Facial_Keypoints_Detection_CNN.ipynb

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    "colab": {
      "name": "Facial Keypoints Detection-CNN.ipynb",
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      "name": "python3",
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      "metadata": {
        "id": "DM-qINHNOmDh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Mount Google Grive for Colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gb_rFPKN30yK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # Get the data from drive\n",
        "# ! cp /content/drive/My\\ Drive/Datasets/facial-keypoints-detection.zip /content\n",
        "# ! unzip facial-keypoints-detection.zip\n",
        "# ! rm -rf sample_data\n",
        "# ! rm -rf facial-keypoints-detection.zip\n",
        "# ! unzip training.zip\n",
        "# ! unzip test.zip\n",
        "# ! rm -rf training.zip\n",
        "# ! rm -rf test.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kh7h3_yx3878",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04lHj3-N4AMs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.read_csv('training.csv')\n",
        "test_df = pd.read_csv('test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPT6vObM4CVl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "d9fc6994-7e4d-4310-afd3-d780e1fd6edf"
      },
      "source": [
        "# Print training data head\n",
        "train_df.drop(['Image'], axis = 1).tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      left_eye_center_x  ...  mouth_center_bottom_lip_y\n",
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    {
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        "id": "7o1JgD7C4FHJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73e7b8a9-02ef-4e5c-cb95-c88f01dae9ee"
      },
      "source": [
        "train_df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7049, 31)"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdnLqw144IL6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# constants\n",
        "IMG_SIZE = 96\n",
        "N_CHANNELS = 1 # Because of gray scale images\n",
        "N_KEYPOINTS = 30\n",
        "m = train_df.shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJfNOMtM4KId",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df['Image']\n",
        "Y_train = train_df.drop(['Image'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLN7E03m4M47",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ee54de5b-a982-4c81-8183-813658452c53"
      },
      "source": [
        "# check if training data contains NaN\n",
        "print (Y_train.isnull().values.any())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OS2cCmI44OmR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This contains NaN, replacing those values with mean of corresponding column\n",
        "if Y_train.isnull().values.any():\n",
        "    Y_train.fillna(Y_train.mean(), inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxl_Zz9t4SI-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "afa0c38e-d605-4893-f9ed-4a74e77993d6"
      },
      "source": [
        "# Check again for NaN\n",
        "print (Y_train.isnull().values.any())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oihc4-L24UOF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "88f118c1-b294-4734-cc8a-e807395c3273"
      },
      "source": [
        "Y_train.tail()"
      ],
      "execution_count": 12,
      "outputs": [
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              "      <td>36.652607</td>\n",
              "      <td>37.989902</td>\n",
              "      <td>22.384504</td>\n",
              "      <td>38.033495</td>\n",
              "      <td>56.068513</td>\n",
              "      <td>29.332678</td>\n",
              "      <td>79.482826</td>\n",
              "      <td>29.734861</td>\n",
              "      <td>39.322137</td>\n",
              "      <td>29.502998</td>\n",
              "      <td>15.871177</td>\n",
              "      <td>30.428166</td>\n",
              "      <td>49.462572</td>\n",
              "      <td>67.515161</td>\n",
              "      <td>63.285735</td>\n",
              "      <td>75.970714</td>\n",
              "      <td>32.900404</td>\n",
              "      <td>76.179768</td>\n",
              "      <td>47.975412</td>\n",
              "      <td>72.919443</td>\n",
              "      <td>49.462572</td>\n",
              "      <td>78.117120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7047</th>\n",
              "      <td>70.965082</td>\n",
              "      <td>39.853666</td>\n",
              "      <td>30.543285</td>\n",
              "      <td>40.772339</td>\n",
              "      <td>59.159339</td>\n",
              "      <td>37.944752</td>\n",
              "      <td>73.330478</td>\n",
              "      <td>37.707008</td>\n",
              "      <td>36.652607</td>\n",
              "      <td>37.989902</td>\n",
              "      <td>22.384504</td>\n",
              "      <td>38.033495</td>\n",
              "      <td>56.068513</td>\n",
              "      <td>29.332678</td>\n",
              "      <td>79.482826</td>\n",
              "      <td>29.734861</td>\n",
              "      <td>39.322137</td>\n",
              "      <td>29.502998</td>\n",
              "      <td>15.871177</td>\n",
              "      <td>30.428166</td>\n",
              "      <td>50.754202</td>\n",
              "      <td>66.724988</td>\n",
              "      <td>63.285735</td>\n",
              "      <td>75.970714</td>\n",
              "      <td>32.900404</td>\n",
              "      <td>76.179768</td>\n",
              "      <td>47.975412</td>\n",
              "      <td>72.919443</td>\n",
              "      <td>50.065186</td>\n",
              "      <td>79.586447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7048</th>\n",
              "      <td>66.938311</td>\n",
              "      <td>43.424510</td>\n",
              "      <td>31.096059</td>\n",
              "      <td>39.528604</td>\n",
              "      <td>59.159339</td>\n",
              "      <td>37.944752</td>\n",
              "      <td>73.330478</td>\n",
              "      <td>37.707008</td>\n",
              "      <td>36.652607</td>\n",
              "      <td>37.989902</td>\n",
              "      <td>22.384504</td>\n",
              "      <td>38.033495</td>\n",
              "      <td>56.068513</td>\n",
              "      <td>29.332678</td>\n",
              "      <td>79.482826</td>\n",
              "      <td>29.734861</td>\n",
              "      <td>39.322137</td>\n",
              "      <td>29.502998</td>\n",
              "      <td>15.871177</td>\n",
              "      <td>30.428166</td>\n",
              "      <td>47.069250</td>\n",
              "      <td>73.033339</td>\n",
              "      <td>63.285735</td>\n",
              "      <td>75.970714</td>\n",
              "      <td>32.900404</td>\n",
              "      <td>76.179768</td>\n",
              "      <td>47.975412</td>\n",
              "      <td>72.919443</td>\n",
              "      <td>45.900480</td>\n",
              "      <td>82.773096</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      left_eye_center_x  ...  mouth_center_bottom_lip_y\n",
              "7044          67.402546  ...                  79.683921\n",
              "7045          66.134400  ...                  77.983023\n",
              "7046          66.690732  ...                  78.117120\n",
              "7047          70.965082  ...                  79.586447\n",
              "7048          66.938311  ...                  82.773096\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhRKu6HI4Ws1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalize the data\n",
        "X_train = X_train.apply(lambda x: np.array(x.split()).astype(int)/255).to_numpy()\n",
        "Y_train = Y_train.apply(lambda x: x / IMG_SIZE).to_numpy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V14cboAM4Zj4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.stack(X_train, axis = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDIiAgrE4cLB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b0ab7e6a-02dc-4978-ebc5-ef47a67e6741"
      },
      "source": [
        "print (X_train.shape)\n",
        "print (Y_train.shape)\n",
        "# Shapes are (m, # of features) and (m, # of keypoints)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7049, 9216)\n",
            "(7049, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdlOpesT8FQU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "66a0abd7-3e6c-4571-b663-98cc1ca01da0"
      },
      "source": [
        "# To feed in CNN, shapes need to be IMG_SIZE * IMG_SIZE\n",
        "X_train = X_train.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
        "print (X_train.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7049, 96, 96, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gfv3kO544eVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split train and validation set data\n",
        "X_train, X_valid, Y_train, Y_valid = train_test_split(X_train, Y_train, test_size=0.20, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8sWUc4r4hFY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "7685dbcf-ae9d-4dc4-866b-8f95425266b9"
      },
      "source": [
        "# Plot a few training images\n",
        "_, ax = plt.subplots(nrows = 2, ncols = 3)\n",
        "count = 0\n",
        "for row in ax:\n",
        "    for col in row:\n",
        "        col.imshow(X_train[count].reshape(IMG_SIZE, IMG_SIZE))\n",
        "        col.scatter((np.take(Y_train[count], range(0, N_KEYPOINTS, 2))) * IMG_SIZE, (np.take(Y_train[count], range(1, N_KEYPOINTS, 2))) * IMG_SIZE, color = 'red', marker = 'x')\n",
        "        count += 1\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q4K5wA9Nz5XYZJKUg6zxjuMkKJijFmbvXmTbgqoAxaZ40+o0i21RoaYyfo/GUFQsjK6vV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\nCq0UPieONVwnzy8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k0u4v6otT+z6IimK18ahk0SHJNZ8ypo8onzBjYvfY0J8pwonn9HhH\nZUXN1BmBXGIq8tMoEorTWvo0LvoF2krz3ejdrOny4zZp6yiVqWyY9XYmNovWqagt3r0nJytkCZ1J\n3qBvC/ZtszDu3iBp+JWiPU+05+Jz+mMtLJhDUaAEdt+AJ78/ELqazXsQTgL22hKdyArbnZA5movM\n+a9lVh/eUbC/9JD9pGP8U9vcDKQkMi9NMLmpiIuK0JlZ1D6bO0f+BccNcxF0pihSnPe0NRWQtWB9\nszzupG+sZZAAdBUxRbulrTytFad71PSsa9HtcMVRD8HKap6ExTKpJu6DNDVc9Av2oaLWgbU9zM1E\nU5Td6ZFn7oqHdsNjd02jhJMOwrxotDBgJiy9NZ6l6WfcHWBpetb27q+xnlXXM65yqS3oecFUhwFy\nmndMceqrk+hyymBQxJzptMGj+MCfcWQOLN2Aj5o8aszCo6qI0hln4xdU8brKs2wGOjdKd60NnFR7\nrsd2LoQ+bW649i1n9Zaf716xsgOfbY/43c9/AYC6NGq99Ec05fbUTPTN5pwX/piI5raoW25Sw4+G\nh0Qkq3A6sI/CetmnmqHoSHRlEfXJsDQ9nR7ozFAcfs2nh+N54T9sakwbWBwfyBmq1cg4Wm42Hfvz\njuzkIl98lmdm1vrD8a5xroRZUkMCu0v0jzvUYUAdrUROuq1R/Ug4blGDR+97kdR9i5YpZaZwt9FE\nrCfmSpTItEjBVpvIuBQBPe0zZkjSzr8TRUY9ROrXPXqIxLM12WmJYp3GLwy7dxSHdzymC2x2Da/O\n11xcLbnZt8SsSh+GXJs7X7F2PWf1lpA154clnfMYnRhKO78om6Y5+9sOFTErKhNpK4/VCe8N42jQ\nOtHVfhbrMiaJtksGpfMcnWdb5AdcFj+jpKBpdxJkhlbNG+TEWiL2WCvai0h7ETF7xf5JxekfH7AH\nhRo0sUts3xNaZyqdpjoIm2b7jmbxiWL/+Mvz0L9eh64UaCN/U/s/gLOkrpJdTlpdmhfUzGr5wtFO\njUHxLlKfKI3T/WykfXaS3iQj+/UdBKtRCtnfUYmGyKSLDHeC+BN90GqJFDSS+vfRCQ1PB07cnrU9\nEJJmiJbWehrjWdhBZHJLul5rz5nd8MReU6nIouiFdHrgmbvmsbuZlReXBVapdRCdbr9gn6q5o3RI\njkZ7mhLtr+zAWbdjfBgZ1oZUWXJtyUXbBWMIBen5qvRcMhmfpyKinMhOZZ7ZK6BQDYGTRxucE2du\ni7jS9eVy/pyjumddyepz2S/QSCSuVeambFSRsuLYHWiN5ya2PKw2nLZ70cDOmiEL//zMCu+80Z5T\nu6PPllfjusAnice1ZANXYcEjdztDLr5QESfu+Uu/ZhMbTuyOmDXfbl6x0v08Fk5FXvfCnnnSCGH4\n7OGGpvFonVgtemmSyqKLTlIor1EJxpW0i/sF9GeO+jZR3yaWn4luivYStcdGU10VQandQbZuHEY2\n339MVrD9hROyVriPzt/quKoEduNn2Y1qkwQGysxNNG6bWb6QXaiqXZLofUxS/Ey5KCZmwsKRWou5\n7VFjQA8RuxPoZfdEMx4ldBdAZfx1g/2owf6oYff5gsvNgqt9y+e7NZ0dedxu+Pyw4oPdg7l4DbIP\n7LoZZr753js2fc2mrxm8nR38WAIzafEXJdW+bLJyGBz9oSJGjXERXRVGnsnkJqFGhT7Inr+yPaV0\niWov3aNTa399nWhuEvszPZ+HbLPICDeG+gLsVvPgW1f4ZWbxMpCMdNTunmmOfyD89Gr7k3WLfb2Q\ny7TpM4iAlC4slsoRF47xyBCaIm5T8Lmp/X+KvuXNzFrnqWy0Om20mxZ31EUVAAMqFIlLLbfzoEVr\nu/DOJyXFykhUmLLALJURp2N04hCcUBgLKXRhB+kojMt516J3F+LAnIpFka+fHXenB4xKrFXPbWpm\nBggEIiLn2hdnPTEzpu8WlotwrDexmSv9cpwi31ufHhiOlqymxbxouyil0G83cPtLLRaQ0ZTjW+iB\nZ+6K97o1f/j5U7w3+MsGvfKcrvdsDjXNcuCsE8z7xWZF4wKNDWwHkUSdtnqbNhGZKJuHKM70/f0Z\njfF8/+GnnNodTkX6bOmzmzMjnw06a4Zk0SrxqLrlJnQYm3jqrnnmruZI/KVffwEemxbaRnmGZLmM\nC5oiE3ATWl70R4zJ0uG58S1aZR4vN9wODQfvOG33fDwcE3cWgkL3mvpSNl1OFVS3wnI4PFDESn7r\n8fuBWGnRZ3kVqa89eozk3R7VSa1JxUT7ssfcHDAHj396LE72o7c4oApypemPDdVO2v390goHnbIX\naC3NQnHatDpm9BhROc8d3WYf8CuRf3avIWstjn2zJ753hl/KNR97i1l4cIlkDK5X1OeGsF1yYzNX\nRwH3zcg7i2s+24lA2rPFDb21M+ff6IRBtpkDvtA3YnRi7x2h0Ba1zhgTRFkxFS58FqVF3tj8IkZz\nt299LCqSSmoHYaGw+5Ltx0nhtbTbpEzoFP2JOC63UWSdqW5Gqm3FuFFc3SzQQZWNPUp0vpukHxTD\nkag6fln72uVzAXIWor4q21VlZ+lPK0IjUbkGCKWBbIZPmDGrCWZRAayXKCErObkTCV8lRSx7hcp2\nX/J+NSrZfXzUkjrZRPYGYyPOxNlxTFrXE059Pi5wJhIKjDLh4te+Y+8dD0pb8tQ9eGT3greWbsU+\nV1zHBb2qeB1W9Nmx0j19rmZoZtpRfsJ9J9unipvCfxZIRpyP1WkWF9Na1PbG44pmCKh9lkjOOcaj\nn2yV/0nNIF2iffYYpP2/UgqfxYGGpHmw3PP51Qq98nTLAaUy1iSeH91wWSiN3lsphBZp2QkLHaJl\njJZX/YraBI7cgUOscPaAVpmFHWfO+CSMYEgszMCPwkOOzIFYeOeNCvP5HZIlOhHumt53ZA64MobT\nDkcrfeAmLoTuGBuMOdDpgSN74KVasXQSPW98jSZz0Xc4nXi6umUz1lgbaU562fzbWMwLzXgMoSmN\nKg3EFhafyO/dPTZyQe8z7esRczuifERpEWELj9bYmx7zZ58w/Oq3qC4PVJ9cktbdWx3XrEWAK+sC\nJUSRtk1OzcXSiYgwLmXPUO0T2kv53Qx3ztT2sRAdFGrS7191XH2vZfdehGWg7kaeHEsvxuerFYfz\nlurC0LwSlVX9ecXHn77DDx89RS89zx9eY3Xkk+uzWRZjalJTiAOf6jOu6C/FpGY8vXYeo++ajWKW\nTS+UyqSkRDY73WHl+Cm4zJAUsRaqYtZq5uHrKBDMuFRkpamvM33hoTfnovVCSjRXkegM/FHLcJY4\nnBiaK1FZXH4e6Y8M7evEzXc0y4/eYlFUKfUu8HeBxzJ0/FbO+b9VSv1XwH8IvC4v/S/Kbihfyibs\nHK1Iy4pYq7kAkIzsj5XMnUzlm3uHwtQim2kuRcQm1tJdF2slglWVko0stBx16IoAfZHLzRpULQI9\nwRtWzUilI2MUJzQmSzKCtW18TYj6CxNmajt3WlzBvPmzDhzZwywANdlCDxgS11F0zhv8HJkPybF5\ngwGTUGVbOpl4KWt8ZWbGxmSHAv0s3UBTeXZLKaRVtUU1NSolcu14/rc+K59711j0Nsc1kvBEDIpa\nWYxS7JPn59wtt0l+17PlDWNhsHx0fsLrfc16tecQ3FxoXnU9u76it3HWnm+M7M3a2ZG6ZEwhGY6d\nLJhHTtQQt7FmnypSVny7eYVRcq5j1uxTxVN3zSULYuEtm7K5RSq1iwlvT0rP+jpHZv/Gb1R0ZsAk\ncVIv/DEfHh7wcr/mpNnTR1u2Gkw8aPeEpDkEx8WuY1GPmEaUMz/ePKR/ULDzEcIyC9PlWs06/G4n\nnZVuF0hGo1orDt1a0rLD7D3qMJDeeYzdjlz+yhH17ZL6YgRwSqnffRvjKlLUGrdP2MNdmjexcdwu\nl929CmNlTBifICSweq5txc7ibkeqlz34MM/LzXdPuPj1yPHTWx4ttzxodtRGlEudiXzuItu6IzYO\ne4D2Y1h/kBmXhv7M8sl7FZdPO2LUHJ1eF4bY3eYiRuUvFEjjFH0zFUelNpazKlvPSROh1lqknaMi\nBY12UjAlTmOkUG8wMGTTeUV0sgjXt2Uj7T6z/nDk4pckG+/OI5ffs/RPOtrPdqjUobKl/+WB8H5L\ne5Fw+4QKeZZamKL1L2tfJkIPwH+Wc/5HSqkV8H+r/4+59w6TrKrz/1/n3FyhqzpNnmEIQ2bIQWBU\nWJXM9IAoQZEgiLrBuMYFBBHXhAEUUEExgzADKGEBUw8gIHFAMkye6Z4OlevGc35/nOqa6f26j+yz\nyPO7PDw01V11b91z77mf8/68gxD3dH53hdb6a691Z9qSpjqHrSEXbmCwc19M8zE3Puh0laDbNoum\n0kLcusZpKpxa0lne2ChXdjG8qc9RniQuWkQlQVwUxGWT3J1pQWqrrmLMkgrXSkk7eYRTXfRa6Hc9\nH3IdylTOipnvTPASM41PS0dM5G2j052CSgZtg61OCSBqysjQi1abjUmv8e+WaVeuvqFdphKb5XvZ\nbVGwYySanIxpKZes0/UNrKRzrIqcm1DpU8QFiZezkVGKFjbkXGbmNv5DxxU6MIuAhA4rAU1OCA71\nt6CQ/E7vhl1WvFztJ667WDlznsJOA6v7OZbC7wT4RolNrKxuDFyiJYNug4IdMZnkUFoSWDGVJCCy\nbJNYJAQt5TGR5vGlwVKraUArc9nRH6WufApWSDU1DJmcNNV10TLr2pZyCZXDgFOnpTzmuePd5uiU\noAvoZJQKarHHnHyV8TRP2sEEUyWpRT610MOWioIbk7dj1lbLyHxC2lEqi0hitzqMro6fNpiGWmvQ\nwvME+Q1tI95xLHQxB7Yk7g9wtUbbEhkm5LakbNnbwR+3YBhez3HNAolbTXEmQrRn4dbSTjj71uIq\n9Y1vi91SiFh1+lzTG3kyTBG1JjrwIM1I5pSo7GTh9dXx3YRev0XejmlnDrXEJ0ptenNtAjfBmqMY\nfX6QqFd2bBE0uc0mUKK2Yw/5xZ1wktTZJhnMuKJOQS9T/4WtdMU4tZDCiN2UkmgNqdj68xQDhimx\nkbXNxDpVuQsD9eoOTVp0/lamHavdWOFVO9V7bJqrjdk2udUpTiPF6jXN17gEXg2cRmqgqZYiLEsK\nG1QXunot29+d0LXWm4BNnZ/rQohngbmveQ/bfta2gzxlles5ZIEJndXC8F2nvIZhG8aK2AqnOHVN\nbjTFaaXY1QgRJWjPMdhdqhBxariuYFYBroMfOMaS17NoD7qEZUlUtogGfNJAM9EyHOhSEGJJRS30\nuskmYWISTXJeTNE2r5klfkq/a+5C2XFZNFxxU/35wmRWvhLP6Pqf+zLhpWhm1+RpQ1hmY7NE3oko\nOBGVOMemeg8TlTyulzKrXGO38ghg2Br1zO9etDkrJhOSwEoYCJpsLJbJPM8o7zwbq5VQ3bWHr8/9\nLRBMSy56PcdVInCEhQNkWtPQCU5nX3Wt2NvdTLPo8duxxUzU8oi2RWlmjTQz6r+0a4S0FSOfqOUp\nFcwkm2qLDa0SWgvGwgJKC+qxR85J6PdNolMlyVGwjQJ0Ks5vfauXQbfe9cRpKY9G5ncapuYmm8jy\njKVFZjum/zFFIZ2iP25Mervw2kQnpWHAqTORFNnU7mFReQuZFoy3c4SJjRRQa/rkg4g5PTVsqbqB\n4fVGgIosZJBiuxmOk9FqeNgbPbQFxXXm2mrNdImLgmA0xhpvoAMXkSrQxvfEHWuZ6zxVpD1mBWRF\nhnECJFrrx16XcU3NPemOhyjfRvmWsR+IFWnOMjF0iWHAWKEyzouZMs14IbDCDoe9nSCSzEzmjlmZ\n17bzaeyc0OvHzC1UCSxDxx1062yxiqRKknRyQpPMwp7doikD8uuMqjzqBW9Sk9soqJTL6PmiC+OV\n/JC8HZv8105F3o4dhNBIqbDZxnERSBKrU6VrA4tlsstwAUxykRbgKUgkZIbxkvmY/l1sIgyt0KAG\n7T4Lp6mx24r6dl7XdTLNWxTXKRpzJUlvQOZIiusi2o8G1BdlFNZLZJThNxLq2+exOjmuYem1s9P+\nVxi6EGIhsC/wEHAY8M9CiDOBv2Cqgsm/8Z7zgfMBvKBsUum7HQaBtixjjN95CumOHL/rx9JxURRK\nE2zpeG5vjLAakVmGNtvm4ZCkBpPX2nhdZOYGwLEhzbCqjW6AsjPqkC/6JGWPsNemPSiJel1aYw4N\ntxMMi6BSjpk9UKVZ87A9A61MTQQt5aKQ9NpNrE5DFWAsKTDgNGgohw1pLy8zg3bmUku9rlHXy7UB\nNtWL5L2YghuzbrxMVPGRQYpKJdaEg1uXWG1YOzvP2HZ59p65kUX5URyRUevAM47IKNltItum7TrM\nHKxS650J60B5FlYroTlbsodr7N7+J2Ou/+u4zp9rkWlNSycUpUuuA+yEOsPB9L5DZSov287Qg218\nJ6USO9QrPcycabTRJS/khXUzqY31ImPBhJ1nzOtDF1OCYkS4OU/pWct0/6saazTjuQU29YWK2buP\nElgJtdSjkuSMX4sWQNEIS7ZZOY0kJWqpTzP12ByVCKwER2y1yw2Vw5p2P4sL6ylabdbHfdNsBKbS\njBJlMRYWKLttZubMQ2JTs4dSoc1O5TH6XAPZ2DIjVRbjfTk2hn2o0CazNCqTUHG6AcnN2eYh4lUy\n7NBwwJlVwmrFJH05nEqIyDRZzkW7RqAio9QkBBWgsH760vz/Oq5urow/kYCEuNcFAd6WkCzndDJ+\njUbEahtIxqmEpAXX+PKnxuYXOhN6pY7uyYMQJAM5ajsIemfW2LV/lLJrQkc2tkvM8Bt4MqXstnlu\nfAa1RmCuGSXQxZS4LHErhh0UlUwj0ZmUVPJ5HD9FdDBxmTfnIu1M6IHbESkldvc1rYx5l8pMaZ11\nHUl0d4UuBGBpVCLRoWVcWTsw7hSCoBzzWpoDoQ0n320q0qDDRW9OCWcgcyyCUU1Scoy3lQJ/QlOz\nNGkOlGt1YvwMq0ik3Qf1a9pe84QuhCgANwMf0VrXhBDfAy41X41Lga8D5/z392mtrwWuBcj3z9c6\nU4bpMsUTtM0yagoazgLRPWlWqLsxVYUNCf6IuUFE3Im7CmMjVMoSM5lPcdzTbGt6T8fcizQ1IINt\nI1pt7EYbq5lHJnmcpk1YtWgPCj70wN0U4zaXv22IlnLZpHr4j5/cQbPH5YZzD+m6/I3GRV6RM1gd\n9hNnFo3EI84scnbCRJynmvg8tWEO6VhgLHtjgdWWaFsjY5Mc3oih6pnDzmUgMgehzAokzZnViv2K\nJJoo88BOPsEuCfP8ya73eqRsWsoIXjyZMTPXYKw0g7hgovvsuqS2yxQXO/ubPPTXY1z339vTpkLP\nSHRGhsYXNhJJRMoWpVnVmoctlElcskyCes5LaDc9xqum8p0QeWg4ODXjQmi3oT1oIcYs+p6xQUBh\nTR1rSxWdD1C+TW4NDEicY6MAACAASURBVD7usuXVWdyx6yDzdhplVr5G0Y6opx5bwgJ7ljaSaIuJ\nNM+WuMh2wRir2/2MtIssyo9SS302ZGUACnbESNRDM3WpZkG3Up+CcKbYSJFymJevkGmBJ022bCXO\nMTtfw5UmOzVvRyZounM/l7yQarlNlklcJ6VeyZl+jjD2qW59a6CCUzdRbFYjRmQZdjUi7gtwJ9qd\nbACBU2ujPBu7lSETm7Bvmx7T63S/Ws0E5dmkOYlby7DqIXGv163KM0/gVuPuytiKLNOn30YQI1sx\namwc6di0dxpkYjePZFGbmX5E0QmpxAFr6r3UQ4/Hagug7mA1JGkxM5Npx47a6o2IZyfIxEXEmqQH\n4jIkvQpiQeZISKGhPTwnxRLaZIkCfUGLVuISJQ5h6PCRX99HT7vNJWccb6AVrbnwZ3dQL7hc+e4j\nAFCphUo6k38iEck2DdIO025KDDaVw5C5gmDCvJh2OOhT5PDUM4ZdXq2TqdBKiUsuMtEEG2xq22uc\npkvPyxlWZPqCSW7r3Phattc0oQshHMzF8TOt9S2dgR/Z5vffB37z9z5HOXQERR0zLq1N+K1jfiey\nzgnqrHDSQOAojV/J8MZDRNSBUZQ2GHzSySfNtKm+tVGhat9DFTxkO0G0Qug0snSamondshBKgVLY\n9djIllOJP64pNduc8dQwVgT/8Z6lfO72Ozjnngf44RGH02w7NArm4io77W4jM0psNtZ60AqkpZnb\nU2PVK3Mp/NWl0DA8VbepULbB9mWsDZ4mBXYzJS45hL2WseH0hMFTW7qTLyjIchpro8d9alcWzR9h\nn771gMHQS5YRMPXYbTbpHrMUdIVJVheCuQv/Z27y6zWuU3WhL2wyrUl0StKh34Ra8WIyQMGK6PVa\nrJVlwz0vt+jLtRlrWyRxx/0ulyJDwayHEuxGQnumRzAmCLYk+M9vNk6SWYbO+QaCa8WIVois28xd\nN07fs4OMHjCbiYNyLOybYF2lzIxio2OeZdKfaqnHmvYAW8IC480ctbLfDSsBeLXVjyU0m1o93XOc\nt01DcyTqIbBitqgC1SToviftOGuaRqhxXGykHk+Nz2F+sdLtyVQjH9dOCZVjLCYGa4xV+9G2aZA6\nTfPwlR2BkHLNnZzlzVPfijKa2xXwx2Kc9ZNkfQW0ZZbp3qSmvsPrO64y1ajAJvUt7LbCHW9DmnVC\n28FuZaZZqk1/LJxTxN9Q48xXVxK4Gd/PHWZW4es3cYF4klo2g6tmHktcBtsx33VNo49W4jLyzAyC\nTZLBEU1hY4xQmrhkE5bsblRdY34OmdP8yz13UcjaXHTWcWAJiOHCG2+nUXL51slvQ3agEltuDV6c\nEn1lmSQLJflaxFn3/BmVSb5w6vFc+PM7OPueB7j+6ENRGShlk7VsZMNCRiYa0Io6IkbR6ecJA3U5\ndeMmaUWG0JH6kvOeuJucDvnejsd1DL40H3rpN7SVz7X7HY03mWLVIhxH4o+2iHt6CPdStAcsimvM\nwzPsNwHgzv+Ci/5aWC4C+CHwrNb6G9u8PruDwwIsA57+e5+lPDpPuW2w3M4J6jokQrfRIDLwagp/\nNEKkCu1OdUUFIkpQOY+0xyfqd82yMyeJpoJyhXE3mwpgtSOjXvMqCVYrRcQpIkwQYYodZtiRReZJ\nrtr5BKwITn96mNM/bTpM1x+2hMuOXYqtmqSdA50SrRSsiLmlKu/+4SMwBhcvHWJClMm/YvGZ/1pB\nmPr8YuCtWM0E2U6M+rXDsReNFkiJDAu4U7mBwixjsQTKksQlGzDJJ3KLy4vM7Kor9yiZ0+9J0zS0\nhUKmxtXOaiua83K8ffaj//BxnRrNUKddpotCkWhFS0OibYpW2HXCc9a72H0NerwQZ5u8RGU5zH5Q\nIZTG3TCJuzpF9fcgmmFH9j4BnmdcOddvAEuiMoXM50AI/CfXUi7twKYZRV5+qoSM4aWFBQb3aBif\nlShvOPsyoRb5ZMqkQuWsiHVhH2AYNGubJdZs6Ke4g1GmGq65RaosXCslzBwm2jkGgiaVKCBMbTIl\nGV/TC4UE6RgrgDSxGB3rwbIV2eYAuyWIZya4xZgtm0u4mxy82CTUpHmIes3qz6kJwxYB0pKH3UxM\nvGIrwQeDU/seViMyRY1tkeZzBq402+szrqkiLjpoG4LNITJMUYWANJAdubpRhgpXmsQhyzRvc8Sc\ntOEhpFvhau8gLogeYVn2HDfahxP2CcKZGbN7miRKsualOZSedpizNqXw9AjpjB7q2wU4bUVh+CXy\nzRai1ANZRv/2c2jP8BkcafHuF1eiHPjC6cdz0S9/wzl/up8fHXsInhchLUNT3JaVBiZNzLYzhO1w\nyenHA3Du3Q9wzt0PmJN25OFc8q7jkKlChTZWxcYfF7gVjV8xAdaGcSe6tgxCgdPWHYqnwEoMdbMY\nt3nXyytxmhlXzzuaD66+k5M3Pcgvd1mCP2GEVbIVIno8rMkmMu4hv9bCioylgluNUZ73v2qIwmur\n0A8D3gusEkI80Xnts8BpQoh9MAXaauADf/eTNCbEopOog1JGqg5dauLUI1Vg6FvB5gi70jKVvG8u\n+CznEPUVifOSpABprmO56RpBklsVnbw/0+hAQKtHmmbNAhunuQ09rJKi3I46tOM7/Z3dT+DU54e7\nh/3FE5citCCqezTKpkIfjYqk2kKi6XHa9DRDTr7zcXRo8dU3LeMz/7WC9zwxzI07HE5mC4RrdSsu\n5Vqmk10PCGd4RkmWmSCD9oA0CScC7Jap7u22Jr9RkOShXbR4dczEpc0OahSsqGtGZcuOm4plzsOm\n/SWfH3gakH8LbnndxlXTgXSwsITowi4ZGkfALKvKb1t7s67Zqc6Lmjk9Nba08iQDCfa4GdfeZ6fY\nTIJoYT/uuknkRN1I3Qs5RH8verICYxOI2TPIXlqNVcijW210HCMKeXqeHifqGQQ0A49VGNuvzCP5\nBQhLM1husGt5tJtikymjEUiUTbsTONJIPaptH3ejS7TAJlLGorWVGkrk5maRkZESouawoZDibnSM\np3Uo6BkHty6JyoLChoyoLGnNEgQjmsJGQ1krvuJQflmRBgK32iLN2aYS7Z3e30hyNnaY4Yy3EK3I\nKA0dE9cmABEn6PWbELkA+sr0PxNT21UAFF6vcTVeLpL8ujYyTEkGcoR95jwZSb/JONACrFaCu75B\nNlDkyj1PRD6WsKz6GMtiY9t887xD+ephQ1T3yNhl5w1UI58tfx1k5mPQ+0zF9MMyRW2HAHXaOOpX\nA9j7bo+3pYV+/lXkrBlYYzUKoxWuz78ZuVBzzp+GOedP5j794dsP44rzjyDfwcoLbvT/hNFMiQOF\n0OjU5tKlQ5x7zwPd33/xhKUQdXxb2hJvUhCMavKbU+yWEUuJ2Jj7WW0bJzDQ5lR8noMRVtlhxpU7\nH0/mCE57bph3rjP7+MVuS/jaIUsZeKJGWjTziEgNhRMB3oS5151ajJxsIPoHiUvSwDavcXstLJeV\nwN96TLxmzvnU5vgJ2goQUbwV2xYmSLa7v22ERFZkqrWsFJDmHVK/I6zISVqDkjRPV9ZutwSFdRp/\nUuGPm0HNXIndTM2yDFP5poGpxNNAEPYJWjPMBTplwK+l4BOP3zbtuC9cfiuXDi1FNC1Gqiaj07NS\nih2Jv0Ky/JP7oLTgnOUrOef3KwH46d5L+M4eJ5IGspP8DXGPJu3tqKZwkLkYKTWul9BfaDHot5kd\nVE3OZVjmiZG5NNb20POShdOENuB3Ltp66nXtBaacGKGDXSpNMi/GEvJv4uev57gCWEJQVzFqCjcF\nEq3JNIxmRTyZsm6ybPxcBiKizCZKbONzojBLc2FTfKmOrDbRlRo6jsnaIfacWei1G2HmAHrhXEQr\nQm8aRTh2N/VKlHrQjSasWc+MMEK7DrgOH155J9YzEV/82NuYna/R5zQ47qurONBZzdVnvJnNcQ8F\na2vwdrvj2Gc3BFuaeebkq2xu9tCMXWpNn3jSJ1hv402ATB3sELxax8dECPzJjMJGRVSyCMYVhQ0G\nZkvyFoOPRyRFG20J8o+vJdl+Fv6aSYIwpifwaOze3x0/K1ZknoUbJuhCgBivIZIUu2GuV5GkkAsQ\n+RxaGbn97N+5rIWG1vp1GVflmP6W1YoRcUrm5YgLkmAsxa3Gxo8lylCeCYhmdBw1u4yyJVfb+zPE\nY93PuvT4k6jvILB6YlaP9SGeKdL/qsZpK7YcUCJzBb0vRChLmASiLSlnrrmPUrPFNcl8VCGHbLT4\nwNjvqUclrtrjOE5Zff/Wzx9aSqCa+HaEZ5umKmxtiiotSDp0RASQwUW/un3a973oxtu4dGgp2gEr\nFN1gjqlUpSSwzYO186BL/alQEolMwa9m2G2F1U4Bmyv2P5HTnttaGF61y/Fkvuicy6DLzNOu3fVV\nN9YBLn7LNYWepssWei3bG6oU7XFCtOeaCR26OLrxhRDd6lxm8OE/3UX/RJNrZh9F1O/RGrD4yGO3\nUvcCrnrz0Sag1TNVbG6zJpjo4I+Jxq7HBrbQ5uZMfQt3MkJbEksI7FaGv0XhlRyikkXYayb4NIBP\n37eCd784zC93XsJXD1vKR564lff9eRgt4JKTTySsmyfrRJCj128x169QtI1d7h2f2otTlm+9iL9w\nylKyHGQ9KcIzocfSNfmkgWe8PtxOuEPBjen1WhTtiPn+JL12k33yazi2bxWPb7cdy2csRryYp/y0\nTWusF4CXFmv2L61lplUFwJUZTt1UDEiB7W11d/xHbgpNS23dl4MgQeMIwYSSuB0pfWsywK7Y+INN\nUiUJ3ISP3nQfPOdw+TuGKL8SkQU2Fzx/D00nx8+K+yOLBUhTdBwjkxQlBAQu2eIdmdw5wG2Yxlz5\n+SatOQFWpImLxislc6H4QJtTHrof9W24+IzjOW75Uxx5z/P85PiDSVJD+SzZ7e6kPhIWqVZzFEOo\nrC7zaCbIlEX0apH8ekmhqQgmM6xQY7cylCNw6gn51QZOS4seQmv8CY1MFTLKzES/voZoR/hT1saB\nj7N+HFWpmpXHplGKLeMRo4s5tGWhco6hLCYZqq8IWpPlXewtddMX6ni6pPP6UbYgGHudx7ujAk16\nA9zRBlY7w2lbOK3UVJZOZ6XjSIQjkXNnIjKF1VZ84L9FJ/3bqlu5ePHxiAmPGPAETOwO2hEoR6EL\nGQiPYEwx/ofZ9PkpvVsqLJv8C0Is4tr1Rc5v3M9Q8iw3zjiMD66e/ny6+Ke3c/EHjyb0YwI76YrQ\nprZEmbALYVSAXHjTbZw9fD8/OnQJl79tiM/duYKzHhxGpnDZcUOA0cHEBYGWjrE0SI1NblIwEXmZ\nB8o1aWgiBX/cJhi1yG8yrLxPPHTr9HPwxG18pbiMcE4R5UlUwQelkK2EYDwl7LOJ84La9i5WksOd\nMPPktorbv7e9oRO6J1OSso9XbXZfy3y7M/lC12BLaXpabU559X6ULfnajkv56KO3csaqYX6y3xJS\n3zgLepPQ+3yMFSu0ELgTbUQzNOyXJMEqGCm0G8ZgW8Tz+rCijLjkoKVhB9iuxPY1mTLxWXU34Oe7\nL+Fbe5+IQPDVw4dAQt0LDAc4b05ywYuIO1adxonR4p1fe3za9/3MPbdy+duXkl/v4FZthOpEdQk6\nUVuC2IK4pNk8mOGUQ3aaOcaCYAJfJIbHbrdw8hnsAi/PHeDJdfNQoz5ozeT6Eo/2LWD7GaM4pFTi\nAKdlqrs0J3nLDi+9IeM65d1iIbAAKQSOpgO5KLazJ3m12Y9wFWk5JWy7tL2YGbk6diXjjIf/jPLg\nin2GuPDW37Ks9RTLe/dH95UMRjwyRnbgbtRnG3aF7KTNZx60Aou4BHGxgD+hac0weY7NBaZZ96nj\njqHxPcnZK4c5+35TLd24bH8e+fxClohXjC2ANo6JALXIR4cWrZmaT9x0D8WozdcOGSJXB7ee8omV\ny2lKjx/t/A5EqrDaGrtqqsGsHCC0qeCcmqlqk94AGWdkBY9wR8OkCdab69/aNEay/yK81WOoZgvd\nbnfO40x0fw/2SAXdaCIKeXS5QHteEaeWoHMeot5ElIqo0THQs5jcxaP3+YjXcxOZpvRyC9lOqe/S\ni1dJyBxBkrexpTCKxiRDaAdn3TjNvWaTW13lgnV3MVR7jOuXLOHSoRP5j3tv5uw7h7HKMZeeeSwI\nQZh3GJxRw5KKNLMYG+mhemBEc7NLYbWmssjmW42T4U8hJ4VPc9LkiwD8ev6hBsp4fiU3bn84X37b\nEJ8cvpWzHhxGeZpvfOStRI6NLVS33wVb7XPD0EHXPepejuveejiXv20p3qTgm/ssxYqg7gTI2KRJ\nZb4hZoTahHWjIS5rkp4M7XVyFTQdkZFGObaZhxoWH31kBe9c9wA3zzuU7+54LB966Q5OWX0/MtV8\n8biTKK7PyHzbsPlGKnijDvW5pU6qE8buQWuSfIDd/P9phV6WMZWdPGa+bGiFuuu3TLdzLAGZCL5x\n8FIEcNqzw7z7RXMj3nDgEi5/+xBZAP4E9P01wm6lyCjFGquRzu7Fim3Uhs0Iy2STinwOXauDgMah\nc2gPSMovJ2ZpUzZLqMztZCFGmmv3OwqRaOyIjkkYfPnIIaI+0LYy3g5AYCfk7IRKkqOd2px+xUMc\nf8vT/OzEA7n0zGP55Pfu5dz7hims1Vy93TFEfW4He1MoV+DW6T7E6vMsrNCmjc+zrTk8v2EmO8we\n47hZq1jojuHLmD2C9ewRrOfA8hp2vmIzuXrM5e8/Gt9KCDObt3z5RTyV8h1/IU41obIo4CMz7+W/\nC4r+EZtC4wlzKUU6RWlNgkYCDpqNmYGpLFuh6y4UTCVZiwN+/JE3Ecc2Z981zNnDZpxvWngYV+10\nHFaqcddOEB64iIldPAZWtXE2V43pWCuksH4jIJA7b8+Wg/vREtyqIvUtgo0WSVGj+uHic47l7JVb\nl74rP78TOwZjXUpiTsas6TRFhdCI2BCN81mb9z00jBXCj4O38eFnb2Wo+hjL+w7AroSkvT4yTVGB\nY3pCQtAecLHbBlNtD+TJjcbYlTbRrGLHSzxDBTZ2pQ25APeZdWitsBfMhUxxRuVB8pue5Nr0SHSm\n0O2QC+K/0GjmuUEeaapirdF9JcREFYTAnmwRl/LUF7iv78AKiPo8wt4cdqRpD7p41QwZG1gh822i\ngQC7ldHcczb5Z0fJygXWLx7g+uKb+Op5R+CLBt//tyUMFpoQwB5zNgOwutLHRKWAZWfsP28d5aBN\nNfRJei0qc3LoyCINPL406yyW3fCJ7iF9e/GJnPXivdw8+038oHwk/qTm0qEhkvug7vq0WgG+mzEZ\nBxSdCFdOBYMLqq3AUGb9jG8ddRR2A+ym7Hg9Cb6591LjxdKGuMeIh+yWMHmfPSabOCkqKKZmEg8l\nIhNoRyNbFk5d4DSN/UY1n+fmeYdy9Zx3oKXkqkXHkfmSMHIIxg0UkQUW/oY6Oh8gGm28urlP0CCj\nBJX3yK9rb+0zvobtDZ3QLSRhafrkkvnmaUjHcAtMczTukVz+9iFOe3brjXjZMUMgBE4TSq+m2M0U\nqx4hxyZpHLAd43vYzL8z5j3xkxRsxTX6cIgTRCHP+aP3MvHoOr74+bcSlwJ6n8uQqSYsmSTyuGR4\n71YotgZopBqZdlg5QhvnxikfdaFpJB5hZgyl6vmAm07aj2+fdwS6KfnSO5aRXydpWj6VRT5xyXxu\n73PGJU9bErvSJsu7JLk8MoHiOovaDjbxTm1eXjeDn7UP4vj5T7NbsIEeK8QRKbOsCjPjJvveuI5M\nS+761B686fJX2eXHozw5NBfhGNxtYrFmDzcg0+p/FBS9XpvoKEUjnSCFMEwXJFUV4wjMasNKUUqg\nB2OKuagrv/btjG9+8EjOvuvB7ud98biTCGcIPvHrOynJNhfvv4ztbngZnSScuvOzTPZ63LX2EM6U\nz5Hv8fjeS4L+l9dg9ZY5x3uCuuPzvUOPpT5fkKzxueiuFdOO9+j/fIYn/2MeBTtk0K51TLgMft3v\nNxmbW6e5scjF7zsOnc8457+GeU9HU3/z3EP51uITCftt4qLAaZrKPvOgPShQjqZntcSKNeN7CCqR\nR27EAwWlV2O89RWYqKDqDWQhjw4jQ8VsNNFpRi6rsEw9DxWbq8U+XBD/haH0WVYU90XWmgilSWaV\nDZyT5BFpRn23PoIR/b+2Wv17m3IEzdmW4VavTgl7LRxhsHUTOqNJ8xZOPcFppOicz+RePXzjqLew\n+9yNzM6M0ZZE84N/OYxW5iEzTc6KmFWskypJHNu8Wu1l/8ENBL0xI1ERNSgZaRdZ11Pigj/eO+2Y\nPrD2t3x9yVKsGPxKhlDgj8GlJy4l6dXYMsSdCqORaddD3eoEW0hpvJuo2liR0TtYoVF1ykwYyX2f\nSVlyGtCzNsEKFbUFrrE6rls0F4Asxwg/NYKnWKBThyxnVLOZrfn+vkfhjyemhwcgBFfucjxOKyO3\nKSQtOGYVV2sakkiaYTcVab5zr2YKZUtkmJLlHV7r9oa7LTb2imC5bXjkjkfqie5EPmXzqixTKX3q\nD9NvxM/etYLL3z6EvwWcaopdacFElWi3eUzsanXtAgqOZllrFfU9ynzp6CEu/8oPWZY8y41pP66X\n0N7e4sN//B0Do22u2O9EoqKF09B88v5baeHzgz2P6vo9o0RX3CQi0U0MbyYueScGZRouvzjnIOqx\ni09GOOHTu0bw9UOWkhSM/WUaGM5qbnUF0Wyjix33xGqb4iuaF84LEF7GzLtdJiyf0n5jjL3cx43R\nvpy5c8J27hhFu01exqy/qIeS1c/xP1rF8TetAuCF9w1y2bJjcH4raM7xeOdb/2wOfxtDrn/kNuWH\nnnRiBSWmUbpt4m6hEFJf24PXX++636VKcOGPp+Oh//r8LVyy/7HYpZB3rVoJN8P3N83kfXuP8p4H\nhrnuiMMRI2P4M3IsW/cAohhztX8w51f+xLL2Kp4dmMNV8mi8Ccnn7ryV9z66kscXLOCU/3w/X7v5\nZoZufJJBt87LF/YzkZow7hmumXw8mdJIPCpezMjGMl98xzLO+a+tTIjv7HY8W/Z1ae2QdN33yqts\nCpsz/u3uOymkbb5x8DJym0PyGyXnb7iLai7H108+mtpOLt74TGY90oO7dgJdqSFcF1HIo8Yn0Crh\ne+yNzHksaz3FMszYrsgt5up5R8HIONkOc8gCG2uiDZZEz+yn+NQo3rxe7PrrC7koG1Jf4E8oA3Ul\nxoJD+YJEOYaFU0uxt9QRScrI2+YyflDKnP4qBSdmXt70dipxQKwsciLmpO8/RqkV8rOPHsTMosVI\nLc+Hr/wjjaLHVae+lSSxsCyFZyd8/oY7OfP3D3H9MYdQcfK84y/PcsaqYZQLXz5yKf/619/QFAE/\nct9h7LLcDM9P6PHCrvhuanOsDK0FYeigGg5uW3T9yz/w6N2UG22u2uk4vIkId0LyoZfvoOEF/GDx\n0dQWuLRmGVWqP6Zx6pI0F5C5cMEjd1FMWlxy0onm85SZR9ra4yfzj0QojVMxvREZO0bB3U6RnoU