KNN.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/negarhonarvar/Machine-Learning/blob/main/KNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "IcHt6fW3s3jS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve"
      ],
      "metadata": {
        "id": "c2FkBoM0TsbG"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "28OFAa0ttpyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Disease_symptom_and_patient_profile_dataset.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "id": "oUgKy6yzkLbJ",
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          "height": 206
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       Disease Fever Cough Fatigue Difficulty Breathing  Age  Gender  \\\n",
              "0    Influenza   Yes    No     Yes                  Yes   19  Female   \n",
              "1  Common Cold    No   Yes     Yes                   No   25  Female   \n",
              "2       Eczema    No   Yes     Yes                   No   25  Female   \n",
              "3       Asthma   Yes   Yes      No                  Yes   25    Male   \n",
              "4       Asthma   Yes   Yes      No                  Yes   25    Male   \n",
              "\n",
              "  Blood Pressure Cholesterol Level Outcome Variable  \n",
              "0            Low            Normal         Positive  \n",
              "1         Normal            Normal         Negative  \n",
              "2         Normal            Normal         Negative  \n",
              "3         Normal            Normal         Positive  \n",
              "4         Normal            Normal         Positive  "
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              "      <td>Positive</td>\n",
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              "      <td>Asthma</td>\n",
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        "data.shape"
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      "outputs": [
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          "data": {
            "text/plain": [
              "(349, 10)"
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          "metadata": {},
          "execution_count": 233
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    {
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      "source": [
        "lblenc=LabelEncoder()\n",
        "zero_one_valued = ['Fever', 'Cough', 'Fatigue', 'Difficulty Breathing', 'Gender', 'Outcome Variable']\n",
        "for col in zero_one_valued:\n",
        "  data[col] = lblenc.fit_transform(data[col])\n",
        "# positive values , yes's and female are 1, the rest of them are 0\n",
        "data['Blood Pressure']=lblenc.fit_transform(data['Blood Pressure'])\n",
        "data['Cholesterol Level']=lblenc.fit_transform(data['Cholesterol Level'])\n",
        "data.head()"
      ],
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        "id": "grnAeRRorlwd",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       Disease  Fever  Cough  Fatigue  Difficulty Breathing  Age  Gender  \\\n",
              "0    Influenza      1      0        1                     1   19       0   \n",
              "1  Common Cold      0      1        1                     0   25       0   \n",
              "2       Eczema      0      1        1                     0   25       0   \n",
              "3       Asthma      1      1        0                     1   25       1   \n",
              "4       Asthma      1      1        0                     1   25       1   \n",
              "\n",
              "   Blood Pressure  Cholesterol Level  Outcome Variable  \n",
              "0               1                  2                 1  \n",
              "1               2                  2                 0  \n",
              "2               2                  2                 0  \n",
              "3               2                  2                 1  \n",
              "4               2                  2                 1  "
            ],
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              "      <th>0</th>\n",
              "      <td>Influenza</td>\n",
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              "      <th>1</th>\n",
              "      <td>Common Cold</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Eczema</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
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              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Asthma</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Asthma</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(data['Disease'].unique())"
      ],
      "metadata": {
        "id": "1n8tqi8H2iVr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a1a6e20-26ae-4970-9c26-fe05627b31c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "116"
            ]
          },
          "metadata": {},
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data['Disease'] = data['Disease'].astype('category')\n",
        "# data['Diseases'] = data['Disease'].cat.codes"
      ],
      "metadata": {
        "id": "yYGp0sBe2Puh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "enc = OneHotEncoder(sparse=False)\n",
        "disease = enc.fit_transform(data[['Disease']])\n",
        "enc_data = pd.DataFrame(disease, columns=enc.get_feature_names_out(['Disease']))\n",
        "# enc_data = pd.DataFrame(enc.fit_transform(data[['Diseases']]).toarray(), columns=onehot.get_feature_names_out(['Disease']))\n",
        "data = data.drop('Disease', axis=1)\n",
        "OneHotData = data.join(enc_data)\n",
        "OneHotData.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "QoraBdeqsxtp",
        "outputId": "1bc04f54-bd1a-4230-b801-5cf5d391addd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Fever  Cough  Fatigue  Difficulty Breathing  Age  Gender  Blood Pressure  \\\n",
              "0      1      0        1                     1   19       0               1   \n",
              "1      0      1        1                     0   25       0               2   \n",
              "2      0      1        1                     0   25       0               2   \n",
              "3      1      1        0                     1   25       1               2   \n",
              "4      1      1        0                     1   25       1               2   \n",
              "\n",
              "   Cholesterol Level  Outcome Variable  Disease_Acne  ...  \\\n",
              "0                  2                 1           0.0  ...   \n",
              "1                  2                 0           0.0  ...   \n",
              "2                  2                 0           0.0  ...   \n",
              "3                  2                 1           0.0  ...   \n",
              "4                  2                 1           0.0  ...   \n",
              "\n",
              "   Disease_Tonsillitis  Disease_Tourette Syndrome  Disease_Tuberculosis  \\\n",
