VAE_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variational Auto - Encoder using CNNs\n",
    "\n",
    "## Importing required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "import torch.optim as optim\n",
    "from torch import nn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the model\n",
    "\n",
    "<img src=\"Architecture_cnn.png\" width=\"800\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAE(nn.Module):\n",
    "  \n",
    "    def __init__(self):\n",
    "        super(VAE, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Conv2d(1, 32, 3)\n",
    "        self.conv2 = nn.Conv2d(32, 16, 3)\n",
    "        self.max_pool = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "        self.fc1_mu = nn.Linear(12 * 12 * 16, 20)\n",
    "        self.fc1_sig = nn.Linear(12 * 12 * 16, 20)\n",
    "        self.fc2 = nn.Linear(20, 12 * 12 * 16)\n",
    "        self.up_sample = nn.UpsamplingNearest2d(scale_factor=2)\n",
    "        self.conv3 = nn.ConvTranspose2d(16, 32, 3)\n",
    "        self.conv4 = nn.ConvTranspose2d(32, 1, 3)\n",
    "  \n",
    "    def encode(self,x):\n",
    "        a1 = F.relu(self.conv1(x))\n",
    "        a2 = F.relu(self.conv2(a1))\n",
    "        mx_poold = self.max_pool(a2)\n",
    "        a_reshaped = mx_poold.reshape(-1 , 12 * 12 * 16)\n",
    "        a_mu = self.fc1_mu(a_reshaped)\n",
    "        a_logvar = self.fc1_sig(a_reshaped)\n",
    "        return a_mu, a_logvar\n",
    "  \n",
    "    def decode(self,z):\n",
    "        a3 = F.relu(self.fc2(z))\n",
    "        a3 = a3.reshape(-1, 16, 12, 12)\n",
    "        a3_upsample = self.up_sample(a3)\n",
    "        a4 = F.relu(self.conv3(a3_upsample))\n",
    "        a5 = torch.sigmoid(self.conv4(a4))\n",
    "        return a5\n",
    "  \n",
    "    def reparameterize(self, mu, logvar):\n",
    "        std = torch.exp(0.5*logvar)\n",
    "        eps = torch.randn_like(std)\n",
    "        return eps.mul(std).add_(mu)\n",
    "  \n",
    "    def forward(self,x):\n",
    "        mu, logvar = self.encode(x)\n",
    "        z = self.reparameterize(mu, logvar)\n",
    "        return self.decode(z), mu, logvar\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = torchvision.datasets.MNIST(root='./data',train=True, transform=transforms.ToTensor(), download=True)\n",
    "test_dataset = torchvision.datasets.MNIST(root='./data',train=False, transform=transforms.ToTensor(),download = True)\n",
    "batch_size = 100\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,  batch_size=100, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiating the model and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = VAE()\n",
    "optimizer = optim.Adam(model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_function(recon_x, x, mu, logvar):\n",
    "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')\n",
    "\n",
    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
    "\n",
    "    return BCE + KLD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ashutoshchaubey/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:177: UserWarning: nn.UpsamplingNearest2d is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.UpsamplingNearest2d is deprecated. Use nn.functional.interpolate instead.\")\n",
      "/Users/ashutoshchaubey/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:122: UserWarning: nn.Upsampling is deprecated. Use nn.functional.interpolate instead.\n",
      "  warnings.warn(\"nn.Upsampling is deprecated. Use nn.functional.interpolate instead.\")\n",
      "/Users/ashutoshchaubey/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1594: UserWarning: Using a target size (torch.Size([100, 784])) that is different to the input size (torch.Size([100, 1, 28, 28])) is deprecated. Please ensure they have the same size.\n",
      "  \"Please ensure they have the same size.\".format(target.size(), input.size()))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 1 , Minibatch : 0 Loss = 54326.6719\n",
      "Epoch : 1 , Minibatch : 100 Loss = 2963207.3535\n",
      "Epoch : 1 , Minibatch : 200 Loss = 1878940.3311\n",
      "Epoch : 1 , Minibatch : 300 Loss = 1469711.3965\n",
      "Epoch : 1 , Minibatch : 400 Loss = 1313537.5762\n",
      "Epoch : 1 , Minibatch : 500 Loss = 1249602.0713\n",
      "Epoch 1 : Loss = (10128098.4697) \n",
      "Epoch : 2 , Minibatch : 0 Loss = 12200.3232\n",
      "Epoch : 2 , Minibatch : 100 Loss = 1188733.4014\n",
      "Epoch : 2 , Minibatch : 200 Loss = 1168597.8184\n",
      "Epoch : 2 , Minibatch : 300 Loss = 1164925.1621\n",
      "Epoch : 2 , Minibatch : 400 Loss = 1150479.5059\n",
      "Epoch : 2 , Minibatch : 500 Loss = 1135445.5137\n",
      "Epoch 2 : Loss = (6940009.0449) \n",
      "Epoch : 3 , Minibatch : 0 Loss = 11324.7793\n",
      "Epoch : 3 , Minibatch : 100 Loss = 1120590.1504\n",
      "Epoch : 3 , Minibatch : 200 Loss = 1118062.9893\n",
      "Epoch : 3 , Minibatch : 300 Loss = 1110201.0215\n",
      "Epoch : 3 , Minibatch : 400 Loss = 1105242.5762\n",
      "Epoch : 3 , Minibatch : 500 Loss = 1103457.6533\n",
      "Epoch 3 : Loss = (6655788.3125) \n"
     ]
    }
   ],
   "source": [
    "num_epochs = 3\n",
    "print_per = 100\n",
    "model.train()\n",
    "for epoch in range(num_epochs):\n",
    "    train_loss = 0\n",
    "    print_loss = 0\n",
    "    loss_record = []\n",
    "    for i, (images, _) in enumerate(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "        recon_batch, mu, logvar = model(images)\n",
    "        loss = loss_function(recon_batch, images, mu, logvar)\n",
    "        loss.backward()\n",
    "        train_loss += loss.item()\n",
    "        print_loss += loss.item()\n",
    "        optimizer.step()\n",
    "        if (i%print_per == 0):\n",
    "            print(\"Epoch : {} , Minibatch : {} Loss = {:.4f}\".format(epoch+1, i, print_loss))\n",
    "            loss_record.append(print_loss)\n",
    "            print_loss = 0\n",
    "    print(\"Epoch {} : Loss = ({:.4f}) \".format(epoch+1, train_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n",
      "torch.Size([1, 1, 28, 28])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model(images)[0].data[0].numpy().shape)\n",
    "image1 = images[8].reshape(1, 1, 28, 28)\n",
    "print(image1.shape)\n",
    "plt.imshow(model(images)[0].data[8].numpy().reshape(28, 28), cmap='gray')\n",
    "plt.show(block=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM7MonLm5mT4zs3TLrLtqZryuN77w+7zb3f0fJa2TtDXbvW1JPv2ZrZWGayqaublZZplZ+q/KXHfVznhdb2WEf1jS8hl/L8uWtQR3H85+n5L0G7Xe7MOjVyZJzX6fKrk/f9VKMzfPNrO0WmDdtdKM12WE/3VJfWb2JTObL+lbkvaV0I/PMbOF2RcxMrOFktaq9WYf3idpILs9IOnlEvvyN1pl5ua8maVV8rpruRmv3b3pP5Lu1fQ3/u9J+mEZfcjp182S3sp+/lh23yQ9r+ndwAlNfzfykKRuSQckvSvpFUnXt1Df/lPSEUmHNR203pL6drumd+kPSzqU/dxb9rpL9KuU9cYRfkBQfOEHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wdlNhI59gnFgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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53zjCDwiKL/yAoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwT1NxgZjahBd0jkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XbqS+ynrdygj/VkkXmdnXzWyCpBWSNpTQx1eYWUf2RYzMrEPSUrXf6sMbJK3KLq+S9EyJvXxJu6zcnLeytEp+7dpuxWt3b/mPpOtU+cb/HUn3ldFDTl/zJb2W/WwvuzdJT6jyNvALVb4b+Y6kP5K0WdIOSf8paWob9faoKqs596kStJkl9bZYlbf0fZJ+m/1cV/Zrl+irlNeNPfyAoPjCDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8LPUbf6qj1uo4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D7n6PpG9I+n7x9raWfOwzW52GaxqaublbJphZ+nNVbrtmZ7xutyrCf1TSsnH3lxbLasHdjxb/npT0W9Vv9uET1yZJLf49WXE/n6vTzM0TzSytGmy7Os14XUX435LUZ2ZfMbNpkr4taUcFfXyJmc0qvoiRmc2StEb1m314h6T1xe31kl6psJe/UpeZm8tmllbF2652M167e9d/JD2qsW/8P5D0T1X0UNLXHZL+t/g5UHVvkl7S2NvAKxr7buRpSbdK2iXpfUn/JWl+jXr7d0n7Je3TWNAWVdTbAxp7S79P0t7i59Gqt12ir0q2G0f4AUHxhR8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaD+H8iwDCMO2ttKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0fXe/MLNW57qbpa9a1lsd4R+RtHrG75/PrmsEdx/Jfo5Kel7Nm3347CeTpGY/R2vu5/81aebm2WaWVgPWXZNmvK4j/K9LusvMvmBmCyR9S9KeGvr4DDO7OfsiRmZ2s6SvqXmzD++RtC27vE3S7hp7+ZSmzNycN7O0al53jZvx2t27/k/Sg5r+xv9/Jf2ojh5y+vpDSf+V/TtSd2+SntH028BxTX838rCkP5C0X9IxSf8m6dYG9fZPmp7N+R1NB22gpt7u1/Rb+nckvZ39e7DudZfoq5b1xh5+QFB84QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/A767XZ5dZLPLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "test_loss = 0\n",
    "print_per = 10\n",
    "fig=plt.figure(figsize=(28, 28))\n",
    "columns = 2\n",
    "rows = 5\n",
    "with torch.no_grad():\n",
    "    for i, (images, _) in enumerate(test_loader):\n",
    "        recon_batch, mu, logvar = model(images)\n",
    "        test_loss += loss_function(recon_batch, images, mu, logvar).item()\n",
    "        if (i%print_per == 0):\n",
    "            plt.imshow(model(images)[0].data[0].numpy().reshape(28, 28), cmap='gray')\n",
    "            plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "image2 = images[1].reshape(1, 1, 28, 28)\n",
    "print(image2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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.imshow(model(images)[0].data[1].numpy().reshape(28, 28), cmap='gray')\n",
