Clustering-KMeans_MallCustomerSegmentation.ipynb

{
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  "metadata": {
    "colab": {
      "name": "KMeans-MallCustomerSegmentation",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeMqhD6d7CG7",
        "outputId": "a00b3ff9-0f7c-448a-f0f5-61aaffe1287b",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c0bbae35-01f4-44e5-af38-f0fee3b4e066\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c0bbae35-01f4-44e5-af38-f0fee3b4e066\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Mall_Customers.csv to Mall_Customers.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZc9knbM7DHB"
      },
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "df=pd.read_csv(io.BytesIO(uploaded['Mall_Customers.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNRR3xWv80L-",
        "outputId": "79e7815f-f245-457a-869c-c171772b38fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JX7NYm4S7n3Z"
      },
      "source": [
        "from sklearn import preprocessing \n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "df['Gender']= label_encoder.fit_transform(df['Gender']) \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYvclGu07pJ-",
        "outputId": "7ff2c586-a0eb-4525-eb88-799d5c2253fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0             1       1   19                  15                      39\n",
              "1             2       1   21                  15                      81\n",
              "2             3       0   20                  16                       6\n",
              "3             4       0   23                  16                      77\n",
              "4             5       0   31                  17                      40\n",
              "..          ...     ...  ...                 ...                     ...\n",
              "195         196       0   35                 120                      79\n",
              "196         197       0   45                 126                      28\n",
              "197         198       1   32                 126                      74\n",
              "198         199       1   32                 137                      18\n",
              "199         200       1   30                 137                      83\n",
              "\n",
              "[200 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4ATDcox9H8U"
      },
      "source": [
        "#X=df[['Gender','Age','Annual Income (k$)','Spending Score (1-100)']]\n",
        "\n",
        "#X=df[['Age','Spending Score (1-100)']]\n",
        "\n",
        "#X=df[['Age','Annual Income (k$)']]\n",
        "\n",
        "X=df[['Annual Income (k$)','Spending Score (1-100)']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yELelLddnFLK",
        "outputId": "39a9e3a6-ef89-4561-89a2-a5e68e15b948",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "# calculate distortion for a range of number of cluster\n",
        "distortions = []\n",
        "for i in range(1, 11):\n",
        "    km = KMeans(\n",
        "        n_clusters=i, init='random',\n",
        "        n_init=10, max_iter=300,\n",
        "        tol=1e-04, random_state=0\n",
        "    )\n",
        "    km.fit(X)\n",
        "    distortions.append(km.inertia_)\n",
        "    \n",
        "# plot\n",
        "plt.plot(range(1, 11), distortions, marker='o')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Distortion')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CfZas-X9-Th",
        "outputId": "0eb9f8c5-c6f7-4ad4-ed4e-04bce4094d6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=3).fit(X)\n",
        "\n",
        "centroids = kmeans.cluster_centers_\n",
        "centroids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[87.        , 18.63157895],\n",
              "       [44.15447154, 49.82926829],\n",
              "       [86.53846154, 82.12820513]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7S0x3fk8-1gE",
        "outputId": "a26ea569-efd1-4bc0-9213-fa65011d2be1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(df['Annual Income (k$)'], df['Spending Score (1-100)'], c= kmeans.labels_.astype(float), s=50, alpha=0.5)\n",
        "plt.scatter(centroids[:, 0], centroids[:, 1], c='red', s=50)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eEPxRVP4vIPnra7IwK+Ig8Y/paiS/H3DA2roh5t6uNaGDXyHPX+zk786cQgEGgo+iLZXmV+65j1SYK18TxEguGw3rapvnl6i2MbQGTOkFlNmqNeRVCTC0hAC+/lqVmIngJRpsW5u5vRJ5FKWmYO74QLF0B6y1fdH+qvxWoGK5IP1jbAK/Ty8QW1uus9XLI+YmMK/uCTfk0oQOfgVcmBjn6dMnaUoksefMde3LZvjWqRN8en84OOHmw9NDPObWXM8QBa9LO3cis87Gz4Kc12kNq2V2gRVPl00uqVZ5bSg/p7tw1aROmVi7l41kRUwk9mFU5NEgf2+BtU0LiC08r/LBPRU0E7naqQcDUPQOEk5y2oCEDn4FvNLTRcS05jl3gMZEkmNDg4s6VENuBswgYs1q/fW5qJx+XRXnR5KSAqkDxgMnGNWpECMJZruWHVgF/EqHbraiBMpA4YNRFeTHl89Fy/RA8GVQqojK/SWUXtVRuj+kX7C26w+lAP+S5whZn4SJ4hUwnMstOfnJEMEQIVsur4FVIZdCRCDybl1lorzZF5Snt9l7WPRvYdYFN4M0mDu1DLBzQH9ttWknuUKUn4H8V4CYVqU0m/VThiqj8l/WQ8Cv9dzF74N7Bqy9YKSDtYSETj15w6CGwNoBxtKDPpTyUW4nfvF5/NIrOpcfsi4II/gV0JpO09/XS3JBh6rn+/hKhfXqNym683QEij9C59LRKYroT2kn555ZcICp55Sql9C5eG+mtl7iv7Qqio+qckyne8wFC7ZGLXi94HVqJ3y151UlKL8a1MtbYN+tq4dUTj+FuMcg+h4k/gtLlojq6P+rgU5+sK0gqOgHkMij60oS4VYkdPAr4KG2LbzS00PRdYkGuu5KKQZyGR5oaQsXWW9SdLXN+1HOg+AG1TZWO2JUa1VDI621UealLEoQeRRiTyKqoFMn5tbVk/P1x1j+31H0usG1oHLMq5c3qsF5RFcH+XmtX5P41WUdtSo8E+jkz+2WrUDx23o9IpQRuKkJHfwKaE2l+eS+u/jGiWOM5D3996/g9oYGfmbPbWttXshlEKMGnPl5ZxEHEp9F5f5cz0dF6RfMTUj8lxHzOk1bMupBKsu8qK59