TransferLearning.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/luohwu/RefreshingML/blob/main/TransferLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#download the dataset \n",
        "%%shell\n",
        "wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
        "unzip hymenoptera_data.zip"
      ],
      "execution_count": 3,
      "outputs": [
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          "text": [
            "--2021-08-22 13:31:42--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
            "Resolving download.pytorch.org (download.pytorch.org)... 52.222.149.28, 52.222.149.79, 52.222.149.112, ...\n",
            "Connecting to download.pytorch.org (download.pytorch.org)|52.222.149.28|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 47286322 (45M) [application/zip]\n",
            "Saving to: ‘hymenoptera_data.zip’\n",
            "\n",
            "hymenoptera_data.zi 100%[===================>]  45.10M   102MB/s    in 0.4s    \n",
            "\n",
            "2021-08-22 13:31:43 (102 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n",
            "\n",
            "Archive:  hymenoptera_data.zip\n",
            "   creating: hymenoptera_data/\n",
            "   creating: hymenoptera_data/train/\n",
            "   creating: hymenoptera_data/train/ants/\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XR2VQpOjBCNx",
        "outputId": "0c3fd360-1193-4b4f-cb88-da869ba38e5a"
      },
      "source": [
        "#make sure that the dataset is available\n",
        "%%shell \n",
        "pwd \n",
        "ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "hymenoptera_data  hymenoptera_data.zip\tsample_data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxiuZn2Z-9yb"
      },
      "source": [
        "\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",
        "#interactive mode\n",
        "plt.ion()\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMyJai43CJVp",
        "outputId": "9ee7ebe9-d17c-4bbb-c5a2-bf311b359cd4"
      },
      "source": [
        "\n",
        "#define transforms for trainning_data and test_data\n",
        "data_transforms={\n",
        "    'train':transforms.Compose(\n",
        "        [\n",
        "            transforms.RandomResizedCrop(224),\n",
        "            transforms.RandomHorizontalFlip(),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "\n",
        "        ]\n",
        "    ),\n",
        "    'val':transforms.Compose(\n",
        "        [\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",
        "\n",
        "#create datasets\n",
        "data_dir='hymenoptera_data'\n",
        "image_datasets={x:datasets.ImageFolder(os.path.join(data_dir,x),data_transforms[x])\n",
        "                for x in ['train','val']}\n",
        "dataLoaders={x: torch.utils.data.DataLoader(image_datasets[x],batch_size=4,shuffle=True,num_workers=4)\n",
        "             for x in ['train','val']}\n",
        "\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')\n",
        "print(f'Using device {device}')\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using device cuda:0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HM2MjES4CqZA"
