
+      "Epoch: 009/010 | Batch 900/921 | Loss: 0.36\n",
+      "Epoch: 009/010 training accuracy: 88.00\n",
+      "Epoch: 009/010 validation accuracy: 88.90\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch: 010/010 | Batch 000/921 | Loss: 0.06\n",
-      "Epoch: 010/010 | Batch 050/921 | Loss: 0.04\n",
-      "Epoch: 010/010 | Batch 100/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 150/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 200/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 250/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 300/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 350/921 | Loss: 0.03\n",
-      "Epoch: 010/010 | Batch 400/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 450/921 | Loss: 0.04\n",
-      "Epoch: 010/010 | Batch 500/921 | Loss: 0.01\n",
-      "Epoch: 010/010 | Batch 550/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 600/921 | Loss: 0.02\n",
-      "Epoch: 010/010 | Batch 650/921 | Loss: 0.04\n",
-      "Epoch: 010/010 | Batch 700/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 750/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 800/921 | Loss: 0.01\n",
-      "Epoch: 010/010 | Batch 850/921 | Loss: 0.00\n",
-      "Epoch: 010/010 | Batch 900/921 | Loss: 0.01\n",
-      "Epoch: 010/010 training accuracy: 99.88\n",
-      "Epoch: 010/010 validation accuracy: 98.90\n"
+      "Epoch: 010/010 | Batch 000/921 | Loss: 0.44\n",
+      "Epoch: 010/010 | Batch 050/921 | Loss: 0.35\n",
+      "Epoch: 010/010 | Batch 100/921 | Loss: 0.41\n",
+      "Epoch: 010/010 | Batch 150/921 | Loss: 0.28\n",
+      "Epoch: 010/010 | Batch 200/921 | Loss: 0.23\n",
+      "Epoch: 010/010 | Batch 250/921 | Loss: 0.30\n",
+      "Epoch: 010/010 | Batch 300/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 350/921 | Loss: 0.38\n",
+      "Epoch: 010/010 | Batch 400/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 450/921 | Loss: 0.31\n",
+      "Epoch: 010/010 | Batch 500/921 | Loss: 0.42\n",
+      "Epoch: 010/010 | Batch 550/921 | Loss: 0.16\n",
+      "Epoch: 010/010 | Batch 600/921 | Loss: 0.33\n",
+      "Epoch: 010/010 | Batch 650/921 | Loss: 0.36\n",
+      "Epoch: 010/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 750/921 | Loss: 0.26\n",
+      "Epoch: 010/010 | Batch 800/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 850/921 | Loss: 0.22\n",
+      "Epoch: 010/010 | Batch 900/921 | Loss: 0.30\n",
+      "Epoch: 010/010 training accuracy: 88.07\n",
+      "Epoch: 010/010 validation accuracy: 89.20\n"
      ]
     }
    ],
@@ -697,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -707,12 +734,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -737,12 +764,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -765,15 +792,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation ACC: 98.90%\n",
-      "Test ACC: 99.10%\n"
+      "Validation ACC: 89.20%\n",
+      "Test ACC: 87.94%\n"
      ]
     }
    ],
@@ -812,9 +839,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tryit",
+   "display_name": "Python 3.6 - AzureML",
    "language": "python",
-   "name": "tryit"
+   "name": "python3-azureml"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/Autoencoder.ipynb b/Autoencoder.ipynb
new file mode 100644
index 0000000..30129e2
--- /dev/null
+++ b/Autoencoder.ipynb
@@ -0,0 +1,796 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Autoencoder with Deconvolutions (without pooling operations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/autoencoder/autoencoder-arch.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
+       " '',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
+       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/Users/ZRC/.ipython',\n",
+       " '/Users/ZRC']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/Users/ZRC\")\n",
+    "sys.path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.utils.data import RandomSampler\n",
+    "from torch.utils.data import Subset\n",
+    "\n",
+    "\n",
+    "from torchvision import datasets\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from torchsummary import summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coke.visualization.image import show_batch"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Hyperparameters\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_EPOCHS = 10\n",
+    "LEARNING_RATE = 0.005\n",
+    "RANDOM_SEED = 7\n",
+    "\n",
+    "# Architecture\n",
+    "NUM_CLASSES = 10\n",
+    "GRAYSCALE = True\n",
+    "\n",
+    "# # other\n",
+    "# torch.cuda.empty_cache()\n",
+    "DEVICE = torch.device(\"cuda: 0\" if torch.cuda.is_available() else \"cpu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_transforms = {\"train\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()]),\n",
+    "                    \"test\": transforms.Compose([\n",
+    "                            transforms.Resize((32,32)),\n",
+    "                            transforms.ToTensor()])\n",
+    "                   }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_indices = torch.arange(0, 59000)\n",
+    "valid_indices = torch.arange(59000, 60000)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_and_valida_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = True,\n",
+    "                                transform = data_transforms[\"train\"],\n",
+    "                                download=True)\n",
+    "\n",
+    "test_dataset = datasets.MNIST(root = \"data\",\n",
+    "                                train = False,\n",
+    "                                transform = data_transforms[\"test\"],\n",
+    "                                download=False)\n",
+    "\n",
+    "train_dataset = Subset(train_and_valida_dataset, train_indices)\n",
+    "valid_dataset = Subset(train_and_valida_dataset, valid_indices)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_dataloader = DataLoader(dataset = train_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=True,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "valid_dataloader = DataLoader(dataset = valid_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "test_dataloader = DataLoader(dataset = test_dataset,\n",
+    "                             batch_size=BATCH_SIZE,\n",
+    "                             shuffle=False,\n",
+    "                             num_workers=4)\n",
+    "\n",
+    "data_loader = {\"train\": train_dataloader, \n",
+    "               \"val\": valid_dataloader,\n",
+    "               \"test\": test_dataloader}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(train_dataloader))\n",
+    "print(batch_samples.size(),labels.size())\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uSoWbdAenhgnaWlDwFd3x6IGKR20+c4HW2Y9HfsnmSev0Ohdq1QOZeE2hfpGuc56ZFTtsHurTeU5DSD8zTj/gvO/rddeXvvJlac3s/pweXHb+vA9tHpOmtfvLuuk2j31Gx2rieDAmP1knIiIiIvIoTtaJiIiIiDwqJdpgXEHnv092NA+xOaeur98xVr48XdIPfkaXXGpn6hJk4yRdWrnoFG0HeGHTcTYPfUkPvBiy7GObA7t0BwCigZQ2utTm5iklNh+cpK1c3//mEzZfllsV8fgcn94vDTnoytrmUTbnb9V6DW7eHv2AiQbQoCWrbfat0wNUbhjzfZvTWvXv/hFr9D09PKgHeqGzM+J5TUAfM2S503721kibz/jmj2x+5vO/s/m/L7rf5h8d+LrN5b/V606oWdtDifoiot23UNsjp+Rpa1aW6N/rX1qj78nSN/R9H6zT3ZT6ate3TrZ53pffs/mXJe/avKhRD216dfFMm0e9sdxmtw4HGj9ZJyIiIiLyKE7WiYiIiIg8ipN1IiIiIiKPSrmeddeMAj0x7oHxE23O6+rOPWj/rJ4Y9/E1ugXQN056y+YpWdqTOMyvp1+NSdMttG4oftPmKzq+bXPBxkJ9MfasUz/zTdPvTlRfVGBz2Tn63YnrS5+xeWianrh4ZlaDzXm+rvvSj+ZzhStsfvbME2wOfXSiju/t1SDyGuNstxuq1OtL1sfO39nONonBlpboX8Tpmw1V6daoObv1tN9dzomn5Wm6BV7nRL3WuFuvEsWKCTob69bpdrsPP3emzYvyzrB55Bt69+yV+h2OYB+3EzWnT7V56IW6xemPhuoc69XW4Tb/9i09qXTK07p9dzCOfeoufrJORERERORRnKwTEREREXlUSrTB5O3RbRm3HS62ecHod2xefPpJNstJx9tsVm+wOa1ct+ICgOqrdBu70vO1PeDe8udtrurU17t51ZU2d+zV9oAHL/6zzTMzdeknc5Bzgmk6lyypf/mHDrV5y1d1Gf3r85bafM1gPfFtbHp3DWNZ3dwO/KmhzOaPDusWjecXrLf5nOy9Nt9+ymKbf/jdL9g8rlZPSQ1u03YDhHi2KXmD6ezoMseSf5Ru3dg8Sq9zx2Vo60t1wGlF26PbAUe0KxDFitvmdVi3BJ3wYL3ex+d8TlyrtwcPaAtlrzinxaeNKYv40car9Tp0+5jXbC7yaw3cvl1PEB77N62f0Hadz3kFP1knIiIiIvIoTtaJiIiIiDwqJdpgBq/RZZYP1+ry+dYReoLp/5myxOabfnSNzcUvzLL54CRdcgGAiy/Uk7Bucr5hnOUszfxkyxU2Z74zyOb2qbos2mn0/4YnD+uJX7JO7++v32MzFy+pXxRq60vRZP0W/7UF2voyKk1bXzqNvhP/0a5tWte/rSfS+fZlRLzEoB36+UDWAV12fOXUz9j83XNfsvnGgq02f/t4rbEnJ56vz1Ol3/Q3bIOhZOfTWms8WXeAKTtB28dK/Nk2v9+muzkVbtBrUzxPY6QU4fx9HNy49Sh37DtzOPIU3tOn64mpZ2Y5u7s4862du4tsnrJcxxfsp7a1vujxk3URKRORZSKyQUTWi8hN4duLROQVEdka/mdhT89FlApYM0TRY90QRYc1kzp60wYTAPBDY8xxAE4F8G0ROQ7ArQCWGmMqACwN/5mIWDNEx4J1QxQd1kyK6LENxhizB8CecG4SkY0ASgFcBuCs8N0WAXgdwC39Mso+Cm2rsrl8if4H5ncHabvLn097yOZnZt9h88/LLrX5qqJtEc/7lcFrbR7iHP5yMKQHTxxXWGvz26fn2vyTz7xq86tNuvvMI2/MtnnCUj0ww+zRZRzytoStmTRdXt+3S9uxfll8gc1NAf0m/fo6PVCibbPef9Jjh2yWXXpoCwCEGvXwJNPhtIJln6rPO0t3WfIVaM35RNtmjC+yJY0SXzzrxl+o1wXJ11Yvc1Dfy0HnvRtPaWW6A8ye2VoHvy7XHS9qg3oNerxOW8aGrHF+B+4Gk/AS9lrTF04bWFqJ7mBWe9HYiLt9c+gim9NFP5f+1b7pNuev0euZV+q7O1H1rItIOYCTALwPoCT8RgGAvQBKunnMQgALASAL0Z9mSJTIWDNE0WPdEEWHNZPcer0bjIjkAXgSwPeMMRH/CWKMMQC6PBvWGHOnMWa6MWZ6OjK7ugtRUmLNEEWPdUMUHdZM8uvVJ+siko4jb4SHjTFPhW+uFZERxpg9IjICgGf7NNxvvWe8+IHNZb4ZNv+s6HKbX5v6oM1/G687U/xv+o17v7PMUujT2+8oW2Zz5fBXbP7zvrNsfuXFk22e/NgBm0OVVZrb9YAk8r5ErBk5dNjmUS9qW8DKD3WnFncHl7INeoBFaLPWVegou0y47QZmynibD0zV551fqO1l7nL+sv2TdBy1ejuMPpYSW7zqJjReW6/qT9JduLL3a8vJoPW6QxL266FDoUNNNptAp97HdDk/ijjIRTJ0tyTfIG2/kczIiVP7RG05qzlVD3u5+KzlNl+eq/X4ZLP+PqvemWjzhI1rdNxsg0kKiXit6Qu/06bWMGeMzV+46dWI+52ZrfX654YTbH76qTk2lz+bOLvs9WY3GAFwD4CNxpjbnR8tAXBdOF8H4JnYD48o8bBmiKLHuiGKDmsmdfTmk/XZAL4KYK2IrA7f9lMAtwFYLCILAFQDuKp/hkiUcFgzRNFj3RBFhzWTInqzG8zbALrbeuHc2A5nYOVUasvJ7rXDbD5woi7jl/j1m8eHQpEb5Tc4q+9Bo/+Kmky6zavbdJnm7u2600twSbHN4x7Upclgq7u8380yKnmaJ2vGWXr3uUvs6fpeDR3UZfScp3UXo5xuDhrqdtnQ+ba+LzfyS0stp06wufoyHdOP5j5v83k5+tpPNOn91/yjwuaKBueQixDrJBnEs26axjk7wFyqy+dZOfr38ZaVo2weXKm7UBRs1etC+qE2myXY9fvSpOmCdkeRtrQ0jtZa7MiP/Ncw4qKPbX50/BM2fyZDH7+jU3cPu2vnZTaXvqnXs1CL3ocSnyevNf3BuaagVFvC9lysbWcLC9a4j0CWaD395eV5Nk96Ug/JDFbuiOUo+1Wvv2BKREREREQDi5N1IiIiIiKPimqf9WRzaKq2opxweqXNJX5tE3BbX36zT79FDABv7dXdLFo7dMmlsU6XVIe+o/+Ki1/UA16CtVtt5l4W1N8iWl8mlNvYOkZ3vkhr0caWzE27bQ7U7tPHui0xbmtNtu6A5CvSHV+ap+puGgBQ8yVdkr/vVD204owsvX1th36G8Ju1unxZcbfT+rIl8oAyor7ozNH38gWjNtn84+L3bM6brDXkczoP7jpUZvPWVt3OujWkO724cv26s9eJOXpo2JcH6fvb3V0MAILOjkch6LXmYFDbWm7Y+iWbA3/QVoGs53THGKJE5C/UQ/cOTNPry7/MfNrmHF96xGPeb9c/F67XejXVu/pjiP2On6wTEREREXkUJ+tERERERB6V0m0w/g79tv6hDl3Grw3qMuXNOy+1ueGmyCX9oirdUB/OQTAjg7pkaTq0jSbYEbmbDNFAEWdXlvpTdBnxd7/4k81NIa2B7z+4wOaxD+vtwW1VNqeNcg6SOVtbAern6Y4YT875Y8Q4xqRpG02eT9sKtgX0MVe+/R2bJ/5Gd+MIOoeEEcVS8VPrbX6n/lSbn/naiTY/eso9Nk9xdlG6Nt/ZUcLJoV40OKaLs8sF/N3e77DRa9K6Dq2bhav+yebCR7T9ctBSPViMbZaU6MwI3a2v7jSdt30lX9vIMiWyDeb763W3yqIten1J1B2R+Mk6EREREZFHcbJORERERORRKd0GM2itfvt+2zt64EWVc0BGTVOBzYNr9dAYAAgccP7czcExRF4gebk275+my4gV6dpmMtin7+Gffelxmx8/Z4bNexom2VxWoO//m0c8rM+ZoXV1fHrkjhh+0SX89R362hcvvUkff6+2lJnNTosBa4z6SbCpyeacNzbaPGab7qryvTJtz9pzur6vh8/W3SWmFmnO8UXX9tgY0Hazl147OeJn2bW6m0XRJj0IZsy2g3qn2hobIw7XI0pA4uxg1nCizsP+5VzdAcZtfTkUinzP+54aYnP6hs02BxP0sEl+sk5ERERE5FGcrBMREREReVRKt8EEd+luLuMe12XNWzd+0+asel1yDO3THQMAAIbfs6fEYJr1G/D5W/W/0TucJUF3SfGKXK2NM8Y+YXNzSB87yKfv/1Fp2joGZHU7jt8fLLf5v5ZeYPP4v2ud+VdtsTnUrrtgEPUbpw5CTksMNmrOqNRrxLhK3Rms/XVdbl+do7tWOOcm9Y5zOanYVhfxI2nWJf7Q/gM2B9vaQJQsJE2npO1n6U5MjZ/XOvz8IG2NPBzSIrtw3Zcjnqt41SGbgwcPIdHxk3UiIiIiIo/iZJ2IiIiIyKNSug3GOEvswQ269D5oQ9f3Z9MLJSrTosvoQ1c127xg69U2zxhSbfO1he/ZPDHdbXFRnUZ3Z3F3dvnFzkts/nBrecRjClZqK8HEd3RpUrZU2Zyoh1ZQcjOdurtLYHuVzX43x+i1uO8RpSL/KG0v23uaXit+cvzTXd0djzSNs7ljcUnEz2Sn7gCTDDuJ8ZN1IiIiIiKP4mSdiIiIiMijUroNhihVhNq05SutcrfNB+8eb/OzxaNtfnTcbH1svh5SFME5W8J/SP8qGfqh3j75o8iDxLD9Y31e5+AWk6AHVRARUWy0lxfbHJyk7Zrn5FTZfMjpR15SO9XmoW/ujXiu0KHG2A8wjvjJOhERERGRR3GyTkRERETkUWyDIUoFzrfhg/v22Zz/qJOdu0d+r74PLxuj5yEiouTWXqgH85UW77e5yKc7wzzbMtTmjSvH2Dxxz9qI5zKBbto3E1SPn6yLSJaILBeRNSKyXkR+Gb59rIi8LyKVIvK4iGT09FxEqYJ1QxQd1gxRdFgzqaM3bTDtAM4xxkwFMA3AfBE5FcCvAfzOGDMBwEEAC/pvmEQJh3VDFB3WDFF0WDMposc2GHNkm4bD4T+mh/9nAJwD4Evh2xcB+FcAf479EIkSD+uGKDqsGaLoJFvNZDRq68r2am13ubngDJufX3+CzZPvqLU52NrWz6OLr159wVRE/CKyGkAdgFcAbAPQYIz55N9sDYDSbh67UERWiMiKTrR3dReipHSsdcOaoVTFaw1RdFgzqaFXk3VjTNAYMw3AKAAzAUzu7QsYY+40xkw3xkxPR+YxDpMo8Rxr3bBmKFXxWkMUHdZMaohqNxhjTIOILANwGoACEUkL/9fbKAC7+mOARImOdUMUHdYMUXSSoWbSluqJehOX6u1bnftUQO8TROrozW4wQ0WkIJyzAcwDsBHAMgCfD9/tOgDP9NcgiRIN64YoOqwZouiwZlJHbz5ZHwFgkYj4cWRyv9gY86yIbADwmIj8CsAqAPf04ziJEg3rhig6rBmi6LBmUoQc+TLxAL2YyD4AzQDqB+xFvaEY3vmdxxhjhvZ8N/KCcM1Uw1vvoYHgpd+XNZNgeK3xBNZNAuG1xhO6rZkBnawDgIisMMZMH9AXjbNU/J0ptlLtPZRqvy/FXiq+h1Lxd6bYSrX3UKL8vr3aDYaIiIiIiAYeJ+tERERERB4Vj8n6nXF4zXhLxd+ZYivV3kOp9vtS7KXieygVf2eKrVR7DyXE7zvgPetERERERNQ7bIMhIiIiIvIoTtaJiIiIiDxqQCfrIjJfRDaLSKWI3DqQrz0QRKRMRJaJyAYRWS8iN4VvLxKRV0Rka/ifhfEeKyWGZK8ZgHVDsZfsdcOaoVhL9poBErtuBqxnPXzC1hYcOQ63BsAHAK4xxmwYkAEMABEZAWCEMWaliAwC8CGAywFcD+CAMea2cBEUGmNuieNQKQGkQs0ArBuKrVSoG9YMxVIq1AyQ2HUzkJ+szwRQaYzZbozpAPAYgMsG8PX7nTFmjzFmZTg3AdgIoBRHfs9F4bstwpE3B1FPkr5mANYNxVzS1w1rhmIs6WsGSOy6GcjJeimAnc6fa8K3JSURKQdwEoD3AZQYY/aEf7QXQEmchkWJJaVqBmDdUEykVN2wZigGUqpmgMSrG37BtB+ISB6AJwF8zxjT6P7MHOk74mD5+DAAACAASURBVH6ZRJ/CuiGKDmuGKHqJWDcDOVnfBaDM+fOo8G1JRUTSceRN8LAx5qnwzbXhXqlPeqbq4jU+SigpUTMA64ZiKiXqhjVDMZQSNQMkbt0M5GT9AwAVIjJWRDIAXA1gyQC+fr8TEQFwD4CNxpjbnR8tAXBdOF8H4JmBHhslpKSvGYB1QzGX9HXDmqEYS/qaARK7bgb0BFMRuRDA7wH4AdxrjPm3AXvxASAicwC8BWAtgFD45p/iSE/UYgCjAVQDuMoYcyAug6SEkuw1A7BuKPaSvW5YMxRryV4zQGLXzYBO1omIiIiIqPf4BVMiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JkvZdEpEBEnhCRTSKyUUROi/eYiLxKRLJEZLmIrBGR9SLyy3iPiSgRiEiViKwVkdUisiLe4yHyMhGZFK6VT/7XKCLfi/e4Yo1bN/aSiCwC8JYx5u7woQE5xpiGeI+LyIvCh0/kGmMOh0+MexvATcaY9+I8NCJPE5EqANONMfXxHgtRIhERP46cvDrLGFMd7/HEUlq8B5AIRGQwgLkArgcAY0wHgI54jonIy8yRTwEOh/+YHv4fPxkgIqL+ci6Abck2UQfYBtNbYwHsA3CfiKwSkbtFJDfegyLyMhHxi8hqAHUAXjHGvB/vMRElAAPgZRH5UEQWxnswRAnkagCPxnsQ/YGT9d5JA3AygD8bY04C0Azg1vgOicjbjDFBY8w0AKMAzBSRE+I9JqIEMMcYczKAzwL4tojMjfeAiLwu3J58KYC/xXss/YGT9d6pAVDjfDL4BI5M3omoB+HvdiwDMD/eYyHyOmPMrvA/6wD8HcDM+I6IKCF8FsBKY0xtvAfSHzhZ7wVjzF4AO0VkUvimcwFsiOOQiDxNRIaKSEE4ZwOYB2BTfEdF5G0ikisigz7JAM4HsC6+oyJKCNcgSVtgAH7BNBr/DODh8FLLdgBfi/N4iLxsBIBF4W/n+wAsNsY8G+cxEXldCYC/H9lMCWkAHjHGvBjfIRF5W/g/bOcB+Ea8x9JfuHUjEREREZFHsQ2GiIiIiMijOFknIiIiIvIoTtaJiIiIiDyKk3UiIiIiIo/iZJ2IiIiIyKM4WSciIiIi8ihO1omIiIiIPIqTdSIiIiIij+JknYiIiIjIozhZJyIiIiLyKE7WiYiIiIg8qk+TdRGZLyKbRaRSRG6N1aCIkhnrhig6rBmi6LFukocYY47tgSJ+AFsAzANQA+ADANcYYzbEbnhEyYV1QxQd1gxR9Fg3ySWtD4+dCaDSGLMdAETkMQCXAej2jZAhmSYLuX14SeqrJhysN8YMjfc4UlhUdcOaiT/WTNzxWpOAWDdxx2tNgjlazfRlsl4KYKfz5xoAs472gCzkYpac24eXpL561TxRHe8xpLio6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL1XRGQhgIUAkIWc/n45ooTHmiGKHuuGKDqsmcTRly+Y7gJQ5vx5VPi2CMaYO40x040x09OR2YeXI0oKPdYNa4YoAq81RNHjtSaJ9GWy/gGAChEZKyIZAK4GsCQ2wyJKWqwbouiwZoiix7pJIsfcBmOMCYjIdwC8BMAP4F5jzPqYjYwoCbFuiKLDmiGKHusmufSpZ90Y8zyA52M0FqKUwLohig5rhih6rJvkwRNMiYiIiIg8ipN1IiIiIiKP6vetGxOSiMaMDJt9BYMj7tY8s9zm3bP9NgeGdeqdnANipU3vk1utuXRZk95p1UZ9aCAQ1bCJiIiIUp2/eIjzB2d+Nm6EzQ0TdbvK5lKd97UVh2yWoN5esEmfcthbdTaHqmtsNu3txz7oo+An60REREREHsXJOhERERGRR7ENJsyXlWWzjB9j8965RTY3jot8zPATa21+cOLfbJ6RKehKXbDF5sebTrD5D5POs7ni7uNt9q/ZanOoRR9LRERElNKclmV/QUHEj2qvnGhz01i9PTBS21Ry8hr1B07LckGGtjIX5bTaXDJbW5aXV0yxecKDOpUOrt/cy8FHh5+sExERERF5FCfrREREREQeldJtMJKZabM5scLmys/n2XzjRS/ZvLBgQ8TjMyXd5hD028P/01Jo865OzbOzK/V5C7TF5ZLz1tl8XvD7Nk++o1zHul4fazo7uvp1iDzPP0XrTBp0STFYf8Bmvr+JiKgr7rzNN260zfUziyPuN+U63VnvhpI3bV60b7bNb7yvbceF67WlJrcuaPP+4bqrzIxvvGPznEuX2HzP5kttLq7KtTnU3Hy0XyUq/GSdiIiIiMijOFknIiIiIvKolGuDkXQ95EimjLd561Xa+vLMF263eUK6/iuq7HS+LgxgW+cgmze360b7f16mu7tk79bN+B85c6/ND015wObRadk2vzX/dzZf8tHNNo+s1V1pAnv0eYg8x6fv+bSRwyN+tPVaXaocvEXzsGVal4Gqj/txcERx4NSEv1AP1zNlWh+BwZmIlq9VD87zN7XpD+obbAzW1+vtJvIaRpQI3NYXmazb8m39su4A84V570Q85urC5Tb/quYim2v+qK2YFY+91+Nr5+ZqW8uTZdpCc9c1f7b58BhtoRlWqGNiGwwRERERUQrgZJ2IiIiIyKNSrg3GXzbS5s1fybf5iSt/b/OYNF3SWNehy4bXrboh4rl87+hy5oh3Dts8eYN+CznUqkuTreum2fzzn1xs86Ixr9lc4teWmIYTdIlz+JvOhv9sgyGvcQ+nGKrfnq+8cXTE3X50+TM2P75rus2Nh7SNLKc3bTBOW4GkR/nXWFC/6W8CgaPckejYSZq+L/0lw2yuna+H7s351gc2/37ECptbQt3viJQu+t5f0qy7jf1q44X62i9OsHn4Y/oeDx482KuxE3mJu+vLlq/qvKvyS9qK0mmCEY/5Wd2pNtf9h7bODHqu59YXl3t9yTjukM3D/driklet88RAza6onr+3+Mk6EREREZFHcbJORERERORRKdcGs2+uLrfPPG2TzSX+Tptv3qO7uWz58XE2j15bFfFcpnWH5g5dtgw6y+zut++zd+uyyTubdJnSX/66c389XOn4yTttbh2q7Tsp938aeV7a6FE2b7lR89NX3x5xP3d3pf/37GU2T1y/z+bIxUzly8rSP0waa2PjRG1nM35BT/K3Od/Q/2Btj/cnOiZTJ9m4YWGOzX8/X1sux6br3/c7OvWd/4f6syKeqiOkdTMhu87m8/L0oL7XT7nP5j+N1ZbLx3LOtXnEf77b6+ETecWhE3Q3vN9e9pDNbuvLO23pEY954cHTbS77SFsro258dNrZvjJB29ZK/AP7WTc/WSciIiIi8ihO1omIiIiIPCrlOiqCevYKalv1UKOLVi+wedBd+m3jnOXr9LEtLX16bQnokicCulwfdFpfQtC2mQyfLti09Ly6TzSg/EN0afLA7FKb/+XyJ22e6BxCBkTuZBEa1m7zvtlDbT58je6cUXL6bpuPK9RdkEZmrrJ5bKa20KSL1sy7TXr4xcfOrhlbntPbS3VVk6jP/Mdr68vGb2rry6Jz77J5U4cehHTFi1+wecTr+tlZfmVTxPNKUK8LlbmTbb5v9nybT7pCr1W/Ln3e5g1Xauvnwad0J5pAdY2+QKi75jOi+Mvep23KP1l9hc23hHRiVPR0TsRjyt7W1pdglDvoude2fZdOtPniQc/a/PO9c23Ore3/XcX4yToRERERkUf1OFkXkXtFpE5E1jm3FYnIKyKyNfzPwqM9B1GqYd0QRYc1QxQ91k1q6E0bzP0A/gjgAee2WwEsNcbcJiK3hv98S+yHF3sly2ptbqvS5fYhLbqMkf6R7hLT19YXVzBHWwJyi2P3vORJ9yOJ6uYT/mI98Gj/Z3V5sOJGPQjs83m6/Jguzg4un/Kb056w+aNpZTaPz9QaPTW72ua9wVybf18zz+ZlHTqOHTXaTpP/YabNeXt0mb+sssFmpzGN4u9+JGDN+HJ0+X3HF7Q+FszSw+5ebjzR5r89N8fmyYv1vSg79DCVYFNkG4xxdhUT50Cw0TW6S9jbpbpz2eArX7V5+uAqHUfOrKP8JpSg7kcC1k00MtbpdaDsd7rbmIS0LvybNkY8JtCoB1VG3eY1TOtYrqy3eZQzY37hNT3Ur2Jbz7uZ9VWPn6wbY94EcOBTN18GYFE4LwJweYzHRZTQWDdE0WHNEEWPdZMajvULpiXGmD3hvBdASXd3FJGFABYCQBZyursbUSroVd2wZogsXmuIosdrTZLp824wxhgjIuYoP78TwJ0AkC9F3d5voAS3brc53ckR94nh66WN0eX9nafpMv51E1/q8bGrNpbbPHmfLulw6T7xHa1uvFYz/kJtd2w4T3dSkWt06e/3Zc/ZnOfT9/nRZPn0ILFMZxeXJ2p1efFfd+jBSek7tY2s+COtAn+H/iuqqG3T2yurbA7u1w+eQibu/0rpGHjqWuO0orSfPsXmsWdW2XwokG3zMy+eavOER/S9GNywRZ+zt+9LZ0nfNGs7pb+96y3D0sW5v3BbsVSTSNea7gTr99ssTo64Tx9fwz9UWyjrZhfb/NvJf7X51Rb9b54R7zh1VbMH/e1Yd4OpFZERABD+Z10P9yci1g1RtFgzRNFj3SSZY52sLwFwXThfB+CZ2AyHKKmxboiiw5ohih7rJsn02AYjIo8COAtAsYjUAPgFgNsALBaRBQCqAVzVn4NMZO3jdGmlfZa2snxt8EfOvXS5tNPo0kr+pnSbffWHbGYbjPclet2Ic5hRx9SxNu/9rLauPDXlYZuznMOOXmzRXVh+vUMPbQGAzqDeb3el1kbOTr09v1rf4ZM26vte9u60OVjb8wdFPOYlsXi+ZpwWkrQSfe9u+ZLefkvJGpt//e5nbZ70lO7uEly/uU/DcA9sqb9Yd0KaNLPK5najbWUbm3XHGN9hbZvhdSQ5eL5uPMyXpbuVtZ6sB4aZy7TVZmqGzttOffcrNo/fqPcJNjf31xCtHifrxphruvnRuTEeC1HSYN0QRYc1QxQ91k1q4AmmREREREQe1efdYKgLzk4Bh0u1nWBuuS5/Fvq09SUE/RL2srZ8m3N360KlGYBlFqJP+AoG27z7DF0q/M4pL9o8LVPbXeqD+v5c26aHVlRvHB75vB3aMpBTr58V5OzVGsjbqTu6SPVum4MN2hJDNNAkTdsSm2aOtvlXs5+0+bWDujPM8KV6eTUr7OGSx8RfMc7mfXN0R4ohX9UDyO4fr4eMbejU68vzG4+3edLeDfqkho0wlNpkvLa+7DxP63vR8Y/ZfGfDVJuHPOVsb1mnBzUNBH6yTkRERETkUZysExERERF5FNtgYsXdKaBcD0I6cLzefnXxezYHnL0qaoPtNn/3vRtsnrRSd7xgCwANJMnT5b62Cm1LuSpfdzE6GNT/1q/s1FaZk7OrbH760j90+xp7A4Nsbgjp6/1qw4U2F9w3yebcV9fbHGJbGA0wSdfLZf2JmoenNdj8+nsn2DxpjXP4UXfPmabP43N2ecGQgoj7bb96iM2fv/Rtm783RK8pbmvlC43O0v3r2q4WatNaJkpF7iF/u8/Vuvry+W/YvLNTb7//yXk2lz+ruz0NxA4wLn6yTkRERETkUZysExERERF5FNtgYsQ/SJf0q67WQyi+eYnunjE3Sw+UORzqtPmZJufb+v+uSyuhqpqYj5OoNwI79Jvug5eX2vzvU86zuSSj0eYHXptrc6azy0vbxMhld1+a7vqSkak1cOOUt2x+6eS7bP5a/hdtDu6bYLO8q8uRRAPBdOpBQ8XrNO/q1GV1FGtLY8ewPJsz9jj3cVpfUKS7Lu367DCbM+fti3jtv0z+q83TM/Vgo2zR1pddQb196R49LGnIGq1TrT6iFOLs0NdwgbZWjryiyuZL81fZfO3Kr9k87gHdkSzQGr82Mn6yTkRERETkUZysExERERF5FNtgYqR9eoXNBXP32rywwDmEArrp/poOXSL9r+d094uKGt3xwnRq2wxRvAy/e6XNVY/qri1VkmvzxNa1+gCji+3ubhf/i093SvrvW7QGxnxeWwAuH77a5v86+zKby97txcCJYsgEdU+XvA37bd7SNsLmJXPusPnHpVfa3NKp7S7fKl9mc75Pl9XHpB20+U/7zo547X/+6zdtrrh4q96v/O82/3L3fJuDj2tLDdZ80NWvQ5QyZOpkm5u+qG1hL1c8ZfNt9TNszntaD6cMVG3SJwp1t69T/+Mn60REREREHsXJOhERERGRR3GyTkRERETkUcnbs+5s1ZM2rFhvz87q4s5H4fTfhurqI34UnKp96k0/1D6oh6Y8qC/nbK21qVO39bp509U2T/yTbtEYOMyTGclbIk497KcTENObtH/9QFC/z3FGTqXNd8zQnl5fltYxT2WkAeH0q4aq9e/sl/4wx+ZHps22OXe0XhPOLtM+85aQnii6uFb7ZFet0K1Jxz2l1woAwKkapxd8bLPfOTn7je36+Emv77I5ENBtJolSRdoo3XJ4w7f0u1Z/PfF+mxc16hzub89pHY9/frPNwTj2qbv4yToRERERkUdxsk5ERERE5FEJ2QbjLoFj8jgbd84vsLm9QNtX8qbo8nlZQUPPz++c89YW1H9FmzedEHG/jCG6/P6bic/ZPDbNWaJ3nuuV5in6vK8NtTlQzX3oKLVlHtQ6Wds8yuYzs7fbPCRXT2hEum6DinanZcDwjEbqf8Z5zw19fpvm5XpSaWexLr2vHHqyzcszT7E5Z5+2qEyqOaDP/6nTq5tv0K3n5uTpEv1Pdl1gc8Fr2nIZ3KXbBxMNJF+ubukbnKZtJpVX6bzIZIRsTmvSlmWfHmqNQTs0D3ujVp9zq14T4LSBpY3ULVQBYMPPtA3mV3OftHl5y3ibH3hWt0id8JC2OQf3ay16BT9ZJyIiIiLyKE7WiYiIiIg8ytttMO6OLqN1SWPfWZoPX9xk81cmLrV5TKYuaZyRXW3zCL8uFXb7stCllXajy5TLRudH3G+Qc/rccenuLi5d7zgzOXOPzR2zdNy7bz7d5mErdHk1Y78u+8sO/XZ/sFF3GSBKVG47W9sQrblJObqEvz2gJz9WVWvr2KQ2rSW2vlA8BWvr9A9Odj8Jy3NP8vXrdc1tpwk59zHTj4t4jYuP1xOC3WuNuwPMhDWH9fE8/ZoGkH+o/t3ccK62mZjr9DTq75S9afPyQ+U2F2XoPGdCttZPZauewvvSWdpCnLVe50vZ+/Tv/v2nRO7a8ttzHrO5KaTXmvtfPsvmise1LTq4UXds8qIeP1kXkTIRWSYiG0RkvYjcFL69SEReEZGt4X8W9vRcRKmANUMUPdYNUXRYM6mjN20wAQA/NMYchyO7vX5bRI4DcCuApcaYCgBLw38mItYM0bFg3RBFhzWTInpsgzHG7AGwJ5ybRGQjgFIAlwE4K3y3RQBeB3BLTAdXoksrdedo68vYr2+x+Y4xS2we5MuweUW7LjU+0fgZmw8E9JvKJenaTnJ6ji6BTMvQfy3pos9zfnb3Bxb5oO017g4wrtOydMnl4Rn32PzW8RNtvuc0XeI5fEDHWvzO8TYPW7bb5sAObfEhb4hnzbh8ObobhW9Ikc0h55vuoZYWxEvrOSfanHeaLpfOz9X6fvTQSTYXfqg7wHCZP/l4pW76g3EPJurmkCLJ1mtI5RdyIn72y6L3bG5x2r581foY38e6K403jnGh/hbPmvEXaIti45m6K1/mAm1R/N2ExTZfs+IGm2X1IJsDufp+fqFEt4M5cYLuiPR/Zug8r+0UvQ6816gtN98e9lrE+KZk6GfRdx/S8RnnI+qDJ+jv0HnqaTa3DtO2zHTtWMbw97TVzPeRHtg3ENfRqHrWRaQcwEkA3gdQEn6jAMBeACXdPGYhgIUAkIWcru5ClLRYM0TRY90QRYc1k9x6vRuMiOQBeBLA94wxEd9wNMYYoOuPk40xdxpjphtjpqcjs6u7ECUl1gxR9Fg3RNFhzSS/Xn2yLiLpOPJGeNgY81T45loRGWGM2SMiIwDUdf8Mx6azXP9jcP+Z+q35d8e+bHO70TaV/69uhs2LV+vBE9mV+ib0O6vnhyt0yaXlNG2hmVakh04cTbvRx6/s0G8bP3NQD8BoDOgy5RmD9XlnZ1fZfGHeeptDFfrfT5tb9PdfVjfN5iFr8nQQzsEB5B3xqpkIFWNs/PgC/X5RyXL9ln3G+p02hxoO2WwCzukUvdltxTmcQpzdLiQt8q+Y9rl6sNje63U3pT9NfsrmNqPP9US1tsEMW67XIO7/kpw8UTcDydnxzFekh/pdP+/1iLtNydAL12/qdbm+wLlURexKQykjbjXjtCnvPVX/zn5r0sM2/3DnJTaX3qHtK+nL19gcatb2Yre1ZveVuiPSSzdom8kDY3RXmQWD3cO/uv+Pjfm5G50/aNx4xkibT8jVtpuvDa6yeVmrzre+X7DA5nE7tV4Hog2mN7vBCIB7AGw0xtzu/GgJgOvC+ToAz8R+eESJhzVDFD3WDVF0WDOpozefrM8G8FUAa0Vkdfi2nwK4DcBiEVkAoBrAVf0zRKKEw5ohih7rhig6rJkU0ZvdYN4GnFOCIp0b2+FEahumyxpjS3W5wz206EBIv1n/8l26k8qUl51DUxwdo7QdoL1Inz/Tp8v+ftEFh5DR79VXdmorDgC81KzLNP+98mybRzytLTWZDTq+N8/RXWlKpuvv0xHUpdCWV7VFIf9jfe2K9z62OVCjBySR98SzZlyNk3RJ8dprX7L5LyfOtbngLT1UpaBSl9rTD2qLigT1fSiBkOa2rndkaR+jO8905Ef+FTPqZt116S+jntXXdtb4btml65TB54bYbFYt7/L1KDl4pW4Gkj9fl9gPnqY7np2dF/lBaJZoHT384SybJ613DkLqjwGSp8WzZkJ5On8KDtJrxM6g3v7eSt3pbtKKDfrYblpfMKzYxkCO/lp+0Xf3oVCrzS+1DLe5sk0zAIScfy05Pr1WFadpO+WcfN15rCGoX7D9zX7dqawpqC3OwSynyny9/spnTAzsqxERERERUa9xsk5ERERE5FFR7bM+0ERX3BEMOa0p3Sz4BZxtQuvO0iWRA3N0CeTG6a/bfE2+fiO5xK+7trQbfeGagLa+fPkj/SYwAOQs0m8DT3pDN8gP1u/vcnzlS7u82TlOCRiMyi7v0/UxGkTdy9mtrSyPbp9u8z/O+qPNg8/Rlq1Fjbp7zD8O6WETh5ydjmpb9DCL3bt0yRIdWp+/OudJm6/Ki9yEwG1hawxp/pOzg9L7z+oSZPlTTl2FeNQLJRdTrq0vx39/rc0nZUT+jd8U0jbNrGqtWX9drc28RtCAcuZncHbw8jvzM5Opd/IN0RZkydT3cMN5FTbvv0J3Vfn1yQ/YfF52vc13H5pi86O3fdbmwsUrI4Zn2nXullaqhyI1f0ZrrjNPr1uDKp2WslW6Q59rLP5h80DXGz9ZJyIiIiLyKE7WiYiIiIg8ytNtMBkNuvRXVV/Q5X1K/PrN4+e++/+6vE+Wc2BLjujOK+mij+10dn15uy3X5m+8fqPNU26POBgMZru20QTbI3eKIYo3//v67fvhPxlr8+zrfmTzrZf83ebP5elOLV8ZVGVzyFnvDDpLnKHjTZe3D/Zp20y6U28AsLRV/3zjB9fbPGSJ9rCNfUNfO1C3D0TJRNK1BaC9WJsgv1ysS+yZEnlp/nq17pA0fLleFwMf14AoHnxN2rKS3pBv87QMfe++O/93Nj81R9tXOp3DLE/PedHmijR9bx8I6XXnis3X6GN/qy3Oha9p64s5yhwssFt3B8yq05aaLJ/ODU2ntxvJ+Mk6EREREZFHcbJORERERORRnm6DSd+oBwEVvqyb639nwhyb/1j6ts0jnB1d3F0nuts95oFG/Vbwr968xOYxznkUx23SZfjgzt0RjzedXR8KQ+QF7vsztGWHzRP/pMuXj7x0kc13l6TbXDdDn8dX4hyQ5HO3AOjmdd2dm+qyIn424m2txQkbDuoP9uy0MXDIaTczPOqFkouvotzm6ou05mZmap21f+ptv/bZyTaXb3RqhfVBcRJy5kMj39SdwS6ccanNL03Rg++uy9c2S9d2p/vkmq1fsHn3s7o72aiXdIe9tI836Rh6237s1Emiztv4yToRERERkUdxsk5ERERE5FGeboMJHmiweeir2hKzufYEm2eXTDvm589s1CX9yducpffN2jIQaGsDUaJzl/4C1bqMnr5XDy0qzNTdkQpX6zfuQ7l6u3P2RbfE2VnJ13wgchw79Vv5wcOHnR9wOZ9Sw+EK3dns2/Nettnv7Fr2m/0nRjxm+Pu63B/cG3nQGFE8uLuv5CzfbnP7v5XbPGPMjehJmtPzlbNXd4Mp21Jtc6Bm17EOM2nwk3UiIiIiIo/iZJ2IiIiIyKM83QaDkC6nu8sgmW6O1UvF6HmIEom7lBlxsNeGxi7u3cvndHKw23sRpQ5/8RCbGyboZffawWttPuRchO5/+ayIx0/apte8AA/gI48J1ju7tbymuagPz+ntI4oGHj9ZJyIiIiLyKE7WiYiIiIg8ytttMERERImuSHeAaR2mjWJZ4rf5/fZcm8teiWwCCNVH7qpERKmFn6wTEREREXkUJ+tERERERB7FNhgiIqJ+JO16KFletR5+dMG6L9m8e0+hzcd9VBPx+EBLSz+Ojoi8rsdP1kUkS0SWi8gaEVkvIr8M3z5WRN4XkUoReVxEMvp/uESJgXVDFB3WDFF0WDOpozdtMO0AzjHGTAUwDcB8ETkVwK8B/M4YMwHAQQAL+m+YRAmHdUMUHdYMUXRYMymixzYYY4wBcDj8x/Tw/wyAcwB8soa3CMC/Avhz7IdIlHhYN0TRSeaaCVTvtHnYHZpxh8aJ7v37f0iUBJK5ZihSr75gKiJ+EVkNoA7AKwC2AWgwxnzyd0oNgNL+GSJRYmLdEEWHNUMUHdZMaujVZN0Y8jOkIQAAIABJREFUEzTGTAMwCsBMAJN7+wIislBEVojIik7wmGRKHcdaN6wZSlW81hBFhzWTGqLautEY0wBgGYDTABSIyCdtNKMA7OrmMXcaY6YbY6anI7NPgyVKRNHWDWuGUh2vNUTRYc0kt97sBjNURArCORvAPAAbceRN8fnw3a4D8Ex/DZIo0bBuiKLDmiGKDmsmdfRmn/URABaJiB9HJveLjTHPisgGAI+JyK8ArAJwTz+OkyjRsG6IosOaIYoOayZFyJEvEw/Qi4nsA9AMoH7AXtQbiuGd33mMMWZovAdBvROumWp46z00ELz0+7JmEgyvNZ7AukkgvNZ4Qrc1M6CTdQAQkRXGmOkD+qJxloq/M8VWqr2HUu33pdhLxfdQKv7OFFup9h5KlN83qi+YEhERERHRwOFknYiIiIjIo+IxWb8zDq8Zb6n4O1Nspdp7KNV+X4q9VHwPpeLvTLGVau+hhPh9B7xnnYiIiIiIeodtMEREREREHsXJOhERERGRRw3oZF1E5ovIZhGpFJFbB/K1B4KIlInIMhHZICLrReSm8O1FIvKKiGwN/7Mw3mOlxJDsNQOwbij2kr1uWDMUa8leM0Bi182A9ayHT9jagiPH4dYA+ADANcaYDQMygAEgIiMAjDDGrBSRQQA+BHA5gOsBHDDG3BYugkJjzC1xHColgFSoGYB1Q7GVCnXDmqFYSoWaARK7bgbyk/WZACqNMduNMR0AHgNw2QC+fr8zxuwxxqwM5yYAGwGU4sjvuSh8t0U48uYg6knS1wzAuqGYS/q6Yc1QjCV9zQCJXTcDOVkvBbDT+XNN+LakJCLlAE4C8D6AEmPMnvCP9gIoidOwKLGkVM0ArBuKiZSqG9YMxUBK1QyQeHXDL5j2AxHJA/AkgO8ZYxrdn5kjfUfcL5PoU1g3RNFhzRBFLxHrZiAn67sAlDl/HhW+LamISDqOvAkeNsY8Fb65Ntwr9UnPVF28xkcJJSVqBmDdUEylRN2wZiiGUqJmgMStm4GcrH8AoEJExopIBoCrASwZwNfvdyIiAO4BsNEYc7vzoyUArgvn6wA8M9Bjo4SU9DUDsG4o5pK+blgzFGNJXzNAYtfNgJ5gKiIXAvg9AD+Ae40x/zZgLz4ARGQOgLcArAUQCt/8UxzpiVoMYDSAagBXGWMOxGWQlFCSvWYA1g3FXrLXDWuGYi3ZawZI7LoZ0Mk6ERERERH1Hr9gSkRERETkUZysExERERF5FCfrREREREQexck6EREREZFHcbJORERERORRnKz3goiUicgyEdkgIutF5KZ4j4nI60SkSkTWishqEVkR7/EQJQIRuUlE1oWvNd+L93iIvCxV5mfcurEXwidajTDGrBSRQQA+BHC5MWZDnIdG5FkiUgVgujGmPt5jIUoEInICgMcAzATQAeBFAN80xlTGdWBEHpUq8zN+st4Lxpg9xpiV4dwEYCOA0viOioiIkswUAO8bY1qMMQEAbwD4XJzHRORZqTI/42Q9SiJSDuAkHDnxioi6ZwC8LCIfisjCeA+GKAGsA3CGiAwRkRwAFwIoi/OYiBJCMs/P0uI9gEQiInkAngTwPWNMY7zHQ+Rxc4wxu0RkGIBXRGSTMebNeA+KyKuMMRtF5NcAXgbQDGA1gGB8R0Xkfck+P+Mn670kIuk48kZ42BjzVLzHQ+R1xphd4X/WAfg7jvThEtFRGGPuMcacYoyZC+AggC3xHhORl6XC/IyT9V4QEQFwD4CNxpjb4z0eIq8Tkdzwl30gIrkAzseRJX4iOorwShREZDSO9Ks/Et8REXlXqszPuBtML4jIHABvAVgLIBS++afGmOfjNyoi7xKRcTjyaTpwpN3uEWPMv8VxSEQJQUTeAjAEQCeAHxhjlsZ5SESelSrzM07WiYiIiIg8im0wREREREQexck6EREREZFHcbJORERERORRnKwTEREREXkUJ+tERERERB7FyToRERERkUdxsk5ERERE5FGcrBMREREReRQn60REREREHsXJOhERERGRR3GyTkRERETkUZysExERERF5VJ8m6yIyX0Q2i0iliNwaq0ERJTPWDVF0WDNE0WPdJA8xxhzbA0X8ALYAmAegBsAHAK4xxmyI3fCIkgvrhig6rBmi6LFukktaHx47E0ClMWY7AIjIYwAuA9DtGyFDMk0WcvvwktRXTThYb4wZGu9xpLCo6oY1E3+smbjjtSYBsW7ijteaBHO0munLZL0UwE7nzzUAZn36TiKyEMBCAMhCDmbJuX14SeqrV80T1fEeQ4rrsW5YM97Cmok7XmsSEOsm7nitSTBHq5l+/4KpMeZOY8x0Y8z0dGT298sRJTzWDFH0WDdE0WHNJI6+TNZ3AShz/jwqfBsRdY91QxQd1gxR9Fg3SaQvk/UPAFSIyFgRyQBwNYAlsRkWUdJi3RBFhzVDFD3WTRI55p51Y0xARL4D4CUAfgD3GmPWx2xkceTL1S9ZdJw62eb6f26JuF9mesDmQbfn25y29MN+HB0lsmStG0nTv0r8pSNs3jt/VMT9Dp4Ysnlw2SGb2zrS9blWD7J59EuNNpsV62IzWEooyVozRP2JdZNc+vIFUxhjngfwfIzGQpQSWDdE0WHNEEWPdZM8eIIpEREREZFH9emT9WTiLyy0+fDcCpuDN9bb/MrxD0Y85m9N2iLzWP6FNvNfKiUrt0UMFWNs3Ht6gc2HZrXZfOFxKyMef16BrsJOSq/r8jUWT5xu8yNZZ9pcviL68RIRUWpLG6vXquYpw2xuLe56tpbe4rRrfrjX5kB1jd4pFIzhCHvGT9aJiIiIiDyKk3UiIiIiIo9K6Y4Nf/EQmxvPnGBz01d1B4o3T3jY5hzJinj83Vtn2zy0rr0/hkgUd2mlI20+OGe0zXvO1qXCS6Z/YPP3hy6z+Y3WcRHP9X83abtYc6sewnHt5OU2LyjU/Pqp2pLmnzDW5mDljt7/AkRJyl8w2ObWWVorh8bp7kpFm/Ta5F8W2ZZGlKz8FXrtqblwuM2dZ+j87jMjdnf52NoW3ZFs6wq9/k24T6fMoR16OKzp7OjbYHuBn6wTEREREXkUJ+tERERERB6Vcm0w7uEtbdPKba7/oh549OYpd9ucJRk2v9OmS4sAEHinyOb0Xfot4QCIEpt/iL63az+r36QffLWeVv3QuKdt3hfUQ8H+s+5cm19aenLE8459RutsaIdWyt03aUvZZXNX21yWd9DmXaN1mT+NbTCUoiRdr0ktsyfavP+GZpu/OF7bXR56+mybx2iHGlHiELHRl52tN4/SA/jaywoiHlI9V9ssTz5vo83/Uqrbzk90aqnT6O4uh02nzavH6vP+uO6fbC59pMnmYG3XO5vFEj9ZJyIiIiLyKE7WiYiIiIg8KjXaYHx+jeVlNu+cp0sgD06/0+YhPl1maQzpAS/fWLEg4mnHP11rc6Dq49iMlcgD2k7SnVciWl8mPmLzfQ2n2Hz3G2fZPPINfZ4Jz66KeN5Qm9aTzzmIDId0OX9fUA9e2tWsS5CZNQ02D+xxFETe4R9WbHP154zNr56s17C33F2YjLYQECUKt93LP1wPMmo+UVtfdp+hU9jJp0e2Rt5f9pzNE9L1ulMb1M+o/96s7Z4NznVnfIbO7U7P0naXSVdutrnxTd1hBvv2a+6nw5L4yToRERERkUdxsk5ERERE5FEp0QbjL9Kl9KprdAnluxfrMsmMTF0qDEGXFrcGdAeYkffpt4sBwOzhjhSUnA5M0ff6rPx9Nv9Hne4s8erfZ9g8+fE9NrsHFumxSf9b52fKbR41Ub9NPyFdD63Y06C7zJRtWdfzwImSXPtEXX4fU1Zv8+g0bd8MGv0cTo5WhEQe5SsfZfOOq/Q9/+0v/4/N3xxc3e3jA87VZ1mrtrv8YPUXbM5aqocfGZ/OAcd+cavNc8e/aPMtpS/YfFP5P9s8aJMemBlq1l2ZYomfrBMREREReRQn60REREREHpUSbTCH54y3efTZumyyYPBW5176r+LDdr31mhe/bfOUFdsinjfY3AKiZDTyUf3W+/ZVk2yWkLaIla/TtpTg4eiX/pqHa6tNeV7DUe5JRJ+oulh3yfh1+Wtd3mdbm+6ekb/ddHkfIq9xD63cebm2vvxlwR02T8/ocB7R/RT27TZtTblx6bU2T3hED+NLX7PB5oYLptg8dbDugOaDtsd8JkN3Fgxk6+2S3v9TaX6yTkRERETkUZysExERERF5VNK2wXSeP93m1ht0if2/xj5lc6boTi8fduhG9gtWXW/zlN/rZvfBA59aqu+nze+J4s19r6etbNUfhPQb9sF2p1/MRL/UfuA4/azg2sItNnc6TxUK8UAXooavnmbz/Lkrbb4gR3dR+rBdr2ePr9cDyya/pUv62gBA5G0BPaMoovUlU3Ta2mr09m/tPD/i8R89eoLNk984qD+odA6wzNCWsubhej1aWLjceQ19vZ/VzrE5f7teF0PH0AYaLX6yTkRERETkUT1O1kXkXhGpE5F1zm1FIvKKiGwN/7PwaM9BlGpYN0TRYc0QRY91kxp60wZzP4A/AnjAue1WAEuNMbeJyK3hP98S++Edu11n6JLgD8e/bfOUDP3vkz1B3c3ltp1X2Fz4SJ7NwcpN+qRse6Heux8JWDeW814PtcRu16O08tE2Byv0eWdk60FK9zfMsjnjfT20gpLe/Ujkmukr0ZYvf0FBxI/q5moDyxVFK2z+/9u78/Aoy3N/4N97JjshgUCAkBDCvgiIigjigrgUbC22WqtHK7a21lO7WOupnvac9vT8enrscll72tpWq5W21p2KOyoFxQ1BEJAdWUwgIYGwBAIhM/P8/mB87mdoQmaSycw7M9/PdfXqN8ksT7zeO+/LPPf7PPmi0/i/2KltAP2f1ZWWgjW74jpM8pyHkCZ1Y5zVxoo+1Dx91bU2twZ0RZb9jdorUzlPvw8A5St15b/gbt3YzwRabQ5NGmnzkamHbO7nL7B5b0jbXea/P9HmMfVOi3Sg+xvMOvxk3RjzOoDGE749G8DccJ4L4PI4j4sopbFuiGLDmiGKHesmM3T2