TRANSFER_LEARNING_FOR_COMPUTER_VISION_TUTORIAL.ipynb

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    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/perfectpanda-works/machine-learning/blob/master/TRANSFER_LEARNING_FOR_COMPUTER_VISION_TUTORIAL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnJfyaQeKh4D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# License: BSD\n",
        "# Author: Sasank Chilamkurthy\n",
        "\n",
        "from __future__ import print_function, division\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "\n",
        "plt.ion()   # interactive mode"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDjbVP1kKq6l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2fc59562-ac54-4498-f64b-bda11641afd7"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Pkn00FJLTYF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 訓練データはデータ拡張してNormalizeを行う。\n",
        "# 検証データにはNormalizeのみをかける。\n",
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        #ランダムなサイズ、                         \n",
        "        transforms.RandomResizedCrop(224),\n",
        "        #50:50の確率で水平に反転\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "    'val': transforms.Compose([\n",
        "        transforms.Resize(256),\n",
        "        transforms.CenterCrop(224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n",
        "\n",
        "data_dir = 'drive/My Drive/hymenoptera_data'\n",
        "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
        "                                          data_transforms[x])\n",
        "                  for x in ['train', 'val']}\n",
        "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
        "                                             shuffle=True, num_workers=4)\n",
        "              for x in ['train', 'val']}\n",
        "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
        "class_names = image_datasets['train'].classes\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v1hf0QcO3P_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "outputId": "ed80790e-3d52-47f5-e2c7-c7fbdf186b93"
      },
      "source": [
        "def imshow(inp, title=None):\n",
        "    \"\"\"Imshow for Tensor.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
        "\n",
        "\n",
        "# Get a batch of training data\n",
        "inputs, classes = next(iter(dataloaders['train']))\n",
        "\n",
        "# Make a grid from batch\n",
        "out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "imshow(out, title=[class_names[x] for x in classes])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cbD_s39iMX7",
        "colab_type": "text"
      },
      "source": [
        "##モデルを訓練する"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gLFZ80fO904",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#訓練の関数を定義\n",
        "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since = time.time()\n",
        "\n",
        "    best_model_wts = copy.deepcopy(model.state_dict())\n",
        "    best_acc = 0.0\n",
        "\n",
        "    #エポックループ\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        #それぞれのエポックで訓練データで訓練→検証データで検証\n",
