sklearn8_CNBayes.ipynb

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        "## Importações e Informações do dataset"
      ],
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          "text": [
            "Primeiros registros do dataset:\n",
            "\n"
          ]
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              "   USMER  MEDICAL_UNIT  SEX  PATIENT_TYPE   DATE_DIED  INTUBED  PNEUMONIA  \\\n",
              "0      2             1    1             1  03/05/2020       97          1   \n",
              "1      2             1    2             1  03/06/2020       97          1   \n",
              "2      2             1    2             2  09/06/2020        1          2   \n",
              "3      2             1    1             1  12/06/2020       97          2   \n",
              "4      2             1    2             1  21/06/2020       97          2   \n",
              "\n",
              "   AGE  PREGNANT  DIABETES  ...  ASTHMA  INMSUPR  HIPERTENSION  OTHER_DISEASE  \\\n",
              "0   65         2         2  ...       2        2             1              2   \n",
              "1   72        97         2  ...       2        2             1              2   \n",
              "2   55        97         1  ...       2        2             2              2   \n",
              "3   53         2         2  ...       2        2             2              2   \n",
              "4   68        97         1  ...       2        2             1              2   \n",
              "\n",
              "   CARDIOVASCULAR  OBESITY  RENAL_CHRONIC  TOBACCO  CLASIFFICATION_FINAL  ICU  \n",
              "0               2        2              2        2                     3   97  \n",
              "1               2        1              1        2                     5   97  \n",
              "2               2        2              2        2                     3    2  \n",
              "3               2        2              2        2                     7   97  \n",
              "4               2        2              2        2                     3   97  \n",
              "\n",
              "[5 rows x 21 columns]"
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      ],
      "source": [
        "# Importações necessárias\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.naive_bayes import CategoricalNB\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Carregamento dos dados\n",
        "file_path = '/content/drive/MyDrive/covid_data.csv'\n",
        "data = pd.read_csv(file_path)\n",
        "\n",
        "# Visualização dos dados\n",
        "print(\"Primeiros registros do dataset:\\n\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pré-processamento"
      ],
      "metadata": {
        "id": "dJQQLiDdJ-pi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tratamento de valores faltantes (se necessário)\n",
        "data = data.dropna()\n",
        "\n",
        "# Manter a coluna de rótulos separadamente\n",
        "labels = data['CLASIFFICATION_FINAL']\n",
        "\n",
        "# Remoção de atributos quantitativos\n",
        "qualitative_columns = data.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Transformação de atributos categóricos para numéricos usando LabelEncoder\n",
        "label_encoders = {}\n",
        "for column in qualitative_columns:\n",
        "    label_encoders[column] = LabelEncoder()\n",
        "    data[column] = label_encoders[column].fit_transform(data[column])\n",
        "\n",
        "# Adicionando a coluna de rótulos de volta\n",
        "data['CLASIFFICATION_FINAL'] = labels\n",
        "\n",
        "# Amostragem do dataset para evitar estouro de memória\n",
        "data_sampled = data.sample(n=10000, random_state=42)  # Ajuste o valor conforme necessário\n",
        "\n",
        "# Separação entre atributos (X) e rótulos (y)\n",
        "X = data_sampled.drop(columns=['CLASIFFICATION_FINAL'])\n",
        "y = data_sampled['CLASIFFICATION_FINAL']\n",
        "\n",
        "# Divisão do conjunto de dados em treinamento e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "eyIHhEpuJMHp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Categorical Naive Bayes (padrão)\n"
      ],
      "metadata": {
        "id": "AWJThPJ9KAex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Criação e treinamento do classificador Categorical Naive Bayes\n",
        "clf = CategoricalNB()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predição e avaliação do modelo\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Avaliação do modelo\n",