              "0                  0.0                        0.0                   0.0   \n",
              "1                  0.0                        0.0                   0.0   \n",
              "2                  0.0                        0.0                   0.0   \n",
              "3                  0.0                        0.0                   0.0   \n",
              "4                  0.0                        0.0                   0.0   \n",
              "\n",
              "   Disease_Turner Syndrome  Disease_Typhoid Fever  Disease_Ulcerative Colitis  \\\n",
              "0                      0.0                    0.0                         0.0   \n",
              "1                      0.0                    0.0                         0.0   \n",
              "2                      0.0                    0.0                         0.0   \n",
              "3                      0.0                    0.0                         0.0   \n",
              "4                      0.0                    0.0                         0.0   \n",
              "\n",
              "   Disease_Urinary Tract Infection  Disease_Urinary Tract Infection (UTI)  \\\n",
              "0                              0.0                                    0.0   \n",
              "1                              0.0                                    0.0   \n",
              "2                              0.0                                    0.0   \n",
              "3                              0.0                                    0.0   \n",
              "4                              0.0                                    0.0   \n",
              "\n",
              "   Disease_Williams Syndrome  Disease_Zika Virus  \n",
              "0                        0.0                 0.0  \n",
              "1                        0.0                 0.0  \n",
              "2                        0.0                 0.0  \n",
              "3                        0.0                 0.0  \n",
              "4                        0.0                 0.0  \n",
              "\n",
              "[5 rows x 125 columns]"
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a1131661-bac7-462c-b40b-460650a21e8b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a1131661-bac7-462c-b40b-460650a21e8b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ec157580-0f58-43a8-b3cb-3c49a114a2d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ec157580-0f58-43a8-b3cb-3c49a114a2d4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ec157580-0f58-43a8-b3cb-3c49a114a2d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "y= OneHotData[\"Outcome Variable\"]\n",
        "# x,y=np.arange(250).reshape((2, 125))\n",
        "# chose the test size based on scikit learn documantation\n",
        "x_train, x_test, y_train, y_test = train_test_split(OneHotData, y, test_size=0.33)"
      ],
      "metadata": {
        "id": "xxQN0MRDP7-f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def knn(x_train, x_test):\n",
        "    k = 7\n",
        "    max_labels = []\n",
        "\n",
        "    for _, test_row in x_test.iterrows():\n",
        "        distances = [(np.linalg.norm(np.array(train_row[:-1]) - np.array(test_row[:-1])), train_row.iloc[-1])\n",
        "                     for _, train_row in x_train.iterrows()]\n",
        "\n",
        "        sorted_list = sorted(distances, key=lambda x: x[0])[:k]\n",
        "\n",
        "        # Find the most common label\n",
        "        maximum = max(set([label for _, label in sorted_list]), key=[label for _, label in sorted_list].count)\n",
        "        max_labels.append(maximum)\n",
        "\n",
        "    return np.array(max_labels)"
      ],
      "metadata": {
        "id": "IvWAJoExZxXy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = knn(x_train , x_test)"
      ],
      "metadata": {
        "id": "UOWP7OHlYd_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, prediction)\n",
        "accuracy"
      ],
      "metadata": {
        "id": "rBFFytyc5L-3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "712cf845-2895-48ea-b098-978dcf425cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.41379310344827586"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sort = []\n",
        "for i in range(0, len(y_test)):\n",
        "    sort.append(i)\n",
        "test = y_test.set_axis(sort)"
      ],
      "metadata": {
        "id": "sMt3yclZxHwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test = []\n",
        "y_pred= []\n",
        "for i in range(len(prediction)):\n",
        "    if prediction[i] == 1.0:\n",
        "        y_pred.append(1)\n",
        "    else:\n",
        "        y_pred.append(0)\n",
        "\n",
        "    if test[i] == 1.0:\n",
        "        Y_test.append(1)\n",
        "    else:\n",
        "        Y_test.append(0)"
      ],
      "metadata": {
        "id": "eaR6zeyryBYe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc"
      ],
      "metadata": {
        "id": "sxIg4j2JwMlY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, threshholds = roc_curve(Y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Zn1_m5cYkdr4",
        "outputId": "434c2775-d18b-4723-ef34-ec52613c70cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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U6ZaP9/vvv3Pw4EEKFSp0033dTI0aNahYsSIrV67kqaeeAq4vcyhYsKDjl3hoaCjh4eF88MEHfPDBByl6jLJly94y8w03/qN2+fLlRB+7/tvNyvF9992XZNvy5csTEBAApO55vlXu6OhopkyZwqJFi/jzzz8TnV7tvwUvtf5bcm6Ul7CwMADHOVnvvffeRNvlypXrph/B/1u+fPmAf57DtMh1Y587d+5kwoQJBAUFcfXq1UTbR0RE4OXl5fj6Zu+HlOzj2LFjAFStWjVV38MNqf35SOl7d9q0afTt2xdfX1/q1KnDQw89RJ8+fShXrlyqM974Q+dOv0fgpqf9K1OmDAsXLsRut3Ps2DEmTZpEaGhokj8iPD09b1tG//uzmC9fPkf21Ga9XaG/0/duSiX3OufKlYtOnTqxfPlyYmJicHNzY+3atcTFxSUqvnfzO1cEVHwlm6lfvz5169YFrs9KNm3alB49enD48GHy5s3rOH/miBEjkp0Fg6RF51bsdjvVqlXjnXfeSfZ2X1/fW96/W7duTJo0iQsXLuDp6UlgYCBPPPGEY4bxRt5evXolWQt8Q/Xq1RN9nZLZXri+BnbdunX8+uuvNGvWLNltfv31V4AUzcL92508z8nlHjJkCIsWLeKll16iUaNGeHl5YbPZ6N69+03PhZpSNzuV1c1KTGpVrFgRgH379lGzZs0U3+92uY4dO0arVq2oWLEi77zzDr6+vri6urJhwwZmzpyZ5HlJ7nlN7T7uVGp/PlL63u3atSt+fn589tlnfPXVV0yfPp2pU6eydu1aHnzwwbvOnVIFChQA/vlj6b/y5MmTaG18kyZNqF27NmPGjGHOnDmO8UqVKhESEsKpU6eS/OFzw39/FitWrMgvv/zC6dOnb/t75t/CwsKS/cP131L73r1ZkU5ISEh2/Gavc/fu3VmwYAEbN26kY8eOBAQEULFiRWrUqOHY5m5/54qo+Eq25ezszJQpU7j//vt57733GDVqlGNGyMXFJdF/kJJzzz33sH///ttus3fvXlq1apWij37/q1u3brz22musWbOGIkWKEBkZ6TiIA6BQoUJ4enqSkJBw27yp9cgjjzBlyhT8/f2TLb4JCQksX74cHx8fmjRpkui233//Pcn2R44cccyEpuZ5vpXVq1fTt29fZsyY4Ri7du0a4eHhiba7k+f+dm5cjODo0aPcf//9jvH4+HhOnjyZ5A+O/3rwwQdxdnbmk08+SdODhL744gtiYmIIDAxMVJJS8xFvSvdxzz33ALB///5b/kF4s+f/bn8+bqVYsWI8//zzPP/88/z999/Url2bSZMmOYpvSh/vxnv1dj/ryblREE+cOJGi7atXr06vXr1YsGABI0aMcDz3jzzyCJ9++in+/v68+uqrSe4XGRnJ559/TsWKFR2vQ/v27fn000/55JNPGD16dIoePz4+ntOnT/Poo4/ecrvUvnd9fHyS/EwCqb6SXbNmzShWrBgrV66kadOmfPPNN4wdOzbRNun5npKcQWt8JVtr0aIF9evXZ9asWVy7do3ChQvTokULFixYwNmzZ5NsHxoa6vj/nTp1Yu/evXz22WdJtrsx+9a1a1f+/PNPFi5cmGSb6Ohox9kJbqZSpUpUq1aNlStXsnLlSooVK5aohDo7O9OpUyfWrFmT7H+Y/503tRo3bkzr1q1ZtGhRsleGGjt2LEeOHOHll19OMkOzbt26RGt0f/rpJ3bt2uUoHal5nm/F2dk5yQzsu+++m2QmKU+ePADJ/sf3TtWtW5cCBQqwcOFC4uPjHePLli276Qzfv/n6+vL000/z1Vdf8e677ya53W63M2PGDM6cOZOqXDdmhP+77GPRokVpvo8HHngAT09PpkyZwrVr1xLd9u/75smTJ9mlJ3f785GchISEJI9VuHBhihcvTkxMzG0z/VehQoVo1qwZH3/8MadOnUp02+1m/0uUKIGvr2+qrmL28ssvExcXl2jGsnPnzlSuXJm33noryb7sdjvPPfccYWFhTJgwIdF9qlWrxqRJkwgKCkryOJcvX05SGg8cOMC1a9do3LjxLTOm9r17zz33EBER4ZiVBjh79myyvztvxcnJic6dO/PFF1+wdOlS4uPjEy1zgPR5T0nOohlfyfZGjhxJly5dWLx4MQMHDmTu3Lk0bdqUatWq8fTTT1OuXDnOnz9PUFAQZ86ccVzSc+TIkY4rgj355JPUqVOHS5cuERgYyPz586lRowa9e/cmICCAgQMH8u2339KkSRMSEhI4dOgQAQEBbN682bH04ma6devG+PHjcXd356mnnsLJKfHfo2+99RbffvstDRo04Omnn6Zy5cpcunSJ4OBgtmzZwqVLl+74ufH396dVq1Z06NCBHj164OfnR0xMDGvXrmXbtm1069aNkSNHJrnfvffeS9OmTXnuueeIiYlh1qxZFChQgJdfftmxTUqf51t55JFHWLp0KV5eXlSuXJmgoCC2bNni+Ij5hpo1a+Ls7MzUqVOJiIjAzc2Nli1bUrhw4Tt+blxdXZk4cSJDhgyhZcuWdO3alZMnT7J48WLuueeeFM02zZgxg2PHjvHCCy+wdu1aHnnkEXx8fDh16hSrVq3i0KFDiWb4U+KBBx7A1dWV9u3b8+yzzxIVFcXChQspXLhwsn9k3M0+8uXLx8yZMxkwYAD16tWjR48e+Pj4sHfvXq5evcqSJUsAqFOnDitXrmTYsGHUq1ePvHnz0r59+zT5+fivy5cvU7JkSTp37uy4TO+WLVvYvXt3ok8GbpYpOXPmzKFp06bUrl2bZ555hrJly3Ly5EnWr19PSEjILfN06NCBzz77LEVrZ+H6UoWHHnqIDz/8kHHjxlGgQAFcXV1ZvXo1rVq1omnTpomu3LZ8+XKCg4MZPnx4oveKi4sLa9eupXXr1jRr1oyuXbvSpEkTXFxc+O233xyf1vz7dGxff/01Hh4etGnT5rY5U/Pe7d69O6+88gqPPfYYL7zwAlevXmXevHmUL18+1QehduvWjXfffZcJEyZQrVq1JKclTI/3lOQwGX8iCZG0d7MLWBhz/cpA99xzj7nnnnscp8s6duyY6dOnjylatKhxcXExJUqUMI888ohZvXp1ovtevHjRDB482JQoUcJxovS+ffsmOrVYbGysmTp1qqlSpYpxc3MzPj4+pk6dOua1114zERERju3+ezqzG37//XfHSfZ37NiR7Pd3/vx5M2jQIOPr62tcXFxM0aJFTatWrcwHH3zg2ObGabpWrVqVqufu8uXLZuLEiaZKlSomd+7cxtPT0zRp0sQsXrw4yemc/n0BixkzZhhfX1/j5uZm/Pz8zN69e5PsOyXP861eu7CwMNO/f39TsGBBkzdvXtO2bVtz6NChZJ/LhQsXmnLlyhlnZ+cUXcDiv8/TzS5sMGfOHFO6dGnj5uZm6tevb3bu3Gnq1Klj2rVrl4Jn9/pVrj788EPj5+dnvLy8jIuLiyldurTp379/otNF3ezKbTeen39ftCMwMNBUr17duLu7mzJlypipU6eajz/+OMl2Ny5gkZyU7uPGto0bNza5c+c2+fLlM/Xr1zeffvqp4/aoqCjTo0cP4+3tneQCFin9+eD/FzZIDv86nVlMTIwZOXKkqVGjhvH09DR58uQxNWrUSHLxjZtlutnrvH//fvPYY48Zb29v4+7ubipUqGDGjRuXbJ5/Cw4ONkCS02vd7AIWxhizbdu2JKdoM8aYv//+2wwbNszce++9xs3NzXh7e5vWrVs7TmGWnLCwMDN+/HhTrVo14+HhYdzd3U3VqlXN6NGjzdmzZxNt26BBA9OrV6/bfk83pPS9a4wxX331lalatapxdXU1FSpUMJ988sktL2BxM3a73fj6+hrAvPnmm8luk9L3lEhybMak0ZEcIpLtnTx5krJlyzJ9+nRGjBhhdRxL2O12ChUqxOOPP57sx62S87Rq1YrixYuzdOlSq6PcVEhICLVr1yY4ODhVB1uKZDda4ysichPXrl1Lss7T39+fS5cu0aJFC2tCSaYzefJkVq5cmeqDuTLSW2+9RefOnVV6JcfTGl8RkZv48ccfGTp0KF26dKFAgQIEBwfz0UcfUbVqVbp06WJ1PMkkGjRoQGxsrNUxbmnFihVWRxDJFFR8RURuokyZMvj6+jJnzhwuXbpE/vz56dOnD2+99Vaiq76JiEjWoDW+IiIiIpIjaI2viIiIiOQIKr4iIiIikiPkuDW+drudv/76C09PT13uUERERCQTMsZw+fJlihcvnuTCTncjxxXfv/76C19fX6tjiIiIiMhtnD59mpIlS6bZ/nJc8fX09ASuP5H58uWzOI2IiIiI/FdkZCS+vr6O3pZWclzxvbG8IV++fCq+IiIiIplYWi9L1cFtIiIiIpIjqPiKiIiISI6g4isiIiIiOYKKr4iIiIjkCCq+IiIiIpIjqPiKiIiISI6g4isiIiIiOYKKr4iIiIjkCCq+IiIiIpIjqPiKiIiISI6g4isiIiIiOYKKr4iIiIjkCCq+IiIiIpIjqPiKiIiISI6g4isiIiIiOYKlxfe7776jffv2FC9eHJvNxrp16257n23btlG7dm3c3Ny49957Wbx4cbrnFBEREZGsz9Lie+XKFWrUqMHcuXNTtP2JEyd4+OGHuf/++wkJCeGll15iwIABbN68OZ2TioiIiEhWl8vKB3/wwQd58MEHU7z9/PnzKVu2LDNmzACgUqVK7Nixg5kzZ9K2bdv0iikiIiIiGcSekMBva95Ll31nqTW+QUFBtG7dOtFY27ZtCQoKuul9YmJiiIyMTPRPRERERDKfs0eP8VDdEbR8Jixd9p+liu+5c+coUqRIorEiRYoQGRlJdHR0sveZMmUKXl5ejn++vr4ZEVVEREREUuHz+f5Ur7WQzSHeXItPn0UJWar43onRo0cTERHh+Hf69GmrI4mIiIjI/10JD2PgIy/T8bkTXIjKDUBhz6vp8lhZqvgWLVqU8+fPJxo7f/48+fLlI3fu3Mnex83NjXz58iX6JyIiIiLW2/PVVupUeoMF6/M4xjo2COfHoGfT5fGyVPFt1KgRW7duTTT29ddf06hRI4sSiYiIiEhqJcTFMXXwJBo+uJ3D57wA8HCN5YPx3qz9YQYFfEumy+NaWnyjoqIICQkhJCQEuH66spCQEE6dOgVcX6bQp08fx/YDBw7k+PHjvPzyyxw6dIj333+fgIAAhg4dakV8EREREUmtyNNcW96WD1eeJ97uDECdsuH88v1jPP3ai9ic0q+eWlp8f/75Z2rVqkWtWrUAGDZsGLVq1WL8+PEAnD171lGCAcqWLcv69ev5+uuvqVGjBjNmzODDDz/UqcxEREREsoJDK8G/OnkufMvyHmtwdY5ndK94fvhtMuXr1033h7cZY0y6P0omEhkZiZeXFxEREVrvKyIiIpIBLl+4QOTGlynx96J/Bj19+bPWh5So90CS7dOrr1l6AQsRERERyd6CAjfSa8BWiuaxsf05J3I526FCd2g9jxLu3hmaJUsd3CYiIiIiWUN8bCyvDZiI32M/cjzUkx9OlmLqdy3hwaXw8HLI4NILmvEVERERkTR2PGQvvbouIuh3H8AGQOPyYfSYNAMqV7csl2Z8RURERCRNGLsd/8lzqdlw5f9LLzg72Xn9aRvb902jbA3rSi9oxldERERE0kDY2bMM7DqdgB1egBsA5QpdZtlHrWnYvp214f5PxVdERERE7krkgS3U9NvEqUtejrF+bS4z59PReBYoYGGyxLTUQURERETuTEIsfDeKfBsf4LEqvwHg43GNgFklWfTV25mq9IJmfEVERETkTlw6DOt7wN/BALz10BauuZVi7Jyh+FaqaHG45Kn4ioiIiEiKGbudhRNm43x0JU/VvV56cXLBvdUk5r8yHGyZd0GBiq+IiIiIpEjoH6d4uutMPv/Jm9wurWnse4xKFQtcPy9vkdpWx7utzFvJRURERCTT+Mp/BdWrv8fnP3kDEB3nwpfhfaF3cJYovaAZXxERERG5hWtRUYzuN4lZa9yBPAAUzBvNxzOq0P6Z3taGSyUVXxERERFJ1r7tO+jZaw37zng7xtrVDGfRmiEULVfOumB3SEsdRERERCQRY09gzshp1Gu92VF63XLFM2ekBxv2zMiSpRc04ysiIiIi/xZ1lqh1TzHjwwrExHsDUN03nGXLOlPVr4m12e6SZnxFRERE5Lqjn4N/dTzPb+STHmtxdrIztHMMuw68keVLL2jGV0RERCTHuxIexpWvxlL49DzHmF+1OI58W4dyzR61MFnaUvEVERERycH2fLWVnn3XUyLvFb5+xoaTk4F7O0KbhZTzKGh1vDSlpQ4iIiIiOVBCXBxTB0+i4YPbOXzOi2+OlmPmzmbQZiE8uhayWekFzfiKiIiI5DinDxykT5cFbDvgAzgDUKdsOO3HvA3V61obLh2p+IqIiIjkIAEzF/Ls2OOER/sAYLMZRvVMYOIHk3HNndvidOlLxVdEREQkB4gMDeWFHlNZssUTcAfA1yeKpQua0rxLB2vDZRAVXxEREZFsLuLQd9RuFsjxUE/HWDe/SOatHIlPsaIWJstYOrhNREREJLuyx8MPE/Ha0JKW5Y4A4OkWg/+Uwny6bXqOKr2gGV8RERGR7Cn8OGzoBWeDAJj56GaicxXj9bmDKFejusXhrKHiKyIiIpKNGLudpW/Nw+XwYp6o/vP1QZszee9/lU9GjQannFv/cu53LiIiIpLNhJ09y8Cu0wnY4UVetzbUL36Me+7xgYeWQfGGVseznNb4ioiIiGQD2wLWUb3KOwTs8AIgKsaN1ed7Q58Qld7/04yviIiISBYWGx3N+AGTmPZpLozJC4B37mt8MOVeurw4weJ0mYuKr4iIiEgWdXjXbnp0X07wSW/HWIvK4fivfhbfShWtC5ZJaamDiIiISBZj7HYWjJtJLb/PHaXXxTmBaUNc2Prr2yq9N6EZXxEREZGs5GooEZ89y8TZ5YiOu35BigpFI1nu/zC127S0OFzmphlfERERkazixCbwr473uc9Y3H0dAAMfuUrwofEqvSmgGV8RERGRTO5aVBRXt44j/9FZjrG2NSPZv6ESVR7sal2wLEbFV0RERCQT27d9Bz16raW059988STYbECZdtBuEVXy5KxLDt8tLXUQERERyYTsCQnMHjGNeq03s/+MF+sPlmf+roZw/xx4fAOo9KaaZnxFREREMpmzR4/Rv8t7bA7x5kZdq+4bjt8Lb0PtJpZmy8pUfEVEREQykc/n+zNg5AEuRHk7xoZ2jmHyojdwz5vXumDZgIqviIiISCZwJTyM4b2msGB9HiA3AMW8rrDk3bq06a0D2NKCiq+IiIiIxcIOB9GoxSoOn/NyjHVsEM7CgGEULOVrYbLsRQe3iYiIiFjFngA/TcVnQzPqFPsDAA/XWBZO8GbtDzNUetOYZnxFRERErBB5Gjb2hjPbAZj7+AainQvx1twBlK9f1+Jw2ZOKr4iIiEgGC5i5ELeD8+lQIfj/Iza8mw9l7eiJ4OxqZbRsTcVXREREJINEhobyQo+pLNniiU/uNvw6/Cglfb3gwaXg29zqeNme1viKiIiIZICgwI3UrHy99AKERefmk1O9oM+vKr0ZRDO+IiIiIukoPjaWN5+bzJuLDQn266XX0y2GuRN96fXyOHDSPGRGUfEVERERSSfHQ/bSq+sign73AWwANC4fxicBT1K2RnVrw+VA+hNDREREJI0Zu50lk+dSo2HA/0svODvZeW2Aje37pqn0WkQzviIiIiJp6VoYYWufZ/ikUkTFeABQrtBlln3Umobt21kcLmfTjK+IiIhIWjm9DZZUJ//ZFXzYNRCAfm0uE3JwtEpvJqAZXxEREZG7FBsdTcz21/D8bRpgAOhY9zw/P1KWOp36WBtOHFR8RURERO7C4V276dF9Ofd6nWFFL4PNBvjeDw/6U8ezpNXx5F9UfEVERETugLHb+WDCbIZOvUh0nDfBePNw5eP0Gdod6g4Hm1aUZjYqviIiIiKpFPrHKQZ0mUngbm/ABYAKRSOp2n861GtpaTa5ORVfERERkVTYvGQF/V4I5lykt2Ns4CNXmfHJeDy8vKwLJrel4isiIiKSAteiohjdbxKz1rgDeQAomDeaj2dUof0zva0NJymi4isiIiJyG5eO7KFFq0/Yd8bbMdauZjiL1gyhaLly1gWTVNGqaxEREZGbMXYIno3P+iaU8zoHgFuueOaM9GDDnhkqvVmMZnxFREREkhN1Fjb3h5ObsQEfdg0kepUPM97vSVW/Jlankzug4isiIiLyH4EL/HHbN5u2ZYMdYwWbPcvmMZMhl7uFyeRuqPiKiIiI/N+V8DCG95rCgvV5KJy3JfuGH6Zw0XzQbjGUecDqeHKXtMZXREREBNjz1VZqV3yTBeuvn7Hh76i8fHy8J/T5VaU3m1DxFRERkRwtIS6OqYMn0fDB7Rw5nw8AD9dYPhjvzSuL54FHQYsTSlrRUgcRERHJsU4fOEjvzgvYftAHcAagTtlwlq/oSfn6da0NJ2lOxVdERERypICZC3l27HHCo30AsNkMo3omMPGDybjmzm1xOkkPKr4iIiKSs8REcuGzF3l6TFEir10/Q4OvTxRLFzSleZcOFoeT9KQ1viIiIpJz/BUES2tS8M/FzHt8PQDd/CLZ+9twld4cQDO+IiIiku3Fx8YSu2MyHiFvgkkAoEfDk5RsWxy/XuOwOWkuMCdQ8RUREZFs7XjIXnp1XURF75N83O166aV4Y3joE5p5lbU2nGQoFV8RERHJlozdztK35jHo9b+IivEhCB8erHSMLgO7QoPR4KQalNPoFRcREZFsJ+zsWQZ2nU7ADi/AFYByhS7j23k6NGpnbTixjIqviIiIZCvbAtbRe+BOzoR5Ocb6tbnMnE9H41mggIXJxGoqviIiIpItxEZHM37AJKZ9mgtj8gLg43GNBZPvpcuLT1mcTjIDFV8RERHJ8i4e/ZUH2iwi+KS3Y+z+KmH4r36OkhUrWBdMMhWdu0NERESyLmNg7wJ8vmhIQdcLALg4JzBtiAtb9s5Q6ZVEVHxFREQka7oaCp93hC0DcbJHs7j7Opre9zc/bmzByDljcHJ2tjqhZDJa6iAiIiJZzlf+K3DfO41mxX9xjBXz6833r04HFw8Lk0lmZvmM79y5cylTpgzu7u40aNCAn3766Zbbz5o1iwoVKpA7d258fX0ZOnQo165dy6C0IiIiYqVrUVEM7Tyatn0P0/OjFoRddYfchaBjILR+X6VXbsnS4rty5UqGDRvGhAkTCA4OpkaNGrRt25a///472e2XL1/OqFGjmDBhAgcPHuSjjz5i5cqVjBkzJoOTi4iISEbbt30H9SuNY9YadwDORHjxweEe0PdXuKe9xekkK7C0+L7zzjs8/fTT9O/fn8qVKzN//nw8PDz4+OOPk93+hx9+oEmTJvTo0YMyZcrwwAMP8MQTT9x2llhERESyLntCArNHTKNe683sO+MNgFuueOaM9OBl/4WQp6i1ASXLsKz4xsbGsmfPHlq3bv1PGCcnWrduTVBQULL3ady4MXv27HEU3ePHj7NhwwYeeuihmz5OTEwMkZGRif6JiIhI1nD26DEeqjuCl2ZEExN//dCkaiUj+PmbdgyZNhKbk+WrNiULsezgtgsXLpCQkECRIkUSjRcpUoRDhw4le58ePXpw4cIFmjZtijGG+Ph4Bg4ceMulDlOmTOG1115L0+wiIiKS/j6f78+AkQe4EOXtGBvaOYbJi17HPW9e64JJlpWl/kzatm0bkydP5v333yc4OJi1a9eyfv163njjjZveZ/To0URERDj+nT59OgMTi4iISKrFXSF01fP0fPEwF6JyA1DM6wqbl1TinVWTVXrljlk241uwYEGcnZ05f/58ovHz589TtGjya3XGjRtH7969GTBgAADVqlXjypUrPPPMM4wdOxanZD7ucHNzw83NLe2/AREREUl75/fA+h4UCjvCrA61eXrVo3SoH8GHq4ZSsJSv1ekki7NsxtfV1ZU6deqwdetWx5jdbmfr1q00atQo2ftcvXo1Sbl1/v/JqY0x6RdWRERE0lVCXBwxO96C5Q0h7AgATzU+xMb3C/BZ0NsqvZImLL2AxbBhw+jbty9169alfv36zJo1iytXrtC/f38A+vTpQ4kSJZgyZQoA7du355133qFWrVo0aNCAo0ePMm7cONq3b+8owCIiIpK1nD5wkD5dFlDV5wjvPhZ/fbBIHWwPLadd/vLWhpNsxdLi261bN0JDQxk/fjznzp2jZs2abNq0yXHA26lTpxLN8L766qvYbDZeffVV/vzzTwoVKkT79u2ZNGmSVd+CiIiI3IWAmQt5duxxwqN92EYDHqx4lIf6dobGE8HZ1ep4ks3YTA5bIxAZGYmXlxcRERHky5fP6jgiIiI5UmRoKC/0mMqSLZ6OMV+fKJZ90BS/zh0sTCaZQXr1NUtnfEVERCTnCQrcSK8BWzke+k/p7eYXwbyVL+NTTBejkPSTpU5nJiIiIllXfGwsrw2YiN9jPzpKr6dbDP5TCvHptrdVeiXdacZXRERE0t3F4wdp324BQb/7ADYAGpcP45OAJylbo7q14STH0IyviIiIpB9j4Dd/vAMbkCs+EgBnJzuvDbCxfd80lV7JUCq+IiIikj6uhcGX3WFTX5wTLrP0ibXULnWRHesaMX7heHK56qwNkrG01EFERETS3PZV68i953XqF/rFMVba7zF+Hv8mNjedVUmsoRlfERERSTOx0dGM7vUq93cL4YkP/bh8zRXcfeCRAGi3SKVXLKXiKyIiImni8K7dNKo8hreWuWCMjeMX8zPvQHfo8ytU6GJ1PBEtdRAREZG7Y+x2Fk6YzUtTLxId5w2Ai3MCk553Z/jMj8HZ2dqAIv+n4isiIiJ3LPSPUzzddSaf/+QNuABQoWgky/0fpnablpZmE/kvFV8RERG5I5uXrKDfC8Gci/R2jA185CozPhmPh5eXdcFEbkLFV0RERFIn/hrnPx9NxwF5uBafB4CCeaP5eEYV2j/T2+JwIjeng9tEREQk5UL3wbJ6FDk5i7ce3gJA25rh7Nv7jEqvZHqa8RUREZHbsickkPDzu7gEjYKEGACGNAuhZJMuPDZsHE7OmkuTzE/FV0RERG7p7NFj9Ov8HjV99jP1keull0LVcXpoGZ0KVrU2nEgqqPiKiIjITX0+35+nRhzg4hVvvrY1oW2FY7Ts3hGaToZc7lbHE0kVFV8RERFJ4kp4GMN7TWHB+jxAbgCKeF6FZlOhhS5GIVmTiq+IiIgksuerrfTos4Ej5/+5vHCH+hF8uGooBUv5WphM5O5oJbqIiIgAkBAXx9TBk2j44HZH6fVwjeWD8d58FvS2Sq9keZrxFRERES6c+J0uj8xl2wEf4PolhuuUDWf5ip6Ur1/X2nAiaUQzviIiIjndoZV4BTYkKvIqADabYXSveH74bbJKr2QrKr4iIiI5VUwkbOwL67vjEn+JZT3XUqlYGN+urMXkpW/gmju31QlF0pSWOoiIiORAQYEb8djzKjXyBTvGyjdtzf7xE3Dy8LEwmUj60YyviIhIDhIfG8trAybi99iPPDG/EVdjXcDVEx70h4c/VemVbE3FV0REJIc4HrKXZlVfZuJHNhLsThz8uxDv7+8MffZC5d5gs1kdUSRdaamDiIhINmfsdpa+NY/Br//J5ZjrM7rOTnYmPOXMS+8tBldXawOKZBAVXxERkWws7OxZBnadTsAOL8ANgHsKRfLJR21o2L6dteFEMpiKr4iISDa1LWAdvQfu5EyYl2Osf5vLzP50DJ4FCliYTMQaKr4iIiLZTUIsZwPH07aHC7EJeQHw8bjGgsn30uXFpywOJ2IdHdwmIiKSnVw6DMsbUez4VCY8sB2A+6uE8euefiq9kuNpxldERCQbMHY79pAPcP5+GMRHA/BKq134NniQnq/OwMnZ2eKEItZT8RUREcniQv84xdNdZ1LL+xcmPHC99OJTAeeHl9O7SG1rw4lkIiq+IiIiWdjmJSvo90Iw5yK9+dKpOQ+UP0ajDu2h+dvg4mF1PJFMRcVXREQkC7oWFcXofpOYtcYdyAOAj0cMl+u8Ba17WhtOJJNS8RUREcli9m3fQc9ea9h3xtsx1rZmOIvXDKFouXLWBRPJ5HRWBxERkSzCnpDA7BHTqNd6s6P0uuWKZ/YIDzb8PEOlV+Q2NOMrIiKSBVz84wQ9O85hc4g3N/7zXa1kBMuXd6KqXxNLs4lkFZrxFRERyeyOfk6edY3481yMY2ho5xh+Ovi6Sq9IKqj4ioiIZFZxV+DrgfB5R9zjz7O85xrKFohk85JKvLNqMu5581qdUCRL0VIHERGRTGjPV1vJs3sUFd1/doxV82vMkfGvkCtfYQuTiWRdmvEVERHJRBLi4pg6eBINH9zOE+/XISbeGXJ5QJuF8OhalV6Ru6DiKyIikkmcPnCQVjVGMmpuPPF2Z0L+Ksb7vz4OvX+B6gPAZrM6okiWpqUOIiIimUDAzIU8O/Y44dE+ANhshlE9Exj0wRLIndvidCLZg4qviIiIhSJDQ3mhx1SWbPEE3AHw9Yli6YKmNO/SwdpwItmMiq+IiIhFggI30mvAVo6HejrGuvlFMG/ly/gUK2phMpHsScVXREQko9nj+fOLN2nRKYHYhOul19MthrkTS9Lr5XHYnHQIjkh60E+WiIhIRgo/DiuaUeLoa4xo8QMAjcuHsXdXd3qPel6lVyQdacZXREQkAxi7HQ4sxfbNYIiLAmBi2+8pVac5T70xjVyurhYnFMn+VHxFRETSWdjZswzsOp163rsY0eJ66cWrHC4PLePZ4g2tDSeSg6j4ioiIpKNtAevoPXAnZ8K8+My5Fa3uO06tB9pByzng6nn7HYhImlHxFRERSQex0dGMHzCJaZ/mwpi8AOR1i+NcpUnQ7kmL04nkTCq+IiIiaezwrt306L6c4JPejrH7q4Thv/o5SlasYF0wkRxOh46KiIikEWO3s2DcTGr5fe4ovS7OCUwb4sKWvTNUekUsdlczvteuXcPd3T2tsoiIiGRZl86cpv/j7xC42xtwAaBC0UiW+z9M7TYtLc0mItelesbXbrfzxhtvUKJECfLmzcvx48cBGDduHB999FGaBxQREcn0TmzCbU1jDp2Mdww91/4qwYfGq/SKZCKpLr5vvvkmixcvZtq0abj+65yDVatW5cMPP0zTcCIiIpla/DX49iVY+yB54s+wrMdaintFEbigHO8HTsXDy8vqhCLyL6kuvv7+/nzwwQf07NkTZ2dnx3iNGjU4dOhQmoYTERHJrPZt38HxmS0geLZjrK5fdY4ff4n2z/S2LpiI3FSqi++ff/7Jvffem2TcbrcTFxeXJqFEREQyK3tCArNHTKNe6830nFuV+AQncHaD++fA4xtwy1/C6ogichOpLr6VK1fm+++/TzK+evVqatWqlSahREREMqOzR4/xYJ0RvDQjmpj4XPz4hy/z9j0KvX6G2kPAZrM6oojcQqrP6jB+/Hj69u3Ln3/+id1uZ+3atRw+fBh/f3++/PLL9MgoIiJiuc/n+/PUiANcvOLtGBvaOYan5y2FvHmtCyYiKZbqGd8OHTrwxRdfsGXLFvLkycP48eM5ePAgX3zxBW3atEmPjCIiIpa5Eh7GwEdepuNzJ7h4JTcAxbyusHlJJd5ZNRl3lV6RLMNmjDFWh8hIkZGReHl5ERERQb58+ayOIyIimdier7bSo88Gjpz/578XHRuEszBgGAVL+VqYTCR7S6++luoZ33LlynHx4sUk4+Hh4ZQrVy5NQomIiFjKnsDpL96i8UPbHKXXwzWWhRO8WfvDDJVekSwq1cX35MmTJCQkJBmPiYnhzz//TJNQIiIilok8Data4XtkNM833g1AnbLh/PL9YwyY+CI2p1T/p1NEMokUH9wWGBjo+P+bN2/G618n5U5ISGDr1q2UKVMmTcOJiIhkJHNwBbatz0FMOABTHtpKqRr1GDRtMq65c1sbTkTuWorX+Dr9/y9cm83Gf+/i4uJCmTJlmDFjBo888kjap0xDWuMrIiL/FRkaygs9plLf8zueb3J9lhdPX3hwKfg2tzacSA6UXn0txTO+drsdgLJly7J7924KFiyYZiFERESsEhS4kZ5PbeXEBU9W5nqA++89QaVmraD1fHD3tjqeiKShVJ/H98SJE+mRQ0REJEPFx8by5nOTeXOxIcHuCYCLs51jZd6g0sPP6mIUItlQqosvwJUrV9i+fTunTp0iNjY20W0vvPBCmgQTERFJL8dD9tKr6yKCfvcBrhfcxuXD+CTgScrWqG5tOBFJN6kuvr/88gsPPfQQV69e5cqVK+TPn58LFy7g4eFB4cKFVXxFRCTTMnY7/m/NY/DrfxEV4wOAs5Od8U86M2buNHK5ulqcUETSU6rPyTJ06FDat29PWFgYuXPn5scff+SPP/6gTp06vP322+mRUURE5K6FnztH9+Yj6Df2AlEx1wtuuUKX2bGuEeMXjlfpFckBUl18Q0JCGD58OE5OTjg7OxMTE4Ovry/Tpk1jzJgx6ZFRRETk7pzehm1FY3b99s9Zifq1uUzIwdE0bN/OulwikqFSXXxdXFwcpzYrXLgwp06dAsDLy4vTp0+nbToREZG7kRAL342CgJZ4JZxg6RNrKZj3KgGzSrLoq7fxLFDA6oQikoFSvca3Vq1a7N69m/vuu4/mzZszfvx4Lly4wNKlS6latWp6ZBQREUm1w7t2k+fHlygZ/4NjzK/5PZwcN5A8RctamExErJLqGd/JkydTrFgxACZNmoSPjw/PPfccoaGhLFiwIM0DioiIpIax21kwbia1/D6nz7v3YbfbwMkFmk2DLltUekVysBRfuS270JXbRESyr9A/TjGgy0wCd3s7xub13s3A6a9DkdrWBRORVEmvvpbqGd+bCQ4OzvSXKxYRkexr85IVVK/+XqLSO/CRq/R5d5lKr4gAqSy+mzdvZsSIEYwZM4bjx48DcOjQITp27Ei9evUclzVOjblz51KmTBnc3d1p0KABP/300y23Dw8PZ9CgQRQrVgw3NzfKly/Phg0bUv24IiKSPVyLimJo59G063eYc5F5ACiYN5rABeWY98VUPLy8LE4oIplFig9u++ijj3j66afJnz8/YWFhfPjhh7zzzjsMGTKEbt26sX//fipVqpSqB1+5ciXDhg1j/vz5NGjQgFmzZtG2bVsOHz5M4cKFk2wfGxtLmzZtKFy4MKtXr6ZEiRL88ccfeHt7p+pxRUQke9i3fQc9e61h3xlvx1jbmuEsXjOEouXKWRdMRDKlFK/xrV69Or1792bkyJGsWbOGLl260LBhQwICAihZsuQdPXiDBg2oV68e7733HgB2ux1fX1+GDBnCqFGjkmw/f/58pk+fzqFDh3Bxcbmjx9QaXxGRbMDY+WPDbCp0vERM/PU5HLdc8Ux7KR+D3xqOk3OareQTEQtYvsb32LFjdOnSBYDHH3+cXLlyMX369DsuvbGxsezZs4fWrVv/E8bJidatWxMUFJTsfQIDA2nUqBGDBg2iSJEiVK1alcmTJ5OQkHDTx4mJiSEyMjLRPxERycKizsLahyh9aBh96uwFoFrJCH7+ph0vTB+p0isiN5XipQ7