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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6Lx3muqILWw"
      },
      "source": [
        "import utils\n",
        "import networks\n",
        "from plot_utils import plot_summary\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision.datasets as datasets\n",
        "from torch.utils.data import DataLoader, WeightedRandomSampler"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zD9VVf7tILXC"
      },
      "source": [
        "train_set = datasets.ImageFolder(root=utils.dirs['train'], transform=utils.transform['train'])\n",
        "val_set = datasets.ImageFolder(root=utils.dirs['val'], transform=utils.transform['eval'])"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9y7jDG-ILXD"
      },
      "source": [
        "# Weighted Random Sampling for dealing with Imbalanced Dataset\n",
        "class_freq = torch.as_tensor(train_set.targets).bincount()\n",
        "weight = 1 / class_freq\n",
        "samples_weight = weight[train_set.targets]\n",
        "sampler = WeightedRandomSampler(samples_weight, len(samples_weight), replacement=True)\n",
        "\n",
        "train_dl = DataLoader(train_set, batch_size=32, sampler=sampler, num_workers=2, worker_init_fn=utils.wif)\n",
        "valid_dl = DataLoader(val_set, batch_size=40)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CoH0oRlUq2d"
      },
      "source": [
        "epochs = 25\n",
        "criterion = nn.CrossEntropyLoss()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "source": [
        "### VGG-16"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "cdd0b5be64924e599833efac7c7237ff",
            "fbd174efbd314c0a884781b98a914a72",
            "ff351d16bbc249f595b355cd779d63d6",
            "1b3232d4c65f45c88c74f4d80fa0e2f5",
            "8b52c61b04b64f31830ae2cfd6e8cf4b",
            "6397a547301d48d18fe82f7418fb8257",
            "f141e3e5dec6448db70069a9404dcb7c",
            "0c36c995c55b44c0865592bc5f9d339f"
          ]
        },
        "id": "Piv8d2I4Uq2e",
        "outputId": "ed548f53-1087-4538-a473-e02b36a3cd9a"
      },
      "source": [
        "vgg16 = networks.get_vgg16(pretrained=True, out_features=4)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cdd0b5be64924e599833efac7c7237ff",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=553433881.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90ol63KFUq2e",
        "outputId": "f0e73074-0519-4bce-ea8a-42d0ccaef80d"
      },
      "source": [
        "utils.fit(\n",
        "    epochs=epochs,\n",
        "    model=vgg16,\n",
        "    criterion=criterion,\n",
        "    optimizer=optim.Adam(vgg16.parameters(), lr=3e-5),\n",
        "    train_dl=train_dl,\n",
        "    valid_dl=valid_dl\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 1/25]: 100%|██████████| 647/647 [05:33<00:00,  1.94it/s, acc=0.907, loss=0.023]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.254330\tAvg valid loss: 0.140339\n",
            "\n",
            "\t\tvalid_loss decreased (inf -> 0.140339)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 2/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.955, loss=0.00411]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.126560\tAvg valid loss: 0.187394\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 3/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.966, loss=0.0118]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.092556\tAvg valid loss: 0.116691\n",
            "\n",
            "\t\tvalid_loss decreased (0.140339 -> 0.116691)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 4/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.973, loss=0.00657]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.074211\tAvg valid loss: 0.147738\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 5/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.977, loss=0.0272]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.069509\tAvg valid loss: 0.227921\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 6/25]: 100%|██████████| 647/647 [05:43<00:00,  1.88it/s, acc=0.978, loss=0.0189]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.061550\tAvg valid loss: 0.099654\n",
            "\n",
            "\t\tvalid_loss decreased (0.116691 -> 0.099654)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 7/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.982, loss=0.00203]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.053340\tAvg valid loss: 0.072426\n",
            "\n",
            "\t\tvalid_loss decreased (0.099654 -> 0.072426)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 8/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.983, loss=0.0504]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.049209\tAvg valid loss: 0.069702\n",
            "\n",
            "\t\tvalid_loss decreased (0.072426 -> 0.069702)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 9/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.984, loss=0.000371]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.045026\tAvg valid loss: 0.092443\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [10/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.986, loss=0.00133]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.038855\tAvg valid loss: 0.081255\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [11/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.988, loss=0.0513]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.034456\tAvg valid loss: 0.108899\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [12/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.988, loss=0.000829]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.032593\tAvg valid loss: 0.124410\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [13/25]: 100%|██████████| 647/647 [05:42<00:00,  1.89it/s, acc=0.99, loss=0.106]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.027338\tAvg valid loss: 0.127904\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [14/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.991, loss=0.176]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.027891\tAvg valid loss: 0.150402\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [15/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.991, loss=0.00629]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.024653\tAvg valid loss: 0.162245\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [16/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.992, loss=0.00157]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.022892\tAvg valid loss: 0.074296\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [17/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.993, loss=0.00143]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.020815\tAvg valid loss: 0.087141\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [18/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.993, loss=9.49e-5]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.018561\tAvg valid loss: 0.150276\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [19/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.994, loss=0.000332]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.019155\tAvg valid loss: 0.145713\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [20/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.995, loss=0.00904]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.014722\tAvg valid loss: 0.160369\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [21/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.994, loss=0.00185]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.018671\tAvg valid loss: 0.159945\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [22/25]: 100%|██████████| 647/647 [05:41<00:00,  1.90it/s, acc=0.996, loss=0.0317]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.012245\tAvg valid loss: 0.184001\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [23/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.994, loss=6.3e-5]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.020863\tAvg valid loss: 0.193386\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [24/25]: 100%|██████████| 647/647 [05:41<00:00,  1.89it/s, acc=0.995, loss=0.00128]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.013835\tAvg valid loss: 0.204753\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [25/25]: 100%|██████████| 647/647 [05:41<00:00,  1.90it/s, acc=0.994, loss=0.000113]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.015361\tAvg valid loss: 0.141700\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZ0I3M55rkCI",
        "outputId": "80ad5ea5-90b1-4136-8b83-d71c9e7bd5fd"
      },
      "source": [
