CIFAR_fm.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CIFAR_fm.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUe31nxbFSMA"
      },
      "source": [
        "Plotting functions. Copied direcly from the code online. Added the line to make it work on notebook\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLAXa9_ypdSR"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "# Plot image examples.\n",
        "def plot_img(img, title):\n",
        "    plt.figure()\n",
        "    plt.imshow(img, interpolation='nearest')\n",
        "    plt.title(title)\n",
        "    plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "\n",
        "def img_stretch(img):\n",
        "    img = img.astype(float)\n",
        "    img -= np.min(img)\n",
        "    img /= np.max(img)+1e-12\n",
        "    return img\n",
        "\n",
        "def img_tile(imgs, aspect_ratio=1.0, tile_shape=None, border=1,\n",
        "             border_color=0, stretch=False):\n",
        "    ''' Tile images in a grid.\n",
        "    If tile_shape is provided only as many images as specified in tile_shape\n",
        "    will be included in the output.\n",
        "    '''\n",
        "    # Prepare images\n",
        "    if stretch:\n",
        "        imgs = img_stretch(imgs)\n",
        "    imgs = np.array(imgs)\n",
        "    if imgs.ndim != 3 and imgs.ndim != 4:\n",
        "        raise ValueError('imgs has wrong number of dimensions.')\n",
        "    n_imgs = imgs.shape[0]\n",
        "    # Grid shape\n",
        "    img_shape = np.array(imgs.shape[1:3])\n",
        "    if tile_shape is None:\n",
        "        img_aspect_ratio = img_shape[1] / float(img_shape[0])\n",
        "        aspect_ratio *= img_aspect_ratio\n",
        "        tile_height = int(np.ceil(np.sqrt(n_imgs * aspect_ratio)))\n",
        "        tile_width = int(np.ceil(np.sqrt(n_imgs / aspect_ratio)))\n",
        "        grid_shape = np.array((tile_height, tile_width))\n",
        "    else:\n",
        "        assert len(tile_shape) == 2\n",
        "        grid_shape = np.array(tile_shape)\n",
        "    # Tile image shape\n",
        "    tile_img_shape = np.array(imgs.shape[1:])\n",
        "    tile_img_shape[:2] = (img_shape[:2] + border) * grid_shape[:2] - border\n",
        "    # Assemble tile image\n",
        "    tile_img = np.empty(tile_img_shape)\n",
        "    tile_img[:] = border_color\n",
        "    for i in range(grid_shape[0]):\n",
        "        for j in range(grid_shape[1]):\n",
        "            img_idx = j + i*grid_shape[1]\n",
        "            if img_idx >= n_imgs:\n",
        "                # No more images - stop filling out the grid.\n",
        "                break\n",
        "            img = imgs[img_idx]\n",
        "            yoff = (img_shape[0] + border) * i\n",
        "            xoff = (img_shape[1] + border) * j\n",
        "            tile_img[yoff:yoff+img_shape[0], xoff:xoff+img_shape[1], ...] = img\n",
        "    return tile_img\n",
        "\n",
        "def conv_filter_tile(filters):\n",
        "    n_filters, n_channels, height, width = filters.shape\n",
        "    tile_shape = None\n",
        "    if n_channels == 3:\n",
        "        # Interpret 3 color channels as RGB\n",
        "        filters = np.transpose(filters, (0, 2, 3, 1))\n",
        "    else:\n",
        "        # Organize tile such that each row corresponds to a filter and the\n",
        "        # columns are the filter channels\n",
        "        tile_shape = (n_channels, n_filters)\n",
        "        filters = np.transpose(filters, (1, 0, 2, 3))\n",
        "        filters = np.resize(filters, (n_filters*n_channels, height, width))\n",
        "    filters = img_stretch(filters)\n",
        "    return img_tile(filters, tile_shape=tile_shape)\n",
        "    \n",
        "def scale_to_unit_interval(ndar, eps=1e-8):\n",
        "  \"\"\" Scales all values in the ndarray ndar to be between 0 and 1 \"\"\"\n",
        "  ndar = ndar.copy()\n",
        "  ndar -= ndar.min()\n",
        "  ndar *= 1.0 / (ndar.max() + eps)\n",
        "  return ndar\n",
        "\n",
        "\n",
        "def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),\n",
        "                       scale_rows_to_unit_interval=True,\n",
        "                       output_pixel_vals=True):\n",
        "  \"\"\"\n",
        "  Transform an array with one flattened image per row, into an array in\n",
        "  which images are reshaped and layed out like tiles on a floor.\n",
        "\n",
        "  This function is useful for visualizing datasets whose rows are images,\n",
        "  and also columns of matrices for transforming those rows\n",
        "  (such as the first layer of a neural net).\n",
        "\n",
        "  :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can\n",
        "  be 2-D ndarrays or None;\n",
        "  :param X: a 2-D array in which every row is a flattened image.\n",
        "\n",
        "  :type img_shape: tuple; (height, width)\n",
        "  :param img_shape: the original shape of each image\n",
        "\n",
        "  :type tile_shape: tuple; (rows, cols)\n",
        "  :param tile_shape: the number of images to tile (rows, cols)\n",
        "\n",
        "  :param output_pixel_vals: if output should be pixel values (i.e. int8\n",
        "  values) or floats\n",
        "\n",
        "  :param scale_rows_to_unit_interval: if the values need to be scaled before\n",
        "  being plotted to [0,1] or not\n",
        "\n",
        "\n",
        "  :returns: array suitable for viewing as an image.\n",
        "  (See:`PIL.Image.fromarray`.)\n",
        "  :rtype: a 2-d array with same dtype as X.\n",
        "\n",
        "  \"\"\"\n",
        "\n",
        "  assert len(img_shape) == 2\n",
        "  assert len(tile_shape) == 2\n",
        "  assert len(tile_spacing) == 2\n",
        "\n",
        "  # The expression below can be re-written in a more C style as\n",
        "  # follows :\n",
        "  #\n",
        "  # out_shape = [0,0]\n",
        "  # out_shape[0] = (img_shape[0] + tile_spacing[0]) * tile_shape[0] -\n",
        "  #                tile_spacing[0]\n",
        "  # out_shape[1] = (img_shape[1] + tile_spacing[1]) * tile_shape[1] -\n",
        "  #                tile_spacing[1]\n",
        "  out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp\n",
        "                      in zip(img_shape, tile_shape, tile_spacing)]\n",
        "\n",
        "  if isinstance(X, tuple):\n",
        "      assert len(X) == 4\n",
        "      # Create an output numpy ndarray to store the image\n",
        "      if output_pixel_vals:\n",
        "          out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')\n",
        "      else:\n",
        "          out_array = np.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)\n",
        "\n",
        "      #colors default to 0, alpha defaults to 1 (opaque)\n",
        "      if output_pixel_vals:\n",
        "          channel_defaults = [0, 0, 0, 255]\n",
        "      else:\n",
        "          channel_defaults = [0., 0., 0., 1.]\n",
        "\n",
        "      for i in range(4):\n",
        "          if X[i] is None:\n",
        "              # if channel is None, fill it with zeros of the correct\n",
        "              # dtype\n",
        "              out_array[:, :, i] = np.zeros(out_shape,\n",
        "                      dtype='uint8' if output_pixel_vals else out_array.dtype\n",
        "                      ) + channel_defaults[i]\n",
        "          else:\n",
        "              # use a recurrent call to compute the channel and store it\n",
        "              # in the output\n",
        "              out_array[:, :, i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)\n",
        "      return out_array\n",
        "\n",
        "  else:\n",
        "      # if we are dealing with only one channel\n",
        "      H, W = img_shape\n",
        "      Hs, Ws = tile_spacing\n",
        "\n",
        "      # generate a matrix to store the output\n",
        "      out_array = np.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)\n",
        "\n",
        "\n",
        "      for tile_row in range(tile_shape[0]):\n",
        "          for tile_col in range(tile_shape[1]):\n",
        "              if tile_row * tile_shape[1] + tile_col < X.shape[0]:\n",
        "                  if scale_rows_to_unit_interval:\n",
        "                      # if we should scale values to be between 0 and 1\n",
        "                      # do this by calling the `scale_to_unit_interval`\n",
        "                      # function\n",
        "                      this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))\n",
        "                  else:\n",
        "                      this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)\n",
        "                  # add the slice to the corresponding position in the\n",
        "                  # output array\n",
        "                  out_array[\n",
        "                      tile_row * (H+Hs): tile_row * (H + Hs) + H,\n",
        "                      tile_col * (W+Ws): tile_col * (W + Ws) + W\n",
        "                      ] \\\n",
        "                      = this_img * (255 if output_pixel_vals else 1)\n",