R0dF4eHgAYLPZcHNzc5zW7E5cuHCBhIQEihQpkmi8SJEiHDp0KNn7HD9+nG+++YaePXuyYcMGjh49yvPPP09cXBwTJkxI9j5Tpkzhtddeu+OcIiKSiRz9HL4aANEXAJj56CZKV63C8Fmv4543r8XhRCSzS9UFLD788EPy/v8XS3x8PIsXL6ZgwYKJtnnhhRfSLt1/2O12ChcuzAcffICzszN16tThzz//ZPr06TctvqNHj2bYsGGOryMjI/H19U23jCIikvauhIcxvNcUGub9mn71rpde8hQjT6fFjC3zgLXhRCTLSHHxLVWqFAsXLnR8XbRoUZYuXZpoG5vNluLiW7BgQZydnTl//nyi8fPnz1O0aNFk71OsWDFcXFxwdnZ2jFWqVIlz584RGxuLq6trkvu4ubnh5uaWokwiIpL57PlqKz37rufwOS+WuT2IX9k/uKdhc2izEDwK3n4HIiL/l+Lie/LkyTR9YFdXV+rUqcPWrVvp2LEjcH1Gd+vWrQwePDjZ+zRp0oTly5djt9txcrq+huvIkSMUK1Ys2dIrIiJZV0JcHG8Pncar82KIt18/JZnd2Nhf9DXueXQw2GwWJxSRrMbSIwCGDRvGwoULWbJkCQcPHuS5557jypUr9O/fH4A+ffowevRox/bPPfccly5d4sUXX+TIkSOsX7+eyZMnM2jQIKu+BRERSQenDxykVY2RjJobT7z9+qd8dcqG88v3j9HhxSEqvSJyR1K1xjetdevWjdDQUMaPH8+5c+eoWbMmmzZtchzwdurUKcfMLoCvry+bN29m6NChVK9enRIlSvDiiy/yyiuvWPUtiIhIGguYuZBnxx4nPNoHAJvNMKpnAhM/mIxr7twWpxORrCzF5/HNLnQeXxGRzOnyhQsMeeItlmzxdIz5+kSxdEFTmnfpYGEyEclolp/HV0REJN38FUTMUj+++tdV67v5RbD3t+EqvSKSZlR8RUTEOvZ4+GEirPCjoP0QS7p/Rj73GPynFOLTbW/jUyz5s/yIiNyJO1rje+zYMRYtWsSxY8eYPXs2hQsXZuPGjZQqVYoqVaqkdUYREcmGjofsJU/QixS5ut0x1qZFUf4Y0xfv0hUsTCYi2VWqZ3y3b99OtWrV2LVrF2vXriUqKgqAvXv33vQiEiIiIjcYu50lk+dSo2EAT84pgTGAzRkavw7dtqv0iki6SXXxHTVqFG+++SZff/11onPntmzZkh9//DFNw4mISPYSdvYs3ZuPoN/YC0TFuLLhUHkW7X8Auu+ARuPAydKTDYlINpfq3zD79u1j+fLlScYLFy7MhQsX0iSUiIhkP9sC1tF74E7OhHk5xvq1uUyXGcuhQAELk4lITpHqGV9vb2/Onj2bZPyXX36hRIkSaRJKRESyj9joaEb1fJWW3UM4E5YXAB+PawTMKsmir97GU6VXRDJIqotv9+7deeWVVzh37hw2mw273c7OnTsZMWIEffr0SY+MIiKSRR36cTeNKo9h6nIXjLl+tbX7q4Tx655+dHnxKYvTiUhOk+riO3nyZCpWrIivry9RUVFUrlyZZs2a0bhxY1599dX0yCgiIlmNMRxfP5fazT4n+KQ3AC7OCUwb4sKWvTMoWVEHsIlIxrvjK7edOnWK/fv3ExUVRa1atbjvvvvSOlu60JXbRETS2dVQ+GoAHAuk1/LHWRZcnQpFI1nu/zC127S0Op2IZAHp1ddSfXDbjh07aNq0KaVKlaJUqVJpFkRERLKBE5tgc3+4cg6AuY+tp3SFcoydNx4PL6/b3FlEJH2luvi2bNmSEiVK8MQTT9CrVy8qV66cHrlERCQLuRYVxeh+k2jsEUiXGtdLL7kL4tXxYybd097acCIi/5fqNb5//fUXw4cPZ/v27VStWpWaNWsyffp0zpw5kx75REQkk9u3fQf1K41j1hp3nlndntPh+aBMO+i7D1R6RSQTSXXxLViwIIMHD2bnzp0cO3aMLl26sGTJEsqUKUPLllq7JSKSU9gTEpg9Yhr1Wm9m3xlvAKLjXPjZazw8vgHyFLU2oIjIf9zxwW03JCQksHHjRsaNG8evv/5KQkJCWmVLFzq4TUTk7p09eoz+Xd5jc4i3Y6xayQiWL+9EVb8m1gUTkWwhvfpaqmd8b9i5cyfPP/88xYoVo0ePHlStWpX169enWTAREcmcPp/vT/VaCxOV3qGdY/jp4OsqvSKSqaX64LbRo0ezYsUK/vrrL9q0acPs2bPp0KEDHh4e6ZFPREQyiSvhYQzvNYUF6/MAuQEo5nWFxXPq8kCfrtaGExFJgVQX3++++46RI0fStWtXChYsmB6ZREQkszm/h8hlT7JmW1vHUMcG4SwMGEbBUr4WBhMRSblUF9+dO3emRw4REcmM7Anw89uw81WKmXg+7BJDj+WdmD26ME+NH4fN6Y5XzImIZLgUFd/AwEAefPBBXFxcCAwMvOW2jz76aJoEExERa50+cJA8QUPIH77VMdahVV5OjOpG4QrVLUwmInJnUnRWBycnJ86dO0fhwoVxusVf9zabTWd1EBHJBgJmLuTZscdpfd8xAnqvwmazQf1R0HgiOLtaHU9EsjlLL1lst9uT/f8iIpK9RIaG8kKPqSzZ4gm4s/rXKiw/GErPiRPBt7nV8URE7kqqF2f5+/sTExOTZDw2NhZ/f/80CSUiIhkvKHAjNSvfKL3XdfOL4KE3P1XpFZFsIdUXsHB2dubs2bMULlw40fjFixcpXLiwljqIiGQx8bGxTHp+Mm8sMiTYr8+HeLrFMHdiSXq9PFAHsIlIhrN0qcO/GWOur/X6jzNnzuDl5ZUmoUREJGMcD9lLr66LCPrdB7j+u71x+TA+CXiSsjV0AJuIZC8pLr61atXCZrNhs9lo1aoVuXL9c9eEhAROnDhBu3bt0iWkiIikMWM4uuljaj92jMsxPgA4O9kZ/6QzY+ZOI5erDmATkewnxcW3Y8eOAISEhNC2bVvy5s3ruM3V1ZUyZcrQqVOnNA8oIiJp7FoYfD2Qew4H0Oq+bqzbX4lyhS6z7KPWNGyvCQwRyb5SXHwnTJgAQJkyZejWrRvu7u7pFkpERNLJ6W2woTdEncFmg4VdvqD0PSV446MxeBYoYHU6EZF0leqD27I6HdwmIjlRbHQ04wdMws99FQ9XPnJ90N0HWi+ACl2sDSci8h+WHtyWP39+jhw5QsGCBfHx8Un24LYbLl26lGbhRETk7h3etZse3ZcTfNKbRXkf5dfh8yhSuT486A+eJa2OJyKSYVJUfGfOnImnp6fj/9+q+IqISOZg7HY+mDCboVMvEh3nDUBYdG52eozl8S6vgE2nKRORnEVLHUREsqHQP04xoMtMAnd7O8YqFI1kuf/D1G7T0rpgIiIpkF59LdV/7gcHB7Nv3z7H159//jkdO3ZkzJgxxMbGplkwERG5M5uXrKB69fcSld7n2l8l+NB4lV4RydFSXXyfffZZjhy5fmDE8ePH6datGx4eHqxatYqXX345zQOKiEjKXIuKYmjn0bTrd5hzkXkAKJg3msAF5Xg/cCoeusiQiORwqS6+R44coWbNmgCsWrWK5s2bs3z5chYvXsyaNWvSOp+IiKRE6D7+/qAli9b/M9SuZjj79j5D+2d6W5dLRCQTSXXxNcZgt9sB2LJlCw899BAAvr6+XLhwIW3TiYjIrRk7BM+GZfUoZXYzr9OXuOWKZ85IDzbsmUHRcuWsTigikmmk+AIWN9StW5c333yT1q1bs337dubNmwfAiRMnKFKkSJoHFBGR5J09eow8QS+Q7+8NjrEnHnCi6cj2+Naob2EyEZHMKdUzvrNmzSI4OJjBgwczduxY7r33XgBWr15N48aN0zygiIgk9fl8f6rXWsgLc1z/GawzFHrsUukVEbmJNDud2bVr13B2dsbFxSUtdpdudDozEcnKroSHMbzXFBasz+MYW/30V3QaMwHKPGBhMhGRtGPplduSs2fPHg4ePAhA5cqVqV27dpqFEhGRpPZ8tZUefTZw5Pw//xHo2CCc5q+ugFK+FiYTEckaUl18//77b7p168b27dvx9vYGIDw8nPvvv58VK1ZQqFChtM4oIpKjJcTF8fbQabw6L4Z4+/XS6+Eay+zRhXlq/DhsTroCm4hISqT6t+WQIUOIiorit99+49KlS1y6dIn9+/cTGRnJCy+8kB4ZRURyrNMHDtKqxkhGzY0n3u4MQJ2y4fzy/WMMmPiiSq+ISCqkeo2vl5cXW7ZsoV69eonGf/rpJx544AHCw8PTMl+a0xpfEckqjmxcSoNOBwiPdgfAZjOM6pnAxA/G4Jo7t8XpRETST6a5ZLHdbk/2ADYXFxfH+X1FROQuxETCxr7cu78vDUqdAcDXJ4pvV9Zi8tI3VHpFRO5Qqotvy5YtefHFF/nrr78cY3/++SdDhw6lVatWaRpORCTH+SsIltaEA/44ORkWdVvHM23D2PvbcJp36WB1OhGRLC3VSx1Onz7No48+ym+//Yavr69jrGrVqgQGBlKyZMl0CZpWtNRBRDKj+NhYJj0/GT+XT2h577Hrg66e0GouVOoFNpu1AUVEMlCmOZ2Zr68vwcHBbN261XE6s0qVKtG6des0CyUikpMcD9lLr66LCPrdhxJeHfh1+Dzy31sLHvoEvMpaHU9EJNtIVfFduXIlgYGBxMbG0qpVK4YMGZJeuUREsj1jt7P0rXkMfv1PLsf4AHDucl6+dR5Fp25jwOmOT7UuIiLJSPFv1Xnz5jFo0CDuu+8+cufOzdq1azl27BjTp09Pz3wiItlS2NmzDOw6nYAdXoAbAOUKXWbZR61p2L6dteFERLKpFB/c9t577zFhwgQOHz5MSEgIS5Ys4f3330/PbCIi2dK2gHVUr/LO/0vvdf3aXCbk4GiVXhGRdJTi4nv8+HH69u3r+LpHjx7Ex8dz9uzZdAkmIpLdxEZHM7rXq7TsHsKZsLwAeOe+RsCskiz66m08CxSwOKGISPaW4qUOMTEx5MmTx/G1k5MTrq6uREdHp0swEZFs5dJhziwawLurmmHM9TM0tKgcjv/qZ/GtVNHicCIiOUOqjpwYN24cHh4ejq9jY2OZNGkSXl7/fFz3zjvvpF06EZGszhj49QPYNpRyRDO7wxWeW/sIk553Z/jMt3FydrY6oYhIjpHi8/i2aNEC223OI2mz2fjmm2/SJFh60Xl8RSSjXDh1Co+gF/A487ljzHhX4FiVhdzb0M/CZCIimZvl5/Hdtm1bmj2oiEh2t3nJCvq9EMzjVeOY+/j/B2s8h63529zr4nHL+4qISPpI9SWLRUTk5q5FRTG082ja9TvMucg8vP9DfdYfqwMdA6H1+6DSKyJiGZ0dXUQkjezbvoOevdaw74y3Y6xdzXDqjAiAcuWsCyYiIoBmfEVE7po9IYHZI6ZRr/VmR+l1yxXPnJEebNgzg6IqvSIimYJmfEVE7sLZo8fo3+U9Nod4c+NXarWSESxf3omqfk0szSYiIomp+IqI3KHDX62kaadfuBDl7Rgb2jmGyYtexz1vXuuCiYhIsu5oqcP3339Pr169aNSoEX/++ScAS5cuZceOHWkaTkQkU4q7Al8P5N69T1C58HkAinldYfOSSryzarJKr4hIJpXq4rtmzRratm1L7ty5+eWXX4iJiQEgIiKCyZMnp3lAEZFM5fweWFobfl2As5Nh6ROf0bvFJX79dQgP9OlqdToREbmFVBffN998k/nz57Nw4UJcXFwc402aNCE4ODhNw4mIZBYJcXFMHTyJHyZ1hrAj1wdzeVCq69v4fzOLgqV8rQ0oIiK3leo1vocPH6ZZs2ZJxr28vAgPD0+LTCIimcrpAwfp3XkB2w/6UDZ/B0KGzSdf6arw0HLIX97qeCIikkKpnvEtWrQoR48eTTK+Y8cOyumUPSKSzQTMXEj1uv5sP+gDwMkwb76yj4AnflDpFRHJYlI94/v000/z4osv8vHHH2Oz2fjrr78ICgpixIgRjBs3Lj0yiohkuMjQUF7oMZUlWzwBdwB8faJYuqApzbt0sDaciIjckVQX31GjRmG322nVqhVXr16lWbNmuLm5MWLECIYMGZIeGUVEMlRQ4EZ6DdjK8VBPx1g3vwjmrXwZn2JFLUwmIiJ3w2aMMXdyx9jYWI4ePUpUVBSVK1cmbxY5fU9kZCReXl5ERESQL18+q+OISCYSHxvLpOcn88YiQ4L9+kowT7cY5k4sSa+XB2Jz0sUuRUQyQnr1tTu+gIWrqyuVK1dOsyAiIpYKP86xD59hypJGJNiv/2psXD6MTwKepGyN6haHExGRtJDq4nv//fdjs9luevs333xzV4FERDKUMXBgKWwdRAVbFNMejmLYF20Z/6QzY+ZOI5erq9UJRUQkjaS6+NasWTPR13FxcYSEhLB//3769u2bVrlERNJd2NmzeAQNxe3ESsfYkIdDaflCU6q2bGNhMhERSQ+pLr4zZ85MdnzixIlERUXddSARkYywLWAdvQfupHv1S0xv///BKv2wtZxDVVfPW95XRESypjQ7UqNXr158/PHHabU7EZF0ERsdzeher9KyewhnwvLy9vYmbP2jOjwSAO0WgUqviEi2dccHt/1XUFAQ7u7uabU7EZE0d3jXbnp0X07wSW/H2P1VwqjwfABUqGBdMBERyRCpLr6PP/54oq+NMZw9e5aff/5ZF7AQkUzJ2O18MGE2Q6deJDrOGwAX5wQmPe/O8JkzcHJ2tjagiIhkiFQXXy8vr0RfOzk5UaFCBV5//XUeeOCBNAsmIpIWQv84xYAuMwnc7Q24AFChaCTL/R+mdpuWlmYTEZGMlarim5CQQP/+/alWrRo+Pj7plUlEJE0c3voZLR4P4lykt2PsufZXeXvpeDz+80e8iIhkf6k6uM3Z2ZkHHniA8PDwNA0xd+5cypQpg7u7Ow0aNOCnn35K0f1WrFiBzWajY8eOaZpHRLK4+Gvw7UuU29MZX69wAArmjSZwQTneD5yq0isikkOl+qwOVatW5fjx42kWYOXKlQwbNowJEyYQHBxMjRo1aNu2LX///fct