BtP+xpiP9xevA9C/vQeKyE0AbgKAPBS09zCiTBBV3bBmiCyea4hix3NNmunyajDGGCMi7S4FYYy5D8B9AFAkJXHfncFdRD84Re/+HXHudpsvLtCVJrKcA/KXDefZ/OG8ETaXv6YbwgTj2Pri79vH5pYJVTYfGKZTmfvG6n+i3h/otGi/1+p0TFudu5nZmpOSTlY33V0zyVTzmQqbPzdG29MKRKcRH1mnKzkNW6AfGOk6NJSJkn2u6W6+/Hyba740JuJn109+zebxOQdt/tCZfX9vU5XNY99xVoBJwBQ9eVdKnWuM/pXv87Ze8xxp6GuzuzFfv4O6Goy8tybipQKtx9AWXw9tnamdpDX349Mf1vdzVplZcqTM5gELnUvmPSdOZnSvzq4Gs1tEygAg/P/1HTyeiFg3RLFizRDFjnWTZjp7sf4MgDnhPAfA/PgMhyitsW6IYsOaIYod6ybNdNgGIyKPAJgOoK+I1AD4IYC7ADwuIjcC2AHgqu4c5D/x6V2//v79bN78VW0bebjyOZsrsnSq4+nDepf9/MWTbR4135k23KN3+UZLsrWVRU4ZbvOe04tt3q83HiNv1AGbz61Ya/MtpYtsvufsi2x+9YxTbB76VInN2a+tstlwutMzPFk3HtDzEzq1eWPJWza/c3Swzf4NOk1pNmhtUHrLyJpxzmW+fjrVX/XprREPu6HXUpsLnE1hbts50+Y+7+gKaME6fpCaKdKqbpzN9YJbdIWwHCe3+9ST/MyXl2dzwFkBpmWyrgBzRQ/dOGlnUK+lHq7T1clK3tHzV+CgPjcROrxYN8Zc086PLozzWIjSBuuGKDasGaLYsW4yA3cwJSIiIiLyqC6vBpMMvh66osvhCeU2P3fur2wema3THtsDuvnKHctvtLnqOb3jN7DdWWElSu5KNK3njrd52+X6/S9N17aWb5SstDlP2v5PnwXdzOL3FUtsPlK+0OaJR261edRaXWEmULc76rETdRe3LjB+VMTPrh+s9VDptKfdVD3N5j4f6ApHbO2idOYvLrJ5/+SBNn+r7JGIx5X4tV3mraO6Udi7i3XVmBGvOK2cLS1xHSdRKjPjtDV562f0Guu28c/b3GL0XPPWkUE2r31Nnzt092p90QSvxMdP1omIiIiIPIoX60REREREHpWabTDO1OHuyXoHfB+/3g/sF/13yJ/2TbW5ZIFOvfsXvd21cYwcanPt13Ta8elJv7X5FGeVmBajU5kbW3UK5ZmDE22+tni5ze4qNoU+bevJrWqy+dgonTr1sQ2GkqWdFZrW/WvkrnhT8nWVi/datHZr3tZ2tuFv6mPYBEPpxl05LDRMj/t9V+nqEjPyd0U8p1D0XPC/2y61uextPY90ppWTKF1lDdBNW7dcrq1jt12iKwXeXLzD5g3ONdnPN11s87DffGhzsFlbqhONn6wTEREREXkUL9aJiIiIiDwqddpgnGn2UIlOafSc3GBzNnRTpKAJ2Tx/m67U0q9GV4CJleTmRny96Uu6OdFPJujd+27ry8HQUZuXHNVNL+54/wqbq/5Hp1923a+bNv1n/1dt7ufXdoJhfXXTpj1lVTbrfxWi6LjHtC9fW63grD7RrqAet+JsOrF3eqXNj13864injMrW1x33mrMy04IjNnNVI0o7oucm/wBtE6s+T1s6V011a8WpRQCHjLZZNryqrTOV726xObFrUxB5jC/ynFVz9TCbvzT7FZu/XNx2m+WTB86w2bygq+wFd2+K4yA7j5+sExERERF5FC/WiYiIiIg8KmXaYNyNkA6O1laRBRPutrnIWTHFbT85urnY5twabZuJZtrQ50zvN1x7WsTP/uNTT9k8u8cem1uMrkrz/boZNr8993Sbh/z5A30hpxWhNdRx+8EH23UFmFHrD9gcauvBRCdwW18OXKHHtP/6epsn9tENVnyiR1bAWdFoSY2uhhRYpTU5ZeYam0dlR67n0ui0zmRt1JrOqanR14ridyBKJf5+pTY3zNANV0Z/dmNUz79x22U291uprZzB+oa2Hk6UGZzWl6yqQRE/GnGFtq9cW6wbUmZBzzsvHdH80Nvn2DxmnrMCTHxG2mX8ZJ2IiIiIyKN4sU5ERERE5FG8WCciIiIi8qiU6VnHMO1H2nWxdhG5fequBc26vFWf1dpDHtpe3eFbSZb+Z5Eh+r4zv/FGxONm99huc2NQ+3ovXfllmwse1l7eskWbbQ4eOmxzVp/eNvfK1t73bGe5L5e/TnuOZceHbT6GKGK5uD66zGjjzBE2X/3vL9l8doEen80hPcaOOX3qffx63N5SusjmugmFNo/K1vsoCpydFwFg0qNft3n40/tsDu6sO9lvQpTSAkPLbG44v9XmJyvnO4+KrBXXxvkjba5cp7suBpz7o4i8wN9L7xFsumC0zYcG6nnEtPMxcWGtc223yrm/cLMut+jr0cPmlmljbO7/o8glFv9j4Iv6M7/Wltun/o2X5tg85reN+n4Nujy2V/CTdSIiIiIij+LFOhERERGRR6VMG4xv/yGb83f0OskjT06c1gB3AtFdotGM1zaBTd/WqZsHSt6OeK3qoP7n++7Wz9mc9bS2tRQv1KW5QgcO2uwfM9zm9f+qv89/937C5p4+3Qn1P+sn6ms6sz0hp52GyOUr0Om+5jN1mcWLbn/T5i8Xb7B52vIv6pMX6TFcUK8tXg2na/18ddbLNt/WW1to4CyNdaISXdURsmOXzaHWzu8sTORF4uxk3TBRa+K2s16wuTJL28f2BZttnrb0pojXqlys547gbi7XSN7im6DtLpuv03PHJy9cZnN57j50pK5FW2g2H9Kdfj+sm2BzoFZrqXSktg3/tFzrCgD6Oq0vC93Wl+dvsHnkQ002Bzdqqw1CXlmwUfGTdSIiIiIij+LFOhERERGRR6VMG0zIuTu3zzq9s35Da4vNI51pxzPzdNWXveN06r7kPWeXq/U6de8r7WtzzTk9bf7rlF/b7E6rAMCnV1+jX8zrY2O/f+hujCakzTZm0libq8/V6c+7LnnY5nE5OtYnDw2w+e/zdXetIW/r1E8w6L3pGvIGKdNpxNqztdR/WPq+zWuP6fGW/ay2Y/Wb7+yseExXr2juP04fL7Efe3tnHbG5cKe2guW8s97mUHMziFLdsfPH29xykbaxfL5onc2tRtsvdwS05bLHC3oOAgDfNq3HIFvGyAPkjFNs3vwverz+4JPayvv5nrU2L2jWFpff10y3uSRX/95fVfquzf/RT1ff21+lrZgrWnQH99E5u20+8frMBz231Qd0fNmH2lllr0TPf8ZpLzbuikvO9ZyvUFelCQ3VMfnrdTW0QLVeC3ZVh5+si8ggEVkkIutEZK2IfCv8/RIReUVENof/v3dHr0WUCVgzRLFj3RDFhjWTOaJpgwkA+I4xZiyAKQBuEZGxAO4EsNAYMwLAwvDXRMSaIeoM1g1RbFgzGaLDNhhjTC2A2nBuEpH1AMoBzAYwPfywuQAWA7ijW0YJIHTkqM2FW/UO3m9u+bzN80Y/ZnNVlt79O/2C1Ta/3qp3FQ9+SacxWp3pjabT9L3O0L1hsOmE6Ucz32l9eXaLft+5k/jQ+bqyTM3FOv1y/hm6LManCvTu/nsadWrpD4tn2DzyWZ1aCW5wNkLiphie45WaaS3Xab2+Z+p0YYvRtpbfNVxgc581uuISeuuU5Z5p/W32XaAbR3yih07n73PqZ12r1tXU3MhWmb9Nvd/ma2u+afPwPZX6oA+clWVivSu/nY3EWCfe55W6iZeGiXryuGHUYpuLnVW+Vh/T4/uLq26wufzdyJUzuOoXtSWZNVN/ZpHN/37p322+slBX+VreopeY337+epv7v6Ovs7tI/2b/eHapzX885S82n5Kt7WKVWfudUTgXaCcxIXenzWdeoC2Xb1XoKmnZ1brxWEGdjkkCTuuLczo6VKGPaRmo59Tyl7TFuUci22BcIlIF4DQASwH0Dx8oAFAHoH87TyPKWKwZotixbohiw5pJb1FfrItIIYCnANxqjDno/swc78Bv86MrEblJRJaLyPJWtLT1EKK0xJohih3rhig2rJn0F9VqMCKSjeMHwsPGmHnhb+8WkTJjTK2IlAGob+u5xpj7ANwHAEVS0vm5aGc6XHZq28i+J3VFifm36kovswt1NZhflL+qj/mc3lX/X5Wftjl3m06zXDhqlb4t9C7kza06RQMAeY36s8DIcpv3jtO7kpsv0taC35/2qM1n52krz4vNumrHg09fZPPIeVpzsl5bX4wHF+ynSF6omUCeri4xspfWTND5u729SVu56ifpHfPN5TqVd8Z03TjpP8uft7nF6OvfWv0Jm1ft1jvjF03SthcAmJyrLTKzZiy3ecGxSTYPqDzD5oJqrROp0xWhUKTjax2gLTvBfB1TyK/TlP5jWqu5H2mLQXCzsxEGJZ0X6qYr/L30WGwu12PurAL9+33UBGx+5qAe96W/1tZNs0HPQQBgAgEQtSVZNXNkgP59vbG4zub6oLaEPL3/LJtH3ets5uW0JTaN1+uqxqPaItZq9LPkkHPO2hfSNuUlR3RlwCVN2sYCAGf31HbK8iz9m/8D5xzWv1Lfo9FZWW/+IV31rPpoic0Nx/S8M7KH/iddvm+wzXWvDUF3iGY1GAHwAID1xpi7nR89A2BOOM8BMD/+wyNKPawZotixbohiw5rJHNF8sj4NwBcArBGRjxdo/h6AuwA8LiI3AtgB4KruGSJRymHNEMWOdUMUG9ZMhohmNZg3ALSzxAIujO9wohPcqytSlD33kc3/dcoVNh+Y8ZLN1zibUFzbU6cuZlzwfzbXnKutKyV+nWbxQdtjxufoVA8A1F6uq8MUF+tmL7eM0PeeUaCrxOwO6nvctedMmx9+d4rNYx7V3y20ZbvmFvaTpQqv1Exuox4zS7YOszlvkJb9LZX/sLnxmzrFN6NA20N6ik7ArWvVevjupitt9v1BpzJ7OyvD/HWkrm4EAF8s1jvxf1m21Oa11y2x+Y8zz7X52RUTbe61WpcKPlyu79FnotZ0eaGumlScrXW8bp/eX7X/ac192QbjGV6pm5j5tPWqeapOxVeO0w1hJuXouaLWmW5fWDvK5h4L37OZaxdRNJJaM1EcpK1Oq+SxgdoitmuankcmfeoDmx8Y+KLNA/363LXH9M0eajzf5ucWaRvZwCXadgYAz5+t11ihgXouGFCq54gppdttHlugq9iEnBacHc3aBrNtv+a33tBzW8U/tE2tZJm238SzYTmm1WCIiIiIiChxeLFORERERORRUa0G4znOaiiBGl3sfuRterfx/d/4pM01X9Dp82/0ecPmns70pbv5kQ/aruJyN1oCgA8v/JPN+4LNNu8P6XTM4wdPs/kPr+smR8P/pi0Ko9/TDZKCR3W6hqgr/DVaDz2W6uYPjefosXd2nnOHPjSHnJnVPx4Yb/PvFutqRcMe06l93xJtafEVaJ38qerSiDHlfVVXCvhUoa7MNMBZueUH/RdrnqUZs9ChJqcFp9X5HW45fLXNhw6yyYDiJ6u/toB99CX9+z1vxCM2+yXb5oXN2irTsExbsnqALVmUOsRZoOhASNuA3U2/vtF3sc2P/Z+u7DW76H2bh2TpdVir8/nxAmeVvB9+cJm+/iParjnsCWd3pRMMbeeWWn9fXQFtXcVom1f11pZLn7N6WHatbsLUv1E3F+x70Nmc0rkm7a61+vjJOhERERGRR/FinYiIiIjIo1KzDaYdplWn5cv/oJtKrFqi0/gzZ5xtc+XM7TbfM/QJm4dltd0Gc6LmkL7fJatusDn7r3rHcO83a2wefWiTzaEmnRIKccML6gbBhj02l7+sGx6dN/Df9DFl2hIjPm0PCR3UafvyhdpOMnqx3ukeOqCbdrmNJaFmbQkb+IcVEWN6+s3Ty+V7AAAgAElEQVQLbL73vNk2N43UGvAVtiIWoVb9zKHiWf2Tlteg9Zm9W8faa6e2nUWuH0AUu/pZugnKFSO0zXJottbQ/MN9bf7pAt2Mb+Rdq23msUipZOBb2vI19bSv2Lxm6p9trnSupW4t0b+72aKtMj6nXfGSNdquGPiLtsEMfl2vo4L1usJeZxoa3dUE0agtLlm+thfVCTitlTBOlZrEtlPyk3UiIiIiIo/ixToRERERkUelVRuMK3T4sM2yWttPBlf30sc8r6vEfL34FptNO9MhJ1O655B+UasbvwQOOt9P4hQKZR7jtFeFturmYSN/o3fum1ydqofocS8Bvafd7NOpwqDT+hLNMRw6YXUjWaNtNBXVukmGFOh0qXFWB4iKO476vfrtY9oG47aamWB33a9PmehwudZNZa4efz7ns7BNR8tsLtil33fPU0SpJHu5/i0v+52ucDSy+ms251R0fHy31OrqYRUL9W95yRt63RbYpxsZuSuvdIp7vjDOec7jfWj8ZJ2IiIiIyKN4sU5ERERE5FFp2wbjMi264kWgThe1h5PdxpfYm2C6byF8oniIqIHqmpM8MnHjCO6uT9o4iOIlr0Gn1fe06qpLjzTphkcPvHWezSOWsPWFUp+7ol3Ou9qyMqpWW76CRXkdvo6/SdsssUvPCcF9+7o4wvTCT9aJiIiIiDyKF+tERERERB6VEW0wRERE3WHAEt1k5UmZYbNxVlcavlo3CvOv1k1dPL4ABVFU3JYYrGtq/4FtYAtxdPjJOhERERGRR/FinYiIiIjIo9gGQ0RE1EnBtRttLl3b8ePZ+kJEseIn60REREREHsWLdSIiIiIij+LFOhERERGRR/FinYiIiIjIozq8WBeRPBF5V0RWichaEflR+PtDRGSpiGwRkcdEJKf7h0uUGlg3RLFhzRDFhjWTOaL5ZL0FwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5oiqZ11E/CLyPoB6AK8A+BDAfmNMIPyQGgDl7Tz3JhFZLiLLW9ESjzETpYTO1g1rhjIVzzVEsWHNZIaoLtaNMUFjzEQAFQAmAxgd7RsYY+4zxkwyxkzKRm4nh0mUejpbN6wZylQ81xDFhjWTGWJaDcYYsx/AIgBTAfQSkY93QK0AsDPOYyNKC6wbotiwZohiw5pJb9GsBlMqIr3COR/AxQDW4/hBcWX4YXMAzO+uQRKlGtYNUWxYM0SxYc1kjqyOH4IyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggY5eaOQZQ/HK8ie6NGDqGhFJ9hAyRVzqhjWTfKyZhOG5Jo2wbhKCNZNGTlYzYoxJ5EAaABwGsCdhb+oNfeGd33mwMaY02YOg6IRrZge8dQwlgpd+X9ZMiuG5xhNYNymE5xpPaLdmEnqxDgAistwYMymhb5pkmfg7U3xl2jGUab8vxV8mHkOZ+DtTfGXaMZQqv29MN5gSEREREVHi8GKdiIiIiMijknGxfl8S3jPZMvF3pvjKtGMo035fir9MPIYy8Xem+Mq0Yyglft+E96wTEREREVF02AZDRERERORRCb1YF5GZIrJRRLaIyJ2JfO9EEJFBIrJIRNaJyFoR+Vb4+yUi8oqIbA7/f+9kj5VSQ7rXDMC6ofhL97phzVC8pXvNAKldNwlrgwkv2r8Jx3fYqgGwDMA1xph1CRlAAohIGYAyY8wKEekJ4D0AlwO4AUCjMeaucBH0NsbckcShUgrIhJoBWDcUX5lQN6wZiqdMqBkgtesmkZ+sTwawxRiz1RhzDMCjAGYn8P27nTGm1hizIpybcHzb33Ic/z3nhh82F8cPDqKOpH3NAKwbiru0rxvWDMVZ2tcMkNp1k8iL9XIA1c7XNeHvpSURqQJwGoClAPobY2rDP6oD0D9Jw6LUklE1A7BuKC4yqm5YMxQHGVUzQOrVDW8w7QYiUgjgKQC3GmMOuj8zx/uOuAQP0QlYN0SxYc0QxS4V6yaRF+s7AQxyvq4Ify+tiEg2jh8EDxtj5oW/vTvcK/Vxz1R9ssZHKSUjagZg3VBcZUTdsGYojjKiZoDUrZtEXqwvAzBCRIaISA6AqwE8k8D373YiIgAeALDeGHO386NnAMwJ5zkA5id6bJSS0r5mANYNxV3a1w1rhuIs7WsGSO26SeimSCJyKYB7APgBPGiM+Z+EvXkCiMg5AJYAWAMgFP7293C8J+pxAJUAdgC4yhjTmJRBUkpJ95oBWDcUf+leN6wZird0rxkgteuGO5gSEREREXkUbzAlIiIiIvIoXqwTEREREXkUL9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixXqURKSXiDwpIhtEZL2ITE32mIi8SkQGicgiEVknImtF5FvJHhOR14nIgyJSLyIfJHssRKkgU841XLoxSiIyF8ASY8wfw5sGFBhj9id7XEReFN4FrswYs0JEegJ4D8Dlxph1SR4akWeJyHkADgH4szFmXLLHQ+R1mXKu4SfrURCRYgDn4fjOVzDGHOOFOlH7jDG1xpgV4dwEYD2A8uSOisjbjDGvA/DUZixEXpYp5xperEdnCIAGAH8SkZUi8kcR6ZHsQRGlAhGpAnAaju8SR0REFHfpfK7hxXp0sgCcDuB3xpjTABwGcGdyh0TkfSJSCOApALcaYw4mezxERJR+0v1cw4v16NQAqDHGfPyvtSdx/OKdiNohItk4/sfzYWPMvGSPh4iI0k8mnGt4sR4FY0wdgGoRGRX+1oUA0urmBaJ4EhHB8Xs81htj7k72eIiIKP1kyrmGq8FESUQmAvgjgBwAWwF80RizL7mjIvImETkHwBIAawCEwt/+njHmheSNisjbROQRANMB9AWwG8APjTEPJHVQRB6WKecaXqwTEREREXkU22CIiIiIiDyKF+tERERERB7Fi3UiIiIiIo/ixToRERERkUfxYp2IiIiIyKN4sU5ERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWLdSIiIiIij+LFOhERERGRR3XpYl1EZorIRhHZIiJ3xmtQROmMdUMUG9YMUexYN+lDjDGde6KIH8AmABcDqAGwDMA1xph18RseUXph3RDFhjVDFDvWTXrJ6sJzJwPYYozZCgAi8iiA2QDaPRByJNfkoUcX3pK6qgn79hhjSpM9jgwWU92wZpKPNZN0PNekINZN0vFck2JOVjNduVgvB1DtfF0D4KwTHyQiNwG4CQDyUICz5MIuvCV11avmyR3JHkOG67BuWDPewppJOp5rUhDrJul4rkkxJ6uZbr/B1BhznzFmkjFmUjZyu/vtiFIea4YodqwbotiwZlJHVy7WdwIY5HxdEf4eEbWPdUMUG9YMUexYN2mkKxfrywCMEJEhIpID4GoAz8RnWERpi3VDFBvWDFHsWDdppNM968aYgIh8HcACAH4ADxpj1sZtZERpiHVDFBvWDFHsWDfppSs3mMIY8wKAF+I0FqKMwLohig1rhih2rJv0wR1MiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH8WKdiIiIiMijurQaDBEREf0zydLTq39Af5uPVZXa3NI3R3OR3+amwRLxWqadj9Vy9msuqA/ZXLirRR+zqdbmwO4GfUIoeJLRE3mIT2sjq19fmw9Mq7K5cazffQaM+6XR6NZMUbXWQM+N+oPguk3Oc50nJxE/WSciIiIi8iherBMREREReRTbYMJ8eXk2S0WZzUeG9bH58IDsyOcEdHqk9ypnbmXrRzaGmpvjOUwiT5LcXJt9wwbb3DS6d8TjjhXq5wPu1H72YWcKv/qIzf7NNTYH9zbGZaxE3SWrotzmI2P1PFJ/utPuMkHPCVX9tUVlakm1zf/db1nE6+ZK5LnnYy836/cf23OWzUu2D9XnrtA88PV+Nst7G2w2rcfafH0iL/AXFdrcNEXPL32/ud3ml4c9G/GcAl8O2vJSs56r7t15gc0fLtA6GbRJ68ortcFP1omIiIiIPIoX60REREREHpXZbTCid9xLpU5ffvRZvXP/9Nkf2HzvoJcjnr4rqHcSz5x/m82VC06xObfhqM2+loDm+n02Bxs1mxa9i5/Ic5y78n09Cmw2o3RqcsMXdMry+5c8HfH0zxZutbnIp61nLx3R17rtvatsLn1ihD7+5fU2Bw8ejHnoRN1t98xKm4uv3mnz3GFP2jwhR2uoxbTavD+k54eGf1qoRc8LjSE9bVdlH7b5xwNfsjmvXD+H23GWvt8VE262eeTPdNofG7fZyHMQeU6ptiPXTdbjedHwF50HRV7ObmrV2vA7y8FMzdM2y4uHv2Dz532X2Nz8dz2fBddv7tyY44yfrBMREREReRQv1omIiIiIPCrj2mDcjSp8xUU27z9dN6qY9tmVNt9b/qbNIUTekT8kS79+afbdNm+9tERfN6jT+8/tPdXmFc+Ptbny5SYd3/sbbfbKXchEH/MX9rD56GRtUdl2tbaUbZl1r80hRG4o4dbQEaPH9yX5+rg3z/6dzTeVXW5zY/Mom3NfXK4v6pFNK4iCl2lL45OjHrU5V/S8UxPQafhlLdp++XTD6VG9x7IdOkXfs1Bf64z+unLSlKIPbb6haJfNb5z3a5vP3X27zaP+T1eJCezQVWmIPMFpWW5vg7AT3bTxWufpeo64snyFzZ8t1NbK6wa8Y/NtN19n88jvOC1iAW1VSzR+sk5ERERE5FG8WCciIiIi8qiMa4PBqTqVvuUKbYP5zKy3bf5e6dvOE3IRjcFZOU4+5PxE86weeufyti8vsPlnl860ef9XqmwObd5uM1tiyAvMEJ223z5b/3ws/cTdzqPy233+0hZtg1l3tMrm0/O323xGjj7/uxW6wsX113/R5mGvaXtZxMZjbImhJGrapJuAPTtKV4ZpCukx/YvXZ9k85Eld9iVvpU63n8yIVl1RCT5tD9hZXmHzT6+bYPMN1/9W30P087msQbpahsmL7jxHlAyt/Xra3Gvc3qie0/jSQJsrnttt87PF023+xc2fsHnJxffYfPV5b9m8/MzTbPYtW2tzolti+Mk6EREREZFH8WKdiIiIiMijMqINxnfqGJs3fFWnz38x/WGbz87TO+YLpP1p/Hbfw/l3jw/S5mMKnCnIMTkhm/9fxbM23/zra2yWH+iKMb7letcyN62gZDE+PYZNtrac9HY2ODpk9Pi8fP3VEc8//LBOTfas1tauP3xTp+SXT/qbzeNydNOYO07VTcnu+dfP2lxx7/s2R7TEECXYqHv1PPLA63qMSkhrZew6nZIP1e+xOXhYa6AzfFXaotba+592Vfrnx/vYMkapwX9EzwN79xae5JHOc5zOYTmo7ci+xv0259ZoW/SuoLaCjc7XOl5cebbNRSt0QyZ4rQ1GRB4UkXoR+cD5XomIvCIim8P/3/tkr0GUaVg3RLFhzRDFjnWTGaJpg3kIwMwTvncngIXGmBEAFoa/JiL1EFg3RLF4CKwZolg9BNZN2uuwDcYY87qIVJ3w7dkApofzXACLAdwRx3F1nbOI/sabdNWXX13wF5svytfpkOxOtL7sDuqGFD+rv8DmkLNq/5Uly2yelqdTOW7bTEWWTr/8fvgjNn/6vO/aXLWzv82B7R/FPFZKrJStmw4cHKV35U+fqHfGu61fLUZbvGrf1rYXABj2eq3NwZ2aD3xhnM1+p10sH7rK0uxC3ejlwA26SswDU6faPPh72oIT3LjlZL8KeUw61Exg2w6bC5wWl4jHdLHdJeK1Zpxh89ardIr+9vNebOvhaHJqM3exnhfRuLuNR1MqSIe66YivWXtafA09T/LIjplmvW4b9A/Nn6/4V5sfm6Eb8x24Sjet7P3uAJuD1Tv1NRPQEtPZG0z7G2M+PtPWAeh/sgcTEQDWDVGsWDNEsWPdpJkurwZjjDEA2r1TRURuEpHlIrK8Fbwxkgg4ed2wZoj+Gc81RLHjuSY9dHY1mN0iUmaMqRWRMgD17T3QGHMfgPsAoEhK4n/7udPu4svVdpL9n5lo8+fOfsfmWQU6pQE4d/a244jR6Zfv1Z0b8bNn39XF8vsu09cS57d8edTpNo+cut3me4c+YXOZX1twqrJ0tZqcKY02t75eoq/PNphUFVXddHvNdEFLsdbbWUW6OUvIORc0OStfFFZHPt8c1PrzVQ3Sx/XSVVyCzlS9+7rFzoozN/faYHPBGD3JPF00o+NfglKJd841MQrFsd3FPc8Fp+t5Z+t1+v3/nabnlE/30LaWZS16brrm5dtsHrOwQce6/0D8xkpekPLnmggBXd3Id6zt1fZOxvTsYXPLBD3v7DxfN+k7a/RGm4dm6XXfD8c/Z/P95Z+xOatWa8zLbTDPAJgTznMAzI/PcIjSGuuGKDasGaLYsW7STDRLNz4C4G0Ao0SkRkRuBHAXgItFZDOAi8JfE1EY64YoNqwZotixbjJDNKvBXNPOjy6M81g6RXJ0tYjDMyfYnH2DTlFc02up84y2f+UWoyu1rDim0+23O5u6HFtQGvGcMa8404hb225NKR2odw9X7x5i8z9uHmrztT1r0ZY5w7V9528jZtncd43exR88eLDN51Jyeb1uYuHvVWzz0RKdghyaozOrbv1saO1rc6/NkX2Q7p34h88Zpq/Vewc64r7H0had1vzZq5+yecyeOpsTu2UFdVU61Uyn+LRdxT9scMSPGif3s3nvJ4/a/NNJT9k8u4euPrPKaRX46urrbB7za213CW3eZnMipvGpe2R83bTjwAhtp2zprddhZoK2Yn5//NM2X9tTz2f79KlY3Vxps79F23GO3wqQOF2+wZSIiIiIiLoHL9aJiIiIiDyqs6vBJJVk6bD9Zbp8aP2/6BT746N0c6Ex2XrHb3vWteoU5LfXXmVzyc90uj171QcRzwk2OSvLtDMl4m6SUfp+H5sf3DHN5mvHPdnmcy8r1Pf7/VCnDaafvg7YBkPdrVSPt+ZBOl1+Zq5Oqbc6h//mFp1yzPnghPYWp3b3jtU8s/f2Nt+62VmN6bUjOo5b39b2tNH/p9OX7kYVRMkk2dqi6cvX1kq4uajQxqNDdMWv7edGnrPOu2S1zXcOWGBzZZauJPZei3729k2nfbPoz07b5Fq3JZQofX3uPG0jPq1Az0Pn5+sSZcU+rdHFR7WWfrD5Spv3vanns6ptumJM8JiemxKBn6wTEREREXkUL9aJiIiIiDwqJdtgfIXamrLvrIE23zlhns2D/CG0xV1RYldQ7+y9q/pym3P+ptORviU6laKP7pzcj3STo11LdNwrR+pYT8vRfz+5U5zHqnRVjUCpTmvKli4OiqgjOc6UfK4eq4U+3YTsUKid3e8k8vOAY6cPt3nghTodeX2v95xH6cZgG1v1T9T3P9AaHfN9bX0J7HRWUwp1tUqJusDZvEjG6mpH+0frikpH+mhNHK7Q/rHR03R1lnmD/x7xssOztQ6ynPo4ZLTu/mu7Tt3nPKjnsIJ5bH2hFOfUlel4L0sAwE/6rbDZ3dxyY6u2vjxycLTNv35bF88Z+6NdNhft0dcJHtWVmBKNn6wTEREREXkUL9aJiIiIiDwqddpgnGkQ9NNNV+o+qdMbn+qh04hFPm0hcbmrvnx3s94xf/jRMptLHnm7S0NtT2DrdpurHtN/J1079is2Lz/vdzYXirYZ3D9trs23rfiqzQO6Z6hEMfE59Zkn2mqGfiURj9v6OW2puadyoc0VWboqxr5gs833119sc8E8bSUIVK/r2oCJuoEvX887G2/UdsV5l/3K5vE5Ha9OBuR2/BAAuwLaRrNpzSCbR85fbnNit24hig/J1RoI9NHzQ6Bf7KuwuBtd3vjODTZX/FlrceRLy/Q9Yn6H7sdP1omIiIiIPIoX60REREREHpUybTC+Qp0GOThe22DumfoXmwtEpzRC7Uz+/W73DJsPPaGtL30f1jt+Ez1t6L5fq9HVNkKiPwkaX9tPIOpmckRXnJDmXjbvC+md8e7mEucXbLb5md+cGvFaj1b91eZx2XogHwrpcX/LR5+0ecv9erd+yePJq1GiaBy+ZJzNYyd8ZPMpOd1zqh2UpeeF3kN1tbHQ5FNslrdWdct7E8WdT9uUmz490ea8r+qKX/OHP+48IQfRuH3952we+Ig+J3fh+zZ7/ZzCT9aJiIiIiDyKF+tERERERB7Fi3UiIiIiIo9KmZ51qRhg8+6z9N8Y5+fvtTlb2u5f+k7tFJtXPDre5ooXd9gcaGlnB8Z4cvqxTL6OtUeBvrcfgrZUt/bRx7R4vbuK0knQ2SG05xbdefcfzRU2X1G4x+ah2XrvyG+Huv2FQH+/HvfZovVwxZZP21z70BCbS5/dqONIRI0SdUHhBw02r92s9fHmIK2J+mBPm3/54UU219X2trnkncjlHX3OWnJTv6bLMv687C2bS3sctvloL13mNLpFIImSQ7L1nLD3C2fY3P/67Tb/asgTNldkxX5Etwb0XFPYovdHmdbYl4FMFn6yTkRERETkUbxYJyIiIiLyqJRpgzHZOo0RKAraXNBO64vrlefOtHno0zX6Orvq4jS66Ph6FNh8aJhOU/5knO5OWuBre3e7n62+xObK9UfbfAxRdzBO+0mP3TqFuGCfLlN3ReFim7OgtVqR1fZOwgDwmc2fsrnur1U2939JdyIO7G0EUaoIVe+yecRD+jf+397WXad9zga/Rdv1b/moQ0f0MdVaAwAQHKrtZ/Ut2kbjcz5vu2zAapt/c+ZgmytfiHr4RAnXep62Jh+97IDNP696ymb3U+Uf1Ov13N836NLAb51zb8Tr9m5nF/tUxU/WiYiIiIg8ihfrREREREQelTJtMO3xtbN6iqvndl09JbD9o5M8snv5+ujd/o1jtFVgZn6zzX5nF9aHm3QFmB6Le9icvXaTzdoQRNQ9fHl5Nhun3Pa1aFuXW4d+0c8AjoQiW7b+5cPLbY5sfdG6DNQmtj2NKF7cljHf0g9s7rvSadd0dusNHdX6MM5qYRhaGfG6m+dord3d790239sPZ5ULfgxHHubrodcz1Rdqbdw++kWb3d15f75XW1+ee+Jsm/tv0CugA2dHrpLX26mB8yu22PxOv0k2FyN1dFjSIjJIRBaJyDoRWSsi3wp/v0REXhGRzeH/793RaxFlAtYMUexYN0SxYc1kjmj+/R0A8B1jzFgAUwDcIiJjAdwJYKExZgSAheGviYg1Q9QZrBui2LBmMkSHbTDGmFoAteHcJCLrAZQDmA1gevhhcwEsBnBHt4wyhfl76z9o952ld/QPumhHWw+P8PqBkTYXb9NdMYJ79rb1cPKItKgZZ0q+5ZxTbK6brtOOc0o/QFtajT7mjaORE401j+qGR2XPbbU5ULe782OltJAWdeMwgUCbOYI47WOF2hqw81NlEQ/7rwt1c7FZBftsrg1q2839H06zueyt1NnshTovVWvG16+vzb1O1Q31Lu2hLb5LW/Tc8ZeVurHl6Kfq9YWclrKXDo+JeI/rivS1vtJnic0vjNOWmj6DdOOyQLWuFOhFMfWsi0gVgNMALAXQP3ygAEAdgP7tPOcmADcBQB4K2noIUdpizRDFjnVDFBvWTHqL+jYUESkE8BSAW40xB92fGWMMANPW84wx9xljJhljJmVz42PKIKwZotixbohiw5pJf1F9si4i2Th+IDxsjJkX/vZuESkzxtSKSBmA+vZfofuE2j4GIx/j/JaSpV+0OzXZRe57tI6rsrlulu6G8fyIJ3R8TpEcCunGGMvqdEWAvk3OThrkeV6umWiEpk2wefsXtMb+MO0hmy/I15UsdDISaDF6rC48ODbidfu9o+cRtr7QiZJVN+6KR1KhLSjHKrSNMXuf/m02azdrjvU84ra+9Cmx+eD04TaPunJjxFOmF2y3Ocv5BPSv+8+wOfAPbS3IWfBWbGOilJWK55pQsbZ8De9Va3OZX4/tX+3Rc1DxMr1GCm760GZ/Xz3mf/HWJyLeY8qF+riJOXpNljW8yeZjVaU2+zzeBhPNajAC4AEA640xdzs/egbAnHCeA2B+/IdHlHpYM0SxY90QxYY1kzmi+WR9GoAvAFgjIu+Hv/c9AHcBeFxEbgSwA8BV3TNEopTDmiGKHeuGKDasmQwRzWowbwDt7jx0YXyH0z5p1RUm/Id1QqDZ6F3vBZKDtjSXOdOO/fvZHNi5K55DtHxDtH1lx4X5Nn/7zOdtLvTptI7bNvDbxok2y3M6RZq9dZvN3dO8Q/HilZqJhq9Apx1loN6DtOMbepTNO/OPNg/J1oaXA86d+MU+bSNwFfgjV6Uwuc7GL+4mMCFu75Xpklk3MlhXhfjoM3qOKLlAp+i3rx5g84j/1r/rpkmn1V0RtVWgj0evIhv3TdaaG/Q1ba15sErPFQCQL/paS47qafv+N863edQSbTHruDmU0kEqnWtite2wsynkbuf8YPToDu0/YPPABc75BMCGc7WdbWJOg80iTnWk0OZhKTRUIiIiIqLMwot1IiIiIiKPimmd9aRqaLSxZI3eAbzycr2reGqebhDhc/4dMvwi3Xxl78Yqm3vO0xukO7UyjDON7y/Wqc0P5+jU5o8+94jNVxTq4v9u68vyFp3ifO5/LrC5/4J1Ngec6R6iLnGOWzN2qM0bv66tWb8//c82u60vi47o3fM7W3WljJuKt9ucLfr6E/KrI956cV/duKUgX1tnQocPRz18ong7OF6n3Ed/UjdT+Xnl0zZ/yXetzb4+euxLTra+kLPSy7Hxg23eN0Jra98pOg3/nYu13eXmYnejvMiWzoMhXXnpK099x+YxD+j0fnDjFhClmpDTxeOu7pfn12uy1gK9nvMX6bWW9NBrp10zI1spR+fUOl+lzqVue/jJOhERERGRR/FinYiIiIjIo1JmbiC4V9tg+tZIG44AAAt1SURBVC7T/J11V9r86sSHbC5yVqf4y7C/2zzt6i/bnLf3VJuzFr4X85j8w6tsbv6NTsEsGfMLm3s743BXrnFXfXn+x9r6Uvz8GpuDzc0xj4moI5KtZd94Sk+bfzDlKZvdDY9uqr7E5jcXj7O5tZ+2ct38ifv0+87d+u8drop47/w6PaZDPL7J40r9Wis/Hf6kzb97dIbNLUF9jE+0ZWxWH13a+tx8bXHJc1plCsVpoTnJ6Xjq21+1ueoZ3ZwpuHlbWw8n8jQ5pi0uNU29bN4T1GP7t5Uv2vzInbpS0t2zLrI5cExbLhdM/1XEewzJctosnfaaYy1ac76W1FmFjJ+sExERERF5FC/WiYiIiIg8KmXaYNyF8M02XWGi5GejbL795zpd/8OBL9lc5tcNKf4w8S823/z162zOrZqquUnfa+84nbLsO3l3xJAG9NAVWn5QvsDmPj59vzdb9N9Dd2zU9zv2d914o//LuupLROuL4dYW1L2M88/1Ap+7mpIe9w1HC21u7a3Tl2eO1Cl4d5qx2Vnp6JGVkyPeb8z+vTYHeXyTR+Q26nG9vl5X88odqlPm47J1yvwnA3WKvr2J9F4+Pb26mxq5dgb17/13q2favHTtsIjHDX9Yx5e1UlerCXEzMUpBZutHNuf/eLTNn7j9Kza/eoZuxndNka50dPbUD20OGT1PuW0vQOQ57Dt1eh4qXqTXZ741q/W1oh9+UvCTdSIiIiIij+LFOhERERGRR6VOG4wjdETvGM5epVMiS5+eYPMrc3Ta5PM9t9t8Wo7+++T3p/7V5leH6ioXh4K6gcX4Am25mZavrwMA2TrLghJnyvOBg7rRzF3/uMzmIfN0KrNknbYQcMMjSqiQtp8U7NFp9N2teld+ALqB1+2DtMXrowElNk/I3akv6fy7f1dQ79Dv/a672gWAA4c6OWii7pO7Rqflix/XFpQz/Nq6+KdT59o8IceZSnem211HnNW/fn9AN0j61Wpd/St7lbaYlb6v7WOjd51QJ1t0fNxAjFJd6KiuNuZftt7mkvv1Gu6sPd+y+bapr9j8tV7RrYD0v3vH2vzKU9oGM/h13QwzmEK1xE/WiYiIiIg8ihfrREREREQelZJtMO4qKcFDOo1ROb/B5p8WfcbmF8/VBfVvd1ZtmZxrnKybEbncKc4Q8iN+9pM9421+a4+2vux4o9Lmkc816RNW6nRPIKAtMUSJZILa+lK4ps7mX704y+bSyx6z+YpCbYmZlqcrIq0+prVxxeZP27zl9Sqbh76uq78AQOjgwU6Omqj7BBv03NFroa4Lkbe3yuYvnnqrza09nCe33QUDcRZqya/Xc83gjdoCkLNlu82BWq0trvJCmcK06Cpk+a9vsHlkQ5XNc5deavO9A9spuBMUVjs1946ew0Lbq9t6uOfxk3UiIiIiIo/ixToRERERkUelZhuMy5kuDK7XdpfhD+lUZvWGETZ/4RTNgb56931nFK3Jsblwp45j6MpaHdOW6O5cJkoYp2YC23WViRF/1o1bftR0jc3/XqmrWrj8jbrSS7/3dMpx6CsbbQ427ot8EjdCIo8L7tHWrexXNZe9Gv/3YjMkkQo1OW3D72prcp939dt9OvG66dBUxk/WiYiIiIg8ihfrREREREQelfptMO0IbtLNknq5ORHvnYD3IIq30Gq9E79ydedfh8c/ERFR/HT4ybqI5InIuyKySkTWisiPwt8fIiJLRWSLiDwmIjkdvRZRpmDdEMWGNUMUG9ZM5oimDaYFwAxjzKkAJgKYKSJTAPwUwC+NMcMB7ANwY/cNkyjlsG6IYsOaIYoNayZDdHixbo47FP4yO/w/A2AGgCfD358L4PJuGSFRCmLdEMWGNUMUG9ZM5ojqBlMR8YvI+wDqAbwC4EMA+40xH688VQOgvJ3n3iQiy0VkeSta2noIUVrqbN2wZihT8VxDFBvWTGaI6mLdGBM0xkwEUAFgMoDR0b6BMeY+Y8wkY8ykbOR2cphEqaezdcOaoUzFcw1RbFgzmSGmpRuNMfsBLAIwFUAvEfl4NZkKADvjPDaitMC6IYoNa4YoNqyZ9BbNajClItIrnPMBXAxgPY4fFFeGHzYHwPzuGiRRqmHdEMWGNUMUG9ZM5ohmnfUyAHNFxI/jF/ePG2OeE5F1AB4VkR8DWAnggW4cJ1GqYd0QxYY1QxQb1kyGEGNM4t5MpAHAYQB7Evam3tAX3vmdBxtjSpM9CIpOuGZ2wFvHUCJ46fdlzaQYnms8gXWTQniu8YR2ayahF+sAICLLjTGTEvqmSZaJvzPFV6YdQ5n2+1L8ZeIxlIm/M8VXph1DqfL7xnSDKRERERERJQ4v1omIiIiIPCoZF+v3JeE9ky0Tf2eKr0w7hjLt96X4y8RjKBN/Z4qvTDuGUuL3TXjPOhERERERRYdtMEREREREHsWLdSIiIiIij0roxbqIzBSRjSKyRUTuTOR7J4KIDBKRRSKyTkTWisi3wt8vEZFXRGRz+P97J3uslBrSvWYA1g3FX7rXDWuG4i3dawZI7bpJWM96eIetTTi+HW4NgGUArjHGrEvIABJARMoAlBljVohITwDvAbgcwA0AGo0xd4WLoLcx5o4kDpVSQCbUDMC6ofjKhLphzVA8ZULNAKldN4n8ZH0ygC3GmK3GmGMAHgUwO4Hv3+2MMbXGmBXh3ARgPYByHP8954YfNhfHDw6ijqR9zQCsG4q7tK8b1gzFWdrXDJDadZPIi/VyANXO1zXh76UlEakCcBqApQD6G2Nqwz+qA9A/ScOi1JJRNQOwbiguMqpuWDMUBxlVM0Dq1Q1vMO0GIlII4CkAtxpjDro/M8f7jrheJtEJWDdEsWHNEMUuFesmkRfrOwEMcr6uCH8vrYhINo4fBA8bY+aFv7073Cv1cc9UfbLGRyklI2oGYN1QXGVE3bBmKI4yomaA1K2bRF6sLwMwQkSGiEgOgKsBPJPA9+92IiIAHgCw3hhzt/OjZwDMCec5AOYnemyUktK+ZgDWDcVd2tcNa4biLO1rBkjtuknoDqYicimAewD4ATxojPmfhL15AojIOQCWAFgDIBT+9vdwvCfqcQCVAHYAuMoY05iUQVJKSfeaAVg3FH/pXjesGYq3dK8ZILXrJqEX60REREREFD3eYEpERERE5FG8WCciIiIi8iherBMREREReRQv1omIiIiIPIoX60REREREHsWL9SiJyEwR2SgiW0TkzmSPh8jrROTbIrJWRD4QkUdEJC/ZYyLyMhHJE5F3RWRVuHZ+lOwxEXldJpxreLEeBRHxA/gtgFkAxgK4RkTGJndURN4lIuUAvglgkjFmHI6v3Xt1ckdF5HktAGYYY04FMBHATBGZkuQxEXlWppxreLEenckAthhjthpjjgF4FMDsJI+JyOuyAOSLSBaAAgC7kjweIk8zxx0Kf5kd/h83QyE6ubQ/1/BiPTrlAKqdr2vC3yOiNhhjdgL4BYCPANQCOGCMeTm5oyLyPhHxi8j7AOoBvGKMWZrsMRF5Vaaca3ixTkRxJyK9cXz2aQiAgQB6iMh1yR0VkfcZY4LGmIkAKgBMFpFxyR4TkVdlyrmGF+vR2QlgkPN1Rfh7RNS2iwBsM8Y0GGNaAcwDcHaSx0SUMowx+wEsAjAz2WMh8rCMONfwYj06ywCMEJEhIpKD4zcvPJPkMRF52UcApohIgYgIgAsBrE/ymIg8TURKRaRXOOcDuBjAhuSOisjTMuJck5XsAaQCY0xARL4OYAGO32n8oDFmbZKHReRZxpilIvIkgBUAAgBWArgvuaMi8rwyAHPDK5D5ADxujHkuyWMi8qxMOdeIMbzRnIiIiIjIi9gGQ0RERETkUbxYJyIiIiLyKF6sExERERF5FC/WiYiIiIg8ihfrREREREQexYt1IiIiIiKP4sU6EREREZFH/X/jBWYG27+ZQwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(valid_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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b45N1AAAAIKSYrAMAAAAhRRtMH2iJHWG1b4adKnf+IDu19MdbL/Z55JO2BVBsr7UWAGEQqbRWro0ftPatL01+3ueqAlvWfK1tkM8711b7PGiVnWzKVqRAooJSq6G2Oqu5z463bX5faStNeMzIBXZ9ce9tT+PogNTR4mKfjzt3s+Wi4CmkRWkfR/DaJqNG+Lj5Yts69e/PfNLnIQU27rjYtsQbopYHNFh2Tc0pG2t3+GQdAAAACCkm6wAAAEBI0QbTB60X244ZJ5zyrs8Fassjb799nM/TXtnic9TZsiYQBgcumebz8efZbkVXDFzt89J2a/36l9V2UumYJ+1b8rS+AN0rGGnL8A1nWj1dMsBq7h/rL094zMCNB3zuj10ygJSI2OfBMwdv9blC+3f+03z+VJ+3fsB2nPmrM173+dpKOzH1sLNxLzlc4fONz/+Nz1PX2En18da21A22Gz1+sq6qo1X1BVVdpaorVfWmztuHqOozqrq+879VPT0XkA+oGSB51A2QHGomf/SmDSYqIl91zk0XkTki8gVVnS4it4jIc865SSLyXOefAVAzwLGgboDkUDN5osc2GOdcg4g0dOYmVV0tInUicoWIzO282z0i8qKIfD0to8ykAtvBJTJ1QsKPGq477PM/1dmOGT+utx1gRrxmSy7RBjsIALkrW2um8cp2n/991FM+10ZsZ5hvbpvrc9Hvh/hc+uJSn60JDOi9bK2bZEWHB3amOMEOEAvuQLFsx8iEx4w9fFiAo+VLzRyLlo/O9nnbFR0+f2f2Iz5fNTC4s5LVX2PMroX/s+sMnyfdbc8jazb56Drs/umSVM+6qo4TkZNF5E0Rqel8o4iI7BCRmm4eM19E5ouIlEp5V3cBchY1AySPugGSQ83ktl7vBqOqA0XkDyJys3PuYPBnzjknIl1+Y8A5d4dzbpZzblaRlHR1FyAnUTNA8qgbIDnUTO7r1SfrqlokR94I9znn/th5c6Oq1jrnGlS1VkR2pmuQ/U6tdSVSZQe/bLpmWMLdbjvtF3a/wA4wG18b6/PE1+0b0OyRkT+ysWaunr7Y51klscBPrBXsrXp7b09YsM3nYOtL8NCX9xPcNcbFYl3eRyP22loY+OuqwD5n0GI7VCM+aYzPsdKe/3or3NdqY1hju3Gwo01mZGPdJKt9kC23nznaDtA77Ow911ZfkfAYbUuYfwFettdMJDDfig60f1MUjgv8XT7U6uHAFMuxYnvs0YZdb7vv/d+6V3w+rdRaXyLa9T9Q1nfY93FfefYEn49723aMibelfweYoN7sBqMicqeIrHbO/TDwo0dE5PrOfL2IPJz64QHZh5oBkkfdAMmhZvJHbz5ZP0tEPikiy1X17c7bvikit4rIg6p6g4hsEZGr0zNEIOtQM0DyqBsgOdRMnujNbjCviEh3aw0XpnY44RAZZN/Wbzpvks9fu/qPCfc7oXifzx9fe53PNW/Zkn50S306hogQy+WaGTvU3vO7zx3tc3FTbdLPVbbTdrgo3NdiPwgcGBatsi89tdYE2msC/+u2D7QFwr3zrK1l6GDbaaM7u1dW+zzl9lH2ups29/hYpFYu142W2HJ7c521bX291nZdagu874e+nfg/gzvYnMbRIVuFvmYC7+l1zcN9brONxKQg0OAx7ATr1nn3Ovv7uHWiXSv+NPcnPh9f3P0UtkithTLmrFEzLmVd3v9A3Npa7t1pO/qN+5YdnJTJnc56/QVTAAAAAP2LyToAAAAQUknts57TAocfRaeN8/nD//Ksz5+oTGxp+X2z7Yyx/091Pte+utbnrve4AMKnJW67VMQCy5eFgUXWx6fagRLy/b693j/umOXzU1um+Rx4ablw7Cqff1T7ZlLP3+rsoIo2Z5UYXMq8ve40n//QMNfn2h9uTuq1gPdTMM5axvZNtzf45KIBPr/bYa0uBdGjdtpzHDWG7ONarbVk7Z9n+rzkRjv0qyay2+cXT3zAHnyixeD1KNj10+F6N8MK3i+461JToK7u3W/Xo8VPT/d5jLzWq9dINz5ZBwAAAEKKyToAAAAQUrTBdIpUDvR5zyTbgeJLVXZoRYkWJTzmX35vuyFNeLbR59ievekYIpBWjyw/yecrBy/x+ZzS9BwQ9M81trz4reGvdHmfIrXPE+Ji9XfYdfjc0U2LwLcbz/P50VV2sEXBLmv3Kdltz1+7pH8PuUD+ODTJtr8YdeIOn/fFbBekH+063+ehLya2XEbZDQZZKN5mu7iMetLmRU9fdbzPs0uf8bk6kp5TVN+NWhvM4812Lbj9Odv1ZfKvrcbGb1nvc1hamflkHQAAAAgpJusAAABASOV1G0xk8CCf91xu3/698Muv+twSt+X26S/OT3j8pD/ZoSvxLVvtB+6ob/IDWWDqd22Z8vPXfs7ntomB9pBujt8oLLZWmZmjtvl817jHEu5XpsVd5p/tm+LzfRvtW/nNB+0AC3fI/rqqfsN2b6rcYkutGrWWmMJm2w1marMd5qQdgbaedqvvePMhywL0TfAgpP0T7L37j2Nf9nlT1G5/9NVTfZ560HZBEhGReFgW44EkBN63bu27Pr992yk+n3PODJ+/cubTPs8fvCFlw/jMyk/5rL8d5vO0BXatijUEWpk70tP62Rd8sg4AAACEFJN1AAAAIKTyug0mOn2cz7susqX0rw17w+fgpvlVL5UmPL5g83s+xw4fFiCbxTbYMuX4+6yVK1Y1oKu7J3AR+3f/7spxPl9Q9+XEO3bTRlO6z5ZLR+yyWioI1JV22M4ZWh9Ysty/PzAQGzetLMiouL0XNdAZua5thM+3v2s7Fk26397f8RbLQC5wgb/Lhz610eeq5dU+3/nOZT7fft5Bn780/QWfLx5gh07+W8M8n194y3aYERHRuF1shi0J5Je3+xwNzOHCjk/WAQAAgJBisg4AAACEVN61wRTWjfR583m2vP/V0/7sc1XEDkXa32Eb5ZftPWphPbCTBJBLops2H/Njg0eHVfVxHK6bDISdi9r1YcRrtqT/aNu5PpfvsmuKLl4aeGz4dqMAUiXWuNP+EMh1O6xFrG1lnc93TP6wz7cNtpaWivesfqasCLRDiojGAm1o+wM792XpoZV8sg4AAACEFJN1AAAAIKTyrg3m8CRbZomfYksjn6m0bycfdrbMsr7DFvIjhxPbYByHHwEAuhK4PrhFK3wetqibu6d7PEDIRRt2+FwYyNXP9/zYXN/9i0/WAQAAgJBisg4AAACEVN61wbRW214VY4fawSrxwCLKsy1DfP7yG9f4PHmr7QwjIiId7AYDAACA9Onxk3VVLVXVt1R1maquVNV/7rx9vKq+qaobVPW3qlqc/uEC2YG6AZJDzQDJoWbyR2/aYA6LyAXOuZNEZKaIzFPVOSLyAxH5kXNuoojsE5Eb0jdMIOtQN0ByqBkgOdRMnuixDcYd2fLkL/0fRZ3/50TkAhH5eOft94jId0Tk9tQPMbWKD8Z8Xvee7Qzz1YHn+/z0shk+T/vGJp9jR2+mz24w6Eau1Q2QbtQMkBxqJn/06gumqhpR1bdFZKeIPCMiG0Vkv3PuL8esbRWRum4eO19VF6nqog45nIoxA1nhWOuGmkG+4loDJIeayQ+9mqw752LOuZkiMkpETheRqb19AefcHc65Wc65WUVScozDBLLPsdYNNYN8xbUGSA41kx+S2g3GObdfVV8QkTNEZLCqFnb+622UiGxLxwBTrfgpO5Fi0lN2++bAfSbLQp9jAvRNLtQN0J+oGSA51Exu681uMNWqOrgzl4nIxSKyWkReEJGrOu92vYg8nK5BAtmGugGSQ80AyaFm8kdvPlmvFZF7VDUiRyb3DzrnHlXVVSLygKp+V0SWisidaRwnkG2oGyA51AyQHGomT6jrxx1NVHWXiBwSkd399qLhMEzC8zuPdc5VZ3oQ6J3Omtki4XoP9Ycw/b7UTJbhWhMK1E0W4VoTCt3WTL9O1kVEVHWRc25Wv75ohuXj74zUyrf3UL79vki9fHwP5ePvjNTKt/dQtvy+vdoNBgAAAED/Y7IOAAAAhFQmJut3ZOA1My0ff2ekVr69h/Lt90Xq5eN7KB9/Z6RWvr2HsuL37feedQAAAAC9QxsMAAAAEFJM1gEAAICQ6tfJuqrOU9W1qrpBVW/pz9fuD6o6WlVfUNVVqrpSVW/qvH2Iqj6jqus7/1uV6bEiO+R6zYhQN0i9XK8bagaplus1I5LdddNvPeudJ2ytkyPH4W4VkYUicq1zblW/DKAfqGqtiNQ655aoaoWILBaRK0Xk0yKy1zl3a2cRVDnnvp7BoSIL5EPNiFA3SK18qBtqBqmUDzUjkt1105+frJ8uIhucc5ucc+0i8oCIXNGPr592zrkG59ySztwkIqtFpE6O/J73dN7tHjny5gB6kvM1I0LdIOVyvm6oGaRYzteMSHbXTX9O1utEpD7w562dt+UkVR0nIieLyJsiUuOca+j80Q4RqcnQsJBd8qpmRKgbpERe1Q01gxTIq5oRyb664QumaaCqA0XkDyJys3PuYPBn7kjfEftlAkehboDkUDNA8rKxbvpzsr5NREYH/jyq87acoqpFcuRNcJ9z7o+dNzd29kr9pWdqZ6bGh6ySFzUjQt0gpfKibqgZpFBe1IxI9tZNf07WF4rIJFUdr6rFIvIxEXmkH18/7VRVReROEVntnPth4EePiMj1nfl6EXm4v8eGrJTzNSNC3SDlcr5uqBmkWM7XjEh2102/nmCqqpeKyI9FJCIidznnvtdvL94PVPVsEXlZRJaLSLzz5m/KkZ6oB0VkjIhsEZGrnXN7MzJIZJVcrxkR6gapl+t1Q80g1XK9ZkSyu276dbIOAAAAoPf4gikAAAAQUkzWAQAAgJBisg4AAACEFJN1AAAAIKSYrAMAAAAhxWS9F1R1iqq+Hfi/g6p6c6bHBYSVqo5W1RdUdZWqrlTVmzI9JiDsVPUuVd2pqisyPRYgW6jqPFVdq6obVPWWTI8nHdi6MUmqGpEjJ3vNds5tyfR4gDDqPAWu1jm3RFUrRGSxiFzpnFuV4aEBoaWq54pIs4jc65ybkenxAGHXOSdbJyIXi8hWOXLA07W5dq3hk/XkXSgiG5moA91zzjU455Z05iYRWS0idZkdFRBuzrkFIhKqw1iAkDtdRDY45zY559pF5AERuSLDY0o5JuvJ+5iI3J/pQQDZQlXHicjJcuSUOAAAUqVOROoDf94qOfjBEJP1JKhqsYh8WER+l+mxANlAVQeKyB9E5Gbn3MFMjwcAgGzDZD05HxSRJc65xkwPBAg7VS2SIxP1+5xzf8z0eAAAOWebiIwO/HlU5205hcl6cq4VWmCAHqmqisidIrLaOffDTI8HAJCTForIJFUd39n98DEReSTDY0o5Juu9pKoD5Mi3jfmEEOjZWSLySRG5ILDl6aWZHhQQZqp6v4i8LiJTVHWrqt6Q6TEBYeaci4rIF0XkKTmykcGDzrmVmR1V6rF1IwAAABBSfLIOAAAAhBSTdQAAACCkmKwDAAAAIcVkHQAAAAgpJusAAABASDFZBwAAAEKKyToAAAAQUkzWAQAAgJBisg4AAACEFJN1AAAAIKSYrAMAAAAh1afJuqrOU9W1qrpBVW9J1aCAXEbdAMmhZoDkUTe5Q51zx/ZA1YiIrBORi0Vkq4gsFJFrnXOrUjc8ILdQN0ByqBkgedRNbinsw2NPF5ENzrlNIiKq+oCIXCEi3b4RirXElcqAPrwk+qpJ9u12zlVnehx5LKm6oWYyj5rJOK41WYi6yTiuNVnm/WqmL5P1OhGpD/x5q4jMfr8HlMoAma0X9uEl0VfPut9vyfQY8lxSdUPNZB41k3Fca7IQdZNxXGuyzPvVTF8m672iqvNFZL6ISKmUp/vlgKxHzQDJo26A5FAz2aMvXzDdJiKjA38e1XlbAufcHc65Wc65WUVS0oeXA3JCj3VDzQAJuNYAyeNak0P6MllfKCKTVHW8qhaLyMdE5JHUDAvIWdQNkBxqBkgedZNDjrkNxjkXVdUvishTIhIRkbuccytTNjIgB1E3QHKoGSB51E1u6VPPunPucRF5PEVjAfICdQMkh5oBkkfd5A5OMAUAAABCisk6AAAAEFJM1gEAAICQYrIOAAAAhGgI0FIAACAASURBVBSTdQAAACCkmKwDAAAAIcVkHQAAAAgpJusAAABASPXpUKRsVFBR4bObMtbn2IAiu89LS9Pz2uXllqsG+xzff8ByS4s9wLm0jAMIg8IRNT63nDTaZ1eoPg9Y2ehzdEu9PZjaQAhpSYn9YfpEHzuGlPpcsqPZZ7d5q8/xQ4fSO7ijaFGxz5GaahvHgYOWm22s1ByQOXyyDgAAAIQUk3UAAAAgpPKuDUYm2HL72r+xtpTioW0+j3spPS+tdSN83nWWtQBU1Lf7XLRguc+uw24HcoEW2l85ey8Y7/OEL67xeUzZXp8fu/tsn2t/tt1nF42ma4hAUoLv6fipU31e/zfWWjlxvLVzbXnOrkHjfm2tL2lrg1FrKysItOl0nDHd5+2nWJtO7avW+qJL1/rsDh9Oz/iArgTet5FBlXZ7oGXLlVjtuVUbEh6ea9cIPlkHAAAAQorJOgAAABBSedcGc2CqLaeMnmBLk3uay7u6e0pFh9tr773Y2m5ay215cfSSAT7H9tEGg9wSqR7mc9s1+32+ddSjPtdEynx+5dIJPusvbPeKXFviRJYJLtGPGulz8/9p8vn5aff6/ODBk32+u9DaYKL1thtMumihtePIFGs9u/SnL/o8u9xaCP5OvujzqI12zYrt2pWeAQKdgi1lBVVVPh887zjLn7DdiiYO2e3z4Wvs2iIiEt1h87tc2MmIT9YBAACAkGKyDgAAAIRUXrTBBJdW9k+yf598pu4dn+9eOyft4yjcZwceFdQP9fn8i1b4vLHSdoyRwOEUEo+ldWxAf2iaPcbn80Yt8bk2Ym1ov26yGtix2PL41s3pHRzwfgKtL8EDvdb8q/1d/qvJv/L5R7vO93nBPaf5PO7Ot32Op3yQ/1tktLXprPrCQJ/vGWQ7j81+7Ms+T312n8+x3dZmAKRb7KwTfF57nc3bfjD3AZ8vKLNdwTZFrTXy20M/mfBcutt2FcuFnfX4ZB0AAAAIKSbrAAAAQEjlRRuMzLSDKiKn2A4UB6KBHWAWD0r7MFxRxOeOwdbW8vEhb/j8zxXX+awRu7+jDQZZqKC0NOHPWz9qu7h8v+otnyNqnxs81Gg7Z4x4M/C+z4Fv9CN7Bf8+PjTTdnSZP/NFn2cV23L79YvsfTz9MVu6j7ZYO2S6RAbb9WzvbGsl+81F/+XzN7Zd4vPYPwdqa1NghxpqDmkWqbZDjjZcYYd2/eR8203pgsBBeSVq15Rxhbar3pqv2k56IiJTbpvks66w3Y6y9XAvPlkHAAAAQqrHybqq3qWqO1V1ReC2Iar6jKqu7/xv1fs9B5BvqBsgOdQMkDzqJj/0pg3mbhG5TUTuDdx2i4g855y7VVVv6fzz11M/vGNXeNw4n1fdaO0un5/0nM8/X3auz5MetWWWdH1D/3C1jePk6e/6PCRiSzkSUUFOuFuysG5SoiDQvjVtQsKP/mrGUp9PCrQMxJx9q3/ltlqfJ72x2e6TyjEijO6WkNVMcCexSJ29L7edZ7efMWC9z1etv9Lnumft7/JYvbXBpEtk6BCf986b7PPEL6zxeXqRXWtefG2Gz1NX7/A52tycriEiPe6WkNVNMvZ+wK4RJ59m7SrB1pcytetDXKw1a6DagV+3n/ObhOf98diLfH7vuVN9HvP4AZ/dMquNsO+41+Mn6865BSKy96ibrxCRezrzPSJypQDwqBsgOdQMkDzqJj8c6xdMa5xzDZ15h4jUdHdHVZ0vIvNFREqlvLu7AfmgV3VDzQAe1xogeVxrckyfd4NxzjlV7fYr4865O0TkDhGRSh3Sb18