1\n3gDbQtcbiGIBK1YkRQunniGiGG0XyXpco7N5jdsbnlj0pTfd0mkGaHTgkgZbTbi63kda86nfr+C9\njw5zwwFL2PVzX+eGA5fwvoeH+dwdK/jgg3fyL8/ciqg2oK9EGkg+c88KPvTAXbQW9HCNewDLc4s5\n8y/DPPfFj7Ms/ivLc4v5jzOWseOMcWwvoZi0Oe3ZYT72l9uwm4pPPLiC058ephCHWLFCduAWmWGc\n5EKBjI37mkqNUCTKbFItjTBFS6SEXq+Fv8FBW4LmXLOca88QiH2r+OOaM8ZWcn7jftgwAnGCKnh8\n+Llb+ejye3nl7dex4z8/x6yHM8SvBtCBIlxX5N7RXbv+6b5IGbQbjF40PW7syc/Nw+uY8bf7JSeX\nHwH4h8MtABqNQnUwc4nSmkineJ2VwWhWJMxs6tUAf8Qi55hkKFtkfODKYU6+5XFuOmk/9r71c/zo\n2EM49977+cL37+LSpSdw/WFLeNdLK7mHm3nPk8M8cMYOfOHU4xHFAj+0DmZ5bjFD9ce5a8vVLGuv\n4un5s9ltbCMff+Q24hIs3rAGgGcWz+SEnVdx+yf35t5TdyXtkYykvQza9W6mq7HfldRjj2bkYo/b\nXPbD6Yync8fvpL1Li/kLxijPrYGlKW5IQGkKaZuTNz7Iv/ErmnN9PvTib3n3SyspqDb59Yr+pzRu\nVTGxq09r50FEPgdpSjK/H7H9fOwZA9gzB7l6/jHT9nlNz+FGgzGjnyww8W1pwTXsnjgByyIccKju\nXHx9x1Uaz/Y0EKSexK1neOMRTjMjCyQiVgQvbYGJilGA7qvYf7dX2b13xGTv2uZfz0pJlUXBDukP\nmyz99ZOc+vVHGG8EfOLaeznztw9RbES06i5Rw6NVDZgc62GcIte9eQmXvOMkesdi9ti4kb/OnEPd\n9vn0fbfy3keH8dw243sZvBvHVOG2VLQzh3ri00pdWqlLpiRxapHGNlbdMivxDhusGLd594vDfOTx\nFWgp+PDzv+HkTQ+S0xGN2RIr0Qw+lTD7gTqDD44x4+Eac/5QZcFt48x9vsE5f1jJpdfdjtWCT96/\nglNfGCanQ9JAmmCPDmVZS5OdigSRKDOZ+50HeinfYcdgGER14xGVORKr/dqb3W9ohb5qYpDPP7qU\nnZwElCItuCQF0X2sdMlWtqAWBPxk/yX85xFDSCW4/G1DyAgaVsBAs8lJIw8hSwnXuG/low/fwrKx\nh/nxQUsYO9jCrezANS+YKmdqu+S9Z+LvP8kLmwexXs5x5aKleBNw2nPDXc75T/dZwrf2WopfVWjL\nNLdgq4RZudOflI7MuiG0UmhaicPL6wcRpYxwlqbnRZv6QnD2nOTQuWt5MtuLPDHLxh+hsZ/P9aWj\n+ednbmOo9jgvDA6yPpF8Ye5vWHbO+xm8Mk+0yqNxSIuNk0XWDvQzaNfoly36RcjAJfVpx3LQl1bj\nn5YQtzUTewj2M2ms1AAAIABJREFU7sCp/2i4pXsuhEWmpzMLqp1AcIVktFWEhkN77tYA7lRbjPl5\nbj5pX67+8JuxQ813PnQEtqWYlC7uDi2+cN4x3WYmwGfOGmIvbz3t+bOxH3qWawqHThvnc695Dx+9\n9necvmKY05/ucJT/6VD+eskcFgWjvBoNct2/HsaOhTEGaFDJct30JzCWxO3YoTGS49KbbuW0Z4e5\nZcbBXD33KM4fu5f3Pj7M/he9yEd+eAob6mXm3Sk4d/x23B0jvr3XMhCCdy1/gHdhBF2/XnAYn/3A\nCSzaYz1LL/ors1+qc+3gkWQ9xl/nAvEkjcee5Advfheua4PWnDd537Tz+IHmA1wzehjkzHvcjVVU\n3ictBTivbgbbpvhijbQ8PYT8/7oZOEMjM21ovc0UqyuOs3BGqmQbNiN32o4Nb3XJza4xJ6iiOmEi\nUzYZtlAUnIiCk3Djxw5AacFJNz3BSbcYmvzPlx7ANR9aQjlpEcYOWWZCm7+59O1YNYmMJV86eila\nwNn3D7P7iHEPvf6wJVx81nFY+YisYeMECa6ddeT+LnKb+HJLKvJ+TBR2aJ8pWG2wI7hytxPwqhkn\nr3+Akzable2K0n5cM/coCpsU/kgbkSi0ZyHqLawoMX2M8Ul+WDwAu9DivU8M894nOvPI4iV8e/GJ\n+BWTlp3lOtCBMYMiKfu4I01UzkVWOxbN2giSRKaxmjE6jLAaMdrykI3XvvJ6Qyt0pwVzbjSVBVKS\nFB2UbSZyLZkWK/fdw4/my0cOmSCM1Ag3vrXPUq7b4yi+s/sJ3LTdYQxVH+POV7/BsrGHuXneoVy1\n6ERkIth8SMC5uaem7fvfVt1K/OdeSnfnGXxckRvL+M6eJ0z7m6+9aSlOqBGZRospszCYSvZG0sV+\njVd30hUv5OwYAXxk+b18beXPsZqC3hcSZAgX/vgOzrr+furbS64d/Cdunnco731smN/9/nOcNPoQ\nP9lvCT/52JvoszxmXFHnR7++gfVHOBTXZ6A1l/7sdvb79hoSbRMqi9LFTXqvazF6doE/vLIja87q\nZbsfTfKxa+5FS0j6MjzhGIHPPyh2bttNILrCIqVNle4I2XFfhOHazmzaUjJKRkdTbftmCQzccNab\nuOID/0SUOfTnmvTnW/z0YwfznVOPII4svrR8+bR9ffi7f0RqxStLPeSsGZw//rtpv//0D+/hU8ef\nPO21uy/ZAymhmuUMXdI28X45GXW9WTyZ4HUqddsytM+6E/CrRYfz3Z2OI56R44oDlvL0/Nnsvnoz\np37lUcp/9Dh/w+2cPTzMwmSciV1drlp03LR9f3fHY9EOxJmkkLU5edODnFf5PSJODQ2v+hhBr0fq\nCyb3KHJ2+HtOWX0/t8w6hMOH/pObFh7GUP0Jzk8eQQUe7oZJqNaRYYwMU3TJVOUiyUhyr299pmzD\nxspcgT8Ro4UgGgig4/ioNm5G9hQYPXwAd48qpVybShLQzFwTbKIt0o7TZZ/bxBYZ9dTn2xccOW0/\n/3nOUWycKDM5XqRV80lTC99P8MohemZMNismmpdy8funr1y+cN4xOMUElQmcUkSx0CbnJNM80Kcy\ngAGS1EJPwadTXiwKhDL49rbbNcXDsV/ZTP7ZLUSDPmuPLbHx8ALZ7D6ITAWtkwQdhlztHTjtvVfu\ndiJeTeNUE5xKiNVKjId9qkFpMk8iGx1fqmYbNT6JHKuCUuZ44hSkxJqsm/fFr71Cf0MndCWh3Wuh\nPBscm6RodZ0VtWSrCY3oTPKWwddlZlSdU5YB7QHH0Am32a7c+XjyIyn9f4256OblnPLq/fxityUs\n/tev89O9l3DWA8N8/jcrsEODB7YGLT74ynRhwad/dyt2w3CL/xZSoTuqU4R54heciJwdU3QiSk4b\nW2T0pw1OvuVxvvDz20BpLv71Ct694lFeWj2AvXiSxqIy1/ZPv6C/tddS9s+vBq1pVSQH/HQtnx2+\nmfHdJBd+57945y2P099q0swcEAJZgolzcqy/sIzC4tnPz2T1Wb1EPQ5JXrBwR2Pb4QjrDanQp+xz\nzT6NKlUiKUmXvBSsafXR19tEFFLQxvmu4EQ40rhPzsg3DIVRKsqeSSPSmz0+f8OdnH7rI9x+yl7s\ne/tnuO4dh/Ku5Y+y7PIn2GXv1Zyy8585Sb3AdUcczk6//AI3LduPZTc/wT2Xf3Pa8X3ku/exwB0j\n01t97h2R4cvEJEdZbeY748x3xgmshAU9kwg/49tvP5qvHjZEYztDfx1b7HDJL45j+cn7cM7dD/D4\ntZ/gfQ8N86uh/bn0/cfSPKjN2dW7pu37PdldbLfDCKtfmsXFp5/ADccfzDvXPsA9D1/Mssm/cNPC\nw/jqkpNoDxhb2boT8PM9lnDJ0Duxm4oflI/kpoWHMT6rRDqQQzdaiMBHew7WRI20P29saccrxD2v\nb7ekUAypHdqmujMoW5IGFnHJpDyJVJkYyZkDTOyl6M+3KHkGK3ZlOs3jHkxvYipM5F+vnv4Q/uQP\n7iHwI0q9TfKlkFwuwnMSbFuZ2GGpsbyUi3813X7msluWU8i1cbyUQi6i6MU4nZCRqeo8URaJsqi0\nfWq1ANV0uoWjcoyZVlwQ/w+v/Rz7UdT8GcZyQgiCMc3c+6qI51ajKlXe03qUC/TjEMV8oH7/tPd+\ncuVytNSkeZus4KIC2+SphqnhVkSqY19iVrC63UbV6qDACpVxjnUd9EQF2UqmWaX8ve0NzhQ1bIDM\ns7B9lyTY5kD11r+ZovNNVe1TKdrZFCtLaf59eDq2ecG6O7li/xMRChqOz893M3ANQvCVNw+hbcMx\nDXstrEjxsUdu5T1PDvOT/ZfwlSVDfOaeFZz+zDBWpPjGQds8LDq+MlpqtKOnzfOOUAROG0to8laE\nZWm+fM5RZJnkrDuGeW+HGfHz3ZfwjV2GaL+qiQ+AS57747Rj/5X4MtvnSjwaCz5x+nmcueZhzr1r\nZZf58ePjDuaJjy/gKOdpfJEy+bGCEZloiURR1wUe+exC7prcE/0TQaUV8Ie25NcTB7JTboT3l56j\nIP1/GONFo5FIMjJCnZFohRQmis5BMMuvMx7mGUt7sGoWk40c84sVBoIG4+HWXkCPGxKmDutG+9CW\noGdOyO9O24XbPro3i9Jxvv+xw0BCjTwvPLGQRvIC1x9zCF9YegK0NZ844WR2+8sm9nx5Ezcu25+7\nP70Hp17xCG//2bOk2uIPn96ZnBWTOB3TKy2YZVfJy4jx1IRVKy0oOSH5YohKfZw2+OMJScEiXtSm\npXx+8++LWXbzE93jHv78IuZmVT684g+c8PgqrnvLEi47bimf//2vOefOYXa4foQvn38UCMEV2x3J\nmb95qPveb+67lMyTZC60ZgqumHc0Pa9mBBOa+nyXyk4uF5ZPQmaCwccjnBl96CQ1xU7Ox3l1BDwX\nNaufzHt9+yV5K0K1bfpegPo8F6+usCKNlSiyvItVLpH1+OhCRqokPW7ITM9Agc3MI+tgqBmCZlwg\nkDEfvPKPLLnpJe541558+4NHcP53hnnPLQ/j2SmXnX00QshOOIUx00qdFG3BZ66/m7PveoDrjjqU\nL77vGC762W95321/xpaKn330YDY2S6RKorUgVdKkjymLRmwmjWbb6wZWaGmIF4aarPjYw7fyrpdX\n8otdl/DNfU/ko4/exqkvDKMtwZc7c4jT0MgwRvQUQQjyW0KWhU+zl7WJnbJxXgpmsVN7My9R5uQN\nD4CAby0+0ZzIKdZcIyQb8HFaKaqUNx7xfSXEZMVYOGhjGyCUBtclqzWQWWYsS17j9oZO6FqCtoW5\ngAseaUC3AtcdZuAUjj4VCD11iWbulNeL5tP3ruCMVcP8dJ8lfPXwIf79j+b/4yJ87ZAhvvWOY0k9\njXY6DJVU8OV/GgJtApOVK6n7AT/bawlX7LMUfwKu2uUE7LamFgSGLxwaKa7uPITSnAZX4XbS6j0r\nJVI2W6ICOTsm6CjUhITL3ncMZ93x5+73/tLRQ6Q5AUrxmftu5bTnVvLjQ5Zw6dKlXHT/Tbz3Rw/x\ny8p2XHbWMdQmC1x+3js493cru+9f/ql96U3DTg5pYvIvhcDv2NVanXCNvBWjXGg/28u/2wZ2GCvn\nWeRt5rhc+I8b121+doQk0goPQag1FppkSuARSsrPC1oLzVJ4PMxTdKOugdJU/N+Os7awOV/k1wfs\nTyBj5vkVgjRlll/jts8uZlOrhHhKcPVBR5O5mhkPQGOeINii+d3Oe/LIngv506d25ti+VSz/+L5M\nhDlqwqelPNa2+1iUHyUnYyayArPsKom2GUlN6HCGZH2zTPR8idImk+beHnRozpaoJiwIJjj3Wyun\nff93f+MvXHbeMYx4RX5zyl784NxDeHPv0/z17XNZkd+bhuuxqV6iuraHC2+avip8/+jtXOucCML4\nEgGMLAHtZ1iTJpC4+GqnmgwsE2jR6e3IVoxWCpGaSi8Ym97D+L9uI5USO73o0J5h9p9/OCMpWkzO\n9slvzsi/Yqh1lp8SOAnFjitlO3OQaOwObU1pSYw57GbB5e53787VH3ozOSvhlx8/EFsqwrzDjGKT\nTEui1CZRklRIHKfT6C85/OSEg/j6Of+ET8pXz3sbjp3SKrisq5fJu6bRDsajJ2ZrLCSYJCwdWYhk\nKy1Z26CUpFowc8HXD1kKCL5+8FJkqqk7xlSpPWhyQ5PCAE6zH6+iuGrsXfBCjmXjDwOwU3szy93d\n+V62Jx+0/0oT4wufeeCvM2wfVfBNTF8jhhfXoHZeiIwSRLGIcF2SgouMMqIFfXgvjyJ3WABAMqsE\nL7y2MXvDaYtpAEnRwoqMQ6K2ME+wKcP4zjaVRzAFw2h7KoJOUM0H3HDAEr7yFvP0/PLbhlAO1PyA\npMd8ZpozwgCmVF5KYIdAZqxKr93XCIisVGDFBs75xoFLSXOGQyw66TDKNfs2GLrG7dh+TmHnrkxR\nWtBWLklmkXcjPvPD6cvuT96/gsuOWwpIqnmf6956OF88/kSyvOI/jj6ZuG1TFx59hTalIOTj107n\n3l76g9v49cf355V4Boucse5FmmhJhqAojcIwsGKiXk06J2LfwfUUrIiF/jjAP5SPLhGozj9bDboU\n+c7+yk6bhcUJVsuZhH3GMC1WNoFtXA9zzpQVhOH39/tNqpHPeJhnTt7YzfbaLSJlMxg0KXkRvGWU\nFyqDKC0Y2bEMVYfWwowbT9mHI2Y+z4HuajanJda2+7jkfUadeCzGKGpLXCRnRfRZ/x97bx5sW3bX\n933WsKcz3XPH916/oVsttYQkhNCI0OCIgIQMSALbKCFGjClclE1VcGKDCbbBMcSYVGHKMRUgThxC\nKUbYIKBIbAQYkCBGgJBAc9Pdr7vfeOcz7XmtlT9+++z7GrWkJ/PU5STsqlvvvfvuPffcs/b5rd/6\n/r7DijpIaME6/3W/GjOMKryVY2Kxo1ncD8kxpDcNX/nDf8TL3vkEH/mG8/zUd34xr/3Bh/mqd3yI\nb85/h+/6mr9EqAw8qbh+cxtaxc++5aWEVYR6HP7eL72Lb/nN3+FfvPp1/JPvfh1/91/8X3zzz70H\n+zL4sWe/hXjR+Y5cM7hMMjl1o1g8GPCRxxaG7JpkcvrEStBy24pCX2ua4T1e3yjQvHzBxa0ZJ3nG\n7HCLeN5Z2d5n0C97FvleRBQtyWxDGzSrNmHVHaXj7k0sJ9iazNS8+9tfyGE1RDdyD9fe8k+//Uuo\nfETUUV8j43oFtnWS9fk/v/01aAKZbikbi/eGH/4v3wBKUTURadTiu7zY2Dg0gdqbs1PCOoWoVZha\nGrt1dNw/e92bJG3JK2wR8LHiv3/DV6NbicX0CVRbjmq720gbQ7Qc8X0v/c/4mn/2vv7l+h9f+XVE\n+wt+YnWOMBqSHIndiNuQYbZyYpu9hlpU48B5wmoFVUX8SEt7/x711BIPUjg6gRAwo7sfdj+jBV2v\nTeAjJFgW+kKuuqLe757rz6muOBvwXRr8P33Dm8AF1LqdV4of+IqvFl8IGwgGXBK6IGkFHmwpEVHr\nUFtvFGu7h3XAhouUBFSHLovUSHbiOr9Z6YDtkokmXYDBNCr6AZAP8N/85K/ytb/4ft7x1lfwd77i\nL/N9P/3LfPN73oNL4b9761v40Te+iZC0EDlU5BlPCn7qu7+I52/u8/roE3zpD32Mv/CvH+aDb7/I\nL/ytL+Qr//Ef87KfeoJIOX7ve+5nESLGdJ0IggXL3zUjU6FrRZI1PG9wmx0754OrK12m6Vng8edk\nbdHojr64xtPL4BjrmHPRnNZrVNZS7mnCKqbYkC5OqdAbKVntMCpwIZkxb1KeOJ0yjks2Ihm2PZgd\n4tBcL6Z9V395fMrl8Skn1YDX7jyCRzEyFWNT8v7l/YB0iA9tH/bDsYkt+gjBuMPSr8TiG/9kuclh\nMcJttuTn4/5UqJzcS9ftlN/5qw/yge++xBV1zG9+z/OovSVMFJcunHK8GpBEDcs8xT0xxG3A9OIc\nrT3swf/+5lfy43/9Vbxs6zo/+C1v4vR0SFllVBPN7LlrOhUC7+2VDIalsH7mA9zDGX4QocsGs5JN\nUMVSPHVZY4t7KyzaHSz4uuf9AVt2xWE74l/e9xd6hXO5pfAmppmIWdwkEnvkO9kt6yvWrdB8u1Bg\nq4MIzVDUzmBMwDvFso5729vInMnd69YSW8cgEovopYnJq5iAJrEtRgsUqpHkMKvWJwNL3XY5r60R\nTUmrUE33GnezOxVEROWtaEV8BM1Yo+u12FCeR9hqCF7hW4WPNX/n3z4V9v32J/4NP37fGzGRbLb2\nYIEfZcIlR3zuQ2eHq7c2pY/tHGRD2+IPDlH3baObgB8lqCcLmZkt7/50/YwWdCmw4nqoS1Fq9oB/\n6OiBd0yffdR17R3LpO/WVUCjcObMXdAlEIxHtarDvWUXNpW4pq0TReQUIH4gPpLuW9ZfFlS39AHT\n62Bq1YKuFG2rexwub7vQYNOQdAHR47giH8X867/0Ev75X38NO+Wc7/uGryTYwGyUEHYblA4kiSjW\nkqhllNQ8MDnhhcPrXIxOOLdzyiPftMMT3zvlQjvjI3/3PBs2J4wh1S2nboDXssCu91AJTHXB2JQE\nA+enc+6LTvjpm6/iIx+8n4NXjvjPx7/1OVvXFt87Os58I3x0DJEKNMGxY+f88fIi442C1YFYIAxt\nTRvkDfnoTFSaO4MVFwenva+KAsqOT7xoE67rKZtRzpXsmEg7LqQzrPb4oHjuSJKHtqMVuUv41eUL\nuLHa4LGDLfamS64Mj7HaczE7ZTNaUfmIJhimJmdAhecseLt2BnsglqbtKBCuFMy3Y+zc8I9e+jV8\n1Ze8j41WXC4vJDPe973P4lq1yXk35wu2hFI3tBXv37mMUZ77R8es2oQP/M0rLOqEVydXmbcpo7Tm\nH7ztzWz9oUU5cIOOMbW3YpjWvZ/37dtTomsx2kFxLmH88U4Obg2scshS0Jpy894ORdf+9YftiGWb\n9O8/H9NrR9pB4NwwZxKV3YauulPrU08LHsWqs0Z1Hc1XE9BGirDzGrqQ56q1/fC6bi2t0zivGEQN\niZWNYO3NMojEiKtyts8J8EFLnmg4e78G11GQXVdnVHfy1kiSGWec+/45R8KAUQ7sUtNi0V6BC/y9\nn/tFvvF3xVvqn3zhW/mu33wXb3vkvSgf+ImdL0WVoFonXfiaaOaD+N9UNWGQosqKkCZ9DVTWovKa\ndF/jRjHxxgTShHA8u/s1u+uvvAeXeKYgzojr5trfwT+/s0PvhqJBAV13ficLZl3METQFn4RumNr9\nGc7cG4MBt96NvUS7tbF8s64lCGPN7tNtEJy920B8Z0uAAmVCn024pka1XfDB+nM//U2vItItmW5J\n7ILRsyp+8NveSFtHKO1Js1q6jaRmFNVspjkXkplY4OqCm9+5gfeKCM/56JQmWH7/e66Q6pZtvSRS\nbZ8+r4EyRJTBsKWkyLdZ4JU7jzP3GR9+5CLaw+188jmFXNbLl4e6/5zHkyrLwtectzM2IunC/W7N\ncFhxUAwZRRLk+5ypdMdt0LTe4INmYGuKMuLm4kwss1+NKVxMolueM7jNheiUJ6ptIh26NKKMg3rE\nvM54+HAH/8ENmvMtX/TQh3rl4NIlPbwyMiUuaIzynDphstwqJ9y+MUUNPK7WNFNHpAKbl2acHo9I\nHkv4rRvP4dUXrlK4CNulFW3FOfvlmGv5lAdGRwx0zZftfYyFS9mwOXm3SUXKUbiYDx1f4HgxxM7F\nn7/a1NKMAOOsomosqZVKM9woWC1Eqh4tHX4Qd8HoMerCHhydiCnUOrvyHl2JbviqyQeY6ppfWn4+\nLhM671qRrVygGQU2k7wv0pmuyeKawp/5yrignvK4RgWMcrigaIPtqKSyPqsmlq69K87r4OeqNVSt\nZRjVaEK/2WkVer+WWDs8itJZQlCdEvnsnle1PA8fnc3slD+Dd0E8XLSTJk46dQmvMKVCOU2z4Qlj\nx2wn5p+/4TX8w694C9m+5gfe9DW0v6koXEo7STFaCxKQ2r4rV0uPaWoJtkliwnKFzjJ8CEJZHA5Q\n8xVmWVC94Dzm/DZ6WRBm87tes2d8KJqcBpKDkmDFD2JduHV7h1JUzk/93118RzGHPqKOIEfT3kM9\nDlJ7GwU+4NdFPEUCdbtOuxmKSEjXirg52zh0C6YB5QO+Yx4EAy4LuJHHpg2DSG68zbiQoGAg0Y42\naGpvSWxLahpi7cjbiK3Ris0k57GTbco6QmtJHo+NYxhVbMU5m9FKkux9xlTnzENKGSJS1VAHQ6xl\n8FkHQ6oa4m7LH+iWYyf2tCApSm7g2YkWfDw/D07hE8+5wfxzTl+MlOm6dI1B9WyXSGm2zYrDasQg\nqSmSiLq2FCZiN5MEqGUjxW5gazLTdBCWZntjxdHpiCxqGcUVZRtRthGjqOL3Zw/w7OEBWgWul1NO\nqgFt0DxysEN1e0B0qtEKdq6cMrElvtt2Fk3KQNcSSaYc90UnHLWjvuAumwRqzfBJQzMCO6mJ4lY6\nQh2otjzl4Zhfrx/iObuH7K9GNE5zcTLnQibp9asuA7P1hszUNF32a+s1TTB8+PQ8N29P0fsJ04eh\nHiuqqZwqQeYzW5O5eLN7Tb5MsDslbTbA5C0+NmjvMcdLwjLvIxXz3Xu7xj4orrdTFrrkraMP8ZNX\nXkPz8ckdJ2iFH7ju/pThd6YCA1NjvWfRdeQ+aBJdsxXVLF3Sz4DmnZKzdobYONF3BIXzihCMeK+Y\nNXwCiW0Z2JpYizgtBEVmG1LboJ2kRNVeirk0B3d06EEGzGh5P6sGdCsNnbdIyPMwEIz4Odml6pl2\nNpcTfTlAalOj+ZGveCNYDzjyGLLrhv/hi2WoqhtQPsXUgXjuSPc7znkIYoWRxGJ9rDS0LSpJxM+n\nKAl1g96YCLXRalRe4tu7H3bfbaboVWABOKANIbxcKbUF/AzwAJKA8ranSxH/01fQUG2npIflGeSy\nLrzdcWhdiHVNb2O5HmAAeBtQTuG7Qq88+KwLY20Vim7hEil8Jtci24+6zSEKnUWmYOlR5+mggvip\ne6vEvD5TuExugGACWoe+k4j0GcYXaUfrNLFuqb3tBjqegZVouHPJgs244OpSHP18UIzjio24xCrX\nd4xjL1123YldGmWJVMvcpRgViFTLUFdE3c7n1nxq5YgIpKohDB2PFzs8PN9FNZpgPa/eeKT7+qd2\n6fdqXesAT7TSgW9pzUDHZCpm7kua4Bl33da5wZK8iikKKW6HxZBpWlA6uQ1T2wgjJmis8tw/kR97\ntBhyxJDt8YrICB0t1o4/OLmCVoFbyzHHsyFuEaFKg6kU9cWa+y8e8cbzH6UKFhc0PiiGVsREGqF9\n5j7hoB33zouVszI4K6HaDrja0BrP5jinKGLcqOWBywc0zvDY8RZKBaoq4nFnuHqyySSt2Bss0Moz\nMhWRcpy0A47rIYs2Qasgm8NpTHooM5/l/dBMW0jlntrKclqvKVspTtoEwpMDhrccdlHRTlN8YjEh\nSEFIE7zWxItwT9d1oFoeiI5pgiZV8BXP+gg/99wvIj0UXxMfgRk3WC3ddt0lPw1M/UmPFSkZdJo1\n1NLdw3dSDV3QtM7gvSao0A0/dQ+vVFHbUxJHnZPi+qRchgitBD9vtRYjODrsHAiNlqM8XXd+Bz3a\nRwK3+Eh0Jm0WJIWpVpiqg2fXwsJWST1pwaUBN/SEYUs7kKH02jl2Hfxt6i6dDQncMUWDHw1QZYUy\nGqoaNcgkdSvPZSaiFPG1E8IwhTjC7O7AjU+3UmfXZ7Olf0kI4QtDCC/v/v3dwK+FEB4Cfq37959f\n/++7/nxd/795/fm6/v/w+rNALm8FXt/9/X8DfgP4rk/7HVqMqqKlJT0InQ/xU7vz/ksb+b8eNukG\npsBT8fG4S/buGDBYj088OPkaVQv9a43Bh1gwdtXIEWzNrtENRLlHhUA1NFSbinoqIQo+DRBJ2sma\nXVG4CBcUSdepa7rJfVD9MAlEIadV4FwyJzEt++WohxZGRrpFkA5m4VIWLsV1+2ykHIaoM4+S7rwM\nEU2QU0KiHE0wNMEQdRF0NpPjWe2Fc2u3K16aXQXu2oLzs17XP1nu8WW/+h3oxDEalcTW8dK9J3nN\n5GFelj4pAizTcFoLfctYj1GB2hlWTdyfeso26oMJduIVR27Ay3au8Wi6zccev8D1k7RfBxaWYAO6\n0D2byU88o/tnbA9zXrx1nQvxjIVL8UF12Lll1mRsRAXb0Ypz0YyFy4jUU8OETa5pB9KBmdgxzCoa\np/GNRieOsrWcGyxxQbEoUrYmK4wSuO9gNqL1mqunW2wPczLbcFpmLCs5lZRFTHhiwPiGaB1mz4Hx\nc0/6LhTog1PWp5mA4LrFliE9FDhHFy2srXc3J7hxIsZP93BdKzSL7uTy/nrCV0//gN994QNc//A5\nxlc13sLGZMU0KmiDKDIbrSl91EW+rWdL8n4oXEzhIjLTkOmzWVBmG8ltbWJ8kDCKNaRSlhGusKCg\nrg21M2zoO2NmAAAgAElEQVSkJUZ5gV+MiJpqb7DaEXcQjdWeqrX4riun0U+FdMOaDCG0RBcHoTcD\n7cDTbDvaWmNXmmZMp+IENwy0mUfVSmpJ4qDW1Bcb6vOKeN+SHCnSk9CH2ZR7Aj3Z3Ek+chKhnKhF\n/XIlHXpRoKwFYySh6+AQnvcs2nMb6KK56w79bgt6AH5FiavSj4cQfgI4d0eK+C3g3Gd8EC10QmGX\n6H4YqjzEC4E/1hRBwjri6UzZ1V86dPxQD7FHWU+oBeOk1WADKvKE0giOnHpUo8EIdCK7g+5xdQLY\nShagGeg+YioY4bKH2GOylvGg7Lm1jTeUzjLtzIg8Ct1xYGtvGNuWzFZsRrkEEQfDVrRiK1px2gz6\nXyW6sygrhw+6oxnSOyxOzYoIodcZfM9uWePCNYYmaJYupa0Mu/GCy6OEx/V59qZLnhe1QPR0OPo9\nWVelAtQafRRRlSnzPcdv1xEfHlzgvtEMqzx/crrDRlpijWNxOiCNZR6x9sQB4SRnpqFwEc8ZiH1B\nEzQXsjnpgw0ffOIS7Cckx3K09XGguVQRgmKyteTzd2+yFedciGd9sTDKU7iERZuyamOs8uzGSy7F\nxxjlORedcruZcrPa6H+fnhnlIEkbQlCcngxhEZFeWGJU4LAY8iXnH+YDp5fEG79OyJuIl1y6xiMn\nO5wej1gVCfUyJr4Z0Y4CulKkR13RGMLqSuDcC/c5N1jw8YO9nvB1ezHuTd/axhAqQ1SKV3fQCrOs\nMScLwQ2MQa0KqgenJIseBrwn66oJDJV4o2zpkjxYvmDrBtOXFTy6/yDJSWC2GHD+/jm36gnTqMCj\nOG0yfNB3rEHoYcVhB8esXIIPZ4rOWZVSNpbGGera0JYR6EBoNWplCHGgqVMOVjHltmV3tOKwGBEZ\nx4XBXBgx3pK3msYbiiZiVcVn7s2xRzmDGwRUo/rZmCkUuqK3HvEWdKUJrRTsZupkthGg1QFdakyr\nxbzPK0LHqqMFs5T3V3GuS1Q7FtbM4LCDhbYjlB8RP3kidg1NC87h9g8wz322BH/PF7jDY3n9H7uO\nObdDu3P3Lpp3W9BfG0K4rpTaA96tlPrYnf8ZQghK3dlfn11KqW8Dvg3AbE4FXzKgWk+Uy67Yahmw\nPOUxbUdfdMgLpp7Kcgk2SAHvdmAV+TOcLARCLS9uiLxg9KO2/1rB2WUA2ueYdlTFeqSoNmVA0oud\nTMAYL+G13a+ZtxGDrrPMupu0CZJReVIP8Ch8xxMv1xi5Kdm0KzZszmPFLvM2Yxrlve/EtlnKpqDc\nU9gsu2bR08CEDdP0Py9SErXWBE3uY8xhzIcXF3jF9CrvGT/EgxuHbOjsc7qudncDk2vcwBMu1rzk\n8jU+b3ybJ4otam+4tpzivOKRa7sEp1G5QW+fzQEWjeziVWtJTYMPivedPovaG140uUGkHI/rLf7T\nhz7B9YsbLGvBoqdJwUExJDaO7XTFVpwzMlU39PQsXYoLGodmK1pxUI64kMlmOTU5Y1Nw6oY0wbAT\ni13pQTmiGXuipbBOtPYoFRiMK/JlRGwdW1nOaZlxu5pwIZtzbTXt8e7TKuPCeI4PsFhm2Kyl3hMq\nlp948vsccdrSNoaX3/9Ez76ZDounvL6R9jxxawtzM5ETagvDGxXKdcKUEKSbs5aQlxQ7RgRy93Bd\nz1+UN5wPigqDIXA+mVF5y0enAkAnSYNRMgDNTNMzWu6cMa2v9f3sgxRdKcLS2c+LlKYxxLE0TMEp\n1MJiaiWn6QXU247BVKw2TvKMSVoxjipKZ/vivhEXTOOC3MZUrZWNAVBLIzqYWnQpdxyiO7aL6prN\ncOb+qqXGBOiphyEOuEjmcOtmUqlAcJpWGcxKAnO8EWMzUwXqTvAV5QFVe5Hy1w2qlbxZZSNUXoI1\nqCxDa4M/neGLEn14gknuPonqrgp6COF69+e+UurngVcCt5VSF0IIN5VSF4D9T/G9PwH8BEBy+XKA\njtddNOJZ0DFa2uzsCKQ8Zy6MZi06UtJds/58ANv923dTCNN15l71OX9nHE+P0gFf2i5irNuNu8c3\npURH1RtnCxtUdwoIojQrmohZBxsMbU2iW9pg2DQ5A1Nzq5pgVGAzzvFBk+ma0kdEyrFhpVOf6IJU\n1fhUs3AplbcY5fFBc9BOiFTLrl0w1g2NsujuTkpVy0C3uKDIO4FG0g1E1x8DLRtL66XQb24v+aKN\nx4CnV4res3V98GJwmy33Xzpkb7BgIyo5aoZd55VxvBqQLxKYRwyvG5LjwMl4RLNRoICi7mxWjafu\nfrehzdnSbc8Oqb3lSnbMuWROpBw3qg0upyccjkQvv2oTNm3OpfiYsgsHf7TYIdEtSXeqmsYFHz05\nx3Qvx6FpggydZ+5swzvMhwLfdMnvi6Mhly8d8eTBGLvUlHXEUTFgFNUcVUNGUcVutiQyjklUUnvD\nUTnk4mROPVwJ3JImbAwKysZyYbwgNQ3jqGInXvLYapuNqOyhAoBxXNF6jTYSgRbP5P4ExGK1aUEp\nQlmeMSUcDG4393RdX/AFZ37RWgUiPJfiYwa65tcvPZd8FLOT1rJpdoX8zqHnugnxKGk+usfyCAsl\nb+O+m24aQ1Nb2sbiSgOVEBnsSpEci0ak3gnsjlcsqpi8TJikYo53VA4ZxZVYCLiIVZNQOYvzCttZ\ndTQjhcu1sNsqdaZJSQOhVbg0yGnchjNIJijCusU3AZV0tUUFKStdeplvjDSGiSfUGr3S+EhOYbpV\n1OtkOaNQISGxWk5Zp9JEBOdEVDQcoAYD1GiABvzJCX65whydPt1SPe31GYeiSqmhUmq8/jvwRuBD\nwC8C39h92TcCv/D0j3DHD2shORLskBCIVl4oPmtWTtdA6+asMw869IpR1cl1dQO60IKTN1pgllah\njEcZj44dxF6gl8yhUifFvOkm3rGXTUFJMbdlkGivoRKBUiQiDz/wkDmU9aL0u+Oy2lF527MlAB7M\nDjkXz3v16OX0mCvJERs2l8Bnk1MHw8JnbJicK8kRVxJ5g5Q+YtbhuZFqSVXDZXvMRJdMdc1YNxgC\nsfIkypEo1zFfPJHyJAo+np8DHdiIS2btgPPjBa8fPL0JxL1cV6VgtJkLfawThMyaTChp3rA1zDm3\nN4NJQzSH6Z/UsIxIbOfTUcUUVUxsHEUbcVwMqLxBK0+kHCMrbpaPrHYpXETlLSNTcdiMSLRY4V5K\nT0h1w8KnHDZjTtphB9/ENMEwMhWndcYkKXuYqw7SdX50cZ7DesRhPWJRJP1sRzmg1hwtB+jY4Uae\nOGpZlAmzKkUrT+ksF9IZ07jguBIoLbMNi1qKSlFHhACjqGYYC55ee8uqjTltBrxocoNxVIrdQZEy\nK1IWdcITJ5u4pcVHcv8PDrw0QK1HlRU4j4oiVBKjJiN2fvsmyYeeBND3al3vvNKu29oyS56T3OJF\nl29w7tmHbKYFVRCobGgqhqYiM3W/iYLMm1atzDAqb1m2MYsuvrFoIorGohSEVuNPYsxxhFkZdKOw\nuWJ4yxOsMGoS0xJbhzFn70eP6h0WG296yiJ08xorNcENPcGK6tdUQi/UrerXWjmFWp/4nRKOkFPy\n+Vb3xVxbqTMgz5mmq0Pr721kZtcOA96CjxU+Frp0uWEozsW00wQ/zNDTDXSWEtoWd3winPMQUOMh\nZndHGEz3mId+Dvh5JZ2uBd4RQvg3SqnfA96plPpW4HHgbZ/pgdZYuWnOOmebB1zaqTQbWNvT9rJc\np+ToA73wgq5DV43uu3aMHHuUcX2Qs04cWgehMHU4GE6hS/F1WGeZ2q77abN12neAjvKIV/1OXLWm\nH56tvVzWOPbIlGyYgqVLSXVD63VXxFc0wZD7hIVPSVXLhpEhWukjtuyy9+P2QTM2BUZ5tnSJUYGp\nPqOAaeTkF69piyhMF8bcBHhytYkb+F6CfSGbc6lb4afBz+/ZutpOqr2/HOGC5pabkJiW3WzZ4+O3\nVhNCLqHcNm9QTcQwrjmZbfaPExlH40xPgVsXgE2bs2oTIi0Uz9vVhCvZMSNTcdQMuV1NSHRLEzQv\nHN1kYCpOmiEjI8EAiRYjtdZr7h8d97BZEyyfqHYZ2pqb+QSAYpFi1xQ2gMSTzzJ07NBTETsNsxrn\npcPcSVfcrqQFmyYFTy6mbGc5sXYcrIaM04qqtRwXA1x3X0Ym4cr0hGkk/jT75Zj9fNwHfzivKfJY\nKJilIj3ykk95vCIYQ8hL1HgIkQXvBWKMLGyM4RYWeO+9WNc7r7EK3PZW8mF1wys3rzKbZDy82CV3\nMZHyvdAHZAA6b8+4/W0wPT11/bllnZDXkax5qwVm6YR/dqmI5530vgrklxwXtua0QRNpz8agIDKO\nZXdfnJYZiW17+1yAWZFSLjrV8VKsunWhSU4VdiUw75oU0Q6FBIFH3mjrUrMe9DnQc4tP5fdcQy3o\nIMPRSuZ10NUVhUA7d5A51jCPixTNyKKalCgEtNaE5Qq/XOHzHBUCerpBGGboJMYffUY2eH99xoIe\nQngUePHTfP4I+NK7/knIL5Qee7KDGkJAtwFbBCrOCjWqg1vuEBbdqSAFOr+WThG6XgAEVoFu1+wK\nuPeq55vq2OG7TcDHEGpJ+I4WLdVW1FsNBIXszLHYQCoTMCaQRi3jSIrEGvscdlxjgCYYHIqRKdGJ\npwwRx+2oZ7IYAltW1J4Ll9EEQ4owWNZ4bqwcD9gTUvXUEwGcKYjXlyHgUNTdTXeYDyHAc4e3yF3C\nlez4U+Ln93JdtQqYzudmWceMYnGefGi4z9Vim5NqQNFY4iPD5idq7P6cy7+ScG1vSjhI2P6APP/H\nX3KB+563T2rbXnBCCvNWuuGhbrHacTE75XY14aqLSIzMGq6tppwbzNmvx4xs1W0GYs/Qeo1XimlS\nMOs8YaYmp/QRJ82AC8mMDx+dl9+/0rSdBF9XGrWwhEmDjRz1LEGNArPZgEvnTthJV+huyHq7muCD\n4gWbt3n//kWGcdNtTi3WOJ69ccRHDs9xfrxgKxGhybloziPlLolpmRUpSWf8drQcELx0kINbgXjp\nJejgdCFe6FoRTmao0ZAwHsDtQ+nexWa1voOq+Gda1z991WimZv3cZ9wXn/S+Ok3Q4KO+wSlc1Cuo\n2yCKTY3p/VvyJqZoLHkZ452mWcRCXOgKYXocGBx4moFiccmwcfmYC8M5szrrzdzW/HWrPTvZktJF\nLOuEZXfiq4oI6rNGJth1YwimCj3Eqx2glATWKwQj7+HdM88oH/u+ucPL0Fqg3e4HeEEU1sIlW6he\nsAhiAb5WoCuvMZXFniooK2G5pIl4oy8Wkli0twNxhB4N4S6b9GfcbdGWXRqQ85i8RXkrmDpI4Q1n\nAqL1v3V3/HHxWaeudLdABsG3jByFXGk7hRAEp9GxIziFth3+ZQKhmzHoRhMvPD7S1EPVebcEcQ8I\nsKbhBK96GtX6hrVKcgsLF3MxOWGga6ZmxUE7YawLIuW4Wu6wF88xBCLd9Ok4rjudaBU6oVBLhGPh\nMsa6YKDcnfsURn36nNgqGPbdiIP9CbrWPBgf8Gi9ywvS68Dn1mkRhGY2Sqr+2DuMKga24Ua1waqN\n+fj+HuVRxsU/9Ni84ejV51ndpzGPWvGvvySvx32/5Wnft8fia2c4Lz4vo6jitB7wvPFtbldjShex\nEZUs2oTaWfbSBfvlmCujE4adfeteNEcz5moxZGhqJtGKjy/PUbYRD4zEfVLjWfmEzDR8ZH6evOqi\nyVIHmSOUBg/YXOObCJ84onHNxrjAaM8gqjksh7xw46awXJqE2/mIeOOIyHhuHU+I4pZlmXD/5gmP\nzLbZyEomcclWvEKrwMKlrNqERZOwmGUs7uAF6P2Y4TVNtPJES4eZFfhzW6hl0T3PBJqG8ORN9O42\nfpCiZ8vP2RofdLONXV1x0J02pyYn0gKJHTdD6IItNAGrPK4bjA5sLZtqUGigCmKPW9YRTWXxhcy1\nVK26mYFCNzJMzC8ols9ueN54QWpa4nRJ3saUTlTZVjlKFzFJSjkFtJZVkdDWXRFJuhCJSIETMWEw\nggi4RERDIQRUC9FCmknfdvM63XXo607KnOHrodF9o6giT9ABnZvO6CsQFRrdiLHgej7sTaeIDxCU\nwjSG1HtCnuPrBq01ylqCDxA8/uQUNRyiRmesuM90PePS/9kDluxIEx1Lgoet1jvemfzfpZ1Hugdn\npMhqp/od9azgK9Ad57zbUXUskItOHNY6vBP1mTIOYx1k4AuLrgymAO0C1dSccd073B6vpJB3GJz3\nIkleQy6VN2SmYWKleJ+3p0x0SaoaPJrYC8b+ZLklzAAnsEoZop5nPtFFX8wnuuRV2WMM1jL+O163\nJvBphx0D3fJosYdaWXwitMaTZsjnjQ+B0edc9u+94ng1wHnNdFAwjCoOihHHesD12QblacrGRyzK\nOx5+e8aVh25xdDilncdgPfZQxmUHL9Wc/78d6mc2WH6d53g14MbphEvTGQ8v9mSY5jWzKiOzDXFn\nl7kVS9e4E0lBW9NIHxrss3QJSyeFOzEtA10zMmUPHfig2E2X2D151z5sdlksM1ynRg5KGgh3mDC4\ntGSUVJwbLMTAS3tOmwE7yZJRVPFEMwVgnFQsEznqV2XEJ27u4VrNKx58nOePbrETLbhebfJHi4to\nAo+ebJMOa6onZMBrC0V8qhjd8CSnrViulpVg51rLQLTzQFdGQwiUF0dkB8efszVe+JhUtaR3bDql\nj4h1S6LObHLXToeZqYn0OkikofaWRZNQewlWb53BOY2vDarSAoe0imxfES3kBJ2fV+RXWjYvzPs5\n1XqtPYqRrUhMCzXczCc0nQfMxqigaQ15GVPn3Sh2DblCX8xdBqEBMwfTypvfDFR3UpeiftbVK5Q3\nZ4NTkG59jc87JXg8HRzcbQLt4OzNa0qFqqSYo8WG2CcROo7RzuHLCp0mmI0Joa7BSRH8jzbgwlso\ndwMozWiSYpe1YKpFoM06X4Wuez7D0EHZbjj6p7AoFei7bmXEy8GhukGII45bijwR/HP9zQFohGds\nC6iHGh931rlrd8c1WB3ARg5jvQy2kprdVIrG2t95M1pxOTri2dERqXI4Fpz6mFvtBolueTTfYWQF\nlrlVbXA5PaYJhoGue6bF1OSMdd0PnvrXq/vz0y3nml1wsxFKKInnj/IrJLplx9xb971P/SQUqzzB\nzWNWScp+NsK13eT/KGHnjxTLS/Ad3/4L/OzNl3FjPqFdRcIHtvTh2+3Ac+tVhsu/WrH8rW2ql+a0\ny4gbWtLcqyZiZ7Si8ZrKWa6Mj7lVCvb94OCQ/XrMxJb963zYjDhtBgxtRWaa3sDr2ek+E11yo91k\nJ1pyrZz2fjK6M1BblZm8iYO8EUFTrGIO1ZDGGa6MT2iDpgmawkXU3rA9zPFBsZnksCHUvTW/+vJk\nxuPzTcad9cDNaoOTcsCyiVkVCc5pstsdvW0pFhTR0pHcWqLKRiiKeSE0xY2x5Ex6T8hh9fw9bOEI\nefF0q3NPrl1TUAbDbReRKscrskf5WHUfl9MTrlWbfdTb2osH6FkuhYt6VsusSlmUkh6kFKh1I5Zr\nbH7WtNUTRXHREW+WzBeD3sBrGhXsJEsZJEcrEt1yYMakpuWJxSarKu6H7U0RoVZdiVMQUkeIAs1G\noB2J5F83YHMY3XQUm4a2EtxXtdJdN5uhj54M5g7LbhPko9H4lUUBbiiCI1N0mQxDzk77dOy+li5Y\nA2wplGqVJvjFAp0mfXIRzokNwHQia3+X1zPboVtoRgEXw/Bmwvhq1412XipAz7n1CWfCo2537XfG\njpEYoD8GrQehxni08b0pj7EO7xXGBFyrpTsvhRKlfMClHbbVFXPlRUVK3HXqSkItkqjlgfEhL59c\nBbqbNQQ2bM62WbLVnavWKT0gb+iNqOCJYosHsiP24kX/fyCCoQfsCZHyT/n8+rqzkH8qH71UeW67\nrLM3led7Nd/mtdOHGanks1me//ArKNSTGXENKhhsLkwRXcHGYw3V1PD3/+q/5MsH17m5M+VXmuez\nCCPWRkd+Qzo8HTuaVrG8FHPx12Z84soIpRFzqolsqusT0iiuhBpqGi4kMw7qMVvxioN6xMSWLNza\nGEpOVZW3TCOhlz5S7jE1wjy6Wm6T6DOlaBrJxmFShwoRdqFIZjIwz9OEQkNVWaz2XBoJney0yRjb\ninGH3Y9Nhe0YMLfqMS/Zu84Tq00S41i0CbfLi1yfTzg5GclNfNp1MetaUUuTYyovuGy87jI9KtGi\nDjWacHiMSlOCUcQ35msM/XN++aBwyAD/Wr2FJjCNCpqge8jFo6g7pkkbNLUzvR1u01iaxuBbTSgN\n2ol3js2FvlxvKKptT8g6yGZYMkxqVk3Mo8vts6Fnk/VCv3FUijq0EvM3QAaW6w7byande4VrZeNY\nn768VbhYE69CJ2zs2G5GaoHirLuXB+Mp5oEhFuGiNCiBdhxwjeo49KJihjP2XtCCQOhGhrBhmKGb\nDcJyBXUj3uhlCVWFSRIZgN/l9Qxj6EGogGiWlwzpUYzNRcTRptKtqXCHKvQOGmMw4O58tv4MZln/\n6ZxGdXCJMYJ5x3HbL3BTROBV7xYXVMdqSbqPTB7HlIpQKZyzNAgh5zt/9te43x3zwe+9CErReM2b\nfujDmKln/zvH3HAxhsAjzW4XFVdwITpFE7hWblJ5y8BUPRXP4Cl9zNV2k7EuuWyWeHha7PxTFfM6\naI59ShNsr8Q7f/6UzDTcF508IwHRAARIjxTpYaDYE+OmoGH2Asf2Rx2Pvx5emz3Jo23M2zb+gIVL\nefLaNsmhoXrwzLzfxo469lz6a4/xJ+98LtOPBvILUPuYKmtoW83xasAgqfsTV+UsR82QNmgOK4Es\nmmC4vpqyly7QKrBqEy6kM66XU/aSBZFyvY3CcT0k0o7bnU2vD0o2jlZB6hncNCIOGYtzZWQccew4\nyTNWXV7l87dvdf7bZ5tH3sYCCyQ1J3XG/mIkxlxeU9QRJwdj1NJ23T9ES0U86xqBVaAZKNqBwY5i\n7P6cYA3h4i7q1pG8LYxBDQcE51EuiChlNIK7J0R8VldEoEQGo+K/X1JqsaXwKDJds/bVFQ76WVpQ\n63VvDeADuFbLrKvW6FKf2XA4sdhuJmJ6hQo4p0msI9ICwdWtEZFf1HZdfmCcVAyjmlmR0jby/0pL\neMraFhsHfinPrzfyC6Bri24katAlZ6ZjbUpvxgWIq2t0x5uxO+kT1Cc3nIEzw8G1ZgZoJtI8imBS\nsHmdV6iiItQNvqrQxqCSBFXXvcbgTO76ma9ntqBrUFmL95Z6Q1NtWezKYythvPioO3LdORTt/nyK\nX/76dVPA+gV3BpWecV+VCtS1xRhPU1t8baBRnZNex0F1Z9xzFEQLRbQEUwSqLUVhFa4y1B7MieNL\nf/ljLJuEn/yO1/Jf/0/v5sX/x3X+8O2XudVMmBsprI/XOyS6IVUNDyW3GJuyx3HXnGmDZ2gats0S\nhyLmqUPQ9fXpBqFAr0Q9cBM+Pt/Dnhpe8fIn+LKND/OieB8Y/Qcs0md/3T894Bu+4d9yq9rgweyA\nB+IDtvWKlySeV11+O+fjBoOEajdBcy6ag5ZBFIsItdEpbUsLXvHxwz3GB55iS2NKyG4ZCj3E5JrV\nVsPGpQIfFHlnpXuz2OhFPWNbYfCMo5JHlzs8ODqkcBE3yw00geN6yH3pKXUwPFlvE2nH1cUWw0Se\nw2meMUgaEbkQ0abi4VFccKhhC0HhvWJ1OmSytWKcVkxsRRM0Vxfboiq18lg3FxOyqGE7yfloYykP\nM7hvzux0IGrZZo23KgY3Qu8zDjC85boTpEVtd8qU1uPPb6NnK8FX0wQiexY991mkw9/NFVBnISrd\nMLAJBqNaIrwMRZWj8QarhMGyVj3rP3XibL2WEIv15dbpQV0hHwTQ0tF6A8QebT3GeCZpSWKE/19V\nEWna4LwiLxNcqykzyy03pioifCOeO3iFWmls3vnJuA47H3rURkM6qKUuLAwqqC5BTTQyoVeuK1Qs\neL63QbQtXSGmG5aq7vcIceio1Kov1iCoRF/GtLBmbKF6Rh/WdDORFpRGGYMaZOg4EsFYfPcqUXim\nI+g6Mr6udXf0UB1N8alHmh5n6lSckul5NmiQAhzQDfjugIcGn1v0QFR0VRmhtEj2fWWglk4gWmhM\n94KuNwndQnoUGF1vieYNdl6CUsyft8Hhiwz1Lvz9t70FowNf/6/ex1v/1QcB+Hdf91z+/d9+kM2Q\n8zuzh3jP9Wfjg+L+zRNeMn2SvWjO1OS9jWracc0bRKE47kRDRz5h4SO2TPNJRXxd6MV5WfVUxfU1\n1TW/W29z43SCywLPG9zipck+U/3MLW2mHN8+/XCXWFTjQqABymAoPrjJ4JW3idSaMx94aXYVHXma\nccAsNb6zUVE6wKBlNcuYdpt8udsdmRtFO3ZEw4ZFmXD/+ISjcogPmmmcY1QgMw1H1YCDasT1+YRz\noyX75RirHdOo4LiWTn7DFEx0yUBXDE3NZpL3eO8wqjkuBkwnOTMdcKdDwkIEJ8p62sbQzGPM0jBv\nR+hzgf1qxLOGR5wbzFk2CSfVgI24ZJRUHK0GfDSco2nEa+j0eCgiFBswp5p4LkNA00DURcilRy3F\nriVeeuLHVxKIYAwhlTd3SOOO7RJw50edbauC+pNta+/VVXanD4fk2Joe8xRIZR0v1zlXo5Xvfc97\nzUZQGB2kDujQzyhgXcQDyoAfeNJxRZbU2K5mpKZhmNQ0zjDORKPRF/Qy4m/8zG8wXlT8g7/yZnRu\niWeK7/2ld7GIM37sNW8iaCi3pcP2jaFpDG1lsY3CxYpqbNCtzOlMI57oLgE3AJd5sRBxT61TqhMd\niV3uGRzsU/n9Qq3RxZlWRncwjCnFaiQYRTtJsYAqS+nOhx2jxQfwgdC6s7jOu7ie0YK+ly4IXmG7\nY6Y3oFzAFh5Tm7MOZQ21tJzFwqHOxB7C+5GvqbuinngISqiFQQkX3XjaTkWqS41dib9x74FsFelR\noNiTuc4AACAASURBVJ4o6rHi6AUR6ZFluB+RHFVMPjEn6AlHLzLUe4Hv//qv5Ot/6SwU9mf+5iu4\njxnvePKVzH/+ApsP1xR7EX/y4CYfe+E5vuDSdV4xfVwUpV2gQqIbKi/hFakSvstU11KoP8W6/WnI\nZX2ULYPh1Cc8UW2RHw6I9greMPwYQ6UZ6bsPlv2zXgFY+BZoaZA3tAswUgmqVdy4toV5oWLhY7ZM\nya5ZcWFnxukfZyLm6H7vbFhTrGKm20ua4RaLZwG7lTAhCkN8ZGiGhiIkzJsUoz2rNmYSF1xbTRlH\nJfv5mIOZDGUnacWDoyNOm4zCxT2evmPnOBQLn2K1YzvJuZ7LrrJOymlaQ32akNUi3bYLRT2MQAVU\nLQNMfU0zazZ4zHj2kiVbneVDGypu5yNR8hpHANK0YVVYUTUHSG9bdAXZQRAPlgDJTDDjatPirSLf\nNUTzEUlV48cD9CIXj+wQYDIUPxAXSE8cqmxQ+t5DbOvmYeEjDEGgwhBRBve0cx8XFLo7a/b3aRv1\n3XnoTjhraT9ehuJr7/E2EzO8QVqxM8hZNTGnRUrVWnGh1B7nNQ4Ehy8svjBMViXf8u7fAaf4R1/6\n1Xzvu97F2z/wHt7xwteRHHt8LIU7WhncQuNthHVyKveRZKSqIOaAvhMXtsOAGzvMsMXVGooucHgN\ns0BPbwTO4i9Th01ammUs5l1rjWLUncLUmaCpD71wHr0tIruwXOFnCwgeFcco+x/pUHTXNPwXr/w1\nfvS9bwBvKTc1UaF76b9ywnIJiqf4uoSejN8d/8qua1fy/z7uFKXWg1e4VefU5o0Ug1YLzNLdQGuF\nqHKQnjpMo1md17gMmrF4pdRjwU4JML4KeW75W7/97qf8Pm/94Q/wy3/7ReRNxOJ+KHYTTC2DX4Ki\ndBGPFjtMbNmxWrqBninJfcKpT0hN/rRvjE8q4l13DsI7Bzn+rn1L9LDl8y/eIF1zL5/BSytFgxRx\n092/8hHQL55x4Z1jfu/1Gzw7OmEVLEPVspXlnHQOd2uov8hjtreW7A2X3I62aScerQM0Cp1r6XBu\nJkTP7rDxJiYxLYfliKq1NG4ob/x5gkokSae5g2nhUX0yVKr2OWzGzJqMVXt2rC3aiMuTE64thIK4\nTrZSXqEX4vqXHBqieSCZB1xsOJ4M+VB8gfPDudAZlWC+p0XKJK2YFbK5mqyFG6m4RI49kdeClWcw\nvO2pJx3NL1Zi7FQHit2I6GSATy3KJai8guNT2JoSBgntMCJadGZdn4WJ02d7nfqMqe5iBLuwkDtP\niuth6PpazxHWr2nlrLhHOk1TiLTfrqQA1mkQwU0WaCcOnbV96lBiW/I64jTP0Npjtcd5RdVEAqN6\nwS6+/6+8heAV3/ru9/Ktv/5eAH76xa/jR14qKULxPJDM5Oc0I2ngQlf92qGEWvgk4LYaTCLaFaUh\njVucU7j27ARC4Exw1P0bg0AwjcLXkj9KByf5M0ucjg4pf49WLWZeQggyD7EG1TqoG+FsKw3ef1bS\n/2doaiZXIPBfbV7lZ974Y7zy9R/l9MVNP5jUrUz311z09UcvNtKhrxS9/4LvGvW1atSp3jKXWkOp\nUaVBF6qnDOn2jD5kmoBLRFw0fbRl+glPeuRBCY3RJd2Hhf/2/3wX3/rv3sv/8uWv5oGf+gHe8daX\n8xff+WG+8h//MZ+3eYuHvuhxtl59i/F/cpvs8045tzXHKs/tYsLKJTTe9MHEEy1eL2WIqMMnL8Gn\nGoKWwbDwETW6+zA8Up/j44tzBK948cZ1Bkqh7zGW+pkuhbitm+64PVCKgYKZL/mRF7+TYOBv/P7X\nsaXFX7sOmodG+xQXxCmSm4l8BMirmMeOtkhPPYwaXGVQiQzI5Kh69nNDUH2hALg9G3N0NJJNu5J3\nze1i0qt6fVDE2jFrB/xxeVmEYa2ldpaijSjaiHFUclwOqRoLNtBmdPMVGF7TpDcN6VF3LzWB4Y1A\nmMfkTcTV2RarJulVrol1oopcJkTGkaYNulFEc4H/ktO1DkJRbmhc10UuLiuh0saKjQ+f4jMpJnpZ\nEgYJbE3xkwzVOFYXYhZXYsIg4dFvunRP1zVAb+0MokxOu+6rvsOzP9Ztz2xZ20hrJcXXavG3B4mQ\nq1sDQfV5B83Yd0NEGYSqUctkXJDFYq1ctpZB3BBbES0pFSjrSHzlGy1ccBOg1fyDr33zU57/P/yq\nr8alAp2YJhAtO4fX8oz67I0Uc5fJzx9ulJzbmjMYVUSd82Nbd0B45jpWncw9dKUxhazl+gMPtKp7\nbuATOXGE2HdNZxdoryXBKMSytiGNhZ5Y1RL63XU5wXlUdven7We0oCsULnhemUT89AO/wXv+4o9w\n/a0tbaqFytN0ktw77C3XKivpkrqOvKMqSrABmJWozFTTBe12wwpdauxc+K1m/f1hTRmi91mX3EAR\ncSQzT7z0pKeOeOGEWmQUp5OM//V1r+P73/ZmQmv4oW/5cn72a17K7XRM7hIeHB3x6r3HuH9yQhq1\n1M7w5HwD2/0ClbeU3bFVK895O2Osz7IuPy3XnDP8eRUiTt2AUzfgRrPJzGXcWo6xkeOLhw9jUAzU\n565Te9rn12F8W9pigDwEIqVoQuAF8QnJN91i8u4hf+3xN/OaVEQ/b918P/c//xY+Cey+H3bfD8Nx\nyfd//i+Rn2QsLhlM5ElHNefOnTJ57gnlrhdjJeM5LbPe+yVvYm6djClnCaE2qNTxnAdvcVwMePx0\nk5uFyPKXTcLNfMIj+S6/N38WL/3Rx/nLP/x+DvMB+/MR+7Mhb/+Rf8//w917h2t2lXX/n7XWbk8/\ndfqkTHoIJBIgtAAiIQgkMxRpFy2giD9QbLwv+ipS/IEgCghi+wEKqIiASSjSfCkTSJAQEkICaSTT\n58yZ0562n13WWu8f936eM0PRQSLh967rmuvknJynnb33ve/1vb/lOe/5Kt2VOrobEKTSNDQPOqZv\nL2ke8JiR0AqjnpPcyKOGrAgmXviZDTi62hQrhDTGZYZBGomjYMdOaLdmJBkAZd2TdxTdU+QfCCSY\ndyDb0kQXDl06XLuOGowkHMFBvrFJf7tmsFmx+kc5n7zizffqcZWhqNgPh2NDuKoLLypjs44Z0A5G\nxGNL5yrku2kyIl1KsVee2JRYpynyQBgoTiAIvyEDBfWDmtZdBr0QS9Gv1tqwRloETNdTGnGOVuCc\nxuYalWlMzxAuBeiR4tUf/Phx7/9/fv5KiqbsukcdzWCzIZ2VvIMJRfnYf4VilEasDWsC5ziBh7Tx\n6HqJia2YAFaq1kmdUmBSjclUZeuN3GhiK9TLwENlV1I2HTaudtpTAfmsWHOorMDXIgm8qKAzlcSo\nKMRv3nDCx+wnLv03SmO9o8SyLWjyvy76BO/+111CISo9LlLHdOXyGH+MWhQk68/iJ3xQudsrXCyU\nRULEhKsi+cMxHNDqXBk7N9pYoa0EVivnKwoTwjCIpGtyAbz1yZdi24VgoEB/WOMdv/KzoBRxXtIJ\nRxRe080T0jzEOcV0I6Ue5GIRahu0/IhOMKwKu6Ol5CKwk33b8evYLa1055GYdlW/u2wl3DgylrO3\nLPDIZECskp8cXfGYZZQck1ApGYp6j6mK+vvO/gCXXfZL3P6hs3jAYzfwe+d+kvtHh9jeXGFPsInD\nl0gn9Pb7XcUHFh5G7e4I9bhl4kpNGGpHrjzxqT2GR+tky3WioKQWFNLcKY9zGlOz2G7I7OY+53QO\n87mVs0jXErIioBaL22FuDQeGHRJdkHQLfuHKG8it4XXPfRL/6+/+lWd+8uu89wkPAwfxsgzvZ75t\nadzTR3mPyetkHVNZRkhHrQsmVMpxV4lXDLIIYxyNmZTRKJw4dpY1T7KoZQhnZRDXPbskWqq8Txri\nM1I7pOlvCZlezQj2HoFKdOJ7fVQ9oWgEFA247LJreeX8NSzZe/e4S8BFFUjhI4YVRmFRcolV5+c4\nQzRwjkEZU3hNVtYmkItRrtpNCRd9LJvXhcKvRphM0b7HUj80YvmcOoN+h8PTLdS8+CYVhVnXlWhP\ns5bhPWSZIVjWxEfht6+7mhd89Rre/dhH8rpnXMYf/MPHedGXdqMtvOHSXTC7vksfQ7WTkKpqJCeh\nFZKMhFdo44T2rPyEKacKLZ12KL7o4wZykqtQKNlhW7M+KB3XsYr2qAtFkHqCrBoWJxHKGLHXLQrs\n4lFUHKPnZsTn5ad1KDpeRskBtd5xYbKHP58Ri8zRlMRaTfDzY4STulTrLJdIirkuZVDqx+5ovpo8\nO8CN7THXn2NM6ldeHN3GN42xx4I3CqsVuhQoJm9osmnFaM5jp0owoIZyFthSuLWxKcmd4ealzWxq\n9uhmCaGxNOs5G+o9mkGO9YrChvTKhMNZR3zRrRTmGbPOw/6P1qC6mHo+JK94nQMXczDrEGjHK7Z9\nlrqOsBPi7U9uaaWIlabnLIlSRErRc54QX1348P4L3suzyl9k5v1t3jzzHPSuozx685384qO+wFPa\n3wDgQ2sP4qYDWznn8XdxWnORb61u4fY9m8gbKdvbazgU3xoINHNyZ4VAOw5221gr1qZ+OaKxrc/J\nnWUGZUy6lkCmuWDzAfb2pllYFfpfoB2rrsYbXnQp/TTmio9dy/OqYfe7L3kEr79sJ/W7AuIVMYiq\nLYxQ3uMig0ktifWMZkN62zTB0JNPOQZZxGoRMNUcEhnL3HSPQRYRaEdhDXFFhRQbaDmHsilFvOKJ\nlxQuNOQz4+2ox3QDXATxmgOtcBum0f0RrtNAO8doS5Oj9w/ITxb/mkWrJ7YR9+aa0BaPAQKP7dIT\nXdAxKQuFKHZjXZCVMpco3XqnbbSwXoxxuNCJStNK3F/YVwQjhw81NhH/99Y9iqJZI5uCdHs5kfCL\ndW1VgY0nn3aUDUXvhhp/+/CLef1TLkNnmjdespNgCH1TI0iFTeON1N5xMzfprqsAeR8qsdgeCxor\nIy7vxQbEC9rC2MMFJ8iBzmFiKjimI3pBJCZFfbwqv6iyBniDMwqTSuC3HpW4ssSXJSqOBVPXWsIv\nTnDdJwV9vIzSnBo68o6idlT+wCJK8RRNKcxeI3FgJevTZFsZ1FfDzbFXepBXAwdd3Y2rqCmqA+ei\nSuo7EKy+flQglbxtUFZ48KbylhlDMTaGfE4GNTKEqd68l8IQKEe/jDDasX+tQxyWE6pV6UwlNtFi\n+4mtOOkJh4sOLZ3S8dmE3gXHd+XHroYqWfI1jBd2Bow5wZ65Wp9HJTlg7pPu3HlP5h2GsVIWWlrJ\nfyvFyHlaquTKB/8Vnz7nXN7xzcdQLrb56G0XsencI9w1nAdgYCN+/vRbJwKhB0wfYP9qh4V90zzo\ngftYLWqctGmZzBp6RcJ80ifPA/JeRLAcYhuOZpJxdmuBbpmgjCeeT9nfn+Kc6QUOHp6GQcCBpUTs\nIhLLa5/9ZK741LWTz/KWBz+V9u2K+hFHslyS7FsDrbGNaOKpkU0FDOc1RbPyBKlbzpxdZGkkir7c\nGtJcbAoGeURWBORZiFJecNeKXRj1PHHPkW40lE03sWwOFkPqhxXBwJMs57J7tBUbwjmyHfMUTUOy\n5Em7ASfFS/8txdyhJkP3RBXrCmhVThxGZ02fZd3kSNaiYXJiXRBqO8HTj12BdtSTnIGHom1QGEwG\n0RqkswHLZ4f0zx/hraJ9U0ztqCPqKoI0oH+qwjesFNzSCw05F0qgbTve8oxLkCpaaVmU4o2X7Jrs\n6v0P3gSL86ITf/TMaYrQQ2hlKJsbbGlwVq37noPUm0KhM0WQSuPowgpCGjvBjrnGwISbOTb7q9KM\nbAQu1CTLBtPNUGt9ERUFgcAt1kEUSlTdCa77rKCPoZemismmPV4rwlQM4W1Udck1gV9MJsqt4+AS\nIWZL13zMlsdkYyUW63dS1o2/lJXOoLWvoHbXEr6RYOM2ZaIxhXT7RV0uXBeJUVhzoyTSrK428EFV\nMJ0iNiX9IqI3ipmpSzRWLSxIC7kI0jJkrZKFj02FxlSuupbtpPsB3PIftELlaCihN+6rEnb2ZrPU\ndM4jZ+4kVOa/3VXxhy2F4OhGKfAeC9SVnNEj70kUDKvj85TWLTz3kd/ml/dcxte+dRpbm2tsq4m8\nceRC9g2nKz8QCcrYMtXlnrva3NGdZ1tDpPZrgxqztSH9MmbjVI8FWui9EfHpfaaSlG6ZcHjUJqwV\n3H/zQW7cfSb9PZtQZ3nqBzWNg468JZ7Ur/rclcd9lj9830d4x7mXCQPqniV8GOCaETorsY0IF2mU\nFx//KBCam+kU3LE0z/apVZxXpEVIHJYsD+qMspAoKiVgOrK4hqVQpprzyDU+mncEcyPcATmuYU8R\ndj21FYcLNNGqMCH8MIVGDVV6aodGDOfq+MhzQbJnolP471rHMrEStV5gWhWffy1PiJIS7Q0aT6RL\nciUXbI7MFQqnCYwjDC1FzeL7phL5eXrbFelpOWdtWyAylpvdNkYHQxoHIV7xFC1DBvhmiYmE6UJG\nlf8pnuSqVDJPq7rxsr4O00od8ZOoS+XXO/UgFUKGsgpb0/hEvNm9PebvWcoQlKpLV1X4xiTDwVc0\n68rRUV6jKuDjP100HgCbSYPpcnBGoVzlrthsCAddK8gL3FQLlZ24vuA+7dBBCnvZsQQjKeTKHTOw\nGLsrVkMkvofKqErQRoq+N3JAdSFwigxdAAdh32Py6gap5ftoLYc4Ipuvi61ANLbU1IwtfEczimzG\n0QxLnEcM7cfK1EKTliH1sCAyckfvjmLSIqAV55O8xLGJ1zhkoaaFi360lLi6RBXMG/HBKLwmVpbM\nm0kikeSGOqyXwj/wIYulbG8X8xZrRcJlUzf+xI/bsWt8vo68hG2MN9ohivyYQtBSnqGHD3XPo3Sa\nnQ++gabJJunwLTOiZkRKPhaqbGuscmdzMyBYeT3MOXfjYVphxreXN7I2qJEvJbCl5P6zSzSDjE1R\nl16Z8ODte7lzdY7W3fL68bKmbEA6r5m9JePl3/4YT9/3FT580sP5q02P51f2foqn7f8KepDxV5su\nkSsxMKiswNUjdOEoWuL9UTYU2awn73j8IKC33OaOLOT8rWJZvH+tw3CQsHF2jaNrTXym0UmBLZUU\n7IHc/PKmFi3Fgdo6tzlAmpnlSnSnxJVPbZ6TxKLSkc3G9HbAppOXMHhyr3+gh/6PszRyfoLcLNbh\nF2lAxsHmU2ZIYkpcJTAKlaMZ5BPIZWSFh+68IjAWrQOU8ZPUsLyjSHfkzMx3WRrKLidq5hQnO3ph\nTNiTwOVgaBjNa8pOKWKeyEN1bwmXDWFPTPe8gV+59lO005Q3/ewuUAqc539+4Uq6tRp//qgnTE5a\nMQD01U7Bk00r8tr4hnuMje7YMqRQQtRwTKxw3QQ5qHxbKkgZKty8WmFN3D1dGExuLDaqbhp5gUtH\nBO0WPh1CGOCtBdOWLv0E131a0B2yPW9u7OPCFl4LlmZDCXRVVv7A3ghUMhajuQDxSqiKOkosR1Up\nPwsGgrEHQ+nuTSbfy53QE2SevBOhmqGIOELZ/tiaFhVqVdDzaQ+dYnLDDCJLXt19o05GHJSc0V7k\n4LAjWZK+TmHFzL83iokCy3eZ5ZTWMmE4oGnEvKlvkwlnt6VTGqqYXCzjYu4QPvzIB8Im0BkWxaqr\ns1zKST8VDlkrkuqi+wk5K/6QVdeGoZM7bqgUmXeT4i4duwxOvzw8mS8snclsPCC1IbEuxQoA6Jgh\noyTEKEe/jOmWNQpnMKmmE6dsSVa5pz/DoUGbu7KIRpyTjypK31CuqpopOJhNcUptidv6GxlkEf0H\nl+iB2CX7k1PCesberVMsHWnyYfUI3m0uwtyzwF/7C1BxnwERajDCTTdxSYjOLf2T6mKWpWDxgQob\nS5xZ56Q1VhdatLZ36fcSrr/zFGqtEdum1sgbkly0XLn1lVmA6Rt0Kba8+ZSifthRtmSbrtOK3dAV\nMZMuPLV9XcrpOsFiD5UXFFtnMIMcXUYUU5aHb7ibls6Pg+3uzTWmLI4tn8ffw3rXbpSb7EAzJ+nu\ngbIThlekLZl2mCqMYqA8Sos3iq1B1vQ0plOyQgbHM/V0wj9fSQ22qSkbmiCVfNF4WcJoxnRDFztM\npqprXWZsnUHK827YTZDCWx66k9++7iqec8tuPnDBxQQD4ZAfG6SjnODyJlOEq2KnbWtOGCpKYGAb\nyP/T2bq5F6qyFdBVYa9w9TE6YEM/8WTXRq6JrGnJClU9r8KFGo4sYaY7+KkWfmVVoJc4wlsvvPQT\nXP9pQVdKnQX80zE/2gG8GpgCfglYrH7+u977T57wK3/PGhPubVTBHdV5owvZgXiz/sef7CzH5/DY\nN0FD1PWEfdla6WLMa698YqqvBeCMwQUi4CjrFZ4Vrd80fCAUoyAuGeUhSVRgj2ERWKvp5xH9MiIJ\nCiJfctbcEYZlxLCIaCYZwyxiVAYspC1awYhN4RpH8jYjFzIX9oh1UQmMatR1xtDFJKogVpZVF1e+\nL54cw6Krc+toKyAXEMDAxjivOCvMgPqPBLfcm8dVK4VBCeSCdOrjNR5hREpxa97i6sULqFeD4qkw\npa7zCeXNKDcJea7rnL6NuWttFjMUyuH+dJqTGius5DWoS+d30E6jM01jxxrntQ+yN50htSGNICPQ\nliQsOe3MvXzznq2wqaTsRxShpXb6Gm/4f36O6RsMs7dmRHceAuCv2o+EqRYYg20IjjnaWCfsW1bO\nCqVZAPx0gR8a1tbqEEhak+uHmL7BNzMGRcRUknLX4pwMQ0OHL7TIwnsG5aC1z5K1NV6744al4UAS\ne4qGpphvYEONiUJJiQ8Ua2e3patsZ5zf2EtL22NN3GKl1LFbtv/ycR1j4IkqWbINGjpjvkosWrBN\nVl2dGdNnq1nj9PoC+0YzEyV05oKJnUJeXcxxUK4rRp0MKm0MfqogDgu8V8RhSTuWIaCnTrdRYPsR\nxcaCotAEK5qopzEpBEMl9iFenLZ8oCgDwCv+6Gd3gofnfWM3z7llNwD/dMYjeedZl9M4LLhJGUtR\nH98LaysOGxuKFpRNi+lrvDVi5uUVKq9QAy3FPEgrpXoIPpK3MQ6110X12WJHVJfzOwyteEzVrETu\nKU+8EhAOS9g4ByBGXVqjGlXSWKDpnz4HPzga+PvWiUTQ3QZcAKCUMsAB4F+AK4C3eu/fcmIv9cPX\naTNLHFFt+UOUHm0qcxwlf7CxHLe67ifTaTdWfFYUJHMMHmZyT5loirqwV6Caco8NwFBS3JtVITdC\nH3OBTL29AR+6iR9MXhjsarTuvlZCVgT0i5jZeDjxrjiaNQi0Iy3DifQ7swFHshbT4XDi/jcoY9pB\nyjCMSLR06CMvlgBjB8VElUSqJKFk1dXouYS70/lJiEBmA05rHqX9X5D539vHdeQtIYpQaTSWoRcr\nA1eJjQbO8+HlB5O7gEDnzEd9El0QKju58DUOW7n5Hco7LI6aHN4/Q6MvWZVrRUIrlGSkb+zdjl2K\noV2gHOyYXmYha3NaQ+rV33/rIWjteMrZN/Hhb/0MD9yxl++uzDL6VpO5i5fYUO/xjbtPx+SKpfMS\n5sqNAKydXidZrYblLU3R0Cyf5wm7Glvz0nRszPAjI8plp6h3UrSCTacssbDYYWOnx9F+g1Pay4y6\nMSp0mMjhF4WhE3VhXbxGFb22bvUar8p1EIykwESLKT40eKNxgVBpTQY2M2wK1iiOZ8dl4wi6H/u4\neiZNRaILDpTTHAA2BWvMmCEzVXEPcSSqoFvWCLWdBDWP17CIyKwM8LMyEAqiEzjD1RzNTjqBKa1X\nLKd1amHBSz6wm2it5A+efZnAJjh+96OfZi2s847HPoGwqwmG8LJrPsVUL+UtD92JLhVB6vj1b1xN\nPzz+unjn2ZeJMjyzE1nzpEN2nmhlhM4bDLYZfv1Tn2ZqKef1O3dCy0PpefU/f4JeVOPPLr1Usk+z\nCjYZOzN6gVjGHHW0R1VBOwC1qKAoRd3qE4saGZJFT7A6otjSIbpzAXvkKLrdnKhGi+mE5bNPHEj5\nUSGXnwPu8t7vUfeCGlFXHcALt3yZ12w8jXjFT6iEXkmX7iIp2Ga0LjLy1UBhPKCAalBRcUxNXtGq\n6vJ7RWMdm5St0DhuTji/ACaVi0R5RGloFBCQAy/71BfopENe8/Sd0BS2wes+dDV5J+DzLz8bgNlw\ngEWzMRL4YP9omplowJGsxVImCfQrRZ0jWYtRGRJoi65VnXYYE6qSpIJexgyCkQ+waOGtK8eMGfAd\nuxldmf3UTMEjmrdPBsw/xkD0xzqu1Z8MGHthQ6IUWkHPeVpa897ueXxjaSundY6yOelicFivaQUj\nZgIJDQlVyZptcLRsspzX2dedpnZPyGje0QwznFd0wpSDgw621NApCKISu9Ezn/SZj3p8dekU7l6c\nxfZC9NRIbIubmWz7r50lP08i5L5+yw7aezUuFM73YJtc/N0disU5RWNPgA/En9tOFdiGZm77Kkf3\nTxGE4hHkQ0v7GzG9Cz3nbzo4KWKjMqC/WuOuZJazTj3EXYfniZOCQS3AdAPCvme4UXaHvZOl07TJ\neoEAiWoMBpZ4cYgq7GTb7UJNMBLjsvb0kNPCFXKviZTDfP+h+7Gv15EPGQGnBH2c1yxZYSAlyjLy\nhn3lFIkqeGzjdtZsnW90t7OYNmmFGe1IrALman1WsjrDIpwEYSvjcLFD1SztmrgppuXYftejvWMm\nHbLz6ptIgpK3v/SxvOxdX+CZn/46f/vEh+LmMkbNAEqIvzbkOddeQ5nA235mJ6/46tU86/bd3N7Z\nctxn+dVbP8a7Tn0iKnco64TgEGpJhCodaE3UK0iOGtq9jCuuuQYfeF77vCfxBx/5GC/60ld4z88+\nEhc6lFMUDYFxbezXMXMjEZteId2MUxMTzHpYcHgg51nYyLHdOrVlhxoVeFXDj0aCm89MQWDwxpBN\nhZNG9kTWj1rQnwX84zHfv1wp9XzgeuC3vPff58aslHoJ8BKAk7b+4Jd7XO0ovzMNjcMyKHGBHdUu\nFAAAIABJREFUZP2hRcU/lgmPoZax3wusD0xlWu0Jhw7lqLoriZkCKd7Sfa/zzqMhRF3p2E0ue9ax\n75cwGRRhN2D6QMELvnodWsMbr7iU33nvp3jeJ/6dD1z2EG5e2AxK0UoytjTXuLCzhx3RIqcnCywU\nHVIbopHhZq9MWEybxKYkCQpWixqrRY26yTmvJqwX6/UEUlks22jl6NkaR8sWc0GPjXGXvpXgil6Z\ncEZ4FDjxzMEfsn6s47plq57ALKG3mIqXPnSWRMGqc/z72ikomCQHTQdD6kY+85jjvFw2sSjWyhrf\nWd7IWq9G6MFvzCavWzpDN4+54JR99IqEO/duYGauxz39GVaiGnfesJ3aEQ0dD0sNPuHOY7oz4Pq9\nJ7Hh0YcpnebA9VvYeItnbQfUjoriM1mU1+hvaZBPS4Ft7vf0TlKoQYBJFYP5iAvv911u/cyZ0rVt\nS+mfGqAXYpa31ZlLBpzWWeKe7gxRveDw3bNw6hJhVDIahaAgHEhXnixJBFnURQarev2clt3oMW58\nK6moB50UHmcMRx+gefCGQ4QKbDWk1N8vQPmxjuuGLcEEM78x28CmYI3tQZfcaw6WLbouoaEzpnTK\nrflGYl1QOk0jzGlH6XG4vlaesBJWjUVCBJIDXFiDCxWhsZI5CTil+YuXPQqF52kf/gZP+6hoFT64\n60Le/PzHExQWq+W5Xv+CJ6JjyxWf2s3zrxd45ZbNW7jfoYP8/f0v5i0P38krv3wVz/nWblDwzjOe\nDEpRtIxkCQeAg2TNiVFgpvjDy3eiHLzoC9fwoi+IN8x7f/6hvHbXk6VxdB4VCb3FBdJsCmVaunRf\niY90ZInD9YqsFET1nCgqKdY0yeJQdAaRRrVbqLUudraJsh49yDC5Izl64o3aCf+mUioCLgf+ufrR\nXwCnIdv2Q8Cf/KDHee//2nv/IO/9g+Znjx/cTaiLOmG0uZQhhRFMyiYI1Wi0ns49EQSMzbz893yt\nLHmLhmY0o3GxDFbFUkC2usHAE61BvOyJVz3JkiPqir9DOPSEQ08wkq9R15MswZ8+eBd/d9HFvOAT\n1/Gdp7+WF3ziq7zn0ofze7+wk9WlFquHW+zbP8s39m/jcwvn8N18noaWMItTkiUe3L6bzUmXVjCi\nU3Ut3TypMkkzrJcufOBiTBWAYb1m4GIKH9AyKdPBgMIb5sI+W+NVtsYSZNGqLpL/and+bxzXuRlN\nokQ9OF6FdxTIgPSTg3M4PGhzzvQCsS45JTnKyfFRQmVpmZQl22TJNmmZEUMbs1bUGBUBfm8dM4IN\nc2JLm9uAg2mbk9srAsGMEhqdEfebO0xWBnz95h107pDd1/wNjrCnUNqxeGCK1ufrHDwyxSCL0AUc\nPV+K6eoZohIuWiFFKyTqevx0TrwKvVPkZr/1C56wr8j2SXeatz2d24B9NWExKM8dBzbwlTt3sJBW\nbo/76hB6Fr47J/zzcWdqEf56osg7cq6HPUW0quncAZ07xAs97JXU7lkl2r8MgYbAcPiSTRx5UEw6\npylOypiL+xRewicMnvCYJvzeOK5TM+vX69ZglZEPuafscFuxgYGPJufqyAecFR7h/sk+NiU9Tmks\n0QxyVrK6dOaleN0M8pBBFokSU0FYL2g3U2ZqQ2qBUFXjoCQOxt4wmne97NHHvb83XvEE8iKgHIW4\nNMAPA3wW8JrLdx33e5+9/7m8/8KL+ZOLdqKc4s0X7+T9D7yY5U6d4caQdD4gnVVkM4psSpHNKvpb\nDHnTYEYek2pe9/TLj3vON//i49eTzKDyTa/me2qMoXtc5LCJx9XFv6cV57TinF4VRN6qZwyHMY39\nnnB5iKtH0shWuLnXClsPcI0YXYhW4UTXj9Kh/zxwg/d+AWD8FUAp9TfAx3/YA/+jNWa6nHnGQY58\n8yRMIcEHNpE0knHCB7Du71KyHhln1znnPoAykUGHC+Gl132KziDlTx+ys0oV8fz2dVcxMAn/33mX\nimI0cxVF0ov7WT/HhwYXG4pmQNaRo/Wmn93FC766e/K+X/vUyyFTUMrk28WevNDckW3gYLfNWXNH\nOLm+TDsYYb3ElO0bTBNpy8aky2LWYiWv0whyNilH7g2NqpCPvdNBeOuhgobO6NmE5bLBoZFYvT68\ncyebzY/dnd8rx9V6T10bes5O+DaJUgy954vLZzIVp9RMzmw4wHlNz9Zw1U1rTI3bn8/QtzELaYv+\nng6zt0HvZKhXJlxROGI2HrI4apIEBTO1IZuaPQ4NOxy8eSO6urDKugy/s2nPVHOE/kSD/nZF/ZYE\nXEJ70dM7RdF74Ij21xJaNx+h2CzuivPXrbF8/izZNDT2efonI66gA6E93lScAdoT96C5V7P6MxbS\nAN+NwMO+u7djcohTGCpQm0cMFxuYdo6qLnSTKvK2CFHybTnhQkTr7nWdRTC00p3PNlClQxUOQkNZ\nU2QzYgzVbKc8pv0dQOCuUDHB0u/N4zpmstycbaOlR8wHXc6NFthmQmIVsOJSht6z2dTYV2q+vbax\n8pdXHOwLvXZLs0s9LEjzkEA7jPEYU9KsZczUhszEoikAGYxr5Ym0OEj+4juuOe79/NbffI7XPPsy\n4Yg7hcrlun71R47/OFP9jD9+2M4JD72sa974+F3i2JlVRIlArdcOA3mr0rGUwpD73c9cfdxz/u57\nP80fPPsybBmKEaCRmYqPhAbnxx4uGpyxmGZBPc5l5wH0Bsl6BsmBGlN3ZajeEN+eESy/GRFs20JR\nC/BGSZi08+j8xBlMP0pBfzbHbN+UUpu994eqb58CfOtHeK7vWy/Z/iVemzyXcMhE1aW8cMZtPFZ/\nye/qAlRSwVbVnXEs8XeRFHUbetppynNv2k04hD8/40n82s1X8/R9X+Ej2x5Oc+9QHqwrvMuoClvz\nqCzH9DzBkkJvacKs4Tc+e/zBfe3ffYI/euwukiVoHrYM5w3DjQGjDYp+arihexK3tTfw2x/5LDPp\nkA++4ELSPGau2efFf3YNg2bEB654KMMyohWMmA4GNHTO0MfgKp8WFzMfZBg8Xx9uZy7ssTedZmSl\nF27o7N7Az3/s4+qAAhg6S89pWtoRKYUGvjbawsF+h3OmF2gHo8l8oGMG7M9nqetsAjEdyKa4bW0j\ndx6aZ/ZGxWCrYsNDD7GaJkTGsrme0StjSq852O9weLnN1rlV9i1OEy9paosSNh4vw2CTpE8Nrpsj\nmpYdWWPBoZxwv8OuwtyasOm6Hr4ek26owiNMi+2fsRy9v6a1v6C2bBhNiyXF3DczTBbRWHD0tht0\n4anfFTE8uaR5V8DgZItNKoqsVcRHNcUWz/SWNVZXGqjA4YzsPm3dY2NPck8slMZEoudAtBDx0gjT\nzyin6zIMjQ0z3yk4OB8wmvHUgSk9JKyGzmN66L15XEX6IfqHC+J9bAvkIrshm+JtRy7km0tbWFxr\nUhyp0fm2wdZEcLX6xCUes+VOlivl7GLaQAGbWj36uRTu0mlqYYHRQlHNyuC4Yh6okhe94ys8+cM3\n84lnnMfbX/pYXvxnX+b5V34V5xSvecblUvCAV3/kY7zoi7t5z2Meyeuefjmv/tDHxMclhzc/Wnjo\nLmQSbGMT1sN1KvaccP89w1AR9D2v+rcreeFXruHdj3sE/++LnsDrP3g1z7v6q1in+IOn7USmoMKG\n84HHjQt1sA4bJIl8vmElNCzzgDAuWesn1A8rwpURPs9RhaVoGaJVxei0ebKpgLBvsbEWhfCPsE6o\noCulGsAlwC8f8+M3K6UuqI77Pd/z/054hZWa7HH1BX6/DnrBU9ZVZVy03rUIm2X9w6lSoccD1HFn\nlqx7HOtS8ZaH7SIcwrNu280z75Du+qMbLuKv535OlKh1Q1EPcJFMu22s0GVSGfWI8ZLH85tfF/zt\nH867mDc9Zhev3H0lL/zKboI+/PHFu+hvMpU8W96XSQNcaOj3De6gZue/3cTyQpvX77ycKz71WXZ+\n7iY++tQLmI97BNpPEo0sipbKaOiMZdtkJugLy0A5NkerHMimGZYRm5IeAA9M9gMnHiD7vevePK5j\nz5ZGVcx7zlNX8M+LD5KttC5xXhHqksIbHLr674BeKYOizAUMioj6TTWm7hiydEHCyrBGmkZYq5lJ\nhoyssCS08oRRyVqa0KhnDGYTlNMoJxessrDxawVHfiYUPveyFPLZrx7BTtWJ12rU7+mCUej+iPpB\nKTSjjTH1PQPMmR3KhiFeKUSwkocUTSMahpYm6noGmzXzNxUcagSYDOIjhvLsIeXBBJMq6oc9WdbA\nPzpDBx63EuEST+kh6IsPUd5xNPfJoHOihPbgagFmLUUVEgidzockyyXBUNwZ0zTivKjHslMkyh/X\nnd9bx9Wj2B50OTNsMHSKNy09hPd/8ZFsvFZRWyyIPZyclgRLS7C0IiHWzQbfPm8r3Q21dYihYrD0\n85hBHpKXAVpLPms7GjEqQ0xltavx1TEOWa7V+ejTLuB9L38oWMXbX/pYrNf04xgVeHwOaEW3kfDe\nR1zM6y+/HBS87hcuQ3noBbUJwy1ekZlF2ZDcUh/ITgcv3ipjb5ZsxmIjzWon4W+f+FD+6AWPJ9CO\nP/7FS7BW06snok6vzP+cEmriOJVojP+aSD6fdZp+vu5DE8cFg14idMfIYJxH9zOG8x2mvnKU/Pwt\noqEZlJSNgNFciD0WS/tP1gkVdO/9AJj9np8974Rf5b5aSvHWCy/nWbetQyV/uf3SCXRzos/Ri2q8\n/8KLeevP7ASl+OOLd8kJE9cqOtUPuYsqxeuecRk4xYs/v5srdsv7eP+TL+J9L3soW1T3hz/2J7D+\nf3tcf9jyx0zOJ9//X/y6P2T933Jc3//Ch6Fw1NWYr6z4k196HMM8guH6773tSY8n6KnqEHhQitdf\nvhOTyZzkv7Le9sTHE24Yrl/fSvHGF12KdQYGP+YH+29c97n0H5h4uqQnFzQOBjgjbBQxgVeV/8r6\ncNQmakJTHLNXXCRE/jFHXeegrOMVNx4Plbxk8d/4i1N/Hq81ZU0sK8eiJq+gqOmJh3Le8pQtzxse\n+jjwntoBTdmUi/X3n385tu3QjRRf3emDSPBjW2qx2uwHJEcC3nbBU3jx59exwFc/8zI2FD0aec6m\nWo/pcEBbpxg8BYYl2yTRBbri9y7bJgbpclthRjuQweoWUwUP3Af+Ld+7Rn494MJ6z4zW/FPvDG5d\n3MjpM0fRypG5gCN5m83RGotlS7B0l0zMufYPpjiwf4aTbyno7qhx2nn7Weg1McYRGEdkStbyhI31\nHgvDFvOtAYtf2IIu4Fe//UlmD2W8Z+aJDDZFtPaO+JW7P8nRpQYfiR+FDxTOaMr5FsEtd9O4y8Ds\nNNn2KSLrCe8+LDfvU05B94Z07q5T1gzRQp90e3vSBCRLltGsKJmn7yh53p7P0bh7xB8/cieNoaJb\nS/i9T1zFWiPhb87/ebIZT7lax1st4QhVBqXYUiiSZUVRh+QoNA6JZ4f4FHnK2QbB0oBiQ4v+VsPK\nmUZizCJPa3eDndPP45Pn/T17SkVDlfd6uEGkLP/jnqdy67U7mL4VWvsyzuz20aNSHAC9h8q722+Y\nRY1y/NFlNly7jRtO2sqpU8sADMtIvFysYaaesjyEJCzZXO+ilWPV1vBekVQePmP2+FhpmruAYRGJ\nD5IS8ZFuelLjhZ6aCtzjwyqo2Sp8CN4J7RSEOedC1g3+nMLrdadWb9Yh3GKuxDRKwsihtZuwcpRW\nFGmAGgZiCtaoQqGzdUtdlZS4UuOsonQCmfRXqjmXVWTGEexJmLt5RHBwGZKYwY5pef00RWcO2zb4\nUB5rcv8jdej3fSWgGowqzYsv2k1ZHw8tqLwR/CT0Yly8ccJaGQ8zJuqs8hgLSzyv/MpVPOdWyRV8\n0AvfwgfPupin7/0yL93zr5R1CdZ1YTVENZXFpq4OfmUv4JJKxec1RdtTdCxFx2JnLLpektRyknrO\n1PSATiulVsuJawVBrcTXLKN5y6/f+C/Hfd7XfPDjxBXob70iOca9rmdrjHzIvnyWPfk8yxUD5FAu\ntrub4zUeUN/HA+r7fqK5of/R0kqRKCYsF4tQ6b6wchYAiSk5PGrTDDK6ZcJC0aZwAT2bsGZrkhxk\nQw6sdWjfHBGklqOXjLjf1CFOmlolCCytJOPwoE0nGqHx9LOY5WGNsAd527FhT8bzr9/Nr33zKmav\nW+DXrv9nfmHPl9lwqE+wMkQVDl048nYIczO4HVvZt2sjJi157sIXeWHzm6w+4iTqRwpGJ0/zihuv\n5iXX/ytea6K1nNqRgtqhFB8oakcttcWC2sEh02sDnnPrbl755asAz2v/4Squ2L2bzmDEcKMl6Cvi\nuxKxYo3dcbTY2hFFc7+nfsQT9yy2ZrA1gw8qG4jZGF+LKJoBjUMObSGfcpihYv7GAe3fjvjlPU9i\nixG47t42Tt6zbyPDV23i9PctMfvJ24nvWECPSihlYOmadVw9gXSEWutDUeLznNkvHyK9bo5+EdMv\nYqzTHFzp0M8iCmuohSXNMKf0mtVc6J7TyZDSmyrlyJIEBfUgJzElpdOERmL9okDcTKOgJI4LdL3E\nJQ6XVLCHlqLuA4mbs3WPrXvKpnx1occZP2GqHKs2VyCpQyI1Ic8NRRHgnGKQxozSCDcIMYPK76mC\napRV8vpJJfEPHEFoKUtDt19D9Q2qb9BJSXmozsbrLeFKim/VIQwmN3i/bSOuUsoXzYC8E0ysSk50\n/VR06GMc/WXTN/De0x7D/PVVZ+6PSSwqjjGyqdbEP35s/lbJcV2ISKTrNT5wwcW8+VG7UE7xpsfs\nxAXQi2r0t4THOaWN17GOjjoX97bKnkICNMxYGOFRRoIVgsASmMoBDknUKQBdK3j1Rz7BFZ+/ln/a\ndSF/+fJH8dJ3fokXXHkd07Uhn3nVuWyI+8SVUjRRBS2dsmjbdMyAvfkcsS7o24ROkDJ0gkmeHR2u\n3m18nzksHru895Nw6KSyAThoFQvDFkZ5GkHOfNSjX8bUTEFqJeAjcwHLRWNiOzvsJswve8ygYMeW\nVQ6P2gTKEYclaRFQC0scYnqWlYZTplfIdvW4fe9G/mLHkwn7nmd89xqeevg6AP5l9iH85ZbHCxbt\nPDYRT5Ajj9400SMsPKgOA3jmjbt5wMpdvOShv8bL7vgkv3DPl7l9agt6Bt679RIAbD0kGMqwykaa\nEMVfbn8CZd3wnFvWJeb/cL+LefMjd9G5A9JNYGOPORKhs2ogmnjCriJvCxunfshjUoetVbFu3RwX\nGkZThqzdFlplE5r7HUVT07oHwn1LoBWLv38q7/uz+/Ps9jc5aO/dpCrTTQnuPCh21O0WPg5F4LRw\nFG8MNOuoLMetdVGNBnjB0Mt79nLSJ5vcvkUi8c44+wBBILL3XhaRhBJOMiwjulnCSXWhwx92mqBi\nfOVWXBtzZ7BOExtJPkqLEO8VhTUoBSa0qLbH9sN1rUrk8KH4qPjKH0dnVQAOMucaN4ySnVCVAS3F\nWRUaV2iMcXgv17j3osydPLZU+Kiyy9Ue3SxwhcavRtApaNQzRnmIHQVQZYo2WyO4sUbz2u9S7thM\ntr1F464VdC7+8MvnT4k6N5XwlDJRJCtOjARPcP1UFHSAwlumTZ0dDzjAyi1yIugMidarOud1NVa1\nZa0YLhPpfyawi4vApPCuRzwBvFhoio+L5g2X7pIOSHGcGf332gp4LZCOKhVKOQmlrjlULN2JMlK4\nlfI4p8iKgMBYosASBSVJWBIYC/Nw5dPP52O/9QDm/YC//42LaIQ5tqOZjlI2hF02BAL0uWPS0hfK\nDoU3rJQNzkkO0HM1FooOYWA585gt2H1dzEGYEBrxazEVlvyepUewliZM1VNKr1kqpGg7r4l1iUVz\naNRhOVunXaqVkJmb1/ChXMzOK5phRmAcRWkY5iHWNzh7+ghFEbBvdYowsKhuSNZRvLv+aJ7BOrT1\nzvMuJ29HuG2NSqgjg/PaisOGiunbHems5q0XXs5D9tzFOUcPsvvjrwLgjuZmzlw9yM21kzGZJRiU\nAs8ZLeda4aqBm+Kd5zz5uDnNGy6tfLiVJxgAKPKOw9Y9yWEx5wKo7RdOc7LmcKEmWpPORKdyMnbu\nGnLwUQ3CPuCge4rC1hzNgxYfhyjriPev8uE/fDzh71ue2Lzl3j2wYYDfKnCYWu3D4jIqSWB2GkYZ\nLCyBs6g4hk4TZR12pkkQhrhv38WmL10AwIHNHR64eR8LwzZHh3XWhjWaYc5JzRU0noNpm2aYUQ/y\nKoBaT/xfxst5Cfj2iLfT5C2Gsh9MczNJQkJ5VCDNl6u6Pm+00AuRok3ljKgLNdG1jMNvqAJylPYE\ngex7ioqpMh6eKqukNuQKInBpgBpp9EhjG5rSaVwFxeqGHM9+t8bmfQ63vIreMo8uQmwrYTQbkiyJ\nejgYQrLi0YWjjCXLNOifuFT0vq8G37N2bb5R2CbVZwhSP/E7H3u0qApymXDQkc58gp8XTCx2BSMX\nGMXG0rnbGIqmp2h6yrqnbPhK7CE3hDJZ7/5f8elP8+qPXo2PLGEzxxjLH/zDx3nFR/6tSjFR1RbQ\n0ogKNjZ7bG502d5e4bTOErf+1la++Dtn0gwLWkHGdJJy1SvP56sv3wHA0EXk3tDSKS2d8q3RdhZK\n4ZkXztCzCfuKWfbnM1gUD659l7qO7rN0oh+0JDxYAi0KPF8cbeCO/gYC4+hEIwLlqJmC2XBAzeQM\nbMSBdIrcyRa7m8d085hN14Je6eFCzZ0H5wmUI9QWox31OMdoEejc3Z1l28wqp04viXVxu6CoeV56\n4NPHva/n2k+zdK5h9XTD6hmBWNWWYNIq+itQRH3PzHdGvPzMlxz32DP6h/johot412lPFF+X6ZjR\nXEJZMxStgDIxjOYThptjfvWWjx332P/xpStJN1pWzreUDcHHQdgQo82WouUnuz7lxNPFG1CFcM59\nFFDWQ7HJnfb0TnEMTvJkM45goIhXMgk/8B5VlEx/eT9/8cEnEf/4bhzft/TaAL24Klh59Zo+MPg8\nx/d6Ul2NrjIxY4Fk4gjCkOl/P8T0vx9CXdfh5iNbxN9Ie4rCsDhokNqQ+aTPsIzoF/EkzHu8xh4/\nwMRi2ShPEpbEgSU0dsLr1qFbD4vPtQRSaA+h/PPx+rXilZ80dMeBGU5mb0Ffo4aGIg0ZjeSfzQzk\nGj0SR0YzkrSlIFXoVKNGVdjGVIkKHMNBLClLpcLlBpcbktsSWnf1wDv0KCfsFqBVpRj2lTusJ+oV\nxMsZysFo1tA/6cS1Jj81HfoYdnlq89u89dQnMXOzmnTKQeYnA1IQmpGvRAFisyvPMcbSx1TGcRD0\nBKM6xmddlQpTZQzasIJtqpNjjKXr0jO1lvL863fjQs+bf+VxvOpvP80Vn7pWcieRoYmu0shrQUEz\nzJiLBjSCjJYZ0TQjjuRtCmtBQ1vLQFMrP7HQHYs3Bi7GoujbhOlgQKgtIxfy3XSe6XDInBlyejhi\nTFX8aejOQS6KYeWFPvKe6wenMhsPiHRJv4jplTE1I5mbTZOR2gg0NIKMpaxB90tijHXyzYv4MCA4\n2scV02ytrXJo1GFjvU/p9aRjH3drAA+c20dWGF7x9Wt4ysr1fGTrw3nN05/Gb95wJVfs3o0PPX/4\nxF0ky5qVc2UwprOAsin++cpCOlPndz9z5fd9rjc99mmUDRH06FIuuGCkyNoaZvTEBOqZd1zDP559\nMW+/4HJ+82tX8fzrd1N0HO/6jUcxf9aAPZ89BR96TKPApTHKQmuvYLxlUxGMrAzmw7G9lsVr6J8s\nF7JtWZK7w8pZEPQwx/f6MDuNKkp8u8Ep/7TA48JXAr917x3Y0krx7jTQawPUdEcK+uFF/ChDz0xB\nqwFLq7jVNVQ9we8/hNcaPTuND+Sa3vaZVQ73Z1h4dJ9T55dIgpLeSARknSglqoQ3g0pjAJJupCte\nutFOumLlCbVkD6TKkxJivTRV2lic1niUDJ4B7/U6+Sj0qGEVUGHXKcbHhuOoKu9T8HFNGWu8qYai\nhXTfE8jFM5nhucRNniNs5pRZgOsHcjMJPBTymdp7HHrPYWxZ4uMQWw8wwxJTePKmIuyLSt30c3Ra\nYPI6NtaCRpzg+qkp6CCwy+agSXRKn/Dfm1KYtXQwEj4hvye8cSZwydjz3MZKbHcreObYDt5XGYZj\nd0aTi+HXZAtVFfFgIDsBVxX5P33wTnQBL/7fu3nx/5bt/Lt/7hG87hlPRpcOVZ1gSVSQWXFfnIsG\nGBwjFzK0EZkLKL3E0QVa5NJjj4y6zmlVDJdVWydUlmnZp6PxhMpSVgZdlzRuY840f3IH5ASX+LQH\nTOmSW/NZDoymaBgJx85dMOnQUxtSeEM7SMlcyP7hFDffcCqnfVE+r48CdFaI6LsbcCCdYnt9BZco\nrlnYQTPK6YQjVvKaDNCU5fCoTWYDunGNf5m/iNftfBpFR/GHl+3EJZZeWMPVPYO5HNU3mKHGJoKp\nmkyhc88rv3Ilz7ptN9+e28I5Rw9OPtffffZtPO1Fv0mQShdVJoA2hH1POBAecz+s8cGzLuZPH7KT\nsqb4s/tfhg0VvU5ILSo5sNYh3WQxA01ZDyB2hH1DmVRh5F52CuHAkU3JlatKUR72txjiZfBatt7R\nGmz64jJ6OMJbB6tdUAqVF/hGjR3/eJQ77s0DGxhUfwgLR2FuBm+0MFlGGcpoaDdBa3wqTQrLa/i8\nQBmNrye4RqX+3L/I5i9a9jVnufN8w8xUH60d/VwsATrxiMBY+kUspnVKwMfECBZSaovT4rHuvGZk\npWw5z7ovjFcTlSa++r6ofgbr3fu4LhwTVDGxnKk6dOXB5YgrYpVXS3XzH1uPKCMKVY+wa1QhEEwx\nCiAzE9YLhZYhKtA4VEjqlBIVaN4JiKtgehtrwqEj6llUKbs0XXjqCwXB6MTjBX+qCvp41eKcoBoM\nuIlPwjoM4ytj+rFpVzjwE4xUebVOA1ZMgqEnmDlMUkbGpjr6mAMVjDxB6jGFONrZSPPjw5HoAAAg\nAElEQVTOcy7nuTetY6Rve8BTad0qg6qi6cmnSoLAYkMRz9zDDFOVZ0sjyAiVCCYCbScT+5mqaGcu\nZLmiKY58JNRFL1BL5gIW8xa9Mua81kFOCeo/FUPQ712RgrNCQ+HhpvQkAuWYCocs53XqQc7IBjIA\nzevUTMGRosVtRzewerDN9O2KbEYu/MZqCukIu32ejdfBnWfMMRf3SW3EeTOHaQTSnd+4Jr7wj9j8\nXe7szXPu/AJ/c8ETsA/xxD+3RHe1gcsMr3/uk0Ar9KKYbamZnLKl0SshtunwfUOcKbpxwq2bt3Du\noYP87cNEoPKxt7+Vcw8f5JVfvoo/uWiXUGWtnDNhF8pYDOT++oGX4pG4RJsoBpsC/rS1k6ULYKM/\nwmCQEMxLvJzqBaiZjLwdCHTSdQQjUbcqv64KtDXD6mkhvR2Oxn5N504YbobO3Q691gejJUg4CKAe\nQ5ajBqkwJu7NZS3eOXEADINJ9222bcZHITi5qXjrUEmMW1nBbN6Eb9ZQ3YEYigF22zz6nsOc9KGU\nheWtLD4wZGprF63kegm0o/SGtAxphaOJWjSuCvrIridRaCUd8yAPGeUhzsnw0jktTo6hw49d/eAY\nFktFK7QKb49RDagJ7D4JydCl1Jsy1YLBe4UeacxQYXJFkEpQUjCWh2pD2bbyJJmR4m4VZJqgr0kW\n5feSu5dwgK7V0EfXiDsxyguLL15zmMITDC2qsGSbW+QtTWtPhumum9P9Z+unqzJU69SpZVyoyJtC\nLQyy/8Pem8TammX5Xb+19/6a09zu9RGZlZlVuMrGLe5t2YWFBIUNgiohythm4AESEzrJTDwAiREM\nGGCQkJAHSEbIWJ4YjGXAVo0AAa4yNlSVTTaVlVmRkRHvxWtud875mr33YrD2951zIyKjIjJevoh8\neZd0dc8997Tf/r7/Xuu//mstG1ThhzJerpz4udp74DkcZK5Lm4BDffrUSMdmlu4bgI3rPR0zeebj\nWuY+D9V15N//P2+G4//B3/rvCdfZBswOIIMjJUcbIpVPOLFqt6jO5oyW4bmNs0x9JWke6vAb/R3O\n05In8Zgn4zGdWoe7PgfO45LHnQ3f/ZmjX/7cAflkz1PLz3715/hd/+O/yy+ef5kvti94b1hzr7mm\ndpHgMk/7NV6Ud3Yn/OP3HnL+5Ah/7enuCn7I+CEjzy+gbQi/8YTjr17x9NkRu1TPk5l2qeKb1/e4\nuFrwcH3F82HJn7j3NX7l8Rvc/4dbqg2c/8pdNDp8m2h+fYEmwb25Q4JSf2OBO6/gQY8/HtGg7B5m\n/ouf+VP8wk/9Tv7KH/lp/pOf+Tnwjn/tT/8F/tvf89Ns8wIQqktrH+Ci1Si40qEztiDIPHw8VyaF\nXX7X8c7jUxbLnnFX2TCGKhN+o6W6togztsK4tB+AsLNuf9v7gd0De/xwqtRXysO/N3L09SuGr9xH\nrzfIcoE0NfQDGqOB7/X2oxfqk5r3cHqMu38X/Y51DZDlYqZSZNejV9eId7iTY6Su0WVrXvvRErft\n7OfXvwt9T372gvv/3f/LT/03Hf3fuzPTZu9tV6zLZn0xLKwRW7Y5pLtU0aUKJ5kuVTzdrbkcGrxT\n2tquod22RjPkLqBbD8Vbdp0zjXhvA6UnV1yibcZuFFwsNS3J1jhslbBVqito33NUzwLh3BM2cmMq\nWnVd+taPmJcfrQjDdY7mvZLQVTj6dTj5ZuLkmwndlEIlEfR4xXgUGNdWqX786zvCJlO/t0Erj99F\n1EFcBrovfPyo/HPpoX95+Zxv3fnJOSE6FjnXVPrve6Ha6AzCkmFY2+5KVlIre3rmIFk+8+sFF9VB\nrpXUWJ+HXIqYwtY4LbLwF37pb/Hz3/rf+Ws/9dP8Z3/gX+bf+3/+Jn/uV/9XhmP4j37+Z4kPIs1R\nT1tb34b7i2vu1FsWzuaITq1um1Ly7kTxkulzRVcmu2wLd964kayOp+N65thrF/ldR9/hDzUWjn8e\nQf3Fbxzj//XEyb8a+OP/9Dd4uz+jcZGzasva93xze2++eN+9PuLqYlH6Iiv3fjmx+LoNpdC7p+Zt\nLlrctufh/3TE4y8fcafe8Ki95Gm/5ruX1r/FRv15/sHFl+h+7ZjhJHL14xDvRBgdaetJb47WnvVF\nw+o3AmdfS8RGEG3wvcnNJENz3vHXT6x47Md+YcfjP7Dk7ldH/vIX/kXi0nqo5CB0Zx7fmcPQXCbq\ni4i6mtRYUhOFuLT++24E3QSud2skCcOdzOKdQHc/EZfC8TesvbMvzgpYLQRYzijXSri02Zb9iXD2\nS8+Rqw2+rsyjXC5MaQIW+u926CcYVfaxTBXOL8nbHeI9cucUbWpk25HPL8hdb9Ppj9boZouOEckZ\nhpKAzCX5fHGFVAEJNifT/fKvcf/e7+A7v+2Y1VFHzo5n9YqUnfHlWDHSdWnxkLJjq7V1JY2BbrwJ\nWz4kUrTKoGkwM7DXmk+WhFmsPxWARtuQ5aDYV7JhgIvgO7kxKc2Nk+jCmntBydf1grsOs2LGWjsI\n41qofr14ncOItA3a9QfD5sENijqhfWKdF3cPF/ghU+0Ul5Rh/fHHS34uAX0deuLSQBu1sluTYNsB\nrK+syMImvljCqr6yXg0uQc52gKTw5bCXPU4683kyd568dSU35sGHhcmH4sLz/HTJX/2dP81/+ses\n7P8//ud/jtTA+XpBOlYWJx131luOm447zZbTajd735VLLBhtmAOW1HtQX9Lniqfjmk1qcCiPx2Pe\nqM/J6vjG7h4xe4JLPN4dczU2fKU2mcTnkW4BcH2E+w1Xf8i4cbBJN8+GNXfra77QnvPW7ozLbk3K\nwtHJjsu0ovl2YP21Z8SHpurZPWw5+ofvoFUAVdZvdWQVng8r7tdXPGWNiPL44siS0PdG3ro8wffw\n3u+pGO8PtCc93bOFcaZ1xr/dkh+MdPczw9uO+jqzeNyRa09cWh/uuPDl4hT6s5rTX4vUL3pkiAx3\nl+TaEXaJ1ZBpzx3D2hJm4xu1FaF5687X3bWLvz5Xdo9KdWHvqB5tGZ8sqC9sQEVcKf0daN8z5yNs\nbA6AG+1k7U6CeX0eJAvH3x6NTgkB7XpI2bxxzeA9utkasC4XL7ksXRDn0JyR+3fRpobgDajH0cA5\nGFCn5+fGq5frVVKG0dBNvLMmVE2DnByTHj9h8XhH3izIq4Guq/iOnlCHRFuPVM7PEkUBk/mqYztW\ndGMw1UksUYJTnNsnJQ9lK5JvgvvUu2V6nGTwYwH1VMB6Floo9LauuZqieDEHMk0FiPYBpcgYm+e2\n/uPK2i1nb+xA/Z7Rr7rbQVXZMOirHfWLhlC6u5Iz7nLH8MVTujPP6nGmuowH5+bHs88loJ/4XZkv\nKjQX1kp0XJlUCEzKaIOdbW7o9JjUeMZV8ZYKZ+67Mt1I9uoXWwSZx85NFE1pYshwosQVhI3wn//J\nP2k7vVphwnCa+Q+/8i+hx4nl8TVNFUtD/46HjenJ+1yREVyhhqKGkrXXWdFyWRpSBZd4pzueFTHP\nelOwxGycohPlkb/gsx4C/ZGWM7ufvM8f+y1f48lwxIPamod1uaJ1I2fthnXo+dXLN/jC8SVvXx4j\n156zryfysiZX9t0WjzsDg1yir93I1969z+/+rW8zqm1ydUhclSTTkD2/4967/G9nZwx3lOXZjhg9\nsoy4xw2+h+Feov12jRusPerpV7f0d1vi0uGi0j4d2D1sZhpv+8Cz/m7kxW9b054nmucD47FnXAXa\nZzbgtgZQa0GhYgmy1FhCbTiB/o6F7+PKo290DBcNbhS2bypuAL81YNk9shB+XAlHb0W2j+wEzDW4\nJNTPYf12Zvnr58j1zqiW1dI47XGAqoa+R+oaxEGMH1iaT21tg/Pe1mXbgXdQBdwbD3FdT77eoM+s\nxB/nTLIIlhwtHDrO2edLCQlWiz+c1LR3d5wsdwx9YLdp6L3S1RVVZd/Di85ig6zCEAM5O9Loybtg\nE4FEbRCzYonLuAftaVykHdRJYy7Fy7bfvive+GjR/hRpzQKKZAOl5/YjhSWYFHfmze/5ehVsME9t\nUurlY8U/scKpOIy20WWbQBWuetzl1ipsVwvyegEKq8cj9Xs74knD7r7/RL2nPleAPo2ka9xo3RMX\n0J94ql3GDX4GyNRKUapYSDIdeDcqYWcHMxVR7sSP6yEelmTp5Oy60TrfQdltMSomYrt2rmA8jfiT\ngaNlmbLjMsdtz3HTUbtI7SLn45LgkvHmbpgH5TrJLGRklyve7k+tJ7QofQpUZbUu4oJRPWf1jve6\nNe/tVpxvFvz+N9/i9zYZ8J9L7xwAVZ799pp/dvWYi7TgneGEr7RPeTIc0buAk5Yj3/FPrJ/y1cuH\nXF0vqM8d7bMe10X8kwt7nSpAP0BdwdUG/ca3aP/eH+bsd254Oh5xWu24v9wQk+d33/suP758yj+6\neoN7X3nO77j7Lu9sj7noW86vl3SnnrxzEDI5KM0LobnIxKOa6y9aaLx8L9Hdq9k8cJx8y0Bk8SxR\nXQyEY09/4oiLllRRHIaaxdOI73Oh9gLVTomtcbL1daa/68wruzBQiC9q5CgiDyP6dkt/LxktcO5Q\nD81TiwYvv1wTOjsHr79kiVc3wNHXr5BugCrYFPhxNA99GBFx0DQG9N7frLh5GZYiut3ZhjEpDVK2\nBGkVkLFw91OCFszbVMXt8ky5AEhr1GM+v0CqmvOfrPnxe++QsmO36rjetKTkGPrAOARCFamqxKav\n2WDgnlRs9N/UWyVZVKUFF1zvcL0Yikz5tLkYUeZ829QT3XfWnrvaWmIybJPJVItazgbMl/oXYAoZ\npkE8UEZjTn2nFuX+DOORUl8Kd/7vF+Sy4blFS951uKMjdGWN/bSu7Dh6x3inxcVM+7Y5RBKrWZzx\nce1zBeiTHU1a7YQNi95BfW0XDjCf+KHLJVSyHhl+nLj2m6PnDrkx3xuVpt5K+6cNwA0yzxfNhWeP\nS7Uz4nTk7HRDU930gGJ2Zc5lx2m1pc+WvGlctB+1ikjYe+RZHZcxcBUbuljxxeW5gXu2pXgxLHi8\nXZOyI/jMHzz+Nu7zmbuebbjfcvYvfJcXcUmfTdHyrc6oo/NxSUbYpYpn3Yp3L48YzxsefB38ZkQu\nN1agAibFy9kAJATCm484+3rk/3j+E9Q+8VvXj2mOIr/39C1O/I7ncUVG+C2nT/n6+X1OWwP8bV8z\nLCJcNzRv1cZHb21tL75S077I1loiK1dfDBx/OxJ2trbDUY2/6q2NshMkKeOZo9ooR9/uiAtPXPtC\ntQhSemkPS2F3X8qFbRFk/ULIX+pJVxVp56l+bIt7vCBshOF+pP1uRX2ls8Oxu7/PFamDo+9E/DtP\n0ViAdbmwRGUIsNsZkGo2MA/BvOeXaYpRLsvWpJHL1toAXG7Q956RdzskBGSxQHcF+GNGknmg2pvz\nk7sef3IM3pGfXhK+8iWuvgyPgIxwttzhnbLtK8bRk5MnJ0efhZyt0Z2rTWyQkrPhFgIaSol4tmRk\n2IkBepo05QeAXpRuk5qt2tqUstCbI+iHg82nVIu6pDfaeFuPJ5lzHW5UiNgM0QBxDanW+T3b54q8\n+x7UpSWD98gw4k5PUKxoCzH5I8OIiyXnUAfSusZfDywey9zb5+PY5xLQa0lz8lIFUu2or5KRijAD\ne7W1EMglJbY2cCB747l8B3Ehc/Jj0p/6nf2OS1OoTJOOLDkh5FrJrVqxAFa2uz7qSq+WfdLGi1L5\nROtHVqHHiXJabTnyHaN6RvVUovPtqJ4vt894MhyxSxVZhV2seLc74qfWT3inP+EKK3G+7hr6PvBH\nvvwtfqJ5TMY89M+rrU53/ImHX+fFaIUwdysritqmmufDksZHVn7grfGU6ycrFt8JLJ6N+PMtjKNR\nB4DkHoonJ0crdLNl9Y0X/IOvfoV/5ff9fS7igne7Yx40Vzwb1gSXWPiR72xO+eLROSs/8Lg74my5\nY9kMvLu7Q3cSab5TUW2V9Vs9V19uigRWefFbK5oXyrh27O7Zhnv9RWH5ZDH3119cZ06/PpjqwIs1\nzxJLZg53HduHQn1hF3FcWB1DXMH1VzKuE/huS/XFLfHZgvisJTzaMjxvqZ6HEu7buThRgbBXbq2/\n9oKpFFIWLYgzGqPyMDaWhFyv0FgomJdtE4USvPHhOSN9tGHGux2a1XqgT4UvItZysyuP6Q8+U0rg\n7Lts/smH6Jd2XA+N9cr3kUU1IqKMwVsPlOhI0Vs1dhZydKQks8YbQEXLJDIIOzFZYZEdThTKYRI0\nNYYNbjCn0I8G5pJtsHNcuz0njhUWusFAPXsDcz1YI1+APhWOHbV+T+pg+Y7j5BsbdNfZhgu2wWk2\nff/lNem9pwbu/YDESDhvGc8WpLU1ZKueb61N8CegXERfYe9mEbkCvvrK3vAHb/eAp5/1h/g+7Muq\nev9lvZiIvIel434Yj8WH2Q/rusJLXNvbdf1c2cda11cN6L+kqn/glb3hD9het+/zaex1Ohav03f5\ntPY6HYvX6bt8L/t8k7O3dmu3dmu39rHtFtBv7dZu7dZeE3vVgP6XX/H7/aDtdfs+n8Zep2PxOn2X\nT2uv07F4nb7Lh9or5dBv7dZu7dZu7Qdnt5TLrd3ard3aa2K3gH5rt3Zrt/aa2CsDdBH5kyLyVRH5\nhoj8xVf1vi/LRORbIvLLIvIPReSXyn13ROTvisjXy++zz/pzvmq7XdfX037Y1xV+NNf2lQC6iHjg\nvwT+FPDbgT8rIr/9Vbz3S7Z/RlX/qQMt618EfkFVfxL4hfL3j4zdruvraa/RusKP2Nq+Kg/9DwHf\nUNVvquoA/DXgZ1/Re/8g7WeBv1Ju/xXg5z7Dz/JZ2O26vp72uq4rvOZr+6oA/QvAWwd/f6fc98Nk\nCvwdEfn7IjKNiH+oqu+U2+8CDz+bj/aZ2e26vp72Oqwr/Aiu7eeyOdfn1P64qr4tIg+Avysi/9/h\nP1VVReRWA/rDZ7fr+vraj9zavioP/W3gxw7+/mK574fGVPXt8vsJ8DewsPSxiLwBUH4/+ew+4Wdi\nt+v6etoP/brCj+bavipA/0XgJ0Xkx0WkBv4M8Ddf0Xt/ahORlYgcTbeBnwF+BfsOf7487M8D/8Nn\n8wk/M7td19fTfqjXFX501/aVUC6qGkXk3wb+F6yx93+tqr/6Kt77JdlD4G+I9aYOwF9V1f9ZRH4R\n+Osi8m8A3wb+9Gf4GV+53a7r62mvwbrCj+ja3pb+39qt3dqtvSZ2Wyl6a7d2a7f2mtinAvTXoZrs\n1j5ot+v6+trt2r7e9n1TLqWa7GvAP4fpVH8R+LOq+o9e3se7tVdtt+v6+trt2r7+9mmSonM1GYCI\nTNVk3/PkqJqVNqs7SFYkKTm4ebiqi4BADge/KdO6g+JDIpQBzSLgyGXmrCKAiP2O6uiiPTm4TFYh\nJs9+3ypTo9WGy5LLb+x9yDZFfBrYi9oUcHX72zcsl//LwYTxg9vTa6uz4bNaYqLD/09TxpEPf+70\nkWUadn04NFaYj6e9t02zB2wob9aD4cMKMbFLlwxp971GiX/ida1dqwt/fOMzzZ99uiFiQ4cpE9u9\nQ52QK7EBydigXQ2Az4SQqV0iuIRH8ZIJknDl9bS8kaA4URwZL5mkjoQjqieqs8fLdAgURVCVMjd8\nfwhkfl27L6uQ1JERsu5/FDtfspbXUZgnBx+aTh9S5jWTaIPJJdl6SVZbH82Q8/6YqUJdkWtPbIXc\ngq8TyzCUQ1nO+f1Bnu3JP37x9CNmT36ita3qlbaLs/n7iNowZRtoLfN5J2OClA/W2dZ6P1A523dS\n4MahEijnAVLOz1wOnAJOyv/k5vNE7PXEhjarL8ObhXm4/I3bYFyEKOKnC1r2mHCABRLLWqkNmpao\nSMq2Vlp+cgZxdj47KWtX/pey3ec9Gjy5knnQN6r4XWR+Y3GodwdYo2hwNoza73FFHXSPv/NR6zrb\npwH0D6sm+8Pvf1Cp0Po3AZrmhD/4+/8t3JC5/PGW7KE9z3SnjmqrpFq4/qLMX2Q8VtJCkfs9Zycb\n1k0PQO0STYi0fqT1I5Vku/BFSSo861dcDAscShMib18ec3G1JA8eHRxU2c6J3iG9Q2K53O+MaOfx\n186ABdBKkUFQr7h+z1CJgt8JvgOXIHvblCQbMPm+PF8g1xCXit8JbrS/p5PbjRB2tmhu3F+gviuv\nFezxKoJLiiRYPrWx8FqmqC/e7ci1t02ydoSrATckxtO2vFbEdZG0qnHdyP/1y//VS13X1q35o/d+\nvlyMgBObTg9oLptwXaOLhrxqycuK/qxmOHJ0dx19wYz+bkbPBo5OdrxxfMkby0vuVBvOqi0nfseR\n39HKCMBQdoHWjdSSaGWklZHL3LLNDedpyUVa4iTjC/A5yYzq6XPFqJ5UrnYved4ocrm6tqlml2