        "print(\"Matriz de Confusão:\\n\\n\")\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(conf_matrix)\n",
        "\n",
        "print(\"\\nRelatório de Classificação:\\n\\n\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(\"\\nAcurácia:\")\n",
        "print(accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Visualização da matriz de confusão\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
        "plt.title('Matriz de Confusão')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "X_U8rjEZJXx6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1f3038e-9696-4d23-b212-e06299a4722d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matriz de Confusão:\n",
            "\n",
            "\n",
            "[[  0   0   4   0   0   0  12]\n",
            " [  0   0   6   0   0   0   0]\n",
            " [  0   0 227   0   2   0 455]\n",
            " [  0   0   0   0   0   0   4]\n",
            " [  0   0  16   0   1   1  49]\n",
            " [  0   0  43   0   0   4 194]\n",
            " [  0   0 143   0   3   0 836]]\n",
            "\n",
            "Relatório de Classificação:\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        16\n",
            "           2       0.00      0.00      0.00         6\n",
            "           3       0.52      0.33      0.40       684\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.17      0.01      0.03        67\n",
            "           6       0.80      0.02      0.03       241\n",
            "           7       0.54      0.85      0.66       982\n",
            "\n",
            "    accuracy                           0.53      2000\n",
            "   macro avg       0.29      0.17      0.16      2000\n",
            "weighted avg       0.54      0.53      0.47      2000\n",
            "\n",
            "\n",
            "Acurácia:\n",
            "0.534\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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KlSv47rvvMGfOHPz444/qPnPmzMGiRYsQFRWFkydPwtzcHJ07d0ZeXp66T2BgIBISEnDgwAHs3r0bx44dw/Dhw4sdh0wQ9O/lAHy9hbTo33dw8UjwMj1JEF9voX1mLb4u1fa5J2YVu+8HH3wAJycnrFixQt0WEBAAMzMzrFu3DoIgwNXVFV988QXGjx8PAMjKyoKTkxNWr16Nfv364cqVK/D09MTp06fRpEkTAMC+ffvQtWtX3Lt3D66urm+MgyM/REREUlbKy14KhQLZ2dkai0KhKPJQLVq0wKFDh/D3338DAM6fP4/jx4/D398fAHDr1i0kJSXBz89PvY21tTWaN2+OmJgYAEBMTAxsbGzUhQ8A+Pn5wcDAACdPnixWyix+iIiIpExmUKolMjIS1tbWGktkZGSRh/rqq6/Qr18/uLu7w8jICI0aNcLYsWMRGBgIAEhKSgIAODk5aWzn5OSkXpeUlARHR0eN9YaGhrCzs1P3eRPe7UVERCRlpbyGHh4ejrCwMI02ExOTIvtu3rwZ69evR3R0NOrWrYv4+HiMHTsWrq6uCAoKKlUcJcHih4iISMpKeau7iYnJK4udf5swYYJ69AcAvLy8cOfOHURGRiIoKAjOzs4AgOTkZLi4uKi3S05ORsOGDQEAzs7OSElJ0djvs2fPkJGRod7+TXjZqwxtjF4P/47t0bSRFwL79cHFCxfEDkknpJr3Cyt/WY6G9epgzuyZYoeiE1I938xbP/OOO3MaY0I/Q8f2rdHIyx1HDh1UrysoKMDCeT+gT8/u8G3WCB3bt8akryciJSVZxIjLt6dPn8LAQLP0kMvlUKlUAAA3Nzc4Ozvj0KFD6vXZ2dk4efIkfH19AQC+vr7IzMxEXFycus/hw4ehUqnQvHnzYsXB4qeM7Nu7Bz/MicSIUSHYuGUH6tRxx8gRwUhPTxc7NK2Sat4vXLp4AVu3bETt2nXEDkUnpHq+mbf+5p2bm4vatd0R/s2UQuvy8vJw5cplDBsxChs2bcPc+T/izu1bGPv5KBEi1aJSzvkpie7du2PmzJn47bffcPv2bezYsQPz5s1Dz549n4cik2Hs2LGYMWMGdu3ahYsXL2Lw4MFwdXVFjx49AAAeHh7o0qULhg0bhlOnTuGvv/5CaGgo+vXrV6w7vQDe6l5mAvv1Qd16Xvh60vN/QCqVCp06tEX/AYMQPKz4zx4ob96FvMX6Dn76NAf9+vTC15Mi8POypajj7o4vv/pGZ8cX41b3d+F8i4F5i5e3Lm91b+TljnkLfkK7Dn6v7JNw6SIG9u+DPfsPw8WleD9o34ZOb3Vv922pts89MrnYfR8/fozJkydjx44dSElJgaurK/r3748pU6bA2NgYwPOHHEZERGD58uXIzMxEq1atsGTJEtSuXVu9n4yMDISGhuLXX3+FgYEBAgICsGjRIlhYWBQrDo78lIGC/HxcuZwAH98W6jYDAwP4+LTAhfPnRIxMu6Sa9wuzZkxH6zZtNfLXZ1I938xbWnm/yePHjyGTyWBpaSV2KGVHhyM/lpaWWLBgAe7cuYPc3FzcvHkTM2bMUBc+wPPRn+nTpyMpKQl5eXk4ePCgRuEDAHZ2doiOjsbjx4+RlZWFlStXFrvwAUSe8JyWloaVK1ciJiZGfXuas7MzWrRogSFDhqBixYpihldsjzIfQalUwt7eXqPd3t4et279I1JU2ifVvAFg357fcPXKZazfuFXsUHRGquebeUsr79dRKBRYNP8HdPHvVqIftO88CT4xVbTi5/Tp0+jcuTMqVKgAPz8/dVWXnJyMRYsWYfbs2fj99981HmJUFIVCUehhSoK8+DPPiUoq6eFDzJk9E1E/r+T3GZFEFBQU4MvxYyEA+HryVLHDKVsSfLGpaMXP559/jj59+iAqKgqyf1WdgiDgs88+w+eff65+ouOrREZGYtq0aRpt30yOwKQpU8s65FeytbGFXC4vNAkwPT0dDg4OOotD16Sa9+XLCcjISEf/vr3UbUqlEmfjTmPThvU4dfYi5HK5iBFqh1TPN/OWVt5FKSgowMTx4/DwwQMsX7Fav0Z9JEq0cu/8+fMYN25cocIHeH69b9y4cYiPj3/jfsLDw5GVlaWxTJgYroWIX83I2BgennVxMvZ/hZpKpcLJkzGo36CRTmPRJanm3dzHB1t3/IpNW3eqF8+69dC1W3ds2rpTLwsfQLrnm3lLK+9/e1H4JCbeQdTPq2BjYyt2SGVPh291f1eINvLj7OyMU6dOwd3dvcj1p06dKvR466IU9XAlMe72GhQ0FJO/noi6deuhnld9rFu7Brm5uejRs9ebNy7HpJi3ubkFatbSnHxnZlYB1jY2hdr1jRTPN8C89Tnvp09zcDcxUf35/v17uHb1CqysreHgUBETwsbg6pXLWLg4CiqVEmlpqQCev2/KyMj4VbstX3jZS3fGjx+P4cOHIy4uDh06dFAXOsnJyTh06BB+/vln/PDDD2KFV2Jd/LviUUYGlvy0CGlpqajj7oEly36BvZ4PD0s1b6mS6vlm3vqb9+WESxj2yf9eqzD3+9kAgO4f9sBno0Jx9I/DAIB+vXtobPfzyjVo0rR4D9R755XT0ZvSEPU5P5s2bcL8+fMRFxcHpVIJ4PmTHr29vREWFoa+ffu+1X7FGPkh8ejfk6qKR4L/X5EE6fI5P+8SnT7np8u8