RF_2m.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/omarseleem92/flood_susceptibility_mapping/blob/main/RF_2m.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Install Geopandas \n",
        "!pip install --upgrade geopandas\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UwK5TyLINy9L",
        "outputId": "0094ec6d-560c-440f-b343-3ee08602dfc5"
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.4)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.21)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2022.6.15)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (22.1.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2022.2.1)\n"
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      "execution_count": null,
      "metadata": {
        "id": "UqiweDj0IJ9r"
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      "source": [
        "## import packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "import geopandas as gpd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Mount google drive to get the data\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWmxBpLvN-ZX",
        "outputId": "481ec70a-afca-4a4f-89bb-54e93c6df57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
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              "   Flooded    Aspect    CN     Curve       DEM  DTDrainage   DTRiver  \\\n",
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          "metadata": {},
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      ],
      "source": [
        "# Read the shapefile\n",
        "# The shapefile includes 3934 locations, half the locations were obtained from the flood inventory while the other half was generated randomly.\n",
        "# Flooded = 0 indicates not flooded location while Flooded = 1 indicates flooded location\n",
        "## Every location has values for the 11 flood predictory features.\n",
        "df=gpd.read_file(\"/content/drive/MyDrive/RF_model_Flood_susceptibility/Merged_RF.shp\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUp_4ZH-IJ9v",
        "outputId": "a58f4415-221a-4534-fe01-54ee1e43f0e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             count      mean       std       min       25%       50%  \\\n",
            "Flooded     3934.0  0.500000  0.500064  0.000000  0.000000  0.500000   \n",
            "Aspect      3934.0  0.490472  0.299935  0.000000  0.230686  0.494488   \n",
            "CN          3934.0  0.765074  0.054014  0.580000  0.720000  0.770000   \n",
            "Curve       3934.0  0.799673  0.001490  0.784468  0.799365  0.799630   \n",
            "DEM         3934.0  0.326524  0.053403  0.248286  0.283137  0.299461   \n",
            "DTDrainage  3934.0  0.016004  0.025404  0.000000  0.004649  0.007977   \n",
            "DTRiver     3934.0  0.069707  0.049146  0.000000  0.026939  0.061735   \n",
            "DTRoad      3934.0  0.013727  0.013888  0.000000  0.005493  0.011653   \n",
            "Freq        3934.0  0.523706  0.204706  0.079168  0.376067  0.540465   \n",
            "Rainfall    3934.0  0.408745  0.202367  0.036162  0.221126  0.427292   \n",
            "Slope       3934.0  0.025746  0.047224  0.000000  0.007828  0.014431   \n",
            "TWI         3934.0  0.223363  0.069369  0.041362  0.173229  0.207372   \n",
            "\n",
            "                 75%       max  \n",
            "Flooded     1.000000  1.000000  \n",
            "Aspect      0.750694  0.999999  \n",
            "CN          0.810000  1.000000  \n",
            "Curve       0.799894  0.842913  \n",
            "DEM         0.369572  0.557868  \n",
            "DTDrainage  0.015516  0.245576  \n",
            "DTRiver     0.107143  0.206167  \n",
            "DTRoad      0.016016  0.231573  \n",
            "Freq        0.708687  0.953356  \n",
            "Rainfall    0.568352  0.978566  \n",
            "Slope       0.026852  0.813648  \n",
            "TWI         0.263089  0.602377  \n"
          ]
        }
      ],
      "source": [
        "print(df.describe().T)  #Values are already normalized in GIS before fitting. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-cliJ3TIJ9w",
        "outputId": "a499259b-dca4-412c-fd42-ce6d5f816a28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Flooded       0\n",
            "Aspect        0\n",
            "CN            0\n",
            "Curve         0\n",
            "DEM           0\n",
            "DTDrainage    0\n",
            "DTRiver       0\n",
            "DTRoad        0\n",
            "Freq          0\n",
            "Rainfall      0\n",
            "Slope         0\n",
            "TWI           0\n",
            "geometry      0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "## Check if there is null values\n",
        "print(df.isnull().sum())\n",
        "#df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "VeN3SPseIJ9x",
        "outputId": "58ff286a-dbe6-4da1-9b06-9185a8982018"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label            int64\n",
            "Aspect         float64\n",
            "CN             float64\n",
            "Curve          float64\n",
            "DEM            float64\n",
            "DTDrainage     float64\n",
            "DTRiver        float64\n",
            "DTRoad         float64\n",
            "Freq           float64\n",
            "Rainfall       float64\n",
            "Slope          float64\n",
            "TWI            float64\n",
            "geometry      geometry\n",
            "dtype: object\n"
          ]
        }
      ],
      "source": [
        "df = df.rename(columns={'Flooded':'Label'})\n",
        "print(df.dtypes)"
      ]
    },
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Understand the data \n",
        "# The dataset includes the same number of flooded and not flooded locations\n",
        "sns.countplot(x=\"Label\", data=df) #0 - Notflooded   1 - Flooded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "lgX_IWDnIJ9y",
        "outputId": "187b2b63-a24b-4b06-a089-9b7c9d59c753"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe150874510>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Correlation between the input features\n",
        "\n",
        "corrMatrix = df.corr()\n",
        "fig, ax = plt.subplots(figsize=(10,10))         # Sample figsize in inches\n",
        "#sns.heatmap(df.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)\n",
        "sns.heatmap(corrMatrix, annot=False, linewidths=.5, ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AzsluB32IJ9z"
      },
      "outputs": [],
      "source": [
        "#Define the dependent variable that needs to be predicted (labels)\n",
        "Y = df[\"Label\"].values\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMYgxAMBIJ90"
      },
      "outputs": [],
      "source": [
        "#Define the independent variables. \n",
        "X = df.drop(labels = [\"Label\",'geometry'], axis=1) \n",
        "features_list = list(X.columns)  #List features so we can rank them later.\n",
        "#from sklearn.preprocessing import normalize\n",
        "# Only needed if the data was not normalized\n",
        "#X = normalize(X, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PQzEFF6DIJ91",
        "outputId": "c1e3c714-4d0c-4220-b2b8-e6c98036662d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Aspect    CN     Curve       DEM  DTDrainage   DTRiver    DTRoad  \\\n",
              "0     0.957001  0.72  0.799850  0.412259    0.008363  0.054802  0.020846   \n",
              "1     0.470967  0.72  0.800776  0.401485    0.006973  0.078309  0.008858   \n",
              "2     0.942324  0.72  0.800335  0.412341    0.009118  0.104160  0.025825   \n",
              "3     0.994332  0.72  0.799277  0.439112    0.017643  0.094123  0.024873   \n",
              "4     0.076357  0.72  0.799542  0.418952    0.006865  0.090165  0.007769   \n",
              "...        ...   ...       ...       ...         ...       ...       ...   \n",
              "3929  0.388047  0.72  0.799542  0.396425    0.012825  0.127760  0.009355   \n",
              "3930  0.142494  0.81  0.799806  0.371613    0.003333  0.164813  0.010987   \n",
              "3931  0.666879  0.81  0.799850  0.360921    0.003465  0.163731  0.011653   \n",
              "3932  0.982427  0.81  0.799057  0.369327    0.002950  0.171007  0.011123   \n",
              "3933  0.501355  0.72  0.799277  0.394303    0.022589  0.151540  0.014842   \n",
              "\n",
              "          Freq  Rainfall     Slope       TWI  \n",
              "0     0.466105  0.164432  0.016718  0.164849  \n",
              "1     0.627576  0.255468  0.091135  0.210387  \n",
              "2     0.705864  0.299589  0.067785  0.164357  \n",
              "3     0.729333  0.349291  0.025067  0.173485  \n",
              "4     0.734826  0.290722  0.010006  0.263441  \n",
              "...        ...       ...       ...       ...  \n",
              "3929  0.114188  0.092143  0.008882  0.232116  \n",
              "3930  0.318662  0.178534  0.013349  0.171602  \n",
              "3931  0.302372  0.198849  0.012433  0.173735  \n",
              "3932  0.299590  0.176342  0.004053  0.294096  \n",
              "3933  0.231982  0.175280  0.000895  0.273471  \n",
              "\n",
              "[3934 rows x 11 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Aspect</th>\n",
              "      <th>CN</th>\n",
              "      <th>Curve</th>\n",
              "      <th>DEM</th>\n",
              "      <th>DTDrainage</th>\n",
              "      <th>DTRiver</th>\n",
              "      <th>DTRoad</th>\n",
              "      <th>Freq</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Slope</th>\n",
              "      <th>TWI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.957001</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.799850</td>\n",
              "      <td>0.412259</td>\n",
              "      <td>0.008363</td>\n",
              "      <td>0.054802</td>\n",
              "      <td>0.020846</td>\n",
              "      <td>0.466105</td>\n",
              "      <td>0.164432</td>\n",
              "      <td>0.016718</td>\n",
              "      <td>0.164849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.470967</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.800776</td>\n",
              "      <td>0.401485</td>\n",
              "      <td>0.006973</td>\n",
              "      <td>0.078309</td>\n",
              "      <td>0.008858</td>\n",
              "      <td>0.627576</td>\n",
              "      <td>0.255468</td>\n",
              "      <td>0.091135</td>\n",
              "      <td>0.210387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.942324</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.800335</td>\n",
              "      <td>0.412341</td>\n",
              "      <td>0.009118</td>\n",
              "      <td>0.104160</td>\n",
              "      <td>0.025825</td>\n",
              "      <td>0.705864</td>\n",
              "      <td>0.299589</td>\n",
              "      <td>0.067785</td>\n",
              "      <td>0.164357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.994332</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.799277</td>\n",
              "      <td>0.439112</td>\n",
              "      <td>0.017643</td>\n",
              "      <td>0.094123</td>\n",
              "      <td>0.024873</td>\n",
              "      <td>0.729333</td>\n",
              "      <td>0.349291</td>\n",
              "      <td>0.025067</td>\n",
              "      <td>0.173485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.076357</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.799542</td>\n",
              "      <td>0.418952</td>\n",
              "      <td>0.006865</td>\n",
              "      <td>0.090165</td>\n",
              "      <td>0.007769</td>\n",
              "      <td>0.734826</td>\n",
              "      <td>0.290722</td>\n",
              "      <td>0.010006</td>\n",
              "      <td>0.263441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3929</th>\n",
              "      <td>0.388047</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.799542</td>\n",
              "      <td>0.396425</td>\n",
              "      <td>0.012825</td>\n",
              "      <td>0.127760</td>\n",
              "      <td>0.009355</td>\n",
              "      <td>0.114188</td>\n",
              "      <td>0.092143</td>\n",
              "      <td>0.008882</td>\n",
              "      <td>0.232116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3930</th>\n",
              "      <td>0.142494</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799806</td>\n",
              "      <td>0.371613</td>\n",
              "      <td>0.003333</td>\n",
              "      <td>0.164813</td>\n",
              "      <td>0.010987</td>\n",
              "      <td>0.318662</td>\n",
              "      <td>0.178534</td>\n",
              "      <td>0.013349</td>\n",
              "      <td>0.171602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3931</th>\n",
              "      <td>0.666879</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799850</td>\n",
              "      <td>0.360921</td>\n",
              "      <td>0.003465</td>\n",
              "      <td>0.163731</td>\n",
              "      <td>0.011653</td>\n",
              "      <td>0.302372</td>\n",
              "      <td>0.198849</td>\n",
              "      <td>0.012433</td>\n",
              "      <td>0.173735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3932</th>\n",
              "      <td>0.982427</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799057</td>\n",
              "      <td>0.369327</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>0.171007</td>\n",
              "      <td>0.011123</td>\n",
              "      <td>0.299590</td>\n",
              "      <td>0.176342</td>\n",
              "      <td>0.004053</td>\n",
              "      <td>0.294096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3933</th>\n",
              "      <td>0.501355</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.799277</td>\n",
              "      <td>0.394303</td>\n",
              "      <td>0.022589</td>\n",
              "      <td>0.151540</td>\n",
              "      <td>0.014842</td>\n",
              "      <td>0.231982</td>\n",
              "      <td>0.175280</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.273471</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3934 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b0d6a030-2e90-4a9e-b864-37a2ca4b79bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b0d6a030-2e90-4a9e-b864-37a2ca4b79bd 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-b0d6a030-2e90-4a9e-b864-37a2ca4b79bd');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# the independent variables\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the dependent variable\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbbTSOQZVLh4",
        "outputId": "ffbb8ef7-c3b1-4ec4-e364-720ec4b3070c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split data into train, validation and test to verify accuracy after fitting the model. \n",
        "# Firstly split the data into train_validation and test datasets then split the train_validation dataset into train and validation datasets.\n",
        "# The training dataset is used to train the model, the validation dataset is used for hyperparamter tuning and the testing dataset is used to test the model.\n",
        "# It is recommended to test the model with a data that the model hasn't seen in the training process\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train_val, X_test, y_train_val, y_test = train_test_split(X, Y, test_size=0.2,shuffle=True, random_state=42)\n"
      ],
      "metadata": {
        "id": "JvSaaEUVMw3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25,shuffle=True, random_state=42)"
      ],
      "metadata": {
        "id": "msedWfEGOpKP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dfsttUO5IJ92"
      },
      "outputs": [],
      "source": [
        "#RANDOM FOREST\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WK0bUaYIJ93",
        "outputId": "b190724d-bc28-450d-ab2b-75b0a80a0b27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Train the model on training data\n",
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpC_2uSHIJ93"
      },
      "outputs": [],
      "source": [
        "# Model prediction\n",
        "prediction = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Frz3lKefIJ94",
        "outputId": "a6f0f5fc-a933-4b39-e173-99f716697cb2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0,\n",
              "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1,\n",
              "       0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
              "       0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0,\n",
              "       1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
              "       0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
              "       0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
              "       0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
              "       1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Prediction are 1 (Flooded) and 0 (Not flooded)\n",
        "prediction "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Wm6R1tRWIJ94",
        "outputId": "5382108c-cea1-46b8-ac52-1906762b25e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Draw the Receiver Operating Characteristics and estimate the Area under the curve (AUC)\n",
        "# for more information about the AUC, please see https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5\n",
        "\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "\n",
        "ax = plt.gca()\n",
        "model_disp = plot_roc_curve(model, X_test, y_test, ax=ax, alpha=0.8)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-uIbXNfkIJ96"
      },
      "outputs": [],
      "source": [
        "# In order to map the flood susceptibility, we need to predict the flood susceptibility (probability between 0 and 1)\n",
        "prediction_prob=model.predict_proba(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IioOkLppIJ96",
        "outputId": "3fb9bb8c-29da-4ecb-8210-adb0e73e023f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.05, 0.95],\n",
              "       [0.88, 0.12],\n",
              "       [0.33, 0.67],\n",
              "       ...,\n",
              "       [0.  , 1.  ],\n",
              "       [0.64, 0.36],\n",
              "       [0.  , 1.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# The probability of being not flooded and flooded for every location ( Their summation equals 1)\n",
        "prediction_prob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPQCt4daIJ96",
        "outputId": "9a0e290b-cc21-4748-f019-fa1b7af3f7b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(787,)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "ls=prediction_prob[:,1]\n",
        "ls.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# As flood susceptibility is classification problem (flooded or not flooded), we can calculate some additional performance indices\n",
        "from sklearn.metrics import classification_report\n",
        "target_names=[\"Not Flooded\",\"Flooded\"]\n",
        "print(classification_report(y_test, prediction, target_names=target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsjM_rS7PDyO",
        "outputId": "ad5b3cb3-7fa1-4b08-d5ea-ce1298c8d307"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " Not Flooded       0.84      0.89      0.87       395\n",
            "     Flooded       0.89      0.83      0.86       392\n",
            "\n",
            "    accuracy                           0.86       787\n",
            "   macro avg       0.87      0.86      0.86       787\n",
            "weighted avg       0.87      0.86      0.86       787\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import cohen_kappa_score\n",
        "print(cohen_kappa_score(y_test, prediction))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnz6y41vPgKD",
        "outputId": "f093cfb6-8ce9-4be5-92f0-3d40f39fd075"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7280167694300268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "La5vuKXJIJ98",
        "outputId": "f2cdcf67-b297-4d02-e719-48a9d1e9c2f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[353  42]\n",
            " [ 65 327]]\n"
          ]
        }
      ],
      "source": [
        "#Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, prediction)\n",
        "print(cm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eCDsQ1hIJ98",
        "outputId": "9744e5b9-9f43-4225-9220-f474578897f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DEM           0.174355\n",
            "DTDrainage    0.155009\n",
            "DTRoad        0.123513\n",
            "DTRiver       0.111450\n",
            "Freq          0.098538\n",
            "Rainfall      0.092725\n",
            "TWI           0.060701\n",
            "Curve         0.059290\n",
            "Slope         0.045487\n",
            "Aspect        0.039515\n",
            "CN            0.039418\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#importances = list(model_RF.feature_importances_)\n",
        "feature_imp = pd.Series(model.feature_importances_, index=features_list).sort_values(ascending=False)\n",
        "print(feature_imp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "HVsOXSYOIJ99",
        "outputId": "b57ed0d6-6ecd-471e-e2b2-65a2416894f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1506e5810>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "feature_imp.plot.barh()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvmJIA9iIJ9_"
      },
      "source": [
        "# Berlin"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install pickle5\n",
        "import pickle5 as pickle\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ao7QCF_HQKu5",
        "outputId": "dfcba265-37f7-47ec-c2ee-a180d4b68d3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pickle5 in /usr/local/lib/python3.7/dist-packages (0.0.12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('/content/drive/MyDrive/RF_model_Flood_susceptibility/district_2_RF_2m.pkl', \"rb\") as fh:\n",
        "  df = pickle.load(fh)\n"
      ],
      "metadata": {
        "id": "1RSmqfebQwC_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V-gmkJ-YQ3GQ",
        "outputId": "7c6a26d8-5c4d-4836-9dfe-a10b07b3ae6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       VALUE    Aspect    CN       DEM  DTDrainage   DTRiver    DTRoad  \\\n",
              "0  34.580002  0.523138  0.81  0.282316    0.002574  0.033522  0.007252   \n",
              "1  34.580002  0.510413  0.81  0.282224    0.003075  0.033429  0.007729   \n",
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              "\n",
              "       Freq     Slope       TWI     Curve  Rainfall  \\\n",
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              "     Aspect    CN       DEM  DTDrainage   DTRiver    DTRoad      Freq  \\\n",
              "0  0.523138  0.81  0.282316    0.002574  0.033522  0.007252  0.510442   \n",
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              "4  0.431156  0.81  0.282189    0.003590  0.033337  0.007593  0.510442   \n",
              "\n",
              "      Slope       TWI     Curve  Rainfall  \n",
              "0  0.009681  0.215963  0.799423  0.623310  \n",
              "1  0.005916  0.250464  0.799536  0.623310  \n",
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              "          const element = document.querySelector('#df-29e7039d-c622-4fe2-ac07-c21c5f51fa49');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "X_hotspot0= df.drop(labels = [\"geometry\",'VALUE'], axis=1) \n",
        "X_hotspot0.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "kvNR89vrIJ-L",
        "outputId": "81b06d2c-073e-4475-8d59-7f42e241f7b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Aspect', 'CN', 'Curve', 'DEM', 'DTDrainage', 'DTRiver', 'DTRoad',\n",
              "       'Freq', 'Rainfall', 'Slope', 'TWI'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "cols=X.columns\n",
        "cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "85akwKz3IJ-M",
