MLBCProjNotebook.ipynb

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          "height": 582
        },
        "id": "sqzCHOP7KgDO",
        "outputId": "f5f2f930-5372-4e4a-c194-266544b2d32c"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
        "import seaborn as sns # for the correlation matrix plot\n",
        "import matplotlib.pyplot as plt\n",
        "import pydot\n",
        "import pydotplus\n",
        "import io\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "\n",
        "from graphviz import render\n",
        "from graphviz import Source\n",
        "\n",
        "from sklearn.tree import export_graphviz\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "# read in the file to import the wdbc dataset \n",
        "wdbc = pd.read_csv(io.BytesIO(uploaded['wdbcwh.csv']))\n",
        "# Print some basic data info\n",
        "print(wdbc.describe())\n",
        "print(wdbc.head())\n",
        "print(wdbc.shape)\n",
        "print(wdbc.columns)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving wdbcwh.csv to wdbcwh (2).csv\n",
            "                 ID  Mean_Radius  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "count  5.690000e+02   569.000000  ...      569.000000               569.000000\n",
            "mean   3.037183e+07    14.127292  ...        0.290076                 0.083946\n",
            "std    1.250206e+08     3.524049  ...        0.061867                 0.018061\n",
            "min    8.670000e+03     6.981000  ...        0.156500                 0.055040\n",
            "25%    8.692180e+05    11.700000  ...        0.250400                 0.071460\n",
            "50%    9.060240e+05    13.370000  ...        0.282200                 0.080040\n",
            "75%    8.813129e+06    15.780000  ...        0.317900                 0.092080\n",
            "max    9.113205e+08    28.110000  ...        0.663800                 0.207500\n",
            "\n",
            "[8 rows x 31 columns]\n",
            "         ID Diagnosis  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
            "0    842302         M  ...          0.4601                  0.11890\n",
            "1    842517         M  ...          0.2750                  0.08902\n",
            "2  84300903         M  ...          0.3613                  0.08758\n",
            "3  84348301         M  ...          0.6638                  0.17300\n",
            "4  84358402         M  ...          0.2364                  0.07678\n",
            "\n",
            "[5 rows x 32 columns]\n",
            "(569, 32)\n",
            "Index(['ID', 'Diagnosis', 'Mean_Radius', 'Mean_Texture', 'Mean_Perimeter',\n",
            "       'Mean_Area', 'Mean_Smoothness', 'Mean_Compactness', 'Mean_Concavity',\n",
            "       'Mean_Concave_Points', 'Mean_Symmetry', 'Mean_Fractal_Dimension',\n",
            "       'Radius_SE', 'Texture_SE', 'Perimeter_SE', 'Area_SE', 'Smoothness_SE',\n",
            "       'Compactness_SE', 'Concavity_SE', 'Concave_Points_SE', 'Symmetry_SE',\n",
            "       'Fractal_Dimension_SE', 'Worst_Radius', 'Worst_Texture',\n",
            "       'Worst_Perimeter', 'Worst_Area', 'Worst_Smoothness',\n",
            "       'Worst_Compactness', 'Worst_Concavity', 'Worst_Concave_Points',\n",
            "       'Worst_Symmetry', 'Worst_Fractal_Dimension'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaRJr7_ZDnkQ"
      },
      "source": [
        "Perform some pre-processing on the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "3VRhYx3kDweH",
        "outputId": "e4cdb051-b498-4d7c-82ef-ca7e742852ef"
      },
      "source": [
        "#Create digital values for the diagnostic labels\n",
        "d={'M':1, 'B':0}\n",
        "wdbc['Diagnosis']=wdbc['Diagnosis'].map(d)\n",
        "wdbc.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Diagnosis</th>\n",
              "      <th>Mean_Radius</th>\n",
              "      <th>Mean_Texture</th>\n",
              "      <th>Mean_Perimeter</th>\n",
              "      <th>Mean_Area</th>\n",
              "      <th>Mean_Smoothness</th>\n",
              "      <th>Mean_Compactness</th>\n",
              "      <th>Mean_Concavity</th>\n",
              "      <th>Mean_Concave_Points</th>\n",
              "      <th>Mean_Symmetry</th>\n",
              "      <th>Mean_Fractal_Dimension</th>\n",
              "      <th>Radius_SE</th>\n",
              "      <th>Texture_SE</th>\n",
              "      <th>Perimeter_SE</th>\n",
              "      <th>Area_SE</th>\n",
              "      <th>Smoothness_SE</th>\n",
              "      <th>Compactness_SE</th>\n",
              "      <th>Concavity_SE</th>\n",
              "      <th>Concave_Points_SE</th>\n",
              "      <th>Symmetry_SE</th>\n",
              "      <th>Fractal_Dimension_SE</th>\n",
              "      <th>Worst_Radius</th>\n",
              "      <th>Worst_Texture</th>\n",
              "      <th>Worst_Perimeter</th>\n",
              "      <th>Worst_Area</th>\n",
              "      <th>Worst_Smoothness</th>\n",
              "      <th>Worst_Compactness</th>\n",
              "      <th>Worst_Concavity</th>\n",
              "      <th>Worst_Concave_Points</th>\n",
              "      <th>Worst_Symmetry</th>\n",
              "      <th>Worst_Fractal_Dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ID  Diagnosis  ...  Worst_Symmetry  Worst_Fractal_Dimension\n",
              "0    842302          1  ...          0.4601                  0.11890\n",
              "1    842517          1  ...          0.2750                  0.08902\n",
              "2  84300903          1  ...          0.3613                  0.08758\n",
              "3  84348301          1  ...          0.6638                  0.17300\n",
              "4  84358402          1  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWsryMWAEsIo"
      },
      "source": [
        "Perform Some Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05yjdLMXFdlG"
      },
      "source": [
        ""
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cTKKcZ--Oo9J"
      },
      "source": [
        "Creating the Model for DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdVtPvDp9Nbp"
      },
      "source": [
        "Get out list of features seperated from the ID and Diagnosis(our target)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6rkIiAsOsro",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68ce481d-79fd-4746-9fb3-216ac92c076c"
      },
      "source": [
        "features = list(wdbc.columns[2:32])\n",
        "features\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Mean_Radius',\n",
              " 'Mean_Texture',\n",
              " 'Mean_Perimeter',\n",
              " 'Mean_Area',\n",
              " 'Mean_Smoothness',\n",
              " 'Mean_Compactness',\n",
              " 'Mean_Concavity',\n",
              " 'Mean_Concave_Points',\n",
              " 'Mean_Symmetry',\n",
              " 'Mean_Fractal_Dimension',\n",
              " 'Radius_SE',\n",
              " 'Texture_SE',\n",
              " 'Perimeter_SE',\n",
              " 'Area_SE',\n",
              " 'Smoothness_SE',\n",
              " 'Compactness_SE',\n",
              " 'Concavity_SE',\n",
              " 'Concave_Points_SE',\n",
              " 'Symmetry_SE',\n",
              " 'Fractal_Dimension_SE',\n",
              " 'Worst_Radius',\n",
              " 'Worst_Texture',\n",
              " 'Worst_Perimeter',\n",
              " 'Worst_Area',\n",
              " 'Worst_Smoothness',\n",
              " 'Worst_Compactness',\n",
              " 'Worst_Concavity',\n",
              " 'Worst_Concave_Points',\n",
              " 'Worst_Symmetry',\n",
              " 'Worst_Fractal_Dimension']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puN9XbyIEjim"
      },
      "source": [
        "constructing our y and x vectors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcXvAuIcErdd"
      },
      "source": [
        "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
        "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n",
        "y = wdbc['Diagnosis']\n",
        "X = wdbc[features]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zx0lkZntsUdB"
      },
      "source": [
        "Show the correlation between the features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "E7t8HY2XscZJ",
        "outputId": "a2212e61-7bf5-4b59-e46d-122cfd5604be"
      },
      "source": [
        "print(\"The number of variables are:\", len(features))\n",
        "print()\n",
        "print(features)\n",
        "# it computes the Pearson’s correlation coefficient.\n",
        "corr_matrix = X.corr()\n",
        "#The parameter ‘ annot=True ‘ displays the values of the correlation coefficient\n",
        "#in each cell\n",
        "sns.heatmap(corr_matrix, annot = False)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of variables are: 30\n",
            "\n",
            "['Mean_Radius', 'Mean_Texture', 'Mean_Perimeter', 'Mean_Area', 'Mean_Smoothness', 'Mean_Compactness', 'Mean_Concavity', 'Mean_Concave_Points', 'Mean_Symmetry', 'Mean_Fractal_Dimension', 'Radius_SE', 'Texture_SE', 'Perimeter_SE', 'Area_SE', 'Smoothness_SE', 'Compactness_SE', 'Concavity_SE', 'Concave_Points_SE', 'Symmetry_SE', 'Fractal_Dimension_SE', 'Worst_Radius', 'Worst_Texture', 'Worst_Perimeter', 'Worst_Area', 'Worst_Smoothness', 'Worst_Compactness', 'Worst_Concavity', 'Worst_Concave_Points', 'Worst_Symmetry', 'Worst_Fractal_Dimension']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04zkGaO8sixB"
      },
      "source": [
        "Splitting the dataset into training and testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwtHa0l9srVA"
      },
      "source": [
        "# Split dataset into training set and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHMF8QhxEzh8"
      },
      "source": [
        "Training the first default tree model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "638U52ahs42P"
      },
      "source": [
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "# Train Decision Tree Classifer\n",
        "clf = clf.fit(X_train,y_train)\n",
        "#clf = clf.fit (X,y)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZajBm0FtH34"
      },
      "source": [
        "Display the tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 759
        },
        "id": "org621CqE3g8",
        "outputId": "f7a678ba-7a46-482f-eaa7-42839aaab2bb"
      },
      "source": [
        "from IPython.display import Image\n",
        "from six import StringIO\n",
        "import pydot\n",
        "dot_data = StringIO()\n",
        "tree.export_graphviz(clf, out_file=dot_data, feature_names=features)\n",
        "graph, = pydot.graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ijTGIRrOJMUWjUUo0PzWJXWPBgitqNCor6gazKia6xhQ1guKDqybRKJtgQ7EGsITglyYKiIUeBji/P3yYzUhxkDKU9+t55o8999xzP+dC5sPej/ec5cuXQ6lUYvXq1XBzc8PAgQM1e65KpVI4OTkhNjYWVlZWeo6YiIiIiIiIqNXYx+IqERERkR7du3cPHTt2xMOHD/Hkn2WGhoZo27Yt4uPj0bZtWz1FSET6cvv2bQQEBGDPnj1wdHRERkYGSktLNcelUin69euHqKgoGBsb6zFSIiIiIiIiolZjH5cFJiIiItKjadOmIT8/v1JhFQDKyspw//59jB07FuXl5XqIjoj0yd7eHrt27cKCBQuQnp6uVVgFHr/hGhMTg6lTp+opQiIiIiIiIqLWh8VVIiIiIj2JiIhAREQE1Gp1tX3UajVOnDiBtWvXNmJkRNRUPHjwANu3b6/2H1iUlZXhm2++wYYNGxo5MiIiIiIiIqLWicsCExEREelBTk6OZjngmt5KlUgkMDB4/O/hYmNj0atXr8YKkYiagFmzZmHbtm01/iMM4PF3RUREBEaNGtVIkRERERERERG1SlwWmIiIiEgfZs6ciby8vCoLqxKJBEZGRgCADh06YOnSpYiPj2dhlaiViY+Px9atW59aWK0wduxYXLx4sYGjIiIiIiIiImrdjPQdABEREVFrc+jQIYSFhWm1VbyhWl5ejhdeeAHvvvsuRo8eDXd3dz1FSUT6dvz4cTg6OiI1NRVCCEilUgghKu29CkDT/sYbb+DSpUtwcHDQQ8RERERERERELR+XBSYiIiJqRPfv30enTp2Qk5MDiUQC4HFhddCgQfDx8cHIkSNha2ur5yiJqCl59OgRrly5gkuXLuHSpUuIjY1FYmIiysvLYWRkBAMDA5SUlAAADAwM0KVLF8TExEChUOg5ciIiIiIiIqIWZx+Lq0RE1CC8vb31HQJRkxQbG4uUlBQYGBigXbt2cHBwgJ2dHYyNjfUdWrOxb98+fYdAVK3Gyn9lZWV49OgRHjx4gIcPHyInJwd5eXmo+L93KpUKnp6emn/EQdQaMD8QEREREVEjYHGViIgahkQiQf/+/bksIdFfZGVl4datW7C3t4etrS2kUqm+Q2pW0tPTERMTA/75Sk2ZPvNfeXk5cnNzNQXXtm3bwtHRsdHjIGpszA9ERERERNSIWFwlIqKGIZFIEBYWBh8fH32HQtRkCCH4FlkdhIeHw9fXlw/PqUlj/iNqfMwPRERERETUiPYZ6DsCIiIiotaChVUiIiIiIiIiIqLmjcVVIiIiIiIiIiIiIiIiIiIdsLhKRERERERERERERERERKQDFleJiIiIiIiIiIiIiIiIiHTA4ioRERERERERERERERERkQ5YXCUiIr3r3bs3JBIJJBIJVCoV5syZ89Rzfv/9d7zwwgtQKBQwMDDAsGHDGiHSurl58yY+/vhjdOvWDebm5jAyMoJSqUTHjh3x1ltv4ezZs/oOsUmIiIiAq6ur5ndCIpFAKpXC3t4e48aNw/Xr159p3B9++AFKpRKHDh2q54ibplOnTmHAgAEwNTWFSqVCQEAA/vzzT53PLy8vR0hICDw9Pas8HhwcjM6dO0Mul8PMzAydO3fGsmXLkJubq9Vv9erVWj/Lik/37t3rND+ilqAl5z8/P78q/9uv6nP48GF9h9usffrpp+jatSssLCxgYmICd3d3LFq0CPn5+ZX61iU3qNVqBAUFwd3dHcbGxrC0tET37t2RnJxc7TnFxcXo3Lkzli5dqtXO3EBERERERM0Zi6tERKR3ly5dwtChQyGRSHD58mV8/vnnWsfLysrwyiuvaLV17NgRsbGxCAoKwtixY/HTTz81Zsi1tmPHDnh4eOC3337DZ599hrS0NBQUFODy5ctYtWoVHj58iLi4OH2H2SSMHj0at27dgpubG5RKY+7kSwAAIABJREFUJYQQePjwIb744gucOnUK/fr1w82bN2s9rhCiAaJtmhISEjB06FD87W9/Q3Z2Ng4cOICvvvoK06dP1+n8xMREDBo0CP7+/igsLKyyz8mTJzF58mSkpqYiMzMTq1atQnBwMMaMGVOfUyFq0Vp6/jt27BgePnwItVqNO3fuAABGjBiBkpISFBQUICsrC5MnT9ZzlM1fVFQUZs2aheTkZNy7dw9BQUHYuHEjvL29tfrVNTf4+vri22+/xe7du1FYWIjr16/Dzc2tyiJuhcDAwGfK2URERERERE0Zi6tERNQk+Pn5QQhR5dsr//3vf/Hf//4X165dq3Tshx9+gK+vb4PHV1RUVO0bfE8TExODqVOnYuDAgThx4gRef/11WFpawsTEBK6urvD19cXy5ctRUlJSz1G3HGZmZhg+fDg+//xz5OfnY9OmTbUe46233sKjR48wfPjwWp9bl5//06jVauzcuRMTJkyotzFXrVoFW1tbrFy5EmZmZnjppZcQEBCAr7/+Gjdu3Kjx3KtXr2Lx4sWYPn06evbsWW0/Y2NjzJw5EzY2NlAoFPD29sbIkSPx888/a4ooFb777jsIIbQ+8fHx9TJXouaupeY/iUSCAQMGQKlUwsjISKtdKpXC1NQUNjY26NOnT32G26Df1/WhIb7zFQoFpk6dCmtra5ibm8PHxwejRo3CTz/9hLS0NE2/uuSGvXv3IjIyEvv27cOLL74IIyMjqFQqHDx4sNq3Tc+cOVPjdz1zAxERERERNVcsrhIRUZPw7rvvwtjYGP/5z38qHTt27Bjs7Oywf/9+rfaioiJcvXoVr7/+eoPHt2PHDmRlZT3TuatXr0ZZWRn+8Y9/aD1g/qvXX38ds2bNqkuIrUK/fv0AoNEfvtbl51+dP//8E1u3bkXv3r2RkJCAtWvX1su4paWlOHLkCF5++WVIJBJN+xtvvAEhBA4ePFjj+T169EBERATGjRsHExOTavsdOHAAMplMq83e3h4AanyLiYi0tdT89/3338PU1PSp/aZOnYq33377WUKrUkN8X9eHhvrOB4DDhw/D0NBQq61t27YAoFl9oK65oSJ2Dw8PnWIqKirCwoULsXHjxtpMhYiIiIiIqFlgcZWIiJoEpVKJ119/HcePH0dRUZGmXa1WAwDGjBlT6eFyxVugxsbGAB4v+/rZZ5+hS5cuMDExgZWVFUaOHKn1Nsa6detgamoKc3NzZGVlYf78+bC3t8fNmzfx66+/ol+/fjA1NYWFhQU8PDyQm5uLuXPnYv78+UhKSoJEIoG7u7vO8yopKcGJEyfQpk0bTWFQF7rMJTQ0FGZmZjA1NcXBgwfxxhtvwMLCAg4ODvj+++8rjfndd9+hb9++kMlkMDMzQ/v27bFq1SoAj5d47dq1K5RKJWQyGTw8PHD06FEAQJcuXSCRSGBgYIA+ffpoHtQuWrRI0//rr78G8HgJy+XLl8PJyQlyuRzPP/88wsLCdJ7305SWlgKAVtFPl3t16tQpODk5QSKRYPPmzbW6f9X9/Kv7fXmagoICfPbZZ+jbty+ysrLw66+/YsOGDVCpVAAeFyaftj9h//79qx3/1q1byM/Ph5OTk1a7m5sbAOC33357aozPKjExEZaWlnB2dm6waxC1NC01/9VWTfnj66+/hkKhgEQigZWVFSIjI3HhwgU4OzvD0NAQY8eOBVD19/Xs2bNhbGwMW1tbzbVmzpwJMzMzSCQS3Lt376n3py65raG/86tz+/ZtyOVyuLi4AKhbbigpKUFMTEyNqxk8KTAwULO6ARERERERUUvD4ioRETUZfn5+KCoqwvHjxzVtP//8M1599VV4e3sjLi4Ov//+u+bYk0sirlixAn//+98RGBiIrKwsREdHIy0tDQMHDkRmZiaAxwVBf39/5OfnIygoCC4uLujfvz/y8/MxYsQIjBkzBvfv30diYiI6duyIkpISbNy4EcOHD4ebmxuEEPjjjz90nlNKSgqKi4vRoUOHWt0LXeYyY8YMzJs3D0VFRTA3N0dYWBiSkpLg6uqKyZMnax7MA8DGjRsxfvx4jBkzBhkZGUhPT8eSJUs0+6BlZmbC19cXycnJyMjIgEKhwLhx4wA8fku0ffv2cHR0xPnz5zVvIq1btw4fffQR1q5di4kTJwIAFi9ejHXr1iEkJAR37tzB8OHDMXbsWFy4cKFW869OdHQ0gMdvV9bmXnl5eeHMmTNaY+l6/6r6+RcUFFT7+1KdR48eYc2aNfD09IQQAjExMfjkk09gbW2t1U+hUFRaJvHJT0xMTLXXuXv3LgDA3Nxcq10mk0Eul2vuSX1Rq9W4ffs2Nm/ejOPHj2PTpk2agk+Fv//977CysoKxsTFcXFwwcuRIxMbG1mscRM1ZS8x/tVVT/pg4cSJiY2NhamqKd955ByNHjkTfvn0xbtw4bNu2DXv27AFQ9ff1v/71L/j4+Ghda8uWLVi5cqVWW3X3RwjxTLmtsb7zq1JYWIioqChMnjxZ831cl9yQkZGBkpISXLx4EUOGDIFKpYJMJkOXLl2wZcuWSnuanz59GklJSZqid3WYG4iIiIiIqNkSREREDQCACAsLq9U5eXl5Qi6Xi0mTJmna5s+fL9RqtSgvLxd2dnZi9erVmmO9e/cWJSUlQgghCgsLhUKhEH5+flpjnj9/XgAQn376qaYtMDBQABBFRUWatvj4eAFAHD58uMrYRo8eLdzc3Go1HyGEuHDhggAgXn31VZ3PqetctmzZIgCIP/74QwghRElJibC0tBRDhgzRGq+0tFRs3LixyhiCgoIEAJGVlSWEECIkJEQAEOHh4Zo+BQUFwsnJSTx69EgIIURRUZEwNTXViruwsFCYmJiIGTNm6Dz/Cm5ubkKpVAohhMjPzxf79+8Xzz33nGjXrp1IT0/XjK/rvUpLSxMAxKZNmzRtutw/ISr//J/2+/KkkpIS4ezsLAYNGiRyc3NrcRdq79ixYwKA+Oyzzyods7CwEJ6enjqP9eKLL4oePXrU2Oe5554TAESbNm3E559/rvlvskJqaqq4dOmSyMvLE3/++ac4e/as6NWrl5DL5SI+Pl7nWIQQIiwsTPDPV2rqmP+qdufOHQFAvPPOO5WO6Zo/vvzySwFA7Nq1S+zZs0f4+/vrFO+4cePEc889p9W2fv16AUBkZ2dr2qq6P8+S2xrzO78qgYGBomPHjlrXrktuiIuLEwDEa6+9Jk6fPi1ycnLEw4cPxeLFizU/jwqFhYWib9++mjydnZ0tAIjAwECtMeszNwjB/EBERERERI0qnG+uEhFRk6FQKPDWW2/h8OHDEELgzz//hJGREYyMjCCRSDB69GjN0ojXrl1Dr169IJVKAQAJCQnIz89H3759tcZ84YUXYGxsjHPnztV4bVdXV7Rr1w7vv/8+VqxYgeTk5HqbE/C/Pc90Ude5VLylUvHm5W+//YaHDx9W2pvP0NAQc+bMqXKMivtaVlYGAJg0aRKUSqXW3mm7du3CyJEjYWFhAQC4efMmCgsL0b17d00fuVwOW1tbraUpa+PRo0eQSCRQKpWYM2cO3nzzTZw/f16zt2dd71VVnrx/Vant74tUKsXVq1fx2muvYeDAgQgJCUFBQUGtY9NFxT6oFUso/1VJSQnkcnm9Xi8tLQ1ZWVnYs2cPvvnmG/Tq1Utrv0NHR0f06tULCoUCxsbG6N+/P3bu3ImioiJs2bKlXmMhaq5aYv6rDV3zx5QpUzBmzBhMmzYN4eHhWLduXZOJ7a8a8zv/SQcOHEB4eDiOHj2q9ZZqXXJDxVL83bp1g6enJ6ytraFUKrFy5UoolUps27ZN03fJkiWYMmWKJk9Xh7mBiIiIiIiaMxZXiYioSfHz88Pdu3cRGxuLH374AcOGDdMcGzNmDK5cuYKkpKRKSyI+fPgQwP+KmX9laWmJvLy8Gq8rl8sRFRUFLy8vrFmzBq6urpplGuuiffv2kMlkWss5Pk1d5/Kkin1ALS0tq+1z5MgRDB48GDY2NjAxMcGiRYu0jisUCkyZMgVnzpzB+fPnAQBbt27F7NmzNX0qHhwvXbpUa6+4lJSUWhWX/0qpVEIIgdLSUqSnp+Orr77S2s+zvu+Vrp7l90WpVGLp0qU4ffo0ysvL0b9/f3z66ad48OCBVr+67r9Xsa/gk/u/FhYWori4WLPPX32RSqWwsbHB0KFDsXfvXiQkJCAoKKjGczw8PGBoaFir/y6IWrqWlv9qozb5Y82aNcjPz9f6RxxNJba/aqzv/L/au3cv1q5di19++QXt27fXOlaX3FBxrGJ/2grGxsZwdnZGUlISgMf7m8fFxWHSpEk6xfsk5gYiIiIiImouWFwlIqIm5a233oK5uTn+85//IDo6GoMGDdIc8/Lygkqlwv79+3HhwgUMGTJEc6yicFjVQ+SHDx/CwcHhqdfu1q0bDh06hIyMDAQEBCAsLAwbNmyo03xMTEzw+uuv4969ezh9+nS1/e7fv695GFkfc/krOzs7AJUfilZITU3FqFGjYGtri3PnzuHRo0cIDg6u1G/27NmQSqUICQlBdHQ0HB0d4ebmpjluY2MDAAgJCam0X9zZs2drFbOu6vte1caz/r6YmZlh/vz5iI2NRdu2bTFo0CAsXLgQd+7cAVD3/fdcXFxgbm6OlJQUrfaKvRKff/75Osy6Zu7u7jA0NERCQkKN/crLy1FeXq55G4qIWl7+qw1d84darcacOXPw2Wef4ezZs1i9enWTia06Df2dX2HTpk3YtWsXoqKiNHn/r+qSGxQKBTp06IBr165VOlZaWgqlUgkA2LFjB06cOAEDAwNNYbji/q1ZswYSiaTGfWqZG4iIiIiIqLlgcZWIiJoUmUyGESNGYP/+/ZDL5TAw+F+qMjAwwLvvvotvv/0W7dq1g5GRkeZY9+7doVAoKj20O3fuHEpKStCnT58ar5uRkaF5aGhjY4N//OMf6N27d5UPEmtrxYoVMDExgb+/f7VvAsXHx2vmU9e5PKl9+/awtrbGsWPHqjweFxcHtVqNGTNmwNXVFTKZDBKJpFI/BwcH+Pj4YP/+/Vi2bBnmzp2rddzR0REymQxXrlypVXx1Ud/3Slf18fsik8kwY8YMXLp0CV26dMHixYvrJTYjIyO8+eabiI6ORnl5uab9xx9/hEQiwYgRI+p8jZycHIwdO7ZSe2JiIsrKyuDo6Khpe3I5agCIjY2FEAIvvfRSnWMhailaYv7Tla754+OPP8bkyZMxb948+Pv7Y9WqVToVOI2MjGpc6r0+YnuahvrOF0IgICAAcXFxiIyMrPINZqDuucHX1xeXL1/GrVu3NG2FhYVISUmBh4cHAGDnzp2VCsPZ2dkAgMDAQAghNMtXMzcQEREREVFzxuIqERE1OX5+frh58ybefvvtSse8vb1x7do1jBo1SqtdJpNh/vz5OHDgAHbt2oXc3FzExcVh+vTpUKlUmDp1ao3XzMjIwLRp03Djxg2UlJTg8uXLSElJ0SzFZ21tjYyMDCQnJyMvL69WD2l79uyJ3bt3Iz4+HgMHDsQPP/yAR48eQa1W4//+7/+wfft2fPTRR5r98+o6lyeZmJhgyZIliI6OxuzZs3H79m2Ul5cjLy8P165dg5OTEwDg+PHjKC4uRmJiYrV79M2fPx+lpaV48OABXnnlFa1jMpkMH3zwAb7//nuEhoYiNzcXZWVlSE9P17yhU9/q+15V58mff0pKSo2/L7UhlUrx4Ycf4ptvvqmXWAFg2bJlyMzMxCeffIKCggKcPXsW69evx8SJE9GpUydNv+XLl0OpVFZbeK+OmZkZjh07hqioKOTm5kKtVuPy5cuYMGECzMzM4O/vr+l7+/Zt7N27Fw8fPoRarcbZs2cxadIkODk5Yfr06fU2Z6KWoKXlP13pkj+2bNkCe3t7vPvuuwCAoKAgdO3aFePGjdNa6raqeN3d3XH//n1ERkZCrVYjOzu70hucdYmtNur7O//atWtYt24dtm/fDqlUWmlJ4b++gVyX3ODv7w9nZ2dMnDgRqampyMnJQUBAAIqKip6pUMzcQEREREREzZogIiJqAABEWFjYM51bUlIievToIcrLyysdKysrEz169BBlZWWVjpWXl4v169eLDh06CKlUKqysrMSoUaPEzZs3NX2Cg4OFXC4XAISjo6P47rvvhBBCJCcnC09PT2FlZSUMDQ2FnZ2dCAwMFKWlpUIIIS5duiScnZ2FXC4XXl5e4u7du7WeV2pqqliwYIHw8PAQCoVCGBoaCktLS9GrVy/x0UcfidOnT9dqLlu2bBGmpqYCgOjQoYNISkoS27ZtExYWFgKAcHZ2Fr///rum/+bNm4WHh4eQyWRCJpOJXr16iS1btgghhAgICBDW1tbC0tJSeHt7i82bNwsAws3NTaSmpmrNY8iQIeLf//53lXP8888/RUBAgHBychJGRkbCxsZGjB49WiQkJOh8n06fPi06duwoAAgAQqVSCW9v72r763KvNm3aJGxtbQUAYWpqKkaMGFGr+/fkz//cuXM1/r40Bb/++qvo16+fMDExESqVSixcuFAUFxdr9Vm2bJkwNzcXR48e1bSdPXtWDBgwQKhUKs3PwNbWVnh6eopff/1V02/EiBHCxcVFKBQKYWJiItzc3ISfn5+Ii4vTusb8+fOFm5ubMDMzE0ZGRsLBwUFMnjxZZGRk1HpOYWFhgn++UlPH/KctNzdXDBo0SFhbWwsAwsDAQLi7u4s1a9Zo9aspfwwfPlxIJBJhbW0tzpw5I4QQYt68ecLAwEAAEEqlUly4cKHaeHNycsSQIUOETCYTLi4u4uOPPxYLFy4UAIS7u7tITU2t9v48LTZ9i4uL03xXV/VZv369Vv9nzQ1CCJGWlibee+89YWVlJUxMTES/fv3Ejz/+WGN82dnZAoAIDAzUaq/P3CAE8wMRERERETWqcIkQQjROGZeIiFoTiUSCsLAw+Pj46DsUImohwsPD4evrC/75Sk0Z8x9R42N+ICIiIiKiRrSPywITEREREREREREREREREemAxVUiIqJaunHjRqU9zar6+Pn56TvUJoP3jIio+eN3ORERERERERFgpO8AiIiImpvOnTtz2bla4j0jImr++F1ORERERERExDdXiYiIiIiIiIiIiIiIiIh0wuIqEREREREREREREREREZEOWFwlIiIiIiIiIiIiIiIiItIBi6tEREStyA8//AClUolDhw416rm1derUKQwYMACmpqZQqVQICAjAn3/+2WDnFhcXo3Pnzli6dKlWu1qtxvLly+Hq6gpjY2PY29tjwYIFKCoqeua5ERFR68hHAFBeXo6QkBB4enpWeVzXPMN8RERERERE1HSwuEpERNSKCCH0cm5tJCQkYOjQofjb3/6G7OxsHDhwAF999RWmT5/eYOcGBgbi5s2bldrnzp2L9evXIygoCDk5Odi9eze2b9+OSZMmPfP8iIio5ecjAEhMTMSgQYPg7++PwsLCKvvommeYj4iIiIiIiJoOFleJiIhakbfeeguPHj3C8OHDG/Xc2li1ahVsbW2xcuVKmJmZ4aWXXkJAQAC+/vpr3Lhxo97PPXPmDOLj4yu137p1C1988QXGjx8PPz8/mJubY/DgwZg9ezb27NmD69ev18t8iYhao5aej65evYrFixdj+vTp6NmzZ5V9dM0zzEdERERERERNC4urRERELZQQAvv27cO2bdv0HYrOSktLceTIEbz88suQSCSa9jfeeANCCBw8eLBezy0qKsLChQuxcePGSsdiY2NRXl6OF198Uat92LBhAICjR4/Wen5ERK1Ra8tHANCjRw9ERERg3LhxMDExqbKPrnmG+YiIiIiIiKhpYXGViIioBSgrK0NQUBA6deoEuVyOtm3bwsXFBUFBQfDx8QHweN84JycnSCQSbN68GQAQGhoKMzMzmJqa4uDBg3jjjTdgYWEBBwcHfP/995rxqzq3Kvn5+ZBIJDV++vfvX+35t27dQn5+PpycnLTa3dzcAAC//fZbvZ4bGBiImTNnwsbGptIxA4PHfybJ5XKt9g4dOgAA3xQiIqoC85HudM0zzEdERERERERNC4urRERELUBwcDCWL1+O9evX4/79+zh27BiKi4thaWkJS0tLAICXlxfOnDmjdd6MGTMwb948FBUVwdzcHGFhYUhKSoKrqysmT54MtVpd7blVUSgUEELU+ImJian2/Lt37wIAzM3NtdplMhnkcjkyMzPr7dzTp08jKSkJY8eOrXK8zp07A6j80LpNmzYAgOzs7GpjISJqrZiPdKdrnmE+IiIiIiIialpYXCUiImoBIiMj0adPH4wYMQJyuRy9e/fGO++8g+joaJSUlOg0hqenJywsLGBjYwM/Pz8UFBQgNTW1gSPX9ueffwIADA0NKx2TSqUoKiqql3OLioowd+5chIaGVjueh4cHhg0bhi1btiAqKgrFxcW4e/cuDhw4AIlEonnQT0RE/8N8pDtd8wzzERERERERUdNipO8AiIiIqO6Ki4shk8m02srKyiCVSqt8MPw0xsbGANDoD2wr5lBaWlrpWElJSaUlEZ/13CVLlmDKlCmwt7evMZ69e/ciICAA48ePx/3796FSqfDiiy9CCKF5Y4iIiP6H+ah2dM0zzEdERERERERNB99cJSIiagHefPNNXLx4EQcPHkRRUREuXLiAyMhIvP3228/0MPtZ1XWPO1tbWwBAbm6uVnthYSGKi4uhUqnqfO6pU6cQFxeHSZMmPXU+SqUSX3zxBdLT01FYWIikpCT885//BADY2dk99XwiotaG+ah2dM0zzEdERERERERNB99cJSIiagFWrFiBixcvYuLEicjPz4dKpYKPjw/WrFnTqHFU7HH3rFxcXGBubo6UlBSt9j/++AMA8Pzzz9f53B07duDEiRMwMKj8b8zWrFmDNWvWIDY2Fn379q3yOrGxsQCAIUOG6DgrIqLWg/mo7nTNM8xHRERERERE+sHiKhERUQuQkJCApKQkZGdnw8io+aZ3IyMjvPnmm4iOjkZ5ebmmAPrjjz9CIpFgxIgRdT53586d2Llzp9a59+7dg42NDQIDA7F69eoaY9y+fTtcXFzw8ssv12WqREQtEvNR3emaZ5iPiIiIiIiI9IPLAhMREbUAs2bNgpOTE/Lz8/UdSp0tW7YMmZmZ+OSTT1BQUICzZ89i/fr1mDhxIjp16qTpt3z5ciiVShw7dqzW5+qqX79+SElJQWlpKZKTk7FgwQIcP34cO3bs0OwDSERE/8N8VDu65hnmIyIiIiIioqaDxVUiIqIWICgoCPHx8bCystLsJWdsbIyuXbviwIEDAIDNmzfjhRdeAAAEBATgnXfeQWhoKEJCQgA8XuLw1q1b2L59O+bPnw8AGDZsGBITE6s8t6F069YNR48exbFjx9CmTRuMHj0aH374IbZu3Vqp75NLPtbmXF1YWlqiZ8+ekMvl6N27N27cuIGTJ09yCUYiomowHz0WExMDLy8v2NnZ4dy5c7h69SpUKhUGDBiA6OhoTT9d8wzzERERERERUdMhEXXZiIaIiKgaEokEYWFh8PHx0XcorUJoaCgSExM1D6YBoKSkBIsXL0ZoaCgePHgAuVyuxwiJ6i48PBy+vr512keRqKG19vzHfET6wPxARERERESNaF/z3QSHiIiIAAB3797F7NmzceXKFa12Y2NjODk5Qa1WQ61W82E2ERE1KOYjIiIiIiIiag24LDAREVEzJ5fLIZVKsWPHDmRmZkKtViMjIwP//ve/sXz5cvj5+cHCwkLfYRIRUQvHfEREREREREStAYurREREzZxSqcSxY8cQHx+Pjh07Qi6Xo2vXrti5cyfWrl2Lb775Rt8hEhFRK8B8RERERERERK0BlwUmIiJqAQYOHIiff/5Z32EQEVErx3xERERERERELR3fXCUiIiIiIiIiIiIiIiIi0gGLq0REREREREREREREREREOmBxlYiIiOpsw4YNaNeuHSQSCb744gt9h1OtTz/9FF27doWFhQVMTEzg7u6ORYsWIT8/X6tfcHAwOnfuDLlcDjMzM3Tu3BnLli1Dbm6uVr/BgwdDIpFU+VEoFI05NSIiQvPJRxXKy8sREhICT0/Pavuo1WoEBQXB3d0dxsbGsLS0RPfu3ZGcnPxM4xEREREREVHdsLhKREREdbZgwQKcOXNG32E8VVRUFGbNmoXk5GTcu3cPQUFB2LhxI7y9vbX6nTx5EpMnT0ZqaioyMzOxatUqBAcHY8yYMTpfy8vLq77DJyKip2gu+QgAEhMTMWjQIPj7+6OwsLDafr6+vvj222+xe/duFBYW4vr163Bzc6v0D4N0HY+IiIiIiIjqxkjfARARERE1FoVCgalTp8LQ0BAA4OPjg4iICISHhyMtLQ2Ojo4AAGNjY8ycORMymQwA4O3tjX379mHfvn24c+cOVCoVAEAmkyE3Nxfm5uZa15k2bRp8fHwacWZERNScXL16FZ9++immT5+OgoICCCGq7Ld3715ERkbi6tWr8PDwAACoVCocPHjwmcYjIiIiIiKiuuObq0RERNRqHD58WFNYrdC2bVsA0HrL58CBA5rCagV7e3sA0HpT6KeffqpUWE1LS0N8fDxeeeWVeo2diIhajh49eiAiIgLjxo2DiYlJtf22bt2K3r17awqrdR2PiIiIiIiI6o7FVSIiombi119/Rb9+/WBqagoLCwt4eHho9gA9efIkunbtCqVSCZlMBg8PDxw9ehQAsHHjRpiZmcHAwAB9+vTBc889B6lUCjMzM/Tu3RsDBw6Eo6MjZDIZLC0tsWjRIs01//Wvf0Emk6Fdu3aYNm0aVCoVZDIZPD09ce7cuafGXFZWhuXLl8PJyQlyuRzPP/88wsLCdJrTk/Lz86vd37Ti079//1rf19u3b0Mul8PFxaXGfomJibC0tISzs3ON/dauXYs5c+bUOg4iouaC+ahh8tGTSkpKEBMTg57wgIurAAAgAElEQVQ9e9Z5LCIiIiIiIqo/LK4SERE1AwUFBRgxYgTGjBmD+/fvIzExER07dkRJSQkAIDMzE76+vkhOTkZGRgYUCgXGjRsHAJg7dy4WLlwIIQS2bt2K//u//8Pdu3cxaNAgXL58GX//+99x+fJl3L9/HxMmTMD69etx9epVAMDs2bMxceJEFBYWYs6cOUhOTsalS5dQWlqK1157DWlpaTXGvXjxYqxbtw4hISG4c+cOhg8fjrFjx+LChQtPndOTFAoFhBA1fmJiYmp1XwsLCxEVFYXJkyfD2Ni40nG1Wo3bt29j8+bNOH78ODZt2lRlvwq3b9/GL7/8gtGjR9cqDiKi5oL5qGHyUVUyMjJQUlKCixcvYsiQIZqCcpcuXbBlyxYu/UtERERERKQnLK4SERE1A8nJycjNzUW3bt0gk8nw3HPPISIiQrOk7ZgxY/DJJ5/AysoK1tbWGDFiBHJycpCdna01TteuXWFqaoo2bdrgvffeAwA4OTmhbdu2MDU1xfvvvw8AuHHjhtZ5RkZG6NKlC0xMTNC1a1eEhoYiLy8PO3furDbm4uJihIaGYtSoURg9ejQsLS2xdOlSSKVS7Ny586lzagxBQUFQqVRYvXp1lccdHR3h4OCAFStWYN26dfD19a1xvLVr1+Ljjz+GgQH/xCKilon5qPFULENvY2ODNWvWICEhAZmZmRg5ciRmzZqFPXv26DU+IiIiIiKi1opP/oiIiJoBV1dXtGvXDu+//z5WrFiB5OTkGvtLpVIAj5dBrE7FG5ilpaWVzlOr1TWO37dvX5iamlZ66P1XN2/eRGFhIbp3765pk8vlsLW1xY0bN2o9p/p24MABhIeH4+jRo5X2Ta2QlpaGrKws7NmzB9988w169eqFrKysKvtmZGTgP//5DyZOnNiAURMR6RfzUeOp2Du1W7du8PT0hLW1NZRKJVauXAmlUolt27bpOUIiIiIiIqLWicVVIiKiZkAulyMqKgpeXl5Ys2YNXF1d4efnh6KiIgDAkSNHMHjwYNjY2MDExERrn7qGYmJiUulNpL8qKCgAACxdulRrH7qUlBQUFhY+dU5Pqs897vbu3Yu1a9fil19+Qfv27avtJ5VKYWNjg6FDh2Lv3r1ISEhAUFBQlX2Dg4MxefJkyGQynWIgImqOmI8ab89VlUoFALh3755Wu7GxMZydnZGUlFTnaxAREREREVHtsbhKRETUTHTr1g2HDh1CRkYGAgICEBYWhg0bNiA1NRWjRo2Cra0tzp07h0ePHiE4OLhBY1Gr1Xj48CEcHByq7WNjYwMACAkJqbQX3dmzZ2ucU1Xqa4+7TZs2YdeuXYiKioKdnZ3Oc3Z3d4ehoSESEhIqHbt79y727NmDGTNm6DweEVFzxXzUOHuuKhQKdOjQAdeuXat0rLS0FEqlss7XICIiIiIiotpjcZWIiKgZyMjI0DxctbGxwT/+8Q/07t0b165dQ1xcHNRqNWbMmAFXV1fIZDJIJJIGjeeXX36BEKLGN3McHR0hk8lw5cqVKo/XNKeGIIRAQEAA4uLiEBkZCYVCUWW/nJwcjB07tlJ7YmIiysrK4OjoWOlYcHAw3n//fVhbW9d73ERETQnzUePy9fXF5cuXcevWLU1bYWEhUlJS4OHhocfIiIiIiIiIWi8WV4mIiJqBjIwMTJs2DTdu3EBJSQkuX76MlJQU9O/fH05OTgCA48ePo7i4GImJiTh37ly9Xr+8vBwPHjxAaWkpfvvtN8ydOxdOTk417i8qk8nwwQcf4Pvvv0doaChyc3NRVlaG9PR03Llzp8Y5NYRr165h3bp12L59O6RSaaUlHCveUDIzM8OxY8cQFRWF3NxcqNVqXL58GRMmTICZmRn8/f21xs3MzMRXX32FefPmNUjcRERNCfNR4/L394ezszMmTpyI1NRU5OTkICAgAEVFRVi8eLG+wyMiIiIiImqVWFwlIiJqBmxsbFBWVgZPT0+Ympri7bffxrRp0zBr1ix4eHggICAAW7ZsgUqlQmBgIAYPHgwA8PLywoIFC7B+/XoAgIeHB06dOoXg4GBMmzYNADBs2DDs2bMHYWFhGDZsGABg9uzZ2Lt3r+b6xcXF8PDwgFwux8CBA9GxY0f897//hYmJCQDgs88+g5eXFwBgwYIFGD16NABg48aNmDdvHoKDg9GmTRuoVCrMnTsXDx48qHFODUEIoVM/mUyGAQMGYNKkSbC3t4e5uTm8vb3Rvn17xMTEoHv37lr9161bhxEjRmiKCkRELRnzUf2IiYmBl5cX7OzscO7cOVy9ehUqlQoDBgxAdHS0pp+VlRVOnjwJBwcH9OzZE/b29jh//jyOHDmCnj171no8IiIiIiIiqjuJ0PVJIxERUS1IJBKEhYXBx8dH36FQHU2bNg379u1DTk6OvkOhVi48PBy+vr46F8qJ9IH5r+EwH1F1mB+IiIiIiKgR7eObq0RERPRUZWVl+g6BiIiI+YiIiIiIiIj0jsVVIiIiIiIiIiIiIiIiIiIdsLhKRERE1VqyZAl27tyJR48ewcXFBfv379d3SERE1AoxHxEREREREVFTYaTvAIiIiKjpCgoKQlBQkL7DICKiVo75iIiIiIiIiJoKvrlKRERERERERERERERERKQDFleJiIiIiIiIiIiIiIiIiHTA4ioREVELs2HDBrRr1w4SiQRffPGFvsPRybRp0yCRSDSf999/HwDw6aefomvXrrCwsICJiQnc3d2xaNEi5Ofna50fHByMzp07Qy6Xw8zMDJ07d8ayZcuQm5tb5fXKy8sREhICT0/POseuVqsRFBQEd3d3GBsbw9LSEt27d0dycjIAIDIyUmtubdu2rfM1iYiaopaUfyroki/27NmDF154Aebm5nB2dsYHH3yAu3fv1imu2uap4uJidO7cGUuXLtVq1yU/Mk8RERERERHVDourRERELcyCBQtw5swZfYdRa9bW1vjxxx9x8+ZN7NixAwAQFRWFWbNmITk5Gffu3UNQUBA2btwIb29vrXNPnjyJyZMnIzU1FZmZmVi1ahWCg4MxZsyYStdJTEzEoEGD4O/vj8LCwjrH7evri2+//Ra7d+9GYWEhrl+/Djc3N00B+J133kF6ejqio6Px5ptv1vl6RERNVUvKP4Bu+SIsLAzjxo2Dt7c30tPTcfDgQURHR+ONN95AaWnpM8XzLHkqMDAQN2/erNSuS35kniIiIiIiIqodFleJiIioSZDL5Rg2bBg6duwIExMTAIBCocDUqVNhbW0Nc3Nz+Pj4YNSoUfjpp5+QlpamOdfY2BgzZ86EjY0NFAoFvL29MXLkSPz888+4c+eOpt/Vq1exePFiTJ8+HT179qxzzHv37kVkZCT27duHF198EUZGRlCpVDh48CC6d+8OAJBIJLC3t8fAgQPRoUOHOl+TiIjqV1X5R9d88eWXX8LOzg4LFy6EUqlEz5494e/vjytXruDcuXO1juVZ8tSZM2cQHx9f5TFd8iPzFBERERERUe2wuEpERERN1uHDh2FoaKjVVrFc4V/f5jlw4ABkMplWP3t7ewDQWkK4R48eiIiIwLhx4zQP0Oti69at6N27Nzw8POo8FhERNR265ou0tDSoVCpIJBJNm6OjIwAgJSWlwa5boaioCAsXLsTGjRurPK5rfiQiIiIiIiLdsbhKRETURHTp0gUSiQQGBgbo06ePpni4aNEiKJVKyGQyfP311wAeL/PXtWtXTbuHhweOHj1a7dizZ8+GsbExbG1tNW0zZ86EmZkZJBIJ7t27p2kvKyvD8uXL4eTkBLlcjueffx5hYWHVjp2fn6+1V1tVn/79+9fx7vzP7du3IZfL4eLiUmO/xMREWFpawtnZud6u/VclJSWIiYmplzdgiYj0ifnn2bm6uiIrK0urrWK/VVdX1wa9NvB4OeCKN1N11dD5kYiIiIiIqKVjcZWIiKiJiI+PR/v27eHo6Ijz58/D1NQUALBu3Tp89NFHWLt2LSZOnAgAyMzMhK+vL5KTk5GRkQGFQoFx48ZVO/a//vUv+Pj4aLVt2bIFK1eurNR38eLFWLduHUJCQnDnzh0MHz4cY8eOxYULF6ocW6FQQAhR4ycmJuYZ74q2wsJCREVFYfLkyTA2Nq50XK1W4/bt29i8eTOOHz+OTZs2VdmvPmRkZKCkpAQXL17EkCFDoFKpIJPJ0KVLF2zZsgVCiAa5LhFRfWP+eXZLlizB3bt3sWnTJuTl5SEhIQEbN27E66+/3uCF3dOnTyMpKQljx459at/GzI9EREREREQtHYurRERETYShoSHmzJmD1NRUHDhwQNNeWFiIiIgIfPjhh5q2MWPG4JNPPoGVlRWsra0xYsQI5OTkIDs7u04xFBcXIzQ0FKNGjcLo0aNhaWmJpUuXQiqVYufOnXUauz4EBQVBpVJh9erVVR53dHSEg4MDVqxYgXXr1sHX17fBYqlYTtHGxgZr1qxBQkICMjMzMXLkSMyaNQt79uxpsGsTEdUn5p9n9/LLLyMgIACzZ8+GhYUFunfvjry8PPz73/9u0OsWFRVh7ty5CA0N1al/Y+ZHIiIiIiKilo7FVSIioiZk0qRJUCqVWnun7dq1CyNHjoSFhUW150mlUgCPl1Ssi5s3b6KwsBDdu3fXtMnlctja2uLGjRt1GruuDhw4gPDwcBw9ehTm5uZV9klLS0NWVhb27NmDb775Br169aq0XGN9qdgLr1u3bvD09IS1tTWUSiVWrlwJpVKJbdu2Nch1iYgaAvPPswkMDMS2bdtw4sQJ5Ofn49atW/D09MRLL72EtLS0BrvukiVLMGXKFM3+qU/TmPmRiIiIiIiopWNxlYiIqAlRKBSYMmUKzpw5g/PnzwMAtm7ditmzZ2v1O3LkCAYPHgwbGxuYmJhg0aJF9XL9goICAMDSpUu19qxLSUnR7MH3pMbY827v3r1Yu3YtfvnlF7Rv377aflKpFDY2Nhg6dCj27t2LhIQEBAUF1ena1VGpVACgtV8gABgbG8PZ2RlJSUkNcl0ioobA/FN7d+7cQXBwMKZMmYJXXnkFZmZmcHFxwfbt25GRkYH169c3yHVPnTqFuLg4TJo0SedzGjM/EhERERERtXQsrhIRETUxs2fPhlQqRUhICKKjo+Ho6Ag3NzfN8dTUVIwaNQq2trY4d+4cHj16hODg4Hq5to2NDQAgJCSk0r51Z8+erfKcht7zbtOmTdi1axeioqJgZ2en83nu7u4wNDREQkLCM1+7JgqFAh06dMC1a9cqHSstLYVSqWyQ6xIRNRTmn9pJTExEWVlZpdxkYWEBa2vrBss/O3bswIkTJ2BgYKApIlfcvzVr1kAikVS7Ty3Q8PmRiIiIiIiopWNxlYiIqIlxcHCAj48P9u/fj2XLlmHu3Llax+Pi4qBWqzFjxgy4urpCJpNBIpE8dVwjIyOo1eoa+zg6OkImk+HKlSt1mkN9EEIgICAAcXFxiIyMhEKhqLJfTk4Oxo4dW6m94qG3o6Njg8Xo6+uLy5cv49atW5q2wsJCpKSkwMPDo8GuS0TUEJh/asfBwQHA4zdY/yovLw/3799vsPyzc+fOSkXkij1vAwMDIYRA37599ZofiYiIiIiIWjIWV4mIiJqg+fPno7S0FA8ePMArr7yidczJyQkAcPz4cRQXFyMxMRHnzp176pju7u64f/8+IiMjoVarkZ2djZSUFK0+MpkMH3zwAb7//nuEhoYiNzcXZWVlSE9Pr/TwuKFdu3YN69atw/bt2yGVSist9bhhwwYAgJmZGY4dO4aoqCjk5uZCrVbj8uXLmDBhAszMzODv71/ra/v5+eG5557DpUuXauzn7+8PZ2dnTJw4EampqcjJyUFAQACKioqwePHiZ5o3EZE+Mf/ozsXFBUOGDMH27dsRHR2NoqIipKWlYerUqQCAjz76SNNX17xSnxoiPxIRERERERGLq0RERE1Sr169MGTIEMyZM6fSMQ8PDwQEBGDLli1QqVQIDAzE4MGDAQBeXl6YN28evLy8AAALFizA6NGjAQAzZszAkCFD8N5776FTp05YtWoV5HI5AOCll15CWloaAGDjxo2YN28egoOD0aZNG6hUKsydOxcPHjxohJn/jxBCp34ymQwDBgzApEmTYG9vD3Nzc3h7e6N9+/aIiYlB9+7dNX1jYmLg5eUFOzs7nDt3DlevXoVKpcKAAQMQHR2t6VdSUoKsrCwcPHiwxmtbWVnh5MmTcHBwQM+ePWFvb4/z58/jyJEj6Nmz57NNnIhIj5h/HtMlX0gkEuzbtw9+fn746KOPYGVlha5duyI1NRUREREYOHCgZjxd84queUoXtcmPREREREREpDuJ0PXJJRERUS1IJBKEhYXBx8dH36FQMzBt2jQcPnwY6enp+g4FAFBeXo7Bgwdj4sSJ+PDDD+tt3Llz52LXrl24d+9evY3ZmoSHh8PX11fnwjuRPjD/NS+NlX8aKq/Ut+aap5gfiIiIiIioEe3jm6tERETUJBQVFeHo0aNITExESUmJ3uIoKytDZGQk8vLy4OfnV+fxhBDIyMjAqVOn8Mcff9RDhEREVJ8aOv/Ud16pb8xTREREREREtWOk7wCIiIiIAOD+/fsYNmwYAGDcuHHYtWuXXuL45ZdfEBERgR9//BGmpqZ1Hu/gwYMYNWqU5n+3adOmzmMSEVH9aej8U995pb4xTxEREREREdUOlwUmIqIGwWURiai+cdlHag6Y/4gaH/MDERERERE1Ii4LTERERERERERERERERESkCxZXiYiIiIiIiIiIiIiIiIh0wOIqERERERERETV7Xl5e2LZtG/Ly8vQdChERERERtWAsrhIRERERERFRs9euXTt8/PHHsLOzw4QJExAVFYXy8nJ9h0VERERERC0Mi6tERERERERE1OwdOHAAmZmZ+Oc//4mkpCS8+uqrcHJywpw5c/Dbb7/pOzwiIiIiImohWFwlIiIiIiIiohbB0tISU6ZMwalTp3Dt2jVMmjQJhw4dQo8ePdC3b198/vnnyM7O1neYRERERETUjEmEEELfQRARUcsjkUj0HQIRtVD885WaMuY/Iv2pLj+Ul5fjzJkz+O6777Bnzx6o1Wq89tprGD9+PEaOHAmpVNrIkRIRERERUTO2j8VVIiJqEOHh4foOoc7KysoQFRWFiIgIFBYWwsfHB2+//ba+w2pxQkJCAADz5s3TcyTUXPj4+Og7BKJqtYT811oxHzV/uuSHoqIiHD58GNu2bcOJEydgZWWFMWPG4P/9v/8HLy+vRoiSiIiIiIiaORZXiYiInlReXo6IiAgEBgYiOTkZH3zwAVasWAGVSqXv0FqkigehLEgQEZE+MR+1Punp6di9ezd27NiBxMREdO3aFePHj8eECRNga2ur7/CIiIiIiKhp2sc9V4mIiP7i+PHj6Nu3L/z8/NCzZ09cv34dX375JQurRERERC2Mg4MDAgIC8Pvvv+PChQt49dVXsWHDBtjb2+O1117Dt99+i8LCQn2HSURERERETQyLq0RERABOnz6Nl19+GUOHDoW9vT0uX76M8PBwuLm56Ts0IiIiImpgffr0weeff4709HRERkbCysoKkyZNgp2dHcaPH4/jx49zz28iIiIiIgLA4ioREbVysbGxGD58OLy8vGBsbIzz58/j0KFDeP755/UdGhERERE1MhMTEwwfPhzh4eFISUnBypUrkZCQgNdeew3t27fH4sWLkZSUpO8wiYiIiIhIj1hcJSKiVun69evw8fHBiy++iHv37uHEiRP4+eef0bdvX32HRkRERERNgEqlwpw5c3Dx4kXEx8fjvffew9dff42OHTvCy8sL27ZtQ15enr7DJCIiIiKiRsbiKhERtSqpqamYOnUqPDw8cO3aNYSFheHMmTN45ZVX9B0aERERETVR3bp1w9q1a3H79m0cPXoUrq6umDdvHtq1awcfHx8cOnQIpaWl+g6TiIiIiIgaAYurRETUKmRnZ2Px4sXo1KkTjh07htDQUFy9ehXe3t6QSCT6Do+IiIiImgFDQ0O8+uqr+Pbbb3H79m18+eWXePDgAd555x20b98ec+bMwdWrV/UdJhERERERNSAWV4mIqEV78OABVqxYATc3N+zevRtr167FzZs3MWXKFBgaGuo7PCIiIiJqpiwtLTF+/Hj8/PPPuH79OiZNmoRDhw6hZ8+e6NatG4KDg5Gdna3vMImIiIiIqJ6xuEpERC1SQUEBgoOD4ebmhs2bNyMwMBC///475syZA2NjY32HR0REREQtSKdOnbBixQr88ccfOHnyJLy8vLB69Wo4ODhg+PDh2LdvH0pKSvQdJhERERER1QMWV4mIqEVRq9XYtm0bOnTogNWrV2PKlClISkpCQEAA5HK5vsMjIiIiohbMwMAAXl5e+PLLL5GVlYVdu3YBAN577z3Y2tpi6tSpOHXqlJ6jJCIiIiKiumBxlYiIWoTy8nLs27cPXbp0wccff4zhw4fjjz/+wNq1a6FUKvUdHhERERG1Mv+fvTuPi6r6/wf+GrZhH0BRQAFZ3FByzQy31NDKtCwQ0iL7ZFpmbmmYqPEpIYlSzC01szQXwDVbPu4bbkG5IC4pymKoIK4w4LCc3x/+mK8jA8wCDMvr+XjweOS958x93xv3vM89Z7jHwsICgYGB2LFjB9LS0hAaGooDBw6gT58+8PHxQXh4ONLT0w0dJhERERERaYmTq0REVO/t2bMHXbp0wahRo+Dn54cLFy5g+fLlaN68uaFDIyIiIiJCy5YtERoaiosXLyIpKQn+/v5YsmQJPD094e/vjzVr1iA/P9/QYRIRERERkQY4uUpERPXWnj178PTTT2PQoEFo27YtUlJSsGbNGnh4eBg6NCIiIiIitbp164aFCxfi2rVr2LZtG+zt7TFmzBi0aNECISEh2LNnD4QQhg6TiIiIiIgqwMlVIiKqd44fP46BAwfC398fdnZ2SEpKQlxcHFq3bm3o0IiIiIiINCKVSjF06FDExcXhxo0b+Oqrr3Du3Dn4+/vD3d0dM2bMwOXLlw0dJhERERERPYGTq0REVG+kpKRgxIgR8PPzQ2FhIQ4cOIDdu3eja9euhg6NiIiIiEhnDg4OGDt2LJKSknD27FmMHDkSP/30E1q3bo3u3btjxYoVuH//vqHDJCIiIiIicHKViIjqgbS0NIwbNw6dOnXChQsXEBsbiyNHjqBfv36GDo2IiIiIqFp16NAB8+bNw7Vr17B79274+PhgypQpaN68OUaMGIEdO3aguLjY0GESERERETVanFwlIqI669q1a5g0aRLatWuHQ4cOYcOGDTh9+jQCAwMNHRoRERERUY0yNjbG888/jzVr1iArKwvLly/HnTt38Morr8Dd3R2TJk3CqVOnDB0mEREREVGjw8lVIiKqc3JzczFjxgy0adMGW7duxbfffovk5GQEBgZCIpEYOjwiIiIiololk8kQEhKC3bt34+rVq5g4cSJ+/fVXdOnSBR06dEBUVBSys7MNHSYRERERUaPAyVUiIqoz8vLyEBUVBS8vL6xatQqfffYZ/vnnH4wdOxYmJiaGDo+IiIiIyODc3d0RGhqKS5cu4fDhw+jduzciIiLg4uICf39/xMfHQ6FQGDpMIiIiIqIGi5OrRERkcAqFAitWrIC3tzciIiLw/vvvIzU1FaGhoTA3Nzd0eEREREREdY6RkRF69+6N5cuXIzs7Gxs2bIC5uTlGjhwJJycnjBs3DgkJCYYOk4iIiIioweHkKhERGUxxcTHWrFmDdu3aYcqUKRg9ejTS09Mxb9482NraGjo8IiIiIqJ6wdzcHIGBgdixYwfS0tLw2Wef4fjx4+jTpw98fHwQHh6OtLQ0Q4dJRERERNQgcHKViIhqnRAC8fHx6NChA8aMGQN/f39cunQJ8+bNg729vaHDIyIiIiKqt1q0aIFJkybh9OnTSEpKgr+/P5YuXQovLy/07t0bK1asQH5+vqHDJCIiIiKqtzi5SkREtWrPnj3o3r07goOD0alTJ5w/fx7Lly+Hi4uLoUMjIiIiImpQunXrhoULFyIzMxPbtm2Di4sLJkyYABcXF4SEhGDPnj0QQhg6TCIiIiKieoWTq0REVCuOHj2K5557Dv7+/nBwcMDff/+NuLg4eHl5GTo0IiIiIqIGTSqVYujQoYiLi8PNmzcRHR2NK1euwN/fH+7u7pgxYwYuX75s6DCJiIiIiOoFTq4SEVGNSk5OxogRI9CrVy8UFxfj0KFD2L17Nzp16mTo0IiIiIiIGh17e3uMHTsWCQkJSElJwX/+8x9s3LgRrVu3Rvfu3bFw4ULk5uYaOkwiIiIiojqLk6tERFQjLly4gBEjRqBTp07IyMjAnj17kJCQgD59+hg6NCIiIiIiAuDj44Pw8HBcuXIFu3fvho+PD8LCwtCyZUsMHToU8fHxKC4uNnSYRERERER1CidXiYioWmVmZmLcuHHw9fVFSkoKYmNjcezYMQwcONDQoRERERERkRpGRkZ4/vnnsWbNGvz7779Yvnw5CgsLERQUBDc3N0yaNAknT540dJhERERERHUCJ1eJiKha3Lp1CzNmzECbNm2wc+dOLFmyBGfOnEFgYCAkEomhwyMiIiIiIg3IZDKEhIRg9+7dSEtLw6RJk/Dbb7+ha9eu6NChA6KionDz5k1Dh0lEREREZDCcXCUiIr08ePAAUVFR8PLywg8//IDw8HBcvHgRY8eOhbGxsaHDIyIiIiIiHbm5uSE0NBSXL19GUlISevfujYiICLRo0QL+/v5Ys2YNCgoKDB0mEREREVGt4uQqERHpRC6XY+HChfD29sZXX32FmTNnIj09HaGhoZBKpYYOj4iIiIiIqlG3bt2wfPlyZGdnY8OGDTA3N8e7774LFxcXjBs3DgkJCRBCGDpMIiIiIqIax8lVIiLSSlFREVasWIHWrVsjLCwM77zzDlJTUxEaGgoLCwtDh0dERERERBqMfMgAACAASURBVDXI3NwcgYGB2LFjB9LT0xEeHo4TJ06gT58+8PHxQXh4OK5evWroMImIiIiIagwnV4mISCOlpaWIj4+Hj48PPvroI7z88su4fPky5s2bBzs7O0OHR0REREREtczFxQWTJk3CqVOncPbsWbzyyitYtmwZvL290bt3b6xYsQJ5eXmGDpOIiIiIqFpJBN/ZQkREVdizZw+mTZuG5ORkvP7664iKioKHh4ehw6J6aOfOndi/f7/Ktt27dwMA/P39Vbb3798fgwcPrrXYiIio8WA+Iqo5CoUCO3fuxNq1a7F9+3YYGxvj5ZdfxtixYzFw4EBIJBJDh0hEREREpI94Tq4SEVGFEhISMHPmTCQkJCAgIABz585FmzZtDB0W1WP79+/HgAEDYGpqCiMj9S/QKC0tRVFREfbt24f+/fvXcoRERNQYMB8R1Y47d+4gPj4ea9aswZEjR9CyZUuMGjUK7777Llq3bm3o8IiIiIiIdMHJVSIiKu/EiRMICwvD3r178fzzz2PevHno1q2bocOiBqC0tBTOzs7Izs6utJyjoyOuX78OY2PjWoqMiIgaE+Yjotp3/vx5xMbG4scff0R6ejq6deuGt956C6NGjULTpk0NHR4RERERkabiueYqEVEjkJmZialTp1ZZ7ty5cxgxYgSeffZZyOVy7N+/H7t37+bEKlUbIyMjvPnmmzAzM6uwjJmZGUJCQjiQTURENYb5iKj2tW/fHuHh4bhy5QoOHz6Mbt26ISwsDC1btsTQoUMRHx+PoqIiQ4dJRERERFQlTq4SETVwN27cQJ8+fRATE4PExES1ZdLT0zFu3Dg89dRTym+UHz16FM8991ztBkuNwhtvvAGFQlHhfoVCgTfeeKMWIyIiosaI+YjIMIyMjNC7d28sX74cWVlZWLFiBQoLCxEUFAQnJyeMGzcOf//9t9afm5ycjLt379ZAxEREREREqvhaYCKiBuzOnTvo1asXLl++DCEE+vTpg3379in35+Tk4JtvvkFMTAxatWqFmTNn4s0336xw7TGi6uLp6YmrV6+q3efu7o60tLTaDYiIiBol5iOiuiMzMxPr16/H999/j8uXL8PHxwchISEYPXo0mjdvXmX9wMBA/P333/jjjz/Qpk2bWoiYiIiIiBopvhaYiKihksvleOmll3D58mUUFRWhuLgY+/fvx759+3D79m3MmDED7u7uWLduHb799lucPXsWISEhnFilWhESEgJTU9Ny283MzDB69OjaD4iIiBol5iOiusPV1RWhoaG4dOkSkpKS8PzzzyM6OhotWrSAv78/1qxZA7lcrrbu3bt3sX37dqSlpaF79+7YvXt3LUdPRERERI0J/3KViKgBUigUGDp0KPbv36+ybpGxsTE8PDyQk5MDqVSKmTNn4v3334dUKjVgtNQYXb58Ga1bt1a778KFC2jbtm0tR0RERI0R8xFR3VZYWIgdO3ZgzZo1+N///gdra2sMHToUISEhGDhwICQSCQBg+fLl+PDDD1FSUgKJRAKJRILIyEiEhoYa+AyIiIiIqAGK5+QqEVEDU1JSguDgYGzbtg3FxcVqy7z11ltYunQprK2tazk6ov/z1FNP4ezZsyjrikgkEvj6+uL06dMGjoyIiBoT5iOi+iErKwvx8fH48ccfcerUKbRt2xbBwcEICQnBG2+8gaSkJJSWlirLSyQSvPPOO1i2bBnMzMwMGDkRERERNTB8LTARUUMihMC4ceOwdevWCidWjY2N