t33q5HQRx3ZyXfX66cbrP1Y/aN4/d6pVpGYeW2Gs019lSzq2jH/c5EljWCR68gdz1fnWTqZpJlYJiW5psOCdxl6W/r1zt88AC+1b/gkAnmG4p8znWuDMNI0Q2ysS1JlIz3Od3P2U7wHzigy/6vD9mk5wtT4/zedyb7/kc7YdDWVydjXXnbPv17xz5hM9fCewAM/7PNqb4jkCdsQNMTgnjtSYyzA4Sazzfdgj73sgXfN4Vs9uXtQ/zud1Zm+VHBtiCwoVlibsszZ70O5//bfBZPj+31w7ArNlgtRtvaur9L5ABx7obTKOq1oqIdP6XKyrQM+oGSA41AySPuskxxzpZf0REru/M14vIw6kZDpDTqBsgOdQMkDzqJsf02AajqveLyFwRGaaqW0Xk2yJyq4g8qKo3iMgWEbk6nYN8X4GdJwpOsG/AD/ygfbt91gDbeeXBJ872edILm3xO1zJlwcRxPu8+xVaZZhRZ/tXBaT5ri23Y71y69qVBuoW+btJIi63dq3lO4tLkuMJ9PncEdoD5t80f9rl6Kcvw+SiMNeMGDfR50JmNPn916BKf//89p/hctc52lIjtSM+HmcEdanT6RJ/fu8x2gzn/9Hd83thhLQfLf2k7wFS/bjszxdsCfWjIKmGsm57EJtb5fOqUzT4PLbDrxec2fMznLa9aC1rMOovlobNtN5evjnwq4TWmFNln0V8ZZq3QT11ih2S2bLW5V9nTy3wO48FJPU7WnXPXdvOjC1M8FiBnUDdAcqgZIHnUTX7gBFMAAAAgpPq8G0ymFZTamsh7l9ohXd8+7j6fX22a5PPgwB740R22rJnaMdkuF7tOt6XJS8+0pdPVHXb//3jdvqE/bY+15rhYuDfpB/4iuDQvdbZL2NXTliTcryZirV0tzlrPNiy1Zc4prwZ20UjlIIFkBXZGicXts61YYAevIYWHfN471doyC1tP8rl0l7WZRHbuT3oYsWGVPreNsB0stl5gr3f1Ba/4/MmqN3z+/+qtxWzoL1/3mSZLZMqhOtvxa0a57ejy671n+LzzD2N8Hvvfb/pcONx2htm4wlpafn1z4m4uX69+0efaQmtn+/b0x3z+xpnX+TzxZZu3xULYBsMn6wAAAEBIMVkHAAAAQio722ACBwdpuS2nDDh7l88zS7b7/PUX7IvQU9456HO69pzQsaN83nOqLTZ+afjzPj/cdKLPE35j94kfsPFxOAWyRcEgW6bfPduWKT8/9FcJ9xsWseXIF1vts4IB9Zaj27YLEAod1oi1Z+9gnw/ErUXx84Ntt7Ehn/qtz29+5Difn1h3vM/Fy63lq7dap1obzflT7GCxH9c85/O0Yquh3xy0137nZWsDHS+7k35tINXahth7tbLQ3tt/fNdax4auC7SiBOot2L489FFrpVzzmcRDWtvsMiSxwM562zusXbqoyeaSrj3QmxxCfLIOAAAAhBSTdQAAACCksrINRiP2DXgZaksaNx73qs8Vai0kw1+1X9MtXZnewYnIntOrfZ5z8pou7/PH+pk+V75gO2bQ+IJspAMH+Lxvut1eHGhZE0lcjvzKymt8HrI6PYeSAX3hGq1tZPT9tq7+qxNP9/mqQYt9vrjcdjK6eqAdivSfIxf6fPg8W25vifdu6f1QoG6GFNhhYuUFthvayvZWn3+4yrbYnniXHRDI/mIIgz2n2jtx9oCNPh8cbTuyPD9ljs81LwYOAgseClY1yOdP172U8BqDC7r+LHrhwbE+l++wGVe8LXw7wATxyToAAAAQUkzWAQAAgJDKyjYYUfs3hiuzJcGzymw55ab3rvB5wPb+XWJvG2pL/1MG2jeXN3fYbgI73rPDkirFxg1kI1duy5eV0/b4XKKJnwdsi7X43LFgqM9lS9b7zFI9wiLe3Oxz6XPv+Pzm1dbr9fSJ5/jcONv+7v+bD7zg8/wqa3X8732n+nzn4rN6NY4RzxT5POOm5T7/YrS1fr7ROt7n6CrbnSm2qetWTCBTal6x68KC2VN8vmDQKp//NPU0n4eca7vE7D7BWr9i5x3w+eyy+oTX2Bs49atArcXlWyOf8HneubZTUvXL1h4TW2+HU4YFn6wDAAAAIcVkHQAAAAgpJusAAABASGVlz7qLWVdr5MAhn+/bN9vnaRW2XdXTw60vqaS83Od4i/XP9lWkyraQPDjDeuT/boht2fU/B+0Uu4q11oMIZKNItW1RuvPswLZ2J/zI54FakvCYFXYgpAzYYU2FsT170zBCoI8Cp0i7w9b3Gt+42edBjXZy9uC3rFd8wf2zfH6h7AyfI622XeO0A71737tyq6OmqOW3Dttzffe1D9nzPmDPG4vzLRCEy9DXbH722qfstN3PDX3N57sv+7nP6y8aYY8ttO+RjCzc5/OPdp2b8BrP3GdbP1ZfutXn/5xopwx/aIZ9D+XlS6xHfjg96wAAAAB6i8k6AAAAEFJZ2QYjgdPcXJMtibyww9pdvjv5IZ9f+rTdvr3GTg6tWtvz6XEuYltxNY2KJPysbZj9rK3Glho/fdrLPg8tKPO5JWbLl4UtnFWK7KYDAu/t4VYLxxfZdqqRo7Zu/OVO2+aubFegJ4alemQRF7X3bmy/bR8nwbzFYrAKgn/z9/Zdv/0fzvT5Q1W2XePXN1zlc93jdn2Kr3u3l88M9L/Ytgafm35/is/f/LS1cv1sjG2xeG6ptc2s67DW5+tXfcpnvcfaMkVERr9qpwlvHDTG5+drp/r80So7ffjRWSf6PLJupM/Rbdvf71fpN3yyDgAAAIQUk3UAAAAgpLK0DSbwDf2WVp9bH7eTsH5eMdfnz419yefFnxzn88oDtT2+VIHaa9UWHU742egy+ybyqQM2+3xmwklatvvMmkM1Ple/fUiAbBarHuRz+/TWLu+zO5b4Pn/5FdsRaXJ9YMeKFI8NyDZaaJfj+OwZCT+bcLmdcj23fJ3P/7HlEp+nrrUWnHhH/57aDSQjuLPSiCdsvrS8zE4Gvuji4T6fMcLaut5oHOdz8V12EnzFk3ayr4hINLDb35in7LTsH429yOf/e+bvfA7uDPPqh2wnp2E/z5I2GFUdraovqOoqVV2pqjd13j5EVZ9R1fWd/63q6bmAfEDNAMmjboDkUDP5ozdtMFER+apzbrqIzBGRL6jqdBG5RUSec85NEpHnOv8MgJoBjgV1AySHmskTPbbBOOcaRKShMzep6moRqRORK0Rkbufd7hGRF0Xk62kZ5fuIt9lySt1D9u3fTS2Tff7GHNt0f+ToPT4PKmnr8fkPx+x/olUrxyT87K3A2n39qfYP1+l19k3nusAGMtsODfa5aPEan9kXJreEvWb6QktsR6MDEwf4fNNM++Z+PPCOXtZuh8SIiIx4I/Bu37pDgL/I5brpDS22XZTqLypP+Nnto57xeX2H7XpRttkeo7s4WCzf5ELNROvtwKJRf7Tbmzfajiyv1llbStlu2w2w4rFlPscPJ7YpB9ulC95a6fOwyXb40e8m2fPeXGs19tQF03we/uv0HKSZrKR61lV1nIicLCJvikhN5xtFRGSHiNR085j5IjJfRKRUyru6C5CzqBkgedQNkBxqJrf1ejcYVR0oIn8QkZudcweDP3POOenmA2Ln3B3OuVnOuVlFUtLVXYCcRM0AyaNugORQM7mvV5+sq2qRHHkj3Oec+8tCRaOq1jrnGlS1VkR2pmuQ7ytwmEpwOWXonZZrnrP2lfbR9q3glgrL3SmI2nt88vOLE34WPBhj+Tfs0IonPm7fbj5+yFqfC9WWbwoGWgtBbB/f3M81oa6ZPohUD/N5/2T7t/7nB9u39VudHTZ2z87zEx5fsd6uI7GDCdcUIGfrpje0uMjnIWcktoiNLGzy+WvL7SCkmkVWa7HGnPyfBT3IpZoJzuFKg7mb+8e7uf1owbna0KX7fX5rqR2YeWiE7Rr4oYkrfF52uh2kWfiK7RgTfM7+0JvdYFRE7hSR1c65HwZ+9IiIXN+ZrxeRh1M/PCD7UDNA8qgbIDnUTP7ozSfrZ4nIJ0Vkuaq+3XnbN0XkVhF5UFVvkCMHK1+dniECWYeaAZJH3QDJoWbyRG92g3lFRLSbH1+Y2uGkR3Sz7RJTEMjJdmi9364tJfvtpw3tg7q8z6ASOzimeYS1E8i+fV3cG9kqF2qmO7FaO4SibXTX7Vu7YrY8+MaLxyf8bNJeaxHr7RIm8kMu1013tMh2c3Gj7JC+r014NOF+pYHD+VpetWvH8GWbfO7fRXmEQT7WTF/F37Gd+OqeO93nf5t6mc//ctxDPv/xulN9nrbarn+xnbvsSV369/Tr9RdMAQAAAPQvJusAAABASCW1zzq659RWoiKBBf6I2r+HdrUO9Ll49fr+GRiQQrFy+yujuMLaYA47W4R/rW2szxN/k3hQS2xHVmxKAPSLyIjhPq//hB2sd3ZpY8L9NkWtabNys11fog2BXWO0m26IfliiB7JGoB4qF9qOM5ufs+tW9SRrWX72Az/y+YaHvuxz2QK7T7zJdmtKFz5ZBwAAAEKKyToAAAAQUrTBpIgGllZigX8DxZwtWTrX3Ze2gezQNNqW4y+ZYIeENQcOQlpwYLLPuvdAwuNdtEMAHBEbbjuHferSF3yuKChOuN93Nl/hc3mj1VBBuR0RX1A12B4QaImJbt2WkrECuSa6bbvPo56znV7mTft7nxeff5vPLZ+3A5VKd9hhm7rMDr9M12FJfLIOAAAAhBSTdQAAACCkaINJkXiRLTuWFNgySFRiPh+ORew+/TMsoO8K7H3bVmX/vj+zYoPP7YE2sH3ttjTv2o86OImdKQATaFcZFGnt9m4tHdYWs+dUu3q0XH6iz5HaFp/L3rCdx0b8mDYYoEuB61Fkve0MM/ypKT6/fIYdQvar4+/1+W+O/4rPQzdV+BxL0yGXfLIOAAAAhBSTdQAAACCkaINJkQMz7Bv651as8flfd53i88GXa3yulI39MzAghYqbbNlwYfN4n08qsaX21bvsfT46xhI80Ff/MvEhnxeNPM7n1/ZZXrpwos/Dl3bfUgPgf4sdOOjz0FfsuvVPP/+0z0986d993jPPaqxq9Sh7ooW0wQAAAAB5hck6AAAAEFK0waTBd9Z82Oe2BfZN4jGP7/U5LkCWCBzsNWSZLRX++ZnZPm87xw5kaVtnB724ji1pHhyQvSK7rZ5+9Pw8nwsufDzhfosOjvP5xZW2U0XlctslZsJC2w0m8o61WXKtAXohbjv3xbY1+Dz6IZsm/9d1Z/r8+RMX+Pw/J17i8/C1lfY8B62++4pP1gEAAICQYrIOAAAAhBRtMCky8lk7OKYgWuXz8IXv+Ryt3ypA1gkcHKGrbXn9uN9N8HnDBluaH7XVDgVz7bZLEoBE8R07fZ58jy2f377t8oT7le+wGpy20Nop3bv19lxthy0HlvQBJMdFA9ew7Y0+P/zg2T5/9hPWqtY8xg43qxk2xJ6INhgAAAAg9zFZBwAAAEKKNpgUGfjgG13eHu3yViA7xdva7A+LV/o4dHHX93dd3wxAjqqnRSt8rFvU/WNocAH6T7zVDj8ac9tyn39abbs3Dd4WuNLF07P/Uo+frKtqqaq+parLVHWlqv5z5+3jVfVNVd2gqr9V1eKengvIF9QNkBxqBkgONZM/etMGc1hELnDOnSQiM0VknqrOEZEfiMiPnHMTRWSfiNyQvmECWYe6AZJDzQDJoWbyRI9tMM45JyLNnX8s6vw/JyIXiMjHO2+/R0S+IyK3p36IQPahboDkUDNAcqiZfhDYDS3e1OTzxK/0b+tzr75gqqoRVX1bRHaKyDMislFE9jvn/jKurSJSl54hAtmJugGSQ80AyaFm8kOvJuvOuZhzbqaIjBKR00Vkam9fQFXnq+oiVV3UIYd7fgCQI461bqgZ5CuuNUByqJn8kNTWjc65/SLygoicISKDVfUvbTSjRGRbN4+5wzk3yzk3q0hK+jRYIBslWzfUDPId1xogOdRMbuvNbjDVqjq4M5eJyMUislqOvCmu6rzb9SLycLoGCWQb6gZIDjUDJIeayR+92We9VkTuUdWIHJncP+ice1RVV4nIA6r6XRFZKiJ3pnGcQLahboDkUDNAcqiZPKHO9d+xJaq6S0QOicjufnvRcBgm4fmdxzrnqjM9CPROZ81skXC9h/pDmH5faibLcK0JBeomi3CtCYVua6ZfJ+siIqq6yDk3q19fNMPy8XdGauXbeyjffl+kXj6+h/Lxd0Zq5dt7KFt+36S+YAoAAACg/zBZBwAAAEIqE5P1OzLwmpmWj78zUivf3kP59vsi9fLxPZSPvzNSK9/eQ1nx+/Z7zzoAAACA3qENBgAAAAgpJusAAABASPXrZF1V56nqWlXdoKq39Odr9wdVHa2qL6jqKlVdqao3dd4+RFWfUdX1nf+tyvRYkR1yvWZEqBukXq7XDTWDVMv1mhHJ7rrpt571zhO21smR43C3ishCEbnWObeqXwbQD1S1VkRqnXNLVLVCRBaLyJUi8mkR2eucu7WzCKqcc1/P4FCRBfKhZkSoG6RWPtQNNYNUyoeaEcnuuunPT9ZPF5ENzrlNzrl2EXlARK7ox9dPO+dcg3NuSWduEpHVIlInR37Pezrvdo8ceXMAPcn5mhGhbpByOV831AxSLOdrRiS766Y/J+t1IlIf+PPWzttykqqOE5GTReRNEalxzjV0/miHiNRkaFjILnlVMyLUDVIir+qGmkEK5FXNiGRf3fAF0zRQ1YEi8gcRudk5dzD4M3ek74j9MoGjUDdAcqgZIHnZWDf9OVnfJiKjA38e1XlbTlHVIjnyJrjPOffHzpsbO3ul/tIztTNT40NWyYuaEaFukFJ5UTfUDFIoL2pGJHvrpj8n6wtFZJKqjlfVYhH5mIg80o+vn3aqqiJyp4isds79MPCjR0Tk+s58vYg83N9jQ1bK+ZoRoW6QcjlfN9QMUizna0Yku+umX08wVdVLReTHIhIRkbucc9/rtxfvB6p6toi8LCLLRSTeefM35UhP1IMiMkZEtojI1c65vRkZJLJKrteMCHWD1Mv1uqFmkGq5XjMi2V03/TpZBwAAANB7fMEUAAAACCkm6wAAAEBIMVkHAAAAQorJOgAAABBSTNYBAACAkGKyngRVjajqUlV9NNNjAcJOVe9S1Z2quiLTYwGyCdcaoPdU9SZVXaGqK1X15kyPJx2YrCfnJhFZnelBAFnibhGZl+lBAFmIaw3QC6o6Q0Q+KyKni8hJIvIhVZ2Y2VGlHpP1XlLVUSJymYj8MtNjAbKBc26BiITqYAkg7LjWAEmZJiJvOudanHNREXlJRD6a4TGlHJP13vuxiPyj2KlXAACkGtcaoPdWiMg5qjpUVctF5FIRGZ3hMaUck/VeUNUPichO59ziTI8FAJCbuNYAyXHOrRaRH4jI0yLypIi8LSKxjA4qDZis985ZIvJhVd0sIg+IyAWq+pvMDgkAkGO41gBJcs7d6Zw71Tl3rojsE5F1mR5TqqlzLtNjyCqqOldE/sE596FMjwUIO1UdJyKPOudmZHgoQFbhWgP0jqoOd87tVNUxcuQT9jnOuf2ZHlcq8ck6gLRQ1ftF5HURmaKqW1X1hkyPCQCQc/6gqqtE5M8i8oVcm6iL8Mk6AAAAEFp8sg4AAACEFJN1AAAAIKSYrAMAAAAhxWQdAAAACCkm6wAAAEBIMVkHAAAAQorJOgAAABBSTNYBAACAkGKyDgAAAIQUk3UAAAAgpJisAwAAACHFZB0AAAAIqT5N1lV1nqquVdUNqnpLqgYF5DLqBkgONQMkj7rJHeqcO7YHqkZEZJ2IXCwiW0VkoYhc65xblbrhAbmFugGSQ80AyaNuckthHx57uohscM5tEhFR1QdE5AoR6faNUKwlrlQG9OEl0VdNsm+3c6460+PIY0nVDTWTedRMxnGtyULUTcZxrcky71czfZms14lIfeDPW0Vk9tF3UtX5IjJfRKRUymW2XtiHl0RfPet+vyXTY8hzPdYNNRMu1EzGca3JQtRNxnGtyTLvVzNp/4Kpc+4O59ws59ysIilJ98sBWY+aAZJH3QDJoWayR18m69tEZHTgz6M6bwPQPeoGSA41AySPuskhfZmsLxSRSao6XlWLReRjIvJIaoYF5CzqBkgONQMkj7rJIcfcs+6ci6rqF0XkKRGJiMhdzrmVKRsZkIOoGyA51AyQPOomt/TlC6binHtcRB5P0ViAvEDdAMmhZoDkUTe5gxNMAQAAgJDq0yfrAAAAQJ8VRHyMnj/T5y3zin2ODYj5XLnWprAjf74k4anibW3pGGHG8Mk6AAAAEFJM1gEAAICQog0mDbTIlmwKKgfa7eXlXT/AOR/j+/b7HD1lsj02Gve5cE+zPXbnbh9j+w8c03gBAOkTqaryWcvLfI4F/v6OjBju8+EJlkVEDo61A2tK91sbQFGz5cKmdsv1u3yONuw41mED6RdofdFTp/u86Wr7LPn3H/iJzxGx+dJHyj7vc12xzbtERIQ2GAAAAAD9gck6AAAAEFK0wfRBYd1In2Mjh/rcPMbaXZpG2RJPu62EJrIOFxlYP8bnvRfaMo6L2/MUNNoS6chX7HXLHn6rdwMHAKScFtolVadO9HnnGfaXf+sw9bm8cazPh0ba7e6kpoTn/cjERT6/uWecPW+TtVk277XrTvmG43westZeo/LtRp+jm+vtBeLWTgP0p8jEcT6v/yt7P396zkt2e3uNz7e9e77PwxZY64trtzawXMQn6wAAAEBIMVkHAAAAQoo2mL8IfCM5MnCA3T6i2sdodUXCQ+rn2LJj66ktPv/1tNd9/mTVGz5PK+56N5gOZ0uQT7faa59UbDsF1ERsB4EitbGOH3Kjz5Mf7vLpAQD9oOA4azlZ95nBPn/rsj/4fEn5Jp+bnLW+dDj77KyiILEtpcM2wJAbhrzW5WuX2FNJ5GLLfz5ku4r9+DdX+jzujkM+x/bstQfQEoN+tPNca+u95EI72Gh8yU6fv/ns1T5P/qXthjdgqc21At3EOYlP1gEAAICQYrIOAAAAhBRtMJ0igyp9bjnDvsXfcP1hnx+fc1vCY8YUdt2akqibg5AC4oEFnPNK9wd+Yv/vOew6uswSC6x9AmEQPOSiIPH9qSV2uIsWBf76iQTqpyPqY/yw1Z8L3M5SPUIj8H7ffLXtc9uv/QAAIABJREFUWvHFeY/7/MkKO5io1dn7fnuHfV72wL7ZPq9tsucREdnVYu2R0VigvtT6Yy4audbnb1bbzmCfqbRdX6be8F92nzV/63Plc7aTBofroT8dHmzXiKHF1uLy0v6pPg97y+rELV3ZPwMLGT5ZBwAAAEKKyToAAAAQUrTBdNJya2nZO7XI5zfO/InPlQWJLS0RPfZ/6+yL2e4xv22a5PNDO2b63BHvrrXGDFxf1ON9gHQrKA/UxiTbEaN9WGLNbDvPDrGoOs2+7T++0najeH2d1cOIp+39XbV4l8+xdRv7NmAgRQqm2/u16mxrd7mmYoXPDYGurfkbbWeLA7fbIXgVm6wFILIn8VCkqoP77A+xwJMFrkELLjzD8o3WyvnSCb/3+ZTiwEF7n7V6ijaMs99n8Rq7T6ANDUiHuF0SpESt1XHRjtE+175z0OfAxkh5hU/WAQAAgJBisg4AAACEVM62wUSGDvF59+VTfA6cQSHDFgaWFvfYLiwaWGXcH7edWjZFA7tRiMjgAvsG/a/22RLkY1uO9/nAZjsYo3KD/dto0GZ7ruKDtrtL4Z5Wn0tczws+Y/baARvR97kfkAqRGjvAovmMcT7Xz7P7XHGaHWxxQvnWhMdPKLbWl9GFtrRZHqjLxpHW+rLmzBE+r2gd5fPT22yngL1rhvo8+b/t+WMbt9iTsnsM0iRWaTscja+0NphhgYPsbtlxms/b/zTO55FP284W8VZrUYl2HPW3eS/ev5XPWvtKU9zq48LPfdTnx6c/6PO90+71+SNz/tHn0e8Ns3Fs3dbj6wLJKhxhux0NOMvascaW2EGQLWts7qSrl/pMGwwAAACAUOlxsq6qd6nqTlVdEbhtiKo+o6rrO/9bld5hAtmFugGSQ80AyaNu8kNv2mDuFpHbROTewG23iMhzzrlbVfWWzj9/PfXD6wW19fOCMlt2XP+P1vpy8QW2hPLE4hN9rlpjy5eywdpgRj5nO1NcWWDLg+WN1hIjItI2xP6tM2CHLVNWN9g36Gv32XPpXjtsIh44eMIF2mviUZpZcsTdEua66UHhWPsm/t6z6nzecZ69zy84aZXP3xr2ps+nldh7e/tRh3bdG2gXe3uvtbVcPXKRz3PK3vW5OtAq89Wh9honl1uLy9I6233mke3n+Dz6D1ZL0c3vCULvbsnCmokX265dZRFrabyvqdbnPz9jBx5NesRaw6IpPIAoeJhRxUvrfd4z0K6FS79jl/w5JZbbTredaKKvWAup0AaTDe6WLKub7VdN8PmTY5/0uVQDLcGtdu2It1mLWCoF23E6Jli9tg+yVkwNTPvKNtl8Lr7F6rg/dk3q8ZN159wCEdl71M1XiMg9nfkeEbkyxeMCshp1AySHmgGSR93kh2P9gmmNc66hM+8QkZru7qiq80VkvohIqZR3dzcgH/SqbqgZwONaAySPa02O6fNuMM45p6rdfkHXOXeHiNwhIlKpQ1LzRd5A60ukosLn+vkzfJ5/2VM+7+6w+5RvsV+5qMG+uR8NLLPomg0+1zVU+hzbG9g9RkSqBtnPgt/kDy6JsAcFuvJ+dZOWmumFpmvm+LzjHHvZmSfaAUTfHrnA57GFVg/PHprm8y93nOvziqdsCV5EZGC9PW/pfquO7330Up9rh1tLWsMOa7X86dn3+Ty92Gq3KHCQxt6PDfD5nS0n2es2NPrMQS/ZKSPXml7Ydl6pz58YZO0nDzWe7PPQZTac/mjJiu2xD1qHvG11+pPtF/t88rgnfP7IlHd8frPadq4JNIoiS4XxWtN6rh36dcGA1T7/pPEinyu2pG44hy+z93Tjqdbi0jbWdvQbVmMtl9UDrC0sFrcGlPWNtlNS5Uun+DziyUBr25b6FIz4fzvW3WAaVbVWRKTzvzt7uD8A6gZIFjUDJI+6yTHHOll/RESu78zXi8jDqRkOkNOoGyA51AyQPOomx/TYBqOq94vIXBEZpqpbReTbInKriDyoqjeIyBYRuTqdgxQRkQL7xn2k2g5B2X/+cT5fft0rPl9RYct68/78FZ8nP2NLHbGt27t8qeDuLMHlxKPFUvhNfuSW0NSNDcjH4K5J7WdY+4r7tB1OcdfkP/k8pchqZkm7LQPeuOYTPu9/yQ4vqqi3r8+PfXh5wjDizba8KIFDv0pmnulzw/5qn2tet3F/Y/BHfH5t1q/sOWWPz/9Ra206Z9TZMmVFcbG9LG0woRS6mvnfA/QxeOje4DnWYnVCiS2Hf3+HnRQ2Zmvm3nMFB1t8XrRkos8tY23njVMH2A5Mr5bZzjW0wYRf6OumkxbZ38EnjrS5V0ngFMoX37br0bQ37YCk7tqJtdCmsJG62oSfNXzQdhvTy+wa8c9TrEX61BLb7Wh/3MZXH7X6PqXEWi7LJ9vfAZ8bfYXPOxttHjqg0a6jqdzFpsfJunPu2m5+dGHKRgHkGOoGSA41AySPuskPnGAKAAAAhFSfd4PpL5HAzivNc8b5XHajLafcMOQ1n7+2xZbMRz8Z+Fbxcvu2vuMAIuSJgpLAgvbkcT7Wf9aWwn8/zXZbGVtoNfOzvaf6/IvXbaeX0U/YkuCYJ5f4HFz6SzxGTBLbccptq7CC9sB9Kq0uDxxn4z7cZLtuDCywfLytXsqrbfaKRc2Buo+xLxP6RgttF4mW2bbsfebwxT5v7rA2MbfRdiYqrrfl9v6+6rjATmUVm6ydtM312+YfyFPBNhU5cZKP4wes9PmlFrt98HK7f2y1zdWCz1NQZTuEdUy1VpctF1p7p4jIl695yOfLB6zzeX10oM8/3T3X58fXT7fXbrBrU9lY27nmhyc+6PP/Gf2ozx+fYq3WFW8M8jm+I3VtMHyyDgAAAIQUk3UAAAAgpLKmDUZGDvdx60W2lL562h98vr/Jlib3/mCcz+UvrPA5zk4QyEM60Jbkd86yZbp7T7/N52lFtsy/st0W63/x6nk+T/wfa5sparRdYtzEcT4HPwHQo5ba4+XWs3KozpYtna3Oy6TRtrvGD86z+h5VGGwgsN/nsLMxzV/6aZ9Hrm21122j7tE3WmotWVsus2vQ96oW+fyDejvca/hia8nqj4OQgLDRQPvlux+2687XKm2XsH/f/EGfK7Z13SQWbH05MNfmefuuOeTza7N/lvCYQ87q794DdkDZz5ec43PNk3Y9mvisHf4X22U7ukQG27i/cpdtqvO7U37hc/ugwHWuPLEdJ1X4ZB0AAAAIKSbrAAAAQEhlTRtMe7Ute4+ZYsvkHc52eXhk50k+l+60b+G64E4Qgd0ohG/DI18E3usa2KJlf9y+9d7h7FvvMbE6qaqzw782/vVgn0tq7bFThttp1vHAY5vaE49VmTvclj+vHbzQ5yKx8UUCJTqm0L65H3PWprMvZge9PNNqh2EMerDCnnPFantsnN1g0DdabO+/q894y+dI4L277kVbop+wJHM7wABhoBHrb4xOsr+zqyPWvrJlj7W4jNofqJTAXK19xmifiz5r879VM2zHl31H/RV/y9YP+bzmrsBhS4/ZAWDRBjvwqLsrhIvZBbNlu12Pds20658GH5ymeSWfrAMAAAAhxWQdAAAACKmsaYNpGWHf2v2vSb/1OXg4yn+O+5PP53zuZp9HPjnT58Gvb/U5utWWKYFcFj9gO7cMf97e99+56nKffzzV6mpGse2w8sIpd9vznGxLfAXBlrKAjsC38Es1kvCzosCfC6Xnb83HAs+1rsNa265feb3Pg75vLXKDltlhG7Hm5h6fH+irJ5pO9Ll6ma2HR7ds7eruQP4ItMGMHGrtlAO058awyPBqnxtOsXne/ZPv9/lAoKVz7uIbEh5f8wObM1YvW+ZztLVVUuGBPXN8HrrCrouxrQ0pef6j8ck6AAAAEFJM1gEAAICQYrIOAAAAhFTW9KxHS60/9oTAFlpBtRHbSudPF9hpVivPHunzrasusQe8PtbH0t3Wc9ReYa9Vsc16EAduTOyB1cCWkK2jbcu44r3tPheuq/c5tmdvl+MG0s1FrUcwts22qxryD+N8/uaov/V5ywftr4aTZ23wefyAPT6vb7JThbc12SlvA4rt/f/BWushFxG5YfDbPg+LDJCePNdqPY+ffeVGnyf+t9VeZNk6n+PBfkS2ZkVfFdj7L3gKcHmB1VBH4PhdjQXec2wXCnjDy21r4JLA150SvvoUyK0zx/hceJ5ddwoCW6VetuITPtf+a+Jnz7pyrc/xtjY5VhoYoCuzmm6OWk984WFrnndR+75XKvHJOgDg/7V352FyV3W+xz/f7vSSpLOvnZCQfWGRJYEAAsoqqCPKqICKDKKMDjowjndkeOZexqvXi8/M6MwdlzsMIHEukInsKltAVsGQsGYjJGQPWchK1k5317l/dHG+v2bSpCtdXfWrqvfreXj4dHUtp3jqy+90/b6/cwAAKcVkHQAAAEipkmmD6b3BTy18de1HYv7l6Gdjrjb/2+P4urpE9lMoRx//y5j/MHlCzNtb/BRnQ7WfMtlwwHdsXL/PsyRlgp8emVj3Vsxr9gyM+fWVY2Me9PTkmAf+8gUBxRCavU2ldbG3kNQu89N6k9eNiXnb77xdbEutf56rE6f++jcllqxr8JaY2dec0O61P/OhZBuM37474zX3w3dOjvmehz4c86T7E21orydaX5qaBHSH6gH+Wd507hExf6yP75z4+91HFXRMOevjx7Zdx3ut1CdO7+/PJFpL6R5DN6iyg3+w2nUrJvL+gT49nTbMl0GtTWzB3ZqYg1W91X6p1NautL70SEyNjxge47dm/D7m+Tv9uFi9PzFwdjAFAAAAKguTdQAAACClSqYNptfCt2Ne/PNjYh53zsSYbzv99pgHVu+N+cgeflri2Npke4yv1JJPyVP6i0f7uf6/Gn5JzE0bT4q57uF53TIOIBcdtcfULD70Y6sHDIh51ye83evCUUva3W9gB18P3LjptJgfvtd3hht/7zs+pjd8VRpWekEhWK23hu1t9FPuR/bwVYdqqg69G2OhVfXx1cl2H+07Qf75tCdj7lPl7+2Hr18Y85Fr/dgJFEu/N3z1mMdfOjrmb398TsxfGD0/5v+46OPtHj/k94mV+Db46k3JldE6Uj1qZMxL/sKPbT/r83rMP336vJgnr/PdWRObqubVIb9ZN7NRZvakmS02s0Vmdm329oFmNsfMlmX/PeBQzwVUAmoGyB11A+SGmqkcnWmDaZH01yGEoySdIukaMztK0vWSngghTJT0RPZnANQMcDioGyA31EyFOGQbTAhhg6QN2bzLzJZIGinpIkkfzd5tpqSnJH23W0YpqeXtDTEPvHuH59f8ityr1/umLplaP03eY/SemIf28xUlThq8OuavDnou5qm1vrnS4Wioqo/5ZO+60bfG+inI//GRS2Me+3CXXg4pk5aa6W7VgwfFvPMsX1mpz5XrY/7rQXPbPaZvVc+YVzZ7Ld73R28Lm5JsfVmyLD+DRepVSt10l+q+fWNuPm58zOvP8u/kvtzvlZhf2O/37/tQgz/PW76ZDNs6pVsqaya5kVg/b8caXu9zuNp2OyF1YLG3PY6721ufLx7i87yfnDA75hFfWdHu4UvHeg2Mudc/38nnrervqz0dONbnkqvP8onbTeffGfM/bD435lGP+BzT1nibdnfJ6QJTMxsj6QRJcyUNy35QJGmjpGF5HRlQBqgZIHfUDZAbaqa8dXqybmYNku6RdF0I4d3k70IIQR2szmpmV5vZfDOb3yzWQ0bloGaA3FE3QG6omfLXqdVgzKxGbR+EO0II92Zv3mRmjSGEDWbWKGnzwR4bQrhZ0s2S1NcGHv4SDonVHzJ7E1erv+arTYx97eAPrT7aV6doHuwbGz3y4caYe3zOr+H90TDfuKUp+GZMj+71UyaS9PhOv0L5mN5+6v+C3r6SxugefvplRM12H8cQf16Un1TUTDeo6u0brOybPi7m3V/048MDk2bFnGx7kaSWxIn1G9/2q/eHPu+nRTPLVuZnsCg5aaubkPHjQmIBGO1NPHsmFHEF5EQ7QWbS6JhXf8JbMS/7qLd47kpsIvONl78Y89gXfePA1m1+nEL6pa1mrMo/Y6Gnt5PU2MGbqgb19Tbl5obBMVc3+6ottc8vivnIXb4C4De+cXnM5x/j95GkxVO8zXLfSG/H6Wn++M0n+3xwz/l+/+uOfjTm1/d6Xf3hjhNjHvmit9O0vtvu76Nu0ZnVYEzSrZKWhBB+nPjVg5KuyOYrJD2Q/+EBpYeaAXJH3QC5oWYqR2e+Wf+wpMslLTCz975yvkHSTZJmm9lVklZL+nz3DBEoOdQMkDvqBsgNNVMhOrMazHOSOrp095z8Dqd7tC7yq9uTpxJGZE6I+b5jj4v5h0NfjnlXxjeK+dc17d/u5t+Mivl30/xq5UEz/HTK6AY/PdIc/CpptXTiamiUpHKomY7Y6BExrzvb//fxh2m3xDy42ltlmkP7U5937fLNJl5NXOE/6pk1Mbd0YtMKlJ9U1s0+3+Cu30r/LL/RPPhg91aoTgw/sSqGMl1bV6Wql69QVjXEV2HK9Pc2y7Xn+uouX7zwqZivGvBizN9Z+6mYG//dWxTC6sRKGmw4VjLSWDPtNh1avynG+xb4fOtPzvBW4z89wvO/T7sg5rErfYUxbdoSoy3zY8XUv/XP8MufOL7dOAbv8c9xs3fBaO2V3s785bOejvkz/Xze97PNZ8X8x7t83CNu9rG27i3s5mFFbLYDAAAA8EGYrAMAAAAp1anVYMpV7SrffKX6Db/it+kMP42TPKU/Z+pv2j3+xsG+Gsyn+vpmE9PqamNOtgG8dcBbCOo2VfR/epSoPRN81+qRx/smF0MTddIafAWNNS2JJTQk/eQXn4151N2rYm5Z3/2bSgC5Sq7y0Odp36DrmV1TYh5f7wtt7B7urS99RwyPOez21sjDceB43+Bl5UV+fDlhuq9I8Y+N98Z8Vk9/vZt3+HFq0/f9eeqe9OXTMs3e7gnkS+tuX+ll6ve8leXb//K5mG879lcxj/uS19LfTPnTmPs8NSnm+u3e3tJa46+1ZXr7VrMzTngj5u82+uouE2p87vXcfl816UuvXhlz/18mNnN64PmY/chWeHyzDgAAAKQUk3UAAAAgpSq6F6N1s7fBDH3ZW1S+uvpjMd859skOH3/DYL8yuEfiP2Wy9eWe3b5qwE1z/iTmKT97y8eRy6CBAkuuRLF5mn/On516R+Je3gazO/hOeBe/8rV2zzXiiW0xt7y9QUCp+3SDt8fsveahmB/73FExb9s3pEuvcfmYOTFf0Ns3AhxW7fW4K+Ptm7fsnBrzj5/x49nUuT7W1hY25kM3S6yC1LJ6XczDv+sb6l3+Q28/ueOE22J+/vSfx7z3w4nWl8RiRcnFl+qt/aI4vcxb0urMW8du3Oyruzzw69NjHnOnj691vc/P0rI2Et+sAwAAACnFZB0AAABIqYpugwkH/Ar4hnmrY37r3/xK/7/8S7+q/opBf2j3+GNq/bTLosTV9P9z7SdjXvqgX8U89dd+mqXlna2HO2yg2yVbX1Z9xzeb+M4lvuLEgKqeMW9v9Q0ifrb9xJiH/pNfbS9JWuEblLH5CkpJSGyQ9NAdp8W84/NeB98Y8lTMF49fGHNzFz/qyXaXOvPX29Lqqy19f9O5MT91v9fgUXeuj7llp69uQ/2hoBItMZnlPt8a+YOJMX/po9+Oedcx3k45Y9LKmL823Dcyuu71S2IOoeONJsN83whp+Fx/3jGLfTOwlk3eFt3VTcy6A9+sAwAAACnFZB0AAABIqYpug0meBmzZ7Av2D3rUb39tu185/Lnz/dSiJI2evCnmDS82xjz8j34KZfTCROvLqjVdHDBQGOFo3zylfpqv4HJZH/8MVyeusJ/X5KcZ77rr7JhHv7qg3fNm9rXfJAkoFZn9fvp89D3eWrJg3XEx3/DnvmnYz8bNjnl8TUOXXvu6DdNjfmKNt1a2zu8f89BXfHWXMW/6SkstK73lAEiDkGgbtte8NXLUpmExt/7eP9ubB42N+X8M9M9/47rE8eQDurpq3l4bcybR7tKyf//B7p5KfLMOAAAApBSTdQAAACClKrsNJilx9W/rps0x93zYWwAmvT253UP2jRga8/g3vCUmsyrR+pI43QOUirXn9on5qvGPxNyryltfXmzy0+7XvfqlmMfcvTHm1j2+SowkVqBA6Upu8JJoLem3xY8R77b4Rkh/MuFvYm6t69pL93srE/PQDV539Ut9HC3rvDUnfWtZAAcXWnwzr+RnWIlck7h/MndWy6Hvknp8sw4AAACkFJN1AAAAIKVogzmE5CkavbSo3e/qX/LMaUeUk/1H+1X2Fzb45i57M/6/jF9tOSPm/nf7ahety/z+QLnL7NoVc++753ouwGuXw+l9AIfGN+sAAABASjFZBwAAAFKKNhgA/0XY4au+rG3xDY/ebvVVKX73yodinvr4sphpCQMAIH/4Zh0AAABIqUNO1s2s3sxeNLPXzGyRmX0ve/tYM5trZsvN7D/NEnuPAxWOugFyQ80AuaFmKkdn2mCaJJ0dQthtZjWSnjOzhyV9W9JPQgizzOz/SrpK0i+6caxAKSnpujnicd+86Nphl8acyVjMox/03Lpla2EGhnJW0jUDFAE1UyEO+c16aLM7+2NN9p8g6WxJd2dvnynp090yQqAEUTdAbqgZIDfUTOXoVM+6mVWb2auSNkuaI+ktSTtCCO8t87pO0sgOHnu1mc03s/nNasrHmIGScLh1Q82gUnGsAXJDzVSGTq0GE0JolXS8mfWXdJ+kKZ19gRDCzZJulqS+NjAc4u5A2TjcuklDzfS8/8WYR99fjBGgEnGsAXJDzVSGnFaDCSHskPSkpFMl9Tez9yb7R0han+exAWWBugFyQ80AuaFmyltnVoMZkv2LTWbWU9J5kpao7UPx2ezdrpD0QHcNEig11A2QG2oGyA01Uzk60wbTKGmmmVWrbXI/O4TwWzNbLGmWmf1A0iuSbj3UE02aNk5z5v+6SwNG15jZoe+EfMhL3VAzxUfNFAzHmjJC3RQENVNGPqhmLITCtSmZ2TuS9kjaUrAXTYfBSs97PjKEMKTYg0DnZGtmtdL1GSqENL1faqbEcKxJBeqmhHCsSYUOa6agk3VJMrP5IYTpBX3RIqvE94z8qrTPUKW9X+RfJX6GKvE9I78q7TNUKu83pwtMAQAAABQOk3UAAAAgpYoxWb+5CK9ZbJX4npFflfYZqrT3i/yrxM9QJb5n5FelfYZK4v0WvGcdAAAAQOfQBgMAAACkVEEn62Z2gZktNbPlZnZ9IV+7EMxslJk9aWaLzWyRmV2bvX2gmc0xs2XZfw8o9lhRGsq9ZiTqBvlX7nVDzSDfyr1mpNKum4K1wWQX7X9TbTtsrZM0T9JlIYTFBRlAAZhZo6TGEMLLZtZH0kuSPi3pzyRtCyHclC2CASGE7xZxqCgBlVAzEnWD/KqEuqFmkE+VUDNSaddNIb9ZP1nS8hDCihDCAUmzJF1UwNfvdiGEDSGEl7N5l9q2/R2ptvc5M3u3mWr7cACHUvY1I1E3yLuyrxtqBnlW9jUjlXbdFHKyPlLS2sTP67K3lSUzGyPpBElzJQ0LIWzI/mqjpGFFGhZKS0XVjETdIC8qqm6oGeRBRdWMVHp1wwWm3cDMGiTdI+m6EMK7yd+Ftr4jluAB3oe6AXJDzQC5K8W6KeRkfb2kUYmfj8jeVlbMrEZtH4I7Qgj3Zm/elO2Veq9nanOxxoeSUhE1I1E3yKuKqBtqBnlUETUjlW7dFHKyPk/SRDMba2a1ki6V9GABX7/bmZlJulXSkhDCjxO/elDSFdl8haQHCj02lKSyrxmJukHelX3dUDPIs7KvGam066agmyKZ2ccl/bOkakm3hRD+V8FevADM7HRJz0paICmTvfkGtfVEzZY0WtJqSZ8PIWwryiBRUsq9ZiTqBvlX7nVDzSDfyr1mpNKuG3YwBQAAAFKKC0wBAACAlGKyDgAAAKQUk3UAAAAgpZisAwAAACnFZB0AAABIKSbrnWRmq8xsgZm9ambziz0eIO3M7FozW2hmi8zsumKPBygFZnaBmS01s+Vmdn2xxwOknZn1N7O7zewNM1tiZqcWe0z5xtKNnWRmqyRNDyFsKfZYgLQzs2MkzZJ0sqQDkh6R9PUQwvKiDgxIMTOrlvSmpPMkrVPbZjWXhRAWF3VgQIqZ2UxJz4YQbslu6tQrhLCj2OPKJ75ZB9AdpkqaG0LYG0JokfS0pIuLPCYg7U6WtDyEsCKEcEBtf/BeVOQxAallZv0knam2nUkVQjhQbhN1icl6LoKkx8zsJTO7utiDAVJuoaQzzGyQmfWS9HFJo4o8JiDtRkpam/h5XfY2AAc3VtI7kn5pZq+Y2S1m1rvYg8o3Juudd3oI4URJF0q6xszOLPaAgLQKISyR9CNJj6mtBeZVSa1FHRQAoNz0kHSipF+EEE6QtEdS2V3rwWS9k0II67P/3izpPrWdrgTQgRDCrSGEaSGEMyVtV1svLoCOrVf7M1BHZG8DcHDrJK0LIczN/ny32ibvZYXJeieYWW8z6/NelnS+2k7zA+iAmQ3N/nu02vrV7yzuiIDUmydpopmNzV4od6mkB4s8JiC1QggbJa01s8nZm86RVHYXZPco9gBKxDBJ95mZ1Pbf7M4QwiPFHRKQeveY2SBJzZKuKceLfoB8CiG0mNk3JT0qqVrSbSGERUUeFpB235J0R/YP3BWSrizyePKOpRsBAACAlKINBgAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWQdAAAASCkm6wAAAEBKMVkHAAAAUorJOgAAAJBSXZqsm9kFZrbUzJab2fX5GhRQzqgbIDfUDJA76qZ8WAjh8B5oVi3pTUnnSVonaZ6ky0IIi/M3PKC8UDdAbqgZIHfUTXnYujQOAAAeLklEQVTp0YXHnixpeQhhhSSZ2SxJF0nq8INQa3WhXr278JLoql3aviWEMKTY46hgOdUNNVN81EzRcawpQdRN0XGsKTEfVDNdmayPlLQ28fM6STPefyczu1rS1ZJUr16aYed04SXRVY+Hu1cXewwV7pB1Q82kCzVTdBxrShB1U3Qca0rMB9VMt19gGkK4OYQwPYQwvUZ13f1yQMmjZoDcUTdAbqiZ0tGVyfp6SaMSPx+RvQ1Ax6gbIDfUDJA76qaMdGWyPk/SRDMba2a1ki6V9GB+hgWULeoGyA01A+SOuikjh92zHkJoMbNvSnpUUrWk20IIi/I2MqAMUTdAbqgZIHfUTXnpygWmCiE8JOmhPI0FqAjUDZAbagbIHXVTPtjBFAAAAEgpJusAAABASjFZBwAAAFKKyToAAACQUkzWAQAAgJTq0mowAADg8FT37xfzntMnx/zO8e0PzXXbPDc+tiHm1uUru29wQEpU9eoVs40aEfOeSQNj3jmmfc00DfAcqg/+vD32ea7fGmLutbk15j5zV8fcsmlz4kn9/oXAN+sAAABASjFZBwAAAFKKNhgAh6fKzy1W9az3PNhPTbY0Dmj3kHfH+enM1lqLuW5nJub6bQdirtmyN+bwlp+OzOzff7ijBtJj6OAYN3yxKea5p/9zu7v9Zs/omP913+diHrxpS8yZXbu6Y4RA3lVPGBtz68CGmA8M9ONIS0//Lnl/f8/bj/H2k1Ef8pawm8Y+0u41zuq5O+YlB/z48vs9U2O+f/1xMa9bNtTHscKnxg2L/JilzYnvt4O3yhQC36wDAAAAKcVkHQAAAEgp2mAAfDDzdpXqPn1iDqP9qvzdk3xVi22TvT2m5Xg/FSlJv57hp/ePrqmN+d93jor5/62ZEfPKhX5qcsKsGn+i+Qs7PXwgrTJ9/LT/F4+aF3Ovqpp29/tyX293+fsT/ZT+4Hkj/U4L3+iGEQL5t+rSxphbj/VjxOlHLon56Ia3Y26o9rbHafWrYt7a2jvm/aF9zfxix8SYf/rqWTH3e8ZrbthzvszSxEVzDzrWwja7dIxv1gEAAICUYrIOAAAApBRtMO9JnOq3Wj89n1yMX7XtT7N0qKUlxrDPT99k9vvV/gp+KjP5euHAgcR9CrvoPnAwVT17xrz7rCkxb/3ynph/eeJPYz7OP87/RXPiI90UvE6u7LfK87GeHx3v7TXX1V8e88T5hxw2kHqtDV4sNw5ZnPhNx8eahlHvxtw03NsAaugMQ4m45HNPxZz83O/N+Pzn4b2+UtJPV3sby48WfSrmhtX+fXPd9vbzpcG/fTPm8VteOeg40tLi0hl8sw4AAACkFJN1AAAAIKVog8mqHuQbuew8268irvnqxpj/aeLsdo/pX+WnbFrlbTT/tOncmJ+774SYj/y1L+Bvzd4CsONkX1Wj3+N+6qZ1p5/uVKaUTtignOz7yNEx77zSN155ctotMTdY8rS9qSP/sHVazJ/o+2rMHbXOnN3Tr9b/zKm+WgZn/FGpah7uH3Pt0y/FTNMkSsXMV06N+WNnLoh5f/CVWq6//4sxT/zfvtLRpAO+MpJafV4U3tc23JpsKS4DfLMOAAAApBSTdQAAACClKq8Npso3bKkef2TMaz89POaLv/R0zF/u7wvlj+7hq2JIUo01xNyaWN3l74fP8fwZv/9jE72dYMiwnTFfPuaRmG8f+PGYh92TaInZsvVg7wboFj3Gem0s+4T/Tf9/j7k75n5VB+9d2Zm4ov/Up77Z7nejZvn/cu4648yYv/7JR2O+ZsDSmGvM67WhR2I1pQ9YLQMoZ1XJBcOay+tUPyrDpJ/55/ZvR14c8w/G3xdzywBvFbY6P9a0bt/ezaNLp0N+s25mt5nZZjNbmLhtoJnNMbNl2X8P6N5hAqWFugFyQ80AuaNuKkNn2mBul3TB+267XtITIYSJkp7I/gzA3S7qBsjF7aJmgFzdLuqm7B2yDSaE8IyZjXnfzRdJ+mg2z5T0lKTv5nFc3abHKF95ZeUl3vpy9WUPxXxJH19rYlmLt7qc++jX2j1X1V4/Ra9MIvb2K5St2f8esj7NMf9g8v0xj6nZEfMvzdtgULpKsW6SrS+rLxkZ8ydP8VVYZtT7CkXbElfi/3KHr/Jyx13nxDz+Kd84SZKqX/cNMCas9Ne4Y+XHYp590Ykx33X07TGf38dXDfjVz78e85TvLok5s8tXq0FpKcWa6aqaLXtjPv11bwd45Jg7292vocpXydhymrcHDFpwTMxhPmskVaKSrJvXvcV347N+7Hi+0Vfi6z3IayMM6OuP3bipe8eWUod7gemwEMJ76xBulDQsT+MByhl1A+SGmgFyR92UmS6vBhPaFrfscIlXM7vazOab2fxmNXV0N6CifFDdUDPAf8WxBsgdx5rycLirwWwys8YQwgYza5S0uaM7hhBulnSzJPW1gUXZt6HHcP+jcs3nRsV8/mdejPmiBj+F+I/v+CoVDz4xI+bJv97d7nmr9iQ+3IkF+ZsH9Y5542m9Yh7/iVUxD6325/rGssv89vl+Gj/sbt9CgJLXqbopVs3smTo05oFn+QZe3xjsqyPVJzY/enS/t5H9+3Mfifmo21fE3LLpnXavkUlu7rVkWYzDNvtqR2/XTo7558PPiPnvhj4f80/P/1XMNyz7Sswj71wec+umDv+3hNJRUseaXGXq/RB85jBv50qugvR+gxt9JbGmwf1i7mBfMVSmVB9rQpPPnXpt8pfd2OSf58Z+3nK5Z3xjzL0zEzr3Ihv8Lbcm51IlusHk4X6z/qCkK7L5CkkP5Gc4QFmjboDcUDNA7qibMtOZpRvvkvSCpMlmts7MrpJ0k6TzzGyZpHOzPwPIom6A3FAzQO6om8rQmdVgLuvgV+d0cHvq7DhjTMyjEq0oXxj4Qsw3vu2rsLz0oF9hP/HX3g7QumJNu+dt7eB0Ss2EsTHvG+pX8f/30b+JeX2rn+555+EjYh658OWYM/v3H/T5kX6lWDdbjvUWlz8/wj+Hk2r8M/xms38m/2WFv5Vx93gttGzY2LkXTLSOJTf9anx6W8z3HeltaJd/1uv19HrfGGPkp1b5U84Z6M9PG0xJKcWa6arWBm9e+eGw1xO/YdMvdE4p1k1VL28P3uXTJZ3YsDrmY3uvi/lHV/hqYft3dG7J+H4LvK1z8AI/btUu9uctpVbJLl9gCgAAAKB7MFkHAAAAUupwV4NJPZvurSwbLzoQ842j5sR8z46TYn75fr//mDu93aVlrZ8y+eAXtBj3TB0S8/Bj/DTLuB6+mcV/bPPXa3w2sQJMi98H6G5V9d7isu/YfTEfW7825kxi1a+Hd/vndvuzvhrMqCd8pZauyrz+hj/v416j/3qGn9X9ycgnYv7IYF9V5sl6b5sBAKRD8ljz7ieOjfnEM5Z6rvPjTlPwFZG+d9yDMa8+MDjm6sSx6ZOJTfMk6d9O9JXEfrPMj1s1r/tqMsNe9NUB61/yVcySm+ulZU7GN+sAAABASjFZBwAAAFKqbNtgln7Frzb+xSkzYz6pzjeU+Iu5J8Q8efb6mA+n9aXHyBExrzvb/wb6PxMeinleYsH/B148MeZJL/rmTCWxkwdKW5WfXrRR/rmdMXZVzBNqfEOK7Rn/nM9e45/b4XO7f8e7+o2+mcXjC6b6LxJtMHVVzTFnahLvLfE+S3UjDAAoWR0ca0Zd562LN4z0OdItW0+P+YGlH/KnWdUz5r4r/elba/zYdPv57Vsgxw3wVcVuOP6RmGecsirmvzznkpi3zpwS84Alvmll1aK3Ys7s81bR5GpmhcA36wAAAEBKMVkHAAAAUqps22CGHOmbpoyr8dMhOzJ+6iKzpS7mlpW+GH+HkqfVJVX3bYh51eVHxvzls5+K+fyefhr/62s/EvP42em4whiVx6r9c7zzBN844tJBz8Q8pNpr45ad4/z+f/T7D5y/KOZCN5lUJb5nmFLnG5fd0+grDjT09la45NX9QClravH67dOSKeJIgA+WPNbsH+sb1p3WZ3HMP1zvG1Ku+ddJMY+f9cfcXuzn7X88MMRX5fv5J/405u+f4xskfevEJ2M++3/+Oua/X/MnMb97o7fH1Mz1lcoK3RLDN+sAAABASjFZBwAAAFKqbNtg8ibR+lI9YUy7X638wrCYv/LZR2O+pv+SmDe1+oZM8zf6AvwjF/ri/6xTgUKy2pqYt031v9eH9jh4q8hPXjk35gkP+31ad757sLsXRI15XZ5e7ys87Rjvt/ed56sv0QaDclHzcP+Ya59+KWZWEkPahGaf/9Q++XrMz5890u/U6q1cffe+4o/t4mu3btkS88C7/Fg1+F5v8bz/w+fF/C+f9ePct2b8PuZeP/OWnVn/zVt2ej6TaIkpwPGFb9YBAACAlGKyDgAAAKQUk3UAAAAgpcq2Z/2dt72vb+2UvjHPqPOlFP/6vN/FPPN3pxz0eaqrvJ9qyoDN7X53zWB//Iy6rTHXme+29fu9vmvX3jd8TK1b3/zgNwAUQEsv7wysN98JNLk0YtVqXw7RFr0WcyjwDm5JyZ71pEzy/2hVdtD7AKWsytuA2/UEA2mW/Ky2btn6AffM1wv68Sk0+W7brYnc8xnvR5+6yudqd5z5sZi//e3ZMc/4wbyYH73ttJhH3r8m5pa167oy6g7xzToAAACQUkzWAQAAgJQq2zaYCb/yHUK/2f8LMd8ybWbMX+izNObzjvFleDrS/31/2nz42WtiHj3Ud0m9Yay3x8zeeFLMI/6QWKQxw4KNKA6r8bLvMW53zP2rfEe2TOLv+Or93k6S2bu3m0fXOa3B29My8mysXwcA6ITMHm+L1tIVMQ5r9vnj9478fMxf+9RjMZ/0JW8JfXbwcTGPvcd3ts+8fuh5ZWfxzToAAACQUkzWAQAAgJQq2zaY6hcWxDy81/ExX7nMW1dqp/iuVo39PO9r9h0eN2zxXRBrVviqGJJUlfhTZ8iR3k7QHPw/6+LVjTFPXeSryfhJFqDAzD+4g/r4acA6S7Zmpfvv+JbEvr87M77KQI9kl04LrWYAgE5ItCZn1r4d84Q7amP+jy2+SsyFlz8f8yWffjrm3607M+ZBvmlrlx3yiGxmo8zsSTNbbGaLzOza7O0DzWyOmS3L/ntA/oYFlC5qBsgddQPkhpqpHJ35+qxF0l+HEI6SdIqka8zsKEnXS3oihDBR0hPZnwFQM8DhoG6A3FAzFeKQbTAhhA2SNmTzLjNbImmkpIskfTR7t5mSnpL03W4Z5WEILd5oUvvo/JgnLhoZc9OEYTHvH+gL4lc3+5ISEzckzqu/6q01krRm1pSYbxj5UMwLmvw1alfXxZzZ9E6nx4/SVao1k0bN/b317MjRW2LeH7y+r1vzyZj7L/eNnTLv7urm0SGfKrFu7ICvZPTEPt/o68z69psdJTcB2znJbx86bkzMLStW5X18SLdKrJlCaLeJ0mLfwPKI9b7B5gOnHBvzrJNuiXn2yI/EPCiPY8qpZ93Mxkg6QdJcScOyHxRJ2ihpWAePuVrS1ZJUr16HO06gJFEzQO6oGyA31Ex56/RVZGbWIOkeSdeFEN5N/i607Tt+0BWOQwg3hxCmhxCm16juYHcByhI1A+SOugFyQ82Uv059s25mNWr7INwRQrg3e/MmM2sMIWwws0ZJmzt+hvRoWbc+5upE7uhvymC+IUxVQ0O731055YWYJ9f4acobVp0c88DFXiPtFuBHWUtzzSRbxN5ePiTmFRMGxjyqx46Y94/w+1dPGh9z65tvddcQo31DfGWma8c8GfPKZv+eYcmdU2NunLcs5tZdtMGUmjTXTXeo3udtW7O3+nHj1BFPt7tfsg2m37FbY947yeu3ljaYilRpNVNUiZXUmpt8+rymxa/ftWZ1i86sBmOSbpW0JITw48SvHpR0RTZfIemB/A8PKD3UDJA76gbIDTVTOTrzzfqHJV0uaYGZvZq97QZJN0mabWZXSVot6fMdPB6oNNQMkDvqBsgNNVMhOrMazHOSrINfn5Pf4aSP9fDT8AdOntTud0N6LI15SeKq/qUvjol50h+91YaNkCpD2msms8dXOJp0q2/m9Q+TLoh5zKS7Yv7aaX5K/s6VPvxR/+Kf7cz+/XkbX/Ugb8fZ0+in/4f32Bnzb3cdF/OIB1bH3LJ1W97GgcJKe93ki9X4Jist/bxPeESdf76rraP/DIAr1ZrpMTxxvWu1/z8+s8NroKhtw4n6q+7TJ+bdH5kY85XHPRPz0+/6yoC93z7o5QFdlu5tCgEAAIAKxmQdAAAASKmc1lmvGMkVYPr6CjBrrmptd7eT6v30+482fCzmIS/5aZCWVWu6Y4TA4cv45zi8sijmpttPifnfvn1mzH837KmY75wx3R871VeGUeJ5DkuVnwrd/jFvNzvqkiUx1ycus799wakxT9q9wp8n075GgbSpHjk85rUf7hnzjUMWJ+5VI6CcWJ23fK35sh87MolZ6BG/97ZM/fH1PL64z+mSrc1Wnfi+OtGOU9XXW1/2He2bXB642tssp/daGfN/+7erYh79sK+Sls/WZ75ZBwAAAFKKyToAAACQUrTBHERVL98iqen4sTH/00l3tbvfhBr/z/fcUr9KeOLqvQJKzYBX/RTf756dFvMxF/iqL8cP97z8aL8Cvt8rXXvtqmO99WXjGb6y0o8afSOkO7d568vYXyQ3G9vXtRcHCmjPUb4SxlmfeamIIwG6V7L1ZeefnhDzdVfeG/M9G0+Medcbo2LuaKPKw1E90Dct2nWmz9V2jvXWl31D/Zgy9VRvcfnLEf8Z8/pmf55rZ30l5vG/fSfmlk3ds/8U36wDAAAAKcVkHQAAAEgp2mAOwhJtMFuO9dM4x9a2P73RI3GiJjT53z3WnFhtozsGCHSDzFu+utGEu/xz/w/Dzo/5pul++nLm1+pjXtv3tJiH/vz5Dl+jx7gxMe8+aqg//rNeM/942qyYVx0YHPNvnvaVaCa97CsFZFp8lRggjXqMGR3zmpN9NYq7hj2RuFeDOjJ+9tdjHndfU8w1r70ZM+sgIc1aa3xFlpE122O+dfzsmO/+/tEx3/JlP6bseatfzJk6n1XVD/eNk/bt9OORJF34oYUxT+3lK7QMq/HWs0HVvvrMroyvzPTC7gkx/81LF/tj/9PvM+EVbwlt3bDJXziwKRIAAABQUZisAwAAAClFG8xBWF1tzMkrhOut/f2e3e//+fq/5qc2q1a8HTOnJlEqQpOfXq9a6KcNR902Nebv7Lg05otnzI+58c92xvzYh06Oeegf/Gp7Sdp2oa/ccvo43/Dobwe/GHOrvND+7rWLYj7yEd9iIrOXFZdQOkKtHx+ae/sxZUfGvy87f+4XYq592E/7S9Lk57b6c63wjfZa9+/P6ziBfArN/v/sIc/4vOiv/p9vItTnJF9JZWhvb0uZPnxtzDMmPx1zsnVleZOvrLRgl29eJElbmnrHfP/O42JescIfU7vJ53A1e/y402+Fr0g2do0fa6oW+cZlLbsTGzh1U+tLEt+sAwAAACnFZB0AAABIKdpgDiL09dMnw6dtjLne2v9t8/2Vn4x50GI/Hdm6fbuAUpZsM6l7blHME/dOjvmxVafEvOcob6E547g3Yt5/jJ/+l6RBzYlNMg741fvfef2zMbcs7Bvz8Ll+GrV+3vKYaS9DSXnHNxwbPcc3Vvn8O9+JecjLXkO1T7ffLKm1+UA3Dg7oJhn/P3XLSl9tbNyvvOVk7x+HxLyjYWDMG/v5fOuF4R/y50xMw2q9+1LV+9u3otS96z/X7PG2lsnrfAWZ6m3v+gOavMZat/ocLiRqz5+l8PhmHQAAAEgpJusAAABAStEGcxCh1v+zTB3gVyTXvK8NZsVq39Rlyg5vGyjmqRIg3zKJFSeq5vkKLqOW+YoVLRNGxPzSR4+Jee+Y9hsWJVdNqt3tpymHrfNTjfVLVvrzbvKVAlozNL+gNCVbI2se81WURjx28PuzmR7KWcuKVTHXJnPiPr0TeVAeXztZWy0d3it9+GYdAAAASCkm6wAAAEBK0QZzELbHT/s/vnRKzK8N/kO7+1Xv8P98dqCUTqgAhyd5ZXzrO96iYol8xAtdew0qCQAAd8hv1s2s3sxeNLPXzGyRmX0ve/tYM5trZsvN7D/NrPZQzwVUCuoGyA01A+SGmqkcnWmDaZJ0dgjhOEnHS7rAzE6R9CNJPwkhTJC0XdJVH/AcQKWhboDcUDNAbqiZCnHINpgQQpC0O/tjTfafIOlsSV/I3j5T0t9L+kX+h1gE232l/YFPDov5q/VXtLvbkPmJH7bu6O5RoYRUZN0AXUDNALmhZipHpy4wNbNqM3tV0mZJcyS9JWlHCOG99tJ1kkZ28NirzWy+mc1vVtPB7gKUpcOtG2oGlYpjDZAbaqYydGqyHkJoDSEcL+kISSdLmnKIhyQfe3MIYXoIYXqN6g79AKBMHG7dUDOoVBxrgNxQM5Uhp9VgQgg7zOxJSadK6m9mPbJ/vR0haX13DLAYWrdsjXngbS8k8gc8pjsHhJJWKXUD5As1A+SGmilvnVkNZoiZ9c/mnpLOk7RE0pOSPpu92xWSHuiuQQKlhroBckPNALmhZipHZ75Zb5Q008yq1Ta5nx1C+K2ZLZY0y8x+IOkVSbd24ziBUkPdALmhZoDcUDMVwtouJi7Qi5m9I2mPpC0Fe9F0GKz0vOcjQwhDij0IdE62ZlYrXZ+hQkjT+6VmSgzHmlSgbkoIx5pU6LBmCjpZlyQzmx9CmF7QFy2ySnzPyK9K+wxV2vtF/lXiZ6gS3zPyq9I+Q6Xyfju1GgwAAACAwmOyDgAAAKRUMSbrNxfhNYutEt8z8qvSPkOV9n6Rf5X4GarE94z8qrTPUEm834L3rAMAAADoHNpgAAAAgJRisg4AAACkVEEn62Z2gZktNbPlZnZ9IV+7EMxslJk9aWaLzWyRmV2bvX2gmc0xs2XZfw8o9lhRGsq9ZiTqBvlX7nVDzSDfyr1mpNKum4L1rGd32HpTbdvhrpM0T9JlIYTFBRlAAZhZo6TGEMLLZtZH0kuSPi3pzyRtCyHclC2CASGE7xZxqCgBlVAzEnWD/KqEuqFmkE+VUDNSaddNIb9ZP1nS8hDCihDCAUmzJF1UwNfvdiGEDSGEl7N5l6Qlkkaq7X3OzN5tpto+HMChlH3NSNQN8q7s64aaQZ6Vfc1IpV03hZysj5S0NvHzuuxtZcnMxkg6QdJcScNCCBuyv9ooaViRhoXSUlE1I1E3yIuKqhtqBnlQUTUjlV7dcIFpNzCzBkn3SLouhPBu8nehre+I9TKB96FugNxQM0DuSrFuCjlZXy9pVOLnI7K3lRUzq1Hbh+COEMK92Zs3ZXul3uuZ2lys8aGkVETNSNQN8qoi6oaaQR5VRM1IpVs3hZysz5M00czGmlmtpEslPVjA1+92ZmaSbpW0JITw48SvHpR0RTZfIemBQo8NJansa0aibpB3ZV831AzyrOxrRirtuinoDqZm9nFJ/yypWtJtIYT/VbAXLwAzO13Ss5IWSMpkb75BbT1RsyWNlrRa0udDCNuKMkiUlHKvGYm6Qf6Ve91QM8i3cq8ZqbTrpqCTdQAAAACdxwWmAAAAQEoxWQcAAABSisk6AAAAkFJM1gEAAICUYrIOAAAApBST9U4ys2vNbKGZLTKz64o9HiDtzOwCM1tqZsvN7PpijwdIOzO7zcw2m9nCYo8FKBVmtsrMFpjZq2Y2v9jj6Q5M1jvBzI6R9DVJJ0s6TtInzWxCcUcFpJeZVUv6maQLJR0l6TIzO6q4owJS73ZJFxR7EEAJOiuEcHwIYXqxB9IdmKx3zlRJc0MIe0MILZKelnRxkccEpNnJkpaHEFaEEA5ImiXpoiKPCUi1EMIzklK1GQuA4mOy3jkLJZ1hZoPMrJekj0saVeQxAWk2UtLaxM/rsrcBAJBPQdJjZvaSmV1d7MF0hx7FHkApCCEsMbMfSXpM0h5Jr0pqLe6oAAAAKt7pIYT1ZjZU0hwzeyN7lqps8M16J4UQbg0hTAshnClpu6Q3iz0mIMXWq/3ZpyOytwEAkDchhPXZf2+WdJ/a2jDLCpP1Tsr+xSYzG622fvU7izsiINXmSZpoZmPNrFbSpZIeLPKYAABlxMx6m1mf97Kk89XWulxWaIPpvHvMbJCkZknXhBB2FHtAQFqFEFrM7JuSHpVULem2EMKiIg8LSDUzu0vSRyUNNrN1km4MIdxa3FEBqTZM0n1mJrXNae8MITxS3CHln4UQij0GAAAAAAdBGwwAAACQUkzWAQAAgJRisg4AAACkFJN1AAAAIKWYrAMAAAApxWQdAAAASCkm6wAAAEBK/X+i3BBDhqQO5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_samples,labels = next(iter(test_dataloader))\n",