        "outputId": "5b21157e-31bc-40ad-81a1-834e03c5b7c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Aspect    CN     Curve       DEM  DTDrainage   DTRiver    DTRoad  \\\n",
              "0  0.523138  0.81  0.799423  0.282316    0.002574  0.033522  0.007252   \n",
              "1  0.510413  0.81  0.799536  0.282224    0.003075  0.033429  0.007729   \n",
              "2  0.500386  0.81  0.799595  0.282241    0.002925  0.033522  0.006224   \n",
              "3  0.292593  0.81  0.799455  0.282350    0.003793  0.033244  0.009160   \n",
              "4  0.431156  0.81  0.799533  0.282189    0.003590  0.033337  0.007593   \n",
              "\n",
              "       Freq  Rainfall     Slope       TWI  \n",
              "0  0.510442  0.623310  0.009681  0.215963  \n",
              "1  0.510442  0.623310  0.005916  0.250464  \n",
              "2  0.510442  0.623310  0.005322  0.229668  \n",
              "3  0.510741  0.623741  0.016196  0.261043  \n",
              "4  0.510442  0.623310  0.003926  0.328652  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2c9b5143-8013-4b14-b7bc-ee000e8c3e14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Aspect</th>\n",
              "      <th>CN</th>\n",
              "      <th>Curve</th>\n",
              "      <th>DEM</th>\n",
              "      <th>DTDrainage</th>\n",
              "      <th>DTRiver</th>\n",
              "      <th>DTRoad</th>\n",
              "      <th>Freq</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>Slope</th>\n",
              "      <th>TWI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.523138</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799423</td>\n",
              "      <td>0.282316</td>\n",
              "      <td>0.002574</td>\n",
              "      <td>0.033522</td>\n",
              "      <td>0.007252</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>0.009681</td>\n",
              "      <td>0.215963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.510413</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799536</td>\n",
              "      <td>0.282224</td>\n",
              "      <td>0.003075</td>\n",
              "      <td>0.033429</td>\n",
              "      <td>0.007729</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>0.005916</td>\n",
              "      <td>0.250464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.500386</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799595</td>\n",
              "      <td>0.282241</td>\n",
              "      <td>0.002925</td>\n",
              "      <td>0.033522</td>\n",
              "      <td>0.006224</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>0.005322</td>\n",
              "      <td>0.229668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.292593</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799455</td>\n",
              "      <td>0.282350</td>\n",
              "      <td>0.003793</td>\n",
              "      <td>0.033244</td>\n",
              "      <td>0.009160</td>\n",
              "      <td>0.510741</td>\n",
              "      <td>0.623741</td>\n",
              "      <td>0.016196</td>\n",
              "      <td>0.261043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.431156</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.799533</td>\n",
              "      <td>0.282189</td>\n",
              "      <td>0.003590</td>\n",
              "      <td>0.033337</td>\n",
              "      <td>0.007593</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>0.003926</td>\n",
              "      <td>0.328652</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c9b5143-8013-4b14-b7bc-ee000e8c3e14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-2c9b5143-8013-4b14-b7bc-ee000e8c3e14 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-2c9b5143-8013-4b14-b7bc-ee000e8c3e14');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "X_hotspot0=X_hotspot0[cols]\n",
        "X_hotspot0.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ky3INRrQIJ-M",
        "outputId": "c2ac2e32-a378-4575-c7e6-7923561d5b54"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aspect        0\n",
            "CN            0\n",
            "Curve         0\n",
            "DEM           0\n",
            "DTDrainage    0\n",
            "DTRiver       0\n",
            "DTRoad        0\n",
            "Freq          0\n",
            "Rainfall      0\n",
            "Slope         0\n",
            "TWI           0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print(X_hotspot0.isnull().sum())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXh1skroIJ-N"
      },
      "outputs": [],
      "source": [
        "X_hotspot0 = X_hotspot0.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fG2QtGAHIJ-N"
      },
      "outputs": [],
      "source": [
        "df=df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7zUFt6VaIJ-N"
      },
      "outputs": [],
      "source": [
        "prediction_prob=model.predict_proba(X_hotspot0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycqAWDb0IJ-N",
        "outputId": "0321e6eb-fbb1-43c1-8ee7-0a58388b338f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1 , 0.9 ],\n",
              "       [0.08, 0.92],\n",
              "       [0.09, 0.91],\n",
              "       ...,\n",
              "       [0.87, 0.13],\n",
              "       [0.9 , 0.1 ],\n",
              "       [0.87, 0.13]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "prediction_prob"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E12pnmUZIJ-O",
        "outputId": "e7bb6068-9869-4ecd-cfeb-1bd5fd30f768"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9 , 0.92, 0.91, ..., 0.13, 0.1 , 0.13])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "ls_hotspot0=prediction_prob[:,1]\n",
        "ls_hotspot0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "j6wHQ58mIJ-O",
        "outputId": "25ba5eaf-691f-4f38-e7b5-743836eb00a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       VALUE    Aspect    CN       DEM  DTDrainage   DTRiver    DTRoad  \\\n",
              "0  34.580002  0.523138  0.81  0.282316    0.002574  0.033522  0.007252   \n",
              "1  34.580002  0.510413  0.81  0.282224    0.003075  0.033429  0.007729   \n",
              "2  34.580002  0.500386  0.81  0.282241    0.002925  0.033522  0.006224   \n",
              "3  34.590000  0.292593  0.81  0.282350    0.003793  0.033244  0.009160   \n",
              "4  34.570000  0.431156  0.81  0.282189    0.003590  0.033337  0.007593   \n",
              "\n",
              "       Freq     Slope       TWI     Curve  Rainfall  \\\n",
              "0  0.510442  0.009681  0.215963  0.799423  0.623310   \n",
              "1  0.510442  0.005916  0.250464  0.799536  0.623310   \n",
              "2  0.510442  0.005322  0.229668  0.799595  0.623310   \n",
              "3  0.510741  0.016196  0.261043  0.799455  0.623741   \n",
              "4  0.510442  0.003926  0.328652  0.799533  0.623310   \n",
              "\n",
              "                         geometry   FSM  \n",
              "0  POINT (389893.708 5818773.972)  0.90  \n",
              "1  POINT (389893.708 5818771.972)  0.92  \n",
              "2  POINT (389895.708 5818771.972)  0.91  \n",
              "3  POINT (389891.708 5818769.972)  0.92  \n",
              "4  POINT (389893.708 5818769.972)  0.93  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25b7a00e-5100-401d-ae58-21ae2e29ddee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>VALUE</th>\n",
              "      <th>Aspect</th>\n",
              "      <th>CN</th>\n",
              "      <th>DEM</th>\n",
              "      <th>DTDrainage</th>\n",
              "      <th>DTRiver</th>\n",
              "      <th>DTRoad</th>\n",
              "      <th>Freq</th>\n",
              "      <th>Slope</th>\n",
              "      <th>TWI</th>\n",
              "      <th>Curve</th>\n",
              "      <th>Rainfall</th>\n",
              "      <th>geometry</th>\n",
              "      <th>FSM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34.580002</td>\n",
              "      <td>0.523138</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.282316</td>\n",
              "      <td>0.002574</td>\n",
              "      <td>0.033522</td>\n",
              "      <td>0.007252</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.009681</td>\n",
              "      <td>0.215963</td>\n",
              "      <td>0.799423</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>POINT (389893.708 5818773.972)</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34.580002</td>\n",
              "      <td>0.510413</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.282224</td>\n",
              "      <td>0.003075</td>\n",
              "      <td>0.033429</td>\n",
              "      <td>0.007729</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.005916</td>\n",
              "      <td>0.250464</td>\n",
              "      <td>0.799536</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>POINT (389893.708 5818771.972)</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.580002</td>\n",
              "      <td>0.500386</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.282241</td>\n",
              "      <td>0.002925</td>\n",
              "      <td>0.033522</td>\n",
              "      <td>0.006224</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.005322</td>\n",
              "      <td>0.229668</td>\n",
              "      <td>0.799595</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>POINT (389895.708 5818771.972)</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34.590000</td>\n",
              "      <td>0.292593</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.282350</td>\n",
              "      <td>0.003793</td>\n",
              "      <td>0.033244</td>\n",
              "      <td>0.009160</td>\n",
              "      <td>0.510741</td>\n",
              "      <td>0.016196</td>\n",
              "      <td>0.261043</td>\n",
              "      <td>0.799455</td>\n",
              "      <td>0.623741</td>\n",
              "      <td>POINT (389891.708 5818769.972)</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34.570000</td>\n",
              "      <td>0.431156</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.282189</td>\n",
              "      <td>0.003590</td>\n",
              "      <td>0.033337</td>\n",
              "      <td>0.007593</td>\n",
              "      <td>0.510442</td>\n",
              "      <td>0.003926</td>\n",
              "      <td>0.328652</td>\n",
              "      <td>0.799533</td>\n",
              "      <td>0.623310</td>\n",
              "      <td>POINT (389893.708 5818769.972)</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25b7a00e-5100-401d-ae58-21ae2e29ddee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-25b7a00e-5100-401d-ae58-21ae2e29ddee 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-25b7a00e-5100-401d-ae58-21ae2e29ddee');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df['FSM']=ls_hotspot0\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install geocube"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BeR5qlyXRsBy",
        "outputId": "5fc0c300-adde-41fc-b2ea-cbd841d8072b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: geocube in /usr/local/lib/python3.7/dist-packages (0.1.0)\n",
            "Requirement already satisfied: xarray>=0.17 in /usr/local/lib/python3.7/dist-packages (from geocube) (0.20.2)\n",
            "Requirement already satisfied: datacube in /usr/local/lib/python3.7/dist-packages (from geocube) (1.8.5)\n",
            "Requirement already satisfied: Click>=6.0 in /usr/local/lib/python3.7/dist-packages (from geocube) (7.1.2)\n",
            "Requirement already satisfied: rioxarray>=0.4 in /usr/local/lib/python3.7/dist-packages (from geocube) (0.9.1)\n",
            "Requirement already satisfied: pyproj>=2 in /usr/local/lib/python3.7/dist-packages (from geocube) (3.2.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from geocube) (21.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from geocube) (1.7.3)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/dist-packages (from geocube) (1.4.4)\n",
            "Requirement already satisfied: rasterio in /usr/local/lib/python3.7/dist-packages (from geocube) (1.2.10)\n",
            "Requirement already satisfied: geopandas>=0.7 in /usr/local/lib/python3.7/dist-packages (from geocube) (0.10.2)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas>=0.7->geocube) (1.3.5)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas>=0.7->geocube) (1.8.21)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas>=0.7->geocube) (1.8.4)\n",
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            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (1.1.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (2022.6.15)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (57.4.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (22.1.0)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.7->geocube) (0.7.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.7->geocube) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.7->geocube) (2022.2.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.7->geocube) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7 in /usr/local/lib/python3.7/dist-packages (from xarray>=0.17->geocube) (4.1.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from xarray>=0.17->geocube) (4.12.0)\n",
            "Requirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (4.3.3)\n",
            "Requirement already satisfied: netcdf4 in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (1.6.0)\n",
            "Requirement already satisfied: psycopg2 in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (2.9.3)\n",
            "Requirement already satisfied: cloudpickle>=0.4 in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (1.5.0)\n",
            "Requirement already satisfied: distributed in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (2022.2.0)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (0.12.0)\n",
            "Requirement already satisfied: cachetools in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (4.2.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (6.0)\n",
            "Requirement already satisfied: lark-parser>=0.6.7 in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (0.12.0)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (1.4.40)\n",
            "Requirement already satisfied: affine in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (2.3.1)\n",
            "Requirement already satisfied: dask[array] in /usr/local/lib/python3.7/dist-packages (from datacube->geocube) (2022.2.0)\n",
            "Requirement already satisfied: snuggs>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from rasterio->geocube) (1.4.7)\n",
            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.7/dist-packages (from snuggs>=1.4.1->rasterio->geocube) (3.0.9)\n",
            "Requirement already satisfied: partd>=0.3.10 in /usr/local/lib/python3.7/dist-packages (from dask[array]->datacube->geocube) (1.3.0)\n",
            "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from dask[array]->datacube->geocube) (2022.8.1)\n",
            "Requirement already satisfied: locket in /usr/local/lib/python3.7/dist-packages (from partd>=0.3.10->dask[array]->datacube->geocube) (1.0.0)\n",
            "Requirement already satisfied: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (5.4.8)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (2.11.3)\n",
            "Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (1.0.4)\n",
            "Requirement already satisfied: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (2.2.0)\n",
            "Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (5.1.1)\n",
            "Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (2.4.0)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed->datacube->geocube) (1.7.0)\n",
            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed->datacube->geocube) (1.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->xarray>=0.17->geocube) (3.8.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->distributed->datacube->geocube) (2.0.1)\n",
            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->datacube->geocube) (5.9.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->datacube->geocube) (0.18.1)\n",
            "Requirement already satisfied: cftime in /usr/local/lib/python3.7/dist-packages (from netcdf4->datacube->geocube) (1.6.1)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->datacube->geocube) (1.1.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jJmfMXMPIJ-O"
      },
      "outputs": [],
      "source": [
        "import geocube\n",
        "from geocube.api.core import make_geocube\n",
        "\n",
        "geo_grid = make_geocube(\n",
        "    vector_data=df,\n",
        "    measurements=['FSM'],\n",
        "    resolution=(-2, 2)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mOpYrfYuIJ-P",
        "outputId": "833bcc1c-65d5-40a4-d7c7-fd788bab1657"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x2263ae86820>"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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zbJOHJvtY8YIdVI/vYdEvpzj7sk8emjE8AEgk/j6+nmp0BEyHDgeJX499FqHriO48ImIgLRtkAEKg5bKgCIyt40hV4C7K4qVN/LgBxTL+XDH0mZgGYmoOETURmoa0bETUDBvIpkNh4wco2QwikwII99U1pOOCoYehxYCs1ULTWCoJrovQtTAsuTsPEtKbdGrLA7ysx9K/3UrrG/1IVaBXLZRaG6mrBKkoWt3BjWuItovwAtSZKgQB+kwDrdREq9moNggPCreF0Q7mRI3qcoE4tsbu50apDypkvlPDyUQY+nWL1kqHN3zrJ+x+cR7Fg9itce6/czlXfOTXVI/vIXrHDs5+5icOyTjudyT4+/h6qtExkXXocBCRhSxiah5yGRQpw1ySbAYZNxG+D7aHPtcgiC5oJAqQSaNm0lCrE0R0FMMIM/s9Dwb6wPORs/NhkmUyAWYEOVcE20EYOn6pjNB0lEwK6brguGGosFDw222UUjnUZNJJEAqyWCa7MUL3F6o0PjrAxOsdbt29jP6qT2y0gZ+KINouUggqR6YQPqR2t2iszhKoguhcDC+mYk42sQsx2t06XhScI9pMV/LoTUljZZbkqKRlpTAFJPcGVJwoVpdGZrKOZprcUF3LFZffzE0fOo25ozWedc49/OztF1A8XSE+tpjYjsNjRYww0fLwpKPBdOhwEHG74ngrB8BxsI9YjHfEMO1lXfiJCF42RpCO4RYSqPNhtr3wJX46RntpjvaxQ6ApBN0ZWMjWF8UyVGugKgS1RuirUZUw90TXQErUrhxKKoEsZP+QxS96C/jVGjKQSMvGX9geTM0gusKAgc13L8F9bxG7GiF7nYniQ2s4iZvQQBVITZAYczAaAVIVJLaXSW2v0u42cNIqqAK9ZpO9dx4vBr6tkpj0Se+yMOds8ncUCQxoDfgYNY8X9m6g8vwmou0w9EWV26aXMOckUN42w/AFI1z7y5NQ3ACzBLteGINWm4uP+LtDMo77F4G/j6+nGh0B06HDQeT6Gz6IVYiAYeDHVPTROaJbp9Hm6uh751HmKhh75miv6kYEAXaXiTo2Q/TePehVh/qSBMpcNTSXKQrBYA/SdhBCQe3tDrWZuWLo+BcC4nGCeiOMIpMSJZ0iqNfxd40ilPCBFbStBf+Mj4hHkbEIjZVZFh83gfL5AkcuH8ePCFTLJ76nRnSkijQ09KkqkdEi8V0V9L3zMDWHKFZJ3zpCZsMcgamjNG2swTQIiGfauHEFfbqGYvmItk1uc0Bsr8rei3V+8IJn0PWdGF5PGiunkzbbvK/ndyxPzzPyu2EW3WRjTNfJ7PIIzIDZ/0oiShXOufDfDvGo/mWEUWRin15PNTomsg4dDjKxsQZ+NhZGkBlGKCzaVlgXzA+NJdGd88i4iVGyCeoNlGwGbcc43oqVYTiyEKBpKE2b9skraBd0VEeitQPiWxTk7DwilwnNaEKA5+GnoqhqL0qpFkac6RqKHQoqFIXayYOkHpwH1yf7jlFGfraUlBEw/c0lRFqhI9/ujmMUW2HYtKaC7SLaDmgacrAPCQjHw09HQUqkoREYCpES2L6Cb0BjbVhGJlluYNR8vJgCEhorMqTWT4Ci4qzsp1JP8uptL2fqnn6cYZfahEFhPKA+oBEfhcZ8Hu+KPP0/2sO5F3yc31/3gUM6rn8uYR7MU0947AsdAdOhw0GmckQaOyWQCsQ2tkEPTU5AKAykDGt6RQ38uI6x4JQnGpqpWCgrYq1dhDHXIrq7RKQYQ6oCxfLwetKIrgRqsYEszqEkEwSLCmhz9bCSQKlG0J9HaVjITBK7O465c47U5hJBLII0NSZ+2k3rpDaBFqX3bgs/oqDPNBBBgJeLo3pBmDRqGgRRHaVhh0JHV0FXkZpAeBICcBIKVhfYFRMzLnBSKrnNDm5/hvqgTmIM9DbEJtu0V/fSWKTTtbFGdrPC5Jn99O70GT9XJTHpgeNQuKeKXYgxd7RO7USLzI4Bonft5IIzPsZ1tz41TWbBU1A72Rc6AqZDh4PMXd94Fys/ehWREky+YAl6QxKf9IjMWzi5CG5CxZx3MEbmUGMG7op+hCcpr4mFD9lYFHsoi+IGKE0Lmi2UIAj9J7EYWj4Lng+VGiKdAstG2C6oCtFdRWQ6jtg5BvEYTNSJ7NUIhvpBAaXaovxxjT6zSPEbi7EzoLZdjK0z4bIBlRre4gxauUWQjiFVFQAvn0CxPaSu4qYMAkNBr7kEQsdoBCy62WPiLAMAxYH5ow2Q0OqXCAnRKUFyRCG6u4RqZ/BjOlqlzaLfVxCuz8q9Kkq5CWYEpW4R8SWpTAqpm4yfJ8n2rSb/w4e4+NgPc+39/3QIR/dPp6PBdOjQYb/i5H1UR2XxNVWUaouZc/vQ2j5GxcaoAEJgr+hBqztITUEqkmgpQLV8gqRJZE8R6g2CWiPMX1FVhBkJzVblaujsVwSy3Q7XlNk7hVQU5PJBvLiOMasTzBfDKgDxGFJTqKxOEpuJUW1Z1NoRsi+ege/1oJaaBL15lPlKmNgpCQMRWi5qw8JPmagNG2loSEUQmapj9yXRR+ewV/Sg11x8U6VnvUd0xsLqNtFaPlrDxc5FUK2AdrdOu8dEn6ygOD5Ky0UUq4hYNLxhI3NhWZtaLczh8X3SD3nY6TxqS5B9+V7qlbWhie8phkTgH6bu8I6A6dDhUBDzUdsqYxeniU+m6NrUQrFclLEZyKQJxicxlg/hdMcxZpsEUR1zNkBqCm4uit+fQLW6aBd00purf3DqB4aGMlchmCv+IYnSr9YQmo6ImsiN29DjsdB/09sNzTYkYjSG4phFj5kTDDzPJXZbnJnTNAptCUGAUlxow/PwTQWj2ArNeYBieUhNQVgO1qIM3lAUs+jRXt0bJmF6Ae2CieJK6sMxImUP31RxExqBJhC+RLMkIpD4hRRO2kDXFJQpP/QP2TYiHoMFQSrbFhSyiJkiXQ/FmHhGjMo3BtFeP0v9S3kuPubvuXbjPx/iAd53Qif/4SlgDs+r6tDhyY4vCAzou7WNrwvchEZtRZLaWcuh1UbJZfGTJpGHxrB7Etg5E6sQwc7qRHbOErt/nMhIicxNI4iWjai3YGQCP6pBLIqyqA+lJx9qN4YR1iRLxFGiZhiGrCphkIEZgVoDK6sS21nkqMu24bZ1/vtdnyV2ZxzVluB52Kt6Cfq6kH0FzOk2zaEkfkzH6U0ycW4WuxCjtK6L0mqNRp9CeaWBk9aYP9KkdEQcKyNwEoJIxUeqAuEGGBWX1MZZVCfASSo4SZXy6jhSF+izdQCC0TFQVIKBboia4fXoGmJqLsz3UWDov3dQXSqYmsriRRXETJFLBt52iAf4T0HgS2WfXk81nno97tDhKc7wf3yKo5aPccEL7qZ4ZJT4jIeQkLlhF9EZi+K5QzSPG8CPqFTPWkpktoniSYyKS6TkhEIhCKDeACGQ07MEs/OIRBw3oYeNVGuhoMlmEEsHEZHwGBE1w0TLRBwSMYhFcVb10/27MVorurj/ulW8+Nh7+JuHXo4Xg9JqFRmPEZmoocxWIAiYPDtJdMZC+BK91EZ1odmv0yoI4tMSLw6BDo1+BT8CVhbsLLT6BM0+DdUJUFwZRpmZOoGuUF4tmDlZ4iQFgSag0QqTUKNRnMU5lHIDdD1cM0aIsLaa56M4PvZRixm83qL/VxrlVYLKuctAyqdMpn+YaKns0+upxlOvxx06PMWJ71XZfM8StrxpLX3XThLfXUUEktaJSwBIjlo4KRU7pxPoAoIAc9M4+lyTQFepHJtn+jlLsY4eYv7CYVg6iNLXA76POdPET5kEA9043QlkJoGoNBCFrrDxVDLUXnQtDARQBE5KQ8aijDwPjr9wCz/78enYrsaXXvcF3CQ0V2TA8wl6soh6i9icxOo2qQ9HGb8wi5WH8mqB1S2pLhNEyqC4YXOqDZEqaM2wVIyTFBTXRLDyOsaOKZxCAinAj0iOX7eL/heM0OhTCXpz0FMAQN+4Gzk7jyyVCSo1ZNsiqNYISmUAFCdg7M0eU2eCb0qOe899NE5cTGx3mXWvv+ogj+6fRyfRskOHDvuF1qKApT9uhWHCc0VEw6J4RAQpoLo8SmCozJwsUd8wzRnvvQv3sy34rsLW96SZODtCo18hOhdgZzRiMx7F4zK4/RlmL1tBcziJm4qglOoYExVQVay1/QTpGKQSSF3D68+F/pNWG0oVFB/GL82jxcOS/kvOG6E5neCfRi4jNgWKKwnSMbykAZZNbkMRNyZodSvkH3Tpu80hvQOW/rhB7x0uvbcsPPhdQIDqSFCg+16Hwn0tjIZEswL8RQWctEZ0+xwrvzDJhvuWkY80aQ7AjlckEPMlRD6HiBiIfBf4QZgI6nkIwyCwbNTxeZS2x/uO/i3fuOw/Gf5lmx1vXU3z9VVmz+imcHeZi477h0M32PuAlIeviazj5O/Q4SCT3ayETnHHh1yGIB0nMREgJDR7BVbWYGjtBHtGujm/ZxtHZyeZtlKsWzXC+mCY7Mll3rj0Bgzh8/7fXkGkt8Fc00CLNGlvjCN8lcjSQTI7bLSmg162cbpiuEmVSNlFcQKCiB46yj2PyjKN5uKAF665j5unl9N2NZJ9dY7MTPF7uZjo3irC8dFdH29ZP/Uhk0a/glmSNPrDyLF2D4wOJjEq4B+fRbXCRdZEAFIVxGYk80capEdVAk1gzrRRS3X0TFhzTSajDF3js/dXK8m/Y4ZyI8aWjy9h+Vdd9LaFTEaRUzNh5eeoibRs1HQKf2YWVVO5vrSG9fows++16f0Xne4PQOWTs8wY3fT+YNuhHfB9IHgKaif7QkfAdOhwEFn2qSvRLmhAkERdZtLsC9e6T0wENPo0zBLoLcnYfJaT1uzhW1tPxGnpHDE8ScmKsXzpNJan87FvXUGkAt9/1+f49+nzuKTrQb49eTIvPu5ufjZ3PA9O9dG4ALyxBPn7IDbjYlQ85o826bm9zq6XZIgUITkeUF3pc8SRe7l+ciVn9O7mrtlhpsaz3Pul46ldEiDVPHYWIhVwz6zRk5pCDVSmHupBBLDyS9PseUkvXQ8FNAYUhn48i1tI4MV1akM6wgez4qNZClZGobochJ+gcIeF4oWVC9xcjFaPhurA1FgOYanE9yqc98Ub+dY3L2Dg2grKYH+odUFYd83zULsL+N1Zbt/UR/dtGi95z+/42qUXoDUSdH/aofQ3DbYcufzQDfg+IBE48vB8FD/1dK4OHZ7CrD15D76nkHjRJPPn2gB4JlSWKzgpkCrUFwtyqSYDsTKfWfd9Ml1NtmwY5syeXaQMm3y0SWwa8hvbvGrDa5i1Evx4dh2btg7yYGuQM7I7kTsS/Ne6b/LMszZwwtvvxXpnhZHXBThn15j/R4dTz97E4meOcMb77qawpMS26W7c3+a5ZucRTM+kUVoqrW4NvbdF7WiXoavr9P9mHndnkr0P9tN2dYK8Q9cDsOOvelF8KB6lYGfA60qw98IYjX4NxZNotsSNKrS6FdoFQWoXRGqSvc8u0BiIsOW9vZgfmcJ5XpnEmMXiX4az+cYKj5vnV9Bc5fDvv/wvtr2xB3dZL8SiqL3dSN9H1huITTtZ/R9N8nfMcfMFy8huk6T3BOx5rsrQlQKjrB7CEX9iDmcn/+EpNjt0eJLyyv7buT25glfnbuPz6fN4IN+P9ZsCIoBoUeJGBe3eAGlFWBGdQSUgCATrTt7BDx5Yx6LeMpf2b2I8WEYQUXFGTaZuTVJfGnDxqfeT1Zt89vcXseQGh3/6zWuYe1eb4JYs0XPmMCIeJ/SP8ba+39Gr2lQClR9X13HZ4EN89cazWfHCHXQZTabaKR7cNIS4osjpXVM0XJPCF+usik3z+/nVLIpVGGtm8a7r4pMf+U/e+uCLaW7LYFQFb335L/iv6cuJzoOVEzhZIIAgAtEZaA75NAcFfbeCb4D74hLKzixOoGL+IMv80YLUqMeqr9QY+3vBnhuGibfgfP8dXHTmRl723Dv48JteR+yhSdSe7jCibG4e+eAOAkWgpFNkr9sBmsar/vkBPtl1AV7VO9TD/oT4nVIxHTp0+Eup+jHOTW3GkhrXb1uFbGkkFTBqYaFKEQgiRQVlUPK5Tedgt3RS601e9NZfMW/FiGkur8xs4EfGeRgli9iUQW2Fj1FUuffzxzH62lH6bwa9bKOW6lgb+5E5qE9meMZR23hefgMv+sVbCaI+iukjWxrHrBnlrJM289FF1/DK17yDQBOc/Y9bmLUS3DqyDMPwWN6lElNdWp7Oy7tu5yP1y/nM27/IN+dP45NH/og3jP4Vnif4/OZn4C6ReGkfY1bD7nUhECAkblaAIukfKDGld5HsrbEoWaWci7NjyyK6BUSLAXZGRbVj5L6pUl4BiSlJc6nCb+4/kug6h6nX2rx09R6u//szSWwroWhqWCTUtgmqYaa/SEf42fnHkv5ym7ry5F6pq5PJ36FDh/3C61beAsDb73sJylQEL+th5yA2B61uFeEDEiK6R6mU4ohl4zT7DHbaPVStKIvzFSY9k+hcQHltktNedD93Tg0RGfZotAtY1TRxBZS2Dc0Wy74+RfmkHppXNJlspfjviTOJ71Xou91i9/OiBLpktpVg73eW0frgr5k50cDKS3KuyfapbvxKBHO7ygNLkuwczOP7Ci/d+iZOP24bS7Ua90wPcsuvjyFY5BI4AvX2FIoKWkvDygdE94aLjQkJXjzAnFOYK3YTKwv8vRm2RzLE65B/0MU3wai4SE1gTNcxDA2ppJg4T3LLsz7NNY2V/OANF5PvNnjDlfew5BNz/K60lr0fWUl0tIpQBEo8Dq5L7eRB1DdMU5xIQQDLP3EVO9/3zkM7+H+E4CkYIbYvdARMhw6HgM8e910+exyc/MorsXLQWCTCpMRun77l8zx3YCP55XU+8dCFrCjMMWblqD3YxV1WF5XzosTfNM5YKcvRiTHuZAg3UAk0OG9wOz87/USmzsmgl/L03hkmcaq/zTCZyODFQWagtjSKn/XIdNeZ3l5ArJC86VVvpVe10csWu5Z3EYm4tAydzG5QPBV2phm4pUJ7keTuylomX3I9/akaWzMZ+n+nMndsaOaJzskwf2fhoSl1ibAFWlPBqIIbF0QqkBpxaXdrRIs+UoHEpjlotiAWxetO4SZ1nIQA0+N