        "      return out_array\n"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udYW8KYi4fGv"
      },
      "source": [
        "import argparse\n",
        "import time\n",
        "import numpy as np\n",
        "import sys\n",
        "import pickle\n",
        "import torch\n",
        "from google.colab import files\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "use_cuda = torch.cuda.is_available()"
      ],
      "execution_count": 205,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBDIYWJ4Fnqd"
      },
      "source": [
        "The upload function allows me to get local data onto colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "BpOB8TFuNtif",
        "outputId": "ec7ee958-d866-4af2-8f30-b79d8dd361f7"
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c0e02c98-fd1e-4440-a246-30b49c5cf1f9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c0e02c98-fd1e-4440-a246-30b49c5cf1f9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FVg1HpIVF02M"
      },
      "source": [
        "Unpickle has been taken directly from original code. Is not needed for smaller test cases, as I preprocessed them locally. This snippet will load all of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOIKH2hrSIgL"
      },
      "source": [
        "def unpickle(file):\n",
        "    fo = open(file, 'rb')\n",
        "    d = pickle.load(fo, encoding='latin1')\n",
        "    fo.close()\n",
        "    return {'x': np.cast[np.float32]((-127.5 + d['data'].reshape((10000,3,32,32)))/128.), 'y': np.array(d['labels']).astype(np.uint8)}\n",
        "\n",
        "train_data = [unpickle('cifar-10-batches-py/data_batch_' + str(i)) for i in range(1,6)]\n",
        "trainx = np.concatenate([d['x'] for d in train_data],axis=0)\n",
        "trainy = np.concatenate([d['y'] for d in train_data],axis=0)\n",
        "test_data = unpickle(os.path.join(data_dir,'cifar-10-batches-py/test_batch'))\n",
        "testx = test_data['x']\n",
        "testy = test_data['y']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bqhmoA-F_Gl"
      },
      "source": [
        "This smaller dataset was used for testing. It does not need unpickling."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEHmHc4BSlPh"
      },
      "source": [
        "f1 = open(\"data_sample_small\",\"rb\")\n",
        "f2 = open(\"test_batch_small\",\"rb\")\n",
        "tr,test = pickle.load(f1),pickle.load(f2)\n",
        "trainx,trainy = tr['x'],tr['y']\n",
        "testx,testy = test['x'],test['y']\n",
        "f1.close()\n",
        "f2.close()"
      ],
      "execution_count": 206,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRARAO5oGCLj"
      },
      "source": [
        "Set some parameters that are used later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUS7i4CG8meU"
      },
      "source": [
        "seed = 1\n",
        "seed_data = 1\n",
        "batch_size = 10\n",
        "lr = 0.0003\n",
        "count=50\n",
        "noise_dim = (batch_size, 100)\n",
        "if use_cuda:\n",
        "    torch.cuda.manual_seed_all(seed)"
      ],
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ntsq2NqGZM0"
      },
      "source": [
        "Setting seeds and creating random objects"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCJETlL_5o6j"
      },
      "source": [
        "# fixed random seeds\n",
        "rng_data = np.random.RandomState(seed_data)\n",
        "noise_rng = np.random.RandomState(seed_data+seed)"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgD9sgObySSx"
      },
      "source": [
        "Creating the custom Dataset to load the data and the corresponding dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3jyw_NLi6mF"
      },
      "source": [
        "class DiscTrainDataset(Dataset):\n",
        "    \"\"\"Face Landmarks dataset.\"\"\"\n",
        "\n",
        "    def __init__(self, data_sample,data_labels, transform=None):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            csv_file (string): Path to the csv file with annotations.\n",
        "            root_dir (string): Directory with all the images.\n",
        "            transform (callable, optional): Optional transform to be applied\n",
        "                on a sample.\n",
        "        \"\"\"\n",
        "        self.d = data_sample\n",
        "        self.lab = data_labels\n",
        "        self.d_unl1 = data_sample[rng_data.permutation(data_sample.shape[0])]\n",
        "        self.d_unl2 = data_sample[rng_data.permutation(data_sample.shape[0])]\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.d)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "        x_lab = torch.tensor(self.d[idx])\n",
        "        lab = torch.tensor(self.lab[idx])\n",
        "        x_unl1 = torch.tensor(self.d_unl1[idx])\n",
        "        x_unl2 = torch.tensor(self.d_unl2[idx])\n",
        "        sample = {'x_lab': x_lab, 'lab': lab, 'x_unl1':x_unl1, 'x_unl2':x_unl2}\n",
        "        #transformations\n",
        "        return sample\n",
        "dataset_loader_train = DataLoader(dataset=DiscTrainDataset(trainx,trainy),batch_size=batch_size, num_workers=8, shuffle=True)\n",
        "dataset_loader_test = DataLoader(dataset=DiscTrainDataset(testx,testy),batch_size=batch_size, num_workers=8)"
      ],
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-LYFH_LDeFH"
      },
      "source": [
        "#Minibatch Layer\n",
        "class Minibatch(nn.Module):\n",
        "    def __init__(self,input_size, num_kernels, dim_per_kernel=5):\n",
        "        super(Minibatch,self).__init__()\n",
        "        self.num_kernels = num_kernels\n",
        "        self.dim_per_kernel = dim_per_kernel\n",
        "\n",
        "        self.bias = torch.nn.Parameter(torch.Tensor(self.num_kernels,))\n",
        "        self.theta = torch.nn.Parameter(torch.Tensor(input_size, num_kernels, dim_per_kernel))\n",
        "        self.log_weight_scale = torch.nn.Parameter(torch.Tensor(num_kernels, dim_per_kernel))\n",
        "    \n",
        "    def forward(self,input):\n",
        "        w = self.theta * (torch.exp(self.log_weight_scale)/torch.sqrt(torch.sum(torch.square(self.theta),dim=0)))\n",
        "        activation = torch.tensordot(input,w,dims=[[1], [0]])\n",
        "        diff = activation.unsqueeze(3) - activation.permute(1,2,0).unsqueeze(0)\n",
        "        nz=torch.Tensor([1e6]) * torch.eye(input.shape[0]).unsqueeze(1)\n",
        "        if use_cuda:\n",
        "            nz = nz.cuda()\n",
        "        abs_dif = torch.sum(torch.abs(diff),dim=2)+ nz \n",
        "\n",
        "        f = torch.sum(torch.exp(-abs_dif),dim=2)\n",
        "        f += self.bias.unsqueeze(0)\n",
        "        return torch.cat([input, f], dim=1)\n"
      ],
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rfwOsm7GrBe"
      },
      "source": [
        "The generator and discriminator models. They have been designed based on the theano implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WRg-VrbgqPi"
      },
      "source": [
        "# Generator Code\n",
        "class Generator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Generator, self).__init__()\n",
        "        self.main1 = nn.Sequential(\n",
        "            nn.Linear(100, 4*4*512, bias=True),\n",
        "            nn.BatchNorm1d(4*4*512),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        self.main2 = nn.Sequential(\n",
        "            nn.ConvTranspose2d(512, 256, 5, stride =2, padding=2, output_padding=1),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.ReLU(),\n",
        "            nn.ConvTranspose2d(256, 128, 5, stride =2, padding=2, output_padding=1),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.ReLU(),\n",
        "            nn.utils.weight_norm(nn.ConvTranspose2d(128, 3, 5, stride =2, padding=2, output_padding=1)),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "    def forward(self, input):\n",
        "        x = self.main1(input)\n",
        "        x = x.view(x.size(0),512,4,4)\n",
        "        x = self.main2(x)\n",
        "        return x\n",
        "\n",
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, minibatch=False):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.minibatch = minibatch\n",
        "        self.main1 = nn.Sequential(\n",
        "            nn.Dropout(p=0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(3, 96, 3, stride = 1, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(96, 96, 3, stride = 1, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(96, 96, 3, stride = 2, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.utils.weight_norm(nn.Conv2d(96, 192, 3, stride=1, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(192, 192, 3, stride=1, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(192, 192, 3, stride=2, padding=1)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.utils.weight_norm(nn.Conv2d(192, 192, 3, stride=1, padding=0)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(192, 192, 1, stride=1, padding=0)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.utils.weight_norm(nn.Conv2d(192, 192, 1, stride=1, padding=0)),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.AvgPool2d(6),   \n",
        "        )\n",
        "        if minibatch:\n",
        "            self.minibatch = Minibatch(192, 100)\n",
        "            self.last_layer = nn.utils.weight_norm(nn.Linear(192+100, 10, bias=True))\n",