+    "print(batch_samples.shape,labels.shape)\n",
+    "show_batch(batch_samples.squeeze(), labels.numpy(), (4,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class AutoEncoderZrc(torch.nn.Module):\n",
+    "    def __init__(self, grayscale = False):\n",
+    "        super(AutoEncoderZrc, self).__init__()\n",
+    "        # calculate same padding:\n",
+    "        # (w - k + 2*p)/s + 1 = o\n",
+    "        if grayscale:\n",
+    "            in_channels = 1\n",
+    "        else:\n",
+    "            in_channels = 3\n",
+    "        \n",
+    "        \n",
+    "        # (w-k+2p) // 2 + 1\n",
+    "        \n",
+    "        # 28x28x1 => 14x14x4\n",
+    "        self.encoder = torch.nn.Sequential(\n",
+    "            torch.nn.Conv2d(in_channels,\n",
+    "                            out_channels=4,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            # 14x14x4 => 7x7x8\n",
+    "            torch.nn.Conv2d(in_channels=4,\n",
+    "                              out_channels=8,\n",
+    "                              kernel_size=(3, 3),\n",
+    "                              stride=(2, 2),\n",
+    "                              padding=1),\n",
+    "            torch.nn.LeakyReLU(inplace = True)\n",
+    "        )\n",
+    "        \n",
+    "        # Hout=(H−1)×stride[0]−2×padding[0]+dilation[0]×(kernel_size[0]−1)+output_padding[0]+1\n",
+    "        \n",
+    "        self.decoder = torch.nn.Sequential(\n",
+    "            torch.nn.ConvTranspose2d(in_channels=8,\n",
+    "                                     out_channels=4,\n",
+    "                                     kernel_size=(3, 3),\n",
+    "                                     stride=(2, 2),\n",
+    "                                     padding = 1,\n",
+    "                                     output_padding = 1),\n",
+    "            torch.nn.LeakyReLU(inplace = True),\n",
+    "            torch.nn.ConvTranspose2d(in_channels = 4,\n",
+    "                            out_channels= in_channels,\n",
+    "                            kernel_size=(3, 3),\n",
+    "                            stride=(2, 2),\n",
+    "                            padding = 1,\n",
+    "                            output_padding = 1),\n",
+    "                            \n",
+    "            torch.nn.LeakyReLU(inplace = True)\n",
+    "        )\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.encoder(x)\n",
+    "        x = self.decoder(x)\n",
+    "        x = torch.sigmoid(x)\n",
+    "        return x\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------------------------------\n",
+      "        Layer (type)               Output Shape         Param #\n",
+      "================================================================\n",
+      "            Conv2d-1            [-1, 4, 16, 16]             112\n",
+      "         LeakyReLU-2            [-1, 4, 16, 16]               0\n",
+      "            Conv2d-3              [-1, 8, 8, 8]             296\n",
+      "         LeakyReLU-4              [-1, 8, 8, 8]               0\n",
+      "   ConvTranspose2d-5            [-1, 4, 16, 16]             292\n",
+      "         LeakyReLU-6            [-1, 4, 16, 16]               0\n",
+      "   ConvTranspose2d-7            [-1, 3, 32, 32]             111\n",
+      "         LeakyReLU-8            [-1, 3, 32, 32]               0\n",
+      "================================================================\n",
+      "Total params: 811\n",
+      "Trainable params: 811\n",
+      "Non-trainable params: 0\n",
+      "----------------------------------------------------------------\n",
+      "Input size (MB): 0.01\n",
+      "Forward/backward pass size (MB): 0.09\n",
+      "Params size (MB): 0.00\n",
+      "Estimated Total Size (MB): 0.10\n",
+      "----------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "def test_nin():\n",
+    "    model = AutoEncoderZrc().to(DEVICE)\n",
+    "    summary(model, (3,32,32))\n",
+    "    \n",
+    "test_nin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoEncoderZrc(grayscale=GRAYSCALE)\n",
+    "model.to(DEVICE)\n",
+    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_model(model, data_loader, optimizer, num_epochs,batch_size, device,metric_func = None, random_seed = 7):\n",
+    "    # Manual seed for deterministic data loader\n",
+    "    torch.manual_seed(random_seed)\n",
+    "    \n",
+    "    loss_list = []\n",
+    "    \n",
+    "    for epoch in range(num_epochs):\n",
+    "        # set training mode\n",
+    "        model.train() \n",
+    "        for batch_idx, (features, targets) in enumerate(data_loader[\"train\"]):\n",
+    "            features = features.to(device)\n",
+    "\n",
+    "            ## forward pass\n",
+    "            decoded = model(features)\n",
+    "            loss = F.binary_cross_entropy(decoded, features)\n",
+    "\n",
+    "            # backward pass\n",
+    "            # clear the gradients of all tensors being optimized\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            ### Login\n",
+    "            loss_list.append(loss.item())\n",
+    "            if not batch_idx % 50:\n",
+    "                print ('Epoch: {0:03d}/{1:03d} | Batch {2:03d}/{3:03d} | Loss: {4:.2f}'.format(\n",
+    "                    epoch+1, num_epochs, batch_idx, \n",
+    "                         len(train_dataset)//batch_size, loss))\n",
+    "    return loss_list, model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 001/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 200/921 | Loss: 0.09\n",
+      "Epoch: 001/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 600/921 | Loss: 0.11\n",
+      "Epoch: 001/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 001/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 150/921 | Loss: 0.09\n",
+      "Epoch: 002/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 300/921 | Loss: 0.11\n",
+      "Epoch: 002/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 800/921 | Loss: 0.11\n",
+      "Epoch: 002/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 002/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 300/921 | Loss: 0.09\n",
+      "Epoch: 003/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 500/921 | Loss: 0.09\n",
+      "Epoch: 003/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 003/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 150/921 | Loss: 0.09\n",
+      "Epoch: 004/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 004/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 005/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 006/010 | Batch 900/921 | Loss: 0.11\n",
+      "Epoch: 007/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 007/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 008/010 | Batch 900/921 | Loss: 0.11\n",
+      "Epoch: 009/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 050/921 | Loss: 0.11\n",
+      "Epoch: 009/010 | Batch 100/921 | Loss: 0.11\n",
+      "Epoch: 009/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 200/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 500/921 | Loss: 0.11\n",
+      "Epoch: 009/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 750/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 009/010 | Batch 900/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 000/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 050/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 100/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 150/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 200/921 | Loss: 0.09\n",
+      "Epoch: 010/010 | Batch 250/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 300/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 350/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 400/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 450/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 500/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 550/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 600/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 650/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 750/921 | Loss: 0.10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 010/010 | Batch 800/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 850/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 900/921 | Loss: 0.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss_list = train_model(model, \n",
+    "            data_loader, \n",
+    "            optimizer, \n",
+    "            NUM_EPOCHS, \n",
+    "            device = DEVICE, \n",
+    "            batch_size = BATCH_SIZE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(loss_list, label='Minibatch cost')\n",
+    "plt.plot(np.convolve(loss_list, \n",
+    "                     np.ones(200,)/200, mode='valid'), \n",
+    "         label='Running average')\n",
+    "\n",
+    "plt.ylabel('Cross Entropy')\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_batch, test_tagret = next(iter(data_loader[\"test\"]))\n",
+    "model(test_batch).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 15)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x180 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "##########################\n",
+    "### VISUALIZATION\n",
+    "##########################\n",
+    "\n",
+    "n_images = 15\n",
+    "image_width = 32\n",
+    "\n",
+    "# axes (2,15)\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=n_images, \n",
+    "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
+    "\n",
+    "orig_images = test_batch\n",
+    "decoded_images = model(test_batch)\n",
+    "\n",
+    "for i in range(n_images):\n",
+    "    for ax, img in zip(axes, [orig_images, decoded_images]):\n",
+    "        curr_img = img[i].detach().to(torch.device('cpu'))\n",
+    "        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\n",
+    "\n",
+    "axes.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reference\n",
+    "\n",
+    "- https://towardsdatascience.com/understanding-and-visualizing-densenets-7f688092391a\n",
+    "- https://github.com/rasbt/deeplearning-models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tryit",
+   "language": "python",
+   "name": "tryit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Network-in-Network.ipynb b/Network-in-Network.ipynb
index 5b1f6df..4728a8e 100644
--- a/Network-in-Network.ipynb
+++ b/Network-in-Network.ipynb
@@ -46,13 +46,13 @@
     {
      "data": {
       "text/plain": [
-       "['/Users/ZRC/miniconda3/envs/tryit/lib/python36.zip',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/lib-dynload',\n",
-       " '',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages',\n",
-       " '/Users/ZRC/miniconda3/envs/tryit/lib/python3.6/site-packages/IPython/extensions',\n",
-       " '/Users/ZRC/.ipython',\n",
+       "['',\n",
+       " '/anaconda/envs/py36/lib/python36.zip',\n",
+       " '/anaconda/envs/py36/lib/python3.6',\n",
+       " '/anaconda/envs/py36/lib/python3.6/lib-dynload',\n",
+       " '/anaconda/envs/py36/lib/python3.6/site-packages',\n",
+       " '/anaconda/envs/py36/lib/python3.6/site-packages/IPython/extensions',\n",
+       " '/data/home/zhangruochi/.ipython',\n",
        " '/Users/ZRC']"
       ]
      },
@@ -106,7 +106,19 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'coke'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-db7f413ee7cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcoke\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvisualization\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'coke'"
+     ]
+    }
+   ],
    "source": [
     "from coke.visualization.image import show_batch"
    ]
@@ -120,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,16 +227,15 @@
      ]
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'show_batch' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-2ff921d4f204>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mbatch_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_samples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mshow_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_samples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'show_batch' is not defined"
+     ]
     }
    ],
    "source": [
@@ -235,29 +246,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "batch_samples,labels = next(iter(valid_dataloader))\n",
     "print(batch_samples.shape,labels.shape)\n",
@@ -266,29 +257,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([64, 1, 32, 32]) torch.Size([64])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "batch_samples,labels = next(iter(test_dataloader))\n",
     "print(batch_samples.shape,labels.shape)\n",
