Image Segmentation by Unsupervised Learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K29I-OwCEYzW",
        "outputId": "db8b297c-a4b8-457a-b43f-7570135306c7"
      },
      "source": [
        "# Image Segmentation by Unsupervised Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBYA3uEoWLV-"
      },
      "source": [
        "## import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "n6A0mthsWLV_"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "from torch.utils.data import Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets, transforms\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive/Colab Notebooks/MachineLearning2022-2/MachineLearning2022-2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYiw5uJyWOk4",
        "outputId": "a50c5cc2-29e5-4e0a-af8d-3072d9938b6b"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/MyDrive/Colab Notebooks/MachineLearning2022-2/MachineLearning2022-2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ewVmC8-WLWC"
      },
      "source": [
        "## load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "a1FXdUp8WLWC"
      },
      "outputs": [],
      "source": [
        "directory_data  = './'\n",
        "filename_data   = 'assignment_10_data.npz'\n",
        "data            = np.load(os.path.join(directory_data, filename_data))\n",
        "\n",
        "image_train     = data['image_train']\n",
        "mask_train      = data['mask_train']\n",
        "\n",
        "image_test      = data['image_test']\n",
        "mask_test       = data['mask_test']\n",
        "\n",
        "num_data_train  = image_train.shape[0]\n",
        "num_data_test   = image_test.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuzunkC1WLWD",
        "outputId": "f9fd1fe6-96c9-427e-c3af-efab5f32f03f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*************************************************\n",
            "size of x_train : (1000, 32, 32)\n",
            "size of y_train : (1000, 32, 32)\n",
            "*************************************************\n",
            "size of x_test : (1000, 32, 32)\n",
            "size of y_test : (1000, 32, 32)\n",
            "*************************************************\n"
          ]
        }
      ],
      "source": [
        "print('*************************************************')\n",
        "print('size of x_train :', image_train.shape)\n",
        "print('size of y_train :', mask_train.shape)\n",
        "print('*************************************************')\n",
        "print('size of x_test :', image_test.shape)\n",
        "print('size of y_test :', mask_test.shape)\n",
        "print('*************************************************')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgGdUNL4WLWE"
      },
      "source": [
        "## plot data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "HKK_LXGjWLWF"
      },
      "outputs": [],
      "source": [
        "def plot_image(title, image, mask):\n",
        "    \n",
        "    nRow = 2\n",
        "    nCol = 4\n",
        "    size = 3\n",
        "    \n",
        "    fig, axes = plt.subplots(nRow, nCol, figsize=(size * nCol, size * nRow))\n",
        "    fig.suptitle(title, fontsize=16)\n",
        "    \n",
        "    for c in range(nCol):\n",
        "        axes[0, c].imshow(image[c], cmap='gray')\n",
        "        axes[1, c].imshow(mask[c], cmap='gray', vmin=0, vmax=1)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "qmagBnErWLWG",
        "outputId": "00d13a2c-e998-4ecf-c9b0-9623f66b25fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_image('training data', image_train, mask_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "6PJ2tFzYWLWG",
        "outputId": "6d5398ba-ad34-410e-c037-81c5962fc202",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plot_image('testing data', image_test, mask_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3fxTKBOWLWH"
      },
      "source": [
        "## custom data loader for the PyTorch framework"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "tBXWn6UEWLWH"
      },
      "outputs": [],
      "source": [
        "class dataset(Dataset):\n",
        "    \n",
        "    def __init__(self, image, mask, use_transform=False):\n",
        "        \n",
        "        self.image          = image\n",
        "        self.mask           = mask\n",
        "        self.use_transform  = use_transform\n",
        "    \n",
        "    def __getitem__(self, index):\n",
        "        \n",
        "        image   = self.image[index]\n",
        "        mask    = self.mask[index]\n",
        "\n",
        "        image   = torch.FloatTensor(image).unsqueeze(dim=0)\n",
        "        mask    = torch.FloatTensor(mask).unsqueeze(dim=0)\n",
        "\n",
        "        # apply a data augmentation scheme\n",
        "        if self.use_transform:\n",
        "            # ==================================================\n",
        "            # add codes for applying data augmentation \n",
        "            #\n",
        "\n",
        "                image   = transforms.RandomRotation(degrees = 45)(image)\n",
        "                mask    = transforms.RandomRotation(degrees = 45)(mask)\n",
        "            \n",
        "            #    \n",
        "            # ==================================================\n",
        "\n",
        "        return (image, mask)\n",
        "    \n",
        "    def __len__(self):\n",
        "\n",
        "        number_image = self.image.shape[0]\n",
        "\n",
        "        return number_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCYs-JWjWLWI"
      },
      "source": [
        "## setting device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "vvkmI42YWLWI"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "4DWf5r6jWLWJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd8a698f-925b-46e2-e8c3-f515904c80a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NRAYkfnfWLWJ"
      },
      "source": [
        "## construct datasets and dataloaders for testing and testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "jgXbkmgVWLWK"
      },
      "outputs": [],
      "source": [
        "# ==================================================\n",
        "# determine the mini-batch size\n",
        "#\n",
        "size_minibatch      = 10 \n",
        "#\n",
        "# ==================================================\n",
        "\n",
        "dataset_train       = dataset(image_train, mask_train, True)\n",
        "dataset_test        = dataset(image_test, mask_test, False)\n",
        "\n",
        "dataloader_train    = torch.utils.data.DataLoader(dataset_train, batch_size=size_minibatch, shuffle=True, drop_last=True)\n",
        "dataloader_test     = torch.utils.data.DataLoader(dataset_test, batch_size=size_minibatch, shuffle=False, drop_last=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ngJeHXH2dfj"
      },
      "source": [
        "## construct a neural network "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "PjlHwUOV-UCh"
      },
      "outputs": [],
      "source": [
        "class Network(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Network,self).__init__()\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        # Encoder\n",
        "        # -------------------------------------------------\n",
        "        self.encoder_layer1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, stride=2, padding=1, bias=True),  \n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "        \n",
        "        self.encoder_layer2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, stride=2, padding=1, bias=True),\n",
        "            nn.BatchNorm2d(16),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        # Decoder\n",
        "        # -------------------------------------------------\n",
        "        self.decoder_layer2 = nn.Sequential(\n",
        "            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "            nn.Conv2d(in_channels=16, out_channels=8, kernel_size=3, stride=1, padding=1, bias=True),\n",
        "            nn.BatchNorm2d(8),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "        \n",
        "        self.decoder_layer1 = nn.Sequential(\n",
        "            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n",
        "            nn.Conv2d(in_channels=8, out_channels=1, kernel_size=3, stride=1, padding=1, bias=True),\n",
        "            nn.Sigmoid(),\n",
        "        )\n",
        "\n",
        "        # -------------------------------------------------\n",
        "        # Network\n",
        "        # -------------------------------------------------\n",
        "        self.network = nn.Sequential(\n",
        "            self.encoder_layer1,\n",
        "            self.encoder_layer2,\n",
        "            self.decoder_layer2, \n",
        "            self.decoder_layer1,\n",
        "        )\n",
        "\n",
        "        self.initialize_weight()\n",
        "\n",
        "    def forward(self,x):\n",
        "    \n",
        "        out = self.network(x)\n",
        "      \n",
        "        return out\n",
        "\n",
        "    # ======================================================================\n",
        "    # initialize weights\n",
        "    # ======================================================================\n",
        "    def initialize_weight(self):\n",
        "            \n",
        "        for m in self.network.modules():\n",
        "            \n",
        "            if isinstance(m, nn.Conv2d):\n",
        "\n",
        "                nn.init.xavier_uniform_(m.weight) \n",
        "                if m.bias is not None:\n",
        "\n",
        "                    nn.init.constant_(m.bias, 1)\n",
        "                    pass\n",
        "                    \n",
        "            elif isinstance(m, nn.BatchNorm2d):\n",
        "                \n",
        "                nn.init.constant_(m.weight, 1)\n",
        "                nn.init.constant_(m.bias, 1)\n",
        "                \n",
        "            elif isinstance(m, nn.Linear):\n",
        "\n",
        "                nn.init.xavier_uniform_(m.weight)\n",
        "\n",
        "                if m.bias is not None:\n",
        "                    \n",
        "                    nn.init.constant_(m.bias, 1)\n",
        "                    pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTXZnsrGLoxg"
      },
      "source": [
        "## build the network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "xsGGOsXKLuc1"
      },
      "outputs": [],
      "source": [
        "model       = Network().to(device)\n",
        "\n",
        "# ==================================================\n",
        "# determine the optimiser and its associated hyper-parameters\n",
        "#\n",
        "learning_rate   = 0.1\n",