        "        else:\n",
        "            self.last_layer = nn.utils.weight_norm(nn.Linear(192, 10, bias=True))\n",
        "\n",
        "    def forward(self, input, matching = False):\n",
        "        x = self.main1(input)\n",
        "        if self.minibatch:\n",
        "            x = x.view(x.size(0),-1)\n",
        "            x = self.minibatch(x)\n",
        "        if matching:\n",
        "            return x\n",
        "        x = x.view(x.size(0),-1)\n",
        "        x = self.last_layer(x)\n",
        "        return torch.squeeze(x)"
      ],
      "execution_count": 218,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3fPDsTAJHbC"
      },
      "source": [
        "gen = Generator()\n",
        "disc = Discriminator()\n",
        "if use_cuda:\n",
        "    gen = torch.nn.DataParallel(gen).cuda()\n",
        "    disc = torch.nn.DataParallel(disc).cuda()"
      ],
      "execution_count": 219,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_KkbPdyyFhW"
      },
      "source": [
        "def log_sum_exp(x):\n",
        "    m = torch.max(x,dim=1)[0]\n",
        "    s = torch.logsumexp(x-m.unsqueeze(1), 1)\n",
        "    return s+m"
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwC5h4fEIyIr"
      },
      "source": [
        "Weight initializations are from the original implementation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meqCLbpNkCrw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e14a8ede-7529-43d2-d4a2-9e59d51df87c"
      },
      "source": [
        "def weights_init(m):\n",
        "    classname=m.__class__.__name__\n",
        "    if classname.find('Conv') != -1 or classname.find('ConvTranspose2d')!= -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.05)\n",
        "    elif classname.find('BatchNorm') != -1:\n",
        "        nn.init.normal_(m.weight.data, 0.0, 0.05)\n",
        "        nn.init.constant_(m.bias.data, 0)\n",
        "    elif classname.find('Minibatch') != -1:\n",
        "        nn.init.normal_(m.theta.data, 0.0, 0.05)\n",
        "        nn.init.normal_(m.log_weight_scale.data, 0.0, 0.05)\n",
        "        nn.init.constant_(m.bias.data, 0)\n",
        "\n",
        "disc.apply(weights_init)\n",
        "gen.apply(weights_init)"
      ],
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataParallel(\n",
              "  (module): Generator(\n",
              "    (main1): Sequential(\n",
              "      (0): Linear(in_features=100, out_features=8192, bias=True)\n",
              "      (1): BatchNorm1d(8192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU()\n",
              "    )\n",
              "    (main2): Sequential(\n",
              "      (0): ConvTranspose2d(512, 256, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), output_padding=(1, 1))\n",
              "      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU()\n",
              "      (3): ConvTranspose2d(256, 128, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), output_padding=(1, 1))\n",
              "      (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (5): ReLU()\n",
              "      (6): ConvTranspose2d(128, 3, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), output_padding=(1, 1))\n",
              "      (7): Tanh()\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcqUyGOzJHvs"
      },
      "source": [
        "The optimiser used is Adam."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pG1agdpU5FnM"
      },
      "source": [
        "# initialize optimisers\n",
        "Doptim = torch.optim.Adam(disc.parameters(),lr=lr, betas = (0.9,0.999))\n",
        "Goptim = torch.optim.Adam(gen.parameters(),lr=lr, betas = (0.9,0.999))"
      ],
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nTbQwmrzN9y"
      },
      "source": [
        "Functions to run the model on a batch, and train the model, computing the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9iP2F6azJ6X"
      },
      "source": [
        "def train_discriminator(x_lab,labels,x_unl):\n",
        "    torch.autograd.set_detect_anomaly(True)\n",
        "    noise = torch.tensor(noise_rng.uniform(size=noise_dim)).float()\n",
        "    if use_cuda:\n",
        "        noise = noise.cuda()\n",
        "        x_lab = x_lab.cuda()\n",
        "        x_unl = x_unl.cuda()\n",
        "        labels = labels.cuda()\n",
        "    disc.train()\n",
        "    gen.train()\n",
        "    output_before_softmax_lab = disc(x_lab)\n",
        "    output_before_softmax_unl = disc(x_unl)\n",
        "    output_before_softmax_gen = disc(gen(noise))\n",
        "    l_lab_ind = [(i,j.item()) for i,j in zip(np.arange(batch_size),labels)]\n",
        "    l_lab = torch.tensor([output_before_softmax_lab[x] for x in l_lab_ind])\n",
        "    l_unl = log_sum_exp(output_before_softmax_unl)\n",
        "    l_gen = log_sum_exp(output_before_softmax_gen)\n",
        "    ll = -torch.mean(l_lab) + torch.mean(log_sum_exp(output_before_softmax_lab))\n",
        "    lu = -0.5*torch.mean(l_unl) + 0.5*torch.mean(nn.functional.softplus(l_unl)) + 0.5*torch.mean(nn.functional.softplus(l_gen))\n",
        "    total_loss = ll+lu\n",
        "    te = torch.mean(torch.ne(torch.argmax(output_before_softmax_lab,dim=1),labels).float())\n",
        "    Doptim.zero_grad()\n",
        "    total_loss.backward()\n",
        "    Doptim.step()\n",
        "    return ll,lu,te\n",
        "\n",
        "def train_generator(x_unl):\n",
        "    noise = torch.tensor(noise_rng.uniform(size=noise_dim)).float()\n",
        "    if use_cuda:\n",
        "        noise = noise.cuda()\n",
        "        x_unl = x_unl.cuda()\n",
        "    disc.train()\n",
        "    gen.train()\n",
        "    output_unl = disc(x_unl, matching=True)\n",
        "    output_gen = disc(gen(noise), matching = True)\n",
        "    m1 = torch.mean(output_unl,dim=0)\n",
        "    m2 = torch.mean(output_gen,dim=0)\n",
        "    loss_gen = torch.mean(torch.abs(m1-m2)) # feature matching loss\n",
        "    Goptim.zero_grad()\n",
        "    loss_gen.backward()\n",
        "    Goptim.step()\n",
        "    return loss_gen.item()\n",
        "\n",
        "def update_historical_average(disc_avg_params):\n",
        "    disc_params = [param.data for param in disc.parameters()]\n",
        "    if disc_avg_params == None:\n",
        "        return disc_params\n",
        "    return [a+0.0001*(p-a) for p,a in zip(disc_params,disc_avg_params)]\n",
        "\n",
        "def test_model(x_lab,labels):\n",
        "    disc.eval()\n",
        "    with torch.no_grad():\n",
        "        if use_cuda:\n",
        "            x_lab = x_lab.cuda()\n",
        "            labels = labels.cuda()\n",
        "        output_before_softmax = disc(x_lab)\n",
        "        test_batch_err = torch.mean(torch.ne(torch.argmax(output_before_softmax,dim=1),labels).float())\n",
        "    return test_batch_err.item()\n"
      ],
      "execution_count": 223,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3Ig8gUIJWpm"
      },
      "source": [
        "Training is made parallel to the original implementation. Theano functions were opened up and replaced by the functions defined above.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBzqCGBVxreC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e5d76d1b-9545-4a91-e292-7d81ae5850b9"
      },
      "source": [
        "for epoch in range(1200):\n",
        "    begin = time.time()\n",
        "    lr = lr*min(3-epoch/400,1)\n",
        "    for param_group in Doptim.param_groups:\n",
        "        param_group['lr'] = lr\n",
        "    for param_group in Goptim.param_groups:\n",
        "        param_group['lr'] = lr\n",
        "    # This entire process needs to be replaced by a dataloader.\n",
        "    \n",
        "    # train\n",
        "    loss_lab = 0.\n",
        "    loss_unl = 0.\n",
        "    train_err = 0.\n",
        "    loss_gen = 0.\n",
        "    disc_avg_params = None\n",
        "    for _,d in enumerate(dataset_loader_train):\n",
        "        x_lab = d[\"x_lab\"]\n",
        "        labels = d[\"lab\"]\n",
        "        x_unl = d[\"x_unl1\"]\n",
        "        ll,lu,te = train_discriminator(x_lab,labels,x_unl)\n",
        "        loss_lab += ll\n",
        "        loss_unl += lu\n",
        "        train_err += te\n",
        "\n",
        "        disc_avg_params = update_historical_average(disc_avg_params)\n",
        "        x_unl = d[\"x_unl2\"]\n",
        "        lg = train_generator(x_unl)\n",
        "        loss_gen += lg\n",
        "\n",
        "    loss_lab /= (_+1)*batch_size\n",
        "    loss_unl /= (_+1)*batch_size\n",
        "    loss_gen /= (_+1)*batch_size\n",
        "    train_err /= (_+1)*batch_size\n",
        "    # test\n",
        "    test_err = 0.\n",
        " \n",
        "    original_params = [param.data for param in disc.parameters()]\n",
        "    for param,avg_param in zip(disc.parameters(),disc_avg_params):\n",
        "        param.data.copy_(avg_param)\n",
        "\n",
        "    for _,d in enumerate(dataset_loader_test):\n",
        "        x_lab = d['x_lab']\n",
        "        labels = d['lab']\n",
        "        test_batch_error = test_model(x_lab,labels)\n",
        "        test_err += test_batch_error\n",
        "    \n",