@@ -311,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,6 +323,15 @@
     "            torch.nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\n",
     "        )\n",
     "        \n",
+    "        for layer in self.modules():\n",
+    "            if isinstance(layer, torch.nn.Conv2d):\n",
+    "                n = layer.kernel_size[0] * layer.kernel_size[1] * layer.out_channels\n",
+    "                layer.weight.data.normal_(0, (2. / n)**.5)\n",
+    "            elif isinstance(layer, torch.nn.BatchNorm2d):\n",
+    "                layer.weight.data.fill_(1)\n",
+    "                layer.bias.data.zero_()\n",
+    "            \n",
+    "        \n",
     "        self.global_avg_pooling = torch.nn.AdaptiveAvgPool2d(1)\n",
     "        \n",
     "    def forward(self, x):\n",
@@ -363,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -420,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,9 +486,232 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 001/010 | Batch 000/921 | Loss: 2.30\n",
+      "Epoch: 001/010 | Batch 050/921 | Loss: 2.30\n",
+      "Epoch: 001/010 | Batch 100/921 | Loss: 2.30\n",
+      "Epoch: 001/010 | Batch 150/921 | Loss: 2.30\n",
+      "Epoch: 001/010 | Batch 200/921 | Loss: 2.24\n",
+      "Epoch: 001/010 | Batch 250/921 | Loss: 2.14\n",
+      "Epoch: 001/010 | Batch 300/921 | Loss: 1.92\n",
+      "Epoch: 001/010 | Batch 350/921 | Loss: 1.29\n",
+      "Epoch: 001/010 | Batch 400/921 | Loss: 1.50\n",
+      "Epoch: 001/010 | Batch 450/921 | Loss: 1.19\n",
+      "Epoch: 001/010 | Batch 500/921 | Loss: 1.25\n",
+      "Epoch: 001/010 | Batch 550/921 | Loss: 1.17\n",
+      "Epoch: 001/010 | Batch 600/921 | Loss: 0.90\n",
+      "Epoch: 001/010 | Batch 650/921 | Loss: 0.82\n",
+      "Epoch: 001/010 | Batch 700/921 | Loss: 0.90\n",
+      "Epoch: 001/010 | Batch 750/921 | Loss: 1.16\n",
+      "Epoch: 001/010 | Batch 800/921 | Loss: 0.56\n",
+      "Epoch: 001/010 | Batch 850/921 | Loss: 0.72\n",
+      "Epoch: 001/010 | Batch 900/921 | Loss: 0.69\n",
+      "Epoch: 001/010 training accuracy: 76.46\n",
+      "Epoch: 001/010 validation accuracy: 78.70\n",
+      "Epoch: 002/010 | Batch 000/921 | Loss: 0.74\n",
+      "Epoch: 002/010 | Batch 050/921 | Loss: 0.92\n",
+      "Epoch: 002/010 | Batch 100/921 | Loss: 0.86\n",
+      "Epoch: 002/010 | Batch 150/921 | Loss: 0.56\n",
+      "Epoch: 002/010 | Batch 200/921 | Loss: 0.48\n",
+      "Epoch: 002/010 | Batch 250/921 | Loss: 0.47\n",
+      "Epoch: 002/010 | Batch 300/921 | Loss: 0.50\n",
+      "Epoch: 002/010 | Batch 350/921 | Loss: 0.74\n",
+      "Epoch: 002/010 | Batch 400/921 | Loss: 0.66\n",
+      "Epoch: 002/010 | Batch 450/921 | Loss: 0.58\n",
+      "Epoch: 002/010 | Batch 500/921 | Loss: 0.53\n",
+      "Epoch: 002/010 | Batch 550/921 | Loss: 0.32\n",
+      "Epoch: 002/010 | Batch 600/921 | Loss: 0.31\n",
+      "Epoch: 002/010 | Batch 650/921 | Loss: 0.38\n",
+      "Epoch: 002/010 | Batch 700/921 | Loss: 0.40\n",
+      "Epoch: 002/010 | Batch 750/921 | Loss: 0.39\n",
+      "Epoch: 002/010 | Batch 800/921 | Loss: 0.41\n",
+      "Epoch: 002/010 | Batch 850/921 | Loss: 0.46\n",
+      "Epoch: 002/010 | Batch 900/921 | Loss: 0.49\n",
+      "Epoch: 002/010 training accuracy: 86.04\n",
+      "Epoch: 002/010 validation accuracy: 87.50\n",
+      "Epoch: 003/010 | Batch 000/921 | Loss: 0.34\n",
+      "Epoch: 003/010 | Batch 050/921 | Loss: 0.40\n",
+      "Epoch: 003/010 | Batch 100/921 | Loss: 0.27\n",
+      "Epoch: 003/010 | Batch 150/921 | Loss: 0.25\n",
+      "Epoch: 003/010 | Batch 200/921 | Loss: 0.42\n",
+      "Epoch: 003/010 | Batch 250/921 | Loss: 0.23\n",
+      "Epoch: 003/010 | Batch 300/921 | Loss: 0.32\n",
+      "Epoch: 003/010 | Batch 350/921 | Loss: 0.22\n",
+      "Epoch: 003/010 | Batch 400/921 | Loss: 0.36\n",
+      "Epoch: 003/010 | Batch 450/921 | Loss: 0.27\n",
+      "Epoch: 003/010 | Batch 500/921 | Loss: 0.44\n",
+      "Epoch: 003/010 | Batch 550/921 | Loss: 0.48\n",
+      "Epoch: 003/010 | Batch 600/921 | Loss: 0.19\n",
+      "Epoch: 003/010 | Batch 650/921 | Loss: 0.29\n",
+      "Epoch: 003/010 | Batch 700/921 | Loss: 0.33\n",
+      "Epoch: 003/010 | Batch 750/921 | Loss: 0.40\n",
+      "Epoch: 003/010 | Batch 800/921 | Loss: 0.33\n",
+      "Epoch: 003/010 | Batch 850/921 | Loss: 0.36\n",
+      "Epoch: 003/010 | Batch 900/921 | Loss: 0.23\n",
+      "Epoch: 003/010 training accuracy: 86.02\n",
+      "Epoch: 003/010 validation accuracy: 86.90\n",
+      "Epoch: 004/010 | Batch 000/921 | Loss: 0.45\n",
+      "Epoch: 004/010 | Batch 050/921 | Loss: 0.38\n",
+      "Epoch: 004/010 | Batch 100/921 | Loss: 0.28\n",
+      "Epoch: 004/010 | Batch 150/921 | Loss: 0.43\n",
+      "Epoch: 004/010 | Batch 200/921 | Loss: 0.31\n",
+      "Epoch: 004/010 | Batch 250/921 | Loss: 0.28\n",
+      "Epoch: 004/010 | Batch 300/921 | Loss: 0.43\n",
+      "Epoch: 004/010 | Batch 350/921 | Loss: 0.44\n",
+      "Epoch: 004/010 | Batch 400/921 | Loss: 0.41\n",
+      "Epoch: 004/010 | Batch 450/921 | Loss: 0.29\n",
+      "Epoch: 004/010 | Batch 500/921 | Loss: 0.35\n",
+      "Epoch: 004/010 | Batch 550/921 | Loss: 0.38\n",
+      "Epoch: 004/010 | Batch 600/921 | Loss: 0.33\n",
+      "Epoch: 004/010 | Batch 650/921 | Loss: 0.34\n",
+      "Epoch: 004/010 | Batch 700/921 | Loss: 0.38\n",
+      "Epoch: 004/010 | Batch 750/921 | Loss: 0.42\n",
+      "Epoch: 004/010 | Batch 800/921 | Loss: 0.27\n",
+      "Epoch: 004/010 | Batch 850/921 | Loss: 0.21\n",
+      "Epoch: 004/010 | Batch 900/921 | Loss: 0.43\n",
+      "Epoch: 004/010 training accuracy: 87.13\n",
+      "Epoch: 004/010 validation accuracy: 88.50\n",
+      "Epoch: 005/010 | Batch 000/921 | Loss: 0.24\n",
+      "Epoch: 005/010 | Batch 050/921 | Loss: 0.16\n",
+      "Epoch: 005/010 | Batch 100/921 | Loss: 0.30\n",
+      "Epoch: 005/010 | Batch 150/921 | Loss: 0.50\n",
+      "Epoch: 005/010 | Batch 200/921 | Loss: 0.33\n",
+      "Epoch: 005/010 | Batch 250/921 | Loss: 0.25\n",
+      "Epoch: 005/010 | Batch 300/921 | Loss: 0.41\n",
+      "Epoch: 005/010 | Batch 350/921 | Loss: 0.45\n",
+      "Epoch: 005/010 | Batch 400/921 | Loss: 0.37\n",
+      "Epoch: 005/010 | Batch 450/921 | Loss: 0.16\n",
+      "Epoch: 005/010 | Batch 500/921 | Loss: 0.29\n",
+      "Epoch: 005/010 | Batch 550/921 | Loss: 0.36\n",
+      "Epoch: 005/010 | Batch 600/921 | Loss: 0.32\n",
+      "Epoch: 005/010 | Batch 650/921 | Loss: 0.25\n",
+      "Epoch: 005/010 | Batch 700/921 | Loss: 0.48\n",
+      "Epoch: 005/010 | Batch 750/921 | Loss: 0.30\n",
+      "Epoch: 005/010 | Batch 800/921 | Loss: 0.42\n",
+      "Epoch: 005/010 | Batch 850/921 | Loss: 0.35\n",
+      "Epoch: 005/010 | Batch 900/921 | Loss: 0.31\n",
+      "Epoch: 005/010 training accuracy: 87.16\n",
+      "Epoch: 005/010 validation accuracy: 88.00\n",
+      "Epoch: 006/010 | Batch 000/921 | Loss: 0.38\n",
+      "Epoch: 006/010 | Batch 050/921 | Loss: 0.55\n",
+      "Epoch: 006/010 | Batch 100/921 | Loss: 0.37\n",
+      "Epoch: 006/010 | Batch 150/921 | Loss: 0.33\n",
+      "Epoch: 006/010 | Batch 200/921 | Loss: 0.25\n",
+      "Epoch: 006/010 | Batch 250/921 | Loss: 0.17\n",
+      "Epoch: 006/010 | Batch 300/921 | Loss: 0.36\n",
+      "Epoch: 006/010 | Batch 350/921 | Loss: 0.46\n",
+      "Epoch: 006/010 | Batch 400/921 | Loss: 0.24\n",
+      "Epoch: 006/010 | Batch 450/921 | Loss: 0.46\n",
+      "Epoch: 006/010 | Batch 500/921 | Loss: 0.19\n",
+      "Epoch: 006/010 | Batch 550/921 | Loss: 0.27\n",
+      "Epoch: 006/010 | Batch 600/921 | Loss: 0.27\n",
+      "Epoch: 006/010 | Batch 650/921 | Loss: 0.37\n",
+      "Epoch: 006/010 | Batch 700/921 | Loss: 0.39\n",
+      "Epoch: 006/010 | Batch 750/921 | Loss: 0.62\n",
+      "Epoch: 006/010 | Batch 800/921 | Loss: 0.38\n",
+      "Epoch: 006/010 | Batch 850/921 | Loss: 0.23\n",
+      "Epoch: 006/010 | Batch 900/921 | Loss: 0.25\n",
+      "Epoch: 006/010 training accuracy: 87.49\n",
+      "Epoch: 006/010 validation accuracy: 88.70\n",
+      "Epoch: 007/010 | Batch 000/921 | Loss: 0.59\n",
+      "Epoch: 007/010 | Batch 050/921 | Loss: 0.33\n",
+      "Epoch: 007/010 | Batch 100/921 | Loss: 0.11\n",
+      "Epoch: 007/010 | Batch 150/921 | Loss: 0.49\n",
+      "Epoch: 007/010 | Batch 200/921 | Loss: 0.34\n",
+      "Epoch: 007/010 | Batch 250/921 | Loss: 0.39\n",
+      "Epoch: 007/010 | Batch 300/921 | Loss: 0.25\n",
+      "Epoch: 007/010 | Batch 350/921 | Loss: 0.41\n",
+      "Epoch: 007/010 | Batch 400/921 | Loss: 0.24\n",
+      "Epoch: 007/010 | Batch 450/921 | Loss: 0.24\n",
+      "Epoch: 007/010 | Batch 500/921 | Loss: 0.32\n",
+      "Epoch: 007/010 | Batch 550/921 | Loss: 0.25\n",
+      "Epoch: 007/010 | Batch 600/921 | Loss: 0.19\n",
+      "Epoch: 007/010 | Batch 650/921 | Loss: 0.30\n",
+      "Epoch: 007/010 | Batch 700/921 | Loss: 0.49\n",
+      "Epoch: 007/010 | Batch 750/921 | Loss: 0.30\n",
+      "Epoch: 007/010 | Batch 800/921 | Loss: 0.36\n",
+      "Epoch: 007/010 | Batch 850/921 | Loss: 0.50\n",
+      "Epoch: 007/010 | Batch 900/921 | Loss: 0.28\n",
+      "Epoch: 007/010 training accuracy: 87.74\n",
+      "Epoch: 007/010 validation accuracy: 88.90\n",
+      "Epoch: 008/010 | Batch 000/921 | Loss: 0.37\n",
+      "Epoch: 008/010 | Batch 050/921 | Loss: 0.39\n",
+      "Epoch: 008/010 | Batch 100/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 150/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 200/921 | Loss: 0.33\n",
+      "Epoch: 008/010 | Batch 250/921 | Loss: 0.34\n",
+      "Epoch: 008/010 | Batch 300/921 | Loss: 0.25\n",
+      "Epoch: 008/010 | Batch 350/921 | Loss: 0.27\n",
+      "Epoch: 008/010 | Batch 400/921 | Loss: 0.34\n",
+      "Epoch: 008/010 | Batch 450/921 | Loss: 0.47\n",
+      "Epoch: 008/010 | Batch 500/921 | Loss: 0.28\n",
+      "Epoch: 008/010 | Batch 550/921 | Loss: 0.38\n",
+      "Epoch: 008/010 | Batch 600/921 | Loss: 0.29\n",
+      "Epoch: 008/010 | Batch 650/921 | Loss: 0.41\n",
+      "Epoch: 008/010 | Batch 700/921 | Loss: 0.26\n",
+      "Epoch: 008/010 | Batch 750/921 | Loss: 0.32\n",
+      "Epoch: 008/010 | Batch 800/921 | Loss: 0.23\n",
+      "Epoch: 008/010 | Batch 850/921 | Loss: 0.47\n",
+      "Epoch: 008/010 | Batch 900/921 | Loss: 0.35\n",
+      "Epoch: 008/010 training accuracy: 87.80\n",
+      "Epoch: 008/010 validation accuracy: 89.20\n",
+      "Epoch: 009/010 | Batch 000/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 050/921 | Loss: 0.41\n",
+      "Epoch: 009/010 | Batch 100/921 | Loss: 0.20\n",
+      "Epoch: 009/010 | Batch 150/921 | Loss: 0.37\n",
+      "Epoch: 009/010 | Batch 200/921 | Loss: 0.42\n",
+      "Epoch: 009/010 | Batch 250/921 | Loss: 0.65\n",
+      "Epoch: 009/010 | Batch 300/921 | Loss: 0.40\n",
+      "Epoch: 009/010 | Batch 350/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 400/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 450/921 | Loss: 0.23\n",
+      "Epoch: 009/010 | Batch 500/921 | Loss: 0.26\n",
+      "Epoch: 009/010 | Batch 550/921 | Loss: 0.20\n",
+      "Epoch: 009/010 | Batch 600/921 | Loss: 0.48\n",
+      "Epoch: 009/010 | Batch 650/921 | Loss: 0.38\n",
+      "Epoch: 009/010 | Batch 700/921 | Loss: 0.40\n",
+      "Epoch: 009/010 | Batch 750/921 | Loss: 0.54\n",
+      "Epoch: 009/010 | Batch 800/921 | Loss: 0.42\n",
+      "Epoch: 009/010 | Batch 850/921 | Loss: 0.23\n",
+      "Epoch: 009/010 | Batch 900/921 | Loss: 0.36\n",
+      "Epoch: 009/010 training accuracy: 88.00\n",
+      "Epoch: 009/010 validation accuracy: 88.90\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 010/010 | Batch 000/921 | Loss: 0.44\n",
+      "Epoch: 010/010 | Batch 050/921 | Loss: 0.35\n",
+      "Epoch: 010/010 | Batch 100/921 | Loss: 0.41\n",
+      "Epoch: 010/010 | Batch 150/921 | Loss: 0.28\n",
+      "Epoch: 010/010 | Batch 200/921 | Loss: 0.23\n",
+      "Epoch: 010/010 | Batch 250/921 | Loss: 0.30\n",
+      "Epoch: 010/010 | Batch 300/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 350/921 | Loss: 0.38\n",
+      "Epoch: 010/010 | Batch 400/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 450/921 | Loss: 0.31\n",
+      "Epoch: 010/010 | Batch 500/921 | Loss: 0.42\n",
+      "Epoch: 010/010 | Batch 550/921 | Loss: 0.16\n",
+      "Epoch: 010/010 | Batch 600/921 | Loss: 0.33\n",
+      "Epoch: 010/010 | Batch 650/921 | Loss: 0.36\n",
+      "Epoch: 010/010 | Batch 700/921 | Loss: 0.10\n",
+      "Epoch: 010/010 | Batch 750/921 | Loss: 0.26\n",
+      "Epoch: 010/010 | Batch 800/921 | Loss: 0.37\n",
+      "Epoch: 010/010 | Batch 850/921 | Loss: 0.22\n",
+      "Epoch: 010/010 | Batch 900/921 | Loss: 0.30\n",
+      "Epoch: 010/010 training accuracy: 88.07\n",
+      "Epoch: 010/010 validation accuracy: 89.20\n"
+     ]
+    }
+   ],
    "source": [
     "loss_list, train_acc_list, valid_acc_list = train_model(model, \n",
     "            data_loader, \n",
@@ -521,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -531,12 +734,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -561,12 +764,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -589,15 +792,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation ACC: 98.90%\n",
-      "Test ACC: 99.10%\n"
+      "Validation ACC: 89.20%\n",
+      "Test ACC: 87.94%\n"
      ]
     }
    ],
@@ -636,9 +839,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "tryit",
+   "display_name": "Python 3.6 - AzureML",
    "language": "python",
-   "name": "tryit"
+   "name": "python3-azureml"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/README.md b/README.md
index 3ad1aa2..8a3d853 100644
--- a/README.md
+++ b/README.md
@@ -18,11 +18,14 @@ Table of Contents
 |[Multilayer Perceptron](./Multilayer-Perceptron.ipynb)|
 |[Convolutional Neural network](./Convolutional-Neural-network.ipynb)|
 |[Fully Convolutional](./Fully-Convolutional.ipynb)|
-|[LeNet-5](./LeNet-5.ipynb)|
+|[LeNet5](./LeNet-5.ipynb)|
 |[VGG16](./VGG16.ipynb)|
 |[ResNet50, ResNet34](./ResNet.ipynb)|
-|[DenseNet-121](./DenseNet-121.ipynb)|
+|[DenseNet121](./DenseNet-121.ipynb)|
 |[Network-in-Network](./Network-in-Network.ipynb)|
+|[Autoencoder](./Autoencoder.ipynb)|
+|[ Convolutional Variational Autoencoder]( Convolutional-Variational-Autoencoder.ipynb)|
+
 
 
 ## Machine Learning
diff --git a/images/autoencoder/autoencoder-arch.png b/images/autoencoder/autoencoder-arch.png
new file mode 100644
index 0000000..f9fdfcb
Binary files /dev/null and b/images/autoencoder/autoencoder-arch.png differ