      },
      "source": [
        "\n",
        "#visualize the images\n",
        "def imshow(inp,title=None):\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=inp*std+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(5.001)\n",
        "\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2U6CHRGDCfd"
      },
      "source": [
        "# main function of training \n",
        "def train_model(model,criterion, optimizer, scheduler, num_epochs=25):\n",
        "    since=time.time()\n",
        "    best_model_wts=copy.deepcopy(model.state_dict())\n",
        "    best_acc=0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print(f'epoch {epoch+1}/{num_epochs}\\n')\n",
        "        print('-'*20)\n",
        "\n",
        "        for phase in ['train','val']:\n",
        "            if phase=='train':\n",
        "                model.train()\n",
        "            else:\n",
        "                model.eval()\n",
        "\n",
        "            running_loss=0.0\n",
        "            running_corrects=0\n",
        "\n",
        "            #iterate over data\n",
        "            for inputs, labels in dataLoaders[phase]:\n",
        "                inputs=inputs.to(device)\n",
        "                labels=labels.to(device)\n",
        "\n",
        "                #zero gradients\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                #forward\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",
        "                    if phase=='train':\n",
        "                        loss.backward()\n",
        "                        optimizer.step()\n",
        "\n",
        "                running_loss+=loss.item()*inputs.size(0)\n",
        "                running_corrects+=torch.sum(preds==labels.data)\n",
        "\n",
        "            if phase=='train':\n",
        "                scheduler.step()\n",
        "\n",
        "            #statistics\n",
        "            epoch_loss=running_corrects/dataset_sizes[phase]\n",
        "            epoch_acc=running_corrects.double()/dataset_sizes[phase]\n",
        "            print(f'phase: {phase} loss:{epoch_loss}  acc:{epoch_acc}\\n')\n",
        "\n",
        "            if phase=='val' and epoch_acc>best_acc:\n",
        "                best_acc=epoch_acc\n",
        "                best_model_wts=copy.deepcopy(model.state_dict())\n",
        "\n",
        "    #training complete\n",
        "    time_elapsed=time.time()-since\n",
        "    print(f'training complete in {time_elapsed//60}m {time_elapsed%60}s \\n')\n",
        "    print(f'Best acc: {best_acc}')\n",
        "\n",
        "    #return the model with best acc\n",
        "    model.load_state_dict(best_model_wts)\n",
        "    return  model\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruNFx3X3DMnO"
      },
      "source": [
        "#visualizing the model\n",
        "\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",
        "            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(f'predicted:{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)\n",
        "\n",
        "\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "42cf25017ce64234b8b886c99d90ee25",
            "00d316d00b384db58fac72322445cb97",
            "5df558c8a55d412a81988bb84038869a",
            "82a3a66b728d4fcdb664e22b90ffc916",
            "0c0d04f11af142b091168bb111c45580",
            "a673f24db807488280451b59a871f9a1",
            "dd4facba491743e6b5f4fae4b1b18a64",
            "3faa78eb28de44abb79326dc17b5fcd9",
            "f6331a18b25b48a7b5c99db903e30801",
            "913495464f7643f4aa05e1f7397ccb60",
            "e2339bc97bab4f0bbfa62d9d172c9bcb"
          ]
        },
        "id": "vszdCn10DYmH",
        "outputId": "cb6482a6-4f45-4819-c3ab-c7c3a02a1e9d"
      },
      "source": [
        "\n",
        "if __name__=='__main__':\n",
        "    model_ft = models.resnet18(pretrained=True)\n",
        "    num_ftrs = model_ft.fc.in_features\n",
        "    model_ft.fc = nn.Linear(num_ftrs, 2)\n",
        "    model_ft = model_ft.to(device)\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "    # all parameters are being optimized\n",
        "    optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
        "\n",
        "    # Decay LR by a factor of 0.1 every 7 epochs\n",
        "    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n",
        "    model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)\n",
        "    visualize_model(model_ft)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42cf25017ce64234b8b886c99d90ee25",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "epoch 1/25\n",
            "\n",
            "--------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n",
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "phase: train loss:0.7745901346206665  acc:0.7745901639344263\n",
            "\n",
            "phase: val loss:0.7647058963775635  acc:0.7647058823529412\n",
            "\n",
            "epoch 2/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8114753365516663  acc:0.8114754098360656\n",
            "\n",
            "phase: val loss:0.8823529481887817  acc:0.8823529411764706\n",
            "\n",
            "epoch 3/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8114753365516663  acc:0.8114754098360656\n",
            "\n",
            "phase: val loss:0.9084967374801636  acc:0.9084967320261438\n",
            "\n",
            "epoch 4/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.799180269241333  acc:0.7991803278688525\n",
            "\n",
            "phase: val loss:0.8888888955116272  acc:0.888888888888889\n",
            "\n",
            "epoch 5/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.7704917788505554  acc:0.7704918032786886\n",
            "\n",
            "phase: val loss:0.8169934749603271  acc:0.8169934640522876\n",
            "\n",
            "epoch 6/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8319671750068665  acc:0.8319672131147542\n",
            "\n",
            "phase: val loss:0.8300653696060181  acc:0.8300653594771242\n",
            "\n",
            "epoch 7/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.7663934230804443  acc:0.7663934426229508\n",
            "\n",
            "phase: val loss:0.758169949054718  acc:0.7581699346405228\n",
            "\n",