8eeff8LS0rKWoyNSFRISAmNjY+W/TUxMEBISYsCIiIioMWI+IqofXFxcMGnSJJw8eRKJiYkYNGgQlixZgjZt2iAxMVFlYhV49Gy0Zs0a9O3bF9nZ2QaKmoiIiIgaIv7lKhFRAzJ9+nTMnz+/3MDCk4yMjLB27VqMHDmyliIjKi8rKwuurq7K31eJRIKMjAy0bNnSwJEREVFjwnxEVH8pFAq8++67iI2NVVkO5XGmpqZwcnLC77//jo4dO9ZyhERERETUAPEvV4mIGoqIiAh88803VU6sAo++xf3pp59WOABBVBtcXFzw7LPPwsjICEZGRvDz8+NANhER1TrmI6L6y9TUFAcOHKj0uaaoqAjXr1/H008/je3bt9didERERETUUHFylYioAVi6dClmzZqFql5GYGZmBhMTEwghkJGRgR9//LF2AiSqwFtvvQWJRAKJRIK33nrL0OEQEVEjxXxEVD8dPHgQ165dq7JccXExHj58iOHDhyM8PLzmAyMiIiKiBo2vBSYiqufWrVuHt956SzmxamJiAolEovz2trGxMVxcXODj44P27dujTZs2aN26Ndq0aQNXV1dIJBJDhk+N3J07d9CsWTMIIXDz5k00adLE0CEREVEjxHxEVD+98847WLt2LUpKSrSqN2rUKKxatQpSqbSGIiMiIiKiBiyek6vUKMXFxSEoKMjQYRDVuJiYGIwfPx6mpqaGDqXO4GQykebYTSTSHPuXRHVDbGwsRowYYegwqIYFBgZi06ZNhg6DiBoYPv8QEZGG4k0MHQGRIcXGxho6BCKdCSFw8uRJODg4wNnZudy3roOCguDs7MyJVTUmT56MZ5991tBh0P+XkJAAiUSCXr16GToU+v+OHTuGmJgYQ4dBVC+xf1l/MR/Vf/yCQ+PSs2dPTJkyRflvhUKhsvZqQUGByl+05ufnK/+7tLQUBQUFsLOzg6ura+0ETER1Fp9/iIhIW5xcpUaN32im+q6yASQOLlXs2Wef5f1fh7z00ksAAGtrawNHQo/j4AKRbphf6i/mo/qP/d/GpWXLlmxziaja8PmHiIi0wclVIiIiMigOYhMRUV3AfERERERERESaMDJ0AERERERERERERERERERE9QEnV4mIiIiIiIiIiIiIiIiINMDJVSIiIiIiIiIiIiIiIiIiDXBylYiIiIiIiIiIiIiIiIhIA5xcJdLA5s2b4enpCYlEovJjbm4ODw8P/Oc//8HVq1f1Ps6YMWNgY2MDiUSCU6dOKbf//vvvkMlk2LFjh97H0NXp06cRHBwMDw8PSKVSNG3aFJ06dcLcuXOVZSq6To//tGrVymDnUJGEhAT06tULlpaWcHZ2RmhoKB4+fKhR3aKiIkRGRsLb2xtmZmaws7NDx44dkZaWVmGdwsJCtGvXDrNmzVLZHhUVhXbt2sHCwgJWVlZo164dZs+ejfv37+tzeqSHrl27Kn93nZ2dMWnSpCrr/PPPP3j66adhbW0NIyMjvPDCC7UQqfaCg4MrvVcf//n1118NHW699vnnn8PHxwe2traQSqXw9vbGJ598gry8vHJldWlTyurNmTMHnp6eMDMzQ4sWLTBt2jQUFBTU0FkRkb4aco5R1yc0NTVFixYtMGrUKJw/f75ajlMX+sj1XWlpKRYsWAA/Pz+9yuiavwBg/fr1ePrpp2FjYwN3d3e88847uHHjhi6nQ1ShJ9ul2bNnV1p+/vz5kEgkMDIyQrt27XDo0KFailQ3Fy9exEcffYQOHTrAxsYGJiYmkMlkaNOmDYYMGYJjx44ZOsQ6oabyU2PLR/qMoehSt6IxFD4DERGRIXFylUgDr7/+Oq5cuQIvLy/IZDIIIVBSUoKMjAx8/vnniI2NRc+ePZGbm6vXcb7//nusXLmy3HYhhF6fq6/k5GT4+fnByckJ+/fvx71793D06FG88MILOHDggLKcuuskhEBxcTHkcjlu3rwJS0tLw52IGikpKRg0aBAGDhyInJwcbNmyBT/88AM++OADjeoHBQVhzZo1WLduHeRyOc6fPw8vLy+1kyZlwsLCcPHixXLbDx8+jPfeew8ZGRm4efMmvvjiC0RFRSEgIEDn8yP9/P333xg0aBAkEglOnjyJhQsXquwvKSnBgAEDVLa1adMGiYmJiIyMxMiRI/G///2vNkPWyq5du3D37l0UFRXh+vXrAIBhw4ZBoVAgPz8f2dnZeO+99wwcZf23b98+TJgwAWlpabh16xYiIyMRExODwMDAcmV1aVMAYPLkyYiOjkZkZCRyc3Oxbt06rFy5EmPGjKmp0yIiPTXkHKOuT3j37l189913SEhIQI8ePdT2hbRl6D5yfXfp0iX07dsXU6dOhVwu17kMoHv+io2NxahRoxAYGIhr165h+/btOHToEF588UUUFxfrdX5Ej3u8XQIePXsXFRWpLVtSUoJvv/0WADBgwABcuHABffv2rbVYtbVq1Sr4+vrizJkzmD9/PjIzM5Gfn4+TJ0/iiy++wN27d5GcnGzoMOuEmspPjSkf6TOGomvdisZQ+AxEREQGJYgaodjYWKHLr7+Xl5eQyWTltn/yyScCgNi4caPesW3YsEEAECdPntT7s6pLSEiIcHFxKbf94cOH4uWXXy63vaLrJIQQr7zyis5xKBQK8cMPP4iQkBCdP+NJQUFBwsPDQ5SWliq3RUdHC4lEIs6fP19p3Q0bNgiJRCLOnDmj8fGOHDkiBg0aJACIsLAwlX3Dhw8XBQUFKtsCAwMFAJGVlaXxMcoAELGxsVrXa+i0vS4//PCDACBWrlxZbt/u3bsFAJGSklJu3+DBg8Uvv/yiV6yakMvl4tlnn9W6XnBwsMjPz1f++/r16wJAuXv0u+++Ezt27NA7zjK6xltbaqKdGTJkiCguLlbZNmLECAFAZGRkKLfp0qYIIURqaqowMjISY8eOVdk+a9YsAUCcO3dO65h1zZNEjZku901DzTFl1PUJt27dKgCIDz/8UN/wap2hclhN5KZTp06J1157Tfz888+ic+fOolOnTjqVEUL3/CWEEP379xcuLi4qffHFixcLACIhIUHrz2P/t/EICAgQAQEBWtfz8vIS3bp1EwBEXFyc2jKxsbHCz89PABADBw7UN9QadezYMWFsbCwGDBggioqK1Jb53//+JxYtWlTLkdVtdSk/1WRuqWtjKLrUrWgMpbqfgfj8Q0REWorjX64SVQNvb28AqJbXV0kkEr0/o7rl5ubi3r17uH37tsp2MzMzrV97s23bNq2P//DhQyxbtgxdu3ZFSkoK5s2bp/VnqFNcXIzffvsN/fr1U7nuL774IoQQ2L59e6X1y2Ly9fXV6HgFBQWYPn06YmJi1O7fsmULzM3NVba1aNECAKr81j/VnNdeew1mZmb45Zdfyu3btWsXXFxcsGnTJpXtBQUFOH36NAYPHlzj8a1atQrZ2dla19uwYYNGf0k+btw4vPzyy7qEppau8da0mmpnAODXX3+FsbGxyramTZsCgMpfAWnbppRJTExEaWkpnnnmGZXtZa8L3blzpy5hE1EtaKg5pjI9evQAAJw9e7ZaP7c21HYOq8nc1KlTJ2zevBmjRo2CVCrVuQyge/4CgMzMTDg7O6v0xV1dXQEA6enpWn8ekSbGjx8P4NHvrjrz58/Hxx9/XJsh6Wzu3LkoKSnBl19+CRMTE7VlBg8ejAkTJtRyZPWPofJTTeSWujiGokvdysZQ+AxERESGxslVompw6dIlAI8GIB53+PBh+Pj4QCaTwdzcHL6+viodPCEEoqOj0bZtW0ilUshkMkyfPl3lMxISEuDm5gaJRILFixcDACZOnAgzMzM4OTkpy3344YewsrKCRCLBrVu3lNsPHjyIHj16wNLSEra2tvD19dV6Dc+nn34a+fn5GDBgAI4cOaJVXX3k5+dj/vz56N69O7Kzs3Hw4EF8/fXXcHZ2BvBowrGqtSJ79uxZ4edfuXIFeXl5cHNzU9le9qqoM2fOVFhXoVDg+PHj6Ny5s8bnExYWhg8//BCOjo4a17l06RLs7Ozg7u6ucR2qXjKZDIMHD8aePXtU1m4pe41YQEBAuYHvvXv3YvDgwTAzMwPw6F6fP38+2rdvD6lUCnt7e7z66qu4cOGCss5XX30FS0tL2NjYIDs7Gx9//DFatGiBixcvVngfT548GR9//DFSU1MhkUiUX/SoCSUlJZgzZw7c3NxgYWGBp556CrGxsQCAH3/8EdbW1pBIJLC3t8e2bduQlJQEd3d3GBsbY+TIkQCgNl5N27PKrk9lsVWlptuZivz777+wsLCAh4cHAN3alDJGRo+6cxYWFirbW7duDQDVtrYhEVW/xphjyl71+vhkXWXteEWxr1q1qlwfOSYmBlZWVjAyMkK3bt3QvHlzmJqawsrKCl27dkWfPn3g6uoKc3Nz2NnZ4ZNPPlGJrbI4KroeusRe1SsnDZWbdKFP/gIAT0/PcpMKZV9Y9fT01Ds+InUGDBiA9u3bY//+/eXuxyNHjkAul2PQoEFq61bV76xsDGDp0qWwsrKCpaUltm/fjhdffBG2trZo2bIlNmzYoPV5KBQK7N27F02aNFFODGpCk7yhbaxr165F9+7dYW5uDisrK7Rq1QpffPFFldekffv2yrVtu3Xrpvzi4SeffKIs/+OPPwKo+trrS11+0uRaqRuz0fT6VZRbdB3HqctjKLrUrWwMhc9ARERkcIb7q1kiw6mu1wLfuXNH/Pjjj8LS0lIMGTKkXPn4+HgRHh4ubt++LXJzc0XPnj1FkyZNlPvDwsKERCIR33zzjbhz546Qy+ViyZIl5V4LnJmZKQCovMpn1KhRonnz5irHi46OFgBETk6OEEKIvLw8YWtrK6KiokRBQYG4ceOGeO2115T7NSWXy0X37t0FAAFA+Pj4iKioKJGbm6vRdRJCiEmTJonk5GSNjnf37l0xd+5c8dRTT4mvv/5a5OXlaRWvpg4ePCgAiOjo6HL7LCwsKn0F1NWrVwUA0blzZ/Hcc88JJycnIZVKRbt27cTixYtVXnMjhBAJCQli2LBhQgghcnJy1L4WuIxCoRDXrl0TixYtElKpVKxdu1an8wNfi6aWLtdl3bp1AoDKKxh/++03sXPnTnH48GEBQFy8eFG574MPPhC///678t9z5swRZmZmYu3ateLu3bvizJkzomvXrqJp06bixo0bynJhYWECgJg0aZJYtGiReO2110RSUlKl9/Hrr78uvLy8dL0cShW9FrjMtGnThFQqFZs2bRJ37twRM2fOFEZGRiIxMVEIIcS5c+eEpaWlePvtt5V1Pv30U/H999+rfI66eDVpz4RQf33Onz9fZWzq1FY7o05+fr6wsbEREydOVG7Ttk153JkzZwQAMXv2bJXtxcXFAoAYPny41jHytVhE2tP1vmnIOUZdn3Dt2rUCgJg+fbpyW1XteEXtv7o+8meffSYAiBMnToj8/Hxx69Yt8cILLwgA4rfffhM5OTkiPz9fTJw4UQAQp06d0jgOdddD19jVMVRueuaZZyp85W9VZfTJX0IIceDAAWFqaiq+/fZbcf/+fXH27FnRvn17MXjwYJ3Ohf3fxkOf1wJfvXpVLFy4UAAQkydPVtk/fPhwsXr1avHgwQO1rwWu6p7XZAwAgNi7d6+4d++eyM7OFn369BFWVlZCoVBodS7//POPACB69uypVT1t80ZVsS5YsEAAEF9++aXIzc0Vt2/fFsuXLxejRo2q8poUFxeLVq1aCTc3t3LLaEyZMkUsWLBA+W9d+vwV0TQ/aXqt1OUjTa/fk7lFl3Gc+jCGom3dqsZQqvsZiM8/RESkpThmDWqU9JlcLZtgLPuRSCRi7ty5Gj0IRUZGCgAiOztbyOVyYWlpKfz9/VXKqFtzVdfJ1bNnzwoA4tdff9X6XJ+kUCjEwoULRbt27ZTn3qxZM3HgwIFyZdVdJwAaTa4qFArh7u4u+vbtK+7fv6933JXZtWuXACDmz59fbp+tra3w8/OrsG5ycrIAIPz9/cWRI0dEbm6uuHv3rpgxY4YAIH7++Wdl2bLJ6WvXrgkhqp5cbd68uQAgmjRpIhYuXKj1Q3YZDi6pp8t1efDggbCwsBBjxoxRbvv4449FUVGRKC0tFS4uLmLu3LnKfV27dlX+f5PL5cLa2loEBwerfOaff/4pAIjPP/9cua3sAfzxtXeruo9rY3K1oKBAWFpaqpyDXC4XUqlUjB8/Xrlt+fLlyt//9evXi6lTp2oUr7aTq49fH01je1xttjPqhIWFiTZt2qgcW5s2RZ0XXnhBODg4iL1794qCggJx/fp1ERcXJyQSidq1savCwQUi7el63zTkHPP44HVeXp7YtGmTaN68uWjWrJmyX6RJO64udiHU95HLJlcfPHig3PbTTz+V64uWXaONGzdqHMeT10Of2J9kyNykz+SqvvlLiP9bH6/sp2XLliIzM1Onc2H/t/HQd3L17t27wsrKStjb2wu5XC6EeLSGY8uWLcXDhw/VTq7q0u98fAxACPVtQtkXrC9fvqzVuSQlJQkA4vnnn9e4jr5548lYFQqFsLOzE/3791f5vOLiYhETE6M2hievSdnk7ONr4Obn5ws3Nzdx7949IYRu174ymuQnba5VZZOrVf2/fjK3aDuOU1/GULSpq+kYSnU+A/H5h4iItMQ1V4m0JZPJIISAEALTp0+HEAIymQympqZV1i0rU1JSgsuXL0Mul2PgwIE1FqunpyeaNWuGN998E+Hh4UhLS9P5s0xNTTFx4kScP38ex48fx6uvvors7GwEBgbizp075co/fp2EEJg0aZLGxzl9+jT8/f3Rp08fLFiwAPn5+TrHXZmy9U3LXv/zOIVCUe71Mo8re1VQhw4d4OfnBwcHB8hkMvz3v/+FTCbDihUrlGVnzpyJsWPHKtdPrUpmZiays7Oxfv16/PTTT+jSpUudXKOyMbG2tsaQIUPw66+/QgiBhw8fwsTEBCYmJpBIJHj99deVr208d+4cunTporzfU1JSkJeXh+7du6t85tNPPw0zMzOcOHGi0mNX532sq4sXL0Iul6Njx47KbRYWFnByclJ5JdbYsWMREBCA999/H3Fxcfjqq6/qTGyPq8125klbtmxBXFwcdu7cCRsbG+V2bdoUdTZu3IjAwECEhITAwcEBvXr1wtatWyGEQJMmTWr0nIhIPw09x9y7dw8SiQQymQyTJk3CSy+9hD///FPZL9KlHddW2SuUH+/zlV3Dslcw6xJHdcZuyNykD33zV1hYGFasWIG9e/ciLy8PV65cgZ+fH5599llkZmbWxilQIyWTyTBy5EjcuXMHGzduBAAsWLAA48ePV7YZT9K13wk8GgOoSNnxytojTVlbWwOA8lW6mtA3bzwZ65kzZ3D37t1y64AbGxtXOAbw5DUZM2YMZDKZyrqaP//8M1599VXY2toCqJlcUVV+0vdaqaPJ/2ttc3N9GUPRpq6mYyh8BiIiIkPi5CqRHmbPng0nJyfMnDlT7cP/b7/9hueeew6Ojo6QSqUq6zpdu3YNALRaf1NbFhYW2LdvH3r37o2IiAh4enoiODhYZU0vXTzzzDPYunUrPvjgA+Tk5GD//v1V1omJiVF5EKqMTCbDrFmzcOTIEZSWlqJnz574/PPPy03i6rteSNkaj0+uXSKXy1FYWKhcl0Sdsn2Pr28LPHpYcnd3R2pqKoBH668kJydjzJgxGp078OjhyNHREYMGDcLGjRuRkpKCyMhIjetTzQgODsaNGzeQmJiI33//HS+88IJyX0BAAE6dOoXU1FT8/vvvCAoKUu67e/cugP8b/HicnZ0dHjx4UOlxa+o+1kbZw/msWbNU7q/09PRygzkRERHIy8urtS8EaBPb42qrnXncxo0bMW/ePBw4cACtWrVS2adpm1LZ+Xz33Xe4du0a5HI5UlNT8c033wAAXFxcNIqPiAynIeeYsi/cFRcX49q1a/jhhx9U1pLXtR2vbrrEUd2xGyI36Uuf/HX9+nVERUVh7NixGDBgAKysrODh4YGVK1ciKysL0dHRNRo70fjx4wEA3333He7evYv4+Hi8//77FZbX5J6vbAygurVq1Qrm5ub4559/NK6jb954UtmztJ2dXYVlqrom1tbWGDt2LI4ePYo///wTALBs2TJMnDhRWaYmckVV+am6r5WmdMnN9WEMRdO62oyh8BmIiIgMiZOrRHqwsbHBvHnz8ODBA+WDWZmMjAwMHz4cTk5OOHHiBO7du4eoqCjl/rJv7T18+LBGY+zQoQN27NiBrKwshIaGIjY2Fl9//bVWn/H666+r/XbhW2+9BUC7b8pqw8rKCh9//DESExPRtGlT9O3bF9OnT8f169cBPHrIefyvY9X9HD9+vMLP9/DwgI2NDdLT01W2X758GQDw1FNPVVjX2toarVu3xrlz58rtKy4uhkwmAwCsWrUKe/fuhZGRkfJhpWxCPSIiAhKJBElJSRUex9vbG8bGxkhJSamwDNWOIUOGwMbGBr/88gsOHTqEvn37Kvf17t0bzs7O2LRpE5KSktC/f3/lvrKBBnUP33fv3kXLli2rPHZ13Mf6KPudXbBgQbl77NixY8pyRUVFmDRpEubPn49jx45h7ty5dSa2itR0O1Nm0aJF+Pnnn7Fv3z61D/qatinaSExMBACV30ciqpuYY3Rvxw0ZR03FXlu5qTrok78uXbqEkpKScnnR1tYWDg4O7P9SjevcuTN69uyJP//8E+PGjUNgYCDs7e0rLF/VPV/VGEB1k0qlGDx4MG7duoUjR45UWO727dvKiarqyBuPK7t/n/yCRRlNr8nEiRNhamqKBQsW4NChQ3B1dYWXl5dyvyFyRXVfK23ompvr8hiKpnX1HUPhMxAREdUWTq4S6SkkJATPPPMMfv31V8TFxSm3Jycno6ioCOPHj4enpyfMzc0hkUiU+zt27AgjIyMcPHhQp+OamJhU+dqgrKws5UCHo6MjvvzyS3Tt2lXt4EdlHj58qLbOxYsXAVTegX7S9evX8c4772h1fHNzc4wfPx5///032rdvjxkzZmhVvyImJiZ46aWXcOjQIZSWliq3//HHH5BIJBg2bFil9YOCgnDy5ElcuXJFuU0ulyM9PR2+vr4AgNWrV5d7WMnJyQHw6DVoQgh0794dubm5GDlyZLljlA06ubq6Vscpkx7Mzc0xbNgwbNq0CRYWFjAy+r8UamRkhNdeew1rSE/3rwAAIABJREFU1qxBs2bNYGJiotzXsWNHWFtbl3sAPHHiBBQKBbp161bpcavrPtaHq6srzM3NcerUqUrLffTRR3jvvfcwZcoUTJ06FV988YVGgx2atGf6xlaVmmpnhBAIDQ1FcnIytm3bpvab72U0aVO0sXLlSnh4eKBfv346xU5EtYc5Rv923BBx1HTsNZWbqpuu+atsYqJs0L/MgwcPcPv2bfZ/qVaUfUl606ZNmDJlSqVlq7rnqxoDqAnh4eGQSqWYOnVqhX/ZePbsWWXu0DdvPKlVq1ZwcHDArl271O7X9Jq0bNkSI0aMwKZNmzB79mxMnjxZZb8hckV1XytNVUdurotjKJrW1XQMpSJ8BiIiotrCyVUiPUkkEnz77beQSCSYOHGi8rUrbm5uAIA9e/agsLAQly5dUlmTw9HRUbmG1qpVq3D//n2cOXOmynWJynh7e+P27dvYtm0bioqKkJOTU+4bgFlZWXj//fdx4cIFKBQKnDx5Eunp6Tq9Jmz48OGIi4vD3bt3ce/ePWzfvh0zZszAK6+8otHkqhACBQUF2Lx5s3LdFG2ZmpriP//5D3766Sed6qsze/Zs3Lx5E5999hny8/Nx7NgxREdHY/To0Wjbtq2y3Jw5cyCTyVQeGqdOnQp3d3eMHj0aGRkZyM3NRWhoKAoKCrR+eLGyssKuXbuwb98+3L9/H0VFRTh58iTefvttWFlZYerUqdV2zqS74OBgXLx4ES+//HK5fYGBgTh37hyGDx+ust3c3Bwff/wxtmzZgp9//hn3799HcnIyPvjgAzg7O2PcuHGVHrOq+9jBwQFZWVlIS0vDgwcPdJ6krIy5uTneeecdbNiwAUuXLsX9+/dRUlKCa9euKQdElyxZghYtWuC1114DAERGRsLHxwejRo1SefWTung1ac/0iU0b1d3OnDt3Dl999RVWrlwJU1PTcq/devwb6Jq2Keraox49eiA9PR3FxcVIS0vDtGnTsGfPHqxatarCdcOIqG5hjqmedrwm43jyehgbG9dK7DXRB65OuuYvDw8P9O/fHytXrsShQ4dQUFCAzMxM5e/tu+++a5DzocZlxIgRaNq0KYYPHw5PT89Ky1bVTlQ1BlATOnfujHXr1uHs2bPo06cPfv/9d9y7dw9FRUW4evUqVq5ciXfffVe5zqm+eeNJUqkUM2fOxKFDhzBx4kT8+++/KC0txYMHD3Du3DmtrsnHH3+M4uJi3LlzBwMGDFDZZ4hcUd3XqiJP5pb09PRqG8epa2MomtbVFJ+BiIjIoARRIxQbGyu0+fU/cuSIaNOmjQAgAAgXFxfx/vvvq5QZPXq0ACDs7OzEl19+KYQQIjQ0VDg4OAg7OzsRGBgoFi9eLAAILy8vkZGRIR48eCDGjBkjmjRpIqytrUXv3r3FnDlzBADRsmVLcfr0abFo0SLh5OQkAAhLS0sxbNgwIYQQubm5on///sLc3Fx4eHiIjz76SEyfPl0AEN7e3iIjI0OkpaUJPz8/YW9vL4yNjYWLi4sICwsTxcXFWl2vXbt2iaCgIOHl5SWkUqkwMzMTbdu2FeHh4aKwsFBZbsuWLcLLy0t5nSr6mTVrllbHr2kHDx4UPXr0EFKpVDg7O4vp06ernJcQQsyePVvY2NiInTt3qmzPzMwUb7zxhrC3txdSqVT06NFD/PHHH5UeLycnRwAQYWFhKtuHDRsmPDw8hLW1tZBKpcLLy0sEBweL5ORknc4LgIiNjdWpbkOmz3VRKBSiU6dOorS0tNy+kpIS0alTJ1FSUlJuX2lpqYiOjhatW7cWpqamwt7eXgwfPlxcvHhRWSYqKkpYWFgIAMLV1VWsXbtWCCGqvI///vtv4e7uLiwsLETv3r3FjRs3tDqn+/fvi759+woHBwcBQBgZGQlvb28RERGhUu7hw4ciNDRUuLm5CRMTE+Ho6Chef/11kZKSIoYOHSokEolwcHAQR48eFUIIMWXKFGFkZCQACJlMJpKSkiqMV5P2rKLrU1VshpacnFxpexgdHa1SXpM2RV175O/vL+zs7ISJiYmwt7cXQ4YMEYmJiTrHrW2eJCL975uGlGOe7Ds7OzuLwMDACstX1o5XFLu6PnJMTIywtLQUAESrVq3E4cOHxbx584RMJhMARPPmzcW6devExo0bRfPmzQUAYW9vLzZs2FBlHBVdD11irwuOHTsmevXqJZydnZX/n5ycnISfn584ePCgxmXK6Jq/bt26JSZPniy8vb2FVCoV1tbWolevXmLr1q06nRf7v41HQECACAgI0Lj848+qTZs2FRMmTFDu++STT5R9WCGEmDVrlrJ9MTIyEj4+PuLw4cNCiKrbicrGAGbMmKFso1q3bi1SU1PFihUrhK2trQAg3N3dxT///KPT9cjIyBDTpk0Tvr6+wtraWhgbGws7OzvRpUsX8e6774ojR44oy2qSN5YsWaJVrIsXLxa+vr7C3NxcmJubiy5duoglS5ZUeU0yMjJUzqN///7i+++/V3uO1dHn1zY/aXKt1OUjba7fk7nlxIkT1TKOU5P0GUPRpO6TKhpDqc5nID7/EBGRluIkQghRHZO0RPVJXFwcgoKCwF9/asgkEgliY2MxYsQIQ4dSp/C6EFWNeZJIe7xviAyP/bzGIzAwEAAQHx9v4EiIqCFgP46IiLQUz9cCExERERERERERERERERFpgJOrRI3QhQsXyq37p+4nODjY0KESkRZ4bxMRUU1hjiEiqnlsa7XHa0ZERESGYGLoAIio9rVr146vOiFqgHhvExFRTWGOISKqeWxrtcdrRkRERIbAv1wlIiIiIiIiIiIiIiIiItIAJ1eJiIiIiIiIiIiIiIiIiDTAyVUiIiIiIiIiIiIiIiIiIg1wcpWoAfr9998hk8mwY8eOWq2rrYSEBPTq1QuWlpZwdnZGaGgoHj58WK11NSlXVFSEOXPmwNPTE2ZmZmjRogWmTZuGgoKCajlPorqkMbQPjyssLES7du0wa9ascvvWr1+Pp59+GjY2NnB3d8c777yDGzdulCtXVFSEyMhIeHt7w8zMDHZ2dujYsSPS0tJ0OTUiIkLDz0dRUVFo164dLCwsYGVlhXbt2mH27Nm4f/++Sjlt+6GlpaVYsGAB/Pz8quX8iIiqU0Nv2zWty7adiIio4ePkKlEDJIQwSF1tpKSkYNCgQRg4cCBycnKwZcsW/PDDD/jggw+qra6m5SZPnozo6GhERkYiNzcX69atw8qVKzFmzJhqPWeiuqChtw9PCgsLw8WLF8ttj42NxahRoxAYGIhr165h+/btOHToEF588UUUFxerlA0KCsKaNWuwbt06yOVynD9/Hl5eXsjLy9P5HImIGruGno8OHz6M9957DxkZGbh58ya++OILREVFISAgQKWcNv3QS5cuoW/fvpg6dSrkcnm1nScRUXVp6G17TYwxsG0nIiKqpwRRIxQbGyv4629YQUFBwsPDQ5SWliq3RUdHC4lEIs6fP18tdTUpl5qaKoyMjMTYsWNVjjFr1iwBQJw7d06v8zQkACI2NtbQYdQ5vC51nz7tw+OOHDkiBg0aJACIsLAwlX39+/cXLi4uKsdYvHixACASEhKU2zZs2CAkEok4c+aMHmdU/zBPEmmP903Do08+Gj58uCgoKFDZFhgYKACIrKwsIYR2/dBTp06J1157Tfz888+ic+fOolOnTvqeXoPEfl7jERAQIAICAgwdBtVDNT0Wwba9fmI/joiItBTHv1wlqueEEIiPj8eKFSsMHYrGiouL8dtvv6Ffv36QSCTK7S+++CKEENi+fbvedTUtl5iYiNLSUjzzzDMqx3nhhRcAADt37tT/hIkMpLG1D48rKCjA9OnTERMTo3Z/ZmYmnJ2dVY7h6uoKAEhPT1duW7ZsGbp27QpfX19dToeIiNA489GWLVtgbm6usq1FixYAoHzzgTb90E6dOmHz5s0YNWoUpFKp7idGRFRNGlvbXhNjDGzbiYiI6i9OrhLVIyUlJYiMjETbtm1hYWGBpk2bwsPDA5GRkRgxYgSAR+t/uLm5QSKRYPHixQCApUuXwsrKCpaWlti+fTtefPFF2NraomXLltiwYYPy89XVVScvLw8SiaTSn549e1ZY/8qVK8jLy4Obm5vKdi8vLwDAmTNn9K6raTkjo0fNoIWFhUq51q1bAwDOnz9fYSxEdQnbB1VhYWH48MMP4ejoqHa/p6cnsrOzVbaVrbfq6ekJAFAoFDh+/Dg6d+6s0TGJiIj5qDKXLl2CnZ0d3N3dAbAfSkT1B9t2jjEQERGRKk6uEtUjUVFRmDNnDqKjo3H79m3s2rULhYWFsLOzg52dHQCgd+/eOHr0qEq98ePHY8qUKSgoKICNjQ1iY2ORmpoKT09PvPfeeygqKqqwrjrW1tYQQlT6c/z48Qrrl01g2NjYqGw3NzeHhYUFbt68qXddTcu1a9cOQPkHnCZNmgAAcnJyKoyFqC5h+/B/jhw5gtTUVIwcObLCMjNnzsSNGzewaNEiPHjwACkpKYiJicHgwYOVAzJZWVlQKBT466+/0L9/fzg7O8Pc3Bzt27fHkiVLam1dKCKi+oT5SFVRURH+/fdfLF68GHv27MGiRYtgZmYGgP1QIqo/2LZzjIGIiIhUcXKVqB7Ztm0bunXrhmHDhsHCwgJdu3bFK6+8gkOHDkGhUGj0GX5+frC1tYWjoyOCg4ORn5+PjIyMGo5c1cOHDwEAxsbG5faZmpqioKBA77qalvP19cULL7yAJUuWYN++fSgsLMSNGzewZcsWSCQS5cMeUV3H9uGRgoICTJ48GUuXLq20XL9+/RAaGoqJEyfC1tYWHTt2xIMHD/D9998ry5S9ttHR0RERERFISUnBzZs38eqrr2LChAlYv369tqdHRNTgMR+pcnV1RcuWLREeHo6vvvoKQUFByn3shxJRfcG2nWMMREREpIqTq0T1SGFhYbm/lCopKYGpqanaDn5Vyr41X9ud+7L1p4qLi8vtUygU5V6fo0tdbY6xceNGBAYGIiQkBA4ODujVqxe2bt0KIYTy26VEdR3bh0dmzpyJsWPHKte1q0hYWBhWrFiBvXv3Ii8vD1euXIGfnx+effZZZGZmAoBy3aMOHTrAz88PDg4OkMlk+O9//wuZTFav1pciIqotzEeqMjMzkZ2djfXr1+Onn35Cly5dVF5Lz34oEdUHbNs5xkBERESqOLlKVI+89NJL+Ouvv7B9+3YUFBQgKSkJ27Ztw8svv6zTA42u9F3nxMnJCQBw//59le1yuRyFhYVwdnbWu642x5DJZPjuu+9w7do1yOVypKam4ptvvgEAuLi4VHotiOoKtg+P1mpKTk7GmDFjKo3x+vXriIqKwtixYzFgwABYWVnBw8MDK1euRFZWFqKjowFAeaxbt26p1DczM4O7uztSU1MrPQ4RUWPEfKTK1NQUjo6OGDRoEDZu3IiUlBRERkYq97MfSkT1Adt2jjEQERGRKhNDB0BEmgsPD8dff/2F0aNHIy8vD87OzhgxYgQiIiJqNY6ydU505eHhARsbG6Snp6tsv3z5MgDgqaee0ruuPscAgMTERABA//79qzodojqB7QOwatUq7N27F0ZG5b87FhERgYiICCQmJkIul6OkpKTcwIatrS0cHByQkpKiPJfWrVvj3Llz5T6vuLgYMplM6/MjImromI8q5u3tDWNjY2WeqQj7oURU17Bt5xgDERERqeLkKlE9kpKSgtTUVOTk5MDEpP7eviYmJnjppZdw6NAhlJaWKidC/vjjD0gkEgwbNkzvuvocAwBWrlwJDw8P9OvXrzpOmajGsX0AVq9ejdWrV6tsu3XrFhwdHREWFoa5c+cCAK5cuQLg0V+wPu7Bgwe4ffs2XF1dlduCgoIQERGBK1euwNPTE8Cjb6enp6fj5Zdf1v+EiYgaGOYjIDc3Fx999FG5tbkvXbqEkpISlTyjDvuhRFTXsG3nGAMRERGp4muBieqRCRMmwM3NDXl5eYYORW+zZ8/GzZs38dlnnyE/Px/Hjh1DdHQ0Ro8ejbZt2yrLzZkzBzKZDLt27dK6rqblevTogfT0dBQXFyMtLQ3Tpk3Dnj17sGrVKuVaMER1HdsHzXl4eKB///5YuXIlDh06hIKCAmRmZmLcuHEAgHfffVdZdurUqXB3d8fo0aORkZGB3NxchIaGoqCgADNmzKieEyYiakCYjwArKyvs2rUL+/btw/3791FUVISTJ0/i7bffhpWVFaZOnaqsy34oEdUHbNu1q8u2nYiIqBEQRI1QbGysqI+//vv27RNNmjQRAJQ/pqamon379mLz5s1CCCEWLVoknJycBABhaWkphg0bJpYsWSIsLS0FANG6dWuRmpoqVqxYIWxtbQUA4e7uLv755x+1dWvSwYMHRY8ePYRUKhXOzs5i+vTporCwUKXM7NmzhY2Njdi5c6fWdTUt5+/vL+zs7ISJiYmwt7cXQ4YMEYmJidV/wrUMgIiNjTV0GHVOQ70ubB/Uy8nJEQBEWFiYyvZbt26JyZMnC29vbyGVSoW1tbXo1auX2Lp1a7nPyMzMFG+88Yawt7cXUqlU9OjRQ/zxxx/Vc6J1VH3Nk0SGxPvmEeajR4YNGyY8PDyEtbW1kEqlwsvLSwQHB4vk5GSVupr2Q48dOyZ69eolnJ2dldfVyclJ+Pn5iYMHD9bMyddDDbWfR+UFBASIgIAAQ4fRaLBt164u2/b6h/04IiLSUpxECD0WKyCqp+Li4hAUFKTXWh2GsHTpUly6dAkLFixQblMoFJgxYwaWLl2KO3fuwMLCwoARUl0ikUgQGxuLESNGGDqUOqWhXhe2D1Sd6mueJDIk3jePMB+RITXUfh6VFxgYCACIj483cCSNA9t2aujYjyMiIi3F19+FEogamRs3bmDixIk4deqUynYzMzO4ubmhqKgIRUVFfKAhaoTYPhARUV3AfERE1PCwbSciIiIqj2uuEtUTFhYWMDU1xapVq3Dz5k0UFRUhKysL33//PebMmYPg4GDY2toaOkwiMgC2D0REVBcwHxERNTxs24mIiIjK4+QqUT0hk8mwa9cunD17Fm3atIGFhQV8fHywevVqzJs3Dz/99JOhQyQiA2H7QEREdQHzERFRw8O2nYiIiKg8vhaYqB7p06cPdu/ebegwiKgOYvtARER1AfMREVHDw7adiIiISBX/cpWIiIiIiIiIiIiIiIiISAOcXCUiIiIiIiIiIiIiIiIi0gAnV4lIY19//TWaNWsGiUSC7777ztDhVOjzzz+Hj48PbG1tIZVK4e3tjU8++QR5eXk6lZs7dy4kEkm5n44dO9bmaRHVO/WlzShTWlqKBQsWwM/PT+1+TdsMIiKqWxpaPmLflIgam4bWjj+psLAQ7dq1w6xZs8rtKyoqQmRkJLy9vWFmZgY7Ozt07NgRaWlp1Rw1ERERaYOTq0SksWnTpuHo0aOGDqNK+/btw4QJE5CWloZbt24hMjISMTExCAwM1KkcEemmvrQZAHDp0iX07dsXU6dOhVwuV1uGbQYRUf3U0PIREVFj09Db8bCwMFy8eFHtvqCgIKxZswbr1q2DXC7H+fPn4eXlxS94EhERGZiJoQMgIqpu1tbWGDduHIyNjQEAI0aMwObNmxEXF4fMzEy4urpqVQ4A1q5dizfffLP2T4aIatzp06fx+eef44MPPkB+fj6EEGrLadNmEBERaUvTfASwb0pEVBdp046XOXr0KM6ePat238aNG7Ft2zacPn0avr6+AABnZ2ds3769WuMmIiIi7fEvV4mowfn111+Vkx9lmjZtCgAq3xzVtBwRNWydOnXC5s2bMWrUKEil0grLsc0gIqKapGk+IiKiuknbdrygoADTp09HTEyM2v3Lli1D165dlROrREREVHdwcpWojjl48CB69OgBS0tL2NrawtfXF/fv3wcAHD58GD4+PpDJZDA3N4evry927twJAIiJiYGVlRWMjIzQrVs3NG/eHKamprCyskLXrl3Rp08fuLq6wtzcHHZ2dvjkk0+Ux/z2229hbm6OZs2a4f3334ezszPMzc3h5+eHEydOVBlzSUkJ5syZAzc3N1hYWOCpp55CbGysRuf0pLy8PLVrSD3+07NnT62v67///gsLCwt4eHhUSzmiuoJtRs20GZpim0FE9AjzkWHzERGRvtiO1347HhYWhg8//BCOjo7l9ikUChw/fhydO3eu1mMSERFR9eDkKlEdkp+fj2HDhiEgIAC3b9/GpUuX0KZNGygUCgDAzZs3ERQUhLS0NGRlZcHa2hqjRo0CAEyePBnTp0+HEALLli3D1atXcePGDfTt2xcnT57Ep59+ipMnT+L27dt4++23ER0djdOnTwMAJk6ciNGjR0Mul2PSpElIS0vD33//jeLiYvj7+yMzM7PSuGfMmIGvvvoKCxYswPXr1zF06FCMHDkSSUlJVZ7Tk6ytrSGEqPTn+PHjWl1XuVyOffv24b333oOZmZlO5T799FPY29vDzMwMHh4eePXVV5GYmKhVHETVjW1GzbQZmtK0bSEiauiYj2o/H7FvSkTVie147bfjR44cQWpqKkaOHKl2f1ZWFhQKBf766y/0799fOfHcvn17LFmyRKNXDhMREVHN4eQqUR2SlpaG+/fvo0OHDjA3N0fz5s2xefNm5WsnAwIC8Nlnn8He3h4ODg4YNmwYcnNzkZOTo/I5Pj4+sLS0RJMmTfDGG28AANzc3NC0aVNYWloq12e6cOGCSj0TExO0b98eUqkUPj4+WLp0KR48eIDVq1dXGHNhYSGWLl2K4cOH4/XXX4ednR1mzZoFU1NTrF69uspzqg2RkZFwdnbG3LlzdSr39ttv45dffkFmZiby8vKwYcMGZGRkoF+/fkhJSanJ0IkqxTbDsDRtW4iIGjrmo9rFvikRVTe247WroKAAkydPxtKlSyssk5eXBwBwdHREREQEUlJScPPmTbz66quYMGEC1q9fX1vhEhERkRqcXCWqQzw9PdGsWTO8+eabCA8PR1paWqXlTU1NATx6FU5Fyv6aqri4uFy9oqKiSj+/e/fusLS0LPfg87iLFy9CLpejY8eOym0WFhZwcnLChQsXtD6n6rZlyxbExcVh586dsLGx0amcq6srunTpAmtra5iZmaFnz55YvXo1CgoKsGTJkpo+BaIKsc0wHE3bFiKixoD5qHaxb0pE1Y3teO2aOXMmxo4dixYtWlRYpmzN1g4dOsDPzw8ODg6QyWT473//C5lMhhUrVtRWuERERKQGJ1eJ6hALCwvs27cPvXv3RkREBDw9PREcHIyCggIAwG+//YbnnnsOjo6OkEqlKmuV1BSpVFru26iPy8/PBwDMmjVLZS2S9PR0yOXyKs/pSdW5zsnGjRsxb948HDhwAK1atdK73ON8fX1hbGyMf/75R6PyRDWBbYZh1kbSpc0gImrImI8Mv+Yq+6ZEpA+247XXjickJCA5ORljxoyptJyzszMA4NatWyrbzczM4O7ujtTUVL1jISIiIt1xcpWojunQoQN27NiB/8fefUdFcfVvAH9mYemINRYURDQSI4otsSVRk9fYQAkgRhB7iRohiYr9JYpGolHRaCxREqyAGnvXGHs3gr1ExS6KYAGFhfv7wx/7SigusOzsLs/nnD0nzt6ZfXbDfu/M3L0zd+/eRXBwMKKiojB9+nTEx8fD09MTlSpVwtGjR5GcnIywsLBizZKeno6kpCRUrVo1zzYVKlQAAMycOTPH/UgOHz6c73vKjbbuczJnzhwsW7YMe/bsQZUqVYrc7t8yMzORmZmp/jUpkVxYM3R7b6TC1gwiImPH/ki+e4AD3DcloqJjHddNHV+8eDF2794NhUKhHrTNei+TJ0+GJEk4ceIEbGxsUKtWLZw/fz7HNlQqFezs7IqchYiIiAqPg6tEeuTu3bvqHecKFSrghx9+QMOGDXH+/HnExcUhPT0dgwcPRo0aNWBhYQFJkoo1z969eyGEyPfXmdWqVYOFhQX+/vvvXJ/P7z0VByEEgoODERcXh3Xr1sHGxqZI7QDg888/z7Hs+PHjEEKgWbNmWstOVFCsGbpTkJpBRFTSsD/SLe6bEpG2sY7rTkRERI5B26wZumPHjoUQAo0bNwYA+Pr64vTp0/jnn3/U66ekpODmzZtwdXWVJT8RERG9xsFVIj1y9+5dDBo0CBcvXkRaWhpOnz6NmzdvomnTpnBwcAAA7Nq1Cy9fvsSVK1dw9OhRrb5+ZmYmnjx5ApVKhdjYWAQFBcHBwQG9evXKcx0LCwv07t0bK1euxLx58/D06VNkZGTg9u3buHfvXr7vqTicP38eP/74IxYtWgSlUpnjMj5Zv1LVtB0A3LlzB6tWrUJSUhLS09Nx+PBh9OvXDw4ODvjqq6+K5X0QaYI1Q3cKUjOIiEoa9ke6xX1TItI21nH99O2338LR0RG9evVCfHw8Hj9+jODgYKSmpmLUqFFyxyMiIirZBFEJFBUVJfTxz//GjRuiefPmokyZMsLExERUqVJFjB07VqhUKiGEEMHBwaJs2bKidOnSwsfHR/z8888CgHB2dhbfffedsLKyEgBE9erVxf79+8XUqVOFnZ2dACAqVqwoli9fLlatWiUqVqwoAIgyZcqIlStXCiGEGDhwoFAqlcLe3l6YmpqKUqVKiS5duohr166p8/3000/qda2trcUXX3whhBDi1atXIjg4WDg4OAhTU1NRoUIF4eXlJc6dO/fW96RtcXFxAkCej2nTphWonRBCfPfdd8LZ2VlYW1sLU1NTUbVqVdG/f39x9+7dYnkP2gJAREVFyR1D7xjT58KaoR2HDx8WLVq0EJUrV1bXgEqVKonmzZuLv/76SwhRsJphDPS1nyTSZyX5e8P+SDs06Y+EMNx9U10wpv08yp+3t7fw9vaWO4bRYB3XDk3r+L8lJCQIAGLs2LE5nrt165b48ssvRZkyZYS5ubn44IMPxNatW4vtPZRUJXkWRIcRAAAgAElEQVQ/joiICiVaEkIILY7VEhmE6Oho+Pr6gn/+/zNo0CDExMTg8ePHckchLZEkCVFRUejatavcUfQKPxftYM0wbuwniQqO3xt5sD+iN3E/r+Tw8fEBAMTExMichIqKdZz0AffjiIiogGJ4WWAiUsvIyJA7AhEZENYMIiLSB+yPiIgMG+s4ERERGRoOrhIRERERERERERERERERaYCDq0SEMWPGICIiAsnJyXBycsLq1avljkREeow1g4iI9AH7IyIiw8Y6TkRERIbKVO4ARCS/KVOmYMqUKXLHICIDwZpBRET6gP0REZFhYx0nIiIiQ8WZq0REREREREREREREREREGuDgKhERERERERERERERERGRBji4SmSgpk+fjnfeeQeSJGH+/Plyx9HIoEGDIEmS+uHv7w8AmDhxIurUqYNSpUrB3NwcNWvWxMiRI/H8+fNs62va7t9evnwJFxcXjBs3rkj5MzMzMXPmTDRv3jzHc+vWrcv23sqXL1+k1yLSBWOqI61atcq2/M2HjY1NgV8nNDQ0123VrVtX3YbfeyKiwjGm/idLfvuJBWlTGLnt67KPIqLiYkw1XNvHEFneVu/DwsLg4uICS0tLWFtbw8XFBePHj8fTp0+ztUtPT8eECRNQo0YNmJmZwd7eHsOHD0dqaqq6Des9ERGRPDi4SmSghg8fjkOHDskdo8DKli2LrVu34tKlS1i8eDEAYM+ePRg6dChu3LiBR48eYcqUKZg1axZ8fHyyratpu38bO3YsLl26VKTcV65cwccff4xvv/0WKSkpOZ7v3Lkzbt++jX379qFDhw5Fei0iXTGmOpKfli1bFksOfu+JiArH2Pqft+0natqmsHLb12UfRUTFxdhqeF4KewyhSb3fv38/+vfvj/j4eDx48ACTJk1CWFgYvL29s7ULCgrCtGnTMGXKFDx+/BjLly/HokWL0K9fP3Ub1nsiIiJ5cHCViHTK0tIS7dq1w7vvvgtzc3MAgI2NDQYOHIiyZcvC1tYWXbt2haenJ7Zt24Zbt26p19W03ZsOHTqEs2fPFinzmTNnMGrUKHz11Vdwc3PLtY0kSbC3t8dHH32EWrVqFen1iCh/udURCwsLPH36FEKIbI+BAwdi5MiRhXqdpUuX5tjem/WE33siopIlt/5Hk/1ETdoUVl77uuyjiIiy08UxhKb13szMDEOGDEGFChVgY2MDHx8fdOnSBTt37sS9e/cAAP/88w/mz5+PgIAAdOvWDba2tmjVqhWGDRuGFStW4MKFCwBY74mIiOTCwVUikt2mTZtgYmKSbVnWpWze/KWnpu2ypKamYsSIEZg1a1aR8tWvXx9r1qyBn5+f+iCMiPTLtm3bYGtrm23ZrVu3cPbsWbRp00amVEREZOw02U8srn1Jbe3rEhGVVNo+htC03q9duxYWFhbZltnb2wOA+rZHx48fR2ZmJj788MNs7dq1awcA2L59e4HzERERkfZwcJVIx9577z1IkgSFQoFGjRqpBwVHjhwJOzs7WFhY4LfffgPw+lIxderUUS93dXXNdwd62LBhMDMzQ6VKldTLhgwZAmtra0iShEePHqmXZ2RkYMKECXBwcIClpSXq1auHqKioPLf9/PnzPO9FkvVo2rRpET+d/7lz5w4sLS3h5ORU6HZjx45V/xqUyJiwjmhm6tSpCAwM1Nr2iIhKOvY/+oX7ukRUEKzhmpHrGOLKlSsoXbo0HB0dAQAKxetTtpaWltnaZc1OzZq5SkRERPLg4CqRjp09exbVq1dHtWrVcOzYMVhZWQEAfvzxR/Tt2xdTp05Fr169AAAPHjyAr68vbty4gbt378LGxgZ+fn55bnv27Nno2rVrtmVz587F999/n6PtqFGj8OOPP2LmzJm4d+8e3N3d0b17d5w4cSLXbdvY2OS4VM6/H0eOHCnkp5JdSkoK9uzZg/79+8PMzKxQ7Q4ePIhr166he/fuWslEpE9YR97uzp072Lt3L7y8vAq9jdGjR6NMmTIwMzODk5MTunTpguPHj2slHxGRIWL/oz+4r0tEBcUa/nbaOIYoiPT0dNy5cwc///wzdu3ahTlz5qjPbbi4uADIOYharlw5AEBCQoJOMhIREVHuOLhKpGMmJiYIDAxEfHw81q5dq16ekpKCNWvWoE+fPupl3t7e+O9//4syZcqgbNmy8PDwwOPHj4u8E/3y5UvMmzcPnp6e8PLyQunSpTFu3DgolUpEREQUadvaMGXKFFSuXBmhoaGFapeamoqgoCDMmzevOGMSyYZ15O2mTp2Kr7/+Wv2L74Lq2bMnNmzYgFu3buH58+dYuXIl4uPj8cknn+DcuXNaTktEZBjY/+gH7usSUWGwhr9dUY8hCqpatWqoWrUqQkJC8OOPP8LX11f9nKurK9q1a4e5c+diz549ePnyJe7fv4+1a9dCkiSkp6frJCMRERHljoOrRDLo168f7Ozsst0fadmyZejSpQtKlSqV53pKpRLA68voFMWlS5eQkpKCunXrqpdZWlqiUqVKuHjxYpG2XVRr165FdHQ0tm/fnuPeJ5q2GzNmDAYMGKC+ZwmRMWIdydvdu3exYcMG9S/vC6NatWpo0KABbGxsYGZmhqZNmyIiIgKpqamYO3eu9sISERkY9j/y474uERUWa3jetHEMUVC3bt3Cw4cPsWLFCvz+++9o0KABHj58qH5+1apV8PHxQUBAAMqWLYsWLVrgjz/+gBBCPYOViIiI5MHBVSIZ2NjYYMCAATh06BCOHTsGAPjll18wbNiwbO02b96MVq1aoUKFCjA3N8fIkSO18vovXrwAAIwbNy7bfUpu3rypvu/Kv+niPierVq3C1KlTsXfvXlSvXr1Q7Q4cOIC4uDj069evSFmI9B3rSN7CwsLQv39/WFhYFHlbb3J1dYWJiQkuX76s1e0SERkS9j/y4r4uERUFa3jeiusYIj9KpRIVKlRA27ZtsWrVKpw7dw5TpkxRP29nZ4f58+fj9u3bSElJwbVr1/DTTz8BAKpUqaKznERERJQTB1eJZDJs2DAolUrMnDkT+/btQ7Vq1eDs7Kx+Pj4+Hp6enqhUqRKOHj2K5ORkhIWFaeW1K1SoAACYOXNmjnuVHD58ONd1ivs+J3PmzMGyZcuwZ8+efA8S3tZu8eLF2L17NxQKhfpAK+v9Tp48GZIk5XkvFyJDwzqS0/3797FixQoMHjy4SNvJTWZmJjIzM2Fubq71bRMRGRL2P/Lhvi4RFRVreE7FeQyhqZo1a8LExOSttyA5fvw4AKB169a6iEVERER54OAqkUyqVq2Krl27YvXq1Rg/fjyCgoKyPR8XF4f09HQMHjwYNWrUgIWFBSRJeut2TU1N33rvjWrVqsHCwgJ///13kd6DNgghEBwcjLi4OKxbtw42NjZFahcREZHjQCvrvjBjx46FEAKNGzcutvdDpEusIzmFhYXB398fZcuWLdJ2Pv/88xzLjh8/DiEEmjVrVqRtExEZOvY/8uG+LhEVFWt4Tto6htDE48eP0b179xzLr1y5goyMDFSrVi3f9RctWgQnJyd88sknxRWRiIiINMDBVSIZfffdd1CpVHjy5AnatGmT7TkHBwcAwK5du/Dy5UtcuXIFR48efes2a9asicTERKxbtw7p6elISEjAzZs3s7WxsLBA7969sXLlSsybNw9Pnz5FRkYGbt++jXv37mnvDWrg/Pnz+PHHH7Fo0SIolcocl/eZPn16gdoVRLdu3VCxYkWcOnVK22+LSGdYR/7nwYMHWLJkCb755ps822j6vb9z5w5WrVqFpKQkpKen4/Dhw+jXrx8cHBzw1VdfaTs6EZHBYf+jfdw3JSJdYQ3/H20eQ2jC2toaO3bswJ49e/D06VOkp6fj9OnT6NmzJ6ytrfHtt9+q237wwQe4efMmVCoVbty4geHDh2PXrl1YvHgxzMzMipyFiIiICo+Dq0QyatCgAVq3bo3AwMAcz7m6uiI4OBhz585F5cqVMXbsWLRq1QoA0LJlS3zzzTdo2bIlAGD48OHw8vICAAwePBitW7fGl19+idq1a2PSpEmwtLQEADRr1gy3bt0CAMyaNQvffPMNwsLCUK5cOVSuXBlBQUF48uSJDt75/wghtNquINLS0vDw4UOsX78+33ZHjhxBy5YtUaVKFRw9ehRnzpxB5cqV0aJFC+zbt0/ruYgKgnXkf3788Ud4eHioTwjlRtPvfbt27TBu3DhUrVoVVlZW6Nq1K1q0aIEjR46gXLly2o5ORGRw2P+8psl+oqb7kpr2UURERcUa/j/aPIbQpN5bWFigRYsW6NevH+zt7WFrawsfHx9Ur14dR44cQd26ddXbK126NNzc3GBpaYmGDRvi4sWL2L9/Py8JTEREpAckURwjFkR6Ljo6Gr6+vsUyYEd5GzRoEDZt2oTbt2/LHQXA6/sntmrVCr169UKfPn20tt2goCAsW7YMjx490to2C0OSJERFRaFr166y5tA3/FwMW1HriLF/77WF/SRRwfF7Y9x0sR/LPqrouJ9Xcvj4+AAAYmJiZE5ChkBfjyG0rSTVe23jfhwRERVQDGeuEpFOpaamYvv27bhy5QrS0tJky5GRkYF169bh2bNn6NatW5G3J4TA3bt3ceDAAVy9elULCYkoL4WtI/zeExFRURTnfiz7KCKi4qUvxxDaxnpPREQkD1O5AxBRyZKYmIh27doBAPz8/LBs2TJZcuzduxdr1qzB1q1bYWVlVeTtrV+/Hp6enup/87KhRMWnsHWE33siIiqK4tyPZR9FRFS89OUYQttY74mIiOTBywJTicTLfVBJwMui5Y6fC9HbsZ8kKjh+b4jkx/28koOXBSYibeJ+HBERFRAvC0xEREREREREREREREREpAkOrhIRERERERERERERERERaYCDq0REREREREREREREREREGuDgKhERERERERERERERERGRBji4SkRERERERERERERERESkAVO5AxDJKTo6uli3r1KpYGrKrxmRvjl8+LDcEYj0Gr8jRIVX3PuXRET02u3bt1lziQxAeno6FAoFTExM5I6SJx7/EBFRQXHUh0o0X19fuSMQkQxmzZqFWbNmyR2DiIiMEPcviYh048iRI6y5RERERCQLSQgh5A5BZEzOnDmDnj174vr165g2bRr69+8PSZLkjiW7rl27AuBsDiJDlJycDCcnJwQFBWHChAlyx5HFhg0b8MUXX2DSpEkYPXq03HGIiIzKixcv0LBhQzg6OmL79u0ldt9ZCIFOnTrh0qVLOH36NGxtbeWORERk8CRJQlRUlPqcBOnezZs3MWPGDCxevBimpqYYNGgQRowYgXLlyskdjYiIqLBieM9VIi1JT09HWFgYmjRpAltbW5w6dQoDBgwosSeHiMh4zJw5E5mZmRg2bJjcUWTj4eGBuXPnYuzYsYiIiJA7DhGRURk2bBgSExPx22+/leh9Z0mSsGTJEjx//hzffPON3HGIiIi0wtHREeHh4bhx4waCgoLw66+/wtHREYGBgYiPj5c7HhERUaFwcJVIC+Li4tC0aVN8//33mDRpEv766y84OzvLHYuIqMiSk5Mxe/ZsfPvttyhdurTccWQ1cOBAjBo1CgMHDsTWrVvljkNEZBT++OMPLFmyBPPmzUOVKlXkjiO7ihUrIiIiAkuWLEFUVJTccYiIiLSmfPnyCAkJwc2bNzF58mT88ccfqFmzJgICAnD+/Hm54xERERUIB1eJikClUiEsLAyNGzeGhYUFzpw5g+DgYCgU/GoRkXHgrNXsJk+eDH9/f3h7e+Pw4cNyxyEiMmh37txB//79MWjQIPj4+MgdR2+0b98eAwYMwKBBgzijh4iIjI61tTUCAwNx7do1/Prrrzh58iTq1q0Ld3d3HDp0SO54REREGuEIEFEhnT9/Hs2aNUNISAgmTpyIffv2oVatWnLHIiLSGs5azUmSJCxYsACffPIJPDw8cPnyZbkjEREZpMzMTAQEBKBMmTKYNm2a3HH0zowZM1CpUiX06NEDGRkZcschIiLSOqVSiYCAAMTFxWH9+vV4/PgxWrRogZYtW2Ljxo0QQsgdkYiIKE8cXCUqoMzMTISHh6Nhw4YwMTHB6dOnERwcDBMTE7mjERFpFWet5k6pVGL16tWoVasW2rdvjwcPHsgdiYjI4EybNg379+/H8uXLYWNjI3ccvWNlZYXly5fjyJEj+Omnn+SOQ0REVGwUCoV61ur+/ftRpkwZdO7cGW5uboiMjIRKpZI7IhERUQ4cXCUqgH/++QetW7fGiBEjMGrUKBw8eBAuLi5yxyIi0jrOWs2flZUVNmzYAFNTU3Ts2BHPnz+XOxIRkcE4deoUJkyYgNDQUHzwwQdyx9FbDRs2xMSJEzFu3DgcO3ZM7jhERETFLmvW6t9//4369eujb9++qFWrFsLDw5Gamip3PCIiIjUOrhJpQAiBhQsXol69ekhKSsKxY8cQEhLC2apEZLQ4a/Xtypcvj61bt+L27dvo0qUL0tLS5I5ERKT3UlJS4Ofnh6ZNm+K7776TO47eGzFiBD766CP4+fnxhzxERFRi1KtXD5GRkbh8+TI8PDwwevRoODo6IiQkBE+ePJE7HhEREQdXid7mxo0b+PTTTzFkyBAMHToUx48fh5ubm9yxiIiKDWetaq5GjRrYvHkzjh49ij59+vC+QEREb/Htt9/i/v37WLp0KX+oqAGFQoHIyEg8efIEw4cPlzsOERGRTjk5OSE8PBw3btzA4MGDMXv2bDg6OiIwMBB37tyROx4REZVgHFwlykPWbFVXV1ckJCTgyJEjmDp1KszMzOSORkRUrDhrtWAaNWqEdevWISYmBuPGjZM7DhGR3tq6dSsWLlyI+fPnw8HBQe44BsPe3h6LFi3CggULEBMTI3ccIiIinXvnnXcQEhKCmzdvYtKkSVizZg1q1KiBgIAAXLp0Se54RERUAnFwlSgX8fHxaNu2LYYMGYIhQ4bgxIkTaNSokdyxiIiKHWetFs6nn36KJUuW4IcffsDs2bPljkNEpHcePHiA3r17o0+fPvD19ZU7jsHx9PREnz59MHjwYNy9e1fuOERERLKwtbVFYGAg/vnnHyxatAjHjh1DnTp14O7uzvuTExGRTnFwlehfYmJi4Obmhjt37uDQoUOYOnUqzM3N5Y5FRKQTnLVaeH5+fggNDcU333yD1atXyx2HiEhvCCHQp08f2NjYYObMmXLHMVizZ89G2bJl0atXL16GnoiISjQzMzMEBATg/PnzWLduHR48eIAPP/wQLVu2xMaNG+WOR0REJQAHV4n+3/3799G5c2f4+vrCx8cHJ06cQJMmTeSORUSkM5y1WnRjxozB0KFD0aNHD+zfv1/uOEREemHmzJnYsWMHli1bBltbW7njGCxra2usWLECe/fuxaxZs+SOQ0REJDuFQqGetbp//36UKVMGHh4eaNiwISIjI5GRkSF3RCIiMlIcXCXC69mqdevWRVxcHP78808sWLAAVlZWcsciItIpzlrVjpkzZ6JTp05wd3dHbGys3HGIiGR17tw5jB07FiEhIWjatKnccQxeo0aNMGHCBIwePRpnzpyROw4REZHeyJq1eurUKdStWxd9+vRB7dq1ER4ejpcvX8odj4iIjAwHV6lEe/jwIby8vODr6wsvLy/Exsbik08+kTsWEZHOcdaq9igUCixduhT16tVDhw4dEB8fL3ckIiJZvHz5Et27d0fjxo0xatQoueMYjTFjxqB58+bo3r07UlNT5Y5DRESkVxo0aIDIyEhcunQJHTt2xKhRo1C9enWEhIQgOTlZ7nhERGQkOLhKJVZMTAzef/99nDp1Crt27cKCBQtgY2MjdywiIllw1qp2WVhYYOPGjShXrhw6dOiAJ0+eyB2JiEjnRowYgZs3b2Lp0qUwMTGRO47RUCgUiIyMxP379zFy5Ei54xAREeklZ2dnhIeH4/r16xg0aBBmzZoFBwcHBAYG4t69e3LHIyIiA8fBVSpxkpKSMHDgQHTt2hXt27dHbGws2rRpI3csIiLZcNZq8bCzs8OWLVvw7NkzdOnShZeiIqISZfv27Zg7dy7mzZuH6tWryx3H6FStWhULFizA3LlzsXHjRrnjEBER6a1KlSohJCQE8fHxmDhxIqKjo+Hk5ISAgABcuXJF7nhERGSgOLhKJcqWLVvw/vvvY+PGjdi4cSMiIyNha2srdywiIllx1mrxsbe3x5YtWxAXFwdfX19kZGTIHYmIqNglJCSgV69e6NGjB7p37y53HKPl7e0Nf39/9O3bF/fv35c7DhERkV4rVaoUAgMDcfXqVYSFheGvv/6Ci4sL3N3dcfLkSbnjERGRgeHgKpUIycnJGDhwIDp27IgWLVrg7Nmz6NSpk9yxiIhkx1mrxe/999/HunXrsGPHDgwdOlTuOERExUoIgT59+sDS0hJz5syRO47RmzdvHkqXLo1evXpBCCF3HCIiIr1nbW2tHmSNiIjA9evX0aRJE7i7u+PgwYNyxyMiIgPBwVUyetu3b0fdunWxfv16/PHHH4iOjkbZsmXljkVEpBc4a1U3Pv74Y0RFRWHRokUICwuTOw4RUbH5+eefsW3bNixbtgylSpWSO47Rs7GxwfLly7Fnzx7MnTtX7jhEREQGQ6lUIiAgAHFxcVi/fj0SExPRsmVLtGzZEhs3buSPloiIKF8cXCWj9fTpUwwcOBDt27dHs2bNcO7cOXTp0kXuWEREeoOzVnXLw8MDP//8M0aPHo3ffvtN7jhERFp3/vx5BAcHY+zYsWjevLnccUqMJk2aYMyYMRgxYgRiY2PljkNERGRQJElSz1rdv38/ypQpg86dO6N+/fqIjIyESqWSOyIREekhDq6SUTpw4AAaNmyIP/74AzExMYiOjka5cuXkjkVEpFc4a1X3Bg0ahJEjR2LAgAHYtm2b3HGIiLTm1atX6N69O+rWrYuxY8fKHafEGTduHBo2bIju3bsjNTVV7jhEREQGKWvW6pkzZ+Dm5oa+ffuiVq1aCA8PR0pKitzxiIhIj3BwlYxKamoqRo0ahU8++QQuLi44c+YMvLy85I5FRKR3OGtVPj/88AP8/Pzg5eWFI0eOyB2HiEgrRo8ejWvXrmH58uVQKpVyxylxTE1NsWzZMty6dYuD20REREXk6uqKyMhIXLlyBR4eHhgzZgyqV6+OkJAQJCYmyh2PiIj0AAdXyWgcOnQIbm5uWLBgAX755Rds2rQJlStXljsWEZFe4qxV+UiShIULF+Ljjz+Gh4cHLl++LHckIqIi2blzJ2bNmoV58+ahVq1acscpsZycnDBnzhzMmjULW7ZskTsOERGRwatevTrCw8Nx48YNDB48GLNnz4ajoyMCAwNx+/ZtueMREZGMOLhKBu/ly5cYNWoUPv74Y9SoUQNnz57FgAED5I5FRKS3OGtVfkqlEmvWrIGzszM6dOiABw8eyB2JiKhQHj16hJ49e8LLyws9evSQO06JFxAQgG7duqF3797sW4iIiLSkQoUKCAkJQXx8PEJDQ7F27Vo4OzsjICAAFy9elDseERHJgIOrZNCOHj2KBg0a4JdffsG8efOwdetW2Nvbyx2LiEivcdaqfrCyssKGDRtgYmKCTp064fnz53JHIiIqsH79+sHExAQLFiyQOwr9v19++QWWlpbo3bs3hBByxyEiIjIaNjY2CAwMxLVr17Bo0SIcP34c77//Ptzd3XH06FG54xERkQ5xcJUMUnp6OkJCQtCiRQs4ODhwtioRkYY4a1W/VKhQAVu2bMGtW7fg6+sLlUoldyQiIo3Nnz8fGzduRGRkJMqWLSt3HPp/dnZ2WLZsGXbs2IGFCxfKHYeIiMjomJmZISAgAOfOncO6deuQkJCApk2bomXLlti4cSN/3EREVAJwcJUMTmxsLD744ANMmzYNP/30E7Zt24Zq1arJHYuIyCBw1qr+cXZ2xqZNm7Bv3z7OMiIig3H16lWMGDECo0ePRuvWreWOQ//SsmVLBAcH49tvv+XlComIiIqJQqGAu7s7jhw5gv3796NMmTLw8PBAw4YNERkZiYyMDLkjEhFRMeHgKhkMlUqFsLAwNGnSBNbW1jhz5gwCAwMhSZLc0YiIDAJnreqvxo0bIyoqCqtWrcKECRPkjkNElK/09HT4+fmhdu3arFl67Pvvv0e9evXg5+eHtLQ0ueMQEREZtaxZq6dPn4arqyv69OmDd999F+Hh4Xj58qXc8YiISMs4uEoG4dy5c2jatCm+//57TJw4Efv27UPNmjXljkVEZFA4a1W/dejQAREREZg8eTLmzJkjdxwiojyNGzcO586dw/Lly2FmZiZ3HMqDqakpli1bhitXrmD8+PFyxyEiIioR3NzcEBkZiUuXLqFTp04YNWoUqlevjpCQECQlJckdj4iItISDq6TXsmarNmrUCGZmZjh9+jSCg4OhUPBPl4ioIDhr1TD4+/tj4sSJCAoKwpo1a+SOQ0SUw759+/DTTz9h9uzZqF27ttxx6C2cnZ0xc+ZMTJ8+Hbt375Y7DhERUYnh7OyM8PBw3LhxA4MGDcKsWbPg6OiIwMBA3L17V+54RERURByhIr114cIFtGjRAiEhIfj++++xf/9+nsAhIiokzlo1HOPGjcOQIUPg7++PAwcOyB2HiEgtKSkJPXr0gIeHB/r06SN3HNJQ37594ePjg549e+Lx48dyxyEiIipRKlasiJCQEMTHx2PixImIiYlBjRo1EBAQgMuXL8sdj4iIComDq6R3hBBYuHAhGjduDAA4deoUgoODYWJiInMyIiLDxFmrhmfWrFno2LEjOnXqhNjYWLnjEBEBAAYNGoSMjAwsWrRI7ihUQPPnz4eJiQn69++f6/PHjx/XcSIiIqKSpVSpUggMDMT169excOFCHDlyBO+99x7c3d1x4sQJueMREVEBcXCV9Mr169fRunVrDB06FF9//TUOHDiA9957T5YeejwAACAASURBVO5YVEALFy6Eo6Mjqlatqn7s2rULu3btyrbM0dERCxculDsukdHjrFXDo1AosHTpUri6uqJDhw64detWjjZz5szBsmXLZEhHRCXR4sWLERMTg99//x3lypWTOw4VUOnSpbF06VJs2LABS5YsUS9//vw5evfujRYtWuDZs2cyJiQiKrouXbpkO+dQtWpV2Nra4uuvv862zMnJCbdv35Y7LpVQ5ubmCAgIwMWLF7Fu3Trcu3cPTZo0QcuWLbFx40a54xERkYYkIYSQOwSREAKLFi3Cd999BycnJ/z+++9o0KCB3LGokK5du4ZatWrhbeVFkiRcuXIFzs7OOkpGVPIkJyfDyckJQUFBmDBhgtxxqICSk5Px8ccfIyMjA/v370eZMmUghMDo0aMRFhaGhg0b4uTJk3LHJCIjd+3aNTRo0ABfffUVwsLC5I5DRRAcHIy5c+fi5MmTSE5ORteuXXH37l2oVCqsXbsWXbp0kTsiEVGhTZ06FaNHj35ru3r16uHMmTM6SESkmV27diEkJAQHDx5Eo0aNMGzYMPj7+0Oh4LwoIiI9FcMKTbK7efMmPvvsMwwZMgRDhgzBiRMnOLBq4JydneHm5pbvTqAkSWjQoAEHVomKGWetGjY7Ozts2bIFT58+haenJ549e4aAgABMmzYNwOtL5586dUrmlERkzFQqFfz9/VGzZk1MmjRJ7jhURJMmTULt2rXh7u6OFi1a4M6dO0hPT4epqSm2bt0qdzwioiLp3r07JEnKt41SqUTPnj11lIhIM5999hkOHDiA/fv3o3LlyujVqxfq16+PyMhIpKenyx2PiIhywcFVklVkZCRcXV3x4MEDHD58GFOnToWZmZncsUgLAgIC8h1cNTExQUBAgA4TEZU8vNeqcbC3t8emTZtw+vRptGjRAitXrkRmZiaA1yeH5s6dK3NCIjJmISEhiI2NxYoVK7ifbgTu378PSZJw7do1qFQqqFQqAEB6ejr++OOPt155hohInzk4OKBJkyb5notQqVTo2rWrDlMRaS7r0sCxsbFo0KAB+vbti1q1aiE8PBwpKSlvXf/ly5eYMGEC+3MiIh3g4CrJ4t69e/Dw8EDv3r3Ru3dvnDx5Eo0bN5Y7FmlRt27d1Cf/c5ORkQEfHx8dJiIqeThr1XhUrFgR9vb2uHjxIjIyMtTL09PTsWzZMiQmJsqYjoiM1YEDBzB16lT89NNPcHFxkTsOFdHq1avh6uqK2NjYXPfTExISEBcXJ0MyIiLtCQgIyHP2qkKhQPPmzVG1alUdpyIqmLp16yIyMhJXr15F586dMWbMGDg6OiIkJCTfY7+IiAhMmjQJ/fv3z/ecHBERFR0HV0nnYmJiULduXZw/fx5//vknwsPDYW5uLncs0rJKlSrho48+gomJSY7nTExM8Mknn6BKlSoyJCMqGThr1Xj8888/aNq0Ka5evZrrJaEyMzPx+++/y5CMiIxZcnIyevTogbZt22LgwIFyx6EiePr0Kfz9/eHj44Nnz57leXlBpVKJzZs36zgdEZF25TcrVZIkXkGLDIqjoyPCw8Nx8+ZNDBkyBHPmzIGjoyMCAwNx69atbG0zMjIwdepUAK8HWXv37p3th7lERKRdHFwlnXnw4AE8PT3h6+sLb29vnDlzBh9//LHcsagY9ejRo1DPEVHRcdaqcTh27BgaN26svideblQqFWbPns1LPxGRVg0ePBgpKSmIiIh46/3rSP9p8v9QpVJhw4YNOkhDRFR8KlSogNatW+f6Q28A8PLy0nEioqIrX748QkJCcPPmTYSGhmLt2rVwdnZGQEAALly4AACIjo5WD7hmZmZi+fLl+PLLL9W3ACAiIu2SBM/EkQ7ExMRg8ODBsLW1xZIlS9CqVSu5I5EOPH36FOXLl88xIKBUKvHw4UPOpiMqJsnJyXByckJQUBAmTJggdxwqpFevXuGjjz7C8ePHIUnSWwdPd+7cic8++0xH6YjImEVGRqJXr17YtGkTOnToIHcc0pLVq1ejb9++SE1NzfMHOwqFAgkJCShbtqyO0xERaU9kZCR69+6d7bKoJiYm+PzzzzlDn4xCWloali5dih9//BFXr15Fly5dcO7cOVy5ciXH333nzp2xatUqKJVKGRMTERmdGM5cpWKVkJAAb29v+Pr64osvvkBsbCwHVkuQUqVKoV27djA1NVUvMzU1Rfv27TmwSlSMOGvVOJibm+PIkSP4/fffUa5cuTx/fQ+8rq1z5szRYToiMlbXr1/H119/jaCgIA6sGhlvb2+cPXsWH374IRSKvE8F7Ny5U4epiIi0z9PTM9t5CAAQQsDf31+mRETaZWZmhr59++LChQuIjo7G1atXcenSpRz3Wc3IyMCGDRvQuXNnvHr1Sqa0RETGiYOrVGw2b96M+vXr48SJE9i5cycWLFgAGxsbuWORjvn7+2e7x0NGRgYPaIi0ZPPmzdi4cWO2GY2816pxUSgUCAgIwI0bNzB58mRYWVnl+otjlUqFTZs24ebNmzKkJCJDI4RAfHx8juUqlQr+/v6oVq0aJk+eLEMyKm7VqlXDvn37MGPGDJiamuYYfFAoFNiyZYtM6YiItMPW1hbu7u7Z9puVSiXc3d1lTEWkfQqFAl5eXrC1tc3Rp2dRqVTYsWMHOnfujJcvX+o4IRGR8eLgKmldUlISBg4ciE6dOqFly5Y4ffo0Pv30U7ljkUw8PDxgaWmp/reFhQU6deokYyIi47Fnzx54eHjAzc1NPcjKWavGydraGsHBwbhy5Qp69eoFhUKRY5DVxMQEixcvlikhERmSv//+G66urli+fHm25aGhoTh58iRWrFiRbf+NjIskSQgMDMSpU6dQs2bNbCdjs+67+u+ZL0REhsbPz099r0lTU1N4enryB/9klI4dO4aDBw/me2/VjIwM7N69G+7u7khNTdVhOiIi48XBVdKYJpeP2LZtG+rWrYsNGzZg/fr1iI6ORpkyZXSQjvSVhYUFvvjiCyiVSiiVSnh7e/NkHZGWXLt2DZIk4ezZs/Dw8ICrqyumT5+Ob775hrNWjVSVKlWwcOFCnD17Vv1DlazLBaenp2PevHlIS0uTMyIRGYAtW7bg+fPn8Pf3x5dffonk5GQcP34cU6ZMwbRp01CvXj25I5IOuLq64tSpU/jqq68gSZL6UsFJSUk4efKkzOmIiIqmQ4cOsLa2BvB6YMnPz0/mRETFIzQ0VKP7qapUKuzduxdt27bF8+fPdZCMiMi4cXCVNPLw4UM0adIEDx48yPX5p0+fYuDAgWjfvj2aN2+uPtFPBADdu3dHeno60tPT8eWXX8odh8hoXL58GUII9eySCxcu4MWLF1ixYgViYmKyXS6YjMt7772HtWvXYufOnXBxcYFCoYAkSXj8+DHWrVsndzwi0nPr169X9xGrV6+Gi4sLvL298Z///AdDhw6VOR3pkqWlJWbPno3169ejdOnSUCqVkCSJlwYmIoNnbm4Ob29vAK8vE9y2bVuZExFp34ULF7Bp0yYIIfK8LPCbVCoVjhw5go4dO+LFixc6SEhEZLwkwTOv9BaZmZlo27at+vIRGzZsyPb8zp070bdvX7x69Qrz58+Hp6enzrJFR0fr7LWo8DIyMtC/f38AwKJFi9SzrEi/de3aVe4I9Ba2tra5/uJUoVAgMzMTrq6uCA0Nhbu7OyRJkiGh7pXEfkEIgX379mHFihVISkpC7dq1MXHiRLljkRFgP2CcEhMTUaFChWyXfTUxMUFmZib69u2LuXPnwszMTMaExev27ds4dOiQ3DH0UnJyMubOnYszZ86gevXqCAsLkzsS6blq1aqhWbNmcscoMNaBkiM2NhaTJ0/Gp59+igEDBsgdh3TAUOvSmw4fPoxbt25p1PbOnTs4f/48kpKSkJiYiMTERDx69AhJSUk5zhWYmppCkiRkZGQgMzMT7777LsaMGcOryxHpCI+vjU4MB1fpraZMmYLx48erT8AsXboU/v7+SElJwejRozFnzhx4e3tj3rx5KF++vE6zlZTBAiI5sHvQb0lJSW+97LokSZAkCRs2bEDHjh11lExe7BeItIf9gHFasWIFevTokes9NU1MTODq6oro6GjUqlVLhnTFLzo6Gr6+vnLHIDIK3t7eiImJkTtGgbEOEBkvQ61Lb/Lx8cHq1avljkFEWsbja6MT8/brBVCJduTIEUyYMEF98kWSJAwaNAjW1tYYOXIkHj16hN9//x09evSQLWNUVBR/+WEADhw4AEmS0KJFC7mj0FvwZINhuH79+lvbSJKEJUuWlJiB1SwlvV9ISEjA1atXDf4X2yQf9gPGbcuWLeqZqv+WkZGBs2fPol69epg3bx569+4tQ0Ld4MmN/J0/fx6pqalo1KiR3FFIT/n4+MgdochYB0qGsLAwDB8+nFfQKgGMoS5lMYZBYiJ6jcfXxouDq5SnxMREeHl5ZVsmhEBaWhr69++Pjz76CPPnz0fFihVlSkiGhIOqRNqV3+Bq1ozV3377TdYfv5A8KlSogAoVKsgdg4j0UGZmJjZv3oz09PR827x8+RIHDx5E9+7dYW5ursOEpC/q1KkjdwQiIq3gwCoREREVBw6uUq6EEAgICEBCQgIyMjKyPZeeno7ExES4u7tzYJU0xkt1EmnXjRs3YGZmhrS0tGzLJUmCiYkJVq1aleMHMkREVLIdO3YMSUlJeT6vVCphbm6OhQsX4ssvv9RhMiIiouLBgVUiIiIqDgq5A5B+mj59OrZs2ZLnr9qFEPj6668RHx+v42RERATkPnM1a2A1JiaGA6tERJTD1q1boVQqc31OoVCgefPmuHjxIgdWiYiIiIiIiPLBwVXK4dixYxgzZsxb7z+Snp6OHj168D4lREQy+Oeff7LNWlUoFDAzM8PmzZvRpUsXGZMREZG+Wr9+fY4fT5qamsLU1BRTpkzBnj17YG9vL1M6IiIiIiIiIsPAwVXK5smTJ/D09NRowFSlUmHfvn2YP3++DpIREdGbLl++rP5vhUIBpVKJzZs3o23btjKmIiIiffXw4UPExsZmW2ZqagonJyccP34cwcHBUCh4eEhERERERET0NrznKqkJIdCzZ89c77MKvL7cpKmpKdLT02FpaYlWrVrh888/R+vWrWVIS0RUst25cwfA63sIZc1YZT0mIqK8bNmyBZIkQQgBhUKBzMxM9O7dG7NmzYKVlZXc8YiIiIiIiIgMBn+aTGrh4eHYtGlTtkuFmZubA3h98r5evXr49ttvsXPnTiQlJWHLli0IDAyEi4uLXJELZM2aNahRowYkSYIkSRg/fny+7WfMmAFJkqBQKODi4oJ9+/bpKKnm/v2esh4WFhZwcnJCnz59cr0vY0H169cPtra2kCQJf//9t3r5li