        "        for phase in ['train', 'val']:\n",
        "            if phase == 'train':\n",
        "                model.train()  # モデルを訓練モードに\n",
        "            else:\n",
        "                model.eval()   # モデルを検証モードに\n",
        "\n",
        "            #各種変数の初期化\n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "\n",
        "            #データローダーからミニバッチを読み込むループ\n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "\n",
        "                #勾配を初期化\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                #順伝播\n",
        "                #※1:訓練データの時はテンソルの勾配を求める\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "                    _, preds = torch.max(outputs, 1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    #訓練データの時に逆伝播＋重みの更新を行う\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                #損失等を計算する\n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)\n",
        "\n",
        "            #学習率の更新を行う\n",
        "            if phase == 'train':\n",
        "                scheduler.step()\n",
        "\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            #検証データで精度が以前より高ければモデルをdeepcopyする\n",
        "            if phase == 'val' and epoch_acc > best_acc:\n",
        "                #ベストの精度を更新\n",
        "                best_acc = epoch_acc\n",
        "                #モデルのコピーを保存して「best_model_wts」に格納\n",
        "                best_model_wts = copy.deepcopy(model.state_dict())\n",
        "\n",
        "        print()\n",
        "\n",
        "    #１エポック終了毎の表示\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    #最も良いモデルを読みだして返す\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_h4rU7Tp0tIc",
        "colab_type": "text"
      },
      "source": [
        "※1:\n",
        "\n",
        "「with torch.set_grad_enabled(phase == 'train'):」\n",
        "は、torch.set_grad_enabled(True)だと自動微分をオンにして勾配を求める設定となるので、(phase=='train')とすることで訓練データのときにTrueとなり、訓練データをいれたときに勾配を求めつつ順伝播を行う設定となる。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdKeKsl3ic_X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#モデルの検証データでの結果を6枚だけ可視化する関数\n",
        "def visualize_model(model, num_images=6):\n",
        "    was_training = model.training\n",
        "    model.eval()\n",
        "    images_so_far = 0\n",
        "    fig = plt.figure()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "\n",
        "            for j in range(inputs.size()[0]):\n",
        "                images_so_far += 1\n",
        "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
        "                ax.axis('off')\n",
        "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
        "                imshow(inputs.cpu().data[j])\n",
        "\n",
        "                if images_so_far == num_images:\n",
        "                    model.train(mode=was_training)\n",
        "                    return\n",
        "        model.train(mode=was_training)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCXY91mG7Gy8",
        "colab_type": "text"
      },
      "source": [