IVeS6Fr9OZLGYgZrCqNBOeIyzt3PQ+WwfqqZV3Fkg8RRpZ+EOjE3OaGDXyH3NLewp66ejrFR3aGaTNOSSoWPrusYMZsg9b9obXI1qSN5c9u8aF15g+BdBATM7brEbyXXtO9AFZ/RkboxR2rBHwny8ddWvijioJx3QOmHCyZOVQK9m0eXP1jlgrLKJZ5SJB7Mcg25FpTywLuA8oYRiYO9e8nqp5USOvhVIG7b7G8MJ9FvJEQssHcv2q6Uhyo8rfPaKIK2S61UGX3/Nd/YxUhA4le0OqTXO/uC2YAkPr2iBiSJvhulxqD81twL6nTTpSJwIxno7riLJRFUDqzFP5+Qy6P1778cPCEGz4gSQcU/hWGv7pyH0MFfI0opLk5OMJTLEbEsdtVuWrKiJmRjocovByP6WufopnhQehZlNiHONfQ+ZDLw1FNIRwfs3AY//34te7PEk8O1IOJA7BMQeVegdmmBtQMx0pc5LqbXKUovLcjBl0EVF82aDbk8Sik9lMYfXqB/n4f8V1Cp39Kpw1UidPDXQLZc5qtvvcG5ibGZFK1jmnzizv3c2RBqZ2xU9OzWHwVdsHMiajH1BKbSj+BqHfyLL8IHPqC1c3I5JJGA3zTgmWfgkdXLb4tIkOq5Oqlpif6ULousnAy2KP1+Y08u2S0bchn8gVmJ57lIHPwJVPnNZQfTXAvr1sEX3QodY2MU3QqNiSSb01U3LO/91yeP0zk5TmsyPXPNQqXCXxx9k988+I5lxcFy5TJnx8dm1CTnar2HrAcqukHKXEJzXhJaTvhqyGS0c8/MqZDJBbIHH/gA9PVBcm2F5kQiEP8MeL0or0c/DVg7Lxv9hyyDPwEso38vUS1LvYqsSwd/aniIv3j7LUquCyIopdhRU8un99+zaGrSajNWyHN8aJCmBc45ZttMloq81tvLB3cvzqMd6evlGyeO4/m+nryjYF9DI5/YdxcRa13+Gm5BbD13VRUDHZo5qLyuhLkannoqUL1cAt/Xr3/2s9dm6ioiImC1IVbbWpuy/jHSgNKL1wudvCoFcwtW8XKrerYbwEg+z5feeoO47dCarqI1laY1laZzcpxvnDh23a8/USzOdKouJGbb9GUW1yt3T07ytWNHqYlGaU2naU1pu48NDfKdjtPX3eaQ1UHEAOcxnT9VcxzzdBesc+kJTIvo6JiN2BeSy8HZs0u/FrJ+MVr0IHY1Mn+7KoAYqz6Fa0UOXkT+jYgcF5FjIvI1EYmKyDYReVVEzorIU7LKmp+H+3pQMG9BU0RoSqQ4PjzESD6/mpdbRCoSwQvmpS6kUHFpSCyWjv1J90Ucy5wXqYsITckUh3p7yIWSBusGibxDyxT4/UGHaa/Oq0Yeu/oF1l27YIm/F0Bv3xnWmG80RARJfEL3NXh9+sPv1Rr+sU+tmq7RNNecGxCRVuBfA3uVUgUR+SvgF4APAP9FKfV1EfkC8FngD1fFWqA3M0VsiZSGBFH1RLFAXTy+6HWlFIO5LP2ZDBHLYkdN7TWlRurjCXZvquP8+CiNidl65ZLr4imf+1sXqxT2ZjIkrMX3OcswUOhZq9c7tRSyOohYEPsYRB5FVc7rqMvacW2DKT7+cfiN31j6NcPQr4dcFUqVtMaOKoPZBEbjTbfOJUYtJD8P7lmUNwSSQOzbdKnsKrPS5K8FxESkAsSBfuDdwCeD178M/C6r6OAbE0k6RkcXbVdK4St/Sf2Xkuvy1PG3eXtwAEQQdNXLL+y7i33XUPXy8Tv28SevH6FnahJDBF8pTMPg43fcSUtq8eJTQyLBqZHhRU7c831QkHJCSYP1hK5IaUGWWmy9GlIpXS0zp4qGREI792eeWfMF1vWGXz4ZTKsqovsTfC0MF//Y5Ucg3mB0n8VtiH19O96v2cErpXpF5P8BuoAC8H3gCDChlHKD3XqApYS3r5n7mlt5/mInJdedF4EP53Nsr6mlPr74LvjtM6c4OjhAa2p+1ctXjr7Bbzz0DpqSqUXHXIp0JMrnHzzI2fExeqYmSdgOt9fVLysN/PDmLbwx0E/F82akhZVSDOWz7G9qCjVr1glK+bp71Z/Sj9jm5pVPQHrkEV0t89RTOue+c6eO3EPnflUobwgKXwWp0nLQoBcyK8dRhW8j8Y+urYFrxEpSNDXAk8A2YAL4BvD+qzj+c8DnALZsufI27OZUio/dcSffOH4MXykMQ/B8n8Zkkk/su2vR41imVOJQbw/Ny1S9vNLTzc/edhWzMANMw2DPpjr2bKq77L7bqmv4md17eKbjzOzkJ+XTXl3Dk3uu/tohNx7lDaHyXwFvJKh+8PWCWeIXV543TSZvimqZ9YwqH9JfzG33F9E6OpXXUf779dzbW4yVpGjeA1xQSg0DiMj/BN4BVIuIFUTxbUDvUgcrpb4IfBHgwIEDi1csL8GBllZ21tZyYmiIXKVC0rGJWjaDuRwxy54X2U8Ui4gIpmGQr5TJlsuYhkF1NErCceiZWvnA5MshIjy+dTv7Gpo4Gcxuba+uZkc4u/W6opTSk47UpI7szLZririVKqNyfxYMApmTlvGHdct58l+vnqpkyLXh9bDkbFwxwZdAHTR08FdDF/CQiMTRKZongMPAc8BHgK8DnwGeXqmRS1EdjXGgtY1vnTzO98/3IggKiFkWn9h3F7fX63rSpOPg+h5HB/sZzOampUOwDIPWVJpdtTduBmRdPM6jW7besOvdyih/TOuYe31B12kQccf/8dULg7lntINYOObPqA8qaS6EsrlrjbEJ3B5ggRNXCvAWT++6Rbjm8FEp9SrwTeB14O3gXF8E/i3wGyJyFtgE/Okq2Lkk3zt7htf6emlOpmlJpWlOpii6Lv/5lZc4May7CmtiMTzf58LEOMmIQ8qJkHIiGCKcHBlm5w108CE3BqVcLffrj2qnbjRr7Rh/FJX/M2aXiK7wfN4QM/8q/mRQBz+n38EfXz3jQ64JcR4Eylolcy5qCKw9q15+uF5YURWNUuo/AP9hwebzwAMrOe+VkK9U+El3F03J1Ex55FuDA5Q9j0LF5Xd/9Bwf3nMbH9i5G0MMNsUSZEplDBEUChRsrkrTNzUFLau6Dhyy1rjn9Si6hVUuRp2OuN2zcDXVC1KlG1HKrwaDNwLhf6Mq0KVZ/fK2kKtDrC2o2M9A4e/QN2MDqIDZhMR/fo2tWzvWbY/8eLEA6FRLoVLhSH8flmGQciJETQtD4M2BfjKlErZpcrBtM6OFAhPFArZh0JBI4qPomppc43eymIrncXFygorn0ZJKh4O7rxLljzCjAjeNnwe/D7wxVOX81ZWnWTvAOx+oA6dm/Dv+GKg8ytzO1VRaKz8b5IxNsLYgEllyW8jVYUQeRVm3oSrHQeUQaxtYuxC5dVVe162DT9g2vlL4StGfzaCUImLqt1PxPaqiEVpSaU6ODM9ICzQkEvM6TYdzOTbVrL7I/ko4OTzEU8ffJl+ZfdR8ePMWPrT7NqxwQfaKEEnopzTQ9eWV18E7FeRjXcj+J3yvGxL/FOMKfqbi96GkBRgMUjOGliqQCBhNiN8HxuWVFZVSqNIPofRscDygLJS5GfyLs9twULGfxXDuvfYfwi2KmPWI+fham3HTsG4dfHU0xm119ZwZHWWiWMQKps74SuH6Pm3pKgwR4rZNdTTKUD5H4xyVR9f3KXouD7ZtXqu3sIi+zBRfevN1qqJRqqP6xuP5Ps9f7CRu2bxv5641tnCdYO/W5XJ+DtxucI8DsdnyRqmHwlO6hC7x5GVPp9yLuhrDvF0LQqmiTsuYDTof748CV+Dgy4eg+N1gXSD416tcgPJT4DysOy9BXyP/dZRRhVzDoO2Qa0MpN6i6qoDZiBhVa23SilnXIeHP3b6XTfEYhUqFbLlMtlwiWy6xvaaW2lgMpRSe8vlHt+2lOhqlZ2qSoVyO/kyGwWyG9+/cybbq1RPXXyk/6e7CMIS4PdvxahoGTckUP+7SzV0hl0ckBvFfBPLgvoGWZ/UAH8xGMCJ6Vmnx65c8j1IKv/QiFP4G3NP6RuGd1xUZVhtMyyzJYmmMxefydeRu1M1OR1I++D3aFq9nzhuIBDNPf3xN7z/k6lHuOVTmP6Gyf4TK/Rkq83/j55++6gX5m411G8GDjuI//+DDPNdwnj994zCbYnFa0mlSjgMIo8Ucm9PV7N5Ux68/+DCnRoY5Pz5O3LG5s6FxSVkBgKlSkYFsFtvQus0Vz6MpmSQduXwufLJYZDCXJWbZtKbTS6pOAkwUCwwFdfvT+3VOjJNyFl/DMU1c32OiWKQx7HC8Igx7J37i83oWKTVgONqRztSrJ8EdxC+fQgw7qJGfn/dWlaPauZvtWutd2YAH5TfAUSDV+knhSkokVUF3wM5b+HV1tC4JXas/F0mD27WCn0DIlaK8Yd3nQGL296M8KL+IEgeJ/fSa2rcS1rWDB+383rdjJ45p8p2O02TLZUquS8nVefjp7taIZbG/qZn9TctPtKl4Ht8+c4pXerrJVcqcGxsDYEdNLUknwsHNm/ngrj0zcgNzKXsef3v6JId6e5BAo35TLM6n7tpPW3r2Ua/kujx9+iSH+3pn9quLx/nUnXdTFY3SMzm1aPSfXmvQ3bchV4FRqyctSVRH7dMopRdImYT8l1AIiI2K/gxG5P5gFwWlfwCp1RG7uRvKL2kRK3woPQfmVkj/zpUtiIqjI3dVgZlFPzOYeVpeQl++GGiHh1xv1PR83bm18mLqFF75RVT08esyEPtGsK5TNNOICO/atp3fPPgI79q6jf2NzXx835381sOPsCkep2dqkrNjo0yVSpc8z3fPnuGl7i7SToSuyUlilk3MtumemiQdcXj+YiffO9ex5LHfPnOSl3u6aUqmaE6maEmlKbguXzzyGpPF4sx+Twc3gbn75SsVvvj6a+xvbCZTKeEvkCIezmfZ19CwpJBayPIYhgGR94AaB3/Oz9TLgRoAY4eO2IwmwITcn+IXXw52quhSS0kEqZRufcMw6vVnUtrBl5/X6ZfLIGKD8yD4Q8FiLwDA6igAACAASURBVIETaQE1AdactSCltM3OI6v0kwi5JO4F9BDcBYgNqHXd57DuI/i5NKdSNKdmO9l6pib5H2+/xUg+PxMtH2zbwod2L47Cs+UyP+nuojmZonNiHF8pIrb+8WS9EgO5LO1VNbzYdZF3b90+TxlyqlTkUE8vLUFN/jTV0Sh9mSmO9Pfy7m07mCgWONzXS0sqvWC/GH2ZKXLlMo9u2cpLXRexTQNTDIquS2Miwc+GmjXXRvKfgHtS1757QeSshnVkH7lfp0gqx4OIvgKZ38N3n4TYzwc59opOrfi52YhauYCvnbLbBV4XWFsva4pEnkB5XeBeBKL6HBIFazugtM4NPlAG+24krKK5MRhV4I+wyMkrpVM16zR6hw3m4OcyWSzyR0dewxSZybV7vs+L3RcRYZHA2Gg+j0LX1Y8XCjjG7A3ANk3GC