Um2fuy/szZ3eMe/EQw4LCgqQlpYGAHBwcICRkVGp9sfiR1rE/w4WB4sfkgIWP9pn5j+/VNvn7h1XRpHozjvxYlMjIyONF5gRERERacs7UfwQERGRSDjhmYiIiCRFgtfQWfwQERFJGUd+iIiISFJY/BAREZGkSPCyl/TKPSIiIpI0jvwQERFJGS97ERERkaRI8LIXix8iIiIp48gPERERSQpHfoiIiEhKZBIsfqQ31kVERESSxpEfIiIiCZPiyA+LHyIiIimTXu3D4oeIiEjKOPJDRET0DjEwkN4PZl1j8UNERESSIsXih3d7ERERkU5Uq1YNMpms0BISEgIAyMvLQ0hICOzt7WFhYYGAgAAkJydr7CMxMRHdunVDhQoV4OjoiAkTJuDZs2clioMjP0RERBKmy5Gf06dPQ6lUqj9funQJHTt2RJ8+fQAA48aNw2+//YYtW7bA2toaoaGh6NWrF/766y8AgFKpRLdu3eDs7IwTJ07g4cOHGDx4MIyMjDBr1qxixyETBEEo29TEl1eyApDKOf37Di4eCY5UE0mGqQ6HJqwHrC3V9lnRg95627Fjx2L37t24fv06srOzUbFiRURHR6N3794AgKtXr8LDwwMxMTHw8fHB3r178cEHH+DBgwdwcnICAERFRWHixIlITU2FsbFxsY7Ly15EREQSVtRlqJIsCoUC2dnZGotCoXjjcfPz87Fu3Tp88sknkMlkiIuLQ0FBAfz8/NR93N3dUaVKFcTExAAAYmJi4OXlpS58AKBz587Izs5GQkJCsXNm8UNERCRhpS1+IiMjYW1trbFERka+8bg7d+5EZmYmhgwZAgBISkqCsbExbGxsNPo5OTkhKSlJ3eflwufF+hfriotzfoiIiCSstHN+wsPDERYWptFmYmLyxu1WrFgBf39/uLq6lur4b4PFDxEREb01ExOTYhU7L7tz5w4OHjyI7du3q9ucnZ2Rn5+PzMxMjdGf5ORkODs7q/ucOnVKY18v7gZ70ac4eNmLiIhIwkp72ettrFq1Co6OjujWrZu6zdvbG0ZGRjh06JC67dq1a0hMTISvry8AwNfXFxcvXkRKSoq6z4EDB2BlZQVPT89iH58jP0RERFKm4ztHVSoVVq1ahaCgIBga/q8Msba2RnBwMMLCwmBnZwcrKyt8/vnn8PX1hY+PDwCgU6dO8PT0xKBBgzBnzhwkJSVh0qRJCAkJKdHoE4sfIiIiCdP1E54PHjyIxMREfPLJJ4XWzZ8/HwYGBggICIBCoUDnzp2xZMkS9Xq5XI7du3dj5MiR8PX1hbm5OYKCgjB9+vQSxcDn/FC5p3/fwcXD5/wQ6S9dPuen4tBNpdo+ddXHZRSJ7nDkh4iISML4bi8iIiIiPceRHyIiIimT3sAPR37K0sbo9fDv2B5NG3khsF8fXLxwQeyQdEKKeScnJ+PriePRtmVzNPeuj949uyPh0kWxw9IJKZ5vgHkzb/3NW4xb3cXG4qeM7Nu7Bz/MicSIUSHYuGUH6tRxx8gRwUhPTxc7NK2SYt7ZWVkYMqg/DI2M8FPUz9j+398QNn4irKysxQ5N66R4vgHmzbz1O28WP/TW1q5ZhV69+6JHzwDUqFkTkyKmwdTUFDu3bxM7NK2SYt6rVv4MZ2dnTJ8RCS+v+nivUmW0aNkKlatUETs0rZPi+QaYN/PW77xZ/NBbKcjPx5XLCfDxbaFuMzAwgI9PC1w4f07EyLRLqnkfPXIYnnXrYXzYaLRr44uPe/fAtq2bxQ5L66R6vpk389b3vFn80Ft5lPkISqUS9vb2Gu329vZIS0sTKSrtk2re9+7dxZZNG1ClSjUsXbYCfT7ujzmRM7DrvzvEDk2rpHq+mTfzBvQ/b6l5p4ufu3fvFvkEyJcpFApkZ2drLAqFQkcRkhSpVALcPepi9NgwuHt4onefj9EroC+2bt4odmhERCUnK+VSDr3TxU9GRgbWrFnz2j6RkZGwtrbWWL7/LlJHET5na2MLuVxeaDJceno6HBwcdBqLLkk174oVK6JGjRoabW7Vq+PhwwciRaQbUj3fzJt5A/qdNy976diuXbteuxw5cuSN+wgPD0dWVpbGMmFiuA6i/x8jY2N4eNbFydgYdZtKpcLJkzGo36CRTmPRJanm3aBRY9y+fUuj7c6d23BxeU+kiHRDquebeTNvfc9bisWPqA857NGjB2QyGV73erE3fWFNTEwKvclVjHd7DQoaislfT0TduvVQz6s+1q1dg9zcXPTo2Uv3weiQFPMeOCgIQwb1xy/Lo9Cpiz8uXbyAbVs3Y3JEyV6sVx5J8XwDzJt563fe5bWAKQ1Rix8XFxcsWbIEH330UZHr4+Pj4e3treOo3k4X/654lJGBJT8tQlpaKuq4e2DJsl9gr6fDpC9IMe96XvUxb8FPWLRwHpZHLcZ771XChIlfo9sHH4odmtZJ8XwDzJt5SyNvKRH1re4ffvghGjZs+MpX0Z8/fx6NGjWCSqUq0X75Vndp4VvdiUjf6PKt7pVD/1uq7e/+VPQAxrtM1JGfCRMmICcn55Xra9asWax5P0RERPR2eNlLx1q3bv3a9ebm5mjbtq2OoiEiIpIeFj9EREQkKSx+iIiISFKkWPy80w85JCIiIiprHPkhIiKSMukN/LD4ISIikjIpXvZi8UNERCRhLH6IiIhIUiRY+7D4ISIikjIpjvzwbi8iIiKSFI78EBERSZgEB3448kNERCRlMpmsVEtJ3b9/HwMHDoS9vT3MzMzg5eWFM2fOqNcLgoApU6bAxcUFZmZm8PPzw/Xr1zX2kZGRgcDAQFhZWcHGxgbBwcF48uRJsWNg8UNERCRhMlnplpJ49OgRWrZsCSMjI+zduxeXL1/G3LlzYWtrq+4zZ84cLFq0CFFRUTh58iTMzc3RuXNn5OXlqfsEBgYiISEBBw4cwO7du3Hs2DEMHz68+DkLgiCULPR3X94zsSMgXdK/7+DikeJQNZFUmOpwUorn1/tLtf3lWZ2K3ferr77CX3/9hT///LPI9YIgwNXVFV988QXGjx8PAMjKyoKTkxNWr16Nfv364cqVK/D09MTp06fRpEkTAMC+ffvQtWtX3Lt3D66urm+MgyM/REREElbakR+FQoHs7GyNRaFQFHmsXbt2oUmTJujTpw8cHR3RqFEj/Pzzz+r1t27dQlJSEvz8/NRt1tbWaN68OWJiYgAAMTExsLGxURc+AODn5wcDAwOcPHmyWDmz+CEiIqK3FhkZCWtra40lMjKyyL7//PMPli5dilq1auH333/HyJEjMXr0aKxZswYAkJSUBABwcnLS2M7JyUm9LikpCY6OjhrrDQ0NYWdnp+7zJrzbi8q9k/9kiB2CKHxq2IkdApHWDdt0XuwQRLE2sIHOjlXa5/yEh4cjLCxMo83ExKTIviqVCk2aNMGsWbMAAI0aNcKlS5cQFRWFoKCgUsVREhz5ISIikrDSXvYyMTGBlZWVxvKq4sfFxQWenp4abR4eHkhMTAQAODs7AwCSk5M1+iQnJ6vXOTs7IyUlRWP9s2fPkJGRoe7zJix+iIiIJEyXt7q3bNkS165d02j7+++/UbVqVQCAm5sbnJ2dcejQIfX67OxsnDx5Er6+vgAAX19fZGZmIi4uTt3n8OHDUKlUaN68ebHi4GUvIiIiCdPl6y3GjRuHFi1aYNasWejbty9OnTqF5cuXY/ny5epYxo4dixkzZqBWrVpwc3PD5MmT4erqih49egB4PlLUpUsXDBs2DFFRUSgoKEBoaCj69etXrDu9ABY/REREkqbLx2Y0bdoUO3bsQHh4OKZPnw43NzcsWLAAgYGB6j5ffvklcnJyMHz4cGRmZqJVq1bYt28fTE1N1X3Wr1+P0NBQdOjQAQYGBggICMCiRYuKHQef80PlXuxNTngm0lec8Kx9DaceenOn14if2qGMItEdjvwQERFJmBTf6s7ih4iISMIkWPuw+CEiIpIyjvwQERGRpEiw9mHxQ0REJGVSHPnhQw6JiIhIUjjyQ0REJGESHPhh8UNERCRlUrzsxeKHiIhIwiRY+7D4ISIikjKO/FCpbIxejzWrViAtLRW167jjq68nw6t+fbHD0jp9ynvvljU4F3MUSffvwNjYBNXdvdAraBScKz1/43DO4yzsiv4FV+JPISM1CRZWtmjo0wYfBQ6HmbkFAODEod+wZuGMIvf//X9+g5VN+X4thT6d7+JY8fMyHDqwH7du/QMTU1M0bNgIY8PGo5pbdbFD0wl9Pt8feDri40Yu2Hc1FevjHgAAvvarAQ8nC41+h66nYfWp++rPRb16YvHxO4i9k6nVeLVFgrUPi5+ysm/vHvwwJxKTIqbBy6sB1q9dg5EjgvHf3ftgb28vdnhao295/33pHN7vFoBqtTygVCqxc20UFkaMxdTF0TAxNUNmRhqyMtIQMDQUrpXdkJ6ShPVL5yArIw0jvpoFAGjSqgPqNvbR2O/qBd/iWUF+uS989O18F8eZ06fwcf9A1PXygvKZEj8unIfPhgVj+67fUKFCBbHD0yp9Pt9udmZoX8sOiY9yC607cj0d2y4kqT8rnqkK9Vkek4gLDx6rPz/NV2onUNIK3upeRtauWYVevfuiR88A1KhZE5MipsHU1BQ7t28TOzSt0re8x0xbgBYdusG1SnVUdquFIWMmISM1CXduXAUAvFe1Bj4Lj0SDZq1R0aUS3Bs0QY+BI3Dh1HEolc/fqGtsYgprW3v1YmBggGsX49CyY3cxUysT+na+i2Pp8hX4qGcv1KxZC3Xc3TF95mw8fPgAVy4niB2a1unr+TYxNMDIllWx4uQ95BRRtCiUKmTlPVMveUUUP0/zlRp9ClTl9x3hMpmsVEt5xOKnDBTk5+PK5QT4+LZQtxkYGMDHpwUunD8nYmTaJYW8c3OeAADMLa1e3edpDkwrmEMuL3ogNfbwXhibmKJxi3ZaiVFXpHC+i+PJ4+e/7VtZW4sciXbp8/kOavoezt/PRkLSkyLXt6hmiyUBdRHZrTb6NnSGsbzwD/jBTSthSUBdTO1cC22ql+8RXZmsdEt5xMteZeBR5iMolcpCw8D29va4desfkaLSPn3PW6VSYfMvC1DDoz7eq1qjyD5PsjPx26ZVaN35o1fu56+Dv6JZm04wNjHVVqg6oe/nuzhUKhXmfDcLDRs1Rq1atcUOR6v09Xz7VLVBNTszROy9XuT6mNuPkJZTgEe5BahiY4qPG7nA2dIEi/68o+6z9fxDXE5+gvxnAuq5WCCo2XswNTLA/mtpukqjTJXX0ZvSEL34yc3NRVxcHOzs7ODp6amxLi8vD5s3b8bgwYNfub1CoYBCodBoE+QmMDEx0Uq8JB0bon7Ag8R/MGH2siLX5z7NwY/Tv4BL5Wro3v/TIvvcvHoRD+/extBxEdoMlXRk1oxpuHn9OlavjRY7FHoLdhWMMNDbFd8d/ueVl6mO3MhQ//1eZh4yc58h3K8GHC0eIuVJPgDgv5dS1H3uPMqFiaEBunpUZPFTjoh62evvv/+Gh4cH2rRpAy8vL7Rt2xYPHz5Ur8/KysLQoUNfu4/IyEhYW1trLN9/F6nt0DXY2thCLpcjPT1doz09PR0ODg46jUWX9DnvDVE/4OKZvxA2YzFsHRwLrc97moNFU8fC1KwCRn49G3LDon+P+Gv/LlR2q4WqNd21HbLW6fP5Lo5ZM6bj2NE/8POqNXBydhY7HK3Tx/PtZmcGazMjfOtfG6v718fq/vXh4WSBTnUcsLp//SIv4dxMewoAcLJ89S/UN9Oewt7cGIYG5bOIkOJlL1GLn4kTJ6JevXpISUnBtWvXYGlpiZYtWyIxMbHY+wgPD0dWVpbGMmFiuBajLszI2BgennVxMjZG3aZSqXDyZAzqN2ik01h0SR/zFgQBG6J+QHzsUYyb8RMcnF0L9cl9moMFEWNhaGiEkEnfw8i46P8U83Kf4sxfh/ViojOgn+e7OARBwKwZ03H40AH8vHINKlWqLHZIOqGP5zsh6QnCd1/DpD1/q5d/0p/ixO1HmLTnbwhFDAZVsXt+uTozt+CV+61qa4Ynimd4Vo4nPUuNqJe9Tpw4gYMHD8LBwQEODg749ddfMWrUKLRu3RpHjhyBubn5G/dhYlL4ElfeM21F/GqDgoZi8tcTUbduPdTzqo91a9cgNzcXPXr20n0wOqRveW+I+gGnju3HqG++g6lZBWQ9ev5br1kFcxibmCL3aQ4WThmDfEUegsMikPs0B7lPcwAAllY2MJDL1fs68+dBqJTP0Pz9LqLkog36dr6LY9a307B3z24s+HEJzCuYIy01FQBgYWkJU9PyPY/rTfTtfOc9U+FeVp5Gm+KZCk8UStzLyoOjhTF8q9ng/IPHeKJ4hso2Zgj0dsXV5Ce4m/l8u0bvWcHK1BA3054iX6mCl4slPqzniD2XU8VIqUxI8bKXqMVPbm4uDF+6XCCTybB06VKEhoaibdu2iI4uP9fVu/h3xaOMDCz5aRHS0lJRx90DS5b9AvtyOjxcXPqW99G92wEAc78O0WgPGjMJLTp0Q+LNa7j19/NbnCeN6KPRZ+bP2+Hg5KL+/NfBX9HI931UsLDUctS6o2/nuzg2b9oAAAgeMkijffqMSHxUTouA4pLa+X6mElDP2RKd3SvCxNAAGTkFOHM3CzsvJmv08avtgEBvY8gAJD/Ox/q4B/jjpblC5Y0Eax/IBKGogT7daNasGT7//HMMGjSo0LrQ0FCsX78e2dnZUCpL9vAoMUZ+SDyxN8vvfzql4VOjfN9eS1QcwzadFzsEURT1FGltab8o5s2dXuPwaN8yikR3RJ3z07NnT2zYsKHIdT/99BP69+8PEWszIiIivccJzzoWHh6OPXv2vHL9kiVLoFIVfrImERERlQ0DmaxUS3nEJzwTERGRpIj+kEMiIiISTzkdvCkVFj9EREQSJsVb3XnZi4iISMIMZKVbSmLq1KmF3grv7v6/J+Dn5eUhJCQE9vb2sLCwQEBAAJKTkzX2kZiYiG7duqFChQpwdHTEhAkT8OxZyW7z5sgPERGRhOl65Kdu3bo4ePCg+vPLz/sbN24cfvvtN2zZsgXW1tYIDQ1Fr1698NdffwEAlEolunXrBmdnZ5w4cQIPHz7E4MGDYWRkhFmzZhU7BhY/REREEqbrq16GhoZwLuL9eFlZWVixYgWio6PRvn17AMCqVavg4eGB2NhY+Pj4YP/+/bh8+TIOHjwIJycnNGzYEN9++y0mTpyIqVOnwtjYuFgx8LIXERERvTWFQoHs7GyNRaFQvLL/9evX4erqiurVqyMwMFD9Ps+4uDgUFBTAz89P3dfd3R1VqlRBTMzzBzHGxMTAy8sLTk5O6j6dO3dGdnY2EhISih0zix8iIiIJk5XyT2RkJKytrTWWyMjIIo/VvHlzrF69Gvv27cPSpUtx69YttG7dGo8fP0ZSUhKMjY1hY2OjsY2TkxOSkpIAAElJSRqFz4v1L9YVFy97ERERSVhJJy3/W3h4OMLCwjTa/v3C8Rf8/f3Vf69fvz6aN2+OqlWrYvPmzTAzMytdICXAkR8iIiIJ+/fdVyVdTExMYGVlpbG8qvj5NxsbG9SuXRs3btyAs7Mz8vPzkZmZqdEnOTlZPUfI2dm50N1fLz4XNY/oVVj8EBERSZiY7/Z68uQJbt68CRcXF3h7e8PIyAiHDh1Sr7927RoSExPh6/v85am+vr64ePEiUlJS1H0OHDgAKysreHp6Fvu4vOxFREQkYbp8P9f48ePRvXt3VK1aFQ8ePEBERATkcjn69+8Pa2trBAcHIywsDHZ2drCyssLnn38OX19f+Pj4AAA6deoET09PDBo0CHPmzEFSUhImTZqEkJCQYo82ASx+iIiISEfu3buH/v37Iz09HRUrVkSrVq0QGxuLihUrAgDmz58PAwMDBAQEQKFQoHPnzliyZIl6e7lcjt27d2PkyJHw9fWFubk5goKCMH369BLFIRMEQSjTzN4BeSV70COVc7E3M8QOQRQ+NezEDoFI64ZtOi92CKJYG9hAZ8cKWBlXqu23feJdRpHoDkd+iIiIJEyK7/Zi8UPlHkdAiPTX8r66GwGRKgnWPix+iIiIpEyXE57fFSx+iIiIJEx6pU8xi59du3YVe4cffvjhWwdDREREpG3FKn569OhRrJ3JZDIolcrSxENEREQ6xAnPr6BSqbQdBxEREYmgtO/2Ko8454eIiEjCOPJTTDk5OTh69CgSExORn5+vsW706NFlEhgRERFpnwRrn5IXP+fOnUPXrl3x9OlT5OTkwM7ODmlpaahQoQIcHR1Z/BAREZUjUhz5KfFb3ceNG4fu3bvj0aNHMDMzQ2xsLO7cuQNvb2/88MMP2oiRiIiIqMyUuPiJj4/HF198AQMDA8jlcigUClSuXBlz5szB119/rY0YiYiISEsMZKVbyqMSFz9GRkYwMHi+maOjIxITEwEA1tbWuHv3btlGR0RERFolk8lKtZRHJZ7z06hRI5w+fRq1atVC27ZtMWXKFKSlpWHt2rWoV6+eNmIkIiIiLSmf5UvplHjkZ9asWXBxcQEAzJw5E7a2thg5ciRSU1OxfPnyMg+QiIiItMdAJivVUh6VeOSnSZMm6r87Ojpi3759ZRoQERERkTbxIYdEREQSVk4Hb0qlxJe93NzcUL169VcuUrYxej38O7ZH00ZeCOzXBxcvXBA7JJ1g3sxbCpi3tPJ+YeUvy9GwXh3MmT1T7FC0RooTnktc/IwdOxZjxoxRL6NGjYKvry+ysrIwfPhwbcRYLuzbuwc/zInEiFEh2LhlB+rUccfIEcFIT08XOzStYt7Mm3nrL6nm/cKlixewdctG1K5dR+xQtEomK91SHskEQRDKYkeLFy/GmTNnsGrVqrLYXankPdP9MQP79UHdel74etIUAM9fBtupQ1v0HzAIwcP0tyhk3sybeTNvbSqbn1Al9/RpDvr16YWvJ0Xg52VLUcfdHV9+9Y3Ojm9mpLNDYeS2y6XafmmAZxlFojslHvl5FX9/f2zbtq2sdleuFOTn48rlBPj4tlC3GRgYwMenBS6cPydiZNrFvJk382be+mrWjOlo3aatRv76SoojP2VW/GzduhV2dnZltbty5VHmIyiVStjb22u029vbIy0tTaSotI95M2+AeesrqeYNAPv2/IarVy5j9NgvxA6FtOStHnL48gQnQRCQlJSE1NRULFmypMQBXLlyBbGxsfD19YW7uzuuXr2KhQsXQqFQYODAgWjfvv1rt1coFFAoFBptgtwEJiYmJY6FiIikLenhQ8yZPRNRP6+UzM+R8jppuTRKXPx89NFHGl8oAwMDVKxYEe+//z7c3d1