1bYGdnh40bNxb5NfRRZmYmZs6ciebNm+f6/MSJE1GnTh2UKlUK5ubmqFmzJkaOHInnz5+/ddsFWTc9PR1TpkxBzZo1YWZmhtKlS6Nu3bq4ceOGuk1oaGiOvwNJklC3bt1Cv3/SXwkJCUhNTYUkSbCyssLevXs5sFpEly5dwtdff433338ftra2MDU1hZ2dHd5991107NgRhw8fljtikclZs8+cOYNu3brByckJ5ubmKF++POrXr4/Q0FB1m7z6tDcf1atX13n2tzlw4ABatGgBKysrVK5cGcHBwXj16pXW1g0LC4OLiwssLS1hbW0NFxcXjB8/Hk+fPi1UOyq5Nm/eDABQKpUoW7Ystm3bhoULF3JgtQDYVxQv9hVFX/dtxy+AZscWlLeSUAe0IbfvqlKphL29Pfz8/HDhwoVCbVdfz0GwfhV9XU3qF+XOGM93ZtHku6XvevfuDQsLC0iShJcvX8odR+8Vd81p1apVnjXYxsZG3S49PR0TJkxAjRo1YGZmBnt7ewwfPhypqalafb9k2HLMXL19+zYOHTokRxaS0bVr1zB+/HgIISBJEgCgWrVqcHNzg6urK2rXrq0eaE1MTMS6des02m61atXQrFmzYstdEF5eXvDy8kLNmjVx7do1/Prrr5gwYQKUSmWOthkZGZg9ezYAoE2bNti1a5eu42rkzff06NEjJCUlITMzE48ePcL27dsxaNAgbN68GefPn0e5cuUK/Tq//vorPvvsM3z55ZfZlhvz/XavXLmC3r174+DBg6hfv36ubfbs2YOhQ4eiW7duUCqV2Lp1K/z9/REXF4etW7fmu/2CrOvr64vz589j+fLlaNSoERISEjBo0CCNBnH1EfuZort69SoAwNLSEuPGjcONGzcM5oSYPvULWRYvXoyvvvoKzZo1w4wZM/Dhhx/C0tISd+7cwfHjxzF79mzExcXpXe6Ckqtmx8XFoXnz5ujfvz/+/PNPVKpUCfHx8Vi8eDH27t2LcePGAci9TwNe98lpaWl49uwZWrVqJct7yMu5c+fQtm1bDB8+HDt27EBsbCw8PDyQkJCAJUuWaGXd/fv3o3///ggICIClpSW2bt0KPz8/HD16FDt27ChwO33AfkD3MjIysGnTJmRmZsLNzQ2DBw9GcnIyoqOj5Y6Wp65du8odIRv2FcWLfUXR19Xk+AUwvmMLXSopdUAbcvuuvnjxQn0c/MEHH+DEiROoXbt2gbarj+cgWL+Kvq6m9YtyZ4znOwHNv1v6LiIiAvb29pg8ebLcUfSermpOXlq2bKn+76CgICxZsgQRERHo2LEjTp48ic6dO+PevXtYvnx5kd4nGRHxL1FRUQIAH3xo5eHt7f3vPzGtAiCioqIKtI6zs7No1KiRACCio6NzbRMVFSWaN28uAIhPP/1UG1GLlbOzs7Czs8uxfOTIkQKAWLVqVZFfY+XKlQKAOH36dJG3pU1paWliyZIlIiAgQGvb/Pvvv8UXX3whli1bJtzc3ET9+vVzbdexY0ehUqmyLevatasAIOLj4/N9DU3XXblypZAkScTGxua7vUmTJomlS5fm20ZTWf1AcWE/U7If+tYvHD58WJiYmIg2bdqI9PT0XNts27ZNzJkzR1sR9UpKSopo1qxZsb5GQECAqFKlSo7lr169Ep06dcqxPK8+TQghOnfuXOgcxdFf+Pr6CicnJ5GZmaleNm3aNCFJkrhw4YJW1vX09BSpqanZ1vXx8REAxN27dwvcThPsB/jQh0dx//0VBPsK9hVFoYu+QtPjF02PLTTh7e1d7Pt1xYV1QHdy+67+8ccfAoAYMmSITrMUVy1j/SrauprWL00Ycl16U2Hfh7Gd7yzod0tf5FZrxo4dKwDkOF4zZIZacz7//HPx9OnTHOsPHDhQ7N69WwghxLVr14RCoRADBgzI1mbcuHECgDh//nyB3ldxH1+TbKLzvCywEIKPEvK4evUqbt68qfXtent75/XnJbvBgwcDAH755Zdcn58xYwa+++47XUYqFjVr1gQA3L9/v8jbyprRrC9evXqFX375BQ0bNsS5c+cwdepUrW27fv36WLNmDfz8/NQztnOzadMmmJiYZFtWvnx5AEBKSkq+r6Hpulnv0dXVtUDvwRDIXfsM+REREYGzZ8/KnsMY+oXQ0FBkZGTghx9+gKlp7rei//zzzzF06FAdJ9ONxYsX4+HDh8X6Go8fP0ZycjISExOzLTczMyvwJd00vXLGm4qrv1CpVNi8eTM++eSTbH1k+/btIYTA+vXrtbLu2rVrYWFhkW19e3t7AMg2y0jTdvpE7ppUkh4xMTG4dOmS7Dk0eURFRcn9p5kD+wr2FYWlq75C0+MXYz62KG4lvQ5o0wcffAAAOHv2rE5ft7hqGetX0dbVtH6RZozpfKc2v1u6lF+t0bdzq4Vh6DVn27ZtsLW1zbb+rVu3cPbsWbRp0wYAcPz4cWRmZuLDDz/M1q5du3YAgO3btxf+jZJR4T1XCc7OznBwcJA7hk61adMG7733Hv78809cunQp23MHDx5ESkoK2rZtm+u6GRkZmDBhAhwcHGBpaYl69eplOwm0f/9+1KlTB3Z2drCwsICrq6u66M6bNw/W1tawsrLC+vXr0b59e5QqVQpVq1bFypUrtf4+r1y5AgA5LquSX0bg9cnOadOmqS8HbWdnhxEjRmTbxoEDB+Dg4ABJkvDzzz8DAIYNGwYzMzNUqlRJ3W7IkCGwtraGJEl49OiRevlff/2FDz74AFZWVihVqhRcXV01ui/cixcvMGPGDDRu3BgPHz7EX3/9henTp6Ny5coAXp9Azu8eJpIkoWnTpgX8JDV3584dWFpawsnJqcjrpqWl4ciRI3Bzc9N2TDJwPXv2xPvvvy93DIOXlpaG3bt3o1y5cuqTPJoQQmDGjBl47733YG5ujjJlyqBLly64ePGiuk1B6/3SpUvRuHFjWFhYwNraGtWrV8ekSZMA5F+z33vvPfX9cho1aqT+ccbIkSPV7X/77bdca3ZQUBC+++47XLt2DZIkoWbNmujXr5+6Vjo7O+P06dMAXt8nxsrKCnZ2dtiwYUOBPucmTZrgxYsXaNOmDQ4ePFigdYuiuPuLf/75B8+fP8+xD+Xs7AwAiI2NLZZ1gdf9e+nSpeHo6KiVdmT8vL298e6778odwyCxr2BfYah9xb/x2KLwSlIdAN5+zqOoVCoVAGQbSNPks8qtRmn6+eVWy4DCn5d4E+uXdtelojGm852afrdmzZoFa2trdX2rWLEilEolrK2t0bBhQ3z00UeoVq0aLCwsULp0aYwcOTLb+prUH03b5VVrAEChUGDz5s1o37497OzsULly5WyXqy3IZ/i2/1f51TZ9PR8rZ82ZOnUqAgMD1f9WKF4PmVlaWmZrV6tWLQAo9H3DyQiJf+E0ZdIWXVyOAyjcZYGvX78uwsPDBQARFBSU7XlPT08REREhnj17lutlMoYPHy7Mzc3F6tWrxZMnT8SYMWOEQqEQx48fF0IIERMTI0JCQkRiYqJ4/PixaNq0qShXrpx6/axLQezevVskJyeLhw8fio8++khYW1uLtLS0Qn0O/76szJMnT8Rvv/0mrKysRMeOHXO01ySjJEnip59+Ek+ePBEpKSli7ty5Ash+WeBbt24JANkufeTn5ycqVqyY7fWmTZsmAIiEhAQhhBDPnz8XpUqVEmFhYSI1NVXcv39ffPHFF+rnc5OUlCRCQ0NFvXr1xPTp08Xz588L/kEVwocffqjxZWlevHghbG1txbBhwwr8Ormte/36dQFAuLm5iVatWolKlSoJc3Nz4eLiIn7++edsl7qYNGmSqFq1qihdurRQKpWievXqonPnzuLYsWMFzqKry0FSyaNv/cLly5cFANG0adMCvcaECROEmZmZWLp0qUhKShKxsbGiYcOGonz58uL+/fvqdprW+5kzZwoA4ocffhCPHz8WiYmJYsGCBcLPz08IkX/NVqlUonr16sLBwSHHpca/+eYbMXPmTPW/c6vZXl5ewtnZOdt6Xl5ewsTERNy5cyfb8u7du4sNGzYU6LMS4vVlkRo3bqy+1GedOnVEWFiYePz4ca7tc7tUWmBgoIiLi9Po9XTVX/z1118CgJg2bVqO5ywtLfO9zFZh1k1LSxO3b98Wc+bMEebm5nleCl7TdvlhP0By0re/P/YV7CuKQtd9hRB5H78U5NhCE4Z8+U3WgfzrwNvOeRREbt/VpUuXCgBixIgRBf6scqtRmn5+/65lhTkvkRvWL+2tW5DzL7kx5Lr0pqJcFtiYzncW5Lv13//+VwAQR48eFS9evBCPHj0S7dq1EwDE5s2bRUJCgnjx4oUYNmyYACD+/vtv9bqa1h9N2+W23/TmZ5OUlCQSExNFhw4dhLm5uXjx4kWBP8P8/l/lV9v0+XysHDVHCCFu374t6tSpIzIyMtTLYmNjBQAxfvz4bG1VKpUAIDw9PTV5S2o8/jVa0RxcpWKjbyfRs2TtbCQlJQlra2tRpkwZkZKSIoR4fU31qlWrilevXuW6s5GamiqsrKxEt27d1MtSUlKEubm5GDx4cK6vN2XKFAFAPHz4UAiR+3X2swYur169WqD38uZ7ytrZyHpIkiRCQ0M12oF5M2NKSoqwsrIS//nPf7K1ye2eq4UdXD179qwAIDZt2qTR+0tLSxOOjo7i448/zvW6+MWpIDv3Y8eOFe+++26hMua2blxcnAAg/vOf/4iDBw+Kx48fi6SkJDFq1CgBQCxbtkzdNj4+Xpw6dUo8e/ZMvHr1Shw+fFg0aNBAWFpairNnzxYoi76d1CTjoW/9wokTJwQA8dlnn2m8/ZSUFGFjY5OtHxBCiGPHjgkAYuLEieplmtT7tLQ0Ubp0adG6dets21OpVGLWrFm5Zvh3v5J1ou3N++q8ePFCODg4iOTkZPUyTU+Y79q1SwAQoaGh6mXJycmiVq1aOU7GaSotLU2Eh4cLFxcXdT/1zjvviL179+Zom1ufBkCjE0667C927NghAIgZM2bkeK5UqVKiefPmWl23YsWKAoAoV66cCA8Pz7N/17RdftgPkJz07e+PfQX7iqLQdV8hRN7HLwU5ttCEIQ9isA7kXQcKc84jP28OJD5//lysXr1aVKxYUbzzzjvi9u3b6u1r+lnlN7j6tnMs/65lBT0vkR/WL+2sy8HV14o6uGpM5zs1/W5lDa4+e/ZMvez333/P8d3KqiurVq1Sv0dN6k9B6lR+g6tvfjaRkZECQLZzdpp8hm/7f5VfbdPn87Fy1BwhhBg6dKj45Zdfcixv166dKFu2rNi9e7dITU0V9+7dE9HR0UKSpALf85fHv0Yr73uuEhk7Ozs7dO/eHU+ePMGqVasAADNnzsTgwYNhZmaW6zqXLl1CSkoK6tatq15maWmJSpUq5bhcRBalUgng9SUb8pL1eunp6YV6L8Dr9yP+/15VI0aMgBACdnZ26tfPz5sZr169ipSU/2PvzqOautb+gX8jZIJAAEWJMsjghGId+qrFoVpXbWtvKVYQav1VrFLHi4gDzkNVCuJ9cVVBrxO3vQ7gVYrt9fpiLVdxwLGoiBPFAiqKqcjwKgAAIABJREFUOCKGIcD+/cEiNSRAApl5PmuxVruz9zn7BHm+yT7JOVKMHj26xXNpjoeHBzp27IhJkyZh9erVyM/Pb3Z+V69exfvvv4/hw4cjLi4Or1+/1tn8WiIlJQUHDhxAWlqa0rX7Wzq2/jJJvXv3hq+vLxwcHCAWi7FmzRqIxWJs375d3tfFxQX9+/eHSCQCj8fDkCFDkJiYiPLycsTHx2vnIAkxMyKRCEDz90h+U05ODsrKyvD2228rtP/P//wPeDwezp8/3+T4hvX+2rVrePHiBT744AOFfhYWFgqXpXlTw1yZNm0axGIxNm3aJO+zZ88e+Pv7w9bWVu1jq/fee++he/fu2L17NxhjAICkpCQEBwcr3StaXVwuF2FhYbh58ybOnTsHf39/PH78GIGBgXj+/LlS/zczjTHW6HOhaj/6yov6+5vWX9ruTVVVVUqXEGrt2Hv37uHx48fYt28fvv/+e/Tv31/lvXzU7UcIUQ9lhWqUFerRd1Y0RZP3FkRRW6oDLVnzaM7Lly/B4XAgFosxd+5cjB07FhcuXJDfG761z5Uq6qyxaLou0RSqX9obS1rPnNY7Nf3bUrXvN/8d1s+5fj7q1h9d1KmGc2nuOOr7Nfe7aqq2GfN6rCFqTlFREX766SeEhIQoPZaUlITAwEB8+eWXcHBwwNChQ/Hjjz+CMYb27durc0ikDaCTq6RNq7/R+7Zt2/DixQv861//wowZMxrtXx8gy5cvV7hmfEFBgfyN1pEjRzBy5Eg4OjqCz+crXctfH1asWAEnJycsXboU9+7dU3q8qTnev38fAODo6Kiz+QmFQqSnp2PYsGFYv349PDw8EBwcjPLy8kbHiMViLF++HGfOnEFtbS2GDBmCb775RunFlCHuuZqUlITo6GicOHECXbt21drY+vsWvHmvWqDuhZWbmxvy8vKa3LaPjw8sLCxw584djeZESFvRtWtXCAQCjf5GXrx4AeDPRbY32dnZ4dWrVxrNof7eJnZ2do32aS5XRCIRvv76a5w9exYXLlwAAGzduhVhYWEazaUeh8PBjBkzcPfuXfz6668AgB9++AFTp05t0fYaGjx4MH788UfMnDkTJSUl+O9//9vsmE2bNim8eWyKvvKi/v7iDe9PI5VKUVFRIa/h2hrL5XLh6OiIMWPGICkpCTk5OYiKimpxP0KIeigrVKOsMM6saEpr31u0ZW2pDqiz5qGp+hOJ1dXVuH//Pnbv3q1wP3htP1fqasm6hDqofmm/fhHNmeN6Z0v+tpqjbv0xVJ1SpbnfVVO1zZjXYw1Rc2JiYhAaGio/OdvwuLdt24b79+9DKpUiLy8Pf/vb3wAAnTt3bnQupG2hk6ukTevXrx+GDBmCCxcuYPr06QgMDIS9vX2j/etPOMbFxSl8ypAxhszMTBQWFmLcuHFwcnLC+fPn8fLlS8TExOjrcORsbGwQHR2NV69eyV9Q1WtujvWBUllZqdM59u7dGz///DOKiooQGRmJ5ORkbNy4sdlx1tbWmD9/Pi5evIgOHTpgxIgRWLhwIR4+fAig7oVOw99Nw59z585p7Tg2b96MPXv2ID09XeNwbW6sSCRCt27dcOPGDaXHqqurIRaLm9x+bW0tamtr5Z9SJ4Qo4vP5+OCDD/DkyROcOXOm0X7Pnj3DtGnTAPy5oKXqzdOLFy/g7Oys0Rzq//YbLnTWUzdXwsLCwOVyERcXh4yMDLi4uMDT01OjubwpJCQEAoEAO3fuxO3bt2Fra6uwCKaJ8ePHq/wE6f/7f/8PgGbfAtGErvPC3d0dNjY2KCgoUGj//fffAQB9+/bVyVgA8PLygoWFBXJycrTSjxDSOMqKxlFWGHdWNNTa9xZtWVuqA82teeiCtp8rTbR0XeJNVL+0O5Zohzmsd+rjb0vd+mPIOtWQOnW6qdpmrOux+q45jx49wr59+5TWzZty8eJFAMCoUaPUHkPMm1mdXP3Pf/4DsViMn3/+Wa9jNXX69GkMHToUVlZWkEgkiIyMVPtEVmvGEtXqi+jBgwcxb968Jvu6uLhAIBDgypUrKh/Pzs6GTCbDrFmz4OHhAYFAAA6Ho/U5q+PLL7/E4MGD8e9//xsHDhxQe459+vRBu3btcPLkyRbt19LSstlLWhQVFcnf1Ds6OuLbb7/FgAEDVL7Rb4xAIMCsWbPw22+/oVevXli8eHGL5ttSjDFERkYiOzsbqampKj+9po2xQUFByMrKwt27d+VtUqkUBQUF8PHxkbc1vDwUUBf6jDG88847as+traL8aLtWr14NPp+PiIiIRj+tef36dVhaWgKoq5EikQiXLl1S6HP+/HlUVVVh4MCBGu2/a9eucHBwwLFjx1Q+rm6uODs7Y8KECTh48CBWrFiB8PBwjebRkL29PYKCgpCamoqNGzciNDS0xduqrKxUWd9v374NQLPFlYcPH2LKlCka7V9XeWFpaYmxY8ciIyMDtbW18vajR4+Cw+HAz8+v1WOfPn2KiRMnKo3Pzc1FTU0NXFxcNOpHNEPZQOpRVqhGWdE8fWSFJtR9b0GUtZU60Nyahy5o+7lSlzbWJQCqX9oeS7TH1Nc7tfm31Rh164+h6pQqzf2umqptxrweq++aExMTg0mTJsHBwUHtOe7YsQPu7u5499131R5DzJtZnVytv9eLvsdqIicnB2PGjMHo0aNRUlKClJQU7N69GzNnztTpWNK4CRMmoEOHDhg3bhw8PDya7CsQCDBlyhTs378fCQkJKC0tRU1NDe7fv4+HDx/C1dUVAHD8+HFUVFQgNze3Rdfd1wYOh4PvvvsOHA4HYWFh8ss1NDdHR0dHjB8/HgcPHsSuXbtQWlqKa9euqX0PHi8vLzx79gypqamQyWQoKSlR+uRQUVERZsyYgVu3bqGqqgpZWVkoKCho0eV6uVwuvvrqK3z//fcaj22NGzduYMOGDdixYwe4XK7SpS7e/NTXypUrIRaL5W+ENRkbEREBNzc3hISEoLCwEE+fPkVkZCTKy8sVXsA8ePAASUlJePHiBWQyGTIzMzFt2jS4urpSjVAD5Ufb1a9fP+zduxfXr1/H8OHD8Z///AcvX76ETCbDH3/8gR07dmDq1Kny+6EIBALMnz8fKSkp2LNnD0pLS5GdnY2ZM2dCIpFg+vTpGu2fz+dj6dKlyMjIQFhYGB48eIDa2lq8evUKN27c0ChX5s+fj+rqajx//hzvvfeeWvt3cHBAUVER8vPz8erVK4UPx8ycOROVlZX497//jU8++USj42po3LhxOHDgAF68eIGXL1/i8OHDWLx4MT799FO13hQzxlBeXo5Dhw616N6AgG7yYsWKFSguLsaqVavw+vVrZGZmIjY2FiEhIejRo4e8X8McUHestbU1jh07hvT0dJSWlkImkyErKwuTJ0+GtbU1IiIiNOpHNEPZQOpRVlBWtIaus0IT6r63IMraSh1obs1DF7T9XDWmYS0rKCjQ2roE1S/d1y+iOXNY72zt31Zz1K0/mtSppl43aUNzv6um1lyNfT1WXzWnuLgYu3fvbvJDB4MGDUJBQQGqq6uRn5+PBQsW4Pjx49i1a1ej9y4mbRBrIDk5maloJloSFBTE3N3dWW1trbwtNjaWcTgcdvPmTZ2NNYSAgAAWEBCg030AYMnJyWr1TUlJYZ6engwA69ChA5szZ478sUWLFrGzZ8/K/3/58uXMycmJAWDt2rVj3t7e7NSpU4wxxiorK1lkZCRzdXVllpaWzNHRkY0fP57l5OQwxhiLjIxkDg4OzM7OjgUGBrItW7YwAMzT05MtXryYWVlZMQCsW7duLC8vj23fvp3Z2toyAMzNzY3duXNH7eM/c+YM6969OwPAALDOnTuzGTNmKPQJCQlhAJidnR379ttvm51jYWEhe/XqFZs2bRpr3749E4lEbNiwYWzlypUMAHN2dmZXr15lmzdvlj9HVlZWzM/PjzHG2NOnT9moUaOYQCBg7u7u7K9//StbuHAhA8C8vLxYYWEhy8/PZ76+vsze3p5ZWFiwzp07s2XLlrHq6mq1j12XMjMz2dChQ5lEIpE/t05OTszX15edPHmSMcZYdna2/DFVP7GxsfLtrVixgtnY2LC0tDSNxzLG2L1799jnn3/O7O3tGZ/PZ4MGDWJHjx5V6DN//nzm6enJrK2tmaWlJXN2dmahoaGsqKhI4+PXdQ5QzrSMOeSHseXCmwoLC9mCBQuYj48PE4lEzMLCgtnZ2bH+/fuzqVOnsjNnzsj71tbWstjYWNatWzfG5XKZvb09GzduHLt9+7a8T3x8vEb1fsuWLczHx4cJBAImEAhY//79WXx8PGOs+Zr9plGjRrGdO3cqHV9jNfu3335jbm5uTCgUsmHDhrFHjx4pjOvfvz9bsmSJxs/nm44dO8aCgoKYp6cn4/P5jMfjsR49erDVq1eziooKeb83c7qpn+XLl7dqPtp28uRJNmjQIMbn85lEImELFy5UOC7GlHNAk7F+fn7M3d2diUQixufzmaenJwsODmbZ2dkt6qcOygHTYQ7Z0JAx//ujrKCsaCldZ4U671/qqfPeQh36eF2nK1QHGq8DjDW/5qGOhmsVEomEBQYGNtpfnedKVY3S5PlrWMvOnz+vlXUJql/6q1/NMeW69CZNj8Mc1zsZU/9va9OmTfJ9d+3alZ06dYpFR0czsVjMALBOnTqxvXv3sqSkJNapUycGgNnb27P9+/czxtSrP5r0a1hrIiIimFAoVHhu9uzZw+zt7eXrrNevX9eonjX1u2pqzdXY12MZ033NYYyxiIgINmnSpCbn8f777zM7OztmaWnJ7O3t2ccff8wuXrzYomOi979m64DJnlytra1lBw4cYH//+98NPRW1yWQyJhKJWEhIiEL79evXGQAWHR2tk7GGYsyL6ISQxhnzoqY2UH6oP1bbKBdMz9ixY9ndu3cNPQ2iZ+aeA6pQNqg/Vtfa4r8/U0dZ0TaZ8kkMqgOEmCdTrktvMpfjIITUodcdZuuASVwWuKamBlFRUejRoweEQiE6dOgAd3d3REVFYcKECQDq7hfk6uoKDoeDLVu2AAASEhJgbW0NKysrHD58GB999BFsbW3h7OyM/fv3y7evaqwqZWVlSpfvbPjT1Nfo7969i7KyMvmlFOp5enoCAK5du6aTsYQQ0lZRflB+EM28ecmia9euQSAQwN3d3YAzIkT7KBsoG0jrUFYQQgghhBBC2jqTOLkaExODlStXIjY2Fs+ePcOxY8dQUVEBOzs72NnZAQCGDRuGs2fPKoybNWsW5s2bh/LyctjY2CA5ORl5eXnw8PBAaGio/E2hqrGqiEQiMMaa/Dl37lyj4x89egQAsLGxUWgXCAQQCoUoLi7WyVhiWm7dutXsQhuHw0FwcLChp0qI0aP8oPwgmomMjERubi7u3LmDKVOmYO3atUp9KKeIqaNsoGwgrUNZQUjb1Fb+rtvKcRKib/S3RQgxN5aGnoA6UlNTMXDgQPj5+QEABgwYgE8//RQ7d+5EVVWVWjcR9vX1hUAgAAAEBwfj1KlTKCwslH86Wx8qKysBABYWFkqPcblclJeX62QsMS09e/YEY8zQ0yDELFB+UH4QzVhZWaFnz57o0qUL4uPj4e3trdSHcoqYOsoGygbSOpQVhLRNbeXvuq0cJyH6Rn9bhBBzYxLfXK2oqFAqvjU1NeByuSoXBJpTv2Dy5uWM9KF+Aaa6ulrpsaqqKgiFQp2MJYSQtoryg/KDaGbdunWoqalBYWEhPvnkE0NPhxCdoGygbCCtQ1lBCCGEEEIIaetM4uTq2LFjcfnyZRw+fBjl5eW4dOkSUlNT8Ze//KVFCyAt1dr7Ijk5OQEASktLFdqlUikqKiogkUh0MpYQQtoqyg/KD0IIaYiygbKBEEIIIYQQQghpDZO4LPDq1atx+fJlhISEoKysDBKJBBMmTMD69ev1Oo/6+yK1lLu7O2xsbFBQUKDQ/vvvvwMA+vbtq5OxhBDSVlF+UH4QQkhDlA2UDYQQQgghhBBCSGuYxDdXc3JykJeXh5KSEshkMhQWFiIhIQH29vaGnppGLC0tMXbsWGRkZKC2tlbefvToUXA4HPl9n7Q9lrQ9//nPfyAWi/Hzzz/rdaymTp8+jaFDh8LKygoSiQSRkZHye4BpoqKiAj179sTy5cv12o8YP8oPyo+2ztzz4JtvvoG3tzdsbW3B5/Ph5eWFRYsWoaysTKHfyJEjG/1moEgkUui7b98+/M///A9sbGzg5uaGKVOm4NGjR1o9XmJYlA2UDaR55p4fMTEx6NmzJ4RCIaytrdGzZ0+sWLFC6dvc6uYMIaRx5l5PGmpqPYFeZxJiOG2hFmkytra2FnFxcfD19VX5+Lp161S+f+7Tp0+Lj40Qc2MSJ1fnzJkDV1dXs3gDs2LFChQXF2PVqlV4/fo1MjMzERsbi5CQEPTo0UPeb+XKlRCLxTh27JjGYwlpzbcg9HVz+ZycHIwZMwajR49GSUkJUlJSsHv3bsycOVPjbS1btgy3b9/Wez9i/Cg/NBtLzI+550F6ejrmzJmD/Px8PHnyBFFRUdi0aRMCAwPV3v+wYcPk/52cnIwvvvgCgYGBuH//Pg4fPoyMjAx89NFHKu9NSUwTZYNmY0nbZO75cerUKYSGhqKwsBDFxcVYu3YtYmJiEBAQoNBPGzlDSFtn7vWkocbWE+h1JiGGZe61SJOxubm5GDFiBCIiIiCVSnVxKIS0DayB5ORkpqLZoNLT01n79u0ZAPkPl8tlvXr1YocOHWKMMbZ582bm5OTEADArKyvm5+fH4uPjmZWVFQPAunXrxvLy8tj27duZra0tA8Dc3NzYnTt3VI7VpZMnT7JBgwYxPp/PJBIJW7hwIauoqFDos2LFCmZjY8PS0tI0HmssAgICWEBAgE73AYAlJyfrdB9EN4KCgpi7uzurra2Vt8XGxjIOh8Nu3ryp9nbOnDnDxowZwwCwZcuW6a2fOdN1DugzZyg/NBura5QLRJXW5MHHH3/MqqurFdomTJjAALDCwkJ52wcffMBKS0uVxk+fPp39+uuv8v8fNWoU69y5s8JctmzZwgCw06dPa3xspsqcckAVygbNxuqbuf/7I9rTmvwYN24cKy8vV2gLDAxkAFhRUZG8Td2caYv08bpOV6gOkIb0sT5BrzN1z5Tr0pvM5TiI5lpTi9Qde+XKFfbZZ5+xPXv2sH79+rG33npL5fbWrl3L/vnPf7byiAhj9LrDjB0wiZOr8fHxLDw8XKGtsrKSzZs3j/H5fCaVSg00M9IUWkTXj9raWnbgwAH297//3dBTUZtMJmMikYiFhIQotF+/fp0BYNHR0WptRyqVMl9fX3bjxo0mT4Zqu5+5M6dFTcoP40K5oFttOQ/eNGvWLAaA3bp1q8l+hYWFbOjQoQptXl5ebODAgQpthw8fZgDY3r17NZ6LqTKnHFCFssG4mfu/P2NE+VEnPDycAWB37txpsp+6OWPuTHnxn+qA7rTletLcegK9ztQ9U65LbzKX4zCktlaLWjp28ODBdHJVD+h1h9k6YPSXBX706BHCwsIwdepUhXYejwdXV1fIZDLIZDIDzY4Q/aqpqUFUVBR69OgBoVCIDh06wN3dHVFRUZgwYQKAuuvru7q6gsPhYMuWLQCAhIQEWFtbw8rKCocPH8ZHH30EW1tbODs7Y//+/fLtqxqrSllZWaP3rqv/GTJkSKPj7969i7KyMri6uiq0e3p6AgCuXbum1vOxbNkyzJ49G46OjnrtR0wD5QcxZ5QHjXvw4AGEQiHc3d2b7BcdHY25c+cqtHl4eODx48cKbfX3wfLw8NB4LsT4UDaQto7yo3G5ubmws7ODm5tbk/3UzRlCzB3VE0XNrSfQ60xCdINqkW5eFxFCmmf0J1eFQiG4XC527dqF4uJiyGQyFBUVYefOnVi5ciWCg4Nha2tr6GkSohcxMTFYuXIlYmNj8ezZMxw7dgwVFRWws7ODnZ0dgLp7x509e1Zh3KxZszBv3jyUl5fDxsYGycnJyMvLg4eHB0JDQ+WLiKrGqiISicAYa/Ln3LlzjY6vfwNhY2Oj0C4QCCAUClFcXNzsHM6cOYO8vDxMnDhRr/2I6aD8IOaM8kA1qVSK9PR0hIaGgsfjNdrvwYMHOHHiBMaPH6/QvnTpUjx69AibN2/Gq1evkJOTg02bNuGDDz5o8o0wMR2UDaSto/xQJJPJ8ODBA2zZsgXHjx/H5s2bm8wPdXOGkLaA6smf1FlPoNeZhOgG1SLtv6+ut2TJEtjb24PH48Hd3R3+/v64ePFii7ZFiDky+pOrYrEYx44dw/Xr19G9e3cIhUJ4e3sjMTER0dHR+P777w09RUL0JjU1FQMHDoSfnx+EQiEGDBiATz/9FBkZGaiqqlJrG76+vrC1tYWjoyOCg4Px+vVrFBYW6njmiiorKwEAFhYWSo9xuVyUl5c3Ob68vBzh4eFISEjQaz9iWig/iDmjPFAtKioKEokE69ata7JfdHQ0/vrXv6JdO8WXwu+++y4iIyMRFhYGW1tb9OnTB69evcLOnTs1mgcxXpQNpK2j/FDk4uICZ2dnrF69Ghs2bEBQUFCT/dXNGULaAqonddRdT6DXmYToBtUi7b+vBoDJkyfjp59+wr1791BWVob9+/ejsLAQ7777LnJycjTeHiHmyNLQE1DH8OHD8csvvxh6GoQYXEVFBQQCgUJbTU0NuFyuygBtTv2nrfV9+bv6Y6iurlZ6rKqqCkKhsMnxS5cuxddff40uXbrotR8xPZQfxFxRHihLSUnBgQMHcOzYMaVP7L6pqKgIP/30E2JjY5UeW7ZsGXbu3Ilff/0VgwcPxuPHj7F48WK88847OHv2LFxcXNSeDzFelA2kLaP8UHTv3j28ePECWVlZWLJkCbZv34709HR07NhRqa+6OUNIW0H1pI666wn0OpMQ3aBapN331fVcXFwU6tKQIUOQmJiIfv36IT4+nr6gQghM4JurhJA/jR07FpcvX8bhw4dRXl6OS5cuITU1FX/5y19a9IKhpVp7HwEnJycAQGlpqUK7VCpFRUUFJBJJo2NPnz6N7OxsTJs2rck5arsfIYQYE8oDRUlJSYiOjsaJEyfQtWvXJvvGxMQgNDRU6Q34w4cPERMTg6+//hrvvfcerK2t4e7ujh07dqCoqEjlyVhCCDE1lB+KuFwuHB0dMWbMGCQlJSEnJwdRUVFK/TTJGULaCqon6q8n0OtMQnSHapH2Xhc1x8fHBxYWFrhz545WtkeIqTOJb64SQuqsXr0aly9fRkhICMrKyiCRSDBhwgSsX79er/Oov49AS7m7u8PGxgYFBQUK7b///jsAoG/fvo2O3bVrF3799VelSzkCwPr167F+/XpcvHhR6/3efvttTQ+TEEJ0hvLgT5s3b0ZaWhrS09MhEoma7Pvo0SPs27cPt2/fVnosNzcXNTU16Ny5s0K7ra0tHBwc6NJHhBCzQPnROC8vL1hYWCjVe01yhpC2hOqJ+usTUqmUXmcSoiNUi3TzukiV2tpa1NbWgs/na2V7hJi6Nv3N1Y0bN6Jjx47gcDjYtm2boafTqG+++Qbe3t6wtbUFn8+Hl5cXFi1ahLKyMp32I8YnJycHeXl5KCkpgUwmQ2FhIRISEmBvb2/oqWnE0tISY8eORUZGBmpra+XtR48eBYfDgZ+fX6NjExMTlW4IX1JSAqDuMjuMMbz99tta70dIS5hKzgB1l7yJioqCl5cXeDwe7Ozs0KdPH+Tn5xt6akQFygOAMYbIyEhkZ2cjNTVVrQXvmJgYTJo0CQ4ODkqPOTs7A6j7ZsGbXr16hWfPntGl2kiLmFIOAHULJnFxcfD19W20z+nTpzF06FBYWVlBIpEgMjJSfp8nYvwoP4CnT59i4sSJSu31H7Kpr/ctyRlC2hKqJ+qvT9DrTEJ0h2pR68Y25oMPPlBqu3jxIhhjeOeddzTeHiHmqE2fXF2wYAHOnj1r6Gk0Kz09HXPmzEF+fj6ePHmCqKgobNq0CYGBgTrtR4zPnDlz4OrqahYnwlesWIHi4mKsWrUKr1+/RmZmJmJjYxESEoIePXrI+61cuRJisRjHjh0z4GwJaRlTyRkACAoKwg8//IC9e/dCKpXi5s2b8PT0NIt6Y44oD4AbN25gw4YN2LFjB7hcrtIllzZu3Kiwn+LiYuzevRvz5s1TOQ93d3eMGjUKO3bsQEZGBsrLy3Hv3j1Mnz4dADB16lQdPQPEnJlSDuTm5mLEiBGIiIiAVCpV2ScnJwdjxozB6NGjUVJSgpSUFOzevRszZ87U82xJS1F+ANbW1jh27BjS09NRWloKmUyGrKwsTJ48GdbW1oiIiACgec4Q0tZQPVEfvc4kRHeoFmk2Vl0PHjxAUlISXrx4AZlMhszMTEybNg2urq702p+QeqyB5ORkpqLZbOXm5jIAbOvWrYaeSqM+/vhjVl1drdA2YcIEBoAVFhbqrF9rBQQEsICAAK1tTxUALDk5Waf7MCbp6emsffv2DID8h8vlsl69erFDhw4xxhjbvHkzc3JyYgCYlZUV8/PzY/Hx8czKyooBYN26dWN5eXls+/btzNbWlgFgbm5u7M6dOyrH6tLJkyfZoEGDGJ/PZxKJhC1cuJBVVFQo9FmxYgWzsbFhaWlpjW6npKSEAWDLli1rcn/a7meudJ0DlDPGZ//+/YzD4bBr167pdD+UC9pDecBYdna2wvE3/ImNjVUYHxERwSZNmtTkPJ48ecLCw8OZl5cX4/P5TCQSsaFDh7Iff/xRuwds5CgHtMsUcuDKlSvss88+Y3v27GH9+vVjb731lsp+QUFBzN3dndXW1srbYmNjGYfDYTdv3tTKXOjfn25RftTx8/Nj7u7uTCQSMT6fzzw9PVlwcDDLzs6W99E0Z9oafbyu05W2Xge0heqJao2tJ9DrTN0z5br0JnM5Dn2hWqTZ2MzMTDZ06FAmkUjkz5eTkxPz9fVlJ09KdoH+AAAgAElEQVSelPebP38+8/T0ZNbW1szS0pI5Ozuz0NBQVlRUpLuDN1P0usNsHaCTqyaw2KHKrFmzGAB269YtvfbTBC2ia198fDwLDw9XaKusrGTz5s1jfD6fSaVSA82MmBNa1NQuU8iZESNGsIEDB+p8P5QL2kN5QHSJckC7TCEH3jR48GCVJ1dlMhkTiUQsJCREof369esMAIuOjtbK/unfn25RfhBtMeXF/7ZeB7SF6gkxNqZcl95kLsehL1SLiLGj1x1m64DOLwt88uRJDBo0CFZWVrC1tYWPjw9KS0sBAKdOnYK3tzfEYjEEAgF8fHyQlpYGANi0aROsra3Rrl07DBw4EJ06dQKXy4W1tTUGDBiA4cOHw8XFBQKBAHZ2dli0aJF8n9999x0EAgE6duyIGTNmQCKRQCAQwNfXF+fPn292zjU1NVi5ciVcXV0hFArRt29fJCcnq3VMDZWVlSldPqjhz5AhQzR+Xh88eAChUAh3d3e99iOG8+jRI4SFhSldLobH48HV1RUymQwymcxAsyPEcChnWpczVVVVOHfuHPr166fW800Mj/KAEEWUA7p5v9HQ3bt3UVZWBldXV4V2T09PAMC1a9davQ+iW5QfhBBtoXpCCDEGVIsIIYak05Orr1+/hp+fHwICAvDs2TPk5uaie/fuqKqqAlB376ugoCDk5+ejqKgIIpEIX3zxBQAgPDwcCxcuBGMMW7duxR9//IFHjx5hxIgRyMrKwpIlS5CVlYVnz55h8uTJiI2NxdWrVwEAYWFhCAkJgVQqxdy5c5Gfn4/ffvsN1dXVeP/993Hv3r0m57148WJs2LABcXFxePjwIT755BNMnDgRly5davaYGhKJREo3t2/4c+7cOY2eV6lUivT0dISGhoLH4+mtHzEsoVAILpeLXbt2obi4GDKZDEVFRdi5cydWrlyJ4OBg2NraGnqahOgV5Uzrc6aoqAhVVVW4fPkyRo0aJT9B0KtXL8THx4Mx1pJfDdEhygNC/kQ5oJv3G6o8evQIAGBjY6PQLhAIIBQKUVxc3Op9EN2i/CCEaAvVE0KIMaBaRAgxJJ2eXM3Pz0dpaSl69+4NgUCATp064dChQ+jQoQMAICAgAKtWrYK9vT0cHBzg5+eHp0+foqSkRGE73t7esLKyQvv27fH5558DAFxdXdGhQwdYWVlh0qRJAIBbt24pjLO0tESvXr3A5/Ph7e2NhIQEvHr1ComJiY3OuaKiAgkJCRg3bhzGjx8POzs7LF++HFwuF4mJic0ekz5ERUVBIpFg3bp1eu1HDKv+RuXXr19H9+7dIRQK4e3tjcTERERHR+P777839BQJ0TvKmdYrKysDADg6OmL9+vXIyclBcXEx/P39MWfOHOzbt08n+yUtR3lAyJ8oB/SnsrISAGBhYaH0GJfLRXl5ub6nRDRE+UEI0RaqJ4QQY0C1iBBiSJa63LiHhwc6duyISZMmYe7cuQgJCUHXrl0b7c/lcgHUXSarMfXfrKyurlYa19zX/N9++21YWVkpLYq86fbt25BKpejTp4+8TSgUwsnJCbdu3dL4mLQtJSUFBw4cwLFjx5Q+Na7LfsQ4DB8+HL/88ouhp0GI0aCcaT0+nw8A6N27N3x9feXta9aswdatW7F9+3b5t7yI8aA8IKQO5YD+CAQCAIrPS72qqioIhUJ9T4m0AOUHIURbqJ4QQowB1SJCiKHo9