        "訓練済みモデルをロードして今回のモデルに組み込みます\n",
        "\n",
        "事前学習モデルは。「ResNet18」を利用するようです。\n",
        "\n",
        "https://jp.mathworks.com/help/deeplearning/ref/resnet18.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vY4cxA4ridfA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "bc0591f077de49e1890c9b65ba8af680",
            "deb3a17710814933a27d11726b3a6b17",
            "7410ca63cedc4b9eaaa1f856f7da904e",
            "a331ade314f44451b396b265dd294725",
            "c838bf6dcf5447f382eb6041d8d027f5",
            "b6fac5f0f24d480189bda7d3184a6876",
            "2a00beb5e09347d480d8d020aba993b6",
            "c85d3cb0a6e743aea6fa5476d294ddce"
          ]
        },
        "outputId": "7de6c4f8-23e9-4a3a-fce9-ba27fcdbe2b1"
      },
      "source": [
        "model_ft = models.resnet18(pretrained=True)\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "# 今回は出力層の分類数は２に固定で設定\n",
        "# 「nn.Linear(num_ftrs, len(class_names))」のように識別クラス数を与えることもできます。\n",
        "model_ft.fc = nn.Linear(num_ftrs, 2)\n",
        "\n",
        "#デバイスの指定を行います\n",
        "model_ft = model_ft.to(device)\n",
        "\n",
        "#損失関数を設定します。交差クロスエントロピーを利用します。\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "#最適化手法をMomentumSGDに設定します。\n",
        "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "# ７エポック毎に学習率を0.1減衰させる「StepLR」です。\n",
        "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc0591f077de49e1890c9b65ba8af680",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=46827520.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNhBrS89iiVP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "48be2266-5439-4995-a8cb-3d7f25727564"
      },
      "source": [
        "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n",
        "                       num_epochs=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/24\n",
            "----------\n",
            "train Loss: 0.6049 Acc: 0.7500\n",
            "val Loss: 0.4479 Acc: 0.8366\n",
            "\n",
            "Epoch 1/24\n",
            "----------\n",
            "train Loss: 0.4446 Acc: 0.8156\n",
            "val Loss: 0.6298 Acc: 0.7451\n",
            "\n",
            "Epoch 2/24\n",
            "----------\n",
            "train Loss: 0.5125 Acc: 0.7664\n",
            "val Loss: 0.2022 Acc: 0.9150\n",
            "\n",
            "Epoch 3/24\n",
            "----------\n",
            "train Loss: 0.6354 Acc: 0.7541\n",
            "val Loss: 0.3502 Acc: 0.8497\n",
            "\n",
            "Epoch 4/24\n",
            "----------\n",
            "train Loss: 0.3900 Acc: 0.8197\n",
            "val Loss: 0.4083 Acc: 0.8693\n",
            "\n",
            "Epoch 5/24\n",
            "----------\n",
            "train Loss: 0.6051 Acc: 0.7992\n",
            "val Loss: 0.3120 Acc: 0.8824\n",
            "\n",
            "Epoch 6/24\n",
            "----------\n",
            "train Loss: 0.5180 Acc: 0.8320\n",
            "val Loss: 0.2723 Acc: 0.8954\n",
            "\n",
            "Epoch 7/24\n",
            "----------\n",
            "train Loss: 0.3050 Acc: 0.8607\n",
            "val Loss: 0.2255 Acc: 0.9020\n",