    "plt.show(block=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADhxJREFUeJzt3X+MFPUZx/HPIz9CIo3xbHoBwdIqISEmleZiKqKpvxo1JIgmBP7xTJteE1HbxD9qJFrUVJvaH/EPQjwCgoZqNXBKxPTXpcE2NsRTKSi2xeoRfgkijQjGtOc9/WOH9sDb7yy7szt7PO9XcrndeXZnngx8bmZ2ZvZr7i4A8ZxVdgMAykH4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ENb6VCzMzLicEmszdrZbXNbTlN7PrzezvZvaOmd3TyLwAtJbVe22/mY2T9A9J10naK+lVSUvcfWfiPWz5gSZrxZb/UknvuPu77v5vSc9IWtDA/AC0UCPhP1/SnhHP92bTTmJmPWY2YGYDDSwLQMGa/oGfu/dK6pXY7QfaSSNb/n2Spo94Pi2bBmAMaCT8r0qaaWZfMbOJkhZL2lRMWwCare7dfncfMrM7JP1W0jhJa9z9rcI6A9BUdZ/qq2thHPMDTdeSi3wAjF2EHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBFX3EN2SZGaDkj6W9JmkIXfvKqIptI8ZM2Yk61dddVWyPnfu3Kq1CRMmJN976623Jutm6cFoUyNQr1ixIvneZcuWJetHjx5N1seChsKfucrdDxcwHwAtxG4/EFSj4XdJvzOz18ysp4iGALRGo7v989x9n5l9SdLvzexv7v7yyBdkfxT4wwC0mYa2/O6+L/t9SFKfpEtHeU2vu3fxYSDQXuoOv5mdbWZfOPFY0rckvVlUYwCaq5Hd/k5JfdnplvGSfuXuvymkKwBNV3f43f1dSV8rsBfUafz46v+MV1xxRfK9N998c7K+ePHiZL2joyNZT/n000+T9ePHjyfrb7zxRrL+yCOPVK3t2bMn+d7h4eFk/UzAqT4gKMIPBEX4gaAIPxAU4QeCIvxAUEXc1YcmmzRpUrL+xBNPVK0tWrSooWVv2LAhWe/r66t73ps3b07Wz4TbZtsZW34gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrz/G1g4sSJyfrq1auT9dS5/P379yffe//99yfra9euTdZTX4/dqIULFybrs2bNStZ37dpVtZZ3/UIEbPmBoAg/EBThB4Ii/EBQhB8IivADQRF+IChr5nnazy3MrHULayN55/HXrVuXrDdyT37eV28/99xzdc+7UXlDdPf39yfrl19+ebL+/PPPV63dcsstyfeOZe6eHrs8w5YfCIrwA0ERfiAowg8ERfiBoAg/EBThB4LKvZ/fzNZImi/pkLtfnE3rkPRrSTMkDUpa5O7/al6bY9s555yTrDf63foPPPBA1Vo737d+ww03JOt55/Hz5A3xHV0tW/61kq4/Zdo9kvrdfaak/uw5gDEkN/zu/rKkI6dMXiDpxGVp6yTdVHBfAJqs3mP+Tnc/kD1+X1JnQf0AaJGGv8PP3T11zb6Z9UjqaXQ5AIpV75b/oJlNkaTs96FqL3T3XnfvcveuOpcFoAnqDf8mSd3Z425JLxTTDoBWyQ2/mT0t6S+SZpnZXjP7jqSfSLrOzHZJujZ7DmAMyT3md/clVUrXFNzLGev2229v6P2ffPJJsp66b314eLihZec566z09mPp0qVVaw8//HBDyz5y5NSTUCe7++67G5r/mY4r/ICgCD8QFOEHgiL8QFCEHwiK8ANBMUR3Aa688spk/b777mto/nlfv719+/aG5p8yadKkZD3vdNqDDz5YZDsnWbVqVbL+wQcfNG3ZZwK2/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOf5CzBu3Lhk3aymEZOruuiii5L17u7uZD1l/vz5yfrQ0FCy3ujXjjdi48aNpS37TMCWHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCMveqI20Vv7DEsF5jWVdXejCivr6+ZH3q1KlFtlOoPXv2JOuHDx9O1ufMmVP3snfu3JmsX3bZZcn6sWPH6l72WObuNV1YwpYfCIrwA0ERfiAowg8ERfiBoAg/EBThB4LKvZ/fzNZImi/pkLtfnE1bLum7kk58Mfq97v5Ss5psdwMDA8n6tddem6zv2LEjWc/7voDUtRoffvhh8r3PPvtssv7QQw8l61dffXWyvn79+mQ9ZcWKFcl61PP4Rally79W0vWjTP+lu1+S/YQNPjBW5Ybf3V+WdKQFvQBooUaO+e8ws+1mtsbMzi2sIwAtUW/4V0q6UNIlkg5I+nm1F5pZj5kNmFn6wBhAS9UVfnc/6O6fufuwpFWSLk28ttfdu9w9ffcLgJaqK/xmNmXE04WS3iymHQCtUsupvqclfVPSF81sr6QfSfqmmV0iySUNSvpeE3sE0ATcz98Gpk2blqznnUvfvXt31dqWLVvq6umEyZMnJ+t51zjMnDmzau29995Lvjfv+ojBwcFkPSru5weQRPiBoAg/EBThB4Ii/EBQhB8IilN9wU2cODFZv+2225L1lStX1r3suXPnJutbt26te96RcaoPQBLhB4Ii/EBQhB8IivADQRF+ICjCDwSVez8/zmxLlixJ1hs5jy9J/f39VWt5Q3CjudjyA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQ3M9/hrvggguS9c2bNyfrs2fPTtaHhoaS9Y6Ojqq148ePJ9+L+nA/P4Akwg8ERfiBoAg/EBThB4Ii/EBQhB8IKvd+fjObLulJSZ2SXFKvuz9mZh2Sfi1phqRBSYvc/V/NaxX1uPPOO5P1vPP4eR5//PFknXP57auWLf+QpLvdfbakb0haamazJd0jqd/dZ0rqz54DGCNyw+/uB9z99ezxx5LelnS+pAWS1mUvWyfppmY1CaB4p3XMb2YzJM2RtFVSp7sfyErvq3JYAGCMqPk7/MxssqQNkn7g7kfN/n/5sLt7tev2zaxHUk+jjQIoVk1bfjOboErw17v7xmzyQTObktWnSDo02nvdvdfdu9y9q4iGARQjN/xW2cSvlvS2u/9iRGmTpO7scbekF4pvD0Cz5N7Sa2bzJP1J0g5Jw9nke1U57n9W0gWSdqtyqu9Izry4pbcJpk6dWrW2bdu25HvPO++8ZH3//v3JeldXeofu4MGDyTqKV+stvbnH/O7+Z0nVZnbN6TQFoH1whR8QFOEHgiL8QFCEHwiK8ANBEX4gKIboHgMWLlyYrC9fvrxqLe88fp5ly5Yl65zHH7vY8gNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUJznb4Hx49Or+dFHH03W77rrriLbOcn69euT9aeeeqppy0a52PIDQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFC539tf6MKCfm//vHnzkvUtW7Y0bdkfffRRsj5//vxk/ZVXXimyHbRArd/bz5YfCIrwA0ERfiAowg8ERfiBoAg/EBThB4LKPc9vZtMlPSmpU5JL6nX3x8xsuaTvSvoge+m97v5SzrxCnucHWqnW8/y1hH+KpCnu/rqZfUHSa5JukrRI0jF3/1mtTRF+oPlqDX/uN/m4+wFJB7LHH5vZ25LOb6w9AGU7rWN+M5shaY6krdmkO8xsu5mtMbNzq7ynx8wGzGygoU4BFKrma/vNbLKkLZJ+7O4bzaxT0mFVPgd4SJVDg2/nzIPdfqDJCjvmlyQzmyDpRUm/dfdfjFKfIelFd784Zz6EH2iywm7sMTOTtFrS2yODn30QeMJCSW+ebpMAylPLp/3zJP1J0g5Jw9nkeyUtkXSJKrv9g5K+l304mJoXW36gyQrd7S8K4Qeaj/v5ASQRfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOEHgsr9As+CHZa0e8TzL2bT2lG79taufUn0Vq8ie/tyrS9s6f38n1u42YC7d5XWQEK79taufUn0Vq+yemO3HwiK8ANBlR3+3pKXn9KuvbVrXxK91auU3ko95gdQnrK3/ABKUkr4zex6M/u7mb1jZveU0UM1ZjZoZjvMbFvZQ4xlw6AdMrM3R0zrMLPfm9mu7Peow6SV1NtyM9uXrbttZnZjSb1NN7M/mtlOM3vLzL6fTS913SX6KmW9tXy338zGSfqHpOsk7ZX0qqQl7r6zpY1UYWaDkrrcvfRzwmZ2paRjkp48MRqSmf1U0hF3/0n2h/Ncd/9hm/S2XKc5cnOTeqs2svRtKnHdFTnidRHK2PJfKukdd3/X3f8t6RlJC0roo+25+8uSjpwyeYGkddnjdar852m5Kr21BXc/4O6vZ48/lnRiZOlS112ir1KUEf7zJe0Z8Xyv2mvIb5f0OzN7zcx6ym5mFJ0jRkZ6X1Jnmc2MInfk5lY6ZWTptll39Yx4XTQ+8Pu8ee7+dUk3SFqa7d62Ja8cs7XT6ZqVki5UZRi3A5J+XmYz2cjSGyT9wN2PjqyVue5G6auU9VZG+PdJmj7i+bRsWltw933Z70OS+lQ5TGknB08Mkpr9PlRyP//j7gfd/TN3H5a0SiWuu2xk6Q2S1rv7xmxy6etutL7KWm9lhP9VSTPN7CtmNlHSYkmbSujjc8zs7OyDGJnZ2ZK+pfYbfXiTpO7scbekF0rs5STtMnJztZGlVfK6a7sRr9295T+SblTlE/9/SlpWRg9V+vqqpL9mP2+V3Zukp1XZDfyPKp+NfEfSeZL6Je2S9AdJHW3U21OqjOa8XZWgTSmpt3mq7NJvl7Qt+7mx7HWX6KuU9cYVfkBQfOAHBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiCo/wJoRYsGzyE3jwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[1].numpy().reshape(28, 28), cmap='gray')\n",
    "plt.show(block=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments\n",
    "\n",
    "All of these are similar to those given in this [notebook](https://github.com/ac-alpha/VAEs-using-Pytorch/blob/master/VAE.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 20])\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    mu1, logvar1 = model.encode(image1)\n",
    "    std1 = torch.exp(0.5*logvar1)\n",
    "    mu2, logvar2 = model.encode(image2)\n",
    "    std2 = torch.exp(0.5*logvar2)\n",
    "    print(mu1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    recon_images1 = []\n",
    "    for ctr in range(0, 100, 5):\n",
    "        eps_val = torch.full_like(mu1, fill_value = ctr * 0.01 )\n",
    "        z_val1 = eps_val.mul(std1).add_(mu1)\n",
    "        recon_image1 = model.decode(z_val1)\n",
    "        recon_images1.append(recon_image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[ 4.0984e-03,  9.7911e-04,  2.0915e-04,  7.5596e-05,  1.3228e-05,\n",
      "            1.3151e-05,  5.3819e-05,  1.8970e-04,  1.5114e-04,  4.6369e-05,\n",
      "            4.2277e-05,  4.5332e-05,  2.3526e-05,  2.7647e-05,  7.6187e-05,\n",
      "            7.2943e-05,  7.8137e-05,  1.3136e-04,  2.0592e-04,  9.2434e-05,\n",
      "            2.7070e-05,  1.8428e-05,  9.4061e-06,  1.4125e-05,  1.1629e-04,\n",
      "            2.2457e-04,  5.0052e-04,  2.3158e-03],\n",
      "          [ 6.9680e-04,  3.4393e-05,  3.2332e-06,  6.1317e-07,  8.4422e-08,\n",
      "            1.2590e-07,  1.2930e-06,  1.0534e-05,  6.6858e-06,  6.3120e-07,\n",
      "            4.1710e-07,  5.7206e-07,  3.6757e-07,  4.7508e-07,  1.6341e-06,\n",
      "            1.4511e-06,  1.5390e-06,  2.3961e-06,  2.8848e-06,  4.5306e-07,\n",
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      "            5.6876e-06,  1.3670e-05,  2.4348e-04],\n",
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      "            1.1224e-07,  7.2249e-07,  2.9414e-06,  1.1269e-06,  3.2572e-07,\n",
      "            1.1375e-06,  2.6919e-06,  2.1600e-06,  3.9187e-06,  8.0521e-06,\n",
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      "            4.8793e-08,  4.5054e-08,  3.0645e-08,  6.1750e-08,  2.6834e-06,\n",
      "            3.6043e-06,  1.2423e-06,  3.4644e-05],\n",
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      "            3.6897e-08,  5.5173e-08,  2.2040e-08,  3.1050e-08,  1.5453e-06,\n",
      "            2.0224e-06,  9.5629e-07,  2.8203e-05],\n",
      "          [ 6.1367e-05,  4.4288e-07,  1.3750e-07,  1.8235e-07,  8.1826e-09,\n",
      "            1.7567e-08,  9.2965e-08,  1.0217e-07,  1.8062e-08,  1.9118e-08,\n",
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      "           -2.5123e-02, -6.8646e-03, -2.7366e-04, -7.3603e-06, -2.4114e-07,\n",
      "            1.0170e-09,  9.5454e-08,  4.4019e-08,  3.7900e-08,  5.3298e-07,\n",
      "            1.7527e-06,  3.1035e-06,  8.6655e-05],\n",
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      "            2.9343e-08,  3.7879e-07,  3.7143e-07,  2.7895e-07,  1.7716e-06,\n",
      "            1.2554e-05,  4.7098e-05,  3.9834e-04],\n",
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      "            2.2637e-07,  3.4685e-07,  7.5254e-07,  4.3183e-07,  1.3935e-06,\n",
      "            1.8371e-05,  1.3374e-04,  6.4403e-04],\n",
      "          [ 8.7216e-05,  1.3057e-06,  3.7573e-07,  3.1622e-07,  1.0095e-07,\n",
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      "           -1.2780e-02, -1.9459e-02, -1.0350e-03, -5.7613e-05, -8.2394e-06,\n",
      "           -2.0511e-08,  5.3933e-07,  1.8209e-06,  1.1040e-06,  1.9019e-06,\n",
      "            1.9011e-05,  9.1558e-05,  5.1044e-04],\n",
      "          [ 9.4486e-05,  2.1372e-06,  1.8813e-06,  3.0044e-06,  1.1015e-06,\n",
      "            4.7608e-06,  7.2782e-06,  4.6519e-06,  1.2263e-05,  1.7057e-05,\n",
      "            3.4369e-05,  2.2723e-04,  2.2949e-03,  9.4811e-03, -1.0567e-03,\n",
      "           -1.2896e-02, -1.0895e-02, -3.2461e-04, -3.1057e-05, -1.0610e-05,\n",
      "           -1.2402e-06,  7.8372e-07,  9.4435e-06,  7.0281e-06,  1.3943e-05,\n",
      "            6.2785e-05,  6.6583e-05,  3.2658e-04],\n",
      "          [ 1.1897e-04,  2.8447e-06,  4.2288e-06,  6.8683e-06,  1.9744e-06,\n",
      "            3.0917e-06,  2.5453e-06,  1.2680e-06,  2.3570e-06,  3.5990e-06,\n",
      "            1.0648e-05,  9.2525e-05,  1.3630e-03,  5.1423e-03, -1.0517e-03,\n",
      "           -1.2722e-02, -5.7606e-03, -1.2196e-04, -1.2055e-05, -5.0832e-06,\n",
      "           -5.7782e-07,  1.9799e-06,  1.7468e-05,  1.7885e-05,  4.5461e-05,\n",
      "            9.1776e-05,  2.8966e-05,  1.7137e-04],\n",
      "          [ 1.8838e-04,  4.8240e-06,  9.1096e-06,  1.1489e-05,  4.2798e-06,\n",
      "            1.3197e-06,  1.1854e-06,  2.3785e-07,  2.1635e-07,  1.1101e-07,\n",
      "           -2.7662e-07, -9.8427e-06, -4.4718e-05, -6.5738e-04, -1.5876e-03,\n",
      "           -1.8729e-02, -3.0501e-03, -4.7186e-05, -4.6620e-06, -1.6436e-06,\n",
      "           -3.8718e-07,  1.5556e-06,  1.9703e-05,  2.4812e-05,  6.4408e-05,\n",
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      "           -2.0074e-06, -4.5073e-05, -8.3449e-04, -4.5050e-03, -1.4890e-03,\n",
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      "           -1.7684e-07,  2.0252e-06,  1.9304e-05,  9.7436e-06,  1.4717e-05,\n",
      "            1.0609e-05,  3.7043e-06,  7.6024e-05],\n",
      "          [ 9.0104e-05,  1.6978e-06,  5.6540e-07,  8.3059e-07, -8.7575e-08,\n",
      "           -4.3374e-06, -2.8788e-06, -1.6136e-07, -4.5915e-08, -6.8671e-08,\n",
      "           -1.6808e-06, -4.3219e-05, -1.6683e-03, -6.2899e-03, -1.7045e-03,\n",