        "plot_summary('outputs/CSVs/vgg.csv')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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i4+PDxx9/TNWqVQEIDw8nNFQV96pcuTJTp041Zqi4ONpxLiWX5MxcPJ3tjbovYVpfrzkmheKEKKe8vb1ZsmQJAN999x0uLi488cQTBY/n5eVhZ1f0R13Dhg1p2LChSeIU1sMc76nly5fTvHlzVqxYYdQEXavVYmtra7TtCyFMIyE1i+gLqURfSCn4F3s5gyqeToQGuqt/ldypG+hOLT9XHOzKPmlar9dz9kpmQTK+IzaJU5fSAXB1sKV5TR/6NamCp7M9m48nsmTvOX7dfgYHWxta1/bhnroBdKrrTy0/VzQaTZnjuV18h86ncPhCCjl5Otyd7PBwssPdyR73a7cezte/d3Oww8bG8LGUldESdK1Wy9ixY5k5cyaBgYEMGjSIqKgo6tSpU/Cc8PBwFixYgLOzM7/++iuff/4533zzDQBOTk4FH5CmEOzvBkBMYhrNqnubbL9CCCEM680338TBwYHo6GiaNWtG7969GTduHNnZ2Tg5OfHxxx9Tu3Zttm/fzo8//si0adP47rvvOH/+PGfPnuX8+fMMHz6cRx55pFj7O3v2LG+99RZXrlzBx8eHTz75hCpVqrBq1SomTZqEjY0N7u7uzJkzh+PHjzN69Ghyc3PR6XR899131KxZ07gHRJSZMd9Ter2eP//8k5kzZ/LQQw+RnZ2No6MjANOnT2fZsmVoNBo6duzIq6++yunTpxkzZgxJSUnY2toyYcIELly4ULBfgLFjx9KgQQMGDBhAVFQUPXv25N9//2XEiBGkp6fzxx9/kJubS40aNRg/fjzOzs5cunSJMWPGEBcXB8D777/P5s2bycrKIjw8HICvv/4aHx8fhg8fbqIjL0TFlqvVEZOYdi0Jv56QX0rLKXhOZU8nwit70CHEnwvJmRyLT2PdkQS0OjXN3M5GQy0/V0IruRMa4E7dSm6EBLpTw8cFO9vbJ+56vZ6YxPRrCflldpxK4nxyFgCezva0rOnDw62r06qWD/UqexTa1tCIGuTk6dgVm8T6owmsP5rIh8sP8+FyqO7jQqe6/twTFkCb2r442Zf8omH+cTl0LuVaQp7M4fMppGTlAWCjAVsbDbnaO0+112jAzcFOJfLO15N4dyc7vF0c+F9kbSp7Opc4vrIyWoK+f/9+atSoQVBQEAC9e/dm3bp1hRL0iIiIgq+bNGnC0qVLjRXOXQX7uwIQkyAJenkTUdvX3CEIUSGsjUnl/U1bDbrNIS2CGNi8WolfFx8fz++//46trS1paWnMmTMHOzs7/v33X77++mu+++67W15z6tQpfv75Z9LS0ujZsycPPvgg9vZ3n1H10Ucfcd9993Hfffcxf/58PvroIyZPnszkyZOZMWMGgYGBpKSkAPD777/zyCOP0K9fP3JyctDpdCX+2SqaBbvPMndXXLGfn5GRgcumq3d8TmneV8Z6T+3Zs4dq1apRvXp1WrduzYYNG+jevTsbN27k77//Zu7cuTg7O3P1qvqZXn31VUaOHEnXrl3Jzs5Gp9Nx4cKFO8bu5eXFokWLADWFf8iQIYBKuOfPn8+wYcP46KOPaNmyJZMmTUKr1ZKRkUFAQABPPvkkb7zxBjqdjhUrVjBv3rwSHTchRPFczcjh8E2J+PH4NHK06nPCwdaGkEA37qkbQHhlD8IruxNeyQNv11uX3WTnaTmZmM6x+FSOxady9GIaB88ls/LABfKXhzvY2VDH343QQLeC0fbUxCy2J51iR6waJc+/EODv7kirWj48VcuHVrV8CA1wv+vIs4OdDW3r+NG2jh9v94a4pAw2HE1gw9FE/tgVx09bT+NoZ0PbYN9ro+sBVPd1uWU76dl5HLmYwuHzKhk/dD6Fo/Gp5OSp4+JoZ0NYZQ/6NK5C/Soe1KvsQVglD5zsbcjO05GSlUtqVt61f7mFblMyc0m56bGE1CxiEvPIztXRo0Gl8pWgx8fHU6lSpYLvAwMD2b9//22fP3/+/ELTurKzsxkwYAB2dnaMHDmSLl26FPm66Ohog8Sr1emxs4EdR07TwDXNINus6LKysgz2/1MWXkB0dIK5wygzSzme5YkcU8PKzcklIyPLoNs8f+E80dGpxXpuYmJiQSLTqFEjjh07VnD/Dz/8UJDEaLVaoqOjOX36NGlpaURHR5OYmEi9evWIiYkBwM3NjW3btuHn51doHzk5ORw/fhwPD4+C+3bt2sWzzz5LdHQ0devW5dNPPyU6OppatWrx/PPP065dOyIiIvDw8MDf359vv/2WAwcOEBERQZUqVW7788j707L06NGjYHp4amoqb7zxBqdPn0aj0ZCbm1vkayIjI3FwcMDHxwcfHx8uX75c6NwIYMWKFfTu3RuAXr16sWTJErp3787WrVsZMGAAzs7q5NDLy4u0tDTi4+Pp2rUrQMFI+9306tWr4Ovjx4/zzTffkJqaSnp6Ou3btwdg27ZtjB8/HgBbW1vc3d0L/h0+fJhLly5Rr149vL1lEEMIgMwcLZfSsklIzSYtO4/MHC1ZuVoyc7Vk5tx0m6sl64avCz03V0t6tpak9Ouj4n5ujoRXduexdjWvJeMe1PZ3xf4OI943crSzLXjdjTJy8jiRkMax+LRriXsqO04lsfi/8zc86zzVvJ3pGOpP61o+tKrlS01flzJPSw/ycWFYm5oMa1OTrFwt208lsf5IwrUR9kOM4RC1/V3pVDcAPzdHDp1P5vCFlGtF59Q2vFzsqV/Fg0fb1qReZQ/qV/Gglp/rbWcCONnb4mRvS4B7mUI3OYtYsb9kyRIOHjzI7NmzC+5bv349gYGBxMXFMXz4cEJDQ6levfotr82fdmUIVdzPckXrZNBtVmTR0dFyLA1IjqfhyTE1tGheHmC+4+nv74+LiwvJyckEBwcX/N/+9NNPdOnShUceeYSzZ8/yyCOPEB4eTkpKCm5uboSHhxe8Nv81zs7O1KpVi2rVCo+yOjg4EBISUqhInK2tLWFhYdjb25Obm4utrS3h4eF8++237Nu3jw0bNjB69GgWLFjAU089Ra9evdiwYQOfffYZH3zwAW3atCny57nx/bl7925jHDKrMLB5tRKNdhvr9zo/UQaYMGECrVu3ZtKkSQXvqaLcWFDO1taWvLy8Qo9rtVpWr17NunXrmDp1Knq9nqtXr5KWVrKBAltb20KzMbKzs28b+5tvvsnkyZMJCwtj4cKF7Nix447b7tq1KwsXLuTSpUsMHDiwRHEJYW2yclXSfSkth8TUbC6lZd/mNoe07Ly7bs9GA872tjg72OHsYKO+vpY0ers6UCX/ewdbavi4FCTV/u7Fu/hWUi4OdjSq5kWjal6F7k/JyuV4fBp7o2PoGVGfql7GHTV2srclMtSfyFB/3qc+py6lFyTrv2w7TU6ejqpeztSv4kG/xlWoX8WT+lU8qOzpZJT165bGaAl6YGAgFy9eLPg+Pj6ewMDAW57377//MnXqVGbPnl3ogyz/uUFBQbRq1YrDhw8XmaAbUpCnPScTZfRcCCHKk9TU1ILPlPxpvobUtGlTVqxYQf/+/Vm2bBktWrQA4MyZMzRu3JjGjRuzadMmLl68SFpaGkFBQTzyyCNcuHCBo0eP3jZBF5bLUO+prVu3UrduXWbMmFFw3xtvvMHatWtp27YtkydPpm/fvgUzQ7y8vKhUqRJr166lS5cu5OTkoNVqqVq1KjExMeTk5JCVlcXWrVtp3rx5kftMT0/H39+f3Nxcli1bVvBztGnThl9//ZVHH320YIq7u7s7rVu35rXXXiMvL48vv/yy1D+rEOam1em5mJLFmcsZxCVlEHclgzNJGVxIzuJSajaJadmkZhWddHs62+Pv7oifmwMNq3nh5+Zw7XtH/N0c8XC2v5aI215Pwh1scLC1sYqE0sPJnuY1vHHJcDV6cl6UWn6u1Gpfi8fb1yIzR0tOng5Pl4pbtNtoCXrDhg2JjY0lLi6OwMBAVqxYccsf9sOHD/Pee+/xww8/4Ot7fZ1wcnIyzs7OODg4kJSUxJ49exgxYoSxQi1QzdOBbWeTycnTGaTaoRBCCPMbMWIEb775JlOmTCEyMrLM2+vXrx82NuozomfPnrz77ruMHj2aGTNmFBSJAxg/fjynT59Gr9cTERFBWFgY33//PUuWLMHOzg4/Pz/+97//lTkeYXqGek+tWLHiliV83bp147fffuOHH37gyJEjDBw4EHt7eyIjI3n55ZcZP3487733HhMmTMDe3p4JEyYQFBREjx496NOnD9WqVaNevXq33eeoUaMYPHgwPj4+NG7cmPR0VYX57bff5t1332XBggXY2Njw/vvv07RpU+zt7WndujUeHh5SAV5YNL1eT3JmLmeSMohLylS3V64l40kZnLuaWahomI0Gqng5U8XTmfDKHnS8loAXJN7Xbn3dHHC0k/e+qTg7qAsdFZlGb8RO8hs3buTjjz9Gq9UycOBAnn76aSZMmECDBg3o3Lkzjz76KMeOHcPf3x+43k5tz549jBkzBo1Gg16v55FHHmHw4MG3bH/37t23vUJcGpOW7+DzLYmsfbkjdaxtsYIFspTpw1tjLtMm2PoLxVnK8SxP5JgalhxPw7p5irshP+8siaF/NnkfGtahQ4d46623mDBhgnQcMAB5f5ZeRk4eCSlqzXdCahbxKdkcOnmODI1zQTJ+8wi4t4s91X1cqObjQnUfF4K81W11HxcqezkVe013RSHvT8MrzWe5UdegR0ZG3nJledSoUQVfz5o1q8jXNWvWjGXLlhkztCJV81RT7E8kpEmCXo5sO1k+EnQhhBDClE6cOMHTTz9Nr169JDkXRqHX60nNzk+8s0hMzSYhJZv4lKyCRDzh2n1Frfm2t9FQ3TeP6j4utKjprZLxa0l4kI8z7k4Vd5q0sF4WUSTOUlTzVL/EMYnpZo5ECCGEEMK86tSpw7Rp02RETRSLTqeS7asZOVzNyOVqZm7B11fy78vIuXa/ui8hJZvMXO0t23KytyHA3YlAD0fCK3nQMcSRAA9HAtydCHB3JNBD3V44feKOSzqEsEaSoN/Axd6Gyp5OxCRIobjy5KWuoeYOQQghhBDC4un1erJydSRn5hb6l1LE9zcm4Plf6+6wcNbdyQ5vFwe8XOzxcnGguo8LAe4q8Q70cMLf/VoC7uGIu6NdsYqrXbSCAmxClJQk6DcJ9nfjhFRyF0IIIYQQ5UhWrpa9Z66y+3QS8SnZpGTdmnSnZOaRo9XdcTvujnZ4ONvj7WqPl7MDlb2c8XZRX+cn394u9gVfeznb4+lsf9te1UKIwiRBv0mwvyvzd59Fr9dbRVsEIYQQQgghbpaalcvu01fYcSqJHaeS2Hf2akEVcy8XlTTn/6vi6YyHc+H7PJztCn3v6WyPu5M9tjZyfiyEMUmCfpPgADfSc7TEp2RTydPJ3OEIIYQQQghxV1fSc9gZm8T2awn5ofPJ6PRgZ6OhYTVPHm9fi9a1fGhewwdPZymeJoSlkrkmN6nj7wZAjExzF0IIizds2DA2b95c6L5Zs2YxZsyYO77mwIEDADz55JOkpKTc8pzvvvuOGTNmFPt+Ub6Y+n2V79577+Wll14qZdSioklIyWLZvvO8u/gg3b/eRNMP1zDyl938su00Lg62PBcVwpwRrdn/fjcWPdOO0T3DiQoLlORcCAsnI+g3CQ5QCfqJhDTa1fEzczTCEL5ec0wKxQlRTvXp04eVK1fSoUOHgvtWrlzJa6+9VqzXf//998YKTVgxc7yvYmJi0Ol07Nq1i4yMDFxcXEq8jeLIy8vDzk5O/0xNr9dz6HwK/8SmcTznPHq9Hr0edHo9Oj2Fvtdz/X6u3epuePxYfCo7TiURezkDAFcHW5rX9KFfkyq0quVDo2qeONrZmvXnFUKUnvyFvkmAuyNujnYygi6EEFage/fufPPNN+Tk5ODg4MDZs2dJSEigRYsWjBkzhgMHDpCdnU337t154YUXbnl9VFQU8+fPx8fHhylTprB48WJ8fHyoXLky9evXL1YMer2e8ePHs3nzZjQaTUHf6ISEBF566SXS0tLQarW8//77NG3alLfffpuDBw+i0WgYOHAgjz76qIGPiigrc7yvli9fTr9+/Th58iTr1q2jb9++AOzfv5+PP/6YjIwMHBwcmDVrFs7OznzxxRcF77khQ4YwbNiwQvs9cOAA48eP55dffuG7777jzJkzxMXFUaVKFV5++WVef/11MjMzAXj33Xdp1qwZANOnT2fZsmVoNBo6duxIs2bNeOutt1i0aBEAsbGxvPTSSwXfi9vLydOx/dRl1hyOZ+3heM4nZ117JKFM2/VysadlTR+GRtSgVS0f6lX2kAJsQpQjkqDfRKPREBzgJgl6ORJR29fcIQhRIXieWgnbXjXsRpsOhSYP3vZhLy8vGjVqxKZNm+jSpQsrV66kZ8+eaDQaXnrpJby8vNBqtTz66KMcOXKEsLCwIrdz8OBBVq5cyeLFi9Fqtdx3333FTtBXr17NkSNHWLJkCVeuXGHQoEG0aNGC5cuX0759e55++mm0Wi2ZmZlER0cTHx/P8uXLAYqcBi1u8t9vsHd2sZ9ePSMdtrne+UkW+L5auXIlM2fO5OTJk8yePZu+ffuSk5PDSy+9xNdff02jRo1IS0vDycmJP/74g3PnzrF48WLs7Oy4evXqXY9LTEwMv/76K05OTmRmZjJz5kwcHR2JjY3l5ZdfZuHChWzcuJG///6buXPn4uzszNWrV7lw4QJubm5ER0cTHh7OwoULGTBgwF33V1GlZOWy4Wgiaw7Hs+FIAqnZeTjZ29AhxJ8Xu4binpNESJ1gNBqw0Wiw0YAGjfreRoOG6/dT8Jzr96NRVdRtpFCbEOWWJOhFCPZ35Z8Tl8wdhjCQNsGSoAtRnvXu3ZuVK1fSpUsXVqxYwbhx4wBYtWoVc+fOJS8vj8TERGJiYm6bSO3atYsuXbrg7OwMqBHQ4tq9eze9e/fG1tYWPz8/WrZsyYEDB2jYsCFvvfUWeXl5dOnShfDwcIKCgoiLi+PDDz8kMjKS9u3bl/0ACKMw5fvqwIEDeHt7U6VKFQIDA3nrrbe4evUq8fHx+Pv706hRIwDc3NQyvK1bt/LAAw8UTFX38vK6688TFRWFk5MqfpuXl8fYsWM5cuQINjY2xMbGFmx3wIABBfF6eXlx4cIFBg8ezIIFCxg9ejQrV65k3rx5xTiCFcf5q5msORzPmsPxbDt5mTydHj83B3o1rEzXeoG0D/HDyV5NOY+OTqPOteWUQghRFEnQixDs78bCPedIzcrF3UkKaQghRHEk1+pFlV6vmHy/nTt35pNPPuHQoUNkZWXRoEED4uLi+PHHH5k/fz6enp68+eabZGdnmzSuli1bMnv2bDZu3Mibb77JY489Rv/+/VmyZAlbtmzh999/Z9WqVXzyyScmjcvqNHnwjqPdNztzbaS3rEz5vlqxYgWnTp0qSODT0tJYvXo1jRs3LtF2bG1t0etVG62b48pPukEVvPPz82PJkiXodLqCCwC30717dyZNmkRERAT169fH29u7RHGVN3q9nsMXUgqS8kPn1UyY2v6uPNGhFt3qBdIkyFvakQkhSkUWrBQh+Fol95OJ6WaOxIzyTHsiK4QQpeXq6krr1q1566236N27NwDp6ek4Ozvj7u7OpUuX2LRp0x230bJlS9auXUtWVhZpaWmsX7++2Ptv0aIFq1atQqvVkpSUxK5du2jUqBHnzp3Dz8+PIUOGMHjwYA4dOkRSUhJ6vZ7u3bvz4osvcvjw4TL97MJ4TPW+0ul0rFq1iqVLl/L333/z999/M3nyZJYvX06tWrVITExk//79gErc8/LyaNu2LX/88Qd5eXkABVPcq1atysGDBwG19OJ2UlNT8ff3x8bGhiVLlqDVagFo27YtCxcuLFibnr9dR0dH2rdvz/vvv19hp7dn52n558Ql3l96iPafraf3t1uYsO44zva2vNkzjHWvRPL3K/cwumc4zWv4SHIuhCg1GUEvQv7Uo5jENBoHeZk3GHM4uwt+7AFP/wP+dc0djRBC3FWfPn149tln+eqrrwAICwujXr169OzZk0qVKhUUwLqd+vXr06tXL+699158fHxo2LDhbZ87ZcoUfvrpp4LvN27cyN69e7n33nvRaDS89tpr+Pv7s2jRImbMmIGdnR0uLi589tlnJCQkMHr0aHQ6HQAvv/yyAX56YSymeF/t2rWLwMBAAgMDC+5r2bIlMTExXL16la+//pqPPvqIrKwsnJycmDlzJoMHDyY2NpZ+/fphZ2fHkCFDGDp0KM899xxvv/02EyZMoHXr1reN66GHHuL5559n8eLFdOjQoaBifMeOHTly5AgDBw7E3t6eyMhIevbsCUDfvn1Zs2ZNhVmWkZGTx94zV6/1FL/M3jNXyc7T4WRvQ/s6/ozqHEJUeAB+bo7mDlUIUc5o9PlzoazQ7t27ad68ucG2l18AJVerI/zdPxnZsTav9yh6XVm5tu5D2PwF9J8CTR4q9WaiDTTNsKy2xlwuF+vQLeV4lidyTA1Ljqdh3Xg8Df15Z0mM9VkuDCP/eM6YMYPU1FRefPFFc4dkFClZueyOvcK2U5fZcSqJA2eTydPpsdFA/SqetKrlQ0RtX9rX8cPZofQtzOT9aVhyPA1LjqfhleazXEbQi2Bva0N1X5eKW8k9dou6vXTMvHEYyLaT5SNBF0IIIczh2Wef5cyZM4Vmjli7y2nZ7IxNujZCnsThCyno9WBvq6FxNS9GdqxNq1o+NK/hLfWIhBAmJQn6bdTxdyOmIq5Bz0mHc7vV15eOmzcWIYQQQpjdpEmTzB1Cmej1ei4kZxVKyE8kqEEYJ3sbmlX3ZlTnEFrV8qFpkHeZRsiFEKKsJEG/jeAAN/4+kkCuVoe9bQWqpRe3A3S54ORVbkbQX+oaau4QhBBCCGFgWp2ey+nZJKRkk5iaTUJqFvEp6jYhJZuEVHV/Ymo2OVpV98Hd0Y4WNb0Z2KwarWr50LCqJw52Feg8Twhh8SRBv41gfzfydHrOJGUUVHWvEGI3g8YWGt0Pu2aANhdsZWqXEEIIIUwrT6vjaHwqB84mcz45i8RriXf8tdvL6TlodbeWUvJ2sSfA3YkAD0dq+7sS4O5EFS8nmlX3Jryyh1RYF0JYNEnQbyPY3xWAmIS0Cpagb4EqTaFqM9gxDZJOgb+MQAshhBDCuJLSc9h75gp7zlxhz+mr7Dt7lYwc1QJOowFfV0cC3B0J8HCkXmUPAj2cCHB3xP9aMq6+dsTRTqaoCyGslyTotxFc0GqtAq1Dz19/3vZ58AtR9106Jgm6EEIIIQxKq9Nz9GKqSsbPXGHvmaucuqTOuWxtNNSr7MHg5tVoVsObJkFeVPVyxq4iLTkUQlRYkqDfhoeTPQHujgVFRCqEuO2gy4Oa7cH3hgRdCCGEEKIMrqTnsDdOjYzvOXOFfXFXSb82Ou7n5kDT6t4MaRFEs+peNKrmJYXahBAVliTodxDs71axWq3FblHrz4MiwNEN3CuXi0ruX685JoXihBBCCBM7Fp/KrH9j2RZzmZM3jI6HVXJnQLNqNKvhRbPq3lT3cUGjkXXhQggBkqDfUZ0ANxb/dw69Xl8xPjhObVZrzx2vrbn3C5ERdCGEEEIUm16vZ/upJKZtjGH90USc7G1oX8ePgc2r0ay6N42DPHFxkNNPIYS4HfkLeQfB/q6kZuWRmJpNgIeTucMxruw0OL9HrT/P5xcK++eBXq+qs1ipiNq+5g5BCCGs1qZNmxg3bhw6nY7BgwczcuTIQo+fO3eOt956i6SkJLy8vPj888+pVKkSAOPHj2fjxo3odDratWvH22+/XTEueFdAWp2evw5dZNrGGPadTcbH1YGXu4YyLKIG3q4O5g5PCCGshiTod5BfKO5EYlr5T9AL1p93uH6fX13IToa0BHAPNF9sZdQmWBJ0IYQoDa1Wy9ixY5k5cyaBgYEMGjSIqKgo6tSpU/Cczz77jP79+3PfffexdetWvvzySz7//HP27NnDnj17WLp0KQAPPfQQO3bsoHXr1ub6cYQRZOVqmbf7LD9sPsnpyxnU8HXho/4NGNS8Gk72so5cCCFKSsph3kGdilTJPXYL2NhB0A0nTn5SKE4IISqy/fv3U6NGDYKCgnBwcKB3796sW7eu0HNiYmKIiIgAICIiouBxjUZDTk4Oubm5Bbd+fn4m/xmEcVxJz2HC2uO0+/Rv3l18EC9ne6Y83Iy/X7mHoRE1JDkXQohSkhH0O6jk4YSLgy0xFaGSe+xmqHLD+nNQU9wBLh2FWh2Kfp0QQohyKz4+vmC6OkBgYCD79+8v9JywsDBWr17N8OHDWbNmDenp6Vy5coWmTZvSunVr2rdvj16vZ+jQoQQHBxe5n+joaIPFnJWVZdDtVXQ3H8+LqbksPJzM6hOpZOfpaVXNhdfb+9Iw0AmN5irHjl41X7BWQN6fhiXH07DkeBpeaY6pJOh3oNFoKkYl9+w0OLcH2o0qfL9HFbB3LReV3IUQQhjH66+/zocffsiiRYto0aIFgYGB2Nracvr0aWJiYti4cSMAjz/+OLt27aJFixa3bCM8PNxg8URHRxt0exVd/vE8cDaZaZtiWHngArY2Gu5tUpWRHWsTGuhu7hCtirw/DSj+MBe3L6dSv3fNHUm5Ie9Pw7vxmO7evbtYr5EE/S6C/V3ZcSrJ3GEYV9w20GtV//MbaTTlopL71pjLsg5dCCFKITAwkIsXLxZ8Hx8fT2Bg4C3PmThxIgDp6emsXr0aDw8P5s6dS+PGjXF1dQWgQ4cO7N27t8gEXVgmvV7P7nMZfLhlG//GXMbd0Y4nO9TmsXa1qORZzmvzCMu36nUqxW6GFn2gSlNzRyOEwcga9LuoE+DG+eQs0rPzzB2K8eSvP68ecetjfqFWP4K+7eRlc4cghBBWqWHDhsTGxhIXF0dOTg4rVqwgKiqq0HOSkpLQ6XQATJ8+nYEDBwJQpUoVdu7cSV5eHrm5uezcufO2U9yFZcjO0/Jf3FVm/nOKUb/vpcP49byz9iIxiWmM7hnGP6OjGN0rXJJzYX4X9qvlmQBbJ5s3FiEMTEbQ7yLYX63JPpmYTsNqnmaOxkhit0DV5uDgeutjfqFwYC7kpBf9uBBCiHLLzs6O9957jxEjRqDVahk4cCAhISFMmDCBBg0a0LlzZ3bs2MFXX32FRqOhRYsWjBkzBoDu3buzbds2+vbti0ajoUOHDrck98J89Ho9Z69ksufMFf6Lu8p/cVc5dC6FHK262BLo4UjTIG+G1HPjqZ4tcLCTMR1hQbZNBntXrla9B69DC6HrB2ppphDlgCTodxFcUMk9rXwm6Nmpav15+xeLfjy/kvvlE1C5scnCMqSXuoaaOwQhhDXQaWHvbEg8At3GgY0kJACRkZFERkYWum/UqOs1S3r06EGPHj1ueZ2trS1jx441enyieFKyctkfl8x/cSoh33vmKpfTcwBwsrehUVUvHm1Xk6ZBXjSp7kVlT2dArZ+U5FxYlNSLcGA+tHiMS/498Dq9CnZMhy7vmzsyIQxCEvS7qOHrgq2NpvwWijuzvej15/n866rbS8etNkEXQoi7ilkPf70NCYfU9/Xvg6BW5o1JiDK4kJzJ+iOJ/Bd3hb1nrnIiMQ29Xj0W7O/KPXUDaFrdiyZBXtSt5I69rSThwkrs+B50edD6KXITciCsN+yaCR1fk9meolwwaoK+adMmxo0bh06nY/DgwYwcObLQ4zNnzmTevHnY2tri4+PDxx9/TNWqVQFYtGgRU6ZMAeDpp5/mvvvuM2aot+VoZ0t1H5fym6DHbgYb+8L9z2/kUxs0NlZfKE4IIYp06TisfgeO/Qle1eHeybBsFEQvkwRdWKX4lCwmrT/B7zviyNHq8Haxp0mQF30bV6FJkBeNq3nh6WJv7jCFKJ3cTNj1I9TtBb7BkBANbZ5Tf7P3/QYtR5g7QiHKzGgJularZezYscycOZPAwEAGDRpEVFQUderUKXhOeHg4CxYswNnZmV9//ZXPP/+cb775hqtXrzJx4kQWLFiARqNhwIABREVF4elpninmwf6unCivvdDvtP4cwM4RvGtC4lGThiWEEEaVkQQbP4OdP4Cds5oa2fppsHeCQwvVyV7XsaqbhRBW4FJaNlM2xDB722m0Oj2DW1Tjifa1CfZ3RSPvY1Fe7PsdMpOgzTPX7wtqDVWawbYp0PxxWZ4krJ7R3sH79++nRo0aBAUF4eDgQO/evVm3bl2h50RERODsrNY4NWnSpKCVy5YtW2jXrh1eXl54enrSrl07Nm/ebKxQ7yrY343YSxnkXSucUm5kp8L5vbef3p6vHFRyF0IIAPJyVMXfb5uqNYtNh8ELe6D9Syo5BwjrA1dOQfwh88YqRDFcSc/h01VH6PDZemb+c4q+javw9yv38MmARtQJcJPkXJQfOp0qDlepEdRod/1+jQbaPKvqJR1fbb74hDAQo42gx8fHU6lSpYLvAwMD2b9//22fP3/+fDp27Hjb18bHxxf5uujoaANFDFlZWUVuzyUvlRytjo27DlLFo/xMC3O98C/V9VpO21Qn4w7HMUDji/elvzl66CDY2BZ7+7c7nqY2+78khjbxMXcYZWYpx7M8kWNqWBZ9PPV63M5vJuC/73BMiyMtsBUJTV4g26sOxF0GrrdjtLUNIQQNlzbP5FID802XtOjjKcwuOTOXGZtP8uM/saTn5NGvcRVGdQ6h9rXuMxZDmwu25efcSZhRzDq15PK+6bfObqp3L6x5D7ZNgrq3Fq0UwppYRJG4JUuWcPDgQWbPnl3i14aHhxssjujo6CK3l+Fyha//TUTvHkh4eKDB9md2Z38DG3tqtBsMDi63f15mBBydQ3hlV/CpVezN3+54mprf2WOEh1t/JXdLOZ7liRxTw7LY43nxAPz1FpzapGYE9ZuHW0hX3O40sri3Df6XtuEf/qXp4rzJjcdz9+7dZotDWJa07DxmbjnF95tPkpKVR6+GlXixSyihge7mDu1W8YdgVm/wD4PuH0PVZuaOSFizrZPArZIq4nkzW3toNRLWjlF/8ys1NH18QhiI0aa4BwYGFkxZBzUqHhh4a3L777//MnXqVKZMmYKDg0OJXmsqdfyvt1orV2K3QLUWd07OQZ3QgtVOc4+o7WvuEIQQ5pAaD0ueg6kd1Albz8/h6X8htNvd15aH94H4g5B00jSxCnEXGTl5TN0YQ4fP/ubLNcdoVcuXFS+0Z/LDzS0zOU8+B7MHqUK0l47D951g4f/U/UKUVPxhOLkeWj0Jdg5FP6f5cLB3UWvRhbBiRkvQGzZsSGxsLHFxceTk5LBixQqioqIKPefw4cO89957TJkyBV/f60lU+/bt2bJlC8nJySQnJ7Nlyxbat7/LOmkj8nSxx8/NsXwl6FkpcP6/u68/hxsSdOus5N4mWBJ0ISqU3EzY9AV810wVFGrzLLywF1qPLP5U27A+6jZ6ufHiFKIYsnK1zNhyio7j1/PpqiM0qubFkmfb8cPwFtSvYp7iuXeVlQxzBqlaN8MWqt+/di+qAozfNYf1H0NOurmjFNZk22RV0LPF47d/jrM3NHkYDsxTF2iFsFJGm+JuZ2fHe++9x4gRI9BqtQwcOJCQkBAmTJhAgwYN6Ny5M+PHjycjI4NRo0YBULlyZaZOnYqXlxfPPPMMgwYNAuDZZ5/Fy8vLWKEWS7C/KzGJ5ejDJO4u/c9v5OIDLn5Wm6ALISoInU4lAGvfh+Q4lWR3Hata8ZSUdw1ViCh6GbR7weChCnE32Xla5u6MY+L6E8SnZNM22JepQ0NpUdPCa6rkZcPvD6tzhqELrk817voBtHhM/X5u/Az2/Ayd34NGD0jVbXFnaYmwfy40fVidk95JxNOqO8fOHyDqbdPEJ4SBGXUNemRkJJGRkYXuy0/GAWbNmnXb1w4aNKggQbcEwQFurNh/Ab1eXz4qop7apKadVStmn1+p5C6EsFR5OWrE5J8JcOmoSqz7T4FaHcq23fB+sP4jSLkAHpUNE6sQxbDpWCKjFx7g3NVMWtb05pv7m1rHbDCdDhY/A7GbVSGv2vcUfty7JgyeBa2fgj9Hw+KnYftU6P4J1GxXxAaFAHbNAG02RDxz9+f6BkPdnuo1HV4Ge2fjxyeEgckly2IK9ncjOTOXy+k55g7FMIq7/jyfX4g68RVCCEuRnaaKBn3bBJY8o6avD5wBIzeUPTkHCO+rbo+uKPu2hCimVQcu8MRPO3F1tOXnx1sx939trCM5B1j3PhycD53HQOP7b/+86hEwYh0M+B7SL8GsXvDHUKn5IG6Vm6VGw0O6qXPR4oh4BjIuq1F3IayQJOjFVCfgWqG4hHKwDj0rBS78BzVLcALrF6r+2KVfvvtzLczWGOuLWQhxB+mX4O9x8HV9VZ3dpzY8vACe2gINB5WoHeQd+dcF3zpqmrsQJrBwz1me/XUPjap5Me+ptnQM9beeWXvbp6tZLC2egPYv3f35NjbQaAg8tws6vQ0n1sHEVvDX25B51ejhCitxcD6kJ6paIsVVs71aWrFtMuj1xotNCCORBL2Ygv1dAThRHgrFndkGel3x1p/nyy8Ud9n6prlvOykJuhDlwpXTsPI1+LoBbBqv/oaNWAePLoeQLnevzF5SGo0aRT+1GTKSDLttIW4ye9tpXp67j4javvz8eCs8na2od3j0Mlj1OtTtDb0+L9nvooMLRL4Oz++BRvdfmxXTFHZ8D9o848UsLJ9eD1snQ2ADqBV59+fn02gg4llIPKJ6pwthZSRBL6Yqns4429sSk3CHQnHZqdZRlTR2E9g6QLWWxX9N/rQiKRQnhDC1iwdhwZPqpH3XTGg4EJ7dCQ/MUUt1jCm8ryqoeewv4+5HVGjTNsbwzuKDdA4L4MdHW+LqaNQSQYZ1ZjssGKF+Fwf+UPoZLB6Vof8ktUQloB6sfBWmtIXjawwarrAiJzdAwiFV+K2kF2AbDFQ907dONkpoQhiTJOjFZGOjoba/6+1bren18FNfmDvctIGVRuwWqFqC9ecAXtXB1tEqE/SXuoaaOwQhREnp9RD7D8wZDFPbwdGV6iRt1D64dxL4m+j3ukoz8Kgq09yFUej1er5ac4xPVh2hT6PKTB3WHCd7Ay3RMIVLx+G3+9XvyIN/lOy84naqNFGzYu6fA7pc1a7tlwHSNqsi2jYZXP2hQSmKRts5QKsRagQ9IdrwsYnyTa+HvXMg+axZdi8JegkE+7vdPkE//S+c36uu9mWnmjSuEslKhgv7Sl5AycZWrcWUSu5CCGPS6eDICpjRVRWOOrcHot6Blw5C93HgWdW08Wg0ql1bzDrrmCElrIZer+ejFdF8u+44Q1pUY8IDTbG3taLTstR4mD0AbOxg6HxwNWAhO40GwvvAM9uh+8dwZuv1vuqiYkg8BsdXQ8snwd6pdNto/jjYOalEX4iSSDyqis+e2myW3VvRJ4H5Bfu7ce5qJpk52lsf3DENNDbqau/JjaYPrrhKs/48n3+oVY6gCyGsgF4P+/6AyRHw+0OQFg+9vlCJecfXwNnbfLGF94W8LDix1nwxiHJFq9Pz1qKDzNhyikfb1uTTAY2wtbGSYnCgOij8OkQVbHzoD1Wo0RjsHFRxsME/QfwhNUtRm2ucfQnLsm2ymrnZ4vHSb8PVFxo/oD5b0i8ZLjZR/h2/tqytNPmSAUiCXgLBAa7o9XDy0k2j6MlnIXq56uvp4A4nLHi9VOzmkq8/z+cXCldiIS/b4GEJISq4bZNh0cjrrdKe3wutnrSMHrbV24Czj0xzFwaRq9Xx8tz/+G3HGZ7tFMyYvvWwsabkXJsL84bDxf2qp3nV5sbfZ2g36PuNmsmybJRU5i7vMpJg3++qyr+bf9m2FfGM6qG+60fDxCYqhmN/qeKEXkFm2b0k6CVQ0Got8aZpjjtnAHqVoNeOhONrLffD49RmlZyX5qTXL1SNvl+OMXxcQoiK69hfsPodCO8H/9usWqXZWlCRLFs7COul4szLMXc0wopl52l5Zs4elvx3nte61+W17mHW00YN1LnN8hfVbJI+X0Nod9Ptu9kjEPkm/DcHNnxiuv0K09v1I+RlquS6rPzrQp2uqiuADDCJ4si8omYch3QzWwiSoJdATV9XNBo4cWMv9NxM2D0LQnuCdw0I6QopZ9XaBUuTeVVd8S7tdA0rreT+9RrrileICiX+MMx/QvWsvW+q6o1sicL7QXYKnNpk7kiElcrM0TLip12sORzP+33r8WynOuYOqeQ2fgZ7Z0PH16H5o6bf/z1vQtOhKo7ds0y/f2F8eTkqmQ6OgsB6htlmm2cgPQEOzDfM9kT5dmKd6t4S2sNsIVjomZBlcrK3JcjbpXChuIMLIDMJWv9PfV+ni7q1xGnuBevPS1ggLp/vtZMJKRQnhDCE9EuqArSDKzzwm7q1VLUiwcENopeaOxJhhVKzchn+4w7+OXGJ8YMa8Wi7WuYOqeT2/KxGrps8DJ3eMk8MGg30+Uaday1/WdoflkeHFkHaRdXH3FBqd1Kt+7ZNttwZrsJyHF+tlrUZu43rHUiCXkJ1AtyIyR9B1+th+zTwD4daHdV9ntXU95bYtzN2syq4UZr156BOnj2DrG4EPaK2ASvLCiEMIy8b/hgKaQnw4K+mr85eUvZOarrb0ZWgK6JQqBC3cSU9h4d/2M6eM1f49sGmDGlhnjWNZXJ8DSx7UY1q9p1Q8p7UhmRrr4rGVWoA8x6Fc7vNF4swLL0etk4Ev7pQp7PhtqvRqOny8QdlFpS4M51W/b0L6ao6WJmJJOglFOzvyqlL6Wh1ejUifXE/tB5Z+MMqpItqCZJ9m5Zs5hK75dr681K2qwA1zd3KEvQ2wZKgC2FR9HpY/pL6O9l/immKTBlCeB9IT4S47eaORFiJhNQsHpi+jSMXU5k2rDl9GlUxd0gl5pQUraqnB9aHIT+rBNncHN3goXng6gdzhkDSSXNHJAzh9D/qvLrNM4a/CNRwsOqpLi3XxJ2c3aVmRptx/TlIgl5iwf5uZOfpOHclU7VWc/KERvcXflKdrqDNsayrdGVdf57PL1RNcZcpQkKI0vr3W1Xo6Z7R0GCAuaMpvpBuqguGVHMXxXDuaiZDpm4l7koGMx9tSefwQHOHVHJJpwja9DK4+MLD88DR3dwRXeceCEMXqrWiswdJG63yYOtk9V67+bzaEOydoMUTcOxPWaopbu/4X6CxNewMjlKQBL2E8iu5nz19HA4vhabDbl03Wb0N2Lta1jr0M1tL3//8Rn6hkJsOKecNE5cQomI5shLWjIH6AyDyDXNHUzKO7mqKb/RyuUgp7ujUpXSGTN3K5fQcfnmiNe3q+Jk7pJLT62HuI2j0Whg6H9wrmTuiW/mFwIN/QMo5+O0ByMkwd0SitC7HqCVELR43XnvNlk+oi6zbphhn+8L6HfsLqkeAs7dZw5AEvYSC/VWC7rz/Z5Xwthxx65PsHFS7tRMW1G4tdkvZ1p/n8wtVt5cssEq9EMKyXTwIC0ZAlabQf7J517GWVlgfSD4DF/aZOxJhoU5fTmfItK1k5mr57ckImtcw74leqZ1cDxf3E9/4BdWqylJVbw0Df1BTUxeMkBoR1mr7VLCxK/q82lDcAlRv9f9+Vb3WhbhR8llVp8CU7SNvQxL0EvJ2daCSC4TEzVfl931uU4m1The4esZyptHEboagVmVbfw43JOgW8nMVw9aYy+YOQQiRlqBGuJw84YFfjTdCYmx1e4HGRqa5iyLpdHpen7+frFwtf4yMoEFVT3OHVHpbJ4NrACk1zLsWs1jC+0LP8XB0Bax8zXIGR0TxZF6FvXPUOnFjz9SIeEb1WN8907j7EdYnvytEiCToVmmo+y7ctFevt1YrSkhXdWsJ09wzr8IFA6w/B3X10dHTqgrFbTspCboQZpWbBb8/rNaIPvgbeFQ2d0Sl5+oLNdrBkeXmjkRYoLm74th+Kom3e4UTEmhB67VLKvGoOn9p9SR6WwdzR1M8rUdCu1GwawZs+drc0YiS2POTWj7Z5hnj7yuwPtS+R/Vaz8sx/v6E9Ti+GrxqWMSMIUnQS0qvp3/OCk5STf2C345XddUmwhLarZ3+F9AbJkHXaKyykrsQwkz0elj2ApzdAQOmQZUm5o6o7ML7QuIRq5pJJIwvISWLcSujiajtw/0trbCV2o22TVbL4lo8bu5ISqbz+9BgEKz7APb9Ye5oRHFoc2H7dKjZASo1