9Y5cx9ZHlRB/ajaEqPOd972b2mTZfOvWbjH1mK7usbm794ClESja7nh9H+IJgNotS0nnl+Tfxna0nMPzFTzHyhvccukF/HAIEjnxy+4n+XDoCpkOHQ0hjkaDdK8lsFpSO8VFshWItzpiV4x5rGLulM/qTpYzbcPKrNnP7jqXsmM+jaz5H9E7zydsuJt9fxXJ0PvyK7/HF0bPIbxBkN7URUtJcHCc25RD76znmfjGI4kJzKKCdV8BWqG/NMnzcBB9Y8muu/NYLaSxJorZ1juvZzU3r1yDNADcmSEwGmG+awLlcMLYpRe8dAe867goCKZBpl+nTdPQqCB8CXaBZktwWiWcKVFfS6BfEZiWRio9ZUjHqflidoBYQHa3C5CytU1cQmWtDANpkCbVaJ5rL0Ort53ZvBWvX7wpvWipJ9o4psveofCL/Mna/XZC4I0b1CptYyiehlVmcrlC2o0xN9vK1e08DCV0bVFb88KPseOGHDu2gPwZPRQf+vnB4XlWHDk8RNv3rO+laOU/pdAe9YCH62yDg6s1HsiQ+z+rF09RW+8hnlqjYUcy4g7c5jeXo3Lt7kMyDOvV787RHk9R9k2f1PURtqWDqzCRO1gSg1WcwVsyy7qUP0DyhTXJphcaJbX5y6edQLYGh+rx/8/PZ9toUU893GDs/xk2bViEjAaKt0s4rNBYp7NzTyxdWfI/Lz1jP8rdvIao7xA2b35zzOZK7FAZ+b5N/yEFvSXI/eRC9FZDe1SY25ZB/wAkXF4sqROdczFmbyGyT5N17EaUacrCP2LZ5pKqECZ2GgbNuGc5gjsFfFVnzgR0EQ71hteggwF6Wh2oNxfFY8gXovqfJuuV7aZZiNLdk2fPLpUxs6aHrAcAXdPdUKR4bkL06dmgH/DGQkk6iZYcOHQ4M/7bmx7zFfgnPX7aR721dx5J8kW2bBii5cUauGyYqoFXMsbeYI16RlNdK5FgCGfXxTIhNQaVL8uXdZ1CuxFE10Btgbplk7sXDNI62URyVW/YsA6BWiRGJufzH7LkgYe47Q1z2lpv5Tv1EBgslXnL8PejC52tjp9J0DLq+GSEwVBQnxhWb3oPiQePUNosKZV60aAOWVHGTEBgKfkQQn3Jon7GGQIXmInNBU/Ewah5OWsecauDk49j5CFFdxU1F8BIq0b0g3ADdsfGzMaycjmcKuiYqyP5ulEoTZ9UiUASVZRHe8oUH+ei1ywkiEi1rYVyzArHYI7VT0BwAxRWU1gpGXvO+/73Zbz5Eg/xHEZ1Ey4OBEGIEqAM+4EkpT3jU92ngW8Biwr5/Skr5VSHEIPANoBcIgC9LKT+7cMwngcsAB9gFvEZKWTkoF9Shwz5w7vA21M9fxc3/cxrB2SrPOGI7M0NJdtW6OOXyB7ht71LcqRjCV/CjAqlKRG+boW/ojF0o0XcKpCY5o3c3P58/hiAV0BxQsFf2EZ2X2OMRPvvi/2GZVuLS77+H5OoySdPm+juP4oRzt3PPpqV8d8s6zlu+DU3x+droqXx37ddZnx3m15uPIGsEBBGVnttK+IkIlZUx/GKE6d39nPuK7/D89a8nszOgeIROcixAqgKtFeCkQr+QUZM0Fhmkd7aJjdTx0iZORiP5wCwybuJHFKJ7G8ydnMFJQXxK0hgQ6HWoL5FULkswlC+x64EBANSWguLCx3/wfFLHlTB+kKW8Os7mjz55nfh/DAlPSe1kX3hSCZgFzpFSPt6qQW8GNkspLxNCFIBtQohvAx7wbinlvUKIJLBBCHGdlHIzcB3wQSmlJ4T4N+CDwPsPxoV06LCv1Na69N1iobWT/Pe157P0xy12vDLNsSdPoG5MoPnhSpDJiQCjouCPxZg7FlI7Ql964Q6Vn+vHsm7FKJONFDPFFLvzBua0YPjUvfz91mfjXJ8nfW6Rxv1dJE6cpu9mcI5VOf+4Tdw8sozbf3AskXPmMVSfZ1z/DmipJHepCL+J2vJw8nFQoHg0RPqaOEGce6wh/vGoq/norS9Fb4TFLJ2UQnZbgJMQBDpIRcEsB7gJDbfPxE4JFB9aqwuY0y28uMq2t8fCJZh9waJnjhPxdPbu7EGxFILdCXY5KqLbpu+HBqrlctOvHqGVPPNQjdr+oxOm/ORAAkkhhAASQIlQ05kCpgCklHUhxBZgEaEw+u0jjr8TeMFB7nOHDk9INGth9UaRCmhNwegz4yR2Qf6MBqdfvpEbdq4k8ATtdRJzYzRcytiBRkYSmxKUjgYk7Cx1YTnhqpOROZX4KfNc1LOZf7/nHIwMcH0X7nCA9pkuxs8TTO4aRDU91vZPU76wTlx3+Nuha3jV1F+hZh1aJ8CuxVG67wIvKvBMgeKAdk+S6BklLoiN8OKtL0NrQ30Y/AgECY9A0zGLEsUTqA40e1RAJTYXUDxWEp1SaOc0as+NYiTbJO+IU1/jIVyBJgL2TnYxeC0ons9Nv3zv/96oFx+K0TmwSARuJ4rsoCCB3wohJPAlKeWXH/X9vwO/ACaBJHCFlDJ45A5CiGHgOOCuxzj/a4HvP1bDQojXA68HWLx48V9wCR06/Olsfd6HOd78EP0Ri7lGAuWWNPVVHsfFRhizssTjFpajE9E97HgUr89m2ecs9nxQxVoK5n0JxHAbz1cRgL4rSuS4MkfnJ/n3e87hxOWjbCiuQEjBsh/Z7HilRuYBgVjVoDKWpvyDIaZOVYivqvCeLS8gk29Q35bFjwfkHxAseetWPrf4V1z+0Csoj+eQQx7DySrn3f03yI0pkm1JbnMYFWcFKoEB0VJAeYWK6wlqR7koVY3g0gpyLol/QpNWI8LywRlmfzlIZrfHg1e+9//elFcckqE46EgO3zwYIeWTJ8tVCNEvpZwUQnQTmrbeKqW8+RHfvwA4HXgXsGxhn2OklLWF7xPATcDHpJQ/edS5/w44AXiefIKLPuGEE+T69ev345V16LDvvOTO13PHAyuIzITzP3MOqic4nLpyF1uKPfi+QnN3Gn1xg6N7pyjZUXbu7kUokOxq0mxGYNpkxfF72XHvYlIry1Rmk6xZNsGWkX6GvyuYPEPHyfnceNmnufDr78OPSOKrKlTnEsRGdJR1VVp7k8i0S3xLhMZyF2NWR3EhdeIcc3MpVCNAjEaJT0F6t0d1qUakLGn2C4QHrcEAbVELISQ8lOTSy+/iml+cTHq3JDHm8PvrP3CI7/RfjhBiw6N9xX8qg0em5dt/eMo+7fvetb/9i9s7mDypNBgp5eTC31khxE+Bk4CbH7HLa4CPLwiInUKIPcBq4G4hhA78GPj2YwiXVwHPAs57IuHSocOh5runfBlOgbOufy890QbrNywHJHfftpqBG33M90ywXU3hTMRZ31qMscdETQcQCPysYHF3iVraZLySQRZs6luyKBrsuX6YmA/zR0KkBG5KcNFX38dzLr+dH9xxMrVKDKFJ2gMeSstAKdgEnsA8ax5uzNNc5hHvaXJO/w5+Uj8Gr2lAv02TCNVVAj3fpGaryKrBccfuZvPvlxPfkkC1JLEZl61f7WdJsoho2dBoHurb/KThcNZgnjQCRggRB5QFH0ocuBD4p0ftthc4D7hFCNEDrAJ2L/hk/gfYIqW88lHnvZjQqX+2lLJ1oK+jQ4f9xc3nfTJ8cxocf82HqJRzjJ+j8sLsONuMAWJjKl7NJLNdUl2qILVwHbG47jA2n0XdGUNTIHlMEX7RxflvvJ27PngijbdWqVTjSFvlpJN2cPP0ckTCZeDHOnZaobJCIIsaUgV1uIl1Ux6jDk1XwdmS5mebTkMJQI1J4isqVG2Vwh0qihdDbwbEptpY/6KwVOwBTaV2wiIaAzpWfoDGIkFmR5gP0+F/eSquVrkvPGkEDNAD/DSUFWjAd6SU1woh3gAgpfwi8M/A14QQDwICeL+Ucl4IcQahxfZBIcT9C+f7WynlNYR+mwhw3cK575RSvuEgXleHDn8x9176UQD+dfOlfGn9WSDBSUu8gsvMAMS2GTSHXPqjFptG+9EmI5x8/iZqrsnGHYvpevY8vxlbDSt07Dvz+P0+Ssrh7t8egRQg8z7uG4o0rQh4KsrmBFoF1Kk4gQ52Dsy5sNRLuwcKGwPslEL3lRUWRS3wPNqretAbHtOnxGm+aAnmbFgSJjYboLckmYcqSJGh1a2S3WEf0vv5ZEJKsV81mIVJ9WcBFfhvKeXHH/X9Y6Z77LcOPIInjYCRUu4GjnmM7V98xPtJQs3m0fvcCo89BZBSPrlXG+rQ4U/gf66+gNSaMm5Spe0nuPioh/jtbcfSWuIRz7WYnk0TTdiIWoQH5/uojGQg5lPekUOvKDjLA2TC5/yjNlO042ycWUbuIZhf7DO9p4vc4grxn6Sws+E6Nam9Pmo7oLxap/93RcRcGcwIrTW9WFmF2UuXUF4rMaoCq9uHhCR7J6R2CGJzAVZWASnJ3FcCXSXQw7VgpCI44/mf4tYfP/lqgx1sJOy3KDIhhAp8AbgAGAfuEUL8YiFl42EeM91DSunsl048gieNgOnQocMTE11doTaZwsi3SQ9VuP6648GUaCUNby6NDkT3mqi2ZG5vGnNOIbNT0OoWaE1wljqsGJjltrElBA+miLah1QvqnM6im332XpIhWRDoDcg/YKHdsRl57EpiMyqNFRmmX5EjuxlSozY9N1eYPL9A/y1BuBSz7bPzxVEaA9C1SZLcWaN9SoZ2XkUfTIEQGHUJEnxTQfE67tAQsT8TLU8Cdi5M2BFCfA94NvBIAfOY6R77qwOPpCNgOnR4CvHhI67mX695OcgE+QcaTJ4BXkKQ2i1xkoL6EkltiSA1IkhvUbEKMHc8qIsbmBEXOZli9qeLcQYlZjNca8VJCJKjUFusseZfRkHTKJ3ej7FnjrG3HE9yPCC5J1yqePm3bES9jd+dAs8nMRXgJBTiuy1aw0kAVOt/+5vb1AYhaBcM3LjALPvYGRW9HZaF6fCwk3+ffTB5IcQjQ1y//Kh0jkXA2CM+jwMnP+ocT5jusb/oCJgOHZ4i/N0Dz+NHP3s5ZgR6b6si2i79t8LccQl8Q1BZE5DZoqDa4JtQOdal73oV1ZZMB3Hyv3bgGJXEhE+gqsTmJa28ID4riZQ8VFuFqIlXSJLe2UQmYwx8dQsilyVIx1DGZiCdonhWP9FZj2jbJTbZprkoijWQIDrVQiYjUDJodguSe3S8pI5edTCLLpGqwI8oWDmBG9fI7NjvFpmnLH9CJv/8E4QpP5akerSqeBFwP3AuC+keQohbHk732J90Qjk6dHiK8N0bT8dZaqFaYfkV0bYRXkDP7WXqw9B1v0Jiyqe4zkdxYe2nKmRvGQMhyG8ErekQKUuclMKi6+bJ3TVLetQnUvXZe6mKmxDMn9aNVEAdn0caGiISQZbKSFUgPY/SSd3Epl1Kq3XcXAxtpkpydx2j7BAYKsiwPExiOqAxFAVf4kc1/JiKH1HQmj6pER+9IZGq4NwLPv6E1324IxEEct9e+8A4MPiIzwOEmsojeQ3wExmyE3g43WO/09FgOnR4iiC7HETRoOuhJn5CJzCzAIxfmiKzQzJ7tkf/tSpDvwrAd3F6k0g1RXTWRt82juzKkr+tGdaHB6SpE522mDotjlR8ikcJutdLhC+RuRTKxBwkE7gr+zFG5iASwTegvMqgvsJH8U26lC78mEo7p+IbAtEI8CMSNybwogInoRCd9/Gi4Rox4+dG6HoojCoTvqQ6HHnMa73wpH9CcTwIAq594KMH7R4fCqTcf05+4B5ghRBiCTBBWFznpY/a5zHTPfZXBx7JEwoYIcTzgH8DugnVLwFIKWXqQHSoQ4cOj41s6EgzoP7hJrWbu+m7w2L2WBO7EODNKAz/ANy4RK86aPfvBFXFOWE5bkpH68oiHBekZO7sPqQqiM2FD37VARI+kSkVzQpwUwalI+Jo7SxeVND9+0lwPdxlvZTXhDXQtC6Ly1+3gZPiu3jPt1/DXz3/t3zp2guQaY+gqtHqVmgN+az5bAk/HQVALTUx55Lo86GQC2IGWq/OBad9lB2vMCHhISo6q75cQhmdRHRl+fWeK//4TTlM+BN8MH+UhaK+bwF+Qxim/BUp5aZ9SffYLx14FPuiwXwCuExKueVAdKBDhw77zqIl88yUkyRqYOV0moOS9FaFnuunaK0ukNhVBwWCtUtQ2mFkl+IE1NdkcVKhNpHZaVFaE8WNK7QKgr6bK6RHEkyeDlIRqG2Pwp01vGyMnS82aAz242QCPvfMr/OQNcA3vncBsbjNN+88jW8Gp9O7NeAHn74A1oI2bRBfW8JbrDL4tQReLo42VydIRPDyCRqLo2QnK1jL8lhdGtVlgtx1UxTuXkbpSIPhX1qI+QrVi49k5uTDM/nw0YQmsv3nrVjI/7vmUdueMN3jQLAvAmamI1w6dDi0nPncT9GdVkhdKUgabdqDBs0+jfiEQEiYuKyP/uvmkaaBlzAwJirUji6Q3FJGRnVS0zWkoUIAotagZ8EgYhw3QGNpCnPOxusNmD/KwFkmOGvFFCvjM8zuPp5mLoIsRvjWzGk0XIOB37eo786yZM7FSapIFXKbm5jlKJNnK5zcu5fbfnQse5/p0XNLlGhKR7N8jM1jZOdTzJ/VR/63I4g1/RTXRhh7xXJSowFqW2Hu+Cj2ucuxFzuMvPrps6rG0zmTf70Q4vvAz4A/pN8+ut5Xhw4d9g+XLHorMpNCuKEP4tc7P0ni/kkSqkpzbTeNPg3NllRXCKSAwv0BZtEDIVCqLYKciYxFSG2YxFlSoNlnkN4qUcZnIWqCrkO7TTDQTezW7YiuLFg2SmkIJxcQS9jc+ZsjuX/uSKQBPXsD6i+vMfIfK2m/qEK3AtNnBOBrmLMKR1y0g+krl6K/cRoxH/qFPvk3/8P7H3w+vpHBTah4cRXv+GGcjIqVFeC6SCFI7g1L/Kfvm2Xm5B5q3T4jr3/vE9yhw4s/MUz5KcW+CJgU0OL/qlQS6AiYDh32I5csXchqj0QIkhECI4ZWbnH+WR/DX9GDbyrEN89izqWYOj1BpAiDV89iLc4gVQFS0l6aI7ptBoRC9cRFJPc0iAmBGJ0CVUXWGghdQ7oeSrGOXNRNYGi0jigQmxA0jnJJ/iCJ1vYZe2bAms/VcbNRtP9JUFwjeNvKm6h/yeSyxINc0ziSVeYkX58+g/FLArpsE1UNyBlNCmoDx1PRdEjfN8PcWb1olqC0RsGLSyrnLafRq9DuBaMKv97+b4f25h9S9q+J7MnEEwoYKeVrDkZHOnR4OnPB6R9FjUbwM1HUSht1toboSuLl4gSGgjlWoT2UwRnIYuUNuja5qJZPfU0XWsvHqNhh9JeuEOSSuJkoqU0lhOcTGZuBVApZriA0DRQFkU6CbUMqhpeMoLiSxGRA/0029WUaiieJjeqUjs3yrPfeyJtz9/LcLS+hV6/yr7deysZVg2T0MPlSU3y67tZpDnRx6kUPcVZyKy/71ttJjEJljaS+pBcvJqlVNJxBB9oqc8eqBIYkiAQwbD3B3Tm8CaPInqYCRggxAHyecB0WCdwKvF1KOX6A+9ahw5OeC0/9Zyqr4gDYGUF9WBKdElg9EilArwu2ffjx14o/7YpPo9d9TMcHQJupIiMGMhXDSxoYY2WcRRn8TAzfVJhdZyAVMKoqgaETnZdEpy3clIFUFWK7ykhdRWu5SEMDBUQuA36AiMcIerK4uSiR8SruYBeKGyACSWz7PO7xPWx/TQykRG2r9NwdECl7fGPzSXzFOo2XHXs3N9ZW8x9nfpOP7XwmmUibl3TdQU5rcNw79rKp0c/NtxzFac/cSbC8SfcPHBpDWb740i/yyb0Xs/3uIRIPGmjPKGFP5EiOgt4SJEZ8eNHBGK0nL09bDQb4KvAd4IULn1++sO2CA9WpDh2erJx2xadJ7G1hFUwiRRshJb4uKB0dAJKjj9vD/GeGaZxZpTKbhB6PlR+7ivgENAZBa4PWgNRYQKtbITfn4KQ0vISBiGrocwFBykQ4HmorDCtWLY/asjiBJmgN+iS3qyQmfUQAyQdnQQi0igqNFpgRhOuhuqHAkkZYdl/YLu7SHtSWizHToLG6C6PsYOcimDMtaLVJXr2RnsixoTYz1kb7l1mOSk/yjuQO3n7TS7n1707l7Z/5Du/5r7/i6jd9gp/Xj+JX1WN5fmY9X62fyX2zA3z82d8GoJBpsP3V3YhA8tpfvZ7Fq6cp3Ldw3usEVq+DXncRgWT2pKd3xsPDiZaHI/siYAqPKuX8NSHEOw5Qfzp02CdOfcmniU/aCDfguts/dMDbO+GvrsSsBKQfnAPLJl6J4hYSKC2fxISLCDQCTeD8R5Lyc1V0wBzX6b5XUBuC+jOafGLdj3nXdS8jsUuluFbB6vFpd5v03eYwdapJ7102rSVZopMNgqiOH1HxB7IobkB9UEFvgHAFvZfvxf/nHryEioybiFINbBt/SR8ATiZCZK4FQuDHDYQXIEwNte6gWE4oXKouesVCtXWCiIa3sh9jj0Z01mXkOSpKWhD/2TDbT+rhx5OncvsVn+KXp61ghTHH19/wWV7wz++j+9Z5jv3uNgqqyzGJvcQHbSbdLBm1xTuWXs/f3/1ShITsZmg82E/PvbPQbIPrYu4ZB13DPn4Z0bmAE197Jfd85V0HfByfrARP4yiyeSHEy4HvLnx+CVA8cF3q0OGJueO77+bod11FZpfHeef+K9f//oP7vY2Hy5h4UZWIIagtVomUM+hzDQhA3zVN6/jFqJaPnRX031ilPZRh6GdztO9No7VaqE2H6KRKczLO5//nxQxpAZUVKo3ugOQuFTcJWsOl784AxQkw5hp42Ri+qaK2PYQvafVHcdLQ7pMIXzD708X0zRVR22a4OmQQQCYNQGCoRHcVQVGQmoq2ewLnuGU0+w3i0w56WaC1fPT5JmK2BIt70Waq+D1pZKOJVAT4gsyNUerDoBsexooKL3nDO5l/XRNWw2e3nMPQDdPQavOTX57Oz+3T+cJrv8hF8Z1c/rH38bfv/hYf23opehN672ij2j5qtQ3NNkGpjDAMRDoFto0XUyitUeg6Y3q/j99Thad7FNlrCatvXkV4L25f2NahwyGldpRLZpeg1WNwwWkfRZuvQ62Ou3IR+niZ9qpubvz1+57wPJes+gBOfxrV8lDaLvUVaSIll8juWWrr+vFNBSchSO31mTvGJLtdxag4KKaGUbKRqmDR78rMr8uQ29wEy8GohuYnEYA2WyMOBKaKNWDi66DXFVontkkm29R2ZbHTgvwDLSpH58jePYOmawgpKR/XRWzWJT4RwSoIzBNKpK9LUjwhR/7GSTAN7FX9qG0PrdzE70vRXFMgtqsCgCh0obVcYrMCY6aBnzBR22FIczDQjZc2UGd91LE5ZF8Bre2TGNFpDEJui8Q9xuFbR32VZ537bt6/+gaumTsK4/o0qDbYTlh37Lgi11SPZUNxgFNft4FJN8vZ/Tu54bQVtEbTCB/0eoTYCMj+HMr4PPg+GAbxXRW8aI7EuU/vBcietj4YKeVe4PKD0JcOHf4kln3dQ+oKsZ0V0JRwJm8Y6NsnkIUs0Xv3cMmqD0CliuzOIRwfPx3DyUWIzLexemK0CxrZWITAUFDbkukzsjSGJPGJCIlsf7gE8LRNoCtoNQvViqN4ErsrgmrrmLvCh6Ws1ChMzIKqQCqJ0rSJVdvg+ci5ItaaPHrVJbNhjswtbVpHLaK506SyMkqi6VNepWKPG7QKAnNZF5HZJu2eOLFZF32+jXVGBDcp6f5yinZepeveCigCpzeJ1nRQS02cRRkAYrsqCHuhUrGioJaaqDNVZCqGVmmFhSsNDT9uoDhBmBszX6Y9mCZ23yj1lw0T36NRWSHwtuR4e+xF9N0uOf75o3zTOpkr3/1FXnfaqwgqXUSnoFyK86P54xn+DjwQ62Pzm6cZ37AIrQVJNyA6Y6FV2uB6qGNzBN05pKkhVQWpgBcVnJQbPVQ/o0OOlALv6SZghBDvk1J+Qgjxef7/cs9IKd92QHvW4ZBw7gUfx87oqE5Au0v9o3bxS5a860+qFXXRiR+huTjOzAkqigd6HZqDAXve+u4/q6/X3/i3LP3MlWS25Mlut7CXpYnOWFjdJgDeUTmMeoBRSqDWLaShEkQ1VMvH6o7hxVQqywXzx6fov0lSXpmkPizx8y5NaZAaBaREaTqojoeYLxHbK8GM4A/kUSeKyEYT0ZVFJBPIag2RiEOtjqhKZD4XWtZ9H73mhdWGASwb1Q1I77YJVJPKMhWzBPVBDb0BY+dr9N2WxM4o5DZWsXvjWL0+asZGtTRyG+uIUo322j5EAEa5RWtFF9GJBsJ2CeIRZMxA2C7C9cOqyABBQBDRUWwXP2ogfEmgKwQxAyWdxJxtAdB3g8L0RQ5r3r+XLR8ZpuUaOK8tcsVP3obWEhRWNXnTsTdxT2UJD/X3sjpb5oyuXdxy5fHsfmGWNRGL+k5QbYmdEjiJKFZXDKlAe12LoS+rREaLWEvzeHGV2hLBlJ3+s34DhwtPRxPZw+Vh1v+RfTocZkhFkNhZQ6k2iXselwy+PZzhNlvguNTOXk5s0kKbr+MM5bnwpH9C6gpzx8UxywGeKfAjgt7rpggycQJDRZup4gxkCbImsck2PfdEMeoexSMiLP2Jw1GjV/H8V9/Id3/6DKQK2//28cN6/7/+Fmy8UTP0WdgB88fGMeoS34DUHgcRhGXh/YSJNj6PSEbDSsSGYOoMQd/aaaY39lAfUCjc30ZxolR0DT8a4BuCxKYScq6ISCYIFvcibBfGZ1CrbYKuNErUhCAIBY0ZQbbaYY6JZSMqtTDnJN+FCCTGXIvGmi4SGx30soXUwtUma0sMnKSg3Q29d3skJiROWsUzBY0lSYy6j15W8RwTJwVGSUVETVQ7wIuqWENZonuryD3jiO4CXm8KqQgi20tgRkBT8NMxRBAQmKGA0YoN/HQUJZDYhRjRtosf01EAK6uwbvko5s9cnhvZwObXrMLqi1M+W7DoRoe33fhWYh+aIGVYvGblHexuF/jdzCr2fsDEdxyOy4wx8/wk/vcLFG6aYvsb+zjn7I28In877/jkGxk/F4auSaPaPnrNRgRJ3EBlyb9/mj1v+fMmG09lnpY+GCnlLxfetqSUP3zkd0KIFz7GIR2e4pz9zE+AJpCmBvMuxKLgOMjpWdA03KOXYNR9tN2TBIsKaHUbpeUgFYW+65v4yShSU9CKDVAUlKaN1KK4/RmMqRpeIUlgqMT3NtjxshT6UJ2x7nAVxG88cDK9DwUEGiz/5JW84KLbufqbp/PQJ/+4sFGmwhLygS7Q6x52VqfZJ3C6PUpH6/ixgBXfdHEyOnZhUegTaXo0+g3MOZjd0ItQJYX7Wqh1B9+Mkr9XoDclWtOHVptg1WLUahtlugRCQNRETs+hpBKh8K02EIYOQiA0DZlJIooBMpVASBnum08hdZXEvRPguihNGz8dRbV9EuMuTkojUhG0CipaW1JaqxCdASurMnEeaDUIYj52WiPVtEEJM/fNmSbtgQTC9miffQSxbXMY0zWaK3KIFb1IIYhM1VBsF/aMo8VjkEwgIxpqqQmej7anDfFY6ODXdSonOVySf5CdVg8//+HpRD5a4oolN/K7mdXsLPTyyhPv4M25u7js79/D3K5h1ly1iT1b+4jvVWmucnlRej0Kkm9deiLRl8Gl2Q1ckt3IF6efwcmvvZfrdq6m8qEW7jUFem8pE5uE20aWIHJPXz/M007APIIPAj/ch20dnuI0ezXiUx7qTBUcBzQVWm1EKknz+EHM6TbGjinQtPBh6nhITUG0nQVbfx0CiYwvrPGhqqhzNYJFWWTUQLE9lFobPxencC8kfqmjthuUjojTrkdJbS1RXJdl2Q/q/Ng7naBXctybrkK1Jev/57FNddrSBhSTTJyt4qUB4WKkHPSdcVafsRtNCbjv5cMs+46HPt5E1JrhAlx2DicXoZVXSY47aFUL0Xbo3tBC2zuHTMfD64LQFJZKIHQdWav9IWmRQBLsnUDpykHbgmQCbBsxXwFANFugqKEJLQhQKk2CQhqlVEfqKupkCSIGalXHnFZpDyYprdJACmLTUD7JIbLXYNnqSUbXD9B7o0Zs1kWqArsvTWmtQeE+mD5Zo0cpEBup01zbjZAQKTkgQ+3Ny8ZCH9I2B9FTwM/EUStN0FSaq/PEbtpCMNiDXmohs0nyN+t85VfPoZ1XGNjYghtMfvORtRzXNc6urf38auwI7iwO0yoI8q+e4d3dv+eh1b2MVwcwkjamCIipNspDCaa1BP7JCr988Ghoq8TGVILegG+d9gUeWNnH+1e8lPdc9AueEdvOM296y0H4lT/5eFrmwQghLgEuBRYJIT73iK9SgHegO9bh4HLKyz5NoujhmwrWigKR6ShipgiGgbW2n9jeOmKuHM7gfR8/pqPZbug4L6RCoeR5EDURlUZYUNHzkMk42nwTGTOQQuAVkihNh/TOJmqpiZ+L03V/jcAMNYCHVzoc/lWLyooo88cHKLZg3V9fhffsEhuf9c//p9+ureHnJF7WI3uvjvrMedq35DHqMPM/S4hUfAaFwBgrhWY+TYMgQJssIfwM0REb4fihRmDZaDvCAhWirSOLZfD9UJhMzUIqgUgmwPMhYkCzhVg6iJyYRUQM3IEs+shc6Oi37DBSStPABZlPgqrSGkyQKDcJTAPVdSFiIBptkBJvRZrCfTaTZ0Vo9UuSDxq0+iWLE2VGGUAq4KQ02oUutFaAmwD/H4po1w8Q316mPZRm/iiNwn0uga7gJTTwJeZUE0VTECuXIBUl1GZUBakp4bgaBsrucUShCwJJcsylttjg0r+5he9efwb5jeD+JMbmuxIszXrsfmmKxM/StK9oszo1w712Lwnd4ecv/xSXf+/dVE7S+c8HzuJrr/4C7932Ql4zdDunrd7NmJfmKKPCiGcy4SeZcdP89fnX873xEyj3xkmknqYlYySHrZP/j13VJKH/xQI2POL1C8I1nTscRiTGLKQqMCouzT6d+oo0ztpBvKFujNkWdnecoDcHZgR3RT9azQ5zLcwwIRDHCR+ovh9Wyk3FkJkEaArS1BCWgwgC1IaNCALwJbTCB6vUFNRqC2G7qK5k5uQk2nSF1B6L4V96JFaXOfKvH6IyneL3I6t4xvXvYeVHr2L4C58mqBn0Hz9Fvq+KcmkRfpJn4IYGZlmSHLWJTrZJbJ4HO5zR43lhHy0btdwKhWEQQK2BzKVCgZBJh4IlmUDkMshWGxSBrDVAKMhsElmpInMZxFwZ0ZMHKdHHy2F9r0YLaTtI2wnbCwKEF+CnYyS2lZCpGK3BGESjoeYjJRgGid017JxOoIE5K6gv9YnMCyKKhxy0UF1JoENqewPfFNg5iaH6tNdY1Ndk0do+qVGJage0eg18XWBUHYTjUVuRAEA02qGGZdl4mWiYpKmpoXCxbGTUIFAFiif59i2nM3TMBLPnOfzibz/BnuekmTsqwk/O+3diIzWWf87DC1Ruqa9i+qtLeP073smpZ23GR+GU4RHe/Q9vIm7YfOM9l/OGN78DH4Xnve/dvOd9b2Z9aylX3ns+177/HPxA4fu7j8fQn57z1od9MPtpyeQnFY8rYKSUG6WUXweOAr4lpfz6wuef84iy/R2e+px92SexCmHOBkDu3hKpTUUUJwjNWvMVzD1F/EQEmYiG9avssNaVNDSMmXror4lFod4AVYXxaYTtIZoWQUQPfTKzlfA4TQFVQCIGQBDRsPtSeLk48V0V+m4o0jiqBy+usecVkupohhsfWE0ka/GGb70B+ZkejjhnJ9lNCgRwdvdOirtzGN/JIVWYPzqBaku0us34BUmCVBTZlQ5NWFKG2lUmFWo0EAofQ0cUq+C44XY/CIWmEIh0CiHCApEyYSLqLejvRng+sqcLqShIzwu1IEUBQ4eBnvDcmRSykKXdFzrZ508t4KVNkpuL2EvytI4ewBkuIBc0uNiUhfAhMCA+qiICWP+fx5G4M0rxeS2iMy6K5VBareBHJNsfHGRRb5nU28bY9UoV9VUz7HqZQvFIBfuvS1RXxKivyWLUg/D6Ahn2r9FCbXt4g/kwEKBSQyZjOF0xvJiCbwiOOWYP/Fs3SHjxO95DpATKuSX+buS5vO1nP2X7GyL88p7jeGnuTj78oa8Rf9s4t921hmHNpWTH+NpHP83fLfkVR/7jRj72uS9R9BIEmmD+GIVTYzsIGjpTp2gMp0rEDJfiTIpln356rGD5aA5XAbMvPpjfAucDjYXP0YVtpx2oTnU4uDS7VcxyQHTrNDKdwM3FUJxQ27B74phjLjgOWtykvShBbHc59L20Qq0Ezw8FS9QMhYwZQTSUhZm5HmZx6yq4Ln4yFwYCzFZxBnM4GZ3EhnG0ZBx7IIVvaqi2T31AJbMzIL0+QqsPssuKvHv5dXzQeS7tHSYzn1uK9eIa3T9L8t3GmURrgvJKiFQg0EB9TpH617rouyPUEGor0yT2aigPl1ZptkNNJpUM/zouJOOhNpOMgxDI2RbCMAgSEZRxG1QV4QUEmSRKtUmQjYfr1ovQOQ4ge7oQtVbo68nnwPUQtQbxRhs/n0QEhKX1hQirJE81kbpKEI/gxXQqyyOoDjSXupgTOsKD6jJB710esdkYgeHTWJ5BtYC4R2SPQfwohz03DFMYkVAvkFyq4psQ/2yadje4cYXYdBuZSyEsFxQNTB9lpoy7ohetbSG7c/hJE6kJEnvqJN5axQlUqsMGCBsnIei/ucq0n8O9z+Stb3opqa4mXzvma7zwB+/AqAoSp81x8Rn3cUu7my3bBnj2zBu55bT/4Nqbj+d1z7uZYX2er33002xyejEVj+OOGGFtcoqtjV78QGCmbGwt+JN/v2c+91M0e1QSkx43/+Kpt5bM4eyD2RfDnymlfFi4sPA+duC61OFgcsmaDxKf9kjeM4ZMxkLtQgjUpk1gahjFNuQyoGmIUo3InAWuF4a9tm2kFprJSKeQ1ToIBacnCck4fi4elpuPRxDzFWR3DjcbBgDUj+4hMBSMihuG+RoagaHQ7taxcwaJCZ/IfBsvBloTKusLfObvX8w/nHA1ZtknPtZmaVeRV37wavIbobDRY9FNNkiwChLn2gKNRQoTZ5kEpk51iYKbjlA5MhP6TxQRmsOchYREKQlGx8PZveeHGkwgoW2h1K1QeHoetNoo4zPIaASxZxLZm0dadmjqmyuGwqrZRDZC/xJChEK32UL4odlOitDxrjgBftJArbeRqoLi+kRqAe2egPguHTcpCQyIlGH2eI3pU6G8Uif54CyKC5ExgyXnjTBVSzHw+zZdG0pEZ23iUxJzHlQnYP5oiE+HvjLh+EhDRxZLfxh/P6JgH7EYuzeJ1W2iWmHezMv772SkmOMV7/g156zeRu6BKku+tIu/edMv0KdreE2N9kNZnv/zt7PsRw2EBMdXuebOY/n4jksY/nGArnu8dfTZBDmXV/znOznGcLi6fjSf2H4RD1oDPHDHMu5+0zo27Byi/fsC3s4ksqE/4W/2nAv/jePfcBVHvvcq3rDhFTR7VZJ7XfS6x/FvvIpTXvbpA/CfcmCRUuzT66nGvgiYphDi+Ic/CCHWAe0D16UOBxN7II3W9P7woK0vSyICiZgpIlWFIKJBuQpC0F7bhx/XwwKLno/XlwlNQkIgTR3Rk0dGIxi7Z8EPwodm20H4friPoYVlShRBpOwSaIJ2waB43jDlY9K08irlVQoIUO2A8fNSDPyuxtDPi2gW6K+b5mMPXMK7P/Ntdrwmwp7fLuHbH3kmMydLyis0xs6P0HtbHcUVdG9o0S6ACGDiGTEiNRCeRGsHtJfnsVb20jp2AG9RFzIRal1KXw+02khTR0aNcKVHNawQIOMxiERCn0kmhagsJFWOT4dl8PsKiFg0vKldWYRpImyfIBPH70og8xmkEKhNl+YiA7XWRrU83KSOW0ggVYHiBvgRQWJUITUSILXQ52J1gZsJULot2gUIUlH0ZliV2fFVhJBIReBlQvOlUfdx4zD+DJP8Rpg+2cDOm7g9SUSrjchlQz9ZpUrs3hEi26cw9xSJlBxmj49i501+MH0i/Z83sKTGTbcdhZ+I8NzcBj7z0LmMvLiX3ps0lvy8TmReweqOYpxa4tjuSRavmmFZZh79A9O8dtUdlOwo6rzOwMWj2PhYgc5wusy/bryYrgfBSelgK6guLP1pg09e8N3H/7ECZzz/U5RWG5ROdXAy8Ptrj6d9YQ0EuCmN0tE+TkLhlJdfyVmXf/LA/vPsRwLEPr2eauyLgHkH8EMhxC1CiFuA7wNPz3jCwxBjtokxOg/JBGKmSGpzCX37RLj8bsvFzkfwlvZBKoHqBBijRaShodRt3JQROrAbVlh6JBPDz8bwFhdoL82hOD7C9VHqFjKbBC/Aj2poszWkJiitDjWL+XUS4UO06POc593G4N9uZ/RZCm4SmoNx2kNpVAvGNvdi3prgjsZyVnzNZvHVZTL3zzP0K4/6Cp+BGx1mT06iNaF4RIyuh0JzS3arjxSw+9XQ6FcprtEx5lo0e1TKq6I0lqawF+cYfXE/7eOGKa3rQrQdgkw89CcZOqLehCDAWzGALFdA10Lh47jg+yjT86EvY2IW5kphwqXjheX2J0uIegtpqKjzNdJbG7SHM6hNh1a3hj5eptkfQWm7BFooFCsrFPSKQmIv6E0wpxWCORMESFUls9PBLMHIvQMEgUKgK7gJHTdl4CTVsEbYGGS21sluDfBiCsKT+D0ZZCwCmoZcthj8AH9RFzIWwYtp5LY4RHdX2Drdg5PSuPb956DXBTtfFOFt3/pr9LsT5B/0ic66OFmTRTe1ie2tk/9ElI3fPJLRkQLbSt1MVtP8x/1nY6oeR560m9Fijh/WVzLnJPmXxT9nZc8sv/rXT/OfX/osR63ey1vf9BN2vUOh7kcf97d67gUfx40rmGWJMWHgDDi8/Nk3IB9IsfjD26m8rs6y1ZOk9rokxiw8U+GE1z35fTpSgh8o+/R6qvGEPZZS3gOsBt4IvAlYI6XccKA71uHAc8nwO7F7Evi9WYJsgvrpy2gtyYSmI1WlNZwkOtFE+EFo6tIUZDSCH9MRtkN0VxE/FQ3zXoIApe2i7ZxAm6liTtaRQoSOZSH+kFPiRRVqx3TTzuvEpwOueMUNqH1tZi+20Rsev/zu6dxx+xoWXQ/pXRI7o2CUHWqrPbSmgnV6g6u/dTo7XmnSWpwkiEeIzLRIbleZO9qg7+ejpPcExGc8zKJH/60WtSGVdgGGv6Ng1CQImPoniRsXxKc9qsMqu58fuiPLqw20tqR4SjfTp6bCOmPagj9JCLTRWUQqBZZNMBcWFZeuG0aMGUYYjaWpiHSoLSilBkQMgmwK4QfIeASlbaPYAW42Su7BGugaqe01RK2FUQ+FrdYCJxuEVZQXhWu/ICGzQzJ/XBKjbOEmwKgK2lsyWPmF/q8wKD+7SfoZMxRPc9n1whRWVsGcd1BsF6Vuw+gksiuN0rSQMhw34XhoLQ8nreF1xej+QZSxZ4I51UR4kNmiEJuG5pE2dlrBKNlEd8zhmyqtoSQ7XqVx5mvWo7RU5K+6SP4gSeHXJpXPD3FZ90Z+cuKXMITHbdNLuPSONzH13SWc+Lu3MualSRkW58Z2ct+ZX+L06G6WfvaxhYKV06msEFSXCQr3BYi6xtd+9wze+eKfcdfYEK6nMlbKMnGWzs4rDCbPD2gMiD9UxX7ysm8O/qein+YJBYwQIga8n3AVyweBYSHEsw54zzoccIJ8GuFL1OkyyvgsyYfmMGfbtI/oR+ZS6A0PpWmjThRxExpOWiOIRwg0BT+XDP0KgQxrXbl+6Dw3jNC8VKyGiZexaDjzL6Tw0hFaBQ1z3sFoBMycAld/8hn44zHUyQi+odC9wSG/EVRXYpZ88nfO4yY0hn8qEQEMfEnHiwGKZHadRmNJkrmTMmg2dN9rUzx3MZk7p3CSKnZOY+JME8WHoasrjLw0wOoSxGck6q+z9F83x9iFGk4WtIaKWQS9IWl3KUTnPLwoOEN52kNpJl44jN+VIMhn8PqzkE4hzEio4QQyLA9TqoQmtGg01GzMCH4+BVLiJ8M8IFEJo+xUy0NxAxpLkjh9YZY/tk1qS5nsdhcvDoonUNuQ3aiiOiDNgEa/IDbv4+RMWv0SqYDegOpShZkTdcrHu+SSLRKGTfJBHaMGekti5cN1YQAY6kdMzxPETUShC1FvIyM6+lQV1ZG0+kxSD82z6osNRp+ZRh5TJ/GcKUrHu5y/ZiuJMQchJdV1vTQW6eh1j7UfL7LpvUcRxH3Kp9gkxixyd0xTXqnwsd88h+f993v47BefR+uuLtyiSXWFZPWVLT5z0WWUrkhwyVfex48ai3nJv7wXxXns36viSZxMgOJBo0/hyGNG6Dtihn+941K0+xIMd5WI6B6xGei7RbD82y5Wj09tyOD0F3zqYP1b/Vkcrj6YfV3RcgNw6sLnccIs/qsPVKc6HCR8GU4x2lbojK7WUKIGZCLYfUmkAEqhcz66q4hcmcfNRIhM1fBToeAQloefiqA2HUjGEa12GIWWNP9gIpOqoDEUJTbtkNvSQrHDfIelP4XyKpOeewJaBYXpUwyGrq6iOD76XDOMPHNczKkGou0w2AwLIqZGJNFZDaMumTlRof9Wj/JyjchklcrKbva8YhFuUgKCnrsDmr0Kez6okbwzQnqPT7tLoV2A5vIshfVQXQorvjJG8ewBkiMWI5ebSEUnNiupLjeRiqBdgFZ/lPmjVHrudvEjGUoX9tB9Tz3MyDcMyEfCSgZTM7B0MDSP1dugh2Yw2m2k7eAt6UarhuHbqY2zePkkflRHLumFQFJepYMEoyLIb3Jo9ehUewRShOHLD5PaJcjfX2fnS+OYMwrtxS7CE5hXZdhzrkE8ALUCRiMgUnJCLVKIUJglwzI2BDIMWKg2kMkYesODhIa1OIO5bZol32kjfxZh9pQ+hsdd7thxDPbFkp67dcx5F+FrqC2X2tEFxi8NUBsq5uIWiqUQpGMM/rYWRhvWm1ir+zA3T4STDtcFoRBMzRCsW01yVFLQahzz2ge4Zc+y/++nev7Z/4I3EGHRDZLqUui/bpaxYCnHveoBirf2olqwecciuvsr6JMBqivxYhpLf+Kx96895HiMpZ/7NLvf9uSrdfa0rEX2CJZJKa8QQrwEQErZFkIcnnfjaYaXjoRFF7uyCNcD10WUakRnwxImQSFD8+SleFGF9M27ie1WkYqC1xVD+BJ/qAfFChfFEl6AaNvgegszTYPYjiJB3MRPREjtbIT1txImSrGOMR+ArpPWcrR6dFQLlnx9DK8vhzFVg7aFt7iAKiXb3htj6NtJolunqa3rR/gQqUkUR5LdAvEHJonf0UbKgJ6f1Zh6/nKy20OHfmLTHMpR3VhbEogAGn0q3XdXmT/bpDVu4CYFigvTlwzixiGz1WPlf80RxAzaAwnUdoCV10iMK5TWqAxdXcXpiqK2PdIjKk4mgjM0SHzCItAExkQFkUlD0/pDbksQjyAzMYSXRGlYBLqKKNXwl/fSGO7GSQoKN00hYxGsRSmkAsm9kuoygVRCwZLYK7EKgvgkuDEFrelTPiKgdSEEVR91uIaoROm7XqcxoKLaUD3WoXCzjlF1UVsufi6OWm2HYdGFFH5ERRMizOw3I7QHUxgVB63uILyA9upeFF+i2D6FDVWU8VkW3x2E+UStcMVMo5BFtB3kUJznHX8vP7/pJFbk59n6gW4UIbBHk0RKAqnkcde0WPKFHrRyCyEEQdJE8Tw8TdD1yr38vraWmhvFvCcOL/q/v1XF8dFbEjulEJuR4Pn0XTfDDWuORIuF4empzTrN3QUWTbZQ2g7Tp2cIzq/g101klwuWekj+z54QGVpgD0f2xWvkCCGiLJTsF0Iso5NoeVigeBKrOxb+CqwwzwMhwIwQdKVwUxH0hkegL8wnag1QBfp4Ga3URLg+dnccAC9tIhMmrWMGUBs20YlG+ABpWqhNJ4xGc9ywom8uEWaSF5JEZhrk7pohtddFJmOojYWy+oU0BJLJ83LIuk5jkYZfyJC6a4zcHVNk75wifdsI+VunwzVXZIDQNMimSe9xiU/aJLaVQNeIlF0Gr2shfHDjhD6kis78uoDasoDee2y8iyvEZiVK20VOTKM0bOK378IcKTFzsqA+BK2+gLl16XABsq4IpZUaIy+VpO+bxc4ZeDGN0qm92Ct6mbqoD7s/RX11DjsfRTgefkyHSo3I3hLOsh6QkvRDRfJ3FrGW5QGIzDYxSxIpQPFA8QLSu9rYGUH33QpeDNyEQPElMulhzcRQSxqOpzLwcw1z3sOPCLQG5G8NQ35bvQZIiXB9mity+DEdtdREcQOUpgWOi1QUYtvmUBwfAnByUZp9OqMXG4xdEGPinDTbPrCc1klLmTunH9mdY+7SZQQxAzlfIjrnsP4f16ENNNm4eYizF+9i8UcDgpxDe4XN0NVVfFtDq9m0B9MEsQhBRMPvz9FYbGL9Wz8/ffBYHpzs47H8/IGhgpREqj6qLRm/vI+tH07zsjNvQ1nWILvdRTyjzN+84lfsepFJ+ahQ2130IZ+BH+n811lfI9lX56xnPzkjy57OUWT/AFwLDAohvg1cDzzxMoEdnvSo5RbmXGh/B5ARA2tNH9WTFmEXYmj1cDabvWc2PCAWDe34iqC+tgsvGUGv2Kh1C63UxM3H0esLeS1qWAyzdkw3ylQRrdgIfTOOh1K3cNYMoFba+EmT2tEFRp6lIiM67aE0c6fmmT05Q31JlPxGG+GJMKR5UVh+JsgmwodiLoWfTyIXMvK9pf3guMTW72HvBVHqa7twCwl2vlylPhSlujLATUFlZZTojAJxn9Wfn0VruAR3ZHBeVGbkeTkmXn80c2d00zx1GU5/mt7bJb13+nQvK5Lb0kIE0OxRaa5rk70rwsw5vZRXaEydrmOWfUYvNTAaknZeQwTh1FTqKlqpSfOkYfA8KiuiGBMV6mtyiCDA3FNC2B7VNWmMeoDqhOvl2GmNdk+ExGRAoBIu1dyQNHsNjEmD6JSK3hB4exJMn6wyc5IBErrvd8hsb9MYEKFgzYZr5JizbRTHD0OWAxnmKvWkCVJmmAO0ULpHrzuIAFRbkN0mic9IVny7jp1RmT/Zx83H8aLhA0/EY2hVi8Qdu8n9NE72fo3fPHAE294STj6UksHEuWkiCRs3G2XmZB03F6X/qj3sfHGCuePhiqt+zXFLxxCKpPcuh7Ufuur//FYDLYyUqy3WaPYq1Fd5fO7U73J/ZYCLlm5l5LmCy4Ye4s7KUqKTClJAbpuDsFzGzxX89U2vpj6VpFV48mkxEnHYRpHty4qW1wkh7gVOAQShs3/+gPeswwHHy8fR5xpITcFbXEBpO0TGa5i7XajUaJ24FM3yMSbnkYUsSInTnSQyWkR1JPpck/bSDMIzMcoW+nwTpycRZoSrAs3xiI+3QFEIEibCcgjiERRAbbiIUgURj5AYaZDclWbvJSnUNkTPnSP30RgT7/WobEyT3A3ReZ/EnXtAUfCW96EoAjFdRI2aYdSboaONz4Om4Q/1ofiguBKt3KJwe47yc5rkr4lTuH2W3S/ppvs+n6GfVAlS0TD6rQmxr2fo3V6meFyG1KiNVrbY+bI0mW2C/PoKsY9E2fPsGN3rfUrnWuR/Y6JZAVKB8pkOfb8wGDtfpfc2iVHzqA0blNYqpHZLKuckGbpGY/pEjUVWD4kJlyARJT5Sx+lNYUzXALAygtyUhxs3cOPQ7FVBQHw6IFoKmF8H2n0qbgIy2yW1yxvITUnUoSbeWJzWchfzToPRS3R67tKIlKGxSCHQDSKGghdV0Jthn1UrwCi28FIRFMulvTiN1g59Zort48YFwoPpC1x+eM5/8ubNL6V9XQpzUiHQAvp+vDPMg0rEGb8wQ/8tBplfPoTIZSjcmyEwVNS2R2B4aLunkF9xIZAs2WrgLelj5iLBSjbjHrOUq6zLWXzyGIOf1VBrLaz8/024bC4yqA2Hmln8olnajRgVP8ZDOwcY705jTmn89Htn0u4LSNoQm/eZPdZgUS3BWSdvpmTHmP7KEpQnabmzw9VEJuTjXJkQYrWUcusjkywfgQRKUsrDcp3TE044Qa5ff/ivs3bxMX9PEAvNJ+pEMSwlUl0o+WK7BLkE1dUpsjfsIejrIohoKE74ALJzJpF5C7Vh4aejOJkIwg+jmqK7S+D5YXXhHZPIfAZhe/iZOIGpolVCrclLGHhRlcpKHdWGVjdkdknS2+pIVdDui6HXPCJ75iGiIxUlTPDMJ9BGZpD5DEFER0iJlzDQKhZBTMfJGJgzLRpLksQm2mizVaYv7Cf+vCkqv+2j5x6LqVNNMjsDUltKbH1LFrPQYvEnwDc1pk+Okt4TYJZd2nmd6KzLyLMMCvdCdM7FHKtROTbH9JkSvayS3SZpPrdG11fiGBWXsfOjuMvbJO6OYmchPgGlY3wAcg+oZLdbzB5vojfAi0JsTuIkBT3XT4OmsvVvk8QeMkmOS9pdgvyDFsZUjW1/n0Q3PdzxONkHFfS2pFUQJMcDTv3Q3fzkxpOh20LVAtzZKPn7BPPHSYh7LP22pNWtE+gCNy5QbYnqQCsvKDxoM3lahEgZWv0QnQ2TO51MAEkXpaLjJ3yiGYvUzxN03T2Hl43jZA3aeY3cxgpb/yZFdCrUDtwERKeh9+4W5VVRhAxze5q9gsSExIsKrCx0bfVwEgqp3S3U+TpBOhb68hrtMPBEVZHJONZgirljDXJbPRZ/cDv3jC3GtTUCW2XJD2H+KJ3YjCQ266FaPoGuYI6UmL6gl/TzJliZnuP31x9LdA6677W4/vcf3G//Q0KIDVLKE/6Sc8RW9MvlV75un/Z98PJ//ovbO5j8MQ3mXcDrgceru9AlhNgopXzF/u9Wh4OBlzbDEvnTtdDBP7tQQsR2IJOivjRJ9tYxpG2jzJRx1vQjTA29aqFZfhiFZDmoxTJmPodw3DABsdWGWBStHGovwvaQCyspAmHdrYTOxFkRvLik7+gpatf04SYljX5BpBRF8SVq28eYrmMtL2COlEBTCJImasMm6OuidHSK/C3TYeLjkcMI30fbPY+6UA8sVWuHSYWKguKC/Z1eEo6k3W3Qf1sbO2ew+4o86U3Qd2OoXXlxjSACxaMUFv/Gpz4YQfg63eslfkQwdbpB971Zps/3ya7XUS2Jrwv6Pq0zcplK7x2CxBiYD0SAAKMmkKoguVulfoSLF1UZvdgkvzGg/Pwm9kyMnrstmouijD+rF82Gpf/dRm3VaQ3EiMqw2sHIMwvkfwPmK6eYAcQDCaysoLbS5wNv/iGTbobPXvY17mws50c/P5PebaG/IvnzAATsfqGKiDnoUY8l/+IhLI+Jf9WwXY3JMyRvXPs7MmqLb4yfwp6NAyiOQM1bDHcX2aPn+dHpX+ZvPvZ25o+VtLu6iVQlyXGX2fNcipeY/4+98w6zpKrW/m9XPDl2zj05z8AwM6RhyEmiggQxoChgQCWIXq8BFBMZUQFRQUWRnATJeYYZmJxnemLndPrkVGF/f1Tfe/28Vx29oPh9vM9TzzlVp2rXPt2n9tpr7bXeF1l0KU+x6Gga4czmVVz7zEl0dfo4/MD1nFnzJtfuOo7sqy3UfWoXRVunUDXpmRfm+wsf5PquYyk843mdNWtKyGQAxXLQ0iVE3qPwlwqUalReXzuFzkkDiO/Wkp6gUw276Hkwsy5awUIfzFFpiTF4VAPZw4q0GBU+VLOMNwbnoVQ9z/ndBin5l0xB3hf8JUXLT42/HvHnzhFCPPNOdOo9/GOg5qo4Ya8aH1WFgB85lkZEIlQbI4R35sCyoakOCSiWiz6UR5Qq6CULBofB54Ng0OMlC5o4ET9Wewz/um4EAZzWWq/qPxkcp0YxyLfoWEGBHZTEtgi0Z5I0pHJkpoUI9ZSpxHUCawe9GW2lipEqUZqQ8GSBAakp5DvDJNbnGD24gdjmkEdPYxrIusR/xhucWBA1X6baFKVmdYYdH4yS2CgpxxVsn7cmoZWAY1M4K33svEQQWKEz4ehdbB2oY5cvQHQ7NH+hi80PT6H1wV4iu5K4ukdnMzbPJr5ao3JshtFD/Ey/ZoBNV9YS2g6xc3op/KyFsWNLiL1+tLyg9lWN4Id6MX7TRGq6QuOdPkZnqQzND+JPSXKzLcLrdQotPrSSS3qCSnBAEugvUbfSR9Nnd3BAbDd3v3YMIwtt2h8H/cQx7j7nRGb8dDML/LuY5u9j0pJdZA7y8d3JD+BIhZv6jiWTThD3Fzm4ZhevXTuBTNnPV6b8gZzjY3W+jft79sd2VJrDGc44YhkrU638cNLv2Fht4DaW8OG3Pk750ApmsEpoRZD+JRKpGMRWQOz0XgZeaaZhueS2n/6WFeU2pvwyT3ZyiBej04jNKbF7YxORFAz9tJNSjaAahvZlVe687iSqB8SoHJOnWjQAP/Vv5EAI8pNi5NpUortsml8usPP9AYxkmfJPmwjnCvjSOnreQbgSPWtTaPFhBjSG5xm0PJ/lw5e8zl07FvGJpRejJKB2rcszK77+z3rc/iL+X01T3pdCS10IcYkQ4oHx7bNCCB1ASnns29kZIcRuIcR6IcQaIcR/i1EJIaJCiMeFEGuFEBuFEOePH28VQrwohNg8fvzzf3RNQgjxrBBi+/hr/O3s8786HJ/mzfKr1n9K/uI6aGNl8p1hZKXiZZZJSTmp4wZN0DXcsOmJb41T9Mug6RVcOh4rs9NcC+UKu08K0/XRGuyQRv8hIQqNOpWoINzj0LBMklyT8zLV/JpHsZ+teNK9gDKa9YoYqzauoUC+iNIzhHAkWtlFFKsk1qa9SnvADfnBdr19XUPNlXAiPoQryXeGseurKLak7q08dkBg5FyvmHJ5gv5Dgqiqi+OD/ns6MVcHsQOSsRmSVcsms98ZG+k7pZmR2T7SkwzqXtQ5e9FyxhZW0V+IUvuKTrU1TnytRqTbxflBA8GeMpFXAphTssS3uRQaBLv7aqh9dZCGFTZDnyxhpqHqJTzR9oigcnAe34jFwFllXBNSpxTZc2KAclxhbqSH21ceRrnGBZ/DyAUFjmjazn4/W09I9RI7X85M49CaLmxHZVWpg7tHDmX7aA03zriPttAYFVdjanSI1FCYOy8+nYfffygv7JpC33CMwxq6KNsaL914EBe1vcy537+cm758Lr0vt1LO+Lj24PtxbIVPXPUIc2ftZsL522g9aye79tRhhyTRf+/GQTDT7KPvsAjmJ/qhrPDopjkANL6Uwj9kERxwKTfbnpRzyUJq4DMtal720tWVbd3YIR2t5FKztsze0ySpmUGm3NqDtjpE/xEuxeYAmQ6FYoNGZOOoJ00tIN+soxVh68dCPNo3h0pVJzAIwT5wzHfvIO66Yp+2fzXsSx3MTwAd+PH4/ofHj+1b0PBvxxF/IYngM8AmKeXJQohaYOt4ZpsNXCalXCWECAMrhRDPSik3AV8GnpdSfk8I8eXx/Svfob7/S0EZzaCbXk0GQY/xl0AA6TdRxrJENlrI5nrvc9PAl7JQx/K40QBqXwoZDyNG0jidTaiZIk40gJotgWWz7aNBEuvDVNsr6L0mo9M0Cm0uwlVoenaEal0Yx1Sw4ibZdoPkmhyRLSXEUIrI4BhuXQxlKO3JE1c0/N15rI5aHJ+GMVYmsGUYGfSRmREl0O/puaB4MsUi44xrzHsMyplOA/+oizKmU6oRRHZCcn0RpCQ8v4R+TxJHh9LuIP6DUlReS1CYYKMUVJyITed9ksH722ga7WfvBxqZcnIXXQ9N4rH7DqHzyB7k7+qpxFQynSalOvCdNELp17UUGnRKBxTpuNGgGpe0PTqCfFpj54cbiHZJtNcijC6uIITEDvio1kL940GK9VD7sMbQfEnkxSBqVZI6qsyyVCetD2kUaxQuvuL3LAns4NJdH+D7HQ+Rdk2WFyfR9bUZrOycS3JjiYeMY9lzvAEtJS6862LMNOTaJa7fZdptWbqPj1NjKFSHFXAFU/wDNBoZbj6wjeu7jqUag8bnRyjW1WGmdZbuNxm3N0DfjDi2q3By3Vq+t+Y4ZkzqxZ6gsGH5BM5KXcApHeuJd9nU+guMNWWJ+svsrdQihlIMnZSgPKuEz7QpTIqj2JIffemHrC+3clfpFAI7yhQPnYJiSfRslUrSxB8rkp2gk/tEK43LbSq7VXqOlKgViS8l6HlfHcF+F/+wTXqSQbkWvn/cvTyZmkP36iaIglMLbv+7c4CW/GtW6e8L9sXALJBSzv2j/ReEEGvfqQ79FUggPF7oGQJSgC2l7Af6AaSUOSHEZqAZ2AScChw+fv3dwEu8Z2A8GDpSUyHkDcYik/N4xRSFwpwmgmt6EaUysrEGkS9jDOQ8+nrHBU2j1BwmUKqiDqSw22oRjosbNEkt6MA/IHA1CK82KdeCWoUpd2U98TFVRa04FBsNAv1VEhsLKF3dXmjLNJCJCK7fQAn4PWlfVYACdlDHGC3j+DVw/TghE/+wxdB+PpK+WrSijZI3kVEdJ2gipJcqnNhURN8xgFptQ8s77DkhRMuLZUZm+4gbaWTGYXiuhm8UYj8Jk2sFp0ejcUkPO3c2UGjUaL6qi9fXTSG6HgZunUDEcug9QrB3VTPm/gL/EFRi3mJ29KsmYscm4q0NbOmMYUUkVlBhxzf9RJ4J0vCGhb8nR6EzgnF0jugNYVy1ila00XvG2H5RM2pZ4Ppcxg6qMv1rg8S21WCJeg774VL6KlHu2nsQ39l0GpGJac657nIqS3JMrh3G1QXRXRalWm9CIJvKuCWN0AEpTu9Yx/ZCHQCDkQ4alnvrG5Pvsai5bg8/eOD9TLh3lI4mh3NuXsriGV1c8upnyE6U7H/gdrZl62h7xmb6Kb08UZzFd946gY/NeYO+SpTDotu4/p6zOfiILiypkp6ocUJsD1GjxGglyA+P+iG7X09y7c4gk6PDTA8O0H7tCH8Ym8WWShM/uu00Yp/oo3ssQrWoMP1bKZyaMHtPhpnJUTbU+tHDVfbUmbQ94TD9ez30n9pBNQyFFolSVUjNMGhcaiFOyvK1353LCSeu4Mxjl3LvmgNQTYdMwvwnPmx/Gf+PJpH9+Syy/zzBS1E+U0q5Y3x/AvCAlPJ/yi7733VGiF3AGN7f+3Yp5R1/8nkYT7J5GhAGzpJS/v5PzukAXgFmSSmzQoi0lDL2R5+PSSn/W5hMCPEpvKQG2tra5u/Z8/9kgtx/wxHHfh9fdxpyBW/Wb2perUvVAtvGHR5FCYzL/5iGxxicy0M0AqqCnQiSnRjAyHmpr0bWZmChSfMxexl4vI3iwiJ1j/oQDvhSFvpICSWdw2pNAqCNFhHZPLJQRCRiXtumiVsf96hoqjaiVKXSnsSKaBTqPJXH5NocSqGCqFRJL2hAK7mMztSIb3WIrB3y2vF5rMEoClLXSC1IMjIXaChT8wcfQ4c4dD7g4moCreyQbTeRCiTW50nNDjF2bImGB3z0HimpfVPBTLsMHKQgOor4lwbJd7pMuT31n8kN3SfV4huVaGXJ2FQFxy/RCgI5J8eEr5YYXFKHWpW4msDVYWxRlak3FlGGx3Br44wcECMw4mCmqqiFKls/7yPxmkn9C/24IT9KucpRD61ixArTZo5y9zUnM/uSdTy3fDbJNQqfueJBZpq9vFnqZHlmArPDvXQV69AUh7hWxJIq74uu5YHUAmqNHAB3rT+QD85cRcnReeL5BRy4eDOvb5rMqfNW8/74Sq45+zwqCR/f+PHPGHVC3H7+++m4YTsHRnbwwAcO40MPPc8TI/PwqRanJlfhUyxeyk5HEZKVqVYW1+xgbmAv3dUEiwI76bbipJ0gDw/uR0ivsGmkniNbtjNSCWGqNpvH6tk/2cOTL85Hzwnc2XncrhCT7hml/8ikl93WWcbs8tF5exf5AzvpOVIgQw6+PTrVmCQ0Mc3ExCgbX5pEeP8RwkaF+kCe3kIE89sxnn/p39625+ftyCLzTWyWbT+4aJ/O3X7G1//q/YQQxwM3Aypwp5Tyv7F9CiEOB27Ci06NSCmX/G293jfsiwdzOfCiEGInXh1MO3D+O9EZ4BApZZ8Qog54VgixRUr5yh99fhywBjgSmDh+zqtSyiyAECIEPAh84T+O7SvGjdkd4KUp/++/yr8GjOEiVl0YbXCEofd1Uv/iANJvIPf2osSiKPEYbm0cVIFSrHoFlGUdaWpITaPY6CO6vUCpwY/tV6hckCXwQA17RhMk+12arrUQ1RJ2PEAlrqOnwGqrQUuXwHYRo2P/RRxZKnviZuUK2C5KpuDp1ISDSE1gjlZwdB/9h4GZCeLqQTITFZIbHPSCQ8160AqOp3VSl0CaOkq+PC7RrBLeWyHb4aPxDypIi8QqAz2XxwobGD1pgmaS0Zk6bT/eSc/Dc3FGTYbPKpIIVEink+gFleaXLLqPDmCmJW6Pws5zajjs+LWsv2UOyuIxaj+T9ShuKiYDZ1RpvVVS2Bxk2zdVIq+AmXFJTVXRC9D0e42tF0SoWR0l2yloWGHjGAr9B/tpu2eIadeFGZtjUJqYxL92L4OnTeL3XziS3k9VsasqtcDaH8+h9swRvn6CRw34+S1nw69q0QsuPYXJjMw2uPhTj3H/547HNRVWp+aiFqp86vGfk3Z8/GbbEbz41MHEPrGXYK/gtQ2T6bxf8vTuhcz9UDc9R4WJdbl0W0lu23UYfR8xiFf93LzlSKofjHHj9WdxwRcfI6YWeXDkAF5fN4X5M3cxLTRAe2iM57+ymHvOL1HJmwQ3GZz3kWcJK2WGiyE2DDfxmfkvMcPXyxd//QmqMReRrLDTLOEfFFz5yd/xvU3H40zOUbyxgvWCp1ZqdvnwD8PmqzqoW6ow7Ts76DlnEggoNzmU18VZZ8YIDUJuZQ0jTRa77XpwBNqny//Mx+3P420acYQQKvAj4Bg83sg3hRCPjS8X/Mc5Mbwlj+OllHvHx9t3BH9xkX+8s3OBycAl49tUKeWL70RnpJR9469DwMPAwj855XzgIemhC9iF580wnnjwIHCPlPKhP7pmUAjROH5OIzD0TvT9XxXKWNbjhjJ0alZ7ITAxOIrQdU+dEZCmiqhYUCx5evQ+k1JblD2neFk+w/uHyDWrjMwRyAdqUKvQdKdJYMCjiBGpLPpQjtCGIZRsCX0471GXuC6yoYZKW+w/6fCxbGTIo1bBZ5A5uN1Tu9QEpXofesll6p0Z9JKLXpTUrvUq5+yAythkDcVyGDukBZEvo6QLSFPHSgZxDY30JB/+IRibYjAy2yB1YJVtH/ex4Nq36DuhAccUFNpcnl82m2KLwxELNtKcSFNemsRuLRMYkBhX9OMfEowcVSG5wUK4sPy3c7ECguBvogzeFqJca1KJKkRf9DO0f8CriN8RoHZlDsWSFNsdHNMTVZt2+xhj06FmnUu2VcP2CxJbHKyOWir1IfSiizlSYuT4iaQO8miGlXUhap/yMXhMFfHBEYSAS1d+kG4rSXtkDP+Qhat6C96hfpfNhSbGphrgSBLX9XDSva/x73tOI6aWUUtwwpdepu+xdqQKp+2/mvNueYLvfOSX3LT1SKae2MV5Vz9Bqz7K96Y8xCn7r2GwFGJR0x58+6XIHVXglntP4Tu3n83KP8xAj1VY39/Ib9YvIKyVOfSaZaiKpzXgH5Hc+YejOSeyjXTBT3vjKEcGN/N6bgp2UBLdptDwiMnwzzuwA/C110+nXNY5d8pbdA8kPM4uBTruH8E9IUVd2xjBj/Sy5/xJmFlJy2+6aHlaoJYhukMQHHCxIy6iquDr0zjj4Ddpusdk8WnvPrqYt5FNeSHQJaXcKaWsAvfiLRP8Mc7FG0f3eveW79iY+BcNjJTSAU6RUlaklOuklGullO8ID5kQIjgeAkMIEQSOBTb8yWl7gaPGz6kHpgI7x9dkfgZsllL+qZjEY8BHx99/FHj0nej/vyqchjjV+hCEgiiliqdHr+uIxjrcphqGj+sgPTVE73E19J3SRv9JbbjRAIVGDf8wlOogsbnsha02SI+E0pEM7aejVF3UbAm7o57C5ATV1gTYNnY84BmqikWlPogUwkuTDgdxw144TlQttn+slmpYUKkP4e/J4RupouUsnKDB2GSN9Pk5SkkVreRSrFHxj0i6jwmgViTDRzRRbYlTTQbQchVcv4ZvzMUKweRztmEdkiO51ABL8NINB6GVoOaK3QS6FYQj0PIKZUdn1+YmpAJtv9NwTEH6l61Y8/OovT72nKqQ3ODVYVgBCO8oUHwzSfepDoFhh0ILZCe7VKKC5peqqKN5tJIDiqR8QIFircbAkiRO0GN8bvzdVnKtglyzihXSKTTq+AcrbL3ET3xjntgKg4FFBnoevnHVz/nQ3BUMDUXJv1KLVfIq38+tf4Nf/uImfnrjjbz/iuf48rd/haY4vP/CF9n7IYdLm56m6Jr4VJt2VeGLH3+Ig4LbiZ/YRzkJBcfkmqdP47JlHyS3O8r63kZ+9c2TKEud9eUWVn1nP4Zea+LlZ+dSWpfAXBEk1C0pzCujFsG3LEj090FCK32sHG1lU66RQ1t2ccT0rUz8xDYA3velS+n8agl5fR0F1+BD8eWoJUHjw7uIrhrA9glOOv0N5k/ZgzNm8osXl3DctE3MPWUzUpf0nlBDeVUC7q3BvDSIkJCaBd3nTUItuXQ8MERwwCEzQaF9Vh/GqIpWhNcGJhC9fC/uZ0ZY8r4f/NOeuT+F5G3NImsGuv9ov2f82B9jChAXQrwkhFgphPjI2/NN/jv2JUS2VAhxK56SZeE/DkopV73NfakHHh4nataA30gp/yCEuGj8frcB3wLuEkKsxwvXXSmlHBFCHIqX3bZeCLFmvL1/k1I+CXwPuE8I8Qk8A3Xm29zvf2mo2TJW1GToyCbiW0voQznGDmvDMQXJt1L40i5WUMFMS4L9NtWoSnZKhNrXh3FiAeqW2XSfEEMrwWi7YPLtfez+UDPtj44iyp7Xo2UKaHukl9JsGGg7+jytlNoISsVBy1VwogGsuIlvVwpcl+KUWpyAy/AhDqmcTnRbAvAqzF0dgj1QXBmDGIxN09EKXk2LloehA1Sc9hI13y6ApjB4SIKaNQXUqkvtWpvc7xN0yjLlVhO9oJGaIUhschm5rhO9CY48dB0VR2P1gzNZcsYG1v5yFnveJ9AyMOm3aaxAnFhXlaH9Ddov3cxbL01HuLD3fWHCeyTtT5TJTg4hBRhjCsE+i2pUY+9FDdSvkNS9Jki+WcROCLTtPVRvrydXieC21NO4rEz/gR5Lgh0MkpoRILwOSs0qjU/sJb9fM5WowmOp/Xjm1f2YOLeHhqk5evNRTgttpdcxOPPKKzDHbKQmeDSsMnCQZPJvSrRHVP7tZ5+inDRQKy5zj/08Bx6wlTfenErjqxBogtXDzUx4qEqhyWBooUDdGiTYV6Ls6tyzdyG5CSqBfggO2HQfK4huV3A1QeIlr64oPV3S9HKGPSfH2NuXJPdGE+sOK3Jgx27OqlvOghN28cvJi8idaeJ/TONL285gcf0OZh7exSZ3EjUHDuA+CttztTT6MyRXqcy6YAMv7J5C6JkQNSXJ6Cyway3ULSql5hDRHS7+YQFI+hbrhNvqqHtgM1KZwqDWghuV5GdZWMvrkNtrGVoomZgrM/+TNxIctHnlsSv+ac8f4FmYfc8iq/mTEo47/mSt+n9q6E8DcBowH2+y7geWCSHekFJu29dO7Cv2xcAcPP569R8dk3jrIG8bpJQ78cJxf3r8tj9634fn2fzpOa/xP/9hkVKOMu71vIf/jvzUBKGNw7hqEqXqYCeCxFeNIBWF4sQY4Td7POqYfAFrYgOBbSPe4n8ygh3QGV0Qwsh6LMWtz1fBtml5oeBpjxRLEAp6r66L1FREroBsqEFqCkrFQt074GmUlCv4Guuwa8MoRQvfUJGGpWGqQZ1y0svQMrIQ7POelXyrQM+AOGIMWTRp/SEIx2XvsSGspgqJl/ygFrGSAeqWp7HDJv6ePCPzYwR9MY8Cf+MwQ/s30rDcpu9QjUA/hPpclj04l7qVVXJnWWy7aQbO2WkiL8XITnLYfWqccpND8gODxG5rZtvPpvOhz7/E769fwoyLN7Lqvln0HB2h5fg9mN9rIteiE9jQhwwHGVxQw9B8QcMbDsWJcUoJlXilgcZbDGy/jRM2sIMaZhp2nR5m0l2DjJ1R733vnRkolQmtH8SZ38Ar3RPR2/Jc1vEMX/zVJ7jozKd4JD+VmWavR4opvTHLP2xx+iGreW3lQhQLjLxCeHuGajIAUmHjcAMTZvXS5W9EKcMlHW8y4WdD3Nm7mMJYgsqGGN/91R2c9eAlmClB7VabSlTFN1RhwoMa5RqdwofHUH+fQC9IYpsFlbogiU0u/ZMllRj41gTYFqnlV/bB5C2Dm2b/jh/2Hs36aRHOatjGgaEuPhR/g4sWfYhjGrfwwSt/zZW7388z26YjZ7useGIWsYOHqZghCsflUaQgvDTEyGywIhBozlHqDpNcI5CaJDXXJdw9ifArXZQSU7GCgik/qbLtU4KBFhCqZGxqgNRcl9BTkgM+cQNqFXyjNkamyrOv//s//Dn8G7jIRv7KIn8P0PpH+y1A3/9wzoiUsgAUhBCv4I29/3gD85cq+d/Dvz6qYYXC9BqCK7u9gT4SAkAGDAJ7c8hYCFG2kLVx7IBGflEDAwdJYlsUYl0WoX6XofkKkR0etTyahh3SUdO6Z/ErFdBUQEXY3gI8loNStclPTRDc2+8Vd4KXuSZByZcoTK9FK7qUEgo1G23yjRrlJPjGvDi8fxAqcdCfjzNxaY6R/cOk5tskmkexcn7MnEupPYqvJ48ymqEwrZX48jGynTHKcY3adRYDxzaiWN7fINoFjgmViMfEOzzXwOyH4rkpilviOJMcps7sYcqBwzz+5n4cWbeNnx3cQngX/PLZJdQ6sP36GTRtS1NsD7FlYjO+gzWaXq2y/bPtTPxdmsk3drH5qg5UC4q1KtlOgVYOEX2zH8PU6fpoHR1PFEnkLGrfssnMraXU4NL6ZBYn7EO1E6AKoqsGKX/U4Lezf85ZP7iCmlMGuP2+E3BVeOX8a3nmuzdiCo393/gYryy6g1WVKIf/+xaW5Sfxvsga7hxagl+tsmvZPIJmlaPrt+BKwWA2zKN9c+hf2sy/n/07vtl3MrKzxKZKE/Uzh8g9W8/wXI3EJgcrotN9tE79m5LELSGM4TTVugDpiQaFBo18i6DmeZNyHIwcDPfEycT93Lvwp3xjz6nMjPZz9ukrqEqVmFJkZbmdmK/EdF8fW6p1rN/ZgjqsM/egHazuaiMkwf++IWZER3mjq5NAFUI9kOsQxIMlRG8UKwyxLRK1qmD7YfADU6l/fCe+gzvYeVqImtcl6amQ2ADJpf3kOhop1glKdQIrCIqlU7Phn8RY/PalFb0JTBZCdAK9wNl4ay5/jEeBW4UQGmAAi4AbeQewL5X8SSHELUKIVePxupuFEMl3ojPv4R+P5b+8FFcTVCc1UJrZBKrATgRR8mVE0avi7zuxgR1nxZG6gm/EwkwphLtt7ICCVnaJdklK78tgdI+RPrAR344RT3xMCNB1sB0Qnq69rI0jKlXKrVEC3XmPpkYR0N6EGw1ghz2RLj1nU4mpJLZWcHSBmXaJ7HYpJQWFekG5BmLbXZLrK6Snh9BKEizB2M44NU/4ib2yB393DlG1qUxu8Ppi2wT6wT8i8e9J4x9xscIwvL8gOGiTmeqSmuviXzxCbIeDkYXQXVEuft/T+IZV8re28tTz80GR3LXpQPSsYNpZW1Gai4zOEri6YOigGJlOjfbHIbHJRTiSCQ/lGLvGwulooPNBl54jVBKrM7S8UKZYp7D3zGayM5O0Pl/BDmoYvWkqNT7CO3MICVbCT77NR7Up7HmGlSrl15J8vecU0gsr3DT1d/zhEz9AqpByBTlpM//WSyj3hjj6+5dzgJnlK+tO48VrD+brn/okXdfOYN3353HBES+i/yjJ05cdjvb1BJalsbu3hshOya1Xf5B7D/opSo+PR4b2o687QXS364Un9xbJTDAQrmB0psLgAoOhg2LsPd8h0m0T2VWm6bUywX4bxYZYVxk1o9J4t48P/+iLzIn2sjVXz2Oj83BR8Ck231rxPhYldhNQKkzUR2hpHqX1eZvsV5sxQlX2q+2lPTLGij3tyLyOq3myBXbMobcvgeMDR4ealWl8KRu94HGwZQ7pwDEgvkUyNg1CewTJN4bIzamjZq1LqUZQaHWQGkgNUlNVDvzQn6NffKewbwv8+7LIL6W0gc8CTwObgfuklBuFEBf90XLDZjwJlnXACrxU5j9d735bsC/m+l5gGPgAcMb4+9+9E515D/84TP3Wf01YzJSFWnHwr9mDyJeRqsBOhrDrIrimR98R3yrpOa+KFVLpvH8UK6igllwyHRqVqGBq7TDbvhWjGlSodCaRftPzVsqV/xzcsSycsA8qVXzdGSq1AUqHTKPvrCn0L4njBA20gg2lMtlOk8iuMuW4jhVScEyBXvR0UowcJDc6hLqLlOp0ol1FRuZ5GvbCEUS3j2epjWYQ2TzGaJHIjgIyGiLWZfHJLz1KqT1GZFMKYUN4t2BonkZiYoq6FYLaLyvkm1WK80r0nOjy8NePxcjAwEGC+CZoelZhYdsepArL10/kotmvYjdUPV4sxwuzZVs1cq0K6Skm6akhkp8qohQqlBMavmEBmoJwXGpXFynXSwYXKfR/uoKeqWA1xTAyVZTuQYJ7FLSi5YVvBnLIoA+7tYbJJ+5g/WADLxxxM1fvOZnDn/sCp574Bu2aRlToNB+zl0BrjvzBnlbOT+b9hrFTC+w536F/MVjnj9JmjFC4KI1wvdocsS1IfLmJYwhGZwvOe+vj3PiBuxj7VhuT7nGIrBum7ekilXo/uXaQmqTSahE/fIDwB/pwUga2T0GxJanpPipxlVIt7D3WR3I9lJIqKHDPGweRrvh489kZ5Bwfu60k31z0OB+LrSDtBFhTaSX/SCNSgewEH413+Vh//RyWJLbi81kQcNCPHUGePcLpC1bS1jKCOytP3ZoKUlcJ7BxjdLqOXpCUEwqxp7fiagIzJQgOulSbo/iGK7g6JDdaTL6nzKSfD+AYYIcg1/IP9mIkSFfs07ZPzUn5pJRyipRyopTymvFjt/3JcsO1UsoZUspZUsqb3pkvtm8GJiGl/JaUctf49m0g9k516D288zj2wKupXeOw6MM3cNwB38TYPYzaPezJ+0aDVGMGwnFR8xWGF4SxApCaLpj87SLluMrwoiTxF3ay+wwvrBTqc+m7fSKTv1MkuT6HUnE8D6Zc8TwYKb2UZylRKp6hoVjC15MhM0Gn0CpxTBidFWB0VgC7vc6jd28y0fMOvhHbm51P1XB0iG2vENxbQJRt+k+w2XVKEDfgUrNSEN8oqCR9FOe1Yk3wVCPF4CiibCNSWQK7xvjJD09j1xnQe1wNUoNq2DNa7hM1VCKC+O2D5NskrfdoTL8lT89xLsUGiG4TjM0APe+wLVVL42sWSMFP1h0GQHqiRrERxAVDhHsdWv4wSubwIooDgye203VektHZCk0vFyjX+XF8Go6pUrMaaldJGn7iQ93V7/GuAfakZnwpSb41gFp2cMJ+xPAYStniwuaX+Pz0Fzlj3SdYv7uZD+73FsdF1uFIiS5URotBVMXFMGz22AoLTYv6X/uYcBtMvK9M4NYYRwX2IqXAVQX5Vh9WxGVsYZXUHJczjn+d6+Y9wAMjC1AsSaHJxEkEQUq6z7VwWspoBUFwm87Yiw30r2givFOl0KB4bYVg6JQK/mFoWO4gJOSbvQGyuXOE/nSUS854nL2VJLd+4WxOCu5lk5UkqeVJqHkCwy5WSCXUa6HlbeyPjPKb7gWcN3kFCMlhjTtoiWRIVYMsrt2Bsj6EMVRAVG2kodG4NAfS0xHKL5lC7YObCAxKsu0KRvcYatFCsaBvsY7WPYIb9NHxZAmpQKHNZcKNf5qM+g5D7uP2L4Z9MTAvCiHOFkIo49sHgd//uZOFEI/tw3bX2/YN3sPfhOMWXIVwJKEdWQKDlldv4vch42EvVTlTQNiSvceH2PbxKLHtVYwcTL6pC1F1qH1zjLpn90I4xLQfF9FK3ppI4nlPDEwdyqJYLtW2hCdGVa162WOqCq78r3oXRSU9J0F6YZX4Zgj1ujQ8N4Av5TI21Y9iebNqI2vh+BVC3VVK9ZL4tipq2Wb3yRGy0yIoac3zakZUSklBqQ7Gpug4PgV9xwBOUw2YBspIGrc+jp0MUWyAGT9Ikdxooeeg/dd7CO91KNVAYUmBlb2tCEcwsEin71uQWK3R/KqNq4FwBIOLdFKbath7goqaV+i4XSG81iC/XxmpwOjSBmy/oOe4JE5Fo1AnGF1gEdkBdmeJ/kOCpKbp7D5ZY3iuiZFzKDQopKYb9J0zBcfn6apUowYAZtpGH8iijRWoTm1ibHaUXw0ezA9+fyrpdACR1jkkvJ0FZp5llRAuLpVXkgR/G2X/ph5+lToYSzogBNkOnzebHy0z6ipcOeVpHL+Cq0F84hidrUME+hQeeegQvnXVxxgpe6qU3uxeJzM5gJvXkUUNKaBUJ3F8YMzIUKrFm42rAiTU/N5jcFBLXj1V7RqL+rcqDKyrx5WCl1NTWXHlAdim4IzzP0dMKTLbGKVOzXHV9+6k/YtbKTTq5NpM8mWT4v2N/HLrIrAVCrbJ4cmtzAt389ADi/GPQKUhhMiVkIqCVBWCA1UUB1xVkDp5OrVPbCe606HSliA7JYzUoOWlKjIWwkr4UaoOHU8UCe1R0POCjp/+I+tlxD5u/1rYlyyyC/G0YX41vq/iZR5cCkgpZeRPzp/OXybCFHiVpu/hH4xjDr0GLV3Aro3ghgyEI6nWhjCG8wjLoTwhiW/rAL7+ArVrwghHkm/WGZ3n4hvrJLKiB5HNQSiIG/GDEMS3VajEdNA1qskAhpSoXb0ozXWUZ7Wglmy0bAVheYWTrt9AUVVkyEexXiG2wsAxoBIVjFzQQNNrNpmJGs0vFchODJKZ5Ce+NkO1NkDrsxbViIbjU2h7ukC51qRuuYJvtIpiScydQ2QWNNN3hESxVMI+E7VvBJnLI+pqcE0Nbc8QkV3tkM0hnDhWEEaObqPmlX4qsUZyahAjDZFul5E5CnXX+Ri6fIzuHTFaXrDRSt6sevcHFIxBDT0Paskmssch3qVRDblEtheoJn3EVo+wx6yj2CKJbNYpNoLe5Se5yUIrOYR7DIysxcAny5iGRd33fDimip6t4Pg1AttHkHotmYkGWiGE1pdCLVoE+xVWvDaNwIDg66c+yDU3nkfgpArDrsMiM0eP7VCzzgJV0PWj6ayYIbjqI0upRBVKCcGHvvICH41sRUGhNTBAw/V3sLQ4iSY9TYOWZtN5LTzaN4dD3r+Te9YuRD/G9IyjXyU1QyDKCmZKodxoE9ijUUlKKmN+zKonWGZmoZL05JZrNpRJTfMhXBCOytg0ndBe+NAJS7l9/aG06Apa0SXfrNNtJbnkW+dw9Vd/ziIzzWNGgU1njFGqGEyvHWbD5DCqo9DSOsJrD83j5AtW06Bl2XlaLauv2Q+ASmcNlYSOVnaxAgpqxcU3aiNVgdPeSDmuEN4whhOowfIruKrAjvkZnW0S7vZqnYIDEqVbMqLvy/D4NuFf0DvZF+xLFln4b2zzq1LKl//SCUKIq/7GNt/D/xLHHHoNoupQmlSDf/swxam1+PdmcP0mYizH2JJ21Ioke0wbsR1lXE3gy1hoBYfROQaRTaMelYsioFJFaiEqtX5cFRRberoxwI5zEkz6cQ6RL2FoiuchCQGqAo6LNpDGaarBiptYIagkJDVrwJeSVBIKStWlZq3Fji8otNxtU0loSJ/G6EyD7DSHifdWGZnjI7Syh2A6RKnDk/kt1Zmg1FGNCCY8YGGMeGm9+QVtBF/cjPR7WvVuTYxgv03uoE7Cb/XAwe3oeUl5Yg35ZkFgEPSCZGCRoOPJMqU6k9xuk1C3wPrsKGPr6kjN1lGz4B+B2tUl9pwYItgL9Q9uZeFzYywb6sC6J4SRDtD2eIr8pCjmmEW2w0SxQSs5CNtl8P1l2n6m4XsxTHJDGW0ozeiRDTg+k4bXs1TaElQiKtEdVXZ/AVynhrZfqGQmGIR3Q3phhTYtxSNf/gEnr/oUd879JQ9kp6MLh2//+A6atQI3Dh3JZ2tf5MVyHdM/vRFbKhwZ3MLL5Rp2V2tIqHl2VOrRhcOy7EQuqXuBzz7ySRQbftOZxAxWqQZ1UnPBeF1Bn5zFHgwSO3CIdMFPOayxqHM3XWM1hNoq9KWjjBEmvMdT8UzP0dDSkoblkv7FoIwztazNtWCuC1BKSqygoJyEPdUatJLksp9/gl9+8mYuqHmFoUqYoq2zfm8T4WljaKrLNZMeYWNrM0k1z4PpA8g7BvkmlWpYxUwDAoStEBxyyLVo6EWVwICN61PRi5LC9BoCO9MY0QDFBpNqzId/2DNItk94XHLTFMzRf+AD+v+rgflbIaW874/3hRDB8XzrP3vOe3jnIVWBlTDRM1VkcHxGWagwemAN6vQQQ4tc6lYoCFdihTVCuzwySWnqTLm6C3tGB5plYzVGvXqZsIGrQjmhMjZV0Eg7vqEywR7TI8WsVFEyReRIChENQ6mMrEsg8lVyk4KoZUnHL/ditdWgFi1cQ0Mrm/QdajDx1u105puxQpBvEpRqQjQ/PUzzg0VkIkJ2kkFDXYzMtAjRbXlEuUp4pIAbNlEsg8wEk9rhPISC+IYq5I+egX+gjLBclIER9IiJmbIp7NdMeI8k16IwenYFZY2Be0KKof4IkU0as65fx3P3LaT5JYdcM7SF03z5/X/g0oc/gp4T1L1ZoPsyF7kZFl+4gm2PJnjioQPxjYLvnCGKdyRJTwyRn2wT2O2j9Q9plGyJ4uQaxi4u0XhXhN0fsQitgZ4jfFTjdYhkieb7dHoPj1C/skyhUWD7DIyVBqE+ya7TXabP2E3VUclsbOZ7PSeyZtUESFSZY0DW3cVsI8PBD15GzUpBNSxYWjmAn3/tBjbdNpNqRND9+WV8r+t47pt5N922nxuvO4vUIRVqXjHY/dXV+IY9D64w5GPquXvYsGIqsR0u1ZBAezUCE10G9iTQxjTMAqzcMZ39jtrKircmo+UVrEaLdEgluVJFuJKRJRa9J0Pz4xpScRneTyFb9VFqcPEPeVF6c1GKgwLbCXytwgO9+3P2sk/ylf3+QNHWOTCxi/VdrVSqOjOaeniz1EmtluPlwjTq9SypapCbL/sJn157LrmKhpU3ELqL8rKBYoNjCOygila08Y3aXjq9ENjB8eFPguJAJeJlE5bjKrVrHF59+PJ/0MPJ31Jo+S+Fd8wHFEIcDNyJR6vfJoSYC1wopfz0O3XP9/A/45hDr0FPFVBCPqSuIMZy0BiiOL2OahisqGDaT8bIzE4QGrYwd4wgIwGwHS+0lYyj9aexWhP0HOmn5fkiStXFn6kS3G0T22JQjRlITSHc63gZY5WKV1w5sQVsF7chjh002P3JBE0vScIbR5DRENraHYy+fxalWoFWBCMDxf07cA2Bb6SKmdbRCy7l1ijGiI5SskisV7zw3FvDXpqz40KxiFPbgn/YxgqrOBE/wnZQShZ6XmdgUZCG5QXQNPoO8RPuHp8513helLkqjJ4H331xgmWJmany7J6p2GHoOdWm9iUFWypc9YOPMumcPfQ93o5SsWn/riD/nQF+/8xC3C9L/AMeZb+8pxYpXBreKNJnBKjMLlHcFEaxQ2g5i+StQYyhHDXPRxk+vMrUm4oo6RylqfX0LdZof6qIayjEtztUQwqtT40hSlWEU0fumVbCq/vwfVjhF52Psao5wO39R/DbXDPfeeQD3HLmz7j7lJ+w6tgOeitxAmqVISfE7d+8iTmGQcYtMzMxQNpVufyKz+DDBVcgHJhtjBIYluSbFC789GM8PjiH5DF9FIcbKTYIStPLND1mMHCQin9oXMp4UpXlayYhgw7VoEPtaxr5Vo8x2shDc2OKvsEYii2xAoLYNsn6eDtqRZBvg9o1DvfN+xlVKRnQM7gIPj5rGWvzbQzf1sEjRifh0zMkgwUMxaHdGOGK586hbpnCqVe8gKnYXNd9HJ+a+hq3PHkiE+b1squnlkpUEN3t4JgC/2AZx6dhBxT0nKTUEkFIeP2Bf5AR+SuQ7j+7B+8M/uwi/3ihzv8GN+KxH48CSCnXAof9L9t8D38HyjUG5dYo6kAKNVui/9R2XFOhnNBQLWh9pkDfMTUEeyuYW/qRYT8iWyQ7r95roFRGhv3sPN1H+2Np9JECSInaP0rfEXFyEwIYmSqZiT7M0QpuLIzb1kBhfitKuoDIeim6wnFpfEUS2j5GtTmK69fp/cRs4lsKxHY4xLdX0Quw9wSVUo2KcFxCvRaR7QWUqsvAITFGFtVgByDfGSa1qA7pN9j6uSZ2fG4yhWaTkdmeHPPYVD9OQKfrywZ6tkrDiiLCcnEbEuhFCH28B6l6WXBTrt1BOSm9mawlcQ2BMVKk9RpBx+N5VNNh2oWbeGtLB+nDSmRv9wqlBw+Mkm8PovyohvicYfSsYPEZq4jushjeH4b3U1GqDuWZJWLP+6mGFUana2Qn+Cg06DgRk/jmPLUv61gJP4XZjVhhjcguL9vO1TwaFisg6D3Ko8oJ9nuqnAiFUqPDoONQqxaI6iVuuu0D1K52easwgbLUcaXC1lw9z31jMc1alve/8BmOvPBCVASfrnuBL+36ANkOhUKDghGqEuqzCAuNytlj2D4YscK0BtLsl+jBCgr0g1JEYkWkCh1PVAkOSuJdDqENBtJwaW0bQR/WyU4QVCaVsYIwNkXQt6WO4FofgwtUBg8E89wBtKyCWhaImTnO/c7vSbuCnFSZqA/z5QlPcWx4AwdFujj/649x+Vd/y4cnreCDLSvZP7IHS2oYwypj7ysyUIkywT9MzChxX/d8fEOCzP0tNDeOgQAr4BXOZif4Ae//W4lrvPzkl3jpqS/9U57H/xFS7Nv2L4a/5ME8AMwXQjwvpfy7qFaklN3j3GL/Aefvaec9/P047oBvkjswSu2aIk5TAqkoBAZdjLRFNawS6nXIdfipxMA1FHZ+qoPwHolejFGsUyglm6h/upvclBitz9jYER+7zovS8IakOLeDphfHyMyIMjI3SCUOwX79vySPKy6V9iT5FoP0yQWsfpPG12Do0BrUsiS5skDjsjyuoeIf8ILzdW+WqX/dRuoqldoAgc1DVNuTpKb7iHVZICA7Qcf2KbTd14tTE8HICPQ8FBoU/COS4fmSROcYY48libwM+TaX0N4iStVb7K1dVWTYbSU73SW2SaG4fzvJ9VA8NUMsPsauFzvItsVofTaLHdIxNpoEJ1WIbNLJzYT+YxwS9WMU30wS63Lw782g3hJj9ASXVx7eH/XiNHX3RYmtHaHaEKbxQQ3/UJFKwiDfrKGVvLBcfF0FaXoV/dEdLoFdOYqdMRxDQwqBkSqRnhzFNiHf6YLrUk7oRJbtobh/G7Nn7eHrPacwUg7QtbOBhWdsY2akj6hWZEO5lVtWHUFgvY8JX9jJNX0noo7oIB1WVQOoSLasbWPCCd04rsLT037DouwXSLs2risQLozZAZ5ZP5Obl/yGl8RCOmJjbH5lIk4r2D6Dclygl1RcHSKbdarNGue97yUe2zub/Kok5Rpw/d7UvNDm4gYdOtuHOLx+Oy2nLkMXDlc/fgaZaX4u2nYu90z7NTou7VoaS7rktDTzzDwFqXN0oJ/3felSHAPu/OaN/OrDt7Cm3Mb2cgPHhTaytViPc3c9NRmbvkM06j+SJ+LvBUUgR8c8T1oIlEyRwaMamPzdG6lZ57Lst5f9sx7N/wvi/8M1GEUI8Q1gynjG2P+F/4G1+E/RPR4mk0IIA4/qf/Pf39X38PdAVCxcAxy/SrHOx8ASl9anJErJJtRdZmyqH9+Y68WqfSrJDS65VoXIrirC9hh6h45pxQ5A47Mpyq0Rml/2Ul4Dgy6ZGVG0kiRcsLH9GrkWndH5CsIS1L+BF7IKCTquBztgYfZlCAcMUnOiVBrDGENFCh0Bj+6+36Z3ic7knw8ydHCCYL+N1ZrADmkYeYlWdhid4aPhDQvHp4B02f6RINNuGaA4tYZ8o0Z6Gkx4xKISjTG4ECb/tJ/itFqssIEvUwJDQ9/WS+Gkyfj7FPSiJD1Zx1VBrohS2BCkcrSLVlDYfUrEYylodNh09RzK5xXwbQiiVqHmdoMJP1rFniMSOFfW4NubIbqlhgnnbWfVyomYJlQaI1RiGnreofuYABNu24mZaiA92U/9W2WGDo7R8NhurFCI6tfHUL4SRs9axHa4DC0Mk5nqopYl/kGBmVKotibItqn0L+7EjVmwvp2lp95AVUqWtraRsoNc98KJzJ+3k1Nq1+Df6EMvwODPOnngu7dyyObplC4e4/LvXoSZc2m0YaCnFTsEdTNDnLbfajKuRnM0w0EfXcNlybVcddxS7shMJ3NAhc2D9WgzMuQHQpSTKm5bieCbfhBQbJC4L9VyT+xwgrNTVBMOHzvsVX79+8Nx/S4fPHwZC4M7aNAyFFyTrZVGTo/swjrpYe645nS+9o27WFWpJ6Hk+ewNn8HV4OZLbqNeFayq+Pnu2Bx85/eR/00Tn/ni5/ntLdcTCOygQc8AUHIMRmcJEptU6la6ZA6bgH+4itE9hqhJ4JgaWu8oVlsNZsbF7VUoJxQWfvQG/CmHSlTljV//t2HuH4N/0RqXfcFfMjBnA6eNn/O3ZpIBXISnqtaMR672DPCZv6Od9/B34oALbmDk8gjakMTZqiAVwaRfV9l1qh//YJjYDofk+jyleh8dD4yy/fwaml5zMLIKu082mHrDXlJL2lCrErUKW64MM+FXkkJCpWbZMKSzpI6eiO0HV1VIbLFwTIWG17ywRHzVMKkFteh5iSjb9Jwcom5lEt9whdRxZQrr/PjGDDKTJC3P2+Raddqeq2AnQwT7bQLdeYYXxABQq5Jcm4ljgNQEoW1jOPUxolsVsnNqGZqvEtkFgT5BeoKnAplvaSQ7rx4rKIjssug/shbFgZo1OhPvy7DryypDUw2SdVl4qIZMg0QtuUy5u8DuU6P4h2De59ay5epZCFsitgeZfmwXq9d2UmwO0PXFaXTcsJ1lh3dQrgkim0qMXt2O+vES1WiQwZNL+E2LzMs1VGochk6cQHR3hXJSMLJEoeZlyd5zO2h9wSa9p4nMgYDw5KV9aYm2RhDusRjaz6CSdNFH8tRsVGj57V6IhIncleb9Gz5K5dE6wj02gV1pJofLjD7WTuLHr7HgtA28vHUyB03eSUXa3PPN61hRbuOa404kvyWM7Ze4fofJ03rJu2XOSy4jodr8ctID6CgUpcv9uSmcF1nPeUeu5yt9x7G8tx2jtsRdJ/yC83/5OfKdLsFuBcUSnHXei/zyySNoDGcpZBNszdejF6BUb3PvyoW8/MxBFBoVbB88fPG1pFz46bdOxwoLYkqR7dUGYkYROwCPfPZadCRrqxEueP7jPHf8jXylVkH9lkAXCuftfD9tgRSnxlfxRG4OI+UgjQv6UF+sYWg/g7pVVYyetFd3ZehoY0Xw+9CyFRTLR6TbxvYphLdlKbaHCO8u/ROf1Hdn+EsIcctf+lxKeclfbWMfJJNPkFI+9Td2TAXullKe97dc927BAQccIN96662/fuK7BEcd8V2ef/Er/+NnRy/5DgDCdrHCOmrVxTEUig06iTdHkX4dO2Kip0pYCT/lhM7gIoXOR4qkpwQI9Vr4NvWy52OdtP9iJ+klHcTe6Kfn9GaaXsmijOVBVclPT9L2lW1s/sUMfCkXIWFktoI5BomTehnMhrG3Rohul6gWBAYsBhcYGFkoNIFrShyfS2Kdin/EoZRUMQqSsamC5AYXVxdIBSI7iihFi8KEMOZoFaNrABQFu7WG7ef5aH0Gci0qiU0VzF0juPEgWz8ZpqFzlKFNtUS7BLYJzU+PUJwUwxytYoU0Bg40KDfZTL2jgNI/yu5PTMA3CuWjs1R2h0lsgNqlw2RnJrH9Cg0X7mT4h52YaZveJQbVhEPNmwrBARvfQIHBg2MIF0J9DlrBoeeTFpO+XmDzlTH8Ow3qVtsIW2IOFVDyFbpPqaf1iSGkoWHF/WQ7fUR2V+hd4sMOgDk1g/F0lMSmEkJC1/kqdx/+M2YZRfZ/8vOEt2kEhiSOLlAcyRGfX8bi8Fa++ODH+NEZd7KjWs9poa2UpeR7A8eyKV2P4ypIKejvTmDGy9yz4GdERJU2zeSAWy4h2C958bs3YwqdIy66kC/d8CtU4XL11z7OxM9t5vW1U0AKtIzK7EO6iOhlNtw5i7aPdrF2bzPmZj/RHS6PX3sDC574Ar5+jZp1Do4h+NkPbuC89efz7Ly7CAidF8tBmtUMynisKKY4FF1BQJGsryZZ4svhSMkex2V7tZayNPjqG6ex4ajbuCM9laNDm3ihMI0b3ziG6BqD+uV5lJLl/farNuSLOM1JyrU+UtO8+qVYVxXf+r0QCvLU9r9PH+btkEw221tl41c+v0/n7rn4iv/1/fYVQogqnibXfXiMzP+XFZRS3v3X2thXPZgb+K8F+peBq6WUmT93gZTSEULUCiGMcVW19/AOQh8tcPzcr4EQOCGT4f0CJLZUvCrxqX5iXWWkolKNapRjCjXr8vjX7kXWxFB6hylOnkD/BwxanpdENo5SjtfRfUyQzvuGyU9NkF/QRstzOZzmWoycS3F6HclNFk5Ap9xQh20KBg5UyN4zg8whFWZ39NL1zASk5mm0lO5qoibvMjwXsp0CxYGh41ykXaXmQYVQr0C4EsdU0HMWvuESwV6PHLOUCNN3GPgHBHWrLLThHHZdhNCOLK5fR9bEGDg0jlTBNwTluCS/uEDDCyXspjjCdkiuVFEnuky9bYhye5xCo44T8xPYmcaqDbHrDAVzENqeACdo4PudxFomaXs8Q6knhFKxGJtmMHSDQvQGGzMDG1+fiH2EQ+fDngaNUlGx/aCWXYqtIYL9Hj2KYwgGDzCofUBjaHGQ5icccq3QfYwCjkDqEZSywEzB0OI65DjVvh2AHYepKFkJdWXMp6LEt5YRrqTniADhDTD1mDybqgGE5lK3usLYZJPggENo+xgfTizjzpHFKFXBnf1L6C+GaehMc9XNH6GS9PR08q3gTi9w4xG/4VvXf4RNc5o4JrAbXaisuuSHLPzu55j50CWceNBqeo9Q+fwbZ7N0ya08f+3N/DbXxtdP+D2/zSygzRhFFzYRtcz60xr5QfvDJDoV9CUKqhA8WWzg58feyTd3nIK62OWWSfezpVrH6/vdgyUVHivU0KqP8tFrL0WtwCPf+AHH/cRbgP/tp27gxj3H8FK8hw7fCBONQZ7LzEQXDpFYkZ+kp7E4sI0t1XoWB7bxs+TB5Ft16pd6rBGibIGqUJncgFr2CFTDPV7xpZEq8VT/u6Tm+92ZRdaIp591FmDjcVA+KKUc29cG9oUq5udADvjg+JYFfrEP1+0GXhdCfE0Icel/bPvasfewbzh+5le9MMD4ZoU04tuqaLkqRhbSUyTG1j6kJohszRAYcXB8GoWFHRQ7YwydPInIlixTf5HDP1jGToZIbC4S7INKcwTbL1CqHitwZmoIPVMlNVUnM0FH7+onNVkjMFAhsRFsPzQ+pdN/VyeOH/SsN4ipVUmxxqM/UatQSbo01Hrzk5HZGuaYhWMoRFcO4PgVKkkflRofjl+nVAfBboX6tywCK3fjhn0ohSoilUUdyYGU1L+exsxIOn8zSHLlGKFXguSmxxmdGUDNlnENSL3aQH5GDYrlkpkk2Ht8EIRAy5Spe13F7izRt1hlxxkmW5+bSPtTFcotIQYWaAzvZ9D47AjpbIDDblxGzxEmdlNlXPJuCOeAHO6cvEcTM9PEDihUogq5ZpVgd4nYdoledInuqFCJKUR3O9Qth4Y3JL5BBTtuU41BZqLnzYV7bbKTHNrvh6ZXXJCQnSgpJ3WG5gewwpJJp3dx4a73c13P8WhDBiOXFFn0iVVUwwrZ6QlOv/dSrqx7mWvPuZv9Y3sYWNnIykInP/jiT0mud7BCnr7Obxbdybdu+AiBIYeD/bs5dd3HKLpVhtwST1z5A1449Tp6izEOOnAzLy/5IS6Qcy3qtQxVFDrNYVJOEEtqvJCZzo0z7qNN9eETKm9V/cy76wss9vUTVipUf95A/lfNTNdVHBTm/uwSTrr4czyfnsEErYx/1GNrXl9N4hvxtH8KUmcoH+KpPTP45e4DufDlj7Irn2Rbro7963t4dmg6vxg5lC89eS7X9h1PPu8DCd3HhZG66onXdcSwgyqjs4KU415W3otPX8nTb33zn/XY/t/4jzqYd1kWmZRydJwk8wjgY3gclBuFEB/e1zb2xYOZKKX8wB/tX/VHqpF/CX3jm8J/reH8P7qU9U+EAqiqJzsMlGt0gn0V1IExaioO4W6PS8rVFHafHqfjh5txJ7SgjxbITouTXJej3BLCHCyh5sqwpw9aGql7NQ/ZPPljOinVaKSmRojtckhP8VO/skQ1prP3vAmEe1y2f9QAHGreUBmZreAbgdC8UdJbE8Q3C0bmKlgRl86HLUp1Or7lNrY/SU1SJbqzTP9BPuz5eaLrNaphBduvUDwrjbM0TsNBfVg/a8C/Zo8n5bynH+H3exxnPtPTsNFVYpvyHsuA7WLkJK4uMHOuJ6jWazM2SzBW9grohAPurDy7zkx6RZEpSegtP9l5FczdJnoe+g7xoYz73v4hSddHaggsh3u3HI5mAXtMIrsk2ZUtBAUUG/CILiUUmhWCvZ4xzUwOoJUk5ZiKU69inz6GflMQtaygFSx8Iyra01WsuGR0pkFyQxUEhHcaVGOCcFcB1zaIbxcEBioMHRDATtis3dPMkVO2sea2OSy4YDOfa3yOm/uOZejoKi2NKc5v3MThb1xMa2KM3StbsGst1mWa2Zav56YbbuXCaz7PkouXs7w4AST0LREMOz7Ue2o41P4Y35r5GJ9/5RxqX9H5+VU3cP5Vl3I606n9yB62rGtDz3rrbOE98PnL7ufs0ABhtcT5j11IcvIoz8/9NburtUR2QVzxccbmM3B1QXaCYF0VfvDtD8FEKCVU/q3hGaKKD8WCwKDDbGMU2w/VsKBJLeG7L87iy5azp5DA0GwmhUYIahUe/+0hPPKZa/lZ6mBaXpB8+KSlvPnKdI4+ahUvPLU/1YQfX7GK1LxJUun4LObzEdR3YUzl3ZxFJoTYHzgHOAZ4Cli5r9fui4EpCSEOHVeNRAhxCLAvK2KbpJT3/0lH35Mrfhtxwox/A1WMa9oLL7wzanuaIpqG69fxLd9Odd4EpIDOX/djT21D609jN8SIrh5E+nTs1iDU+jDSCpUl0xG2ZHieTvuvS4R3lzH6PNqVzVc3M+2mDMWOCL2LVeKbJdkzc8hUAMV0UByVhhUOpaSCrtkYWcHoATbxtRo5XWAM5bBDMUo1GkbeJdchGDvWZfK3R+gr1VBt0PClHCoxldZomp5SnMrdjaSnCyJbazzyTNtldF4UI+dSjSgU6wTx7Q5jk1X0QtijfUkI7ICXamuFPXGy0E4wM9B/sEF4r6RUCKEXoRqCUo0gsamKcE2k4umKKDaU9ytx1KStNPnSvD4yge1764m/YVJs9Cq/862e1LJv1MIKmYh5Weru9DO4QCdzQAXGebOK9zciXDzNkhcSqKUiwpBYIR2pCwYXhFEsxjP5FPaeKNCyUGgRZCaECWyD1ByXwLAJwIHTd7J9rIaCbZCaBX3XTObcMyfS0TzMDw/5DVf+/Hx+OiVBMFqmPxPBNSVtjyuM+DsYmi84s7ad+796Mx2axXHfuhwz67LljB+hCoVircB4NMb++w9S96KOmXVJOz5GDnBp/73k0y0v8rmtH2byoXvoeqmT0Jl9NGhpDvz25/jll69nvwN28KP2R7BQODG4kx/qYOPw0qyHyHynTFwJ4OKlZte/ZZNr1WjRgmTcMmrVpRJVWVWp58ufuZcFvm4sBMKRfDr5Cifd/yWEBQsveJXrtx1DrMulzw7xhzsOYfIV2/jO5R9DLID+UpRqvU2uTccOxNFKLsKVqEsj5Nsl5jrouO06Wp6F1x58dxRavhun3uOUXifhZf/eC3xlXG9m39vYh0X+ucAvgej4oTHgo1LKdX/lulVSyv3/2rF3I/5VFvmPn/1VUBSkoiBcl61fCiAthbaHFNSyi+NX8A2VKdf6CG4exq6PovWOguPi1scptgQJbRohtbAO/4hNepJOYlMFK+J5EnrBpRpSSKwZQ4xmsCY2YIU07ICKf6hCsd6k0KQw6YPbSX2znUKTTjUsMDOSfJMgMCTJN3sV4nrR4+3KtftQLEmxXsFVPfGv+LoMe06NUf+mhe1XyHSq2CEI9o5/UQlm1iXXrGBmvAr8yF6bakRFrXiaH1YAtLLH+hscdLFNQSUuqMRAnZNBfymKnpOkZkFiIxxw8WreGOigsjyBfxBiXRX2Hmcy+Sfd7Dy/FcUGZX6G0AMR0qfnabrN9Ioma020stf+wEEKdbOH8F8bY88JBv5+QSUJgX4wjh+m6qjYrycI7/V0bNITFMK9klJSkJnu0PmQgzlUYGxOjOiWPMXWALkWFSvkGSOpgHAgMAC5Tok5KtAXp5iaGCZuFHh62TykJml7UjLl6xvZPFZPW3iM5SumIuoq6IaNsipMqEfiSzlIBXqOVtAbisxsHEATLp9qfIkrf/Apnv3a9Rz3tUsp1Qh++ukf4hM2H7n1i+gFeOar13HMdy8HCa9/9SZO2PRBuLmOYq1KqU7QcvweHpryMINOlUbVR1FaRBUflnQYcysAlKXkodwcLohuAuATu0/mpvaHcSQEFQVHSspSYiFQkTyZn05Z6hwZ3IyDgiUVylLnmexszo8vpV5V2W6rrC23MtkY5PHMfoTUMr/auBAAfVOAhsN6GXy5mcAAFE/I4v9DhGoUatdU0Yo2qRkBSjXQuLTE8y/929/1DL4ti/xtrbLpii/s07m7L7n8H7nI7wI7+S+H4j+MhcAjOp7z19rYF7LLtcBcIURkfD/7Vzp1AnAi0PwnaW4RvIWi9/AOQBoavi0+Sk0O5mgZNV1CFIo4dXF8w2WkT0cpVD11SdtGVG1Cb3VDKODR9kuJUh1PAV47QHlSLeZgHmlopPaLo1ZjFBoUtCJYIRie6ye+TVK7ukR//0TKnQrFBtCKUE4IqtNKhJ70YYz/WoQLQ/v7cXVwdeHpw+fAn3IYOihGYBDKCQ0rKDCy4JrefRTLG2TzTQr+lEQ4MHKAgx3USGzxMrTMNFSiGmbGRi3ZFBt8xPaWSE0PkJll0XJPGCsgCQxahPoEaslm/XfnYk1USexwKNWo7DxbJfkmjBzeQufvRth8WZTQG1HGpkPYX8EKBbBCOrlWQcMbFsNzTRIbJNMOG+TNefVMmr+HkV+3YwfBCkIhF8C/PEhhbpXCdInI6iTXSKQCSAjtVBmdqWIvNGh6tYTUFfyDFcYmBUhutFEsj1kg16YT31om3+ojstflw5NeY8wO8ovfH4VZgPKEKtErenhu43RETmMo14CigumvEngkgpF3QUpsv0Lf4RJjVKESMDlm9ia+//oJnFP/Brd++YeoCB6++lqWPHg5C02FIcfmns/ewEVf/gIRYZKZ6pJcI3i5HOb6yffz66sO4sTYWtaXW7nv+8cxZ/bnWXHODRSlxQ0jC9mWr2frA1OQwOOX/oBHc7M5K7IO0MhJm1UrJ6K2C8788uX86nvXccvQEaz8/v7oRRdhuVhhjVOveo6pumDJ1y4hdE4f35n0EBclXueIly5BpAyWfuB6nrGiLPDt5rPJV1hfrcWZofDbpw4jfOAwFUfDmlVgdKJOcHmE1AE2zc8oHheZlET2VIltcxg4yM+Mr95IbLuLasl/HAfZH0Hso5jYPxj/WzaXv+7B/M0Neh7PPOBq4Ot/9FEOePFvyUD4Z+FfwYM5fuZXAc+wCNel2BHB9inoRRdfTx43oKOULZSeIYhFPTZj2xP9kmNp3AktqMPp/zQ4qKon9OW4SE1FqdiIXb1QX0ulOYKeqyJVhVK96YV7DEGhXvEkfYOeYUHxjIJa4j8TGv0jkkKD8OLe0os1h3pdRmco2AFAkVhJGzNSoeb+AL4Ri2ynQXJ1hkpdEF9fDjGa8RiZQwHIFbz+hoIeu7OueVxkfh92XYRq3MAcKSOqNgOHxEjPteh4CPoP1ml9vvyfKdqZiQJm5vC9HEYte1lbZkYyOk8S3qnQ9OQAqQPrqUQFlTgsOXUVTy+dx5z9drHp1Ym4uqTlRRsrqJJvUZB4YbNqFE8BdLtDoKeA78Zh1q7rwBxRQYA5BrYPfKPePRXL81QUB8y0V+SKhIYVZXacrxDaYBAYlIzOhY8f9wJ3vngEBx6wlZXPTSex2SU1Q6HaUkXv8xiWw302ll9BcSSj5xWwbRVVdVHfDBM7coCRXJBK1uTjB7zOvb85AisMt59zG4f5HBQUsm4Jv9CpSJvllSB9dpwmbQyfsPjwKxfw9BG3EFbgD4UJTDP7SCplAoo3hiQUA0dKTKGhC428W8ZFkpM2ZSloU32UpMXpWz7I7k1NBLoVVAuu/9wdXPTgJ3HCLncfdweX3HAxwUGXx2+8ER2F4y7/PNWgglQ8hmbfkEp5YoWOewSfuvUh5pk9lKVKTLHQx393Txcmcc2bJ3L1wkd5cnQOy3d2EFnmxzEgucnCHCmhDKQYObqdfKtAqYKZBl/KZel9+17d/7Z4MK2tsvmyL+7Tubu+eNk/zIP5UwghknjZxHullPu0DvNOsCmvBdYKIX4z3n6blHLr232f/59xwtQvI1QFKQTSp5FrC5CepKAXoOn+3djt9Tg+jWrMINArqLTEMAZy4NNBURBKAjVXAl1HZnIIv88rQuvqhUgY6TdwfQZycit9i0OEu12MVImdp4Vo/4M38Bk9Bg3Lbfa+D0LbNXITJDWrASm8dN0KJDZXGVxooB2QJuirUH6knlwHND05hBWsJ39alrZvuozuHyMzUUeqLlqhil7QGVoUIz3dZdoN4wbF78MNmiiFErJUhvokbiLsfY98kZ6TG2h4o0DvoRp1KwOEuosEB11KAzo9R0l8w5D49l7eWjWJ0C5Bpc6B/iCVqQ5tT0usgEL/YZLOhx1S0wxKExL4RmwGD1EI7FV5+ZH9ieSgPEej8Q2bkfMLpHuiLDhvLRtunM1RV77OPcsOJrpRpbi4gOzysfX8IGJ1iPhmBdeAStxLBLJDwBjYQdDzEN1po1ZcRmca2EFofL1KtsMkvlyQnSAJDIFTU+WFoSmE2zOse3g6bcuKdJ1rUrtcMhrWsGpsfGsUqiGFYp1CZUmO9oQ3l9vWU098DOSddXzjmvv4xv1n8/NXDsOYn8fdE+TTqz6E8lYYM+NNFowcuBr89Iqb+epVn+Seb19HQlHwbzepOVrBJ1R+/IP34+oCvSARLpRjAuuYDN+Z/QgJJc9HH7mI2JQURzdv5craZTSrAQD2OmWenfEoygyFNdUyTapNxoVfnXkrADnXx2+vuI6JmomNSpflMjxPIblBYqYdbv/qjzBw+OTXvsjuU1x6qgnu+NwHOOem3zPD7GWyXuTVciNHB7q480mDe1sXckLdBpalp5Ce5RDqUrEDCtn5EVKzQwR7BErF+875NpDqP1gu+T/w7lyDeQL4spRygxCiEVgFvAVMFELcsS9Sy++kos7xwHWAAXQKIebh1c+c8g7e8/8bSCEQlo06mKG0X4j6N6uYb3WBaXiMyY6Lv6eA21yLMZRDDI1CbQJyXmqvjIYRFYvqfhNRLAe9e9RTnszlETKIOjiK09lI/YoSWsFCKgqNy2x2nKORfE2jVAvGWJVpt3rh2S2Xh6h+sABPJ4gdNcDQygaqvRqtz+bZVh+hZfZeinsshNRxkiFCvVUiN+iIUo7ojjL+ER2l6rLtIpOaVwVGXhLbrNB9ZiuhPs8LwoVCa4TJv8wgpEQdSCHDQWR9HCMHqRlBwnsAAamZQRxT4Pgl7U9VybUaDF/dwcRShe6j/cQ2qBg5CQgy7QKtDASr9B5uIGzQV1pYEYPOBx2K9QrpKYLoTpueP7RjtID6apR8m2T17XMJFB1e/dpBTNudYWRBnNbbNHadb+Hf5iOx2aVY6w3YRhqyUxy0rEol6g3mahFGZ2hI1fMCfcMwOsugkgBhQ2SnoFgH1x5yP8+nZwCQPtxhqFqDGi8yvESjo9ULB9V+Lk+m4qP0cDOuFHS92YZwBKHpaawTyxTWxPht/0LqF/QT1Kv8e/sT+BbYdFtxynMMfrjzCIr5ANaGMFZYElOqWAHBG6U22vQUT134A3R0TKFj+4THC/aVm2jVHPpshahi82RhGv9+x0dp2uWgvBDl0p+8zh5b46QvX0g5rvDYv/2AxZdejBRw83du5dF8JwGlwvW3nIVWkjx99Q2cvvlc9vbW8MG5b/Hvtctpmd/L9896kJ8OL2HYCRFTipSSAqO+xFRfP5Nu/S3//vOPcOXH7ufW/jmE9TKxZBHrvBSbV3UQO6jEDcfdw9c2nIrcFaUaVig0e2HbE85+g4deXIR/BMwRge375zzP79Issk4p5Ybx9+cDz0opPyKECAOvAzf9tQb+qoERQgSAy/A8kU8KISYDU6WUT/yVS78JLAReApBSrhFCdPy1+72Hv4wTplw5LuAlcA0flYYggSEH3+4U1NdAvoiWLoHrIkbSUBtHpLK4HY2Iqo3w+3CDPpRMARk0MTfu9WpoImHv1XEhlwfTRN0ziBoJe4SBhkZwe4XJd/sZWKRRrnfRB3OUOuP4urMkXzVJzdFp31bFuqOOGt3L3OldEiK0B/o7IjjTdRwf5K4qIKVA3F1DZXoAxfaq+6NbSky/OsvgMc34xhwSr/ax/eJWclMcJtzvUqrVqdlggQIiV4GgR8kvMhYQJTBsY45ZDM33E9/q8ZWFuyVqwabQaDI8XwNVJbjnv8oKhAu1a4pIVSHUryEVm6H9Na9ORnqpxs0vFRlcbDC0v0bHQ6OUWiOUkxoNJ3cz0NfG6MEurY8KtlwYhZBFfIvD5FscXJ/LyNwA0d02wnIp1+jkOxSaX7HQcxY7zvSReM0hPVnH9nvMzorthc2CPVCugXLSM05XbzyJ93VspPLDJrILVKozbZRhH2iS3TvrMZMl+vvjJGpzZCdKYv4KmVaFzvoR+jJRKhUNZUae7myM+lCOubFeXBQ2VZpI2SEsqfLNyY9xgJnlh1Pmc1RoIz4hiZ3Rw5ZSE1tKTQxXw9SbWb6QfJMbLr+N3dVaYorF4/mJHB7YQUUKbt28hJWX38KgU+Zrve/j5HUf48m5d6MVXYrTFeoUP0jQPj7IZzedw50zf0VYsVn1ieW8+a0D2GMrFO5toqHo0j0lzpznPk3DMwafUz9H50XbeCU3jY2ZRh669FrSrsEEzea5YiPf/8Qv+MKKszmwcxcr+ttY1tvB+VOWcczMTdw+soStlUZ0zcGtQmq6YP5hW2nyp+kvRzj+sFU83zYF7c0wG7+7b6Gqtx3vTgNj/dH7o4CfAkgpc+MJAH8V++LB/AIv7/mg8f0e4H7grxkYW0qZ+RM25ffwv4UQoHgyr91HBzDTEBga/3WmsxAOeRXMFRtcFzvmR4R8qLkS0tCodCTwdae9psZyoKpQtZBjaUQyAbEIbiyIMpDy6kwsG2wbq6EexXLBlUT2OjSsqLDl61FiSw32fjRI40MuU36eJTclQrFuPNsrILBCULtogGzJR26mRdsjgu76WsI7FRq3ZSg1BxlYpNH6TIlSk59QrkT9S4NIv4HVVkOwF+oe9aj+zb4sbsiHUqySnl9Htk0hudkLL6XmuOjLFCpRH/EtnlxwNeQpFJaSKlYYCNlEVxqk59hEN2lYIUE1DJHdgtQMHzVri1hhnegOSWJpP9ovymx5bQJqtsT0mwr0HVPL0MFJIrurBAYs5DdqSURsXF2nWAvNLzj4Rl0KzT6MjE16soFaBj1Tpf+QAJWERMsL0pN0io06ja+5+HvzBHbZZGYlKdUoCBdScxwCfSrmohTWqwnMFNRHM2zN1VO8II2yMsmkX1uMXlEil/cRixTx6xY5w6JQNgj2CioTNELBMr3PtKGVofWkHoafasF39BAHxPfy1I8X874vr6VBS7OrUstRoY1c/vVPe3IFmuBp+zCevv5m7pt2L0defzk//tyPaNUKHP78FzjnyBVcuvGDjA2E+VFThmzez9UlnUOmdrF04c/ISxefEJxT9wYTm1KUpeRXt97A53adwZhb5rkbbqHHtpish8i7AheNx59dSCLk/Yz1oks1rBA3ikRXmjiGRM9LPtv4HM/kZhPSKzyQncenYuvQUci5Pq58/iwC9Xk23DODiWd1seveSTwRms3kzkFmBXv5wfLj8W83KM8vc+7sN/n1mwdy0UEv8cZQBwG9imMrbP1nGRd4txqYbiHE5/DG/P2BPwAIIfyAvi8N7Guh5VlCiHMApJQlsW9WY4MQ4lxAHfd6LgGW7kun3sNfh96fARGgEgetKKBYQpYruK31qHsHPPVIO4iWLiFNHZHOI8JBfNsGwDSRoylEOISMRRCZnHe9pmInw+g7Bz1DBtiNMZSqg6srpKb7CAw59B4J7b/XmXxzmXKDRsM3s1TaYlTqAwgXGl5JMXRQguSGIpFdKsXNdTTuKJC7WFJ75W6sH06kEoGtnwmAcOn8TRVjKE9qRi1SSXqFhq/tQMbbSG6oICyXwYPC+FIhhhZJGl8JoFYlia02wS0jYDtM+3aRwqJOArsylJvC+Pry+AZUSk0BXFWgZ1XAILuwTMujBkP7Q3yrJNcB5aRBZqpLYNDEN1JlZI5GdFuYXS80keiSDC9KotiS2HYL35V9lG5oxtUEru4ZL7UC1Qikj6qi7vBjpsCIGNg+qFlXovuYAFoezJTANwKuAWYKgj0leo+O40t5HGL+EZdivUJinZeqnLzWD5RQLJdtU1tJrhVU4wLD8WSqc1vjOLVV0puS5MqC8B7wm1ANeyG86Al9jEoviWBXdx1RC8xb4iy9rJPPXvogCi4Pp+bjSoXP3f4ZZAyskKA4rUo0kacobaKKiV6Ef/vChTimgjlToSxVXt//l6ytqlxx2acxVYFacdkenY7y3aeJCJMdToVvbT+J26bdwwkPfJGaVXDbt25iVTXBsf4yNarDmFtkq2Uw7EQ447il9B4W44eDR/HFq+5FES4+YfHZy1+kVoVP7z6FnPTx283zOWf6SrbkGzm1bxbva9rAYz1zaHkGjr5qNU/6ZuJKQakOfLbOZQ9+hDvOvJ1TZ69hRUM7ipDkHZPrDv8dv+w7mLpAnlmRPvozEfa/8EZW3f6PNzJCvmuzyD6Bl6x1NHCWlDI9fvxA9o3NZZ+oYqrjFksCCCEmApV9uO5zwMzxc3+LRzHzhX3p1Hv483hq6/e8N5pK+5N5IrskWnl8TSUURM0Uqc7wBLEwdcTwmBf6cj2PVsbCONEAwjAg4IWY8JmI2iRidAw7oIFheAkA0RBaqoBUBeZQgdpVOXJtKv4BlcGF3gTGyFgMHF1PeqLJ7g9JBj5QYcunoyi2JD3Fj+NTsIIK/YeEMXoNVm7tYOAQCA45NDynMvXmIr6eDNmZSUL9DoojPebkQyfSc6SPsWkm6tAYDa9niK9NMe3bO4i+1U9wbwH/QIlKa5zypFoIBalEVaxEAD1nMXhwnJ6jIug5m3LcW2QP74HkSyZjkxUcv6TQKHCCLt0nSowxhaGzyuw4x0CxBP2HhAn2eGErxwd6QeLvydL/SDtq2UUrOPiGSkgForscwt0uTlkl1APO4gyFE3I0Li2w9xg/gX4v9CUcSB9dpJz01lcKzX7MtGT48CqOD0q1Xqp3JerV8+z8gEnPUX4GFwaJb1I47pLXKNdCqQGPBqckCGw2iU5PIVXPkEh1fBNQ6y+QPLqP+Dab4CaDagRGZut0hFNsKzeQdgMcFt3G2TVvkD6gyq+vuB5fCmIrDPy6Tbdj8ItsB8KBakTF1QVGBhKKRca16NDKHPfNV8ifl2He19dw3zXXstlSOXvnCdSqMLKqno9eeynJNd4gesF3v8jXv/1xLutfwMJ7L2Xxjy9jP0MyTR9CFw5vPjmTc2veYGe1lg59FJ+wGHX89NkKFze9wCOj8wFIWwEWRHZxaN1OftO1gDum/5pf3HwDH4mtQFcdzm1YTsuh3ZzfsZSnzrmOF3Mz+P1zC5iZGCD/RAM78zV8c8PJaIrLaDnAG6MdBM0qtSszHHvg1f/oR9qD3MftHwuflPIiKeWpUspn/rOrUr4opbxuXxrYFw/mm3iuUasQ4h7gELwFn78IKWUR+Or49h7eTozTo7iGSq5V4B8GUaqAELgBE320CD2DyJZ6RDCAMppF5gsIRUE2JLxphaZi10XQhnPgSm8xwu/DHC4CIEM+RKFMtTmGnioyOj9BoUlQ/1aV7iMNJjyco39xmHyHS3yDt14QW2ES3W3jqtLLCCu63kD3xigiW6DaWYdrKNh+l0pMxQoKlEIZqzFK5M0exg5txcg6dJ9jEVzlKRBGdlkMHt+GXpAEB6qYxYqXmmy7KBUHo+yJk8mgj/jqUUodMXAlwUGHSLfE9nuL+eE9oBcl1ZAg1Cep2eiw91iF9sddXEMh3wj1D1UotpuMXZCm+d8deo9NEuqTqBUI78x5jNQhKNXqRDdmvFRwC9SSQ3qigdltkFlSovaBCPlGwa5TIbZVUokJSg0S/4DAqWioZbAikGtVcHze3801wBxx0YqCXLvwvLhdCqWDCtT+WKWS0InrBeJbvOJVI+9STUrskIJdMhG250UptpcmLRzY+MxkfAtSyE4NvQhycQrn5QS7cwm+1vgHfjF2IBcnlnP8dVcQVmHn4hqu+/JtLDLLHP5vn+fcqZ/HmJ4h/IEh7p5xN7qQhBWF7ZYfRyp020l+seYgap8zGfhUhG47yGc3nMPL+9/F4d+9HGeCJD3Dm9ioBQUnaqMUVB59c3+mzt/LmY0rOWP7yWxa2077kw7VYyWffPIChAu+o22OC23k3O9fTrjHJtuu8cAV19JfilBr5Hg5NZXPNj1PeGIZVwq+tPd0Tq5dy6c6XqXbSnBO85ss8O1mtxXl1y8dyhWnPM5Ms5dPX/YCj2X3Y/sLE/j4hx/CJyx2Vuv47vITSJ1t4NRZzLzyRsw0IGDVbV/k0A9cR7FOBentvyOP9LszRPYIXmgMIcSDf0IZtk/Yl0LLZ4QQK/HcIgF8Xko58teuE0IcAPwb0PHH99mX6s/3sG9QSjbmghRje6M0POXiJqMIKRGpLISCSEVB+g2E7UJbI1Rsj14/kwOfiZoZNyZBE+FI7LoIaq6MGw3ihA30XBHhSsrNEbSKpPXJDNXaAC0vWYzNCCEVmPLLvCdq5jOQmoJSsXD9hucJCXB9ClsujKNYCSbdm6ecDFCOq9SuSP1fnhWmiW/URrFdpn47z9j+Pnwpjzol3Gvj68t75IXlCrm59YS2jFHqiJKZoKOMl+/qBYlvxKaS0PAPWR57btIkMFDG1VVybQb+UY/lOLh9jERjLSguxlgVpcZPqS1CrlUjOxykxU6T2GrTfbRKcI/ACkWx/aAVYODYKtGtCpUa0+Mea9RxDGh6rUoffvzDVVxNxzfmsUfbQQkCYjtsIns18s1exth/1A/FuqqkJxseQWY7NKxw6D1CEOxRqGwOkpkoGVlS5c10J8U6jwIn36QQ2gENy4tUv5Flb5uBsd7ACoJ/xKO7USvAHxJUkl7iwKL6HtKnpegareG8yy7nDzfezLJKDK0EVgC+vfVE7p/9CzZZJqEP93L35HvRx0e+9ZV6Xs1NZUFoF83aGGk3wPXbj6b1fg3HkLzZ1c6Huz6JMqrz+NRmvn/pT3k4NZ8jolvIuT6+u/p4ZM7g08c97f2vhMMi/y5mtPcy0Bxl61GNXBBby8IXPkvzQzq/mXQAd5QPwWkAM6NSXpJju5Vg04pOLv/A00S1Ir9LLSSpFzj1hc9S/7zOHe4ETv3q8xwd2sSHVnwCdW2I1y6+nlfefx11ip9Xyib3ji2iRs/zgdNfpdtKUnF12o1hLjzgFX6x6SCkpdL2vt24UrB9dRsdd30fZbFO48wBFtbueece5nengfnjuN2Ev6eBfcki+w/J5N//D8f+Eu4BrgDW824lo/4Xh1KqUKmaIAWZBc0Ee0poPSPg90GxBLt6EH4f9qRmtKEsWBb4TJxJzQCoXb247Q1IxTMMStFC6ipWwo/ZkyE/txHHUBidpdDyYpn0rCi+lE2+SUdIiVaGzNQwatnFHLOxIhpK1UArOSi2i6srlOMqgV5BcrNDqcFPeFua6gEJzxhlS8igD713jGpLHKXqoqdKVOtCxFaPIsaLgN2giajaOCETWmpQSy49J9YQGJIE+z3tGF/aRa24FBs0wt1V7xcnBK4KVlj/z0w1PeegFaqk5yUJ9dv4d6ZBFRgFk3yDhlaU1C7VKLVFsQIKk+ftZXR9G/4RB6kKHEOQm6YyMi9CqNfC0cHxCYwcjMw2aHqtihVSGZ0DwhJEu6DQPC5BPFnDOGIUQ7gUXqslussl16KQmm4gFchNtTEHNXLNKsKRlOohulOiViTT2/tZvrUT44A8wRdCCAeyE6DrXBN6a2hrHmEkEoRVUZSqRM9DJumldou5WcQbEVYPtZDfkCDaBeUEzLvvC9x+6k/59pU/45I3z6aYDXLyjV/6L4N9heTl4kQW+XfxtZ9+BA7K8MoTixg5yOK1425k6bzfcgwXs/hry+guxbmj7Rn2+8UXmO/rJqZIJtc/x247QliWOHP6aur1LMeFNhIQLrWqQVFKWlWLc584DwIO1gKNaw58BGeRoEHL4CL49I4LyEwSBJ8JE96/TPsfqnyx62LKcTj8jJWkrQC/Ovyn5Jb4uHT1B1k6OpED/LsIPef9jd6qRBiyIwA8k5rFVc2/57TVn+TYtq1UXJ2Hv3wMqakaxRaXgxZtYccPp5MS7aSnCBYft4Fl3R3sN3UX6aqPi5Kv0HHHtYiqwmmHvsmN8+59ex5k+a71YOSfeb/P+LMGRgjhAwJAjRAizn9ZswjQtA9tD0spH/t7OvUe/jxOmPplryp/HKW0D7UqiGweRaTzyEwWUZMEIRABP9gO6roupKEjdB0cF6s1hmK7qKqCMpRGxsOIsoXIFyDgxxxKY7fXUU6oBPssatapOKbi0e7Xa+hFl0KjQmKrjVJ1UaoujqkQ3JXFCZkoFRvXUNFHCuhZE60UwPk/7L13nKRVlf//vk+unDqn6ZnpiTAMzBCHjCKgICIqYkRXXVEBQRBdc1aCgCgoGBBxFyNBRckZhhxmmBkmT+dYXTk86f7+uE1Y18B3F1xXf+f1qldXV1e4VU/1Pc855xNMQWJLmTBm07J2Gmlo+K1JjKEpKqt7cKaaGNsnQdcwdIFouuD7uPPbsEYK6j1LiZ+wkIag/dEGtQ6L6HgTsDHqIVJXxL9qh4VVDrDzAaEloA6aF6J5kol9bFqf0mhkFCkxtjmksEcL5W6NtiebTO+ulAoaOYOp45q4V/cRmwwwiy71dof4rjptD0RJba2RXx5V8y9XQijIbmqSX6pcN0MrJJIXzKwO6LhPo55VBFT3rhxmBazn5iRPuRTnW1gVSb3dIL1ZyclUOup4tSjRcY/Rgy2igc5rdt/ALQ+vwF0ZkNqo4ycDsk/qiEBn4QemWZ4e5851qygsEeTWSfQ6WBXIpossP2kzN23ejdY9J8kPRGkWbd68+jFq0ubTl70bIw6NjoDk0eNMleL4TZ0PbT2JnliBgyPbsGchdlWcqT0hud5k3eHKBOzt5/2WdyR3cuBXPsoRxWWwDH5b3oPfju1O/ZpOvv35S/nMOR9gdpFSffhu9Bh+/57zeKARpU2voAnJkydczFAg+ens/uT0MpN+Eg+dPa08QYvHiUc+yv2TCxgPUpR7LYyapHFwg0OTz3LR50/m9K/fyXv+40M4U4J1u1t83T+GT55zDW16maq02NMZ5u0XfQwOn+US6zD2ah/h7JZ7uWJ2bwJLI7014MbTL+SG8goWfWKSjFHlW799LXsmhli7YXemvz0PEYSc5p2K+LDPkksq3ODty5P5l874/6vx95lgVgohSqi9PzJ3HV7QIkv+tSf4s1IxQogzUEP5LmCEFxJMCbhSSvntv/jEQrwKJfF8Oy8CBUgpf/3XFvW/HX+PUjHHLD4XdA1paIhAInV1OLa+K0f7QyHJp6dgtkhYryMcG3o6IAjUGezMLBiGqmyaLkhJ2JJG+D6MTEJ3GwDSMKguSJB4aoL6olYaWR3dhXqLRnQywKiFFAZMrJLEqoTo9RCzqjges8uiaAHExjzM2SbS0Gi22jRTOtFxD2u2gTY2A4kYoWOpVl6hTGNxB86uWcKojZ9xEIGk1mGramysQJhLEERNNDdACoHW9CBEtcs0CC0dhECr+1TmxwgsgZtUApvPSbHk1tUJbR2pCazZhkpSmmBmNwsnL9F8KPUL2h9xCQ1B4Cg/l8iM8mr3ooJGTvmTZJ8sUhlI0kwJSvMF868vIVyfmdUZRAixUY9Sv4XUIb/Kx5w20JvgTKuz1MLSkPnXe0yvtEntCLCKHqGpMXqQRXRMtc2cWUl0KmBsf4O+Wxp86aor+drg61j/VD9WXunBaT6kjh6jcW0HUhMsfu9G1k924j2RBsAsK5mewBKkTx7i1e2b+N79h2NNGqS2SRppQWRG8p0vfYukcNnstXD6796N8AVCQnKrgphHJyWTR3j0dU8ztKGD7x77A1ZaBaZCDUeENKTGkJ/Gkwa9Rp4tbjtviM0wGDR43dXn4EyBXVBJU/fAd9Tz6k2oHlHhJ3v/kDM+dRqNjOBjp/+M8y8/Cb0BXzz7R/zb+hOoDCZZf+K3eKjp0KFXOOXTH8OshrSduYO9UkPc9PXDWHjaRr7S/Vveeu7ZtJ+6g6e29NHWWcC6MsuJX76F7/7iGDoe8sgvNfnqh3/E1z/+Lm781sU82kzy71MHsK2U45S+Bznv52/k/Lf9mKdqfZiaT9GPkndjHJ15mu+efAKho1Nvs4lvK/Pp637Kwf1bXxapGKe7V8774Euzytr82bP+16Ri/jvxZysYKeUlwCVCiNOklJf+N577PcBSFF76uRaZBP7uE8zfUxwzf+6LZ5pITQNNI3QMhOtTnZ+k7RGJVQ6gWAJdQ2trVdIqtaYiTMaihL3taDNl1SKLR6FUQZvMQzSigABTs/gLOjEmisR2QmnPdqxSQDOt0fpYmdiwUHMcKYkmExj1ED+iYQaS0NKo9To0coLEsBq8V/ui2LM+0ysM+m6cQVQahJkYNF3C1gxapU59oAXHC3A2jCBTCbSZImYQIPIlksW0miMZBo3WCJHhEqLuPs/P8RZ2KOWBiIOmC0SlTphW60psqxLOCXuKMGT8kCxe0lTVTUlVVqEuqLeaJHcFTK7WSW6XtD7lY8428dI2bkooxeYdPl5UI7O5iRSC8QNssusEseEazVSMrnuVsYjUNErzBS1Ph+gNn8Qgqk3oOTSTUOuR+DHQXEH2aY1dx1hkN0icyQa1rgilXg07rwiVrU+5TOxjUWs3iExBeZ7N3dWlTHx/PguGmgwf5lBvl8SHBEvTk2w+WTLyZCcxw+XMpbdzpXMQTd8gPx3n1Xs8SdW3ufO2PfnFjl7Y2+fSt13J4/V+vvfooVTzJu/59hlceOqVfPFLp5BMCeotEBuDaje4rT6f+9driWlNtjQ7iPU2KYUR9r/jXbTdbvHRT/+MNc4gB9klnvU1HBGwwh7lSdfCETrr3nMps2GDqVAjJgJSms5TbowtzQ62NDr4bOuDXFNeiFkJ0DyNxdYEicGA4gKdp+t9iDvTdIyHHPbYGXzjc99jm5fDqCuvn8Nyz3JwdDPPntbO0z9bzoXvqDH+6oBv9t7EB248Df2WHJoXsndkO7lnFJBjj7ds4FCnwL2feYgP7TqW41uf4NzOP3Ds2rO45oJj6bB8zll6Il/f69ecc907eetr7uOw9CbWVgb4yi9/yJvvOpXF367xrRuvxP477Wn9vcVLGfJfKoTYHVgOOC+6/eq/8tCVUsoV/8P1/dPGMb1nPM9FwVCHKUhHEF7IyOEJpAmxEUnL7YPIWg0ZSkR7iyJGRhyoVBVRMpTUu6KYCQu97qMPT6sKpiOrmP2lKmFPG9LQqOzeRnSkRnSsgZcw0RuS2eVxWu4cJn9wD+kNJUSozsST6/MU9soRmBCZCWibCLBHSgSpCMmHhpCZBD23uEztr2Yd0SeGkN1taNUGMuYQeXpIocFiUUS5qpLf6CS0tVDvihPxArz2ONEdswpAkIwo35swxJyqgGURRky0WfU+pamjeRI3F6GZNohMCPyYQetjVfRKA6SkvCRDdDxAbwSktroEtk7LkzbjawTVLoNGa4yuuyXRMZeJfWwi0xazSzRCw0aEErMEz54WYcnFNcyqMgjLbtbwHY3uu5TNQb3dwagGuClTGY1lBMmtAqssiY802HaSScsjGmZF4qYtZpZrxIeVBUFogJsyaH/UZXp3i/Q2H6vgkdAamFU10+p4yGNsjUmowz03rySISIKsz9OX7sEj6ZWsesfTnNJ6PyN+hl5jhm+OHMVrj3qYm/6wL+mnDE7LnIz2TJyWIbW2yIxkpVVQVthtOuaKImKVz6nzH+a18fXs9NOstAqssYu8/oOnU2vT6axJ7H8ZpRw43FFbyD6RnTRCi3dedQZGDW4543wKoeD2RpRPf/00rEpII6sRmVatVTcBC1+1g2i7xQdSQ6y4+Hv0m1WiQufj513DjB9n2M1yzod+Rrc5ywd/+kFMAk67+x20OwLfEZyUULL/JddBf1WeM9vu4CtH3cuuICR71CjNH3cy+FpBIYxy+fkXc0d1GbdMLWM4CPi31rUc+dQ7WdM7yHvfcwZvvOBh1i6fxxcHbiCQGldNHsS8m13uvfcA/tBmcNKZt3BbZTmnrHqAE375OF8dO4Yf7/uDl/cf/h80X70UP5jPAYehEsxNwDHAfVLKN/2Vx10JXCSl3PDyLPVvF/+bLbJj+j6q0FW6pmRbQFUvEZudb27BzYZodY3WJ0PST80weWArbbePIJNRgqiFMV2G2SJkUmDoSm3Z9QmjFtp0kcpe3c/PSoyZCng+QUsCaehU+hzSTyiAYGVpjvhjwxCPUpufRndD7E1jVFb3oDdCpCaI7pjF7UziJQy0ZogzViGMWkyvjBMaij9iFaHt9hHCXEIN9SMWojbXMQ1DZMSi2ZnEytfRRqbUbR0t4If4LTHctIkz2SCwVSvMGi8hDQ1pm8+DE0TDI0xG0Ep1aotzWDNNNNcHKan1xoltKSDqTZoLWzALTaQucNPKDbM43yS91cXZOMrGz/aw7BuTBNkEWtPDbYkR2BqzS02yG73njdKiw1VKi+KMHeXTcatJaZ6g88EGWjNAL9SpDaSRApppnchMQGCpE4Viv45ZUZ9Lo0VVLJoHHQ95TOxt0ndLBalr1DodzLLP5GqLrntreCmLSoeBm4SW9S7mbJOhoxM0WkPsvJrHeTFJEAvRs03MTVG0JnQeOcSOsRYSD0UUUiyu2ma+LWh9ZBYv7WCNFtnyvg569x7mnT0P8aWHX8e791zLVY8cSGKdiVWBxEkjvKXnMX543nFUu5SdgubDv511DfvYo+RDkwdqA9wwvpLy93uZXiG44qTvsrdVZ6sP7/vKmYhQKU1LoYzfAht+ce759OkOB33qI9RaBXedcQHHfuJjiLdP8ZPdfsyFE6/i65138UAzRateIac10YB23aEpfT48dDQbrl7O7PKQQ/fZwOe6biKrGTRkQENKfjC7P4cnNrDILNGiOdxST/GZb7+b8qoGmWyVDw3czVcfO4Yl3ROUvtXLbZd+hzNHD2Lj5/dACjBLHpf+5Ds81ujlqz85iZ++7yLuqC7j7GU3P///+rK0yLp6Zf+/vrQW2bOf/7/VInspRMs3oXRoxqWU7wFWAvZLeNxBwJNCiGeFEE8LIdYJIf6iSdk/axyz6OMcvednlQS/JlTlEr4o8esajb4UrU8FCFegDVSYWiXY+OEcVjXE7c+ps3nXV4+1LfA86vPS+EkbqQukoVFe3U1s8yx+0sbYNamk+jWN2eVxjMkS6bt3EqQiVBdn0RshYX4W8gWiTw5R7bBA14juqijnwYxOeVmOZsZUSsRrLGZWZ5haFceLQ2w8AKk2lTCXwI+ZyKkZxNQsO97ewfDrO5g6rBNRqOA8sQMtX1GOlYk4YraMqNXRK02iO0vohRrWaBGj1Jj7PHS0oQk1j6o2kJbyXg8yUaKbpjAKNaSm4ScdYnc/C6YOton12Da0iVlm9khgFV2MqkdufQ294SOzSZZ9M8/4a7oIIwbjB2dp5EwFaR4LKfeapDeViYzVkEJg1EK6f6OTXypwU+CmTEJLR9Tqyu9l4wy6O3cMJRQWKLmaajdkN7lK56ypEozmKdTX5ndGCGyd8f0FVqFJy3ofL25iT9SJTvmUBwKmVlqIIKD/F5PKtlgDPwLRMUH2KZ34/VGa2ZAgAsP39dL2O5vIjCQ66ZMYCklvLNN26yCi2sAoNvn8rT/nnOOv55TeB1lqjxI2DK6+7VAAmi0wu0zyq2XX0gxNSgsEoQWnfPD32K+fBOCtz5zCgAFrolu5eenveN9nr0MbqLDILHFdtYfdTINzz/l3Cq+uUV1VZ/Wb19PIzNk6IDlj9EC8mOAnH1bS/FLAV5f8mpiAp8/bk2M+9lE+//n3srspuKawD00p2Oz5BEjGawnOOfNanj7hEs7ouJWoEJSlT01KalLjo7lHOMyRvO7x9+PJgKMjJZoZ6GgrAvDlu1/Puatu5uL5v2Dxuc/wy0o7mpDc8L1voXmSa//9OwD88CMn0HtrhQGD/5RcXtb4+yRa/o/jJVkmSylDIYQ/Zzo2yUvDRB/9P1vaP3Ycs+BsCAMAZCKq9MNCRXak3lAJxjQp7t1JZLKJPV0niJoYtQgLzi1R3c1hYm+D7Ad38MyGPhb+HMxSQz1WSoLWNGbJpdliY8zU0EfzxJoBGBrmdBWZjiMNA222RO7GTRCNMntYP8ltVcySjzlZQaSSFA7px6yFZNeOI2NR0KCZVhuv3gyZ3sPELEPPXQ1K/TZSqMGwWQmIjwpKfRoT+ydpe6jM7OuWUe0QdN3TVPYBpo7f14oxUVSAhHgM8gWk6yIX9aGVVfKQpo4IQvW7YyINjWBhN0JKZMSiuCJLYkcNreYSxiMI3yeIm2jNgOY+i3B25glycUS0Cy9h0/rwLKXFKVIPDqpkbBrU+zMMvdpAGpKO60bQlg2Q2FZi04djZB/TaFtbQAQSrdjEb00Q3zjD9Jo24iPgzIRM7akz/6dTyGiE2PoJ6kva8B2BWYF6i069XXFhomOCwNZIbZPUW5Xx2sQ+FgD2tM7M7jpSl0zsl8AqSTKbqtS7o8S2ziLNNJUBnyEzTd+N08z/ZYEdb0pjVpSbZyOrkpYzqWGVFe8m1KGe08k8NKU+G9ensKaHIz5xPy1mhd+W9uT1qSfwpMYCo8Hvj7qYhBYSSNjg5Vhk5jExOC2znXe85wIOffj9vCP1DB/Y41l2+T7fXPozopqFI+oM+mXenvB4+5ofAg4nxIbZ5EkWWFOsPfgyksKmIl0ufkeePwwvYypwOL3tDh54zXyymkc+BDcp6DdKlEOB5kuMD41z5ZJ/ZzqUnJtbT1Ma2MKgLj0+Ou82bsivoteYQRch+TBgPEjw2bPeT36pzqLXbiNt1cl9O8bWK+G7kwfTnNdk39Zd3DG0mFxPga/e9zqOO/pilsXG2dFs4+Y7VjF2QIofXnkRjzRzXPietyEjgv2ueJzf19o56RXYCwSK5/yPGC8lwTwqhEijlDQfAyrAw3/uzs/ZIksp/ywr6f+KdfLLHcfMP0uJS764OpmbrwhPJRuEgIiD1DSCTJTUo2MELUmaOYd6m0nfLTVkPILwJIEF2+6Yj2nD/hc/wDWP7o+ddOi42iGwNaKjdaJbC3jtcZpLMySeGFfKyDGHIOFgbBkm7G1HJBUpM/PEDIWVORI7qgjPp7zfPErzNIy6RqWjk+h0QH6Jjl2AyLSCJkfH1XspzbPxYoLcujrFhQ6ziy2aGTCrgISpfRJYZUnr0y5GVQ3HxXQBo2gg4xGkYyGaHsKxEZkUYrIAsQj13hTRrTNI00B4PsIPqfXHiG2dBdcDTSP95AxSCLz2OOZMDVF30Wse+sgM/vIumr0Z7E0jYBpYI0rxQAwkcQc60KtKnTmybYa2TBteVKgqKoBGV5wF14ZYMwUF507HCVNRjNE8BCGp7Q30UhOvJUJkwqY2kCO6LY+MO9iTVbxYgshYjclVSYREWSpnIb/EoJlThzv9rMTJQ3JHnam9ojSykHlGYDQkye01qj1Rkk9OMPGqTub/ykMKqLdK6r1KjsfOQ8+/b4VIhMGTutGbakZWb4GOuyto5RpxXQdDZ2ZVigM+/BhJYyNR3WXCS3LfF/bnptwhXPWZCylLSGuSA+86nfgTDqGpiKXfOutyPnT5qQgJzdU1DrvkbKwynPyRW/jV147Ef9sMd678CXtcfyYi6fHF/W5gqT3ORz71UV7z8Xv5XOs6KmFAXXocd+aZuAkFoqgus1igu3xg0X1YQhAgcWYlAYJ2XefqSy4koWkEElKawy31OPs7MxRDj1+WVmAKn3arxL+d+UHMkk/s8yOs29rLgqJP2+MBq982yK93rSQbShKaz233rqR1Hfw+tpwD+7bz7Pm7k+jXGHq1zQfS6/l+cTn2rOBVuY3cVVvIwdHtNHMWpT6dR2f7+NKK6165zeH/YHXyUuKlDPk/NHf1u0KIPwBJKeVfanUt+yutMAGk/uQfhNiJcr4MUGrMe//R31PANUDf3NovkFL+aO5vPwSOBSallLu/6DF7At9FARR84ENSyj+bIP+7cczAOQq9FbzoVERKlTAMfW4xcx1J+aL7hH/0zQpC8HxKe3eSWj+DjNnosxUiYzM0s31Kb8pQjoV+VxO/rtN/neTuR9fQXw9xU1FK8zRKB9RJ3R+jOBDFmRE0s5Li/G4lU6+BMwt29wBTe2oMfG8QqnWk6+L0ptCLdSiVSTwjKPe0k97hM7vIQExIUjtCQlNQ7tWUnfHc8t0EWCWY3DuCCFSvPzkUUu7WMWuSlrXTeC0xjNkapWVZ7IKNZRlIx4BAIqRkds8s2duLhAkHbbao7Jw3TyFjDqLhIW0DPxMlMqYUCMKUMrHSKk1EvYHVdBW4IZVE3zUByQTOxjEF727NKCDATBkMneTDw6BrhLkk2lSJoD1Nal0ehGDjZzIkH4fhw3QW/qKOl44gEjbWYB7pWIQtKZASc8Mgs69ZxNQqwYLrakgNZg5oQ/Oh3K0G6I1sks4HXGrtJpU5D5LcBo+xA02673YVP6cRkN8tRse9s+w4MYPUlBaZ8JTUzuThnaS3NSnOt5k5qAmVkK67dFLrZ+gsR5TVgibo++UYSEllRTtdd5SUE6hjg5Rs/ESGU1bfzXx7igsvfzOaC7N7eWQ7lJTOzZXdcITHftHt3HjId8gdFnLMN85BhPDue/4F+kI0V/Dl1TfytUfeilmV7B3dztWdgvnxEhs9nZuOu4iFhs02v8kZ295CIyfmkkuTQ79xFneceyH3XHI5B/zbh+g5cQc5rcbrzz6LZlLjXz63A08G/PhrF9Cj25w/s4L3ZB6hEEr65mYu/Waet7zndPKnVThl4UO0m0V+eMlxpGouesPnukW/xxsIOOKWj9DyoZ10WbMUpuK0uAF3VBchOhskfyPI+xqrE7vYNbOEf/3Kjcwzmrz+g2cS2Bo3X3wet1YXcnRsO+dPHcI7v/obfjm2it8fcsnLvWW8aJ94eYmWQoijgUsAHfi+lPLrf+Z++wBrUUKWv3z5VvBCvCTDMSFENzDvufsLIQ6RUt7zZ+6+9CU8ZfAX/nb4X5Ci+TCwQUp5nBCiFXhWCPFTKaULXAV8G/hjdNt5wBeklL8XQrx27vfDXsIaX1IcM3COuhIE/zVZaC8acZmmmnkEgUoi2hxCLAwQxeoL9zMMmvNbSD0xoe4Xs1XikpLIlIc5VqS0sg1n2oWahTWjs/P16uPsuU3DKgVoniRzlY41WyKzycYou+RXxEFIMhtrVLsjNFMalW6N1FbJ7IE9pJ/MgwbOkzvVWi0LqjW6bholzMTJeg6RDWNK2iUaIR1zGDssjebODW4LEJ0KMRohvi0wq+EcX0cn+1QRPB8vZeFmLKodGkIalHtTZDdU1ewIMOqS2qo+IjsLSKmG/HptzpJiDlHnpSz08lwF5PqIhofflkR4UUQQoFVrSEtHxKIKuh2NEk5MogUhAnAXtmNtHVefqWYhdo4iQ4nu+chcipnVGeLrBF23TCP8gPr8DPZ0nYkDUpTe0MXiK2cQnrJCCPs7ST+ZJzqZoDzPYXapOt49t9dJbvbZdVwCADdhUW+D7EbFuXFTOlKDidUW6W0hSEjtbCJcn9iwEgKdeXcFs5Zg5sQarT+LUlhgY9Ykqcdskrt84pvzUG+gGarS1efaoxu/2sWiK5owPE6woIfqvBjj+wvm945w1SMHsnLRIFZRUlii9M6MuiQ25rK52sFdOwdY2bWE7/b/hg1ulM+dcTUHRyZ5pJnh09/4F/JrXNY4g1z2ke/wwe9+mE3NLt51yi28K/UUj7tZ9rBgs+/SocN4KUnsmEk2e3WOv+ZsbjrnfKLCpha6PPjVy9DQuKo0j+9+/WLurw+w0QuYCuLsZ5cphk3Wlbp56xcOpdamccunLiCjRXjT2nfR+fEpLh/4NQuMBieecRZx18eP6mw/SXBdNcVluw5jYj+B7doMu1m6bjKY/niFWmjT+QsL8PjFAd8jq3n8xyf24fHKPBZZ43zikh/z2S+/l7d95GM0Mhpv/PJFvDXzEMvMgH9dcvfLsFP8lXiZEowQQge+AxyJktl/RAhx4x+Drebu9w3gFRoqqfirQ34hxDdQ7mWfRkm/nAOc/efuL6Xc9RIuw//N9UogMWcXEAfyqKqEuYSX/zOPeY5xmgJG/5uv/Z/imEUfV8kllCppSPlC0oA5UzBNtcCEpjZmqbgkz0coVdKRUl1si8L+ndhjJXUmbpuIcg05oxSRrSe2q7Pvu7ei13w67tVofTwguVkHXVKap2PUAgVptTUarRGCqM7EmgTVLkF0MkAv1UlsL9N23yStTzRovWOYWpvG7KocQTKi/GRiUWg2kakEVKqIpoc90yDMpdTfiiXCqKkIfx40s2AXQ8VncEOsUoDeDBBSPi87ImMOblyx53MbmxjVgFqHoNwfpdERI4haJLYUCUyhVAVME2O2plqHQiBqTULHwBmt4GUjFPbMIZo+0jEx8lW0epPQNgl72hANT7UikwmQIVo6RXNJB7JQRITyhYoyGsHbYz5yfhdYFhMHZsk+UaTnhxtVEmk2cUZKVOYniMyEhNGA6qI0hb3bGDyxC61UR9Tq2DtmsAsBPXc0aPY3kbqG8AI0F9oeD7ALSjBz5ugG9Zwg1AVel4vUley+FihTtKkDWkgOevgRgb8hxYGfeJjbD7iM0IRmGtLri3RduxmzHDB5UCthNoGYzEMIpeVZwvYMfb/UEX5AY++FBFGD+I4yi36cp/CrHv51v7t4d+f9eHGBHw+IpBt4MYFe9akHJmyK0xUp8LWpA/mXn36YVr2MhqBbL3LDZ87jq2t+TY8Rw9F8/v1D3ySnV/jJD1/Dfr8/k7RWY+UPT6cWKpXtR/f5Cbes+ClZTXLTO89HR/KEK7i53spmr84zXo1+a4qoFnD1zv2phhZnf+f9HHreWRyy9lTW3byY2cUawoeoMKiETWK3JoiYHntZkppUVs0jh+qMHqgRGTK5fnoV+Rt6WLTXIDs3d/BMqYvItIv18wzTXpzJVTprLnmYVt0jH5qEUnDzjfvyrp+dxriX5qxP/oxLLvkWVlly/L+eTpfR4Irispdju/jr8fIN+fcFtkopt8+deF8LHP8n7nca8CvUTP0Vi5dSwbwB5WD5UiT6/6chgVuEEBL4npTyij/6+7eBG1FJIoEq7f7aeOyjwM1CiAtQCXXNn7qTEOIDwAcA+vr6/uITHr3bpxCm8UKyeHFbLAxVcnnuAi8kHiFeeEwo1Wp8/3nBx8b8HPHBBpQqaoP0A8LpPFokgqzVQQi81jjGyDh6vkzqWcXot4sW6e06M8thYh+HzgdqhIagOODgO4LOqzcgF3SjDU9CMkG9N0Vk2FeoLMOg+6YJpg5uo97hUFgTIzYuyd5ZQxSUaCaahh+31KC94YIQGFtHaCvnmDg4R+6ZABFIAlsNrUNTgNDVRmmD1HXGD06QedZjcpWJ5pvozZDcMz5mVTH0palRHkgS31mZU0sOkLaJVqwhynUwdLRyE2kZ6M2AxHZXJQohYLaI0HU0wyBI2oiaiYyYaNUmMqpcrOwtExCLojV99dnGopAvUF3VQsINGT08Tc+1O9Tzpecg3tMVqFRJThcgHiN9bxNsi6nDOvHjMHp0O+0PldGnyxi1AM0NWHC1wc7jTbJPWcRGJZUuHb0pSQxKtO0OoS7JPjSB1Nsp96H+3gCpRYiPeDQzBl5EcOnJV/KrmX34wthRaB5ktgRotSZYFva2CczOXrRijaCvg4kDEkgN8svS9P2hRBAxiAwWkZPTiFyWIBNFcyGl1/ne8KFEp0LK+7scNm8LDx8zj7PP+jVXjB1G4EjuHF6EvDODNz/klLXvxXkqwumnXM8j5fnsn9zGZFDjzb89C6utzuf3/C0igB+9+vuYIuTSt11JVPO4prgbxyfWESDIahoJQs4deS2X9txKvzFGVUI1NElrdUwktZvb6N+9SmpHwMxuOt5olNadUlW07RoX51dw5X2HkRGwcUMvj/bp3FNdzfAxkg8deCud5iyfWfsGViaGOeRDmxnz0mz2e7F0n3+54np+NbmazZV23nDsAzycn8frU0+wwPA5tns9A2+/nTWRYV574cfxY3DnB8/nxksuYr9rPsY1xb248UuvYvlXd+e189fzSsb/Q4usRQjxYg7FFX+0T3YDQy/6fRjY7z+9lupInQAcAezz/7zY/4d4KQlmO4qN/7dIMAdKKUeFEG3ArUKITX/UijsKeBL1wSycu8+9UsrSn3iu5+JU4Ewp5a+EEG8BfoAy0PlPMXeQrgDFg/lzT3bMgrMR2hyM+I8rEiFUspkb3D+fWDypEGP/Zd4y1ynUNIKeFjQ/xJgoInMpGJlAaHNcGF1TdZrvKwSYEISpqDpLHiugZZPY+RLR7QmaXUlmVkRppqDvJ9sJC0VEXzfl+QkSTTUUd6bqCC9AeIGab0zM0HpznfHj+ml/pI6XNAk7c/gxE2v7JASWQpVVa6DpYJp489sYOyCKWVVzBaOu5gUIgeZJRCjJLzHVxE1X0i2NnEHLeh/hSaShhCO9mI6dd9HcgPguD9H0IF+AloxCi+Xi6KUGblsca7JCEDXRyw3wQ8Kohah76vN2bLRCGQwNDA2t3ABNIFxfqUfbNv7CLozxAo2FLTjbpiGbVpweXdB5T1l9D0plRHsrTOWRrpLVkY2mSvK5FGK6QMujBcrzMrQ8VWf7CQk6H4yguZLqApvMphptDykL5EqPILFLQYSLC0wlpPngNI3+LLU2QXxICXAmt1URrs/s7imcQkBxgcH5p7yD6RURmhlwWiH3TB23M4k1qiqwzN272HJaP+0PB4Q6hCaI1UUaj0WVw+fqFrxYK9GpgMQjw7RNljjv4KNIZmo4luD7B/yYQGqsHe/nk19/P1/+xA/47KYB4nvVKHgZEv1FDurezs3Dq/j2lW+g2i1Z87qtNKTkhDWP8ob04zzT7CZ33DD3VZZwUupRui2X8UAhzq4uLeTSS05E+JBfGZDYprPfQX3Uyzb93dNcv+xnPOVG6NB9rj3zAob8CLd/+zJmwgaPNVvof4NqRmxodjLkZTnjkFtIHF4nkBrPup30WdN86bBfsbnRSUCWyw66hnEvzbuS41TC7fQclafXnOG0x06m/RqHQ774ID+9fw3zrw/Qrwz55vS+3PeJ/fl1j8knzr2G+v5Vfr7/FRz/ybP57dcv5B2vvYuVkUF+kdTY0OjmjK9fxJZPvEJmZJL/Fzng6b/Cg/lTzmV/vJ9dDJwrpQxeacfhl5Jgaig+yx9rip3+ci9GSjk693NSCHEdqtx7cYJ5D/B1qdihW4UQO1Azn780tH83cMbc9V8A3//vru+Yxee+kDT+VHKBF5LLcxH+leQiBLOHzsPO+0SGioTxiDpTFRroGsI0kE0X0ZKFRhNKFUQiTnl+guQzM0p6pVCGIEC4Ac6WKezxCHLbIMSiVI7ZA6sUkHx6Ss0TJmr4CQu/I4pvC5xpF8Nso94Vpf3uaZiYon7EEkoDcdJrRyAeJUhG0KsuMhYlSEcYPDqGMwVOHhLDHn5Ew4vr2LMe9RYLzZfMLtGROsSHJMILaH28ipe0mF1k4sxKnBkfp+QRWjrS0AgBvT7XT4s4BIkI9Y4IzrSreCcVlzBmo1eaiozpGMoXxzJwl3QhdYEzVFAJZWRCVSmpJJTKqlU2WwQyhOkYzjMjhK0ZKgNJJldrDFxdUUkNkIvnIaVEK6mEIywLkU4iC0VkKgaNBgQB8y/ZSOlVS2h/JMCLadhuQHQ6QAQhXkwju6FGpTeKF1NJtPXxKqGlE6QieHGdzvurIASBrVNaGKOeEwQW5B6cRoQtTO8RodYBbpeHv4dL66MhbtZGRm0aHTEi2wMWXT5E/uAedA+kAbWhBHrTw94whD0UB9dl/LV96Cu7GdvfQLdrNNZlqKyU/HjyIO7bsYD2XzuMHOnzcHUhVlkyOJmlcyygv22MW7YuJWjxePWrn+D2ocX8ZHg/HkgOMFJLcT2raDErBBd38Iv3xTlq5ToumjyIj7Xfzs8rnewf2YH+0V/wuTtPACskdcwke2VHMLSA9+fuZYtvUgodtnigETLqZxjyYJfbwrGJp7kqv4bdoiM0QpO0XuPh0gJWJ3ayf2Q7Z2w5idnfdNP/pm28reMh7ist5kcbDmC/3l1c9o03ktrRZNtJJj856rtkr4sCIW/NPMztC5bwwe/cxiP1+RyW2MiHr1zLTt/hw+edxm4nb+Pxxjxu+NoFlEPJVfcezE1rD+VnXzmf4x/9AL99+/msG7qQFb3/3e7+X46Xccg/DPS+6Pce/utYYG/g2rnk0gK8VgjhSymvf9lWMRcvhWh5I/AllN3xYy+6vKwhhIgJIRLPXQdeA/xxXTqIIn0ihGgHlqAqrL8Uo8Chc9ePALb8txcZhi/MXIK566F8Ibno+gv31cTcbGbufs8nJvmCB0o0QmO3btLrZoluyyP8EDE0BtWamoUYBqKzHel5UK7QXNypHp+Mk1w7CPm5DRXwFnUhDR38OfmXPReDaZLYOIuzdQo0gTNaxm2Nolc8CCTRsYaaSUhJbMss0jEQbS2kHh2jmdKYelUP44e1Uu9wqCxMMnJ0C6MHxYiOQXw0IDbhI0IlZKj5Ei9uYBd8CgM6gQN6Deqtz71vmFxlYlYlRj0kiGjUOm2smRrmTA297qNVGgg/REYj6PmyInWWGwgpCW3jeWIlQiACiZe20aoN7C3j6A2lVtDsiKtjY9uEMUcBFmp1wv5O9IoapKNpVBYmCCzovbWJ1xKjuVsftdX9aLMVxM5RghnlVyMScWQqjojHVOL3FLCgsc8As0s0pvbSyS9TCXZibwM/YZHa1qTRaqPXoP3Nu2hkNPSqq1qM1SbR4Rpa02f0oChTe9qkn5qh6/fj9N4wjozac+rUkFsf8sk1v+Og/m2MHpqguNCCMMSo+YTJCHg+2ftGkTrExiUdD0Ctw6SyXz+4LoU1PUQnA6Jrt9FzR5OgaSjL5ljA3Y8v430r7mdiH8G3j/gJgdTwooKz9rqNkVfBKW33cfD8bawcGOLhqXkc1K3+zW57fDc2PtvDdQ+v5srHDgZgVccwN5dXcMfOxdxbW4ApfBJaSCM0EVEfYYSMjGe46a7V3HfJvgDsYWoc6hRYYJZZYTU5NDKCo3nYmnKxvO+r+/OdL78JUwQsscZ4+qI9WGqP8VhjHgBOXnLtwt9xe2E5v9+6jI/vcQtf6FaOIl7c4NbXfZPlVoPLvnYJX734e1xXXMWnFv2Ob1z4NtaWFvDRp0/imPPOYS9Lcu0nzufc3pv44