      "           -3.1290e-02, -1.1566e-03, -1.4904e-05, -1.6371e-06, -4.6886e-07,\n",
      "            7.5421e-08,  3.8071e-06,  1.5600e-05,  5.2126e-06,  9.1869e-06,\n",
      "            1.0027e-05,  1.1900e-05,  2.4489e-04],\n",
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      "           -2.7217e-02, -7.6080e-04, -1.3880e-05, -1.5934e-06, -3.1060e-07,\n",
      "            4.0262e-07,  3.6616e-06,  4.6978e-06,  1.5524e-06,  5.8434e-06,\n",
      "            1.3837e-05,  4.3841e-05,  7.0034e-04],\n",
      "          [ 6.8027e-05,  1.2985e-06,  5.2314e-07,  6.0697e-07,  2.4174e-08,\n",
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      "           -8.6309e-06, -1.2805e-04, -3.0369e-03,  1.8227e-04, -2.9606e-03,\n",
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      "            1.0299e-05,  3.9388e-05,  6.4598e-04],\n",
      "          [ 9.4679e-05,  1.9650e-06,  7.0247e-07,  6.4361e-07,  3.2578e-08,\n",
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      "           -1.8863e-06, -7.8857e-06,  4.4937e-04,  1.1578e-03, -5.2624e-03,\n",
      "           -1.8124e-02, -4.1375e-04, -6.9931e-06,  1.0692e-06,  2.3617e-06,\n",
      "            8.0276e-06,  2.1323e-05,  6.2315e-06,  8.5049e-07,  2.1535e-06,\n",
      "            6.0287e-06,  2.0332e-05,  3.9023e-04],\n",
      "          [ 1.7366e-04,  3.8047e-06,  1.0869e-06,  1.2023e-06,  2.7708e-07,\n",
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      "            1.6709e-05,  3.2318e-05,  7.5053e-06,  5.3968e-07,  1.3892e-06,\n",
      "            3.5139e-06,  1.2279e-05,  2.2697e-04],\n",
      "          [ 2.2581e-04,  6.1762e-06,  2.2767e-06,  3.2623e-06,  1.3339e-06,\n",
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      "            4.1525e-05,  6.2279e-05,  7.9459e-06,  3.5859e-07,  8.8326e-07,\n",
      "            3.0483e-06,  1.1270e-05,  1.9155e-04],\n",
      "          [ 2.9976e-04,  8.9432e-06,  3.0598e-06,  3.4651e-06,  1.1558e-06,\n",
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      "            1.0651e-06, -3.5093e-07,  2.7884e-08,  1.0621e-08,  8.8488e-08,\n",
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      "          [ 1.2848e-04,  1.4698e-06,  1.5540e-07,  5.1662e-08,  8.2374e-09,\n",
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      "           -3.6449e-06, -2.5405e-04, -3.0623e-02, -9.5698e-03, -2.9016e-03,\n",
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      "            1.9027e-07, -2.2416e-08,  1.7390e-08,  1.0519e-08,  7.7746e-08,\n",
      "            2.9220e-07,  7.8890e-07,  4.6721e-05],\n",
      "          [ 1.0191e-04,  1.2495e-06,  9.5335e-08,  2.1541e-08,  1.3369e-09,\n",
      "            1.2302e-09,  2.6435e-09,  1.3575e-08,  1.0746e-07,  4.5075e-09,\n",
      "           -8.2864e-06, -4.8183e-04, -2.6045e-02, -1.2142e-02, -3.6284e-03,\n",
      "           -7.9941e-04, -4.1077e-05, -1.7080e-06,  5.9764e-07,  7.2608e-07,\n",
      "            1.3636e-07,  5.8011e-08,  1.7015e-08,  7.8944e-09,  7.1255e-08,\n",
      "            3.5365e-07,  1.2225e-06,  6.5878e-05],\n",
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      "           -2.0564e-06, -3.2500e-04, -2.4186e-02, -1.1697e-02, -6.0201e-03,\n",
      "           -1.6520e-03, -5.9318e-05, -1.5024e-06,  1.8738e-07,  2.4931e-07,\n",
      "            5.6130e-08,  2.7654e-08,  9.5070e-09,  6.8613e-09,  8.5267e-08,\n",
      "            6.6577e-07,  1.1482e-06,  4.2351e-05],\n",
      "          [ 2.0253e-05,  7.7437e-08,  5.6241e-09,  6.9507e-10,  8.1185e-12,\n",
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      "           -7.8626e-07, -1.6021e-04, -1.3638e-02, -1.0613e-02, -3.9049e-03,\n",
      "           -7.1243e-04, -3.9275e-05, -2.8843e-07,  2.1608e-07,  6.3724e-08,\n",
      "            5.4834e-09,  2.9549e-09,  4.3051e-09,  1.1448e-08,  1.9825e-07,\n",
      "            9.7227e-07,  8.4299e-07,  1.9501e-05],\n",
      "          [ 2.2389e-05,  3.7262e-08,  9.5816e-09,  4.4193e-09,  4.9943e-11,\n",
      "            3.8558e-10,  2.8353e-08,  1.0038e-07,  2.6349e-07,  2.9203e-07,\n",
      "            1.5181e-06, -4.2375e-07, -1.0919e-03, -1.0928e-03,  1.0255e-02,\n",
      "            2.2239e-03,  7.8710e-05,  9.9324e-06,  8.8120e-07,  6.9705e-08,\n",
      "            4.0216e-09,  2.5240e-09,  2.7034e-08,  9.3595e-08,  1.2113e-06,\n",
      "            2.7106e-06,  1.0060e-06,  2.1752e-05],\n",
      "          [ 2.7835e-05,  3.2521e-08,  4.7809e-09,  7.7616e-09,  6.6107e-10,\n",
      "            9.1009e-09,  2.8825e-07,  2.5538e-07,  4.2440e-07,  6.5072e-07,\n",
      "            2.1476e-06,  2.0163e-05,  2.3422e-04,  9.2218e-04,  1.1772e-03,\n",
      "            2.6562e-04,  2.3297e-05,  4.6232e-06,  8.6202e-07,  1.7466e-07,\n",
      "            2.2248e-08,  4.5597e-08,  2.3475e-07,  1.8922e-07,  7.8419e-07,\n",
      "            1.2747e-06,  1.0086e-06,  4.1878e-05],\n",
      "          [ 2.5630e-04,  6.5189e-07,  2.6038e-08,  3.1201e-08,  3.2501e-08,\n",
      "            5.3939e-07,  2.3683e-06,  1.0995e-06,  1.2318e-06,  1.7457e-06,\n",
      "            3.2721e-06,  9.8285e-06,  4.1628e-05,  9.4449e-05,  1.2332e-04,\n",
      "            7.0505e-05,  1.8217e-05,  5.8566e-06,  2.3558e-06,  1.4601e-06,\n",
      "            7.9082e-07,  1.2995e-06,  1.0719e-06,  1.4530e-07,  1.4377e-07,\n",
      "            6.3902e-07,  8.1917e-06,  5.2915e-04],\n",
      "          [ 3.4159e-03,  1.5712e-04,  1.2454e-05,  7.9236e-06,  9.4312e-06,\n",
      "            4.2895e-05,  8.4197e-05,  5.1486e-05,  3.4929e-05,  3.2455e-05,\n",
      "            3.5065e-05,  5.4970e-05,  1.1962e-04,  1.7551e-04,  2.4345e-04,\n",
      "            2.1780e-04,  1.2869e-04,  7.2386e-05,  4.8335e-05,  4.7914e-05,\n",
      "            3.5566e-05,  2.8708e-05,  8.8024e-06,  1.4361e-06,  1.5644e-06,\n",
      "            8.3593e-06,  2.1370e-04,  3.4393e-03]]]])\n"
     ]
    }
   ],
   "source": [
    "print(recon_images1[0] - recon_images1[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x2016 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(28, 28))\n",
    "columns = 4\n",
    "rows = 5\n",
    "for i in range(1, columns*rows +1):\n",
    "    img = recon_images1[i-1].detach().numpy().reshape(28, 28)\n",
    "    fig.add_subplot(rows, columns, i)\n",
    "    plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    recon_images1 = []\n",
    "    eps_val = torch.randn_like(mu1)\n",
    "    for ctr in range(0, 100, 5):\n",
    "        eps_val[:, 7] = ctr * 0.05 * std1[:, 7] + mu1[:, 7]\n",
    "        z_val1 = eps_val.mul(std1).add_(mu1)\n",
    "        recon_image1 = model.decode(z_val1)\n",
    "        recon_images1.append(recon_image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x2016 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(28, 28))\n",
    "columns = 4\n",
    "rows = 5\n",
    "for i in range(1, columns*rows +1):\n",
    "    img = recon_images1[i-1].detach().numpy().reshape(28, 28)\n",
    "    fig.add_subplot(rows, columns, i)\n",
    "    plt.imshow(img, cmap = \"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_any = torch.randn_like(mu1)\n",
    "z1 = eps_any.mul(std1).add_(mu1)\n",
    "z2 = eps_any.mul(std2).add_(mu2)\n",
    "all_recons = []\n",
    "for i in range(20):\n",
    "    z_bet = z1 + torch.full_like(mu1, fill_value = 0.05*i).mul(z2 - z1)\n",
    "    recon_image = model.decode(z_bet)\n",
    "    all_recons.append(recon_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QLQAM77aQL8CMVYv8Uso3IuLFKZwFgDkiXwAYmmwBYBLkC9CDndwj/zOZ+c9b3150yp/ykJk3ZeadmXnnDvYCYH7IFwCGJlsAmAT5AkzNdov8/xIR74yID0TEoYj4T6caLKXcXEq5tpRy7Tb3AmB+yBcAhiZbAJgE+QJM1baK/FLKM6WUjVLKZkT8QUR8cNhjATCP5AsAQ5MtAEyCfAGmbVtFfmZefsIfPxUR955qFgBayRcAhiZbAJgE+QJM21JtIDP/NCKuj4hLMvOJiPiNiLg+Mz8QESUiHouIX5zgGenIhRee8pZvb1pYqH99aH19vWm/hx56qDpTSmlaC+iLfOFE5513XnWmJV82Njaa9rvvvvsGWwvoh2zhRHv37q3ODPna5Z/+6Z8GWwvoi3zhREtL1Tp10Hy58876j1Y4duxY01qMW/Ujr5Ry40kevmUCZwFgjsgXAIYmWwCYBPkC9GC7P+wWAAAAAACYAkU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMeWZn0AxuWxxx6rziws1L8+tLTU9qH38ssvN80BMG6PPvpodaYlXzKzab/nn3++aQ6A8Wp57dKSG8vLy037HTp0qDpTSmlaC4B+HTx4sDozZL489dRT1Rn5Mh9ckQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0bGnWB2Bcjh8/Xp1ZWKh/fWhzc7NpvyNHjjTNATBu6+vr1ZmWfCmlNO13+PDhpjkAxqvlNcfS0nAviV988cXB1gJg3IbMl6effnqwtRg3V+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVua9QEYl927d1dnMrM6s7Ky0rTf8ePHm+YAGLc9e/ZUZxYW6tcfHDlypGm/1dXVpjkAxmvfvn3VmZZsaX3tcvTo0aY5AMZt//791ZmlpXrleuzYsab9Dh8+3DTH2c8V+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANCxpVkfgHF57rnnqjOrq6uDrBMRcfz48aY5AMbtpZdeqs6sra1VZ1rzpWUtAMbt8OHD1Zn19fXqzAsvvNC0X8vrIADGb2VlpTqzsbFRnWnNlyNHjjTNcfZzRT4AAAAAAHSsWuRn5lWZ+fXMvC8zv5OZv7z1+EWZeUdmPrj1+4WTPy4AZwv5AsDQZAsAkyBfgB60XJG/HhG/Ukq5OiL+h4j4d5l5dUR8NiK+Vkp5d0R8bevPANBKvgAwNNkCwCTIF2DmqkV+KeVQKeXurbdfjYj7I+LtEfGJiPj81tjnI+KTkzokAGcf+QLA0GQLAJMgX4AenNEPu83Md0TENRHxDxFxoJRyaOuvno6IA6f4NzdFxE3bPyIAZzv5AsDQZAsAkyBfgFlp/mG3mbk/Iv4iIv59KeXwiX9XSikRUU7270opN5dSri2lXLujkwJwVpIvAAxNtgAwCfIFmKWmIj8zl+P1J6o/LqV8cevhZzLz8q2/vzwinp3MEQE4W8kXAIYmWwCYBPkCzFq1yM/MjIhbIuL+Uspvn/BXX4qIT2+9/emI+MvhjwfA2Uq+ADA02QLAJMgXoAct98i/LiJ+PiL+JTPv2XrsVyPityLizzPzFyLi8Yi4YTJHpCdHjx6tzhw7dqw6s7m52bTf0lL9Q3Rtba1pLaA78oU3HTlypDqzvr4+yExExOLiYtMcMDqyhTe1vHbZ2NiozrS+3lhYaL5zLTA+8oU3raysVGda8mV1dbVpv9e/jgQNRX4p5b9FxKk+Yn5i2OMAMC/kCwBDky0ATIJ8AXrgkgEAAAAAAOiYIh8AAAAAADqmyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY0uzPgDjUkoZZJ3du3c3ze3atas6c/To0Z0eB4AZW1ioX1uQmdWZxcXFpv1a5wAYr5bXHC15sL6+PsRxADhLXHjhhdWZ5eXl6szq6mrTfpubm01znP1ckQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0bGnWB2Bc1tfXqzNHjhypzjz33HNN+62trTXNATBux48fr86srq5WZw4dOjTYfgCM27Fjx6ozLa83Dh482LSfbAGYDy2vS1oy4eGHH27ar6WLYz64Ih8AAAAAADqmyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADo2NKsD8C4rK+vV2d+5Vd+pTrz6KOPNu23urraNAfAuK2srFRnfu3Xfq06861vfatpv2PHjjXNATBeL7/8cnXmN3/zN6szf/3Xf920X8trJQDG74knnqjO/M7v/E515o/+6I+a9tvY2Gia4+zninwAAAAAAOiYIh8AAAAAADqmyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADoWJZSprdZ5vQ2Y2YyszozzY87+ldKqX/QwGnIl/kgXzhT8oWdkC3zYWGhfm3b5ubmFE7CiNxVSrl21odgvOTLfJAvnKmW1y6uyAcAAAAAgI5Vi/zMvCozv56Z92XmdzLzl7ce/1xmPpmZ92z9+tjkjwvA2UK+ADA02QLAJMgXoAfVW+tk5uURcXkp5e7MPDci7oqIT0bEDRHxWinlPzZv5tuH5oJbH3Cm3PpgPskXzpR84UzJl/kjWzhTbn3ANri1zhySL5wp+cKZanntstSwyKGIOLT19quZeX9EvH3nxwNgnskXAIYmWwCYBPkC9OCM7pGfme+IiGsi4h+2HvpMZv5zZt6amRee4t/clJl3ZuadOzopAGct+QLA0GQLAJMgX4BZqd5a583BzP0R8X9HxH8opXwxMw9ExPMRUSLif43Xv8Xof66s4duH5oBbH3Cm3PpgvskXWskXzpR8mV+yhVZufcA2uLXOHJMvtJIvnKmW1y5NV+Rn5nJE/EVE/HEp5Ytbiz9TStkopWxGxB9ExAd3clgA5o98AWBosgWASZAvwKxVi/x8/fK3WyLi/lLKb5/w+OUnjH0qIu4d/ngAnK3kCwBDky0ATIJ8AXpQvbVOZn4oIr4ZEf8SEW98z8evRsSNEfGBeP3bhx6LiF/c+uEfp1vLtw8B/z9ufTCf5AswafJl/sgWYArcWmcOyRdg0lpeuzTfI38InqyAk1G0sFPyBTgZ+cJOyBbgFBT57Ih8AU5msHvkAwAAAAAAs6HIBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjinwAAAAAAOjY0rQ3XFxcPO3fb2xsTOkkwDQsLJz+64Wbm5tTOglnO/kC80W+MA0+zmC+ZGZ1ppQyhZNwtpMvMF9q+dKaLa7IBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjinwAAAAAAOiYIh8AAAAAADq2NOX9nt/Y2Hj8LY9dEhHPT/kcQxjruSOcfVbm8uybm5u1ke/ezrrwFm/Nl7n8fOuAs8/GXJ5dvjAFz29ubp4tr10inH0WxnruiDk9eymlZUy+sFNvzZe5/HzrgLPPxlyevSFfmrIlG4NqYjLzzlLKtTM9xDaM9dwRzj4rzg7TM+aPWWefDWefjTGfnfk05o9ZZ5++sZ47wtlhmsb8Mevss+Hss9HD2d1aBwAAAAAAOqbIBwAAAACAjvVQ5N886wNs01jPHeHss+LsMD1j/ph19tlw9tkY89mZT2P+mHX26RvruSOcHaZpzB+zzj4bzj4bMz/7zO+RDwAAAAAAnFoPV+QDAAAAAACnoMgHAAAAAICOzazIz8yPZOa/ZuZDmfnZWZ1jOzLzscz8l8y8JzPvnPV5Ticzb83MZzPz3hMeuygz78jMB7d+v3CWZzyVU5z9c5n55Nb7/p7M/Ngsz3gqmXlVZn49M+/LzO9k5i9vPd71+/405x7F+x0i5Mu0yJfpG2u2RMgXxk+2TIdsmY2x5ots4WwgX6ZDvkzfWLMlou98mck98jNzMSL+n4j4nyLiiYj4x4i4sZRy39QPsw2Z+VhEXFtKeX7WZ6nJzP8xIl6LiP+jlPKDW4/9bxHxYinlt7aC4sJSyv8yy3OezCnO/rmIeK2U8h9nebaazLw8Ii4vpdydmedGxF0R8cmI+LfR8fv+NOe+IUbwfgf5Mj3yZfrGmi0R8oVxky3TI1tmY6z5IlsYO/kyPfJl+saaLRF958usrsj/YEQ8VEp5pJSyFhF/FhGfmNFZzmqllG9ExItvefgTEfH5rbc/H69/MHbnFGcfhVLKoVLK3VtvvxoR90fE26Pz9/1pzg1jIV+mRL5M31izJUK+MHqyZUpky2yMNV9kC2cB+TIl8mX6xpotEX3ny6yK/LdHxMET/vxEdPIOaVQi4m8y867MvGnWh9mGA6WUQ1tvPx0RB2Z5mG34TGb+89a3F3X3LThvlZnviIhrIuIfYkTv+7ecO2Jk73fmlnyZrdE8x53CaJ7nxpotEfKFUZItszWq57iTGNVz3FjzRbYwUvJltkbzHHcKo3meG2u2RPSXL37Y7fZ8qJTywxHx0Yj4d1vf5jJK5fV7K03//krb918i4p0R8YGIOBQR/2m2xzm9zNwfEX8REf++lHL4xL/r+X1/knOP6v0OIyZfZmc0z3NjzZYI+QIzIltmZ1TPcWPNF9kCMyNfZmc0z3NjzZaIPvNlVkX+kxFx1Ql/vnLrsVEopTy59fuzEXF7vP7tUGPyzNb9nt6479OzMz5Ps1LKM6WUjVLKZkT8QXT8vs/M5Xj9E/6PSylf3Hq4+/f9yc49pvc7c0++zFb3z3GnMpbnubFmS4R8YdRky2yN4jnuZMb0HDfWfJEtjJx8ma3un+NOZSzPc2PNloh+82VWRf4/RsS7M/N7MnNXRPxMRHxpRmc5I5l5ztYPOojMPCciPhwR957+X3XnSxHx6a23Px0RfznDs5yRNz7Zt3wqOn3fZ2ZGxC0RcX8p5bdP+Kuu3/enOvdY3u8Q8mXWun6OO50xPM+NNVsi5AujJ1tmq/vnuFMZy3PcWPNFtnAWkC+z1fVz3OmM4XlurNkS0Xe+5OvfxTB9mfmxiPidiFiMiFtLKf9hJgc5Q5n5vfH6VxojIpYi4k96Pntm/mlEXB8Rl0TEMxHxGxHxf0bEn0fEd0XE4xFxQymlux+ccYqzXx+vfwtLiYjHIuIXT7i3Vjcy80MR8c2I+JeI2Nx6+Ffj9Xtqdfu+P825b4wRvN8hQr5Mi3yZvrFmS4R8Yfxky3TIltkYa77IFs4G8mU65Mv0jTVbIvrOl5kV+QAAAAAAQJ0fdgsAAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRsaZqbZWaZ5n7AOJRSctZnYNzkC3Ay8oWdkC3AKTxfSrl01odgvOQLcDItr112dEU94FRMAAAgAElEQVR+Zn4kM/81Mx/KzM82/pvT/gLOLj7n2Q75AtT4nOdMyRagpvY5v/V5//isz0lftpMvANux7SI/Mxcj4j9HxEcj4uqIuDEzrx7qYADMJ/kCwNBkCwCTIF+AadrJFfkfjIiHSimPlFLWIuLPIuITwxwLgDkmXwAYmmwBYBLkCzA1Oyny3x4RB0/48xNbj/13MvOmzLwzM+/cwV4AzA/5AsDQZAsAkyBfgKmZ+A+7LaXcHBE3R/iBHgAMR74AMDTZAsAkyBdgCDu5Iv/JiLjqhD9fufUYAOyEfAFgaLIFgEmQL8DU7KTI/8eIeHdmfk9m7oqIn4mILw1zLADmmHwBYGiyBYBJkC/A1Gz71jqllPXM/ExEfCUiFiPi1lLKdxr+3Xa3BEbI5zxnSr4ALXzOcyZkC9DC5zxnarv5ArAdOc2gch8w4GRKKTnrMzBu8gU4GfnCTsgW4BTuKqVcO+tDMF7yBTiZltcuO7m1DgAAAAAAMGGKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjinwAAAAAAOiYIh8AAAAAADqmyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY0uzPgDzKTMHW6uUMthaAMyHlhySLwAAAPTCFfkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAxxT5AAAAAADQsaVZH4Czz9JS/cPqYx/7WNNai4uL1ZmvfOUrTWsdPXq0aQ6APrXky4//+I83rbV79+7qzN/+7d82rfXqq682zQHQn5Zsue6665rW2rdvX3Xm7//+75vWeuWVV5rmAOhTS5/1Iz/yI01r7d+/vzpz9913N6310ksvVWdKKU1rMX2uyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6tjTrAzAumVmdee9731ud+b3f+72m/Y4cOVKdOXjwYNNad999d3Vmc3OzaS0AhtWSL+9///urM7//+7/ftN/a2lp15pd+6Zea1vrqV79andnY2GhaC4DhtGTLtddeW535wz/8w6b9SinVmV//9V9vWusLX/hCdaYlywAYXku+/OiP/mh1pjVfFhbq12H/7u/+btNat9xyS3WmpYtjNlyRDwAAAAAAHdvRFfmZ+VhEvBoRGxGxXkqpX84AABXyBYBJkC8ADE22ANMyxK11/k0p5fkB1gGAE8kXACZBvgAwNNkCTJxb6wAAAAAAQMd2WuSXiPibzLwrM2862UBm3pSZd2bmnTvcC4D5IV8AmITT5otsAWAbvHYBpmKnt9b5UCnlycx8W0TckZkPlFK+ceJAKeXmiLg5IiIzyw73A2A+yBcAJuG0+SJbANgGr12AqdjRFfmllCe3fn82Im6PiA8OcSgA5pt8AWAS5AsAQ5MtwLRsu8jPzHMy89w33o6ID0fEvUMdDID5JF8AmAT5AsDQZAswTTu5tc6BiLg9M99Y509KKf91kFPRrcXFxerMj/3Yj1VnLr300qb9nn++/kPf19bWmtZaWKh/3Wpzc7NpLWCi5MscWlqq/yfJhz/84erMgQMHmvZ7+OGHqzPHjh1rWmt5ebk6s7Gx0bQWMFHyZc7s3r27OvOpT32qOnPZZZc17Xfw4MHqTGu27Nmzpzpz/Pjx6kwp7uABEyZb5tDevXurMz/7sz9bnbniiiua9nv66aerMy2ZEBGxf//+6szKykp1Rn82G9su8kspj0TE+wc8CwDIFwAmQr4AMDTZAkzTju6RDwAAAAAATJYiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjS7M+AOOyvLxcnbnmmmuqM4uLi037Pfjgg9WZp59+ummtjY2NpjkApm/Pnj3Vmfe9733VmYWFtmsU7rvvvurMww8/3LTW2tpa0xwA07Vv377qzNVXX12dycym/b797W9XZ+65556mtVZWVqozpZSmtQBo0/p8f/7551dnfuAHfmCnx3nTXXfdVZ35u7/7u6a1XnnlleqMfOmXK/IBAAAAAKBjinwAAAAAAOiYIh8AAAAAADqmyAcAAAAAgI4p8gEAAAAAoGOKfAAAAAAA6JgiHwAAAAAAOqbIBwAAAACAji3N+gCMy65du6oz733ve6szGxsbTfs98MAD1ZnDhw83rVVKaZoDYPr27t1bnXnnO99ZnTl27FjTfnfffXd15oUXXmhaa3Nzs2kOgGFkZtPc/v37qzNXXHFFdea1115r2u+b3/xmdebQoUNNa62vrzfNATCc1nw5//zzB5lpfb3x5S9/uTrzyCOPNK3V8npJf9YvV+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAx5ZmfQDGZXNzszqzuLhYnVlYaPsa0sbGxiAzAPRtfX29OnP8+PHB9ltbW6vOtJwJgOkrpTTNHTt2rDpz+PDh6kxrHhw9enSwtVr/NwIwnNbn3iNHjlRnnnrqqerMrl27mvZ76aWXqjNeu8wHV+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVua9QEYl42NjerMsWPHqjOllKb9jhw5MthaAPRrfX29OrO6ulqdkQkAvKElW1ZWVgZZJyJi79691ZmFBdfSAYzd8ePHqzOvvfZadaalP4uIOOecc6ozS0ttFW/rnvTJf0UAAAAAAEDHqkV+Zt6amc9m5r0nPHZRZt6RmQ9u/X7hZI8JwNlGvgAwCfIFgKHJFqAHLVfk3xYRH3nLY5+NiK+VUt4dEV/b+jMAnInbQr4AMLzbQr4AMKzbQrYAM1Yt8ksp34iIF9/y8Cci4vNbb38+Ij458LkAOMvJFwAmQb4AMDTZAvRguz/s9kAp5dDW209HxIFTDWbmTRFx0zb3AWC+yBcAJqEpX2QLAGfAaxdgqrZb5L+plFIys5zm72+OiJsjIk43BwAnki8ATMLp8kW2ALAdXrsA09Byj/yTeSYzL4+I2Pr92eGOBMAcky8ATIJ8AWBosgWYqu0W+V+KiE9vvf3piPjLYY4DwJyTLwBMgnwBYGiyBZiq6q11MvNPI+L6iLgkM5+IiN+IiN+KiD/PzF+IiMcj4oZJHpJ+LCzUv/Zz3nnnVWeWltru6vSe97xnkDMB/ZEvnKglFy666KLqzJ49e5r2u+6666ozt9xyS9Naa2trTXPAdMgX3rBr167qzIEDp7yl9ZsuuOCCpv0+/vGPV2duv/32prWeeuqp6kwp7s4B0yJbONG+ffuqM9/1Xd9Vnbnqqqua9vv5n//56sy3v/3tprUeeOCB6sz6+nrTWkxf9VVzKeXGU/zVTwx8FgDmiHwBYBLkCwBDky1AD1zKDAAAAAAAHVPkAwAAAABAxxT5AAAAAADQMUU+AAAAAAB0TJEPAAAAAAAdU+QDAAAAAEDHFPkAAAAAANAxRT4AAAAAAHRsadYHYFyWluofMvv37x9knYiIyy+/vDqzsODrUQBjt2vXrurM+eefX51ZXl5u2u+7v/u7BzkTAP3at29fdebiiy8eZJ2IiHe9613VmZYsi4g4dOhQdaaU0rQWAMM677zzqjNXXHFFdeaCCy5o2u+HfuiHqjMtr28iIh588MHqzMbGRnVmyAzKzKY5ueeKfAAAAAAA6JoiHwAAAAAAOqbIBwAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjS7M+AONyySWXVGcuvvji6kxmNu23vLw82FoA9OvKK6+szlx00UXVmYWFtmsUWuZa86VlrpTStBYAda3Pz+9617uqMxdeeGF1pjVbWs61ubnZtBYA09f6fP+DP/iD1ZmW1y4tnVdExNJSvb6VL/PBFfkAAAAAANAxRT4AAAAAAHRMkQ8AAAAAAB1T5AMAAAAAQMcU+QAAAAAA0DFFPgAAAAAAdEyRDwAAAAAAHVPkAwAAAABAx5ZmfQDG5YorrqjOZGZ1ZnNzs2m/lrnl5eWmtVZWVprmABhOSyZEtOVLi9Z8WV1drc4sLLjeAaBHrc/Pl156aXWmlFKdac2W1157bZD9etWa6WP+3wjMt8XFxaa5c889tzqzsbFRnWnNl6NHj1ZnWjuvaT9Ht2YHbbxCBQAAAACAjinyAQAAAACgY4p8AAAAAADomCIfAAAAAAA6psgHAAAAAICOKfIBAAAAAKBjinwAAAAAAOiYIh8AAAAAADqmyAcAAAAAgI4tzfoA9CEzm+bOOeec6szGxkZ1Zm1trWm/xx57rDqzubnZtBYA09eaL4uLi9WZ48ePV2da8+WRRx6pzqyvrzet1aPW93spZcInAZidltw4duxYdWb37t1N+7W8dllZWWlaq/V5fCjT3g+gR6390quvvlqdWV1drc7s2bOnab8nnniiOvPiiy82rTXUf//LjdlwRT4AAAAAAHSsWuRn5q2Z+Wxm3nvCY5/LzCcz856tXx+b7DEBONvIFwCGJlsAmAT5AvSg5Yr82yLiIyd5/P9l7+5i7bzqO/H/ln18bGPHre0UJ2QSkpCIJhSRFCsNJZ0yrQZ1uAFu6HAxYjRI4aKoVJqLQdyUalqpqgrzb0VLFRQa/i1TSAudhoq0UEgIpEqok5A3Oy9AnTiJX+KX2I5fjs85e82FT5Antb1Wznn23us5+/ORIh9vf73WYnP8fL1/3uc5/yvnfN3Cf1/v9lgATIDbQr8A0K3bQrcA0L3bQr8AY1Yc5Oec74mIuhstAUAl/QJA13QLAMOgX4AWLOUe+R9NKT2y8OVFG88VSindnFLallLatoS9AJgc+gWArukWAIZBvwAjs9hB/mcj4k0RcV1E7I6IT50rmHO+Jee8Nee8dZF7ATA59AsAXdMtAAyDfgFGalGD/Jzz3pzzfM55EBGfi4gbuj0WAJNIvwDQNd0CwDDoF2DUFjXITyldfMZP3x8Rj50rCwC19AsAXdMtAAyDfgFGbaoUSCn9VUS8KyIuTCk9FxG/HRHvSildFxE5InZGxEeGeEZGIOdclZubmytmUkqdrBMR8cADDxQzJ06cqFoLaIt+mQy1/fLyyy93st9gMKjKPfLII8XMkSNHqtaq/d84Si2eCUZBt0yG2mv9/v37i5ma62XtNfXJJ58sZvbt21e11vz8fFWuK3oDzk+/TIbaa++uXbuKmdnZ2aUe5yd27txZzDzzzDNVa9XM43RCu4qD/JzzB8/y8K1DOAsAE0S/ANA13QLAMOgXoAWL/Wa3AAAAAADACBjkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANmxr3AeiXXbt2FTMrVpT/fWh6erpqvxdeeKGYGQwGVWsBMHo556rcqPtlz549xcz8/HzVWgCMVm23PP/888XMypUri5nVq1dX7bdv375iZnZ2tmotAEavtl92795dzKxataqYqe2XAwcOFDMnT56sWqv2fyNt8o58AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANCwqXEfgH5ZsaL8bz/T09PFTEqpar9du3ZV5QDot6mp8l9JuuyXH//4x8VMzrlqLQDaVNMta9asKWZqu+WJJ54oZnQLQP/VdMe6deuKmZoZW0TEY489VszMz89XrUW/eUc+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjY1LgPQL9s3LixmJmaKn9azc7OVu138ODBYibnXLUWAO3avHlzMTM9PV3MzM3NVe23Z8+eYka/APTbRRddVMzUdMv8/HzVfjt37ixmdAtA/1166aXFzJo1a4qZ2n7ZsWNHZ2vRb96RDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANmxr3AeiXmZmZYmYwGBQzBw8erNrv8OHDVTkAiIg4dOhQVe7FF18c8kkAGLfVq1cXMymlYuall16q2m/Xrl3FTM65ai0A2rVp06ZiZsWK8nunjxw5UrXfE088Uczol8ngHfkAAAAAANCw4iA/pXRpSumulNL2lNLjKaWPLTy+KaX0zZTS0ws/bhz+cQFYLvQLAF3TLQAMg34BWlDzjvy5iPjvOedrI+LGiPiNlNK1EfHxiPhWzvnqiPjWws8BoJZ+AaBrugWAYdAvwNgVB/k559055wcXPj4aETsi4pKIeG9EfGEh9oWIeN+wDgnA8qNfAOiabgFgGPQL0ILX9M1uU0qXR8T1EXF/RGzJOe9e+KU9EbHlHL/n5oi4efFHBGC50y8AdE23ADAM+gUYl+pvdptSWh8RX4mI38o5/z/fVjmf/tbIZ/32yDnnW3LOW3POW5d0UgCWJf0CQNd0CwDDoF+Acaoa5KeUVsXpC9UXc85fXXh4b0rp4oVfvzgi9g3niAAsV/oFgK7pFgCGQb8A41Yc5KeUUkTcGhE7cs6fPuOX7oiIDy18/KGI+LvujwfAcqVfAOiabgFgGPQL0IKae+S/MyL+S0Q8mlL6wcJjn4iI34+I21NKH46IZyLiA8M5Ii2ZnZ0tZk5/Ndn5HTt2rGq/wWBQlQN6Sb/wE6dfGy3d0aNHq3Jzc3Od7Ac0R7fwE2vXri1mavrn8OHDVfvNzMxU5YBe0i8ToPY1yebNm4uZFSvKN0E5ePBg1X4nTpyoyrH8FQf5OefvRcS5PpN/tdvjADAp9AsAXdMtAAyDfgFaUP3NbgEAAAAAgNEzyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGjY17gPQL5dddlkxMzVV/rQ6cuRI1X7z8/NVOQDalFKqyr3lLW8pZmr65cUXX6zab3Z2tioHQHtWrKh7P9o73vGOYqamW3bt2lW136lTp6pyALSpphMiIn7pl36pmKnpqh/96EdV++kXXuEd+