LtK99+/bho48+goWFBZ4+fYodO3Zg8ODBaNCgwfNry506Yf/+/a8tgCIjIzFt2jSNtm8mR2DSlKkliqU0bG1sIZfLC00CTE9Ph4ODg87i0DXmzbwB5q2vpJr35csJyMhIR/++vdRtSqUSZ+NOY9OG9Th19iLkcrmIEZa9MrsEVI6UuPiZOnVqmR18+vTpmDBhAmbMmIGNGzdiwIABGDlyJGbOfH5LYXh4OGbPnv3a4ic8PBxhYWEabYJct9W6kbExPDzr4mRsDNp38APwfGLgyZMx6Nd/oE5j0SXmzbyZN/PWN819fLB1x68abVMmhcPNrTqGBg/Tu8IHkObIT4kLPrlcjpSUlELt6enpJf6mSEhIwJAhQwAAffv2xePHj9G7d2/1+sDAQFx4wzMlTExMYGVlpbGIMVQ5KGgotm/djF07d+CfmzcxY/pU5ObmokfPXm/ctjxj3sybeesvKeZtbm6BmrVqayxmZhVgbWODmrVqix2eVoj1VvfZs2dDJpNh7Nix6ra8vDyEhITA3t4eFhYWCAgIQHJyssZ2iYmJ6NatGypUqABHR0dMmDABz56V7DbvEo/8vOrOeIVCAWNj45LuTl1xGhgYwNTUFNbW1up1lpaWyMrKKvE+xdDFvyseZWRgyU+LkJaWijruHliy7BfY6/HwMMC8mTfz1mdSzVtqSlPAvK3Tp09j2bJlqF+/vkb7uHHj8Ntvv2HLli2wtrZGaGgoevXqhb/++gvA80uQ3bp1g7OzM06cOIGHDx9i8ODBMDIywqxZs4p9/GI/52fRokXqwL799ltYWFio1ymVShw7dgy3b9/GuXPFvwWyQYMG+O6779ClSxcAwKVLl+Du7g5Dw+c12Z9//omgoCD8888/xd4nIM5zfoiIqOyJ9ZwfsenyOT9hu66Wavt5H5Zsvu+TJ0/QuHFjLFmyBDNmzEDDhg2xYMECZGVloWLFioiOjlZfBbp69So8PDwQExMDHx8f7N27Fx988AEePHgAJycnAEBUVBQmTpyI1NTUYg/CFHvkZ/78+QCej/xERUVpXOIyNjZGtWrVEBUVVezkAWDkyJFQKpXqz/Xq1dNYv3fv3jfe7UVERERvr7Rzfoq669rE5NV3XYeEhKBbt27w8/PDjBkz1O1xcXEoKCiAn5+fus3d3R1VqlRRFz8xMTHw8vJSFz4A0LlzZ4wcORIJCQlo1KhRsWIudvFz69YtAEC7du2wfft22NraFnfTV/rss89eu74kQ1hERERUcqW97FXUXdcRERFF3iC1ceNGnD17FqdPny60LikpCcbGxrCxsdFod3JyQlJSkrrPy4XPi/Uv1hVXief8HDlypKSbEBER0TuqtDd7FXXXdVGjPnfv3sWYMWNw4MABmJqalu6gpVTiu70CAgLw3XffFWqfM2cO+vTpUyZBERERkW4YyGSlWop713VcXBxSUlLQuHFjGBoawtDQEEePHsWiRYtgaGgIJycn5OfnIzMzU2O75ORkODs7AwCcnZ0L3f314vOLPsXKuYRfIxw7dgxdu3Yt1O7v749jx46VdHdEREQkIoNSLsXVoUMHXLx4EfHx8eqlSZMmCAwMVP/dyMgIhw4dUm9z7do1JCYmwtfXFwDg6+uLixcvajxy58CBA7CysoKnZ/FfsFriy15Pnjwpcja1kZERsrOzS7o7IiIikgBLS8tCNzaZm5vD3t5e3R4cHIywsDDY2dnBysoKn3/+OXx9feHj4wMA6NSpEzw9PTFo0CDMmTMHSUlJmDRpEkJCQkr0jL8Sj/x4eXlh06ZNhdo3btxYoqqLiIiIxPcuvdV9/vz5+OCDDxAQEIA2bdrA2dkZ27dvV6+Xy+XYvXs35HI5fH19MXDgQAwePBjTp08vWc7Ffc7PC7/++it69eqFAQMGqG9DP3ToEKKjo7F161b06NGjRAFoA5/zQ0SkH/icH+2bvO96qbb/tkutMopEd0p82at79+7YuXMnZs2aha1bt8LMzAwNGjTA4cOHYWdnp40YiYiISEsk+Gqvko/8/Ft2djY2bNiAFStWIC4uTuOhhWLhyA8RkX7gyI/2Td1fupGfqZ3K38jPW7/J/tixYwgKCoKrqyvmzp2L9u3bIzY2tixjIyIiIi0r7a3u5VGJLnslJSVh9erVWLFiBbKzs9G3b18oFArs3LmTk52JiIioXCj2yE/37t1Rp04dXLhwAQsWLMCDBw/w448/ajM2IiIi0rJ36W4vXSn2yM/evXsxevRojBw5ErVqlb/re0RERFRYad/tVR4Ve+Tn+PHjePz4Mby9vdG8eXP89NNPSEtL02ZsREREpGWyUv4pj4pd/Pj4+ODnn3/Gw4cPMWLECGzcuBGurq5QqVQ4cOAAHj9+rM04iYiISAsMZKVbyqNS3ep+7do1rFixAmvXrkVmZiY6duyIXbt2lWV8b4W3uhMR6Qfe6q59c47cLNX2X7arUUaR6E6pn/MDAEqlEr/++itWrlzJ4od0TqmS5v+O8vL6KxdRCTyR6H/oDhYlfgbxW5Ni8VMmX125XI4ePXq8E6+2ICIiouKTlddbtkpBd6UlERERvXOkOIjM4oeIiEjCJDjww+KHiIhIysrrKypKg8UPERGRhEnxstdbv9iUiIiIqDziyA8REZGESfCqF4sfIiIiKTMop6+oKA0WP0RERBLGkR8iIiKSFClOeGbxQ0REJGFSvNWdd3sRERGRpHDkh4iISMIkOPDD4oeIiEjKpHjZi8UPERGRhEmw9uGcn7K0MXo9/Du2R9NGXgjs1wcXL1wQOySd0Pe8486cxpjQz9CpfWs09nLHkUMHC/X555+bGPv5SLTxbYIWzRphYL/eePjwgQjRap++n+9/iztzGp+P+gx+77dCg7p1cLiI86+PpJj32lU/o6V3XSz4IVLddu9uIsK/GI1uHVqhY5tmmDwxDBnpaSJGWfYMSrmUR+U17nfOvr178MOcSIwYFYKNW3agTh13jBwRjPT0dLFD0yop5J2Xm4vatd3x1TdTilx/924iggcPQDW36li+8j/YtO2/GDZiFEyMTXQcqfZJ4Xz/W27uU9SpUwfhkyLEDkWnpJb3lYSL+O/2LahZq7a6LTf3KcaFDAdkMiyKWomoFetQUFCAL8eFQKVSiRht2ZLJZKVaSmLp0qWoX78+rKysYGVlBV9fX+zdu1e9Pi8vDyEhIbC3t4eFhQUCAgKQnJyssY/ExER069YNFSpUgKOjIyZMmIBnz56VKA4WP2Vk7ZpV6NW7L3r0DECNmjUxKWIaTE1NsXP7NrFD0yop5N2ydRuEjB6L9h06Frl+8aIFaNm6LcaGTYC7hycqV66Ctu3aw87eXseRap8Uzve/tWrdFqFjxqGDX9HnX19JKe+nT3MwbdJETJw0DZZW1ur2C/HnkPTwPiZNnYkatWqjRq3amDRtFq5eTkDc6ZMiRlx+VapUCbNnz0ZcXBzOnDmD9u3b46OPPkJCQgIAYNy4cfj111+xZcsWHD16FA8ePECvXr3U2yuVSnTr1g35+fk4ceIE1qxZg9WrV2PKlKJ/OX2Vd674EQRB7BBKrCA/H1cuJ8DHt4W6zcDAAD4+LXDh/DkRI9Muqeb9MpVKhePH/kDVqtUwakQwOrRtgcED+hZ5aay84/kmfTV39gz4tmqDps19NdoLCvIhk8lgZGysbjM2MYGBgQEuxJ/VdZhaIyvlUhLdu3dH165dUatWLdSuXRszZ86EhYUFYmNjkZWVhRUrVmDevHlo3749vL29sWrVKpw4cQKxsbEAgP379+Py5ctYt24dGjZsCH9/f3z77bdYvHgx8vPzix3HO1f8mJiY4MqVK2KHUSKPMh9BqVTC/l+/6dvb2yMtTb+uDb9Mqnm/LCMjHU+fPsWqlT+jRcvWWLJsBdq198P4cZ8j7vQpscMrUzzfpI8O/r4Hf1+9gs9CxxVaV9erAUxNzbBk0Vzk5eYiN/cpflrwPZRKJdLTUkWIVjsMZLJSLQqFAtnZ2RqLQqF443GVSiU2btyInJwc+Pr6Ii4uDgUFBfDz81P3cXd3R5UqVRATEwMAiImJgZeXF5ycnNR9OnfujOzsbPXoUXGIdrdXWFhYke1KpRKzZ89W/wc7b9681+5HoVAU+iILchOYmOjffAt69wj/f93//ffbY+DgIQCAOu4eOH/+HLZu2Qjvps1EjI6IXic56SEW/DAbC5b8XOTPDFtbO3z73Tz8EPkttm5cDwMDA/h17oo67p6Qyd65sYO3VtqbvSIjIzFt2jSNtoiICEydOrXI/hcvXoSvry/y8vJgYWGBHTt2wNPTE/Hx8TA2NoaNjY1GfycnJyQlJQEAkpKSNAqfF+tfrCsu0YqfBQsWoEGDBoWSFAQBV65cgbm5ebEmUhX1Rf9mcgQmTZlahtG+nq2NLeRyeaFJn+np6XBwcNBZHLom1bxfZmNrC0NDQ1SvUVOj3c2tBuLPxYkUlXbwfJO+uXblMh5lpOOTwD7qNqVSifizZ7B98wYciTmH5r4tsWXXPmQ+egS5oRyWllbo3qkNOlTyFzHyslXaW93Dw8MLDWi8bgCiTp06iI+PR1ZWFrZu3YqgoCAcPXq0dEGUkGjFz6xZs7B8+XLMnTsX7du3V7cbGRlh9erV8PT0LNZ+ivqiC3LdjvoYGRvDw7MuTsbGoH2H