0V21OgHntFCfp6DH8nnQVhSVyZuO1yY0BHAcC6PwNLToCybrqkJPn3XPdzT1MzRwQHylQp7NtWxt74hHOt3jRhGEr/693VKpfQjHY17F3XKRVXAPaadtyQB0c7c7YD8V8E5qI/z53RHKvR4Pmv37DZ/GNh6WVvEiEPic+B2oConAAtx9qGMzYh3FlU5ObMNc/vKlSpDrghxHkRV3gZVPWeNBh0IWLsQ4+YpxLhaNqzXONLXS9F1aZ2zkGoaBi3JFC/3dPPEth3zZHpjtoXv60lNEcsiU55N53i+T9QyA815tSgXHrUs/GDK00I1y7LnUhUszsZsa2H7zex+vt5PRNhaXcPWm6i6Z71jGA7EfgoVeRiV+ytw3wT3de3gVQ7MNl1CqTy9GGs263K56BPgbYfSIZ0T903A1ZUwM/NJZXH+/BKI2GDvRezZAEMAjDsQ+47VfNshV4q1GyKPQekFwNapGVUAowaJ/9xaW7ciNqyDPzc+TtJePCVpOsoezufmOfj6eIItVVUM5bO0ptMMZDP4SiHomvnWVJrRQp6tVdXUxeaXxTUH+fTxYoHaOa+5vk/J8zgQSCG0pNI0JBKMFwrUxGLz9qt4Pvc2r3CARMiyKOVr5Ue/D8zbtECYsnSKxh/UejUqN78ixp9AEp9F2fdB9j8DcbBaA71x0ZG8OGCF/QnrGRGB6IfAvgtVeVN3PVu7EOeOdbu4Os2GfQZMRyKUPG/R9unJTzFrfhQuInx83504pkWhUqYuFmckn2Mon6U+niDvVohYFh+7485FUbqI8Il9d2EYBr1TU4wXCgzmMvRnM7xv+07aq6oBnZr5xJ37EdFNTdP7DWQzvH/nLjan139jxU2Ld15H5UYTWO16YAfFYPBPNhAmqwVrdjaBSBwRW1fWpH47mHjvgj8B3oDuao1/6qabFhRy9YgIYrVjxJ7ESHwCI3Jg3Tt32MAR/P2trRzq68Hz/Xm58fFigZZUmqYl6skbEkl+46F38NbgAB2jI7qxSCBiWuzeVMddjU3Lzk5tTqX4rYOP8NZAP+fGx0hHItzb3MLmdNW8G0JrKs1vHnyENwf6uTAxTjoS4b7mVtrS6UU3jpDVQ7kDs9+IBfY9OmqvvKIXTa12sPeiZQgK+jF9TmRuOPtRZguq8jp4Q2A2I86910WlUPlj+qZj1Nw0QyqUKun3LTYYDeH6wDpBlFouK3zjOHDggDp8+PCqnlMpxTMdZ3iu8zyOaeKYJrlKmYTt8M/ue2BetU3IxkeVX0fln1qs6e4OQOWQFhQzW4GgpDX2SQxn34210Z9A5b+pF3kxQRTY9yKxD6+Z+JjWz3kBSj8IqocUmHVI7KOI1b4mNoXMIiJHlFIHlnt9w0bwIsIHdu1mb0MDR/p6yZRK7KjdxD1NzeEM1FsRa3fQaFScvyhqVoPs1zXnakprkNj3IWb9DTVPT436U12uabTMLvqWD6NUAeK/eMOe8JQqB5VBFqrSAcW/1aktI3h69adQuT+G5Odv+M8p5OrYsA4etJPfVl1zU+nNhKwNYiRRsY9B/uuB6FgMKOha5/inMZz9a2ug2wHeoK7omWa6EapyXC8ET4uRXSeUUqjyT6D4faAMvgdeB1h3zOrugO4H8Auo8stI7MPX1aaQlbGhHXxIyFx0Hr0RVT4C/oCW+nUOIGbjWpuGcjuBJaQopqN27wY4+PKrUPgWGI1a8IwsVIq6V8B4aL6omqShckbfJ0NuWkIHH3JLIWYTEvvgWpuxGIkDi6u+Zl9fenF/tVDK1Xl2oyFw7ugFVbH1U47bDfaeOQeUway7rjaFrJxbdil8vFBgIJsJJXhDbgrE3geoJWaKFubIGVxH/Eld/z13fUIiuuZf/EDzftompdcr7Ieur00hK+aWi+CHczn++uRxzo2PIhg4psET23bwzq3blpX2DQm53ohZj4p+EIrfQXdTxoCcfjH2i9e/ikYc9A3G1wqX09h7oDQKBHLHuLohzL4Lce68vjaFrJhbysFnSiW+cOQQJdelJanrzkuey7fPnMJTPu/ZfgW63muEUoqJYpGK71Ebi4fj+zYgRvQxlNWuJz/5Y2DuR5z7b8gagRgplLULvE498WqGmH56sO4EcUGiiHMArD2I3FLuY11yS/2G3hjoZ6pUpDU12zEaMS2akyl+eOE879jcflNqrvdlpvjWyRN0Tk4gAgnb4ad37ub+ltawOWqDIVb7mtWXS+xJVO4L4PXp4eK4oLLg3I3EP3VLD69er9xSDv7UyDBJe/Gjrm2auMpnMJe96US+xgp5/vDwIQBakilEhEKlwtePHUWA+1vbLn2CkJArRMw6SH5eVxm5J3UO3j6A2HvDaH2dckv91uK2jesvM5xB6elQNxsvd3dT9lyak7OqmDHbpj6e4JmzZ7inuSVM14SsGmKkkOjjwONrbEnIanBLeYb7WlrJVcoslGeYLBbZFI/TlLz55AuOjwxRFVlcbByzbXLlMuOFwhpYFbLaKOWj/HGUP7no7zMk5Fq5pSL4PZvqtAhZby9pJ4JjmkyVS5hi8Et333tTVtHELIvxSnHR9mknYJu31D16Q+KXT0Lx74JSRAXmFoh9GJk7xi8k5Bq4pbyDIcLH9t7JL999L61VaWK2zSOb2/k3Bx+mvbp6rc1bkodaNzNRLCyK6sYKBbZV11AdDVsJ1zN+5TTk/1zXv5stWprAH0Hl/gjlDa61eSHrnFsqggc98OOuxibuary+bd+rxf6mZt4Y6Of06AhVkQiWYTBZLBGxzUVjB0PWF0opKH5Xt/0bgXy1iJ4X6w+hSs8j8Y+urZEh65pbzsGvJp7vk6uUcUyTqHV9Ssgc0+SX7r6Xo4MDvNDVSb5S4Z1bt3Kwbcu8qVAh6xCV14NDjObFr0kNVE7ceJtCNhShg78GlFK81tfL35/rIFMqgcDdjc18cNceqqKrP91nslTk7aEB+jMZAN4eGmRzVXXo4Nc7YqJHSqng81xcrQMTErICbqkc/GrxQlcnXz92FFMMWlJpGuNJjg4O8IUjhyhUKpc/wVUwWSzyB6+9SsfYKE3JFC2pNGXP40tvHuGtgf6Z/TzfZ6pUouS6FN0K2fLiaqG1xA3sqywxRvFWRSSqp0ipkcUv+qPgHLzxRoVsKMII/iopuS5/f+4sTcnUTN28aRg0JVP0ZiY5OjjAg22rV/3wSm83hUp5Xh18yolgIHyn4zR76xt4ra+HZy+cZziXpTeTwTIMWlNpWlNpPrhrN7fVN6yaPVeL5/s8f7GTH1+8QL5SwTZNHm7bwnu27yBihX9+Ev1pVK4TvH49J1b5oMbBbEGcB9favJB1zooieBGpFpFvisgpETkpIgdFpFZEfiAiHcHnm6s1dIX0ZTO4vr9kU1TCdjg2vLqVD28PDlAVWZz2STgOE8Uif3P6BN88cRzX8+menKLkumRKJfoyU2TKJf74jSMcHxpa8twl1yVfqVDxPAazWfLl8qraDvD06ZN8+8wpYpZNSypNdSTKc53n+crRN/CXeMLwfJ9subxsQ1rRraz6U9JaImYdkvw1iL4TEDCiEP0ZJPE5xIhf9viQkEux0hDq/wO+p5T6iIg4QBz4X4FnlVK/JyK/Dfw28G9XeJ2bBkuMZVMfnq9wjNXthrVNk0JlsaSxUoqK5/GTri5a01VcGB+j7HukHC3FMFUqUvE8aqJRvt1xitvr62fq/IfzOZ7pOMPbA/2cGRtlIJvFMU1itsUjm7fy+YcOrkr55Ug+z8s9XbSlq2aubZsmrak0p0dG6JwYZ3uNHlrt+T4vdHXyo04d6Ucsi0c2t/OubdtxTJPezBTPnDlNx9goCtheU8MHd+1hS9XNWd56NYhRjUTfD9H3r7UpIRuMa47gRaQKeAz4UwClVFkpNQE8CXw52O3LwM+u1MibiZZUilQkQm5BtKuUIu9WuLe5ZVWvd39LGxOlxY1OE8UiyUgE2zSxDIOhfI6oOXu/nt6WdCKM5fNMFHXH63ihwB8ceoXTI8Ocnxjn4uQESilKnotjmDzXeZ5f/+53KK+CTn735ASerxbdEEUEUww6xmY1xv/m9En+9vQpokGkn3IifP/8Wf7H22/Rn5niDw69wsXJCb0OkUzRn8nyB6+9Svfk5JLXLrluqPUfcsuzkhTNNmAY+HMReUNE/kREEkCjUmp69W8AWFLrVEQ+JyKHReTw8PDwCsy4sZiGwUdvv4PJUpGhXI6y55Etl+iemuSOhgb21K3uEOL7mlvYVl1D99Qk+UqZkusymMtQ8X3eu21W3lhEmOtGFWDOedowA43vn3R3UXArRE2LCxPjJGyHiGXp+vpSicZEgguTEzzbeX5Fdg/lsnz3bAevD/Txw87zHO7rmbnJaPsUVmDTcC7HK93dtKWriAZ5ecc0aUulOTY0yDdPHAegLp7AEEFEqI3FcAyT7509M++6vZkp/uT1w/z75/6Bf//Df+CPj7xG79TUit5LSMh6ZSUO3gLuBf5QKXUPejrBb8/dQWnvsmQ+Qyn1RaXUAaXUgfr69TWZ/bb6Bv71gwe5o76eglshZtt87I59/OKdd6+68FfEsvgn997Hk7tvwzQMSr7Hfc2t/PpDB3mwrQ3HtCi6Lq3JFEV3Ojet8HyfxkSC8WKRLVXVpCM6dfPWYD810Rh9WV1yOZ06McWg7Hn4CmzD4KWui1dkn1KKkuvOy6eP5PP8t9deYbyQJ2E7xC2LTLnMa329TBQLeL6PrxS3B7/3i5MTqDm2TCMiiMBrfb1sii3OR9fEYpwZGyVfLlP2PPozGf7g0Ct0TozTnEzRnErRNTnJf3vtFXozoZMPufVYSQ6+B+hRSr0afP9NtIMfFJFmpVS/iDQDS6/wrXPa0lV86q67b8i1opbNY1u38djWbYte+7nb9/KXb79F1LJJOA5jhTwKnUoqeR6GGPzsbbfP6MYbYuArpZ3pUrdeAV+pyypr+kpxqLeHZ8+fY7JUJGpZPNa+lcfat/H8xQuUXI/WdBWu73NqdATHNDFFODY0yJaqah5r30pzIO6mHfvS6xrKB9MQfKVYaFGmVOLc+Ci/86NnERFy5TK2abJtjuTzpnic4XyOfzh3ls/cfe8l31NIyEbjmh28UmpARLpFZI9S6jTwBHAi+PgM8HvB56dXxdKQJbm3uYXqqK5MEYFMuYQgVEej3NnQyGPt22hMJmf2v7+lhe90nKE1rbXlPd/HNAxc3ydqWQjaeT++dRtlz8MyjCVF2L53toMfnO+gPp6gJZWm5Lp892wHXVOTdI6Pz0TcW6qqSTgOnRMTZEpFCq7Lx/fu4/62zTM3nR01tRgiuL6HOecG5CuFCLxzyzZOjAzRkpotFc2Wy7zQ3UltNEZTMoVSir/v7yNqWdTF4zOLzQCbYnGODw/NvNeQkFuFlVbR/Brwl0EFzXngl9Fpn78Skc8CF4GPrfAaIZdhe03tTDXKciileL2/j1d6ujk5MkzF