73fy5ElGjBiBn59fmmURkWwgdB8sqwfBs3FxtrOsxxoeb3CRfXufof0zva1OJyIiFrIZY0xq7rBp0yZGjx7NG2+8QZ06dciTJ0+i21N7PeUGDRpQr1493nvvPQDsdju+vr4MGTKEUaNGJXufhIQEmjVrxpNPPsn3339PeHg469atS9Hjpde1n0XEWvaEBN4bNQM/M5daxU5dH3R2g2bTodZguMUVJ0VEJHNJr76W4hnf119/nStXrvDQQw+xd+9eHn30UUqWLImPjw8+Pj54e3unet1vbGwse/bsoXXr1v8EcnKidevWBAUF3TJL4cKFeeqpp277GDExMURGRib6JyLZy9mjx3io7ghefDuaHkvbczXWBQpVh14/Q+0hKr0iIgKk4uC21157jYEDB/Ltt9+m2YNfuHCBhIQEihQpkmi8SJEiHDp0KNn77Nixg48++oiQkJAUPcaUKVN47bXX7jaqiGRSn8/3Z8DIA1yI8gbg0N+F2Bj7Ep16vA65dG5xERH5R4qL740VEc2bN0+3MLdz+fJlevfuzcKFCylYsGCK7jN69GiGDRvm+DoyMhJfX9/0iigiGeRKeBjDe01hwfo8QG4AinldYfGcujzQp6u14UREJFNK1enMbGn8cWHBggVxdnbm/PnzicbPnz9P0aJFk2x/7NgxTp48Sfv27R1jdrsdgFy5cnH48GHuueeeRPdxc3PDzc0tTXOLiLX2fLWVHn02cOT8P+u+OjYIZ2HAMAqW0h+2IiKSvFQV3/Lly9+2/F66dCnF+3N1daVOnTps3brVcUoyu93O1q1bGTx4cJLtK1asyL59+xKNvfrqq1y+fJnZs2drJlckm0uIi2P6S9MYNz+GePv10uvhGsusUYUYMGEcNqdUn6hGRERykFQV39deey3Jldvu1rBhw+jbty9169alfv36zJo1iytXrtC/f38A+vTpQ4kSJZgyZQru7u5UrVo10f29vb0BkoyLSDYTeZpDHzzLuPl1ibdfv8RwnbLhLF/Rk/L161ocTkREsoJUFd/u3btTuHDhNA3QrVs3QkNDGT9+POfOnaNmzZps2rTJccDbqVOncNIsjkjOdmglbBlIFVs4b7S7zJiNrRjVM4GJH0zGNXduq9OJiEgWkeLz+Do7O3P27Nk0L74ZTefxFck6Ll+4QO4fh5PrsL9jLCFPKX4p+R51H2l/i3uKiEhWZvl5fFN5nQsRkbsSFLiRmpXf4s05p/4ZrNAN5357VXpFROSOpHipw42zJ4iIpKf42FgmPT+ZNxYZEuyevLGlGQ9UOUvjp8dCpV66GIWIiNyxVK3xFRFJT8dD9tKr6yKCfvcBrhfchvdFUKxfAFSubm04ERHJ8nTUmIhYztjt+E+eS82GK/9fesHZyc5rA2xs3zeNsjVUekVE5O5pxldELBV29izPdZvOyu+9gOsXmylX6DLLPmpNw/btrA0nIiLZioqviFjm8LYNtHn8W06H/XN+8H5tLjPn09F4FihgYTIREcmOtNRBRDJeQix8N4rSP3bA2/0KAD4e1wiYVZJFX72t0isiIulCM74ikrEuHYb1PeDvYNxdYHmPNbzyTTcWLB9EyYoVrE4nIiLZmIqviGQIY7ezcMJsmsa/Q+WCZ64POrlQtdsI1k8fDjZ9ACUiIulL/6URkXQX+scpOjYczrNvRtLD/xFi4p3BpwL0+BHqjVTpFRGRDKH/2ohIutq8ZAXVq79H4G5vAPb+VZQvrw6B3sFQpLa14UREJEfRUgcRSRfXoqIY1XcSs9e6A3kAKJg3mo9nVKH9M72tDSciIjmSiq+IpLl923fQo9da9p/55zRlbWuGs3jNEIqWK2dhMhERycm01EFE0ow9IYHZI6ZRr/VmR+l1yxXP7BEebPh5hkqviIhYSjO+IpI2os6yb8Eghr1TDbu5/qulWskIli/vRFW/JhaHExER0YyviKSFo5+Df3Vq8BljWn0PwNDOMfx08HWVXhERyTQ04ysid+xqRDjuP47Caf8Cx9j4jkd44Onn8eva2cJkIiIiSWnGV0TuyJ6vtlKrwhvMeHffP4P3dsSlf4hKr4iIZEoqviKSKglxcUwdPImGD27nyPl8jN3UiuCzZaHNB/DoWvAoaHVEERGRZGmpg4ik2OkDB+ndeQHbD/oAzgBUL3WZvF0DoHpda8OJiIjchoqviKRIwMyFPDv2OOHRPgDYbIZRPROY+MFkXHPntjidiIjI7an4isgtRYaG8kKPqSzZ4gm4A+DrE8XSBU1p3qWDteFERERSQcVXRG7q8HdbeKjzJo6HejrGuvlFMD/gFbyLFrEwmYiISOrp4DYRScoeDz9MpOTO9uQiBgBPtxj8pxTi021vq/SKiEiWpBlfEUks/Dhs6AVng8jjCst7rmHEV134ePlTlK1R3ep0IiIid0zFV0QAMHY7S9+aR5NrU7jH68/rgzZn6nR9jm/eHoXN2cXagCIiIndJSx1EhLCzZ+nefAR9x16g55IHiUtwAq9y0H0HNBqn0isiItmCiq9IDrctYB3Vq7xDwA4vAHadKsmXlwdCnxAo3tDacCIiImlIxVckh4qNjmZUz1dp2T2EM2F5AfDxuMaqWSV57I254Op5mz2IiIhkLVrjK5IDHd61mx7dlxN80tsxdn+VMPxXP0fJihWsCyYiIpKONOMrkoMYu50F42ZSy+9zR+l1cU5g2hAXtuydodIrIiLZmmZ8RXKKq6H8Mm8IA9+sBFw/WK1C0UiW+z9M7TYtrc0mIiKSATTjK5ITnNgE/tWpzUqGNfsBgOfaXyX40HiVXhERyTE04yuSjcVcicJ116vYfpntGJv8eAjtnuxDm749LUwmIiKS8TTjK5JN7du+g7oVxzPvvR/+GSzTDrcBe1V6RUQkR1LxFclm7AkJzB4xjXqtN7P/jBfDv3iAA6HF4f458PgGyFPU6ogiIiKW0FIHkWzk7NFj9O/yHptDvLnx431f0avQfhXUbmxpNhEREaup+IpkE5/P92fAyANciPJ2jA3tHMPkRa/jnjevdcFEREQyCRVfkSzuSngYw3tNYcH6PEBuAIp5XWHxnLo80KerteFEREQyERVfkSzsyA/baf94IEfO53OMdWwQzsKAYRQs5WthMhERkcxHB7eJZEX2BPhpKkW2PUxsbAIAHq6xLJzgxdofZqj0ioiIJEMzviJZTeRp2NgbzmzHyw0+6bGW4Zsex395L8rXr2t1OhERkUxLxVckC1k1ayENL7+Or8eZ/4/YaNKtF0GzJmDL5WZpNhERkcxOSx1EsoDI0FD6tRlB16F/0WdJKxLsNvD0ha7fgt9klV4REZEUUPEVyeSCAjdSq8pUlmzxBGDbsbJ8GfEk9PkVfJtbnE5ERCTr0FIHkUwqPjaWSc9P5o1FhgT79dLr6RbD3IklefTlceCkv1tFRERSQ8VXJBM6HrKXXl0XEfS7D2ADoHH5MD4JeJKyNapbG05ERCSL0pSRSCZi7Hb8J8+lZsOV/y+94Oxk57UBNrbvm6bSKyIichc04yuSWVwL4+f3X6Lv2HLA9YPVyhW6zLKPWtOwfTtrs4mIiGQDmvEVyQxOb4Ml1aln/Hm24c8A9GtzmZCDo1V6RURE0ohmfEUsFHctmly7XsP28zTAADCjyy4e6vMYjz73pLXhREREshnN+IpY5PCu3TSsNIYl8zZzo/Tiez95ng5W6RUREUkHKr4iGczY7SwYN5Nafp8TfNKbIese5OjFwtBsGnTZAp4lrY4oIiKSLWmpg0gGCv3jFAO6zCRwtzfgAkAJnxiiH1gJ9VpYGU1ERCTbU/EVySCbl6yg3wvBnIv0dowNfOQqMz4Zj4eXl3XBREREcggVX5F0di0qitH9JjFrjTuQB4CCeaP5eEYV2j/T29pwIiIiOYiKr0g6OroriMc7B7DvjLdjrF3NcBatGULRcuWsCyYiIpID6eA2kfRg7BA8G58tD3Ex8vqPmVuueOaM9GDDnhkqvSIiIhbQjK9IWos6C5v7w8nNFHCHxd3XMXLTo3zySWeq+jWxOp2IiEiOpeIrkoa+WOBPvUvjKOp6yjHW5okO7JnzJs5uHhYmExERES11EEkDV8LDGPjIyzw68ARP+vthDJCnGHTaDC3eUekVERHJBFR8Re7Snq+2UrvimyxYf/2MDRsP3ceXYX2gz69Q5gGL04mIiMgNWuogcocS4uJ4e+g0Xp0XQ7w9HwAerrHMHl2IR8YvAif9XSkiIpKZqPiK3IHTBw7Su/MCth/0AZwBqFM2nOUrelK+fl1rw4mIiEiyVHxFUmnlOwsZ+OpxwqN9ALDZDKN6JjDxg8m45s5tcToRERG5GRVfkZSKieTH90fQfXgJwB0AX58oli5oSvMuHazNJiIiIrelRYgiKfFXECytSUP7QnrX2QtAN78I9v42XKVXREQki9CMr8gt2OPjcPppEvz4JpgEAN7r+h0PP9GGrkPHYdMBbCIiIlmGiq/ITRwP2Uuvrot4qcEWuta8Xnop3ph8D31CN6+y1oYTERGRVFPxFfkPY7ez9K15DH79Ty7H+HDwzCM0KvMXvg8NhQajwUk/NiIiIlmR/gsu8i9hZ88ysOt0AnZ4AW4A5M8bz0W/Ffg20sUoREREsjIVX5H/2xawjt4Dd3ImzMsx1q/NZeZ8OhrPAgUsTCYiIiJpQcVXcrzY6GjGD5jEtE9zYUxeALxzX+ODKffS5cWnLE4nIiIiaUXFV3K048F76NLpE4JPejvGWlQOx3/1s/hWqmhdMBEREUlzOheT5EzGwN4F5P6yHadCXQBwcU5g2hAXtv76tkqviIhINqTiKznP1VD4vCNsGUixPBf4qGsgFYtF8OPGFoycMwYnZ2erE4qIiEg60FIHyVG2LF1JrfOvUMD2h2Ps0R4tefC913Dx8LQwmYiIiKS3TDHjO3fuXMqUKYO7uzsNGjTgp59+uum2CxcuxM/PDx8fH3x8fGjduvUttxcBuBYVxdDOo2nT5xDP+jfAGCB3QegYCK3fV+kVERHJASwvvitXrmTYsGFMmDCB4OBgatSoQdu2bfn777+T3X7btm088cQTfPvttwQFBeHr68sDDzzAn3/+mcHJJavYt30H9SuNY9YadwDW7KvMprDu0Hcf3NPe4nQiIiKSUWzGGGNlgAYNGlCvXj3ee+89AOx2O76+vgwZMoRRo0bd9v4JCQn4+Pjw3nvv0adPn9tuHxkZiZeXFxEREeTLl++u80vmZU9I4N1XZvDK7MvExF9f1eOWK57pQ/Mx+K3h2Jws/7tPREREkpFefc3SNb6xsbHs2bOH0aNHO8acnJxo3bo1QUFBKdrH1atXiYuLI3/+/MneHhMTQ0xMjOPryMjIuwstWcLZo8fo3+U9Nod4c+NtXq1kBMuXd6KqXxNLs4mIiIg1LJ3yunDhAgkJCRQpUiTReJEiRTh37lyK9vHKK69QvHhxWrduneztU6ZMwcvLy/HP19f3rnNL5ha4wJ/qtRb+v/ReN7RzDD8dfF2lV0REJAfL0p/1vvXWW6xYsYLPPvsMd3f3ZLcZPXo0ERERjn+nT5/O4JSSYeKusHPWEDoMPMGFqNwAFM13hc1LKvHOqsm4581rcUARERGxkqVLHQoWLIizszPnz59PNH7+/HmKFi16y/u+/fbbvPXWW2zZsoXq1avfdDs3Nzfc3NzSJK9kYuf3wPoeNI4/wmNVu/HZ/kp0qB/Bh6uGUrCUZvlFRETE4hlfV1dX6tSpw9atWx1jdrudrVu30qhRo5veb9q0abzxxhts2rSJunXrZkRUyaRMQjz8NBWWN4SwI9hssPCJr1n0ujefBb2t0isiIiIOll/AYtiwYfTt25e6detSv359Zs2axZUrV+jfvz8Affr0oUSJEkyZMgWAqVOnMn78eJYvX06ZMmUca4Hz5s1LXn2UnaOcPnCQPl0WMLzhRh6pHH99sEgdCjy0nH75y1sbTkRERDIdy4tvt27dCA0NZfz48Zw7d46aNWuyadMmxwFvp06dwulfp52aN28esbGxdO7cOdF+JkyYwMSJEzMyulgoYOZCnh17nPBoH377owO/Dp9H0dZDoPFEcHa1Op6IiIhkQpafxzej6Ty+WVtkaCgv9JjKki3/XGnN1yeKdZ80p/ZDj1iYTERERNJKtjyPr0hqBAVupOdTWzlx4Z/S280vgnkrX8an2K0PhhQRERHJ0qczk5whPjaWiU9NxO+xHx2l19MtBv8phfh029sqvSIiIpIimvGVTO3kr7/So/PHBP3uA9gAaFw+jE8CnqRsjZufxk5ERETkvzTjK5mTMfCbP05r23Hg9PWLUTg72XltgI3t+6ap9IqIiEiqqfhK5nMtDL7sDpv6UsrzLPM7fUm5QpfZsa4R4xeOJ5erztogIiIiqaelDpKpfL96HTXODCNfwgnHWPdeden4/ljc8/lYmExERESyOs34SqYQGx3NqJ6v0rxrCEM+qXF90N0HHgmAdotUekVEROSuqfiK5Q7v2k2jymOYutwFY2z476nJV5cehz6/QoUuVscTERGRbEJLHcQyxm7ngwmzGTr1ItFx3gC4OCcw6Xl3Wr8eAM7O1gYUERGRbEXFVywR+scpBnSZSeBub8AFgApFI1nu/zC127S0NJuIiIhkTyq+kuE2L1lBvxeCORfp7Rh7rv1V3l46Hg8vL+uCiYiISLam4isZJ/4a3899lXYveQJ5ACiYN5qPZ1Sh/TO9rc0mIiIi2Z4ObpOMEboPltWjadwM2lX4HYB2NcPZt/cZlV4RERHJEJrxlfRl7PDLu/DdK5AQg80Gi3ps5LPoGgycNA6bk/72EhERkYyh4ivp5tyxYzzV9V2G1Quk1X0x1wcLVado32U8V7CqteFEREQkx9F0m6SLwAX+VKu5kA3BPvRd8RgXr+SGOkOhxy5Q6RURERELaMZX0tSV8DCG95rCgvV5gNwA2HHmZK1PKNDicWvDiYiISI6m4itpZs9XW+nZdz2Hz/1zSrKODcJZGDCMgqV8LUwmIiIioqUOkgYS4uKYOngSDR/c7ii