PCA_Sklearn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PCA Sklearn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNely0R3+JkoTuTWrQminQq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/philsaurabh/Tutorials/blob/main/PCA_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VexRB3wbzKYU"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2iRX35_B1ib"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po3bju8yEGAT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "b0cd61e0-93da-404e-ec38-28944620c06c"
      },
      "source": [
        "# To read data as dataframe\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/philsaurabh/Tutorials/main/Credit.csv')\n",
        "df.head()"
      ],
      "execution_count": 11,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.891</td>\n",
              "      <td>3606</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>104.593</td>\n",
              "      <td>7075</td>\n",
              "      <td>514</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>580</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>55.882</td>\n",
              "      <td>4897</td>\n",
              "      <td>357</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>331</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   Income  Limit  Rating  ...  Married  Ethnicity  Balance Defaultee\n",
              "0           1   14.891   3606     283  ...      Yes  Caucasian      333         0\n",
              "1           2  106.025   6645     483  ...      Yes      Asian      903         0\n",
              "2           3  104.593   7075     514  ...       No      Asian      580         0\n",
              "3           4  148.924   9504     681  ...       No      Asian      964         0\n",
              "4           5   55.882   4897     357  ...      Yes  Caucasian      331         0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQ9PGErh8kb0"
      },
      "source": [
        "# PCA in scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81eDuBTa8VM0"
      },
      "source": [
        "# Machine learning systems work with integers, we need to encode these\n",
        "# string characters into ints\n",
        "encoder = LabelEncoder()\n",
        "# Now apply the transformation to all the columns:\n",
        "for col in df.columns:\n",
        "    df[col] = encoder.fit_transform(df[col])\n",
        "X = df.iloc[:,1:12].values\n",
        "y = df.iloc[:,0].values\n",
        "X_std = StandardScaler().fit_transform(X)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAY-CKYkU9TT",
        "outputId": "2435ce5a-571c-4ee7-f026-c4e89e635586",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-gJMHZoRiPD"
      },
      "source": [
        "In scikit-learn estimator api,\n",
        "\n",
        "fit() : used for generating learning model parameters from training data\n",
        "\n",
        "transform() : parameters generated from fit() method,applied upon model to generate transformed data set.\n",
        "\n",
        "fit_transform() : combination of fit() and transform() api on same data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwWQsVak8nOg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "117a2623-9b64-4f80-e334-8760e8904bd5"
      },
      "source": [
        "sklearn_pca = PCA(n_components=5)\n",
        "Y_sklearn = sklearn_pca.fit_transform(X_std)\n",
        "print(Y_sklearn.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(400, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9NVJO-gXGGs"
      },
      "source": [
        "### components_\n",
        "Principal axes in feature space, representing the directions of maximum variance in the data. Equivalently, the right singular vectors of the centered input data, parallel to its eigenvectors.\n",
        "The components are sorted by ***explained_variance_***.\n",
        "\n",
        "### explained_variance_\n",
        "\n",
        "The amount of variance explained by each of the selected components. The variance estimation uses n_samples - 1 degrees of freedom.\n",
        "\n",
        "Equal to n_components largest eigenvalues of the covariance matrix of X."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-8L38qkUIp0",
        "outputId": "43552993-bd43-49c1-ff74-0de99978eb23"
      },
      "source": [
        "# Variance\n",
        "pca = PCA()\n",
        "pca.fit_transform(X_std)\n",
        "pca_variance = pca.explained_variance_\n",
        "print(pca_variance)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.28475263 1.17163109 1.13492056 1.04879032 1.01122112 0.97859626\n",
            " 0.95166312 0.82164462 0.5719286  0.04772453 0.00469608]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ID7ItD9CVh32",
        "outputId": "098e815e-74b2-4b0d-bb34-80b0b51966d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pca.singular_values_"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([36.202435  , 21.62130441, 21.27988028, 20.45647419, 20.0867426 ,\n",
              "       19.76005838, 19.48624088, 18.1062476 , 15.10627387,  4.363724  ,\n",
              "        1.36884475])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w0_APU7s0YXp",
        "outputId": "8b48c808-9509-42db-aebd-3f934136eeb7"
      },
      "source": [
        "# Visualization\n",
        "\n",
        "plt.figure()\n",
        "plt.bar(range(11), pca_variance, label='individual variance')\n",
        "plt.legend()\n",
        "plt.ylabel('Variance ratio')\n",
        "plt.xlabel('Principal components')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIrVmtz28-o3"
      },
      "source": [
        "# Additional"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTzkR1mePWUV",
        "outputId": "d8465f5c-ccf7-48de-bbb6-541d7ee0ce48"
      },
      "source": [
        "pca2 = PCA(n_components=2)\n",
        "pca2.fit(X_std)\n",
        "x_3d = pca2.transform(X_std)\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.scatter(x_3d[:,0], x_3d[:,1], c=df['Defaultee'])\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v0aEBEAqG+N/zX9CtWk+uSL+RJ694gY3/GHPBUKlmhbjH7Q4bdpctydHsF1waabwQw4P2TEh6OEZStuZQ9lUwVQZsgB2cnVBlXzA6NFGCSEIWCfPWPR/gc/sPLg0Jh8J483y89+BogyOLeO32dxn30kSytu/D5/YxZ/JC7m3xBLs27056LLcMuDYm8dpddro93Amz2aA1ljoE5DNVroNJDSUVmJyXoCr9hqo8A1VlLqayz6OUQRdLxwEd2kk45y3CWX0I57yFDu0yOqSUJwlZJITfF2DLqvjdjVbMXp3kaGLt3LSbGZ/Pinpuq7XG5/Ezcej3SY/n/K4tufvN2ylbMQOLzYIrw0n3R6/kuieuSXosB1kbE/8rwolyXp3saFKCUgplKl8i+iYnkg6uRu+6FPKGg+8XyBuO3nUJOrjG6NBSmhR1iYSwWM3YnDa8eb6Y1zIqpBsQUbT1yzZhtVvxe6M3fw/6g6yYvcqQmDrc3o5Lbr0Ad7YHZ4bDuDvj/ZSyQuZQdNZdRNbn+kG5wHo6OK80NDaR2nT2gEjtwcFlYn7QAXT2QFT51JghS0VyhywSwmQycfkdF2F3xk7Ddu13hUFR/ad6gyoEfIGY42aLmXpNahsQUYTJZCI9M83wZHyAsp+HqjQF0u8HVw9U5luociMjyVqIOLTW4J9D9JptIr/3/2VESMcNuUMWCdPzxRvI2ZPL9HG/Y7FZCPqDXHHHRVx93+VGh0b1BlU5rW0jFk3/O+ou2Wq3cM39xseXSpS5Miq9p9FhiOOEUgqt7KA9cV6Uqf4jUUb0Ym3evLmeO3du0scVxsjencP29Tup3qAKaWXTjA7nIK/bx7v3jWLqJzMIBkLUaVSL+4f14tSWJxsdmhDHtfC+Z8HzBXDoIys7OLthKvu0QVEZSyk1T2vd/IjvKW0JOXt3Dt+NmMLyP1dRr0ltOt55MRVrxF9yIkqHUChEKBDC5pCKWSGKg9YedFYf8C8AZYlU5duaocoNK7VtRiUhH2bbuh3cfdZjeN0+/B4/VrsFi83C4GkDOLFZ/aTHI4QQJZkOrIxsxGFugLKeaHQ4hipIQi5VRV3DHxpDblYufk9kqUvAF8ST42XIHcMNjkwIIUoeZT0J5bi01CfjgipVCXnuT4sIh2NnBNYsXIffG28fVCFESaGDG9CBv9E6trpeiFRQqqqsHU4b3jh9gU1mE2ZLaiwzOR74PD5mfPYnaxavo84pNWl77bk40xP7XChrxz7GvjCRWZPmklE+nc73X8GF17cqMduuicTRoa2RntzBf0GZARO6zPOYnB0SP3ZwE+hssJwgXb3EUZWqhHx57/Z8/vq3+Dz/3Q1b7RZaXdNCEnIB7dmWxT3nPE5uVh6eXC+ONDsjnxzLm7MGUa1elYSMmZOVy53NHmbfzmyCgRDb1u7gjT7DWb1wLXe8enNCxhQlg9Y6sltVaD0Q/m9p7L5H0Za6KOspiRk3tAO9924IrIgUNaHQGc9gcklDFZG/UjVlfcPTXWh2UVNsThuuMk7sLjsnNqvPfe/KGsuCGvbAR+zZmnVwByJvno/sXdkM7TMiYWNOeu8ncvbkEgyEDh7z5vn4+p0fyNq+N2HjihIgsBjC24ndy9iPdn+SsGF1Vk8ILAV8kZ2ydC5k90cHFidsTHH8K1V3yFablYFfPcrGfzbz7+IN1DixKiecXs/osI4rsybNIxSM/nILhzULfllKKBRKSIepBT8viWlxCWCzW1i9YC1nXXpGsY8pSojwbuLfd4QhFL/XelHpwD8QXE+k3eihvOi8j1CZrydk3OOBDudCcDmYKqMsdYwOJ+WUqoR8QK2Ta1Dr5BpGh3FcMpnjP7NVSiXseW7VupUwmU2EQ9EXAqFgmArVyydkTFFCWJuCjlew6QD7+YkZM7wr8qw6pn5UQ2hbYsY8DoRzh0HuMFDWSF9r66mRdcmmckaHljJK1ZS1KLoLurfCaou+jjNbzLTs2ByTKTH/nK6+7/LYMa1map1cnfpN5Spb5E+ZK4LrNuDQokM7mCujnJ0TM6i1UT4XAXawt0nMmClOe6dA7nuAF3RO5NfAYnRWX6NDSymSkEWh9H7lRmo1rIEz3YHVbsGZ4aBq3Ur0HdYrYWPWb1qHJ8beT9lKZXCk2bHarTRpdQovTH4yYWOKkkNl9ENlvgLWs8ByMqT1RlWYiDIlpo2rMmVCeh+I6khlA1N5lOv6hIyZ6nTeKODw3tZBCCxAh7YbEVJKKlWdukTxCIfDLPxlKeuWbqTGSdVofslpSdmdKBQKsWX1NtLKuihfVaa5RGrT3qmRRBTOAkd7VNqtpXZ6NrzzEgitjX1BpaHKj0dZT0p+UElWkE5dpfIZcqrQWrNp5RaC/iB1GtVK2JRvcTOZTDRr35Rm7ZsmdVyz2SzP/sVxQznaoRztjA4jNdjPB/cm4PDiTBNY6hoQUGqShGyQdX9v5NmrX2HXlixMJoUjzc6TYx/gtLaNjA5NpLhQMMTiGcvw5vlo0voU0jNTZwetgtBaS0OXUkal9UZ7v4VwNuAHFGCHjP7SMOUQMmVtAL8vwPW1+pC9O5tD//gdaXY+WvkWFaqVzmktcXSr5v/LEx0G4fdF7jSC/hB3DrmFK+642ODIjm7Znyt5654PWLNgHY50Ox37XMxtz1+HxSr3BaWBDu9B540G30wwV0el3Y6ylZ4li7K5RIqa/d18/L4Ah18LhUNhpoyZbkhMIvUFA0Eeu+R59u7Mxp3twZ3twe/1816/0axeEOf5XArZsGIzj7YfyOr5a9Fa48nx8vXbPzC4xzCjQxNJokzlMWU8gKniF5jKvVWqknFBSUI2wJbVWwn6Yxtd+L0Bdm3abUBE4niwYOoSgoFgzPGAL8D3H/x81M9r78+Ed3UkvP1MwruvQ/uTN0s1/uWvDt7VH+Dz+Jnx+Sz2bMtKWhxCpDJJyEkUCoUYetf7fNR/PAFf7BerM93B6Rc2MSAycTxwZ3tiZlUg0ikte3fuET8bdn+F3tsPgv9E1oEG5qH33I72z0lQtNHWLFoX09gFwGq3smWNLHsx0uoFa/lp9HSWzlyOEY8wxX/k4U0SffH6t0wZ8ytBf2wytjtt1D6lBi07HvERgyjFmrZtRCjOHbIjzU7ra87J93Naa8h9BTh8pzMvOudVVIUJxRtoHCc2q8/aJRtikrLfG6DGidUSPr6I5ff6ebrTy/z9xwpMJhMaqFa/Mq9NfZYyFTKMDq9UkjvkJJo49Dt8bl/McaXgpme7MXj6ANl1SuSrXOWy3PxsN+wuOweKlB1pdk5q3oBWR0jI6DwI57MJR3Bl8Qcax7WPXInNEV1Na3fauPCGVpSrXDYpMYhoYwZ8xtKZy/G5/XhyvXhzvWxcsZkhvYcbHVqpJXfISZSblRf3uFKKq+/tEPOFJcThrn3kKhqd15Dv3/+ZvH1uzu/akjZdWx75Qk45QdlBx95dY6qauGAPUfOk6gye/izv9B3JP3NW4yrj4sp7LuXGp7okZXwR68eRv8Rs2hL0h/jz27kE/AGsNqtBkZVekpCT6NRzT2bB1CUxx2ueXF2SsSiwxuc1pPF5DQv8fqXM6LQekPs+0e0LHaj05PUSPunMBgz9fVDSxhNHdniR3QHhsCYUDGOVr6SkK/KUtVKqllJqmlJqmVLqb6XUfcURWEnUZ/AtONMdmC2RP3aTSWF32en7buL6QAsBoNLugrQeoFyADVQmlHkS5bws38/4fQFGPDKGq8vfymXO63iiwyA2rUrMloUi+c65vBkmc2wKOLFZfRwuuwERiSI3BlFKVQOqaa3nK6UygHnAVVrrZfl9pjQ3BtmyZhvjX/maf+aspm6jWlz7yJXUayI7Fonk0DoIOhdUGZQ68vX4Ux1fZMHU//aiVkqRVtbFyBVD5blvCbBr827uOusx3NlufG4/NocVi83CkBnPyS5qCVCQxiDF3qlLKfU18LbWekp+7ykJCdnr9jFt7EwW/LKUavUqc1mv9lSpU8nosIQoFhv/2cydzR7B54neRtDmsHLto1dx8zPdDIpMFKe8bDdTxvzK8tmrqHtqTS7t0U4uthIk6ZtLKKXqAmcAs+O81hvoDVC7du3iHDbpcvfmcc/Zj7F7axbePB8Wm4WJQ7/j+UmPSy9qUSKs+3sTZqs5Zsc8vzfAP3NWGxOUKHZpZVxcdU8Hrrqng9GhCIpx2ZNSKh34Arhfa519+Ota6xFa6+Za6+aVKh3fd5LjXv6SHRt34c2LLGEK+oN483y8dPObsrBelAi1Tq5OKBiKOW61W2hwWt3kByREKVAsCVkpZSWSjD/RWk8sjnOmshmf/xm301bOnly2/itdh8Txr26jWjQ850Ss9uilL1ablU53XWJQVEKUbMVRZa2AD4HlWuvXix5S6rM741cghkMau1QnihLiuW8eo/1NbbDarSilOLXlSbw+YyAVa1QwOjQhSqTiqLJuBfwGLAEO9MV7Qmv9fX6fOd6Luia99xPDHxoT1XXLZDZxUvMGvDXrBQMjE6L4aa0Jh8OYzdJFTohjlZSiLq31TCK7TZcal/Vqx9+/r+C3L/6MdEhSULZiGZ6e0M/o0IQodkopScZCJEGxL3sqiOP9DvmATSu3sHz2KirWKM9pbRthMklrcCGEELGSvuyptKl5UnVqnlTd6DCEEEKUAJKQhRAiRWjtB+9PaP/vYKqKcnVBmWsYHZZIEknIQgiRArT2oHdfB8F1gBuwovNGQrl3UPZWBkcnkkEeegohRArQef+D4L9EkjFAAPCg9z6I1rFNWkTJIwlZCCFSgfc7wBvnBT8E/0l2NMIAkpCFECIVqHyaCukQKEdyYxGGkIQshBApQLmuB+U8/CiYq4G5niExieSShCwEsOKvVTx68UC6VOlB33OfYO5Pi4wOSZQ2jo7guAywA05QaWCqgCr3LpEOxaKkk8YgotRb+vsKHrvkOXzu//b+tbtsPDzqHs7v2tLAyERppINrwT8PTBXA3orI3j3ieFeQxiByhyxKvfcf+TgqGQP43H7ee3C0bKcpkk5Z6kXWHzsukGRcykhCFqXemkXr4x7P2paFNy9e1asQQhQ/Scii1KtQrVzc4zanTbbTFEIkjSRkUerd8FTnmMRrd9npfP8VsmGIECJppHWmKPUuvqUt2Xty+XjABELBEKC48p5LubF/F6NDS6jVC9YyfcIfmEyKNl1bcsLpsrRGCCNJlbUQ+wUDQbK276NsxQxsDpvR4STUqP7j+OL1SQS8AVAKq83CtY9exU39uxodmhAlklRZC1EIFquFSjUrlPhkvH7ZRj4fPAmf2084rAmHwvg8fsa99BWbVm4xOjwhSi1JyEKUMn98PZdQIHazgnAoxB9fzzEgIiEESEIWotSxWM0oU2znJ5PZhMUmZSVCGEUSshClTJuuLTHFScgArTu3SHI0QogDJCELUcpUqVOJe97qgc1hxea0YXNYsdos9H23F5VqVjA6PCFKLUnIQpRCHXq04+GP7sZiMaO1RpkUnw2exKZVW40OTYhSSxKyEKXQ9vU7ee32YbhzPAR8QfzeABuWbeKhC57ZvxZbCJFskpCFKIV+GPlLTOLVWuPO8TJvymKDohLi2GgdJpw3hvCOtoS3NyO8pzc6uMbosApNSiqFKIV2btxF0B+MOa7DYfZs22tAREIcO50zCNyfA57IAf+v6N1zoMIklKWmobEVhtwhH0cC/gCeXI/RYYgS4Ix2TXGkxW6cEQ6FaXTuSQZEJMSx0eEscE/gYDKOHAXtQ+d9aFRYx0QS8nEgb18eg64bQqcyN3N1+dvo1bQfy2evMjoscRxr07UF1RtUxeb4b79dR5qd8689l1on1zAwMiEKKfgvqHjd9YIQWJj0cIpCelkfB+5v9RQr560h4PtvitGR7uCDJa9TpU4lAyMTxzNPnpcv3/ye6eN+x+awUa9xLdYu3UAoGKb9TW3o2OfiEt9GVBz/dGgbeudFgO+wV0zguBxT5mAjwopRkF7WkpBT2N6d+5g27nfef/R/kU0ADmGxWbi672X0fuUmg6ITAEt+W864l75k69odNG7VkOufuIaqdSsbHVahPd99CLO/m4c3L/KlZnfZaHBaXV6fMRCz2WxwdKKg/l28nhmfzQLg/G4tqdekjsCRp5kAACAASURBVMERJUc4627wzSA6KTtQFSagrA2NCitKQRKyFHWlqI8HfsbYF79EmVRMMgYI+oOs/3ujAZGJA6aN/53BPYbhc0e+BLas3sqMz2bxzpyXqHFCNYOjK7jVC9fy57dz8bn9B4/53H7WLtnA7O/mc26nswyMThTUxwM/Y/zLXxHYX6z3+euTuPaxq7jp6ZK/g5fKHIzOHgieSUAIzNVRZQakTDIuKHmGnILm/LCACa9+TcAXwO/xx32PzWHllJZSfGOUUCjEO/eOPJiMAULBMJ4cD6OfGW9gZIW3dOYKdDh2psyT62XRtKUGRCQKa8OKzYx7+St8Hj/hULjU7eCllANT2RdQVeahKv+FqjgFZT/P6LAKTRJyCvrq7ckHpw7jMZkUjjQHHftcnMSoxKF2b8nCm+eNOR4OaxZN+9uAiI5duSqZmK2x09I2h5Xy1csbEJEorD++nhO3oUs4WLp28FLKhjKlo1T8Xu2pThJyCsrZkxv/BQUOl51WnVvwzpyXKFuxTHIDEwelZ6YRjnNXCZBZpWySoymalh3PxBpnlyeT2cRFN7UxICJRWGaLGVOcJKRMJswWqQE4XkhCTkGtO7fA7oytbnWkOfhi10ieHt/vuCwcKklcGU5aXXNO1LIhiFwwdX/0aoOiOjY2h43Xpg2gWv0qOFx2nOkOylXJ5PlvH6d81XJGhycKoE2XFnG31FQKWnc+x4CIxLGQoq4UdMUdF/HjqGlsW7cTn9uHyaSwOqzc+3YPWYaSQvq93we/189fkxdgtVkIBcNc98TVtL32XKNDK7R6jWszetVbbFixmaA/SL0mtTGZ5Hr9eFGlTiXuGnob79436mBi1mHNPW/1oHJtWRp5vJBlTynK6/YxZfR0Zk2aS4Vq5eh096Wc2Ky+0WGJOLK272X3lixqnFQNZ5rD6HBEKbZ7axazvol8t7bs1JwK1WSGI1XIOmRxVHN+WMBHT49j8+pt1GpYg9uev45m7ZoYHZYoglAoxNolG7DaLNQ+peZxW+AiREkiCVkc0cwvZ/PSjW/iO2Rpld1p45kvHuKsS88wMDJxrBZOW8qg697A5/ahtaZclUwGfPUI9RrXLpbzh8Nhxr/8FV8M+ZacrDwanF6Xu964jcbnHV/rPYVINknI4ohuPuEetv67PeZ43Ua1eH/J6wZEJIpi15Y93HpS36i10QBlKmQwdtNwbHZrPp8suHcfGMX370+NGsPusjH090E0OK1ukc8vRElVkIQsVRulVDgcjpuMATb+U/IbCZREU8ZMJxwKxxwP+oPM/nZekc+fl+3mu+FTYhK+3xPgf899XuTzC1HaSUIupUwmE2UrZsR9rXzVzISPv29XNmuXrMfrzr8Biiic3Vv2EvDFabMaDJK1fV+Rz79j/U4s1tiFGVpr/l28vsjnF6K0k4Rcil33xDU4XNF74jpcdm54unPCxvR5fDx/3RCuq9WH+1o9TdcqPRj/ylcJG680OePCxjjTY6u8FYomrYv+jLdy7YoEA8GY40op6jUpnmfUQpRmxZKQlVIjlVI7lFLS+PY4cs19l3PD051xlXFitVtJz0zjtkHduaxn+4SN+cad7zPr6zkEfAE8OR68eT4+fu5zpo//PWFjlhYtrjiTuo1rYXf9t1bdkWbnvKvPLpZdf9LKptGhZ7uo8wPYnDZufKpLkc8vRGlXLEVdSqk2QC4wRmvd+Gjvl6Ku1BIKhsjb5yYt05XQrfY8uR46V+oRd1q1/ml1GL7gtYSNXVr4vX4mvfcTU/83A6vdyuW9L6L9TW2KrclHKBTi00FfMPGN78nb56Zuk9rcPfQ2Tju/UbGcX4iSKqlV1kqpusC3kpBFfnZu2s1tJ/eNWmZ1QGblsny27QMDokpdWmvmTVnMD6N+IegLcuH1rWh1zTkp00FLay1rnIUooJTaD1kp1RvoDVC7tjxvKo3KV8vEkWaPScjKpGjS5hSDokpdwx8czXfv/3xw5695Uxbxy6e/8cwXD6dEIkyFGIQoSZJ2qa21HqG1bq61bl6pkvRWLY3MZjN3vXl71DNIk9mEzWFj27/b6VTmJm5reB+/fPqbgVGmhk0rtzDpvZ+ituH05vmYN2UxC2WPYiFKpNSY+xKlxoXdWzHo2yc486KmVD+hKmddejo6rFk1fy2eXC+bVm7h9d7D+fKt7w2Lcdu6HbzRZwQ9Gz/AM1e/wrI/VyY9hvk/L4l73JvnY/b385McjRAiGWS3J3FMtNb8/cc/bF2znQan16V+04JX8Z7WthGntY0UAT168UD83ugpbJ/bx+j+4+l05yVJ38t106qt3H3Wo/jcfkLBEBuWb2LelEU89nFfWl2dvG3sXGWc+3/26AI4i81CemZa0uIQQiRPcS17GgvMAk5WSm1SSvUojvOK1LRvVzZ3nPEQj3cYxFv3fEDfc5/gicsG4Y9TPX00axbFbygR9AfJ2lH0ZhaFNfLJT/HkegkFQwBoDT63n7fu+YBwOLYLVqKce+VZEOcRrdlsov2NbZIWhxAieYolIWutr9NaV9NaW7XWNbXWHxbHeUVqGtxzGBuXb8ab68WT68Xn9rPo12V8OuiLQp+rat3K8V9QijLl04sYaeEt/nUZOhy78iA3K69Yul0VlCvDyfOTHic9Mw1XGSeuMk7sLjsPf3RP/n9mQojjmkxZi0Lxe/3MmbyAYCAUfdzjZ/KHU7l1YPdCne/mZ7sxsOtr+NyH7DjlstPprkuwOWxH+GRiZFYqw76d2THHtY5MIydT0zanMmHb+yz+dRnBQIim558q+y0LUYJJUdcxysnKZcfGXRixW5aRgoEQ+f3Ifk/hp6zP7nAG/T64k/LVMrFYzTjSHVxz32X0ePH6IkZ6bLo+1CmmnajNYeX8ri0NSYZWm5UzLzqNcy5rJslYiBJO7pALKXt3Di/d+CYLpi3FZFKUrViGBz+8kzMvOs3o0JLCleGkbuNarFm4Luq4yaxwZji4tnovKtWqyPVPXsO5nc4q0Dkv7N6KC649j7x9bpzpjoQXcgX8AWZO/It5Py2kQo3yXHr7hVSrVwWAi29py9a12/ns1UlYbGYCviBnXXoG973XO6ExCSGE7IdcSHef/Rj/LloXNWVrd9l5d+7L1G5Yw8DIkmf1wrU8eP4zBANB/N4AVoeVgC+AQh2cMbC77Nw55BYu73WRwdFG87p99GvzNBtXbsWb68ViM2O2mOn/2UOc3eGMg+/Ly3az6Z8tVKxZgQrVyhXL2Ll788jdm0elWhUS2qK0uGz8ZzMLpi4lvVwaLTs1lzt0IYogqa0zC+N4TchrFq3j/lZPRTVrADBbTFzWqz193+mV1HhCwRDzpiwma/teGp3XkJonVkva2Hu2ZTH5w6msX7aZdX9vYN2SjTHT9+mZaXy+48OkL106ks8Gf8Po/uNjuoVllE/ns20fJCTWvGw3r976Dn9Nno/JbMKZ7qTvOz1p3blFsY9VHLTWvHXPB/w4ahpKKUwWE0opXvzhKU5tcVLCxl2/bCOTR/5C9u4cWnY8i3M7NU+pfztCFEVKtc4sCXZu3I3JHPvYPRQMs3n1tqTGsmnlFh684Fm8uV7C4TDhUJgLr29Nv/f7JKWlYfmq5bjhycgOP9dW7xX3WXrQH2TX5j1UqZM6ndmmjfs9bi/tYCDIv4vXc2Kz+sU+5sAug1ny2zICvsjWhT63n5dveYtKtSrQ8OwTi328opo1aS5TxvyK3xtdE9C/08uM3zIiIUny5//9yht3jCAYCBIKhvnti9mc3LwBL/34VNw9mIUoiaSoqxBOOKPuwS/VQ9kc1oONLpJBa03/q14ha9te3Pu3MPR7A0wf/ztTP0l+28lKtSrGPR4OhylTIflLl47E7oxfua1DGls+rxXF9vU7WTpzRcy/G78nwGevfVPs4xWHyR9OjZkFAgj4AixPQNcyT66HN/qMwOfxEwpG1np7c738M2c108bJtpyi9JCEXAgVa1Tg4lvPj6rCNVvMpJV1ccUdyXtWunnVVnZsiK3w9ub5mDTsx6TFccANT3XGflhlst1po/2NbXCmJ3ep0NF07HMxjrToWJWCCjXKJ6QGYOem3VjtsXd4Wmu2/ru92McrDvEuOgFQEPDn81oRLJ25Iu5dtzfPJwlZlColPiH/+e08Hmj9NDefcA+v936PHRt2Ful8fd/pxR2Db6F2wxpUqF6eS267gGHzX6VM+YxiivjofB4/JlP8aWmvO/bOJtFadmzOnUNuIT0zDbvLjs1h5cIbWnPP26nXsO2C61px4fWtsDmsONLsuMo4yaxcloFfP5qQqf56jWvF3f/ZYjPTNEX3EG5/Y5uYixaIXEQ0Oq9hsY9nd9khn1IWV3pqFpIt+3MlA7q8xl3NH+G9h0aze2uW0SGJEqBEF3VNfPM7Rj4xFt/+JGW2mHBmOBm+8DUq5zPNaoRdW/Ywfdzv5O7No/klp9Po3JOPmBxCoRDdqvUie1dO1HGb08pN/bvR/dGrEh1y/LiCIXZv2UNGhYyUr8jdtGorS2euoFyVspx5UdOEPqcc1X8cE4d8e3Aa2GQ2kVbWxYjFg6lYvXzCxi2o3Vuz+P79n1m/bBOnnHMC7W86n5dvfoslM1fgzfVitVkwmU08Mfb+Ai9lO0BrDzr3ffB8FZmKcF6DSuuJUv8l/FAoRPcad7D3sFapjjQ7A756lGbtmhTLz3nkODW/ffEnX7/zA+5sN226tOSqezvEneH5dcIfvHr7O/g9frSO9Bd3ZTgYNv/VlPpeEamlVFdZb/13Oz0aPRBzd2K2mrmsZ7ukV0TnZ9akuQzqPoRwWBPwB3C47Jxz+Zk88el9R9yIfs6PCxnQ+TVCwRBBfxBHuoPqDarwxsznUz4ZHm+01vzx9Rx+GPULOqy56Kbzad2lxRH/fg7//C+fzmTCa1+zb1cOZ17UlJuf6ZYSxW6HL2Gzu2y4Mpy8NftFNq7Ywl+T51O2YgYX3XQ+lWsXLl6tQ+jd3SC4Ejgwc2MHayNU+bFRF50r563hsUueJxQIEdaaUDBE14c6cVshO78dq/ceGs13w6ccvGiyOaxUrVeFd+e+hN0ZffHQrWpPsnfnRn3ebDFx8a0X0G9En6TEK44/pTIha60Z/uBovn7nR4KB+M+7ap1cnZHLhyZk/MLweXx0rdITT6436rgjzc6jY+496u5C29bt4IeRv7Bj4y6aX3Qarbu0wGqzJjLkUum129/h189mHfyydqTZObtDM54a/0BSKtoT6c7mj7B6/tqoYyazibbXnsvj/7vvmM65Z1sWC6YupWqN5TRs+C4Kd/QblAuVOQxlbxl1OOAPMPfHReTuzeP0CxpTqWaFYxq/sHZu2s0tJ94bc/HuSLNz1xu30aFHu4PHNq/eSp8zHo5b9Fa5TkU+WTss4fGK41OpXPY0bexMvhvxc77JGPKvCk62xb8uQ8V5FuzN8zFlzK9HTchV61YudO/oeNw5HvbtzKZSrQoJnbpd9udKhj84mtUL1lK2Uhm6P3Y1HftcnNJJbfXCtUyf8EdUr21vno+/Js9n2ayVNDr3ZAOjKxpProe1i2N32wqHwsz+7tj2XB7/6teMenosOhTG4Qxy0ulVufmhbTQ6+5CkrH0QWAyHJWSrzUrLjkf8vkqIZbNWYrVZYhKyN8/HX9/Pj0rIGeXSD+4EdrjMSmUSGqco+UpcUdeXb00+YmGT3WXnWoOesR7O7/UTzKdqNRkNEfy+AK/1eJcuVXrQ+7QH6VK5B5PeK3iVdjgcZvZ383ijz3A+fPwTNv6zOd/3rl6wlkfaD2TZrJX4vQF2btzN+w9/zP+e+7w4fpSEmT9lMaFA7Bew1+1j7k8LDYio+JitlnwvhmyOws+0LJ+9itH9xxHyhwiHNO5cMwtnZvD4dQ2YO/2QokflAHPVYw272GVWLoOOU1VmtpioeNhdepkKGTS76DSstugLV0eana4PdkponKLkK3EJOW+fO9/XnOkO7h56W4GKRPbtymbcy18x6LohTHj1a7J35xz1M4Wx4JclvHTTWzHNFyDyP/clt11QrOPF8+Zd7zNt3O8EvAG8eT7y9rkZ/tDH/PHNnKN+NhQM8eTlL/D8dW/w3Yif+WzwJPo0e4SfP5kR9/2jn52A3xN9oeR1+xj/ytf4PMmvDC+o9HLpWGyxswY2u5WMcqm1xrqwbHYrLa5ojsUaffFnc1i5rFf7Qp9v8odT4y6Z8nlMDHu6+v7fKcAGjouPIeLEaNL6FMqUz4iZrbLYLFzRJzbOx8bcS+PWDbE5rLjKuLA5bHR9qBPndzs3WSGLEqrETVmfd9VZfDFke8wXQ1qmi3GbR+Bwxi7nONzGfzbT99wn8XsD+D1+Zn0zl3Evf8Vbf75AjROK3p7S7/Xz7DWvxn0OZbFZaHdj66i+yoURCob49MWJfPPOD7izvTRp3ZA7h9xKnVNrRb3Pk+vhl7EzCRx2QeBz+/h00MSjVtP++tksls5ccfBnCAVDhIIh3rhjOOddeVZMdeqahWvj7hJlMil2btpz1LafWmtmTpzNd+//TNAfpP1N59P+xtYJ7+LUuvM5DHtgVMxxZTJxQffzEjp2Mjzw/h080n47m1dvQxGZrm7athHXP9m50Oc6vBbiUJvW2AmHbZhs9VGZQ1CqeNan/7t4PRNe/ZpNK7fQuFVDuvTrSMUahXv2bDKZeHXqMzx95ctsW7sDs9mEyWziwQ/vpM4pNWPen56ZxitTnmHbuh3s3pJF3UY1SSubViw/jyjdSlxC7vpQJ6aN+529O/bhc/sxmU1Y7RYe+eieAiVjgDfv/oC8ve6DjTd8Hj9+X4B37h3JC5OfLHKMi6b/ne9rzdo14f5hdxzzuV/r8S6/ffHnwWee839eTN+WT/L+ksFRVbL7duVgNpmIt2Hizk27jzrO9PG/x72gMFvMLJ6xnIZnn8AfX88hGAhxzuXNqHlSdXZujD1vKBimQrXMo443pPdwpo2beXDMf+asZtrYmbz4w5MFrnY+Fhnl0nnum8cY0Pk1wuHwweNPjXuAclWOHndBLZ+9ijHPTmDd3xupc2pNbn62W0L7Rh9QpnwGw+a9wvLZq9i6Zjv1T6tDvca1j+lc53dtyfTxv8ddU5xW1oW5yi8oc+UiRvyfuT8t4tlrXiHgDRAOa9YsWsePo6bzzpyXqN6gcFPi1epX4YMlr7Pxn814cr3Ub1rnqBd7VetWpmrd4vt5hChxCblM+QxGLBrMDyN/Ye6PC6latzJX3nNpzB1ifrTWLP51WUwXLB3WzP9lSbHEeKA9YDyONDvhcJhdm/eQnpmGK6PgdxK7Nu/m1wmzoopTtAa/N8DEod/RZ/CtB49XqlkhMhV72PN2ZVI0OvfoiSC/NpNaa6Z+MoNnr34Fk8WMAt7r9xEdelzIsj/+ieojbXfZ6NCj3VG7ea1ftpGpn/6G3xNdWLXsz5XM+2kRZ116bLMJBXX6BY35bPsHLJ25gnBY07hVQ2z24qtmXzhtKU9d8eLBP5tdm3azdOZyBn79WFLW4CqlOLXFSUW+AGjZqTm1T6nJhmWboo5bbBa6PnRlsSZjrTVD7ngvqtgu6A/hDrr58IlPeXp8v2M6b62TS8eObSWFDm0D/19gKgO2c1Gq+NvfJlOJe4YMkT17r7nvcl74/kn6vturwMn4gMOfqR1QXF/Cp7U9NW5SdqTZqVK3MtdW783tDe+jS+UeDLpuCJ5cT4HOu2H55rjFOMFAkOWzV0cdM1vM9HrlRuyu//4BK5PC4bJzSwEqty/r2T5uNydPrpdpY38nGAjh9/gjswveAJNHTqP3qzdRrX4VTKbI3snX3H85fV6/5ahjLZz2N/Hmu725Xub+tOiony8OFquF0y9oTLN2TYo1GQO8e/+omA0vfG5/3KnyVGY2mxmx6DXaXnsuZosJs9WM1WHlmvsv57rHry7Uueb+tIiejR/gYks3ulXrxVdvT466SN63K5usbXtjPhcOaxYW04WzSG3hnKHonReh9/VH730AvaMVOrDsiJ/RwQ1o9xdo71S0jt1kxmgl7g65qJRSXHh9a6Z+MiPqObTVbqX9TecXyxjOdCePfHQ3L9/yNjoUJuAP4kiz0/CcE/nm3R+irvp//2oO3jwfz33z2FHPW/2EqnHbNJotJuo3iZ2GvKxneypUK8cngyayc+MuTj33ZG5+tlvc52aHa9auCVfd24GJb3yHyWxCmUz43L6oad1DBf1Bdm3JYszqt/F7/VhslgJPNWeUT8dsNUOcArg5kxdw5d2XFnqKMpWs+3tj3OPr/96E1jqll4Udzmw28+TYB/B5fOzekkX5auWier8XxOIZy3j26lcOXqRkbd/Lh499Qt4+Nzfsf7btOELzm/TjvNhOHJ32/QF5IznYcGb/tZrO6gmVfkOp6JsqrTU6ZxC4x4MyEykutEL5MShr8beDPVYl8g65qO4ccisnnlkfR5odZ7oDu8vOyWc3oNcrNxbbGG26tGTU8je46ZmuXPvIlQz67gmc6Y6oaVmI7LAz/+fF7Nqy56jnrFq3MmdedFrMXbLVbqVzv45xP3PO5Wfy5h+DGLtxOE+P71egZHxAjxduYNSKodw99HbuePWmyFKtfPrM6LA+uMTL5rAV6rlvy07N833/xpVbuPvsx8g6rO3i8aRMhfh90DMqpB9XyfhQdqed6g2qFjoZA4x6elzMjIHX7WP8y18R8EcuyhwuO607t8B62GyF3WWn8wNXHHvgR6F1GO1fhPb/hdapuzqgpNPucUCcmUPtgcCC2OO+n8HzGeAD7QadB3ovOqs3Wuf/CDHZJCET6SU9uNcwutfsTZ8zHmbODwsZMuM5Xps2gL7v9mLIjIEM+fW5Ym9JWbl2Ja57/Bp6vnQjTducysq5a+JWIltsFnZvPnpCBnhy3P1cfOsF2BxWlElR/7Q6vDylf1QVs9aaXVv2FHgq/Gg/w6W3X8gZ7ZpgMuefPGxOG607tzimMZxpDl7+6WnKVIyTuDT4PX6+G/7TMZ07FXR7uFPsblkuO90eKp3rWjcu3xT3eDgUZt/O7IO/v/+93px+QSNsDhtpZV2R5Vo929ExzlKl4qADy9A7z0dn3YrO6oPe0YKw54eEjCWOQufm84KKJOXD3+4eF/c4OhuC+RfZJluJa51ZWFnb93JTg3sObkABkdaB3R+9ktuev75Yx1r390b27czmhGb1SCvjinpt8odTeaPPCMKh2Ks1u9PGhG0fFKrAKxwOEwqGYlppzpo0l6F3jiBnTy5aa8676mz6vd+nyNskaq25sd5d7NiwK+Y1s8VMxzsv5u6htxdpjBmf/8krt74d9Xd1gKuMk1AwROXalbjtue7HnPyNEA6HGfXUWL4c+j0ms4lwKMxlvSKPExb/toxaJ1fnyrs7UK1+FaNDTYoH2jzN0pkrYo470h1M3DUy5t/0tnU72LFhF3VOrUnZionplqW1H72jFejDn1s7UBW/QVnqJmRcEZ92T0RnDyD2LtmJqjwLZYr+fg3vvh4CcXKOSkeVG4GyJb5DXKnsZV1Y/a96mVnfxMZiMikm7h5VLOsLd23Zw1OXv8imVVuxWEwE/CFufe7aqM4+3Wvdke9dcPNLTufS2y6gZafm2BzHXkW4ct4a+p3fP+oZtdVu5fQLG/PCd08c83kPWDpzOY93GEQoGCbgC2C2mnFlOOn/+YOc3rZxkc+/dsl67m35RFT88dhdNu5/rzftbyyeZ/7J4nX72L1lD8qkuL/V07j3ufF5/FisZixWC4O+f4KmbU4t8Pm01iybtZIlM5aRWSWTNl1aFOqiziiLpv/Nk1e8EPX3bHfZufaRK7mpf1dDYtLeqeh9D0WmOqNYIO12TBkPGRJXaaW1H73nVggui0xBYwasUGYgJldsJ8Zw3ljIeYmYBK7SUZX/TEp1tiTkArgi/YZ8v+Bf+P6JYllSc1fzR1izaH3U3a/DZeeZiQ/T/OLT0FpzsaVbvs9fzRYTNqcNu8PG6zMGHvPSjOe7v86Mz/6MWdJlc1gZtWJo3N18jlZUtHPTbub+uBCbw0aLjmfiyfHww6hpbF+7g9MuaEybri2LtSq5X9v+rJi9Om7x2qHKVyvHuE3DDX0G+8unvzFmwGfs2rybuo1q0evlmzit7dH3QB7caxhTRv8a0zO5Wv3KjF71doF+plAwxDPXvMqiaUsJ+AJY7VbMZjMv/9yfk5s3OOafKRl08F82Lx3Ost/nM2WCmbUrqtP9savpfP8VR/3Ztdb8NHo6Y1/8kr079tHw7BPo+fKNnHB6vaLF5J6IzhkQf9rT2RlT2RcPjr905gpW/LWaijXKc95VZxXpIro001pD6N/In7mlIUpZDns9CL6f0d6fwVQO5eyGsp6Yz7n86D03Q3DF/gRuASyozMEox0WJ/2GQhFwgl7muj+lWdcDg6QMKdUcSz6aVW+jT7OG4Sf+sDqfzwneRRiPda/Ri99bYZRyHUkpRr0lthi987ZhiufPMR1i9YG3McVdZF4O+fZzGh2w+/+tns/jgsf+xbd0OKlYvz80DutHh9nZRnxv/6teMeWZ8pMpaKcJhzTNfPMRZl5x+TPEVhDvHwzv3jWTa2JkEAyHQxFxgQOSxwzfZY6K2zkumSe/9yPCHPo6aXrc7bbz4w1M0aX3KET/brVpPsrbHFqlZ7Vb+t/Ydylctd9TxvxsxhWH9RsdM71eqVYFP1g1L2WKxsPtzyB4IBPf/5wL7+fu7ex295OXTF75g7ItfRjWtcaTZeevPF6nbqHDLHw+lg5vQuy4FDvv/WLlQZV9GOS7B7wvwRIdB/DNnNUF/EKvDis1hY0gRLqJ1cEMkKZnroSx1jjn+440Orkdn9YHQFlAmwLL/z/nCYz+nDoJvKto3HUyVUM4uKMuxNcE5FgVJyKW+qKvp+fETrtliptF5Rd/JZ+/ObEJxngsD7N3+X4FKi45H3/hda82mlVsK1EkrniatT4m7xjroC1Dn1P+qVqNqIAAAIABJREFUq2d+OZtXb32bbWt3gIZdm/fwTt9RfPf+lIPvWTX/Xz5+dgL+/X2wPblefG4fA7u8VizFYvlxZTh5eOTdfJv3Cd97PqV6g/jPVdPKOA27MwmFQnz09LiYZOjz+Png8U+O+nlnevziQa11TPFXfn4Y+UvcZ+05e3JZt3RDgc6RbDq8D7IHAF4iyRjADf5fwffrUT/v8/hiknHkuJ+PB35WpNiUpSa4bgAOmfJXTrCcAvbIherEId+yYvYqvHk+goEQnhwv2btyGNT9jUKPp7WfcNbd6F2Xo/f2Q++6gnBW71JR2a11CL3npsiFCJ79FdH70HvvRwfXHfN5lbKgHJdgKvsipox+SU3GBVXqE/J97/TC5oyeUlUmxb3v9sRsLvqOSz9+NI1gnIb7FpuFFh3PPPj7Nl1bFmiHHaVUvtu/HU3XhzrhSHNENdF3pNm5qu9lURsljHh4TJxGFT5G9x9/8G50yphf8ceZNlYmE399H2fZQTEzmUxYrBZuGdg9qrkJRJ43XvfkNYbdBebtdefb13nd0vhrjg/V6e5LYxKvxWqmWfsmMcWA+cl34mv/TEZK8v8BKk5rBO1Ge78/6se3r98VdztTHdb8M2d1nE8Ujsp4DJX5BtguAGsLVMaTqPKjD06l/jDql5j/b7TWbPhnc4GWLUZ9LucN8P1GZJlObuRX3yx0zitF/jlSnv9P0DnEPsMLot3jjYgoaUp9Qq5Wvwqjlg/lsl7tqFa/Cme0a8Lr0wdwec/C73ZzuL079zH1k9/ivmaxmbm672UHf3/6BY2oVKsiZsuR/0oq1ihPlTqxz3oLolLNCrw792XaXnsumZXLUPuUGtw99HZ6vnjDwfcE/AG2/rsj7uf37sg+uM+03+tHx/li11rHTdSJckH387jnrR5kVi6L2WIiPTONm5/tRpcH4q+7Toa0sq58101Xq3f0v7ur7u1A6y4tsDmsODMc2F026jauzSMf3VPgGC6+pW3MhQpEZg7qxWkSkxpsRBo2HE6BOvrMQIVqmZHHGHEUR+MYpRTKcQGm8sMxVRiDcnWLKgbK70LnwKYdheIZT2SmIEJrmPqFk3vbzub2U+9n1FNjyd17eIFZCRHO7+IlCOGtSQ0l2aRTF5G1tA8M7wPAplVb/8/eWYc3db5v/HPiaUuBtrjrcHe3DR0MBowNGDCcAWMMGQyHDXeHMWwMZ7g7FNfh7pSWQqnFc87vj9BAyEmbCkx+3/u6uC6a5JzzJjl5n/d9nue+b6JfxmA2mpNdf3x84ykarVq2Rp05T0aXXalCoWDKoZFMbD+bCwcuI+Fo5hIQMBstaPUalColg1f2SdbOL1PuDAxe0cfj8ye2nEUQBNm6rD6Vzim4X615RfatOOKWHhRtdsp4UUOOjohh7aTNRL2Iod43NSlQTr4ZwxvU61CLuu1rYjKY0eoTJzryPrBu6lbZLIZGr/ZKllSpVNJrZkcMkQZObjvnXOQ8uvHUa1pPg861Cd54iqvHb2KKNaH10aJQCgxb+0OKfj52m517lx6i0WvI9lHm5GUltJWR72zUIeibJXi4b2pf6rSuyv4/jrppprcZ2jzp4/IStVtXZc2ETW6WqhlypiN9tiCvzyNJEqI9lre/ptk/ZWb36gDMRiXwhLWTt3BwdTDzLkxKcX2Evx2aUiDJLOoFHwRttQ8/ng+I/wXk1wh/+pJhjcfx8NoTlGoloijRY1p7t0amxCBj7gyy3cAKpYI8xXO6PR6QMS1jdw7BGGPEZrXjk0rPia1nuXLsBumzB1Hrqyr4B8irOqUUQu6GegzIRSoXcE64JWsXpUqz8hzdcBKzwYygUKDSqOgyoQ1p06f2eH6HKcB8dvy6z/nYtgV7KFQpP1MPj05ysBAE4R8xMcVGxrJs+BrZnVqFRmWo+Kl3fMdhn43n6rEbzsD+8OpjBtUbw7zzE72yAFWpVYzbNYQLBy5z6fA1BAFyFstOphSUGD214zzj2s7AZrUh2kXSZ0/HqE0DE7TS9ARB0EGaOUivuuMQeBABEfw6IWhKeXWO3nM6o/XRsv3XfYg2kTTp/ekx/ZtkN2d6g5b9m3By61ke3wzBGONYBKnUSgb/4XkBHAeL2crxTad5eieUy8eu83k7X4pVikahgLAnanatDMRifvPbsJqtvAiJYM/SQzTuUfd9vq0PDkGZBcnnCzCs4w1NSQvKbKBr+HcO7b3j/32XdRy6lujH/SuPXFJLWh8NE/YMo1DFpDd3jWo5mZPbzmIxvgnMWh8ts04mr+vzfeHsnouM/HySbA30iwFN6DTujXxonDPWkQ0n0PnqqNOmWoLvaX6/ZaybskX2uY5jW9NqoDuH8N+ECwcuM6LZRGIjDW7PFar0EdOPjknwHA+vP6FH6QFu9UilSkmjbh/Tc0ZHr8cTGxnL8M8mcP3UbVQaFRazlYad69BjWodk7WZD7obSuVhfF/aAIAgEZErDivtzHTKqSYQkxoD5gIOeoq2CoEx8h7LV4mg29Evj+0F7Cex2Oye3nePaiVukzx5EzVaV8UsTv5ZB2KNwvqv0E7FRBswGC6JdRK0R+S34GmmCbJzck5opfbNhiHH/TCs1LsvIjQPe19vxCMkeihSzwFH3V6ZH8O2MoK2ScueXJDDtQDL87rgPdA0QfNq4CX78m+BNl/X/dsjAvcsPeXrnmVudx2K0sGH6tmQF5B+X92ZBv2Xs+G0/NrOVbAWy0HtO5xQJxjarDUEQXCa/2xfusXf5YcwGM4UqfUTOItnIVSR7gt6ucShZuyhZP8rMvUsPsFlcd3mbZu1E76ej9RBH+k8QBIrXKOwVtxYg6mU0G2Zs8/j8xpnb//UBOU361NhldseCAEGZE6YrgSPYqTQqt4Bst9l5cFVeVtITJn4zh6vHb2K12Jzn27FoPzkLZ6Nhl6TzL7ct3OP2PiVJwhht4ty+S8mivgkKP9AnrwdArVG7KXrJIepFNGGPwsmcJ2OKiKYolUoqNS5LpcYJsybiMLnjXF4+e+Uy/1gtCjpVL0CbvqFoNCJWi/uiQqlSkCFn0vpJPEGSJLD+BeJzUBdFULqzGCR7KFJ449eNVzaw30GyXkDyG4DCt7X7SZMAQRBA3wBB3yDhF/+H8L+ADLwKi5Rd0UsShD+J8OocoihijDaiT6V3SbtqtGp6zuxIj+kdsFlsKULFCbkbypTOc/nr8DUEQaBs/ZL0XdCVfX8cZcmQlVjMViRRYuv8PShVCrS+Wn5Y2J1qzSu6nSvqZTS3z90jbcY05CqSHYVCweQDI/ix3hiuHrvp8lqTwcyKXzbQ+Nt6LvVvb3Hnwn2UCgUi8g0uVot7N/q/DTkLZyNLvkzcu/zQZYLV6LU06+Od6UGuotllG+PUWnWiPIsN0UZObTvn9rmaDWbWT9vqdUA2xpp4FRpJYJYAp8hL+OOXsml5SZJ4JcOh/qfBarEytct8Dq05hkqjwm6106xPQzqM+fKD7qgtJgsXD16WbfoyG5QsGpPZ+bcguHbPqzRqPu2ecrrdkv2ZQ/1KfAYoQLIg+XyFkGqQy2cixSx4E4ydDxohZiKST3MELxrw/gd5/C8gA/lL55at9Wr0aio0Ki1zxBtIksS6KVtY8fN6zLFm9Kn0tBvZkibf1nd5nUKhSJFgbIwx0rviYCJfRDu7nE/vOE/P8oOICI10ex92m4gh0siE9rPImj8zuYu9ERdYNnINq8dvRK1VY7fayfpRZn7ZPpi0GdJ4rOVqtGpunbtHqdpFnY/dv/KIB1cekfWjzLK18TgEZg6Qb6J9jSpNyyX4/h/ffMrlo9dJmzENZT4pnuTUaNTLaOw2Md56d1IxZtsghjUZz8Orj539CN0mf82lI9cY3XIKhigDxaoXouukr2UFI9JnC6J6i4ocWX/CmRIWFAJaHw1NetbzehyGaKMsDQggOiLhDl273c6CfsvYumAvCoWAIAh89VMzvhjwGWXqliB44ym3pj67zU6RKv8cOztPmN9vGYfXHsdisjqbsDZM305QlgAa9/D+M04uvC0ZanQaMufNwNPbz1ColGj1Gvr/1iNBwRFJkrh24ia3zt0jU+4MZMyVnj9+2cDVYzfIlCcDXw1uRvHqjgyXFNET7A+AtxZahtWgLgb6txaTlmO4BGMnBLDdAfX7r9f/V/G/gIyjO/PrES35fdQ6TK/FFNRaNWnTp+HTbvHvIjbO3MHS4WucIgzRL2NYOHAFWh8t9TokXVXGEw6uPobJYHahHNltdiLCIolvYW8129g0eyffz+8KOMQ/1k7a7DIh3bv0kJHNJzHtyBiCsgbKNnfZrHbSZnAEMbPRzPCmE7l85BpKlRK7XSRf6Vz8sm2w06zi7l8PWDt5M3cu3CdVoB9p0qd2CJu8Mw/pU+npOqmdx/GLosiUzvM4sCoYhUJAoVCg89Uy6cAIr1SQzu+/xMaZOwh/8pKoF9GEP3mJIDi63X9c3pu8JZMnrfg2gjIHMOf0eJ7cDiHqRQy5i2Vndu/f2L/yqDPAntp+nstHr7Pw0hTSZQ10O0e/RT3IUTArm2bvxBBtpPTHxeg0ro1XKl1xCMyUFv8gf8LfEZJRKBWU+aR4gscvGbqKbQv3uliC/j56PanT+VO7dTXWTt7M45tPnf0ROl8ttdtU/cebYNhtdnYucucMmw1m1kza/EEDslavpUiVglw6fNWVNiU40rZavQZ9Kj39FvWgXP2ShD18jiHaRLYCmWV1EiTJCrZ7oPDHYknL4Aa/cPPMHUS7hEIpOO4/wcHNDrkbypXg6/ywqAc1mucC2w1cgjEARiTDMoS3A7IyPdjvuL8ZyQYK13tZkkQkw1KIXexwVlKXRvAfhKDKiyRGg2kniK9AUw7Uxf6xCnIfCv9r6noLp3eeZ8P0bbx6HkXFT8vQ9B3BDDk0z9DRxRIuDumzB7Hi/lzZY26evcPqCZt4fPMpRSoXoGX/Jl5zi+f1W8Z6maYopVKBQqWMV+O5TN0SjN3hkOr05Kij0alZfGMGL55G0L/2CJemHaVKQa6iOZh7dsLrsSxly5xdLjQPtVZF7dbV+OHX7pzedYGRn090kw0VBAFB8Sb9VvHTMgxe2QdtPBmEPcsPMaPHQrcdWVCWAH6/PydeEZe1kze7LJrehY+/nmW3Z703p6AXIRF8nedbNzqMSqOkSc/6dItnIZJcxH0HVrOjE1qtVaHz0zH3zIR47zm73U7TtO1lm/sy5U7PstuzMcaa2Dx7JwdWBaPz1dK4e11qflnlvU2qL59FsHHmDi4HXyd7gSx8/n2jJElSGqKNNAtsj93mnibW++nYHLU8JYbrNZ7dD6N3xcFOxTt9Kh2BmQMY+Wd/FEolmfNk8Ip9IBq3QdRwwA6SjcUTCrFhntLtvnsXadKnZtWDfgivWr3WeX4Hytwo0r2xmZTMR5EivsXVqEENmvIoAn5zHVPUKDCsf+u1Agg+4D8Woga97qS3ABrQVkNIMw1BSL4gE4Bku+ewXRRDEbTVQdfwg5hIeML/mro84Mzui2ydvxtTjIkarapQp01VVGoVZeuVTJSZhCiKssEYHJOwHE5uP8folpOxGK1IksSDq4/Zt+IIs06OJWv+zLLHvI28JXKi99O5TZRqnTpe8QGNXo2vv54Rn08kXdZAXjyVH59SrST6ZQwFy+ejz7wuzOy5CCSw2ezkKZ6DERv6Y7fbuXH6DtsX7HX7sVvNNvatOML3C7oyrdt8WQ1vSZJQa9RMPjiKfCVzeZV23jpvt1swBoesZ/fSA5hx7Bd0MrKSMa9iWTJ0VbyTkiHayMZZO2g34osExxGHFyERzO+3lBNbzqJUKandpiodf/nKmRmIeRXL/SuPCMyclpC7Yai1arcx2Cx2rh2/KXd6wFGeOL//MgqFgpK1iySJF1+2bglmHv+F9VO38uhmCMWrF6Jp7wakzZAm3uMsRgsWk7zpystnjhqx3lfHFwM+44sB778RL+ReKN+W/RFTrAmr2caV4BvsW3GEMVsGed