JurQqEQ6enpGDZsGNavXw8PDw8EBwfLP9V85MgRjBw5Eo6OjuDz+Qr3MdIVPp+v9En1N71+/RoAsHz5coX7FBUUFEAqlTZ7TA1p8x5ISUlJiI6OxokTJ5pcYNF2P0IIMVaUM63PGYlEAgB48uSJQjuPx4Obmxvy8vI0fQoIIURvKAf0d89VJycnAFC696tUKkVFRYU8TwghhBBCCCGEEHOn05OrQN03YX7++WcUFRUhMjISycnJ2LhxIwoLCzFu3Dg4OTnh/PnzePnyJWJiYnQ6F5lMhhcvXsDZ2bnRPo6OjgCAuLg4pXsVZWZmNnlMqmjrHkibN2/Gnj17kJ6ejs6dO+utHyGEGDvKmdbljEgkQrdu3XDjxg2lx6qrqyEWizV5CgghRO8oB/Rzz1V3d3fY2NigoKBAof33338HAPTt27fV+yCEEEIIIYQQQkyBTk+uFhUVyRdrHR0d8e2332LAgAG4ceMGsrOzIZPJMGvWLHh4eEAgEIDD4ehyOjhx4gQYY01+ctvFxQUCgQBXrlxR+XhTx6QLjDFERkYiOzsbqampEIlEeulHSGts3LgRHTt2BIfDwbZt2ww9nUbFxMSgZ8+eEAqFsLa2Rs+ePbFixQqlb2QQ40U5ox1BQUHIysrC3bt35W1SqRQFBQXw8fHR2X6J+TOVPADqTopFRUXBy8sLPB4PdnZ26NOnD/Lz8w09NdIEygH9sbS0xNixY5GRkYHa2lp5+9GjR8HhcODn52fA2RFTZipZsW7dOpXfDH/zEt+EEP0xldpRr7a2FnFxcQq3YnkT1RhCTJO51SJN10qb2x4h5kznJ1dnzJiBW7duoaqqCllZWSgoKMCQIUPg6uoKADh+/DgqKiqQm5uL8+fPa3X/tbW1eP78Oaqrq3Ht2jWEh4fD1dUVISEhjY4RCASYMmUK9u/fj4SEBJSWlqKmpgb379/Hw4cPmzwmXbhx4wY2bNiAHTt2gMvlKr3Iqv8Eu7b7EdIaCxYswNmzZw09jWadOnUKoaGhKCwsRHFxMdauXYuYmBgEBAQYempETZQz2hEREQE3NzeEhISgsLAQT58+RWRkJMrLy7F48WKd7ZeYP1PJA6DuQwY//PAD9u7dC6lUips3b8LT0xNlZWWGnhppAuWAfq1YsQLFxcVYtWoVXr9+jczMTMTGxiIkJAQ9evQw9PSIiTKlrCCEGA9Tqh25ubkYMWIEIiIiIJVKDT0dQogWmVst0mStlGobaet0enLV0dERNTU18PX1hZWVFf7yl79gxowZmDNnDnx8fBAZGYn4+HhIJBIsW7YMI0eOBAAMGzYMCxYsQGxsLADAx8cHp0+fRkxMDGbMmAEA+PDDD7Fv3z4kJyfjww8/BACEhYUhKSlJvv+Kigr4+PhAKBRi+PDh6N69O/773/+Cz+cDAP73f/8Xw4YNA1BXCMePHw8A2LRpE+bNm4eYmBi0b98eEokE4eHheP78eZPHpAuMMYP0I6Qt4PF4mD17NhwdHSESiRAYGAh/f3/88ssvePjwoaGnR9RAOaMd9vb2OHXqFJydndGvXz906dIFFy5cwJEjR9CvXz+d7ZcQY5GUlITU1FT861//wuDBg2FpaQmJRILDhw/TtwWMHOWAdpw7dw7Dhg1D586dcf78eVy9ehUSiQRDhw5FRkaGvF/v3r2RlpaGY8eOoX379hg/fjy++uorbN26VWdzI8SY/POf/1S67Pb169cNPS1CiBG7evUqFi9ejJkzZzb73opqDCFEV9StRequlWpS2wgxV5a63LibmxvOnDnT6OPR0dGIjo5WaJs9e7b8vxt+i3LYsGGIjIxU2k5QUJDK7dvY2OD+/fuN7j8iIgIRERFK7TweT+Xc6jV1TNrWp08ftU6IarsfIW1BSkqKUluXLl0AgL6pZCIoZ7TH2dkZ+/bt0/t+CTEGW7duxYABA+gy2CaIckA7hgwZgtOnT6vVd8SIEVr/BjAhhBBirt566y0cOnQIALB582ZUVFQYeEaEkLZI3Vqk7lop1TZCdPzNVUOrqakx9BQI0ZmTJ09i0KBBsLKygq2tLXx8fOTXvz916hS8vb0hFoshEAjg4+ODtLQ0AHXflLC2tka7du0wcOBAdOrUCVwuF9bW1hgwYACGDx8uvxeYnZ0dFi1aJN/nd999B4FAgI4dO2LGjBmQSCQQCATw9fVVa5GtpqYGK1euhKurK4RCIfr27Yvk5GS1jqmhsrIylfcjefOnJZfPy83NhZ2dHdzc3DQeS9oeyhliDCgPWpcHVVVVOHfuHH3alrQI5QAxFZQVunnvQIi5o9pBtYMQY0C1yLC1iNZKCVHNrE+uEmKuXr9+DT8/PwQEBODZs2fIzc1F9+7dUVVVBQAoLi5GUFAQ8vPzUVRUBJFIhC+++AIAEB4ejoULF4Ixhq1bt+KPP/7Ao0ePMGLECGRlZWHJkiXIysrCs2fPMHnyZMTGxuLq1asA6i6FFxISAqlUirlz5yI/Px+//fYbqqur8f777+PevXtNznvx4sXYsGED4uLi8PDhQ3zyySeYOHEiLl261OwxNSQSiZQul9Pw59y5c2o9nzKZDA8ePMCWLVtw/PhxbN68GTweT91fByGEGAzlQevzoKioCFVVVbh8+TJGjRolf9Pcq1cvxMfH0xU/CCEmj7JCe+8dlixZAnt7e/B4PLi7u8Pf3x8XL17U5NdBiMmg2qHddQd1UI0hRBnVIv3XIoDWSglRh1meXF26dCkSExPx8uVLuLu74+DBg4aeEiFalZ+fj9LSUvTu3RsCgQCdOnXCoUOH0KFDBwBAQEAAVq1aBXt7ezg4OMDPzw9Pnz5FSUmJwna8vb1hZWWF9u3b4/PPPwcAuLq6okOHDrCyssKkSZMAALdu3VIYZ2lpiV69eoHP58Pb2xsJCQl49eoVEhMTG51zRUUFEhISMG7cOIwfPx52dnZYvnw5uFwuEhMTmz0mXXJxcYGzszNWr16NDRs2NHrpP0LqUc4QY0F50Hr1lzZydHTE+vXrkZOTg+LiYvj7+2POnDl0uWyiEuUAMSWUFdoxefJk/PTTT7h37x7Kysqwf/9+FBYW4t1330VOTo7O9kuIoVDt0C+qMYSoRrXIMGitlJDmmeXJ1aioKFRWVoIxhj/++AMBAQGGnhIhWuXh4YGOHTti0qRJWL16NfLz85vsz+VyATR96br6Tx9VV1crjZPJZE1u/+2334aVlZXSC5A33b59G1KpFH369JG3CYVCODk54datWxofkzbdu3cPjx8/xr59+/D999+jf//+ePz4sd72T0wP5QwxFpQHrcfn8wEAvXv3hq+vLxwcHCAWi7FmzRqIxWJs375dp/snpolygJgSygrtcHFxQf/+/SESicDj8TBkyBAkJiaivLwc8fHxOt8/IfpGtUO/qMYQohrVIsOgtVJCmmeWJ1cJMXdCoRDp6ekYNmwY1q9fDw8PDwQHB6O8vBwAcOTIEYwcORKOjo7g8/kK9wzQFT6fr/SpsDe9fv0aALB8+XKFewIUFBRAKpU2e0wNafN+A1wuF46OjhgzZgySkpKQk5ODqKgozZ8EQgjRM8qD1ueBRCIBADx58kShncfjwc3NDXl5eZo+BYQQYlQoK3R3rzIfHx9YWFjgzp07Go8lxNhR7TD8fQ6pxhBCtQgwTC2itVJCmkcnVwkxUb1798bPP/+MoqIiREZGIjk5GRs3bkRhYSHGjRsHJycnnD9/Hi9fvkRMTIxO5yKTyfDixQs4Ozs32sfR0REAEBcXp3RfgMzMzCaPSRVd3W/Ay8sLFhYWdNkdQojJoDxoXR6IRCJ069YNN27cUHqsuroaYrFYk6eAEEKMEmWFbt471NbWora2Vn4VBELMDdUO/d/n8E1UYwipQ7XIsLWI1koJUc1oTq5u3LgRHTt2BIfDwbZt2ww9HbXMmDFD4RMi9ddm/+abb+Dt7Q1bW1vw+Xx4eXlh0aJF8nt61dN2P02cPn0aQ4cOhZWVFSQSCSIjI1FZWSl/PDU1VeHYjOma7wQoKiqSLwI7Ojri22+/xYABA3Djxg1kZ2dDJpNh1qxZ8PDwgEAgAIfD0el8Tpw4AcZYk5+ScnFxgUAgwJUrV1Q+3tQx6cLTp08xceJEpfbc3FzU1NTAxcVFJ/slhmNOOQPUvaCPioqCl5cXeDwe7Ozs0KdPnxZfToZywTRRHmhHUFAQsrKycPfuXXmbVCpFQUEBfHx8dLZfol/mlgNA3aJrXFwcfH19G91Gc/VdU5QXpoeyQjs++OADpbaLFy+CMYZ33nlHZ/slxFCodugX1RhCVKNapD+0VkqIZozm5OqCBQtw9uxZQ09DYw4ODjh69Chu376NXbt2AQDS09MxZ84c5Ofn48mTJ4iKisKmTZsQGBioMFbb/dSVk5ODMWPGYPTo0SgpKUFKSgp2796NmTNnyvt8+umnuH//PjIyMjB27NgW7YfoTlFREWbMmIFbt26hqqoKWVlZKCgowJAhQ+Dq6goAOH78OCoqKpCbm4vz589rdf+1tbV4/vw5qqurce3aNYSHh8PV1RUhISGNjhEIBJgyZQr279+PhIQElJaWoqamBvfv38fDhw+bPCZdsLa2xrFjx5Ceno7S0lLIZDJkZWVh8uTJsLa2RkREhE72SwzHnHIGqDsZ9MMPP2Dv3r2QSqW4efMmPD09W/TBG8oF00V5oB0RERFwc3NDSEgICgsL8fTpU0RGRqK8vByLFy/W2X6JfplbDuTm5mLEiBGIiIiAVCpVOVad+q4JygvTRFmhHQ8ePEBSUhJevHgBmUyGzMxMTJs2Da6uri3+myLEmFHt0C+qMYSoRrVIf2itlBANsQaSk5OZima9yM3NZQDY1q1bDbJ/TU2fPp116dJFqf3jjz9m1dXVCm0TJkxgAFhhYaHO+qkrKCiIubu7s9raWnlbbGws43A47ObNm0r9586dy9q3b6/xfgICAlhAQIDG4zQBgCUnJ+t0H8YoPz+f+fr6Mnt7e2ZhYcE6d+7Mli1bJv93EhkZyRwcHJidnR0LDAxkW7ZsYQCYp6cnmz9/PrOysmIAWNeuXdmpU6dYdHQ0E4vFDADr1KkT27t3L0tKSmKdOnViAJi9vT3bv38/Y6zu3z2Xy2VdunRhlpaWzNbWlvn7+7O8vDz5/P72t7/Jx1pbW7PPPvuMMcZYZWUli4yMZK6urszS0pI5Ojqy8ePHs5ycnGaPSRf8/PyYu7s7E4lEjM/nM09PTxYcHMyys7N1tk9ToOscoJxRX2M5s3//fsbhcNi1a9e0sh/KBdNFeaA99+7dY59//jmzt7dnfD6fDRo0iB09elSn+zRWlAPGo7EcuHLlCvvss8/Ynj17WL9+/dhbb72lcrym9b05+sgLc/73ZyiUFdoxf/585unpyaytrZmlpSVzdnZmoaGhrKioSGf7NGb6eF2nK22xDrQE1Q7tyMzMZEOHDmUSiYQBYACYk5MT8/X1ZSdPnpT3oxrTeqZcl95kLsehLVSLtEPdWqTuWqm62yP0usOMHaCTq63Q2GKHKrNmzWIA2K1bt/TaryGZTMZEIhELCQlRaL9+/ToDwKKjo5XG0CI6edP06dOZg4ODoadBdMicFzXNJWdGjBjBBg4cqJV9UC6QlqI8MF+UA8ZDnfcbgwcPVnlytSX1vSn6ygtz/vfXFlFWmC9TXvynOmD8qHaQljDluvQmczkOc0C1iGgDve4wWwdafVngXr16gcPhoF27dhg4cKD8klSLFi2CWCyGQCDAP/7xDwDAqVOn4O3tLW/38fFBWlpao9sOCwsDj8eDk5OTvG327NmwtrYGh8PBkydP5O01NTVYuXIlXF1dIRQK0bdvXyQnJze67bKyMoV7Aan60eZX8R88eAChUAh3d3e99mvo7t27KCsrk182oZ6npycA4Nq1axptj7RNNTU1hp4CaUMoZxRVVVXh3Llz6Nevn8ZjVaFcIK1BeUD0gXKgZbRd3ykvSEtRVhBCWoJqByHEGFAtIoQ0ptUnV69fv46uXbvCxcUFFy5cgJWVFQBgw4YNmDp1KqKjo+XXIC8uLkZQUBDy8/NRVFQEkUiEL774otFtf/fdd5gwYYJCW3x8PNasWaPUd/HixdiwYQPi4uLw8OFDfPLJJ5g4cSIuXbqkctsikQiMsSZ/zp0718JnRZFUKkV6ejpCQ0PB4/H01k+VR48eAQBsbGwU2gUCAYRCIYqLizXaHiGE6BrljKKioiJUVVXh8uXLGDVqFCQSCQQCAXr16oX4+HgwxjTaHuUCIcTYUQ60jLbrO+UFIYQQQgghhBBSp9UnVy0sLDB37lwUFhYiJSVF3i6VSnHo0CF89dVX8raAgACsWrUK9vb2cHBwgJ+fH54+fYqSkpJWzaGiogIJCQkYN24cxo8fDzs7OyxfvhxcLheJiYmt2rY2REVFQSKRYN26dXrtp0plZSWAut9bQ1wuF+Xl5Rpvk7QdS5cuRWJiIl6+fAl3d3ccPHjQ0FMibQDljKKysjIAgKOjI9avX4+cnBwUFxfD398fc+bMwb59+zTaHuUCaQnKA6JPlAMto+36TnlBNEVZQQhpCaodhBBjQLWIENKcVp9cBYBp06ZBLBZj06ZN8rY9e/bA398ftra2jY7jcrkAWv/1+tu3b0MqlaJPnz7yNqFQCCcnJ9y6datV226tlJQUHDhwAGlpaUqf8tZlv8YIBAIAQHV1tdJjVVVVEAqFGm+TtB1RUVGorKwEYwx//PEHAgICDD0l0kZQzvyJz+cDAHr37g1fX184ODhALBZjzZo1EIvF2L59u0bbo1wgLUF5QPSNckBz2q7vlBdEU5QVhJCWoNpBCDEGVIsIIc3RyslVkUiEr7/+GmfPnsWFCxcAAFu3bkVYWJhCvyNHjmDkyJFwdHQEn8/HokWLtLF7vH79GgCwfPlyhXsYFRQUyO/J1JA+7oGUlJSE6OhonDhxAl27dtVbv6bU30+qtLRUoV0qlaKiogISiaRF2yWEEF2inPlTfZ1+8z6AAMDj8eDm5oa8vDyNtke5QAgxBZQDmtN2fae8IIQQQgghhBBC6mjl5CoAhIWFgcvlIi4uDhkZGXBxcYGnp6f88cLCQowbNw5OTk44f/48Xr58iZiYGK3s29HREQAQFxendB+jzMxMlWN0fQ+kzZs3Y8+ePUhPT0fnzp311q857u7usLGxQUFBgUL777//DgDo27dvi7dNCCG6RDnz53a7deuGGzduKD1WXV0NsVis0fYoFwghpoJyQDParu+UF4QQQgghhBBCSB2tnVx1dnbGhAkTcPDgQaxYsQLh4eEKj2dnZ0Mmk2HWrFnw8PCAQCAAh8NpdruWlpaQyWRN9nFxcYFAIMCVK1dadQzawBhDZGQksrOzkZqaCpFIpJd+6rK0tMTYsWORkZGB2tpaefvRo0fB4XDg5+fXqu0T47Nx40Z07NgRHA4H27ZtM/R01DJjxgyFb3VMmjQJAPDNN9/A29sbtra24PP58PLywqJFi+T3oGyp2tpaxMXFwdfXV+mx1NRUhbl06NChVfsiLUc586egoCBkZWXh7t278japVIqCggL4+PhotC3KhbbDnPKgXlP1WxPr1q1T+a3CNy8BS3lgeJQDmtF2fae8aBsoK9RXUVGBnj17Yvny5fI2ygpC/mRu9UQmkyEqKgpeXl7g8Xiws7NDnz59kJ+fr/F+ZDIZVq5cCQ8PD/B4PHTp0gULFixQuH851RNCNGdudQfQ/uuY5rYXExODnj17QigUwtraGj179sSKFSuUrl4DAKdPn8bQoUNhZWUFiUSCyMhIVFZWyh+nOkbMndZOrgLA/PnzUV1djefPn+O9995TeMzV1RUAcPz4cVRUVCA3Nxfnz59vdpteXl549uwZUlNTIZPJUFJSovRpaYFAgClTpmD//v1ISEhAaWkpampqcP/+fTx8+FB7B6iGGzduYMOGDdixYwe4XK7SIt3GjRt10g8AgoOD0alTJ/z2229NznHFihUoLi7GqlWr8Pr1a2RmZiI2NhYhISHo0aOH7p4cYhALFizA2bNnDT0NjTk4OODo0aO4ffs2du3aBQBIT0/HnDlzkJ+fjydPniAqKgqbNm1CYGBgi/eTm5uLESNGICIiQuVl/T799FPcv38fGRkZGDt2bIv3Q7SDcqZOREQE3NzcEBISgsLCQjx9+hSRkZEoLy/H4sWL5f0oF8ibzCkPgObrt7ZRHhgHygHNqFvfKS9IPcoK9S1btgy3b99WaKOsIORP5lZPgoKC8MMPP2Dv3r2QSqW4efMmPD09W/Rh7/DwcMTGxiIqKgpPnz7F3r17sWPHDkybNk3eh+oJIZozt7qj7dcx6mzv1KlTCA0NRWFhIYqLi7F27VrExMQo3XM2JycHY8aMwejRo1FSUoKUlBTs3r0bM2fOlPehOkbMnVZPrvbv3x+jRo3C3LlzlR7z8fFBZGQk4uPjIZFIsGzZMowcORIAMGzYMMybNw/Dhg0DUFcIx48fDwCYNWsWRo0ahc8//xw9evTA2rVrIRQKAQDvvPMO7t27BwDYtGkT5s2bh5iYGLRv3x4SiQTh4eF4/vy5Ng+xWYwxg/QDgKqqKjx+/BiHDx9usl/v3r2RlpaGY8eOoX379hg/fjy++uorbN26Ve19EaJrQqEQH374Ibp37w4+nw+g7vJ606dPh4ODA2xsbDBhwgSMGzcO//d//yevBZq4evUqFi9ejJkzZ6Jfv34q+3A4HHTp0gXDhw9Ht27dWnVMpPUoZ+rY29vj1KlTcHZ2Rr9+/dClSxdcuHABR44cUfi3TLlAzIGqPFCnfmvqn//8p9IlW69fvy5/nPLAOFAO1Dl37hyGDRuGzp074/z587h69SokEgmGDh2KjIwMeT916zvlBTF1+sqKemfPnlXIiHqUFYSYPlX1JCkpCampqfjXv/6FwYMHw9LSEhKJBIcPH1a40ok67t69i23btuHLL79EcHAwbGxsMHLkSISFhWHfvn24efMmAKonhLQl+ngdo+72eDweZs+eDUdHR4hEIgQGBsLf3x+//PKLwodK165dCycnJ6xZswbW1tZ45513EBkZiX/84x+4desWAKpjxPxZanuD6enpjT4WHR2N6OhohbbZs2fL/zsuLk5pjIODg8ptbtiwQeH/eTyeyu3rW58+fdQ6IartfgBw8OBBjBw5Em5ubs32HTFihFqf5CfEmPz73/9Waqu/pERLPsH11ltv4dChQwDq7mtcUVHRugkSvWjrOVPP2ZK+PmYAACAASURBVNkZ+/bta7IP5QIxV1S/2zbKAWDIkCE4ffq0Wn3Vqe+UF8Qc6SorysvLsXDhQuzcuRPe3t5a2SYhxLht3boVAwYM0PgWLKpcvHgRtbW1GDx4sEL7hx9+iHXr1iEtLQ29evVq9X4IIaZN269j1N1eSkqKUluXLl0AQP5N/erqahw5cgQBAQEKt2H56KOPsHDhQhw+fBg9e/Zs1XwJMQVa/eZqW1ReXo60tDTk5uaiqqrKYPOoqalBamoqXr16heDg4FZvjzGGoqIinD59Gr///rsWZkg01atXL3A4HLRr1w4DBw6UnzxctGgRxGIxBAIB/vGPfwCou2SDt7e3vN3HxwdpaWmNbjssLAw8Hg9OTk7yttmzZ8Pa2hocDgdPnjyRt9fU1GDlypVwdXWFUChE3759kZyc3Oi2y8rKVN637s2fIUOGtPLZ+dODBw8gFArh7u6utW0SYkxamjOUC+aD8oCQtk3X7zcoL8wDZYX+LFu2TP6NDkLMEdUTRVVVVTh37pzWvgHfrl3dUmz9VTLq1X+rq/6bq4S0JVR3jFtubi7s7OzkH8S8e/cuysrK5Ldlqefp6QkAuHbtmt7nSIgh0MnVVnr27Jn8a/tfffWVweZx4sQJHDp0CEePHoWVlVWrt3f48GH51/aPHDmihRkSTV2/fh1du3aFi4sLLly4IP+9btiwAVOnTkV0dDRCQkIAAMXFxQgKCkJ+fj6KioogEonwxRdfNLrt7777DhMmTFBoi4+Px5o1a5T6Ll68GBs2bEBcXBwePnyITz75BBMnTsSlS5dUblskEildVrHhz7lz51r4rCiSSqVIT09HaGgoeDyeVrZJiLFpac5QLpgPygP9WLJkCezt7cHj8eDu7g5/f39cvHjR0NMiROfvNygvzANlhX6cOXMGeXl5mDhxoqGnQojOUD1RVFRUhKqqKly+fBmjRo2CRCKBQCBAr169EB8fr9HtvADIv83V8CRq+/btAQAlJSUaz5EQU0d1x/jIZDI8ePAAW7ZswfHjx7F582b52uujR48AADY2NgpjBAIBhEIhiouL9T5fQgyBTq62wrZt2xSK5Z49eww2l9GjR2Pv3r0Kn8JpDX9/f4Vje/NTPEQ/LCwsMHfuXBQWFipckkEqleLQoUMKi2sBAQFYtWoV7O3t4eDgAD8/Pzx9+rTVL8orKiqQkJCAcePGYfz48bCzs8Py5cvB5XKRmJjYqm1rQ1RUFCQSCdatW2foqRCiE63JGcoF80F5oHuTJ0/GTz/9hHv37qGsrAz79+9HYWEh3n33XeTk5Bh6eqQN08f7DcoL80BZoXvl5eUIDw9HQkKCoadCiE5RPVFUfxlMR0dHrF+/Hjk5OSguLoa/vz/mzJnT7K1aGvLx8cGHH36I+Ph4pKeno6KiAo8ePUJKSgo4HA5kMpkuDoMQo0Z1x/i4uLjA2dkZq1evxoYNGxAUFCR/rLKyEkDd760hLpeL8vJyvc2TEEOik6uEGLFp06ZBLBZj06ZN8rY9e/bA398ftra2jY7jcrkA6i5n0Rq3b9+GVCpFnz595G1CoRBOTk7ym5MbSkpKCg4cOIC0tDSlT0oRQoi5oTzQLRcXF/Tv3x8ikQg8Hg9DhgxBYmIiysvLER8fb+jpEUKIWigrdGvp0qX4+uuv5fcdI8ScUT35E5/PBwD07t0bvr6+cHBwgFgsxpo1ayAWi7F9+3aNt5mUlITAwEB8+eWXcHBwwNChQ/Hjjz+CMSb/BishbQ3VHeNy7949PH78GPv27cP333+P/v374/HjxwDqvqEK1N17taGqqiqly54TYq7o5CohRkwkEuHrr7/G2bNnceHCBQDA1q1bERYWptDvyJEjGDlyJBwdHcHn87Fo0SKt7P/169cAgOXLlyvcL6CgoEB+/4OG9HG/gaSkJERHR+PEiRPo2rVrq7ZFCCGmgPJA/3x8fGBhYYE7d+4YeiqEEKIWygrdOX36NLKzszFt2jRDT4UQvaB68ieJRAIASldi4PF4cHNzQ15ensbbFIvF2Lbt/7N353FRVY3/wD8DDJus5gbihpp7LmmJIioqiguIyJK7PmllpeQSmkv0PGWaC5lbaWqZqSyiSO6IiCia5o6K5oKi4IYIyDbA/f3hz/lGLIIwc2aGz/v18g/vMnwuc+45l3vuPedHJCUlISsrCzdu3MDSpUsBALa2thX+PCJdwHpHs8jlctSuXRsuLi7Ytm0b4uPjsWDBAgBQjniTnp5eZJ+srCzk5OQo600iXcfOVSINN2XKFMjlcgQGBiImJgYNGjRQThAOAHfu3IGHhwfq1auHkydP4tmzZ1i0aFGV/OzatWsDAAIDA4vNGRAXF1fiPqqeb2DFihXYvHkzoqKi+EcHEVUrbA/Uq7CwEIWFhcq3FYiItAHbCtVYv349Dh06BD09PeXN0pfH+80330Amk5U6HxuRtmJ98n+f27x5c1y+fLnYuvz8fFhaWlb4M0ty6tQpAEDv3r2r5POItBHrHc3UrFkz6OvrK6fMadKkCczNzZGYmFhku7///hsA8NZbb6k9I5EI7Fwl0nB2dnbw9vZGaGgo5s2bBz8/vyLrL168CIVCgcmTJ8Pe3h7GxsaQyWSv/FwDA4NXzuXRoEEDGBsb49y5c5U6hqogSRL8/f1x8eJF7Ny5E2ZmZqIjERGpFdsD1enfv3+xZadOnYIkSXBwcBCQiIjo9bCtUI2NGzcWu1n6cm63OXPmQJIkdO7cWXBKoqrF+uT/+Pj44OzZs7h586ZyWVZWFhITE9GuXbsq+Rnr1q1DkyZN0LNnzyr5PCJtxHpHrCdPnmDEiBHFll+/fh0FBQVo0KABgBe/z4EDByImJgaFhYXK7fbu3QuZTAY3Nze1ZSYSiZ2rRFpg+vTpyM/Px9OnT+Hs7FxkXcOGDQEAkZGRyMnJwfXr13Hy5MlXfmazZs2QmpqKnTt3QqFQ4NGjR8WeODI2Nsb48eOxdetWrF69Gunp6SgoKEBSUhKSk5Or7gDL4fLly/juu++wbt06yOXyYsNsLFmyRLmtr68v6tatizNnzqg1IxGRqrE9qJjytgf37t3Dtm3bkJaWBoVCgbi4OLz//vto2LAhPvroIzWlJSKqGmwrKoZ/OxCVjvXJC9OmTUOjRo0wbtw43LlzB0+ePIG/vz+ys7Mxa9Ys5XblrU/eeecdJCYmIj8/H7dv38aMGTMQGRmJ9evXw9DQUNWHQ6TRWO9UTFVex9SoUQMHDhxAVFQU0tPToVAocPbsWYwdOxY1atTAtGnTlNvOmzcPDx48wJdffonnz58jLi4Oixcvxrhx49CiRYtKZyHSBgalrfDy8lJnDtJBJ06c0Npx5TVNx44d0bt3b4wcObLYunbt2sHf3x+rVq3CihUr0K9fP/Tq1QuhoaFwdHTEkCFDsHXrVgDAjBkzcPDgQWzfvh2TJ09GVFQU3nvvPdjY2GDw4MHKCccdHBwQFRWFBg0a4Pvvv4eFhQUWLVqEqVOnwtraGk5OTvjvf/+r1jH0JUkq97Z5eXl4+PAhwsPD0alTp1K3O3HiBGbMmIGbN28qL5RsbGxgb2+Pb7/9Fk5OTpXOTaVjO1P9sF2oPLYHL5S3/i5vezBgwADMnTsX77//PnJzc1GvXj24urriq6++whtvvKGWY6qO2A5QSZKSkkRH0HpsK16o6raCqDpiffKCtbU1jh49is8//xwdOnRAVlYW2rdvj927d6NDhw7K7cpbn1hZWaFDhw7IzMyEubk5unXrhqNHj/INeCKw3nmpqq9jyvN5xsbG6N69O95//308evQICoUCdnZ26NKlC9atW4e2bdsqP69NmzbYv38/Zs6cicWLF6NmzZqYMGEC/ve//6nwt0KkWWTSv3os4uLisGzZMlF5SMc4ODgUeaqlqslkMgQFBcHb21tlP4PU78MPP8Qff/zx2jfXCgsL0atXL4wbNw4TJkyoslx+fn7YvHkzHj9+XGWfqYmCg4Ph4+NToQ7timA7U72xXaCKYHsgBtsB0gQhISEq+VxVl29SP7YVYrx8QEZV56oqsR6g0rA+0W7aXC/9k64cB5WPptY7Va0612O87tBZIcXeXHVwcGDlTUTCZWdnY//+/bC3t0ejRo3KPTROQUEBwsPDkZGRAV9f30rnkCQJycnJuHnzpnJidqoctjNEVBFsD3QP2wEiqmpsK4ioqrA+ISJ105R6p6qxHiNdV+qwwEREIqWmpmLAgAEAgJEjR2Lz5s3l2i86Ohrbt2/H3r17YWpqWukc4eHh8PDwUP6fw0MSEakX2wMiInoVthVEVFVYnxCRumlKvVPVWI+Rris2LDCRNuHwj0RVj8NVkDZju0BUeWwHSJexfBNVDW0etpL1AJFu0uZ66Z905TiI6AVed+isED3RCYiIiIiIiIiIiIiIiIiItAE7V4mIiIiIiIiIiIiIiIiIyoGdq0RERERERERERERERERE5cDOVSIiIiIiIiIiIiIiIiKicmDnKhERERERERERERERERFRORiIDkBUWYGBgQgJCREdQ+NIkoScnByYmJiIjlJphYWF0NPjsyDqkpSUJDoCUaWwXShZVlYWTE1NRceotIKCAujr64uOodPYDlB14OXlJTqCSmRmZsLExIT1JKnciRMn0LVrV9ExKkVX6wFtpVAoIJfLRcfQOOnp6TA3N4dMJhMdRePpQr300okTJ1hHkVbKzs6Gnp4ejIyMREfRGPz7WnfpBwQEBIgOQfS64uPjYWFhITqGRrp48SIuXLiApk2bavVF+NWrV3HhwgXY29uLjlJtWFhYoHXr1vD29hYdhajC2C6U7MmTJzh06BBq166t1R2sz549w759+9CwYUMYGhqKjqOz2A6QLktPT8ezZ89Ex1CJp0+fIiYmBgqFAnXr1hUdp1J27doFc3NzmJubi45CpbCzs4ODgwMcHBxER6kwXa4HtNmpU6dw//591K9fX3QUjZGfn4/IyEikpaXB1tZWq+/tqIM210v/xI6Y6kMXr3dOnTqFhIQE1K1blx2s/x//vtZZl2WSJEmiUxBR1QoPD4eHhwc2bNiAcePGiY5TKXv37sWgQYNw584d2NnZiY5DRKR1nj59ik6dOqF169b4448/tPqmTH5+PmxtbTF9+nT4+/uLjkNEpDGio6Ph7u6Ozp07Y+fOnVp/k04mkyEoKIg3oYiqiRs3bqBFixbYunUr39b7l5iYGAwePBg9e/ZESEgIjI2NRUcioiqii9c7T548gYeHBy5duoSwsDD06tVLdCQiVQnhOJtEOubatWsYM2YMPvzwQ63vWAUAZ2dnmJmZISIiQnQUIiKtI0kSxo8fj/z8fPz6669a3bEKAAYGBhg6dCiCg4NFRyEi0hjh4eFwdXVFnz59sHv3bq3vWCWi6mfhwoVo3Lgxhg0bJjqKxnFycsKhQ4dw/PhxuLq6IjMzU3QkIqJSvfHGGzh48CBcXV3Rv39/bNq0SXQkIpVh5yqRDsnMzMSwYcPQsmVLBAYGio5TJYyMjODi4oLw8HDRUYiItM7ixYuxe/dubNu2DbVq1RIdp0p4eXnhzJkzuH79uugoRETCbdq0CcOHD8f48eMRGhrKN5qISOukpKRg8+bNmDVrFueLLkWXLl0QGRmJ+Ph4uLq6Ij09XXQkIqJSGRkZYfPmzZg9ezbGjh2LqVOngoOnki5i5yqRDpk8eTKSk5MRFBSkU+Pau7u7IyoqCmlpaaKjEBFpjZMnT2LevHlYsGABunfvLjpOlXF2dkadOnUQEhIiOgoRkVDLly/HuHHjMH36dKxevRp6evzznoi0z5IlS2BlZYVRo0aJjqLROnbsiJiYGNy6dQvOzs54/Pix6EhERKWSyWQICAjA+vXrsWbNGvj4+CAnJ0d0LKIqxb++iHREYGAgfv/9d2zZsgWNGzcWHadKDRo0CJIk4cCBA6KjEBFphadPn8LX1xd9+/bFjBkzRMepUvr6+hg2bBg7V4mo2pIkCZ9//jk+++wzLFmyBAsXLhQdiYjotaSmpmLt2rWYMWMG37wvh5YtWyI2NhZPnz5Fz549cf/+fdGRiIjKNGHCBOzduxcHDx5Enz598OjRI9GRiKoMO1eJdEBcXBxmzZqFr7/+Gv379xcdp8rVrFkTjo6O2LVrl+goREQaT9fmWS2Jl5cXzp07h6tXr4qOQkSkVgUFBZg4cSICAwOxfv16TJs2TXQkIqLXtnLlSsjlckyaNEl0FK3RuHFjHD58GAqFAs7Ozrh7967oSEREZerTpw9iY2Nx//59ODg4ICEhQXQkoirBzlUiLffgwQN4eXmhf//+mDVrlug4KuPm5obdu3dDoVCIjkJEpNF0cZ7Vf+vVqxdsbGwQGhoqOgoRkdrk5ubCx8cHW7duRXh4OMaPHy86EhHRa8vKysLKlSvxySefwNzcXHQcrdKwYUMcPXoURkZG6NGjB/7++2/RkYiIytSmTRucOnUK9erVQ7du3XDkyBHRkYgqjZ2rRFosPz8f3t7eMDExwaZNm3Ty7aSXPDw8kJaWhqNHj4qOQkSksXR1ntV/09PTg4eHB4cGJqJqIzMzE4MHD8ahQ4ewf/9+DBw4UHQkIqJKWbduHZ4/f45PPvlEdBStVLduXURHR6NevXro0aMHLl26JDoSEVGZatWqhcjISLi4uKB///7YvHmz6EhElcLOVSIt9vnnn+PUqVMIDg6GlZWV6Dgq1bhxY7Rt2xbh4eGioxARaSRdnme1JF5eXrhw4QIuX74sOgoRkUo9ePAATk5OuHTpEqKjo+Ho6Cg6EhFRpSgUCgQGBmLixImoXbu26Dhay9raGvv370fTpk3Rp08fnDt3TnQkIqIyGRsbY8uWLZg1axbGjBmDgIAASJIkOhbRa2HnKpGW2rlzJ77//nusWbMGHTt2FB1HLdzd3dm5SkRUguowz+q/OTk5wdbWlm+vEpFOu337NpycnJQjuLRv3150JCKiSvv9999x//59fPbZZ6KjaD1LS0scOHAAHTp0QO/evREXFyc6EhFRmWQyGQICArBu3TosWLAA48ePR15enuhYRBXGzlUiLZSQkICxY8fi448/xtixY0XHURs3NzckJibiwoULoqMQEWmU6jDP6r/p6elh2LBh2LZtm+goREQqcfnyZfTo0QOGhoaIjY1Fs2bNREciIqo0SZKwZMkSjBw5Eo0aNRIdRyeYmpoiIiICvXv3Rr9+/RAZGSk6EhHRK/3nP//B7t27sXPnTjg7O+Px48eiIxFVCDtXibRMZmYmhg0bhlatWmHp0qWi46hVly5dYGdnx7dXiYj+obrMs1oSb29vXL16FfHx8aKjEBFVqT///BNOTk6wt7dHbGwsbG1tRUciIqoSO3bswOXLl6vFNBbqZGhoiODgYAwbNgyDBw/Grl27REciInqlfv36ITY2FklJSXBwcMC1a9dERyIqN3auEmkRSZIwYcIEPH78GKGhoTA0NBQdSa1kMhkGDRrEzlUiov+vus2z+m/du3dH/fr1OTQwEemUQ4cOoU+fPnBwcMC+fftgaWkpOhIRUZVZsmQJhg4dijZt2oiOonMMDAywceNGjBw5El5eXti+fbvoSEREr9S2bVvExcXBysoK3bp1w9GjR0VHIioXdq4SaZFly5YhLCwMv/32G+zs7ETHEcLd3R1nzpzB3bt3RUchIhKqOs6z+m96enoYPnw4hwYmIp2xZcsWuLq6YujQoQgLC4OJiYnoSEREVebQoUOIi4vDzJkzRUfRWfr6+vj5558xefJk+Pj44JdffhEdiYjolWxsbBAdHQ1HR0f069cPW7ZsER2J6JXYuUqkJY4fP47Zs2djwYIFcHFxER1HGGdnZ5iZmSEiIkJ0FCIioarjPKsl8fLyQkJCAi5evCg6ChFRpaxatQqjR4/Ghx9+iF9//RVyuVx0JCKiKrVw4ULlm/mkOjKZDIGBgZgzZw4mTJiAFStWiI5ERPRKNWrUwI4dOzBr1iyMGjUKAQEBoiMRlclAdAAierWUlBR4eXnB1dW12j/haWRkhP79+2PXrl2YPHmy6DhEREJU53lW/61bt25o2LAhgoOD0a5dO9FxiIhey6JFizB79mzMnz+fN5KISCedPXsWhw4dwoEDB0RHqTa++uormJqaYurUqVAoFJg2bZroSEREZZLJZAgICED9+vUxefJk3LlzBz/99BMfOiSNxDdXiTRcfn4+vL29UaNGDWzatKlaDvv4b25uboiKikJaWproKEREalfd51n9N5lMBk9PTwQHB4uOQkRUYZIkYdq0aZgzZw5+/PFHdqwSkc765ptv0L59e/Tp00d0lGrF398fK1aswIwZMzBr1izRcYiIymXixInYvXs3tm/fDldXV94DJo3EzlUiDTdjxgycOXMGYWFhsLS0FB1HIwwaNAiSJPGJVyKqdjjPasm8vb1x7do1nDt3TnQUIqJyy8vLw4gRI7Bq1Sps3boVkyZNEh2JiEglEhISsGPHDsydO5fXrwJ8/PHH+Omnn7B48WL4+/uLjkNEVC4uLi6IjY3F9evX0b17d9y+fVt0JKIi2LlKpMGCgoKwfPlyrF69Gm3bthUdR2PUrFkTjo6OCA8PFx2FiEitOM9qyd599100btwYISEhoqMQEZXL8+fP4e7ujoiICERERMDLy0t0JCIilfnuu+/QvHlzeHh4iI5SbU2cOBGbN2/GsmXL8NFHH6GwsFB0JCKiV2rXrh3i4uJgYmKCLl264NixY6IjESmxc5VIQyUkJGDSpEmYMmUKxowZIzqOxnF3d8fu3buhUChERyEiUgvOs1o6mUyG4cOHY9u2baKjEBG90tOnT+Hi4oI///wTkZGRcHFxER2JiEhlkpKSsHnzZnz++efQ0+NtSJHee+89hIWF4ZdffsGYMWOQn58vOhIR0SvZ2toiOjoaDg4O6Nu3L//uJ43BqxoiDZSZmQkPDw+0bt0aixcvFh1HIw0dOhTPnj3D0aNHRUchIlI5zrP6al5eXrh58ybOnDkjOgoRUamSk5PRq1cvJCUl4fjx4+jatavoSEREKrV06VLUqVMHo0aNEh2FAAwZMgQ7duxAWFgYRo4cyQfWiUgrmJmZYceOHZg4cSJGjBiBgIAA0ZGI2LlKpGlezqeXmpqK0NBQGBoaio6kkRo3boy2bdtyaGAi0nmcZ7V83nnnHTRt2hTBwcGioxARlejmzZvo0aMH8vLyEBsbixYtWoiORESkUqmpqfj5558xbdo03tvQIAMGDMC+ffuwb98+eHh4IDs7W3QkIqJX0tfXxw8//IDAwEB8/fXXeP/99/mACAnFzlUiDbN48WLs2LEDv/32G+rXry86jkZzd3dHeHg4JEkSHYWISGU4z2r5eXp6Ijg4mO0CEWmcM2fOwMHBAdbW1oiJiUGDBg1ERyIiUrkffvgBhoaGmDhxougo9C9OTk44dOgQTpw4gYEDByIjI0N0JCKicpk6dSr++OMPBAcHY9CgQXj27JnoSFRNsXOVSINER0djzpw5WLhwIfr16yc6jsZzd3dHYmIiLly4IDoKEZFKcJ7VivHy8sKtW7dw+vRp0VGIiJSOHDkCZ2dntGnTBlFRUahdu7boSEREKvf8+XOsWrUKU6ZMgZmZmeg4VILOnTvjyJEjSEhIgKurKzsoiEhrDBgwALGxsbhy5QocHR2RmJgoOhJVQ+xcJdIQKSkpGDFiBAYNGoTp06eLjqMVOnfuDDs7O+zatUt0FCKiKsd5Viuuc+fOaNasGUJCQkRHISICAOzatQuurq7o3bs39uzZA3Nzc9GRiIjUYu3atcjOzsbHH38sOgqV4eWDP4mJiXB2dsbjx49FRyIiKpe33noLJ06cgFwuh4ODAx+yJrVj5yqRBlAoFPD29oa5uTnn06sAmUyGQYMGcd5VItI5nGf19Xl5eWHbtm0cGpiIhNu0aRM8PT3h4+ODkJAQGBsbi45ERKQWCoUC33//PSZNmsRpLbRAy5YtcfToUTx79gxOTk64f/++6EhEROVSv359xMTEoFOnTujVqxfvEZNasXOVSANMmzYNZ8+eRVhYGCwtLUXH0Sru7u44c+YM7t69KzoKEVGV4Tyrr8/Lywt3797Fn3/+KToKEVVjy5cvx7hx4zB9+nRs3LgRBgYGoiMREanNpk2bkJycDD8/P9FRqJwaN26Mo0ePQk9PD46Ojrh165boSERE5WJmZobw8HCMGzcOHh4eCAgIEB2Jqgl2rhIJtnXrVqxcuRKrV69GmzZtRMfROs7OzjAzM0NERIToKEREVYLzrFZOx44d0bJlSw4NTERCSJKEWbNm4bPPPsN3332HhQsXio5ERKRWhYWFWLZsGUaPHo2GDRuKjkMVYGNjg6ioKFhYWKBXr164fv266EhEROWir6+PlStXIjAwEP/73/8wadIk5Ofni45FOo6dq0QCXbp0CRMnToSfnx9Gjx4tOo5WMjIyQv/+/TnvKhHpBM6zWjU8PT0RHBzMoYGJSK0KCgrwwQcfYMmSJfj5559ZjxNRtRQWFoarV69i+vTpoqPQa6hTpw4OHz4MW1tbODk54eLFi6IjERGV29SpUxEaGorff/8dgwYNQnp6uuhIpMPYuUokSEZGBry9vfHWW29h0aJFouNoNTc3N0RFRSEtLU10FCKi18Z5VqvOy6GB4+LiREchomoiNzcXvr6+2LRpE0JCQjBhwgTRkYiIhFi0aBGGDRuG1q1bi45Cr8na2hoHDx5E69at0atXL063QURaxcPDA4cPH8aFCxfg6OiIO3fuiI5EOoqdq0QCvLyBnpqaipCQEBgaGoqOpNUGDRoESZJw4MAB0VGIiF4b51mtOu3bt0erVq04NDARqUVmZiaGDBmCyMhIREZGwsPDQ3QkIiIhDh48iNOnT8Pf3190FKqkl9Mvde7cGS4uLjh+/LjoSERE5fbOO+/g9OnT0NfXR9euXfHXX3+JjkQ6iJ2raoF+ogAAIABJREFURAIsWrQI4eHhCAoKQv369UXH0Xo1a9aEo6MjwsPDRUchInotnGe16nl5eSEoKAiFhYWioxCRDnv48CF69uyJixcv4vDhw3B0dBQdiYhI5TIyMkqcfmHhwoXo168fOnfuLCAVVTVTU1NERESgT58+cHFxQWRkpOhIRETlVr9+fcTExKBDhw7o2bMnIiIiREciHcPOVSI1O3z4MObNm4fvvvsOPXv2FB1HZ7i7u2P37t3Iy8sTHYWIqEI4z6pqeHl5ITk5mU/ZE5HKJCYmokePHnj69Knyxg0RUXXw+++/o02bNvjtt9+gUCgAAKdOnUJUVBRmzZolOB1VJUNDQwQHB8PT0xODBw/Gzp07RUciIio3c3Nz7Nq1C2PGjIGHhwdWrFghOhLpEHauEqlRcnIyRo4cicGDB8PPz090HJ0ydOhQPHv2DEePHhUdhYio3DjPquq0bdsWrVu35tDARKQSV65cgaOjI+RyOY4ePYrmzZuLjkREpDb3799HQkICxo4diyZNmmDlypX4+uuv0aVLFzg7O4uOR1VMX18fGzZswKhRo+Dj44PQ0FDRkYiIys3AwACrV6/G0qVL4efnhw8++AD5+fmiY5EOYOcqkZooFAp4eXnBwsKCN9BVoHHjxmjXrh127dolOgoRUblxnlXV8vLyQnBwMAoKCkRHISIdcurUKTg5OaF+/fo4cuQIp/kgomonKSkJenp6kCQJ9+7dg5+fH/bu3Qt7e3ukpqaKjkcqoK+vj3Xr1mHy5Mnw9fXFxo0bRUciIqqQqVOnIjg4GL/99huGDBmCjIwM0ZFIy7FzlUhN/Pz8cP78eYSFhcHCwkJ0HJ3k5uaG8PDwEud+ISLSNJxnVfV8fX2RkpKCY8eOiY5CRDoiKioKffr0QdeuXREVFYU33nhDdCQiIrVLTEws8tZPQUEBFAoFtm/fjvr162Pq1Km4d++ewISkCjKZDIGBgZg7dy7+85//4IcffhAdiYioQjw9PXH48GGcPXsWjo6OuHv3ruhIpMXYuUqkBlu2bMGaNWuwfv16tG7dWnQcneXu7o7ExERcuHBBdBQiojJxnlX1aNmyJdq2bYvg4GDRUYhIB+zYsQMDBw6Em5sbwsLCYGpqKjoSEZEQSUlJJS7Pz89HTk4O1qxZoxwumHRPQEAAvv32W/j5+WHJkiWi4xARVci7776LuLg4KBQKdO3aFWfPnhUdibQUO1eJVOzixYuYOHEipk2bBm9vb9FxdFrnzp1hZ2eH8PBw0VGIiErFeVbVy9vbG6GhoRwamIgqZfXq1Rg+fDgmTpyITZs2QS6Xi45ERCRMcnLyK7d58803MWLECDWkIRH8/f2xcuVK+Pv7Y9asWaLjEBFVSJMmTXDs2DG0aNECTk5O+OOPP0RHIi3EzlUiFcrIyIC3tzc6dOiAb7/9VnQcnSeTyTB48GDOu0pEGm3JkiXYvXs3tm7dynlW1cDHxwcPHjxATEyM6ChEpKUWLVqEjz/+GDNnzsSKFSugp8c/o4mo+srOzi5znjq5XI6mTZsiOjoaNWvWVGMyUrfJkyfjl19+wdKlS/Hpp59yiiYi0irW1tY4cOAARo4ciaFDh2LVqlWiI5GWMRAdgEhXSZKEcePG4enTpzh06BCfblcTNzc3/PTTT7h79y4aNGggOg4RURF//vkn5s6di2+++QaOjo6i41QLb775Jtq3b4+QkBD07t1bdBwi0iKSJGHGjBlYvnw5fvzxR3zwwQeiIxERCVfWXKpyuRwNGjTA4cOH+RBhNTF69GjI5XKMGTMGCoUCq1ev5kNIRKQ1DAwM8OOPP6JVq1aYMmUKrl27hsDAQNZjVC4sJUQq8u2332LXrl0IDg6Gra2t6DjVhrOzM8zMzBARESE6ChFREU+fPoWPjw/69OmDmTNnio5TrXh5eSE0NBT5+fmioxCRlsjPz8eECROwcuVKbNmyhR2rRET/3/3790tcLpfLYWdnh9jYWNSrV0/NqUgkX19fhIWF4ddff8Xo0aN5zU1EWmfq1KnYtm0b1q1bh+HDhyMrK0t0JNIC7FwlUoGoqCjMnz8fS5YsgZOTk+g41YqRkRH69+9f4ryrnG+PiET55zyrmzZt4jyraubr64tHjx7hyJEjxdYpFAoBiYhIk2VlZcHNzQ0hISHYtWsXvL29RUciItIY9+7dK3YtK5fLUb9+fcTGxsLGxkZQMhJp8ODB2LNnD3bt2gVPT0/k5uaKjkREVCFeXl44dOgQjh07ht69eyMlJUV0JNJw7Fwlek2XL1/G48ePiy2/e/cufH19MXz4cEydOlVAMnJ3d8fhw4eRlpaGrKwshIeHY8KECbC1teUcIESkUnv37kVOTk6x5ZxnVaymTZuiY8eOCA4OBgA8evQIP/74I3r27Mm2moiKSEtLg4uLC06ePImDBw+if//+oiPpvLS0NDx9+rTIPwB4/vx5seV8G4pIvHv37hWZ9kgul8PW1haxsbEctaua6927N/bs2YPo6GgMGzYM2dnZoiMRaQxe72gHBwcHxMXFIT09HZ07d8a5c+dERyINxs5Vote0dOlStG/fHqdPn1YuUygUeO+991CzZk2sXbtWYLrq7Z133kFhYSH69++PmjVrwsPDA5s2bcKzZ8/4thgRqYwkSZgwYQK6dOmC69evK5dznlXNMHjwYISFhcHZ2Rn16tXDJ598gqNHj7JdIKpmrl+/josXL5a4LiUlBb169cLNmzcRHR0NBwcHNaernjw9PVGzZs0i/wBgwoQJRZbVqVMHT548EZyWiP45LLBcLketWrUQHR2N+vXrC0xFmqJHjx6IiorCyZMn4erqioyMjGLbSJKE5cuX8+F3qlZ4vaM97O3tcezYMTRv3hxOTk7Ys2dPqdsmJiZypMRqjJ2rRK9BoVAgNDQUycnJ6Natm7Ij9dNPP8X58+cRFhYGCwsLwSmrl5s3b2L58uXo1asXWrduDUmS8NdffyE3NxeSJKGgoAAmJiaiYxKRDvvzzz+RkpKCK1euoEOHDggODuY8q4KlpaVh06ZNGDhwIBYsWICnT58iJiYGhYWFKCgogFwuh76+vuiYRKRGX3zxBfr27Ytbt24VWX7r1i306NEDubm5OHHiBNq1aycoYfXz3nvvvfJBFz09PTg5OaFu3bpqSkVEpbl37x4UCgUMDAxQu3ZtxMXFoXHjxqJjkQZ5++23ERMTg+vXr6NPnz5ITU0tst7Pzw9+fn6IiIgQlJBI/Xi9o11q1qyJ/fv3Y+jQoXB3d8eaNWuKbfP48WM4Oztj/fr1AhKSJmDnKtFrOHjwINLT0yFJEhQKBT788EP06NEDa9euxS+//ILWrVuLjlitrFu3Dk2bNsWMGTMQExODgoIC5Y3zfzI2NhaUkIiqg7CwMMjlchQUFCArKws+Pj7o0KED8vPz8dtvv/ENSTV7//33UatWLUyYMAEHDhxAQUGB8t8/sXOVqPr466+/sH37djx+/Bi9e/fGgwcPAACXLl2Co6MjLC0tERMTg4YNGwpOWr14enrCwMDglduNGTNGDWmI6FUSExMhSRJq1aqFo0ePolGjRqIjkQZq3bo1oqKikJycjL59++LRo0cAgDlz5mDFihWQyWT44osv+PYqVRu83tE+hoaG+PXXXzFnzhx8/PHHmDp1KgoLCwEAOTk5GDJkCG7dugV/f3+kpaUJTksiyCS2YkQVNm7cOGzZsgUKhUK5zMDAAHXr1kVMTAzs7e0Fpqt+CgsL4ezsjLi4OOTl5ZW6XZMmTXDz5k01JiOi6sTe3r7Ym1D6+vpo3Lgx9u7di+bNmwtKVj3FxsaiV69eZQ7RY2hoiE8//RRLlixRYzIiEsXZ2RmxsbFQKBSQy+Vo1qwZFi9ejJEjR6Jjx44IDw/n6DOCuLm5Ye/evaXOMSaXy/Ho0SNYWlqqOZnuWbZsGeLi4kTHIC22Z88eFBYWolevXjAzMxMdh/5h2rRpGjekfWJiIvr27QsDAwP4+Pjgq6++Uq6TyWQICQmBp6enwIRE6sPrHe0VFBSEcePGYdCgQdi0aRMmTpyI4OBg5OfnQy6X45NPPsGyZctExyT1CuGbq0QVlJeXh7CwsCIdqwCQn5+Phw8fol27dti5c6egdNWTnp4egoKCYG5uDj290qs1U1NTNaYiouokPj6+WMcqABQUFODOnTto3749tm7dKiBZ9eXo6IglS5aU+cawTCbjm6tE1UR0dDQOHz6svIZXKBT4+++/8cknn6B3797Yt28fO1YFGjlyZKkPwxgYGGDw4MG80VhF4uLicOLECdExSMs5OTmxY1XDhIaG4u7du6JjFNOoUSNER0cjJyenSMcqAOXbqy/fBCPSdbze0V4+Pj7Yt28fDh8+DEdHR2zbtk3ZSa5QKPDDDz8gPj5ecEpSt1e/i05ERRw4cAAZGRklrlMoFMjPz8ewYcMwb948fPnll2V29lHVqVu3LjZv3oyBAweWuk2NGjXUmIiIqpOdO3dCLpcXe/AGeNE2KBQKjBgxAjExMQgMDOQw5Wri5+eH06dPIygoqNSng9m5SqT7JEnC9OnTYWBgUKQuUCgUSEpKQsuWLVkXCDZkyBAYGxsjOzu72LqCggKMGjVKQCrd1bVrV4SEhIiOQVooJycHt2/fRsuWLUVHoX/R5ClIIiMjkZiYWGx5YWEhrl+/juDgYPj6+gpIRqRevN7Rbj179sTMmTMxe/bsYuv09PTw6aefIioqSkAyEoW9PkQVFBQUBLlcXup6SZIgk8kQGBiI48ePqzEZDRgwADNmzCj15hifrCUiVQkODi6xY/WfZDIZrl27htTUVDWlIgD48ccfYW9vX+r8NuxQIdJ9ISEhOHv2bIkPWeTn5yMyMhJjx47lmzMCmZqawsPDo8S/s0xMTODq6iogFRH9m7GxMTtWqUK2b9+OCRMmlDq3qkwmw+zZs0t9EJJIl/B6R7vFxMRg/vz5Ja5TKBQ4fPgw9uzZo+ZUJBI7V4kqIDc3Fzt27Cj1Brqenh5kMhlcXFwQHx8PR0dHNSekBQsWoFOnTiVeqPDNVSJShcTERFy8eLHU9XK5HEZGRggMDERkZCRsbW3VmI7MzMwQEREBIyOjEp/oZ+cqkW7Lz8/H7Nmzy3yjJz8/H9u2bcOMGTPUmIz+bcSIEcX+zpLL5Rg+fDhMTEwEpSIiotcVHh4OHx+fUjtWgRdvr965cwdbtmxRYzIicXi9o52uXr2KwYMHlzqsM/CiX2Dy5MnIzc1VYzISiZ2rRBWwd+9eZGVllbhOLpfDysoKv/zyC/bu3YsGDRqoOR0BL+YoCA0NhYmJSZGbaDKZjG+uEpFK7Nixo9QOOj09Pbz99tu4dOkSpk6dqtHDdemyN998E7///nuJ69i5SqTbNmzYgNu3b7/yrVSZTIY1a9bg2LFjakpG/9a/f39YW1sXWaZQKDBy5EhBiYiI6HVJkoTIyEjo6emVOoLMP82bN++VIwER6QJe72ifhw8fon///sjMzCzzb4rCwkIkJSVh1apVakxHIrFzlagCgoODi10Uvrwp6+bmhoSEBIwZM0ZENPqHhg0bYsOGDUWejtTX14epqanAVESkq0JCQopdYL98W3XBggU4duwYmjVrJigdveTu7o5p06YV60xl5yqR7srOzsb8+fNLfWPm5agztWrVwty5c3H37l10795dzSnpJQMDA/j4+MDQ0FC5zMrKCs7OzgJTERHR65DJZFixYgXu3buHL774AlZWVtDX1y/xYdOXHRKbNm0SkJRIvXi9o33kcjk+++wzNG/eHACKfHf/VlBQgPnz5+Phw4fqikcCsXOVqJxycnIQHh5e5Ek6AwMD2NjYYP/+/QgNDUWtWrUEJqR/8vT0xAcffKDsDNfT02PnKhFVucePH+PkyZNFOlf19PTQqVMnXLx4Ef7+/tDT4+WWpli4cCEcHByKDB1fnifpiUg7LV++HI8fPy7WufqyDmjXrh1++eUX3L9/HwEBAbyW1wDvvfce8vLyALy4cTV69GjW00REWqx27doICAhASkoKNmzYAHt7e8hksmIPOEqShHnz5nE4TaoWeL2jXaytreHn54eEhAScPn0aY8eOhYmJCfT09Eq835OXl4c5c+YISErqxrt9ROW0e/duZGdnA3hxI1Ymk2H8+PG4cuUKXFxcBKejkgQGBqJ58+bKCxTOXUBEVS08PFx5014ul8PQ0BALFizA8ePHlU81kuYwMDBASEiI8sl5SZLY+U2ko9LS0vDtt98WmRfJwMAABgYGcHNzw/Hjx3Hu3DmMGTOmyAMXJJajoyPq1asH4MWNKV9fX8GJqFOnTpDJZJDJZLCxscHUqVNfuc+1a9fQpUsXmJmZQU9PDwMGDFBD0td3/vx5+Pr6okmTJjAyMkKtWrXQvn17fP3116Kjldv48eNhbGwMmUyGnJwc0XE0XmxsLLp37w5TU1PY2NjA39+/3J165dlXoVBg/vz5sLe3h6GhIerXr48ZM2Yo7yn905YtW9ClSxeYm5ujUaNGGD9+PFJSUqrkODWJkZERxowZg2vXriE8PByOjo4A/u+BJ0mS8ODBA2zYsEFkTCK14PWO9nr77bexdu1aPHjwABs3blTWZf98m1WhUGD9+vU4ffq0qJikJrybRFROwcHBAF4MH9i8eXOcPHkSa9eu5TyeGszExATh4eGQy+XIy8vjm6tEVOW2b9+OwsJC5dyqly9f5tuqGq5evXoIDw+HTCZDXl4enxAm0lELFy5Eenq6cujfN954A/PmzUNSUhJCQ0Ph4OAgOiKVQE9PD6NHjwYA2Nra8nvSAGfOnIGLiwtkMhnOnj2L5cuXF1lfUFBQbCjDN998E6dOncKCBQswYsQI7Nu3T52RK+TixYvo1q0b6tWrh8OHD+PZs2c4fvw4BgwYgOjoaNHxym3jxo2YMWOG6BhaIT4+Hi4uLujTpw8ePXqEsLAwbNiwAR999FGV7evn54fFixdjwYIFePLkCX7//XesW7cO77//fpHtgoKCMHLkSHh5eSEpKQnh4eGIiYmBq6sr8vPzq/S4NYWenh6GDBmC6OhoHD9+HIMGDYJMJoOhoSEKCwvx1Vdf8QEB0nm83tF+5ubmGDNmDI4cOYJz585h0qRJsLCwgJ6envLN/ClTpghOSaomk0qbgIa0gpeXl+gI1UJBQQF27doFSZLQqlUrtGjRQiNunIeEhKjkc+Pi4rBs2TKVfLYIt2/fxunTp9GuXTu0aNFCdByN5+DggGnTpomOQZXE9kH18vPzsWvXLgAvhpZs1qxZiXMIicD24dWuX7+O8+fPo0OHDpwTt5zYPpSM9a3myc7Oxr59+1BQUAArKyu8+eabsLOzU8n1O+vbqpeWlobIyEi0bNkSbdu2FR1HCFXWty/rrIqU3Y0bN2LChAkldg5FRkaiX79+iI+PR+vWrYusGzBgAD7++GMMGTKk8sHLkJ2djT59+uD48eMV3nfs2LGIjIzEvXv3iizPy8uDp6cnIiIiqipmlSrpmOfOnYtvvvkG2dnZMDY2Fpiu6igUCmzevBnR0dH49ddfq+QzfX198eeff+LGjRvKa/clS5bg888/x+XLl9GyZctK7Xvz5k00b94c77//Pn766SflvvPmzcPXX3+Ny5cvo1WrVgAAZ2dnJCQkICkpSfl5q1atwieffKJ8Q7a8ZDIZgoKC4O3tXeHfSXmo8nonMzMT165dw+3bt1FYWIj27dtzFCANw+udqsfrHdX/fanuv9MKCgpw79493LhxA0+ePAEAdO3aFXZ2dmrNQapRQj0YIr53iColNDQUSUlJomPovOTkZFhZWaFv375o1aqV8I7Vl0/cq8rdu3dV+vnq1rhxYzRs2LDYnB5U3IkTJxAXFyc6BlUBtg+ql5KSAmtra7i4uKB58+Ya0bHK9qH8mjdvjkaNGmnE96YN2D6UjvWt5rl69SpsbW3h7OyMvn37omHDhlV+/c76VnWsrKxgYWGBBg0aiI4ihCbWt8OGDYOhoaHyobJ/OnDgAGxtbYuV1+zsbJw/fx79+/dXeb7169fj4cOHr7XvkydP8OzZM6SmphZZbmhoqLEdq0DZx6wL1za5ublYs2YNOnXqhPj4eCxcuLBKPjc/Px+7d+9Gz549i/yeXF1dIUkSwsPDK73vqVOnUFhYiHfffbfI/i+Hx96/f79y2d27d2FjY1Pk817WfYmJiZU40qqnyusdMzMzdOrUCYMHD0abNm1w+/btIsP6kzi83lEdXu+o/npH3X+n6evro2HDhujduzcGDBiAFi1a4Nq1a6zPtFyZ9aBEWg2AFBQUJDqGzrt06ZJUUFAgOoZSUFCQpMrTV9WfL0J6eroUHR0tOobGGz58uDR8+HDRMagKsH1QvQsXLmhU2yBJbB8qKiMjg21DObF9KB3rW83z4MEDlf8M1reqtX37dtERhFF1ffu6nz9kyBDJxMREysrKUi7Ly8uTZs6cKU2ZMkVq165dke0jIiKksWPHKv9fWFgoLV26VGrZsqVkaGgoWVlZSe7u7tKVK1eU2yxatEgyMTGRzMzMpAcPHkjTpk2TbG1tpatXr0rR0dFSly5dJBMTE8nc3Fxq27at9OzZM2nq1KmSoaGhBEACIDVt2rRCxxUQECABkNq3by/FxsaWul1gYKBkamoqyWQyqVOnTlKdOnUkAwMDydTUVOrYsaPk6Ogo2dnZSUZGRpKlpaU0c+bMIvuX5/jLu11pxzxnzhxJT09P2r59uzRgwADJwsJCqlevnrR+/XrlvqtWrZJMTU0lExMTaefOndKAAQMkc3NzqX79+tKWLVuKZMnPz5fmzZsnNWjQQDI2NpbatWsnbdu2Tbm+tO/kVevKkpmZKS1dulRq27atFBAQID158qTI+oyMDOVxl/bv3XffLfXzExISJADS/PnziyzPzs6WAEgjRoyo9L7BwcESgGK/zwcPHkgApEmTJimXubi4SA0aNCiy3U8//SQBkOLi4krNUhJVX4+o83onOztbSk1NVcvPorLxeke1eL2j2r8vNeHvtLy8PCkjI0NoBqqcMuqpYL65SlQObdq0Ef62KlWOubk5evbsKToGEemQdu3asW3QcmZmZmwbiHRQnTp1REegSho2bJjoCPQvvr6+yM7ORmRkpHLZwYMH0bdvX3h5eeHixYu4du2act2ePXvg4+Oj/H9AQABmz56NOXPm4OHDh4iJicHdu3fRo0cPPHjwAADw+eefY9q0acjMzMSCBQvQpEkTdO3aFZmZmXBzc8Pw4cORmpqK69ev480330ReXh6+//57DBkyBE2bNoUkSfj7778rdFyff/45OnfujPPnz8PR0RFt2rTBd999V+xNVj8/P8ycOROSJGHNmjW4desWUlJS4OTkhLNnz2L27Nk4e/YsUlNTMXbsWCxevBjnz5+v0PGXd7uyjrmwsBBWVlbYtm0bbt++jU6dOmHy5MnIysoCAEyePBmfffYZsrOzYW5ujqCgINy4cQP29vaYOHEiFAqF8rNmzZqF7777DoGBgUhOTsaQIUMwYsQInD59Gs+fPy/1OylrXWmePXuGb775Bt26dYMkSThx4gS+/PJL1KxZs8h2ZmZmkCSpzH8nTpwo9eekpKQAeHF/4J+MjY1hYmJS5Lt43X1fDit85cqVItu98cYbAIBHjx4pl33xxRdISUnBihUrkJGRgfj4eHz//ffo378/unbtWmoWXWdsbAxra2vRMYhUjtc7uk8ul8PMzEx0DFIR3hEkIiIiIiIiIqJSubm5wcTEpMjQwFFRUXB2dkb37t1ha2tbZC6qkydPom/fvgBeDBG8bNkyDBs2DKNGjYKlpSXatWuHH3/8EY8fP8batWuL/byFCxfik08+wfbt22FsbIz09HS0adMGxsbGqFu3LrZv345atWpV+rhMTExw/PhxLF++HC1btsTly5fh7++PVq1a4ciRIyXu07p1a5iamuKNN97Ae++9BwBo2LAhatWqBVNTU4waNQrAiyHKK3L8r/N7Kkm3bt1gaWkJa2tr+Pr6Ijc3F7du3SpxOwsLC9SuXRu+vr54/vw57ty5AwDIycnB6tWr4eHhAU9PT1hZWWHu3LmQy+XYuHEjbt++Xep3Uta6kigUCrRv3x4HDhxAbGwspk+fjho1apTrWCsqNzcXAEqcMkgulyM7O7vS+7Zr1w4DBgzAqlWrEBUVhZycHKSkpCAsLAwymaxIB3bPnj3h7++PKVOmwMLCAm3btkVGRgZ+/vnnSh0nERERqR47V4mIiIiIiIiIqFRmZmYYNGgQ/vjjD0iShNzcXBgYGMDAwAAymQyenp7K+aguX76Mjh07Qi6XAwDi4+ORmZmJzp07F/nMLl26wNDQECdPnizzZ9vb26NOnToYNWoUAgICcPv27So9NrlcjilTpuDKlSs4ceIEhg4diocPH8LLywtPnz4tc19DQ0MAL+bj/OfnAVB2opX3+Cv7eyrt2P6Z5VXH8XK7hIQEZGVloW3btsptTExMUK9ePVy9erXM76Si35dcLsf58+fRr18/9OjRA4GBgXj+/HmFj7U8jI2NART9vl7Ky8uDiYlJley7bds2eHl5YcyYMahZsya6d++OHTt2QJIk5RusADBnzhysXbsWhw4dQmZmJm7evIlu3brBwcEBd+/efe3jJCIiItVj5yoREREREREREZXJ19cXKSkpOHXqFPbs2YMBAwYo1w0fPhznzp3DjRs3ig0JnJaWBgAlDotnZWWFjIyMMn+uiYkJoqKi4OjoiG+++Qb29vbKYYqr2rvvvosdO3bgo48+wqNHj3D48OFKf2Z5j7+yv6eq9LJzc+7cuZDJZMp/iYmJyMrKKvM7eZ3vy9LSEnPnzsWxY8dQWFiIrl274r///W+xzu3MzMwieUr6V9ZwuvXq1QMApKenF1melZWFnJwc2NjYVMm+lpaW+PHHH5GUlISsrCzcuHEDS5cuBQDY2toO3w1LAAAgAElEQVQCAJKTk7Fo0SJMmjQJzs7OqFGjBpo0aYJ169bh/v37WLx4calZiIiISDx2rhIRERERERERUZkGDRoEc3Nz7Nq1CzExMXByclKuc3R0hI2NDUJDQ3H69Gn07t1buc7KygoASuwcTEtLg52d3St/dps2bRAREYH79+/D398fQUFBWLJkSaWPydPTs8Q3EUePHg0AyrlKK6O8x18Vv6eqUrt2bQBAYGBgsTlN4+LiAJT9nbzu91WjRg1Mnz4dp06dQq1ateDk5ISZM2ciOTkZQOXnXG3SpAnMzc2RmJhYZPnLeWvfeustlewLAKdOnQIA5blx/fp1FBQUKDtbX7KwsEDNmjURHx9f5ucRERGRWOxcpSq1Z88eWFpaIiIiQq37VlRsbCy6d+8OU1NT2NjYwN/fXzl/hir3pdfHskWk3XgOk6qwbBEVxXOCVIVli4yNjeHm5obQ0FCYmJhAT+//binp6elh2LBh2LRpE+rUqQMDAwPlurZt28LMzAynT58u8nknT55EXl4e3n777TJ/7v3793H58mUALzr9vv32W3Tq1Em5rDJyc3NL/JyEhAQAr+4wK4/yHn9lf09VqUGDBjA2Nsa5c+dKXF/Wd1IV35exsTEmT56MM2fOoFWrVpg1a1blDwqAgYEBBg4ciJiYGBQWFiqX7927FzKZDG5ubirZFwDWrVuHJk2aoGfPngCg7Cx/2XH8UkZGBlJTU9GgQYMKH191wjaJVIVli0RjGdQe7FylKiVJkpB9KyI+Ph4uLi7o06cPHj16hLCwMGzYsAEfffSRSvelymHZItJuPIdJVVi2iIriOUGqwrJFwIuhgRMSEjB48OBi67y8vHD58mV4eHgUWW5sbIzp06cjLCwMmzdvRnp6Oi5evIiPPvoINjY2+OCDD8r8mffv38eHH36Iq1evIi8vD2fPnkViYqJy+NeaNWvi/v37uH37NjIyMl45v+i/eXh4IDg4GGlpaXj27BnCw8Mxa9YsuLu7V0nnanmPvyK/p8oec3kyjx8/Hlu3bsXq1auRnp6OgoICJCUlITk5uczv5FXfV0XI5XJMmDABv/76a5Ud27x58/DgwQN8+eWXeP78OeLi4rB48WKMGzcOLVq0UG43f/58WFpa4sCBAxXe95133kFiYiLy8/Nx+/ZtzJgxA5GRkVi/fr1yftsmTZqgd+/eWLduHWJiYpCdnY27d+8qv+f//Oc/VXbMuohtEqkKyxaJxjKoRSTSagCkoKAg0TG0io+Pj9SkSROpsLBQuWzx4sWSTCaTrly5orJ9q1JQUJCkytNX1Z+vq3ShbA0fPlwaPny42n4eqQ7bh4rThXOY7YNm0oWyxfahdKxvK04XzgnWt5pJF8qWquvbyn5+Xl6e1L59+yK/p5cKCgqk9u3bSwUFBcXWFRYWSosXL5aaN28uyeVyydraWvLw8JASEhKU2yxatEgyMTGRAEgNGjSQfvvtN0mSJOn27dtSt27dJGtra0lfX1+ytbWV5syZI+Xn50uSJElnzpyRGjVqJJmYmEiOjo5SSkpKuY/nwIEDko+Pj9S0aVPJyMhIMjQ0lFq0aCEFBARIOTk5yu2+//57ydTUVAIgNW7cWDp69Ki0cOFCydLSUgIg1a1bV/r999+lbdu2SXXr1pUASNbW1tLWrVvLffwV2e7fxzxt2jTl76558+bSjRs3pM2bN0vW1tYSAMnOzk66dOmStGrVKuVxvNxu7dq1koWFhQRAatSokXTt2jVJkiQpNzdX8vf3lxo2bCgZGBhItWvXljw9PaX4+Pgyv5NXfV+a4MiRI9I777wjGRkZSTY2NtLMmTOLfN+SJEnz5s2TzM3Npf3791d43379+klWVlaSgYGBZG1tLQ0aNEg6depUsRyPHz+W/Pz8pGbNmklGRkaSmZmZ1L17d2nHjh0VPiZVX4/weqfidKFN4vWOZtKFsqWOvy9Zb6mOLpTB8iqjngpm7aXlRFYShYWFUnBwsPTTTz8J+fmvQ6FQSGZmZtK4ceOKLL906ZIEQFq4cKFK9q1qun5xw7JV/n2rGm+e6w62DxWjK+cw2wfNoytli+1D6VjfVoyunBOsbzWPrpQtTe9cJSLNpMudq2yTyr9vVeP1jubRlbLFztUXWAbLv68oZXWuclhgKpeCggIsWLAALVq0gImJCWrVqoUmTZpgwYIF8Pb2BvBirOyGDRtCJpNh5cqVAIDVq1ejRo0aMDU1RXh4OFxdXWFhYQE7Ozts3bpV+fkl7VuSzMxMyGSyMv+VNdTMzZs3kZmZiYYNGxZZ3rRpUwDAhQsXVLIvlY5li2WLtBvPYZ7DqsKyxbJFRfGc4DmhKixbLFtERJqCbRLbJFVh2WLZEo1lUPfKIDtXqVwWLVqE+fPnY/HixUhNTcWBAweQk5MDKysrWFlZAQAcHR1x/PjxIvtNnjwZn332GbKzs2Fubo6goCDcuHED9vb2mDhxonJekJL2LYmZmRkkSSrz34kTJ0rdPyUlBQBgbm5eZLmxsTFMTEzw4MEDlexLpWPZYtki7cZzmOewqrBssWxRUTwneE6oCssWy5YuuXr16itvGMpkMvj6+oqOSkQlYJvENklVWLZYtkRjGdS9MsjOVSqXnTt34u2334abmxtMTEzQqVMnuLu7IyYmBnl5eeX6jG7dusHCwgK1a9eGr68vnj9/jjt37qg4eVG5ubkAAH19/WLr5HI5srOzVbIvlY5li2WLtBvPYZ7DqsKyxbJFRfGc4DmhKixbLFu6pGXLlq+8YShJErZt2yY6KhGVgG0S2yRVYdli2RKNZVD3yiA7V6lccnJyIElSkWUFBQWQy+UlngyvYmhoCADKJyvUxdjYGACQn59fbF1eXh5MTExUsi+VjmWLZYu0G89hnsOqwrLFskVF8ZzgOaEqLFssW0REmoJtEtskVWHZYtkSjWVQ98ogO1epXAYOHIi//voL4eHhyM7OxunTp7Fz504MHjz4tU7+11XZMcHr1asHAEhPTy+yPCsrCzk5ObCxsVHJvlQ6li2WLdJuPId5DqsKyxbLFhXFc4LnhKqwbLFsERFpCrZJbJNUhWWLZUs0lkHdK4MGogOQdggICMBff/2FcePGITMzEzY2NvD29sY333yj1hwvxwR/XU2aNIG5uTkSExOLLP/7778BAG+99ZZK9qXSsWyxbJF24znMc1hVWLZYtqgonhM8J1SFZYtli4hIU7BNYpukKixbLFuisQzqXhlk5yqVS3x8PG7cuIFHjx7BwEB7i42BgQEGDhyImJgYFBYWQk/vxcvbe/fuhUwmg5ubm0r2pdKxbLFskXbjOcxzWFVYtli2qCieEzwnVIVli2WLVGfPnj147733sHnzZgwZMkRt+1ZUbGws/P39cfbsWVhaWmLMmDH473//CyMjo3LtX1hYiOXLlyMkJATHjx8vcZstW7YgMDAQV69eRc2aNfH/2Lvz+Kjqe//j7yHbTDY2IwlCNICCaFzQKoZFKLcu3F4UCMtFfz+pFVm0CAjEsrpUJIQ2VFmsiNRfsRK2wq2WCyqVgIRFBQ1BMGIhIFuUzeyT5Pv7g5u5jAmQZU4mk3k9H4/8wZmzfE54f7/fk3xzzvn5z3+uV155xXUXCyAxJtV1W1wa2SJb3kYGG18GeSwwquXpp59WbGys8vLyvF1KnU2fPl0nT57UzJkzlZ+fr4yMDKWkpGj48OHq2LGja70ZM2aoadOm2rhxY423RfWRrZptCzQ0tOGabYvqI1s12xaNH22iZtui+shWzbYFaqIud4bUZduayMrK0n333ac+ffooNzdXa9as0VtvvaXRo0dXa/vs7Gz17NlTEyZMUEFBQZXrpKWl6ZFHHtGgQYN09OhRrVu3Tunp6XrwwQerfO8a/BdjUs22RfWRrZptC88jgzXb1icY+DRJJi0tzfLjbNq0ybRs2dJIcn0FBQWZG2+80axevdoYY8xrr71moqOjjSQTGhpq+vXrZxYsWGBCQ0ONJHP99debgwcPmjfeeMNERkYaSebaa681X3/9dZXbWmnz5s3mrrvuMiEhISYmJsZMmjTJFBUVua0zffp0ExERYTZs2FDjba2WlpZmrGy+Vu//YmSrZttaLTEx0SQmJtbrMWENxofa8fU2zPhAtqzC+HBp9Le14+ttgv6WbFnF6v6W/hw/NWTIEBMXF2fKy8tdy1JSUozNZjNfffXVZbfds2ePGTBggFm2bJm57bbbzK233lrler179zatW7d2O8b8+fONJLN161bPnIifs/p6hOud2vH1MYnrHbJllfq4Hqmvfqu6yGDNtm0oLtNPrWBy1cfVVyexYMECM27cOLdlxcXFZvz48SYkJMQUFBRYXgP+V2O6uCFbDQu/bGk8GB/8E+MDrML4cGn0t/6J/hZWYXLVd5WXl5sVK1aYP/3pT94updqcTqcJDw83w4cPd1u+d+9eI8nMnj272vu6++67Lzm52qFDB3PHHXe4LVu3bp2RZN55552aF45KGsvkKmNSw8L1Dqzij5OrZNA3XW5ylccC44pOnDihsWPH6te//rXb8uDgYMXGxsrpdMrpdHqpOvgysgX4NtowrEK2AHe0CViFbAG1U1ZWplmzZqljx45yOBy66qqrFBcXp1mzZmnw4MGSLrzHNDY2VjabTfPnz5ckLVy4UGFhYQoNDdW6dev04IMPKjIyUm3atNG7777r2n9V21YlLy9PNpvtsl9du3a95Pbffvut8vLyFBsb67a8ffv2kqQvv/yy1t+ji7Vr106nTp1yW3bixAnXZ4DEmATrkC14GxlsnJhcxRU5HA4FBQVpyZIlOnnypJxOp44dO6Y333xTM2bM0NChQxUZGentMuGDyBbg22jDsArZAtzRJmAVsgXUTnJysmbMmKGUlBSdPn1aGzduVFFRkZo1a6ZmzZpJkrp3765t27a5bTdmzBiNHz9ehYWFioiIUFpamg4ePKh27dppxIgRrl+sVrVtVcLDw2WMuezX9u3bL7l9xQRnRESE23K73S6Hw6GTJ0/W6PtyKVOmTNGJEyf02muv6ccff1RWVpbmzZun+++//7KTv/AvjEmwCtmCt5HBxonJVVxRxUuH9+7dqxtuuEEOh0OdO3fW0qVLNXv2bL399tveLhE+imwBvo02DKuQLcAdbQJWIVtA7axdu1Z33HGH+vXrJ4fDoS5duuihhx5Senq6SkpKqrWPhIQERUZGKioqSkOHDlV+fr5ycnIsrtxdcXGxJCkgIKDSZ0FBQSosLPTIce69914lJSVp7NixioyM1M0336wff/xRb775pkf2j8aBMQlWIVvwNjLYOAV6uwD4hh49euiDDz