affAEAhNLn0qKs59py7uHp2P05c+BS2kFwx9dw28grEyzeDeQRYJISYL4SwgLei9vAXXkrK+VLKfillP/BLFLL2+pflffxRvBSY8o9fiRf+E9EOXDeXVQ3g36WUfxBCfHBuHd9FJbqrhBDrUKXguc8hzoQQ/4FCh7UIIYaBz0kpfwC8HyXaaQAN5uYs/69xzKKPqyt/auQThGCp4ebIG7px8pLc3cMvwJTVAkEXz7PnK6t7sIoezjMjapZQb7zwfHNS9RgGNF20dAocG7PYIOhrQ6s0IJNAOhb4IUEmh7F+p2rnCIFIJTB2ThB25tCmCkpkMhNDG5zA8QJCxyK6aVIN8tNJGJsE08DbfR6Tr0rTdU8ZP6o2Lb0J5W5VjYgQMlsDQl2gBWrWIqRGaAl0T84pGZuYFUhtD5hapZNbrzbVelcMswLxEY/ZpRap7T5GNaDZFsMZq6AVqlCr48/vUN41oVRViutTXZDCnmliTCu2v2h4eB1JRdYsliCbxtw2jkwncQYLeHsswI8aRB7foeYpgZrzhDELY7rC2PHzKM+TZJ+B/HKbzt+P4nWlsZ8ZxF/QhZ4vIGwbYZqE+Vm0Oe02bbqokIFS0kzrtD/sojeVRli10yS3PqTSbdE4sUDsP1LEJiTbHu6jb6uqukJbRzR9Cntlyd05SO9vFEJOlKuqKms0EFUbLROh9Ykm1mQZUwQcmNrK/HfPcOdZByJtE3Oqgp+JESxoxxwt0PmjdUyevILQFHTcNc3U/i3UjuzDqihJH1qz7DzOwhxVqgPmjIHb5vH7TxzOW798H1ubHbwmsY6DPvss29x2TjjgUZ5tdvKG3BP8+8T+jD7bRmlHB14Sll0zRpiOMbFfkshMiOb63H/X7tzd0eTtez6MLkLO23wUC9J5tl+5mDYfjFqIF1eWBYEF8wyDJb85lR++5vuc89Uzuf3z3+TeRidHRVRZfHO9ldsv/jZHrDuJhF5nbzug1q6xu1XjPTeeQGRU4/wv/IArigvZLT7ChmtXcPLBg9zdyBHJK/Rij27zcNPk89uPo/6DLqb3FPwoIumaCjit/XY+8L2P4swGHPzp07DKkp9ddAFHf/ZuQqAQRrCEzxPFXr7V/2uuyO+PLuDSVT/972wdLylergpGSukLIT6CQofpwA+llM/80T76N4u/5AfzcynlW+Y28//y9l9uZ0op5XaUDM0f3/7dF10fRVU2f+rxJ/+Z2+8DVr8MC3zh+ouH+i+SdgnakmQ2eYS2RtCaRp+tqETxIv8WIg5eZ4r4hmlVySTj4PnIUkXNDDJp5MTU84lJtLUgozai1kRsG1Zsf12HzjZEQ8F1jZEypBKKeBhxVCstGkEbmsBb3I05ViR0TOjvRNRce5ni4QAA1ABJREFUxK5RpCYQto3XGsM0O6l1x4k+O4U10MXYgQm8hFIJlhokhkK8mJqjmOVAOVVaChGmz8GSpaZTmiewZ8Eqq4H/wh+OErQmqQ1k0dyQ1A4fEUL72jJeykb4EqPmgjeHpEslMGYqyKiNNNRGLvyQ+PpJwnQMNEGQiiIC+Xw7TbbnaHQniTxeUBVYzcUcmcVf1Kqg1qU5985yHap1cF067jZotwx2nJBi4Y9GQdcxx4pI18NPmGgLutXnv3UIrbMdSmXE5l2K7NnRSmGvFqxCQGhphLaGWfLJLxeEvQ1a/+AQ3J/Bjauqru0RSb3dRBoJ7PEKhCG5BydU+7NUUTwVz1MnB/M6EeN5zNk6Ynya6n7z+faFJ/LeM3+Do3m45+TZsaOF2I4kvb+fRdRdZvfvJLk1ARJyzzQoL83SunYaLxdjYp8I0oDkJo2+P3jUW00aOUG1C+ItNQZfG+c/1u/NwQPbmPCSHJ7YgE7I0amn+cyzb2DPlhEeG+qh7SGB3gxxk4KpQztoebRAdDJkYn/Bwp83yWw0KTVs/tC6jAcu3o+Z1xl89tjfMnTus+wf2caNpb04MLaZtF7nZ7P7UQ49fvyaK5lnlLn+c+eR1BJ842tv58tRwVmn/YILvvdmpt5/I7et+A+GfQ9TRJl3wnZCJHpVQ+rwuS+9lzeffSs/veI1RCIhjzZtLtp1JCOHaFzw+p8yGzZ5srGQrliJ8bEWqu0OzRbB5N5KokZ3wYtpc99lwbFPvI/LV/yUQij4yJUfxDlwBv2XWdZ9Jsc1T+3L3mt2/Ke+08seLyMPRkp5E0o38sW3/cnEIqU85eV75f8af6mCeW5ucewruYD/U/EcHPm5eC65CI2pw7vIbqhiVn30KU9VGd7cADoIQGjImE15SYbkfdufn6uEERMGR9CyGWShCLU6wrYhHkXGIxBIGBqHjlaEY+Mv6ETfsBNRb+J3pJWviqXmEVqhCqah0ENSSc0YG3ZBJo1eqiNtE+H5VA9aTGz9BNTrmOt3IhwHK2ax6y1dxMYkLfdX0OoeQUxJzg2/OobUFas7sCxSO11qrTrNtMBNaAQWNC0w6sq2OLW1yshhcZLbEjRaHWJb8tT7MzQyOpFpidQ19MbcDEpKNZgPDaQQc5wOBULQpATPp7a0lei2WTWDKSo0nTQ0xGwZmU3ijJQIqzVVXTQ9aDaJ7JxF1uuIthY1XzEMVbEFAc3OBIGtsfDqCVV5PjfQNw2cpweRbVkYHEO05pD5WUQmDY0mIpvBa42T2Fpl69vjzLvJo5E1EKFBz50u0jSpdArsguKXmLWQyFgDzQsIIgbSMiAMFfS8UlUGcBUfmVXHEU0D22ZiTQajniYyE9B+006ue/bVdHxjB1mnxogUODMgZor489pIbSox/KoUqR0hg0c6dN/jIcZnCLuStKxzFfu+I4HeVCdFVklSO7BGdSxOz8AkIxMZ3tjyGF9+9rW8a/cH0ZCcdvUHSG2TPG61EB7RoDRfkNyhHttMa3hph3KfRssTksAxqHYLIlNQeaSF5LnDHJGcwZM6Pzz/OM7bJyT3mM5N7qEEpuDzn/wRLXqU/yj1896nD+XWA7/Nl6eXEupCwbYJSe4Kuei613PlEPzu385no9fglwO/49rKPG5+53kUQpO05nFvfT4Pn3spPy13Me6nuHLgWh7v7WDUy9ARq3Jc/BnWRLfylhNOQyZcYpsslr9+M/fXB5h+bYO2G22MmqTWrnPdnt8npen8oLAH557yCxaYkyz8Uo2pQOeew79Fb/fYK7ev/B/VGXsp8WdnMFLKsbmff5LL8rdb4t9ZhPI/VyS6DrpGaofCP3gxgyBiqI1OiOeTi9ebJb8qR36Zjr+wS1UvQYjYNQZL+qHZRGTToOtqs4s4SNskHBxRkOhKDaTEmK7grVwAjoWxfRRt0y60Yk29VtOFpqecLIMAWa0hEnGoNxD5khKULJWJrZ8gaEsqqXxHmZBN7h2j/VGXllt3oo/MqDPongj1TofUdklsRCGV3CSMHmQpU68Q7EJIZotLdFLxEpppweTqOG2Pu/gJpbHV6E1T7TAwqyFGPVAMdU2ZhIlmoNp8qYiC0zY8/GyMIKNgxI35OSK7itTnpRGujzSVQnQYs/HntREkHPXe0im0mTKNgVZkUx0Lf/k8lVyi0eePnYhGcdYPE3tIzRTk2KRqUZoGwnGQTVch+FIJZH6WcFEvYTJC/ZBlyKhNcWGEal+UJZdNMrG3RTOlYU/UcYZKjL27QcsjBUITyr2C6FANzfUJbUPNYBouhT2y0HTVa2XTlNfMJ4zZNHpSBFGT+pI22u/Pk9lYITJaRSbj7Pwg5JtRliQn6V4wRdvvdyJzKaQGXsYhtTMktAS9dzQJHI1d71/C5J4Woa3hR3U1wzI1Mk/Pkt5cZ+DMSQgEUw920vtzg4/98t3MbMnxjbGj2e620faYj1kNiY35mNsi2LMKJagFChEXWjrZZ30Su+rMLonQfVeN1serZDeF7Bxt4eGfruTyocN47zm/YdmyIaw3TXDyuX8gcKDfzLPvFz7MsYn1xB6K4gjB775xGEEEah2wf2SQay++gE+f+Avu+/TFbPISvOcLZ3HA50/j4MgORvwYJ9z6EY64/aMUgii/q6X4zgUnssIe5U2fO4cLz307V3/tWEaCMnfUBnjHo+/lnjdewPkH/xx3dZU9U0PcXxggeU+EZkLjsm9ewpEfeIB23WKfuz7EftFtXPbFN/GZbW/g9lofjgi4tbrwFd1SBKpF9lIu/9fizyYYIURZCFH6c5e/5SL/LuKPqxdd+0/zldDQ8JLKztccmnkBZeYHBG1JvJiBXQzw96iqzaa7A2o1woEeJVMPqk0mJfR1qnbXph3q+SOOuj0RJ0w4WJuG1cxG1xEdrQqy3PSorehWr2tZCiQgJbJQUj9bM+B5yFwGZIg+XVZn77Eowfwuum4aU2CAIIB4lNkjF5F6cpLE05Nkbt+GXQyxiuDHITYCRk05FQa2oNphUupXlsCR6RCnILEma2iuggA3WgySO5sYTUm1y1aulAK8jE2jKwamTjPrKBkc60VFdTathCoTDpGt0wg/JEg4EKq5jrFrUjldZpMQjRBm4zg784hEHD8bw9wyinQ9pb82MYXsbIFmE3wf2WgiJ6fVMfQD1aYsVxARh2BJnwJYOA7C9SktSWJUfcYPzhAb83CTij3fc3uZ2IQyWJta08KCzzXYeUKa1ser9N5SRAQBoW0g3AA3F0XUXSJTPt7ibrAsxl7dQj2nU1oUY3aJhRQgQomYzKOVVZLc/rYWoo9GGPpNP794dDWL05ME3TmkoWFs2IUXUz44mcen8SM6WjNk4THbkQco0mpkpEal26LaZVJcnmZmtyi1lT1kn9bovVXxpcLeOrknBA/9fncGmzkqnQZIGH6VgR+VBCb4EY1yt05sexFn8ziEkuk9orQ+VpxrFeqEhqC/a5rD3/UwbZEK5z/6GrY82E9btMK/pDZx/bnncfnUYVxy7ncY8hPcc/Y3iQodNymwipJ73nE+MQFv+uQ5XHjZm7mm1E9OU7JAhd1CnnFb+cg3P8zr9nqaR478Fns6g5gi4NrPns8mtw3xpmmV8EshnXqMQ6Pb6PiBw+sefz85vcL397maK+86nDe0PI6bAo6foRTa3Dm6iFXf/yhCg4TWQL59mk8t+B0nJ6Z5/0fOZE9n8JXeXf5hE8xfcrR8DtH1RWAc+Akq2b4dRXL85wqhgfwjhRtdEf3y+7UTG1cS7F5ce2G4H4SE7Rm0cpPJI9VHFr3PQMxMqfaN0NB3TeAv7EJ/dlBBkUtlRCAJU1G0alwlCs9T92800XwfolHceTmsjUNKLiSUSNsgsnYLwZI+1Z7TNGWdHIaqLVOp01zcib1hCNmSJozZaA1fCUMWqri9GaxnBiEagXKVzAOq1YRQ4o+pp6dJxB2qfTHKPTqBBcldEt0NmVxlYBXBKkMjq6G5UFqaJPXMLNVum+i4i1Fx1bcnlEjLYGY3h8xmF6voUetLEB0sgxfg56LoVRc/YSv0V2saI1/F7cmg1z2kLgjiDprrK2mY6SJhOoGQrvKJiVhgGxizamMS2bQCOSydjz408ULLEjXfolBEBoGqHBd0M7ssSbVDYFZjdNw+gVZukH7CZWbfVjruyTP6qizdf5hi8sBWpA7td4xTW9JCZnOd8vIs3fc2qXU6xHeUaXTEMGoBWDrOjhn89hTOrlmafWnl32NDrRNa5pT7Gi0W8Z0Vpl67kOSgy87jTMwiNHLQ/mjA8e9cy6wfZXplAqsqqa9Jkd3kUlxoUe5rxZhTHNq1rQdnp8XYQZIl3yqh+XF8B3IPTrHzpHbMukHm2TqzS6LU20DfFcGshThTglu/eRCNLoiPS8yyILc+ZGpPQWgKnDz46QheTxKjFpDZIplelSIx6CJ1QXTcY+TBHsrbu5l9TZ3OGyxq7TD2/QUcUzmdWy69lDPb7uCdnzibUp/ggY98k6QW4byzr8ASASnNpBh6TO4jie8UnJzYwcX5vWhkBHe88Xx6jBhfLEvu+Y/VvOqQBczP5Jm8bD5XfeNCjokWWLT71YwuTdJvFKiFBlOBQ2hp2L9Oc9oDH+TM9/yaTx95PRNeik/9y7UsNCcJEJw+cAef2f5m0ok6H9r4do7vfZp+s8hkAOZHx1jV98onmH+6FtmL4igp5WVSyrKUsiSlvBw48ZVe2N9dGLranGxL/TQM1TMXAt2VCC/EHnlRYeeqwa1WcwkTNr23lkkMSrpuGlOtK9MkrNcV7HRoGtGaU2fS0ShybBKpz72elMhyRT1nxEEmoshCEWvDoGLZN+agszNFRCyKPlVCTBcQdUUSxLII+jug0cSaqUEygRifQZ+tIcan1RBcCKxnBvEX9ajX9Dx2ndyt3oNpgKETpKNoE7PEN8/SaAE/Bn5EUBiYSy4l1T6zSpLQgmqnRq0/RXJjiXqLxdhBSeqtFs5EjXpXlNR2D3usTK3TwagH+ClHqSWH4KUc9IZP0JHBj1uEjoWZr6HVPczxEsbUnOlYSxosC7FrFAolddYfKhQR5SoYhko4ho4+OK4G6kGgPoN47IXkAohohPE1KfyIwMlLnEKoqsRSmUZfmtTWGtvfkiWwYeToVmITPu2/2Y7MzyIF+FGD5DMz1NqV5E1htxTOZB2j4mJtGYMgxNg+xswBbdjDRULHQHfBbfWYXqHR9lBZ8ZpsAy8uGD3IJrFNo+f2Os4UjO+rc9sFB3HndatxCiHOjI9dkkyutnDyIVZJYtYldimk/2cCPwpokslXd5O5b4j4sAeuS/+14zjTPpovKSyVNFolfixk5MiQ2ESI5kNtsYt95iiNBU1KfRpd97poHtilkEpfBHRBpcem3GOS3OVi5Zt4MZ3A0Vh26DamD/bozBWZ/7FNHP3++zn+nDvoOmsbN1Q72OS2MLkPmDV43I2y/PJTOffrH+D0i05lNPBwpWTfvbfwwzMu4be1Ts7JreOWj55Pu+4wHaiTBmnAyQsfZfNUK5or6TNsxoIG7/3sWZx55fvpM2zubGSphjZ3X34F+aMapLaHfPn+41hkTdBr5VlqjaEJyfuuOI2v/PQkzJJGxPTwQo3ffeFwHCE58M7T+ObCX/xt9peXD6b8dxUvhWgZCCHejtK0kcDJ/GWxyn/MEOK/8lzmbnemPfSaYowntjsv3N/QwfUI7TjmZJncw3UolBC2RVgqoyXiKmmUK4g5kzAaTURHK9qzO18gCoZSiTc6tpLZj0WR1ZqqYsYtRK2O39+Ovn474bJ+RDqGNjKlEGTbJ9BKihyIFyiuTHebSkCGgazWkKUyWk+XggcHIaRTRCcUqstvS2FMl9VwXdfIr8oRmurMxE0KvITSI6MEoQ61NoEfg+gYREYqeC0RGlmN9HYfL6ZR6Y9j1EOiW2Zo9maU/ljMQi/VCbIxAltXas39USLTHtZklTBi4kccrG0T1HbvIrppUrX4dF1ppeUyyIkpRFn53IQRAwYLaJk0olhFJmKQiCLKNcLOVrSxKWRrBjGZR+i6ail6HlZFYpUlblwdW7+vFWPXJI2sQSSU9N9QJoiZjK1xiO4qgW2z9fQFmFXovqtGbWGGzNox0HWc2QBtbEahCLNp3I4EesUjtbVOkIoQ2gapHT7VTpOBn0xRXJHDd+JEJ30KKz2SG0xan6wjAknbEw1GD3aY2V0QGwEvJhjf3yC3juftlosLBPOvL1JckqDeYtD5gM/0HgblPrD37SF1zzamXztApUeQeyZAm/RY9JMmXjbC+D4ObUeN0nZWlYlanDe1DLF2ah6f3u93XPrIG6l2mWgB1HMaVllS7jaed8ec2NuiZZ1G4AiMJuwsZJj/7/DObz/A0bHtHH3hOVhFSWgJEufcz3eHDwMJ537kWhYYJW543/mYQvKlsaOphTpvu/ws4sOSU63FfOuz32bZrf9Kd+csVy69hiE/zQ1fvgBbaMSFxQ+2HIkXlwz6TU7ffhKVHuW2ecBXTufBT32LjV7AOley9pDvsHONSTm0caXOTreFrFbhIxd/mE9++GccHNnFzdXFvCmxjY8OHc2lF1/NUKBzzPJnyOr+K7+3/B9tf72UeCkJ5m0o6edLUAnm/rnb/qkijNrK4EoI0OaUfAP1ux/XsQcrYJkEjoEehKq6iUag4eJHdGRnEmuzko4Pe9rQpu05/oxFsKQPY6qs2lmaUBVJPKbOoOdgx7JeR5gmWsNTCKSOVsx8HZEvIJsueiKKiMcQzYAwakJPG8b6nfjL5mEMThH0d6CPz6pWkBlRzx2NKBRaOom0dGb3yhGYiu8SmoKxo7twkzDvVxUqS7LUWnS1sUwqYyurLKl2qU1P8yVuWhlsGVUwmpL8yhSxUY/MpjqNFkudObsBaAK/NYGQEi8bwSi7uB0JCgts5YOyo44908DNOEhTR5+t4SfSAESGigQtSfSRqednSPgBIplQc5OGh5+OYHZ3Eo6MEe6xiNDSsJ4dIexuRZspzQmTaiqBzxbx5rcRRAxyv1wH/d0ECQc/ZlLpdYjp7aSezhOkI5QWxah2akQnJNtPyhI4kp47fSK7igTJCFa+CUFAmIgSXbsNHBvZmqHZmcSoqY1qdkmE5KBLqAsm9zJoezxker8WigMCvQmNnEl8C3SsrSF1oeYbpoKLd9/tEd0yw8wB7XSsVYlF8yWVbg0hobwggTPjU2818WIa9UVNIpttCos1vPgAmY1VSv1x5QvkeRTX9OE76iRhx1Abr9nnbnZZOWzh0R6tcPfsEopLA0TSw9ns4MxArVWghWCWITruUc9aFAYM3IPKNMajRB/Isvr8tZjC59ZaP985/TvcUFxNm1lihTXFxNX9pA2Bc5zLDZXduPza17H2A9/kjbnHuL8+wCfe+zOKQZT9ottphAbZB2xqegfeuRqfOP/95Pf2uPqI7zPPKPPGox7gV/oBPOO2M1pMYRcACWZN4skARwR88CMfxYtqFOdrNHOSq068jOPjz5DVTGoHVjkuNsIVhT1phCb5MOTTXTfxrGezzAz4asfdTIWvrJTKc/FPaTg2J+n8YSnl8X+j9fzdxouTy3My/FJXjPLA0gjTMYTrUxxwaB111AylUMXryWBWfIzxgkL+bBtEG5qAbBo/G8PYOYFuq7aUrNXVa4VzbH9b2RSj69DdjvTVt1Au6EZMzKKVKpBJKaJevkTY1YI2MYsI4ojJPDIIMKbKhC1ptJpLbVkHkbVbEJXq82Kast6AbFIhtyyodagz3PwSJSmf3BUyc0AblR6BF1fDfalBYpdEiucSTUgzrZEYhFK/+meJjylPlJk9THpvEUTHm3hxA6PYRAQBQcLBzNdpdMSwSnUsL6C10MRPWGiNgNDUCW0NrapagGa+DpZJGHfQnh1UycVFMfUNHTlbQ7ZmcFujODvzUKujtbWiTZdVm7FWV5DviANCQZyxTLBMRCCxxysMfXgPOh5qYE5V0KsujZYU295ss+R7VRqtNpEpn8ROD63mkT/CJn1PBKPsMXZYjq5bJ5UcTcRBm8wjZYhsU5+7UfOpddjMLlUJ2i4psc+ue5s0MwYiFLQ9HqD5kl3HQXKDgQhCQCO0NIr9FoENxfkmu17XSt/NAb6joTclpXkaycGQwBIUBjT6flOimcliVkO0GQtt7yLi/hTleYJmKk7LuhCvR+m8+Y6gOCCITsC8g8b5wW+P5MDD1nP7RXsxs5vgvcffyo7FWcZmUjQWNzCSDVovibDrdRa5p2BsjUWj22PvZTsZumKAT37ql9w4tSe/fHIVNz+1P7X9apy6xz28Of3wnCxMmh989iJGghTXz6zmofE+/Cgc/pWz+P2/nc9nNh6P9fMMk4f53Pzqi0kbLl4U4mMhWd3HqkiMgsHZX/wg7/j4Tdxw4xpuOuV8JoMov151JUeOnEX7gxqaJylKj8ca87j5u5cREvKsJ5kJYuTDOGetewsfX3wzd6+5jC2+xUGxZ5kKkpRDg4Tmc9bZHyE0IbAEX/nclSz6W2ww/4wVzJwY2v+cpPiPEFKZYkk0lViea5f5Hs5UEz9mYkhJfERJyAPIqE3gGJhlF2lbquppzanqwfUwtgyr9szoJCSTCMsk7GpTumJhAJpOmI0jyhXCqKWea7KCqDeRmQRialbNGoRAFktojo0sVwg7ssgFStiRag1tbi1WyXtB00xKBY2ORSFfwmz6xCM6XtxidpGOH4XMsyFOPlAGZXnBxD4G2pwaXbVLES9DQyVasyLRXUlgazRyMLObalekNwnqbQbRrTNodQetUldw66Y6o7enBH5LXKGnvEA5UtaauL0ZnNHK80g9bWSK+l79it/S2abkdKp1CBV5UXS2getjTdVA1wh6W9GnSipxJ6KIVBIxU8Trb0WvmoQRC2NwCndxF5N7OZT3MtCnJNYzg4R9HZTnx5ECFv+gxMzeOVrWTlGbn0Gvumw6I07rrQaNHNTbbNrXltVasml1bH0fOlvRSnVkxKLeblPt0Ol4sEmjxWTkOJ8V84cZ/MUCzJqk1iYIIjrugga27dP6lInwAvyoSWhoNDPQfY9Hrc0gvkNn+AhJcqvAyYcMHLuNVqfCvb9fiTMDg8e10LrOJ7/EoOvegKk+jeXHb+WphxcSGxFU2zVKfVGcvKTllh1IfT6zy6Dj9BjRr8yyODbB3a9ejDVs4YUGUgo+udcfyPtxvrP2cJqnNlnQMsFgf4b47xN0HTTB6V238e0PSG7J78bk+QtoT2jE3zNER7TMSmeQD1x4Ou889Q/87PyjECHc8tWLIKfUpm4rRZjt1Dhk7anIzXFMXaLnDTp1g6dci2YL1LoFv6kspTCgCM3FBYJ9ItvJrQ9552fO5oP/9mtur+T43tE/pON1ZZaaJrOh4ODILnb4Gv9R2Jc3pR5FFyEzXpx3z1/LlJ/ksLVvAKA563D30d9kl5+gIX2uuuhC+nSHinQJ/0Y7/z9zi+wJIcSNKKHI552xpJS/fsVW9XcWR6/8jLryHLteSkLHUITBIKDa45B5fBppaDijZUS+BC1pRN3FmqqhFavgutB08Zb1KlmTQlG1yFwP+joJIib6jiparYm0dNVem8or1Fgui1Zzn3d+xLEQU7PIpksw0I2xc0JpZuULEIbo0yXVavN9ZDqppOuHJhDzlJZZbfcuoutH1dk8gKYhp/NYO3bRObSQ0de0YJXUGVytzSCwBW5SzVpEqAb8qe2SSqcgPgTNpMCoS7yohlGFRFniJtUsptIL8WEFBRaBVMnNddX7M03QNIzZGmHEVI6TNZcwFcXaOoFMxRShUoa4y3pxhotqhmWaal71HAs+nUKOTSIcm3BeO0xW0MtVws6cSuqTeUgnFVjg2WGIxdAmC4RtaaQh6LqzwKblcey8gFgMN+sQHW9grN/J+NuX03FPnjBmM7m3SW1enOVfHGfs2F4SQyGpp6bU+9E1qNWfV9P2cjHMfI16TxKr5FM+QEMaNnZBIl0NTUisY6Zo+gYtVyYozjeIjjvk1tfRa038hI2X0PEjGh0PNZjZXVkYewmIDqvkPrmPoPGzhWxJgtUAsyyJTkrc02Y4e/49bG20c93Vh+AeN83BBz7D4yO7Ex8Nie+qM3pQjNnD+olO+MTGoNGVoLrZ5Mfufkhfw1xRpBLYHN/9NN/bdjBrOnaw79IdrErv4uHZ+Ry/cB35f40R0Tzefde/kF1r4UdhxSc20O0UODr1NF/afixf2HYcxaUBK51BrsqpNuCet32E1QO72PSbRcw7cpjRQoqvrbyOffYf55C7Tid7t8Nt9Rwfu+kdWD507juOTkh2/wlGh7NcevjVLDKbhKbAtwX7RHbyze+fyLlnPs0Trsk9DZtVtktDBrzpsffT3Jpk/uunuPTiE0mdOMKxXeu49IFX0Xm7jtQE9bcUOOrhUzlt+V0UgyhX3n8onz3sBgDeFP8bIcj+iRNMFphBCUU+FxL4p0gwR+/52f98w3Oux7oGEho9KWKjTUVmfE6TzNBhcAxasmjj02ojnUMwmVMVgt5W3IyjGOhRS9n7zmsHy0JGLGWulXDQ4tEXDMmEUO2iqbx6LsOAXAapC2StjkjGEXZKbXT1Bs0lXQDYg3mkYxD2tKHvGEX6PtFGUykFaJqSinlqOyKXQdME0jHovmGE/IFdNFOCehu0P+bTTKuvipuG+DA0sgI5N6/xEhDYigdj1gApaGbU3zQXKt3Qlogg8iVkSbWsxNxnEtoGoukpLpBlITMJpKlTWd1DfNMMbmcSkYlijRYV4CGt5ibPod0QAoolRC4Dvq+G/0Io75fxvEpoEYfQsdCqDTV3KVfAD9Bmq5jA+KFpln9hJ8Nv6ae+uJVGi0nmtl3Q0Upqp4+Xi2INzTLvVx7CDxg7tpfcMw2soVnwfcKpGbS2VqU1V1WWzNZgnuE3dJPeEWBWPLK7TVObaUWvC3KPGuxct5Diapf0wxbFBYpTlNpap7QgglW20JohfkS1wYaOdGh9IiSwoPXxpvLO6bGQhiS9xaO4wMKPqLmZORUQXNHCFeJEKp0auoDuaBFb8wkOKlJ8OIXmOvT9bJDqHl3Y+SZazcVPR7BnLOpWFKOtQV9mFlvzaTeLzBTiTGYSxM0GD8/O5+mHFrL81WM8MdXN8b3riD9jYbxhikolwjPTHay7fTm/19bQzMABR65nj/1HOW/X0cw7bgendN+PJQKm/ASjhyfJ39jDnic9y8ZGN7vcVu4/7FI2HZigQ6/Q8oSgmYIz5t/OanuUb935BnIHz3CQXcIUFl5EcOLpt/N4o4/P/es13Fht4caZPbE0H1rWoouQ2/a5ghN+fTZfTB5P/w6P9/Q9wJSfwJow0fwQvRmQfybD2Sdcz/k3Hs9hhz9Nz4Ip3hQfpCZ9KvIFt8JXNP5BE8xfhSlLKd/zJy7v/Vss7n87jtrrc//1xudMw4Rgcp8Yet3HGi7MyboEqjKp1hVqq1RG1upK6t22IBJRmmUREzehgzY3Y5ASbfMQslJVGly6hjY6rdpnQDg6rp7P0JBBQNiSBsdGuJ46+x/oVRupYyknzGgEe9MI9uYxhbJyfaRlIJvN52VP8HyElFi7phGxqCJrJuKIrUOgCbJ37aLj3jx6Ayb2NoiPSvyoaon5EdT8pQFOISQ2LmHONllvKKVbzQfNBy8hERKChEN9eaeqMpbPR1YV5NQYmcFtjRP2tClEnabRzNpERmqEcYdql405lFc6ZP0d6n20ZZVnjmki5xLIc6rW4fik0ncrFAk7spBJgWGglWsKsuwHyEaTsL8TrzeLNjhBx/0limuUyZzeCLBKAWFnK4U9c4SGwNo5g9udprgyS21hFrMi1UxISmReodXQhPpcAdmWxZ2Xo/WpJs5kgx3HR6g0bJK7QqQBSCgNhHTcbCBC6Li/RGJng3q7TWgoNN7sEpPAEjRTGlKAG1fXiwOq6tRdSf9vfPRGQGLERwsgs7lBqc/AqIck10/jR6DRAk9ftAdrJ+bx+gXraXnVKFIXlPbuJrZ1Fqlr1PuSVLts+n47gzOpETQMwrMyPFno4YLL30z2Nod9UzvZ+tXd2HHtAIkdgt9+/2B+seKHJPQGH3rfjezdNkjszjjlcoTKPnXKCwLCRVUeH+/hoOQWfrX4V+yWGuM7Ow/njuIyfja6Ny2RKoe861Ee2dHHyanH+MnFx3DMN86hIU1MIfGiQqHWgghTgQIZuHfmAJgIGiRPHuZNycc5NLKD1fYoh0ZG+PG8O/la182cdtUHOPXqD7LNizJzbI3cIwb1FoNfjq/mtfH1/PYd5zP6qpAVn30Kr8tl2M3yu7deQNqsc/tuv6QifRyhc+7wa1/BHWZuS+Efl2j5UiyTe4QQ1wkhJoUQE0KIXwkhev4Wi/u7iReb8kiJNHVmdlN9bGu0qDzh666qJHJZJVqZy6okkEkhUkkYVdpf1OpYu2ZoZDVEpa4AA6aJSMbx9lz4vCw9uo7fEseb34bW142XiyFGp/D2mK+WMTUDhRKhY6EXqshchiA5J3VSLBF25hRIIBFHFCroMxVFLATF/XA9dakomRltsqAseBNxlaQMHVGs4Myot13PCZwpMKvqZMsqSxJDIfWsknnXG+DFlRZZuU9gFSA2IpGWRG/A1pMcnKGSak9tHkJEI8iJaYKODHrDV3OkiIVWaxLdUUAEAZX+ONExF7cvi9uewI+auC0xpK4rnTTbQiTiuPNaCMcmoFJDa2tFjk/hD3Qj3DlZ+2qVMBVVx7FcQXS0Uu+MYszWkJ0tSu05p2GVIbR0zJKHdAysYkD84V3IuEO93UZzJVbJw2hIdYyEUGg/05xL8DaNVfOpdyeYXOVQWGQTRA38VED23+OMvipgdk+fag/EBjWqnRptDxYILR0vZTK7SGfNaY+w2yfWkdnq48UEbkIQHYPCa2vs+Z6nye8hEYGk1qYxs9xi6MgItVadlqcbGMUG0gDNk3itcSIzknm/K5N+Mo//21Yemp7H/i07QYKb1JTPzvA0eiOg1K8xcXCOwIbFlzdptkTYef0CxdVJCtrNIm5cbReNFmi0wjs+djZ7R7ZzUmILK2ND3PapC3n37mvRBiOYRZ1wPMKJC56kVS9xZyPLUD3DzqFWbrt2X3ZM5tg81cr94+r7/GSzg2s+fQGFPdRsLh/Y+BH1PTspMcQvCvviR8CZlZhCJ60ZXDjwC7K6IKHpvPb7H2cq1Fj99Q+TDwW6q9CO+9oh6w7+AQ996TuMHxrwvQW/YpuXI6XB08deysfbbueuIy7mXzJradd17hwdYM1nT+NV3z2HNZedRS2w/hY7jJpBvoTL/7V4KUTLH6H8BLpQdpy/mbvtHzqO2ucL//mGF8nCTO0Vx6xJsnftUiS+imK8i2gEOTmtNnBHETJlSln2iliUsLtV9etRZ6CEEjE+TdCZQ6bi6FUPYzSvklWLEqgUgSS0TczZOsSjWGMlMDRENo3syCm4rpSKAV+sIyKOYvHvHH1h3VLNPp4jHfor5oNlqplNJgXRiHLUHM8/LycjCyXwA7Iba2Q3hjh5SXRKIgIIosrnZXaJRnGJsrZ9roIRocQugF1S/wxGWanfap5g6NgWwkwMkUqoQTgoyZfJkhLq9ALcziS4HtpUkcSGPPU2k9klEcyZKvZgHr2prIONWQVuCNuVAoFcsQjZ1ao0xVqyql05OIaoe8jOVry0atE9V2lERqv46QhBwiGMmEQnA9Kb6zg781gjBfTpMoGjUTpgHqOvyuFHBGY1JDQEqQ0F5Zjp+0r3rFxB5lLkD+gksNW/VHlBiNSg3GvRfp9OfqmG8DTa7teJjitdt9IyjyBhUe2J0PtvmzEPzvPq1DM8NtVDrVVndk8fuyiprKnjTUa45+4V6FWNbe9RREo/og6x7wi8uIHUdUoDIfZ4GXOiTKVHUBqII8pVOm4epfjzHp4pdjL15jp2IWRqTQsyFmVmmY3UlMxP7x0NRCCZ2MciPKRIx53TNA6q8N1dh6D5ksACexbMEhQGNN7/+LvY+9dncsmP38A1paXsF9uK3+5y9FGPQiC4f3oB3x07nBXWBA9t7weg1hOibY3RnIlgX5Ohrz3P2Q+/mXtrA3x4zR3saeUZD1JIQ7Ve76inuOGm/fEOL/KDz17EdNigIQNOvOEMymHIuaOvxo/Cu9edglmWJLQQ4YNdlOhC46flHophg1uOuoiqhNdEKgwFFqbQ6TFi5EOTHiPGEV88C3lDy/Pf5doil1+tuewV2F3+KF4qyfL/Xn55SQmmVUr5IymlP3e5Cmh9hdf1vx/yPx9NKQRSF9R646S3uiR2qQ1fCqE2LkNH5lKEi/oUwqxcVYgiISARU9WMHyqkkaaEImXEVtXD9mGkpqHVn4No1RCVOqLhqSH3zhHETJGwJYXblVKmWUGINAxIxgmTESU3Pz7HDQlDhDHn+GgbqppxXcT4DJQraG6A151BtGTx25Lgugr6axr4A90wOoEwDMLZAua2cdL37CI27tJMqWG/UVPVipdQUOVyr5ibAYDRkCSGfexiSGQmwE+EihdTFQgJY4ek8buyirMSjyG8QG3UMQcZMZUtcjZB2J5B+AGpDQXa75kkjChlZ2PXJLWFWdVqzMVVizGdRJoajEyoBJ+fRds6TLC8nzBhI6oN7E0j4LqEFcXwF9tH0MsuxuAU2lQRN6FRXOhAucLoMZ243WkSj49SeGcFBCR3NLBmGljbJpRe2pwWHEFI8fBF1PqTipfihuSXmYi0i12SVDsEXkwlhNaHNErzBYk3jpLeGiJ0yY7jI6z5t4d5bKSXwmSCb5zzLur3tRDqgsxTBkZDEn8gQupZjdQWQXQcUo/Z6vNPQmaTkp63ih7N9giJbaqy8nNxYiOQuXOH+i7XG7Rfv4XBG+aze9cYw68B97gC40e0EMmr73poQGmezc7XJwn2rOBYHhgaLT+PMnNrF6EplF6cQMncm9CSrGBUNEILbplczscufz+fXfMbVsYH+dyxv+T0vttp+AZHXnsOuUyFSKqB1V3FS4ZEdxlU2zX2yg1z3YGX86Oda8gaFTRglT3BgSc+yfSe0G0UuOCkH5OMNNnp57i1upDXf+ps2teCJwV337qS0Ja8d8EDzC5XDuWBo9ZXCZu8PTHMEV//GG/8zjlcV1rJTr/BbZXlHPzp09j9gXexh2XRlB6//cz5GHVJrQPqbbDXwN9gwD8X/7QtMmBaCPEOIYQ+d3kHauj/DxubN/4ZaW4hmNlNSWIYszUoVxHF8vMDc6nraFuH1ADa99XcZHgcOTymZjBhSDg0ipyaIffgJOI53ksui9b0VMus3lCDbEBaJuamIdW28n0FB56pIooVZCqGVlES/NrotAIZdLQS5pIqqSUTSMtE+KGqDhJxBQ7QdbRqE73mQbWGXqqreUw6pZJdzUOkky8oFQQBslbHTRpEZhQPx4spwcvUFsGSyyfoXNsgNFR7TAoITEFxvs7UngaJ7RrlpT6ap5JSMwtDR8YYfusCyvv0QFPBiAlDQsdUXCMAP3xBYFQI9PE8jQWq7Rd9epgw4SC8ALcjgd+WxJgsEQ70QFc7uB7Bkj5EEKJvG1VyNxFHSessW6CQaLqONHV2vbsfb34bTiEk82yd2t792CWJNVJg05daiV6fpLyqwY7XO+iVBpimYuh7qsUoF/aoGZMp0Dy1AwQR6P25wdRxdWpLXeyixJmB7PoyegPCy9qxSj5fOvA6ssun+fUzKzmobzuJZxS8XXMhvzKksDREdyXZTS7pbR52OaTapRJBbAS8dMDs8VV8B6ZWRpje3UDqMLVvlqEjY4QmDL1tAaX9egnbspCI03PjGOvuH6Bj/gzlmRiVg6vERpqkdoQUFmnkVyiOU8R2KT/RgijXMcsBte6QRloZzT13LPUGvLP3IdYcsZ5wRYXuaBERwBOVeXz5juPR5ozmf77oBuy8oPRoKycveoz+XJ6lewzipiTVHkm/M80Npb2Ymk2Q9+P8rLycIx86lWpg8cljr+Pmyu70GzOMDWX58pffhSd1fEcwsT9s8tqITkBsfhFNSGIjghE/hubBPV+5lLhmMx020I+ewazATWO7kdUFh8Y20cgJvrfqGgD2veSjPNZs4eIvfhtnRlVol/df97LvLX82/kErmJeCInsv8G3gornf75+77R83BM/zXoAXCJZCIDwlaCmqTTU/cWxVxTRdxfDvaFUop0xKwVZnG4hkAjk6Qbh0HmLpfMSQMpxqDrRh5utoo9NIz0OkU8iOFkTTI8gmVPtL01TCijjokyXCVEz5xPshcmYWYgp6XN+9m8hgEWnqNHfrI7A1IiNlaCjXRDk5jYg4SN+HIKGSUjyGKNfw+9rQHt+IsCy0Sg3pugjHRu42ADtGEbaF5kkmV+sYNeX7UusEPyIJjQ4Ky0LMMphbQ/LLdUQIIlA98EYOIoMGQqqNq/WBKabWtNJ6yy41IwKCuINRUHYEYWKuqiuoCiGM2YiGjyYjWNM1sEzcPlXBmFNV6gstUo+MgGWp2U48Rrikn9DUMDfsVO3K8Snkgm7V1gokGDq1veaheyF6DWodNlYpwBidRavGcMYEzf4sLXdY6K4ksslh3rWjUKkqH594DHQdd6AdL2HQTAryuwtSW1WV1vFgk+k9bCyrRn3aodQvMEvw7IciRLfB5CqdxC6dzz36esSogzMreOThlVT7JYGj40xD9FGBHxE00pJk0UNqGvW3F+j6XpLBYyG2zaDvJsnk6jj+q4p0XGRRHHAoLBJ03lcGkVCAARNqbTqhmUSESTRf0vK0pDHUxsI3DrF1WycText031Ygfec0Oz44QPP1BcTtWRbePAUyZOhoHZnw8SOWAik46mzaqMHXHjqGpd+s0bYwwinfvJfPnHUL500cwTsPvJ9pP0mbUeJzk/tz+QcuZ4FZ5pA7T+fz+/2GJ6t9aPtJ9svuJK3XuG1qGUu7xrns0cM4/8Cfc9mqnzITxDkyMsmvK/O4t7aIW466iE1HtOEIl5s+cz5bvCgP1RciNfjNqito1S1+WJTUpMWjZ3yLpvQ56HOns/oDTyKuz6FLWJkdwZw7rw5N2Mtq4EkdJLw6UiYk5OpPXEhOCylL6PhbbTn/B5PHS4mXYpk8CLz+b7CWv68QQrUCXhRSF+ge6I25xFOpQlqx5kkln7+PmLPVlXPWuwAinUQfmoJYlLCvA210GssyYHhcocs8j6A1CVKiV+qKyxKNKEXhVAzh+2DpaKOTYNvPt/DEHAzanmkgDQ19aApdCILOHKLWpL4whzNWIVixEK3pg5S4uQiO50O+QHPPfoyysmWW9YaqXDQN2dmKvmOU0qED1Np1minF4DdqUOuSxHcpImWtA6IjGpoP0yt1IpOAhMBWZ7l+RKHJpICWR/IQhLTeO4EsllTCazQxpPLGCbqTSCHIL7doe1RDBMo3Rq818XNxjEKN0DbRmgHG4BTYFlogVSVRriDaWxTfxvWxxmYgk1YmY/UG2tiMmpcMN3D3W4ZR85naK0KtW1LaPWD5V6eYPrSL6HSAUfGpdlqU+wSNBS59v9RfUG9Y2ENoaGjPDmKNlzHKNpobwS7p1NoEg0ea9N7mISR0fttG6j6FRRb1Nkg/blLpU1UeJ8wQ7szw2eN+zX+M7sOOR3pxpgT1zpDkDjWEb2TArAgq3TaT+0kiD2bRPA+9YhAfk0TGaiT2bdL0DTRfgoTWp0L0UgO7EKPco9H+mIvz1C78gW7cpEl+mUmtU5JbJxm5p5e9j9xCY7HB5HQ/uV8NMv/aSYYqbSCgsjhDfP0kie0a+mvKtJ2vbKyF6yPcgMqyHF7cprhcVV7vf/JdnLrkHo7NPIErDYa8HB/52fsBuHlqf2rdkuvfdBHjQYLNpb1pi5RpN4sktDrHtK3nwMhWBnsy1EKbnW4r3Wae31S7GXaz3PT1w7gydxzLT97IZ7p/x0E/PYf73n4+SW0Db/vYOjZ5STp1j9u+dBH50Oe+RpqzLv0AMgXbyjnqrWp2FEqNmgz4Wf4gYqOSivRp0yyqu7kMB01ee8053PyO88hpEVJ/K8lF+Y8rFfP/o8heSsxVL2pgrvgGaiOem79ICZUqXkscUakTdrU8T7gjk5rjwIQEXS2qMto6hEwnCaIWwjCUtpjjoBdqaqYghOKzFErIZhNtpgig5E2eIxlGI8+rCch6HfHsTrxcTK3XttC2DEKjifPgs4hqA63uos2U0SYLOM+MIPOzBAu7sJ/cqUicloWwLOWTMtDLzKo0o29bwuwS9RqpHSF2XnFe0psU0S+zsUL3XRX6frKdWocktVWiuRCbCJXNcgK67vcwyzDvN0XETBGEYMfbOtj5sT0YPGUxO89awdixvchEjPH9bAJHI7PJJbQ09JEZQlNj8pB2ZRUtJVpNqSaga7h9WRKPj+L3t6uZlG2qKnO2TNiZQ07PEExOqXalaSB0Ha2zHfPRzVjbJ7GLkswG6LtRHdvcY3ns6QaNnKng2MurRLbaqvVVKiPrDeW0WVNVjLQMZpcnlIVzM6Tt4RLRcfVcqe0+1S4LBKS2e8y7sYBVlmQ2QnYDBKGG017j608fhRfo+FFJdFxiT2kUFmsIX6kGG3UFqJCxgMiESvJ6X5V6TpBfoSwgapvSAPhvnGXsUEkYs2mmNNwMmMUmpYMXMr5vlGqXQevTTaQl8eKC+d/fycbfLeJdXQ9Se0OJsVP2QNom8dEQ3YPYtgIyYhE5ZpLSM1me/WCWnZ/Sqc1PM72mjVqbgVWG2TdVqWc13I0pvvPT47guv5qNjW4s4XPvO8/HmVRq26EpOeH6j/LpL72PU7rvJ2fVSOs1GtJCJ+Sb46+h2yjwdK2XuwtLcKXBJV9/CzNeDKscYFYk3+67iawmaXlK7ciOCHjDv53NAbbLBfnleIQ0pEafUeQ3Z53HI2d9i58svpYL3vcDioskXc4sttC4+fp9CU1BSlPJ0ZgwmQoUBHybl6YiXda7L2WC8DLFP3GL7EfAvwNvnvv9HXO3HflKLep/Pf7cgQwkzqzEKgVKw0poyEZDocE0DWtC+b8Lb04CPpt+oZJJJdBHpxUxb0mf2pSqTdVySScJ0wm0OcZ50JJA3zpC2NcOIeD6akCvCTBNaqvmEXlwi3oNeH5+Yq7bAa05yBeUBEzEQSRiSFNXLbWYjZiqIV1PJa7NQ0hATMwgXQ9hW0y8YwVtP3gUucdqOm+bprpIuVHW2tU/m1mGtvum8LMx9FnFLQm7Wljw6xp6paFUmmt1gt5WkrcOUj9oCT0/30ll717iNZftJ7eS2qbO4r24Ajuktnnk98kRHZeM72tg5yG0IZ3tIbG5QG59gD5ZUklXSsyCIrZawwVqyzuwZxo09luEM1pWx8m2lMtnIo6m64TVGvpz9gpz6DUMg5a7Rhk/uptSn4EWtDK2v8H8XxYwmpLYhgrpTRrCL6OPzUAqqexstu5CWBZhfzf1vjheTGDUfELDZNfrkqS3SpoZndEjQoThsfCqQCXVTxpYDwmiU5LJwz2shkXqdzEi0wHSt+ENIVMHQGKzQaU/ILcOGnPiopU+iTFt4MwGlHtM/BGD0AC7GDI0mqZtI1S7HexrI7TpSoh1ej+f+FaDIGKSX6Zsrf2YIDqpY81otDyh+FJmBc7/2skceOpTLDt1jJ+4x2AXQzQPho9poef300yta2XeLS5aM2DsgARGtUmy5DG2JoIIof37DsOvluSegsJiwT3XrWLtAXmkhC9NHscXT/0FayI7OX37mxm+vp96C5xzz0l8fM3vcTSPH44exJfnXU/WqPD96UN4YGw+1aez9LxuFrMu8aROI6OrGSQaX546CC+mMRoYnLv9RMrzBBNBg5FmmsMuOZuP/cuv+PrTR+FWLU5Z/QCnZR9jH3uWJ0+6mInQJ0Twh/efx+PNDmrSJx+63HLy+fQbCe5+5/mkNJO7G2k8+VK2x/95PMeD+UeMl/IJtkopXwxLvkoI8dFXaD1/H/HHAqohYAjF1Ab8qAYNV80QWtKIUk0NkstVpRFWn5MOCZWnSLi4Fz1fVYPmfAEAbU6eXizsQVSVACQpdUYqXB8pQ6X8a1lI01CznmIJXA9nova8aZbs7VAtujBUcjHjU2qGEnGQrRnqvXH0hvKqkTuHEMmEsgYAlWSyGWRd+dyXDl6I3pSMfWhvRADlZRm0ANoemEFUGgBMHd7F9re20vGQhzHYQDSbyEwSY7qI15tD13U0IdRwPZchsqsIYYidd9n6rlbSz0rMqlIC1psQmFBvM6h2CAJbtdNCGwILZhfrJNf5qlLTNWg0CTpz+AkLd9V8nMEiblIn8tAw1kAPYcxGL6tZjkzGwEwi4jE0S0eGQES9b39eK+aOSXBdOm4do74wR2GRRXqLpNEZo57TaWTipLZU1RwsFoVmk7C7FeFlqfcmsacaSE1gNKSyee506P/1DGgaQ8dkMPMarU+ENFp0plfqhNugvMzDqphEt1gsPGqYZwcW4EcMKj2QXqfaJKntHtlNgnKPgVGF4l4esU0m3r4VShNx3BTEBgX1DkkwIjCnlXCm5kum99CID0IYtbDTDbxEnKFXO7i9Lsa4xcKrRkHXSaXbaLbajB8Qw6hDbMzn8ctXMv9jU7z59Fv53l1HsPiqMsOvTrDpIxla18LIITa59SGJ4ZBqt0X2niFqb+0mtUHHaAS07jbLVFeChZcEFBbHiD0SR3NDunZNcq12IF9995t5+wl3kv3AOrJGFUe4/H/svXe8bGlV5/19dqycT44339u3cw6kbhSQYCIo5oDKOyPOiOEdHcfRcfQ1jjMKDChiJiogOXWD0IGmc7g5nRwr5x2f949V94KMju0MtKT1+dTn3jqnTtWuXbue9ay1fuFzvX1kjQEvm3iQM/4Yv/1fv5ef/6W/5nNvuJYX/+T9vLbyOT6aegZziTqPeHLN/E13nvfefQNjnmY3ymAojVfS/EXzBu7/n9fhmlC2uoSBSfqkw8ff/0we+KkFTt+zhzATs3h0g9cs3MWv/uEPkN6IWfzd/8FQu0yaA7rxkIe8CmWzy+2JHn/Rnv+yLjP/IPTXZob5Borsnwr1+YQCyAUQadxWjFYwODhG4zmLKC8gLmdFfBGpBqg3wQsExur5wtdIJ6DbR0+UJdk0W5iru6jz61BtEKccqTyqdWnB5HLC7E86KM8nbjRR5SJqrIw6v44arwi67MKatLX8AF1roCwLI5eFmQk2bi/Q2mvh50ziXAJjakLk+j0xI1OOg253UIkE26+8jGFBZimFsyHagN6USWfGlPdkWyPrZ8Xev9igOyOthWiyiIpjgrky9loDFcd4C0VJCJ5wfshl2L0mydR9IU5HFkMj0GRXA2JXJOCTVUFa5ZY0pieETtOD7dsnUH7IcG8ZXciI3TLgND20Y5H/yAlUIY+5UZfXimJwXYF4OxbUm+iEI3I5afm52fYgnRRi5N4ydleMzlQMXtEkNiG5E2CeXRe+UNJBZ1O0juTxxzKkjm+h4pjaCNAQ5lzSa31WXlKmdm2B9IZm4SND3GZE7CjKT8SMPxxz2W/WiGzoH/LYesseSsdi8ucDkjvg58EaaMKkgfvaDZLVmGEFjLYAJMbelqR0IiB/VgscfF+X+hED0wfTh81XeFg9UVbQhkJrRXIbKo/HaN8gvYEgERM2aHAaPqUTAYlGTOpcHaereffvfBMAb3nhH3P2+9KEGcCJSTQi0NCdFgh047Bi+fvmISUKAnZ9QPXRcewTScxqh90bNGHKZPO2BIODY+w+ZwqnBRN2i02/gEnMrYlt3vHpWyhbXRbtKmnDwysoEiogthQf/NBNAHh5xZaXx8sZ/N7PvZGC2aP4hEHkKu7rHaD2l/OYA0XK9Gh9c4/2Hrivu59jz/ljkjuazpzJsePzBKWI7GKL5a0yn+kc4j0/+9s0v6fDVpRjzByyGmYJiPmpD/0gtoqpxkPmnKdvmft6hin/CPAKxDZ5E3gZX+sosn8ilNb0K2KTnFxpUfzctmhaVWVGEtcbxL0+8eyEJJ1mCz1VwVzaIiwkpK3W6DDcUwIQ5n8qSTw/gbm8Lf7xjkOcdiGOUKmUODc2WyjDIN4ZOVAawkj3Dk3L3x+Yl6rENMFQ9K9fJMommLqvS3I3pjdl0jyYRqdHJEzTRA8GglybHCceKxK5kKxHJBoR1iDC6Ugby+lotl56kLWXTLD5jCzT71+FTpfxD10A38fcbgJgr9bwF8oMZrK46216N+2RechA7IaTVfl2BBnxNnE6MUHGJLOmiZICcY5taO0bzbksyC9FJKsRYSWN1Qvp7SsAYHV8mVd1+qhKiTibgkwKc2nrkkp0nBXl5nhhUiykEZZ+nEkSZR1otvEOTJA4s0v1qhSZjYjYhCBl0J1XuNUBemacxm2zaMsgyiVJr3u4Z7Y4+TMzdPZmsQZgd2OGZYe1OzLYPUHPJRoRrb0uQdogd6JN/oF18o9XCcey+Hlwl1wKp/qYPvSmLIKsJNba1ZrwJ2psfnCe7RsVU/cFpFcVqR1Ne8HEK5hoUwAU+fdmyJ/VWH1Aa7J3pwjT0DxgsPGsNKnPpOk9o0f1CgM75xFbMDg8yblXlvBzCnu7Q+pMldSmR+P6MbZuVXgFxTtf/83c2T7Kzz7/AxRPatKnbXZ+YIDbhMrjHolaSPnJmNiG3CMujaMRsSOzmCCrodlm/HOKjWcYLLx9g+6sTXozxG1oLnhjvOPktdzZvIyUsnjypX/AMLb5mf/8bzg+nOXVr34fN7hVPvXLv88Pf/vHedET388Pvuoj3JE/Qe4V69zf38dfbt2K5Yls0WOtWYKMwhrA6x68HffhNErDwcQWv1W7kshV2D344+f/CdkzJnyySBwZDCKb573t5xiuZEkrn35s8tPHX8F3n345udMGBcNnN7LZDZ8WFbKvb6Kl1npFa/2tWusxrfW41vrbtdbLT8fB/WvGJT4GkliItNgbDzXJHY/uoZLsBjtdWdQsE6OQl9tuQxa1bAaUaGg5q3XhwmRSJB5bhnJRNMQsU+CzIAx32xbeiuejCxmCQvLScRjjFYHuttro/gD32ApkM5idAfHsONHRPfRu3otT8/CLDtpQRK4isx4xLCs2by+NnkhhLMxh5HNo1yLKuWTWY4KUQegaeHmx87UG4p4YuTD7vi1m37cl8jKxFgHJ2XHiQhbCmGisgHNmk+RqCwZD0p9bAs+Dbo/Se5+kOUocyR0xagMxKXPbEalNcFoRuZUIty7VC0BqvU/ueB17vYGft8k8tilaZLYhem+ej96pSsLoSmuMIBBPlkYP+gPUmRX0+hZGZ4i92QJTYa/WiCcrODs9tp8/Q7IaY3dD8ueHjL/vNHv+fB3lhwymMwzKivXnFkY79wQ7L1gQva6EongmRBuKYdHA6kNyN6a9R/zrK397