1UGAe9n450WYN4dt9dAKdgImr0KYWMOdOz/O5sDq87wbO7YeQqlp2Kn5ZJVDlGsv4FkYN4W0xk90ozFlPCjW2GKAMRLwIJUGqAdwOyBnSutCpBWwUp1QCImYhju20DTVmHitlbCHtwmzSKlajUb++6JZBMEDnAZaxgBPNhMG0BffLvJ9G4ByJ/IE4TXTIfhNjfIGD1P7pT+/9dQF40eAUbZ+5wTu5Xjt1g95IDTNw3PNH1SIVCQbpsgTx/5K4tnUWGiylJEtO7L3AJUnarHUO0kUWD/2D4uoQt3Ko1r+AMMHEcVbVWRZZ8mej4y1eMajEZm9WO7a1GHpVGhd0mcmLbOcwGM0qVAkly0GjeFbBQKBROXes6bapTvWUlHlx5jF9aXzLmTM+VYzfoWqI/FqPFozykzWojIiySiGeezTKUKiXP7oZSoGzeBN8z4JZefBuPbzxl3ZQttBniLv5w7eQtVBr3YOgCCXb8us/rgGwymOlZ7kciQl85d1nbF+7j+qnbzDj2M8tHrWXNhE2otWqsZit5SuSSzVwoVQpyeOi2P7LhJOO/nulUbpNEiaFr+ibJfSxX0Rz0++3bRB2j89URmCmAsEfu/tYpmd5/F5L1KpJhLUhRCLpPQFsHQVDy648riH0V60zrinYRU6yZKV3mseTGjETtygVBoNfsToxqPsmhOCc50vgavYYuE9u+r7cWL7R6LR+3TbpdqBS7GHi3nu/9Z+KXxh9BMRbp1feAFUfqWgfKIARfdx9zhW9rJJ/mjpqxItCtG/tFSARTOw1k0GwJP7c1gR15ARgjkmEdQjIDsiRZIOpHXAK+ZADbAyTDSgQ/72mDHxr/r6Qzwx6Fs37qNpedlinWzK1zdzm26XSizmW325nadb7sTlir19B14tdYLVZehLzk6omb3P3rAa+eR/EqzL0DVRIlLh684tV1NToNX49oiW9qPQiOSb3yZ+WYfHAkZeuVZPXThQxY0pMm39ajQPl85CqanUIV86NQCM6Urd0mItod/+L0swXB0TTUe04nF4qUWqMmb8lcZMyZntjIWAbVH8OrsEgM0Z4NJApV/ChhzWUpcXKCNVtVRu3BxtFqtrFnmbz5gH9gKkQx4etEhEYSG+mdFOGBlUeJeRXrkvK0mq08uPKI30etY93kLVhMVmIjDVhMVm6fu4uPv95Nq1ylUdPiB3d6T/iTF4xvOwOzwYwhyoghyogxxsTI5pOJehHtcVzRETEc23yac/suERtlYMeifUz8ZjYrx24gIjR+J7F3IQgC3ae1d5HfFAQHN7/rpK8TdS5vIcb+gfSiFRhXgmkLUuRApIhvkCQb5/f+JStNGfYgnOiImERfq1z9kkw6MIIKjcqQNX9man5ZhTmnx8XblPiPhj2Ed4NclQavUKnjL0lq9GpqfVUVnY8WQVcbIXA96L8ATXVI9QNC4GYEhXzjoyBoEdSFZKlRG6Zt49EN0Ghk5HUR8Bx6EikxKgfbdQ/nMYHJM+3yn4D/VzvkiwevoFS711lNsWaObzlD1c8reH2udZO3sG/FEcR36lCpg1IxYFkvzu39i+HNJjiMJQRQa1QEZkrrUfTfP9A7Ba7Nc3ayYMDvzuAqihInt53jZUgEvv4++KTSU7NVZWq2quw8pnOxvlhlDC60vhrqtqvJnYv3SZ89iGbfNeSjeHash9edkNWvjoNaq0atVfHd3M7ofLRUaVqew+tPyPJy7TY7Zep5z1Vt0rM+e5Yd8sjD9bRDyl86N4GZ0vD0Tmi8YxcUgte0q1vn7sqmz0VRYufi/W7PWS02Yl7FUq15JY5uOIHdZidLvkz0mddVtgZ6YNUxj7rIR9afkKUrbZ67i/k/LEWlUSGJEiaDGbVGhcVkRaNTs3LcRibuGx6vF3JslIHnj16QPnsQPqn0VGlanjFbBrF81Fqe3AohT4lctB/1BflK5U7oI0o0JDEKosfissuTDGC9CKZd+KT2ke8MFxwL4KSgQLl8jNo0MGkD/qdBUxmsV3j782s/8BlnD/nz6mUqTDFmtD4ax29AAKVKhd1qo0rT8vSa9WbHKKjzIaQekezh/HX4KqGPFZzc60+52lFo9W/uZ0lUIyh1jiYvF+gR9Ckhcap/XZuWgRC/SMvfjf9XAdkvjS8KmYlbqVKQOl3iGnr+nLFdtknIGGPm1I5z7Fi4743Lk+TYxT27/xy1ToVa56pvrfXR0qKfZyEEm9XG9oX72LX4ALfP33WZrCVRwmwws2zEGn5a+b3s8T7+8oIHkl3i8+8bed0VG/UiRjawA2T9KDM1W1WmUdePnZ3A3y/oStTLaM7vu4Rod4xZUAiotWp6z+mUKBlQnY+Wuecm0C5vLzf7Sa1eQ932NWSPEwSBsTuH8FODX3j++AVWi012gZAlbybSpPOOApWjcDa0Plq371+pUnisu6rUKtqNbMmAJd9iMVvjlfk0RBtcSg5xsNvsGKLdywS3zt1lQb9lLh3zgPP/FpMVTFYmtJvFoitT3c9rtzOv71K2L9yLUu3g5Db+ti6dx7ehRM0ilKhZxONYE4IkSRzfcobdSw5it9v5uG11qjQr796kZDkJghretReUDEimnTTtVZ/FQ1e7fOZqrYrKTcsnurZutViRJJyc6n8qJMkGCF41OQm+bZGMqxwdyzi+d/8ADQtP1+TIzpJcP3WLrPkyUbtNNTQ6DSF3QwnMnPa9SfFmzZ+Jm6dvM75XdrqNeMLHLSNQqiVCH2nRZxxHYJZ0SBGdX3d2mh1WluqyoG+c/Iur8oIyI9jv45I1EPQIKSRc8r6QIgFZEIR6wHRACfwqSdK4lDhvSqNM3eKoNO43t0qton7HxGlWx7yS6UbE8WPfsXCfx5qlSq0iR6Gs3L344HWjj4VStYqQp0QuJEly2+lJksTgBr9w9fhNj13Coihx5dgN2edehEQ4Jn8Bl3tToRDI+lHmRFFUStQq8roe7RrQdL5a+szr4uQzxkHvp2fczqGE3AvlzO6LPLkVQuogf2p8UYlMuRJPjVFr1Izd+RN9qw/DarY5TTfylszF5309L2gy5crAoqvTuPvXA57dD2POd0uIehGFKdaMRqdGqVYxYKn3Tkp1Wldl2fDVWIwWJy1MqVYSmDmAIlUKsGfpQfcOXkHg3uWHpMsWmKDmdtl6JVk7aYvb961QKigrk1XY/uveeLvr4xBy9xkRYZFu3OsVY9azY9F+Z+AG2DJ3N2nS+/NF/+TV86Z2mc+BVUedWYML+y9zaO1xhqz63vVeFzyVOARQ+NL0u4Y8uPqEfSsOo9aqsVlsFKiQz0nj8wYvn0Uwtct8Tu+8gCRJFKqUnx9+7ZFk7e33Bcn2CClqCFhOAQKStjqC/2gEpWeDCkGRBgI3I8UudMiOKtIi+HZAp6vLx21xq0/nKvJ+Hb+a9/30NY9eYuagbMwZmhWfVErylCzExL2v9ajTHQDjdpAiQFMe1GVTpENfEARIO9/hqSzFvJ73bKBvCdp/tu53smlPgmP5dhP4GHgMnAa+lCTpqqdj/k7a0+3z9xjc8BdMsSYEQUC0ifRZ0JXaX1VN1HkG1f+Zs7svuKWgs+TLSPiTCI/BU+er5dsZHSlRszATv5nNtRO3UGtUiHaR7AWyMG73UBeq1bl9lxjedAImDw1UcShU6SN6zeqIQqEgV9HsCIJARFgkXYr2JSoixiW1rtKoCMoSwKT9IxJt5Tim1VRObjvrnGB1vlqK1yzC6E0DXdV8JIkrwdc5+ucpNDo1tdtUI0fBrIm6lieYDGaObjhJ+OMXFKyQn2LVCyXqh2wymDm4Kpi/jlwlW/7M1PumVoJdx+/i8a0QpnaZx+Wj1xEUAhUbleG7eZ2xmm10K9kfQ7TRZZer0alRqVUo1UrG7x4ab9pXkiQmdpjNkfUnXD7nuh1qyupYj2k1lUNrjiU4ZqVaybrQRW7ayp8FtCNWZoGZJr0/a58tcnlMFEXO7LrIsU2nuXHmNoYoI/lL56b1kOZucrBXT95kQK2Rbg15Ol8tY3cOoUjlNwIikmRFCqsM0ru1bj1CwGKnuUT405fcv/yIjDnTecVKiIPdbuebAt8R+iDcuaAUBIFUAX4suzPLa5/p9w1JNCCF1wYxgjd1UBUoMyEE7U4xStD7hmS9SvitCUQ//4srp/Ssn5+R3CUr8sOiHh/ss5Yku8NbWXwJmtIISu/vl/cBb2hPKRGQKwIjJEmq+/rvQQCSJI31dMzfzUO22+1cO3ELs8FM4coFZOkyCeHB1Uf0rvgTFpMFm9Xu6NLUqRm1aSAjP58kay4Ajol54aUpHFgVzMpfNrhMViqNigoNSzN8/Ztu66UjVvP7qHXxjkWtU6PVa7Bb7UiSRKoAP4av70/wnydZN2WLW5pZpVGy+unCJKWrRFHk4Kpgti7ci91io2GXj6nduqpLh7okSUzpPI+Dq4MxGywolAIqtYouE9t+UNGFDwGrxYpCoXB5/+FPX7Bq3EZO77zAs3thbs1raTOkZuXj+fFypyVJ4tSO8+xbcQSFQuCTdjUoWbuo7MLj0JpjTOo4R7au/TYEhUCJmkUYuXGAc5cuiiJ1VfLd5UqVgp2WN4bwJoOZfjWHc+/SQ5cMkKAQ0Oo0TNw/nALl8vHoxhMmdpjD9VO3PNbt/QP90PnpHM5nxXLQsn8TCpYxI73siFMFSrKBX28Ufp3jfV/e4NSO84xpNQXjOyl/na+WLhO/5tNuKSdBmRxIhnVIUWNwox8JvgippyDoaiZ4juiIGJYNX8OhdcdRqpTU7VCTrwY1TRGlQG/whqdsBkQkSQmCDkXQWgSVd6yK/yI+FA85C/Dorb8fA+VlBtMF6AKQPXvKpEuunbzFxpnbiXj2igqflqF+x1pOHmh8iH1l4OntZ4iihDHGlKSAnKNQNuZfnMS6KVu4duIWuYpko0W/xuQolI32o79g4cAVLgpHABq9hk/a1SBznoxsnbfbbedgs9g4se218IimAAAgAElEQVQsFpPF+eNJmz4NWh+NLJ9XpVE5n4t5q+HFFGtmYJ1RZM6bQb6ZS6/l/uVHSbKkMxvMHN96lusnboIgsHjISvwDU7lIjP516CoHVwc7A4TdJmG3WZjfbxlVP6+Q6N3oPxnvGhjsWnKAX39cQcyrWERRlO0kNxnMXD12k6JVC3o8ryAIlKlbnOwFs+Af4Idvas/NKFWalWfz3F3cPHMHU6zZKbwiipJLmUISJS4fvc7cPkvou9Dh/61QOKhXD648cjtvnhI5Xf5ePWGjWzCOO6/JYGZe36X8vH0w31UeQkxEbLyykFEvYoh64eiOfnY3jDO7L9JvUQ+qtwwGczBIsaCpiKB0VzFLCp7cCnEzSwHHb+Xh9Scpco2UgGS7gzsXGJAsYL8HxB+QLWYrvSoMJuzBc2eT4tqJm7h85BoT9w1/L6ItkmQG8zHAjqQu/1qc5A0LQxDsgAEpeiJC2vkpfv3/Ej4Y7UmSpAWSJJWRJKlMunTJdyjZ/ute+tcewYGVwZzff5nffvqDHmUGxkvHATiwOpgvs3VjZq9FzP5uEW1ydmfHon3xHiOHE1vPMrb1dI5vPkPW/Jn4cnAzchRypOw+69mAAYu/JXvBrKh1anz89eQvk4cBS3rSe3YnAKdE57uQRMmlHlijVSVZlR6fVHrmnp1AuxFfOLmqb8Nud+za5X6ANouNQC+dh97F6C+mErzxFFazDavJSviTl4xpNZWbZ99I6R1ef1w2Za9QKTm980KSrguO5rZDa44xo+evrJqwkZfPvDP++FAI3niKmT0X8SosEpvF5taBHwdBEDDFxl+C2PfHEVpm7ETnoj/QImNnx+7OwzFKlSMN/v38rlT+rCx12lZjyqHR+KZ2Tw1azVb2/n7YhQr27fQOaH00TunVOApc96kdXI7du/xwvHzuG2fusP+Po1hMVq81muNgNliY2fNXRFGJoKuJoG+UYsEYIHfxHKjU7hkJvZ+O/KVTvms8qRDUBeXr6YIaVAm7zh1df4IXIS9dGAMWk5Ubp29z7YRnEZqkQjIHI4VVRIr8ASmyPzyvDHY5JoQElsRRS/8/IiV2yE+At4tHWV8/9t5gMpiZ+/0Sl12j2WAh7NELts7bTcv+TWSPe/ksgknfzHHrhJ3V+zdK1CridaPRlnm7mN9vuTPoHFwVzImtZ5lzZrzTPL56y0pUb1nJ4znK1S/JwdXH3HZQ2QpkRqFUsHrCRo5sOEmqNL60GdqcDdO2ERtthLdS0jkLZ+PQmmOyu2er2UaBcvm4f+WRy/MqtZJ8pXN7ZXL/LsIehXPxwGWXDnFwqDqtmbiJIav6Ao5do5wOtiA4dvVJgTHGSJ8qQwm5G4oxxoRGp2bF6PWM2zWEwpWSbo+Zklg2wrM859uw20SKxLM7vnjoClO7zHP53o7+eYqYVwbG7Rwie4xKraLWV1Wp9VYvhKeOeJvVIaMZt9ArWasokw+O4vdRa7l/5RF5iuekzbDm5C3hKgCS0O7KP8CPxzefev4M3mksfBcWk5XQ+8/JnCdjvNfxBMl63UGTUqQHbVUE4c29VqxaIbIVzMK9vx46F7xKlZJUgX5Ub+Hugva3QVcXoqc4dsROAwcNKLODJuFxXj1xC1OMDCXPLnLz7N1kWcm+C0mMQnrVw+H05A0UKZ8Zk+xPkGKXO3S41cURfFojKFPWkvJDIiV2yKeBfIIg5BIcQqGtgM0pcF6PuHX2Lgql+9AtRgtH/zzp8bijG07Jmg2JNjuH1hz36tpWi5Vff1zhMumIooQp1szyBGq9b6PTuDb4B6ZycijVGhV6Px29ZnWkZ/lBLBu5lhunbnNm90WWj1pLvY41mX50DDOO/cyK+3OdfNKi1Qqh83Pv2lVrVdRqXZX+v31LqgBHvU6tVVO8RuEkG5qHPQyXFeeQJIknt545/67Tpprs60S7SPmGpZJ07bWTt/Do5lOnOpjFZMUUa+LnL6clejf2vhD64Hm8zytVCrR6DX3mdY6303rV+I1uiyy71c7Z3ReZ+M1sr99viZqFZYNo/tJ53PyxPyqTh9Gbf2T5ndmM2NDfLRgDfPx1dTR6eaqQ1kdL876f8lGZPOjl7kedOsFuZrtNxC9t4nmikmRDjOiJ9KIlUtTPSJF9kZ7XRLI9dL5GEAQm7RtOwy51SBXgh4+/nlpfVWHWyXGJqq0+vvmUBQOWMb79LA6uDsZmTVnLUEHQIgSuA10Dx05Z8AN9M4SAFQhCwtN1lnwZZXnZSrWSjDnTp+hYMe0FSW5GVeAg3LwNPfh0kHlt4iHZHiJZziCaTyKFNwTDcrAEQ+yvSOH1kWz3UuQ6fweSvUOWJMkmCEJPYBeOb+E3SZK8k51KIvzS+npUeUod5O9U3lLr1NT8orKT2mOz2GRVm0S76BVtBCD0QbisaINoF7l02GNjuRvSZQ3kt2vT2LFoP1eCr5OjUFYadfuEk9vOEfog3KX+bIo1s2biFpr0bOBGWSlZqwgFK+Tj6rE3tCitj5aStYtSsHw+ClXIT5Vm5Xl65xl+af2SZTeYo1BW2c9JpVa51EPzlsxF2+EtWDZiDcJrZybRLjJkVd9EdVg+ux/GshFrOL//MpHPo2SvHfUimqd3niVpx5/SyFU0u6xhh95PR+Wm5Ugd5E/9jrWcpQ1PCL3vObAfXBVM8eqF+aRdjQTH031Ke64eG4zZaMZqtqHSKFFr1Hw3N2lNUi37N+bM7ovcvXj/TQPZa9GbRl0/5vO+jbBZ7SwZthqr2er0S1Zr1eQpnpP2o75geNOJsjtotVZF2XolktRoKBlWgvkITqlECZCMSK++Qwj60/k6vZ+eb6d/w7fT3aUgvcHhdceZ0G4WNqsdu83O0fUn2TB9G5MOjExRTrOgDEJIMylJx9ZpU42lw1a79KcolApSpfWTpcwlC1IM8jaMIihyghgCgsax2/dpieDTJnmXE185msWsl15z1mNwTblYQLIiRY1FCFiQrGv9XfhXuj1JkkTnon15dP2JS3DU+mgp80kxzuy+iNVkRfHaAenb6R1o0KkOT26H0KXYD251MK2PhmlHx8juCt5FzKtYWmbqLBscilQuwNQjozm2+TQLBiwn5E4oAZnS8PXwll7znH9qNJZT28+5Pe7jr2fg0l5UalLW7bnnj8OZ1fs3rh6/iV9qH5r3/ZR639RKsldwfFg4cDmb5+xyTsgKhYDeX8/Cv9xtBMMehXN6x3k0Og0VG5dxo9vEh7BH4XQt0Q9DlDF+0wydmsXXp5M++9+fprocfJ0f64522d1qfTT0mOa4/+IQEfqKOX0Wc2zzGQSgyucV6D6lndNtamrX+ez8bb/H952vVG7mnBnv1ZhePotg8+ydXD99h9zFcvBZz3rJ+qwkSeL8/svcOHWbVAG+5C6ekxwFs7g0nUW9iGbR4D84sv4ESqWC2m2r0W7kF2j1GqZ0msue5YeRJAlJlFAoBJRqJcWqF2bomsQt2OIgPm8I9lsyz2gR0u1BUCYtBf42LCYLzTN0dOvS1vpo6Trp/XdpW0wWfh+zjl2LD2A126jUpCwdx7aWXWDfu/SA8V/P4sG1xyBJFKr0EQOX9SJ9Ns885qRAst1FCm/CuxraCHqEtL+CMptD0lOVy8GTTup1JBuSYT1Ejwe8kUnVosh4KcnXe1/4ILSnpCAlaE/P7ocxqN4YXjyNQFAosFmsfNK+JnuWHXJbgWt0an6/P5e06VOzfNRaVo/fiNX8Wq1Hr6FRt48TZYH3S+vpBP950iWwa320DF3jqKGObjn5nUlZ66D8dE+YlD6lyzx2LT7gNhnrU+n4ZftPLtxNcJhjDKo3BrvNjsVkRe+nI3PejEw9MjpBAYqkQJIkdv62n7WTNxP1IoaStYrQYcyXSa77ecLMXovYNn+PmwjJ2xAEgVxFszP/QtJ2E+8Dl4Ov8+uPv3Pv0kPSZQ3k6xEt8fH34ffRa3l2L4y8JXNx69w9Ip9HOd+bQykuNWN3DCZ3sZyEPnhO52J93Sb/OGTOm5GlN2d+yLeVbEiSxMjmkzi7+6JzMafRaSj1cVF6zeyYrEWC+LwO2B/KPKNDCNqKoEo+q+Ovw1cZ2ngchij3emnRqgWZcmhUsq8RH/rXGcnVYzecc45SpSQgUxoWXZ3m8Xce9SIapUoRb4d+ciFGjQPDSpxd1YIPaGs6KFrJ6OiWRAPYbiEpAiH6Z0fnPfE3QjohpEWRwXPp8u/Cf9p+MWPO9Px2bTq3zt0lMjyaAuXysnzUWjeqEThSNqe2n6Nu+5q0HdaCCo1Kc2DVUew2keotK1EoEZZrkiRRu3UVrp+6zbN7oag1KtRaNZ3Gt6F8g1J0Kf6DW/3PbDCzbPhqPu32SYI3aeMeddn/xxGXcwgKgdRB/m7NS5Ik8fOX01xcl4wxJh5df8KGaVup+GlZDq8/jiAIVGte0St1HovJwu6lhzi4Jhi/NL407l6XUnWKvRmLIFC/Y223Hf/zxy9YNnINZ3ZeIFWgHy36NqZO22pJ/lH+deiKx2Cs1qpQaVTofXUMXftDks7/vlCkcgGmHRnj/PvA6mBGNJvg/D7lbC/tNpGXIRF8W24QJWsXZcSG/sw5M54uxfq5ZWLUGhVVP3djFaYI7v71gJVjN3Dv0kPylc5Nqx+bppiYy6Uj11yCMTjutbO7LhLzKjZ5GQ5dI4hdhNtOTRHo2KWlALQ+Wo+carmaeUrixpk7XD95y2UDYLfZiX4Zw4E/jtKgcx3Z47zVx08OhFQDQVsdybgBsCHoPgVtjWQFYzF2EURPB0H1WkrVjvemEzrwaZXka//d+NcGZHAEh/yl34jlK5UK2U5OQRBcmsDylcqdJIF8SZKY3HEOh9Yed/A9FY6brlmfhjR6Lfj/9M4z2WNjXhkwxZoS5EnnLZGL7xd0Y3r3BQiCgN0mkj57ED9vHeR2kz+5/YxoGfcfi8nKhmnbWfnLn29xETfTesjnfDmomcdrWy1Wvq82jIdXHztpWWd3X6Rl/ya0HdbC43ERoa/oVrI/sZEOB6TwJy+Z8e1C7l99ROdxbYiNjGXr/D2c3fMX6bMH0bR3gwRddTLmTM/9y+7cWJVGRdthLchROBvl6pd0a076J0EUReb2WSLbBS8Hm8XGxYOXWTZ8NZ3GtWHEhv4OO02LFbtNROujIU361G4sghtn7rB13i4iw6Op1KQctb6qkuia5l+HrzK4wS8OO0JR4tGNpxzdcJJJB0bGa0jhLc7sviArXGK12BjbegbzLkyMVyhFFEUHv1pmohd8OyGZdoP41GFIgQYEFUKaySnGu81XKhd+aX3dLEd1vloaJTJd/fzxC0Lvh5GtQBZnmSI+3LlwX7Y73RRr5trJWx4D8oeAIAigrYigTZlOdcl0AKJnAKZ4O/JdoXTszCULaKsj+CXOavSfhH/ubJYE1PyyClvmugtu2O2ii3BFUnEl+LozGIODM2wxWVk9fiN129ckQ450ZMqdQTaQ+KbWo/VSgKT2V1Wp2qw8t87dwze1DzkKZXWbWERR5NH1J9hs8l2eUS+jXW5os9HC76PXUa1FRY8NUAdXHePhtccuHGlTrJmV4/6kUdePPQp6bJi+DWO00UW/2RRrZuOM7TToWJv+dUYSGR6NxWhBoRA4uCqYgct6xeuu9cWAJpzff9mt/CDaRfR+Oio1dq+l/9MQ/TKGmFeJswa0GK1s/3Ufnca1oVz9ksw9O57Nc3bx7H4YpeoUo16Hmi6Luq0L9jCv7xIsRgf39+TWsywbsZq5Z8eTOsj7Br5ZvRa5Mgde+w3P7bOYaUfHxHOkd/APSIVKo5I1zXhyO4RDa45T68sqbs+FPQpnRo+FnN55AUEhUKlJWXrN6uRSOxUUfhC0EUy7kCynQJkFQd8MQZn0rmJDtJF9K45w+/w9chfLTp021fh522D61x6J1WxFFCXsNjsNu37s9dxiNpoZ2Xwy5/ddQqNz6HHX71SbHtM6yGoNxCFT7vTOxf/b0Og1ZCvg7hb2b4YUu5C3RUXihxKE1JB2JoIYDaq8KVKe+Dvxn/JDzl86D18M/AyNTo1aq0bro0Gj1zBgSU8XfeikInjTaVlBD0EQOLXjPADf/PyVi4csOJyK2gxtnih6jkanoXClj8hZOJtbML594R6tc3RnbOvp7iYGOCgOggzBSxQljm3yXLs/tvm07C5GrVZx6cg1j8ddOHBF1rpQrVWzZNgqXoVGOksJoihhNlqY2nV+vPXhIlUK0mdBF959G6JdZOGPKzi9K+kCIx8KPv56hHgmWk94u+yS7aMsfDv9G0Zv+pGmvRq4BGNDtJF5r/n4cfeWKEo8f/SCTkX6JiiSEwe73c79y3I1WLhx+o7s44lFjVaVZSmHEOdnfdDtcbPRTK8Kgzm98wKiXcRutXNs42n6VP7J7d4RBA2C/lMUqUej8OuW5GAsSSKht7fTPl9HFvT7je0L9/LrwBV8na8XWr2GVY/nM/iPPvSe3YnF16bTbVI7r3bhdpudbqUGcHrHeWwWG4YoIxaTlR2L9rNx5o54jy1eozCBWQLcmjRVaiV12lZjzcRNtMrahUZ+bRhUbwz3ZVTX/jUQw718oQJ8WiMEbUOhKYugq/WvD8bwHwvIAG2HtWDhpSl0GtearpPa8fu9OSlG/Nf5aGTTagqlwsn9q/hpGQYu7UXGXOlBcAj0Z8yVnrl9l9JQ/xVDG48j/OnLJI/BYrIwoPZIwp+8xBhjcqlrORYhWrLmz4xCRr1LoRBkVb3ikCadvyy/GyBVgOuCRpIkbpy+zc7f9uOTSucWOMGRjrx+6rZssLZZbLKSheFPX/JL62k0Tt2WKZ3myaatLEYLaye9V6q7LMIehbP398Oc3HYWqyVhmpxao6Z+x1qJuoagECj9SXGvXnvl2A2UMupTAJHh0Wydv8er8ygUCnQeGoP80qaMEUBQ5gDajfJc25NjBBxeewJjtGuXvd1mJyIs0rkATklIkgnpZSvmfDeTyBcWTAZH0DcZzES/jGF694Wo1CrKNyjFx22re2XMYrPaMMYYWTx0JY9vPHV73mK0sH7atnjPoVAomHJwJKU/KYZKrUSpVpKvVG6mHh7NsuGrWTZyDS+eOgxtzu65SO9KP/Hsfli853z5LIKzey7y+FZIgu/hg0JTFfnErQDoHalpRXqEgD9Q+A/xSs1NksxI3oqX/M34T6Ws45A5T0aafdcwxc9bu3U11k7a4rY6F0XJhY5U9fMKVP28AmaThfb5evHw+hMkUcIuOgwDvqv0E0tuznDTQfYGJ7edwyazK1aoFJSsXZQOY1rh6+9DpyLfy/r+pk7nT6ei3/Po+lNSB6Wi3ehWNOhYG0EQaNj1Y0eX+ls7NEEAnZ+OYtXf6F4bY00Mrv8zt887CPiSKLmV7lUaFQXK5sVqsfLsnvvkYLfZ3WhQxhgjPcv9yKuwSNmd/9t41xPZG4Q/fcmTmyFkyZeRoCyJk2VcNHgF66duQ6VWIigEVBoVE/YMS7AW3n1Ke07vPM/T26Euj2t9tDTtXZ9Ns3Zis9qxmq1odGp0vlq6Tfau49/XXy/7HYPjOwn+8yQt+yXsLysIAp92/4RNs3a6fPdaHw3N+qTc76h530asnrCR6JeuaXyHk5X7wuXhtcduNVtw9Eg8vvEUPDtuJglS7CJE81VO7/8I0f6ODaooceHAZURRjDe9HAdjrIlZvRZxYGUwol2U1T+IQ0xEwmWNtBnS8PNWB6fcZrXj6+9DRFgku5cdclHOkyTHon3tpM30mtXJ7TyiKDKr1yJ2/nbAmTYvUD4fIzcO+Ec4Xgl+XZFMW19zjOPelx5SDUHQlADsoMrvlUiKZA9DihwMlmOAhKQuhpB6LILqnyOV+i7+czvk94ms+TPTfVp7NDo1ej8d+lQ6dK/pTnIc2+A/TxEbaXBZ4Yt2keiIGI5vThrtK+pFNKLdfRIWbSIZcgSRt0QuMuXOQNfJ7dC8doHS6jVodGqa9KzHpA6zeXDlMaJdJCI0kuldF7B0mMPRJ2+JXPSa3QmtjxYffz16Px1BWYOYsGeoS2Zg8U8ruXHaYWRgijVjNlpQKBXOMoFaq6LMJ8UZuXEAn/dphM7XtXauVCnIWyq3G2957+9HiI00JBiMAZcFQkKwWW380no6X+fpyfCmE/g6by9++WqaV7tcgNM7z7Nx5g6sZivGGBOGKCNR4dH81HBsvBMtOHZ+c89OpFiNwmi0Dl1ztU5N3Q416DDmSxbfmEHLAU2o9FlZ2gxtzuLrM7z2qC5QPh8+/p6bBN/NasSHDmO+pEaryqh1anxT+6DRqanboSZfDEieH/LbUKqUjPxzAHo/HTo/ndMcpVrzilRpWs7t9bmKZpftYNbo1OQonDLd029DMvyJQmFBpZIvLXnShpfD6JZTOLAyGKvZit1m99ihDSTK5EWr1zoD5+MbT2Wb9+xWO9dPyvGyYcu83exeegir2UpspAGz0cLVYzeY0mmu12N4nxCU6RGCtoLP16AqAJoaCAELUfi2QFDnQ1AX8C4YS3akl60cCl7YADtYLyC9aIEYNQ4xagKS5eJ7fz+JxX9yh5xcvAiJ4NqJm6RJn5rClT5y+RE27PwxVZqW58yui6jUSkrXLc7diw84vP4EZesWd6nxPbkZIrvCN8eaeXwzaamiYtULuXkwg2MXW+aTN0o8jbvXpVLjMo7ALziaYQZ+MtqpnhQHSZL4Y+wG2gxvgUqlpG77mlRrUZFrJ26h99NRoFxet0lo99KDbnQcu83RBbvgyjRSpfVz1uyrtajIrfP3+HP6NlSvfZ8z58nIMBm60s3TtxO0EARH6v3r4Z67vt/FkmGrObbxFFaz1Tnu4E2nST98DZ3Gtk7w+K3z98iOyxBt4Pqp207a3OObT7kcfIO0GVJT5pPizjSsTyo9k/eP4OmdZ4Q+eE7OItmdTUmBmdLSfqS8/WFCUCgUTNg7nG4l+8kuYq4ev8mz+2FeSSaq1Cr6LepB5/FteHb/OZnzZEhW30XI3VCWj1rLpcPXCMoSQKsfP6N8w9IUrVqQlY/mcWTDKaJfxlCqTlGPWYYqzcqzaPAfWExWZ1ZKpVGRLmsgpT8pJntMcmCKtaDXQ82mEexZE4DV8mbiVygEqjWv4FVADrkb6tB890L9T6EQ6DyhbZLGmzFXeiwy11AoFR4XLBumbXNrlLRabBzffAZjrOm9aBckFoIyHYL/wOSdxHLktaf02/OdBFI0GBY7/jL8juTTAoX/0ORdKwXxv4D8FiRJ4tcff+fPGTtQa1VIokSaDKmZuHe4S70odZA/tVtX5caZ23yZtavLZN2o+8d8N7sLADmLZEPvp3MLylofLTmLJG2Fn+2jLNRpW439K444r6v10ZKvZC7KN3LViQ7KEsinb4mRPL7pXsMCRzrur0NXKFXbMcnpfXWUql3U4xg86ffa7SIZsqdzqQcKgkCnsa35/PtG3Dp7l4BMachTPKfsxJajcDa0eo1bl/zbUKqUDFnzPYGZAjy+5l3IWV1ajBa2zN3lVUCWW1QBSCJM7TKPxzdDUCgEbDY7ao0KhVKJ3lfL5IMjyZr/jSl65jwZU1xAJWfhbPx2bTrdSg3A+E4TV+yrWMa2mcH0RHRJpw7y94qKA3B2z0V+/XEFj248JUOOdLQf3YqqzcoTci+U7qUHYIwxIdpFnt0PY/QXU53iOL6pfanXIWFfX41Ow8wTvzCnzxKObz6NoFBQvUVFuk7+Ol6KVFIR8rQiWTJvpNPQEG5f9uHhTS2S5CjGqLX+9JzZ0bvz3A1FrVV7cMaSiONmKlUwZEkusiexUzpd1kDK1y/FqR3nXK6l0ao9GuzEvpL3aUdwMCP+CQE5RWB7CJKnBVHcjsYEhnVIusYIGu/6Nt43/heQ38LRDSfZPGeXy07KbDAzrMl4NzUoi9nK91WHua2Ct87dQ74SuWnQuQ4VG5chbYY0WM3PnTtTlVpJYJYAyjdImskCQJ+5XSjzcXG2LtiDxWihdutq1O1QI8FJSqNVY/RQc/SUJo6NjOXRjacEZQ0kKLMjCJarX5LgjaddUvGCIFCkcgGPcp1p06emXP2S8Y7vk/Y1WPHzegcX9vVvRqly6PDmLZ2b7B9loUnPeokOap4CqqfH30XNVpW5duKm2y7ZFGtyo7iZbY7Ab4oxMeyzCSy6MvW9eNC+jcx5MuLjr3MLyKIocfP0bWJexSZKttQbnNl9kRFNJzgXOg+vPWb81zMwG7tyYf9lZzCOg9lgZtGgFdT7plaiONIBGdMyZNX3KTp2j9fK8z23T+8hd0EDUzfd4kKwHw9u6Nj2eyY6T+rhdcYgR+FssrtjpUokUw4LggLyFjHy5Xeh5CgQf/PVu7h94R5//LKB+5cekqdETr4c3JQ0GVKze+lBbBYbWfJl4ru5ncn5zg5ZkkQw/kmp6hEc+lOBKLrekwGZ0pImnXcLsX8F1AVfC4skpAFgQjLt/F9A/qcg5lUs+/84ytO7zzi947zbpCuKEk9uh/D4VoiLW82xTac8pqSWjlhDg851UKlVzDj+M/P7LePI+hMOxawWFekysW2ydKYFQXA2jiUGtVpXZZtM561CpaBQRVe1MkmSWDJ0FeumbEGlUWE12yhbrwSDVnxHtyntuXTkOsYYI2aDBa1eg1qnps/8rgmO4XLwdbYt2ENspIFqzStS44tKTnEP/4BUTDs6hqmd53Ht5C0EhUDFRmXoM7+L17s2OXxUNq+sF6x/gB87Fx+gZqtKaPWeOeJ12lZj15ID3LngMFVQqhRIEk49ZjlIkkTYw3Ce3Apx2SW/L0ieStmC4AyMdrudc3sv8eJpBAXL503Q5CI+LByw3C3rYDZY+HXg787ShNsYJYmQu6Eppv6V0gjImJE914aycsZv5CkcRXiIitMHMpC/TOFE6RgEZkpLra+qcmDVUacojCBI6Hwkxq+5Q1CmtzJMgnfaBPCWeIvRQXN7fCuE41vOMmHvMHrO/AabxZd4uT0AACAASURBVObxPpaihoFpC+0H2DizLz8mgwKrReHs/ei7oNt7Xzh+UKjLgCo/WK8C8QVlhcOo4h+Cf62WdUrg3qUH9K0+HKvFhtngUN6Sm2B9UumZuH+4iyrYmkmbWDjgd9nzavRqtsX+8d7GnVQYY010KNDbId8YtwNVK+k6sS1Ne7t20+5afICZ74hFaHRqqrWoyMClvYiNMrBn2SFunrlDrqLZqduhZoJOPasnbmL5yLVYjGYkydFdm69UbibsHeamuGUxORrFvFHiCrkbyqrxG7l+8hbZCmah1cDPXIxCbp27S98aw7GaLG6ZAJ2vlqAsAcw8MTbeXaTdZufon6c4sfUMqdP5c3LbOVkay9vQp9Ix9fDoBDuxUwJz+y5hy9xdLh7IggB5SuRi7tkJPLsfxg81hhMdEYtkF5EkiQqflmHQit5JSv820H8luyBVKAQKVsrPlaM33J5Ta9Wsejz/g0g6JgdXT9xkx6J9GKNNVG9RkUoNfRDMK8EeDto6CD5NEIT4U7t2u511k7ewceYOYiMNFK9spvOQG2TN83YWQwd+fVD4eec+1aVEP+799cDt8Y/K5WXWibEej5Nsj5HC6xMnLRrxXMXGX4O4dDIVWQsUpMWA79wWZy9CIgi584ws+TJ5FAT6p0MSDUgxM8C4EbCCFIu7BKcOIXA9gjrfex/Pf9pcIiXQpfgP3LskL4jwNnzT+LAudJFLcHh88ykdCnwn+/qPyuZh1slxKTbOlIQx1sTORfs4+ucp0mZITZNv67tYJ8bhm0Lf8ei6e8BRa9VseLEYnZeqY3GICIukTc7ubnU1pUpBxtwZKFGjMJ9/34hsHyWunnb/yiN6VxqMxegItoJCQKNTM2rjQErVKcb9K484tOYYkeFRRIRGcmLrWTe1KLVWRbM+Dek01nt7uHFtZ3Bg5VFZK844+AelYk3IwvdS73wXsZGxfFd5CGEPwzHGmND6aFFrVUw7MpochbLxbbmB3D53z80drfOENjTpUS/R12uXvxdPb7vLxKYK8OPH5b0Y1WKK22KufKPSDFvjWXvcbrdz8eBVIp69omCFfCleb08KRMM6iBqFY5clAnpQZUcIXIMgxC+D+zZO7zzAbz/O4MkdBRlzWGk/8DkVGpVCSDMdQUh40SmKInVV8s1/SpWCnZbVHo+VjNuRon56HZDegfYTFGlnOf+0WqxM+mYORzacRPO6Dl7rqyp8P7/re3GP+5AQjTshcoBjpSpJgASpvkfhmzQ7zsTiP20ukVy8eh6Z4A7Hkc5R0XdBN7edWtb8mSleszAXD7haPwsKwavUbUrDbrPz+OZT/NL6EZgprcfX6X11NO3d0G1H/C6iwt01sgEQwBBlSHRAvnT4Kiq1yi0g220iT26GEHInlH2/H2Hc7qFuJhrxYX6/ZZhiTM6asyRKmA0WpvdYSINOtVk+cq1DmESSUKlVshkQq9nGoTXHExWQW/3YlOA/T8kqt8XB11+PaBc/SED2Te3LvPMTOb75DNdP3SJznozUaFUZX38fwp++5P7lR26LB7PBzNZ5u2UDssVk4fnjFwRkTCOrv95u5BdM6TzPJehqfbR89VMzytUvRbcp7Vg4YDmSKGGz2infqDT9F3vWGA65F0q/miOIfs3JtVvt1PqqKt8v6OoV7/d9QBINEDUaV5chI9geIBnWIvh+7dV5Tu04z6gWv2I2OOaQe1e1/NItFwOWtqFac++mYIVCgY+/XtZtKsH+AGV65IWhVaB0LR/89tMfHP3zBFaTzclvPrgqmPTZg/h6eEuvxvpPhUJfD0lbHkz7AJvDBCMFrDlTEv9vecgKpcKjdnmqtH5UaFSahl3qMPPEWKo1l1f6mrh3OF8OaoqPvx6VWkmuYjmYfWqsV77KKYlDa47RPENHelUYTNvc39Kv1ggiw6OSdc5i1QvJ6uf6B/glKYXl4+8jq+YVB9EuYjKYmd49ccbiV4Kvy9LAnt0LY+lwh1G7Q5hBwmK2epTrjFNa8xY5C2dj8MrvPCqbAUSERnJ47YlEnTc5UKlVVP28Ap3Ht6Vhl4+dfFWrySr7XYJDO/ttSJLEip/X83m6b+heagDN03diVu9Fbp9brS+r8O30DqRO549SpcAvjS9fj2jJ530aAdCoy8esC1vE7NPjWPVkPsPW/BBvB+/IzycR/vgFxmgTxmgTFpOVg6uD2bv8cHI+kuTBegkEucWUCUw7vTrF5eDrjG0z3d0BzmhlQf/liRpO094N3GR5tT5amr3+zD1CXRoU6YF334sKwedL51+iaGPbvK1YjK4ZJLPRwqZZ8ct7/lsgKNIi+DRH8Gn1jwvG8P94h+wfkIr8ZXJz/cQtl52DRq/ms971XVaDVouVNRM3s33hXqxmK1WalafdyC9IHeTPNz9/xTc/f/V3vAXA4fQz8ZvZLj/4K8HXGdJoLDNf15Ue3XjC76PXc/3kTTLny0Trwc0oUsU9Tf02vvmlNWf3/MX/sXeWgVEdXRh+7vpuBIK7uxQobsUdWqBIcYpTihSKlFKKa6EtUGhL0UKLFdfi7u7uFgiBkGT9zvdjQ8qyd+MJ8vH8y2Z37mx2c8/MmXPe1xpuw+lwIkkuMfte0zvHqfijaJWCEcpkUUvY3Tx3B5vFhs4QswDpl8JXsVo6qjlKkuSmK6436WnQPfYG82f3XUStVikWMIGrjeTIvyeo1qpirMdOSNJlT4N/Sj8ehburm2n1Wio1c19sbpi1lUXjVrgVN26cvR2jj4GOr7SI1elYjdodqmIJc6XIX97JCiH4d94OFo9fSXBgCHmK56DLxLaKzlEPbgRy+8Jdjx28JczKql82UrNd5Sjfn8PuwGq2YfIzJmxhksoXr7Z/qugXpX8MWsCqXzZ67a1/ePMRTqczxhmUNkOb8jTwGZvn74w06qjdoQrNByq3OL1AkiRIMR/xtBfYzwMqUPkgJZuApMka+Txh3ogl/EVbljthCjvztxVh3YcInQyOa6DOjOT3FZK+8uueFvB/vEMG+GZBbwLSJcfoZ0Sr12Dw0ZO3ZC4+G+iuTjSs8Q/8PWY5gbceE/zwGRv+2EqPkoOiTFcmFf/8uNZjl+OwO7l+5hY3z9/h+plb9Cg5iB2L93Lv6kOObDzBoFqj2LMiagPvTLnT89uJH6jTsSrZC2ehXMNS/LBtWJxdljRaDeM2DSF5an9Mfkavu+UXWr0xpUnf+h4uWjqjlvxlcisaO6g0KnySmVwqaz56dEYdZeoXj7WFHsCxzacVdbpfoNGpPdTIYsP2RXvpVLgvjVN/zrf1x3D9tGdBT0yQJIlvFvTG4OM6VwaXkEzabKlpPsD9Zv732BUeAcQabmXltA04FRTiJEnC6Gv0SCsvGLWMGV/N5f61QCyhFk7tPMfXlb9XfA/WcKvXTIMlzHtrmtVsZXLnGXySrC2fpupA+7y9OL7ttNfnxxpNAVClwvPLakQyRd2/fvviXVZMWR+l0E2y1P6xOs5Qa9T0+bUri+7+zqTtw1l8byZfTukYozEkdTpUKZcgpd6MlGolUuo9SHp3dy3Jto4cBZUDb76ScXfOig/CcQf52XDkx42Rnw5A2D27JWI1nnU3Irgb2E+55Dkd5xHBvZDNmxJoxvHj/3aHDC7P3QXXp3Ng7VECbz4mT4kcFCyfz22VffXkDU7uOOPW4uGwO3n2OITtf++hTsdqiT7PO5fv8/vX8zmx/QwmfxMNv6xN0/4fo1arCbz1SNFFSqPV8OR+MMsmr8ESZnFL61rNNqb1nEX5hqWi3FGky5aG3jO6JNj7yFUsO4vu/s7p3efZMHsbe5YfdHM20hm0VG9bKVY3qU++rMOD64Gs/W2zS4zBaqdM/RJ0GteaTgX7eDz/RSvao9tBPLoTRL7SuePchpMmayquHL+mmDIHUGs01OkUt+/HsslrmDt0ceQZ7eENxzm18zzTDo6JU7tS4Yr5mXPhZzbM3saD64EUrVyISs3KemQingY+U3y93WrHZrFj9In+s7GEW/l7zHK3im9wfe/mD1/K98u+dns8U94MGHwMHsFLZ9BSqVk5r9cZ3eInjmw6EXmde1ce8N3H45m6fzTZC2f1+jpvCCE4vu0Mm+fvwOmQqdayAiVrzoTgz0E8AySX2IRvdyR9+SjHOrzhhNfvBbgc4Fp9+2ms5wi4KeHFlijTtJKenmPvMqhZDmw2CdmpQq0WaPWCL36IXsgloRGOK4igpiCsgAMc5xCWTRDwO5K+dNzGfD4e95oAXD+HjgNjLaWXJCn/1wEZXDfoCo28f7iXjlxVDFqWMCun95xP9ID8+N4Tviw9iPBnZoQQmEMtLBi1jFsX7jJg7pd8WP0DLh+95lEsZbPayVk0G+f2X1K8MYQEPSck6Hm8+nvjglqjpmiVQhSumB+NRs32RXvRGbTYrXY+rP4BX/zYnuunb7J7+UHUGjWVmpaNso9XpVLRfuRnfPpVfYLuPyVt1lSkSOcqauv9axd+7va7a6csBEIIukxoTea8GWNdza1E034fc2TTSQ8pQpVawje5LwPn9yR99pjpUr+MzWpn/rAlbuMK4doRzh++lO8W943TfFNlTEmb76KWHE2VMYWirKvOoGNsy58p06AE1VpViLJv+8S20x7BGFwB79IRTztHtVrNwPk9GdZ4Ik67E4fdgcFHT+rMqfj0K+Xz0WunbnJgzVGPxajdamfJD6sZOK9nlO9Tid++nse637dELgz2rz5M+UalGTB3K5LjBMhPQVcMSRW9SpzepEOtVqGkVKAzaGk7vBkNe9aJ9RwTE8nYlPzFtzN142WWTk/NtbNGchcx07SHmYyl6iKcD0DydflPJwEiZCyIcP4rSJMBMyJkKFLqOO5oHdeUH3feRQgnkmLNQNLxfx+QoyNtNi/m4AYtmRUChSy7NJ0T6ixrxc/rsb3kdwsuAYYdi/fRYXQLGn5Zh3W/byHkcUikGpjBR8+nfevjn8KP5KmTERrs2e4gqVQY/WLetpHQqDVq+s/pweejW3Dr/F0y5ExLumxpmPv9Ipb9sAa7zYEkSfw1ZjkdRreILBZ6mfvXHjKh3TTOH3IJ6Rcom8etkrdm28qUrFWUfatcqmJlGpSIVwr5VQqWy0uf37ow7UtXv7bTIZM8jT+dJ7SmasuKca6uDrz5SPFxIQuvpgEJQVhIOA+9XNscamH/miMc33aa5T+vY+qBMV6LtKKyRvRPqXwzL1GzCDNPTWLdzM0E3nzMh9U/oEqL8l4D/4T20xQzQ7JT5tZ5T1vP6Lh5/g5rft3slrGxhFnZu+IgF7rXpEDZmAuDgEuHe8ZXcz0e1xq0/HH2xzgt1JR4IWMbk3796JD05RGm1mTJPZ9+PwaBpAIk8OkBj6sh5GeAjNBXQUo2LvEDs/0IitXhzlsIYY5V21kkqjQgK3TXSAGvPRjD//kZckwoWqUgKdIFePgIa7Qaanf4zzLu3tUHDKw5kjr6FtQ1tmRs6ymRLRzx4czu84rnlDqDlhtn7+Cf0o9fj03g4x61yZArHfnL5Obr2T1oN8zVs/jZoIYKZ6w6ararHCsJQ3D1/H7faALN0neie/EB7F4e9Tl0TEiVIQUfVitMumxpuH76Jst+WBNZGe10OLGZbcwe/JdHoLCarfQq9y1n91/EaXfitDs5u+cCfcoPwWb576YakDY59brUoEH3WgkajF9QoEyeyCNGIQTPHj9nyhd/cGbPhTiPGZAuOQ4v1eDpsifeWd7JHWfRRvOdsIRZuX/tIWt//dfrc8KeedFLBsrU996GmT5HWjqNbc3gv/pQu0NVr8H48d0gbp67ozyIBPnLRC/ycPPcbXYs3svlY64d09FNJxVb4izhVg6sPRrteK+SLJU/Qxb3RW/SY/QzYvIzojPq6DuzW4IE48BbjxhUayT1fFpRz6cVg+uMjpMl6auo/PsjpVqP5P8tUrIxEPAHhP4MciAuYRE7WHcgnnpvYUswJG/ZOw0QR3Ut3x7Aq4HcCL7d4jZeAvN+hxwNKpWKSTuGM77tVE7vPo8EZMidngFze0S2/4Q+DaNnmcE8Dw5FyALZKbNr2X5unL3Fr8cmxmm3LIRg7tBFXDh8RfH3dqud9DlcN+eAtMnpPrk93Se393hezXaVCbz9mMXjV6FSq3DaHXzUpAzdf/R8blTcPH+HXmUHYwmzIoQg+OEzxredStC9IBp+WTe2b0+R3csPei2S2r/6iFuKb/c/B7GGW91uorLsSunvWXGIqi0qKA2T4PwxaAHhIebIechOGUuYlZ+6/sacC1PiNKaPv4lqLSuy/e89Hv7ErYY0ifV4QgjO7LnA5WPXSJctDaXqFlPcUcW0st1mtrFr6X6a9lP2Wi5TvwR7Vx7yaPVRaVTU61I91vN/lZCgULR6jYfAC7iKzLzNC1zHAcMaT+TUjrOoNWpkWSZ74SyujIZGhf2VGiyNVoNPsrj5BJepX5ylD2ZyeOMJZKdMiVpFE0RT3Gax0avctwQ/fBZZ4X90yyl6lR3MvCvTYr3QfhVJkxk0rjoF8bQvntKTNrAdQzhuIWmyxOtaUWJqB6FTce/M0IOxUYzEVJSQjE0QwuwaV4SDZACfrkim9gkx43jzPiC/xIG1R/lr9D8E3g6iYPm8tBvenCz5MpIyfQATNg8lLCQch83hce66+c+dWM02t+DgsDm4f/Uhp3ado0ilgrGey/ZFe1n+0zrFlhqtXssHlQqQMVd6hVe6I0kSbb5rSpO+DXhwPZCUGQKilbhUYv6wJVjCra+kzq3MGbKIel1qRLQ0xQ+1Rq28eJEkj2ODe1ceKFtbhlt5cD12gv3x4djW04o7q/vXA72aOoQ+DeP8wcv4p/Ald/EcnN51nqsnbpAuRxpK1/0QtUZNr+md0Og0/DtvB0IIfPxNdP+xXZQuXEpYzVYG1hzJ1RM3cDqcaHRafJOb+Gn3SNJkSe323CKVC8RYhCOqwFLx09KsmLKOa6duRZ6D6006GvWqS6qM8c9SZM6XQfF7IklQvfVHbs5srzJ/2BJObj/jVnNx5dh1UmdOpVhroVKrqBKPxZ3R1+hVxyCu7Fl+kPAQs4fPelhIOPtXHY6yEC6mCOcDsG4D23EUW78kHcgPkJ1aeDYEbMdcPduGT5D8eiOp4l+bIvl0QDhvgXm5S/Nb2ED/EZL/4LiPKUlIPm0RplYuK0bJ741IVb/gfUCOYN3Mzcz4al7kDWT3Pwc4vOE40w6Ni7RHeyG08CrXT9/yKOwB147tzsV7cQrIS39Y7bVlonyjUvSdGbsUi9HHQPZCcV/Nnt9/STHwyE6ZwFuPY7Q4iI6PmpThrzHLPcU7hKD8Kwb2J3acURxDZ9SRs2i2eM8lpvj4mxQt7VQqCZ2C2MjSSauZ+92iSAMGp8OJSq1Cdsho9Br8Uvjy855RpMqYkt7TO9N9cjvCnoWTLLV/nBSrFo7+x63oz2516baPbzeNSduHuz1Xq9Mycs0gvq03BqvZitOu3IOrNWj5uId3uU2NVsMP24fz79wd7FiyF5OfkQbdalKydjFuX7zLxtnbePb4OaXrFafcxyViLcmo1Wn54ufPmdpjVqQuulavwTe5D53HR622tuGPrR4FkHabg32rDjNseX9Gf/Zj5OLP6XDSf3YP0mROFe2chLCBdQ/Ij0FXAkmTI1bvKTbcvfIAs0I7mDXMyp3LcfNZfxk5bCE8H4frLMaLhaGwIoQdHlUl0nNYAOY/EdadkHo9khQ7sZ1XkSQVUrIRCL8+4Ljq6hlOIDEPSVKD9OZpdL8PyLgKI2YOWOBe1SoLLOFW5n2/ONqq1lxFs2Pw0XsEUEnCq1H4y9c+u+8islOmYLm8kWnDkCBl6UqDj57PR36WZL6lZ/ZeYNUvGwkPVe5PtFnsTGg/jWLVPuCTHrUJSJMs8nchQc959jiE9DnSRll0YjVbCbz1mBTpA/h8VAvmfBthzBGhOdvzl06R1o/gMotQqtYFV/q+RC3vVmr3rj5g09zthASFUqbeh5SsUyxe0oyNetV1a08CV3Co+GlZj9ThyR1nmff9EmwWu6JXrsvkxMYPHaYzbpPLNF1n0MU4lazE5nk7Pa4lO2XO7b/Ew1uPOLzhBCFBzylSuSAFyuahUPl8LL43kwHVRyg6ZIHLftOb+9GLokadXkv9rjWo37VG5O+2L9rLpI7TcdidOB1Odi7ZR57iORm/+btYFyXValeFjLnSs2zSagJvBVGiVhEa96lH8tTJonydzYvXtuxwUqxaYZY+/IPjW88gO2WKVSukKBv6Kq72nDaAJcJ2S0YY6yP5j0aSEr5MJ3vhLBh9FHzWffRkLxy/FLJw3IoIxlFpLBjB2AKefUNkMI7A6QRr2H18/DaC0fvRQWyQVClAF3P/87eZ9wEZeHQ7SFFSUciCs3s9XWtepXqbj/hzxFJsZluk2pBWryFLgUxR6jKf2XOeoQ0n/HdtAYP/6k3pesUpUasom+Zs95iX3qgnbTbvKbmEQJZlDm84zuKJqzi376JXr2SIuLnvu8Tlo9dZM30TM46Oxy+FLxPa/cLB9cfQaNWo1Cq6/tDWo0VMCMGCUctc59sqCYfDSa12lZl5ejIH1x1DpVZRvlEpt2AMcGL7Wa8SmOUblfJa3bxz6X4mtp+Gw+EqAtuyYBcFy+Vl9Npv4iyc36h3XW6eu8PWv3aj02ux2xwULJeX9iOb82PX39i74hBavYY6natz9cR1xUzKy8hOmRPbz2I1W6NsLYop3v5OQhZ0KvgVCNe5qs6g5cPqHzB0WT8MJj2fflWfHzr84rHI1Oo19Puju0fK+PzBy0zpMZOrx2+gN+mo16U6Hca0ilyUWMKtTOo03e1M3BJm5cLhK2xZsJvan8e+z7VQ+XwUKp8vVq8pXrMI+1cf9lAFy1MyV+RcY2O1KIRABHcH8QS3imDLetCVTbCg9DJlG5QgZYYAHtx4FHmOrtFpSJUxJaXrxd1nHQDLJpTVyVQgGUGdAUyfg64cmOd7PEuthpAnsGHxJpoOjv69C8ctRNg8cFwG3YdIptZI6ugzEu8q7wMyLsUcpxf5w9SZoz/zMvkZ+eXQWKb3mcuhDcfR6NRUa1mRzhPaeC3oCgsJZ3DdMR6r3JHNJjP38lTaDG3C3hWus6IXLUA6o47eMzrHqZ3mzJ7zbJi1DWu4lUrNy1O+YUnFnaHdZueb2qO5ePiK15S5RquObLGKfF2ETvTsIYswPzdzeOMJ7FZ7pEXfL71nkyZLKorX+G/3umHWVpaMX+UWpDbP34nRz0iXCW28vhf/lL4R0oHuc9AZtASkTcb2RXt5HCH6UaiCS+jFEm7lhw6/uAeEUAtn915gx+J9cZa3VKvV9PujO22HN+PKsWtkzJOB1JlS0qngVwTdD8YZ8XdaPH4lOkPMz9mjcpGKDR81Lcv637coFMsJt8/XEmbl2JZTbFu4hxptK1GhUSnW/7GFc/svYQm1uIxWdBq6TGzjIUpx59I9BlQfHjmeJczKml838/juE4YscmWXzu276PF5gWvHunne9jgF5LjQdVJbTu8+jyXcis1sQ6vXotVp6PNrHAVwnFfBGYhHe44wI8L/QkqEgKzWqPl532hmDlzArqX7I33WO49vnQBGJg6UA7IGybcXks/nAAjnY69eAA6binmjH1Oiwc0oBVqE7SjiSQdcaXEH2I8hwhdCyn8St1jsDeZ9QMYVUCs3K8fOpfvcZCj1sVDTSZMlNcOW94/xNfeuOKT4uCwLti7cTfP+nzDz9GSW/7yOE9vOkDZ7Gpr2+1hRCzg65g9fwpKJqyPP2w6uP0axaoUZtry/R1DeNGcHFw5diXInl7t4Ti4fu+pxg5Wdrp21OdTi4ZVrDbcx57tFhAaHkTlfRnJ8kNWlmfzKdaxmG2umb6Lj2JZeby4VGpfml96zPX8hSSwatxKHzYHNaker05CvVG5Grx/MmT0XFOUZLWFWti7cFS+96d3/HGBG37k8uR+MzqijcMX8PHscEhmMgQh7SAdagzbSRUcJSZLIWzJngh1JtBvenKP/niToXnCkLaMqwvfb6XD/21vCrGycs40abSuh1qgZs34w+1cfYfc/B/FNbqJOx2rkKuZpnLLkh9WewjRmG/tXH+Hx3SBSZUxJ6LNwr7v1Jw+V1cESg/TZ0zLnws+sn7mFC4eukP2DLNTvWjNKh7QoETZXv65SdBKJJ63rn8KPfjO7029m94Qd2FADQmfwaioaJND/l+GS1KkQmvzIttO8fAuxhEusX5ACu02w5c9ddI5iYS2eDcG9gtoGwoF4PhEpYGpCvJu3jvcBOYI+v3ZBkiS2L96LWq1CrVHTaVyrWKWvYkNocFhkU//L2K12nkecHwekTU7HMVFr5kZH4K1HLBq30i1AWsKsHFx3lM/z9cY/hS85imSj4Ze1yV44K1sX7ooyGEsSpMmSkivHlBVvDCY9dptd0bz+0uGrTOo8A9kpyFcql9cbsd1mx2a2eT2/8/E3MW7jEL5vNBGr2YokSag1anySmXh4IzCyWtZpd3LuwCVW/LyOfKW996a+2qcdG45tOcX4dlMjW3zMzy0c2XRSsTre1ULjQ9izMKzhNpfJBS5bSIfNpU6lM2ijtCmMLX4Bvvx+ahJ7VxziwuErZMyZjkx5M/B9owmKz3+5il6tVlOhUekolewArp284bUb4O6VB6TKmBKNTu2qEVIIXLHJHCQE/in9+GxQo4QZTJMH5Z5YAxiicWGKQAgnWHeB8zpo8oKubKKcPccESZML4dMRwmbhaneSAC349vDYtUrJf8J2uzGy8zkqtQABJ/f6snJWapcNqkX5vB5AyKHgVNJml8G2NyHf0lvF+4Acgc6go/+cHvSY0oFnj0NInSllgqjfeKNY9cKovvX8pzP46ClZu1iCXefYltOK/ZWyU3DvygPuARcOX2Hrwl0M+rMXWl3U71ln1NOoVz2cdpmD6466pUL1Jj2Nv6rH3KHKZulCCMzPXSn6c/sv2yBIsQAAIABJREFU4ZfCF6tCWjxlxhQYotkhFiibl0V3f+PSkWvITplUGQPokL+PR+uKzWxjw6xtNOnbQNFtyuCjp24c9abBlX14td/Wm/uTSq2i38xu3Lv2kH2rDhOQNhl1O1fnyf2nXDp6lUy501OlRQWX+UYMEUJwbMsptizYhSRJVG9TiWJVC7kdlWh1Wio3L0+y1P7MH7aEu5fvKxaVGXz01P68qsfj0ZGneE6uHL/hsQO2W+1kzutSs8uUOwNandZjoSapJD74qECsr/mmIEkaSD4ZEdwDV7rXDpIJ1DmQfKJfTAvnY8STz0AOithta0GdGVL8haSKfXtiQqDy640w1EJYNgIqJEMdJK3nglbSZMZsWM+Uzu1IkcrMheMmrpx2daIYfPRU/LSM94tIOrw6zEjx79V+W3kfkF/BFKGqk9hkL5SFaq0rsu2vPZFnb3qjjvQ50hJ4+zHhz81RzuPG2dvM+Goup3efQ6VW4Z/Sj7wlc/Fpn3pu1opGP6NXL9xIhCul/GOXX+nxcwfOH7ysWDGu0WnpNLYlBcvlJWuBTHz38TguH7uGRqvBZrFTvU1FGvWqi0qtYtY3f0W507Zb7YQGh2Iw6V093BGRVG/S0eOnDjESU1Gr1eSP2PkG3nrkmqQCsiyj1qgZuWYQ39QehZBlnE6BkGXqda1BiVpFo72WN+5deRCj56k1alKmD6BE7aKoVCoafumuY1zls6jNCrzxU/ff2bZwd+TntfufA9T6vApfTuno9rw9Kw4yrs0Uj8WDVq/FaXegM+ooVq0w1VrHPnXf9OuP+XfeDreArFJLlGtYKlJXPEu+jBQsl5ez+y646VzrjToa964X62u+SUj6CpB6AyJ8GcgPkfQVEOq8YD+P0ORFUnkXFhEhQ8F5D1cwx2Ve4biGeD4BKdnIpHkDL89HDkOEzQTLGkANxqag8X4OnDJ9aorU+ILZg/+OqHWR0Zv0VGpaNsqFliTpEIZaYPkXd+ERA5iibl17l5GU9GATmxIlSogjR44k+XXfNIQQ7F9zhI2zt3H1xA2C7gejUkmunbkEY9YNVvQtDrz1iE6F+0buNl9Gb9LTbXI76ndxtZtYwq00S98Z8/Po/UyNvgZ+3jea5T+uZfuivQhEZPVxh9EtqdHmI3ySua9eb124y8Obj8jxQVa3c7j9a46waNwKgu4HE3jzsaLusEqtYvqR8SwYsYxLx66SKU8GWg9pQuGKUXs1KyGEoEOBPty56K5TqzNo+WxQI9oMdZkqWM1WDqw9RtjTMIpVK0z6HPGTMRxUexRH/z3p8bjBR0/67Gm5fekeCEHRKoXoP7dHZIBKCC4dvUrfSt97LHz0Rh1TD4yJLKgRQtA62xcE3n7sMUa67Gmo3aEqRSoXpGC5vHFSlXt48xEdC/RxK5hTqVXkL52bn/aMinzMHGpmSo8/2Ll4H06nTNYCmejzaxcKlPXeifC2IZxBiKdfuHyHJQ0IJ/j1Q+XT1vO5wol4WJjIYPwykg+qtN41wRMDIRyIoMbguM5/bU8GV/VzwJwIH3EzWLaCCHGl1jWumoLrp2+y5c9dWC12Kn5amg8+KhDtd0nIoa4KdftJV2ZA2MBQGynZ2Dgrcb3JSJJ0VAjhXTuW9wH5jeDg+mOMaj7ZY1fql8KXiVuH8u+8nYQGh1H24xKU/bgEA6qN4NSuc17HM/joWfpwFoaIs9Eze84zpME4LGFWr4U14Apecy5OIU3mVNw8d5uTO85h9DUgqcBhlyle44M46UEPrDmS41tPuwVlSYIilQsxcev3sR7PG1dOXOfrKsNw2J1Yw60YfQ1kypuBSTuGJ1rf9sXDV+hX+ftXJC71tB3WlGZff8Lz4FA0WnWM+lljy8JR/zB/+BKPFLlaq+bzkS0ivY4t4VY+SdZWMZWuN+lYG7owXvP4rf98Vk7Z4FEToTfpFa0QHXYHdpsjyXrpkxI5qDnYT+MeZI1IAdM9LBuFcEQEZIX/ScmIKq3nQi8xEZZNiGcDIxyWXsaIlGIeICGCPweEa6GBAGMTJP+h8TLTEY6r4LwNmjxIau/Obm87MQnI794y5CUOrD3Kql82EvYsjI+alKVe1xpv5E1g4+ytii1GlnArPcsMxumQI/WxM+RMx7VojOpVahVXT9yI7IEuVCE/Sx78wYop65k3dJGiNZ6kkshZNFukKlHWApl5+iiE7z4eB7j6Vp1OmVaDG8daT/nLqR3oVfZbbBYbNour51Wr1/LltI5eX+N0OHny4Cn+KX1j3I+bq2h2Ft6Yzra/9xJ4+zEFyuShVN1iCdAK4