82hIJOjLZDAEbMMkFonQn82wrbqGn3R18e0zp4nbNo9t2cqj7VtnovqJYoHnOs/TmqqaSUlFLIu2VJoTQ0P4SpGwHUCnWeriCeriCXylGMhm2N/cPO+mURWN8r4dO/nym28wXizgKYUg1MSi/Nxte3n3tu30vDZJX2aKTbE4IsJbA/34vn7SeK7zvF7krlQwRDg3NsbdTc3z3rsEufuQkFuJFTl4pdSbwIElXnpiJecNuTye788sOF4Jz3We5+/OnKY2FuOd7du4MD7G+Ylx6uNxLNOg4vukIlHubW4mV6ngKkVrEJk/c/YMnZMT/HIgqXxxchJQgXNX+AoMCapjDINqx2G0kKcllZ5Z5BURRgt5bqurxzGt2TRRwHQTVNSyKHkejmliGyb5ikt1NMavPXCQF7o6OdzXi68UnlJETIvJYolkcDOZKpUYyecBuKuxaeb8I4Uc9zQ135Ry0CEh15OwlXAdoZTi2NAgPzh/lr5shrjt8Ojmdh5r33rJrtBsucz3znZgAEcHB2dy1bdvqsM0Tf7FgQeojyf4Tscp/vzN13FMi4liiZ21taQjUdpSaU4OD3F+fIydtZu0aooS+rNTnB8fJ1cpEzEt2quqcQyTO+obONLfx0vdnWTLeuE3Zlk0JZK4VVX8u2e/j1KKO+obeM+OnURNixe6OqmJRZkqFfGUj1IGNdEobw728fjUNjZXVfGh3bfxod23oZTi49/8OoYhJBxn5n02JpJcnBxnrFCg4nkYwU0lalk39UD1kJDrRejg1xEvdXfxP08epzoaozWpNWm+d66DCxPj/Mo9982kS5RS86pSuicn6M9kGAmcXdJx8JTi7PgYUcvitd4eLkyM0zU5ScS0SEUiTBaLHOrt4UBLK9XRGJZhcHJkmJ21m9hWXcNAboqhXJ6EbZN2Iri+z+mRYaK2zafu2s/h/j5sw8Q2vCBFAqfHRjBNk/aqagwRzoyNcmp0hMfat9I9OclkqUTcskk7UVzf4+TIMAnbpmN0hM1VVfN+FiKyqL7eNk3qYglKnqvXIkR4oLWNd7ZvY1M87AoNufUIHfw6oVCp8EzH6XkaONN579OjI3SMjdKaSvGjzgu82ttD2fO4va6e92zfQaFSoTfIX08vMloipJwIQ/kch/t6tXOMJ+jNTCEIMdum6FY4PTrCg8Fg7+kEh0KBEopuhcliEdfX0XLUsonZNq/19eJ6Pg+0zmrynBwZYqpYYqJYYEewXlAfTzCaz/OjC+cZzGZpSCQRESqex0SxSKZcojczxd+dPcPehoZ5IxVbU2nOumNkyyXitlZdzFcqOJbJ7rpN/O7jT4SzakNuecL/gHVC99Tkkho4IkLUsjjc28N/f+0QL3ZdpDoSpTmZ4sL4OP/t0Cv0BTLDCxVpRfSmvmyGmmiM6mgUQef3AaKWxVSpRNF1cX2fvYFo2YWJCXzlIwiG6NLL6UYqAV7surgoYh7IZqmORhkrFHD9WUXJ2liMvkwGH10oWfE8ejNTZColbMPAQOibmuL3D70yI5csIhxs28yO6hpa02mKrkvBdWlJpdlZW8sDrZtD5x4SQujgNwYKzo+PM5LXC5u2aWKIsCkeJ2E7/KTnIpurqsiWy5Q8F1C4vsdUqURNNEYiiIAd02Rn7SayQcesUgrX9+nNTHFXYxNbg/ryfKVMbyZDwnYwph276Hz4aKFA0a3oKP/KTMc2TZqSSTLlEiOFnK55F4Oy75GMODQmEqDg7891zBz3rm07qIpGqYnGeKx9W5CGiRF3HN4b5ttDQoAwRbNu2JzWJYnloMJkGqUURbeCq3xqY4tVJ9ORCF1TBWqjMRoTSbonJ5kql4iYJrtrN2EYBnc2NHJyZJiYbdNeVU3M0jIGo/kCVdEIH719Hwc3b5nJ6SsFBbdCtlzCNkwc08RXiuFcFoC2VAujwc1mmuZkinNjozSlUlhzBNnGCnnubmqme3ICgB9f7NQ5e7Q0wfTnhONwfHho5v3XxeP8qwcO8tyFc7w+0I9Sirubmnn3tu00JGbr/kNCbmVCB79OiNk2H9q9h2+eOE5VNEraiVDyPIbzWWpjcY4O9TNZLJFwHNqrqmlLp2eia9sweWf7Np69cI7b6xtwDOHc+DhvDw1SFYuxp64O0zDoy0zRkEhQn0hiGgZbqlx+9f6HZiL3uSgFrq8ouWU8pUs2TTGwDGFHzSaGi3n6s1PUx5MYIiRtB8MQqqJRKp7HSD7HiZFhCpUKBzdvoS1dxYWJcTbFY0RNi4lSiYGsfkrompygvbp60cyjunicj95xJx/Zuw8grHMPCVlA6ODXEQfbtpCORPmH8+fomZok6TjsqK7lzNgoSTtCpqRLEk+ODDNWKLC/sYmpconGZJKf3rmLzVVVPNNxmuc6L+ArxZ66OrZW13BxYoKSW2FvfSPnxsfwfJ/b6up5346dtKWrFtkxnbufrtaZFTpTIAZxx+Zf3fkgP7xwfqZu/c6mJn753ns5OTzM06dO0j01SXMqyYGWVkwx6JwcpyWdZjSf5+jQALZh0phIko5EGMhluDg1wQd37VlSQiF07CEhSxM6+HWEiLCvoZF9DY34SlFyK/yfL/yY5pQW1pooFSm6LinHYTCboTMSIWJZfHLfXRiGQcSyGMnnqHgum2Jx4rbDZLFE1+QEQ7k8vZkMn3/gIAda2y6jRTPr0EUE5SsMI3CyQUlkbSzOR/bu4+dvv2NeyWZbqopXe3u4q6kJe06qJm7b9GWmeKC1jYFcRlfkWHYQtQu+r7tbQ0JCrpzQwa9TDBG6pqaoeB6RQAf+/pY2LoyP0ZfNBMqQil+9/0G2VtfwcncX3zxxnNOjo1RHYvgKDvX2UPE9GhNJqqMRxgsFvnbsKJ2TE/zCvruW7fycrmsXMVC+h5ruZjUMRAx8f3aBVWS+Wz4/Pgaoec59+v0YIhwZ6OORzVvpnpqkP1C8rI8n2NasJZMrnod9GSG0kJAQTejg1zELG32ilsXt9Q3cVlfP2bFRnHyeN//jf6R/YIAzNdW0fvhn6LS0czQQSp5L2XUZyuWC9n9d+nikr5eDm7fMU2Wci3b8gmUIcTuCEejOlzwXhUIuUaKoAruH8zk6J8bJlMvELZv2qioUejiIZRpELBPb0OeNWhae79M9NcF/fOFHGMDdTc081r6V2iVkhENCQjShg1/HbE5XzTQGzY1q+zJTyEsv8X/9v7+PoRROscjdkQj82Zfgd3+HZ9taMEWCqUceSpWJ2haWGHSMj5ByIhwbHFjWwfvKJ25bxKwYE8Ui5UAWYFMsTsnzLplIaa+qZiCbZSSvu2qjpkXJdTk6OEAqEuGh1s18//xZTMMgbtkgcHFinEO9PTSnUiSCbS/3dPPmQD//8oGHqI8nVvcHGxKyQQgd/Dom4Ti8f8cuvn3mFJticRKOQ75S4cT583zzv/xXosXZUX+xUgmAX/3d/4PDf/j79KO0M9bBOL5SNKdSRE2L3uwUT58+xeG+PqpjUR5r38o9TS0zKZuEE6EpmWaiWKA5lcIyDHzlkytXqHGcS8oC2KaB6yuKrks2GNTh+p4WDyvkaYhrqYGkGZm5Xrasy0CrgjUF0GWXg7kMPzjXwSfvvDG6/CEh642w0Wmd8/jWbXzyzv2YhtCXmaIvM8WTbx1dNBxjBt/nUydOUxON4SuFr9TMHNSYZZMpF5ksFhkvFkg6DtlSmb88+hZ/feL4TEpoS7qKtnSa2+vqQRE4ap/tNTVsq67htk11y9p7fnwc29Sa79O19PlKBeUrLMPk9YF+IoZJ2omQq5TJlEu4ymdrVTWTpRK+8mfOVRdL8NbgIK7vL3u9kJBbmTCCX+eICAdaWrmvuYWS53J2bIzhL38Vp7h4UDdAtFSivr+fHbXvpntqCs/3sA2DgWwWESFbLhGzbOpicWzTxDZNEo7Dod5uHmhto726mppYjHe2b+Xp0ydxTANXGURMLe27v6mZbZfQpp8qleiZmqIpmWS8UGACIWJZKKXIlEqIIRQrZSZKJaqjUSKmzsVLcOyLXRcREZqSSdpSaXy1eKh3SEiIJozgNwgSiH21V1Uz2tpCObq4qxWgFI1ysU5H2I1Bh2jR01ozhUoZX2md9/rEbF7bCHTeTwzPTl+sjcUpui4TpRJlz2OqXCZXKVMT6Nksh698XUqJkAlkiwFKnksxyN8XPY/xYoGhXG4mjXN6dISiqwd6CNA5McHzXZ20BtIMISEhiwkj+HVG1+QEz1/spHNinKpolEc2t3NXY9OMSmQqEqHm05/B/8IXlzzesW0++L//Dj+dSPAvvvM02UqZmGVTDhQhXb+sR+ctqIQRhEogEpYrl/nb06fYnK7CQJgoFUjYDluqqnm5p4eDbe20ptNLXZ5UJEJd/P9n773D4zqvO//Pe2fu9IbeAQIg2MVOqlGV6pKtSO623Nf2OnEep3h3nX2SPLvZ326SJ8V2krVjx94kLokcW7aKJVuSJcuWRIm9dxJE78AMps9t7++PAYeEALCCaLyf59FDYTBz77kzg3PPe95zvsfPUCZFUtcKzl4bW0m4Hc6xWbJ5EbW4lsM5NsbPcZ6omVMoJHUdl8OOUWxspsL+65hHHBzo5+93vM3RwUFUxcFIJsP3DuzjR0cOYZ2XprhrzVpOfe975Hw+cmORvOH1IoNBxAsv4CvK15SrQkECaUPHMC0My0QCTkVhMJUqHE9KiW6ZLBnLrZ+JRelPJdjX10ssm8WlOEnrOvv7++iJxzk80D/lNdSH8lo3mmmBBFNKdGkhpcShKFhI/KqKx+HEsCx0yyJnGoWKnoSeI6lrBN0ubq1roH101M7B29hMgR3BzxM00+RHhw9S7PHiHVN/dDudBF1udvZ0s7mmtjCXVRGC8D338PSrv8T71FNU9w9QsXYt8n3v543oMO2v/5qUrnFiZJjKQIAqJYA5NkJvOJWmP50klskAefne/lSSpqIiFheXAPkIvj0Ww+9yEc/lyJkGTkUh7HbTm4jTn06Ns/1MLMrr7W10x+MEXC6iuSw+VaXI7aEvlUQ3DTJSYkkLwxI4hKDI6yVrGGiGji4hls3mABF/9AAAIABJREFUO3nLKijz+VAd+bx9TzIx7uZmY2NzDtvBzxM6RmNkDWNCY48iBG6Hg/19vQUHf6C/j+/t34fqcBB697vZY+i0x2Kk3nqdxcUlhNwehtNpRnNZ3E4HZT5/IfVR4vOS1DV0y6QnEcehKNzWsIj7mhYXNNbP5usTWg6FfH5eM036UkkEAtM8p/e+o7uLHx4+iNfpJOByc2xokHg2S0UgQM40iXg8xLIZdMvCoSi4HA50yyKhaZiWSWYsXTOayxH2uDk00M/i4mKaikqIZjM0FxdfRFbBxub6xXbw84QLpSEcQiFnmuQMgx3dnfz1W2+iCKgOhgi5XQRcboYzaXKGgWtssHWR10t4zNH7VRc+1YUlJSldp8If4H0rVvFAy1I8TucEB6oqCqbM186f1Z4XQmAY1piyZP5GkNI0fnL0MOV+f0FOIeBy43OpJDWNm2rrcDudOBWFQ/39nI6OEHC56EsmkUKS1nXcDgduRz4XrwB+1cWpkeGCvQ80t1yz99zGZr5jO/h5Qk0wlHeiljVhWlHG0GmOFPGdvbvZ29dDRtcJut20x2J0x+M0FxVjjjnjgVSKiMdLyO0m5PHgEPnyQ3NMg702FMKnqqyqqCTkdk9qi2ZZ+FQVt9M5NrLPQgiIeDwYloUcK6PJK1PKgnOHvD69EApIyXAmU8itrywrJ2ca9CYTFHk8xLJZHGMCaW6Hk0afj2g2Q0rXyBgmumnyO5snlzK2sbHJYzv4eULQ7ebORY28fPoU5f5AQZ9lIJ2iKhgkbRicjg5T5vPTk0jkB167HKR1jdZYFGCsSubsOL58SeXxoUEawhGWlZYhgKFsmtpgmJaxfPuktrhclPsDjGTSuJ0ONNPK68aQd+DFZzd2LQvE+Py43+WiOhDkVHSEnGGMPS+f3rm9oZHW6Ahht5uRTIbDgwMEXW4cY0qVCoLb6hsYSKV4sGVJISVlY2MzObaDn0fc39yCz6nyalsrI9k0AsG6yioealnKd/buIuLxFlQarbFNU5+qEstmQeSHdJR4vYXjtRSXEM9m8blUhrNpFASbqmp4qGXpBWvLa0Jh/C6V3oRB2tDzZY5j6Z9Sn4+WsWqb2rFSybO2nGVZSSkpTcOpKPQk4zhQuLWugQdbWviPw4c4PjRExONFEUpB2Cxr6BR5vXicKg5FsZ27jc0lYDv4eYQiBHcsauSWunqSmobH6SxU1OQME6dQ8Did1IcjtMWiBFQXDkUpOPqkphHy5NMupmUxkEqxsbqGz27YhGaaeFUnHqd6UTsCLpWsYeBTXVQEzo3HS+k6KU0j4HIBUO4PsKGqhp09XVT6g6gOB4Zl0Z9OcVtDA59cu2HsOGpBY2ZrUzMH+vsYTCfJGBqD6RRBlwunolDq8/HKmdMUe310jo4Scrk5MTLMnt5uLGBdRRUbqmvwqRe/Bhub6wHbwc9DVIeDovMicYAVZWVs6+zAq6q0FJegKkp+UpOWQwKPLl1OxONlR08XsWwOZF5y95ElS/G7XFyOHuPpkRFqgiFKvD46RmPk5d8ltaEwQZfKsaHBwiSo9yxfSdjj4Y2O9nyuHrixppaHWpbiVdXCDeosRR4vAZeLI4MDOMdGAI5k8quVnGnSEAnTGInw9LEj/P2Ot6gOhgpqks8cP8q2rg4+v/HGKfcPbGyuJ2wHP884MtjPDw8f4uRwPo3x6LLl3NvYzC11Dezo7mI4k6bY46WpqJgyn5++ZIKPrF7LLXX1ANy/uIV4LodPVcdFupppcnCgj1093WimyQ3llWyoqiY4iaPUxzZVKwMBcqZBNJPvZK0OBtEMg8xYbh3yN6MHFy/h7kVNJDQN/5hT707EeeHkcTpGY4XnC6AtGqUzMUp1MES5P4AADvb30REfxed00hQpJuLxMpRKM5xJU+LzFWwMut30JOK80nqax5avuHYfgo3NPEHMBaGmjRs3yl27ds22GXOeX54+yZ+/+RsAfE4XmmmSMXS2NjbzJ7ffSW8qydPHjtAWiyIQBN1uHlq8hPVV1RecW5ozDP553x5ODg8RdHtwCEF8rO788xtvnCD/O5hK8UevvDRWJy9QFSeGZWJYFkUeL398+50sLyuf8nz7env4waEDgKR1JEo0m0EzDKSAZE7D7XRS5veR0jQkgpSmFTaIKwIBlpWU0hqL4hQCzbK4e1FT4foMy2Q4k+F/333vlBOpbGwWCkKI3VLKjVP93o7g5wlZw+Ar29/C53ThH8tx+4GQ5eZXba3c27yYzTW1bKmtR0pJxjDYUFXNkpLSiw6l3tXTzcmRIWrHBohAPhruTyX42cnjfHzNunHPD7ndJLQcOcNAArqVwzkmSBbNZgi5pxA6Mwx29nTzN2+9gdvhRBGQ0jVKvD7aR2PolomFJKXl8iqRADI/71UdKw31qyrHh4fyMsduN1kz/xrHmMSZQyjopolpWSh2A5TNdY7t4OcJ27s6SWv6uE1NAIei4FAUXjh5nKNDgxwZ7CegunEoCr84dZI3O/M56dILDOF4q6uTIo9vwo2gzBfg8MAAaV0fl845HR0hoLroseKF6NqSjHXFFnFsaHCC2FjW0Pn23t3s6ekhmcuBO9+d6xprlNJNk6x5NlUjSGg5BPlyTif5TlmPmm+KEgJcwsFoTqPIm19xnGU0l2VRpMhWmLSxYQE5eCklJ0eG2dnTTTyXpaW4hA1VNRM2I+craUNHTCEN5xT5DdW0rhfG+EE+0h5IJXn2+FE+tW7DlMfOGDouZaJDzKc4JLppwnkOPqlptI3GKPcFUPwCU0ocY92s/alkYVj2+Wzr7OBMNEqJ10d/KoEgn3LJ6joZXSdrGChCwe1Q0Ky8EJkilPx0J5dKUtcpVV3kM/WCsNdDRyxGZcDPnt5eJBBxu3EqCk+svvoJT/3tgxz49WGGe6KU1ZWw+o6VlNVO3RtgYzMXuWoHL4RwALuAbinlI0KIRuBJoATYDXxUSqld7XkuhJSSZ44f5fX2Nryqisvh5EwsymttZ/jchs3UhcPX8vQzworSMqTMlze+U8o3Zxq4nA4iHu+EKLzU5+fo0CCJXG7SDVOAJSUl7Ovro8I5fnWQ0jQiHu+E12mGiSktVIcCiHHTo6SUmJPIKmzr7KTU68vr2BgGA8nUWBpGkNZ1TClxCnC73Tgsk4xhYFpWXiZYUbilpo7BdJp4LkvG0FlSXErI7ebU8DAWEhD0JxNsqKqhKhC85Pd1Mg6+foSff+dVVJcTj99Dz+l+9rxykEc+dy/Lb1xyVce2sZlJpiOC/yJwFDi7Jv9L4CtSyieFEP8IfBr4xjScp4CUktboCDt7uknkcnhVle3dnSwKFxWcX8jtJpbN8O+H9vOlW26b9xtuDZEibq6t482Odsr8fpyKAyktRjJZwh4PdaHwpNroZwdkaOcJgL2T2+sb2dvby2guS8jlRoh8Y9FwNs0TN6yd8N4F3C6KvT7iuRwBV352qkSS0jT8LhfhSYaNpA2NYo8PhxBkdQML8DlVkrqGkBIFMJFIaSGEYFEozEAmjWZYrCyrYElJKc2WRftolOpgiBvKK3i17QwPtCwhresgwedS6UkkePVMK+9auuyK3udENMlL//IaxVVFuNz5VYs/7COX0fj5d15l0co6vIGFsSrMJDMc23GKU/vacLlVVt66lMYb6nHY6a0Fw1XpwQshaoGHgW+P/SyAu4Efjz3lX4HfuppzvBMpJT87cZyv79rBwf5+epIJnjl2lONDQ6T08QuFiMfLUDo9acpgPvLHt93JPU2LiWazDKSS9KdTNETCfOX+B7mhojLfsfoOMro+pdM9S1UwyGc3bMKnqvQmE/Qm42QNgw+uXM26yqoJz68MBFgUjlAXCpPWNZJajqSmUerz01xcMqnMwZLiUmLZDLFclpDHQ8TjQQKqUBCKgtPhQBWCjGEScrlBUVheWk5TcRGmlPQmE/SlkmyqruMLm29me0835T4/AoFfzW88CwTlPj9vd3dcsYRw6/42TFMWnPtZ3F4XpmbQdrjrio471xgdivPd//EfvPzd3zDQNkj74U6e+tuf8bN/fAnTmDoYsJlfXG0E/1XgvwJn18QlQExKebYQuguomeyFQojPAp8FqK+vv+QTnolFea39DNWBYCFaz3dp5jg40M8tdfWI84bGCSEKmifzHZ/LxZ/ccRe/k76RttEYRR4vjUV5sS2/6mZXT9e4KDxnGAykU7x/xaoJAmXvpKmomC/dvIXBdArDsijz+afcqKwMBFlXWcX+/j62RBrQpYUqFEa1HCVeb2EwyPnc3djE4cF+srqBQ0DE5yfkcpHQNFaXV9CdTNCbSLC0tJS6UARVURjNZakKVPPxtetIaTpBt4uQ25OvEtJ1IpNU6ziVvLLmlVbRZNMaijL5ak8CWvaaZhtnjFf//Q1S8QyVi8oKj4XLQhzdfpLmtY2suvXKVkA2c4srjuCFEI8AA1LK3Vfyeinlt6SUG6WUG8vKyi7+gjH29PbgdjjG5aGLvHndkrSuk8yd+wM82zlZ7g9McqT5S7HPx/qq6oJzB6gIBPjshs3nReEJkrrGe5av5Kbauks6rhCCcn+A6kuYc/r+lTdwU20d0VyWlKYxks3QUlzMZ9ZvmvS19eEIn163kYDbTVrXSeSySGB9VTX1kSI2VtVQ7s9H5GldI5rNsLysjE+t20DE46UmFCqUXwohaC4qZnSSFUs8l6MuGL7oDW0qKheVISfZQ8j3i4gFsdGaTmQ4vbeN4srIuMeFEISKg+z71aFZssxmurmaCP5W4N1CiIcAD/kc/NeAiBDCORbF1wLdV2/mORK53AR98qpgkLZYlKSmoZsG4MawLHoSce5a1FTQRlnoNEaK+NLNW+hPJQtR+FmNl+nG7XTy3hWruL+5hVg2S8DlumjF0rLSMv6/u+7hq9u30RqN0hiJjO0lSIazaW6pq+eTazeQ1PIj+SKeqY93b1Mz39i1A7fTWSjhzOg6o7kcH1i56qK1/1NRu7SayqYKBjqGKKkuRlEElmUx2DVM/fIaqpoqrui4cwktq4EAZZKboOpWScczs2CVzbXgiiN4KeUfSSlrpZSLgA8Cr0opPwL8Cnjv2NM+Djxz1VaeR3NxyYRcu9vhZF1lNW6nk7im0ZuMM5ROsbWxmQdbrq+qByEElYEgtaHwNXPu5xN0u6kLhy+5HNWhKPz2xhvZUpeX/e1NJuhJxllSXMJ/Wr+RIq+XunD4gs4d8t+Dj61ZV5g81ZOIkzMNnli9hmUX6KK9qH0OB4//3sM0rWlgsGuIwa5hhrpGWLqpmUd/5/4rvnHMJYJFATx+N7nMxHRTMpqkYUXtLFhlcy2YFqkCIcSdwJfGyiSbyJdJFgN7gSeklLkLvf5ypAoSuRx/89YbmFJSPFYWaFgWvckEW+rquaWugaxhUOrzFTo+5zuWlJyJjrC3r5esYbCstIxV5eWXpPw4lxnNZollswTHqnKuBMOy6BvbRK/wB6a1wSk+nCA2GGeoZ5ieU/0IAUs3LaZxVT0O5/yuNNnzygFe+pfXKKsrRXXlA4HUaJp0IsPH/sf7F0Qq6nrgYlIF81KLpjeR4MnDB+hJJMaWIIJb6up5qGXJgutgtKTkqSOHeLu7C7fDgVPJ7zWU+fx8buOmi0a6NldONp3jJ1/5GZ0nevH43SAluXSOxtUNPPqFBydU2swnpJTs+Pletj2zE9MwkVISKgny4Ke3Ur9s0roImznIgnTwkP+C9qeSZAyDMp//kvPsUkq64nH29fUS13I0FxWxuqKqkMdN6zoH+/s4FR0h5HKztrKK2lBoVpbmSU3jp0cP85NjR6gKBKgKhgi68k1Hfckky8tK+fia9TNu13zDNEzaDndycncrAC0bmli0su6iUfivf7SN7c/voaKhrPD5Synpae1j2eYWPD43ikNhyYYm6lfUzsv68Vwmx1D3CE7VSVldyaR5eZu5y4J18FeClJKXTp/i5dZTOBUF1eEga+iE3R4+t2EzCPjmrh2M5rJ4nCqGZaKbFvc0NXN/c8uMOvmeRJxv7d7Jnt4eNNPEqShIme86bYgUYUlJXzLBn9x+15Qdqjag5XR++ncv0H6oA5c3/z5pmRwNq+p57HcfxOWZPDCwLIu//51vEygKFFIYAIZucOj1o4wOJVh9xwqkhFxGo2V9I+/6/H2orvkb1dvMP2w1yfM4E4vyUuspqgLBcWV0Q5kUTx7aP9bBaVATPCdtYFgWL7eeZklJ6YyNibOk5PsH9iFlvlpFVRyoDgeWtDgxPEyxz0ew0HFq2A7+Aux6cR9thzqoXFSe7w1I50jF07z5k+1kEhke/72HCRVPlDYwdBMtq+NUx0flXSd6ScYzOFwOIhVhBCKvg7S7lf2vHWbjfVevg2NjM11cV+uxnd3duB3OCTXSJR4fJ4aHOT48RMk7NvucioLH4WRH98x1MHbFRxlKpynyeinyeNGsfGehIvJKir2JRF5/xuG4YIfq9Y6Ukj0vH6CkqgghBCN9Mfb88iCdR7vRcjq/+fFbfPu/fZ/Wg+0TXqu6nBRXF5FOnCsZtCxJX9sATqdCuCRYaKgTQlBUEWH3S/tn7NpsbC6F68rBR7MZPJPkXYUQWDIvbDVZGsbtdE4qA3CtSOt6wY66cBgpZUFLxjG2ydqfTHLXosYJPQE257Asi3Qyg+pW0TWDE7tO4/Ko+MM+fCEvqlvFG/Ly7NdfJJseX+glhODWRzcxOhhHz+kASMtCz+kYuknt0vEbkS6vSjKWnrFrs7G5FK4rB99YVDShhh7yCo1up4rb4ZxUCTGlaeO6Rq81ZT5/4YYTHNvolUgSWo54Lj9j9Z6mxdzR0DhjNs1HHA4HFfVlpEbTxAZGMQ0T51g+Xc/q+MM+/EEfWkbnzMGOCa9fumkx9338TuIjCfo7hhjqGUFxOKhbVkNR+XiF0mQ0RVXTldff29hcC66rHPym6hp+09Y2TjrXkpKeZILb6hcB8GZnO9XBUEFBMZHL4VAUNlaNj9hyhsGRwQHaR2MEXC5uKK+cMIzjSinx+VhbWcW+vl6qAiFKfX5uq19EV3wUU1p8+dY7qApenSTu9cItj27iqa8+jzEWhQOYpkkuo9G0ZhGQV5jPJCZ2bwohWH/PalbeupS+tsF8mqc/xi++/Qq6ZhQ2X7WsTmo0zcOfu3cmLumKOFtJ1Ha4A9XtomV9Y2Ffwmbhcl05+GKvj89s2Mj3D+yjOxFHESAl3FRTy8NLlgJgSosd3V0IAZaEsNvNZzZsHDeXdDid5lt7djKSTuNyODGkyYunT/FIy1LuWDQ9UfV7lq8EYF9fb2FiUoU/wBOr19rO/TJYvK6R+z9xJ899I5+GEYpAKILmtYsorhrTYhFQXDX1Cs3tddOwPN/dWbe0Gj2r8/pTb2MZFghwqg4e+sxWGlddumjeTJJJZvjx3/6M3tZ+VLeKZVq8/dwu1m29ga0fuc0ujVzAXFdlkmcxLYuO+ChZw6DSH5jQZh/NZOhLJfE4ndSHwuOEzaSUfH3ndnqSccp8+Yg9qeXoTSQYSqf57IZN3N6waNoarobSaQbTKXxOlbpweN7r2s8W6USaf/zD75KKpahuqURVVaSUjPTFCJeF+Pj/fP9l1bFnUln6zgygKIKqpoopyy3nAr/451c5+PpRKurPifpZlkXfmQEe++LDLN3YPIvW2VwNdpnkJDgUhcbI1BFbkdc7pbbKYDpFWyxGdTCIlJIzsSinoyMI8oOx/3H3Dvb29/KZdRunpcKl1Oe74DxVm0vDF/Tx6f/zYZ77xkv0tvYjRL68saqpgnf/9v2X3aTk9XvmbMR+Ptl0jsNvHqe0enyJr6IoBIoC7H55v+3gFzDXpYO/GpKahkPJzx+NZjKcGhkuTDVSHQ68TpWhdIqfHD3MJy8wB9Vm5gmXhvjIH7+HgY4hEtEkwaIA5fWlCzoPnUvnkFJO2rXr8blIDC+MYTg2k2M7+Muk2OvFkhJLSjrjozgVpZA20U2TsMdNuS/A0aFBYtmMrRUzxxBCUNFQRkXDpc8gmM+cLQfVcvoE7ZxkLE3jDXN/FWJz5di7K2MMp9O83tHGL06d4OjgAMYk5ZKQHwO4vqqa3mSctK7jVPKRkWaaCCGoDuQrcBQhSGn6pMewmRkyqSwH3zjKb556m4NvHCWTmrlehrmC6lLZ/OA6hrtHsMxz32ktq6FlNTbev2YWrbO51tgRPPBGRzvPHj8K5IdUm5ZFVTDIf1q/sTBF6Hx+a9lyNNOkPTZKLJvBq6qoisL6yiq8qophWUiwu0xnke5Tvfzkq8+TTeVwOB2YhonL6+I9v/8ItS0T58wuZDY/tI7UaJq9rxzk7DRL1eXkkc/dS83i6+u9uN64LqtozqdzdJSvbd82QUu8P5VgcXEJn1435QY1B/v7+LvtbxHxeqkKBFCEgpSS7kScW+rqeXys1HE6GEynODY0SM4wWBQpoqmo2K6omQItq/HNL30Xp+rEHz63QZ0aTaPrBp/7q4/i9l5/+j3x4QR9bQM4nA5ql1Rdl+/BhYgNjtJ6oJ1sWqO6qYK6ZdVzXiHUrqK5CNu7O1EdjglljeW+AMeGhhhOp8fVwJ/PDRWVfPGmW3jy0AF6k0lAIiWsrazi4Zal02KflJJfnWnl56dPwJj6iYWkOVLMJ9aux6va6oXv5MyhTrLJHBWLQuMe94d99LUN0Haok6WbFs+SdbNHqCRIqMTuoZiMXS/t41dPvgkShCKQpkVlUwXv+f1H8AXn7z7ade/gB9MpvJNMRhJjefSkpk3p4AFWlVfwJ7ffRWt0BM00qQoGp3XI98mRYZ4/dZyqQKggkialpDUW5WcnjvG+lTdM27kWCqlYasrfCSGIR5MzaM3FkVLSd2aArhM9KE6FRSvrKblA45XN9NJ1spdX/+0NSqqLx0lD97cN8svv/5p3f/6BWbTu6rjuHXxtMEx7LEboHZK7ZytlIpeQR3c7nSy/ijmgF+LNjg58qmucAqYQggp/gN29PTy8ZFlhWIlNnnBZqJBrfidSygk6MrOJrum88E+/5PjO0+PKNTc9uI473nfzgi7hnCvsf+0QLo9rnHMHKK0t5sSuVpKxFIGIf5asuzquewe/uaaW1zvbyOh6Id0hpaQ3mWB9ZfWsbJRGMxlODA+hmSYnR4YITjKtyqkoSPJdtLaDH0/DilrCZSFig3EiZefSNKODccKlQRatrJs126SUDHQM0XWyB0VR6G8b4NiOU+N0YcwxKYGqxvLrMpU00wz3RPMjGd+BoigIBKnRtO3g5ysVgQAfW72Ofz90gJHsmOCUlCwrK+e3li2fcXt+097Gz04cw5IAstAle0tdfaEkE8CwTBTEhJWHDThVJ+/5/Uf4yVefp799sPB4pDzEY198GKc6O197Qzf4+Xde5ejbJxAILMvi2I6T1C2vHbficDgUgsVBdvxir+3gZ4CKhjKObDsxIddumhYSSbB4+lKuM8117+DhbB79Tk6NjJA1DKoCQaqDwRlfHrdGR3jm+FEqz6vo8asqr7W3cWRwkNUVlcBYzjaZ5I6GRjyT7B/YQElVEZ/6Px+i63gPiWiKYJGf2qWzWxWx8xf7OLLtOJWN+Whdy+moHhd9rf1ESoOU1pQUnusNeBjpjc2ardcTa+9axYFfH0HLagVNISklQ11DrL5jpb3JuhDwOFVWlVfMqg1vdLTjdTrHVfQUeX2sr6rmQF8vYY8HpyJAwpqKSu5rtqO7C+FwOGhYMXvpmPMxDZOdv9hHSXVxIXBwqg5UlxNpWXSf6hvn4DOJDGW1MzMi8nqnoqGMhz97D7/4519haCaQLx1vXruIOz9wy+wad5XYDn4O0Z9K4lMn5tsbwhEE8EjLUoIeNzXBEFWBmV9h2Fw5WlZDy2jj9gQURaFmcSVnDnUg4+emQZmGSSKa4r5P3Dnjdp7dI+hrG8CpOmlYUTtv88+Xw4qbl9J4Qz3tR7rQcwbl9aULQqfIdvBziKpAkKNDgxM2TQ3LQnU4uKmu3t5Qnae4vC5cPte4NABA9eJKogNx4iP5JiTIO/473n8zi9fN7MQuLafz/Lde5uTuVqQEIfL7AXd9aAvr71k9o7bMBt6Al2WbW2bbjGnFdvBziFvq6tnb14tmmoVZq/l8e4JbbOc+r3E4HGx+YC2v/cc2KhdVoCj5yFBakpKqCO/9g0fwBjwoDoX65TWEime+IenNn27n5K5WKhaVFSJXXTN4+Xu/RigKTtWB0+WkfnkN/pAtYT0fsB38HKKpqJjHl63gmeNHscYkJASwvKych1qWzK5xNlfNpgfWMdIX4/AbY1VSIq99dOtjm9n84LpZTQfkMjn2vnqI0tqScXYoiqC3dYDv/PfvU78sP9XK4XSw9YnbWHvnqtky1+YSsR38HOPW+gZuqKjgxPAwmmlQGwpTFwrP+1zgQsA0TDqOdZOKpQiWBKldUnVZVTkOp4MHP72VTQ+so/tkLw6ng7pl1UTKZr/xKh3PYJkWTnX89XQc7SIZTeELe6lclG/m03M6L/7za5TWlFx3wm3zDdvBz0FCbg8bq2su/kSbGWOgc4iffu15RofzmkMCQXFlhMd/72GKKiKXfBwhBGW1JZTVllz8yTOIN+hFKALTMAvDQQzdpK9tENXjHFcqqLpV3F4Xe17ebzv4OY6tB29jcxG0nM6P//Y59JxBZUMZlQ3lVDSUkRpN89RXn8c0zdk28arx+Nysvn05Q93DnFWY1XM6lmlhGibVzeNLiH0hLwMdQ7Nhqs1lYDt4G5uLcOZAO8loaoISY6Q8TLQvRuexnlmybHq5/b0307Cynv72QfrbBxnpi5LL5KhfVpPX9zmPbDJLcbUtiDbXsVM0NjYXYbgvhuJQsCxJYiSBltVxe10EiwNIKYlf4VxT0zDpOtlLOp4hXBqkqqliVvda3F437/vDd9F9qo/e0304XU6W3djCqb1n8r0/Y6YZukk6mWX91oVfOjnfuWIHL4SoA74vhYVJAAAgAElEQVQLVJD/+L8lpfyaEKIY+CGwCGgD3i+ljF69qTY2s0O4JEgmkWHfqwfHjf3zhXyUVEfwhS6/lX2gc4if/t0L+ZuDzJfDVjWV8+gXHpyVEsmzCCGobakq5NaX37SEp//uBTqOdaMo+YE2CLjz/TfTsKJ21uy0uTSuJoI3gD+UUu4RQgSB3UKIl4FPAK9IKf9CCPFl4MvAf7t6U21sZoe6ZdV0nuhBCEEgfK6rMz6cQMto1C6tvqzj5TI5fvQ3zyJNSUV9fvh3XvtkhKf//uc88SfvRVHmRvbU43Pz/v/6KF0neuk+1YvLo9K0etGckly2mZordvBSyl6gd+z/E0KIo0AN8Chw59jT/hV4DdvB28xj+s4MUNVYwWDXMMnRFEIIpJS4fS7K68vobxukYfmlR7On9p4hPZqhoqGs8JiqZbmxby+Ot1oZVTso+t3PQXBuTF9SFIX6ZTXUL7Mru+Yb05KDF0IsAtYB24GKMecP0Ec+hWNjM28Z6okSLA5Qv7yGkb4Y2VQOX9BDUWWEoa4RRgfjcBnK0kPdI+Pqzcs7jrH1B38OUqLqOcz/9Sb8xZ/BCy/Ali3X4IpsZhspJcM9I4z0xfD4PdS0VF4TpdOrdvBCiADwFPB7Usr4+ZtEUkophJh0qrcQ4rPAZwHq6+uv1gwbm2tGqDiAtCxUl1pIqZxFCHHZcrKh0iCGni+tdOYybP3Bn6Nq53L7jmwGssBDD0FPDwTmrx65zUQyqSzPf+tlzuxvRzjy+xrBogCPfuEBqhqnNx6+qkSfEEIl79x/IKX8ydjD/UKIqrHfVwEDk71WSvktKeVGKeXGsrKyyZ6yoBlMpzgyOEBrdATTsmbbHJsL0LymAdXjIp3MkIglGemLkhpNkRxN4Q16qb/MzcaWdU04nA6yqRyLDm8DOWkMBJYFP/zhNFzB1RMfSXBq3xnaj3Sia/psmzOv+fm3X+HMwQ7KG8ooryulor4M07D40V8/R+o8VdHp4GqqaATwHeColPJvz/vVs8DHgb8Y+/eZq7JwgZE1dH50+BAH+vsReWl3wh4PH71hLQ2RS++ItJk5vAEvd39oC9/4/X8hFc/gUASmZREqCfLFr38Gl/vyROACET/v+u37eO7rL+I404qq5yZ/YioFp05NwxVcOaZh8toP32TvKwcZGzKG2+fmwU/fTcv6plm1bT4y3Bvl9L62CVLEwSI//e2DHNt+kg33rpm2811NBH8r8FHgbiHEvrH/HiLv2O8VQpwE7hn72WaMnxw9woGBPqqDQaqDIWqCISzL4p/27GQ0m734AWxmHEM32P78bppWN9B4Qx2ldSU0r22kblk1bz27C+sKVmAt65r47F99jMp7b8VwTzH31++HxbM71GX787vZ9eJ+SmtKqKgvo6KhDI/PzTP/8Itx4xDnCqZh0nWih9YD7Vfcn3AtiQ2MIhQxab+D2+em78ykCY8r5mqqaN5gytn1bL3S4y5kRjJp9vX1UhUIjfuAQ24PPYk4e/p6uGuRHRXNNdoOddLfMUS0L0YylkIgiA8nCJeEMHWT7pO91C29/AqTQMRP4H/+IXz7byE3yc1dUeADH5iGK7gytJzOjp/vpbS2BMVxLhb0+N2k4mn2/PIAD3567vypd53o4blvvERqNAVCIC3J6jtWcPeHt6C65obUtjfgKUhBvBM9pxMqnd7Kqeuqk3U0m6UvlcTtcFAXCuOY4Vrj4XQGIQTKJHdvr1OlY9SewTkX6W0boP1IF6rqwB/yFcokE9Eko8NxhnujV+TggXwp5Asv5DdULQtSKSyfD4Qg+b0fEroGG6yWZTHQMUQmmSVSHp6ypj0ZTWLoJqpropvwh330nOqbdtuulOjAKD/6m+fw+NyUj22EW5bF/l8dxqk62PqR22fZwjyVjeUUV0YYHUoQPs+Z6zkdy7JYftP0yoJfFw5eN01+duI4b3V1FB4Lud18+IY1NBXN3NxLn0vFsiRSyglLtJxpEHHP3+G+C5nkSJJcOkfovJmpZ6tnhrqHSUZTV3eCLVugp4fUP/0zp596hR7p53jzBrSnW2lue54HP3033sD0fDeGuod59hsvMtQdRVEElmWxdNNi7v/EXXh87nHP9fg9IPOO8p2NV1pGo7yhdFpsmg4Ovn4Uy7Dwh88NIlEUhbK6Evb96jA3v3vTnBierSgK7/7tB/iPv36G/vZBXB4VXTOQUnLfx++ktHp6/dGCd/CWlPzbwf283tlOU1ERPmd+XFoil+Pbe3bxBzdvodR3ZdNpRrNZBlIpPE4nNaHQpJH5+VT4/PhVlTOxKLWhEC5H/u3XTRPDsmyJ4DmKJ+BBURQM3cCpnvuT0TUdxangC06RQ78MNKeL73eHyK1+mHBZiKKxVULrgXae/caLvP9Lj06pU5NJZRnoGMLhdFC5qGycje983g//6hks3aJibJPPsiQndp5GWpLf+sKD457vC3pZsqmJk7vPjJM3tiyL5GiaB+aQFk3XiZ5JJSMcTgdIyehgfE44eICy2hI+9b8/zIldp+lrGyBYFGDZ5sUUV06/eNuCdvCdo6P86749vNrWisfppDeRoDYUYmlJKUG3m6SusaO7k4dall7WcTXT5LkTx9je1YkgXwlT4vXx4RvWUBeefLnbGh3h3w8eYDiT5sTIMAcH+qkLhqgMBpFI3rVkGTWh0KSvtZldiisj1C2tZrBrmGw6h8PpwDTM/Hi9ZbUEiq4+jXJ6fzvx4eS47taz2vHth7sY6Bga9zvIN8tsf343257ZiWUB0sIT8PDAp+5m8dqJ81xP7Do9oYNWUQTl9aWc3N3KSF90gpO5+0O3MdQ9Ql/bAG6vC9Ow0DWdDfeupmlNw1Vf93QRKg4w0D40LoKH/HtkWRKP3z3FK2cHr9/DmjtWsuaOldf0PAvWwceyGb65ZwcZ3cCnqgRcbgzT5NTIMFndYG1lFQHVRWv08nXQnj9xnG2d7VQHQoU8fiyb5Zt7dvBfbr6NsGd8RDeYSvFPu3fiU100F5dQH47Qk0zQPhqjxOvlY2vWUzVH2tJtJrJ47SKKKiKUN5SRGEmQSWbxBb34I36QkkUr6676HF3Hu8imc8RHEjgcCoZu4va58fjcCCEY6YtNcPD7XzvMaz/cRnl9aSFqz6Sy/PRrL/DEn753QtNMz+m+cQO/zyJEvqpjpDc2wcEHIn6e+JP30bq/jfbDXbi8Kks3LZ515ct3svqOlRzednzcwBKAaP8otUuqiFyn2jkL1sHv7ulBMyxKvD4sKRlKpYhr+XrjA5k+4lqO+lCYxqLLWxbFczne7u4c59wBIh4P3Yk4u3u6ubupedxr3urqQAJBdz6KUB0OGsIRqgNBYrksxd65sXS0mRx/2M8j//lenvvGS7jcLgKRALlMDkMzePyLD0/qNC8VKSV7fnmAV5/cRtfxHnIZDVM3CZUEcHldlNYUEygK4PGNP4dpmrz5zA5KqorGpWS8fg+ZRJadv9jHuz9//7jXBIsCGLoxuR1MHeW63CrLNrewbHPLFV/ntaZuaTU3v3sTbz+3C6fqxKk6yGU0QiUBHvj01jl1M5pJFqyDb41G8asqXqcTS0qGs2l8TrXwQWcNg4OD/Tyxeu1lHXcond9Qm6wCJ6C6OB2Ncvc7Hj8dHSHomvjHozocGJbFSCZjR/BzGF3TCUT8PPK5exnsGiI2EKesrpTlN7VctbTv4W3Hefm7v6a6sZyTu1sRQqB6VJKjacpDvnyOdjRNzZLxipXpeIZMIkuwbmJ6KFjkp+v4xCEkyzYv5q1nd6FrxrjKmEQ0Rag4QFXz/JWNEkJw2+M30rK+kWPbT5KOZ6hfXkPLhuYJm8fXEwvWwYfcbs7E8puXQoDb4US3LBQBumViWhYVvkDBYV8qXqeKJa0pK2HCnolfppDLQzSTxf+Ox6XMV9R4p9gUs5l9Dr5xlF/9+xtoGR0JeANu7v3oHSzddPUNSJZl8ebTOyiqiJAaTeELesmmsliGibQshnuiFFWGCZeFifXHCuV/AC6PigBM08LheEeFS1afkIsGKK0p4e4Pb+HVf3sDxaHg8qjk0jlUj4vHv/juayJ2NZMIIahqrJh2PZf5zIL1LJtqatjR00VSy+EQCvXhMElNI57LEXC5uaWuHocQnL7MHHxlIEB1MMRIJkOJ99wfkWlZ5Exz0kqYm+vqODzYT8TjGVdpM5hO0VJSQsRjp2iuNYZuMNg1DOSrGKaqNDmfU3vP8MK3fklxVRGRsnwON5vK8fQ//JwPfvmxy5IInoxsKkd8OEFFfRl9bQP4wz4iZSFS8Qy6piOEYP3W1cQGRhnuHe/g3V43y29ewpG3TlBed65cUUrJ6FCCLY9vnvScG+5dQ92yGo68dZz4cJKqxnKW37SEQOSd4YfNQmDBOvjGSBFbG5t44eQJ0rqOY2wjqdTvZ2NVNQGXm+F0mrD78pZvQgg+uHI139yzk+5EHJ9TRbdMNNNka2MzjZGJOf1lpWXcUtfAW10duB0OVMVBxtAJezw8vuza7qLbwNHtJ3jlB6+TTeb3YDwBN1s/chvLb5y6qURKyRs/3U6wOIDbey7/7fG78QV9vP3crqt28E6XE0VRMA0T1a1iWRaqWyVSlq+NVhyicO7zbTjLHe+7mb62AfraBvD6PZimhZbJsfymFlbcPHVlWHld6bibgs3CZcE6eCEEDy5ewvLSMv5q2+sMpNI0h8NUBoK4HA4sKUnrGptrL78CoioY5A9vupV9fb2cjo4QcrtZX1VNQzgy6WaOIgTvWb6CdZWV7OnrIaMbLCkpYXVFFT51brRQL1TOHOrg2a+/SFF5pJAvz6ZzPPv1F/EGvIUKmPhIglQsTaDIT7AogK4ZDHYOU14/0REGiwN0HOueNE13ObjcKqu2LOPgb45SXBHm9L4zZNM53F4X2VSW5rWLSCcyuP1u6pZNnBrlD/t54o/fy4ndrZze14bqdrL8piXUL6+Z9+kWm+lhwTp4yDv5xqJi/sedW/nH3TtJZHOkdY1Y1iJnGmypb6CluOTiB5qEoNvNbQ2LuK1h0SXb0lxcQvMVns/mytj2zE78Yf+4ChGPz40/7OfNp3dQXl/Ky9/9NSd2nUZxKEhLsnTzYu768BacLuekrfp6TscX9E5LZcatj21m76sH2fHzvWTTOYa6R/K55OZyFEUhncjw+BcfnlJLxeVxserWZay6ddlV22Kz8FjQDv4s5f4AfzAWcZ8YHsKvuthYXU1TUfF1Wz51PWCaJt2neqmoL8OyJJlkBsjL/waL/HSd7OGpr/6M/jODlNWVFlr3j+84RSqWYu1dK9n54j4qG8oLx5RSMtIX5bb33jwtNnYe68YyLVo2NBEfSaLndPScjqEbrLh5Cbc+trmQ/7exuVyuCwcPEHC52FLfwJb6udN9Z3NtURQFt8fNQMcQHce60XL5QRUut0r9shqMrEnv6X4qGsoKN/qz+iUdx7rZ9OA6Oo5109c2gC/oRUpJOpGhblkNG+654artsyyLN36yg+KKInwhL+dvz4/0xTAM03buNlfFdePgba4/hBDULa/m6b/7OZHyMIFQvupJ1wwOvn6UdVtXYWjmhFXc2Z8TI0k+9OXHOLmnlWM7TqMosPymJTSvXTQt8rPZVI7Rwfikef5QSYD2w11XfY75hK7pxAbiqG4n4dKQvbqeBmwHb7OgScZShEoCZNNZLDPvlLWcTqgkgKEZU07LEyJfa+7yuFh5yzJW3jL9OW6ny4lQJq9l1zUDb+D6aNCRUrLrpf1se2Ynek5HSqhuLue+j981TuTM5vKZWUF0G5sZxDRN+s4MsP