nNxnVzCaHdGpU4rGkRT+5X2m7g2IXRMvr4gtAUx4OYVTM/DfM07pdMje9w6oHbUxPdi9XtO5yqc/MZLl8eTcCZxeQCeVx/pYfeFvZJ+zg9LwnL1nyZ/XpD6bxvRg+cUWlcdiUjsx9Ac0r59g+YVJig9XSW4o2ociWvs19/3MjfztxjXUX9Jn7kN1Fn8tYliC9K+ss/Rdmvphg9x5zeRnu8x9HHZvyDIc02SXFd1n7Mfqx2gTOldNkFv2WbvDorOg+Pgf3ob9ZJqrsytc/alX04oDbkhsEWQU1yaXWHQEAXnrf/9p/vTd38zvHHoXf/nGFzCMbX5+70fY8XO8evqT9McU5vNq/NDUPVh9GMyGEBr0Z2OcJmyHOd77pufQfnafn/n5t/OW7WdSftLHGkC+2CNnDSkeh/Ijivv7+zjmT/PvDtzFm/a/ne5tfWINEQZ/snLbl3+hubjGRE/t9tUWTwVF9udKqcIX3C8qpd7yZT2qf+U4eHjq0v8vcmGU1hBD4USb3auSRI5C9T1IpSAIRfUYZOhvGNIiGwwZTmdlppJOCCmz0xPJGK0FitwbiDxLry+8ll5PFvGMaG65q42R86VNvCtQW7NURI9QVfQHxCmH1qEsXsnBafhESQurE1C7Ik1qKyS5PaT8pM/0u87Lom9Z0GzhH5iiu79Ab8bF7kY4nQg98ncxAs34JzeY/NvTzLz9rGifjciFmMZIScAQgmh/gNkZEM1WhB8Uiu9999Z96Ikyg2ccZvH1J0hu9rF7IZlTddKnahihRoWaYRmaB2xae6TllKxGWH0wL2ziT2TR6QSmHxPOlC8BKPA8OR7bwnzgpJxDz4dcFoYewWxRZlqzU6jZKYKpPOFEHr+UJC5k6S9k2Lm5RPHkECPU7F7pYj1xAZSifts0QSnFYNyic/MA81l1Tv+ITW8a8hc8chc0iUZE5Ci6swZhCqY+3cTpxgJgMJSoCEyVIYxoHylx5gfHqF2tST6UEkHN9SZeXtHeIxWJ24rJrsi5QMP5b5cBfKKhqTykSJ51iC2ZTfX2hZiexm1GRAlp7W3dnCJRlUow8+sZhiU4/wuHcZsxnT0x2dWQ2U/EdOYMjCCmc+Mcdj8mTsT0DpSYfMEq8x/WjD+oWXqJjfFLJW7fc4bnv/1+2odzqBiOPbgHe8tBGxAmFSvPy+DWfdKbEdnzCm1CkBIU3djDUD9kEtkGxROw9w1nSFUjEjX4nQeex0uOPIEB5JVN4aVrHLK7vGn9Obyrc5BnffdDTN+yzhPeHJmNmA/UruKXf/1HeGb2FFc4TVI7mlftu4f316/GbcXkTlrgGxhDUUxYcGq8/NV3EvZs/nTtVmJtsHmrQ/nxHgPPoRmkGJYVpq/50OZR5qwaa36Jc0GBygeSDLXJL730h6neOf20rTlfzy2yK7XWzYt3tNYN4Jov2xF9hcTHPvfLUrV8cUSaypNDelNfcOr8kRulbYnIZTIBnkc0M0bybJUw6xK7NrqYlYom7YhY4kWGuR+IfIkrJMNwrkKcT6FtU8iHhxdgdhIjm7lks2wkk8Tzk5DLYHSGZJcGxLZiWHHxihat/QnSWyF2NyDI2jLk7w/Q4yVhtOey2I9fAECFsgsO0gbWQJN/dJfujMxQ9IjJz+yEmJnNTsgiHoaYFzZldhSE6PUtgRXn0+hiljibIHOyBnFM6rE1VLGA6vtY9R5hOYNO2KQeWyd2DMYf9okcYbAXzgbUj5jkl0L01JhYCWxVcR9dwlrZRdsm1nYLHXzeK92YmybudME00PUGcSGLdXwZGi100hbk2Wqd2uUp3J0eQSnJoGIy8ZldtKVIbnvMvuMC9W89wsYrDrB9a8zO9Un644rFNxuYHyiROWPjNCCyDSIX1p9lsv4cRfmYT5iE7t4c6fsvkNoJcOsB/akE2zfn2HrJItWrDZ7z3Mf4vmffTWpbU7s8QeOGccYe9YhSmiCn6U4bDIuK4qmI9oJFlNQUTw4IE7KYm8OLbxYSGxZWP6Y3YWG3YfsGRWxB/aqI0vEeg4kETkeOVZsKc6jYutki89Aasx+uApB9fIeNWy3SywYr36JYuX8OFWjyJ9voks/2TWk+duwyWlGSZ//CffjFmLjsE8x4VB6PRbmhC2HKQpuK1G6M1RcPIT+rKD5aZ+HPz5P83FnKj7bxD8/iNHxMT1O8O8Fjv3gNN3/wp7nq3f+ODx15N6ZSLKZrvOn138pyt8TP7/0I3545ReQqXlx+jNhS/PYvfx+fGszxiv/wUX44t8R/m76LzowkeG3H2B1FmJZW3F+cupGZj5pcXtikHbhkVzRmzyebGvJ4bZrnf/9naRwymMm0+G/rz+ePP/cs/v3j30XuXJ9Xvum1gMDEn5bQyGbxqdy+yuKpJBhDKVW8eEcpVeKpoc++ZsNqe5RO+LSunSTe2YVMSuYiPZFwp92BTFoqFNvC2WjJTr/aBJBWTQwYCl3MEs2PyxNnRoKYIOZbngzujVMrRCmHcM/USLbfJO50MNZ2iNMuOuWiophE1cet+2hDiS95xsQIpP0Tp112X36U7WdVCCZEXmX3ZZdhDmNUrLEGMamtALfu07xuDMNnJHcTy9ym0UEpA2O3QVRIi5DnRBmj2UP3+gI4SLioOCbKjIzMvOAS+kw3mqidmpwDrdFnl8GxyTy6gVe0KB8LMD2oH7IpnYxIP7GJavcFQZZKSmKzLGlddroCw7YsIYcOPYxK6dLnY3QHkqy1xugMiTMJeS8aorRDb8bB7ml6B4oEGQuzPeDEL84zLCrKxzz2vDcivaXpHAxp7XOJbZj/u128EvQnLYKMIszFlB9V9CdsJu8fkvvUGaL5SQYVi+0bXdrzJm5L0zwS442HfOKJy3j7B59FmFT0ZmDrNk3rZzs4TYWueCTqmmRN47ZC+lNQfsxgWHEIMkqEOM9G2F1Ib8YMp0JML2I4Bp0DEaYvShPZcybGIMBtBjLw3+zROGTg1tTn+/emSXJrgN7eJbUN+QsRB/5qiDmEzpwNMez7Yy3w9Y7F8qCEScz3PPse8g+7jH/CYVgy6OwRNW2tpGrJnu1RPDWgeLJH/uwANfCpPn8PKpGAE+exHjyFs91h7J5dxu+rk3zwPE7V4vm3PMoz/vNPYaC4b3sPSsPyB/awaDX4rZ1n439PnZLZJVmNiC3F63/hFdyRPkFfB/xFez9mIKAJw41AgdNSvPnR25h9nY3TCvn4W29iLtUkTCj8sRQ/f+Bj1NppPvN7NzF9t88j77+MRy/MgtIMlnKs/VzEsKJZvz3HiBbztMTXagXzVBLF7wH3KqX+BrlMXwH8+pf1qL5C4qOP/Oo/NBz7gnRsNzy2bnLInltE+yHqog2x78vsxTTAiyAIiQtpYsfE2h7tQoYe6qLsiG1hTJSJDi9gnVuHQhbzyfPEhxaFxGlb4DjyO9tGOzY6laT7kmvJPbiOsb5LcGAaq+0RJW36k0LuS6979CddejNJEvWA4aSL3YvJn+4S2yZ+0SHIiO9I6WSAe99JVErmPd7hA5RO+pccI1UyKWROUxjtZndIVMkRpW2cdg8WpmFkkha7NlazT1hKE82VxEEymUBpVxwmw1hY+PMzciI9n/yju0T5FNqWmUT202dFwNKx0fk0anUTY+SNE6VtTCWKyNr3JeE6IpevlHEJABFPjaFWN0XvLZsgWBzDCDX96SRBSpGoC4gh88Q2/lyJ8iMmqWpEf8qheoVi7NGY/W8NMbsD1p+bJU672D1oHlDse0eDyc8oWkfy5E+0UBcE/jucSjKoGIRJiEqazJam9LhB9ZaIub9TNPcKUqz8hKa1zyR+soL+pg7zf5okda5KlE8yHE/glyOskwZmoMmuSktw92qTqXt9Vl5gYwwNgoyFiiC1bjJ1z5CNn/Ip5zpEny2weYtL+VmbhGeKjD0WsnulxdxdHrrXp3/NrFQc3jxuUxOmDLyKS/6cJn9WvIMGvzSkc97h2dec4NjrruDvr4FX3nG3zJnqEcsvgb3viFCxxq52aRyqEGVsrLYnLUhLYaUT2F0tvj1TZZlJRjH9QxXhcKVSzH1swMeda4hu9Xnt2vOpHa9Q7GucNnz3f/tZ3vXa3+Gm7HliFH/9338PTysMBb+3/VxeUf4cC06VT//C7/OHjSv5jtwjTN9u8Ks7t2GriA9ffStORwieg8gmcqC5z+Vqdx2/4/AHv/aHvKd1He8+fRWJxzNkVjX9CUUvkWbvB3yMMGbnus/PP7/s8VWYPJ5KPBWpmL9QSj2IOFoq4Du11se/7Ef2FRJaqUtzGEAqD1NmMnMf69C4PE/5ziVBZClxk2ToyQyl3SW4ei+xJcgpc6IsUvz2CDIbx6hWH9XsEk1kMCslsVfOjUiZI8Y+/QFxr49RKcnOvNnG7uRo3jKD04lJLjWJMwm8kkCH80/UUWFMtufT2ZclcgwStUB8V7RG2wZOw6fyWMzuVS7NfTZTZ8eg2YaxEhN3bcr/HVtafs2WqDNblsB7AXN1G713inisgNEeEI5lsVarqH4fSkWslV2xJJ6syKxmpyntv5yL7aVkRmWZxJMlEd/caZBeGaHAshniTBLlhxidPoyVRVXa9zH8SNB2M+NSDZmmJJkoAtchbrYwxscwtqr4ly/ibHfQlkF/KkF6M6Q/YeG2Y5I7HrFr0j8yjpc3MQORXmEqxYE373Dh+6ZwWibb1yUYXDHgXCkLCz3Cvs35lxdZ+FCPwpNNsQQo5NGZBF7OHKkca5ymwhrEJHc9knWLze8Zkv2URaIZ4WcMMmtiKz3xZ0m6kxZWJ4PdGJB+uMqRJ2x6V0xQvdzCCGD8EY/sivi/mAPInVdUrxAbhDAJ51/qoFsGtXtyJA5p/KN9XDPE8GMsHeK0LAwvQpWLNPdZtPfHHH5jSOQoWnvB7glxM7ticu3bTvHRN91GOgcn7j1K44hi/qMBf9t8Jm/4xTfyaz/xIxx+fZedW0qgILITTHyuR2cxScI1R8itiDhpkz3XljllLFJA9Pq41SF6YYrBZJrYVuz72x7m0ha3fuos9wSXo03wR+v6D/zHn+W3/8sbiVA877P/hvG3JjFCTXve4vqfWsJWEW9szvBwa568KTSBfYkdbCXVndMRLbyzrQrf+kOfwVCaX1z9dg7v2+C1p1/Bd8w+SrCZgqu6ZO+xKT06YDCXkbmnUmTW3KdljflaJlo+JS0ErfVxrfXrtNZ/+PWUXECqmC8M9QW9UBVEDEviSaL3zKDDED1eIp4dJ5wswNQ4VsdHRZrEZhdWt6DRQtUaGD0Po+ehq3WIItxTWwTjGaLxnCSVdEoUkTtdSCUxCnlwXWkJpVMk7j1J4YFtYhMGiwU6iyn8rEH2XE+4Lq7NcCKF3Y2IEgaGH2EMAmLXwuwHhFmb7pyD04GJe5qSGPNZ4fC0R4x4P0Bvbl8S3IymKyLHojXa87GWtolS9kjCJRADsGQSLJNosoTRGaJaI5SdY6NOL2Of2xIJnFodvVtjOJ6UttfQExkcy0Jnkxi9IWqnRlTKSjLa3MXbP451eg3d78PyBrpUEHh2FEM2gx4OMbIZ4lKGcO+UIK0aLcxzG9j9GNOLiVxF/YjB5r/3aS84LL9IkagG0j5L2qy+UHPi30/ws698N9O/fJaxx3ys80kyKwrjbJqZD5iMPxxhVbuo3hC9b5bVl81w+odL8hpDsDuK4umY3oSJCmIa+02S96cJMhCbivpRg9wFj8K5kO0bLBqXafyiQ+26Ip0b59CZBOnju5SPS6tu+Vsc+uMK55urBDM++jtqBFmwupBdgdLjBqmzDiqCQUWReiBF9T1zVK9M0TiUILZh6yYxXAvTcOR1VZQvEHBrAIlnVcncukuYNPnAX95G83BM4UyE247Z/0frRK5i6l6fH3/XT3DhlTCczjLxsXUStZj+lIBCgrSBu9PDrftYLZ/qVRmGk2nxIIqBoUf/qjnUo6dR59dJbPVQGvy8w85L9vOJ2mVk1iD33Wu0DsXYz6vSfWmbnzv5Mq5wurzx+r/io6//Qxqv6hKmYBjbvO7sc1hwqvzG3Ht55/p1TNsNXvf2lzDn1AhKEb/yC3/KM77jUW4eX+LjG4dJmR6Wiim5faqfneSv3vAC/p/nfoKZYpPmfpc4adNatEZcIZHTeVpCPzWzsa9Gw7Gv61nKU404YWEMQ4j/IXwZYOruNt09WXJ3n4fxCmzVUKZBdGgWTEVnTwanE6FqLamC81nCiTzWThs8X2YXWqO7PZz1Jjrloh0T1ezK3CDWkmT4AoGBVlvgyoU0meNVdNLBv6xAshZhdgaE5Qy9mQQq0qQ2hoRZG7PnE5SSGH5Me3+G7LkeVsHC9DXtwzmyCQujO0RnUxibu5DPCGjBC0QwspSTOVJD5HCYn4Ltmsj+A0EpCZUk7loLolgS8WAIrsNgJoMxnibp+ehR8tKhMPST954mumwRq1onHi8QlJK4x9dFbHSyjDH0BVkXa9wnVog7HTiyH9XpEy+tSvvNtgWSPJoLxQkba6uJdh0BNWhN8u5TbH7/UQoXQtLbit0wz7AMC+8TYmLuwgBjGOGUQpTS/PpHvx2nYZA8CGFWM/aRAfGTBl7JJnv3OQD61+2hdtSme7nP5MdsWosm7aMBe96pMSJNbDn0ZlwmHxgSuSbdaRGlnPvEEGdpF6ucY/ozSao/2ad7tkD+nM/St5o415SZvidAG5CoheRP2dh9TdLxaCUCals5yhdG18IIquwVNWOPiKVD/ZBFajcmszrE6PnUrsvTWVCc+rcTTN0ds3vbGE5HJI+8Kz1Kby5idyL8nImKoPyoIne8RvdgkY2XzBCbYrMw88mAzR/xufm3HuPe/3QTdjfC9Ay0ZVB5uIUahgz25sjdv4rpZYhcA9UWSRrd7eFWhxjzM9DuED9xhlS4D28yQ+8FHTL2kG//iU/xmtJDdA5G9LXBgmXRigM6sWY1KNOLu/z04TuJDyuaUYoXzJ6gF7s85M3xmsW72AiK/PL3vZ3HBvP88u3v5a72ZXxv5T5+e+UFTGXa/MkHv4k7bn+M060xLn/uGc6+8wDfkXuMVpjkfdlZoqRF5II2TRQBfvZpSjDw9dsi+0bAxz77yzz/+l+BSCT8NSOXSKVQQURnziRn2+IDvziFsbaDVqBdGzPQuDsjC+J0Cp1JYjX6REVxujTrHXkey5THjIQt44mivN7yBiqRIG62ULmcVBlBCFs7KD8kHM/RnUuQXerTn07iTefpTzkU7zwnrYlYY5UK4Ps4vSF0uuSPB6hUkvzKFgDdm/fgF1wsx6RxKMn4J0dghVh4JbqUQwUR2jAE9dZqozpd0fpq9YlKaez6QM6Na0tLbGVLWmpak/7cBfRYUd5nwiXu9S8N4ZVlYT55nujQIubKFmZyBgyDaKaMMQgkMU9W0OdWUMkEat8ianMX7fkYmZG4ZxBAMU9QSWN2fIyezGaiYgqz1UftNtBKMf3OMwyuWcQMYoqnFf0JA6flo2JNbBssvySL/TmY/kyPOOEzLNloUxG5Bmd+wuTwb3fYuaZC9ngBgJ3rbcpPRnQPGrT2KLwjA4r3Jmn/VA3eVya7EpBc77B5ewm7o0ntRgyLJirUBFdMkVpqkVhrMfMLCm/Gp3bUIbENc3f22b06hdPRmK7C8jSp7YCl41NQ8tm7uE3v0zLDGlQUyV3N5Gc1difAKznYfcgfb4p8UKuHlyuw5101VKPD4Og0ufs3iccL4va5USZIicOq045AmWTXRAVh9TsjrC2Lic9F5N61ytl/u8jcm2LuS9zEyosU2dMWTptLRnxGqLG7EcMjU2LQdmYTXcihzy2hXBd1/JyojmuNOTPJcCKDu9lhuFMgs+jz8V99Fu/a+xx6R32+++oHeP9bb8NtiXr5O37td2jGNmWzy4/82Wu47gXHWWqXiP78mQyLisZVIZXPWXzkV36X/zmc4NxwnO8v3csT3iwpK+BbKk8Q36w4066w281wx8RpnvETZ+nHJvNuDT8/eh/TmijrYAx9IbE+TfG12iL7ZxOMUuongb8ewZO/bkNbBooYws9fCdowUDoiuxYTT5ZQF9YxGl0wTayWhz+eElJhDLrVIbhqL0HeJrneI05a2Bd2iCZLmMub0hYDEcOcHccvJbBbHkYyAbkMNJroWl2qnb1zqEoZGh16hwuYvkYFEaav6c46lB6pi4pzvQkG6Fpd0Dx90RxThbyAEYp5aLbJnG4IHDrlMP7hbYFTG4Yg2bo9tGOhGg1UIHBqXS4SFZJiD9CVVp8/nsW9UCWq5DBbfaK90xgnl1FhiI4iVLOLzmVQWmNkMyIAOtrZhlfvx97pEHd7mCeX0YC5USeu1QW+7fmCGgtDVKsjrcihJ541QSD2CLt14qkcVhyjWl3iSh6z6+FN5XAcC6PvEycc7LZAcDPrMVOfakiFFoQYmSQTDzr0xi3O/ECC+Q9pepMmiaZmWIE9M7uo14cMtpJcyI0z9aw1XlC6nw9edZTMZzMMxzWJ40m8EuTfWkIbmt60RXJD4bQ0nUVFf9IiuQNOOyByTc6/osy+v9iif6RCbIlbqF/UbN6aorsn4vAbW5z+0QKJHcXOjSalxQbdgcvyk9OMRcKJSdS1EFabAWY/YLgvieFDUE7hrNQhmSC3EtG4qkTp7j5R0iScH8O6sEW4OEH5cZHqMT2D/INLTHgzWM0Bp36sQPF+A7elyR2rcfrfL3Dwd89y4j/twQgV+9/m0dqXwOlo3LUW9eeOUz42JHINutM2hTMh/sFpnDObqGwWrWOUbcmMLJOG/gCnMaRzsMChN3f5YP4oB862eeWvfYZ+7PKmD30zi89fZetjcxi+4mRQ5la3xesbl/GqV3yE+xr7+NvL/pLf+tlnYauIC70KqSt9/qB2I3995zNxa4p3pp9JogqTL14hbXgsN4t88No/5o57/y3XpJY4748z1BaH3U1+6KUfJ/Vyn3WvyN+YN5NcCOhWg6dpcUE2c1+D8VQqmEngAaXUw8BbgI9q/VUIyP4ShLYMsT0G2eGPJljZk3WCSgbHNKHVQc+Mj2Y1wi8xekMoF3HObBJfPkv7YJbMyHLZ3KqD4xBOlbB2RBgyzDgkVltiu+zYhIUURnIPan1XEGpbVYJDs8Lv6MeoICbMurjbA/xsWtprhhJzMYAoxj8whXNhR9pWnlgC6O2qzDA6I6ixoaBUlDaY6xBNCAIIJXOmMJ/A6vhoy8AvOCQ2ehh9T0Q7R9WOcWoJbZoMD5exk/twlmqXWnuq3kSPFVEDH5UTTo8aK2MdW0JPj8v9VEpacK02RiZN3OujOx1UMom6iGobK6OHGyK1UyrIrCj0sO8/ITL6nk88XUZZMU5jiLG+O1JIaBPvnSK7EuG0xTeGwZDas+eoPn/I3jf6eHmT8cU668+uoCJoXB3xAzfdy8ONOc7sjPFdhx/m7Nw4j25OU+ulSTgBmeWY8UcDgrQpwqK7fXavLzAsw/ZtRQwfzL4MnRONiChp0pt0mHwgpHn9uIhm9mMWPtSmfSBD45Aid8YkKKXY984hsW1w8/94gL++/xawNHsu36B1bJbsRohT91BBDKbCG0sRW4pkLcLe7YoWXMKlN2WSrMZgmiR2PbySQ3d+ET9jUDouScc9vk48M0aUMAmnMpQeN4gtSO4GBONZ9r2jy9Kr9rP/HQOCtEX9cJKX/7uP8+aPfBOJWg5roLEbA2ytsbsJzEaf9nVlSqsuejiUDYFhiE5eLos/nccchiR3PM69PE82W2f5P9mMWR0SRp0ffMGn+NjWYQZXDnjF0Ye5yW1y12CMQJsUjR6vGH+Avx/M8EB1no3dAoljSXp7Avbv3eKjL/tdpk2b5TDkV9dewg2FC9zdPkivl+CkXyDo2XyidZQPnjlK9uohrzv7HII7y5SPBzznd+4hf9LAejTH8HnDL14CvoyLy9P3Uk9nPBWpmF8CDgB/AvwQcEYp9RtKqX1f5mP7igx9UQDzi2Yx3TkXPRR/eOKYOGkT2wqnLYuYTieIx0tYnQCnFdE8mMKbyeEdmCSaKWPVugQzRchliW0DbyYn7Sjbxjq/IYnNcdCN5qXjMIYR7vaAxGpbrJTjGLs38qFJJWF6HFUuocYrGF4kPB3bFmKn1pfMxS4u2iqVkioHiAtptKkYTqTk9RyLyDVRXoC5ukvqTE0QXnGMV3IJM7JXUakkKp8ldc8ZmVe5NgwGoGP8QzMElfTnT1oci86aaaLPr2KUCtLuimMBS/T6GOkUmCZqalwIosmEVFQLszA7hc5nxIBtblKUoUezGGu3jdkaYDS6xK2OgAgMA2uzidsKSTy2hD+ewds3QeFUl/RDSdaem6I7pagdr2Au9sisAobmLx++mdqbFnjR/mO85/yVPHD/QXSs6CznsT5YpL1oQAyZB1ZIbHRQfkh7L6INdkTmUf3ZGLcV4zZChkWb0v3bpJbatPYY1I8oqleZQr4FSsdj0psx1SsThGmLpRe7vOPktSjPoDzZ4sK5SeJvrdM4YDGYSLDywiyNI2nsTkDl4SZRQrH9zAqEId5iiURDE7mK8z8wTW8mgTZgUDYY/8Q6g3EXw4+pPm8PQTGBCjVRwsDua8Ye7hAmDezz27QPpJn7RJeNZyTxiiaZrZC/+41v4rnPeoyln4gJMoreYpYo7WJ2PcJyiuzSQMRaczmUbaOSCcI9U2x+8wTnfthg6UUZgrzD9D0h106sYRgxv/+fv5vf+JUf4F1/djuNT0yRfijJp37rFt7WPsifb97G+//gOaQNj6G2+a9v+B42dgvcceA04dVdnF2bPznwNt7TvorXNy7jbc0bGUYWJ3tTPFid48bFJe7pHaQw1mVvchfXDbkjdY6j5S1yKzFewSSITQYTIpG0d3L3y7aOfHF8KYf8SqkXKKVOKaXOKqX+wz/y++9VSj0+ut2rlLrqS/6GRvFUUWQaEbvcAkKgCPyNUuq3v1wH9hUZF03HDEPaY+FIrsUwSG2PFu1QSI3mdovEdh9tKMK5MdRWFaPaxFqrknpiQ77kFVs+gRiifEocJ9sdnMYQFWl0vSELo1KwsilyMftniafGcNZbMgA3FVgGemQ+Zg5i6rdNizJzp492LTBF0iU4MgdRhNaxtJtyOYH5Wpa0zOIYXc7L/EcpzH4gx2oZaNMQAUjXFoTbxePq9kiutnGrQ7lfyIl5mWniLtfkZ9kM/p4xnJU6zkpdkqRS8jxTY+gglDZXHAsjXynUMMDI52TOMz2JVgpdrRNv7aA6A1SrQzAhcyzdbAlCL+HK+8lm0DtV0SdrdcS6YKoiJm+VHLGpINY4J1ZRUUztiiy55Yj0GgQ3dIkdzcwfOaQ3IyY+bZE67ZA73+eRX7gW73yOifs1fs/BbhmMPdhicO2A5R+LxYK63gbDICxETH/GJ7fYpPSkgDQiV13adJz//klalxVwG6JgkF2SNqfdi8meblO4fxOnrenM2rgNxdh7kyx8IKT32QqpsR7pPyuQXYlIrfUpnorpTSn6ky71K/NEtqJ0XDYTYsEckF32mL7bJ7nt0Zu0KB8b0j88TnLHw2oNKT3ZweoGbL1myO41Fts3aYbjSdIX2qAUxXvX8SoJFt62TvHhKm7VI3uhz2N/cBVRyyG2ob1oESWFDGt2fIYVV7Tj0i5qcozuLXtZekma5tGIn7zhLqbvCVChZutmi7uX9uH7ljh5FoSnlKiD29D4OYP3b1/JzaXzeHl4oj9HyvDIbMXk7kly9wevIvWpDKkt+MxggTefuJU3fOa5vPWjz6TlJfjE8SO0+glO1Cb4hfITvGDuBC/JHGOk/sRUokVr0aB+xKARpnj+t36O2Iaby0tP39LyJSJaKqVM4PXAtwCXAa9USl32RQ+7ADxba30l8GvAH31p383n46nMYH4K+EGgCrwZ+DmtdaCUMoAzwM9/uQ7uKy20UqgvrFxGkOWgksZueKhCXsQfQdwuBwFWzyZKW0SXL9CdczA9jRHC2F1rBHNlOgtJ8i2fKGOLCZnjEKZtDD++NAjHtqXNNBhiLvcvWSwHUxOYXoQ3kcH0IgxPZPZjC9ntD4bCQ3BsjEYPozMkWpxEDUP5eX8AljmyIhgx9ts94skisW1gDkJ0IYO5VgXXIZguoGIwRrMc7dioTFrUBAwl7TcA15GkMRgSF9IYq9uY5QzBbBHrySWZp9i2IObOLGHMTEEYyaxozyx6eQMdhBiFHLREPVopBcUCyrHRmQRstHEu7BLvVDHGyuh0CmoNVD6LbrfRBxdQ2w0oFWBrh7CYwqr36M+lcBtiuIZlYT9ylvFjDo3nHcAMNNFKGmN6yNrtSUwPxh8OaRyyOPWjCYpTbayHSjQOK9wVh/GHQog0M2+12b7BYu0lSew7arSOFzn0x21UEJF9S57ugmLPewN2rnWpfHKbaKzA5H2KIGOSWw0Zv69LfzFLbz5DZ97EaQpHKr0Z0DjsUD4WsXWTyeXPXKHxrv10z+cwvYj02QaxazMoG1SeCFEavAJYnsZ+cgmUQerkLv5MAXMYYg6gP5MivRXirDWxUy67NxSxvCS5s338ooP9SZvJTzdZ/6YCRhChdupUv2Uf5QfrpE7XoNejefN+Co/W8KZyYm2wahFkYDgTUjohjHnDj4htxYWXFjAHMDjgoQNNotjBWEvzugdvZ0+k2b3KwW5D4Z1J1r4lor1Xk9pQVK/R2C3Ff/qudxJoi+0wRxBbfMcPfJqX5R/kzdVn8S2/+Pc81p5lOtFi0m1xY+ocG2GR/3jlh+lECX73ky/kmvIaW5+egWsGtE8XecPe/dyePY4B3DSzTIRiuV/mGd/9MB9+9ApMNA/+xvVE+yBrPk0tMs2XskV2I3BWa30eQCn1duDbgEv0Eq31vV/w+M8CXzaH4qcyg6kg5Mp/oKCstY6VUi/+8hzWV1Y87+b/ggpjlI6+oIoBRjPArRsSzN4pHiNx2sEY+MIs7/Wx+gOswVBQTrkyKhYGefOWGXJnuhQfq9NfzGN3Q6ymoLdi18RZ3pYnH3rSygJJGv0B4VQBbRnEjineKn5E5JpoBX7OIrfsQatDtH+GKGHhnFglnh1HG4Z43Ld68pwJV1oY2zVIpyEMiSaLRK6J3RyiekNJPubIzXK7Q+9wGfQY1moVtnehVMRsDSRBJVx0No3q9UUZWhkY2w1IJjDXa4SHJokPzqFNAxXEmI2uEDkDOZEqn4NhADOTqJEIKIWc8HIcR2ZHti0zJtMUGZ1sZtRKFBsFXW+iDy0K2CKZIF7bQO2dw16tQxBgd3LS5lRKjNlsGx2GZJeH7F6TQoUw9ncufha6c7BztZAdJ//eZPuOLLYjPjXDqRBrEINlYA5jFt7fIUpZrOXKzD9jjZ0Ls/QnYPH9bVTfZ/e2ComGZveOWaKEwulo+mOK9Bb4h3PkTrdpHc6RvxAyGHNIhTGNgw7FUwGbt9oEUz7n/mY/RgSVR8AcxvQXCyy/HPb/2YAgY5E6sUNzzwymjxBjE6Jt5xdsElVNa58QFQdjJuklF22bFM4MaO9JYAwCBgdSZDZiWpflKJwX2LI7O07lM1v4MwWck2tgWWTPity11Q9oHHbJrEHxlM+F7wWnPiTMOhihZv15EXZ2gNdxUF2L/BmT9gEDciHz0zUWf71K7/VXYPXlOzH3QYMgBW47JFE3CRPwa4+9iLF8l/+w78Oc9Sb5u40ryVt9/n59H1orcskhbd9lxSpy185B5jMN2kGSstPjZ2//EAfcLR561hzbrSyJqiJv9jk+nKUXu3xn+SHu6h0gaw+5PL3O/lt3SKiAO7+3w75yjaVh+WlZX4Ro+SXLMDPA6hfcXwNu+t88/keBD3+pXvyL46kw+X/5f/O7E1/Kg1FKLQEdIAJCrfX1X/T7PPBXwDxy7L970W1zpPD8YmBHa335F/3da4CfRNp7H9Ra/4uqLhXGnxe+NLTAdRldFIbB7F1tjGqLweFJkmer0OmiJyvCAej2ZQEMI1KrXYz2gN4RGSC392dwmxFGoFGRlvYKYATxiKE+hLGSiEnWGgSzRZyTPcyeT+tInuyFHtowCLMOyZU2QTmFtiByDKL9M8SOCYpLEGFzfVeOxTSIyhlJUMMIY6IsM5aUjVdyyBzbRTu2VCS2JTMO30enXNInqngLRfSecez1BuFYFsOPUEOfaGwc5UeQdsUYTGvidF4SUBjhrrdFhTnpEGVcgumCQFYHocxKdmsY42OiXDBeQoWhqDNnM9DqEA9kPmQkk1LVuK7MpXZrQvAMI/HHMZUYwNk2xtSEbBAzCYhcwrRJ+nQDHUWCyPPFEM48vsRke4Lh7w+pVmdI1DUc7VL56xTWv9nC/bs0pbtFen/7WRWGUxA7Bqd+OE35EZPxpTpKp5i618T6aJGxuMva7Rn8YgJHaxL1iNzju3gLRYK0SXJ7SOnBAc2rypieJnZtig9XRQw1k6B2dZbYhuTZKgu1DF4lQZSISK90iV0Lq97Dn8px8I9CerMpnHbI2nfOXPKjAYimSpgrOySqPsMx0VQLkwbZ1Yjm0TzaUMQ2VD6zBUOP0ntXYX6KKJ+i9osDSr+ZlGThWtj1PoOrFzAijTmI2HjeGNN/t8qBPx+wdfsYfs6k8hmDpW912PtX23jzBRbep0if7BLnkqihz3A2z9hjEc6FXaLJIlvrAbs/pzEnBmitiAOD1BMJ7B/cpVYtkMkMcGODX97/ftpxkhWvxMH8Ln+zci1DzyZcTeMNCtgd6B3xWJypMoxsHlma5bZ95/kfT9zBD132WVaPT6I0/Jcffiu92OVEb4q//INvwW3G3PXfXkegTf7sd19MkFFc/T1PorXimeWzVIPMv2SZ+L+L+Ck/sjJSVrkYf6S1/sIW1z9G3vlHs5dS6nYkwTzjKb/6vzCe0gzmaY7btdZXf3FyGcW/BY5rra8CngP8nlLKGf3uz4AXfPEfjE7ityGq0EeB3/2XHMwLrv5lSTAGn3e4NNWl+cv2bSWMjSr0BiSfXCcuZiCbET6LL+x23e+jG02xQG61SZ1tkn9gE22AVpA8V8Nq9Annx0ApnLPbhBN5canc2hVHxYVpnKVd2ZWubJI/1sAYhhhegJc3qV9TZFiyKTxaw93uE6ZsYkfmRKrnyXH7YiFAEIg8S89HRRFxwmEwk8bwIzLHqzKf6Q8EAKAMWfBLWXnPYYR7RqornXRQQSzD/Bj8nI3Z7GFu1UUcszvEaA+h3RW5/1YHfyqHqrexdtpoU2EdX0adWkInHYzJcXQ2CbkMamkd1epJsm62JKHsnUPtmxfhy5EnjbTPDMgKACDcO41xamU0C7KJSlmxpN5tgGWQeWSdsJxCOY6QWAMR9NS+T/twke7bpokSUP7+FULfwhzE7Nw5w/pzC2w/fw7V6tHar8mctVBBTGGhxeBFLba+aZLda3Msv8giyDlUr84Qu+ButkFr2oumCIxe6RJbit1rMzSuKZM72yX75C6GH9I9XIb+gNUXZMmsB4TPkA1HdzFN9UqbtecqOvuyWDttwlIasxsIEg5w19qoCHLLEYnNHv1rF1FhjB4rsnZHipUXxyRrMZXHurg1f6ScHRM5SmwilEJNjKEGPlaty8TPRPRmEzhbHfyiQ39PHj9vEVsGVr3H9Ie35VrcrZNbCbE8TXo7YvauIZvfPCGmcMOY4WJRoOCGgZ8zcbY6EEZ4JZf1ly4w9pBi30SVhT8ysZcTzL5wmV/f/16ef/A4P3Hgbq6dXOPnf//HCLTF/zv2aQwVs7le5EX7jzF1+TZaQX8m5sjiJjOZFkWnzzP3n+Mzjx4m9EyOd6f4ued9gOuuP8ufbdzKPmeH/2/6TgYV6E0ZfKBfItAWvWnFX7z2v7EnVeXHD9/Dh7cu41hr6ouXgy9bKK2f0g2oaq2v/4LbF89P1oC5L7g/C2z8L6+n1JXIyOPbtNZfNuvOrzaipQaySgYhGaCOVCVorT+tlFr8R/7m/wF+U2vtjR6383/0yjFgSYLRSgm/RevRMNUC20IXshir29JWymXR27uoXFZgtdtVaXeVCqidGjoMSa+X8Au2EOLaPeJKGu/wFO56G2u1ir64eG7XUOWRoHV/gHIdWkeKaBOMQJKUisQXpHuohDmIsNtiamUOQoK5krhTGkrQZZ6P2qqhinkIAvqXTZJc72H0fWlLXRSNHIlb4toYu61L7TJv3wTaVNjVJoObFkmt9/Ens9jdAJ2wUa22VD2mgWq2wQ8Ir9yLCmOcU+voXl/mQmNZce2cSKMtC4WPPrci85kgIN7aIXzmldj1Aaxuok0TY2MHlc0Q7Z/BPL8hMxbPR69totIpzCfPobIZ9MY2OoowYy1KALZ9adZktYbohWk4cVYEPAFlGOQ/u8rw8BTV2xRnH5gnva4IsjHjD/ssvcSi9LjBhR9Z4MBbBU4cJU0GD5dYfG8TjDZbt+VY+GBIZ84hUYvpzBuc+g8ZZt5tEyVg+4YUxbMh6c+eJ1cqULtx7JI6tDH0CA7nGByepPxkxPIrNId/NaJ26ySFE13SGybtPUnS6wPaV42TO14nyiWIbZvMhQ5Ka6b+6BEG33QFRJrkWpvhdJYLL1eoKGTqLpPGAYPV5yUxPIPseTADTaIRo0boPOKYeGtH2p2X7SW97hHlkzgNHz9nk1tuCWm1lEO1+3ItlQok1roMp9MYoVgll58cECVtEqe26B+dQnWH6LRLf9wgpxTKtkmutEjetyPn/xMOxr6YfW9cYntlkV87+UMsvTjBs7/jFMf/51E6V2uGsc3zf+vnCNNgF+CXxu/m4fxjLC9UGMY21TDLvFPj/btX8dsL78E2Ig6nNzngbvMHy3dwTWmdnDngdRvP5YFH9/HT3/tB/vT1L+L+7n4+eP4ykjfX+JXVb+WbK8fZ9AtcXVpnx8v+Hy0V/+K4SGr+0sQDwAGl1B5gHfhu4Hu+8AFKqXng3cD3a61Pf6le+B+Lr7QEo4GPKaU08KZ/JDu/DngfkpGzwHdprf+54vIg8Eyl1K8DQ+BntdYPfPGDlFI/Dvw4wPz8PICw9y3jEiRZm0pkJCLhHcQJF/Mi/Nd1UPU20Z4pzFoXbVvC0Ugl0TtV+SKZpjy2VED1+mjTIHIN4mIGdXYV51TI8Kio3Q4vm8LuBFg7baJyBnOrgW60ZOdtQP6JGlE+SXc+RWbdAw2DMYcooTCSisRGByOw8coJkstNokJKCI9KSRIZqQ7rsSLJNVET8CeymL0EKoow6h2pdjJptGuhTENaXpU8KopxdnqQTJJ5fBt/sSznAVA7dWlpDUdVUyqJd3gGd7MtyC7bRpUK6HYXa2mbcM8k1pk1giNzOJ3+CGwQid9MMY+52kDbFnpuCmOrKuCBVgfT84mnx1HnV1H5nDxnvSmIOKVQ4xVoNIl3dkXHTSnUZlWSnh+iwojYNKXNdpEv5Pu4213s7RKmB05bk39gkzO/WSDtDqj8ucFgR2ysjQDcmkdqK8WF7ygQ5GNmPhWRPLmF0yqx8oIM+95eZ/sZJQYlWHj7Bu2rJ0DDyg8fpHAupnzvFlExw+CqeexuiFsXqZfsPecovarMyZ/Okn1Mkc3YEGu2b4nZfpbD4rvF7M3cbqJTSbyZHO75dZovvZrkTkBvT5bso1soneXgW0Qg9NRrXJJZj7de++e8t3kd70zeRPlYBBpaN8+Rf3gLb6GMO0LxRaZJPCLSetMjtJ5tQjkvn3EuSziWRQUx51+apfKEeMkExQR+wSa5MSAaL0rSmSqgYk12NRS1h2aL3edeRqJRJHf3eZrPXKDw0A7BnnHySz7dWZfZOwNed88rqDy6SuVjIb/Zeyn7PrIlrc8w5I0vvpp3vPm5qFhALe0jIROfMXnZ//txXvH//RyDcfjkoYO8/dY/4mcWP8ac1eQPt59LyvIxBwZveOeLeOGP3k83dLCtiOE9ZR45mGU+VSfWBh9fOsQNMytPcdn6v48vFZNfax2OyPEfBUzgLVrrY0qpV49+/0bgl4Ey8IYRaOl/GUd8qUJ9JXEmlVLTWusNpdQ48HHgNVrrT3/B718G3Aa8Ftg3esxVWuv26PeLwAe+cAajlHoSuAv4d8ANwDuAvf87suj111+vK+ELL93XSkl7TKlLZEsVRPT25Mjce04MuPoDuCh2mXQxd5vi7JhKyg6l0ZJdYmKk0DoY4h2cEr0mDcnHRVdLuw7KDySp1HvE+RTGRlXgwylHTL6mhcgZZVxUGBElbQw/wmwOiApJhmMudifC6vl45QSpC01Z3KP480x+z4N8Fm0q/PEsdmtI7FrEtiRU5/SGVCEJMUdTQSRzmoyL2R6gLiaDMIR0ijifIk46GD0fY7eBHg5RysA/Oo9zfkfe++gcMaoaotkK2jAwTy5LhWaZ6PUtOT6t0aOZiwAW4dJeYmoMNndF+BMwxsrE21WUbaEmxsDziesNgTfXG5JEoujz5m6ZNHE2gbG+S9xsoxypbtTkOM3rxjECzfpzofyIyd4fOs3peoXK7yYF+FBvE48XMEbOneFYjq2b02S2YpxWhDWICBMmqbM19PYuvTsuoztlklsO8HMm+Sfro2rYoL+QI3WuiU7IPq+7J0tmuYfyAm7462N8/DefQfHedcLpEmde5WA2LEpPQmo3whxGuE+uQjrFYH8Fr2SRXhvSm0mQe99jADRedjWl959AJRMMj87Sm7ZxW9La9XMmTicmtdolzLmjinQg0kSuw+Cq+VGLVTOsWORPdjGGPto20Y6FeXYdxkoM5vMMK7aIbO7EZM/3MFsDvNk8hh+LQkNKZm5WtScbsxNn4brLMI5fkM996BHPjNG8LIcRaAqP1WAwpHXjDPm7l+hfNUeYGgFNenIN+FmT+lGDIKux2+J3M5yKuOaKC3xz5TjPTJ3hfFDhvu5+5t0aCRWwGRT4408/h/SySW9fyPOueZIbc+f5r/e/iMR5F7cBz/nBz3G+W+HYQ3s4/+9f+1TWrIf+bxfnXHZG33jNv3lKj73zM7/0f/16T2d8Rc1gtNYbo393gPcgkLsvjB8G3q0lziJ47sP/zNOufcHffA75elee8jF9EaFSG0r4IrZJ5skdkY4fQXwv8jfMRpe4nCOarRDnRglmZEoWp2Xgj23jbrYxvRjTi/APTkMYEoylCaby4g6ZTVxCYQEYHakKjM1dCCJUGKEijdUcYvZ8sAys85ukVru4F6qYG3WpTtpdYcln0qh8ljgvZMewJBBj04uIHesSQk5FMb3r5wU91mihml3ilIM/nsbseqihyKsQxzLrGAwFBu2FxEmb/lWzwqifHsPZ6RKPF0Sg0/cFCef56FKOKGFhPHIKZVvQHxCMZWDPLLrVFlkZ0xTy6giYoLLyM9XzUJk0RiGPMT0JrosxOYbeNw+eT7S1I5VeoyUSOaPqUbekUqPZwqi2iKcr8togP49l8epPmNiVIYVXrnL8fQcpvCGLs7RL7FhEk6VLag5xJom1vM3s+zboTklbsTfl4LQDtG0xuO0wRqiZvHOb1IMXSG35xEkX1Wyj/JDNWy1Ov0qQSnHCZvsmA2OrjgoiPvSHzyKz5jE4NCEOnjGUnoDSIy2cukfkGJfOpenH5E60sWp9CvdvglIY5RKlh2tSOYchdtuj/JGzZJ7YJn2hTfHxJsOiQZSy6cy5hCkTpTWdOw7hXTYHSuHWPNzqkPSmT5S2CQtJuT78EBIuYSFF8rEVkjsBkQ1eVqqqOOWgIo293mD9BeMYfV/mfYORZ5IyMI5fILjuoPglzYwRFBM47YhELcSbyRHMV8idaoFtkVpqkl7pk3t4C68osHq3FVI8FXPw9y6QP6/Z884djvzaMo8+vJe3/PZLeNV/fC3nvXFOtCcxicmaA+6u7QNTM6xokisWd547yLs2r2N+ukZswo+9+v14scWxtSkSu0+v0KWKn9rtqy2+YhKMUiqtlMpe/D/wPODJL3rYCvDc0WMmgEPA+X/mqd+LeNmglDoIOAin55+MM8e/aCZ2sXoxlchyxNBdSEO7c4l3gesIlPai8vFAGO9GdyiIrJEtstEbSbV0RMbePbmOc2ZLZGTKOZxz20QJi+Fl0xitvpAeDQPWtlGB6HqRcFH15iV1ZxVFEEQQxpBMirKxZQpkuN0Tpn5KTMPiQhaj2sQ/OI0x8CWR2ULENDtDnLUG9ooQ6eJ8Sjg3lkWYS+Ds9lEdqSqIRpDt3kAEOD3x/bA267i1IXExRzjSKzN2mpKIDYN4Y4vhtXsksQ1DcaJMJiCKsB49i+oNUY6DCkKiUUswrjeIp8bQmaRUhL0eupAV4unGlixSlTwqjtH9PmY+h3IdAQMEgYAnMmlJeqWcnENbuEGqVJAWWyaNTrk47YjGTR5Bz2bl/jmCLGzeZuPvvVg12kJ67Q1EySCZICplmflEndTZOjs3Ki78lKZ9tITSsH2txXChyPCqReya7OCJY5pXV9j3l1XyZxWtwzlQisX3D+RcNNvUrovYvi6JOYzYffYUR376BKavhVRrGbLYDIb4+yZkHmiA2qmJArZtiwJ2wrn45cJcEmFTnU6MPIFMyg/UCNM26a0AFcPqC4oMKib1Iy7DssXWLWkufGeW1qLLYMzBLzgQaeKkQzg/RpixoZAjdg1ih0vH1J/PYnYDME2mP9nAnxKBS51JoPwIc98CqpDH2e7Qv2YOo9WnccAl8+AqSmucB89y5gctYseieesseqfK1jOy0OkSJBXtBQenNiCyFSQTpLaC0SYqYu+7fcY+tkzxsQbv/8k7OPvBfVTDLH+0+ixeOvkwt19znMLRGkpDKulzfXGFmXSLcN+A37n3BXzkoSuZ+yuLMM3TG1/HlslPV0wAdyulHgM+h8CJP6KUevXF/iHCOr1VKfUEcCfw/2qtqwBKqbcB9wGHlFJrSqkfHf3NW4C9o1bZ24EffEpaaqMP9B/g05VCxTE7txbIPbSBrpTkZ6kUDIbE5TwA4XgO7YzkWrwA1enhH5iSJNMfjDxdktI2c6VlpisFiKF/xQyJExu4D52TymC3iT9TEFOregOmxyGM8A/N4E0kMXpSUahaA386SzhVwD27I7bC1TrRRIHBtXtk4Zoaw1jbJp4sYVe7Mmex7VFVJu9PlAlMQXlZBv58iWg8h13toroi+XKR/EkoMji0OuC6eGMp/MUxjLNraNcU1eisVF8qnyXudFEHFnG3e5jrNczlbfB9wpmyzGaSCRl2FnLohI25fxEAI5Mmyrmo7oB4a5d4enykbOBgpFIwGGBs1dGnl9Ddnohh9gcYuw3i6XGss+uSELVGdYcj50sDnXSl4pkpg+vSOVigP2HxgqPHoG9SPKkZfzgiUQM0rD4/h7PakEq0VpdjdWxUHBPm5diTm4riR1IMSwb9cYv5D3dEdv+xJWktrdfQpYIoF5sKa6BJb/oA2LtdekcqxJMVKg8YzNxZx97t4RWV8E+WhwQFF/vcFl5BzNo6Cwns9QZqeRMqxUtSP5SLeOUE8WCAHivSvWUvOgwZTmfxJrMYPY/151WIXEXiQp3Ug0vMv3MdpxNTPOMT2WB3YOyRGG1AlFD4WQMMaBxO0jyUIkqIIGqQNFARxDZgmiMbbrlOqtcXxH8on5Bhf8IWxe1ujzhpk3rgAgD58764pfZCVLnIgT8TblRki+qCV5QWaXYtAAXD8RTZFanovaIl7d5ykTBjoVtt1HaNg799jFf9wIe5PLlG/a3z/MH//E7uKJzgZw58guGY5uV7HyHQJjuDDMW7ksx81ET5BsvfHeNPPU1ClxdDP8XbV1l8xQz5R8zT/0UTZzSUuvj/DaSy+cf+/pX/xM994Pv+jw5KqUstMj1afKOUjduM8RfHUFGMXWtIqyIyMHbq0t93TKxqV5JJpYBq97GfXCLaP4sxDNGqiBr66EJWuC9KESdthuOiBaXLeVgZ8WeUwtnu4O2p4DzcRAURZNMYfoxT88A2Be48N4HhSw0dTRYxd0WyxBgEJFd3haVvmlAqEKZtTMsgKqdld+tFGL0hOuUK4GDoo1o9zGqHaK6EX3ChmCB5riaLaqxFAUApqVxGg3KrH+IXHOyJMbRlorZrRBML6MUxzEGIykh1ZTWGKJAW1TDEavTR5Tyq1iKayKMeP4ORSqEnyzA7QWyb2Kt1dKuDMTcNzQ5MVNBbuzI/SSYFygwYUxPCPXJsgWNv7AgwYOiJ9UAug+r3Be028MU9dHWXYO8E2lDc8JMP87Gzhzn4Z320ZbB7bRoN7F6TECb8gQqpJ32U64py9E6DndvHSFZj6renqDwRYXcjOnM2iWbE0rdm2feX21CQzYe/bwLnyWVS9SYU8yRrEcOSzcZzFHv/1iC53sMfS1F+VK4LfzLL7Pu30fNTeCWHnWss9pwyKHzqPOHeKcqfuAB+IIRRy6B33SyZB1dl4c+YJGwb5UdkHlpDx5rkchN/Ikvs2sx8vCbAh8xIYNQ0KN63Sf/wOMUTXczWgLCUJkilCF1F5CqKnSFOJzci+MasvqhM/nyM6YOfh/YtC2SWezQuz5Gop6l8TiDrRq9P/5YDpI6La+nudx6hcl9NrnHLFE7Obg3mxwgn8ti7XYbzBfLnhHysQlC5HFY3YPxtp1COI2oMlTJBSo1UHHIMCyaptOjnfeT4UTb35Hn1vjOM/cc/5E3btwNgEvPn3/YGfvwt/5Zf+YG3YijN226aQjkxViKglOvzmn138XSKlKj4q7D/9RTiKybBfCXGJWFLkBZEpGnvSeK2Y6zmELW6KYtrrKXHP5aR+YylCMeymK6F8kP8hTLOaU/AAZu7qHwOnXJRTZFBYTDE6A6xMjbOdhe1WUXPTsC2wNPjdhdXz8q8A/Cm87JgxzFR2iF2zUu2zEZk4OUt1LQQDq1hTGp9G6UM4okiRmcosiG1LqbnE85WBNmTdEBrcdl0LHHpNBUq1iRW2+ikTTCZw6r10QkLbyyFW5Mhe5Sysao9zEFIeqvNYF+Z5MkttB/gPHhWzMnCWPxZ+gFhIYG9tI6RTIhWWtdDjQQ4ze2WQLz7g0u6Xsp1xCsnL22ScN801qlVkZxxHEkuWmPsmUOvb4k4ppMaqSyH4sMzHIrgYqeH9nxY3wZlSBstmyDI2GgL7vmL65g77mMM2hDD5N0+F76zSKIK2c1YhvfZtDD4mz3iYo7JT+4SjGWI3ATbN5rs/8NlEstpolIaI8pw4XsmmPtoD2unRevqHM7YAYxQk9roo0JNenMAWhSpjdVt3NND1NQEAN3ZAu6ZIareJL1VZXG5fKlVYnY9etfNYw5jDD8CQ+HWfYZHZzD8mEQ9QA892lePkTteh2aLsCR8p6CSJMhYuMUU9koV/IB4axdjokJqqSkVeCjIwNLjLYJCEneljj9XlGvqbJM465JdGS0hWuwDvLxBNogoPdpEdQdEpSzeFTOkTu+SvPc08eIMxtCn9GQX1WyLuvZ2jdSSIj4q+rnWuQ3iqTESjy6J+GsQkL+gCZdX8Y+M47/wKPnjTfqLOax+ROmJNt1b9hKkDex+TPVb9lG7Ap596Bglp8fPb97KILa5+zNH2XlThWv/5gzPr2zxTd/+IM9OrvNAby+vfcZH6ccOC06NPzh3B+e8iS/38vL50PxLiJZfVfGV1CL7ygr1BW2jizetUbEo0bYuywnaCaTVMhiitqpEuSTuEysYwwDVlQXY2WihCzlBac2OS3LpDQnmR1iDUoGwlMZueqIYMFYS8UaAXBYjlxEeTRBCb4C71hTklRehghirMcSp9UW6PdQkagFuIyC17eHlTenJuw5GZyT9EkMwXWBwdBprdRez2UfbJn4lLbvhVvcScsw+v43qDzB2mpgdn2BcIKuJ1TbGRhVjs4a9WhefFstAuxbubl/Oh2PLYH4YwJoQS42eh/3QaTmf7Q5qGKCTLtHKOno4mk9ZlpBMuz3i3SrRyjrBZE522QlXPGOiSB4/kB2uHnoCB0+4olfW6UI+iyoWiCeK8lmlUzKPmhqXaq6QQ61u0t2TZetmm81na7KrojigLQs2d1DrO+z53ccZe2xAernHcE8JbJPYtQWmG0XErk39SJJENSS5Axsv24u2TIyTy8x9rEdswfrtaRo3T1F+rMPGiwM2v8ujeTiD3Q0xu0PmPyaIKwp5cGwa149BFJFd9Wg8c46406H2/P00ryhCwqV92x7CQpL0iV2ZW5xcG20yFFYnYFhxMPsBav88+cerqE4f/6ZDoogdxNSOuFh9kRha/445SCYwZqcE5l1rCOCh3cHabKKGIXZrCJ6Ps97CHMZ0DxeJEhapnYAwqVCRoMuNUKp9bRnolItfcokdJbOhsTLG2jb+VE5QeMkE3kRa5pe7dYzNGmarT7h/hsZVecJ9M6PryCG5E2Dmc6TON8Rqoe+RuucM7okNtm/Jk/rEkxTvPEf2s8uoGA6+ucbfHzvE1ekVPnz2COuvHOfgm7bxZwvc/5PX8ezfei2/NvlpHvMLrA/yrHplHm/PcsEb42XzD/O87BNP31LDUyNZfgnlZJ62+EaC+SdCX9QbG7XJtGmw8ew86Q2fygMN0hu+ECwNQ9AxSqHHiljrNeLZcYxqSzTDlBIL5F4fq97D6HpSGVki04LrghcQJk0hXO7U0LYpPI5SAW+xBNkM4eWLMuz3fej1pTVkyjHGaQe/nMLwQ2JH+CpaKcz2iItiWQJA8H2ZmRgQ5GzMYcTwyDQMPYy+jxHG+PMldCYpSKEYGYrnM5BwMWot7GoXbZt4sznhyAyHYBoMrpglTNmoMBbC5si5kkxKpFwyaezT67IzXhDEHLmsaLaFEebCrCz6ji0Q48kxQb1l0piz09hrDfT6FtFkUYb6B+dEibk