        "    for param,o_param in zip(disc.parameters(),original_params):\n",
        "        param.data.copy_(o_param)\n",
        "    \n",
        "    test_err /= (_+1)*batch_size\n",
        "    # report\n",
        "    print(\"Iteration %d, time = %ds, loss_lab = %.4f, loss_unl = %.4f, train err = %.4f, test err = %.4f\" % (epoch, time.time()-begin, loss_lab, loss_unl, train_err, test_err))\n",
        "    sys.stdout.flush()\n",
        "    # generate sample\n",
        "    gen.eval()\n",
        "    with torch.no_grad():\n",
        "        noise = torch.tensor(noise_rng.uniform(size=noise_dim)).float()\n",
        "        if use_cuda:\n",
        "            noise = noise.cuda()\n",
        "        sample_x = gen(noise)\n",
        "        sample_x = sample_x.cpu().numpy()\n",
        "        img_bhwc = np.transpose(sample_x[:100,], (0, 2, 3, 1))\n",
        "        img_tile_o = img_tile(img_bhwc, aspect_ratio=1.0, border_color=1.0, stretch=True)\n",
        "        img = plot_img(img_tile_o, title='CIFAR10 samples')\n"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration 0, time = 15s, loss_lab = 0.2550, loss_unl = 0.0949, train err = 0.0936, test err = 0.0930\n",
            "Iteration 1, time = 15s, loss_lab = 0.2552, loss_unl = 0.0952, train err = 0.0944, test err = 0.0940\n",
            "Iteration 2, time = 15s, loss_lab = 0.2561, loss_unl = 0.0969, train err = 0.0932, test err = 0.0950\n",
            "Iteration 3, time = 15s, loss_lab = 0.2546, loss_unl = 0.0961, train err = 0.0938, test err = 0.0940\n",
            "Iteration 4, time = 15s, loss_lab = 0.2528, loss_unl = 0.0964, train err = 0.0928, test err = 0.0940\n",
            "Iteration 5, time = 15s, loss_lab = 0.2564, loss_unl = 0.0972, train err = 0.0932, test err = 0.0950\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-225-8a8272873a67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m         \u001b[0mdisc_avg_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_historical_average\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisc_avg_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mx_unl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x_unl2\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0mlg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_unl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m         \u001b[0mloss_gen\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-223-6ee6e58476ba>\u001b[0m in \u001b[0;36mtrain_generator\u001b[0;34m(x_unl)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mdisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0moutput_unl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_unl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatching\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0moutput_gen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdisc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatching\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mm1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_unl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m<ipython-input-217-7fb334145fab>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_weight_scale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mactivation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensordot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mdiff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mnz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/functional.py\u001b[0m in \u001b[0;36mtensordot\u001b[0;34m(a, b, dims)\u001b[0m\n\u001b[1;32m    934\u001b[0m         \u001b[0mdims_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m         \u001b[0mdims_b\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 936\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_VF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensordot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims_b\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcartesian_prod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mformat_stack\u001b[0;34m(f, limit)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mformat_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mextract_stack\u001b[0;34m(f, limit)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0mstack\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStackSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwalk_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(klass, frame_gen, limit, lookup_lines, capture_locals)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mco_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m             \u001b[0mfnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m             \u001b[0mlinecache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlazycache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_globals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m             \u001b[0;31m# Must defer line lookups until we have called checkcache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qsDd5CYx0ahue51hrr5/nHp5VbgVp6d/GJ86j4sBulmNkq8S5uqljHpM+WD3IuaTrR+Ge5Hz3pjlGdSfHzKwixU+0E9cd4uo1PdI551KKcjPkxF0KM0yNi55XZBAXx/clbTgK03RsXIONBXCKY0AS+ixJwz/bBpEpigPw5AVwFP+SqyBHcweI+8lSXi8f5Bpk53ENjPz5B8Y5x+qTwMRM4p4aIefdkuGYzV2cwIml8sp0EvcjGXl+hvguGx8THxaXT8mzk8nCEc756FCLu7VF2UhaK2UJ96K97yjVuT/N+T83kUL9Qc7n6TZ6DlJPUCL+9kru8ZJ7eRZvv7WH89tNCfljr+A8V3yP93V9/RgAoGE3JYlbXm2w7nc8B6Mfpgev5AR/E9/r5xlr20wJ2rlK705zrB0693UjOuc1krL8OOxP28hzI+dQKFGEsUAvYYOYlkBUSsCw7roLO7lJ+5dQPJobIzHoXjkDH9rDxbz/eh7uFWgFABy/iO7M727mgj34HhpYlgWULXe+lt//0UZu3s9u4NjrQMLywmW1uGYvD+y9l+0AAFT7HPNIKw/evB4uzJ6UFkNutiCuxbAun5gNN7YGrzg8iZ3F7p7SmHWn8d31pIUYIcool4ox1sTNWdvN+R+cyx/PglgF+pu4Jtd18O+LyynyNvhcx64WHoxXdnD+2+bwb3XAAzFQwx/zDIWfd1ZRtIzlDNIx+8PgMzl7AEI3p1x7Ad8ZM9bVJ7fnNG7OpLQNX+tXodDe4Vr+bUgQ59FaqgvnqMD+4bmcZ60MpyPN/FFdr6ChzatOYpbP9h9dC7iQbzrG+W1cxD1qDrhWnTP4I1iswLl9DfxxxGV4HE5xbpVpEoxxxw+D24x1+foKMLLu7TDkXoRvClff/LjCqWVY/8u9DVh/Bdd5VZLn9tBl/IF/+zni88sP8IBcGtANuvNtPNevfZiE4/HPc5GW5BYBALa+k/i/ar3Br99LlWJ1wDO/+WqO+cqdPIP3XP0iAKDO8NwfbOV8F3WSGG0Xs3K0x5kyF4kMz0pWLnPflyvUBu3Z1IHIzRlBBBG8XJiWfPTJIJdIk+usr1c4aTcp3g6FCDcfOIiPL18AAKjaxkCoe5eRwi5+mhLF968mNW+8ZzcAoPMaiV73MAjkH99IY86CJ/cBAH6ymtRy1bNd+O1F5Bple6h2lNXz2YYujvmsXGUJideO5u3a8F0jN5zCox0ZrXJugJhn3X6FuFtXI2TU6SlTcFNarlUFaZUNcC021pJLxuWCOp4tQ20XpaFfNMigdoL37quj5FB3qgMAcFej8Osn1e8q4dilw+SuveVi6UreSgYOYnIppqFwc+vOmtLN6RdeD92cVgzP4z6upDebiNZv3bRp4jOod5YPkqs/X02u7wxw/kdLKDnMOM21+LlqS7sdA9g5gxy1uZ2i9g8lZVTrTB2qINeu6aYK+tgMGWZHON+SBOeUzMhlbCReuxk4NsHLJqzJNR3i7lmXqFSMSVy8ALBLxtWKTuL7jdZqmD2c7/3LOeaSDc8BAH7wR2wpMuthcvl/vWIlAODcn3Ft/uHdcwAA9XdsAQD0vJbrve6/WJH/EzfMwsKHGET1bxdTtVi5/jgA4PZXcG1qt1KlLpnHvWw+xt/UA1UyemfyyYcA4PqZMAjPMTZsX650GZZjUh9zedv0pBBJEBFEEMGUMK0E0eAqOUufL28lxdonA9JVNTTU5Gon8PG1sjEkGUq6dt4KAMDJ2dQRv3k5JYqnKjna0gW8v3sxdbulF1O3e0Qd4xYvJOU9vGMA511GjvtYCSl/eYzTPtnVDgCoXEyqvvsAqXagMF4/S8nHS0j3zMr4ZnXVIB8o4hTRylJZdTwhX1PJZ8dHuAblFaTIngy3l8+kzrzrtNxhdfUYHOD/37SUXHNzOyWCmhnUU8fkAm5bxPk/d4Act7qK+A7283NdA/Ht7FOSVAxI98mOUsq/EzLQ+kUGOF+JPDFr1ESh2zNnDbf5jDeUSG8NZNCqUrr92AhxrUpxvtbFe8lcShB7OolPYz3xG9Z837qUn58+OoHZTdSzh0/y3jVy5T2ynxJEUwPPRX83dfrWhVzvgxQ0YcT1R0dlJLZlBob90OBmBQLP2lv0TBDaZwolhzNxB4B1KdoAMqW84X2ra/DUoVYAwIpZlJTHWjn/r1zE7x8v4Xk/t42dAI8t5fXvn8feyFvK2AVw8RxKCd1LuLfzLmzAYz4N+ksX8rdz5FlKyqsuo53iiQquY5kMsieOUsJoXEYpYcs+/saQkH1haAK5lEozWMO9JAebN2FtVsW4F0MkQUQQQQRTwrQSxOkUuY/fS4nh9l4l5+zn5w1LOgEAiYMt+MIpUrn5vyRn/f8+zwSUkhdJMd9xjFRu0T+S65z+0QEAQO3jCwEAB1/Hd636tgJl/nkPAKDyhXk4cRV1wbbfkUK6f047Rs1AKwAgO8rr9XKnmUol6dh0cFvYBPYvwQQmDKop1kNHjXX1kWsOjGupFMw00SBX5SC556ZRSTeH+c4ds3rh9jLw5S6tTcNW6fLX0fpcepquvrso+KD5MDmzv44cxB3n9fQQuWV5D/GPNY4iJdw8a2dRwFQ+SEg4OoVuTScodP3F5BLMnsEqJmyylsdn+7SujnA/0UxbgztAqemZARVWOcy92zmLCDkDxP/OTs6xdnMC6bcwt7m8k1LVTwaJW9N24f5q2mViA5ROH+shXlWHiHuyjdw2mSWXDyTiJeHB2LRt36bCW3uM0utDbikbxRRuzmdLqOObHs7xjqMOZj7IM/9vazm/sk2UcHdcTyll6Zfkvv/pfgBAyXpKAwf/hFLkwps5p977eXbrH6REfegd41j5ZZ6LkR+wbWrVdgVTKWCq7SfEI3ETfzPVp/nusSFKIc2DSguv42cnkwKUNm8kA1j3twmDw4hrmMI/BUQSRAQRRDAlTCtBzHKU7FJP6vgXRygNPP5KehouKCGVPPpHC/GGTaS2v/oErc9Xx6ir7Xwfv7/mCeqhT32P19fGaP3d8zleP/ch+Xy/fpRju+cDALa/twKX7iX3uO8vHgEANAe0FL+4hhbvlYdJBR+vJXeJObw/XUL0klmlDNtiI1aSCGJhGTIXhbpYRVw6fAnHWtXHNTi2gNxxZjm5e+8Ccsm/Osa1euIicowlpTPRvpo65GtOEff7rqfluy1GDrH/Yn5/RTu56F0Xc21mOrTHnFxILrlU3oBts8m5U14JhtTZMpkmtxszhSG1rk33DqZK97aShHMW/mVxhWVr/ZarQEnHfEpqjSniPriQ+P1FR6lw5zwXubQznFhBe8JtJ7lG975hExb6PEPtl9NrdfVB4v6rqyg1LTatvH4O13tdF/F6Zin76JSCa9ZdTXxqhyiNnUzmECrYpjAOIr8WNlisMHisOBbgFY7iOFZSivnatkW46xZy81emlgIA9n6A87/yPm7EQ9/j9ctjxG//Fzn/Vc/xXGy7i3u7DGxavu9f+I4VDzt4+Hv0kJyXvAgAsOMDfPaCrdybB25iAFWz4W9m28Uc0577RxuOC29+P1EeQ2qCUvS4YllsQR5bYMgLbGnCyAYRQQQRvEyYVoLYr9Da6h5yqW+2Uu8OttOk/OtzySGW37MTX/lTUtaZv6JP9x/eQL3q8q+Sq7z7NnKu2m+Si556P7nORZ8i59j0ZXKd2T98HgDQ9wa++6Lvx/Gh95OONdxJCSF1MSns4p3UB5+ex+v1/aKKKi6SVPzGhAq0QFGTri3h5vj5OIAi3HtllEiNc4wtSozJKWpzt6H+3bCfPuovL+b8K/Zwjo8scbBoE/XnBy4hdynZzGt3LuM7FjxNrvLAWq5FxV5+3txA/GYd4Xz3z+Q4pkfFaFIeypRmPhEj14C1QfiF4cRuoDgI18YC2JRneTMmiYPoz1jcec+OKn5O9/Ic7FVj7sZDxOcb8ykFlh3iXj7VooKuL3IOj1wgfXhjB4bXKfr1cZ6LOxQ12LC9R8/y3tk7KS09vVIJbO3ch2QdP9eMqtCMisQmTBaucIGNkHRsDIg8NUUxIlPFQfxWqeRzFH/w1suaUHInz/UX3sR9vvhrlCTfcdti4vqtxwEAhz5wJQDgivdxDx/5L0pPMz75NOf76UsBABfdzHc9++8z0PIvmwAAA+9cAwC49F8575tYOxiNv6aEkLiYa9W2nXv/2ELiVc+lQ1zSQiw9howK4BqdC1OUCj/VuS+GSIKIIIIIpoRpJYhVSXKKXJKU6W+Wk7I9tZO2h9UNpKaZFVl853zqo8+lLwAArGilP/j0deQEP1zB6+1/+WoAwKxGPjt0GTly3QpSx/2vvwoA0LKI7zi2/BQWrKG+95yKsdSXcR57XqRFeOYK6nPrt5CjGcXST6gUeVzp0uPyCdtUV9fzkQn9wYW0ssm10Za899JZvH7gNN89q5p6uNdIav4hpaQ/mqRUtXJWGzrnk8tceB710g2lnM+cJuq2p+rJBVevI+6PBowBqWvU9RPEZ95i7sOuI6T3CcQwOMo4gaBMBYSHbZk4zt8Wo7UpzzZiNMzF8G2xWuF/hg2iQbksqsyGNjJBHOsh7k1VtAMEtdTxz9f6P7WLN86bQ67a306bxeU6G0+WtGHhDO5lt7xA56zgM4+M855GxQJ0HaO3Y+UirvNmqKBrgu/q7yZ3N4rS7Dnt58u+yxQRWPuLZ0snqgBOUFgkqDgG5i+SPJsjzdzbL59bhY3uWgDAugZG+I5fy7F/tJxxHAffznPdqjiJoXfy3Ne2UtLs/vgrAQB1SukefTXPfcWiChy+kTESM+fznJxcSe/g3FU8By9muNc1lTxj+1+QF28pJc+NW7gWuaQKOo1l4VZIwkzLNiXJwcj2kAu9GVG6dwQRRPAyYVoJYnuJ0nbl7/5pF1nKvEdJuf71alpfqx5diE1v5DPnforXjjxJn23qv8gRDr2VFG3h68jpuvbRW1H141YAQMct/P78G8jJjm9hbHvt44vQfiMp6oVfUon3r+8CANSfIjcaGia1XtxFfdRVujRy0s89W2KMVNuxFeoDJ/QP+0X+4C41u0E3dckfD3PsukPilsupb5crXf1rq7k2bffxnbd/7CBKt5Ob3HI+x1rxE17r/wzXrXYP1+aAirquXc+xx27k2iR7yUHu7uW8mw9ybd0l3UjlVEZPqeClKkdn+YEtJ+8obyJry7D5Nptv6nTv0yqOY/opufxmXHEOx7gWzy+m0ps6yvl9a5WyCzfw+q/XtAMAEvvpr//YSUoxK3/soPeLzNWp2tNK3JWpuu4Bvqv/Zsa/lJ3k9e+ephS2eIvK5F9Ci37pOPFPy85U5Tkwsi0Ya4twbKEY21yHYJvGuFOkPN9Rxb01hyhJfOS0j2Xf5Dru/x1zKKp/zKzMPTdw7GVv4ruOHeH1km8R984buT9zL+bf7g7ubeUPeG66PuxhzZv53lPbeOarHuPYJ/6aUtQFn5G0909bAQD1XZRiRodos1qoc+/MUpkAL4nAt0WR9BuWxGBswxwbB/ESIkIkQUQQQQRTwrQSxGqxlbEF5AyffYEU9bG/oU50Xo764bF3zcGVv1DprZ+T4q/yqFMe/ob8wb8m9zyseImWLDlA+y/JFRfdRQq89SHqtwtH+a4dXyrFBU9Sb3v6M+Jco/Qlb76altpLd5Ic3k2GBpMlRU1X2uKjpKzDSRsdKdutccOIw7NsEEpzy9bw+9eppsHWS0iRr/c4/651nOc3NlIXvfsv5e/2Z+Hwaznv12/g37tvpiQ0f1RxEK9jvMO1z/Jdd7+BnKtmTLECa4nfhR2SWpZwbWN+GforyFFrVDa+W5WF/bBgq2oeqKhIPCz9rjoAQWFW55k1EZr1/3QV53VtB3HfvZbPzpd9pnMN53/bM9z7e9/G+S3JcSPaX801uX0D8xHuu6UbrSPc/0NvVx2QDZRofvmn9Ei1jFAqPHYhJYrLJTVtuIASadbj9z1N3OOZfcSvPeaH7eagOJhAZe8Tiia1LRgtrnn7S+HP4J1pntm+K7lvd9zXjPXfpj3lvAxx6vgS933ZjzmPfc/z3C7w+OyJ3/D7ub9RHs1u4j1DZ/PkfZxTy30O9j3O30rLBNdt+9f4Wzn/SY753G20TaWyPDdbruZ5uHA3pa87m+t+VXwAACAASURBVCUVSDSeqHSRShP3UdeecPvXxsPYjNcoDiKCCCJ4mTCtBLFZhVHLjtKaftMKxj+kNjC3/Z8uugwAMP8Xz+IvP0gr7/yv3QMAOPhBWm3X3EoOteF7zLlo+vDvAAD9n+P1c28m13nyB9TX277A633vvQQAsOqbQ9j6t7TyL/glfdGlF5BjzX2B+ugjraTaLcNq16dqS/Ecdd8xFbk1smq7oqKe44cFTXNuoQ3ipG20O0pKfIfNP2jnO59sbiU+uxg/f8fFpO6zH6OF+ceXLcSChyhNfOe1lLjqHqHtZGIt752zgfrqdy+lxDB3Mz/n5pNL1u9TFOFczremk3OIJ12UTvD/x5TZaovQ5n39haXfz64HMXUswAklB8bH+J876/VFJ63lz6ii14z9xPWu1ZQG6p8nl79rKfFp2UCJ6D9VC6TykV3IrOU5mP0I7TDfuop2mNbnGSk5uphcu+kApcX7FpDjzmjn54RaBJQPkavuUHWrpDFwPUkOxnJH2WnCjNVCe0x8khgQAPj+KM/JjKdoL/mn1zeg9va7AQB7//ytAIA1X90MAFj/HUb1LrrlFwCAQ9+4HgBwwdv5zg0PcK0a3nUnAGDwX+ilW3cjY2Oe/ulsLL31twCA7g/TA7juqzwvz3+cktuiX78AAChbxc9zt/A385Aqqs0c1dmUzSUejGJcZ9xYL50t2CzjQzyKg4ggggj+pzCtBHG+yrNbX/vHlpBTP5VtBQCsnkfq76/tR9tKcvXtbyEVXNxKiWD41aSGjeLyJz9GyWHGbFLWiWvILSsWkDoe/ktS2MY2WoF71x3HTEVsvqC4hrJmco3jO5XFuU51/zbR6mvzJ4JuRusFFeQYE6qtGKhFn+udwVmLaOVMRSj6ajZ7XRN1xj2K03+NYhmCelLvf1jBz09midfCeYvR10pKf/65sqF4jKVvnk89e2AX59t2EfXZJ7K8r2Q+dfuhE7R4z1pMCWr3PkozXrmL9HE1R1bJ/OF+ciyrabpTxkHY0u+26W9hQVcAaFZdATlGsFK471eB3itncH5uFdf3gpWc92NZrsH8BbTCDxwll7xtHXXsDd5izFnYSty20R6zdJ3iIDJcg+q5tFeMHiLuLUv57PYMbS052UUmhiTVqBXDQI8Pz7EtDGzx2oxwLWwJEA8L+55d8h8A3lfGOU3M4f3fWV2JTWnmSaxu4f5mr+W5rl3A83DiY4xlaGri9bEbOb+KBmUn/yPjJCqa+DuZuI7fl86vxLFbGH1Z18Zn+y87wrFW8fM22ZliM5UTs5eRl3Wrufnrn6VU65WqDsrgMEwpcUuP68zb6FGtTT4GJLJBRBBBBC8TprdBJKjnBUPkGD9uV/75Y+Qw/3gJdaPU44uw543Ut9tuJYU68dBOAEDiZ+QIx97JZ1vfSqreqziHil9RH+/6ADlx2828PvjgRgBA1X1t6Pxj2j6W/zOptrmVzzZ1yh98imPP6iE1jDXzvlhWNQYyNqNRHMa3dSFM6AvPFsVBnHI5JjKUbH6oxikl+8gxnlhI/TTeTn37Cx1cq9l3cW2Glu+Bu4OSwuG9pPCL7uCQ6U9q3fZQH+88yuttT8k2UkfdvuQ0Jaex45QcapS0F1vQh1JZ2j3VT4xL2TQ2rqM4DsJWMbZl74sqT53Zgu2Uo/LpE8R9u3IwylWv4ul57XznCa7/N44T9zlP8PrIYs7f3cm1+eh+SUq/8zD8d8zFKTnEfT99XJ6dx7l36T/l2pR0qeZBO59tOKJ8jiVqh5Ah/jnli5TCga3z4Ps2J0NSUmh/KW4uJNxRuPffS9HW4h2kRHTznn7M/wavHbmYdrCSn6oJ77u4NgvezHd07mUdlOS/cO9O/wXXctZVOtcHWJm9/CfEv/fDY1jyV7w29NizAIDKX7UCALpfzXO/8ts8i97HafeYeYrrOqYcqdnDskOleO7dTAk8ebXCM2+RE6r5OijT14OYPtRaQTTjLRSHPrubotf6d1B0XD6hEuXvmIlXrqe489SPuIErPS7uka9QPF77CH9Yh+5XunSOLpx93+L11Q9xKptVHKPVZ/LX7r8rw4VPUoV47O/0o/RIdHZfpAKgu3kwH2ukOA31ssiq41NKPSJHEyq6e0YX6NwU7p4ZtmSbXKXXdnHMvetIKBrS6n51LkXG1+ykKHzvDTxci0fr0XEdcbt+N8Xnx/+aKlHzODd49/V89toXlep+DcXqUpAgn1Zo+/IOvnvbXGXl5OIY5W8XpRK1rQs3TPe2RiiVvY/Ddja3hWUK3Zxnpjw3qeScV87vrj3Nvdu3muvYoEIy/WuI3xd28u9jbyaTmCVVqfNKno8/3sEfyxNv70HdOM/FwVdzT6/bQtzvvZZGy8ocn+1eTmK5/BgRfWGxauvLhTlSowI9A8R/IOHly9iLMAQqt+foPAS2q3tQZMi1ncIFfy7D9fBF3Iefb2rCxq9x71ZluWfHv8T9XvUI1d0dChJry8kNegf3bv6TfPepTWSA9Tmq3ofv4Jlte8LFtp/x/S3+EgDArr/nMxc+ync89n4af5M+ieauC3j/yr0kLE/UCk91BEuXAclxEqa0bfsaFLqz8+c+KnsfQQQRvEyYlny8MKx+iV3kXO9vUwjqk1QfvnXxxQCApl+9gL/+8CoAwNyvrgcAHHvfNQCAJZ/js5u/T65f/3dMix359BW8/1aOuft75ByLv8K02KEbVvPzjwMc+gAp49zfMpimZB2pdP0uUvWNy0jnZp6W6KgEK+RIYUdldHPD8ucqmuHm3ZzFXY671cXKHaHEcJeotH+UIu+mNKl5xS4Wz/nVJXKNPU5X5X+dl8Ksxyny/fPryOXq7uO9waWtAICq9XTt3X41ucnMPeSSpolcs/IoRcvtFDhQ1q8eozEXMY/XRmWQdW1TGInXmdAopc7coaSg0u/TlL0/KdyTE5z3PbU0JHsnuFfPZ6k6le2ixHP/ahXjfYb7M7KcY9Zuogj846u5NtVPHkZuHUXrqqeJ+x1XENc5KiHgzaJkUXmCuG4W7imr4skQF1P7geHSpPDMwvEKpaKYbXdg9z2wuBe6eLNFJef+Ux3O6uR2/s51FUj9J4sVdbzjDQCA2V+ky3rrv5Prz77lQQDAia9cBwBoew+lqW13Uwqpu/ExAEDm268gvn/Dc7TnF7VY+hmqFiM30Ii95D+4Xodu4Xzn3Kt0byWGNe7n2M8tpvRSrzIAxvaZ9dPIyMhu1JHdro2ntP/8uY9KzkUQQQQvE6aVIJbGSIHLSkitb6ihHrWvjBxklhqmlK49hWWzSIl2rWY1lMYWpQRfQ5dNizpcH3ktA0uqm0kNs1fQSFU/k2PuvZQusvg86u3ZBfsws5XGsOeWUtfN1lA36yml3lpTRR13n1x9EykZK4f42eqatoy7dW/FPH/KDs81Kp0fT1CXW5Ei7kPD1MdLq0mhK5qVMFPHd+2r59wqGqvhziXlXzaDy7yzhdcStUoVb6FNpWE2P+84SY6QbeRndPL50krZekY510ypC79XodUqEpJLy80pNOK2vHkgt6Zvy67JeOVMXfp9hkKTY+qKPjfF/e8doSSRVHObskbiPqdRHK+LSX1OPf+Wz+Hets3gO3fVNaBEafJBE/Xqhkbu6d6jPGuZGfzsdtGWU1bFd55UL850TC5sJeD5DvHLpj142su4dXNKqrIJaV6Ie6HU5BbxydenKNVUtFAC+uKcLHYvYln71ibi5r6O0uHsJg7Sfv06AEB9k87omyhN1ao7+em/ZjGYshkcO3M916aqaSb2X0VpNLaQ9pnxZUxYa56vloULKVWl9Wxngkbt2nLuw4Fe9XwtUf/Y4TRyMlLaSCgbYm+bC2WFq+NHbs4IIojgZcK0EsQxXzaHMZXPOkUPxOyD9GKcWE2XT2xPPXYcJTe84FFStwOX89nEc60AgP37qX9f8l3qrcfOJfcveZqSQvshUs2rfkfK275cZfOPtKB/O9NcVz3L949cTc5aIa6SGaI+VzOhUnJK0rFhxQhsBRH+sbpaACd0cxa7e0Z8SgxGZe9PDCutWiXoh/s4B1fp4D8/SbznHSe37Vy4FbEuUv7TR4nLgh20MfS1KPnmuBrkHKOUtaCDHHuknPp5fIzcKtfH+8s1By/IIGFLiNmCH5II8vjwa1cFXLOm0FtzdofrPO6DIA4mTQ51bIwSQsWwvEHD3Dunh7jfdUJuztO0WfR100ZlhP/tx2hfaD3ch765lDoSkjZGjvM8zOmSN6aWZeMdnbmgh2H+FaOSkFzb0kB7GOT1b+vitTt5VndvffaKy94X2Z82THBvfbUl+MeDJ7DsUc7jyKV0v7sP0Diy41XtAIDLfkAc917F85u6ny7sw2/j7+Lyz/HvoTWUnKoeoVR89E/24uqfU5roaOM5KT9AabprB9dxzXM89+MpnfsRJVEO83dYr83zx3l+HC8Go2DAoKgxki1rkMc9skFEEEEELxOmL3vvSb+rIRW6/hC9BvsuJue7RCXSBq+Yi288wnsf+RNee1ean0++kdTu1Q9Tv97wMXKO2hy5ftc7aal9xROkZHe+nbroa8kocPpVE7hgv0rKXcb3JZTW2z2H3zdJsjmWUFmtOK8H0j2R5WB+zLafs41u43luUrQUZdJffZWAb+khVx2ZqQaqDjlepoV64XWHaMnftYJ4Lh7PYGIhxzh3H+e1TQVbE+A6ji3nui5QcdqdrXx3o6adbuC8y4c49qBiH+Bm4MVVJESp7DmLm7Gt9Wy6t8KNA9tGMVdwfbJ070pFWQUlfHYemT6GFOLeFBPuc7iXV6iZ7CG1ZmzRmk7M4VxWHeA79p4Tw4KAuvuYyujNP0aud2CuPB+ab3YGx6xQi72hKinPcV7PJVQ8Z5ySXs51Q26ZL+3OZ23Jf/s5TNbD2TEgALDW43pPLOA4n9wUw9N/TmniL3Pk2t1/zvlf+3PuwyN/T9vDonHifvyDPPdX/pb3PfsVSkRVWZ3V93IOr3jQxb03EJeaLPfmuLw+F+zl9+svoTQej3H+p2dzrEY1OD6RkEVBDZyDIAFkua5B0bkIY0Bs2n8Uah1BBBG8XJhWgugbsU1cSc0frifVy/VQn9o5Smmh5NQOPC5LfMVB6kmPZvi5fB+5/s/EMSpf5LP+SV0/wLiIOxaTPc44RB3OV2JQoq8Lm5WQlOgiF4/JthAH9b7BmEp8W+6ZFve0n9Ui3n62beCzboC4TXcuwj2dkY6vK52VapuXIcfyJ8iR3Sz1x0criZ8Z5ByPjSaRHCPlP1Qur0o/nw1GFBqbpv59VHEacemQRtITVCJvVLqzr/JyJhtDoCQy1+IWRgUWRUjaOAiZr12/KA7CL7ToA8B42ur3fKajVO8Q7l5XTLhTCtwoa7qjuAnnhMK9JYE+XC7Pw9gYzBCvxTzuXbe8Eu6EyrMrBytQoZuJwDZRlqSTVcGTmE1Cs/h7oZ7tOVYqLCnA1QnkJSpO90Yh7O/RuRmgXenLM33E97cDAO4aoe2hbhvjfb5zHm0J9U8zvHx8hDa1uqdop/nHS7j+9U/QBucdp6eibgfH+8HqCjTu42/G6SZuiW6+97lazq+km+uetHuUpYdnWKXtLZd3s/lzH3PVmtHi7tuw86I0f0wPkQQRQQQRTAnTShBJG5klXa06Tu44NqQiHXX8m6gYxhXlpLpHBxQ/UE2dLKECoC3yBx/vJ7fx6ykxuJ2kljVUZ3G0T1SwRjrmcC+S5XzPkJUMKpS6PCiJIEFuk8na1FaOZdN+Q707LH/O5xK+g6wttFJEK93QFm7b9JG1lWT4bnU5g6vioCWOOJ64rylJALK4V8T5zJg65GaFT0zlzH21+csp8cpL6B2e7CB2/jnbXi+PW9YXLradHAp9/TbdOy57jE33ztsgbEHXvC4aZiaE11QEWA11UKrYESU/JV3qzBm1FfAVJxEblUShWA0vA+RSxN0dG9L8CEHOxnXozGkvbSxDQiJOWFJPdhLvjDiPvE1BuPrWBmFtEnbfbRShxa/wZ9Ag7uvKBnNe9RA65VU5v5qSYrye+/0uhvtgn6SoWCMjgk0zpcPFstvsVLNizOLzfjsl64aqOPYrfiFdx3c4g4w+LqvgswNKMcqWKKckJ5uamuIEOeJti+/G/QCepE2brGXPfFznxCaoOUGUixFBBBG8TJiWfGRsvJVKpR9PS3+0FCtH33BmuBQ7hildVKiBy3hvOx8doCSxoYPX60+L09bR928GSFHH+2gdrpXVOj1I3d6MlQFDpOQpcVjflf6ZI6UPlMkIy13yxd/5NazPt7B5a9YJzii5VugPDjkbbHxBIYeGL0OB4iQGlT8bF0f3MkNARlmPshfExRU9cU+TkySk9bQxCYGs2UZc33FssxMrzeQb5NhCpZmipr25EHd9nqrk3CRl7+06WS+QCbmQ1sjjfgQZnotRjR32zs0MF6yNDd8zngF0zdjycDbv2JZll60hF0p2ihK0sQxhSIvWRvdljB96J+w8jSnUu92p9PCive/zZCsap5h4ZAAoHeC53DtGG5rXRcPYb7r5G5jdSWlp6BRtEaaTIvHPD1IamH2UnqvRGt4fV0TuSG8PGjR2upRn3h2l9GwGuc6JCXnnDNfO2mGspByYwnMfGMAJ418Kz4WNh7GSRZSLEUEEEbxsmFaCSMBSLlK4ihFF8lWSGhmV9PKqStHYQ8lgoEmUXbEIqOc9Laf4zNE5ipeXv3t8Nu9r7OLnrio9H5P+XZlFqSL4hlVGyzZI8VXfITZOiSIbtxTUZm2KWyqyMowNCGP04/kouyJ/sGt96rbIiGoLJNXWz+rnUFnxMuXf50oUnxDPAkkVEVWj3UzK4sbPXkoSmbwXWd2PmG2Txo++/PKxmC22mwtx8229B9VJ8I2Nc9Bn237OL/qMqeMg3FA31zpn9f5krAB3oybJUPMakxRni+m69tBM8LqfioUl/OzrnKykERXftesJa28JbNk4W5BWY9vIQGuDQCy0r7h+UQxIiHt+34n75HtfoXgCo5aNS3rG0Deb96TU/nFU5/g1h3jvgaXc96T2I9vKZxd1cM/3L5ZEJDfN6GxKzq2dY2hXnpKRNOXVEo+SEY41orom0HoGMjY4GZ4bL14U6+DH4GndEmed+anrgEwGkQQRQQQRTAnTko+cjfcXpfVLlfsg6mSGrFV1DMdS4m7iFs6EovE8UrWBCl1XKXUzIqruUR8fFecNPFJFd9By7nFMxC23FPeYsBZtG59vfeGKDRB3zeuetuW91cPt52DKFvC+KG+o37nURzOWu2YKr3sx+Z1tc5ZRF74kLxvl6Ml+YcYLIzrts5b7m2xh3HzMKfTfO37ijGpIJQXXYhY3O2+nsOx98VpMVg/Cs1zHxu0rziSj+bkZq+uLY1ncbcbgeCFHiyUS4ZzcTNHY2ju7l45X1BrQFM3fK7IjKNYha7xQcihem6lxn3zvxyd07nNqylNWgswEPTmBKouZcdoSnlCMiDuk+JhBzsHJ8PpJ1avwFR/jjkhaydC7tzUWh5vWb0JmLXvOJ1wb/6J9H7Ol6+2ZzMeAnLk2OcdHLIz5sL+BwrWZbN8ng0iCiCCCCKaEaSUI13JsURnPJ6UrtT70uG2n7ue5h57x1dDE9bM4E8RgEVh9XPaDvIW8MBYgkfaQk26ZVByB7c0aD+s7qNKSZ8uZ20xFSSFBYdSgF+rlbl4PDQr1UEe6rc0Q9OS1SFifevicrTWQ1RzyHpOw3Lp80vlYBEJYONSWovdsUVXhZ8uzh23s8+3zYqbovTYiMij2/WcLcPeL6kNMVv7ctRGbdv56JincbHReQvPP+lp/FHoFwvXXGUjAPav0vFccq6DViaMQP8vxQn9+WOMhE+If1n8oftaffC1yk+DOd1vpRrYgjCGljMmgjH9jasokZwE8lZfPKUYnKU+PowLAOXsuUooTylIi8WIBfBvzIVuCq9oeWUlsto90zja90QHKn7niYrzmDFyt9G1tENPjXgyRBBFBBBFMCdNKEJ78yLaXvLV0T1jvhrh9znfhiCKFPmiVms+IMxtxccs9jepF2ob1tixgYMUVGzNg3NDXnbbvDdu2F1qh87EAhW3djVPkE4bViYN8HEARqfSMjSe0uryNNyjMgrMJo27xHGDCOHdHUY0Z26glmLxuQTbMRrRx8sLP2iDCdyfy16y/e4o4B2ulzhZZrbPTxEH4GjMIcY8X4JaPK7H4Fa5NvvaAxU/XHf+M0vMEE87fWuALPQz5ehZ53AvW5gz88k15p8c9n4cyuQ0iF0p2NhbDwJMdAGkb/yLPg/Vm6TfiK7t2wnpO9JyrhtH+uGxUqgkZBnYAQNYrei/HtOfe8QsltPBchec+f27cs858UT1Ov7BuyFQwvYphf84Kaw3FOxs4E+LoIbALYQvG2r9+Yekr68rxrfhmDXJaQNe6MHWf8Qz8wIqdOlx61rHht2GKszVeagFtaLVTJJY6+VJkVmwzKFwp2/fb2NJtJhs+c+b8bQkvv0is9p0Arj/5NRsc5OYsIbPzsfhZY6veFb5bY7seXK8Idytmhj+0wnfH/cLr4QGZBH/HHvoQNxumq/k4lkHY26zBEAXXnUnmkH/W4lakfhXtpV33eFCEXxH+bmDO+P9L4Q7hruAsFEI89C+LqXkBEjIYBvbcujYknPPz1Q/WFmqBZw26YnQKIQ9sarb9PfgebM0fey5CBmhL5KHw3Di2jFxI6PTZtXtvzlbFztr3yXEvhkjFiCCCCKaEaSUI26EoTH6CTX6yaoANATZwXMsB9LBnS15JhSjUVsJnA9dSMmuksi+3iUkmVCGKr3khFbQSjiXFk1+3n61wn3NMyG1swdP8K2IFuIfvMIXv8IvGPHMOdsyz5ufn38/r1vg3/fz9M15V/N6cmRzX8LMpvN9enwx/358cd69ofYOXWv/iOcGctW4h7kXh8ZM+O93awEz9rPP74w4Ani1nZ1WkWJB/f8ZeEzeP2fKFAmnO9tmca+OhdX3Crq1+F4jln7WFeuz8TaFRO/xNFZ+54nNjzMva98kgkiAiiCCCKcEEIUufFKbP5Iggggj+b4FJRYlIgoggggimhGltEJ/84kcB5NN+Y7ZBrAKWkgohzk0YlFWSAI2mZZVWeHRGZesaZ1AY6VH58jI1pskM83pdA8fuVIHWUiVFjQwFqKpRspbKkVkPiSeXUaBIkiBT6Lqx1C90NYXBRdZ9Z5CTYu/o2hc/+RUAwK1f/Jge5veJlFxQaeKRStBtlFFYblUlvx9S4lI8kUB6mGPXqqnO4Egg3DnmmNamojan6/LWJJyCtSupIP7K6YJxHXgZ4WyT4mwQWejis4FIXiHuKFyLyfD/xG3cd1uUJp8EZC35NllPViGbXGa9BXLbGrsRSk4zvhOW57eb5NuELuuVsN6BUN+2BYadguvGs7YMuVZ99yzcc74tOfj74w4At972EZwJgfHCkm2BNabZCgMxr+Cz7Sjuyw3q2gLKnk3Uk0dCtgqT8hBkrOfOrpu1z1k3UehP5vWwEJJNqis618bJ426bBAWFa5Gz89d8Le7FEEkQEUQQwZQwfbKWbVknapjJKdklQ6o4Uk7O4AyXoVvBMTHmrMA0iav0sfjFiSwlgmQvk1ZyizhG6hQLZxyuY2BJknVj4LQolLW7HL0KKjFKN0+oka7r2YQvm0qr8NbQsl1Y8CRbVFjDQxD6mO21EHf5uwPFIk8o0ScQ5x6qlp97kHPo1lzitihrcwa+Gs90pVWAd5AiQPccSRndHPOUxk6OMQjHlEtUyPH5IeFnJJ3FKjJhA+JAQUxQPIGl+F7YUMYGYRUG0+RjF6zUlcff+tf9wHoDeEzS1nLvSiKwhUss17QNhG2qvoKFjJ/QZz9sSRA+G4a422ozelZeJGMDv2wAXlgCUJzYpnYbPwwazgVWkigulvPSuHMsa+LXlJx4WKINbr5wMABkXXklFDRoUopzULGgrNoN+Cqph4TOqH5LgW/CIjkmbmNvbJKkxg5smQNdV+yQcQulA+vc8ODnCwXZIKui4LtYGOxWiHsxRBJEBBFEMCVMK0GkYpbTkhrVjZGedM8g1WlIsujFSH0NFnXz2s5WprHOMyxK2zWXJef++Bh19mdWUMRYGGfD3ROLWP5+zWFyz/WL+LfV5ffds6vQepLlunY3sulIuUuOPCL6VqIWZINWFzaWu0iXU8GYuMJ0bZq1G8SmLJxRGrfchpyufkCSwmxKLzNKWWB0SI12l6uZ7rZ55P4tbjW6Z7Hs2GqViX9u8TEAwFyXTXw753CMRce5Jlvn0cjSAI45VEs8m/sZvntEhVJL/QQmbNLQGCWyCe2V5QeOuKOv0vlxtyhFu6hwyJkFY2wkoW+5j8LejZoiJ8K14TMlY5zXaJkiF22zFnHX0hFyzaFKD6UqepOV5JMcUQGVUr3b6sw6c3EV4smoUG5ce5uzxVZzMky5sbOawwQKSrCFgizuNoEpi7NxB4CYLbknfd3NZOHZIsUqbBOouE9CxYAmyjS2uLqX4n2VknoH63hfqU2/Vxp76dAYBmS/KtF6ZTSfpJoCjetdsTDK2IoKxN13CssyOL4LD7wWC8vwyTaoQkLZSfZ9MogkiAgiiGBKmJZ8DEjPTUnvPplU0ZQxUqNtorAzTvfhMbVtT/SQ4j9WQ27dfIhc/3a1CzNHSdleaCHXWbCbDVT+fb5ayneRau5Xu79ZRwO0N5D6BgP8rruEn6tVim4kYRvnqJCN1b+Fh2s4ds5avFVMIwcvbJ5i054t9MrqXKJCucfV/GVCRXk7pBc2qHHtvU0sYprqpoRxuDaGpnZKNr9qpvRkTvDanibOe/ZRSlsHm22TIM6/o1Sen26+q6tGhWVGOclBByhXmnxahWw8a3+xRV3DRCnink+GsoVDxO09a5/J456xxWqtLq4y8Bl5FCbUVCghW1Qmyfn7Afd0TGtaMs539idVcCY9ihGNXabixONqoeerI9bJZAAAIABJREFUpN+4Jm7P3HjMclzil5Y9KmFLETgWv1yIWz6fo7CYjr1e3DinsCABkLVJRhb/RDIsluNL/49rfjnhltb8bS2CpOxFvSmu3YS8WwPyUFWM8HcyUpZETg2GdLRQPsFn07HSAtxtQRhXZ88xum49PTbh0QkQ89QwyeZrwEoOXiHuL5GMEUkQEUQQwZQwrQRR79qCl6RCcxr4uauPVHFZinaGibIM3ryI9ojnT/DvnDq2IDt5mpT1vefy83o1uZ3XxJZlnTXUr28+j/r6I7spYTRVUQ/vODWIlUvYdORFtXSLOXz/0BD175JKUthueUAC6ch+xlp/5RGxRUdshiCArM0VCQppZbNbWLaubRbHOt5Dyai+lB1T0lWk3m9t41psOsa/LTUz0VFKaeivVrHd2oajHHtWHW0Q/R2c9zlryOme3ENbS30l8e3sI5dZ2kbqv7OD15PGxeAouXWykrgMqLeNNeXbZiq+I4+JZ0vkWV20KGX7DF5ha9HaDOekLQ4s331M+rWnWIQqNU0aGRO3ipN7ZSR9zazm2D0jCcRKlLouKbSmSvExY9Kf47bFIa9XVHCM0XGbLSkbhRg2tJduLoBnC+HaXBfZIGwRn7AYTWA9V5PvvZg8fOGXiOfgKlbBSVj7imJakipIrIIxcZWgy0rCaKrmO7rUbrFc0taEbAIzaw06hlUcSQ2Z0yqVUFnBZwfGtCFq0eiN2kK/wt0WTrJhJwGQVRGluPXy2EJBTqHEWFwoqRgiCSKCCCKYEqaVIHqszzpD+8GBMVK4ym6Sn92ttC8ktzbhtwuoC7ZuJOd/8Ea2Jk90zAIAfGuEXHLpPbRVdH+E12sPUrL43KWkvKsfJYUdfTcbjpSenImfjpEaL9hB6QQXsSBo6SjHGmsip621fuuETamTL90WrQnLuYuKIsjHBRSRyk6VPA96iNeuEUoGNSc45tb5xD21idLB7Ys57wXP0hZx/ztOo+zEXADAD9SAZeEjnP/ATbQ91B6lFPUvKmS67DniM/EWrk2ym56ehweIT/MBtSNcdRrJLO9Ny6JdKa6JuG12JNxtTTRb38JatXW7U1RcBwAyVpmVrz8jrm3jAjzFOaC/VGtFqapMMSFerThcJ/E9UqVo0z4XGbVBiHdwPQ8q4q+mX9b/OeR8Ti/PQ69elVTzGNRQwkyMSdIokVSZ9vIRhmGEpPApbipUXDSlaO9th0HbMCrjx8LIzqzOlhkh7v3yMKRkY8hV8ywG+s20ZzjPcpqqcLSR80+NU2LeDR+1PZL25MVyx7huHZLMUqM6v3XySCmuxBZDjmUUj6QYh2zghHEQ2TC61NpbbG0S/QZeQkSIJIgIIohgSphWgpgl3/lYDf9ec4JUf9cqcvD5CXLPk2tm4dZt5Jx3/gmp3FqnFQBw8Erq6j98mtzwnvfTULDSnwMA2PPH5KLXPkmy/Yu3k9SuzFKy2HZpLV65j9fuuYSctTLNBqlH55I6z+4kBT1UKmuv9ZUrNsC1zU5tzQoblx7EwmpOxf7gFkXAjc+g1PTGY3znlvPaAQBtsUbOYR3x/IfNpPq/fgONAedhJvZfwWf+7Rn+vecviNtSr43reA3X5NpNnP+vX8O1afMoHexbw+cuPU4O8+janQCA0nQTTjWSIzT3Uxk/nijkIr41JGRUUj1mYwFsQdrC8vJOvmUvkmK9nooSVwzzmbE6eSCUR5NWg+Xlfbz/qLh/vS8bURO56FWd3Kcd806iXl6V/haepdZO7s3+mXy2KuCYAzWUumYNEa8T1WyPkNDYo2riU5XhemdiTpi3YWzcg1ooxN2z41+Iu63JUKiHJ4uqmpWOZjEmO0q1coQm1MahsY/3nJrJv5X2uub/im6eiz0LiGe9wzXpm83fyytPTGBnC/eo2ue17nqu0Zxhzv94nUrma48mkvKEKE5iwjaMslKiE4PvFzYcyjdttiX9osY5EUQQwf8QpiUf7cpcLB0m9bu3WfpVBzn5cw2k9nO278PN6+YBAOqfOQAA+M1aUqzlj9OW8O+vJkWrvZ9csPvy5QCAlQ+fBAB87VWUJFo37gUA/GIVuevCLR24ay0pa9U+6v3xOnKRpu4uAMAL5aTWtiy71att6frANmdRdpyTs3H0HhJhVF0hHJYVuqSXHOpHM+lBiKkZ68YWUvmWrcTnXRe3AgAat+wDAPxuZStWPMn5/uhV4hCPtQMA+i/g/FZsYAzIDy+ntDRnG68/MJ9ejLnbyTUfXkmqX3aQ45TXOqjpJ3fZo/YAMcsNbcNf39ZOLC3CvbD5ivVunNnAdjxnx+DngRLimlHj3T7ZNcoUwbqlktfjAzwfXSm+s7KX+N9fx+tO7wQOVRGXGSe5d0frtXfD9lnubfkAcd9ZqSOq9BQbTZhUw6UxKKfBycKRbh42O3KKYkC8pD6rbP8UsQATtiCtbBpDpUnk1ERnVNHEKTVg3l/Fs+mI258W7tWD5O4P1Wtfevj5QDXxrzl1CgBwuDGFVB+fPaaM4Qo1q35BUoqRN8i2FUioufCQYmBsa0wnjAPxz2o4ZJtF2ejReFH5+6kgkiAiiCCCKWFaCWKu/Nm2gN11i0iRdx+ljjy/iTaIidM5fHodLfYPJqhnr1l0DgDgxC5S2m9e2gIAeKr6IgDAwjn0bvQ1kxouExd9yFnC6wtbAQAdO3twzsV83+Ml1CHLk+QUJzs7AAD1raSOew7zXZ7Vt+VL9+K2CrbNVLAx+06+fHpQSEoXpag7eqW8/tZl1Bk3HyZ3Xzyb88+eIvX/3HmUoO5PkDOuWXwOOvaS437t4vkAgCcqeO+i2bQt9Gwl7isuY1zE/Ybt3+cv4FoeVRTq2vNpx3mylPNOJlz09lKSqVBT2YPHFZdvqyurSnjOzccJAPm2BImwarSNyc/r4SVhgiXvqa7kWMNq/VaR4DmAw7Evaia32t/DOdRUc61Gh2kfWbOA9oYXT2VQV0tcR3Rv23yesc3H9GwV93p4mJLbjAZy0VN9tukvP6d7ZEfiqzA+7MM3tv6j9Wao6TBs3UhJDmH5+LNxB4ASY/NalCNR4od1P8rVRtBL84wtl43q+AC/X1zFCQ0Pca9fJUlzRze/r63nXk50U2JasKACLx4e0zXeM6j4nsaZHPOEYol8netMrzwpFZx/Wl4+W7PUDZBv1murh8NKIYUtFl6qcc60BOJoUj0ARrnBG07pwGzjj+e+FTRWJnfNwt9fxEnP/zEX5J++RlUj/nwrAGDv9ToQX+IYfT/Yw7GeWQQAOPJmvmvl97koo199EQBQsWchOhSy3PaA0rrfdBAAUDtMNWRCh6lmnM+6ldycmE0zDl18NplL/RURhISh2NV1IM4x/X7i/nAH51e3mQf6tyuOAwDKdvDH/LeXCf8f8YD/61f2I/GicD9CwtX2dR64vn+jGtK4eQHfdR0PwPJfcO3Sf8/rqUMc+yvtfL71Mf4QU9cfQdkYCVV6nO+tyvGHEwuLs4RRQwDO6IptS6gLTzcMGsqrGBOOLffOdeofl8uU5xad9fxPbJSuuhflhis9obDvJqqgzgB/DI+RlqFudwrjr6KLt6yX839whPOqP6xsqHW8OT7MZ7cqnL6ik+seayHRTGQVRq3ixwnk4NrU5dCtbcVo24+k0M3phr0hCisrjtueAUpJH84m4anQUa9cjeYU93n3OO8pPcn5tc+gKmpG+GPfOMCDVbufTGxipn4zvWQS9w34mHGIuPj1ZC7xUa7NNhVPKjvNZ+MzeQ7iWZ4Tz7NtKOzeEkyQT9HPE4LCfiJT9QQphkjFiCCCCKaEaSWIhXJnZZtpiLlZHO3RN+wAAJwfJwc8+MeteOPTpHp3fp7c/lKX3H3fB6mGXPEEKe7GfyOHWGHk6vsUKe1F6zmV3375MMeOrQUAvPCOelyzj5T/zj95AgAww1vMZ1eQsrYdozj1XJU1NcqNlVQQTYaUWHE/CGSSdIJYSFGL3T1tMmxm5pJLfmT3QuL+pu2cb4pqw+63tAIA3rCe9/36M1SxrnJnYuv7KE6/8nni/tQ/E/flUqN2fohBZZc8Qzr9848zHfzC1GoAwItvJBe96jDxuPf1GwAAtWYe9i0m55orY9+2UltijPPPKl7YZClN2fyjMIXbuvqEr3uGqFkqUT1I8h2LRygxnmghd5yV5J4NtHL+b+1SqPhavqstrnT25Zz/xzp4Nu65aguWuVyvw+t4zyuOUAK6+wJyx9kJfn9yAcdecUrJffNozE7KDTqqZMDSMZ63/lguZKGhPBBw30NXny1oE3JX3lbs4i6z/TblKm4dTKJLuDeUcC8HZxGnN3VyHk+t5jwXJTn/rsWUED7UwTV44IqtAICFpTw37efz+hUHM7jrEp6LuTGq2ScW8R3nyAX8/DzG6FeogFCfAs/Kx/qFu3VvSlr0YvkSB7ZgDdJFuFv3dhRqHUEEEbxMmFaC2DlBKlPdSZ3y8/NIsZxNNA7+9EJKCUvv3Ikv3UiuOPunmwAAH3/DhQCAS79OXf6vPs97a7/G66ffy1ef9wVyhuc/y+tzfrgNAND3mlUAgAtv78Ft7yIHqHuI+mvJWrlX95FjbWshnatRuTvrCnNzlBRyKtLhy2BnW/Z5jh92jbZ9NC28oESv6mOUCD66SC7WTYcAAD+4jPNd9nPO91N/Rfzn/Yy2k1vfZPCKr5G7/c1nadCs+Ravdf8153/+1+jquvHj5BzzfkWX6ZevpnR14e/IWb75OuG3geOllsQx7xA5xB4yIlRNFBpbXQXKGEMjWljc1XZAF7eJeWfrogMKw01OcIzd5eo+rcI1Rx3Ov/4o9envziOXLDvCc/L8bN4/dwdX9fGVWvcXu/HwSu5F63O0Y9x7HqWT2oPE7bkG2nzmHKJktHueUvd7OFZJCedWPkqJY0AJTq7jw9Z4ta0XY75Nh7Zp/tbNOXWqOwAMKKU7qQStQxVAVqn4RyWB1Z3iWfxhMyWJUiXSbWniuZ69k9fXr1Ln863E58GVlEznPc37Hzi/BnUyZm9p5ntbDpLb75wrw+JpJdjJTFOt0oRdMZU5UAdx5wy7kytJy5ZOdBRgFvZknWTfJ4NIgoggggimhGkliLVJ2h6CclKw9y8jxXoyYJj0qiZyvmDNBFasIxfZlKbksGLRCgBA9zWklAuWktIevvEKAEDzTI4xtI7Us2Eldc7tQ3SDzmmjzn90wVEsXUMOvDFHEaEipUK3DoOq6uaTOj6/xzZKFddMyz2U0N+ihq+Onw+Qcopo5cVxcrZAbs6PnyPcQbvLuTM4p7E1nNP3pVNvnjgPALB83lJ0Xkvc/2Mlw7IPvvMaAEBLM+0yvav4bPMars2WCa5d60LifnhOOwBgxSWUVp6QV6k+UY5D+3cDAMpaqWNu30cOHMhH6anMmy2yalOXbUFa29A2xP+MlOd6G6osTruyln9P9pKFNZXSBpGr4XxuWMzPzx7gHs5taAUA9KhE3sfP49lYXzIvn+bfQGlk3Tm89igoLc2olw3iOD+3LaBtZ2u77AlK9R/olzRbS3yGBj34SmV2wxZ0esazrRsLU93ze1+oh9fYsHNJHnOrA5xWiYOKFOcbL6fN4fzltBc8c5BrsHAmbQzdHbx+xTqe8yeV+j+3kfh3N3Ju566pxSMBz0fjbO5zxzHZqpbwzG05pFRtSYP9fcQ9Vkfc+wfldZI0YHwgJ5uDDYiyJRtiYZp/ZIOIIIII/ocwrQTxYgmpnuklZ7i9k/Rk3iPUb751KWMdKh9bhOdfTyq35tOkvsceYZxD6S9JUfe/ldxk2TtVvm4zff11dysO4hZyvIv/ju8+8dDzAIDaFxfgSCf1twu+K9vBR+lJqBpUsJIKt7YMq7RblQqZyoJrU54dFRCxjYL98H9nNAYWbCpVBZY+cobvnSLFnX8X8fviFeTgNQ/T9rD5LRz7vE+S6h99Yg+SPyPuB99C3Jf+mUrIbVEcxz2UFNo/yLW75KOc58n7twAAKjdzbY6eJEe5+Puyl/ztLlQNUHpLp6mLt4ypIExchVpzsl6rHX2+JYBN2iLkWwDkoU8BacEQ9/mhNHEu76QEsX0B7TLJ4+Sm/97GMRdvoP3g7ptoV3LaWwEAn+tUaPmv4xj4JO1XlTvIWT9xBd+8+iFKhSM3tQMAEqfpMfuFKuHM3a2w4jV8dypHCS+ncPoypMOGtLZcv22IkzGFniprbwpLvxd1mBxIqRqNvDcHMylUMIYNg4toZ0nJM/Pvgxyz7Un+Ru5ZyRvdQwwM/JTKBi75