7e1SzZ0Iw/4scf8bNkQzPr711NLquh53TiI4lxQxi0rI7D6aRhGjRmLoTLrbL8piWM9I7vxZBSEusfZd3Wq08DzQd2vbSfV77/G/xh31gJZwnD3VGe/MuniY/MvalM8wk7grdZsCiKgsvjQloTa78HuoboPNpDcXWEE7tO4/G5aVyzCLdHxdBNHvrMVvyhK5ORvhxue89NdJ/spa9tEH/Yh7QsUqNpGlbUsvr2Fdf8/LONltPZ9swOympLUMdm2wohiJSH6e8Y5NDrR7nl0cmbtmwuzoJw8IZlEc1kcDkcBSVH3TSJZbPjHrO5vhBCsPauVez8+d5xSoyjQwkO/voItUuqWbppMXVLamg/2kXboQ7u+tCt3PWBW8d1jV5LgkUBnvjT93H07ROc2HUah9PByluX0bK+cc6MmbuWxPpj6Dmj4NzPJxDx03qww3bwV8G8dvBSSt7u6uTF0yfJGPncXVNREXWhMDt7uguPNRcX89iyFZT7p3/8mc3cZvOD62g71EHvmX4Ckfznf3jbMQIRf0HPPBDxs/LmpcQG4wihzJhzP4vX72H91tWsn0MDNGYKp8tZ0GR656aqoeUlk22unHmdg3+zs4MfHTmEx+mkKhCiKhBkR3cXX3l7Gwqi8Fjn6Cjf2LWD+GSDjS9CIpdjKJ3GsKxrcAU21xpf0MuH/ugxHvjUXRRXRoiUhyiqCLP+vtU4XU6y6Ry5TA6JJFQSpO1QB9GBGPHhxJTDkacirwMTv6LXXq8UVUQory8lPjw+1y6lJBVPXxdpqmvJvI3gNdPkpdMnqfAHcDvzl2FJyXA6g1NRGMqkCXk8ef0Zn5+eRJxd3RO12qdiKJ3m2eNHOTo0iCLAq7q4v2kxN9XW2eVb8wy3182aO1ax5o5VWJbF1z4/yOhggs5j3aQTWUDiD/kIl4foPd3Pt7/8AyAvSnbPE7dTu2SiTMA7aT3Yzqv/9gax/lGklFQ0lLH1iduoWVx1ja9ufiOE4MFP3c2Tf/k0A51DBCJ+DM0gOZpi+Y1LWLxu0WybOK+ZtxH8UDpFzjQLzh0gpesY0sKnqgy+QwY46HJzZGjgko6dyOX4xq7tnIoOUxUIUhUI4XE4+NGRQ7ze0XbB1+b13dOkNG3S3yc1jWgmg2mvCGYFRVGoW1rN/l8dwtAM/GEv/rCP5GiK3S/uxxfyFTZkU6NpnvzLp+k903/BY7Yf6eTHf/McelanvL40H5GOJHnyL55moGNwhq5s/lJeX8Yn/uyD3PjwenxBLxUNZTz2uw/xyOfutTV1rpJ5G8GrimNC7s4x9q9pWTjfMZDDsCw8zkvbtNrb20M8l6UmeK6L0ONUqQgEeOn0aW6sqRt3Y4H8kvLNzg5+2XqKtJ7vmLyhvJJ3LV1KxONlMJ3i2WNHOTY8hCCvV//g4iWsr6q2VwQzTDaZxRfyomX1fMG7JN9g41FxOETh8wgWBTA0k23P7uI9X3x40mNJKfnNj9/GF/IVNNiFEISKA4zkdN5+fs+EyUo2EwmVBLnt8Zu47fGbZtuUBcW8jeBLfT6qgkFi2XN5dZ+q4ldVkrpOTfDcAGvNMBjOpllfeWnL5cNDA5NOYHI7nOimwUBq4pCQX7W18pOjh/E4nVQHQ1T4Axwe7Oebu3bSn0zy9Z3baY2NUBUIUh0MoQjBDw7uZ2dP9xVcvc2VYpomPWcG2HDfWhpvqMflVlHdTjw+F7UtVcRHkkjO5c/DZUHOHGifMqeu53T6zgwQLJqopx4uC3F6X9u1uhQbm4sybx28EIL3Ll+FIS36kgnSuk5Cy1Hk8RLxeLCkJJrJsKO7kxdOn6AnHue5E8d5u6vzohtgHocTw5r8ORImrA4yus4vW09TFQgWVgkORaEyEGQok+Kpo4dIahplvkBh4IdPdVHm8/PCqRP2Bu4MIoTA6XSgKArVzZWsuXMlq+9YiT/kQ8KEjlLTsHC6pl7oKg4FxSGwJvm+mIY1afmfjc1MMW8dPEBdOMzv33Qrt9Y14HQoFHt9fGbDJv7hwUfYXFPLgYF+kprGpuoattQvQnU4+I/DB/lNe9sFj7uhuoaklptwIxjNZSn1+akIjC+37E0mMKVEnSRf6HO62NHdTdg9sRbfq6qkde2yxwbaXDmKonDD7csZ6Yue95igvKGM0cE4FYvKEZxLmUX7Yqy5c8WUaTSn6mTZjS1E+yZOBhvpj7L2LrsKxGb2mLc5+LOU+ny8a+ky3sV4rZDaUIjFxUXj8ug+VaUyEOSl1lPcVDsxj36WFWXlrK6o5EB/HxGPB1VxMJrLIYTkY2vWjRu7B/mIfqpVgSkt3E7HpJuq+T2EyQd421w7bnpkI2cOdtLfPkioJD8ExOVxESjy43KrZJL5tN/oUIKiyjCbH1h3weNteexGOo5209+RP560JPHhBKW1xay/Z801vx4bm6mY9w5+Kg4PDOBXJ445czkcGJZFTzIx6Xg9yDvsj9ywhlXlFbzV1UFK09lcU8OW+oZJm6VqgiGCbjdJTSPgOndOKSUZQ+e+5hbe7Ggn5HaPiwRHc1mqg0FKvde+Jd7mHMGiAB/54/dw4DdHOLLtBIoCW5+4jcVrGzm5t7Xw2B3vv5nVty/HGzg3M1fLaowOJQiXBnF58p91uDTER//0fRz49WGObj+Joijc9aEtrNqyDK/f7qK2mT3EXGjI2Lhxo9y1a9e0HvN7+/dyYniYEt9E59mTiPO7m2+mLjx9WtvHhwb5zr7dOIVC2O0hZ5rEshnWVlbxvhWr+O7+vZwYGR5bESjEsjkUIfjPGzdRH45Mmx0214Z0MsP3/+xHbH9+L4Zu4PKo3Ppbm/nwf3+84OhtbGYaIcRuKeXGqX6/YCP49VXV7Ovvo/gdLdBJTSPk9lAdDE7r+ZaWlvHFG2/hjfY2TkVHiHg8PNSyhLWVVTgVhU+sXc+e3h7e7uokY1x4RWAzt7Asi7/6xP/l1L4zRMrCuNwqWlbjlR+8zmDXMP/l//3ObJtoYzMpC9bBLy0tY1V5BQf7+yjyenE5HIxmsxiWxafXbbgmee+aYIgPrJpcT8TtdHJzXT0319VP+3ltri37XzvM6X1tlNYUo4j898blcVFaU8zB149yYk8rS9Y3zbKVNjYTuSa7e0KIB4QQx4UQp4QQX74W57gYTkXhiRvW8IGVNxBwuciZJqvKK/jijbewtHRmxaRs5jcHXz+KUETBuZ9FURSQcHTb8VmyzMbmwkx7BC+EcAD/F7gX6AJ2CiGelVIeme5zXQzV4eDG2jpurL22gxtsFjZnFQ+nwqEu2IWwzTznWkTwm4FTUspWKaUGPAk8eg3OY2MzI2y8fy0Ahjm+1NXQDYQiWHfPqtkwy8bmolwLB18DdJ73c9fYY+MQQnxWCLFLCLFrcNAWZLKZuyxZ38SND61juHuExGgKQzdIRJOM9MW464O3UtNsK0bazE1mbW0ppfwW8C3Il0nOlh02NpfC57/ySZrXNvLyd3/N6GCckuoiPvjlx7jzA7fMtmk2NlNyLRx8N3B+0rt27DEbm3mLoig88Mm7eeCTd8+2KTY2l8y1SNHsBFqEEI1CCBfwQeDZa3AeGxsbG5sLMO0RvJTSEEJ8AXgRcAD/T0p5eLrPY2NjY2NzYa5JDl5K+QLwwrU4to2NjY3NpWHLGNrY2NgsUGwHb2NjY7NAmRNqkkKIQaB9Bk9ZCgzN4PmuFfZ1zB0WwjWAfR1zjYtdR4OUckrtlTnh4GcaIcSuC0lszhfs65g7LIRrAPs65hpXex12isbGxsZmgfL/t3c+oXVUURz+fliJtIJJLNZoColalFrUBhcJuhAV20qpCC4iBSsW3AhWKYixILgURa2gVfFPQEIVY9UQ0FJj1xGrNo1NYysNmtKaLrSCblo4Lu59dnzJS95rq/PuvPPBkJl7JuH87pl73rxzJ3M9wTuO4xSURk3wb+XtwAXCddQPRdAArqPeOC8dDVmDdxzHaQQa9Q7ecRyn8HiCdxzHKSiFTvCSlkvaK+mgpB8kbYntrZL2SDocf7bk7Ws1SLpI0neShuNxp6TRuDTih/HlbnWNpGZJg5IOSZqQ1JNiPCQ9Ga+pcUk7JV2SQjwkvStpRtJ4pm3O/lfg1ahnTFJXfp7/mwo6XojX1ZikTyQ1Z2x9UcekpDX5eD2buXRkbFslmaSl8bjmeBQ6wQNngK1mthLoBh6TtBJ4GhgxsxXASDxOgS3AROb4eeBlM7sO+A3YnItXtbEd+MLMbgBuJuhJKh6SrgYeB241s1WEl+r1kkY8+oG1ZW2V+n8dsCJujwI7/icfq6Gf2Tr2AKvM7CbgR6APII75XuDG+Duvx6VF64F+ZutA0nLgHuDnTHPt8TCzhtmAzwhrxU4CbbGtDZjM27cqfG8nDL47gWFAhP9wWxTtPcDuvP1cQMNlwFHi5H6mPal4cHbVslbCC/uGgTWpxAPoAMYX6n/gTeDBuc6rh61cR5ntfmAg7vcBfRnbbqAnb//n0wEMEm6ApoCl5xqPot/B/4OkDmA1MAosM7Pj0XQCWJaTW7XwCvAUUFoY9HLgdzM7E4/nXBqxzugETgLvxVLT25KWkFg8zOwY8CLh7uo4cArYR3rxKFGp/6tafrNOeQT4PO4npUPSfcAxM9tfZqpZR0MkeEmXAh8DT5jZH1mbhY/Cun5WVNJ6YMbM9uXty3myCOgCdpjZauBPysoxicSjhbCQfCdwFbCEOb5mp0gK/b8QkrYRyrMDeftSK5IWA88Az16Iv1f4BC/pYkJyHzCzXbH5V0lt0d4GzOTlX5XcBmyQNAV8QCjTbAeaJZXe6Z/C0ojTwLSZjcbjQULCTy0edwNHzeykmZ0GdhFilFo8SlTq/+SW35T0MLAe2Bg/rCAtHdcSbhz2x/HeDnwr6UrOQUehE7wkAe8AE2b2UsY0BGyK+5sItfm6xcz6zKzdzDoIk0VfmdlGYC/wQDwtBR0ngF8kXR+b7gIOklg8CKWZbkmL4zVW0pFUPDJU6v8h4KH49EY3cCpTyqk7JK0llDE3mNlfGdMQ0CupSVInYZLy6zx8XAgzO2BmV5hZRxzv00BXHDu1xyPvCYb/ePLidsLXzTHg+7jdS6hfjwCHgS+B1rx9rUHTHcBw3L+GcKEeAT4CmvL2rwr/bwG+iTH5FGhJMR7Ac8AhYJIyaCEAAAB+SURBVBx4H2hKIR7ATsK8wemYPDZX6n/CRP5rwE/AAcJTQ7lrmEfHEUKNujTW38icvy3qmATW5e3/fDrK7FOcnWStOR7+qgLHcZyCUugSjeM4TiPjCd5xHKegeIJ3HMcpKJ7gHcdxCooneMdxnILiCd5xHKegeIJ3HMcpKH8DbqIsgkpHcYYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CIHGOxCBSf7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}