7wdhlohMgW4Ntow7AK2QLc0SZgFbIF1FxRUZHsdrvbsrKyMgUFBVU5UXklwcHBklTvjwSsOIfS0tJKn5WUlMjhcHjkOFOnTtWbb76pjz76SHfffbdOnTql5557Tvfcc4+2bdumtm3beuQ48H2MSbAK2YK3kcHGhztXAQAAAAAAgGrq27evPvvsM61bt06FhYX69NNPtXbtWv3yl7+s1eRqbdX1navR0dGSpPPnz7stLygoUFFRkWJiYupc4/Hjx5WcnKwnn3xSP//5zxUWFqa4uDgtXrxYx44dU0pKSp2PAQAAUN+4cxUAAAAAAACopueff16fffaZhg8frry8PMXExGjw4MF6+eWX67WOineu1lZcXJwiIiJ0+PBht+XffPONJOmWW26pU32SlJ2drbKyMrVu3dpteWRkpFq0aKGsrKw6HwMAAKC+cecqfMLcuXN19dVXy2az6fXXX/d2OVdUXl6u1NRUJSQkeLsUXIGvZOvFF19U586dFRkZqZCQEHXo0EGTJ09WXl6et0sDvMpX2nAFxgff4SvZ+t3vflflXSo333yzt0tDI+MrbaIC/a3v8LVsVSgqKlKnTp00bdo0b5cCL8jKytLBgweVm5srp9OpnJwcLVy4UM2bN/d2aTUSGBiovn37Kj09XeXl5a7l69evl81mU79+/ep8jDZt2ki6cAfrxX788UedPn2aRwKjQfGVMYmfAXyPr2SrAtfSvqOxZSs5OVmdOnWSw+FQWFiYOnXqpOnTp1d6yoYkbd26Vd26dVNoaKhiYmKUlJTkep98fWByFT5h4sSJ2rZtm7fLqJbs7Gz17NlTEyZMUEFBgbfLwRX4SrY2bdqkp59+WocOHdL333+vWbNmad68eRo0aJC3SwO8ylfasMT44Gt8KVtAffClNkF/61t8KVsXmzp1qg4cOODtMuAlTz/9tGJjYxvFH7tOnz5dJ0+e1MyZM5Wfn6+MjAylpKRo+PDh6tixo2u9GTNmqGnTptq4cWON9h8XF6fevXtr8eLFSk9PV2FhoY4cOaKRI0dKkn7961979HyAuvDVMQkNny9li2tp39LYsrVlyxaNGDFCOTk5OnnypF566SUlJycrMTHRbb2srCzdd9996tOnj3Jzc7VmzRq99dZbGj16dH2ciiQmVwGP+uKLL/Tcc89p9OjRuu2227xdDhqR8PBwjRw5Ui1atFBERIQGDx6s/v3767//+7915MgRb5cH4AoYH2Clv/zlLzLGuH3t3bvX22UBXkF/i/qwbds2+lk/N2vWLO3du1fNmzd33TEWHByszp07a82aNZKk+fPn62c/+5kkKSkpSQ899JAWLlyo1NRUSRceufvtt99q8eLFevbZZyVJDzzwgLKzs6vc1io33XSTNmzYoI0bN6ply5YaOHCgHn/8cS1atKjSuj99BPH27dvVvXt3tW7dWjt27NAXX3yhmJgYdevWTenp6ZIkm82mlStXaujQofr1r3+t5s2bq3PnzsrJydHq1avVo0cPy84NaMz4GQBW4FoaVqlutoKDg/XUU08pKipK4eHhGjRokB5++GF98MEHbk/BeOmllxQdHa0XXnhBYWFhuueee5SUlKQ///nP2r9/f32cEu9cBTzp1ltv1erVqyVJr732moqKirxcERqL9957r9Kyq666SpL4KzLABzA+AED9oL+F1QoLCzVp0iS9+eab6ty5s7fLgZd89dVX+j//5/+4JkolqaSkRM8995yGDRumM2fO6Omnn9bTTz9dadsxY8a4/btdu3YaMWKE27Lrr7++ym2t0rNnT+3YseOy67z44ot68cUX3ZZ17dpVW7duveL+W7ZsqdTUVLfvFwCg4eFaGlapbrYq/kjtYtdcc40kuZ4YUlpaqvfff1+JiYmy2Wyu9R588EFNmjRJ69atU6dOnTx9CpVw5ypcNm/erLvuukuhoaGKjIxUfHy861nWW7ZsUefOndW0aVPZ7XbFx8drw4YNkqR58+YpLCxMTZo00R133KFWrVopKChIYWFh6tKli3r06KG2bdvKbrerWbNmmjx5suuYr776qux2u66++mqNGjVKMTExstvtSkhIuOKFvSSVlZVpxowZio2NlcPh0C233KK0tLRqndNP5eXlVfm+gou/unbtWpdvsd8iW9Zk67vvvpPD4VBcXFyNtwVqgjbM+GAVskW24I42QZuwCtnyXLamTp3q+mt6+KcTJ05o7NixlR5nGxwcrNjYWDmdTjmdTi9VBzR8jElc71iFbJEtq5At72YrOztbzZo107XXXitJ+vbbb5WXl6fY2Fi39dq3by9J+vLLLy2rxY2BT5Nk0tLS6ryfvLw8ExkZaZKTk01hYaE5ceKEGTBggMnNzTXGGLNy5Urz/PPPm9OnT5sffvjBdO3a1bRs2dK1/cyZM40ks2PHDpOfn2++//5788ADDxhJ5v333ze5ubkmPz/fjB071kgye/bscW07cuRIExYWZvbt22eKiopMVlaW+dnPfmYiIiJMTk6Oa73s7GwjySxatMi1bOLEiSYkJMSsWrXKnDlzxkyZMsU0adLE7Nq164rnZLW7777b3HrrrZbsOy0tzVjZfD25f7Jljfz8fBMREWHGjh3r0f0mJiaaxMREj+4T3sH40HDbMOPDBWTLM1566SXTpk0b06xZMxMUFGSuu+4689BDD5mdO3d69DiMD5dGf9uw2sTF6G8vIFues3XrVtOvXz9jjDG5ublGkpk6dapHj2F1f0t/Xndnz541drvdjBs3zpw4ccKUlJSY7777zixevNhERESYYcOGebtE+CFPXY9YvX/GJM+or58BuN7xv2xdzMpr6fq4HqHf8u1slZSUmKNHj5rXXnvNhISEmL/85S+uzzZv3mwkmZSUlErbORwO06dPH4/Vepl+agWTqz7OU53E3r17jSTz3nvvVWv9WbNmGUnm1KlTxpj/7SR+/PFH1zpvv/22kWQyMzNdy3bu3GkkmeXLl7uWjRw50jRt2tRt/7t27TKSzAsvvOBa9tNOorCw0ISGhpqhQ4e61ikoKDAhISFmzJgxNT4nT+OXOReQLWtMnTrV3HDDDeb8+fMe3S+/bGk8GB8abhtmfLiAbHlGTk6O+fzzz82PP/5oiouLTUZGhrn99tuNw+Ewe/fu9dhxGB8ujf62YbWJi9HfXkC2PKOgoMDceeed5ujRo8YYJlf9XXp6uvm3f/s3ExkZaQICAkzTpk1NQkKCWbBggXE6nd4uD37IVyZXGZM8o75+BuB6x/+ydTEmVy8gW55XnWy1atXKSDItW7Y0f/zjH01JSYnrs40bNxpJ5g9/+EOl7SIjI01CQoLHar3c5CqPBYakC+/4uPrqq/Xoo4/q+eef16FDhy67flBQkKQLt5dfSnBwsKQLz8D+6XZXekTOnXfeqdDQ0Mu+fPjAgQMqKCjQzTff7FrmcDgUHR2t/fv31/icYA2y5Xlr1qzRihUrtGHDBkVERNTrseF/aMOwCtnyjLZt2+r2229XeHi4goOD1bVrVy1dulSFhYVasGCB5ceH59AmYBWy5RlTpkzRk08+6XrnE/xbjx499MEHH+jcuXMqLS3V2bNn9cknn2jMmDEKDAz0dnlAg8WY5Bn8DFAZ2YJVyJZ3HDlyRKdOndJf//pXvf3227r99tt16tQpSZLdbpfk/v2rUFJSIofDUS81MrkKSRca16ZNm9S9e3e9/PLLateunYYOHarCwkJJ0vvvv69evXopKipKISEhbs//tkpISIhyc3Mv+Xl+fr4kadq0aW7P9z58+LAKCgqueE4/5e1nhzdWZMuz2Vq+fLlmz56tjz/+WNddd12Nzx2oKdow44NVyJZ12YqPj1dAQIC+/vrrGm8L76FN0N9ahWzVPVtbt25VZmamnnjiibqdOAD4OcYkfgawCtniWtoqZMs72QoKClJUVJTuu+8+LV++XFlZWZo1a5YkKTo6WpIqvSO2oKBARUVFiomJ8Wgtl8LkKlxuuukm/f3vf9exY8eUlJSktLQ0zZ07Vzk5Oerfv7+io6O1Y8cOnTt3TsnJyZbW4nQ6dfbsWbVp0+aS60RFRUmSUlNTZYxx+8rIyLjsOVUlPDy80n5++rV9+3bPn6wfIFueydZrr72mZcuWadOmTWrdunUtzh6oHdow44NVyJY12SovL1d5eblCQkJqvC28izZBf2sVslW3bC1ZskQfffSRmjRp4voFUkWNL7/8smw2mz799NPafksAwK8wJvEzgFXIFtfSViFb3s1Whw4dFBAQoKysLElSXFycIiIidPjwYbf1vvnmG0nSLbfcYlktF2NyFZKkY8eOad++fZIuNL5XXnlFXbp00b59+5SZmSmn06kxY8aoXbt2stvtstlsltbz8ccfyxhz2b94aNu2rex2u/bs2VPl55c7J9QfslV3xhglJSUpMzNTa9euVXh4uCXHAapCG4ZVyJZn3H///ZWW7dq1S8YY3XPPPZYdF55Hm4BVyFbdLV26tNIvkCruFpg6daqMMbrzzjstOTZQlblz5+rqq6+WzWbT66+/7u1yrqi8vFypqalKSEi45Dpbt25Vt27dFBoaqpiYGCUlJam4uLgeq0R9YEzyDH4GqIxswSpkq/788MMPGjZsWKXl2dnZKisrU9u2bSVJgYGB6tu3r9LT01VeXu5ab/369bLZbOrXr1+91MvkKiRdaFCjRo3S/v37VVJSot27d+vw4cPq2rWrYmNjJUkffvihioqKlJ2drR07dnj0+OXl5Tpz5oxKS0v15Zdfaty4cYqNjdXw4cMvuY3dbtevfvUrvfvuu1q4cKHOnz+vsrIyHT16VMePH7/sOaH+kK2627dvn+bMmaPFixcrKCio0mMXLvVXRYAn0IZhFbLlGd99952WL1+us2fPyul0KiMjQ0888YRiY2M1evRoy44Lz6NNwCpkC2h8Jk6cqG3btnm7jGrJzs5Wz549NWHCBBUUFFS5TlZWlu677z716dNHubm5WrNmjd566y2uZRohxiTP4GeAysgWrEK26k9YWJg2btyoTZs26fz583I6ndq9e7cee+wxhYWFacKECa51p0+frpMnT2rmzJnKz89XRkaGUlJSNHz4cHXs2LF+CjbwaZJMWlpanfdz6NAhk5CQYJo3b24CAgJM69atzdSpU01paakxxpikpCTTokUL06xZMzNo0CAzf/58I8m0b9/ePPvssyY0NNRIMtddd53ZsmWLmT17tmnatKmRZFq1amXeeecds3z5ctOqVSsjyTRv3ty8++67xhhjRo4caYKCgsw111xjAgMDTWRkpHn44YfNwYMHXfX9/ve/d20bFhZmBgwYYIwxpri42CQlJZnY2FgTGBhooqKizMCBA01WVtYVz8kKGRkZplu3biYmJsZIMpJMdHS0SUhIMJs3b/bYcdLS0oyVzdeT+ydbdZeZmenKU1VfKSkpHjtWYmKiSUxM9Nj+4D2MDw2nDRvD+FAVsuUZzz77rGnfvr0JCwszgYGBpk2bNmbEiBHm2LFjHj0O48Ol0d82rDZBf1sZ2bJGbm6ukWSmTp3q0f1a3d/Snzce2dnZRpJZtGiRt0u5pD179pgBAwaYZcuWmdtuu83ceuutVa43ZMgQExcXZw1w12oAACAASURBVMrLy13LUlJSjM1mM1999VV9levTPHU9YvX+GZM8o75+BuB6x/+yVV/X0vVxPUK/5ZvZ6tevn4mLizPh4eEmJCTEtG/f3gwdOtRkZmZW2ufmzZvNXXfdZUJCQkxMTIyZNGmSKSoq8mjdl+mnVjC56uOsvniqDyNHjjQtWrTwdhk+xZcubryJbNUcv2xpPBgf/BPjQ/WQrZpjfLg0+lv/RH9bPWSr5phcRXX5wuTqxe6+++4qJ1edTqcJDw83w4cPd1u+d+9eI8nMnj27vkr0ab4yuepNjEk1x/VO9ZCtmvOlyVVvIlved7nJVR4LjAahrKzM2yWgkSJbgG+jDcMqZAtwR5uAVcgWfNXmzZt11113KTQ0VJGRkYqPj9f58+clSVu2bFHnzp3VtGlT2e12xcfHa8OGDZKkefPmKSwsTE2aNNEdd9yhVq1aKSgoSGFhYerSpYt69Ojhehdas2bNNHnyZNcxX331Vdntdl199dUaNWqUYmJiZLfblZCQUK3HDJaVlWnGjBmKjY2Vw+HQLbfcorS0tGqd00/l5eVVeiXNT7888fjAb7/9Vnl5ea5HK1Zo3769JOnLL7+s8zGACoxJsArZglXIVsPF5CoAAAAAAADwP/Lz89WvXz8lJibq9OnTys7O1g033KCSkhJJ0smTJzVkyBAdOnRIx44dU3h4uB555BFJ0rhx4zRp0iQZY7Ro0SL961//0okTJ9SzZ0/t3r1bv/3tb7V7926dPn1ajz32mFJSUvTFF19IksaOHavhw4eroKBAzzzzjA4dOqTPP/9cpaWl+sUvfqEjR45ctu7nnntOc+bMUWpqqo4fP67/+I//0LBhw/Tpp59e8Zx+Kjw8XMaYy35t3769zt/rEydOSJIiIiLcltvtdjkcDp08ebLOxwAAAPA0JlfhVVOmTNHSpUt17tw5xcXFadWqVd4uCY0E2QJ8G20YViFbgDvaBKxCtuDLDh06pPPnz+umm26S3W5Xq1attHr1al111VWSpMTERM2cOVPNmzdXixYt1K9fP/3www/Kzc1120/nzp0VGhqqli1b6j//8z8lSbGxsbrqqqsUGhqqRx99VJK0f/9+t+0CAwN14403KiQkRJ07d9bChQv1448/aunSpZesuaioSAsXLlT//v01cOBANWvWTNOmTVNQUJCWLl16xXPyluLiYklSQEBApc+CgoJUWFhY3yWhEWJMglXIFqxCtho+JlfhVbNmzVJxcbGMMfrXv/6lxMREb5eERoJsAb6NNgyrkC3AHW0CViFb8GXt2rXT1VdfrUcffVTPP/+8Dh06dNn1g4KCJF3+0X3BwcGSpNLS0krbOZ3Oy+7/zjvvVGhoaKVJ2IsdOHBABQUFuvnmm13LHA6HoqOjtX///hqfU32x2+2S3L8vFUpKSuRwOOq7JDRCjEmwCtmCVchWw8fkKgAAAAAAAPA/HA6HNm3apO7du+vll19Wu3btNHToUNddlO+//7569eqlqKgohYSEuL031SohISGV7oy9WH5+viRp2rRpbu9FPXz4sAoKCq54Tj9VX+9cjY6OlqRK734tKChQUVGRYmJi6nwMAAAAT2NyFQAAAAAAALjITTfdpL///e86duyYkpKSlJaWprlz5yonJ0f9+/dXdHS0duzYoXPnzik5OdnSWpxOp86ePas2bdpccp2oqChJUmpqaqV3o2ZkZFz2nKpSX+9cjYuLU0REhA4fPuy2/JtvvpEk3XLLLXU+BgAAgKcxuYoamzt3rq6++mrZbDa9/vrr3i6nWkaNGuX215UV7zWRpK1bt6pbt24KDQ1VTEyMkpKSXO/8qK3y8nKlpqYqISGh0mdr1651q8Xb7zdpSBpTtl588UV17txZkZGRCgkJUYcOHTR58mTl5eXV+lhXyirZgrc1pjZc4XL9eU3RhmuvsWXLk9cev/vd76q8k+TiRwKSrcansbUJif62oWhs2bLiZ70KRUVF6tSpk6ZNm+ZaRrYaj2PHjmnfvn2SLkxavvLKK+rSpYv27dunzMxMOZ1OjRkzRu3atZPdbpfNZrO0no8//ljGmMveKdq2bVvZ7Xbt2bOnys8vd07eFBgYqL59+yo9PV3l5eWu5evXr5fNZlO/fv28WB28qTGNSdW5Zq8prndqrzFlqwLX0g1DY8tWfc+bSFJycrI6deokh8OhsLAwderUSdOnT6/0hIvq1Gd1VplcRY1NnDhR27Zt83YZNdaiRQutX79eBw4c0JIlSyRJWVlZuu+++9SnTx/l5uZqzZo1euuttzR69OhaHyc7O1s9e/bUhAkTVFBQUOnzhx56SEePHlV6err69u1b6+M0Ro0pW5s2bdLTTz+tQ4cO6fvvv9esWbM0b948DRo0qFbHqE5WyRa8rTG1YenK/XlN0IbrpjFly4prjyshW41PY2oTEv1tQ9KYsmV1fzt16lQdOHDAbRnZajyOHTumUaNGaf/+/SopKdHu3bt1+PBhde3aVbGxsZKkDz/8UEVFRcrOztaOHTs8evzy8nKdOXNGpaWl+vLLLzVu3DjFxsZq+PDhl9zGbrfrV7/6ld59910tXLhQ58+fV1lZmY4eParjx49f9py8bfr06Tp58qRmzpyp/Px8ZWRkKCUlRcOHD1fHjh29XR68pDGNSZ7G9U7dNLZscS3dcDSmbHlj3kSStmzZohEjRignJ0cnT57USy+9pOTk5ErvnG0IWWVyFX7D4XDogQce0A033KCQkBBJ0ksvvaTo6Gi98MILCgsL0z333KOkpCT9+c9/1v79+2t8jC+++ELPPfecRo8erdtuu63KdWw2m6655hr16NFD119/fZ3OCQ1DVdkKDw/XyJEj1aJFC0VERGjw4MHq37+//vu//1tHjhyp8TGqk1WyBdROVW24Ov15TdCG/VN9XHtI0l/+8pdKj+nbu3ev63OyhYaC/hZWqa/+tsK2bdvc+tkKZKvxiIqKUllZmRISEhQaGqpf/vKXGjVqlJ5++mnFx8crKSlJCxYsUExMjKZOnapevXpJkrp3766JEycqJSVFkhQfH6+tW7cqOTlZo0aNkiQ98MAD+utf/6q0tDQ98MADkqSxY8dq+fLlruMXFRUpPj5eDodDPXr00A033KB//vOfrnz/4Q9/UPfu3SVd+EXuwIEDJUnz5s3T+PHjlZycrJYtWyomJkbjxo3TmTNnLntOVtm+fbu6d++u1q1ba8eOHfriiy8UExOjbt26KT093bXeTTfdpA0bNmjjxo1q2bKlBg4cqMcff1yLFi2yrDbAKlWNSdKVr9lrgusd/8S1NKzSUOZNJCk4OFhPPfWUoqKiFB4erkGDBunhhx/WBx98oOPHj7vWawhZZXIVfqu0tFTvv/++7r33XrdH+Dz44IMyxmjdunU13uett96q1atX65FHHnG7gIL/ee+99xQQEOC2rOLRAzX9KzIrsgrg8jzZn9OGUYEsAJXR38IKVmahsLBQkyZN0rx58zxRKhqoa6+9Vp988olOnz6t0tJSfffdd/rd737n+hlv9uzZ+uGHH3TmzBmtWLFCTz31lIwx+uabbzR37lzl5+fLGKN//etf6t69u5KSknT27FkZY3TixAkNGzZMQ4YM0YkTJ2SM0enTpzV06FDX8SMiInT06FE5nU6dO3dOf/vb39SuXTvX5xMmTHBtm5eXp9WrV0u68AvJ2bNn6/Dhw3I6nTp16pRWrVqlzp07X/GcrNC1a1dt3bpVx44dc00mHT9+XJ988ol69uzptm7Pnj21Y8cOFRUV6dixY5ozZw6/VwGqwPUOLsa1NKzgzXmTNWvWyG63uy275pprJMn1ur2GklUmV/3IjTfeKJvNpiZNmuiOO+5wTfBMnjxZTZs2ld1u15///GdJF26/7ty5s2t5fHy8NmzYcMl9jx07VsHBwYqOjnYte+qppxQWFiabzabvv//etbysrEwzZsxQbGysHA6HbrnlFqWlpV1y33l5eVW+l+Dir9o8xubbb79VXl6e65E+Fdq3by9J+vLLL2u8T39Ftqrnu+++k8PhUFxcXI22I6uwGm3YWv7chsmWO3/OAi6gTVjLn9sY2XJnZRamTp3q+mt6wCplZWXeLgGoNcYka3G9Q7asQrbIVoWGloXs7Gw1a9ZM1157bYOqj8lVP7J3715dd911atu2rXbu3KnQ0FBJ0pw5c/TrX/9as2fPdr2/4+TJkxoyZIgOHTqkY8eOKTw8XI888sgl9/3qq69q8ODBbssWLFigF154odK6zz33nObMmaPU1FQdP35c//Ef/6Fhw4bp008/rXLf4eHhlR6b8dOv7du31/j7ceLECUkX/iL0Yna7XQ6HQydPnqzxPv0V2bqygoICbdq0SSNGjFBwcHCNtiWrsBpt2Fr+3IbJljursvDb3/5WzZs3V3BwsOLi4vTwww9r165dtdoXrEWbsBb9LdmqYFUWPvnkEx08eFDDhg2r1fYA4A8Yk6rmqWt2rnfIllXIFtmq0BCy4HQ69d1332n+/Pn68MMP9dprr7l+p94Q6pOYXPUrAQEBeuaZZ5STk6M1a9a4lhcUFGj16tV6/PHHXcsSExM1c+ZMNW/eXC1atFC/fv30ww8/KDc3t041FBUVaeHCherfv78GDhyoZs2aadq0aQoKCtLSpUvrtO+aKi4ulqQqH4ETFBSkwsLCeq3Hl5GtK5s1a5ZiYmL0u9/9rsbbklVYjTZsLX9uw2TLnRVZeOyxx/Rf//VfOnLkiPLy8vTuu+8qJydH9957r7KysupcMzyLNmEt+luyVcGKLBQWFmrcuHFauHBhnesDLmXKlClaunSpzp07p7i4OK1atcrbJQE1xphUmSev2bneIVtWIVtkq0JDyELbtm3Vpk0bPf/885ozZ46GDBnSoOqTmFz1O0888YSaNm3q9n6YZcuW6eGHH1ZkZOQltwsKCpJU90fTHDhwQAUFBbr55ptdyxwOh6Kjo2v1IuS6qHh2d2lpaaXPSkpK5HA46rUeX0e2Lm3NmjVasWKFNmzYUOkvaqqDrKI+0Iat4+9tmGz9Lyuy0LZtW91+++0KDw9XcHCwunbtqqVLl6qwsFALFiyoc83wPNqEdehvyVYFK7IwZcoUPfnkk653PgFWmDVrloqLi13va01MTPR2SUCtMCa58+Q1O9c7ZMsqZItsVWgIWThy5IhOnTqlv/71r3r77bd1++2369SpUw2mPonJVb8THh6uJ598Utu2bdPOnTslSYsWLdLYsWPd1nv//ffVq1cvRUVFKSQkRJMnT/bI8fPz8yVJ06ZNc3v29+HDh13PMv8pq54dXvGc8/Pnz7stLygoUFFRkWJiYmq8T39Gtqq2fPlyzZ49Wx9//LGuu+66Wu2DrKI+0Iat4+9tmGz9r/rKQnx8vAICAvT11197ZH/wLNqEdehvyVYFT2dh69atyszM1BNPPFHjWgDAHzEmXVltr9m53iFbViFbZKtCQ8hCUFCQoqKidN9992n58uXKysrSrFmzGkx9EpOrfmns2LEKCgpSamqq0tPT1bZtW9fLfiUpJydH/fv3V3R0tHbs2KFz584pOTnZI8eOioqSJKWmplZ6/ndGRkaV21j17PC4uDhFRETo8OHDbsu/+eYbSdItt9xS4336O7Ll7rXXXtOyZcu0adMmtW7dutb7IauoL7Rha9CGyVaF+spCeXm5ysvLFRIS4pH9wfNoE9agvyVbFTydhSVLluijjz5SkyZNXL+oqjjfl19+WTab7ZLvwgIAf8WYdHm1vWbneodsWYVska0KDS0LHTp0UEBAgOsx6g2lPiZX/VCbNm00ePBgrVq1StOnT9e4cePcPs/MzJTT6dSYMWPUrl072e122Wy2K+43MDBQTqfzsuu0bdtWdrtde/bsqdM5eEJgYKD69u2r9PR0lZeXu5avX79eNptN/fr182J1volsXWCMUVJSkjIzM7V27VqFh4fXaX9kFfWFNmwN2jDZqmBFFu6///5Ky3bt2iVjjO6555461Qvr0CasQX9Ltip4OgtLly6t9IuqivdqTZ06VcYY3XnnnR49B/i2uXPn6uqrr5bNZtPrr7/u7XKqZdSoUW53ujz66KNun5eXlys1NVUJCQl1Oo7T6dSMGTPUrl07BQcH65prrtHEiRPd3o+2du1at1quuuqqOh0T3sGY9L88ec3O9Q7ZsgrZIlsVvJWFH374QcOGDau0PDs7W2VlZWrbtq1X6/spJlf91LPPPqvS0lKdOXNGP//5z90+i42NlSR9+OGHKioqUnZ2tnbs2HHFfXbo0EGnT5/W2rVr5XQ6lZubW+mvB+x2u371q1/p3Xff1cKFC3X+/HmVlZXp6NGjOn78uOdOsJqmT5+ukydPaubMmcrPz1dGRoZSUlI0fPhwdezY0bXe0KFD1apVK33++ef1XqOvIVvSvn37NGfOHC1evFhBQUGVHscwd+5c17rVzVZ1swrUFW24ZmjD1Ue2LvD0tcd3332n5cuX6+zZs3I6ncrIyNATTzyh2NhYjR492urTQR3QJmqG/rb6yNYF/KwHb5o4caK2bdvm7TJqrEWLFlq/fr0OHDigJUuWuJZnZ2erZ8+emjBhwiUfTVhd48aNU0pKimbNmqUffvhB77zzjhYvXuz22O2HHnpIR48eVXp6uvr27Vun48G7GJMuqO41O9c71Ue2aoZsVR/ZusAb19JhYWHauHGjNm3apPPnz8vpdGr37t167LHHFBYWpgkTJtS4PksZ+DRJJi0trVbb9u7d27z55ptVfpaUlGRatGhhmjVrZgYNGmTmz59vJJn27dubcePGmVatWhlJJiwszAwYMMAYY8wPP/xgevfubex2u4mLizO/+c1vzKRJk4wk06FDB5OTk2OMMaa4uNgkJSWZ2NhYExgYaKKioszAgQNNVlZW7b4J1TBy5EhzzTXXVPnZ5s2bzV133WVCQkJMTEyMmTRpkikqKnJbp3///kaSmTFjxmWPk5GRYbp162ZiYmKMJCPJREdHm4SEBLN58+ZK6z/zzDOmZcuWNT6ftLQ0Y2Xzrev+/T1bmZmZrv//qr5SUlJc61Y3W8ZUL6sVaputxMREk5iYWOPt0PAwPlTPpcaH6vbnDa0NMz40/GwZ49lrj2effda0b9/ehIWFmcDAQNOmTRszYsQIc+zYsSrXZ3zwPPrb6qG/rd/9k60LPNnf/lRubq6RZKZOnVrl5w21v6U/rz/Z2dlGklm0aJG3S6mWS7WlPXv2mAEDBphly5aZ2267zdx66621PsbBgwdNkyZNzJNPPum2fNq0aUaS2bdvX6VtatuW/E1drkes3j9jUvWv2bneqRmy5bvX0vVxPUK/VT0Nbd6kX79+Ji4uzoSHh5uQkBDTvn17M3ToUJOZmVmr+ipY0A+uYHLVx1l98dRYXK6TqI6ysjLTo0cPs2TJEg9W1XgvbvxJY8sWv2xpPBgfqqextWHGh4ajsWWL8eHS6G+rp7G1CfrbhqOxZYvJ1cajsUyuXuzuu++u0+Tq8uXLjaRK7W3r1q1GkklNTa20DZOr1dOQJ1f9SWMbk7jeaTgaW7Ya+uSqP2mo2fI0KyZXeSww/EZhYaE2bNig7OxslZSUVHu7srIyrV27Vj/++KOGDh1a5zqMMTp27Ji2bt3qeskyfBvZAnwbbRhWIVuAO9oErEK24Ck33nijbDabmjRpojvuuMP1CNzJkyeradOmstvt+vOf/yxJ2rJlizp37uxaHh8frw0bNlxy32PHjlV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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpLdvj5A8D48"
      },
      "source": [
        "The predicticton result for head data, this should really be fresh data not reusing training data \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNMqjgIo8GeD"
      },
      "source": [
        "#When we use our training data above we should use our validation data here to test the predictions\n",
        "print(\"Making predictions for the following 5 patients:\")\n",
        "print(X_test)\n",
        "print(\"The predictions are\")\n",
        "print(clf.predict(X_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nw2RUXzKFNyr"
      },
      "source": [
        "Desicion tree test results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NJ2sZ89GeQL"
      },
      "source": [
        "#Predict the response for test dataset\n",
        "y_pred = clf.predict(X_test)\n",
        "print(y_pred)\n",
        "# Test decision tree accuracy\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCymTgE61GQt"
      },
      "source": [
        "Random forrest "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X57wsace1MpE"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfclf = RandomForestClassifier(n_estimators=10)\n",
        "rfclf = rfclf.fit(X_train,y_train)\n",
        "#Predict result from the data given\n",
        "y_pred=rfclf.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "#Test Random forrrest accuracy\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65WRbzxYH1Q5"
      },
      "source": [
        "So we can see that the Random forrest is more accurate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3p16AuhK46z7"
      },
      "source": [
        "We also explored the alternative to the decision tree using entropy criterion rather than the default gini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wkr0D9dF43t9"
      },
      "source": [
        "entclf = tree.DecisionTreeClassifier(criterion=\"entropy\")\n",
        "\n",
        "# Train Decision Tree Classifer\n",
        "entclf = entclf.fit(X_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juBJcNLt6nnf"
      },
      "source": [
        "Display the entropy tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBlkRFh66scc"
      },
      "source": [
        "from IPython.display import Image\n",
        "from six import StringIO\n",
        "import pydot\n",
        "dot_data = StringIO()\n",
        "tree.export_graphviz(entclf, out_file=dot_data, feature_names=features)\n",
        "graph, = pydot.graph_from_dot_data(dot_data.getvalue())\n",
        "Image(graph.create_png())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5nVVFGu61Xe"
      },
      "source": [
        "Entropy Desicion tree test results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C35aqW7i616e"
      },
      "source": [
        "#Predict the response for test dataset\n",
        "y_pred = entclf.predict(X_test)\n",
        "print(y_pred)\n",
        "# Test decision tree accuracy\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7K8ri8n62W-"
      },
      "source": [
        "Random Entropy forrest\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmGtjvui626G"
      },
      "source": [
        "rfentclf = RandomForestClassifier(criterion=\"entropy\",n_estimators=10)\n",
        "rfentclf = rfentclf.fit(X_train,y_train)\n",
        "#Predict result from the data given\n",
        "y_pred=rfentclf.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "#Test Random forrrest accuracy\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HF1Rba9NDwN"
      },
      "source": [
        "testing via crossvalidation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsvHzc_pNKVc"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "np.random.seed(1)\n",
        "scores = cross_val_score(clf, X, y)\n",
        "print('gini tree accuracy = {} +/- {}'\\\n",
        "        .format(scores.mean(axis=0), scores.std(axis=0)))\n",
        "scores = cross_val_score(rfclf, X, y)\n",
        "print('gini random forest accuracy = {} +/- {}'\\\n",
        "        .format(scores.mean(axis=0), scores.std(axis=0)))\n",
        "scores = cross_val_score(entclf, X, y)\n",
        "print('entropy accuracy = {} +/- {}'\\\n",
        "        .format(scores.mean(axis=0), scores.std(axis=0)))\n",
        "scores = cross_val_score(rfentclf, X, y)\n",
        "print('entropy random forest accuracy = {} +/- {}'\\\n",
        "        .format(scores.mean(axis=0), scores.std(axis=0)))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YN1_GZwN-MPP"
      },
      "source": [
        "Creating the Model For DecisionTreeRegression() (Problematic due to M/B classification rather than a series of numeric variables, may not be a viable model for this data)"
      ]
    }
  ]
}