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYVPjPgD9Mjs7W8zMz88XM9u3b6/ar2YtAPpvxYryewtqOuiBBx6o2k+/ACx/69atK2ZquuWf//mfq/bTLQD9llKqym3YsKGYqemXb3/721X76Rde4R35AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhU+M+AP3y8MMPFzN/+qd/Wsz8yZ/8SdV+8/PzVTkA2pRzrsp985vfLGb+7M/+rJj51Kc+VbXfYDCoygHQntpr+Je//OViZmZmppj5zGc+U7VfbecB0Ka5ubmq3Be/+MVi5pFHHilmbr311qr99Auv8I58AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGhYyjmPbrOURrcZY7NiRfnfhwaDwQhOQl/knNO4z0C/6Zf+S6l8GajJ6BfOpF9YCt3Sf7qFIXkg57x13Iegv/RL/9XMvWroF85U89rFO/IBAAAAAKBhxUF+SunSlNJdKaXtKaXHU0ofW3j8kyml51NKP1j47z3DPy4Ay4V+AaBrugWAYdAvQAuKt9ZJKV0cERfnnB9MKV0QEQ9ExPsi4gMR8XLO+Q+rN/PlQxPBrXV4rdz6YDLpF87k9gcMg36ZPLqFM+kWhsStdSaQfuFMbq3DMNS8dpmqWGR3ROxe+PhoSmlHRFyy9OMBMMn0CwBd0y0ADIN+AVrwmv4JKaV0eURcHxH3Lzz00ZTSIymlz6eUNp7j99ycUtqWUtq2pJMCsGzpFwC6plsAGAb9AoxL8dY6PwmmtD4ivhMRv5dz/mpKaUtE7I+IHBH/M05/idF/K6zhy4cmgFvr8Fq59cFk0y9EuP0Bw6FfJpduIUK3MDRurTPB9AsRbq3DcNS8dqn6zEsprYqIr0TEF3POX11YfG/OeT7nPIiIz0XEDUs5LACTR78A0DXdAsAw6Bdg3IqD/HT6LQq3RsSOnPOnz3j84jNi74+Ix7o/HgDLlX4BoGu6BYBh0C9AC4q31kkp3RQR342IRyPila/5+EREfDAirovTXz60MyI+svDNP863li8fAv4Ntz6YTPoFGDb9Mnl0CzACbq0zgfQLMGw1r12q75HfBRcr4GwMWlgq/QKcjX5hKXQLcA4G+SyJfgHOprN75AMAAAAAAONhkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANGxq1BuuWHH+fzsYDAYjOgkwCiml8/56znlEJ2G587kGk8WfeUbB5xlMltKf+Qh/7umGfgEWwzvyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDpka83/7BYPDMqx67MCL2j/gcXejruSOcfVwm8uw551LkjYtZF15lf875zH6ZyD9vDXD28ZjIs+sXRuDV3RIxoX/eGtDXs/f13BETevaKbonQLyyd1y5tcPbxcPazq+qWVFlUQ5NS2pZz3jrWQyxCX88d4ezj4uwwOn3+nHX28XD28ejz2ZlMff6cdfbR6+u5I5wdRqnPn7POPh7OPh4tnN2tdQAAAAAAoGEG+QAAAAAA0LAWBvm3jPsAi9TXc0c4+7g4O4xOnz9nnX08nH08+nx2JlOfP2edffT6eu4IZ4dR6vPnrLOPh7OPx9jPPvZ75AMAAAAAAOfWwjvyAQAAAACAczDIBwAAAACAho1tkJ9S+rWU0pMppR+mlD4+rnMsRkppZ0rp0ZTSD1JK28Z9nvNJKX0+pbQvpfTYGY9tSil9M6X09MKPG8d5xnM5x9k/mVJ6fuG5/0FK6T3jPOO5pJQuTSndlVLanlJ6PKX0sYXHm37uz3PuXjzvEKFfRkW/jF5fuyVCv9B/umU0dMt49LVfdAvLgX4ZDf0yen3tloi2+2Us98hPKa2MiKci4j9GxHMR8S8R8cGc8/aRH2YRUko7I2Jrznn/uM9SklL69xHxckT8/znnn1t47A8i4mDO+fcXimJjzvl/jPOcZ3OOs38yIl7OOf/hOM9WklK6OCIuzjk/mFK6ICIeiIj3RcR/jYaf+/Oc+wPRg+cd9Mvo6JfR62u3ROgX+k23jI5uGY++9otuoe/0y+jol9Hra7dEtN0v43pH/g0R8cOc849zzqci4ksR8d4xnWVZyznfExEHX/XweyPiCwsffyFOfzI25xxn74Wc8+6c84MLHx+NiB0RcUk0/tyf59zQF/plRPTL6PW1WyL0C72nW0ZEt4xHX/tFt7AM6JcR0S+j19duiWi7X8Y1yL8kInad8fPnopEnpFKOiG+klB5IKd087sMswpac8+6Fj/dExJZxHmYRPppSemThy4ua+xKcV0spXR4R10fE/dGj5/5V547o2fPOxNIv49Wba9w59OY619duidAv9JJuGa9eXePOolfXuL72i26hp/TLePXmGncOvbnO9bVbItrrF9/sdnFuyjn/fET8p4j4jYUvc+mlfPreSqO/v9LifTYi3hQR10XE7oj41HiPc34ppfUR8ZWI+K2c85Ezf63l5/4s5+7V8w49pl/GpzfXub52S4R+gTHRLePTq2tcX/tFt8DY6Jfx6c11rq/dEtFmv4xrkP98RFx6xs//3cJjvZBzfn7hx30R8bdx+suh+mTvwv2eXrnv074xn6daznlvznk+5zyIiM9Fw899SmlVnP4D/8Wc81cXHm7+uT/bufv0vDPx9Mt4NX+NO5e+XOf62i0R+oVe0y3j1Ytr3Nn06RrX137RLfScfhmv5q9x59KX61xfuyWi3X4Z1yD/XyLi6pTSFSml6Yj4zxFxx5jO8pqklNYtfKODSCmti4h3R8Rj5/9dzbkjIj608PGHIuLvxniW1+SVP+wL3h+NPvcppRQRt0bEjpzzp8/4paaf+3Oduy/PO4R+Gbemr3Hn04frXF+7JUK/0Hu6Zbyav8adS1+ucX3tF93CMqBfxqvpa9z59OE619duiWi7X9Lpr2IYvZTSeyLi/4uIlRHx+Zzz743lIK9RSunKOP0vjRERUxHxv1s+e0rpryLiXRFxYUTsjYjfjoj/ExG3R8RlEfFMRHwg59zcN844x9nfFae/hCVHxM6I+MgZ99ZqRkrppoj4bkQ8GhGDhYc/EafvqdXsc3+ec38wevC8Q4R+GRX9Mnp97ZYI/UL/6ZbR0C3j0dd+0S0sB/plNPTL6PW1WyLa7pexDfIBAAAAAIAy3+wWAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABhSoBEAACAASURBVBpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMOmRrlZSimPcj+gH3LOadxnoN/0C3A2+oWl0C3AOezPOf/MuA9Bf+kX4GxqXrss6R35KaVfSyk9mVL6YUrp40tZCwBeoV8A6JpuATryzLgPQFv0CzAqix7kp5RWRsSfRMR/iohrI+KDKaVruzoYAJNJvwDQNd0CwDDoF2CUlvKO/Bsi4oc55x/nnE9FxJci4r3dHAuACaZfAOiabgFgGPQLMDJLGeRfEhG7zvj5cwuP/T9SSjenlLallLYtYS8AJod+AaBrugWAYdAvwMgM/Zvd5pxviYhbInxDDwC6o18A6JpuAWAY9AvQhaW8I//5iLj0jJ//u4XHAGAp9AsAXdMtAAyDfgFGZimD/H+JiKtTSleklKYj4j9HxB3dHAuACaZfAOiabgFgGPQLMDKLvrVOznkupfTRiPjHiFgZEZ/POT/e2ckAmEj6BYCu6RYAhkG/AKOUch7drbncBww4m5xzGvcZ6Df9ApyNfmEpdAtwDg/knLeO+xD0l34BzqbmtctSbq0DAAAAAAAMmUE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaNjXuA7D8rFy5spi55pprqtbasGFDMfPkk09WrXXo0KFiZjAYVK0FwOitWFF+/8Fll11Wtdb69euLmRdeeKFqrZdeeqmY0S8AbUopFTMXXnhh1Vqve93ripnDhw9XrXX06NFiZn5+vmotAEavpl9qXpNERKxZs6aYmZmZqVrr+PHjxczc3FzVWoyed+QDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIZNjfsA9MuKFeV/+3nnO99ZzHz2s5+t2m96erqY+cu//MuqtT7zmc8UMwcPHixmcs5V+wFQr6ZffvEXf7GY+aM/+qOq/dauXVvMfP3rX69a64//+I+Lmeeee66YGQwGVfsBUKemW2644YZi5g/+4A+q9lu/fn0xc//991etVfN6aceOHcXM7Oxs1X4A1Kvpl61btxYzv/M7v1O130/91E8VM0888UTVWn/xF39RzHz/+98vZk6cOFG1n9c43fKOfAAAAAAAaNiS3pGfUtoZEUcjYj4i5nLO5X9uAoAC/QLAMOgXALqmW4BR6eLWOv8h57y/g3UA4Ez6BYBh0C8AdE23AEPn1joAAAAAANCwpQ7yc0R8I6X0QErp5rMFUko3p5S2pZS2LXEvACaHfgFgGM7bL7oFgEXw2gUYiaXeWuemnPPzKaXXR8Q3U0pP5JzvOTOQc74lIm6JiEgp5SXuB8Bk0C8ADMN5+0W3ALAIXrsAI7Gkd+TnnJ9f+HFfRPxtRNzQxaEAmGz6BYBh0C8AdE23AKOy6EF+SmldSumCVz6OiHdHxGNdHQyAyaRfABgG/QJA13QLMEpLubXOloj425TSK+v875zzP3RyKpq1fv36YubDH/5wMXPZZZdV7bd/f3ff9H3z5s3FzNGjR4uZ2dnZqv1y9tVysEj6ZRlZ+P+xaOPGjcXMb/7mbxYzV111VdV+hw4dKmbWrl1btdbll1/eyX4nTpyo2m9ubq4qB/wb+mWZqO2Wiy66qJj5xCc+Ucz83M/9XNV+hw8fLmY2bdpUtdZb3/rWYubgwYPFzIEDB6r2O3nyZFWuhtdBTBjdsoysWFH3fucrrriimPnd3/3dYubnf/7nq/Y7cuRIMfPSSy9VrXXjjTcWMzV99uyzz1btV/M6qLY3anLLvYMWPcjPOf84It7W4VkAQL8AMBT6BYCu6RZglJZ0j3wAAAAAAGC4DPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIZNjfsAtGHFirp/07nwwguLmSuvvLKYGQwGVfvdd999xcydd95Ztdbzzz9fzMzPz1etBUCd2n659NJLi5krrriimJmbm6va75577ilmbr/99qq1Hn300WJmZmammMk5V+0HMOlWrVpVlbv++uuLmZpuOXXqVNV+//RP/1TMfOUrX6la6+GHHy5mjh49WszUvu4CIGLt2rVVuXe/+93FzOWXX17MnDhxomq/v//7vy9m/vEf/7Fqrccff7yYefnll4uZmtc3dM878gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAw6bGfQDakFKqym3atKmYmZ6eLmZ27txZtd/tt99ezOzYsaNqrRMnThQzOeeqtQCoMzVV91eNiy66qJg5depUMfPggw9W7ffnf/7nna11/PjxYqamX2q7GGDSrV+/vip35ZVXFjNHjx4tZmr74LOf/Wwx8/TTT1etdfLkyWKmpltWrKh7715NB3mtBPRZzfXwkksuqVrr2muvLWZefPHFYubuu++u2q/mtcvevXur1pqZmSlmap6rVatWVe1Xs9b8/HzVWnrIO/IBAAAAAKBpBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGFT4z4A/XLw4MFi5sknnyxmLrjggqr99u3bV8zMzs5WrVUj51zMpJQ62w+A03bv3l3MPPjgg8XMkSNHOtuvy37pUk0P1fQZQItqrnG1fx//4Q9/WMzce++9xUxN/0R02y1dXce7fO2iW4A+W7lyZTGzZs2aqrUef/zxYubFF18sZr7xjW9U7Vez1qlTp6rWGnW/eO3SLe/IBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANmxr3AeiXubm5Yubo0aPFzPT0dNV+69at62yt2dnZYialVLUWAN2quUbX9MupU6eq9lu/fn0xs2bNmqq1BoNBMTM/P1+1Vo2arso5d7YfwCjVXONWrKh7P1pNbxw8eLCYqb2Gr127tpiZmZmpWqsm12W31Dyntd2ig4AWrVy5spip7Zfdu3cXMzWvEWpeA0XUzb1qz16jpotr52c1r89q19Iv3pEPAAAAAABNKw7yU0qfTyntSyk9dsZjm1JK30wpPb3w48bhHhOA5Ua/ADAM+gWArukWoAU178i/LSJ+7VWPfTwivpVzvjoivrXwcwB4LW4L/QJA924L/QJAt24L3QKMWXGQn3O+JyJefePA90bEFxY+/kJEvK/jcwGwzOkXAIZBvwDQNd0CtGCx3+x2S875le/ssCcitpwrmFK6OSJuXuQ+AEwW/QLAMFT1i24B4DXw2gUYqcUO8n8i55xTSuf8tsE551si4paIiPPlAOBM+gWAYThfv+gWABbDaxdgFGrukX82e1NKF0dELPy4r7sjATDB9AsAw6BfAOiabgFGarGD/Dsi4kMLH38oIv6um+MAMOH0CwDDoF8A6JpuAUaqeGudlNJfRcS7IuLClNJzEfHbEfH7EXF7SunDEfFMRHxgmIekHRs3bixmfvZnf7aYueyyy6r2m56eLmbm5+er1rrvvvuKmRMnThQzOfsqOOiCfpkMKaWq3JYt57yl6E9cd911xczmzZur9rvqqquKma9//etVa919993FzMGDr/7eaP/W3Nxc1X41PVT7vOs0liP90m8116Xaa9dFF11UzLz97W8vZt7xjndU7XfTTTcVM//6r/9atdZdd91VzDz77LPFzPHjx6v20wdwfrql/waDQTFz8uTJqrVqZmPXX399MfOWt7ylar8DBw4UM1NTdXdPf+yxx4qZ7du3FzN79uyp2q/meade8f/lnPMHz/FLv9rxWQCYIPoFgGHQLwB0TbcALVjsrXUAAAAAAIARMMgHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABo2Ne4D0Iacc1Xu9a9/fTFzxRVXFDNbtmyp2m/dunXFzL333lu11oMPPljMnDx5spipfa5qpJSqcl3uCdCiN77xjcXMNddcU8xs2rSpar/LLrusmDlx4kTVWjX9cujQoWJGvwDUqb12XXnllcXM9ddfX8xs2LChar+3v/3txcz27dur1nr66aeLmWeffbaYGQwGVfvVWLlyZVVubm6usz0BulLTHbXXzDe96U3FzC//8i8XMxdccEHVfjVnP3jwYNVaNdfohx9+uJiZmZmp2q9Gbb902Wl95R35AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhU+M+AG1YtWpVVe76668vZjZv3lzMTE9PV+23evXqYmYwGFStVZvrSkqpk0xERM55qccB6FzNNWzdunVVa91www3FzIUXXljM1PZLTSesWFH3foeZmZlipuY6XtsJNWrPPupuBChZuXJlMfMzP/MzVWvdeOONnaw1NVX3srkmV3utP3LkSDHT5TW8pjdqu2Vubm6pxwHo3Nq1a4uZq6++umqtm266qZh5wxveUMzUdF5ExPz8fDFz+PDhqrX27t1bzJw8ebKYqZ1Tddkvs7OzVbnlzDvyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDpsZ9ANowNVX3qbBy5cpiZnZ2tpjJOVftd/LkyWJm//79VWvNzc0VM7XnqpFS6mwtgBbVXOdqeiOi7no/MzPT2X6nTp0qZnbt2lW11vHjx4uZwWBQzNR2UM3z3mWfAYxSl3+HPnz4cDFT0z9r1qyp2q/mddCzzz5btdaePXuKmZou67IParoMoM9q/l4fEXHo0KFi5sSJE8XM9PR01X4186wXXnihaq0dO3YUM0eOHOnkTLXm5+c7W2u58458AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjY1LgPQBtmZ2ercnv27Clmjh8/XsysXr26ar+9e/cWMy+88ELVWoPBoCoHQJ2cczHz8ssvV6311FNPFTNHjhwpZqam6v5qc+DAgWLmRz/6UdVap06dKmZqnqsujXo/gK7Mz88XM7t3765aa9u2bcXML/zCLxQzmzdvrtrv8OHDxcwTTzxRtdaxY8eKmbm5uWKmyz7QLUCf1cyqHn/88aq17rrrrmLmjW98YzFz4YUXVu1XM7PbsWNH1Vr79+8vZmZmZoqZmr6OqOsO/VLPO/IBAAAAAKBhxUF+SunzKaV9KaXHznjskyml51NKP1j47z3DPSYAy41+AaBrugWAYdAvQAtq3pF/W0T82lke/1855+sW/vt6t8cCYALcFvoFgG7dFroFgO7dFvoFGLPiID/nfE9EHBzBWQCYIPoFgK7pFgCGQb8ALVjKPfI/mlJ6ZOHLizaeK5RSujmltC2lVP5OQwCgXwDonm4BYBj0CzAyix3kfzYi3hQR10XE7oj41LmCOedbcs5bc85bF7kXAJNDvwDQNd0CwDDoF2CkFjXIzznvzTnP55wHEfG5iLih22MBMIn0CwBd0y0ADIN+AUZtUYP8lNLFZ/z0/RHx2LmyAFBLvwDQNd0CwDDoF2DUpkqBlNJfRcS7IuLClNJzEfHbEfGulNJ1EZEjYmdEfGSIZ2QE5ubmqnJPPfVUMXPy5MliZsWKun9D2rNnTzGzffv2qrVmZmaKmcFgULVWjZxzZ2vBcqRf+q/mOlfTCRERDz30UDFz5MiRYmbz5s1V+x06dKiY+f73v1+11ssvv1zM1PYssDS6pf9q/j5+9OjRqrXuvffeYubXf/3Xi5nXv/71VfudOHGimLnnnnuq1jpw4EAxMzs7W7VWDa9d4Pz0S//V/H285tobEfGNb3yjmPmVX/mVYqa2X2rOfvfdd1ettWvXrmKmps9q6ZduFQf5OecPnuXhW4dwFgAmiH4BoGu6BYBh0C9ACxb7zW4BAAAAAIARMMgHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABo2Ne4D0IbBYFCV27NnTzGzatWqYmb16tVV+7300kvFzNGjR6vWqv3fCEB3aq+9e/fuLWbWrFlTzLzuda+r2u/YsWPFzMGDB6vWmp+fr8oB0I3abjlw4EAxs27dumJm/fr1VfudPHmymKl5PRURcerUqWIm51y1FgB1av9ef+jQoWJmw4YNxcymTZuq9quZe+3cubNqrePHjxcz+qVd3pEPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABo2Ne4D0C9r164tZjZs2FDMrFy5smq/Rx99tJg5depU1VoAtGv9+vXFzKZNm4qZ2n7Zvn17MXPs2LGqtXLOVTkARuuCCy4oZi6++OJiZtWqVVX7PfXUU8XMwYMHq9YaDAZVOQBGr2budeWVVxYzq1evrtpv586dxcxzzz1Xtdb8/HxVjjZ5Rz4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaNjUuA9Av1x99dXFzPr164uZwWBQtd9DDz1UzMzNzVWtBUC73vrWtxYzGzZsKGZyzlX73XfffcXMzMxM1VoAtGnr1q3FzMaNGzvb73vf+14xc+zYsc72A6BbKaWq3I033ljMbNmypbP9vvvd7xYzL730UtVa9Jt35AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAw6bGfQD65corryxmVq5cWcwcPXq0ar+HHnqomBkMBlVrATB6KaWq3Nve9rZiZtWqVcXMsWPHqva75557ihn9AtCm2m656aabipnp6eli5vjx41X73XnnncXM/Px81VoAjF5tv7znPe8pZlavXl3MnDx5smq/L3/5y8XM3Nxc1Vr0m3fkAwAAAABAw4qD/JTSpSmlu1JK21NKj6eUPrbw+KaU0jdTSk8v/Lhx+McFYLnQLwB0TbcAMAz6BWhBzTvy5yLiv+ecr42IGyPiN1JK10bExyPiWznnqyPiWws/B4Ba+gWArukWAIZBvwBjVxzk55x355wfXPj4aETsiIhLIuK9EfGFhdgXIuJ9wzokAMuPfgGga7oFgGHQL0ALXtM3u00pXR4R10fE/RGxJee8e+GX9kTElnP8npsj4ubFHxGA5U6/ANA13QLAMOgXYFyqv9ltSml9RHwlIn4r53zkzF/LOeeIyGf7fTnnW3LOW3POW5d0UgCWJf0CQNd0CwDDoF+Acaoa5KeUVsXpC9UXc85fXXh4b0rp4oVfvzgi9g3niAAsV/oFgK7pFgCGQb8A41Yc5KeUUkTcGhE7cs6fPuOX7oiIDy18/KGI+LvujwfAcqVfAOiabgFgGPQL0IKae+S/MyL+S0Q8mlL6wcJjn4iI34+I21NKH46IZyLiA8M5IqOwYkXdXZYuvfTSYmblypXFzIEDB6r2O3LkSDkE9JV+mQC1/XLVVVd1stb+/fur9jt06FBVDugd3TIBVq1aVZV785vfXMx02S0vvvhiVQ7oJf0yAaanp6ty1157bTFT0y/79tV9Acfu3bvLISZCcZCfc/5eRKRz/PKvdnscACaFfgGga7oFgGHQL0ALqr/ZLQAAAAAAMHoG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAw6bGfQDasGbNmqrcjTfe2Ml+jz76aFXu1KlTnewHwHj89E//dFXubW97Wyf73X///VW5mZmZTvYDYPTe8IY3VOXe/OY3d7Lft7/97aqcbgFoV0qpmLn22mur1rr88suLmZxzMfO1r32tar+TJ09W5Vj+vCMfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADRsatwHoA2rVq2qym3YsKGYOXXqVDHzD//wD1X7zc/PV+UAaNP69eurcuvWrStmZmZmipk77rijar/BYFCVA2C0UkrFzEUXXVS11urVq4uZEydOFDN/8zd/U7WfbgFoV02/XHPNNVVr1czQjh07Vsx86Utfqtov51yVY/nzjnwAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0LCpcR+ANpw4caIq99d//dfFzPT0dDHzpS99qWo/APrt0KFDVbk777yzmHnuueeKma997WtV+wHQppxzMVPTBxER3/nOd4qZ733ve8XMXXfdVbUfAO2q6Zcnn3yyaq377ruvmLn99tuLmW3btlXtB6/wjnwAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaFjKOY9us5RGtxlDMTU1VcwMBoNOMkyOnHMa9xnoN/3Sf6tWrSpm5ufnixn9wpn0C0uhW9qVUt0f7ZpumZubK2Z0C6/yQM5567gPQX/pl3atWFH3fueafpmdnS1m9Atnqnnt4h35AAAAAADQsOIgP6V0aUrprpTS9pTS4ymljy08/smU0vMppR8s/Pee4R8XgOVCvwDQNd0CwDDoF6AFxVvrpJQujoiLc84PppQuiIgHIuJ9EfGBiHg55/yH1Zv58qHec2sdhsGtDyaTfuFMbq3DMOiXyaNbJoNb6zBmbq0zgfTLZHBrHcap5rVLcSqbc94dEbsXPj6aUtoREZcs/XgATDL9AkDXdAsAw6BfgBa8pnvkp5Quj4jrI+L+hYc+mlJ6JKX0+ZTSxnP8nptTSttSStuWdFIAli39AkDXdAsAw6BfgHEp3lrnJ8GU1kfEdyLi93LOX00pbYmI/RGRI+J/xukvMfpvhTV8+VDPubUOw+DWB5NNvxDh1joMh36ZXLpleXNrHcbMrXUmmH5Z3txah3Gqee1S9RmaUloVEV+JiC/mnL+6sPjenPN8znkQEZ+LiBuWclgAJo9+AaBrugWAYdAvwLgVB/np9Nsdbo2IHTnnT5/x+MVnxN4fEY91fzwAliv9AkDXdAsAw6BfgBYUb62TUropIr4bEY9GxCtf8/GJiPhgRFwXp798aGdEfGThm3+cby1fPgT8G259MJn0CzBs+mXy6BZgBNxaZwLpF2DYal67VN8jvwsuVsDZGLSwVPoFOBv9wlLoFuAcDPJZEv0CnE1n98gHAAAAAADGwyAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjY1Kg3TCmd99dzziM6CQDLiX4BAAAAlivvyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADZsa8X77c87PvOqxCyNi/4jP0YW+njvC2cfF2c/ujUNal8ny6n7x5208nH08nP3s9AtLtT8ilstrlwhnH4e+njvC2c9Hv7BUr+4Xf97Gw9nHw9nPrqpbUs55SPvXSSltyzlvHeshFqGv545w9nFxdhidPn/OOvt4OPt49PnsTKY+f846++j19dwRzg6j1OfPWWcfD2cfjxbO7tY6AAAAAADQMIN8AAAAAABoWAuD/FvGfYBF6uu5I5x9XJwdRqfPn7POPh7OPh59PjuTqc+fs84+en09d4Szwyj1+XPW2cfD2cdj7Gcf+z3yAQD+L3v3H+t3Xd8L/PXuOS2lP4BKoRRoQRlFmXFyZZVteIcuLvhjwWXGQJYbzBbZkploNNl1xmzGG5flblP/mDFjk6AZyow/7ky2ecGNXZhGpAUspWABLbS1tLT0t+05Pee87x893PSSnr7f9Hx/vD89j0dievrtk/f77ZfyeZ7Pq99+vwAAAMDMWnhFPgAAAAAAMAODfAAAAAAAaNjQBvkppRtTSj9OKT2dUvrYsM5xOlJKF4vkAgAAIABJREFUW1JKj6WUHk0prRv2eU4lpXRHSmlXSmnjCY+9KqV0b0rpqekflw3zjDOZ4eyfTCltn37uH00pvXOYZ5xJSmlVSum+lNKmlNLjKaUPTT/e9HN/inN34nmHCP0yKPpl8LraLRH6he7TLYOhW4ajq/2iWzgT6JfB0C+D19VuiWi7X4byHvkppZGI2BwRb4+IbRHxUETcknPeNPDDnIaU0paIuDbnvHvYZylJKf3XiDgUEV/OOb9++rH/GREv5pz/YrooluWc//swz3kyM5z9kxFxKOf8V8M8W0lKaWVErMw5P5xSWhoR6yPiPRHx/mj4uT/Fud8XHXjeQb8Mjn4ZvK52S4R+odt0y+DoluHoar/oFrpOvwyOfhm8rnZLRNv9MqxX5K+NiKdzzj/JOY9HxN0RcdOQznJGyznfHxEvvuzhmyLiS9NffymO/2Zszgxn74Sc846c88PTXx+MiCci4pJo/Lk/xbmhK/TLgOiXwetqt0ToFzpPtwyIbhmOrvaLbuEMoF8GRL8MXle7JaLtfhnWIP+SiNh6ws+3RSNPSKUcEfeklNanlG4b9mFOw4qc847pr5+PiBXDPMxp+GBKacP0Xy9q7q/gvFxK6fKIuCYiHowOPfcvO3dEx5535iz9MlyducbNoDPXua52S4R+oZN0y3B16hp3Ep26xnW1X3QLHaVfhqsz17gZdOY619VuiWivX3zY7em5Puf8XyLiHRHxR9N/zaWT8vH3Vhr8+yudvi9ExBUR8caI2BERfz3c45xaSmlJRHwjIj6ccz5w4q+1/Nyf5Nydet6hw/TL8HTmOtfVbonQLzAkumV4OnWN62q/6BYYGv0yPJ25znW1WyLa7JdhDfK3R8SqE35+6fRjnZBz3j79466I+FYc/+tQXbJz+v2eXnrfp11DPk+1nPPOnPNkznkqIv4uGn7uU0rz4/h/8HflnL85/XDzz/3Jzt2l5505T78MV/PXuJl05TrX1W6J0C90mm4Zrk5c406mS9e4rvaLbqHj9MtwNX+Nm0lXrnNd7ZaIdvtlWIP8hyLiypTSq1NKCyLi5oj49pDO8oqklBZPf9BBpJQWR8RvRsTGU/9Tzfl2RNw6/fWtEfFPQzzLK/LSf+zTfjsafe5TSikivhgRT+ScP3PCLzX93M907q487xD6ZdiavsadSheuc13tlgj9QufpluFq/ho3k65c47raL7qFM4B+Ga6mr3Gn0oXrXFe7JaLtfknH/xbD4KWU3hkRn4uIkYi4I+f86aEc5BVKKb0mjv9JY0TEaER8peWzp5S+GhE3RMTyiNgZEX8WEf8rIr4WEasj4tmIeF/OubkPzpjh7DfE8b/CkiNiS0T8wQnvrdWMlNL1EfFARDwWEVPTD388jr+nVrPP/SnOfUt04HmHCP0yKPpl8LraLRH6he7TLYOhW4ajq/2iWzgT6JfB0C+D19VuiWi7X4Y2yAcAAAAAAMp82C0AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADRsd5GYppTzI/YBuyDmnYZ+BbtMvwMnoF2ZDtwAz2J1zvmDYh6C79AtwMjX3Ll6RDwAAAFDn2WEfAIC5aVaD/JTSjSmlH6eUnk4pfaxXhwJgbtMvAPSabgGgH/QLMCinPchPKY1ExOcj4h0RcXVE3JJSurpXBwNgbtIvAPSabgGgH/QLMEizeUX+2oh4Ouf8k5zzeETcHRE39eZYAMxh+gWAXtMtAPSDfgEGZjaD/EsiYusJP982/dj/J6V0W0ppXUpp3Sz2AmDu0C8A9JpuAaAf9AswMKP93iDnfHtE3B7hk7kB6B39AkCv6RYA+kG/AL0wm1fkb4+IVSf8/NLpxwBgNvQLAL2mWwDoB/0CDMxsBvkPRcSVKaVXp5QWRMTNEfHt3hwLgDlMvwDQa7oFgH7QL8DAnPZb6+ScJ1JKH4yI/x0RIxFxR8758Z6dDIA5Sb8A0Gu6BYB+0C/AIKWcB/fWXN4HDDiZnHMa9hnoNv0CnIx+YTZ0CzCD9Tnna4d9CLpLvwAnU3PvMpu31gEAAAAAAPrMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0bHfYBmJsWLlxYlVuwYEExMzU1VbXW2NhYMTMxMVHM5Jyr9gNg8ObPn1+VGx3t3bdANd2hXwC6a2RkpCo3b175dXI1mYiIycnJnmR0C0C7ajshpdSztWpmaDXdUTuLo7e8Ih8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANGx02AegW+bNK//ZzzXXXFPM/Mmf/EnVfq961auKmeeee65qrX/5l38pZu67775iZu/evVX7TU5OVuVq5Jx7thZAi2r65Q1veEMx86EPfahqvwsvvLCY2blzZ9Va999/fzHzH//xHz3bb2xsrCpXY2pqqmdrAbSmpluuvvrqYuYDH/hA1X4rV64sZvbt21e11vr164uZBx98sJipvVc6dOhQMVPbGTX3Qe5vgC6r6Zc1a9YUM7feemvVfpdeemkxc+TIkaq1Nm3aVMw89thjxcwzzzxTtd+ePXuKmYmJiaq1au6DzvT7G6/IBwAAAACAhs3qFfkppS0RcTAiJiNiIud8bS8OBcDcpl8A6Af9AkCv6RZgUHrx1jpvzTnv7sE6AHAi/QJAP+gXAHpNtwB95611AAAAAACgYbMd5OeIuCeltD6ldNvJAiml21JK61JK62a5FwBzh34BoB9O2S+6BYDT4N4FGIjZvrXO9Tnn7SmlCyPi3pTSkznn+08M5Jxvj4jbIyJSSj6aHoAa+gWAfjhlv+gWAE6DexdgIGb1ivyc8/bpH3dFxLciYm0vDgXA3KZfAOgH/QJAr+kWYFBOe5CfUlqcUlr60tcR8ZsRsbFXBwNgbtIvAPSDfgGg13QLMEizeWudFRHxrZTSS+t8Jef8nZ6cioGbN6/uz3Re//rXFzOf/exni5nXve51Vfvt37+/mJmamqpa67rrritmfv7znxczmzdvrtrvueeeK2YmJiaq1qr5/1j7PEAH6JczyMjISFXuzW9+czHzmc98pph59atfXbVfTb8sXLiwaq2afpk/f34x89RTT1Xtt2HDhmLmyJEjVWuNj48XM5OTk1VrQQfolzPE6Gjdbezb3va2YubTn/50MXPppZdW7XfgwIFiZvv27VVrLViwoJhZvnx5MbN169aq/b73ve8VM3v27Kla69ChQ8VM7X0QdIBuOYPUXHsjIt71rncVM3/6p39azFx00UVV+x08eLCY2bFjR9VaS5YsKWZWr15dzOzdu7dqv/vuu6+YeeaZZ6rWqumhmvubLjvtQX7O+ScR8Us9PAsA6BcA+kK/ANBrugUYpFm9Rz4AAAAAANBfBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMNGh30A2nDeeedV5d7//vcXM5dddlkxc/Dgwar9vva1rxUz999/f9VaTz/9dDEzNjZWzIyPj1ftl3MuZlJKPVsLYNBqrmGXXHJJ1Vof/ehHi5nVq1cXM/v27ava78477yxmfvCDH1SttXXr1qpcybx5da+vmJqa6sl+vV4LoBdquuXqq6+uWusTn/hEMVPTLbt27ara72//9m+LmXXr1lWttXv37mJm4cKFxczixYur9puYmKjK1dAtQItqvtd+y1veUrXWpz71qWLmoosuKmZ+9rOfVe33hS98oZh55JFHqtbas2dPMVMzI1yxYkXVfrUztBq97Kqu8op8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANCw0WEfgP4bHS3/a167dm3VWr/4i79YzOzYsaOYueuuu6r2+/rXv17M7N+/v2qtiYmJYmb+/PnFzKJFi6r2GxkZKWYmJyer1so5V+UABmnhwoXFzO/8zu9UrXXllVcWM1u3bi1mPve5z1Xt953vfKeY+fnPf1611tTUVDFT81ydf/75VfvV9EvNmSL0C9Cec889t5j54Ac/WLXWqlWripnNmzcXM5/61Keq9vv+979fzIyPj1etVWPx4sXFzOrVq6vWSikVM8eOHataq7aDAHql5hp2ySWXFDN//Md/XLXf8uXLi5nHH3+8mPn4xz9etd/DDz9czNTMvGqdc845xUxNB0XUdcKRI0d6ttaZzivyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBho8M+ALOTUipmFi9eXMysWLGiar8NGzYUM88//3wx88///M9V++3du7eYmZycrFqrRs3zOX/+/Kq15s0r/znZ1NRU1VoAg1ZzDbv44ouLmcsuu6xqvx/96EfFzAMPPFDM3HvvvVX7HTx4sJjJOVetVfNcjYyMFDMLFy6s2u/w4cPFTC+7EaBXar6PXrt2bTFT2y2PPvpoMfPlL3+5mPn+979ftd+RI0eqcjVquqXm+TzvvPOq9qvplmPHjlWtBTBoNd9Hv/e97y1mVq5cWbXfD3/4w2Lms5/9bDGzfv36qv3Gx8ercjV6dV9y4YUXVu337LPPFjNHjx6tWguvyAcAAAAAgKYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADRsd9gGYnXnzyn8Ws3jx4mJmfHy8ar+NGzcWM/v37y9mxsbGqvYbHS3/Fj3rrLOq1lq4cGExc/bZZxczCxYsqNrv8OHDxUxKqWotgF6pve7UXOsuueSSYqbmWhgR8eSTTxYzNR1U22c1/Tl//vyqtc4999xi5sILLyxmlixZUrXfvn37qnIAg1JzTY2IWLZsWTHzS7/0S8VMzf1GRMR3v/vdYuaxxx4rZiYmJqr2q+nY2m654IILipk1a9YUM8uXL6/ab9u2bcVMzrlqLYBeqZkJRURcddVVxcyv/uqvFjMvvPBC1X5f/epXi5nHH3+8mOllv9Q+VytXrixm3vzmNxczNfeCERGbN28uZqampqrWwivyAQAAAACgacVBfkrpjpTSrpTSxhMee1VK6d6U0lPTP5ZfWgEAJ9AvAPSDfgGg13QL0IKaV+TfGRE3vuyxj0XEv+Wcr4yIf5v+OQC8EneGfgGg9+4M/QJAb90ZugUYsuIgP+d8f0S8+LKHb4qIL01//aWIeE+PzwXAGU6/ANAP+gWAXtMtQAtO98NuV+Scd0x//XxErJgpmFK6LSJuO819AJhb9AsA/VDVL7oFgFfAvQswUKc7yP9/cs45pTTjx9fnnG+PiNsjIk6VA4AT6RcA+uFU/aJbADgd7l2AQah5j/yT2ZlSWhkRMf3jrt4dCYA5TL8A0A/6BYBe0y3AQJ3uIP/bEXHr9Ne3RsQ/9eY4AMxx+gWAftAvAPSabgEGqvjWOimlr0bEDRGxPKW0LSL+LCL+IiK+llL6/Yh4NiLe189DMjvj4+PFzJ49e6rWWrVqVTHzute9rpi59NJLq/abnJwsZi6//PKqtQ4dOlTMbNy4sZjZvHlz1X4TExNVOZir9EvbUkrFzIEDB4qZp59+umq/Y8eOFTM1HXTOOedU7bdw4cJi5k1velPVWuedd14xs23btmLm0Ucfrdqv5rmCuUy/DN68eXWvDxsdLb+z6/PPP1/MPPLII1X77dpVfnHs8uXLi5mzzjqrar+lS5cWM29961ur1qq5p9q/f38x89BDD1Xtd+TIkaoczFW6pfdq7jcWLFhQtdaSJUuKmZrvx5966qmq/Xbs2FHMLFq0qJip7c9zzz23mLnxxhur1rrhhhuKmampqWKmtl9quipn7zZVq/idVM75lhl+6Td6fBYA5hD9AkA/6BcAek23AC043bfWAQAAAAAABsAgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABo2OiwD8Ds5JyLmYmJiWLm6NGjVfutWbOmmPn1X//1YmbZsmVV+82fP7+YqXkOIiJ+9KMfFTOPPPJIMXPgwIGq/WrONTpa95/g5ORkVQ6gV2quYUeOHClmDh06VLXfG9/4xmLmDW94QzGzYsWKqv3OOeecYmbx4sVVaz3//PPFzD/+4z8WMzt37qzar6YTavozou57BGBuSyn1JBNR1y379u0rZpYsWVK1X01vvOtd7ypmVq1aVbVfTQctX768aq2a+7N77rmnmNm6dWvVfuPj48VM7b3LsWPHqnIAJbXf09Zcn7Zv317M1M5eavqlZjZ25ZVXVu13xRVXFDOXXXZZ1Vrz5pVf0/3QQw8VM88880zVfjX3jCMjI1Vr6RevyAcAAAAAgKYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADRsd9gGYnZRSMbN8+fJi5pd/+Zer9nvrW99azFxxxRXFzMjISNV+NQ4ePFiV27FjRzGze/fuYmZsbKxqv5r/j/Pm1f1Z2vj4eDGTc65aC5jbanojImJ0tPwtwmte85pi5vrrr6/a7y1veUsxc/HFFxcz8+fPr9qv5vo7OTlZtdaLL75YzGzfvr2Yqe2zmrPXPg9Hjx4tZvQLzG0114Dablm0aFExc/XVVxczb3rTm6r2W7NmTTFz/vnnFzMLFiyo2q/m2lv7/X/NfcnWrVuLmb1791btV3Oumu8NIup+P+gWoOZaUXvdOeecc4qZmn6pud+IiFi5cmUxc9555xUzCxcurNqvpodqn6uae5ef/exnxcyuXbuq9quhX+p5RT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaNjosA9A/42Pjxcz+/fvr1rr0KFDxczRo0eLmdHR3v3We+GFF6pyDz74YDGzffv2YubnP/951X5TU1PFzOTkZNVaOeeqHEBJ7fWk5vpU0x01vRERMTY21pPMsWPHqvZbsGBBMVN79h/84AfFzKZNm4qZvXv3