NM0+I56F1AtweLFp9icsX24mnNyoprdbwPI7SdBL6uxOqmUe5cfcriRl5N75uSFdVcuInHTTxHY7+evPq7YwzPbyK7lbiZe6hpo7BCEqri1fwf4/IOodqHevuaMxjLDe6lamuYsbjFl6iOw8HZ8MaITGAk7wSi39Muz7HRrfr9qYWRMbG1XfomYHWPIsnNxg7ojE3RxeAilnoc2zpttnnS7qXHbrRFkOIZRTm9XSBwtYfw6SoJfc9qnk2buxUNuBmMS79Dmv01m1W4vdYprYbsdQ68/z+YWoK49ZKYbZnhCifDq8FNaNVesKO7xq7mgMx7Oa6t0uCbq45s+DF1l18CIvdgmhlp/r3V9gyXb/CHlZaq2uNbJzhPtnq3OV34fCxQPmjkjcjl6vZmv4BJt23a+NDUQ8rYp9nv7XdPsVluvYn2DvAjUMMNvYACRBL4mUC3B4CZn1HyQDJ2IS7pKg12in/rPNOc0984r6cKrZwXDbzF+bcdl6RtGFECZ2YR8s+p+audNvokVMGTOosD5wfo+q+ioqtOTMXN5bcpDwyh482aG2ucMpm7xstTY3uDMEhJs7mtJz9oKH56vWiHMGw9U4c0ckihK3A87tVsmyjYlTkkYPqFZa2yabdr/C8uj1av157U6GG8wsI0nQS2LXj6DT4tL+aRztbO4+gm7nCLU6qkIr5ppCc3orBlt/ns8KK7kLIUwo9SL89iA4+1yr2G4ZH3gGFd5P3R5ZYd44hNl9uuoIl9Ky+WxgQ+xtrfy06uBCSIs3TbEuY/Osqvq356TDnEFqwEJYlm2TwMkLmjxk+n07uKgaC0dWQNJJ0+9fWI6Ew5AcB6GW07HCztwBWI28bNWSIaQbtn7B1PI7x4m7jaCDWudy7E+4HAN+dYwf581iN4OdE1Qz0PpzAO+aqldlovRCF0LcJDcTfn9IdY944i9wDzR3RMbhVwf8w9Q09zt19BDl2raTl/ltxxme7FCLRtW8zB1O2ej1KmHyD1Mj6OVBYH14YA78MkD9K+tgRUA9aPKgYWIrrejl6hzMQopZldr5/9Tfz3ajwMFMy0JaPgn/fAvbpkKv8eaJAdTv3r7foVYHtYRKmFZBezVJ0K3PocWQnqjaeADBAW4cOJt899fV6aJuT6wxX4Ie1EqN5huKrT341LaaSu5frzkmheLKm9xMsHUAG1tzRyJupNerwkzn9qg1oKaqyGsu4X1h85eqfZy1FdMSZZaVq2X0wgME+TiXj8+Y2C1qSVzfb6078btZrY6qg8SKVyAhuvTb0etAmw1u/tfP7Uwt4QjMGw66PLV0sfs4qNzYPLGUVvol2PAJ7JqpRs9bjTRfLB6VodH9agAu4il1bmsO0ctg8VNQvQ08tqp8/f5Zg2N/QaVG4FHF3JEUkAS9uLZPBd8QqB0FQB1/N1YeuEBWrhYn+zskCT61wLcOnFir1tiYUkYSXDwInd4y/LatrJK7KCdyM2HLN/DPN9DxVej4mrkjEjfwOzwTDi6ALu9DeB9zh2N84X1h0+dwdBU0G2buaISJfff3cU5dSueXJ1rh4lAOTqe2TgIXX2g0xNyRGF6DgepfWeRlw+Q2sOoNeHor2Jm4P7xeD6teV6PNHV9XHTKmRarp4VHvqmTTkuVlw/ZpsOkLyElT08vvGQ2uvuaNK+ptOLQI/nwLHvrd9PvPyYC/3gYHdzizFQ7MK5+/g5YqI0m1gbWwQrbl4BPFBM7uUsWAen5eUMQiOMANvR5iL6cTVsnjzq+v01VdncvNBHtnEwR8zRkjrD/P5xeirjhpc9WIugWLqG3mP/6i7PR6iF4Kf70DyWeuFV9cKwm6IeRPrbuwD2ztrs1MsFe/17b2hb8ueKyI5yVE439wOjR+ENq9aO6fyjQqNQLP6mr0QxL0CuXw+RSmbTzJwGbV6BDib+5wyu5yjFqO1/E1056nWBM7R+jxKfw6GLZPUVOzTSl6KZzaqM5FW4+EpkNh8xdqevahxdD+RWjznFpbbUnyP7/XvAdXYtU04q4fQkCYuSNTPKpA5Guw9n1V1Dmkq2n3/88EdV4zfDmseRdWvwt1e6oCh8L4TqxVs2MspL1aPknQi2P7VHVl64Z1R8H+ar3MiYS0uyfoIV3UH/PYLab9xT91bf151eaG37ZfKOhy4cpp80zdL4E2wZKgW7WEI2rU4NRGCKgPj65QJ5Lbp6sr8oZcvlHRZCTBkufg6Ap10UOvUxfd9NrSbc6vES59J1Sc6XkajRpF3/m9ajvpdJfPAiu0adMmxo0bh06nY/DgwYwcWXg66rlz53jrrbdISkrCy8uLzz//nEqVKgFw/vx53nnnHS5cuIBGo2H69OlUq2b96yu1Oj1vLtyPp7M97/S24krnN9o2RV1oaznC3JFYttBuENoDNo6HhkNMN2qdP8oaUF+NPIOqVN/tI/X9mjGwfhzsngWdx6jWlqauil6U83tV3Kf/Af9wGLpQtSC2NBHPwJ5f1OyIWh1Nd15x5bSaEdhgoFp/3usL+KGzmpnVdaxpYqjojv0FLn5QpZm5IylEEvS7SY1XVyZbPlHoalZtPzc0GohJSL/7Nmq0Bztn01+Zi91i+PXn+QoquR+z+ARdWKmsZNjwqZoS5+imRg1aPK5GbzOvwL/fqSIz1VubO1LrFLsFFo6EtATo/jG0vqHNjU6nLsBpc0Gbo9Y7Fvo6R31/49d6LWcyvAmraBdMwvuqwlon1pR9Cq2F0Wq1jB07lpkzZxIYGMigQYOIioqiTp3rf/M/++wz+vfvz3333cfWrVv58ssv+fzzzwF44403eOqpp2jXrh3p6enYWELCYAAz/znF/rPJfPdgU7xdTTzN2Rgyr8B/c1RSV16LOhpS949hcoQaER74vWn2+c83qsr0oyvUZ+CNfGrD/b9A7D/w11uwaKQaWOrxCVSPME18N0s5D+vGwr7fVPLT52to+sitsVsKO0fo+Zmq9r9tMrR/yTT7/est0NioGQWgCjo3eRi2Toamw9RsVWE82jz12V23t2Vc0LqBZUVjiXbPVCeqLZ8sdLezgy1VvZzv3moNVIuhWh3Um8BUMpIg/iDU7Gic7ef/0bCSQnHCiuh0sHc2fNdcjeo0GwbP71VT+vI/3IOuJeVx280Xp7XS5sH6j+GnvuqkZMQaaPNs4Q8nGxv1mKMbuPiAW4BqWeRTS/3uB9aHKk3UyUSNtlA7EoKj0NuVw3ZqdxPUClz91TT3cmb//v3UqFGDoKAgHBwc6N27N+vWrSv0nJiYGCIiVBIQERFR8PiJEyfIy8ujXbt2ALi6uuLsbP1Tp+OSMvhy9TE6hwXQp5GFr/ktrt2zIDfD9HVyrJVvMLR9Hg7MvdbK1siuxKraKw0G3nnJYs128OR66D8VUi/Aj91h7nD1elPJSVcX1r9rruqRtHsRXth7/eK6JQvpCqE9YePn6gKDscX8DUeWq3o6nlWv39/lfbXM5M83zdeiuaI4u0MNBlnY9HaQEfQ7y8tRvc/rdC1ylDjY3614rdZAVfw8vlqt8/INNnCgRTj9L0Zbfw7g5AlulaRQnDCsc7th5etwbhdUawUPz4MqTW99nlsAeNeSBL2krsbBwidVfYrGD0Kvz2WdW1nZ2EJYbzgwH3KzLLbn+99//80999xTolHs+Pj4gunqAIGBgezfv7/Qc8LCwli9ejXDhw9nzZo1pKenc+XKFWJjY/Hw8OC5557j7NmztGnThldffRVb26KLqkZHl6G69k2ysrIMur18er2ed9ZeBL2O4Q2cOHLkiMH3YXK6POr8M4mcgBacuWIHV249bsY6ntZME9CHYOdf0C56gVNdZ5aoo0hJj2e1LW/gioaYWsPJK87rHJug6fYrvkdm43tkNhxZQVLoA1yu9yg6eyO1M9Pr8Iz9E/8DU7DPTCQlqDMJjZ4l160KnDoHnDPOfjHs+9M+5Alqn1hH6vxRnG9jxCnm2lxq//UiGrdqnPTugv6m+H3CHyPwvwnErZtOWlUjDbTdRkX6ffffNwdfjS3H8qqiM+LPXJpjKgn6nRxeAmnxt+1xG+zvxvZTl9Hp9NjY3GXNZUG7tXWmSdBjt6hp9VWNuKbCLwQuWX4v9K0xl2UduqVLS4R176uRc7dAuG+aWt93p2SiesS14h76irPmuSwOL4Wlz6tp6fdNh8b3mzui8iO8rxqFPLkB6vYwdzRFWrlyJR9//DHdunVj4MCBBAcb5nPo9ddf58MPP2TRokW0aNGCwMBAbG1tycvLY9euXSxevJjKlSvz0ksvsXDhQgYPHlzkdsLDDbeWOzo62qDby7dwz1n2nD/F2Hvr07FFTYNv3ywOzIfMROz7TyS8btHHzFjH0+ppPsV+/uOEZ2wr0dr9Eh3PE+vg3Ebo/B4hzSNLFl/DZpDyKqwbi9++X/A7s0pVLDf0VPPT/8Kfo+HCf2od74Oz8agegakqchj2/RkOKS/gufkLPDu/pGaIGcO/30HqaXhoLmGhRbTJC30Xzv1F0KHJEDncpBd+K9Tv+9+7oWY76jZuadTd3HhMd+/eXazXyBT3O9k+FXyCIbjoghZ1AtzIytVxPjnz7tvyDVbrhEw1zd0Y/c9v5heqprhb+BScbScvmzsEcTvaXDWN/bvmqpJ42+fhuV3Q+IG7rwcKagXpiZB00jSxWqvcTFj+Eswdpqao/2+TJOeGVrMjOHpa9DT3L774gsWLF1O9enVGjx7N/fffzx9//EFa2u1ngQUGBnLx4sWC7+Pj4wkMDLzlORMnTmTx4sW89JJat+nh4UGlSpUIDw8nKCgIOzs7OnfuzOHDh43zw5nApbRsxi4/TLPqXgxtXcM0OzX2Z6teD1snqlawId2Mu6/yqP4A1Yv874/UskJDy8tRRct8aqvq7KXhUQXum6qmvvuFqs+CaR1U4p+XXbZ/l2Pgj2Ews6f6LB7wPYxYZ75174bS4WXwqKZm8+lKVzD1jlIvwobPIKT77adW29qrNfFXYlUyb0oWfk5vMFdOQ2K0+n+wQEYdQb9b9dedO3fy8ccfc/ToUb766it69Lg+8hAeHk5oqCpEVrlyZaZOnWrMUG91dreaZtvjs9smCjdWcq/mXYy2FnW6wp6fjT8NMn/9ead3jLcPUH/ss5LVH2a3AOPuS5Q/pzapD8DEaAiOUr9r/qHFf33QtZOAuB2mmZVijRKiYd5j6hi3fR6i3jN9796KwM5BnWgdXanW+FvoWks3Nze6d+9OVlYWP//8M2vWrGHGjBkMGzaMYcNubRPXsGFDYmNjiYuLIzAwkBUrVvDll18Wek5+9XYbGxumT5/OwIEDC16bkpJCUlISPj4+bN++nQYNGpjk5zSGD5cfJj07j88GNrr7jLmyij8Mq9+BxKPw6DKVoBnDmW2qynbvLy2uQJJV0GhUEjW1A/z9oSqEZkjbp8Ll4/DQ3LIPtlRtBo+tVBcR17wLswcYJkZ7F+j0tmW2dystB1fo9iHMf0wtc2315N1fUxJr3wdttiridye174HwfrD5SzVo4RVk2DiKsnMGdf7+BAJWlf8CdcdXq9tQy5z1ZrSziOJUf61cuTKffPIJP/744y2vd3JyYsmSJcYK7+52TAMHN2jy0G2fEhzgBkBMYjr31C3GNkO6qu2e3nJ9yrsxnP5H3dbqYLx9QOFCcRacoL/UtQRJnzC+5LOq7crhxeBVAx74Fer2Kvk0df8wNWoZt61QC0SrotdDdqrh23Pp9arA5Z+j1RrzoQuM+zdHqGnuB+bCmX9Vmx4Ls27dOhYuXMiZM2e49957mTdvHr6+vmRmZtK7d+8iE3Q7Ozvee+89RowYgVarZeDAgYSEhDBhwgQaNGhA586d2bFjB1999RUajYYWLVowZswYAGxtbXnjjTcYPnw4APXr17/t9HZLt/5IAkv+O8+LXUIICTRizYa0RNUqa89P12pDaGD2QHhiDbj6GX5/2yaBk5eqRyFKJ7C+mt6+Yzo0fxQqFzFduTRSL8LGu4yylpRGA/X6qe3tnwvpCWXbnq0DNBhkulZzplT/PpWc//2RminhaqBlkme2q8r27V8q3sBC93GqA9Tqd2DIT4aJ4Xail8GKV7BHr2ZuDF1QvpcPHvtTXfy00E5URkvQb6z+ChRUf70xQc/vh2pxrVfSEuDgQmjx2B1PnH1dHfBysS9eJXeAGu3A1lFNLTLmyXL++nNj9/S7sdWasYrRifLlymmY0U3NvOj0thrVtS9lZWcbGwhqqUbQrdXOH2Dlq+pCRVBrNW0/qLU66StB0aFCMq/A0hcgeinU7qTW80vrJOOr0xnsnNRJjgUm6KtXr+bRRx+lZcvCa+2cnZ0ZN27cbV8XGRlJZGThta+jRo0q+LpHjx6FZr/dqF27dixbZrnT/osjLTuPtxcdICTAjafvMdJMndwsNVq6+UtVBbvlk3DPm6oI68/94Nf7Yfgyw45QXomFIyug3Sg1YihKr9NoODhfzQh7/E/DJDVrxqgWlncbZS0NO0fVHUXcnkYDPcfD1PZqdkTfb8q+TZ0WVr0G7lWgw6vFe41XdZXMb/gYTm5UHVOM4cx2WDACqrUgwbs5AQemqhlhYb2Nsz9zy0mHU5tVC20LZbQEvTjVX+8kOzubAQMGYGdnx8iRI+nSpeiE1hiVX/0O/Yi/LpcY387k3GX7VVxtOBCbUOw4gvybYn9oBSdrPGKIkItU68ha8nwbEHc8xmj7AECvo66tE1eObSfBtc0tD1ekSpCmYO3H0zY7mRrrRmKXnc7pzj+Q7RUMJ2LLtE0/p9r4nVjHsX070DmUfGTL3Me0xvafsHOtQpZbbVyOr8PuwFwAtHYuZPnWI8O3EZl+jcj0rV+sn8858T+qbhuDXeYlEho/R1Ldh+BsEmCE9ZFFMPfxNLdqga1xOrCIEzUfVb1ty8iQx/O5554jIOD6TKesrCwuXbpEtWrVaNPm1r/fQvnir6NcSMli/lNtcbQr5UWz29Hr1UyiNWPg6mk11bLrh9eX+lRvDQN/UOt8FzwB988u/YW7m22fpt6jrUbe/bnizpy9ofMYWPaCGpkua42PM9th/+/Q/mVZvmVOgfXU9Pbt09TsiCpNyra9PT/DhX0wcIZqYVpc7V6A/2arUe2nNqv16YZ06Tj8dj94VIUH/+By7HkCLm5QM/CCo0o/iGLJTm5UywwsuPaGZS6UA9avX09gYCBxcXEMHz6c0NBQqlevfsvzDF75NSQYViyF4M4Et777uoQGh3L4+0hC8eNI7g9/vkl4JWfwrlmmeIuUdBKST0Dzd0xThXFzXXx1l/AtYl8VqhKkCVj18czJUCNBGRfhkSXUrmGghMCxDxycTl2XFAhpVeKXm/WYplyAywcg6h0cOr6mTtavnoG4HdjGbcc1bjuu0bNArwM0EBB+fYQ9qLWampU/UqPTqtG3DZ+oK+4PrSGwWnNMPW5u1e9RQ8h+EBY/RbhHFlRrXubNlaby6+2MGjWK33//veB7GxsbRo0axYIFC8q03fJsz5kr/LQ1lkciatC8hrdhN35uN/z5llqiE1Afhi2G4E63Pi+8r2qHuPJVWPmaWi9e1hHarGSVLNQfoIqIibJrOkwtK1rzHoT1Kn37Sp1W/V+7V4EOrxg2RlFy94xWnQ5WvQ6P/1X6372MJFg3Vs2kbTCwZK+1d4bun8AfD6tZdxFPly6GoqTGq2U0NnYwdL6aym+ToGYP/NxPFaiLfN1w+7MUx/9Sy5hrtDN3JLdltLnlxan+erfXAwQFBdGqVSvTVX+NXgppF2/bWu1mwf5uXErL4WpGTvG2X6eruj1uhGruej0sexEc3KHx7dfOG1R+JXchbkebB/Mfh7O71GiQoZJzgKrNQWOrTnKtzdEV6jasr7rVaMC7BjQaDL2/UFfK34yDR5aq5QAeVeHQIlj8NHzXDD4Pht8ehC1fw0/91NrVBgPhf5sNkhyKUgjtrk50opeaO5JbaLVaHByuFwh0cHAgNzfXjBFZtpw8HW8u2E8lDyde6xFmuA0nn4OFI+H7KEiKgb4T1O96Ucl5vlZPqqnou2ao3/ey2vML5KRBm2fKvi2h2NhAry/U+ePG8aXfzp6f4OJ+VaSsJKOswjicvaDLGIjbDvv/KP121n8MWVdVUcHSJPlhvdVo9vqP1TJcQ8hOg1+HqELPD/1RuBhl7Uiody9s/koNHJQnej0cW63+5lpw0VyjJeg3Vn/NyclhxYoVREVFFeu1ycnJ5OSohDcpKYk9e/YUWrtuVNungXet64n0XdS5oVBcsfgGq5HzE2tLGeAd7J0NpzZC1w/As6rht18Uv1C4GqdGSC3U12vkAoLZ6PWw4mU4tkqNAtXrZ9jtO7pBpQbqw9PaRC8D3xDwv0OFSUc39UEZ+Zq6uv16LDyzTZ3Uh/ZQF8fWvq8qMfefotrcGLrgnCg+Fx/Vdil6mcW1qvHx8WHdunUF369duxZvbwOPCpcjUzfGcCw+jY/6N8DN0QCTDbPT4O9xqqXkocVq+vLze9TU2eJMW+/8PjQcDOs+UC0pS0ubp85zqreFKk1Lvx1xq2otoMnDqnXopeMlf31GEqz7EGq0L/koqzCeJkNVTac170FWSslff/GAurjW4gmo1LB0MWg0qtNNbgas/aB027iRNhfmPapiGzxLDXbcrNu12iSrjdwRytQuHoDU8xZbvT2f0RL0G6u/9urVi549exZUf80/Sdi/fz8dO3bkzz//ZMyYMfTurYoRxMTEMHDgQPr168fw4cN58sknTZKgOyUdgbM71JqsYhauC/a/lqAnFLNQnEajCsSd2qQKwxhKygVVGbtGO2j+mOG2ezd+IYAeLp8w3T6F9dg4Xo0IdHjF8K1K8gVFqLaI2jzjbN8YMpJUMcfwPiW7mm5jo6a6N38U+k+G53fDazHw8iHVcaI8V1y1FuF91Mho4hFzR1LIBx98wLRp07jnnnuIjIzkhx9+YOzYseYOyyKdSEhl4t8n6Nu4Cp3Dy7hQRKeDvXNUYr5pPNTtCc/tVKNyJbmYZmMD905SF4CWPAsx60sXz5HlkHwG2jxbuteLO+vyvpqSvOqNkl+kWz+ubKOswjhsbNQAQ1q8+h0uCb1eFQ908oJOb5UtDv9QNb39v9lqRmJp6fWw/CU4sQb6fHX7LgFeQaon/OElcHJD6fdnaY79pW6LORBrLkZdg3636q+NGjVi06ZNt7yuWbNmZqn86n18Lti7QtOHi/2aat7OONjaFL+SO6g3xc4f4MzWO09rK4mVr6qCB32/NW0/0xsruVduZLr9lkBEbQO1xxAls/snVXm08UMQ9a7x9hPUSrUvjD9Y9iIupnLsL9DlqfWlZWWM9kui9ML6wIpX1Sh6gOWsx69evTpz584lPV3N9nJ1lcrdRdHp9Ly54AAujraM6VuvbBuL3aIKLV3cD1VbwP2/qL9XpWXnCA/MgR97qsJxj68q+Yjc1klqFl/dnqWPQ9yeW4CqwP/XWyWrgn3xgGrr1XKEmhUmLEu1FmokfdsUaPrI9UKOd3NwgWq92ecbNcOqrDq+rgoRrnwNRqwr3fn+xs9g7y9qW80fvfNz276gZueuegOe2mL4AnXmcPwvNSPCwrvbFOt/NiMjA51OB8CpU6dYt25d+Vu7lpaIx5k1qp+yk2exX2Zna0MtP9eSJei1Oqj+kYaa5n54iboqfs+bpu/n5xMMaEo3nctE2gRLgm5yR/9UV2jrdIF+3xp3NKB6hLq1pmnuR5arNeXGboUoTM+9kkrCYv42dyS32LBhA7/++iszZ85k4sSJTJw40dwhWZxD51PYdfoKr3Sri5+bY+k2knkVfn8YZvVWs2UGzlC9zMuSnOdz8oSH56nR9zmD1RKz4jq7S80SbP204arBi1u1Ggn+YeriTG7m3Z+fP8rq7F32UVZhPF3GgL0L/FnM2RHZaWp6eOXG0MxAnZucPKDrWDi/R42kl9SeX1Qx2SYPF++9Zu+kWv0lHoEd35d8f5Ym/ZL6O3i7WQMWpFgJ+tChQ8nOziY+Pp4nnniCJUuW8Oabbxo7NtPa/zs2utxStRwJDnAt/hp0UD1Ha7QzTKG4jCQ1WlO5MbR5vuzbKyl7J1XYSgrFiXxxO9XapsqNYPBPxr/i6llNJbtnrKRQXE66ujgXVsLp7cJ69P5S9a61IO+99x4rV65k9mx1UvfXX39x/vx5M0dleU4kpgLQpnYZRrv++UaNnka9A8/vgoaDDDuzzbMqPDxf1X6ZMwgyrxTvdVsngaNHiWYJilKwtVfT1K+eVlWw7+bAfDXK2vk9laQLy+QWoKq6x/wNR1bc/fmbv4DUC6p4oCEviDW6X3VyWfuBuhhYXMfXwLJRENxZ1bEp7vlH3V7qNRs+MVyBOnM5vgbQl58EXa/X4+zszOrVq3nwwQf59ttvOXGinK05rtqC+MbP37lg020E+7tx+nI62Xna4r8opCtcOlr26oir34GMy9BvItiaqWueX6hFj6ALE7p0XFUFda8ED80zXRXaoNYQt8M0+yqrE2shL8sw09uFZarU0OJOAPbu3cv48ePx8PDgueee4/fffyc2NtbcYVmcmIR0bG00VPcp5RKAnHTYNVNdgOv4mvF6CAfWgwdmq9aqvz8Medl3fv7VODXbrvnw0rcAE8VX+x4I73f3KtjZabDmXajcRLVqE5at1ZNqdsRfd5kdcTkG/p0IjR80zMyZG2k0qg1axmWVNBfH+b0wdzgE1ochJRw40WjUBafcTMMUqDOnY3+CWyBUamzuSO6q2An63r17WbZsGffccw9AwZT3cqNGG5LCSndVuU6AGzo9nL5cgkrmdbqo27KMop9YB//NgfYvmnf9t18oXD6uiuGIiis1HmYPAI0NDF0Abv6m23dQa0g5C8lnTbfP0opeDs4+UN2A7eaEuAtHRzVd29nZmfj4eOzt7UlMTDRzVJbn5KU0qvu44GBXyhHv/35Vhb7aPGfQuIpUq6Pq3nD6H1j0vzt/Bu+Ypm5bFa+FrDCA7sWogr3pc+OMsgrjsLVXyfHVM/DPt7d/3p9vgp0TdDFSQluliVo/vuN7iL9LG+orsTBnCLj4quUxpblA5xdimAJ15qTNVbMfQrqZtlZXKRUrwrfeeotp06bRpUsXQkJCiIuLo3Xr1saOzWqUuJI7qKTWs7pKsksjO031PPcNUYUezMkvVI0IJpdgLZwJbY25bO4Qyr+sFDXVMv0SPDxXtRM0perX/h5Z+jr0vBxVIC6sl/lmvIgKqVOnTqSkpPDEE08wYMAAoqKi6NOnj7nDsjgxCekE+5dy9FynU0WkqjY3/KjZ7TQcpNakHlqkRmKLkp0Gu39WbS69gkwTlwCv6mqpy+2qYF86oZYdNH4IglqaPDxRSvk9wrfcZnbE0T/h+Gq45w3jFiLr/J5Ktle9fvs18RlJMHsgaHNUq1b3SqXfX+Tr4FZJFaizxgG5M1shO8XiZrfdTrES9FatWjF16lRGjhyJTqfD29ubd94pZ33xyqCWn/owP1GSBF2jgZAuqm95Xk7Jd/r3RyohvneiWgduTjdWcrdA205Kgm5UeTkwdxjEH4IhPxfdT9PYAhuo4i1nLDxBj90E2ckQJtPbhenodDratGmDh4cH3bt3Z/369axatapQVxUBWp2eU5fTCy66l9jx1arFXsQzpq0v0fYFNTK+daK6QHCz/+aovzsR0lrN5Nq9oBL1VW+oEbx8ev0No6zvmy08UUrdxgEa1d74RrlZ6v/VL9T4s1VcfFSdi9jN6gLdzXIz4df71fKWB38v1RLeQhzdy1agztyO/aUKdNe+x9yRFEuxEvRXXnmFtLQ0MjIy6NOnD7169eKHH34wdmxWw9XRjiqeTiWr5A6q3VpOmrqqUxJxO2D7VNWOI7+CtTlZeIIujEinUz15T26Aft+p2grmYGuvLgxY+gh69DJwcLOaDwhRPtjY2BTqee7g4IC7u6xDvtm5K5nk5OmoXdoR9K0TVcHKevcaNrC70WhUpeWwPqpy+KHF1x/TaVXSXq2VjNKag70zdC+iCvaxP1Uf6nvetPh2T6II+T3Co5cWnh2xdSJcOaXWbNs5GD+OFo9DYEO1jCLnhmLVOi0sGAFnd8LA76GGgZbUNRoCQRElL1BnCY79pQp0W0kNjmIl6CdOnMDNzY21a9fSsWNH1q1bx5IlS4wdm1UJDnArWSV3UOvHbOzVH+niysuGJc+pk4AuY0q2P2Nx9VVrai00QX+pazH7VYqSWzsGDsxVfc7NXRk4qLXqJZtdwgtlpqLTwpGV6iKGuWe9iAqnTZs2/PXXX+iL0x6ogsq/yF6qEfQL+9VIVuv/madXsI0tDPxBTa1fOBJO/6vuP7pKJQxtnjF9TEIJ6w3BUQVVsDXa7GujrHXV+0VYp7YvgFcN1SJPm6tq4Gz+Ul0oC44yTQw2ttBrPKScUwUJQc3OWPWGaufa41PDXjDUaNT+SlKgzhJcjlG1skJ7mDuSYitWgp6Xl0dubi5r164lKioKe3t7NNIeqJBgfzdiEtNKdvLj6Kauah0vQT/0TV+o6u99v7Gsq0BSyb3i2TYF/v1WzeTo8Iq5o1GzSfRaNf3KEsXtgPQEqd4uzOL3339n1KhRNGzYkGbNmtG0aVOaNWtm7rAsSn6CXrs0Cfq2KWDvCs2GGziqErB3VlNZvarDbw9C4lHYNlnVu5FlNeaj0UCPzyA3A9Z+gM/RX1Xhrp6fmedijjCM/B7hl47Cjumw+l3Q66D7x6aNo0ZbaDhYnY8lnYR/JsDO71WhyoinDL+/yo2hxWPFK1BnKY6vVreh3cwbRwkUq0rR/fffT1RUFGFhYbRs2ZJz587h5mai9klWIjjAjYwcLReSs6jiVYK2KnW6qsIuyWdVP+c7uXhQFaVo9ID5phLfjl+ImrIlKoaDC9VUyrA+qqKpJVywq9ZC3Z7ZrmanWJojy9X6pzoW9rsrKoS9e/eaOwSLF5OYjreLPT6uJZyamnoRDsxTJ63OXkaJrdhcfFQxqB+6wqzekJ4I3T6SopTm5h+qqmD/+x1+to7qQm1wJ3NHJcoqv0f4ug8hLxMi3wTvGqaPo+tYNUPv1/vVbNb6A6Drh8bbX9S7at37qtdh+DLLOAe8k2N/qYFEn9rmjqTYijWC/sgjj7B582a+//57NBoNVatW5eeffzZ2bFalTn4l95KuQ89PtE/cZRRdmwdLnwMnL3XFztL4haoTgYwkc0cijO3UZtXSJ6i1mlJpKa1hnL3BP9wy16Hr9WqtWu1O4ORh7mhEBbRz584i/4nrTiamlW56+84fQJcHrY0wWlUa3jVVN42cDFXzotkj5o5IgOq443ZtvXm3ceaNRRhGfo9wXZ6aqdL+RfPE4VEFIl9TyXmN9nDfVOO2ErtbgbqyiP0HfugCU9qpJTplXZaVnapaUYZYz+g5FHMEPTU1lYkTJxZ8mLdq1Ypnn31WiszcIDhAFZWJSUijQ0gJ+j/7h4FHNdUPvfmjt3/e9ilwfi8Mmql+MSxNfnXIyyfAxUTtZYTp6HSqa8D5vbD0BfCuBQ/+pqZUWpLqrdWHhU5nWX0uLx5Q7Vg6vmbuSEQFNWPGjIKvs7Oz2b9/P/Xr15eL7TeISUwnKqwEn9+gKiXv+lGNpJm6veSdVGkKj69SNTmcPM0djQB1cXbYIs4c2UdNc4yyCuPwC4GhC1QLM3OeE7V5TrVBC+sFdo7G31/zx2D3LFWgLrQ7OJSyuGa+pJOw5j1VTNejqjqWvz0AtSLVsoFKDUq33ZMbVJs5K1p/DsVM0N966y1CQkKYMGECAEuWLGH06NFMnDjRqMFZE383R9yd7DhR0hH0/HZrBxaodlVFVX28HAN/j1MnAPXvM0zAhuYXom4vHTNd/9di+nrNMSkUVxLpl1TLtIRoSMi/jVYdB0D94Ry6wDIvFAW1Vh8Yl45CQLi5o7kuehlobNTvsBBmMHXq1ELfX7hwgY8/NvFaSQuWnJnLpbTsko+g7/9DFUyyxCJslRubOwJxs8D6ZCZZ0MVjYRi1I80dgapn0ORB0+3PxhZ6fg4ze6gCdZ3fLd12Mq/Cps9h+zS1DLDTO9DmWfXz7JyhitFN6wBNh6rHStr14Nhf4OhpGV2vSqBYCfqZM2f47rvvCr5/7rnnuPdeE7cRsXAajYawSu5sOX6JnDwdDnYl+ANcp4tKKuK2Q60OhR/T62HZKPVG7f2l5a7z8KqhfrESj5o7ElFc2Wmq9UvCYVXoI+Hav/TE689x9oHA+tDkIQiop/5VaggOLuaL+06CWqvbM9ssK0E/shyqtwVXP3NHIgQAlSpVIiYmxtxhWIyTpSkQp9fD1slQqZFq3yOEEBVJjTbQcIgqUNf04ZKt8dbmwe6ZsP5jyLwCTR5W0+Y9Kl9/TsRTqrXbps9VIb6DC1V7u4hnijdbQadTBeLqRFldQcZiJehOTk7s2rWLFi1UEabdu3fj5CRtgm72zD11eGzWTmb9e4qRHUsw1a1WJNjYqXXoNyfoe35Sazz6TlBrTCyVjS341rHISu4RtX3NHYJluHoG9vx8bXT8sKoim8/eRS23COkOgfVUchtQH9wCLPeiUFF8aoOLn6qY3uIxc0ejXDqhjnePz8wdiajAPvzww4LuKzqdjujoaOrVq2fmqCxHfpvU4JL0QD+xTs3WuW+adf2dFEIIQ+k6Fo6uhL/eVksfi+P4GvX8S0ehZgfoPu72M35cfFTtrRZPqKLa68bCrlmq1XSDgXf+23txH6TFq3NbK1OsBP2DDz7g9ddfJy1NXWH28PDg008/NWpg1qhTWACdwwKYsPY49zapSqBHMS9iOHlA9TYqQe/6wfX7U86rtg01O5i3dUtx+YWoSvMWpk2wJOgcWw0Ln1TFMvxC1PrEJkNVIh5YD7xqWtaa7dLSaNQ0prht5o7kuiPL1G1Yb/PGISq0Bg2ur9+ztbWld+/eNG/e3IwRWZaTiWnY22oI8inB7KBtk9Saz/oDjBeYEEJYMo/Kqr7O2jGqbXRIl9s/NyFaJeYx69SAyv1z1LlRcS5w+tVRFwBOblTbWPAEbJ8K3T+BoJZFv+bYX4DG8jpfFUOxEvSwsDCWLl1akKC7ubkxa9YswsLCjBqcNXq3Tz26fb2JT1cd4ev7mxT/hXW6qDd3ynk1Uq7Xw4pXQJurRs+t4eq8XyhEL4e8bNMUqBB3p9PChk9h03gIbAj3/2xVbSZKJaiVmlKelghuJSz4ZAzRy9UFEa8gc0ciKrDu3bvj6OiIra3quqDVasnMzMTZ2cIKPZpJTGIa1X1csLct5oXKhGiI+Vu1GyqqdowQQlQUEc/A3l/gzzeg1tZb/yamX4L149RyXkd3VfSt5ZOl+9tZOxL+txH+m6Pa283oAg0GqRF1r+qFn3vsL9WC1wqXF5ZoyMzNza2g//msWbOMEY/Vq+nnysiOtVm09xw7Y0vQcqzOtStO+e3WDi1SU0ai3rasyrB34hcKei0knTJ3JALUH8TZA1Vy3mQojFhT/pNzgKBrhUAsod1aynk4t0v1ixfCjB599FGysrIKvs/KyuKxxyxkGYgFiElML1mBuG2Twc4ZWjxuvKCEEMIa2DmoZXyXT6i/jfnysuGfCfBtU9j9k0rKX/hPFYEry4VNG1vVPvKFPdDhVTUoM7Glmv6enaqekxoP5/eoCvNWqNRzWvVl7UtXjj3TKZgqnk6MWXIIra6YxymwPrhXUesyMpJg5Wtq1K3108YN1pBurOQuzCtuJ0zrCKf/hX7fQf9JltcSzVgqN1YFCy1hmvuRFeo2vJ954xAVXnZ2Nq6u19dXu7q6kpmZacaILEeeVsfpy+nFLxCXlgj7/oDGD1hmNwshhDC1kC6qU82mz9XgxKHFKmle855axvvMNug13rB/Mx3dVfX453ZBeF/Y/CV820xdDDj257W4KliCrrGGKddm4uJgx9u963H4Qgq/7jhTvBdpNFCns+rXt/I1yLoK/SaCbbFWIVgGX8tM0LfGXDZ3CKaj16tWFTN7qsKDT6xWVxkrEnsndXErboe5I4HopWpmib+0+RPm5ezszKFDhwq+P3jwYPks9nrsL+zSL5boJXFXMsnV6otfIG7Xj6DNVtM6hRBCKN0/VktzJ7eBecPBwQ2GLYKH5xr3PMgrCAb+AE+sBe+asOwFWPGyGvis1NB4+zWiO2Z/TZs2LTIR1+v1ZGdnGy2o8qBXw0q0qe3LF38dpXfDyvi4FmMqR0hXtYbj4Hzo+DpUanD311gSRzfwqGZxCfq2k5crRqG47DT1R+ngAgjtCfdNAWdvc0dlHkGt1IWK3CyVsJtDRhLE/gPtXzTP/oW4wVtvvcWoUaMICAhAr9dz6dIlvv76a3OHZXh/vUWNrHSou7HY/XLzW6wFBxRjBD0vG3b+ACHd5MKbEELcyKcWdBoN26dDl/fVAJGNren2H9RSDUwdWqjat92tyrsFu2OCvnfvXlPFUe5oNBo+uLc+PSds5ovVR/n4vmJcwal9jxr19AmGjq8aPUaj8AuxuAS9Qkg8Cn8Mg8vHofN70O6l8lGVvbSCIuDf7+DCPqje2jwxHF2lajKE9zXP/oW4QaNGjVi1ahWnTqkaIbVq1cLe3rr6whbLgOnY/dgLfh0Mj65QUyDvIiY/QfcrRoJ+YD6kJ8jouRBCFKX9S+qfuWg0KjFvMNB8MRhABT6DN77QQHeGt6nJbzvOcPBc8t1f4OQJQ35RbQSstQq6X6jqhW5BNQpe6lrORzkOzIfpnSDjMgxbDB1eqdjJOUDQtaTcnOvQjywHzyCo3MR8MQhxzZw5c8jMzCQ0NJTQ0FAyMjKYM2eOucMyvKrNOdv2Y9Xyc96jarrlXZxMTMfPzQFPl7tcsNDrYeskCKivLqgLIYQQRlDBz+KN78WuIfi6OvDekoPoilMwLqyX9VRtL4pfCOSkQeoFc0dS/uXlwMrXVS/ISg3gqc2q/YRQ7dV8aptvHXp2GpxYp6q3W+n0KlG+zJ07Fw8Pj4LvPT09mTdvnhkjMp70Km2hz9eqK8qyF+96wTgmMY3axRk9P7UREg5BxNPyey2EEMJoJEE3Mg8ne97oEcaeM1dZtPecucMxPr9ro9Uyzd24ks/BrF6wYxpEPKumcnpUMXdUliUoAs5sM89sjhNrVRGpcGmvJiyDTqcr1H1Fq9WSm3v30WWr1Xw4RL4B/82GDZ/e8akxiekEBxSjQNzWyeDqDw0HGyhIIYQQ4laSoJvAwGbVaFrdi09WHSElqxyfEMENCfpx88ZRnsWsh2kdICEaBs+CHh+DbTlcS1pWQa0g4xIknTT9vqOXgYufai0ihAVo3749L774Ilu3bmXr1q28/PLLdOzY0dxhGdc9o6HJUNj4qWq7U4Qr6TkkpefcvQf6peNw/C9oOcJ8hSeFEEJUCJKgm4CNjYYP+tXncno2364t54mreyVw9JARdGPQ62Dj5/DLfWoU58n1UP8+c0dluapHqNu47abdb142HF8NdXuatnqpEHfw2muvERERwW+//cZvv/1GaGgoWVlZ5g7LuDQa6PsNBHeG5S/BsdW3POXkJVUgrvbdWqxtmwy2jtDiCSMEKoQQQlwnCbqJNKrmxQMtg5j1byzH41PNHY7xaDQWV8n96zWWE0upZacRtPlVWP8RNBwEI9ZJi5+78aurCi+aOkE/tQmyUyC8n2n3K8Qd2NjY0LhxY6pWrcqBAwfYtm0bwcFWXO+kuGztYchPqk7HvOFwbk+hh2MS0wHuPIKekQT//QaNBqv6FkIIIYQRSYJuQq92q4uLgy3vLztUaC1gueMXConlICm2JCtfxfXiNuj1BQz4XvWcF3dmYwPVWsEZEyfo0cvAwV0K9gmLcOrUKSZOnEiPHj348MMPqVJF1ar45ZdfGDp0qJmjMxFHd3hoHrj6wa9DIOlUwUMxiWk42NpQzdvl9q/fPRPyMlW9DyGEEMLIJEE3IV83R17tXpd/Tlzmz4MXzR2O8fiFQOp5bHLTzR0JABG1fc0dQtns+wP2/caleo9DqyelenBJBLWGxGjIvGqa/em0cGQFhHaz3laJolzp2bMn27ZtY9q0afz2228MGzYMm4rYhtE9EB5eALo8mD0Q0i8DEJOQTk0/F