Oddy6LO021Tt4tk9eI3B6gd4ZsbeRy9W6Sle+0kv57Fgl40RUQyJUsnTPs9VRWDtTbYlgANY3EMvXXEMiG20hGkhkiAiiCCCKWF6G4SsueMqKPu556k7P/huUtGLctS/jt/Yiit/Q0775B3kdquy1CUPfIH3nH+XwrQfpNl97gQ53KF/ls/3Tk7l2TvIlVqHqI9tvaUcV20kB3v0vaTOZox2gD1r+P2aA6SkGypsRJkavag8WCJLLpR1bRSheEgQPyPduVAXu0CFWccXk1p/9TnO57EP0a5wfo7csv0m6pSX/4a68sZfUD88x5uH/V/nPevuIvfY/RCfbU2TQxz+LnFfcR/p9LM/4prNV4To5lv43DXP8PuH30PuWeLPxTaVqVulBK4NCru1ne2yYQwI7QT5GJDC8OLcJLEAUuuRIUq4/BQ53CF5I1pU0q9rKaWYT20h93zgzTZdnfM99Ar+/dGzvO/+v+nC/DF+t/+tPAd/9CzPwZ1v5t42jlJyOHQez8sl7Xz3U8s5tpMj3j21SvoaoJTbGfPDcnoxJaD54qxxYz8rxN7GyUxR+r3GFhSq4Nm4vKsEB1cR9yaVte9bWYj7/W+hFLs4Q8ni2NW0K/xYv5kHtHfzR3ie9r6ee/xHz8Rx159R4qod43npuJh/Lz/I+T6xmlIHMjwvp+s5v8V9xP2EWmIayYGBiYUFhOOmsOy9bQEwWbuDySCSICKIIIIpYVrysUVqeGkHKdhNK+m3Tz3LqLDvXHAJAGDhbzbippuZnDXvGw8DAA6/5zIAwOqvM2fhqa9S35792bsBAH238PqqL9MW8cznqG8v/9cHAAC9b2Mk5cqfjOAjN1AXXPAw7y1fRApbf5jcemsjuUp9WrHpUimNOIjvkAt5ji3ymdRnP5/yW0QqnxklhS07Ss524+p2AEDJ45sBAF+67FIAwOJfPsPrH6TXpvVL9wIAjvztlVj3GY7x5D9R4mn+DK8NfOgVXJtbaYt4+kvkmiv+9X7i/qeMpFxxO6WDj76N+M8X/qXLKjDzEKW4bWpZOCNXmMruCPecLZYSlr23+Bb7w/N6eE9apczGqAM/qhJyfif3f7NHCWjG0f0AgA8sIzetf542qbtXUP+eu5FzvOMaHqTah/ZhbB2jYFuU5v/9q8hp525hfMmEojMb93PdN84jflVdtLWkVCimdJz6+GFFvMaNE/r686nsk5e9z71ELECvxV9NcR6tn0BOdrAtOeJef5getPcL15rnuRb3nkPbSuPTlHh+eCXnXfv4LgBAZjVjXGauZ3Tk966chbmbaK/LLKR0VbeH6/zofM6jtkPxL0rtL1URoANKpoyHqf5qZ+j4cL3idG8bB1FY8j+Kg4ggggheNkwrQZybUrMbNar5yEJSrPWZVgDA6tn8a1b2YslK2g6efz05f1srJYKhy0n95iyiQnvkPZcDABrn8dmRdeSODecog+0tjCNoXkIPQNfidixcS6r8zDgpZ7yB1PDUCXozKpZRonjhRVJ5pJSSO6IqIyrH5mWtv1hpvzD50ud+ISm9OMb5BhXU3f7/9r48yLKzuu/c5b3X7/Xe090zPZt6RrNpGYFAW0mALKCEMKSMF4gLjItUHC9ll1Ox4wQTxy5CQgXiwi6ylZ0idiBlF5giJiAUhIR2DUhCIGkkzUgzo1k0PdMzvW9vu0v++P3O9/re7vuEUZUrFZ3fHwxP9917v+/e19853zm/8zufOIj93yMRxnI95x5fx333Yaz+z37orSIicuUV+2T5Lhzbzrmf/e2fwlz34tzVt1O89k2Y+/O/CI9s69WwStPXvSIiIntvQ5zj+7qvHA3l3BnseQcPc+5PY4+bMvbArmyScO6phl0Y1S7lmhav54GMsqBD6zdu4Z73lVm8q1uGYfXDIdzkd67G+B9sIa6wdxLe4tIZxKP+7Y2U/ZeDsotl/AvPMdd/A/bi97fw7oYmce3l87CwE2x58MJR3KvdR2n4c3gW6RY2U56NJfGURYpxxxqD0JJncgE2Sr9n7eSwKxvHF24e9OU0LfL4CMZT6sP+/59dgzk/3MZ/3zOJ3+3qKcaqbsB8jqTwnCb2Y/5rR+ElXHXzLrm/BeGg4b14zwtn8XnyWnhTP3yWWaVBjKt+EnP3RvEu5y/xd80Ym5ekEmvbQVfurb95nbtWeVoMwmAw/ITounw8U8IeP1mFd/Dl09hTbr0Pp/3nmxCLqD6xT56fggW79t/j3PNfQY1C+e5JERF55UM4vu+3sBrOfRt7977vYE829WsUq/0UVra1L6Bmo/r9SZm5E/nh/V/BXjb8KPZsw7NYndszWC23MDMSkHEYknvvOsD4WsWpq6pIWKAH8VQFFi5ehGfwpZcx94m7YVY/dyPqKmqPYX7P/QLGf+iTOH7xG89I6W8gInOa0f0Dv4K5zTzwhIiI9N+N2MSFX8VzvuYPMY/l//k4jj8Iq3PpDliUA3+F+Zf/yWkZuYy5tyg8so1pCp+sUZ9aDgndAN+J8/KRaFc91URYl8SZJwsvacNj/AbFWnrOYr//9BXgMpTOYwz/9QKeza4j+P7iQXBEgucx/98/Rov4VU9WPo7fTA85EjOs5zj4IDMLH0I9SukCrOkqazS2nGZG6iC+X61TTo7t/Xq8wMncp3ynnqfS7sz9qx7Ea0i/L7JDUEqv4VtLkfRMkdW4BzG10kV4RX92HuPZcQTfXTyA32ZwHJ7mx1/C8Su+imsv/UvEsPpPwNO4cGZKrvwuMw3bUCVdnsK5a2fwu9hyitb/EGuR1uDNRmw+1MOMlOfEjwLH/YmcDkhO9p48iHaQ5YDkYR6EwWAoRFcP4hpy2puUFPv4MXgSj38EVutgE6vq1C9slfd9FzyIb/8xVszrW9g/nfxd7L/f9ghWvWf/O6zNzib2qS/8Ea5523cwlPs+i3+viGFdj32sKnccwWp9zwexeoceLNe5qyjzdRwW7ym2oUtEWYVcUckFiErZWowgCd2Kmt+LHeba2dqOuX/yJewxH/kYotJXNTGfqV9CVPvOhzD/B/4M0fc3tcfl1L/Cd26+D7Gco1+GRdjdRHzmhU/hnJvvhwV56HMYw2R0NY7/Y1jk2x/HPvfeX8L8/XhcTl6Hue8/gb3wD/pYWUCp/7hCS8GId1QiezRVT4Ly536WZSgi0qsVgDVc69ZZ3Ov01bCsY/RO5q7BvP71c3g2938A89uV4JlcvB3xkZ95Du/rwV9ekG3LOHb8Dvw+7noG973nztO4d4zvXrgG97huGuN+7uAcxw+vcBmPVmpLmN9SOXZitWotU+pBBKqJkNODKJJ+71WuSA9jEJdEzhzC70G5Qcuc+x8cwzy++178LvaR/3P+dvzu3/8s3t13Pgbrv7OBjM/J9+J3/57HK3L3++EhD6f47rm3YjzXn8I1nj6IcxOK1C6zBmPgEuY+N6CSc6r1UZKUrR5Ctq5IXbsDbd5ssvcGg+F1onsMYgnrR+0yVrDf34tl2/se9kpfePNNIiKy7dtPySf+EXgQ2/8csYMLH8OxK6kg9eFPYT86/tmHRURk7VdRubjzP+Lax/4IK+/BLz4tIiIr70ZsYu83RT7zD7GSb38E1rnGNn+D5Gc8v5tamUuMzLZ0T8b9V9iRRhfp1M23gsRFuPN6ED9axjVr0xj/P92PcfoPI3MyfSt4HONfQzzhN3/jBhER2fOnUH06+2vvlAN/gvE99cfYb277Q3BEVn7nnSIisvsz1Dz4U8z9wOcx97WfR+zhyq/Bun/yoxjjju8i8t27r1+GuHd/fie1MqiF4bkNtYrWksfvZO/ZBp68iM14IIvkAZTYTu4RNihuT+MZHCUPovoyLN/vXodrDh0Bt2GW3s3447CqX7wTlnzovlPSvhH76+EjiC186V2wxLufxXySHbCsfWeQBaCsp/RfgkWsULS21MKElyvwKEIvEl+bMyvHg5oIHR4Efge6Lw8LuACrGs+JMf8jI740Z+AdvZjAw+l5GXGYB9+Ck0d+AO/u7msROxk/gkzD/3gX5j7wADgirbfAexx7DLyJL9w5KLt+iLnHu+GNDFPn5ImD+L0OXGT2ipW6pQbnPqBz17gT362XSkgNFKcHQe5P5Od4ENZ6z2Aw/KR4DU1KrMC9VUTPP8gWYCenkbveMYrVv3poWa7ajlX5masnRURkbIJSR7dgpdw3gbXo2NuuEhGRoV04HrGd3MRu1l4cwL/BJOIO7R0nZOcO1ibsYlv5YXgyKwEsWoU6gedhlKRNtp1H5R1uGzuKQ64NmUiLysdBbq08QNWk2gDm/JFRWMOXZ8BR2D4CTkDteli6qydx8eeuQ3Xntu1jEt0OpuQeKl6duPN6zH07LEXrHZf5XVjVZ2+E9fEmMd9oEs9u1yQs9g8msSetjw/L3Dl6U/2Y69klWM0W6yTCJlWg1ItiiELb05WcmrU2GeqY0WFV/Q5hmbaXMffFOp5JDy1X7yh+Fwfovhyn5HuF0u61SVjVfRMYw8s7R6W8FcdkGzge42OY0/P98AobW3GN8jR4ED181xeWyQGoMb7A+aXMOESNRBiYl1BFqRNMuuz0IKir4KTfN5e97+OfRchszmgwL8tU0a5W4YXUxlhFO4Df2LnaAOeO8VZ34v0c2gLv8PkR/J57RlngMn5aRER2jmyRZ/vZ3GgMz8a/iGODI7jW6QWOt4ce0CrbKTJj1WBjY21AHUR0q1GeAAAgAElEQVSJRGnWS3IcEKcHoZoor0P2/kIM9zpdw0JwZAp/LKNn8Udx7yxSmcHJMTl2GlTZtzzBjkk34ccfHMWP/fSLoAnf+DW8+LPXIvXV/yxcttPHQF192+M479whXKfvwqjMvYxz9z+PB1MPkFLsW2P3ZXZ8GqC/lHrsEK3dvRmIbOVSPknaSXXFOenz8xEDQ5T8f+gi5ryVBJhL1yDNGRzDQnf0JH7wNzyEbcDZt89K8CQWkxMvwb285UsQ5D3/JvzhVI8gYHX6PUgLvuN+zGtqP47XzuDZzR/DInrwWcyzUZqSfnYEb6xirkP860hi9gJhwZLyzrVvoz4TRbBJd+8Vj9LuLSwEP+IfY3UN/66tYCsh7PVwzzSeya4LWDBmL2ML6l/AD376FL6/58VZmZtgwdk0hW9fxfO68jLe7eIwU6Qr+EOKZ/AeBpjSi32622Q5JUzTBr4v+gpj0f+WWwi0L6Wb++bvfo1tvDy2TZhtJdJDGv/qCgsGWQp/3yXMZwf7fMxfQprTvwQj8MXzeJeTJxBkXdgNK1aewu9q8fxp2cPnNrcVdOzyEo61Se8eWuTcAwrFcD+Yxhpgz3ZCS72g093b76Q+8Ww05cuFwrYYBoPhJ0VXD2IrCUbRIFbPO9j96Ax7Zd7gwXKsvnWHfOIIS1PfA9f7fSx0mXkn1qD3PoZrPvzLOD4UIzBz+R/Aff6px/G9u+/E8duaWGlnb4jk+lNY/Z46zOATy1vn2OF6y0WswNNl7iEosyZa3s0uz0GQ7XAdSEki2by79zhdtGQYx99DeuupGzD3W1OMb/VWbAs+/QC+f//PwXJ8ZG1Npj8Ai/W+e2GJH/h1WMXhGFZn+kNwGX/qPszjf/80+zauwXJN34Z7vvU43O/vvWWB809lbgLXHJ+BqzvFuXsqMacdn2O4oV5Ir0kJU4mWAW9M8/bGpO5W8A63L+Ccla0Y7xZhj9ZdOPeOk9hivEIp/t0UzG1eiet8mCKsL1xflXIKS7p8CB7ElafhFR3bifEO0+JFWymGQuu5wEBpym1P0oQFD9jhOgqDTkEaPQWlWoda3u1KnjWYt3nJcy1h2TS3L2NLIo0RzH3Ix28tHsPv9KbzuP+5PbjGLgol1yfx78+/hHscfwtJfD7eYetq/I72n27K0Svx2x9jI5zWGL3CBdx/ZpjvyMfnVhnvvsQtdIsKea67t5Q6pe5pkeS/pTkNBsPrRNflY3GVVqgNz+HxYZIs5hE/OFHHKlqaOy5PbIF17D0Hz+CHGtCagpdx/zaswP0nsIeLZmFBalNIi943ge8PXsT5EXsdVpaX5WUSdvwlrJjlBd1z4fMaySDaOcVjyzQlzmin49gFZrACt7xUSo5+m5v7CjflESzGg2O0SrPYM79Yx3wrlxAfeHAHglF93G8/Xh+S3pOwjn+7D9ai73nEINqXEbcYOv2oiIh8nf0pR87i3GQB4y3PwdoeHcJYSnMsVvM8CRkjOUlroit9wLknbu6a2iOJyKW3tOR54/yb2gGchKFLvZRQJ+lK+1L6LXhCjw5zDHw/Qvk7r4kxfp0dvYPFVfEWGACMEIu6wDSl36R0e0P50vpuGVthb1FpkD5P6x4kfLd+LIGKoTiZtWpm7hp/aeVTfbk0Z4v/XQvbFmoVabG7t7fAjtsx3s1z7EaeNnB8hil2vwUP4jt9fFbsXh4t8l1GSJNOhaEEa2xuxNS6J/jNrZIqHsdsp9hSAiCLD0lYi/0O8Q+f003mnpX8N9l7g8HwutHVg6gwBeaRbNEbYr/VbGAVLbPLtl+qy5t6GHPgPt+rIUVTqiHy+rZB/PfpVYq3DLLhyRxWy34EbuUyG8FG/VgB/bVlqfbhPotwRiRhGlMa2I+mtAjazNRl7FLtaM20ZqKWUYVlgkL57wpX74BpwV428404d7+KMZRruPatvbjmxTmObaBPyoOwMndx/3p2Fp6Ez+Yw4UU8m4FhxBheXaSXMsTu2Ss4v7eKZzFPJfu4pyzJCq01LW3MeFHia8mzlrZruks7VtOz0GIdznf9/APXno+W2MN7L7XYzo+EHSUSVeh/tFVan73r/Ajvp4exoGbLk5gW1VthbIT513abkXq2+/ObtJJ8P2WaupQW0GO1mUtlii+xzs1TqXfGIPS9M65UcsIxm3e4DrQ2nr//NFmTmoq8UrQl5H09ZsyEneMTelsBve6AuXWGgiRmuXqwuqo3k7jNeBev7bErfcLnX9YUdajl3CTBaad2TeNqatPzJE6y8RcnOZdkvSyTvTcYDD8xunoQTbUvXHVWmTQNdLVvISIbNypyivn4vgatyTL306tYUV+cwX68toRrtnqxlw/WYB3XVmClBtlmvVmD9QzrNfF4bd2fqkXTWIOUmet3UWmu/E4QhJFbR7nt7MPDgvZrrVTNNc5dbnGfRysZc/8drcLav7jEpj51zn/+nMTz8Bi+P4VMw/AMrMbaGDgTwQo8i/oMXKMRtrpvVLF3L/HZJL0wP+TFiHjLEpBWK6Hm8LOt91JaBrUAruR5g+Qcy73XSb8rJyLVsvg0G5OQRHkS8OyWezR7QA+iSevI44uaKYoSSZsk+XA/rawmLc2Wtvo0HH+qHBaVdNd9Oh8FvYWWl6yTtVep/6zMmjue24fn333sZa2q5/nS4Dh8eg5RrOw7/vb0x0YKuLDtQErv29M+Aywc9No8Hibi8S15lA3U5+5zzjofz/Um0DYOnps75teJqejc85JzTm4v1zyoCOZBGAyGQnT1IErKvuNerLTGcuqaimMyJ91XkitWYDWWh2nZSoz6UrJteJ7yWGzj5jMKHY/Ciu6bxco6O4h79XO/JX0tKdMqUyPExRxStj0PIl7L9zLHtSjLSY8lKn+u+7FSp5FIbi9WZuzB49z7V7kn7mVzHlrFmMzZfYylLI1p3KYtQjm0HTMYz9Qu3L8Uw8rUt8OKjM3g2tNs6z6kz5X8k55lPMs6667SMJWEe96gje9GGu3XUnZlzsW6R1duQHYvGivVet38Q9cWQNmZ3M+W9efCfDtp3UGdoraMy+h78bTtAGNFzVpJAhbMuVgDz40plZfSIvs06zE5LOWQJdvK49D5pUqfDp3Mmub+ozwXQGXYXAxicy5AQLvqaewiaUi5RIvPjIGnsRDyHrySenR0FbQ4rs75kiKuMRdWoIvfbEtMzsq6zj/4h8+9FOrfG+fuZP2b2flpvCkJJUnb7v9n5q7PxngQBoPh9aLr8hHHyl1nhLZHGWhcyVa4qsdNuUQuQtym5zBPi8Aoe53Wpc1V0VtUPjms6ZpaiJj5cB6P01hK5NRH3P8FbW0uS08iZa6fLELNB3ekxnhceelqQfykMB8cRbpqczVnvrul7fvYci1NsKc+z3x+wvn7cyJJgrjLcg/HS0sqdV47JhOVXAY39yXNvuBzk96Bm3/Dc6zBUHSvmZe112atmLtKi3XKfovz4VGqzWvVu9L3ruX0nI9aITZLVqai31DWJj3OcsdqRS3lGHC8fO+xeqtt3VdzfH5Wul6l9DrHOzwPn+XcLubgZ6XfXbm3PpuCdx+Lmne1slX3N+AzdqCfwyDLM/Gb9Lr0/dCziF3RnHpCvHZQlpieQdDO1oZ05q7vtszP+l5Yzs44RxB3fvdhmp1rp9Rds1gme28wGF4nunoQmg/mNtXtByuaE9aCQUmlTevRwyh/pMIejErrClojIy7W/Ssjt1EAS6zlFBGj86V24vbNPVrBF+heS6O83KfGugJnc/0b9t2i5wfrWpDlc+FxZu4JdeQr3Jen3DeW2szHpzgeso6hHZalJ9GwMyPdnHvao9ZQQ9usbSCPI6bVCVqa78b3eqKOwGyJc1erXc5lI7Ss13EBXNkvy73V09iEB+Leuzb25TUqrmxaPTPek8ddPp7XLOnzZ06+HHkuQ0Ij6Jrvuu8WjL8UZ99dSToepgjepcYjXO0F2wSEicZfmu67IuvbDubeveM4dObvuBN6f2ZdIo2RKPeC11Ax5ITXUtamcnCcWHDSllKkIsrZuUep/q712uoV6nPtcEAwzo60fZSoh5mN1eizcRyQ1HgQBoPhJ0T3GAT3/spI1Pp6zbtqbjf2Ai2clJbmymkdG762gsse15xvW1SwJGv9xTEBfdfGveF45dmNk0p8uz2yi9TquHP5cIaQIy+VUFfy/D5U56459Zy2gBep9+JnxtTQMUWRNDS/LRpTYOS7lc0S+HpcmauReg70smidmqpnkXr6lJ0IaWfu2VZ6vpcddyD6LLLy5+vnrzwAxzXg82sWMA99Pv9WLoOi1wwYQ2l6iXtObq5+/tzs+wgYY3DHRWMAOj8V3407sSXnFeViEpLjAmwydxGRxFehV41l9HS4Bjo+fjfIcS18ukhOvEV/N/q7z9lk3+94seqx6bWVy9B5NvSElBOiv3s/K/7S9jrvJN+kd8PcTQ/CYDD8pOgeg3DMOd3vcvXhupIwThBEiSTc8wTKX6eR8WJVNCJvXDSOoFF1zTUzkssVTjkNYNjr3kzP5aoYqTXSSG02RuE+675R97kaYU48iXy18gVzV+YkP7u9r5u7Muk4Rn/d/OlFcWsoIY8lKsevXlKkLeHwPRpC8XmicvL1ZSVBIr7bt/K5KWchP3df607UQ+vMXaSTzfDWPYHAuYa8nzZ81Wu4jAKP6/vhNWONqie546nn3p3Hd+eeq75bl3lSpSuVyOOQNKKvtSVe233uNCjWcefGpdmCnPXMv3vfTVyv03b7fjc+94jS7Hg2jF/jBvy+/u7dbzfqvDvNOMTZczvPnZ7CJnPPj80J97q552IfBXPPwzwIg8FQiK4ehLZt02XE7ZV13VHr6nku4uuOKRvMMfXUEmfPjbThqEZbc+fHnrfxvknWy9A9vrICvTR7b8kdd599z5HXOsf0FpvPPT9+d89g4/FU4yu8hu4tPWY+Ijd+jeBL9tnotT31wjrXjjecm527G1fB3PXem80/oQuTOiuTm1u6+fOPpPvzj8RzVZhJ0bmy+e8myr3b/L3X917Waxe996TL3EVEknTj/JP83HPvJpbNn7daYPduN/x2na9a+Fxf67lveNe+l/n/mWfxGnPPwzwIg8FQCE/3zgXoetBgMPx/g01dia5bjE/8u3/BM7lO5GTLVAjVi0W8EkUv6B4L0zspyU0hS4Jj0knV7U5bdI8qFMlgwCXl+V47lTTMFs+kTHt6KqrrUlC4ppJxNBmnYiqBI7NoQNWXiL6pplI//Qef4dz/Of47rxExYKjEE3Vp9akmeg9fyUO4vois64ep48+dq+NJs1sNDfi6HgduO5G6Y25uvl/wWecaZK/l6OvC+QeZ+RsMIrbFMBgMXdA9SOlISxoEUboroJbdb5UlJeElJQHKrzEkR5HRhgpsqMnq57lNFippQI+pQY/CnGmrJH6i3Zm1lFbLXrWEVoU/lEwjHGc2ANohjXQscajkFKfowbnx30iFQvLdmJwcmQarmDrToh6vQ3318/fnd3U6nhKLNH3o7i2bnu+Jt04yLH9f/Zwdd0c4JMh8P8xd22BYD/MgDAZDIbp6EKGm17hZLlHKu13jZxZkxeWy9LVQVLPUh3N6fFJie/Hv8DKs+/QIvjfErtNr/Tg+uIBip8uDsGT9pNDWqxXpocBnvaZkK0BFUiRCyXjqGuMoJVsLZFikI/kmIqUNVFSF30lK4toqAhooA0x9DJZGJ9qcRyX31xHKqFgaBOrxaCGYlozr+LSMWsk2jFlQ4i1w8v6Jk9FzBUhOFEU/q7eXFQ5xxU+SK91/DeEQwxsT5kEYDIZCdDUbrVSFT2ktSyh+aXqwQmsUbqk0W3KZgikpvYwVlkP3UKrtfFUb7eJaVHiXoUVY3qleXDuJILKifWsGGm1ZotRZmxH4BithKqRnewGFM1SSyxF9lJarx5UC3ClcKRYN4QA1Q8LmO5FShTUQ4wrCoIenwi1e6pi6UuK5LcdN1mIonuvp+OL1t3QVbmGSHb+flBzdWRukuLmlWpC0uUiKfm6/hmiKwSBiHoTBYOiCrh6E7vWVS1UJWTjDmuZSL/e95Vi29GOtma/DFFUrsFQNehA7JvDdqTn8O9ALi1tfobArxV5fmYd17avi+GK9KaMj+G9ziywsonydKox7AXkS2rjFWWLSS7lPzwtvlFLfSZ5rmbQiLxij4iOVSOXqmHXRohwVEHWKKL5zIVTcV+/vCpa0oIrNV8pJlm6u7dwj0WtrjKXdKQZSQRKNdzhRERUK4bmavXACLZ3SYMyju3CI4Y0J8yAMBkMhuscgXFkvvrbIyLgTS6ErEcz2yau9WGtqsxT42IushN9CC74Xl5mtQL8cOflmZCaqy1tERORpxhPGLqHZzPlDbF3W7JdzbMZTXWHbsy04N6SwrEqmB9rkNDePjmhKjhsgaaFwxkaxHIy/6b7BDA/38GnAvT/JDamXiKdFP46ZoZkPfa5aEEbREZ6rXkukTWL8rMiLJ77z6ryckM2G5jGSEzRxAjN/N+EQwxsT9rMwGAyF6OpBVHX/TU9ieBlWZ2UU+/M+yrW3Ryty+CK+fHoXrP0WyoGvjCMC/7PT8BSeOHxWRES2JuMiInLpCvz3D5yDV/C9vciQjEbwPC5v7Ze9C7DbJ8bRkq7Mlu/sTCflBryNup+10MozSFS81PEglHEZOlGOPA8gIA/CxQlU9JMy7Z5rcsITInAVtAGrH6SuNNlvYk6RNr3lsqxlKyGb38Ra2+LKu9WLoafmKccicXUpjgeh/AspaJiic881UCmav8EgYh6EwWDogq5mo5FoySX+WehTLgOs0Dy/17ewKE+MogddZQk8hnMJWtwPX0Sq4a+3cT9+Dmcd3Yprj59FY9u/3IXYw9AsGgKf7IfnMXIhkke28kbMWvQEuH+VlrlFYdM0YEv51ImziYhIkCIj4hqQxJ0mrkU8gI5oiGYFclyLWCsztQEK7hFTBiyOOnwGr0yOhEYweLMgUcl/jN9JimkDXuVBuPF3GgM5WbeY13bCptmmQcqhiHLtCDvNY368BiqGNybMgzAYDIV4jea9BD2IwX5YnfoyThuoshJz1Ze3j/eJiMgLM/AgrhsZERGRpSVYvV+8elhERJ46i/301nHEIFan4BZccy1iEQ+9uMjjYyIiMjNTl317cJ/jFzVSj3VtpYHveqzfWNWWdV6u8Qt5Bp3mJZ0GJEXNU3zGKZzKnUcuQ4trKpmiKTMPQch6CxWKCEtaniGVChvU8mba1DZuaOWqejYqzsCxuGJabR6Dz6nXaRqUSI5DwfFoYxfXOMfVYmSbCP24DVQMb0yYB2EwGArR1YPQfWzK1mVn64zQr2FduTCO7IGsDMuTTViggbPY876wA7EE7xI8iW9egOUafwLXaH5oWkREes7Dk/jLWViyXc9zvz1xUUREyvNb5eF57OuHTiJtUbnqEo414bWkkbYcozXM61hocxNXj6CaCZ02Zm1XnQnEKoevcQRmRJpOyJTVnm1tbsJb0gsIypEkbKBa16pMTEPCaovnMvPgQiaMMYQxv68ZEbIkXTOcdJ2iaq55StrheOC72qxXYxCbN49pm6kwbAL7WRgMhkJ09SDYtV6SMszjgXl4B1N74R1M1BBXWNizXd55Btb93ttw7NrSNhEROftmeAifOjkqIiL3/OyPRETkkOwUEZGX371dRETuPI57feUOeA77gj0iInLq8JDcMo39/aPXviQiIn0xMiYzIxhg/8qciIhcVsvrzD4zETE4CiXlRWjz3jTsVD1u4EG4ck38L6tUSxVY6LCs2pRYY6tke6704bxyELoHOFjHPWYGMY8asxatAXgQvWvwxBZ7qHeh8QN20vHJsWiH+ByKSEJ2aBqtcpzkObhGLVrXkeNFpFleRNH8DQYR8yAMBkMXdDUbK21YoVILVuY4qA2yNgeLdiyAJZ94+bz8h4MgK9ReAs/h4StgJQ88Ccv667eQzfjoGRER+atbwUg8cC94EF9+F641/BzYkg/uwT13vbAqjx9mi/szsH79g6wIXca5M7TUZbIEtXYkVa0GT7kA2uS0U69Q1MA11sCAWuSQmg7Uw2xFZD2yEW+Lx6MYWZk1SaWnDuu9WuE9Grj/SgnXrK3gOa6RkZqk+Nyk81Jigx2P6lsRORZR6jm2pepQxEFubq6Nm+pBpLnjjEGYHoShC7ouEIMq285vHSS56dXL+MHeVgUduj3syW9fgzTlo89ja7F/8pCIiFwkEerf3Iotw6N9+CM4sHWHiIic3w0X+c3XYwtyX4Ktx8Tu3bjXyxfl2oMTIiLyBIlG1RB/8BdmSFrC2iIzs2lmvFpU5ujIscrQ84/eX5/mzDpTIXOM2jfE48JQbTMlWcUfnvCPuLcXf7yNBmX+q70S+fhvWwaxACyuYByVHjy/xgqODwzjnLll/FuhuM5qC/eqVXAPrjfi+SIR45yp5Oam/VO1HFxLxXNpTv3sCtgszWnYBLbFMBgMhejqQSyUKZqyCot99zIs4cCrSC8+cTW2Ez1nd8ifrOJSh+5B4HLmxldERKR89AoREfmtt8OSXf3nuMbC50/jWj/YLyIiJ38a97z5f+Fe8793QkRE+qZ2y38hXXv/E1TLvX1KRERqLbgObbrmA9wW+LEyiji9QLcUKgLTSQW6ZjU5F7vNNKd2Uo5Uqj7OisF6bXgDl2iBq6u89mBdPKZhX6HITI1B3mSYIrR17NlOkvQ0uMhu3hPYjvkRtg/zbIZTbuHaQW/bSeWnWuClzXmYrdVel1qE1VIqtqZ8JZvmtHJvw2awn4XBYChEVw9iiMol7RrWkY9chEU8eiO8gbvY0ObCjdvlc0cQg/jWbyDNebCFoOW5nwNl+v2PI+Zw/+/h+J4VHD/+UaRB73wQlvBvP4Zy8O3LtK5vr8k7X8J9v3PrrIh0RGgujGGvvm0GHsY5BurEz6YoU8rOeyqL72Tvw0LZ+5JSrYlKQ58FU6ss9474eQ/jH5fH8b1aWpL6KMZ382VY7RM74RlUWUBVH0ccY8dlBBROj/HeFJZt9eOZ9DP4sKLNiBKRiPcPYlxT060paeZO9j4nc69l6yUnHGPl3oZimAdhMBgK0dVsLJJX7LewZ/7GKCx18irITEcEmYbxo0flqzdNiojItgeOiojIX7x1r4iI7P3mjIiIfOaDIANtvecHIiKy9rYDIiKy55unRETk0+9D9mL/Yzh/5WpkLnY+k8rXD2OYo6cQ86gNwHPpX4ZHcayCzyV6PFrynDpqMuIErpxa6cZd0pyRRvWVQk3J/5aHZ1FvwiKHTczrVB/uka7h+Fy5IuUlHHt0kGSrRZKaKogtVBYw/ukBnFtaYVEXCVJlFnMtk6gWai1X2xdtgJhS9j4OlBCVpVaHwhSvFmVpTEKFexMr9zYUwzwIg8FQiK4exEBIkdomrM3tE7A2L16EVbpzBPEDf8ua3PIm8BgejHeJiMihgwdFRGT+NGION10PfsSR+CoREdl5CB7Gyo/Oi4jIvpvhMTxUnxQRkeF9+Dx78lWZPIz7PN3EfjslxXp5ER6NP4rPs5eYBaAkvUq+qVBLGGnRE62rV8yDCJxmPb5QClWElw2BWOouDVjukX7ca5bZnGp/v8RrGO+OcTy383MsNhtkkdkyvI2RMfz384ylBH0UgaF3UhnA2NZWKX9XFZE1cjxCeB2BSv5rdkJL3V1bP1LDVQbfy5V7WwzCsAnMgzAYDIXoajaWKJKSMB//14y295+FxXtyP2jT4dkJ+fQZlGDv+T+wVJf2oSir9iQYlMdvAHfhyr+AZV761BFc6wXwIC6chR7+oXth+VoTiEX0nt0hiyewV584Rirh9SgVH6gjmxE1KEGXW+88xhc8lbnXkmfRQqZ0Xbl3du6xp14I/q0zYxPTUtd9xBPSGN7ARbYUDJYoszOwKEkdMnovzcNKVy6wofEQisvCNXBGWmwuVFuAV+L3gH3qN+GppauMpdQ5n1okAUVn1UtKScP2neg/GaBebu4F5d5RrtzdYBAxD8JgMHRBVw+ij7Q8Dw6E3H4ZkfzTh7G3Hk9hEZdvGJPPHoMwzLc+DGt/qIkYwrm7YCXf/zR4D4/+JjyN8RZiFS+9F/yJux6DZfvmz4AHMZggZvHqjVW54QQ8lu9dh+yJkCewMAarODSDvfxsFZbaSblT6Cah9ptmN7Tcez0PYkO5t+NBYA0tteBNpb200Nq4hk17xqC0J/PbtIGwL+kQ7r+H7QTP7GL7viae29oY5rHzEsZ/bkxbCJLDMMhmRIxlrFZxPE2Sdd11cG6qojLalMe13lPJOdxTWwAoL8J4EIZuMA/CYDAUonsMoonDASP19w3BqjbPwwv4foL4Qt9zx+TLN4LXMPEgPIC/uRHnjj+ASPznPwhLNvwNiL6033EVj+Pzf/oA3JS9T57D8T3wLEZOzstjV5JROAXr19MLb6O8BrO9RDZjmVbTi7T+QAVcWS4dZFsJRn4qpbggBiHKpWA2o8Ry7wTz0JoMifH5lTK7+KzAg2iHFQlXML7pfsQivHl89mtgiaZLiEVMDWM+NVZ3SsCaC3otyz2Mh3CsYRqIBOqp4L7JOkl8zE09o2rms0/vq+1k763c21AM8yAMBkMhunoQ/YyAl8jSO1QGp2FlFXGFWi+sUW1iTn6FArYvjcLy948hw+DvgwDM4WF4H89MIjbRswXX8K58WUREtm1DIcJzQ7C2zTHwKvxzZ2R0DFWbJxcx3AYFYuLlKDOLRkNjEPhcZgYicnoQKnuP75XSwHkOfprjQbBew9MahxQchRqFZNMexiZi1j6U4d1E1PFNaiUpUQp/qIJjS014CimfW7gK2f4yW/JRP0ZaNTJDW8yUKGeBVZ1JKRWPWhEqgsPCW4nXSfqLbORBxNpCMMegND0Iw2YwD8JgMBSiqwexxoYzXgyLdnGVqkprsEprK6+KiEg60ytfZVxiLxWk5vaAx5BOwaP4bydRczH5HLIc8xOo0e9/9xwAAAhtSURBVCifowDtGeg/7H8VlnpxHOeHy8PSngbvYWQB9489fCdsKzuQ+21PeQ+s4uQ8fB/j14h9wOxLex0PIq+HEPt8NNqAV3BOk/dKIjyDpraua1JwljyJdrTsJPEvrsFq99Tp4awi9pA21BMij4MhiKhF1mZETkWojYTx0Ys9EY230Etq5xiRkc5d9R9y2ZoNPAgzFYZNYD8Lg8FQiK4eRJVcAK8EezSxCO9gbRxR+K0eovLJzl559yuotDx1AFHzK1qw8tF+mMU3n8I1XrwBVrHkI56xegiximtegtX84T6sWdvYgKY1sSpbLuP/X9pCu8jxRJSgL9exeW9oxzrXyJbS7tyHlwJyAdiuLkxLHc7EBh4EOQn0MBLqLoQl1mCoohQbB9eaiBdEPbhnJU0kqWKcI3XMrT1ApmRIGfs+xFt6VnDuWg3eQJn2n313xGuzBqVEjyOIJIjJ8aCkvxdulPQXWacHwWeRUNg3TLOy98aDMGwG8yAMBkMhurfeI59AG9TO9rPNXBvR93QWVshvz8g09RC8NRwTtuKTBuIHZ6uw3t4C4wdLvHUb2pVnmZko1SnXzMpFL1mRZbWObeb+ee00gGcQpZSN95UbQB6ESrunqomgLMOOFHwRDyAm01B5EGGAazjmoVpweisJPYuYn6Om55oIRwGPae+9FeVY0LMo6XNmrIGxiUS5Cr5em81+o7K0Aq1IVUl85TloK76cHoTTwtC5Kw/C9CAMxei6QDi6sUb7+IOuatl0Gf8GfltKPtzgmD004ip+mGEb55RClirjb1paNfxQy4vYpqQUiW2zz0Rc5oLSiN0foXa3TgIKw3Ab0tYthJY4qxiKdO9wXUrCdbLvuS1GolsIpkoTlWrTVCkQkvKcJOzepfLzXiA+F9aUW4aKPjeOXyiuG3Hh0A7d2iCcf8OuY3fI/G0iSWduHFennFtTuPnjOvdm5ll0unvbFsOwEbbFMBgMhehqNmIlCWmnaC2bZupP3YF2O5S6c1H5fxqsXmKqLmmSCkxfNuFWpB4x6BepICvPbzfdEH3aa+1Y7WmqkVbP41ai5dKYuGfsSp411ceSbOl87nS3zpY7J352i+FrF20NgKq3wjSjT1JZ0xGVUkm4/vr0Mpr6HJ1jxvFzXO64pmnVk/CyYwySoDM3P+C4NI1ZzjwrHfdrd/e2cm/DRpgHYTAYCtE9BsFUnk8rkyhtN2S6kCIlEvriN2nxGWxM2WlbRVu04CshrVhI39Y4Rsjzo7I2hNFgoCcxC6L8IJvKU6udsICqpMc5Li1cipnaK6tnwRhE2KXc22fcQtOcroWdC16qvHy2i3Z5XXCzxABgrOPjc0u1gQ89spRxgo7YLoORaZYere3yEondfaO8rH0ujdmJv6jsvXX3Nvz4MA/CYDAUonsMQq2Qlg57lH7Xz231JGJJKQsfi6YiNVVH4k6F1px7X6+lFo9pwjCbJgzanVRl6FJz9ByYYtQO1VrS3NZ75VN9adVda/3xyE8K03yJp9Y+Wzbt9vqu2EnTidkxBEkobS6/G0quOf7YdejOlmAHae7aXm7+SXkdbXzzcm7nGfjZ44GlOQ1/B5gHYTAYCtE9BpHjAmhOvexEX9V6BhKRB6DHolxjFhcn0Py9RtFT5RHotbMiqqXU73AQ4tx9E831t7Lj0j27pzGA3LhT9TSCdbL32XJnn9fUcm8XR8jNvaRxAsradeafSuh4GAXjT/PjzzYZ7tyLMnHq+UhzHZWasRH1aNI874EUcY1fFJV75+ZvMIiYB2EwGLqgqwfhuAC6D/dzreSlwzfwSQfOH3P5eJZcd47TOqoFy1877XgSPiXYHM9hw7XJa3D78GxJc2fcG0uei1rvJX4uBuErf0Dp2tl7uDG44/66+2e5Cv6G8TMG4ViN5ez40/zxysb7Fs1dsjEJv6jc22IQhk1gHoTBYChEVw/CFy1+YFs3T0uJyYtwWQVPYk/3towpBPQE9BJ6rnIqWGykxzUmUWL9wfrzE3eMsZAN16a11FoFFXDVcWotB+MdsXIYEt9lQjzJmtDO3HPj17iBZiv0ns7z6Xx2/1+9EP284dz25seT7HEXc/Fj95xd1ifOn8txu8wNv+9HmWupUIw5EIbNYB6EwWAoRPcYhDL7nJXRBrHaPIaffa9zzMsei7WR7o95PPaKr637ZO0SV3jt3LU2XFuv53sS6LW8rA3VuXeWUF5jk7mvP56sa32Xv3/+uW2Yu2SbDncbf5y/b75cPX9tf+PcRaRw/gaDiHkQBoOhC7xUKzM3h5X4GQxvDGzqQpoHYTAYCmELhMFgKIQtEAaDoRC2QBgMhkLYAmEwGAphC4TBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhELZAGAyGQtgCYTAYCmELhMFgKIQtEAaDoRC2QBgMhkLYAmEwGAphC4TBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhELZAGAyGQtgCYTAYCmELhMFgKIQtEAaDoRC2QBgMhkLYAmEwGAphC4TBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhELZAGAyGQtgCYTAYCmELhMFgKIQtEAaDoRC2QBgMhkLYAmEwGAphC4TBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhELZAGAyGQtgCYTAYCmELhMFgKIQtEAaDoRC2QBgMhkLYAmEwGAphC4TBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhELZAGAyGQtgCYTAYCmELhMFgKET4Gse9v5dRGAyG/ydhHoTBYCiELRAGg6EQtkAYDIZC2AJhMBgKYQuEwWAohC0QBoOhEP8XCp44Pfdoz34AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P06exDb95BAb"
      },
      "source": [
        "    # Code to save params\n",
        "    #np.savez('disc_params.npz', *[p.data for p in disc.parameters()])\n",
        "    #np.savez('gen_params.npz', *[p.data for p in gen_parameters()])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}