p2\nqWKTai6Hlu1Yz8Dex0A3BoYhEEdvGJTtueKUPHqIAlFwg8N3Qn0urN5W2heJ5sWA2464fkQ2O3Tb\nQYx2zIYBeeMB46MTzn9qweVPQPqJHb/tzcfcaTYchx5XdnVXdn9fRsP/pd/317/9adb2xvXanvL7\n/+i/M//PDZnUOMYjjzqhvoi071wj7zyDnCAEZLmwdW4q8HY83OUOGSPEZGAX7fylrhjfPGPzZkN/\n6lAH1bXSXGaqq4ioOX2pccSlbRCSwSUlVTIDX6qEXENqhLiAtLDrLS0zWpeNJiiuSvgqIQIpevLo\nbM0USIL0jrBxdp0OsHiqLJ5m2ucDfjPidiP0A9INUIX5e7puQLadfa/g0a5HVkvGRyds32jxg61R\ndRXxXcQ/u55fIy9b8romBzsX4yowrj39sWNcy7wh/cpf+gsfta6z/cBli6r6lymTQo7XX9DhJNAf\nOZqrTPN8ZHe/wkVoziOXX67ItS1aapUcQL3ifaIOkcbbCR9cZhkGamd/55vbPl9ePue6aXhrc8Zm\nrFlUEV3v2HY1g1S4oOQ07Zpy8FkBr+QHA9p7wrOAjgbmiAH37GFPDknxrrWGWJXdPUFq7LFxMe3G\nMJ4o1aWdhOog12qbSfEGUmO3q60S+r1nKx2oU0JnP35XQDIpqfUMZzUSldAlfJfQ4OjOGgCqq9G8\nYS+Ep9fIMJpH8RLX9aR+qOIcmgxsyQ6Cg+D3zkdTo8uG8U7L9kHF7r6jvwP93YScmue5Pup4dHTF\nWbvlpNpxv77mTtjwZvWC1o0ziAOsJBUgztRiANFpRcbhJNO6EdgyaiAdnB9Zb6aNJjD3ZUHb8loO\npRKLEIJLBuIp4IEgmeAywSc2BVBVQbPDB3t+8koaPJo96syzzRWMK/CDI+w8ZEVUYQiUcHM6uMhm\nR/Wi5ug7AUkVL+oF316c4e4op9Vu/i5ZbaOqnH3eT2uH63p08sWbIYCFwgasSQmbiLvuyJsNEgLS\ntmgVUOfse8X909U7JGfIig4jEjxaBfo7NddverZvKvEo4Tee6jpQnwfqK8WNoB7iAnKQOfrL9T5q\nBnOicmW4EdcZbTNuEfG+bOY+MxWF5nzgnU/fKyiqmQjkSnC94KIgySG5onZCEEHmSLM8dSzHfBjR\nmOD0CMlqzpba5lNf2nURXuwY7y7xVYBdD2NEuh6pPARHXBkc+0FxEYZT++6++/jr92kA/RNXk+Xa\n0T4baF4IqfFc/ViDOjj7x9c8+z1rhlMDT4mC3wmpyXAUWS0GKpepvR282kVqF6kkz2AesweXqCTz\nfFwB8Mbikvf6NU6UmB2pjnif6bsajSXUEcUPjlwpJIGQaZcDvauAgO8NaH0vSNpHT6lRUq3kWghb\nMU+6UnJV6CM1wEZARsHFEjrdy/h+ikWBrHYCjRB2aqCebVFTLbNHEnrFRTtRxiMDNt/Zc3PlqK6L\n1y6Q24D6acMCiYl43CCLyk62dz9y2b+PKkEF55DFYh9uewdVsNuVJ65rhtOa3T3P7p7Q3VfinUh7\n2nH3aAPAg+UV99tr1r5n4UeOfMfadyxdTysjSRckZKZgJjB3ZDLuBuA78h7g3gfq9n/jQSYwnx7r\nJJPV2X0uUWkiO7FI0E2PUbxmKhUqn4jeG/3iEs4pzimqQvaCijMQdOX8qIVUQ2ocLjp0sGhGnNsT\nKDkbOPQD4WqkXTjq88DmYsHVuoGVfQYnttF8AvvEazvTJgq5Mi/ZD0rYZcJlh2629oC6sp8qGJj3\n494Th5le0mEwiqluSXdWbB55dm8o6VHP6qjnzmrLIoxcDg3vfveM+p2K+sKcoPFIGY8zukwwWmQt\noyBR0KD2U2ekTYQq40Oar9e2HqlCoh8DKTuSL9dO2oOzNoJmSIMn7zyFpwF8oXAEXznczuOGCMOI\niwliIj19Dppx98/QoyUaHC5mmhcj1ZNrOwaXG9yqseNSPpjsepxzaGNRz0SX5gDdw4QuErLdn9e/\nmX0aQJ+rybCT4s8Af+6jniBJSZVjPApsHjqO34o0z3o2P7bEDfYl1NmupN52XFdlmipS+ZveR1ZH\nFsWhxnMixOwZ3veV7jYbrpx5q22I9Mnzoixids68cA/pzsjyuGMcPVVI5Fro70ZkcOahX3nyUtGm\neLcK4cojEcaVmrddePC0ADLkdbJNQkB6R3VpJ58kkFx+T5jj7bbvFJcgtkJsxThXBTd5O7qnZnIl\niCpha3+nyhmg9AnXJ7r7Dd2DhuXbW8J5jzYedz18MA/wKdfV+E6HHq/IrYH45ALlJhBXge39wPaR\noz9ThjsRfzpw/2TDveWGh4srAO5UG45DNwNsJWmmEjKOhJiHXZIRTjKDeuqPwDQnSkUkaUUuz6nm\nz+1mz3yiMLK6mY5JB968F51zOGBe+ogvdGk2AM+Cc3rTAxTAWaRpOQNmQPe9wzuLZGY+Fsrxy0jK\nuCHiu4rqCrrzisu7DX0OHIeOpR+4jg0JR1Zh5De98D/52h6YOqNDq+tEdTngrjs0RvPOm8Y2cVXz\nPCfeHPb35wwpIW1LunfM5gsLdveEuEqEJnK63HHa7liGgZNmR/XFzOZBRUyeppER0NwAACAASURB\nVIqM0VuUfV0ji4h4RZOBsqsS3itNO1L5hHOZnB1J9zmRfgyMsThDPlNV5THJEUdfoiyx110kooJk\nhyTBFVrN/ABFx4SLCd3ukBBwd06NZtz1kIub6T3omni2tAN4uiRc7IwrXzRoXeEurpFdj38OVaFd\nkgZc9GhQju9uWLwx8rH4Fj4FoH8/1WTqhe2jCkmwfifh+oyKkGrjwHyPJSuc7cbaZNLg2PQ1p+3u\nIz+Pe1+CyKEEl2hcxKHULnEdGy77lrvHGwQ43y7onlbkhXnn4xA4Wu9oq8iiHrl0yu6yBVGSKDK4\nfeLKKXGdCJSQ+u5A3gXc1hZFFIiCpOI9NJm4clTXUk4Q+76SKV4PpFrMCxfoTxypgfpCCR1kL4Rt\ntvBzWd4jWSjvC9jHtae6SrikaG1hrySQIeIut2gVzIv6CMrl+6oSDJ5895jcBnJwaOVIjSe1Qmwc\ncSFsHwq7R5l8OrI66bi33vBodcm9esNpZV7eke9o3EhWN3vUo3pGDWTcB+iST2rzhvAhNt0/gblx\n7aFw5va/2kWi+htesYglZTP7SFxV0Fz4WQEVRWYv3eiCVFtkNUdShza5lTHhukh1HWnOK4ZnjssH\nSy7uLFj7nkoSTpSkkLBN4KPspVWAKrjtALvO6JOmnr1zRJDJM5+80KxGSQyjJRPriuHugus3Pd39\njK4SIWTWdU89Xa8+8sX1OY23aHzte56PK767OeGd5mh28HK2zSz4hHdK7e2YZBWG5KEAeC6JcOfU\nksolsWzALzifUVzJtyriTacwKrjoLUKf+VaoYkZ7byCeM1JV9n2H8SCMz7jLHRxZLgvn9v+vAtoG\ntKuQfkRiwsVsm2a0aNxvHP0QOGr7j700n4pDV9W/Dfztj/14MR4sjEp1bV7keFJTX2WGI0/Y6Azu\nk6LDN4nKJy76lvvLzQdeMyMfSGgdgntUP98fJLGue/oUePfyiM1FiwtY8moBdROJybNTwTvlZLWj\nrUfGZCfF5rLFFU5Ok6DbgAqkVcYHJbcJTYL0UjLmJlvx18549WUm17ZgU2bdpYk/N8/NjULYYZ5+\nA8OJkBb2+asrR73Js4cuWfG90TTjyuNHZTzypNoRdql489m856ZG+uFGGPyy1jU3nt0bK3yf0SCk\n2tGfOnb3HbE1EOsfJNpHG+4dbXhzfcGD5ooH9RVr382Jzon22OYG1JMQOq3Y5Hrm0OsDnjiXMorh\ne0QcXopyRBw5O/pcfeD/6QDIAVO2qJSNxBOzZyxqmdonXFa6ZK/jRPEuU4dETOV50RcP3dYep+Z5\nF7VCDgbmqaYofFxRcRyoIaYIJ0boB/yVsHhakdrAsy/UvOiWPGiu5ihmLJtM1Jvf78Psk67tFM2Z\n88CsTKIfjG5JaVZ0qDcuWHK2+xr7PLLt0K6znMF6hbY1/Yln+wWFBz11lQghUXujkIZsa7EKA6vQ\ns3QDVXHO7lQbfvK44umwIhYwH8rGm3I5H7InJ2+01BRVlf95v6epFNt8nbNEmIjOyqWcPL5NJIEh\nW2IbcWRvDihApYpTRfpxjj4A29zARAIXV/irglveo229V8SIQF0ZWk3egFjUXW0z7ZPAtlryzvDx\nYfrVts8VWL0zUl2NSMzk2ry4VDtWTxLXb3jiWk0WlQv3LErKzkLag/DJSTaPKfsbQD7qPjQf1bHw\n4wzwwWVqb15APhLqEHkeT/CXniRKHSL9GGxXXPYM0ZOyYxiCJVRKQmb6LjI4U8EsEnUzkoIj+kze\nVLjuQD1TKZKE6soy+Rog1grZAD1c7z12MA/OqBcxtU0HfrT3T7Xg+4lnE4a1eXmSjWc3NUwmtZ7U\nHEiiwIDd3UzqvAzLQdg+CHu5mIe4MMXBcKzE00Q4Hjhedpy2O06qnVEGbqCVcVZqgHmaE5iO6sHB\nqAE+fR6XdECx3Pj8yAzsk1c+eeiw997fHwVOJgU4cjIuPSeH7hmH6U1mSo4DWvHmC5XIqwC75mxg\nIUJ9OdKcO/yV50W34GrV0lUGHJUk+w6voBPsRP9JygbgqkgI5tXmjEYMuJzspYzemwLGOfLZmv7R\nms0jz3g2crTuaKtI7ZMBtIhdnwhRHTF7esK8aU0b78oPjM7+H1wmZkd0hU7JGSdKyA6T3oNqnqWm\nkxgrZ0dmH0E5Z7RZzoJmxXnTLyag00BcCOFICJ1nWDua00DzoqV60ZnSpR/NA4dCoYFotT8RUkJ2\nveVUABYH/8sZ6RNOI25r13P73JMWnt7VH3t9XimgS1JEobvf4KKSGgO4xXsDu3sV3V2TAcaVkhsD\nsMnrWbf9BxJAXpSdutljWoWhZP6nx00LngjqyEVWFtV277aKNCcdfVrA4OYd3vvM8/MVdRMZR8/R\nqkPEuNFxWw7uKFRTMnR0DH0ForTLgTFkxrqC3lFdmGguniaaxwEfYTjJ88Ws2S7s5plQXess2XSj\nceXqSkIqwLgS8+ALZ67zfVBfGbC6UelPPGGnVFdpTqDKGGG7M69qjC91XdXD9pEwHhU10FAAyRuY\nHz+6YtkMtCGyDMMBXZANrMs5XUlkVLt4u1zZRp1hnC5UyQwKNcw8un25/WeZgLmWZJ67ULTplmeZ\nnnMI7EndDN6TVz4BO+wjv+CSeX9qYJNV8KLgMqq+eHyFckmu1DTYd3AldzKXQDiTooL9lkm/DTN1\nMSURRRV/1dFcBKprz9W25eq44bpuaFxk6YYbfP8PzCaFSy58uBMkWM5EsqIxIYF9ci+/Lxr0nrRq\n2N0LdPeV6qSnrSJt2OfIXMlVOHR20MjVnKwey/cMLuFUqSQzqmOQgJuQmkBWe73qwAlMBdCl3Ock\nz5HVFIqYKsZBMBrI+0x0mREYGkdcO1wvjCthOPX0J472JFBfRKqrAXex3VNOYGv5PgqKlJAx7hUy\nEzWVEhLL2bYR6suG2ApueDVJ0e/L+hNPDsLiWWRcyVxgsX3gTVFSQypyvrxKVFWiCh9OE/QpMOQw\nA3qQXLL/xYNVoc/BFjwHYna0hZOLzlE5z73jDc+d0r29pusrqiohoixXPcMQcKJsu5rjVcebdy55\nEtYA7J4vyMEULXrlyVuPVpnxTPEhk+pEHhy5yI7qZ4Vrd4LvhNQAzjx311sI3t3bK1PcaK/tBsDB\ncFT07gHisgDMbq+oGZfYa3nj4UWV6jrSPtkiu2EOB91qCdcvwd09sFwUCKkxEHfTWVUUPpfPVgzH\ngUenl3ZhYQU7o3oyOickZ/F/sUk66GQvKzQ1yx7U/ffwmt9vvgDE9DoTVfEybPL6psSoZkuukaQk\n0axgxY0G6lO9wlSsIu+v1pabjgsp2XUSFTdA1weuhpZdW8+A7j4k8njZNitepoIoAF+SuqpITKWW\nqoSyk+PgxM6/uiYtAt0dx3CaOFkM5SXyrCCqXSRIpvGRUO4bS4XQHClJNiC3v+a1nbjz72W+eNsO\nbjzu/WeQUTD2uZyzvzVHU8UsHDk60soxbh3DsTCceJrnjsVzz0LVdPcxWfLTuzniOvTUSRmJB2v2\nvsdIzFSbTPscqs3HVzK9Wg89w+LpSHenYns/EDqlfTbSn1a4UQlboburVNdCXCluEamriHfvV5qX\nD+8SC8ZCwSgLP86e1mRD9gQRYjZQb33EibIMA1mFLlZ85e5zvtpVDM9a5N6O1aJnUUXOdYEqbC8W\nPB88+RTSJHOKQjpKsHHmQbcJiY74tDX57XFE2mQn0OjtOy0UiRAGi0TQSZaIyRwDpIUSV0U7O8hc\njCRqwKlVeTzMxUhSwnkXTeKIQH/sCdvpgYq2DXQ9enFpF+FLNA0UqqyobYLO1ILbOqpnnu5NQU+u\nzLvNnjF7eqkMsPelrPa9Jd9Qn3hLiZo3DyB8uKf+PcyTZ236IfBNjPOHn13f+/5DS4UKjMmTknH1\nOTrLyxTvXKLJVi3qooB6SVgfVCDui7HKhV8qfTUnC8mz6bLzLnDRt2xSzd36mqUbWLr+Q+mkl2pS\nNqJYPHQRpKoMtPpx3pw0eEjJJIpgXnypoIxLz3AMehRZNQO7IiN0wTzz4PKcCG3cPpLsc5jXrpK8\nT1hj9QGHFb1DsvMrZcdYom498NSnDXhSweTymEmlBEW5hD1mHP38fBcyVBmtHLF1xBNhOHWMK0eu\nPb5vaWJGLi0SFoIdD7BEqHOW+cnZPPeJSy85CPXlscGZ1r9XPsmyvlJAVy9cv1mTK6E9TzRPB8Jl\nx3B6TLVVLr9cAG4U0jpShYOL70C2GJzt0JO13vbqXaqs3LvIGcEWMGL3tX68UcbtRGnDyHHd8aWH\nz/lWd5/+2YLw0ORMU5KkWozEwfP8vWPkyg6ZU8gnI7oajWYokicXpiySknpbnPE0MZ5SEisFfxwz\nr6pFw55WlsRznUODFl276dxl9vaYFzjVRrn4AZrnSmyF5lIZjhzVpni0dcDHZMmYkyO43sDLZVxm\nflvbbAdidJb/2FoyOFcK/vvjd3Mp5fffZx85RwbBEq8HLzHJHyuMcpnBI1N4e4crgAEG3LEk65wo\nQTJJ9mCRZ+9c5oS3rW8B9XQI6lqAfd+y4eOYqElaScJuqNjE2oBrziH94L10YC9BnMrfp1L86f6y\nwcpBoo+6Ih+t2N31DKdKsxpYVQMxOxZhZBkGhgNnbLq+J6/8UJ00wg0wn+gvo8L2qpYJvKe/7aPv\nwfzw/5NNtOzE4+UspCknMlUCe0W8fT51QhZl7IVhJ/SngXBdIVtvIoQxHnYRQaqw99qzSVMnTl3q\nUj8ignrB95nujmf78OOf+68U0F1UVu+OM687HlWMJxWxcfTHbgar4aRo+eYqvJsHfQrJkgpj9lQu\nkVTo0r6HC2Ih+7SQwWWCZGI5MSYePavwoluyGyvOHlxx/nxN/7Vjto8G7t2/ZIyeGD0uKOoSelJ2\n8HZk2UROFh2VT5xvFwzRE7zRPkP0RJ9JTVG+eCVHx3gHmsU462RzKT3W0UN0MDryYmomYWXNliyz\n1gF+Z/QMWHGTHaJC4QDNJfiDIqTw3qXJFR8/RU6O4c2H8PVPX1F4aFO1rF9GRJQoAQY/tzTINUjI\n80U2Hmi94dBT1hse9D5RWd0A46ltwg0v/UNsr4hJFi3o/nwYCTiEjM6bKzDfzoXPnpLuMfv53HGy\nl76BsQ8pOVL0aNl4mbjaA/58oluMTlOLpiYg/F52UEEqsWwEgzCMgeuxYZvruRDKqmNfkTkH7CPA\nGcyLt2uRRalodg5pG+JJS3dXiMeRdWN68eAyTYjULtGlaua6R3VUMHPmkwNmt/cnw6FnPoH5RLse\nnuXzc6Gs+4d9pfKYvAf6GXsExOmcr9F8gEkCuVbiQuiPhPq4xm8bS4DmPCdKpRQhTX2MJOWZXxeA\nIUJwqPeIJovOYzVf2x/HXimgZw+bRxXDsVBdg1sqsRFCp+QGS2AtdG7M5b0SfCpSoo8Of3PxzD+M\nQzvk1IPkAg6xnAgVfUl0iSgnZxsufUY3Fc++dpfcZNOSe4VF4vjMNNOLesRP2fXsOFl0dDGwG+yk\ndE4JwSoHNUAo0cY4elJ0Vjg1eXPFxKlhVZJyMAAVcqVIFlKrNxiGuFbcYPxsroWwKxugN8+het4V\n+VjAHR+hF5dIXZvq4CWaeshthsHN9Kk2mf7BRB0oVRMZkmcbrT9KNRUPuTQDt/N55konLXhG6IrK\npZI4r2VSdxPU4cZm4N7n4TmMukFMvjgpoRJyg0ufippG8ZZgf58XCMbzmneX6AjE5G+AwFSBDDJ7\n6NP9knWu/jX5qs5qkAkA52Si2mYuwVvCMeV5M5v170X5kfTT6/Q/ytRPG5XelFgWKujQZhppLydB\nV2u6ezXdA8UfjbR1kar6ROtHFn7kfFgYNSoBL0p+30b9odc2+5xZmCDcMSdFD5+bShI7ZWdyxnxT\nt7SfF+2IRaEmoka/TDUGRXFniW+xWpNotGtqlPFY6LZGvdRDRK62aDfJ15J57Gcn5mTFVDZB+xFV\nmLz34HFXHc1xRX35OVW5TM2kJJvGOjqjC8aVnfRxoXOGQhaREKxYYFI/TQvqShOuyWoXGQsoH0rg\nPswql3CacRhYBJdJ2XFnsWUZBp5sj3Cnmepu5mrX0Pcl4RoS3h/QPCGyGWq6MbCoR15sFzRVZNkM\nXO1aS/RUpa9HdniXZz37oaXkyOMUohYPoIToEgEt3e3cwe0pH9XZRZYWStru9etTlzl/fo0uW8ig\nVbAGWeO4f4GXZU6tU+Xg5gaD0mRCO87ReAiZMTn6ZEnsqaHakmFOiB3a3vMyrt07pZJPxxUd6s4n\nMP/NQDCV0P6QpgsuU2v8gJ75Qx3tvVO3/zuX36o35ZgzAE4bIfsnq+6TaGIcrwG6e2nJ3d/MJuDW\nIKbeGEeTVqYCTLBPgAIS9p8rrxq29zzx0cDRqqMpMkVgjtqcqEXOWekJ4PctPsayYU2PnSiZ4JJF\nVLKXNR9GYpOlXCg0FVRKbx3K3nuAGa6olpyTG2s6ee8qmZQc4jOq++reXCnSCOMahmPBDxUyLgnO\n4cpjdNuhqUdiIclKHmJysHS7M5pqqh5OmcU3n9M8WXzsNXrFgF7AG+jPjC9v38rs7ogphXYGWLnO\ns+easuAExuxYvg+sp4KCq7Gduc7rsaH21mrUic7FCtNOvom2200XZ1bhqO5mqdSb6wtaPxKz53xh\nB/Kyb+cNpClcfuMj1BBKZZp9TsWLsj6o7Moq1CGRsqMfZZ9UzWJrWRQ8OkncQkYXisbSCbJc9BLN\n6VO/h2M/QGqZ2336znhZN1pV4uQF6FGLu97OWuB5h3xJJqNQPQ2WCyg9NRgdY1908AJpYWBc+cy2\nqgmSWfiRUTzVQXDsJOMQKzIqh6o5SGbm4rUetgSYCos+ylNn9mLlhs59apE72RwZFGnikAND8nM4\n37px9tCDZvxBMcpEtSBaJH460y6HbV1FdQZ1HPskdan01NJ1UUSgFnDV7BXnYNHPqhm422w4Djsr\nvJH4UppzfRxTVwqiJnufh06eEn0FXpwwnDX0d4Szu1ccNQONjx+IvC03USpAObw/0xTZYp/Dhyqb\npjbJh06fE52dhaw3o+GPMgGqkEqRmJv7vUzvOtEtLuQ5XUIuXT6yY1wJYesIx9Y7aVIxSVYrFsvl\nwTlbEZK3JDK9WoM7KV56Ssi2w3fDx/rc8BkUFsWlsHuoLJ7A8nGmOxU02ImaWqMntM1U7YgW9UAd\nPtiEaEpQAXM4HNWy0TE7tgW4+2jhcuUM5Kd+HHN5cA6sQ8+QPUMOHFcdMRu/fq+9nm9P3kRfePo+\nhfm+bgxUPuOdZcbbYFxyFwOoRQFWfBJuePnGw0Kedv+Jl5uuFbHqPEGQQXh/XlCSqSAk7sFCvRB6\nqybNRy3+3Re4qYItRmv69JJVLi5C+0yIS0vU5rq0/XRlYwlKdp4YEmNyjMkTg12czft4X48yYrLC\nub0teuMinkAd5cMLhUrPlsPXhKloaX/Kpw/xbvdJczcrWKZk3dyqtoBFOADQudT/fTb7IMIHC4m+\nl+Up4eb2sDbx1A4ImUU1clLtWHpTuLRu/NQRzMexPSvhrKDosHvnlATNGatOLtmRuqI/8fRnypvL\n7Sw2mCpCp2t7EjdMZrJVi0Q8mSSWYzGFi63/lBB+vyIpFIdgzplkZSzYkLL7wB50+Kc5aTBGP7dx\ntwcdbNxSvHafyCFb5bizBpNx6UtXTY/k2qq1ATeMSIxGreU8e+PqBMGX6ObgMx0+7mPaK6hGuLVb\nu7Vbu7VXYa9chy4Z7vyqVUCmWhjXQrVR0pmQWoxTVGiam97GYcIzqqPCkhyNj5AC16khZseq6hly\nYBer+fEpuzkEM+mim19nG92++i+72Wuwbdnur13EiaePFn5PNlWW1mGfqa4KBeNEaXxiIl+cqEkv\na7sdi2ZZRKnryDAEe8uPCgtn7rUcz9IHRrJFOHEBoSuVpBnSusY3Nby4QCe6RfMHQ+RPaZKgeaH4\nThiPhKHSA6+m/BSKCZilpU3RGk9UQVbHiJ954SlBeehtd1rRl8rBShJLN3DqN3NL3S5XbLLJArxk\njlzHyvWW/EwWEVRqFalTcVGXqxuVoodl/3AzqT71RY/ZKpRdaRkRq5IYLf17CBPnytyXCC0yN79P\nXM+yvwMuVaaktff24xwyRtzljuryCLkO1C7xpeY5S9ezdD3Hbt8T52WaHqhscihFa2NG68qqj4cR\n7fuSvAUicwfGyeLJwkr9TyOXQzPTLMFlky6qRccnVUef/VzdfXjco9qaNy5yt9rwIi5NgZRquoNq\n8TnPIalct/YaMTt6grXPVUGApjKMSdmYACdKU1l0PcR9XYFzmdViwJU82G7bGFvmrZJ06CskKOoj\nOVjBke+liK881VWpdE7ZvO5Uio5EYBhMqFBXFvFQ5J51BV0HKd+kt34Te7WUi8LivUx35lg+zYxL\nK3fPtTCcZuoXjrhWqKzMXma1QHn6gaZ0zB4nmZUf5gsslQz5xJkjtpBTccE21h+4OKfbEx8/nRiT\n1S6yDCO5JMGW7C+aqSFQzG6WYEEZmlDeZ3rv2idScHhnDZ2G6OnHQChUTUqmw5h00N/zEHrmAh7t\nBa3Ab4xPR8EPGUlKtY3ImElnK+S9Z7DrTLYYI9/vHNnvZZKh2kzDCITxaGZE9la63M0dYlEaN86V\njmDh86Q6mSWNU0WnpFne2JUmW62zKUWH/dHhoNEW02vsnYNaIqnUJUwJyVEOkncHG+ohtXJD34yd\nL30KZaO2C1smPlVNqzwnsym0+lRINB84Zt0xh1OefAm/ZSqtF6ss7Af8YAVnlU/cCdc4MqvSE2fp\nPn5Xvu/XVLBJRNM56sR4BjGKwJJ9uk/4AeNxRVwCTaZymWU1zMnlSSV2PZpDNmn8G5c4Dru5PqDP\nYe7zc110fK70sw+S5xoTu/999BzM4/uG5MnjnkJb1cMsZx7T3rGLyZc1NXpo11ezQ+JDsp4vyc2i\nBvEZ8YqrMvE0sa0dw4nQXzlSbbm44yDU34zzZCpCsOIr722D9P4mcTRt7PI5BXTJMC6Fo+9GxoWN\nWEKhuwPVhcNFK/uvFiZrislblWiRGl2PxotPutWsjk2qOao6uhR43B8xdvuLU1U4bjvuLkxq+Mbi\nghfDkm2saf1I7RNXY2sJ0XLxTgs/PWZKrDh01r5P/590s7tYcd03BJ9Y1/b86XnLCsbkGZJHZC/D\nnLz7MXpGysxTUaRWyJZQM4LdjtE0Y9BS8yXyaHXfU72oJ9QJoYuoE1xWXBeRO6fkp89NKdG2vHSV\nCwWwtLQymKY8IWitaK24JlHXkUU1sqoGjqqOdeg5CVuOnI1kmVrl4mCbGvOUo/V2OQk7jqpu1lpP\nKpVOK87Tkkoiq7IxLF1v/dPVscm1eeFaGSBkP/eLSYhJGEvEABiAZMAP7FI9J0DJtpFMfPqQrBKx\nLyCQ85QMnWR91oJVvVrztFl/bo3WbHZlmcc5TfI5VIqI3LyQvUOXLcOxkE5H7jSbGcC9ZBJiXSpf\ngen7pvZ86GMOWjSPa8/U6HI3VizCSFtEC9Z3Za9OC5LnLosLP87KpMbFG6A+2VQhPuq+bxPs6wcO\nN2gnyrIyubGW3FYQw5dKUuHW95tCVXJ3MXobM4iBeVUlUnKMg4G5TkqmImWUNpGrzLB0pIVDy7GS\n3HDnvSVycc3UC4dJ4glzwZGO4weHnnxMe+VJ0fW7ke7U0921QqLhGNKyjGZzBbiceXITmIPttFNT\nnaksOCNsYs07wzFPrtdsdg1VleaOaTkLMTm6xuY/7mKFFxtf15Zxdo2PHFcdTrKF0OxDtCiOzdiw\nDfaY42o/C2oCc2BOeja+6KqzuxEJjNkxlMHVJl90JeEipLSnIhAI5WTRWOaYplKUogdKl7LK6guI\n+31SNBdZYw6CE7E2p7V1ddMYkaM14n5wMjfJpo3PAbQq0USdqWor9V7XPevQs/Z9mUY0sCrA1GmF\nU2VMRrdM9Mdhlz1HZukGKzYq921zw9LBqEbPHJdWu1ldab8brPBG/n/mzjzMsqo897+11p7OWGOP\n0EyKQUHFGWiICCpjd1fjgAOgYKTRqHG65pobMALOQ1QM0BgQFUyUGFpQoyhI6BZUBEGNmDiATD1W\nV9UZ97TWun+svXdVAVG8t/XJep7zdFV11T777LX3t771fu/3vikZXlUYXUhZrGAfUQQXDUbmVTPR\nwrGw+P6YPRLCup6CYrdl5388D5tVJtE8LtaR9T1s6JO1oDYSM+oPCRYUjp0A2R93OMYGLhtfyEV/\nxA7D2f5Q3ah5TTh2z1AxPd0kGVG0o4CGn+IrTdNPaHlJJZVbavdkRjGssnFbZevz90VZ/Jb4GHxV\nMp8ELGhUAtgdN0i0YqI2YGkh+9HLQuaSyPURWBbFnHk4RpIIO9+h7zk7zEzM9x5YW5AbcoHte1B2\nRvsG3YC4uoUkrd/WCWY6mE4XUa9jBwPIg4LR5GOT1O2iwRmHSFmxnh7P+NPSFgXEY4re3hIVu+cx\nmTBFR5TDgG1UrIBGLAiMBimg7hdyPMIy1D6pUUzHDTpxSJp781i0EQXdSDDbC5hVzu17draBH+aM\ntQZEnmt0GQljciurC1E+wNpIBjZgLolo4SR3felXGXqsfbeiF1lAK0yoeRml3K+xgkwXuHvukRZC\n+yU1t7wZHHTqMgYtXSBwlDZRye+aoGwVFwVH3R1DmCKAB7aytZO6YLoMclQ3cSp4ke8ai+IY2+09\nghi9Z+ZVB2CUww1V4hYU41msbwkaKWOtAXs15xjxY1p+TNuLqyAaFzreC2GWukwXMWAUljldLzTA\n8+pV1lYczGWIREZbxhX7YVY3yKxiXPVoy5hZU6dfmEgPTPgoLnqFqQsn6lby5mF+226sIMGr7gXH\nYLL//WUtGC7OI7egMCpRZLo41tFCyKUSaiqbThwbQtd90lHLEyd2s1c4SyQy0sKlKH4cWuj/L+Ox\nSjpWCFCOulgFHGOxFIyMMuPw3VPVXy6Jl2vkWEIQ5IzXhxXsMt/lKelmfJQnmgAAIABJREFUEfgU\nENx8Jl4G83J3pq2kk0cYIaBs+19EPZ2XaSihsvGoX71fyYDzpOHA0Z3UCqP53WmdrYM22+daDHfV\n8eacBpMaQqiLayEg8RxdWNcspuEs8YRnsFoiY4mRpli4i+e37QJyLDwGKyP8HQ1EmjpjEKNBKrcD\nU0UTmdEucy8z+z+A5fJ7A7oQYhXweWAZLtm4zFr7SSHE3wGvB3YWv/o3hXj+7xzDCVkU8iAZA1Oz\nRLtk4W5vEZGmFmSVFrkR1p3kAkW2WPt005Cd3SZaS1r1mJH6kEwrAi93TShKE3k53SRk92zTNe/M\nBqQqYNt0DXyDCAwz9ZSx5oCJ2gBPzLuduCDvmo6GmV/deE3fZZO5keR2geSuNPRSl03U/XRR8Swv\nMnJYUGArdSGKgG6MQBXNS1niQS7w+o7HrUOQicDrC6Sm6nQ3ykkNVxm6dVmUFSCHObJf2F0Vrddm\negYRhXt+XoUL5iXX2imeWnTToNopy0a7rGzOsVc0S01lRDKrtNAB0ipjXrg9NhVMG4msKoZqBL5w\nD3eJmRshKyw9eAwudloUWF0hNUeJxcFvEW8dschjtByuyKYXSEfMa/RXKovgHmLj5HOtnZ+rko//\n6FeJocv/Hsaw1rlChYq8aXhiaydjXr/wVHWfb/5q4Qshvsseel4fdSoCrCewhQn4fzuyvNp56KjY\nqVnXh6GkoekneMJ5Apdm70Pt0/JjfKlpqhiFnacl2nmd/IFe3DlpCoJEOR4LbgmUduJ9Rd0j1j6Z\nVuwYtki0RycOmZluobYHhLsFkzst3tC6mpQpHKY8qmQoazhCRxYr0kxg6oXMgS6gtwXKCJSSAoFB\nB9JZzwWBW/ASVUEvQhdeslnu+g/y/HffF48xHk+GngPvsNbeKYRoAXcIIb5d/N/fW2s/+njfzKpC\nZ2TgsHRds8hY4A0ga7rWWekbtBEo6bQ/ysJJO0yrre1DnTZJ5qOkYUmrR83L8KQhKWCNsNDdDmRO\nXlN0Gj2Guc/WZtutvLMKlXhOr9sL2VprsnUkozEyZGW74y6+ddK7gZdX3aTdLKo+i7GCQeYTKl1h\n/ElhcOAX0Ev583JrDlSLjucZktjHL3YVWktMqshi5c4vdTeHleD157W0F4pJCg2qbMZSzszaSuEs\n6PyyGifAczeFbDfd94M9O68lfm8C12eQjlrSyZz2sh4HTuzkOaO/ZUQNKj7xwlHh5sXX8+Yk88Ei\nk6rKol32ntBWMXUxD9WUJtEdE9EhqgqnsQ0YGBcAIpnR1TW6JiIxPrHxKx57uACbr8w1cJ2IZS2l\npjKG2qenw2IHJhfAoKLCUMkkVrtaSGkQXnnHFnCFW/hcYLSyaCR5pGyumJ9DG3jkNQXNnANqOxlV\n/YVBvNJ8L8aemdfHGgWJx8oFC9Ejh7GFqUXRAalA5AIdO+lsKNyIlNvx9HVQ7IQ81/eBZUQNaUtn\nO1nWSQZ5wEAHDHVQNJsV/QVFRm6q4D/f2Vv2CpQsl1LTvp8F7Ow26c/U8KZ9wmnBsgcMjYcTvDl3\nX5nII2/5pG1F1pjXUBJF36NMwe+CTCVZWzjZ7wKSAveZRVownwA1kKjkEZz98nrl+fwLXKE0TpwU\nQPj4ayO/N6Bba7cCW4uvu0KIe4C9Hvc7LDoYhB1LVnNBSKbg9Z3JhQ7B+FRdlaqQ0vRKXMwKdvSc\nFnmSedTClCUNZ+1UZsm+0q5ppaiaB9K5mUQqo58FNGsJY/sN2DnTIn+4hj8nIQV/TmB3hcStgP8a\nr+M3Mmq1ohKvDFlRME0e0bqvjSQvZAjKQqcoMnxtJHHusHtrRVUxL88xTXxM3yPbFaKGgmBQwCnF\nfOd1S960mNBgg6LTLHWuR16vvGOYl98trq+VIDKn4mYDH9HpOdUA34OJMezWHY4KtSfnFffQ6tA1\nF2UjhnAsZtXoLPvWd7PCnyEQ2mHkmKpgCc6NqAxEZZESFrsLLRJjsq45KLWKSMgiS3VmFqowknbm\nGPPvEYnM+ZKaxcH6sWiRjxyqYF0AlYtOqhVxAaUt3HFZA+jCRxYKhUVR4eYLKad/8PX1FXld4tcS\nlntzj7kTKUZmrb0T9sy8VmPBWmOlwPpyvvHJ2MXMLK1h2SS9A0cBiFfkyGZW6RtNRH3HTjMeM2mN\n+ztjdAcRUZCxY9CiHcZMRj3Gg0GFqYPznB3z+tRlyv3JBJ08csbwlffrYpEuoGr+G+Z+tdPePdeA\nh2q07hPs9+uMcGcHOcwwNR9d8+k8qcXMQZJkqcabGLJ0tMe4n1ZMmDj32D3XQHcCvFlFfbsjAhjf\nERi8vvNN9vvg9W21UHsDaP+yi5zpOEu+JHHCXUphrXE7GiUR7SZmpIGc6VUSxI93/EEYuhBiP+AZ\nwA+A1cCbhBBnAD/CZQUzj/E3ZwNnA/jNsSJ4C+IljqHhd3DdocpiffOoTkpwbbjdYUQcu4e91Ygd\nDueldLNokTWYWIDLZTp0XPPMJ80Vae7RySKCMCdenhA3fFTXqQIK7ZQM1cM+yRIJwlKLskoEqTyX\nhXosJfUqSQNKSc7SwzDRHnHqkyRecSM7amKaeqQzEXLosrtwp8SLqQrEyZjBNHPCdoIv3G67FmZu\nkZquo1NRMQZk5rIAFZeG07bA+YTT/VDSbeE6XZflDYbIdgvixdP+/zuvQWOMPHImHOmoQY45zHxJ\n1APg/nSygjDmNa0XeIMuCNilSfTC/1+kwIhgV95iTterDsm6TCrqogKUnL8fVIGrx7Zgs+CCeElp\nLHcMZZDvmqg4J7dTKM3Gc6Po50FVSOsnAfEwQBUCbM52rmAlGRCIKohLLSraYgmLPWosVF2080Gg\n1PXQdc/ZnoXz9MRygSw/56Mc7f4/5zWMRhfXW8rss8zQJY/eVYArElo7z2EXpoAT3XP0cG+E++bG\nq6AYbfPwepDUILGwW8C9CvLIYgInJWE9ixrJGGn32avdYSJ0ydxQ+4ukGRYG9lITvRzD1KezrUXz\nVx7t+w2Nh4YIbUgmawwnW/RWSZIxSz6ikY2EesNxz2cHNbb2RzCpquQ5SBRyKPFigRpavEJ+wxsW\n4muZxYstKjFVp6jXyxBbd7lo5XmF6YfvDKaVcgXRsTbZkhbDpSGNBzzkbB/+GK3/Qogm8BXgrdba\njhDiEuCCYpovAD4GnPUYk3sZcBlAfckqm9VdFmc8UAaiGctgadkq7tppjRVI69QPy0x9Nplvmx+p\nxShh2B038AvIoxxlUTItWvZdE4+DQmqBk+yMU2cXp1oZWlmQChm7h8h6uKaQQuDHWofPlXzUqpmo\n0HRxJsFFRVy7m6gTO3EurHUi+rkActJBAB0PmTs8PJp2N3xec8qJWdtgmzkq1OS5IggcNBOnvmPD\n+Bo9jtOBAEQikIl0mKZ0yn5WCnRN4XUFYphgR1uIOMXsnsEMBoj6YqGfPTGvjSWrrONTF1tRYfGl\nqdhIC4udwGNqcZRjYdt++XsuAAtm8ga9PKST11z9wgpCqWl4CSPekKaKH/UeoUidTVwxMiPJKL04\nc5SgYtSUw8EX88U2hSHFFUcHWUCceA4mK0XVfP076WVWLtyHF81FnsD4hcersIsDo33E90KQ1T3S\ntiDyH5vx8Egt9D0xr62RvR/zY1UF3d8xFmqYsABNynPJ9t1t8r6PmvXwDPMJiQW/a1GJo3bOQ+AC\nKyXJmEd/MuKny0bYb//tTNQGFZPFFHReW9S/Mq3IjFwU0OPEJ9ilaGw1RNMZFstg7xq9FYrhcks6\nniFbudthzQUMd4V4A4HfFYzOWLAuGc1ahZCgBIxLrPyhM3YO5nJkrJG5U2gU2iKHhXxut++YZpFL\nGmyeu8Be9hpYg418krGA4bgk2u0j+94fZBn5uAK6EMLH3RxXW2v/FcBau33B/38G+NrjOpYtLkBP\n4PUBLHnDmUCImvMWVNJWKobGOgeSIMgIg/kPlmmHTSvpxJxkwSXPtMJXzjR3kPnU/YzQczSgMggL\nYRlrDhx1qR7y+q9todVJOf9la3AVPsvfXPktes2AS171fLfIGEHo53hqntZYjqafcuoVP6TVS/jY\n2S+kl0QEKuOdn/kOu8M6H1/zYlLtQ9fD7zpPwpLVkzecdZuJDDYshaREJdqV5wqtBW/58ndp9hPO\nf8XJEDsa4nnXXE+nGfGpY453Rgl9h5+roUYMU/JlI6huAgNHt1SjIwVOV16HPTOvloKumDl7vWzo\nMcxc0AxlzrjXXyR9W2bFGokxEAsHTmrrHkJfUnWC+iKnLhPmdIPptMn9gzF29Jv04pA49pHSEASa\nZpQwGg0rSqonNad85k4avZR/fttzaAUJTRlz4od/StL2ufmNf1YxacoMHpz2+lC7V2ZldYyNbzrK\nyf+mHm+/7DvMhg0+fuJxGGUrHrJQ1gXvUpiLxYViZ2TisFaZOzjmrIe+S2vQ57KRPy+4zJYNvdvo\ny4Crms9xx1KSZFQxXGqZCBMy61WNVCWOXhfzjkV77Hl9LIZLoc+DYDEObA2gKv65DTyyWrHghSlY\nS5Z66J6HSCQyF+iGZr8Dt7Oi3sGTmu2DNvftGiedjghmFH5XEM5YohlLtCulvlOSbpWccf2N1Jd0\nuPJth7vPby1vuuRmeo2Qz56x2uHluUdSiGsBDAcBdi4gzAT9lYIz7vsuDYb83bEnIzR4c4L3/8PX\niLOIf5p8Pt7MDEzPYrs9TJygmg1YtYLhqja9lR6D5cJl83VD80FB0DUEnQxvLkGkOa+e3kJdZnz6\noDX4heToGx7eQl9GXB08DzsYYuMEERW0VmPR07tRo21kZhBWuWP1hr+7+PyI8XhYLgK4HLjHWvvx\nBT9fUeCwAOuBn/3edxOuWowAvwfewJKXFXBl8YL5rNeThrrvoIbXX7WZZj/ho69/octWtODNl/07\ng2bAFWesxhQWc1JY13yU4TJE5RqCRqMhkXKF0239NsMkcLi2tEzU++yrdrP+prsQvuG9p67hvC9+\njbO+cyufPf5w8kzi+cZlzJ4mKbrMPKWJ/EJTHah3M0796h3kRnLha07gHZffyOlf+wFXHH8EGLCp\nQiWu7dvULUFHkNctumYLZUTH8PF8jVROejfLnLmGkprWYMiZ3/w+NhdcsG4d533lOs66ZQtXvOBI\nwGCFxBs4GMt6ApFmTkjfVwgpkUsmnAVdHIOn9ui8Sg1Bxxb+pgKrfHYHDR5ojLn5FKaySZsXuDI8\n+6L7kHOWG//3QSAcU+TYD/2CYdPnO294SsVHrsuUubzGjqTJbFxjrl8j6QfQ9bBaMAT6UYPtgUEo\n48xIjOWYh37BKd+8i4dmRvnA647jvKu+zpHX/5qr1jyXGx76M+pBzkTUp+UlVUMKFNt445FrQdDJ\nOe6an5MaxafOOYa3X3Yjr/r67Vzx4iOKNneJ8RboYxeFUFG0jcrMdXZW9nP5Aksxa2nmMafs+AEi\nzbmsuZoNc5uZSn7GtbWnui7RwMeGAcmIIF2SMxYOyKxyTB+RMbDzMgcNUW3N98zzWiJA0j2zJXfe\n9T4IRJpjO10HGxSFO5tmnDa8k9bDio8c9AoH/0nDe676OrvDOp98zhr3e75TorzvN0u5L5hEhRrP\n02TDooXfd3Uk4wnicYE4IHRMON8QPTTkNV//PvUg5fK3rOZNF9/Myf/6U65Z/0zqXsJMUncQa+qR\npe55tXMBqusctGRiGZsd8uqfbaa+3XLxE07mLXd/lZdsvY1NrUPxZh6qPruo11CNhvt6rkctzYi2\nRWRjEWnbQwfuHvFijRrkiNSpKTZ0winbfkDe8PjUIWv4q59cx/rdP+La8edAkoKxDv5UilJjWo2P\nIfpDavfO4HebyOmOC/rB46ekPp4MfTVwOvBTIcRdxc/+BnilEOLQYtrvAzb83iNZV/x0Ad3dGHlN\nuKzGL8yVjUQb16WVaGc+W++mvGzTnWRa8vGzX8jbP3MTL9t0J/9yyjPcyq9dt2VmJL04RBvJSH3I\nXs05PGHYPmxx3+5x0lSRxz42VuQjCc1GDELw9xuOJTeSs669lbO+dSsAVxx/BBecdiLK2qKLeR6b\nB7dDCop2/0x7fGLDMWgtOf26H/Dq624H4LPHH+4y6kQhY+nMKIobJa9ZsrZFjCeFg5GDl1TBqMm1\nrIpI1gre/9oTsEbyum99j9d953vuHI8+kvOn1iEzR2903qOWvK4IhcDb0cHWQ8x4C7m7C2GAqEXQ\ns3t0XmVmaD6ckjU8/L5EJZK+jbg3mmCY++RGMeoPGPMHVWDHWsJOzlO+sA2N5Ob//SSe/8H/4oir\nf8MNpz6Z+/rjGCGrLsJUK2aSOt04JEs97EARdCRe39UQhJXzW3QBIodPPvElRIcozrphM2fd4Ob1\n8885ivc/fYr8LtjeNNw7khE1UkYajlERFfBZufB86g3HoK1i3TV3s+aan1bz+t6Xr0HEEmscrGaN\nC+4idabf4BY6NXTn5w1BpXbegi53piX/cOBJiExzyvYfsH72DgCurT2Vy8aPhijE1CN0OySeFIwu\n7zIWDOmbkFE1KArNLktviLSEm5p7al4XDkeLLaA+U+Dj1mK1RqhoPlu3lqbImJq7G/2DkEv2P5H3\nXHwDp936Q648/CgmQoMJJDoU6EDiDaRTRSx2L6rhArmuWychUXTYqrSErATvP3EdJrC87totvOza\nOwH4+ssP4ZvvOISDg23c01lOnPpkiYedK7rLdyqCWfBiSzhruWzvk6jtzDj1l1s49Zdb3HX3n8yl\nHIqIk4Kl47mFqpCytXEMwyFiRhDO1Anq0byipHGSGyJ2tnOXTb4AGyhO/eVmTv3lZgCu2Xc1lwdH\nIOKCgx74Dkcvu0WFcEYmgxhvznMslzxfpKfz+4bY07oev2vUl66yTzjt7cjcBfQ8ck736YhFrupX\nglyBlxP5OU3f6bTEmeKvLr2pCpQA16x/Jhf/5fNJtI9lcbEyyxWDOHATGivIBaqvXAYpnLyAqTvy\nvz+W0KgnrGjN8c0XXFQd44CrL3QUxDCvoJ6x+rDC6534Vr5IWne6X+eOkz9QHWO/K9+PTTzkQDoz\nCor3r1nUsiHNRlxpudhH0ODi1KcRpeRa0huECAF5Irn39L+tjr/vpR9CpG5rioX6Vier4PU0tf/c\nXuhsuMILpcyp53Hrw1cxl+18/HfJ7xm15avs/me93TVbBIXwWsNAO6s8VoWwKE/TqCW0o4QV9Q57\nRTO89GN3cvjVv6mOteVVT+Czf7Wavg6Jtceg9M00rrgdZx5J4pP2AuScR9CR+B2Hwap0/l4WRYu9\nig23XfOu6ufPOf2jGL/gEDchGbfodo4/4gqNQaCRC0zJPaVRwvCjkz5YHeOAqy9E933kUDmnplAX\nPqoSr+sWGdfa75gN3sASdgtOc1EYlblFJRqZaFQ/5Vt3XVAd/4S93oytR9goRLdD0pGAB1+oOPS5\nv+JJzR08IdrBfsFOWjJmt24SW5+V3gyR0Dxj3wfvsNY+e0/Ma2tkb/uMo95SZehl7VoYl63Xf9uB\nB7YiWs15WCDNsGnKht6trI/nNwHX7Luaz0wei/zVg+73A6fxbrv9+Rb4JEE/ZT9mDmowdyCkExqR\nC/xZSX1rITpHwYjz4CcXvaM6/ut+cAarR39FS8Z8Y/fTuH3bKnoPtIm2u/NqPGSp79R4A40/E6Pm\n+tjZOb45/ZnqGMcvOafq07Cxc/sqRcZsSSks8ewwdJlz4LtnrKRoDobuWQt8mBjlm//x/ur4xz7/\nQvyH55xZtFIgQQwSZ7Cd5wUEE2KbdcxIHfnAjupY39p28eOa1z9t6z9utQXIms4HU4cWPZbhF9i0\nrzS+MkReTiuIUXmAkh6ffuPRiwL6p994NP00pO5nxLlHlivmZuswVxS8OpJaX2ACwEI6ZtC+I/cT\nuWDu7fKxvTrdlYK/vvxbi87z3C98nQtOPwmlTAWvhCpfFNCNFUQqI1KWXYMGb9t44+JjXP01Ljxp\nPbLMLoo2cBMYPGkZJgFxL4BSf8aziFBTbybUw4zuICLwc5cg5IJzv7C4D+Q9X7qeC09eh5VifsGg\noMb5nmv7TzNEoU8NOBrUHm7990dSDjrpv1hZm6Ofh/zn7FJ2dRokMxFy2qO2UyBTF8Ti8RadMcv9\nK8fZa8kou97cXBTQN7/7QNpZjEHSy0MS7fD4UkfdWkEUOvZRqiyp8J0FmHZZcOXmlDkD5rfded2i\nc33XLddy0cFrSNseaewoG6n2yMo+gVA7Z3fAGoEQhnOvXnzd//Zz3+C9U+uQqStCWxQiFaihxJ8r\n7BUzi0od6yHoGcJdCTLO55khpUlwmnPOg4vvvQ2dLVwydiJ6JCJt+yRjCj2ecXB7KyPK7SR8oYmE\nRmIKez1L9Ec2uJjPFF2R1yqB8L3FBVwpwfO4bOz5rN86H9CvyJ+J+OUD6G4Xmabgu+zUxAlCKWQt\nQjTqeL/dweR0ndH/bNHfOyJtuZ1X2HUFR6+fI3LDG+77t0Xntvpd9/LBv3wxQajpbmtRv99j6f2G\n2k4HQ4XTMXJu4KDIQYzNMjb0b110jHOG32dj/WjHRPL9+V2HWaBQWgpmGY1NTGFYYV1wLzXiAazl\nnN8uvm/e8JtvcNFT1tBdFWB8qO221LcniMw4uuPc0BGjPIkJFHJyDDGIIX78omt/8oAutBMryiPI\nG64oGI0kKOVwarfVdVz0bf02njQooXnnZd9ZdJzXf2oLHzv7WKbTOr3pOmrWo7FdVsbE3tCiElvJ\nl+qHXJflcNJjuMy6pqKlGSSC9/zjN3nVd2/ncyc9jw//xYv563+8gTO/fhsml3zo7BdhvPnij3xE\nQM+toilj3n7Zd5i6/m6uPPEw3vvKkzn3C9/gdTfcikgU7z9+HV4sSSYMYmmMLw15prBzAX7HZe8y\npbCX84lHQ+KVrpAphCWLFedd9XXOuuFWLn/has5/+RrO+9L1vO7GLYjUbUGFdgVR135vKxswjHZ0\nuixD+L6DXPSeffBL+qaxEk9qVjbnCL2c2VrCXKtOZzRAxu5zWunmxHR8HszGOPtTtyw61gHn7uSf\nz3wWWdFBJYQl8vOCiqgrvQ1LUTBuSFKDqxcoMZ+pJ5a3//CrvPzXW/jy/kdy0cFrePN/XM/L792C\n9QSfeMZaVKxQSVGgHxbdqoEia5mCwWD522uv46ybbuXKEw/jireu5nWf3MJZX78VMVR88JgpZCYx\nPng9F8jDWUvQNXhDB6voqMDSc+OEuMostzCI3rDt2w5bHXsWl+51POds/Tbrp39I1q3zsYOnyBqS\nvCGImimTXs/J/4qc2PjMFtes7LiN/8hWdPPOOxSZibtGi+4nYxBKsWFu86K/PXvXd9kYPgc1OeF+\nYC1kAtX0EI165W5kh0PYNYPX6dHujqPbYZGw5MhBCknKOTtuYqpzJ/+69Hl8+uC1vOE33+Csf99M\nOA0fXb2OlbNQ2xET7OxTmW+Unqzasc82xN9nffpzNjWezsax57Nh7hamuneBtWxsHzm/SJXJkDQg\nHztcLrTaIwwQvseGnTcx1buLTc1DufjJU5xz/zd56QO3Mlyi+LsXrEMat6irxEdmjkGjhhkiyYp7\nA0zgIVO1Z1v/9/goeKyl2bFpODEtKaEeJQSeUyyseZlzGzKCcz59Cy/Z9GO+NPUsPnHOMbz10ps4\nbdMPSYY+H/rz9bRnJMGcYxjkDYHXt0SzpihYaKaf6hfMGovxLHoiQ3Q95+FZN3TGAq447gjOf/lJ\niD6cf/qJaK3oijq59jBWE2ceI2Fc6XkAlTM8QmBGBF+aehYXnnoiNlW896VrIZP0bQ2VSOIlGjlR\naCr3AtACmQp0WHBtBZi6QWQu0wt/WsP4EC8JQMGcX+fyY1dz/qlrQEvOf+laRCbpee74ZU2v7MSz\ngxihpHvYpELUavMqfn9A1fzxDG0k2wctZuI6rSBmLBiysjZHfcI1j/R0yHTSYMegxfbZFul0hBoI\nzvvCv3HGrT/gij8/igum1nLupus4a9NmBjvrvG/NOidr0NR4Ez1GanGFp5f1jET50MgxoSYJfYwv\n8XsCegK/LxgQcc1+q7nokDUYX/KJZ63FBIJuELkgkVpU6rpwg07RIOTDcJlCR+6h6qk6V7zgSC48\n7ThWyVnOf/XJmE5AT9bw+wKTOmg5mrbUdhv8riaYS1HTjoM/PGAcHbnFRlrrsn5jEHGKlYK+DLl2\n4rlcuuo48tGIT0+sJXsgYGakTmd/5Z6REEabA5Z4nYrSOWvqBFZTF46LrxdiIn+sYef/rbj0Ws/L\n5oILlp0tTPXu4traIWxsHMGGuc2sT36G8D0unTzeQQxphpUZIgyc762SiNkuCOla5fMcOT2LnHV1\ntFIvBinoGcWm5qF8pnkk/q4hl+53AmqYY7dZlt28E9EfOnbKMEZNjrvTajWwtbAyYe7HdRfMx48G\nJd2/FnpGzeu5L1CMFIUu/aLdSGnw7HlVD4Hj5yt6MuTa6BA21g7DhB4XP/Ekhkt9dq0MyZfliL4i\nbSvSslCbW2pQwDdu8ZynfT5+dPRPH9DLbkbr9D5UK6vMlEuoJVQ5kcqoe6nTOR+VfOWUZ/CB1x4H\nmeDvTj+JXi+ibxrUtwlUbDE+dPeHfK+Y6JcRje2WM39xA5O/2sp5TziTvCFozhje/oPryB/MueRV\nz2fXPZMYDz4x9UJqjZiWF9Odq5ENQ973muPJOhFSO+pkkvmVzG45Su1mg+CiVx3D7l4NmVpsLEBL\nLjx5HeG0Qtcs4QpnvTXsOA6qSJ3apMiE6zRLASFRsUWH83hhbYckbQkuOewEsmax9bMAgvedvA6v\nX2jjFNCe8SBte4RRiNk9g2w1McvGMVIijEFu3006umeFnERRT/AKdlIocxROe8eXujKyqHtuod4R\nNekPA2aWBFz+otWc/7KTQBje+6qTQVoXcAvJWfqKWd1izm8stnjLBCKRoMD6Bpk5YTCRO3kJlVo+\nv++x6NDxvVXitrWffNoadOQeooUJbRULi+Ky8R1W+/frXoSt5zSTWR3yAAAgAElEQVRqMYHU+KHm\nva9YQ7Tdo/GQrR5GL3E7QoQgr/sYr40wrrHE6zuhNNIMwsDJqXoKjOHq1vOwnmM6lDvBjx22jv4K\nRTJuycdyorGYp4xtRwrDqBwQyYzMKicyZupgXJb+O7pH99xcGx7dGKU1dsHPeipiU/NQNvrPBmO5\n1H82KEVfhi5DjhNX8NPa9U8M3DHMzCxybBRqDVdsHCbY/tB1VJZMmsDn6sbzsb0+DIaIWkhv74hL\nByegthV9UlKC7yFyr4JNTCPC1DxUTyC04arGsx3bZDiklCq+tHE4WDitfztNkbOxtbpioGzobKan\nalw9sdq9h7VOPGPhMMb1fuQ5VwVPd4024yMMlzpc/oIXTKGf1odZgRpIrIK0VRTQUzG/gJSiXEnm\ndhX/Yw0uiuxcZpYsFOi6JQwc99xbICZfeoIm2rX0fub0o5yv6sA5JCdJyPkvWYvqewSzgBSkIwa7\nPCEMMnQQogNomJj18c8Qn72K9699FX/1k+t45T2buXLFYWgtkCv7mN116PgkvmHl6C5CP6+w62wu\nwsQeshUT+Dme0JXvoSoUH2fTWqWuKCRO06MMLEPp4J7Ioqx1kFKgsVog5zwaD0qiXY7tkYwodAjN\nhzJkbukvDxgsF7TvzalvBxNIBksV/ZWWvFFkA4V8riwKcO5nzlWGwg3HJilimJLsN+Y61dpNJrzO\nHp3WUkfek874WS4w+HBGFi6gj/kDloVdkhGPXUmTf3vrIcwOIxpJodkxDHjvGSdBphCpQcYClUrE\nrHTdlvl8g44oRMp0DdIRxywRuXC49cCgYotMNXk9wHgQdAxqmGPCEE2pBlkQN6TToQGXYckURCDQ\noxrb0EStlHqYoqShFiTErQDxsEc0YwhmM/xeRt7w0ZHC+IK85qEDH6kt9YdivOmew0KlwNYCVxAD\nxDB2Tu9RiK0FmEJ/J69J17wymbHfPjs5fvnPmfS6KCwTqs+4TJkrdNwfzsbomogRNST4I3qKVp61\nEij49XiyoN1Jx0MXEhH4XDX652Cty8QBPI+N9kiE7yEGMTZzDTUicgG6LDTKiXH3vbWc9uBNNNI+\nG5tHIFpNbKvO2du+Q7/vc5V/qDvucIiQEm84hvEVZp9Jdj6j4bLdaUPztwN40GmRye4AmQZuUU0z\nlxh5xbkXjJ2yrtEwCVNDx2jaOHIUG2ZvYap/N5tah7q/LYJ8JUldZu/GYNO0mNMIlozTPXiSuf3d\nfKfjBt0PquspjNM+SsYsJoDGjjb1ezvI/tCx66IAKQSiO3jc8/Qnl88tt7rGA1NzGUWaO83rRmEO\nkWiPOBfMDaNK2EpriS46JK0FNZrx1hv/jcZMxvkvXesuctfjXdd8g75o8IWVx/HJp69FWHjlPZuZ\nuurHAFx5+FG87zUv5M2fvZlmN+H8k6fAE+gZn1d/9oeo5YZ/Ouu53D8zhsgLSiUuC421X7Wzg+Mr\nZ4V9nTsvd55v/eq3GZlL+dDRU060KtScf/X1TEcNPrLmOMTuAONbsiaM3KudPvS9MVPq3znmrnv4\n+cg+fLK+lp3PFrzjtut4xr33c/v4QfzzzqOp7wgYLHUPfjJawFYeKO262FRiMb7ANuvIXLsbrj/E\nijF0zWPXsUtZumVe131PDAGVZo5XuLOXrde+1Au6N2HEG+ILTduLmQwjdoQOqgHYHdTphwFp6vHm\nf/4uI3MJ7zthCpVLZGp597c30anV+OSLj0NqgUycHlAwIx0W3oc33PpN2sMhn37KGvKmD1je9qOv\nMjQhX1j5AmTqmC9WFYttDrkvyBrzLCRR6phHhubYgCcv2U4vC5naeCf+DsuHV59CbbvF6+e86b++\nRl+EbHzOiSQjxfZZu65Bv6PxdnVhtsure7fTlDmXNk90xTNj2DD9XfrG5/Orjqe7f4Nk1M1r2na1\ngPqvA+7LlvHPgxrdXg2dKA4+4CFeuOQeDqv9mgP9GdoyZoduMTBhZb33xxpOAx0o3MCsEg7WW2hu\nLEQlm1uqLYrAx6YZNtec1ruNhk24bPT5LnhLyYa5zfSsx9VjR1A6HTVNwlT8M4RSbGwdy9/f93me\nPHyYTc2nY9M+st3i7PR2+jMBl8/thYkUvb0C5p4bY43A2x6QtJss6buCougOIMlc8NDa4d7SMW1e\nPfd9miZxGTqWjY0jeFq2laneXUz1HPNzU+PpbGwfVQXyMps+rftDmmRsHP1z95nTjHPyO+jbEf5x\n/3XMHaAYLC/YOQ2DGCqsZ7CeBetkPLIxgxhJGY5H1B5UkOUIITAjNQe9LIB+ft/4k0MutjBgyOsW\n2ciR0jqlQWnpJgGRn7vuTpUzWesRa7+wkXMt9QC9YUjkp+xtZjnlxrtotGLe/ZL1nPuZb3HWLd/j\n8mOOpLM/1HZKPvbcdbzynvkCzXtPXYvdBq1tOWd+71ZET3H+y9bynq98jTO3uKLjrn6dNFXYyCAC\n7ahrj4BbynZyVTz9s8OIQRyAgfYg5qx//x5qCB984RTnXXM9p950B1ccfSR2VwDjKdKzJHnoIJYl\noLoJq361k4O7D3DwzAPI1NDZoTjzVseR/dnkvuhAUtuZEnTcij97gE8yQSWfWwqcGeVU/ITWCCnJ\nV00ic0s64nHx//o0f7llz1JVRQE9+YVELVA5y/joRbosdZkyovos8yWp9XjIH2Ob3wagFcRMhw16\nccBY1ue1N38foQUfOnqKd9+wiTN+tJnPPe8oV3OwgHGBL+y5IrjXN7QHQ151z2aEgYsOXsNlN32a\nQ2bu5yt7H+Gyxtzwtju/Tjeocdkzj3PBW7gCfTm8PhhlkbWcle0Ox4z/gtvn9iOc0Zx5423UH5au\nGeWn1/GSh2/jK3sfQTwiGC4T+H3XNBfOWILdrsBnOh0aKmZq8DPsVp9L936xK4bO3MG1o88inoyY\nO0A5T12cjHQ4LahvN8jMo9sdo3m/oDZt+PnqfYielvHUlQ+wv68YV0OW6B38NF1OxyyWddjTo6It\n2jK4O6hCSIktC6PCtekLrecpfsa4DD7XNHTi8HTPY2PtWDbs+m6Btz8VvX0ncmwU0Wpw6T4nYLd5\nrN/9I6buvXv+HNIMazM29G5lKv4Zm5qH4u8eMti3yeyTBGsP+Qn9POSny1awM5qkvsMJhDV+kcH0\nrNOdjyLXsKXkPG++dxfWGDbWnseGwW08Id+16LNf2jzi0S34AppkTPXvrn5nQ3Y769Of8y/LVzNz\noE9vX41tzt//Ys5zxi+erbqHbaSp11OsjCoWlA19TKSQmf6fa3DhnFpwD1DL4vm60iNqhMWW3c8Y\nCwc0vJSaytiVNHi4N8Ig9SvRpVY9RknDh17/YjKjeMWmO3jFJteUccULjuSCl6wFa0gmLOd+9auL\nTuHcTV/l/cdNccH6tZjQ8rqbNnPmrS7gf+Hk5/G5txxGXeToRszubki9lbCk4cTx235cWZDVCg9D\nKSzW2qqRQQjL+a84GTmQnHnrZl7zQ3fsy485ko8+bz3NBwR6uyIds4QzAh0Z6js03Se1uaj9EpKH\n6pz6q+/xkodvg4fdOX/h0KP46JEu229sM6RN92TpGpW2vPHdzbFQsxmlsCNNsqZPMuax62mSFOWE\no/bgKPXjTSl9C5TeqJlRxIV2iin8QjOrqsBflynLQwcBlTj7Lq/JJW92Gc+Z39jMmZvdNbzy8KN4\n34nr3OcU7oHIG5AAwZwg9CQfO3yd25X9YjOv/MX8Qj6ckHSeUONtP7qOl/96C198ylEkbUjGhNPR\naRXZZC4QxmXvdi5gR7tJ10TsU5/hW+cejJCW135jc5UkfHn/I/no6nVkbacaGu02NLYWHqmNAH+0\njQx8NrZOgN011k/fzvrdjn577eizuHSf4+mt8kmePqBed9mkpzQz940hc0U8YbErYvInpFCLOXHy\nQZ7R/C0Ky11JQmwDOiYiEhlL9zCU9sghdbFb1ThHHmvBGmyOy8Zl8TMJxLljrAAiilwQtZbLGscg\n5nymuj92rBJgU/NQLltyDCrXbpHIckSWc1nrSNbv/lH1/pvqT2P94Ces5+eQub+7+MC1mLpPb4Ui\n2StjzBvw8HDEseOWD5g90Cm0Bt1RgoUiVwWd1xrDpdHzsFnujj34CQC/8iZ54oKgfk7/tgpjr8xI\nlHLFVCFcNl8E9n/ZZzUXnrSe3j5ul1dqL2HABhaRSayyDPYtoKZA05+pMb5bk07U0CsbJG2JSi2N\nWCP/AFbaH7ksvniIojquA4EJDbpw1gYXFHylafkxuZEMtc+upIGxguWNDivbHUIvd1rnfkao3OR/\n8pxjFr3HR998DNHyAdbXnPeV63ndjd/js6uPYr+LPszlL1zNWTdv4d03bEI3DRe89oRFf/vJN7yA\n3Co6cUi3V0PWcpqRe8jaQUxNZQQyrwT5F5phSGnxghyhLGjF+05et+jY5790DYP9cwYrDMPlrsFE\nJTBzoEfSVsRjgrn9a1z8xDWPum4fPnoKHYqKiZGOCNKRAuYRLog7IaCSPSTQzRDCgHykhvElwwnJ\nja/5CM8M9izcAmAfYSbgJG6dx2NivCqQD7TTs57TNXo6IrOKUGZMej0mvR57hbPsX59mr/ocK5rd\nKqiX44L16zChwPjOpzRvavLRnHxJRjJhSEcEeV3y989eu+jvPnfi8zjt7i388Kr/xSt/sZmrn3YU\nHzhuimRMOu32CY0YSxFjKWYkd+waAV5P0h8G9HTEimCWF0/+nO+ed9CiY3/ssHUMJxV5zd3f0W5N\n8OAsMtHoUGEaNRgfIZ9s8Q8HLT6vy8aPxkYBwyWC4w68h5cd8GNedsCPOWHVPQTLBmQNyEc1S8a7\nvOmgm7nxqf/Eectv4uj6r9AIfpEu57/SZWzLR5AYxlWPP8Ww0u0CTei5QJ0kLsgFgWORaOvglLER\n98I16tgkASHY2Dxi0fE2jv65k3cu4RsAY9iw++bFb/wItsfGkaOQg8SxhfaCJcvn2Jm22DlskhtJ\nLcrorTLutVeIHWk63DuOsWlW8cYdrLN60bGfmO9iU/NQTtj7LWxqHcpU7y7O6d1aQTY219gsw2aZ\nK/ouGBesPYX+KkE+oouFj+LlNH6sKhY9YRG1ImZkEr+nGU56zO3nEU+4uCgzXTFoHs/4kwZ047nO\nOV34FJvCT9NanPdnYVBRSmEG0rnElPjs0nqXpfUuzSAhVDlNP+Gdn/n2ovc47/Nf54CJXUzu3SFZ\nAV9c9xw+8o4XUN+7z+XvPIIvTT0LvQJqkwMu+PLippO3bbyR6U6dma1tsl7A8sm5+VbwymtUOTNa\noQmk6xQdZL6DjbTC5BKM5f98bfHO4D2bvsoTn7CVcP8utb275CsTkgnLcLlh1zMsM08zzD7Jcubs\nozWT3nXzJurbNCqDpC2dHkipCSIcfJWOWvKaw89dgC+r5zkyM/h9lzQ3ZbSnHejcXFqBxBYF0VKS\nttAfL+oMvtAVpu4ci5weSSjdy8ExQ5aFHVZGc5x7xeLmkb/51rXk7RwxlkIrK1QxBaQS41uScUvn\nAM05Dy6+9rq3mNXz/hdPEU9CvMyQL8nw2im1hnuFrYRsIidvFbhnrujlIT0d0c0jTv34jxYd6613\nXUcwZ2g8bGnfa5CpwTRryFTjdxKEtbx65lbO3v4tXr9jcQPRxx/+ooPHlBMj25aMsC0Z4fbpfcke\natD+rSHYqZjt1fhZf29uS2rclYzyy2yCrqnhC81Sr8tyb462jBdqueyZsVDwcQGMW6pF6prnOlrz\n3DWrBU4GltRpi2fLR8iWj0AYOA3+QpDq7OnvLnqbDbO3OFhGlywu6wqR3bvmg2rj6VUGXP3d3GZE\np0fe9EmX5uzTnuE3vQl6SUimJYGnEcsSxLKE/gpJurThipep68xc6BR1Tm9xk9GvvEk2jhwFnmLj\n5DFsah1KT5YyAAaMdpRgbdiQ/HDR37717k2kk5nrIK4klUW1a0VaF8xTOX+ZDS7xmpQMl1vyupMp\nkIP0fy5tsazsZg3At8hCH9kY1wVYC7JFv19mv+XXOwYtoHAfSRXv/Mx3OOVrd/GlqWfxD288mr+8\n+GZesekOQqX5xDnH8K9nPwuB4aBgB900ItEe//DGowH4P5/6Jqd/7QdcvfY5vO+1J/B/rvw3Xn3d\n7cRDnwvPOJGw7qQHDM41yeAcjEobPCksTS9l4KXMUHOm1JnEpoL3fPl6zrx1C5977lF85Kgp/vrm\nTZx1w2a8QHPhGScgpKA1MqQnLWbgOU56Ivjb6zbx2u87zPyqQ4/CKMsZd2zh9Ls2Iyy878QpJ7y1\nYAfm2svdqiiMmMfQVSGpm2j8Too35pFZ0PbxF1ge9xBUUsW+MGTVnEmMlSTGK/7PiaWVei6PtHkL\nZUZIhkSz5sN3c/g1v+FrLzuEj5z9Yv7iU1t43fXfQ/qGT7zhGDLjMSDEpgq0cBm7n/N3mzZx5s23\nccXxR3D+mine88XrOeuWxU0u77plE+85fQ1iLCOKcgI/JyjULVOpEKMxsQoRmV8Yllu0haM/+Cue\n/eX7uWrNczn3mJdxwZXXcdrdm5GZ5ZIDTsIbFEbTdR+hXSORCRV1mfKS39wGwJcPWI0aal6y9fs8\nOd7KG+7/Ju/2TsEXmq2JgwcemBkl2impb0vorQwZpB7b4ha/Tpc5OqjICQrnprpMKvu93yVL/P86\nFuqIGDX/tfNILQqgqrBRKwKy0AITthguc0VaXZ8kesBH7Jhmw9wtrE9/zrXRIVy25FjO3v5t1vfu\ngm2wceJoF/jznJ5RXBse4qiPw2Gl4niPv4y3L38FZ3e3sH72Dkwt5D0HHYCIEqbjhmPJFbt+pKlU\nS4dLLTMHhozZpYT3e9hO173ynHOyHzGV/pxNrUPZOH40G6ZvZqp3F2fv/i4bm0eCklwaHebqAInT\neSlx+LO7W1g/+AlXPf0oLlwzxbtu2cRZt2zGRMbpOOkFwbgM5saZoIhcYGLl6smhYfopAcmErWwl\nVVLQIP+A+fqTBnQRGqaf7lx4/FZSLJCWPBPk+aObXaSwSGylpLi84TDCbhoxY2t0GiGfPf4I3nfq\n8QR9zfmvOZEk8+h6IdvmRmjWEnItmfVzRsKYyVqP6bjBWDhAjwv+ad2z+eSGYyARXHDGiSSxz3DU\nZ9XyWUTRwOILU0EsuZX4lIwNgyc0dS9lJIzpxSGZMlip6IQ1rjziKD54zBTCCj7woimwMK0CpCp0\n03PFxHgPf4lmRaPDr3dPMrsi4Mf7ruLH++/LhSevdRXwtuWZv3mA3ZM18hY4Iv8jrlMqKpPoyupM\nuaKVSFKUMai0RmolSuz5TZnAFhx99woLpkuGyzw1Em0hMR51lTKiBpUv6KxuVBZxZUBqqQRvVHPr\nqw/gK29+FrYr+fjZxwKQNBUj9YTcZJWQGTjKo+55zKoGVxx9JO8/eopoW0HhBO5evg+vXfcW3n77\nV3nNDzeTNy3v/4vjyDNFnin6peia0oRhTtRKSH3NqslZDq4/RF0miBHL9179BK5749OxP4aLDl5L\n0LXEeUCwK0b2Y0y7RtYOGSyLyOpOJvkDh7yEJ33jIZ66835e/hsnrHbt2LOxgWK21SAbgT+rb6s0\n2Y0RCA/yukLXIIwyVtbmeEKwvVoEM6vQVhIbn0woVLE7+iNM7mN+XxqpWFEoSxYdo2Xg1ZFHf3lh\nGrKPotUep9kI6d/T4Fr7ZDbWDkNY66COvqRvPGfwUIyr2s9DCOEogULQz+psUodWbJKN/nMQI5a5\ndpPugYbmyNDpK3lZJZ8bZ14F6erRnO5+HsaPaI4tI9o9huqkqNke/e11rhWHsFE8A7trN5fwNGhY\n+qLQDRnGbhdSBvLRFunKEeLxgB33jvDZJx3Fe1+5BhvknH/WCRBpOrXo0Y1eJalCWofA1BxbSChL\nWE/oP1M77+NUAq7xKKpH0H38UNqfNKAHXo5aNSAqGonSVJGmCqkMUZDhl0yS4nOnpvR1dDdJidOW\nxheXvur5JInCK0yW89znwtecQC3KiciIMw9faWq+M7YAqHkZqfH44pnPwxOaWp6zcyZACPjUm46m\nHuZgoOZntIMYTxg8qVHF4lKO0ipNFdz5wMtJPB9rBJ9Y+2LC7U7zGZyK3AePnWK4DNrBbpLMY9gL\nyXNJEGhm+zWMkXzhLw7js/nh9AchJs+wueT8V60pMviCHmRYpA1jPIuoWWenh8uc/KHb+rus3WLq\ninA2I0OyQ/cxeziTE1gCmRcsl/LYhoVAR+nRClTBXOFqEFUgK4KSL3L+4y0r2JW1aCSO/60UXPKX\nz8f3DONhn9S4uS29WncYwbDv8akXHY/Xc1o+KoFOVOfKw4/kY4etx+/CB4+dIo+g0wpQvnHKjbnE\nFmYVJhAEgaYeJTRrCfu3p1npzaCE4ZdvXcqDyThLkx5IUBlcsv+J+HMJsjNEDBNoRJhAkrYFyZjj\ntKtU8Jq1b+HOy99ZXY+NK15EPlans38NU9Ms9TqEcpmbQyvAs2QNSdawTDaGTPh9JuSAsAjoA+vR\nNREdE2GsM8mWf4zd1+8YJYe/1DexuXZQhHHCY2U86x6gGS6TdPdqs3H5y6j/dg7ZLxqtfJ+NZbFx\nGLuMXxYZv1fi6pKrx48oirC4+9r3uHifk5l++gj1FR3G60N86RIsbZxNYJ4rFyABAkM2rulLRdZS\n+L0aXj+iNtPgirlT8LopcpghewPXDNU8cR7qEAKUwNQD0pGAeMKjv1ySjFs+cMyL/i97bx50WXrX\n932e7Zxzt3fpfnuZHvVMayQBYjMBDBJICTgIISSkCYWpJKRAoCqbKqBSSaVsYweMFBMoO6awKjFL\npQgESNm4UmKxsRBgcCSQBEnkwoFIAkkjzUz39PZudzvLs+SP33POvW/PjDSDeqachFPV1d3veu9Z\nfs/v+f6+C3q/wVqZTaWo+Xvf8Y1EbyDI83r2pHF2kTRp+DXjScN6XYhOQoOvjNhfP49r8qIW9NZb\ngjc8sH/K0WoEGGIwaL0xvYIMtWwFSPSd+sJnJ7SkKE0g5PQWo6NkiXpNuy7xOdk7eI2x8rkTU6FU\nYm9Uc1pX2Z5W0oCMla5sZyTK1IRg5jEprAm4raLep4t3SQ//99srsUL8SowiBbFQJUlRH92CE7eP\nubTmyqVjlk3BunGUhWfZOA7riQxIQGwJTEQVCaUTYZVQK4MOEq4NSCBGAt3oM3YKoZDUIlcWpM6j\nT5Y012bEpChfiA4933Fd0meKSi8wcirQ5YC0kDNBiQw5o/3iWOoOjXxcq4jTkR1bs1ushxDuUgX2\nC2FPnJiKo2bMaV2J73VUxFGkHYFfgV0r3vn610uBaRV+BO1+4oe+/U24fbGZ8FljMnh/t4bVaUWc\nKq7uH3POLamToyAQk+bJdp8/OnwQIqwONLqzuNNWHPRSAqtRMTF5KlAdZvMunfjPP3R2XvPXr7+H\nf7zzZsGmg+I4jIcFLyUIk8TyisZfaHl454ixabgbx8x0TUEcArX7TFbJVr2/1zXd4ybQbwBUSiiX\nHRidQeW0HQA6T0oJd/2InQPZedWvXfPqhx6jCZYPfuIa0//jPPsf7hh/7BC1QlSm3ksXnBIqO3Sm\nEDYmWUpJcTeaZDT+wg6nj4w4fTlcmqyobDfAfoNAsTMkn99Abq66nUB3LsmwElBLg12V2GWFWUNx\neo7qKDK63eHHhnrf0Oxrmv0cD7nnKadLDnaW7FVrCu356J2LdJ2RGMKkspA7Dz4tgp+D3GNROPsS\nHanxF6Qrr09GFIeGNEmw6xntryFNxV/9eQxFX1wMvdOEuWO15whRY23EJwk2aL2lCf5MJuC9Mvve\nR6VWbujSje5TWoTPbiatBPYmcKUnJcV6XbDOhfjoaCr0SS2BGs4Fzu8uh+FmL27qs0TvPXzcgoZ0\nGEQ08k1puHixlASf/oiF/LFzhU8jbgHn9xZURUfTWWaTmtabnFC0eYqSlwVG2USaeYLXw9ZNl4G4\ncCgvalS7FPm7LyWwNo4cuu2gaSnvtrRoYgro+z8WPXNuej66VgmDWAEggTYArGJJqTV14kyeqDgG\nbuYope6odMd+sZbAiXqE08KE6lOP2mhZdQ5XeHxp0DaiTcIbh9dGPOi9osu4ZNjx6IlE++ktjDX1\nDotBk1pNjIpL41MuFnNZgJAFqomOk7Xwhds9Rbi7pTJ0NlsWJNw84BQEp/jej/xzvvXj7+OfvuI1\n/NRL3sD3/smv8s03P0j8ZMHff+Sb5X0kOxR0gDCK1AeKyf6aq6MjZrpmGUuBpXQzZKT2R5HTne77\n8Qy3SlKiFI1OEQuLKbL60QeBJQDqhuqWMMTCY1P+aPQAD+8ecfXiEYf/Qc3jX1qRjg8Y3TBMrifG\ntzymiZg6oNuAWndnQlrCpMTPnKhox5rjz9XUV1t2zi9Zti4nXUVOm4o2GNaty6rtvKtvNLrREro+\nCrhRR1l62mk2ZVMJbSPrqJh7ITho02BtZFS2HBSbXX5MitpbbsxntN7ivc56I0X0G0xKKbZi9LYO\nLQVdNzDZrWUxuFVw+QOBm19hsA+veXD3hPZOBTfv/Ls7FJWBAISoGJct69bhOwNZYQnygIao8doM\nUua+W+4LrCgSA220g5fKPJVE57EmsqyzqX3u4Jdr+b/ND22MCmslC9ToNIiE+og6EJjHPgMm2dsQ\nhbwQbdP1zvj2WHnIo2GQ6IdKuNNmreCxMTevGHb3VviomZRi3LWKemNe1zc+YXOTyA/Pi8bSgRIh\nirRTm9/fD+iUF1yzuH7MB1cv55GdP7nvkAvkYq7l3EYlRVyrMKQO9ZmfIPhvF6TDdCoMlrBj3TDR\nDTpj8PKxlodGh1gdZMekJZpOfv6SiW14YHTC7cmUG5MdfDDEBCdpQmxz0qaDWEYoI7rMqVA6oYDd\n6ZrKesZOFvLjesTNm7tYG3lodMSBnRNQGDQ7uuZCMefidMHJZEKoBN/V9cYnW3UBHQzd2BJLuU4L\nKyZhP/FSGZz+5ENvgAin4zHtjoZRm4edfXMCahxoNVyZLRlPqwwAACAASURBVHhZdYs9syKiaZOB\nWBJQEmyhGzSRHV0/bch8P47t7NBeFAjZOsEpUqGhKsXNUGtJr1eatFzhHhce987HHuLw/A6zsqU0\nnmv7R9hzAf+Q4dbLptw5nnL7uEDXDlMXmCYHn9fIPWVEc+EniVAl4igwuTDnkZ05u8WaP3nqMr7w\njHJz5KOWIunVJn/XC5lAd4rQiT2t9yJQMyZRFR3nJqvBR2rqGmkioh60Jz1y0EUjEGwmAxSFx3uD\nB1I00hRkmuKzPWrJiRNnqSPGKLpRpN3RdDuJUkc+9tQFHjntxP66LJ7z9XpRC/q4arjyyJ2hOHuj\n8dbQtZaRE+xr7R1OB9pgaLzgpDYHDo+yj8rdZkIbbfYiF0y8CxKCUFjPSgmNsIdttE7DAFapRFUE\nSucxOZwiIUk1VomfTGEC0xxLFpMmqsTSS+BCfxQ6UCfNoivRKjFynnmC1GhU3CzN2Q1Y/s5dao89\n6jsFJ6cOZp52JKKkrrGkvLilqFBato9qrUkuZfHG1iLSSBKSafOWOMrvsvMWPa/lQSsK4s3b/Pfv\n+ka+77s+CZxVwX22R0qy0LXRCnUxX6e+8AIDLbGJji4vijGT50vdf70fBnttMhgiU1Mz1g0Rxe16\nOvxOpwKV7QhbgqYhxNlbjA2EIkrod1KoKmCKgNJxuA8KG9iv1kzcJhqvCRY36phWDQduzsysiUkT\nstx9bBrOV0s+UXWEopBLESNYI6ZbuQAmowhOEqp+5gu+HrOOYpMKJKv5yWtvYPXgmHam0DZyo9vj\nNEOKWieqaYPbC1ybHbJnVpkh1KtwFSHp4f89RHX/WS7qDOTSpxSptMVFN5rkrAxGOz+EQsT5nPCk\nKOP2P3KJ05eWHJ0fMS5bdlVib7TiodER584vGV+THcedbsYilMy7ipOuYuWlkPW785ltmNiGqWn4\n+OpA7pNgaNcOpaCwsuiHXFBTZpNsH7pTpJUhREUoNXSazia6yqB1ZFJ06DJhlOhh5m3FsitYd5tn\n32ipEZOiZVII627dWRrtqKMmBTZUxe1LkuhltlLQC89yUUknPwkcfoGDg5rFvGL2ByPM/FB81u83\n5KKUegyYI44NPqX05Uqpc8A/Ba4hkVbfmlI6+vQ/R7I+V51jp1pTdxZjIl2GN0JUAsWoKMrDpDA6\n0gbztG45JpU79E0OKYiV62zUSPffF8ak6M+sVglrJK+0/z1DzqWOjG1LoYOkEYlP6PDvdsu3JaKo\ng6MJsqMI23urKGyT3os9WekyBi9scpMdFWatSK2jHZtNsVZSPEiQHKgyp8oH6ThU7x1jMp81iRWv\nzph9UqBaL4ZQXQejCn3xgJf+r6e86TVvIPKT9/26xiTno9AS0DxRkbHJismkaaJjpWSB7AuPUQFD\n2gyYdSRGnfHgMLgHahWZx2rQJ3TJSNdv1qxiwa12xtyXLNqSdU410jrhZo2EBCeFdX4I+45ROv1J\n0bJbrolJ8bHDg/xaFRf3Fjyye4eZlh1FnRx1csxjhSFxsZpTVR3rvPsCiOOCMHEZjhBPetNBKLRA\nYFZ8dlRK6DqhfSRaRbsv1/p9d15G7eU8WBu4unfMl+4/zoGbUyfHWDfs5NfTF2/ZOaTNaxyux/25\nrr2ZXn9EQ25WoDfqSjZj233weF7U1GwmwcqAuTmnOK0IGRbRKtFGy81mh48uLqJVYmJaHqhO2LVr\n9u0KRmKr0QvS1qFgGQpuNTMe8+e5vZpQt2ILom3CGHn+SyfakBgd2kZixsqxkILCrMnFVm/mlbUi\ndI7jMGHuAnfshCfdLs4GKutz6E6gC4Y2GDpvqDs7DOQnZUuMWhaSoMQVNGzgns35zLh970llYPKh\nkVC5p4LRmycqdj4GF9/1EbG8ripJT3qOx/OZkH1tSulLtmKQ/hbw2ymlVwC/nf//F8f/+46/uK7/\n3zz+4rr+//D4bCCXtwBfk//9c8DvAn/z032DJuFMYIx0Y623mZImTBQHG7Vo1BLAvEVd9N1Z86Fl\nV1Aa2dJbJVFxIWpmZYPVkWVToHWkzeKQwgYK6yXcWclAtdT+Htm6oo0Gn2SnMPx8Lda520PRmBSN\nt8OqTVLD9JzN7JLoxK9dUnBkZZbueoOeqE6RHLkz39qqtZImr4ogX9/qYXKvGnH3o0+QybCOSqB8\nJB6foMYjsQl1Fv3YdT7yB5/HBrh4xuN5X1fFZoAsOKM5M+xsosXkgSJwBh4IWy2gMDc0hfK06SyU\n4LIyt42WdXDUxjHWbfaFEdrkyEocYd85CbQifZhzYaC/gnRyvYPmsivEWC1/3ZXpCQ+NjpjoPNTb\nei86zwOcCayywRJKiX2r7amjW7izytYM+WO61aguEitLKBV+JHDg3dWEbZvxqWt4eXWTSnU5bq57\nGs98G2LZPt/Pcjzv67r9Hs5+QERsfbYoPqsmYyR1ObtWSxcMwK27mPoCrRc64VE9Yu0dC1ey7ArO\nV0uiUdyodwW+NO0wUG+i49SXHDYTTpuKZetoOif1o7P4zrC/u6T1htYbCiuZsM4JpbWfUcZOE42F\ntcY08tpDMNmOBHkujy1Rw3oW6HZaqpHsMMeuG571urN0nRHSTbb8Pl1V+DxIjZ1kGA/KUNi0zf3O\n2kXwCnti2P+IpzxsSE7TjS2miRR3lqTFUhg+z/N4rgU9Ae9Rwi38qZTSTwOXUko38uefAi490zcq\npf4a8NcAxpcnPDA+oQ6Ou/WEEDRF2aKUCACUStio6bK/eIryMaMiddi8uZ7t0ng7FGaA0gS8StTe\nooCD6XKASkA46DtFjSZhs+KzjXZg1dTBMe8qGcLmQjOyHVMrD3ZEDTh+E2Rg4qMW24LWiSe63Txk\nSQmKojuFn0WSzmrOMm62shlTU53QwCjDWXVZytu3Uze4P/YFXXuGwt/fmCpzdVXdEuZzjLWkUSEP\nnrWESw1uszG7L9d1cnkixVb1fHLFOjhWQYpkTCrTFfNimMwwwNNbsEGHxeEJCA2zTVAA5KI+cw3z\nDuZdNShQS+3ZtQKb9MPtphPmge+sQFIIa2lWNeyUNZXpBmz0ydMdWm8HmHJUdFwdHfFQeZdKd7R5\nkTFEzNbi4kwc1JIpF3IVk0AuSvDmng1CXp+TFgWvn8hj104VqYg5gQl8Djr33si9GEbMXM2DdoOM\ntGyxrJ79uC/XtRjvbQmI8g/WoJIMGPuGRfmQVaK5guVkod6XJc1XuEVivihpSnl+QhSrj9KEvEjH\nATMX2C4nMzUjjusRR/Mxvs0FUyUmu/XmmrmOmCQoXuqFaF6USkN4TlM7KbqFRnUZ0tRJYvDWMoTV\nXh43v7Q0QbHsNGEiVMSYyIwWybUVhbtgUs3aZcsPJWHhPaQydHT55GalqCoiKUq4+/hTp6jHb5La\nFhsCOCdpSdPJINJ6PhnAz7Wgvyal9KRS6iLwm0qpD29/MqWU1DaR/Oznfhr4aYDdz72UjtsxbTAs\n2yJjmprZuGHVnF2NzNaP2+7+ABZdydo7QlLSpeeUo51M7m9ylz8raqyWG2VqG84Vq8E0KiLeI1FF\nDtuxdPRdwfFqREyKUdExKxt8lLBimxkbofdIyYOaxsmCoHUkRSuDmNwpR5ewtRqSieIkwthDp2UV\nLyPaBWJnZOAUlajEjPBkVcbUB0lTUKS1Ra9yAQn5wcpc9/6P6VIWXxTih954VNsRjk9g8ch9v64H\nrzxI/c7FJ4OOiU5vJP+GKAvmIMaKQ9drVDzDzug79jOdOjIwPSgWWBVpoqGJhsN2wsh0jExLIO+o\nrB8sJLa7qJ5y1g/X7tYT5nXJunbEYHCF7MR2qprL5Ql7ZjkoMYf7kCj2BMlSmECykuGaXA6s6CI4\nLTOOjDH3fjtinJaGTl1yAaC8ZWhiyVGnKcfyuqejhgvlggv2lJlen2H9PMfjvlzX6bmrqZf+DwuT\ngqBBezEwC4WWBKIYxeiqyh7A25SvomByM7B8wrGeOnYmteDo3nBhvOS4HvGUn3EwXrLqMkMtB7Jf\nP91hfntKcdNiEYvosOeJUVEUnuQCR6sRVkfGRTfQjrtgqH2i6Xpuv6yqfhKFXOAiZuTxS4e+ZSmP\nxPwuOvHVL44M9UXN+ryl3bE4FyQw3kod6LyQOUJrSK0eOnJx61Tid27IO+Z8DpFirkyEYLFrYUUB\nOes3oqYTUlXIIHS+lM/db5ZLSunJ/PctpdS7gK8AbiqlHkgp3VBKPQDc+ow/B5lKd1uwRdNZtJah\ngjUbHq3VcZD890cvwe+79T6cuB+YWhUynBLyNjzkB75jZutsxzuliXZgrJy0I+4ux3TBsDwZkaJi\nur/KoceKtXcbn5JsFiavT37XyHa0QbZ6jU4oE0lWi99xFNpidGyGly6Ci8ROo4uAtYFOyXAptVq6\nca8h5O18L0ZIMpBSrRqUogKt5EEVuUPv4RcfIEX0ZEyYlujaEF71Bbzi857kMHfE9+u6RkRc5ZPZ\nDKtjHDrosfHSnedL6fL16b93gDQ0A0feZBtWQySicSrwYHnE1DRcb3aZdxWLUHKiqmHHZjL0slet\nGbmOlIfqVkVOmora590YimVbsFqV+IUDm5hNhTp5aTTnkj2hUh3HYUyXbO7+BQLqkixARkeSi8RC\nEwsrYhufQ7pD37purpFpxWGzT/xRIVEdBYqFYnVBs3xJSfuwfN/Dl2/wBdPrfH55gzoZVrFEqygx\nc/lH92LhvtBvQ1f367oCA8tFRQbTt56+qEOSoai1KK3EMTG77anOD4NSCkdx6imPLMtOYAuFNGin\nTUVMaqAM97veJhjm64rVE1PGNwxuCetLibDnmR4sh/unZytNipaxa9kpaoF1mjFNZ1muejFiJhco\nwCS0E2gmaAnb8SMgKWIBochU4wD21BBCST32mFK+J0Ytvk0hN2a5iBNF5KdSFqvdC1X1ryMTHnSL\nnCOtUM6RTNy4TSr1vMKh++MzFnSl1ATQKaV5/vfXA+8AfhX4DuBH89+/8uw/ZesX5il3dIqmsxJg\nEZ7blqLfhlV0w6JQGj9cXJ8MY9WBkeI/tS1WhYE+t4oFc18OuGm/7Wu9pV4XcOJQey27o5p1Z1m1\nThzbckEvjD4TEi22AJKD2nqLtQEPhCpKce7XJyVQi64hrCzFTsNo3FBYP7z3GBVBZ6lyp6X4hb4i\nJFTQqBxoPKA1IRf5sFXIEel/vHsoCryqpN2vGP3xk/zZ9+7yiVf+c/490n29rvcqe/v/t9Fmsy4p\n9IGN0Vp/zbaLOxEq3QlPXsmCKvFm0smfMwsqJXmaTkVuNVNWvhioo3q4TpuFvV9kms6ybhxPqD0q\n6zmej/BrKw+WC5ybSMzXxWo+KFgPg0wbxlqueQ+/xKRlYbfZCroy6DqgWy8JPj1fO0lXPuQAJCRz\ndss8TbpcRbcTOL8nHdkX7Nzg4eIODtFYxGfAzz/NoZVSs/v1vG4fKgEpEfvkomFnmLJcX5Sc4o2u\n6WOMVFliTxvKw5LT2tBYh87ZBifrCmfleq26gjYIpbALhuWdMeMbhtEtCX/vdiKj/fXwfLZ5XrJu\nNTFtdvD9c915Q2juqS35ukSfC3J+1pIRb/1QiCgwus3X6pUmBktoNaGIaBs3c664Vcw7JfkECUAR\nbHpG2knvMGuaJDoRGOieaE1v2dHH2j2f47l06JeAdynZQlngf0kpvVsp9YfALyml3gZ8EvjW5/pL\ntZJQ4b3xmsOkBqoRcGYwtD3EAjbURZ2TgrRsoX0yw8rudECnyE7mkbe5S2yi5bAdD1mldR5wLNfl\noPBSAUazBmcC87ochEkpKZk75u4TGDr1ykiHbo2IVawNJKdIxpBy6dXNpsMG6ArLzqRmUrSsOsHe\nYxRlWdBJqFYJUtACu6hEqoGoh5sHckfeY5kp2wwApk5gDPbaVfzFHWKhSdMxB1ePWcS6v8fu23VN\nef7QF9R+JtEEO1jqyrXNBV2pM744veFZVALLaCMeBj2mjhIMe8fUTHQ72Amsg2PRlRzXo2GRmBQt\n56slVkm+6TJIwV83juak4tZaHBTj3MnWeBQYTVquTQ8BeKA4YRlLumS41e4wNi17Rop9nQpWsaBL\ngtUqG4kF+JGhaCMqJHFZjOIJnjeUgyWD7iQ0OjpFNzX4kUTfrR5InH/pEa++/AkAvnryUXZ0TZMH\nvruqoclS/15/X6Sn4+l5p2OB993P5xUY/PZhGPvkhSqRuk4WMqDP5QTow46xBnO0ZHxrjJ5bfBHQ\nRgyqYlSMK/HrWTcFPutJ6nVBcdtSHSZMC8sHwV5Yc2l3DshOHwQbD51mrQvmLlAUY7SO+AyJDNTB\nHtvOnXNSCYLI77WXgh4LEf+FMhHHwv9VrXyNm2tirQmTCDstykZSv31JDO6JpslwW++DzhaWvnWo\noHCrJH42kG0NzGZ3A8+7mMNzKOgppY8Df+kZPn4X+A+fzy8L2SZ3VtQ5xcZzbrLiZC3brhA1Ln8d\nMAyz+gLRe7n0K7FRkWVXnomHm3clhQ5i4ZrPx512ytIXnLZVFmQk1q3jdD7OSrFIagyUG6z1YCrd\n0qpzmC0Mdt7Kaxjn7j8mwYn3qzVGJVatYxE0XRWJnXRiKojqLTq5kKk1nCwq4gQq5/FGo2wi2iDJ\nRyBc1iRKNoXKAxVg+95I4uanW8jqdBEVLaVTTEbjJ47iqGX+hRf4xS/6MUT4r+7rde1f0jY85pOh\nzauzFPqNEvIzHTGDzxoFyVIqERC1eahaqZZzdsmiKFlnLUDjs0IwGE7bahhcLdqSZVPQrgpUo6HR\nwtOPkFyimLYcTJecK+R6OxWYx2pg5PSCnjjsLvTArDEuEiropga7Dpj52ffXs0CiUzQ7RlKllNwH\noZRr55YJt1Acn445Pi8sLpOtEwKKOtqBh/4chUPtFlVxc43+PNdVZn5b7yedsdNVUWAXvAdvUUa6\nzSEcOmeLJqNRq5riuKU4mlDvCJ4RO02yis4ZbIhDMV+dVtinCmYfB9NCs6eor7U8uLeg0IEb8xkv\n2T0hjhR33YTTZUXw4gvV1BqlIylnEctFEHaC2M0kFDpL7/UgyEsGQpFE2T101olUQCCSsgeSahVx\n7vL7VxuxoOlXiq0T2C8cfZeuhNGklLyWYh4l9KPzsqsxRiCYHDmXejjmeUAvL3qmaEQxbytKKwEV\nY9uyaEqMDnnrJMV72LUmlafMZ7f1PTXNJy3MjnzUweVuTQrvyjuWXcmiK8ScygTuLsesliUpKsqR\n3Hy+DCQX2R3VzAp5XW0wlEZojSmpgcoImYKZIZdLozmfP73O4/U5/ujwQVZ1gXJxmE4PbIAoeCpB\nEbxh3RQ4E4cdSheEeqWVJipJNyHqpwvFtrG5lH9uh2z7WrDzRqAKJQvK6kpFKBWHoeKK6Z4J2vus\nDkWeZ+QWziMwx8oXxMwKKo3HZaGRdOpx6OZ74y6d5M1IFilPg17mYTQ4NUbWPFhu2B/H7YhFW9IG\nQ+0FP+28oW0coTaotUE3avDX8ZOIqgLndpY8NDvkwIlFqVaREz8eirbLAqc2b+lDTl+yOmJdwE8S\nza6iOJFmRAURD6mU8dIoTJj6nKI5n+hmSYZyCUY3DLMnA9EYjs5VPHb+PADzgxET1VIoz91Ycd3v\nc9Xd5bzeSn9XUbp09Zwoi3/u4wxlMbN2kiZTbwVTH1gYISP53oO1Q/FX2ePcLBtGN8c0F7MTT/bM\nacxGWFbXDnXomDyhKOeRek+xvpyY7q/QKrH20mD1O7JpKQy0nk7ovcGvCmg1/8W7fpPdRc3f+6a3\nSFMUEn/nX/wKp6MR73zd6wezsVhANIk4isI6KgPlqBNL76QGk8fYGmg1eiXdfrJyLZOR+Vjqm67h\n5D3zOdUmkoLCrsURVb423zDeC/VT6eddzOFFLug+y/Frb9kBxpW4G24bbG0zWlKS9EtnRB2Ztu6u\nnlbYU+JSSsP3t1H4qZCHLEmLmjNqFnXJ4ngEQeGmrUAkSWGLjeK0Mp0EMdiOK6NT1sHRJcGEe2pV\nHSxWC5+90J5KeaamobKdqNa2utUhIi6BaRQ+F5W2tSx0ybRqBkhHcnf7apCl/77H69hgdwy7b4CM\n3SVMm1BP3ITC0V7ZZX3eUswjdh05jmNKtbrvAvGUr4NPZijq/eGTwaeIfRqZ+dmPfkiq1dOhF5Kl\nCzZTCZNQFis1zFKO6xGndUnTOKG5eRk0J52GDhkNaq9lZ2fNg9MTzhWbQtlnnhoV2bfLAT8H6db7\nLtmqQOE883GknRnCSJPMPXhtZoWYNjG6E3ErkZv3/j7lccKuIumSIe5KKDowGJS12T+mKm6dMS17\ntsO8EH7o97yX/u+kxXgsOiXydMh4qXgHnbnavc9N3TG9EVhcM3QqobwwuLyWs+pcIJwWjA415VEk\nFLC6rPAPr3lgsqIJQhncHdUDDDqyHSPbseoKlm2Ru3WFXir2Tmre+v73Yhr4ka97lO//rV/h2//w\nvfzcq16LbpNY4hqIVSJWETXyuNJTVR2TshWzvCALu9aRxaqiDeXwaCeThFOeR17Cesqf+0ynM4FZ\n+Z6KNXw8xVzkzQs0FL2vRyvDpFNfcsePKa0Xz5bumV9GyDx0H7WY9NwjAAKhJ+kslde5i9ruqPuh\n57oVHmpb5630RNzWxN1QZR48zJtS2CwmZG66zd1nRJMGSuRpEK+JqWu4U0/4+PwAjThAFjZQ6w3T\nwY8SyQoGadaZc55E7LAOBV1nKAqRGCuV0DqRUpShWR6wfKZDtxJJZ9pEuHuI+pLP5/hlJXsfb3js\njQVv/6Z/xlV7zFGM+Pvsm50QuEyrhN/Cdftr4aPGq6cPvjdDz7j5P3oAnXvohYwfV3REDHVyxKTR\nSrxepkYYTBqBWNrW0tWWVJvNqTPSSaVCgUkc7C956d5dro6O2HergSMfouzwZqblijvCkIbP9V4z\nIYnobVR0nI4i3czQjTTkYSeZ5ZI0YBS6jcye6FBdHLjqsTTSyXcRXxXsHSx4aCI7jr5418lx2Sw5\np+EkJubxLLVXb3XpTwtTuF/H1q3Xs65Urjm9VTNWYueGdPoQNoM9kLBoY1CdZ/ypJdXLdknaiKmc\nh4QMLwNQPWUpTgSSOn0ZFJ9/zKseeJyTtuLGcoeR6/ii/ev82fwCMSnGtpU5WmFYlAK/3g0anxzv\n+JY3kxR85++/l2//wxw0/lWv5Yff+BbRD7gk+bSl7NaKUUdZePGbKWt0JffT8bri+PYOam1EkDSJ\nUAb62XDK0It07JmiuGX1cfZ8JmLQ4pO/7oTdosJZmqc2G1FRSs8LS39RC7pp4aMfvcKXfeHHhQfc\nlgM9sWms2Fnm5CLt0oCN95DLvXa6/dF42886nnb0hb8v5kolikmHtRKKEbzAPD3n+/BkwrIusGaT\nhtPb9Jps6gTiGFl7SxMs687RZd8Yl3nPKQgBz08yLucSulXEIglGF8XzITWGZAW6sSZmz/j8mvpm\nPCkZ7mj5OWnwgkgbGhlyE00eX2H29jj+nBnlaaS4tSRegG+b3WURFaWqcPfZEz0lCSNRKg0spu1D\n6KWf2QlQoJZAX9S7vK8vdZe3ONAlSxPdGU67U2G4PyorD2VKCq83ryN68XRRJuAqz4XJgovlYhCK\n9YdRccg97b1kumQG1Sphc08plVAuyoKdr4EKabPDQq5NVJou+7uoBLqN6CbIA71YU5yOWQczWAKP\ndUNBpMJTJ8P1+2+i+LwPSSaS85mJVyQrhTeVhYiLeppihvsGOGELHzaHp4xvzuhmmnonoDoZJgKo\nTixlk4XVJYV/aM2V6ZKlL7iznuKDdPa3mpnsorMWZGQ6yPTkynTsVWtW5wvWneOd3/8avvObNhGE\nf/dt3wht3Jh2KVBek5aKZm1pTOLUJG7qRKoN9tTgThU7S+HAtzuJ7oJQGEmK0G4ot8nIAkGUxSLZ\nfKL6wmTz3KXTFEuFPlltxEP3Fu7t8/c8jhe3Q09w8AeG/3PyEJcunOCDobCe5eMzUpGYXjkZMPD+\nAQ1buDlbyUV90UhJEXp4Zqv4bx+rxomLode4Sbtloys4yADzRE30mrUvRY3ZKcg0JWWEwdKbAKWk\naE3MkmMri0PQpJiN9VdGrDpLweb0WpN0Ikiuh6zq+Zx0i4IlUJR+cw0/HUShz36JihALRXUYMcuW\n+ssewXSJ6m6L36l44NIxXQqUyuGUETzxPh59h96pjU1Dfw16rYBRz35zhuENRZpks0Pj1nWPDI6M\nAcUqFqxCnoloj9OBLhlKLXOZWSXuibWWop8SeGNIUVGUntm45oHRKReK+fAr7rWeHeuGsWqIaGrc\nwAMXM7Gem57QRZAtu+2vZxoKNwjGHB2kSg0PqVsqqkWLPlkSb92hPL3ISWuG81CpwER5NHA7ljzl\nd7lo5ox19/S9/HaX/gIfSTPY6cpwV+EriJMK03lUjAK3OCdd+/bLzHS8tGoY3Q0srmrUyEM2JJOw\nDFkYfSVBJMYFbp7MuH64i+9E/FU7y7wWkWthRQ9QWT/oDfoa4EzAac/3/ePfOfM6fvAX/wU//IZH\n0dvGWT1CmrbIBR6mn4LxbYEr1+cNi6uaMEpiw4Es6KYMQo3sA8WKvJgbRCCoNwVdO7Fu9guHWwAn\ni2F3mrzfRPmB8NF9fF7dObzoHXpidBh44Nccq4uXaHcgHiaufaLj+lcXlA/7AX4JUQZQ/VA9bGHt\n2wVbqzR4YJtsNdu7NoY8UFwvS1JjUEWgKPywUAwkfxjYLlXVMSo6sbL1RhKNjHinp6SoW7kBg9eS\nhWoNxkRC0IROE1qLmlv6xk9FMEstStEybWT/QWwCkknQarpFQQga6z59Oyb4Zf9Uq82gFagOA93+\niPUFx/R6w9HLK772ez7A377wezg1/qyv37MdPdQEstD5zDDonSx91ASthvBoYCuebrsrkc/3IqRt\njnoIemCYgHyuSVbySKP4sXdJdALnRjJAC0lR1w5f90UjDUEEXbYOACi158Buivs8VtldUVgnWp11\nf+ySEd/+POOJVmCUHmpRZApjFuP06l0VMjSYc07TrMC4RwAAIABJREFUuEI9dIXlZcPVg+NhyFtn\n7rxWiYLIZXtC8bQss63zn4v6C3UkpVBJFq0BRzfSsYIijh26LlAxygB0VMr8p6ffWbvBiVOkvFtj\n1g7jIr63yjAJPfJ0ywoVJOAjnZRwZHEnCsaJ5nyg0eCODUnBMkvphXIYwUmxVVqq8w/+wq/zze/+\nN/xP3/BVvP1Nj/JDv/hrvO1fvZfqtuIfvuotMs8oNrOBbUFRcQqXfvcm6VNPonZ3OH30ZSyveUYH\nK7GVaCyu8kzHNcdhIh7oFtF+IK9LOVkpVH5elUk452lqTXGaSPM5qiiEDRTzQtDDoTEJhBUTyj4d\nrny248Xt0BVMPnHK4mW7oODhX7kDbUd3eRdTF5uHNxfvlITpkTm1BH/25SqVSEp8MGIU5kTK9MfO\nm8EqNQWFKgPjqYh5tIK622yb5W/5mc6IgVdlPcFpnA2EuNkB9B369/yT32FnVfMj3/kNtI3FN5of\n+Plf57Qc8c6v/4ZN+5yxoDCLGz5qTv6GJKlD2W4zdlrUYvecM/k7/yC1kY8PHwbsKmHXgdWVivI0\nQIRrb/1T/sHlD/F/t7D/3O+J532orMoVtajGsrE/jls7ph733j7OMjQ2zBeAjg0EExBorQ/MGMRk\n0bDeUg47FTlfLrO9hGMVKsHSew+N/LPbKIuBQSCWSm+gl1UsiUkzj9XTTLECaujQt3ePAkPItjmx\nUcX2h2kSuo0kK5g6IJa7Y0t9Hr5o/zpX3eHw+2sVKVJgrDsuqIYORZ1ewIv4LMd2wAWwGY4mMI28\nLwC1qkl1g5qMUSHKILTvLg/2SD6gfECZMWrdMXsy0PzZCP25SzHnay1+6YgXOlFetjI363YCSRuK\nY8X5f62ZPNWg2oYbr5nQ7iV0q3ELSFoTHUTnBl+W9cmUn/+S1/Jjn/so+/8W3vmFb8G0sA4jklHU\nF4R3rltRin7FV32YD378Gu4TFcwVcVaRvuRzOH7FhKNXt0x319KoNBZbeoyJNF3eBfaWuXqzQKXs\n9aIy3GiM7FRHNwyzJ+RcETP5wUpYiipLkvek1UoUuNYIffE5Hi9uBJ1ShFlFdadl8dY18QMF/uKU\nUGr656nIXOxkciHY6sZNPlk2hwN3QQIx+pBmoxOrxmU4Sop5yGwXbWU71qvL4j1exdbGoTMH2QU4\nE9jNPshNkIl3zPzw2aLlre/+ANFr3v7Nb+EHf+nXeNu/+j1+5mtfQ1KRNFaZm5q551tDUjkZchPY\nMsLIExduY8qlEtbJjiBFIwuBUoNDo6k3uF10UJyIiMWPDaGQ/8dC8189+BvcCjVX7AtHa5O3ogYD\ntP49usEiQYrvyhcskki8R6YjZq712R8kr1PSiBKlaocCDtDEYiiUfbh0l3cBTTS0wbLoxBf9cDVi\ntawIS4tqFewEirHAbTGJH9DcVExswyoUfKK5cOaljHVLmyx1FC/0nt1yp5vJIuKdDNqPC0ZPGdxS\n3AaJoAgSkmyFJtlT/WKh0V1EefkDWXQGHDjxbgE4Z1Ycx4pbYcaD6phKwzwaVtGJMVge0AOEJEPi\n7kUo9r3Hf3ICt6gk7CrdBhHI9EPRvpj3i4HWGz44oJdrxjcqprsj7l51HFyYY8Y1d9IMQPxx8r2u\nXCSMA6sdg5tbilNLtVzT7CfiwzW68CwznJqigk6hlwJ3/tibX487BZSiOUh0U/ivv+xNVE85up3I\nwStvMylaDpciRnr13sf5INckUSzB4qVTVgea01ckLl48kQzgVUFqDHrUDTtzZZLATTlzrhcDKp1y\nSE0+f7m22BrMOjNc+htaD18kyU+dUD+f7/GiFvRQwPpSyfq8ZrkOqK7DHde010RiXXc2szzyzZ67\ntTR0eBvcvP+4M4E2M1lCjDnfT9ghMdOc+uDl3tEx5s6q77pBBp/3YvC9ZabVEoihi3Yw//mx7/4r\nKB35rl//fb7rPb8PwM+8/qt4x3/yjSSfNpzCqJ4Oe+ZQ55QS3pp8AyAFOuP6PRSxTdUEhigt+dny\nkIVSMb4VaKeG6ZMt7Y6lHmuqjMM+v9zwP8eRNq+zP5/bUnwQfcDaO0ZWVu6R6SjuMZ3qz7tkkm66\nkm33y95GQL5+871tlGJ+dz3mZDkSB7y1lU4PMEVgb7qmCxqjhfM/z0K1ORVLvzFAmtmGPbcaTLHm\noRp+56kfsQwFq86xXhe4Y8PodsItcjGPIgtXIUpRz1oAPzJZLYpg7J18TnmB4A7sfOCZz5RnRWCV\no+aMym6gyQ1Yv8v3R70Vvh2fkVbxWRzbDBelxLvF5LxaJyQH7RP4KF1k54Xh4iW5qBcW9bTFpBWq\nCaRmhb2pmU0tpzcL/DnNw7uHhKg5Oh2jLdhRQ7sqMEVgOqnZHdV8sjig3S3Y/bihu+B55ZWbfOW5\nx7jkTljFkjvdlMdW51n4Ukz12pInb+0B8MCFE165f5MmGv7NzQd56e4Jf+Ohd9Mmw4fW1/jo8hKP\n1edJJwWmEejl+OWG1YOB6dVTChO4uy6IS0ufF9rXC60jWDXALcpIDVMKYhChE2SoLxhsNmojJTlf\n2yrbz/J40TF0twjc/ApNfHwCHKO6QDsRxVbdOiZVS0pC31NZWNNjsj3uGlQPOSmcDTSdWI52yOAr\nZny8Z4NoKyc4BD1AJtsd+gZHf/oJbYMRpaE/2wF5b/jh73gDb/31Dwwfe8e3vXGgMJEENxdmSmTb\nHzm5HieRRYQk/xZKk2zTJO4rv4fMclE58HhwQOgUugW3SEMXuLpYsP/B69x454gr1nMY4cqLuFPv\nU9e3bQBWvhhmGoP5EvaM/B8YHDC7pLGEgfUSlWgNejaLT27A42OGeRZdyZ3VhJPliHpe5vOb0NOG\nqup4cPeEK5MTTtpqGLzfWs340/UFluuC7jR/TwI99kxmNdf2jzhfLpllF8+YNE/VM26tZtw5mhEP\nS6oTRXEacfMOVYvIRfBjK3J4lUDpAT8XCE7ClbGablbgJ4nL7oRZ3qYaBZXynDMLYtLcDYKnnzMr\nHGcHzBWBgsjEtGcMuu7XcSaCrrcwUWSXT5kTpNKgqnIQEqUui9dyQVedFwdBo6FuSU0LN+8wAnYf\nvMzhgyPsQWSnqjnJkWz7sxW3O4s2wv7aKWs+5+Gn+NRsnzuzKfbI8uFPXQbg6y58mC8dPcal6YJm\nz3DOdMxyW/yvH7kIwEyvuWCWVCrw4YMLPGiP+bKy4MePHuLXnvwirj+1LzvjhaabCdXYX+iYnV9y\nfrISF9agwCVs1eGcp+ssbW2HhpE899ZDzCFA3IS+J0UMmqKVBZ3OyzkyZsNq2SrqSmvp3O+xQPl0\nx4ta0LVPmJXn3L+18uaXNf7iDqGQrrPrDGYcM9zSDznTAJH4fJF0xtd91OAZCjjIsDLlTM/kpaDq\nohOr3pAVmEkNLBNlZFB2LzOmP8auw5tAVShab1iu5cH3reYHfuHXz3ztD/zsv+Qd3/JN8kTCoAhU\n2S5Xd4DKk3CkY0lBDwOUFLPjogZMfnp6GCYKdNNP40EeKu0Fy+wmom6Y3PR89Lsf5J990Y8zjwkD\nuGfggL9YR481t9EMAbttlF1P/AwFaFCR3nM/dz1HHVEKN8Gy9k4W3tqi1oZUBUwVONhb8NDOEVdG\nJxy4BXfclJNuxO16yqpznJ6OSEcF1Z2s6o3gx5b5OccngWZmuTSW0I42Gp5a7nD7ZEo4LClvG8qj\nRHkSMMsOVbfyQDqL6gLKGWErxCTdrdrqzIwijC3djiGMIzO9xvUEgARj7bnIQlg9SUzOJv30m427\noiM+M1/3fh3bZBCnRE+RQDd56GsU3U6BXk1Rq2YwmkoxototSqizuXhplLUieb97zN5Hd1lcHfMn\ns0tc2z9iMhKxYd06dNZlrFvHjfkO50YrLu3OefKqIT05wtwo+fDJVf7s4gFffvVhvnz3k5yzC2a6\nZs8scSrwf61fAilx4BbctQuud/s8tj6P05Hfcgt+8WNfzuKJHeypJowToUj4aYRZR5XtjO8uxzSN\nQ5uELVqqUgIvJIh6WxiZqayqbw6ltmzDxillA68uZDGkGmL7zjBajNnAMM/jeNGl/yomllcEctgf\nl7R7BW6VaHcVxgj7xActBjsZInEm0ESR9vZH762yqgvpynORjp3BVILBem3kImTmiJzALe8gI8km\nzoSBY16YTbJN7S1j12VeuWDo53eWkBLf/bPv5Tve/QF+/k1fydu/7Y387Z97N2979/tAJf6bNz8K\nDhnoJiVDnpQn6Jn1EkuxHk1RmBLJ53AMm5kYQW/cFk2Cba5u5qjZtQxD/QiKRWL22JpYGH71P/5H\nfI6rWKRIpSz6fm/Fn3ZRN9vP7cOqgEcKbsz00u1BaXiGRTSg0CmxDk6w9Czs6TnaAS0OjmwGoouu\nZNkWNI340ScbsSPP3s6KR3bv8pd2nsheMtkTXbeDhcOqKVjWFj/OzYLP1LUIdeO4paactiVtp2l8\nwfJohL3rGN+B0Z3E6G6gOGrRy1o6U2uh84KTdwGcgTZi8yBU+43cO5SGeteQSk+XLId5uNuiOa8b\nXmI9EahTok6KZbbybdFn4KZ+FnH/Q6LPsmdTr3gOYpiVsjV0OzPYeYn1MgxVzpG6LaFRTDDOPula\nQ+FQIZDaluKx2+z96VVu7e/id0+4OF1w0lScLEcYsyEhLNYlIyeq0P3dJbfnjuKWpXrcEG5P+UDz\nUq4/sMvFsbCVdl3NxDZ86Y8/xnTR8E/+y79MZT0fPbzA9/wPv8vpeMQ7/+pfITwxppyL71MYZcXo\nODCeNRgtWpS2NQQvtEnnAtYEVnU54OUpgdJixWdsX2tUnuP1EKqsjUrHzEyLL8DVerGHokahG8/s\nU5Hjl+u89RTBRX0+oRG6olIpd+eCbXcw2G3CWVFR8IY+4ZsEym4YENokqlE7BLymbHYVgyFFBpfD\nkIeG3usBX9c91q4SYWUlcMIkFnO5MU/HFT/7ja/i7775UVgo3v6Wt8DaMLeVrLYhoUxm6WhkWOOV\nUAwH5ko+L4ksNJIbY+jK+9U9MkAt2jO4+GmfByxN4mv+1u/zeaPrHIYpRiWM0uyqEeE+q0Kf6egj\n6O4t0Ns2DvfugPqoumc6xMRLVLR9seqNvbSKQ4feJS3D1rZk3TpizAOqsWc6qbk0nXNtfJcH3BF1\nKmiiY2rqM5zz00lFCJo6ynVVXstArpSvqVvH2/7n9zE9bXn7o2+hOLSMbsH3v+eXqbuCX7z0NdiT\nNappSSGbUzknha1PMoLMdc47M6WkQy81zTmFm2U/kkyCXqaCPd0yVo4meQKJOp+zvpi3mIE11FMW\nX5ArvV3Q9YbhQtoMSH2l5Os6LwKjwqGUIrXZNkHLTCFlj3RVFFLY25bw1E32/mhGN9nn5sumfPGF\nGyiVuH13xmRWU2XH01VTiCVu1ISo2Ls85zjsUJxYRjcToSj5ZH2Bx8f7pCBZA1XZ8iVPfYpvec+H\nmK8q3vGfvZEf/Inf4Dt+74P83Ktei/3QBOMU0SXCGOKOl+YkDzudCcQtFlxKSqDd7BmjTaAoI23j\nhmI+CBaDJK6RFNr0LBfRsgBnWUDAICzqu/I/py/6i8xyAdUETl+qGd1OqFVDM9uj2Vd05wLWb154\nrxJ1OmJ0P+jMmHiGXGJUGBvyudjAMTEK7NLzx0GKdddalE6i7uo0REVstCgKk2DSbD0cvdd42A2Y\npUEHwdYA/uFr3wRERp+ysg2N8A+++lFAMXoqvwklPORQJRl4ZjnwdnTccGLyHDQNZhnQW3+qduMM\nZ2rBzUF+5/hmx5Nf6/hvL/0RIUWadJ3uhe7I7zmUEvOtBruxOIYzQ1GQAIrt/z8b5NIlvek2kyYk\nxRrpyEvjB5x90ZWcNhVHqxFtZ9E6Uo07dqqGi+M5LxkfU2rPYZjmYaoUwD4XtNSe8pzndKfk8IIk\n1G97ap+2JfNVyfS05bve8/vYQ8M/+uK38Dd/55f51k+8j3ed+8tYMxeaXq+GBEhio4vfNtvRZx/Y\nlGinmsXVyLWDY/b0alhoQtQcxwJDw3EsmMeKPb1mV3d0nN3ZmLyQNsnQvgBD0e11WHkgq0P7xUl5\nsHXCnDZwugCtSZMRlG5zdfPwT3WetF7Lgqc1qigk5eip25z745LHHzjgj78ycTBeMp427GSBWBsM\nLjPb6tZRt47dyRp3rmZVj4mFNF72xJJO8zM+MdQzxdv/6ptJUfG2d/8e3/nu9wMi//+R1z2KXSl8\n5qEnIyKxnpHiOwNFh9WRYCONly49GSnafWEWMSEiFkwiLgxBZULG0+ecKYnLpvICuYgJVyZpxPjn\n8m/ZPl5kyEXxkb++S3KebmLQr7lEdRxZXzDovZbge6l9GixrbWafBKvPdHld/tpx1Q44bY+j12vh\nASu9gXDa2gkfOYFZGEwtxVq3Is9WXvzEo2EYXiWJL8RPFGEWiJ7hChUXVvhPjeXzo4RdKEyrsMuE\nXWdBicp0NauIpXBdo4Vk00Z6DNKRp35omoYOCBh8luUmUENXTv7S4qTFjw1N6vh413HeJHb1hrFh\n7rPM/5mOvkPvi/m90Ms2t3wbcnm2uYWPZmPuzib31Wcs3upIGwwn7UigllZuY2MSO1XDS2bHXCzn\nXCzmhKQ58SKqMiriYCjqY9NwoYAL7pSXje8MXxOi4q6f8mfzCyzqkrd/y5vRc8Nb3/9evv1/Fxn5\nu/a+jJ9yX45a1QyhBFqTojgLKmukY8WSdJ6L5KKuADq5L9L5hmvTQ2Ek5cVuR8sFXibLMhXUyRGQ\njxnSma7ZIc9Kh6Z4gXdjmaNAKLKrZw12LUQHFfL12i5IvSCmzxwNgS0On/i7TCekdY375G0u/WHB\nE7vnWb20YFo1w866CxprhG7aL2aFCezN1hxfgXUxGpK83FqhgiKsFWGhKGrNj37Nf8Tbfuv3hpf1\nI1/3aI6JQ3DzSvzPq8oPwrO09bti3v2jBClwVnZiXQ6p7qmJMWhiOPv120l/0rkbZj5tbBLu8/Gi\nFvRuBj/7xp/if7z57+Oj4f2XX87Lf8Fz+MoRouITyCOpNNAF++DXkevosql9Qjr4XvSjtgqDVonO\ninHOuGrROrJclaSVpbhrGN+QwtntQHGc6HZkuOjH0AcnxVKKqtDMFG6u+b7f/A12mjU/8rpHQSna\nZswP/dKvsrAj3vm6b9i8xx0xro9OMG6QDiZ0YNaQrKKdiadLiGTKjhTu4dJnI/7BNL/eZIdGwxBx\nVh4n9KJFxTGlclw2HWP93PMH7/ehVBqSpFRMg91xz3rposb2Q1Jl2UQ6nT20SfhosDoMsEwf4t0v\nArV3LNsiQy1KdARly1615nJ1mjvwjhM/ZhFKdu2amRYmSE9FbKLldT/xx8yWDe///kdAKcaq5tV/\n/xPcHU/4sf/0df0740e/7lHe+v6NJ8hPT74KFkvpwrYzH/vC1nYbUkvhiD38UkdU06LqFh32GM8a\nLpWngpsrgV4uWE+HDEcnZkGrVzTJcBiL4fXrIbA64ogvyHA0wRkHhhRzc+Jkd1gsIuWRx9RBOu6J\nLJyqD4zeTqsJ2YWxKhmsYpVC7cwgRMKtO4z/sOHC3iu4zYxzX7giZdpwFwzjoiNmtoi1gS5qZmXD\npGi5aSPreUmaW2xt0B1832//BrN2zX/36rfwN957Npzp7/zLX+aH3/Ao3UyES2riKUcdVdGJqjmI\nrW/XbSw9lAadu/PCBmkSE8ROowYrkZ6ZplEmonQaMHUQeDh0WjJ/45asv2/jYzr7sf7Pv6uQCybx\nvy0+jz23ZmQ6Pjjy1OcK1pclRR0kY7Qqcop31BQmPI3TvO202GJyZx4HTmhRCAzTBUO7KgcIozhS\nnP/jmvWFgutfGFlf1nBQYz5V4T73lHOTNdef2qcYt3zxlf+nvfcOt+yozrx/q2qHc84NnWhFMogg\nGBA5tskgISQ1wSIZCZMsGDz2+LO/cQB/DPnDQ/BgMMLGWEZjgp8BSYAJBmOPZNki2LKNiAIMSGrF\n7r7hhB2qav5YVfuc2wG6pVZLNHc9z33uPeees1PtvWrVWu9632v416uPp9kxgBBYqMa8+FKl4nzr\nE7fzmgsv4kX/cjEfevA28uUQm0iARluixWu7dxBNsbhSsBXIWAUP2jmUEjglI0G7zHIPs7nlFKl7\nidvUbQFs+O4KPzhzExdtfwdVyJg3isAxCC54rJguh35rRuqz7f6pPyAT38m/GQk4NJXQSkBk6qRn\nTfVhf3Lk0njbSYxVEUpqbaDMWxbKmvm86lIXqXA4LahKRx/gg9B4Q7nS8pgPf49SGr742/fhkW/9\nAQ/60I/5wvPuw/KkZDIukFr43c9esOY4fmXpYt5nHrT2OiTqUx8I4hBjkIT4mLTKORYCZJbQK2jm\nhOM3LHFssbRmO7kIEx9YCRlz0jJnPDYE7RQNJhZKtYBaBCUNK9ibFO2WWsr9Awq/zeKPhXwU6N3U\nkO+a4HsZ7WIPWSgxkxZz0zJhUpHa2KUsO46XsDBAhmP9P8CGeaTqI3VNGE/YfNm1tL1juf5O8+S5\nPvtl5thQTtg16eO93ks3Lc2xWrZsGoyZ71fM9WrqTZbJ0TmT3QW9y0acdeklPGTH97jf1ddwxXHH\ncb9rruHrdzyOs75yMc1Gxzte9STcJIrmeGFltY+NkpPea0eoGOWUKQrt6G5bw6pyHiigqR8RdF5i\nhK5ABjHQ6zXk1nWBjXaZWm3QaluC84hZ+wx0aZeb+bweiKbovYGPzrx1d+D3gY3Ay4Eb4vu/G0L4\na36CmbHwrdVjaINR8Yhv9QnWEUrfqbNXk5z5frVPTHhqSpkl7wI6Pu4uxy4NdWuZVHrTl72aSYDx\n0cKue5X0b4ypAeMpihZ772UGZcMDtlzD7lGfk465mlce87d8uHwU/9C7G3NlzZ//94dR/tGEF114\nMS/62pSK8/972dMRUxEaS7Yzo3eTMD5KaXLrDUIzD3NXB+av1s6wetGQjcAsMKUCCBERU3iVNUtC\n0YYu6koRuqmUMAjAXnUDGx864T55SYuLujoGT/ipUMVDOa4+CJM2X/O69rYLwJPzbFrbkak5M4V7\nJSttizfSEXql96pIYdyzChcb1gVVk3WNZfPlhLmyYVM5ojAty41KyI2kYGDrjh53qR10Lf7agRq4\n6DcfiCHwuPO/y0PP/xEAn33uibz5JadwzU0bcbtyXveXn+Ssr1zM+Q/cxntPOI3fvOTjPOvafyIM\nPO/PHwch8m44R6gbzRcbicgli0yCYtSNISzO4edKmg0lw+OF7Vuv5IH9H1LgGYbEY+S41s1zQ7vI\n8dkuttoxCxLYajwroWW3z6i9ikfvjh2ic6abyEoRufxQjGtKO6YmVNfT+s2Gaz0LPxxDgHpLn8mW\nnGLZUd40QVbHWgQtiw7N0xX2QkDGlaZfUJx1qGqN7vs9/Xs8YeP3J9T/sMDKvRxzxy6z2JswbhO9\ngwMsWaYB3O5xDx+pQjLr6JUQNsAbX3oyUnpe8llt+rvfNdfwZ099NG940Sm89kOfYXW+R9NmHXzZ\neU3HOme6hY7E7tbgDd57rHGYGC2nKD4tNvR8tJPC2KAyimjaJuHQfaToDoY42U/rKRqx67lpei5F\n50InIHIAdiASdN8GTtITFAtcDXwC+GXgnSGE/3GgOzMNfOVL96Xe2rLw7ZwN13hWj7MQ8+SgfCbK\nb7422jDoAw10up5reD+ctsgLKA+LL7vBahrLb1zwN8wt17zh5GdSL1r6VwV+79MXcOMdS979wscz\nKBt+NNzEnTft4hc2fYfPr/wnStNwyp2/QW4cS22fS197D15w4Ve6Y3rjrz6FbXf9Ds97x1fY2Z/j\nLdtPZthfYNPdd7Lzmg2YocUPPL/+mS+w5cYR77nXM7CVYDP4vb++kJ1be7zz2U9Ck21aXbdJkzT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I3jX752T+58vx3cfeEmfnDDFkJttaKez3QcinJhW6Ddk8Q/qHPv0i8SqLzpljp51BwUEVbn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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgHXPH0PVbeQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define CNN layers\n",