2xtbvN3NS8HdnwPtTtBYD0TBiyEEKKiqoCf0ub1UKvqhFVy56MV0WTmaM0djnFcKxTnkHrGzIEobYKtOEG/HAMrXobqbblU71FzR2N9ql/rh352p2n2d2abKkwXJtXbhWWYOHEi/v7+PPLII7zzzjts3bq1xDO4Nm3aRPfu3enatSvTp0+/5fFz584xfPhw+vbty7Bhw7h4sfAF6LS0NDp27MjYsWPL9LOUmX8oPPg7JJ+F3+6HnAxOXkq78/T2Q4tU29A2MnouhBDCNCRBNzE7Wxs+6Fefc1czmbLhhLnDMY78BD0l1rxxWLu8HNXj3MYOBn6vbkXJVG0OGlvTrUM/slwVkgrpapr9CXEXXbp04euvv2bVqlW0bt2an376iaSkJMaMGcOWLVvu+nqtVsvYsWP54YcfWLFiBcuXL+fEicKfXZ999hn9+/dn2bJlPPPMM3z55ZeFHv/mm29o2bKlQX+uUqseAQN/gLO70C0YwdnLabcvEKfXw7ZJ6jMtuLNp4xRCCFFhSYJuBq1r+3JvkypM3XSSM5czzB2O4XnXAo0tjqmnzR2Jdfv7Qzi/F+6dCJ7VzB2NdXJwhUoN1ci2sen1av15cCe15lUIC+Li4kLfvn2ZOnUqGzdupF69enz//fd3fd3+/fupUaMGQUFBODg40Lt3b9atW1foOTExMUREqK4JERERhR4/ePAgly9fpl27dob9gcqiXj/o+Rk2R1fwjs0sgv1uk6Cf/hcu7IOIZ1RNCyGEEMIEjPqJc7dpcTt37uS+++6jXr16/Pnnn4UeW7RoEd26daNbt24sWrTImGGaxeie4djZaBi7/LC5QzE8OwfwqYVDiiTopXZiLfz7rWrpE97X3NFYt6DWcG43aPOMu58L+yA5Tv6/hMXz9PTk/vvv56efiu4NfqP4+HgqVapU8H1gYCDx8fGFnhMWFsbq1aqF2Zo1a0hPT+fKlSvodDo+++wz3njjDcP+AIbQ+n/Ehj7OI3ZriLgwu+jnbJsMzj7Q+AHTxiaEEKJCM9qc2fxpcTNnziQwMJBBgwYRFRVFnTp1Cp5TuXJlPvnkE3788cdCr7169SoTJ05kwYIFaDQaBgwYQFRUFJ6ensYK1+QqeTrxQucQPl11hPVHE+hUN8DcIRmWX10cLkSbOwoAtsZctq516GkJsOgpCKgH3ceZOxrrV7017JgG8QegSlPj7Sd6GWhsILSn8fYhhAV6/fXX+fDDD1m0aBEtWrQgMDAQW1tbfv31Vzp27Fgowb+d6GjDfV5kZWUVa3tzbQfRVBtNv12fcs7WiZSaPQoes0+NI/jICi7XG07iiViDxWaNins8RfHI8TQsOZ6GJcfT8EpzTI2WoN84LQ4omBZ3Y4JerZqatntzRdktW7bQrl07vLy8AGjXrh2bN2+mT5/yVXjp8Xa1mLszjrHLDtM22BdHO1tzh2Q4fiE4Hv8LctLVNGMz2nbSihJ0nU4l59mpMHwZ2DubOyLrF6Sm3nJmu3ET9CPLoUY7cLWS95oQxRAYGFio6Ft8fDyBgYG3PGfixIkApKens3r1ajw8PNi7dy+7d+/mt99+Iz09ndzcXFxcXHj11Vdv2U94eLjBYo6Oji7W9tIO7uMzx1H0q2ZH1Z3jqFq3KdS+Rz24cibY2OHXczR+7ne/wFCeFfd4iuKR42lYcjwNS46n4d14THfv3l2s1xgtQS9qWtz+/ftL/dqbp9TlM8dVd0N6rIk77669yKcLdzCkoZdJ921Mzo7h1NBpufrb/7jQ6m2zxnLpUhLR0dZRMd/nyBwCY9ZxofkbXL0MXL7+fpSrmqVXxyWQjENrOO9ZuDe5oY6pQ8ppghOPcLFaL65U4P8jeY8aliUcz4YNGxIbG0tcXByBgYGsWLHiliJwSUlJeHl5YWNjw/Tp0xk4cCBAoectXLiQgwcPFpmcm0tMYhpBAV5w/2yY2RP+GAaPrVI1P/bOhoaDoIIn50IIIUzP6stCm+OquyGFh8Om87v44+Al/te9KZU8nUy6f6MJD+fShX/xi/4Jr2b91YmOmYyzlguB5/bAgakQ3pfKfUZT+aZ+53JVswwOtcfzzDY8bzp+Bjumm1cBUOmeJ6hUgQv6yXvUsEpz1d3Q7OzseO+99xgxYgRarZaBAwcSEhLChAkTaNCgAZ07d2bHjh189dVXaDQaWrRowZgxY8wSa0no9XpiEtPp06gyOHvBw/Pgh64wZzDUuxdy01VxOCGEEMLEjJagF2da3J1eu2PHjkKvbdWqlcFjtBTv9q5Hl6838vHKaL590IhTcE0sscGT+KUdhWWj1NRi32Bzh2S5slNVSzW3QOj7LdyUnIsyCmoNBxeo/sfGSKCjl0GVZlJtX5RLkZGRREYWnn0yatSogq979OhBjx49bn5ZIQMGDGDAgAFGia80ktJzSM7MpXZ+D3TPajB0PvzYA7ZPgZodoHIj8wYphBCiQjJaFfcbp8Xl5OSwYsUKoqKiivXa9u3bs2XLFpKTk0lOTmbLli20b9/eWKGaXXVfF56KDGbpvvNsP3nZ3OEYjo2d6jdrYwfzH4O8bHNHZLlWvApXYlW/cxcfc0dT/gS1VrfGaLeWfBbO75Hq7UJYkZjEdACCb+yBHlhfTXd3C4SOljMVXwghRMVitAT9xmlxvXr1omfPngXT4vJ7pO7fv5+OHTvy559/MmbMGHr37g2Al5cXzzzzDIMGDWLQoEE8++yzBQXjyqunI4Op6uXMmKWHyNPqzB2O4XgFQf8pqgXVGsuf9mgW+36H/b9D5JtQo625oymfAhuAvSvE7bj7c0vqyAp1G97P8NsWQhjFycQ0AILzR9Dz1Y6EV45eLxYnhBBCmJhR16DfbVpco0aN2LRpU5GvzU/OKwpnB1ve6R3O03P2MHH9CV7sEmrukAwnrBe0fkpNG6zVUX1vLbR5sHYMeAZBi8dVj3dDuhwDK15R1b9lxMZ4bO2gWnOIM8IIevQy8A8Dvzp3f64QwiLEJKbhaGdDVa8iOmXIEiMhhBBmZLQRdFFyPRpU4t4mVfhm7XE+WRWNXq83d0iG03UsVG4MS55RU4JN6Os1x0r3Qr0eVrwEWyfCn2/A5NZweKm63xDyctS6cxs7GDAdbMpRmz1LFNQaLh6E7DTDbTP9Mpz+R6a3C2FlTiamU8vPFRsbScaFEEJYFknQLYhGo+GrIU0YFlGDaRtP8vr8/eVnurudIwyaCdpcmP+EGpm2dBvHw56focOr8PB8sHWAucNUEaGzu8q+/b/Hwvm9cO8kKS5mCkERoNfCOQNWwz66EvQ6COtjuG0KIYwuJjHt1untQgghhAWQBN3C2NpoGHtvfV7sEsK83Wd5avZusnKto4f3XfkGQ59v1DTjDZ+YbLcRtX1L/qK9s2HDx9D4QYh6B0K6wlP/qPiTTsIPnWH+46qwW2mcWAv/fgctR0C4JHcmUa0FoDHsOvQjy8GzupodIoSwCtl5Ws4kZRQuECeEEEJYCEnQLZBGo+HFLqF82L8B644kMGzGdpIzcs0dlmE0GgxNh8LmLyFmvUl22Sa4hAn6ibWw9AWo3alwyzNbO2jxGLywBzq+BkdWwsSWsPpdyLxa/O2nJcCipyCgHnT7qGSxidJz9oKAcMOtQ89OVe/h8D6yZlUIK3LmcgY6PQQHyAi6EEIIyyMJugUbFlGDiQ8247+4q9w/fSvxKVnmDskweo4Hv1BYOFIlq5bk/H/wxyMQWA+G/Fx0UThHdzWq/vxuaDhYjYR/2xS2T1NT+O9Ep4NF/1PJ3aAfwb6IAkXCeIJaQdxO9f9QFhcPwE/9QJsN9e8zTGxCCJOIuVbBvbafJOhCCCEsjyToFq53o8rMeqwVcUkZDJzyb0FrGKvm4AqDZ0F2ikpWy5osGcqV0zBnsOpD/tA8cPK48/M9q0L/yfC/TVCpIax6HSa1hujlty8kt3UixPwNPT5Ro7nCtIIiIDsZEo+U7vW5mbD2A5h+DyTHqYssQa0MGqIQwrjye6DXlinuQgghLJAk6FagXR0/fh/ZhswcLYOnbuXA2WRzh1R2gfWg52cqWf3nG3NHAxlJMHugGhF9eD54VC7+ays3gkeWqKTexg7+eBhm9b61GNm53bDuA9Uvu/ljho1fFE/11uo2bnvJX3tqE0xpC1u+gkYPwLM7oMFAw8YnhDC6mMQ0Knk44epo1E6zQgghRKlIgm4lGlbzZP7TbXF2sOWB6Vv558Qlc4dUds2GQ/0B8PdHcKYUCVMxbY25fOcn5GbCbw/A1dPw4O8QEFbynWg0ENoNnv4X+nwNl47B91GwYARcPaOmtM9/AtwqQb9vZc2yuXjXAlf/kiXomVdgyXPwU181M+KRJdB/kpppIYSwOjGJ6QQHyOi5EEIIyyQJuhWp5efKgqfbEuTjwmMzd7Ji/wVzh1Q2Gg30nQBeQaofeEaSUXaz7eQdEnSdFhY+qRK2AdOhRtuy7czWDlo8Ds/vUe3ZopfBdy1gZk91AWDgD+DsXbZ9iNLTaFQ/9OIk6Ho9HFoEE1vBf79CuxfVBZja9xg7SiGEkej1ek5KizUhhBAWTBJ0KxPo4cQf/2tD4yBPnvttD79sO23ukMrGyUP1R0+9CEufv/3abWPQ6+Gvt1QS3f1jwxb7cvKAzu+qQnINBsDFg3DPW1CjjeH2IUonqLVqlXenAoXJ5+C3B2Heo+BRBUauh64fgIOLycIUQhheYlo2qVl51PaTEXQhhBCWSRJ0K+TpbM8vT7Smc1gA7y4+yNdrjqE3ZWJraFWbqeTnyHLY8b3BN/9S19CiH9g6EbZPhYhnoc2zBt8vAJ7V4L6p8PpJiHzNOPsQJVM9Qt0WNYqu06n34KTWcHKDaoM3Yp30OReinDh5rUCctFgTQghhqSRBt1JO9rZMHdqcwc2rMWHdcd5bcgitzoqT9IhnILQHrH5btToztgPzYfU7UK+/aXqRy3ply1G5Mdg63pqgJxyBmT1g5atQrQU8sxXaPq+WLQghyoWCFmsyxV0IIYSFkgTditnZ2jB+UCOeigzml22neeG3vWTnac0dVuloNHDvZHDxg/mPqaJqxnJqMyx+Gqq3hfumgY38GlQodo5Qpen1woR52bD+E5jaXhX3u28aDFsEPrXMG6cQwuBiEtJxtrelsoeTuUMRQgghiiSZiZXTaDS82TOMd3qHs+LABR6ftZO07Dxzh1U6rr4waAZciYXlLxtnPXpCNPz+sKrm/cAcsJeTtAopqBVc+A+X+F0wtQNs/FTVIHhuFzR+QKrsC1FOnbyURm1/V2xs5HdcCCGEZZIEvZwY0aE2Xw1pzPaTSTw4fRubjiWSmpVr7rBKrkZbuGc0HJgL/80x7LZTzqte5/bOMHS+TDuvyKpHgDaHGhueU232Hp4PA78HVz9zRyaEMKKYxDSZ3i6EEMKiSYJejgxoVo3vH2lBTGIaj/y4g8YfrKb3t5t5f+khlu8/z8XkLHOHWDwdXoFaHWHla5B4tMyb+3rNMchKhjmD1e3D88CrugECLX/Gjx9vkO28+eabJCYmsnDhQk6cOFHosSeeeKLQ99u3b2f69OkG2W+x1WgL/mFcDn1ArTUP6Wra/QshTC4rV8vZK5kE+0sFdyGEEJZLqh+VM53CAtjxdhf2nrnCztgr7IpN4o+dccz6NxaAat7OtKzpQ4ua3rSs6UMdfzfLm+pnYwsDvocp7eD7zuBfV60H9q6ppqbnf+1WqXjrx3Va+GMYJB5RyXnlRsb+CazW66+/btDtDRgwwKDbMxhnb3h2OwnR0fg6ymiaEBVB7OV09HopECeEEMKySYJeDrk52tEhxJ8OIf4A5Gp1HD6fwq7TKmHffPwSi/aeA1TLtuY1vAsS9oZVPXGytzVn+Ip7JRi6AHbPhKRTELcDDi4Ave76c+ycriXtNQsn7t61wLuGKgam1xNx4WeI3Qj9p0JwlJl+IMuwcOFCNmzYwJUrV2jWrBkJCQkcOnSI8ePHExYWxhNPPMGMGTMYMGAAQUFBnD17lqlTp+Lvr95LZ8+e5YcffuD999/n5MmT/Pbbbzz66KO88cYbZGVlMXjwYO6///6C/X333Xd06NABb29vXn31VSpXrkxycnKRseXk5PDKK69w+fJl2rRpw/PPP89nn33Gvn37cHBwYNasWbd8L4QQxRWTcK3FmoygCyGEsGCSoFcA9rY2NA7yonGQF0+0r4Ver+f05Qx2xiaxK/YKu04n8feRBAAcbG1oVM2TlrV86N+kKnUruZsv8CpNoMqE699rc+HqGVVE7soplbhfiVX/Tm2G3PQbXqwBjyrg7EOb+AMQ9Q40edCk4VuqOnXq8MILL9CrVy+mTZtGUlISy5cvJywsrOA5V69eZe7cufz555+sW7eOBx54AIBq1apx/vx5ANatW0eXLl3w9/fn559/BtT09RsT9HwzZsxg3LhxBAUF0b179yLjWrNmDS1atGD48OG88MILJCQkcPjwYebMuV6L4ObvhRCiuE7mt1jzkxF0IYQQlsvqE/Sv1xwD4KWuobd9LKK2L22CfQs9tjXmMttOXq6wr63p50pNP1cGtwgC4OMVhzl/bY36uauZfL/pJFM2xNCsuhetavpga6PBztbGvD+vrT34Bqt/+a/1hYiWvrSp7QPpiSpZTzrF1hMJbDufC6lXeSmyF3R41TwxG/C1s/9Lwu/ssVK99v/t3Xl8VPW9//HXzCQz2WeyThKyQFg0JCxh0ahFriDCBREU7W3V6s/l2vqwUuUnWrRUpEqlVlHaR+8Pa39t/V2X1lpANhGxFa+CLIIxEAWDkIXsJCGTkG1yfn9MmIKAQpgwM+H9fDzmwSxnznzm+zjMN+9zvud7jjdwoKf9UlJSSE9Px2Qy0dR04mXtBgwYQEhICImJid5AfkxOTg6FhYV88skn3HHHHdTU1LBgwQKam5vZt2/fKT+zoqKCIUM8dWVmZp5ymfLycvLy8gDIzs7m0KFD3HbbbcydO5f09HRmz5590mPTaWZbP9N2npLW8/cG0rah9/b9916pOS3PWXGNi36OcMKtATBKTERE5HSMILZ9+3afrm/Pnj0+XV8wq21qNV58v9i46tf/MDIfWW3k/vxt47HlBUZhecMZr0Pt6Vu+aM8333zTWL16tWEYhnHnnXcahmEYpaWlxvz580947ti/W7ZsMZYtW3bCOj777DNjwYIFxk9/+lPDMAzjpZdeMtavX2+43W5j6tSphmEYxiOPPGJUV1cbS5cuNXbu3GnMnz/f2Lt3r9HS0mJceeWVJ6zv2GesXr3aePnllw3DMIz777/fqKysNFpbWw3DMLzv//rjc6Vt1LfUnr51fHv6ur8LJOerL5/+mw+MW1/a4tPPuhDo/7VvqT19S+3pW2pP3+tJXx70R9Cld8RH2fjPK7O4e9wAth2o57WtJfx1exn/vaWE4Wl2vjc2g+tGphJl0yZ0ocnNzWX27Nk88sgjAFx66aXMmzePdevWERERccr33HnnncydO5eUlBSSk5NPucykSZOYM2cOa9euJT8/H6fTyV133UVLSwt2u53MzEzuvffeEx6LiJwJwzAornZ5R42JiIgEKpNhGIa/i+ipHTt2MHr0aJ+tr6ioiOzsbJ+tr69pbOlg+c4yXttayhdVTURYLVw3IpXvXZLBiDT7ScONA6U9NxfXnTSkNBgFSnv2JWpT31J7+tbx7enr/i6QnI++vLKxlfxfbuQXM3L4wWX9ffZZFwL9v/YttadvqT19S+3pez3py3X4U86YPSKU/3XFAG6/vD87Sxt4fWsJK3cd4vVtpWSnxPD9S9KZMbIf9vBQf5d6gi37+0ZAFxGRnjk2QdxAXWJNREQCnAK6nDWTycSojFhGZcQy/9qh3SG9hJ+v3M2itUVMG5bK9y9JJyJ4B2eIiEgfUnxsBncFdBERCXAK6HJOosNCuTU/k1vzM/msrJHXtpXw1q5DvPlJGRn2UO6oD+eGUWl+Par+bbOai4hI31Zc00yk1YIzxubvUkRERL6R2d8FSN8xLM3OouuH8fGjE1k8axhhIWaeWLWH/EUbeeRvBRSUNfi7RBERuQAV17jISow67aUZRUREAoWOoIvPRdpC+I+xGQyPaqYzOpVXPj7Iyl2H+Mv2Uob1s3NrfgbTR6QSYdXmJyIivW9/TTNj+8f6uwwREZFvpSPo0quGpdl5etZwPn5sIk9cl0Nbp5tH3vyMSxdtZMFbu9lX1eTvEkVEpA872u6mvOGoJogTEZGgoEOYcl7EhIVy++X9ue2yTLYdqOeVjw/y6scl/OmjA1wyII5bLs1gSm4ythCLv0sVEZE+ZH+tJogTEZHgoSPocl6ZTCYuGRDHC9/LY/O8Cfz03y+msrGVn7y+i8t/+R5Pr/uc0sMtPv3MJRv2+nR9IiISPPbXNAMwMCnSz5WIiIh8Ox1BF7+Jj7Lxo/EDuWdcFh98Wct/bznIi5uKWbapmCsHJ3JrfiZXXZRIiEX7kUREpGeKa1yYTNA/XgFdREQCX68G9E2bNvHUU0/R1dXFTTfdxD333HPC6+3t7Tz88MPs3r0bh8PBkiVLSEtLo6ysjKlTpzJgwAAARowYwcKFC3uzVPEjs9nE+CGJjB+SSEXjUV7bWsrrW0v4z5e3Yw8PZXianRFpDoZ1/5tsDzur9ednxfdS5SIiEuj21zSTFhtOWKhOoRIRkcDXawHd7XazcOFC/vjHP+J0OrnxxhuZMGECgwYN8i7zxhtvEBMTw4YNG1izZg2//vWvef755wHIyMhg5cqVvVWeBKgUezhzJg3h/gmD2FhUxT8+r6GgvJH/er8Yd5cBQFK0jeFpdoYfF9rjIq2nXedlAxXQRUQuVMU1Lk0QJyIiQaPXAnpBQQGZmZmkp6cDMG3aNDZu3HhCQH/vvff48Y9/DMDkyZNZuHAhhmH0VkkSREItZqbkpjAlNwXwzMK7p6KRgrJjtwY2fl7Nsc0lLTbcG9qHp9nJ7WcnJizUj99ARCT4fdtIuPLych599FEOHz6Mw+HgmWeeITk5maKiIhYsWIDL5cJsNnPvvfcyderU815/V5fB/ppmLh2gHbUiIhIcei2gV1VVkZyc7H3sdDopKCg4aZmUFE8ACwkJITo6mvr6egDKysqYOXMmUVFRPPDAA4wZM6a3SpUgEG61MDozjtGZcd7nmlo7KCw/QkFZgye0lzew9rNK7+tZiZEM7+cJ7SPSHeSkxmiIo4jIGTqTkXCLFy9m5syZXH/99WzevJlnn32WZ555hrCwMBYvXkz//v2pqqpi1qxZfOc73yEmJua8fofKI60c7XBrgjgREQkaATlJXFJSEv/4xz+IjY2lsLCQ++67jzVr1hAVdfIQtaKiIp99bmtrq0/Xd6E7H+3pAK5MgiuTwmFUOI2tbr6sa2NvbRv76tr44IsqVuw6BIDFBJmxVobE27gowcaQBBuZDisWs6lXa/QVbZ++pzb1LbWnb/m7Pc9kJFxxcTHz5s0DID8/n/vuuw/AO4cMeHbQx8XFcfjw4fMe0Itrui+xlqAh7iIiEhx6LaA7nU4qK/91NLOqqgqn03nSMhUVFSQnJ9PZ2UlTUxOxsbGYTCasVs85xbm5uWRkZPDVV18xbNiwkz4nOzvbZzUXFRX5dH0XOn+1Z/7XHq/9rIIQs4lPu4+0f1TawNv7mgAICzWTk+o5j31Euudoe//4CEymwAvt2j59T23qW2pP3zq+PXfs2HHeP/9MRsJdfPHFvPPOO9x+++1s2LCB5uZm6uvriY2N9S5TUFBAR0cHGRkZp/2s3trZ/lFRIwBdjYcoKqr22WdcSPy9o6ivUXv6ltrTt9SevteTNu21gD5s2DAOHDhAaWkpTqeTNWvW8Oyzz56wzIQJE1i+fDl5eXmsX7+e/Px8TCYThw8fxm63Y7FYKC0t5cCBA949+CJn64vKJh6cNIRrcjx/aBqGwYG6FgrKGvi0tJFPyxp4detB/u+HXQDemeOPzR4/It2BM+bsZo4XEbkQPPzww/ziF79g+fLljBkzBqfTicXyr1OJqqurmTt3LosXL8ZsPv0lM3trZ/trewuJDmvk8rzcgNzxGgy048231J6+pfb0LbWn7/VkZ3uvBfSQkBB+/vOfc/fdd+N2u5k1axaDBw/mhRdeIDc3l4kTJ3LjjTcyd+5cJk2ahN1uZ8mSJQBs27aNpUuXEhISgtls5oknnsDhcPRWqXKBMZlMDEiIZEBCJDNG9gOg093F3iqXJ7R3B/f/8/5+78zxWYmRTMp2Mmmok7yM2KAZFi8i0lNnOhLut7/9LQDNzc2888473mHsLpeLH/7whzz44IOMHDnyvNV9vOIaF1mJUQrnIiISNHr1HPTx48czfvz4E577yU9+4r1vs9lYunTpSe+bPHkykydP7s3S5ALy4KQh37pMiMXM0NQYhqbG8L1LPMMwWzvc7D50hJ0l9by/t4Y//M9XLNu0n/hIKxMuTmLSUCfjBicSbtXEcyLS95zJSLhjs7ebzWZefPFFZs2aBUB7ezv33XcfM2bMYMqUKf4oH/BcA12X2hQRkWASkJPEiQSCsFALozNjGZ0Zy93jsjjS2sH7X9SwYU8Vb++u5I0dZdhCzIwbnMDV2U4mZjtJjLb5u2wREZ84k5FwW7du5bnnnsNkMjFmzBgef/xxANatW8f27dtpaGhg+fLlADz99NPndehkc1snFY2tuga6iIgEFQV0kTMUExbK9BGpTB+RSoe7i61fHWbDnio27Kni3aJqTKbPGJnuYNJQJ5OynQxK0rBKEQlu3zYSbsqUKac8Qj5jxgxmzJjR6/V9k69qmwEYmKhLrImISPBQQBfpgVCLmSsGJXDFoAQenz6Uooom3i3yhPVfvf0Fv3r7C/rHRzBpqJOrs52MzowlxHL6CZJERMS3jl1iTUfQRUQkmCigi5wjk8nkPX999sTBVDQe5d2iajbsqeJPHx3g9x98RWxEKGP6x5GX4SAvPZbhaXYibfrvJyLSW4qrXZhNkBEf4e9SREREzpgSgvR5SzbsPaOJ4nwlxR7OD/Iz+UF+Jk2tHWzaW8t7n1fzSUk9G/ZUAWA2wUXJMd2B3UFeRixZCZGYNTu8iIhPFNc2kxEXgS1EE3mKiEjwUEAX6UXRYaFMG57CtOEpANQ3t7OrtIGdJfXsLG1g1a5DvPpxCQAxYSGMzIjtDuwORqY7cERY/Vm+iEjQKq72XGJNREQkmCigS5+XnxU4l9iJjbRy1cVJXHVxEgBdXQb7a118UtLAzhJPcP/Ne/vovvw6WYmR5KXHkpfhIKazjURXG3ERVh1pFxH5Bl1dBl/VNjNucIK/SxERETkrCujS5wXyNXDNZhODkqIZlBTNd8ekA+Bq66Sg7Fhgb+CfX1Tz5idlnjesLsdiNhEfaSUhykZitOd2/P3EKBuJ0VYSo8KICQ/RTPIicsEpbzhKW2eXJogTEZGgo4AuEmCibCFcPjCBywd6jvwYhkFZ/VHWfbwba0wCta52apraqHG1UetqY29VE7WuNjrcxknrslrMJERZveG9nyOcS7PiuSwrnthIDZ8Xkb7p2AzuGuIuIiLBRgFdJMCZTCbS4yIY1z+K7OwBp1zGMAwaj3Z4gnt3ePeG+KZ2alxtlDe0srm4jj9vPojJBENTYrhiUAKXDYznkv5xmlVeRPqM/TW6BrqIiAQn/UUu0geYTCYcEVYcEVYGO6NPu1yHu4uCskY++rKWD4tr+dOHB3hx035CLSZGpju4fKDn2u4j0x1YQ3TddhEJTsU1LuzhocRppJCIiAQZBXTp8zYX1wX0eejnU6jFzOjMWEZnxnL/xMEcbXez/eBhPvyyjo+Ka1n63j5e2LiPCKuFsf3juGJQPJcPTGBoSowmphORoLG/ppmBiZGag0NERIKOArr0eVv2K6CfTrjVwrjBiYwbnAhAY0sHm/d7wvqHX9ayaG0NAI6IUC7LiufyQQlcnBxNR2cXbe4u2juPu7lPvN/W4T7tMvbwUAYlRXlvqfZw7QAQEZ8prnExfkiiv8sQERE5awroIuJljwhlSm4yU3KTAahsbO0O657Qvq6w8qzWZ7WYsYZ03467f7i5nde3lXqXCw+1MDApkkGJUQx2RjMw0RPcM+MjCLVoqL2InLnm9i6qm9o0QZyIiAQlBXTp8x6cNMTfJQStZHsYN4xK44ZRaRiG57rCpfVHsR0Xur33vxbCrRbzNw4vrXO18WW1iy9rXJ5/q11s/eowK3Yd8i4TajGRGX8suHtC+8BEzy3cajkfTSAiQabsSDugCeJERCQ4KaCLyBkxmUxkJUb57KhUfJSN+Cgbl2adePqBq62T4u7A/mWNi31VLr6oauKdPZV0GcdqgbgIK/FRnuvBx0fZSOi+nxBlJT7S5n0tIcqmMC9yASlr7AB0iTUREQlOCugiElCibCGMSHcwIt1xwvNtnW4O1LZ4j7ZXHmmlztVGXXM7BWUN1LnacbV1nnKdkVaLN8QfH+a7mhsp7arEGRNGsj2MhCgbFp0LLxLUyho7CDGbyIyP8HcpIiIiZ00BXUSCgi3EwkXJ0VyUfPrLyB1td1PX3Eatq90T3l2ea8DXudqpdbVR19xGSV0LO0vqOdzc7jki/3Gd9/1mEyRG23DGhHXfbCTHhJEUE0Zy93PJMWHEhIdodmiRAFV6pIMMzV8hIiJBSgFdRPqMcKuFNGsEabHffuTM3WWweWchMc50qo60UXmkleojrVQ2tlLV1Ebp4Ra2HThMQ0vHSe8NCzV7Anx0GCkOz9H3VHs4KfYwUuzhpDjCiI+0KsSfRw0t7fxlWyn/82Ut+VnxXDcilfQ4HUG9EJU1tjM4Jc7fZYiIiPSIArr0eUs27NVEcXISi9lEfEQI2WmOb1yutcNN9ZE2qppaqeoO8NVNbVQdaaWisZWdJQ1UNrbS7u464X1Wi5lkexgp9jBSHeHdId4T4JO7n4uNCD0pxBuG4b0cXVv3zXPf7X3u2OO2Ds+l646dFpAQZfN1MwW8zyuP8OePDrB8ZzmtHV30j4/gg321PLP+C/IyHMwYkcq04akkRl94bXMhcncZlB/pYMoITRAnIiLBSQFdROQbhIVayIiPIOMbzmft6jI43NJORUMrhxqPUtFwlIojrVQ0tFLReJRtBw5TdaSVDrdxwvtsIWbiIq10uE8M4z2VERfBqAwHeRmxjMqI5eKU6F4Z5msYBiWHWygsP0LhoUYKyxv5vLKJhDC4vsbK5JxkMuN7LyC5uwzeLariTx8eYPP+OmwhZq7P68ftl/cnOyWGsvoWVn1awVufHmLBqj0sXL2HKwYlMH1EKlNyk4kJC+212sS/yupb6OyCgQmaIE5ERIKTArr0eflfmyVcxNfMZpN3xvhhafZTLtPVZVDraqOi0RPaDzW0UnmklfrmdkJD/nW5OluIBdsJj//1vNVixhbaff+41+pc7ewqreeTgw18VFznvVSdLcTM8DQ7ozJiyctwMCojlqSYsLP6bl1dBl/VNVNY3th984TyplbPhHyhFhNDnNGMG5zApwdqWLT2cxat/ZzslBgm5ziZkpvMRc5onwz3PzaM/eXNBylvOEo/Rzg//feL+Y8x6cRGWr3LpcVGcO+/DeTefxvIvqom3vr0ECt3HeLhvxXwsxWFXHVRIteN6MfE7CTCQjXDf1+yv6YZgIFJOoIuIiLBSQFd+rzLBiqgi/+ZzSaSuiec+/oM9edqYCJcMsBzzq1hGBxqbGVniSew7yyt548fHmDZJs+R+X6OcEZ2h/W8DAc5qTHYQjwhtdPdRXGNJ4x/Vt7I7kON7Dl0hOZ2NwDWEDPZydFcNyKV3H52clPtDEmO8r6/qKiIKGcm63dXsn53JS9s3Mfz7+6jf3wEk3OTmZyTzMg0B+aznCn/68PYLx0Qx/xrs7k620nIt4wQGOyM5n9fcxFzJg3h07JGVu4qZ3VBBet3VxFlC+GaoU6uG5nKFYMSznm0QVeXQePRDupb2kmKCSPKpi72fCuucQGQpSPoIiISpPTXg4hIH2IymejnCKefI5xrh6cCnkvU7T50hJ0lDXxSUs+ukgbWFFQAnnPlc/rFYBhQVHGEtu4h9uGhFoamxnDj6DRy+tkZ1s/OoKSobw2x6XER3D0ui7vHZVHd1MqGPVW8XVjJHz74imXv78cZY2NyTjJTcpK5ZEDcaQO2u8tgw54q/vzRqYex96RdRqY7GJnu4GfThrJlfx1v7TrE2sIK/r6znLhIK9OGpTBjZCqjMmLpMgwajnbQ0NLO4WZP6K5vbqe+5RT3ux83Hu3wXBkAuGJQPK/cnX/Wdcq5Ka5xEWMznzCiQkREJJgooIuI9HG2EAujus9Lv4sBAFQd6T7KXtLAzpJ6TCYTt+ZnktsvhtxUO1mJUed8Tfik6DBuuTSTWy7NpLGlg/e+8IT1v273DFN3RIQyKdvJ5JxkvjM4gbBQy0nD2FPtYTwy5WK+NzbdZ6HLYjZxxaAErhiUwMKZObz/RQ0rPz3EGztK+X9bDhIWaqa14/RzAVhDzMRFWImNtBIbEUp2cgyxkaHERlg9t8hQxmRqFnF/KK5pJt2ucC4iIsFLAV1E5ALkjAljSm4KU3JTzsvn2SNCuT4vjevz0mhp72TT3hreLqzk7d2VvLGjjEirhVGZsWw7cPish7GfC1uIhWtykrkmJxlXWycb9lRSUNaIPbw7cHeH8OPvh4dadAm9ALW/xsXoFM3YLyIiwUsBXfq8zcV1Og9dJIBEWEO8OwfaO7vYvL+Otwsr2bK/jhkjPMPYh6ae/TD2cxVlC/HuRJDgNDLdweWpvbdDR0REpLcpoEuft2W/ArpIoLKGmBk/JJHxQxL9XYr0AS/dPpaioiJ/lyEiItJj2s0sIiIiIiIiEgAU0KXPe3DSEH+XICIiIiIi8q0U0EVEREREREQCgAK6iIiIiIiISABQQBcREREREREJAAroIiIiIiIiIgFAAV36vCUb9vq7BBERERERkW+lgC4iIiIiIiISAEyGYRj+LqKnduzY4e8SREREzovRo0f7u4Reob5cREQuFGfSlwd1QBcRERERERHpKzTEXURERERERCQAKKCLiIiIiIiIBAAFdBEREREREZEAEOLvAgLFpk2beOqpp+jq6uKmm27innvu8XdJQW3ChAlERkZiNpuxWCz8/e9/93dJQWXevHn885//JD4+ntWrVwPQ0NDAgw8+SHl5Of369eP555/Hbrf7udLgcKr2/M1vfsNf//pX4uLiAJgzZw7jx4/3Z5lBo6Kigocffpi6ujpMJhPf/e53uf3227WN9tDp2lPb6NlRP+576svPjfpy31Jf7lvqy33Lp325IUZnZ6cxceJEo6SkxGhrazOmT59u7Nu3z99lBbWrrrrKqKur83cZQWvr1q1GYWGhMW3aNO9zixcvNpYtW2YYhmEsW7bM+NWvfuWv8oLOqdpz6dKlxksvveTHqoJXVVWVUVhYaBiGYTQ1NRnXXHONsW/fPm2jPXS69tQ2eubUj/cO9eXnRn25b6kv9y315b7ly75cQ9yBgoICMjMzSU9Px2q1Mm3aNDZu3OjvsuQCNnbs2JP2Vm7cuJGZM2cCMHPmTN59910/VBacTtWe0nNJSUnk5OQAEBUVRVZWFlVVVdpGe+h07SlnTv24BCL15b6lvty31Jf7li/7cgV0oKqqiuTkZO9jp9OpP4584K677uKGG27gL3/5i79L6RPq6upISkoCIDExkbq6Oj9XFPxeeeUVpk+fzrx582hsbPR3OUGprKyMoqIiRowYoW3UB45vT9A2eqbUj/ce9eW+pd9J39Pv5LlTX+5b59qXK6BLr3jttddYvnw5v//973nllVfYtm2bv0vqU0wmEyaTyd9lBLXvf//7bNiwgZUrV5KUlMTTTz/t75KCTnNzM7Nnz+bRRx8lKirqhNe0jZ69r7entlHxN/XlvUu/k+dOv5PnTn25b/miL1dAx7OnvbKy0vu4qqoKp9Ppx4qC37H2i4+PZ9KkSRQUFPi5ouAXHx9PdXU1ANXV1d7JJqRnEhISsFgsmM1mbrrpJj777DN/lxRUOjo6mD17NtOnT+eaa64BtI2ei1O1p7bRM6d+vHeoL/c9/U76ln4nz436ct/yVV+ugA4MGzaMAwcOUFpaSnt7O2vWrGHChAn+LitotbS04HK5vPc//PBDBg8e7Oeqgt+ECRNYsWIFACtWrGDixIn+LSjIHet8AN59911to2fBMAwee+wxsrKyuOOOO7zPaxvtmdO1p7bRM6d+3PfUl/cO/U76ln4ne059uW/5si83GYZh9EqVQeb9999n0aJFuN1uZs2axb333uvvkoJWaWkp9913HwBut5trr71W7XmW5syZw9atW6mvryc+Pp7777+fq6++mgceeICKigpSU1N5/vnncTgc/i41KJyqPbdu3crnn38OQL9+/Vi4cKH3nCv5Ztu3b+eWW25hyJAhmM2e/bxz5sxh+PDh2kZ74HTtuXr1am2jZ0H9uG+pLz936st9S325b6kv9y1f9uUK6CIiIiIiIiIBQEPcRURERERERAKAArqIiIiIiIhIAFBAFxEREREREQkACugiIiIiIiIiAUABXURERERERCQAhPi7ABE5f7KzsxkyZIj38bRp07jnnnt8su6ysjJ+9KMfsXr1ap+sT0RERE6mvlykb1NAF7mAhIWFsXLlSn+XISIiIj2kvlykb1NAFxEmTJjAlClT+OCDD7DZbDz77LNkZmZSVlbGo48+Sn19PXFxcfzyl78kNTWV2tpaHn/8cUpLSwFYsGABSUlJuN1ufvazn7Fz506cTie/+93vCAsL4+WXX+b111/HYrEwaNAglixZ4udvLCIi0reoLxfpG3QOusgFpLW1lRkzZnhva9eu9b4WHR3NqlWruPXWW1m0aBEATz75JNdffz2rVq1i+vTpPPnkk97nx44dy1tvvcXy5csZPHgwAAcPHuSWW25hzZo1REdHs379egBefPFFVqxYwapVq3jiiSfO87cWERHpO9SXi/RtCugiF5Bjw+KO3aZOnep97dprrwU857Lt2rULgJ07d3qfnzFjBjt27ABgy5Yt3HzzzQBYLBaio6MBSEtLIzs7G4CcnBzKy8sBuOiii3jooYdYuXIlFoul97+oiIhIH6W+XKRvU0AXEZ+xWq3e+xaLBbfbDXj2ut98883s2bOHG2+8kc7OTn+VKCIiIt9AfbmIfymgiwgA69atA2Dt2rXk5eUBkJeXx5o1awBYtWoVY8aMAeCyyy7j1VdfBcDtdtPU1HTa9XZ1dVFRUUF+fj4PPfQQTU1NtLS09OZXERERuSCpLxcJfpokTuQCcuy8tWPGjRvHQw89BEBjYyPTp0/HarXy3HPPATB//nzmzZvHH/7wB+/EMgCPPfYY8+fP580338RsNrNgwQISExNP+Zlut5u5c+ficrkwDIPbbruNmJiYXv6mIiIifZP6cpG+zWQYhuHvIkTEvyZMmMDf/vY34uLi/F2KiIiI9ID6cpG+QUPcRURERERERAKAjqCLiIiIiIiIBAAdQRcREREREREJAAroIiIiIiIiIgFAAV1EREREREQkACigi4iIiIiIiAQABXQRERERERGRAPD/AVQanuI4zNISAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "### ResNet-18"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "6d746ef5587f46a9974db3b23f7bcdc3",
            "9eff7a6d11324bb6a663261d5a610bac",
            "4900022210344626a9a2bdbede43d81d",
            "e489eb2f853e4bdba96be6d874fe7384",
            "f34b063cf3694e9b8da9052d3b211d8b",
            "161d99ac073e44a99770a26b611ae48d",
            "83b1f8a5efbb416c8a3185b9a1e010b7",