9Hq6xLJzgxdofZqj0ioiISKagGV+5K2cOHqJ35/lsO+ADXL/EcJ2y4Sxf0ZPy9etaG05ERETkXzTjK3fu0EqiP32Q3UevX4zCZjOM7hXPD79NVukVERGRTEfFV1IvJhI29oX13bnP6yRzOm7E1yeKb1fWYvLSN3DNndvqhCIiIiJJaKmDpMpP6zdR9eQQPK4ddYz171WernNHkrdgYQuTiYiIiNyaZnwlReJjY3ltwEQaPxrEiGUVrg+6esKD/tge+VSlV0RERDI9zfjKbR0P2UuvrosI+t0HsDEvqB5dWsL9I98Fr7JWxxMRERFJERVfuSljt7P0rXkMfv1PLsf4AODsZGf8k874jV8Lrq4WJxQRERFJORVfSVbY2bM81206K7/3AtwAKFfoMss+ak3D9u2sDSciIiJyB1R8JYntq9bR+9mdnA775wps/dpcZs6no/EsUMDCZCIiIiJ3TsVX/pEQy/a5E7n/JVeMyQuAj8c1Fky+ly4vPmVxOBEREZG7o7M6yHWXDsPyRjSNeYtmZf8A4P4qYfy6p59Kr4iIiGQLmvHN6YyBXz+AbUMhPhpnJ1jaM5BVkcN5acYMnJydrU4oIiIikiZUfHOw0D9OMbD7Owyrs5YmZaOvD/pUwLfXcoYVqW1tOBEREZE0pqUOOdTmJSuoXv091v7oQ6/ljxN5zQ1qPAe9g0GlV0RERLIhzfjmMNeiohjVdxKz17oDeQCIinPnSJXF1G3d3dpwIiIiIulIxTcH2bd9Bz16rWX/mX9OU9auZjiL1gyhaLlyFiYTERERSX9a6pAD2BMSmD1iGvVab3aUXrdc8cwZ6cGGPTNUekVERCRH0IxvNnf26DH6d3mPzSHe3Hi5q5WMYPnyTlT1a2JpNhEREZGMpBnf7Ozo51xa/BDb9ud1DA3tHMNPB19X6RUREZEcR8U3O4q7Al8PhM87UsXnCNMf+Yqi+a6weUkl3lk1Gfe8eW+/DxEREZFsRksdspm933xLxd+fxy3qkGNscN/i9HrvBXxKlLQwmYiIiIi1NOObTSTExTF18CTqtvmWsZ+Wuj6YywPafICtw1qVXhEREcnxNOObDZw+cJDenRew/aAP4MyM7Y3p6GdoOvQ9yF/e6ngiIiIimYKKbxYXMHMhz449Tni0DwA2m2FUzwTqj/kccue2OJ2IiIhI5qHim0VFhobyQo+pLNniCbgD4OsTxdIFTWnepYO14UREREQyIRXfLCgocCO9BmzleKinY6ybXwTzVr6MT7GiFiYTERERybxUfLMSezzb3n+T1i8aEuzXS6+nWwxzJ5ak18vjsDnpWEURERGRm1FTyirCj8OKZjS5+gZ1SvwFQOPyYezd1Z3eo55X6RURERG5Dc34ZnbGwIGlsHUQxEXh4gzLeq1j5cUhvPLuNHK5ulqdUERERCRLUPHNxMLOnmXwE9MZVmsFdUpGXR/0Kse9TyxjbPGG1oYTEcnmjDHEx8eTkJBgdRSRbMnFxQVnZ+cMfUwV30xqW8A6eg/cyZkwL/Yc6ETw0AV41OoJLeeAq+ftdyAiIncsNjaWs2fPcvXqVaujiGRbNpuNkiVLkjdv3gx7TBXfTCY2OprxAyYx7dNcGHP9jfD3lbz8dt+H1GvXx+J0IiLZn91u58SJEzg7O1O8eHFcXV2x2WxWxxLJVowxhIaGcubMGe67774Mm/lV8c1EDu/aTY/uywk+6e0Yu79KGP6rn6NkxQrWBRMRyUFiY2Ox2+34+vri4eFhdRyRbKtQoUKcPHmSuLi4DCu+OhVAJmDsdhaMm0ktv88dpdfFOYFpQ1zYsneGSq+IiAWcdLYckXRlxScpmvG1WOgfpxjQZSaBu70BFwAqFI1kuf/D1G7T0tJsIiIiItmJ/py10olNnF7wMBv2/HOw2nPtrxJ8aLxKr4iIiEgaU/G1Qvw1+PYlWPsgtQvs581231AwbzSBC8rxfuBUPLy8rE4oIiKSoxw+fJiiRYty+fJlq6NkC7GxsZQpU4aff/7Z6iiJqPhmsEM//EDckvoQPNsxNqKvJ7/9+iztn+ltYTIREcnq+vXrh81mw2az4eLiQtmyZXn55Ze5du1akm2//PJLmjdvjqenJx4eHtSrV4/Fixcnu981a9bQokULvLy8yJs3L9WrV+f111/n0qVL6fwdZZzRo0czZMgQPD2TnjK0YsWKuLm5ce7cuSS3lSlThlmzZiUZnzhxIjVr1kw0du7cOYYMGUK5cuVwc3PD19eX9u3bs3Xr1rT6NpK1atUqKlasiLu7O9WqVWPDhg233H7btm2O99G///33+587dy5lypTB3d2dBg0a8NNPPzluc3V1ZcSIEbzyyivp8j3dKRXfDGJPSGD2iGnUbL6RN1cWuD7o7Ab3z8G5ywYKly1rbUAREckW2rVrx9mzZzl+/DgzZ85kwYIFTJgwIdE27777Lh06dKBJkybs2rWLX3/9le7duzNw4EBGjBiRaNuxY8fSrVs36tWrx8aNG9m/fz8zZsxg7969LF26NMO+r9jY2HTb96lTp/jyyy/p169fktt27NhBdHQ0nTt3ZsmSJXf8GCdPnqROnTp88803TJ8+nX379rFp0ybuv/9+Bg0adBfpb+2HH37giSee4KmnnuKXX36hY8eOdOzYkf3799/2vocPH+bs2bOOf4ULF3bctnLlSoYNG8aECRMIDg6mRo0atG3blr///tuxTc+ePdmxYwe//fZbunxvd8TkMBEREQYwERERGfaYf/1+1LSt+ZKBiQYmGifbeLNrfHNjQvdlWAYREUmZ6Ohoc+DAARMdHW11lFTr27ev6dChQ6Kxxx9/3NSqVcvx9alTp4yLi4sZNmxYkvvPmTPHAObHH380xhiza9cuA5hZs2Yl+3hhYWE3zXL69GnTvXt34+PjYzw8PEydOnUc+00u54svvmiaN2/u+Lp58+Zm0KBB5sUXXzQFChQwLVq0ME888YTp2rVrovvFxsaaAgUKmCVLlhhjjElISDCTJ082ZcqUMe7u7qZ69epm1apVN81pjDHTp083devWTfa2fv36mVGjRpmNGzea8uXLJ7m9dOnSZubMmUnGJ0yYYGrUqOH4+sEHHzQlSpQwUVFRSba91fN4t7p27WoefvjhRGMNGjQwzz777E3v8+233xrglrnq169vBg0a5Pg6ISHBFC9e3EyZMiXRdvfff7959dVXk93HrX7W0quv6awO6ezz+f4MGHmAC1HejrEXOsVRfeSXkIFXKhERkbv0SV24kvSj7nSXpyj0urN1kvv37+eHH36gdOnSjrHVq1cTFxeXZGYX4Nlnn2XMmDF8+umnNGjQgGXLlpE3b16ef/75ZPfv7e2d7HhUVBTNmzenRIkSBAYGUrRoUYKDg7Hb7anKv2TJEp577jl27twJwNGjR+nSpQtRUVGOq31t3ryZq1ev8thjjwEwZcoUPvnkE+bPn899993Hd999R69evShUqBDNmzdP9nG+//576tatm2T88uXLrFq1il27dlGxYkUiIiL4/vvv8fPzS9X3cenSJTZt2sSkSZPIkydPkttv9jwCLFu2jGefffaW+9+4ceNNMwUFBTFs2LBEY23btmXdunW3zV2zZk1iYmKoWrUqEydOpEmTJsD12fc9e/YwevRox7ZOTk60bt2aoKCgRPuoX78+33///W0fK6Oo+KaTK+FhDO81hQXr8wC5ASia7wpL3q3LA326WhtORERS78o5iPrT6hS39eWXX5I3b17i4+OJiYnBycmJ9957z3H7kSNH8PLyolixYknu6+rqSrly5Thy5AgAv//+O+XKlcPFxSVVGZYvX05oaCi7d+8mf/78ANx7772p/l7uu+8+pk2b5vj6nnvuIU+ePHz22Wf07t3b8ViPPvoonp6exMTEMHnyZLZs2UKjRo0AKFeuHDt27GDBggU3Lb5//PFHssV3xYoV3HfffVSpUgWA7t2789FHH6W6+B49ehRjDBUrVkzV/QAeffRRGjRocMttSpQocdPbzp07R5EiRRKNFSlSJNn1yjcUK1aM+fPnU7duXWJiYvjwww9p0aIFu3btonbt2ly4cIGEhIRk93vo0KFEY8WLF+ePP/64Zf6MpOKbDvZ8tZUefTZw5Hw+x1iH+hF8uGooBUv5WphMRETuWJ6iWeJx77//fubNm8eVK1eYOXMmuXLlolOnTnf00MaYO7pfSEgItWrVcpTeO1WnTp1EX+fKlYuuXbuybNkyevfuzZUrV/j8889ZsWIFcL1gXr16lTZt2iS6X2xsLLVq1brp40RHR+Pu7p5k/OOPP6ZXr16Or3v16kXz5s159913kz0I7mbu9HkE8PT0TNVjpYUKFSpQocI/F89q3Lgxx44dY+bMmale1507d26uXr2a1hHvmIpvWrIn8M38qbQdco14+/XS6+Eay6xRhRgwYRw2XQVIRCTrusPlBhktT548jtnVjz/+mBo1avDRRx/x1FNPAVC+fHkiIiL466+/KF68eKL7xsbGcuzYMe6//37Htjt27CAuLi5Vs765c+e+5e1OTk5JymBcXFyy38t/9ezZk+bNm/P333/z9ddfkzt3btq1awdcX2IBsH79+iSzoG5ubjfNU7BgQcLCwhKNHThwgB9//JGffvop0ZkJEhISWLFiBU8//TQA+fLlIyIiIsk+w8PD8fr/6Unvu+8+bDZbktnQlLjbpQ5Fixbl/PnzicbOnz9P0aKp+4Oqfv367NixA7j+fDk7O6dov5cuXaJQoUKpeqz0pCaWViJPw6pWNLk8nspFQgGoUzacX75/jKdfe0mlV0REMpyTkxNjxozh1VdfJTo6GoBOnTrh4uLCjBkzkmw/f/58rly5whNPPAFAjx49iIqK4v333092/+Hh4cmOV69enZCQkJue7qxQoUKcPXs20VhISEiKvqfGjRvj6+vLypUrWbZsGV26dHGU8sqVK+Pm5sapU6e49957E/3z9b35J661atXiwIEDicY++ugjmjVrxt69ewkJCXH8GzZsGB999JFjuwoVKrBnz54k+wwODqZ8+fIA5M+fn7Zt2zJ37lyuXLmSZNubPY9wfanDvx8/uX/JLdO4oVGjRklOl/b11187loKkVEhIiGN5jKurK3Xq1Em0X7vdztatW5Psd//+/becbc9waXqoXBaQLkcJHlxhzLvexryNMW9j9o8obMb2ftXEXL2ado8hIiIZIrud1SEuLs6UKFHCTJ8+3TE2c+ZM4+TkZMaMGWMOHjxojh49ambMmGHc3NzM8OHDE93/5ZdfNs7OzmbkyJHmhx9+MCdPnjRbtmwxnTt3vunZHmJiYkz58uWNn5+f2bFjhzl27JhZvXq1+eGHH4wxxmzatMnYbDazZMkSc+TIETN+/HiTL1++JGd1ePHFF5Pd/9ixY03lypVNrly5zPfff5/ktgIFCpjFixebo0ePmj179pg5c+aYxYsX3/R5CwwMNIULFzbx8fHGmOtniihUqJCZN29ekm0PHDhgALN//35jjDE7d+40Tk5O5s033zQHDhww+/btM2PGjDG5cuUy+/b9c/amY8eOmaJFi5rKlSub1atXmyNHjpgDBw6Y2bNnm4oVK940293auXOnyZUrl3n77bfNwYMHzYQJE4yLi0uibKNGjTK9e/d2fD1z5kyzbt068/vvv5t9+/aZF1980Tg5OZktW7Y4tlmxYoVxc3MzixcvNgcOHDDPPPOM8fb2NufOnUv0+KVLlzb+/v7JZrPirA4qvnezr7//NgPaDjP7RxRylF6zwNeYU9vSIKmIiFghuxVfY4yZMmWKKVSoUKJTaX3++efGz8/P5MmTx7i7u5s6deqYjz/+ONn9rly50jRr1sx4enqaPHnymOrVq5vXX3/9lqe7OnnypOnUqZPJly+f8fDwMHXr1jW7du1y3D5+/HhTpEgR4+XlZYYOHWoGDx6c4uJ7o3yWLl3a2O32RLfZ7XYza9YsU6FCBePi4mIKFSpk2rZta7Zv337TrHFxcaZ48eJm06ZNxhhjVq9ebZycnJKUuBsqVapkhg4d6vh68+bNpkmTJsbHx8dx6rXkHu+vv/4ygwYNMqVLlzaurq6mRIkS5tFHHzXffvvtTbOlhYCAAFO+fHnj6upqqlSpYtavX5/o9r59+yZ67qdOnWruuece4+7ubvLnz29atGhhvvnmmyT7fffdd02pUqWMq6urqV+/vuN0dTf88MMPxtvb21y9yUSgFcXXZsxdrLjOgiIjI/Hy8iIiIoJ8+fLd/g43ERS4kV4DtnI81JPqxc7x04sLcavSGVrPB3fvtAssIiIZ6tq1a5w4cYKyZcsme8CTZE9z584lMDCQzZs3Wx0l2+jWrRs1atRgzJgxyd5+q5+1tOpr/6WD21IpPjaWSc9P5o1FhgT79aMsT1zy4dcy86j38ACw2SxOKCIiIqn17LPPEh4ezuXLlzP8LArZUWxsLNWqVWPo0KFWR0lExTcVjofspVfXRQT97gNcL7iNy4fxScCTlK1R3dpwIiIicsdy5crF2LFjrY6Rbbi6uvLqq69aHSMJnWogBYzdjv/kudRsuPL/pRecney8NsDG9n3TVHpFREREsgDN+N5G2NmzPNdtOiu/9wKunwOwXKHLLPuoNQ3bt7M2nIiIiIikmGZ8b+X0Ng7OfIRVO/5Z69OvzWVCDo5W6RURyeZy2LHfIhnOip8xFd/kJMTCd6MgoCWNiwQzttX3eOe+RsCskiz66m08CxSwOqGIiKSTGxdDyEyXWRXJjmJjYwFwdnbOsMfUUof/OPHLHkr9+gzOF4IdY+P623j2vX6U+Nd1q0VEJHtydnbG29ubv//+GwAPDw9sOmOPSJqy2+2Ehobi4eFBrlwZV0dVfP/P2O18MGE2Q6deZEKb3LzSEnBygaaTcKk7nBI2TY6LiOQURYsWBXCUXxFJe05OTpQqVSpD/7BU8QVC/zjFgC4zCdztDbjw6qaWPFA3gVrPz4Uita2OJyIiGcxms1GsWDEKFy5MXFyc1XFEsiVXV1ecnDJ2YjHHF9/NS1bQ74