        "def conv_relu(inp, filter_size, n_filters):\n",
        "    weights = tf.get_variable(name='weights', shape=[filter_size, filter_size, N_CHANNELS, n_filters], initializer=tf.contrib.layers.xavier_initializer(seed=0))\n",
        "    biases = tf.get_variable(name='biases', shape=[n_filters], initializer=tf.zeros_initializer())\n",
        "    \n",
        "    conv2d = tf.nn.conv2d(input=inp, filter=weights, strides=[1, 1, 1, 1], padding='SAME')\n",
        "    return tf.nn.relu(tf.add(conv2d, biases))\n",
        "\n",
        "def max_pool(inp, filter_size):\n",
        "    return tf.nn.max_pool(value=inp, ksize=[1, filter_size, filter_size, 1], strides=[1, filter_size, filter_size, 1], padding = 'SAME')\n",
        "\n",
        "def fully_connected(inp, n_neurons):\n",
        "    weights = tf.get_variable(name='weights', shape=[inp.shape[1], n_neurons], initializer=tf.contrib.layers.xavier_initializer(seed=0))\n",
        "    biases = tf.get_variable(name='biases', shape=[n_neurons], initializer=tf.zeros_initializer())\n",
        "    \n",
        "    return tf.add(tf.matmul(inp, weights), biases)\n",
        "\n",
        "def fully_connected_relu(inp, n_neurons):\n",
        "    return tf.nn.relu(fully_connected(inp, n_neurons))\n",
        "\n",
        "def dropout(inp, dropout_rate = 0.20):\n",
        "    return tf.nn.dropout(x=inp, rate=dropout_rate, seed=0) # rate = 1 - keep_prob"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jN0HVym8baHA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_minibatches(X, Y, batch_size = 64):\n",
        "    \n",
        "    assert X.shape[0] == Y.shape[0]\n",
        "    \n",
        "    shuffeled_indices = np.random.RandomState(seed=0).permutation(X.shape[0])\n",
        "    X_shuffeled = X[shuffeled_indices]\n",
        "    Y_shuffeled = Y[shuffeled_indices]\n",
        "    \n",
        "    complete_batches = X.shape[0] // batch_size\n",
        "    X_batches, Y_batches = [], []\n",
        "    \n",
        "    # First create all complete batches of size \"batch_size\"\n",
        "    for batch in range(complete_batches):\n",
        "        X_batches.append(X_shuffeled[batch * batch_size : (batch + 1) * batch_size])\n",
        "        Y_batches.append(Y_shuffeled[batch * batch_size : (batch + 1) * batch_size])\n",
        "        \n",
        "    # In case of incomplete last batch\n",
        "    if X.shape[0] % batch_size != 0:\n",
        "        X_batches.append(X_shuffeled[batch_size * complete_batches : ])\n",
        "        Y_batches.append(Y_shuffeled[batch_size * complete_batches : ])\n",
        "        \n",
        "    return X_batches, Y_batches"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxjzqOqddo9o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_placeholders(n_outputs):\n",
        "    X = tf.placeholder(name='X', shape=[None, IMG_SIZE, IMG_SIZE, N_CHANNELS], dtype=tf.float32)\n",
        "    Y = tf.placeholder(name='Y', shape = [None, n_outputs], dtype=tf.float32)\n",
        "    is_training = tf.placeholder(name='is_training', dtype=tf.bool)\n",
        "    \n",
        "    return X, Y, is_training"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xrw_oEnajSSR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def calculate_cost(logits, labels):\n",
        "    # Need to use one of the regression cost functions as this is a regression problem\n",
        "    return tf.reduce_mean(tf.squared_difference(tf.squeeze(labels), tf.squeeze(logits)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygqADFBrnIaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_validation_cost(session, X_valid, Y_valid):\n",
        "    X_valid_placeholder = tf.placeholder(name='X_valid_ph', shape=[None, IMG_SIZE, IMG_SIZE, N_CHANNELS], dtype=tf.float32)\n",
        "    Y_valid_placeholder = tf.placeholder(name='Y_valid_ph', shape=[None, N_KEYPOINTS], dtype = tf.float32)\n",
        "    is_training_placeholder = tf.placeholder(name='is_training_ph', dtype=tf.bool)\n",
        "    Z = forward_propagate(X_valid_placeholder, is_training_placeholder)\n",
        "    cost = calculate_cost(Z, Y_valid_placeholder)\n",
        "    return session.run(cost, feed_dict={X_valid_placeholder: X_valid, Y_valid_placeholder: Y_valid, is_training_placeholder:False})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs8Bzwrlqj5y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(X, session):\n",
        "    X_pred = tf.placeholder(name='X_pred', shape=[None, IMG_SIZE, IMG_SIZE, N_CHANNELS], dtype=tf.float32)\n",
        "    is_training_placeholder = tf.placeholder(name='is_training_ph', dtype=tf.bool)\n",
        "    Z = forward_propagate(X_pred, is_training_placeholder)\n",
        "    return session.run(Z, feed_dict={X_pred: X, is_training_placeholder:False})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqAM1IdSdtRy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def forward_propagate(X, is_training):\n",
        "    \n",
        "    with tf.variable_scope(name_or_scope='conv0', reuse=tf.AUTO_REUSE):\n",
        "        X = conv_relu(X, filter_size=5, n_filters=32)\n",
        "        X = max_pool(X, filter_size=2)\n",
        "        # Apply dropout only if training the CNN\n",
        "#         X = tf.cond(is_training, true_fn=lambda: dropout(X, dropout_rate=0.20), false_fn=lambda: X)\n",
        "\n",
        "    with tf.variable_scope(name_or_scope='conv1', reuse=tf.AUTO_REUSE):\n",
        "        X = conv_relu(X, filter_size=5, n_filters=64)\n",
        "#         X = max_pool(X, filter_size=2)\n",
        "        # Apply dropout only if training the CNN\n",
        "#         X = tf.cond(is_training, true_fn=lambda: dropout(X, dropout_rate=0.20), false_fn=lambda: X)\n",
        "\n",
        "    with tf.variable_scope(name_or_scope='conv2', reuse=tf.AUTO_REUSE):\n",
        "        X = conv_relu(X, filter_size=3, n_filters=128)\n",
        "        X = max_pool(X, filter_size=2)\n",
        "        # Apply dropout only at training time\n",
        "#         X = tf.cond(is_training, true_fn=lambda: dropout(X, dropout_rate=0.20), false_fn=lambda: X)\n",
        "\n",
        "    \n",
        "    # To feed this output in fully connected layer, flatten it\n",
        "    shapes = X.get_shape().as_list()\n",
        "    flattened = tf.reshape(X, [-1, shapes[1] * shapes[2] * shapes[3]])\n",
        "    \n",
        "    # Feed this flattened output to fullyconnected layer of size 1000 neurons\n",
        "    with tf.variable_scope(name_or_scope='fc0', reuse=tf.AUTO_REUSE):\n",
        "        X = fully_connected_relu(flattened, n_neurons=1000)\n",
        "\n",
        "    # Feed this flattened output to fullyconnected layer of size 1000 neurons\n",
        "    with tf.variable_scope(name_or_scope='fc1', reuse=tf.AUTO_REUSE):\n",
        "        X = fully_connected_relu(X, n_neurons=1000)\n",
        "        \n",
        "    # Finally add the output layer\n",
        "    with tf.variable_scope(name_or_scope='output', reuse=tf.AUTO_REUSE):\n",
        "        Z = fully_connected(X, n_neurons=N_KEYPOINTS)\n",
        "    \n",
        "    return Z"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpCFRRciiHcT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def CNN_model(X_train, Y_train, X_valid, Y_valid, learning_rate=0.01, epochs = 20, batch_size=64):\n",
        "    \n",
        "    # Reset tensorflow graph\n",
        "    tf.reset_default_graph()\n",
        "    \n",
        "    # First get the placeholders for data\n",
        "    X, Y, is_training = get_placeholders(N_KEYPOINTS)\n",
        "    \n",
        "    # Propagate forward\n",
        "    Z_L = forward_propagate(X, is_training)\n",
        "    \n",
        "    # Calculate the cost/loss\n",
        "    cost = calculate_cost(Z_L, Y)\n",
        "    \n",
        "    # Propagate backward. Define optimizer\n",
        "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
        "    \n",
        "    # Create tensorflow session to run the graph built above\n",
        "    with tf.Session() as session:\n",
        "        \n",
        "        session.run(tf.global_variables_initializer())\n",
        "        train_costs, valid_costs = [], []\n",
        "        \n",
        "        # Run gradiant descent over given number of epochs\n",
        "        for epoch in range(epochs):\n",
        "            \n",
        "            n_minibatches, batch_cost = m // batch_size, 0\n",
        "            \n",
        "            # Divide the training data into mini batches of given size to run mini batch gradiant descent\n",
        "            X_minibatches, Y_minibatches = get_minibatches(X_train, Y_train, batch_size)\n",
        "            \n",
        "            # For each mini batch, take a step of gradiant descent\n",
        "            for X_minibatch, Y_minibatch in zip(X_minibatches, Y_minibatches):\n",
        "                \n",
        "                _, minibatch_cost = session.run([optimizer, cost], feed_dict={X: X_minibatch, Y: Y_minibatch, is_training: True})\n",
        "                batch_cost += minibatch_cost / n_minibatches\n",
        "                \n",
        "            valid_cost = get_validation_cost(session, X_valid, Y_valid)\n",
        "            train_costs.append(batch_cost)\n",
        "            valid_costs.append(valid_cost)\n",
        "            print (f'Error after {epoch}th epoch: Training error: {batch_cost}, Validation set error: {valid_cost}')\n",
        "            \n",
        "        return train_costs, valid_costs, predict(X_valid, session)\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wf9gx_GOmb_i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "65f48762-4454-46ba-cfa0-c6997247bddd"
      },
      "source": [
        "train_costs, valid_costs, Y_preds = CNN_model(X_train, Y_train, X_valid, Y_valid, learning_rate=0.001, epochs=500, batch_size=20)"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Error after 0th epoch: Training error: 0.008432744542667273, Validation set error: 0.0008628186769783497\n",
            "Error after 1th epoch: Training error: 0.0007851485158673974, Validation set error: 0.0008453458431176841\n",
            "Error after 2th epoch: Training error: 0.0007871924367050683, Validation set error: 0.0009311602334491909\n",
            "Error after 3th epoch: Training error: 0.0007936253628444817, Validation set error: 0.0009379579569213092\n",
            "Error after 4th epoch: Training error: 0.0007927815503618066, Validation set error: 0.0007969118887558579\n",
            "Error after 5th epoch: Training error: 0.0007448123286105165, Validation set error: 0.0007538819336332381\n",
            "Error after 6th epoch: Training error: 0.0007118665707821489, Validation set error: 0.0007147221476770937\n",
            "Error after 7th epoch: Training error: 0.0006584692053531235, Validation set error: 0.0006149144028313458\n",
            "Error after 8th epoch: Training error: 0.0006117492073992087, Validation set error: 0.0005650365492329001\n",
            "Error after 9th epoch: Training error: 0.000555714894703256, Validation set error: 0.000528817530721426\n",
            "Error after 10th epoch: Training error: 0.000519492494442437, Validation set error: 0.0005752636352553964\n",
            "Error after 11th epoch: Training error: 0.0004600538200479188, Validation set error: 0.0004957540659233928\n",
            "Error after 12th epoch: Training error: 0.0004077250816242833, Validation set error: 0.0004772052343469113\n",
            "Error after 13th epoch: Training error: 0.00036814358596033876, Validation set error: 0.0004432694404385984\n",
            "Error after 14th epoch: Training error: 0.00035261865960737015, Validation set error: 0.0005643834010697901\n",
            "Error after 15th epoch: Training error: 0.0003333958959294915, Validation set error: 0.0004255541425663978\n",
            "Error after 16th epoch: Training error: 0.00027835843870475826, Validation set error: 0.00041213774238713086\n",
            "Error after 17th epoch: Training error: 0.00024307821410151192, Validation set error: 0.00040669506415724754\n",
            "Error after 18th epoch: Training error: 0.00021200113087616057, Validation set error: 0.00042880652472376823\n",
            "Error after 19th epoch: Training error: 0.0002010396334009618, Validation set error: 0.0004728997591882944\n",
            "Error after 20th epoch: Training error: 0.00019762798456213212, Validation set error: 0.00047685718163847923\n",
            "Error after 21th epoch: Training error: 0.00016970141535404605, Validation set error: 0.0004359163576737046\n",
            "Error after 22th epoch: Training error: 0.00014871017790276463, Validation set error: 0.0005229379166848958\n",
            "Error after 23th epoch: Training error: 0.0001448054921514664, Validation set error: 0.00041536203934811056\n",
            "Error after 24th epoch: Training error: 0.00013861250301322974, Validation set error: 0.00047457683831453323\n",
            "Error after 25th epoch: Training error: 0.0001419265628845262, Validation set error: 0.0005092713399790227\n",
            "Error after 26th epoch: Training error: 0.00013971537708295552, Validation set error: 0.0004527561250142753\n",
            "Error after 27th epoch: Training error: 0.00012869700830461545, Validation set error: 0.00041231702198274434\n",
            "Error after 28th epoch: Training error: 0.00012041467907279603, Validation set error: 0.000530996301677078\n",
            "Error after 29th epoch: Training error: 0.00013396608001543925, Validation set error: 0.0004010982811450958\n",
            "Error after 30th epoch: Training error: 0.00014261520325155964, Validation set error: 0.00047159037785604596\n",
            "Error after 31th epoch: Training error: 0.00013455254005831116, Validation set error: 0.0004379591846372932\n",
            "Error after 32th epoch: Training error: 0.0001310947465795338, Validation set error: 0.00043266155989840627\n",
            "Error after 33th epoch: Training error: 0.00011454069001295203, Validation set error: 0.0004664389998652041\n",
            "Error after 34th epoch: Training error: 0.00011957606266770944, Validation set error: 0.0003846180625259876\n",
            "Error after 35th epoch: Training error: 0.0001074926278761268, Validation set error: 0.00046091905096545815\n",
            "Error after 36th epoch: Training error: 0.00011001472878226854, Validation set error: 0.0004517772176768631\n",
            "Error after 37th epoch: Training error: 0.00010810900818631289, Validation set error: 0.0004916771431453526\n",
            "Error after 38th epoch: Training error: 0.00011016620712987123, Validation set error: 0.00047753227408975363\n",
            "Error after 39th epoch: Training error: 8.853083350211154e-05, Validation set error: 0.00045446824515238404\n",
            "Error after 40th epoch: Training error: 8.395286213478606e-05, Validation set error: 0.000516611326020211\n",
            "Error after 41th epoch: Training error: 9.105171681188621e-05, Validation set error: 0.000451903382781893\n",
            "Error after 42th epoch: Training error: 9.863349617427794e-05, Validation set error: 0.0005343382363207638\n",
            "Error after 43th epoch: Training error: 0.00010310201305814684, Validation set error: 0.00047233037184923887\n",
            "Error after 44th epoch: Training error: 0.00011096785266834717, Validation set error: 0.0005022602272219956\n",
            "Error after 45th epoch: Training error: 9.58132470197433e-05, Validation set error: 0.0003939433372579515\n",
            "Error after 46th epoch: Training error: 8.32945207569289e-05, Validation set error: 0.00044704513857141137\n",
            "Error after 47th epoch: Training error: 8.023445401373793e-05, Validation set error: 0.00039522460428997874\n",
            "Error after 48th epoch: Training error: 9.792356582817201e-05, Validation set error: 0.00046525633661076427\n",
            "Error after 49th epoch: Training error: 0.0001183771711442784, Validation set error: 0.0004411441332194954\n",
            "Error after 50th epoch: Training error: 8.463941332014325e-05, Validation set error: 0.0004452014109119773\n",
            "Error after 51th epoch: Training error: 7.158057708652038e-05, Validation set error: 0.0004016744205728173\n",
            "Error after 52th epoch: Training error: 7.512907752077974e-05, Validation set error: 0.0004632938653230667\n",
            "Error after 53th epoch: Training error: 7.976647534426531e-05, Validation set error: 0.0004675365053117275\n",
            "Error after 54th epoch: Training error: 7.869956364588271e-05, Validation set error: 0.0004298923595342785\n",
            "Error after 55th epoch: Training error: 9.217250586020367e-05, Validation set error: 0.0004614734498318285\n",
            "Error after 56th epoch: Training error: 8.58139548592424e-05, Validation set error: 0.0004836606385651976\n",
            "Error after 57th epoch: Training error: 8.532026315192945e-05, Validation set error: 0.0004252638027537614\n",
            "Error after 58th epoch: Training error: 7.866484432642295e-05, Validation set error: 0.0005593643290922046\n",
            "Error after 59th epoch: Training error: 8.05228784201972e-05, Validation set error: 0.00047804071800783277\n",
            "Error after 60th epoch: Training error: 7.948599950809282e-05, Validation set error: 0.0004891807911917567\n",
            "Error after 61th epoch: Training error: 7.23675594000187e-05, Validation set error: 0.00042262597708031535\n",
            "Error after 62th epoch: Training error: 9.030905069225402e-05, Validation set error: 0.0005529593909159303\n",
            "Error after 63th epoch: Training error: 8.58318947249162e-05, Validation set error: 0.0004138470394536853\n",
            "Error after 64th epoch: Training error: 9.746309649472101e-05, Validation set error: 0.00047423160867765546\n",
            "Error after 65th epoch: Training error: 7.75207092002265e-05, Validation set error: 0.00043247354915365577\n",
            "Error after 66th epoch: Training error: 6.16610438493643e-05, Validation set error: 0.00047311626258306205\n",
            "Error after 67th epoch: Training error: 6.0774388636921416e-05, Validation set error: 0.00042418617522343993\n",
            "Error after 68th epoch: Training error: 7.109200680588401e-05, Validation set error: 0.0004984825500287116\n",
            "Error after 69th epoch: Training error: 9.623543923515336e-05, Validation set error: 0.00040592404548078775\n",
            "Error after 70th epoch: Training error: 9.747374537544765e-05, Validation set error: 0.00042277760803699493\n",
            "Error after 71th epoch: Training error: 6.8321574055765e-05, Validation set error: 0.00039896060479804873\n",
            "Error after 72th epoch: Training error: 5.5236503297609566e-05, Validation set error: 0.00046817641123197973\n",
            "Error after 73th epoch: Training error: 5.425702572252207e-05, Validation set error: 0.0004943227977491915\n",
            "Error after 74th epoch: Training error: 5.9702880869074546e-05, Validation set error: 0.0004011722339782864\n",
            "Error after 75th epoch: Training error: 5.764170307739648e-05, Validation set error: 0.0004454090667422861\n",
            "Error after 76th epoch: Training error: 4.7467426642552645e-05, Validation set error: 0.0004038604092784226\n",
            "Error after 77th epoch: Training error: 4.662508111798822e-05, Validation set error: 0.0004864086222369224\n",
            "Error after 78th epoch: Training error: 5.080009800001953e-05, Validation set error: 0.0004511313163675368\n",
            "Error after 79th epoch: Training error: 4.9542018308040306e-05, Validation set error: 0.000454987253760919\n",
            "Error after 80th epoch: Training error: 5.223285227306072e-05, Validation set error: 0.0003996384621132165\n",
            "Error after 81th epoch: Training error: 5.8390337456289286e-05, Validation set error: 0.0005360699142329395\n",
            "Error after 82th epoch: Training error: 6.753318839710944e-05, Validation set error: 0.0005638145375996828\n",
            "Error after 83th epoch: Training error: 7.484865217189838e-05, Validation set error: 0.00040507878293283284\n",
            "Error after 84th epoch: Training error: 6.834824588705403e-05, Validation set error: 0.0004527720157057047\n",
            "Error after 85th epoch: Training error: 5.9857967884950524e-05, Validation set error: 0.0004309279902372509\n",
            "Error after 86th epoch: Training error: 5.301609932282437e-05, Validation set error: 0.0004359095182735473\n",
            "Error after 87th epoch: Training error: 5.296650486612137e-05, Validation set error: 0.0004889811389148235\n",
            "Error after 88th epoch: Training error: 4.716121597167245e-05, Validation set error: 0.00041567819425836205\n",
            "Error after 89th epoch: Training error: 4.306596223490157e-05, Validation set error: 0.0004876363673247397\n",
            "Error after 90th epoch: Training error: 4.648066312607959e-05, Validation set error: 0.0004100651713088155\n",
            "Error after 91th epoch: Training error: 5.045709828395546e-05, Validation set error: 0.0005263658822514117\n",
            "Error after 92th epoch: Training error: 5.871505193640171e-05, Validation set error: 0.00041543037514202297\n",
            "Error after 93th epoch: Training error: 5.7905687748933096e-05, Validation set error: 0.000487465993501246\n",
            "Error after 94th epoch: Training error: 4.368511459708976e-05, Validation set error: 0.0004624957509804517\n",
            "Error after 95th epoch: Training error: 4.109198865039202e-05, Validation set error: 0.000409472530009225\n",
            "Error after 96th epoch: Training error: 4.043006628700657e-05, Validation set error: 0.00041358903399668634\n",
            "Error after 97th epoch: Training error: 4.749704526251332e-05, Validation set error: 0.0004341863968875259\n",
            "Error after 98th epoch: Training error: 6.008123395159521e-05, Validation set error: 0.0005736107123084366\n",
            "Error after 99th epoch: Training error: 6.669004034682237e-05, Validation set error: 0.0003920523449778557\n",
            "Error after 100th epoch: Training error: 6.583537763886351e-05, Validation set error: 0.0004275833780411631\n",
            "Error after 101th epoch: Training error: 5.170210794796569e-05, Validation set error: 0.00040371474460698664\n",
            "Error after 102th epoch: Training error: 4.687253370796252e-05, Validation set error: 0.0003817757242359221\n",
            "Error after 103th epoch: Training error: 4.5228046272685866e-05, Validation set error: 0.00047544785775244236\n",
            "Error after 104th epoch: Training error: 4.481820631822713e-05, Validation set error: 0.00038980646058917046\n",
            "Error after 105th epoch: Training error: 4.2709934038240016e-05, Validation set error: 0.00048078285180963576\n",
            "Error after 106th epoch: Training error: 4.304418502504799e-05, Validation set error: 0.00038227016921155155\n",
            "Error after 107th epoch: Training error: 4.381183516567367e-05, Validation set error: 0.00047417578753083944\n",
            "Error after 108th epoch: Training error: 4.679560199023897e-05, Validation set error: 0.0003981468908023089\n",
            "Error after 109th epoch: Training error: 5.063391469403458e-05, Validation set error: 0.0004471199936233461\n",
            "Error after 110th epoch: Training error: 5.07703719511648e-05, Validation set error: 0.00047474916209466755\n",
            "Error after 111th epoch: Training error: 5.264865823371358e-05, Validation set error: 0.00039498088881373405\n",
            "Error after 112th epoch: Training error: 5.217077055228643e-05, Validation set error: 0.00047205662121996284\n",
            "Error after 113th epoch: Training error: 5.5379866503037846e-05, Validation set error: 0.00040789952618069947\n",
            "Error after 114th epoch: Training error: 5.48992656077148e-05, Validation set error: 0.0004527460259851068\n",
            "Error after 115th epoch: Training error: 5.27759574424636e-05, Validation set error: 0.0004812629777006805\n",