58N1KpUKJ0/GoF//gTqNRZekmvfLjIyM4Vm3Hm7fvqXRnnjnNlxcXEWKSjt4vknfeDfzwdpNOzXaZk77BlWrVcfAoGDI5XJ1u42tLQAg7lQsHmVkoFWbdroMVatKesfWv5mYlOxqi7GxMWrWfP4Lo7e3N06fPo2FCxfi448/Rn5+PjIzMzUGRpKTk+Hs7AwAcHZ2xqlTmlMKXtwN9qJPcYhW/Hz11Vfo0KEDBg4ciO7duyMyMhJGRkYl3k9RX/S8kt3xViYGBQ3F5K8nom7deqjnVR/r1q5Bbm4uevTs9eaNyzEp5P30aQ7uJiaqP9+/fw/Xrl6BlbU1XFxcMXhoML4aH4bG3k3QpFlznDj+J44dPYLlK/8jYtTaIYXz/W9Pc3KQ+PL5v3cPV69cgbW1NVxc9avAfZkU8jY3N0f1mrU02szMKsDK2lrd/tuuHajqVh02NrZIuHgeC36IxMcDBqNqNTcxQtZLKpUKCoUC3t7eMDIywqFDhxAQEAAAuHbtGhITE+Hr+3wyuq+vL2bOnImUlBQ4OjoCAA4cOAArK6tiD5oAIj/huWnTpoiLi0NISAiaNGmC9evXl7oCFUsX/654lJGBJT8tQlpaKuq4e2DJsl9gr+eXA6SQ9+WESxj+SZD687zvZwMAun/YA9Nmzkb7Dh3x9ZSpWPXLcnw/eyaqVnPD9/MWoVFjb7FC1hopnO9/S0i4hE+HDlZ//mHO8wfgffhRT3w7a7ZYYWmdVPP+t8TbtxD103xkZ2XBxfU9BH0yHB8HBr15w3JEl7OXwsPD4e/vjypVquDx48eIjo7GH3/8gd9//x3W1tYIDg5GWFgY7OzsYGVlhc8//xy+vr7w8fEBAHTq1Amenp4YNGgQ5syZg6SkJEyaNAkhISElGn2SCe/IveUbN27E2LFjkZqaiosXL5aogvs3MUZ+SDxK1TvxLaxzcoPy+YsCUUk8keh/6A4Wuhub2BxfuqfR921Y/JHA4OBgHDp0CA8fPoS1tTXq16+PiRMnomPH58+TysvLwxdffIENGzZAoVCgc+fOWLJkicYlrTt37mDkyJH4448/YG5ujqCgIMyePRuGhsX/mr0zxQ8A3Lt3D3FxcfDz84O5uflb70ei/1Yki8UPkf5i8aN9W0pZ/PQpQfHzrninXmxaqVIlVKpUSewwiIiIJKO8TjcpjXeq+CEiIiLd0p8nFhWfFHMmIiIiCePIDxERkYTxshcRERFJivRKHxY/REREkibBgR8WP0RERFJmIMGxHxY/REREEibFkR/e7UVERESSwpEfIiIiCZPxshcRERFJiRQve7H4ISIikjBOeCYiIiJJ4cgPERERSYoUix/e7UVERESSwpEfIiIiCePdXkTlkKJAJXYIoqhgIhc7BNIhQRA7AnHcSskROwRROFhY6+xYBtKrfVj8EBERSRlHfoiIiEhSOOGZiIiISM9x5IeIiEjCeNmLiIiIJIUTnomIiEhSOPJDREREkiLFCc8sfoiIiCRMgrUP7/YiIiIiaWHxQ0REJGEGMlmplpKIjIxE06ZNYWlpCUdHR/To0QPXrl3T6JOXl4eQkBDY29vDwsICAQEBSE5O1uiTmJiIbt26oUKFCnB0dMSECRPw7Nmz4udcoqiJiIhIr8hKuZTE0aNHERISgtjYWBw4cAAFBQXo1KkTcnL+9xqTcePG4ddff8WWLVtw9OhRPHjwAL169VKvVyqV6NatG/Lz83HixAmsWbMGq1evxpQpU4qfsyDo3xtj8opf/JEeeKpQih2CKPhuL2nRv/+pi+fS3SyxQxBF0+q6e7dX7M3MUm3vU8PmrbdNTU2Fo6Mjjh49ijZt2iArKwsVK1ZEdHQ0evfuDQC4evUqPDw8EBMTAx8fH+zduxcffPABHjx4ACcnJwBAVFQUJk6ciNTUVBgbG7/xuBz5ISIikjBZKf8oFApkZ2drLAqFoljHzsp6Xtza2dkBAOLi4lBQUAA/Pz91H3d3d1SpUgUxMTEAgJiYGHh5eakLHwDo3LkzsrOzkZCQUKzjsvghIiKSMJmsdEtkZCSsra01lsjIyDceV6VSYezYsWjZsiXq1asHAEhKSoKxsTFsbGw0+jo5OSEpKUnd5+XC58X6F+uKg7e6ExER0VsLDw9HWFiYRpuJickbtwsJCcGlS5dw/PhxbYX2Shz5KUMbo9fDv2N7NG3khcB+fXDxwgWxQ9IJKeX9n1U/w7exJ+Z//7/fambPiEDvDzujrW8j+LdviS/HheD2rX9EjFK7pHS+XybVvF9Y+ctyNKxXB3NmzxQ7lFK5evEs5kaEITSwKwb6N8OZE39orM96lI5lc6chNLArPunRGt9NGo2k+4lF7ksQBMyZPKbI/ZQnpZ3wbGJiAisrK43lTcVPaGgodu/ejSNHjqBSpUrqdmdnZ+Tn5yMzM1Ojf3JyMpydndV9/n3314vPL/q8CYufMrJv7x78MCcSI0aFYOOWHahTxx0jRwQjPT1d7NC0Skp5X064iJ3bNqNmrToa7e4edfFNxExs3LYbCxb/DEEAxoZ8CqVS/yZiS+l8v0yqeb9w6eIFbN2yEbVr13lz53ecIi8PVarXQtCoCYXWCYKA+dMnICXpPsZN+QEzfloHB0cXRH4diry83EL99+3coB+vhtDh7V6CICA0NBQ7duzA4cOH4ebmprHe29sbRkZGOHTokLrt2rVrSExMhK+vLwDA19cXFy9eREpKirrPgQMHYGVlBU9Pz2LFweKnjKxdswq9evdFj54BqFGzJiZFTIOpqSl2bt8mdmhaJZW8nz7NwdRvvsRXk6fB0spKY12PgL5o5N0ELq7voY6HJ0aMGo3kpCQ8fHBfpGi1Ryrn+9+kmjfw/Hv/668mYMrUGbC00t0dSNrSoGkL9AkaiaYt2xVal3Q/ETeuXsLQ0ImoUccTrpWqYmjoRBQoFIj543eNvndu/o0926IxbNwkXYWuNaWd8FwSISEhWLduHaKjo2FpaYmkpCQkJSUhN/d5cWltbY3g4GCEhYXhyJEjiIuLw9ChQ+Hr6wsfHx8AQKdOneDp6YlBgwbh/Pnz+P333zFp0iSEhIQU63IbwOKnTBTk5+PK5QT4+LZQtxkYGMDHpwUunD8nYmTaJaW8f5g9Ay1atUWz5i1e2y839yl279oB1/cqwamYw6/lhZTO98ukmvcLs2ZMR+s2bTXy11fPCgoAAEZG//sBamBgAEMjI/ydcF7dpsjLw+LvJmNIyATY2DnoPM6yVtoJzyWxdOlSZGVl4f3334eLi4t62bRpk7rP/Pnz8cEHHyAgIABt2rSBs7Mztm/frl4vl8uxe/duyOVy+Pr6YuDAgRg8eDCmT59e7Dg44bkMPMp8BKVSCXt7e412e3t73NLjuR9SyfvA73tw7eplrFy7+ZV9tm3egMULf0Bubi6qVHPDwiW/wMjozc+aKE+kcr7/Tap5A8C+Pb/h6pXLWL9xq9ih6IRL5Wqwd3TGptWLEfx5OExMzbB3RzQy0lKQmZGm7rdu+XzU8vSCt29bEaMtO7q8cFecRwuamppi8eLFWLx48Sv7VK1aFXv27HnrON6p4icnJwebN2/GjRs34OLigv79+xf6D+ffFApFoecJCHKTYg99Eb1OctJDzP8+EouW/PLa76nO/h+gmY8v0lLTEL12FSZNDMOyVev5fUjlVtLDh5gzeyaifl4pme9jQ0NDjJ30HX5eMAMj+vrBwECOuo2aokGTFhDw/Id2XOwxXD5/BjN/WitytFQaohY/np6eOH78OOzs7HD37l20adMGjx49Qu3atXHz5k18++23iI2NLTQh6mWRkZGYNm2aRts3kyMwacpULUf/P7Y2tpDL5YUmP6anp8PBofwPib6KFPK+eiUBjzLSMSSwt7pNqVQi/uwZbNscjaOx8ZDL5bCwtISFpSUqV6mGevXro1NbXxw9chCdunQTMfqyJYXzXRSp5n35cgIyMtLRv6/mawXOxp3Gpg3rcersRcjl+veUcbdaHpi1eD2e5jzBs4ICWNnYImLsULjV8gAAXI4/g5SH9zC8dweN7RbO/Ap16jbEpDlRYoRdOnowZ7ukRC1+rl69qn4RWXh4OFxdXREfHw9ra2s8efIEPXv2xDfffIPo6OhX7qOo5wsIct3+lmJkbAwPz7o4GRuD9h2eP5VSpVLh5MkY9Os/UKex6JIU8m7SzBfrNv9Xo23m1G9QtZobBg75tMj//AUBECCgID9fV2HqhBTOd1GkmndzHx9s3fGrRtuUSeFwc6uOocHD9LLweVkFcwsAzydB/3P9CnoPGgEA6N53MN7v8pFG3/CR/TFw+Dg0at5K53GWBb24Y62E3pnLXjExMYiKioK19fO7CSwsLDBt2jT069fvtduZmBS+xCXGu70GBQ3F5K8nom7deqjnVR/r1q5Bbm4uevTs9eaNyzF9z9vc3Bw1atbSaDM1M4OVtQ1q1KyF+/fu4uD+vWju0xI2trZISUnG2lXPL5H5tmojUtTao+/n+1WkmLe5uQVq1qqt0WZmVgHWNjaF2suTvNynSH5wT/05NfkB7tz8G+aWVnBwdMbJPw/C0toWDhWdcff2DayNmocmvm3h5f38TiMbO4ciJznbV3SCo/N7OsujLJV00rI+EL34kf3/Vz0vLw8uLi4a69577z2kpqaKEVaJdfHvikcZGVjy0yKkpaWijrsHliz7BfZ6PCwOSDfvF4xNTHD+XBw2Ra/F4+ws2Nk7oGFjbyxfFQ07u9fPVyuPpHq+pZq3Pvrn+hXMmjhS/Xn98gUAgNZ+3TDiiwhkZqRj/fIFyMrMgI2dA1p16Iqe/YNFilY3JFj7iPtWdwMDA9SrVw+Ghoa4fv06Vq9ejYCAAPX6Y8eOYcCAAbh3795r9lIY3+ouLXyrO0kB3+ouLbp8q/v5u49LtX2DypZlFInuiDryExERofHZwsJC4/Ovv/6K1q1b6zIkIiIi0nOijvxoC0d+pIUjPyQF+vc/dfFw5Ef7Ltx9Uqrt61e2eHOnd4zoc36IiIhIPJzwTERERJIiwdqHxQ8REZGkSbD6YfFDREQkYVJ8yCHf6k5ERESSwpEfIiIiCeOEZyIiIpIUCdY+LH6IiIgkTYLVD4sfIiIiCZPihGcWP0RERBImxTk/vNuLiIiIJIUjP0RERBImwYEfFj9ERESSJsHqh8UPERGRhHHCMxEREUmKFCc8s/ihci85K0/sEETh5mgudgikQ1L8AQUAbQK+ETsEUeSe+0lnx5Litxbv9iIiIiJJ4cgPERGRlElw6IcjP0RERBImK+Wfkjh27Bi6d+8OV1dXyGQy7Ny5U2O9IAiYMmUKXFxcYGZmBj8/P1y/fl2jT0ZGBgIDA2FlZQUbGxsEBwfjyZMnJYqDxQ8REZGEyWSlW0oiJycHDRo0wOLFi4tcP2fOHCxatAhRUVE4efIkzM3N0blzZ+Tl/W9uZ2BgIBISEnDgwAHs3r0bx44dw/Dhw0uWsyAIQslCf/flPRM7AtKlWyk5YocgCk54JimwbRoqdgii0OWE59tppbtppJqD6VttJ5PJsGPHDvTo0QPA81EfV1dXfPHFFxg/fjwAICsrC05OTli9ejX69euHK1euwNPTE6dPn0aTJk0AAPv27UPXrl1x7949uLq6FuvYHPkhIiKSMlnpFoVCgezsbI1FoVCUOIxbt24hKSkJfn5+6jZra2s0b94cMTExAICYmBjY2NioCx8A8PPzg4GBAU6ePFnsY7H4ISIiorcWGRkJa2trjSUyMrLE+0lKSgIAODk5abQ7OTmp1yUlJcHR0VFjvaGhIezs7NR9ioN3exEREUlYaZ/wHB4ejrCwMI02ExOTUu1T21j8EBERSVhpH6BpYmJSJsWOs7MzACA5ORkuLi7q9uTkZDRs2FDdJyUlRWO7Z8+eISMjQ719cfCyFxERkYSVcspPmXFzc4OzszMOHTqkbsvOzsbJkyfh6+sLAPD19UVmZibi4uLUfQ4fPgyVSoXmzZsX+1gc+SEiIpIwXb465cmTJ7hx44b6861btxAfHw87OztUqVIFY8eOxYwZM1CrVi24ublh8uTJcHV1Vd8R5uHhgS5dumDYsGGIiopCQUEBQkND0a9fv2Lf6QWw+CEiIpI43VU/Z86cQbt27dSfX8wVCgoKwurVq/Hll18iJycHw4cPR2ZmJlq1aoV9+/bB1PR/t9OvX78eoaGh6NChAwwMDBAQEIBFixaVKA4+54fKPT7nh0h/8Tk/2nfvUX6ptq9ka1xGkegOR36IiIgkTJeXvd4VLH6IiIgkTIK1D+/2Kksbo9fDv2N7NG3khcB+fXDxwgWxQ9IJfcs74XwcZnw9BkN7d0KPdo0Re/zIK/sunTcTPdo1xq6t6zXaZ34zFp9+3BV9OvlgaEAnzJ81CRlpqdoOXSf07Xy/yeaN0ejdsztaNGuMFs0aY9CAj3H8z6Nih6Uz+nS+DQxkmDKqG67snoqMmHlI2BWBr4Z10ejzzYiuiN8+CWkn5uLB0Tn4LSoUTetVLbSvLq3q4th/xiMjZh4eHJ2DzfOG6SqNMqfLd3u9K1j8lJF9e/fghzmRGDEqBBu37ECdOu4YOSIY6enpYoemVfqYd15eHtxq1MaIMV+9tl/sn4dx7fJF2DlULLTOq2ETTIiYjcX/2Y6J075H0oN7+G7qBG2FrDP6eL7fxNHJGWPGjceGLdsRvXkbmjX3wZjQENy4cf3NG5dz+na+vxjSEcN6t8a42VvQsNcMTFr0X4QF+WFU/7bqPjfupGDcd1vQpM8sdBg6D3ceZODXJaFwsLVQ9+nRoSFWzBiM/+yKRbOPZ6P90HnYtPeMGCmVCV2+1f1dweKnjKxdswq9evdFj54BqFGzJiZFTIOpqSl2bt8mdmhapY95ezdvicDgEPi0bv/KPumpKfh50RyEfTMTcnnhq8cf9hmIOp714ejsCvd6DRDQfyj+vnwRz54VaDN0rdPH8/0m77drj9Zt2qJq1WqoVs0Nn48ZhwoVKuDC+XixQ9M6fTvfPg2qY/fRC9h3PAGJDzOw42A8DsVeRZO6/xvZ2bTvDI6cvIbb99Nx5Z8kTJy7HdaWZqhX6/lt1HK5AX6YEICvF+zEL1uP40ZiCq7+k4RtB86JlVbpvSsP+tEhFj9loCA/H1cuJ8DHt4W6zcDAAD4+LXDhfDn+B/EGUs1bpVJhQeQk9Ph4MKq41Xhj/8fZWTh6cA/c6zaAoaGRDiLUDqme75cplUrs3fMbcnOfokGDRmKHo1X6eL5jz/+Dds3qoGaV5++G8qr9HnwbVsf+vy4X2d/IUI7gXi2R+fgpLv59HwDQyL0y3nOyhUolIGbDRPyzfyZ2/jQSnjVcitwHvZs44bkMPMp8BKVSCXt7e412e3t73Lr1j0hRaZ9U896+YTUM5Ib4IKD/a/utWbYQe3ZugiIvD3U8vfDNrIU6ilA7pHq+AeD639cwaEA/5OcrUKFCBcxftBg1atYUOyyt0sfz/cOqA7CyMMX5HZOgVAqQy2WIWLwbG/91ycq/dT38Z/ZQVDA1QlJaNj747CekZz5/pIZbJQcAwKTPumLi3O248yAdYwZ1wO8/j0H9HtPxKPupzvMqrXI6eFMqoo78nD17Frdu3VJ/Xrt2LVq2bInKlSujVatW2Lhx4xv3oVAokJ2drbEoFApthk0SduPaZezetgFjJk6D7A0z/Xr2G4x5yzdg6vdLYGAgx8LIKdDDx2pJQrVqbti8bSfWbdiMPh/3x+SvJ+LmS0+ppfKhd6fG6OffFEO+XgPfAd/h0ylrMXZQBwR213wtwtHTf6N5v0i0GzIP+09cxro5n6Di/8/5Mfj/f/ff/fI7dh6Kx7krdzE8Yh0ECOjVsXyOBnLCs44NHToUN2/eBAD88ssvGDFiBJo0aYJvvvkGTZs2xbBhw7By5crX7iMyMhLW1tYay/ffReoifDVbG1vI5fJCkwDT09Ph4OCg01h0SYp5X754DlmZGfj0467o1aEpenVoitTkh1i9dD6G9eum0dfK2hbvVa6Khk188MWUSMSdPI5rl8vvnTJSPN8vGBkbo0rVqvCsWw9jxn2B2nXcsX7df8QOS6v08XzPGtsDP6w6gC2/xyHhxgNs+O00flx/GBOGdtTo9zQvH//cTcOpi7cxclo0nilVCOr5/PLfw7QsAMDVfx6q++cXPMPte+mo7Gynu2TKkBQnPIt62ev69euoVasWAGDJkiVYuHAhhg373+2CTZs2xcyZM/HJJ5+8ch/h4eHqx2O/IMhL/3bZkjAyNoaHZ12cjI1B+w5+AJ7PCzl5Mgb9+g/UaSy6JMW83+/YDQ28NX9LnPZlCN7v2A0dunz4yu0ElQoAUFBQfic8S/F8v4pKpUJBfumeivuu08fzbWZqDJWg0mhTqgQYGLx+HMBAJoOJ0fMfl+eu3EWeogC1qjnhRPzzy3+Ghgao4mqHxIcZ2glc28pn/VIqohY/FSpUQFpaGqpWrYr79++jWbNmGuubN2+ucVmsKCYmJjAx0Sx2xHi9xaCgoZj89UTUrVsP9bzqY93aNcjNzUWPnr10H4wO6WPeublP8fD+XfXnlIf38c+Na7C0tEJFJxdYWdto9JfLDWFjZ4/3qlQDAPx9+SKuX0uAh1cjWFhYIunBPUSvXApn10pw96yvw0zKnj6e7zdZOH8uWrVuA2cXFzzNycGe33bjzOlTWLp8hdihaZ2+ne89xy5iYnBn3H34CJdvPkRD90oYPbAd/rMzFgBQwdQYEz/tjN+OXkRSWhbsbSwwom8buDraYPuBswCAxzl5+GXrcUz+rCvuJT1C4sMMjAt6Xhy+6FPeSLD2Ebf48ff3x9KlS/HLL7+gbdu22Lp1Kxo0aKBev3nzZtQsJ5MKu/h3xaOMDCz5aRHS0lJRx90DS5b9AvtyOjxcXPqY941rlzF53HD155VL5gEA2nXujjFfTXvj9sampoj98zA2rl6GvNxc2No7oHGzFugz8DsYGZe/d+C8TB/P95tkZKRjUvhEpKamwMLSErVr18HS5Svg26Kl2KFpnb6d77DvtiBi1AdY+PXHqGhrgYepWVix9S/MWr4XAKBUqVCnmhMGdm8OextzZGQ9xZmEO/D7ZD6u/JOk3k/4gh14plRhxYzBMDMxwulLd+A/fBEyH+eKlRqVkKgvNn3w4AFatmyJKlWqoEmTJli6dCm8vb3h4eGBa9euITY2Fjt27EDXrl1LtF++2FRa+GJTIv3FF5tqX3pO6X5o2puXvxvHRZ3w7OrqinPnzsHX1xf79u2DIAg4deoU9u/fj0qVKuGvv/4qceFDRERExSfFCc+ijvxoC0d+pIUjP0T6iyM