            "\n",
            "Epoch 8/24\n",
            "----------\n",
            "train Loss: 0.3307 Acc: 0.8607\n",
            "val Loss: 0.2398 Acc: 0.9085\n",
            "\n",
            "Epoch 9/24\n",
            "----------\n",
            "train Loss: 0.2960 Acc: 0.8730\n",
            "val Loss: 0.2421 Acc: 0.9085\n",
            "\n",
            "Epoch 10/24\n",
            "----------\n",
            "train Loss: 0.2494 Acc: 0.8770\n",
            "val Loss: 0.2651 Acc: 0.9020\n",
            "\n",
            "Epoch 11/24\n",
            "----------\n",
            "train Loss: 0.2826 Acc: 0.8689\n",
            "val Loss: 0.2148 Acc: 0.9346\n",
            "\n",
            "Epoch 12/24\n",
            "----------\n",
            "train Loss: 0.3792 Acc: 0.8361\n",
            "val Loss: 0.2151 Acc: 0.9346\n",
            "\n",
            "Epoch 13/24\n",
            "----------\n",
            "train Loss: 0.3594 Acc: 0.8566\n",
            "val Loss: 0.2098 Acc: 0.9281\n",
            "\n",
            "Epoch 14/24\n",
            "----------\n",
            "train Loss: 0.2917 Acc: 0.8689\n",
            "val Loss: 0.2346 Acc: 0.9150\n",
            "\n",
            "Epoch 15/24\n",
            "----------\n",
            "train Loss: 0.2417 Acc: 0.9180\n",
            "val Loss: 0.2240 Acc: 0.9216\n",
            "\n",
            "Epoch 16/24\n",
            "----------\n",
            "train Loss: 0.2475 Acc: 0.8975\n",
            "val Loss: 0.2217 Acc: 0.9281\n",
            "\n",
            "Epoch 17/24\n",
            "----------\n",
            "train Loss: 0.3311 Acc: 0.8566\n",
            "val Loss: 0.2194 Acc: 0.9346\n",
            "\n",
            "Epoch 18/24\n",
            "----------\n",
            "train Loss: 0.2213 Acc: 0.9098\n",
            "val Loss: 0.2411 Acc: 0.9346\n",
            "\n",
            "Epoch 19/24\n",
            "----------\n",
            "train Loss: 0.3391 Acc: 0.8443\n",
            "val Loss: 0.2155 Acc: 0.9216\n",
            "\n",
            "Epoch 20/24\n",
            "----------\n",
            "train Loss: 0.3420 Acc: 0.8279\n",
            "val Loss: 0.2666 Acc: 0.9085\n",
            "\n",
            "Epoch 21/24\n",
            "----------\n",
            "train Loss: 0.1747 Acc: 0.9344\n",
            "val Loss: 0.2184 Acc: 0.9216\n",
            "\n",
            "Epoch 22/24\n",
            "----------\n",
            "train Loss: 0.2663 Acc: 0.8811\n",
            "val Loss: 0.2956 Acc: 0.8824\n",
            "\n",
            "Epoch 23/24\n",
            "----------\n",
            "train Loss: 0.2565 Acc: 0.9139\n",
            "val Loss: 0.2409 Acc: 0.9085\n",
            "\n",
            "Epoch 24/24\n",
            "----------\n",
            "train Loss: 0.2607 Acc: 0.8934\n",
            "val Loss: 0.2191 Acc: 0.9281\n",
            "\n",
            "Training complete in 2m 39s\n",
            "Best val Acc: 0.934641\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSKv9GKeip_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "1b82e6d3-aa44-4637-bb34-cd2d39fe896b"