            "Error after 116th epoch: Training error: 4.8090522088014845e-05, Validation set error: 0.00038075901102274656\n",
            "Error after 117th epoch: Training error: 4.543551774945213e-05, Validation set error: 0.0005331224529072642\n",
            "Error after 118th epoch: Training error: 4.117659079321872e-05, Validation set error: 0.0004055917670484632\n",
            "Error after 119th epoch: Training error: 3.751547568630155e-05, Validation set error: 0.0004195963265374303\n",
            "Error after 120th epoch: Training error: 3.267382300009559e-05, Validation set error: 0.0004429039836395532\n",
            "Error after 121th epoch: Training error: 3.3055100878052386e-05, Validation set error: 0.00042266538366675377\n",
            "Error after 122th epoch: Training error: 3.3765997205715714e-05, Validation set error: 0.0003948927333112806\n",
            "Error after 123th epoch: Training error: 4.15492007629258e-05, Validation set error: 0.0004521268419921398\n",
            "Error after 124th epoch: Training error: 3.888418599341424e-05, Validation set error: 0.00042714265873655677\n",
            "Error after 125th epoch: Training error: 3.645047856469896e-05, Validation set error: 0.0004936936311423779\n",
            "Error after 126th epoch: Training error: 3.376673915702419e-05, Validation set error: 0.00038330178358592093\n",
            "Error after 127th epoch: Training error: 3.438503148345261e-05, Validation set error: 0.00047228223411366343\n",
            "Error after 128th epoch: Training error: 3.612139035815493e-05, Validation set error: 0.00042553048115223646\n",
            "Error after 129th epoch: Training error: 3.639527776819954e-05, Validation set error: 0.00046665084664709866\n",
            "Error after 130th epoch: Training error: 3.937377241217375e-05, Validation set error: 0.00041652272921055555\n",
            "Error after 131th epoch: Training error: 4.2049821393613306e-05, Validation set error: 0.0005135913379490376\n",
            "Error after 132th epoch: Training error: 4.8233902563507274e-05, Validation set error: 0.000415198941482231\n",
            "Error after 133th epoch: Training error: 4.485645000200253e-05, Validation set error: 0.0004689663473982364\n",
            "Error after 134th epoch: Training error: 4.850481486135488e-05, Validation set error: 0.00039425757131539285\n",
            "Error after 135th epoch: Training error: 5.239334672585922e-05, Validation set error: 0.00046180494246073067\n",
            "Error after 136th epoch: Training error: 4.857052603959098e-05, Validation set error: 0.00046068019582889974\n",
            "Error after 137th epoch: Training error: 3.8855001295442845e-05, Validation set error: 0.0004068093840032816\n",
            "Error after 138th epoch: Training error: 3.3787345707671833e-05, Validation set error: 0.0004430376866366714\n",
            "Error after 139th epoch: Training error: 3.057115567757635e-05, Validation set error: 0.0004028666589874774\n",
            "Error after 140th epoch: Training error: 2.7402682891616586e-05, Validation set error: 0.00045754670281894505\n",
            "Error after 141th epoch: Training error: 2.8410332700704274e-05, Validation set error: 0.0003833748633041978\n",
            "Error after 142th epoch: Training error: 3.1503020569413265e-05, Validation set error: 0.0004883853835053742\n",
            "Error after 143th epoch: Training error: 3.34389867113962e-05, Validation set error: 0.00040669547161087394\n",
            "Error after 144th epoch: Training error: 3.497474919682149e-05, Validation set error: 0.00043809154885821044\n",
            "Error after 145th epoch: Training error: 3.281587877071698e-05, Validation set error: 0.00041933561442419887\n",
            "Error after 146th epoch: Training error: 3.528356149279882e-05, Validation set error: 0.0004502739757299423\n",
            "Error after 147th epoch: Training error: 3.395363465456698e-05, Validation set error: 0.00039527082117274404\n",
            "Error after 148th epoch: Training error: 3.548993123558829e-05, Validation set error: 0.0005046576843596995\n",
            "Error after 149th epoch: Training error: 4.16450283802822e-05, Validation set error: 0.0004153258923906833\n",
            "Error after 150th epoch: Training error: 4.637928634048709e-05, Validation set error: 0.000487185810925439\n",
            "Error after 151th epoch: Training error: 4.093890990216919e-05, Validation set error: 0.00042622198816388845\n",
            "Error after 152th epoch: Training error: 3.478222129677473e-05, Validation set error: 0.0004729870706796646\n",
            "Error after 153th epoch: Training error: 3.5266077707712414e-05, Validation set error: 0.000400743680074811\n",
            "Error after 154th epoch: Training error: 3.685615803306062e-05, Validation set error: 0.0005024303682148457\n",
            "Error after 155th epoch: Training error: 3.552834506721907e-05, Validation set error: 0.00043320280383341014\n",
            "Error after 156th epoch: Training error: 3.351892761119498e-05, Validation set error: 0.0004612183547578752\n",
            "Error after 157th epoch: Training error: 3.758930309190455e-05, Validation set error: 0.00046329008182510734\n",
            "Error after 158th epoch: Training error: 3.649924317724899e-05, Validation set error: 0.00043322538840584457\n",
            "Error after 159th epoch: Training error: 3.229265296960709e-05, Validation set error: 0.00044395230361260474\n",
            "Error after 160th epoch: Training error: 3.108039667991267e-05, Validation set error: 0.0004383082559797913\n",
            "Error after 161th epoch: Training error: 3.320316712709182e-05, Validation set error: 0.00042341643711552024\n",
            "Error after 162th epoch: Training error: 3.217301471377968e-05, Validation set error: 0.0005110387573949993\n",
            "Error after 163th epoch: Training error: 3.558789287374318e-05, Validation set error: 0.00039814168121665716\n",
            "Error after 164th epoch: Training error: 3.478405679660765e-05, Validation set error: 0.0004888357361778617\n",
            "Error after 165th epoch: Training error: 3.129537783634492e-05, Validation set error: 0.0004098940989933908\n",
            "Error after 166th epoch: Training error: 3.7271080016539194e-05, Validation set error: 0.0005331085412763059\n",
            "Error after 167th epoch: Training error: 3.831184185607773e-05, Validation set error: 0.0004242498835083097\n",
            "Error after 168th epoch: Training error: 3.177950866922237e-05, Validation set error: 0.00046333231148310006\n",
            "Error after 169th epoch: Training error: 2.7178137468230182e-05, Validation set error: 0.00040517025627195835\n",
            "Error after 170th epoch: Training error: 2.865439368675155e-05, Validation set error: 0.0004556827188935131\n",
            "Error after 171th epoch: Training error: 2.9161069361097443e-05, Validation set error: 0.00040805351454764605\n",
            "Error after 172th epoch: Training error: 3.2206799703131294e-05, Validation set error: 0.0004498623020481318\n",
            "Error after 173th epoch: Training error: 3.307607989261834e-05, Validation set error: 0.0004163306439295411\n",
            "Error after 174th epoch: Training error: 2.89036376169737e-05, Validation set error: 0.00045707717072218657\n",
            "Error after 175th epoch: Training error: 2.9089812228862248e-05, Validation set error: 0.00041993247577920556\n",
            "Error after 176th epoch: Training error: 3.118265398039841e-05, Validation set error: 0.00046799887786619365\n",
            "Error after 177th epoch: Training error: 3.533148691303722e-05, Validation set error: 0.0004105869447812438\n",
            "Error after 178th epoch: Training error: 3.8722543479252636e-05, Validation set error: 0.00047459578490816057\n",
            "Error after 179th epoch: Training error: 3.876578939422291e-05, Validation set error: 0.0004078187339473516\n",
            "Error after 180th epoch: Training error: 3.632814213005984e-05, Validation set error: 0.0004559871449600905\n",
            "Error after 181th epoch: Training error: 3.528128890841549e-05, Validation set error: 0.0003961740876547992\n",
            "Error after 182th epoch: Training error: 3.365227393136264e-05, Validation set error: 0.00048160177539102733\n",
            "Error after 183th epoch: Training error: 3.346387332860765e-05, Validation set error: 0.0004064162203576416\n",
            "Error after 184th epoch: Training error: 3.221189512652763e-05, Validation set error: 0.0004806269134860486\n",
            "Error after 185th epoch: Training error: 3.361653489781184e-05, Validation set error: 0.00045406067511066794\n",
            "Error after 186th epoch: Training error: 3.274511440374475e-05, Validation set error: 0.00044429904664866626\n",
            "Error after 187th epoch: Training error: 3.082867143885696e-05, Validation set error: 0.0004707193002104759\n",
            "Error after 188th epoch: Training error: 2.9778184889230704e-05, Validation set error: 0.00039463513530790806\n",
            "Error after 189th epoch: Training error: 2.816305479313997e-05, Validation set error: 0.0004810728132724762\n",
            "Error after 190th epoch: Training error: 2.590048706772244e-05, Validation set error: 0.00039227044908329844\n",
            "Error after 191th epoch: Training error: 2.864278912862905e-05, Validation set error: 0.0004759745788760483\n",
            "Error after 192th epoch: Training error: 2.7637561562311358e-05, Validation set error: 0.00042136062984354794\n",
            "Error after 193th epoch: Training error: 2.9353315111083326e-05, Validation set error: 0.000458319642348215\n",
            "Error after 194th epoch: Training error: 3.1545883824822916e-05, Validation set error: 0.00044681152212433517\n",
            "Error after 195th epoch: Training error: 3.4688790590693806e-05, Validation set error: 0.0004582689725793898\n",
            "Error after 196th epoch: Training error: 3.225011759737258e-05, Validation set error: 0.00043866533087566495\n",
            "Error after 197th epoch: Training error: 2.676107129673255e-05, Validation set error: 0.0004106435808353126\n",
            "Error after 198th epoch: Training error: 2.644664480623183e-05, Validation set error: 0.00046594589366577566\n",
            "Error after 199th epoch: Training error: 2.5372632235716573e-05, Validation set error: 0.00043059661402367055\n",
            "Error after 200th epoch: Training error: 2.9296561595257032e-05, Validation set error: 0.0004901508218608797\n",
            "Error after 201th epoch: Training error: 2.9357213580024946e-05, Validation set error: 0.00041984027484431863\n",
            "Error after 202th epoch: Training error: 2.9051546055763405e-05, Validation set error: 0.0004633278585970402\n",
            "Error after 203th epoch: Training error: 2.6902676265453082e-05, Validation set error: 0.0004302190791349858\n",
            "Error after 204th epoch: Training error: 2.7029295014554843e-05, Validation set error: 0.0004888952826149762\n",
            "Error after 205th epoch: Training error: 3.2971459066030496e-05, Validation set error: 0.0004263478913344443\n",
            "Error after 206th epoch: Training error: 3.770176486610081e-05, Validation set error: 0.00047683840966783464\n",
            "Error after 207th epoch: Training error: 3.481572751049745e-05, Validation set error: 0.00043995497981086373\n",
            "Error after 208th epoch: Training error: 3.106294139404947e-05, Validation set error: 0.00048232238623313606\n",
            "Error after 209th epoch: Training error: 3.315760008862604e-05, Validation set error: 0.0004204934521112591\n",
            "Error after 210th epoch: Training error: 3.581674576480508e-05, Validation set error: 0.00048588201752863824\n",
            "Error after 211th epoch: Training error: 3.4735243358144615e-05, Validation set error: 0.0004133196489419788\n",
            "Error after 212th epoch: Training error: 3.585551327618799e-05, Validation set error: 0.0005156135302968323\n",
            "Error after 213th epoch: Training error: 3.981953087035931e-05, Validation set error: 0.0004041605570819229\n",
            "Error after 214th epoch: Training error: 3.644182231193389e-05, Validation set error: 0.0004557371430564672\n",
            "Error after 215th epoch: Training error: 3.140560999461492e-05, Validation set error: 0.0004311107040848583\n",
            "Error after 216th epoch: Training error: 2.942596235429433e-05, Validation set error: 0.00045435293577611446\n",
            "Error after 217th epoch: Training error: 2.834112082310144e-05, Validation set error: 0.0004366768407635391\n",
            "Error after 218th epoch: Training error: 2.6201917276838784e-05, Validation set error: 0.0004313692625146359\n",
            "Error after 219th epoch: Training error: 2.9153622426230674e-05, Validation set error: 0.0005054585635662079\n",
            "Error after 220th epoch: Training error: 2.8412959587533574e-05, Validation set error: 0.00041156160295940936\n",
            "Error after 221th epoch: Training error: 2.5307921677350272e-05, Validation set error: 0.0004630528565030545\n",
            "Error after 222th epoch: Training error: 2.2804988493486586e-05, Validation set error: 0.0004092864110134542\n",
            "Error after 223th epoch: Training error: 2.0762970070687856e-05, Validation set error: 0.0004727202467620373\n",
            "Error after 224th epoch: Training error: 2.077478558248912e-05, Validation set error: 0.00040905780042521656\n",
            "Error after 225th epoch: Training error: 2.297446832761291e-05, Validation set error: 0.00046162327635101974\n",
            "Error after 226th epoch: Training error: 2.204374556992083e-05, Validation set error: 0.0004039540363010019\n",
            "Error after 227th epoch: Training error: 2.4038183681795993e-05, Validation set error: 0.0004673573130276054\n",
            "Error after 228th epoch: Training error: 2.392208703192656e-05, Validation set error: 0.000430458749178797\n",
            "Error after 229th epoch: Training error: 2.3274192828508092e-05, Validation set error: 0.0004546954878605902\n",
            "Error after 230th epoch: Training error: 2.3804254725105647e-05, Validation set error: 0.00042468690662644804\n",
            "Error after 231th epoch: Training error: 2.3530448926707813e-05, Validation set error: 0.000432276981882751\n",
            "Error after 232th epoch: Training error: 2.7416264636961118e-05, Validation set error: 0.0005084014846943319\n",
            "Error after 233th epoch: Training error: 2.870280637136024e-05, Validation set error: 0.00043072865810245275\n",
            "Error after 234th epoch: Training error: 2.5326079367005157e-05, Validation set error: 0.00045027179294265807\n",
            "Error after 235th epoch: Training error: 2.2585011673858055e-05, Validation set error: 0.00042585170012898743\n",
            "Error after 236th epoch: Training error: 2.8408869049847195e-05, Validation set error: 0.0005228507216088474\n",
            "Error after 237th epoch: Training error: 3.3495369073286625e-05, Validation set error: 0.000409605068853125\n",
            "Error after 238th epoch: Training error: 2.7175252354667954e-05, Validation set error: 0.0004648916656151414\n",
            "Error after 239th epoch: Training error: 2.4742782391678542e-05, Validation set error: 0.00044932778109796345\n",
            "Error after 240th epoch: Training error: 2.5928669910771614e-05, Validation set error: 0.0004802703333552927\n",
            "Error after 241th epoch: Training error: 2.5867033235210145e-05, Validation set error: 0.0004188886668998748\n",
            "Error after 242th epoch: Training error: 2.6085274028404936e-05, Validation set error: 0.0005001312238164246\n",
            "Error after 243th epoch: Training error: 2.471341074862143e-05, Validation set error: 0.00043961621122434735\n",
            "Error after 244th epoch: Training error: 2.3956214642401565e-05, Validation set error: 0.0004731426597572863\n",
            "Error after 245th epoch: Training error: 2.409807422571414e-05, Validation set error: 0.00044852629071101546\n",
            "Error after 246th epoch: Training error: 2.2276703669438742e-05, Validation set error: 0.0004284283786546439\n",
            "Error after 247th epoch: Training error: 2.3909469467236544e-05, Validation set error: 0.0004997252253815532\n",
            "Error after 248th epoch: Training error: 2.657356411232697e-05, Validation set error: 0.00041406718082726\n",
            "Error after 249th epoch: Training error: 2.814572025044981e-05, Validation set error: 0.00045241526095196605\n",
            "Error after 250th epoch: Training error: 2.725805631795002e-05, Validation set error: 0.0004638161917682737\n",
            "Error after 251th epoch: Training error: 2.6080032615606194e-05, Validation set error: 0.00045467837480828166\n",
            "Error after 252th epoch: Training error: 2.4873103804814246e-05, Validation set error: 0.0004462634096853435\n",
            "Error after 253th epoch: Training error: 2.3053057093007233e-05, Validation set error: 0.0004166330327279866\n",
            "Error after 254th epoch: Training error: 2.4343007921019808e-05, Validation set error: 0.0004776918503921479\n",
            "Error after 255th epoch: Training error: 3.138842342309544e-05, Validation set error: 0.00045865896390751004\n",
            "Error after 256th epoch: Training error: 2.653911006301645e-05, Validation set error: 0.00043502365588210523\n",
            "Error after 257th epoch: Training error: 2.1673049068547027e-05, Validation set error: 0.00043856105185113847\n",
            "Error after 258th epoch: Training error: 2.1552926687656356e-05, Validation set error: 0.0004744792531710118\n",
            "Error after 259th epoch: Training error: 2.503184318863363e-05, Validation set error: 0.0004136732895858586\n",
            "Error after 260th epoch: Training error: 2.767099776044482e-05, Validation set error: 0.00046912749530747533\n",
            "Error after 261th epoch: Training error: 2.413190395342017e-05, Validation set error: 0.0004485308309085667\n",
            "Error after 262th epoch: Training error: 2.1172170492759904e-05, Validation set error: 0.000441902520833537\n",
            "Error after 263th epoch: Training error: 1.8997754515546384e-05, Validation set error: 0.00043698534136638045\n",
            "Error after 264th epoch: Training error: 1.845428187411098e-05, Validation set error: 0.0004489367420319468\n",
            "Error after 265th epoch: Training error: 1.8511708788082608e-05, Validation set error: 0.0004321980813983828\n",
            "Error after 266th epoch: Training error: 2.3106300468291688e-05, Validation set error: 0.0004902698565274477\n",
            "Error after 267th epoch: Training error: 2.3143779414298976e-05, Validation set error: 0.0004148627631366253\n",
            "Error after 268th epoch: Training error: 2.2363692832786092e-05, Validation set error: 0.00047961287782527506\n",
            "Error after 269th epoch: Training error: 2.3356435550877816e-05, Validation set error: 0.00041685227188281715\n",
            "Error after 270th epoch: Training error: 2.307010384005149e-05, Validation set error: 0.0004871450364589691\n",
            "Error after 271th epoch: Training error: 2.1422741846002026e-05, Validation set error: 0.0005198216531425714\n",
            "Error after 272th epoch: Training error: 2.3133928240523264e-05, Validation set error: 0.0004202319250907749\n",
            "Error after 273th epoch: Training error: 2.118856119698812e-05, Validation set error: 0.00045344026875682175\n",
            "Error after 274th epoch: Training error: 1.906622132320412e-05, Validation set error: 0.0004462607903406024\n",
            "Error after 275th epoch: Training error: 1.963199085253109e-05, Validation set error: 0.0004427895473781973\n",
            "Error after 276th epoch: Training error: 2.463477148818459e-05, Validation set error: 0.0004431629495229572\n",
            "Error after 277th epoch: Training error: 2.6793903592967813e-05, Validation set error: 0.00047778550651855767\n",
            "Error after 278th epoch: Training error: 2.789525185355822e-05, Validation set error: 0.0004204183351248503\n",
            "Error after 279th epoch: Training error: 2.6664741155131714e-05, Validation set error: 0.0004625250003300607\n",
            "Error after 280th epoch: Training error: 2.6102771796972366e-05, Validation set error: 0.0004252103390172124\n",
            "Error after 281th epoch: Training error: 2.675035875654431e-05, Validation set error: 0.000485859636683017\n",
            "Error after 282th epoch: Training error: 2.50678760509841e-05, Validation set error: 0.0004341161693446338\n",
            "Error after 283th epoch: Training error: 2.178272003758055e-05, Validation set error: 0.0004596601938828826\n",
            "Error after 284th epoch: Training error: 2.1640599194163507e-05, Validation set error: 0.00045550253707915545\n",
            "Error after 285th epoch: Training error: 2.272563018447149e-05, Validation set error: 0.0004062463704030961\n",
            "Error after 286th epoch: Training error: 2.1122954583512905e-05, Validation set error: 0.0004703413578681648\n",
            "Error after 287th epoch: Training error: 1.9189031475393264e-05, Validation set error: 0.00042540603317320347\n",
            "Error after 288th epoch: Training error: 1.8464959670051344e-05, Validation set error: 0.0004390586691442877\n",
            "Error after 289th epoch: Training error: 1.804928702659419e-05, Validation set error: 0.0004473573062568903\n",
            "Error after 290th epoch: Training error: 2.1011226287080033e-05, Validation set error: 0.0004902135697193444\n",
            "Error after 291th epoch: Training error: 2.411383484389839e-05, Validation set error: 0.00040921560139395297\n",
            "Error after 292th epoch: Training error: 2.7939458174263247e-05, Validation set error: 0.0004646028974093497\n",
            "Error after 293th epoch: Training error: 2.2435262451381284e-05, Validation set error: 0.00042498152470216155\n",
            "Error after 294th epoch: Training error: 2.008387490739303e-05, Validation set error: 0.0004652683564927429\n",
            "Error after 295th epoch: Training error: 1.778248893784126e-05, Validation set error: 0.0004291870864108205\n",
            "Error after 296th epoch: Training error: 1.714505857948593e-05, Validation set error: 0.00047935062320902944\n",
            "Error after 297th epoch: Training error: 1.851318946871121e-05, Validation set error: 0.00040859568980522454\n",
            "Error after 298th epoch: Training error: 2.2449772519518215e-05, Validation set error: 0.0004890247364528477\n",
            "Error after 299th epoch: Training error: 2.400175704441279e-05, Validation set error: 0.00043364387238398194\n",
            "Error after 300th epoch: Training error: 2.261028132269612e-05, Validation set error: 0.00046511268010362983\n",
            "Error after 301th epoch: Training error: 2.0893542685226653e-05, Validation set error: 0.00044529835577122867\n",
            "Error after 302th epoch: Training error: 2.0603227870775623e-05, Validation set error: 0.0004254568775650114\n",
            "Error after 303th epoch: Training error: 2.218316599954629e-05, Validation set error: 0.0004985064151696861\n",
            "Error after 304th epoch: Training error: 2.2563831852163623e-05, Validation set error: 0.0004164569836575538\n",
            "Error after 305th epoch: Training error: 2.2977300667228277e-05, Validation set error: 0.0004601115651894361\n",
            "Error after 306th epoch: Training error: 2.2846781041090996e-05, Validation set error: 0.00043791226926259696\n",
            "Error after 307th epoch: Training error: 2.1674010771345238e-05, Validation set error: 0.0004542748793028295\n",
            "Error after 308th epoch: Training error: 2.1704781305102583e-05, Validation set error: 0.00041921663796529174\n",
            "Error after 309th epoch: Training error: 2.375148794054769e-05, Validation set error: 0.00047499267384409904\n",
            "Error after 310th epoch: Training error: 2.3994538124001835e-05, Validation set error: 0.00044523217366077006\n",
            "Error after 311th epoch: Training error: 2.2759191564732227e-05, Validation set error: 0.0004724351456388831\n",
            "Error after 312th epoch: Training error: 2.258841339332982e-05, Validation set error: 0.00041945729753933847\n",
            "Error after 313th epoch: Training error: 2.3678201066765955e-05, Validation set error: 0.00047816408914513886\n",
            "Error after 314th epoch: Training error: 2.1923277382091478e-05, Validation set error: 0.00043846500921063125\n",
            "Error after 315th epoch: Training error: 2.0502870267013026e-05, Validation set error: 0.0004711963701993227\n",
            "Error after 316th epoch: Training error: 2.0625169518304836e-05, Validation set error: 0.0004542471142485738\n",
            "Error after 317th epoch: Training error: 2.0870085705837496e-05, Validation set error: 0.00044385381625033915\n",
            "Error after 318th epoch: Training error: 1.8070681009744888e-05, Validation set error: 0.00043344817822799087\n",
            "Error after 319th epoch: Training error: 1.7577140250130636e-05, Validation set error: 0.0004600365355145186\n",
            "Error after 320th epoch: Training error: 1.967075098331282e-05, Validation set error: 0.0005330599960871041\n",
            "Error after 321th epoch: Training error: 2.3759806348328475e-05, Validation set error: 0.0004178427334409207\n",
            "Error after 322th epoch: Training error: 2.6167330963227712e-05, Validation set error: 0.00046203829697333276\n",
            "Error after 323th epoch: Training error: 2.1229769484679405e-05, Validation set error: 0.00045004248386248946\n",
            "Error after 324th epoch: Training error: 1.959483149960305e-05, Validation set error: 0.00043098675087094307\n",
            "Error after 325th epoch: Training error: 1.9529305596031257e-05, Validation set error: 0.0004654381482396275\n",
            "Error after 326th epoch: Training error: 1.8048025342988125e-05, Validation set error: 0.0004356908320914954\n",
            "Error after 327th epoch: Training error: 1.87483966620696e-05, Validation set error: 0.0004989695153199136\n",
            "Error after 328th epoch: Training error: 1.9462658860385837e-05, Validation set error: 0.00042473047506064177\n",
            "Error after 329th epoch: Training error: 2.0711124596511408e-05, Validation set error: 0.0005079600377939641\n",
            "Error after 330th epoch: Training error: 2.3004752038828382e-05, Validation set error: 0.00043881338206119835\n",
            "Error after 331th epoch: Training error: 2.6382480443358385e-05, Validation set error: 0.0005177468992769718\n",
            "Error after 332th epoch: Training error: 2.6492178573210953e-05, Validation set error: 0.00042987483902834356\n",
            "Error after 333th epoch: Training error: 2.4634485568371527e-05, Validation set error: 0.00047720473958179355\n",