            "20b7af98e43c40f79fda7c870fce01d8"
          ]
        },
        "id": "EOS__UpqUq2f",
        "outputId": "d8aa6756-0ef7-4a74-9b09-e7acc33fcd8d"
      },
      "source": [
        "resnet18 = networks.get_resnet18(pretrained=True, out_features=4)"
      ],
      "execution_count": 8,
      "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": "6d746ef5587f46a9974db3b23f7bcdc3",
              "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoH23wktILXG",
        "outputId": "20a553f0-eafa-4347-8e17-4e019c4cab45"
      },
      "source": [
        "utils.fit(\n",
        "    epochs=epochs,\n",
        "    model=resnet18,\n",
        "    criterion=criterion,\n",
        "    optimizer=optim.Adam(resnet18.parameters(), lr=3e-5),\n",
        "    train_dl=train_dl,\n",
        "    valid_dl=valid_dl\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 1/25]: 100%|██████████| 647/647 [01:33<00:00,  6.92it/s, acc=0.908, loss=0.37]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.247413\tAvg valid loss: 0.198641\n",
            "\n",
            "\t\tvalid_loss decreased (inf -> 0.198641)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 2/25]: 100%|██████████| 647/647 [01:33<00:00,  6.90it/s, acc=0.959, loss=0.011]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.119524\tAvg valid loss: 0.112521\n",
            "\n",
            "\t\tvalid_loss decreased (0.198641 -> 0.112521)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 3/25]: 100%|██████████| 647/647 [01:33<00:00,  6.91it/s, acc=0.971, loss=0.0196]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.088386\tAvg valid loss: 0.140252\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 4/25]: 100%|██████████| 647/647 [01:33<00:00,  6.92it/s, acc=0.978, loss=0.0869]\n"
          ],
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            "\t\tAvg train loss: 0.046880\tAvg valid loss: 0.140529\n",
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            "\t\tsaving model...\n",
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            "\t\tAvg train loss: 0.013085\tAvg valid loss: 0.100938\n",
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            "\t\tAvg train loss: 0.007794\tAvg valid loss: 0.106966\n",
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            "\t\tAvg train loss: 0.011099\tAvg valid loss: 0.075002\n",
            "\n",
            "\t\tvalid_loss decreased (0.077828 -> 0.075002)\n",
            "\t\tsaving model...\n",
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            "\t\tAvg train loss: 0.011808\tAvg valid loss: 0.164304\n",
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          "output_type": "stream",
          "text": [
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            "\t\tAvg train loss: 0.013112\tAvg valid loss: 0.088991\n",
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.006676\tAvg valid loss: 0.102371\n",
            "\n"
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          "text": [
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            "\t\tAvg train loss: 0.008749\tAvg valid loss: 0.141883\n",
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          "text": [
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            "\t\tAvg train loss: 0.007406\tAvg valid loss: 0.178468\n",
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        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.007592\tAvg valid loss: 0.120552\n",
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    {
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      "metadata": {
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        "outputId": "7e3997b2-391a-4abd-feaf-248124b48fc3"
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      "source": [
        "plot_summary('outputs/CSVs/resnet.csv')"
      ],
      "execution_count": 10,
      "outputs": [
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "source": [
        "### DenseNet-121"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "d35a815585474b51b6536c4ffa0f29be",
            "2b7b95a2e2094af08d3ee8183c9012ae",
            "8fb00930ba2c4fa6ba0278bf1d1d70bb",
            "5b3dbe2bf6f54aef80669851a423b322",
            "b53dda61e0b14b1f91caf9bba852ae1c",
            "37b41d2c3b55473aa34856f190128d2f",
            "8e38aeac91a34e75b1575b2e0c57fa3d",
            "d993019389974ed4b886be207e17e65d"
          ]
        },
        "id": "eD0K2-rZUq2g",
        "outputId": "117cf89b-f277-483a-eed9-46a09587e591"
      },
      "source": [
        "densenet121 = networks.get_densenet121(pretrained=True, out_features=4)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.cache/torch/hub/checkpoints/densenet121-a639ec97.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d35a815585474b51b6536c4ffa0f29be",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=32342954.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lW-RcxAGUq2h",
        "outputId": "5cd82883-d747-4a97-ea54-471182bc2c19"
      },
      "source": [
        "utils.fit(\n",
        "    epochs=epochs,\n",
        "    model=densenet121,\n",
        "    criterion=criterion,\n",
        "    optimizer=optim.Adam(densenet121.parameters(), lr=3e-5),\n",
        "    train_dl=train_dl,\n",
        "    valid_dl=valid_dl\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 1/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.901, loss=0.26]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.288723\tAvg valid loss: 0.187355\n",
            "\n",
            "\t\tvalid_loss decreased (inf -> 0.187355)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 2/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.959, loss=0.112]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.122695\tAvg valid loss: 0.144584\n",
            "\n",
            "\t\tvalid_loss decreased (0.187355 -> 0.144584)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 3/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.97, loss=0.227]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.090146\tAvg valid loss: 0.125367\n",
            "\n",
            "\t\tvalid_loss decreased (0.144584 -> 0.125367)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 4/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.976, loss=0.163]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.072722\tAvg valid loss: 0.126661\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 5/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.98, loss=0.00282]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.064221\tAvg valid loss: 0.117544\n",
            "\n",
            "\t\tvalid_loss decreased (0.125367 -> 0.117544)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 6/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.986, loss=0.00575]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.042977\tAvg valid loss: 0.138251\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 7/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.986, loss=0.224]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.041941\tAvg valid loss: 0.132365\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 8/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.989, loss=0.0084]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.034838\tAvg valid loss: 0.148224\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [ 9/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.992, loss=0.0292]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.028194\tAvg valid loss: 0.160335\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [10/25]: 100%|██████████| 647/647 [04:01<00:00,  2.67it/s, acc=0.992, loss=0.0257]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.025700\tAvg valid loss: 0.109081\n",
            "\n",
            "\t\tvalid_loss decreased (0.117544 -> 0.109081)\n",
            "\t\tsaving model...\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [11/25]: 100%|██████████| 647/647 [04:01<00:00,  2.67it/s, acc=0.993, loss=0.00172]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.022214\tAvg valid loss: 0.120966\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch [12/25]: 100%|██████████| 647/647 [04:01<00:00,  2.68it/s, acc=0.995, loss=0.0219]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t\tAvg train loss: 0.017896\tAvg valid loss: 0.148889\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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            "\n",
            "\t\tAvg train loss: 0.009338\tAvg valid loss: 0.137126\n",
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    {
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      "metadata": {
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        "outputId": "a23e25f9-dab9-474c-af2b-5a91609b82ce"
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      "source": [
        "plot_summary('outputs/CSVs/densenet.csv')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}