p28JXMxfvNQZg74kysnbpAiXXJaffspNdtXBoizb21MMPoa0GjV2F4JtBqNGqOv63seHPiMmQP+9HqunSJd/NWKbp69rVigqNaouX8t0CMga7SaRK3PeF0Ix22wn8Nzx2tGhM32CMiSpEHoyoBtP+6tRhrQx1/zW3GOQnalyFV+SCp3ERVhO64QjAGcrt+F/QbiFcMcy3LQVwBD3OswJE1OhCq9y7RC7Ad9+QRT5HrbePf+KyKY893fLP9pXWSgu3byJv/O28G0g2PjpXyUGCgFSMCjPcYSZuX6mVvKLRYv4XQ4PWzudHotzft/gkolMfe7RciywGFzIEkuKcQMOdIx9CXjeJvFxtBPxnukxf8et5Ji1QrHKs2YOW9GZl/4mXW/b+bysWvkLpadel1qRJpzvMrqGZuY/e1fOGwOhIC6narRbVK7GIl3+CTzoUG3mjGeW0JQsFxet9RsUvFRs7LMfqFq9goVm5TBarbyZelBBN0NVnyO3qSnxeDG8Z5HgbJ5OLnjrEehmMNmJ1uhqJXq3lTMoWbmfreYLQt3ITtkKjQqRafxraP2D5efRKReFWonZM/jAgDJfyTiSVOQzUC4qyBMSo7k903CvJGXp2DeDM+HghwGOBH6ikjJJvynO63OABiAV47CpBfFkArvS5gR5qVI8QjIwnYIEdwFV5GXDCEywvcLVL4J3NL1FvBOBuQnD4JZ+sMat4pOq9nG/WsP2bpwt4di1OumeuuPOLnjrFchjpdR0pN+GZVaRaY8GcicN6Pi75v2+5jaHapydu9FZFkGJFJlDCD3hznc0k7HtpxWfL3NbGPjnO2xPvcLSJOM1jHYWe9atp/f+//pdi66YdZW1Bo13Sa1i9U1E5urJ2/wS6/ZnDtwCZOfkY971KL1kCZJtvtLlSEFA+f3ZHy7qZGLFdkpM/ivPgSkSca/83YQEhSqeEyhN+loM7RJnKqqX6V+t5osn7Ieh80RKWiiM+rIWSQbx7e6vkcZcr49Ox4hBF9XG871U7ci7yFbFuzixI6zzDr3k3c3JW0eFNPP6EBfSfElkiYTpNoKlvUIx1UkbX4w1EKSPLNCQjjAcQ1UvrFO7Qr7GXjWD7dga92DCO6BlPJP11yMHyNCf3plwR+h0KX9AK9V0fHotxbCggju5rkzD52B0JVF0sW94PJt5J30Qz6775Ji+44lzMqBdbH3Nk1sKjYpQ9GqhTFEpBm1Og1anUbRnCAq9CYd2QpmZtSaQVE+zy/AlzL1i1Pu45KU+7gEeYrn9DgDsllsijtxIUS08o/xYcHIZR7jW8NtrP3tX2wKvc2vi/vXHvJVxe84vfs8TruT509CWfbDGiZ1nJGk86j4aRmW3P+D/nN6MGDulyx58AdlG7iOqS4dvYZFwRFLq9dEnDE3jNfZ3wuSp07GL4fGUb5xaUz+RvxT+iFkmetnbjKj7zw6F+7Lr/3mKhb2vYmc2nmO2+fvui3oHXYnzx6FsOefA15fJ0lG8B3Ay367TqeGZ0EqhrUOZMfivYp/A0llQjI1QeU/EMn4sWIwls2bEYFlEU+aIR7VQg5qinA+jPF7EmGz8Nzh2sB+0qVfDUiq5Egp/gR1NkAP6ECTDynFX0i6kiin5oxIxk9iPA8PrHu9jGtDmP+J+7hvKe/kDtk/pS9C4UNWqVUJcmaW0KjVakasHMDxbWc4vPE4vgE+VP2sPF+WGewhhq8z6pAk3NtXImznmvX/JF6OTi9TrFphxXNBg48hQezdvOHNZF12CsKehaNLE7V5QFKxdJKnOpXVbGPHkn10HNfKQ387MTH5GRXFO7Lky4jBpPdQQ9PoNGTOp5xBiSvps6dl6JJ+OB1OmmXojN3qcDuKWff7Fj6sXoSUGQJw2J3kKpYtUc/248PVkzcUv/vmUAuXjl2jakvvrWEqn1YITU7ksFncvXCSvRuMLJuRgpAnVzm+bQZHN5+i3x+xS8UK+0XP3a39DOLJ55BqXcwWVY7bKAY+SQvyQyBLxI8FIdUmkB8AaiR1mv+em2xihD+yk8h+a22xGAugKONNPEQG4bmYfNd5J3fIhSvmxze5r8cXVavT0KBbrdc0q6iRJIkPqxWm68S2tBr8KelzpGPMusH4JvfB5GfE6GtwtfAMbEie4jndZSCFS4rzRSFPQuAX4EuPKR3QG3WoNa5rGXwMFK9RONHUy8AlMqGEyc9AslQxE0qwhFvZOHsb49tPY8GoZTy+9yQhpwjA5aPXFFPBDpuDgdVH8OxxSIJfM7ZUa1UBrUHr9n+g1qgJSJOM4jU/SJRrntl7IdLw4GUsYVaGN5lIn4rf0b/aMJql78zxbcrHIq+bDDnToVHIsBl89GTOE32qWNKX4eDuLnxZOy+zR6ch5IlrLEuYlW1/7+HWhbuxmo8IX4Bn4HKCfA8cyjakHuhLo6goJqwRamMvzV+SkNTp3YMxIBmqI6XeAD7dwdgWKfkUpIBZ8WtR0pUFodT2ZUIy1PF83AtCDkbYzyLk1/9/Fx/eyR2ySqViwpahDKk/lqB7T1CpVQgh6PNrF3J84L3J/U0jb8lcLL4/kyObThAeYqZo1UJYwqwsGr/So2rWbrWzYsp6uk9un2DXr9upOgXL5eXfeTsJf26mfMNSFK/xQYKkOb3RcWxLzu676Ja21pv0dJ7YJkY2iSFPnvNlqW8IfvgUS5gVrV7LkgmrGPfvdxQokyfa18eUHEWycenoNcXq5btXHjCq+Y+xaunau/IQSyet4WngM0rWKkqLwY3i3bPsk8yHKftGM6njDM7uv4ikkihStRD9Z3Xn4NpjXD99i4y501GuYSnv56KxRCkYv8ClE+/KKpifW/ju4/HMvzI1QXuzE4KStYuSLKV/hP646/OVJAmdQUuVFhViNMaRzSc9jGNc47hS4llik6Fw3sWrA5PzkbJy56vXNbV3CZeIEP475zaCqa1HtXWU46gzIvl9GePnRzueKjnCfwiEjMb13XC6dt668qCvHO3rhbC7xFXMayN7mYXpMyS/wW7yo8J5H+SnoMkZb5/mxOSd7kMWQnD52DWCHzylSJWCGExvXstTbDm4/hhjW/1M2DPP9oSiVRK2r/d1ceX4dWZ/+zeXj10lbdY0tBnahNL1YrYrn9F3Lmumb/JwN8qQKx1zL05JsMXEnUv36F58gNdCPK1ey5/XfomRacGi8StYOOqfyLE0WjW+ERrUL/ylhRA8efAUk58h1j3Nx7acYuLnvxDyJBTZ6UStUSNJElazDYNJj08yExO2DOXB9UB0Bh2FKuSLsx2lJdxK07QdY1SgqDNoaTu8Oc37x+MMMpF4fO8Jkzv/yrHNpwBB/tJ5+GpmV7LkyxSj1y8YuYy/Rv/j8T00+Rn5evYXUctKvoIcOhtCf8Kj+hkdUurtSOqY2bEK531E6DSw7gZVAJJPBzB8nKgL7JgiHFcQ5hUgP0cy1ARd+RjNSw6ZAOELcP/bGMG3JyrfTgj5CSL4S1dvuKQBJPAbgsoU/+6C2JLowiCSJDUFhgH5gVJCiBhF2aQIyA67g5kD/mTdzC3IThnf5D50m9yOqi3i7uoTV+5ff8iBNUfRaNVUaFzaa7tPTHhwI5COBfp4nF9qdRoaf1WPTmP/f2XnAFpm6aZ4Dq0zaJl3eSqpMsZe2MQbFw5dpm+loYptawZfA9MOjiVr/qhv4GEh4TRP39lNWARcn+enfevTcUwrDq47yo9df+P5k1CELCj7cQn6zfoiRhKvdy7fp1ux/lEbhkRIqxp9DSBAo9cweu035Cvl3ZAjKnYu3c+E9tOQHU4cdidanQaH3alY0PRp3/p0+yH+1fM3zt5m4+xtPH8SSpkGJSj3SYkEOaO2We1snL2NhSOX8eTBU1JlTEH7UZ9Rq13UdpGBtx/TIX9vD6lSvxS+LLrzm9fWy9CnYWz+cyc3ztwm94c5qNaqAgaTE/G4AciP+C91bQRTS1T+A91eL8sy+1cfYdtfu9HotNRqX5li1Qq/EUE3oRFCIAKLKfdOq1KjSrMX+XETcLzaG25ASjEXSRdP7+hYkhTCIGeAxsBv8RwnwZneZw7/ztsR+Q8R/PAZkzv/in9Kf0rULBLNqxOOv8etYMGIpYDrxvfr1/PpN6s7VT+LWerrVdJlS0OZ+sU5uO5Y5E1cklzFXg171k2web+JhD83s3flIUKDXbKX2Qp69rjqjMr5OyEE2gRKy74gX6nc1O9WkzXTN3n4Q2t1GjLlTh/tGNdP30Kj03gEZLvNwdF/T1GpaTlGNp/sdmPfv+YII5r8wLhN30U7/upfNkaZRob/WunCQyLkVZ+7JEGX3JsZp579Sk3Lkqd4DjbN3U7Ik1CyFcjEb/3nu1mbgmsBUKxq4ViP/yqb5m1n6hd/YLc5IgR0DpCvVC7GbRrittMPf25m9fRN7Fl+EL+UvjTqWZdSdaI2ctm6YJdbG97ju0+Y2uMPNBpNlJadaTKnYujSrxnb+mdkh4wQAt8AH0auHuT1b3rn8n16lXMVclrDbRh89Pw5Yim/HBpLyvQrXJXSls2g8kcytQOD+/+7EIJRzX/k8MbjkRmKfasOUb9bTbpO9JTufPtxgPAiCSw/Qziug+MSnkItVkTYnCQPyDEhXgFZCHEeeONWX+YwC5vmbPesgg23sWDk0iQLyNdO3WThyGUe85jUYTofVitM8tRxqxj+ZmFvFoxcxtpf/8UcaqFI5UJ0m9wuSSt7k5qz+y7yTZ3RIAROhxNJkqjWuiJ9fu3q9v2r37Umc4cucgtgKrWKvKVyRy3qEEdaDGrEzsX7CA0Ow2a1I6kkdHotvWd0iVHaN2X6AI+0JkTYXGZNpeg1bLc6OL37PA9uBJIuWxqP177MvSsPopRL9YZwCg6uP07FxlFbL3ojfY60tB/xWeTPZ/ddZN+qw/8ZqZh05P4wByVrx6/PNPy5malf/OG2oLGEWbhw6DI7Fu+LDJrmMAs9Sg4k8NbjyL/nmd3n+WxgwyiV5+Z9v1ixDW/ud39H66Fdqk4xlj74g8vHrqPVachZNFuU98qfuv5G6JOwyEyCJcyKzWJnRt95fLe4L5JfP/Dr5/X1p3aecwvGL8ZY/ctG6nWpEaMF4tuEJGkR6hzgvOr5S+0HLjEWSaNQXC7AqWwKI+TniOcTwLLWJQ+qr4rkP9ijwC2xSLIqa0mSukiSdESSpCOPHj1K1Gs9DXymaEYP8OB64l77ZbYv2qN4s1WpVRxYE/d+aI1WQ/sRn7EscDbrwv9izPrBsSsSectwOpwRqmFmzKEWbBY7VrONbX/tYd+qw27PbdSrLiVqFUVv1GHw0WP0M5A2a2oGL+ydKHMLSJucmacn03xQQwqWz0uVzyowaecIKjUtG6PXp8+Rlrwlcrr8gl9CZ9TRtN/H3L18X1EMRqvXEnhLWf3pZYpUKRgnv2FZlhXrFOLKwPk96TW9M4Ur5idf6dx0mdCWcf8OiVGhXlSc3n0etdZz4WMJs7J90Z7InzfO3sajO0FuixtLmJWFo5cTEuE//ipCCILuKaucBd5Wbs97FY1WQ/7SuclVLHuUwdjpcHJ693mPtP6HHz2lXvO/kB/VQQ4Zi3B6/8wPrDvq9WhCyQDlXUDy/x6XutiLv60KMCL5fwOa/C73LA90oPdcTAkhEE/agHkFiDDAAtZNiKBPXaYaSUC0O2RJkrYASjI73wohVsX0QkKI34HfwXWGHOMZxoFUGVMo/qNLEuQpniMxL+2G7JS9Kmt50xZ+jyfn9l/y2kqzcfY2yjcsFfmYWqNm2D/9uX7mFhcPXyVNllQUrVIw3jf+qPBP6Ufb75vR9vtmXp9z5fh1zh+4RMqMKShVp5ibmtewFf0Z1fxHzuy5gEanRqVS8eXUDhQsl5fCH+Xn2qkbOGyeXsNKKftXqdupGn+NWa7ofwyuxaEsyx67CNkp82G1QtGO7w2nw8niCStZM8OVxSlRqyidx7emRhtlxaq4YjDpvUrJHttyiiENxtJhdEsOrj3qcZ4LroXN+YOXKV3XM30pSRJpMqfy6pKVoEgRZ/kvfcwNOwXy+aAHGEwCnCEQfgthWQ0p1yCpU3kM4ZvchFqr9viuqDVqTP6Jbyn7OpD0ZSDlIkToDHBcAW0BJN/uSJpcAAjfLyFs+kupbS2okiH5tPEczHYAnDdwbzFzgngO5g2QBIVg0QZkIUTiqJwnIlqdljbDmjH3u0Xu7TNGPe1GNE+yeXzUpCyrftnksWqVnTKlE7GX913D6XB6Ve1TEnAAl990QomkxAenw8mwTydyfOsZhJDRaDQYfA38uGtEpJykfwo/JmweStD9YJ4/CSVj7nRoda5dbZO+Ddg0ZzvhjvBIWUqDj54G3WvinzL6vmyfZD50GtuKaT1neZxzSxJ8WKMwDpuTCy95YBt89DT8sg5pssSseleJcW2nsn/V4chU8u5l+zm+9TSzz/+UoEcHhSrkQ2vQgoK9qN3q4ND6Y5zccZaiVQshqSSPBbIsy1H2t3cY25Ifu/zqFsz1Jh0dx7RMsPcALnGg8g1LsW/lIRx2J3qj879g/N87AjkEET4Hya+/xxhVW1bk7zErcLwi3ymAcp+UTND5vklI2gJIAVMVf6fy7YrQ5EaEzQY5CAxVkHw6IakUuh8cl5V7okU4wnEOicQPyO+kMAhAk6/q03dmN7IWyIRvch+K1/iAybtGkLNItiSbQ96SuWjQvSZ6kw6VSkKtVaMz6ug6qV2M2mHe46JAOWXdbIOPnuqtE3bHldCs+mUjx7eewRpuxWa2E/7cTPDDp4xsNtnjuSnTB+CXwpcFI5YxqPYoZg5cgJAF04+Mp3KLCiRPk4ws+TPyxU+f03m8wgrfC1VaVFAUutAZdHQc3YpxG4fw1W9dKV3vQyo1LcvwFQPoOLZVnN/z/WsP2bfykNu5riwLLGEW1vz6b5zHVUKtUTN63WD8Anww+nm2NQrhOvMNDzF79FqrVBIp0ycnb8lcXsev1rIiX8/6ggw506HWqMmYJz0D5/fioyYxO5KIDb2ndyZjngwYfQ3kLuLE6VS6PdtdbUsKpM+elq9nf4HepMPkb8Tkb8QnmYmRqwbi429K8Pm+LUiGqqhSLkCVegMqvwFIKi+1NprsEUYar2JC0sSt4yC2xLftqREwFUgNPAVOCCGilcL6f/NDvnLiOntXHEKr11CpWTky5nq3iiuSgoPrjjKy+WRkp4zd6sDga6BIpQIMXzngjZVgBOhYsA+3znsqM2kNWua/0oZ168JdepUdjM1ix261o9Fp0Oq1/JgAC8nDG48zvMkkVCoJIQSyU060HuC9Kw8xof0vhId4nkGXrF2UMeu/TfBr2qx2tvy5k2k9Zym2oaVIH0CHMS34pedsJJWE7JBJlz0NI9cMirYwLikRQnByx1mCbp+hUvVRqFQK0pK6SqhSzPQ6hjnUzIntZ9Fo1RSpUijBRF/edYSQEY/ruLyZIyuzVS73rdRbkVQ+8Ro/0fuQ48r/W0B+15BlmUe3gzD5GxPV7/dVHt8NYsuCXYQEhVKiVlGKVS30xlX4v0q73D25d9WzolNv1DHzzGTSZ08b+djAmiM5vvUUr/5LJpS9Y1hIOAfWHMVmsVGydtEE7cl+meunb9Kz7LceRzUarYZPetZOkL5jJcyhZpqk6ah4Xp6/TB6m7BuNzWLj8rHr+CY3kbXAm20NKQe1Avtx3Nt2jEgBv7nOTt+T4Aj5CeLZMLBuAWTQlUXyH4Gkif93JSn6kN/zf8b+NUf4setvhD8LR5ZlitcowoB5XyZJYE6VMSWfDWzk8XhYSDjrftvM/rVHSZE2GY1616NQ+XyJPp+YUKVlBZZOXOURJALSJffYmZ3aec4jGAOcO3AJWZbjXZjm42+KtlUnIcheOCu5imbj0pGrbl0GGp2aT3rUTrTrGn2N1GhbiS1/7nJLl+tNOlp/52pt0hl0FPRyBPKmIQVMQwT3AvuJCJUpwG9QjIPx7Yt3WfvbZh7dDqJEzSJUa10RvdHTSeo9/yGpUiAFTEEIGRBIUtJm397vkN8yhBBcPXmD4IfPyFsyJ/4pYma4kBBcOXGdPhWGuBW4aHSuto7JO0d4fd2DG4FsmrOdx/eeUKJmUco3LJlgnsFhz8LoXnwgQfeDsZltESIperpNbkf9LjUS5BrxIfy5mT4VhnD/eiCWUAs6gxa1Rq2ord0woJ1iq5HOqGNt6II3PhvwMmHPwvix6+/sXXkIIQsy5c1A35ndElRPXAm7zc70PnP5d+52kCQMJh1dfmgbrbLWm4xwPgD5SYQOc8wC6oG1RxnVfDIOuxOnw4nBR0/qzKmYdnBsjBTe3pPwvE9Zv2M8vhvEN3VG8+B6ICqNGofVTotvGtH6u6ZJcv3x7aaybeHuyGrfF+iNOn49PpFMCk44rrPLH3A6nDhsrhtD1gKZmLxzRJxUoF7lr7HLFcVX9CY9Sx/+gdHn9euX22129q44xOnd50mbLTU12laO1Kh+md8HzGfVtI1u70Vn0FKzfRV6T++clFNOMGxW13l4UhcVWc1WQp+GkzyNf5LXGNw8f4dlP6zm5vk75C+Th0+/qk+azJ5tSomF0+GkabpOPH8S6va4zqCl5beNafWtdyGU9yQeMQnI72yV9evG6XTidMZMIenK8ets+3sPV05cj/J5QxtO4Nb5u1jCrIQ/C8dmsbP49qzDAAAbUElEQVR4wir2r0maxc29qw89gjG4dslK+tFOh5OxradgDbdF9kZawqzcOHObtb9tTpA57V99RPHMUK1RcfV41H/PpEKr01K5eXl6TutEs68/UQzGAO1HtqB4jSLoDFp8/I3ojToKf1SArj+8vbKHOr32tVT46o16UqYPSPJgfGrXOXqUHMS/83dy/sBlVv+ykS4f9OP2xdhZLsaHG2dvK7YD2ix2di7ZnyRzEPazyME9kB/VRA7ug7BfSpLrvu28P0NOYAJvPeLHLr9xbOtpJAlK1vmQPr92UWxzMoeaGVx3DJePXUellpCdMvlK5mLk2m+QJIlV0zaw7a89aA1aKjQsxa1zdzwERSxhVpb/vI6yDaJceCUIRSsX5PLRqx5VrHarXdHW8sqJGzjtnosSq9nG1oW7ady7XrznlDy1ck+r0yHjlyLpCs4SAp1ey4hVA7lz+T63zt0hU94M77QC27vIT91+dytmc9idOEPM/N7/T0auHpQkczD46JEdysJDxiRIVwvrfkRwV8CKS6byFsK6HVLMR9IlnY/A28j7gJyAWM1Wepb9lqeBzyID5+ENx+hd7lvmXpricW7629fzuXj4Knbrfzu8cwcuM7P/n1w4fIWb5+5giyhOuX7yJk4v6l4hj5Wl/xKahr3qsu73zYQ6wyJ9Yg0+eup3q6ko9qAzaF0qUAroTQnjSdqoV12ObzvjdhNUqVVkyJn2ja2iPbzpBL/3/5M7F++SMkMK2gxr6nbGmSl3+rdSd1gIwd6Vh9g0ZzuyU6ZG20qUb1yKE9vOcu/KA3J8kJVCFfK9VWfhscEcaubelfsej79oZUoqMuZKT4Zc6bh59rZbRuuF4EtiI0JG4G6HKANmxPMxSCkXJ/r1o0II4XJ/ct4CTT4kTfbXOp9XeR+QE5Dd/xzE/Nzstot1OmRCnjznwNqjVGj0n1C/EIItf+5yC8bg2m1umrsdtUYdGYzBdRanhFavpVzDpFHhCUiTjBnHJjJ/+BKObDyBXwpfmvRtQI22yuIc2QpmJkW65Ny/9tCtetjgo6dBt2jb1WPEh9U/oP2