Vu1X0+u1z4N+AXqh5vveiIgjR44UM7t37y5mJiYmqvarOVfN9bJ2v7PPPruYqf3+/5FHHilmau5vduzYUbVfzT1cTQ9H6BagTs21oub73oiIffv2FTOHDx8uZhYvXly1X831PqVUzNRce2vVdtXmzZuLmQceeKCY2bJlS9V+NTO02n/P+sUr8gEAAAAAoGkG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYaPDPgCzMzU1Vczs2rWrmPnud79btd+5555bzJx11lnFzPLly6v2Gx0t/xZ96qmnqtbasmVLMXPw4MFi5tixY1X71fy7qckA9FLOuSo3Pj5ezGzatKmYOXz4cNV++/fvL2be/va3FzPnn39+1X41ffbss89WrVXzPOzcubOYOXLkSNV+ExMTxYx+AQap9prz4osvFjP33HNPMfOTn/ykar+1a9cWM9ddd10xU9stK1euLGZq7jciIh577LFi5rnnnitmDh06VLVfTe9PTk5WrQVQo+a+5OjRo1Vr1Xw/ftdddxUz69evr9rvta99bU8y55xzTtV+q1atKmZqr9E1z9UTTzxRzOzbt69qv5p7HPcu9bwiHwAAAAAAGlYc5KeU7kgp7UopbTzhsU+mlLanlB6d/t87+3tMAM40+gWAXtMtAPSDfgFaUPOK/Dsj4saTPP7ZnPMbp//3L709FgBzwJ2hXwDorTtDtwDQe3eGfgGGrDjIzznfHxHlNzMEgFdAvwDQa7oFgH7QL0ALZvMe+R9MKW2Y/utFy2YKpZRuSymtSymtm8VeAMwd+gWAXtMtAPSDfgEG5nQH+V+IiCsi4o0RsSMi/nqmYM759pzztTnna09zLwDmDv0CQK/pFgD6Qb8AA3Vag/yc886c82TOeSoi/i4i1vb2WADMRfoFgF7TLQD0g34BBu20BvkppZUn/PS3I2LjTFkAqKVfAOg13QJAP+gXYNBGS4GU0lcj4oaIWJ5S2hYRfxYRN6SU3hgROSK2RMQf9PGMnELOuZg5evRoMbN169aq/b7xjW8UM1dddVUx87a3va1qvxoPPPBAVe7JJ58sZg4cOFDM1DznryQHc5V+advk5GQxs2/fvmLm4MGDVfvt2rWrmFm8eHExc9NNN1XtNzIyUsysW1f3FqY1ud27dxczNc95hH6BU9EtwzE1NVWVq7kv2bZtWzGzY8eOqv02bNhQzNR8/3/zzTdX7bdgwYJi5oknnqha63vf+14xU3MPNzExUbVf7b9DmKv0y3AcO3asKlfzvfaLL5Y/q3j9+vVV+y1durSYecc73lHM/O7v/m7VfjX98uMf/7hqrX//938vZmq6amxsrGo//dJbxUF+zvmWkzz8xT6cBYA5RL8A0Gu6BYB+0C9AC073w24BAAAAAIABMMgHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABo2OuwD0IbJycmq3N69e4uZpUuXFjMXXnhh1X4vvvhiMfPUU09VrbVv375iZmpqqmotAOrU9sv+/fuLmZp+Wb16ddV+Y2NjxcwTTzxRtdaOHTuKmYmJiaq1AOa6nHMxU3tNPXjwYDGzcOHCYmbNmjVV+42Olm+vn3zyyaq1Nm/eXMwcO3asmKl5PgHmgpp5T+1M6MCBA7M9TkREXH311VW5mvugZ555pmqtH/7wh8VMzb2S+dlweEU+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjY6LAPQLcsWbKkmHnDG95QzCxatKhqv40bNxYzmzZtqlrr2LFjVTkABq+mF66//vpiZunSpVX7bdu2rZj5/ve/X7XWkSNHqnIADNbZZ59dzLz73e8uZs4///yq/Z577rli5l//9V+r1jpw4EAxk3OuWguA3jrrrLOKmfe+973FzEUXXVS13wsvvFDMfP3rX69aa9euXcXM1NRU1VoMnlfkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGjQ77ALQhpVSV+5Vf+ZViZvXq1T3b73vf+14xs3Pnzqq1cs5VOQB6p/Z6f9111xUzV111VTEzMjJStd+DDz5YzGzZsqVqLf0CMFi13bJ27dpi5pprrilmRkfrbpsffvjhYubxxx+vWmtqaqoqB0Dv1PZLTXe85S1vKWYWLFhQtd/GjRuLmR/84AdVa01OTlblaJNX5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAw0aHfQDaMG9e3Z/p3HLLLcXMokWLipnx8fGq/f7+7/++Z2sBMHgjIyNVudtuu62YWbJkSTFT2wmf+9znipmxsbGqtQAYrNHRutvYD3/4w8XMueeeW8xMTExU7ffnf/7nxcyRI0eq1gJg8Gr75ROf+EQxs2zZsmJmcnKyar9PfvKTxczhw4er1qLbvCIfAAAAAAAaVhzkp5RWpZTuSyltSik9nlL60PTjr0op3ZtSemr6x/IfNQHANP0CQK/pFgD6Qb8ALah5Rf5ERHw053x1RFwXEX+UUro6Ij4WEf+Wc74yIv5t+ucAUEu/ANBrugWAftAvwNAVB/k55x0554envz4YEU9ExCURcVNEfGk69qWIeE+/DgnAmUe/ANBrugWAftAvQAte0YfdppQuj4hrIuLBiFiRc94x/UvPR8SKGf6Z2yKi/Al2AMxZ+gWAXtMtAPSDfgGGpfrDblNKSyLiGxHx4ZzzgRN/LeecIyKf7J/LOd+ec74253ztrE4KwBlJvwDQa7oFgH7QL8AwVQ3yU0rz4/iF6q6c8zenH96ZUlo5/esrI2JXf44IwJlKvwDQa7oFgH7QL8CwFQf5KaUUEV+MiCeEGcGBAAAgAElEQVRyzp854Ze+HRG3Tn99a0T8U++PB8CZSr8A0Gu6BYB+0C9AC9Lxv/lzikBK10fEAxHxWERMTT/88Tj+XmBfi4jVEfFsRLwv5/xiYa1Tb8bQLFmypCr3yCOPFDO/8Au/UMw8++yzVfu99rWvLWaOHj1atRbtyjmnYZ+BwdMvc8OyZcuqchs2bChmLr300mLmueeeq9rvqquuKmb0S/fpl7lHt8wNF110UVWuplsuuOCCYqa2W9asWVPMjI2NVa1F09Z7e5S5R7903/E/izm1yy67rGqthx9+uJipuQ/aunVr1X5XXnllMaNfuq/m3qX4Ybc55/+MiJkW+o1XeigAiNAvAPSebgGgH/QL0ILqD7sFAAAAAAAGzyAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjY6LAPQP+llIqZ66+/vmqtiy++uJjJORczd999d9V+Y2NjVTkABm9kZKSYec973lO11gUXXFDMTE1NFTN33HFH1X5Hjx6tygEwWKOj5VvU3//9369a67zzzitmJicni5nPf/7zVfu5dwFo14IFC4qZj370o1VrLV26tJip6Ze//Mu/rNpPv/ASr8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0bHfYB6L+RkZFi5td+7dd6ttbBgweLma985StV++Wcq3IADN6CBQuKmRtvvLFqrXnzyq8t2LdvXzHzD//wD1X7AdCmc845p5h597vfXbVWSqmY2bNnTzFTe+8CQLtWrlxZzNTeu9T0y44dO4qZu+++u2o/eIlX5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAho0O+wD039TUVDGzYcOGqrXWr19fzPzN3/xNMbNp06aq/QBo1+TkZDHz6KOPVq21evXqYuZTn/pUMbNly5aq/QBo07Fjx4qZ2nuJefPKr1v7yEc+Usz87Gc/q9oPgHbV3Lv89Kc/rVpr7969xcwHPvCBYmb37t1V+8FLvCIfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABqWcs6D2yylwW3GKzIyMlKVGx0dLWbGx8eLmUH+vqN9Oec07DPQbfqlXfPnz6/KzZtXfm2BfuGV0i/Mhm5p18KFC6tyNZ0wNjY22+Mw96zPOV877EPQXfplOFIqf1u4aNGiqrUmJiaKGf3CK1Vz71K8a04prUop3ZdS2pRSejyl9KHpxz+ZUtqeUnp0+n/v7MWhAZgb9AsAvaZbAOgH/QK0oPzy6oiJiPhozvnhlNLSiFifUrp3+tc+m3P+q/4dD4AzmH4BoNd0CwD9oF+AoSsO8nPOOyJix/TXB1NKT0TEJf0+GABnNv0CQK/pFgD6Qb8ALXhFH3abUro8Iq6JiAenH/pgSmlDSumOlNKyGf6Z21JK61JK62Z1UgDOWPoFgF7TLQD0g34BhqX6w25TSksi4v9ExKdzzt9MKa2IiN0RkSPif0TEypzz7xXW8IEejfJhtwyTDyOc2/TLmc2H3TJM+mXu0i1nNh92y5D5sNs5TL90lw+7pXU9+bDbiIiU0vyI+EZE3JVz/ub04jtzzpM556mI+LuIWDubwwIw9+gXAHpNtwDQD/oFGLbiID8d/yOrL0bEEznnz5zw+MoTYr8dERt7fzwAzlT6BYBe0y0A9IN+AVpQfp+UiF+LiP8WEY+llB6dfuzjEXFLSumNcfyvD22JiD/oywkBOFPpFwB6TbcA0A/6BRi66vfI78lm3gcMOAnvYcxs6RfgZPQLs6FbgBl4j3xmRb8AJ9Oz98gHAAAAAACGwyAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGjY64P12R8SzL3ts+fTjXdPVc0c4+7A4+8ld1qd1mVte3i/+exsOZx8OZz85/cJsnUn3LhHOPgxdPXeEs5+KfmG23Lu0wdmHw9lPrqpbUs65T/vXSSmtyzlfO9RDnIaunjvC2YfF2WFwuvx71tmHw9mHo8tnZ27q8u9ZZx+8rp47wtlhkLr8e9bZh8PZh6OFs3trHQAAAAAAaJhBPgAAAAAANKyFQf7twz7AaerquSOcfVicHQany79nnX04nH04unx25qYu/5519sHr6rkjnB0Gqcu/Z519OJx9OIZ+9qG/Rz4AAAAAADCzFl6RDwAAAAAAzMAgHwAAAAAAGja0QX5K6caU0o9TSk+nlD42rHOcjpTSlpTSYymlR1NK64Z9nlNJKd2RUtqVUtp4wmOvSindm1J6avrHZcM840xmOPsnU0rbp5/7R1NK7xzmGWeSUlqVUrovpbQppfR4SulD0483/dyf4tydeN4hQr8Min4ZvK52S4R+oft0y2DoluHoar/oFs4E+mUw9MvgdbVbItrul6G8R35KaSQiNkfE2yNiW0Q8FBG35Jw3DfwwpyGltCUirs057x72WUpSSv81Ig5FxJdzzq+ffux/RsSLOee/mC6KZTnn/z7Mc57MDGf/ZEQcyjn/1TDPVpJSWhkRK3POD6eUlkbE+oh4T0S8Pxp+7k9x7vdFB5530C+Do18Gr6vdEqFf6DbdMji6ZTi62i+6ha7TL4OjXwavq90S0Xa/DOsV+Wsj4umc809yzuMRcXdE3DSks5zRcs73R8SLL3v4poj40vTXX4rjvxmbM8PZOyHnvCPn/PD01wcj4omIuCQaf+5PcW7oCv0yIPpl8LraLRH6hc7TLQOiW4ajq/2iWzgD6JcB0S+D19VuiWi7X4Y1yL8kIrae8PNt0cgTUilHxD0ppfUppduGfZjTsCLnvGP66+cjYsUwD3MaPphS2jD914ua+ys4L5dSujwiromIB6NDz/3Lzh3RseedOUu/DFdnrnEz6Mx1rqvdEqFf6CTdMlydusadRKeucV3tF91CR+mX4erMNW4GnbnOdbVbItrrFx92e3quzzn/l4h4R0T80fRfc+mkfPy9lQb//kqn7wsRcUVEvDEidkTEXw/3OKeWUloSEd+IiA/nnA+c+GstP/cnOXennnfoMP0yPJ25znW1WyL0CwyJbhmeTl3jutovugWGRr8MT2euc13tlog2+2VYg/ztEbHqhJ9fOv1YJ+Sct0//uCsivhXH/zpUl+ycfr+nl973adeQz1Mt57wz5zyZc56KiL+Lhp/7lNL8OP4f/F05529OP9z8c3+yc3fpeWfO0y/D1fw1biZduc51tVsi9AudpluGqxPXuJPp0jWuq/2iW+g4/TJczV/jZtKV61xXuyWi3X4Z1iD/oYi4MqX06pTSgoi4OSK+PaSzvCIppcXTH3QQKaXFEfGbEbHx1P9Uc74dEbdOf31rRPzTEM/yirz0H/u0345Gn/uUUoqIL0bEEznnz5zwS00/9zOduyvPO4R+Gbamr3Gn0oXrXFe7JUK/0Hm6Zbiav8bNpCvXuK72i27hDKBfhqvpa9ypdOE619VuiWi7X9Lxv8UweCmld0bE5yJiJCLuyDl/eigHeYVSSq+J43/SGBExGhFfafnsKaWvRsQNEbE8InZGxJ9FxP+KiK9FxOqIeDYi3pdzbu6DM2Y4+w1x/K+w5IjYEhF/cMJ7azUjpXR9RDwQEY9FxNT0wx+P4++p1exzf4pz3xIdeN4hQr8Min4ZvK52S4R+oft0y2DoluHoar/oFs4E+mUw9MvgdbVbItrul6EN8gEAAAAAgDIfdgsAAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAw0YHuVlKKQ9yP6Abcs5p2Geg2/QLcDL6hdnQLcAMduecLxj2Iegu/QKcTM29i1fkAwAAANR5dtgHAGBumtUgP6V0Y0rpxymlp1NKH+vVoQCY2/QLAL2mWwDoB/0CDMppD/JTSiMR8fmIeEdEXB0Rt6SUru7VwQCYm/QLAL2mWwDoB/0CDNJsXpG/NiKezjn/JOc8HhF3R8RNvTkWAHOYfgGg13QLAP2gX4CBmc0g/5KI2HrCz7dNP/b/SSndllJal1JaN4u9AJg79AsAvaZbAOgH/QIMzGi/N8g53x4Rt0f4ZG4Aeke/ANBrugWAftAvQC/M5hX52yNi1Qk/v3T6MQCYDf0CQK/pFgD6Qb8AAzObQf5DEXFlSunVKaUFEXFzRHy7N8cCYA7TLwD0mm4BoB/0CzAwp/3WOjnniZTSByPif0fESETckXN+vGcnA2BO0i8A9JpuAaAf9AswSCnnwb01l/cBA04m55yGfQa6Tb8AJ6NfmA3dAsxgfc752mEfgu7SL8DJ1Ny7zOatdQAAAAAAgD4zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMNGh30A5qaUUs9y8+bV/XlUzrknmampqar9AGjXoPull+v0aj8AequmW2rvg3q13zDuXfQUwOD1cs5WkxnGvYt+8Yp8AAAAAABomkE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANCw0WEfgG6ZN6/8Zz9XXnllMfO+972var/LL7+8mJmcnKxa6+mnny5mfvrTn/YkExGxffv2Yubw4cNVa9Xkap8HgBbV9MtrXvOaYua3fuu3qvar6aqRkZGqtbZs2VLM7Nmzp5ip6Y3a/V544YWqtfbu3VvMHDt2rGotgNbUdMvq1auLmRtvvLFqvzVr1hQzixYtqlqrphPGx8eLmZr+iai7x6m5n4qIeP7554uZsbGxqrUAWlTTLxdffHExc8MNN1Tt97rXva6YWbZsWdVau3btKmbmz59fzBw6dKhqv61btxYz69atq1qr5j7o6NGjVWt1lVfkAwAAAABAw2b1ivyU0paIOBgRkxExkXO+theHAmBu0y8A9IN+AaDXdAswKL14a5235px392AdADiRfgGgH/QLAL2mW4C+89Y6AAAAAADQsNkO8nNE3JNSWp9Suu1kgZTSbSmldSmluk8uAAD9AkB/nLJfdAsAp8G9CzAQs31rnetzzttTShdGxL0ppSdzzvefGMg53x4Rt0dEpJTyLPcDYG7QLwD0wyn7RbcAcBrcuwADMatX5Oect0//uCsivhURa3txKADmNv0CQD/oFwB6TbcAg3Lag/yU0uKU0tKXvo6I34yIjb06GABzk34BoB/0CwC9pluAQZrNW+usiIhvpZReWucrOefv9ORUDNxZZ51Vlbv55puLmY985CPFzAUXXFC13+HDh4uZXbt2Va113nnnFTNr1qwpZsbGxqr2+8///M9i5qGHHqpaa+vWrcXM5ORk1VrQAfrlDHL22WdX5X7v936vmPnDP/zDYub888+v2q+mX/bs2VO1Vk2n1XTHvHl1r694+OGHi5n/296dx9hdnvcCf17PjNdhTDACGwdiluBSsEwSslSlt5aucklo1VDS0NAoJb1VSZtQQEoCNPmjbqIqyS1NrtJKSKmSkts2QW0JaSrdtKAst7QppJiwm30xxhvGOzb2LO/9g0lkRbbfF/vMOe+Z+Xwk5PGZr9/35Wj8+/o8c+ac73znO1Vr7dq1q5gZHR2tWgv6gH6ZJhYsWFCVu+qqq4qZK664ophZuHBh1X779u0rZrZt21a11uLFi6tyJXPnzq3KPf7448XMLbfcUrXW5s2bq3IwTeiWaWR4eLgq9/GPf7yY+c3f/M1iZmRkpGq/mn7ZsWNH1VobNmwoZoaGhoqZ2rOvX7++mKmd6z333HNVuensqAf5OeenI2JlB88CAPoFgCmhXwDoNN0CdNMxvUY+AAAAAAAwtQzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGDfb6AEy9gYGBYubSSy+tWmv16tXFzMjISDGzbt26qv1uuummYua+++6rWmvbtm3FzIknnljMvPGNb6zab9++fcVMzrlqrbGxsaocQDcNDpb/GfF7v/d7VWvdcMMNxczcuXOLmWeeeaZqv7/8y78sZh588MGqtbZv317MLF26tJh585vfXLXf6OhoVa7bawF0wtDQUDFz/fXXV631kY98pJipeaz0xBNPVO1X0y0PPPBA1Vq7du0qZt7whjcUM6tWrarar+Z+n5iYqFpLtwAtmj17djHz2c9+tmqtD37wg8d6nIiIeOSRR6pyf/EXf1HMPPzww1Vr7dy5s5g566yziplf/dVfrdqvZkZYa//+/R1bq195Rj4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaNhgrw/AsUkpFTMrVqwoZm644Yaq/UZGRoqZ++67r5j52Mc+VrXf2rVri5mxsbGqtWruq+3btxczixcvrtpvfHy8mNm9e3fH1gLopJpr5i/90i8VM9dee23VfgsWLChm7r777mLm6quvrtrvqaeeKmZqr70199WuXbuKmTPPPLNqv9mzZxczNX0WoV+A7po1q/w8sl/7tV8rZq688sqq/ebMmVPM/OAHPyhmrrnmmqr91q1bV8xMTExUrVXTLXv27Clm3vKWt1Ttt3DhwmJm69atVWvpFqDbavrliiuuKGY+8IEPVO03NDRUzNx+++3FzFVXXVW136ZNm4qZTvbLvn37iplVq1ZV7Tc8PFzMbNiwoWqt2v/H6cwz8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYYO9PgDH5vjjjy9mPvKRjxQzCxcurNrv3//934uZP/mTPylmHnrooar9xsfHq3I1Zs0qf99qeHi4mFm6dGnVfk8//XQxs2fPnqq1ALpt8eLFxcwnPvGJYmbevHlV+91xxx3FzCc/+cli5sknn6zab2JioipXY2BgoJgZGRkpZs4444yq/Wr6Zfv27VVrAXTTaaedVsx8/OMfL2Zq/l0fEfGtb32rmLn++uuLmY0bN1btl3OuytWo6ZYTTjihmFm+fHnVfs8++2wxs2XLlqq1ALrt7LPPLmauvvrqju13yy23FDPXXXddMfPSSy914jivSUqpmDnppJOKmXPPPbdqvxdeeKGY2bBhQ9VaeEY+AAAAAAA0zSAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGjYYK8PwKHNmTOnKvfOd76zmLnggguKmQ0bNlTtd9NNNxUzjz32WDEzPj5etV+NgYGBqtwpp5xSzFx00UXFzOmnn1613/3331/MdPJ+AKgxPDxclfvQhz5UzJxzzjnFzDPPPFO13+c+97mOrDUxMVG1X41Zs+qe77BkyZJi5r3vfW8xc8YZZ1Tt96Mf/aiYGR0drVoLoBMWLlxYlbvuuuuKmWXLlhUza9eurdpv9erVxcymTZuKmZxz1X41UkpVuZNPPrmY+e3f/u1iprZbfvjDHxYz+/fvr1oLoFMWLVpUlfvMZz5TzNT8m/3ee++t2u9Tn/pUMbNt27aqtTqltl9OOumkYuYP/uAPipnafrnrrruKmb1791athWfkAwAAAABA04qD/JTSV1NKW1JKDx102wkppTtSSk9M/vq6qT0mANONfgFgKugXADpNtwAtqHlG/s0R8a6fue2GiPhuzvmNEfHdyd8DwGtxc+gXADrv5tAvAHTWzaFbgB4rDvJzzv8WET/7wk7viYivTX78tYi4pMPnAmCa0y8ATAX9AkCn6RagBUf7Zrcn55w3Tn68KSIO+048KaUrI+LKo9wHgJlFvwAwFar6RbcA8Bp47AJ01dEO8n8q55xTSvkIn/9yRHw5IuJIOQA4mH4BYCocqV90CwBHw2MXoBtqXiP/UDanlJZEREz+uqVzRwJgBtMvAEwF/QJAp+kWoKuOdpD/7Yi4YvLjKyLinzpzHABmOP0CwFTQLwB0mm4Buqr40joppW9ExKqIODGltD4i/jgiPhcRf59S+t2IeC4iLpvKQ043KaViZuHChVVrLVmypJjZuHFjMXPvvfdW7bd+/fpiZnCw/IpN8+fPr9pvwYIFxcxFF11UtdZll5W/TIeHh4uZ+++/v2q/rVu3FjMTExNVa8F0pF86b9as8vfnTznllKq1zjzzzGJm06ZNxcw3v/nNqv02bNhQzNT0Z00HRUTMmTOnmFm1alXVWn/4h39YzCxevLiYeeyxx6r2e/7554sZ/cJMpl86q6ZbzjnnnKq1zjrrrGKm5rHLV77ylar9tmzpzJNja+6DiLoOeutb31q11urVq4uZM844o5ip6YyIug7SLcxkuqXzaq6tv/ALv1C1Vs318IUXXihmvvjFL1btt3379qpcpwwMDBQzK1asqFqr5v/x537u54qZmplXRMSaNWuKmbGxsaq1qBjk55wvP8yn/nuHzwLADKJfAJgK+gWATtMtQAuO9qV1AAAAAACALjDIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaNtjrA8xEs2aVv38yPDxctdbs2bOLmaeeeqqYeemll6r2O++884qZlStXFjMrVqyo2u9Nb3pTx9aaO3duMfP4448XM48++mjVfrt27SpmBgYGqtYaHR2tygEzW8015bTTTqtaa86cOcXMQw89VMzU9ssZZ5xRzCxbtqyYqemgiIhVq1YVMxdeeGHVWiMjI8XMhg0bipma+zMiYtu2bcVMzb81AGrUPN54y1veUrXW0NBQMbNmzZpiprZblixZUsyMj48XM+eee27Vfr/yK79SzFx88cVVay1atKiY2bFjRzHz4IMPVu1X01Mppaq1AGosWLCgmLnooouq1qrpl//4j/8oZmr75fjjjy9mavrlrLPOqtrv0ksvLWYuv/zyqrVOOumkYubll18uZmofuzzzzDNVOep4lAcAAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGjbY6wPMRLNmlb9/Mn/+/Kq1hoeHi5kzzzyzmDnvvPOq9jvhhBOKmeOOO66YWbBgQdV+c+bM6UgmImLv3r3FzIsvvljMbNiwoWq/nHMxMzAwULUWMLOllKpyQ0NDxUzNdTwiYvbs2cXMySefXMwsW7asar+RkZFi5vjjj+/IOhF13VHbxaOjo8XMjh07ipnnn3++ar/x8fFiRr8AJbXdUvPv9sWLF1etNThYfvhZ0xsf/vCHq/areVyyaNGijmQiIubNm1fM1D4OqnkssXv37mLm6aefrtqvpltqHscC1PZLzbX11FNP7dieNf1yzTXXVO1X85ij5rHSSSedVLVfzeyvJhNRd1+98sorxcyTTz5Ztd+BAweKGf1Szz0FAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABo22OsDzETj4+PFzN69e6vW2r59ezEza1b5+zVLliyp2m94eLiYGRwsf1nV3AcRERMTE8XMgQMHqtbasGFDMXPnnXcWM48++mjVfnv27Clmas8OzGw556rc2NhYMfPSSy9VrVXTL6eddloxs3jx4qr9FixYUMzU9FlNb9SuNTo6WrXWtm3bipm77rqrmPnxj39ctd/u3buLGf0ClNR2y/79+4uZLVu2VK1V8+/jmsclixYtqtqv290yNDTUsbVqHg8+8MADxczdd99dtd/OnTuLmdpeBKjxyiuvFDMvvvhi1Vqvf/3rO5JZuXJl1X7z588vZgYGBoqZ2k6ombPV9nrNtfzJJ58sZmrmZxF1/VLzOJZXeUY+AAAAAAA0zCAfAAAAAAAaZpAPACJzvEUAABUOSURBVAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADRssNcHmIlyzsXM1q1bq9b6/ve/X8zs3LmzmFm+fHnVfosXL+5IZu7cuVX7nXbaacXMrFl134968skni5kf//jHxcyLL75Ytd++ffuKmfHx8aq1AGqMjY0VM48//njVWv/6r/9azGzbtq2YOfPMM6v2Gx4e7kimthNq+mVoaKhqrfXr1xczd911V0fWiYjYu3dvMaNfgE555ZVXipn//M//rFpr3rx5xcyKFSuKmVNPPbVqv4GBgWKm5lpf8/gtIuL1r399MVP7OOill14qZn74wx8WM0899VTVfjXdMjExUbUWMLPVXjN37NhRzNx+++1Va9U8Djr99NM7sk6tmvuhdr+lS5cWM/Pnz69aq+Z6/6Mf/aiYeeSRRzq2n36p5xn5AAAAAADQsOIgP6X01ZTSlpTSQwfdtjql9EJK6b7J/y6e2mMCMN3oFwA6TbcAMBX0C9CCmmfk3xwR7zrE7V/MOZ8/+d//7eyxAJgBbg79AkBn3Ry6BYDOuzn0C9BjxUF+zvnfIqL8IrgA8BroFwA6TbcAMBX0C9CCY3mN/KtSSg9M/njR6w4XSildmVK6J6V0zzHsBcDMoV8A6DTdAsBU0C9A1xztIP+miDgzIs6PiI0R8eeHC+acv5xzviDnfMFR7gXAzKFfAOg03QLAVNAvQFcd1SA/57w55zyec56IiL+KiLd19lgAzET6BYBO0y0ATAX9AnTbUQ3yU0pLDvrtr0fEQ4fLAkAt/QJAp+kWAKaCfgG6LeWcjxxI6RsRsSoiToyIzRHxx5O/Pz8ickQ8GxEfzjlvLG6W0pE346dSSh3LzZpV/n7N4OBg1X7Dw8PFzC//8i8XM7/1W79Vtd9b3/rWYmbz5s1Va918883FzN/8zd8UM3v27Knab2JioipHRM657gueaUW/9EZNJ9TmBgYGipmhoaGq/ebPn1/MvP3tby9mLrvssqr9Vq1aVcy8/PLLVWvddtttxcyNN95YzOzYsaNqv/Hx8aoc+mUm0i29UdMHtbnZs2cXM7XdUrPW+eefX8y8733vq9rvoosuKmZqHyN873vfK2Y+9alPFTO1j5V0y2uyxsujzDz6pTdqr/c1uZpOqMlE1D1WOuecc4qZSy65pGq/Sy+9tJipva/WrFlTzFxzzTXFzDPPPFO1n36pV/PYpTi9zTlffoibv3JUJwKASfoFgE7TLQBMBf0CtOBo3+wWAAAAAADoAoN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhg70+AIeWc+5YbmJiopgZGxur2q8mt3fv3mLm3HPPrdpv4cKFxcw999xTtdYdd9xRzLz88svFTM39CdCq2mtYp7pj//79Vfvt27evmNm+fXsxU9svxx9/fDHz1FNPVa116623FjM7d+4sZsbHx6v2A2hN7fWrJnfgwIFjPc5PzZpVft7apk2bipnly5dX7VfTLY8//njVWt/4xjeKmRdffLGY0S1APxsdHe1YrmZWVaumX4aHh4uZs88+u2q/E044oZhZv3591Vp/+7d/W8ysW7eumDEb6w3PyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADRvs9QHoL0NDQ8XM+9///mLmDW94Q9V+u3fvLma+/vWvV6319NNPFzPj4+NVawHQWQMDA8XMb/zGbxQzy5cvr9rvwIEDxUxtv9x///3FzNjYWNVaAHTOrFnl561dcsklxczKlSur9ss5FzO33HJL1Vo/+MEPipnR0dGqtQDorJRSMfPud7+7mHnHO95Rtd/ExEQxc+utt1atddtttxUzNY+V6A3PyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaJhBPgAAAAAANMwgHwAAAAAAGmaQDwAAAAAADRvs9QFoQ0qpKrdixYpi5l3velcxM2fOnKr97rnnnmLm9ttvr1prbGysKgdA95199tnFzPve975iZt68eVX7Pfroo8XMP/zDP1StNTo6WpUDoLuWLVtWzPzO7/xOMTM8PFy138MPP1zM/PVf/3XVWgcOHKjKAdB9p5xySjFz9dVXFzMjIyNV+z399NPFzJe+9KWqtV555ZWqHG3yjHwAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMM8gEAAAAAoGEG+QAAAAAA0DCDfAAAAAAAaNhgrw9AG4aGhqpyn/70p4uZE088sZiZmJio2u+P/uiPipldu3ZVrQVA982ePbsq99nPfraYWbx4cTGTc67a77rrritmtm/fXrUWAN1V2y1/9md/VsyceuqpxUxtt3ziE58oZrZu3Vq1FgDdV9svN954YzFz+umnFzMppar9avpl06ZNVWvR34rPyE8pnZpS+n5K6ZGU0sMppWsmbz8hpXRHSumJyV9fN/XHBWC60C8AdJpuAWAq6BegBTUvrTMWER/LOf98RLwjIj6aUvr5iLghIr6bc35jRHx38vcAUEu/ANBpugWAqaBfgJ4rDvJzzhtzzvdOfrw7ItZGxNKIeE9EfG0y9rWIuGSqDgnA9KNfAOg03QLAVNAvQAte05vdppSWRcSbIuLuiDg557xx8lObIuLkjp4MgBlDvwDQaboFgKmgX4BeqX6z25TScETcGhHX5px3HfyGDDnnnFI65DsApZSujIgrj/WgAExP+gWATtMtAEwF/QL0UtUz8lNKQ/Hqhervcs7fnLx5c0ppyeTnl0TElkP92Zzzl3POF+ScL+jEgQGYPvQLAJ2mWwCYCvoF6LXiID+9+u3Fr0TE2pzzFw761Lcj4orJj6+IiH/q/PEAmK70CwCdplsAmAr6BWhBzUvr/GJEfDAiHkwp3Td52ycj4nMR8fcppd+NiOci4rKpOSIA05R+AaDTdAsAU0G/AD2Xcj7ky3dNzWaHea0wptbBr9l2OMuXL69a6+677y5mRkZGipnnn3++ar+zzjqrmDlw4EDVWrQr51z+IoUj0C+9UdMvK1eurFrrzjvvLGaGh4eLGf3CwfQLx0K39MasWeVXf33b295WtdYdd9xRzNR0y7p166r2q+mW0dHRqrVo2hovj8Kx0C+9UdMvF154YdVa3/nOd4qZ+fPnFzP6hYPVPHapeo18AAAAAACgNwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDBnt9AKbe/Pnzi5nVq1dXrTU8PFzMjI2NFTOf/vSnq/Y7cOBAVQ6A7hsZGSlmPv/5z1etVdNVo6Ojxcz1119ftZ9+Aei+lFIxs2jRomLmC1/4QtV+CxYsKGZquuXaa6+t2q9mLQA6r6Zfli5dWsx86Utfqtqv5rFLzeON3//936/aT7/wE56RDwAAAAAADTPIBwAAAACAhhnkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaNtjrA3BsUkrFzIoVK4qZVatWdeA0r1q3bl0x84//+I8d2w+AzhsYGChmLr744mLm7W9/eyeOExERTzzxRDHzz//8zx3bD4DOmj17djHz0Y9+tJhZuXJlJ44TEREPPPBAMfMv//IvHdsPgM4bGRkpZj7/+c8XM+ecc07VfjnnYuauu+4qZr73ve9V7Qc/4Rn5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhg70+AMcmpVTM5JyLmY0bN1bt99xzzxUzH/jAB4qZnTt3Vu0HQG/U9MvgYPmfEdu2bavab+3atcXMZZddVszs2bOnaj8Auq+mN+bPn1/M7Nq1q2q/e++9t5h573vfW8zs27evaj8AOqvmMUlExLx584qZ4447rph5+eWXq/Z78MEHi5lLLrmkmNm/f3/VfvATnpEPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA1LOefubZZS9zbjpwYGBoqZ4eHhqrX27dtXzBw4cKBqLfiJnHPq9Rnob/qlN4aGhoqZkZGRqrV2795dzOgXXiv9wrHQLZ2XUvmv5Ny5c4uZ2m7Ztm1bMTM6Olq1FhxkTc75gl4fgv6lXzqvZu513HHHFTO1s7HNmzcXM/qF16rmsUvxGfkppVNTSt9PKT2SUno4pXTN5O2rU0ovpJTum/zv4k4cGoCZQb8A0Gm6BYCpoF+AFgxWZMYi4mM553tTSsdFxJqU0h2Tn/tizvnGqTseANOYfgGg03QLAFNBvwA9Vxzk55w3RsTGyY93p5TWRsTSqT4YANObfgGg03QLAFNBvwAteE1vdptSWhYRb4qIuydvuiql9EBK6asppdcd5s9cmVK6J6V0zzGdFIBpS78A0Gm6BYCpoF+AXql+s9uU0nBE/L+I+NOc8zdTSidHxNaIyBHxmYhYknP+n4U1vKFHD3izW1rnzQhnNv3Sv7zZLa3TLzOXbmmTN7tlmvBmtzOYfmmTN7tlOujIm91GRKSUhiLi1oj4u5zzNycX35xzHs85T0TEX0XE247lsADMPPoFgE7TLQBMBf0C9FpxkJ9efdrEVyJibc75CwfdvuSg2K9HxEOdPx4A05V+AaDTdAsAU0G/AC0ovtltRPxiRHwwIh5MKd03edsnI+LylNL58eqPDz0bER+ekhMCMF3pFwA6TbcAMBX0C9Bz1a+R35HNvA4YcAhew5hjpV+AQ9EvHAvdAhyG18jnmOgX4FA69hr5AAAAAABAbxjkAwAAAABAwwzyAQAAAACgYQb5AAAAAADQMIN8AAAAAABomEE+AAAAAAA0zCAfAAAAAAAaZpAPAAAAAAANM8gHAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABpmkA8AAAAAAA0zyAcAAAAAgIYZ5AMAAAAAQMMGu7zf1oh47mduO3Hy9n7Tr+eOcPZecfZDe8MUrcvM8rP94u9bbzh7bzj7oekXjtV0euwS4ey90K/njnD2I9EvHCuPXdrg7L3h7IdW1S0p5zxF+9dJKd2Tc76gp4c4Cv167ghn7xVnh+7p569ZZ+8NZ++Nfj47M1M/f806e/f167kjnB26qZ+/Zp29N5y9N1o4u5fWAQAAAACAhhnkAwAAAABAw1oY5H+51wc4Sv167ghn7xVnh+7p569ZZ+8NZ++Nfj47M1M/f806e/f167kjnB26qZ+/Zp29N5y9N3p+9p6/Rj4AAAAAAHB4LTwjHwAAAAAAOAyDfAAAAAAAaFjPBvkppXellB5LKT2ZUrqhV+c4GimlZ1NKD6aU7ksp3dPr8xxJSumrKaUtKaWHDrrthJTSHSmlJyZ/fV0vz3g4hzn76pTSC5P3/X0ppYt7ecbDSSmdmlL6fkrpkZTSwymlayZvb/q+P8K5++J+hwj90i36pfv6tVsi9Av9T7d0h27pjX7tF93CdKBfukO/dF+/dktE2/3Sk9fITykNRMTjEfHOiFgfEf8VEZfnnB/p+mGOQkrp2Yi4IOe8tddnKUkp/beI2BMR/yfnfN7kbf8rIrblnD83WRSvyzlf38tzHsphzr46IvbknG/s5dlKUkpLImJJzvnelNJxEbEmIi6JiA9Fw/f9Ec59WfTB/Q76pXv0S/f1a7dE6Bf6m27pHt3SG/3aL7qFfqdfuke/dF+/dktE2/3Sq2fkvy0insw5P51zPhARt0TEe3p0lmkt5/xvEbHtZ25+T0R8bfLjr8WrX4zNOczZ+0LOeWPO+d7Jj3dHxNqIWBqN3/dHODf0C/3SJfql+/q1WyL0C31Pt3SJbumNfu0X3cI0oF+6RL90X792S0Tb/dKrQf7SiHj+oN+vj0bukEo5Im5PKa1JKV3Z68MchZNzzhsnP94UESf38jBH4aqU0gOTP17U3I/g/KyU0rKIeFNE3B19dN//zLkj+ux+Z8bSL73VN9e4w+ib61y/dkuEfqEv6Zbe6qtr3CH01TWuX/tFt9Cn9Etv9c017jD65jrXr90S0V6/eLPbo3NhzvnNEfHuiPjo5I+59KX86msrdf/1lY7eTRFxZkScHxEbI+LPe3ucI0spDUfErRFxbc5518Gfa/m+P8S5++p+hz6mX3qnb65z/dotEfoFekS39E5fXeP6tV90C/SMfumdvrnO9Wu3RLTZL70a5L8QEace9PvXT97WF3LOL0z+uiUibotXfxyqn2yefL2nn7zu05Yen6daznlzznk85zwREX8VDd/3KaWhePUv/N/lnL85eXPz9/2hzt1P9zsznn7preavcYfTL9e5fu2WCP1CX9MtvdUX17hD6adrXL/2i26hz+mX3mr+Gnc4/XKd69duiWi3X3o1yP+viHhjSun0lNLsiHh/RHy7R2d5TVJKCybf6CBSSgsi4n9ExENH/lPN+XZEXDH58RUR8U89PMtr8pO/7JN+PRq971NKKSK+EhFrc85fOOhTTd/3hzt3v9zvEPql15q+xh1JP1zn+rVbIvQLfU+39Fbz17jD6ZdrXL/2i25hGtAvvdX0Ne5I+uE616/dEtF2v6RXf4qh+1JKF0fE/46IgYj4as75T3tykNcopXRGvPqdxoiIwYj4estnTyl9IyJWRcSJEbE5Iv44Ir4VEX8fEadFxHMRcVnOubk3zjjM2VfFqz/CkiPi2Yj48EGvrdWMlNKFEXFnRDwYEROTN38yXn1NrWbv+yOc+/Log/sdIvRLt+iX7uvXbonQL/Q/3dIduqU3+rVfdAvTgX7pDv3Sff3aLRFt90vPBvkAAAAAAECZN7sFAAAAAICGGeQDAAAAAEDDDPIBAAAAAKBhBvkAAAAAANAwg3wAAAAAAGiYQT4AAAAAADTMIB8AAAAAABr2/wElM+yEYsQLdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2016x2016 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(28, 28))\n",
    "columns = 4\n",
    "rows = 5\n",
    "for i in range(1, columns*rows +1):\n",
    "    img = all_recons[i-1].detach().numpy().reshape(28, 28)\n",
    "    fig.add_subplot(rows, columns, i)\n",
    "    plt.imshow(img, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}