            "epoch 8/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8360655307769775  acc:0.8360655737704918\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "epoch 9/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8729507923126221  acc:0.8729508196721312\n",
            "\n",
            "phase: val loss:0.8954248428344727  acc:0.8954248366013072\n",
            "\n",
            "epoch 10/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8524589538574219  acc:0.8524590163934427\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "epoch 11/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8319671750068665  acc:0.8319672131147542\n",
            "\n",
            "phase: val loss:0.9411764740943909  acc:0.9411764705882353\n",
            "\n",
            "epoch 12/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.868852436542511  acc:0.8688524590163935\n",
            "\n",
            "phase: val loss:0.9281045794487  acc:0.9281045751633987\n",
            "\n",
            "epoch 13/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8401638865470886  acc:0.8401639344262295\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "epoch 14/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8073769807815552  acc:0.8073770491803279\n",
            "\n",
            "phase: val loss:0.915032684803009  acc:0.9150326797385622\n",
            "\n",
            "epoch 15/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8852458596229553  acc:0.8852459016393444\n",
            "\n",
            "phase: val loss:0.9346405267715454  acc:0.934640522875817\n",
            "\n",
            "epoch 16/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8893442153930664  acc:0.889344262295082\n",
            "\n",
            "phase: val loss:0.9346405267715454  acc:0.934640522875817\n",
            "\n",
            "epoch 17/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8893442153930664  acc:0.889344262295082\n",
            "\n",
            "phase: val loss:0.9411764740943909  acc:0.9411764705882353\n",
            "\n",
            "epoch 18/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.9098359942436218  acc:0.9098360655737705\n",
            "\n",
            "phase: val loss:0.9346405267715454  acc:0.934640522875817\n",
            "\n",
            "epoch 19/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.9016392827033997  acc:0.9016393442622951\n",
            "\n",
            "phase: val loss:0.9346405267715454  acc:0.934640522875817\n",
            "\n",
            "epoch 20/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8729507923126221  acc:0.8729508196721312\n",
            "\n",
            "phase: val loss:0.9411764740943909  acc:0.9411764705882353\n",
            "\n",
            "epoch 21/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8729507923126221  acc:0.8729508196721312\n",
            "\n",
            "phase: val loss:0.9019607901573181  acc:0.9019607843137255\n",
            "\n",
            "epoch 22/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.9098359942436218  acc:0.9098360655737705\n",
            "\n",
            "phase: val loss:0.915032684803009  acc:0.9150326797385622\n",
            "\n",