VgzkV6O8YGPBRDhWGbwMvLumAiImI5Z2fnDF1/KCLpK1N8fj937lzKlCmDu7s7DRo04Keffrrl9qtWraJixYq4u7tTrVo1NmzYkOrHvBYVxUudRtOu32HOReYBoGDeaAIXlGPeF1PxUOkVERERyVYsL74rV65k2LBhTJgwgeDgYGrUqEHbtm1vuq7qhx9+4IknnuCpp57il19+oWPHjnTs2JH9+/en6nFb1JvE7LX/XBe6Xc1w9u19hvbP9L6r70dEREREMiebsfhEhQ0aNKBevXq89957wPWj/Hx9fRkyZAijRo1Ksn23bt24cuUKX375pWOsYcOG1KxZk/nz59/28SIjI/Hy8gJGAe645Ypn+tB8DH5rOLYMXmciIiIiIknd6GsRERHky5cvzfZr6Rrf2NhY9uzZw+jRox1jTk5OtG7dmqCgoGTvExQUxLBhwxKNtW3blnXr1iW7fUxMDDExMY6vIyIibtxC5eKRfPRRByo3bsjlqKi7+l5EREREJG1ERkYCaX+RC0uL74ULF0hISKBIkSKJxosUKcKhQ4eSvc+5c+eS3f7cuXPJbj9lyhRee+21ZG6ZyYG/oNGD799RdhERERFJXxcvXvz/J/VpI9uf1WH06NGJZojDw8MpXbo0p06dStMnUjKnyMhIfH19OX36dJp+VCKZk17vnEWvd86i1ztniYiIoFSpUuTPnz9N92tp8S1YsCDOzs6cP38+0fj58+cdJw//r6JFi6Zqezc3N9zc3JKMe3l56QcnB8mXL59e7xxEr3fOotc7Z9HrnbOk9Xl+LT2ay9XVlTp16rB161bHmN1uZ+vWrTRq1CjZ+zRq1CjR9gBff/31TbcXEREREYFMsNRh2LBh9O3bl7p161K/fn1mzZrFlStX6N+/PwB9+vShRIkSTJkyBYAXX3yR5s2bM2PGDB5++GFWrFjBzz//zAcffGDltyEiIiIimZzlxbdbt26EhoYyfvx4zp07R82aNdm0aZPjALZTp04lmuZu3Lgxy5cv59VXX2XMmDHcd999rFu3jqpVq6bo8dzc3JgwYUKyyx8k+9HrnbPo9c5Z9HrnLHq9c5b0er0tP4+viIiIiEhG0BUbRERERCRHUPEVERERkRxBxVdEREREcgQVXxERERHJEbJl8Z07dy5lypTB3d2dBg0a8NNPP91y+1WrVlGxYkXc3d2pVq0aGzZsyKCkkhZS83ovXLgQPz8/fHx88PHxoXXr1rd9f0jmktqf7xtWrFiBzWajY8eO6RtQ0lRqX+/w8HAGDRpEsWLFcHNzo3z58vqdnoWk9vWeNWsWFSpUIHfu3Pj6+jJ06FCuXbuWQWnlbnz33Xe0b9+e4sWLY7PZWLdu3W3vs23bNmrXro2bmxv33nsvixcvTv0Dm2xmxYoVxtXV1Xz88cfmt99+M08//bTx9vY258+fT3b7nTt3GmdnZzNt2jRz4MAB8+qrrxoXFxezb9++DE4udyK1r3ePHj3M3LlzzS+//GIOHjxo+vXrZ7y8vMyZM2cyOLncidS+3jecOHHClChRwvj5+ZkOHTpkTFi5a6l9vWNiYkzdunXNQw89ZHbs2GFOnDhhtm3bZkJCQjI4udyJ1L7ey5YtM25ubmbZsmXmxIkTZvPmzaZYsWJm6NChGZxc7sSGDRvM2LFjzdq1aw1gPvvss1tuf/z4cePh4WGGDRtmDhw4YN59913j7OxsNm3alKrHzXbFt379+mbQoEGOrxMSEkzx4sXNlClTkt2+a9eu5uGHH0401qBBA/Pss8+ma05JG6l9vf8rPj7eeHp6miVLlqRXRElDd/J6x8fHm8aNG5sPP/zQ9O3bV8U3C0nt6z1v3jxTrlw5Exsbm1ERJQ2l9vUeNGiQadmyZaKxYcOGmSZNmqRrTkl7KSm+L7/8sqlSpUqisW7dupm2bdum6rGy1VKH2NhY9uzZw//au/+gmPM/DuDPtmwlG9OR2iu/os74cfSDy49xXHflDp1Qd5qEyEkyHKfxq+Jb4shgOJxTzjUKw2lEEbqr5e78aOtG2aTCjXKDG4m6fuz7+4dp51bl7Eq59vmY+fzx+Xze7/fn9d7X7PTad5/ProeHh+aYRCKBh4cHLl682GSfixcvarUHAE9Pz2bb05tDn3w/7+nTp6itrYWVldXrCpNaiL75Xrt2LaytrREUFNQaYVIL0SffKSkpcHd3x4IFC9C9e3cMHDgQMTExqK+vb62wSU/65HvEiBG4cuWK5naI4uJinDx5Eh9//HGrxEytq6XqtTb/5baWdP/+fdTX12t+9a1B9+7dcf369Sb7lJeXN9m+vLz8tcVJLUOffD9v+fLlkMvljd5M9ObRJ9/Z2dn47rvvoFQqWyFCakn65Lu4uBjnzp2Dv78/Tp48iaKiIoSEhKC2thYRERGtETbpSZ98T58+Hffv38eoUaMghEBdXR2++OILrFixojVCplbWXL1WUVGBqqoqmJubv9Q47WrFl0gXsbGxSEpKwrFjx2BmZtbW4VALe/z4MQICAvDtt9+ia9eubR0OtQK1Wg1ra2vs2bMHLi4u8PPzw8qVK7Fr1662Do1eg8zMTMTExGDnzp24evUqjh49itTUVKxbt66tQ6M3WLta8e3atSuMjY1x7949reP37t2DjY1Nk31sbGx0ak9vDn3y3WDTpk2IjY1FRkYGBg8e/DrDpBaia75v3ryJ0tJSTJw4UXNMrVYDAExMTKBSqeDg4PB6gya96fP+trW1RYcOHWBsbKw51r9/f5SXl6OmpgZSqfS1xkz60yffq1evRkBAAObMmQMAGDRoEJ48eYLg4GCsXLkSEgnX9tqT5uo1S0vLl17tBdrZiq9UKoWLiwvOnj2rOaZWq3H27Fm4u7s32cfd3V2rPQCcOXOm2fb05tAn3wCwceNGrFu3DmlpaXB1dW2NUKkF6Jrvd955B7///juUSqVmmzRpEsaOHQulUgl7e/vWDJ90pM/7e+TIkSgqKtJ8wAGAwsJC2Nrasuh9w+mT76dPnzYqbhs+9Dx7Xorakxar13R77u7Nl5SUJExNTUVCQoLIz88XwcHBokuXLqK8vFwIIURAQIAIDw/XtFcoFMLExERs2rRJFBQUiIiICH6d2X+IrvmOjY0VUqlUHDlyRJSVlWm2x48ft9UUSAe65vt5/FaH/xZd83379m0hk8lEaGioUKlU4sSJE8La2lr873//a6spkA50zXdERISQyWTi4MGDori4WJw+fVo4ODgIX1/ftpoC6eDx48ciJydH5OTkCAAiLi5O5OTkiFu3bgkhhAgPDxcBAQGa9g1fZ7Zs2TJRUFAgduzYwa8za7B9+3bRo0cPIZVKxbBhw8Qvv/yiOTdmzBgRGBio1f7QoUPC0dFRSKVSMWDAAJGamtrKEdOr0CXfPXv2FAAabREREa0fOOlF1/f3P7Hw/e/RNd8XLlwQw4cPF6ampqJPnz4iOjpa1NXVtXLUpC9d8l1bWysiIyOFg4ODMDMzE/b29iIkJET89ddfrR846ez8+fNN/j1uyHFgYKAYM2ZMoz5DhgwRUqlU9OnTR8THx+t8XSMh+P8AIiIiImr/2tU9vkREREREzWHhS0REREQGgYUvERERERkEFr5EREREZBBY+BIRERGRQWDhS0REREQGgYUvERERERkEFr5EREREZBBY+BIRAUhISECXLl3aOgy9GRkZ4ccff3xhm5kzZ+LTTz9tlXiIiN5ELHyJqN2YOXMmjIyMGm1FRUVtHRoSEhI08UgkEtjZ2WHWrFn4888/W2T8srIyjB8/HgBQWloKIyMjKJVKrTZbt25FQkJCi1yvOZGRkZp5Ghsbw97eHsHBwXj48KFO47BIJ6LXwaStAyAiakleXl6Ij4/XOtatW7c2ikabpaUlVCoV1Go1cnNzMWvWLNy9exfp6emvPLaNjc2/tuncufMrX+dlDBgwABkZGaivr0dBQQFmz56NR48eITk5uVWuT0TUHK74ElG7YmpqChsbG63N2NgYcXFxGDRoECwsLGBvb4+QkBBUVlY2O05ubi7Gjh0LmUwGS0tLuLi44PLly5rz2dnZGD16NMzNzWFvb4+wsDA8efLkhbEZGRnBxsYGcrkc48ePR1hYGDIyMlBVVQW1Wo21a9fCzs4OpqamGDJkCNLS0jR9a2pqEBoaCltbW5iZmaFnz55Yv3691tgNtzr07t0bADB06FAYGRnh/fffB6C9irpnzx7I5XKo1WqtGL29vTF79mzN/vHjx+Hs7AwzMzP06dMHUVFRqKure+E8TUxMYGNjg7fffhseHh6YNm0azpw5ozlfX1+PoKAg9O7dG+bm5nBycsLWrVs15yMjI7F//34cP35cs3qcmZkJALhz5w58fX3RpUsXWFlZwdvbG6WlpS+Mh4ioAQtfIjIIEokE27Ztw7Vr17B//36cO3cOX331VbPt/f39YWdnh0uXLuHKlSsIDw9Hhw4dAAA3b96El5cXpkyZgry8PCQnJyM7OxuhoaE6xWRubg61Wo26ujps3boVmzdvxqZNm5CXlwdPT09MmjQJN27cAABs27YNKSkpOHToEFQqFRITE9GrV68mx/3tt98AABkZGSgrK8PRo0cbtZk2bRoePHiA8+fPa449fPgQaWlp8Pf3BwBkZWVhxowZWLRoEfLz87F7924kJCQgOjr6pedYWlqK9PR0SKVSzTG1Wg07OzscPnwY+fn5WLNmDVasWIFDhw4BAJYuXQpfX194eXmhrKwMZWVlGDFiBGpra+Hp6QmZTIasrCwoFAp06tQJXl5eqKmpeemYiMiACSKidiIwMFAYGxsLCwsLzTZ16tQm2x4+fFi89dZbmv34+HjRuXNnzb5MJhMJCQlN9g0KChLBwcFax7KysoREIhFVVVVN9nl+/MLCQuHo6ChcXV2FEELI5XIRHR2t1cfNzU2EhIQIIYRYuHChGDdunFCr1U2OD0AcO3ZMCCFESUmJACBycnK02gQGBgpvb2/Nvre3t5g9e7Zmf/fu3UIul4v6+nohhBAffPCBiImJ0RrjwIEDwtbWtskYhBAiIiJCSCQSYWFhIczMzAQAAUDExcU120cIIRYsWCCmTJnSbKwN13ZyctJ6Df7++29hbm4u0tPTXzg+EZEQQvAeXyJqV8aOHYtvvvlGs29hYQHg2ern+vXrcf36dVRUVKCurg7V1dV4+vQpOnbs2GicJUuWYM6cOThw4IDm3/UODg4Ant0GkZeXh8TERE17IQTUajVKSkrQv3//JmN79OgROnXqBLVajerqaowaNQp79+5FRUUF7t69i5EjR2q1HzlyJHJzcwE8u03hww8/hJOTE7y8vDBhwgR89NFHr/Ra+fv7Y+7cudi5cydMTU2RmJiIzz77DBKJRDNPhUKhtcJbX1//wtcNAJycnJCSkoLq6mr88MMPUCqVWLhwoVabHTt2YN++fbh9+zaqqqpQU1ODIUOGvDDe3NxcFBUVQSaTaR2vrq7GzZs39XgFiMjQsPAlonbFwsICffv21TpWWlqKCRMmYP78+YiOjoaVlRWys7MRFBSEmpqaJgu4yMhITJ8+HampqTh16hQiIiKQlJSEyZMno7KyEvPmzUNYWFijfj169Gg2NplMhqtXr0IikcDW1hbm5uYAgIqKin+dl7OzM0pKSnDq1ClkZGTA19cXHh4eOHLkyL/2bc7EiRMhhEBqairc3NyQlZWFLVu2aM5XVlYiKioKPj4+jfqamZk1O65UKtXkIDY2Fp988gmioqKwbt06AEBSUhKWLl2KzZs3w93dHTKZDF9//TV+/fXXF8ZbWVkJFxcXrQ8cDd6UBxiJ6M3GwpeI2r0rV65ArVZj8+bNmtXMhvtJX8TR0RGOjo5YvHgxPv/8c8THx2Py5MlwdnZGfn5+owL730gkkib7WFpaQi6XQ6FQYMyYMZrjCoUCw4YN02rn5+cHPz8/TJ06FV5eXnj48CGsrKy0xmu4n7a+vv6F8ZiZmcHHxweJiYkoKiqCk5MTnJ2dNeednZ2hUql0nufzVq1ahXHjxmH+/PmaeY4YMQIhISGaNs+v2Eql0kbxOzs7Izk5GdbW1rC0tHylmIjIMPHhNiJq9/r27Yva2lps374dxcXFOHDgAHbt2tVs+6qqKoSGhiIzMxO3bt2CQqHApUuXNLcwLF++HBcuXEBoaCiUSiVu3LiB48eP6/xw2z8tW7YMGzZsQHJyMlQqFcLDw6FUKrFo0SIAQFxcHA4ePIjr16+jsLAQhw8fho2NTZM/umFtbQ1zc3OkpaXh3r17ePToUbPX9ff3R2pqKvbt26d5qK3BmjVr8P333yMqKgrXrl1DQUEBkpKSsGrVKp3m5u7ujsGDByMmJgYA0K9fP1y+fBnp6ekoLCzE6tWrcenSJa0+vXr1Ql5eHlQqFe7fv4/a2lr4+/uja9eu8Pb2RlZWFkpKSpCZmYmwsDD88ccfOsVERIaJhS8RtXvvvvsu4uLisGHDBgwcOBCJiYlaXwX2PGNjYzx48AAzZsyAo6MjfH19MX78eERFRQEABg8ejJ9++gmFhYUYPXo0hg4dijVr1kAul+sdY1hYGJYsWYIvv/wSgwYNQlpaGlJSUtCvXz8Az26T2LhxI1xdXeHm5obS0lKcPHlSs4L9TyYmJti2bRt2794NuVwOb2/vZq87btw4WFlZQaVSYfr06VrnPD09ceLECZw+fRpubm547733sGXLFvTs2VPn+S1evBh79+7FnTt3MG/ePPj4+MDPzw/Dhw/HgwcPtFZ/AWDu3LlwcnKCq6srunXrBoVCgY4dO+Lnn39Gjx494OPjg/79+yMoKAjV1dVcASail2IkhBBtHQQRERER0evGFV8iIiIiMggsfImIiIjIILDwJSIiIiKDwMKXiIiIiAwCC18iIiIiMggsfImIiIjIILDwJSIiIiKDwMKXiIiIiAwCC18iIiIiMggsfImIiIjIILDwJSIiIiKD8H9ZkEsbdGLaxQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}