        "alpha           = 0.0005\n",
        "number_epoch    = 1000\n",
        "optimizer       = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "#\n",
        "# =================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yE9LChkQ5G2"
      },
      "source": [
        "## compute the prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "9mgS8kzGQ502"
      },
      "outputs": [],
      "source": [
        "def compute_prediction(model, data):\n",
        "\n",
        "    prediction = model(data)\n",
        "\n",
        "    return prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQVz0ChdM9KL"
      },
      "source": [
        "## compute the loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "qA_l4ep8WLWN"
      },
      "outputs": [],
      "source": [
        "def compute_estimate(data, prediction):\n",
        "    # ==================================================\n",
        "    # fill up the blank\n",
        "    #\n",
        "    (b, c, h, w) = data.size()\n",
        "    estimate = torch.zeros(2, b, c).to(device)\n",
        "\n",
        "    estimate[0] = torch.sum(torch.sum(data*prediction, axis=2), axis=2)/ torch.sum(torch.sum(prediction, axis=2) , axis=2)\n",
        "    estimate[1] = torch.sum(torch.sum(data*(1-prediction), axis=2) ,axis=2) / torch.sum(torch.sum((1-prediction), axis=2), axis=2)\n",
        "\n",
        "    #\n",
        "    # ==================================================\n",
        "\n",
        "    return estimate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "xjiBzKUrWLWO"
      },
      "outputs": [],
      "source": [
        "def compute_loss_data_fidelity(data, prediction):\n",
        "    # ==================================================\n",
        "    # fill up the blank\n",
        "    # \n",
        "    (b, c, h, w) = data.size()\n",
        "    estimate = compute_estimate(data, prediction)\n",
        "    estimate_in = torch.unsqueeze(torch.unsqueeze(estimate[0], dim = -1), dim = -1)\n",
        "    estimate_out = torch.unsqueeze(torch.unsqueeze(estimate[1], dim = -1), dim = -1)\n",
        "    residual_in = torch.square(data - estimate_in)\n",
        "    residual_out = torch.square(data - estimate_out)\n",
        "    fidelity_in = torch.sum(prediction*residual_in)\n",
        "    fidelity_out = torch.sum((1-prediction)*residual_out)\n",
        "    loss = (fidelity_in + fidelity_out)/(b*c*h*w)\n",
        "\n",
        "    #\n",
        "    # ==================================================\n",
        "\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "kI2onbA-WLWO"
      },
      "outputs": [],
      "source": [
        "def compute_loss_regularization(prediction):\n",
        "    # ==================================================\n",
        "    # fill up the blank\n",
        "    # \n",
        "    (b, c, h, w) = prediction.size()\n",
        "    gradient_yaxis = torch.abs(prediction[:,:,1:,:]- prediction[:,:,:-1,:]).sum()\n",
        "    gradient_xaxis = torch.abs(prediction[:,:,:,1:]- prediction[:,:,:,:-1]).sum()\n",
        "    loss = (gradient_yaxis+gradient_xaxis)/(b*c*h*w)\n",
        "\n",
        "    #\n",
        "    # ==================================================\n",
        "    \n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "XbvdtlxKNAmm"
      },
      "outputs": [],
      "source": [
        "def compute_loss(data, prediction, alpha):\n",
        "    # ==================================================\n",
        "    # fill up the blank\n",
        "    #\n",
        "\n",
        "    loss_data_fidelity  = compute_loss_data_fidelity(data, prediction)\n",
        "    loss_regularization = compute_loss_regularization(prediction)\n",
        "    loss                = loss_data_fidelity + alpha*loss_regularization\n",
        "\n",
        "    #\n",
        "    # ==================================================\n",
        "\n",
        "    return (loss, loss_data_fidelity, loss_regularization)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnpYjSeaWLWO"
      },
      "source": [
        "## compute the loss value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "c_B24SfOWLWP"
      },
      "outputs": [],
      "source": [
        "def compute_loss_value(loss):\n",
        "    \n",
        "    loss_value = loss.item()\n",
        "    \n",
        "    return loss_value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zdr0nGPYWLWP"
      },
      "source": [
        "## compute the accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "5md5RCYaWLWP"
      },
      "outputs": [],
      "source": [
        "def compute_accuracy(prediction, mask):\n",
        "    \n",
        "    prediction1 = prediction.squeeze(axis=1)\n",
        "    prediction2 = 1 - prediction.squeeze(axis=1)\n",
        "    binary1     = (prediction1 >= 0.5)\n",
        "    binary2     = (prediction2 >= 0.5)\n",
        "    mask        = mask.squeeze(axis=1).bool()\n",
        "    \n",
        "    intersection1   = (binary1 & mask).float().sum((1, 2))\n",
        "    intersection2   = (binary2 & mask).float().sum((1, 2))\n",
        "    union1          = (binary1 | mask).float().sum((1, 2))\n",
        "    union2          = (binary2 | mask).float().sum((1, 2))\n",
        "\n",
        "    eps         = 1e-8\n",
        "    correct1    = (intersection1 + eps) / (union1 + eps)\n",
        "    correct2    = (intersection2 + eps) / (union2 + eps)\n",
        "    accuracy1   = correct1.mean() * 100.0\n",
        "    accuracy2   = correct2.mean() * 100.0\n",
        "    \n",
        "    accuracy    = torch.max(accuracy1, accuracy2)\n",
        "    accuracy    = accuracy.cpu() \n",
        "    \n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UIl4gCp2hE6"
      },
      "source": [
        "## Variable for the learning curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "NXs-w_99-cuZ"
      },
      "outputs": [],
      "source": [
        "loss_train_mean     = np.zeros(number_epoch)\n",
        "loss_train_std      = np.zeros(number_epoch)\n",
        "accuracy_train_mean = np.zeros(number_epoch)\n",
        "accuracy_train_std  = np.zeros(number_epoch)\n",
        "\n",
        "loss_test_mean      = np.zeros(number_epoch)\n",
        "loss_test_std       = np.zeros(number_epoch)\n",
        "accuracy_test_mean  = np.zeros(number_epoch)\n",
        "accuracy_test_std   = np.zeros(number_epoch)\n",
        "\n",
        "loss_train_data_fidelity_mean   = np.zeros(number_epoch)\n",
        "loss_train_data_fidelity_std    = np.zeros(number_epoch)\n",
        "loss_train_regularization_mean  = np.zeros(number_epoch)\n",
        "loss_train_regularization_std   = np.zeros(number_epoch)\n",
        "\n",
        "loss_test_data_fidelity_mean    = np.zeros(number_epoch)\n",
        "loss_test_data_fidelity_std     = np.zeros(number_epoch)\n",
        "loss_test_regularization_mean   = np.zeros(number_epoch)\n",
        "loss_test_regularization_std    = np.zeros(number_epoch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTvfw4jUWLWP"
      },
      "source": [
        "## train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "ZkqFMlBhWLWQ"
      },
      "outputs": [],
      "source": [
        "def train(model, optimizer, dataloader):\n",
        "\n",
        "    loss_epoch                  = []\n",
        "    loss_data_fidelity_epoch    = []\n",
        "    loss_regularization_epoch   = []\n",
        "    accuracy_epoch              = []\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    for index_batch, (image, mask) in enumerate(dataloader):\n",
        "\n",
        "        image       = image.to(device)\n",
        "        mask        = mask.to(device)\n",
        "        \n",
        "        # ==================================================\n",
        "        # fill up the blank\n",
        "        #\n",
        "        prediction  = compute_prediction(model, image)\n",
        "        (loss, loss_data_fidelity, loss_regularization) = compute_loss(image, prediction, alpha)\n",
        "        \n",
        "        loss_value                  = compute_loss_value(loss)\n",
        "        loss_data_fidelity_value    = compute_loss_value(loss_data_fidelity)\n",
        "        loss_regularization_value   = compute_loss_value(loss_regularization)\n",
        "        accuracy                    = compute_accuracy(prediction, mask)\n",
        "        #\n",
        "        # ==================================================\n",
        "        \n",
        "        loss_epoch.append(loss_value)\n",
        "        loss_data_fidelity_epoch.append(loss_data_fidelity_value)\n",
        "        loss_regularization_epoch.append(loss_regularization_value)\n",
        "        accuracy_epoch.append(accuracy)\n",
        "\n",
        "        # update the model    \n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    loss_mean   = np.mean(loss_epoch)\n",
        "    loss_std    = np.std(loss_epoch)\n",
        "\n",
        "    loss_data_fidelity_mean = np.mean(loss_data_fidelity_epoch)\n",
        "    loss_data_fidelity_std  = np.std(loss_data_fidelity_epoch)\n",
        "\n",
        "    loss_regularization_mean    = np.mean(loss_regularization_epoch)\n",
        "    loss_regularization_std     = np.std(loss_regularization_epoch)\n",
        "    \n",
        "    accuracy_mean   = np.mean(accuracy_epoch)\n",
        "    accuracy_std    = np.std(accuracy_epoch)\n",
        "\n",
        "    loss                = {'mean' : loss_mean, 'std' : loss_std}\n",
        "    loss_data_fidelity  = {'mean' : loss_data_fidelity_mean, 'std' : loss_data_fidelity_std}\n",
        "    loss_regularization = {'mean' : loss_regularization_mean, 'std' : loss_regularization_std}\n",
        "    accuracy            = {'mean' : accuracy_mean, 'std' : accuracy_std}\n",
        "\n",
        "    return (loss, loss_data_fidelity, loss_regularization, accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKvKiivoWLWQ"
      },
      "source": [
        "## test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "WX9Mp8yyWLWQ"
      },
      "outputs": [],
      "source": [
        "def test(model, dataloader):\n",
        "\n",
        "    loss_epoch                  = []\n",
        "    loss_data_fidelity_epoch    = []\n",
        "    loss_regularization_epoch   = []\n",
        "    accuracy_epoch              = []\n",
        "\n",
        "    model.eval()\n",
        "\n",
        "    for index_batch, (image, mask) in enumerate(dataloader):\n",
        "\n",
        "        image       = image.to(device)\n",
        "        mask        = mask.to(device)\n",
        "        \n",
        "        # ==================================================\n",
        "        # fill up the blank\n",
        "        #\n",
        "        prediction  = compute_prediction(model, image)\n",