            "epoch 23/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8893442153930664  acc:0.889344262295082\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "epoch 24/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.9057376384735107  acc:0.9057377049180328\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "epoch 25/25\n",
            "\n",
            "--------------------\n",
            "phase: train loss:0.8770491480827332  acc:0.8770491803278689\n",
            "\n",
            "phase: val loss:0.9215686321258545  acc:0.9215686274509804\n",
            "\n",
            "training complete in 2.0m 19.522504091262817s \n",
            "\n",
            "Best acc: 0.9411764705882353\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ej1mQMBgcoYVCWR7CsFmMFrhMKeKIIg6odFa51thAlwlSFgz7Q2y/w0ufuUGRxnzlK1/hvfd3qFRrHN59n0V/nyReIAyJYdpIy8FzKyRJSHd1k+5Kj5pn0Wj3OD56gGWaHO0dEi8CGvU2T954id76Ffxqi1KlHO7c4vBwf2k8LlWUil3yI688j9YG797ZYxyk2AJKJXDtAqVLQtPAaaxw9fp1tCq4c/cdXFdz99ZXqDVXaXZXWFndZHX9caLU5PR0F9OA+bjPvfv3ef6FT9NaWSELQwo9RGuTJJyTlVMsM8cgZTqPWH/sM6xtbDEf7COKgPe/8Rqb2zc5OXxAGMyod1bIkjm+Z2KYJvV6k1zlrKxt0F5ZZzwacTIOCVKw05x6awO0JIgiTo4PWd18jLwssW2XVnedS8n0wwn6iFiqKIvFBFlqihIMKTENRZwWJKMFqDnSNGmvdHlme4ut7StEixSlbabjU5J4RlGUtFfXqDU6lEpTaolfaaCKhLW1DRbzMVGaIh0XE0Fj7RLN1k12Dk4ZHTwAnUI+xXBr+NUmjlvBtiW1mgOej2VqbK9CrdXi8lPPEYz3CaYDykwjDUm7XsOQgnA+wrMtep0qvYZNGKREccJ0NieIp9y5c4das8fG1hXqzQZSCtbX1pbG41JFaTerPLi9z3B4hmUIDNPFMGGxCEjTnEatwfUbz7CysoXnuAz7faTU9FY2GOsEpU1WV7aQhiRPE9IkplLxMXAxRc7N5z5Nhs/RcIbrOjTbm8yyBf1BHyFMTNMgKQsuXX2GWlWQhFOOjo7Y2L4BTodM1vCaqyBKvGoVQ7TRRUYYJhTS4XQ4JprtUanV6bbrPH21geX1uHcwJJ1NMEwPw3AYDkd85bXf4fEnj3n+pefRZY7gER0lDuYjHuzu4diS9soWdqVBlgpuf/A+1Zrm5nMvsbKyTZ4WTEdHVH2LIopIFxkCwcal6+SFZDpNMERMFs9JggDbEoynZ9Tal1ld3UBpiLISpRSz+RnDwSloQb1eob26TaXaYz4LidMhiYIbL77C3tmCMkhY6bVIajZSCMJU8tiNV5iMzzg+2mc+GRNMBsThHKF6LHJJkmmGs5SD/X2SRUgczklTC7/a4ujojEb3DFumnB2d8DM//aeXwuNSRXnzgwF+9xo31l0ub18lWYyYTwOUuszK5hVWV7ZwHQ+AOE1xHYvpaEQSh9RWn0R4m+wez8jTCFREND0lzzM6nR53P3gXvzHmj61f4rHHrpIXiuFwAkWX3uZjnB3tcjKcYLc2ORqHHB0cUK1X0dpg/uCIk+NdJAXkBUEQIVnnqSsbDKcpXv0S5eExaZzg+k2qtSqm1+TtO2ekxQgNxEnGfD7GsWyKUjGbz3Bcj3ffeo/J8T2yJPnu5PwhsNyh++Y6V7c2cBwLw7RQFZe19oxex0L5l7l9Z5fZdITUGZYpENhEaUJn62WqK1fRwiErpyjhgTDImROHY94/3CWJQyajGW+/0aHXadHtdhDdOnmZcXbWo8wjTs9OODjp092oMVlEhEmO51c4Pn6HcHqEQUI4iygLSZbGONYmUQDr2yvU2xsUaYrr2lQbLbKiZDAc47gtDKmxHY+VtU2kYeLWfEhiZmc7xEGE6Tq0Nq59OEEfEUsVpdNuYts1DJnhOFWkV0MUDXQ65M13fovXv3yfeRjRqpm06yZepcnq9R+k0b1Ks7FKrnLW1mqMRyc0qzU2nr6CSuaM995nvPN7fOPekG++/n8RhnN+8LM/weXL26x2mjRrDnncxqw02T2e0D8bYlguUTxnPh+RhFOyOKaIFwhhIIUgS3LOTiMM0+Hu7XeYzyY4fg3HNaCMaNTa5yUkCCjiGWm4oNlpcuXx62RByNntN5lNJ+BU2d6+wrOf+J6l8bhUUYYH79Ftr+HXOkhpYZgGZIpKrc21zVPeb2pGo5SxUpQK1vwapVJMpyMsy6Hb7eDZgscvP4FraO6++zWSwqKwVkgmx1xZafPWYcb7b34VYTp8/x/7EQw0RZ4zTSTDaUp35TJJGmM6q2TBhLOjO1RrTaQBVrNFHocUymLryjbD0xNO+n2ktGl2N3CrTYLZGUkxZzYaEicpEokuIra3NynykMHtb6DTjFpvlczyyKKAq+tVntt6RHv0s/mQ+eyYlc3vRQqFJEOYHir26fU2+J4XxgRhzt2jENurYtgVosUEw67g2222110avstkcMK9g31ic43u1iUW0wl3xFXmkxFrV57gbDCkXq2TZ6CFRpYJxXSX6cERV9a+jyceq3BwPKbz9EucnG5yNjhmMBgwHRxRZOD6DSr1GvPZGbZtYDselinI0pBKa5X5Wch8dMJ8OsSQFpsbXaLj24RBhNvc5PFnPo1wbea33iWNIqLc4ORsuDQelyqKFj55mpzPl3sOqAQKiV/fRJQZV65c4VOh4nh8BzApigLHhk88e40bN54mnI05fLDP6WDC2SSmkCVOrY1pVyi9VcZ7h2w91uNa5zJxFnJ2tke308VyHJ557tOsrR/g2opquo95+FscpQmt3jqXblwhurbNa18p6ZcnFKpk/6CPIWt01z2moxEnex8gDIeVzS3KIkPrAlXkSBNENGU6nZC4XRA+kXYJRyFBXBInKfsnQ1a3v9N2mj88lipKb/0a3bXroEpQIA0L0CANTLeD3xBcverQbRwwjSIub67y2T/xUxSF4Nd++e9zsPMBUVIwnwfno8qOSzr7FH69gxIGhVKkiwnrj2+jihkHe+8heIIgiml1mmxcus7w6D73bu9yPEg53P8ClfbK+Xw/Ltev9Li82SPMTaQ2uX/3A472H0BREC2mFLkiieasbT1ObIzR5fl4nknEpLCo2d65oEenSNMgi0OkYZIVgp0HO0vjcami1NtbKOlxcO/rrG3fpNHqgYqhjBECTLuCZSRc326wvvU8aS74u//r3+Jgf5cwCCizjFIpVFliSImQgvnpLrVajTxLKUvN8Og+TtWj1eqwdekqltuiCDSngxE6Txke7pKfHRPnDhawuXmdXreNY0hO7n2NYF6iK2u0u+s8eXUNiwXJvM9RKomUJosDwmmfSq3JtN/HkwWjcYxT61FrtLHcKpnKkGmB57uYFZckzTjYebA0HpcqymQSsf/gNeq1EoXG9V/BKAOKIiZLQ+L5LmU+5eYL38+DBwMmo1OGxycsxjMcxydKIrRSoPXFdifNXKXIbEIU52jToxAml8KIOEu4v5hy5UaLre3rSATvvfNVjvcfILOQze2ryNkY1zqfdHONgqduvsz//Zu/woMP7vHTf+ZnuXHjJb72e3ByUiGNFpzmM/K8YDY+AwGGKSjzhHkGtl2QJBHCtIjGx5RZQl4ULOYzqr6L7zyijn46GDHsHyGubHDv9tvUmiv0uh2m4xPKDNIkY39Y5ytvvMH4dJ/1tSbtZoP5PCGO5limiRCCIs/QSqFVCUqDUsiyJIhSiqJgf2+HT3ziFd47OGaYfJV6pUa10mA2OiXJUnq9bcaTCM9rMB1P+OprfwfLMLh8eQWrscqWk/HUjSf53G/8I77+1W8SJxm5kviOSWnCaDIhyjSrrSphnCEMG5VnpOGc2bjPYrFAoLFsizyNyUyJKZayYhVYsihZHDCajHBbLeb9E5T+HZ594fsI+xNqjs27dzO+/s4D5pMpGkGwCFhfa9Jb6zE4zojjBLvSQsYBRZqAIbBMjSnBMg3MLKeMQ/rHO7z+dRNz82n80uDw3jvMp3Ns0+D6tad5+Xt/kPvvv4POQwbjCaPjI9I0Zj4e4LVWuHr9KvMg5OjkjMODU/Isx7UtDNtGa+g0qhTDGWQhs1IyDQJsy0ZQkGcJhlD4fgVpgKFTbJlhSWdpPC511f3Nx3pcW2vTKFNapqQ4u8/i7u+wahecTTKC0sX3Kriug2m5aC0oVYHvN3BcD8OQZOEUXaQ4ro1rgWdLWq7GMjXSAK1ysnDB7OyAZDZmfHCPWb8PizPWWk1qnsve21+i16jww//Sv0bVr4IuMSjJ4oBgMsEyPXZ2d6k1Oly5uoXUitlkwXg4Jo0i6pZkpVkFlRHECa5jUqvXqNYaWLZJq9XC8zwsKaj6HrZlY3mVpfG41JISzQ65XAtp1Aqc9QaonErFYxBIInMd2z2j3mxRahCGg0oD0lJiux6lPl+K6rsVgvkYXaT4nsmzV22SJOZgapKWklyBzkqcIsOxTSZHh1gC1u0ZzYqP1DnR8TcpFof8yoM9gukYXSoMIUFLXNvluZdf4Z03X+Pg/j6jSYzt2HRXVhn2BxjSOJ//MQWjWJPmGkMVFLMpeX7eVE7TMVIILKkxLRspBWmaLY3HpYpyMDZQeYtsFiNEwubKFperl7h/dojXVTRbqzz+1AuMRhPu3L7P3u03yZXEEgqBxjA0RRoAYJgmj1+us94K+dItk1SZpEqjlUaJEgAVz0mnI+rtGqZtsrO3Tzg5wixDpHlKpgyw61iOTcWySVOFyhPCKGLY79Nce5Zpsc/+wTGPPXUZx3M4u3eH1IDCsMFwcGyNQpMXBaPxmLIsMKSB6xjUqz4qS5FCU5bF0nhcqii5sJBuC51nTCYTmo7LnvR5+84Oq7MpN198hVd/6LPosuSXfumX2b39FtPhGcnsCEOWWLYkKzQ2Fs1Wj0US8PaeQgsD05CUCigLHFeTh2MGd7+GVppWu0cWBJwe7RDHGeiUmq/wKzbSMnnu0z/ERt1k74M3eXfvhA/eep1gMWN4ukuUCUyhmfaPcDybimdi6ILVzgqPXVvn3v4Ro0lAoc8/BCEkeV5iGJISQRYnmBI06dJ4XKoo0hSUaUSwmBDMRvRNxdbm06xffoxWu4Njmrzz5jc4Phnywbtfx5SCJAkxzPPFbXGsKeOMVrWDIxKCIMCyBY4jsVOBFAItwLUlUijKLMZtbbJ66Rpnd3Yo8oxCKbQ2SLWBqStsb16j3V3j9Ow+T3/mVXaHv8rh/TuUwoBigVdkrFZN4kmfWT9DFZokjRiFe2xf6vLkpQZfW0ToWKNKTVlqSgVJknN2NkEgME3BEhtfy3X0RbrAMGxyUcGrr+K0t5gHAbVGnZc+/Rlst8Hvfv6f8MXP/zrj43tE8yGgMGRB1dVIQ+P7VSpuiSPnuK6gUzep+mDIEiVASolja2ypsW1JveZCPicIQoIwIstTlCEQpke7s4Iocn7387/Ob7/2eyh/latXr6GlwSdf+SSWYyOkQbXqIaVCihLQmI5NURQcnIzJFhF1z0JpjWGANASmCUprilJTlIqiVOiPfFLIh2Opomxe2sRxHNYvP4kwLMbjAQ3bpshzvviF3+a3P/857rz9NZLxERVbY5kSy5QYhiBMckpcXNvGYoHlmvgVGwUUWYEpFBKBEAK0Js4VcVbSaHVQyZAwKim1BsPAcxzW19Zpd1oc3X+byWjAbB4ynU1prV5ieLrL737pa9x44TksW2JaAsc16XTr1GtVVpsudVfi2YKskKzXbEBRlCWgON9QfV46pCFBCIzl9R2XK4pWKWW0i2cHlMmAVrODMlzqRk5//wGj0z3yLMOxSyoVH9cV51vxKh62V2OtXcNiTpIp0kIShAWTWUaZZ3iWwpICQ0CifSLlEOeKVruJpWckpca2wHYk3XqVx598CpUuKPKIXJUkec7h6Smbl7fZXGlxcO8ub7xxi0ang1ApvqEwS0We5KSzBY7OMYuMaZBjS0HXtzGlgWmYCCExpcCxTWzLAKXJ0uU5+qWKkkULptM5Bzv3efzGE7x88wrJcIf+6SGPb3i0KhZC5EhRYpkxrl3g+jaW5bC50kWqKSfjhP60ZDzNCSJNlECYWswiiedbVNzzbXwaTb1WZW2liSxnpLmm0ALfNri8tYbvGYynQ5RTIS/BMiX37tym0V3hqadfoNuwOTs55dbdIxJZwaivU212efxKm26nRtM2MLSkKBWNRo/vv7GO75porTEkVCsOrm1iSvAdiWc/opuG+qMZ1ZUbWI6FX80I410s5mRJSRBEpOkc17FI05wkiShLhWnYbHdq6GyfRWaTKO98mEVrTEtiGpJFnDMLE9pdn0a9gSEF/XFIo92gwYA5EY5nkyxSVipdtF3l9u13Gc2mDMcLCl1Sr/oM+yccHve5dv06Z4e7jL5xnzTLWaWhH3wAABFTSURBVEQFWmTIms2Nq3X2TyQjXWLOYiIBGzc/wc1LVU717/Du+/dI8oKiKJCGgVYltYqFeFSHWSrdp6k1uoSzA4LEoLQVmRERJQOSRDOehThOhbzQCOFQqozLPY1tCXaGLspbod2dk8UBpc4xHQeJwjULwnTIeDQjqfg89cQqh7OM3kqTPJpwq+8hPUGlKhjNx8zeHOM4JnleEOUlvbaHYdcJw4ivffk1Xvg3f4ZXP3mT/eMxR+OUVqeFVBl5ofjgWGPbLXRDkeUGSpq8ffs9mps/xmd/4sepVH6bt2/dJc9LbNsiCUO0IVDqQ0+M+shY7vY6v47rVUgCG6FNFkFClpqsrHaYjkf0mhb1zirRYkIYhVze3GbbP+TLd0ZMQgtpzxCGQSkUllNFWjZSKwyZUa1V0VpiOS7HZwHSrOJ6HebxlNOpwnU8NtbXcCs+88mIwWgB0qHim1hS4xgxlaZk3D9gMT6jXTF5rO0wDhRlqXBcB1OURIUkE5JZYZNbNWzbZT4P+c3f+HWuXLnMi5/5LE89/wpvffOrHO7tgCoptaJY4nEqyz22VsfkKaTJgjSLcWwTy6tTLmIMLBzbZjYdYXs+FV2ybp9xf2gwj02EgCxN0YCkxLLPWzcqVyAtmu01qu1V8iRgMR1ieSbXrl2lv/M1TNPEcV1My6a3ukpZpEi7hmlaOI6HLiJcxyBPF/SHM872brHQMa++8jLD7BtEZoM8Pv9QkC4qikiSlLIsIYdqvc1iNuHtb/bZuXef73n1R/mJn/rTfPX13+Lrv/c6udJ4/iPqU4anD7AtC9v2qPgV0Od7Os76Y0qjTmfrSewkRKUB19ccyrzP7thBixyDHC0MLNek27tKpbZBOJvgOB5KCpIkYBEEzMcjSm3h+x6rqx3ufmAgDQMhNZqSLI9pNrv4aXI+EmBaSOEzHAdMZooolvwfX/wmj281+NTTa7z6yWu8fi9gEka4tgVSooRJUaSUeYEqFfPZlLLIz/fsY/Da736eKI554VM/hO243Lv9FovgEZ2jt1TAavMK3Y2nyLKMyeAuO3uHTMMMvw6tzjqjwQmX29CSp/zybw8YLhSe67CxuUWl2sQ0BI6R4VVqOG4FpERKg4Zskj/YIfEbzBdz1loVWjWbaaTxKk1QCYZdZXA24tLlDYRKzzt0hiSOYuIowrIMDKm5vztiOs8YLW7xo5/aBhGSUiFLY2wrRZguWmscxyMtzsuukBZFnhHGIc1Gg7e+/hqz+YKbNz9BkRfs3H93aTwuVZTHb3yGVnsLLU2ci+NcNxYBShvM5guGgwO6vmDTG/D6XZvTGSgMOo11Kp0nsAyFUWbk8YA8OcTwqwjTwbRsomBOrVYny2IWs5BGwyVPp0RRyGIR4NmCeDZEFSUH+0eoQoMhybKCJEnOp5l1hhZguhWCRHMwmCP8FjK7zzzWoF3CKAEdIYWFUiUlAp2nFHlGWZTo9Lz0eK7D3p23sYTgyZufIEuipfG43I2oXpM0y9BCY1oeXrXHxuPfS629wje/9BvM99/ixedqPJi0mKY2Tzz5HJPRCVIUCJVSZBF5NqHR8FnM5pj4zEcHIG2UOh8CicI5Nb9Cr+0TTQ4pVUwpJIvMQkuLstRMxwm246JVSZYm55tiHbBtmywruRjFJ4hCJlHJc9dbfLA3QFgV0B5xFFAqBWhUmWCgUEqRZzmOW8GybEzTRJcZp4e3aTZrbG49sTQelypKHCUYpsK0FFmeo0pFURYgfa5vr7NWm5PTJjA3Wd0sMWSJI0tKYraurHH3nTdxbY/JbM50PCE+PkUL83wAMFoghDzfstCp45UnjAaCeqONNgsWiTjv/2Q5ZVHiuDaGpYmTGMMy6XRXGA1OyPPsfAGHZSGk4pu3dnn1yQY3NnxunaRIed5Lj9OcNMvRSiFVDsJAGBZ5nmHbLqubG9x+702icIYlFM+8/Nml8bhUUY533qLRbCGkDVpRFAlIC3P6Lpcru7x3ZDKnhled4fguG5dW6R/eJkstPnjvA3QWU5oaaVRxvIx5GJBlGWlWIqWg06zS29zk+vVtrloPKPwWL9QEo1HGrVtH9MMIyzAxpU2eZ1RrdVwvwTQ048mIIEoR0qTq22gskIqdnRPW3ZJXthXrnuDNU8VponEsSRgWaKDIcnSZYFomruuR5QmD013Wuj5CaVABR/tvLY3HpYpydPerHBsWyvRAFaz4ml41Jc0z3gibLFKTMBrixCWOZxMHxyAyOqvbhLECy8IwNH69gWnnNNcucbi7w3QWc/nSCgKF41fwjBzPGDIQ65hmgqEHGCKl4miiKCaXHp7voVRGb3WFYD5BGpJ6vUWWFZg6J0kTtJKURcFr39zDfK7NJ1/YQOzM+bXfeZMgKigLhTRMDNMGo0SrkiJLyZKQrfUu7apFOAsplaSIJ0vjcamiJFlCtyqp26dYTgWhSo7nVU6GkrRMWdleoxRnpMkEv9IhCWNUljMb7pKLCkIYrKyvo3VJXhoMD/YZjYZkWc5sXqHTrlMTivLsLtHlNbS9il0OSBYBaTTDrzRw7ZIgUqRpBKZNXgqUPl8lozEIwgmtep2m55HHc/IkJ8HkC7em3DlTpIWmKAS242I7gqIoqDZ66DJmMR1jGAIDRRJGZLaL7beQpkNZPKLTwc2GxVPPPk8w6jOY5bjNS5h4NPQ+J6fHHB+cICnRZUKS5mTTUyq2wfr20xweHpDkOfdvn6LKHGlYzBcpCossKzg8GSJNj6e2m1Dm3BlMYX7AwYMHDI7HWHYb063h1CReteT0bEAYZfSjCM+v4Ls2eZaSxgkLwyJQJagMw7aR0iHOcnb6M7ZWelheFcvxSZMYz1VkWYRj2dQqLtWKR61Rx6t6KLuO6zgYhsH5/1lYDpYqyquf2OKN9+6TFYppXFKM7+I6NmmuWb/yONVajZODXWbTjMXkjDLNcPwVykJS9yzCOGY4nOLXWnhOi0q7xCtyhDWnyDP6wxG/9XtzPFuwiAMoj6lValj+Goblog0HhCYvIhxbMg8itOGcH0WFgiLFskzSNMfzqxhGE0MqsjyjKHMEmqPxgs3txwgWc6JgihQ5UpsUBWxd2Uar4rz/ZAqElAjTPp8sE/7SeFyqKMJZYzB4ncGiJIgzLNM9X5Kzsskzn/xBfK+C5boM9nPOTg6pVJusbD2JaXkcne0RJjmPP/tJmr1LJOGCJJ4wGx1S8QR4LkVeEMQJaeGQpwrf8/AbXaQAz2synY6ZRfOLqQGDZtUgw8HxfIIwwDQMhFAgFJWqh0Qzm04pVYllCLI0w7ZbqCLCkBnoBKUVtlEQJil53kTokjxPEMJBCrAtC8oU/eFH2H9kLFWUN24dsFAuly63KQrFaDRhOgtYtSQnh3eRUuC7Et9zuHbzVfJkSrw4YRYkhJmg0urRWd2ke+lJDAlnx/eZTga4nkBrgyjok2UZWVpioSgMTZIWGKZEJyGlLsmL4vzQN7+KYQiSJKNQNlJIyvL8pIs4WjDsH+FYJq5tIZQiTVOatRrNeoXpZMDJwQG25VCrSVS6IDc84iDA9210WQIFeRoSE+NY8tEdujfcdV58+SnisI80BC+8/BKLyZBEm4zObqFVjjRsDH+Dy9dfZD66Q7fb4+77b3M63melvcJiOmI8/CKtbo/hoI/nNjibHCNFhjAskjQhy1IsCWYO2hjR7bQw/RqOPl+qpFWJNE2aFY94PmIWJVR9h6JQlFpR8T20KimzCFmeN3N910ELg+nwgMl4jG+ZmLYBSYYjTBxfUOYxttvGkAZCK7I0xpIWyvCRS5wuXKooW9dfIJgPUWVAcXH6w8vf+8PceuPznEYDWqs38etr+O1NFvMxs9kYqSOKUp0fiVvEDA72MKRgePg+wm1x45lnWCzm58tOhwvSLMMwLExDI4RAFRmOJUjiENMocT0Pz3cIplMoImwjZmO1he/Xmc2mpJlCGC4KSZlV0MX5YrxSgZYm09GUKEpwbAdVlFj2+eE6575RodMYq+Ihyc83RgkTVSaUabk0HpcqitIlzc46kanRxZRmZ4MsjUlySWftKT756p+kUvF565uvkych9eY2wjBY3VxjY9vCIGD3rub4aJ9FpPGKhMMdRavT5HA/IMtLVJljGQ6OY2M6Fo1GjSxXKJWgySlVxGISo5IQ37OpNDsIQ5KkKc3OJkkcoHWJQl4sILdQRUoUZpR5gmHblBREOfiWgTYMtOmgLRsoiDJNTkHVLqi456e+CruCW6kvjcflzjxWW6ALau1VbNnFNE3u37/D5uPfT2M+xndhMjojChMst4Ht2HiujeO4eF6N4eltLl3eIpwcUWiTam8VbThMRmPGwzF5ofErdUxDQlnw5BMvsba5wbvf+CJJVuC4HqbOSIOAaq2CXakSBSEK8PwKeamQVgOlNdV6nTQYUaaQ5xlppghiEFaHRqd2vl+mLMlKgW+5aCFIlSIvSwqVY5o+IskxU4ljW8TTR3TovixzHNvCNGo4joEoMyzDo7exgeNKRoM9hLVBrbnFIgyJohBBiS5zTAPqjTaeK6nWfBYlWHaNLJ3iyZj1jstwWmDZHq5rkSUxs/kYYcHapSvMJwNmkwGKHNM8XzZk2Tau56BLRakKijxHGBKtFdHkEFROGs1I4oS8FGD52JaH7SparRZRsGDQPyOKUyoVl3q1gYM6F990QMPpySHmyMK0HtH9KZ12A8e2aTRa6DJj0D9k+4mXGY5C0rhkc/0ap4MZ9XaHXCvCIEFIA8NQZNGYPD7m9GiPsizp1h1KNcE0S7x2hZUm1P2C42FKmJSY5MwmR6TRGVXfJk1KVJaQ64JarYHnOtSrDfxWD8u2SRZ9+oM+lhEhrDpFLsizgjDMEbJOXsxJihizBK1LXMfBtAx832U2m1GxDOw0QSpNc3MLQwiiuERLm9l0dn4I9pKwVFGefvoGzsWJEovZmOHwhDiTSF2wvnKV9978CkhJgcnZ8R6WCYPdA8oiwHXrnJ3ep1Zt01i5jut6FGlAHC0w3SaubZMWO5RGwe7hANfR1HwD1xJInVPxbCyzSZqGCDTHh0MMr42eHZLlijwtyMMZwjax6nUwG6RFgbA75FlClmXEQYTl+HiVOv9Pe2ezG7cNhdEjURRJSSNpVE8mcNI2aYGgy77/KxQoggAJimbhAHXR2O6MZVujX/504XUWBWYxizmPwLMhyO/ejxDQWpNcNMh4pDAapWJ0Ikj8PUW1oa42HLqOqSopq291vv1/jryXWKF1SvARIdSsmw33nQNrWeUKPz1yt9vTvHjF5aahqCrGYuDjhy+MKMr6h+d3pWEmBElVv2A67Dh0D1jTUH/3I+3jZ4yEi6akKAyrXPMcjExxfmE4wO1dSzvOVLPg9uYry2yRqUbLnLq+xNsDwzSzWMvkI9qHPSF4nLOEoSO4GUxOUWQYLVHrimUZEXmByiXrdUNebwgejClIpSI+YkTyuAHvWBHFCikF3oMxGSG2yIuavMj46ZdfmT++5/L1G9zcM00ds4tQ1Sua7c9Escc7S9cdCN7Rtf8wThIvVwSRY4qMl9sDJo0py5J+6PBesMwL87hDacnj/l/adsC7lM/vf2O9rlEqIyCIZEqIHBGWMOzIpKbd73HLghAJKtUM/UAcLNIEhJ0QkUPGCSGWZEajlSESGal6vnB4O4ObcHY42jked45+6ojimSEk/H19hUwLXNdydfWJl6/fMizw9t07/vrzd6b+nqzcYp1iu/0e7x5YxpEoXeHtjFsWnEwJaUOSKKyLaLsJsdpyYQrurv+AKCaNe5JgQTja/RM3t088TYKszHl86pHDxORBzANClNx8vYalJ00TRNSTSY+TEc7PxDhUKsh1ijKGEIGPxfN39DTgrCMWkr5vYReTmQLcSKIz/BFXS53rn06Qc9PQCXKWcoKcpZwgZyknyFnKCXKWcoL8BzDGO39FoFJSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMtM7WZxG1Nq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}