      },
      "source": [
        "visualize_model(model_ft)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vXyQxJ+TzeXJzKxxsbWFGY7JRzMrGJVbWNzi6f4v3tg4ofeJ5qisbaLkFwkGC50+wowkijhkHHqOxS8m2UDTBNBG4UsHBRS1nubD8MqsrC5g5hyiMsS0bRVEhTRiNxkxNi/XaMmEc4/se7cMm8aiDaWicO3+JOE3Zuv8hqiI4t3qOfK6MYhQRqnEyfjzpUanlmatW6OWfkDZJXXkWSw0R3gH5pUs8/ewQtzfmqHvEfrfHYlaSy1ZImDLttmg97GHmqrizCFdRSU0bLwrIjLdZr1gUykukio6qaiiaiV45B6Mm0u0TzQa0Oi6xXWI2GVMpGhwe7hMnIboaYWUVqo08+eU6enkBOR1DFIChIZKUwd4ueuMi/u4W9rRNr71POmzTHsYchSb74S5PX1hGy9cZjXsU62Wcpcuks2NSVUNz5tGLy9QuG3xmboO8+oR0wck3EF4fUh1yZfJXPkWus4PRr5OfenRauySBTybvkLF0sgsNMpkM3bHHeDYjY005HLTJl3OUigVMM6FUX0HJrYBVgnCMnpsHzSGO9ymobTrNHZLCKv0+uCMXS43ImAaJSHFDj/F4hBanZKvzqEqONPKQep5YuYPfvMPz51ewogLBYpnf+8rrTEY9jEKGtasv8+DgISvzDexSBaOxgTBKEHkIUiQpmHmESEjbTRaufeqx03kmIs02XycJEpxcASVOkE6eqeEg6VBrVJg/t07qu8jsHEwOMTQTv9OkslHGyOgohsnu9jYiSnGyOfKVGrlqDYyT5dOKUQa9iLRK2JUN5nI3MM27HO3vkZKevICaRdqjLm4oyWJQEAWODzqUgpC5pRWEmpIOjjh/7Sor7ozxwRZbnS7CC1GlpJKz6Iczpn6PfL7K6GiHz37qVQq1ecTsGJFbRagWoCCJkLGPNX+FpRX7sfN5JiJt377PZPsOG9dfJPfij7H/2m9wcOhSmV9EDcbsvXOTq1cvUa2CZlTR0hir9ALSziATD6WwxoXVTwEKiqYhlAikAsN90tQgnY4Iu/tgKViNJbT561TKl8gUX2e6fQMhdDAVKkuv0G7t406H9PttZp7L5H6bbHGOXLmGanRRsxVme29w7/YdWqHkYqXOtYsXSRH0Z1N2du9x2BlQreYpZjQ0wwR/RhrfRilfBaEj3eOTSUIRInwX7Npj5fNMRNp/sIXr67gfbLPs/QaKU2H+wgalxUWkYfPNN2/SefirXP70T/L8j/40RGNwx0h/QBQLzLkqKhKEBv4xhDGoBkKzIJEozNByFkp5EYEPXhfFmcNZfwFDRLC/yXA2JW+HyHqJYNrm+GCbjKmjxBKmA2SxhAw9FG+EWSqy9vIrXMwVyOk6YjzCUASJVFm8ew9vPaC8tEhh+QLCzBJPh9x69xZ27m1WVy6iayaKTIg1E8N6QuaTqqYH7gQnCtHHCrPBPqNxwGyvTH88Rpu0URDs3vgaly6ex1m7BN4+AoEwSyT+AEUECLMMZhVkcDKFrQgQBqK0iI5yMhA6O4Z4Bm6bdDZk/3jMnQcjIkVQCbbIWBLf7THs9ZGpQqVWo9frEgdjcqaFommo1QZFy8SqLEAao1drxK1dEi9g5eJ5tEwGff0qfveQaOd9tjoTRmmV3/7Nr1BSfpd//8//B+iWhW5lkTnnsfN5NiMOhoVtuDTWL+FOmigSwmmHMPTotFtkNRVVNVioWIStuzgLFxFLLyCDEWngIYMZZr4K4RiSCIzcSalSnBNB0uhkGtssg6aBkoXWu8wGPTbvb1KoLFBafYpua4ete+9RKVnkGjV6oxg9ieg8vE1QLpBUy5QQaIaNGoE6HpJGPoMowp+60O+AVDA8m5IVQ26V7k6L3//iN1GrCzz36c/x+//3L3Pz1ntcf+aT6AZE7Xto68vfn6QfAGci0p1jn0mzR+WyTXZulfbRiHLD5qg7Rsmv0Dk6oGjHrF2+Tn7jOnKwizA2mA6PeO21b2IIlc/+u/8hWtAFo3Ay3xaMIIkQvU3SQRfK85AtIYI+6XSEOhuhzGYUwjH3vvEtDt/9EoqVI8BhjM+zL75M943XSHCQmkO2uohlJkxCj3jq0xp55NOAfq+Pm0acrxeRfp9YKzPY36UzSxiZOvd3j5lNAgrWAFMXCKvA62+8x+WNi4y7LQadY15c/2N9Ivuv4WzWOJhVlLrCu+99wOXVOlKxiHIV8HWcwKd47Xn2jg85GCaUVAOhG/izIc37H6LEgg9vfYurl9eZq+ahXAEhkFEAikE68xnceY/QfIC1dpFitYCIU6LhCCZTnr58DVyX/f6AaHiMGqXkK0+zu/kQS8tgWiWUyhr9kcuR73HoT5BCsNOZoiQB3aFLTo1oHx5ytaJh53N09TnubvvE3rco2iqfvbZK30/wBsdYUY/+YMTdB9vEaZadY5cXHzOfZyLS5OHbYJh4hs574wGWKcnkCmSqc+SLZexCgdC0eHDzXRbrRUKrQK/T5c6tD/ns5z/Dy89fI1dvIL0e9LeIIpfuvVvoukEUhuiGjmUaxL0jZraDPb9B1D1CcyyURKFYqNCfRVQaVfLlCrl6gf7mbebXL6BIHWyIpM3eOMVTdCbjAVa2hjAM8pkEGXm8s3uP5mGPpUWTYagx3f6Ac0sF1ufmWb60QafjcePm11gtmnw4hgd7x9QWM9jVtcfO55mIdPnSIkLaFIsmd+8/xDGzGOmAqOtSW1qmrKvo+phxxWbaug/zV9jb2ifqt7HMDJlKBZH4BGGEDAN29/bxppKZ16ez3ySvBjz77DMo0ZS0u0+sgLV6GXfzFtNmi8FwguPkOP/sy6jFGq0br+PHDlkixkddpBshCiVGx03ef/9DpmHE3IULuBMPu7xAyVbRYo/UmxH1WxTjCQu1lPVGQNaYcfvNNykWatS0BE03sKTCu6+/RSZ/GyWV8Bf+i8fK55mI9GE/5UI9x8VnnyO7uEgg8swtzaMrKUZpGV1TWUgmeKMpqmlz86tf5ujdr7C6eO6kjTp8l957X8WNJb3pjNisMnfpOnQnFP0UEQ556+Y2F8+togYeTthBK85IjAyyUiZj2JTyOaSpok7aWNk8Mg1Jw5hOe4TpC3q7TbzxACuaEbo+m+/cQBEq5WqHatXmvJhwbqOASEFNbPb3O7zeCcmZLrn5NQRjMo6NiuDaWhU3yDHzAgzl8a+tPxORrHPP8uv/z6/hTia8+oVP4iyex66vgqIRJILx3l1sJ4O9fAGpqvjGbVRpc+XTn0QJO8z27jMb+riKRsYyaB/fpZsMOX74kKxlIAtL9FstjgtlokmbxKmh5apgOizPL7N4sY5QdBS/jxaEZGKDbNbGtFRyah4tU6DVHZHZl2RI0ZQKqaoR6za6pZPEKd7OIceWwbAzRsoUYWSJlJhMqcjKuXOsPv8KajDFnwxYPGyTKilJBDLyvz9BPyDOZt1d8z6ry/Mkmg65BoaTBRJIJYZhMkxmjA6H9N74MpZtEW7d5tXPv8rcteuMb76GP5ix8MqfRrFUwsmQYfd3qC6skdcTpseHjLr7bFy6iHRbaIFPrVrEdAJEsYxtKKerfQRSqIiF81Sef5rh7od0b/0WjVoNs1LHiFXmi3mumSrecETzoMU4kRTn5zh+uIVr5zAMh6c/cYnq+gamImnvblIoFSgWyshRF6tRwV69QH7hiGAyxLRthPOErLv7yZeeojscYxaLxJ0HJIU8orhOMGox7e/xzo33ORoKDEOn+c13eb5msfD0NbzdO8jjA7LVBTQlReSqzFo71Deex+u36R/08XyFwuIyeUbU1urMBjMiGVPMgrNcR6uuI3Wb6f4HRNMBpp1FRj6ZxiJd3yPUJWkwxSmXKOVVCvMbRO0DRmGMgs65Z57j8rVn8IZDCsUChuOgL5xHJjHlpVWMdEaqGGjFBkJXIb+AKnQsoaNIF6k/fkrPRKTj0Zi1jRruaMpoEGLPArThASkG777xZZzcOdbrBd780m/ht5qUrn4Wo7ZGu7lPxinizM0hEp8oSslmc1See5VUBkzuvoYIQqSM8bodMtUl9LpB/7DJ4HgfF51GYQHNKmJmykTTMf7+HeJej163zdHuQ/rZMvnzRfquz73XvsVq4xZPrS+xuLbAwdGAvAVaECEMQeC7WHNLEE4Rio5qmKiqglZeBTN/MuIRTMAfI4IJwWQAUudxD7GeiUhud5vk/BKNC2tIvYKazXG8t4vTWCcyqtiFBltv/RbjzVucW1hl9coVdGIaqyt09tvk8xUiL0BVDYLRCO3wLqSQX7gAoUtv+x5hrCKLDYQfMpn4PHx4SKPvkfY76KVVNrcfMmp3WDQkSRgSCoGrCHZafa4up5Sqiyyvzuh2u/TfucdLV8+xdfcm/dYmV69ex/VC1GBEamepLi4jjx8gvQC58QLCzMGsB4UFpDGHGHeBkMTzsZLvtHXDHw9n8zJbu4TML6AUllDLF0niKaF/Fw7vMleuc+PdL3H7nVus52yuXKjiHTwg1gOU2jIBR0h3hJapIIeHqInk8L03MQsNqvM1Em+GajkMhk0WFYFuwdryAqaMSB2byuoq/e4IM/BYX5zHUAWTTofI98kUCkyDETs3Xsf3Eipz88w6Q7qtfRh3OBynjIRGtNXm2rlFAs+je/998qaG8H0i34fmfazzz4CdB9VGRC6yvATRFDOC1H1Cps+/duNd3ntwj6dXz/HiJ16Gcg1DEfjuhIcPdhls3+WVKzVmrTbHW8csVOYI3Bh73iZTLiONHAKIs/OIBYOsZpOtVJhufYim6VhKysLqMhJJ0Dokms2YX11CFOo45y9h+xNqy3NojYtIzca9903G0yn5QobB/h7tdpP2cYfmrbcJpiOeWl1FyohGtQyqhswtceveA8xgTGLn8d5+h7yRUlhcxll9FqQAd0A6HRMPD1HnL4HmIB2PJA4fO59nItL8Uy8zcX3+6e+/xYMHLT79hR+hOt8gPPiQbNLjh66vow363PYSVp7ZYNJrM+h0uJiv4WQLRFJBGQ+ROQWtukTOtGHSw5hfQ0+mTI/bWPkCaqFMuPch2ZVlJoM+uaXLYBQRpo0pBdJ2CL0Z2vxFluYWke4AXSo4joOVgiZ0bK3CykoVqeZoDwPGYxdnvMnYLNGe+JxfrmGpAjtjQJySGjkUrw+BR9A+IlQVtFigKCaGoiDSJ+Q96eVPfZ6JP+HurXe4tXvE+f1t5uaL5CsVNmId4Y2ZRX1eeeUTmFmdbjuisbbK+OEHuGFE9fwGqSoQioGj6yjRiMguYSw1iO69jlqoYl98Djlokl9YQyxepngxi0hjMLIgTaQ6gSTAKNQwKxrCPSQ1c2iGRW7xHAQzUt3i+LDNxNNRMjbBuMPS2jmEkUMVRaxcmem0gyoidC9hmEYUxvvIOD5ZhCIUjh/c5/0vv0arucuP/eirnF9/6rHzeTYzs/c+wCchYwjqGR3HjLl38w0a8/MYBRu9oKKEIZlciXDmU2g0KJSL7O1sYeTqWKU64+YmcaSSziYQuuzfeY2Na8/gFEsYmSrxoEV/c4tSvcTkndcpXH0ZTUsg8pH+BKw8aAYiCUAGSMUGM0uo22CZZC+/gDb7JqPxFrX5BprqoJomk6MWiZml7+0TKw791iZPzVfw05C1555DTIbEcYzUNBJFYTab4XVaOJkq775xg0atQvEx83kmImnjLW7fusdKVmW1msMIeuztHzE/v0r9XINJ9xixZJKNIrxUMptO2d3Zw5vNWH3hIkZ1jpKhI3WHNIrwRwNWLlzCVFPkoIecjEnDkEzOIZhMGfcHZA/vo9YXEGkC7pA0lihOFvI1SEMwM6T9A+IwwCzW2bz1HjuDmE5iEuwesLd1k7lqhqWyg1OAMHVoHe0zHE6plXNgOgzff4B/6yGWjHj+k5+mvLhM7fzT9H7/q1jVBF96NN9/n+InHjOfjze6Ezw1n6NuruIe7dEZubhjk0reRlEFsciQnbuAZ/ZI3BF+OKYrFTLZHJdfeInMhWcRaYiYWwd0cI8xK/NIRSPtN5H+FNXvoxkmQmoMOy1S1UStr4CaImVCGoUIS0EqGiKYIa0SEJMYGTIL6xzuNdGzRS4891mMbAPbkHTDW0yDEYepTT3W8GcuoTvFtGzutX20fAahgF2us3PjbZr7v8K/92d+nLTdZfFiQToAABI4SURBVLnq0Bm2CEXKwdYW1x4zn2ciku9PqBYLzLoW8/MO5WqNVBFkynliz0XTFcyMjahdYP7SC1QnQ8xcFSUKIFNFBpOTGVhVkEYpSjRAyVYQ5aUTsXbeRLoTdm99izRJmLv2PCopTHukhomozCOcGkyaSFGByCV2ByffucYzvNZtDls9hqM+/U6PxYUKhkiwqzX2N/doxkfMLc9B7JHRNYg9lmyNSDjcuXeDUbfHzjDgna99jXrWIKeHhPkMhqYQJ09Ix0EJPHKVCvm5ZYIwZDBLWXrqMiKNUBUDoWZIZiOQA1RpY5kmQtMRqgZ+D0YtSDl5/yDB39/EWjwHc1cQ4Rgqy6SzuyxefwmrOo9CQNraJo18Uk2BfIxmlhCldWR/C2YtFLcHZgb0Auc/+afIvvc6W7c7aAWVeHBM3Pe5eWtA3tYplXO47ogodEliKORNpqMOYQw1AqxKgqXrGJZKfe0p/Mhj2J0xCxOM5PHzeSYitZotYumQK9VpFBya+weMhyNsvYJiCZLQR/enyCghCR20YgUxGyNzDUQSgTDAUGDaxpuMMCoNhOMgExfSFKFbaGvXUAMfYRaQXh9UFZGqROMJwo8JJ2MyF55BKa2BpiGzY2QqkL37KLkaxXKDcrZILl9h++5NCo6gVjJIhCBII0aDAbahkHcEhpJiGjp+MEWkIboCxZxB4vcI/BGaUyHSNAb9HpbwHjufZyKSF+i8/trbXL5wmeULq9TKFaxCGZ0IOZkSSx0LwexgCz9VKFx5EatcREYeZBogTYSdhWkLu3EOJYmQcQjuELrbUKghNAupQDJoIZMUtTwHR9tMe12aB4dkavOct220889CMCXptfDcmDQO8e5/E+FP2PjMZ3D9hON+h0FvTLaakGoGjmLR9aega6SaQFUTktBHSyV+GFDO6ShpQjCdMBt1MApLeD1QHI9p9ISMOARJysJiDTsr0ISK4pSxM2WkN0CQYpKiJpJUkSSqgVHIkmo2Sm4OvBGEAegaxAFK6CKcPMQhMk4QpgV6BmmWoPkNFMtB+iOEsJipDnY+y8XFF2kPQ7xeGy27w+atW9x87xZHR21MHRbKDuv1EoWNK0SjNvPrG6xez/HNb7zBg4cH9OMhRsEgSAK6KXhSUjYV8oU8quogYhdd19F0g1AqDAYTZqMh8WSMbjwh80mGleXi1WuYJmhpjIzGBFMFp1hAiSMURRIM2gjLwM6UEN6YeNIDd4Bi5xDB5GRFqFWEtHPydTkp0gvBLsDoGLKSNAWlsIBSW4b2Fs75p5G5DAhQ+7sc7T7gcL/HQazw5Q93OD7qEYcpV69d4sbDTWZhgqakXPns58ifu872wwO2N5uEImUy9FBUlTiGUFWwpEDIPrlSDkfN4xgWSq7Gvmdw56DL1uYuz9R18uXHu3oVzuqrCuGy+/4HVJbmmFtZxMwoSCmQMw/MLIqIsGpLWJVFpJFFCtCyOdLOEagGaDoMDqDcIG1vMdp8SOG5zyI0FdKQVAgY91BzeZh0IFOEfAWReMjxEYkwcA82OSCDvrbOlZU1fvv3XkdqUxQlpdPtUzJSHrbavPT0Es3bb2F0h2xv7uKPPWZhyiyRqFpKJmeTaSySxj6q4eEYNjnLRrccNveG3DvaxTWyaHGA1BySJ2VYqN8bUc2HBEGW2PfR0FDVCG8sMcwhWsZBccrIbJG0f8C4PSGzXCOd9lBLq8jcHMKekR5+yKzVJPVjhNARZhY52COxyojhAaJQQyQ+YtqG6irhcMhRs43rR0xjj8PQYPjeTdpffJ29nQM0Q6Fia9STCRXbQY1D7GyDG298le3uh9y8u0vNTjFNlYfHEYahUYsERtomX7ZQVQXHMJGAP4sY9Ye4gxGm5WHGEenUZSa+687Rf2Sczcvss69gKjO6hyPGkxlZu4SqmWjlOkQjUkVDibswjZGTPgZgFFeQ5XMIIU62nVFUhOEgU4vi+gKoAjk5RGbK6KaDjAtg5xB6EcIZyXTM9vY2X98d0RkOwXXxBkcM3Bnbm02E71PJmzzdyFPNaKTBFO9gwJu/9xpJ4uM129TNhHLRIpPPs9sf4EcpOil5NSRjZEj8KaFvUSgUSaXK1WsbVOeHhDMXIXR0VWI6T8oGUKZKEuoYqkrWzKIV5hBJiKrpKKpD6npQsEDR0coVtKmP0LMnu2jpWZgNQLcRAhxbI+z3sOorYFgkrQO0q5+CSQdFFUhhkDRv05/GtCMTNVsknHjca+4wLwN+8voat5I+WgSlSpFSKU/iDqiUbYLIYOAPMeSMC6sZ4jSlM1FxRwnrBYtZlJDLmFiGjq4L6svrkKRkq4vYpoOZdZhv95hMxiiWhT9zmbpPSBd81GqzcWGDhbkllMgj3rqDWZ1HLy8ji5dQeg+R/gxRm0MmPWQ2RsQusnUPaWZRNBWpGggZo5SqWHMWctRB1BZQ422YDiGKSNtNlMoCkV2g2emgZm3sbB5V7ZDJ2Mxnczz30iusVHPs7O6TRgGlQhWtUsQftSBJ6UYOO7tTygWVYt5BVVLSOGRtLo+GwDAVSpUiVj6LXVliOpqwvd8nTPtkC3kcPNLpCDsVTIcuivWElKSrqxVE6DKbedRX11DyPlp9BQiR738JpVYlGXZIZjMMA4SVBSEhmkEyI9Z0FD0DcR/pJiiFPEI3Tja6ICTYepcgCMiduwThBC8M2D48JnV82oct/OmEJdugbkJ/b5sgiEkUk4WNFZo7x1giIpVZQi2msVwniQUZNaJYzpHP59jeO2ZxqcFcPkeunGM8mWFni+iJT+oPubXb4+bRhI4Xk85cPv/MIp+4lGeWJBwddvnTj5nPMxHJjlP6vUPCFO7v7lCs1qkFEqOYQclloLyCMApokY8cHSF0i+ToHsKIUPQihAGKnSNpNxFoMOiDaSD7RyRSZXR8SKqq5GIJ9VVkZwCxT7/r09vbZM1MaVR1+sMRO/v76KpCuZiDKCGTz0CaIH0FHcHiQp1CMEMzVUq1KpmMg2FnyRUyVGsNspaOlXGRQmd/e4+HvsZ47jzVUoK7s0drPOX332+SpimaovKw94S8J838KaDixx77e00KtTn0vHkypGMbJPe+SUIGfeNp0uEByaiH9Fy0jasQeyi6heweglpCWahD6CFDj0m7i9sfkyQh5fPnUfw+qOsoUciFRoW5KOEZYwUx7qEbOuN+j+7QpbbQQM9mUEiplnLEUYKrJBRqiziaJM5ZpGlKtZBn4KVUCzmOD5toioUnJD4wbLVoDUb08muUGis0bIfxwOVIP6TneXzlgwM2yhkU5fFTeiYb5WadDDNvynjs8+xLzzN36QKKnUXJF5GZCqkfoOcs8Pq4nQHheIqi2ye7ncTRSVfbn5J2DpHTKTgVhGoSaTqbD7fJ1uexSg0wHQgmFI2Yc/Us1+pFXn7pOdavXKbdOiBOUqIowDA1us0DVMOhVq+jJgG2lUWPQyzbZnllgVyhROjNGPZ7lOdqFDMauggxbZVk0sZQA/IZjcM7d3j/rTdoNfdRDYMUlVTodAJBICXPbzwhX/rJNKVYrbPywjmslQ1EHEI4A3+I0FfQFtcQQpJ6MzTLRmgSkTERbg9JijRyBOhICbP7H5BfOweZLF5rn/LyAmbWIjraQS+UUcrLpK6PbVkIfETgUl67SL1zTO9b38IwsyRTl6yjYzo6lmORdSzUfI5oFhBJleksJZUpmUYV2evjj/tARKlaZTYaM3BdHh679ELBeOpixj7790KUbAFFaOimJGOozJcdMk9Kx8Fev45tGcjpABnEiGhMMh7hdzrYl2oohTmkphM9/ABVRqhSh1RBpgYi7xCmBq2DHkUtxJ+MsSYjNNMgCkLydpYknpEoCmY+DwcfIkkgCNAKeVLDRLfLnH/+Vbpb92kejRlKwYXLGyhCkCQpluNQqDfotI/Z3t3DMXXmalUU1YJgytSLGU8nJFv32Ot4DJwCyuoa6WDKdH9MXaSMIo9EZtBViS0jFgsGaRDT7gweO59ns49DpkjsDQi7XZQgwa5VkLMBermCEDFyPEY05tFKOaQhURSBiGaITB6/c0Tz9haVShYrVyRNAmLfRw98KhefQpsMsUtlCANkLBGqipbNkI6GSFlAKS/B8Bi9u0stX8E0c7gzj9B1cTIOneMj9g97zO432T3us3/Q5/JSkcFRmzRN8HtbZEo1rPoF7uy3aCtlTKVIOIjoHY6wkggnSZh4ESPXZ04NmC8YCFVQKeonW1Q/ZpzNVEVrG8UwsecXkUEACahzq6iGA4PDkyW6sxHMZqiWBdkKHO2DTPBSnWMvQI0KlIMZw+4QzBKWBDuTOVnXJrMIXRAc7xOlDrkL64jJeyS9NlqhBu6ETMZmcWWNzGTM0AsIJj26RzHv7XW5ezjAyBh0xh5eCvffb1G2VEQU8UwFlgybO0c+R0GOzsEB4fgOjvRQDYWNgkYtZ5NHY7M1wSkYzNWLdAczshkTw7AeO59nItKg2aLxyc8h+y1QNSbDLof7bcxMhlzewfE6aNEMbzRBty3UwQ6aYyDdGYXGPM/8UIF3v/46tjOPbitM2i1ylQKaAH1uBf+ohVXMo6oz3GEfma4hynPQ78L0GLfXR4lC8lkTI1PCGs9ohh7v7nZwC3UuLJ3Dchzk/W2mkyloQ+LE52iWYnoaxu0DRsKi/eEmpD4LRYO1JQ3SlCTRSTWHcBKQ1w10Q0fEKUVTQRE6tvWEbLYxOXwP66aJkzPZ64x5d/+AN751G1sTGKnGldUlfuSHXyZfqxJOJ6imBpqF8Cakg2NMYVLJWIy7fRw7S6ZcZdDaQ5WCQqGEbhmkoY+UFtI2SWddUDV6R8dkgwSZy+Dtt8nO1Sg6GWJvk+1mm17s8FM//edIk4jDg0MO2j5SHpMzdcadLrmsRHEUJof7KElCxYwoVEtYukZj3iT1PaZehsE0wR3PUFOBOwjQzSyJbtGZhLhe/7HzeSYizT31FIf3PkCpb9AvzGGvFhi/t8fR4T4ZmWDGLlkt5blPfxLHgGA2BXeGls+inH46slTOEAz7hF6ImE1RhCCMApLYg1AikAjbYdobkpn4kPjINOFoexvFcnBMFSlMxvsHjPsDdo8GHIUe/c6YwaDP3Q9uMpn5RMMxdTNiFvjULEldT8jIGZEUZIo2QsYEYYofFShXGzAOcb0RWUPBdGw6rSGzQBLFCd2DPnPmY/0PCMBZrXHY6zLxhnTbEyJZ5WDrIbLfo6ALFrMW1ayk3dxh/06O1Y0NytU8UbeLki+iygTVyZGbn2fcO+ag0yNqdciXC3ijycnMqBqiJQm5mkpBD0gGHaSmEc7GqJZJr3PEseez5PsIU8UoFHlmpcD4Xpsv/cr/xv/X3rnsuG0EUfQ0m2y+SYl6jEcaJICTLLIJjPx4viA/kJUNJAgQ2NA4Y43jyBKloUSxyWYzi9l7YSALATq/cFCLQt3C3Vctm+0OKRzcviWKBYUSOIGL0i3HTmBlgO0k40gRxhGdllRPDbPbBa5tCWXGvnFw7EB3rOlDifAUfXIh/0m//Po7maPJZgP/vnnNfr/nh9ShmMzIPUmkHMb5hCJ1EG3NMKTIcQ75lGFo6LePfHx45M2792x2G7QxtH8N3BUFxvR4SU7iu5xNhwokTlpQrd8/72ejEZ21PKxqdocnwjzDCsl3d0vm8yl/bw98Ts5UWYbXtdB7pGmAG3ho3aIPNVvjkkQ+o9TH8wOCOCMPBJ8+PyGHD4i2JAkGdOexLGIOpiEJDGoZoM2FTFLQlaBcXHviRSpRrSDNEiZ5ShjGzGcF+WTGNJWIusbiEBRTRH0EDzbre357/Sdv1xtM1xKFLoM48U9psV2FHR5Jk4QwipFiIE62CDR6X6K1QdsBaRoQOcdyRzKdEM5vCfWZwPH5+dVPmEGyW63wAsVRn1FxClbw6X5F/bBFRQF+HCF7i2sNvZF4KsCKANNFz1Hm7sTNcsr5/gOBEPjKxw5fLLr8Kv6fo9/LKeWh5+X333KqKkLXZfHNHbFr8JMJxfyGMBsRTUY07/6gLbcop4dzB6MR94976nSBcxNQrd5imj1dM/Dqxxluqzk+1eysYMyAlBKrzjRliYpCKqkQ3Qnlu6g4wRE9bd1yO1tQfVzTd4bMT57TqcsF0nF4Ebp4jkt1bHB6zbqsMdZi+55REuEpjzBN0a7B6Jo4yTmjCEVDVbWMxxG9blGe8+Uu0q/kWhd3AVybyC6Aq6QL4CrpArhKugCuki6Aq6QL4D+SSENfU56bSQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nox2zElwoApS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}