/ELXoTBo1XpHEM1EWeR3XQdfqGO0BdHsyKyrk0JvSNtPVOvgBubvP4xdjSEZsfdcQnbQxNkftt3QSlUxgPX4eo+eRWGoQFBKYzR7D8QT+RIaz35dnWIHUyR2mPt1i6q6qXBOzE1gbdWb+3kcbsH2TZjCdYvoDNlPvcAldhX1hR/glIErVUYRemCJ3XpSgzUFImFTs/Oj1lO/dIrkToKt1sneeJLYM4kwS5/5TItzZ9HDWm1hn1khUfeFjXVgTX6Eoxj2xQXuPg7YN0lsxsalEOmcrlpZifyDADjVSqjAMdKOJGngY7YEoUjgmdtsntTHAWWtg9kIiR2FEkLsQk2hGqFiuAdXukTxbJbXWkyF8EKCnx7AfPQ9r20SVLOYwwts3IYjFSFCRerQqadsQI790CiPSMDdJnHDEYfTHpojbXXR/gN3TGOkU/esW2X7xXklySZv8YzbD2CasC7AB3ydMmpdAEkll04tdTv3pEV6Yf5SXjz3AWz58B3/z688nrZ7uGcw3hvxfN3HgsulL//9CmHJuOcIIIoJSitgxiDMJSCWJi2kwDIJymnCmLJIwSmH4EcO5nHA2uqMvWast0GOlUFoz2FtCJ12CrIWxsi0e852B7OQB90IN3Wxhn1hF9/sy77EtqWzaomps1Xs4u33ipI0RxGhbCJxhQQbs4cI4xBGEEdFUmShlizKuAiOMCeYrBGMZ7I0maNCOhTEIpC3oh4T5BN5CkWimjLbkd+5Wl6iYgulxvPkSdifAXalL738sg+72MIoF2K3DzLi0qwZD1Pl1ScrJBFExhW61Jel1e+A4xJvbMn+pN4nrTUk6lgmeR3j1fszNOvGhRUFOrW1LOyyVIl4UEIWyLJGpSSVROzVJ1N0Rk/8i7DqVlIomitC+jw4CMksGmSccsp9K0z6QFU5Sf4BuC/GQKBLk3mCI1fHwZwv4WYPNm11S64qpe4ZSyQKq2gTPQ9Xb6GwKd7uLEcLe9/gMyiaJWsDgxxokGjFnfnKB5W8rQawFRDEYUr22QOOwsOvN1gA05JYCzvz4JMYIIq2HHs5WRzYj2Qy63cHsDAim8pBM4GyJRI2ybXEoHXqQTTP26W36Uwnyj2xLm26pQ3I3oHFtRa69IBJb41ZHLvp4xHXSWpB2CQflh3J92Bbm6RUS9QgVgdONSW70UX2P3oEiOpuif2gMY7tBPF1Bd7oC+pibRC/OYK5Vae1N4D6+JBugZILOkRL2dofyPVvYx1cIMw46l8Fdb4t9dsIi++Qu+97RpPdt1+Fftw+toHPbPrrTNr1Z0AZsPSPP+LevcHfzAK+45X4ufO801Tvm2LzVpnEgwaHvOcVSOOSR/iL953f4WPsK/rZ6Pdffdppn/Nz9TFtPc8/qGwnm6yxG0GRAzpLWDEuy00RrEudrkiiiCKPVB0PhbLQuST4MD06ghj7J45tC9AP0WFGEJ9MpGAoJMkoYKK3JXOjQfvZ+SSKWKdyaTk84HDMTMoOwbaKxUatIa8LxHEbfl0FsJUk0IuwRCzO4cSiBEWms9Zq4G6aSaNvA2WxDFOGc3cZqDIVQF2mRde8FGLst4qQNCFfGPreF4UVESQuj2oTVLdR2DbPeI8omUFpjLe+AUniT2c9XTr4vaLnNXbHMnZ5AZTOCuBolKjUuC0/cFkSbOrjn0kdgZNLgusS5JP6haewL8hrmTgNOCDpdJWV3qoLokt00CVcSCkCzLTOdQkbOZTIhlaChZIZjmhBrZt+5RLKqmfzIOvnjTaKxgiQnQ13yqAHQrTbGyjaxbdCdUZhD8IvQ2uty4bsqqDAm3N6RAXRepEa6+/JoJZuVyFZs3+Ay/jOa/JM1Cqc1w/GYKGFw+tVTeJfN0JtGFLGB7sEi6a2Q9qLNwoc9sVDYP4dRyKH8ALPeEXTiSJzUenJJNidRLJJAqaQkrmKeqJACyySzJG6XiceXCQsJhkWbzOpQyLhay79KQS4ratdRJICVdAqj1iFO2Kj1HUmKlkVvSkRV7U5IbJs0r58gfbpO64oSqdNVdK+P0R6Ie+lWk90bSwLXByofOSfiqzNlgrky2Sd2iVOuVFOzEzhrDZQfMFgoiMRQpIlXN+DYWTLvfxhnu0t6JyLzyZOM/e1x9ryrRumTy0zeIzpuFbfLh5cvwwgERj32aEz7uT3y9oDX7d5OyvS4Ze4CKcPntsJZHrznIB//k5u54X0//SVbSv7Z0DLffSq3r7b4xpD/nwqtuSjLr0dy9ZVH2oRpm/6Ui7NjwdCje/k4meNVdD6D8kOsnRZxOUfizK5UNvm07KgtCzZ3RUqm22RweBKnNsTcqApXY2Ke5K5PeNkCsW3iPr4kA2rHQQ2G0k7SGrPZh2YbPV6Sw7QMzM6QyE2jLXVJJ60/bpHaicjev4weDC/J5Fu7HUFQxRrSAkJwz+4Ql7LojItfTpBsJTB2W4RzFWnnRRHW8WXCyxaIZiuY1Y6QJW1LeC5+TDg3Jq2ZOJYWm+tCOiV6bOOjWZMXSMts5KSpdurEkxWi6b2SPEY/Dy6bx2x7giqbLhHkhDODbROXsxitvhAsgwDdbMvO+tQF4isOYPihkDpTSbEPaHdQngduGl3IoZfXMJLCCcIPUMmEGK8NBlQ+swm+jxqYmANfhDKzKYF2DwayYCcTkEyQuFBnpp2hvS+NeyykdtTGmww5991F5srX0S9a5B7aQGlN7WiZIAtBzkJbkNnQbD9zjMxmSPmRFmOf8ajfOE7puCw0i++ucurHSxx+QxIj0KTO1nEaGayNGv6ecZwTq0S1Oo0X7CO2FcMSzL9nG+KY6LJFzEYfVWtI8gxD4nFZ0M2z6/Ru3Uf6RFU4UjtDnNMbOBcs/D3j4NgE5TT2iig+6FpdVKoNg7jRxMhloN/HOj8g7nRkd5pMgAZroDG8iDBrk73Qk+QTIdp56ZTMxybH8OdKjP/9lkD008JritMJjJPLso+bmxQ0putirG2D6+AdmWb7BpvZ9qKQTpMJIeGGIarTJ3VsCFPjYmGRcjCDFLFt8ucH3sHH+4u8p3Yt6Qia+xUqVqTvTcNeuCazzLu+/VkEYxnW/b14RZfUq1v80IvuY8UrPf3rzddgfCPB/BOhTfMSdFDFWpKM1nhlByPQhKU0VhCSOV7Fn84TuyaJM7vQ62H0+pe8OFQYoztd9MGFS86QzVtmyD/ZwJ/IYG4ZYovcGaHMtoUnMbx2D4lHl9BJF9UJ8Wfy8qXVYFcbBOUUdrVHUEljeBFWR0zLtKXwihZ2X+M2Rj4rpQJmoy/KtkFadr0AA1lghwcnsDoBWkFipUU4lsXaCLGqXYKJrLQ37BGvJZckziTRxYwM7Hc6EMUM9pWxivnRydNoTzzYVTEv1VMcE82NycB4tyctG8tE+SH25pBopoxZ7aBqLUzblAoyijC3GgT5Sezz2xCGGK7IumjHhoSNLmVRazuo/fMYW3XiegNVLIyUrF3iwRCzXJIKJ9ZSvWQz0GhKm8w0IOGIzE+9KQCBah3lOkSzBexacKmiVLYli5oyZK6UTZBdGjAccxl/2KN8zKC9B8K0Se6eCzA6H4PpCCNQJLcGbNyWJlFTDMua/JKQdle+fQJtwdyHmmw/o0Ci4bL33R5hOYPVE8UEv+xiDHJs3ZxgrlHB1JrUTkhyqSUEy3xWOCitPv50FrfelKrGMDC2qsSdLkYhT/p0He1adA6XiK8skX+yznAmR2K9DbOTWL1AiKi5jCRq0xTzt0wavEDQfa6LMVZB9/qoOCa1E2NEGmMY4HY9glIK42yd7PmkcL7CEDUzCd0+zpMNKBXo37AXtzrE3KyhXFGRGNxygNSjq5euHe37qHwWu+kxHLOlErcsQRMmXIYLBRIbHTaeWya/9AXzm0lRHPjRO36AV3/4I/zSbe/nb/ddy/XFFR58xWGINSsfn+XVH/4kP3L8N57eheUfja/O9tdTiW8kmP9NXEwIkmAERdaZMSme9mWYPpIXcbY7hKW0LFaxhkJWWmdbdbEPzufAD1HDmP7eAvnjLQazOaxhRDhXkYU77WC1hvIF8nysTgCFvPjPI5BppSFMGNiVIvYj52B+Cqs1pLs3R+5YDW0bdGdd8md6Uum0u7KTV4r+vhKppeaotZYnzNokTm/jT0v7BsDe7V1CqoTTRcxWH3urTe9wBRVqUnUxmgrzLto2CGZSpNZ6qL5P8pEl4qkxIR0WkthVV3a3vT6kU3gLRezGUGRuMmmpZPIZ4qSNubKDeZEzNPQwNmtS/ZTzRNkE7mdPoQGVzUCsZTjcGqANE3V2VVo5dVkM1ah9SDoF7Q7mxBgEAfHOrtgymyb0evJcCVeSyUUARBiKEGmrAwkXZ7Mt2lsTZWm1ua5om1l9ASDsthhcPYPT8HHWm3SuHCd3ISB2FHq8RH8xx6BkQiIkfdZGDUP2v6ND+0CG2Y+10aaJdk0mPzdEW4o4ZZM/FxC7BkYQs/zCFFN3B2y8cor0ukYFKVHPPnkOVSyQXGmhl1bhwCJhPoG91UYNfOx7j1N9xdU4XU32EyeIu12MbPYSg181OmRPC0mye7BI5kwTtVUToub5VdkcbWwLP6vdFWfRYl7mV+XiyCfIxr9uH+6pLYYlg7EHmgIKsQzMYSizsJUtQeAe3Yu53aJ/5QzJz5yge3mFzJmmvP92h42XL1I8mSf10BJLP36A2bv6tPckCdIKe2RGduE1r4XX/J98i38TgFcdHN39Bw5Wv/5/8oRf+tB8zSaYb8xg/rn4os+9cEGQWvZ2l6icQWdEDsVsDqRPPV5GWybRWA7/oPhJxOMFtGEQZZMktgcQx6SWmqgwxqu4DGfzWNWe9MozMhcwex6DPUWirEM0WcLZ6mAOAuGedPswP4WqtTAaPSJXWmODCZHNMAaBDPTnx4lzSbRtkXpkWRKOF2CtVUGDvyCtK3e7j9kdChelPxDrZVOhLYtgKk/6xC6mF+NP5wnTtuhUrbcF5pqyxXd9ooyKY5QfEqQt4vES2rakMkg6mINRwrUs6fEPPYJiUk5qLiO74c3dkWbbSNzSsQQmm8/B4gy63RHfmPZQWoF9D0xT5HRcF13MokpFdK9PvC6aXAQBcaP5eaHPUZIinRS5HkOhE7Ykqdkp6PVFCWD0uWrXRrX7krg8Twh+gyFxo4lutUmudqV9NxiSvfscqZM7pD95An3qPMk7n6Ty9xtMfdykdDKgu1/gudmzPYxaG7PVJ3Yttm5MoA1FdyEp8jI3mGhTMXV3QP2wzfz7m4x9dInERodETdN66bVQzNO8qgxRRPW6gkgY+T6YJkYmTfnBGtnjdVSlRPN7bhREXCQWyHowYDidxdhpkvnkKQYL+Uuupcp1UcWCcI9ME2OiIotffyDnupSFVAqddHHXWmx8xwITd21BDFvPLEj7su9DamSLvTCNcWYVdEzq1C7eLYfJHq/JsN9UqJlJZj68Q/KzZ4inxpi836e1L0npvk3ae2H4igbhd9Wflq/6v2rET/H2VRbfSDD/u1AKKR/0pZlMYrMHCsJKWqC8QYTqeQRjafpXz0kFsLkju0VT0T8yjhoEMtRUo+eMNNoysHe7pJ/Yxt3qoGoNkZtvtIhKaVTfGzkOigghSqGCCGOzRjRTFnXj0RA5f6KFN5MTf4+Tm8JQt0y8okucdIgKSZnhRBH0+6MZQg2rF2AEMUEpKRBZpURdwAuwmgO8iRRRwmS4t4y7XMOu9zHCGBVroozMIezdLkZX5EnipLDJ3YYnCsGmQk+NSZXSGV6SxVeuC8U8zpb41qOUzKiUgqEn0OikQ+xa4qRZyAiybs8scdIW9NrqhsC6HVscRg0lWmyWoKrUSCBUjxBQmIYkh35fFtN2VyqpIBTez8gQTXs+enFGhqrdAcbKFvT7qEpJ7A4cG+U4GGlJhIYXEKZF80sHoXjPjAQdtR+gG00Kd54l9cQGuSdliD6YSdG+QWzQhxWHwaSmP25jeprWHpt9/3MJe6VK7Bok6xrtmuhynqCUIlWNxPmy1iB/qkP9ldcx9vebmN0h3aunibMJgsNzgmBLCvQ4vRWw+v0H6N68RzTZTJPkmV3isTx6YZrk+SYXFb91GMp14AcjeaKmwNxHKDRjEIDv07ymjD+RZfoj2wK57w2pPD6geeMkYT5BOFWAdAqj0ye8bIGV75pDZ5I49SGtKyt055NSHe5Uodmm9+xDRDkXZ6dPeisgqmRJbkP599PUNvJP33f+Xym+wYP5egyt/2EFM5qpeDkTqzXE6AxRA4+4KCzsxEZPMPYFsQl211poS9G5TAaGKtaywx9L449lZFE3lCSEYl4Y78kE5mYd3WiSOr2LXR/Ibj2IRMQSMPo+/nhGBrmujVHvMKxY9CumLJ7NFjTbuPUh1laTMG0T5VPoUkEQVZZAf71yAq/kYPUDvLKw6tXARzW7IqM/kp6JTUU4kZdjVgqvaAuSS+tLtsqqM5B2YqePX3BFqgbEzTAeQXezGeHNZOV5CEKM9oAokxBy6QjlpfxQ5i8nltH5rBA1Ew4qiDCbfcxjF2DPrFQ6mTTG+TV5rYGQLQFJWJn0JUn/i9IiqiAwXmwL2h15jihGpZKScGbGLyHodDYlPKV8Du3YomGWTEj1YxiohEtQSYsXzkVB0cmKLMg6RhlKnlepEbjBoL+/xKBskLt/lf7BMrGlKB2DcKT1Fblw4VWLoAzS51t05hVBziUoJrF3eySXW+jtKtoPMLYbFM4M2P6maerXlLC7IWvfnKe1L8HgyjmMWpu43sS5+xizbz5GarUrgqEj2LXR6qPOrch1BFLlmYZAk6PoUlWDJQrGemsXfzyDLmbJH2th9kO51oIQggBnuYrTirDPbIj+m+8LxH6jSWZDo6pNjKVNjFCTPdejP50UEEE6RXvexOx6XHh5AXejzfZNWbrXDLHrA7Knv8Y7+Ro590/l9lUW30gw/0wYQfT5O1oqj4v/j9MJwukig5kMYcqit0cqiuGBMWHe9wYkNgfkHlhDuxZeUQb/vdkEfkG8VXTKFZmWIBRb3yAUiY7pcWkHDX0BG3gBhh8T7JskKMuipifKxAlH4L0hVD6xLItiqTCSrBHiYuJCDbM7JM4lZJ4ShHQPldC2gVvzRDZmGDGcyRFM5tCFDFHWwa71SZyrktzoYrYHBFkT99iKVFaVvCzSSsAPGEoEMrMp3J0ecTFDUBJNKK+SIFyckDbLxJgsvvWmtKqUwmyIAnWwb1JmWVpL0p0aF8M3rWVo3+6it6uoQg7VG6J3a+LAOD0uaKmdXVQiQVyrixzM0MOYmsDIZmTQPF6RnXjCRbc6knzMEXk1jqE/IMqnRrv0AFVvMzw8JZXSCPChs6nP2zMkXDGNS5rowVDafHVJrMbcDBzZLwt1KoU2Fe3LS/QrJu1Fhb9vgs6sLJymB4UzA8r3bDHzgU2ZiemY4VSGxb9aJ7HaxFlroJdW0edX8a/ZR/eOw5BMYK3XCJNg+hp3ucH8u7ep/NCy+KdEMergIsZ4Be/GA4S5xKXjIQih28O7+bDMWuYnBSgwJX46KpclziSlpRhGYpuwME2Qt/HHs0RZB7MzEO5SThB74XRJ3C8Pz6KDUEixgLdQxurHcr0UcmRONjCXN1GRpnftLHg+0+9fp3lZnvGHIurXljFCOPhjxzF2G1z10uNf3i/5v3o8RQ7MNyqYr51QUSRs5lF7yvAjSS6Rxs/KVDzKuVg7bZLrXZLn69i9CFVvkzhXFXTW4SnMrToYJoPFAqmTwtp2OhHZJ6v4+YuIKAtMUxZhQ3bJygtltuEKO16nXeydDmbXw/Dl2FTfw5uQRTx3uiWL5KgyIJvBbAkbnP5AlHyrHYxAE5VF7iV1bBujH4ig5nqT5IUGsWMIAKHWlzZcOkF/PstwNk/kGHhXzJN9dIvhhCya/nwJqo1L8v2q3saodzDWdnDWm/T3l0hs9ohSlgzeLUHNYY1spmt1dK2BP57B8CPCtAX1JuFkgWAigwoi/NmRXXQ6JarMpawkDMtC7V8AECHMTFoqCcsSG2B3ZJ+Qzcjsp9aQY+gNPi//E4zcLBOuEFJBYLJD8S5xHzwriDX4/I4+mRD168EQw4/Em8U0pYLp9uTfWgN1bkXe50g6f1gwCFOKyQdCmvsTNI/E9KYMOguK2uVJTr1mgu7RMabv8SGMsDsB2988A2FEXEiz/HPXYJSKWJ95HKsXEUxkQRlM/9kxCnedJRzLEp1fovd7s9QPmZBO0t2XZ/t5c2zc5mA9cBLlR0SVHNF0RVq+ay2at+9l/Tk5Nl48LeThhIsu56Wtaxjg+QxvOQRrW6SWOzhbHaxaX/x7whB1YYPhfAFru4XV6FE7moAwxKskUKkU7mqDRC1geHBC2qSrG3Ru28egbJI+WUPnMjRvniJyIHuiTvmTK+gX1jn1368imhvjvvuOfPm/8P/a8Y0E83Uao5nJP/jRSKPM8GRxMnaaslv2InRJBClxbJTW9K6eAcsksdG9BMtNH9shmMzhtAJUpDGWt6RlMRSeSDiRJ6xkMLqe2PsOfFRDoMVG18Na2hYWv9YkT22LVUDSlsQ08AVpVZKkEE+WIJVE1VpCrtvsEmZsElVf2k5xLBWUYaAtE6vlg6loHy3RurxMUE5h+DHuVo/8Y7tEjoHOJHAaPmqUENrPOSAD4HxK4K0JV9pNgyGJjS7Gxg6RbaATNlE2QX9/Wdou/YFAimcn0KaB0fdJHFtHV0pY6zWcYysQhFjtoSz4Ix+XMJcQiX9DwcqmJNuLSCfXRR+Yl+fv9qSa6w9gdgJVKcn/DSX38znUWFlmJa2O+OI8ckqQd8GoirGtEWQ6Fu+XY2eI1zeljTeCIaceWpLkc7FKSrgCHojjzz+PF9Derymc9Ulu9Bn/6DJ73uNz0/c9itWH/iSklwwMX2ME0qoyvJBUNaJ91TjKD1l8V5Xzr1rALBVo7ndo700yPDCGmhxDj5cwvBAjn8PuhCz87Ta7t42R/dwKTk+T3IVz/+lqrnjnOYr/Y0M2D54P9SZGAL35mNiA3WsyLH33FOvfVCROJ2QeNlkm+dgKKpuhdSRPMJZGJyyipLQh1UQF557jYBh0D5eJEoL4c7dFmSAqpLF3pErtXTmFqpTJfOIY6a2QOJtgsJinvWCQ2QipX19h/WULTP20z5Hf3KB+NM0NN31ZbeO/MuJrNMF8RVkmf6XE9ddfr8vRi+SOgSy+I9Kl8iL8sRRhysCt+6Iu7Fho26SzL03+REsWn4vn1QsuSYiElbTwRnoDGVwPAzAN/KmcLKbWCHUVRcT7ZzFrwrjGNEY7YR8cG51yGU5nsTsB1kYdnUqi2l10KYeqNkWSZXJMlHN7PkZvSJxLiiNj2sJuDInSop4c2ybOTkfmLrkM2KY8R6XAYCaDisWeNkooCsdFfiQoJBlWbIxQk17qMJjNYPUj3OPrkM/KID7jog1xx7TPbMhO2HXpHx4ndaFB91CJ9PkWcdolSlii8NzsC9s80jLjcRz86Tx2vY/aqeMfnsXqBcSuhbXVlHmC1qjeUJKPH0hlAQIKMA2wTKJ8ErMtSdhY2xHE225DEkQkoo7EsZBgN+qftyDQWuT9g1CY5dUaOtYYCVf01EZoN9UbjqwLxKlTnzwnnxdgJJNE+2eIEhaDMYf8ozv403mc9aYkwEyaMz82xfhDMWjIP1GFakP4NpNjct2U0uxcnyZIw+xdHTZvyzJ1bxfj1Io8Rmvh5YxXoD/gxK9OcuBNAWZnpEjtuui0S+PqMsOSIkyBNRBJlemPVuW6bvXQ2TTr31Khsz8itWJiDaE/AVYfxh8NWX15SOG+BJPvW5IWbH+Ad+MBEqvyOcauxe7VKbQlfzf5uYjIVritEHdzJGsTxmjHYuPZOToHQ9IXLObfLZukYM849SNJIhfCFPQO++z5a5j8L+d4281/9DR9+/9l8aWwTM67k/rWmafmKPKRC7/3Dcvkr+VQWmO3PYK0gdUa0t9fIE7ZxEmL3qQBQSRzBC+4BJPt7yuAAfbJVUEvuQ7tI0WiQppgPIu93RVmvmGIJhNgXhCY7aWKqD8A1yHOp1DDgOSxDWE1Z1MM53L4+yclqbkuqlIiGMvg522RdgGpuIIIYlE0thp9vLKLijX9vQXIpgWlEsfSPgESGz2SSw2y5zuktgN6CxmMnaYMXs+2cRsB7UM50qfr2PWBcFdsk95iFqvewy/YGMNI5HFMEwxF6ol1tGGQqP7/7Z15vF1ldfe/a+8zz/fceUpukpsEAoQgYRKtMxBeKlp9xWqr2Ne2tvVVLOJcB2ptnevQymudrSJFq6I2QRRBFBDClAHIeOd5PvOw937eP56dm5tLCCG5N8kN+/v57M/d83nWueectZ/nWeu3KgeLkdmOtrdUxsiXMQfHUfEITjJCYNcAMjSOvaIZDMEYnsQOH5TskaqtJ/YLRezWeqyUK9RZqej3LFfAKFlgOxj9o9jLm5FswZ2jMnSejmHMFo/DNHTei63r7kilCo6DMzEFYiCGHh6Viq0lbyxHD8NZFgQDOqhB3No7SkFdDeZMkb1v9FFOGjzxzga63wb5dfUMv7qT7k9FMctQjQrjGwwqjbpSpyTjOvemXMXc1Uu8z2b5zX2U0yGa78nSfVVMS91UqjjRoJ4rmclSWdHAmR8a1KoOXf26N6b00Gn6rh7MMpQ2FMi3KqwI9F1Vx+AnTMqrm2j85jDZc8uc+Yl+lv3PFMoAs6KdUe+VQv0dQWq3F3T4cV2KyvM6CXVP6ax8pZg4O4I/ryg2QOeX9xPbOUZy+ziBiSJStXXOkghG9xC1O6ss+wWIBVZtDLsljZkr4/hBmdB2xwydX7cY2RjAcswT9O0+WSj9PzqaZYnhOZijxVVWBjBKFsV6Q4v+VRT51jBGxSbRY4PPwNc1rIdGAEJBIj0ZXb42nYJEjEprishQCaOoM+FlWicJEgnrHzXThNoaLbNSKGun4z4h46DH2ltrcVJR7Wy29+snYsPQ8xohP/6hGYJjJZ1X4jiU68JU0iGCfVNQKlNpjCGO0oq4RYeZc2p1BFzZwpzMUmyJYZQqKMPAmCnoErUBoXBO66zScmDPMGLr+jSFdm1XtTZCtCeHEw4SmK5q1eGWOOW1TZRW6nF/yeTw9Y7h6x1DKlq/Cr8fFY/owAARJFugXKfLNdudrWArAntH9ByHrXDiEdeRVHXxr+V6vss/MKV7iNmcHh6MRbQjceVjzLFpNxEzejA0uy6lFZ1HZnRibNXW/49sXldgHJvQX25D9FxLNAKmoevMT07jtDfqIbyynjvB1CHKYpo4kQDZM9MEUyVyy6D2UYOGnwaJPjJANQr134pQarYo1gqND1iU0wGoVJm5qI3Cxg7wmZQ2riI8UoJAgMjD3WQ7o6z8zE6IRyGbo1wfwUnH6X/9Sny5CpVVjVSTAYxlrZQ70oy+ol0Pkfr9NNwzzpobxuj4eYmmP5SpXpjDuaeG8fVBtv7obCj4iPygwvTZKewgmEVI7rdJPm4y0yn0vSLCwKtaUX4TO2TokPGaJIW2KI0/3YuvrKh/2Kb/T1fqnpVh6JLQ+SLKsii2x3XpCMsh+lAf5VowcyWMvf0YI1M03TlOvM8hvyzG1PsLhMfAZ9iH/06eLpzGUWTeENlh2Lhxo9q6detT9l9+wcf0EyuQOSNBcsckyufTNUEiAax4AGUIof0TOLHwbJVIKxnSiZSOQ7GjhsB0WcvpZ/Xku5QsGBnT8xGGQbUpgb93HJXNIfW1VOtj+HZ0I6ZJ5ZzloCDQN6kdioh+Cq9WIZ0ivyZN9P79lNcvJzCYRbJ5cs9rxagqQiNadLCwupbATFX3gKIhZCZH8YwmgmN6Yr9SF8XxC+G94/p4voSdjpNZE8Of1/ZXowbJXVmsWIBK0qdrnHdnkFIVJxHW+Tpt2qGYM0WoWhRX1WKHDB0uG/CRWRkh+WQGo3+U8vrlBHunUeEAUqyQW1dH7P4uss9fQfxBHYZMKEi5vYZg1zhYFtUVDfh3D+hJ6URUJxGWK3reJJnQcvyAWtWGMZHVNeGrtg5KqKvBCQX0RHa+oMU0x/NIWYdIq1gYKVZQw6P6HuWy7pmsXo7kSjjpGLK7B5a36KHO6YyeewFURs+XSSiIakgzc3aaStyg5c/3M5BNYt6appQWKpdmWfmeLCMvbyHz0jyR+6Kk9llE906BZTN1YSOpRyaQbJ7qigZ8UwXXUfioLq/HKFu67HQoiNWcxszpSXkpV6nWx9j/6jDxLsGwYOYMhxU/rmCUbabWRQhNOkR7skjFotSWJLy9H+JR8l+26O2rwxz3ExoTQpMw8cIy9b8JkFkpBCfBikLtDgt/3mbokiD1j1kEpioon+7VAxiD4zqnaGWrDnkfGNMh3oATj6BCPhy/gW+qgBMNMnJRnNTeKpnlfpq2DIByKJ7RRN/LfLz/lT/mrWvuWZwv+3GyIENkgUb1/MbXH9W5W/q/uKSGyDwHcxiezsHM549e+WnEUlTjJvfecv3s/hf98acJ98zoL/vIBNbKZnwjM1iNSVBQrgsS6dYlfmUqS2ldC6H9E5RW1BLa2Y/dXo85Mq11oCL6R4u+YarnrsQ/VUSmsnpIoX9cT6pXqvo8pfSPxZ4xVDAApmi9s7yFr28MlU7MlhoI7dP5IjhKh48WiqjaJFYypJUKpssY03mcRJhia4xK3CCxv4BRrGLHglhRH9l2P+LoIlPpreNkz0hr9eacLmtglGydHFquzkbjFZcldSRbxCQ0lMcYntQZ+NkCKpPTNo7nUD4fVk0IM1fFHM/oSXvTwIkGMabzVNp1blFgYFo75K5RLX5ZKOpqkMUSqlBA6mu1nVWdbe80pDGmXQVi0DbXRPBNF7FjQa3IcEA0sXtYqwCIoUOrg0EkFsXqaMR8vNtVBXDcc9zebWpO+HbQz8gfNWBFIHOmRWDUh68IpUYHO2qT2uan5WcDZM5vppg2SPRUyC4LMHWmouUeh2y7Se3jZYL9M3po1bKYuGI1tfdrYdCxS+uZXgud35tGZnLY9SlGLonTsnkYiiUKG9oIDeWR7kGkJjXr4MODRQw3eGTm3HqSj43hDAxhhMPMvHQ1wWmL1f/0OL985GxqH/TRcOcgu/6umTXfmCC7toZy0qCUFkKTimyHUI0rwiNCwyv6sb/QpCPDAnpYS8b157hwbhviQGgwS7kxBgJm0dIVThO6XMX0Sv3+REZtrIiBFYTabVm63mew+zX/cFzf6cViwRxMwzVHde6WgS8tKQfjDZEdB7+97Qbu/p/3HOJcAO7+2Q1k19YgFQvVWItvPKcn503BzJUITuiyxFLQORmhJ4YACPXPaBXZXb26IqM7VOSEAkg8hi+vczMI+FE+k9K6Vq0gG48gEzNIsUJgukylrQY7FabUmsAs2vjGMqh0gkpDFLNYJdQ1qYdz/DohkFxeh976DJ1B7jPIL4vq6DK0/lkw42AOT2nZlIrtTuCDUYX0/SPYyTDK0BF2xQY/RsWm2BohuyZBqc0VfVyeYvLMAAMvDpJr8mH0jeDU12DVRLCb05QuWq11rMoW+ZVxndA5nddq0w0J7GQYYzKH1ZQCIDCc0WWcZ0pucqCj5V5MQ/f+wq4Uje0W1HIcjIkZPZxlmqhUQuu0NQVxgn6k6iAFHZ1nTuaxx3XJ6lnnEgmjapP4RjM6Ig2gXMGoSSGGgbGsVQ+5TU5D0K8TIk2o2VUl3O+j5XcVooOKtl85mHkTs6Tvndg2hq+kyLYHqHmyQOtvHUYuMAnO6MJxdlyHPxMIkP7JDqxaXeG0/u5hOn5eovfKGrBszKGJWV0rpyFF5IlRpGrT+1dnuhF9WWKPDmIOTyLj0ziRIMFpi76rGzFqUqiGWgY3WYT2T9D3JzWYiQq+kqK8opa1n+li9AV1FOtNig1CzR6L4IxDvFtRu1ZLD/HJBiJ7p90kTROZydPzlk6c5loGX+Cn+2oDGR4n9y79cFWN6zD9wN4RonumCE8oci/PEe3J4s87ZK7Ms++1CZx9scX5Ap8yeHkwHs8WpSisqsEJ6Giial0Us6hlOHxTBd3bWFWnEwRrk/rDYztU29M65NZ2ZvMvzJkCRMMYfSOoVAwVDOD4DUJ7x7Tqs2XpqKdcnszKCJWUH1/vGEZF5/GocECXJhbBdEN+VW1S15txJ2zLy2v0uckYod4Znb1fE0Esh+SjoxgVh/w5zdgNCZywD7NkE8jq4bLxSxvJt4apxgz8MxUSuzNUk0GskBAZKuHLVbFqY0y/PUvdDl3nZOL5VUgmKDVH8T3eg7FzP/5sVScupqLE9kwTHipR6Kyl0tmEOVXA7BrCaqnB8RsEdg9i1cbw900gg2Oz9VecoJ9qfUzXmalN6pwX08BO6mRApz4JKR04oUI62TW5XZfqNSe1yKVYDk7/IMpRKEfNlnB2mup0QMD4pF4Kug4Qfh/SUIfT06+Hx0xTz8UYQsMDM4w9z0+5zqF6/STL/nIvVtggscegnNafk/6rdP33RG8Ff98E0a4Mq34wiVlWlFM+rTjslmuQeAxf3xhOZzv4fYyvD+uQ5MuWQ6VC4wNZLY1fsvSDRixI+x0Zxi/rABEGXr2MSmcjdns9Qy9OMbMyQKFV6c9EJECoO6DVGUyD1X/XQ3rrOLmWAOUzWkntLePPKxofKFONmcTv3IW/qIh+IUn6SQs7aOAkQpDLI8PjqGSU5nuLGOMzLN9SpG6rQeH8FdS+30c1apJr9bP/tSmmX9COGh3nRdffR+xXMRAhdt9+mr8RpOFhR6cFnM4o9APS0SxLjNNcg+HkERkqkV0RITgCKmBiFi2MqRwYBlZdnDvu/dARr9+04u8pticI7z1Q5lggENAVJctVlE9AOdjxABDAZ5oQDRKctok81k9pXSvBkRyF5Qn8QVM7pD1j+j6AZAo6Qk0prGQIo2xjjE5jN9dCwEd8TwYrGaTSECXfXEMgY2NUFL+8/8OHbe8V53yQyfNr8Q/NUGmrwSzZhCah0BzCsBTVqIG6I8L+11ngq7L661VyZ9UTHiogfv9stru/b5JSZz2hngK+3DROIE1gJIsyDKzVbfgmdMlmlUrgG5zSjrihVsvETM8gYxPIWSt16PfAtA4HL+rywVQtjEGtB4bPp4tguXNqlMta3Tno13MZ4Ebt6JoqRkM9lHTeiAI90e9iN9VgDkxgxKJaj8wQnf+xdhnZjjAXXb2NxmCWB67fSH7ET6JnJ5PXnUPjVi1KapZ1yLAvV8VuTOl5lXCYeJcfc2BC17RJp2az5pUIZt8IpBI0/7yP/Ppm6rZO4bQ1MHl2DF/n2dT8pgtfTz+yXGuepR+ZYWp9DaFJRWD3EGOXd9B68x6UZVG99kxIp9h7TZzOWzJU1jSTawsys0JYcfMw6V88yZOfW0nn1x0iw1VdIK+3iMSipO7pxRkbZ/ivN9Ly097Z7H1iUQYuqyPZbeMfC2MWqtTduhdp1GHVsX2KYm2aeDfEeks4K9u480tnUE3ByCVJ/LkEmRVC7Q6H+PqJY/oOLimWoPM4GjwHs0gMXxxFHIj1mih/GDNTBL+P/Jo0v/3pDc94/eauzx2yfdnFN2KH/QT2jUClglEXpbyygeCuQYrntOlekQh3/+I9R9W+TW3vgP4ZqhtWYhYtyjVB/H4/2c4o933/+me+wTy2bNfS5xe/8bMEp22Ck1pxoBozmeo0afpDkbHzQ4Rqi6T+O4bjt4num0aZJqVzlmGWLIoNQfz7Kviny9jpKEamRGAkq7PLQetbRSJIuYqKh3VosKmfmiXkw26rwag6+KYLWt8sm0MsnZnO1AykU/rp3s1xIZvTodnFEiS0oKZZdGVjHC1YCSCRiB5C9JtINIqkfKihEZ0LMzSG8XiXjhBMp5BsXkd3lcqU64I4fqFgBfnxlvXElymkLU3lxbUkuxT5Jh+hu6bxFRX5qzMk/jVAYKow28MyRzM6NymX122sWhjFEkTDFM5bTmTPOJgm0Qe62f83nay4ZZz6346w5/80YVQ7CE5bZNv9pHYX8Y/nSd6yFfvSc1DJOLGBKpUz2rAiPgrNChX0Ee8WnIBJ4PE+orKM5JMVCqtrMSs1rP1SmYGXJDArEJpQ1Dw6oQNLTBPrkrNpvC+rQ5BLOvk4e24jwRlFfNsoKuTXc22GgRoe1VVNW1Kknyjg6xmltK6FQL5C/b3jjD2/jvSOHCMXx6l2FhnuBCMXetafx6WFmq2Ee7rhOZhFIjqiCE5ZTJ4ZITpqEzQFf9cov/3pPx/T/eb3HDatfg+b93zqmNu3uf+Lx3ztkbj/e9o5XX7eR7DjAUbPN6l/xGHgJWGivZC6K0LsyTH2XNtAxy8cSvU6iMGfKRN/clJnwzsOVjSIkRfIl1DJMMowcFa1aJmcclUrFgQDqIAfc2QGqzmFfyQLVQu7MYmRL2N3NGHkyzqsub1R91jCYV0ZMx3DSgQJ9E7qwIBkHKulBl/PKM7Y+GyiJKCl7isVJJPVemMNdSjHQfpHtKhmS6MebqxUdah5qayHHg3BqCoevH8NTdsdkvfpp32jJoXTVKeHNk0TX0nR8P8iBPboPBECAR3xli8i43ntcCpVVDKOEw1Srg8RunsnmU3rSTw6gqqvoeO2GVQkQKE5RcPDDrGuPD1XxWm+r8rU2ggNQzMYsSjmvhFmLmnHqCrCj/Yw9NbVrLolS7E9QcMDWWbWxjBbVxEe1TlJ+fUxam9+lOG/PI/aHbrnbIUN1N5eXUSsWEIZOiIS0KHutkP8oQHsS9ogk0UlGnTl0EoFZRuUzm4jMFli5KIEM9cso/l3ENqTZ/CqVv1+l21mzrRp+kWQcsog88LionxWTxkUqCWY43I0eA5mkbj/P/+eK875IIGpMPn2MP79Iwv6o348zuVEcPsjH5tdP/+vPo9/BgptCrNikm1tYPnmIsWGIIltY1TaUkjVJt9ZQ1SEak0IHCguT2KFaoh1ZTGHxqisbdE9tYkpnTDpOFTSYUI9U7ocgGngJOKYYxldlGyqoKOZlEK6BiChhwSpVjGyJQKZop78b2vECQXwjWZwGt38I0BMUydWhoI4qThGyS3BnNfDeuLz6eGwXEEHZbQ1I8WydjRKkW0zabprnFxLHZnlQqGhAzvYQbbTpuUuITpQwmxvITxuMbohwLL9unyAGp9AzHqcySk9NFcooBrSSCaPYQq+gh8jGmH4YkHsBuLbRtiy7eMAXL7xo5QaIkjVpuX3unBdJKilbiSdQkWCxPdmKDdEGXrtatJP2JjZIpHRKVQiSnisij9n4Rucwm5IUPejx6leeCaVOMys8tP8s16mLm3DqEu74d2tBCYK9L6ygWXf3oNUItjT05h1NaQe00NbUrV1FKCj5xmVodXBG7ZmqdvuQ2xFZVmaxvuz9PxxnPzKOMt/rghMlgimAiT3++DooniXLl4PxuPZcmDY6LnOQ1991yHbK7//CQK5EImuEtXmJIG9I+Dz4U+FkVyJYKlK5twGon15qA9jxQIEEjECw1mcoB/aGxGlcEJ+lIHOu5nI6sgtt6aMOVDBWlaPr2dU5ws11mvhzwM6Y6CLi6UTSLaA2FrFgB17tMy+W2PGaGrQatUVC6YzWva/VNY9jVBQD4eBnkcandASL1Et8x/vs1BBP44fmu8tEhjKYNdEaLstR9efNZO8b4ziumYqCR9tv57BrnGVnKMtkC8z8LcbSO+yiP6hC+fJ/RSvPE9ryCnF5pGvaBvmjWbevvWjT3n/L7vwRiodtRTrA0ycbbDyu8OU1ySopEAGYN+fN+ArQHRQEe+p4BucwmpKIUpRvmA1yoDwmJZvwbJJ/uQxMpvWY1iK2H37KVy4kmXf2g2ppI58DAS08nWhSOmi1YQGs4xfVEfNk2EGXxijcWsJKxHEPziNaZpg25RW1uGfKhIZgtjuKegZxNrQSaRrmkJHakE/j6ckSzBC7GjwHIzHCWf/Gz4Ab9Dr5//l50kEmwlMlfBnykxe2kKiq0hkoIg5msFIBBEF+dVpojtGMCpVystrUT4htK0Xo7V+toSCCviwY36MdAIpW0jV0UNa8ZgWrBTBiYcwskAuj0pp7TaUmlVPxjT1D6Rt6zmYYgmnNoHsH9ASLqmEDkM2TVS+AMUiEgrpJMJkHHGU1jKrWkS7MpSaY9hh8GXLFDrTOH5h+HqD9M8UxCKEHx+i8NJlGIUKTtiv829miqipadq+NsOuG8/gjN1JpjZ18uA3//6Y3u9fPnDo8OoVP/owRlVRWFOh+Xc2RjlM2y+zOGGfjqCrjTO9NoJhQSBr48/aNNwzTvfr6mZ/CBO/76J47jJIxIls7QYRqk0JRs8P07y7W0v/pxME796BNNVTt2UKlU7R/tM8djKC2TWoe4+2zeDrV9N81xR2PETjzTv1/3LNcvyD00xf2ETiicwx2b1kcCWaTkc8B+NxUnnoPw7t3Wxa+z7KbSl8+QoAwaEMdkLn2JRX1WH7DeyQgT9nU17XTmA8jzGZ0+WMg36djZ8roSYmMZ06PX8RCMDgqHYYB5xMterWpHEOGZ4Q00SVyojfh1Ffi6qJY0xmUS2NqP4hXTQuHNIOpljSQz41cd0DqlpQLKAKRSiXGby6lfrHSrTcU2HX26M03ukjNGHR9L0gxTqgUiV/XivFtBvZV7WZ2JAk/ThknreO4IxN19uvh7cv7Hu+5dEbZ9dfcdPHqd9mI0rhm8wz+vw6REFoyiGQsbS6dW0SOxkm1qe17oyGOtTEFOEnh7Ha6vDZDhSL+PeP0DoYhLo0lYYYge4xrbIwMYU01FFaliT8xBBGyQfhEMV1zVhRk6bfZ7QKwJ5+XWIhECCzMk5iSw+x7uQhw62nK8o+PeVwPAfjcUqxede/zK5fdvGNlOvD+DNVqjETUTB+jo/ooCI4WdFJmcUK1bYa/MMZmJzG7mzF7BuGFW2oqo1Uq7qYWyKhJ/ZjAQL7R7VjmTcs4RQKs85GwiFUPIrymzAxhVg2tDbh9A5g1NdqZ2II4vNDtqATV0EPp7UkKLclKddA95VBaC9yxt90I+mULmkgQt+mGKHXC797+Q2s+8DnGb+4jge/cWw9lONhbrj8xX/2OeL9VcJ9Gfa8KU3nzQUK57XjmBDbNUn9ndM6ek8pXd3T78PXPcLQq1bQ/NNurTxdrqAyWQK9WltOjenwbQJ+wj3T4ChkOkdlZQPTqwM03TmOWLaOyqutATGYuaCZxI5J3WM8TZ/sD2VpJlEeDZ6D8ThlmR85t+bjn6eadAiPGhRagogVRNoixJ+YcmvGx/DtG4T6WpxdXRjtLSCC1ZzSczFAYM+QHv4KBNzKlKKXOb0Yo61ZZ8MnQph9Yzq8eWoGqVYx0jWQK2jl5IgWvSQc0lUfDwzHZXIExvxYsQCxbsHcE2HL5H88rZ2Pf+JdT3vsRHL/fx7q4C77rxvxZXUp5MqKel0mfCpPcW0D4Z05LaxaKtF8yy6oSSLTGaZe3EHN3WVUIgJVGyMe00rYQPdVMWK9EMg5xLuLNP+kW+dizWTJv3QdkZ4cRqlC4slppGqhGuu1jt3pjsKb5PfwONns/tDBH+KNb/0c5YTgBMBXTjF+Vh0tvyvgGwY7FcXM6PLVKhnDt7sf1VSLMTCmLzZd+fcDjsU0wGdgxKJsHvkKmzpvoNKeJrBnCFUq6fo64RBONqe1xnymDhSIRnUuTkbL+myZ+tqJfksWlblzNy9/0SdwQn6cppRWepic0j0520E5FmpwGFnRRupnO1Ar2qB3CKezHdNJ6iTXYommB0L4cjZ2xKTQHCK+q4gUglRXNRG7bz/5i1YSfbAbAaxVLZjb90Fr08l7A04kXpiyh8epw9avHfq0fen//qwOM05FtQBSKAilMk4qitFQq6Vy6muQTH52eAvDAMeeFeI8EJm1ee+n2bTmvahSCUnXQMivQ20Ng807n5uRgb+6+wOHbG9adh3V9lrUch39ZZSqbN7+T1xR91eo3d0678V2lauTcVRtkkhPFhmbwupoRBIBJJVEjY7j9/sontcBQO6iDiJ3bMc3PK3nb0w58caeYBRoSaIFQkSuAL4AmMDXlFL/Mu+4uMevBArAtUqphxesAXPwHIzHacHvbz2y+sDlF3wMK+rHak8Q7p3RsvyOjho7XH7S5t2fXKymnhZs7v3X2fVNnTewee+nAdgy/lU21b+NzWM3cXn0TZRecBah3mnY34cEg6hKBXNXL76alJ6PSacgm8esxAndtwvnzA5wFCoYQPKGrtlzuqPUgvVgRMQE/g14BdAPPCgitymlHp9z2iZgtbtcBHzF/bvgeA7G4znB7Q9+5GQ34bTlgHOZ3R67CYDb899hU9PfoirV2WqhmCaqowUpVCiu0irb4Z5pAv3TEItijmagrZmpDWnKqTrSO58DczAsaBTZhcBepdR+ABH5AXA1MNfBXA18R+laLfeLSEpEmpVSQwvViAN4DuYwPPTQQ+Mi0nMCX7IOGD+Br7eYeLacepwadhxIZ3nI/fvEEc7de3BV5INzj5wathxk+fHeIMvU7b9SP6w7ytNDIjK3WNVXlVJfnbPdCvTN2e7nqb2Tw53TCngO5kSglKo/ka8nIluXUhGhI+HZcupxutgBp5ctB1BKXbGAtzvcpNX8CZ6jOWdB8OrBeHh4eJw+9APtc7bbgMFjOGdB8ByMh4eHx+nDg8BqEVkhIgG0TOht8865DXiTaC4GZhZj/gW8IbJTha8+8ylLBs+WU4/TxQ44vWxZcJRSloi8HbgdHab8DaXUThF5m3v8JuB/0CHKe9Fhym9ZrPaIOk0lCjw8PDw8Ti7eEJmHh4eHx6LgORgPDw8Pj0XBczDHgYiEROQBEXlMRHaKyMfc/beIyKPu0i0ij7r7/SLybRHZLiJPiMj759zrfHf/XhH5oivngIgE3fvtFZE/iEjHnGveLCJ73OXNi2DHBhG537Vjq4hcOOea97tt2iUil58KdhyLLSLyChF5yG3zQyLy0qVqy5zrlolITkTevZRtEZH1InKfe/52EQmdCrZ4PAuUUt5yjAs6njzmrvuBPwAXzzvns8CH3fU3AD9w1yNAN9Dhbj8AXOLeczOwyd3/t8BN7vrrgVvc9TSw3/1b467XLKQdwC/ntONK4C53fR3wGBAEVgD7APNk23GMtpwHtLjrZwMDc+61pGyZc92PgFuBdy9VW9ABSNuAc93t2lPlM+YtR794PZjjQGly7qbfXWajJtwnq9cBNx+4BIiKiA8IAxUgIyLNQEIpdZ/S34jvAK9yr7ka+La7/kPgZe59LwfuUEpNKqWmgDuAY0rYOoIdCki4+5McjJW/Gu0oy0qpLnQ0yoUn245jsUUp9YhS6oBdO9GZ0sGlaAuAiLwK/QO6c86+pWjLZcA2pdRj7vUTSin7VLDF4+jxwpSPE9Hicg8BncC/KaX+MOfwC4ERpdQed/uH6C/BELoH8y6l1KSIbEQnPx3ggHQDzJF1UDoEcQb9NPd0cg8LZoeIXAfcLiKfQQ+nPn9Om+4/zGtXT7Ydx2DLXF4DPKKUKotI61KzRUSiwHvRQofvnnObJWcLsAZQInI7UI9+oPnUqWKLx9Hh9WCOE6WUrZTagM6GvVBEzp5z+E852HsBLURnAy3ooaXrRWQlR5ZueLpjCyr38DR2/A3aCbYD7wK+fhxtOiF2wLO2RTdO5Czgk8BfP0N7j3TsZNvyMeDzc3oKB1iKtviAFwBvdP++WkRe9gztOmG2eBwdnoNZIJRS08BduF1vdxjsT4Bb5pz2BmCLUqqqlBoFfg8c6L20zTlvrnTDrKyDe88kMMkiyT3Ms+PNwH+7h25FO8hD2jTvtU8ZO56FLYhIG/Bj4E1KqX1z2rvUbLkI+JSIdAPXAR8QnXS3FG3pB+5WSo0rpQro5MDnnWq2eDwDJ2Pi53RZ0F33lLseBu4BrnK3r0B/Qeae/17gm+gnqihaQnu9e+xB9KTngYnLK939f8ehE5f/5a6ngS70pGWNu55eSDvQercvdve/DHjIXT+LQyf593NwAvak2XGMtqRcW15zmHstKVvmXftRDp3kX1K2uK/5MHoo2Qf8Cvhfp4It3vIs/u8nuwFLeQHWA4+go1124EaLuce+Bbxt3vkx9FPaTrRzuWHOsY3uPfYBX+agykLIvWYvOnpm5Zxr/sLdvxd4y0LbgR6aeAj9A/wH4Pw513zQbesu3Ciek23HsdgCfAjIA4/OWRqWoi3zrv0ohzqYJWcL8Gfo78oO4FOnii3ecvSLJxXj4eHh4bEoeHMwHh4eHh6LgudgPDw8PDwWBc/BeHh4eHgsCp6D8fDw8PBYFDwH4+Hh4eGxKHgOxmNJIiI592+LiPzwOO5znYhEFqhNZ7iqwI+IyKqFuOece39NRNYdw3UbROTKOduvFJH3LWTbPDyeDi9M2eOUR0R8Silr3r6cUiq2APfuBjYqpcYX4F7vA8JKqY8c4/VPsXMB2nQt2r63L+R9PTyOBq8H43HUiMgFIrLNre0Rdet0nH2Y897knveYiHzX3bdcRH7t7v+1iCx7hv3fEpHPichvgE+KyAq3NsiDIvKPc16rQ0R2uOvXish/i8gWt+7Hp+ac9xXR9Ubm1iJ5B1oX7jfu6yAil7mv87CI3CoiT3FicrCGyTYR+bGI1Li9hOuAtx6417xrciLyWfe+vxaRenf/XSLyCRG5G3iniLzM7QFtF5FviEhwznkbj9RG9/9zr/u+PyAiSeBG4Bq3Z3WN+x59+Sje+y+699ovIq99Fh8TD4+DnOxMT29ZWgvwceAzwL8B7z/M8bPQ2f117nba/fsz4M3u+l8AP3mG/d8Cfs5BCZrb0FphoCVBcu56B7DDXb8WLVuTRGd19wDt89phonWwDkj0dM9pax3wWyDqbr+XOeoMc2zcBrzIXb8R+Fd3/aPMyZ6fd40C3uiufxj4srt+F/Dv7noIrfq7xt3+DnDdnPM2Pl0bgYBr+wXu/gRaYuXaA6815z068NpHeu9vRT+ArgP2nuzPnbcszcXrwXg8W25Ey8FvBD51mOMvBX6o3CEnpdSku/8S4Pvu+nfREiFH2g9wq1LKdtcv5aAy9XeP0L5fK6VmlFIltBzPcnf/60TkYbRcyVnoH875XOzu/73oKqRvnnM9AG6vIKWUutvd9W3gj47QngM4HBQ+/U8OtfPA/rVAl1Jq9xHu/XRtXAsMKaUeBFBKZdQzD7cd6b3/iVLKUUo9DjQ+s3keHk/Fqwfj8WxJozXV/Ogn7vy848LRSaE/3Tlz98+/99Hctzxn3QZ8IrICXR/lAqXUlIh8C932+Qi6MNWfHsXrHC+Hs/NwsvLzOWwbRWQ9xy9BP/f6ue/j0bTLw+MpeD0Yj2fLV4F/AL6Hrp8yn1+jewu1ACKSdvffi1a4BV3j43fPsH8+v5933rMhgf4RnxGRRmDTnGNZIO6u3w9cKiKdbtsjIrJm7o2UUjPAlIi80N3158DdPDMGcGAu4w0c3s4ngY4Dr/809366Nj4JtIjIBe7+uGjJ+rn2zedo33sPj2PC68F4HDUi8ibAUkp9X3R1wntF5KVKqTsPnKOU2iki/wTcLSI2ekjqWuAdwDdE5AZgDHiLe8nT7Z/PO4Hvi8g70TXnjxql1GMi8ghamXc/2lkd4KvAZhEZUkq9RHTU1c0HJtfRasu7OZQ3AzeJDm/ef4Q2zyUPnCUiDwEzwDWHaWdJRN4C3Oo6hweBmw49RY0dro1Kqd0icg3wJREJA0Xg5cBvgPe5w2n/PO8lj/a99/A4JrwwZQ+PE4AcZ1i1iGwHXqmU6lrAZnl4LCreEJmHxymOiNwBbPeci8dSw+vBeHh4eHgsCl4PxsPDw8NjUfAcjMdpj5vtX3Qnuo/3Xodoez2L664Rkb0i8vPjbYOHx1LBczAezxX2KaU2LMB9NgCHdTBu5NdhUUrdArx1AV7fw2PJ4IUpezynEJEOYAs65+Ni4DHgm8DHgAa0nMsDIhIFvgScg/6efBTYjFYyCIvIC9Bhv2ei9cw6gHE3jPomYJn7ktcppeaGRXt4PGfwejAez0U6gS8A64Ez0ImPL0Bn+3/APeeDwJ1KqQuAlwCfRqsXfBi4RSm1we2VAJwPXK2UeoN738+7170G+NqJMcnD49TD68F4PBfpUkptBxCRnWj9MuXmmnS451wGvFJE3u1uhzjYK5nPbUqporv+cmCdyKy6SkJE4kqp7EIb4eFxquM5GI/nInN1tpw52w4HvxMCvEYptWvuhSJy0WHuN1czzQAumeNwPDyes3hDZB4eh+d24P+K2xURkfPc/UfS9gL4JTBb3EtENixWAz08TnU8B+PhcXj+ET3nsk10QbMDRc5+gx4Ce9TV/prPO4CNbhGvx4G3nZjmeniceniZ/B6nPW7k2M+VUk+pvnmC2/FidEGyq05mOzw8ThReD8bjuYANJBci0fJYcXs7/w5Mnaw2eHicaLwejIeHh4fHouD1YDw8PDw8FgXPwXh4eHh4LAqeg/Hw8PDwWBQ8B+Ph4eHhsSh4DsbDw8PDY1H4/7ThiQVrDJ4lAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "geo_grid.FSM.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9RgfrriIJ-P"
      },
      "outputs": [],
      "source": [
        "geo_grid.FSM.rio.to_raster(\"FSM.tif\")\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}