        "        (loss, loss_data_fidelity, loss_regularization) = compute_loss(image, prediction, alpha)\n",
        "        \n",
        "        loss_value                  = compute_loss_value(loss)\n",
        "        loss_data_fidelity_value    = compute_loss_value(loss_data_fidelity)\n",
        "        loss_regularization_value   = compute_loss_value(loss_regularization)\n",
        "        accuracy                    = compute_accuracy(prediction, mask)\n",
        "        #\n",
        "        # ==================================================\n",
        "\n",
        "        loss_epoch.append(loss_value)\n",
        "        loss_data_fidelity_epoch.append(loss_data_fidelity_value)\n",
        "        loss_regularization_epoch.append(loss_regularization_value)\n",
        "        accuracy_epoch.append(accuracy)\n",
        "\n",
        "    loss_mean   = np.mean(loss_epoch)\n",
        "    loss_std    = np.std(loss_epoch)\n",
        "\n",
        "    loss_data_fidelity_mean = np.mean(loss_data_fidelity_epoch)\n",
        "    loss_data_fidelity_std  = np.std(loss_data_fidelity_epoch)\n",
        "\n",
        "    loss_regularization_mean    = np.mean(loss_regularization_epoch)\n",
        "    loss_regularization_std     = np.std(loss_regularization_epoch)\n",
        "    \n",
        "    accuracy_mean   = np.mean(accuracy_epoch)\n",
        "    accuracy_std    = np.std(accuracy_epoch)\n",
        "\n",
        "    loss                = {'mean' : loss_mean, 'std' : loss_std}\n",
        "    loss_data_fidelity  = {'mean' : loss_data_fidelity_mean, 'std' : loss_data_fidelity_std}\n",
        "    loss_regularization = {'mean' : loss_regularization_mean, 'std' : loss_regularization_std}\n",
        "    accuracy            = {'mean' : accuracy_mean, 'std' : accuracy_std}\n",
        "\n",
        "    return (loss, loss_data_fidelity, loss_regularization, accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-VwAY52WLWR"
      },
      "source": [
        "## train and test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "l6Qtqgv8-fMW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0f56772-8586-4ab5-abdf-75c39870e24c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [24:00<00:00,  1.44s/it]\n"
          ]
        }
      ],
      "source": [
        "# ================================================================================\n",
        "# \n",
        "# iterations for epochs\n",
        "#\n",
        "# ================================================================================\n",
        "for i in tqdm(range(number_epoch)):\n",
        "    \n",
        "    # ================================================================================\n",
        "    # \n",
        "    # training\n",
        "    #\n",
        "    # ================================================================================\n",
        "    (loss_train, loss_data_fidelity_train, loss_regularization_train, accuracy_train) = train(model, optimizer, dataloader_train)\n",
        "\n",
        "    loss_train_mean[i]  = loss_train['mean']\n",
        "    loss_train_std[i]   = loss_train['std']\n",
        "\n",
        "    loss_train_data_fidelity_mean[i]    = loss_data_fidelity_train['mean']\n",
        "    loss_train_data_fidelity_std[i]     = loss_data_fidelity_train['std']\n",
        "\n",
        "    loss_train_regularization_mean[i]   = loss_regularization_train['mean']\n",
        "    loss_train_regularization_std[i]    = loss_regularization_train['std']\n",
        "\n",
        "    accuracy_train_mean[i]  = accuracy_train['mean']\n",
        "    accuracy_train_std[i]   = accuracy_train['std']\n",
        "\n",
        "    # ================================================================================\n",
        "    # \n",
        "    # testing\n",
        "    #\n",
        "    # ================================================================================\n",
        "    (loss_test, loss_data_fidelity_test, loss_regularization_test, accuracy_test) = test(model, dataloader_test)\n",
        "\n",
        "    loss_test_mean[i]  = loss_test['mean']\n",
        "    loss_test_std[i]   = loss_test['std']\n",
        "\n",
        "    loss_test_data_fidelity_mean[i]    = loss_data_fidelity_test['mean']\n",
        "    loss_test_data_fidelity_std[i]     = loss_data_fidelity_test['std']\n",
        "\n",
        "    loss_test_regularization_mean[i]   = loss_regularization_test['mean']\n",
        "    loss_test_regularization_std[i]    = loss_regularization_test['std']\n",
        "\n",
        "    accuracy_test_mean[i]  = accuracy_test['mean']\n",
        "    accuracy_test_std[i]   = accuracy_test['std']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOiuOUbEWLWR"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOAnh07VWLWR"
      },
      "source": [
        "## functions for presenting the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFXi9mcZWLWR"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "Tp50r3lh-o-P"
      },
      "outputs": [],
      "source": [
        "def function_result_01():\n",
        "    \n",
        "    title           = 'loss (training)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_train_mean)), loss_train_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_train_mean)), loss_train_mean - loss_train_std, loss_train_mean + loss_train_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "HlU7A834WLWS"
      },
      "outputs": [],
      "source": [
        "def function_result_02():\n",
        "    \n",
        "    title           = 'loss - data fidelity (training)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_train_data_fidelity_mean)), loss_train_data_fidelity_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_train_data_fidelity_mean)), loss_train_data_fidelity_mean - loss_train_data_fidelity_std, loss_train_data_fidelity_mean + loss_train_data_fidelity_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "kUk0E_f_WLWS"
      },
      "outputs": [],
      "source": [
        "def function_result_03():\n",
        "    \n",
        "    title           = 'loss - regularization (training)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_train_regularization_mean)), loss_train_regularization_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_train_regularization_mean)), loss_train_regularization_mean - loss_train_regularization_std, loss_train_regularization_mean + loss_train_regularization_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "R9z1SbyNWLWS"
      },
      "outputs": [],
      "source": [
        "def function_result_04():\n",
        "    \n",
        "    title           = 'loss (testing)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_test_mean)), loss_test_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_test_mean)), loss_test_mean - loss_test_std, loss_test_mean + loss_test_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "bK4ELyiVWLWT"
      },
      "outputs": [],
      "source": [
        "def function_result_05():\n",
        "    \n",
        "    title           = 'loss - data fidelity (testing)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_test_data_fidelity_mean)), loss_test_data_fidelity_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_test_data_fidelity_mean)), loss_test_data_fidelity_mean - loss_test_data_fidelity_std, loss_test_data_fidelity_mean + loss_test_data_fidelity_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "aGyrJ_xvWLWT"
      },
      "outputs": [],
      "source": [
        "def function_result_06():\n",
        "    \n",
        "    title           = 'loss - regularization (testing)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'loss'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(loss_test_regularization_mean)), loss_test_regularization_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(loss_test_regularization_mean)), loss_test_regularization_mean - loss_test_regularization_std, loss_test_regularization_mean + loss_test_regularization_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "SrTMRb1DWLWT"
      },
      "outputs": [],
      "source": [
        "def function_result_07():\n",
        "    \n",
        "    title           = 'accuracy (training)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'accuracy'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(accuracy_train_mean)), accuracy_train_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(accuracy_train_mean)), accuracy_train_mean - accuracy_train_std, accuracy_train_mean + accuracy_train_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "51q4pBXFWLWU"
      },
      "outputs": [],
      "source": [
        "def function_result_08():\n",
        "    \n",
        "    title           = 'accuracy (testing)'\n",
        "    label_axis_x    = 'epoch' \n",
        "    label_axis_y    = 'accuracy'\n",
        "    color_mean      = 'red'\n",
        "    color_std       = 'blue'\n",
        "    alpha           = 0.3\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(title)\n",
        "\n",
        "    plt.plot(range(len(accuracy_test_mean)), accuracy_test_mean, '-', color = color_mean)\n",
        "    plt.fill_between(range(len(accuracy_test_mean)), accuracy_test_mean - accuracy_test_std, accuracy_test_mean + accuracy_test_std, facecolor = color_std, alpha = alpha) \n",
        "    \n",
        "    plt.xlabel(label_axis_x)\n",
        "    plt.ylabel(label_axis_y)\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "rPmQijSkWLWU"
      },
      "outputs": [],
      "source": [
        "def function_result_09():\n",
        "\n",
        "    nRow = 10\n",
        "    nCol = 4 \n",
        "    size = 3 \n",
        "    \n",
        "    title = 'training results'\n",
        "    fig, axes = plt.subplots(nRow, nCol, figsize=(size * nCol, size * nRow))\n",
        "    fig.suptitle(title, fontsize=16)\n",
        "\n",
        "    number_data = len(dataset_train)\n",
        "    index_image = np.linspace(0, number_data-1, nRow).astype(int)\n",
        "\n",
        "    image       = torch.FloatTensor(dataset_train.image[index_image]).unsqueeze(dim=1).to(device)\n",
        "    mask        = torch.FloatTensor(dataset_train.mask[index_image]).unsqueeze(dim=1).to(device)\n",
        "    prediction  = compute_prediction(model, image)\n",
        "\n",
        "    image       = image.detach().cpu().squeeze(axis=1)\n",
        "    mask        = mask.detach().cpu().squeeze(axis=1)\n",
        "    prediction  = prediction.detach().cpu().squeeze(axis=1)\n",
        "    binary      = (prediction >= 0.5)\n",
        "\n",
        "    for r in range(nRow):\n",
        "\n",
        "            axes[r, 0].imshow(image[r], cmap='gray')\n",
        "            axes[r, 1].imshow(prediction[r], cmap='gray', vmin=0, vmax=1)\n",
        "            axes[r, 2].imshow(binary[r], cmap='gray', vmin=0, vmax=1)\n",
        "            axes[r, 3].imshow(mask[r], cmap='gray', vmin=0, vmax=1)\n",