            "Error after 334th epoch: Training error: 2.337406464582934e-05, Validation set error: 0.00045695234439335763\n",
            "Error after 335th epoch: Training error: 2.180448481422819e-05, Validation set error: 0.00045615059207193553\n",
            "Error after 336th epoch: Training error: 2.1878502552267907e-05, Validation set error: 0.0005045876605436206\n",
            "Error after 337th epoch: Training error: 2.2955866354963792e-05, Validation set error: 0.0004338883445598185\n",
            "Error after 338th epoch: Training error: 2.4071885338695538e-05, Validation set error: 0.0005126468022353947\n",
            "Error after 339th epoch: Training error: 2.3766770301459784e-05, Validation set error: 0.0004430339322425425\n",
            "Error after 340th epoch: Training error: 2.234519972303547e-05, Validation set error: 0.0004820946778636426\n",
            "Error after 341th epoch: Training error: 2.0459325634709042e-05, Validation set error: 0.00045390010927803814\n",
            "Error after 342th epoch: Training error: 1.9671743718742046e-05, Validation set error: 0.0004427026433404535\n",
            "Error after 343th epoch: Training error: 1.9982742574339596e-05, Validation set error: 0.000496333057526499\n",
            "Error after 344th epoch: Training error: 2.1065136340067416e-05, Validation set error: 0.00043092845589853823\n",
            "Error after 345th epoch: Training error: 2.1575661311213807e-05, Validation set error: 0.0004977437783963978\n",
            "Error after 346th epoch: Training error: 2.173927237498158e-05, Validation set error: 0.0004725999606307596\n",
            "Error after 347th epoch: Training error: 2.229711267887069e-05, Validation set error: 0.00048608880024403334\n",
            "Error after 348th epoch: Training error: 2.2235643740486925e-05, Validation set error: 0.00042680971091613173\n",
            "Error after 349th epoch: Training error: 2.0472799713450673e-05, Validation set error: 0.0004623302083928138\n",
            "Error after 350th epoch: Training error: 1.80336441259628e-05, Validation set error: 0.0004433313151821494\n",
            "Error after 351th epoch: Training error: 1.796394930796519e-05, Validation set error: 0.00048540756688453257\n",
            "Error after 352th epoch: Training error: 1.850551410856578e-05, Validation set error: 0.0004589503805618733\n",
            "Error after 353th epoch: Training error: 1.9148889087070768e-05, Validation set error: 0.00047587076551280916\n",
            "Error after 354th epoch: Training error: 2.043912613720005e-05, Validation set error: 0.00043034349801018834\n",
            "Error after 355th epoch: Training error: 2.0202462689088538e-05, Validation set error: 0.0005350934225134552\n",
            "Error after 356th epoch: Training error: 2.0288279684295573e-05, Validation set error: 0.0004256340325810015\n",
            "Error after 357th epoch: Training error: 2.174462304858276e-05, Validation set error: 0.0005046115256845951\n",
            "Error after 358th epoch: Training error: 1.9635324693209352e-05, Validation set error: 0.00045862715342082083\n",
            "Error after 359th epoch: Training error: 2.0156740997766654e-05, Validation set error: 0.00048198667354881763\n",
            "Error after 360th epoch: Training error: 1.845520999020004e-05, Validation set error: 0.0004693949013017118\n",
            "Error after 361th epoch: Training error: 1.7349600055767522e-05, Validation set error: 0.000490051053930074\n",
            "Error after 362th epoch: Training error: 1.9733411131090363e-05, Validation set error: 0.0004885937087237835\n",
            "Error after 363th epoch: Training error: 2.124728023938423e-05, Validation set error: 0.0004686989705078304\n",
            "Error after 364th epoch: Training error: 2.2579640226571114e-05, Validation set error: 0.00044368740054778755\n",
            "Error after 365th epoch: Training error: 2.2062937577257707e-05, Validation set error: 0.0005288083921186626\n",
            "Error after 366th epoch: Training error: 2.1437339442668816e-05, Validation set error: 0.00043266350985504687\n",
            "Error after 367th epoch: Training error: 2.117702964390199e-05, Validation set error: 0.0004926593974232674\n",
            "Error after 368th epoch: Training error: 2.1446334309096283e-05, Validation set error: 0.0004920686478726566\n",
            "Error after 369th epoch: Training error: 2.2441593813482548e-05, Validation set error: 0.000416804279666394\n",
            "Error after 370th epoch: Training error: 2.2789743646647108e-05, Validation set error: 0.0004945935215801001\n",
            "Error after 371th epoch: Training error: 1.9817955161856425e-05, Validation set error: 0.0004381313337944448\n",
            "Error after 372th epoch: Training error: 2.07420781324882e-05, Validation set error: 0.00047080600052140653\n",
            "Error after 373th epoch: Training error: 2.0402404986312317e-05, Validation set error: 0.00045403384137898684\n",
            "Error after 374th epoch: Training error: 1.9112902969389083e-05, Validation set error: 0.0004808971134480089\n",
            "Error after 375th epoch: Training error: 1.8180336160805215e-05, Validation set error: 0.00045645219506695867\n",
            "Error after 376th epoch: Training error: 1.8898382168427744e-05, Validation set error: 0.0004475699388422072\n",
            "Error after 377th epoch: Training error: 2.1241071765003166e-05, Validation set error: 0.0005239950842224061\n",
            "Error after 378th epoch: Training error: 2.0561209143207552e-05, Validation set error: 0.0004267111362423748\n",
            "Error after 379th epoch: Training error: 1.9959897865750146e-05, Validation set error: 0.0004728849744424224\n",
            "Error after 380th epoch: Training error: 1.9986785159434564e-05, Validation set error: 0.00044750122469849885\n",
            "Error after 381th epoch: Training error: 1.8716755657909417e-05, Validation set error: 0.0004864573129452765\n",
            "Error after 382th epoch: Training error: 1.7277340917458293e-05, Validation set error: 0.0004507785488385707\n",
            "Error after 383th epoch: Training error: 1.7451650664539063e-05, Validation set error: 0.00043985951924696565\n",
            "Error after 384th epoch: Training error: 1.939684464440978e-05, Validation set error: 0.0005117361433804035\n",
            "Error after 385th epoch: Training error: 2.226539554716179e-05, Validation set error: 0.00047710773651488125\n",
            "Error after 386th epoch: Training error: 2.3541103832836952e-05, Validation set error: 0.00042719938210211694\n",
            "Error after 387th epoch: Training error: 2.2151077403846508e-05, Validation set error: 0.00048247442464344203\n",
            "Error after 388th epoch: Training error: 1.930160308140991e-05, Validation set error: 0.00043537578312680125\n",
            "Error after 389th epoch: Training error: 1.8539810701606076e-05, Validation set error: 0.00048498212709091604\n",
            "Error after 390th epoch: Training error: 1.9890290567239148e-05, Validation set error: 0.0004967647837474942\n",
            "Error after 391th epoch: Training error: 1.9033600595590048e-05, Validation set error: 0.0004340590676292777\n",
            "Error after 392th epoch: Training error: 2.095552017842484e-05, Validation set error: 0.0005758990882895887\n",
            "Error after 393th epoch: Training error: 2.3414570441879967e-05, Validation set error: 0.0004337468999437988\n",
            "Error after 394th epoch: Training error: 2.285343893246053e-05, Validation set error: 0.0004759846779052168\n",
            "Error after 395th epoch: Training error: 2.0366414534319934e-05, Validation set error: 0.00047464281669817865\n",
            "Error after 396th epoch: Training error: 1.8339351844980964e-05, Validation set error: 0.00045471524936147034\n",
            "Error after 397th epoch: Training error: 1.871733490655846e-05, Validation set error: 0.0004817405715584755\n",
            "Error after 398th epoch: Training error: 1.814534622145526e-05, Validation set error: 0.0004753827815875411\n",
            "Error after 399th epoch: Training error: 1.8298421521607942e-05, Validation set error: 0.00043815228855237365\n",
            "Error after 400th epoch: Training error: 1.893091251758959e-05, Validation set error: 0.0005152372759766877\n",
            "Error after 401th epoch: Training error: 1.7888562086651202e-05, Validation set error: 0.0004337278369348496\n",
            "Error after 402th epoch: Training error: 1.8511162781206543e-05, Validation set error: 0.0004844092472922057\n",
            "Error after 403th epoch: Training error: 1.825830191716127e-05, Validation set error: 0.0004777427529916167\n",
            "Error after 404th epoch: Training error: 2.0010096883218194e-05, Validation set error: 0.00042445884901098907\n",
            "Error after 405th epoch: Training error: 2.168326982402132e-05, Validation set error: 0.0005141012370586395\n",
            "Error after 406th epoch: Training error: 2.2098150760091986e-05, Validation set error: 0.00048411142779514194\n",
            "Error after 407th epoch: Training error: 1.9397742929330967e-05, Validation set error: 0.00044205159065313637\n",
            "Error after 408th epoch: Training error: 1.8344808946256876e-05, Validation set error: 0.00045806137495674193\n",
            "Error after 409th epoch: Training error: 1.671727470409223e-05, Validation set error: 0.0004884150694124401\n",
            "Error after 410th epoch: Training error: 1.657187992908472e-05, Validation set error: 0.00043496923171915114\n",
            "Error after 411th epoch: Training error: 1.7487317012151763e-05, Validation set error: 0.0005072515923529863\n",
            "Error after 412th epoch: Training error: 1.874582461751247e-05, Validation set error: 0.0004373813862912357\n",
            "Error after 413th epoch: Training error: 1.839165426143941e-05, Validation set error: 0.000504339870531112\n",
            "Error after 414th epoch: Training error: 1.8287198589529322e-05, Validation set error: 0.00044408542453311384\n",
            "Error after 415th epoch: Training error: 2.1160623524772085e-05, Validation set error: 0.0005122593720443547\n",
            "Error after 416th epoch: Training error: 2.00255768576922e-05, Validation set error: 0.00045120148570276797\n",
            "Error after 417th epoch: Training error: 1.9950400716662774e-05, Validation set error: 0.0005208668881095946\n",
            "Error after 418th epoch: Training error: 2.3629383540744385e-05, Validation set error: 0.00044760038144886494\n",
            "Error after 419th epoch: Training error: 2.057323074340772e-05, Validation set error: 0.000509269826579839\n",
            "Error after 420th epoch: Training error: 1.7541110591681978e-05, Validation set error: 0.0004452975408639759\n",
            "Error after 421th epoch: Training error: 1.659235546469518e-05, Validation set error: 0.0004481885116547346\n",
            "Error after 422th epoch: Training error: 1.6873768147589054e-05, Validation set error: 0.0005018106894567609\n",
            "Error after 423th epoch: Training error: 1.802554638950162e-05, Validation set error: 0.0004376735305413604\n",
            "Error after 424th epoch: Training error: 1.6801884244314753e-05, Validation set error: 0.0005159955471754074\n",
            "Error after 425th epoch: Training error: 1.5933831764155813e-05, Validation set error: 0.0004344645421952009\n",
            "Error after 426th epoch: Training error: 1.5412502897695706e-05, Validation set error: 0.0004836374137084931\n",
            "Error after 427th epoch: Training error: 1.6390247359574698e-05, Validation set error: 0.0004455980670172721\n",
            "Error after 428th epoch: Training error: 1.8094784327228236e-05, Validation set error: 0.00047603851999156177\n",
            "Error after 429th epoch: Training error: 1.890517024156163e-05, Validation set error: 0.00046174367889761925\n",
            "Error after 430th epoch: Training error: 1.8550876637329402e-05, Validation set error: 0.0004609193711075932\n",
            "Error after 431th epoch: Training error: 1.797546971844936e-05, Validation set error: 0.0005194863770157099\n",
            "Error after 432th epoch: Training error: 1.6606900034203548e-05, Validation set error: 0.00045354891335591674\n",
            "Error after 433th epoch: Training error: 1.566220416422806e-05, Validation set error: 0.00047616069787181914\n",
            "Error after 434th epoch: Training error: 1.6344209087874752e-05, Validation set error: 0.0005294344155117869\n",
            "Error after 435th epoch: Training error: 2.0842881635406795e-05, Validation set error: 0.00042058914550580084\n",
            "Error after 436th epoch: Training error: 1.9891950123771753e-05, Validation set error: 0.0004964423715136945\n",
            "Error after 437th epoch: Training error: 1.759303194148742e-05, Validation set error: 0.0004486326652113348\n",
            "Error after 438th epoch: Training error: 1.8171382051109095e-05, Validation set error: 0.000494401203468442\n",
            "Error after 439th epoch: Training error: 1.8068073502645756e-05, Validation set error: 0.00048668106319382787\n",
            "Error after 440th epoch: Training error: 1.639363613424973e-05, Validation set error: 0.00043948693200945854\n",
            "Error after 441th epoch: Training error: 1.5098119888534711e-05, Validation set error: 0.0004930099239572883\n",
            "Error after 442th epoch: Training error: 1.483150706798659e-05, Validation set error: 0.0004374419804662466\n",
            "Error after 443th epoch: Training error: 1.4885924681028547e-05, Validation set error: 0.0005198539001867175\n",
            "Error after 444th epoch: Training error: 1.567240686804118e-05, Validation set error: 0.000478389993077144\n",
            "Error after 445th epoch: Training error: 1.786082609617716e-05, Validation set error: 0.0004326009948272258\n",
            "Error after 446th epoch: Training error: 2.078763552307547e-05, Validation set error: 0.0005062760901637375\n",
            "Error after 447th epoch: Training error: 1.9948152825577388e-05, Validation set error: 0.0004606990551110357\n",
            "Error after 448th epoch: Training error: 1.726007072926967e-05, Validation set error: 0.0005135402898304164\n",
            "Error after 449th epoch: Training error: 1.8073840617592978e-05, Validation set error: 0.000540119013749063\n",
            "Error after 450th epoch: Training error: 2.0614181695774196e-05, Validation set error: 0.0004314428078942001\n",
            "Error after 451th epoch: Training error: 2.066516346779033e-05, Validation set error: 0.0005241631297394633\n",
            "Error after 452th epoch: Training error: 1.9519244021508214e-05, Validation set error: 0.00047727132914587855\n",
            "Error after 453th epoch: Training error: 1.7270692993065495e-05, Validation set error: 0.00046083112829364836\n",
            "Error after 454th epoch: Training error: 1.6399586406443874e-05, Validation set error: 0.00047704961616545916\n",
            "Error after 455th epoch: Training error: 1.7109138211553315e-05, Validation set error: 0.0004699690034613013\n",
            "Error after 457th epoch: Training error: 2.000485918074446e-05, Validation set error: 0.0004347406211309135\n",
            "Error after 458th epoch: Training error: 2.3203877966373273e-05, Validation set error: 0.0005296979215927422\n",
            "Error after 459th epoch: Training error: 2.3774250789446437e-05, Validation set error: 0.0004418700118549168\n",
            "Error after 460th epoch: Training error: 2.6665490408235705e-05, Validation set error: 0.000578013772610575\n",
            "Error after 461th epoch: Training error: 2.420961889852563e-05, Validation set error: 0.00047120204544626176\n",
            "Error after 462th epoch: Training error: 2.0855678110882018e-05, Validation set error: 0.00045100695569999516\n",
            "Error after 463th epoch: Training error: 1.840545304141662e-05, Validation set error: 0.00047293805982917547\n",
            "Error after 464th epoch: Training error: 1.6351798517565513e-05, Validation set error: 0.0005075420485809445\n",
            "Error after 465th epoch: Training error: 1.6359794203295174e-05, Validation set error: 0.00047424613148905337\n",
            "Error after 466th epoch: Training error: 1.7325285526226266e-05, Validation set error: 0.0005461567197926342\n",
            "Error after 467th epoch: Training error: 1.8708636632295964e-05, Validation set error: 0.00043162802467122674\n",
            "Error after 468th epoch: Training error: 1.9975937143292622e-05, Validation set error: 0.0005465262220241129\n",
            "Error after 469th epoch: Training error: 2.0792673276239604e-05, Validation set error: 0.0005419121007435024\n",
            "Error after 470th epoch: Training error: 2.1765890597759035e-05, Validation set error: 0.00044833135325461626\n",
            "Error after 471th epoch: Training error: 1.8972164833092556e-05, Validation set error: 0.0004767787759192288\n",
            "Error after 472th epoch: Training error: 1.6304598598925305e-05, Validation set error: 0.00045433436753228307\n",
            "Error after 473th epoch: Training error: 1.585582502240289e-05, Validation set error: 0.0005119988927617669\n",
            "Error after 474th epoch: Training error: 1.6377076881533182e-05, Validation set error: 0.00043497950537130237\n",
            "Error after 475th epoch: Training error: 1.6127201621171398e-05, Validation set error: 0.0005417451029643416\n",
            "Error after 476th epoch: Training error: 1.611451635596951e-05, Validation set error: 0.00046563753858208656\n",
            "Error after 477th epoch: Training error: 1.5711303132351617e-05, Validation set error: 0.0004908764385618269\n",
            "Error after 478th epoch: Training error: 1.4819323194036432e-05, Validation set error: 0.0004631156043615192\n",
            "Error after 479th epoch: Training error: 1.525092542661614e-05, Validation set error: 0.0005290088010951877\n",
            "Error after 480th epoch: Training error: 1.6589403331450242e-05, Validation set error: 0.00043033211841247976\n",
            "Error after 481th epoch: Training error: 1.8780407176874093e-05, Validation set error: 0.0005364310345612466\n",
            "Error after 482th epoch: Training error: 1.841499310986902e-05, Validation set error: 0.0004297157283872366\n",
            "Error after 483th epoch: Training error: 2.247892672228729e-05, Validation set error: 0.0004991161986254156\n",
            "Error after 484th epoch: Training error: 1.9054332320613635e-05, Validation set error: 0.00044636891107074916\n",
            "Error after 485th epoch: Training error: 1.8256536479425868e-05, Validation set error: 0.00048503937432542443\n",
            "Error after 486th epoch: Training error: 1.6815595732179066e-05, Validation set error: 0.000505138945300132\n",
            "Error after 487th epoch: Training error: 1.7764058151731834e-05, Validation set error: 0.0005060384282842278\n",
            "Error after 488th epoch: Training error: 1.7836385431089436e-05, Validation set error: 0.0004625589062925428\n",
            "Error after 489th epoch: Training error: 1.7807899687547682e-05, Validation set error: 0.0004832773993257433\n",
            "Error after 490th epoch: Training error: 1.7776398856674618e-05, Validation set error: 0.000548439915291965\n",
            "Error after 491th epoch: Training error: 1.9601941054568005e-05, Validation set error: 0.00043760862899944186\n",
            "Error after 492th epoch: Training error: 1.9863901738662306e-05, Validation set error: 0.0005206666537560523\n",
            "Error after 493th epoch: Training error: 2.0048954377733192e-05, Validation set error: 0.0004548114666249603\n",
            "Error after 494th epoch: Training error: 2.0736305161376955e-05, Validation set error: 0.0005358930793590844\n",
            "Error after 495th epoch: Training error: 2.1065839823884087e-05, Validation set error: 0.00044525929843075573\n",
            "Error after 496th epoch: Training error: 1.9515569779183665e-05, Validation set error: 0.0005662199691869318\n",
            "Error after 497th epoch: Training error: 2.1617725689214822e-05, Validation set error: 0.0004676055978052318\n",
            "Error after 498th epoch: Training error: 1.873083457258997e-05, Validation set error: 0.0005115153617225587\n",
            "Error after 499th epoch: Training error: 1.730743065567449e-05, Validation set error: 0.0004818225570488721\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_8P2906mqiL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "47c85d96-d177-4947-82f0-cc66e6b86bb1"
      },
      "source": [
        "plt.plot(train_costs)\n",
        "plt.title('Training Costs (Cost vs. Epochs)')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cost')\n",
        "plt.show()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D6RNaeeUhE+io1FizZRfb2iscPHEMMyaN4WcPPc/SpzZy8iunM2tKG1+7YwXb\n2it0Vms8umYbU8Y2M661zMKXTeHo2ZNZsXY796/czIOrt9DRVWXeIRNoEtz77GaaBCfNn85rZkzk\nl8vXsWrTzp7/hOUmMWFMmQljmmlLvZXntuxizZZ2qrX8z8+EMWUmtJYpl5p4tuB8m1KTCrd3oJk4\npkwEbOuo9JSVmrIveQI605d3by3lLOzau6oprLMfGmNaSnRVa3R01ajUatRi9+ewL82lLExE9oNG\nKP2bBUKlVqOzUitcv1rLfhhI9IRw9xd9NbIQ7t5297K19EVf/9r0vPbuujRJtHdVqaRKtJabKDeJ\nnV1VImBsS6knkLtjY2dnlc70A2R8a7nn9Sq1oLNS6/eH295Sjej5AQAwriUL+lpApRZUa7WeHwYR\n2Q/JWnqfsun6YN7dsu5c7G7n1PGt/OuHfn9QddyTIGnkja1mAvVXNlwFvL6/ZSKiImkLMDWV/6bX\nujPTdNE2AZB0AXABwOGHHz64FjSYJEp1P4iyX8wtALxm5qQXLVsuNTHv4AkvKjv1qMM49ajDep5/\nefGxg6pHpVqjUoueXVrdN/MqUq0FL2xt5/mt7Uwc08yktma27Oqio1Jl8tgWpoxtZmzL7o/Y6s27\neGb9Drbs6mJMc4kp41p6fo0fNK6Fg8a18OT67VTTL/eSRHtXjc27OhnbUmZca4nmUhNNEo+/sI2O\nSo3xY8q0lJrY1l6hraX0oi+GjkqVzkqNzmr2ZddRyf4tNYmxLSW6qsHWXV27GyQY31pm3sHj0/JV\nmlNPplLNehftXdWe7XT/21WtUS5l9QWYPLY5+zICZk1pY/XmdiKCJolaBF3VICJobS7RUhKd1aBS\nrTFtfCvrt3ews7PK5LHNrN/WyaZ0e+fJY5spSXSlL5lKLSDgkIljGNea/d0qtWBXZ5UdHVV2dlXo\n6KrR1lLi0IljaO+qsmZLO+1d1Z6QKZeaaEpfxNl3kRjT3IQQXdWsJ9ZZDYLstYLdv/KzaSiX1NPb\nK6cPc62WLVOpZb2EiKBcynpiEUF7pUZHVxVJNEk0afcXYASUStmPFyGCF79ef3VpLTcxrrVMR9p2\npRaMa8nGILd3VKhUaz3bAGhrKdFabko92AqlJvX8aGopNfXbi9lbJJjc1sL41qz3uXFHF1vbuyhJ\nPe3vqgbtXdWesOz5W4megIWs/d3vXX1JRPZ5Hg6j9g6JEXElcCVkPZIRrs4+rVxqYjA961KTmDG5\njRmT23rKpqddRX2ZObmNmXXL9uVVh04c0GvPnTZuYJXci3qHu5llGnlY0Gpgdt3zWamsz2XSrq1J\nZIPu/a07kG2amdkwamSQ3A3QaIDmAAAGO0lEQVTMlzRXUguwGFjSa5klwHlp+izgjsh2/C0BFktq\nlTQXmA/cNcBtmpnZMGrYrq005vF+4FayQ3W/HREPS/ossCwilgBXAddIWgFsJAsG0nLfAx4BKsCF\nEVEF6GubjWqDmZkV8wmJZmb2Enty1JZPnTYzsyFxkJiZ2ZA4SMzMbEgcJGZmNiQHxGC7pHXAM4Nc\nfRqwfi9WZ3/gNh8Y3OYDw2Db/LKImD6QBQ+IIBkKScsGeuTCaOE2Hxjc5gPDcLTZu7bMzGxIHCRm\nZjYkDpJiV450BUaA23xgcJsPDA1vs8dIzMxsSNwjMTOzIXGQmJnZkDhI+iFpkaTlklZIumik67O3\nSPq2pLWSHqorO0jSLyQ9nv6dksol6avpPXhA0nEjV/PBkzRb0p2SHpH0sKQPpfJR225JYyTdJen+\n1Oa/TeVzJS1Nbbsx3Y6BdMuGG1P5UklzRrL+QyGpJOleSTen56O6zZKelvSgpPskLUtlw/rZdpD0\nQVIJuAI4FVgAnCNpwcjWaq/5DrCoV9lFwO0RMR+4PT2HrP3z0+MC4BvDVMe9rQJ8LCIWACcAF6a/\n52hudwfwloh4LXAMsEjSCcBlwOURMQ/YBJyflj8f2JTKL0/L7a8+BDxa9/xAaPObI+KYuvNFhvez\nnd332I/6B/AG4Na65xcDF490vfZi++YAD9U9Xw4clqYPA5an6W8C5/S13P78AH4M/OGB0m5gLPBb\n4PVkZziXU3nP55zsHj9vSNPltJxGuu6DaOsssi/OtwA3AzoA2vw0MK1X2bB+tt0j6dtMYGXd81Wp\nbLQ6JCLWpOnngUPS9Kh7H9Lui2OBpYzydqddPPcBa4FfAE8AmyOikhapb1dPm9P8LcDU4a3xXvFl\n4BNALT2fyuhvcwA/l3SPpAtS2bB+tht2h0TbP0VESBqVx4RLGg98H/hwRGyV1DNvNLY7sruKHiNp\nMvBD4FUjXKWGknQasDYi7pF08kjXZxidFBGrJR0M/ELS7+pnDsdn2z2Svq0GZtc9n5XKRqsXJB0G\nkP5dm8pHzfsgqZksRK6LiB+k4lHfboCI2AzcSbZbZ7Kk7h+Q9e3qaXOaPwnYMMxVHaoTgdMlPQ3c\nQLZ76yuM7jYTEavTv2vJfjAczzB/th0kfbsbmJ+O9mghu5f8khGuUyMtAc5L0+eRjSF0l/95OtLj\nBGBLXXd5v6Gs63EV8GhEfKlu1qhtt6TpqSeCpDayMaFHyQLlrLRY7zZ3vxdnAXdE2om+v4iIiyNi\nVkTMIfs/e0dEvJtR3GZJ4yRN6J4G/gh4iOH+bI/0QNG++gDeDjxGtl/5b0a6PnuxXdcDa4Ausv2j\n55PtF74deBy4DTgoLSuyo9eeAB4EFo50/QfZ5pPI9iM/ANyXHm8fze0GjgbuTW1+CLgklb8cuAtY\nAfw/oDWVj0nPV6T5Lx/pNgyx/ScDN4/2Nqe23Z8eD3d/Vw33Z9uXSDEzsyHxri0zMxsSB4mZmQ2J\ng8TMzIbEQWJmZkPiIDEzsyFxkJgNkqRquuJq92OvXSVa0hzVXaHZbF/mS6SYDd6uiDhmpCthNtLc\nIzHby9L9If4+3SPiLknzUvkcSXek+0DcLunwVH6IpB+me4fcL+mNaVMlSf+U7ify83SGOpI+qOze\nKg9IumGEmmnWw0FiNnhtvXZtnV03b0tEHAV8jeyKtAD/CFwdEUcD1wFfTeVfBf4tsnuHHEd2hjJk\n94y4IiKOBDYDf5LKLwKOTdv5q0Y1zmygfGa72SBJ2h4R4/sof5rsplJPpotFPh8RUyWtJ7v3Q1cq\nXxMR0yStA2ZFREfdNuYAv4jsxkRI+iTQHBGfk/QzYDvwI+BHEbG9wU01y+UeiVljRD/Te6KjbrrK\n7jHNd5BdL+k44O66K9uajQgHiVljnF3376/T9H+RXZUW4N3Ar9L07cD7oOdmVJP626ikJmB2RNwJ\nfJLs0ucv6RWZDSf/kjEbvLZ0B8JuP4uI7kOAp0h6gKxXcU4q+wDwz5I+DqwD/iKVfwi4UtL5ZD2P\n95FdobkvJeDaFDYCvhrZ/UbMRozHSMz2sjRGsjAi1o90XcyGg3dtmZnZkLhHYmZmQ+IeiZmZDYmD\nxMzMhsRBYmZmQ+IgMTOzIXGQmJnZkPx/yg1TOgvCX0oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "026cCXpLpxb_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c45dc5b8-7059-4a4f-f001-d37be0acf77b"
      },
      "source": [
        "plt.plot(valid_costs)\n",
        "plt.title('Validation Costs (Cost vs. Epochs)')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Cost')\n",
        "plt.show()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JzDQYwzCMLEzANIIK02AMwzByYgKmEcTEBIxhGEYuTMA0AvPBGIZh5MYETCNICRjzwRiG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\nWfj/jAZDxD91p4gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oZQkWg1p0qS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "4aa83e71-b9e4-4018-efde-45f716e28fec"
      },
      "source": [
        "print (f'Y_pred: {Y_preds[0]}')\n",
        "print (f'Y_valid: {Y_valid[0]}')\n",
        "\n",
        "# Plot a few training images\n",
        "_, ax = plt.subplots(nrows = 2, ncols = 3)\n",
        "count = 0\n",
        "for row in ax:\n",
        "    for col in row:\n",
        "        col.imshow(X_valid[count].reshape(IMG_SIZE, IMG_SIZE))\n",
        "        col.scatter((np.take(Y_preds[count], range(0, N_KEYPOINTS, 2))) * IMG_SIZE, (np.take(Y_preds[count], range(1, N_KEYPOINTS, 2))) * IMG_SIZE, color = 'red', marker = 'x')\n",
        "        count += 1\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y_pred: [0.7359011  0.4129554  0.28436756 0.40333223 0.615662   0.3958459\n",
            " 0.7634907  0.393638   0.38198537 0.39656988 0.23355198 0.39601856\n",
            " 0.58536685 0.30623865 0.8290638  0.3122245  0.40965107 0.30856347\n",
            " 0.16604048 0.31682596 0.50369895 0.7224025  0.65811354 0.7919372\n",
            " 0.3414854  0.7924669  0.4977064  0.75922644 0.49688467 0.91024673]\n",
            "Y_valid: [0.74040625 0.4123355  0.27428725 0.39639988 0.61624312 0.39525784\n",
            " 0.76385915 0.39278134 0.38179799 0.39572814 0.23317192 0.39618224\n",
            " 0.58404701 0.30554873 0.8279461  0.30973813 0.40960559 0.3073229\n",
            " 0.16532477 0.31696006 0.4934025  0.727065   0.65922641 0.79136161\n",
            " 0.34271254 0.79353925 0.49974387 0.75957753 0.48941875 0.8465825 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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loUtXeOh7SRGWKFECpS35RIo1L7u/l8NAT44MQlgUy9xT4iklKL4PGUUhjR56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Tu7n8mEP51KKTwQjO+dntvONnjoL5jTe8lq+cczSzgwZXf3BfAN7yw1/zlut/Dbj+k38/ZCHx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a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\nOzj0gx3sYAe7Jvb/Aa1eavN5x66sAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhRMMSVn_CKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}