/2vfoqbJU29tWkJdRJLrDJzwTERGRpJS/C3VERERUZsrrgwpLgyM/REREJCkc+SEiIpKw8jppuTRY/BAREUmYFC97sfghIiKSMAnWPix+iIiIJE2C1Q8nPBMREZGkcOSHiIhIwjjhmYiIiCSFE56JiIhIUiRY+3DODxERkaTJSrmU0OLFi1GtWjWYmpqiefPmOHXqVBkkUTIsfoiIiCRMl29137RpE8LCwhAREYGzZ8+iQYMG6Ny5M1JSUrSUXdFY/BAREZFOzJs3D8OGDcPQoUPh6emJqKgoVKhQAStXrtRpHCx+iIiIJEwmK92iUCiQnZ2tsSgUikLHyc/PR1xcHPz8/NRtBgYG8PPzQ0xMjC5TBgQqM3l5eUJERISQl5cndig6xbyZtxQwb+ZNRYuIiBAAaCwRERGF+t2/f18AIJw4cUKjfcKECUKzZs10FO1zMkEQBN2WW/orOzsb1tbWyMrKgpWVldjh6AzzZt5SwLyZNxVNoVAUGukxMTGBiYmJRtuDBw/w3nvv4cSJE/D19VW3f/nllzh69ChOnjypk3gB3upOREREpVBUoVMUBwcHyOVyJCcna7QnJyfD2dlZW+EViXN+iIiISOuMjY3h7e2NQ4cOqdtUKhUOHTqkMRKkCxz5ISIiIp0ICwtDUFAQmjRpgmbNmmHBggXIycnB0KFDdRoHi58yZGJigoiIiGIN/+kT5s28pYB5M28qvY8//hipqamYMmUKkpKS0LBhQ+zbtw9OTk46jYMTnomIiEhSOOeHiIiIJIXFDxEREUkKix8iIiKSFBY/REREJCksfsrQ4sWLUa1aNZiamqJ58+Y4deqU2CFp1bFjx9C9e3e4urpCJpNh586dYoekE5GRkWjatCksLS3h6OiIHj164Nq1a2KHpXVLly5F/fr1YWVlBSsrK/j6+mLv3r1ih6Vzs2fPhkwmw9ixY8UORaumTp0KmUymsbi7u4sdlk7cv38fAwcOhL29PczMzODl5YUzZ86IHRaVIRY/ZWTTpk0ICwtDREQEzp49iwYNGqBz585ISUkROzStycnJQYMGDbB48WKxQ9Gpo0ePIiQkBLGxsThw4AAKCgrQqVMn5OTkiB2aVlWqVAmzZ89GXFwczpw5g/bt2+Ojjz5CQkKC2KHpzOnTp7Fs2TLUr19f7FB0om7dunj48KF6OX78uNghad2jR4/QsmVLGBkZYe/evbh8+TLmzp0LW1tbsUOjsqTTN4npsWbNmgkhISHqz0qlUnB1dRUiIyNFjEp3AAg7duwQOwxRpKSkCACEo0ePih2Kztna2gq//PKL2GHoxOPHj4VatWoJBw4cENq2bSuMGTNG7JC0KiIiQmjQoIHYYejcxIkThVatWokdBmkZR37KQH5+PuLi4uDn56duMzAwgJ+fH2JiYkSMjHQhKysLAGBnZydyJLqjVCqxceNG5OTk6Pyx9GIJCQlBt27dNP6d67vr16/D1dUV1atXR2BgIBITE8UOSet27dqFJk2aoE+fPnB0dESjRo3w888/ix0WlTEWP2UgLS0NSqWy0BMqnZyckJSUJFJUpAsqlQpjx45Fy5YtUa9ePbHD0bqLFy/CwsICJiYm+Oyzz7Bjxw54enqKHZbWbdy4EWfPnkVkZKTYoehM8+bNsXr1auzbtw9Lly7FrVu30Lp1azx+/Fjs0LTqn3/+wdKlS1GrVi38/vvvGDlyJEaPHo01a9aIHRqVIb7egqgUQkJCcOnSJUnMhQCAOnXqID4+HllZWdi6dSuCgoJw9OhRvS6A7t69izFjxuDAgQMwNTUVOxyd8ff3V/+9fv36aN68OapWrYrNmzcjODhYxMi0S6VSoUmTJpg1axYAoFGjRrh06RKioqIQFBQkcnRUVjjyUwYcHBwgl8uRnJys0Z6cnAxnZ2eRoiJtCw0Nxe7du3HkyBFUqlRJ7HB0wtjYGDVr1oS3tzciIyPRoEEDLFy4UOywtCouLg4pKSlo3LgxDA0NYWhoiKNHj2LRokUwNDSEUqkUO0SdsLGxQe3atXHjxg2xQ9EqFxeXQsW8h4eHJC75SQmLnzJgbGwMb29vHDp0SN2mUqlw6NAhycyHkBJBEBAaGoodO3bg8OHDcHNzEzsk0ahUKigUCrHD0KoOHTrg4sWLiI+PVy9NmjRBYGAg4uPjIZfLxQ5RJ548eYKbN2/CxcVF7FC0qmXLloUeXfH333+jatWqIkVE2sDLXmUkLCwMQUFBaNKkCZo1a4YFCxYgJycHQ4cOFTs0rXny5InGb4G3bt1CfHw87OzsUKVKFREj066QkBBER0fjv//9LywtLdXzuqytrWFmZiZydNoTHh4Of39/VKlSBY8fP0Z0dDT++OMP/P7772KHplWWlpaF5nOZm5vD3t5er+d5jR8/Ht27d0fVqlXx4MEDREREQC6Xo3///mKHplXjxo1DixYtMGvWLPTt2xenTp3C8uXLsXz5crFDo7Ik9u1m+uTHH38UqlSpIhgbGwvNmjUTYmNjxQ5Jq44cOSIAKLQEBQWJHZpWFZUzAGHVqlVih6ZVn3zyiVC1alXB2NhYqFixotChQwdh//79YoclCinc6v7xxx8LLi4ugrGxsfDee+8JH3/8sXDjxg2xw9KJX3/9VahXr55gYmIiuLu7C8uXLxc7JCpjMkEQBJHqLiIiIiKd45wfIiIikhQWP0RERCQpLH6IiIhIUlj8EBERkaSw+CEiIiJJYfFDREREksLih4iIiCSFxQ8RERFJCosfInqtIUOGoEePHurP77//PsaOHavzOP744w/IZDJkZmbq/NhEpF9Y/BCVU0OGDIFMJoNMJlO/bX369Ol49uyZVo+7fft2fPvtt8Xqy4KFiN5FfLEpUTnWpUsXrFq1CgqFAnv27EFISAiMjIwQHh6u0S8/Px/GxsZlckw7O7sy2Q8RkVg48kNUjpmYmMDZ2RlVq1bFyJEj4efnh127dqkvVc2cOROurq6oU6cOAODu3bvo27cvbGxsYGdnh48++gi3b99W70+pVCIsLAw2Njawt7fHl19+iX+//u/fl70UCgUmTpyIypUrw8TEBDVr1sSKFStw+/ZttGvXDgBga2sLmUyGIUOGAABUKhUiIyPh5uYGMzMzNGjQAFu3btU4zp49e1C7dm2YmZmhXbt2GnESEZUGix8iPWJmZob8/HwAwKFDh3Dt2jUcOHAAu3fvRkFBATp37gxLS0v8+eef+Ouvv2BhYYEuXbqot5k7dy5Wr16NlStX4vjx48jIyMCOHTtee8zBgwdjw4YNWLRoEa5cuYJly5bBwsIClStXxrZt2wAA165dw8OHD7Fw4UIAQGRkJP7zn/8gKioKCQkJGDduHAYOHIijR48CeF6k9erVC927d0d8fDw+/fRTfPXVV9r6shGR1Ij8VnkiektBQUHCRx99JAiCIKhUKuHAgQOCiYmJMH78eCEoKEhwcnISFAqFuv/atWuFOnXqCCqVSt2mUCgEMzMz4ffffxcEQRBcXFyEOXPmqNcXFBQIlSpVUh9HEAShbdu2wpgxYwRBEIRr164JAIQDBw4UGeORI0cEAMKjR4/UbXl5eUKFChWEEydOaPQNDg4W+vfvLwiCIISHhwuenp4a6ydOnFhoX0REb4NzfojKsd27d8PCwgIFBQVQqVQYMGAApk6dipCQEHh5eWnM8zl//jxu3LgBS0tLjX3k5eXh5s2byMrKwsOHD9G8eXP1OkNDQzRp0qTQpa8X4uPjIZfL0bZt22LHfOPGDTx9+hQdO3bUaM/Pz0ejRo0AAFeuXNGIAwB8fX2LfQwiotdh8UNUjrVr1w5Lly6FsbExXF1dYWj4v3/S5ubmGn2fPHkCb29vrF+/vtB+Klas+FbHNzMzK/E2T548AQD89ttveO+99zTWmZiYvFUcREQlweKHqBwzNzdHzZo1i9W3cePG2LRpExwdHWFlZVVkHxcXF5w8eRJt2rQBADx79gxxcXFo3Lhxkf29vLygUqlw9OhR+Pn5FVr/YuRJqVSq2zw9PWFiYoLExMRXjhh5eHhg165dGm2xsbFvTpKIqBg44ZlIIgIDA+Hg4ICPPvoIf/75J27duoU//vgDo0ePxr179wAAY8aMwezZs7Fz505cvXoVo0aNeu0zeqpVq4agoCB88skn2Llzp3qfmzdvBgBUrVoVMpkMu3fvRmpqKp48eQJLS0uMHz8e48aNw5o1a3Dz5k2cPXsWP/74I9asWQMA+Oyzz3D9+nVMmDAB165dQ3R0NFavXq3tLxERSQSLHyKJqFChAo4dO4YqVaqgV69e8PDwQHBwMPLy8tQjQV988QUGDRqEoKAg+Pr6wtLSEj179nztfpcuXYrevXtj1KhRcHd3x7Bhw5CTkwMAeO+99zBt2jR89dVXcHJyQmhoKADg22+/xeTJkxEZGQkPDw906dIFv/32G9zc3AAAVapUwbZt27Bz5040aNAAUVFRmDVrlha/OkQkJTLhVTMZiYiIiPQQR36IiIhIUlj8EBERkaSw+CEiIiJJYfFDREREksLih4iIiCSFxQ8RERFJCosfIiIikhQWP0RERCQpLH6IiIhIUlj8EBERkaSw+CEiIiJJ+T/4rNl422zOTAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}