        "\n",
        "            axes[r, 0].xaxis.set_visible(False)\n",
        "            axes[r, 1].xaxis.set_visible(False)\n",
        "            axes[r, 2].xaxis.set_visible(False)\n",
        "            axes[r, 3].xaxis.set_visible(False)\n",
        "            \n",
        "            axes[r, 0].yaxis.set_visible(False)\n",
        "            axes[r, 1].yaxis.set_visible(False)\n",
        "            axes[r, 2].yaxis.set_visible(False)\n",
        "            axes[r, 3].yaxis.set_visible(False)\n",
        "            \n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "R8mIBDKZWLWU"
      },
      "outputs": [],
      "source": [
        "def function_result_10():\n",
        "    \n",
        "    nRow = 10\n",
        "    nCol = 4 \n",
        "    size = 3 \n",
        "\n",
        "    title = 'testing results'\n",
        "    fig, axes = plt.subplots(nRow, nCol, figsize=(size * nCol, size * nRow))\n",
        "    fig.suptitle(title, fontsize=16)\n",
        "\n",
        "    number_data = len(dataset_test)\n",
        "    index_image = np.linspace(0, number_data-1, nRow).astype(int)\n",
        "    \n",
        "    image       = torch.FloatTensor(dataset_test.image[index_image]).unsqueeze(dim=1).to(device)\n",
        "    mask        = torch.FloatTensor(dataset_test.mask[index_image]).unsqueeze(dim=1).to(device)\n",
        "    prediction  = compute_prediction(model, image)\n",
        "\n",
        "    image       = image.detach().cpu().squeeze(axis=1)\n",
        "    mask        = mask.detach().cpu().squeeze(axis=1)\n",
        "    prediction  = prediction.detach().cpu().squeeze(axis=1)\n",
        "    binary      = (prediction >= 0.5)\n",
        "\n",
        "    for r in range(nRow):\n",
        "    \n",
        "            axes[r, 0].imshow(image[r], cmap='gray')\n",
        "            axes[r, 1].imshow(prediction[r], cmap='gray', vmin=0, vmax=1)\n",
        "            axes[r, 2].imshow(binary[r], cmap='gray', vmin=0, vmax=1)\n",
        "            axes[r, 3].imshow(mask[r], cmap='gray', vmin=0, vmax=1)\n",
        "\n",
        "            axes[r, 0].xaxis.set_visible(False)\n",
        "            axes[r, 1].xaxis.set_visible(False)\n",
        "            axes[r, 2].xaxis.set_visible(False)\n",
        "            axes[r, 3].xaxis.set_visible(False)\n",
        "            \n",
        "            axes[r, 0].yaxis.set_visible(False)\n",
        "            axes[r, 1].yaxis.set_visible(False)\n",
        "            axes[r, 2].yaxis.set_visible(False)\n",
        "            axes[r, 3].yaxis.set_visible(False)\n",
        "            \n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "swGkMHekWLWV"
      },
      "outputs": [],
      "source": [
        "def function_result_11():\n",
        "    \n",
        "    print('final training accuracy = %9.8f' % (accuracy_train_mean[-1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "QGbTlIuiWLWV"
      },
      "outputs": [],
      "source": [
        "def function_result_12():\n",
        "    \n",
        "    print('final testing accuracy = %9.8f' % (accuracy_test_mean[-1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyT7tCwcWLWV"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPQJy0tUWLWW"
      },
      "source": [
        "## results "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzGmAVmIWLWW"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "LXFmjlEJWLWW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "54a32299-0c80-4f35-f526-8a41495b1bfe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 01\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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wxhwN4PsATrbWdqY2fwLAS9baFmttCzzPzsFFLGtuYjFOEyeEEEJKiGIKnFcBzDLGzDDGVAM4DcADegdjzHwAv4MnburUVx8AONwYU2mMqYKXYJwVouo7DH+LihBCCCkhiiZwrLVxAF8H8Cg8cfIXa+0SY8yVxpiTU7tdA2AogL8aY94wxogAuhfACgBvA3gTwJvW2geLVdZ82FiMKxkTQgghJURlMU9urX0IwEPOtkvV56NDjksAOKeYZSsIYxiiIoQQQkqIAZFkPOChB4cQQggpKShwomCYg0MIIYSUEhQ4UYjFYDlNnBBCCCkZKHCiYGKwcXpwCCGEkFKBAicKxiDJlYwJIYSQkoECJwI2FoNNMERFCCGElAoUOFGgB4cQQggpKShwoqA8ON3dnFBFCCGEDHQocKKgfk08kQDa2vq5PIQQQgjJCQVOFGIx2KSFtZ7AaWnp7wIRQgghJBcUOFEwMcAm0dXlCZz29v4uEPmwwOWXCCGkZ1DgRMEYmGQSq1Z5Aqezs78LRD4sJBL9XQJCCClNKHAiYGMxABadnV6CcVdXf5eIfFigwCGEkJ5BgROFlAcnHqcHh/QtFDiEENIzKHCikPLgiMAZ6B6csGns3d19Ww6y41DgEIF1gZDCoMCJgvLgJJMZD05X18BcEyceD94+0IUZySbsXZIPH6wLhBQGBU4UjOfB6e72RlFiaDo6escr0tuelbCRXkdH716HFJ9EYsdG7qUwC6sUyjgQoAeHkMKgwIlCLObLwRGB097eO+KktXXHz6EJG+kxd6j0SCR2rI6Vwqi/3DruYj3zUniX5MNDKQxMKHAiYI0BbEbgiEEu1IMTZqD6SuD0VYhqIIbteoug913MDnpHc776UzxEMYCyeKZLKXfmxSp7f7zLUujEwiilsicSpWc3S6GNUuBEIRaDsZkkYwDYts0TJpKXA2R3RK5BCvOgiMDprQaZSASfq688OFE65IFsfETEBHUoQfdWTMO0owKnp0aoNzrTpqb8+1gb/PzyDRxKof64WFt63rjeDJ/3dflLoQMWdGSgVCiF8lLgRCHlwbEWWLXK29TcDGza5BkAWdnY/Y0qbRx0crKL5Mb0VoVJJoM7jZ4InJ50JFE65IGc8Czvo7k5+zu33GHPurfY0RBVIUJF34eui/F4z+pmR0d+kZNMBtexfPfcWx1vMd5dWNm2bg2uU1HpDw9Ob7bTbdt671xRKKXQZzyO9Er5wkC2kUBpzMqlwIlCyoMDZF5qe3um8xEPjBtq0hWguzs4yddar3KHueqj4HY+yWRw5euJwOlJI4tS8Ts7CxNPxRQR7vOT9yQhSF3OIIGT6z521Mj2lQenvR3YvNl/Xf1dT8qQTPrP6Z5X9umJByfX94XUlWKMQsPK1tGxY17UfGXdkXsJq8O96fXdvr33zhWFUhM4InKEgT4phB6cciHlwdG0t2eEhAibXB6ceDy7wlrrGRCJv/a0wkhZ9HmDGkdviJUwQ6jPHeU6nZ3RjWdTU8/ylKKOMNxnJeWyFnj3Xf97KdSDs6OjnEJXznbrUFQjr9+He087InAaGnKXJ+j5xeM7JnAK6RiKMQoNa8fJ5I79jl0xBU6Yp8197+7zKiSUmOu9FCPk+GEWOL09IMyVJyffNTYOvGdOgRMFE8tqgVEEjq6s3d3ZHbp0XoUKHNcYtLf7rx0UDutpjLery2+Uw0SJdr27wi6Izs7s5+Ui56mr65lrv6MjWkN3Ox1Z30g6JJ1nFfQO5X1ETZaN8mv0cj3J24hqsNwOR1/frTe67FpI6YRHEeE9yasKqtPuM7I2+7jeFDhBHWdYKC4XhbSdsByuXGHqKOTrPHIJq3yEhY/c8roeOakXYQKltTW87eS6TlTknoPusbc8DH0RihH7rNvZjtSV3p64ksu2STk3b86dv9gfUOBEIRaDcTw40nnG415lCkogdENUQZ2j9uBErRRuww0SOK767+l6Kl1d/sYSNKpwvVNhoxD9PKIIHPntr5aWaKJAI+HDfB1zkIdEkslF5CQSwOrVwe+4qytj3IMMYTyevZzA+vXRO3CpY/rd5TLcuQRO0HfyvQgZuaZ0GJs3R/ciufU7KOzqdkRBIqi7e8cEjhasQfu1tWU8S1E7r54IHLeTCQsda3KN2vOVIezcEgLPRVtbfq9vMpl9T/J9WGfc0pIptx4ouPTUsyU2ZEdmN+YTgG5uTC56KoaCVsnvqQfH2nB72dPy5RKQUmY9q3ig5OdQ4ETAplYy1ogHR8REd3e2IXEFTlDnWKgHx+3spCz5PDhBx6XvL4fxCxI47n2Il0Mfo78TdJk6O/MbNcnTaWsrzIMjHXJnZ/6RTJDxkgUdZYaPjKzcfaVsUjWC3l887pVj0SL/feUzmK7AySVUgo4TtDcmSIDLu9IiRguc9et7LnCieHCk03e9Kvk8AzsicBKJTH2Kaoh7shyE28nky9cCcov+qB4cd2aa2Ca3LJrOzmgCx50xKs8lrDNuacmuy0G4tqCQ3LGg/fWAUfIcZbuL1Nuwd9zV5YVfotBTr4vrwUkm8w8AgXCb4z7PXIMwuV6+8rn7yza5587OzPkHSn4OBU4UYtkhKqkQ8uOb7ho5QP6ZKNIJ5xM4uvK1tWVPA+/qym4cQSGqzk5gy5bs87sNyc3k14a6szNbbEjCtaAbkf45C20EtfjIFcaShh7Fg6MTwLu7vXLK/YaJqaDwj2yTf/pHVvW+8tzzeXC6u/05Dvo8uUbd0iG69SpIbOhFKHXdiMcz7zfIU6KFRFeX/951OfIZbjleo41gWBmk85HrydILIjLDhJWb/K3p6Mh8F3S8DtfuSIgq7N11dwP19Zk6K/fm5hsFHe/m08nzizIAkvOJXdFlDxpcuX+HdZaCLr+0Jz16d5EBgA5bhHm5dfssJFepvR3YsCH4/nQHLM/DtWX6HsIGUTLIyydyE4ns57B9e7DNdXEXke3o8Gbd5btemC0ICrsDuUVcvmtpgsSurkOFhNWLCQVOFIzJClEJIizESOuKoP8OGkXl8+BIB7Rpk9dBdnV5Fd/9RXMx9lKpJdygOwARRUFCwd2mG5brwYkicHRjkUaoQyCAZ/jkOkFeB7mWGLuwBquR84tRbWzMGJe1a7P3l/O4jdcNUcm71Tk9Eq7SHW1QQrZ0LvrdaoHT3Ay8/773WRsEEXZBISr33uXdB4nsRMJ7v2Gdi+vBkXvUAj7K+i1BOV66TovACwtRSVtZujQzGMgVqtLLM7jokWSYB0cb4qDvg7a5gipM9CWTwLJlmc5OnrPrwQk6XoszIFP3W1ujeXDkWq59cIWKez9hOXquPdMCR9eLsJC4FldhNk7OB2SEcliH676D+npgzZpggaPFgjwPbcvq6rz/5buwWW7yPPOFjERIud7IMPGn0XVeytLRkVvoBbVpsTnuNXVKRVjZwwjyPOq6oD04YjejhJn7AgqcKAR4cAQZMYtidQ2JNqT6hcsxuTw47e3Axo3AihXAO+9kDHci4Q8LybWlMWi3v4iXXC5aV+CsXp0RMV1dXr7Cxo2Z82zfnjF8LS25Q1S645RG19rqnUOPODWyn+vp0N6JIAMo28SD09SUcS3rJErXC+N2HPI+dIjK9eBs2+YXtvJsNFJmV6Do84hw1fctZZTcLjk+7N5FZIeFe0Qo6fonnZxsk45FyuZ6cII6VxGSDQ3B9TdI4OQKUbW2ZkKgUp9zeXCCwo/yvIJGrHJt6Xw7OqJ7ZoIETpjoSCa9ZyLfBT1XILjz+uCD7H3efz+8rECmXNJBuknhrvc4SHSH5eiFeXDckK3boYqnWdu9XHmK8iw6OjLnDrpf11aJ/czlwZFzusevWuUfLIqdcgkSOEH1QfbT79UdjALBHlF5H1rghLU7IcgzJ+cJEjhafLiEiTdtB/X96rLpPCzxOGtR359EEjjGmG8ZY4Ybj1uMMa8bY44tduEGDtnTxAU3gS6RyBhz7bZ3O+goAkcapvYgSMPVxl+8CBIGkEqcSHgeDO1hCapwbv7O5s1+Vd7ZmRE4yaQnGkQANTTkDlFJWWR0JOJGJ6C6I05tcNxppq5bWeN6cKRjt9YfQ9+yxT/6EOMux4d5cLRXbMuWzHXCPDhi3N2Ql4QhWloyYSF3ar8WsXL9rVszHjFXUIoxcQWbuKvlPej654Y1pT4GeXB0fofQ3g48/3y4h0jOY21GjEjZtOAOEpRS3/U5pQ7Ksw1aAVzepdyrPl4Si+WZNjcHe1bd3Dl9L/rempuDQ40yM0x7b10PjuvRlHO2tPiv09DgeR+lgxLhoMun7UBXl7ePK2h0h6RDgPrahXhwpE3nEzi6E3a9RPKdHpy1tmbaRNCgzF12QO4ryDsu28SGxeP+Z97UlG1TJByuc+vEPulr6AGbINcReypt0bVVmzdnH6sHQ7qvyCVwct23tt96JqSUxxXXYR4cOd5az25LuV2xq8PjWtgGibC+JKoH50vW2iYAxwIYBeDzAK4qWqkGGgGzqARX4LS3ZzpQ6XC0h0cbRj3ylr/lfDq3R86tR03t7f7kOOkQAP+oWAxZLoEjYmD7dn88FchcQ4/Au7q8e7A2I3BcwyVIxW9v98rX3JxZ8Es3kHXrMsfI6MX1RkgytesREeRZ6o5RhJ8YL8Bza8v30qFv3OhPWJTOSBqsCBwpT2srsHixX7SGeXDkWvG4ZzzlnGvXZgsLXVe0UUokMl6Brq7MitqAJ96kQ5dn4NYjeQ8iCqRj0Dk4iURmWm+YB0cb6+5u7370+TVa+Ek70J2+7CPXkOvIP7kvYeNGf1hT7kWHN+Rcrss/kfC8blIGETjay6nvSz+75cv9njp5ftrD5nrf9P9BHhz5qReNK/5kP6nTck96wTxpD/p+RVzoEJl+9mJ3wrzNGi0y5J/2EuhwskZ7DfW7dr2n0n70avBasIu9FNxkX92hSjnl3FI2sZXabsh1tTdFpwOsWpV5fjo3TQiawCAeILEzsi6MHjhJ6Fx7N+QZac+Hbr8i+lxc8aDfqy57Q0PG9sj9NjbmD5fKdnlOW7ZkQv168KLvUdqWlEVsRH8RVeCY1P8nAvhfa+0Sta38yROikv+TyYxIEAEgjUZ/r136EsvW3h/t8dBGQBqAFjiu4QH8RkiMm+7YXGT/5uZsgSP7S4ORCizeB4kTi+HYsiUjCPT+cj9a4Mj5pRHKIxbD6XYqbW25BY50tO7IXwyOdI4NDf7wmHioxA0tHawWnm6ISt6TNnxh7mItcGQEKoZG3qmcW3v09MhRv/uuroyhtzYT5tIeHP3zHzp8JZ2F1CfdmQCZTt/14AR1ZHqELOd3w5PyvYwqxShqA+56uORcDQ3+87W2eiEcSSqVOi0GtK4ucy5pS11d3v8ym0faUyKRuVd3kTs3TCDi3PUUSYfR0hIucOT56nsHvHvTYSz9PPW5pL7oEMOKFZnv163LtEd51yJw3ngj016CnrnraXVDWVrYbtiQeTeNjdnv0+2A9Urv2i5pD5Z0gFIPZTAitkLKLJ2x9vQI2i5qsaIHhtqbqweO+h1Z69mHpibvu9Wr/WLJ9XhpISPnkXYs2/WARL4ToSrbxSunbYXrcRcxLOfVaQf6HUodl/NLfZGlHqS+S15lR4c3S9L1bOlnK+9Yiy8pl7wPOSfgvy8pS2+vyVMIUQXOa8aYx+AJnEeNMcMABLs0ypCgaeKCNBRpAM3NXsVpbMw0uNWrM0ZGvpcGob0unZ0ZYymGUSqJNiQicMR4iKfBHRVLnksymenc9W1o0eN2xq7A0Z1QPJ4xBCJy5DksXuwXONpTJdO93VCMNBzt7pbj3PJqY+WiRYI21NJ4JeFYxImIxNZW7/nIZ/28dTmkXDpEJ+9VdyCCGw+PxzOJjdpDpUMYUm+0+NAhTDEmev2PtrbMvev6Ic9XCxCd7Ct1SD/P5uZsgapH93IvMtKW5yvvMaijF4MtZdN1VTpmLW7key1E5flt3uyNrmVwIPVCyqa9o62t3j5atEkbkzrsegjkmUrIUJ6bO7tJhI20ad0BSicuQlNy57RwFI+pKyb1M9ZhGr3gpHzf3p55Fs3NmXYmuUHbt3vCXeqo60lwPa1dXf7wsXSUch15LtJ+dGjS9czJsXokHzTw0QJIhGBjY8aT2NTk3c3BQmIAACAASURBVJvUsaCwnh5E6Y5WyivPTDp9eVZ6v23bMjZNey31ZAItcMRWaM+FvG/XgyPPVgscqVdib/QgZvt2vwBrbvb227TJ2ybeW93O5Vzajsp1JNdIBLmEqLZt80Sw5BO66LCqLrP2autzSnm1N6pUBM5ZAC4BsL+1tg1AFYAvFq1UA41YDECwB0c3bGmQTU0Zz4xME9QjEBkNuqMlqaDSUPWIRY7XHZgeRelOSBrm1q1+z5FcR8qs3Y1SKbVgk+8EMTJApvFKJyFGVIyeK3CSyYzAcUeO0viDBI4eNYsxCBM48mz0LBrZDmQaoxiUJUs8oyHCSQscV9BJ56jd6fKc5N24ZZIGrkNfeuqwNgRyPRE4evSvR1BiLHWcX0a9OkSlhbMIDNlPzr9pU+Z7ncCsxYYYODHU+hnIveQTONLh6ueq64srcOTZ6H/6mUlnJdu1UNfJmeIxlOcmx8g2HaLS9UxEn5xLOoRkMjPjTd6DtFsppxbl0k7l+cuzlGtob6zUD7feyLl0xyj3IeUWL4fUURFVUt+kjsoqxPIexE7IsxPb1dGR8YaJUGpoyLS7bdu8kf8HH/i9J1J2uYZ+521tmYGAfK87dql70sGLwOnq8soSJnDk/vWARZ9f59jJs9M2SOqbnkShvSe6vur3J0JZ2pA8B/EyA96zdL2x2oOjPdJaDIholTrU1ubdmwxO9PIa69dntoUJHLme2GaxK/KMRYS6z3XlymCBo9urK3D0QEbeZykInIMBLLPWNhhjzgDwAwARlz4qA3J4cAQJO0knLsJjwwbvez0C1qJA0CNx6aB1Z6oNv7iIdYcjlXDz5oy3prExY1z1iEDCG2LwdGcrjUNfV9DTpMWAiqEQgaBHr/K/NoCuOBHj4BpzCSXofcX1rn/zpKXFa4iAP0yjBY4bH5byulNdu7oyRkA3YjdEpY2BO9oDMoZI7kF3YGHGU84jdUOu4454RZBo74/ujOU9BuVjiQGU+5Z3oZ+9Fhm6rHJO+dfc7J9pJ+/ZXc8EyAgc8VTpUbGe3i5CUYv/7m6vPuswmdyrtBPtIZH6Lu9LPE26E2puznQ+8k/PstNtUcoqokpGz3JvEv6VdiseENlHRvTS0clxugNz14OSTkSHQPRz0kJPjhOBI528HKuFkXgA3NG1eJKamry2JL/9pkUK4PcEugJdOmLd0WlbIflKuj5LJ6jvWzpEncOj87w6O/0hRbF9nZ2ZiRdbtmTC1NKJa5GuPThSL7TAcb07+n8920sEyqJF/qn8Ykd0OF3qh/YkvfOOP5Snn7MunzuYlfarB3MyqA4SOEBmICTPVMqo71PT0pJZ5FMLPC3s6+sz/V6uEJUeQPU1UQXOTQDajDH7ALgIwAoAfypaqQYaJtyDI7iGTSdnAX5Xs8RUXYGjR+dSiaRz1h4cGdGI8tbi54MPMhW3tdXfmKVsEtPWuRtSGWXf5uZst7w7g0OP1qWj0d/p+K1udBpXRHR1eR2a/K6Je/143Fv3QraLG146Iu1WFrTA0YLMHbVI5yef3SRjKY+baKsTg4FMJyjPRPZ3jacOP4kBl3filk8bDL1onlxDe3D0uXWd1C50LWi0INQdsWzTibRyre3bM89KC78gD44WjfK89Kg7yIOjPQJbt3rG1C1zMunVEzHWIij0qFTvK5+10JT3pr2sMtp1Q1QycNGdiLSnzk6vXetBgJTHvUc3H8ZdykHKp0e+cg9yHi0MZF894JE6onNwpPyS5Cr3rsN6HR1+Qarza9xEaV0vRUBJ/dHPWN9jkMDRgyod6tCeBe3t2bDBLwCkbCJK16/36oW0Fe29kQGSPl7ek65b8lnanJxDey2lTB0dGU+wfLdpkz/MKR4cnVMnAxMtoPRzlf9lQKMFp26znZ2ZfDVdL1yBo9upCEfZT3uigUy91fVV2xCpFyJctY2VPkrKLdv6g6gCJ26ttQBOAfAba+0NAIYVr1gDjAgeHF05gUyFEbTAAbITYbW7XRq6Pl53OjIqE9el9uC4azBoQyPn2bw5E7LSalsLEZklpI8VAynkEjjiUtXrmoQ9N+31Ea+SduvrZ6hH6IDXqei8Fd1xCUECI2iNFW00pMyueAny4Lj7yD24RktfXwsm7c4VI60TvmV/bYjk/HqkLufUcX3docjoT4+oxFDpcmnRq48VgdPenr2OkZQtSohKe3C0h0Get37n3d2ZVYH1s5TyL12aCZeJ8ZVnqmfHaJGq0SN06XySyYxXRncI8j608Jd22Nnpjch1PdBeL7m+iA8px6ZNGaGo664ISRct2nRHJs9T2xHAv1SClF3CT1oMa1wvpu5IpR4K2sbU1fnbodyPhCL1e5P6qQWOhFn0u9FtVzw3+tnotqCnmMs7FeGr7ad474BswSbnl++0XdNCTeqtlHHrVv9SCGvW+J+/Tj2Q5yUDGi3KBXcgJM9LjtX5RuLVElsb5sHR5xNxot+rTm7Wz0ALHBmMuXbFPb8r5oKSmPuCqAKn2RjzXXjTw/9ljInBy8P5cBCLIWwdnDDcTl0qjp6+53pwAM/YSaPQx+vOVmhs9CcquiLDvbacR5JqdTmlYUiZrM3El4UNG7Ld5voarnFqb8+MlsIEjttYxI0unYjr8ZHOQI6REZr80yN/fQ05v2yXqZMabRz0+9OhODderTsBazOjeUnE1PetQ07u85b6IIJDr7Giy66feTyeCa1IxwJkkiVdYSbGSa+1Is9YPwvxOLjhBulYZJQvJJNefXIFju5UxciJcJfy6Y5UP2/9TnQIykXCH9K5yrXWrvULHHk+QQJHvtM5ExLm0M9VjtUdgfZyaPe9fqdSN0W86XVqGhoydV4LlrBFDoM8OBs3+gWwPpcWjkDGe9zdnVnOwrUZOv9F7kOedS4Pjg696zYv70U+y3Hu85IZmPpe9f2IzRKvBuDPHRNRqkWRiGt5j+6il0ECR88Y1TZBBI6IFbnP7u7MIEsQO6nDVtLpa/Eny2zod6Y/6zCp9uJI2E7Km0h459LiLUjgaMEu1xCPjLYLrsDRnl3tpQwTOHKs9sz3B1EFzmcAdMJbD2cTgMkArilaqQYYNhaDKTCIKJ2VoEczgL+CAf5GKXFNfbw7sgW8xiPTSKXRBAkcN0TlusVdV2wYbrKYO8J0f+JBz/QKEzjSMck91Nd7n6XRuw1DL2YIZHsRgjpJ3fhkf51zIeh3oj0lrudH8qoEbaiXLPG2uaN5wP9u3B971B2WHl3pe3BH9fG4f10QfW8iNnQnozs52a5nzAji5XE7S3mn8o40Ep7QAswdZcuvqIvB1nU+TOCIMc43AhShLx2CdCZyDjl/kNdO2oQspCgd8rZtftEsx7a1ZWbk6Y5I9tHiQuyACF7J2dEDGu01lP/DfvvIDSkDmbrshjUF/e7XrcsIMhEk7jG6U9blcgUfkKk78sz1YEN7SfTASQtb7cFx26S+n+5uv5jXISUph4T2db0VweMKGd2Bhwkc1+MnA4y1a/0hw1z2TQtoEa06x0balL4XuR9dFi2mkkm/h1dP0ND1RttDfb/6Gcl3Irx1ErV+Pq4HR3vdwwSOftcD2oOTEjV3ABhhjPlPAB3W2g9RDk74SsZh5PJaANkCR1c41+ADwQ1RYrzyvWxzcT048Xi2wBFDk0vguOQSODpclssAyGgR8I+M5digdS/knvQCg26ILciDoz8H3ac2tq4HJ8j4CHp6sjRuPXtF7ycECRz92X2P2kMhyMKHghZhW7Z4HZi+X7mHpiZ/bo7rwZHkSVfgiGtcprpr3H2bm7MXZZPRr06QFHT91s9XC5ZcuB48wO+BkO9d0SDXFYGjcw2COjjAE1N6RpKUE/CEnhu6BfzT1XX+nXQqutOOx70QR5gHRzxg7jNx36O+R3nf8k50e3HPo6f16+eoc+oEVxy7Xjm5vhY7a9d639fV+ducW+d1yE17DaUeuu1LhLDuYMXb4pbb9U5qXG+HoD2Ygh5EhCF1TDxM2vMKBP9quPauyL3pdy55VPKdLoMWyu6gQYsrfQ86zK49xVIvc4WodPvUHkHtwRnQScbGmFMBvALg0wBOBfCyMeZTxSzYgKIHHhy38blo16zsr9FTKoFggaPJJUzcmHmYB8c1VvlwxYc2Bnpk4t6rRj8nveKl3E/YKpjJZPaCfUGjUX0uwL/IYFBZ9EgoqMMNQpL8dAhRT/UXtGGU+wrz4ARdwz2HTrwF/O9OQoxayLijLtkeFKIKEzj5moEWOGHfS4euO1Z9L0F1MIrACatjuTw4QKbzk05D36dul3Ks/qVwLbCDyuleT2ZGus/RHRC4OXqC7rzca7k2Q293n43cY5BQAvziTv7XoR59HtfjpsNycn0tcPQ6RLkGVTpM6s7O6+jIrEztbnftUFC5tYhxr6/bifbWik3TbdC1gUFoES3PWrePoHO4AkdEiPwtP8Ej++p7cHOmNPX12ferc6uSSW8hSQmbyqBKC5yoISo3N7E/qIy43/fhrYFTBwDGmHEAHgdwb7EKNqDogQfHzZdxCcoF0LiCJshIaXJ1wkGVXxtmbcwK8eDk6nS6u/2L0eXqfKqrvc9B6yWEraEgHZIWCEDwaEgj60YEoZ+5iBa5Vi5k32HD/LF9MUpCUAKunFvyTPS9BKGNoetJceuQHiW6XiHZV8/oEeTv1av9ZYwifrVnIgg9U0k/j82bgREjwq+TL4afawCgO7igNul6bMI8q26HH1ZWTVC7DjomHveP1oHge3bDL1HQ4tZFEk5dtMjT37vP2X2mMkjQ9+naMdeLEEXg6PokA7RBg7LvS4dP5BxB5c4lcPQ+ekq6tFE3/ywfua4h9xO2za0TeqFOef+ufZV76+jwnpEmLOwJZESMO7gTgaM9rzpioOuA/izCp7+8N0B0gRMTcZNiKz5Mv0Ruwn+qIRe5jLJb0dx9XaO0IwInzGADmcobi+X2tASRLwTnxo/D9qtM1cIgb01YR6lj/0DG6ORbVKqQ1xg1MU5G2+5Iyr1W0KwYQY+Ec11Xn8Pdz33G+vruKFEboaCy6mO0Bycf1npiLyiMJcgS9m7oVI8CXfJ15kFCTeOONDW6A3LzE8I+C4UKHO3NcM/jdmb5OqNc9UmTT+CETUxwvVOy3c0tdO2LeGW1uNCeRHeCRS7vrg7xudcMevZh9xnmwckVYgrKgwOyf9ojCoV4xoHgUHvYtbXnWR/T3g4MH57/WtpbGfScKiq88huTHaICwsVqPmHXF0QVOI8YYx4FcFfq788AeKg4RRqAGBP6Y5u5yOXBydc5BY1ICx0FBH3nXldckUGGZEfIlWDr7ldT430OEjO5RuX6vqTh5/PgFAOdYJqLfOIxisDJJdBy1bcwgSMdbq7zynOOaqwaG/35WC6yjk5trX+77rxdonQQ+TymnZ2esXbR3ga38w0KURVSLve5hhl9vQpvWE6Kvp7bqeUil8AJC6WHeXWD6rBu29ojFCZwtLfSPb9GD2D0dcU25nrfLtb6RbeUMdeAKOy7nngkCilrLoIEjusl0d6fQgasYrN1ne7sBKqqvHPGYv50BsENE+rt/RmeAiIKHGvtxcaYTwL4SGrTzdba+4tXrAFGLJZ3HZwg8nk4NG6jCYqn5xuhhpFrtCSCoLfdiPk8VLo8Qa7sfLg5F/0pcNxkzJ4SJUSVi1z1wxU4kmyqwzNh6NlkUQjLvxFk9O16IHIlgEchVyciP36by5Mo//emB8clzIOjE79z4YaYo5BP4AQhM3aioAWOzg3SM6cESfyNEqIKu77OHyoECTkD0ULavfkTA70lcPIJTE1YHlcYegKA3maM96wrKjL9Utg7c8VwSQgcALDW/g3A34pYlgGLzfFbVD0lX+MMMkq94cHpK/IJOE1PphC6HhydoNnXBI0ye0JPhF7Q8UG4AkdmAeUTN5qo9SifCz9MyEk96KnYzvXu85Upigcnl0clKmGdg+58c9ETgZPLQxcmpt1cpFzkEzgaGdDoY3J5cIKQ+9mRti7PI5fHutB3m4vePJdLmP2U5QmiEubBicX83jQgmsAJS3rvS3IKHGNMM4J7dgPAWmsjRPjKgAgrGfc2Qeo3VyOJEmLoSwo1wD05f3+PDnqbHb2fXAY/LNxZyLOPum8UD04QveUBK+SagjyfsHyGnp43iKBzulPqe3KOMPLNMAs7f9T37Xq5coUaRZy4OTX5zqsR29gboqGvbEgxBU6ucxcicPSaUHqb5Ejq7bnEpzDgPTjW2g/PzzHkoggenHwEqd+eCpViNq4wiq3cc00v/bBS7Pcc9fz5hEpYx7WjAqc3jKlbhmIInKBjCs1962nSqkuuMFBP0B6cXNPc+9uD09f0l60q5BkFeXDCnnPY/RQyKO8LPjwzoXYESR/vQwpNMs5FOQqBQkIrHxaKbUx6qx6FlXMgvE/XmOd7pv1lwAu9blhH19siQYefciWLR0kyDqsPQWsUkd4hKAm+EIETdL7+hAInCqbwhf52lKAcnJ4a0/5W0cWAHpxsSkXgDOSOqVAvUn+1rULNUb7k6t4kl8AJW4eoJ5SjXetvggaOvZF71l8UVeAYY443xiwzxiw3xlwS8P2Fxpilxpi3jDFPGGOmpbYfYYx5Q/3rMMZ8vJhlzUkPfmxzR6EHJzcUOH1Pb2n8gSxwCjXcpWLo+7KcUaa5B+1fKKXy7EuJqBM+SsX2Fk3gGGMqANwA4AQAcwCcboyZ4+y2CMACa+1ceKsiXw0A1tqnrLXzrLXzABwJoA3AY8Uqa156uA7OjtCbGej97SYsBqUWgycZBnLHVGibK5W2NVCeeW8KHNL7RF08slTeWTE9OAcAWG6tXWmt7QJwN4BT9A4pISMrEbwE71fKXT4F4GG1X59jYz1byXhHGAgZ6AMdCpzSZKB0th8mBsozD7JppdJZfhiImuxeKu+smAJnEoC16u91qW1hnAXg4YDtpyGzgrIPY8zZxpiFxpiF9fX1PS5oXvrBg5NrcS7iQQFISDQGisAJolQ6yw8D9OAUAWPMGQAWALjG2b4TgL0BPBp0nLX2ZmvtAmvtgnHjxhWvgPTgEEJKGAocEoWoHpyBXJ80kVcy7gHrAUxRf09ObfNhjDka3q+VH26tdVOcTgVwv7W2f4MRJgaT7NtWOBBWgSSElAcDuUPiQG7gEHUWYam8s2J6cF4FMMsYM8MYUw0v1PSA3sEYMx/A7wCc7PxauXA6QsJTfYmtqkIs3rcaiyEqQkhvQVtCepNSqU9FEzjW2jiAr8MLL70D4C/W2iXGmCuNMSendrsGwFAAf01NB08LIGPMdHgeoGeKVcbIVFbBJPpe4BBCCCGkZxQzRAVr7UMAHnK2Xao+H53j2NXInZTcd/SDB4cQQgghPWdAJBkPdGxVdZ97cAghhBDScyhwolBVhVgiXjqBR0IIIeRDDgVOBGxlFQDAMExFCCGElAQUOFGo8gROjGEqQgghpCSgwImAraoGAMTiBf7UMCGEEEL6BQqcKFQxREUIIYSUEhQ4EZAcHIaoCCGEkNKAAicK1V6IynQzREUIIYSUAhQ4EaAHhxBCCCktKHCiIDk4FDiEEEJISUCBE4G0B4dJxoQQQkhJQIEThWpOEyeEEEJKCQqcCHAlY0IIIaS0oMCJAlcyJoQQQkoKCpwIcCVjQgghpLSgwIkCVzImhBBCSgoKnAhwHRxCCCGktKDAiULag8MQFSGEEFIKUOBEIJ2Dw59qIIQQQkoCCpwIJMdNAAAM2ra+n0tCCCGEkChQ4ETADh+B9rGTMXzN4v4uCiGEEEIiQIETkaZpe2P8aw+hqmlrfxeFEEIIIXmgwInImuPPRXXzNox9+6n+LgohhBBC8kCBE5HGXfYFAAzZuByz/vITIJns5xIRQgghJIzK/i5AqdA1bAwAYI8/fRcAUD/3KDTsflB/FokQQgghIdCDE5FkzWAkqgf3dzEIIYQQEgEKnALoHjIi/bmyo6UfS0IIIYSQXFDgFMCg7ZvSnylwCCGEkIELBU4BbNv9kPTninYKHEIIIWSgQoFTAC9d+Rie/O17AIDKztZ+Lg0hhBBCwqDAKYDEoCHoGDkRAD04hBBCyECGAqdAEjW1ALwcnMlP3I7B9R+E72xtH5WKEEIIIRoKnAgYo/6oqECiejAG163G/OvPxIKf/j8AwLhFj+Gkkw2GfrAEAFDdWI+TTolh6iM390OJBybDVywqOdFX3VCHk042GPP20/1dlB1iwssP4KSTTXn81Ii12OmFe4FEor9LQggZwFDgRMAncADEBw/FiJVvAAAqWxsBwDO4AMYsfR4AUJOacTXzwev6qJQDmzFvPonDL9gX0x6+qb+LUhCjlr0EAJj5j1/2c0l2jF3uvxYAMPyD0v/B2J2fvRsLfvZpzHyAbYsQEg4FTg/oHjoaI1Z5AqdrxHiMevdFmETct4+NeY+2qnlbn5cviOqmLZj7m7NR0dE/ydG1dasBACPff7Vfrt9jlMdpzOJncMKnh0R/p4kEdn727gHxsx5SH/u7LId/Y2/s/qfv7dA5Bm1bn/p/Q28UiRBSplDgRMD14LSPnZz+XLt5JT7634dg6hO3AQAsvJ0rujoAANUteTpDa3Hs58dj9h2X9lp5d7/9u17Hqph1z48x7bHfY/KTt/fadQohWVkNAIjFu/rl+j3FICVwjMFud12Bys42jFjxeqRjpz98I/a79nRMefy2nl8/3o3DvzEX41/9V4/P4Z3Ia+rGFiBwkknUblje40vWblqJif++z7dt+JrFmHXvT9N/733jV7Hgfz5R0HljqcGErSi/X5oZ89ZTGLns5f4uBiFlAQVOBFyB0zEmI3D04n9651hK4MTi3QCAif++DyedbFDdUOfbffTS51HTWI/d7vlRr5V31t+uwn7Xno7ajSuyvqvobNuhc8e6OnLm0ZjuLsy8/+cw3X4hk6xKCZzuzuwydbSmQ30u415/NB3+6ytm3n8txi98yPsjda/WmLQ4E7GWj5rGegDA4K3rCi7DnFsuxLRHfofp/7oBw9e8jXm/+lLB50iXY9vGtAfHJKPnrcz8xy9x1LmzMGzVWz267mHfmof9r/pkzvoy/ZHfYqeX/l7QecVbamMVPSpXoUx+4nbs/MxdO3yeKf93K6qbtuTc55AfHIlDL/Z+4850dw0YDzDpW8Ytegx7/v5b/V2MHjF81ZuY/OSf+rsYAChwIuEKnOqm+vB9bRLV2zdj4isP+LbPfMDL4RidytEBgKqW7Tj4h0cB8DrQqY/+vmcFTCSwxx+/g5ptG32bDzt/fmaX6kEAgFh3bg/KkA3vY9LTdwR+V9W8DR/71GDsct81GPrBUgxZtyxrn2mP/g573vZtzHzwegDArL/8BMNXLIJJhUaCPDhHnLc7Tjh9ZOA1D7r8eCz42acDvxu99HmcdLKJ7FEJY/jKN3werz1vuxgHXvkx749052xgUmWP6gVJVlQB8N7znFu/XdDIfJd//BJzbzwXe91yAQCgoofrLo1943Ece+bOGPfmEwCAWGd74H61G1dkhS9HLvfCicPXvI0RK14veGmEqvZm75opsd9bCeamjz04868/E/v9/LM7dI6arRsw79dn4aPfPjDyMfN+9SUc/7kx/R5W3BFMIo4Z/7gua2BT1bIdg7au76dSRWf8wodw0skmsiezt1IADrrsOMx88FeZtjPAMN1doRMWDv/WPMy/7gt9XKJgKHAi4AqctUeGv7xYZzsO+f5/YNf7rk5vm/TUn9PJx3pa+ax7fpz28Bhrsc8NZwd2IoM3rcKsu69EzdbgnIMxS5/DrvddjX1+82XfzJKq9mZUtLeguqEONh0iyvagaA655FDs+4sz0h6YwZtWYf4vPo9YVweqUx6JWX/5MY74+p44+IdHoXbjCux21xVpIyweourGOlR0tmH3P/8AH734IMS6/R4t3/1tyfZw7PT8XzHvujPTfwd5niamRv67/+/3A8XW9H/+OpL35/Dz52O/a08HgFBPEpQHp6IrWCS4iNdq8pO3Y5e//xwzUqJPqGra6s2yi9Dxy/0PX/kGjvzKTN/IvmbrBhz15RkYsuF97HbXFZjwckZcu+IvzIN31Dm74qivzPBt6x7iic65N52Lwy7YD/v+/HN5yxlEVZv3TIO8dz1BvFBJJXAqWxpw0skGk565s1eukaaXRFllSuwN2bQy8rV2fuEvAAr3AO5x23/juM+NLayAAdRs24gp/3dr3mdQ2dqI/a76dHpihWbqozdjr1suwIwHrsN+V30qPav0yHN2xTFfnOzZjWQyy7Pdmwxd+w5OOtlg1LsvRto/1tmOoeveRXXTFkx6+s8AgFHv5R+cDFn/Hk48dSgmP5GdBjBo6/q8QnXmP37pPW9kcuYGBdjGfFS2NqJ6+2YA3izQaQ//tuBz5GP/n34Cx5+RqWNj3n4aJ35yULqPGChQ4PSAjR89FQ/+I7iyVnS2Ydi6d33bJr6cccHvdcsFmPaQN5MoqKOsatmetW36wzdh9zsvw+RUY9PEujtxyPePAABUtjVndWCHf3MujvuvCemRQND5NdXNnioftN3zBu1/1Scx+ek/Y8Ty19LllZH54K3rse/PP4vZd12eFhvJyprUvXWkjVZFvAsVKc+BiXehdsNy7PmHC7Km+eqyj3vzcUxR+UJDnWeqGf/6IzjyvN2ztu998zdDvT+BJBJZ6xqJt0aHqGKd7YC12PXeqzD/55/D3F9/GZWtjVmGTURldWsDAKCqrcn7v2U7qrdvxpHn7Ip9bjwHUx6/Le/0bZPqZHa7+0oM2bwqPW19/Kv/xJzbv4PautWY8eCvMPuuy3HAT05JH+eG03KFKGsa630eHhE4lSnRPfL9V3KWMQwRjVGFYT4yIaqMwBmy2RMOu6iBRW+g24s7kUAzZvEz6c7IxLt9I+/JT9zuq5/yro87fRTm3HJheruIIKF97FQAXsdpursw+Yk/Rpoav+v916TbcRAjl72M8a89nPc8s/7yY8z7XFBLQgAAIABJREFU9VmY8MqDOfeb+tjvsfO/7w189jUpG1DZ0Yqd//037HPjOQCA6pRAP+5zY3DSxytw3H9NyPbopMRPzjLefSVOOtnkFGETX7w/9f992V9ai9FLngOsxYjlr6Fm20Yc+dXZOOK8PXDcGePS7c4kExiy/r2cZRm+2gvlTnz57xix/LX09kH1a3HMFydjt7uvDD12xPLXsOctF2Ler8+CiXeja5gnHmrr1+S8ZhCHXrgAx31hImAt9v3FGZh701cx9IOlBZ8nFxNSIfyKNq/O7nLf1ajo7sTod17I7DQAlgShwImA68GRje/810+zNgf9hENNw2bf33vd/A3sc/2XMD1gynRVy3bMvP9a7HHbxZlzpn7Yc7d7rsT8n38Oo5c+D5PyhOiO3yQTWUZyyOZV3jnavc514isP5DTU3UNHAQAGb1kLABixchEAr3Nyz902fnragO1/1Sdh4t2+XBt93+nzdHdi7k3nYuYD12HUey/7XLrSQVQ3bUFlm9+TMvPvv8hqpDbwxXj4chdyGMljPz8+c0xbo0/gVLS3+J6vhPcqOttQu3kV9vjTdzH5mTsx7f9uwbxffQnzrz8Tw1a/DQAY+sES7Kk6L+/4Dgzasg7Hf3Y0jvvCxLTwmffrs3wev1yI98LGKlDVtBUH/uiktPANeq/yPtL31JUROJWtjdjrt19LGykAvhyRZPVg51w1WeePdbbndd9L3Ss0/+uAK07EYd+an7XdJLPv06aSqGvrVuOIc2Zh9h2XYtzrj2btt9Pzf4nUuQsy+w8A9vjjf+Okk03gfRzyvf/Af3xjLwDAQZcegyPO2wOA57Gdf/2Zvn2HrXsHgCd8d1HLD7j5Oe3jpgAADr70GOz9u69h/vVfxPSHb0o/71hnOypbGkLLPnrxs4Eh3EMvPggHXnGib9vEf9+XHvUL8ZTAHbP4meALJBLY8+ZvppdSiA8elv5qzJtPoqppa3pQIO/HRdoAgHSIfczbT2On5/+CE08dgsMuXBB6fyYRx+53XuadJ4egq2r1RGr3kFFZ341/7WF85LuHYfq/foPDLlyAY8/cOW37vIJ7tmP+dV/AkV+dnbMOSxl2eunvOOzCBemJAfJed3rxPsS6Ojyh6nT++j4nPXMHuoZ7AmfI+vcw6+4fRQ59xTrbMXSjVz+Grns37VHprcEFgHTfA2RmMiarvBQI7T3uLY/tjkCBE4GwfnT5py5B95ARvm2z/vo/WfuNUXk3gGcIZNaVS3XLNux528XYNbVuCQBUpAROZUcrJj9zJz5yyaFYcNUnvf2VUTTJeKg7uzLlPaitW4MhG8M7JBE4rmvUEx1Nvm3Jyipfbsig7RvTs8dcgTPtMS+/aPQ7L6Q9AoO2bfCN2mo3rQSsxXFnjMOk5+7xXWvyM3fgiK/vGVpul+M/Nyb9ecim7GRrwGuoNcqlWtWy3SdwDrr0GOx+xw9l73R4r6KzLaszGrrWE18iQNx3Lscd9eXpgWWZ/NSf0jPpTEAYL43k/8RiWd44bXzHL3zI8xQ4HYveZ9d7r8KMh27EjH/9Jr2ttm419r7pPBx06bFZ8X8xYnLszs/dg4N/eDSOOndWztFaVcqDE5b/AwCD61Ij1WQSc284B0d+ZSYmvPZwejmGio5W7H3TeRj53iuZxP1XHshcN/V/VWsjhm5cjt3u+REOuvz4rOssuPozOPCKE/GR/z4EVU1bMf7Vf+YUCeNeeyT9WcRImLetqrUR1U1bMHbxM6itW43Bm1Zh32uzc3cG138Q+Cxc76F40ABg2mN/AADsffM3cNS5s1DdWI9Dvnc4TvhsptM28W4Mql+rjkm1ucXPYlD92sDw98z7f46dnv8r9r/qk/jodz7i/zJVl2VgM3LZy2nBNOGlf2CnF/+Gmf/8NXb+998AAPHBwwF4i5we8sOjMO9XX0x3coV0sId8/wgsuPozqOjqSA+Mgphz67fTn3Pl80g7qWrd7nlZrcWUx27B6MXPpm2reCRcjFOvw979oK3rMffGc33bRr7/KkwinvYix7raMefWizD/+i9izFtPAQDm3nAOTvjMMN9x4954PC1wdv/z97H7nZcGen/GLvq/zGSIFFqcjVjxejpPzSQTqNm+CcPWLMb+P/k49rvKK9PoJc9hr99+Lfu+nboEALAWg7as89mQjMDxBj+6XxsI+UPlN8+yCORwFODJm97DnNsuxpSnomeNJwYPA1qDjar2PIx4fyHitcPToQ3NxFceRO2mlT4hYhJxHBqSxFilrpdl6KxFVct2JKsHobvWE2yjlz6P+vnHpnfZ79rTsW33g32HDd3wPgBg0wEnYeIrD2Lk+69iz1svAuB5K1zPlSBJ2oPrVqNdzUgbs/hpbNvrsMBjhLFvPoFkVU2WN0mzy33X+P4evvINwFoc+dXZeOu832LN8ed4U6DV6BwAqlq2pTsZawxGp0amADB8zVvpXKExi5/G7Lsv9x0r4i9Xo65qbUQsZBbToO2bsNs9P8KaY78SOtNmxPuv+pLU3WcgI1UAmSRph1n3/hQrPvkd3/Gz/vLj9Pcf+W7m+TdPmeM7VhLVAWCPP33XX7YVr2Ovm7+JN87/I1p3nuUvV1v+ENXRX56OR2/fhEnP34Npj/pX/65urMekp+/A9Idv8kKFKU/VqPdexomfHoLHb/2g4NHi6HdfxJ63XoQpT96O9jGT8PhtwQOD0e++gK6ho1CtxKTrpdWCdHDKYwoAR589EwnHCwZ4Yi0oVCw5WslKLzk915IK0/91A0al1pQa/9rDaJq6F445a6pvnyHrvby07qEjccxZU9E4Yx6evd4vGPa8LSMShmxagWkP3Yg1J57nlTP1virbGrHbnZdhdqqTHbfosaz3D2RmtUl4pqahDu3jpnnnyNFehUK9DBNe/Wf686BtG1DVvA1b9zwMqPDPrqtp9LzMMmhsnbQb5v3mywCA9YeeBgAYqdq6xn0HVS3b0ZHyrAmz//zDwEkA1U31GL7qzfQAa+jG5WnvypCNy9G4y75ZdR3wnrfkLEq9G+zaqqatOPgyzz4/+IDFiBWvo330JJ+np7ZudfqdzLv+ixi21u8Bfw2Z9r7krF/CJBMwNonEoCHY8w/nY8ZDN+LhuxsRr/WE69TH/oB9bjgbL1+aWbJChGVQfa7o7kA8kfAGKfP2BZCjIy0SFDgRyCVwukaOx/Y9DokscBpnzENt3arQ7/W6OYddtD8AYPtuBwTuO3jzap9il4YchJ7OLiEv4fBv7oPha95G2/hp6Ez9mOiMh27IOt/ogCQ9G4thzXHnYOIrD2LnZzNTaavamlAb4jmRRMtR772ClqkZr8yYpc+j6eUHsvav3+eo9Cygg394dHr78k9827ffbnddgVi8K8uLtuDqU9Of5954LoatfhtVbY1ZOU3VzdsweIsncNyR21AVf5/8bPaU4apUGCbd+QV4NFwDE4TbSWkOuyhTDw748cl45Xv+6dVVOTwR6X1SCYhdoyZkOrAQt7sYWUF7cFz2/8kpGLx1PUYvfhatO8/yieidn70LVc3b0DR9bnrbfj87FatO+qbvHAt+9qlAz9fQ9cvSi/vNeOhG33cVXe2Yd92ZoW1k1t1Xom6/E9E4a0HW0gWS4zV463rvfaUa+s7P3o3Zd12O9Yd+BhMWPoStcw7F0HXvoCYlPCvbmoBEApOfvRMbPvJpX3uqcZJlgzrtio7W7PWxrMWYJc8CADpHeGHTXGJ59t1XpD+74SZBBNCeKU+HeMOEcYseyzpm7m+/hjUnnodJz9yJmQ/+CoBXZ2YrD0KQuAFUIvzqNwEALZN2SwuEoHB81vEdrYHh5KqmregePgajlr6AoRvew9qjvwjAH36d9PQdmPzMHXj3cz/C+5/5Afb/0UmoW/AxrDnh3KzEV50fMyy1snd1yIDTDZUP2bwSh/zgCCz50i+w7qgvINbZjt3UAEEz9q0n0azqvGafG8/BlCf/GPjdsLXvZHmddQhu2Oq38R/f9J/3sAv2Q+eIcVj43Uye0WAlcIJsz9SUVxDw+oQjvjobNU1b8OADNi0eB21dj5aUwBm5fCEAYMzbT6WPm/Tc3Vh/xBmoachOMN/phb+ibaddceAVJ6J5yfnAH/p+NXiGqHqB7pRrFgDWHvFfeOTO8LUrln7xmpxx3EFb1mZtG/WeP7mzOxXrrmnY7JuyHjQbSRixchFaJs0G4IUv5v7mKzDdXRi2ZjGGr/HyRmrr1qRdtgCw8wt/DT2fsOnAj2PbnI8C8IfLKtuaMGbJczmPHf/aQ2nj07LTrhj9zgvp2Uz+a5yCRRdkC0gdxgOA2Xdd7hM3icpqdA0b4x6GGQ/dEJiwPWTjctTWFZ7UB2QScdMdex8k2B3wPx/3/S3TuvMxaPtGDNq6Pqf7H8jckzDqvZcx8x+/xNiU2NQMTo3kjE2iurHeZzx3/vffsM8NZ/tWHt75hb9ir5v9Aicskby6sd7ngXSZsPBf6VwMl93vvAwHXXoMAODIr84OPYeexTPv+jMxdP2ydKeeqBmMjrGZUXtlWxOmPHk75v/yv3DgFSfguDPGpb/LNchIH9/RkuWlq928CtXN25CMVWDw1vU44qu7Y9xbT+Y9VxR8+SmqXh502XGB++/5+2/5Zsy5nXwYkt8lQtvGKsLzdwKoadiEkz6evbbR8WeMxUE/PAYfveSjmPerL6VzxmQZBsALYQOpNVieuB0TX/0n5t70Ve+8zjvROUk5fywZmfBq+tjlr6G6eVs6r6p2c/hgddi6d7FbSL0EggeMQHBIXbcdV2DunArn1zTW+/qW2s2rcy6lsM9vvpL+fPznxqQFPJD5UWkd+usYtVPqvN49d9cOx8jU7LJBzhIlgDfJY9ojv/O+fyo4BFhsKHAikMuDAwDJGs8N/c4ZP8EbF9zui527bJl3NFaeFL6A08SX/5G3PO3jPbfvfj//LAaFTB0PQkIOu95/DaY99gdMfeI2f0IdgOGpHwsVRBSF0TlyAuK1w5GoqvGFfCrbGjF81RvYeFD4KrWVHa3pn25oHxfuuUgMGor2sVNCvw9jxf+7GC1OuCQXu9x/LYauexdNU6Pn+riIByeWyJFHUyQqIsa8B23fiEMuOTSvwKnZvjEtpoU9b7nQ50VzqWxrwnGfH4+9brkAiepBPoG5f+qHaYUmZ3Tres2EKY/fukNrpshsNl0/61T4FQCO+8JEL5SJjHEXktWDfbl2lW1N6Y57rPMjrFEWZByz+Jn0zEdBcpCap3mJykPXZy970BvITJ9ciOdGCAqRB1HZ0YqKtua0GB25fGHwfYQk/c+//ouh5x735uOZz295AtsGLLgZ6+7wJXUP2rIu24Oj6n3YvclSIG4bcfMXw/L70tcPCdMDQOeIcaHfuQxb+46XtN3VgbHqWQDAfteclv4sEzHax0xCTcPmtFewEOb+5ivpTm+QyumMDx4KAOmcq44xk1DV2oDKlobQteGkPzNt/fMTQRQ4EcgncDYdcDJeveRvWJ7KbXAPePFH/goZ1JmvTLnrR6x6M295OkZPSn+e8Kp/Cucjd27DUzcsDfRcuMJr7o3n5p0+uHXOR/HonzaHCp1ETS1gDLpGjPN5PwZvWYvKzjZsn517YTNJcO0IEDDykxidIyf4cnWiYpJJvH7RnWjYNXwmhrDilAtQW7ca1c1bsfnAU/Lu3zF6p8DtNds2YugHSwJnPbx17g1YfFb//2hnzbaN6dl1uaitW4OuAowwkJkxBXihltaJM0P3neL+bEjIAooTX/0nJhQw88mlprEOs+7xhxG27n1E1n673X0FBtWv9eXbAEC8phbxWiVw2ptyLvaZi/jgob7OGgA+cvHBmPLEHwFk24bGmfPx6O2bsG32QWiZtFvW+ZZ+4arQa7VOmJG17fBvzSu4zGGhZgDoGjY6/bm6YTNOPG04pj/irbsyfE3wD7vu6GrqI1Me7aAVxSeqvBzAm7lU1d6M5il7YONBH0fzlDlZoTphyZd+nv7csOt+gftogTPhlQdzenBcJLdKqClwzZixi5/G8JVvoCJHvpksDNo5ckLaM18o0x77A4at9Wb6DftgKXZ64V7M/PsvsgZQHaN2QiwRxwmfHRU6OBFi7Tv2znsKBU4E8gkcxGLYdMj/y0puA7wEsC37HOXbFjRdcclXMovArT4+k42vE7oEPcJ0K3v30FFombIHVpziTVFeeElmobvtTpIw4E8yDKJpxjx0jRyfnlGx6YCTfN9L9rw7RVlGRpJkCHgiIghrjM9QCq0Td8GzP38Vdfsejw71+1/JiCvYmkQc7RNn4Llf+EM3z/7ytax9NxyaGQVt2fsIJPL8HMM7X/gZHv3f7HDEnrd9G0d8fS+MWJF9jTUnnoeWKXtEKnsxiIvbedsGtI2flvX9FqfTH7JpRUEGHPAnk3aOnIC2gE42DO31euGnz+Lts3+d95jl/++/I507MxsuU7YVp1yAtvHT09tq69akZydqbEVlOvke8BI/c3VOuUbmMshIVA/CppSQHr3spXQOX4cj5Cs629A1agJeuObFwEGGbl8udfudEPpdIYR1qE/89n2fLQrKTXM7dQBZszHz8fbZv/blWO304n0Y9c6/Mfrdf4ces+j82xEfNASjUuuyrDzpfCz83v1omLV/ep/20Tv7jtFJ9ImaIVnnTFTVpPNQAC8PbvJT/xv5PjpHToi8b9a1qwdhwVWfwt6/y57xFPVaa4/4fPpzfFD2/QUx9bHfY8HPPo09b73Il74AAJ2jggd5QZi2ln5ZF4cCp0i8dPkjWHZa8A9oxp2p5Q277Ov7u2n63unPjTPn47Vv3+UL0Wzb81As/vJ1Wed99M8Zo7v81O/hwQds2jB3DRmZTs4DEDl0Uz/Py1+QmTDbZ/tFkoyidBLzB0edmf7coYzIUjVCAoC3z/Zc4cZaJAYNzbp2ZUcrGmctAGIxJFPGZ8Mhn8Tjt2bnKQHAtt0P8f2tf3dpyRcz+TqNM7JHsc2TM8KjdaddEQtYa0XTPmYyukaMy/LOCeLGdamfd4w306MP0Ab8qd8swVM3LUP72CnY/Y4forZuDZZ/4mLf/u+dflmWh8lYG/i8wtA5C50jJ/gM7bufvSJLIGt0zk980FCs/s+v572e/uHbQth04ClYetYv8MTvMysLVzdtSS9wqTHWpusf4IXpdEenSVZU4v9uyw6n1acGOdJxtk7cJSvsmqysQnftcN+2fD/R0TQz+92sO+x0PHfNS1gSYCPCaMsRIg7ivVO/j7aJM8NX/pbyTfPbMsA/lblxxj6+/Rt23Q8P37nd54GODx6Kun0zYm3o+mXZU9oduoeMRNfwcRiWCrl3pwZQrRN3Se/TttOuzlGZkezmBR9DoxNCtQFiLaweBLHo/D+lBxleGUdg0QV/wos/ejywr9Bh1OZU2HzkitfROTyzgnDdvsen60yHamtBAmf1Ceep7ydGKrNOvp71t5/5vgvzYgdhuruBpsKEbW9QVIFjjDneGLPMGLPcGHNJwPcXGmOWGmPeMsY8YYyZpr6baox5zBjzTmqf6cUsay7yenACqN/3OLz32cxMh5cufwRPX++Fn+rnH5tepG79oafhuV/4G4lueImaWmw47LS0q3/dYadj5cnnY9XJ38K7Z/jd7l2q4rs0zZzvSzh75tdvY8Mhnwrc99//8zSeu/pFPPPL19E6eXa6nIA3xVIT5CZuV6PijjGZcJp+kB2jJqa9Jiv/8xvpRcIadt0Pz13rJa65I4Z/3duO1y6+J71WDwC0jZ+WNhofHHMWVn3s63j+qudRP+8YrPj4Ren9Vn4i8xmxmO+7Z3/5GhK1mVyTjrGTsej83LPiOlONOygUmJNYDEu/8LP8+wF4/9PfS39+97NXYOXJ5+c9Rnv/Xv3+3/HKDx7A6xfdgZapc9AxdjISNZlpy52OgUpUDUL30Oz8sWevX4Ttu3mhxpUnfROP/HkLlp12Gd4++1d4/mr/KHrq47emP8eHjEwvFrfm2K/g/dMu9QneXLh5MIA3K6dd1ycgveKrZv1HT8XTv3oLi771x8BzLz3z6nSHp+vkoK3rgr0L1mbVxTDaJu4CW1mFDYd8CnXzvSTeRPVgvHz5I3jonub/396Zh0lSlPn/G1XVd/d090zPfTPMACMOjA6HgMslKqjArhcowiq7uF67iq6yXiiiruKq6E9RVlbRRXEB1wVFTpFDZHRguIb7ZmBkDuaGObo7fn9kfivfjIrMyuqu6q6ufj/P009315EZGRnxxjfe943IYk7KhlceWczv4MC/a0JfcQk8iYVzPDPgbdMXYtX7/gNPHhcNXnd/9GJs2usg74BMln8uHspxl/a7cGUXefiUc4FczruxqeRB0dafPC7wPkxdEXilb//SjVj7qvheRbd+cwX6O3tiHkZbaC72+fWvPCJ1g08AePJNH8a6pa/HYKGpmFPIpc6bF0bhancbhI17HVzMM9k9YRJuOf/uopcNKE26d5HiaeVHSx/XsGHJkfjdZVF9XfOLTVh95Huwfr+j8djbP41Vp38TD707eOjydRevwTPHnF787EBz1B+2C0/e8s//tuglXfHp/y2+7hM4L4lw8eN/968l77uUm9i4Hhy3jUgGeicBG9N30a8FNRM4xpg8gO8BOBbAYgAnG2MWOx9bCWCZtXYJgMsByO1cfwrgPGvtPgAOBFC7h5WUYSgCx2Xdq96ArfODGcFgUwvu/UAQp2YOi0R2FBp6eiM27v2a4ufXLYmHvnxwmap0v7IMrlFb/8oj8OyR78GGfQ7Dpr0PxpYF0S6yD5z+TVxzyQasOehEPPKOz2D1EacUjwMA94sQGweJF/c+BC9NX4Cbv3UX7vx4/BlBN3/3PuzqnozrfvI8Vv3j+UWBM9DcXhQNBSePZbC5FcjnYzvq3vS9B4uruHb2TsP97/8uNi4+FHeccx12TooPpjJ08MD7vlE0lO5gavMFPHfEu3G94ylafvbVWP7532LbzEXF0IYvR2XFp9JXn21xZq0AsGHxa52yTsFDp5yL275+Ox59+6fx6EmfL8lnue7Hz+H2c+MrbTYJV/5gcxteOPAteO7waLO5ez8U7Luxq7O3xOM12NyKl0TbA1AUNlxuurujF7snTMIj7/oCnnrzR7xhT7L6iFOivK8wv8a9p0kMhC70qy/dgpu/vRKrjzgFfzr398Xyk90TSgXmlvn7Y+u8V2L10afFBsMXlr0Jzx/6djzz+n+Iff6hd30R/S3tMNbGkk7XvObvimXPGlbZNit4JMOdZ12G5V+8Brf8x59x0wUPweYLGGiLvJQPvefLeOzvPond7ROKk4fBQkvxga7El69Cr8jvv/8QkM/jiRPPxOMiVCeFDScLLluFlxgIvJbk6TecEXvvpu8/iPveH20GKevUlHmUwktT5mEwX0B/a0fxXi36ZTCI7+qZEksUloJU5jwN5puKgtTmCrGwosuNFz6O+9//XQw2x5+LRE/2+n2PKL62VSwmuPqy7diyYCluPW857j/9W8Fk0Bj85TPBVgzbZu5VDLE/fczpeOC0r+Hhk78AIPKU5ndsw3U/fg43XPhE8Z4CyOSxHWxqwRMnfAyPvv3TuObnL2Jn77SiV7vfGSO2zRCTzFyuOHFlHwWA3Z3xkP+q9/1HbAL89Bvfj99csRPXXly6vJs89zelK1olUkQ9d9g7vDudkzX3rgfmzUs9Xi2opQfnQACPWWufsNbuAnApgFj2prX2Jmste/AdAGYBQCiECtba68PPbROfG3GqIXBcnj/sHVi339F4RLgm/3rAmwHEGw6NFV3/u4XHYPPCZSVuVJf1S47C2qWvx6ow9LD87KsDwwiU5L089O5zcffHfurNJbL5QmBk8nk8fMq5RbVOcfDkW/4Zt553B1acdXlxWSlF0JYFS/H84UFnWXHW5fjTl6KdOndOnA4YAxPmX2yfvmfxvY17HZx6bUAwKFP8lVuVcNP3H8INF0WJ0OyQ3Ixt3f7HxDYz3Nk7DatFJ1/76mOxdtlxuOmCh4shC2k0bv/SjbjhP5/EmkPfhmt/thYv7n1INEjKMre0Yd3+x8RCeY+cHF9O+vxh7wCMwca9X4OH3vPlkmPs7J6CnZNmYMOSI/HIOz5bfF1uTObzgmzY93BcdaXFtT9/EZv2Ogi3/Xu074zN5fHiK16LP3822ouIHhoOtNJ7lsa9H/wB1r3qDcWBpfig0pQBW8KyD7R3Ycse+2PlmT8LvIHCi7H8c7/xetBefEUkFpn8uHn+fnjknZ/DnZ/6n5JrePSkz+P6i0tDU7x3xtqiJ5Ih2yTcPJnNCw+IJQ4PhG2uv30CXjjoeFxz6ebidwZa2koeRuvLMXroPV/GVVfaoncVgHdDQSAQvMy3kJ/Z4YQoNu71Giz/3FW4+tItuPdDP4xf06y9i0Zww+LDcO0lpbv53vp1/5LnHZNn45pLN+O6n60tuVc7u6cUH+Hw8Emfx+qjo4cY94tQ3ct9s7ErvGe5/l14JBQW5GrhFZFeChleYVrAYEsbbv3Gctx15n/HJnhsb9vmLMaTJ8Q9pb/7xSbc/O2VeOC938Bvr9iBez/yIzz+1k9izSFvxaYFr8J9H7igeIydk2bg5WnzYcUePbef+3v89oooQfeWb67ADRcmPHQ1lyu2z/5Q4OzsnRbLO9w2K97Gnnld4OmR7ay/LR7yf+LEM4FcfLi3Tc1F0f3XA48vehKBwFu6+ohTig/99MF2e8+HLsRdn/xlWc/aaFDLjf5mApBT4NUA0pbUnA6ASyUWAdhkjPkVgPkAbgBwlrW2/JPmakAt7lt/Zw/ucPI37jzrcuRf3uYdmDrWBLsGb5mzb/E1my/glu/cg9b1q9HiyR0Agpnw8i9Gz+SRiYcy2fnmb6/0xvOTePSdn4UZ7MezR/998bVNex2ETTgIm/dYis7nH8Xzr31nyffWHFKaxAlED+ncsORI9Hd04+bz747NKhMxBvd8+EepntrcAAAgAElEQVSYe+2F2LzAv/KB7O6aGIUmEITX8rt2FA3/HefENz6z+QJWfuLn3uTJ4jHErGXDfkcV/97VPRl//HqQ4PiW40sbEM81J1w9M+BsoidnY4Tej+de+86iUQWAh0/5EnZ3TcQrLjqzuFcFkDzoSTYuPhRb5r4SE56+rygUX5B5MqGBY4jGJ3C4k/X1//Usjnnf7Ni5uYUCB27f6rIt85aUrPjw9QH5+rNHnoq1B7wpesRDyFW/HogZ8rs/chH2/NXXcMv5/pUzpL99Al549bGx1VrR85MsHnjvN7B+ydF44YA3Y9Gl52DhFf7VS/QmJnHjhU+U7I5Lz8ZAc1vxvSeO/yhW/UM8H2r9/sdg2l9+4+0Xg83JmzCyLd37ge8Xl2Jb5xllu7onY+2yaMPAay7ZgEn3/aHo6aGXYMu8/bxt4GVP0vpTb/ynWP9wBc6urknFxHL2f0Jh/OxRp2HT3gcHD8RE8LDe1Uedio2LDio+wDSpraxd+gZMWXlt7HhAIPo2LTqw7MMzicyZHMxF5dw6d1/cGi5YWPXeb+CFcIIqGcwXAq+zmDRuTlih5cIH/e7smYb7PnBBcR+n7TP3wvZpexQ3TH362H/CM8ecDtvUjOcPeStm3H5FsFt+BgZaO/CH796H7dMWYPLd1+PAL5+AXR09uOmCIJS6cdFBifv1vDRtD/zmip0lbemOs3+HA776t3j+sHeWrpQcYepiJ2NjzCkAlgE4PHypAOC1AJYCeAbALwH8PYCLnO+dAeAMAJgzp7IkuXpksKkl0c1HI0UXuGRH36zYKqOsSENVibjhd+XKL8lL0/bA7V/NvsEXEOTP7OibVRRgvjBOEi9PnYeHTi19Blg5/nrQCZh908/KCoGHTzq74tVEkvX7Ho4XE9zUj59wJjYtOrBkgPLNnCgyts3cu2SQeeIt/4Ktc14R8zBkETgA8PiJH8fS8/8+dS8i5lr4VrutOOsK5Pp3YaC1A9tmLETn89HqGm7GRg+du4s2ELjCp//xMuTFwJ/UDza88gjcd8Z3sTpcEVLiwXHq7dlj3odnjym/Nw1Quo1CcWZjLQbaOrHmsOD5PY+97SzMueEiPHzyF7HkBx/EbV/7IxZdeg6mrLw2FgLx4YZNgSBUMpjL4+F3fwkzbr0UAGKzafLkmz+C5w95m/cYafeatsPmm/DIOz6DaeFu4bu6Jhaf6t3vJDfv7poYrAwN+esBb8Gq956Hp4/9gPccPtFz3wfjuxeX3Kt8vviIDfehsMz1Y44OQ+wUgLuddnj/6d8qEckrzrocx4XPeXKvD4jyjrKuykwjluMXcsu37oxNOCqF5XvixDOxfeYibJm7LyY8fT9emjofN3/n3mina2OKIuPOT12GOwHMvsH/rMN7P/iDkudpce8l9tnVR0WetBf3ObRE4Lw0ZR7a1z6FwebWuLgJ+8uu7sm4+vIgNWL27y8uuyK1ltRS4DwHQC4TmBW+FsMY8zoAnwFwuLWW6xFXA7jbWvtE+JlfAzgYjsCx1l4I4EIAWLZsWc3WoNWD5+22r92O7qfuKVHLw8E1EqNKPh+bQY4E93z4R3jkpLNjCcY+HnnXF8oeKy3Z+E9f+UPiew+cHqws63Q2WLS50q759LEfQN89NwbP0nLJ5WLPDgOSZ7Yuq48+LRYe8EHPi2+LA1towkAYSuUqIZ7bFp+r5PfgrDjrcqw94E1Yv9/RmHrn7/DHr9wMA5vc6YyJra5irs7O7sm468xLUq+hLOLBpP1tnUWR6eaZ9Hd047pwi4CnjwsG/OVfvCbYwC7FpZ/E7s5e/PbXQXJxV7h3zEu+/YOM8YobwL/Cp/heKHAGC014+JRzgwRhBPuYFAVOuRl/Po8n/jZ5S4nB5las+NRl2N3Zi9d87nXFhGKJtDe3hAsrIoETF7SPvfVTeLlvdhCqRbR8fu2yN3nL64aVAMRynrx9wRhc89/ra/bU683O6thK2TFpJq66MhrW7v7nH2PBr7+BrbMXB33Ot9Q77Dc+gQzAbztC1i85Css/d1VsxdpOz6KA27/yB/TdfUOifZH38pqfvwhrcigNQo8MtRQ4fwGw0BgzH4GwOQlA7NG6xpilAH4I4I3W2rXOd3uMMZOttesAHAUg+3q8KlMPAmfr/CXFJOVqwRUtWz1eoXrn+oueyfQAvzRsUzNemr6g/AfLcPUvtw5pYJOYgXj01Rei2jFpJv54nt9dLPnjv9+KmX+4JHXQK4f7aIyiwCmTg0Ojy5DbhsWvxa7O3uImmFvmLYlttLYrTIa86+M/x4zb/ifIn6mkwxmDm/7f/dgxcSb6PSvAKoFepr8e+Baset83i+G1pxK8FiUMsw0AwON/+wmsX3IkNov9WoZNKNxsPt4eNi08ABOeWYUX9z7EmweVhdV/c3IxhLvm0GBV5lX/N+i9h7I9MlGYHhk3RGXzBaw+6tTi/zsnTsd1P3m+mPuXltDqJaFN+ZLU65XNC5fhrn+9NNNntyxYiqt+PYC3nJjPbt9zOax1wmys753dk4t7P708ZS6eff3pJV/nMnt5b7Lm7NWKmgkca22/MebDAK4FkAfwX9baVcaYcwCssNZeCeA8AJ0ALjNBA3zGWnu8tXbAGPMJADea4I07AfxnrcpajnoQOLVg+8xFuO3rt6cu76tX3Cf6jiYDbaV7+FTKzt4g6XPNwSdi+h2/xoYlpTvtZuXFxYeVzQUpx2qxKRiA4lPQyxksJrTm+4NZ8e4Jk3CteDbbfR+8AE8d90Ec9qlDYAYHi7P6/s4ePPPGM0oPmIFtw3i0hoSD7eojT8VLM4I8FzmDHhFyuWGJG3cfHQCw+ciDI7n/n76HDUuOChYDDNHIrfzEz7HyE/EVkmnHYr4Xw4FcNeaGqHzEtjXIWN7bv/IHdD01tB19xzy5HK792drMnlwf9EI99tazgg1QMxzLNzkbLWqag2OtvRrA1c5rnxd/Jz7UJlxBVV2XxRBpVIED+Hc3VkaeXT1TgoS9QhMK2zcP2xtRK8oJnK1z9sWUldcl5oQMtLRj014HFcM+uzrrJ0zKsKC7pcJYISnnQ+bgSAZa2kuEbK3549duCx53EIqudfu/HnNu/Ak271F5OGflx35aspeNy4Z9D8eGfQ9P/UwjU+mjVly2zd4HN1z0dPConDIeSity1uqFukgyVkq5+fy760oJK7WH+VX1Km6A9NU6APDgqV/Fxn0OyTyo1FMe2P3/eD52TJyOdUv9T9mud5JyPoo5OPmhhyyrRX/7hFg5nz/8ZKxddlzJ7u5ZGGlxNl5JW3wgeeTkL2Dpt08r2UB0NFGBk4HR8OBUsopIUWrNLd9ckWlbetvUnLgVgI/huM+rzc5JM7DqjO+U/+AYg8vdh5OTVUuGIm6U+mP1UafG8qbqARU4GWjkEJWiZGHznq/OvH9HFv50zvXou/f32rlGgHry4CjKSKICJwNqgxWluqzf/3VYv39iCp5STZiDU6ceHEWpFfo0cUVRlAZGPTjKeEUFTkbUi6MoylikuGEh6md1i6KMBCpwMqICR1GUsUhxNWaZJ38rSqOhAicjKnAURRmLUODwYaqKMl5QgaMoitLAPHLS2QCiB1gqynhBV1FlRD04iqKMRV446PiRf+SEotQB6sHJiAocRVEURRk7qMDJiAocRVEURRk7qMDJiAocRVEURRk7qMBRFEVRFKXhUIGTEfXgKIqiKMrYQQVORlTgKIqiKMrYQQVORlTgKIqiKMrYQQVORlTgKIqiKMrYQQVORlTgKIqiKMrYQQVORnJaU4qiKIoyZtBhOyMqcBRFURRl7KDDdkZU4CiKoijK2EGH7YyowFEURVGUsYMO2xnRJGNFURRFGTuowMlIPj/aJVAURVEUJSsqcDKiHhxFURRFGTuowMmI5uAoiqIoythBh+2MaIhKURRFUcYOKnAyoiEqRVEURRk7qMDJiHpwFEVRFGXsoAInI+rBURRFUZSxgwqcjGiSsaIoiqKMHXTYzogKHEVRFEUZO+iwnZFcDmhrG+1SKIqiKIqSBRU4GWltBRYtGu1SKIqiKIqShcJoF2Cs0N0N7Ngx2qVQFEVRFCUL6sHJSGdnEKLq6BjtkiiKoiiKUg4VOBkxJhA4U6aMdkkURVEURSmHCpwKyOcDT46iKIqiKPWNCpwKyed1V2NFURRFqXdU4FRIoQC0tIx2KRRFURRFSUMFToXk8ypwaoE+CkNRFEWpJipwKkQFTm1oahrtEiiKoiiNhAqcCtEQVUC1PS6FIe7INNTvjUf0cSOKoown1ORVSD4f7Go83mlqqlzkJImRXG7oidsqcLKj7VZRlPGECpwKUQ9OQFNT5R6BpHobrsCptWeinvODKimbCpyhUSgAzc2jXQplLDGSK23r2T6NNipwKqS5uTKvQaM2vqEIi6QBNpcbukhpaqp9/k49bwvAgTdLO1NhPjSamoCurtEuhTKWGMmcwkrP1ahjkg8VOBWSNhjLhtPUBEyd2rhPIG9urryj1MqDU+swVT2HwVinafXH+6QenKFRKKjAUSpjJD1+afbJN1YNx96mHbceGSPFrC/kzZWDthxA2tuD51a5Dakag0w9DFTV9ODk80PvMIVCZTOYJGOQdoyxIHDSysj36qHdVAtfe6mVp62pqb7bQD0xnutJXnu9CBzaVtlfhuMxJ2Nl4q4CZwjQkDY1xR/dIG96R0cw+LgNaY89/MesxBvS05P9s7VC5uC0t2f7TlYPTiUDFT04WesvyfCkddh6NdotLZFo8ZWR9UgxWk95JMOtU19bqtV96uqK2no9hytHA7c+Ku27I0UuB0ycWNtzyEmS29eqfa3yeGmTMwocWZ5qeHDGymSppgLHGPNGY8zDxpjHjDFned4/0xjzgDHmXmPMjcaYueK9AWPM3eHPlbUsZ6XQ2PX0xBsKB8lcLmgAzc2lDckdSHmsSp5x1d1dWXlrEXOleGtvByZNSv6cvN6ksJbb4WSHLTfToNDK2mF9g3wul56fUi3jVG2j0N4e1amvjB0dwe9CIWhfQ5m1DSWXIO1eVMubxPslr6kWA6YxQX/jeWqdW+G2z6lTy38nS33XCvf4lZxvJAfJ1tbKHpScz6ev+vSR5sHp68t23qx9VE4qy3mf8/lSm6oenGFijMkD+B6AYwEsBnCyMWax87GVAJZZa5cAuBzA18V7L1tr9w9/jq9VOYcCDcrcuX6B09wcNKgsAofvT5iQfk6ZA9DSkixafAP4tGmlr6U18CxGfO7c4BjlknylGMvnkwWGLA8/k8sBCxakl4PeCX6/nNH0nb+paWiDRBYj0dYWDcaup6utbXjis709MsTlBM6cOcnXmHYdQ/H6pH2HZaqWwJHiuhbiI5eLe3BGWuBkGRjT6rtcYvlwJz9DFThJtqBWtLbG713afWxtDezxjBn+95PuSZoHZ+LEbJMw3/3y3SP2I6DUgy3/5rMTXYHjK0slXp1xL3AAHAjgMWvtE9baXQAuBXCC/IC19iZr7Uvhv3cAmFXD8lQNuju7uqIBxpjIaHOlFQWObLRuw2DDcwWOO+jMmRN/L8mQtLXFG6ox/plLmsdIljGp0UvPQZpRk8IsKUziCgx+pr092cgAQX1zhpLVE5Z0/nJxbB9ZViX19AT3dv78UoEzZ05y8mqWgbS9PapTWX7Wi8zPSXtIbFoC7VBc7dwjySecWAflBE65gdcVOFlXN/J6sgosChyWp5YChxMGWW9ZzpcmFMqJiOF6eIYqcNgmszLcem9rix8jbYDu7g7qbb/9/PXX0+MvT5oHp6Ulm9fFVy5fGdrbo/O5Aka27aQQla9vVrIitZzAqZeVWrUUODMBPCv+Xx2+lsTpAH4n/m81xqwwxtxhjDnR9wVjzBnhZ1asW7du+CXOSHt7IBooJvr6IsNUKESzhfb2oGHPCmWbnLWwAbCRSiPq20xQDqZJCtyYYDB1ZxK+RpvmMZLnTtoDhANYOYEjz5MUCnLDJxSMnZ3B95OO39kZeXBYt77jy+vxXYvP0yZxxYPv7yR6e4NydnUF7cUdvJLyqbKIJwocaeyAIKwh7zsFZNI1um0hSy5B2sBJ4Zvkwcyyr0y5gdL1ivG45eA9yJo3xsGD15FUbr4/HM8U27Mr9l3vkcz9o6c4qTzl2tFwhcNwBE4l4iprCF8eU9bjrFnZBU6hEPfGE0623HvEcyYJnNbWeL4ckHxffO0nSeDIFZTyM+4EVU7CgdLxQ7avRYui/9NWC7e2ptdhveT71UWSsTHmFADLAJwnXp5rrV0G4F0Avm2MKQlWWGsvtNYus9Yumzx58giVNmgc3d1R45k8OTJE++wTdMZJk4JG0NoauRMpgoyJwkZyMGcHaWkpNTyuKvcl1hYKwXnlLDBJ4KTN2qU7N5eLN2QOhpwFpBmqfD67wGGH6+iIjsvwVpJHqasrLnDouXCRgxk7nuuuzrIKieckWZZoc0DlfZDnlcbHJctGihQQ7ux08uT4wCfryIcrcOQ9ckUjBbw7I5Xw/rltj/e/tbX87L3cwMsy8RqzCpyWlqjOsoSd+Bl3QpJUXg7EroGXIQXi9t+urtL7Luuf97GnB5g+PXiNeVguPF81PDhSWJX7vu94aV5TX/icyPpJEzju5Mh3XydOjJctaXDu6ysVODwOvYXuPWI/kLu7yz4zaVLQx5IEjmwbPoHjs5ktLZUJHNn/XVtAb3xra3AtrOsksc5xjZN8H/Wy8rCWAuc5ALPF/7PC12IYY14H4DMAjrfW7uTr1trnwt9PAPgDgKU1LGvF9PYGv/P5wDAxTs8bL13xbIhsyFOmRLklFCVyFt7bW2pMXA+Ou6MyRcnEicHvmTODRusTS0D6DLatLR6CYiPu6AgGT3cmIBuyuxdQR0f0vi9ExcGGdcPcnkIhqmN3dRqPTYGTz0er1nzXKg2IHBCNiTxRcsWRKx59AscNSfqMATs5xSDrleei8Vm0qPS7rnCU55MDjs+Dw0FPCpy0++SKUB6fIkXWf6EQ1bsrkuXMj991BxUer5zAKRRKHwfiy6FgH3BzLHg+F4or6UXyrXYkclBwyyDh62xr7rknTy4Vku5nKHBk/2pqCsrMNtrUFAj/6dPjixlcsgqcckKytTXyMrqflffXXWUm26tv9RLb9157Jde97FNuqNstB5Ht3BXXsu/Jumd9GwPsuWepwHHDmq59kLluLS3Rtcl+3tISTd6kOAGAffeNroltsa8vqrcsAkdeq7TtLIdciOCGqNi+aK95v5PECydUrodW3gfZNkZz5WEtBc5fACw0xsw3xjQDOAlAbDWUMWYpgB8iEDdrxeu9xpiW8O8+AIcCeKCGZa0YNih6Dnp6IiMkk9CodoEoVNXXV5oAakw0w58xwy8EZAN1BQ49QBQUM2ZEXiRpfGlk09yLTIxleXiM7u7gmDIJmAMRkeVubw+u69BDgXnz4oMnoUeAsyN28EIhHn6g4e/sDP6eMiX6O5eLQlnurKGrKzqOMXFjJUNc0s38ilcAhxzin7Wz/jo7416EBQtKZ+T0TFFE0a0sV9vRy+V+V4aUKFRZDil0aDxlCEV6FnltFIH8HI0Y61kOCnIbBN5/lkMKHF4/60SubKIBlPe7oyP435cnJmFfkOFdIJrtG1MaMnI9OC0t/twzOQBRxDc3l3oIXGHjEzhSIMv6k32e52TfkciBiBMlWf+8j+yLFNK9vZFHwOehlffGtSP8LAWD21/4Pq9twoSoDfu8yjLELo83f370mm+VJdvdhAnJIkxOWOT9kV4aeY3sT7L9EtemtLZG7a67O3qP9tMVOOyDvvPTG8g2SxsmJwqsE07MeCzmSEqhTjHG9utOFFgWWR45meExgMh7I9uoz4PDc/Ke8Lp8cDyQ5wGi++yOC93do5eTUzOBY63tB/BhANcCeBDA/1hrVxljzjHGcFXUeQA6AVzmLAffB8AKY8w9AG4C8O/W2roSOIQeCAochqCINHbspOzUbkOg8GGYSSKTSWWuD+FgDwS/J0wIjsdOBQTekblzo3IlQbc4Bzger1AIjsnvyhCVdM3yfbrRJ0wIOqsvTMLr4MyaxkS6OPv6guN2dQXXOWVKUN89PVF9cNCVxonnpZiU7nyKUhpv6cGZMyeYPbGjS+PCY/X2RqvZOMi4gyQFBQcLCkx5TgpXaXB5Tp6Xg5mc5cn6528aTrZD3gt+xpjIK0bj2dERlJ9LoV0XvwxFcTCXoZQJE6I6ccvluqk5eLh5Ve4MlfXT3h6INNYBvZ5yYE0SOPPnx/+n2KfopGBoafELHLYhKXR8AwNFhzvhkQLj0EOD8vf2xlfVyXvKepw2rXRw4vVycKQgpqfXHQBZrsmTS+uWE5SZYTakLyQGBHW/116RePblcMk6ZziZ/8+bF9XJxImR2Jbn4USQ/cgtqxRvsq6kV0KWmX2L7UaG2Xkeii7ZNrlghMnDPoHT0RGfWLi5dLwHFB5yMiUnT7Slrlefn2FeXaEQTULo/SHso7KN0QZzsrI4XK/M+yY9amzLsl5oe+VE2s0Z5H3gfZf3ZP78yF6y3bJeaGNGg5rm4Fhrr7bWLrLWLrDWfjl87fPW2ivDv19nrZ3qLge31t5urX2ltXa/8PdFtSzncKDbkQOEzwi0tgbGmQMFGwINFxvU9Olx9x8Qd1cXClFCruvB4SyEx+EAThEABMdkCMududHbwgGAhp+dkseRhq6zMyhvV1fcWM2cGQ0mhAOvr354rRxw3Nye6dOjDj91auQ9kYaXAodu1lwuulZ2bBnSoiiVnRGIl8+3SzBFC1fQyfyrffeNX5v04FC0sZ3wN+uE7cQVPmwvkyaVenBYbh57ypTI6MhBUnoF3BAKvVjMFZOf5eBNgyZDbVJYuuEiDsqu61wKHCkcfN5Kiieen+Xi56WRlmUi8hzyOqUQpsfTHUBk/UgPDs9POJi5AkeGNXp6Ird/by8we3b0WTkwdXcH5+ztjd87CiYO9r29kR3p6orOReHKsvT1BYOcW7cTJwbf5zHk7J9lopdt7tx4f2R9uvaL18nj0WsqhYcUPEBwvTwO+/3ChdH7tIG8X1LEsS24+U7sz5xIuALHmKAf8Xrk+Vtb47lrMhxFuyk9Q9IOyvw6tgfZrl1R3NkZHZ8TDZ/AkWFGaUspsNjfaAuld5T2mPctl4smC9Lm8D7KPB4pcFzR2dYWlIWClBMn3ieWl7a4oQXOeICdhI3OHcDZyaQbUg7ockCR3589O/i8zPXJ5SKBw84ERCt1pCAAolmV9IjQ8MhyFgpBKGD27KATSLc4f5hUzc8DUWipqSmIXTME09sbKHp39ZK8PtaBdF9LgSMTUTn7aGkJ6lsaTw5wMu+koyMQWfvsUypw8vlISFIU8nzy2nhs9zUen4Zk7tyoPuW15HLRgE6Bw3tCYyMFGhP8OJi5MezFi6NBmYMYRRDrgLNMKXBctz2NmjuD7OiICxy2sc7OwKMl3e4sGwcZn8CZMCHuFgeC6+MMNovAoUeK4o6innXKrRNorN08BNnGWVfSg0MxLGf2xF1Fw2v3CRwprlyBI4/b3h5MdKRoY31MmRIPIfC7bBv8rFx1x/qhwMnlgn7IAZmeAjnAU0ixvbOv87yso56e4Di8V/zp6Ih7gaVnCYi8B7Q58l7K+pP7Y1FQSNHF/tzXF3i1ZH9g2+M5pdeJOV6+/CAgSjamHeDxKHB4HLktAMuRywW/3dVusk/QTvlCVLxn0nbznKxH9l96kShkWCbpZXUFDts87xMnSxQh0uPNz9PTJPuhFDhu32xvj+pdllVe7+TJcYFDuz4aqMAZJjJO6RM4ZNKk0jgkjbVP4HR0AAcdFLnl+R4FDvdqAKJB0w1rycbJwYszA9dQ77lnYLjkzFsaVikq5GDJAbKvLwolMZYrG7XrweEOra7AYWdz478cFHK5eBI3DakMyXR2BgZfdjKWt7U1mj1SmPC7MlTFOgfigxcHCRoT1guNCEUrw2nSgwNEr1EgceCl4ZNlZFn4W8a+pRGloJFuY/ldObBIoyZnhpxlSYHJ71FY0AhSbFBUuQKHxlR6cHg83hPZ5n05JGzrra2RwKJ44Lnlij4OioQzYekBlS542a5bWkqX63OmykTPXC4oh/SuSA8ORZd09be2RmJAXltnZyDAeQ84sSCsx/nz4+KeYogwrMUQI+tM5jxIAcPPSg8Fy8m/2RY5sZo0KWqnuVw0OeAAnM/HvcQytMI6BqJ+wb4oPWYUibSHclDkPlLSo8n24woc9se2tuj+897JOmP7kmFZmUrgJuk2N0ftmXZJtmGZO1VO4PD8XORA+1coBPcbiOrXmEhksO1xkiBtB/sGvcu8V1KYsq9SCFMI8hg+D46bDM26oc1wbRD75dSpUb2xDagHpwFIEzi+JCuZ8AiUusC7u4PZiwyVsONOmRJvuNIA+JAuRBpTaXxoqChw8vngHGygctbnhkrI1KmRB4IzY+KGSjgjkoZOJuu5q7xkyIvH53FlgjJFl9wxl2Ec6QWQnZKDyKxZ8TqUgwSPD8RXvUlRBgT3bOLEoC44A5cCh+WQHj8aW2kg5bJW1+BQ5LBOeXwpRORALEV0T09k2CZNKhU4shxSGMl7I40qjZgcICjAafBYL7Kty/JJ4yc9U5wpM3+Gg7U04DwWc5yAYFLAepW5NHIgp9HlwCrbDMs0Z05UP7lccE9YtwyfUeDwPkuB09XlXx7e2RmFd5krJ/s9B0y5iSHzbeTnKMI4ALO+5eaYUuDI8IcU2Lx/FO304ABxe8Bra28PysyVmt3dcVvieqhlO5QiRtYHyyPFTnt7cGyKrUIhsF30ftHGSIFDOyV3Cc85oxzrifVAgUPk3zI8JhOtpefUDX37JhmEAofine9RWAJxwcj6knteycmi9MhLwQLEbQzFEttaV1d80iHDrLwWnzBxQ3/SI8RysvwyAuDeg5FCBU4VcT0A5Ujz4Ehkcpt0W7LBs/G7M2GJzJWgwOGjJuFQLOoAABQdSURBVGQjliGO6dOjRi6Th6UHx70eGjjfRoK8Vg7U0n0ORF4ddzbOcvkGCymGOPhzg0BZdzJxW3pu+LtQCLxYUpTxdemyBaLcHhpieR177hmUc/bsqD3QtSzrQc7CKHCki5ttQ9Y3B2LWoTQwckm/T+Dw/46OoJ7z+eA6kgQOBxFX4NDYT50anZ+eBRnq4Hu8fhpG95pyuUDEMxdButml2JSza+kt4bFk35FhXXo86ZVim5Hihu1fJt42NcV3mua1y0GGfUL2GekxS9q7pbMzqO/u7iCc44bHuFJKJtHOmVM62DAPgmWQngnC65ainvW1cGFU1zNmRF4KhkrlMXhsegoYguT/bGOu90MOhmxXrt2gNzqXA/beO/BkyLbO68nngVe/OvLg0lMqRSw9ODJB1idwpAB2BY77WSnUpJeD35deZLYNd+ECYT+RCf+y7EAUAgciQc/PMEwPxEWwFDj8fHNzPEwFRMnerLfm5uAYfX2l10ZBKnHbqgxD8vO0EdKDM1qkzPmVSpGNKwsylwGID0wS6TmRS2xpXOQziZKQOTpspAy1uAJHdgjONuQqB9mBJFLAJMEyS7FG5OaHPoGTtvKLnwHiMXS+NmNG9DcFjrXRYAnEhREQN+xStLmJutII9fUB27bFyy9DJUA0wFKU0oNEMeV6bqTA4YDtHp/X4n5XXgOhy1961Chiu7uB9evjoTdeqwzzyNkivycFDsuwe3c02LvikfdnyhRg61bgpZfinhQJ711HB/Dyy34PjryPrDe2XSnkadhdccrvFQrR7uS+2SrbL6+ntRXYsSMqU1KYlchEZx8tLXEvDAeqpIFCbmfg2g+WR+ZtyfbE6+rsBHbuBHbtKq1/eb9po2aHO5yxHgqFQCDKdubaJAqewcH48bu7gQ0bIqE1OBi0Z+lR4/fpedy4MQpF8V5JAUVhDSQLHCCynUkhFDmAs87ktchcHenxkLbc3UeKMCTF16XXmTZatk/eKxkC5GSV/XnnznhY2J10sy2xn61ZE9kV2T+YR+PWi2vbpViU/UWmDajAaRBkp8qCHOiA8gKHrmRro89T7XOAS0IKHPmaG2dlp2A55Czel4grkYYgCQ46aa5L34yqnLgB4jkg8nxAMKjLmD0Fjltmd/bLDtzXF9+TReLeN3fmLgdfXh8Npy+MJwWCfGK99KBJgSNDVNLAyGPK83NTStfwMJlXGiufwJHXzUGlszOavboDEnN9ZB1RlEgPEgeq2bMDwyuRq7527Sr14PD6OjriAoeeksHBuACmwHbzk1h3riF3PThS8Hd1BeKM7QVI74/l2nJLS3wPH15nuYHCJ3AY+tixI54UCkQz7O3bg/6xbVtQT+7kIp8H9tgDeO65uDDg8enFoFdHfk+WmXW+cydi0N5IO+h7Bp20lcwFoVCUS8Sl8JW/ZZ3I6yjnwZHXIL0c7e1BvcprYyiIfYNJye4xgfgGiFOnxoWJb/LkEzhcLZXPB9/bti0qo7TlHDNcj4pMhpftlY9scEW626ZlDqD00LKOZY7eaKACp4pUeiPd7PKkEJXcvVcOlnTfNjUBAwPp55deDXlcGTMHgobJgd+9Jjmg+MjiwWKjl8miLr6BIYvASRNXcjM9dnzpWibujqnsqFxyn3TeoQgcVzDwdWkk5BPrOVMeGCj14AwOxsNaMg7vtgsKHLeepavf3YyPMzFZXs7QpZdAeto4A5wwwb+Rnkx27e4O2t2uXXGjTqQHZ8uWePnldTAvhOeYMiU47saN0THYFmTuDBCJMJ/nRRry9vbgHjDU0NQUJW+2tgbX0N+fPuFIQ+ZmuGVIw/XUkb6+4PplEioQTW7y+aBNDAz42wU9Cr7+LcWB9IYCpSKaImDr1tIyZrEd0lZSTLCs7rUBkafCtQuuUErzprltXnpwpE2SSc3S8yqXvqchxY68TqYJ8HhSNMprpdBkmwSiOsrnI68ZvZMk6bEMfJ05Rfl85DmVuB4c2Sc5IVeB0yBUEp4CSgfXJA+IFDgSJvwBfsMk8XmW6EFwZ6tyduxrnNXw4Lgzo3LHH24ncQ2fPJfrWZB/8x6lPbvLFTiusXAFTlJcmoJBzhJ7ewPDwuMaE8y45ffpDXHDAYB/sJYzeB8zZ0ZeEuKuguF5ZdgGiJIXeR7mHLj3lOKOba+tLTDC27f7B2qelzN0ORmQx5bXy0TVHTsi0cZjuN8DguubMcMfYmV9U9z098cnDcxD4XcHB4cucJJCvOVWoiRNGJjYKp/zBkS5OTKfJCkpGvCLEOkddPu/L0Tl7lckP1vOdsh+JVczAaXbAgDB8SZOjPqPexyK2bR69dlcvi7L47MPrgcrK/L+y+PKZG7fueSEAYgEjvTgAPHcn7SHLrMsEycGx+nvL32fbY4/vC+07zLZezRQgTOK+EI0vs4vcwMkNFBAfOfMSvAJHKB0yaBkOB4cJqSmlXeoA8NQSBoUgPgMtBKB4+Lz4Pig8JECgbk5QDxfxp0pu+dnuZPyQHicpNcHBkpj964olXXHgZEr8VgGrqpzYXxetj05205qRxRySUmcrsBhOXwhOR/S+PtgEivL4r7Hdr1799DbcdK1DyVEJd9zJ0rSYwdEISwXGbrzeXDk3254WL7P0F/SpKncQOh6cOT56WGScLsCNyQmRYBPfLvH8JWB3+H7vmf7udeflaR6kHvQ+M4FxNt/a2uUAycFjhuuTqO1NQhdDwwE4S8XN0dJ3hfXTo0GKnBGETc3AvB3CGlgJOUSwLLgLud2y5HkafDhehF8yNl5JR6cWuG7B0QOimlCISm0SLIKHOaK8H7IPWckvtU0Se0o7aGqae3FHXBosOQA6BMu3d2lIQvf5zjjdMvgDrpJZZN1LtuRvE/SlZ+1TcmcpySS2otcUj7UwS2Nch4OnwCR7yV57tycFReZSFtO4LgeHPl5egB9/T6L+HRXdsnvTZ4cDMIShgzd60paEp4FN8eE1+drE+W86pXC/bF8+AROW1vgdcnnS+uGpNkHHrenJ/Cs+sS/Ww/SOzqSdjyJOijC+MVn+H0d3Y3tErdTDcUdSvdjUjmy5siQLF4kufzVx0h7cNLey1KWLB4c+X7SMTs7456TJFHl3i+GqCRpIaqk40h8CfOugUsSOG579QkphqjSkjvLlc1NvgaSrzerdzMpzyoL0t3PJPaRJClpH4jXp2/1YxZcgQuUhl4lPg+ODMFKKhU4RHo3fR4cmRvjO06laQXyvPm8P2wjj11tgZOWHiB/8/PbtgX9L62c5eB9k5tREhmiApLDv6NFnRRjfOIzRmkdvZyRHoo7MGmASev4ae9VIrKSrmeoRmcopHVEOetNo1ohKiB+7WnixD1/ksAZ6g6ivuvhMn6S5Jkh5YRsUnjUPY6L6ynIMmANxbtZKb4tBkaStHudJnCy9rc0DxHfd4/rywsbqgenUmGUxYMzVJhvleQZ4WeqacvSyu07DycC7e2leUiVwImez3vENiET1/mdeqBOiqGQch6FeqBa5RjN7HpSKCTPtLMaqHL14UsyzkJWweoLUY1E3WYtX5K4cDd6lFQisLMMWEMVepUgV4plSZqtNuVCjsQtV9ZBuFwdum3OFXl8f6genEonhMwZc8Mw1egbrufCR7XbXLn9xXzQe5x1suSDEz2fnePrI93Ws1InQ6ZC0jpfPQgCoHqzkpEYdMpRKJRuPCbfq4aYcz0stcjNcMXGaCf3SZLuc6UzUuJLtq8H3MTvkSarwKnkPUlTU3JfSTqOLJO7R5NkOKGiJHge7lBdTbLUWbW9hmn2Mqm/c/LjWx2XFS4I8NHUFD0vsR5RgVNnpHWcelHJKnAqo5IQ1VAoN5McbdJCVElkDeMB9SNwRpusISqXrP15KO1MlinNgzNUynlwgORHZgyHLBOIepiQ0vYM12Yn9dV6tjsA9FlU9UY9zbyTqNYAXQ8ht7S8gmrF0H0hpGpS6+MPl6Q6rJYHZyRztuqZagnGaiLLJFdjVYu0AbYe7MtoUy0BUs/2JQ0VOErFVMsg1kOnSVviXSsPTi0Yi4N82kx+qEnu45m0NpbF01ELfAPsSPX7evCgjDb17mGpNeP88pWhUA/CpJrU2oMzEgPyWBz0y608G8p7ip+xEPquNurBadx7m5VxfvmKkky19rEYCSMz3g2Zks54HOy1T2gdjPPLV5R0xorAUZQ01Os1PhnvtmecX76ipDNWQlSKoiguKnAURUlEPTiKooxVxrvtGeeXryjpVMNAjHcjoyjK6DDebc84v3xFqT3j3cgoijI6jHfbM84vX1Fqz3g3MoqijA7jPblcTa+iKIqiKA2HChxFURRFURoOFTiKoiiKojQcKnAURVEURWk4VOAoiqIoitJwqMBRFEVRFKXhUIGjKIqiKErDoQJHURRFUZSGQwWOoiiKoigNhwocRVEURVEaDhU4iqIoiqI0HCpwFEVRFEVpOFTgKIqiKIrScKjAURRFURSl4VCBoyiKoihKw6ECR1EURVGUhsNYa0e7DFXBGLMOwNM1PEUfgPU1PL5Sitb5yKN1PvJonY88WucjTy3rfK61drL7YsMInFpjjFlhrV022uUYT2idjzxa5yOP1vnIo3U+8oxGnWuISlEURVGUhkMFjqIoiqIoDYcKnOxcONoFGIdonY88Wucjj9b5yKN1PvKMeJ1rDo6iKIqiKA2HenAURVEURWk4VOAoiqIoitJwqMApgzHmjcaYh40xjxljzhrt8jQKxpjZxpibjDEPGGNWGWP+JXx9ojHmemPMo+Hv3vB1Y4z5Tngf7jXGvGp0r2DsYozJG2NWGmN+E/4/3xizPKzbXxpjmsPXW8L/Hwvfnzea5R6rGGN6jDGXG2MeMsY8aIx5jbbz2mKM+VhoV+43xvzCGNOq7by6GGP+yxiz1hhzv3it4nZtjDkt/PyjxpjTqllGFTgpGGPyAL4H4FgAiwGcbIxZPLqlahj6AXzcWrsYwMEAPhTW7VkAbrTWLgRwY/g/ENyDheHPGQAuGPkiNwz/AuBB8f/XAHzLWrsngI0ATg9fPx3AxvD1b4WfUyrnfADXWGv3BrAfgrrXdl4jjDEzAfwzgGXW2n0B5AGcBG3n1eYnAN7ovFZRuzbGTARwNoCDABwI4GyKomqgAiedAwE8Zq19wlq7C8ClAE4Y5TI1BNbaNdbau8K/tyIw+jMR1O/F4ccuBnBi+PcJAH5qA+4A0GOMmT7CxR7zGGNmAXgTgB+F/xsARwG4PPyIW+e8F5cDODr8vJIRY0w3gL8BcBEAWGt3WWs3Qdt5rSkAaDPGFAC0A1gDbedVxVp7C4AXnZcrbddvAHC9tfZFa+1GANejVDQNGRU46cwE8Kz4f3X4mlJFQpfwUgDLAUy11q4J3/orgKnh33ovqsO3AXwSwGD4/yQAm6y1/eH/sl6LdR6+vzn8vJKd+QDWAfhxGBb8kTGmA9rOa4a19jkA3wDwDAJhsxnAndB2PhJU2q5r2t5V4CijijGmE8AVAD5qrd0i37PBHga6j0GVMMa8GcBaa+2do12WcUQBwKsAXGCtXQpgOyK3PQBt59UmDHGcgEBczgDQgSp6BZRs1EO7VoGTznMAZov/Z4WvKVXAGNOEQNxcYq39VfjyC3TJh7/Xhq/rvRg+hwI43hjzFIJw61EI8kN6Qlc+EK/XYp2H73cD2DCSBW4AVgNYba1dHv5/OQLBo+28drwOwJPW2nXW2t0AfoWg7Ws7rz2VtuuatncVOOn8BcDCMPu+GUGi2pWjXKaGIIxxXwTgQWvtN8VbVwJgJv1pAP5PvH5qmI1/MIDNwhWqZMBa+2/W2lnW2nkI2vLvrbXvBnATgLeFH3PrnPfibeHn1dNQAdbavwJ41hizV/jS0QAegLbzWvIMgIONMe2hnWGdazuvPZW262sBvN4Y0xt63l4fvlYdrLX6k/ID4DgAjwB4HMBnRrs8jfID4DAE7st7Adwd/hyHIPZ9I4BHAdwAYGL4eYNgRdvjAO5DsEJi1K9jrP4AOALAb8K/9wDwZwCPAbgMQEv4emv4/2Ph+3uMdrnH4g+A/QGsCNv6rwH0ajuveZ1/EcBDAO4H8DMALdrOq17Hv0CQ47Qbgafy9KG0awDvC+v+MQDvrWYZ9VENiqIoiqI0HBqiUhRFURSl4VCBoyiKoihKw6ECR1EURVGUhkMFjqIoiqIoDYcKHEVRFEVRGg4VOIqiNDzGmCNM+PR0RVHGBypwFEVRFEVpOFTgKIpSNxhjTjHG/NkYc7cx5ofGmLwxZpsx5lvGmFXGmBuNMZPDz+5vjLnDGHOvMeZ/w51QYYzZ0xhzgzHmHmPMXcaYBeHhO40xlxtjHjLGXKJPjFaUxkYFjqIodYExZh8A7wRwqLV2fwADAN6N4GGJK6y1rwBwM4Czw6/8FMCnrLVLEOyOytcvAfA9a+1+AA5BsNsqEDyx/qMAFiPY1fbQml+UoiijRqH8RxRFUUaEowG8GsBfQudKG4KH9Q0C+GX4mf8G8CtjTDeAHmvtzeHrFwO4zBjTBWCmtfZ/AcBauwMAwuP92Vq7Ovz/bgDzANxW+8tSFGU0UIGjKEq9YABcbK39t9iLxnzO+dxQny+zU/w9ALV/itLQaIhKUZR64UYAbzPGTAEAY8xEY8xcBHaKT4F+F4DbrLWbAWw0xrw2fP09AG621m4FsNoYc2J4jBZjTPuIXoWiKHWBzmAURakLrLUPGGM+C+A6Y0wOwVOKPwRgO4ADw/fWIsjTAYDTAPwgFDBPAHhv+Pp7APzQGHNOeIy3j+BlKIpSJ+jTxBVFqWuMMdustZ2jXQ5FUcYWGqJSFEVRFKXhUA+OoiiKoigNh3pwFEVRFEVpOFTgKIqiKIrScKjAURRFURSl4VCBoyiKoihKw6ECR1EURVGUhuP/A4usM0Fhjz3JAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 02\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 03\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 04\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 05\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 06\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 07\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 08\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 09\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x2160 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 10\n",
            "#\n",
            "################################################################################\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x2160 with 40 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 11\n",
            "#\n",
            "################################################################################\n",
            "\n",
            "final training accuracy = 80.23637390\n",
            "\n",
            "################################################################################\n",
            "#\n",
            "# RESULT # 12\n",
            "#\n",
            "################################################################################\n",
            "\n",
            "final testing accuracy = 84.61300659\n"
          ]
        }
      ],
      "source": [
        "number_result = 12 \n",
        "\n",
        "for i in range(number_result):\n",
        "\n",
        "    title           = '# RESULT # {:02d}'.format(i+1) \n",
        "    name_function   = 'function_result_{:02d}()'.format(i+1)\n",
        "\n",
        "    print('') \n",
        "    print('################################################################################')\n",
        "    print('#') \n",
        "    print(title)\n",
        "    print('#') \n",
        "    print('################################################################################')\n",
        "    print('') \n",
        "\n",
        "    eval(name_function)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "zJr6STSBWLWW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "897087eb-ba83-4d17-92d7-296d25ed1555"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hit:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease\n",
            "Hit:2 http://security.ubuntu.com/ubuntu bionic-security InRelease\n",
            "Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Hit:7 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu bionic-updates InRelease\n",
            "Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease\n",
            "Hit:12 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Hit:13 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
            "Reading package lists... Done\n"
          ]
        }
      ],
      "source": [
        "!apt-get update"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install texlive texlive-xetex texlive-latex-extra pandoc\n",
        "!pip install pypandoc"
      ],
      "metadata": {
        "id": "B4JaGkHc21C4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acd02ca4-cce4-42ec-9e97-0f49a42b0a5f"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "pandoc is already the newest version (1.19.2.4~dfsg-1build4).\n",
            "texlive is already the newest version (2017.20180305-1).\n",
            "texlive-latex-extra is already the newest version (2017.20180305-2).\n",
            "texlive-xetex is already the newest version (2017.20180305-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 11 not upgraded.\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pypandoc in /usr/local/lib/python3.7/dist-packages (1.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!jupyter nbconvert --to PDF '/content/drive/MyDrive/Colab Notebooks/MachineLearning2022-2/MachineLearning2022-2/assignment_10.ipynb'"
      ],
      "metadata": {
        "id": "Y4qgcsPV3jRj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "845198f1-354d-4acb-d056-357d006c4843"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/MachineLearning2022-2/MachineLearning2022-2/assignment_10.ipynb to PDF\n",
            "[NbConvertApp] Support files will be in assignment_10_files/\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Making directory ./assignment_10_files\n",
            "[NbConvertApp] Writing 120397 bytes to ./notebook.tex\n",
            "[NbConvertApp] Building PDF\n",
            "[NbConvertApp] Running xelatex 3 times: ['xelatex', './notebook.tex', '-quiet']\n",
            "[NbConvertApp] Running bibtex 1 time: ['bibtex', './notebook']\n",
            "[NbConvertApp] WARNING | bibtex had problems, most likely because there were no citations\n",
            "[NbConvertApp] PDF successfully created\n",
            "[NbConvertApp] Writing 537803 bytes to /content/drive/MyDrive/Colab Notebooks/MachineLearning2022-2/MachineLearning2022-2/assignment_10.pdf\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "PBYA3uEoWLV-",
        "9ewVmC8-WLWC",
        "SgGdUNL4WLWE",
        "Q3fxTKBOWLWH",
        "TCYs-JWjWLWI",
        "NRAYkfnfWLWJ",
        "8ngJeHXH2dfj",
        "6yE9LChkQ5G2",
        "UQVz0ChdM9KL",
        "bnpYjSeaWLWO",
        "Zdr0nGPYWLWP",
        "9UIl4gCp2hE6",
        "b-VwAY52WLWR",
        "hOAnh07VWLWR"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3.9.12 ('base')",
      "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": "3.9.12"
    },
    "vscode": {
      "interpreter": {
        "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
      }
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}