I5swZsgitXoPT4SRt1tSMWvtNgoyf0BzbcorhjSdGVgE/vPmIqT1mYQ2z8vEXiVeBnBRM6jidnUv3Ryp+ndx5jkmdZoAEslOgUqvIVjAzE7YMfSPO9hMarV6LSq2OXKy+jE+ypE3bD185gP5VhxPy5DkSEnabg1rtq1C5eblEva4QDnBeU/6l/UyiXjs6hByCeNIBnJcBNQg7Qv8RUvKfkBQFQZKe9wE5Abl98R7m0FdNxMFmtnPn0n8rZ5vVzp5/DnhY7r3AbnUon4tqXcbyTocTIQv0Rh0BaZMnSOo3pqTOlJJ+M7vH6LmSJDFi1UC+rjIMm8WO7JSRZZmqLSok6I2hSd8G1OlYlUtHr5EslT/ZC2d5Y3dhs75Z6PG5W8OtzPt+MfW71UxUze3E5MqJ6+xYss+tAl/J6ODqiRvM/W4R3Se3T8LZJQ0arcuScdtfu93+f/UmPY16Ja01avrsaZl/dRqnd58n+MFTCpTNEy8p1JijBskUYc7wCqq4+8AnBOLZEHCcB166t1p3I0J/RfLr+drm9TLvA3ICkqNwFoy+Bo+grDNqyV7IlT598iCYnmUH8zwoVGkIJEkifc60PLr92OOsVm9w9VNePXmDR3eCKF3vQ+p1ro5PsvgpyCQmWQtk5q/bv3Jk00meBj6jcMX8itXY8cUnmU+CeOsmNrcv3VN8PPy5hfAQM77J39zPMiqObzkdI5tHu9XO5vk738mALMsy5RqW5PSe8zy4FojepMNudVCtVQU+7Vs/yeejUqkoUqlgkl5TkiSEsTWEz8M9bW0E0+dJOpeXEcIG1q24BWMALGBeBO8D8rtHuYYl+eObhdis9shiJo1OQ+pMKSkR4fs6vc9cgu4GK968dEYdOoOWr2d3Z3CdMW4B2dVfq6VhrzqRxgNvC1qdNkmKzt4G0mdPy7VTNz0e15t0ilrMbwu+AT5otBoPpyEl4uLP/KZjs9gYUGMk107ewBxmQW/UITtlRq4eSPEa/1+FTJJfb4QIBvNKkHQuC0TTZ0g+HV7fpIQd11m20u+SxloxJryd+bE3FK1Oy9QDY6jyWflIL97qbT7ipz2jItsv9q85onxDkqDl4MbMvTiFwhUKMHL1IALSJsPoa0Bv0pExTwYm7Rjx1gXj97jz+agWHgVtepOeFt80fqM1uaOj4qdlXKvGaFBr1G52me8KK6as58qxa67sWIShhSXMypQef3h4HL/rSJIGVbJRSGn2IqVYiJRmPyr/b5Ck1xduJJUPaJRMRFSgf3MMat4LgyQxDfxaRxa9vIxao2aD9W+3s09Zlrl59jZavZaMudO/seei7xIH1h5l1jcLuXf1AWmzpaHD6BZuxXgJwc6l+5k54E8Cbz3CL6UfLQc3pnHvem/953ty51mGNZ4YWdQoZBFZ0GUNt6IzaFGpVVRqXo4GXWtG6bIUX05sP8O87xdz59I9shXMQvuRnyWqZKY3ExG9UcfM05NJnyOtwqvek5QI+2nEkzYRFos2wACSESnVCiR1wh+jvcp7pa43kAntp7H97z1u3rRqjZpyn5Rg6NKvX+PM3rNv9WHGtPzJw/v261lfULl5+QS/ntPhRK15e3fFSjjsDs7uvYgsyxQsnw+7xcaWBbtYOW0jD64H4rA5kFSSSzVqcGNaDv4UIQTLflzDkgmrePb4OVkLZOaLn9rHuSbghWzkq3rWo9Z8Q9EqhRLqrbrRqdBX3Dx3x+NxJRORtxUhXH3FkvT2Hq0I5wNE+EJwXAFtUSRTc6QkKjZ7r9T1BtJtcjsy5EqH0c+AWqPG6GcgTZZU9Pzl7ZRGjA9CCLYu3E3vCkPoWuxrFo1bgTnMs0o9qZg54E+3YAyu1OMfgxYmyvXetWAMrkrjIpULUqxqYZdKVzIfshfOyuM7QTgijCaELLCG21gw8h8Cbz1i7veLmTd0CU8DQxCy4MaZW3z38TjO7b8YpznM+GqOQiW7jd++nhfv9+eNmu2roDd69tanzpLqjbJ3jAvC+QD5yeeIh8UQD4shB7VCOG697mnFCUmdDpVfP1QBM1D5dk2yYBxT3hd1JTH+Kfz4/dQkjmw6yc2zt8mUNwOl6374Tt6co+PnL35n64LdkSn8Oxfvs33RXqYeHPtaPFzvXX2o+PjDm48SxG3p/5W9Kw8ptkCp1Cr2rznC8h/XevzeGm5j3veLGf/v0Fhdy+l0ev0cr5+5HauxYkPDnnU4tP4YF49cxWa2oTPq0GjVfLe471t9FCGEDRHUDORAIoui7Eddj6XehqRKelnUd5n3Afk1oFarKV33Q0rX/fB1T+W1ce/qAzbP2+nWr2mz2Lh39QG7lu6neuuPknxOqTKmIPDWY4/HU6RL/j4YxwODSR8hmOFezKhSSdisnprLL1BKAUeHSqXCN8CH0GDPPlhvGuIJgU6vZeLW7zm18xzn9l8iZYYAKjYp8/YLoFi3g3iOe4WyDMIClo1gavy6ZvZO8v4u857Xwtm9FxWzApYwK0c2nXgNM4J2I5qjN7nb2+lNetp8nzRuWu8idpsdn+Q+ipXGsiyo1rKCogc0QJb8mWJ1LSEERzadIG3W1KjU7rc2vUlPi8GJGzwkSaJI5YK0+KYRNdtVfvuDMYDzNgjP7AaEI5ye7XvviR/vd8jveS34p/ZTvElrtBpSJ6FV3cvUbFsZh83BnCGLCAl6jl+AD22HNaPeG+Cr/DYSFhJO73Lf8vDWYzc5Wb1JhyRJDF3ajxTpAvi0Tz3++WmdW9pab9TRbnjzGF9LCMGE9tPYs/ygWxeDSqNCb9TTYlBDGnSrmTBv7P8JTb6IXuJXMhmSCUmb//XM6R3mfUB+T5JzcsdZJrafhkXhXFGtVVO3U7XXMCsXdTtVp07HatgsNnQG3Vt9/ve6WTJhFfeuPvTQazf4GJh/ZSomP9f5Y7sRzfFJbmLJhFWEBIWSpUBGuk9uH6s2pXP7L3kEY3AdD03ZP4psBbLE/w39P6IrB+qs4LiKq1UIQAuqNKB/ff+n7yrvA/J7kpTgh08Z0mCsYi+2XwpfBv3Z67X3bEqShN6oj/6J74mS7Yv3egRjAEuYhWePnkcGZJVKRbOvP6HZ/9q78yCpynuN499f93T3rOAMIOCAbKKgiCAIWISyBJFdXGKUaCzF9Qq5qKiJuJEE40L0qogxRiXeiBENqDdBVDZFRTYFycgmBhVN2IIII7N2v/ePGRGcGRiml9N0P5+qqaJ7Tp9+3hmmf+e85z3ve/NInHMNOghaOvuDWg/wzGDlvKJ6FeRwOEwkHNHkO/sx80HBdFzx/0DJ34AIZA7B8sYnzYIMqUQFWRJq3vR3aqzpDFVdlNc/fDm9hnT3IJXEQ12FzUUcgVDtHz0N7ZHIys0kI8N/wP39UDWSOyv34NdyS4pLmDpuGguef4dwRZj23dpy4x+u5fgeHRqUxSubir7g8XHPUPTuejJzQwy/9mwum3hh1AcY5svFGt0Jje6MUVKpiwZ1SUJ9vWVXrStZRSKO3TtqX3BDjkzDrxtYY5pQn89oc1JrmhY2iel7nTnqR/hqGSToHPQ97+BTdd418gEWPP8uFWWVRCKOjR9u4uYzJ7L18+0xzXgwzpXgKj7BRb5p0Ou3bd7BDX3vYNXCj6msqKT462+Z9chs7vvZlKhyhSvDvDXjPSaeP5n7LpvC6kVrotqfHJwKsiRU9/5daj1j8fl9dD3jRA8SSbyc81+D6DmoG6HsYPXiGVkUtMznzhdvivl7tWh7NOOfuo5QVpDsvCyyG2WRlZvJ3X8dT15+bp2v+3zNZtYu2VBzXfLySl55bE7Mc/6Qc45I8eO4rX1wO3+C29aXyK5bq1YnOgyzHplNedmBrykvKWfJ31Y0+MAiHA5zx/B7efCq3/PeK8tYMH0RE4b+lj//+qUG7U8OTV3WklA9zj6F43t2YN2yjftG1WbmhOg9rAfHdW/ncTqJJX+Gn4kzb+Gfqz9n7ZINNC0soOegbnGbBKf/qH70HtaDD+euxp/h59SBXcnMPvhYgK82bsEfzIAfzOxVWV7Jpn/EfzYqV/IKFP8BKIHvbjoofR1n2VjjifXez4YVn9a60lYgFOCLdV/RvM3hr4W8dPaHFC1ev2+8h3NVa1y/cN/LDLmyf8x7OUQFWRJo754SZj0ym9079lDQ4ijMjPwWjRl29UAGXNrP63hyCKV7y9izs5iCFkcdVlFt37UN7bu2iWOy7+U0yq5aeaqe2p7UmspaBp4FQgE69Yrf4hf7fPsE8MPl/0qhZCau0QTMak7HWZsOp7Rl7fsbalxDryiroFXHlg2KtvjV5ZQW15zK1pfhZ+X8IgZeljyrJKUKFWRJiLKSMsb2vo2tn23bdw05MyfEaUO66Q87yVWUV/D4uGm8+exbmBnBrCBXP3ApQ0Yf+be9HNOhBb2GnsryOSv3zX9tPiOUFWTkmMHxDxD5Tx3fcOCKwQrqtZvzbxjGG9MWHlCQg5lBegzs2uC7FnLzc/D5fTUGYfp8RnajrAbtUw5O15AlIeY/9w7bN+84YEBX6bdlzHlqPtu+SNzgGTl8U8c9w9z/rZrmtKyknD07i5n639NYOvsDr6PFxITnx/Hj8SNo3KwRoewgvYf1YMrSe8lvnoCFBwLdgVpGlvuOAsuv925atmvO7xZOpFPvjlW37WWHGHpVf+6YcWODow2+oj+BYM1zNvMZpw3u1uD9St20/GKMVVZUUlFWQVaujiD39+sLH+SdmUtqPJ+dl8WNT14bl+UNJXol35by42ajax0Z36nXcUxZcq8HqVKHq1iP23lR1dzQ++aLzoTGk/FlDWrQPiORCGYWk0ltXp+2gCljnyYjUHWJwp/h557ZE+jcu2PU+0439Vl+UV3WMVJWUsbj46Yx77lFhCsjtGh3NDc8cU3c1l890jRr3QR/Rs0FBhyO/BbJtQSafG/3jj1YHQtrbK1lIQ45PBY4AZrMxBVPhYqPwH8slns9FjytwfuM5UIog6/oT78L+rD67TWEsoJ0PeNEMgIqG/GiLusYufeSR5n33CLKSysIV4b56pN/c8eI+9hUdGSuGxprw68duO8o+zs+n9GoII+T+2lO3GTVtLCgxu8Nqibw0FlSbFhGB3xHPYSv2Xx8BdOiKsbxkNMom9NH9OTUs7qqGMeZCnIMbP/yPyx/fWWNbr2KsgpenPyqR6mSS+sTCrn9hRvJK8glKy+TUFaQtl2OZfKCu7W0YRLzZ/i58t6fHrAKllnVAhGX/+ZiD5OJpB4d7sTAlk3bCIQCNQpyJBxp0Jquqer0ET15actTfPbxZjJzQhQe17DbMSSxRlw3iIIW+Uyf9Fe2f7mTTr07MnrSxbTrogUbRGJJBTkGju1cWOsk+v6An859jvcgUfLyZ/jpcEpbr2PIYep7bi/6nnvwKShFJDrqK4yBxk0bMWh0/5rdellBfnLzOR4mExGRI4XOkGNk7KOjKezQnJkPz6Z4115O7teJayZf1qAp60REJP3oPmQREZE4q899yOqyFhERSQIqyCIiIklABVlERCQJqCCLiIgkARVkERGRJKCCLCIikgSiKshmNtnM1pnZajN72cy0bI+IiEgDRHuGPBfo4pzrCmwAbos+koiISPqJqiA75950zlVWP1wCtIo+koiISPqJ5TXk0cCcur5pZteY2QozW7F9+/YYvq2IiMiR75AF2czmmVlRLV8j99vmdqASmF7XfpxzTzrnejrnejZrpvmdBcKVYZ6dOIPzmlzOoMBF/Pz0Caxb9onXsUREPBH1XNZmdjlwLTDAObe3Pq/RXNYC8OBVv2fhX96lrKR833OZOSGmLr+fYzsVJjRLOBxm8asrWPzqMvLycxhy5QDandwmoRlEJHXVZy7rqFZ7MrPBwK3AGfUtxiIAu7Z/w/zn36Gi9MB1pMtLK5hx/yvcMm1MwrKEK8P8ctAk1i37hNJvy/D5fbz2x/mMmTKaIaMHJCyHiKS3aK8hPwbkAXPNbJWZPRGDTJIG/vXpVgLBmseDkXCEjSs3JTTLWzMW7yvG32UoKynnsZ8/w7e7dZwpIokR1Rmyc+64WAWR9NKyfXMqyiprPO/z++jQrW1M3mPLZ9t4+8X3KS8tp8/wHnQ8tX2t27390uJ9xXh/GQE//1i0lj7De8Qkj0iyCIfDrHjjI4reW0ezwiacOaovefm5XsdKe1EVZJGGyj+6MWeO6svbLy6mbO/315CDoQAX/eLcqPf/xp8W8uiYp4iEI0Qqw8x44BWGXDmAMY+MrrFtVm4mZlDbcIrMnFDUWUSSSVlJGbcM+BWfFW2mpLiUUHaQpydM54F5d3NCzw5ex0trmjpTPHPTk9dxwQ3DyWmcjfmMjj3ac/+8u2jTObrb2b/ZsZtHr/8j5SXlVJZXEok4yvaW8/ozCyh6b12N7YdefRbBrJqFNxAMcHK/zlFlEUk2sx6ezacffU5JcSkAZXvL2bu7hEkXPUS0g3wlOjpDFs/4M/xcMWkUV0wahXMOM4vJfpfNWYk/ww8cOGCsbG85b814jy59Ox3w/ClnnMTFvxjJ8799mYyAHzPDH/Bzz2sTqvcjkjrm/nkR5fvd2fCdr7fu4l+fbqHwuJYepBJQQZYkEatiDODz1d7xY1b39y6980KGXHUWqxYUkdM4mx5ndyUQDMQsk0iy8Pvr6Bh1VWM4xDv66UvK6TW0O5FwpMbzgcwA/X/6ozpf16RlPgMu6Uef4T1UjCVlDR59JqGs4AHPmcHRbZrRsl1zj1IJqCBLCsrLz+XWZ8cSzAoSyg4SCGUQzAxwwU0j6NSro9fxRDw1cuwQTu7XmcycEBkBP1m5meQV5HLXS+O9jpb2op6pqyE0U5ckwtdbd/HurKWUl1bQe9iptDr+GK8jiSQF5xxr3t/AmsXraXJMPn3P60WoloGNEjv1malLBVlERCTO6lOQ1WUtIiKSBFSQRUREkoAKsoiISBJQQRYREUkCKsgiIiJJQAVZREQkCaggi4iIJAEVZBERkSSggiwiIpIEVJBFRESSgCdTZ5rZduDzBL5lU2BHAt/Pa2pv6kqntkJ6tTed2grp1d6mQI5zrtnBNvKkICeama041ByiqUTtTV3p1FZIr/amU1shvdpb37aqy1pERCQJqCCLiIgkgXQpyE96HSDB1N7UlU5thfRqbzq1FdKrvfVqa1pcQxYREUl26XKGLCIiktTSpiCb2W/MbLWZrTKzN83sGK8zxZOZTTazddVtftnMjvI6U7yY2YVm9rGZRcwsZUdtmtlgM1tvZhvN7Jde54knM3vGzLaZWZHXWeLNzFqb2UIzW1P9/3ic15nixcwyzWyZmX1U3dZfeZ0pEczMb2YrzezvB9subQoyMNk519U51w34O3CX14HibC7QxTnXFdgA3OZxnngqAs4HFnkdJF7MzA9MBYYAJwKjzOxEb1PF1Z+AwV6HSJBKYLxz7kSgDzAmhX+3ZUB/59wpQDdgsJn18ThTIowD1h5qo7QpyM653fs9zAFS+uK5c+5N51xl9cMlQCsv88STc26tc2691znirBew0Tn3T+dcOfACMNLjTHHjnFsE7PQ6RyI45/7tnPuw+t97qPrgLvQ2VXy4KsXVDwPVXyn9WWxmrYBhwFOH2jZtCjKAmd1jZpuBS0j9M+T9jQbmeB1ColIIbN7v8Zek6Id2OjOztkB3YKm3SeKnuvt2FbANmOucS9m2VnsYuBWIHGrDlCrIZjbPzIpq+RoJ4Jy73TnXGpgOjPU2bfQO1d7qbW6nqktsundJo1eftoocycwsF5gJ3PCDHr2U4pwLV186bAX0MrMuXmeKFzMbDmxzzn1Qn+0z4pwnoZxzZ9Vz0+nAa8DdcYwTd4dqr5ldDgwHBrgj/P62w/jdpqqvgNb7PW5V/ZykADMLUFWMpzvnZnmdJxGcc7vMbCFVYwVSdfBeX+AcMxsKZAKNzOw559yltW2cUmfIB2NmHfd7OBJY51WWRDCzwVR1k5zjnNvrdR6J2nKgo5m1M7MgcDHwfx5nkhgwMwOeBtY65x7yOk88mVmz7+74MLMsYCAp/FnsnLvNOdfKOdeWqr/ZBXUVY0ijggzcV93FuRo4m6pRb6nsMSAPmFt9q9cTXgeKFzM7z8y+BE4HZpvZG15nirXqAXpjgTeoGvTzonPuY29TxY+Z/QV4HzjBzL40syu9zhRHfYGfAf2r/1ZXVZ9RpaKWwMLqz+HlVF1DPuitQOlEM3WJiIgkgXQ6QxYREUlaKsgiIiJJQAVZREQkCaggi4iIJAEVZBERkSSggiwiIpIEVJBFRESSgAqyiIhIEvh/YUFPsBp4l1UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNE0Kq1iZmhD",
        "outputId": "161ae491-0c9a-47eb-f273-0c04a15d43aa"
      },
      "source": [
        "pca2 = PCA(n_components=6)\n",
        "pca2.fit(X_std)\n",
        "x_3d = pca2.transform(X_std)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_std, y, train_size = 0.8, test_size = 0.2)# random_state = 100\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "reg = LinearRegression().fit(X_train, y_train)\n",
        "print(reg.score(X_train, y_train))\n",
        "reg.score(X_test, y_test)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.05326649904817493\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.16422334897111554"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg-w5oEWZ3AN",
        "outputId": "2ffa3b80-fd61-4dcd-fd1b-7e81a378c861"
      },
      "source": [
        "pca2 = PCA(n_components=2)\n",
        "pca2.fit(X_std)\n",
        "x_3d = pca2.transform(X_std)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_std, y, train_size = 0.8, test_size = 0.2)# random_state = 100\n",
        "reg = LinearRegression().fit(X_train, y_train)\n",
        "print(reg.score(X_train, y_train))\n",
        "reg.score(X_test, y_test)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.037402964189240384\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.11672811310813813"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    }
  ]
}